@article{ WOS:000774014800008,
Author = {de Lima Araujo, Henrique Cesar and Martins, Fellipe Silva and Philippi
   Cortese, Tatiana Tucunduva and Locosselli, Giuliano Maselli},
Title = {Artificial intelligence in urban forestry-A systematic review},
Journal = {URBAN FORESTRY \& URBAN GREENING},
Year = {2021},
Volume = {66},
Month = {DEC},
Abstract = {Environmental quality and the citizens' well-being largely depend on the
   urban forests. But managing this natural capital is challenging for its
   biological complexity and interactions with other environmental, social,
   and economic aspects of the cities. In line with the current digital
   revolution with the rise of Smart Cities, the use of Artificial
   Intelligence (AI) is becoming more common, including in urban forestry.
   In this systematic review, we evaluated 67 studies on the interplay
   between AI and urban forestry surveyed on Science Direct and Scopus to
   provide an overview of the state of the art and identify new research
   avenues. The sample includes studies in 23 countries and 85 cities,
   including 5 megacities, comprising the remote assessment of canopy cover
   and species distribution; ecosystem services assessment; management
   practices; and socioeconomic aspects of urban forestry. Most studies
   focused on extant urban forests, with few examples evaluating temporal
   trends, and only one focused on future scenarios despite the predictive
   potential of AI. A total of 22 AI methods were employed in these
   studies. Only half of them point to clear advantages of the chosen
   methods, such as robustness against missing data, overfitting,
   collinearity, non-linearity, non-normality, the combination of discrete
   and continuous variables, and higher accuracy. The choice of these
   methods depends on the various combinations of aim, timescale, data
   type, and data source. The application of AI in urban forestry is in
   full growth and will support decision making to improve livability in
   the cities.},
Publisher = {ELSEVIER GMBH},
Address = {HACKERBRUCKE 6, 80335 MUNICH, GERMANY},
Type = {Review},
Language = {English},
Affiliation = {Locosselli, GM (Corresponding Author), Inst Bot SIMA SP, Cluster Ecol, Sao Paulo, Brazil.
   de Lima Araujo, Henrique Cesar, Univ Fed Sao Paulo, Sao Paulo, Brazil.
   Martins, Fellipe Silva, Nove de Julho Univ UNINOVE, IT \& Knowledge Management Grad Sch, Sao Paulo, Brazil.
   Philippi Cortese, Tatiana Tucunduva, Nove de Julho Univ UNINOVE, Smart \& Sustainable Cities Grad Sch, Sao Paulo, Brazil.
   Philippi Cortese, Tatiana Tucunduva, Univ Sao Paulo, Inst Adv Studies, Sao Paulo, Brazil.
   Locosselli, Giuliano Maselli, Inst Bot SIMA SP, Cluster Ecol, Sao Paulo, Brazil.},
DOI = {10.1016/j.ufug.2021.127410},
Article-Number = {127410},
ISSN = {1618-8667},
EISSN = {1610-8167},
Keywords = {Deep learning; Governance; Green infrastructure; Machine learning;
   Nature-based solutions; Urban planning; Urban trees},
Keywords-Plus = {TREE SPECIES CLASSIFICATION; LAND-SURFACE TEMPERATURE; ECOSYSTEM
   SERVICES; DECISION-MAKING; CARBON STORAGE; GREEN SPACES; LIDAR DATA;
   COVER; BENEFITS; IMAGERY},
Research-Areas = {Plant Sciences; Environmental Sciences \& Ecology; Forestry; Urban
   Studies},
Web-of-Science-Categories  = {Plant Sciences; Environmental Studies; Forestry; Urban Studies},
Author-Email = {locosselli@yahoo.com.br},
Affiliations = {Universidade Federal de Sao Paulo (UNIFESP); Universidade Nove de Julho;
   Universidade Nove de Julho; Universidade de Sao Paulo},
ResearcherID-Numbers = {Cortese, Tatiana Tucunduva P./T-3516-2018},
ORCID-Numbers = {Locosselli, Giuliano/0000-0002-2178-2027
   Cortese, Tatiana Tucunduva P./0000-0003-2915-5084},
Funding-Acknowledgement = {FAPESP {[}2019/08783-0, 2020/09251-0, 2017/50341-0]},
Funding-Text = {We thank Dr. Domingos Marcio Rodrigues Napolitano and Dr. Wonder
   Alexandre Luz Alves for double checking the list of AI methods. We also
   thank FAPESP for the financial support (2019/08783-0, 2020/09251-0,
   2017/50341-0).},
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Number-of-Cited-References = {143},
Times-Cited = {6},
Usage-Count-Last-180-days = {30},
Usage-Count-Since-2013 = {56},
Journal-ISO = {Urban For. Urban Green.},
Doc-Delivery-Number = {0A5SS},
Web-of-Science-Index = {Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)},
Unique-ID = {WOS:000774014800008},
DA = {2023-04-22},
}

@article{ WOS:000914747100001,
Author = {Koutra, Sesil and Ioakimidis, Christos S.},
Title = {Unveiling the Potential of Machine Learning Applications in Urban
   Planning Challenges},
Journal = {LAND},
Year = {2023},
Volume = {12},
Number = {1},
Month = {JAN},
Abstract = {In a digitalized era and with the rapid growth of computational skills
   and advancements, artificial intelligence and Machine Learning uses in
   various applications are gaining a rising interest from scholars and
   practitioners. As a fast-growing field of Artificial Intelligence,
   Machine Artificial Intelligence deals with smart designs, data mining
   and management for complex problem-solving based on experimental data on
   urban applications (land use and cover, configurations of the built
   environment and architectural design, etc.), but with few explorations
   and relevant studies. In this work, a comprehensive and in-depth review
   is presented to discuss the future opportunities and constraints in
   meeting the next planning portfolio against the multiple challenges in
   urban environments in line with Machine Learning progress. Bringing
   together the theoretical views with practical analyses of cases and
   examples, the work unveils the huge potential, but also the potential
   barriers of the complexity of Machine Learning to urban planning
   strategies.},
Publisher = {MDPI},
Address = {ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND},
Type = {Review},
Language = {English},
Affiliation = {Ioakimidis, CS (Corresponding Author), Inteligg PC, Karaiskaki 28, Athens 10554, Greece.
   Koutra, Sesil, Univ Mons, Fac Architecture \& Urban Planning, 88 Str Havre, B-7000 Mons, Belgium.
   Ioakimidis, Christos S., Inteligg PC, Karaiskaki 28, Athens 10554, Greece.},
DOI = {10.3390/land12010083},
Article-Number = {83},
EISSN = {2073-445X},
Keywords = {case-study analysis; machine learning; urban planning},
Keywords-Plus = {ROAD NETWORK EXTRACTION; LAND-USE; BUILDING ENERGY;
   ARTIFICIAL-INTELLIGENCE; CELLULAR-AUTOMATA; NEURAL-NETWORKS; GROWTH;
   MODEL; CLASSIFICATION; PREDICTION},
Research-Areas = {Environmental Sciences \& Ecology},
Web-of-Science-Categories  = {Environmental Studies},
Author-Email = {cioakim@inteligg.com},
Affiliations = {University of Mons},
ResearcherID-Numbers = {IOAKIMIDIS, CHRISTOS/O-2114-2013},
ORCID-Numbers = {IOAKIMIDIS, CHRISTOS/0000-0002-4139-7046},
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@article{ WOS:000807169600001,
Author = {Kamrowska-Zaluska, Dorota},
Title = {Impact of AI-Based Tools and Urban Big Data Analytics on the Design and
   Planning of Cities},
Journal = {LAND},
Year = {2021},
Volume = {10},
Number = {11},
Month = {NOV},
Abstract = {Wide access to large volumes of urban big data and artificial
   intelligence (AI)-based tools allow performing new analyses that were
   previously impossible due to the lack of data or their high aggregation.
   This paper aims to assess the possibilities of the use of urban big data
   analytics based on AI-related tools to support the design and planning
   of cities. To this end, the author introduces a conceptual framework to
   assess the influence of the emergence of these tools on the design and
   planning of the cities in the context of urban change. In this paper,
   the implications of the application of artificial-intelligence-based
   tools and geo-localised big data, both in solving specific research
   problems in the field of urban planning and design as well as on
   planning practice, are discussed. The paper is concluded with both
   cognitive conclusions and recommendations for planning practice. It is
   directed towards urban planners interested in the emerging urban big
   data analytics based on AI-related tools and towards urban theorists
   working on new methods of describing urban change.},
Publisher = {MDPI},
Address = {ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND},
Type = {Review},
Language = {English},
Affiliation = {Kamrowska-Zaluska, D (Corresponding Author), Gdansk Univ Technol, Fac Architecture, PL-80233 Gdansk, Poland.
   Kamrowska-Zaluska, Dorota, Gdansk Univ Technol, Fac Architecture, PL-80233 Gdansk, Poland.},
DOI = {10.3390/land10111209},
Article-Number = {1209},
EISSN = {2073-445X},
Keywords = {artificial intelligence; big data; urban design and planning; urban
   change},
Keywords-Plus = {ARTIFICIAL-INTELLIGENCE; NEURAL-NETWORKS; KNOWLEDGE; MANAGEMENT; GROWTH;
   METHODOLOGY; PERCEPTIONS; CHALLENGES; SCENARIOS; AUTOMATA},
Research-Areas = {Environmental Sciences \& Ecology},
Web-of-Science-Categories  = {Environmental Studies},
Author-Email = {dzaluska@pg.edu.pl},
Affiliations = {Fahrenheit Universities; Gdansk University of Technology},
ORCID-Numbers = {Kamrowska-Zaluska, Dorota/0000-0002-3083-6576},
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Number-of-Cited-References = {109},
Times-Cited = {5},
Usage-Count-Last-180-days = {22},
Usage-Count-Since-2013 = {45},
Journal-ISO = {Land},
Doc-Delivery-Number = {1X0QT},
Web-of-Science-Index = {Social Science Citation Index (SSCI)},
Unique-ID = {WOS:000807169600001},
OA = {gold},
DA = {2023-04-22},
}

@article{ WOS:000458227000020,
Author = {Grekousis, George},
Title = {Artificial neural networks and deep learning in urban geography: A
   systematic review and meta-analysis},
Journal = {COMPUTERS ENVIRONMENT AND URBAN SYSTEMS},
Year = {2019},
Volume = {74},
Pages = {244-256},
Month = {MAR},
Abstract = {Artificial neural networks (ANNs) and their latest advancement in deep
   learning are blooming in computer science. Geography has integrated
   these artificial intelligence techniques, but not with the same
   enthusiasm. The main reason for hesitation is that ANNs are still
   confronted as complex and black boxes. However, ANNs might be more solid
   methods than conventional approaches when dealing with complex
   geographical problems. This study considers the great potential of ANNs
   for research in urban geography. First, using the PRISMA protocol, it
   provides a statistical review of 140 papers on studies that employed
   ANNs in urban geography between 1997 and 2016. Second, it performs a
   quantitative meta-analysis using non-parametric bootstrapping. 45 (of
   the 140) papers were assessed regarding ANNs' overall accuracy (OA)
   achieved when used for urban growth prediction or urban land-use
   classification. Third, a new guideline for reporting ANNs is proposed.
   Statistical review indicated that ANNs performed better in 75.7\% of
   case studies compared to conventional methods. Meta-analysis found that
   on bootstrapped averages, the median OA achieved when using, ANNs was
   higher than the median OA achieved by other techniques by 2.3\% (p <
   .001). ANNs also performed better when used for classification compared
   to prediction. Analysis also identified inadequate presentation of ANNs
   and related results when used in urban studies. For this reason, a new
   guideline for reporting ANNs is suggested in this work to ensure
   consistency and easier dissemination of individual lessons learned.
   These findings aim to motivate further studies on ANNs and deep learning
   in urban geography.},
Publisher = {ELSEVIER SCI LTD},
Address = {THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND},
Type = {Review},
Language = {English},
Affiliation = {Grekousis, G (Corresponding Author), Sun Yat Sen Univ, Sch Geog \& Urban Planning, Dept Urban \& Reg Planning, 135 Xingang Xi Rd, Guangzhou 510275, Haizhu, Peoples R China.
   Grekousis, George, Sun Yat Sen Univ, Sch Geog \& Urban Planning, Dept Urban \& Reg Planning, 135 Xingang Xi Rd, Guangzhou 510275, Haizhu, Peoples R China.},
DOI = {10.1016/j.compenvurbsys.2018.10.008},
ISSN = {0198-9715},
EISSN = {1873-7587},
Keywords = {Artificial Neural Networks; Deep Learning; Urban Geography;
   Meta-analysis; Trends; Guidelines on Reporting results},
Keywords-Plus = {LAND-COVER CLASSIFICATION; MODEL; GIS; TRANSFORMATION; URBANIZATION;
   SIMULATION; ATHENS},
Research-Areas = {Computer Science; Engineering; Environmental Sciences \& Ecology;
   Geography; Operations Research \& Management Science; Public
   Administration},
Web-of-Science-Categories  = {Computer Science, Interdisciplinary Applications; Engineering,
   Environmental; Environmental Studies; Geography; Operations Research \&
   Management Science; Regional \& Urban Planning},
Author-Email = {graikousis@mail.sysu.edu.cn},
Affiliations = {Sun Yat Sen University},
Funding-Acknowledgement = {Sun Yat Sen University {[}37000-18821113]},
Funding-Text = {This work was supported by Sun Yat Sen University Starting Research
   Grant for George Grekousis (37000-18821113).},
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Number-of-Cited-References = {63},
Times-Cited = {55},
Usage-Count-Last-180-days = {16},
Usage-Count-Since-2013 = {135},
Journal-ISO = {Comput. Environ. Urban Syst.},
Doc-Delivery-Number = {HK8GI},
Web-of-Science-Index = {Social Science Citation Index (SSCI)},
Unique-ID = {WOS:000458227000020},
DA = {2023-04-22},
}

@article{ WOS:000844329200002,
Author = {Liu, Pengyuan and Biljecki, Filip},
Title = {A review of spatially-explicit GeoAI applications in Urban Geography},
Journal = {INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION},
Year = {2022},
Volume = {112},
Month = {AUG},
Abstract = {Urban Geography studies forms, social fabrics, and economic structures
   of cities from a geographic perspective. Catalysed by the increasingly
   abundant spatial big data, Urban Geography seeks new models and research
   paradigms to explain urban phenomena and address urban issues. Recent
   years have witnessed significant advances in spatially-explicit
   geospatial artificial intelligence (GeoAI), which integrates spatial
   studies and AI, primarily focusing on incorporating spatial thinking and
   concept into deep learning models for urban studies. This paper provides
   an overview of techniques and applications of spatially-explicit GeoAI
   in Urban Geography based on 581 papers identified using a systematic
   review approach. We examined and screened papers in three scopes of
   Urban Geography (Urban Dynamics, Social Differentiation of Urban Areas,
   and Social Sensing) and found that although GeoAI is a trending topic in
   geography and the applications of deep neural network-based methods are
   proliferating, the development of spatially-explicit GeoAI models is
   still at their early phase. We identified three challenges of existing
   models and advised future research direction towards developing
   multi-scale explainable spatially-explicit GeoAI. This review paper
   acquaints beginners with the basics of GeoAI and state-of-the-art and
   serve as an inspiration to attract more research in exploring the
   potential of spatially-explicit GeoAI in studying the socio-economic
   dimension of the city and urban life.},
Publisher = {ELSEVIER},
Address = {RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS},
Type = {Review},
Language = {English},
Affiliation = {Biljecki, F (Corresponding Author), Natl Univ Singapore, Dept Architecture, Singapore, Singapore.
   Liu, Pengyuan; Biljecki, Filip, Natl Univ Singapore, Dept Architecture, Singapore, Singapore.
   Biljecki, Filip, Natl Univ Singapore, Dept Real Estate, Singapore, Singapore.},
DOI = {10.1016/j.jag.2022.102936},
EarlyAccessDate = {AUG 2022},
Article-Number = {102936},
ISSN = {1569-8432},
EISSN = {1872-826X},
Keywords = {Urban studies; Deep learning; Socio-economics; Location encoder; Graph
   neural network},
Keywords-Plus = {RESOURCE-ALLOCATION; BIG DATA; INFORMATION; PATTERNS; LIFE},
Research-Areas = {Remote Sensing},
Web-of-Science-Categories  = {Remote Sensing},
Author-Email = {pyliu93@nus.edu.sg
   filip@nus.edu.sg},
Affiliations = {National University of Singapore; National University of Singapore},
ResearcherID-Numbers = {Liu, Pengyuan/HCH-0879-2022
   },
ORCID-Numbers = {Liu, Pengyuan/0000-0002-5443-5910},
Funding-Acknowledgement = {Singapore Ministry of Education Academic Research Fund Tier 1},
Funding-Text = {We thank the editor and 5 anonymous reviewers for their helpful and
   constructive comments and suggestions. We are grateful to the members of
   the NUS Urban Analytics Lab for the discussions. This research is part
   of the project Multi -scale Digital Twins for the Urban Environment:
   From Heartbeats to Cities, which is supported by the Singapore Ministry
   of Education Academic Research Fund Tier 1.},
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Number-of-Cited-References = {142},
Times-Cited = {12},
Usage-Count-Last-180-days = {39},
Usage-Count-Since-2013 = {44},
Journal-ISO = {Int. J. Appl. Earth Obs. Geoinf.},
Doc-Delivery-Number = {3Z3PH},
Web-of-Science-Index = {Science Citation Index Expanded (SCI-EXPANDED)},
Unique-ID = {WOS:000844329200002},
OA = {gold},
DA = {2023-04-22},
}

@article{ WOS:000702075400001,
Author = {Chaturvedi, Vineet and de Vries, Walter T.},
Title = {Machine Learning Algorithms for Urban Land Use Planning: A Review},
Journal = {URBAN SCIENCE},
Year = {2021},
Volume = {5},
Number = {3},
Month = {SEP},
Abstract = {Urbanization is persistent globally and has increasingly significant
   spatial and environmental consequences. It is especially challenging in
   developing countries due to the increasing pressure on the limited
   resources, and damage to the bio-physical environment. Traditional
   analytical methods of studying the urban land use dynamics associated
   with urbanization are static and tend to rely on top-down approaches,
   such as linear and mathematical modeling. These traditional approaches
   do not capture the nonlinear properties of land use change. New
   technologies, such as artificial intelligence (AI) and machine learning
   (ML) have made it possible to model and predict the nonlinear aspects of
   urban land dynamics. AI and ML are programmed to recognize patterns and
   carry out predictions, decision making and perform operations with speed
   and accuracy. Classification, analysis and modeling using earth
   observation-based data forms the basis for the geospatial support for
   land use planning. In the process of achieving higher accuracies in the
   classification of spatial data, ML algorithms are being developed and
   being improved to enhance the decision-making process. The purpose of
   the research is to bring out the various ML algorithms and statistical
   models that have been applied to study aspects of land use planning
   using earth observation-based data (EO). It intends to review their
   performance, functional requirements, interoperability requirements and
   for which research problems can they be applied best. The literature
   review revealed that random forest (RF), deep learning like
   convolutional neural network (CNN) and support vector machine (SVM)
   algorithms are best suited for classification and pattern analysis of
   earth observation-based data. GANs (generative adversarial networks)
   have been used to simulate urban patterns. Algorithms like cellular
   automata, spatial logistic regression and agent-based modeling have been
   used for studying urban growth, land use change and settlement pattern
   analysis. Most of the papers reviewed applied ML algorithms for
   classification of EO data and to study urban growth and land use change.
   It is observed that hybrid approaches have better performance in terms
   of accuracies, efficiency and computational cost.},
Publisher = {MDPI},
Address = {ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND},
Type = {Review},
Language = {English},
Affiliation = {Chaturvedi, V (Corresponding Author), Tech Univ Munich TUM, Chair Land Management, D-80333 Munich, Germany.
   Chaturvedi, Vineet; de Vries, Walter T., Tech Univ Munich TUM, Chair Land Management, D-80333 Munich, Germany.},
DOI = {10.3390/urbansci5030068},
Article-Number = {68},
EISSN = {2413-8851},
Keywords = {urban growth; land use change; earth observation; modeling},
Keywords-Plus = {CELLULAR-AUTOMATA; LOGISTIC-REGRESSION; CLASSIFICATION; GROWTH; COVER;
   PERFORMANCE; EXPANSION; MODELS},
Research-Areas = {Environmental Sciences \& Ecology; Geography; Public Administration;
   Urban Studies},
Web-of-Science-Categories  = {Environmental Sciences; Environmental Studies; Geography; Regional \&
   Urban Planning; Urban Studies},
Author-Email = {vineet.chaturvedi@tum.de
   wt.de-vries@tum.de},
Affiliations = {Technical University of Munich},
ResearcherID-Numbers = {de Vries, Walter Timo/D-4256-2009},
ORCID-Numbers = {de Vries, Walter Timo/0000-0002-1942-4714},
Funding-Acknowledgement = {TUM open access publishing fund},
Funding-Text = {This research was funded by TUM open access publishing fund.},
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Number-of-Cited-References = {42},
Times-Cited = {14},
Usage-Count-Last-180-days = {20},
Usage-Count-Since-2013 = {54},
Journal-ISO = {Urban Sci.},
Doc-Delivery-Number = {UZ2YH},
Web-of-Science-Index = {Emerging Sources Citation Index (ESCI)},
Unique-ID = {WOS:000702075400001},
OA = {gold},
DA = {2023-04-22},
}

@article{ WOS:000855633600008,
Author = {Tekouabou, Stephane Cedric Koumetio and Diop, El Bachir and Azmi, Rida
   and Jaligot, Remi and Chenal, Jerome},
Title = {Reviewing the application of machine learning methods to model urban
   form indicators in planning decision support systems: Potential, issues
   and challenges},
Journal = {JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES},
Year = {2022},
Volume = {34},
Number = {8, B},
Pages = {5943-5967},
Month = {SEP},
Abstract = {Modern cities dynamically face several challenges including
   digitalization, sustainability, resilience and economic development.
   Urban planners and designers must develop urban forms that address these
   chal-lenges. With the integration of new communication and information
   technologies (Smartphone, GIS, Drones, IoT, Sensors, etc.), urban
   activities have generated large volumes of urban data. The rapid growth
   in terms of collection and big data storage capacities combined with the
   ever-increasing computational power of modern machines have made
   possible their efficient treatment using machine (ML) and deep learning
   (DL) algorithms. The emergence of such groundbreaking methods has in
   turn helped to address the challenges of modern-day cities in several
   domains (health, security, mobility, etc). ML algorithms have been
   proposed to model the urban form's indicators for intelligent urban
   planning decision making. They have been proven to perform better than
   the traditional methods. However, the potential of ML has not yet been
   fully explored in research for urban planning decision support. This
   paper presents a com-prehensive review of ML applications for mitigating
   the challenges of modern cities planning. First and foremost, an
   overview of the urban forms, sources of urban data, the ML and DL
   techniques as well as their potential in solving the aforementioned
   challenges. For each ML method, we will highlight it work-ing principle,
   advantages, disadvantages and potential applications using comparative
   tables. Finally, we will discuss the issues and challenges of ML methods
   in urban form's modeling while ultimately advocat-ing some future
   research directions.(c) 2022 Published by Elsevier B.V. on behalf of
   King Saud University. This is an open access article under the CC
   BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).},
Publisher = {ELSEVIER},
Address = {RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS},
Type = {Review},
Language = {English},
Affiliation = {Tekouabou, SCK (Corresponding Author), Mohamed VI Polytech Univ UM6P, Ctr Urban Syst CUS, Lot 660,Hay Moulay Rachid, Ben Guerir 43150, Morocco.
   Tekouabou, Stephane Cedric Koumetio; Diop, El Bachir; Azmi, Rida; Chenal, Jerome, Mohamed VI Polytech Univ UM6P, Ctr Urban Syst CUS, Lot 660,Hay Moulay Rachid, Ben Guerir 43150, Morocco.
   Jaligot, Remi; Chenal, Jerome, Ecole Polytech Fed Lausanne EPFL, Urban \& Reg Planning Community CEAT, Lausanne, Switzerland.},
DOI = {10.1016/j.jksuci.2021.08.007},
EarlyAccessDate = {AUG 2022},
ISSN = {1319-1578},
EISSN = {2213-1248},
Keywords = {Urban form; Urban planning; Artificial intelligence; Machine learning;
   Deep learning; Urban models; Urban data; Neural networks; Ensemble
   methods; Urban big data},
Keywords-Plus = {LAND-USE; ALGORITHM; REGRESSION; NETWORK; METRICS; ORANGE},
Research-Areas = {Computer Science},
Web-of-Science-Categories  = {Computer Science, Information Systems},
Author-Email = {ctekouaboukoumetio@gmail.com},
Affiliations = {Swiss Federal Institutes of Technology Domain; Ecole Polytechnique
   Federale de Lausanne},
ResearcherID-Numbers = {azmi, rida/O-7311-2017
   Tekouabou Koumetio, Cédric Stéphane/AAK-8507-2020
   },
ORCID-Numbers = {azmi, rida/0000-0002-4903-176X
   Tekouabou Koumetio, Cédric Stéphane/0000-0003-3627-5746
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Number-of-Cited-References = {168},
Times-Cited = {9},
Usage-Count-Last-180-days = {28},
Usage-Count-Since-2013 = {30},
Journal-ISO = {J. King Saud Univ.-Comput. Inf. Sci.},
Doc-Delivery-Number = {4P8IM},
Web-of-Science-Index = {Science Citation Index Expanded (SCI-EXPANDED)},
Unique-ID = {WOS:000855633600008},
OA = {gold, Green Published},
DA = {2023-04-22},
}

@article{ WOS:000526210700001,
Author = {Li Ziwei and Lin Borong and Zheng Shanwen and Liu Yanchen and Wang Zhe
   and Dai Jian},
Title = {A review of operational energy consumption calculation method for urban
   buildings},
Journal = {BUILDING SIMULATION},
Year = {2020},
Volume = {13},
Number = {4},
Pages = {739-751},
Month = {AUG},
Abstract = {Rapid urbanization has driven economic and social development, but it
   has also led to continued growth in building energy consumption. It is
   of great significance to ensure the user comfort while controlling the
   growth of building energy use. Accurate quantification of urban
   buildings' energy demand can support energy efficient and sustainable
   community design, assist urban morphology generation and optimization,
   building layout optimization, building shape and construction design,
   HVAC system optimization, assessment of the energy program and policy.
   In recent years, researchers worldwide have carried out research of
   urban scale energy consumption calculation methods from different
   perspectives, and encountered different technical difficulties. This
   paper provides a critical review on the energy modeling methods at urban
   neighborhood scale from the following three aspects: database, models
   and platforms. Through literature review, the authors indicate the
   advantages and limitations of current urban building energy calculation
   methods and tools, and propose the following possible approaches to
   improve the operational energy consumption calculation method for urban
   buildings: (1) develop micro-environment data generation methods that
   can be directly applied to energy consumption calculation of urban
   buildings; (2) improve the capabilities to collect, filter and convert
   the building information data by introducing the data mining technique;
   (3) introduce the cluster analysis and artificial intelligence
   technology to improve the speed of energy consumption calculation; (4)
   develop a visualization platform to realize real-time editing and
   calculating of urban design.},
Publisher = {TSINGHUA UNIV PRESS},
Address = {B605D, XUE YAN BUILDING, BEIJING, 100084, PEOPLES R CHINA},
Type = {Review},
Language = {English},
Affiliation = {Dai, J (Corresponding Author), Beijing Univ Technol, Coll Architecture \& Urban Planning, Beijing 100124, Peoples R China.
   Li Ziwei; Zheng Shanwen; Dai Jian, Beijing Univ Technol, Coll Architecture \& Urban Planning, Beijing 100124, Peoples R China.
   Lin Borong, Tsinghua Univ, Dept Bldg Sci, Beijing 100084, Peoples R China.
   Liu Yanchen, Guangzhou Univ, Coll Civil Engn, Guangzhou 510006, Peoples R China.
   Wang Zhe, Lawrence Berkeley Natl Lab, Bldg Technol \& Urban Syst Div, Berkeley, CA USA.},
DOI = {10.1007/s12273-020-0619-0},
EarlyAccessDate = {APR 2020},
ISSN = {1996-3599},
EISSN = {1996-8744},
Keywords = {urban buildings; operational energy consumption; building dataset;
   energy model; energy modelling platform},
Keywords-Plus = {RESIDENTIAL BUILDINGS; STATISTICAL-ANALYSIS; MODEL GENERATION; LASER
   SCANNER; HEAT-ISLAND; PERFORMANCE; SIMULATION; CITY; STOCK; CLIMATE},
Research-Areas = {Thermodynamics; Construction \& Building Technology},
Web-of-Science-Categories  = {Thermodynamics; Construction \& Building Technology},
Author-Email = {daijian@bjut.edu.cn},
Affiliations = {Beijing University of Technology; Tsinghua University; Guangzhou
   University; United States Department of Energy (DOE); Lawrence Berkeley
   National Laboratory},
ORCID-Numbers = {Wang, Zhe/0000-0002-2231-1606
   Lin, Borong/0000-0003-3848-2837},
Funding-Acknowledgement = {China Postdoctoral Science Foundation {[}2019M650408]; National Key
   Research and Development Project {[}2019YFE010332]},
Funding-Text = {This research is supported by the China Postdoctoral Science Foundation
   funded project (No. 2019M650408), and the National Key Research and
   Development Project (2019YFE010332)},
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   {[}No title captured].},
Number-of-Cited-References = {94},
Times-Cited = {25},
Usage-Count-Last-180-days = {19},
Usage-Count-Since-2013 = {101},
Journal-ISO = {Build. Simul.},
Doc-Delivery-Number = {MI2PR},
Web-of-Science-Index = {Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)},
Unique-ID = {WOS:000526210700001},
DA = {2023-04-22},
}

@article{ WOS:000725706800001,
Author = {Chew, Louis and Hespanhol, Luke and Loke, Lian},
Title = {To Play and To Be Played: Exploring the Design of Urban Machines for
   Playful Placemaking},
Journal = {FRONTIERS IN COMPUTER SCIENCE},
Year = {2021},
Volume = {3},
Month = {NOV 15},
Abstract = {Within the paradigm of the smart and playable city, the urban landscape
   and street furniture have provided a fertile platform for pragmatic and
   hedonic goals of urban liveability through technology augmentation.
   Smart street furniture has grown from being a novelty to become a common
   sight in metropolitan cities, co-opted for improving the efficiency of
   services. However, as we consider technologies that are increasingly
   smarter, with human-like intelligence, we navigate towards uncharted
   waters when discussing the consequences of their integration with the
   urban landscape. The implications of a new genre of street furniture
   embedded with artificial intelligence, where the machine has autonomy
   and is an active player itself, are yet to be fully understood. In this
   article, we analyse the evolving design of public benches along the axes
   of smartness and disruption to understand their qualities as playful,
   urban machines in public spaces. We present a concept-driven speculative
   design case study, as an exploration of a smart, sensing, and disruptive
   urban machine for playful placemaking. With the emergence of artificial
   intelligence, we expand on the potential of urban machines to partake an
   increasingly active role as co-creators of play and playful placemaking
   in the cities of tomorrow.},
Publisher = {FRONTIERS MEDIA SA},
Address = {AVENUE DU TRIBUNAL FEDERAL 34, LAUSANNE, CH-1015, SWITZERLAND},
Type = {Review},
Language = {English},
Affiliation = {Chew, L (Corresponding Author), Univ Sydney, Sch Architecture Design \& Planning, Design Lab, Sydney, NSW, Australia.
   Chew, Louis; Hespanhol, Luke; Loke, Lian, Univ Sydney, Sch Architecture Design \& Planning, Design Lab, Sydney, NSW, Australia.},
DOI = {10.3389/fcomp.2021.635949},
Article-Number = {635949},
EISSN = {2624-9898},
Keywords = {play; playable city; urban machines; placemaking; interaction design;
   public bench; urban prototype; smart city},
Keywords-Plus = {ARTIFICIAL-INTELLIGENCE; SPECULATIVE DESIGN},
Research-Areas = {Computer Science},
Web-of-Science-Categories  = {Computer Science, Interdisciplinary Applications},
Author-Email = {louis.chew@sydney.edu.au},
Affiliations = {University of Sydney},
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Number-of-Cited-References = {80},
Times-Cited = {0},
Usage-Count-Last-180-days = {5},
Usage-Count-Since-2013 = {17},
Journal-ISO = {Front. Comput. Sci.-Switz},
Doc-Delivery-Number = {XH8VZ},
Web-of-Science-Index = {Emerging Sources Citation Index (ESCI)},
Unique-ID = {WOS:000725706800001},
OA = {gold},
DA = {2023-04-22},
}

@article{ WOS:000816793700001,
Author = {Sapienza, Martina and Nurchis, Mario Cesare and Riccardi, Maria Teresa
   and Bouland, Catherine and Jevtic, Marija and Damiani, Gianfranco},
Title = {The Adoption of Digital Technologies and Artificial Intelligence in
   Urban Health: A Scoping Review},
Journal = {SUSTAINABILITY},
Year = {2022},
Volume = {14},
Number = {12},
Month = {JUN},
Abstract = {As more people live in cities, the impact of urban settings on
   population health has been increasing. One of the main strategies to
   cope with urbanization is adopting artificial intelligence (AI) and new
   digital technologies to develop new urban services that improve
   citizens' health and well-being. The aim of this study is to review
   urban interventions and adopting digital technologies and AI-based
   algorithms to improve population health. A scoping review of the
   literature was conducted by querying MEDLINE, Web of Science, and Scopus
   databases. The included studies were categorized into one urban health
   area, suggested by the WHO, according to the type of intervention
   investigated. Out of 3733 records screened, 12 papers met all inclusion
   criteria. Four studies investigated the ``outdoor and indoor
   pollution{''} area, one ``climate change{''}, one ``housing{''}, two
   ``health and social services{''} and four ``urban transport{''} areas.
   Only one article used a comprehensive approach to public health,
   investigating the use of AI and digital technologies both to
   characterize exposure conditions to health determinants and to monitor
   population health effects, while the others were limited to
   characterizing exposure conditions to health determinants, thus
   employing a preliminary public health perspective. From this point of
   view, countries should foster synergy for the development of research on
   digital technologies to address the determinants of health in the urban
   context. From a global health perspective, sharing results with the
   scientific community would also allow other countries to use those
   technologies that have been shown to be effective, paving the way for
   more sustainable living conditions worldwide.},
Publisher = {MDPI},
Address = {ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND},
Type = {Review},
Language = {English},
Affiliation = {Riccardi, MT (Corresponding Author), Univ Cattolica Sacro Cuore, Dipartimento Sci Vita \& Sanita Pubbl, I-00168 Rome, Italy.
   Sapienza, Martina; Nurchis, Mario Cesare; Riccardi, Maria Teresa; Damiani, Gianfranco, Univ Cattolica Sacro Cuore, Dipartimento Sci Vita \& Sanita Pubbl, I-00168 Rome, Italy.
   Nurchis, Mario Cesare; Damiani, Gianfranco, Fdn Policlin Univ A Gemelli IRCCS, Dept Woman \& Child Hlth \& Publ Hlth, I-00168 Rome, Italy.
   Bouland, Catherine; Jevtic, Marija, Univ Libre Bruxelles, Sch Publ Hlth, Res Ctr Environm \& Occupat Hlth, B-1050 Brussels, Belgium.
   Jevtic, Marija, Univ Novi Sad, Fac Med, Novi Sad 21000, Serbia.
   Jevtic, Marija, Inst Publ Hlth Vojvodina, Novi Sad 21000, Serbia.},
DOI = {10.3390/su14127480},
Article-Number = {7480},
EISSN = {2071-1050},
Keywords = {digital technologies; artificial intelligence; urban health},
Keywords-Plus = {SYSTEM},
Research-Areas = {Science \& Technology - Other Topics; Environmental Sciences \& Ecology},
Web-of-Science-Categories  = {Green \& Sustainable Science \& Technology; Environmental Sciences;
   Environmental Studies},
Author-Email = {martina.sapienza01@icatt.it
   nurchismario@gmail.com
   mt.riccardi@gmail.com
   catherine.bouland@ulb.be
   marija.jevtic@uns.ac.rs
   gianfranco.damiani@unicatt.it},
Affiliations = {Catholic University of the Sacred Heart; IRCCS Policlinico Gemelli;
   Catholic University of the Sacred Heart; IRCCS Policlinico Gemelli;
   Universite Libre de Bruxelles; University of Novi Sad},
ResearcherID-Numbers = {Damiani, Gianfranco/K-2782-2016},
ORCID-Numbers = {Riccardi, Maria Teresa/0000-0003-3506-922X
   Nurchis, Mario Cesare/0000-0002-9345-4292
   Damiani, Gianfranco/0000-0003-3028-6188},
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Number-of-Cited-References = {47},
Times-Cited = {0},
Usage-Count-Last-180-days = {8},
Usage-Count-Since-2013 = {13},
Journal-ISO = {Sustainability},
Doc-Delivery-Number = {2L1PI},
Web-of-Science-Index = {Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)},
Unique-ID = {WOS:000816793700001},
OA = {gold, Green Published},
DA = {2023-04-22},
}

@article{ WOS:000888573700001,
Author = {Liu, Jinping and Cho, Hyo-Seob and Osman, Sazali and Jeong, Hyeon-Gyo
   and Lee, Kwonmin},
Title = {Review of the status of urban flood monitoring and forecasting in TC
   region},
Journal = {TROPICAL CYCLONE RESEARCH AND REVIEW},
Year = {2022},
Volume = {11},
Number = {2},
Pages = {103-119},
Month = {JUN},
Abstract = {With the impacts of rapid urbanization and climate change, the urban
   flood has increasingly become a major hazard risk faced by human being
   in recent decades. The catastrophic urban flood events appear every year
   in the world, especially in Asia and Pacific region due to its
   geographical composition, density population and un-even economic and
   social development. To reduce the urban flood risk and enhance the
   resilience of vulnerable communities, especially coastal communities,
   the Members of ESCAP/WMO Typhoon Committee (TC) have made their great
   efforts including engineering and non-engineering measures based on
   their different national conditions. As a key part of non-engineering
   measures, it is recognized that improving urban flood monitoring and
   forecasting is a measure with high benefit related to cost on urban
   flood risk reduction. In recent years, TC Members enhanced their
   capacity building on urban flood monitoring, forecasting and simulation,
   inundation mapping, etc. In order to enhance the technical cooperation
   and exchange on this aspect, Typhoon Committee Working Group on
   Hydrology (WGH) conducted two projects on ``Urban Flood Risk Management
   in Typhoon Committee Area (UFRM){''} and ``Operation System for Urban
   Flood Forecasting and Inundation Mapping (OSUFFIM){''} in the past
   years. This paper generally reviewed the situation and causes of urban
   flood in TC region; briefly summarized the progresses and shortages on
   urban flood monitoring and forecasting in TC Members; and initially
   discussed the areas to be enhanced in future for improvement of urban
   flood monitoring, forecasting and simulation, and inundation mapping
   with up-to-date development of weather radar and satellite monitoring,
   image-based monitoring, information technology (IT), Internet of Things
   (IoT), big data and artificial intelligence (AI).(c) 2022 The Shanghai
   Typhoon Institute of China Meteorological Administration. Publishing
   services by Elsevier B.V. on behalf of KeAi Communication Co. Ltd. This
   is an open access article under the CC BY-NC-ND license
   (http://creativecommons.org/licenses/by-nc-nd/4.0/).},
Publisher = {KEAI PUBLISHING LTD},
Address = {16 DONGHUANGCHENGGEN NORTH ST, BEIJING, DONGCHENG DISTRICT 100717,
   PEOPLES R CHINA},
Type = {Review},
Language = {English},
Affiliation = {Liu, JP (Corresponding Author), UNESCAP, WMO Typhoon Committe, Macau, Peoples R China.
   Liu, Jinping, UNESCAP, WMO Typhoon Committe, Macau, Peoples R China.
   Cho, Hyo-Seob; Jeong, Hyeon-Gyo, Minist Environm, Han River Flood Control Off, Seoul, South Korea.
   Osman, Sazali, Minist Nat Resources \& Water, Dept Irrigat \& Drainage, Putrajaya, Malaysia.
   Lee, Kwonmin, Ewha Womans Univ, Dept Climate \& Energy Syst Engn, Seoul, South Korea.},
DOI = {10.1016/j.tcrr.2022.07.001},
ISSN = {2225-6032},
Keywords = {Urban flood; Monitoring; Forecasting},
Research-Areas = {Meteorology \& Atmospheric Sciences},
Web-of-Science-Categories  = {Meteorology \& Atmospheric Sciences},
Author-Email = {jpliu@typhooncommitee.org},
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Times-Cited = {0},
Usage-Count-Last-180-days = {6},
Usage-Count-Since-2013 = {6},
Journal-ISO = {Trop. Cyclone Res. Rev.},
Doc-Delivery-Number = {6M0NF},
Web-of-Science-Index = {Emerging Sources Citation Index (ESCI)},
Unique-ID = {WOS:000888573700001},
OA = {gold},
DA = {2023-04-22},
}

@article{ WOS:000660769900001,
Author = {Kakderi, Christina and Oikonomaki, Eleni and Papadaki, Ilektra},
Title = {Smart and Resilient Urban Futures for Sustainability in the Post
   COVID-19 Era: A Review of Policy Responses on Urban Mobility},
Journal = {SUSTAINABILITY},
Year = {2021},
Volume = {13},
Number = {11},
Month = {JUN},
Abstract = {The COVID-19 pandemic has put lifestyles in question, changed daily
   routines, and limited citizen freedoms that seemed inalienable before. A
   human activity that has been greatly affected since the beginning of the
   health crisis is mobility. Focusing on mobility, we aim to discuss the
   transformational impact that the pandemic brought to this specific urban
   domain, especially with regards to the promotion of sustainability, the
   smart growth agenda, and the acceleration towards the smart city
   paradigm. We collect 60 initial policy responses related to urban
   mobility from cities around the world and analyze them based on the
   challenge they aim to address, the exact principles of smart growth and
   sustainable mobility that they encapsulate, as well as the level of ICT
   penetration. Our findings suggest that emerging strategies, although
   mainly temporary, are transformational, in line with the principles of
   smart growth and sustainable development. Most policy responses adopted
   during the first months of the pandemic, however, fail to leverage
   advancements made in the field of smart cities, and to adopt
   off-the-shelf solutions such as monitoring, alerting, and operations
   management.},
Publisher = {MDPI},
Address = {ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND},
Type = {Review},
Language = {English},
Affiliation = {Kakderi, C (Corresponding Author), Aristotle Univ Thessaloniki, URENIO Res, Thessaloniki 54124, Greece.
   Kakderi, Christina; Oikonomaki, Eleni; Papadaki, Ilektra, Aristotle Univ Thessaloniki, URENIO Res, Thessaloniki 54124, Greece.},
DOI = {10.3390/su13116486},
Article-Number = {6486},
EISSN = {2071-1050},
Keywords = {urban planning; COVID-19; urban mobility; sustainability; smart cities;
   smart growth; pandemic; resilience},
Keywords-Plus = {ARTIFICIAL-INTELLIGENCE; CITIES; TRANSPORT; AI},
Research-Areas = {Science \& Technology - Other Topics; Environmental Sciences \& Ecology},
Web-of-Science-Categories  = {Green \& Sustainable Science \& Technology; Environmental Sciences;
   Environmental Studies},
Author-Email = {kakderi@plandevel.auth.gr
   Eleni.Oikonomaki@urenio.org
   Ilektra.Papadaki@urenio.org},
Affiliations = {Aristotle University of Thessaloniki},
Funding-Acknowledgement = {European Union {[}823952]},
Funding-Text = {This research received funding from the European Union's Horizon 2020
   Marie SklodowskaCurie (MSCA-RISE-2018) project TREND-{''}Transition with
   Resilience for Evolutionary Development{''}. Grant agreement 823952.},
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Number-of-Cited-References = {70},
Times-Cited = {18},
Usage-Count-Last-180-days = {18},
Usage-Count-Since-2013 = {75},
Journal-ISO = {Sustainability},
Doc-Delivery-Number = {SR0XT},
Web-of-Science-Index = {Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)},
Unique-ID = {WOS:000660769900001},
OA = {gold, Green Submitted},
DA = {2023-04-22},
}

@article{ WOS:000880885400001,
Author = {Zhu, Shoupeng and Yang, Huadong and Liu, Duanyang and Wang, Hongbin and
   Zhou, Linyi and Zhu, Chengying and Zu, Fan and Wu, Hong and Lyu, Yang
   and Xia, Yu and Zhu, Yanhe and Fan, Yi and Zhang, Ling and Zhi, Xiefei},
Title = {Observations and Forecasts of Urban Transportation Meteorology in China:
   A Review},
Journal = {ATMOSPHERE},
Year = {2022},
Volume = {13},
Number = {11},
Month = {NOV},
Abstract = {Against the backdrop of intensified global warming, extreme weather
   events such as dense fog, low visibility, heavy precipitation, and
   extreme temperatures have been increased and enhanced to a great extent.
   They are likely to pose severe threats to the operation of urban
   transportation and associated services, which has drawn much attention
   in recent decades. However, there are still plenty of issues to be
   resolved in improving the emergency meteorological services and
   developing targeted urban transportation meteorological services in
   modern cities. The present review briefly illustrates the current
   cutting-edge developments and trends in the field of urban
   transportation meteorology in China, including the establishment of
   observation networks and experiments and the development of early
   warning and prediction technologies, as well as the related
   meteorological commercial services. Meanwhile, reflections and
   discussions are provided in terms of the state-of-the-art observation
   channels and methods and the application of numerical model forecasts
   and artificial intelligence. With the advantages of various advanced
   technologies from multiple aspects, researchers could further expand
   explorations on urban transportation meteorological observations,
   forecasts, early warnings, and services. Associated theoretical studies
   and practical investigations are also to be carried out to provide solid
   scientific foundations for urban transportation disaster prevention and
   mitigation, for implementing the action of meteorological guarantees,
   and for the construction of a high-quality smart society.},
Publisher = {MDPI},
Address = {ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND},
Type = {Review},
Language = {English},
Affiliation = {Yang, HD; Liu, DY (Corresponding Author), China Meteorol Adm, Key Lab Transportat Meteorol, Nanjing Joint Inst Atmospher Sci, Nanjing 210041, Peoples R China.
   Zhu, Shoupeng; Yang, Huadong; Liu, Duanyang; Wang, Hongbin; Zhou, Linyi; Zhu, Chengying; Zu, Fan; Wu, Hong, China Meteorol Adm, Key Lab Transportat Meteorol, Nanjing Joint Inst Atmospher Sci, Nanjing 210041, Peoples R China.
   Lyu, Yang; Zhu, Yanhe; Fan, Yi; Zhang, Ling; Zhi, Xiefei, Nanjing Univ Informat Sci Technol, Collaborat Innovat Ctr Forecast \& Evaluat Meteoro, Key Lab Meteorol Disaster, Minist Educ, Nanjing 210044, Peoples R China.
   Xia, Yu, China Meteorol Adm, Inst Urban Meteorol, Beijing 100089, Peoples R China.},
DOI = {10.3390/atmos13111823},
Article-Number = {1823},
EISSN = {2073-4433},
Keywords = {urban meteorology; transportation meteorology; observation; forecast;
   early warning; review; China},
Keywords-Plus = {AIRCRAFT SYSTEMS UASS; BOUNDARY-LAYER; AIR-POLLUTION; WEATHER;
   ENVIRONMENT; TEMPERATURE; CHALLENGES; TURBULENCE; INTERNET; PROGRESS},
Research-Areas = {Environmental Sciences \& Ecology; Meteorology \& Atmospheric Sciences},
Web-of-Science-Categories  = {Environmental Sciences; Meteorology \& Atmospheric Sciences},
Author-Email = {huadonyang@hotmail.com
   liuduanyang@cma.gov.cn},
Affiliations = {China Meteorological Administration; Nanjing University of Information
   Science \& Technology; China Meteorological Administration},
ResearcherID-Numbers = {Zhi, Xiefei/AGU-6880-2022
   Yang, Huadong/HJG-6479-2022
   },
ORCID-Numbers = {Zhi, Xiefei/0000-0003-4414-0497
   ZU, FAN/0000-0003-3627-273X},
Funding-Acknowledgement = {Basic Research Fund of the Chinese Academy of Meteorological Sciences
   {[}2022Y027, 2021Y011]; Jiangsu Meteorological Bureau {[}KQ202209,
   KQ202114]; 333 Project of Jiangsu Province {[}BRA2018420]; Beijing
   Foundation of NJIAS {[}BJG201906, BJG202104, BJG202209]; General Program
   of Key Science and Technology in Transportation, Ministry of Transport
   {[}2018-MS4-102, ZL-2018-04]},
Funding-Text = {This research was jointly funded by the Basic Research Fund of the
   Chinese Academy of Meteorological Sciences (Grant Nos. 2022Y027 and
   2021Y011), the research project of the Jiangsu Meteorological Bureau
   (Grant Nos. KQ202209 and KQ202114), the 333 Project of Jiangsu Province
   (Grant No. BRA2018420), the Beijing Foundation of NJIAS (Grant Nos.
   BJG201906, BJG202104 and BJG202209) and the General Program of Key
   Science and Technology in Transportation, the Ministry of Transport
   (Grant Nos. 2018-MS4-102 and ZL-2018-04).},
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Number-of-Cited-References = {131},
Times-Cited = {1},
Usage-Count-Last-180-days = {8},
Usage-Count-Since-2013 = {8},
Journal-ISO = {Atmosphere},
Doc-Delivery-Number = {6A8FV},
Web-of-Science-Index = {Science Citation Index Expanded (SCI-EXPANDED)},
Unique-ID = {WOS:000880885400001},
OA = {gold},
DA = {2023-04-22},
}

@article{ WOS:000790501700002,
Author = {Piadeh, Farzad and Behzadian, Kourosh and Alani, Amir M.},
Title = {A critical review of real-time modelling of flood forecasting in urban
   drainage systems},
Journal = {JOURNAL OF HYDROLOGY},
Year = {2022},
Volume = {607},
Month = {APR},
Abstract = {There has been a strong tendency in recent decades to develop real-time
   urban flood prediction models for early warning to the public due to a
   large number of worldwide urban flood occurrences and their disastrous
   consequences. While a significant breakthrough has been made so far,
   there are still some potential knowledge gaps that need further
   investigation. This paper presents a comprehensive review of the current
   state-of-the-art and future trends of real-time modelling of flood
   forecasting in urban drainage systems. Findings showed that the
   combination of various real-time sources of rainfall measurement and the
   inclusion of other real-time data such as soil moisture, wind flow
   patterns, evaporation, fluvial flow and infiltration should be more
   investigated in real-time flood forecasting models. Additionally,
   artificial intelligence is also present in most of the new RTFF models
   in UDS and consequently further developments of this technique are
   expected to appear in future works.},
Publisher = {ELSEVIER},
Address = {RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS},
Type = {Review},
Language = {English},
Affiliation = {Behzadian, K (Corresponding Author), Univ West London, Sch Comp \& Engn, St Marys Rd, London W5 5RF, England.
   Piadeh, Farzad; Behzadian, Kourosh; Alani, Amir M., Univ West London, Sch Comp \& Engn, St Marys Rd, London W5 5RF, England.},
DOI = {10.1016/j.jhydrol.2022.127476},
EarlyAccessDate = {FEB 2022},
Article-Number = {127476},
ISSN = {0022-1694},
EISSN = {1879-2707},
Keywords = {Artificial intelligence-based models; Data -driven models; Real -time
   flood forecasting; Urban drainage systems; Urban flood},
Keywords-Plus = {ARTIFICIAL NEURAL-NETWORKS; RAINFALL DATA; OPTIMIZATION TECHNIQUES;
   PRECIPITATION PRODUCTS; DATA-DRIVEN; RADAR DATA; GAUGE; RESOLUTION;
   URBANIZATION; COMBINATION},
Research-Areas = {Engineering; Geology; Water Resources},
Web-of-Science-Categories  = {Engineering, Civil; Geosciences, Multidisciplinary; Water Resources},
Author-Email = {kourosh.behzadian@uwl.ac.uk
   amir.alani@uwl.ac.uk},
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Number-of-Cited-References = {120},
Times-Cited = {13},
Usage-Count-Last-180-days = {53},
Usage-Count-Since-2013 = {83},
Journal-ISO = {J. Hydrol.},
Doc-Delivery-Number = {0Y6LZ},
Web-of-Science-Index = {Science Citation Index Expanded (SCI-EXPANDED)},
Unique-ID = {WOS:000790501700002},
OA = {Green Accepted, hybrid},
DA = {2023-04-22},
}

@article{ WOS:000482939700011,
Author = {Wagner, Magdalena and de Vries, Walter Timo},
Title = {Comparative Review of Methods Supporting Decision-Making in Urban
   Development and Land Management},
Journal = {LAND},
Year = {2019},
Volume = {8},
Number = {8},
Month = {AUG},
Abstract = {This paper discusses how and where technologies supporting
   decision-making can play, or are already playing, a role in both urban
   development and land management. The review analyzes and compares three
   types of technologies: cellular automata (CA), artificial intelligence
   (AI), and operational research (OR), and evaluates to which extent they
   can be useful when dealing with various land planning objectives and
   phases. CA is one of the most useful models for simulating urban growth,
   AI displays great potential as a solution to capture the dynamics of
   land change, and OR is useful in decision-making, for example to conduct
   locational analyses. The evaluation relies on a collection of relevant
   articles, selected on the basis of both content and actuality. The paper
   offers new perspectives towards innovative methods in urban planning and
   land management and highlights where and when which type of tool can be
   considered useful and valid. The existing gaps, i.e., phases or areas in
   spatial planning or land management where the methods have not been
   applied, are also discussed.},
Publisher = {MDPI},
Address = {ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND},
Type = {Review},
Language = {English},
Affiliation = {Wagner, M (Corresponding Author), Tech Univ Munich, TUM Dept Civil Geo \& Environm Engn, D-80333 Munich, Germany.
   Wagner, Magdalena; de Vries, Walter Timo, Tech Univ Munich, TUM Dept Civil Geo \& Environm Engn, D-80333 Munich, Germany.},
DOI = {10.3390/land8080123},
Article-Number = {123},
EISSN = {2073-445X},
Keywords = {cellular automata; artificial intelligence; operational research; land
   management; urban development},
Keywords-Plus = {CELLULAR-AUTOMATA; ARTIFICIAL-INTELLIGENCE; GROWTH; MODEL; SIMULATION;
   PREDICTION; DYNAMICS; NETWORK; AREA; GIS},
Research-Areas = {Environmental Sciences \& Ecology},
Web-of-Science-Categories  = {Environmental Studies},
Author-Email = {m.wagner@o2.pl},
Affiliations = {Technical University of Munich},
ResearcherID-Numbers = {de Vries, Walter Timo/I-7765-2019
   },
ORCID-Numbers = {de Vries, Walter Timo/0000-0002-1942-4714
   Wagner, Magdalena/0000-0002-9919-8728},
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Number-of-Cited-References = {77},
Times-Cited = {10},
Usage-Count-Last-180-days = {4},
Usage-Count-Since-2013 = {17},
Journal-ISO = {Land},
Doc-Delivery-Number = {IT5WT},
Web-of-Science-Index = {Social Science Citation Index (SSCI)},
Unique-ID = {WOS:000482939700011},
OA = {Green Published, gold},
DA = {2023-04-22},
}

@article{ WOS:000406817800033,
Author = {Hawas, Mohamed A.},
Title = {Are We Intentionally Limiting Urban Planning and Intelligence? A Causal
   Evaluative Review and Methodical Redirection for Intelligence Systems},
Journal = {IEEE ACCESS},
Year = {2017},
Volume = {5},
Pages = {13253-13259},
Abstract = {The chronic growth of networked complexities in today's world, now
   require highly efficient evolvable systems. However, diverse open issues
   and inabilities are facing urban planning practice and social sciences
   due to the limitations of artificial intelligence planning tools. These
   incapacities have relatively limited our ability to perceive and handle
   possible present and future temperamental situations in socio-physical
   contexts and in real- time modes. Here, we theoretically present two
   simple philosophical and systematic causal models to help software
   engineers to understand this philosophical and complexity dilemma from
   an urban planning perspective. The first model evaluates the reliance on
   perceptual and bounding trajectories. It discusses discrete and
   finite-expert systems that perceive specific parts of
   self-organization's complexities, while bounding limited facets only of
   general intelligence to address certain issues in urban planning and
   social contexts. This implies the second causal model that is based on
   aligning to urban self-organizational happenings, by putting
   philosophical foundations for a responsive artificial superintelligence
   (ASI). This proposed ASI is based on connecting between complex adaptive
   systems in our contexts by open-endedly hosting and operating in finite
   expert systems to refiect different fields and functions, toward
   asymptotic in finite intellectual capacity.},
Publisher = {IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC},
Address = {445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA},
Type = {Review},
Language = {English},
Affiliation = {Hawas, MA (Corresponding Author), Cairo Univ, Fac Engn, Dept Architecture, Urban Design \& Community Dev Sect, Giza 12613, Egypt.
   Hawas, Mohamed A., Cairo Univ, Fac Engn, Dept Architecture, Urban Design \& Community Dev Sect, Giza 12613, Egypt.},
DOI = {10.1109/ACCESS.2017.2725138},
ISSN = {2169-3536},
Keywords = {Philosophical considerations; artificial intelligence; artificial
   superintelligence; urban planning; complexity theory; causal evaluation;
   expert systems},
Keywords-Plus = {SELF-ORGANIZATION; COMPLEXITY; MODEL; SUITABILITY; SIMULATION; CITIES;
   LAND; LIFE},
Research-Areas = {Computer Science; Engineering; Telecommunications},
Web-of-Science-Categories  = {Computer Science, Information Systems; Engineering, Electrical \&
   Electronic; Telecommunications},
Author-Email = {m\_hawas@live.eom},
Affiliations = {Egyptian Knowledge Bank (EKB); Cairo University},
ORCID-Numbers = {Hawas, Mohamed/0000-0003-0574-1177},
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Number-of-Cited-References = {57},
Times-Cited = {2},
Usage-Count-Last-180-days = {7},
Usage-Count-Since-2013 = {23},
Journal-ISO = {IEEE Access},
Doc-Delivery-Number = {FC4NY},
Web-of-Science-Index = {Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)},
Unique-ID = {WOS:000406817800033},
OA = {gold},
DA = {2023-04-22},
}

@article{ WOS:000831582400004,
Author = {Casali, Ylenia and Aydin, Nazli Yonca and Comes, Tina},
Title = {Machine learning for spatial analyses in urban areas: a scoping review},
Journal = {SUSTAINABLE CITIES AND SOCIETY},
Year = {2022},
Volume = {85},
Month = {OCT},
Abstract = {The challenges for sustainable cities to protect the environment, ensure
   economic growth, and maintain social justice have been widely
   recognized. Along with the digitization, availability of large datasets,
   Machine Learning (ML) and Artificial Intelligence (AI) are promising to
   revolutionize the way we analyze and plan urban areas, opening new
   opportunities for the sustainable city agenda. Especially urban spatial
   planning problems can benefit from ML approaches, leading to an
   increasing number of ML publications across different domains. What is
   missing is an overview of the most prominent domains in spatial urban ML
   along with a mapping of specific applied approaches. This paper aims to
   address this gap and guide researchers in the field of urban science and
   spatial data analysis to the most used methods and unexplored research
   gaps. We present a scoping review of ML studies that used geospatial
   data to analyze urban areas. Our review focuses on revealing the most
   prominent topics, data sources, ML methods and approaches to parameter
   selection. Furthermore, we determine the most prominent patterns and
   challenges in the use of ML. Through our analysis, we identify knowledge
   gaps in ML methods for spatial data science and data specifications to
   guide future research.},
Publisher = {ELSEVIER},
Address = {RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS},
Type = {Review},
Language = {English},
Affiliation = {Casali, Y (Corresponding Author), Delft Univ Technol, Fac Technol Policy \& Management, Bldg 31,Jaffalaan 5, NL-2628 BX Delft, Netherlands.
   Casali, Ylenia; Aydin, Nazli Yonca; Comes, Tina, Delft Univ Technol, Fac Technol Policy \& Management, Bldg 31,Jaffalaan 5, NL-2628 BX Delft, Netherlands.},
DOI = {10.1016/j.scs.2022.104050},
EarlyAccessDate = {JUL 2022},
Article-Number = {104050},
ISSN = {2210-6707},
EISSN = {2210-6715},
Keywords = {Machine learning; Urban areas; Review; Spatial analyses; Geospatial data},
Keywords-Plus = {WATER DISTRIBUTION NETWORKS; LAND-USE; DATA ANALYTICS; PRINCIPAL
   COMPONENT; NEURAL-NETWORKS; ENERGY USE; SCALE; MODEL; GIS; ENVIRONMENT},
Research-Areas = {Construction \& Building Technology; Science \& Technology - Other
   Topics; Energy \& Fuels},
Web-of-Science-Categories  = {Construction \& Building Technology; Green \& Sustainable Science \&
   Technology; Energy \& Fuels},
Author-Email = {ylenia.casali@gmail.com},
Affiliations = {Delft University of Technology},
ResearcherID-Numbers = {Comes, Tina/G-2076-2016},
ORCID-Numbers = {Comes, Tina/0000-0002-8721-8314},
Funding-Acknowledgement = {Delft University of Technology (TU Delft)},
Funding-Text = {This work was supported by the Delft University of Technology (TU Delft)
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Number-of-Cited-References = {217},
Times-Cited = {3},
Usage-Count-Last-180-days = {42},
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Journal-ISO = {Sust. Cities Soc.},
Doc-Delivery-Number = {3G8EY},
Web-of-Science-Index = {Science Citation Index Expanded (SCI-EXPANDED)},
Unique-ID = {WOS:000831582400004},
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@article{ WOS:000652901900001,
Author = {Feng, Tian and Fan, Feiyi and Bednarz, Tomasz},
Title = {A review of computer graphics approaches to urban modeling from a
   machine learning perspective},
Journal = {FRONTIERS OF INFORMATION TECHNOLOGY \& ELECTRONIC ENGINEERING},
Year = {2021},
Volume = {22},
Number = {7},
Pages = {915-925},
Month = {JUL},
Abstract = {Urban modeling facilitates the generation of virtual environments for
   various scenarios about cities. It requires expertise and consideration,
   and therefore consumes massive time and computation resources.
   Nevertheless, related tasks sometimes result in dissatisfaction or even
   failure. These challenges have received significant attention from
   researchers in the area of computer graphics. Meanwhile, the burgeoning
   development of artificial intelligence motivates people to exploit
   machine learning, and hence improves the conventional solutions. In this
   paper, we present a review of approaches to urban modeling in computer
   graphics using machine learning in the literature published between 2010
   and 2019. This serves as an overview of the current state of research on
   urban modeling from a machine learning perspective.},
Publisher = {ZHEJIANG UNIV PRESS},
Address = {Xixi Campus, Zhejiang University, No. 148 Tianmushan Road, Hangzhou,
   Zhejiang, PEOPLES R CHINA},
Type = {Review},
Language = {English},
Affiliation = {Feng, T (Corresponding Author), La Trobe Univ, Dept Comp Sci \& Informat Technol, Bundoora, Vic 3086, Australia.
   Feng, Tian, La Trobe Univ, Dept Comp Sci \& Informat Technol, Bundoora, Vic 3086, Australia.
   Fan, Feiyi, Chinese Acad Sci, Inst Comp Technol, Beijing 100190, Peoples R China.
   Bednarz, Tomasz, Univ New South Wales, Expanded Percept \& Interact Ctr, Sydney, NSW 2021, Australia.
   Bednarz, Tomasz, CSIROs Data61, Sydney, NSW 2015, Australia.},
DOI = {10.1631/FITEE.2000141},
EarlyAccessDate = {MAY 2021},
ISSN = {2095-9184},
EISSN = {2095-9230},
Keywords = {Urban modeling; Computer graphics; Machine learning; Deep learning;
   TP399},
Keywords-Plus = {NEURAL-NETWORK; DESIGN},
Research-Areas = {Computer Science; Engineering},
Web-of-Science-Categories  = {Computer Science, Information Systems; Computer Science, Software
   Engineering; Engineering, Electrical \& Electronic},
Author-Email = {t.feng@latrobe.edu.au},
Affiliations = {La Trobe University; Chinese Academy of Sciences; Institute of Computing
   Technology, CAS; University of New South Wales Sydney; Commonwealth
   Scientific \& Industrial Research Organisation (CSIRO)},
ResearcherID-Numbers = {Bednarz, Tomasz/A-7376-2011},
ORCID-Numbers = {Bednarz, Tomasz/0000-0001-9240-0922},
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Number-of-Cited-References = {98},
Times-Cited = {4},
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Journal-ISO = {Front. Inform. Technol. Elect. Eng.},
Doc-Delivery-Number = {TQ9KH},
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Unique-ID = {WOS:000652901900001},
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@article{ WOS:000931227300001,
Author = {Yang, Liu and Iwami, Michiyo and Chen, Yishan and Wu, Mingbo and van
   Dam, Koen H.},
Title = {Computational decision-support tools for urban design to improve
   resilience against COVID-19 and other infectious diseases: A systematic
   review},
Journal = {PROGRESS IN PLANNING},
Year = {2023},
Volume = {168},
Month = {FEB},
Abstract = {The COVID-19 pandemic highlighted the need for decision-support tools to
   help cities become more resilient to infectious diseases. Through urban
   design and planning, non-pharmaceutical interventions can be enabled,
   impelling behaviour change and facilitating the construction of lower
   risk buildings and public spaces. Computational tools, including
   computer simulation, statistical models, and artificial intelligence,
   have been used to support responses to the current pandemic as well as
   to the spread of previous infectious diseases. Our multidisciplinary
   research group systematically reviewed state-of-the-art literature to
   propose a toolkit that employs computational modelling for various
   interventions and urban design processes. We selected 109 out of 8,737
   studies retrieved from databases and analysed them based on the pathogen
   type, transmission mode and phase, design intervention and process, as
   well as modelling methodology (method, goal, motivation, focus, and
   indication to urban design). We also explored the relationship between
   infectious disease and urban design, as well as computational modelling
   support, including specific models and parameters. The proposed toolkit
   will help designers, planners, and computer modellers to select relevant
   approaches for evaluating design decisions depending on the target
   disease, geographic context, design stages, and spatial and temporal
   scales. The findings herein can be regarded as stand-alone tools,
   particularly for fighting against COVID-19, or be incorporated into
   broader frameworks to help cities become more resilient to future
   disasters.},
Publisher = {PERGAMON-ELSEVIER SCIENCE LTD},
Address = {THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND},
Type = {Review},
Language = {English},
Affiliation = {Yang, L (Corresponding Author), Southeast Univ, Sch Architecture, Si Pai Lou 2, Nanjing 210096, Jiangsu, Peoples R China.
   Yang, Liu, Southeast Univ, Sch Architecture, Nanjing, Peoples R China.
   Yang, Liu, Southeast Univ, Res Ctr Urban Design, Nanjing, Peoples R China.
   Iwami, Michiyo, Imperial Coll London, Fac Med, Dept Infect Dis, London, England.
   Chen, Yishan, China IPPR Int Engn CO LTD, Architecture \& Urban Design Res Ctr, Beijing, Peoples R China.
   Wu, Mingbo, Chinese Acad Sci, Inst Geog Sci \& Nat Resources Res, State Key Lab Resources \& Environm Informat Syst, Beijing, Peoples R China.
   Wu, Mingbo, Univ Chinese Acad Sci, Beijing, Peoples R China.
   van Dam, Koen H., Imperial Coll London, Ctr Proc Syst Engn, Dept Chem Engn, London, England.
   Yang, Liu, Southeast Univ, Sch Architecture, Si Pai Lou 2, Nanjing 210096, Jiangsu, Peoples R China.},
DOI = {10.1016/j.progress.2022.100657},
EarlyAccessDate = {FEB 2023},
Article-Number = {100657},
ISSN = {0305-9006},
EISSN = {1873-4510},
Keywords = {Urban design; Urban planning; Decision -support tool; COVID-19;
   Infectious disease; Computer modelling; Resilience},
Keywords-Plus = {VIRUS TRANSMISSION; EPIDEMIC; NETWORK; CITIES; FLATS; MODEL},
Research-Areas = {Environmental Sciences \& Ecology; Public Administration},
Web-of-Science-Categories  = {Environmental Studies; Regional \& Urban Planning},
Author-Email = {yangliu2020@seu.edu.cn},
Affiliations = {Southeast University - China; Southeast University - China; Imperial
   College London; Chinese Academy of Sciences; Institute of Geographic
   Sciences \& Natural Resources Research, CAS; Chinese Academy of
   Sciences; University of Chinese Academy of Sciences, CAS; Imperial
   College London; Southeast University - China},
Funding-Acknowledgement = {National Natural Science Foundation China {[}5210081743]; Natural
   Science Foundation of Jiangsu Province {[}BK20210260]; China
   Postdoctoral Science Foundation {[}2021M690612]; Fundamental Research
   Funds for the Central Universities {[}2242021R20003]},
Funding-Text = {Acknowledgments Dr Liu Yang is supported by the National Natural Science
   Foundation China (No. 5210081743) , the Natural Science Foundation of
   Jiangsu Province (No. BK20210260) , the China Postdoctoral Science
   Foundation (No. 2021M690612) , and the Fundamental Research Funds for
   the Central Universities (No. 2242021R20003) . Dr Koen H van Dam works
   on Climate Compatible Growth (CCG) , a ?38 m UK ODA-funded research
   programme. The authors thank Prof Jianguo Wang (South-east University,
   China) and Dr Bani Anvari (University College London, UK) for giving
   insightful suggestions and research ideas for the project. The search
   terms of this review were refined through test runs of searching in
   October/November 2020 with assistance from two library managers (Rebecca
   S Jones and Georgina Wildman) at Imperial College London. We benefited
   with the support from Dr M. Trent Herdman (Medical Entomology and
   Zoonoses Ecology, UK Health Security Agency) at the revision stage.},
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Number-of-Cited-References = {172},
Times-Cited = {2},
Usage-Count-Last-180-days = {24},
Usage-Count-Since-2013 = {35},
Journal-ISO = {Prog. Plan.},
Doc-Delivery-Number = {8W3HB},
Web-of-Science-Index = {Social Science Citation Index (SSCI)},
Unique-ID = {WOS:000931227300001},
OA = {Green Published, Bronze},
DA = {2023-04-22},
}

@article{ WOS:000887624400001,
Author = {Bhuyan, Bikram Pratim and Tomar, Ravi and Cherif, Amar Ramdane},
Title = {A Systematic Review of Knowledge Representation Techniques in Smart
   Agriculture (Urban)},
Journal = {SUSTAINABILITY},
Year = {2022},
Volume = {14},
Number = {22},
Month = {NOV},
Abstract = {Urban agriculture is the practice of growing food inside the city
   limits. Due to the exponential amount of data generated by information
   and technology-based farm management systems, we need proper methods to
   represent the data. The branch of artificial intelligence known as
   ``knowledge representation and reasoning{''} is devoted to the
   representation of information about the environment in a way where a
   computer system can utilise it to accomplish difficult problems. This
   research is an extensive survey of the knowledge representation
   techniques used in smart agriculture, and specifically in the urban
   agricultural domain. Relevant articles on the knowledge base are
   extracted from the retrieved set to study the fulfillment of the
   criteria of the system. Various interesting findings were observed after
   the review. Spatial-temporal characteristics were rarely approached. A
   generalised representation technique to include all domains in
   agriculture is another issue. Finally, proper validation technique is
   found to be missing in such an ontology.},
Publisher = {MDPI},
Address = {ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND},
Type = {Review},
Language = {English},
Affiliation = {Bhuyan, BP (Corresponding Author), Univ Petr \& Energy Studies, Sch Comp Sci, Dehra Dun 248006, Uttarakhand, India.
   Bhuyan, BP (Corresponding Author), Univ Paris Saclay, LISV Lab, 10-12 Ave Europe, F-78140 Velizy Villacoublay, France.
   Bhuyan, Bikram Pratim, Univ Petr \& Energy Studies, Sch Comp Sci, Dehra Dun 248006, Uttarakhand, India.
   Bhuyan, Bikram Pratim; Cherif, Amar Ramdane, Univ Paris Saclay, LISV Lab, 10-12 Ave Europe, F-78140 Velizy Villacoublay, France.
   Tomar, Ravi, Persistent Syst, Pune 411016, Maharashtra, India.},
DOI = {10.3390/su142215249},
Article-Number = {15249},
EISSN = {2071-1050},
Keywords = {knowledge representation; sustainable agriculture; smart urban
   agriculture; ontology; Semantic Web; artificial intelligence in
   agriculture},
Keywords-Plus = {PRECISION AGRICULTURE; ONTOLOGY; INTERNET; MODEL; IOT},
Research-Areas = {Science \& Technology - Other Topics; Environmental Sciences \& Ecology},
Web-of-Science-Categories  = {Green \& Sustainable Science \& Technology; Environmental Sciences;
   Environmental Studies},
Author-Email = {bikram23bhuyan@gmail.com},
Affiliations = {University of Petroleum \& Energy Studies (UPES); UDICE-French Research
   Universities; Universite Paris Saclay},
ResearcherID-Numbers = {Bhuyan, Bikram Pratim/AAT-5369-2020
   Tomar, Ravi/Q-5206-2019
   },
ORCID-Numbers = {Tomar, Ravi/0000-0002-8957-6756
   Amar, RAMDANE-CHERIF/0000-0001-8289-747X
   Bhuyan, Bikram Pratim/0000-0001-5373-8912},
Funding-Acknowledgement = {IDEX ParisSaclay {[}ANR-11-IDEX-0003-02]},
Funding-Text = {This work is supported by the ``ADI 2022{''} project funded by the IDEX
   ParisSaclay, ANR-11-IDEX-0003-02.},
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Number-of-Cited-References = {142},
Times-Cited = {2},
Usage-Count-Last-180-days = {7},
Usage-Count-Since-2013 = {7},
Journal-ISO = {Sustainability},
Doc-Delivery-Number = {6K6QQ},
Web-of-Science-Index = {Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)},
Unique-ID = {WOS:000887624400001},
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}

@article{ WOS:000701987300003,
Author = {Kaginalkar, Akshara and Kumar, Shamita and Gargava, Prashant and Niyogi,
   Dev},
Title = {Review of urban computing in air quality management as smart city
   service: An integrated IoT, AI, and cloud technology perspective},
Journal = {URBAN CLIMATE},
Year = {2021},
Volume = {39},
Month = {SEP},
Abstract = {Cities foster economic growth. However, growing cities also contribute
   to air pollution and climate change. The paper provides a perspective
   regarding the opportunity available in addressing the urban air quality
   management (UAQM) issues using smart city framework in the context of
   `urban computing'. Traditionally, UAQM has been built on sparse
   regulatory monitoring, enhanced with satellite data and forecast models.
   The `Fourth Industrial Revolution' (4IR) technologies such as Internet
   of Things (IoT), big data, artificial intelligence, smartphones, social
   and cloud computing are reshaping urban conglomerates, worldwide. Cities
   can harness these ubiquitous technologies in concert with traditional
   methods for betterment of air quality governance and to improve quality
   of life. This paper discusses the role of urban computing in UAQM
   through a review of scientific publications and `grey literature' from
   technical reports of governments, international organizations and
   institutional websites. It provides an interdisciplinary knowledge
   repository on urban computing applications for air quality functions. It
   highlights the potential of integrated technologies in enabling data
   driven, strategic and real-time mitigation governance actions and
   helping citizens to take informed decisions. It recommends `fit for the
   purpose' multitechnology framework for UAQM services in emerging smart
   cities.},
Publisher = {ELSEVIER},
Address = {RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS},
Type = {Review},
Language = {English},
Affiliation = {Kaginalkar, A (Corresponding Author), Ctr Dev Adv Comp, Pune 411008, Maharashtra, India.
   Kaginalkar, Akshara, Ctr Dev Adv Comp, Pune 411008, Maharashtra, India.
   Kaginalkar, Akshara; Kumar, Shamita, Bharati Vidyapeeth Deemed Univ, Inst Environm Educ \& Res, Pune, Maharashtra, India.
   Gargava, Prashant, Parivesh Bhawan, India Cent Pollut Control Board, New Delhi 110032, India.
   Niyogi, Dev, Indian Inst Technol Roorkee, Ctr Excellence Disaster Mitigat \& Management, Roorkee 247667, Uttarakhand, India.
   Niyogi, Dev, Univ Texas Austin, Dept Geol Sci, Jackson Sch Geosci, Austin, TX 78712 USA.
   Niyogi, Dev, Univ Texas Austin, Cockrell Sch Engn, Dept Civil Architectural \& Environm Engn, Austin, TX 78712 USA.},
DOI = {10.1016/j.uclim.2021.100972},
EarlyAccessDate = {SEP 2021},
Article-Number = {100972},
ISSN = {2212-0955},
Keywords = {Air pollution; ICT; Urban computing; 4 IR technologies; AI; ML; Internet
   of things (IoT); Big data; Social media},
Keywords-Plus = {PARTICULATE MATTER; BIG DATA; POLLUTION EXPOSURE; SENSOR NETWORK;
   PERSONAL EXPOSURE; SPATIAL VARIATION; NEXT-GENERATION; CARBON-MONOXIDE;
   HYBRID MODEL; CFD MODEL},
Research-Areas = {Environmental Sciences \& Ecology; Meteorology \& Atmospheric Sciences},
Web-of-Science-Categories  = {Environmental Sciences; Meteorology \& Atmospheric Sciences},
Author-Email = {akshara@cdac.in},
Affiliations = {Centre for Development of Advanced Computing (C-DAC); Bharati Vidyapeeth
   Deemed University; Indian Institute of Technology System (IIT System);
   Indian Institute of Technology (IIT) - Roorkee; University of Texas
   System; University of Texas Austin; University of Texas System;
   University of Texas Austin},
ResearcherID-Numbers = {Niyogi, Dev/H-6326-2013
   },
ORCID-Numbers = {Niyogi, Dev/0000-0002-1848-5080
   /0000-0001-7807-2280},
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Number-of-Cited-References = {305},
Times-Cited = {22},
Usage-Count-Last-180-days = {14},
Usage-Count-Since-2013 = {49},
Journal-ISO = {Urban CLim.},
Doc-Delivery-Number = {UZ1QW},
Web-of-Science-Index = {Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)},
Unique-ID = {WOS:000701987300003},
OA = {Bronze},
DA = {2023-04-22},
}

@article{ WOS:000754067600001,
Author = {Reja, R. K. and Amin, Ruhul and Tasneem, Zinat and Ali, Md Firoj and
   Islam, Md Robiul and Saha, Dip Kumar and Badal, Faisal Rahman and
   Ahamed, Md Hafiz and Moyeen, Sumaya Ishrat and Das, Sajal Kumar},
Title = {A review of the evaluation of urban wind resources: challenges and
   perspectives},
Journal = {ENERGY AND BUILDINGS},
Year = {2022},
Volume = {257},
Month = {FEB 15},
Abstract = {Wind energy in urban areas is a prominent alternative source of
   renewable energy that is becoming increasingly popular. The
   technological development, low cost of installation, and availability of
   wind make it a promising and essential renewable energy sector. Wind
   farms in urban settings, on the other hand, have yet to be
   commercialized in a meaningful way. In metropolitan areas, the urban
   wind becomes turbulent and unpredictable to some extent due to high-rise
   buildings and variations in their structures, railway tracks, and roads.
   Hence traditional wind mapping technologies have proven ineffec-tive in
   metropolitan areas, preventing its commercial use. This paper presents a
   thorough analysis of the most recent urban wind-resource evaluation
   techniques, covering multiple strategies, methodologies, software
   instruments, and the impact of site and building on the accurate
   installation of wind farms. Noise and vibrations, as well as aesthetic
   and shadow effects, ecological impact, high investment prices, and data
   availability, have all been considered, with potential solutions
   proposed. It has been found that in metropolitan settings, the
   traditional method of analyzing wind resources is hampered by the
   inability to collect data in a turbulent environment. As a result, this
   study predicts that artificial intelligence, such as machine learning,
   deep learning, and multiple neural networks, will dominate this industry
   in order to provide precise forecasts while minimizing the operational
   time and costs of the utilization system.},
Publisher = {ELSEVIER SCIENCE SA},
Address = {PO BOX 564, 1001 LAUSANNE, SWITZERLAND},
Type = {Review},
Language = {English},
Affiliation = {Reja, RK (Corresponding Author), Rajshahi Univ Engn \& Technol, Dept Mechatron Engn, Rajshahi, Bangladesh.
   Reja, R. K.; Amin, Ruhul; Tasneem, Zinat; Ali, Md Firoj; Islam, Md Robiul; Saha, Dip Kumar; Badal, Faisal Rahman; Ahamed, Md Hafiz; Moyeen, Sumaya Ishrat; Das, Sajal Kumar, Rajshahi Univ Engn \& Technol, Dept Mechatron Engn, Rajshahi, Bangladesh.},
DOI = {10.1016/j.enbuild.2021.111781},
Article-Number = {111781},
ISSN = {0378-7788},
EISSN = {1872-6178},
Keywords = {Urban wind; Site location; Wind speed mapping; Wind turbulence; Building
   structure},
Keywords-Plus = {ATMOSPHERIC BOUNDARY-LAYER; ENERGY-CONVERSION SYSTEMS; PEDESTRIAN-LEVEL
   WIND; ELECTRICITY-GENERATION; SOCIAL ACCEPTANCE; WEIBULL PARAMETERS;
   POWER-GENERATION; COMPLEX TERRAIN; ECONOMIC-FEASIBILITY; POTENTIAL
   ASSESSMENT},
Research-Areas = {Construction \& Building Technology; Energy \& Fuels; Engineering},
Web-of-Science-Categories  = {Construction \& Building Technology; Energy \& Fuels; Engineering, Civil},
Affiliations = {Rajshahi University of Engineering \& Technology (RUET)},
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Number-of-Cited-References = {212},
Times-Cited = {5},
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Usage-Count-Since-2013 = {34},
Journal-ISO = {Energy Build.},
Doc-Delivery-Number = {YX4HX},
Web-of-Science-Index = {Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)},
Unique-ID = {WOS:000754067600001},
DA = {2023-04-22},
}

@article{ WOS:000644016800001,
Author = {Severino, Alessandro and Curto, Salvatore and Barberi, Salvatore and
   Arena, Fabio and Pau, Giovanni},
Title = {Autonomous Vehicles: An Analysis Both on Their Distinctiveness and the
   Potential Impact on Urban Transport Systems},
Journal = {APPLIED SCIENCES-BASEL},
Year = {2021},
Volume = {11},
Number = {8},
Month = {APR},
Abstract = {Autonomous driving is a technological innovation that involves the use
   of Artificial Intelligence (AI) in the automotive area, representing the
   future of transport and whose applications will influence the concept of
   driving and many other features of modern society. Indeed, the
   introduction of Autonomous Vehicles (AVs) on the market, along with the
   development of related technologies, will have a potential impact not
   only on the automotive industry but also on urban transport systems. New
   mobility-related businesses will emerge, whereas existing ones will have
   to adapt to changes. There are various aspects that affect urban
   transport systems: in this work, we highlight how road markings,
   intersections, and pavement design upgradings have a key role for AVs
   operation. This work aims to describe how contemporary society will be
   influenced by Autonomous Vehicles' spread in regard to urban transport
   systems. A comprehensive analysis of the expected developments within
   urban transport systems is hereby presented, and some crucial issues
   concerning benefits and drawbacks are also discussed. From our studies,
   it emerges that the detection performed by vehicles is mainly affected
   by road markings characteristics, especially at intersections. Indeed,
   the need for a new cross-sections type arise, since vehicles wandering
   phenomena will be reduced due to AVs position-keeping systems.},
Publisher = {MDPI},
Address = {ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND},
Type = {Review},
Language = {English},
Affiliation = {Severino, A (Corresponding Author), Univ Catania, Dept Civil Engn \& Architecture, I-95123 Catania, Italy.
   Severino, Alessandro, Univ Catania, Dept Civil Engn \& Architecture, I-95123 Catania, Italy.
   Curto, Salvatore; Barberi, Salvatore; Arena, Fabio; Pau, Giovanni, Kore Univ Enna, Fac Engn \& Architecture, I-94100 Enna, Italy.},
DOI = {10.3390/app11083604},
Article-Number = {3604},
EISSN = {2076-3417},
Keywords = {autonomous vehicles; intelligent transportation system; road safety;
   urban transport system; artificial intelligence},
Research-Areas = {Chemistry; Engineering; Materials Science; Physics},
Web-of-Science-Categories  = {Chemistry, Multidisciplinary; Engineering, Multidisciplinary; Materials
   Science, Multidisciplinary; Physics, Applied},
Author-Email = {alessandro.severino@unict.it
   salvatore.curto@unikorestudent.it
   salvatore.barberi@unikore.it
   fabio.arena@unikore.it
   giovanni.pau@unikore.it},
Affiliations = {University of Catania; Universita Kore di ENNA},
ResearcherID-Numbers = {Pau, Giovanni/K-2944-2015
   },
ORCID-Numbers = {Pau, Giovanni/0000-0002-5798-398X
   Severino, Alessandro/0000-0003-0688-5113
   Arena, Fabio/0000-0002-6656-1797
   Curto, Salvatore/0000-0003-2517-0711},
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Number-of-Cited-References = {48},
Times-Cited = {16},
Usage-Count-Last-180-days = {8},
Usage-Count-Since-2013 = {40},
Journal-ISO = {Appl. Sci.-Basel},
Doc-Delivery-Number = {RS8IN},
Web-of-Science-Index = {Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)},
Unique-ID = {WOS:000644016800001},
OA = {Green Published, gold},
DA = {2023-04-22},
}

@article{ WOS:000791965300001,
Author = {Kaginalkar, Akshara and Kumar, Shamita and Gargava, Prashant and
   Kharkar, Neelesh and Niyogi, Dev},
Title = {SmartAirQ: A Big Data Governance Framework for Urban Air Quality
   Management in Smart Cities},
Journal = {FRONTIERS IN ENVIRONMENTAL SCIENCE},
Year = {2022},
Volume = {10},
Month = {APR 8},
Abstract = {Rapid urbanization across the world has put an enormous burden on our
   environment. Cities from developing countries, in particular, are
   experiencing high air pollution levels. To address this challenge, the
   new WHO global air quality guidelines and various nations are mandating
   cities to implement clean air measures. However, these implementations
   are largely hindered by limited observations, siloed city operations,
   absence of standard processes, inadequate outreach, and absence of
   collaborative urban air quality management (UAQM) governance. The world
   is experiencing transformative changes in the way we live. The 4th
   industrial revolution technologies of artificial intelligence, Internet
   of Things, big data, and cloud computing bridge gaps between physical,
   natural, and personal entities. Globally, smart cities are being
   promulgated on the premise that technologies and data aid in improving
   urban services. However, in many instances, the smart city programs and
   UAQM services may not be aligned, thereby constraining the cumulative
   advantage in building urban resilience. Considering the potential of
   these technologies as enablers of environmental sustainability, a
   conceptual urban computing framework ``SmartAirQ{''} for UAQM is
   designed. This interdisciplinary study outlines the SmartAirQ
   components: 1) data acquisition, 2) communication and aggregation, 3)
   data processing and management, 4) intelligence, 5) application service,
   6) high-performance computing- (HPC-) cloud, and 7) security. The
   framework has integrated science cloud and urban services aiding in
   translating scientific data into operations. It is a step toward
   collaborative, data-driven, and sustainable smart cities.},
Publisher = {FRONTIERS MEDIA SA},
Address = {AVENUE DU TRIBUNAL FEDERAL 34, LAUSANNE, CH-1015, SWITZERLAND},
Type = {Review},
Language = {English},
Affiliation = {Kaginalkar, A (Corresponding Author), Ctr Dev Adv Comp, Pune, India.
   Kaginalkar, A (Corresponding Author), BharatiV idyapeeth Deemed Univ, Inst Environm Educ \& Res, Pune, India.
   Kaginalkar, Akshara; Kharkar, Neelesh, Ctr Dev Adv Comp, Pune, India.
   Kaginalkar, Akshara; Kumar, Shamita, BharatiV idyapeeth Deemed Univ, Inst Environm Educ \& Res, Pune, India.
   Gargava, Prashant, Cent Pollut Control Board, New Delhi, India.
   Niyogi, Dev, Indian Inst Technol Roorkee, Ctr Excellence Disaster Mitigat \& Management, Roorkee, India.
   Niyogi, Dev, Univ Texas Austin, Jackson Sch Geosci, Dept Geol Sci, Austin, TX USA.
   Niyogi, Dev, Univ Texas Austin, Cockrell Sch Engn, Dept Civil Architectural \& Environm Engn, Austin, TX USA.},
DOI = {10.3389/fenvs.2022.785129},
Article-Number = {785129},
EISSN = {2296-665X},
Keywords = {urban computing; frontier technologies; data governance; AI; cloud
   computing; machine learning; smart cities; air pollution},
Keywords-Plus = {STREET VIEW CARS; ARTIFICIAL-INTELLIGENCE; PARTICULATE MATTER; DATA
   ANALYTICS; SOCIAL MEDIA; LAND-USE; EMISSIONS INVENTORY; LEARNING
   APPROACH; CITIZEN SCIENCE; NEURAL-NETWORK},
Research-Areas = {Environmental Sciences \& Ecology},
Web-of-Science-Categories  = {Environmental Sciences},
Author-Email = {akshara@cdac.in},
Affiliations = {Centre for Development of Advanced Computing (C-DAC); Central Pollution
   Control Board Delhi; Indian Institute of Technology System (IIT System);
   Indian Institute of Technology (IIT) - Roorkee; University of Texas
   System; University of Texas Austin; University of Texas System;
   University of Texas Austin},
ResearcherID-Numbers = {Niyogi, Dev/H-6326-2013
   },
ORCID-Numbers = {Niyogi, Dev/0000-0002-1848-5080
   /0000-0001-7807-2280},
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@article{ WOS:000909346600001,
Author = {Chitwatkulsiri, Detchphol and Miyamoto, Hitoshi},
Title = {Real-Time Urban Flood Forecasting Systems for Southeast Asia-A Review of
   Present Modelling and Its Future Prospects},
Journal = {WATER},
Year = {2023},
Volume = {15},
Number = {1},
Month = {JAN},
Abstract = {Many urban areas in tropical Southeast Asia, e.g., Bangkok in Thailand,
   have recently been experiencing unprecedentedly intense flash floods due
   to climate change. The rapid flood inundation has caused extremely
   severe damage to urban residents and social infrastructures. In
   addition, urban Southeast Asia usually has inadequate capacities in
   drainage systems, complicated land use patterns, and a large vulnerable
   population in limited urban areas. To reduce the urban flood risk and
   enhance the resilience of vulnerable urban communities, it has been of
   essential importance to develop real-time urban flood forecasting
   systems for flood disaster prevention authorities and the urban public.
   This paper reviewed the state-of-the-art models of real-time forecasting
   systems for urban flash floods. The real-time system basically consists
   of the following subsystems, i.e., rainfall forecasting, drainage system
   modelling, and inundation area mapping. This paper summarized the recent
   radar data utilization methods for rainfall forecasting,
   physical-process-based hydraulic models for flood inundation prediction,
   and data-driven artificial intelligence (AI) models for the real-time
   forecasting system. This paper also dealt with available technologies
   for modelling, e.g., digital surface models (DSMs) for the finer urban
   terrain of drainage systems. The review indicated that an obstacle to
   using process-based hydraulic models was the limited computational
   resources and shorter lead time for real-time forecasting in many urban
   areas in tropical Southeast Asia. The review further discussed the
   prospects of data-driven AI models for real-time forecasting systems.},
Publisher = {MDPI},
Address = {ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND},
Type = {Review},
Language = {English},
Affiliation = {Chitwatkulsiri, D (Corresponding Author), Shibaura Inst Technol, Dept Civil Engn, 3-7-5 Toyosu,Koto Ku, Tokyo 1358548, Japan.
   Chitwatkulsiri, Detchphol; Miyamoto, Hitoshi, Shibaura Inst Technol, Dept Civil Engn, 3-7-5 Toyosu,Koto Ku, Tokyo 1358548, Japan.},
DOI = {10.3390/w15010178},
Article-Number = {178},
EISSN = {2073-4441},
Keywords = {urban floods; real-time forecasting; methodology; physical-process-based
   models; artificial-intelligence-based models; regional implementation},
Keywords-Plus = {SPATIAL-RESOLUTION; DRAINAGE SYSTEMS; TIPPING-BUCKET; RADAR;
   PRECIPITATION; INUNDATION; GAUGE; SCALE; ROUGHNESS; IDENTIFICATION},
Research-Areas = {Environmental Sciences \& Ecology; Water Resources},
Web-of-Science-Categories  = {Environmental Sciences; Water Resources},
Author-Email = {na20109@shibaura-it.ac.jp},
Affiliations = {Shibaura Institute of Technology},
ORCID-Numbers = {Miyamoto, Hitoshi/0000-0003-3368-5715},
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@article{ WOS:000502885500038,
Author = {Ibrahim, Mohamed R. and Haworth, James and Cheng, Tao},
Title = {Understanding cities with machine eyes: A review of deep computer vision
   in urban analytics},
Journal = {CITIES},
Year = {2020},
Volume = {96},
Month = {JAN},
Abstract = {Modelling urban systems has interested planners and modellers for
   decades. Different models have been achieved relying on mathematics,
   cellular automation, complexity, and scaling. While most of these models
   tend to be a simplification of reality, today within the paradigm shifts
   of artificial intelligence across the different fields of science, the
   applications of computer vision show promising potential in
   understanding the realistic dynamics of cities. While cities are complex
   by nature, computer vision shows progress in tackling a variety of
   complex physical and non-physical visual tasks. In this article, we
   review the tasks and algorithms of computer vision and their
   applications in understanding cities. We attempt to subdivide computer
   vision algorithms into tasks, and cities into layers to show evidence of
   where computer vision is intensively applied and where further research
   is needed. We focus on highlighting the potential role of computer
   vision in understanding urban systems related to the built environment,
   natural environment, human interaction, transportation, and
   infrastructure. After showing the diversity of computer vision
   algorithms and applications, the challenges that remain in understanding
   the integration between these different layers of cities and their
   interactions with one another relying on deep learning and computer
   vision. We also show recommendations for practice and policy-making
   towards reaching Al-generated urban policies.},
Publisher = {ELSEVIER SCI LTD},
Address = {THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND},
Type = {Review},
Language = {English},
Affiliation = {Ibrahim, MR (Corresponding Author), UCL, Dept Civil Environm \& Geomat Engn, Room 1-02,Chadwick Bldg,Gower St, London WC1E 6BT, England.
   Ibrahim, Mohamed R.; Haworth, James; Cheng, Tao, UCL, Dept Civil, SpaceTimeLab, London, England.},
DOI = {10.1016/j.cities.2019.102481},
Article-Number = {102481},
ISSN = {0264-2751},
EISSN = {1873-6084},
Keywords = {Cities; Computer vision; Deep learning; Convolutional neural networks
   (CNN); Urban studies},
Keywords-Plus = {SEMANTIC SEGMENTATION; IMAGES; SCALE; SCENE; FRAMEWORK; TRACKING;
   NETWORK},
Research-Areas = {Urban Studies},
Web-of-Science-Categories  = {Urban Studies},
Author-Email = {mohamed.ibrahim.17@ucl.ac.uk},
Affiliations = {University of London; University College London},
ORCID-Numbers = {Ibrahim, Mohamed/0000-0001-7733-7777
   Cheng, Tao/0000-0002-5503-9813},
Funding-Acknowledgement = {UCL; Road Safety Trust {[}RST 38\_03\_2017]; EPSRC {[}EP/G023212/1,
   EP/J004197/1] Funding Source: UKRI},
Funding-Text = {This research outcome is a part of a PhD study for the first author at
   University College London (UCL). It was supported by funds from UCL
   Overseas Research Scholarship (ORS) and the Road Safety Trust (RST
   38\_03\_2017).},
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Journal-ISO = {Cities},
Doc-Delivery-Number = {JW2KB},
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Unique-ID = {WOS:000502885500038},
OA = {Green Published},
DA = {2023-04-22},
}

@article{ WOS:000580580900039,
Author = {Zhang, Junyi and He, Shenjing},
Title = {Smart technologies and urban life: A behavioral and social perspective},
Journal = {SUSTAINABLE CITIES AND SOCIETY},
Year = {2020},
Volume = {63},
Month = {DEC},
Abstract = {The explosive advancements of sensing technologies, Artificial
   Intelligence (AI), and Internet of Things (IoT) have given rise to
   various smart technologies that brought profound changes in various
   aspects of urban life. Such changes may suggest a paradigm shift in the
   ways of experiencing and researching urbanism. However, we still know
   very little about what to expect and how to cope with these changes
   based on scientific investigations from a behavioral and social
   perspective. To fill this void, this special issue ``Smart technologies
   and urban life: A behavioral and social perspective{''} presents a
   collection of ten papers covering four themes, namely smart
   technologies-generated big data and urban research, smart technologies
   and travel behavior, smart technologies and energy consumption, and
   smart technologies and urban life. Specific topics include the
   application of open source data and deep learning techniques, the
   development of smart street lighting system, the usage and social
   implications of smartphone applications, the ownership and application
   of emerging mobility services, household decisions on investments in
   renewable energy equipment, travel behavior, smartness and
   sustainability etc. Building upon findings from these studies, we
   speculate on the prospects and future applications of smart technologies
   and their implications for social and environmental sustainability.},
Publisher = {ELSEVIER},
Address = {RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS},
Type = {Review},
Language = {English},
Affiliation = {Zhang, JY (Corresponding Author), Hiroshima Univ, Mobil \& Urban Policy Lab, Grad Sch Int Dev \& Cooperat, Hiroshima, Japan.
   Zhang, Junyi, Hiroshima Univ, Mobil \& Urban Policy Lab, Grad Sch Int Dev \& Cooperat, Hiroshima, Japan.
   He, Shenjing, Univ Hong Kong, Fac Architecture, Dept Urban Planning \& Design, Hong Kong, Peoples R China.
   He, Shenjing, Univ Hong Kong, Fac Architecture, SIEW Lab, Hong Kong, Peoples R China.},
DOI = {10.1016/j.scs.2020.102460},
Article-Number = {102460},
ISSN = {2210-6707},
EISSN = {2210-6715},
Keywords = {Smart technologies; Smart cities; Life-oriented approach; Social
   exclusion; Sustainability; Big data},
Research-Areas = {Construction \& Building Technology; Science \& Technology - Other
   Topics; Energy \& Fuels},
Web-of-Science-Categories  = {Construction \& Building Technology; Green \& Sustainable Science \&
   Technology; Energy \& Fuels},
Author-Email = {zjy@hiroshima-u.ac.jp
   sjhe@hku.hk},
Affiliations = {Hiroshima University; University of Hong Kong; University of Hong Kong},
ResearcherID-Numbers = {Zhang, Junyi/HDN-7815-2022
   He, shenjing/AHC-5435-2022
   Zhang, Junyi/B-4865-2012},
ORCID-Numbers = {He, shenjing/0000-0001-5692-2088
   Zhang, Junyi/0000-0002-3267-542X},
Funding-Acknowledgement = {Japan Society for the Promotion of Science (JSPS) {[}18KT0007];
   Grants-in-Aid for Scientific Research {[}18KT0007] Funding Source: KAKEN},
Funding-Text = {This research was supported by a Grants-in-Aid for Scientific Research
   (B), Japan Society for the Promotion of Science (JSPS) {[}No. 18KT0007].},
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Number-of-Cited-References = {16},
Times-Cited = {11},
Usage-Count-Last-180-days = {9},
Usage-Count-Since-2013 = {55},
Journal-ISO = {Sust. Cities Soc.},
Doc-Delivery-Number = {OE5PA},
Web-of-Science-Index = {Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)},
Unique-ID = {WOS:000580580900039},
DA = {2023-04-22},
}

@article{ WOS:000699674500001,
Author = {Aldebei, Faisal and Dombi, Mihaly},
Title = {Mining the Built Environment: Telling the Story of Urban Mining},
Journal = {BUILDINGS},
Year = {2021},
Volume = {11},
Number = {9},
Month = {SEP},
Abstract = {Materials are continuously accumulating in the human-built environment
   since massive amounts of materials are required for building,
   developing, and maintaining cities. At the end of their life cycles,
   these materials are considered valuable sources of secondary materials.
   The increasing construction and demolition waste released from aging
   stock each year make up the heaviest, most voluminous waste outflow,
   presenting challenges and opportunities. These material stocks should be
   utilized and exploited since the reuse and recycling of construction
   materials would positively impact the natural environment and resource
   efficiency, leading to sustainable cities within a grander scheme of a
   circular economy. The exploitation of material stock is known as urban
   mining. In order to make these materials accessible for future mining,
   material quantities need to be estimated and extrapolated to regional
   levels. This demanding task requires a vast knowledge of the existing
   building stock, which can only be obtained through labor-intensive,
   time-consuming methodologies or new technologies, such as building
   information modeling (BIM), geographic information systems (GISs),
   artificial intelligence (AI), and machine learning. This review paper
   gives a general overview of the literature body and tracks the evolution
   of this research field.},
Publisher = {MDPI},
Address = {ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND},
Type = {Review},
Language = {English},
Affiliation = {Aldebei, F (Corresponding Author), Univ Debrecen, Fac Econ \& Business, Inst Econ \& World Econ, Dept Environm Econ, H-4032 Debrecen, Hungary.
   Aldebei, Faisal; Dombi, Mihaly, Univ Debrecen, Fac Econ \& Business, Inst Econ \& World Econ, Dept Environm Econ, H-4032 Debrecen, Hungary.},
DOI = {10.3390/buildings11090388},
Article-Number = {388},
EISSN = {2075-5309},
Keywords = {urban mining; material stock; circular economy; building information
   modeling (BIM); geographic information systems (GISs)},
Keywords-Plus = {MATERIAL FLOW-ANALYSIS; IN-USE STOCKS; BUILDING MATERIAL STOCKS; FUTURE
   ENERGY DEMAND; RESIDENTIAL BUILDINGS; CIRCULAR ECONOMY; DEMOLITION
   WASTE; SOCIOECONOMIC METABOLISM; ANTHROPOGENIC RESOURCES;
   DYNAMIC-ANALYSIS},
Research-Areas = {Construction \& Building Technology; Engineering},
Web-of-Science-Categories  = {Construction \& Building Technology; Engineering, Civil},
Author-Email = {faisalaldebei@mailbox.unideb.hu
   dombi.mihaly@econ.unideb.hu},
Affiliations = {University of Debrecen},
ResearcherID-Numbers = {Dombi, Mihály/HOF-7767-2023
   },
ORCID-Numbers = {Dombi, Mihály/0000-0002-3386-0889
   aldebei, faisal/0000-0003-4534-3309},
Funding-Acknowledgement = {National Research, Development and Innovation Office-NKFIH {[}K-135907]},
Funding-Text = {This research was supported by the National Research, Development and
   Innovation Office-NKFIH, K-135907.},
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@article{ WOS:000940073700001,
Author = {Yan, Yuna and Zhang, Na and Zhang, Han},
Title = {Applications of Advanced Technologies in the Development of Urban Flood
   Models},
Journal = {WATER},
Year = {2023},
Volume = {15},
Number = {4},
Month = {FEB},
Abstract = {Over the past 10 years, urban floods have increased in frequency because
   of extreme rainfall events and urbanization development. To reduce the
   losses caused by floods, various urban flood models have been developed
   to realize urban flood early warning. Using CiteSpace software's
   co-citation analysis, this paper reviews the characteristics of
   different types of urban flood models and summarizes state-of-the-art
   technologies for flood model development. Artificial intelligence (AI)
   technology provides an innovative approach to the construction of
   data-driven models; nevertheless, developing an AI model coupled with
   flooding processes represents a worthwhile challenge. Big data (such as
   remote sensing, crowdsourcing geographic, and Internet of Things data),
   as well as spatial data management and analysis methods, provide
   critical data and data processing support for model construction,
   evaluation, and application. The further development of these models and
   technologies is expected to improve the accuracy and efficiency of urban
   flood simulations and provide support for the construction of a
   multi-scale distributed smart flood simulation system.},
Publisher = {MDPI},
Address = {ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND},
Type = {Review},
Language = {English},
Affiliation = {Zhang, N (Corresponding Author), Univ Chinese Acad Sci, Coll Resources \& Environm, Beijing 100049, Peoples R China.
   Zhang, N (Corresponding Author), Univ Chinese Acad Sci, Beijing Yanshan Earth Crit Zone Natl Res Stn, Beijing 101408, Peoples R China.
   Yan, Yuna; Zhang, Na; Zhang, Han, Univ Chinese Acad Sci, Coll Resources \& Environm, Beijing 100049, Peoples R China.
   Zhang, Na, Univ Chinese Acad Sci, Beijing Yanshan Earth Crit Zone Natl Res Stn, Beijing 101408, Peoples R China.},
DOI = {10.3390/w15040622},
Article-Number = {622},
EISSN = {2073-4441},
Keywords = {artificial intelligence technology; remote sensing; crowdsourcing
   geographic data; Internet of Things; spatial data management and
   analysis; distributed smart flood simulation system},
Keywords-Plus = {SYNTHETIC-APERTURE RADAR; SOIL-MOISTURE RETRIEVAL; REMOTE-SENSING DATA;
   REAL-TIME; ARTIFICIAL-INTELLIGENCE; INUNDATION; AREAS; INFORMATION;
   PREDICTION; MANAGEMENT},
Research-Areas = {Environmental Sciences \& Ecology; Water Resources},
Web-of-Science-Categories  = {Environmental Sciences; Water Resources},
Author-Email = {zhangna@ucas.ac.cn},
Affiliations = {Chinese Academy of Sciences; University of Chinese Academy of Sciences,
   CAS; Chinese Academy of Sciences; University of Chinese Academy of
   Sciences, CAS},
Funding-Acknowledgement = {Beijing Natural Science Foundation {[}8181001]; Special Fund for
   Scientific Research Cooperation between Colleges and Institutes of the
   University of Chinese Academy of Sciences {[}Y65201NY00]},
Funding-Text = {This research was supported by the Beijing Natural Science Foundation
   {[}8181001] and the Special Fund for Scientific Research Cooperation
   between Colleges and Institutes of the University of Chinese Academy of
   Sciences {[}Y65201NY00].},
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Number-of-Cited-References = {137},
Times-Cited = {0},
Usage-Count-Last-180-days = {8},
Usage-Count-Since-2013 = {8},
Journal-ISO = {Water},
Doc-Delivery-Number = {9J3EE},
Web-of-Science-Index = {Science Citation Index Expanded (SCI-EXPANDED)},
Unique-ID = {WOS:000940073700001},
OA = {gold, Green Published},
DA = {2023-04-22},
}

@article{ WOS:000788276000001,
Author = {Deng, Tianhu and Zhang, Keren and Shen, Zuo-Jun (Max)},
Title = {A systematic review of a digital twin city: A new pattern of urban
   governance toward smart cities},
Journal = {JOURNAL OF MANAGEMENT SCIENCE AND ENGINEERING},
Year = {2021},
Volume = {6},
Number = {2},
Pages = {125-134},
Month = {JUN},
Abstract = {Many countries and governments consider smart cities a solution to
   global warming, population growth, and resource depletion. Numerous
   challenges arise while creating a smart city. Digital twins, along with
   the Internet of Things, fifth-generation wireless systems, blockchain,
   collaborative computing, simulation, and artificial intelligence
   technologies, offer great potential in the transformation of the current
   urban governance paradigm toward smart cities. In this paper, the
   concept of a digital twin city (DTC) is proposed. The characteristics,
   key technologies, and application scenarios of a DTC are elaborated
   upon. Further, we discuss the theories, research directions, and
   framework regarding DTCs. (c) 2021 China Science Publishing \& Media
   Ltd. Publishing Services by Elsevier B.V. on behalf of KeAi
   Communications Co. Ltd. This is an open access article under the CC
   BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).},
Publisher = {SCIENCE PRESS},
Address = {16 DONGHUANGCHENGGEN NORTH ST, BEIJING, 100717, PEOPLES R CHINA},
Type = {Review},
Language = {English},
Affiliation = {Deng, TH (Corresponding Author), Tsinghua Univ, Beijing 100084, Peoples R China.
   Deng, Tianhu; Zhang, Keren; Shen, Zuo-Jun (Max), Tsinghua Univ, Beijing 100084, Peoples R China.
   Shen, Zuo-Jun (Max), Univ Calif Berkeley, Berkeley, CA 94720 USA.},
DOI = {10.1016/j.jmse.2021.03.003},
ISSN = {2096-2320},
EISSN = {2589-5532},
Keywords = {Digital twin; Smart city; Urban governance; IoT; Blockchain},
Keywords-Plus = {INFORMATION; INTERNET; FUTURE; RECONSTRUCTION; SUSTAINABILITY;
   FRAMEWORK; IOT},
Research-Areas = {Business \& Economics; Operations Research \& Management Science},
Web-of-Science-Categories  = {Business, Finance; Economics; Management; Operations Research \&
   Management Science},
Author-Email = {deng13@tsinghua.edu.cn},
Affiliations = {Tsinghua University; University of California System; University of
   California Berkeley},
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Number-of-Cited-References = {58},
Times-Cited = {63},
Usage-Count-Last-180-days = {42},
Usage-Count-Since-2013 = {75},
Journal-ISO = {J. Manag. Sci. Eng.},
Doc-Delivery-Number = {0V3WG},
Web-of-Science-Index = {Emerging Sources Citation Index (ESCI)},
Unique-ID = {WOS:000788276000001},
OA = {gold},
DA = {2023-04-22},
}

@article{ WOS:000803500600006,
Author = {Noaeen, Mohammad and Naik, Atharva and Goodman, Liana and Crebo, Jared
   and Abrar, Taimoor and Abad, Zahra Shakeri Hossein and Bazzan, Ana L. C.
   and Far, Behrouz},
Title = {Reinforcement learning in urban network traffic signal control: A
   systematic literature review},
Journal = {EXPERT SYSTEMS WITH APPLICATIONS},
Year = {2022},
Volume = {199},
Month = {AUG 1},
Abstract = {Improvement of traffic signal control (TSC) efficiency has been found to
   lead to improved urban transportation and enhanced quality of life.
   Recently, the use of reinforcement learning (RL) in various areas of TSC
   has gained significant traction; thus, we conducted a systematic
   literature review as a systematic, comprehensive, and reproducible
   review to dissect all the existing research that applied RL in the
   network-level TSC domain, called as RL in NTSC or RL-NTSC for brevity.
   The review only targeted the network-level articles that tested the
   proposed methods in networks with two or more intersections. This review
   covers 160 peer-reviewed articles from 30 countries published from 1994
   to March 2020. The goal of this study is to provide the research
   community with statistical and conceptual knowledge, summarize existence
   evidence, characterize RL applications in NTSC domains, explore all
   applied methods and major first events in the defined scope, and
   identify areas for further research based on the explored research
   problems in current research. We analyzed the extracted data from the
   included articles in the following seven categories: (i) publication and
   authors' data, (ii) method identification and analysis, (iii)
   environment attributes and traffic simulation, (iv) application domains
   of RL-NTSC, (v) major first events of RL-NTSC and authors' key
   statements, (vi) code availability, and (vii) evaluation. This paper
   provides a comprehensive view of the past 26 years of research on
   applying RL to NTSC. It also reveals the role of advancing deep learning
   methods in the revival of the research area, the rise of using
   non-commercial microscopic traffic simulators, a lack of interaction
   between traffic and transportation engineering practitioners and
   researchers, and a lack of proposal and creation of testbeds which can
   likely bring different communities together around common goals.},
Publisher = {PERGAMON-ELSEVIER SCIENCE LTD},
Address = {THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND},
Type = {Review},
Language = {English},
Affiliation = {Noaeen, M (Corresponding Author), Harvard Univ, Harvard Business Sch, Boston, MA 02115 USA.
   Noaeen, Mohammad, Harvard Univ, Harvard Business Sch, Boston, MA 02115 USA.
   Noaeen, Mohammad, Univ Toronto, Dept Civil \& Mineral Engn, Toronto, ON, Canada.
   Naik, Atharva; Goodman, Liana; Abrar, Taimoor; Far, Behrouz, Univ Calgary, Dept Elect \& Software Engn, Calgary, AB, Canada.
   Crebo, Jared, Univ Calgary, Dept Mech \& Mfg Engn, Calgary, AB, Canada.
   Crebo, Jared, Univ Calgary, Dept Phys \& Astron, Calgary, AB, Canada.
   Abad, Zahra Shakeri Hossein, Harvard Univ, Dept Biomed Informat, Boston, MA 02115 USA.
   Bazzan, Ana L. C., Univ Fed Rio Grande do Sul UFRGS, Inst Informat, Porto Alegre, RS, Brazil.},
DOI = {10.1016/j.eswa.2022.116830},
EarlyAccessDate = {APR 2022},
Article-Number = {116830},
ISSN = {0957-4174},
EISSN = {1873-6793},
Keywords = {Reinforcement learning; Traffic light control; Urban network;
   Multi-agent system; Intelligent transportation system; Artificial
   intelligence},
Keywords-Plus = {CELL TRANSMISSION MODEL; NEURAL-NETWORKS; FUNCTION APPROXIMATION;
   MULTIAGENT SYSTEM; ALGORITHMS; OPTIMIZATION; INTELLIGENCE; COORDINATION;
   EFFICIENCY; DESIGN},
Research-Areas = {Computer Science; Engineering; Operations Research \& Management Science},
Web-of-Science-Categories  = {Computer Science, Artificial Intelligence; Engineering, Electrical \&
   Electronic; Operations Research \& Management Science},
Author-Email = {m.noaeen@utoronto.ca
   atharva.naik@ucalgary.ca
   liana.goodman@ucalgary.ca
   jared.crebo@ucalgary.ca
   taimoor.abrar@ucalgary.ca
   zahra\_shakeri@hms.harvard.edu
   bazzan@inf.ufrgs.br
   far@ucalgary.ca},
Affiliations = {Harvard University; University of Toronto; University of Calgary;
   University of Calgary; University of Calgary; Harvard University;
   Universidade Federal do Rio Grande do Sul},
ORCID-Numbers = {Bazzan, Ana/0000-0002-2803-9607},
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Number-of-Cited-References = {258},
Times-Cited = {13},
Usage-Count-Last-180-days = {34},
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Journal-ISO = {Expert Syst. Appl.},
Doc-Delivery-Number = {1R6TQ},
Web-of-Science-Index = {Science Citation Index Expanded (SCI-EXPANDED)},
Unique-ID = {WOS:000803500600006},
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@article{ WOS:000665912200001,
Author = {Saad, Mohamad Hanif Md and Hamdan, Nurul Maisarah and Sarker, Mahidur R.},
Title = {State of the Art of Urban Smart Vertical Farming Automation System:
   Advanced Topologies, Issues and Recommendations},
Journal = {ELECTRONICS},
Year = {2021},
Volume = {10},
Number = {12},
Month = {JUN},
Abstract = {The global economy is now under threat due to the ongoing domestic and
   international lockdown for COVID-19. Many have already lost their jobs,
   and businesses have been unstable in the Corona era. Apart from
   educational institutions, banks, privately owned institutions, and
   agriculture, there are signs of economic recession in almost all
   sectors. The roles of modern technology, the Internet of things, and
   artificial intelligence are undeniable in helping the world achieve
   economic prosperity in the post-COVID-19 economic downturn. Food
   production must increase by 60\% by 2050 to meet global food security
   demands in the face of uncertainty such as the COVID-19 pandemic and a
   growing population. Given COVID 19's intensity and isolation, improving
   food production and distribution systems is critical to combating hunger
   and addressing the double burden of malnutrition. As the world's
   population is growing day by day, according to an estimation world's
   population reaches 9.6 billion by 2050, so there is a growing need to
   modify the agriculture methods, technologies so that maximum crops can
   be attained and human effort can be reduced. The urban smart vertical
   farming (USVF) is a solution to secure food production, which can be
   introduced at any adaptive reuse, retrofit, or new buildings in vertical
   manners. This paper aims to provide a comprehensive review of the
   concept of USVF using various techniques to enhance productivity as well
   as its types, topologies, technologies, control systems, social
   acceptance, and benefits. This review has focused on numerous issues,
   challenges, and recommendations in the development of the system,
   vertical farming management, and modern technologies approach.},
Publisher = {MDPI},
Address = {ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND},
Type = {Review},
Language = {English},
Affiliation = {Sarker, MR (Corresponding Author), Univ Kebangsaan Malaysia, Inst IR 4 0, Bangi 43600, Selangor, Malaysia.
   Sarker, MR (Corresponding Author), Nebrija Univ, Ind Engn \& Automot, Campus Dehesa Villa,Calle Pirineos,55, Madrid 28040, Spain.
   Saad, Mohamad Hanif Md; Hamdan, Nurul Maisarah; Sarker, Mahidur R., Univ Kebangsaan Malaysia, Inst IR 4 0, Bangi 43600, Selangor, Malaysia.
   Sarker, Mahidur R., Nebrija Univ, Ind Engn \& Automot, Campus Dehesa Villa,Calle Pirineos,55, Madrid 28040, Spain.},
DOI = {10.3390/electronics10121422},
Article-Number = {1422},
EISSN = {2079-9292},
Keywords = {automation; smart vertical farming; sensors; Internet of Things; urban
   farming},
Keywords-Plus = {BUILDING-INTEGRATED AGRICULTURE; SOIL-MOISTURE; LOW-COST; PERFORMANCE
   ANALYSIS; ABOVEGROUND BIOMASS; FOOD SECURITY; IOT; IRRIGATION; SENSOR;
   FIELD},
Research-Areas = {Computer Science; Engineering; Physics},
Web-of-Science-Categories  = {Computer Science, Information Systems; Engineering, Electrical \&
   Electronic; Physics, Applied},
Author-Email = {hanifsaad@ukm.edu.my
   nmaisarah.hamdan@gmail.com
   mahidursarker@ukm.edu.my},
Affiliations = {Universiti Kebangsaan Malaysia; Universidad Antonio de Nebrija},
ResearcherID-Numbers = {Saad, Mohamad Hanif Md/AAO-9476-2020},
Funding-Acknowledgement = {Ministry of Higher Education of Malaysia {[}LRGS/1/2019/UKM-UKM/5/2]},
Funding-Text = {This work was carried out with the financial support from the Ministry
   of Higher Education of Malaysia under the research grant
   LRGS/1/2019/UKM-UKM/5/2.},
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Number-of-Cited-References = {201},
Times-Cited = {10},
Usage-Count-Last-180-days = {15},
Usage-Count-Since-2013 = {75},
Journal-ISO = {Electronics},
Doc-Delivery-Number = {SY5EX},
Web-of-Science-Index = {Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)},
Unique-ID = {WOS:000665912200001},
OA = {gold},
DA = {2023-04-22},
}

@article{ WOS:000629356000003,
Author = {Su, Fang and Chang, Jiangbo and Li, Xi and Zhou, Dan and Xue, Bing},
Title = {Urban Circular Economy in China: A Review Based on Chinese Literature
   Studies},
Journal = {COMPLEXITY},
Year = {2021},
Volume = {2021},
Month = {MAR 4},
Abstract = {Circular economy is a critical approach to realize the coordinated
   development of society, economy, and ecological environment. Given the
   fact that urban is a complex system in which human beings integrate
   material, energy, information, and natural environment and interact and
   influence each other, reviewing the urban circular economy research and
   development could benefit for having a better and comprehensive
   understanding on urban complexity. Mainly based on the Chinese
   literature studies from 1999 to 2020, this study aims to present an
   in-depth analysis of the research themes, policy systems, and index
   system of China's urban-scale circular economy, discuss the changes and
   evolution trends of themes, levels, and perspectives in time series,
   sort out the policy systems at both the national and local levels, and
   analyze the design principles and application fields of indicators.
   Finally, we propose that future development of an urban circular economy
   should be built based on modern techniques, technologies, and models.
   The construction of development mechanism on the circular economy should
   be framed as ``government-led, market-driven, legal norms, policy
   support, technological support, and public participation{''} and inject
   concepts such as ``Internet +,{''} ``sharing economy,{''} ``Internet of
   Things,{''} and ``artificial intelligence.{''}},
Publisher = {WILEY-HINDAWI},
Address = {ADAM HOUSE, 3RD FL, 1 FITZROY SQ, LONDON, WIT 5HE, ENGLAND},
Type = {Review},
Language = {English},
Affiliation = {Xue, B (Corresponding Author), Jiangxi Univ Sci \& Technol, Jiangxi Key Lab Min \& Met Environm Pollut Control, Ganzhou 341000, Peoples R China.
   Xue, B (Corresponding Author), Chinese Acad Sci, Inst Appl Ecol, Key Lab Pollut Ecol \& Environm Engn, Shenyang 110016, Peoples R China.
   Su, Fang; Chang, Jiangbo; Li, Xi, Shaanxi Univ Sci \& Technol, Sch Econ \& Management, Xian 710021, Peoples R China.
   Zhou, Dan; Xue, Bing, Jiangxi Univ Sci \& Technol, Jiangxi Key Lab Min \& Met Environm Pollut Control, Ganzhou 341000, Peoples R China.
   Xue, Bing, Chinese Acad Sci, Inst Appl Ecol, Key Lab Pollut Ecol \& Environm Engn, Shenyang 110016, Peoples R China.},
DOI = {10.1155/2021/8810267},
Article-Number = {8810267},
ISSN = {1076-2787},
EISSN = {1099-0526},
Research-Areas = {Mathematics; Science \& Technology - Other Topics},
Web-of-Science-Categories  = {Mathematics, Interdisciplinary Applications; Multidisciplinary Sciences},
Author-Email = {sufang@sust.edu.cn
   18335927039@163.com
   821693615@qq.com
   zhoudan1122@163.com
   xuebing@iae.ac.cn},
Affiliations = {Shaanxi University of Science \& Technology; Jiangxi University of
   Science \& Technology; Chinese Academy of Sciences; Shenyang Institute
   of Applied Ecology, CAS},
Funding-Acknowledgement = {Key Program of ``Thousand People Plan{''} of Jiangxi Province and
   Shaanxi Province; Social Science Foundation of China {[}17XJY018];
   Natural Science Foundation of China {[}41971166]; MOE Project of
   Humanities and Social Sciences of China {[}19YJAZH076]},
Funding-Text = {This work was supported by the Key Program of ``Thousand People Plan{''}
   of Jiangxi Province and Shaanxi Province, the Social Science Foundation
   of China (Grant ID: 17XJY018), Natural Science Foundation of China
   (Grant ID: 41971166), and MOE Project of Humanities and Social Sciences
   of China (Grant ID: 19YJAZH076).},
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Number-of-Cited-References = {76},
Times-Cited = {2},
Usage-Count-Last-180-days = {10},
Usage-Count-Since-2013 = {63},
Journal-ISO = {Complexity},
Doc-Delivery-Number = {QX4ZI},
Web-of-Science-Index = {Science Citation Index Expanded (SCI-EXPANDED)},
Unique-ID = {WOS:000629356000003},
OA = {gold},
DA = {2023-04-22},
}

@article{ WOS:000879561000004,
Author = {Fu, Guangtao and Jin, Yiwen and Sun, Siao and Yuan, Zhiguo and Butler,
   David},
Title = {The role of deep learning in urban water management: A critical review},
Journal = {WATER RESEARCH},
Year = {2022},
Volume = {223},
Month = {SEP 1},
Abstract = {Deep learning techniques and algorithms are emerging as a disruptive
   technology with the potential to transform global economies,
   environments and societies. They have been applied to planning and
   management problems of urban water systems in general, however, there is
   lack of a systematic review of the current state of deep learning
   applications and an examination of potential directions where deep
   learning can contribute to solving urban water challenges. Here we
   provide such a review, covering water demand forecasting, leakage and
   contamination detection, sewer defect assessment, wastewater system
   state prediction, asset monitoring and urban flooding. We find that the
   application of deep learning techniques is still at an early stage as
   most studies used benchmark networks, synthetic data, laboratory or
   pilot systems to test the performance of deep learning methods with no
   practical adoption reported. Leakage detection is perhaps at the
   forefront of receiving practical implementation into day-to-day
   operation and management of urban water systems, compared with other
   problems reviewed. Five research challenges, i.e., data privacy,
   algorithmic development, explainability and trustworthiness, multi-agent
   systems and digital twins, are identified as key areas to advance the
   application and implementation of deep learning in urban water
   management. Future research and application of deep learning systems are
   expected to drive urban water systems towards high intelligence and
   autonomy. We hope this review will inspire research and development that
   can harness the power of deep learning to help achieve sustainable water
   management and digitalise the water sector across the world.},
Publisher = {PERGAMON-ELSEVIER SCIENCE LTD},
Address = {THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND},
Type = {Review},
Language = {English},
Affiliation = {Fu, GT (Corresponding Author), Univ Exeter, Ctr Water Syst, Exeter EX4 4QF, Devon, England.
   Sun, SA (Corresponding Author), Chinese Acad Sci, Inst Geog Sci \& Nat Resources Res, Key Lab Reg Sustainable Dev Modelling, Beijing 100101, Peoples R China.
   Fu, Guangtao; Jin, Yiwen; Butler, David, Univ Exeter, Ctr Water Syst, Exeter EX4 4QF, Devon, England.
   Sun, Siao, Chinese Acad Sci, Inst Geog Sci \& Nat Resources Res, Key Lab Reg Sustainable Dev Modelling, Beijing 100101, Peoples R China.
   Yuan, Zhiguo, Univ Queensland, Adv Water Management Ctr, St Lucia, Qld 4072, Australia.},
DOI = {10.1016/j.watres.2022.118973},
EarlyAccessDate = {AUG 2022},
Article-Number = {118973},
ISSN = {0043-1354},
EISSN = {1879-2448},
Keywords = {Artificial intelligence; Data analytics; Deep learning; Digital twin;
   Water management},
Keywords-Plus = {NEURAL-NETWORKS; DEFECT CLASSIFICATION; ANOMALY DETECTION; PREDICTION;
   MODEL; LOCALIZATION; FRAMEWORK; TIME},
Research-Areas = {Engineering; Environmental Sciences \& Ecology; Water Resources},
Web-of-Science-Categories  = {Engineering, Environmental; Environmental Sciences; Water Resources},
Author-Email = {g.fu@exeter.ac.uk
   suns@igsnrr.ac.cn},
Affiliations = {University of Exeter; Chinese Academy of Sciences; Institute of
   Geographic Sciences \& Natural Resources Research, CAS; University of
   Queensland},
ResearcherID-Numbers = {Butler, David/I-2991-2012
   yuan, zhiguo/C-4980-2013},
ORCID-Numbers = {Jin, Yiwen/0000-0002-7002-2518
   Butler, David/0000-0001-5515-3416
   Fu, Guangtao/0000-0003-1045-9125
   yuan, zhiguo/0000-0002-7566-1482},
Funding-Acknowledgement = {Royal Society under the Industry Fellowship Scheme {[}IF160108]; UK
   Engineering and Physical Sciences Research Council under the Alan Turing
   Institute {[}EP/N510129/1]; National Natural Science Foundation of China
   {[}42071272]},
Funding-Text = {This work was supported by the Royal Society under the Industry
   Fellowship Scheme (Ref: IF160108), the UK Engineering and Physical
   Sciences Research Council under the Alan Turing Institute (Ref:
   EP/N510129/1) and the National Natural Science Foundation of China
   (Grant No. 42071272).},
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Number-of-Cited-References = {134},
Times-Cited = {2},
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Journal-ISO = {Water Res.},
Doc-Delivery-Number = {5Y8WD},
Web-of-Science-Index = {Science Citation Index Expanded (SCI-EXPANDED)},
Unique-ID = {WOS:000879561000004},
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@article{ WOS:000798828000001,
Author = {Chen, Kexin and Yang, Mingxia and Zhou, Xiaolu and Liu, Zelin and Li,
   Peng and Tang, Jiayi and Peng, Changhui},
Title = {Recent advances in carbon footprint studies of urban ecosystems:
   overview, application, and future challenges},
Journal = {ENVIRONMENTAL REVIEWS},
Year = {2022},
Volume = {30},
Number = {2},
Pages = {342-356},
Month = {JUN},
Abstract = {Urban ecosystems are complex systems with anthropogenic features that
   generate considerable CO2 emissions, which contributes to global climate
   change. Quantitative estimates of the carbon footprint of urban
   ecosystems are crucial for developing low-carbon development policies to
   mitigate climate change. Herein, we reviewed more than 195 urban carbon
   footprint and carbon footprint related studies, collated the recent
   progress in carbon footprint calculation methods and research
   applications of the urban ecosystem carbon footprint, analyzed the
   research applications of the carbon footprint of different cities, and
   focused on the need to study the urban ecosystem carbon footprint from a
   holistic perspective. Specifically, we aimed to: (i) compare the
   strengths and weaknesses of five existing carbon footprint calculation
   methods {[}life cycle assessment, input???output analysis, hybrid life
   cycle assessment, carbon footprint calculator, and Intergovernmental
   Panel on Climate Change (IPCC)]; (ii) analyze the status of current
   research on the carbon footprint of different urban subregions based on
   different features; and (iii) highlight new methods and areas of
   research on the carbon footprint of future urban ecosystems. Not all
   carbon footprint accounting methods are applicable to the carbon
   footprint determination of urban ecosystems; although the IPCC method is
   more widely used than the others, the hybrid life cycle assessment
   method is more accurate. With the emergence of new science and
   technology, quantitative methods to calculate the carbon footprint of
   urban ecosystems have evolved, becoming more accurate. Further
   development of new technologies, such as big data and artificial
   intelligence, to assess the carbon footprint of urban ecosystems is
   anticipated to help address the emerging challenges in urban ecosystem
   research effectively to achieve carbon neutrality and urban
   sustainability under global change.},
Publisher = {CANADIAN SCIENCE PUBLISHING},
Address = {65 AURIGA DR, SUITE 203, OTTAWA, ON K2E 7W6, CANADA},
Type = {Review},
Language = {English},
Affiliation = {Peng, CH (Corresponding Author), Hunan Normal Univ, Sch Geog Sci, Changsha 410081, Peoples R China.
   Peng, CH (Corresponding Author), Univ Quebec Montreal, Inst Environm Sci, Dept Biol Sci, CP 8888, Montreal, PQ H3C 3P8, Canada.
   Chen, Kexin; Yang, Mingxia; Zhou, Xiaolu; Liu, Zelin; Li, Peng; Tang, Jiayi; Peng, Changhui, Hunan Normal Univ, Sch Geog Sci, Changsha 410081, Peoples R China.
   Peng, Changhui, Univ Quebec Montreal, Inst Environm Sci, Dept Biol Sci, CP 8888, Montreal, PQ H3C 3P8, Canada.},
DOI = {10.1139/er-2021-0111},
EarlyAccessDate = {MAR 2022},
ISSN = {1208-6053},
EISSN = {1181-8700},
Keywords = {accounting systems; climate change; carbon sequestration; CO 2 emissions
   assessment; hybrid life cycle assessment},
Keywords-Plus = {INPUT-OUTPUT-ANALYSIS; LIFE-CYCLE INVENTORY; FOREST CARBON;
   ENERGY-CONSUMPTION; HUMAN-SETTLEMENTS; DRIVING FACTORS; DIOXIDE FLUXES;
   METABOLISM; EMISSIONS; ECONOMY},
Research-Areas = {Environmental Sciences \& Ecology},
Web-of-Science-Categories  = {Environmental Sciences},
Author-Email = {peng.changhui@uqam.ca},
Affiliations = {Hunan Normal University; University of Quebec; University of Quebec
   Montreal},
ORCID-Numbers = {chen, kexin/0000-0003-4081-6592},
Funding-Acknowledgement = {Key Research and Development Program of Hunan Province {[}2021NK2031];
   National Natural Science Foundation of China {[}41901117]; Research and
   Development Project of Hunan Province; Natural Sciences and Engineering
   Research Council of Canada (NSERC) Discovery Grant},
Funding-Text = {This study was financially supported by the Key Research and Development
   Program of Hunan Province (2021NK2031), the National Natural Science
   Foundation of China (41901117), the Research and Development Project of
   Hunan Province, and a Natural Sciences and Engineering Research Council
   of Canada (NSERC) Discovery Grant.},
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Number-of-Cited-References = {195},
Times-Cited = {1},
Usage-Count-Last-180-days = {108},
Usage-Count-Since-2013 = {200},
Journal-ISO = {Environ. Rev.},
Doc-Delivery-Number = {1N0IP},
Web-of-Science-Index = {Science Citation Index Expanded (SCI-EXPANDED)},
Unique-ID = {WOS:000798828000001},
DA = {2023-04-22},
}

@article{ WOS:000825514600002,
Author = {Zhou, Xiao-min and Jiang, Guojing and Li, Fangzheng and Gao, Wei and
   Han, Yufu and Wu, Tao and Ma, Wenzhu},
Title = {Comprehensive Review of Artificial Ground Freezing Applications to Urban
   Tunnel and Underground Space Engineering in China in the Last 20 Years},
Journal = {JOURNAL OF COLD REGIONS ENGINEERING},
Year = {2022},
Volume = {36},
Number = {3},
Month = {SEP 1},
Abstract = {Artificial ground freezing (AGF) has been applied widely in the
   construction of urban transportation infrastructure in China for the
   last two decades. This study provides a comprehensive review of the
   regional application, distribution, new developments, existing problems,
   and challenges of AGF. The innovative achievements of AGF in theory and
   technology are presented using selected prominent or typical engineering
   examples. Accidents or problems occurring in previous projects and the
   engineering risks of AGF technology were analyzed. In addition, the
   competition and integration of AGF technology with other construction
   methods are discussed. Furthermore, future research needs, including
   freezing tube installation issues related to directional drilling,
   miniaturization of construction equipment, artificial intelligence,
   information technology, and construction safety, are proposed. Finally,
   the expectations of AGF technology in urban development are predicted
   based on the new policies of urban groundwater protection issued in
   recent years. (C) 2022 American Society of Civil Engineers.},
Publisher = {ASCE-AMER SOC CIVIL ENGINEERS},
Address = {1801 ALEXANDER BELL DR, RESTON, VA 20191-4400 USA},
Type = {Review},
Language = {English},
Affiliation = {Jiang, GJ (Corresponding Author), Univ Sci \& Technol Beijing, Sch Civil \& Resource Engn, Beijing Key Lab Urban Underground Space Engn, Beijing 100083, Peoples R China.
   Zhou, Xiao-min; Jiang, Guojing; Wu, Tao; Ma, Wenzhu, Univ Sci \& Technol Beijing, Sch Civil \& Resource Engn, Beijing Key Lab Urban Underground Space Engn, Beijing 100083, Peoples R China.
   Zhou, Xiao-min, Univ Alaska Anchorage, Anchorage, AK USA.
   Jiang, Guojing, Univ British Columbia, Vancouver, BC V6T 1Z4, Canada.
   Jiang, Guojing; Li, Fangzheng, Beijing China Coal Mine Engn Co Ltd, Beijing 100013, Peoples R China.
   Gao, Wei; Han, Yufu, Beijing China Coal Mine Engn Co Ltd, Ground Freezing Res Inst, Beijing 100013, Peoples R China.
   Wu, Tao, China Construct Infrastruct Co Ltd, Beijing 100022, Peoples R China.},
DOI = {10.1061/(ASCE)CR.1943-5495.0000273},
Article-Number = {04022002},
ISSN = {0887-381X},
EISSN = {1943-5495},
Keywords-Plus = {CLAY; THAW},
Research-Areas = {Engineering; Geology},
Web-of-Science-Categories  = {Engineering, Environmental; Engineering, Civil; Geosciences,
   Multidisciplinary},
Author-Email = {sean\_ustb@ustb.edu.cn
   jiangguojing2006@126.com
   Lifangzheng@tdbmc.com
   ccrigw@126.com
   hanyufu82@163.com
   w616000360@126.com
   294085338@qq.com},
Affiliations = {University of Science \& Technology Beijing; University of Alaska
   System; University of Alaska Anchorage; University of British Columbia},
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Number-of-Cited-References = {78},
Times-Cited = {2},
Usage-Count-Last-180-days = {20},
Usage-Count-Since-2013 = {36},
Journal-ISO = {J. Cold Reg. Eng.},
Doc-Delivery-Number = {2X9KB},
Web-of-Science-Index = {Science Citation Index Expanded (SCI-EXPANDED)},
Unique-ID = {WOS:000825514600002},
DA = {2023-04-22},
}

@article{ WOS:000677675100003,
Author = {Takahashi, Hiroyuki and Inoue, Eiji and Inoue, Nobuyuki and Komori, Yuji
   and Konishi, Masakazu and Noda, Mizuki and Mizue, Taichi},
Title = {``Urban Mines{''} Recycling in Astec-irie Co., Ltd.},
Journal = {JOURNAL OF THE JAPAN INSTITUTE OF METALS AND MATERIALS},
Year = {2021},
Volume = {85},
Number = {8, SI},
Pages = {279-284},
Abstract = {This paper reviews recycling technologies in Astec-irie Co., Ltd.
   Repellent substances and valuable metals, such as precious metals and
   rare metals, contained in waste electronics were separated and
   concentrated using superheated steam, an aqueous iron (III) chloride
   solution, and AI. The electronic components and substrates were
   separated by melting the solder with superheated steam, and the
   repellent substance in noniron smelting was removed from them. The
   gold-plated parts contained in the electronic components were separated
   back into gold by dissolving the copper and nickel in the aqueous iron
   (III) chloride solution. The copper and nickel dissolved into the
   solution were separated and recovered respectively as solid components
   by adding iron powder. The useful metals were concentrated from the
   separated electronic components, which are sorted out using AI. As a
   result, valuable metals such as precious metals could be concentrated
   while reducing the concentration of repellents, thus making it possible
   to treat the removed repellents as useful substance.},
Publisher = {JAPAN INST METALS \& MATERIALS},
Address = {1-14-32, ICHIBANCHO, AOBA-KU, SENDAI, 980-8544, JAPAN},
Type = {Review},
Language = {Japanese},
Affiliation = {Takahashi, H (Corresponding Author), Astec Irie Co Ltd, Kitakyushu, Fukuoka 8080002, Japan.
   Takahashi, Hiroyuki; Inoue, Eiji; Inoue, Nobuyuki; Komori, Yuji; Konishi, Masakazu; Noda, Mizuki; Mizue, Taichi, Astec Irie Co Ltd, Kitakyushu, Fukuoka 8080002, Japan.},
DOI = {10.2320/jinstmet.JA202103},
ISSN = {0021-4876},
EISSN = {1880-6880},
Keywords = {urban mines; gold; recycle; aqueous iron chloride solution; copper;
   nickel; printed circuit board; electronic components; artificial
   intelligence (AI); superheated steam; iron powder},
Research-Areas = {Metallurgy \& Metallurgical Engineering},
Web-of-Science-Categories  = {Metallurgy \& Metallurgical Engineering},
Cited-References = {{[}Anonymous], 2000, DENKIKAGAKUBINRAN, P92.
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Number-of-Cited-References = {13},
Times-Cited = {0},
Usage-Count-Last-180-days = {0},
Usage-Count-Since-2013 = {7},
Journal-ISO = {J. Jpn. Inst. Met. Mater.},
Doc-Delivery-Number = {TP5YO},
Web-of-Science-Index = {Science Citation Index Expanded (SCI-EXPANDED)},
Unique-ID = {WOS:000677675100003},
OA = {gold},
DA = {2023-04-22},
}

@article{ WOS:000770290800003,
Author = {Pokhrel, Sarin Raj and Chhipi-Shrestha, Gyan and Hewage, Kasun and
   Sadiq, Rehan},
Title = {Sustainable, resilient, and reliable urban water systems: making the
   case for a ``one water{''} approach},
Journal = {ENVIRONMENTAL REVIEWS},
Year = {2022},
Volume = {30},
Number = {1},
Pages = {10-29},
Month = {MAR},
Abstract = {An urban water system (UWS) has three main service components: (i)
   drinking-water; (ii) waste-water; and (iii) storm-water. Historically,
   each component in urban water development evolved over time with
   different objectives for ``different{''} types of water. Even today, the
   trend continues, as different urban water services are managed in silos.
   This trend is less sustainable, resilient, and reliable, mainly because
   of significant pressures on freshwater supplies exerted by the
   increasing population, demand for high living standards, rapid
   urbanization, and climate change. To cope with these challenges, the
   conventional thinking needs to change. This paper identifies a number of
   significant research gaps related to inter-relationships among various
   UWS service components. An innovative paradigm, the ``one water{''}
   approach (OWA), which considers ``urban water{''} as a single entity, is
   investigated herein. Currently, Australia, the USA, and Singapore are
   leading the implementation of the OWA, whereas only a few Canadian
   municipalities have embraced OWA at a very basic level. Among the EU
   nations, the Netherlands have emphasized the need for integrated water
   resource management in an urban environment. This review highlights the
   challenges in adopting the OWA, and also proposes guiding principles in
   ongoing water management practices. Institutional complexities involving
   an intricate regulatory structure for different UWS service components,
   a wider fragmentation in decision making at government levels, and
   insufficient stakeholder engagement within and between water utilities
   and other institutions present serious challenges. Various strategies
   such as, data sharing between water utilities, use of novel technologies
   (e.g., artificial intelligence, sensor technologies), and visionary
   leadership at different government levels have been identified as key
   drivers for the adoption and implementation of the OWA. The authors
   believe that a paradigm shift from ``conventional{''} approach to OWA is
   needed to increase resiliency and reliability of water services and
   assist decision-makers of UWSs.},
Publisher = {CANADIAN SCIENCE PUBLISHING},
Address = {65 AURIGA DR, SUITE 203, OTTAWA, ON K2E 7W6, CANADA},
Type = {Review},
Language = {English},
Affiliation = {Pokhrel, SR (Corresponding Author), Univ British Columbia, Fac Appl Sci, Sch Engn, Okanagan Campus, Kelowna, BC V1V 1V7, Canada.
   Pokhrel, Sarin Raj; Chhipi-Shrestha, Gyan; Hewage, Kasun; Sadiq, Rehan, Univ British Columbia, Fac Appl Sci, Sch Engn, Okanagan Campus, Kelowna, BC V1V 1V7, Canada.},
DOI = {10.1139/er-2020-0090},
ISSN = {1208-6053},
EISSN = {1181-8700},
Keywords = {drinking-water; waste-water; storm-water; one-water approach;
   sustainability},
Keywords-Plus = {CLIMATE-CHANGE; RESOURCES MANAGEMENT; CITY BLUEPRINT; SURFACE-WATER;
   GROUNDWATER; IMPACTS; QUALITY; LEAKAGE; DESIGN; PHARMACEUTICALS},
Research-Areas = {Environmental Sciences \& Ecology},
Web-of-Science-Categories  = {Environmental Sciences},
Author-Email = {pokhrelsarin@gmail.com},
Affiliations = {University of British Columbia},
Funding-Acknowledgement = {Natural Science and Engineering Research Council of Canada's
   Collaborative Research and Development Grants},
Funding-Text = {The authors would like to thank the Natural Science and Engineering
   Research Council of Canada's Collaborative Research and Development
   Grants for providing the financial assistance to conduct this research.},
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Number-of-Cited-References = {143},
Times-Cited = {6},
Usage-Count-Last-180-days = {6},
Usage-Count-Since-2013 = {13},
Journal-ISO = {Environ. Rev.},
Doc-Delivery-Number = {ZV1JI},
Web-of-Science-Index = {Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)},
Unique-ID = {WOS:000770290800003},
DA = {2023-04-22},
}

@article{ WOS:000524096800021,
Author = {Yigitcanlar, Tan and Desouza, Kevin C. and Butler, Luke and Roozkhosh,
   Farnoosh},
Title = {Contributions and Risks of Artificial Intelligence (AI) in Building
   Smarter Cities: Insights from a Systematic Review of the Literature},
Journal = {ENERGIES},
Year = {2020},
Volume = {13},
Number = {6},
Month = {MAR},
Abstract = {Artificial intelligence (AI) is one of the most disruptive technologies
   of our time. Interest in the use of AI for urban innovation continues to
   grow. Particularly, the rise of smart cities urban locations that are
   enabled by community, technology, and policy to deliver productivity,
   innovation, livability, wellbeing, sustainability, accessibility, good
   governance, and good planning has increased the demand for AI-enabled
   innovations. There is, nevertheless, no scholarly work that provides a
   comprehensive review on the topic. This paper generates insights into
   how AI can contribute to the development of smarter cities. A systematic
   review of the literature is selected as the methodologic approach.
   Results are categorized under the main smart city development
   dimensions, i.e., economy, society, environment, and governance. The
   findings of the systematic review containing 93 articles disclose that:
   (a) AI in the context of smart cities is an emerging field of research
   and practice. (b) The central focus of the literature is on AI
   technologies, algorithms, and their current and prospective
   applications. (c) AI applications in the context of smart cities mainly
   concentrate on business efficiency, data analytics, education, energy,
   environmental sustainability, health, land use, security, transport, and
   urban management areas. (d) There is limited scholarly research
   investigating the risks of wider AI utilization. (e) Upcoming
   disruptions of AI in cities and societies have not been adequately
   examined. Current and potential contributions of AI to the development
   of smarter cities are outlined in this paper to inform scholars of
   prospective areas for further research.},
Publisher = {MDPI},
Address = {ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND},
Type = {Review},
Language = {English},
Affiliation = {Yigitcanlar, T (Corresponding Author), Queensland Univ Technol, Sch Built Environm, 2 George St, Brisbane, Qld 4000, Australia.
   Yigitcanlar, Tan; Butler, Luke, Queensland Univ Technol, Sch Built Environm, 2 George St, Brisbane, Qld 4000, Australia.
   Desouza, Kevin C., Queensland Univ Technol, QUT Business Sch, 2 George St, Brisbane, Qld 4000, Australia.
   Roozkhosh, Farnoosh, Guilan Univ, Sch Arts \& Architecture, Rasht 4199843653, Guilan, Iran.},
DOI = {10.3390/en13061473},
Article-Number = {1473},
EISSN = {1996-1073},
Keywords = {artificial intelligence (AI); AI technologies; AI algorithms; disruptive
   technology; smart city; smart urban technology; urban informatics;
   sustainable urban development; climate change},
Keywords-Plus = {BIG DATA ANALYTICS; PUBLIC-SECTOR; SURVEILLANCE; TECHNOLOGY; CHALLENGES;
   MANAGEMENT; EDUCATION; INTERNET; CLIMATE; FUTURE},
Research-Areas = {Energy \& Fuels},
Web-of-Science-Categories  = {Energy \& Fuels},
Author-Email = {tan.yigitcanlar@qut.edu.au
   kevin.desouza@qut.edu.au
   luke.butler@hdr.qut.edu.au
   farnoosh\_r@msc.guilan.ac.ir},
Affiliations = {Queensland University of Technology (QUT); Queensland University of
   Technology (QUT); University of Guilan},
ResearcherID-Numbers = {Yigitcanlar, Tan/J-1142-2012
   },
ORCID-Numbers = {Yigitcanlar, Tan/0000-0001-7262-7118
   Roozkhosh, Farnoosh/0000-0003-2523-149X
   Butler, Luke/0000-0003-0708-8896},
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@article{ WOS:000938634800001,
Author = {Vallejo-Gomez, David and Osorio, Marisol and Hincapie, Carlos A.},
Title = {Smart Irrigation Systems in Agriculture: A Systematic Review},
Journal = {AGRONOMY-BASEL},
Year = {2023},
Volume = {13},
Number = {2},
Month = {FEB},
Abstract = {This research aims to carry out a systematic review of the available
   literature about smart irrigation systems. It will be focused on systems
   using artificial intelligence techniques in urban and rural agriculture
   for soil crops to identify those that are currently being used or can be
   adapted to urban agriculture. To this end, a modified PRISMA 2020 method
   is applied, and three search equations are formulated. From those
   filters, and after a screening process, 170 articles are obtained. These
   articles are analyzed through VantagePoint, a text processing software.
   After this, they are taken through a detailed analysis phase in which 50
   sources are selected as the most relevant to be read and analyzed by
   topic. Finally, the different phases of the analysis are used to draw
   conclusions that might be interesting for researchers working in this
   specific field or for the general public interested in rural and urban
   agriculture and its automation.},
Publisher = {MDPI},
Address = {ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND},
Type = {Review},
Language = {English},
Affiliation = {Vallejo-Gomez, D (Corresponding Author), Univ Pontificia Bolivariana, Grp Invest Gest Tecnol \& Innovac GTI, Maestria Ingn, Medellin 050031, Colombia.
   Vallejo-Gomez, David, Univ Pontificia Bolivariana, Grp Invest Gest Tecnol \& Innovac GTI, Maestria Ingn, Medellin 050031, Colombia.
   Osorio, Marisol, Univ Pontificia Bolivariana, Ctr Ciencia Bas, Grp Invest Gest Tecnol \& Innovac GTI, Medellin 050031, Colombia.
   Hincapie, Carlos A., Univ Pontificia Bolivariana, Fac Ingn Agroind, Grp Invest Agroind GRAIN, Medellin 050031, Colombia.},
DOI = {10.3390/agronomy13020342},
Article-Number = {342},
EISSN = {2073-4395},
Keywords = {agriculture; urban agriculture; smart irrigation system; Internet of
   Things (IoT); fuzzy logic; artificial intelligence (AI); machine
   learning (ML)},
Keywords-Plus = {NETWORK PATTERN-CLASSIFICATION; NEURAL-NETWORK; LOW-COST; BIG DATA; IOT;
   MODEL; INTERNET; THINGS; MANAGEMENT},
Research-Areas = {Agriculture; Plant Sciences},
Web-of-Science-Categories  = {Agronomy; Plant Sciences},
Author-Email = {david.vallejo@upb.edu.co},
Affiliations = {Universidad Pontificia Bolivariana; Universidad Pontificia Bolivariana;
   Universidad Pontificia Bolivariana},
Funding-Acknowledgement = {Ministerio de Ciencia, Tecnologia e Innovacion, Colombia {[}Minciencias
   852]},
Funding-Text = {This is a product of the Tecnologias en Agricultura Urbana (Urban
   Agriculture Technologies) program, call Minciencias 852. It is funded
   with resources from the ``Patrimonio Autonomo Fondo Nacional de
   Financiamiento para la Ciencia, la Tecnologia y La Innovacion Francisco
   Jose de Caldas{''} (Francisco Jose de Caldas National Fund for Science,
   Technology and Innovation), Ministerio de Ciencia, Tecnologia e
   Innovacion, Colombia. Research Program: ``Technologies in Urban
   Farming{''}. (Grant Number: 127-2021).},
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Number-of-Cited-References = {90},
Times-Cited = {0},
Usage-Count-Last-180-days = {10},
Usage-Count-Since-2013 = {10},
Journal-ISO = {Agronomy-Basel},
Doc-Delivery-Number = {9H1YL},
Web-of-Science-Index = {Science Citation Index Expanded (SCI-EXPANDED)},
Unique-ID = {WOS:000938634800001},
OA = {gold},
DA = {2023-04-22},
}

@article{ WOS:000916165200001,
Author = {Wang, Tong and Liu, Yang and Li, Qiyuan and Du, Peng and Zheng, Xiaogong
   and Gao, Qingfei},
Title = {State-of-the-Art Review of the Resilience of Urban Bridge Networks},
Journal = {SUSTAINABILITY},
Year = {2023},
Volume = {15},
Number = {2},
Month = {JAN},
Abstract = {With the rapid advancement of the urbanization process, the bridge
   networks in cities are becoming increasingly optimized, playing an
   important role in ensuring the normal operation of cities. However, with
   the gradual deterioration of bridges and the further attenuation of
   their capacity, many bridges are prone to damage or even collapse under
   extreme loads. After a natural disaster or human-derived accident occurs
   in a city, the normal operation of the bridge network in the city will
   play an irreplaceable role in emergency rescue and long-term recovery
   after the disaster. In this paper, the resilience of urban bridge
   networks, as a comprehensive indicator that integrates predisaster early
   warning, disaster response and postdisaster recovery information, is
   considered. This indicator has been applied in many disciplines, such as
   civil engineering, sociology, management and economics. The concept of
   resilience is expounded, and functional and resilience assessment
   indicators for bridge networks are established. Additionally, the
   research progress on bridge network resilience is described. Finally,
   combined with research hotspots such as big data, artificial
   intelligence and bridge structural health monitoring, the development
   trends and prospects of bridge network resilience research are
   discussed.},
Publisher = {MDPI},
Address = {ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND},
Type = {Review},
Language = {English},
Affiliation = {Gao, QF (Corresponding Author), Harbin Inst Technol, Sch Transportat Sci \& Engn, Harbin 150090, Peoples R China.
   Wang, Tong; Liu, Yang; Li, Qiyuan; Gao, Qingfei, Harbin Inst Technol, Sch Transportat Sci \& Engn, Harbin 150090, Peoples R China.
   Du, Peng, Elect Engn Co Ltd, China Railway Bur Grp 5, Changsha 410006, Peoples R China.
   Zheng, Xiaogong, Heilongjiang Dingjie Rd \& Bridge Engn Co Ltd, Harbin 150090, Peoples R China.},
DOI = {10.3390/su15020989},
Article-Number = {989},
EISSN = {2071-1050},
Keywords = {resilience; bridge network; multihazard; structural health monitoring of
   bridge},
Keywords-Plus = {SEISMIC FRAGILITY; TRANSPORTATION NETWORK; ROADWAY NETWORKS; EARTHQUAKE;
   RISK; SUSTAINABILITY; PERFORMANCE; FRAMEWORK; CURVES; IMPACT},
Research-Areas = {Science \& Technology - Other Topics; Environmental Sciences \& Ecology},
Web-of-Science-Categories  = {Green \& Sustainable Science \& Technology; Environmental Sciences;
   Environmental Studies},
Author-Email = {gaoqingfei@hit.edu.cn},
Affiliations = {Harbin Institute of Technology},
ORCID-Numbers = {Liu, Yang/0000-0002-1187-6388},
Funding-Acknowledgement = {Key Research and Development Program of Shandong Province of China
   {[}2019JZZY010427]; Key Research and Development Program of Heilongjiang
   Province of China {[}GY2021ZB0063]},
Funding-Text = {This study is supported by the Key Research and Development Program of
   Shandong Province of China (Grant No: 2019JZZY010427) and the Key
   Research and Development Program of Heilongjiang Province of China
   (Grant No: GY2021ZB0063).},
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Number-of-Cited-References = {104},
Times-Cited = {0},
Usage-Count-Last-180-days = {9},
Usage-Count-Since-2013 = {9},
Journal-ISO = {Sustainability},
Doc-Delivery-Number = {8A3TX},
Web-of-Science-Index = {Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)},
Unique-ID = {WOS:000916165200001},
OA = {gold},
DA = {2023-04-22},
}

@article{ WOS:000795423800001,
Author = {Joseph, Kiran and Sharma, Ashok K. and van Staden, Rudi},
Title = {Development of an Intelligent Urban Water Network System},
Journal = {WATER},
Year = {2022},
Volume = {14},
Number = {9},
Month = {MAY},
Abstract = {Water and wastewater services have been provided through centralised
   systems for more than a century. The operational and management
   approaches of the water systems face challenges induced by population
   growth, urbanisation, and ageing infrastructure. Recent advancements in
   water system engineering include the development of intelligent water
   networks. These intelligent networks address management and operational
   challenges associated with pressure and flow variations in the water
   network and it reduces the time for identification of pipe bursts and
   leakages. Research is required into the development of intelligent water
   networks to ensure consistent data collection and analysis that can
   filter and aggregate into actionable events to reduce water leakage,
   leakage cost, customer disruptions, and damages. Implementation of an
   intelligent algorithm with an integrated Supervisory Control and Data
   Acquisition (SCADA) system, high-efficiency smart sensors, and flow
   meters, including a tracking mechanism, will significantly reduce system
   management and operational issues and ensure improved service delivery
   for the community. This paper discusses the history of water systems,
   traditional water supply systems, need for intelligent water network,
   and design/development of the intelligent water networks. A framework
   for the intelligent water network has also been presented in this paper.},
Publisher = {MDPI},
Address = {ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND},
Type = {Review},
Language = {English},
Affiliation = {Sharma, AK (Corresponding Author), Victoria Univ, Inst Sustainable Ind \& Liveable Cities ISILC, Melbourne, Vic 3011, Australia.
   Joseph, Kiran; Sharma, Ashok K.; van Staden, Rudi, Victoria Univ, Inst Sustainable Ind \& Liveable Cities ISILC, Melbourne, Vic 3011, Australia.},
DOI = {10.3390/w14091320},
Article-Number = {1320},
EISSN = {2073-4441},
Keywords = {intelligent water network; smart water systems; leakage detection; water
   pipeline burst detection; cyber-physical security; artificial
   intelligence; IoT; wastewater; smart water management; smart water
   grids; drinking water networks},
Keywords-Plus = {ARTIFICIAL-INTELLIGENCE; SMART METERS; WASTE-WATER; MANAGEMENT;
   CHALLENGES; EFFICIENCY; OPTIMIZATION; INTERNET; TECHNOLOGIES; AQUEDUCTS},
Research-Areas = {Environmental Sciences \& Ecology; Water Resources},
Web-of-Science-Categories  = {Environmental Sciences; Water Resources},
Author-Email = {kiran.joseph2@live.vu.edu.au
   ashok.sharma@vu.edu.au
   rudi.vanstaden@vu.edu.au},
Affiliations = {Victoria University},
ResearcherID-Numbers = {Sharma, Ashok/A-4945-2008
   },
ORCID-Numbers = {Sharma, Ashok/0000-0002-0172-5033
   Joseph, Kiran/0000-0003-4992-3933},
Funding-Acknowledgement = {Victoria University, Melbourne Australia; Greater Western Water,
   Melbourne, Australia},
Funding-Text = {This research is funded by Victoria University, Melbourne Australia and
   Greater Western Water, Melbourne, Australia.},
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Number-of-Cited-References = {122},
Times-Cited = {1},
Usage-Count-Last-180-days = {38},
Usage-Count-Since-2013 = {70},
Journal-ISO = {Water},
Doc-Delivery-Number = {1F8PL},
Web-of-Science-Index = {Science Citation Index Expanded (SCI-EXPANDED)},
Unique-ID = {WOS:000795423800001},
OA = {gold, Green Accepted},
DA = {2023-04-22},
}

@article{ WOS:000531562000002,
Author = {Yan, Yu and Hou, Xiaomeng and Fei, Honglu},
Title = {Review of predicting the blast-induced ground vibrations to reduce
   impacts on ambient urban communities},
Journal = {JOURNAL OF CLEANER PRODUCTION},
Year = {2020},
Volume = {260},
Month = {JUL 1},
Abstract = {Drilling and blasting is widely used in mining and construction
   industries, and ground vibration is one of the detrimental effects
   caused by blasting. To determine the fragmentation of rocks and to
   ensure the safety of ambient urban areas and residents, it is necessary
   to determine the parameters that have a reasonable influence on
   blast-induced vibration. This review firstly presents the mechanism of
   influential parameters governing the ground vibration. The collected
   results show that these parameters such as free faces, decoupling, hole
   diameter, hole depth, charge type, delay blasting and geological
   conditions affect the prediction of ground vibration. Meanwhile, the
   influence of burden and hole numbers on the ground vibration is
   controversial in the current literature, and the few studies on the
   stemming and spacing are not enough to prove the impact of these factors
   on the prediction of peak particle velocity. While several methods are
   used for predicting ground vibration, four methods are summarized in
   this review, namely, empirical equation, AI algorithm, multiple
   regression analysis and numerical simulation methods. The details of the
   four methods for predicting the blast-induced vibration are discussed.
   Finally, performance assessments and sensitivity analysis are described,
   respectively, to compare the four methods for the accuracy of predicting
   the blast-induced vibration and to evaluate the effectiveness of various
   parameters in predicting the PPV. (C) 2020 Elsevier Ltd. All rights
   reserved.},
Publisher = {ELSEVIER SCI LTD},
Address = {THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND},
Type = {Review},
Language = {English},
Affiliation = {Hou, XM (Corresponding Author), Harbin Inst Technol, Minist Educ, Key Lab Struct Dynam Behav \& Control, Harbin 150090, Peoples R China.
   Yan, Yu; Hou, Xiaomeng, Harbin Inst Technol, Minist Educ, Key Lab Struct Dynam Behav \& Control, Harbin 150090, Peoples R China.
   Yan, Yu; Hou, Xiaomeng, Harbin Inst Technol, Key Lab Smart Prevent \& Mitigat Civil Engn Disast, Minist Ind \& Informat Technol, Harbin 150090, Peoples R China.
   Fei, Honglu, Liaoning Tech Univ, Inst Engn Blasting, Fuxing 123000, Liaoning, Peoples R China.},
DOI = {10.1016/j.jclepro.2020.121135},
Article-Number = {121135},
ISSN = {0959-6526},
EISSN = {1879-1786},
Keywords = {Ground vibration; Influence factor; Peak particle velocity; Empirical
   equations; Artificial intelligence; Numerical simulation},
Keywords-Plus = {PEAK PARTICLE-VELOCITY; NEURAL-NETWORK; ROCK; MODEL; SIMULATION;
   FREQUENCY; MINE; FRAGMENTATION; REGRESSION; EXCAVATION},
Research-Areas = {Science \& Technology - Other Topics; Engineering; Environmental
   Sciences \& Ecology},
Web-of-Science-Categories  = {Green \& Sustainable Science \& Technology; Engineering, Environmental;
   Environmental Sciences},
Author-Email = {houxiaomeng@gmail.com},
Affiliations = {Harbin Institute of Technology; Harbin Institute of Technology; Liaoning
   Technical University},
ResearcherID-Numbers = {YAN, Yu/GWQ-9653-2022},
Funding-Acknowledgement = {National Key R\&D Program of China {[}2017YFC0806100]; National Natural
   Science Foundation of China {[}51578184]; Excellent Youth Foundation of
   Heilongjiang Province {[}YQ2019E028]},
Funding-Text = {The study is supported by National Key R\&D Program of China
   (2017YFC0806100), the National Natural Science Foundation of China
   (No.51578184), and Excellent Youth Foundation of Heilongjiang Province
   (No. YQ2019E028).},
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Number-of-Cited-References = {124},
Times-Cited = {22},
Usage-Count-Last-180-days = {21},
Usage-Count-Since-2013 = {72},
Journal-ISO = {J. Clean Prod.},
Doc-Delivery-Number = {LL4XZ},
Web-of-Science-Index = {Science Citation Index Expanded (SCI-EXPANDED)},
Unique-ID = {WOS:000531562000002},
DA = {2023-04-22},
}

@article{ WOS:000946297200001,
Author = {Liu, Yiqi and Ramin, Pedram and Flores-Alsina, Xavier and Gernaey, V,
   Krist.},
Title = {Transforming data into actionable knowledge for fault detection,
   diagnosis and prognosis in urban wastewater systems with AI techniques:
   A mini-review},
Journal = {PROCESS SAFETY AND ENVIRONMENTAL PROTECTION},
Year = {2023},
Volume = {172},
Pages = {501-512},
Month = {APR},
Abstract = {Recent advances in artificial intelligence (AI) and data analytics (DA)
   could provide opportunities for the fault management and the
   decision-making of the urban wastewater treatment systems (UWS)
   operations. The UWS is typically a large system, including Sewer
   networks (SNs), Wastewater Treatment plants (WWTPs) and also considering
   the Receiving media (RM). However, applications of AI and DA in the UWS
   can be challenging due to the complexities and size of systems, the
   large variation in the level of UWS instrumentation, and the relatively
   poor data quality. This review goes beyond the state of the art by
   critically analyzing previous work on AI-based data-driven methodologies
   to system-wide fault detection, life cycle fault management and
   transformation of big and small data into analytics, particularly,
   considering two different points of view: process faults (such as
   bulking sludge, sewer corrosion \& technology specifics) and
   instrumentation faults (such as sensors and actua-tors), thereby
   offering more opportunities to distinguish complex patterns and
   dynamics. Our analysis reveals the relative strengths and weaknesses of
   the different approaches to design fault diagnosis tools and to apply
   these in the UWS. Finally, the opportunities and challenges about the
   inter-play among UWS, data and AI are discussed.},
Publisher = {ELSEVIER},
Address = {RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS},
Type = {Review},
Language = {English},
Affiliation = {Liu, YQ (Corresponding Author), South China Univ Technol, Minist Educ, Sch Automat Sci \& Engn, Key Lab Autonomous Syst \& Networked Control, Guangzhou 510641, Guangdong, Peoples R China.
   Liu, Yiqi, South China Univ Technol, Minist Educ, Sch Automat Sci \& Engn, Key Lab Autonomous Syst \& Networked Control, Guangzhou 510641, Guangdong, Peoples R China.
   Liu, Yiqi; Ramin, Pedram; Flores-Alsina, Xavier; Gernaey, Krist., V, Tech Univ Denmark, Proc \& Syst Engn Ctr PROSYS, Dept Chem \& Biochem Engn, Bldg 228 A, DK-2800 Lyngby, Denmark.},
DOI = {10.1016/j.psep.2023.02.043},
EarlyAccessDate = {FEB 2023},
ISSN = {0957-5820},
EISSN = {1744-3598},
Keywords = {Fault detection; Fault diagnosis; Fault prognosis; Data analytics;
   Artificial intelligence},
Keywords-Plus = {CONCRETE CORROSION; ANOMALY DETECTION; BIG DATA; MODEL; PERFORMANCE;
   PREDICTION; REGRESSION; SEWERS},
Research-Areas = {Engineering},
Web-of-Science-Categories  = {Engineering, Environmental; Engineering, Chemical},
Author-Email = {aulyq@scut.edu.cn
   pear@kt.dtu.dk
   xfa@kt.dtu.dk
   kvg@kt.dtu.dk},
Affiliations = {South China University of Technology; Technical University of Denmark},
Funding-Acknowledgement = {Horizon 2020 Framework Programme -Marie Sklodowska-Curie Individual
   Fellowships {[}891627]; National Natural Science Foundation of China
   {[}62273151, 61873096, 62073145]; Basic and Applied Basic Research
   Foundation of Guangdong Province {[}2020A1515011057, 2021B1515420003];
   Guangdong International Scientific Cooperation Research Foundation
   {[}2020A0505100024, 2021A0505060001]},
Funding-Text = {Yiqi Liu thanks for the support of Horizon 2020 Framework Programme
   -Marie Sklodowska-Curie Individual Fellowships (891627) . This work was
   partially supported by the National Natural Science Founda- tion of
   China (62273151, 61873096, 62073145) , the Basic and Applied Basic
   Research Foundation of Guangdong Province (2020A1515011057,
   2021B1515420003) , the Guangdong International Scientific Cooperation
   Research Foundation (2020A0505100024, 2021A0505060001) .},
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Times-Cited = {0},
Usage-Count-Last-180-days = {1},
Usage-Count-Since-2013 = {1},
Journal-ISO = {Process Saf. Environ. Protect.},
Doc-Delivery-Number = {9S4FG},
Web-of-Science-Index = {Science Citation Index Expanded (SCI-EXPANDED)},
Unique-ID = {WOS:000946297200001},
DA = {2023-04-22},
}

@article{ WOS:000858733500001,
Author = {Ndaguba, Emeka Austin and Cilliers, Jua and Ghosh, Sumita},
Title = {A Systematic Review of a City in a City: An Aerotropolitan Perspective},
Journal = {LAND},
Year = {2022},
Volume = {11},
Number = {9},
Month = {SEP},
Abstract = {The purpose of this research is to demystify literature on aerotropolis
   using systematic review. Literature on aerial life and aeronautical
   studies suggests that airports are frequently cited outside urban
   centres. However, recent events surrounding the growth of aerotropolis
   contradicts existing realities. In fact, the pull and push factors
   constitute the life cycle of aerotropolis in urban enclaves. In
   generating data for this study, Dimensions, an artificial intelligence
   databank, was adopted, and a hybrid method which combines both VOSviewer
   and Citespace software was the preferred analytical tool for analysis.
   Key findings were imperative in establishing certain parameters
   regarding aerial life, including but not limited to knowledge about the
   technologies adopted, quality of stakeholders, in addition to existing
   relationships of urban space, urbanisation, and geography. Furthermore,
   two recurrent themes were identified, such as the development in ICT,
   and smart technologies, which corresponds with the multiple potentials
   that exist for developing sustainable airports, such as eco-innovation,
   greenovation, and social innovation. This study contributes to the
   concept of transit-bound tourism, a concept we coined to depict the role
   tourism can play in transit philosophy and economics.},
Publisher = {MDPI},
Address = {ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND},
Type = {Review},
Language = {English},
Affiliation = {Ndaguba, EA (Corresponding Author), Univ Technol Sydney, Fac Design Architecture \& Bldg DAB, Sch Built Environm, Ultimo, NSW 2007, Australia.
   Ndaguba, EA (Corresponding Author), Univ South Africa, Coll Econ \& Management Sci, Sch Publ \& Operat Management, ZA-0002 Pretoria, South Africa.
   Ndaguba, Emeka Austin; Cilliers, Jua; Ghosh, Sumita, Univ Technol Sydney, Fac Design Architecture \& Bldg DAB, Sch Built Environm, Ultimo, NSW 2007, Australia.
   Ndaguba, Emeka Austin, Univ South Africa, Coll Econ \& Management Sci, Sch Publ \& Operat Management, ZA-0002 Pretoria, South Africa.
   Cilliers, Jua, North West Univ, Unit Environm Sci \& Management, ZA-2531 Potchefstroom, South Africa.},
DOI = {10.3390/land11091499},
Article-Number = {1499},
EISSN = {2073-445X},
Keywords = {aerotropolis; urbanisation; transit-bound tourism; VOSviewer; citespace;
   technology; new urban extension; transportation; transitional cities;
   built cities},
Keywords-Plus = {URBANIZATION},
Research-Areas = {Environmental Sciences \& Ecology},
Web-of-Science-Categories  = {Environmental Studies},
Author-Email = {emeka.ndaguba@uts.edu.au},
Affiliations = {University of Technology Sydney; University of South Africa; North West
   University - South Africa},
ResearcherID-Numbers = {Ndaguba, Emeka/R-2389-2019},
ORCID-Numbers = {Ndaguba, Emeka/0000-0002-7447-5565},
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Number-of-Cited-References = {52},
Times-Cited = {0},
Usage-Count-Last-180-days = {11},
Usage-Count-Since-2013 = {12},
Journal-ISO = {Land},
Doc-Delivery-Number = {4U3YM},
Web-of-Science-Index = {Social Science Citation Index (SSCI)},
Unique-ID = {WOS:000858733500001},
OA = {gold},
DA = {2023-04-22},
}

@article{ WOS:000720107800001,
Author = {Khanna, Abhirup and Sah, Anushree and Bolshev, Vadim and Jasinski,
   Michal and Vinogradov, Alexander and Leonowicz, Zbigniew and Jasinski,
   Marek},
Title = {Blockchain: Future of e-Governance in Smart Cities},
Journal = {SUSTAINABILITY},
Year = {2021},
Volume = {13},
Number = {21},
Month = {NOV},
Abstract = {In recent times, Blockchain has emerged as a transformational technology
   with the ability to disrupt and evolve multiple domains. As a
   decentralized, immutable distributed ledger, Blockchain technology is
   one of the most recent entrants to the comprehensive ideology of Smart
   Cities. The rise of urbanization and increased citizen participation
   have led to various technology integrations in our present-day cities.
   For cities to become smart, we need standard frameworks and procedures
   for integrating technology, citizens and governments. In this paper, we
   explore the potential of Blockchain technology as an enabler for
   e-governance in smart cities. We examine the daily challenges of
   citizens and compare them with the benefits being offered by Blockchain
   integration. On the basis of a comprehensive literature review, we
   identified four key areas of e-governance wherein Blockchain can provide
   monumental advantages. In the context of Blockchain integration for
   e-governance, the paper presents a survey of prominent published works
   discussing various urban applications.},
Publisher = {MDPI},
Address = {ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND},
Type = {Review},
Language = {English},
Affiliation = {Khanna, A; Sah, A (Corresponding Author), Univ Petr \& Energy Studies, Sch Comp Sci, Dehra Dun 248007, Uttarakhand, India.
   Bolshev, V (Corresponding Author), Fed Sci Agroengn Ctr VIM, Lab Power Supply \& Heat Supply, Moscow 109428, Russia.
   Bolshev, V (Corresponding Author), Don State Syst Univ, Lab Intelligent Agr Machines \& Complexes, Rostov Na Donu 344000, Russia.
   Khanna, Abhirup; Sah, Anushree, Univ Petr \& Energy Studies, Sch Comp Sci, Dehra Dun 248007, Uttarakhand, India.
   Bolshev, Vadim; Vinogradov, Alexander, Fed Sci Agroengn Ctr VIM, Lab Power Supply \& Heat Supply, Moscow 109428, Russia.
   Bolshev, Vadim, Don State Syst Univ, Lab Intelligent Agr Machines \& Complexes, Rostov Na Donu 344000, Russia.
   Jasinski, Michal; Leonowicz, Zbigniew, Wroclaw Univ Sci \& Technol, Fac Elect Engn, Dept Elect Engn Fundamentals, PL-50370 Wroclaw, Poland.
   Jasinski, Marek, WWSIS Horyzont, PL-54239 Wroclaw, Poland.},
DOI = {10.3390/su132111840},
Article-Number = {11840},
EISSN = {2071-1050},
Keywords = {Blockchain; IoT; e-governance; smart cities; urban planning},
Keywords-Plus = {ARTIFICIAL-INTELLIGENCE; HEALTH-CARE; MANAGEMENT; SYSTEM;
   TRANSPORTATION; TRANSACTIONS; TECHNOLOGY; INTERNET; DECISION; THINGS},
Research-Areas = {Science \& Technology - Other Topics; Environmental Sciences \& Ecology},
Web-of-Science-Categories  = {Green \& Sustainable Science \& Technology; Environmental Sciences;
   Environmental Studies},
Author-Email = {abhirupkhanna@yahoo.com
   asah@ddn.upes.ac.in
   vadimbolshev@gmail.com
   michal.jasinski@pwr.edu.pl
   schkolamolen@gmail.com
   zbigniew.leonowicz@pwr.edu.pl
   jasinski.lubin@gmail.com},
Affiliations = {University of Petroleum \& Energy Studies (UPES); Federal Scientific
   Agroengineering Center VIM; Wroclaw University of Science \& Technology},
ResearcherID-Numbers = {Jasinski, Michal/B-1775-2019
   Leonowicz, Zbigniew M./K-8650-2017
   Bolshev, Vadim/M-8440-2018
   },
ORCID-Numbers = {Jasinski, Michal/0000-0002-0983-2562
   Leonowicz, Zbigniew M./0000-0002-2388-3710
   Bolshev, Vadim/0000-0002-5787-8581
   Khanna, Abhirup/0000-0003-4451-0558
   VINOGRADOV, Aleksandr/0000-0002-8845-9718
   Jasinski, Marek/0000-0001-8648-8219},
Funding-Acknowledgement = {Chair of Electrical Engineering Fundamentals, Wroclaw University of
   Technology, Wroclaw, Poland {[}K38W05D02]},
Funding-Text = {This research received funding from the Chair of Electrical Engineering
   Fundamentals (K38W05D02), Wroclaw University of Technology, Wroclaw,
   Poland.},
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Number-of-Cited-References = {105},
Times-Cited = {5},
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Journal-ISO = {Sustainability},
Doc-Delivery-Number = {WZ6XA},
Web-of-Science-Index = {Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)},
Unique-ID = {WOS:000720107800001},
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DA = {2023-04-22},
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@article{ WOS:000652733900001,
Author = {Paiva, Sara and Ahad, Mohd Abdul and Tripathi, Gautami and Feroz,
   Noushaba and Casalino, Gabriella},
Title = {Enabling Technologies for Urban Smart Mobility: Recent Trends,
   Opportunities and Challenges},
Journal = {SENSORS},
Year = {2021},
Volume = {21},
Number = {6},
Month = {MAR},
Abstract = {The increasing population across the globe makes it essential to link
   smart and sustainable city planning with the logistics of transporting
   people and goods, which will significantly contribute to how societies
   will face mobility in the coming years. The concept of smart mobility
   emerged with the popularity of smart cities and is aligned with the
   sustainable development goals defined by the United Nations. A reduction
   in traffic congestion and new route optimizations with reduced
   ecological footprint are some of the essential factors of smart
   mobility; however, other aspects must also be taken into account, such
   as the promotion of active mobility and inclusive mobility, encouraging
   the use of other types of environmentally friendly fuels and engagement
   with citizens. The Internet of Things (IoT), Artificial Intelligence
   (AI), Blockchain and Big Data technology will serve as the main entry
   points and fundamental pillars to promote the rise of new innovative
   solutions that will change the current paradigm for cities and their
   citizens. Mobility-as-a-service, traffic flow optimization, the
   optimization of logistics and autonomous vehicles are some of the
   services and applications that will encompass several changes in the
   coming years with the transition of existing cities into smart cities.
   This paper provides an extensive review of the current trends and
   solutions presented in the scope of smart mobility and enabling
   technologies that support it. An overview of how smart mobility fits
   into smart cities is provided by characterizing its main attributes and
   the key benefits of using smart mobility in a smart city ecosystem.
   Further, this paper highlights other various opportunities and
   challenges related to smart mobility. Lastly, the major services and
   applications that are expected to arise in the coming years within smart
   mobility are explored with the prospective future trends and scope.},
Publisher = {MDPI},
Address = {ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND},
Type = {Review},
Language = {English},
Affiliation = {Ahad, MA (Corresponding Author), Jamia Hamdard, Dept Comp Sci \& Engn, New Delhi 110062, India.
   Casalino, G (Corresponding Author), Univ Bari Aldo Moro, Dept Comp Sci, I-70125 Bari, Italy.
   Paiva, Sara, Inst Politecn Viana Do Castelo, P-4900367 Viana Do Castelo, Portugal.
   Ahad, Mohd Abdul; Tripathi, Gautami; Feroz, Noushaba, Jamia Hamdard, Dept Comp Sci \& Engn, New Delhi 110062, India.
   Casalino, Gabriella, Univ Bari Aldo Moro, Dept Comp Sci, I-70125 Bari, Italy.},
DOI = {10.3390/s21062143},
Article-Number = {2143},
EISSN = {1424-8220},
Keywords = {smart mobility; sustainability; smart cities; smart services},
Keywords-Plus = {INTELLIGENT TRANSPORT-SYSTEMS; PRIVACY PRESERVATION; ICT INTENSITY;
   VEHICLE; CITIES; SERVICE; CITY; DECARBONIZATION; RESPONSIBILITY;
   BLOCKCHAIN},
Research-Areas = {Chemistry; Engineering; Instruments \& Instrumentation},
Web-of-Science-Categories  = {Chemistry, Analytical; Engineering, Electrical \& Electronic;
   Instruments \& Instrumentation},
Author-Email = {sara.paiva@estg.ipvc.pt
   itsmeahad@gmail.com
   gautami1489@gmail.com
   noushaba.feroz@gmail.com
   gabriella.casalino@uniba.it},
Affiliations = {Polytechnic Institute of Viana do Castelo; Jamia Hamdard University;
   Universita degli Studi di Bari Aldo Moro},
ResearcherID-Numbers = {Paiva, Sara/O-9328-2015
   Tripathi, Gautami/AAG-7185-2019
   Casalino, Gabriella/N-6374-2017
   },
ORCID-Numbers = {Paiva, Sara/0000-0002-0041-8939
   Tripathi, Gautami/0000-0001-7140-1563
   Casalino, Gabriella/0000-0003-0713-2260
   Feroz, Noushaba/0000-0002-5016-341X
   Ahad, Mohd Abdul/0000-0002-3880-3680},
Funding-Acknowledgement = {Italian Ministry of Education, University and Research through the
   European PON project AIM (Attraction and International Mobility)
   {[}1852414]},
Funding-Text = {Gabriella Casalino acknowledges funding from the Italian Ministry of
   Education, University and Research through the European PON project AIM
   (Attraction and International Mobility), nr. 1852414, activity 2, line
   1.},
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Number-of-Cited-References = {158},
Times-Cited = {38},
Usage-Count-Last-180-days = {24},
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Journal-ISO = {Sensors},
Doc-Delivery-Number = {SF4NF},
Web-of-Science-Index = {Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)},
Unique-ID = {WOS:000652733900001},
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@article{ WOS:000717749300001,
Author = {Rani, Sita and Mishra, Ram Krishn and Usman, Mohammed and Kataria, Aman
   and Kumar, Pramod and Bhambri, Pankaj and Mishra, Amit Kumar},
Title = {Amalgamation of Advanced Technologies for Sustainable Development of
   Smart City Environment: A Review},
Journal = {IEEE ACCESS},
Year = {2021},
Volume = {9},
Pages = {150060-150087},
Abstract = {The concept of smart city evolved with the integration of information
   and communication technology (ICT) in various sub-systems and processes
   in urban environment. The development of the smart cities is the best
   possible solution to major urban issues. It contributes towards economic
   and social development of the residents. It aims to provide the cordial
   environment in the domains of healthcare, education, transportation,
   power generation and dissipation, security, living, industry, etc., to
   the inhabitants to make their lives comfortable. Sustainability of these
   services is another major objective in a smart city framework. Along
   with the true realization of the idea of a smart city, advanced
   computational and communication technologies are contributing hugely
   towards its sustainable development. Communication technologies act as
   backbone to ensure connectivity at the various levels in a smart city
   framework. Novel smart city solutions for different application domains
   are designed and deployed by the industry using advanced computational
   technologies like IoT, Artificial Intelligence, Blockchain, Big Data and
   Cloud Computing. In this work, authors discuss the concept of smart
   city, its architecture and sustainability. Different operational domains
   in a smart city ecosystem are elaborated. The cyber physical aspect of
   the smart cities is discussed in brief. The role of various
   computational and communication technologies in the sustainable
   development of smart cities is presented. Limiting factors in the
   deployment of various advanced technologies in different smart city
   domains are highlighted. Security issues associated with the
   technological sustainable development of different smart city services
   along with existing solutions are discussed. The article is concluded by
   highlighting the future research directions.},
Publisher = {IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC},
Address = {445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA},
Type = {Review},
Language = {English},
Affiliation = {Mishra, RK (Corresponding Author), Birla Inst Technol \& Sci Pilani, Dept Comp Sci, Dubai Campus, Dubai, U Arab Emirates.
   Kumar, P (Corresponding Author), Manipal Acad Higher Educ, Manipal Inst Technol, Dept Elect \& Commun Engn, Manipal 576104, India.
   Rani, Sita, Gulzar Grp Inst, Dept Comp Sci \& Engn, Khanna 141401, Punjab, India.
   Mishra, Ram Krishn, Birla Inst Technol \& Sci Pilani, Dept Comp Sci, Dubai Campus, Dubai, U Arab Emirates.
   Usman, Mohammed, King Khalid Univ, Dept Elect Engn, Abha 61411, Saudi Arabia.
   Kataria, Aman, CSIR CSIO, Opt Devices \& Syst, Chandigarh 160030, India.
   Kumar, Pramod, Manipal Acad Higher Educ, Manipal Inst Technol, Dept Elect \& Commun Engn, Manipal 576104, India.
   Bhambri, Pankaj, Guru Nanak Dev Engn Coll, Dept Informat Technol, Ludhiana 141006, Punjab, India.
   Mishra, Amit Kumar, DIT Univ, Sch Comp, Dehra Dun 248009, Uttarakhand, India.},
DOI = {10.1109/ACCESS.2021.3125527},
ISSN = {2169-3536},
Keywords = {Smart cities; Urban areas; Sustainable development; Buildings; Wireless
   sensor networks; Statistics; Sociology; Artificial intelligence; big
   data; blockchain; cloud computing; Internet of Things; smart cities;
   sustainability; wireless sensor networks},
Keywords-Plus = {BIG DATA-ANALYSIS; ARTIFICIAL-INTELLIGENCE; COMMUNICATION TECHNOLOGIES;
   WASTE MANAGEMENT; CITIES; INTERNET; CHALLENGES; FUTURE; TRENDS; THINGS},
Research-Areas = {Computer Science; Engineering; Telecommunications},
Web-of-Science-Categories  = {Computer Science, Information Systems; Engineering, Electrical \&
   Electronic; Telecommunications},
Author-Email = {therkmishra@gmail.com
   p.kumar@manipal.edu},
Affiliations = {King Khalid University; Council of Scientific \& Industrial Research
   (CSIR) - India; CSIR - Central Scientific Instruments Organisation
   (CSIO); Manipal Academy of Higher Education (MAHE); Guru Nanak Dev
   Engineering College Ludhiana; DIT University},
ResearcherID-Numbers = {Kumar, Pramod/AAG-9154-2021
   Mishra, Ram Krishn/AAX-2543-2021
   Usman, Mohammed/G-9132-2011
   Mishra, Dr. Amit Kumar/AHB-4242-2022
   Usman, Mohammed/GPX-1230-2022
   Bhambri, Pankaj/D-6024-2016
   },
ORCID-Numbers = {Usman, Mohammed/0000-0002-3352-472X
   Mishra, Dr. Amit Kumar/0000-0001-8079-5066
   Usman, Mohammed/0000-0002-3352-472X
   Bhambri, Pankaj/0000-0003-4437-4103
   Kumar, Pramod/0000-0003-0321-7187
   Rani, Sita/0000-0003-2778-0214
   Mishra, Ram Krishn/0000-0002-7384-6635},
Funding-Acknowledgement = {Deanship of Scienti\~{}c Research at King Khalid University
   {[}RGP.1/376/42]},
Funding-Text = {The authors extend their appreciation to the Deanship of Scienti\~{}c
   Research at King Khalid University for funding this work through General
   Research Project under grant number (RGP.1/376/42).},
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@article{ WOS:000816827300001,
Author = {Shivaprakash, Kadukothanahally Nagaraju and Swami, Niraj and Mysorekar,
   Sagar and Arora, Roshni and Gangadharan, Aditya and Vohra, Karishma and
   Jadeyegowda, Madegowda and Kiesecker, Joseph M.},
Title = {Potential for Artificial Intelligence (AI) and Machine Learning (ML)
   Applications in Biodiversity Conservation, Managing Forests, and Related
   Services in India},
Journal = {SUSTAINABILITY},
Year = {2022},
Volume = {14},
Number = {12},
Month = {JUN},
Abstract = {The recent advancement in data science coupled with the revolution in
   digital and satellite technology has improved the potential for
   artificial intelligence (AI) applications in the forestry and wildlife
   sectors. India shares 7\% of global forest cover and is the 8th most
   biodiverse region in the world. However, rapid expansion of
   developmental projects, agriculture, and urban areas threaten the
   country's rich biodiversity. Therefore, the adoption of new technologies
   like AI in Indian forests and biodiversity sectors can help in effective
   monitoring, management, and conservation of biodiversity and forest
   resources. We conducted a systematic search of literature related to the
   application of artificial intelligence (AI) and machine learning
   algorithms (ML) in the forestry sector and biodiversity conservation
   across globe and in India (using ISI Web of Science and Google Scholar).
   Additionally, we also collected data on AI-based startups and
   non-profits in forest and wildlife sectors to understand the growth and
   adoption of AI technology in biodiversity conservation, forest
   management, and related services. Here, we first provide a global
   overview of AI research and application in forestry and biodiversity
   conservation. Next, we discuss adoption challenges of AI technologies in
   the Indian forestry and biodiversity sectors. Overall, we find that
   adoption of AI technology in Indian forestry and biodiversity sectors
   has been slow compared to developed, and to other developing countries.
   However, improving access to big data related to forest and
   biodiversity, cloud computing, and digital and satellite technology can
   help improve adoption of AI technology in India. We hope that this
   synthesis will motivate forest officials, scientists, and
   conservationists in India to explore AI technology for biodiversity
   conservation and forest management.},
Publisher = {MDPI},
Address = {ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND},
Type = {Review},
Language = {English},
Affiliation = {Shivaprakash, KN (Corresponding Author), Nat Conservancy Ctr, 37 Link Rd Lajpatnagar 3, New Delhi 110024, India.
   Shivaprakash, Kadukothanahally Nagaraju; Mysorekar, Sagar; Arora, Roshni; Gangadharan, Aditya; Vohra, Karishma, Nat Conservancy Ctr, 37 Link Rd Lajpatnagar 3, New Delhi 110024, India.
   Swami, Niraj, Nature Conservancy, Arington, VA 22201 USA.
   Jadeyegowda, Madegowda, Keladi Shivappa Nayaka Univ Agr \& Hort Sci, Coll Forestry, Ponnampet 571216, India.
   Kiesecker, Joseph M., Nature Conservancy, Global Lands Program, Ft Collins, CO 80524 USA.},
DOI = {10.3390/su14127154},
Article-Number = {7154},
EISSN = {2071-1050},
Keywords = {forest; artificial intelligence; forest resource management; machine
   learning; biodiversity conservation},
Keywords-Plus = {WATER-QUALITY ASSESSMENT; NEURAL-NETWORK MODEL; SELF-ORGANIZING MAP;
   FUZZY SYSTEM; FISH; PREDICTION; MANAGEMENT; ABUNDANCE; FLOW;
   CLASSIFICATION},
Research-Areas = {Science \& Technology - Other Topics; Environmental Sciences \& Ecology},
Web-of-Science-Categories  = {Green \& Sustainable Science \& Technology; Environmental Sciences;
   Environmental Studies},
Author-Email = {shivaprakash.kn@tnc.org
   niraj.swami@tnc.org
   sagarmysorekar@hotmail.com
   roshni.arora@tnc.org
   a.gangadharan@tnc.org
   karishma.vohra90@gmail.com
   mjadegowda@gmail.com
   jkiesecker@tnc.org},
Affiliations = {Nature Conservancy; Nature Conservancy},
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@article{ WOS:000867038500001,
Author = {Li, Zhichao and Dong, Jinwei},
Title = {Big Geospatial Data and Data-Driven Methods for Urban Dengue Risk
   Forecasting: A Review},
Journal = {REMOTE SENSING},
Year = {2022},
Volume = {14},
Number = {19},
Month = {OCT},
Abstract = {With advancements in big geospatial data and artificial intelligence,
   multi-source data and diverse data-driven methods have become common in
   dengue risk prediction. Understanding the current state of data and
   models in dengue risk prediction enables the implementation of efficient
   and accurate prediction in the future. Focusing on predictors, data
   sources, spatial and temporal scales, data-driven methods, and model
   evaluation, we performed a literature review based on 53 journal and
   conference papers published from 2018 to the present and concluded the
   following. (1) The predominant predictors include local climate
   conditions, historical dengue cases, vegetation indices, human mobility,
   population, internet search indices, social media indices, landscape,
   time index, and extreme weather events. (2) They are mainly derived from
   the official meteorological agency satellite-based datasets, public
   websites, department of health services and national electronic diseases
   surveillance systems, official statistics, and public transport
   datasets. (3) Country-level, province/state-level, city-level,
   district-level, and neighborhood-level are used as spatial scales, and
   the city-level scale received the most attention. The temporal scales
   include yearly, monthly, weekly, and daily, and both monthly and weekly
   are the most popular options. (4) Most studies define dengue risk
   forecasting as a regression task, and a few studies define it as a
   classification task. Data-driven methods can be categorized into single
   models, ensemble learning, and hybrid learning, with single models being
   further subdivided into time series, machine learning, and deep learning
   models. (5) Model evaluation concentrates primarily on the
   quantification of the difference/correlation between time-series
   observations and predicted values, the ability of models to determine
   whether a dengue outbreak occurs or not, and model uncertainty. Finally,
   we highlighted the importance of big geospatial data, data cloud
   computing, and other deep learning models in future dengue risk
   forecasting.},
Publisher = {MDPI},
Address = {ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND},
Type = {Review},
Language = {English},
Affiliation = {Li, ZC (Corresponding Author), Chinese Acad Sci, Inst Geog Sci \& Nat Resources Res, Key Lab Land Surface Pattern \& Simulat, Beijing 100101, Peoples R China.
   Li, Zhichao; Dong, Jinwei, Chinese Acad Sci, Inst Geog Sci \& Nat Resources Res, Key Lab Land Surface Pattern \& Simulat, Beijing 100101, Peoples R China.},
DOI = {10.3390/rs14195052},
Article-Number = {5052},
EISSN = {2072-4292},
Keywords = {dengue; risk forecasting; big geospatial data; data-driven models;
   review},
Keywords-Plus = {GOOGLE EARTH ENGINE; MODEL; DISEASES; BURDEN},
Research-Areas = {Environmental Sciences \& Ecology; Geology; Remote Sensing; Imaging
   Science \& Photographic Technology},
Web-of-Science-Categories  = {Environmental Sciences; Geosciences, Multidisciplinary; Remote Sensing;
   Imaging Science \& Photographic Technology},
Author-Email = {lizc@igsnrr.ac.cn},
Affiliations = {Chinese Academy of Sciences; Institute of Geographic Sciences \& Natural
   Resources Research, CAS},
ResearcherID-Numbers = {Dong, Jinwei/C-4949-2009},
ORCID-Numbers = {Dong, Jinwei/0000-0001-5687-803X},
Funding-Acknowledgement = {Key Research Program of Frontier Sciences of the Chinese Academy of
   Sciences (CAS) {[}QYZDBSSW-DQC005]; CAS {[}XDA19040301]; Institute of
   Geographic Sciences and Natural Resources Research of the CAS
   {[}E0V00110YZ]},
Funding-Text = {This study was supported by the Key Research Program of Frontier
   Sciences (QYZDBSSW-DQC005) of the Chinese Academy of Sciences (CAS), the
   Strategic Priority Research Program (XDA19040301) of the CAS, and the
   Institute of Geographic Sciences and Natural Resources Research of the
   CAS (E0V00110YZ).},
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@article{ WOS:000457127300189,
Author = {Abduljabbar, Rusul and Dia, Hussein and Liyanage, Sohani and Bagloee,
   Saeed Asadi},
Title = {Applications of Artificial Intelligence in Transport: An Overview},
Journal = {SUSTAINABILITY},
Year = {2019},
Volume = {11},
Number = {1},
Month = {JAN 1},
Abstract = {The rapid pace of developments in Artificial Intelligence (AI) is
   providing unprecedented opportunities to enhance the performance of
   different industries and businesses, including the transport sector. The
   innovations introduced by AI include highly advanced computational
   methods that mimic the way the human brain works. The application of AI
   in the transport field is aimed at overcoming the challenges of an
   increasing travel demand, CO2 emissions, safety concerns, and
   environmental degradation. In light of the availability of a huge amount
   of quantitative and qualitative data and AI in this digital age,
   addressing these concerns in a more efficient and effective fashion has
   become more plausible. Examples of AI methods that are finding their way
   to the transport field include Artificial Neural Networks (ANN), Genetic
   algorithms (GA), Simulated Annealing (SA), Artificial Immune system
   (AIS), Ant Colony Optimiser (ACO) and Bee Colony Optimization (BCO) and
   Fuzzy Logic Model (FLM) The successful application of AI requires a good
   understanding of the relationships between AI and data on one hand, and
   transportation system characteristics and variables on the other hand.
   Moreover, it is promising for transport authorities to determine the way
   to use these technologies to create a rapid improvement in relieving
   congestion, making travel time more reliable to their customers and
   improve the economics and productivity of their vital assets. This paper
   provides an overview of the AI techniques applied worldwide to address
   transportation problems mainly in traffic management, traffic safety,
   public transportation, and urban mobility. The overview concludes by
   addressing the challenges and limitations of AI applications in
   transport.},
Publisher = {MDPI},
Address = {ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND},
Type = {Review},
Language = {English},
Affiliation = {Abduljabbar, R; Dia, H (Corresponding Author), Swinburne Univ Technol, Dept Civil \& Construct Engn, Hawthorn, Vic 3122, Australia.
   Abduljabbar, Rusul; Dia, Hussein; Liyanage, Sohani; Bagloee, Saeed Asadi, Swinburne Univ Technol, Dept Civil \& Construct Engn, Hawthorn, Vic 3122, Australia.},
DOI = {10.3390/su11010189},
Article-Number = {189},
EISSN = {2071-1050},
Keywords = {Artificial Intelligence; Genetic algorithms; Simulated Annealing;
   Artificial Immune system; Ant Colony Optimiser; Bee Colony Optimization;
   public transport; Auto Urban Mobility; traffic management},
Keywords-Plus = {NETWORK DESIGN PROBLEM; NEURAL-NETWORKS; AUTONOMOUS VEHICLES;
   PUBLIC-TRANSPORT; BLACK-BOX; BUS; MODEL; OPTIMIZATION; PREDICTION;
   SYSTEMS},
Research-Areas = {Science \& Technology - Other Topics; Environmental Sciences \& Ecology},
Web-of-Science-Categories  = {Green \& Sustainable Science \& Technology; Environmental Sciences;
   Environmental Studies},
Author-Email = {rabduljabbar@swin.edu.au
   hdia@swin.edu.au
   sliyanage@swin.edu.au
   sasadibagloee@swin.edu.au},
Affiliations = {Swinburne University of Technology},
ResearcherID-Numbers = {Liyanage, Sohani/Q-6258-2019
   abduljabbar, rusul/AAO-1128-2020
   },
ORCID-Numbers = {Liyanage, Sohani/0000-0002-1875-5300
   abduljabbar, rusul/0000-0002-5943-8176
   Asadi Bagloee, Saeed/0000-0001-6078-6314
   Dia, Hussein/0000-0001-8778-7296},
Funding-Acknowledgement = {Iraqi Government; Swinburne University of Technology},
Funding-Text = {Rusul Abduljabbar acknowledges her Ph.D. scholarship provided by the
   Iraqi Government and Swinburne University of Technology. Sohani Liyanage
   acknowledges her Ph.D. scholarship provided by the Swinburne University
   of Technology.},
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Number-of-Cited-References = {154},
Times-Cited = {99},
Usage-Count-Last-180-days = {40},
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Journal-ISO = {Sustainability},
Doc-Delivery-Number = {HJ4ER},
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Unique-ID = {WOS:000457127300189},
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DA = {2023-04-22},
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@article{ WOS:000811546000001,
Author = {Band, Shahab S. and Ardabili, Sina and Sookhak, Mehdi and Chronopoulos,
   Anthony Theodore and Elnaffar, Said and Moslehpour, Massoud and Csaba,
   Mako and Torok, Bernat and Pai, Hao-Ting and Mosavi, Amir},
Title = {When Smart Cities Get Smarter via Machine Learning: An In-Depth
   Literature Review},
Journal = {IEEE ACCESS},
Year = {2022},
Volume = {10},
Pages = {60985-61015},
Abstract = {The manuscript represents a comeprehensive and systematic literature
   review on the machine learning methods in the emerging applications of
   the smart cities. Application domains include the essential aspects of
   the smart cities including the energy, healthcare, transportation,
   security, and pollution. The research methodology presents a
   state-of-the-art taxonomy, evaluation and model performance where the ML
   algorithms are classified into one of the following four categories:
   decision trees, support vector machines, artificial neural networks, and
   advanced machine learning methods, i.e., hybrid methods, ensembles, and
   Deep Learning. The study found that the hybrid models and ensembles have
   better performance since they exhibit both a high accuracy and low
   overall cost. On the other hand, the deep learning (DL) techniques had a
   higher accuracy than the hybrid models and ensembles, but they demanded
   relatively higher computation power. Moreover, all these advanced ML
   methods had a slower processing speed than the single methods. Likewise,
   the support vector machine (SVM) and decision tree (DT) generally
   outperformed the artificial neural network (ANN) for accuracy and other
   metrics. However, since the difference was negligible, it can be
   concluded that using either of them is appropriate.},
Publisher = {IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC},
Address = {445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA},
Type = {Review},
Language = {English},
Affiliation = {Moslehpour, M (Corresponding Author), Asia Univ, Dept Business Adm, Coll Management, Taichung 413, Taiwan.
   Moslehpour, M (Corresponding Author), Calif State Univ San Bernardino, Dept Management, San Bernardino, CA 92407 USA.
   Pai, HT (Corresponding Author), Natl Yunlin Univ Sci \& Technol, Int Grad Inst Artificial Intelligence, Touliu 64002, Taiwan.
   Mosavi, A (Corresponding Author), Obuda Univ, John von Neumann Fac Informat, H-1034 Budapest, Hungary.
   Mosavi, A (Corresponding Author), Slovak Univ Technol Bratislava, Inst Informat Engn Automat \& Math, Bratislava 81107, Slovakia.
   Band, Shahab S., Natl Yunlin Univ Sci \& Technol, Coll Future, Future Technol Res Ctr, Touliu 64002, Taiwan.
   Ardabili, Sina, J Selye Univ, Dept Informat, Komarom 94505, Slovakia.
   Sookhak, Mehdi, Texas A\&M Univ, Dept Comp Sci, Corpus Christi, TX 78412 USA.
   Chronopoulos, Anthony Theodore, Univ Texas San Antonio, Dept Comp Sci, San Antonio, TX 78249 USA.
   Chronopoulos, Anthony Theodore, Univ Patras, Dept Comp Engn \& Informat, Patras 26500, Greece.
   Elnaffar, Said, Canadian Univ Dubai, Fac Engn Appl Sci \& Technol, Dubai, U Arab Emirates.
   Moslehpour, Massoud, Asia Univ, Dept Business Adm, Coll Management, Taichung 413, Taiwan.
   Moslehpour, Massoud, Calif State Univ San Bernardino, Dept Management, San Bernardino, CA 92407 USA.
   Csaba, Mako; Torok, Bernat, Univ Publ Serv, Inst Informat Soc, H-1083 Budapest, Hungary.
   Pai, Hao-Ting, Natl Yunlin Univ Sci \& Technol, Int Grad Inst Artificial Intelligence, Touliu 64002, Taiwan.
   Mosavi, Amir, Obuda Univ, John von Neumann Fac Informat, H-1034 Budapest, Hungary.
   Mosavi, Amir, Slovak Univ Technol Bratislava, Inst Informat Engn Automat \& Math, Bratislava 81107, Slovakia.},
DOI = {10.1109/ACCESS.2022.3181718},
ISSN = {2169-3536},
Keywords = {Smart cities; Databases; Urban areas; Regression tree analysis; Medical
   services; Machine learning; Taxonomy; Smart city; big data; machine
   learning; ensemble; artificial intelligence; deep learning; data
   science; smart grid},
Keywords-Plus = {NEURAL-NETWORK ANN; SUPPORT VECTOR MACHINE; OF-THE-ART; HEALTH-CARE;
   CITY; INTERNET; CLASSIFICATION; INTELLIGENCE; PLATFORM; MODELS},
Research-Areas = {Computer Science; Engineering; Telecommunications},
Web-of-Science-Categories  = {Computer Science, Information Systems; Engineering, Electrical \&
   Electronic; Telecommunications},
Author-Email = {writetodrm@gmail.com
   htpai@yuntech.edu.tw
   amir.mosavi@uni-obuda.hu},
Affiliations = {National Yunlin University Science \& Technology; Texas A\&M University
   System; University of Texas System; University of Texas at San Antonio
   (UTSA); University of Patras; Canadian University Dubai; Asia University
   Taiwan; California State University System; California State University
   San Bernardino; University of Public Service; National Yunlin University
   Science \& Technology; Obuda University; Slovak University of Technology
   Bratislava},
ResearcherID-Numbers = {Sookhak, Mehdi/D-4684-2016
   S. Band, Shahab/ABB-2469-2020
   Mosavi, Amir/I-7440-2018
   Moslehpour, Massoud/N-5923-2018
   },
ORCID-Numbers = {Sookhak, Mehdi/0000-0001-5822-3432
   S. Band, Shahab/0000-0001-6109-1311
   Mosavi, Amir/0000-0003-4842-0613
   Moslehpour, Massoud/0000-0001-8808-2407
   Pai, Hao-Ting/0000-0002-6993-1343},
Funding-Acknowledgement = {European Union's Horizon 2020 Research and Innovation Programme under
   the Programme SASPRO 2 COFUND Marie Sklodowska-Curie {[}945478]},
Funding-Text = {This work was supported by the European Union's Horizon 2020 Research
   and Innovation Programme under the Programme SASPRO 2 COFUND Marie
   Sklodowska-Curie under Grant 945478.},
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Number-of-Cited-References = {123},
Times-Cited = {4},
Usage-Count-Last-180-days = {25},
Usage-Count-Since-2013 = {37},
Journal-ISO = {IEEE Access},
Doc-Delivery-Number = {2D4VE},
Web-of-Science-Index = {Science Citation Index Expanded (SCI-EXPANDED)},
Unique-ID = {WOS:000811546000001},
OA = {gold},
DA = {2023-04-22},
}

@article{ WOS:000816853300001,
Author = {Kappel, Coralea and Rushton-Marovac, Moira and Leong, Darryl and Dent,
   Susan},
Title = {Pursuing Connectivity in Cardio-Oncology Care-The Future of Telemedicine
   and Artificial Intelligence in Providing Equity and Access to Rural
   Communities},
Journal = {FRONTIERS IN CARDIOVASCULAR MEDICINE},
Year = {2022},
Volume = {9},
Month = {JUN 13},
Abstract = {The aim of this review is to discuss the current health disparities in
   rural communities and to explore the potential role of telehealth and
   artificial intelligence in providing cardio-oncology care to
   underserviced communities. With advancements in early detection and
   cancer treatment, survivorship has increased. The interplay between
   cancer and cardiovascular disease, which are the leading causes of
   morbidity and mortality in this population, has been increasingly
   recognized. Worldwide, cardio-oncology clinics (COCs) have emerged to
   deliver a multidisciplinary approach to the care of patients with cancer
   to mitigate cardiovascular risks while minimizing interruptions in
   cancer treatment. Despite the value of COCs, the accessibility gap
   between urban and rural communities in both oncology and cardio-oncology
   contributes to health care disparities and may be an underrecognized
   determinant of health globally. Telehealth and artificial intelligence
   offer opportunities to provide timely care irrespective of rurality. We
   therefore explore current developments within this sphere and propose a
   novel model of care to address the disparity in urban vs. rural
   cardio-oncology using the experience in Canada, a geographically large
   country with many rural communities.},
Publisher = {FRONTIERS MEDIA SA},
Address = {AVENUE DU TRIBUNAL FEDERAL 34, LAUSANNE, CH-1015, SWITZERLAND},
Type = {Review},
Language = {English},
Affiliation = {Dent, S (Corresponding Author), Duke Univ, Duke Canc Inst, Div Med Oncol, Durham, NC 27708 USA.
   Kappel, Coralea; Leong, Darryl, McMaster Univ, Dept Med, Hamilton, ON, Canada.
   Rushton-Marovac, Moira, Univ Ottawa, Ottawa Hosp Canc Ctr, Div Med Oncol, Ottawa, ON, Canada.
   Leong, Darryl, McMaster Univ, Dept Hlth Res Methods Evidence \& Impact, Hamilton, ON, Canada.
   Leong, Darryl, McMaster Univ, Populat Hlth Res Inst, Hamilton, ON, Canada.
   Leong, Darryl, Hamilton Hlth Sci, Hamilton, ON, Canada.
   Dent, Susan, Duke Univ, Duke Canc Inst, Div Med Oncol, Durham, NC 27708 USA.},
DOI = {10.3389/fcvm.2022.927769},
Article-Number = {927769},
ISSN = {2297-055X},
Keywords = {cardio-oncology; telehealth; artificial intelligence; innovation; care
   delivery model},
Keywords-Plus = {HEALTH-CARE; HEART-FAILURE; CANCER CARE; TELEHEALTH; PEOPLE;
   RADIOTHERAPY; EXPERIENCE; MORTALITY; SERVICES; BENEFITS},
Research-Areas = {Cardiovascular System \& Cardiology},
Web-of-Science-Categories  = {Cardiac \& Cardiovascular Systems},
Author-Email = {susan.dent@duke.edu},
Affiliations = {McMaster University; University of Ottawa; Ottawa Hospital Research
   Institute; McMaster University; McMaster University; Population Health
   Research Institute; McMaster University; Duke University},
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Number-of-Cited-References = {73},
Times-Cited = {1},
Usage-Count-Last-180-days = {5},
Usage-Count-Since-2013 = {7},
Journal-ISO = {Front. Cardiovasc. Med.},
Doc-Delivery-Number = {2L2MG},
Web-of-Science-Index = {Science Citation Index Expanded (SCI-EXPANDED)},
Unique-ID = {WOS:000816853300001},
OA = {gold, Green Published},
DA = {2023-04-22},
}

@article{ WOS:000603196300001,
Author = {Serey, Joel and Quezada, Luis and Alfaro, Miguel and Fuertes, Guillermo
   and Ternero, Rodrigo and Gatica, Gustavo and Gutierrez, Sebastian and
   Vargas, Manuel},
Title = {Methodological Proposals for the Development of Services in a Smart
   City: A Literature Review},
Journal = {SUSTAINABILITY},
Year = {2020},
Volume = {12},
Number = {24},
Month = {DEC},
Abstract = {This literature review analyzes and classifies methodological
   contributions that answer the different challenges faced by smart
   cities. This study identifies city services that require the use of
   artificial intelligence (AI); which they refer to as AI application
   areas. These areas are classified and evaluated, taking into account the
   five proposed domains (government, environment, urban settlements,
   social assistance, and economy). In this review, 168 relevant studies
   were identified that make methodological contributions to the
   development of smart cities and 66 AI application areas, along with the
   main challenges associated with their implementation. The review
   methodology was content analysis of scientific literature published
   between 2013 and 2020. The basic terminology of this study corresponds
   to AI, the internet of things, and smart cities. In total, 196
   references were used. Finally, the methodologies that propose
   optimization frameworks and analytical frameworks, the type of
   conceptual research, the literature published in 2018, the urban
   settlement macro-categories, and the group city monitoring-smart
   electric grid, make the greater contributions.},
Publisher = {MDPI},
Address = {ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND},
Type = {Review},
Language = {English},
Affiliation = {Fuertes, G (Corresponding Author), Univ Santiago Chile, Ind Engn Dept, Ave Ecuador 3769, Santiago 9170124, Chile.
   Fuertes, G (Corresponding Author), Univ Bernardo OHiggins, Fac Ingn Ciencia \& Tecnol, Ave Viel 1497,Ruta 5 Sur, Santiago 8370993, Chile.
   Serey, Joel; Quezada, Luis; Alfaro, Miguel; Fuertes, Guillermo; Ternero, Rodrigo; Vargas, Manuel, Univ Santiago Chile, Ind Engn Dept, Ave Ecuador 3769, Santiago 9170124, Chile.
   Fuertes, Guillermo, Univ Bernardo OHiggins, Fac Ingn Ciencia \& Tecnol, Ave Viel 1497,Ruta 5 Sur, Santiago 8370993, Chile.
   Ternero, Rodrigo, Univ Amer, Inst Matemat Fis \& Estadist, Ave Republ 71, Santiago 7500975, Chile.
   Gatica, Gustavo, Univ Andres Bello, Fac Ingn, Antonio Varas 880, Santiago 7500971, Chile.
   Gutierrez, Sebastian, Univ Cent Chile, Fac Econ Gobierno \& Comunicac, Lord Cochrane 417, Santiago 8330507, Chile.
   Gutierrez, Sebastian, Univ Mayor, Fac Ciencias, Manuel Montt 318, Santiago 7500628, Chile.},
DOI = {10.3390/su122410249},
Article-Number = {10249},
EISSN = {2071-1050},
Keywords = {artificial intelligence; internet of things; city services; smart cities},
Keywords-Plus = {DATA ANALYTICS FRAMEWORK; BIG DATA ANALYTICS; ARTIFICIAL-INTELLIGENCE;
   HEALTH-CARE; E-GOVERNMENT; TRAFFIC MANAGEMENT; MONITORING-SYSTEM; CITIES
   LESSONS; PUBLIC VALUE; INTERNET},
Research-Areas = {Science \& Technology - Other Topics; Environmental Sciences \& Ecology},
Web-of-Science-Categories  = {Green \& Sustainable Science \& Technology; Environmental Sciences;
   Environmental Studies},
Author-Email = {joel.serey@usach.cl
   luis.quezada@usach.cl
   miguel.alfaro@usach.cl
   guillermo.fuertes@usach.cl
   rodrigo.ternero@usach.cl
   ggatica@unab.cl
   sebastian.gutierrez@ucentral.cl
   manuel.vargasg@usach.cl},
Affiliations = {Universidad de Santiago de Chile; Universidad  Bernardo O'Higgins;
   Universidad de Las Americas - Chile; Universidad Andres Bello;
   Universidad Central de Chile; Universidad Mayor},
ResearcherID-Numbers = {Fuertes, Guillermo/Q-2341-2016
   },
ORCID-Numbers = {Fuertes, Guillermo/0000-0003-3044-5919
   Alfaro, Miguel/0000-0002-1633-8853
   Vargas Guzman, Manuel Eduardo/0000-0003-4161-6621
   Gustavo, Gatica/0000-0002-1816-6856},
Funding-Acknowledgement = {DICYT (Scientific and Technological Research Bureau) of the University
   of Santiago of Chile (USACH); Department of Industrial Engineering;
   Project FONDEF: ``Multiuser VLC for Underground Mining{''}
   {[}IT17M10012]},
Funding-Text = {This research was supported by DICYT (Scientific and Technological
   Research Bureau) of the University of Santiago of Chile (USACH) and the
   Department of Industrial Engineering. Project FONDEF IT17M10012:
   ``Multiuser VLC for Underground Mining{''}.},
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Number-of-Cited-References = {196},
Times-Cited = {8},
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Journal-ISO = {Sustainability},
Doc-Delivery-Number = {PL5YE},
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Unique-ID = {WOS:000603196300001},
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@article{ WOS:000845100100001,
Author = {Li, Yongchang and Peng, Li and Wu, Chengwei and Zhang, Jiazhen},
Title = {Street View Imagery (SVI) in the Built Environment: A Theoretical and
   Systematic Review},
Journal = {BUILDINGS},
Year = {2022},
Volume = {12},
Number = {8},
Month = {AUG},
Abstract = {Street view imagery (SVI) provides efficient access to data that can be
   used to research spatial quality at the human scale. The previous
   reviews have mainly focused on specific health findings and
   neighbourhood environments. There has not been a comprehensive review of
   this topic. In this paper, we systematically review the literature on
   the application of SVI in the built environment, following a formal
   innovation-decision framework. The main findings are as follows: (I) SVI
   remains an effective tool for automated research assessments. This
   offers a new research avenue to expand the built environment-measurement
   methods to include perceptions in addition to physical features. (II)
   Currently, SVI is functional and valuable for quantifying the built
   environment, spatial sentiment perception, and spatial semantic
   speculation. (III) The significant dilemmas concerning the adoption of
   this technology are related to image acquisition, the image quality,
   spatial and temporal distribution, and accuracy. (IV) This research
   provides a rapid assessment and provides researchers with guidance for
   the adoption and implementation of SVI. Data integration and management,
   proper image service provider selection, and spatial metrics
   measurements are the critical success factors. A notable trend is the
   application of SVI towards a focus on the perceptions of the built
   environment, which provides a more refined and effective way to depict
   urban forms in terms of physical and social spaces.},
Publisher = {MDPI},
Address = {ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND},
Type = {Review},
Language = {English},
Affiliation = {Zhang, JZ (Corresponding Author), Univ Mons, Fac Architecture \& Urban Planning, Rue Havre 88, B-7000 Mons, Belgium.
   Li, Yongchang; Peng, Li; Wu, Chengwei, Nanjing Forestry Univ, Coll Art \& Design, 159 Longpan Rd, Nanjing 210037, Peoples R China.
   Zhang, Jiazhen, Univ Mons, Fac Architecture \& Urban Planning, Rue Havre 88, B-7000 Mons, Belgium.},
DOI = {10.3390/buildings12081167},
Article-Number = {1167},
EISSN = {2075-5309},
Keywords = {adoption strengths; artificial intelligence; building environment;
   critical success factors; computer vision; deep learning; street view
   image; visual analytics},
Keywords-Plus = {THERMAL COMFORT; URBAN GREENERY; MENTAL-HEALTH; WEB; SCIENCE; AUDIT;
   LANDSCAPE; STRENGTHS; PHOENIX; QUALITY},
Research-Areas = {Construction \& Building Technology; Engineering},
Web-of-Science-Categories  = {Construction \& Building Technology; Engineering, Civil},
Author-Email = {jiazhen.zhang@umons.ac.be},
Affiliations = {Nanjing Forestry University; University of Mons},
ORCID-Numbers = {Peng, Li/0000-0001-6856-488X
   /0000-0001-7455-0548},
Funding-Acknowledgement = {National Social Science Foundation in Art, PRC {[}21ZD11]; Theoretical
   and Practical Innovation Research Artistic Evaluation System},
Funding-Text = {This research was funded by the National Social Science Foundation in
   Art, PRC, grant number 21ZD11. The APC was funded by the Theoretical and
   Practical Innovation Research Artistic Evaluation System.},
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Number-of-Cited-References = {150},
Times-Cited = {5},
Usage-Count-Last-180-days = {41},
Usage-Count-Since-2013 = {51},
Journal-ISO = {BUILDINGS-BASEL},
Doc-Delivery-Number = {4A4VD},
Web-of-Science-Index = {Science Citation Index Expanded (SCI-EXPANDED)},
Unique-ID = {WOS:000845100100001},
OA = {gold},
DA = {2023-04-22},
}

@article{ WOS:000690510700001,
Author = {Ghazal, Taher M. and Hasan, Mohammad Kamrul and Alshurideh, Muhammad
   Turki and Alzoubi, Haitham M. and Ahmad, Munir and Akbar, Syed Shehryar
   and Al Kurdi, Barween and Akour, Iman A.},
Title = {IoT for Smart Cities: Machine Learning Approaches in Smart Healthcare-A
   Review},
Journal = {FUTURE INTERNET},
Year = {2021},
Volume = {13},
Number = {8},
Month = {AUG},
Abstract = {Smart city is a collective term for technologies and concepts that are
   directed toward making cities efficient, technologically more advanced,
   greener and more socially inclusive. These concepts include technical,
   economic and social innovations. This term has been tossed around by
   various actors in politics, business, administration and urban planning
   since the 2000s to establish tech-based changes and innovations in urban
   areas. The idea of the smart city is used in conjunction with the
   utilization of digital technologies and at the same time represents a
   reaction to the economic, social and political challenges that
   post-industrial societies are confronted with at the start of the new
   millennium. The key focus is on dealing with challenges faced by urban
   society, such as environmental pollution, demographic change, population
   growth, healthcare, the financial crisis or scarcity of resources. In a
   broader sense, the term also includes non-technical innovations that
   make urban life more sustainable. So far, the idea of using IoT-based
   sensor networks for healthcare applications is a promising one with the
   potential of minimizing inefficiencies in the existing infrastructure. A
   machine learning approach is key to successful implementation of the
   IoT-powered wireless sensor networks for this purpose since there is
   large amount of data to be handled intelligently. Throughout this paper,
   it will be discussed in detail how AI-powered IoT and WSNs are applied
   in the healthcare sector. This research will be a baseline study for
   understanding the role of the IoT in smart cities, in particular in the
   healthcare sector, for future research works.},
Publisher = {MDPI},
Address = {ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND},
Type = {Review},
Language = {English},
Affiliation = {Alzoubi, HM (Corresponding Author), Skyline Univ Coll, Sch Business, Sharjah 1797, U Arab Emirates.
   Ghazal, Taher M.; Hasan, Mohammad Kamrul, Univ Kebansaan Malaysia UKM, Ctr Cyber Secur, Fac Informat Sci \& Technol, Bangi 43600, Selangor, Malaysia.
   Ghazal, Taher M., Skyline Univ Coll, Sch Informat Technol, Sharjah 1797, U Arab Emirates.
   Alshurideh, Muhammad Turki, Univ Jordan, Dept Mkt, Sch Business, Amman 11942, Jordan.
   Alshurideh, Muhammad Turki, Univ Sharjah, Coll Business, Dept Management, Sharjah 27272, U Arab Emirates.
   Alzoubi, Haitham M., Skyline Univ Coll, Sch Business, Sharjah 1797, U Arab Emirates.
   Ahmad, Munir; Akbar, Syed Shehryar, Natl Coll Business Adm \& Econ, Sch Comp Sci, Lahore 54000, Pakistan.
   Al Kurdi, Barween, Hashemite Univ, Dept Business Adm, Fac Econ \& Adm Sci, Zarqa 13115, Jordan.
   Akour, Iman A., Univ Sharjah, Dept Informat Syst, Coll Comp \& Informat, Sharjah 27272, U Arab Emirates.},
DOI = {10.3390/fi13080218},
Article-Number = {218},
EISSN = {1999-5903},
Keywords = {smart cities; IoT; machine learning; sensor networks; artificial
   intelligence; healthcare},
Keywords-Plus = {ARTIFICIAL-INTELLIGENCE; INTERNET; SCHEME},
Research-Areas = {Computer Science},
Web-of-Science-Categories  = {Computer Science, Information Systems},
Author-Email = {ghazal1000@gmail.com
   mkhasan@ukm.edu.my
   malshurideh@sharjah.ac.ae
   haitham.alzubi@skylineuniversity.ac.ae
   munir@ncbae.edu.pk
   syedshehryar@live.com
   barween@hu.edu.jo
   iakour@sharjah.ac.ae},
Affiliations = {University of Jordan; University of Sharjah; Hashemite University;
   University of Sharjah},
ResearcherID-Numbers = {Ghazal, Taher M./AAS-7443-2021
   Hasan, Mohammad Kamrul/F-4782-2015
   Al Kurdi, Barween/AAA-4558-2022
   Alzoubi, Haitham M./A-9678-2018
   Ahmad, Munir/F-7482-2018
   },
ORCID-Numbers = {Ghazal, Taher M./0000-0003-0672-7924
   Hasan, Mohammad Kamrul/0000-0001-5511-0205
   Al Kurdi, Barween/0000-0002-0825-4617
   Alzoubi, Haitham M./0000-0003-3178-4007
   Ahmad, Munir/0000-0002-5240-0984
   Alshurideh, D. Muhammad Turki/0000-0002-7336-381X
   Akbar, Syed Shehryar/0000-0002-3024-9419},
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Number-of-Cited-References = {40},
Times-Cited = {59},
Usage-Count-Last-180-days = {10},
Usage-Count-Since-2013 = {32},
Journal-ISO = {Future Internet},
Doc-Delivery-Number = {UI3KN},
Web-of-Science-Index = {Emerging Sources Citation Index (ESCI)},
Unique-ID = {WOS:000690510700001},
OA = {gold},
DA = {2023-04-22},
}

@article{ WOS:000909133200001,
Author = {Zinno, Raffaele and Haghshenas, Sina Shaffiee and Guido, Giuseppe and
   Rashvand, Kaveh and Vitale, Alessandro and Sarhadi, Ali},
Title = {The State of the Art of Artificial Intelligence Approaches and New
   Technologies in Structural Health Monitoring of Bridges},
Journal = {APPLIED SCIENCES-BASEL},
Year = {2023},
Volume = {13},
Number = {1},
Month = {JAN},
Abstract = {The challenges of urban administration are growing, as the population,
   automobiles, and cities rise. Making cities smarter is thus one of the
   most effective solutions to urban issues. A key feature of the ``smart
   cities{''} of today is that they use cutting-edge technology in their
   infrastructure and services. With strategic planning, the smart city
   utilizes its resources in the most efficient manner. With reduced
   expenses and enhanced infrastructure, smart cities provide their
   residents with more and better services. One of these important urban
   services that can be very helpful in managing cities is structural
   health monitoring (SHM). By combining leading new technologies like the
   Internet of Things (IoT) with structural health monitoring, important
   urban infrastructure can last longer and work better. A thorough
   examination of recent advances in SHM for infrastructure is thus
   warranted. Bridges are one of the most important parts of a city's
   infrastructure, and their building, development, and proper maintenance
   are some of the most important aspects of managing a city. The main goal
   of this study is to look at how artificial intelligence (AI) and some
   technologies, like drone technology and 3D printers, could be used to
   improve the current state of the art in SHM systems for bridges,
   including conceptual frameworks, benefits and problems, and existing
   methods. An outline of the role AI and other technologies will play in
   SHM systems of bridges in the future was provided in this study. Some
   novel technology-aided research opportunities are also highlighted,
   explained, and discussed.},
Publisher = {MDPI},
Address = {ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND},
Type = {Review},
Language = {English},
Affiliation = {Zinno, R (Corresponding Author), Univ Calabria, Dept Environm Engn, Via Bucci, I-87036 Arcavacata Di Rende, Italy.
   Zinno, Raffaele, Univ Calabria, Dept Environm Engn, Via Bucci, I-87036 Arcavacata Di Rende, Italy.
   Haghshenas, Sina Shaffiee; Guido, Giuseppe; Vitale, Alessandro, Univ Calabria, Dept Civil Engn, Via Bucci, I-87036 Arcavacata Di Rende, Italy.
   Rashvand, Kaveh; Sarhadi, Ali, Tech Univ Denmark, Dept Wind \& Energy Syst, Riso Campus,Frederiksborgvej 399, DK-4000 Roskilde, Denmark.},
DOI = {10.3390/app13010097},
Article-Number = {97},
EISSN = {2076-3417},
Keywords = {smart cities; SHM; bridge; IoT; AI; drone technology; 3D printer},
Keywords-Plus = {DAMAGE DETECTION; SMART CITIES; IDENTIFICATION; CHALLENGES; MANAGEMENT;
   SYSTEM; MODEL},
Research-Areas = {Chemistry; Engineering; Materials Science; Physics},
Web-of-Science-Categories  = {Chemistry, Multidisciplinary; Engineering, Multidisciplinary; Materials
   Science, Multidisciplinary; Physics, Applied},
Author-Email = {raffaele.zinno@unical.it},
Affiliations = {University of Calabria; University of Calabria; Technical University of
   Denmark},
ResearcherID-Numbers = {Shaffiee Haghshenas, Sina/J-3688-2019
   Rashvand, Kaveh/AAO-6762-2020
   Guido, Giuseppe/P-3632-2015},
ORCID-Numbers = {Shaffiee Haghshenas, Sina/0000-0003-2859-3920
   Rashvand, Kaveh/0000-0002-7384-2335
   Guido, Giuseppe/0000-0001-7570-4123},
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Number-of-Cited-References = {155},
Times-Cited = {1},
Usage-Count-Last-180-days = {17},
Usage-Count-Since-2013 = {17},
Journal-ISO = {Appl. Sci.-Basel},
Doc-Delivery-Number = {7Q1BU},
Web-of-Science-Index = {Science Citation Index Expanded (SCI-EXPANDED)},
Unique-ID = {WOS:000909133200001},
OA = {Green Published, gold},
DA = {2023-04-22},
}

@article{ WOS:000798064000048,
Author = {Yaminifar, Leila and Altisench Jane, Barbara and Tarraga Lopez, Pedro
   Juan and Doudaran, Pooya Jafari and Endo Milan, Jesus Yasoda and
   Hivechi, Nafiseh},
Title = {A review on endometrial cancer; artificial intelligence, imaging
   modalities},
Journal = {MEDICINA BALEAR},
Year = {2022},
Volume = {37},
Number = {3},
Pages = {102-107},
Month = {MAY-JUN},
Abstract = {Endometrial cancer is one of the most common cancers among women
   especially in urban areas. The appearance of symptoms such as abnormal
   uterine bleeding or infertility on clinical examination increases the
   risk of endometrial lesion. Vaginal ultrasound and diagnostic
   hysteroscopy are common gynecological examinations for endometrial
   lesions. Endometrial cancer is often diagnosed in the early stages, in
   which case surgical removal of the uterus often cures endometrial
   cancer. However, screening and treatment programs are important in the
   timely diagnosis and treatment of this disease. Due to the importance of
   the topic, the present study was conducted to investigate the prevalence
   of this disease, the relationship between artificial intelligence as
   well as various imaging methods and the diagnosis of endometrial cancer.},
Publisher = {REIAL ACAD MEDICINA ILLES BALEARS},
Address = {CALLE COMPANER 4, PALMA DE MALLORCA, 07003, SPAIN},
Type = {Review},
Language = {English},
Affiliation = {Hivechi, N (Corresponding Author), Univ Tehran Med Sci, Imam Khomeini Hosp Complex, Obstet \& Gynecol Dept, Tehran, Iran.
   Yaminifar, Leila, Shahid Beheshti Univ Med Sci, Tehran, Iran.
   Altisench Jane, Barbara, Atenc Primaria Mallorca, Palma De Mallorca, Illes Balears, Spain.
   Tarraga Lopez, Pedro Juan, Univ Castilla La Mancha, Ciudad Real, Spain.
   Doudaran, Pooya Jafari, Qom Univ Med Sci, Fac Med, Qom, Iran.
   Endo Milan, Jesus Yasoda, Univ Ciencias Med Villa Clara, Santa Clara, Cuba.
   Hivechi, Nafiseh, Univ Tehran Med Sci, Imam Khomeini Hosp Complex, Obstet \& Gynecol Dept, Tehran, Iran.},
DOI = {10.3306/AJHS.2022.37.03.102},
ISSN = {1579-5853},
EISSN = {2255-0569},
Keywords = {Artificial intelligence; endometrial cancer; imaging modalities},
Keywords-Plus = {PREOPERATIVE RISK STRATIFICATION; MYOMETRIAL INVASION; MRI; MODEL},
Research-Areas = {General \& Internal Medicine},
Web-of-Science-Categories  = {Medicine, General \& Internal},
Author-Email = {unicahinvestigacion20219@gmail.com},
Affiliations = {Shahid Beheshti University Medical Sciences; Universidad de Castilla-La
   Mancha; Tehran University of Medical Sciences},
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Number-of-Cited-References = {30},
Times-Cited = {0},
Usage-Count-Last-180-days = {3},
Usage-Count-Since-2013 = {10},
Journal-ISO = {Med. Balear},
Doc-Delivery-Number = {1J6YY},
Web-of-Science-Index = {Emerging Sources Citation Index (ESCI)},
Unique-ID = {WOS:000798064000048},
DA = {2023-04-22},
}

@article{ WOS:000520407400011,
Author = {Hong, Tianzhen and Wang, Zhe and Luo, Xuan and Zhang, Wanni},
Title = {State-of-the-art on research and applications of machine learning in the
   building life cycle},
Journal = {ENERGY AND BUILDINGS},
Year = {2020},
Volume = {212},
Month = {APR 1},
Abstract = {Fueled by big data, powerful and affordable computing resources, and
   advanced algorithms, machine learning has been explored and applied to
   buildings research for the past decades and has demonstrated its
   potential to enhance building performance. This study systematically
   surveyed how machine learning has been applied at different stages of
   building life cycle. By conducting a literature search on the Web of
   Knowledge platform, we found 9579 papers in this field and selected 153
   papers for an in-depth review. The number of published papers is
   increasing year by year, with a focus on building design, operation, and
   control. However, no study was found using machine learning in building
   commissioning. There are successful pilot studies on fault detection and
   diagnosis of HVAC equipment and systems, load prediction, energy
   baseline estimate, load shape clustering, occupancy prediction, and
   learning occupant behaviors and energy use patterns. None of the
   existing studies were adopted broadly by the building industry, due to
   common challenges including (1) lack of large scale labeled data to
   train and validate the model, (2) lack of model transferability, which
   limits a model trained with one data-rich building to be used in another
   building with limited data, (3) lack of strong justification of costs
   and benefits of deploying machine learning, and (4) the performance
   might not be reliable and robust for the stated goals, as the method
   might work for some buildings but could not be generalized to others.
   Findings from the study can inform future machine learning research to
   improve occupant comfort, energy efficiency, demand flexibility, and
   resilience of buildings, as well as to inspire young researchers in the
   field to explore multidisciplinary approaches that integrate building
   science, computing science, data science, and social science. (C) 2020
   Elsevier B.V. All rights reserved.},
Publisher = {ELSEVIER SCIENCE SA},
Address = {PO BOX 564, 1001 LAUSANNE, SWITZERLAND},
Type = {Review},
Language = {English},
Affiliation = {Hong, TZ (Corresponding Author), Lawrence Berkeley Natl Lab, Bldg Technol \& Urban Syst Div, One Cyclotron Rd, Berkeley, CA 94720 USA.
   Hong, Tianzhen; Wang, Zhe; Luo, Xuan; Zhang, Wanni, Lawrence Berkeley Natl Lab, Bldg Technol \& Urban Syst Div, One Cyclotron Rd, Berkeley, CA 94720 USA.},
DOI = {10.1016/j.enbuild.2020.109831},
Article-Number = {109831},
ISSN = {0378-7788},
EISSN = {1872-6178},
Keywords = {Machine learning; Artificial intelligence; Buildings; Building life
   cycle; Building control; Building performance},
Keywords-Plus = {CLOSE-RANGE PHOTOGRAMMETRY; ARTIFICIAL NEURAL-NETWORKS; MODEL-PREDICTIVE
   CONTROL; ONLINE FAULT-DETECTION; DATA-DRIVEN; ENERGY PERFORMANCE;
   THERMAL COMFORT; AUTOMATED MEASUREMENT; DATA ANALYTICS; FDD STRATEGY},
Research-Areas = {Construction \& Building Technology; Energy \& Fuels; Engineering},
Web-of-Science-Categories  = {Construction \& Building Technology; Energy \& Fuels; Engineering, Civil},
Author-Email = {thong@lbl.gov},
Affiliations = {United States Department of Energy (DOE); Lawrence Berkeley National
   Laboratory},
ResearcherID-Numbers = {Hong, Tianzhen/D-3256-2013
   },
ORCID-Numbers = {Hong, Tianzhen/0000-0003-1886-9137
   Wang, Zhe/0000-0002-2231-1606},
Funding-Acknowledgement = {Laboratory Directed Research and Development (LDRD) from Lawrence
   Berkeley National Laboratory; Office of Science, of the U.S. Department
   of Energy {[}DE-AC02-05CH11231]},
Funding-Text = {This work was supported by Laboratory Directed Research and Development
   (LDRD) funding from Lawrence Berkeley National Laboratory, provided by
   the Director, Office of Science, of the U.S. Department of Energy under
   Contract No. DE-AC02-05CH11231},
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@article{ WOS:000615635500001,
Author = {Feroz, Abdul Karim and Zo, Hangjung and Chiravuri, Ananth},
Title = {Digital Transformation and Environmental Sustainability: A Review and
   Research Agenda},
Journal = {SUSTAINABILITY},
Year = {2021},
Volume = {13},
Number = {3},
Month = {FEB},
Abstract = {Digital transformation refers to the unprecedented disruptions in
   society, industry, and organizations stimulated by advances in digital
   technologies such as artificial intelligence, big data analytics, cloud
   computing, and the Internet of Things (IoT). Presently, there is a lack
   of studies to map digital transformation in the environmental
   sustainability domain. This paper identifies the disruptions driven by
   digital transformation in the environmental sustainability domain
   through a systematic literature review. The results present a framework
   that outlines the transformations in four key areas: pollution control,
   waste management, sustainable production, and urban sustainability. The
   transformations in each key area are divided into further
   sub-categories. This study proposes an agenda for future research in
   terms of organizational capabilities, performance, and digital
   transformation strategy regarding environmental sustainability.},
Publisher = {MDPI},
Address = {ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND},
Type = {Review},
Language = {English},
Affiliation = {Zo, HJ (Corresponding Author), Korea Adv Inst Sci \& Technol KAIST, Sch Business \& Technol Management, Coll Business, Daejeon 34141, South Korea.
   Feroz, Abdul Karim; Zo, Hangjung, Korea Adv Inst Sci \& Technol KAIST, Sch Business \& Technol Management, Coll Business, Daejeon 34141, South Korea.
   Chiravuri, Ananth, United Arab Emirates Univ, Coll Business \& Econ, Al Ain 15551, U Arab Emirates.},
DOI = {10.3390/su13031530},
Article-Number = {1530},
EISSN = {2071-1050},
Keywords = {digital transformation; environmental sustainability; artificial
   intelligence; big data analytics; Internet of Things; systematic
   literature review},
Keywords-Plus = {BIG DATA ANALYTICS; ARTIFICIAL-INTELLIGENCE; DYNAMIC CAPABILITIES;
   SMART; WASTE; FRAMEWORK; TECHNOLOGIES; CHALLENGES; INDUSTRIAL; POLLUTION},
Research-Areas = {Science \& Technology - Other Topics; Environmental Sciences \& Ecology},
Web-of-Science-Categories  = {Green \& Sustainable Science \& Technology; Environmental Sciences;
   Environmental Studies},
Author-Email = {akferoz@kaist.ac.kr
   joezo@kaist.edu
   ananth.chiravuri@uaeu.ac.ae},
Affiliations = {Korea Advanced Institute of Science \& Technology (KAIST); United Arab
   Emirates University},
ResearcherID-Numbers = {Zo, Hangjung/C-1786-2011},
ORCID-Numbers = {Zo, Hangjung/0000-0002-2892-1659},
Funding-Acknowledgement = {BK21 FOUR program - Ministry of Education (MOE, Republic of Korea)
   {[}5199990114726]; National Research Foundation of Korea (NRF)},
Funding-Text = {This research was supported by the BK21 FOUR program (Project No.
   5199990114726) funded by the Ministry of Education (MOE, Republic of
   Korea) and National Research Foundation of Korea (NRF).},
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@article{ WOS:000610927300001,
Author = {Farzaneh, Hooman and Malehmirchegini, Ladan and Bejan, Adrian and
   Afolabi, Taofeek and Mulumba, Alphonce and Daka, Precious P.},
Title = {Artificial Intelligence Evolution in Smart Buildings for Energy
   Efficiency},
Journal = {APPLIED SCIENCES-BASEL},
Year = {2021},
Volume = {11},
Number = {2},
Month = {JAN},
Abstract = {The emerging concept of smart buildings, which requires the
   incorporation of sensors and big data (BD) and utilizes artificial
   intelligence (AI), promises to usher in a new age of urban energy
   efficiency. By using AI technologies in smart buildings, energy
   consumption can be reduced through better control, improved reliability,
   and automation. This paper is an in-depth review of recent studies on
   the application of artificial intelligence (AI) technologies in smart
   buildings through the concept of a building management system (BMS) and
   demand response programs (DRPs). In addition to elaborating on the
   principles and applications of the AI-based modeling approaches widely
   used in building energy use prediction, an evaluation framework is
   introduced and used for assessing the recent research conducted in this
   field and across the major AI domains, including energy, comfort,
   design, and maintenance. Finally, the paper includes a discussion on the
   open challenges and future directions of research on the application of
   AI in smart buildings.},
Publisher = {MDPI},
Address = {ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND},
Type = {Review},
Language = {English},
Affiliation = {Farzaneh, H (Corresponding Author), Kyushu Univ, Interdisciplinary Grad Sch Engn Sci, Fukuoka 8168580, Japan.
   Farzaneh, Hooman; Malehmirchegini, Ladan; Afolabi, Taofeek; Mulumba, Alphonce; Daka, Precious P., Kyushu Univ, Interdisciplinary Grad Sch Engn Sci, Fukuoka 8168580, Japan.
   Bejan, Adrian, Duke Univ, Dept Mech Engn \& Mat Sci, Durham, NC 27708 USA.},
DOI = {10.3390/app11020763},
Article-Number = {763},
EISSN = {2076-3417},
Keywords = {artificial intelligence; smart buildings; energy efficiency},
Research-Areas = {Chemistry; Engineering; Materials Science; Physics},
Web-of-Science-Categories  = {Chemistry, Multidisciplinary; Engineering, Multidisciplinary; Materials
   Science, Multidisciplinary; Physics, Applied},
Author-Email = {farzaneh.hooman.961@m.kyushu-u.ac.jp
   malehmirchegini.ladan.982@s.kyushu-u.ac.jp
   abejan@duke.edu
   afolabi.taofeek.203@s.kyushu-u.ac.jp
   mulumba.alphonce.203@s.kyushu-u.ac.jp
   daka.preciousp.203@s.kyushu-u.ac.jp},
Affiliations = {Kyushu University; Duke University},
ResearcherID-Numbers = {Farzaneh, Hooman/AAS-7993-2020
   },
ORCID-Numbers = {Farzaneh, Hooman/0000-0002-3753-5928
   Mulumba, Alphonce/0000-0001-6237-3038
   Bejan, Adrian/0000-0002-2419-2698
   Afolabi, Taofeek/0000-0002-4983-3236},
Funding-Acknowledgement = {Kyushu Natural Energy Promotion Organization; Hitachi Global Foundation},
Funding-Text = {This research was supported by the Kyushu Natural Energy Promotion
   Organization and Hitachi Global Foundation for supporting of financial
   support of this research.},
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@article{ WOS:000925307900001,
Author = {Bahramian, Majid and Dereli, Recep Kaan and Zhao, Wanqing and Giberti,
   Matteo and Casey, Eoin},
Title = {Data to intelligence: The role of data-driven models in wastewater
   treatment},
Journal = {EXPERT SYSTEMS WITH APPLICATIONS},
Year = {2023},
Volume = {217},
Month = {MAY 1},
Abstract = {Increasing energy efficiency in wastewater treatment plants (WWTPs) is
   becoming more important. An emerging approach to addressing this issue
   is to exploit development in data science and modelling. Deployment of
   sensors to measure various parameters in WWTPs opens greater
   opportunities for exploiting the wealth of data. Artificial intelligence
   (AI) is emerging as a solution for automation and digitalization in the
   wastewater sector. This review aims to comprehensively investigate,
   summarize and analyze recent developments in AI methods applied to the
   modelling of WWTPs. The review shows that among the standalone models,
   Artificial Neural Networks (ANN) was the most popular model followed by,
   in descending order: Decision Trees (DT), Fuzzy Logic (FL), Genetic
   algorithm (GA) and Support Vector Machine (SVM). In the case of
   incomplete data, FL was the most frequently used method as it uses
   linguistic expert rules to find an approximation for the missing data.
   Regarding accuracy and precision, hybrid models demonstrated relatively
   better performance than the standalone ones. Among these models, the
   Machine Learning (ML)-metaheuristic, which integrates an AI model with a
   bioinspired optimization method, was the most preferred type as it was
   used in more than 45\% of the hybrid models. Correlation coefficient
   (R), Correlation of Determination (R2) and Root Mean Square Error (RMSE)
   were the frequently used metrics for model performance evaluation.
   Finally, the review shows that despite recent developments, industrial
   deployment is still lacking. The industrial application requires close
   interaction of interested parties, among which research institutes,
   private sector and public sector play an inevitable role. The future
   research should focus on mitigating the barriers for more in-depth
   collaboration of interested parties and finding new paths for more
   cooperative and harmonized activity of them.},
Publisher = {PERGAMON-ELSEVIER SCIENCE LTD},
Address = {THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND},
Type = {Review},
Language = {English},
Affiliation = {Bahramian, M (Corresponding Author), Univ Coll Dublin, Sch Chem \& Bioproc Engn, Dublin, Ireland.
   Bahramian, Majid; Dereli, Recep Kaan; Giberti, Matteo; Casey, Eoin, Univ Coll Dublin, Sch Chem \& Bioproc Engn, Dublin, Ireland.
   Zhao, Wanqing, Newcastle Univ, Sch Comp, Urban Sci Bldg,1 Sci Sq, Newcastle Upon Tyne NE4 5TG, England.},
DOI = {10.1016/j.eswa.2022.119453},
EarlyAccessDate = {JAN 2023},
Article-Number = {119453},
ISSN = {0957-4174},
EISSN = {1873-6793},
Keywords = {Artificial intelligence; Machine learning; Modeling; Optimization; Deep
   learning; Wastewater treatment},
Keywords-Plus = {RESPONSE-SURFACE METHODOLOGY; EFFLUENT QUALITY PARAMETERS;
   NEURAL-NETWORK APPROACH; TREATMENT-PLANT; FUZZY-LOGIC;
   ARTIFICIAL-INTELLIGENCE; GENETIC ALGORITHM; REVERSE-OSMOSIS;
   ANAEROBIC-DIGESTION; PREDICTION MODEL},
Research-Areas = {Computer Science; Engineering; Operations Research \& Management Science},
Web-of-Science-Categories  = {Computer Science, Artificial Intelligence; Engineering, Electrical \&
   Electronic; Operations Research \& Management Science},
Author-Email = {Majid.bahramian@ucd.ie
   Wanqing.Zhao@newcastle.ac.uk},
Affiliations = {University College Dublin; Newcastle University - UK},
Funding-Acknowledgement = {Science Foundation Ireland under the SFI Strategic Partnership Programme
   {[}SFI/15/SPP/E3125]},
Funding-Text = {This publication has been financially supported by Science Founda- tion
   Ireland under the SFI Strategic Partnership Programme Grant No.
   SFI/15/SPP/E3125. The opinions, findings and conclusions or recom-
   mendations expressed in this material are those of the author (s) and do
   not necessarily reflect the views of the Science Foundation Ireland.},
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Times-Cited = {0},
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Journal-ISO = {Expert Syst. Appl.},
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@article{ WOS:000903564700001,
Author = {Megahed, Naglaa A. and Hassan, Asmaa M.},
Title = {Evolution of BIM to DTs: A Paradigm Shift for the Post-Pandemic AECO
   Industry},
Journal = {URBAN SCIENCE},
Year = {2022},
Volume = {6},
Number = {4},
Month = {DEC},
Abstract = {The architecture, engineering, construction, and operation (AECO)
   industry is evolving rapidly. In particular, technological advancements
   and lessons learned from the COVID-19 pandemic are shaping the
   industry's future. Various artificial intelligence (AI), building
   information modeling (BIM), and Internet of Things (IoT) techniques have
   contributed to the industry's modernization by enabling more
   self-reliable, self-automated, self-learning, time-saving, and
   cost-effective processes throughout the various life cycle phases of a
   smart building or city. As a result, the concept of digital twins (DTs)
   has recently emerged as a potential solution to optimize the AECO sector
   to achieve the required cyber-physical integration, particularly
   following the pandemic. Based on a systematic review, the study develops
   and proposes theoretical models that examine the evolution of DTs in the
   context of BIM, cutting-edge technologies, platforms, and applications
   throughout the project's life cycle phases. This study demonstrates DTs'
   high potential as a comprehensive approach to planning, managing,
   predicting, and optimizing AECO projects that will achieve more
   Sustainable Development Goals (SDGs). However, while DTs offer many new
   opportunities, they also pose technical, societal, and operational
   challenges that must be addressed.},
Publisher = {MDPI},
Address = {ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND},
Type = {Review},
Language = {English},
Affiliation = {Megahed, NA (Corresponding Author), Port Said Univ, Fac Engn, Architectural Engn \& Urban Planning Dept, Port Said 42523, Egypt.
   Megahed, Naglaa A.; Hassan, Asmaa M., Port Said Univ, Fac Engn, Architectural Engn \& Urban Planning Dept, Port Said 42523, Egypt.},
DOI = {10.3390/urbansci6040067},
Article-Number = {67},
EISSN = {2413-8851},
Keywords = {artificial intelligence; building information modeling; digital twins;
   life cycle; smart building and cities; Sustainable Development Goals},
Keywords-Plus = {DIGITAL TWINS; CONSTRUCTION; CHALLENGES; INTERNET},
Research-Areas = {Environmental Sciences \& Ecology; Geography; Public Administration;
   Urban Studies},
Web-of-Science-Categories  = {Environmental Sciences; Environmental Studies; Geography; Regional \&
   Urban Planning; Urban Studies},
Author-Email = {naglaaali257@eng.psu.edu.eg},
Affiliations = {Egyptian Knowledge Bank (EKB); Port Said University},
ResearcherID-Numbers = {Megahed, Naglaa Ali/L-5089-2019
   },
ORCID-Numbers = {Megahed, Naglaa Ali/0000-0001-5388-5066
   M. Hassan, Asmaa/0000-0001-5047-2442},
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Number-of-Cited-References = {92},
Times-Cited = {2},
Usage-Count-Last-180-days = {8},
Usage-Count-Since-2013 = {8},
Journal-ISO = {Urban Sci.},
Doc-Delivery-Number = {7I0DA},
Web-of-Science-Index = {Emerging Sources Citation Index (ESCI)},
Unique-ID = {WOS:000903564700001},
OA = {gold},
DA = {2023-04-22},
}

@article{ WOS:000912755700001,
Author = {Masoumi, Homa and Shirowzhan, Sara and Eskandarpour, Paria and Pettit,
   Christopher James},
Title = {City Digital Twins: their maturity level and differentiation from 3D
   city models},
Journal = {BIG EARTH DATA},
Year = {2023},
Volume = {7},
Number = {1},
Pages = {1-46},
Month = {JAN 2},
Abstract = {The emerging field of City Digital Twins has advanced in recent years
   with the help of digital infrastructure and technologies connected to
   the Internet of Things (IoT). However, the evolution of this field has
   been so fast that a gap has opened in relation to systematic reviews of
   the relevant literature and the maturation of City Digital Twins on an
   urban scale. Our work bridges this gap by highlighting maturity in the
   field. We conducted a systematic literature review with bibliometric and
   content analysis of 41 selected papers published in Web of Science and
   Scopus databases, covering five areas: data types and sources, case
   studies, applied technologies and methods, maturity spectrum, and
   applications. Based on maturity indicators, the majority of the reviewed
   studies (90\%) were at initial to medium stages of maturity (up to
   element 3), most of them focused on 3D modelling, monitoring and
   visualisation. However, digital twins cannot be limited to 3D models,
   monitoring and visualisation, for they can be developed to include
   two-directional interactions between humans and computers. Such a high
   level of maturity, which was not found in the reviewed studies, requires
   advanced technologies and methods such as cloud computing, artificial
   intelligence, BIM and GIS. We also found that further studies are
   essential if the field is to handle the complex urban challenges of
   multidisciplinary digital twins . While City Digital Twins extend by
   definition beyond mere 3D city modelling, some studies involving 3D city
   models still refer to their subjects as City Digital Twins. Among the
   research gaps we identified, we'd like to highlight the need for
   near-real-time data analytics algorithms, which could furnish City
   Digital Twins with big data insights. Other opportunities include public
   participation capabilities to increase social collaboration, integrating
   BIM and GIS technologies and improving storage and computation
   infrastructure.},
Publisher = {TAYLOR \& FRANCIS LTD},
Address = {2-4 PARK SQUARE, MILTON PARK, ABINGDON OR14 4RN, OXON, ENGLAND},
Type = {Review},
Language = {English},
Affiliation = {Shirowzhan, S (Corresponding Author), Red Ctr, West Wing,UNSW Kensington Campus, Kensington, NSW 2052, Australia.
   Masoumi, Homa, Shahid Beheshti Univ, Dept Urban \& Reg Planning, Tehran, Iran.
   Shirowzhan, Sara, UNSW Sydney, Sch Built Environm, Sydney, Australia.
   Eskandarpour, Paria, Monash Univ, Fac Informat Technol, Clayton, Vic, Australia.
   Pettit, Christopher James, UNSW Sydney, Fac Arts Design \& Architecture, City Futures Res Ctr, Sydney, NSW, Australia.
   Shirowzhan, Sara, Red Ctr, West Wing,UNSW Kensington Campus, Kensington, NSW 2052, Australia.},
DOI = {10.1080/20964471.2022.2160156},
EarlyAccessDate = {JAN 2023},
ISSN = {2096-4471},
EISSN = {2574-5417},
Keywords = {City Digital Twins; big data; IoT; artificial intelligence; city
   information modelling},
Keywords-Plus = {BUILT ENVIRONMENT; SMART CITY; GENERATION; BLOCKCHAIN; BIM},
Research-Areas = {Computer Science; Geology; Remote Sensing},
Web-of-Science-Categories  = {Computer Science, Information Systems; Geosciences, Multidisciplinary;
   Remote Sensing},
Author-Email = {s.shirowzhan@unsw.edu.au},
Affiliations = {Shahid Beheshti University; University of New South Wales Sydney; Monash
   University; University of New South Wales Sydney},
ORCID-Numbers = {Shirowzhan, Sara/0000-0003-1511-3617},
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Number-of-Cited-References = {128},
Times-Cited = {0},
Usage-Count-Last-180-days = {14},
Usage-Count-Since-2013 = {14},
Journal-ISO = {Big Earth Data},
Doc-Delivery-Number = {9E5RJ},
Web-of-Science-Index = {Emerging Sources Citation Index (ESCI)},
Unique-ID = {WOS:000912755700001},
OA = {gold},
DA = {2023-04-22},
}

@article{ WOS:000628582900001,
Author = {Lee, Keeheon},
Title = {A Systematic Review on Social Sustainability of Artificial Intelligence
   in Product Design},
Journal = {SUSTAINABILITY},
Year = {2021},
Volume = {13},
Number = {5},
Month = {MAR},
Abstract = {Emerging technologies such as artificial intelligence help operations
   management achieve sustainability. However, in sustainable operations
   management studies, scholars pay less attention to product design, which
   can be highly affected by artificial intelligence. In addition,
   sustainability is perceived as maintaining economic development while
   limiting environmental harm caused by human activity. Therefore, social
   sustainability is treated as peripheral compared to economic and
   environmental sustainability. However, social sustainability now has
   gained more attention because it is the basis on which meaningful
   economic and environmental sustainability can be valid. Thus, I
   systematically reviewed present studies on product design considering
   artificial intelligence to understand what types of social
   sustainability are achieved when applying artificial intelligence to
   product design. This study discovered artificial intelligence can
   improve social sustainability in product design, but social
   sustainability diversity is necessary. These findings can contribute to
   the inclusion of different types of social sustainability in product
   design when using artificial intelligence.},
Publisher = {MDPI},
Address = {ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND},
Type = {Review},
Language = {English},
Affiliation = {Lee, K (Corresponding Author), Yonsei Univ, Creat Technol Management, Underwood Int Coll, Incheon 21983, South Korea.
   Lee, Keeheon, Yonsei Univ, Creat Technol Management, Underwood Int Coll, Incheon 21983, South Korea.},
DOI = {10.3390/su13052668},
Article-Number = {2668},
EISSN = {2071-1050},
Keywords = {sustainable operation; product design; artificial intelligence; social
   sustainability; systematic review},
Keywords-Plus = {SEMANTIC NETWORK ANALYSIS; DECISION-SUPPORT-SYSTEM; SUPPLY CHAIN;
   ENGINEERING DESIGN; LIFE; OPTIMIZATION; ROBOTS; AI},
Research-Areas = {Science \& Technology - Other Topics; Environmental Sciences \& Ecology},
Web-of-Science-Categories  = {Green \& Sustainable Science \& Technology; Environmental Sciences;
   Environmental Studies},
Author-Email = {keeheon@yonsei.ac.kr},
Affiliations = {Yonsei University},
Funding-Acknowledgement = {Korean Ministry of Education through National Research Foundation of
   Korea {[}NRF-2017R1C1B1010094]; Yonsei University through Yonsei
   Future-leading Research Initiative {[}2017-22-0067]; AI-Factory Research
   Center, Urban Communication Center, and Design Thinking Research Center
   in ICONS (Institute of Convergence Science), Yonsei University},
Funding-Text = {This research was funded by the Korean Ministry of Education through
   National Research Foundation of Korea, grant number
   NRF-2017R1C1B1010094; by Yonsei University through Yonsei Future-leading
   Research Initiative, grant number 2017-22-0067; by AI-Factory Research
   Center, Urban Communication Center, and Design Thinking Research Center
   in ICONS (Institute of Convergence Science), Yonsei University.},
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Number-of-Cited-References = {142},
Times-Cited = {3},
Usage-Count-Last-180-days = {31},
Usage-Count-Since-2013 = {90},
Journal-ISO = {Sustainability},
Doc-Delivery-Number = {QW3WB},
Web-of-Science-Index = {Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)},
Unique-ID = {WOS:000628582900001},
OA = {gold, Green Published},
DA = {2023-04-22},
}

@article{ WOS:000617436500026,
Author = {Guo, Jonathan and Li, Bin},
Title = {The Application of Medical Artificial Intelligence Technology in Rural
   Areas of Developing Countries},
Journal = {HEALTH EQUITY},
Year = {2018},
Volume = {2},
Number = {1},
Pages = {174-181},
Abstract = {Background: Artificial intelligence (AI) is a rapidly developing
   computer technology that has begun to be widely used in the medical
   field to improve the professional level and efficiency of clinical work,
   in addition to avoiding medical errors. In developing countries, the
   inequality between urban and rural health services is a serious problem,
   of which the shortage of qualified healthcare providers is the major
   cause of the unavailability and low quality of healthcare in rural
   areas. Some studies have shown that the application of computer-assisted
   or AI medical techniques could improve healthcare outcomes in rural
   areas of developing countries. Therefore, the development of suitable
   medical AI technology for rural areas is worth discussing and probing.
   Methods: This article reviews and discusses the literature concerning
   the prospects of medical AI technology, the inequity of healthcare, and
   the application of computer-assisted or AI medical techniques in rural
   areas of developing countries. Results: Medical AI technology not only
   could improve physicians' efficiency and quality of medical services,
   but other health workers could also be trained to use this technique to
   compensate for the lack of physicians, thereby improving the
   availability of healthcare access and medical service quality. This
   article proposes a multilevel medical AI service network, including a
   frontline medical AI system (basic level), regional medical AI support
   centers (middle levels), and a national medical AI development center
   (top level). Conclusion: The promotion of medical AI technology in rural
   areas of developing countries might be one means of alleviating the
   inequality between urban and rural health services. The establishment of
   a multilevel medical AI service network system may be a solution.},
Publisher = {MARY ANN LIEBERT, INC},
Address = {140 HUGUENOT STREET, 3RD FL, NEW ROCHELLE, NY 10801 USA},
Type = {Review},
Language = {English},
Affiliation = {Li, B (Corresponding Author), Georgetown Univ, Med Ctr, Dept Neurosci, 3800 Reservoir Rd NW, Washington, DC 20007 USA.
   Guo, Jonathan; Li, Bin, Washington Inst Hlth Sci, Dept Social Med, Arlington, VA USA.
   Li, Bin, Georgetown Univ, Med Ctr, Dept Neurosci, 3800 Reservoir Rd NW, Washington, DC 20007 USA.},
DOI = {10.1089/heq.2018.0037},
EISSN = {2473-1242},
Keywords = {artificial intelligence; developing countries; healthcare; rural areas;
   service network},
Research-Areas = {Public, Environmental \& Occupational Health},
Web-of-Science-Categories  = {Public, Environmental \& Occupational Health},
Author-Email = {bl444@georgetown.edu},
Affiliations = {Georgetown University},
Funding-Acknowledgement = {Washington Institute for Health Sciences {[}G20171003]},
Funding-Text = {This study was supported by a grant from the Washington Institute for
   Health Sciences (G20171003). The authors would like to thank Editage
   (www.editage.com) for English language editing.},
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Number-of-Cited-References = {53},
Times-Cited = {71},
Usage-Count-Last-180-days = {11},
Usage-Count-Since-2013 = {33},
Journal-ISO = {Health Equity},
Doc-Delivery-Number = {VJ6UP},
Web-of-Science-Index = {Emerging Sources Citation Index (ESCI)},
Unique-ID = {WOS:000617436500026},
OA = {gold, Green Published},
DA = {2023-04-22},
}

@article{ WOS:000594149700003,
Author = {Dawood, Thikra and Elwakil, Emad and Mayol Novoa, Hector and Garate
   Delgado, Jose Fernando},
Title = {Artificial intelligence for the modeling of water pipes deterioration
   mechanisms},
Journal = {AUTOMATION IN CONSTRUCTION},
Year = {2020},
Volume = {120},
Month = {DEC},
Abstract = {Water pipes deterioration modeling has been a prevalent research topic
   in the last two decades due to high water break incidents and
   contamination rates. Failure processes are de facto very intricate to be
   diagnosed since there is a time lag between the failure incidence and
   consequences. Artificial intelligence (A.I.) techniques have gained much
   momentum during the last two decades, specifically for the deterioration
   modeling and assessment of water distribution networks. However, a
   comprehensive critical review on water infrastructure modeling via
   artificial intelligence and machine learning techniques is missing in
   the literature. This paper aims to bridge the gap in the body of
   knowledge and address the aforementioned limitations.
   The intellectual contributions of this paper are twofold. First, a
   comprehensive literature review method is presented through sequential
   steps that systematize and synthesize the literature in a scientific
   way. The state-ofthe-art of AI-based deterioration modeling for urban
   water systems is revealed along with models' methodologies,
   contributions, drawbacks, comparisons, and critiques. Second, future
   research directions and challenges are recommended to assist the
   construction automation research community in setting a vibrant agenda
   for the upcoming years.},
Publisher = {ELSEVIER},
Address = {RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS},
Type = {Review},
Language = {English},
Affiliation = {Dawood, T (Corresponding Author), Purdue Univ, Sch Construct Management, 401 N Grant St, W Lafayette, IN 47907 USA.
   Dawood, Thikra; Elwakil, Emad, Purdue Univ, Sch Construct Management, 401 N Grant St, W Lafayette, IN 47907 USA.
   Mayol Novoa, Hector; Garate Delgado, Jose Fernando, Natl Univ St Augustin Arequipa, Sch Civil Engn, Arequipa, Peru.},
DOI = {10.1016/j.autcon.2020.103398},
Article-Number = {103398},
ISSN = {0926-5805},
EISSN = {1872-7891},
Keywords = {Artificial intelligence; Machine learning; Pipe failure; Condition
   assessment; Water Main deterioration; Infrastructure; State-of-the-art
   review},
Keywords-Plus = {NEURAL-NETWORK; LEAK DETECTION; FAILURE RATE; PREDICTION; LOCALIZATION;
   REPLACEMENT; RELIABILITY; PERFORMANCE; INFERENCE; SYSTEM},
Research-Areas = {Construction \& Building Technology; Engineering},
Web-of-Science-Categories  = {Construction \& Building Technology; Engineering, Civil},
Author-Email = {dawoodt@purdue.edu},
Affiliations = {Purdue University System; Purdue University; Purdue University West
   Lafayette Campus},
ResearcherID-Numbers = {Novoa, Héctor Mayol/AAE-7687-2021},
ORCID-Numbers = {Novoa, Héctor Mayol/0000-0002-1333-8903},
Funding-Acknowledgement = {Universidad Nacional de San Agustin (UNSA) in Arequipa, Peru; Purdue
   University in Indiana, U.S.A. through Discovery Park's Center for the
   Environment (C4E)},
Funding-Text = {This work is supported by the collaboration of the Universidad Nacional
   de San Agustin (UNSA) in Arequipa, Peru, and Purdue University in
   Indiana, U.S.A. through Discovery Park's Center for the Environment
   (C4E).},
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Times-Cited = {20},
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@article{ WOS:000806912200001,
Author = {Abid, Sheikh Kamran and Sulaiman, Noralfishah and Chan, Shiau Wei and
   Nazir, Umber and Abid, Muhammad and Han, Heesup and Ariza-Montes,
   Antonio and Vega-Munoz, Alejandro},
Title = {Toward an Integrated Disaster Management Approach: How Artificial
   Intelligence Can Boost Disaster Management},
Journal = {SUSTAINABILITY},
Year = {2021},
Volume = {13},
Number = {22},
Month = {NOV},
Abstract = {Technical and methodological enhancement of hazards and disaster
   research is identified as a critical question in disaster management.
   Artificial intelligence (AI) applications, such as tracking and mapping,
   geospatial analysis, remote sensing techniques, robotics, drone
   technology, machine learning, telecom and network services, accident and
   hot spot analysis, smart city urban planning, transportation planning,
   and environmental impact analysis, are the technological components of
   societal change, having significant implications for research on the
   societal response to hazards and disasters. Social science researchers
   have used various technologies and methods to examine hazards and
   disasters through disciplinary, multidisciplinary, and interdisciplinary
   lenses. They have employed both quantitative and qualitative data
   collection and data analysis strategies. This study provides an overview
   of the current applications of AI in disaster management during its four
   phases and how AI is vital to all disaster management phases, leading to
   a faster, more concise, equipped response. Integrating a geographic
   information system (GIS) and remote sensing (RS) into disaster
   management enables higher planning, analysis, situational awareness, and
   recovery operations. GIS and RS are commonly recognized as key support
   tools for disaster management. Visualization capabilities, satellite
   images, and artificial intelligence analysis can assist governments in
   making quick decisions after natural disasters.},
Publisher = {MDPI},
Address = {ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND},
Type = {Review},
Language = {English},
Affiliation = {Abid, SK (Corresponding Author), Univ Tun Hussein Onn Malaysia, Fac Technol Management \& Business FPTP, KANZU Res Resilient Built Environm RBE, Batu Pahat 86400, Malaysia.
   Abid, Sheikh Kamran; Sulaiman, Noralfishah; Chan, Shiau Wei; Nazir, Umber, Univ Tun Hussein Onn Malaysia, Fac Technol Management \& Business FPTP, KANZU Res Resilient Built Environm RBE, Batu Pahat 86400, Malaysia.
   Abid, Muhammad, Harbin Engn Univ, Coll Aerosp \& Civil Engn, Harbin 150001, Peoples R China.
   Han, Heesup, Sejong Univ, Coll Hospitality \& Tourism Management, Seoul 05006, South Korea.
   Ariza-Montes, Antonio, Univ Loyola Andalucia, Social Matters Res Grp, Cordoba 414004, Spain.
   Vega-Munoz, Alejandro, Univ Autonoma Chile, Publ Policy Observ, 425 Pedro de Valdivia Ave, Santiago 7500912, Chile.},
DOI = {10.3390/su132212560},
Article-Number = {12560},
EISSN = {2071-1050},
Keywords = {disaster management; artificial intelligence; geographic information
   system},
Keywords-Plus = {VULNERABILITY ASSESSMENT; FLOOD; HAZARD},
Research-Areas = {Science \& Technology - Other Topics; Environmental Sciences \& Ecology},
Web-of-Science-Categories  = {Green \& Sustainable Science \& Technology; Environmental Sciences;
   Environmental Studies},
Author-Email = {shkamranabid@gmail.com
   nora@uthm.edu.my
   swchan@uthm.edu.my
   ambernazir4@gmail.com
   abidkhg@hrbeu.edu.cn
   heesup.han@gmail.com
   ariza@uloyola.es
   alejandro.vega@uautonoma.cl},
Affiliations = {University of Tun Hussein Onn Malaysia; Harbin Engineering University;
   Sejong University; Universidad Loyola Andalucia; Universidad Autonoma de
   Chile},
ResearcherID-Numbers = {Ariza-Montes, Antonio/G-8882-2017
   Vega-Muñoz, Alejandro/AAX-7468-2021
   SHIAU WEI, CHAN/H-7571-2014},
ORCID-Numbers = {Ariza-Montes, Antonio/0000-0002-5921-0753
   Vega-Muñoz, Alejandro/0000-0002-9427-2044
   Han, Heesup/0000-0001-6356-3001
   SHIAU WEI, CHAN/0000-0002-9134-5025},
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Number-of-Cited-References = {86},
Times-Cited = {4},
Usage-Count-Last-180-days = {26},
Usage-Count-Since-2013 = {36},
Journal-ISO = {Sustainability},
Doc-Delivery-Number = {1W6WD},
Web-of-Science-Index = {Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)},
Unique-ID = {WOS:000806912200001},
OA = {gold},
DA = {2023-04-22},
}

@article{ WOS:000857642800001,
Author = {Siregar, Riki Ruli A. and Seminar, Kudang Boro and Wahjuni, Sri and
   Santosa, Edi},
Title = {Vertical Farming Perspectives in Support of Precision Agriculture Using
   Artificial Intelligence: A Review},
Journal = {COMPUTERS},
Year = {2022},
Volume = {11},
Number = {9},
Month = {SEP},
Abstract = {Vertical farming is a new agricultural system which aims to utilize the
   limited access to land, especially in big cities. Vertical agriculture
   is the answer to meet the challenges posed by land and water shortages,
   including urban agriculture with limited access to land and water. This
   research study uses the Preferred Reporting for Systematic Review and
   Meta-analysis (PRISMA) item as one of the literary approaches. PRISMA is
   one way to check the validity of articles for a literature review or a
   systematic review resulting from this paper. One of the aims of this
   study is to review a survey of scientific literature related to vertical
   farming published in the last six years. Artificial intelligence with
   machine learning, deep learning, and the Internet of Things (IoT) in
   supporting precision agriculture has been optimally utilized, especially
   in its application to vertical farming. The results of this study
   provide information regarding all of the challenges and technological
   trends in the area of vertical agriculture, as well as exploring future
   opportunities.},
Publisher = {MDPI},
Address = {ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND},
Type = {Review},
Language = {English},
Affiliation = {Seminar, KB (Corresponding Author), IPB Univ Indonesia, Dept Agr Technol, Bogor 16680, Indonesia.
   Siregar, Riki Ruli A.; Wahjuni, Sri, IPB Univ Indonesia, Dept Comp Sci, Bogor 16680, Indonesia.
   Seminar, Kudang Boro, IPB Univ Indonesia, Dept Agr Technol, Bogor 16680, Indonesia.
   Santosa, Edi, IPB Univ Indonesia, Fac Agr, Dept Agron \& Hort, Bogor 16680, Indonesia.},
DOI = {10.3390/computers11090135},
Article-Number = {135},
ISSN = {2073-431X},
Keywords = {vertical farming; artificial intelligence; machine learning; deep
   learning; internet of things (IoT)},
Keywords-Plus = {SMART AGRICULTURE; INTERNET; THINGS; CHALLENGES; FRAMEWORK},
Research-Areas = {Computer Science},
Web-of-Science-Categories  = {Computer Science, Interdisciplinary Applications},
Author-Email = {kseminar@apps.ipb.ac.id},
ResearcherID-Numbers = {Santosa, Edi/S-1975-2017
   },
ORCID-Numbers = {Santosa, Edi/0000-0001-5560-4598
   Siregar, Riki/0000-0002-9751-0415},
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Number-of-Cited-References = {71},
Times-Cited = {1},
Usage-Count-Last-180-days = {25},
Usage-Count-Since-2013 = {28},
Journal-ISO = {Computers},
Doc-Delivery-Number = {4S7VD},
Web-of-Science-Index = {Emerging Sources Citation Index (ESCI)},
Unique-ID = {WOS:000857642800001},
OA = {gold},
DA = {2023-04-22},
}

@article{ WOS:000711438200006,
Author = {Owolabi, Titilayo Abimbola and Mohandes, Saeed Reza and Zayed, Tarek},
Title = {Investigating the impact of sewer overflow on the environment: A
   comprehensive literature review paper},
Journal = {JOURNAL OF ENVIRONMENTAL MANAGEMENT},
Year = {2022},
Volume = {301},
Month = {JAN 1},
Abstract = {Sewer networks play a pivotal role in our everyday lives by transporting
   the stormwater and urban sewage away from the urban areas. In this
   regard, Sewer Overflow (SO) has been considered as a detrimental threat
   to our environment and health, which results from the wastewater
   discharge into the environment. In order to grapple with such
   deleterious phenomenon, numerous studies have been conducted; however,
   there has not been any review paper that provides the researchers
   undertaking research in this area with the following inclusive picture:
   (1) detailed-scientometric analysis of the research undertaken hitherto,
   (2) the types of methodologies used in the previous studies, (3) the
   aspects of environment impacted by the SO occurrence, and (4) the gaps
   existing in the relative literature together with the potential future
   works to be undertaken. Based on the comprehensive review undertaken, it
   is observed that simulation and artificial intelligence-based methods
   have been the most popular approaches. In addition, it has come to the
   attention that the detrimental impacts associated with the SO are
   fourfold as follows: air, quality of water, soil, and business and
   structure. Among these, the majority of the studies' focus have been
   tilted towards the impact of SO on the quality of ground water. The
   outcomes of this state-of-the-art review provides the researchers and
   environmental engineers with inclusive hindsight in dealing with such
   serious issue, which in turn, this culminates in a significant
   improvement in our environment as well as humans' well-beings.},
Publisher = {ACADEMIC PRESS LTD- ELSEVIER SCIENCE LTD},
Address = {24-28 OVAL RD, LONDON NW1 7DX, ENGLAND},
Type = {Review},
Language = {English},
Affiliation = {Mohandes, SR (Corresponding Author), Hong Kong Univ Sci \& Technol, Fac Construct \& Environm, Dept Bldg \& Real Estate BRE, Hung Hom,Kowloon, Hong Kong, Peoples R China.
   Owolabi, Titilayo Abimbola, Hong Kong Univ Sci \& Technol, Dept Civil \& Environm Engn, Kowloon, Hong Kong, Peoples R China.
   Mohandes, Saeed Reza; Zayed, Tarek, Hong Kong Univ Sci \& Technol, Fac Construct \& Environm, Dept Bldg \& Real Estate BRE, Hung Hom,Kowloon, Hong Kong, Peoples R China.},
DOI = {10.1016/j.jenvman.2021.113810},
EarlyAccessDate = {OCT 2021},
Article-Number = {113810},
ISSN = {0301-4797},
EISSN = {1095-8630},
Keywords = {Sewer systems; Sewer overflows; Environmental concerns; Scientometric
   analysis; Artificial intelligence},
Keywords-Plus = {REAL-TIME CONTROL; ARTIFICIAL NEURAL-NETWORKS; DECISION-ANALYSIS;
   WATER-QUALITY; MANAGEMENT; MODEL; RIVER; CONTAMINATION; OPTIMIZATION;
   DESIGN},
Research-Areas = {Environmental Sciences \& Ecology},
Web-of-Science-Categories  = {Environmental Sciences},
Author-Email = {taowolabi@connect.ust.hk
   saeedreza.mohandes@polyu.edu.hk
   tarek.zayed@polyu.edu.hk},
Affiliations = {Hong Kong University of Science \& Technology; Hong Kong Polytechnic
   University; Hong Kong University of Science \& Technology},
ORCID-Numbers = {MOHANDES, SAEED REZA/0000-0001-9149-3928},
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Number-of-Cited-References = {101},
Times-Cited = {12},
Usage-Count-Last-180-days = {8},
Usage-Count-Since-2013 = {46},
Journal-ISO = {J. Environ. Manage.},
Doc-Delivery-Number = {WM9ZZ},
Web-of-Science-Index = {Science Citation Index Expanded (SCI-EXPANDED)},
Unique-ID = {WOS:000711438200006},
DA = {2023-04-22},
}

@article{ WOS:000687457800002,
Author = {Wang, Jindong and Jiang, Shengchuan and Qiu, Yue and Zhang, Yang and
   Ying, Jianguo and Du, Yuchuan},
Title = {Traffic Signal Optimization under Connected-Vehicle Environment: An
   Overview},
Journal = {JOURNAL OF ADVANCED TRANSPORTATION},
Year = {2021},
Volume = {2021},
Month = {AUG 11},
Abstract = {Traffic signal optimization is a significant means for smoothing urban
   traffic flow. However, the operation of traffic signals is currently
   seriously constrained by the data available from traditional point
   detectors. In recent years, an emerging technology, connected vehicle
   (CV), which can percept the overall traffic environment in real time,
   has drawn researchers' attention. With the new data source, traffic
   controllers should be able to make smarter decisions. A lot of work has
   been done to develop a new traffic signal control pattern under
   connected-vehicle environment. This paper provides a comprehensive
   review of these studies, aiming at sketching out the state of the arts
   in this research field. Several basic control problems, communication,
   control input, and objectives, are briefly introduced. The commonly used
   optimization models for this problem are summarized into three types:
   rule-based models, mathematical programming-based models, and artificial
   intelligence-based models. Then some major technical issues are
   discussed in detail. Finally, we raise the limitation of the existing
   studies and give our perspectives of the future research directions.},
Publisher = {WILEY-HINDAWI},
Address = {ADAM HOUSE, 3RD FL, 1 FITZROY SQ, LONDON, WIT 5HE, ENGLAND},
Type = {Review},
Language = {English},
Affiliation = {Jiang, SC (Corresponding Author), Tongji Univ, Key Lab Rd \& Traff Engn, Minist Educ, Shanghai, Peoples R China.
   Wang, Jindong; Jiang, Shengchuan; Qiu, Yue; Du, Yuchuan, Tongji Univ, Key Lab Rd \& Traff Engn, Minist Educ, Shanghai, Peoples R China.
   Wang, Jindong; Ying, Jianguo, Jinqiao Grp Co Ltd, Shanghai, Peoples R China.
   Zhang, Yang, Shanghai Urban Rural Construct \& Transportat Dev, Shanghai, Peoples R China.},
DOI = {10.1155/2021/3584569},
Article-Number = {3584569},
ISSN = {0197-6729},
EISSN = {2042-3195},
Keywords-Plus = {INTERSECTION CONTROL; TIMING OPTIMIZATION; CONTROL ALGORITHM; NETWORKS;
   TECHNOLOGY},
Research-Areas = {Engineering; Transportation},
Web-of-Science-Categories  = {Engineering, Civil; Transportation Science \& Technology},
Author-Email = {shengchuanjiang@tongji.edu.cn},
Affiliations = {Tongji University},
Funding-Acknowledgement = {Shanghai Science and Technology Committee, China {[}YDZX20193100004845,
   21DZ205100, 19DZ1208700]},
Funding-Text = {This study was jointly supported by Shanghai Science and Technology
   Committee, China (YDZX20193100004845, 21DZ205100, and 19DZ1208700).},
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Journal-ISO = {J. Adv. Transp.},
Doc-Delivery-Number = {UD8MU},
Web-of-Science-Index = {Science Citation Index Expanded (SCI-EXPANDED)},
Unique-ID = {WOS:000687457800002},
OA = {gold},
DA = {2023-04-22},
}

@article{ WOS:000947567500001,
Author = {Pavaloaia, Vasile-Daniel and Necula, Sabina-Cristiana},
Title = {Artificial Intelligence as a Disruptive Technology-A Systematic
   Literature Review},
Journal = {ELECTRONICS},
Year = {2023},
Volume = {12},
Number = {5},
Month = {MAR},
Abstract = {The greatest technological changes in our lives are predicted to be
   brought about by Artificial Intelligence (AI). Together with the
   Internet of Things (IoT), blockchain, and several others, AI is
   considered to be the most disruptive technology, and has impacted
   numerous sectors, such as healthcare (medicine), business, agriculture,
   education, and urban development. The present research aims to achieve
   the following: identify how disruptive technologies have evolved over
   time and their current acceptation (1); extract the most prominent
   disruptive technologies, besides AI, that are in use today (2); and
   elaborate on the domains that were impacted by AI and how this occurred
   (3). Based on a sentiment analysis of the titles and abstracts, the
   results reveal that the majority of recent publications have a positive
   connotation with regard to the disruptive impact of edge technologies,
   and that the most prominent examples (the top five) are AI, the IoT,
   blockchain, 5G, and 3D printing. The disruptive effects of AI technology
   are still changing how people interact in the corporate, consumer, and
   professional sectors, while 5G and other mobile technologies will become
   highly disruptive and will genuinely revolutionize the landscape in all
   sectors in the upcoming years.},
Publisher = {MDPI},
Address = {ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND},
Type = {Review},
Language = {English},
Affiliation = {Pavaloaia, VD (Corresponding Author), Alexandru Ioan Cuza Univ, Fac Econ \& Business Adm, Dept Accounting Business Informat Syst \& Stat, Iasi 700506, Romania.
   Pavaloaia, Vasile-Daniel; Necula, Sabina-Cristiana, Alexandru Ioan Cuza Univ, Fac Econ \& Business Adm, Dept Accounting Business Informat Syst \& Stat, Iasi 700506, Romania.},
DOI = {10.3390/electronics12051102},
Article-Number = {1102},
EISSN = {2079-9292},
Keywords = {artificial intelligence; disruptive technology; disruptive innovation;
   blockchain; IoT},
Keywords-Plus = {FUTURE; SMART; INNOVATION; DIRECTIONS; IMPACT},
Research-Areas = {Computer Science; Engineering; Physics},
Web-of-Science-Categories  = {Computer Science, Information Systems; Engineering, Electrical \&
   Electronic; Physics, Applied},
Author-Email = {danpav@uaic.ro},
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Number-of-Cited-References = {109},
Times-Cited = {0},
Usage-Count-Last-180-days = {5},
Usage-Count-Since-2013 = {5},
Journal-ISO = {Electronics},
Doc-Delivery-Number = {9U2SO},
Web-of-Science-Index = {Science Citation Index Expanded (SCI-EXPANDED)},
Unique-ID = {WOS:000947567500001},
OA = {gold},
DA = {2023-04-22},
}

@article{ WOS:000522824300002,
Author = {de Souza Groppo, Gustavo and Costa, Marcelo Azevedo and Libanio, Marcelo},
Title = {Predicting water demand: a review of the methods employed and future
   possibilities},
Journal = {WATER SUPPLY},
Year = {2019},
Volume = {19},
Number = {8},
Pages = {2179-2198},
Month = {DEC},
Abstract = {The balance between water supply and demand requires efficient water
   supply system management techniques. This balance is achieved through
   operational actions, many of which require the application of
   forecasting concepts and tools. In this article, recent research on
   urban water demand forecasting employing artificial intelligence is
   reviewed, aiming to present the `state of the art' on the subject and
   provide some guidance regarding methods and models to research and
   professional sanitation companies. The review covers the models
   developed using standard statistical techniques, such as linear
   regression or time-series analysis, or techniques based on Soft
   Computing. This review shows that the studies are, mostly, focused on
   the management of the operating systems. There is, therefore, room for
   long-term forecasts. It is worth noting that there is no global model
   that surpasses all the methods for all cases, it being necessary to
   study each region separately, evaluating the strengths of each model or
   the combination of methods. The use of statistical applications of
   Machine Learning and Artificial Intelligence methodologies has grown
   considerably in recent years. However, there is still room for
   improvement with regard to water demand forecasting.},
Publisher = {IWA PUBLISHING},
Address = {REPUBLIC-EXPORT BLDG, UNITS 1 04 \& 1 05, 1 CLOVE CRESCENT, LONDON,
   ENGLAND},
Type = {Review},
Language = {English},
Affiliation = {Libanio, M (Corresponding Author), Univ Fed Minas Gerais, Dept Sanit \& Environm Engn, Belo Horizonte, MG, Brazil.
   de Souza Groppo, Gustavo, Univ Fed Minas Gerais, Belo Horizonte, MG, Brazil.
   de Souza Groppo, Gustavo, Companhia Saneamento Minas Gerais COPASA MG, Rua Mar Espanha 525, BR-30330900 Belo Horizonte, MG, Brazil.
   Costa, Marcelo Azevedo, Univ Fed Minas Gerais, Dept Prod Engn, Belo Horizonte, MG, Brazil.
   Libanio, Marcelo, Univ Fed Minas Gerais, Dept Sanit \& Environm Engn, Belo Horizonte, MG, Brazil.},
DOI = {10.2166/ws.2019.122},
ISSN = {1606-9749},
EISSN = {1607-0798},
Keywords = {Artificial Intelligence; Machine Learning; Soft Computing; water demand
   prediction},
Keywords-Plus = {ARTIFICIAL NEURAL-NETWORK; TIME-SERIES PREDICTION; FEATURE-SELECTION;
   CONSUMPTION; REGRESSION; ENSEMBLES; MODELS; FILTER; DETERMINANTS;
   OPTIMIZATION},
Research-Areas = {Engineering; Environmental Sciences \& Ecology; Water Resources},
Web-of-Science-Categories  = {Engineering, Environmental; Environmental Sciences; Water Resources},
Author-Email = {marcelo@desa.ufmg.br},
Affiliations = {Universidade Federal de Minas Gerais; Universidade Federal de Minas
   Gerais; Universidade Federal de Minas Gerais},
ResearcherID-Numbers = {Libanio, Marcelo/GVS-9471-2022
   },
ORCID-Numbers = {Costa, Marcelo/0000-0002-2330-5056},
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Number-of-Cited-References = {118},
Times-Cited = {31},
Usage-Count-Last-180-days = {16},
Usage-Count-Since-2013 = {86},
Journal-ISO = {Water Supply},
Doc-Delivery-Number = {KY8KC},
Web-of-Science-Index = {Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)},
Unique-ID = {WOS:000522824300002},
DA = {2023-04-22},
}

@article{ WOS:000543394800155,
Author = {Shi, Wenzhong and Zhang, Min and Zhang, Rui and Chen, Shanxiong and
   Zhan, Zhao},
Title = {Change Detection Based on Artificial Intelligence: State-of-the-Art and
   Challenges},
Journal = {REMOTE SENSING},
Year = {2020},
Volume = {12},
Number = {10},
Month = {MAY},
Abstract = {Change detection based on remote sensing (RS) data is an important
   method of detecting changes on the Earth's surface and has a wide range
   of applications in urban planning, environmental monitoring, agriculture
   investigation, disaster assessment, and map revision. In recent years,
   integrated artificial intelligence (AI) technology has become a research
   focus in developing new change detection methods. Although some
   researchers claim that AI-based change detection approaches outperform
   traditional change detection approaches, it is not immediately obvious
   how and to what extent AI can improve the performance of change
   detection. This review focuses on the state-of-the-art methods,
   applications, and challenges of AI for change detection. Specifically,
   the implementation process of AI-based change detection is first
   introduced. Then, the data from different sensors used for change
   detection, including optical RS data, synthetic aperture radar (SAR)
   data, street view images, and combined heterogeneous data, are
   presented, and the available open datasets are also listed. The general
   frameworks of AI-based change detection methods are reviewed and
   analyzed systematically, and the unsupervised schemes used in AI-based
   change detection are further analyzed. Subsequently, the commonly used
   networks in AI for change detection are described. From a practical
   point of view, the application domains of AI-based change detection
   methods are classified based on their applicability. Finally, the major
   challenges and prospects of AI for change detection are discussed and
   delineated, including (a) heterogeneous big data processing, (b)
   unsupervised AI, and (c) the reliability of AI. This review will be
   beneficial for researchers in understanding this field.},
Publisher = {MDPI},
Address = {ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND},
Type = {Review},
Language = {English},
Affiliation = {Zhang, M (Corresponding Author), Hong Kong Polytech Univ, Dept Land Surveying \& Geoinformat, Hung Hom, Hong Kong, Peoples R China.
   Zhang, M (Corresponding Author), Wuhan Univ, Sch Remote Sensing \& Informat Engn, Wuhan 430079, Peoples R China.
   Shi, Wenzhong; Zhang, Min; Zhang, Rui; Chen, Shanxiong; Zhan, Zhao, Hong Kong Polytech Univ, Dept Land Surveying \& Geoinformat, Hung Hom, Hong Kong, Peoples R China.
   Zhang, Min; Chen, Shanxiong; Zhan, Zhao, Wuhan Univ, Sch Remote Sensing \& Informat Engn, Wuhan 430079, Peoples R China.
   Zhang, Rui, China Univ Min \& Technol, Sch Environm Sci \& Spatial Informat, Xuzhou 221116, Jiangsu, Peoples R China.},
DOI = {10.3390/rs12101688},
Article-Number = {1688},
EISSN = {2072-4292},
Keywords = {artificial intelligence; change detection; remote sensing; deep
   learning; neural network; unsupervised learning; SAR; hyperspectral;
   multispectral; street view},
Keywords-Plus = {IMAGE CHANGE DETECTION; UNSUPERVISED CHANGE DETECTION; REMOTE-SENSING
   IMAGES; CHANGE DETECTION FRAMEWORK; MULTIPLE-CHANGE DETECTION;
   NEURAL-NETWORK APPROACH; URBAN CHANGE DETECTION; LAND USE/COVER CHANGE;
   MARKOV RANDOM-FIELD; SAR IMAGES},
Research-Areas = {Environmental Sciences \& Ecology; Geology; Remote Sensing; Imaging
   Science \& Photographic Technology},
Web-of-Science-Categories  = {Environmental Sciences; Geosciences, Multidisciplinary; Remote Sensing;
   Imaging Science \& Photographic Technology},
Author-Email = {lswzshi@polyu.edu.hk
   007zhangmin@whu.edu.cn
   rzhang@cumt.edu.cn
   shanxiongchen@whu.edu.cn
   zhanzhao@whu.edu.cn},
Affiliations = {Hong Kong Polytechnic University; Wuhan University; China University of
   Mining \& Technology},
ResearcherID-Numbers = {CHEN, Shanxiong/GQQ-0535-2022
   SHI, wen/GPW-9531-2022
   ZHANG, Min/AAV-9787-2020
   },
ORCID-Numbers = {ZHANG, Min/0000-0003-1643-5271
   Chen, Shanxiong/0000-0002-9235-6340
   ZHAN, ZHAO/0000-0002-5092-715X},
Funding-Acknowledgement = {Ministry of Science and Technology of the People's Republic of China
   {[}2017YFB0503604]},
Funding-Text = {This research was funded by the Ministry of Science and Technology of
   the People's Republic of China, Grant No. 2017YFB0503604.},
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Number-of-Cited-References = {240},
Times-Cited = {148},
Usage-Count-Last-180-days = {100},
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Journal-ISO = {Remote Sens.},
Doc-Delivery-Number = {MC6LB},
Web-of-Science-Index = {Science Citation Index Expanded (SCI-EXPANDED)},
Unique-ID = {WOS:000543394800155},
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@article{ WOS:000831858600001,
Author = {Yang, Liping and Driscol, Joshua and Sarigai, Sarigai and Wu, Qiusheng
   and Chen, Haifei and Lippitt, Christopher D.},
Title = {Google Earth Engine and Artificial Intelligence (AI): A Comprehensive
   Review},
Journal = {REMOTE SENSING},
Year = {2022},
Volume = {14},
Number = {14},
Month = {JUL},
Abstract = {Remote sensing (RS) plays an important role gathering data in many
   critical domains (e.g., global climate change, risk assessment and
   vulnerability reduction of natural hazards, resilience of ecosystems,
   and urban planning). Retrieving, managing, and analyzing large amounts
   of RS imagery poses substantial challenges. Google Earth Engine (GEE)
   provides a scalable, cloud-based, geospatial retrieval and processing
   platform. GEE also provides access to the vast majority of freely
   available, public, multi-temporal RS data and offers free cloud-based
   computational power for geospatial data analysis. Artificial
   intelligence (AI) methods are a critical enabling technology to
   automating the interpretation of RS imagery, particularly on
   object-based domains, so the integration of AI methods into GEE
   represents a promising path towards operationalizing automated RS-based
   monitoring programs. In this article, we provide a systematic review of
   relevant literature to identify recent research that incorporates AI
   methods in GEE. We then discuss some of the major challenges of
   integrating GEE and AI and identify several priorities for future
   research. We developed an interactive web application designed to allow
   readers to intuitively and dynamically review the publications included
   in this literature review.},
Publisher = {MDPI},
Address = {ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND},
Type = {Review},
Language = {English},
Affiliation = {Yang, LP (Corresponding Author), Univ New Mexico, Dept Geog \& Environm Studies, Albuquerque, NM 87131 USA.
   Yang, LP (Corresponding Author), Univ New Mexico, Ctr Adv Spatial Informat Res \& Educ ASPIRE, Albuquerque, NM 87131 USA.
   Yang, LP (Corresponding Author), Univ New Mexico, Dept Comp Sci, Albuquerque, NM 87106 USA.
   Yang, Liping; Driscol, Joshua; Sarigai, Sarigai; Lippitt, Christopher D., Univ New Mexico, Dept Geog \& Environm Studies, Albuquerque, NM 87131 USA.
   Yang, Liping; Driscol, Joshua; Sarigai, Sarigai; Lippitt, Christopher D., Univ New Mexico, Ctr Adv Spatial Informat Res \& Educ ASPIRE, Albuquerque, NM 87131 USA.
   Yang, Liping, Univ New Mexico, Dept Comp Sci, Albuquerque, NM 87106 USA.
   Wu, Qiusheng, Univ Tennessee, Dept Geog, Knoxville, TN 37996 USA.
   Chen, Haifei, Univ New Mexico, Interdisciplinary Sci Cooperat, Albuquerque, NM 87131 USA.},
DOI = {10.3390/rs14143253},
Article-Number = {3253},
EISSN = {2072-4292},
Keywords = {Google Earth Engine (GEE); artificial intelligence (AI); machine
   learning; deep learning; computer vision; remote sensing; cloud
   computing; geospatial big data; review},
Keywords-Plus = {BIG DATA APPLICATIONS; SENTINEL-2 IMAGERY; SURFACE-WATER; RANDOM FOREST;
   LANDSAT; EXPANSION; CLASSIFICATION; EXTRACTION; SYSTEMS; TRENDS},
Research-Areas = {Environmental Sciences \& Ecology; Geology; Remote Sensing; Imaging
   Science \& Photographic Technology},
Web-of-Science-Categories  = {Environmental Sciences; Geosciences, Multidisciplinary; Remote Sensing;
   Imaging Science \& Photographic Technology},
Author-Email = {lipingyang@unm.edu
   joshuadr@unm.edu
   rsa@unm.edu
   qwu18@utk.edu
   hfchen@unm.edu
   clippitt@unm.edu},
Affiliations = {University of New Mexico; University of New Mexico; University of New
   Mexico; University of Tennessee System; University of Tennessee
   Knoxville; University of New Mexico},
ResearcherID-Numbers = {Wu, Qiusheng/C-7976-2016
   },
ORCID-Numbers = {Wu, Qiusheng/0000-0001-5437-4073
   Lippitt, Christopher/0000-0001-7979-7857
   Yang, Liping/0000-0002-9240-5501
   Chen, Haifei/0000-0003-3807-3734},
Funding-Acknowledgement = {US National Aeronautics and Space Administration {[}80NSSC22K0384];
   College of Arts and Sciences at University of New Mexico},
Funding-Text = {This material is partly based upon work supported by the US National
   Aeronautics and Space Administration under Grant number 80NSSC22K0384,
   and supported by the funding support from the College of Arts and
   Sciences at University of New Mexico.},
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Number-of-Cited-References = {264},
Times-Cited = {9},
Usage-Count-Last-180-days = {52},
Usage-Count-Since-2013 = {83},
Journal-ISO = {Remote Sens.},
Doc-Delivery-Number = {3H2GM},
Web-of-Science-Index = {Science Citation Index Expanded (SCI-EXPANDED)},
Unique-ID = {WOS:000831858600001},
OA = {gold},
DA = {2023-04-22},
}

@article{ WOS:000580815300001,
Author = {Lee, Kwang-Sig and Ahn, Ki Hoon},
Title = {Application of Artificial Intelligence in Early Diagnosis of Spontaneous
   Preterm Labor and Birth},
Journal = {DIAGNOSTICS},
Year = {2020},
Volume = {10},
Number = {9},
Month = {SEP},
Abstract = {This study reviews the current status and future prospective of
   knowledge on the use of artificial intelligence for the prediction of
   spontaneous preterm labor and birth ({''}preterm birth{''} hereafter).
   The summary of review suggests that different machine learning
   approaches would be optimal for different types of data regarding the
   prediction of preterm birth: the artificial neural network, logistic
   regression and/or the random forest for numeric data; the support vector
   machine for electrohysterogram data; the recurrent neural network for
   text data; and the convolutional neural network for image data. The
   ranges of performance measures were 0.79-0.94 for accuracy, 0.22-0.97
   for sensitivity, 0.86-1.00 for specificity, and 0.54-0.83 for the area
   under the receiver operating characteristic curve. The following
   maternal variables were reported to be major determinants of preterm
   birth: delivery and pregestational body mass index, age, parity,
   predelivery systolic and diastolic blood pressure, twins, below high
   school graduation, infant sex, prior preterm birth, progesterone
   medication history, upper gastrointestinal tract symptom,
   gastroesophageal reflux disease, Helicobacter pylori, urban region,
   calcium channel blocker medication history, gestational diabetes
   mellitus, prior cone biopsy, cervical length, myomas and adenomyosis,
   insurance, marriage, religion, systemic lupus erythematosus,
   hydroxychloroquine sulfate, and increased cerebrospinal fluid and
   reduced cortical folding due to impaired brain growth.},
Publisher = {MDPI},
Address = {ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND},
Type = {Review},
Language = {English},
Affiliation = {Ahn, KH (Corresponding Author), Korea Univ, Anam Hosp, Dept Obstet \& Gynecol, Seoul 02841, South Korea.
   Lee, Kwang-Sig, Korea Univ, Anam Hosp, AI Ctr, Seoul 02841, South Korea.
   Ahn, Ki Hoon, Korea Univ, Anam Hosp, Dept Obstet \& Gynecol, Seoul 02841, South Korea.},
DOI = {10.3390/diagnostics10090733},
Article-Number = {733},
EISSN = {2075-4418},
Keywords = {preterm birth; early diagnosis; artificial intelligence},
Keywords-Plus = {GASTROESOPHAGEAL-REFLUX DISEASE; DIABETES-MELLITUS; RISK; PERIODONTITIS;
   ASSOCIATION; PREDICTION; WOMEN},
Research-Areas = {General \& Internal Medicine},
Web-of-Science-Categories  = {Medicine, General \& Internal},
Author-Email = {ecophy@hanmail.net
   akh1220@hanmail.net},
Affiliations = {Korea University; Korea University Medicine (KU Medicine); Korea
   University; Korea University Medicine (KU Medicine)},
ORCID-Numbers = {Lee, Kwang-Sig/0000-0002-0576-0098
   AHN, KI HOON/0000-0002-6314-4621},
Funding-Acknowledgement = {Ministry of Science and ICT of South Korea under the Information
   Technology Research Center support program {[}IITP-2018-0-01405]},
Funding-Text = {This research was supported by the Ministry of Science and ICT of South
   Korea under the Information Technology Research Center support program
   supervised by the IITP (Institute for Information \& Communications
   Technology Planning \& Evaluation) (IITP-2018-0-01405).},
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Number-of-Cited-References = {47},
Times-Cited = {22},
Usage-Count-Last-180-days = {6},
Usage-Count-Since-2013 = {17},
Journal-ISO = {Diagnostics},
Doc-Delivery-Number = {OE9AW},
Web-of-Science-Index = {Science Citation Index Expanded (SCI-EXPANDED)},
Unique-ID = {WOS:000580815300001},
OA = {gold, Green Published},
DA = {2023-04-22},
}

@article{ WOS:000644682100001,
Author = {Song, Yongze and Wu, Peng},
Title = {Earth Observation for Sustainable Infrastructure: A Review},
Journal = {REMOTE SENSING},
Year = {2021},
Volume = {13},
Number = {8},
Month = {APR},
Abstract = {Infrastructure is a fundamental sector for sustainable development and
   Earth observation has great potentials for sustainable infrastructure
   development (SID). However, implementations of the timely, large-scale
   and multi-source Earth observation are still limited in satisfying the
   huge global requirements of SID. This study presents a systematical
   literature review to identify trends of Earth observation for
   sustainable infrastructure (EOSI), investigate the relationship between
   EOSI and Sustainable Development Goals (SDGs), and explore challenges
   and future directions of EOSI. Results reveal the close associations of
   infrastructure, urban development, ecosystems, climate, Earth
   observation and GIS in EOSI, and indicate their relationships. In
   addition, from the perspective of EOSI-SDGs relationship, the huge
   potentials of EOSI are demonstrated from the 70\% of the infrastructure
   influenced targets that can be directly or indirectly derived from Earth
   observation data, but have not been included in current SDG indicators.
   Finally, typical EOSI cases are presented to indicate challenges and
   future research directions. This review emphasizes the contributions and
   potentials of Earth observation to SID and EOSI is a powerful pathway to
   deliver on SDGs.},
Publisher = {MDPI},
Address = {ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND},
Type = {Review},
Language = {English},
Affiliation = {Song, YZ (Corresponding Author), Curtin Univ, Sch Design \& Built Environm, Bentley, WA 6102, Australia.
   Song, Yongze; Wu, Peng, Curtin Univ, Sch Design \& Built Environm, Bentley, WA 6102, Australia.},
DOI = {10.3390/rs13081528},
Article-Number = {1528},
EISSN = {2072-4292},
Keywords = {sustainable infrastructure; earth observation; remote sensing; earth big
   data; Sustainable Development Goals (SDGs); SDG targets; bibliographic
   analysis},
Keywords-Plus = {URBAN GREEN AREAS; POWER-PLANT; DEVELOPMENT GOALS; SATELLITE IMAGERY;
   TRANSPORT INFRASTRUCTURE; ARTIFICIAL-INTELLIGENCE; REGIONAL INEQUALITY;
   CULTURAL-HERITAGE; WASTE MANAGEMENT; SPATIAL-ANALYSIS},
Research-Areas = {Environmental Sciences \& Ecology; Geology; Remote Sensing; Imaging
   Science \& Photographic Technology},
Web-of-Science-Categories  = {Environmental Sciences; Geosciences, Multidisciplinary; Remote Sensing;
   Imaging Science \& Photographic Technology},
Author-Email = {yongze.song@curtin.edu.au
   peng.wu@curtin.edu.au},
Affiliations = {Curtin University},
ResearcherID-Numbers = {Song, Yongze/F-1940-2018
   },
ORCID-Numbers = {Song, Yongze/0000-0003-3420-9622
   wu, peng/0000-0002-3793-0653},
Funding-Acknowledgement = {Australian Government through the Australian Research Council
   {[}DP180104026]},
Funding-Text = {This research was partially supported by the Australian Government
   through the Australian Research Council's Discovery Project grant number
   DP180104026.},
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@article{ WOS:000744086200002,
Author = {Cohen, Abigail Rae and Chen, Gerry and Berger, Eli Matthew and Warrier,
   Sushmita and Lan, Guanghui and Grubert, Emily and Dellaert, Frank and
   Chen, Yongsheng},
Title = {Dynamically Controlled Environment Agriculture: Integrating Machine
   Learning and Mechanistic and Physiological Models for Sustainable Food
   Cultivation},
Journal = {ACS ES\&T ENGINEERING},
Year = {2022},
Volume = {2},
Number = {1},
Pages = {3-19},
Month = {JAN 14},
Abstract = {Inefficiencies and imprecise input control in agriculture have caused
   devastating consequences to ecosystems. Urban controlled environment
   agriculture (CEA) is a proposed approach to mitigate the impacts of
   cultivation, but precise control of inputs (i.e., nutrient, water, etc.)
   is limited by the ability to monitor dynamic conditions. Current
   mechanistic and physiological plant growth models (MPMs) have not yet
   been unified and have uncovered knowledge gaps of the complex interplay
   among control variables. Moreover, because of their specificity, MPMs
   are of limited utility when extended to additional plant species or
   learning (ML) can uncover latent interactions across conditions,
   phenotyping bottlenecks have hindered successful application. To bridge
   these gaps, we propose an integrative approach whereby MPMs are used to
   construct the foundations of ML algorithms, reducing data requirements
   and costs, and ML is used to elucidate parameters and causal inference
   in MPM. This review highlights research about control and automation in
   CEA, synthesizing literature into a framework whereby ML, MPM, and
   biofeedback inform what we call dynamically controlled environment
   agriculture (DCEA). We highlight synergistic characteristics of MPM and
   ML to illustrate that a DCEA framework could contribute to urban
   resilience, human health, and optimized productivity and nutritional
   content.},
Publisher = {AMER CHEMICAL SOC},
Address = {1155 16TH ST, NW, WASHINGTON, DC 20036 USA},
Type = {Review},
Language = {English},
Affiliation = {Chen, YS (Corresponding Author), Georgia Inst Technol, Sch Civil \& Environm Engn, Atlanta, GA 30332 USA.
   Cohen, Abigail Rae; Berger, Eli Matthew; Grubert, Emily; Chen, Yongsheng, Georgia Inst Technol, Sch Civil \& Environm Engn, Atlanta, GA 30332 USA.
   Chen, Gerry; Dellaert, Frank, Georgia Inst Technol, Sch Interact Comp, Atlanta, GA 30308 USA.
   Warrier, Sushmita, Georgia Inst Technol, Sch Elect \& Comp Engn, Atlanta, GA 30313 USA.
   Lan, Guanghui, Georgia Inst Technol, H Milton Stewart Sch Ind \& Syst Engn, Atlanta, GA 30332 USA.},
DOI = {10.1021/acsestengg.1c00269},
EISSN = {2690-0645},
Keywords = {Artificial Intelligence; Phenotyping; Automation; Precision Agriculture;
   Plant Growth Modeling},
Keywords-Plus = {NITRATE UPTAKE KINETICS; NUTRIENT-UPTAKE; SOILLESS CULTURES; SECONDARY
   GROWTH; PHOSPHATE-UPTAKE; PLANT NUTRITION; NITROGEN UPTAKE; WATER;
   LETTUCE; SYSTEM},
Research-Areas = {Engineering},
Web-of-Science-Categories  = {Engineering, Environmental},
Author-Email = {yongsheng.chen@ce.gatech.edu},
Affiliations = {University System of Georgia; Georgia Institute of Technology;
   University System of Georgia; Georgia Institute of Technology;
   University System of Georgia; Georgia Institute of Technology;
   University System of Georgia; Georgia Institute of Technology},
ResearcherID-Numbers = {Lan, Guanghui/HPF-8429-2023
   },
ORCID-Numbers = {Chen, Gerry/0000-0002-1026-1760},
Funding-Acknowledgement = {U.S. Department of Agriculture {[}2018-68011-28371]; National Science
   Foundation {[}2112533]; National Science Foundation-U.S. Department of
   Agriculture {[}202067021-31526]},
Funding-Text = {This study was partially supported by the U.S. Department of Agriculture
   (Award No. 2018-68011-28371); National Science Foundation (Award No.
   1936928); National Science Foundation-U.S. Department of Agriculture
   (Award No. 202067021-31526); and National Science Foundation (Award No.
   2112533).},
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Times-Cited = {3},
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@article{ WOS:000331019600030,
Author = {Afram, Abdul and Janabi-Sharifi, Farrokh},
Title = {Theory and applications of HVAC control systems - A review of model
   predictive control (MPC)},
Journal = {BUILDING AND ENVIRONMENT},
Year = {2014},
Volume = {72},
Pages = {343-355},
Month = {FEB},
Abstract = {This work presents a literature review of control methods, with an
   emphasis on the theory and applications of model predictive control
   (MPC) for heating, ventilation, and air conditioning (HVAC) systems.
   Several control methods used for HVAC control are identified from the
   literature review, and a brief survey of each method is presented. Next,
   the performance of MPC is compared with that of other control
   approaches. Factors affecting MPC performance (including control
   configuration, process type, model, optimization technique, prediction
   horizon, control horizon, constraints, and cost function) are elaborated
   using specific examples from the literature. The gaps in MPC research
   are identified, and future directions are highlighted. (C) 2013 Elsevier
   Ltd. All rights reserved.},
Publisher = {PERGAMON-ELSEVIER SCIENCE LTD},
Address = {THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND},
Type = {Review},
Language = {English},
Affiliation = {Afram, A (Corresponding Author), Ryerson Univ, Dept Mech \& Ind Engn, 350 Victoria St, Toronto, ON M5B 2K3, Canada.
   Afram, Abdul; Janabi-Sharifi, Farrokh, Ryerson Univ, Dept Mech \& Ind Engn, Toronto, ON M5B 2K3, Canada.},
DOI = {10.1016/j.buildenv.2013.11.016},
ISSN = {0360-1323},
EISSN = {1873-684X},
Keywords = {Model predictive control (MPC); HVAC control review; Theory;
   Applications; Performance; Comparison},
Keywords-Plus = {BUILDING THERMAL STORAGE; REINFORCEMENT LEARNING CONTROL;
   AIR-CONDITIONING SYSTEM; ARTIFICIAL-INTELLIGENCE; ADAPTIVE CONTROLLER;
   NEURAL-NETWORKS; PI-CONTROLLER; ENERGY; OPTIMIZATION; STRATEGIES},
Research-Areas = {Construction \& Building Technology; Engineering},
Web-of-Science-Categories  = {Construction \& Building Technology; Engineering, Environmental;
   Engineering, Civil},
Author-Email = {abdul.afram@ryerson.ca
   fsharifi@ryerson.ca},
Affiliations = {Toronto Metropolitan University},
ResearcherID-Numbers = {Janabi-Sharifi, Farrokh/AAD-8442-2021},
Funding-Acknowledgement = {Ryerson Center for Urban Energy (CUE), Toronto Hydro and
   Mitacs-Accelerate Program},
Funding-Text = {This research was financially supported by Ryerson Center for Urban
   Energy (CUE), Toronto Hydro and Mitacs-Accelerate Program.},
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Number-of-Cited-References = {132},
Times-Cited = {664},
Usage-Count-Last-180-days = {67},
Usage-Count-Since-2013 = {481},
Journal-ISO = {Build. Environ.},
Doc-Delivery-Number = {AA3UK},
Web-of-Science-Index = {Science Citation Index Expanded (SCI-EXPANDED)},
Unique-ID = {WOS:000331019600030},
ESI-Highly-Cited-Paper = {Y},
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DA = {2023-04-22},
}

@article{ WOS:000858287300001,
Author = {Lagos, Ana and Caicedo, Joaquin E. and Coria, Gustavo and Romero Quete,
   Andres and Martinez, Maximiliano and Suvire, Gaston and Riquelme, Jesus},
Title = {State-of-the-Art Using Bibliometric Analysis of Wind-Speed and -Power
   Forecasting Methods Applied in Power Systems},
Journal = {ENERGIES},
Year = {2022},
Volume = {15},
Number = {18},
Month = {SEP},
Abstract = {The integration of wind energy into power systems has intensified as a
   result of the urgency for global energy transition. This requires more
   accurate forecasting techniques that can capture the variability of the
   wind resource to achieve better operative performance of power systems.
   This paper presents an exhaustive review of the state-of-the-art of
   wind-speed and -power forecasting models for wind turbines located in
   different segments of power systems, i.e., in large wind farms,
   distributed generation, microgrids, and micro-wind turbines installed in
   residences and buildings. This review covers forecasting models based on
   statistical and physical, artificial intelligence, and hybrid methods,
   with deterministic or probabilistic approaches. The literature review is
   carried out through a bibliometric analysis using VOSviewer and Pajek
   software. A discussion of the results is carried out, taking as the main
   approach the forecast time horizon of the models to identify their
   applications. The trends indicate a predominance of hybrid forecast
   models for the analysis of power systems, especially for those with high
   penetration of wind power. Finally, it is determined that most of the
   papers analyzed belong to the very short-term horizon, which indicates
   that the interest of researchers is in this time horizon.},
Publisher = {MDPI},
Address = {ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND},
Type = {Review},
Language = {English},
Affiliation = {Riquelme, J (Corresponding Author), Univ Seville, Dept Ingn Elect, Seville 41092, Spain.
   Lagos, Ana; Coria, Gustavo; Romero Quete, Andres; Martinez, Maximiliano; Suvire, Gaston, Univ Nacl San Juan, Inst Energia Elect, RA-5400 San Juan, Argentina.
   Caicedo, Joaquin E., Univ Dist Francisco Jose de Caldas, Fac Ingn, Bogota 110311, Colombia.
   Riquelme, Jesus, Univ Seville, Dept Ingn Elect, Seville 41092, Spain.},
DOI = {10.3390/en15186545},
Article-Number = {6545},
EISSN = {1996-1073},
Keywords = {wind speed forecasting; wind power forecasting; distributed generation;
   microgrid; urban; residential},
Keywords-Plus = {ARTIFICIAL NEURAL-NETWORK; TIME-SERIES PREDICTION; ENERGY MANAGEMENT;
   OPTIMIZATION ALGORITHM; UNCERTAINTY ANALYSIS; MODEL; GENERATION;
   FRAMEWORK; SELECTION; STRATEGY},
Research-Areas = {Energy \& Fuels},
Web-of-Science-Categories  = {Energy \& Fuels},
Author-Email = {jsantos@us.es},
Affiliations = {Universidad Nacional de San Juan; Universidad Distrital Francisco Jose
   de Caldas; University of Sevilla},
ResearcherID-Numbers = {Riquelme, Jesus/L-6720-2014
   },
ORCID-Numbers = {Romero Quete, Andres Arturo/0000-0002-6530-852X
   Riquelme, Jesus/0000-0001-6280-6796
   Coria, Gustavo/0000-0001-8975-9547
   Caicedo Navarro, Joaquin Eduardo/0000-0003-2168-829X},
Funding-Acknowledgement = {DAAD; CONICET; CYTED {[}718RT0564]; CERVERA program for Outstanding
   Research Centers {[}CER20191019]; PDTS 2020-2022 of UNSJ; USE of Junta
   de Andalucia {[}PYC20 RE 078]; SECITI},
Funding-Text = {This work was supported, in part, by DAAD, CONICET, and CYTED (through
   the network 718RT0564) and by the CERVERA program for Outstanding
   Research Centers (CER20191019). In addition, the research was funded
   under grant PDTS 2020-2022 of UNSJ and SECITI, and under project PYC20
   RE 078 USE of Junta de Andalucia.},
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Number-of-Cited-References = {109},
Times-Cited = {2},
Usage-Count-Last-180-days = {18},
Usage-Count-Since-2013 = {21},
Journal-ISO = {Energies},
Doc-Delivery-Number = {4T7JA},
Web-of-Science-Index = {Science Citation Index Expanded (SCI-EXPANDED)},
Unique-ID = {WOS:000858287300001},
OA = {Green Published, gold},
DA = {2023-04-22},
}

@article{ WOS:000907048500004,
Author = {Lehtola, Ville V. and Koeva, Mila and Elberink, Sander Oude and Raposo,
   Paulo and Virtanen, Juho-Pekka and Vahdatikhaki, Faridaddin and Borsci,
   Simone},
Title = {Digital twin of a city: Review of technology serving city needs},
Journal = {INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION},
Year = {2022},
Volume = {114},
Month = {NOV},
Abstract = {Digital twins (DTs) have been found useful in manufacturing,
   construction, and maintenance. Adapting DTs to serve cities, the
   question arises of what an urban digital twin should contain and how it
   should be orchestrated to serve a city's dynamical ecosystem, along with
   how to enhance the efficiency of the city. We are aligning with the
   commonplace idea that the main advantage of using DTs is economical as,
   for example, DTs can improve the planning of activities thus saving
   money and time. But how can they be useful for a city? Instead of
   looking at the DTs as solutions in search of problems to be solved, we
   start from city needs. Our approach is two-fold. We start by briefly
   reviewing existing possibilities for meeting some specific needs, but
   keep the focus on identifying and attempting to close the gap between
   the needs arising from everyday city functions and the latest DT
   techniques useful for meeting those needs. DTs are technically different
   and serve different applications, yet they share a common identity and
   name, as well as several technical similarities. Adopting computer
   science terminology, we see a back-end city DT as the container of all
   information, while any single front-end, visualized or used either by
   humans or robots, offers a limited but meaningful representation of the
   DT for a specific application. Alas, there are multiple open questions
   regarding the realization and benefits of such back-end DT.
   Nevertheless, we discuss how the back-end DT (or any specific DT) could
   be updated autonomously from sensor data using artificial intelligence
   techniques, and how the front-ends could be used for large benefits to
   the entire city ecosystem.},
Publisher = {ELSEVIER},
Address = {RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS},
Type = {Review},
Language = {English},
Affiliation = {Lehtola, VV (Corresponding Author), Univ Twente, ITC Fac, Dept Earth Observat Sci, Postbus 217, NL-7500 AE Enschede, Netherlands.
   Lehtola, Ville V.; Elberink, Sander Oude, Univ Twente, ITC Fac, Dept Earth Observat Sci, Postbus 217, NL-7500 AE Enschede, Netherlands.
   Koeva, Mila, Univ Twente, ITC Fac, Dept Urban \& Reg Planning \& Geoinformat Managemen, Postbus 217, NL-7500AE Enschede, Netherlands.
   Raposo, Paulo, Univ Twente, ITC Fac, Dept Geoinformat Proc, Postbus 217, NL-7500 AE Enschede, Netherlands.
   Virtanen, Juho-Pekka, Forum Virium Helsinki, Unioninkatu 24, Helsinki 00130, Finland.
   Vahdatikhaki, Faridaddin, Univ Twente, Fac Engn Technol, Postbus 217, NL-7500 AE Enschede, Netherlands.
   Borsci, Simone, Univ Twente, Fac Behav Management \& Social Sci, Postbus 217, NL-7500 AE Enschede, Netherlands.},
DOI = {10.1016/j.jag.2022.102915},
EarlyAccessDate = {NOV 2022},
Article-Number = {102915},
ISSN = {1569-8432},
EISSN = {1872-826X},
Keywords = {Digital twins; City; Artificial intelligence; Human factors; Sensor
   systems; Point clouds},
Keywords-Plus = {SOCIAL INNOVATION; 3D; CHALLENGES; MODELS; CITIES; STATE; BIM;
   VISUALIZATION; MANAGEMENT; INTERFACE},
Research-Areas = {Remote Sensing},
Web-of-Science-Categories  = {Remote Sensing},
Author-Email = {v.v.lehtola@utwente.nl},
Affiliations = {University of Twente; University of Twente; University of Twente;
   University of Twente},
ResearcherID-Numbers = {Elberink, Sander Oude/D-3829-2009},
ORCID-Numbers = {Elberink, Sander Oude/0000-0002-4511-2095},
Funding-Acknowledgement = {European Union?s Horizon 2020 research and innovation program; 
   {[}101004590];  {[}731297]},
Funding-Text = {Acknowledgments JPV acknowledges funding from the European Union?s
   Horizon 2020 research and innovation program under grant agreements No.
   101004590 and 731297.},
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Number-of-Cited-References = {184},
Times-Cited = {5},
Usage-Count-Last-180-days = {12},
Usage-Count-Since-2013 = {12},
Journal-ISO = {Int. J. Appl. Earth Obs. Geoinf.},
Doc-Delivery-Number = {7N0PD},
Web-of-Science-Index = {Science Citation Index Expanded (SCI-EXPANDED)},
Unique-ID = {WOS:000907048500004},
OA = {Green Published, gold},
DA = {2023-04-22},
}

@article{ WOS:000375722300007,
Author = {Zhao, Wanqing and Beach, Thomas H. and Rezgui, Yacine},
Title = {Optimization of Potable Water Distribution and Wastewater Collection
   Networks: A Systematic Review and Future Research Directions},
Journal = {IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS},
Year = {2016},
Volume = {46},
Number = {5},
Pages = {659-681},
Month = {MAY},
Abstract = {Potable water distribution networks (WDNs) and wastewater collection
   networks (WWCNs) are the two fundamental constituents of the complex
   urban water infrastructure. Such water networks require adapted design
   interventions as part of retrofitting, extension, and maintenance
   activities. Consequently, proper optimization methodologies are required
   to reduce the associated capital cost while also meeting the demands of
   acquiring clean water and releasing wastewater by consumers. In this
   paper, a systematic review of the optimization of both WDNs and WWCNs,
   from the preliminary stages of development through to the
   state-of-the-art, is jointly presented. First, both WDNs and WWCNs are
   conceptually and functionally described along with illustrative
   benchmarks. The optimization of water networks across both clean and
   waste domains is then systematically reviewed and organized, covering
   all levels of complexity from the formulation of cost functions and
   constraints, through to traditional and advanced optimization
   methodologies. The rationales behind employing these methodologies as
   well as their advantages and disadvantages are investigated. This paper
   then critically discusses current trends and identifies directions for
   future research by comparing the existing optimization paradigms within
   WDNs and WWCNs and proposing common research directions for optimizing
   water networks. Optimization of urban water networks is a
   multidisciplinary domain, within which this paper is anticipated to be
   of great benefit to researchers and practitioners.},
Publisher = {IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC},
Address = {445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA},
Type = {Review},
Language = {English},
Affiliation = {Zhao, WQ; Beach, TH; Rezgui, Y (Corresponding Author), Cardiff Univ, Cardiff Sch Engn, Cardiff CF24 3AA, S Glam, Wales.
   Zhao, Wanqing; Beach, Thomas H.; Rezgui, Yacine, Cardiff Univ, Cardiff Sch Engn, Cardiff CF24 3AA, S Glam, Wales.},
DOI = {10.1109/TSMC.2015.2461188},
ISSN = {2168-2216},
EISSN = {2168-2232},
Keywords = {Artificial intelligence; hydraulics; network optimization; wastewater
   collection networks (WWCNs); water distribution networks (WDNs)},
Keywords-Plus = {EVOLUTIONARY MULTIOBJECTIVE OPTIMIZATION; HARMONY SEARCH ALGORITHM;
   RELIABILITY-BASED DESIGN; GENETIC ALGORITHM; CELLULAR-AUTOMATA;
   DIFFERENTIAL EVOLUTION; COST DESIGN; TABU SEARCH; DECOMPOSITION; MODEL},
Research-Areas = {Automation \& Control Systems; Computer Science},
Web-of-Science-Categories  = {Automation \& Control Systems; Computer Science, Cybernetics},
Author-Email = {zhaow9@cardiff.ac.uk
   beachth@cardiff.ac.uk
   rezguiY@cardiff.ac.uk},
Affiliations = {Cardiff University},
ResearcherID-Numbers = {Zhao, Wanqing/AGZ-4360-2022
   Rezgui, Yacine/ABE-6712-2020
   },
ORCID-Numbers = {Rezgui, Yacine/0000-0002-5711-8400
   Beach, Thomas/0000-0001-5610-8027
   Zhao, Wanqing/0000-0001-6160-9547},
Funding-Acknowledgement = {EU Seventh Framework Programme (FP7) {[}619795]},
Funding-Text = {This work was supported by the EU Seventh Framework Programme (FP7)
   under Grant 619795 with partners from Universities, Institutes, local
   authorities, water utilities, and Information Communication Technology
   companies. This paper was recommended by Associate Editor K. W. Hipel.},
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Number-of-Cited-References = {172},
Times-Cited = {35},
Usage-Count-Last-180-days = {4},
Usage-Count-Since-2013 = {74},
Journal-ISO = {IEEE Trans. Syst. Man Cybern. -Syst.},
Doc-Delivery-Number = {DL6BK},
Web-of-Science-Index = {Science Citation Index Expanded (SCI-EXPANDED)},
Unique-ID = {WOS:000375722300007},
OA = {Green Accepted},
DA = {2023-04-22},
}

@article{ WOS:000492779400008,
Author = {Falamarzi, Amir and Moridpour, Sara and Nazem, Majidreza},
Title = {A review of rail track degradation prediction models},
Journal = {AUSTRALIAN JOURNAL OF CIVIL ENGINEERING},
Year = {2019},
Volume = {17},
Number = {2},
Pages = {152-166},
Month = {JUL 3},
Abstract = {The increase in traffic congestion in urban areas as a result of more
   private transport intensifies the importance of promoting public
   transport and rail transport in particular. The increasing demand for
   rail transport will lead to more pressure and stress on rail components
   and consequently higher rates of degradation. If degradation of railway
   components is not treated by means of effective maintenance strategies,
   catastrophic human casualties and massive financial losses are
   inevitable. Degradation prediction modelling is the key element in the
   establishment of cost-effective and efficient maintenance strategies in
   railway systems. This paper reviews different models proposed in the
   literature for rail track degradation prediction, including mechanistic
   models, statistical models and Artificial Intelligence (AI) models. In
   addition, the advantages and disadvantages of each model are explained,
   and factors influencing rail track degradation are investigated.},
Publisher = {TAYLOR \& FRANCIS LTD},
Address = {2-4 PARK SQUARE, MILTON PARK, ABINGDON OR14 4RN, OXON, ENGLAND},
Type = {Review},
Language = {English},
Affiliation = {Falamarzi, A (Corresponding Author), RMIT Univ, Sch Engn, Civil \& Infrastruct Engn Discipline, Melbourne, Vic, Australia.
   Falamarzi, Amir; Moridpour, Sara; Nazem, Majidreza, RMIT Univ, Sch Engn, Civil \& Infrastruct Engn Discipline, Melbourne, Vic, Australia.},
DOI = {10.1080/14488353.2019.1667710},
ISSN = {1448-8353},
EISSN = {2204-2245},
Keywords = {Railway; degradation; prediction models; rail infrastructure; rail
   maintenance},
Keywords-Plus = {DECISION-SUPPORT-SYSTEM; INFRASTRUCTURE MAINTENANCE; STOCHASTIC-MODEL;
   MANAGEMENT; WEAR},
Research-Areas = {Engineering},
Web-of-Science-Categories  = {Engineering, Civil},
Author-Email = {S3349323@student.rmit.edu.au},
Affiliations = {Royal Melbourne Institute of Technology (RMIT)},
ResearcherID-Numbers = {falamarzi, amir/I-1266-2013
   Nazem, Majid/A-4267-2013},
ORCID-Numbers = {Nazem, Majid/0000-0002-6433-3416},
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Number-of-Cited-References = {62},
Times-Cited = {16},
Usage-Count-Last-180-days = {3},
Usage-Count-Since-2013 = {15},
Journal-ISO = {Aust. J. Civ. Eng.},
Doc-Delivery-Number = {JH4ZR},
Web-of-Science-Index = {Emerging Sources Citation Index (ESCI)},
Unique-ID = {WOS:000492779400008},
DA = {2023-04-22},
}

@article{ WOS:000379861800013,
Author = {Niu, Jiqiang and Tang, Wenwu and Xu, Feng and Zhou, Xiaoyan and Song,
   Yanan},
Title = {Global Research on Artificial Intelligence from 1990-2014:
   Spatially-Explicit Bibliometric Analysis},
Journal = {ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION},
Year = {2016},
Volume = {5},
Number = {5},
Month = {MAY},
Abstract = {In this article, we conducted the evaluation of artificial intelligence
   research from 1990-2014 by using bibliometric analysis. We introduced
   spatial analysis and social network analysis as geographic information
   retrieval methods for spatially-explicit bibliometric analysis. This
   study is based on the analysis of data obtained from database of the
   Science Citation Index Expanded (SCI-Expanded) and Conference
   Proceedings Citation Index-Science (CPCI-S). Our results revealed
   scientific outputs, subject categories and main journals, author
   productivity and geographic distribution, international productivity and
   collaboration, and hot issues and research trends. The growth of article
   outputs in artificial intelligence research has exploded since the
   1990s, along with increasing collaboration, reference, and citations.
   Computer science and engineering were the most frequently-used subject
   categories in artificial intelligence studies. The top twenty productive
   authors are distributed in countries with a high investment of research
   and development. The United States has the highest number of top
   research institutions in artificial intelligence, producing most
   single-country and collaborative articles. Although there is more and
   more collaboration among institutions, cooperation, especially
   international ones, are not highly prevalent in artificial intelligence
   research as expected. The keyword analysis revealed interesting research
   preferences, confirmed that methods, models, and application are in the
   central position of artificial intelligence. Further, we found
   interesting related keywords with high co-occurrence frequencies, which
   have helped identify new models and application areas in recent years.
   Bibliometric analysis results from our study will greatly facilitate the
   understanding of the progress and trends in artificial intelligence, in
   particular, for those researchers interested in domain-specific
   AI-driven problem-solving. This will be of great assistance for the
   applications of AI in alternative fields in general and geographic
   information science, in particular.},
Publisher = {MDPI},
Address = {ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND},
Type = {Review},
Language = {English},
Affiliation = {Niu, JQ (Corresponding Author), Xinyang Normal Univ, Sch Urban \& Environm Sci, Xinyang 464000, Peoples R China.
   Niu, Jiqiang; Xu, Feng, Xinyang Normal Univ, Sch Urban \& Environm Sci, Xinyang 464000, Peoples R China.
   Tang, Wenwu, Univ N Carolina, Dept Geog \& Earth Sci, Charlotte, NC 28262 USA.
   Tang, Wenwu, Univ N Carolina, Ctr Appl Geog Informat Sci, Charlotte, NC 28262 USA.
   Zhou, Xiaoyan; Song, Yanan, Wuhan Univ, Sch Resource \& Environm Sci, Wuhan 430079, Peoples R China.},
DOI = {10.3390/ijgi5050066},
Article-Number = {66},
ISSN = {2220-9964},
Keywords = {Artificial Intelligence; bibliometric analysis; scientific outputs;
   research trends; SCI-expanded; Conference Proceedings Citation
   Index-Science},
Keywords-Plus = {KNOWLEDGE MANAGEMENT RESEARCH; PARTICLE SWARM OPTIMIZATION;
   NEURAL-NETWORKS; GENETIC ALGORITHMS; IMMUNE-SYSTEM; FUZZY-LOGIC; BEE
   COLONY; PERFORMANCE; TRENDS; MODEL},
Research-Areas = {Computer Science; Physical Geography; Remote Sensing},
Web-of-Science-Categories  = {Computer Science, Information Systems; Geography, Physical; Remote
   Sensing},
Author-Email = {njq8196@163.com
   WenwuTang@uncc.edu
   xu-f88@163.com
   zhouxiaoyan@whu.edu.cn
   yanansong@whu.edu.cn},
Affiliations = {Xinyang Normal University; University of North Carolina; University of
   North Carolina Charlotte; University of North Carolina; University of
   North Carolina Charlotte; Wuhan University},
ResearcherID-Numbers = {Niu, Jiqiang/F-8497-2019
   },
ORCID-Numbers = {Tang, Wenwu/0000-0001-7870-177X
   niu, Jiqiang/0000-0002-3315-2970},
Funding-Acknowledgement = {National Sciences Foundation of China {[}41201387, 41001219]; Key
   Natural Science Research of Department of Education, Henan, China
   {[}15A170012]},
Funding-Text = {This study is funded by National Sciences Foundation of China (No.
   41201387, 41001219) and the Key Natural Science Research of Department
   of Education, Henan, China (No. 15A170012).},
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Number-of-Cited-References = {76},
Times-Cited = {49},
Usage-Count-Last-180-days = {15},
Usage-Count-Since-2013 = {177},
Journal-ISO = {ISPRS Int. Geo-Inf.},
Doc-Delivery-Number = {DR4HI},
Web-of-Science-Index = {Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)},
Unique-ID = {WOS:000379861800013},
OA = {Green Published, Green Submitted, gold},
DA = {2023-04-22},
}

@article{ WOS:000519323900001,
Author = {Yu, Manzhu and Bambacus, Myra and Cervone, Guido and Clarke, Keith and
   Duffy, Daniel and Huang, Qunying and Li, Jing and Li, Wenwen and Li,
   Zhenlong and Liu, Qian and Resch, Bernd and Yang, Jingchao and Yang,
   Chaowei},
Title = {Spatiotemporal event detection: a review},
Journal = {INTERNATIONAL JOURNAL OF DIGITAL EARTH},
Year = {2020},
Volume = {13},
Number = {12},
Pages = {1339-1365},
Month = {DEC 1},
Abstract = {The advancements of sensing technologies, including remote sensing, in
   situ sensing, social sensing, and health sensing, have tremendously
   improved our capability to observe and record natural and social
   phenomena, such as natural disasters, presidential elections, and
   infectious diseases. The observations have provided an unprecedented
   opportunity to better understand and respond to the spatiotemporal
   dynamics of the environment, urban settings, health and disease
   propagation, business decisions, and crisis and crime. Spatiotemporal
   event detection serves as a gateway to enable a better understanding by
   detecting events that represent the abnormal status of relevant
   phenomena. This paper reviews the literature for different sensing
   capabilities, spatiotemporal event extraction methods, and categories of
   applications for the detected events. The novelty of this review is to
   revisit the definition and requirements of event detection and to layout
   the overall workflow (from sensing and event extraction methods to the
   operations and decision-supporting processes based on the extracted
   events) as an agenda for future event detection research. Guidance is
   presented on the current challenges to this research agenda, and future
   directions are discussed for conducting spatiotemporal event detection
   in the era of big data, advanced sensing, and artificial intelligence.},
Publisher = {TAYLOR \& FRANCIS LTD},
Address = {2-4 PARK SQUARE, MILTON PARK, ABINGDON OR14 4RN, OXON, ENGLAND},
Type = {Review},
Language = {English},
Affiliation = {Yang, CW (Corresponding Author), 4400 Univ Dr, Fairfax, VA 22030 USA.
   Yu, Manzhu; Cervone, Guido, Penn State Univ, Dept Geog, University Pk, PA 16802 USA.
   Bambacus, Myra; Duffy, Daniel, NASA Goddard Space Flight Ctr, Greenbelt, MD USA.
   Clarke, Keith, Univ Calif Santa Barbara, Dept Geog, Santa Barbara, CA 93106 USA.
   Huang, Qunying, Univ Wisconsin, Dept Geog, Madison, WI 53706 USA.
   Li, Jing, Univ Denver, Dept Geog \& Environm, Denver, CO USA.
   Li, Wenwen, Arizona State Univ, Sch Geog Sci \& Urban Planning, Tempe, AZ USA.
   Li, Zhenlong, Univ South Carolina, Dept Geog, Columbia, SC 29208 USA.
   Liu, Qian; Yang, Jingchao; Yang, Chaowei, George Mason Univ, NSF Spatiotemporal Innovat Ctr, Fairfax, VA 22030 USA.
   Liu, Qian; Yang, Jingchao; Yang, Chaowei, George Mason Univ, Dept Geog \& GeoInformat Sci, Fairfax, VA 22030 USA.
   Resch, Bernd, Univ Salzburg, Dept Geoinformat, Salzburg, Austria.
   Resch, Bernd, Harvard Univ, Ctr Geog Anal, Boston, MA 02115 USA.},
DOI = {10.1080/17538947.2020.1738569},
EarlyAccessDate = {MAR 2020},
ISSN = {1753-8947},
EISSN = {1753-8955},
Keywords = {GeoAI; geography and geoscience; human dynamics; digital earth;
   computational challenges; cloud computing; internet of things},
Keywords-Plus = {SYNTHETIC-APERTURE RADAR; WIRELESS SENSOR; DATA FUSION; BIG DATA;
   SYSTEM; SIMULATION; FRAMEWORK; TWITTER; EARTH; CLASSIFICATION},
Research-Areas = {Physical Geography; Remote Sensing},
Web-of-Science-Categories  = {Geography, Physical; Remote Sensing},
Author-Email = {cyang3@gmu.edu},
Affiliations = {Pennsylvania Commonwealth System of Higher Education (PCSHE);
   Pennsylvania State University; Pennsylvania State University -
   University Park; National Aeronautics \& Space Administration (NASA);
   NASA Goddard Space Flight Center; University of California System;
   University of California Santa Barbara; University of Wisconsin System;
   University of Wisconsin Madison; University of Denver; Arizona State
   University; Arizona State University-Tempe; University of South Carolina
   System; University of South Carolina Columbia; George Mason University;
   George Mason University; Salzburg University; Harvard University},
ResearcherID-Numbers = {Li, Zhenlong/M-1065-2017
   Resch, Bernd/ABE-4625-2021
   Yang, Chaowei/A-9881-2017
   Yu, Manzhu/J-6953-2019
   Clarke, Keith/E-1863-2011},
ORCID-Numbers = {Li, Zhenlong/0000-0002-8938-5466
   Resch, Bernd/0000-0002-2233-6926
   Yang, Chaowei/0000-0001-7768-4066
   Yu, Manzhu/0000-0001-6769-7517
   Clarke, Keith/0000-0001-5805-6056},
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Number-of-Cited-References = {229},
Times-Cited = {37},
Usage-Count-Last-180-days = {7},
Usage-Count-Since-2013 = {72},
Journal-ISO = {Int. J. Digit. Earth},
Doc-Delivery-Number = {PA4YF},
Web-of-Science-Index = {Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)},
Unique-ID = {WOS:000519323900001},
OA = {Green Published},
DA = {2023-04-22},
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@article{ WOS:000832308400001,
Author = {Seng, Jasmine Kah Phooi and Ang, Kenneth Li-minn and Peter, Eno and
   Mmonyi, Anthony},
Title = {Artificial Intelligence (AI) and Machine Learning for Multimedia and
   Edge Information Processing},
Journal = {ELECTRONICS},
Year = {2022},
Volume = {11},
Number = {14},
Month = {JUL},
Abstract = {The advancements and progress in artificial intelligence (AI) and
   machine learning, and the numerous availabilities of mobile devices and
   Internet technologies together with the growing focus on multimedia data
   sources and information processing have led to the emergence of new
   paradigms for multimedia and edge AI information processing,
   particularly for urban and smart city environments. Compared to cloud
   information processing approaches where the data are collected and sent
   to a centralized server for information processing, the edge information
   processing paradigm distributes the tasks to multiple devices which are
   close to the data source. Edge information processing techniques and
   approaches are well suited to match current technologies for Internet of
   Things (IoT) and autonomous systems, although there are many challenges
   which remain to be addressed. The motivation of this paper was to survey
   these new paradigms for multimedia and edge information processing from
   several technological perspectives including: (1) multimedia analytics
   on the edge empowered by AI; (2) multimedia streaming on the intelligent
   edge; (3) multimedia edge caching and AI; (4) multimedia services for
   edge AI; and (5) hardware and devices for multimedia on edge
   intelligence. The review covers a wide spectrum of enabling technologies
   for AI and machine learning for multimedia and edge information
   processing.},
Publisher = {MDPI},
Address = {ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND},
Type = {Review},
Language = {English},
Affiliation = {Seng, JKP (Corresponding Author), Xian Jiaotong Liverpool Univ, Sch AI \& Adv Comp, Suzhou 215123, Peoples R China.
   Seng, JKP (Corresponding Author), Queensland Univ Technol, Sch Comp Sci, Brisbane, Qld 4000, Australia.
   Seng, Jasmine Kah Phooi, Xian Jiaotong Liverpool Univ, Sch AI \& Adv Comp, Suzhou 215123, Peoples R China.
   Seng, Jasmine Kah Phooi, Queensland Univ Technol, Sch Comp Sci, Brisbane, Qld 4000, Australia.
   Ang, Kenneth Li-minn, Univ Sunshine Coast, Sch Sci \& Engn, Petrie, Qld 4502, Australia.
   Peter, Eno, Fed Univ, Dept Comp Sci, Oye Ekiti 370112, Nigeria.
   Mmonyi, Anthony, Afe Babalola Univ, Dept Elect \& Comp Engn, Ado Ekiti 360102, Nigeria.},
DOI = {10.3390/electronics11142239},
Article-Number = {2239},
EISSN = {2079-9292},
Keywords = {multimedia processing; edge multimedia; intelligence edge; edge AI; edge
   computing; edge multimedia analytics},
Keywords-Plus = {CONVOLUTIONAL NETWORKS; SMART CITIES; DEEP; INTERNET; CLASSIFICATION;
   SURVEILLANCE; CHALLENGES; TRENDS; QOE},
Research-Areas = {Computer Science; Engineering; Physics},
Web-of-Science-Categories  = {Computer Science, Information Systems; Engineering, Electrical \&
   Electronic; Physics, Applied},
Author-Email = {kahphooi.seng@qut.edu.au
   lang@usc.edu.au
   eno.peter@fuoye.edu.ng
   mmonyica@abuad.edu.ng},
Affiliations = {Xi'an Jiaotong-Liverpool University; Queensland University of Technology
   (QUT); University of the Sunshine Coast},
ORCID-Numbers = {Mmonyi, Anthony Chukwunonso/0000-0002-1542-7520},
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Journal-ISO = {Electronics},
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Unique-ID = {WOS:000832308400001},
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@article{ WOS:000823890600001,
Author = {Badidi, Elarbi},
Title = {Edge AI and Blockchain for Smart Sustainable Cities: Promise and
   Potential},
Journal = {SUSTAINABILITY},
Year = {2022},
Volume = {14},
Number = {13},
Month = {JUL},
Abstract = {Modern cities worldwide are undergoing radical changes to foster a
   clean, sustainable and secure environment, install smart
   infrastructures, deliver intelligent services to residents, and
   facilitate access for vulnerable groups. The adoption of new
   technologies is at the heart of implementing many initiatives to address
   critical concerns in urban mobility, healthcare, water management, clean
   energy production and consumption, energy saving, housing, safety, and
   accessibility. Given the advancements in sensing and communication
   technologies over the past few decades, exploring the adoption of recent
   and innovative technologies is critical to addressing these concerns and
   making cities more innovative, sustainable, and safer. This article
   provides a broad understanding of the current urban challenges faced by
   smart cities. It highlights two new technological advances, edge
   artificial intelligence (edge AI) and Blockchain, and analyzes their
   transformative potential to make our cities smarter. In addition, it
   explores the multiple uses of edge AI and Blockchain technologies in the
   fields of smart mobility and smart energy and reviews relevant research
   efforts in these two critical areas of modern smart cities. It
   highlights the various algorithms to handle vehicle detection, counting,
   speed identification to address the problem of traffic congestion and
   the different use-cases of Blockchain in terms of trustworthy
   communications and trading between vehicles and smart energy trading.
   This review paper is expected to serve as a guideline for future
   research on adopting edge AI and Blockchain in other smart city domains.},
Publisher = {MDPI},
Address = {ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND},
Type = {Review},
Language = {English},
Affiliation = {Badidi, E (Corresponding Author), UAE Univ, Dept Comp Sci \& Software Engn, Coll Informat Technol, POB 15551, Al Ain, U Arab Emirates.
   Badidi, Elarbi, UAE Univ, Dept Comp Sci \& Software Engn, Coll Informat Technol, POB 15551, Al Ain, U Arab Emirates.},
DOI = {10.3390/su14137609},
Article-Number = {7609},
EISSN = {2071-1050},
Keywords = {edge computing; edge intelligence; Blockchain; smart grids; smart
   mobility; smart energy},
Keywords-Plus = {ENERGY MANAGEMENT; INTERNET; INTELLIGENCE; VEHICLES; TECHNOLOGY;
   CHALLENGES; ARCHITECTURE; CONVERGENCE; INFORMATION; PERFORMANCE},
Research-Areas = {Science \& Technology - Other Topics; Environmental Sciences \& Ecology},
Web-of-Science-Categories  = {Green \& Sustainable Science \& Technology; Environmental Sciences;
   Environmental Studies},
Author-Email = {ebadidi@uaeu.ac.ae},
Affiliations = {United Arab Emirates University},
ORCID-Numbers = {Badidi, Elarbi/0000-0001-9121-8766},
Funding-Acknowledgement = {UAEU Program for Advanced Research Grant {[}G00003443]},
Funding-Text = {This work is supported by the UAEU Program for Advanced Research Grant
   N. G00003443.},
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Number-of-Cited-References = {140},
Times-Cited = {5},
Usage-Count-Last-180-days = {10},
Usage-Count-Since-2013 = {16},
Journal-ISO = {Sustainability},
Doc-Delivery-Number = {2V5MR},
Web-of-Science-Index = {Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)},
Unique-ID = {WOS:000823890600001},
OA = {gold},
DA = {2023-04-22},
}

@article{ WOS:000934877500008,
Author = {Urban, Barbara},
Title = {THiNkiNG AND pRocEssiNG. A REviEw oF DEMocRATic sysTEMs BAsED oN
   ARTiFiciAl iNTElliGENcE FRoM REcoGNiTioN, coMMiTMENT AND jusTicE},
Journal = {REVISTA DE FILOSOFIA},
Year = {2022},
Volume = {79},
Pages = {190-200},
Abstract = {This paper proposes a double reflection on the cognitive differences
   between humans and machines from ethics. In the first place, I will try
   to differentiate between human thought and machine processing through
   etymological, semantic and comparative methods that will reveal why it
   is not possible to attribute thinking capacities to artificial
   intelligences. On the other hand, these terms will be related to the
   praxis of representative democracy through various examples. This
   analysis is based on three aims: the need for mutual recognition through
   language; the commitment acquired through the word; and democracy as an
   expression of justice.},
Publisher = {UNIV CHILE, FAC FILOSOFIA \& HUMANIDADES},
Address = {DEPT LINGUISTICA, AV CAPITAN IGNACIO CARRERA PINTO 1025, CUARTO PISO,
   NUNOA, SANTIAGO, 00000, CHILE},
Type = {Review},
Language = {English},
Affiliation = {Urban, B (Corresponding Author), Univ Jaume 1, Dept Filosofia \& Sociol, Avda Vicente Sos Baynat S-N, Castellon de La Plana 12071, Spain.
   Urban, Barbara, Univ Jaume 1, Dept Filosofia \& Sociol, Avda Vicente Sos Baynat S-N, Castellon de La Plana 12071, Spain.},
ISSN = {0034-8236},
EISSN = {0718-4360},
Keywords = {KEywoRDs; Thought; processing; democracy; reciprocity; Arendt; Habermas;
   language; commitment; representative democracy},
Research-Areas = {Philosophy},
Web-of-Science-Categories  = {Philosophy},
Author-Email = {barbara.urban@uji.es},
Affiliations = {Universitat Jaume I},
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Number-of-Cited-References = {16},
Times-Cited = {0},
Usage-Count-Last-180-days = {0},
Usage-Count-Since-2013 = {0},
Journal-ISO = {Rev. Filos.},
Doc-Delivery-Number = {9B6XC},
Web-of-Science-Index = {Arts &amp; Humanities Citation Index (A&amp;HCI)},
Unique-ID = {WOS:000934877500008},
DA = {2023-04-22},
}

@article{ WOS:000625391400003,
Author = {Liu, Zhen and Wu, Kefeng and Wu, Binhua and Tang, Xiaoning and Yuan,
   Huiqing and Pang, Hao and Huang, Yongmei and Zhu, Xiao and Luo, Hui and
   Qi, Yi},
Title = {Imaging genomics for accurate diagnosis and treatment of tumors: A
   cutting edge overview},
Journal = {BIOMEDICINE \& PHARMACOTHERAPY},
Year = {2021},
Volume = {135},
Month = {MAR},
Abstract = {Imaging genomics refers to the establishment of the connection between
   invasive gene expression features and non-invasive imaging features.
   Tumor imaging genomics can not only understand the macroscopic phenotype
   of tumor, but also can deeply analyze the cellular and molecular
   characteristics of tumor tissue. In recent years, tumor imaging genomics
   has been a key in the field of medicine. The incidence of cancer in
   China has increased significantly, which is the main reason of disease
   death of urban residents. With the rapid development of imaging
   medicine, depending on imaging genomics, many experts have made
   remarkable achievements in tumor screening and diagnosis, prognosis
   evaluation, new treatment targets and understanding of tumor biological
   mechanism. This review analyzes the relationship between tumor radiology
   and gene expression, which provides a favorable direction for clinical
   staging, prognosis evaluation and accurate treatment of tumors.},
Publisher = {ELSEVIER FRANCE-EDITIONS SCIENTIFIQUES MEDICALES ELSEVIER},
Address = {65 RUE CAMILLE DESMOULINS, CS50083, 92442 ISSY-LES-MOULINEAUX, FRANCE},
Type = {Review},
Language = {English},
Affiliation = {Zhu, X; Luo, H; Qi, Y (Corresponding Author), Guangdong Med Univ, Marine Biomed Res Inst, Guangdong Key Lab Res \& Dev Nat Drugs, Zhanjiang, Peoples R China.
   Liu, Zhen; Wu, Kefeng; Wu, Binhua; Tang, Xiaoning; Yuan, Huiqing; Pang, Hao; Huang, Yongmei; Zhu, Xiao; Luo, Hui; Qi, Yi, Southern Marine Sci \& Engn Guangdong Lab Zhanjian, Zhanjiang, Peoples R China.
   Liu, Zhen; Tang, Xiaoning; Yuan, Huiqing; Huang, Yongmei; Zhu, Xiao; Luo, Hui; Qi, Yi, Guangdong Med Univ, Marine Biomed Res Inst, Guangdong Key Lab Res \& Dev Nat Drugs, Zhanjiang, Peoples R China.
   Liu, Zhen; Wu, Binhua; Yuan, Huiqing; Huang, Yongmei; Zhu, Xiao; Luo, Hui; Qi, Yi, Guangdong Med Univ, Key Lab Zhanjiang R\&D Marine Microbial Resources, Zhanjiang, Peoples R China.
   Liu, Zhen; Wu, Binhua; Yuan, Huiqing; Huang, Yongmei; Zhu, Xiao; Luo, Hui; Qi, Yi, Marine Biomed Res Inst Guangdong Zhanjiang, Zhanjiang, Peoples R China.},
DOI = {10.1016/j.biopha.2020.111173},
Article-Number = {111173},
ISSN = {0753-3322},
EISSN = {1950-6007},
Keywords = {Cancer; Imaging genomics; Radiogenomics; Magnetic resonance; Computed
   tomography; Artificial intelligence},
Keywords-Plus = {CT TEXTURE ANALYSIS; LOWER-GRADE GLIOMAS; BREAST-CANCER; MRI FEATURES;
   GLIOBLASTOMA-MULTIFORME; COMPUTED-TOMOGRAPHY; LUNG ADENOCARCINOMA;
   MOLECULAR SUBTYPE; MUTATION STATUS; EGFR MUTATIONS},
Research-Areas = {Research \& Experimental Medicine; Pharmacology \& Pharmacy},
Web-of-Science-Categories  = {Medicine, Research \& Experimental; Pharmacology \& Pharmacy},
Author-Email = {xzhu@gdmu.edu.cn
   luohui@gdmu.edu.cn
   qiyi7272@gdmu.edu.cn},
Affiliations = {Southern Marine Science \& Engineering Guangdong Laboratory; Southern
   Marine Science \& Engineering Guangdong Laboratory (Zhanjiang);
   Guangdong Medical University; Guangdong Medical University},
ResearcherID-Numbers = {Wu, Ke/HCQ-3503-2022
   },
ORCID-Numbers = {Zhu, Xiao/0000-0002-1737-3386},
Funding-Acknowledgement = {Guangdong Science and Technology Department {[}2016B030309002,
   2019B090905011]; Fund of Southern Marine Science and Engineering
   Guangdong Laboratory (Zhanjiang) {[}ZJW-2019-007]; Science and
   Technology Program of Zhanjiang {[}2017A06012]; Public Service Platform
   of South China Sea for R\&D Marine Biomedicine Resources {[}GDMUK201808]},
Funding-Text = {This work was supported partly by Guangdong Science and Technology
   Department (2016B030309002 and 2019B090905011); The Fund of Southern
   Marine Science and Engineering Guangdong Laboratory (Zhanjiang)
   (ZJW-2019-007); The Science and Technology Program of Zhanjiang
   (2017A06012); The Public Service Platform of South China Sea for R\&D
   Marine Biomedicine Resources (GDMUK201808).},
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Number-of-Cited-References = {70},
Times-Cited = {3},
Usage-Count-Last-180-days = {3},
Usage-Count-Since-2013 = {15},
Journal-ISO = {Biomed. Pharmacother.},
Doc-Delivery-Number = {QR7JO},
Web-of-Science-Index = {Science Citation Index Expanded (SCI-EXPANDED)},
Unique-ID = {WOS:000625391400003},
OA = {gold},
DA = {2023-04-22},
}

@article{ WOS:000855074900001,
Author = {Verma, Deepak and Okhawilai, Manunya and Dalapati, Goutam Kumar and
   Ramakrishna, Seeram and Sharma, Abhishek and Sonar, Prashant and
   Krishnamurthy, Satheesh and Biring, Sajal and Sharma, Mohit},
Title = {Blockchain technology and AI-facilitated polymers recycling:
   Utilization, realities, and sustainability},
Journal = {POLYMER COMPOSITES},
Year = {2022},
Volume = {43},
Number = {12},
Pages = {8587-8601},
Month = {DEC},
Abstract = {From the environmental perspective, efficient plastic utilization and
   its recyclability become significant issues that need to be resolved for
   deploying urban and sustainable technologies. It is estimated that
   approximately 400 million tons of plastic are produced each year for
   different applications. This number will be doubled by 2050, which is a
   serious problem. The primary issue that arises in a recycling process is
   associated with optimum supply chain management. The comprehensive and
   transparent supply chain methodologies will help stockholders to make
   conclusive policies and precise strategies. Transparency in supply chain
   management assists in captivating planning, pricing, purchasing, and
   inventory management decisions. Environmental sustainability requires
   recycling, which should have innovative concepts like Artificial
   Intelligence (AI) and Block-chain Technology. Manual methods of sorting
   and segregating the waste have outdated and not much efficient. The
   inclusion of AI and Blockchain Technology brought a revolution by
   increasing the efficiency and accuracy of the recycling process. This
   critical review focused on recycling plastics and plastic waste using AI
   and Blockchain Technology. Various plastic regulation policies and AI
   utilization for plastic recycling are discussed. An overview of the
   blockchain and its classification for waste management or plastic
   recycling has been discussed. The utilization of Blockchain Technology
   for a plastic circular economy, its types, and critical benefits has
   also been systematically demonstrated.},
Publisher = {WILEY},
Address = {111 RIVER ST, HOBOKEN 07030-5774, NJ USA},
Type = {Review},
Language = {English},
Affiliation = {Verma, D (Corresponding Author), Chulalongkorn Univ, Int Grad Program Nanosci \& Technol, Bangkok 10330, Thailand.
   Verma, Deepak, Chulalongkorn Univ, Int Grad Program Nanosci \& Technol, Bangkok 10330, Thailand.
   Okhawilai, Manunya, Chulalongkorn Univ, Met \& Mat Sci Res Inst, Bangkok, Thailand.
   Okhawilai, Manunya; Biring, Sajal, Chulalongkorn Univ, Res Unit Polymer Mat Med Practice Devices, Bangkok, Thailand.
   Dalapati, Goutam Kumar, Mingchi Univ Technol, Organ Elect Res Ctr, New Taipei, Taiwan.
   Dalapati, Goutam Kumar; Ramakrishna, Seeram, Natl Univ Singapore, Ctr Nanofibers \& Nanotechnol, Mech Engn Dept, Singapore, Singapore.
   Dalapati, Goutam Kumar, Sunkonnect, Singapore, Singapore.
   Sharma, Abhishek, Manipal Univ Jaipur, Dept Chem Engn, Jaipur, Rajasthan, India.
   Sharma, Abhishek, RMIT Univ, Sch Engn, Chem \& Environm Engn, Melbourne, Vic, Australia.
   Sonar, Prashant, Queensland Univ Technol QUT Brisbane, Ctr Mat Sci, Sch Chem \& Phys, Brisbane, Qld, Australia.
   Sonar, Prashant, Queensland Univ Technol QUT, Ctr Waste Free World, Brisbane, Qld, Australia.
   Krishnamurthy, Satheesh, Open Univ, Sch Engn \& Innovat, Milton Keynes, Bucks, England.
   Biring, Sajal, Mingchi Univ Technol, Dept Elect Engn, New Taipei, Taiwan.
   Sharma, Mohit, ASTAR, Inst Mat Res \& Engn, Singapore, Singapore.},
DOI = {10.1002/pc.27054},
EarlyAccessDate = {SEP 2022},
ISSN = {0272-8397},
EISSN = {1548-0569},
Keywords = {artificial intelligence; Blockchain technology; plastic circular
   economy; plastic recycling},
Keywords-Plus = {WASTE; CHALLENGES; PLASTICS},
Research-Areas = {Materials Science; Polymer Science},
Web-of-Science-Categories  = {Materials Science, Composites; Polymer Science},
Author-Email = {dverma.mech@gmail.com},
Affiliations = {Chulalongkorn University; Chulalongkorn University; Chulalongkorn
   University; Ming Chi University of Technology; National University of
   Singapore; Manipal University Jaipur; Royal Melbourne Institute of
   Technology (RMIT); Queensland University of Technology (QUT); Queensland
   University of Technology (QUT); Open University - UK; Ming Chi
   University of Technology; Agency for Science Technology \& Research
   (A{*}STAR); A{*}STAR - Institute of Materials Research \& Engineering
   (IMRE)},
ResearcherID-Numbers = {sharma, mohit/B-4205-2011
   Krishnamurthy, Satheesh/AAE-7577-2019
   Verma, Deepak/H-4643-2019},
ORCID-Numbers = {sharma, mohit/0000-0002-6045-9716
   Krishnamurthy, Satheesh/0000-0001-7237-9206
   Verma, Deepak/0000-0002-6432-320X},
Funding-Acknowledgement = {Chulalongkorn University},
Funding-Text = {Chulalongkorn University},
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Number-of-Cited-References = {60},
Times-Cited = {0},
Usage-Count-Last-180-days = {14},
Usage-Count-Since-2013 = {18},
Journal-ISO = {Polym. Compos.},
Doc-Delivery-Number = {6U8QD},
Web-of-Science-Index = {Science Citation Index Expanded (SCI-EXPANDED)},
Unique-ID = {WOS:000855074900001},
DA = {2023-04-22},
}

@article{ WOS:000906806200013,
Author = {Urban, Barbara},
Title = {THiNkiNG AND pRocEssiNG. A REviEw oF DEMocRATic sysTEMs BAsED oN
   ARTiFiciAl iNTElliGENcE FRoM REcoGNiTioN, coMMiTMENT AND jusTicE},
Journal = {REVISTA DE FILOSOFIA},
Year = {2022},
Volume = {79},
Pages = {211-221},
Abstract = {This paper proposes a double reflection on the cognitive differences
   between humans and machines from ethics. In the first place, I will try
   to differentiate between human thought and machine processing through
   etymological, semantic and comparative methods that will reveal why it
   is not possible to attribute thinking capacities to artificial
   intelligences. On the other hand, these terms will be related to the
   praxis of representative democracy through various examples. This
   analysis is based on three aims: the need for mutual recognition through
   language; the commitment acquired through the word; and democracy as an
   expression of justice.},
Publisher = {UNIV CHILE, FAC FILOSOFIA \& HUMANIDADES},
Address = {DEPT LINGUISTICA, AV CAPITAN IGNACIO CARRERA PINTO 1025, CUARTO PISO,
   NUNOA, SANTIAGO, 00000, CHILE},
Type = {Review},
Language = {English},
Affiliation = {Urban, B (Corresponding Author), Univ Jaume 1, Castellon De La Plana, Spain.
   Urban, Barbara, Univ Jaume 1, Castellon De La Plana, Spain.},
ISSN = {0034-8236},
EISSN = {0718-4360},
Keywords = {KEywoRDs; Thought; processing; democracy; reciprocity; Arendt; Habermas;
   language; commitment; representative democracy},
Research-Areas = {Philosophy},
Web-of-Science-Categories  = {Philosophy},
Author-Email = {barbara.urban@uji.es},
Affiliations = {Universitat Jaume I},
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Number-of-Cited-References = {16},
Times-Cited = {0},
Usage-Count-Last-180-days = {0},
Usage-Count-Since-2013 = {0},
Journal-ISO = {Rev. Filos.},
Doc-Delivery-Number = {7M7AQ},
Web-of-Science-Index = {Arts &amp; Humanities Citation Index (A&amp;HCI)},
Unique-ID = {WOS:000906806200013},
DA = {2023-04-22},
}

@article{ WOS:000702934400001,
Author = {Hameed, B. M. Zeeshan and Prerepa, Gayathri and Patil, Vathsala and
   Shekhar, Pranav and Zahid Raza, Syed and Karimi, Hadis and Paul, Rahul
   and Naik, Nithesh and Modi, Sachin and Vigneswaran, Ganesh and Prasad
   Rai, Bhavan and Chlosta, Piotr and Somani, Bhaskar K.},
Title = {Engineering and clinical use of artificial intelligence (AI) with
   machine learning and data science advancements: radiology leading the
   way for future},
Journal = {THERAPEUTIC ADVANCES IN UROLOGY},
Year = {2021},
Volume = {13},
Month = {SEP},
Abstract = {Over the years, many clinical and engineering methods have been adapted
   for testing and screening for the presence of diseases. The most
   commonly used methods for diagnosis and analysis are computed tomography
   (CT) and X-ray imaging. Manual interpretation of these images is the
   current gold standard but can be subject to human error, is tedious, and
   is time-consuming. To improve efficiency and productivity, incorporating
   machine learning (ML) and deep learning (DL) algorithms could expedite
   the process. This article aims to review the role of artificial
   intelligence (AI) and its contribution to data science as well as
   various learning algorithms in radiology. We will analyze and explore
   the potential applications in image interpretation and radiological
   advances for AI. Furthermore, we will discuss the usage, methodology
   implemented, future of these concepts in radiology, and their
   limitations and challenges.},
Publisher = {SAGE PUBLICATIONS LTD},
Address = {1 OLIVERS YARD, 55 CITY ROAD, LONDON EC1Y 1SP, ENGLAND},
Type = {Review},
Language = {English},
Affiliation = {Patil, V (Corresponding Author), Manipal Coll Dent Sci, Dept Oral Med \& Radiol, Manipal, India.
   Patil, V (Corresponding Author), Manipal Acad Higher Educ, Manipal 576104, Karnataka, India.
   Hameed, B. M. Zeeshan, Father Muller Med Coll, Dept Urol, Mangalore, Karnataka, India.
   Hameed, B. M. Zeeshan, Manipal Acad Higher Educ, Kasturba Med Coll, Dept Urol, Manipal, India.
   Hameed, B. M. Zeeshan, Manipal Acad Higher Educ, KMC Innovat Ctr, Manipal, Karnataka, India.
   Hameed, B. M. Zeeshan; Naik, Nithesh; Prasad Rai, Bhavan; Somani, Bhaskar K., Int Training \& Res Urooncol \& Endourol iTRUE Grp, Manipal, India.
   Hameed, B. M. Zeeshan; Naik, Nithesh, Curiouz Techlab Private Ltd, Govt Karnataka Bioincubator, Manipal, Karnataka, India.
   Prerepa, Gayathri, Manipal Acad Higher Educ, Manipal Inst Technol, Dept Elect \& Commun, Manipal, Karnataka, India.
   Shekhar, Pranav, Manipal Acad Higher Educ, Manipal Inst Technol, Dept Comp Sci \& Engn, Manipal, Karnataka, India.
   Zahid Raza, Syed, Dr BR Ambedkar Med Coll, Dept Urol, Bengaluru, Karnataka, India.
   Karimi, Hadis, Manipal Acad Higher Educ, Manipal Coll Pharmaceut Sci, Manipal, Karnataka, India.
   Paul, Rahul, Harvard Med Sch, Massachusetts Gen Hosp, Dept Radiat Oncol, Boston, MA 02115 USA.
   Naik, Nithesh, Manipal Acad Higher Educ, Manipal Inst Technol, Fac Engn, Manipal, Karnataka, India.
   Modi, Sachin; Vigneswaran, Ganesh, Univ Hosp Southampton NHS Fdn Trust, Dept Intervent Radiol, Southampton, England.
   Prasad Rai, Bhavan, Freeman Rd Hosp, wDept Urol, Newcastle Upon Tyne, England.
   Chlosta, Piotr, Jagiellonian Univ Krakow, Dept Urol, Krakow, Poland.
   Somani, Bhaskar K., Univ Hosp Southampton NHS Fdn Trust, Dept Urol, Southampton, England.
   Patil, Vathsala, Manipal Coll Dent Sci, Dept Oral Med \& Radiol, Manipal, India.
   Patil, Vathsala, Manipal Acad Higher Educ, Manipal 576104, Karnataka, India.},
DOI = {10.1177/17562872211044880},
Article-Number = {17562872211044880},
ISSN = {1756-2872},
EISSN = {1756-2880},
Keywords = {artificial intelligence; data science; data science in radiology; deep
   learning; machine learning; machine learning in radiology; radiology},
Keywords-Plus = {OF-THE-ART; NEURAL-NETWORKS; AUTOMATED DETECTION; CT; SEGMENTATION;
   DIAGNOSIS; SOCIETY},
Research-Areas = {Urology \& Nephrology},
Web-of-Science-Categories  = {Urology \& Nephrology},
Author-Email = {vathsala.mcods@manipal.edu},
Affiliations = {Father Muller Medical College; Manipal Academy of Higher Education
   (MAHE); Kasturba Medical College, Manipal; Manipal Academy of Higher
   Education (MAHE); Manipal Academy of Higher Education (MAHE); Manipal
   Academy of Higher Education (MAHE); Manipal Academy of Higher Education
   (MAHE); Harvard University; Harvard Medical School; Massachusetts
   General Hospital; Manipal Academy of Higher Education (MAHE); University
   of Southampton; University Hospital Southampton NHS Foundation Trust;
   Newcastle Freeman Hospital; Newcastle University - UK; Jagiellonian
   University; University of Southampton; University Hospital Southampton
   NHS Foundation Trust; Manipal Academy of Higher Education (MAHE);
   Manipal Academy of Higher Education (MAHE)},
ResearcherID-Numbers = {Naik, Nithesh/W-5086-2019
   },
ORCID-Numbers = {Naik, Nithesh/0000-0003-0356-7697
   Vigneswaran, Ganesh/0000-0002-4115-428X},
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Number-of-Cited-References = {64},
Times-Cited = {4},
Usage-Count-Last-180-days = {0},
Usage-Count-Since-2013 = {2},
Journal-ISO = {Ther. Adv. Urol.},
Doc-Delivery-Number = {WA5ON},
Web-of-Science-Index = {Science Citation Index Expanded (SCI-EXPANDED)},
Unique-ID = {WOS:000702934400001},
OA = {Green Accepted, Green Published, gold},
DA = {2023-04-22},
}

@article{ WOS:000668529800001,
Author = {Russell, Frances M. and Ehrman, Robert R. and Barton, Allen and
   Sarmiento, Elisa and Ottenhoff, Jakob E. and Nti, Benjamin K.},
Title = {B-line quantification: comparing learners novice to lung ultrasound
   assisted by machine artificial intelligence technology to expert review},
Journal = {ULTRASOUND JOURNAL},
Year = {2021},
Volume = {13},
Number = {1},
Month = {JUN 30},
Abstract = {Background: The goal of this study was to assess the ability of machine
   artificial intelligence (AI) to quantitatively assess lung ultrasound
   (LUS) B-line presence using images obtained by learners novice to LUS in
   patients with acute heart failure (AHF), compared to expert
   interpretation.
   Methods: This was a prospective, multicenter observational study
   conducted at two urban academic institutions. Learners novice to LUS
   completed a 30-min training session on lung image acquisition which
   included lecture and hands-on patient scanning. Learners independently
   acquired images on patients with suspected AHF. Automatic B-line
   quantification was obtained offline after completion of the study.
   Machine AI counted the maximum number of B-lines visualized during a
   clip. The criterion standard for B-line counts was semi-quantitative
   analysis by a blinded point-of-care LUS expert reviewer. Image quality
   was blindly determined by an expert reviewer. A second expert reviewer
   blindly determined B-line counts and image quality. Intraclass
   correlation was used to determine agreement between machine AI and
   expert, and expert to expert.
   Results: Fifty-one novice learners completed 87 scans on 29 patients. We
   analyzed data from 611 lung zones. The overall intraclass correlation
   for agreement between novice learner images post-processed with AI
   technology and expert review was 0.56 (confidence interval {[}CI]
   0.51-0.62), and 0.82 (CI 0.73-0.91) between experts. Median image
   quality was 4 (on a 5-point scale), and correlation between experts for
   quality assessment was 0.65 (CI 0.48-0.82).
   Conclusion: After a short training session, novice learners were able to
   obtain high-quality images. When the AI deep learning algorithm was
   applied to those images, it quantified B-lines with moderate-to-fair
   correlation as compared to semi-quantitative analysis by expert review.
   This data shows promise, but further development is needed before
   widespread clinical use.},
Publisher = {SPRINGER},
Address = {ONE NEW YORK PLAZA, SUITE 4600, NEW YORK, NY, UNITED STATES},
Type = {Review},
Language = {English},
Affiliation = {Russell, FM (Corresponding Author), Indiana Univ Sch Med, Dept Emergency Med, 720 Eskenazi Ave,FOB 3rd Floor, Indianapolis, IN 46202 USA.
   Russell, Frances M.; Sarmiento, Elisa; Nti, Benjamin K., Indiana Univ Sch Med, Dept Emergency Med, 720 Eskenazi Ave,FOB 3rd Floor, Indianapolis, IN 46202 USA.
   Ehrman, Robert R.; Ottenhoff, Jakob E., Wayne State Univ, Dept Emergency Med, Sch Med, 4021 St Antoine Ave,Suite 6G, Detroit, MI 48201 USA.
   Barton, Allen, Boone Cty Emergency Physicians, Zionsville, IN 46077 USA.},
DOI = {10.1186/s13089-021-00234-6},
Article-Number = {33},
ISSN = {2036-3176},
EISSN = {2524-8987},
Keywords = {Artificial intelligence; Point-of-care ultrasound; Lung ultrasound;
   Acute heart failure; Novice learner},
Keywords-Plus = {HEART-FAILURE},
Research-Areas = {Radiology, Nuclear Medicine \& Medical Imaging},
Web-of-Science-Categories  = {Radiology, Nuclear Medicine \& Medical Imaging},
Author-Email = {framruss@iu.edu},
Affiliations = {Indiana University System; Indiana University Bloomington; Wayne State
   University},
ORCID-Numbers = {Sarmiento, Elisa/0000-0001-7283-9938},
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Number-of-Cited-References = {18},
Times-Cited = {6},
Usage-Count-Last-180-days = {0},
Usage-Count-Since-2013 = {1},
Journal-ISO = {Ultrasound J.},
Doc-Delivery-Number = {TC3HC},
Web-of-Science-Index = {Emerging Sources Citation Index (ESCI)},
Unique-ID = {WOS:000668529800001},
OA = {Green Published, gold},
DA = {2023-04-22},
}

@article{ WOS:000817670100001,
Author = {Syed, Abbas Shah and Sierra-Sosa, Daniel and Kumar, Anup and Elmaghraby,
   Adel},
Title = {Making Cities Smarter-Optimization Problems for the IoT Enabled Smart
   City Development: A Mapping of Applications, Objectives, Constraints},
Journal = {SENSORS},
Year = {2022},
Volume = {22},
Number = {12},
Month = {JUN},
Abstract = {One of the prime aims of smart cities has been to optimally manage the
   available resources and systems that are used in the city. With an
   increase in urban population that is set to grow even faster in the
   future, smart city development has been the main goal for governments
   worldwide. In this regard, while the useage of Artificial Intelligence
   (AI) techniques covering the areas of Machine and Deep Learning have
   garnered much attention for Smart Cities, less attention has focused
   towards the use of combinatorial optimization schemes. To help with
   this, the current review presents a coverage of optimization methods and
   applications from a smart city perspective enabled by the Internet of
   Things (IoT). A mapping is provided for the most encountered
   applications of computational optimization within IoT smart cities for
   five popular optimization methods, ant colony optimization, genetic
   algorithm, particle swarm optimization, artificial bee colony
   optimization and differential evolution. For each application
   identified, the algorithms used, objectives considered, the nature of
   the formulation and constraints taken in to account have been specified
   and discussed. Lastly, the data setup used by each covered work is also
   mentioned and directions for future work have been identified. This
   review will help researchers by providing them a consolidated starting
   point for research in the domain of smart city application optimization.},
Publisher = {MDPI},
Address = {ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND},
Type = {Review},
Language = {English},
Affiliation = {Syed, AS (Corresponding Author), Univ Louisville, Dept Comp Sci \& Engn, Louisville, KY 40208 USA.
   Syed, Abbas Shah; Kumar, Anup; Elmaghraby, Adel, Univ Louisville, Dept Comp Sci \& Engn, Louisville, KY 40208 USA.
   Sierra-Sosa, Daniel, Hood Coll, Dept Comp Sci \& Informat Technol, Frederick, MD 21701 USA.},
DOI = {10.3390/s22124380},
Article-Number = {4380},
EISSN = {1424-8220},
Keywords = {smart cities; Internet of Things (IoT); Artificial Intelligence;
   optimization; genetic agorithm; particle swarm optimization; heuristics},
Keywords-Plus = {ARTIFICIAL BEE COLONY; VEHICLE-ROUTING PROBLEM; ADAPTIVE DIFFERENTIAL
   EVOLUTION; PARTICLE SWARM OPTIMIZATION; ANT COLONY; GENETIC ALGORITHM;
   TIMING OPTIMIZATION; ENERGY MANAGEMENT; TRAFFIC CONTROL; SYSTEM},
Research-Areas = {Chemistry; Engineering; Instruments \& Instrumentation},
Web-of-Science-Categories  = {Chemistry, Analytical; Engineering, Electrical \& Electronic;
   Instruments \& Instrumentation},
Author-Email = {m0syed03@louisville.edu
   sierra-sosa@hood.edu
   anup.kumar@louisville.edu
   adel.elmaghraby@louisville.edu},
Affiliations = {University of Louisville},
ResearcherID-Numbers = {Elmaghraby, Adel S/B-3353-2014
   Sierra-Sosa, Daniel/AAP-4610-2020},
ORCID-Numbers = {Elmaghraby, Adel S/0000-0001-5274-8596
   Syed, Muhammad Zaigham Abbas Shah/0000-0002-3953-7134
   Sierra-Sosa, Daniel/0000-0003-1326-0867},
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Number-of-Cited-References = {154},
Times-Cited = {3},
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Journal-ISO = {Sensors},
Doc-Delivery-Number = {2M4KK},
Web-of-Science-Index = {Science Citation Index Expanded (SCI-EXPANDED)},
Unique-ID = {WOS:000817670100001},
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@article{ WOS:000624032700001,
Author = {Majumdar, Sreyashi and Verma, Rohit and Saha, Avishek and Bhattacharyya,
   Parthasarathi and Maji, Pradipta and Surjit, Milan and Kundu,
   Manikuntala and Basu, Joyoti and Saha, Sudipto},
Title = {Perspectives About Modulating Host Immune System in Targeting SARS-CoV-2
   in India},
Journal = {FRONTIERS IN GENETICS},
Year = {2021},
Volume = {12},
Month = {FEB 16},
Abstract = {Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), the
   causative agent of coronavirus induced disease-2019 (COVID-19), is a
   type of common cold virus responsible for a global pandemic which
   requires immediate measures for its containment. India has the world's
   largest population aged between 10 and 40 years. At the same time, India
   has a large number of individuals with diabetes, hypertension and kidney
   diseases, who are at a high risk of developing COVID-19. A vaccine
   against the SARS-CoV-2, may offer immediate protection from the
   causative agent of COVID-19, however, the protective memory may be
   short-lived. Even if vaccination is broadly successful in the world,
   India has a large and diverse population with over one-third being below
   the poverty line. Therefore, the success of a vaccine, even when one
   becomes available, is uncertain, making it necessary to focus on
   alternate approaches of tackling the disease. In this review, we discuss
   the differences in COVID-19 death/infection ratio between urban and
   rural India; and the probable role of the immune system, co-morbidities
   and associated nutritional status in dictating the death rate of
   COVID-19 patients in rural and urban India. Also, we focus on strategies
   for developing masks, vaccines, diagnostics and the role of drugs
   targeting host-virus protein-protein interactions in enhancing host
   immunity. We also discuss India's strengths including the resources of
   medicinal plants, good food habits and the role of information
   technology in combating COVID-19. We focus on the Government of India's
   measures and strategies for creating awareness in the containment of
   COVID-19 infection across the country.},
Publisher = {FRONTIERS MEDIA SA},
Address = {AVENUE DU TRIBUNAL FEDERAL 34, LAUSANNE, CH-1015, SWITZERLAND},
Type = {Review},
Language = {English},
Affiliation = {Saha, S (Corresponding Author), Bose Inst, Div Bioinformat, Kolkata, India.
   Majumdar, Sreyashi; Saha, Sudipto, Bose Inst, Div Bioinformat, Kolkata, India.
   Verma, Rohit; Surjit, Milan, Translat Hlth Sci \& Technol Inst, NCR Biotech Sci Cluster, Vaccine \& Infect Dis Res Ctr, Virol Lab, Faridabad, India.
   Saha, Avishek, CSIR Cent Sci Instruments Org, Ubiquitous Analyt Techn, Chandigarh, India.
   Bhattacharyya, Parthasarathi, Inst Pulmocare \& Res, Dept Resp Med, Kolkata, India.
   Maji, Pradipta, Indian Stat Inst, Biomed Imaging \& Bioinformat Lab, Machine Intelligence Unit, Kolkata, India.
   Kundu, Manikuntala; Basu, Joyoti, Bose Inst, Dept Chem, Kolkata, India.},
DOI = {10.3389/fgene.2021.637362},
Article-Number = {637362},
EISSN = {1664-8021},
Keywords = {SARS-CoV-2; genetic variations; host immuno-modulation; repurposed
   drugs; vaccines; medicinal plants; CT scans; artificial intelligence},
Keywords-Plus = {COVID-19; INFECTION},
Research-Areas = {Genetics \& Heredity},
Web-of-Science-Categories  = {Genetics \& Heredity},
Author-Email = {ssaha4@jcbose.ac.in},
Affiliations = {Department of Science \& Technology (India); Bose Institute; Department
   of Biotechnology (DBT) India; Translational Health Science \& Technology
   Institute (THSTI); Council of Scientific \& Industrial Research (CSIR) -
   India; CSIR - Central Scientific Instruments Organisation (CSIO); Indian
   Statistical Institute; Indian Statistical Institute Kolkata; Department
   of Science \& Technology (India); Bose Institute},
ResearcherID-Numbers = {Maji, Pradipta/HHR-9037-2022
   },
ORCID-Numbers = {Saha, Sudipto/0000-0001-9433-8894
   Majumdar, Sreyashi/0000-0002-1555-3105},
Funding-Acknowledgement = {Bose Institute Intramural Fund},
Funding-Text = {Research support and publication charges are funded by Bose Institute
   Intramural Fund.},
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Number-of-Cited-References = {177},
Times-Cited = {3},
Usage-Count-Last-180-days = {1},
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Journal-ISO = {Front. Genet.},
Doc-Delivery-Number = {QP7SM},
Web-of-Science-Index = {Science Citation Index Expanded (SCI-EXPANDED)},
Unique-ID = {WOS:000624032700001},
OA = {Green Published, gold},
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@article{ WOS:000942039100001,
Author = {Zeng, Xuming and Wang, Zinan and Wang, Hao and Zhu, Shengyan and Chen,
   Shaofeng},
Title = {Progress in Drainage Pipeline Condition Assessment and Deterioration
   Prediction Models},
Journal = {SUSTAINABILITY},
Year = {2023},
Volume = {15},
Number = {4},
Month = {FEB},
Abstract = {The condition of drainage pipes greatly affects the urban environment
   and human health. However, it is difficult to carry out economical and
   efficient pipeline investigation and evaluation due to the location and
   structure of drainage pipes. Herein, the four most-commonly used
   drainage pipeline evaluation standards have been synthesized and
   analyzed to summarize the deterioration and breakage patterns of
   drainage pipes. The common pipe breakage patterns are also summarized by
   integrating the literature and engineering experience. To systematically
   describe the condition of drainage pipes, a system of influencing
   factors for the condition of pipes, including physical, environmental,
   and operational factors, has been established, and the mechanism of
   action of each influencing factor has been summarized. Physical,
   statistical, and AI models and their corresponding representative models
   have been categorized, and the research progress of current mainstream
   drainage-pipe deterioration and breakage prediction models are reviewed
   in terms of their principles and progress in their application.},
Publisher = {MDPI},
Address = {ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND},
Type = {Review},
Language = {English},
Affiliation = {Wang, H (Corresponding Author), Fuzhou Univ, Zijin Sch Geol \& Min, 2,Wulongjiang North Ave, Fuzhou 350108, Peoples R China.
   Zeng, Xuming; Zhu, Shengyan; Chen, Shaofeng, Powerchina Huadong Engn Corp Ltd, Bldg 35,Fuzhou Software Pk,Tongpan Rd, Fuzhou 350108, Peoples R China.
   Wang, Zinan; Wang, Hao, Fuzhou Univ, Zijin Sch Geol \& Min, 2,Wulongjiang North Ave, Fuzhou 350108, Peoples R China.},
DOI = {10.3390/su15043849},
Article-Number = {3849},
EISSN = {2071-1050},
Keywords = {pipeline condition assessment; pipeline deterioration and breakage;
   influencing factors; artificial intelligence model; machine learning},
Keywords-Plus = {OF-THE-ART; STRUCTURAL DETERIORATION; CONCRETE CORROSION; SEWER PIPES;
   INSPECTION; MANAGEMENT; FAILURE; SYSTEM; CLASSIFICATION; PERFORMANCE},
Research-Areas = {Science \& Technology - Other Topics; Environmental Sciences \& Ecology},
Web-of-Science-Categories  = {Green \& Sustainable Science \& Technology; Environmental Sciences;
   Environmental Studies},
Author-Email = {h\_wang@126.com},
Affiliations = {Fuzhou University},
Funding-Acknowledgement = {Fujian Province University-Industry-Research Cooperation Innovation
   Project {[}2022Y4002]; Fujian Province Transportation Technology Project
   {[}202202]; China Electric Power Construction Group East China Institute
   of Technology Project {[}2021032304]},
Funding-Text = {This research was funded by Fujian Province University-Industry-Research
   Cooperation Innovation Project: Key technologies for disaster mechanism
   and risk prevention of abandoned soil field in complex environments
   (2022Y4002), March 2022-February 2025; Fujian Province Transportation
   Technology Project: Research and application of intelligent construction
   technology for a new type of low-carbon assembled anchoring structure
   (202202), July 2022-December 2024; China Electric Power Construction
   Group East China Institute of Technology Project: Key technology
   research on investigation of hidden culverts under complex conditions
   (2021032304), January 2021-December 2022.},
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   {[}周倩倩 Zhou Qianqian], 2021, {[}中国给水排水, China Water \& Wastewater], V37, P114.},
Number-of-Cited-References = {142},
Times-Cited = {0},
Usage-Count-Last-180-days = {4},
Usage-Count-Since-2013 = {4},
Journal-ISO = {Sustainability},
Doc-Delivery-Number = {9M2AL},
Web-of-Science-Index = {Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)},
Unique-ID = {WOS:000942039100001},
OA = {gold},
DA = {2023-04-22},
}

@article{ WOS:000878591800002,
Author = {Valle-Cruz, David and Gil-Garcia, Jose Ramon},
Title = {Emerging Technologies in Local Government: A Systematic Literature
   Review Using the PRISMA Methodology},
Journal = {REVISTA MEXICANA DE ANALISIS POLITICO Y ADMINISTRACION PUBLICA},
Year = {2022},
Volume = {11},
Number = {21},
Pages = {9-28},
Month = {JAN-JUN},
Abstract = {Emerging technologies have the potential to transform public
   administration in unimaginable ways. In this regard, this paper aims to
   analyze emerging technologies in local governments using the PRISMA
   methodology. The questions guiding the research are: what are the
   emerging technologies used by local governments? And what are the
   challenges and consequences of using emerging technologies in local
   governments? The findings show three types of emerging technologies: 1)
   Basic (such as mobile technology, Web 2.0, websites and ICT), 2) Cutting
   edge (such as Blockchain, artificial intelligence, Big Data, and
   Internet of things) and 3) Specific and applied to smart cities, urban
   agriculture, environmental awareness, and telehealth. Although the
   implementation of emerging technologies can result in benefits for the
   public sector, one of the challenges lies in bridging the gap between
   technology developers and decision makers. Also, the inscrutable status
   of some algorithms and the mass surveillance capabilities of some
   emerging technologies threaten the freedom of societies and may
   dehumanize some processes in the public sector.},
Publisher = {UNIV GUANAJUATO},
Address = {LASCURAIN RETANA 5, CENTRO, GUANAJUATO, 36000, MEXICO},
Type = {Review},
Language = {Spanish},
Affiliation = {Valle-Cruz, D (Corresponding Author), Univ Autonoma Estado Mex, Ciencias Econ Adm, Toluca, Spain.
   Valle-Cruz, David, Univ Autonoma Estado Mex, Ciencias Econ Adm, Toluca, Spain.
   Gil-Garcia, Jose Ramon, Univ Estatal Nueva York Albany, Adm \& Politicas Publicas, Albany, NY USA.
   Gil-Garcia, Jose Ramon, Univ Estatal Nueva York Albany, Rockefeller Coll Publ Affairs \& Policy, Albany, NY USA.
   Gil-Garcia, Jose Ramon, Univ Las Americas Puebla, Puebla, Mexico.},
DOI = {10.1080/14719037.2020.1838787},
ISSN = {2007-4425},
EISSN = {2007-4638},
Keywords = {Emerging Technologies; Public Administration and Technology; Local
   Governments and Technologies},
Keywords-Plus = {COMMUNICATION TECHNOLOGY; INFORMATION},
Research-Areas = {Government \& Law},
Web-of-Science-Categories  = {Political Science},
Author-Email = {davacr@uaemex.mx
   jgil-garcia@albany.edu},
Affiliations = {Universidad Americas Puebla (UDLAP)},
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Number-of-Cited-References = {56},
Times-Cited = {0},
Usage-Count-Last-180-days = {8},
Usage-Count-Since-2013 = {8},
Journal-ISO = {Rev. Mex. Anal. Politico Publica},
Doc-Delivery-Number = {5X4SW},
Web-of-Science-Index = {Emerging Sources Citation Index (ESCI)},
Unique-ID = {WOS:000878591800002},
OA = {hybrid},
DA = {2023-04-22},
}

@article{ WOS:000451219000003,
Author = {Loetsch, Joern and Ultsch, Alfred},
Title = {Machine learning in pain research},
Journal = {PAIN},
Year = {2018},
Volume = {159},
Number = {4},
Pages = {623-630},
Month = {APR},
Publisher = {LIPPINCOTT WILLIAMS \& WILKINS},
Address = {TWO COMMERCE SQ, 2001 MARKET ST, PHILADELPHIA, PA 19103 USA},
Type = {Review},
Language = {English},
Affiliation = {Lotsch, J (Corresponding Author), Goethe Univ, Inst Clin Pharmacol, Theodor Stern Kai 7, D-60590 Frankfurt, Germany.
   Loetsch, Joern, Goethe Univ, Inst Clin Pharmacol, Theodor Stern Kai 7, D-60590 Frankfurt, Germany.
   Loetsch, Joern, Fraunhofer Inst Mol Biol \& Appl Ecol IME, Project Grp Translat Med \& Pharmacol TMP, Frankfurt, Germany.
   Ultsch, Alfred, Univ Marburg, DataBion Res Grp, Marburg, Germany.},
DOI = {10.1097/j.pain.0000000000001118},
ISSN = {0304-3959},
EISSN = {1872-6623},
Keywords-Plus = {NEUROPATHIC PAIN; MOLECULAR-MECHANISMS; POSTOPERATIVE PAIN;
   FEATURE-SELECTION; DATA SCIENCE; CLASSIFICATION; PREDICTION;
   IDENTIFICATION; RECOGNITION; ALGORITHMS},
Research-Areas = {Anesthesiology; Neurosciences \& Neurology},
Web-of-Science-Categories  = {Anesthesiology; Clinical Neurology; Neurosciences},
Author-Email = {j.loetsch@em.uni-frankfurt.de},
Affiliations = {Goethe University Frankfurt; Fraunhofer Gesellschaft; Philipps
   University Marburg},
ORCID-Numbers = {Lotsch, Jorn/0000-0002-5818-6958},
Funding-Acknowledgement = {European Union {[}602919]; Landesoffensive zur Entwicklung
   wissenschaftlich-okonomischer Exzellenz (LOEWE), LOEWE-Zentrum fur
   Translationale Medizin und Pharmakologie},
Funding-Text = {This work has been funded by the European Union Seventh Framework
   Programme (FP7/2007-2013) under grant agreement no. 602919
   ({''}GLORIA{''}, J.L.) and by the Landesoffensive zur Entwicklung
   wissenschaftlich-okonomischer Exzellenz (LOEWE), LOEWE-Zentrum fur
   Translationale Medizin und Pharmakologie (J.L.).},
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Number-of-Cited-References = {80},
Times-Cited = {78},
Usage-Count-Last-180-days = {4},
Usage-Count-Since-2013 = {21},
Journal-ISO = {Pain},
Doc-Delivery-Number = {HB6YD},
Web-of-Science-Index = {Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)},
Unique-ID = {WOS:000451219000003},
OA = {Green Published, hybrid},
DA = {2023-04-22},
}

@article{ WOS:000586208600001,
Author = {Nosratabadi, Saeed and Mosavi, Amirhosein and Puhong Duan and Ghamisi,
   Pedram and Filip, Ferdinand and Band, Shahab S. and Reuter, Uwe and
   Gama, Joao and Gandomi, Amir H.},
Title = {Data Science in Economics: Comprehensive Review of Advanced Machine
   Learning and Deep Learning Methods},
Journal = {MATHEMATICS},
Year = {2020},
Volume = {8},
Number = {10},
Month = {OCT},
Abstract = {This paper provides a comprehensive state-of-the-art investigation of
   the recent advances in data science in emerging economic applications.
   The analysis is performed on the novel data science methods in four
   individual classes of deep learning models, hybrid deep learning models,
   hybrid machine learning, and ensemble models. Application domains
   include a broad and diverse range of economics research from the stock
   market, marketing, and e-commerce to corporate banking and
   cryptocurrency. Prisma method, a systematic literature review
   methodology, is used to ensure the quality of the survey. The findings
   reveal that the trends follow the advancement of hybrid models, which
   outperform other learning algorithms. It is further expected that the
   trends will converge toward the evolution of sophisticated hybrid deep
   learning models.},
Publisher = {MDPI},
Address = {ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND},
Type = {Review},
Language = {English},
Affiliation = {Mosavi, A (Corresponding Author), Ton Duc Thang Univ, Environm Qual Atmospher Sci \& Climate Change Res, Ho Chi Minh City, Vietnam.
   Mosavi, A (Corresponding Author), Ton Duc Thang Univ, Fac Environm \& Labour Safety, Ho Chi Minh, Vietnam.
   Band, SS (Corresponding Author), Duy Tan Univ, Inst Res \& Dev, Da Nang 550000, Vietnam.
   Band, SS (Corresponding Author), Natl Yunlin Univ Sci \& Technol, Coll Future, Future Technol Res Ctr, 123 Univ Rd,Sect 3, Touliu 64002, Yunlin, Taiwan.
   Nosratabadi, Saeed, Szent Istvan Univ, Doctoral Sch Management \& Business Adm, H-2100 Godollo, Hungary.
   Mosavi, Amirhosein, Ton Duc Thang Univ, Environm Qual Atmospher Sci \& Climate Change Res, Ho Chi Minh City, Vietnam.
   Mosavi, Amirhosein, Ton Duc Thang Univ, Fac Environm \& Labour Safety, Ho Chi Minh, Vietnam.
   Puhong Duan, Hunan Univ, Coll Elect \& Informat Engn, Changsha 410082, Peoples R China.
   Ghamisi, Pedram, Helmholtz Inst Freiberg Resource Technol, Helmholtz Zentrum Dresden Rossendorf, D-09599 Freiberg, Germany.
   Filip, Ferdinand, J Selye Univ, Dept Math, Komarno 94501, Slovakia.
   Band, Shahab S., Duy Tan Univ, Inst Res \& Dev, Da Nang 550000, Vietnam.
   Band, Shahab S., Natl Yunlin Univ Sci \& Technol, Coll Future, Future Technol Res Ctr, 123 Univ Rd,Sect 3, Touliu 64002, Yunlin, Taiwan.
   Reuter, Uwe, Tech Univ Dresden, Fac Civil Engn, D-01069 Dresden, Germany.
   Gama, Joao, INESC TEC, Fac Lab Artificial Intelligence \& Decis Support L, Campus FEUP,Rua Roberto Frias, P-4200465 Porto, Portugal.
   Gandomi, Amir H., Univ Technol Sydney, Fac Engn \& Informat Technol, Sydney, NSW 2007, Australia.},
DOI = {10.3390/math8101799},
Article-Number = {1799},
EISSN = {2227-7390},
Keywords = {data science; deep learning; economic model; ensemble; economics;
   cryptocurrency; machine learning; deep reinforcement learning; big data;
   bitcoin; time series; network science; prediction; survey; artificial
   intelligence; literature review},
Keywords-Plus = {PREDICTION; HYBRID; MODEL; CLASSIFICATION; PERFORMANCE},
Research-Areas = {Mathematics},
Web-of-Science-Categories  = {Mathematics},
Author-Email = {saeed.nosratabadi@phd.uni-szie.hu
   amirhosein.mosavi@tdtu.edu.vn
   puhong\_duan@hnu.edu.cn
   p.ghamisi@hzdr.de
   filipf@ujs.sk
   shamshirbandshahaboddin@duytan.edu.vn
   uwe.reuter@tu-dresden.de
   jgama@fep.up.pt
   gandomi@uts.edu.au},
Affiliations = {Hungarian University of Agriculture \& Life Sciences; Ton Duc Thang
   University; Ton Duc Thang University; Hunan University; Helmholtz
   Association; Helmholtz-Zentrum Dresden-Rossendorf (HZDR); J. Selye
   University; Duy Tan University; National Yunlin University Science \&
   Technology; Technische Universitat Dresden; INESC TEC; University of
   Technology Sydney},
ResearcherID-Numbers = {Nosratabadi, Saeed/P-7552-2016
   Filip, Ferdinánd/AAA-1439-2019
   Reuter, Uwe/HLG-6096-2023
   S.Band, Shahab/ABI-7388-2020
   Ghamisi, Pedram/ABD-5419-2021
   Gandomi, Amir H/J-7595-2013
   S. Band, Shahab/ABB-2469-2020
   S.Band, Shahab/AAD-3311-2021
   Mosavi, Amir/I-7440-2018
   Gama, Joao/A-2070-2008},
ORCID-Numbers = {Nosratabadi, Saeed/0000-0002-0440-6564
   Filip, Ferdinánd/0000-0003-1439-4330
   Reuter, Uwe/0000-0002-8527-0725
   S.Band, Shahab/0000-0002-8963-731X
   Gandomi, Amir H/0000-0002-2798-0104
   S. Band, Shahab/0000-0001-6109-1311
   Mosavi, Amir/0000-0003-4842-0613
   Ghamisi, Pedram/0000-0003-1203-741X
   Gama, Joao/0000-0003-3357-1195},
Funding-Acknowledgement = {Hungarian-Mexican bilateral Scientific and Technological
   {[}2019-2.1.11TET-2019-00007]; project in the framework of the New
   Szechenyi Plan {[}EFOP-3.6.2-16-2017-00016]; European Union; European
   social fund},
Funding-Text = {This research in part by the Hungarian-Mexican bilateral Scientific and
   Technological (2019-2.1.11TET-2019-00007) project, and also
   EFOP-3.6.2-16-2017-00016 project in the framework of the New Szechenyi
   Plan. Completing this project is supported by the European Union and
   co-financed by the European social fund.},
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@article{ WOS:000490665800001,
Author = {Kwon, Heeseo Rain and Silva, Elisabete A.},
Title = {Mapping the Landscape of Behavioral Theories: Systematic Literature
   Review},
Journal = {JOURNAL OF PLANNING LITERATURE},
Year = {2020},
Volume = {35},
Number = {2},
Pages = {161-179},
Month = {MAY},
Abstract = {The term ``behavioral{''} has become a hot topic in recent years in
   various disciplines; however, there is yet limited understanding of what
   theories can be considered behavioral theories and what fields of
   research they can be applied to. Through a cross-disciplinary literature
   review, this article identifies sixty-two behavioral theories from 963
   search results, mapping them in a diagram of four groups (factors,
   strategies, learning and conditioning, and modeling), and points to five
   discussion points: understanding of terms, classification, guidance on
   the use of appropriate theories, inclusion in data-driven research and
   agent-based modeling, and dialogue between theory-driven and data-driven
   approaches.},
Publisher = {SAGE PUBLICATIONS INC},
Address = {2455 TELLER RD, THOUSAND OAKS, CA 91320 USA},
Type = {Review},
Language = {English},
Affiliation = {Kwon, HR (Corresponding Author), Univ Cambridge, Dept Land Econ, LISA, 19 Silver St, Cambridge CB3 9EP, England.
   Kwon, Heeseo Rain; Silva, Elisabete A., Univ Cambridge, Dept Land Econ, LISA, 19 Silver St, Cambridge CB3 9EP, England.},
DOI = {10.1177/0885412219881135},
EarlyAccessDate = {OCT 2019},
Article-Number = {0885412219881135},
ISSN = {0885-4122},
EISSN = {1552-6593},
Keywords = {behavioral theories; behavioral science; data-driven research;
   theory-driven research; agent-based modeling; urban and environmental
   planning; data science; complexity theory},
Keywords-Plus = {ARTIFICIAL-INTELLIGENCE; DECISION-MAKING; BOUNDED RATIONALITY;
   SELF-DETERMINATION; PROSPECT-THEORY; MODEL; COST; RISK; ANTECEDENTS;
   INTEGRATION},
Research-Areas = {Public Administration; Urban Studies},
Web-of-Science-Categories  = {Regional \& Urban Planning; Urban Studies},
Author-Email = {hk394@cam.ac.uk},
Affiliations = {University of Cambridge},
ResearcherID-Numbers = {Kwon, Heeseo/AAD-9889-2019},
ORCID-Numbers = {Kwon, Heeseo/0000-0003-1780-2328},
Funding-Acknowledgement = {Economic and Social Research Council {[}RG76702/JPAG254]; Cambridge
   Humanities Research Grant {[}GASR009831/JPES.AHAS]},
Funding-Text = {The author(s) disclosed receipt of the following financial support for
   the research, authorship, and/or publication of this article: Economic
   and Social Research Council (Grant No. RG76702/JPAG254) and the
   Cambridge Humanities Research Grant (Grant No. GASR009831/JPES.AHAS).},
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Number-of-Cited-References = {155},
Times-Cited = {34},
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@article{ WOS:000595021100001,
Author = {Rasheed, Faizan and Yau, Kok-Lim Alvin and Noor, Rafidah Md. and Wu,
   Celimuge and Low, Yeh-Ching},
Title = {Deep Reinforcement Learning for Traffic Signal Control: A Review},
Journal = {IEEE ACCESS},
Year = {2020},
Volume = {8},
Pages = {208016-208044},
Abstract = {Traffic congestion is a complex, vexing, and growing issue day by day in
   most urban areas worldwide. The integration of the newly emerging deep
   learning approach and the traditional reinforcement learning approach
   has created an advanced approach called deep reinforcement learning
   (DRL) that has shown promising results in solving high-dimensional and
   complex problems, including traffic congestion. This article presents a
   review of the attributes of traffic signal control (TSC), as well as DRL
   architectures and methods applied to TSC, which helps to understand how
   DRL has been applied to address traffic congestion and achieve
   performance enhancement. The review also covers simulation platforms, a
   complexity analysis, as well as guidelines and design considerations for
   the application of DRL to TSC. Finally, this article presents open
   issues and new research areas with the objective to spark new interest
   in this research field. To the best of our knowledge, this is the first
   review article that focuses on the application of DRL to TSC.},
Publisher = {IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC},
Address = {445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA},
Type = {Review},
Language = {English},
Affiliation = {Yau, KLA (Corresponding Author), Sunway Univ, Dept Comp \& Informat Syst, Subang Jaya 47500, Malaysia.
   Rasheed, Faizan; Yau, Kok-Lim Alvin; Low, Yeh-Ching, Sunway Univ, Dept Comp \& Informat Syst, Subang Jaya 47500, Malaysia.
   Noor, Rafidah Md., Univ Malaya, Fac Comp Sci \& Informat Technol, Kuala Lumpur 50603, Malaysia.
   Wu, Celimuge, Univ Electrocommun, Grad Sch Informat \& Engn, Tokyo 1828585, Japan.},
DOI = {10.1109/ACCESS.2020.3034141},
ISSN = {2169-3536},
Keywords = {Reinforcement learning; Deep learning; Neurons; Computational modeling;
   Analytical models; Complexity theory; Licenses; Artificial intelligence;
   deep learning; deep reinforcement learning; traffic signal control},
Keywords-Plus = {REAL-TIME; CONTROL-SYSTEM; SIMULATION; INTELLIGENCE; ALGORITHMS;
   NETWORKS; MODEL},
Research-Areas = {Computer Science; Engineering; Telecommunications},
Web-of-Science-Categories  = {Computer Science, Information Systems; Engineering, Electrical \&
   Electronic; Telecommunications},
Author-Email = {koklimy@sunway.edu.my},
Affiliations = {Sunway University; Universiti Malaya; University of
   Electro-Communications - Japan},
ResearcherID-Numbers = {Noor, Rafidah Md/B-5445-2010
   Rasheed, Faizan/ABC-9488-2020
   Yau, Kok Lim Alvin/AFO-7004-2022
   Wu, Celimuge/P-1232-2019
   Wu, Celimuge/I-4703-2013
   Rasheed, Faizan/HNQ-9857-2023
   },
ORCID-Numbers = {Noor, Rafidah Md/0000-0001-6266-2390
   Yau, Kok Lim Alvin/0000-0003-3110-2782
   Wu, Celimuge/0000-0001-6853-5878
   Rasheed, Faizan/0000-0002-8908-8883
   Low, Yeh Ching/0000-0003-3450-2538},
Funding-Acknowledgement = {Novel Clustering algorithm based on Reinforcement Learning for the
   Optimization of Global and Local Network Performances in Mobile Networks
   - Malaysian Ministry of Education through Fundamental Research Grant
   Scheme {[}FRGS/1/2019/ICT03/SYUC/01/1]; Sunway University
   {[}CR-UM-SST-DCIS-2018-01, RK004-2017]; University of Malaya
   {[}CR-UM-SST-DCIS-2018-01, RK004-2017]},
Funding-Text = {This work was supported in part by A Novel Clustering algorithm based on
   Reinforcement Learning for the Optimization of Global and Local Network
   Performances in Mobile Networks funded by the Malaysian Ministry of
   Education through Fundamental Research Grant Scheme under Grant
   FRGS/1/2019/ICT03/SYUC/01/1, and in part by the Partnership between
   Sunway University and the University of Malaya under Grant
   CR-UM-SST-DCIS-2018-01 and Grant RK004-2017.},
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Number-of-Cited-References = {116},
Times-Cited = {21},
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Journal-ISO = {IEEE Access},
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@article{ WOS:000744157100004,
Author = {Munawar, Hafiz Suliman and Mojtahedi, Mohammad and Hammad, Ahmed W. A.
   and Kouzani, Abbas and Mahmud, M. A. Parvez},
Title = {Disruptive technologies as a solution for disaster risk management: A
   review},
Journal = {SCIENCE OF THE TOTAL ENVIRONMENT},
Year = {2022},
Volume = {806},
Number = {3},
Month = {FEB 1},
Abstract = {Integrating disruptive technologies within smart cities improves the
   infrastructure needed to potentially deal with disasters. This paper
   provides a perspective review of disruptive technologies such as the
   Internet of Things (IoT), image processing, artificial intelligence
   (AI), big data and smartphone applications which are in use and have
   been proposed for future improvements in disaster management of urban
   regions. The key focus of this paper is exploring ways in which smart
   cities could be established to harness the potential of disruptive
   technologies and improve post-disaster management. The key questions
   explored are a) what are the gaps or barriers to the utilization of
   disruptive technologies in the area of disaster management and b) How
   can the existing methods of disaster management be improved through the
   application of disruptive technologies. To respond to these questions, a
   novel framework based on integrated approaches based on big data
   analytics and AI is proposed for developing disaster management
   solutions using disruptive technologies. (c) 2021 Elsevier B.V. All
   rights reserved.},
Publisher = {ELSEVIER},
Address = {RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS},
Type = {Review},
Language = {English},
Affiliation = {Munawar, HS (Corresponding Author), Univ New South Wales, Sch Built Environm, Sydney, NSW 2052, Australia.
   Munawar, Hafiz Suliman; Mojtahedi, Mohammad; Hammad, Ahmed W. A., Univ New South Wales, Sch Built Environm, Sydney, NSW 2052, Australia.
   Kouzani, Abbas; Mahmud, M. A. Parvez, Deakin Univ, Sch Engn, Geelong, Vic, Australia.},
DOI = {10.1016/j.scitotenv.2021.151351},
EarlyAccessDate = {NOV 2021},
Article-Number = {151351},
ISSN = {0048-9697},
EISSN = {1879-1026},
Keywords = {Disaster management; Smart cities; Wireless communication; Cloud
   computing; Internet of Things; Big data},
Keywords-Plus = {BIG DATA ANALYTICS; SMART CITIES; SYSTEM; SECURITY; INFRASTRUCTURE;
   CHALLENGES; INSPECTION; INTERNET; NETWORK; MODELS},
Research-Areas = {Environmental Sciences \& Ecology},
Web-of-Science-Categories  = {Environmental Sciences},
Author-Email = {h.munawar@unsw.edu.au},
Affiliations = {University of New South Wales Sydney; Deakin University},
ORCID-Numbers = {Hammad, Ahmed WA/0000-0001-6190-0078},
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Number-of-Cited-References = {99},
Times-Cited = {14},
Usage-Count-Last-180-days = {24},
Usage-Count-Since-2013 = {62},
Journal-ISO = {Sci. Total Environ.},
Doc-Delivery-Number = {YI9JK},
Web-of-Science-Index = {Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)},
Unique-ID = {WOS:000744157100004},
DA = {2023-04-22},
}

@article{ WOS:000671050900001,
Author = {Sharifi, Ayyoob and Allam, Zaheer and Feizizadeh, Bakhtiar and Ghamari,
   Hessam},
Title = {Three Decades of Research on Smart Cities: Mapping Knowledge Structure
   and Trends},
Journal = {SUSTAINABILITY},
Year = {2021},
Volume = {13},
Number = {13},
Month = {JUL},
Abstract = {The concept of smart cities has gained significant momentum in science
   and policy circles over the past decade. This study aims to provide an
   overview of the structure and trends in the literature on smart cities.
   Bibliometric analysis and science mapping techniques using VOSviewer and
   CiteSpace are used to identify the thematic focus of over 5000 articles
   indexed in the Web of Science since 1991. In addition to providing
   insights into the thematic evolution of the field, the three-decade
   study period is divided into two sub-periods (1991-2015 and 2016-2021).
   While splitting the dataset into more sub-periods would have been
   desirable, we decided to only examine two sub-periods as only very few
   papers have been published until 2010. The annual number of publications
   has progressively increased since then, with a surge in the annual
   number of publications observable from 2015 onwards. The thematic
   analysis showed that the intellectual base of the field has been very
   limited during the first period, but has expanded significantly since
   2015. Over time, some thematic evolutions, such as further attention to
   linkages to climate change and resilience, and more emphasis on security
   and privacy issues, have been made. The thematic analysis shows that
   existing research on smart cities is dominated by either conceptual
   issues or underlying technical aspects. It is, therefore, essential to
   do more research on the implementation of smart cities and actual and/or
   potential contributions of smart cities to solving societal issues. In
   addition to elaborating on thematic focus, the study also highlights
   major authors, journals, references, countries, and institutions that
   have contributed to the development of the smart cities literature.},
Publisher = {MDPI},
Address = {ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND},
Type = {Review},
Language = {English},
Affiliation = {Sharifi, A (Corresponding Author), Hiroshima Univ, Grad Sch Humanities \& Social Sci, Hiroshima 7398511, Japan.
   Sharifi, A (Corresponding Author), Hiroshima Univ, Network Educ \& Res Peace \& Sustainabil NERPS, Hiroshima 7398511, Japan.
   Ghamari, H (Corresponding Author), Calif State Univ Northridge, Coll Hlth \& Human Dev, Dept Family \& Consumer Sci, Interior Design Program, Northridge, CA 91330 USA.
   Sharifi, Ayyoob, Hiroshima Univ, Grad Sch Humanities \& Social Sci, Hiroshima 7398511, Japan.
   Sharifi, Ayyoob, Hiroshima Univ, Network Educ \& Res Peace \& Sustainabil NERPS, Hiroshima 7398511, Japan.
   Allam, Zaheer, Univ Paris 1 Pantheon Sorbonne, IAE Paris Sorbonne Business Sch, Grp Rech Gest Org GREGOR, Chaire Entrepreneuriat Terr Innovat ETI, F-75013 Paris, France.
   Feizizadeh, Bakhtiar, Univ Tabriz, Dept Remote Sensing \& GIS, Tabriz 51368, Iran.
   Ghamari, Hessam, Calif State Univ Northridge, Coll Hlth \& Human Dev, Dept Family \& Consumer Sci, Interior Design Program, Northridge, CA 91330 USA.},
DOI = {10.3390/su13137140},
Article-Number = {7140},
EISSN = {2071-1050},
Keywords = {smart city; internet of things; big data analytics; urban planning;
   science mapping; bibliometric analysis},
Keywords-Plus = {BIG DATA; SUSTAINABLE CITIES; ARTIFICIAL-INTELLIGENCE; CITY; INTERNET;
   FUTURE; IOT; TECHNOLOGY; PRIVACY; SYSTEM},
Research-Areas = {Science \& Technology - Other Topics; Environmental Sciences \& Ecology},
Web-of-Science-Categories  = {Green \& Sustainable Science \& Technology; Environmental Sciences;
   Environmental Studies},
Author-Email = {sharifi@hiroshima-u.ac.jp
   zaheerallam@gmail.com
   bakhtiar.feizizadeh@gmail.com
   hessam.ghamari@csun.edu},
Affiliations = {Hiroshima University; Hiroshima University; University of Tabriz;
   California State University System; California State University
   Northridge},
ResearcherID-Numbers = {Feizizadeh, Bakhtiar/ABC-9664-2021
   Sharifi, Ayyoob/M-7584-2013
   },
ORCID-Numbers = {Feizizadeh, Bakhtiar/0000-0002-3367-2925
   Sharifi, Ayyoob/0000-0002-8983-8613
   Allam, Zaheer/0000-0001-7682-5912
   Ghamari, Hessam/0000-0001-8848-1969},
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Number-of-Cited-References = {134},
Times-Cited = {21},
Usage-Count-Last-180-days = {19},
Usage-Count-Since-2013 = {98},
Journal-ISO = {Sustainability},
Doc-Delivery-Number = {TF9RB},
Web-of-Science-Index = {Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)},
Unique-ID = {WOS:000671050900001},
OA = {gold, Green Published},
DA = {2023-04-22},
}

@incollection{ WOS:000661534400003,
Author = {Ashraf, Chowdhury and Joshi, Nisarg and Beck, David A. C. and
   Pfaendtner, Jim},
Editor = {Doherty, MF and Segalman, RA},
Title = {Data Science in Chemical Engineering: Applications to Molecular Science},
Booktitle = {ANNUAL REVIEW OF CHEMICAL AND BIOMOLECULAR ENGINEERING, VOL 12, 2021},
Series = {Annual Review of Chemical and Biomolecular Engineering},
Year = {2021},
Volume = {12},
Pages = {15-37},
Abstract = {Chemical engineering is being rapidly transformed by the tools of data
   science. On the horizon, artificial intelligence (AI) applications will
   impact a huge swath of our work, ranging from the discovery and design
   of new molecules to operations and manufacturing and many areas in
   between. Early adoption of data science, machine learning, and early
   examples of AI in chemical engineering has been rich with examples of
   molecular data science-the application tools for molecular discovery and
   property optimization at the atomic scale. We summarize key advances in
   this nascent subfield while introducing molecular data science for a
   broad chemical engineering readership. We introduce the field through
   the concept of a molecular data science life cycle and discuss relevant
   aspects of five distinct phases of this process: creation of curated
   data sets, molecular representations, data-driven property prediction,
   generation of new molecules, and feasibility and synthesizability
   considerations.},
Publisher = {ANNUAL REVIEWS},
Address = {4139 EL CAMINO WAY, PO BOX 10139, PALO ALTO, CA 94303-0897 USA},
Type = {Review; Book Chapter},
Language = {English},
Affiliation = {Beck, DAC (Corresponding Author), Univ Washington, Dept Chem Engn, Seattle, WA 98195 USA.
   Beck, DAC (Corresponding Author), Univ Washington, eSci Inst, Seattle, WA 98195 USA.
   Ashraf, Chowdhury; Joshi, Nisarg; Beck, David A. C.; Pfaendtner, Jim, Univ Washington, Dept Chem Engn, Seattle, WA 98195 USA.
   Beck, David A. C., Univ Washington, eSci Inst, Seattle, WA 98195 USA.},
DOI = {10.1146/annurev-chembioeng-101220-102232},
ISSN = {1947-5438},
EISSN = {1947-5446},
Keywords = {molecular data science; chemoinformatics; machine learning; artificial
   intelligence; continuous representation of molecules; molecular design},
Keywords-Plus = {GENETIC ALGORITHM; NEURAL-NETWORKS; MULTIOBJECTIVE OPTIMIZATION;
   SOFTWARE NEWS; DESIGN; EXPLORATION; GENERATION; PREDICTION; GRAPHS;
   EXPERIMENTATION},
Research-Areas = {Chemistry; Engineering},
Web-of-Science-Categories  = {Chemistry, Applied; Engineering, Chemical},
Author-Email = {dacb@uw.edu
   jpfaendt@uw.edu},
Affiliations = {University of Washington; University of Washington Seattle; University
   of Washington; University of Washington Seattle},
Funding-Acknowledgement = {NRT-DESE: Data Intensive Research Enabling Clean Technologies (DIRECT)
   {[}1633216]; HDR: I-DIRSE-FW: Accelerating the Engineering Design and
   Manufacturing Life-Cycle with Data Science {[}1934292]},
Funding-Text = {The authors would like to thank Dr. Wesley Beckner and Orion Dollar for
   thoughtful comments on language. J.P. would like to acknowledge the
   funding from NRT-DESE: Data Intensive Research Enabling Clean
   Technologies (DIRECT) under grant no. NSF \#1633216. C.A., D.A.C.B., and
   J.P. would like to acknowledge support from HDR: I-DIRSE-FW:
   Accelerating the Engineering Design and Manufacturing Life-Cycle with
   Data Science under grant no. NSF \#1934292.},
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Number-of-Cited-References = {144},
Times-Cited = {1},
Usage-Count-Last-180-days = {12},
Usage-Count-Since-2013 = {66},
Journal-ISO = {Annu. Rev. Chem. Biomol. Eng.},
Doc-Delivery-Number = {BR6JU},
Web-of-Science-Index = {Book Citation Index – Science (BKCI-S); Science Citation Index Expanded (SCI-EXPANDED)},
Unique-ID = {WOS:000661534400003},
DA = {2023-04-22},
}

@article{ WOS:000946929300001,
Author = {Strunga, Martin and Urban, Renata and Surovkova, Jana and Thurzo, Andrej},
Title = {Artificial Intelligence Systems Assisting in the Assessment of the
   Course and Retention of Orthodontic Treatment},
Journal = {HEALTHCARE},
Year = {2023},
Volume = {11},
Number = {5},
Month = {MAR},
Abstract = {This scoping review examines the contemporary applications of advanced
   artificial intelligence (AI) software in orthodontics, focusing on its
   potential to improve daily working protocols, but also highlighting its
   limitations. The aim of the review was to evaluate the accuracy and
   efficiency of current AI-based systems compared to conventional methods
   in diagnosing, assessing the progress of patients' treatment and
   follow-up stability. The researchers used various online databases and
   identified diagnostic software and dental monitoring software as the
   most studied software in contemporary orthodontics. The former can
   accurately identify anatomical landmarks used for cephalometric
   analysis, while the latter enables orthodontists to thoroughly monitor
   each patient, determine specific desired outcomes, track progress, and
   warn of potential changes in pre-existing pathology. However, there is
   limited evidence to assess the stability of treatment outcomes and
   relapse detection. The study concludes that AI is an effective tool for
   managing orthodontic treatment from diagnosis to retention, benefiting
   both patients and clinicians. Patients find the software easy to use and
   feel better cared for, while clinicians can make diagnoses more easily
   and assess compliance and damage to braces or aligners more quickly and
   frequently.},
Publisher = {MDPI},
Address = {ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND},
Type = {Review},
Language = {English},
Affiliation = {Strunga, M; Thurzo, A (Corresponding Author), Comenius Univ, Fac Med, Dept Orthodont Regenerat \& Forens Dent, Bratislava 81250, Slovakia.
   Strunga, Martin; Urban, Renata; Surovkova, Jana; Thurzo, Andrej, Comenius Univ, Fac Med, Dept Orthodont Regenerat \& Forens Dent, Bratislava 81250, Slovakia.},
DOI = {10.3390/healthcare11050683},
Article-Number = {683},
EISSN = {2227-9032},
Keywords = {orthodontics; AI; ChatGPT; AI Treatment Assessment; Teledentistry
   Cephalometrics},
Keywords-Plus = {TELEDENTISTRY; ACCURACY; HEALTH},
Research-Areas = {Health Care Sciences \& Services},
Web-of-Science-Categories  = {Health Care Sciences \& Services; Health Policy \& Services},
Author-Email = {strunga2@uniba.sk
   thurzo3@uniba.sk},
Affiliations = {Comenius University Bratislava},
ResearcherID-Numbers = {Thurzo, Andrej/AAX-7034-2021},
ORCID-Numbers = {Thurzo, Andrej/0000-0002-7810-5721},
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Number-of-Cited-References = {93},
Times-Cited = {0},
Usage-Count-Last-180-days = {21},
Usage-Count-Since-2013 = {21},
Journal-ISO = {Healthcare},
Doc-Delivery-Number = {9T3KO},
Web-of-Science-Index = {Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)},
Unique-ID = {WOS:000946929300001},
OA = {gold, Green Published},
DA = {2023-04-22},
}

@article{ WOS:000945979200001,
Author = {Goswami, Rupak and Dutta, Sudarshan and Misra, Sanchayeeta and Dasgupta,
   Shubhadip and Chakraborty, Somsubhra and Mallick, Kousik and Sinha,
   Aditya and Singh, Vinod K. K. and Oberthur, Thomas and Cook, Simon and
   Majumdar, Kaushik},
Title = {Whither digital agriculture in India?},
Journal = {CROP \& PASTURE SCIENCE},
Number = {SI},
Abstract = {Agriculture is central to the Indian economy and suffers from widespread
   operational inefficiencies that could be corrected by the use of digital
   agriculture technologies (DA). We review and synthesise available
   literature concerning digital agriculture in India and anticipate its
   transformative potential in the coming decade. Although the initial
   growth of DA was more conspicuous in the downstream sectors and
   high-value crops, reaching smallholder farmers upstream is slowly
   emerging despite significant obstacles such as small fragmented
   holdings, inadequate data infrastructure and public policy, and unequal
   access to digital infrastructure. Agri-tech enables innovation at many
   locations within value chains, and a steady shift is occurring in change
   from individual farms to the whole value chain. Technology in the sector
   is progressing from information and communication technology-based
   solutions to Internet of Things and artificial intelligence-machine
   learning-enabled services. India's public policy shows signs of a
   longstanding investment and collaboration in the sector, with an
   explicit focus on data infrastructure development. We find smallholder
   predominance, diversity in production systems, the predominance of
   commodity crops, proximity to urban markets, and public policy as the
   major factors of DA's success in India. A stocktake of the available
   technologies and their applications by the public sector, tech giants,
   information technology leaders and agri-food tech startups in India
   strongly indicates a digital transformation of Indian agriculture.
   However, given the federal structure of governance and agriculture being
   a state (province) subject, we need to wait to see how DA policies are
   rolled out and taken up across the country.},
Publisher = {CSIRO PUBLISHING},
Address = {UNIPARK, BLDG 1, LEVEL 1, 195 WELLINGTON RD, LOCKED BAG 10, CLAYTON, VIC
   3168, AUSTRALIA},
Type = {Review; Early Access},
Language = {English},
Affiliation = {Dutta, S (Corresponding Author), African Plant Nutr Inst, Benguerir 43150, Morocco.
   Dutta, S (Corresponding Author), African Plant Nutr Inst, Benguerir 43150, Morocco.
   Goswami, Rupak; Misra, Sanchayeeta; Mallick, Kousik, Ramakrishna Miss Vivekananda Educ \& Res Inst, Fac Ctr Integrated Rural Dev \& Management IRDM, Narendrapur 700103, West Bengal, India.
   Dutta, Sudarshan; Oberthur, Thomas; Majumdar, Kaushik, African Plant Nutr Inst, Benguerir 43150, Morocco.
   Dutta, Sudarshan; Oberthur, Thomas; Majumdar, Kaushik, Mohammed VI Polytech Univ, Benguerir 43150, Morocco.
   Dasgupta, Shubhadip; Chakraborty, Somsubhra, Indian Inst Technol Kharagpur, Agr \& Food Engn Dept, Kharagpur 721302, West Bengal, India.
   Dasgupta, Shubhadip, Bidhan Chandra Krishi Viswavidyalaya, Mohanpur 741252, West Bengal, India.
   Sinha, Aditya, Bihar Agr Univ, Dept Extens Educ, Sabour 813210, Bihar, India.
   Singh, Vinod K. K., ICAR Cent Res Inst Dryland Agr CRIDA, Hyderabad 500059, India.
   Cook, Simon, Murdoch Univ, Coll Sci Hlth Engn \& Educ, Murdoch, WA, Australia.
   Dutta, Sudarshan, Kosher Climate, Nat Based Solut Agr, Bengaluru, Karnataka, India.},
DOI = {10.1071/CP21624},
EarlyAccessDate = {MAR 2023},
ISSN = {1836-0947},
EISSN = {1836-5795},
Keywords = {agri-startup; artificial intelligence-machine learning; data
   infrastructure; data policy; digital technologies; factors of adoption;
   proximal sensing; smallholder systems},
Keywords-Plus = {FARMERS},
Research-Areas = {Agriculture},
Web-of-Science-Categories  = {Agriculture, Multidisciplinary},
Author-Email = {sudarshandutta@gmail.com},
Affiliations = {Mohammed VI Polytechnic University; Indian Institute of Technology
   System (IIT System); Indian Institute of Technology (IIT) - Kharagpur;
   Bidhan Chandra Agricultural University; Indian Council of Agricultural
   Research (ICAR); ICAR - Central Research Institute of Dryland
   Agriculture; Murdoch University},
ResearcherID-Numbers = {Goswami, Rupak/E-1030-2013},
ORCID-Numbers = {Goswami, Rupak/0000-0002-5998-4396},
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Journal-ISO = {Crop Pasture Sci.},
Doc-Delivery-Number = {9R9PD},
Web-of-Science-Index = {Science Citation Index Expanded (SCI-EXPANDED)},
Unique-ID = {WOS:000945979200001},
DA = {2023-04-22},
}

@article{ WOS:000832059900001,
Author = {Li, Wenwen and Hsu, Chia-Yu},
Title = {GeoAI for Large-Scale Image Analysis and Machine Vision: Recent Progress
   of Artificial Intelligence in Geography},
Journal = {ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION},
Year = {2022},
Volume = {11},
Number = {7},
Month = {JUL},
Abstract = {GeoAI, or geospatial artificial intelligence, has become a trending
   topic and the frontier for spatial analytics in Geography. Although much
   progress has been made in exploring the integration of AI and Geography,
   there is yet no clear definition of GeoAI, its scope of research, or a
   broad discussion of how it enables new ways of problem solving across
   social and environmental sciences. This paper provides a comprehensive
   overview of GeoAI research used in large-scale image analysis, and its
   methodological foundation, most recent progress in geospatial
   applications, and comparative advantages over traditional methods. We
   organize this review of GeoAI research according to different kinds of
   image or structured data, including satellite and drone images, street
   views, and geo-scientific data, as well as their applications in a
   variety of image analysis and machine vision tasks. While different
   applications tend to use diverse types of data and models, we summarized
   six major strengths of GeoAI research, including (1) enablement of
   large-scale analytics; (2) automation; (3) high accuracy; (4)
   sensitivity in detecting subtle changes; (5) tolerance of noise in data;
   and (6) rapid technological advancement. As GeoAI remains a rapidly
   evolving field, we also describe current knowledge gaps and discuss
   future research directions.},
Publisher = {MDPI},
Address = {ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND},
Type = {Review},
Language = {English},
Affiliation = {Li, WW (Corresponding Author), Arizona State Univ, Sch Geog Sci \& Urban Planning, Tempe, AZ 85287 USA.
   Li, Wenwen; Hsu, Chia-Yu, Arizona State Univ, Sch Geog Sci \& Urban Planning, Tempe, AZ 85287 USA.},
DOI = {10.3390/ijgi11070385},
Article-Number = {385},
EISSN = {2220-9964},
Keywords = {artificial intelligence; deep learning; CNN; transformer; LSTM},
Keywords-Plus = {GOOGLE STREET VIEW; HIGH-RESOLUTION IMAGE; REMOTE-SENSING IMAGES;
   SYSTEMATIC SOCIAL OBSERVATION; TEMPERATURE CONDITION INDEX; SATELLITE
   VIDEOS; MESOSCALE EDDIES; BIG DATA; SEMANTIC SEGMENTATION; CONVOLUTIONAL
   NETWORK},
Research-Areas = {Computer Science; Physical Geography; Remote Sensing},
Web-of-Science-Categories  = {Computer Science, Information Systems; Geography, Physical; Remote
   Sensing},
Author-Email = {wenwen@asu.edu
   chsu53@asu.edu},
Affiliations = {Arizona State University; Arizona State University-Tempe},
ResearcherID-Numbers = {Hsu, Chia-Yu/GPF-4406-2022},
ORCID-Numbers = {Hsu, Chia-Yu/0000-0002-8923-1213},
Funding-Acknowledgement = {US National Science Foundation {[}BCS-1853864, BCS-1455349, GCR-2021147,
   PLR-2120943, OIA-2033521]},
Funding-Text = {This research was funded in part by the US National Science Foundation,
   grant numbers BCS-1853864, BCS-1455349, GCR-2021147, PLR-2120943, and
   OIA-2033521.},
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@article{ WOS:000500371200009,
Author = {Zhang, Kun and Chui, Ting Fong May},
Title = {A review on implementing infiltration-based green infrastructure in
   shallow groundwater environments: Challenges, approaches, and progress},
Journal = {JOURNAL OF HYDROLOGY},
Year = {2019},
Volume = {579},
Month = {DEC},
Abstract = {Urban water problems (e.g., increased runoff, inhibited infiltration,
   and groundwater recharge) are becoming increasingly serious, and green
   infrastructure (GI) has been demonstrated to be effective in tackling
   these problems and restoring the pre-development hydrologic cycle.
   However, shallow groundwater limits the implementation of
   infiltration-based GI. Although projects and studies have been
   conducted, knowledge of the hydrologic performance of infiltration-based
   GI, and the impact on shallow groundwater environments, has not been
   comprehensively summarized. In this review, we first identify the
   challenges of implementing infiltration-based GI in shallow groundwater
   environments, and then evaluate and compare the potential approaches to
   evaluating GI in such environments. We also summarize progress in the
   understanding of the performance and impact of GI in shallow groundwater
   environments from previous and ongoing engineering projects and academic
   studies. The main topics include the evaluation of the potential
   reduction in runoff control performance of GI, the formation of
   groundwater mounds, and groundwater contamination. We also assess the
   exploration of strategies for optimally allocating and designing GI in
   shallow groundwater environments. The distance between the bottom of the
   GI and the groundwater table, the selection of the media soil, and the
   design of underdrain pipes are the main considerations when implementing
   GI in shallow groundwater environments. The review is concluded with
   recommendations for future studies, which include conducting tracer
   monitoring and variably saturated modeling to track subsurface flows of
   GI, improving existing hydrological models, developing new multi-scale
   models, and utilizing more advanced data-driven artificial intelligence
   models to predict the performance and impact of GI and to process the
   monitoring data for model calibration.},
Publisher = {ELSEVIER},
Address = {RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS},
Type = {Review},
Language = {English},
Affiliation = {Chui, TFM (Corresponding Author), Univ Hong Kong, Dept Civil Engn, Pok Fu Lam, Hong Kong, Peoples R China.
   Zhang, Kun; Chui, Ting Fong May, Univ Hong Kong, Dept Civil Engn, Pok Fu Lam, Hong Kong, Peoples R China.},
DOI = {10.1016/j.jhydrol.2019.124089},
Article-Number = {124089},
ISSN = {0022-1694},
EISSN = {1879-2707},
Keywords = {Green infrastructure; Low impact development; Best management practices;
   Stormwater; Groundwater recharge},
Keywords-Plus = {LOW-IMPACT DEVELOPMENT; VEGETATIVE FILTER STRIPS; SENSITIVE URBAN
   DESIGN; STORMWATER INFILTRATION; PESTICIDE TRANSPORT; WATER MANAGEMENT;
   AQUIFER RECHARGE; MODEL; PERFORMANCE; SURFACE},
Research-Areas = {Engineering; Geology; Water Resources},
Web-of-Science-Categories  = {Engineering, Civil; Geosciences, Multidisciplinary; Water Resources},
Author-Email = {maychui@hku.hk},
Affiliations = {University of Hong Kong},
ResearcherID-Numbers = {Zhang, Kun/AAA-1493-2021
   /A-7652-2013},
ORCID-Numbers = {Zhang, Kun/0000-0002-1062-8323
   /0000-0003-3322-8848},
Funding-Acknowledgement = {Seed Funding Programme for Basic Research of The University of Hong Kong
   {[}201611159011]},
Funding-Text = {This work was funded by the Seed Funding Programme for Basic Research of
   The University of Hong Kong (Project code: 201611159011).},
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Number-of-Cited-References = {109},
Times-Cited = {23},
Usage-Count-Last-180-days = {9},
Usage-Count-Since-2013 = {75},
Journal-ISO = {J. Hydrol.},
Doc-Delivery-Number = {JS5UK},
Web-of-Science-Index = {Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)},
Unique-ID = {WOS:000500371200009},
DA = {2023-04-22},
}

@article{ WOS:000958616900001,
Author = {Fernandes, Elizabeth and Moro, Sergio and Cortez, Paulo},
Title = {Data Science, Machine learning and big data in Digital Journalism: A
   survey of state-of-the-art, challenges and opportunities},
Journal = {EXPERT SYSTEMS WITH APPLICATIONS},
Year = {2023},
Volume = {221},
Month = {JUL 1},
Abstract = {Digital journalism has faced a dramatic change and media companies are
   challenged to use data science algo-rithms to be more competitive in a
   Big Data era. While this is a relatively new area of study in the media
   landscape, the use of machine learning and artificial intelligence has
   increased substantially over the last few years. In particular, the
   adoption of data science models for personalization and recommendation
   has attracted the attention of several media publishers. Following this
   trend, this paper presents a research literature analysis on the role of
   Data Science (DS) in Digital Journalism (DJ). Specifically, the aim is
   to present a critical literature review, synthetizing the main
   application areas of DS in DJ, highlighting research gaps, challenges,
   and op-portunities for future studies. Through a systematic literature
   review integrating bibliometric search, text min-ing, and qualitative
   discussion, the relevant literature was identified and extensively
   analyzed. The review reveals an increasing use of DS methods in DJ, with
   almost 47\% of the research being published in the last three years. An
   hierarchical clustering highlighted six main research domains focused on
   text mining, event extraction, online comment analysis, recommendation
   systems, automated journalism, and exploratory data analysis along with
   some machine learning approaches. Future research directions comprise
   developing models to improve personalization and engagement features,
   exploring recommendation algorithms, testing new automated jour-nalism
   solutions, and improving paywall mechanisms.},
Publisher = {PERGAMON-ELSEVIER SCIENCE LTD},
Address = {THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND},
Type = {Review},
Language = {English},
Affiliation = {Fernandes, E (Corresponding Author), ISTAR, ISCTE Inst Univ Lisboa ISCTE IUL, Ave Forcas Armadas,Edificio II,D615, P-1649026 Lisbon, Portugal.
   Fernandes, Elizabeth, ISTAR, ISCTE Inst Univ Lisboa ISCTE IUL, Ave Forcas Armadas,Edificio II,D615, P-1649026 Lisbon, Portugal.
   Moro, Sergio, ISTAR, Inst Univ Lisboa ISCTE IUL, Lisbon, Portugal.
   Cortez, Paulo, Univ Minho, ALGORITMI Res Ctr, Guimaraes, Portugal.},
DOI = {10.1016/j.eswa.2023.119795},
EarlyAccessDate = {MAR 2023},
Article-Number = {119795},
ISSN = {0957-4174},
EISSN = {1873-6793},
Keywords = {Data science; Digital journalism; Text mining; Systematic literature
   review; Media analytics; Machine Learning},
Keywords-Plus = {NEWS ARTICLE RECOMMENDATION; AUTOMATED JOURNALISM; SENTIMENT ANALYSIS;
   ONLINE; SYSTEM; INTELLIGENCE; POPULARITY; PERCEPTION; 3RD-PERSON;
   ALGORITHM},
Research-Areas = {Computer Science; Engineering; Operations Research \& Management Science},
Web-of-Science-Categories  = {Computer Science, Artificial Intelligence; Engineering, Electrical \&
   Electronic; Operations Research \& Management Science},
Author-Email = {elizabeth.fernandes.data@gmail.com},
Affiliations = {Instituto Universitario de Lisboa; Instituto Universitario de Lisboa;
   Universidade do Minho},
Funding-Acknowledgement = { {[}UIDB/04466/2020];  {[}UIDP/04466/2020];  {[}UIDB/00319/2020]},
Funding-Text = {Acknowledgements This work was supported by the FCT-Funda?a ? o para a
   Cie ? ncia e Tecnologia, under the Projects: UIDB/04466/2020,
   UIDP/04466/2020, and UIDB/00319/2020.},
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Times-Cited = {0},
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@article{ WOS:000748636800001,
Author = {Khan, Syed Abdul Rehman and Shah, Adeel Syed Ali and Yu, Zhang and
   Tanveer, Muhammad},
Title = {A systematic literature review on circular economy practices:
   challenges, opportunities and future trends},
Journal = {JOURNAL OF ENTREPRENEURSHIP IN EMERGING ECONOMIES},
Year = {2022},
Volume = {14},
Number = {5, SI},
Pages = {754-795},
Month = {NOV 30},
Abstract = {Purpose The circular economy (CE) is an evolving subject transitioning
   from conceptualization to empirical testing. Over the past decade,
   researchers have done an exhaustive study to understand the concept of
   CE and its realized values both financially and environmentally on
   organizations that have traditional business models based on linear
   consumption. For understanding the transitional phenomena completely,
   the paper aims to review the current and emerging research trends in CE
   to ascertain future direction. Design/methodology/approach The research
   was conducted on 91 articles published in the study area during the past
   decade (2016-2021) in renowned peer-reviewed journals. The criteria set
   to review literature are based on the following assortment: CE drivers,
   CE barriers, definitions by different authors, yearly distribution of
   the publication, research publisher and journals, google citation and
   methodology used in the selected research articles. Findings The study
   suggests that researchers from the selected years are keen to understand
   the transition and its critical factors by bringing forward frameworks
   and incorporating CE with digital technologies. The digital technology
   implied are Industrial Technology (IR) 4.0, Big Data, Internet of Things
   (IoT), Artificial Intelligence, Blockchain and Data Analytics to
   increase organizational and environmental performance. CE researchers
   need to use empirical testing in different sectors to understand and
   bring forward more improvised business models and practices according to
   the dynamics of the industry. Originality/value The literature review
   suggests gaps exist to integrate the micro, meso and macro levels to get
   CE implementation's system-wide benefits. The study has also identified
   that many CE frameworks available in the literature for implementation
   must be empirically tested to yield performance results.},
Publisher = {EMERALD GROUP PUBLISHING LTD},
Address = {HOWARD HOUSE, WAGON LANE, BINGLEY BD16 1WA, W YORKSHIRE, ENGLAND},
Type = {Review},
Language = {English},
Affiliation = {Khan, SAR (Corresponding Author), Xuzhou Univ Technol, Sch Engn \& Management, Xuzhou, Jiangsu, Peoples R China.
   Khan, Syed Abdul Rehman, Xuzhou Univ Technol, Sch Engn \& Management, Xuzhou, Jiangsu, Peoples R China.
   Shah, Adeel Syed Ali, Coll Business Management, Dept Supply Chain \& Logist Management, Karachi, Pakistan.
   Yu, Zhang, Changan Univ, Sch Econ \& Management, Changan, Peoples R China.
   Yu, Zhang, ILMA Univ, Dept Business Adm, Karachi, Pakistan.
   Tanveer, Muhammad, Prince Sultan Univ, Riyadh, Saudi Arabia.},
DOI = {10.1108/JEEE-09-2021-0349},
EarlyAccessDate = {JAN 2022},
ISSN = {2053-4604},
EISSN = {2053-4612},
Keywords = {Sustainability; Systematic literature review; Meta-analysis; Circular
   economy; Green technology; Systematic review},
Keywords-Plus = {SUPPLY CHAIN MANAGEMENT; SUSTAINABLE DEVELOPMENT; BIG-DATA; DIGITAL
   TECHNOLOGIES; WASTE MANAGEMENT; BUSINESS MODELS; PRODUCT DESIGN;
   INDUSTRY 4.0; TRANSITION; FRAMEWORK},
Research-Areas = {Business \& Economics},
Web-of-Science-Categories  = {Business},
Author-Email = {sarehman\_cscp@yahoo.com
   adeelshah@mail.com
   Zhangy19@foxmail.com
   mtanveer@psu.edu.sa},
Affiliations = {Xuzhou University of Technology; Chang'an University; Prince Sultan
   University},
ResearcherID-Numbers = {Yu, Zhang/AFV-9605-2022
   },
ORCID-Numbers = {Yu, Zhang/0000-0002-9892-7642
   khan, syed abdul rehman/0000-0001-5197-2318},
Funding-Acknowledgement = {Beijing Key Laboratory of Urban Spatial Information Engineering
   {[}20210218]; Research Center of ILMA University},
Funding-Text = {This research is supported by the Beijing Key Laboratory of Urban
   Spatial Information Engineering (No. 20210218) and Research Center of
   ILMA University.},
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Number-of-Cited-References = {132},
Times-Cited = {7},
Usage-Count-Last-180-days = {24},
Usage-Count-Since-2013 = {82},
Journal-ISO = {J. Entrep. Emerg. Econ.},
Doc-Delivery-Number = {6R9HY},
Web-of-Science-Index = {Emerging Sources Citation Index (ESCI)},
Unique-ID = {WOS:000748636800001},
DA = {2023-04-22},
}

@article{ WOS:000594559300001,
Author = {Barmpoutis, Panagiotis and Papaioannou, Periklis and Dimitropoulos,
   Kosmas and Grammalidis, Nikos},
Title = {A Review on Early Forest Fire Detection Systems Using Optical Remote
   Sensing},
Journal = {SENSORS},
Year = {2020},
Volume = {20},
Number = {22},
Month = {NOV},
Abstract = {The environmental challenges the world faces nowadays have never been
   greater or more complex. Global areas covered by forests and urban
   woodlands are threatened by natural disasters that have increased
   dramatically during the last decades, in terms of both frequency and
   magnitude. Large-scale forest fires are one of the most harmful natural
   hazards affecting climate change and life around the world. Thus, to
   minimize their impacts on people and nature, the adoption of
   well-planned and closely coordinated effective prevention, early
   warning, and response approaches are necessary. This paper presents an
   overview of the optical remote sensing technologies used in early fire
   warning systems and provides an extensive survey on both flame and smoke
   detection algorithms employed by each technology. Three types of systems
   are identified, namely terrestrial, airborne, and spaceborne-based
   systems, while various models aiming to detect fire occurrences with
   high accuracy in challenging environments are studied. Finally, the
   strengths and weaknesses of fire detection systems based on optical
   remote sensing are discussed aiming to contribute to future research
   projects for the development of early warning fire systems.},
Publisher = {MDPI},
Address = {ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND},
Type = {Review},
Language = {English},
Affiliation = {Grammalidis, N (Corresponding Author), Ctr Res \& Technol Hellas, Inst Informat Technol, Thessaloniki 57001, Greece.
   Barmpoutis, Panagiotis; Papaioannou, Periklis; Dimitropoulos, Kosmas; Grammalidis, Nikos, Ctr Res \& Technol Hellas, Inst Informat Technol, Thessaloniki 57001, Greece.},
DOI = {10.3390/s20226442},
Article-Number = {6442},
EISSN = {1424-8220},
Keywords = {early fire detection; multispectral imaging systems; terrestrial;
   aerial; satellite; artificial intelligence},
Keywords-Plus = {CONVOLUTIONAL NEURAL-NETWORKS; UNMANNED AERIAL VEHICLES; DYNAMIC TEXTURE
   ANALYSIS; REAL-TIME FIRE; SMOKE DETECTION; DETECTION ALGORITHM; WILDFIRE
   DETECTION; COMPUTER VISION; FLAME DETECTION; SURVEILLANCE},
Research-Areas = {Chemistry; Engineering; Instruments \& Instrumentation},
Web-of-Science-Categories  = {Chemistry, Analytical; Engineering, Electrical \& Electronic;
   Instruments \& Instrumentation},
Author-Email = {panbar@iti.gr
   ppapaioa@iti.gr
   dimitrop@iti.gr
   ngramm@iti.gr},
Affiliations = {Centre for Research \& Technology Hellas},
ORCID-Numbers = {Dimitropoulos, Kosmas/0000-0003-1584-7047},
Funding-Acknowledgement = {European Union, project i-FORESTER ({''}Reinforcement of Postdoctoral
   Researchers-2nd Cycle{''}); European Union, project eOUTLAND
   ({''}INTERREG V-A COOPERATION PROGRAMME Greece-Bulgaria 2014-2020{''})
   {[}1672]},
Funding-Text = {This research was funded by Greece and the European Union, projects
   i-FORESTER ({''}Reinforcement of Postdoctoral Researchers-2nd Cycle{''})
   and eOUTLAND ({''}INTERREG V-A COOPERATION PROGRAMME Greece-Bulgaria
   2014-2020{''}, grant number 1672).},
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Number-of-Cited-References = {122},
Times-Cited = {87},
Usage-Count-Last-180-days = {40},
Usage-Count-Since-2013 = {131},
Journal-ISO = {Sensors},
Doc-Delivery-Number = {OY9LD},
Web-of-Science-Index = {Science Citation Index Expanded (SCI-EXPANDED)},
Unique-ID = {WOS:000594559300001},
OA = {Green Published, gold},
DA = {2023-04-22},
}

@article{ WOS:000760587900001,
Author = {Parsons, Miles J. G. and Lin, Tzu-Hao and Mooney, T. Aran and Erbe,
   Christine and Juanes, Francis and Lammers, Marc and Li, Songhai and
   Linke, Simon and Looby, Audrey and Nedelec, Sophie L. and Van Opzeeland,
   Ilse and Radford, Craig and Rice, Aaron N. and Sayigh, Laela and
   Stanley, Jenni and Urban, Edward and Di Iorio, Lucia},
Title = {Sounding the Call for a Global Library of Underwater Biological Sounds},
Journal = {FRONTIERS IN ECOLOGY AND EVOLUTION},
Year = {2022},
Volume = {10},
Month = {FEB 8},
Abstract = {Aquatic environments encompass the world's most extensive habitats, rich
   with sounds produced by a diversity of animals. Passive acoustic
   monitoring (PAM) is an increasingly accessible remote sensing technology
   that uses hydrophones to listen to the underwater world and represents
   an unprecedented, non-invasive method to monitor underwater
   environments. This information can assist in the delineation of
   biologically important areas via detection of sound-producing species or
   characterization of ecosystem type and condition, inferred from the
   acoustic properties of the local soundscape. At a time when worldwide
   biodiversity is in significant decline and underwater soundscapes are
   being altered as a result of anthropogenic impacts, there is a need to
   document, quantify, and understand biotic sound sources-potentially
   before they disappear. A significant step toward these goals is the
   development of a web-based, open-access platform that provides: (1) a
   reference library of known and unknown biological sound sources (by
   integrating and expanding existing libraries around the world); (2) a
   data repository portal for annotated and unannotated audio recordings of
   single sources and of soundscapes; (3) a training platform for
   artificial intelligence algorithms for signal detection and
   classification; and (4) a citizen science-based application for public
   users. Although individually, these resources are often met on regional
   and taxa-specific scales, many are not sustained and, collectively, an
   enduring global database with an integrated platform has not been
   realized. We discuss the benefits such a program can provide, previous
   calls for global data-sharing and reference libraries, and the
   challenges that need to be overcome to bring together bio- and
   ecoacousticians, bioinformaticians, propagation experts, web engineers,
   and signal processing specialists (e.g., artificial intelligence) with
   the necessary support and funding to build a sustainable and scalable
   platform that could address the needs of all contributors and
   stakeholders into the future.},
Publisher = {FRONTIERS MEDIA SA},
Address = {AVENUE DU TRIBUNAL FEDERAL 34, LAUSANNE, CH-1015, SWITZERLAND},
Type = {Review},
Language = {English},
Affiliation = {Parsons, MJG (Corresponding Author), Australian Inst Marine Sci, Perth, WA, Australia.
   Parsons, Miles J. G., Australian Inst Marine Sci, Perth, WA, Australia.
   Lin, Tzu-Hao, Acad Sinica, Biodivers Res Ctr, Taipei, Taiwan.
   Mooney, T. Aran; Sayigh, Laela, Woods Hole Oceanog Inst, Dept Biol, Woods Hole, MA 02543 USA.
   Erbe, Christine, Curtin Univ, Ctr Marine Sci \& Technol, Perth, WA, Australia.
   Juanes, Francis, Univ Victoria, Dept Biol, Victoria, BC, Canada.
   Lammers, Marc, Hawaiian Isl Humpback Whale Natl Marine Sanctuary, Kihei, HI USA.
   Li, Songhai, Chinese Acad Sci, Marine Mammal \& Marine Bioacoust Lab, Inst Deep Sea Sci \& Engn, Sanya, Peoples R China.
   Linke, Simon, CSIRO, Dutton Pk, Qld, Australia.
   Looby, Audrey, Univ Florida, Fisheries \& Aquat Sci, Gainesville, FL USA.
   Looby, Audrey, Univ Florida, Inst Food \& Agr Sci UF IFAS Nat Coast Biol Stn, Cedar Key, FL USA.
   Nedelec, Sophie L., Univ Exeter, Coll Life \& Environm Sci, Biosci, Hatherly Labs, Exeter, Devon, England.
   Van Opzeeland, Ilse, Helmholtz Zentrum Polar \& Marine Res, Ocean Acoust Lab, Alfred Wegener Inst, Bremerhaven, Germany.
   Van Opzeeland, Ilse, Carl von Ossietzky Univ Oldenburg, Helmholtz Inst Funct Marine Biodivers HIFMB, Oldenburg, Germany.
   Radford, Craig, Univ Auckland, Inst Marine Sci, Leigh Marine Lab, Warkworth, New Zealand.
   Rice, Aaron N., Cornell Univ, Cornell Lab Ornithol, K Lisa Yang Ctr Conservat Bioacoust, Ithaca, NY USA.
   Sayigh, Laela, Hampshire Coll, Amherst, MA 01002 USA.
   Stanley, Jenni, Univ Waikato, Sch Sci, Coastal Marine Field Stn, Tauranga, New Zealand.
   Urban, Edward, Univ Delaware, Sci Comm Ocean Res, Newark, DE USA.
   Di Iorio, Lucia, Univ Perpignan Via Domitia, CNRS, Ctr Format \& Rech Sur Environm Mediterraneens, Perpignan, France.
   Di Iorio, Lucia, CHORUS Inst, Grenoble, France.},
DOI = {10.3389/fevo.2022.810156},
Article-Number = {810156},
ISSN = {2296-701X},
Keywords = {soundscape; bioacoustics database; artificial intelligence;
   biodiversity; passive acoustic monitoring; ecological informatics},
Keywords-Plus = {MULLOWAY ARGYROSOMUS-JAPONICUS; BOTTLE-NOSED DOLPHINS;
   OPHIDION-MARGINATUM OPHIDIIDAE; FISH SOUNDS; SPAWNING AGGREGATION;
   AUTOMATIC DETECTION; PASSIVE ACOUSTICS; WHALE SONG;
   BALAENOPTERA-PHYSALUS; MARINE MAMMALS},
Research-Areas = {Environmental Sciences \& Ecology},
Web-of-Science-Categories  = {Ecology},
Author-Email = {m.parsons@aims.gov.au},
Affiliations = {Australian Institute of Marine Science; Academia Sinica - Taiwan; Woods
   Hole Oceanographic Institution; Curtin University; University of
   Victoria; National Oceanic Atmospheric Admin (NOAA) - USA; Chinese
   Academy of Sciences; Institute of Deep-Sea Science \& Engineering, CAS;
   Commonwealth Scientific \& Industrial Research Organisation (CSIRO);
   State University System of Florida; University of Florida; State
   University System of Florida; University of Florida; University of
   Exeter; Helmholtz Association; Alfred Wegener Institute, Helmholtz
   Centre for Polar \& Marine Research; Carl von Ossietzky Universitat
   Oldenburg; University of Auckland; Cornell University; University of
   Waikato; University of Delaware; Centre National de la Recherche
   Scientifique (CNRS)},
ResearcherID-Numbers = {Barrela, Cristina/HJY-8958-2023
   Lin, Tzu-Hao/GLT-9168-2022
   },
ORCID-Numbers = {Lin, Tzu-Hao/0000-0002-6973-3953
   Looby, Audrey/0000-0003-1833-8643
   Di Iorio, Lucia/0000-0002-3354-830X},
Funding-Acknowledgement = {U.S. National Science Foundation {[}OCE-1840868]},
Funding-Text = {Support for the initial author group to meet, discuss, and build
   consensus on the issues within this manuscript was provided by the
   Scientific Committee on Oceanic Research, Monmouth University Urban
   Coast Institute, and Rockefeller Program for the Human Environment. The
   U.S. National Science Foundation supported the publication of this
   article through Grant OCE-1840868 to the Scientific Committee on Oceanic
   Research.},
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Journal-ISO = {Front. Ecol. Evol.},
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@article{ WOS:000797382100001,
Author = {Jukic, Marko and Bren, Urban},
Title = {Machine Learning in Antibacterial Drug Design},
Journal = {FRONTIERS IN PHARMACOLOGY},
Year = {2022},
Volume = {13},
Month = {MAY 3},
Abstract = {Advances in computer hardware and the availability of high-performance
   supercomputing platforms and parallel computing, along with artificial
   intelligence methods are successfully complementing traditional
   approaches in medicinal chemistry. In particular, machine learning is
   gaining importance with the growth of the available data collections.
   One of the critical areas where this methodology can be successfully
   applied is in the development of new antibacterial agents. The latter is
   essential because of the high attrition rates in new drug discovery,
   both in industry and in academic research programs. Scientific
   involvement in this area is even more urgent as antibacterial drug
   resistance becomes a public health concern worldwide and pushes us
   increasingly into the post-antibiotic era. In this review, we focus on
   the latest machine learning approaches used in the discovery of new
   antibacterial agents and targets, covering both small molecules and
   antibacterial peptides. For the benefit of the reader, we summarize all
   applied machine learning approaches and available databases useful for
   the design of new antibacterial agents and address the current
   shortcomings.},
Publisher = {FRONTIERS MEDIA SA},
Address = {AVENUE DU TRIBUNAL FEDERAL 34, LAUSANNE, CH-1015, SWITZERLAND},
Type = {Review},
Language = {English},
Affiliation = {Jukic, M; Bren, U (Corresponding Author), Univ Maribor, Fac Chem \& Chem Engn, Lab Phys Chem \& Chem Thermodynam, Maribor, Slovenia.
   Jukic, M; Bren, U (Corresponding Author), Univ Primorska, Fac Math, Nat Sci \& Informat Technol, Koper, Slovenia.
   Jukic, Marko; Bren, Urban, Univ Maribor, Fac Chem \& Chem Engn, Lab Phys Chem \& Chem Thermodynam, Maribor, Slovenia.
   Jukic, Marko; Bren, Urban, Univ Primorska, Fac Math, Nat Sci \& Informat Technol, Koper, Slovenia.},
DOI = {10.3389/fphar.2022.864412},
Article-Number = {864412},
EISSN = {1663-9812},
Keywords = {artificial intelligence; machine learning; computer-aided drug design
   (CADD); infectious diseases; antibacterial drug design; antibacterial;
   antibacterial target discovery; antibacterial drug resistance},
Keywords-Plus = {ANTIMICROBIAL PEPTIDES; BIG DATA; DATABASE; DISCOVERY; PREDICTION;
   RESOURCE; NETWORK},
Research-Areas = {Pharmacology \& Pharmacy},
Web-of-Science-Categories  = {Pharmacology \& Pharmacy},
Author-Email = {marko.jukic@um.si
   urban.bren@um.si},
Affiliations = {University of Maribor; University of Primorska},
Funding-Acknowledgement = {Slovenian Ministry of Science and Education; Slovenian Research Agency
   (ARRS) program {[}P2-0046, J1-2471, J1-1715, N1-0209, P1-0403, L2-3175,
   J1-9186]},
Funding-Text = {This work was supported by the Slovenian Ministry of Science and
   Education infrastructure project grants HPC-RIVR and RI-SI-ELIXIR and by
   the Slovenian Research Agency (ARRS) program and project grants P2-0046,
   J1-2471, J1-1715, N1-0209, P1-0403, L2-3175 and J1-9186.},
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Journal-ISO = {Front. Pharmacol.},
Doc-Delivery-Number = {1I7BJ},
Web-of-Science-Index = {Science Citation Index Expanded (SCI-EXPANDED)},
Unique-ID = {WOS:000797382100001},
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}

@article{ WOS:000603487500015,
Author = {Kumar, Prashant and Kalaiarasan, Gopinath and Porter, Alexandra E. and
   Pinna, Alessandra and Klosowski, Michal M. and Demokritou, Philip and
   Chung, Kian Fan and Pain, Christopher and Arvind, D. K. and Arcucci,
   Rossella and Adcock, Ian M. and Dilliway, Claire},
Title = {An overview of methods of fine and ultrafine particle collection for
   physicochemical characterisation and toxicity assessments},
Journal = {SCIENCE OF THE TOTAL ENVIRONMENT},
Year = {2021},
Volume = {756},
Month = {FEB 20},
Abstract = {Particulate matter (PM) is a crucial health risk factor for respiratory
   and cardiovascular diseases. The smaller size fractions, <= 2.5 mu m
   (PM2.5; fine particles) and <= 0.1 mu m (PM0.1: ultrafine particles),
   show the highest bioactivity but acquiring sufficient mass for in vitro
   and in vivo toxicological studies is challenging. We review the
   suitability of available instrumentation to collect the PM mass required
   for these assessments. Five different microenvironments representing the
   diverse exposure conditions in urban environments are considered in
   order to establish the typical PM concentrations present. The highest
   concentrations of PM2.5 and PM0.1 were found near traffic (i.e.
   roadsides and traffic intersections), followed by indoor environments,
   parks and behind roadside vegetation. We identify key factors to
   consider when selecting sampling instrumentation. These include PM
   concentration on-site (low concentrations increase sampling time),
   nature of sampling sites (e.g. indoors; noise and space will be an
   issue), equipment handling and power supply. Physicochemical
   characterisation requires micro- to milli-gram quantities of PM and it
   may increase according to the processing methods (e.g. digestion or
   sonication). Toxicological assessments of PM involve numerous mechanisms
   (e.g. inflammatory processes and oxidative stress) requiring significant
   amounts of PM to obtain accurate results. Optimising air sampling
   techniques are therefore important for the appropriate collection
   medium/filter which have innate physical properties and the potential to
   interact with samples. An evaluation of methods and instrumentation used
   for airborne virus collection condudes that samplers operating cyclone
   sampling techniques (using centrifugal forces) are effective in
   collecting airborne viruses. We highlight that predictive modelling can
   help to identify pollution hotspots in an urban environment for the
   efficient collection of PM mass. This review provides guidance to
   prepare and plan efficient sampling campaigns to collect sufficient PM
   mass for various purposes in a reasonable timeframe. (C) 2020 The
   Authors. Published by Elsevier B.V.},
Publisher = {ELSEVIER},
Address = {RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS},
Type = {Review},
Language = {English},
Affiliation = {Kumar, P (Corresponding Author), Univ Surrey, Global Ctr Clean Air Res GCARE, Dept Civil \& Environm Engn, Fac Engn \& Phys Sci, Guildford GU2 7XH, Surrey, England.
   Kumar, Prashant; Kalaiarasan, Gopinath, Univ Surrey, Global Ctr Clean Air Res GCARE, Dept Civil \& Environm Engn, Fac Engn \& Phys Sci, Guildford GU2 7XH, Surrey, England.
   Kumar, Prashant, Trinity Coll Dublin, Dept Civil Struct \& Environm Engn, Dublin, Ireland.
   Porter, Alexandra E.; Pinna, Alessandra; Klosowski, Michal M., Imperial Coll London, Dept Mat, London SW7 2AZ, England.
   Demokritou, Philip, Harvard Univ, Ctr Nanotechnol \& Nanotoxicol, Dept Environm Hlth, TH Chan Sch Publ Hlth, 665 Huntington Ave,Room 1310, Boston, MA 02115 USA.
   Chung, Kian Fan; Adcock, Ian M., Imperial Coll London, Natl Heart \& Lung Inst, London SW3 6LY, England.
   Pain, Christopher; Dilliway, Claire, Imperial Coll London, Dept Earth Sci \& Engn, London SW7 2AZ, England.
   Arvind, D. K., Univ Edinburgh, Sch Informat, Ctr Speckled Comp, Edinburgh EH8 9AB, Midlothian, Scotland.
   Arcucci, Rossella, Imperial Coll London, Dept Comp, Data Sci Inst, London SW7 2BU, England.},
DOI = {10.1016/j.scitotenv.2020.143553},
Article-Number = {143553},
ISSN = {0048-9697},
EISSN = {1879-1026},
Keywords = {Particulate matter; Ultrafine particles; Mass collection;
   Physicochemical characteristics; Toxicological assessments; Artificial
   intelligence},
Keywords-Plus = {PARTICULATE MATTER PM2.5; X-RAY-FLUORESCENCE; AIR-QUALITY;
   CHEMICAL-COMPOSITION; OXIDATIVE STRESS; COMPOSITION DISTRIBUTIONS; GREEN
   INFRASTRUCTURE; SOURCE APPORTIONMENT; HOSPITAL ADMISSIONS; PERSONAL
   EXPOSURES},
Research-Areas = {Environmental Sciences \& Ecology},
Web-of-Science-Categories  = {Environmental Sciences},
Author-Email = {P.Kumar@surrey.ac.uk},
Affiliations = {University of Surrey; Trinity College Dublin; Imperial College London;
   Harvard University; Harvard T.H. Chan School of Public Health; Imperial
   College London; Imperial College London; University of Edinburgh;
   Imperial College London},
ResearcherID-Numbers = {Kumar, Prashant/C-6357-2011
   },
ORCID-Numbers = {Pain, Christopher/0000-0003-4194-2590
   Pinna, Alessandra/0000-0002-4789-5898
   Kumar, Prashant/0000-0002-2462-4411
   Adcock, Ian/0000-0003-2101-8843},
Funding-Acknowledgement = {EPSRC {[}EP/T003189/1]; EPSRC {[}EP/T003189/1] Funding Source: UKRI},
Funding-Text = {This work has been supported by the EPSRC funded project, Health
   assessment across biological length scales for personal pollution
   exposure and its mitigation (INHALE; Grant No. EP/T003189/1).},
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Number-of-Cited-References = {242},
Times-Cited = {27},
Usage-Count-Last-180-days = {8},
Usage-Count-Since-2013 = {66},
Journal-ISO = {Sci. Total Environ.},
Doc-Delivery-Number = {PM0FY},
Web-of-Science-Index = {Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)},
Unique-ID = {WOS:000603487500015},
OA = {Green Published, hybrid},
DA = {2023-04-22},
}

@article{ WOS:000358218600040,
Author = {Jordan, M. I. and Mitchell, T. M.},
Title = {Machine learning: Trends, perspectives, and prospects},
Journal = {SCIENCE},
Year = {2015},
Volume = {349},
Number = {6245, SI},
Pages = {255-260},
Month = {JUL 17},
Abstract = {Machine learning addresses the question of how to build computers that
   improve automatically through experience. It is one of today's most
   rapidly growing technical fields, lying at the intersection of computer
   science and statistics, and at the core of artificial intelligence and
   data science. Recent progress in machine learning has been driven both
   by the development of new learning algorithms and theory and by the
   ongoing explosion in the availability of online data and low-cost
   computation. The adoption of data-intensive machine-learning methods can
   be found throughout science, technology and commerce, leading to more
   evidence-based decision-making across many walks of life, including
   health care, manufacturing, education, financial modeling, policing, and
   marketing.},
Publisher = {AMER ASSOC ADVANCEMENT SCIENCE},
Address = {1200 NEW YORK AVE, NW, WASHINGTON, DC 20005 USA},
Type = {Review},
Language = {English},
Affiliation = {Jordan, MI (Corresponding Author), Univ Calif Berkeley, Dept Elect Engn \& Comp Sci, Dept Stat, Berkeley, CA 94720 USA.
   Jordan, M. I., Univ Calif Berkeley, Dept Elect Engn \& Comp Sci, Dept Stat, Berkeley, CA 94720 USA.
   Mitchell, T. M., Carnegie Mellon Univ, Machine Learning Dept, Pittsburgh, PA 15213 USA.},
DOI = {10.1126/science.aaa8415},
ISSN = {0036-8075},
EISSN = {1095-9203},
Keywords-Plus = {NEURAL-NETWORKS},
Research-Areas = {Science \& Technology - Other Topics},
Web-of-Science-Categories  = {Multidisciplinary Sciences},
Author-Email = {jordan@cs.berkeley.edu
   tom.mitchell@cs.cmu.edu},
Affiliations = {University of California System; University of California Berkeley;
   Carnegie Mellon University},
ResearcherID-Numbers = {Jordan, Michael I/C-5253-2013
   },
ORCID-Numbers = {Jordan, Michael/0000-0001-8935-817X},
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Number-of-Cited-References = {31},
Times-Cited = {2751},
Usage-Count-Last-180-days = {421},
Usage-Count-Since-2013 = {3376},
Journal-ISO = {Science},
Doc-Delivery-Number = {CN1ZB},
Web-of-Science-Index = {Science Citation Index Expanded (SCI-EXPANDED)},
Unique-ID = {WOS:000358218600040},
ESI-Highly-Cited-Paper = {Y},
ESI-Hot-Paper = {N},
DA = {2023-04-22},
}

@article{ WOS:000394541200005,
Author = {Tian, Yong-hong and Chen, Xi-lin and Xiong, Hong-kai and Li, Hong-liang
   and Dai, Li-rong and Chen, Jing and Xing, Jun-liang and Chen, Jing and
   Wu, Xi-hong and Hu, Wei-min and Hu, Yu and Huang, Tie-jun and Gao, Wen},
Title = {Towards human-like and transhuman perception in AI 2.0: a review},
Journal = {FRONTIERS OF INFORMATION TECHNOLOGY \& ELECTRONIC ENGINEERING},
Year = {2017},
Volume = {18},
Number = {1},
Pages = {58-67},
Month = {JAN},
Abstract = {Perception is the interaction interface between an intelligent system
   and the real world. Without sophisticated and flexible perceptual
   capabilities, it is impossible to create advanced artificial
   intelligence (AI) systems. For the next-generation AI, called `AI 2.0',
   one of the most significant features will be that AI is empowered with
   intelligent perceptual capabilities, which can simulate human brain's
   mechanisms and are likely to surpass human brain in terms of
   performance. In this paper, we briefly review the state-of-the-art
   advances across different areas of perception, including visual
   perception, auditory perception, speech perception, and perceptual
   information processing and learning engines. On this basis, we envision
   several R\&D trends in intelligent perception for the forthcoming era of
   AI 2.0, including: (1) human-like and transhuman active vision; (2)
   auditory perception and computation in an actual auditory setting; (3)
   speech perception and computation in a natural interaction setting; (4)
   autonomous learning of perceptual information; (5) large-scale
   perceptual information processing and learning platforms; and (6) urban
   omnidirectional intelligent perception and reasoning engines. We believe
   these research directions should be highlighted in the future plans for
   AI 2.0.},
Publisher = {ZHEJIANG UNIV PRESS},
Address = {Xixi Campus, Zhejiang University, No. 148 Tianmushan Road, Hangzhou,
   Zhejiang, PEOPLES R CHINA},
Type = {Review},
Language = {English},
Affiliation = {Huang, TJ (Corresponding Author), Peking Univ, Sch Elect Engn \& Comp Sci, Beijing 100871, Peoples R China.
   Tian, Yong-hong; Chen, Jing; Wu, Xi-hong; Huang, Tie-jun; Gao, Wen, Peking Univ, Sch Elect Engn \& Comp Sci, Beijing 100871, Peoples R China.
   Chen, Xi-lin, Chinese Acad Sci, Inst Comp Technol, Beijing 100190, Peoples R China.
   Xiong, Hong-kai, Shanghai Jiao Tong Univ, Dept Elect Engn, Shanghai 200240, Peoples R China.
   Li, Hong-liang, Univ Elect Sci \& Technol China, Sch Elect Engn, Chengdu 611730, Peoples R China.
   Dai, Li-rong; Hu, Yu, Univ Sci \& Technol China, Dept Elect Engn \& Informat Sci, Hefei 230027, Peoples R China.
   Hu, Wei-min, Chinese Acad Sci, Inst Automat, Beijing 100190, Peoples R China.
   Chen, Jing, Beijing Inst Technol, Sch Optoelect, Beijing 100081, Peoples R China.},
DOI = {10.1631/FITEE.1601804},
ISSN = {2095-9184},
EISSN = {2095-9230},
Keywords = {Intelligent perception; Active vision; Auditory perception; Speech
   perception; Autonomous learning},
Keywords-Plus = {SPEECH RECOGNITION; PARAMETERS},
Research-Areas = {Computer Science; Engineering},
Web-of-Science-Categories  = {Computer Science, Information Systems; Computer Science, Software
   Engineering; Engineering, Electrical \& Electronic},
Author-Email = {yhtian@pku.edu.cn
   tjhuang@pku.edu.cn},
Affiliations = {Peking University; Chinese Academy of Sciences; Institute of Computing
   Technology, CAS; Shanghai Jiao Tong University; University of Electronic
   Science \& Technology of China; Chinese Academy of Sciences; University
   of Science \& Technology of China, CAS; Chinese Academy of Sciences;
   Institute of Automation, CAS; Beijing Institute of Technology},
ResearcherID-Numbers = {Xing, Junliang/HGE-9630-2022
   },
ORCID-Numbers = {Xing, Junliang/0000-0001-6801-0510
   Xiong, Hongkai/0000-0003-4552-0029},
Funding-Acknowledgement = {Strategic Consulting Research Project of Chinese Academy of Engineering
   {[}2016-ZD-04-03]},
Funding-Text = {Project supported by the Strategic Consulting Research Project of
   Chinese Academy of Engineering (No. 2016-ZD-04-03)},
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Number-of-Cited-References = {51},
Times-Cited = {22},
Usage-Count-Last-180-days = {3},
Usage-Count-Since-2013 = {51},
Journal-ISO = {Front. Inform. Technol. Elect. Eng.},
Doc-Delivery-Number = {EL3TC},
Web-of-Science-Index = {Science Citation Index Expanded (SCI-EXPANDED)},
Unique-ID = {WOS:000394541200005},
OA = {Bronze},
DA = {2023-04-22},
}

@article{ WOS:000434005700036,
Author = {Reynolds, Jonathan and Ahmad, Muhammad Waseem and Rezgui, Yacine},
Title = {Holistic modelling techniques for the operational optimisation of
   multi-vector energy systems},
Journal = {ENERGY AND BUILDINGS},
Year = {2018},
Volume = {169},
Pages = {397-416},
Month = {JUN 15},
Abstract = {Modern district energy systems are highly complex with several
   controllable and uncontrollable variables. To effectively manage a
   multi-vector district requires a holistic perspective in terms of both
   modelling and optimisation. Current district optimisation strategies
   found in the literature often consider very simple models for energy
   generation and conversion technologies. To improve upon the state of the
   art, more realistic and accurate models must be produced whilst
   remaining computationally and mathematically simple enough to calculate
   within short periods. Therefore, this paper provides a comprehensive
   review of modelling techniques for common district energy conversion
   technologies including Power-to Gas. In addition, dynamic building
   modelling techniques are reviewed, as buildings must be considered
   active and flexible participants in a district energy system. In both
   cases, a specific focus is placed on artificial intelligence-based
   models suitable for implementation in the real-time operational
   optimisation of multi-vector systems. Future research directions
   identified from this review include the need to integrate simplified
   models of energy conversion units, energy distribution networks, dynamic
   building models and energy storage into a holistic district optimisation
   framework. Finally, a future district energy management solution is
   proposed. It leverages semantic modelling to allow interoperability of
   heterogeneous data sources to provide added value inferencing from
   contextually enriched information. (C) 2018 The Authors. Published by
   Elsevier B.V.},
Publisher = {ELSEVIER SCIENCE SA},
Address = {PO BOX 564, 1001 LAUSANNE, SWITZERLAND},
Type = {Review},
Language = {English},
Affiliation = {Reynolds, J (Corresponding Author), Cardiff Univ, Sch Engn, BRE Ctr Sustainable Engn, Cardiff CF24 3AA, S Glam, Wales.
   Reynolds, Jonathan; Ahmad, Muhammad Waseem; Rezgui, Yacine, Cardiff Univ, Sch Engn, BRE Ctr Sustainable Engn, Cardiff CF24 3AA, S Glam, Wales.},
DOI = {10.1016/j.enbuild.2018.03.065},
ISSN = {0378-7788},
EISSN = {1872-6178},
Keywords = {Energy modelling; Multi-vector energy systems; Power-to-Gas; Building
   energy modelling; Urban energy systems; Energy management; Optimisation},
Keywords-Plus = {POWER-TO-GAS; ARTIFICIAL NEURAL-NETWORKS; TERM WIND-SPEED; SMALL-SCALE
   CHP; OF-THE-ART; PREDICTIVE CONTROL; HEAT-PUMP; PERFORMANCE PREDICTION;
   SIMULATION PROGRAMS; ELECTRICITY SYSTEM},
Research-Areas = {Construction \& Building Technology; Energy \& Fuels; Engineering},
Web-of-Science-Categories  = {Construction \& Building Technology; Energy \& Fuels; Engineering, Civil},
Author-Email = {ReynoldsJ8@Cardiff.ac.uk
   AhmadM3@Cardiff.ac.uk
   RezguiY@Cardiff.ac.uk},
Affiliations = {Cardiff University},
ResearcherID-Numbers = {Ahmad, Muhammad Waseem/AAL-9468-2021
   Rezgui, Yacine/ABE-6712-2020
   },
ORCID-Numbers = {Rezgui, Yacine/0000-0002-5711-8400
   Ahmad, Muhammad Waseem/0000-0002-7269-4369},
Funding-Acknowledgement = {EPSRC (Engineering and Physical Sciences Research Council); BRE
   (Building Research Establishment); European Commission {[}723562,
   731125]},
Funding-Text = {The authors would like to acknowledge the financial support of EPSRC
   (Engineering and Physical Sciences Research Council) and BRE (Building
   Research Establishment) as well as the European Commission as part of
   the Horizon2020 THERMOSS (Project Id: 723562) and PENTAGON (Project Id:
   731125) projects.},
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Number-of-Cited-References = {159},
Times-Cited = {23},
Usage-Count-Last-180-days = {2},
Usage-Count-Since-2013 = {22},
Journal-ISO = {Energy Build.},
Doc-Delivery-Number = {GH9RD},
Web-of-Science-Index = {Science Citation Index Expanded (SCI-EXPANDED)},
Unique-ID = {WOS:000434005700036},
OA = {hybrid, Green Accepted},
DA = {2023-04-22},
}

@article{ WOS:000674591100001,
Author = {Raita, Yoshihiko and Camargo, Carlos A. and Liang, Liming and Hasegawa,
   Kohei},
Title = {Big Data, Data Science, and Causal Inference: A Primer for Clinicians},
Journal = {FRONTIERS IN MEDICINE},
Year = {2021},
Volume = {8},
Month = {JUL 6},
Abstract = {Clinicians handle a growing amount of clinical, biometric, and biomarker
   data. In this ``big data{''} era, there is an emerging faith that the
   answer to all clinical and scientific questions reside in ``big data{''}
   and that data will transform medicine into precision medicine. However,
   data by themselves are useless. It is the algorithms encoding causal
   reasoning and domain (e.g., clinical and biological) knowledge that
   prove transformative. The recent introduction of (health) data science
   presents an opportunity to re-think this data-centric view. For example,
   while precision medicine seeks to provide the right prevention and
   treatment strategy to the right patients at the right time, its
   realization cannot be achieved by algorithms that operate exclusively in
   data-driven prediction modes, as do most machine learning algorithms.
   Better understanding of data science and its tasks is vital to interpret
   findings and translate new discoveries into clinical practice. In this
   review, we first discuss the principles and major tasks of data science
   by organizing it into three defining tasks: (1) association and
   prediction, (2) intervention, and (3) counterfactual causal inference.
   Second, we review commonly-used data science tools with examples in the
   medical literature. Lastly, we outline current challenges and future
   directions in the fields of medicine, elaborating on how data science
   can enhance clinical effectiveness and inform medical practice. As
   machine learning algorithms become ubiquitous tools to handle
   quantitatively ``big data,{''} their integration with causal reasoning
   and domain knowledge is instrumental to qualitatively transform
   medicine, which will, in turn, improve health outcomes of patients.},
Publisher = {FRONTIERS MEDIA SA},
Address = {AVENUE DU TRIBUNAL FEDERAL 34, LAUSANNE, CH-1015, SWITZERLAND},
Type = {Review},
Language = {English},
Affiliation = {Raita, Y (Corresponding Author), Harvard Med Sch, Massachusetts Gen Hosp, Dept Emergency Med, Boston, MA 02115 USA.
   Raita, Yoshihiko; Camargo, Carlos A.; Liang, Liming; Hasegawa, Kohei, Harvard Med Sch, Massachusetts Gen Hosp, Dept Emergency Med, Boston, MA 02115 USA.
   Camargo, Carlos A., Massachusetts Gen Hosp, Harvard Med Sch, Div Rheumatol Allergy \& Immunol, Dept Med, Boston, MA USA.
   Camargo, Carlos A.; Liang, Liming; Hasegawa, Kohei, Harvard TH Chan Sch Publ Hlth, Dept Epidemiol, Boston, MA USA.
   Liang, Liming; Hasegawa, Kohei, Harvard TH Chan Sch Publ Hlth, Dept Biostat, Boston, MA USA.},
DOI = {10.3389/fmed.2021.678047},
Article-Number = {678047},
EISSN = {2296-858X},
Keywords = {big data; data science; causal inference; the ladder of causation;
   machine learning},
Keywords-Plus = {RESPIRATORY SYNCYTIAL VIRUS; BRONCHIOLITIS; CHILDREN; RISK;
   PREDNISOLONE; ASSOCIATION; MEDICINE; EFFICACY; ASTHMA},
Research-Areas = {General \& Internal Medicine},
Web-of-Science-Categories  = {Medicine, General \& Internal},
Author-Email = {yraita1@mgh.harvard.edu},
Affiliations = {Harvard University; Harvard Medical School; Massachusetts General
   Hospital; Harvard University; Harvard Medical School; Massachusetts
   General Hospital; Harvard University; Harvard T.H. Chan School of Public
   Health; Harvard University; Harvard T.H. Chan School of Public Health},
ResearcherID-Numbers = {Camargo, Carlos A./C-2145-2008},
ORCID-Numbers = {Camargo, Carlos A./0000-0002-5071-7654},
Funding-Acknowledgement = {National Institutes of Health (Bethesda, MD) {[}R01 AI-127507, R01
   AI-134940, R01 AI-137091, R01 AI-148338, UG3/UH3 OD-023253]},
Funding-Text = {This work was supported by grants (R01 AI-127507, R01 AI-134940, R01
   AI-137091, R01 AI-148338, and UG3/UH3 OD-023253) from the National
   Institutes of Health (Bethesda, MD). The funding organization was not
   involved in the conception, preparation or approval of the manuscript,
   or decision to submit the manuscript for publication.},
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Number-of-Cited-References = {63},
Times-Cited = {8},
Usage-Count-Last-180-days = {6},
Usage-Count-Since-2013 = {28},
Journal-ISO = {Front. Med.},
Doc-Delivery-Number = {TL1CF},
Web-of-Science-Index = {Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)},
Unique-ID = {WOS:000674591100001},
OA = {Green Published, gold},
DA = {2023-04-22},
}

@article{ WOS:000794463500001,
Author = {Giuffrida, Nadia and Fajardo-Calderin, Jenny and Masegosa, Antonio D.
   and Werner, Frank and Steudter, Margarete and Pilla, Francesco},
Title = {Optimization and Machine Learning Applied to Last-Mile Logistics: A
   Review},
Journal = {SUSTAINABILITY},
Year = {2022},
Volume = {14},
Number = {9},
Month = {MAY},
Abstract = {The growth in e-commerce that our society has faced in recent years is
   changing the view companies have on last-mile logistics, due to its
   increasing impact on the whole supply chain. New technologies are
   raising users' expectations with the need to develop customized delivery
   experiences; moreover, increasing pressure on supply chains has also
   created additional challenges for suppliers. At the same time, this
   phenomenon generates an increase in the impact on the liveability of our
   cities, due to traffic congestion, the occupation of public spaces, and
   the environmental and acoustic pollution linked to urban logistics. In
   this context, the optimization of last-mile deliveries is an imperative
   not only for companies with parcels that need to be delivered in the
   urban areas, but also for public administrations that want to guarantee
   a good quality of life for citizens. In recent years, many scholars have
   focused on the study of logistics optimization techniques and, in
   particular, the last mile. In addition to traditional optimization
   techniques, linked to the disciplines of operations research, the recent
   advances in the use of sensors and IoT, and the consequent large amount
   of data that derives from it, are pushing towards a greater use of big
   data and analytics techniques-such as machine learning and artificial
   intelligence-which are also in this sector. Based on this premise, the
   aim of this work is to provide an overview of the most recent literature
   advances related to last-mile delivery optimization techniques; this is
   to be used as a baseline for scholars who intend to explore new
   approaches and techniques in the study of last-mile logistics
   optimization. A bibliometric analysis and a critical review were
   conducted in order to highlight the main studied problems, the
   algorithms used, and the case studies. The results from the analysis
   allow the studies to be clustered into traditional optimization models,
   machine learning approaches, and mixed methods. The main research gaps
   and limitations of the current literature are assessed in order to
   identify unaddressed challenges and provide research suggestions for
   future approaches.},
Publisher = {MDPI},
Address = {ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND},
Type = {Review},
Language = {English},
Affiliation = {Giuffrida, N (Corresponding Author), Univ Coll Dublin, Sch Architecture Planning \& Environm Policy, Richview Campus, Dublin D04 V1W8, Ireland.
   Giuffrida, Nadia; Pilla, Francesco, Univ Coll Dublin, Sch Architecture Planning \& Environm Policy, Richview Campus, Dublin D04 V1W8, Ireland.
   Fajardo-Calderin, Jenny; Masegosa, Antonio D., Univ Deusto, Fac Engn, DeustoTech, Av Univ 24, Bilbao 48007, Spain.
   Werner, Frank; Steudter, Margarete, Software AG, Altenkesseler Str 17, D-66115 Saarbrucken, Germany.},
DOI = {10.3390/su14095329},
Article-Number = {5329},
EISSN = {2071-1050},
Keywords = {city logistics; freight transport; vehicle routing problem},
Keywords-Plus = {VEHICLE-ROUTING PROBLEM; VARIABLE NEIGHBORHOOD SEARCH; TIME WINDOWS;
   SIMULTANEOUS PICKUP; ANOMALY DETECTION; META-HEURISTICS; ALGORITHM;
   DELIVERY; VRP},
Research-Areas = {Science \& Technology - Other Topics; Environmental Sciences \& Ecology},
Web-of-Science-Categories  = {Green \& Sustainable Science \& Technology; Environmental Sciences;
   Environmental Studies},
Author-Email = {nadia.giuffrida@ucd.ie
   fajardo.jenny@deusto.es
   ad.masegosa@deusto.es
   frank.werner@softwareag.com
   margarete.steudter@softwareag.com
   francesco.pilla@ucd.ie},
Affiliations = {University College Dublin; University of Deusto},
ResearcherID-Numbers = {Masegosa, Antonio D./E-3302-2012
   Giuffrida, Nadia/V-6875-2018},
ORCID-Numbers = {Pilla, Francesco/0000-0002-1535-1239
   Masegosa, Antonio D./0000-0001-7759-9072
   Fajardo Calderin, Jenny/0000-0002-9355-3610
   Giuffrida, Nadia/0000-0002-4446-8292},
Funding-Acknowledgement = {European Commission {[}861,540]},
Funding-Text = {This article was partially funded by the European Commission through the
   SENATOR project (H2020MG-2018-2020, RIA, project n. 861,540).},
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Number-of-Cited-References = {83},
Times-Cited = {5},
Usage-Count-Last-180-days = {29},
Usage-Count-Since-2013 = {58},
Journal-ISO = {Sustainability},
Doc-Delivery-Number = {1E4LZ},
Web-of-Science-Index = {Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)},
Unique-ID = {WOS:000794463500001},
OA = {gold},
DA = {2023-04-22},
}

@article{ WOS:000781297500001,
Author = {Rosario, Alberico Travassos and Dias, Joana Carmo},
Title = {Sustainability and the Digital Transition: A Literature Review},
Journal = {SUSTAINABILITY},
Year = {2022},
Volume = {14},
Number = {7},
Month = {APR},
Abstract = {The digital transition processes have demonstrated an enormous capacity
   to develop and implement sustainable solutions, which allow solving
   several problems such as poverty, high rates of species extinction and
   lack of equal opportunity. However, little attention is paid to the
   connection between the digital transition and sustainability. Thus, a
   systematic bibliometric literature review was developed to fill this
   knowledge gap and demonstrate the potential contributions of the digital
   transition to environmental, economic, and social sustainability
   aspects. In environmental sustainability, the digital transition
   involves the application of technologies such as Artificial Intelligence
   (AI), big data analytics, Internet of Things (IoT), and mobile
   technologies that are used to develop and implement sustainability
   solutions in areas such as sustainable urban development, sustainable
   production, and pollution control. In economic sustainability, emerging
   digital technologies can drive transformation into a more sustainable
   circular economy, the digital sharing economy, and establish sustainable
   manufacturing and infrastructure design. In the digital transition to
   social sustainability, the studies analyzed demonstrate the need for
   multidimensional policy perspectives to address the current digital
   divide. For effective management of the digital transition that achieves
   sustainability goals, the study discusses alternative approaches that
   include innovation through experimentation and dynamic and sustainable
   advantages achievable through temporary benefits.},
Publisher = {MDPI},
Address = {ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND},
Type = {Review},
Language = {English},
Affiliation = {Dias, JC (Corresponding Author), Univ Europeia, Ctr Invest Org Mercados \& Gestao Ind COMEGI, P-1200649 Lisbon, Portugal.
   Rosario, Alberico Travassos, Univ Europeia, Res Unit Governance Competitiveness \& Publ Polici, P-1200649 Lisbon, Portugal.
   Dias, Joana Carmo, Univ Europeia, Ctr Invest Org Mercados \& Gestao Ind COMEGI, P-1200649 Lisbon, Portugal.},
DOI = {10.3390/su14074072},
Article-Number = {4072},
EISSN = {2071-1050},
Keywords = {sustainability; digital transition; sustainable development; systematic
   bibliometric literature review (LRSB)},
Keywords-Plus = {TECHNOLOGIES},
Research-Areas = {Science \& Technology - Other Topics; Environmental Sciences \& Ecology},
Web-of-Science-Categories  = {Green \& Sustainable Science \& Technology; Environmental Sciences;
   Environmental Studies},
Author-Email = {alberico@ua.pt
   joana.carmo.dias@universidadeeuropeia.pt},
Affiliations = {Universidade Europeia; Universidade Europeia},
ResearcherID-Numbers = {Rosário, Albérico/AAT-7696-2020
   },
ORCID-Numbers = {Rosário, Albérico/0000-0003-4793-4110
   Carmo Dias, Joana/0000-0001-5900-2121},
Funding-Acknowledgement = {FCT-Fundacao para a Ciencia e a Tecnologia, I.P. {[}UIDB/04005/2020]},
Funding-Text = {This research is supported by national funding's of FCT-Fundacao para a
   Ciencia e a Tecnologia, I.P., in the project << UIDB/04005/2020 >>.},
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Number-of-Cited-References = {52},
Times-Cited = {11},
Usage-Count-Last-180-days = {92},
Usage-Count-Since-2013 = {161},
Journal-ISO = {Sustainability},
Doc-Delivery-Number = {0L2GC},
Web-of-Science-Index = {Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)},
Unique-ID = {WOS:000781297500001},
OA = {gold, Green Published, Green Submitted},
DA = {2023-04-22},
}

@article{ WOS:000515446900002,
Author = {Sohail, Ayesha and Arif, Fatima},
Title = {Supervised and unsupervised algorithms for bioinformatics and data
   science},
Journal = {PROGRESS IN BIOPHYSICS \& MOLECULAR BIOLOGY},
Year = {2020},
Volume = {151},
Pages = {14-22},
Month = {MAR},
Abstract = {Bioinformatics refers to an ever evolving huge field of research based
   on millions of algorithms, designated to several data banks. Such
   algorithms are either supervised or unsupervised. In this article, a
   detailed overview of the supervised and unsupervised techniques is
   presented with the aid of examples. The aim of this article is to
   provide the readers with the basic understanding of the state of the art
   models, which are key ingredients of explainable machine learning in the
   field of bioinformatics. (C) 2019 Elsevier Ltd. All rights reserved.},
Publisher = {PERGAMON-ELSEVIER SCIENCE LTD},
Address = {THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND},
Type = {Review},
Language = {English},
Affiliation = {Sohail, A (Corresponding Author), Comsats Univ Islamabad, Dept Math, Lahore Campus, Islamabad 54000, Pakistan.
   Sohail, Ayesha; Arif, Fatima, Comsats Univ Islamabad, Dept Math, Lahore Campus, Islamabad 54000, Pakistan.},
DOI = {10.1016/j.pbiomolbio.2019.11.012},
ISSN = {0079-6107},
EISSN = {1873-1732},
Keywords = {Machine learning; Evolutionary bioinformatics; Support vector machine
   learning; Algorithms},
Keywords-Plus = {SUPPORT VECTOR MACHINE; GENE-EXPRESSION DATA; DNA MICROARRAYS;
   CLASSIFICATION; MODEL; CLASSIFIERS},
Research-Areas = {Biochemistry \& Molecular Biology; Biophysics},
Web-of-Science-Categories  = {Biochemistry \& Molecular Biology; Biophysics},
Author-Email = {ayeshasohail81@gmail.com},
Affiliations = {COMSATS University Islamabad (CUI)},
ResearcherID-Numbers = {Sohail, Ayesha/AAP-8462-2021},
ORCID-Numbers = {Sohail, Ayesha/0000-0001-6835-6212},
Funding-Acknowledgement = {HEC/PSF/ICT {[}PSF/ILP/P-HIT/Engg (085)]},
Funding-Text = {This research was partially supported by HEC/PSF/ICT/Project number
   PSF/ILP/P-HIT/Engg (085) titled Design Improvement of High Torque
   Low-Speed Diesel Engine (Phase -1).},
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Number-of-Cited-References = {59},
Times-Cited = {18},
Usage-Count-Last-180-days = {12},
Usage-Count-Since-2013 = {39},
Journal-ISO = {Prog. Biophys. Mol. Biol.},
Doc-Delivery-Number = {KO3KI},
Web-of-Science-Index = {Science Citation Index Expanded (SCI-EXPANDED)},
Unique-ID = {WOS:000515446900002},
DA = {2023-04-22},
}

@article{ WOS:000854198700001,
Author = {Liu, Yusha and Yang, Kun},
Title = {Communication, sensing, computing and energy harvesting in smart cities},
Journal = {IET SMART CITIES},
Year = {2022},
Volume = {4},
Number = {4},
Pages = {265-274},
Month = {DEC},
Abstract = {A smart city provides diverse services based on real-time data obtained
   from different devices deployed in urban areas. These devices are
   largely battery-powered and widely placed. Therefore, providing
   continuous energy to these devices and ensuring their efficient sensing
   and communications are critical for the wide deployment of smart cities.
   To achieve frequent and effective data exchange, advanced enabling
   information and communication technology (ICT) infrastructure is in
   urgent demand. An ideal network in future smart cities should be capable
   of sensing the physical environment and intelligently mapping the
   digital world. Therefore, in this paper, we propose design guidelines on
   how to integrate communications with sensing, computing and/or energy
   harvesting in the context of smart cities, aiming to offer research
   insights on developing integrated communications, sensing, computing and
   energy harvesting (ICSCE) for promoting the development ICT
   infrastructure in smart cities. To put these four pillars of smart
   cities together and to take advantage of ever-increasing artificial
   intelligence (AI) technologies, the authors propose a promising
   AI-enabled ICSCE architecture by leveraging the digital twin network.
   The proposed architecture models the physical deep neural network-aided
   ICSCE system in a virtual space, where offline training is performed by
   using the collected real-time data from the environment and physical
   devices.},
Publisher = {WILEY},
Address = {111 RIVER ST, HOBOKEN 07030-5774, NJ USA},
Type = {Review},
Language = {English},
Affiliation = {Yang, K (Corresponding Author), Univ Essex, Sch Comp Sci \& Elect Engn, Colchester, Essex, England.
   Liu, Yusha; Yang, Kun, Univ Elect Sci \& Technol China, Sch Informat \& Commun Engn, Chengdu, Peoples R China.
   Yang, Kun, Univ Essex, Sch Comp Sci \& Elect Engn, Colchester, Essex, England.},
DOI = {10.1049/smc2.12041},
EarlyAccessDate = {SEP 2022},
EISSN = {2631-7680},
Keywords = {Energy harvesting; IoT and mobile communications; networks and
   telematics; smart cities},
Keywords-Plus = {UNMANNED AERIAL VEHICLES; JOINT COMMUNICATION; RESOURCE-ALLOCATION;
   WIRELESS; DEPLOYMENT; PLACEMENT; NETWORKS; CITY},
Research-Areas = {Computer Science; Engineering; Telecommunications},
Web-of-Science-Categories  = {Computer Science, Information Systems; Engineering, Electrical \&
   Electronic; Telecommunications},
Author-Email = {kunyang@essex.ac.uk},
Affiliations = {University of Electronic Science \& Technology of China; University of
   Essex},
Funding-Acknowledgement = {Department of Science and Technology of Sichuan Province {[}22QYCX0168];
   Ministry of Science and Technology of the People's Republic of China
   {[}2021YFB2900204]; National Natural Science Foundation of China
   {[}62132004]},
Funding-Text = {Department of Science and Technology of Sichuan Province, Grant/Award
   Number: 22QYCX0168; Ministry of Science and Technology of the People's
   Republic of China, Grant/Award Number: 2021YFB2900204; National Natural
   Science Foundation of China, Grant/Award Number: 62132004},
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Number-of-Cited-References = {40},
Times-Cited = {0},
Usage-Count-Last-180-days = {7},
Usage-Count-Since-2013 = {10},
Journal-ISO = {IET Smart Cities},
Doc-Delivery-Number = {6U3ZF},
Web-of-Science-Index = {Emerging Sources Citation Index (ESCI)},
Unique-ID = {WOS:000854198700001},
OA = {Green Published},
DA = {2023-04-22},
}

@article{ WOS:000526928500002,
Author = {McClements, David Julian},
Title = {Future foods: a manifesto for research priorities in structural design
   of foods},
Journal = {FOOD \& FUNCTION},
Year = {2020},
Volume = {11},
Number = {3},
Pages = {1933-1945},
Month = {MAR 1},
Abstract = {A number of major challenges facing modern society are related to the
   food supply. As the global population grows, it will be critical to feed
   everyone without damaging the environment. Advances in biotechnology,
   nanotechnology, structural design, and artificial intelligence are
   providing farmers and food manufacturers will new tools to address these
   problems. More and more people are migrating from rural to urban
   environments, leading to a change in their dietary habits, especially
   increasing consumption of animal-based products and highly-processed
   foods. Animal-based foods lead to more greenhouse gas production, land
   use, water use, and pollution than plant-based ones. Moreover, many
   animal-based and highly-processed foods have adverse effects on human
   health and wellbeing. Consumers are therefore being encouraged to
   consume more plant-based foods, such as fruits, vegetables, cereals, and
   legumes. Many people, however, do not have the time, money, or
   inclination to prepare foods from fresh produce. Consequently, there is
   a need for the food industry to create a new generation of processed
   foods that are desirable, tasty, inexpensive, and convenient, but that
   are also healthy and sustainable. This article highlights some of the
   main food-related challenges faced by modern society and how scientists
   are developing innovative technologies to address them.},
Publisher = {ROYAL SOC CHEMISTRY},
Address = {THOMAS GRAHAM HOUSE, SCIENCE PARK, MILTON RD, CAMBRIDGE CB4 0WF, CAMBS,
   ENGLAND},
Type = {Review},
Language = {English},
Affiliation = {McClements, DJ (Corresponding Author), Univ Massachusetts, Dept Food Sci, Amherst, MA 01003 USA.
   McClements, David Julian, Univ Massachusetts, Dept Food Sci, Amherst, MA 01003 USA.},
DOI = {10.1039/c9fo02076d},
ISSN = {2042-6496},
EISSN = {2042-650X},
Keywords-Plus = {SLOWLY DIGESTIBLE STARCH; DELIVERY-SYSTEMS; RESISTANT STARCH; GUT
   MICROBIOTA; DIETARY FIBER; CULTURED MEAT; REDUCED-FAT; EMULSIONS;
   SAFETY; BIOAVAILABILITY},
Research-Areas = {Biochemistry \& Molecular Biology; Food Science \& Technology},
Web-of-Science-Categories  = {Biochemistry \& Molecular Biology; Food Science \& Technology},
Author-Email = {mcclements@foodsci.umass.edu},
Affiliations = {University of Massachusetts System; University of Massachusetts Amherst},
ResearcherID-Numbers = {McClements, David J/F-8283-2011},
ORCID-Numbers = {McClements, David J/0000-0002-9016-1291},
Funding-Acknowledgement = {National Institute of Food and Agriculture, USDA, Massachusetts
   Agricultural Experiment Station {[}MAS00491]; USDA, AFRI {[}2016-08782]},
Funding-Text = {This material was partly based upon work supported by the National
   Institute of Food and Agriculture, USDA, Massachusetts Agricultural
   Experiment Station (MAS00491) and USDA, AFRI Grants (2016-08782).},
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Number-of-Cited-References = {103},
Times-Cited = {31},
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Journal-ISO = {Food Funct.},
Doc-Delivery-Number = {LE7UH},
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Unique-ID = {WOS:000526928500002},
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@article{ WOS:000579994300001,
Author = {Huang, Yingjing and Fei, Teng and Kwan, Mei-Po and Kang, Yuhao and Li,
   Jun and Li, Yizhuo and Li, Xiang and Bian, Meng},
Title = {GIS-Based Emotional Computing: A Review of Quantitative Approaches to
   Measure the Emotion Layer of Human-Environment Relationships},
Journal = {ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION},
Year = {2020},
Volume = {9},
Number = {9},
Month = {SEP},
Abstract = {In recent years, with the growing accessibility of abundant contextual
   emotion information, which is benefited by the numerous georeferenced
   user-generated content and the maturity of artificial intelligence
   (AI)-based emotional computing technics, the emotion layer of
   human-environment relationship is proposed for enriching traditional
   methods of various related disciplines such as urban planning. This
   paper proposes the geographic information system (GIS)-based emotional
   computing concept, which is a novel framework for applying GIS methods
   to collective human emotion. The methodology presented in this paper
   consists of three key steps: (1) collecting georeferenced data
   containing emotion and environment information such as social media and
   official sites, (2) detecting emotions using AI-based emotional
   computing technics such as natural language processing (NLP) and
   computer vision (CV), and (3) visualizing and analyzing the
   spatiotemporal patterns with GIS tools. This methodology is a great
   synergy of multidisciplinary cutting-edge techniques, such as GIScience,
   sociology, and computer science. Moreover, it can effectively and deeply
   explore the connection between people and their surroundings with the
   help of GIS methods. Generally, the framework provides a standard
   workflow to calculate and analyze the new information layer for
   researchers, in which a measured human-centric perspective onto the
   environment is possible.},
Publisher = {MDPI},
Address = {ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND},
Type = {Review},
Language = {English},
Affiliation = {Fei, T (Corresponding Author), Wuhan Univ, Sch Resource \& Environm Sci, Wuhan 430000, Peoples R China.
   Huang, Yingjing; Fei, Teng; Li, Jun; Li, Yizhuo, Wuhan Univ, Sch Resource \& Environm Sci, Wuhan 430000, Peoples R China.
   Kwan, Mei-Po, Chinese Univ Hong Kong, Dept Geog \& Resource Management, Shatin, Hong Kong, Peoples R China.
   Kwan, Mei-Po, Chinese Univ Hong Kong, Inst Space \& Earth Informat Sci, Shatin, Hong Kong, Peoples R China.
   Kwan, Mei-Po, Univ Utrecht, Dept Human Geog \& Spatial Planning, NL-3584 CB Utrecht, Netherlands.
   Kang, Yuhao, Univ Wisconsin, Dept Geog, Geospatial Data Sci Lab, Madison, WI 53706 USA.
   Li, Xiang, Informat Engn Univ, Inst Surveying \& Mapping, Zhengzhou 450000, Peoples R China.
   Bian, Meng, Wuhan Univ, Sch Remote Sensing \& Informat Engn, Wuhan 430000, Peoples R China.},
DOI = {10.3390/ijgi9090551},
Article-Number = {551},
EISSN = {2220-9964},
Keywords = {human-environment relationship; collective emotion; GIS-based emotional
   computing},
Keywords-Plus = {LIFE SATISFACTION; FACIAL EXPRESSIONS; NEGATIVE AFFECT; DEPRESSED MOOD;
   AIR-POLLUTION; PERCEPTIONS; TWITTER; SCALE; SENSE; CONNECTEDNESS},
Research-Areas = {Computer Science; Physical Geography; Remote Sensing},
Web-of-Science-Categories  = {Computer Science, Information Systems; Geography, Physical; Remote
   Sensing},
Author-Email = {huangyingjing@whu.edu.cn
   feiteng@whu.edu.cn
   mpkwan@cuhk.edu.hk
   yuhao.kang@wisc.edu
   leejun@whu.edu.cn
   liyizhuo@whu.edu.cn
   lixiangzzchxy@163.com
   bian@whu.edu.cn},
Affiliations = {Wuhan University; Chinese University of Hong Kong; Chinese University of
   Hong Kong; Utrecht University; University of Wisconsin System;
   University of Wisconsin Madison; PLA Information Engineering University;
   Wuhan University},
ResearcherID-Numbers = {Kwan, Mei-Po/S-4162-2016
   Huang, Yingjing/AAX-1361-2021
   Kang, Yuhao/U-2821-2019
   fei, teng/AAF-5448-2021},
ORCID-Numbers = {Kwan, Mei-Po/0000-0001-8602-9258
   Huang, Yingjing/0000-0002-8772-1403
   Kang, Yuhao/0000-0003-3810-9450
   fei, teng/0000-0002-3415-1654},
Funding-Acknowledgement = {State Laboratory of Information Engineering in Surveying, Mapping and
   Remote Sensing, Wuhan University {[}19E02]},
Funding-Text = {This work is supported by Open Fund of State Laboratory of Information
   Engineering in Surveying, Mapping and Remote Sensing, Wuhan University
   (Grant No. 19E02).},
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Number-of-Cited-References = {123},
Times-Cited = {11},
Usage-Count-Last-180-days = {20},
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Journal-ISO = {ISPRS Int. Geo-Inf.},
Doc-Delivery-Number = {OD6XN},
Web-of-Science-Index = {Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)},
Unique-ID = {WOS:000579994300001},
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@article{ WOS:000955359500001,
Author = {Albahra, Samer and Gorbett, Tom and Robertson, Scott and D'Aleo, Giana
   and Ockunzzi, Samuel and Lallo, Daniel and Hu, Bo and Rashidi, Hooman H.},
Title = {Artificial intelligence and machine learning overview in pathology \&
   laboratory medicine: A general review of data preprocessing and basic
   supervised concepts},
Journal = {SEMINARS IN DIAGNOSTIC PATHOLOGY},
Year = {2023},
Volume = {40},
Number = {2},
Pages = {71-87},
Month = {MAR},
Abstract = {Machine learning (ML) is becoming an integral aspect of several domains
   in medicine. Yet, most pathologists and laboratory professionals remain
   unfamiliar with such tools and are unprepared for their inevitable
   integration. To bridge this knowledge gap, we present an overview of key
   elements within this emerging data science discipline. First, we will
   cover general, well-established concepts within ML, such as data type
   concepts, data preprocessing methods, and ML study design. We will
   describe common supervised and unsupervised learning algorithms and
   their associated common machine learning terms (provided within a
   comprehensive glossary of terms that are discussed within this review).
   Overall, this review will offer a broad overview of the key concepts and
   algorithms in machine learning, with a focus on pathology and laboratory
   medicine. The objective is to provide an updated useful reference for
   those new to this field or those who require a refresher.},
Publisher = {W B SAUNDERS CO-ELSEVIER INC},
Address = {1600 JOHN F KENNEDY BOULEVARD, STE 1800, PHILADELPHIA, PA 19103-2899 USA},
Type = {Review},
Language = {English},
Affiliation = {Albahra, S; Rashidi, HH (Corresponding Author), 9500 Euclid Ave, Cleveland, OH 44195 USA.
   Albahra, Samer; Gorbett, Tom; Robertson, Scott; D'Aleo, Giana; Ockunzzi, Samuel; Lallo, Daniel; Rashidi, Hooman H., Cleveland Clin, Pathol \& Lab Med Inst PLMI, Cleveland, OH USA.
   Hu, Bo, Cleveland Clin, Dept Quantitat Hlth Sci, Cleveland, OH USA.
   Albahra, Samer; Gorbett, Tom; Robertson, Scott; D'Aleo, Giana; Ockunzzi, Samuel; Lallo, Daniel; Hu, Bo; Rashidi, Hooman H., Cleveland Clin, PLMIs Ctr Artificial Intelligence \& Data Sci, Cleveland Hts, OH USA.
   Albahra, Samer; Rashidi, Hooman H., 9500 Euclid Ave, Cleveland, OH 44195 USA.},
DOI = {10.1053/j.semdp.2023.02.002},
EarlyAccessDate = {MAR 2023},
ISSN = {0740-2570},
EISSN = {1930-1111},
Keywords = {Artificial intelligence; Machine learning; Pathology; Laboratory
   medicine; Supervised; Learning; Predictive modeling},
Keywords-Plus = {STATISTICS},
Research-Areas = {Medical Laboratory Technology; Pathology},
Web-of-Science-Categories  = {Medical Laboratory Technology; Pathology},
Author-Email = {albahrs@ccf.org
   rashidh@ccf.org},
Affiliations = {Cleveland Clinic Foundation; Cleveland Clinic Foundation; Cleveland
   Clinic Foundation},
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Number-of-Cited-References = {54},
Times-Cited = {0},
Usage-Count-Last-180-days = {3},
Usage-Count-Since-2013 = {3},
Journal-ISO = {Semin. Diagn. Pathol.},
Doc-Delivery-Number = {A5ET2},
Web-of-Science-Index = {Science Citation Index Expanded (SCI-EXPANDED)},
Unique-ID = {WOS:000955359500001},
OA = {hybrid},
DA = {2023-04-22},
}

@article{ WOS:000518682300001,
Author = {Ruehle, Fabian},
Title = {Data science applications to string theory},
Journal = {PHYSICS REPORTS-REVIEW SECTION OF PHYSICS LETTERS},
Year = {2020},
Volume = {839},
Pages = {1-117},
Month = {JAN 21},
Abstract = {We first introduce various algorithms and techniques for machine
   learning and data science. While there is a strong focus on neural
   network applications in unsupervised, supervised and reinforcement
   learning, other machine learning techniques are discussed as well. These
   include various clustering and anomaly detection algorithms, support
   vector machines, and decision trees. In addition, we review data science
   techniques such as genetic algorithms and topological data analysis.
   This first part of the review makes some reference to concepts in
   physics, but the explanations and examples do not assume any knowledge
   of string theory and should therefore be accessible to a wide variety of
   readers with a physics background. After that, we illustrate
   applications to string theory. We give an overview of existing string
   theory data sets and describe how they can be studied using data science
   techniques. We also explain the computational complexity involved in the
   investigation of string vacua. Example codes that illustrate the
   techniques introduced in this review are available from Fabian Ruehle
   (0000). (C) 2020 The Author. Published by Elsevier B.V.},
Publisher = {ELSEVIER},
Address = {RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS},
Type = {Review},
Language = {English},
Affiliation = {Ruehle, F (Corresponding Author), CERN, Theoret Phys Dept, 1 Esplanade Particules, CH-1211 Geneva 23, Switzerland.
   Ruehle, Fabian, CERN, Theoret Phys Dept, 1 Esplanade Particules, CH-1211 Geneva 23, Switzerland.
   Ruehle, Fabian, Univ Oxford, Dept Phys, Clarendon Lab, Rudolf Peierls Ctr Theoret Phys, Parks Rd, Oxford OX1 3PU, England.},
DOI = {10.1016/j.physrep.2019.09.005},
ISSN = {0370-1573},
EISSN = {1873-6270},
Keywords-Plus = {NEURAL-NETWORKS; LEARNING ALGORITHM; CLASSIFICATION; MODELS; DYNAMICS;
   GAME; GO},
Research-Areas = {Physics},
Web-of-Science-Categories  = {Physics, Multidisciplinary},
Author-Email = {fabian.ruehle@cern.ch},
Affiliations = {European Organization for Nuclear Research (CERN); University of Oxford},
ORCID-Numbers = {Ruehle, Fabian/0000-0002-8409-9823},
Funding-Acknowledgement = {EPSRC, UK network grant {[}EP/N007158/1]; ICTP Trieste; Microsoft
   Research; BCTP at Bonn University; Simons Center for Geometry and
   Physics, USA; Banff International Research Station; Casa Matematica
   Oaxaca; Universidad Nacional Autonoma de Mexico; University of
   Pennsylvania; Northeastern University; Aspen Center for Physics; EPSRC
   {[}EP/N007158/1] Funding Source: UKRI},
Funding-Text = {I also thank the EPSRC, UK network grant EP/N007158/1 for support during
   the time I started to work on machine learning, and the ICTP Trieste,
   Microsoft Research, the BCTP at Bonn University, and the Simons Center
   for Geometry and Physics, USA for financial support for schools and
   conferences we organized on machine learning. I am furthermore thankful
   for support and hospitality by the Banff International Research Station
   and the Casa Matematica Oaxaca, Universidad Nacional Autonoma de Mexico,
   University of Pennsylvania, Northeastern University, and the Aspen
   Center for Physics, where part of this work was carried out.},
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Number-of-Cited-References = {224},
Times-Cited = {48},
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Journal-ISO = {Phys. Rep.-Rev. Sec. Phys. Lett.},
Doc-Delivery-Number = {KT0DY},
Web-of-Science-Index = {Science Citation Index Expanded (SCI-EXPANDED)},
Unique-ID = {WOS:000518682300001},
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DA = {2023-04-22},
}

@article{ WOS:000679379300007,
Author = {Huang, Di and Wang, Shuaian and Liu, Zhiyuan},
Title = {A systematic review of prediction methods for emergency management},
Journal = {INTERNATIONAL JOURNAL OF DISASTER RISK REDUCTION},
Year = {2021},
Volume = {62},
Month = {AUG},
Abstract = {With the trend of global warming and destructive human activities, the
   frequent occurrences of catastrophes have posed devastating threats to
   human life and social stability worldwide. The emergency management (EM)
   system plays a significant role in saving people's lives and reducing
   property damage. The prediction system for the occurrence of emergency
   events and resulting impacts is widely recognized as the first stage of
   the EM system, the accuracy of which has a significant impact on the
   efficiency of resource allocation, dispatching, and evacuation. In fact,
   the number and variety of contributions to prediction techniques, such
   as statistic analysis, artificial intelligence, and simulation method,
   are exploded in recent years, motivating the need for a systematic
   analysis of the current works on disaster prediction. To this end, this
   paper presents a systematic review of contributions on prediction
   methods for emergency occurrence and resource demand of both natural and
   manmade disasters. Through a detailed discussion on the features of each
   type of emergency event, this paper presents a comprehensive survey of
   state-of-the-art prediction technologies which have been widely applied
   in EM. After that, we summarize the challenges of current efforts and
   point out future directions.},
Publisher = {ELSEVIER},
Address = {RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS},
Type = {Review},
Language = {English},
Affiliation = {Liu, ZY (Corresponding Author), Southeast Univ, Sch Transportat, Jiangsu Prov Collaborat Innovat Ctr Modern Urban, Jiangsu Key Lab Urban ITS, Nanjing, Peoples R China.
   Huang, Di; Liu, Zhiyuan, Southeast Univ, Sch Transportat, Jiangsu Prov Collaborat Innovat Ctr Modern Urban, Jiangsu Key Lab Urban ITS, Nanjing, Peoples R China.
   Huang, Di; Wang, Shuaian, Hong Kong Polytech Univ, Dept Logist \& Maritime Studies, Hung Hom, Hong Kong, Peoples R China.},
DOI = {10.1016/j.ijdrr.2021.102412},
EarlyAccessDate = {JUN 2021},
Article-Number = {102412},
ISSN = {2212-4209},
Keywords = {Emergency management system; Disaster; Prediction methods; Resource
   demand; Artificial intelligence; Review},
Keywords-Plus = {ARTIFICIAL NEURAL-NETWORKS; SUPPORT VECTOR MACHINE; BIG-DATA; SPATIAL
   PREDICTION; DEMAND PREDICTION; DECISION-MAKING; TIME-SERIES; EARTHQUAKE;
   MODELS; RISK},
Research-Areas = {Geology; Meteorology \& Atmospheric Sciences; Water Resources},
Web-of-Science-Categories  = {Geosciences, Multidisciplinary; Meteorology \& Atmospheric Sciences;
   Water Resources},
Author-Email = {zhiyuanl@seu.edu.cn},
Affiliations = {Southeast University - China; Hong Kong Polytechnic University},
Funding-Acknowledgement = {National Key R\&D Program of China {[}2018YFE0102700]},
Funding-Text = {This study is supported by the National Key R\&D Program of China
   (Project No. 2018YFE0102700) .},
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@article{ WOS:000572537200011,
Author = {Liu, Yingli and Niu, Chen and Wang, Zhuo and Gan, Yong and Zhu, Yan and
   Sun, Shuhong and Shen, Tao},
Title = {Machine learning in materials genome initiative: A review},
Journal = {JOURNAL OF MATERIALS SCIENCE \& TECHNOLOGY},
Year = {2020},
Volume = {57},
Pages = {113-122},
Month = {NOV 15},
Abstract = {Discovering new materials with excellent performance is a hot issue in
   the materials genome initiative. Traditional experiments and
   calculations often waste large amounts of time and money and are also
   limited by various conditions. Therefore, it is imperative to develop a
   new method to accelerate the discovery and design of new materials. In
   recent years, material discovery and design methods using machine
   learning have attracted much attention from material experts and have
   made some progress. This review first outlines available materials
   database and material data analytics tools and then elaborates on the
   machine learning algorithms used in materials science. Next, the field
   of application of machine learning in materials science is summarized,
   focusing on the aspects of structure determination, performance
   prediction, fingerprint prediction, and new material discovery. Finally,
   the review points out the problems of data and machine learning in
   materials science and points to future research. Using machine learning
   algorithms, the authors hope to achieve amazing results in material
   discovery and design. (C) 2020 Published by Elsevier Ltd on behalf of
   The editorial office of Journal of Materials Science \& Technology.},
Publisher = {JOURNAL MATER SCI TECHNOL},
Address = {72 WENHUA RD, SHENYANG 110015, PEOPLES R CHINA},
Type = {Review},
Language = {English},
Affiliation = {Shen, T (Corresponding Author), Kunming Univ Sci \& Technol, Fac Informat Engn \& Automat, Kunming 650500, Yunnan, Peoples R China.
   Wang, Z (Corresponding Author), Cent South Univ, Light Alloy Res Inst, Changsha 410083, Peoples R China.
   Liu, Yingli; Niu, Chen; Zhu, Yan; Shen, Tao, Kunming Univ Sci \& Technol, Fac Informat Engn \& Automat, Kunming 650500, Yunnan, Peoples R China.
   Liu, Yingli; Shen, Tao, Kunming Univ Sci \& Technol, Comp Technol Applicat Key Lab Yunnan Prov, Kunming 650500, Yunnan, Peoples R China.
   Wang, Zhuo, Cent South Univ, Light Alloy Res Inst, Changsha 410083, Peoples R China.
   Gan, Yong, Chinese Acad Engn, Beijing 100088, Peoples R China.
   Sun, Shuhong, Kunming Univ Sci \& Technol, Fac Mat Sci \& Engn, Kunming 650093, Yunnan, Peoples R China.
   Wang, Zhuo, Chengdu MatAi Technol Co Ltd, Chengdu 610041, Sichuan, Peoples R China.},
DOI = {10.1016/j.jmst.2020.01.067},
ISSN = {1005-0302},
EISSN = {1941-1162},
Keywords = {Materials genome initiative (MGI); Materials database; Machine learning;
   Materials properties prediction; Materials design and discovery},
Keywords-Plus = {ARTIFICIAL NEURAL-NETWORK; MECHANICAL-PROPERTIES; MATERIALS DISCOVERY;
   DATA SCIENCE; DESIGN; PREDICTION; INFORMATICS; COMPOSITES;
   INFRASTRUCTURE; SIMULATION},
Research-Areas = {Materials Science; Metallurgy \& Metallurgical Engineering},
Web-of-Science-Categories  = {Materials Science, Multidisciplinary; Metallurgy \& Metallurgical
   Engineering},
Author-Email = {wangzhao@mat.ai
   shentao@kust.edu.cn},
Affiliations = {Kunming University of Science \& Technology; Kunming University of
   Science \& Technology; Central South University; Kunming University of
   Science \& Technology},
Funding-Acknowledgement = {National Natural Science Foundation of China {[}61971208, 61671225
   and51864027]; Yunnan Applied Basic Research Projects {[}2018FA034];
   Yunnan Reserve Talents of Young and Middleaged Academic and Technical
   Leaders; YunnanYoung Top Talents of Ten Thousands Plan {[}2018 73];
   Scientific ResearchFoundation of Kunming University of Science and
   Technology {[}KKSY201703016]},
Funding-Text = {This work was financially supported by the National Natural Science
   Foundation of China (Nos. 61971208, 61671225 and51864027), the Yunnan
   Applied Basic Research Projects (No. 2018FA034), the Yunnan Reserve
   Talents of Young and Middleaged Academic and Technical Leaders (Shen
   Tao, 2018), the YunnanYoung Top Talents of Ten Thousands Plan (Shen Tao,
   Zhu Yan, Yunren Social Development No. 2018 73) and the Scientific
   ResearchFoundation of Kunming University of Science and Technology (No.
   KKSY201703016).},
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Number-of-Cited-References = {149},
Times-Cited = {58},
Usage-Count-Last-180-days = {68},
Usage-Count-Since-2013 = {308},
Journal-ISO = {J. Mater. Sci. Technol.},
Doc-Delivery-Number = {NS8WW},
Web-of-Science-Index = {Science Citation Index Expanded (SCI-EXPANDED)},
Unique-ID = {WOS:000572537200011},
DA = {2023-04-22},
}

@article{ WOS:000612766700011,
Author = {Quer, Giorgio and Arnaout, Ramy and Henne, Michael and Arnaout, Rima},
Title = {Machine Learning and the Future of Cardiovascular Care JACC
   State-of-the-Art Review},
Journal = {JOURNAL OF THE AMERICAN COLLEGE OF CARDIOLOGY},
Year = {2021},
Volume = {77},
Number = {3},
Pages = {300-313},
Month = {JAN 26},
Abstract = {The role of physicians has always been to synthesize the data available
   to them to identify diagnostic patterns that guide treatment and follow
   response. Today, increasingly sophisticated machine learning algorithms
   may grow to support clinical experts in some of these tasks. Machine
   learning has the potential to benefit patients and cardiologists, but
   only if clinicians take an active role in bringing these new algorithms
   into practice. The aim of this review is to introduce clinicians who are
   not data science experts to key concepts in machine learning that will
   allow them to better understand the field and evaluate new literature
   and developments. The current published data in machine learning for
   cardiovascular disease is then summarized, using both a bibliometric
   survey, with code publicly available to enable similar analysis for any
   research topic of interest, and select case studies. Finally, several
   ways that clinicians can and must be involved in this emerging field are
   presented. (C) 2021 The Authors. Published by Elsevier on behalf of the
   American College of Cardiology Foundation.},
Publisher = {ELSEVIER SCIENCE INC},
Address = {STE 800, 230 PARK AVE, NEW YORK, NY 10169 USA},
Type = {Review},
Language = {English},
Affiliation = {Arnaout, R (Corresponding Author), Univ Calif San Francisco, Dept Med, Div Cardiol, Bakar Computat Hlth Sci Inst,Ctr Intelligent Imag, 555 Mission Bay Blvd South, San Francisco, CA 94158 USA.
   Quer, Giorgio, Scripps Res Translat Inst, La Jolla, CA USA.
   Arnaout, Ramy, Beth Israel Deaconess Med Ctr, Dept Pathol, Div Clin Pathol, Beth Israel Lahey Hlth, 330 Brookline Ave, Boston, MA 02215 USA.
   Henne, Michael, Univ Calif San Francisco, Dept Med, Div Cardiol, San Francisco, CA 94143 USA.
   Arnaout, Rima, Univ Calif San Francisco, Dept Med, Div Cardiol, Bakar Computat Hlth Sci Inst,Ctr Intelligent Imag, 555 Mission Bay Blvd South, San Francisco, CA 94158 USA.},
DOI = {10.1016/j.jacc.2020.11.030},
EarlyAccessDate = {JAN 2021},
ISSN = {0735-1097},
EISSN = {1558-3597},
Keywords = {artificial intelligence; bibliometric analysis; cardiology; deep
   learning; literature search; machine learning},
Keywords-Plus = {ARTIFICIAL-INTELLIGENCE; HEART-FAILURE; CLASSIFICATION; VALIDATION;
   CARDIOLOGY},
Research-Areas = {Cardiovascular System \& Cardiology},
Web-of-Science-Categories  = {Cardiac \& Cardiovascular Systems},
Author-Email = {rima.arnaout@ucsf.edu},
Affiliations = {Harvard University; Beth Israel Deaconess Medical Center; University of
   California System; University of California San Francisco; University of
   California System; University of California San Francisco},
Funding-Acknowledgement = {National Institutes of Health (National Center for Advancing
   Translational Sciences, NCATS) {[}UL1TR002550]; National Science
   Foundation Convergence Accelerator {[}OIA2040727]; National Institutes
   of Health {[}R01HL15039401]; Department of Defense {[}W81XWH-19-1-0294];
   American Heart Association {[}17IGMV33870001]; Chan Zuckerberg Biohub},
Funding-Text = {Resources at Scripps Research (to Dr. Quer) were provided by the
   National Institutes of Health (UL1TR002550 from the National Center for
   Advancing Translational Sciences, NCATS) and in part by the National
   Science Foundation Convergence Accelerator (OIA2040727). Drs. Ramy and
   Rima Arnaout were supported by the National Institutes of Health
   (R01HL15039401), Department of Defense (W81XWH-19-1-0294), and the
   American Heart Association (17IGMV33870001). Dr. Rima Arnaout is
   additionally supported by the Chan Zuckerberg Biohub. Mr. Henne has
   reported that he has no relationships relevant to the contents of this
   paper to disclose.},
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Number-of-Cited-References = {92},
Times-Cited = {89},
Usage-Count-Last-180-days = {24},
Usage-Count-Since-2013 = {71},
Journal-ISO = {J. Am. Coll. Cardiol.},
Doc-Delivery-Number = {PZ5FV},
Web-of-Science-Index = {Science Citation Index Expanded (SCI-EXPANDED)},
Unique-ID = {WOS:000612766700011},
OA = {hybrid, Green Accepted},
ESI-Highly-Cited-Paper = {Y},
ESI-Hot-Paper = {N},
DA = {2023-04-22},
}

@article{ WOS:000900750300006,
Author = {Ray, Saikat Sinha and Verma, Rohit Kumar and Singh, Ashutosh and
   Ganesapillai, Mahesh and Kwon, Young-Nam},
Title = {A holistic review on how artificial intelligence has redefined water
   treatment and seawater desalination processes},
Journal = {DESALINATION},
Year = {2023},
Volume = {546},
Month = {JAN 15},
Abstract = {In the modern era, deep learning (DL), and machine learning (ML), have
   emerged as potential technologies that are widely applied in the fields
   of science, engineering, and technology. These tools have been
   extensively used to optimize seawater desalination and water treatment
   processes to achieve efficient performance. Indeed, automation has
   played a key role in redefining the issues of water treatment and
   seawater desalination. Artificial intelligence (AI) has been developed
   as a versatile tool for processing data and optimizing smart water
   services while addressing the issues of monitoring, management, and
   labor costs. Recently, specific AI tools, such as artificial neural
   networks (ANNs) and genetic algorithms, have been implemented for
   self-monitoring and modeling applications in the field of water
   treatment and seawater desalination. In the present article, the
   application of AI in the water treatment and seawater desalination
   sectors is thoroughly reviewed. Additionally, conventional modeling
   approaches are compared with ANN modeling. Furthermore, the challenges
   and shortcomings are discussed, along with future prospects. Moreover,
   the applications of AI mechanisms in data processing, optimization,
   modeling, prediction, and decision-making during water treatment and
   seawater desalination processes are underscored. Finally, innovative
   trends in seawater desalination and water treatment with AI tools are
   summarized.},
Publisher = {ELSEVIER},
Address = {RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS},
Type = {Review},
Language = {English},
Affiliation = {Kwon, YN (Corresponding Author), Ulsan Natl Inst Sci \& Technol UNIST, Dept Urban \& Environm Engn UEE, Ulsan, South Korea.
   Singh, A (Corresponding Author), Shiv Nadar Univ, Sch Nat Sci SONS, Dept Life Sci, Delhi Ncr, India.
   Ray, Saikat Sinha; Kwon, Young-Nam, Ulsan Natl Inst Sci \& Technol UNIST, Dept Urban \& Environm Engn UEE, Ulsan, South Korea.
   Verma, Rohit Kumar; Singh, Ashutosh, Shiv Nadar Univ, Sch Nat Sci SONS, Dept Life Sci, Delhi Ncr, India.
   Ganesapillai, Mahesh, Vellore Inst Technol VIT, Sch Chem \& Environm Engn SCHEME, Vellore, Tamil Nadu, India.},
DOI = {10.1016/j.desal.2022.116221},
Article-Number = {116221},
ISSN = {0011-9164},
EISSN = {1873-4464},
Keywords = {Artificial intelligence (AI); Machine learning (ML); Artificial neural
   networks (ANNs); Desalination; Water treatment},
Keywords-Plus = {NEURAL-NETWORK MODEL; WASTE-WATER; TREATMENT-PLANT; OSMOSIS;
   PERFORMANCE; PREDICTION; REMOVAL; SYSTEMS; OPTIMIZATION; SIMULATION},
Research-Areas = {Engineering; Water Resources},
Web-of-Science-Categories  = {Engineering, Chemical; Water Resources},
Author-Email = {ashutosh.singh@snu.edu.in
   kwonyn@unist.ac.kr},
Affiliations = {Ulsan National Institute of Science \& Technology (UNIST); Shiv Nadar
   University; Vellore Institute of Technology (VIT); VIT Vellore},
Funding-Acknowledgement = {National Research Foundation of Korea (ROK) - Ministry of Education,
   Science and Technology {[}NRF-2018R1D1A1B07043609]; Korea Institute of
   Energy Technology Evaluation and Planning (KETEP) - Korea Government
   (MOTIE) {[}20202020800330]},
Funding-Text = {This study has been financially assisted by the National Research
   Foundation of Korea (ROK) as supported by Ministry of Education, Science
   and Technology (NRF-2018R1D1A1B07043609) and by the Korea Institute of
   Energy Technology Evaluation and Planning (KETEP) grant financed by the
   Korea Government (MOTIE) (20202020800330).},
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Number-of-Cited-References = {110},
Times-Cited = {0},
Usage-Count-Last-180-days = {18},
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Journal-ISO = {Desalination},
Doc-Delivery-Number = {7D8RF},
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Unique-ID = {WOS:000900750300006},
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@article{ WOS:000706184900001,
Author = {Balogun, Abdul-Lateef and Tella, Abdulwaheed and Baloo, Lavania and
   Adebisi, Naheem},
Title = {A review of the inter-correlation of climate change, air pollution and
   urban sustainability using novel machine learning algorithms and spatial
   information science},
Journal = {URBAN CLIMATE},
Year = {2021},
Volume = {40},
Month = {DEC},
Abstract = {Air pollution is a global geo-hazard with significant implications,
   including deterioration of health and premature death. Climatic
   variables such as temperature, rainfall, wind, and humidity impact air
   pollution by affecting the strength, transportation, and dispersion of
   pollutants in the atmosphere. Emerging data science tools, particularly
   Machine Learning (ML) big data analytics, are being utilized to predict
   air pollution intensity and frequency under varying climatic conditions
   for effective mitigation plans. However, comprehensive documentation of
   these digitalization approaches and outcomes in terms of correlating
   future air pollution with climate change remains scant. This study
   addresses this gap by systematically reviewing pertinent literature on
   climate change and air pollution studies. We also investigated the
   potentials of integrated spatial data science for spatial modelling and
   identifying cities vulnerable to air pollution hazards. Our findings
   show that climatic factors and seasonal variations are critical
   predictors of air quality in urban areas. A strong correlation exists
   between climate change and air quality, and air quality in urbanized
   regions is projected to deteriorate with climate change in the future.
   Therefore, climatic variables remain essential factors for the
   prediction of air quality. Also, air pollutants tend to have higher
   concentration in the warm season, making the consideration of seasonal
   changes crucial in air quality management. The study also revealed that
   machine learning algorithms such as random forest, gradient boosting
   machine, and classification and regression trees (CART) accurately
   predict air pollution hazard when integrated with spatial models. The
   detailed review},
Publisher = {ELSEVIER},
Address = {RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS},
Type = {Review},
Language = {English},
Affiliation = {Tella, A (Corresponding Author), Univ Teknol PETRONAS, Dept Civil \& Environm Engn, Geospatial Anal \& Modelling GAM Res Lab, Seri Iskandar 32610, Perak, Malaysia.
   Balogun, Abdul-Lateef; Tella, Abdulwaheed; Adebisi, Naheem, Univ Teknol PETRONAS, Dept Civil \& Environm Engn, Geospatial Anal \& Modelling GAM Res Lab, Seri Iskandar 32610, Perak, Malaysia.
   Baloo, Lavania, Univ Teknol PETRONAS, Dept Civil \& Environm Engn, Seri Iskandar 32610, Perak, Malaysia.},
DOI = {10.1016/j.uclim.2021.100989},
EarlyAccessDate = {OCT 2021},
Article-Number = {100989},
ISSN = {2212-0955},
Keywords = {Air pollution; Climate change; Digitalization; Sustainable cities;
   Machine learning; Spatial data science},
Keywords-Plus = {FINE PARTICULATE MATTER; ARTIFICIAL NEURAL-NETWORKS; LOW-EMISSION ZONES;
   SPATIOTEMPORAL PREDICTION; ENERGY EFFICIENCY; RANDOM FOREST;
   HEALTH-RISK; GREEN INFRASTRUCTURE; PM2.5 CONCENTRATIONS; OZONE
   CONCENTRATIONS},
Research-Areas = {Environmental Sciences \& Ecology; Meteorology \& Atmospheric Sciences},
Web-of-Science-Categories  = {Environmental Sciences; Meteorology \& Atmospheric Sciences},
Author-Email = {tellaabdulwaheed01@gmail.com},
Affiliations = {Universiti Teknologi Petronas; Universiti Teknologi Petronas},
ResearcherID-Numbers = {Tella, Abdulwaheed/ABF-8657-2020
   Adebisi, Naheem/GPF-8462-2022
   Tella, Abdulwaheed/CAA-2751-2022},
ORCID-Numbers = {Tella, Abdulwaheed/0000-0002-4380-3343
   Tella, Abdulwaheed/0000-0002-4380-3343},
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Times-Cited = {12},
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@article{ WOS:000698879200003,
Author = {Tariq, Zeeshan and Aljawad, Murtada Saleh and Hasan, Amjed and Murtaza,
   Mobeen and Mohammed, Emad and El-Husseiny, Ammar and Alarifi, Sulaiman
   A. and Mahmoud, Mohamed and Abdulraheem, Abdulazeez},
Title = {A systematic review of data science and machine learning applications to
   the oil and gas industry},
Journal = {JOURNAL OF PETROLEUM EXPLORATION AND PRODUCTION TECHNOLOGY},
Year = {2021},
Volume = {11},
Number = {12},
Pages = {4339-4374},
Month = {DEC},
Abstract = {This study offered a detailed review of data sciences and machine
   learning (ML) roles in different petroleum engineering and geosciences
   segments such as petroleum exploration, reservoir characterization, oil
   well drilling, production, and well stimulation, emphasizing the newly
   emerging field of unconventional reservoirs. The future of data science
   and ML in the oil and gas industry, highlighting what is required from
   ML for better prediction, is also discussed. This study also provides a
   comprehensive comparison of different ML techniques used in the oil and
   gas industry. With the arrival of powerful computers, advanced ML
   algorithms, and extensive data generation from different industry tools,
   we see a bright future in developing solutions to the complex problems
   in the oil and gas industry that were previously beyond the grip of
   analytical solutions or numerical simulation. ML tools can incorporate
   every detail in the log data and every information connected to the
   target data. Despite their limitations, they are not constrained by
   limiting assumptions of analytical solutions or by particular data
   and/or power processing requirements of numerical simulators. This
   detailed and comprehensive study can serve as an exclusive reference for
   ML applications in the industry. Based on the review conducted, it was
   found that ML techniques offer a great potential in solving problems in
   almost all areas of the oil and gas industry involving prediction,
   classification, and clustering. With the generation of huge data in
   everyday oil and gas industry activates, machine learning and big data
   handling techniques are becoming a necessity toward a more efficient
   industry.},
Publisher = {SPRINGER HEIDELBERG},
Address = {TIERGARTENSTRASSE 17, D-69121 HEIDELBERG, GERMANY},
Type = {Review},
Language = {English},
Affiliation = {Alarifi, SA (Corresponding Author), King Fahd Univ Petr \& Minerals, Dhahran, Saudi Arabia.
   Tariq, Zeeshan; Aljawad, Murtada Saleh; Hasan, Amjed; Murtaza, Mobeen; Mohammed, Emad; El-Husseiny, Ammar; Alarifi, Sulaiman A.; Mahmoud, Mohamed; Abdulraheem, Abdulazeez, King Fahd Univ Petr \& Minerals, Dhahran, Saudi Arabia.},
DOI = {10.1007/s13202-021-01302-2},
EarlyAccessDate = {SEP 2021},
ISSN = {2190-0558},
EISSN = {2190-0566},
Keywords = {Oil and gas industry; Systematic review; Machine learning; Future of
   data science in oil and gas},
Keywords-Plus = {ARTIFICIAL NEURAL-NETWORKS; COMPRESSIVE STRENGTH PREDICTION; WATER
   SATURATION PREDICTION; PVT PROPERTIES; WELL LOGS; COMMITTEE MACHINE;
   RESERVOIR; MODEL; PRESSURE; INTELLIGENCE},
Research-Areas = {Energy \& Fuels; Engineering; Geology},
Web-of-Science-Categories  = {Energy \& Fuels; Engineering, Petroleum; Geosciences, Multidisciplinary},
Author-Email = {salarifi@kfupm.edu.sa},
Affiliations = {King Fahd University of Petroleum \& Minerals},
ResearcherID-Numbers = {Alarifi, Sulaiman A./AAD-3222-2021
   Tariq, Zeeshan/ABD-8533-2020},
ORCID-Numbers = {Alarifi, Sulaiman A./0000-0003-3643-4014
   Tariq, Zeeshan/0000-0001-5456-7115},
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@article{ WOS:000611850000003,
Author = {Mahmood, Asif and Wang, Jin-Liang},
Title = {Machine learning for high performance organic solar cells: current
   scenario and future prospects},
Journal = {ENERGY \& ENVIRONMENTAL SCIENCE},
Year = {2021},
Volume = {14},
Number = {1},
Pages = {90-105},
Month = {JAN 1},
Abstract = {Machine learning (ML) is a field of computer science that uses
   algorithms and techniques for automating solutions to complex problems
   that are hard to program using conventional programming methods. Owing
   to the chemical versatility of organic building blocks, a large number
   of organic semi-conductors have been used for organic solar cells.
   Selecting a suitable organic semi-conductor is like searching for a
   needle in a haystack. Data-driven science, the fourth paradigm of
   science, has the potential to guide experimentalists to discover and
   develop new high-performance materials. The last decade has seen
   impressive progress in materials informatics and data science; however,
   data-driven molecular design of organic solar cell materials is still
   challenging. The data-analysis capability of machine learning methods is
   well known. This review is written about the use of machine learning
   methods for organic solar cell research. In this review, we have
   outlined the basics of machine learning and common procedures for
   applying machine learning. A brief introduction on different classes of
   machine learning algorithms as well as related software and tools is
   provided. Then, the current research status of machine learning in
   organic solar cells is reviewed. We have discussed the challenges in
   anticipating the data driven material design, such as the complexity
   metric of organic solar cells, diversity of chemical structures and
   necessary programming ability. We have also proposed some suggestions
   that can enhance the usefulness of machine learning for organic solar
   cell research enterprises.},
Publisher = {ROYAL SOC CHEMISTRY},
Address = {THOMAS GRAHAM HOUSE, SCIENCE PARK, MILTON RD, CAMBRIDGE CB4 0WF, CAMBS,
   ENGLAND},
Type = {Review},
Language = {English},
Affiliation = {Wang, JL (Corresponding Author), Beijing Inst Technol, Sch Chem \& Chem Engn, Beijing Key Lab Photoelect Electrophoton Convers, Key Lab Cluster Sci,Minist Educ, Beijing 100081, Peoples R China.
   Mahmood, Asif; Wang, Jin-Liang, Beijing Inst Technol, Sch Chem \& Chem Engn, Beijing Key Lab Photoelect Electrophoton Convers, Key Lab Cluster Sci,Minist Educ, Beijing 100081, Peoples R China.},
DOI = {10.1039/d0ee02838j},
ISSN = {1754-5692},
EISSN = {1754-5706},
Keywords-Plus = {FINGERPRINT SIMILARITY SEARCH; MOLECULAR DESIGN; FULLERENE; EFFICIENCY;
   ACCEPTOR; PHOTOVOLTAICS; MORPHOLOGY; CHEMISTRY; CANDIDATES; ENERGIES},
Research-Areas = {Chemistry; Energy \& Fuels; Engineering; Environmental Sciences \&
   Ecology},
Web-of-Science-Categories  = {Chemistry, Multidisciplinary; Energy \& Fuels; Engineering, Chemical;
   Environmental Sciences},
Author-Email = {jinlwang@bit.edu.cn},
Affiliations = {Beijing Institute of Technology},
ResearcherID-Numbers = {Mahmood, Asif/S-5579-2019},
ORCID-Numbers = {Mahmood, Asif/0000-0001-9412-1011},
Funding-Acknowledgement = {National Natural Science Foundation of China {[}21672023, 21971014,
   21950410533]; National Key Research and Development Program of China
   {[}2018YFA0901800]; Thousand Youth Talents Plan of China; BIT Teli Young
   Fellow Recruitment Program},
Funding-Text = {The authors acknowledge the support from the National Natural Science
   Foundation of China (No. 21672023, 21971014, and 21950410533) and the
   National Key Research and Development Program of China (2018YFA0901800).
   Jin-Liang Wang was supported by the Thousand Youth Talents Plan of China
   and BIT Teli Young Fellow Recruitment Program. The authors thank the
   Analysis \& Testing Center, Beijing Institute of Technology.},
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Number-of-Cited-References = {99},
Times-Cited = {117},
Usage-Count-Last-180-days = {112},
Usage-Count-Since-2013 = {452},
Journal-ISO = {Energy Environ. Sci.},
Doc-Delivery-Number = {PY1ZW},
Web-of-Science-Index = {Science Citation Index Expanded (SCI-EXPANDED)},
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DA = {2023-04-22},
}

@article{ WOS:000467513500032,
Author = {Toivonen, Tuuli and Heikinheimo, Vuokko and Fink, Christoph and
   Hausmann, Anna and Hiippala, Tuomo and Jarv, Olle and Tenkanen, Henrikki
   and Di Minin, Enrico},
Title = {Social media data for conservation science: A methodological overview},
Journal = {BIOLOGICAL CONSERVATION},
Year = {2019},
Volume = {233},
Pages = {298-315},
Month = {MAY},
Abstract = {Improved understanding of human-nature interactions is crucial to
   conservation science and practice, but collecting relevant data remains
   challenging. Recently, social media have become an increasingly
   important source of information on human-nature interactions. However,
   the use of advanced methods for analysing social media is still limited,
   and social media data are not used to their full potential. In this
   article, we present available sources of social media data and
   approaches to mining and analysing these data for conservation science.
   Specifically, we (i) describe what kind of relevant information can be
   retrieved from social media platforms, (ii) provide a detailed overview
   of advanced methods for spatio-temporal, content and network analyses,
   (iii) exemplify the potential of these approaches for real-world
   conservation challenges, and (iv) discuss the limitations of social
   media data analysis in conservation science. Combined with other data
   sources and carefully considering the biases and ethical issues, social
   media data can provide a complementary and cost-efficient information
   source for addressing the grand challenges of biodiversity conservation
   in the Anthropocene epoch.},
Publisher = {ELSEVIER SCI LTD},
Address = {THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND},
Type = {Review},
Language = {English},
Affiliation = {Toivonen, T; Heikinheimo, V (Corresponding Author), Univ Helsinki, Dept Geosci \& Geog, Helsinki, Finland.
   Toivonen, T; Heikinheimo, V (Corresponding Author), Univ Helsinki, Helsinki Inst Sustainabil Sci HELSUS, Helsinki, Finland.
   Toivonen, Tuuli; Heikinheimo, Vuokko; Fink, Christoph; Hausmann, Anna; Hiippala, Tuomo; Jarv, Olle; Tenkanen, Henrikki; Di Minin, Enrico, Univ Helsinki, Dept Geosci \& Geog, Helsinki, Finland.
   Hiippala, Tuomo, Univ Helsinki, Dept Languages, Helsinki, Finland.
   Di Minin, Enrico, Univ KwaZulu Natal, Sch Life Sci, ZA-4041 Durban, South Africa.
   Toivonen, Tuuli; Heikinheimo, Vuokko; Fink, Christoph; Hausmann, Anna; Hiippala, Tuomo; Jarv, Olle; Tenkanen, Henrikki; Di Minin, Enrico, Univ Helsinki, Helsinki Inst Sustainabil Sci HELSUS, Helsinki, Finland.},
DOI = {10.1016/j.biocon.2019.01.023},
ISSN = {0006-3207},
EISSN = {1873-2917},
Keywords = {Social media; Nature conservation; Biodiversity; Spatial analysis;
   Content analysis; Machine learning; Artificial intelligence},
Keywords-Plus = {CULTURAL ECOSYSTEM SERVICES; BIG DATA; COMMUNITY DETECTION; NETWORK
   SITES; TWITTER; CHALLENGES; INTENSITY; PATTERNS; TRADE; SPACE},
Research-Areas = {Biodiversity \& Conservation; Environmental Sciences \& Ecology},
Web-of-Science-Categories  = {Biodiversity Conservation; Ecology; Environmental Sciences},
Author-Email = {tuuli.toivonen@helsinki.fi
   vuokko.heikinheimo@helsinki.fi},
Affiliations = {University of Helsinki; University of Helsinki; University of Kwazulu
   Natal; University of Helsinki},
ResearcherID-Numbers = {Hiippala, Tuomo/F-1088-2013
   Fink, Christoph/AEX-0106-2022
   Järv, Olle/N-5312-2019
   Tenkanen, Henrikki/N-2183-2019
   Di Minin, Enrico/J-6904-2013
   Järv, Olle/L-3488-2016
   },
ORCID-Numbers = {Fink, Christoph/0000-0003-1251-9726
   Järv, Olle/0000-0003-3446-1545
   Tenkanen, Henrikki/0000-0002-0918-4710
   Di Minin, Enrico/0000-0002-5562-318X
   Järv, Olle/0000-0003-3446-1545
   Heikinheimo, Vuokko/0000-0001-5119-0957
   Hiippala, Tuomo/0000-0002-8504-9422
   Hausmann, Anna/0000-0002-9639-9532
   Toivonen, Tuuli/0000-0002-6625-4922},
Funding-Acknowledgement = {Kone Foundation; University of Helsinki; Helsinki Institute of
   Sustainability Science (HELSUS); Academy of Finland {[}296524]; Helsinki
   Metropolitan Region Urban Research Program},
Funding-Text = {All authors would like to thank the Kone Foundation and the University
   of Helsinki for support. A.H. and E.D.M. would like to thank the
   Helsinki Institute of Sustainability Science (HELSUS) for support. C.F.
   was funded thanks to a University of Helsinki grant to E.D.M. E.D.M.
   would also like to thank the Academy of Finland 2016-2019, Grant 296524,
   for support. H.T. was supported by the Helsinki Metropolitan Region
   Urban Research Program.},
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@article{ WOS:000544029600001,
Author = {Keyes, Timothy J. and Domizi, Pablo and Lo, Yu-Chen and Nolan, Garry P.
   and Davis, Kara L.},
Title = {A Cancer Biologist's Primer on Machine Learning Applications in
   High-Dimensional Cytometry},
Journal = {CYTOMETRY PART A},
Year = {2020},
Volume = {97},
Number = {8},
Pages = {782-799},
Month = {AUG},
Abstract = {The application of machine learning and artificial intelligence to
   high-dimensional cytometry data sets has increasingly become a staple of
   bioinformatic data analysis over the past decade. This is especially
   true in the field of cancer biology, where protocols for collecting
   multiparameter single-cell data in a high-throughput fashion are rapidly
   developed. As the use of machine learning methodology in cytometry
   becomes increasingly common, there is a need for cancer biologists to
   understand the basic theory and applications of a variety of algorithmic
   tools for analyzing and interpreting cytometry data. We introduce the
   reader to several keystone machine learning-based analytic approaches
   with an emphasis on defining key terms and introducing a conceptual
   framework for making translational or clinically relevant discoveries.
   The target audience consists of cancer cell biologists and
   physician-scientists interested in applying these tools to their own
   data, but who may have limited training in bioinformatics. (c) 2020
   International Society for Advancement of Cytometry},
Publisher = {WILEY},
Address = {111 RIVER ST, HOBOKEN 07030-5774, NJ USA},
Type = {Review},
Language = {English},
Affiliation = {Davis, KL (Corresponding Author), Lokey Stem Cell Res Bldg SIM1,265 Campus Dr, Palo Alto, CA 94305 USA.
   Keyes, Timothy J., Stanford Univ, Sch Med, Med Scientist Training Program, Stanford, CA USA.
   Keyes, Timothy J.; Domizi, Pablo; Lo, Yu-Chen; Davis, Kara L., Stanford Univ, Sch Med, Dept Pediat, Stanford, CA USA.
   Nolan, Garry P., Stanford Univ, Sch Med, Baxter Lab Stem Cell, Dept Microbiol \& Immunol, Stanford, CA USA.},
DOI = {10.1002/cyto.a.24158},
EarlyAccessDate = {JUN 2020},
ISSN = {1552-4922},
EISSN = {1552-4930},
Keywords = {machine learning; mass cytometry; cancer; computational cytometry; data
   science},
Keywords-Plus = {ACUTE LYMPHOBLASTIC-LEUKEMIA; FLOW-CYTOMETRY; MASS CYTOMETRY; CELL
   SUBSETS; IDENTIFICATION; IMMUNE; SELECTION; REGULARIZATION;
   NORMALIZATION; VISUALIZATION},
Research-Areas = {Biochemistry \& Molecular Biology; Cell Biology},
Web-of-Science-Categories  = {Biochemical Research Methods; Cell Biology},
Author-Email = {tkeyes@stanford.edu
   kardavis@stanford.edu},
Affiliations = {Stanford University; Stanford University; Stanford University},
ORCID-Numbers = {Domizi, Pablo/0000-0003-2903-0337
   Keyes, Timothy/0000-0003-0423-9679},
Funding-Acknowledgement = {US National Institutes of Health (National Cancer Institute)
   {[}1F31CA239365-01]; V Foundation; Andrew McDonough B+ Foundation;
   Hyundai Scholar Hope Grant; Maternal and Child Health Research
   Institute; NIH {[}U54CA209971, U54HG010426, U19AI100627]; Parker
   Institute for Cancer Immunotherapy; Leukemia and Lymphoma Society},
Funding-Text = {We thank Dr. Nima Aghaeepour (Stanford University School of Medicine)
   and Dr. Brice Gaudilliere (Stanford University) for useful discussions.
   This work was supported by the US National Institutes of Health
   (National Cancer Institute 1F31CA239365-01 to T.J.K.). Kara Davis's work
   on this publication was funded by The V Foundation, the Andrew McDonough
   B+ Foundation, the Hyundai Scholar Hope Grant, and the Maternal and
   Child Health Research Institute. Pablo Domizi and Yu-Chen Lo's work was
   funded by the Leukemia and Lymphoma Society. Garry Nolan's work on the
   publication was funded by NIH: U54CA209971, U54HG010426, U19AI100627,
   and the Parker Institute for Cancer Immunotherapy.},
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Number-of-Cited-References = {106},
Times-Cited = {10},
Usage-Count-Last-180-days = {2},
Usage-Count-Since-2013 = {20},
Journal-ISO = {Cytom. Part A},
Doc-Delivery-Number = {MU4BU},
Web-of-Science-Index = {Science Citation Index Expanded (SCI-EXPANDED)},
Unique-ID = {WOS:000544029600001},
OA = {Green Accepted, Bronze},
DA = {2023-04-22},
}

@article{ WOS:000922676400005,
Author = {Kataria, Suchitra and Ravindran, Vinod},
Title = {Digital Health and Rheumatology: The Indian Context},
Journal = {INDIAN JOURNAL OF RHEUMATOLOGY},
Year = {2022},
Volume = {17},
Number = {7, 3},
Pages = {377-383},
Month = {DEC},
Abstract = {India as a country of contrast and diversity has witnessed digital
   evolution in different waves and stages. The technology is already an
   integral part of lives of millions in India; however, its application in
   the health management remains limited unlike developed economies.
   COVID-19 pandemic has plunged the country into universal, regional, or
   local lockdowns repeatedly since the last year. An unexpected and
   unforeseen impact of this has been the usage of technology for
   doctor-patient interactions through telemedicine. Hitherto limited to
   certain pockets, virtual interactions with doctors, ordering laboratory
   investigations through an application or procuring medicines through
   internet, are now part of mainstream patient behavior. This is a crucial
   change in the mindset but requires a lot more to be done at various
   levels to tap its full potential with rheumatologists being at the
   forefront and leading the change in their specialty. The pool of
   rheumatologists is very small and mostly concentrated in few urban
   areas, leading to diagnostic delay, suboptimal treatment, and poor
   outcomes. Technology could, therefore, become a catalyst for change and
   harbinger for greater clinician access. There are plenty of discussions
   about the impact and potential of deep learning, artificial
   intelligence, remote monitoring with wearables, etc., but plenty of them
   may not be relevant to Indian patients in the current scenario. Hence,
   the context, relevance, and applicability are the key for
   rheumatologists when making a judgment.},
Publisher = {WOLTERS KLUWER MEDKNOW PUBLICATIONS},
Address = {WOLTERS KLUWER INDIA PVT LTD , A-202, 2ND FLR, QUBE, C T S  NO 1498A-2
   VILLAGE MAROL, ANDHERI EAST, MUMBAI, Maharashtra, INDIA},
Type = {Review},
Language = {English},
Affiliation = {Ravindran, V (Corresponding Author), Ctr Rheumatol, Kozhikode 673009, Kerala, India.
   Kataria, Suchitra, Melange Commun Pte Ltd, Singapore, Singapore.
   Ravindran, Vinod, Ctr Rheumatol, Kozhikode 673009, Kerala, India.},
DOI = {10.4103/injr.injr\_127\_21},
ISSN = {0973-3698},
EISSN = {0973-3701},
Keywords = {Digital health; disease management apps; e-pharmacy; fifth-generation;
   patient data; remote monitoring; telemedicine; virtual care; wearables},
Keywords-Plus = {RECORDS},
Research-Areas = {Rheumatology},
Web-of-Science-Categories  = {Rheumatology},
Author-Email = {drvinod12@gmail.com},
ResearcherID-Numbers = {Kataria, Suchitra/ACP-3967-2022},
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Number-of-Cited-References = {18},
Times-Cited = {2},
Usage-Count-Last-180-days = {0},
Usage-Count-Since-2013 = {0},
Journal-ISO = {Indian J. Rheumatol.},
Doc-Delivery-Number = {8J8QF},
Web-of-Science-Index = {Emerging Sources Citation Index (ESCI)},
Unique-ID = {WOS:000922676400005},
OA = {gold},
DA = {2023-04-22},
}

@article{ WOS:000531147900001,
Author = {Pourmohammad, Parisa and Jahani, Ali and Zare Chahooki, Mohamad Ali and
   Goshtasb Meigooni, Hamid},
Title = {Road impact assessment modelling on plants diversity in national parks
   using regression analysis in comparison with artificial intelligence},
Journal = {MODELING EARTH SYSTEMS AND ENVIRONMENT},
Year = {2020},
Volume = {6},
Number = {3},
Pages = {1281-1292},
Month = {SEP},
Abstract = {Increasing urban demand and population growth in cities have led to an
   increase in demand for developing new ways. Parchin-Pasdaran Road, which
   runs from the heart of Khojir National Park, is a big threat to this
   park. Despite these environmental threats, the development and creation
   of new highways is unavoidable. This research was carried out to study
   the effect of the road on Smith-Wilson evenness index and Simpson
   diversity index in Khojir National Park. The Land Management Units were
   created using the ArcGIS software. Using appropriate algorithm in
   artificial neural network structure and linear regression of species
   evenness and diversity was modelled. For modelling of species evenness
   and diversity, factors like bulk density, particle density, moisture
   content, porosity and distance from the road were used. Finally,
   considering that the amount of R-2 in artificial neural network method
   was statistically significant for Smith-Wilson and Simpson (0.54),
   (0.71) and in the regression method, respectively (0.25), (0.75), was
   obtained, the neural network model was selected as the optimal model.
   Based on the analysis of sensitivity analysis, humidity factors at 5 and
   10 cm from the soil surface, the actual 5 cm particle density on the
   Smith-Wilson index and the porosity at 10 cm from the soil surface had
   the most effect on the Russian Simpson index.},
Publisher = {SPRINGER HEIDELBERG},
Address = {TIERGARTENSTRASSE 17, D-69121 HEIDELBERG, GERMANY},
Type = {Review},
Language = {English},
Affiliation = {Jahani, A (Corresponding Author), Coll Environm, Nat Environm \& Biodivers, Karaj, Iran.
   Pourmohammad, Parisa; Jahani, Ali; Goshtasb Meigooni, Hamid, Coll Environm, Nat Environm \& Biodivers, Karaj, Iran.
   Zare Chahooki, Mohamad Ali, Univ Tehran, Nat Resources Fac, Dept Rehabil Arid \& Mt Reg, Karaj, Iran.},
DOI = {10.1007/s40808-020-00799-6},
EarlyAccessDate = {MAY 2020},
ISSN = {2363-6203},
EISSN = {2363-6211},
Keywords = {Artificial neural network; Sensitivity analysis; Simpson's index;
   Smith-Wilson index; Regression},
Keywords-Plus = {DECISION-SUPPORT-SYSTEM; PROTECTED AREAS; NEURAL-NETWORKS; PREDICTION},
Research-Areas = {Environmental Sciences \& Ecology},
Web-of-Science-Categories  = {Environmental Sciences},
Author-Email = {ajahani@ut.ac.ir},
Affiliations = {University of Tehran},
ResearcherID-Numbers = {Zare Chahouki, Mohammad Ali/AAW-4194-2021
   Jahani, Ali/L-4850-2019},
ORCID-Numbers = {Zare Chahouki, Mohammad Ali/0000-0002-2538-0332
   Jahani, Ali/0000-0003-4965-3291},
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Number-of-Cited-References = {31},
Times-Cited = {18},
Usage-Count-Last-180-days = {0},
Usage-Count-Since-2013 = {7},
Journal-ISO = {Model. Earth Syst. Environ.},
Doc-Delivery-Number = {MA5KL},
Web-of-Science-Index = {Emerging Sources Citation Index (ESCI)},
Unique-ID = {WOS:000531147900001},
DA = {2023-04-22},
}

@article{ WOS:000938601500001,
Author = {Thurzo, Andrej and Strunga, Martin and Urban, Renata and Surovkova, Jana
   and Afrashtehfar, Kelvin I. I.},
Title = {Impact of Artificial Intelligence on Dental Education: A Review and
   Guide for Curriculum Update},
Journal = {EDUCATION SCIENCES},
Year = {2023},
Volume = {13},
Number = {2},
Month = {FEB},
Abstract = {In this intellectual work, the clinical and educational aspects of
   dentistry were confronted with practical applications of artificial
   intelligence (AI). The aim was to provide an up-to-date overview of the
   upcoming changes and a brief analysis of the influential advancements in
   the use of AI in dental education since 2020. In addition, this review
   provides a guide for a dental curriculum update for undergraduate and
   postgraduate education in the context of advances in AI applications and
   their impact on dentistry. Unsurprisingly, most dental educators have
   limited knowledge and skills to assess AI applications, as they were not
   trained to do so. Also, AI technology has evolved exponentially in
   recent years. Factual reliability and opportunities with OpenAI Inc.'s
   ChatGPT are considered critical inflection points in the era of
   generative AI. Updating curricula at dental institutions is inevitable
   as advanced deep-learning approaches take over the clinical areas of
   dentistry and reshape diagnostics, treatment planning, management, and
   telemedicine screening. With recent advances in AI language models,
   communication with patients will change, and the foundations of dental
   education, including essay, thesis, or scientific paper writing, will
   need to adapt. However, there is a growing concern about its ethical and
   legal implications, and further consensus is needed for the safe and
   responsible implementation of AI in dental education.},
Publisher = {MDPI},
Address = {ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND},
Type = {Review},
Language = {English},
Affiliation = {Thurzo, A (Corresponding Author), Comenius Univ, Fac Med, Dept Orthodont Regenerat \& Forens Dent, Bratislava 81250, Slovakia.
   Afrashtehfar, KI (Corresponding Author), Ajman Univ, Coll Dent, Clin Sci Dept, Evidence Based Practice Unit, Box 346, Ajman City, U Arab Emirates.
   Afrashtehfar, KI (Corresponding Author), Univ Bern, Sch Dent Med, Dept Reconstruct Dent \& Gerodontol, CH-3010 Bern, Switzerland.
   Thurzo, Andrej; Strunga, Martin; Urban, Renata; Surovkova, Jana, Comenius Univ, Fac Med, Dept Orthodont Regenerat \& Forens Dent, Bratislava 81250, Slovakia.
   Afrashtehfar, Kelvin I. I., Ajman Univ, Coll Dent, Clin Sci Dept, Evidence Based Practice Unit, Box 346, Ajman City, U Arab Emirates.
   Afrashtehfar, Kelvin I. I., Univ Bern, Sch Dent Med, Dept Reconstruct Dent \& Gerodontol, CH-3010 Bern, Switzerland.},
DOI = {10.3390/educsci13020150},
Article-Number = {150},
EISSN = {2227-7102},
Keywords = {AI; generative AI; AI academic implementation; dentistry; AI plagiarism;
   ChatGPT; Midjourney; health professions; AI detectors; education
   curriculum},
Keywords-Plus = {DENTISTRY; 3D},
Research-Areas = {Education \& Educational Research},
Web-of-Science-Categories  = {Education \& Educational Research},
Author-Email = {thurzo3@uniba.sk
   kelvin.afrashtehfar@unibe.ch},
Affiliations = {Comenius University Bratislava; Ajman University; University of Bern},
ResearcherID-Numbers = {Thurzo, Andrej/AAX-7034-2021},
ORCID-Numbers = {Afrashtehfar, Kelvin Ian/0000-0002-6053-8967
   Thurzo, Andrej/0000-0002-7810-5721},
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Number-of-Cited-References = {50},
Times-Cited = {1},
Usage-Count-Last-180-days = {59},
Usage-Count-Since-2013 = {59},
Journal-ISO = {Educ. Sci.},
Doc-Delivery-Number = {9H1LQ},
Web-of-Science-Index = {Emerging Sources Citation Index (ESCI)},
Unique-ID = {WOS:000938601500001},
OA = {gold},
DA = {2023-04-22},
}

@article{ WOS:000395596400002,
Author = {Lin, Eugene and Lane, Hsien-Yuan},
Title = {Machine learning and systems genomics approaches for multi-omics data},
Journal = {BIOMARKER RESEARCH},
Year = {2017},
Volume = {5},
Month = {JAN 20},
Abstract = {In light of recent advances in biomedical computing, big data science,
   and precision medicine, there is a mammoth demand for establishing
   algorithms in machine learning and systems genomics (MLSG), together
   with multi-omics data, to weigh probable phenotype-genotype
   relationships. Software frameworks in MLSG are extensively employed to
   analyze hundreds of thousands of multi-omics data by high-throughput
   technologies. In this study, we reviewed the MLSG software frameworks
   and future directions with respect to multi-omics data analysis and
   integration. Our review was targeted at researching recent approaches
   and technical solutions for the MLSG software frameworks using
   multi-omics platforms.},
Publisher = {BMC},
Address = {CAMPUS, 4 CRINAN ST, LONDON N1 9XW, ENGLAND},
Type = {Review},
Language = {English},
Affiliation = {Lane, HY (Corresponding Author), China Med Univ, Grad Inst Biomed Sci, Taichung, Taiwan.
   Lane, HY (Corresponding Author), China Med Univ Hosp, Dept Psychiat, Taichung, Taiwan.
   Lin, Eugene; Lane, Hsien-Yuan, China Med Univ, Grad Inst Biomed Sci, Taichung, Taiwan.
   Lin, Eugene, Vita Genom Inc, Taipei, Taiwan.
   Lin, Eugene, TickleFish Syst Corp, Seattle, WA USA.
   Lane, Hsien-Yuan, China Med Univ Hosp, Dept Psychiat, Taichung, Taiwan.},
DOI = {10.1186/s40364-017-0082-y},
Article-Number = {2},
EISSN = {2050-7771},
Keywords = {Genomics; Pharmacogenomics; Single nucleotide polymorphisms; Machine
   learning; Multi-omics; Systems genomics},
Keywords-Plus = {GENE-GENE INTERACTIONS; TAIWANESE POPULATION; HAPLOTYPE ANALYSIS;
   FEATURE-SELECTION; PUBLIC-HEALTH; DRUG EFFICACY; MODEL;
   PHARMACOGENOMICS; CLASSIFICATION; REGULARIZATION},
Research-Areas = {Oncology; Research \& Experimental Medicine},
Web-of-Science-Categories  = {Oncology; Medicine, Research \& Experimental},
Author-Email = {hylane@gmail.com},
Affiliations = {China Medical University Taiwan; Vita Genomics Incorporated; China
   Medical University Taiwan; China Medical University Hospital - Taiwan},
Funding-Acknowledgement = {Ministry of Economic Affairs in Taiwan (SBIR) {[}S099000280249-154];
   Taiwan Ministry of Health and Welfare Clinical Trial and Research Center
   of Excellence {[}MOHW105-TDU-B-212-133019]; China Medical University
   Hospital, Taiwan {[}DMR-101-091, DMR-102-069]},
Funding-Text = {This work was supported by the Ministry of Economic Affairs in Taiwan
   (SBIR Grant S099000280249-154), Taiwan Ministry of Health and Welfare
   Clinical Trial and Research Center of Excellence
   (MOHW105-TDU-B-212-133019), and China Medical University Hospital,
   Taiwan (DMR-101-091 and DMR-102-069). The funding supports had no role
   in study design, data collection and analysis, decision to publish, or
   preparation of the manuscript.},
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Number-of-Cited-References = {70},
Times-Cited = {97},
Usage-Count-Last-180-days = {35},
Usage-Count-Since-2013 = {98},
Journal-ISO = {Biomark. Res.},
Doc-Delivery-Number = {EM8YG},
Web-of-Science-Index = {Science Citation Index Expanded (SCI-EXPANDED)},
Unique-ID = {WOS:000395596400002},
OA = {Green Published},
DA = {2023-04-22},
}

@article{ WOS:000600933500001,
Author = {Fang, Jie and Swain, Anand and Unni, Rohit and Zheng, Yuebing},
Title = {Decoding Optical Data with Machine Learning},
Journal = {LASER \& PHOTONICS REVIEWS},
Year = {2021},
Volume = {15},
Number = {2},
Month = {FEB},
Abstract = {Optical spectroscopy and imaging techniques play important roles in many
   fields such as disease diagnosis, biological study, information
   technology, optical science, and materials science. Over the past
   decade, machine learning (ML) has proved promising in decoding complex
   data, enabling rapid and accurate analysis of optical spectra and
   images. This review aims to shed light on various ML algorithms for
   optical data analysis with a focus on their applications in a wide range
   of fields. The goal of this work is to sketch the validity of ML-based
   optical data decoding. The review concludes with an outlook on
   unaddressed problems and opportunities in this emerging subject that
   interfaces optics, data science, and ML.},
Publisher = {WILEY-V C H VERLAG GMBH},
Address = {POSTFACH 101161, 69451 WEINHEIM, GERMANY},
Type = {Review},
Language = {English},
Affiliation = {Zheng, YB (Corresponding Author), Univ Texas Austin, Walker Dept Mech Engn, Austin, TX 78712 USA.
   Zheng, YB (Corresponding Author), Univ Texas Austin, Texas Mat Inst, Austin, TX 78712 USA.
   Fang, Jie; Swain, Anand; Unni, Rohit; Zheng, Yuebing, Univ Texas Austin, Walker Dept Mech Engn, Austin, TX 78712 USA.
   Fang, Jie; Swain, Anand; Unni, Rohit; Zheng, Yuebing, Univ Texas Austin, Texas Mat Inst, Austin, TX 78712 USA.},
DOI = {10.1002/lpor.202000422},
EarlyAccessDate = {DEC 2020},
Article-Number = {2000422},
ISSN = {1863-8880},
EISSN = {1863-8899},
Keywords = {data decoding; machine learning; optical data; optics},
Keywords-Plus = {NEURAL-NETWORK ANALYSIS; RAMAN-SPECTROSCOPY; LISTERIA-MONOCYTOGENES;
   RAPID IDENTIFICATION; LIGHT-SCATTERING; WAVE-GUIDE; CLASSIFICATION;
   MICROSCOPY; BACTERIA; CANCER},
Research-Areas = {Optics; Physics},
Web-of-Science-Categories  = {Optics; Physics, Applied; Physics, Condensed Matter},
Author-Email = {zheng@austin.utexas.edu},
Affiliations = {University of Texas System; University of Texas Austin; University of
   Texas System; University of Texas Austin},
ResearcherID-Numbers = {Fang, Jie/ABD-4939-2020
   },
ORCID-Numbers = {Fang, Jie/0000-0002-0793-9323
   Zheng, Yuebing/0000-0002-9168-9477},
Funding-Acknowledgement = {National Aeronautics and Space Administration Early Career Faculty Award
   {[}80NSSC17K0520]; National Science Foundation {[}NSF-ECCS-2001650,
   NSF-CMMI-1761743]; National Institute of General Medical Sciences of the
   National Institutes of Health {[}DP2GM128446]},
Funding-Text = {J.F. and A.S. contributed equally to this work. The authors would like
   to thank Dr. Kan Yao, Mr. David Zhang, Mr. Akash S. Nivarthi, and Dr.
   Zilong Wu for the helpful discussions. They acknowledge the financial
   support of the National Aeronautics and Space Administration Early
   Career Faculty Award (80NSSC17K0520), the National Science Foundation
   (NSF-ECCS-2001650 and NSF-CMMI-1761743), and the National Institute of
   General Medical Sciences of the National Institutes of Health
   (DP2GM128446).},
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Number-of-Cited-References = {191},
Times-Cited = {12},
Usage-Count-Last-180-days = {8},
Usage-Count-Since-2013 = {78},
Journal-ISO = {Laser Photon. Rev.},
Doc-Delivery-Number = {QK1XH},
Web-of-Science-Index = {Science Citation Index Expanded (SCI-EXPANDED)},
Unique-ID = {WOS:000600933500001},
OA = {Green Accepted},
DA = {2023-04-22},
}

@article{ WOS:000604758900001,
Author = {Pena-Guerrero, Jose and Nguewa, Paul A. and Garcia-Sosa, Alfonso T.},
Title = {Machine learning, artificial intelligence, and data science breaking
   into drug design and neglected diseases},
Journal = {WILEY INTERDISCIPLINARY REVIEWS-COMPUTATIONAL MOLECULAR SCIENCE},
Year = {2021},
Volume = {11},
Number = {5},
Month = {SEP},
Abstract = {Machine learning (ML) is becoming capable of transforming biomolecular
   interaction description and calculation, promising an impact on
   molecular and drug design, chemical biology, toxicology, among others.
   The first improvements can be seen from biomolecule structure prediction
   to chemical synthesis, molecular generation, mechanism of action
   elucidation, inverse design, polypharmacology, organ or issue targeting
   of compounds, property and multiobjective optimization. Chemical design
   proposals from an algorithm may be inventive and feasible. Challenges
   remain, with the availability, diversity, and quality of data being
   critical for developing useful ML models; marginal improvement seen in
   some cases, as well as in the interpretability, validation, and reuse of
   models. The ultimate aim of ML should be to facilitate options for the
   scientist to propose and undertake ideas and for these to proceed
   faster. Applications are ripe for transformative results in
   understudied, neglected, and rare diseases, where new data and therapies
   are strongly required. Progress and outlook on these themes are provided
   in this study.
   This article is categorized under:
   Structure and Mechanism > Computational Biochemistry and Biophysics
   Structure and Mechanism > Molecular Structures},
Publisher = {WILEY},
Address = {111 RIVER ST, HOBOKEN 07030-5774, NJ USA},
Type = {Review},
Language = {English},
Affiliation = {Nguewa, PA (Corresponding Author), Univ Navarra, Dept Microbiol \& Parasitol, ISTUN Inst Trop Hlth, IdiSNA Navarra Inst Hlth Res, E-31008 Navarra, Spain.
   Garcia-Sosa, AT (Corresponding Author), Univ Tartu, Inst Chem, Ravila 14a, EE-54011 Tartu, Estonia.
   Pena-Guerrero, Jose; Nguewa, Paul A., Univ Navarra, Dept Microbiol \& Parasitol, ISTUN Inst Trop Hlth, IdiSNA Navarra Inst Hlth Res, E-31008 Navarra, Spain.
   Garcia-Sosa, Alfonso T., Univ Tartu, Inst Chem, Ravila 14a, EE-54011 Tartu, Estonia.},
DOI = {10.1002/wcms.1513},
EarlyAccessDate = {JAN 2021},
Article-Number = {e1513},
ISSN = {1759-0876},
EISSN = {1759-0884},
Keywords = {Artificial intelligence; drug design; data science; drug discovery;
   machine learning; molecular design; neglected diseases},
Keywords-Plus = {LARGE-SCALE; MOLECULAR DESIGN; DISCOVERY; PREDICTION; BINDING;
   IDENTIFICATION; SIMILARITY; PEPTIDES; BIOLOGY; DOCKING},
Research-Areas = {Chemistry; Mathematical \& Computational Biology},
Web-of-Science-Categories  = {Chemistry, Multidisciplinary; Mathematical \& Computational Biology},
Author-Email = {panguewa@unav.es
   alfonso.tlatoani.garcia.sosa@ut.ee},
Affiliations = {University of Navarra; University of Tartu},
ResearcherID-Numbers = {Garcia-Sosa, Alfonso T./E-4551-2015
   Nguewa, Paul/T-6982-2017},
ORCID-Numbers = {Garcia-Sosa, Alfonso T./0000-0003-0542-4446
   Nguewa, Paul/0000-0002-2193-7316},
Funding-Acknowledgement = {``la Caixa{''} Foundation {[}LCF/PR/PR13/11080005]; Departamento de
   Educacion, Gobierno de Navarra; EU COST Action {[}CA18217, CA18218]; EU
   Project unCoVer {[}DLV101016216]; Fundacion Caja Navarra; Fundacion
   Roviralta; Gobierno de Navarra-Salud {[}12/2017]; Inversiones Garcilaso
   de la Vega; Spanish Ministry of Education, Culture, and Sport (PhD
   fellowship) {[}FPU17/03304]; Ubesol; Haridus-ja Teadusministeerium
   {[}IUT34-14]; Laser Ebro},
Funding-Text = {``la Caixa{''} Foundation, Grant/Award Number: LCF/PR/PR13/11080005;
   Departamento de Educacion, Gobierno de Navarra; EU COST Action,
   Grant/Award Numbers: CA18217, CA18218; EU Project unCoVer, Grant/Award
   Number: DLV101016216; Fundacion Caja Navarra; Fundacion Roviralta;
   Gobierno de Navarra-Salud, Grant/Award Number: 12/2017; Haridus-ja
   Teadusministeerium, Grant/Award Number: IUT34-14; Inversiones Garcilaso
   de la Vega; Laser Ebro; Spanish Ministry of Education, Culture, and
   Sport (PhD fellowship), Grant/Award Number: FPU17/03304; Ubesol},
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@article{ WOS:000793483600004,
Author = {Chen, Chen and Yaari, Zvi and Apfelbaum, Elana and Grodzinski, Piotr and
   Shamay, Yosi and Heller, Daniel A.},
Title = {Merging data curation and machine learning to improve nanomedicines},
Journal = {ADVANCED DRUG DELIVERY REVIEWS},
Year = {2022},
Volume = {183},
Month = {APR},
Abstract = {Nanomedicine design is often a trial-and-error process, and the
   optimization of formulations and in vivo properties requires tremendous
   benchwork. To expedite the nanomedicine research progress, data science
   is steadily gaining importance in the field of nanomedicine. Recently,
   efforts have explored the potential to predict nanomaterials synthesis
   and biological behaviors via advanced data analytics. Machine learning
   algorithms process large datasets to understand and predict various
   material properties in nanomedicine synthesis, pharmacologic parameters,
   and efficacy. ``Big data{''} approaches may enable even larger advances,
   especially if researchers capitalize on data curation methods. However,
   the concomitant use of data curation processes needed to facilitate the
   acquisition and standardization of large, heterogeneous data sets, to
   support advanced data analytics methods such as machine learning has yet
   to be leveraged. Currently, data curation and data analytics areas of
   nanotechnology-focused data science, or `nanoinformatics', have been
   proceeding largely independently. This review highlights the current
   efforts in both areas and the potential opportunities for coordination
   to advance the capabilities of data analytics in nanomedicine.(c) 2022
   Elsevier B.V. All rights reserved.},
Publisher = {ELSEVIER},
Address = {RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS},
Type = {Review},
Language = {English},
Affiliation = {Heller, DA (Corresponding Author), Mem Sloan Kettering Canc Ctr, New York, NY 10065 USA.
   Chen, Chen; Yaari, Zvi; Heller, Daniel A., Mem Sloan Kettering Canc Ctr, New York, NY 10065 USA.
   Chen, Chen; Heller, Daniel A., Mem Sloan Kettering Canc Ctr, Triinst PhD Program Chem Biol, New York, NY 10065 USA.
   Apfelbaum, Elana; Heller, Daniel A., Cornell Univ, Dept Pharmacol, Weill Cornell Med, New York, NY 10065 USA.
   Grodzinski, Piotr, NCI, Nanodelivery Syst \& Devices Branch, NIH, Bethesda, MD 20892 USA.
   Shamay, Yosi, Technion Israel Inst Technol, Dept Biomed Engn, Haifa, Israel.},
DOI = {10.1016/j.addr.2022.114172},
EarlyAccessDate = {MAR 2022},
Article-Number = {114172},
ISSN = {0169-409X},
EISSN = {1872-8294},
Keywords = {Nanotechnology; Artificial intelligence; Nanoparticles; data mining;
   Cancer therapeutics; Particle characterization; Data curation},
Keywords-Plus = {PROTEIN CORONA; COLLOIDAL STABILITY; NANOPARTICLES; PREDICTION; SHAPE;
   SIZE; DELIVERY; DESIGN; NANOINFORMATICS; FORMULATION},
Research-Areas = {Pharmacology \& Pharmacy},
Web-of-Science-Categories  = {Pharmacology \& Pharmacy},
Author-Email = {hellerd@mskcc.org},
Affiliations = {Memorial Sloan Kettering Cancer Center; Memorial Sloan Kettering Cancer
   Center; Cornell University; Weill Cornell Medicine; National Institutes
   of Health (NIH) - USA; NIH National Cancer Institute (NCI); Technion
   Israel Institute of Technology},
ORCID-Numbers = {Chen, Chen/0000-0002-4803-0183},
Funding-Acknowledgement = {NCI {[}R01-CA215719, P30-CA008748]; NINDS {[}R01-NS116353]; American
   Cancer Society Research Scholar Grant {[}GC230452]; Louis and Rachel
   Rudin Foundation {[}901/91]; Alan and Sandra Gerry Metastasis Research
   Initiative; Commonwealth Foundation for Cancer Research; Experimental
   Therapeutics Center at Memorial Sloan Kettering Cancer Center; Israeli
   Science Foundation grant ISF},
Funding-Text = {This work was supported in part by the NCI (R01-CA215719, P30-CA008748)
   , NINDS (R01-NS116353) , the American Cancer Society Research Scholar
   Grant (GC230452) , the Expect Miracles Foundation-Financial Services
   Against Cancer, Emerson Collective, the Louis and Rachel Rudin
   Foundation, the Alan and Sandra Gerry Metastasis Research Initiative,
   the Center for Metastasis Research Scholars Fellowship Program, Mr.
   William H. Goodwin and Mrs. Alice Goodwin and the Commonwealth
   Foundation for Cancer Research, and the Experimental Therapeutics Center
   at Memorial Sloan Kettering Cancer Center. YS would like to thank
   funding from Israeli Science Foundation grant ISF\#901/91. Figure 1 and
   2 were created with BioRender.com .},
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Number-of-Cited-References = {133},
Times-Cited = {10},
Usage-Count-Last-180-days = {16},
Usage-Count-Since-2013 = {43},
Journal-ISO = {Adv. Drug Deliv. Rev.},
Doc-Delivery-Number = {1D0EN},
Web-of-Science-Index = {Science Citation Index Expanded (SCI-EXPANDED)},
Unique-ID = {WOS:000793483600004},
OA = {Green Accepted},
DA = {2023-04-22},
}

@article{ WOS:000456614000014,
Author = {Chan, Stephen and Siegel, Eliot L.},
Title = {Will machine learning end the viability of radiology as a thriving
   medical specialty?},
Journal = {BRITISH JOURNAL OF RADIOLOGY},
Year = {2019},
Volume = {92},
Number = {1094},
Abstract = {There have been tremendous advances in artificial intelligence (AI) and
   machine learning (ML) within the past decade, especially in the
   application of deep learning to various challenges. These include
   advanced competitive games (such as Chess and Go), self-driving cars,
   speech recognition, and intelligent personal assistants. Rapid advances
   in computer vision for recognition of objects in pictures have led some
   individuals, including computer science experts and health care system
   experts in machine learning, to make predictions that ML algorithms will
   soon lead to the replacement of the radiologist. However, there are
   complex technological, regulatory, and medicolegal obstacles facing the
   implementation of machine learning in radiology that will definitely
   preclude replacement of the radiologist by these algorithms within the
   next two decades and beyond. While not a comprehensive review of machine
   learning, this article is intended to highlight specific features of
   machine learning which face significant technological and health care
   systems challenges. Rather than replacing radiologists, machine learning
   will provide quantitative tools that will increase the value of
   diagnostic imaging as a biomarker, increase image quality with decreased
   acquisition times, and improve workflow, communication, and patient
   safety. In the foreseeable future, we predict that today's generation of
   radiologists will be replaced not by ML algorithms, but by a new breed
   of data science-savvy radiologists who have embraced and harnessed the
   incredible potential that machine learning has to advance our ability to
   care for our patients. In this way, radiology will remain a viable
   medical specialty for years to come.},
Publisher = {BRITISH INST RADIOLOGY},
Address = {36 PORTLAND PLACE, LONDON W1N 4AT, ENGLAND},
Type = {Review},
Language = {English},
Affiliation = {Chan, S (Corresponding Author), Harlem Hosp Med Ctr, Dept Radiol, New York, NY 10037 USA.
   Chan, S (Corresponding Author), Columbia Univ, New York, NY 10027 USA.
   Chan, Stephen, Harlem Hosp Med Ctr, Dept Radiol, New York, NY 10037 USA.
   Chan, Stephen, Columbia Univ, New York, NY 10027 USA.
   Siegel, Eliot L., VA Maryland Hlth Care Syst, Dept Diagnost Radiol \& Nucl Med, Baltimore, MD USA.},
DOI = {10.1259/bjr.20180416},
Article-Number = {20180416},
ISSN = {0007-1285},
EISSN = {1748-880X},
Keywords-Plus = {COMPUTER-AIDED DETECTION; MAMMOGRAPHY; MODEL},
Research-Areas = {Radiology, Nuclear Medicine \& Medical Imaging},
Web-of-Science-Categories  = {Radiology, Nuclear Medicine \& Medical Imaging},
Author-Email = {sc56md@gmail.com},
Affiliations = {Columbia University},
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Number-of-Cited-References = {69},
Times-Cited = {44},
Usage-Count-Last-180-days = {1},
Usage-Count-Since-2013 = {25},
Journal-ISO = {Br. J. Radiol.},
Doc-Delivery-Number = {HI7CW},
Web-of-Science-Index = {Science Citation Index Expanded (SCI-EXPANDED)},
Unique-ID = {WOS:000456614000014},
OA = {Green Published},
DA = {2023-04-22},
}

@article{ WOS:000727577100002,
Author = {Kong, Sung Hye and Shin, Chan Soo},
Title = {Applications of Machine Learning in Bone and Mineral Research},
Journal = {ENDOCRINOLOGY AND METABOLISM},
Year = {2021},
Volume = {36},
Number = {5},
Pages = {928-937},
Month = {OCT},
Abstract = {In this unprecedented era of the overwhelming volume of medical data,
   machine learning can be a promising tool that may shed light on an
   individualized approach and a better understanding of the disease in the
   field of osteoporosis research, similar to that in other research
   fields. This review aimed to provide an overview of the latest studies
   using machine learning to address issues, mainly focusing on
   osteoporosis and fractures. Machine learning models for diagnosing and
   classifying osteoporosis and detecting fractures from images have shown
   promising performance. Fracture risk prediction is another promising
   field of research, and studies are being conducted using various data
   sources. However, these approaches may be biased due to the nature of
   the techniques or the quality of the data. Therefore, more studies based
   on the proposed guidelines are needed to improve the technical
   feasibility and generalizability of artificial intelligence algorithms.},
Publisher = {KOREAN ENDOCRINE SOC},
Address = {101-2503, 109 MAPO-DAERO, MAPO-GU, SEOUL, 04146, SOUTH KOREA},
Type = {Review},
Language = {English},
Affiliation = {Shin, CS (Corresponding Author), Seoul Natl Univ, Dept Internal Med, Coll Med, 101 Daehak Ro, Seoul 03080, South Korea.
   Kong, Sung Hye; Shin, Chan Soo, Seoul Natl Univ, Dept Internal Med, Coll Med, 101 Daehak Ro, Seoul 03080, South Korea.
   Kong, Sung Hye, Seoul Natl Univ, Dept Internal Med, Bundang Hosp, Seongnam, South Korea.
   Shin, Chan Soo, Seoul Natl Univ Hosp, Dept Internal Med, Seoul, South Korea.},
DOI = {10.3803/EnM.2021.1111},
ISSN = {2093-596X},
EISSN = {2093-5978},
Keywords = {Osteoporosis; Data science; Medical informatics},
Keywords-Plus = {ARTIFICIAL-INTELLIGENCE; NEURAL-NETWORKS; AUTOMATED CLASSIFICATION;
   TEXTURE ANALYSIS; FRACTURES; RISK; DENSITY; RADIOGRAPHS; DIAGNOSIS},
Research-Areas = {Endocrinology \& Metabolism},
Web-of-Science-Categories  = {Endocrinology \& Metabolism},
Author-Email = {csshin@snu.ac.kr},
Affiliations = {Seoul National University (SNU); Seoul National University (SNU); Seoul
   National University (SNU); Seoul National University Hospital},
ORCID-Numbers = {Kong, Sung Hye/0000-0002-8791-0909},
Funding-Acknowledgement = {Korea Research Foundation {[}2021R1A2C2003410]},
Funding-Text = {This study was funded by the Korea Research Foundation (Proj-ect number
   2021R1A2C2003410) .},
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Number-of-Cited-References = {75},
Times-Cited = {3},
Usage-Count-Last-180-days = {5},
Usage-Count-Since-2013 = {12},
Journal-ISO = {Endocrinol. Metab.},
Doc-Delivery-Number = {XK6MK},
Web-of-Science-Index = {Science Citation Index Expanded (SCI-EXPANDED)},
Unique-ID = {WOS:000727577100002},
OA = {Green Published, gold},
DA = {2023-04-22},
}

@article{ WOS:000594311100001,
Author = {Ben Atitallah, Safa and Driss, Maha and Boulila, Wadii and Ben Ghezala,
   Henda},
Title = {Leveraging Deep Learning and IoT big data analytics to support the smart
   cities development: Review and future directions},
Journal = {COMPUTER SCIENCE REVIEW},
Year = {2020},
Volume = {38},
Month = {NOV},
Abstract = {The rapid growth of urban populations worldwide imposes new challenges
   on citizens' daily lives, including environmental pollution, public
   security, road congestion, etc. New technologies have been developed to
   manage this rapid growth by developing smarter cities. Integrating the
   Internet of Things (IoT) in citizens' lives enables the innovation of
   new intelligent services and applications that serve sectors around the
   city, including healthcare, surveillance, agriculture, etc. IoT devices
   and sensors generate large amounts of data that can be analyzed to gain
   valuable information and insights that help to enhance citizens' quality
   of life. Deep Learning (DL), a new area of Artificial Intelligence (AI),
   has recently demonstrated the potential for increasing the efficiency
   and performance of IoT big data analytics. In this survey, we provide a
   review of the literature regarding the use of IoT and DL to develop
   smart cities. We begin by defining the IoT and listing the
   characteristics of IoT-generated big data. Then, we present the
   different computing infrastructures used for IoT big data analytics,
   which include cloud, fog, and edge computing. After that, we survey
   popular DL models and review the recent research that employs both IoT
   and DL to develop smart applications and services for smart cities.
   Finally, we outline the current challenges and issues faced during the
   development of smart city services. (C) 2020 Elsevier Inc. All rights
   reserved.},
Publisher = {ELSEVIER},
Address = {RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS},
Type = {Review},
Language = {English},
Affiliation = {Driss, M (Corresponding Author), Univ Manouba, Natl Sch Comp Sci, RIADI Lab, Manouba, Tunisia.
   Ben Atitallah, Safa; Driss, Maha; Boulila, Wadii; Ben Ghezala, Henda, Univ Manouba, Natl Sch Comp Sci, RIADI Lab, Manouba, Tunisia.
   Driss, Maha; Boulila, Wadii, Taibah Univ, Coll Comp Sci \& Engn, Medina, Saudi Arabia.},
DOI = {10.1016/j.cosrev.2020.100303},
Article-Number = {100303},
ISSN = {1574-0137},
EISSN = {1876-7745},
Keywords = {Internet of Things; Deep Learning; Smart city; Big data analytics;
   Review},
Keywords-Plus = {CONVOLUTIONAL NEURAL-NETWORKS; FALL DETECTION; INTERNET; THINGS;
   CHALLENGES; CLASSIFICATION; ARCHITECTURES; REQUIREMENTS; AGRICULTURE;
   PREDICTION},
Research-Areas = {Computer Science},
Web-of-Science-Categories  = {Computer Science, Information Systems; Computer Science, Software
   Engineering; Computer Science, Theory \& Methods},
Author-Email = {maha.idriss@riadi.rnu.tn},
Affiliations = {Universite de la Manouba; Taibah University},
ResearcherID-Numbers = {Ben Ghezala, Henda Hajjami/AAK-7052-2021
   Boulila, Wadii/AGY-5718-2022
   },
ORCID-Numbers = {Ben Ghezala, Henda Hajjami/0000-0002-6874-1388
   Driss, Maha/0000-0001-8236-8746
   Boulila, Wadii/0000-0003-2133-0757
   Ben Atitallah, safa/0000-0003-0796-3507},
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Number-of-Cited-References = {201},
Times-Cited = {85},
Usage-Count-Last-180-days = {14},
Usage-Count-Since-2013 = {76},
Journal-ISO = {Comput. Sci. Rev.},
Doc-Delivery-Number = {OY5UF},
Web-of-Science-Index = {Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)},
Unique-ID = {WOS:000594311100001},
DA = {2023-04-22},
}

@article{ WOS:000721506900001,
Author = {Ranasinghe, Kavindu and Sabatini, Roberto and Gardi, Alessandro and
   Bijjahalli, Suraj and Kapoor, Rohan and Fahey, Thomas and Thangavel,
   Kathiravan},
Title = {Advances in Integrated System Health Management for mission-essential
   and safety-critical aerospace applications},
Journal = {PROGRESS IN AEROSPACE SCIENCES},
Year = {2022},
Volume = {128},
Month = {JAN 1},
Abstract = {Integrated System Health Management (ISHM) is a promising technology
   that fuses sensor data and historical state-of-health information of
   components and subsystems to provide actionable information and enable
   intelligent decision-making regarding the operation and maintenance of
   aerospace systems. ISHM fundamentally relies on assessments and
   predictions of system health, including the early detection of failures
   and estimation of Remaining Useful Life (RUL). Model-based, data-driven
   or hybrid reasoning techniques can be utilized to maximise the
   timeliness and reliability of diagnosis and prognosis information. The
   benefits of ISHM include enhancing the maintainability, reliability,
   safety and performance of systems. The next evolution of the ISHM
   concept, Intelligent Health and Mission Management (IHMM), delves deeper
   into the utilization of on-line system health predictions to modify
   mission profiles to ensure safety and reliability, as well as efficiency
   through predictive integrity. This concept is particularly important for
   Trusted Autonomous System (TAS) applications, where an accurate
   assessment of the current and future system state-of-health to make
   operational decisions (with or without human intervention) is integral
   to both flight safety and mission success. IHMM systems introduce the
   capability of predicting degradation in the functional performance of
   subsystems, with sufficient time to dynamically identify which
   appropriate restorative or reconfiguration actions to take in order to
   ensure that the system can perform at an acceptable level of operational
   capability before the onset of a failure event. This paper reviews some
   of the key advancements and contributions to knowledge in the field of
   ISHM for the aerospace industry, with a particular focus on various
   architectures and reasoning strategies involving the use of artificial
   intelligence. The paper also discusses the key challenges faced in the
   development and deployment of ISHM systems in the aerospace industry and
   highlights the safety-critical role that IHMM will play in future
   cyber-physical and autonomous system applications (both vehicle and
   ground support systems), such as Unmanned Aircraft Systems (UAS) Traffic
   Management (UTM), Urban Air Mobility (UAM) and Distributed Satellite
   Systems (DSS).},
Publisher = {PERGAMON-ELSEVIER SCIENCE LTD},
Address = {THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND},
Type = {Review},
Language = {English},
Affiliation = {Sabatini, R (Corresponding Author), Khalifa Univ Sci \& Technol, Coll Engn, Dept Aerosp Engn, POB 127788, Abu Dhabi, U Arab Emirates.
   Ranasinghe, Kavindu; Gardi, Alessandro; Bijjahalli, Suraj; Kapoor, Rohan; Fahey, Thomas; Thangavel, Kathiravan, RMIT Univ, STEM Coll, Sxhool Engn, Melbourne, Vic 3001, Australia.
   Sabatini, Roberto, Khalifa Univ Sci \& Technol, Coll Engn, Dept Aerosp Engn, POB 127788, Abu Dhabi, U Arab Emirates.},
DOI = {10.1016/j.paerosci.2021.100758},
EarlyAccessDate = {NOV 2021},
Article-Number = {100758},
ISSN = {0376-0421},
EISSN = {1873-1724},
Keywords = {Avionics; Integrated system health management; Prognostics; Diagnostics;
   Health and usage monitoring systems; Artificial intelligence; Machine
   learning; Intelligent health and mission management; Unmanned aircraft
   system; UAS; Satellite systems; UAS Traffic management; UTM; Distributed
   satellite systems},
Keywords-Plus = {USEFUL LIFE PREDICTION; ADAPTIVE NEURO-FUZZY; HIDDEN MARKOV-MODELS;
   OF-THE-ART; FAULT-DIAGNOSIS; KALMAN FILTER; GAUSSIAN-PROCESSES; CHARGE
   ESTIMATION; DATA-DRIVEN; PART 1},
Research-Areas = {Engineering},
Web-of-Science-Categories  = {Engineering, Aerospace},
Author-Email = {roberto.sabatini@ku.ac.ae},
Affiliations = {Royal Melbourne Institute of Technology (RMIT); Khalifa University of
   Science \& Technology},
ResearcherID-Numbers = {Sabatini, Roberto/Q-6752-2016
   },
ORCID-Numbers = {Sabatini, Roberto/0000-0002-3399-2291
   Fahey, Thomas/0000-0003-2847-2280
   Ranasinghe, Kavindu/0000-0001-5132-4230
   THANGAVEL, KATHIRAVAN/0000-0002-3401-9493},
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@article{ WOS:000663353600001,
Author = {Groft, Stephen C. and Posada, Manuel and Taruscio, Domenica},
Title = {Progress, challenges and global approaches to rare diseases},
Journal = {ACTA PAEDIATRICA},
Year = {2021},
Volume = {110},
Number = {10},
Pages = {2711-2716},
Month = {OCT},
Abstract = {Rare diseases occur globally at every stage of life. Patients, families
   and caregivers have many unmet medical and social needs leading to
   extraordinary psychosocial and economic burdens. Efforts to improve
   diagnostic capabilities and to develop therapies for an estimated 7000
   rare diseases have met with considerable success. In the United States,
   a rare disease or condition is one affecting fewer than 200,000 people.
   In the European Union (EU), a rare disease is any disease affecting
   fewer than 5 people in 10,000 (less than 1 in 2000 people). However,
   there are no effective treatments for 90 per cent of rare diseases.
   There is a need to expand awareness, advocacy and outreach to everyone
   including those with low incomes, poor literacy, minority ethnic status
   and living in underserved and marginalised populations in urban and
   rural areas as well as in developing nations throughout the world. The
   acceptance of patients as research partners complements the increased
   research emphasis and major regulatory initiatives leading to expedited
   review and approval programmes for products for serious or
   life-threatening conditions. The pipeline of new therapies provides hope
   to untreated patients. Advances in medical bioinformatics, artificial
   intelligence and machine learning with access to big data continue to
   identify novel therapeutics for screening and evaluation. Advanced
   analytics can identify the patterns of disease occurrence, predict
   disease progression, identify patient response to treatments, establish
   optimal care guidelines and generate research hypotheses with the
   narrowly identified research patient populations.},
Publisher = {WILEY},
Address = {111 RIVER ST, HOBOKEN 07030-5774, NJ USA},
Type = {Review},
Language = {English},
Affiliation = {Groft, SC (Corresponding Author), NIH, Nat Ctr Adv Translat Sci, Bethesda, MD 20814 USA.
   Groft, Stephen C., NIH, Nat Ctr Adv Translat Sci, Bethesda, MD 20814 USA.
   Posada, Manuel, Inst Salud Carlos III, Inst Rare Dis Res IIER, Madrid, Spain.
   Taruscio, Domenica, Natl Ctr Rare Dis, Ist Super Sanita, Rome, Italy.},
DOI = {10.1111/apa.15974},
EarlyAccessDate = {JUN 2021},
ISSN = {0803-5253},
EISSN = {1651-2227},
Keywords = {global needs; Orphan Drugs; Patient Advocacy Groups; Patient-Centric
   Research; rare diseases},
Keywords-Plus = {NETWORK},
Research-Areas = {Pediatrics},
Web-of-Science-Categories  = {Pediatrics},
Author-Email = {stephen.groft@nih.gov},
Affiliations = {National Institutes of Health (NIH) - USA; Instituto de Salud Carlos
   III; Instituto de Investigacion de Enfermedades Raras (IIER); Istituto
   Superiore di Sanita (ISS)},
ORCID-Numbers = {Groft, Stephen/0000-0002-7532-8962},
Cited-References = {{[}Anonymous], 2020, CELL GENE THERAPY IN, V6, P137, DOI {[}10.18609/cgti.2020.018, DOI 10.18609/CGTI.2020.018].
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Number-of-Cited-References = {15},
Times-Cited = {19},
Usage-Count-Last-180-days = {4},
Usage-Count-Since-2013 = {14},
Journal-ISO = {Acta Paediatr.},
Doc-Delivery-Number = {UN4XP},
Web-of-Science-Index = {Science Citation Index Expanded (SCI-EXPANDED)},
Unique-ID = {WOS:000663353600001},
DA = {2023-04-22},
}

@article{ WOS:000345734700045,
Author = {Araghi, Sahar and Khosravi, Abbas and Creighton, Douglas},
Title = {A review on computational intelligence methods for controlling traffic
   signal timing},
Journal = {EXPERT SYSTEMS WITH APPLICATIONS},
Year = {2015},
Volume = {42},
Number = {3},
Pages = {1538-1550},
Month = {FEB 15},
Abstract = {Urban traffic as one of the most important challenges in modern city
   life needs practically effective and efficient solutions. Artificial
   intelligence methods have gained popularity for optimal traffic light
   control. In this paper, a review of most important works in the field of
   controlling traffic signal timing, in particular studies focusing on
   Q-learning, neural network, and fuzzy logic system are presented. As per
   existing literature, the intelligent methods show a higher performance
   compared to traditional controlling methods. However, a study that
   compares the performance of different learning methods is not published
   yet. In this paper, the aforementioned computational intelligence
   methods and a fixed-time method are implemented to set signals times and
   minimize total delays for an isolated intersection. These methods are
   developed and compared on a same platform. The intersection is treated
   as an intelligent agent that learns to propose an appropriate green time
   for each phase. The appropriate green time for all the intelligent
   controllers are estimated based on the received traffic information. A
   comprehensive comparison is made between the performance of Q-learning,
   neural network, and fuzzy logic system controller for two different
   scenarios. The three intelligent learning controllers present close
   performances with multiple replication orders in two scenarios. On
   average Q-learning has 66\%, neural network 71\%, and fuzzy logic has
   74\% higher performance compared to the fixed-time controller. (C) 2014
   Elsevier Ltd. All rights reserved.},
Publisher = {PERGAMON-ELSEVIER SCIENCE LTD},
Address = {THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND},
Type = {Review},
Language = {English},
Affiliation = {Araghi, S (Corresponding Author), Deakin Univ, CISR, Melbourne, Vic 3216, Australia.
   Araghi, Sahar; Khosravi, Abbas; Creighton, Douglas, Deakin Univ, CISR, Melbourne, Vic 3216, Australia.},
DOI = {10.1016/j.eswa.2014.09.003},
ISSN = {0957-4174},
EISSN = {1873-6793},
Keywords = {Traffic signal timing; Machine learning; Q-learning; Neural network;
   Fuzzy logic system; Isolated intersection},
Keywords-Plus = {FUZZY-LOGIC; MULTIAGENT SYSTEM; APPROXIMATION; ARCHITECTURE},
Research-Areas = {Computer Science; Engineering; Operations Research \& Management Science},
Web-of-Science-Categories  = {Computer Science, Artificial Intelligence; Engineering, Electrical \&
   Electronic; Operations Research \& Management Science},
Author-Email = {saraghi@deakin.edu.au
   abbas.khosravi@deakin.edu.au
   douglas.creighton@deakin.edu.au},
Affiliations = {Deakin University},
ResearcherID-Numbers = {Khosravi, Abbas/AAQ-8102-2021},
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Number-of-Cited-References = {88},
Times-Cited = {71},
Usage-Count-Last-180-days = {5},
Usage-Count-Since-2013 = {177},
Journal-ISO = {Expert Syst. Appl.},
Doc-Delivery-Number = {AU6TN},
Web-of-Science-Index = {Science Citation Index Expanded (SCI-EXPANDED)},
Unique-ID = {WOS:000345734700045},
DA = {2023-04-22},
}

@article{ WOS:000669902500011,
Author = {Pedrero, Victor and Reynaldos-Grandon, Katiuska and Ureta-Achurraz,
   Joaquin and Cortez-Pinto, Erick},
Title = {Overview of machine learning and its application in the management of
   emergency services},
Journal = {REVISTA MEDICA DE CHILE},
Year = {2021},
Volume = {149},
Number = {2},
Pages = {248-254},
Month = {FEB},
Abstract = {The processes associated with health care generate a large amount of
   information that is difficult to analyze using standard statistical
   procedures. In this context, disciplines such as Data Science became
   relevant, mainly through strategies such as Machine Learning (ML). The
   latter groups a series of tools whose purpose is to develop algorithms
   to extract information from data, whether for explanation,
   classification, or prediction. Despite its usefulness as support for
   clinical decisions, its potential in health care management has been
   less explored. Also, there are difficulties in understanding these types
   of studies. This work tries to offer a nontechnical overview of the ML
   concept and its advantages for health care management. It collects
   examples of ML applications in emergency department management.},
Publisher = {SOC MEDICA SANTIAGO},
Address = {BERNARDA MORIN 488 PROVIDENCIA, CASILLA 168 CORREO 55, SANTIAGO 9,
   00000, CHILE},
Type = {Review},
Language = {Spanish},
Affiliation = {Reynaldos-Grandon, K (Corresponding Author), Ave Republ 217, Santiago, Chile.
   Pedrero, Victor; Reynaldos-Grandon, Katiuska, Univ Andres Bello, Fac Enfermeria, Santiago, Chile.
   Cortez-Pinto, Erick, Serv Salud Metropolitano Sur, Dept Gest TIC, Santiago, Chile.},
ISSN = {0034-9887},
EISSN = {0717-6163},
Keywords = {Emergency Medical Services; Health Information Management; Machine
   Learning},
Keywords-Plus = {HEALTH-CARE; MODEL; RISK},
Research-Areas = {General \& Internal Medicine},
Web-of-Science-Categories  = {Medicine, General \& Internal},
Author-Email = {katiuska.reynaldos@unab.cl},
Affiliations = {Universidad Andres Bello},
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Number-of-Cited-References = {26},
Times-Cited = {1},
Usage-Count-Last-180-days = {6},
Usage-Count-Since-2013 = {19},
Journal-ISO = {Rev. Medica Chile},
Doc-Delivery-Number = {TE3GO},
Web-of-Science-Index = {Science Citation Index Expanded (SCI-EXPANDED)},
Unique-ID = {WOS:000669902500011},
OA = {gold},
DA = {2023-04-22},
}

@article{ WOS:000668603600004,
Author = {Bianchi, Jonas and Ruellas, Antonio and Prieto, Juan Carlos and Li,
   Tengfei and Soroushmehr, Reza and Najarian, Kayvan and Gryak, Jonathan
   and Deleat-Besson, Romain and Le, Celia and Yatabe, Marilia and Gurgel,
   Marcela and Al Turkestani, Najla and Paniagua, Beatriz and Cevidanes,
   Lucia},
Title = {Decision Support Systems in Temporomandibular Joint Osteoarthritis: A
   review of Data Science and Artificial Intelligence Applications},
Journal = {SEMINARS IN ORTHODONTICS},
Year = {2021},
Volume = {27},
Number = {2},
Pages = {78-86},
Month = {JUN},
Abstract = {With the exponential growth of computational systems and increased
   patient data acquisition, dental research faces new challenges to manage
   a large quantity of information. For this reason, data science
   approaches are needed for the integrative diagnosis of multifactorial
   diseases, such as Temporomandibular joint (TMJ) Osteoarthritis (OA). The
   Data science spectrum includes data capture/acquisition, data processing
   with optimized web based storage and management, data analytics
   involving in-depth statistical analysis, machine learning (ML)
   approaches, and data communication. Artificial intelligence (AI) plays a
   crucial role in this process. It consists of developing computational
   systems that can perform human intelligence tasks, such as disease
   diagnosis, using many features to help in the decision-making support.
   Patient's clinical parameters, imaging exams, and molecular data are
   used as the input in cross-validation tasks, and human
   annotation/diagnosis is also used as the gold standard to train
   computational learning models and automatic disease classifiers. This
   paper aims to review and describe AI and ML techniques to diagnose TMJ
   OA and data science approaches for imaging processing. We used a
   web-based system for multi-center data communication, algorithms
   integration, statistics deployment, and process the computational
   machine learning models. We successfully show AI and data-science
   applications using patients' data to improve the TMJ OA diagnosis
   decision-making towards personalized medicine. (Semin Orthod 2021;
   27:78-86) (c) 2021 Elsevier Inc. All rights reserved.},
Publisher = {ELSEVIER INC},
Address = {525 B STREET, STE 1900, SAN DIEGO, CA 92101-4495 USA},
Type = {Review},
Language = {English},
Affiliation = {Bianchi, J (Corresponding Author), Univ Pacific, Dept Orthodont, Arthur A Dugoni Sch Dent, San Francisco, CA 94103 USA.
   Bianchi, Jonas, Univ Pacific, Dept Orthodont, Arthur A Dugoni Sch Dent, San Francisco, CA 94103 USA.
   Ruellas, Antonio; Deleat-Besson, Romain; Le, Celia; Yatabe, Marilia; Gurgel, Marcela; Al Turkestani, Najla, Univ Michigan, Sch Dent, Dept Orthodont \& Pediat Dent, Ann Arbor, MI 48109 USA.
   Prieto, Juan Carlos, Univ N Carolina, Dept Psychiat, Chapel Hill, NC 27515 USA.
   Li, Tengfei, Univ N Carolina, Dept Biostat, Chapel Hill, NC 27515 USA.
   Soroushmehr, Reza, Univ Michigan, Dept Computat Med \& Bioinformat, Ann Arbor, MI 48109 USA.
   Univ Michigan, Dept Orthodont \& Pediat Dent, Ann Arbor, MI 48109 USA.
   Paniagua, Beatriz, Kitware Inc, Carrboro, NC USA.},
DOI = {10.1053/j.sodo.2021.05.004},
EarlyAccessDate = {JUN 2021},
ISSN = {1073-8746},
EISSN = {1558-4631},
Keywords-Plus = {MACHINE; BIOMARKERS; DISORDERS; CRITERIA; NETWORK},
Research-Areas = {Dentistry, Oral Surgery \& Medicine},
Web-of-Science-Categories  = {Dentistry, Oral Surgery \& Medicine},
Author-Email = {jbianchi@pacific.edu},
Affiliations = {University of the Pacific; University of Michigan System; University of
   Michigan; University of North Carolina; University of North Carolina
   Chapel Hill; University of North Carolina; University of North Carolina
   Chapel Hill; University of Michigan System; University of Michigan;
   University of Michigan System; University of Michigan; Kitware, Inc.},
ORCID-Numbers = {Gryak, Jonathan/0000-0002-5125-7741
   Li, Tengfei/0000-0001-6142-3865
   Gurgel, Marcela/0000-0001-9978-0542
   AL TURKESTANI, NAJLA/0000-0002-7650-3638},
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Number-of-Cited-References = {61},
Times-Cited = {3},
Usage-Count-Last-180-days = {2},
Usage-Count-Since-2013 = {10},
Journal-ISO = {Semin. Orthod.},
Doc-Delivery-Number = {TC4IG},
Web-of-Science-Index = {Science Citation Index Expanded (SCI-EXPANDED)},
Unique-ID = {WOS:000668603600004},
OA = {Green Accepted},
DA = {2023-04-22},
}

@article{ WOS:000878982900001,
Author = {Liu, Yang and Wei, Yu-Shen and Yan, Hong and Li, Guan-Bin and Lin, Liang},
Title = {Causal Reasoning Meets Visual Representation Learning: A Prospective
   Study},
Journal = {MACHINE INTELLIGENCE RESEARCH},
Year = {2022},
Volume = {19},
Number = {6},
Pages = {485-511},
Month = {DEC},
Abstract = {Visual representation learning is ubiquitous in various real-world
   applications, including visual comprehension, video understanding,
   multi-modal analysis, human-computer interaction, and urban computing.
   Due to the emergence of huge amounts of multimodal heterogeneous
   spatial/temporal/spatial-temporal data in the big data era, the lack of
   interpretability, robustness, and out-of-distribution generalization are
   becoming the challenges of the existing visual models. The majority of
   the existing methods tend to fit the original data/variable
   distributions and ignore the essential causal relations behind the
   multi-modal knowledge, which lacks unified guidance and analysis about
   why modern visual representation learning methods easily collapse into
   data bias and have limited generalization and cognitive abilities.
   Inspired by the strong inference ability of human-level agents, recent
   years have therefore witnessed great effort in developing causal
   reasoning paradigms to realize robust representation and model learning
   with good cognitive ability. In this paper, we conduct a comprehensive
   review of existing causal reasoning methods for visual representation
   learning, covering fundamental theories, models, and datasets. The
   limitations of current methods and datasets are also discussed.
   Moreover, we propose some prospective challenges, opportunities, and
   future research directions for benchmarking causal reasoning algorithms
   in visual representation learning. This paper aims to provide a
   comprehensive overview of this emerging field, attract attention,
   encourage discussions, bring to the forefront the urgency of developing
   novel causal reasoning methods, publicly available benchmarks, and
   consensus-building standards for reliable visual representation learning
   and related real-world applications more efficiently.},
Publisher = {SPRINGERNATURE},
Address = {CAMPUS, 4 CRINAN ST, LONDON, N1 9XW, ENGLAND},
Type = {Review},
Language = {English},
Affiliation = {Lin, L (Corresponding Author), Sun Yat Sen Univ, Sch Comp Sci \& Engn, Guangzhou 510006, Peoples R China.
   Liu, Yang; Wei, Yu-Shen; Yan, Hong; Li, Guan-Bin; Lin, Liang, Sun Yat Sen Univ, Sch Comp Sci \& Engn, Guangzhou 510006, Peoples R China.},
DOI = {10.1007/s11633-022-1362-z},
EarlyAccessDate = {NOV 2022},
ISSN = {2731-538X},
EISSN = {2731-5398},
Keywords = {Causal reasoning; visual representation learning; reliable artificial
   intelligence; spatial-temporal data; multi-modal analysis},
Keywords-Plus = {CONVOLUTIONAL NETWORKS; GRAPH},
Research-Areas = {Automation \& Control Systems; Computer Science},
Web-of-Science-Categories  = {Automation \& Control Systems; Computer Science, Artificial Intelligence},
Author-Email = {liuy856@mail.sysu.edu.cn
   welysh8@mail2.sysu.edu.cn
   yanh36@mail2.sysu.edu.cn
   liguanbin@mail.sysu.edu.cn
   linliang@ieee.org},
Affiliations = {Sun Yat Sen University},
ResearcherID-Numbers = {Li, Jiaxi/HTS-3430-2023
   Lin, L/HKO-8213-2023
   l, j/HNC-5728-2023
   },
ORCID-Numbers = {Li, Jiaxi/0000-0002-8197-8590
   Yan, Hong/0000-0003-4100-6751
   Liang, Lin/0000-0003-2248-3755},
Funding-Acknowledgement = {National Natural Science Foundation of China {[}62002395, 61976250,
   U1811463]; National Key R\&D Program of China {[}2021ZD0111601];
   Guangdong Basic and Applied Basic Research Foundation, China
   {[}2021A15150123, 2020B1515020048]},
Funding-Text = {This work was supported in part by National Natural Science Foundation
   of China (Nos. 62002395, 61976250 and U1811463), the National Key R\&D
   Program of China (No. 2021ZD0111601), the Guangdong Basic and Applied
   Basic Research Foundation, China (Nos. 2021A15150123 and
   2020B1515020048).},
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Number-of-Cited-References = {236},
Times-Cited = {0},
Usage-Count-Last-180-days = {9},
Usage-Count-Since-2013 = {9},
Journal-ISO = {Mach. Intell. Res.},
Doc-Delivery-Number = {6G6SK},
Web-of-Science-Index = {Emerging Sources Citation Index (ESCI)},
Unique-ID = {WOS:000878982900001},
OA = {hybrid, Green Submitted},
DA = {2023-04-22},
}

@article{ WOS:000922993000001,
Author = {Cantarero-Garcia, Guadalupe and Molla, Marina and Alonso
   Gonzalez-Lezcano, Roberto},
Title = {Smart citizen in architecture and landscape. Method design based on
   spatial intelligence and universal accessibility learning by students
   with intellectual disabilities},
Journal = {FRONTIERS IN BUILT ENVIRONMENT},
Year = {2023},
Volume = {9},
Month = {JAN 13},
Abstract = {The Smart Citizen concept is at an early stage of research in the field
   of architecture and landscape architecture. ICTs and their use by
   citizens have been studied exclusively in the fields of engineering and
   telecommunications sciences as well as Artificial Intelligence. This
   study seeks to publicize the Smart Citizen concept in architecture and
   landscape from a spatial understanding and the perception of citizens
   with intellectual disabilities. A real case study developed by
   architects in collaboration with psychologists focused on the
   development of spatial intelligence for people with intellectual
   disabilities is presented in this study. ``The students that
   participated in these activities belong to San Pablo CEU University
   course financed by ONCE Foundation (FONCE) and the European Social Fund.
   `` The purpose is to question the effectiveness of the use of
   technologies for said cognitive development and how even the increased
   use of GPS navigator systems could be a detriment to the citizen when
   trying to orient him or herself in open spaces. The result to highlight
   in this review is to know if a person with a disability is equally
   capable of understanding a space and navigate it without the aid of GPS
   as a person without an intellectual disability. Both analogical tools
   (2D plan) and digital tools (GPS) are used under the same conditions.
   New lines of research are presented in the study of spatial intelligence
   through innovating tools or TICs in relation to urban elements where
   concepts like scale, proportion, light, and shade were identified.},
Publisher = {FRONTIERS MEDIA SA},
Address = {AVENUE DU TRIBUNAL FEDERAL 34, LAUSANNE, CH-1015, SWITZERLAND},
Type = {Review},
Language = {English},
Affiliation = {Cantarero-Garcia, G (Corresponding Author), CEU Univ, Univ San Pablo CEU, Escuela Politecn Super, Urbanizac Monteprincipe, Madrid, Spain.
   Cantarero-Garcia, Guadalupe; Molla, Marina; Alonso Gonzalez-Lezcano, Roberto, CEU Univ, Univ San Pablo CEU, Escuela Politecn Super, Urbanizac Monteprincipe, Madrid, Spain.},
DOI = {10.3389/fbuil.2023.1094760},
Article-Number = {1094760},
EISSN = {2297-3362},
Keywords = {smart citizen; smart city; architecture; landscape; intelectual
   disability},
Keywords-Plus = {CITIES; INCLUSIVITY; CITY},
Research-Areas = {Construction \& Building Technology; Engineering},
Web-of-Science-Categories  = {Construction \& Building Technology; Engineering, Civil},
Author-Email = {gcgarcia@ceu.es},
Affiliations = {San Pablo CEU University},
ResearcherID-Numbers = {GARCIA, GUADALUPE CANTARERO/E-7035-2017
   Gonzalez Lezcano, Roberto Alonso/K-5670-2014},
ORCID-Numbers = {GARCIA, GUADALUPE CANTARERO/0000-0003-3169-8547
   Gonzalez Lezcano, Roberto Alonso/0000-0002-6185-4929},
Funding-Acknowledgement = {CEU San Pablo University;  {[}EC01/0720- MGI22RGL]},
Funding-Text = {The authors wish to thank CEU San Pablo University Foundation for the
   funds dedicated to the ARIE Research Group, through the Project Ref.
   EC01/0720- MGI22RGL provided by the CEU San Pablo University.},
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Number-of-Cited-References = {26},
Times-Cited = {0},
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Usage-Count-Since-2013 = {2},
Journal-ISO = {Front. Built Environ.},
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Web-of-Science-Index = {Emerging Sources Citation Index (ESCI)},
Unique-ID = {WOS:000922993000001},
OA = {gold},
DA = {2023-04-22},
}

@article{ WOS:000524758400006,
Author = {Falini, Stefano and Angelotti, Giovanni and Cecconi, Maurizio},
Title = {ICU management based on big data},
Journal = {CURRENT OPINION IN ANESTHESIOLOGY},
Year = {2020},
Volume = {33},
Number = {2},
Pages = {162-169},
Month = {APR},
Abstract = {Purpose of review The availability of large datasets and computational
   power has prompted a revolution in Intensive Care. Data represent a
   great opportunity for clinical practice, benchmarking, and research.
   Machine learning algorithms can help predict events in a way the human
   brain can simply not process. This possibility comes with benefits and
   risks for the clinician, as finding associations does not mean proving
   causality. Recent findings Current applications of Data Science still
   focus on data documentation and visualization, and on basic rules to
   identify critical lab values. Recently, algorithms have been put in
   place for prediction of outcomes such as length of stay, mortality, and
   development of complications. These results have begun being implemented
   for more efficient allocation of resources and in benchmarking
   processes, to allow identification of successful practices and margins
   for improvement. In parallel, machine learning models are increasingly
   being applied in research to expand medical knowledge. Data have always
   been part of the work of intensivists, but the current availability has
   not been completely exploited. The intensive care community has to
   embrace and guide the data science revolution in order to decline it in
   favor of patients' care.},
Publisher = {LIPPINCOTT WILLIAMS \& WILKINS},
Address = {TWO COMMERCE SQ, 2001 MARKET ST, PHILADELPHIA, PA 19103 USA},
Type = {Review},
Language = {English},
Affiliation = {Falini, S (Corresponding Author), Ist Clin Humanitas, Via Alessandro Manzoni 56, I-20089 Rozzano, Italy.
   Falini, Stefano; Cecconi, Maurizio, Humanitas Clin \& Res Ctr, Dept Anesthesia \& Intens Care, Rozzano, Italy.
   Angelotti, Giovanni, Humanitas Clin \& Res Ctr, Data Sci Core Facil, Rozzano, Italy.
   Cecconi, Maurizio, Humanitas Univ, Milan, Italy.},
DOI = {10.1097/ACO.0000000000000834},
ISSN = {0952-7907},
EISSN = {1473-6500},
Keywords = {benchmarking; big data; clinical prediction model; data science;
   intensive care medicine},
Keywords-Plus = {CARE; TRIALS; MEDICINE; SEPSIS},
Research-Areas = {Anesthesiology},
Web-of-Science-Categories  = {Anesthesiology},
Author-Email = {stefano.falini@humanitas.it},
Affiliations = {IRCCS Humanitas Research Hospital; IRCCS Humanitas Research Hospital;
   Humanitas University},
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   Cecconi, Maurizio/HDN-8785-2022
   Angelotti, Giovanni/HNC-2169-2023},
ORCID-Numbers = {Cecconi, Maurizio/0000-0002-4376-6538
   Cecconi, Maurizio/0000-0002-4376-6538
   Angelotti, Giovanni/0000-0003-3264-2721},
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Number-of-Cited-References = {54},
Times-Cited = {3},
Usage-Count-Last-180-days = {1},
Usage-Count-Since-2013 = {13},
Journal-ISO = {Curr. Opin. Anesthesiol.},
Doc-Delivery-Number = {LB6QN},
Web-of-Science-Index = {Science Citation Index Expanded (SCI-EXPANDED)},
Unique-ID = {WOS:000524758400006},
DA = {2023-04-22},
}

@article{ WOS:000651380300008,
Author = {Hassan, Nehal and Slight, Robert and Weiand, Daniel and Vellinga, Akke
   and Morgan, Graham and Aboushareb, Fathy and Slight, Sarah P.},
Title = {Preventing sepsis; how can artificial intelligence inform the clinical
   decision-making process? A systematic review},
Journal = {INTERNATIONAL JOURNAL OF MEDICAL INFORMATICS},
Year = {2021},
Volume = {150},
Month = {JUN},
Abstract = {Background and objectives: Sepsis is a life-threatening condition that
   is associated with increased mortality. Artificial intelligence tools
   can inform clinical decision making by flagging patients at risk of
   developing infection and subsequent sepsis. This systematic review aims
   to identify the optimal set of predictors used to train machine learning
   algorithms to predict the likelihood of an infection and subsequent
   sepsis.
   Methods: This systematic review was registered in PROSPERO database
   (CRD42020158685). We conducted a systematic literature review across 3
   large databases: Medline, Cumulative Index of Nursing and Allied Health
   Literature, and Embase. Quantitative primary research studies that
   focused on sepsis prediction associated with bacterial infection in
   adults in all care settings were eligible for inclusion.
   Results: Seventeen articles met our inclusion criteria. We identified
   194 predictors that were used to train machine learning algorithms, with
   13 predictors used on average across all included studies. The most
   prevalent predictors included age, gender, smoking, alcohol intake,
   heart rate, blood pressure, lactate level, cardiovascular disease,
   endocrine disease, cancer, chronic kidney disease (eGFR<60 mL/min),
   white blood cell count, liver dysfunction, surgical approach (open or
   minimally invasive), and pre-operative haematocrit < 30 \%. All included
   studies used artificial intelligence techniques, with average
   sensitivity 75.7 +/- 17.88, and average specificity 63.08 +/- 22.01.
   Conclusion: The type of predictors influenced the predictive power and
   predictive timeframe of the developed machine learning algorithm.
   Predicting the likelihood of sepsis through artificial intelligence can
   help concentrate finite resources to those patients who are most at
   risk. Future studies should focus on developing more sensitive and
   specific algorithms.},
Publisher = {ELSEVIER IRELAND LTD},
Address = {ELSEVIER HOUSE, BROOKVALE PLAZA, EAST PARK SHANNON, CO, CLARE, 00000,
   IRELAND},
Type = {Review},
Language = {English},
Affiliation = {Slight, SP (Corresponding Author), Newcastle Univ, Sch Pharm, King George VI Bldg, Newcastle Upon Tyne NE1 7RU, Tyne \& Wear, England.
   Hassan, Nehal; Slight, Sarah P., Newcastle Univ, Sch Pharm, King George VI Bldg, Newcastle Upon Tyne NE1 7RU, Tyne \& Wear, England.
   Slight, Robert; Weiand, Daniel, Newcastle Tyne Hosp NHS Fdn Trust, Freeman Hosp, Newcastle Upon Tyne NE7 7DN, Tyne \& Wear, England.
   Vellinga, Akke, Natl Univ Ireland Galway, Sch Med, Univ Rd, Galway H91 TK33, Ireland.
   Morgan, Graham, Newcastle Univ, Sch Comp, Urban Sci Bldg, Newcastle Upon Tyne NE4 5TG, Tyne \& Wear, England.
   Aboushareb, Fathy, Northumbria Healthcare NHS Fdn Trust, Rake Lane, N Shields NE29 8NH, Tyne \& Wear, England.},
DOI = {10.1016/j.ijmedinf.2021.104457},
EarlyAccessDate = {APR 2021},
Article-Number = {104457},
ISSN = {1386-5056},
EISSN = {1872-8243},
Keywords = {Sepsis; Prediction; Machine learning; Decision-making; Artificial
   intelligence},
Keywords-Plus = {PREDICTION; ONSET; MODEL},
Research-Areas = {Computer Science; Health Care Sciences \& Services; Medical Informatics},
Web-of-Science-Categories  = {Computer Science, Information Systems; Health Care Sciences \& Services;
   Medical Informatics},
Author-Email = {n.a.m.hassan2@newcastle.ac.uk
   bob.slight@nhs.net
   dweand@nhs.net
   akke.vellinga@nuigalway.ie
   graham.morgan@ncl.ac.uk
   fathy.aboushareb@hs.net
   Sarah.Slight@newcastle.ac.uk},
Affiliations = {Newcastle University - UK; Newcastle Freeman Hospital; Newcastle Upon
   Tyne Hospitals NHS Foundation Trust; Newcastle University - UK},
ResearcherID-Numbers = {Vellinga, Akke/H-2130-2011},
ORCID-Numbers = {Slight, Sarah P/0000-0002-0339-846X
   Morgan, Graham/0000-0002-0089-0395
   Hassan, Nehal/0000-0002-8302-5769
   Weiand, Daniel/0000-0001-5854-3452
   Slight, Robert/0000-0003-3255-0640
   Vellinga, Akke/0000-0002-6583-4300},
Funding-Acknowledgement = {Newcastle University, UK},
Funding-Text = {The first author (NH) is awarded NUROS scholarship by Newcastle
   University, UK towards tuition fees, as this project is a part of a
   doctorate degree.},
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Number-of-Cited-References = {38},
Times-Cited = {8},
Usage-Count-Last-180-days = {3},
Usage-Count-Since-2013 = {15},
Journal-ISO = {Int. J. Med. Inform.},
Doc-Delivery-Number = {SD4YS},
Web-of-Science-Index = {Science Citation Index Expanded (SCI-EXPANDED)},
Unique-ID = {WOS:000651380300008},
DA = {2023-04-22},
}

@article{ WOS:000663012500001,
Author = {Li, Shunning and Liu, Yuanji and Chen, Dong and Jiang, Yi and Nie,
   Zhiwei and Pan, Feng},
Title = {Encoding the atomic structure for machine learning in materials science},
Journal = {WILEY INTERDISCIPLINARY REVIEWS-COMPUTATIONAL MOLECULAR SCIENCE},
Year = {2022},
Volume = {12},
Number = {1},
Month = {JAN},
Abstract = {In recent years, we have witnessed a widespread application of machine
   learning techniques in the field of materials science, owing to the
   increased availability of research data and sophisticated algorithms. At
   the core of this technology lies the ability to encode material
   structures into descriptors that are understandable for a computer.
   Although significant advances have been made in this area, there is a
   continued need to explore efficient structure-encoding strategies so as
   to maximize the predictive power of the machine learning models. Here we
   present a revision of the exciting progress in four representative
   structural features that are capable of describing the structures of
   diverse materials: structure graph, Coulomb matrix, topological
   descriptor, and diffraction fingerprint. Particular attention is given
   to the studies of crystalline solids, which appear more challenging to
   be encoded than molecules. By summarizing previous works and presenting
   critical appraisals of these descriptors, this review could offer some
   guideline for the selection of structural features and stimulate
   inspiration for the design of powerful descriptors suited towards
   different tasks.
   This article is categorized under:
   Structure and Mechanism > Computational Materials Science
   Data Science > Artificial Intelligence/Machine Learning},
Publisher = {WILEY},
Address = {111 RIVER ST, HOBOKEN 07030-5774, NJ USA},
Type = {Review},
Language = {English},
Affiliation = {Pan, F (Corresponding Author), Peking Univ, Sch Adv Mat, Shenzhen Grad Sch, Shenzhen 518055, Peoples R China.
   Li, Shunning; Liu, Yuanji; Chen, Dong; Jiang, Yi; Nie, Zhiwei; Pan, Feng, Peking Univ, Sch Adv Mat, Shenzhen Grad Sch, Shenzhen 518055, Peoples R China.},
DOI = {10.1002/wcms.1558},
EarlyAccessDate = {JUN 2021},
Article-Number = {e1558},
ISSN = {1759-0876},
EISSN = {1759-0884},
Keywords = {atomic structure; crystalline solids; machine learning; structural
   feature},
Keywords-Plus = {PREDICTING PROPERTIES; MOLECULAR DESCRIPTOR; PERSISTENT HOMOLOGY;
   CRYSTAL-STRUCTURES; DESIGN PRINCIPLES; NEURAL-NETWORKS; DISCOVERY;
   MODELS; ELECTROCATALYSTS; REPRESENTATIONS},
Research-Areas = {Chemistry; Mathematical \& Computational Biology},
Web-of-Science-Categories  = {Chemistry, Multidisciplinary; Mathematical \& Computational Biology},
Author-Email = {panfeng@pkusz.edu.cn},
Affiliations = {Peking University; University Town of Shenzhen},
ResearcherID-Numbers = {Li, Shunning/ABB-4187-2021
   },
ORCID-Numbers = {Li, Shunning/0000-0002-5381-6025
   Nie, Zhiwei/0000-0002-2781-5248},
Funding-Acknowledgement = {Chemistry and Chemical Engineering Guangdong Laboratory {[}1922018];
   National Key R\&D Program of China {[}2016YFB0700600, 2020YFB0704500];
   Shenzhen Science and Technology Research Grant {[}JCYJ20200109140416788]},
Funding-Text = {Chemistry and Chemical Engineering Guangdong Laboratory, Grant/Award
   Number: 1922018; National Key R\&D Program of China, Grant/Award
   Numbers: 2016YFB0700600, 2020YFB0704500; Shenzhen Science and Technology
   Research Grant, Grant/Award Number: JCYJ20200109140416788},
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@article{ WOS:000526785800022,
Author = {Boje, Calin and Guerriero, Annie and Kubicki, Sylvain and Rezgui, Yacine},
Title = {Towards a semantic Construction Digital Twin: Directions for future
   research},
Journal = {AUTOMATION IN CONSTRUCTION},
Year = {2020},
Volume = {114},
Month = {JUN},
Abstract = {As the Architecture, Engineering and Construction sector is embracing
   the digital age, the processes involved in the design, construction and
   operation of built assets are more and more influenced by technologies
   dealing with value-added monitoring of data from sensor networks,
   management of this data in secure and resilient storage systems
   underpinned by semantic models, as well as the simulation and
   optimisation of engineering systems. Aside from enhancing the efficiency
   of the value chain, such information-intensive models and associated
   technologies play a decisive role in minimising the lifecycle impacts of
   our buildings. While Building Information Modelling provides procedures,
   technologies and data schemas enabling a standardised semantic
   representation of building components and systems, the concept of a
   Digital Twin conveys a more holistic socio-technical and
   process-oriented characterisation of the complex artefacts involved by
   leveraging the synchronicity of the cyber-physical bi-directional data
   flows. Moreover, BIM lacks semantic completeness in areas such as
   control systems, including sensor networks, social systems, and urban
   artefacts beyond the scope of buildings, thus requiring a holistic,
   scalable semantic approach that factors in dynamic data at different
   levels. The paper reviews the multi-faceted applications of BIM during
   the construction stage and highlights limits and requirements, paving
   the way to the concept of a Construction Digital Twin. A definition of
   such a concept is then given, described in terms of underpinning
   research themes, while elaborating on areas for future research.},
Publisher = {ELSEVIER},
Address = {RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS},
Type = {Review},
Language = {English},
Affiliation = {Boje, C (Corresponding Author), Luxembourg Inst Sci \& Technol, Esch Sur Alzette, Luxembourg.
   Boje, Calin; Guerriero, Annie; Kubicki, Sylvain, Luxembourg Inst Sci \& Technol, Esch Sur Alzette, Luxembourg.
   Rezgui, Yacine, Cardiff Univ, BRE Trust Ctr Sustainable Engn, Cardiff, S Glam, Wales.},
DOI = {10.1016/j.autcon.2020.103179},
Article-Number = {103179},
ISSN = {0926-5805},
EISSN = {1872-7891},
Keywords = {Digital Twin; BIM; Review; Industry Foundation Classes (IFC); Internet
   of Things (IoT); Framework; Artificial Intelligence (AI); Big data;
   Construction safety; Construction site},
Keywords-Plus = {BUILDING INFORMATION MODELS; 4D BIM; HAZARD IDENTIFICATION; APPLICATION
   FRAMEWORK; AEC INDUSTRY; SIMULATION; MANAGEMENT; ONTOLOGY; DESIGN; 3D},
Research-Areas = {Construction \& Building Technology; Engineering},
Web-of-Science-Categories  = {Construction \& Building Technology; Engineering, Civil},
Author-Email = {calin.boje@list.lu},
Affiliations = {Luxembourg Institute of Science \& Technology; Cardiff University},
ResearcherID-Numbers = {Rezgui, Yacine/ABE-6712-2020
   Kubicki, Sylvain/I-6965-2019
   },
ORCID-Numbers = {Rezgui, Yacine/0000-0002-5711-8400
   Kubicki, Sylvain/0000-0003-2985-0378
   Boje, Calin/0000-0002-5150-9355
   Guerriero, Annie/0000-0002-8804-1995},
Funding-Acknowledgement = {Fonds National de la Recherche Luxembourg; Agence Nationale de la
   Recherche France {[}11237662 (LU)/ANR-16-CE10-0006-01 (FR)]; EPSRC
   {[}EP/T019514/1] Funding Source: UKRI},
Funding-Text = {The authors acknowledge financial support from Fonds National de la
   Recherche Luxembourg, and Agence Nationale de la Recherche France, to
   4DCollab project, grant reference: 11237662 (LU)/ANR-16-CE10-0006-01
   (FR).},
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Times-Cited = {241},
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Unique-ID = {WOS:000526785800022},
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@article{ WOS:000543385900327,
Author = {Gotovtsev, Pavel},
Title = {How IoT Can Integrate Biotechnological Approaches for City
   Applications-Review of Recent Advancements, Issues, and Perspectives},
Journal = {APPLIED SCIENCES-BASEL},
Year = {2020},
Volume = {10},
Number = {11},
Month = {JUN},
Abstract = {There are a number of significant changes taking place in modern city
   development and most of them are based on the number of recent
   technological progress. This paper provides a review and analysis of
   recent approaches of biotechnology that can find a place in today's
   cities and discusses how those technologies can be integrated into a
   city's Internet of Things (IoT). Firstly, several biotechnologies that
   focus on rain gardens, urban vertical farming systems, and city
   photobioreactors are discussed in the context of their integration in a
   city's IoT. The next possible application of biofuel cells to the sensor
   network's energy supply is discussed. It is shown that such devices can
   influence the low-power sensor network structure as an additional energy
   source for transmitters. This paper shows the possibility of
   bioelectrochemical biosensor applications, discusses self-powered
   biosensors, and shows that such a system can be widely applied to
   rainwater monitoring in rain gardens and green streets. Significant
   attention is paid to recent approaches in synthetic biology. Both
   cell-based biosensors and bioactuators with synthetic genetic circuits
   are discussed. The development of cell-based biosensors can
   significantly enhance the sensing possibilities of a city's IoT. We show
   the possible ways to develop cyber-physical systems (CPSs) with the
   systems mentioned above. Aspects of data handling for the discussed
   biotechnologies and the methods of intelligent systems, including those
   that are machine learning-based, applied to the IoT in a city are
   presented.},
Publisher = {MDPI},
Address = {ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND},
Type = {Review},
Language = {English},
Affiliation = {Gotovtsev, P (Corresponding Author), Natl Res Ctr Kurchatov Inst, Biotechnol \& Bioenergy Dept, 1 Akad Kurchalova Pl, Moscow 123182, Russia.
   Gotovtsev, Pavel, Natl Res Ctr Kurchatov Inst, Biotechnol \& Bioenergy Dept, 1 Akad Kurchalova Pl, Moscow 123182, Russia.},
DOI = {10.3390/app10113990},
Article-Number = {3990},
EISSN = {2076-3417},
Keywords = {Internet of Things; smart city; wireless sensor networks; biofuel cells;
   biosensors; biotechnology; synthetic biology; cyber-physical systems},
Keywords-Plus = {MICROBIAL FUEL-CELL; NEURAL-NETWORK-MODEL; SYNTHETIC BIOLOGY;
   ARTIFICIAL-INTELLIGENCE; GREEN ARCHITECTURE; RAIN GARDENS; BIG DATA;
   INTERNET; DESIGN; OPTIMIZATION},
Research-Areas = {Chemistry; Engineering; Materials Science; Physics},
Web-of-Science-Categories  = {Chemistry, Multidisciplinary; Engineering, Multidisciplinary; Materials
   Science, Multidisciplinary; Physics, Applied},
Author-Email = {Gotovtsev\_PM@nrcki.ru},
Affiliations = {National Research Centre - Kurchatov Institute},
ResearcherID-Numbers = {Pavel, Gotovtsev/A-5384-2014},
ORCID-Numbers = {Pavel, Gotovtsev/0000-0003-2172-5839},
Funding-Acknowledgement = {NRC ``Kurchatov Institute{''}, Thematic Plan 1.11, ``Development of
   Nature-Like Bioenergy and Hybrid Energy Sources for Different
   Application Fields{''}},
Funding-Text = {This work was supported by the NRC ``Kurchatov Institute{''}, Thematic
   Plan 1.11, ``Development of Nature-Like Bioenergy and Hybrid Energy
   Sources for Different Application Fields{''}.},
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Number-of-Cited-References = {126},
Times-Cited = {6},
Usage-Count-Last-180-days = {8},
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Journal-ISO = {Appl. Sci.-Basel},
Doc-Delivery-Number = {MC6HQ},
Web-of-Science-Index = {Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)},
Unique-ID = {WOS:000543385900327},
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@article{ WOS:000568826300059,
Author = {Zounemat-Kermani, Mohammad and Matta, Elena and Cominola, Andrea and
   Xia, Xilin and Zhang, Qing and Liang, Qiuhua and Hinkelmann, Reinhard},
Title = {Neurocomputing in surface water hydrology and hydraulics: A review of
   two decades retrospective, current status and future prospects},
Journal = {JOURNAL OF HYDROLOGY},
Year = {2020},
Volume = {588},
Month = {SEP},
Abstract = {Neurocomputing methods have contributed significantly to the advancement
   of modelling techniques in surface water hydrology and hydraulics in the
   last couple of decades, primarily due to their vast performance
   advantages and usage amenity. This comprehensive review considers the
   research progress in the past two decades, the current state-of-the-art,
   and future prospects of the application of neurocomputing to different
   aspects of hydrological sciences, i.e., quantitative surface hydrology
   and hydraulics. An extensive literature survey, by running over more
   than 800 peer-reviewed papers, outlines and concisely explores the past
   and recent tendencies in the application of conventional neural-based
   approaches and modern neurocomputing models in relevant topics of
   hydrological and hydraulic sciences. Apart from segregated descriptions
   and analyses of the main facets of surface hydrology and hydraulics,
   this review offers a practical summary of prevailing neurocomputing
   methods used in different subfields of hydrology and water engineering.
   Six relevant topics to modelling hydrological and hydraulic sciences are
   articulated and analysed, including modelling of water level in surface
   water bodies, flood and risk assessment, sediment transport in river
   systems, urban water demand prediction, modelling flow through
   hydro-structures, and hydraulics of sewers. This review is meant to be a
   mainstream guideline for researchers and practitioners whose work is
   associated with data mining and machine learning methods in various
   areas of water engineering and hydrological sciences to assist them to
   decide on suitable methods, network structures and modelling strategies
   for a given problem.},
Publisher = {ELSEVIER},
Address = {RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS},
Type = {Review},
Language = {English},
Affiliation = {Zounemat-Kermani, M (Corresponding Author), Shahid Bahonar Univ, Pajoohesh Sq, Kerman 7616914111, Iran.
   Zounemat-Kermani, Mohammad, Shaihd Bahonar Univ Kerman, Dept Water Engn, Kerman, Iran.
   Matta, Elena, Tech Univ Berlin, Dept Water Engn, Campus El Gouna, Berlin, Germany.
   Matta, Elena; Zhang, Qing; Hinkelmann, Reinhard, Tech Univ Berlin, Chair Water Resources Management \& Modeling Hydro, Berlin, Germany.
   Cominola, Andrea, Tech Univ Berlin, Chair Smart Water Networks, Berlin, Germany.
   Cominola, Andrea, Einstein Ctr Digital Future, Berlin, Germany.
   Xia, Xilin; Liang, Qiuhua, Loughborough Univ, Sch Architecture Bldg \& Civil Engn, Loughborough, Leics, England.},
DOI = {10.1016/j.hydrol.2020.125085},
Article-Number = {125085},
ISSN = {0022-1694},
EISSN = {1879-2707},
Keywords = {Artificial neural networks; Machine learning; Hydroinformatics;
   Hydrosciences; Artificial intelligence; Soft computing},
Keywords-Plus = {ARTIFICIAL NEURAL-NETWORK; SUSPENDED SEDIMENT CONCENTRATION;
   SHORT-TERM-MEMORY; LEARNING-MACHINE; DEMAND PREDICTION; HYBRID MODELS;
   RIVER; LEVEL; ANN; SIMULATION},
Research-Areas = {Engineering; Geology; Water Resources},
Web-of-Science-Categories  = {Engineering, Civil; Geosciences, Multidisciplinary; Water Resources},
Author-Email = {zounemat@uk.ac.ir},
Affiliations = {Technical University of Berlin; Technical University of Berlin;
   Technical University of Berlin; Loughborough University},
ResearcherID-Numbers = {Cominola, Andrea/AAU-7028-2020},
ORCID-Numbers = {Cominola, Andrea/0000-0002-4031-4704},
Funding-Acknowledgement = {Alexander von Humboldt Foundation},
Funding-Text = {The first author, as a Georg Forster Research fellow, would like to
   express his gratitude to the Alexander von Humboldt Foundation for
   supporting him during his research visit at Technische Universitat
   Berlin.},
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@article{ WOS:000654951400001,
Author = {Zippel, Claus and Bohnet-Joschko, Sabine},
Title = {Rise of Clinical Studies in the Field of Machine Learning: A Review of
   Data Registered in ClinicalTrials.gov},
Journal = {INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH},
Year = {2021},
Volume = {18},
Number = {10},
Month = {MAY},
Abstract = {Although advances in machine-learning healthcare applications promise
   great potential for innovative medical care, few data are available on
   the translational status of these new technologies. We aimed to provide
   a comprehensive characterization of the development and status quo of
   clinical studies in the field of machine learning. For this purpose, we
   performed a registry-based analysis of machine-learning-related studies
   that were published and first available in the ClinicalTrials.gov
   database until 2020, using the database's study classification. In
   total, n = 358 eligible studies could be included in the analysis. Of
   these, 82\% were initiated by academic institutions/university
   (hospitals) and 18\% by industry sponsors. A total of 96\% were national
   and 4\% international. About half of the studies (47\%) had at least one
   recruiting location in a country in North America, followed by Europe
   (37\%) and Asia (15\%). Most of the studies reported were initiated in
   the medical field of imaging (12\%), followed by cardiology, psychiatry,
   anesthesia/intensive care medicine (all 11\%) and neurology (10\%).
   Although the majority of the clinical studies were still initiated in an
   academic research context, the first industry-financed projects on
   machine-learning-based algorithms are becoming visible. The number of
   clinical studies with machine-learning-related applications and the
   variety of medical challenges addressed serve to indicate their
   increasing importance in future clinical care. Finally, they also set a
   time frame for the adjustment of medical device-related regulation and
   governance.},
Publisher = {MDPI},
Address = {ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND},
Type = {Review},
Language = {English},
Affiliation = {Bohnet-Joschko, S (Corresponding Author), Witten Herdecke Univ, Fac Management Econ \& Soc, Chair Management \& Innovat Hlth Care, D-58448 Witten, Germany.
   Zippel, Claus; Bohnet-Joschko, Sabine, Witten Herdecke Univ, Fac Management Econ \& Soc, Chair Management \& Innovat Hlth Care, D-58448 Witten, Germany.},
DOI = {10.3390/ijerph18105072},
Article-Number = {5072},
EISSN = {1660-4601},
Keywords = {machine learning; digital health; registry analysis; ClinicalTrials;
   gov; device regulation},
Keywords-Plus = {BIG DATA ANALYTICS; ARTIFICIAL-INTELLIGENCE; TRANSLATIONAL RESEARCH;
   DATA SCIENCE; HEALTH-CARE; TRIALS; MEDICINE; CANCER; CLASSIFICATION;
   INFORMATION},
Research-Areas = {Environmental Sciences \& Ecology; Public, Environmental \& Occupational
   Health},
Web-of-Science-Categories  = {Environmental Sciences; Public, Environmental \& Occupational Health},
Author-Email = {claus.zippel@uni-wh.de
   Sabine.Bohnet-Joschko@uni-wh.de},
ORCID-Numbers = {Bohnet-Joschko, Sabine/0000-0002-1119-9786},
Funding-Acknowledgement = {Ministry of Economic Affairs, Innovation, Digitalization and Energy of
   North Rhine-Westphalia {[}ITG-1-1]},
Funding-Text = {The APC is paid for under the ATLAS project which is funded by the
   Ministry of Economic Affairs, Innovation, Digitalization and Energy of
   North Rhine-Westphalia (funding code: ITG-1-1).},
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Times-Cited = {5},
Usage-Count-Last-180-days = {1},
Usage-Count-Since-2013 = {8},
Journal-ISO = {Int. J. Environ. Res. Public Health},
Doc-Delivery-Number = {SI6PY},
Web-of-Science-Index = {Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)},
Unique-ID = {WOS:000654951400001},
OA = {gold, Green Published},
DA = {2023-04-22},
}

@article{ WOS:000898561500011,
Author = {Hall, Ola and Ohlsson, Mattias and Rognvaldsson, Thorsteinn},
Title = {A review of explainable AI in the satellite data, deep machine learning,
   and human poverty domain},
Journal = {PATTERNS},
Year = {2022},
Volume = {3},
Number = {10},
Month = {SEP 9},
Abstract = {Recent advances in artificial intelligence and deep machine learning
   have created a step change in how to measure human development
   indicators, in particular asset-based poverty. The combination of
   satellite imagery and deep machine learning now has the capability to
   estimate some types of poverty at a level close to what is achieved with
   traditional household surveys. An increasingly important issue beyond
   static estimations is whether this technology can contribute to
   scientific discovery and, consequently, new knowledge in the poverty and
   welfare domain. A foundation for achieving scientific insights is domain
   knowledge, which in turn translates into explainability and scientific
   consistency. We perform an integrative literature review focusing on
   three core elements relevant in this context-transparency,
   interpretability, and explainability-and investigate how they relate to
   the poverty, machine learning, and satellite imagery nexus. Our
   inclusion criteria for papers are that they cover poverty/wealth
   prediction, using survey data as the basis for the ground truth
   poverty/wealth estimates, be applicable to both urban and rural
   settings, use satellite images as the basis for at least some of the
   inputs (features), and the method should include deep neural networks.
   Our review of 32 papers shows that the status of the three core elements
   of explainable machine learning (transparency, interpretability, and
   domain knowledge) is varied and does not completely fulfill the
   requirements set up for scientific insights and discoveries. We argue
   that explainability is essential to support wider dissemination and
   acceptance of this research in the development community and that
   explainability means more than just interpretability.},
Publisher = {ELSEVIER},
Address = {RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS},
Type = {Review},
Language = {English},
Affiliation = {Hall, O (Corresponding Author), Lund Univ, Dept Human Geog, Lund, Sweden.
   Hall, Ola, Lund Univ, Dept Human Geog, Lund, Sweden.
   Ohlsson, Mattias; Rognvaldsson, Thorsteinn, Halmstad Univ, Ctr Appl Intelligent Syst Res, Halmstad, Sweden.
   Ohlsson, Mattias, Lund Univ, Dept Astron \& Theoret Phys, Div Computat Biol \& Biol Phys, Lund, Sweden.},
DOI = {10.1016/j.patter.2022.100600},
Article-Number = {100600},
ISSN = {2666-3899},
Keywords-Plus = {POPULATION; SYSTEMS; IMAGERY; WILL},
Research-Areas = {Computer Science},
Web-of-Science-Categories  = {Computer Science, Artificial Intelligence; Computer Science, Information
   Systems; Computer Science, Interdisciplinary Applications},
Author-Email = {ola.hall@keg.lu.se},
Affiliations = {Lund University; Halmstad University; Lund University},
Funding-Acknowledgement = {Swedish Research Council {[}2019-04253]; Riksbankens Jubileumsfond
   {[}MXM19-1104:1]},
Funding-Text = {The authors would like to thank financial support during the project
   from the Swedish Research Council 2019-04253 and Riksbankens
   Jubileumsfond MXM19-1104:1. We would also like to thank the reviewers
   for their insightful comments on the paper.},
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Number-of-Cited-References = {75},
Times-Cited = {0},
Usage-Count-Last-180-days = {8},
Usage-Count-Since-2013 = {8},
Journal-ISO = {Patterns},
Doc-Delivery-Number = {7A6KC},
Web-of-Science-Index = {Emerging Sources Citation Index (ESCI)},
Unique-ID = {WOS:000898561500011},
OA = {Green Published, gold},
DA = {2023-04-22},
}

@article{ WOS:000691223200001,
Author = {Munawar, Hafiz Suliman and Hammad, Ahmed W. A. and Haddad, Assed and
   Pereira Soares, Carlos Alberto and Waller, S. Travis},
Title = {Image-Based Crack Detection Methods: A Review},
Journal = {INFRASTRUCTURES},
Year = {2021},
Volume = {6},
Number = {8},
Month = {AUG},
Abstract = {Annually, millions of dollars are spent to carry out defect detection in
   key infrastructure including roads, bridges, and buildings. The
   aftermath of natural disasters like floods and earthquakes leads to
   severe damage to the urban infrastructure. Maintenance operations that
   follow for the damaged infrastructure often involve a visual inspection
   and assessment of their state to ensure their functional and physical
   integrity. Such damage may appear in the form of minor or major cracks,
   which gradually spread, leading to ultimate collapse or destruction of
   the structure. Crack detection is a very laborious task if performed via
   manual visual inspection. Many infrastructure elements need to be
   checked regularly and it is therefore not feasible as it will require
   significant human resources. This may also result in cases where cracks
   go undetected. A need, therefore, exists for performing automatic defect
   detection in infrastructure to ensure its effectiveness and reliability.
   Using image processing techniques, the captured or scanned images of the
   infrastructure parts can be analyzed to identify any possible defects.
   Apart from image processing, machine learning methods are being
   increasingly applied to ensure better performance outcomes and
   robustness in crack detection. This paper provides a review of
   image-based crack detection techniques which implement image processing
   and/or machine learning. A total of 30 research articles have been
   collected for the review which is published in top tier journals and
   conferences in the past decade. A comprehensive analysis and comparison
   of these methods are performed to highlight the most promising automated
   approaches for crack detection.},
Publisher = {MDPI},
Address = {ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND},
Type = {Review},
Language = {English},
Affiliation = {Hammad, AWA (Corresponding Author), Univ New South Wales, Sch Built Environm, Sydney, NSW 2052, Australia.
   Munawar, Hafiz Suliman; Hammad, Ahmed W. A., Univ New South Wales, Sch Built Environm, Sydney, NSW 2052, Australia.
   Haddad, Assed, Univ Fed Rio de Janeiro, Pea POLI \& EQ, Programa Engn Ambiental, BR-21941909 Rio De Janeiro, Brazil.
   Pereira Soares, Carlos Alberto, Univ Fed Fluminense, Engn Civil, BR-24210240 Niteroi, RJ, Brazil.
   Waller, S. Travis, Univ New South Wales, Sch Civil \& Environm Engn, Sydney, NSW 2052, Australia.},
DOI = {10.3390/infrastructures6080115},
Article-Number = {115},
EISSN = {2412-3811},
Keywords = {crack detection; machine learning; artificial intelligence; image
   processing},
Keywords-Plus = {ARTIFICIAL NEURAL-NETWORK; ALGORITHM; SYSTEM},
Research-Areas = {Construction \& Building Technology; Engineering; Transportation},
Web-of-Science-Categories  = {Construction \& Building Technology; Engineering, Civil; Transportation
   Science \& Technology},
Author-Email = {h.munawar@unsw.edu.au
   a.hammad@unsw.edu.au
   assed@poli.ufrj.br
   capsoares@id.uff.br
   s.waller@unsw.edu.au},
Affiliations = {University of New South Wales Sydney; Universidade Federal do Rio de
   Janeiro; Universidade Federal Fluminense; University of New South Wales
   Sydney},
ResearcherID-Numbers = {Soares, Carlos Alberto Pereira/Q-1507-2019
   Haddad, Assed N./C-9206-2014
   },
ORCID-Numbers = {Soares, Carlos Alberto Pereira/0000-0002-1332-5854
   Haddad, Assed N./0000-0002-4793-0905
   Munawar, Hafiz Suliman/0000-0001-8492-0274
   Hammad, Ahmed WA/0000-0001-6190-0078},
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Number-of-Cited-References = {71},
Times-Cited = {37},
Usage-Count-Last-180-days = {20},
Usage-Count-Since-2013 = {66},
Journal-ISO = {Infrastructures-Basel},
Doc-Delivery-Number = {UJ3XV},
Web-of-Science-Index = {Emerging Sources Citation Index (ESCI)},
Unique-ID = {WOS:000691223200001},
OA = {gold},
DA = {2023-04-22},
}

@article{ WOS:000781406800001,
Author = {Jiang, Huiwei and Peng, Min and Zhong, Yuanjun and Xie, Haofeng and Hao,
   Zemin and Lin, Jingming and Ma, Xiaoli and Hu, Xiangyun},
Title = {A Survey on Deep Learning-Based Change Detection from High-Resolution
   Remote Sensing Images},
Journal = {REMOTE SENSING},
Year = {2022},
Volume = {14},
Number = {7},
Month = {APR},
Abstract = {Change detection based on remote sensing images plays an important role
   in the field of remote sensing analysis, and it has been widely used in
   many areas, such as resources monitoring, urban planning, disaster
   assessment, etc. In recent years, it has aroused widespread interest due
   to the explosive development of artificial intelligence (AI) technology,
   and change detection algorithms based on deep learning frameworks have
   made it possible to detect more delicate changes (such as the alteration
   of small buildings) with the help of huge amounts of remote sensing
   data, especially high-resolution (HR) data. Although there are many
   methods, we still lack a deep review of the recent progress concerning
   the latest deep learning methods in change detection. To this end, the
   main purpose of this paper is to provide a review of the available deep
   learning-based change detection algorithms using HR remote sensing
   images. The paper first describes the change detection framework and
   classifies the methods from the perspective of the deep network
   architectures adopted. Then, we review the latest progress in the
   application of deep learning in various granularity structures for
   change detection. Further, the paper provides a summary of HR datasets
   derived from different sensors, along with information related to change
   detection, for the potential use of researchers. Simultaneously,
   representative evaluation metrics for this task are investigated.
   Finally, a conclusion of the challenges for change detection using HR
   remote sensing images, which must be dealt with in order to improve the
   model's performance, is presented. In addition, we put forward promising
   directions for future research in this area.},
Publisher = {MDPI},
Address = {ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND},
Type = {Review},
Language = {English},
Affiliation = {Zhong, YJ (Corresponding Author), Guangdong Surveying \& Mapping Inst, Lands \& Resource Dept, 13 Guangpu Middle Rd, Guangzhou 510663, Peoples R China.
   Jiang, Huiwei, Natl Geomat Ctr China, Beijing 100830, Peoples R China.
   Jiang, Huiwei; Xie, Haofeng; Lin, Jingming; Hu, Xiangyun, Wuhan Univ, Sch Remote Sensing \& Informat Engn, 129 Luoyu Rd, Wuhan 430079, Peoples R China.
   Peng, Min; Hao, Zemin, Geotech Invest \& Surveying Res Inst Co Ltd, Shenyang 110004, Peoples R China.
   Zhong, Yuanjun; Ma, Xiaoli, Guangdong Surveying \& Mapping Inst, Lands \& Resource Dept, 13 Guangpu Middle Rd, Guangzhou 510663, Peoples R China.
   Hu, Xiangyun, Wuhan Univ, Inst Artificial Intelligence Geomat, 129 Luoyu Rd, Wuhan 430079, Peoples R China.},
DOI = {10.3390/rs14071552},
Article-Number = {1552},
EISSN = {2072-4292},
Keywords = {deep learning; change detection; high-resolution; remote sensing images},
Keywords-Plus = {CONVOLUTIONAL NEURAL-NETWORK; SCENE CHANGE DETECTION; FUSION NETWORK;
   CLASSIFICATION; SATELLITE; ACCURACY; SEGMENTATION; PIXEL; AREA; CNN},
Research-Areas = {Environmental Sciences \& Ecology; Geology; Remote Sensing; Imaging
   Science \& Photographic Technology},
Web-of-Science-Categories  = {Environmental Sciences; Geosciences, Multidisciplinary; Remote Sensing;
   Imaging Science \& Photographic Technology},
Author-Email = {huiwei\_jiang@whu.edu.cn
   minpeng@whu.edu.cn
   yuanjun.zhong.gd@gmail.com
   xiehaofeng@whu.edu.cn
   haozemin@whu.edu.cn
   linjingming@whu.edu.cn
   xiaoli.ma1010@gmail.com
   huxy@whu.edu.cn},
Affiliations = {Wuhan University; Wuhan University},
Funding-Acknowledgement = {Chinese National Natural Science Foundation Projects {[}92038301,
   41771363]; Guangdong Surveying and Mapping Institute of Lands and
   Resource Department, Shenyang Geotechnical Investigation \& Surveying
   Research Institute Co., Ltd.},
Funding-Text = {This research was funded by the Chinese National Natural Science
   Foundation Projects (Grant Nos. 92038301 and 41771363), and was
   supported by the fundings of Guangdong Surveying and Mapping Institute
   of Lands and Resource Department, Shenyang Geotechnical Investigation \&
   Surveying Research Institute Co., Ltd.},
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@article{ WOS:000454356400002,
Author = {Weichenthal, Scott and Hatzopoulou, Marianne and Brauer, Michael},
Title = {A picture tells a thousand...exposures: Opportunities and challenges of
   deep learning image analyses in exposure science and environmental
   epidemiology},
Journal = {ENVIRONMENT INTERNATIONAL},
Year = {2019},
Volume = {122},
Pages = {3-10},
Month = {JAN},
Abstract = {Background: Artificial intelligence (AI) is revolutionizing our world,
   with applications ranging from medicine to engineering.
   Objectives: Here we discuss the promise, challenges, and probable data
   sources needed to apply AI in the fields of exposure science and
   environmental health. In particular, we focus on the use of deep
   convolutional neural networks to estimate environmental exposures using
   images and other complementary data sources such as cell phone mobility
   and social media information.
   Discussion: Characterizing the health impacts of multiple
   spatially-correlated exposures remains a challenge in environmental
   epidemiology. A shift toward integrated measures that simultaneously
   capture multiple aspects of the urban built environment could improve
   efficiency and provide important insights into how our collective
   environments influence population health. The widespread adoption of AI
   in exposure science is on the frontier. This will likely result in new
   ways of understanding environmental impacts on health and may allow for
   analyses to be efficiently scaled for broad coverage. Image-based
   convolutional neural networks may also offer a costeffective means of
   estimating local environmental exposures in low and middle-income
   countries where monitoring and surveillance infrastructure is limited.
   However, suitable databases must first be assembled to train and
   evaluate these models and these novel approaches should be complemented
   with traditional exposure metrics.
   Conclusions: The promise of deep learning in environmental health is
   great and will complement existing measurements for data-rich settings
   and could enhance the resolution and accuracy of estimates in data poor
   scenarios. Interdisciplinary partnerships will be needed to fully
   realize this potential.},
Publisher = {PERGAMON-ELSEVIER SCIENCE LTD},
Address = {THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND},
Type = {Review},
Language = {English},
Affiliation = {Weichenthal, S (Corresponding Author), McGill Univ, Dept Epidemiol Biostat \& Occupat Hlth, Fac Med, 1110 Ave Pins Ouest, Montreal, PQ H3A 1A3, Canada.
   Weichenthal, Scott, McGill Univ, Dept Epidemiol Biostat \& Occupat Hlth, Montreal, PQ, Canada.
   Hatzopoulou, Marianne, Univ Toronto, Dept Civil Engn, Toronto, ON, Canada.
   Brauer, Michael, Univ British Columbia, Sch Populat \& Publ Hlth, Vancouver, BC, Canada.},
DOI = {10.1016/j.envint.2018.11.042},
ISSN = {0160-4120},
EISSN = {1873-6750},
Keywords-Plus = {LAND-USE REGRESSION; GOOGLE STREET VIEW; SPATIAL-DISTRIBUTION; ULTRAFINE
   PARTICLES; NEURAL-NETWORKS; SATELLITE; HEALTH; CLASSIFICATION;
   PREDICTION; VALIDITY},
Research-Areas = {Environmental Sciences \& Ecology},
Web-of-Science-Categories  = {Environmental Sciences},
Author-Email = {scott.weichenthal@mcgill.ca},
Affiliations = {McGill University; University of Toronto; University of British Columbia},
ResearcherID-Numbers = {Brauer, Michael/Y-2810-2019
   },
ORCID-Numbers = {Brauer, Michael/0000-0002-9103-9343
   Weichenthal, Scott/0000-0002-9634-5323},
Funding-Acknowledgement = {NSERC Discovery Grant; CIHR Foundation Grant; Quebec Ministry of
   Economy, Science and Innovation; FRQS (Fonds de Recherche du
   Quebec-Sante); British Columbia Lung Association Professorship; GRePEC
   salary award - Cancer Research Society},
Funding-Text = {Dr. Weichenthal received research support from an NSERC Discovery Grant,
   a CIHR Foundation Grant, and a GRePEC salary award funded by the Cancer
   Research Society, the Quebec Ministry of Economy, Science and
   Innovation, and FRQS (Fonds de Recherche du Quebec-Sante). Dr. Brauer is
   supported in part by the British Columbia Lung Association
   Professorship.},
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Usage-Count-Last-180-days = {6},
Usage-Count-Since-2013 = {44},
Journal-ISO = {Environ. Int.},
Doc-Delivery-Number = {HF6OB},
Web-of-Science-Index = {Science Citation Index Expanded (SCI-EXPANDED)},
Unique-ID = {WOS:000454356400002},
OA = {gold},
DA = {2023-04-22},
}

@article{ WOS:000688578200001,
Author = {Martinho, Vitor Joao Pereira Domingues and Guine, Raquel de Pinho
   Ferreira},
Title = {Integrated-Smart Agriculture: Contexts and Assumptions for a Broader
   Concept},
Journal = {AGRONOMY-BASEL},
Year = {2021},
Volume = {11},
Number = {8},
Month = {AUG},
Abstract = {The innovative technologies developed in the different fields of science
   (nanotechnology, artificial intelligence, genetic modification, etc.)
   opened new and infinite possibilities for the several stakeholders that
   carry out their activities in the different economic sectors. For
   agriculture, these new approaches are particularly relevant and may
   bring interesting contributions, considering the specificities of the
   sector, often dealing with contexts of land abandonment and narrow
   profit margins. Nonetheless, the question in these unstopped evolutions
   is about the interlinkages with sustainability. In this context, the
   objectives of this study are to highlight the main insights from the
   available scientific literature about the interrelationships between the
   new trends in the agriculture and the sustainability. To achieve these
   aims, a search on the Web of Science Core Collection (WoS) and Scopus
   databases was carried out, on 15 May 2021, for the topics `smart
   agriculture' and `sustainability'. A total of 231 documents (102 from
   WoS and 129 from Scopus) were obtained, remaining 155 documents after
   removing the duplicated, which were surveyed through systematic review
   following the PRISMA (Preferred Reporting Items for Systematic Reviews
   and Meta-Analyses) approach. As main insights, the concerns of the
   researchers with the impacts on the sustainability from the
   transformations in the farming organization are highlighted. On the
   other hand, it was shown the relevance and the new opportunities,
   including in terms of food supply, arising from the precision
   agriculture, agricultural intelligence, vertical/urban farming, circular
   economy, internet of things, and crowdfarming. We suggest the new and
   wider concept of `integrated-smart agriculture', better than
   `climate-smart agriculture'.},
Publisher = {MDPI},
Address = {ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND},
Type = {Review},
Language = {English},
Affiliation = {Martinho, VJPD (Corresponding Author), Polytech Inst Viseu IPV, Agr Sch ESAV, P-3504510 Viseu, Portugal.
   Martinho, Vitor Joao Pereira Domingues, Polytech Inst Viseu IPV, Agr Sch ESAV, P-3504510 Viseu, Portugal.
   Polytech Inst Viseu IPV, CERNAS IPV Res Ctr, P-3504510 Viseu, Portugal.},
DOI = {10.3390/agronomy11081568},
Article-Number = {1568},
EISSN = {2073-4395},
Keywords = {agriculture; new technologies; sustainability; systematic review; PRISMA},
Keywords-Plus = {CLIMATE-CHANGE ADAPTATION; ENVIRONMENTAL SUSTAINABILITY; DIGITAL
   AGRICULTURE; CROP PRODUCTION; SYSTEM; PRODUCTIVITY; IMPACT; FARM;
   TECHNOLOGIES; INNOVATIONS},
Research-Areas = {Agriculture; Plant Sciences},
Web-of-Science-Categories  = {Agronomy; Plant Sciences},
Author-Email = {vdmartinho@esav.ipv.pt
   raquelguine@esav.ipv.pt},
ResearcherID-Numbers = {Martinho, Vítor João Pereira Domingues/Y-6540-2019
   Guine, Raquel/N-1834-2013},
ORCID-Numbers = {Guine, Raquel/0000-0003-0595-6805},
Funding-Acknowledgement = {FCT-Foundation for Science and Technology, I.P. {[}UIDB/00681/2020]},
Funding-Text = {This work is funded by National Funds through the FCT-Foundation for
   Science and Technology, I.P., within the scope of the project
   Ref<SUP>a</SUP> UIDB/00681/2020.},
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Number-of-Cited-References = {187},
Times-Cited = {6},
Usage-Count-Last-180-days = {17},
Usage-Count-Since-2013 = {46},
Journal-ISO = {Agronomy-Basel},
Doc-Delivery-Number = {UF4XH},
Web-of-Science-Index = {Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)},
Unique-ID = {WOS:000688578200001},
OA = {Green Published, gold},
DA = {2023-04-22},
}

@article{ WOS:000449273000032,
Author = {Sanchez-Pinto, L. Nelson and Luo, Yuan and Churpek, Matthew M.},
Title = {Big Data and Data Science in Critical Care},
Journal = {CHEST},
Year = {2018},
Volume = {154},
Number = {5},
Pages = {1239-1248},
Month = {NOV},
Abstract = {The digitalization of the health-care system has resulted in a deluge of
   clinical big data and has prompted the rapid growth of data science in
   medicine. Data science, which is the field of study dedicated to the
   principled extraction of knowledge from complex data, is particularly
   relevant in the critical care setting. The availability of large amounts
   of data in the ICU, the need for better evidence-based care, and the
   complexity of critical illness makes the use of data science techniques
   and data-driven research particularly appealing to intensivists. Despite
   the increasing number of studies and publications in the field, thus far
   there have been few examples of data science projects that have resulted
   in successful implementations of datadriven systems in the ICU. However,
   given the expected growth in the field, intensivists should be familiar
   with the opportunities and challenges of big data and data science. The
   present article reviews the definitions, types of algorithms,
   applications, challenges, and future of big data and data science in
   critical care.},
Publisher = {ELSEVIER},
Address = {RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS},
Type = {Review},
Language = {English},
Affiliation = {Churpek, MM (Corresponding Author), Univ Chicago, Pulm \& Crit Care, 5841 S Maryland Ave,MC 6023, Chicago, IL 60637 USA.
   Sanchez-Pinto, L. Nelson, Northwestern Univ, Dept Pediat Crit Care, Feinberg Sch Med, Chicago, IL 60611 USA.
   Sanchez-Pinto, L. Nelson; Luo, Yuan, Northwestern Univ, Dept Prevent Med Hlth \& Biomed Informat, Feinberg Sch Med, Chicago, IL 60611 USA.
   Churpek, Matthew M., Univ Chicago, Dept Med, 5841 S Maryland Ave, Chicago, IL 60637 USA.},
DOI = {10.1016/j.chest.2018.04.037},
ISSN = {0012-3692},
EISSN = {1931-3543},
Keywords = {big data; critical care; data science; machine learning; prediction
   models},
Keywords-Plus = {ELECTRONIC MEDICAL-RECORD; PRECISION MEDICINE; DECISION-SUPPORT;
   CRITICAL ILLNESS; CLASSIFICATION; DISEASE; SEPSIS; IMPLEMENTATION;
   VALIDATION; PREDICTION},
Research-Areas = {General \& Internal Medicine; Respiratory System},
Web-of-Science-Categories  = {Critical Care Medicine; Respiratory System},
Author-Email = {matthew.churpek@uchospitals.edu},
Affiliations = {Northwestern University; Feinberg School of Medicine; Northwestern
   University; Feinberg School of Medicine; University of Chicago},
ResearcherID-Numbers = {Sanchez-Pinto, L. Nelson/AAO-9748-2021
   Luo, Yuan/K-5563-2016},
ORCID-Numbers = {Sanchez-Pinto, L. Nelson/0000-0002-7434-6747
   Churpek, Matthew/0000-0002-4030-5250
   Luo, Yuan/0000-0003-0195-7456},
Funding-Acknowledgement = {National Heart, Lung and Blood Institute; National Institute of General
   Medical Sciences},
Funding-Text = {The authors have reported to CHEST the following: M. M. C. has a patent
   pending for risk stratification algorithms for hospitalized patients; he
   is also supported by a career development award from the National Heart,
   Lung and Blood Institute and a research project grant program award from
   the National Institute of General Medical Sciences. None declared (L. N.
   S.-P., Y. L.).},
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Number-of-Cited-References = {59},
Times-Cited = {114},
Usage-Count-Last-180-days = {6},
Usage-Count-Since-2013 = {68},
Journal-ISO = {Chest},
Doc-Delivery-Number = {GZ3FR},
Web-of-Science-Index = {Science Citation Index Expanded (SCI-EXPANDED)},
Unique-ID = {WOS:000449273000032},
OA = {Green Published},
DA = {2023-04-22},
}

@article{ WOS:000862338700001,
Author = {Velayudhan, Nibi Kulangara and Pradeep, Preeja and Rao, Sethuraman N.
   and Devidas, Aryadevi Remanidevi and Ramesh, Maneesha Vinodini},
Title = {IoT-Enabled Water Distribution Systems-A Comparative Technological
   Review},
Journal = {IEEE ACCESS},
Year = {2022},
Volume = {10},
Pages = {101042-101070},
Abstract = {Water distribution systems are one of the critical infrastructures and
   major assets of the water utility in a nation. The infrastructure of the
   distribution systems consists of resources, treatment plants,
   reservoirs, distribution lines, and consumers. A sustainable water
   distribution network management has to take care of accessibility,
   quality, quantity, and reliability of water. As water is becoming a
   depleting resource for the coming decades, the regulation and accounting
   of water in terms of the above four parameters is a critical task. There
   have been many efforts towards the establishment of a monitoring and
   controlling framework, capable of automating various stages of the water
   distribution processes. The current trending technologies such as
   Information and Communication Technology (ICT), Internet of Things
   (IoT), and Artificial Intelligence (AI) have the potential to track this
   spatially varying network to collect, process, and analyze the water
   distribution network attributes and events. In this work, we investigate
   the role and scope of the IoT technologies in different stages of the
   water distribution systems. Our survey covers the state-of-the-art
   monitoring and control systems for the water distribution networks, and
   the status of IoT architectures for water distribution networks. We
   explore the existing water distribution systems, providing the necessary
   background information on the current status. This work also presents an
   IoT Architecture for Intelligent Water Networks - IoTA4IWNet, for
   real-time monitoring and control of water distribution networks. We
   believe that, these components need to be designed and implemented
   effectively to build a robust water distribution network.},
Publisher = {IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC},
Address = {445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA},
Type = {Review},
Language = {English},
Affiliation = {Velayudhan, NK (Corresponding Author), Amrita Vishwa Vidyapeetham, Ctr Wireless Networks \& Applicat WNA, Amritapuri 690525, India.
   Velayudhan, Nibi Kulangara; Rao, Sethuraman N.; Devidas, Aryadevi Remanidevi; Ramesh, Maneesha Vinodini, Amrita Vishwa Vidyapeetham, Ctr Wireless Networks \& Applicat WNA, Amritapuri 690525, India.
   Pradeep, Preeja, Univ Coll Cork, Sch Comp Sci \& Informat Technol, Insight Ctr Data Analyt, Cork T12 K8AF, Ireland.},
DOI = {10.1109/ACCESS.2022.3208142},
ISSN = {2169-3536},
Keywords = {Water resources; Monitoring; Distribution networks; Statistics;
   Sociology; Internet of Things; Ocean temperature; Internet of Things;
   IoT communication technologies; IoT services; water distribution network},
Keywords-Plus = {BIG DATA ANALYTICS; THINGS IOT; DECISION-SUPPORT; INTERNET; MANAGEMENT;
   EDGE; FRAMEWORK; ARCHITECTURE; DESIGN; CHALLENGES},
Research-Areas = {Computer Science; Engineering; Telecommunications},
Web-of-Science-Categories  = {Computer Science, Information Systems; Engineering, Electrical \&
   Electronic; Telecommunications},
Author-Email = {nibikv@am.amrita.edu},
Affiliations = {Amrita Vishwa Vidyapeetham; Amrita Vishwa Vidyapeetham Amritapuri;
   University College Cork},
ORCID-Numbers = {Devidas, Aryadevi/0000-0002-1440-671X
   Pradeep, Preeja/0000-0001-6708-4964},
Funding-Acknowledgement = {Project ``Fast Forward to SDG6: Acceptable and Affordable Water in
   Secondary Indian Cities (4WARD)''; Department of Science and Technology
   (DST), Water Technology Initiative-National Consortia on Urban Water
   System (UWS) {[}DST/TM/EWO/WTI/2K19/UWS-03(G2)]},
Funding-Text = {This work was supported in part by the Project ``Fast Forward to SDG6:
   Acceptable and Affordable Water in Secondary Indian Cities (4WARD),''
   and in part by the Department of Science and Technology (DST), Water
   Technology Initiative-National Consortia on Urban Water System (UWS)
   under Project DST/TM/EWO/WTI/2K19/UWS-03(G2).},
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@article{ WOS:000802044600006,
Author = {Tang, Ruifan and De Donato, Lorenzo and Besinovic, Nikola and Flammini,
   Francesco and Goverde, Rob M. P. and Lin, Zhiyuan and Liu, Ronghui and
   Tang, Tianli and Vittorini, Valeria and Wang, Ziyulong},
Title = {A literature review of Artificial Intelligence applications in railway
   systems},
Journal = {TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES},
Year = {2022},
Volume = {140},
Month = {JUL},
Abstract = {Nowadays it is widely accepted that Artificial Intelligence (AI) is
   significantly influencing a large number of domains, including railways.
   In this paper, we present a systematic literature review of the current
   state-of-the-art of AI in railway transport. In particular, we analysed
   and discussed papers from a holistic railway perspective, covering
   sub-domains such as maintenance and inspection, planning and management,
   safety and security, autonomous driving and control, revenue management,
   transport policy, and passenger mobility. This review makes an initial
   step towards shaping the role of AI in future railways and provides a
   summary of the current focuses of AI research connected to rail
   transport. We reviewed about 139 scientific papers covering the period
   from 2010 to December 2020. We found that the major research efforts
   have been put in AI for rail maintenance and inspection, while very
   limited or no research has been found on AI for rail transport policy
   and revenue management. The remaining sub domains received mild to
   moderate attention. AI applications are promising and tend to act as a
   game-changer in tackling multiple railway challenges. However, at the
   moment, AI research in railways is still mostly at its early stages.
   Future research can be expected towards developing advanced combined AI
   applications (e.g. with optimization), using AI in decision making,
   dealing with uncertainty and tackling newly rising cybersecurity
   challenges.},
Publisher = {PERGAMON-ELSEVIER SCIENCE LTD},
Address = {THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND},
Type = {Review},
Language = {English},
Affiliation = {Lin, ZY (Corresponding Author), Univ Leeds, 34-40 Univ Rd, Leeds LS2 9JT, England.
   Tang, Ruifan; Lin, Zhiyuan; Liu, Ronghui; Tang, Tianli, Univ Leeds, Inst Transport Studies, 34-40 Univ Rd, Leeds LS2 9JT, England.
   De Donato, Lorenzo; Vittorini, Valeria, Univ Naples Federico II, Dept Elect Engn \& Informat Technol, Via Claudio 21, I-80125 Naples, Italy.
   Besinovic, Nikola; Goverde, Rob M. P.; Wang, Ziyulong, Delft Univ Technol, Dept Transport \& Planning, POB 5048, NL-2600 GA Delft, Netherlands.
   Flammini, Francesco, Linnaeus Univ, Dept Comp Sci \& Media Technol, Vaxjo, Sweden.
   Flammini, Francesco, Malardalen Univ, Sch Innovat Design \& Engn, Vasteras, Sweden.
   Tang, Tianli, Southeast Univ, Jiangsu Prov Collaborat Innovat Ctr Modern Urban T, Sch Transportat, Jiangsu Key Lab Urban ITS, Nanjing 211189, Peoples R China.
   Lin, Zhiyuan, Univ Leeds, 34-40 Univ Rd, Leeds LS2 9JT, England.},
DOI = {10.1016/j.trc.2022.103679},
EarlyAccessDate = {MAY 2022},
Article-Number = {103679},
ISSN = {0968-090X},
EISSN = {1879-2359},
Keywords = {Artificial Intelligence; Railways; Transportation; Machine Learning;
   Autonomous driving; Maintenance; Smart mobility; Train control; Traffic
   management},
Keywords-Plus = {AUTOMATED VISUAL INSPECTION; CONDITION-BASED MAINTENANCE; BIG DATA;
   TRANSPORTATION SYSTEMS; TRACK MAINTENANCE; LEARNING APPROACH; DEFECT
   DETECTION; PREDICTION; OPTIMIZATION; MODEL},
Research-Areas = {Transportation},
Web-of-Science-Categories  = {Transportation Science \& Technology},
Author-Email = {Z.Lin@leeds.ac.uk},
Affiliations = {University of Leeds; University of Naples Federico II; Delft University
   of Technology; Linnaeus University; Malardalen University; Southeast
   University - China; University of Leeds},
ResearcherID-Numbers = {Wang, Ziyulong/GPW-8486-2022
   Tang, Tianli/GRX-7209-2022
   Flammini, Francesco/C-1589-2008
   Goverde, Rob/H-9055-2013
   },
ORCID-Numbers = {Wang, Ziyulong/0000-0003-4664-2087
   Flammini, Francesco/0000-0002-2833-7196
   Goverde, Rob/0000-0001-8840-4488
   Tang, Tianli/0000-0003-2182-6525
   Besinovic, Nikola/0000-0003-4111-2255},
Funding-Acknowledgement = {Shift2Rail Joint Undertaking (JU) {[}881782 RAILS]; European Union; UK
   Rail Safety and Standards Board (RSSB) {[}RSSB/494204565]},
Funding-Text = {This research has received funding from the Shift2Rail Joint Undertaking
   (JU) under grant agreement No 881782 RAILS. The JU receives support from
   the European Unions Horizon 2020 research and innovation programme and
   the Shift2Rail JU members other than the Union. Co-authors Ronghui Liu
   and Zhiyuan Lin are also partially supported by the Assisted Very Short
   Term Planning (VSTP) /Dynamic Timetabling Project (RSSB/494204565)
   funded by UK Rail Safety and Standards Board (RSSB) .},
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@article{ WOS:000707357700008,
Author = {Vivanco-Benavides, Luis Enrique and Martinez-Gonzalez, Claudia Lizbeth
   and Mercado-Zuniga, Cecilia and Torres-Torres, Carlos},
Title = {Machine learning and materials informatics approaches in the analysis of
   physical properties of carbon nanotubes: A review},
Journal = {COMPUTATIONAL MATERIALS SCIENCE},
Year = {2022},
Volume = {201},
Month = {JAN},
Abstract = {Machine learning has proven to be technically flexible in recent years,
   which allows it to be successfully implemented in problems in various
   areas of knowledge. Carbon nanotubes have been studied to describe their
   properties or predict possible material responses during their synthesis
   process or in different conditions and environments. In this review, we
   analyze the machine learning approaches used in modeling physical
   properties in carbon nanotubes. This work reveals a remarkable match
   between the amount of experimental data, the number of parameters, and
   the algorithms used to model uncontrolled physical properties exhibited
   by carbon nanotubes. The importance of artificial neural networks,
   support vector machines, decision trees, random forests, and K-Nearest
   Neighbors is highlighted, mainly in analyzing these nanostructures. The
   evaluation of mechanical, thermal, electrical, and electronic properties
   of carbon nanotubes has been reported. Regarding the thermal,
   electrical, and electronic properties, it is still necessary to
   complement the molecular dynamics and density functional theory results,
   respectively, with machine learning. Mechanical properties present an
   open line of research regarding vibrational properties, where chiral
   geometric parameters are used to study the vibrational response of
   carbon nanotubes; therefore, more accurate estimates are required to
   predict these frequencies. There is conclusive evidence that there is a
   relationship between detecting defects in carbon nanotubes and the
   number of iterations required to describe thermionic and vibrational
   properties using machine learning. An understanding of the vibratory
   behavior in these nanomaterials would be helpful in the development of
   nanosensors. Finally, using simulation models and machine learning would
   help reduce cost and experimentation time studying these properties.},
Publisher = {ELSEVIER},
Address = {RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS},
Type = {Review},
Language = {English},
Affiliation = {Martinez-Gonzalez, CL (Corresponding Author), Inst Politecn Nacl, ESIME Zacatenco, Secc Estudios Posgrad \& Invest, Ciudad De Mexico 07738, Mexico.
   Vivanco-Benavides, Luis Enrique; Martinez-Gonzalez, Claudia Lizbeth; Torres-Torres, Carlos, Inst Politecn Nacl, ESIME Zacatenco, Secc Estudios Posgrad \& Invest, Ciudad De Mexico 07738, Mexico.
   Vivanco-Benavides, Luis Enrique, Tecnol Estudios Super Coacalco, Div Ingn Sistemas Comp, Coacalco Berriozabal 55700, Edo De Mexico, Mexico.
   Mercado-Zuniga, Cecilia, Tecnol Estudios Super Coacalco, Subdirecc C, Coacalco Berriozabal 55700, Edo De Mexico, Mexico.},
DOI = {10.1016/j.commatsci.2021.110939},
EarlyAccessDate = {OCT 2021},
Article-Number = {110939},
ISSN = {0927-0256},
EISSN = {1879-0801},
Keywords = {Artificial intelligence; Carbon nanotubes; Materials informatics;
   Materials data science; Statistical learning},
Keywords-Plus = {ARTIFICIAL NEURAL-NETWORK; DENSITY-FUNCTIONAL THEORY; CONVECTIVE
   HEAT-TRANSFER; THERMAL-CONDUCTIVITY; RAMAN-SPECTROSCOPY; GENETIC
   ALGORITHM; ELECTRONIC-PROPERTIES; VIBRATIONAL ANALYSIS; MATERIALS
   DISCOVERY; SINGLE},
Research-Areas = {Materials Science},
Web-of-Science-Categories  = {Materials Science, Multidisciplinary},
Author-Email = {clmartinezg@ipn.mx},
Affiliations = {Instituto Politecnico Nacional - Mexico},
ResearcherID-Numbers = {Martinez-Gonzalez, Claudia Lizbeth/T-9559-2018},
ORCID-Numbers = {Martinez-Gonzalez, Claudia Lizbeth/0000-0002-1307-0847},
Funding-Acknowledgement = {Instituto Politecnico Nacional; Consejo Nacional de Ciencia y Tecnologia
   (CONACyT)},
Funding-Text = {Authors acknowledge the support of the Instituto Politecnico Nacional
   and Consejo Nacional de Ciencia y Tecnologia (CONACyT) .},
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Journal-ISO = {Comput. Mater. Sci.},
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@article{ WOS:000956310000001,
Author = {Zhang, Jiawei and Liu, Zhen and Jiang, Weidong and Liu, Yongxiang and
   Zhou, Xiaolin and Li, Xiang},
Title = {Application of deep generative networks for SAR/ISAR: a review},
Journal = {ARTIFICIAL INTELLIGENCE REVIEW},
Abstract = {Military, agricultural, and urban planning have all made extensive use
   of SAR/ISAR in the realm of remote sensing. SAR/ISAR images are more
   capable of identifying the details of the targets than optical images
   and can be taken in any condition. Due to the challenges associated with
   SAR/ISAR imaging, the lack of data causes many jobs relying on
   data-driven deep learning algorithms to perform less than
   satisfactorily. Cropping, rotation, and other procedures are examples of
   classic data augmentation techniques now in use, although they do not
   fundamentally differ from basic replication and cannot increase the
   model's stability and robustness. Deep generative models are used to
   generate SAR/ISAR images, which is a more efficient way than the
   conventional ones. The generation techniques are outlined and organized
   depending on the application fields in this review, including SAR/ISAR
   data augmentation (26 papers), SAR/ISAR image translation (29 papers),
   SAR/ISAR image enhancement (22 papers), azimuth interpolation (9
   papers), and deceptive jamming (1 paper). The connected works are then
   summarized based on several deep generative models. 87 linked studies
   and 5 associated survey papers from 2017 to 2022 are compiled in this
   review. Finally, the summarized works are systematically analyzed. There
   are 27 papers using MSTAR for image generation, which is the mostly
   applied dataset. For evaluation, the combination of SSIM and PSNR is
   applied most widely (32.19\%). In conclusion, this review offers fresh
   perspectives on the direction in which deep generative models for
   SAR/ISAR image generation are headed. The cutting-edge methods outlined
   in this paper are also available to researchers in other domains.},
Publisher = {SPRINGER},
Address = {VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS},
Type = {Review; Early Access},
Language = {English},
Affiliation = {Liu, Z (Corresponding Author), Natl Univ Def Technol, Coll Elect Sci \& Technol, Changsha 410073, Peoples R China.
   Zhang, Jiawei; Liu, Zhen; Jiang, Weidong; Liu, Yongxiang; Zhou, Xiaolin; Li, Xiang, Natl Univ Def Technol, Coll Elect Sci \& Technol, Changsha 410073, Peoples R China.},
DOI = {10.1007/s10462-023-10469-5},
EarlyAccessDate = {MAR 2023},
ISSN = {0269-2821},
EISSN = {1573-7462},
Keywords = {Synthetic aperture radar image; Deep learning; Generative adversarial
   network; Image generation; Artificial intelligence},
Keywords-Plus = {CONVOLUTIONAL NEURAL-NETWORK; OPTICAL IMAGE TRANSLATION; SAR IMAGES;
   ADVERSARIAL NETWORK; POLARIZATION; SIMULATION; GAN},
Research-Areas = {Computer Science},
Web-of-Science-Categories  = {Computer Science, Artificial Intelligence},
Author-Email = {zhen\_liu@nudt.edu.cn},
Affiliations = {National University of Defense Technology - China},
Funding-Acknowledgement = {National Key Research and Development Program of China
   {[}2021YFB3100800]; National Natural Science Foundation of China
   {[}62022091,62201588, 61921001]},
Funding-Text = {AcknowledgementsThis work was supported in part by National Key Research
   and Development Program of China under Grant 2021YFB3100800, in part by
   the National Natural Science Foundation of China under Grants
   62022091,62201588, and 61921001.},
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Times-Cited = {0},
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Journal-ISO = {Artif. Intell. Rev.},
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Unique-ID = {WOS:000956310000001},
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@article{ WOS:000878280200001,
Author = {Loetsch, Jorn and Ultsch, Alfred and Mayer, Benjamin and Kringel, Dario},
Title = {Artificial intelligence and machine learning in pain research: a data
   scientometric analysis},
Journal = {PAIN REPORTS},
Year = {2022},
Volume = {7},
Number = {6},
Month = {NOV-DEC},
Abstract = {The collection of increasing amounts of data in health care has become
   relevant for pain therapy and research. This poses problems for analyses
   with classical approaches, which is why artificial intelligence (AI) and
   machine learning (ML) methods are being included into pain research. The
   current literature on AI and ML in the context of pain research was
   automatically searched and manually curated. Common machine learning
   methods and pain settings covered were evaluated. Further focus was on
   the origin of the publication and technical details, such as the
   included sample sizes of the studies analyzed with ML. Machine learning
   was identified in 475 publications from 18 countries, with 79\% of the
   studies published since 2019. Most addressed pain conditions included
   low back pain, musculoskeletal disorders, osteoarthritis, neuropathic
   pain, and inflammatory pain. Most used ML algorithms included random
   forests and support vector machines; however, deep learning was used
   when medical images were involved in the diagnosis of painful
   conditions. Cohort sizes ranged from 11 to 2,164,872, with a mode at n =
   100; however, deep learning required larger data sets often only
   available from medical images. Artificial intelligence and ML, in
   particular, are increasingly being applied to pain-related data. This
   report presents application examples and highlights advantages and
   limitations, such as the ability to process complex data, sometimes, but
   not always, at the cost of big data requirements or black-box decisions.},
Publisher = {LIPPINCOTT WILLIAMS \& WILKINS},
Address = {TWO COMMERCE SQ, 2001 MARKET ST, PHILADELPHIA, PA 19103 USA},
Type = {Review},
Language = {English},
Affiliation = {Lotsch, J (Corresponding Author), Goethe Univ, Theodor Stern Kai 7, D-60590 Frankfurt, Germany.
   Loetsch, Jorn; Mayer, Benjamin; Kringel, Dario, Goethe Univ, Inst Clin Pharmacol, Frankfurt, Germany.
   Loetsch, Jorn, Fraunhofer Inst Translat Med \& Pharmacol ITMP, Frankfurt, Germany.
   Ultsch, Alfred, Univ Marburg, DataBion Res Grp, Hans Meerwein Str, Marburg, Germany.},
DOI = {10.1097/PR9.0000000000001044},
Article-Number = {e1044},
EISSN = {2471-2531},
Keywords = {Data science; Machine learning; Biometrics; Knowledge discovery; Pain;
   Precision medicine},
Keywords-Plus = {LOW-BACK-PAIN; FEATURE-SELECTION; VARIABLES},
Research-Areas = {Neurosciences \& Neurology},
Web-of-Science-Categories  = {Neurosciences},
Author-Email = {j.loetsch@em.uni-frankfurt.de},
Affiliations = {Goethe University Frankfurt; Philipps University Marburg},
ORCID-Numbers = {Lotsch, Jorn/0000-0002-5818-6958},
Funding-Acknowledgement = {Deutsche Forschungsgemeinschaft {[}DFG LO 612/16-1]},
Funding-Text = {J. Lotsch was supported by the Deutsche Forschungsgemeinschaft (DFG LO
   612/16-1: ``Generative artificial intelligence-based algorithm to
   increase the predictivity of preclinical studies while keeping sample
   sizes small{''}). This public funder had no role in study design, data
   collection and analysis, decision to publish, or preparation of the
   manuscript. Data availability statement: The R code used for information
   retrieval is available at https://github.com/JornLotsch/AI4pain},
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Number-of-Cited-References = {97},
Times-Cited = {0},
Usage-Count-Last-180-days = {3},
Usage-Count-Since-2013 = {3},
Journal-ISO = {Pain Rep.},
Doc-Delivery-Number = {5X0EL},
Web-of-Science-Index = {Emerging Sources Citation Index (ESCI)},
Unique-ID = {WOS:000878280200001},
OA = {gold, Green Published},
DA = {2023-04-22},
}

@article{ WOS:000557537700004,
Author = {Chae, Dongwoo},
Title = {Data science and machine learning in anesthesiology},
Journal = {KOREAN JOURNAL OF ANESTHESIOLOGY},
Year = {2020},
Volume = {73},
Number = {4},
Pages = {285-295},
Month = {AUG},
Abstract = {Machine learning (ML) is revolutionizing anesthesiology research. Unlike
   classical research methods that are largely inference-based, ML is
   geared more towards making accurate predictions. ML is a field of
   artificial intelligence concerned with developing algorithms and models
   to perform prediction tasks in the absence of explicit instructions.
   Most ML applications, despite being highly variable in the topics that
   they deal with, generally follow a common workflow. For classification
   tasks, a researcher typically tests various ML models and compares the
   predictive performance with the reference logistic regression model. The
   main advantage of ML lies in its ability to deal with many features with
   complex interactions and its specific focus on maximizing predictive
   performance. However, emphasis on data-driven prediction can sometimes
   neglect mechanistic understanding. This article mainly focuses on the
   application of supervised ML to electronic health record (EHR) data. The
   main limitation of EHR-based studies is in the difficulty of
   establishing causal relationships. However, the associated low cost and
   rich information content provide great potential to uncover hitherto
   unknown correlations. In this review, the basic concepts of ML are
   introduced along with important terms that any ML researcher should
   know. Practical tips regarding the choice of software and computing
   devices are also provided. Towards the end, several examples of
   successful ML applications in anesthesiology are discussed. The goal of
   this article is to provide a basic roadmap to novice ML researchers
   working in the field of anesthesiology.},
Publisher = {KOREAN SOC ANESTHESIOLOGISTS},
Address = {101-3503, LOTTE CASTLE PRESIDENT, 109 MAPO-DAERO, MAPO-GU, SEOUL, SOUTH
   KOREA},
Type = {Review},
Language = {English},
Affiliation = {Chae, D (Corresponding Author), Yonsei Univ, Dept Pharmacol, Coll Med, 50-1 Yonsei Ro, Seoul 03722, South Korea.
   Chae, Dongwoo, Yonsei Univ, Dept Pharmacol, Coll Med, 50-1 Yonsei Ro, Seoul 03722, South Korea.},
DOI = {10.4097/kja.20124},
ISSN = {2005-6419},
EISSN = {2005-7563},
Keywords = {Artificial intelligence; Data science; Electronic health record; Machine
   learning; Predictive analytics; Risk score system},
Keywords-Plus = {PREDICT; PAIN},
Research-Areas = {Anesthesiology},
Web-of-Science-Categories  = {Anesthesiology},
Author-Email = {dongy@yuhs.ac},
Affiliations = {Yonsei University; Yonsei University Health System},
ORCID-Numbers = {, Dongwoo/0000-0002-7675-3821},
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Number-of-Cited-References = {20},
Times-Cited = {6},
Usage-Count-Last-180-days = {0},
Usage-Count-Since-2013 = {4},
Journal-ISO = {Korean J. Anesthesiol.},
Doc-Delivery-Number = {MX2DW},
Web-of-Science-Index = {Science Citation Index Expanded (SCI-EXPANDED)},
Unique-ID = {WOS:000557537700004},
OA = {Green Published, gold},
DA = {2023-04-22},
}

@article{ WOS:000531366700037,
Author = {Kamkar, Shiva and Ghezloo, Fatemeh and Moghaddam, Hamid Abrishami and
   Borji, Ali and Lashgari, Reza},
Title = {Multiple-target tracking in human and machine vision},
Journal = {PLOS COMPUTATIONAL BIOLOGY},
Year = {2020},
Volume = {16},
Number = {4},
Month = {APR},
Abstract = {Author summary
   Multiple-target tracking (MTT) is a challenging task vital for both a
   human's daily life and for many artificial intelligent systems, such as
   those used for urban traffic control. Neuroscientists are interested in
   discovering the underlying neural mechanisms that successfully exploit
   cognitive resources, e.g., spatial attention or memory, during MTT.
   Computer-vision specialists aim to develop powerful MTT algorithms based
   on advanced models or data-driven computational methods. In this paper,
   we review MTT studies from both communities and discuss how findings
   from cognitive studies can inspire developers to construct higher
   performing MTT algorithms. Moreover, some directions have been proposed
   through which MTT algorithms could raise new questions in the cognitive
   science domain, and answering them can shed light on neural processes
   underlying MTT.
   Humans are able to track multiple objects at any given time in their
   daily activities-for example, we can drive a car while monitoring
   obstacles, pedestrians, and other vehicles. Several past studies have
   examined how humans track targets simultaneously and what underlying
   behavioral and neural mechanisms they use. At the same time,
   computer-vision researchers have proposed different algorithms to track
   multiple targets automatically. These algorithms are useful for video
   surveillance, team-sport analysis, video analysis, video summarization,
   and human-computer interaction. Although there are several efficient
   biologically inspired algorithms in artificial intelligence, the human
   multiple-target tracking (MTT) ability is rarely imitated in
   computer-vision algorithms. In this paper, we review MTT studies in
   neuroscience and biologically inspired MTT methods in computer vision
   and discuss the ways in which they can be seen as complementary.},
Publisher = {PUBLIC LIBRARY SCIENCE},
Address = {1160 BATTERY STREET, STE 100, SAN FRANCISCO, CA 94111 USA},
Type = {Review},
Language = {English},
Affiliation = {Moghaddam, HA (Corresponding Author), KN Toosi Univ Technol, Machine Vis \& Med Image Proc Lab, Fac Elect \& Comp Engn, Tehran, Iran.
   Lashgari, R (Corresponding Author), Inst Res Fundamental Sci IPM, Brain Engn Res Ctr, Tehran, Iran.
   Kamkar, Shiva; Moghaddam, Hamid Abrishami, KN Toosi Univ Technol, Machine Vis \& Med Image Proc Lab, Fac Elect \& Comp Engn, Tehran, Iran.
   Kamkar, Shiva; Ghezloo, Fatemeh; Lashgari, Reza, Inst Res Fundamental Sci IPM, Brain Engn Res Ctr, Tehran, Iran.
   Borji, Ali, HCL Amer, New York, NY USA.},
DOI = {10.1371/journal.pcbi.1007698},
Article-Number = {e1007698},
ISSN = {1553-734X},
EISSN = {1553-7358},
Keywords-Plus = {ARTIFICIAL BRAIN PROJECTS; VISUAL WORKING-MEMORY; OBJECT TRACKING;
   IDENTITY TRACKING; EYE-MOVEMENTS; SPATIAL-RESOLUTION; ATTENTIVE
   TRACKING; POSITION TRACKING; WORLD SURVEY; MOTION},
Research-Areas = {Biochemistry \& Molecular Biology; Mathematical \& Computational Biology},
Web-of-Science-Categories  = {Biochemical Research Methods; Mathematical \& Computational Biology},
Author-Email = {rezalashgari@ipm.ir
   moghaddam@kntu.ac.ir},
Affiliations = {K. N. Toosi University of Technology},
ResearcherID-Numbers = {Abrishami Moghaddam, Hamid/AAW-9288-2021
   Kamkar, Shiva/HTP-4665-2023
   },
ORCID-Numbers = {Kamkar, Shiva/0000-0002-3074-3396
   Borji, Ali/0000-0001-8198-0335
   Lashgari, Reza/0000-0003-1768-8342
   Ghezloo, Fatemeh/0000-0003-3888-2793},
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Number-of-Cited-References = {123},
Times-Cited = {11},
Usage-Count-Last-180-days = {13},
Usage-Count-Since-2013 = {49},
Journal-ISO = {PLoS Comput. Biol.},
Doc-Delivery-Number = {LL2EG},
Web-of-Science-Index = {Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)},
Unique-ID = {WOS:000531366700037},
OA = {gold, Green Published},
DA = {2023-04-22},
}

@article{ WOS:000909574400001,
Author = {Malik, Sumbal and Khan, Manzoor Ahmed and El-Sayed, Hesham and Khan,
   Jalal and Ullah, Obaid},
Title = {How Do Autonomous Vehicles Decide?},
Journal = {SENSORS},
Year = {2023},
Volume = {23},
Number = {1},
Month = {JAN},
Abstract = {The advancement in sensor technologies, mobile network technologies, and
   artificial intelligence has pushed the boundaries of different
   verticals, e.g., eHealth and autonomous driving. Statistics show that
   more than one million people are killed in traffic accidents yearly,
   where the vast majority of the accidents are caused by human negligence.
   Higher-level autonomous driving has great potential to enhance road
   safety and traffic efficiency. One of the most crucial links to building
   an autonomous system is the task of decision-making. The ability of a
   vehicle to make robust decisions on its own by anticipating and
   evaluating future outcomes is what makes it intelligent. Planning and
   decision-making technology in autonomous driving becomes even more
   challenging, due to the diversity of the dynamic environments the
   vehicle operates in, the uncertainty in the sensor information, and the
   complex interaction with other road participants. A significant amount
   of research has been carried out toward deploying autonomous vehicles to
   solve plenty of issues, however, how to deal with the high-level
   decision-making in a complex, uncertain, and urban environment is a
   comparatively less explored area. This paper provides an analysis of
   decision-making solutions approaches for autonomous driving. Various
   categories of approaches are analyzed with a comparison to classical
   decision-making approaches. Following, a crucial range of research gaps
   and open challenges have been highlighted that need to be addressed
   before higher-level autonomous vehicles hit the roads. We believe this
   survey will contribute to the research of decision-making methods for
   autonomous vehicles in the future by equipping the researchers with an
   overview of decision-making technology, its potential solution
   approaches, and challenges.},
Publisher = {MDPI},
Address = {ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND},
Type = {Review},
Language = {English},
Affiliation = {El-Sayed, H (Corresponding Author), United Arab Emirates Univ, Coll Informat Technol, Abu Dhabi 15551, U Arab Emirates.
   El-Sayed, H (Corresponding Author), United Arab Emirates Univ, Emirates Ctr Mobil Res ECMR, Abu Dhabi 15551, U Arab Emirates.
   Malik, Sumbal; Khan, Manzoor Ahmed; El-Sayed, Hesham; Khan, Jalal; Ullah, Obaid, United Arab Emirates Univ, Coll Informat Technol, Abu Dhabi 15551, U Arab Emirates.
   Malik, Sumbal; Khan, Manzoor Ahmed; El-Sayed, Hesham, United Arab Emirates Univ, Emirates Ctr Mobil Res ECMR, Abu Dhabi 15551, U Arab Emirates.},
DOI = {10.3390/s23010317},
Article-Number = {317},
EISSN = {1424-8220},
Keywords = {autonomous driving; decision-making; behavioural planning},
Keywords-Plus = {DECISION-MAKING; BEHAVIOR},
Research-Areas = {Chemistry; Engineering; Instruments \& Instrumentation},
Web-of-Science-Categories  = {Chemistry, Analytical; Engineering, Electrical \& Electronic;
   Instruments \& Instrumentation},
Author-Email = {helsayed@uaeu.ac.ae},
Affiliations = {United Arab Emirates University; United Arab Emirates University},
ORCID-Numbers = {Khan, Manzoor/0000-0002-0319-8126
   Malik, Sumbal/0000-0003-2759-3144
   Ullah, Obaid/0000-0003-1434-7948
   Khan, Muhammad Jalal/0000-0002-6230-1760
   El-Sayed, Hesham/0000-0002-7488-0915},
Funding-Acknowledgement = {Emirates Center for Mobility Research (ECMR) of the United Arab Emirates
   University {[}31R151]},
Funding-Text = {This research was funded by the Emirates Center for Mobility Research
   (ECMR) of the United Arab Emirates University (grant number 31R151).},
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Number-of-Cited-References = {109},
Times-Cited = {0},
Usage-Count-Last-180-days = {28},
Usage-Count-Since-2013 = {28},
Journal-ISO = {Sensors},
Doc-Delivery-Number = {7Q7NT},
Web-of-Science-Index = {Science Citation Index Expanded (SCI-EXPANDED)},
Unique-ID = {WOS:000909574400001},
OA = {Green Published, gold},
DA = {2023-04-22},
}

@article{ WOS:000560033900003,
Author = {Schleder, Gabriel R. and Padilha, Antonio C. M. and Acosta, Carlos Mera
   and Costa, Marcio and Fazzio, Adalberto},
Title = {From DFT to machine learning: recent approaches to materials science-a
   review},
Journal = {JOURNAL OF PHYSICS-MATERIALS},
Year = {2019},
Volume = {2},
Number = {3},
Month = {JUL 1},
Abstract = {Recent advances in experimental and computational methods are increasing
   the quantity and complexity of generated data. This massive amount of
   raw data needs to be stored and interpreted in order to advance the
   materials science field. Identifying correlations and patterns from
   large amounts of complex data is being performed by machine learning
   algorithms for decades. Recently, the materials science community
   started to invest in these methodologies to extract knowledge and
   insights from the accumulated data. This review follows a logical
   sequence starting from density functional theory as the representative
   instance of electronic structure methods, to the subsequent
   high-throughput approach, used to generate large amounts of data.
   Ultimately, data-driven strategies which include data mining, screening,
   and machine learning techniques, employ the data generated. We show how
   these approaches to modern computational materials science are being
   used to uncover complexities and design novel materials with enhanced
   properties. Finally, we point to the present research problems,
   challenges, and potential future perspectives of this new exciting
   field.},
Publisher = {IOP Publishing Ltd},
Address = {TEMPLE CIRCUS, TEMPLE WAY, BRISTOL BS1 6BE, ENGLAND},
Type = {Review},
Language = {English},
Affiliation = {Schleder, GR; Fazzio, A (Corresponding Author), Fed Univ ABC, Ctr Nat \& Human Sci, BR-09210580 Santo Andre, SP, Brazil.
   Schleder, GR; Fazzio, A (Corresponding Author), CNPEM, Brazilian Nanotechnol Natl Lab, BR-13083970 Campinas, SP, Brazil.
   Schleder, Gabriel R.; Acosta, Carlos Mera; Fazzio, Adalberto, Fed Univ ABC, Ctr Nat \& Human Sci, BR-09210580 Santo Andre, SP, Brazil.
   Schleder, Gabriel R.; Padilha, Antonio C. M.; Acosta, Carlos Mera; Costa, Marcio; Fazzio, Adalberto, CNPEM, Brazilian Nanotechnol Natl Lab, BR-13083970 Campinas, SP, Brazil.},
DOI = {10.1088/2515-7639/ab084b},
Article-Number = {032001},
EISSN = {2515-7639},
Keywords = {machine learning; artificial intelligence; materials informatics;
   density functional theory (DFT); high-throughput; data science; big data
   screening},
Keywords-Plus = {DENSITY-FUNCTIONAL THEORY; NEURAL-NETWORK POTENTIALS; GENERALIZED
   GRADIENT APPROXIMATION; ELECTRONIC-STRUCTURE CALCULATIONS; CALCULATING
   WAVE-FUNCTIONS; INITIO MOLECULAR-DYNAMICS; TOTAL-ENERGY CALCULATIONS;
   HIGH-THROUGHPUT; CRYSTAL-STRUCTURE; UNREASONABLE EFFECTIVENESS},
Research-Areas = {Materials Science},
Web-of-Science-Categories  = {Materials Science, Multidisciplinary},
Author-Email = {gabriel.schleder@ufabc.edu.br
   adalberto.fazzio@lnnano.cnpem.br},
ResearcherID-Numbers = {Schleder, Gabriel R/C-7986-2016
   Fazzio, Adalberto/I-9933-2017
   Schleder, Gabriel/GLU-0316-2022
   Costa, Marcio/J-1890-2016
   },
ORCID-Numbers = {Schleder, Gabriel R/0000-0003-3129-8682
   Costa, Marcio/0000-0003-1029-8202
   Fazzio, Adalberto/0000-0001-5384-7676
   Mera Acosta, Carlos Augusto/0000-0002-9148-2142
   Michejevs Padilha, Antonio Claudio/0000-0003-1697-2800},
Funding-Acknowledgement = {FundacAo de Amparo a Pesquisa do Estado de SAo Paulo (FAPESP)
   {[}2017/18139-6, 18/05565-0, 18/11856-7, 16/14011-2, 17/02317-2];
   Fundacao de Amparo a Pesquisa do Estado de Sao Paulo (FAPESP)
   {[}16/14011-2, 18/11856-7] Funding Source: FAPESP},
Funding-Text = {GRS, ACMP, CMA, MC, and AF acknowledge financial support from the
   FundacAo de Amparo a Pesquisa do Estado de SAo Paulo (FAPESP), project
   numbers 2017/18139-6, 18/05565-0, 18/11856-7, 16/14011-2, 17/02317-2.},
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Unique-ID = {WOS:000560033900003},
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@article{ WOS:000600551200003,
Author = {Qin, S. Joe and Dong, Yining and Zhu, Qinqin and Wang, Jin and Liu,
   Qiang},
Title = {Bridging systems theory and data science: A unifying review of dynamic
   latent variable analytics and process monitoring},
Journal = {ANNUAL REVIEWS IN CONTROL},
Year = {2020},
Volume = {50},
Pages = {29-48},
Abstract = {This paper is concerned with data science and analytics as applied to
   data from dynamic systems for the purpose of monitoring, prediction, and
   inference. Collinearity is inevitable in industrial operation data.
   Therefore, we focus on latent variable methods that achieve dimension
   reduction and collinearity removal. We present a new dimension reduction
   expression of state space framework to unify dynamic latent variable
   analytics for process data, dynamic factor models for econometrics,
   subspace identification of multivariate dynamic systems, and machine
   learning algorithms for dynamic feature analysis. We unify or
   differentiate them in terms of model structure, objectives with
   constraints, and parsimony of parameterization. The Kalman filter theory
   in the latent space is used to give a system theory foundation to some
   empirical treatments in data analytics. We provide a unifying review of
   the connections among the dynamic latent variable methods, dynamic
   factor models, subspace identification methods, dynamic feature
   extractions, and their uses for prediction and process monitoring. Both
   unsupervised dynamic latent variable analytics and the supervised
   counterparts are reviewed. Illustrative examples are presented to show
   the similarities and differences among the analytics in extracting
   features for prediction and monitoring.},
Publisher = {PERGAMON-ELSEVIER SCIENCE LTD},
Address = {THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND},
Type = {Review},
Language = {English},
Affiliation = {Qin, SJ (Corresponding Author), City Univ Hong Kong, Ctr Syst Informat Engn, Sch Data Sci, 83 Tat Chee Ave, Hong Kong, Peoples R China.
   Qin, SJ (Corresponding Author), City Univ Hong Kong, Ctr Syst Informat Engn, Hong Kong Inst Data Sci, 83 Tat Chee Ave, Hong Kong, Peoples R China.
   Qin, S. Joe, City Univ Hong Kong, Ctr Syst Informat Engn, Sch Data Sci, 83 Tat Chee Ave, Hong Kong, Peoples R China.
   Qin, S. Joe, City Univ Hong Kong, Ctr Syst Informat Engn, Hong Kong Inst Data Sci, 83 Tat Chee Ave, Hong Kong, Peoples R China.
   Dong, Yining, Stanford Univ, Dept Elect Engn, Stanford, CA 94305 USA.
   Zhu, Qinqin, Univ Waterloo, Dept Chem Engn, Waterloo, ON N2L 3G1, Canada.
   Wang, Jin, Auburn Univ, Dept Chem Engn, Auburn, AL 36849 USA.
   Liu, Qiang, Northeastern Univ, State Key Lab Synthet Automat Proc Ind, Boston, MA USA.},
DOI = {10.1016/j.arcontrol.2020.09.004},
ISSN = {1367-5788},
EISSN = {1872-9088},
Keywords = {Data science; Latent variable analytics; Machine learning; Process data
   analytics; Kalman filtering; Multivariate time series},
Keywords-Plus = {PARTIAL LEAST-SQUARES; SLOW FEATURE ANALYSIS; PRINCIPAL COMPONENT
   ANALYSIS; FISHER DISCRIMINANT-ANALYSIS; TIME-SERIES; FACTOR MODEL;
   SUBSPACE IDENTIFICATION; FAULT-DIAGNOSIS; MULTIBLOCK PLS; REGRESSION},
Research-Areas = {Automation \& Control Systems},
Web-of-Science-Categories  = {Automation \& Control Systems},
Author-Email = {joe.qin@cityu.edu.hk},
Affiliations = {City University of Hong Kong; City University of Hong Kong; Stanford
   University; University of Waterloo; Auburn University System; Auburn
   University; Northeastern University},
ResearcherID-Numbers = {zhu, qin/HIR-6291-2022
   Qin, S. Joe/A-4234-2010
   },
ORCID-Numbers = {Qin, S. Joe/0000-0001-7631-2535
   Wang, Jin/0000-0002-7638-8537},
Funding-Acknowledgement = {City University of Hong Kong {[}9380123]},
Funding-Text = {Financial support for this work from the City University of Hong Kong
   under Project 9380123, SGP: Bridging between Systems Theory and Dynamic
   Data Learning towards Industrial Intelligence and Industry 4.0, is
   gratefully acknowledged.},
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Number-of-Cited-References = {139},
Times-Cited = {46},
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}

@article{ WOS:000736485400001,
Author = {Jasinska-Piadlo, A. and Bond, R. and Biglarbeigi, P. and Brisk, R. and
   Campbell, P. and McEneaneny, D.},
Title = {What can machines learn about heart failure? A systematic literature
   review},
Journal = {INTERNATIONAL JOURNAL OF DATA SCIENCE AND ANALYTICS},
Year = {2022},
Volume = {13},
Number = {3},
Pages = {163-183},
Month = {APR},
Abstract = {This paper presents a systematic literature review with respect to
   application of data science and machine learning (ML) to heart failure
   (HF) datasets with the intention of generating both a synthesis of
   relevant findings and a critical evaluation of approaches, applicability
   and accuracy in order to inform future work within this field. This
   paper has a particular intention to consider ways in which the low
   uptake of ML techniques within clinical practice could be resolved.
   Literature searches were performed on Scopus (2014-2021), ProQuest and
   Ovid MEDLINE databases (2014-2021). Search terms included `heart
   failure' or `cardiomyopathy' and `machine learning', `data analytics',
   `data mining' or `data science'. 81 out of 1688 articles were included
   in the review. The majority of studies were retrospective cohort
   studies. The median size of the patient cohort across all studies was
   1944 (min 46, max 93260). The largest patient samples were used in
   readmission prediction models with the median sample size of 5676 (min.
   380, max. 93260). Machine learning methods focused on common HF
   problems: detection of HF from available dataset, prediction of hospital
   readmission following index hospitalization, mortality prediction,
   classification and clustering of HF cohorts into subgroups with
   distinctive features and response to HF treatment. The most common ML
   methods used were logistic regression, decision trees, random forest and
   support vector machines. Information on validation of models was scarce.
   Based on the authors' affiliations, there was a median 3:1 ratio between
   IT specialists and clinicians. Over half of studies were co-authored by
   a collaboration of medical and IT specialists. Approximately 25\% of
   papers were authored solely by IT specialists who did not seek clinical
   input in data interpretation. The application of ML to datasets, in
   particular clustering methods, enabled the development of classification
   models assisting in testing the outcomes of patients with HF. There is,
   however, a tendency to over-claim the potential usefulness of ML models
   for clinical practice. The next body of work that is required for this
   research discipline is the design of randomised controlled trials (RCTs)
   with the use of ML in an intervention arm in order to prospectively
   validate these algorithms for real-world clinical utility.},
Publisher = {SPRINGERNATURE},
Address = {CAMPUS, 4 CRINAN ST, LONDON, N1 9XW, ENGLAND},
Type = {Review},
Language = {English},
Affiliation = {Jasinska-Piadlo, A (Corresponding Author), Southern Hlth \& Social Care Trust, Craigavon Hosp, CVD Res Unit, 68 Lurgan Rd, Portadown BT63 5QQ, North Ireland.
   Jasinska-Piadlo, A (Corresponding Author), Ulster Univ, Fac Comp Engn \& Built Environm, Shore Rd, Jordanstown BT37 0QB, North Ireland.
   Jasinska-Piadlo, A.; Brisk, R.; McEneaneny, D., Southern Hlth \& Social Care Trust, Craigavon Hosp, CVD Res Unit, 68 Lurgan Rd, Portadown BT63 5QQ, North Ireland.
   Jasinska-Piadlo, A.; Bond, R.; Biglarbeigi, P.; Brisk, R., Ulster Univ, Fac Comp Engn \& Built Environm, Shore Rd, Jordanstown BT37 0QB, North Ireland.
   Campbell, P., Southern Hlth \& Social Care Trust, Craigavon Hosp, Cardiol Dept, 68 Lurgan Rd, Portadown BT63 5QQ, North Ireland.
   McEneaneny, D., Ulster Univ, Ctr Adv Cardiovasc Res, Shore Rd, Jordanstown BT37 0QB, North Ireland.},
DOI = {10.1007/s41060-021-00300-1},
EarlyAccessDate = {DEC 2021},
ISSN = {2364-415X},
EISSN = {2364-4168},
Keywords = {Heart failure; Machine learning; Data analytics; Data science; Heart
   failure dataset},
Keywords-Plus = {RISK PREDICTION MODELS; EJECTION FRACTION; ESC GUIDELINES; READMISSION;
   DIAGNOSIS; MORTALITY; CLASSIFICATION; MANAGEMENT; EVENTS},
Research-Areas = {Computer Science},
Web-of-Science-Categories  = {Computer Science, Artificial Intelligence; Computer Science, Information
   Systems},
Author-Email = {jasinska\_piadlo-a@ulster.ac.uk},
Affiliations = {Ulster University; Ulster University},
ORCID-Numbers = {Jasinska-Piadlo, Alicja/0000-0002-3603-5999
   McEneaney, David/0000-0002-1734-0736
   Brisk, Rob/0000-0002-3865-0792
   Biglarbeigi, Pardis/0000-0002-5908-6334
   Bond, Raymond/0000-0002-1078-2232},
Funding-Acknowledgement = {Public Health Agency and Research and Development Department of the
   Health and Social Care in Northern Ireland, UK},
Funding-Text = {Dr Jasinska-Piadlowas awarded a Doctoral Fellowship Award by Public
   Health Agency and Research and Development Department of the Health and
   Social Care in Northern Ireland, UK.},
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Number-of-Cited-References = {138},
Times-Cited = {3},
Usage-Count-Last-180-days = {2},
Usage-Count-Since-2013 = {6},
Journal-ISO = {Int. J, Data Sci. Anal.},
Doc-Delivery-Number = {0I4BM},
Web-of-Science-Index = {Emerging Sources Citation Index (ESCI)},
Unique-ID = {WOS:000736485400001},
OA = {Green Published, hybrid},
DA = {2023-04-22},
}

@article{ WOS:000462661000046,
Author = {Liyanage, Sohani and Dia, Hussein and Abduljabbar, Rusul and Bagloee,
   Saeed Asadi},
Title = {Flexible Mobility On-Demand: An Environmental Scan},
Journal = {SUSTAINABILITY},
Year = {2019},
Volume = {11},
Number = {5},
Month = {MAR 1},
Abstract = {On-demand shared mobility is increasingly being promoted as an
   influential strategy to address urban transport challenges in large and
   fast-growing cities. The appeal of this form of transport is largely
   attributed to its convenience, ease of use, and affordability made
   possible through digital platforms and innovations. The convergence of
   the shared economy with a number of established and emerging
   technologiessuch as artificial intelligence (AI), Internet of Things
   (IoT), and Cloud and Fog computingis helping to expedite their
   deployment as a new form of public transport. Recently, this has
   manifested itself in the form of Flexible Mobility on Demand (FMoD)
   solutions, aimed at meeting personal travel demands through flexible
   routing and scheduling. Increasingly, these shared mobility solutions
   are blurring the boundaries with existing forms of public transport,
   particularly bus operations. This paper presents an environmental scan
   and analysis of the technological, social, and economic impacts
   surrounding disruptive technology-driven shared mobility trends.
   Specifically, the paper includes an examination of current and
   anticipated external factors that are of direct relevance to
   collaborative and low carbon mobility. The paper also outlines how these
   trends are likely to influence the mobility industries now and into the
   future. The paper collates information from a wide body of literature
   and reports on findings from actual use cases' that exist today which
   have used these disruptive mobility solutions to deliver substantial
   benefits to travellers around the world. Finally, the paper provides
   stakeholders with insight into identifying and responding to the likely
   needs and impacts of FMoD and informs their policy and strategy
   positions on the implementation of smart mobility systems in their
   cities and jurisdictions.},
Publisher = {MDPI},
Address = {ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND},
Type = {Review},
Language = {English},
Affiliation = {Liyanage, S; Dia, H (Corresponding Author), Swinburne Univ Technol, Dept Civil \& Construct Engn, POB 218, Hawthorn, Vic, Australia.
   Liyanage, Sohani; Dia, Hussein; Abduljabbar, Rusul; Bagloee, Saeed Asadi, Swinburne Univ Technol, Dept Civil \& Construct Engn, POB 218, Hawthorn, Vic, Australia.},
DOI = {10.3390/su11051262},
Article-Number = {1262},
EISSN = {2071-1050},
Keywords = {Flexible Mobility on Demand (FMoD); Mobility-as-a-Service (MaaS); shared
   mobility; Internet of Things (IoT); Cloud and Fog computing; sustainable
   public transport},
Keywords-Plus = {ROBUST OPTIMIZATION MODEL; TIME-SERIES ANALYSIS; PUBLIC TRANSPORTATION;
   NEURAL-NETWORKS; BIKE SHARE; RESPONSIVE TRANSPORT; AUTONOMOUS VEHICLES;
   PREDICTION MODEL; CAR DEPENDENCY; SAN-FRANCISCO},
Research-Areas = {Science \& Technology - Other Topics; Environmental Sciences \& Ecology},
Web-of-Science-Categories  = {Green \& Sustainable Science \& Technology; Environmental Sciences;
   Environmental Studies},
Author-Email = {sliyanage@swin.edu.au
   hdia@swin.edu.au
   rabduljabbar@swin.edu.au
   sasadibagloee@swin.edu.au},
Affiliations = {Swinburne University of Technology},
ResearcherID-Numbers = {abduljabbar, rusul/AAO-1128-2020
   Liyanage, Sohani/Q-6258-2019
   },
ORCID-Numbers = {abduljabbar, rusul/0000-0002-5943-8176
   Liyanage, Sohani/0000-0002-1875-5300
   Dia, Hussein/0000-0001-8778-7296
   Asadi Bagloee, Saeed/0000-0001-6078-6314},
Funding-Acknowledgement = {Swinburne University of Technology; Iraqi Government},
Funding-Text = {Sohani Liyanage acknowledges her PhD scholarship provided by the
   Swinburne University of Technology. Rusul Abduljabbar acknowledges the
   Iraqi Government for her PhD scholarship.},
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@article{ WOS:000707933500001,
Author = {Bai, Qifeng and Liu, Shuo and Tian, Yanan and Xu, Tingyang and
   Banegas-Luna, Antonio Jesus and Perez-Sanchez, Horacio and Huang,
   Junzhou and Liu, Huanxiang and Yao, Xiaojun},
Title = {Application advances of deep learning methods for de novo drug design
   and molecular dynamics simulation},
Journal = {WILEY INTERDISCIPLINARY REVIEWS-COMPUTATIONAL MOLECULAR SCIENCE},
Year = {2022},
Volume = {12},
Number = {3},
Month = {MAY},
Abstract = {De novo drug design is a stationary way to build novel ligands in the
   confined pocket of receptor by assembling the atoms or fragments, while
   molecular dynamics (MD) simulation is a dynamical way to study the
   interaction mechanism between the ligands and receptors based on the
   molecular force field. De novo drug design and MD simulation are
   effective tools for novel drug discovery. With the development of
   technology, deep learning methods, and interpretable machine learning
   (IML) have emerged in the research area of drug design. Deep learning
   methods and IML can be used further to improve the efficiency and
   accuracy of de novo drug design and MD simulations. The application
   summary of deep learning methods for de novo drug design, MD
   simulations, and IML can further promote the technical development of
   drug discovery. In this article, two major workflow methods and the
   related components of classical algorithm and deep learning are
   described for de novo drug design from a new perspective. The
   application progress of deep learning is also summarized for MD
   simulations. Furthermore, IML is introduced for the deep learning model
   interpretability of de novo drug design and MD simulations. Our paper
   deals with an interesting topic about deep learning applications of de
   novo drug design and MD simulations for the scientific community. This
   article is categorized under: Data Science > Chemoinformatics Data
   Science > Artificial Intelligence/Machine Learning},
Publisher = {WILEY},
Address = {111 RIVER ST, HOBOKEN 07030-5774, NJ USA},
Type = {Review},
Language = {English},
Affiliation = {Bai, QF (Corresponding Author), Lanzhou Univ, Inst Biochem \& Mol Biol, Sch Basic Med Sci, Key Lab Preclin Study New Drugs Gansu Prov, Lanzhou 730000, Gansu, Peoples R China.
   Xu, TY (Corresponding Author), Shenzhen Tencent Comp Ltd, Tencent AI Lab, Shenzhen, Peoples R China.
   Perez-Sanchez, H (Corresponding Author), UCAM Univ Catolica Murcia, Dept Comp Engn, Struct Bioinformat \& High Performance Comp Res Gr, Murcia, Spain.
   Bai, Qifeng, Lanzhou Univ, Inst Biochem \& Mol Biol, Sch Basic Med Sci, Key Lab Preclin Study New Drugs Gansu Prov, Lanzhou 730000, Gansu, Peoples R China.
   Liu, Shuo; Tian, Yanan; Liu, Huanxiang, Lanzhou Univ, Sch Pharm, Lanzhou, Gansu, Peoples R China.
   Xu, Tingyang; Huang, Junzhou, Shenzhen Tencent Comp Ltd, Tencent AI Lab, Shenzhen, Peoples R China.
   Banegas-Luna, Antonio Jesus; Perez-Sanchez, Horacio, UCAM Univ Catolica Murcia, Dept Comp Engn, Struct Bioinformat \& High Performance Comp Res Gr, Murcia, Spain.
   Yao, Xiaojun, Lanzhou Univ, Coll Chem \& Chem Engn, Lanzhou, Gansu, Peoples R China.},
DOI = {10.1002/wcms.1581},
EarlyAccessDate = {OCT 2021},
Article-Number = {e1581},
ISSN = {1759-0876},
EISSN = {1759-0884},
Keywords = {de novo drug design; deep learning; explainable artificial intelligence;
   interpretable machine learning; MD simulation},
Keywords-Plus = {SCORING FUNCTION; NEURAL-NETWORK; SCREENING LIBRARIES; GENETIC
   ALGORITHM; FORCE-FIELDS; DOCKING; DISCOVERY; PROGRAM; MODEL;
   INTELLIGENCE},
Research-Areas = {Chemistry; Mathematical \& Computational Biology},
Web-of-Science-Categories  = {Chemistry, Multidisciplinary; Mathematical \& Computational Biology},
Author-Email = {baiqf@lzu.edu.cn
   tingyangxu@tencent.com
   hperez@ucam.edu},
Affiliations = {Lanzhou University; Lanzhou University; Tencent; Universidad Catolica de
   Murcia; Lanzhou University},
ResearcherID-Numbers = {Xu, Tingyang/AHA-6587-2022
   Bai, Qifeng/A-2950-2019
   Xu, Tingyang/HGU-8709-2022
   Perez-Sanchez, Horacio/O-5017-2016
   Banegas-Luna, Antonio Jesus/O-7331-2016
   },
ORCID-Numbers = {Bai, Qifeng/0000-0001-7296-6187
   Perez-Sanchez, Horacio/0000-0003-4468-7898
   Banegas-Luna, Antonio Jesus/0000-0003-1158-8877
   Liu, Huanxiang/0000-0002-9284-3667},
Funding-Acknowledgement = {Tencent AI Lab Rhino-Bird Focused Research Program {[}JR202004]; Lanzhou
   University},
Funding-Text = {Tencent AI Lab Rhino-Bird Focused Research Program, Grant/Award Number:
   JR202004; Lanzhou University},
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@article{ WOS:000480685300012,
Author = {Bradley, Alison and van der Meer, Robert and McKay, Colin},
Title = {Personalized Pancreatic Cancer Management A Systematic Review of How
   Machine Learning Is Supporting Decision-making},
Journal = {PANCREAS},
Year = {2019},
Volume = {48},
Number = {5},
Pages = {598-604},
Month = {MAY-JUN},
Abstract = {This review critically analyzes how machine learning is being used to
   support clinical decision-making in the management of potentially
   resectable pancreatic cancer. Following PRISMA (Preferred Reporting
   Items for Systematic Reviews and Meta-analyses) guidelines, electronic
   searches of MEDLINE, Embase, PubMed, and Cochrane Database were
   undertaken. Studies were assessed using the checklist for critical
   appraisal and data extraction for systematic reviews of prediction
   modeling studies (CHARMS) checklist. In total 89,959 citations were
   retrieved. Six studies met the inclusion criteria. Three studies were
   Markov decision-analysis models comparing neoadjuvant therapy versus
   upfront surgery. Three studies predicted survival time using Bayesian
   modeling (n = 1) and artificial neural network (n = 1), and one study
   explored machine learning algorithms including Bayesian network,
   decision trees, k-nearest neighbor, and artificial neural networks. The
   main methodological issues identified were limited data sources, which
   limits generalizability and potentiates bias; lack of external
   validation; and the need for transparency in methods of internal
   validation, consecutive sampling, and selection of candidate predictors.
   The future direction of research relies on expanding our view of the
   multidisciplinary team to include professionals from computing and data
   science with algorithms developed in conjunction with clinicians and
   viewed as aids, not replacement, to traditional clinical
   decision-making.},
Publisher = {LIPPINCOTT WILLIAMS \& WILKINS},
Address = {TWO COMMERCE SQ, 2001 MARKET ST, PHILADELPHIA, PA 19103 USA},
Type = {Review},
Language = {English},
Affiliation = {Bradley, A (Corresponding Author), Univ Strathclyde, Business Sch, DuncanWing 606,199 Cathedral St, Glasgow G4 0QU, Lanark, Scotland.
   Bradley, Alison; van der Meer, Robert, Univ Strathclyde, Business Sch, Dept Management Sci, Glasgow, Lanark, Scotland.
   Bradley, Alison; McKay, Colin, Glasgow Royal Infirm, West Scotland Pancreat Canc Unit, Glasgow, Lanark, Scotland.},
DOI = {10.1097/MPA.0000000000001312},
ISSN = {0885-3177},
EISSN = {1536-4828},
Keywords = {machine learning; pancreatic cancer; decision-analysis; predictive
   modeling; personalized medicine},
Keywords-Plus = {ARTIFICIAL NEURAL-NETWORKS; BAYESIAN NETWORKS; PREDICTION; MODELS;
   HEALTH; RISK; ADENOCARCINOMA; RESECTION; THERAPY; TRIALS},
Research-Areas = {Gastroenterology \& Hepatology},
Web-of-Science-Categories  = {Gastroenterology \& Hepatology},
Author-Email = {bradley\_alison@live.co.uk},
Affiliations = {University of Strathclyde; University of Glasgow},
ResearcherID-Numbers = {Van Der Meer, Robert B/O-1731-2016},
ORCID-Numbers = {Van Der Meer, Robert B/0000-0002-9442-1628},
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Number-of-Cited-References = {57},
Times-Cited = {11},
Usage-Count-Last-180-days = {1},
Usage-Count-Since-2013 = {22},
Journal-ISO = {Pancreas},
Doc-Delivery-Number = {IQ3XN},
Web-of-Science-Index = {Science Citation Index Expanded (SCI-EXPANDED)},
Unique-ID = {WOS:000480685300012},
OA = {Green Accepted},
DA = {2023-04-22},
}

@article{ WOS:000821876800001,
Author = {Basu, Bikramjit and Gowtham, N. H. and Xiao, Yang and Kalidindi, Surya
   R. and Leong, Kam W.},
Title = {Biomaterialomics: Data science-driven pathways to develop
   fourth-generation biomaterials},
Journal = {ACTA BIOMATERIALIA},
Year = {2022},
Volume = {143},
Pages = {1-25},
Month = {APR 15},
Abstract = {Conventional approaches to developing biomaterials and implants require
   intuitive tailoring of manufacturing protocols and biocompatibility
   assessment. This leads to longer development cycles, and high costs. To
   meet existing and unmet clinical needs, it is critical to accelerate the
   production of implantable biomaterials, implants and biomedical devices.
   Building on the Materials Genome Initiative, we define the concept
   `biomaterialomics' as the integration of multi-omics data and
   high-dimensional analysis with artificial intelligence (AI) tools
   throughout the entire pipeline of biomaterials development. The Data
   Science-driven approach is envisioned to bring together on a single
   platform, the computational tools, databases, experimental methods,
   machine learning, and advanced manufacturing (e.g., 3D printing) to
   develop the fourth-generation biomaterials and implants, whose clinical
   performance will be predicted using `digital twins'. While analysing the
   key elements of the concept of `biomaterialomics', significant emphasis
   has been put forward to effectively utilize high-throughput
   biocompatibility data together with multiscale physics-based models,
   E-platform/online databases of clinical studies, data science
   approaches, including metadata management, AI/ Machine Learning (ML)
   algorithms and uncertainty predictions. Such integrated formulation will
   allow one to adopt cross-disciplinary approaches to establish
   processing-structure-property (PSP) linkages. A few published studies
   from the lead author's research group serve as representative examples
   to illustrate the formulation and relevance of the `Biomaterialomics'
   approaches for three emerging research themes, i.e. patient-specific
   implants, additive manufacturing, and bioelectronic medicine. The
   increased adaptability of AI/ML tools in biomaterials science along with
   the training of the next generation researchers in data science are
   strongly recommended.
   Statement of significance
   This leading opinion review paper emphasizes the need to integrate the
   concepts and algorithms of the data science with biomaterials science.
   Also, this paper emphasizes the need to establish a mathematically
   rigorous cross-disciplinary framework that will allow a systematic
   quantitative exploration and curation of critical biomaterials knowledge
   needed to drive objectively the innovation efforts within a suitable
   uncertainty quantification framework, as embodied in `biomaterialomics'
   concept, which integrates multiomics data and high-dimensional analysis
   with artificial intelligence (AI) tools, like machine learning. The
   formulation of this approach has been demonstrated for patient-specific
   implants, additive manufacturing, and bioelectronic medicine. (c) 2022
   Acta Materialia Inc. Published by Elsevier Ltd. All rights reserved.},
Publisher = {ELSEVIER SCI LTD},
Address = {THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND},
Type = {Review},
Language = {English},
Affiliation = {Basu, B (Corresponding Author), Indian Inst Sci, Mat Res Ctr, Bangalore, Karnataka, India.
   Basu, B (Corresponding Author), Indian Inst Sci, Translat Ctr Excellence Biomat Orthoped \& Dent Ap, Bangalore 560012, Karnataka, India.
   Basu, Bikramjit; Gowtham, N. H., Indian Inst Sci, Mat Res Ctr, Bangalore, Karnataka, India.
   Basu, Bikramjit, Indian Inst Sci, Translat Ctr Excellence Biomat Orthoped \& Dent Ap, Bangalore 560012, Karnataka, India.
   Kalidindi, Surya R., Georgia Inst Technol, George W Woodruff Sch Mech Engn, Atlanta, GA 30332 USA.
   Xiao, Yang; Leong, Kam W., Columbia Univ, Dept Biomed Engn, New York, NY 10027 USA.},
DOI = {10.1016/j.actbio.2022.02.027},
EarlyAccessDate = {APR 2022},
ISSN = {1742-7061},
EISSN = {1878-7568},
Keywords-Plus = {ELECTRIC-FIELD STIMULATION; GENE REGULATORY NETWORK; STRUCTURE-PROPERTY
   LINKAGES; HIGH-THROUGHPUT DISCOVERY; STATIC MAGNETIC-FIELD; STEM-CELL
   FATE; SUBSTRATE CONDUCTIVITY; ACCELERATED DEVELOPMENT;
   MECHANICAL-PROPERTIES; MOLECULAR SIMULATION},
Research-Areas = {Engineering; Materials Science},
Web-of-Science-Categories  = {Engineering, Biomedical; Materials Science, Biomaterials},
Author-Email = {bikram@iisc.ac.in},
Affiliations = {Indian Institute of Science (IISC) - Bangalore; Indian Institute of
   Science (IISC) - Bangalore; University System of Georgia; Georgia
   Institute of Technology; Columbia University},
ResearcherID-Numbers = {Leong, Kam W/O-9302-2019
   },
ORCID-Numbers = {Leong, Kam W/0000-0002-8133-4955
   Xiao, Yang/0000-0001-7878-4923},
Funding-Acknowledgement = {Department of Biotechnology, Government of India (NIT Rourkela, India);
   Abdul Kalam National Innovation Fellowship of Indian National Academy of
   Engineering; Chiranjib Bhhatacharyya, Indian Institute of Science,
   Bangalore, India {[}CRG/2020/001145]; Department of Science and
   Technology-Science Engineering Research Board-(DST-SERB) , Ministry of
   Science and Technology, Government of India {[}IMP/2018/000622]; SERB,
   Government of India; Government of India},
Funding-Text = {One of the authors, BB acknowledges the financial support pro-vided by
   the Department of Biotechnology, Government of India, under the `Centre
   of Excellence and Innovation in Biotechnology' scheme, the Translational
   Centre on Biomaterials for Orthopedic and Dental applications (in
   particular, collaboration with Deabsish Sarkar, NIT Rourkela, India) ,
   Abdul Kalam National Innovation Fel-lowship of Indian National Academy
   of Engineering, and ongoing research projects, ``Investigation on
   Machine learning approaches for optimization of process parameter for
   additive manufacturing and accelerated design of patient specific hip
   implants, based on Finite Element Analysis' (CRG/2020/001145,
   collaboration with Chi-ranjib Bhhatacharyya, Indian Institute of
   Science, Bangalore, India) , and IMPRINT project entitled ``Development
   of new generation Ac-etabular Socket Liner and Femoral Head Prototypes
   with unique 3D microstructures and better fracture resistance for
   Osteoporosis and Osteoarthritis treatment.{''} (IMP/2018/000622) ,
   sponsored by the Department of Science and Technology-Science
   Engineering Re-search Board- (DST-SERB) , Ministry of Science and
   Technology, Gov-ernment of India. SK would like to thank the SERB,
   Government of India for the Vajra fellowship. BB and KWL acknowledge the
   SPARC research program, ``Electrical stimulation with electroactive
   biomaterials as therapeutic strategy for intractable bone and
   neu-rodegenerative diseases{''}, funded by the Government of India. BB
   ackowledges Professors P. Balaram and Ramray Bhat, Indian Insti-tute of
   Science, Bangalore for their suggestions. BB also appreciates the help
   from Nitu Bhaskar, Soumitra Das, Srimanta Barui, Swati Sharma and
   Prerana S. during manuscript preparation.},
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Number-of-Cited-References = {250},
Times-Cited = {7},
Usage-Count-Last-180-days = {9},
Usage-Count-Since-2013 = {22},
Journal-ISO = {Acta Biomater.},
Doc-Delivery-Number = {2S6CA},
Web-of-Science-Index = {Science Citation Index Expanded (SCI-EXPANDED)},
Unique-ID = {WOS:000821876800001},
DA = {2023-04-22},
}

@article{ WOS:000836472200016,
Author = {Stein, Helge S. and Sanin, Alexey and Rahmanian, Fuzhan and Zhang,
   Bojing and Vogler, Monika and Flowers, Jackson K. and Fischer, Leon and
   Fuchs, Stefan and Choudhary, Nirmal and Schroeder, Lisa},
Title = {From materials discovery to system optimization by integrating
   combinatorial electrochemistry and data science},
Journal = {CURRENT OPINION IN ELECTROCHEMISTRY},
Year = {2022},
Volume = {35},
Month = {OCT},
Abstract = {Insight generation from electrochemical experiments augmented by data
   science requires broad, systematic, and well-defined parameter
   variations which build upon automation, data management, and flexible
   instrumentation interfaces. Combinatorial electrochemical synthesis of
   interfaces and interphases with liquid electrolytes by automated
   high-throughput robots offers the required high reproducibility.
   However, automation of electrochemistry is not enough as data needs to
   be collected in ways that make it machine readable and interpretable.
   Once established this integration allows scientists and algorithms to
   transfer knowledge and insights from interfaces and interphases to
   systems like batteries. Herein, we present an overview of recent
   innovative methods of combinatorial electrochemistry and synthesis which
   have been integrated into our platform for accelerated electrochemical
   storage research (PLACES/R), targeting the entire battery research value
   chain.},
Publisher = {ELSEVIER},
Address = {RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS},
Type = {Review},
Language = {English},
Affiliation = {Stein, HS (Corresponding Author), Karlsruhe Inst Technol KIT, Inst Phys Chem IPC, Fritz Haber Weg 2, D-76131 Karlsruhe, Germany.
   Stein, HS (Corresponding Author), Helmholtz Inst Ulm HIU, Helmholtzstr 11, D-89081 Ulm, Germany.
   Stein, Helge S.; Sanin, Alexey; Rahmanian, Fuzhan; Zhang, Bojing; Vogler, Monika; Flowers, Jackson K.; Fischer, Leon; Fuchs, Stefan; Choudhary, Nirmal; Schroeder, Lisa, Karlsruhe Inst Technol KIT, Inst Phys Chem IPC, Fritz Haber Weg 2, D-76131 Karlsruhe, Germany.
   Stein, Helge S.; Sanin, Alexey; Rahmanian, Fuzhan; Zhang, Bojing; Vogler, Monika; Flowers, Jackson K.; Fischer, Leon; Fuchs, Stefan; Choudhary, Nirmal; Schroeder, Lisa, Helmholtz Inst Ulm HIU, Helmholtzstr 11, D-89081 Ulm, Germany.},
DOI = {10.1016/j.coelec.2022.101053},
EarlyAccessDate = {JUN 2022},
Article-Number = {101053},
ISSN = {2451-9103},
Keywords = {Combinatorial; High-throughput; Electrochemistry; Data science; Machine
   learning; Integration; Batteries},
Keywords-Plus = {SEMICONDUCTOR MATERIAL LIBRARIES; THROUGHPUT},
Research-Areas = {Chemistry; Electrochemistry; Materials Science},
Web-of-Science-Categories  = {Chemistry, Physical; Electrochemistry; Materials Science,
   Multidisciplinary},
Author-Email = {helge.stein@kit.edu},
Affiliations = {Helmholtz Association; Karlsruhe Institute of Technology},
ResearcherID-Numbers = {Stein, Helge/M-5595-2019
   },
ORCID-Numbers = {Stein, Helge/0000-0002-3461-0232
   Sanin, Alexey/0000-0003-1796-9224},
Funding-Acknowledgement = {German Research Foundation (DFG) {[}390874152]; European Union
   {[}957189]; Bundesministerium fur Bildung und Forschung (BMBF)
   {[}03XP0323D, 03XP0363A]},
Funding-Text = {This work contributes to the research performed at CELEST (Center for
   Electrochemical Energy Storage Ulm-Karlsruhe) and was funded by the
   German Research Foundation (DFG) under Project ID 390874152 (POLiS
   Cluster of Excellence). This project received funding from the European
   Union's Horizon 2020 research and innovation programme under grant
   agreement No 957189. This project received funding from
   Bundesministerium fur Bildung und Forschung (BMBF) in the framework of
   the BMBF-Kompetenzcluster InZePro for the projects DataBatt No 03XP0323D
   and InForm No 03XP0363A.},
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Number-of-Cited-References = {61},
Times-Cited = {2},
Usage-Count-Last-180-days = {6},
Usage-Count-Since-2013 = {8},
Journal-ISO = {Curr. Opin. Electrochem.},
Doc-Delivery-Number = {3N9NW},
Web-of-Science-Index = {Science Citation Index Expanded (SCI-EXPANDED)},
Unique-ID = {WOS:000836472200016},
DA = {2023-04-22},
}

@article{ WOS:000701169800003,
Author = {Calzolari, Giovanni and Liu, Wei},
Title = {Deep learning to replace, improve, or aid CFD analysis in built
   environment applications: A review},
Journal = {BUILDING AND ENVIRONMENT},
Year = {2021},
Volume = {206},
Month = {DEC},
Abstract = {Fast and accurate airflow simulations in the built environment are
   critical to provide acceptable thermal comfort and air quality to the
   occupants. Computational Fluid Dynamics (CFD) offers detailed analysis
   on airflow motion, heat transfer, and contaminant transport in indoor
   environment, as well as wind flow and pollution dispersion around
   buildings in urban environments. However, CFD still faces many
   challenges mainly in terms of computational expensiveness and accuracy.
   With the increasing availability of large amount of data, data driven
   models are starting to be investigated to either replace, improve, or
   aid CFD simulations. More specifically, the abilities of deep learning
   and Artificial Neural Networks (ANN) as universal non-linear
   approximator, handling of high dimensionality fields, and computational
   inexpensiveness are very appealing. In built environment research, deep
   learning applications to airflow simulations shows the ANN as surrogate,
   replacement for expensive CFD analysis. Surrogate modeling enables fast
   or even real-time predictions, but usually at a cost of a degraded
   accuracy. The objective of this work is to critically review deep
   learning interactions with fluid mechanics simulations in general, to
   propose and inform about different techniques other than surrogate
   modeling for built environment applications. The literature review shows
   that ANNs can enhance the turbulence model in various way for coupled
   CFD simulations of higher accuracy, improve the efficiency of Proper
   Orthogonal Decomposition (POD) methods, leverage crucial physical
   properties and information with physics informed deep learning modeling,
   and even unlock new advanced methods for flow analysis such as
   super-resolution techniques. These promising methods are largely yet to
   be explored in the built environment scene. Unavoidably, deep learning
   models also presents challenges such as the availability of consistent
   large flow databases, the extrapolation task problem, and over-fitting,
   etc.},
Publisher = {PERGAMON-ELSEVIER SCIENCE LTD},
Address = {THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND},
Type = {Review},
Language = {English},
Affiliation = {Liu, W (Corresponding Author), KTH Royal Inst Technol, Dept Civil \& Architectural Engn, Div Sustainable Bldg, Brinellvagen 23, S-10044 Stockholm, Sweden.
   Calzolari, Giovanni; Liu, Wei, KTH Royal Inst Technol, Dept Civil \& Architectural Engn, Div Sustainable Bldg, Brinellvagen 23, S-10044 Stockholm, Sweden.},
DOI = {10.1016/j.buildenv.2021.108315},
EarlyAccessDate = {SEP 2021},
Article-Number = {108315},
ISSN = {0360-1323},
EISSN = {1873-684X},
Keywords = {Artificial intelligence; Neural networks; Fluid mechanics; Turbulence},
Keywords-Plus = {PROPER ORTHOGONAL DECOMPOSITION; ARTIFICIAL NEURAL-NETWORKS; INVERSE
   DESIGN; MODEL IDENTIFICATION; TURBULENCE MODELS; HEAT-TRANSFER;
   SIMULATION; FLOWS; MACHINE; CABIN},
Research-Areas = {Construction \& Building Technology; Engineering},
Web-of-Science-Categories  = {Construction \& Building Technology; Engineering, Environmental;
   Engineering, Civil},
Author-Email = {weiliu2@kth.se},
Affiliations = {Royal Institute of Technology},
ResearcherID-Numbers = {Liu, Wei/B-3532-2016
   },
ORCID-Numbers = {Liu, Wei/0000-0003-1285-2334
   Calzolari, Giovanni/0000-0001-9287-6103},
Funding-Acknowledgement = {Digital Futures, C3.ai Digital Transformation Institute;
   Energimyndigheten (Swedish Energy Agency) {[}50057-1]},
Funding-Text = {This work was partially supported by the Digital Futures, C3.ai Digital
   Transformation Institute, and the Energimyndigheten (Swedish Energy
   Agency, grant No. 50057-1) .},
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Number-of-Cited-References = {99},
Times-Cited = {18},
Usage-Count-Last-180-days = {42},
Usage-Count-Since-2013 = {157},
Journal-ISO = {Build. Environ.},
Doc-Delivery-Number = {UX9QF},
Web-of-Science-Index = {Science Citation Index Expanded (SCI-EXPANDED)},
Unique-ID = {WOS:000701169800003},
OA = {hybrid},
DA = {2023-04-22},
}

@article{ WOS:000852001600001,
Author = {Travaini, Guido Vittorio and Pacchioni, Federico and Bellumore, Silvia
   and Bosia, Marta and De Micco, Francesco},
Title = {Machine Learning and Criminal Justice: A Systematic Review of Advanced
   Methodology for Recidivism Risk Prediction},
Journal = {INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH},
Year = {2022},
Volume = {19},
Number = {17},
Month = {SEP},
Abstract = {Recent evolution in the field of data science has revealed the potential
   utility of machine learning (ML) applied to criminal justice. Hence, the
   literature focused on finding better techniques to predict criminal
   recidivism risk is rapidly flourishing. However, it is difficult to make
   a state of the art for the application of ML in recidivism prediction.
   In this systematic review, out of 79 studies from Scopus and PubMed
   online databases we selected, 12 studies that guarantee the
   replicability of the models across different datasets and their
   applicability to recidivism prediction. The different datasets and ML
   techniques used in each of the 12 studies have been compared using the
   two selected metrics. This study shows how each method applied achieves
   good performance, with an average score of 0.81 for ACC and 0.74 for
   AUC. This systematic review highlights key points that could allow
   criminal justice professionals to routinely exploit predictions of
   recidivism risk based on ML techniques. These include the presence of
   performance metrics, the use of transparent algorithms or explainable
   artificial intelligence (XAI) techniques, as well as the high quality of
   input data.},
Publisher = {MDPI},
Address = {ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND},
Type = {Review},
Language = {English},
Affiliation = {De Micco, F (Corresponding Author), Campus Biomed Univ Rome, Bioeth \& Humanities Res Unit, I-00128 Rome, Italy.
   De Micco, F (Corresponding Author), Campus Biomed Univ Hosp Fdn, Dept Clin Affairs, I-00128 Rome, Italy.
   Travaini, Guido Vittorio; Pacchioni, Federico; Bellumore, Silvia; Bosia, Marta, Univ Vita Salute San Raffaele, Sch Med, I-20132 Milan, Italy.
   Bosia, Marta, IRCCS San Raffaele Sci Inst, Dept Clin Neurosci, I-20132 Milan, Italy.
   De Micco, Francesco, Campus Biomed Univ Rome, Bioeth \& Humanities Res Unit, I-00128 Rome, Italy.
   De Micco, Francesco, Campus Biomed Univ Hosp Fdn, Dept Clin Affairs, I-00128 Rome, Italy.},
DOI = {10.3390/ijerph191710594},
Article-Number = {10594},
EISSN = {1660-4601},
Keywords = {machine learning; recidivism; crime prediction; artificial intelligence;
   explainable artificial intelligence},
Keywords-Plus = {BIG DATA; LIMITS},
Research-Areas = {Environmental Sciences \& Ecology; Public, Environmental \& Occupational
   Health},
Web-of-Science-Categories  = {Environmental Sciences; Public, Environmental \& Occupational Health},
Author-Email = {f.demicco@policlinicocampus.it},
Affiliations = {Vita-Salute San Raffaele University; Vita-Salute San Raffaele
   University; IRCCS Ospedale San Raffaele; University Campus Bio-Medico -
   Rome Italy; Fondazione Policlinico Universitario Campus Bio-Medico},
ResearcherID-Numbers = {Bosia, Marta/K-5388-2016},
ORCID-Numbers = {Pacchioni, Federico/0000-0002-5220-2574
   TRAVAINI, GUIDO VITTORIO/0000-0002-3033-834X
   Bosia, Marta/0000-0002-9658-2759},
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Number-of-Cited-References = {50},
Times-Cited = {2},
Usage-Count-Last-180-days = {9},
Usage-Count-Since-2013 = {11},
Journal-ISO = {Int. J. Environ. Res. Public Health},
Doc-Delivery-Number = {4K5PO},
Web-of-Science-Index = {Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)},
Unique-ID = {WOS:000852001600001},
OA = {Green Published, gold},
DA = {2023-04-22},
}

@article{ WOS:000486393900023,
Author = {Le, Vincent},
Title = {SPIRIT IN THE CRYPT NEGARESTANI VS LAND},
Journal = {COSMOS AND HISTORY-THE JOURNAL OF NATURAL AND SOCIAL PHILOSOPHY},
Year = {2019},
Volume = {15},
Number = {1},
Pages = {535-563},
Abstract = {Iranian philosopher Reza Negarestani's first 2008 book Cyclonopedia was
   written under the influence of Nick Land's nihilistic and antihumanist
   philosophy which seeks to critique anthropomorphism by confronting us
   with our coming extinction beyond which our concepts of reason cannot
   reach. Since Cyclonopedia's publication, however, Negarestani has left
   behind Landian nihilism to develop in his 2018 book Intelligence and
   Spirit a neorationalist philosophy of mind whose primary influences are
   Sellars, Brandom, and Hegel. At 579 clearly written yet dense pages, it
   is difficult even for a review article to encapsulate the book in its
   entirety. The first half of this article instead aims to provide a sense
   of the book's overall project by focusing on how Negarestani outlines
   and develops his neorationalist philosophy through a critique of Land's
   antihumanism.
   Never one to remain silent whilst others seek to resurrect Hegel from
   the dead, since December 2018, Land has been releasing a draft on his
   blog Urban Futures 2.1 of his new monograph Crypto-Current: Bitcoin and
   Philosophy, which proffers the most up to date articulation of his main
   antihumanist tenets. Having organized Intelligence and Spirit around
   Negarestani's objections to Land, this article's second half turns to
   Crypto-Current to see how Land is able to provide convincing responses
   to each of Negarestani's objections, showing some to be based on
   strawman characterizations, others to stem from misunderstandings of
   Land's position, and still others to lack traction at all. By putting
   Negarestani and Land's new books in combat, we will ultimately see that
   the grounds for Negarestani's efforts to move continental philosophy
   from its Kanto-Landian phase to a renewed Hegelian phase is unsuccessful
   in that antihumanism is able to respond to each of his objections in
   kind.},
Publisher = {COSMOS PUBL COOPERATIVE},
Address = {C/O ARRAN GARE, PHILOSOPHY \& CULTURAL INQUIRY, PO BOX 218, HAWTHORN,
   VIC 3122, AUSTRALIA},
Type = {Review},
Language = {English},
ISSN = {1832-9101},
Keywords = {Reza Negarestani; Nick Land; Intelligence and Spirit; Crypto-current;
   Bitcoin; cryptocurrency; antihumanism; neorationalism; Hegel; Kant;
   artificial intelligence; Sellars; Brandom; nihilism; blockchain;
   accelerationism},
Research-Areas = {Philosophy},
Web-of-Science-Categories  = {Philosophy},
Author-Email = {eltnecniv@gmail.com},
Cited-References = {Land N., 2012, FANGED NOUMENA COLLE.
   Land Nick, CRYPTO CURRENT BITCO.
   Negarestani Reza, 2018, INTELLIGENCE SPIRIT.
   Negarestani Reza, 2008, CYCLONOPEDIA COMPLIC.},
Number-of-Cited-References = {4},
Times-Cited = {0},
Usage-Count-Last-180-days = {0},
Usage-Count-Since-2013 = {1},
Journal-ISO = {Cosm. Hist.},
Doc-Delivery-Number = {IY4WO},
Web-of-Science-Index = {Emerging Sources Citation Index (ESCI)},
Unique-ID = {WOS:000486393900023},
DA = {2023-04-22},
}

@article{ WOS:000680488900001,
Author = {Xiong, Guoli and Shen, Chao and Yang, Ziyi and Jiang, Dejun and Liu,
   Shao and Lu, Aiping and Chen, Xiang and Hou, Tingjun and Cao, Dongsheng},
Title = {Featurization strategies for protein-ligand interactions and their
   applications in scoring function development},
Journal = {WILEY INTERDISCIPLINARY REVIEWS-COMPUTATIONAL MOLECULAR SCIENCE},
Year = {2022},
Volume = {12},
Number = {2},
Month = {MAR},
Abstract = {The predictive performance of classical scoring functions (SFs) seems to
   have reached a plateau. Currently, SFs relying on sophisticated machine
   learning techniques have shown great potential in binding affinity
   prediction and virtual screening. As one of the most indispensable
   components in the workflow of training a machine learning scoring
   function (MLSF), the featurization or representation process enables us
   to catch certain physical processes that are important for
   protein-ligand interactions and to obtain machine-readable descriptors.
   Currently, according to how they are derived, the descriptors used in
   MLSFs for both continuous and binary binding affinity estimates can be
   grouped into two broad categories: handcrafted features and
   automated-extraction features. Moreover, the automated-extraction
   features emerge as a new featurization trend along with the application
   of deep learning algorithms. Here, we make a thorough summary of the
   advances in the featurization strategies for protein-ligand interactions
   in the context of MLSFs, with emphasis on the recently rising
   automated-extraction features. We also discuss the similarity between
   protein-ligand interaction representations and small-molecule
   representations, and the challenges confronted by the scientific
   community in characterizing protein-ligand interactions. We expect that
   this review could inspire the development of novel featurization
   approaches and boosted MLSFs. This article is categorized under: Data
   Science > Artificial Intelligence/Machine Learning Software > Molecular
   Modeling Molecular and Statistical Mechanics > Molecular Interactions},
Publisher = {WILEY},
Address = {111 RIVER ST, HOBOKEN 07030-5774, NJ USA},
Type = {Review},
Language = {English},
Affiliation = {Cao, DS (Corresponding Author), Cent South Univ, Xiangya Sch Pharmaceut Sci, Changsha 410003, Peoples R China.
   Hou, TJ (Corresponding Author), Zhejiang Univ, Hangzhou Inst Innovat Med, Coll Pharmaceut Sci, Hangzhou 310058, Zhejiang, Peoples R China.
   Chen, X (Corresponding Author), Cent South Univ, Dept Dermatol, Hunan Engn Res Ctr Skin Hlth \& Dis, Hunan Key Lab Skin Canc \& Psoriasis,Xiangya Hosp, Changsha 410008, Hunan, Peoples R China.
   Xiong, Guoli; Yang, Ziyi; Cao, Dongsheng, Cent South Univ, Xiangya Sch Pharmaceut Sci, Changsha 410003, Peoples R China.
   Shen, Chao; Jiang, Dejun; Hou, Tingjun, Zhejiang Univ, Hangzhou Inst Innovat Med, Coll Pharmaceut Sci, Hangzhou 310058, Zhejiang, Peoples R China.
   Jiang, Dejun, Zhejiang Univ, Coll Comp Sci \& Technol, Hangzhou, Peoples R China.
   Liu, Shao, Cent South Univ, Xiangya Hosp, Dept Pharm, Changsha, Peoples R China.
   Lu, Aiping; Cao, Dongsheng, Hong Kong Baptist Univ, Inst Adv Translat Med Bone \& Joint Dis, Sch Chinese Med, Hong Kong, Peoples R China.
   Chen, Xiang, Cent South Univ, Dept Dermatol, Hunan Engn Res Ctr Skin Hlth \& Dis, Hunan Key Lab Skin Canc \& Psoriasis,Xiangya Hosp, Changsha 410008, Hunan, Peoples R China.},
DOI = {10.1002/wcms.1567},
EarlyAccessDate = {AUG 2021},
Article-Number = {e1567},
ISSN = {1759-0876},
EISSN = {1759-0884},
Keywords = {artificial intelligence; feature engineering; machine learning;
   protein-ligand interaction; scoring functions},
Keywords-Plus = {BINDING-AFFINITY PREDICTION; NEURAL-NETWORK; INTERACTION FINGERPRINTS;
   DRUG DISCOVERY; RANDOM FOREST; DOCKING; INHIBITORS; COMPLEXES;
   REPRESENTATIONS; IDENTIFICATION},
Research-Areas = {Chemistry; Mathematical \& Computational Biology},
Web-of-Science-Categories  = {Chemistry, Multidisciplinary; Mathematical \& Computational Biology},
Author-Email = {chenxiangck@126.com
   tingjunhou@zju.edu.cn
   oriental-cds@163.com},
Affiliations = {Central South University; Zhejiang University; Zhejiang University;
   Central South University; Hong Kong Baptist University; Central South
   University},
ResearcherID-Numbers = {Hou, Tingjun/C-7492-2011
   Shen, Chao/AAV-2938-2020
   Xiong, Guoli/GWV-7207-2022
   },
ORCID-Numbers = {Hou, Tingjun/0000-0001-7227-2580
   Shen, Chao/0000-0003-2783-5529
   Xiong, Guoli/0000-0001-9949-2313},
Funding-Acknowledgement = {Changsha Municipal Natural Science Foundation {[}kq2014144]; Changsha
   Science and Technology Bureau project {[}kq2001034]; HKBU Strategic
   Development Fund project {[}SDF19-0402-P02]; Key R\&D Program of
   Zhejiang Province {[}2020C03010]; Leading Talent of ``Ten Thousand
   Plan{''}-National High-Level Talents Special Support Plan; National
   Natural Science Foundation of China {[}21575128, 81773632]; Zhejiang
   Provincial Natural Science Foundation of China {[}LZ19H300001]},
Funding-Text = {Changsha Municipal Natural Science Foundation, Grant/Award Number:
   kq2014144; Changsha Science and Technology Bureau project, Grant/Award
   Number: kq2001034; HKBU Strategic Development Fund project, Grant/Award
   Number: SDF19-0402-P02; Key R\&D Program of Zhejiang Province,
   Grant/Award Number: 2020C03010; Leading Talent of ``Ten Thousand
   Plan{''}-National High-Level Talents Special Support Plan; National
   Natural Science Foundation of China, Grant/Award Numbers: 21575128,
   81773632; Zhejiang Provincial Natural Science Foundation of China,
   Grant/Award Number: LZ19H300001},
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Number-of-Cited-References = {148},
Times-Cited = {5},
Usage-Count-Last-180-days = {19},
Usage-Count-Since-2013 = {72},
Journal-ISO = {Wiley Interdiscip. Rev.-Comput. Mol. Sci.},
Doc-Delivery-Number = {ZP6TE},
Web-of-Science-Index = {Science Citation Index Expanded (SCI-EXPANDED)},
Unique-ID = {WOS:000680488900001},
DA = {2023-04-22},
}

@incollection{ WOS:000684003800005,
Author = {Sengupta, Abhishek and Naresh, G. and Mishra, Astha and Parashar, Diksha
   and Narad, Priyanka},
Editor = {Donev, R and KarabenchevaChristova, T},
Title = {Proteome analysis using machine learning approaches and its applications
   to diseases},
Booktitle = {PROTEOMICS AND SYSTEMS BIOLOGY},
Series = {Advances in Protein Chemistry and Structural Biology},
Year = {2021},
Volume = {127},
Pages = {161-216},
Abstract = {With the tremendous developments in the fields of biological and medical
   technologies, huge amounts of data are generated in the form of genomic
   data, images in medical databases or as data on protein sequences, and
   so on. Analyzing this data through different tools sheds light on the
   particulars of the disease and our body's reactions to it, thus, aiding
   our understanding of the human health. Most useful of these tools is
   artificial intelligence and deep learning (DL). The artificially created
   neural networks in DL algorithms help extract viable data from the
   datasets, and further, to recognize patters in these complex datasets.
   Therefore, as a part of machine learning, DL helps us face all the
   various challenges that come forth during protein prediction, protein
   identification and their quantification. Proteomics is the study of such
   proteins, their structures, features, properties and so on. As a form of
   data science, Proteomics has helped us progress excellently in the field
   of genomics technologies. One of the major techniques used in proteomics
   studies is mass spectrometry (MS). However, MS is efficient with
   analysis of large datasets only with the added help of informatics
   approaches for data analysis and interpretation; these mainly include
   machine learning and deep learning algorithms. In this chapter, we will
   discuss in detail the applications of deep learning and various
   algorithms of machine learning in proteomics.},
Publisher = {ELSEVIER ACADEMIC PRESS INC},
Address = {525 B STREET, SUITE 1900, SAN DIEGO, CA 92101-4495 USA},
Type = {Review; Book Chapter},
Language = {English},
Affiliation = {Narad, P (Corresponding Author), Amity Univ Uttar Pradesh, Amity Inst Biotechnol, Noida, India.
   Sengupta, Abhishek; Naresh, G.; Mishra, Astha; Parashar, Diksha; Narad, Priyanka, Amity Univ Uttar Pradesh, Amity Inst Biotechnol, Noida, India.},
DOI = {10.1016/bs.apcsb.2021.02.003},
ISSN = {1876-1623},
ISBN = {978-0-323-85319-4},
Keywords-Plus = {MASS-SPECTROMETRY; GEL-ELECTROPHORESIS; 2-DIMENSIONAL ELECTROPHORESIS;
   SAMPLE PREPARATION; PROTEINS; IDENTIFICATION; BIOMARKERS; DATABASE;
   PEPTIDEATLAS; TECHNOLOGIES},
Research-Areas = {Biochemistry \& Molecular Biology; Mathematical \& Computational Biology},
Web-of-Science-Categories  = {Biochemistry \& Molecular Biology; Mathematical \& Computational Biology},
Author-Email = {pnarad@amity.edu},
Affiliations = {Amity University Noida},
ResearcherID-Numbers = {Narad, Priyanka/ABI-2361-2020},
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Number-of-Cited-References = {80},
Times-Cited = {1},
Usage-Count-Last-180-days = {6},
Usage-Count-Since-2013 = {17},
Journal-ISO = {Adv. Protein Chem. Struct. Biol.},
Doc-Delivery-Number = {BS0RD},
Web-of-Science-Index = {Book Citation Index – Science (BKCI-S); Science Citation Index Expanded (SCI-EXPANDED)},
Unique-ID = {WOS:000684003800005},
DA = {2023-04-22},
}

@article{ WOS:000744234900015,
Author = {Kriza, Christine and Amenta, Valeria and Zenie, Alexandre and Panidis,
   Dimitris and Chassaigne, Hubert and Urban, Patricia and Holzwarth, Uwe
   and Sauer, Aisha Vanessa and Reina, Vittorio and Griesinger, Claudius
   Benedict},
Title = {Artificial intelligence for imaging-based COVID-19 detection: Systematic
   review comparing added value of AI versus human readers},
Journal = {EUROPEAN JOURNAL OF RADIOLOGY},
Year = {2021},
Volume = {145},
Month = {DEC},
Abstract = {Purpose: A growing number of studies have examined whether Artificial
   Intelligence (AI) systems can support imaging-based diagnosis of
   COVID-19-caused pneumonia, including both gains in diagnostic
   performance and speed. However, what is currently missing is a combined
   appreciation of studies comparing human readers and AI.
   Methods: We followed PRISMA-DTA guidelines for our systematic review,
   searching EMBASE, PUBMED and Scopus databases. To gain insights into the
   potential value of AI methods, we focused on studies comparing the
   performance of human readers versus AI models or versus AI-supported
   human readings.
   Results: Our search identified 1270 studies, of which 12 fulfilled
   specific selection criteria. Concerning diagnostic performance, in
   testing datasets reported sensitivity was 42-100\% (human readers, n = 9
   studies), 60-95\% (AI systems, n = 10) and 81-98\% (AI-supported
   readers, n = 3), whilst reported specificity was 26-100\% (human
   readers, n = 8), 61-96\% (AI systems, n = 10) and 78-99\% (AI-supported
   readings, n = 2). One study highlighted the potential of AI-supported
   readings for the assessment of lung lesion burden changes, whilst two
   studies indicated potential time savings for detection with AI.
   Conclusions: Our review indicates that AI systems or AI-supported human
   readings show less performance variability (interquartile range) in
   general, and may support the differentiation of COVID-19 pneumonia from
   other forms of pneumonia when used in high-prevalence and symptomatic
   populations. However, inconsistencies related to study design, reporting
   of data, areas of risk of bias, as well as limitations of statistical
   analyses complicate clear conclusions. We therefore support efforts for
   developing critical elements of study design when assessing the value of
   AI for diagnostic imaging.},
Publisher = {ELSEVIER IRELAND LTD},
Address = {ELSEVIER HOUSE, BROOKVALE PLAZA, EAST PARK SHANNON, CO, CLARE, 00000,
   IRELAND},
Type = {Review},
Language = {English},
Affiliation = {Kriza, C (Corresponding Author), European Commiss, Joint Res Ctr JRC, Directorate Hlth F, Consumers \& Reference Mat,Unit Consumer Prod Safe, Via E Fermi 2749 TP 281, I-21027 Ispra, Lombardy, Italy.
   Kriza, Christine; Amenta, Valeria; Zenie, Alexandre; Panidis, Dimitris; Chassaigne, Hubert; Urban, Patricia; Holzwarth, Uwe; Sauer, Aisha Vanessa; Reina, Vittorio; Griesinger, Claudius Benedict, European Commiss, Joint Res Ctr JRC, Via E Fermi 2749 TP 281 Ispra, Lombardy, Italy.},
DOI = {10.1016/j.ejrad.2021.110028},
EarlyAccessDate = {NOV 2021},
Article-Number = {110028},
ISSN = {0720-048X},
EISSN = {1872-7727},
Keywords-Plus = {DEEP; RADIOLOGISTS; PERFORMANCE; AGREEMENT},
Research-Areas = {Radiology, Nuclear Medicine \& Medical Imaging},
Web-of-Science-Categories  = {Radiology, Nuclear Medicine \& Medical Imaging},
Author-Email = {Christine.KRIZA@ec.europa.eu},
Funding-Acknowledgement = {European Commission Joint Research Centre (JRC) within the Consumer
   Products Safety Unit of Directorate for Health, Consumers and Reference
   Materials through the JRC's Work Programme for-2020-2021, running under
   Horizon 2020; current EU Framework Programme},
Funding-Text = {This work was supported by the European Commission Joint Research Centre
   (JRC) within the Consumer Products Safety Unit of Directorate for
   Health, Consumers and Reference Materials through the JRC's Work
   Programme for-2020-2021, running under Horizon 2020, the current EU
   Framework Programme for research and innovation funding.},
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Number-of-Cited-References = {35},
Times-Cited = {3},
Usage-Count-Last-180-days = {0},
Usage-Count-Since-2013 = {6},
Journal-ISO = {Eur. J. Radiol.},
Doc-Delivery-Number = {YJ0NB},
Web-of-Science-Index = {Science Citation Index Expanded (SCI-EXPANDED)},
Unique-ID = {WOS:000744234900015},
OA = {hybrid, Green Published},
DA = {2023-04-22},
}

@article{ WOS:000468128200001,
Author = {Bock, Frederic E. and Aydin, Roland C. and Cyron, Christian J. and
   Huber, Norbert and Kalidindi, Surya R. and Klusemann, Benjamin},
Title = {A Review of the Application of Machine Learning and Data Mining
   Approaches in Continuum Materials Mechanics},
Journal = {FRONTIERS IN MATERIALS},
Year = {2019},
Volume = {6},
Month = {MAY 15},
Abstract = {Machine learning tools represent key enablers for empowering material
   scientists and engineers to accelerate the development of novel
   materials, processes and techniques. One of the aims of using such
   approaches in the field of materials science is to achieve
   high-throughput identification and quantification of essential features
   along the process-structure-property-performance chain. In this
   contribution, machine learning and statistical learning approaches are
   reviewed in terms of their successful application to specific problems
   in the field of continuum materials mechanics. They are categorized with
   respect to their type of task designated to be either descriptive,
   predictive or prescriptive; thus to ultimately achieve identification,
   prediction or even optimization of essential characteristics. The
   respective choice of the most appropriate machine learning approach
   highly depends on the specific use-case, type of material, kind of data
   involved, spatial and temporal scales, formats, and desired knowledge
   gain as well as affordable computational costs. Different examples are
   reviewed involving case-by-case dependent application of different types
   of artificial neural networks and other data-driven approaches such as
   support vector machines, decision trees and random forests as well as
   Bayesian learning, and model order reduction procedures such as
   principal component analysis, among others. These techniques are applied
   to accelerate the identification of material parameters or salient
   features for materials characterization, to support rapid design and
   optimization of novel materials or manufacturing methods, to improve and
   correct complex measurement devices, or to better understand and predict
   fatigue behavior, among other examples. Besides experimentally obtained
   datasets, numerous studies draw required information from
   simulation-based data mining. Altogether, it is shown that experiment-
   and simulation-based data mining in combination with machine leaning
   tools provide exceptional opportunities to enable highly reliant
   identification of fundamental interrelations within materials for
   characterization and optimization in a scale-bridging manner. Potentials
   of further utilizing applied machine learning in materials science and
   empowering significant acceleration of knowledge output are pointed out.},
Publisher = {FRONTIERS MEDIA SA},
Address = {AVENUE DU TRIBUNAL FEDERAL 34, LAUSANNE, CH-1015, SWITZERLAND},
Type = {Review},
Language = {English},
Affiliation = {Bock, FE (Corresponding Author), Helmholtz Zentrum Geesthacht, Inst Mat Res Mat Mech, Geesthacht, Germany.
   Bock, Frederic E.; Aydin, Roland C.; Cyron, Christian J.; Huber, Norbert; Klusemann, Benjamin, Helmholtz Zentrum Geesthacht, Inst Mat Res Mat Mech, Geesthacht, Germany.
   Cyron, Christian J., Hamburg Univ Technol TUHH, Inst Continuum \& Mat Mech, Hamburg, Germany.
   Huber, Norbert, Hamburg Univ Technol TUHH, Inst Mat Phys \& Technol, Hamburg, Germany.
   Kalidindi, Surya R., Georgia Inst Technol, Sch Mech Engn, Atlanta, GA 30332 USA.
   Kalidindi, Surya R., Georgia Inst Technol, Sch Computat Sci \& Engn, Atlanta, GA 30332 USA.
   Klusemann, Benjamin, Leuphana Univ Luneburg, Inst Prod \& Proc Innovat, Luneburg, Germany.},
DOI = {10.3389/fmats.2019.00110},
Article-Number = {110},
ISSN = {2296-8016},
Keywords = {machine learning; materials mechanics; data mining;
   process-structure-property-performance relationship; knowledge discovery},
Keywords-Plus = {STRUCTURE-PROPERTY LINKAGES; VISCOPLASTIC MATERIAL PARAMETERS; SPHERICAL
   INDENTATION DATA; ARTIFICIAL NEURAL-NETWORK; DATA SCIENCE; ELASTIC
   LOCALIZATION; MULTIOBJECTIVE OPTIMIZATION; RESIDUAL-STRESSES; GENETIC
   ALGORITHM; FATIGUE LIFE},
Research-Areas = {Materials Science},
Web-of-Science-Categories  = {Materials Science, Multidisciplinary},
Author-Email = {frederic.bock@hzg.de},
Affiliations = {Helmholtz Association; Helmholtz-Zentrum Geesthacht - Zentrum fur
   Material- und Kustenforschung; Hamburg University of Technology; Hamburg
   University of Technology; University System of Georgia; Georgia
   Institute of Technology; University System of Georgia; Georgia Institute
   of Technology; Leuphana University Luneburg},
ResearcherID-Numbers = {Klusemann, Benjamin/AAC-5939-2019
   Klusemann, Benjamin/G-9265-2014
   Huber, Norbert/I-4004-2013
   Cyron, Christian Johannes/ABE-2064-2021
   },
ORCID-Numbers = {Klusemann, Benjamin/0000-0002-8516-5087
   Huber, Norbert/0000-0002-4252-9207
   Cyron, Christian Johannes/0000-0001-8264-0885
   Bock, Frederic E./0000-0002-6541-2036},
Funding-Acknowledgement = {Helmholtz-Association via an ERC-Recognition-Award {[}ERC-RA-0022];
   Deutsche Forschungsgemeinschaft (DFG, German Research Foundation)
   {[}192346071-SFB 986]; NIST {[}70NANB18H039]},
Funding-Text = {FB and BK acknowledge support from the Helmholtz-Association via an
   ERC-Recognition-Award under contract number ERC-RA-0022. From CC and NH,
   support from Deutsche Forschungsgemeinschaft (DFG, German Research
   Foundation)-Projektnummer 192346071-SFB 986 is acknowledged. SK
   acknowledges support from NIST 70NANB18H039.},
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Times-Cited = {135},
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Journal-ISO = {Front. Mater.},
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@article{ WOS:000838158100002,
Author = {Heidari, Arash and Navimipour, Nima Jafari and Unal, Mehmet},
Title = {Applications of ML/DL in the management of smart cities and societies
   based on new trends in information technologies: A systematic literature
   review},
Journal = {SUSTAINABLE CITIES AND SOCIETY},
Year = {2022},
Volume = {85},
Month = {OCT},
Abstract = {The goal of managing smart cities and societies is to maximize the
   efficient use of finite resources while enhancing the quality of life.
   To establish a sustainable urban existence, smart cities use some new
   technologies such as the Internet of Things (IoT), Internet of Drones
   (IoD), and Internet of Vehicles (IoV). The created data by these
   technologies are submitted to analytics to obtain new information for
   increasing the smart societies and cities' efficiency and effectiveness.
   Also, smart traffic management, smart power, and energy management, city
   surveillance, smart buildings, and patient healthcare monitoring are the
   most common applications in smart cities. However, the Artificial
   intelligence (AI), Machine Learning (ML), and Deep Learning (DL)
   approach all hold a lot of promise for managing automated activities in
   smart cities. Therefore, we discuss different research issues and
   possible research paths in which the aforementioned techniques might
   help materialize the smart city notion. The goal of this research is to
   offer a better understanding of (1) the fundamentals of smart city and
   society management, (2) the most recent developments and breakthroughs
   in this field, (3) the benefits and drawbacks of existing methods, and
   (4) areas that require further investigation and consideration. IoT,
   cloud computing, edge computing, fog computing, IoD, IoV, and hybrid
   models are the seven key emerging de-velopments in information
   technology that, in this paper, are considered to categorize the
   state-of-the-art techniques. The results indicate that the Conventional
   Neural Network (CNN) and Long Short-Term Memory (LSTM) are the most
   commonly used ML method in the publications. According to research, the
   majority of papers are about smart cities' power and energy management.
   Furthermore, most papers have concentrated on improving only one
   parameter, where the accuracy parameter obtains the most attention. In
   addition, Python is the most frequently used language, which was used in
   69.8\% of the papers.},
Publisher = {ELSEVIER},
Address = {RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS},
Type = {Review},
Language = {English},
Affiliation = {Navimipour, NJ (Corresponding Author), Kadir Has Univ, Fac Engn \& Nat Sci, Dept Comp Engn, Istanbul, Turkey.
   Heidari, Arash, Islamic Azad Univ, Dept Comp Engn, Tabriz Branch, Tabriz, Iran.
   Navimipour, Nima Jafari, Kadir Has Univ, Fac Engn \& Nat Sci, Dept Comp Engn, Istanbul, Turkey.
   Unal, Mehmet, Nisantasi Univ, Dept Comp Engn, Istanbul, Turkey.
   Heidari, Arash, Islamic Azad Univ, Dept Comp Engn, Shabestar Branch, Shabestar, Iran.},
DOI = {10.1016/j.scs.2022.104089},
EarlyAccessDate = {AUG 2022},
Article-Number = {104089},
ISSN = {2210-6707},
EISSN = {2210-6715},
Keywords = {Smart cities; Sustainable city; Power management; Machine learning; City
   management; Deep learning; Review},
Keywords-Plus = {ENERGY MANAGEMENT; CITY; SECURITY; INTERNET; OPTIMIZATION; GENERATION;
   NETWORK; DESIGN; THINGS; MODEL},
Research-Areas = {Construction \& Building Technology; Science \& Technology - Other
   Topics; Energy \& Fuels},
Web-of-Science-Categories  = {Construction \& Building Technology; Green \& Sustainable Science \&
   Technology; Energy \& Fuels},
Author-Email = {nima.navimipour@khas.edu.tr},
Affiliations = {Islamic Azad University; Kadir Has University; Nisantasi University;
   Islamic Azad University},
ResearcherID-Numbers = {Heidari, Arash/AAK-9761-2021
   Jafari Navimipour, Nima/AAF-5662-2021},
ORCID-Numbers = {Heidari, Arash/0000-0003-4279-8551
   Jafari Navimipour, Nima/0000-0002-5514-5536},
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Times-Cited = {27},
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Journal-ISO = {Sust. Cities Soc.},
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Web-of-Science-Index = {Science Citation Index Expanded (SCI-EXPANDED)},
Unique-ID = {WOS:000838158100002},
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@article{ WOS:000782283800001,
Author = {Ahmad, Kashif and Maabreh, Majdi and Ghaly, Mohamed and Khan, Khalil and
   Qadir, Junaid and Al-Fuqaha, Ala},
Title = {Developing future human-centered smart cities: Critical analysis of
   smart city security, Data management, and Ethical challenges},
Journal = {COMPUTER SCIENCE REVIEW},
Year = {2022},
Volume = {43},
Month = {FEB},
Abstract = {As the globally increasing population drives rapid urbanization in
   various parts of the world, there is a great need to deliberate on the
   future of the cities worth living. In particular, as modern smart cities
   embrace more and more data-driven artificial intelligence services, it
   is worth remembering that (1) technology can facilitate prosperity,
   wellbeing, urban livability, or social justice, but only when it has the
   right analog complements (such as well-thought out policies, mature
   institutions, responsible governance); and (2) the ultimate objective of
   these smart cities is to facilitate and enhance human welfare and social
   flourishing. Researchers have shown that various technological business
   models and features can in fact contribute to social problems such as
   extremism, polarization, misinformation, and Internet addiction. In the
   light of these observations, addressing the philosophical and ethical
   questions involved in ensuring the security, safety, and
   interpretability of such AI algorithms that will form the technological
   bedrock of future cities assumes paramount importance. Globally there
   are calls for technology to be made more humane and human-centered. In
   this paper, we analyze and explore key challenges including security,
   robustness, interpretability, and ethical (data and algorithmic)
   challenges to a successful deployment of AI in human-centric
   applications, with a particular emphasis on the convergence of these
   concepts/challenges. We provide a detailed review of existing literature
   on these key challenges and analyze how one of these challenges may lead
   to others or help in solving other challenges. The paper also advises on
   the current limitations, pitfalls, and future directions of research in
   these domains, and how it can fill the current gaps and lead to better
   solutions. We believe such rigorous analysis will provide a baseline for
   future research in the domain. (C) 2021 Elsevier Inc. All rights
   reserved.},
Publisher = {ELSEVIER},
Address = {RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS},
Type = {Review},
Language = {English},
Affiliation = {Ahmad, K (Corresponding Author), Hamad Bin Khalifa Univ, Coll Sci \& Engn CSE, Informat \& Comp Technol ICT Div, Doha, Qatar.
   Ahmad, Kashif; Al-Fuqaha, Ala, Hamad Bin Khalifa Univ, Coll Sci \& Engn CSE, Informat \& Comp Technol ICT Div, Doha, Qatar.
   Maabreh, Majdi, Hashemite Univ, Fac Prince Al Hussein Bin Abdallah Informat Tech, Dept Informat Technol, POB 330127, Zarqa 13133, Jordan.
   Ghaly, Mohamed, Hamad Bin Khalifa Univ, Coll Islamic Studies, Res Ctr Islamic Legislat \& Eth, Doha, Qatar.
   Khan, Khalil, Pak Austria Fachhochschule, Inst Appl Sci \& Techn, Dept Informat Technol \& Comp Sci, Haripur, KPK, Pakistan.
   Qadir, Junaid, Qatar Univ, Fac Engn, Dept Comp Sci \& Engn, Doha, Qatar.
   Qadir, Junaid, Informat Technol Univ, Dept Elect Engn, Lahore, Pakistan.},
DOI = {10.1016/j.cosrev.2021.100452},
Article-Number = {100452},
ISSN = {1574-0137},
EISSN = {1876-7745},
Keywords = {Smart cities; Machine learning; AI ethics; Adversarial attacks;
   Explainability; Interpretability; Privacy; Security; Data management;
   Data auditing; Data ownership; Data bias; Trojan attacks; Evasion
   attacks},
Keywords-Plus = {BIG-DATA; ADVERSARIAL ATTACKS; SPECIAL-ISSUE; SOCIAL MEDIA; HEALTH-CARE;
   MACHINE; PRIVACY; AI; CLOUD; REPRODUCIBILITY},
Research-Areas = {Computer Science},
Web-of-Science-Categories  = {Computer Science, Information Systems; Computer Science, Software
   Engineering; Computer Science, Theory \& Methods},
Author-Email = {kahmad@hbku.edu.qa
   aalfuqaha@hbku.edu.qa},
Affiliations = {Qatar Foundation (QF); Hamad Bin Khalifa University-Qatar; Hashemite
   University; Qatar Foundation (QF); Hamad Bin Khalifa University-Qatar;
   Qatar University},
ResearcherID-Numbers = {Qadir, Junaid/Q-6329-2019
   Qadir, Junaid/GQH-4631-2022
   },
ORCID-Numbers = {Qadir, Junaid/0000-0001-9466-2475
   Qadir, Junaid/0000-0001-9466-2475
   Khan, Khalil/0000-0002-0864-5255
   Ahmad, Kashif/0000-0002-0931-9275},
Funding-Acknowledgement = {NPRP grant from the Qatar National Research Fund (a member of Qatar
   Foundation) {[}13S-0206-200273]},
Funding-Text = {This publication was made possible by NPRP grant \# {[}13S-0206-200273]
   from the Qatar National Research Fund (a member of Qatar Foundation) .
   The statements made herein are solely the responsibility of the authors.},
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@article{ WOS:000383773600049,
Author = {Kubota, Ken J. and Chen, Jason A. and Little, Max A.},
Title = {Machine learning for large-scale wearable sensor data in Parkinson's
   disease: Concepts, promises, pitfalls, and futures},
Journal = {MOVEMENT DISORDERS},
Year = {2016},
Volume = {31},
Number = {9},
Pages = {1314-1326},
Month = {SEP},
Abstract = {For the treatment and monitoring of Parkinson's disease (PD) to be
   scientific, a key requirement is that measurement of disease stages and
   severity is quantitative, reliable, and repeatable. The last 50 years in
   PD research have been dominated by qualitative, subjective ratings
   obtained by human interpretation of the presentation of disease signs
   and symptoms at clinical visits. More recently, wearable, sensor-based,
   quantitative, objective, and easy-to-use systems for quantifying PD
   signs for large numbers of participants over extended durations have
   been developed. This technology has the potential to significantly
   improve both clinical diagnosis and management in PD and the conduct of
   clinical studies. However, the large-scale, high-dimensional character
   of the data captured by these wearable sensors requires sophisticated
   signal processing and machine-learning algorithms to transform it into
   scientifically and clinically meaningful information. Such algorithms
   that learn from data have shown remarkable success in making accurate
   predictions for complex problems in which human skill has been required
   to date, but they are challenging to evaluate and apply without a basic
   understanding of the underlying logic on which they are based. This
   article contains a nontechnical tutorial review of relevant
   machine-learning algorithms, also describing their limitations and how
   these can be overcome. It discusses implications of this technology and
   a practical road map for realizing the full potential of this technology
   in PD research and practice. (c) 2016 International Parkinson and
   Movement Disorder Society},
Publisher = {WILEY},
Address = {111 RIVER ST, HOBOKEN 07030-5774, NJ USA},
Type = {Review},
Language = {English},
Affiliation = {Kubota, KJ (Corresponding Author), tranSMART Fdn, Dept Data Sci, 401 Edgewater Pl,Suite 600, Wakefield, MA 01880 USA.
   Kubota, Ken J., tranSMART Fdn, Dept Data Sci, 401 Edgewater Pl,Suite 600, Wakefield, MA 01880 USA.
   Chen, Jason A., Verge Genom, San Francisco, CA USA.
   Chen, Jason A., Univ Calif Los Angeles, Interdept Program Bioinformat, Los Angeles, CA USA.
   Little, Max A., Aston Univ, Birmingham, W Midlands, England.
   Little, Max A., MIT, Media Lab, Cambridge, MA 02139 USA.},
DOI = {10.1002/mds.26693},
ISSN = {0885-3185},
EISSN = {1531-8257},
Keywords = {machine learning; artificial intelligence; data science; wearables;
   digital sensors},
Keywords-Plus = {LEVODOPA-INDUCED DYSKINESIAS; SYMPTOMS; MOVEMENT; UPDRS},
Research-Areas = {Neurosciences \& Neurology},
Web-of-Science-Categories  = {Clinical Neurology},
Author-Email = {Ken.Kubota@transmartfoundation.org},
Affiliations = {University of California System; University of California Los Angeles;
   Aston University; Massachusetts Institute of Technology (MIT)},
Funding-Acknowledgement = {Parkinson\&apos;s UK {[}J-0901] Funding Source: Medline;
   Parkinson\&apos;s UK {[}J-0901, J-1403] Funding Source: researchfish},
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Number-of-Cited-References = {42},
Times-Cited = {108},
Usage-Count-Last-180-days = {3},
Usage-Count-Since-2013 = {51},
Journal-ISO = {Mov. Disord.},
Doc-Delivery-Number = {DW6PO},
Web-of-Science-Index = {Science Citation Index Expanded (SCI-EXPANDED)},
Unique-ID = {WOS:000383773600049},
OA = {Green Accepted},
DA = {2023-04-22},
}

@article{ WOS:000954714300013,
Author = {Hidalgo, Edgar M. and Wright, Leah and Isaksson, Mats and Lambert, Gavin
   and Marwick, Thomas H.},
Title = {Current Applications of Robot-Assisted Ultrasound Examination},
Journal = {JACC-CARDIOVASCULAR IMAGING},
Year = {2023},
Volume = {16},
Number = {2},
Pages = {239-247},
Month = {FEB},
Abstract = {Despite advances in miniaturization and automation, the need for expert
   acquisition of a full echocardiogram, including Doppler, has restricted
   access in remote areas. Recent developments in robotics, teleoperation,
   and upgraded telecommunications infrastructure may provide a solution to
   this deficiency. Robot-assisted teleoperated ultrasound examination can
   aid medical diagnosis in remote locations and may improve health
   inequalities between rural and urban settings. This review aimed to
   analyze the status of teleoperated robotic systems for ultrasound
   examinations, evaluate clinical and preclinical applications, identify
   limitations, and outline future directions for clinical use. Overall,
   robot-assisted teleoperated ultrasound is feasible and safe in the
   reported clinical and preclinical studies, with the robots able to
   follow the hand movements performed by sonographers and researchers from
   a distance or in local networks. Moreover, multiple types of ultrasound
   examinations have been performed in remote areas, with a high success
   rate nearly comparable to that of conventional sonography. The studies
   showed that although a low-bandwidth link can be used to control a
   robot, the bandwidth requirements for real-time transmission of video
   and ultrasound images are significantly higher. Furthermore, if haptic
   feedback is implemented, the bandwidth requirements are increased.
   Haptically enabled systems that improve robotic control are necessary
   for accelerating the introduction to clinical use. Haptic feedback and
   enhanced front-end interface control for remote users are vital aspects
   required for clinical application. The incorporation of artificial
   intelligence through either aiding in window acquisition (knowledge of
   anatomical landmarks to adjust scanning planes) or through measurement
   and disease identification is yet to be researched. However, it has the
   potential to lead to dramatic advances. A new generation of robots is in
   development, and several projects in the preclinical stage reveal a
   promising future to overcome the shortage of health professionals in
   remote areas. (J Am Coll Cardiol Img 2023;16:239-247) (c) 2023 by the
   American College of Cardiology Foundation.},
Publisher = {ELSEVIER SCIENCE INC},
Address = {STE 800, 230 PARK AVE, NEW YORK, NY 10169 USA},
Type = {Review},
Language = {English},
Affiliation = {Marwick, TH (Corresponding Author), Baker Heart \& Diabet Inst, 75 Commercial Rd, Melbourne, Vic 3004, Australia.
   Hidalgo, Edgar M.; Isaksson, Mats; Lambert, Gavin, Swinburne Univ Technol, Dept Mech Engn \& Prod Design Engn, Melbourne, Vic, Australia.
   Wright, Leah; Marwick, Thomas H., Baker Heart \& Diabet Inst, 75 Commercial Rd, Melbourne, Vic 3004, Australia.
   Lambert, Gavin, Swinburne Univ Technol, Iverson Hlth Innovat Res Inst, Melbourne, Vic, Australia.},
DOI = {10.1016/j.jcmg.2022.07.018},
ISSN = {1936-878X},
EISSN = {1876-7591},
Keywords = {robotics; tele; echography; telemedicine; telerobotics; ultrasound;
   imaging},
Keywords-Plus = {TELE-ECHOGRAPHY; CLINICAL-PRACTICE; FEASIBILITY; ARM; SYSTEM;
   ECHOCARDIOGRAPHY; NETWORK; TIME},
Research-Areas = {Cardiovascular System \& Cardiology; Radiology, Nuclear Medicine \&
   Medical Imaging},
Web-of-Science-Categories  = {Cardiac \& Cardiovascular Systems; Radiology, Nuclear Medicine \&
   Medical Imaging},
Author-Email = {tom.marwick@baker.edu.au},
Affiliations = {Swinburne University of Technology; Swinburne University of Technology},
ResearcherID-Numbers = {Marwick, Thomas/C-7261-2013
   },
ORCID-Numbers = {Isaksson, Mats/0000-0003-3047-6811
   Lambert, Gavin/0000-0003-0315-645X
   Wright, Leah/0000-0003-3269-6287
   Marwick, Thomas/0000-0001-9065-0899
   HIDALGO FLOREZ, EDGAR MAURICIO/0000-0003-1832-4305},
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Number-of-Cited-References = {46},
Times-Cited = {0},
Usage-Count-Last-180-days = {3},
Usage-Count-Since-2013 = {3},
Journal-ISO = {JACC-Cardiovasc. Imag.},
Doc-Delivery-Number = {A4FY3},
Web-of-Science-Index = {Science Citation Index Expanded (SCI-EXPANDED)},
Unique-ID = {WOS:000954714300013},
DA = {2023-04-22},
}

@article{ WOS:000434868800219,
Author = {Bai, Lu and Wang, Jianzhou and Ma, Xuejiao and Lu, Haiyan},
Title = {Air Pollution Forecasts: An Overview},
Journal = {INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH},
Year = {2018},
Volume = {15},
Number = {4},
Month = {APR},
Abstract = {Air pollution is defined as a phenomenon harmful to the ecological
   system and the normal conditions of human existence and development when
   some substances in the atmosphere exceed a certain concentration. In the
   face of increasingly serious environmental pollution problems, scholars
   have conducted a significant quantity of related research, and in those
   studies, the forecasting of air pollution has been of paramount
   importance. As a precaution, the air pollution forecast is the basis for
   taking effective pollution control measures, and accurate forecasting of
   air pollution has become an important task. Extensive research indicates
   that the methods of air pollution forecasting can be broadly divided
   into three classical categories: statistical forecasting methods,
   artificial intelligence methods, and numerical forecasting methods. More
   recently, some hybrid models have been proposed, which can improve the
   forecast accuracy. To provide a clear perspective on air pollution
   forecasting, this study reviews the theory and application of those
   forecasting models. In addition, based on a comparison of different
   forecasting methods, the advantages and disadvantages of some methods of
   forecasting are also provided. This study aims to provide an overview of
   air pollution forecasting methods for easy access and reference by
   researchers, which will be helpful in further studies.},
Publisher = {MDPI},
Address = {ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND},
Type = {Review},
Language = {English},
Affiliation = {Wang, JZ (Corresponding Author), Dongbei Univ Finance \& Econ, Sch Stat, Dalian 116025, Peoples R China.
   Bai, Lu; Wang, Jianzhou; Ma, Xuejiao, Dongbei Univ Finance \& Econ, Sch Stat, Dalian 116025, Peoples R China.
   Lu, Haiyan, Univ Technol Sydney, Fac Engn \& Informat Technol, Sydney, NSW 2007, Australia.},
DOI = {10.3390/ijerph15040780},
Article-Number = {780},
ISSN = {1660-4601},
Keywords = {air pollution forecast; forecasting models; statistical methods;
   artificial intelligence methods; numerical forecast methods; hybrid
   models},
Keywords-Plus = {FUZZY TIME-SERIES; EMISSION INVENTORIES; NEURAL-NETWORK; MODEL; PM10;
   PREDICTION; LEVEL; URBAN; DECOMPOSITION; PROJECTION},
Research-Areas = {Environmental Sciences \& Ecology; Public, Environmental \& Occupational
   Health},
Web-of-Science-Categories  = {Environmental Sciences; Public, Environmental \& Occupational Health},
Author-Email = {baildctg@hotmail.com
   wjz@lzu.edu.cn
   xuejiaomadufe@163.com
   haiyan.lu@uts.edu.au},
Affiliations = {Dongbei University of Finance \& Economics; University of Technology
   Sydney},
ResearcherID-Numbers = {Lu, Haiyan Helen/T-8426-2019
   Wang, Jianzhou/ABE-8452-2020
   },
ORCID-Numbers = {Wang, Jianzhou/0000-0001-9078-7617
   Bai, Lu/0000-0001-9010-381X
   Lu, Hai Yan/0000-0001-5655-0237
   Ma, Xuejiao/0000-0002-6984-2455},
Funding-Acknowledgement = {National Social Science Foundation of China {[}17ZDA093]},
Funding-Text = {This work was supported by Major Program of National Social Science
   Foundation of China (Grant No. 17ZDA093).},
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Number-of-Cited-References = {113},
Times-Cited = {123},
Usage-Count-Last-180-days = {16},
Usage-Count-Since-2013 = {123},
Journal-ISO = {Int. J. Environ. Res. Public Health},
Doc-Delivery-Number = {GI9TK},
Web-of-Science-Index = {Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)},
Unique-ID = {WOS:000434868800219},
OA = {Green Published, Green Submitted, gold},
DA = {2023-04-22},
}

@article{ WOS:000858012500001,
Author = {Blaziak, Mikolaj and Urban, Szymon and Wietrzyk, Weronika and Jura,
   Maksym and Iwanek, Gracjan and Stanczykiewicz, Bartlomiej and
   Kuliczkowski, Wiktor and Zymlinski, Robert and Pondel, Maciej and Berka,
   Petr and Danel, Dariusz and Biegus, Jan and Siennicka, Agnieszka},
Title = {An Artificial Intelligence Approach to Guiding the Management of Heart
   Failure Patients Using Predictive Models: A Systematic Review},
Journal = {BIOMEDICINES},
Year = {2022},
Volume = {10},
Number = {9},
Month = {SEP},
Abstract = {Heart failure (HF) is one of the leading causes of mortality and
   hospitalization worldwide. The accurate prediction of mortality and
   readmission risk provides crucial information for guiding decision
   making. Unfortunately, traditional predictive models reached modest
   accuracy in HF populations. We therefore aimed to present predictive
   models based on machine learning (ML) techniques in HF patients that
   were externally validated. We searched four databases and the reference
   lists of the included papers to identify studies in which HF patient
   data were used to create a predictive model. Literature screening was
   conducted in Academic Search Ultimate, ERIC, Health Source
   Nursing/Academic Edition and MEDLINE. The protocol of the current
   systematic review was registered in the PROSPERO database with the
   registration number CRD42022344855. We considered all types of outcomes:
   mortality, rehospitalization, response to treatment and medication
   adherence. The area under the receiver operating characteristic curve
   (AUC) was used as the comparator parameter. The literature search
   yielded 1649 studies, of which 9 were included in the final analysis.
   The AUCs for the machine learning models ranged from 0.6494 to 0.913 in
   independent datasets, whereas the AUCs for statistical predictive scores
   ranged from 0.622 to 0.806. Our study showed an increasing number of ML
   predictive models concerning HF populations, although external
   validation remains infrequent. However, our findings revealed that ML
   approaches can outperform conventional risk scores and may play
   important role in HF management.},
Publisher = {MDPI},
Address = {ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND},
Type = {Review},
Language = {English},
Affiliation = {Blaziak, M; Kuliczkowski, W (Corresponding Author), Wroclaw Med Univ, Inst Heart Dis, PL-50556 Wroclaw, Poland.
   Blaziak, Mikolaj; Urban, Szymon; Wietrzyk, Weronika; Iwanek, Gracjan; Kuliczkowski, Wiktor; Zymlinski, Robert; Biegus, Jan, Wroclaw Med Univ, Inst Heart Dis, PL-50556 Wroclaw, Poland.
   Jura, Maksym; Siennicka, Agnieszka, Wroclaw Med Univ, Dept Physiol \& Pathophysiol, PL-50368 Wroclaw, Poland.
   Stanczykiewicz, Bartlomiej, Wroclaw Med Univ, Dept Psychiat, Div Consultat Psychiat \& Neurosci, PL-50367 Wroclaw, Poland.
   Pondel, Maciej, Wroclaw Univ Econ \& Business, Inst Informat Syst Econ, PL-53345 Wroclaw, Poland.
   Berka, Petr, Prague Univ Econ \& Business, Dept Informat \& Knowledge Engn, Churchill Sq 1938-4, Prague 13067, Czech Republic.
   Danel, Dariusz, Polish Acad Sci, Ludw Hirszfeld Inst Immunol \& Expt Therapy, Dept Anthropol, PL-53114 Wroclaw, Poland.},
DOI = {10.3390/biomedicines10092188},
Article-Number = {2188},
EISSN = {2227-9059},
Keywords = {artificial intelligence; machine learning; deep learning; heart failure;
   predictive model; systematic review},
Keywords-Plus = {NATRIURETIC PEPTIDE; MORTALITY; RISK; VALIDATION; IDENTIFICATION;
   READMISSION; BIOMARKERS; PHENOTYPES; FRAMEWORK; DISEASE},
Research-Areas = {Biochemistry \& Molecular Biology; Research \& Experimental Medicine;
   Pharmacology \& Pharmacy},
Web-of-Science-Categories  = {Biochemistry \& Molecular Biology; Medicine, Research \& Experimental;
   Pharmacology \& Pharmacy},
Author-Email = {m.blaziak@umw.edu.pl
   wiktor.kuliczkowski@umw.edu.pl},
Affiliations = {Wroclaw Medical University; Wroclaw Medical University; Wroclaw Medical
   University; Wroclaw University of Economics \& Business; Prague
   University of Economics \& Business; Polish Academy of Sciences;
   Hirszfeld Institute of Immunology \& Experimental Therapy of the Polish
   Academy of Sciences},
ResearcherID-Numbers = {Biegus, Jan/AAF-4207-2021
   Pondel, Maciej/Q-4595-2018
   Berka, Petr/K-6013-2015
   Stanczykiewicz, Bartlomiej/J-5742-2017
   },
ORCID-Numbers = {Biegus, Jan/0000-0001-9977-7722
   Pondel, Maciej/0000-0002-1978-6571
   Danel, Dariusz/0000-0001-6175-3928
   Berka, Petr/0000-0003-0464-2257
   Jura, Maksym/0000-0003-4316-8623
   Blaziak, Mikolaj/0000-0001-8207-1723
   Iwanek, Gracjan/0000-0002-8574-9963
   Stanczykiewicz, Bartlomiej/0000-0001-9221-3502
   Siennicka, Agnieszka/0000-0003-0988-5821
   Zymlinski, Robert/0000-0003-1483-7381},
Funding-Acknowledgement = {European Union's Horizon 2020 research and innovation programme
   {[}857446]},
Funding-Text = {This research and APC were funded by the European Union's Horizon 2020
   research and innovation programme, grant number 857446. Presented
   analyses were conducted in cooperation with experts from the consortium
   HeartBIT\_4.0-Application of Innovative Medical Data Science
   technologies for heart diseases.},
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Number-of-Cited-References = {64},
Times-Cited = {2},
Usage-Count-Last-180-days = {1},
Usage-Count-Since-2013 = {4},
Journal-ISO = {Biomedicines},
Doc-Delivery-Number = {4T3HI},
Web-of-Science-Index = {Science Citation Index Expanded (SCI-EXPANDED)},
Unique-ID = {WOS:000858012500001},
OA = {Green Published, gold},
DA = {2023-04-22},
}

@article{ WOS:000957399100001,
Author = {Pitafi, Shahneela and Anwar, Toni and Sharif, Zubair},
Title = {A Taxonomy of Machine Learning Clustering Algorithms, Challenges, and
   Future Realms},
Journal = {APPLIED SCIENCES-BASEL},
Year = {2023},
Volume = {13},
Number = {6},
Month = {MAR},
Abstract = {In the field of data mining, clustering has shown to be an important
   technique. Numerous clustering methods have been devised and put into
   practice, and most of them locate high-quality or optimum clustering
   outcomes in the field of computer science, data science, statistics,
   pattern recognition, artificial intelligence, and machine learning. This
   research provides a modern, thorough review of both classic and
   cutting-edge clustering methods. The taxonomy of clustering is presented
   in this review from an applied angle and the compression of some
   hierarchical and partitional clustering algorithms with various
   parameters. We also discuss the open challenges in clustering such as
   computational complexity, refinement of clusters, speed of convergence,
   data dimensionality, effectiveness and scalability, data object
   representation, evaluation measures, data streams, and knowledge
   extraction; scientists and professionals alike will be able to use it as
   a benchmark as they strive to advance the state-of-the-art in clustering
   techniques.},
Publisher = {MDPI},
Address = {ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND},
Type = {Review},
Language = {English},
Affiliation = {Pitafi, S (Corresponding Author), Univ Teknol PETRONAS, Comp \& Informat Sci Dept CISD, Bandar Seri Iskandar Pera 32610, Malaysia.
   Pitafi, Shahneela; Anwar, Toni; Sharif, Zubair, Univ Teknol PETRONAS, Comp \& Informat Sci Dept CISD, Bandar Seri Iskandar Pera 32610, Malaysia.},
DOI = {10.3390/app13063529},
Article-Number = {3529},
EISSN = {2076-3417},
Keywords = {clustering algorithms; taxonomy of clustering algorithms; challenges in
   clustering algorithms},
Keywords-Plus = {EFFICIENT ALGORITHMS; SELECTION; SPLIT},
Research-Areas = {Chemistry; Engineering; Materials Science; Physics},
Web-of-Science-Categories  = {Chemistry, Multidisciplinary; Engineering, Multidisciplinary; Materials
   Science, Multidisciplinary; Physics, Applied},
Author-Email = {shahneela\_22000124@utp.edu.my},
Affiliations = {Universiti Teknologi Petronas},
Funding-Acknowledgement = {Universiti Teknologi PETRONAS (UTP) under the YUTP {[}015LC0-350]},
Funding-Text = {This research work is supported and funded by Universiti Teknologi
   PETRONAS (UTP) under the YUTP grant scheme with the cost center of
   015LC0-350.},
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Number-of-Cited-References = {93},
Times-Cited = {0},
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Journal-ISO = {Appl. Sci.-Basel},
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@article{ WOS:000884583700001,
Author = {Zhang, Yaolong and Lin, Qidong and Jiang, Bin},
Title = {Atomistic neural network representations for chemical dynamics
   simulations of molecular, condensed phase, and interfacial systems:
   Efficiency, representability, and generalization},
Journal = {WILEY INTERDISCIPLINARY REVIEWS-COMPUTATIONAL MOLECULAR SCIENCE},
Abstract = {Machine learning techniques have been widely applied in many fields of
   chemistry, physics, biology, and materials science. One of the most
   fruitful applications is machine learning of the complicated
   multidimensional function of potential energy or related electronic
   properties from discrete quantum chemical data. In particular,
   substantial efforts have been dedicated to developing various atomistic
   neural network (AtNN) representations, which refer to a family of
   methods expressing the targeted physical quantity as a sum of atomic
   components represented by atomic NNs. This class of approaches not only
   fully preserves the physical symmetry of the system but also scales
   linearly with respect to the size of a system, enabling accurate and
   efficient chemical dynamics and spectroscopic simulations in complicated
   systems and even a number of variably sized systems across the phases.
   In this review, we discuss different strategies in developing highly
   efficient and representable AtNN potentials, and in generalizing these
   scalar AtNN models to learn vectorial and tensorial quantities with the
   correct rotational equivariance. We also review active learning
   algorithms to generate practical AtNN models and present selected
   examples of AtNN applications in gas-surface systems to demonstrate
   their capabilities of accurately representing both molecular systems and
   condensed phase systems. We conclude this review by pointing out
   remaining challenges for the further development of more reliable,
   transferable, and scalable AtNN representations in more application
   scenarios. This article is categorized under: Data Science > Artificial
   Intelligence/Machine Learning Molecular and Statistical Mechanics >
   Molecular Interactions},
Publisher = {WILEY},
Address = {111 RIVER ST, HOBOKEN 07030-5774, NJ USA},
Type = {Review; Early Access},
Language = {English},
Affiliation = {Jiang, B (Corresponding Author), Univ Sci \& Technol China, Dept Chem Phys, Sch Chem \& Mat Sci,Anhui Higher Educ Inst, Key Lab Surface \& Interface Chem \& Energy Catalys, Hefei 230026, Anhui, Peoples R China.
   Zhang, Yaolong; Lin, Qidong; Jiang, Bin, Univ Sci \& Technol China, Dept Chem Phys, Sch Chem \& Mat Sci,Anhui Higher Educ Inst, Key Lab Surface \& Interface Chem \& Energy Catalys, Hefei 230026, Anhui, Peoples R China.},
DOI = {10.1002/wcms.1645},
EarlyAccessDate = {NOV 2022},
ISSN = {1759-0876},
EISSN = {1759-0884},
Keywords = {machine learning; neural networks; potential energy surface;
   representability; data sampling},
Keywords-Plus = {POTENTIAL-ENERGY SURFACES; DISSOCIATIVE CHEMISORPTION; FORCE-FIELD;
   QUANTUM DYNAMICS; H-ATOM; SCATTERING; METHANE; STATE; WATER; EXCITATION},
Research-Areas = {Chemistry; Mathematical \& Computational Biology},
Web-of-Science-Categories  = {Chemistry, Multidisciplinary; Mathematical \& Computational Biology},
Author-Email = {bjiangch@ustc.edu.cn},
Affiliations = {Chinese Academy of Sciences; University of Science \& Technology of
   China, CAS},
ResearcherID-Numbers = {jiang, bin/F-2391-2014},
ORCID-Numbers = {jiang, bin/0000-0003-2696-5436},
Funding-Acknowledgement = {Anhui Initiative in Quantum Information Technologies {[}AHY090200]; CAS
   Project for Young Scientists in Basic Research {[}YSBR-005]; National
   Natural Science Foundation of China {[}22033007, 22073089]; Fundamental
   Research Funds for Central Universities {[}WK2060000017]},
Funding-Text = {Anhui Initiative in Quantum Information Technologies, Grant/Award
   Number: AHY090200; CAS Project for Young Scientists in Basic Research,
   Grant/Award Number: YSBR-005; National Natural Science Foundation of
   China, Grant/Award Numbers: 22033007, 22073089; The Fundamental Research
   Funds for Central Universities, Grant/Award Number: WK2060000017},
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Number-of-Cited-References = {215},
Times-Cited = {0},
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@article{ WOS:000774626800001,
Author = {Casadio, Rita and Martelli, Pier Luigi and Savojardo, Castrense},
Title = {Machine learning solutions for predicting protein-protein interactions},
Journal = {WILEY INTERDISCIPLINARY REVIEWS-COMPUTATIONAL MOLECULAR SCIENCE},
Year = {2022},
Volume = {12},
Number = {6},
Month = {NOV},
Abstract = {Proteins are ``social molecules.{''} Recent experimental evidence
   supports the notion that large protein aggregates, known as biomolecular
   condensates, affect structurally and functionally many biological
   processes. Condensate formation may be permanent and/or time dependent,
   suggesting that biological processes can occur locally, depending on the
   cell needs. The question then arises as to which extent we can monitor
   protein-aggregate formation, both experimentally and theoretically and
   then predict/simulate functional aggregate formation. Available data are
   relative to mesoscopic interacting networks at a proteome level, to
   protein-binding affinity data, and to interacting protein complexes,
   solved with atomic resolution. Powerful algorithms based on machine
   learning (ML) can extract information from data sets and infer
   properties of never-seen-before examples. ML tools address the problem
   of protein-protein interactions (PPIs) adopting different data sets,
   input features, and architectures. According to recent publications,
   deep learning is the most successful method. However, in
   ML-computational biology, convincing evidence of a success story comes
   out by performing general benchmarks on blind data sets. Results
   indicate that the state-of-the-art ML approaches, based on traditional
   and/or deep learning, can still be ameliorated, irrespectively of the
   power of the method and richness in input features. This being the case,
   it is quite evident that powerful methods still are not trained on the
   whole possible spectrum of PPIs and that more investigations are
   necessary to complete our knowledge of PPI-functional interactions. This
   article is categorized under: Software > Molecular Modeling Structure
   and Mechanism > Computational Biochemistry and Biophysics Data Science >
   Artificial Intelligence/Machine Learning Molecular and Statistical
   Mechanics > Molecular Interactions},
Publisher = {WILEY},
Address = {111 RIVER ST, HOBOKEN 07030-5774, NJ USA},
Type = {Review},
Language = {English},
Affiliation = {Martelli, PL (Corresponding Author), Univ Bologna, Biocomp Grp, Bologna, Italy.
   Casadio, Rita; Martelli, Pier Luigi; Savojardo, Castrense, Univ Bologna, Biocomp Grp, Bologna, Italy.},
DOI = {10.1002/wcms.1618},
EarlyAccessDate = {MAR 2022},
Article-Number = {e1618},
ISSN = {1759-0876},
EISSN = {1759-0884},
Keywords = {deep learning; machine learning; protein-protein interactions},
Keywords-Plus = {INTERACTION SITES PREDICTION; STATISTICAL-ANALYSIS; PHASE-SEPARATION;
   CONSERVATION; FINGERPRINTS; COVARIANCE; NETWORKS; RESIDUES; ACCURATE},
Research-Areas = {Chemistry; Mathematical \& Computational Biology},
Web-of-Science-Categories  = {Chemistry, Multidisciplinary; Mathematical \& Computational Biology},
Author-Email = {pierluigi.martelli@unibo.it},
Affiliations = {University of Bologna},
ResearcherID-Numbers = {Martelli, Pier Luigi/I-8824-2012
   },
ORCID-Numbers = {Martelli, Pier Luigi/0000-0002-0274-5669
   SAVOJARDO, CASTRENSE/0000-0002-7359-0633},
Funding-Acknowledgement = {Italian Ministry of University and Research {[}2017483NH8\_002]},
Funding-Text = {The work was supported by PRIN2017 grant (project 2017483NH8\_002),
   delivered to CS by the Italian Ministry of University and Research.},
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Number-of-Cited-References = {118},
Times-Cited = {7},
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Unique-ID = {WOS:000774626800001},
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@article{ WOS:000757657800001,
Author = {Wigh, Daniel S. and Goodman, Jonathan M. and Lapkin, Alexei A.},
Title = {A review of molecular representation in the age of machine learning},
Journal = {WILEY INTERDISCIPLINARY REVIEWS-COMPUTATIONAL MOLECULAR SCIENCE},
Year = {2022},
Volume = {12},
Number = {5},
Month = {SEP},
Abstract = {Research in chemistry increasingly requires interdisciplinary work
   prompted by, among other things, advances in computing, machine
   learning, and artificial intelligence. Everyone working with molecules,
   whether chemist or not, needs an understanding of the representation of
   molecules in a machine-readable format, as this is central to
   computational chemistry. Four classes of representations are introduced:
   string, connection table, feature-based, and computer-learned
   representations. Three of the most significant representations are
   simplified molecular-input line-entry system (SMILES), International
   Chemical Identifier (InChI), and the MDL molfile, of which SMILES was
   the first to successfully be used in conjunction with a variational
   autoencoder (VAE) to yield a continuous representation of molecules.
   This is noteworthy because a continuous representation allows for
   efficient navigation of the immensely large chemical space of possible
   molecules. Since 2018, when the first model of this type was published,
   considerable effort has been put into developing novel and improved
   methodologies. Most, if not all, researchers in the community make their
   work easily accessible on GitHub, though discussion of computation time
   and domain of applicability is often overlooked. Herein, we present
   questions for consideration in future work which we believe will make
   chemical VAEs even more accessible. This article is categorized under:
   Data Science > Chemoinformatics},
Publisher = {WILEY},
Address = {111 RIVER ST, HOBOKEN 07030-5774, NJ USA},
Type = {Review},
Language = {English},
Affiliation = {Lapkin, AA (Corresponding Author), Univ Cambridge, Dept Chem Engn \& Biotechnol, Cambridge CB3 0AS, England.
   Wigh, Daniel S.; Lapkin, Alexei A., Univ Cambridge, Dept Chem Engn \& Biotechnol, Cambridge CB3 0AS, England.
   Goodman, Jonathan M., Univ Cambridge, Yusuf Hamied Dept Chem, Cambridge, England.},
DOI = {10.1002/wcms.1603},
EarlyAccessDate = {FEB 2022},
Article-Number = {e1603},
ISSN = {1759-0876},
EISSN = {1759-0884},
Keywords = {chemoinformatics; fingerprints; machine learning; molecular
   representation; variational autoencoder},
Keywords-Plus = {DRUG DISCOVERY; DESIGN; LANGUAGE; SYSTEM; RETROSYNTHESIS; DESCRIPTORS;
   GENERATION; ALGORITHM; CHEMISTRY; SELECTION},
Research-Areas = {Chemistry; Mathematical \& Computational Biology},
Web-of-Science-Categories  = {Chemistry, Multidisciplinary; Mathematical \& Computational Biology},
Author-Email = {aal35@cam.ac.uk},
Affiliations = {University of Cambridge; University of Cambridge},
ResearcherID-Numbers = {Lapkin, Alexei/CAG-1632-2022
   Goodman, Jonathan M/A-2123-2008
   },
ORCID-Numbers = {Goodman, Jonathan M/0000-0002-8693-9136
   Wigh, Daniel/0000-0002-0494-643X
   Lapkin, Alexei/0000-0001-7621-0889},
Funding-Acknowledgement = {Engineering and Physical Sciences Research Council {[}EP/S024220/1]; UCB
   Pharma},
Funding-Text = {This work is co-funded by UCB Pharma and Engineering and Physical
   Sciences Research Council via project EP/S024220/1 EPSRC Centre for
   Doctoral Training in Automated Chemical Synthesis Enabled by Digital
   Molecular Technologies'.},
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Times-Cited = {21},
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@article{ WOS:000496444000002,
Author = {Li, Xiang and Yan, Ziming and Liu, Zhanli},
Title = {Combination and application of machine learning and computational
   mechanics},
Journal = {CHINESE SCIENCE BULLETIN-CHINESE},
Year = {2019},
Volume = {64},
Number = {7},
Pages = {635-648},
Abstract = {With the steady development of computer science, machine learning and
   data science have made significant progress in recent decades. These
   techniques generally rely on a substantial amount of data samples to
   extract the abstract mapping hidden within the data. Hence, these
   technologies have gradually attracted the attention of researchers in
   the field of computational mechanics. Combining the recent studies of
   the authors and other researchers, this paper aims to interpret several
   forms of applications that integrate machine learning and data science
   with computational mechanics. In the first application, the core
   algorithm of the convolutional neural network is implemented to solve
   the linear elastic finite element problem. A standard finite element
   equation is transformed into an optimization problem in this method. The
   method is verified by a plane strain linear elastic finite element
   problem. The method demonstrates promising accuracy by comparing the
   results obtained by traditional finite element solver. However, some
   limitations of this method need to be addressed. First, though the
   optimization process can be accelerated by GPU, the efficiency of the
   proposed method is still lower than most mainstream numerical solvers.
   And, the framework of convolutional neural networks requires that the
   input layer data should be a constant matrix. This is a major challenge
   for solving nonlinear finite element equations whose stiffness matrices
   contain variables. These are the issues worth considerations in future
   studies. In the second application, a method is proposed to establish
   the implicit mapping between the effective mechanical property and the
   mesoscale structure of heterogeneous materials. Shale is employed in
   this paper as an example to illustrate the method. At the mesoscale, a
   shale sample is a complex heterogeneous composite that consists of
   multiple mineral constituents. The mechanical properties of each mineral
   constituent vary significantly, and mineral constituents are distributed
   in an utterly random manner within shale samples. Large quantities of
   shale samples are generated based on mesoscale scanning electron
   microscopy images using a stochastic reconstruction algorithm. Image
   processing techniques are employed to transform the shale sample images
   to finite element models. Finite element analysis is utilized to
   evaluate the effective mechanical properties of the shale samples. A
   convolutional neural network is trained based on the images of
   stochastic shale samples and their effective moduli. The trained network
   is validated to be able to predict the effective moduli of real shale
   samples accurately and efficiently. Not limited to shale, the proposed
   method can be further extended to predict effective mechanical
   properties of various heterogeneous materials. In the third application,
   the authors discuss a data-driven computational mechanics framework
   proposed by Kirchdoerfer and Ortiz. The most outstanding feature of the
   framework is that explicit material constitutive equations are no longer
   required. More specifically, experimental material response data are
   employed in the framework to replace constitutive equations. Combined
   with traditional compatibility and equilibrium equations, the framework
   is able to find the optimal stress-strain combination from a material
   response dataset to best fit the current element. With this framework,
   the errors and uncertainties induced by the empirical constitutive
   functions of traditional computational mechanics approaches can be
   avoided.
   The aforementioned applications are only the tip of an iceberg in the
   recent advancement of computational mechanics. Hence, researchers have
   reasons to believe that there would be more application scenarios that
   integrate data science and machine learning with computational mechanics
   in the future. Hopefully, computational mechanics methods with more
   robustness, efficiency, and fidelity will be developed.},
Publisher = {SCIENCE PRESS},
Address = {300 WEST CHESNUT ST, EPHRATA, PA 17522 USA},
Type = {Review},
Language = {Chinese},
Affiliation = {Liu, ZL (Corresponding Author), Tsinghua Univ, Sch Aerosp Engn, Appl Mech Lab, Beijing 100084, Peoples R China.
   Li, Xiang; Yan, Ziming; Liu, Zhanli, Tsinghua Univ, Sch Aerosp Engn, Appl Mech Lab, Beijing 100084, Peoples R China.},
DOI = {10.1360/N972019-00005},
ISSN = {0023-074X},
EISSN = {2095-9419},
Keywords = {machine learning; data-driven; artificial neural network; computational
   mechanics; finite element method},
Keywords-Plus = {ARTIFICIAL NEURAL-NETWORKS; CONSTITUTIVE MODEL; HOMOGENIZATION;
   PREDICTION; FLOW; INFORMATION; RECOGNITION; REGRESSION; FRAMEWORK;
   ALGORITHM},
Research-Areas = {Science \& Technology - Other Topics},
Web-of-Science-Categories  = {Multidisciplinary Sciences},
Author-Email = {liuzhanli@tsinghua.edu.cn},
Affiliations = {Tsinghua University},
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Doc-Delivery-Number = {JM8FM},
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Unique-ID = {WOS:000496444000002},
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@article{ WOS:000958886700001,
Author = {Nigam, Nikhil and Singh, Dhirendra Pratap and Choudhary, Jaytrilok},
Title = {A Review of Different Components of the Intelligent Traffic Management
   System (ITMS)},
Journal = {SYMMETRY-BASEL},
Year = {2023},
Volume = {15},
Number = {3},
Month = {MAR},
Abstract = {Traffic congestion is a serious challenge in urban areas. So, to address
   this challenge, the intelligent traffic management system (ITMS) is used
   to manage traffic on road networks. Managing traffic helps to focus on
   environmental impacts as well as emergency situations. However, the ITMS
   system has many challenges in analyzing scenes of complex traffic. New
   technologies such as computer vision (CV) and artificial intelligence
   (AI) are being used to solve these challenges. As a result, these
   technologies have made a distinct identity in the surveillance industry,
   particularly when it comes to keeping a constant eye on traffic scenes.
   There are many vehicle attributes and existing approaches that are being
   used in the development of ITMS, along with imaging technologies. In
   this paper, we reviewed the ITMS-based components that describe existing
   imaging technologies and existing approaches on the basis of their need
   for developing ITMS. The first component describes the traffic scene and
   imaging technologies. The second component talks about vehicle
   attributes and their utilization in existing vehicle-based approaches.
   The third component explains the vehicle's behavior on the basis of the
   second component's outcome. The fourth component explains how
   traffic-related applications can assist in the management and monitoring
   of traffic flow, as well as in the reduction of congestion and the
   enhancement of road safety. The fifth component describes the different
   types of ITMS applications. The sixth component discusses the existing
   methods of traffic signal control systems (TSCSs). Aside from these
   components, we also discuss existing vehicle-related tools such as
   simulators that work to create realistic traffic scenes. In the last
   section named discussion, we discuss the future development of ITMS and
   draw some conclusions. The main objective of this paper is to discuss
   the possible solutions to different problems during the development of
   ITMS in one place, with the help of components that would play an
   important role for an ITMS developer to achieve the goal of developing
   efficient ITMS.},
Publisher = {MDPI},
Address = {ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND},
Type = {Review},
Language = {English},
Affiliation = {Nigam, N (Corresponding Author), Maulana Azad Natl Inst Technol, Comp Sci \& Engn Dept, Bhopal 462003, Madhya Pradesh, India.
   Nigam, Nikhil; Singh, Dhirendra Pratap; Choudhary, Jaytrilok, Maulana Azad Natl Inst Technol, Comp Sci \& Engn Dept, Bhopal 462003, Madhya Pradesh, India.},
DOI = {10.3390/sym15030583},
Article-Number = {583},
EISSN = {2073-8994},
Keywords = {intelligent traffic management system (ITMS); vehicle detection; vehicle
   tracking; traffic signal control systems (TSCSs); simulators},
Keywords-Plus = {VEHICLE TRAJECTORY PREDICTION; ANOMALY DETECTION; OPTIMIZATION;
   CLASSIFICATION; SURVEILLANCE; RECOGNITION; IMAGES; MODEL;
   IDENTIFICATION; ALGORITHMS},
Research-Areas = {Science \& Technology - Other Topics},
Web-of-Science-Categories  = {Multidisciplinary Sciences},
Author-Email = {193112013@stu.manit.ac.in},
Affiliations = {National Institute of Technology (NIT System); Maulana Azad National
   Institute of Technology Bhopal},
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Number-of-Cited-References = {169},
Times-Cited = {0},
Usage-Count-Last-180-days = {1},
Usage-Count-Since-2013 = {1},
Journal-ISO = {Symmetry-Basel},
Doc-Delivery-Number = {C0JM7},
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@article{ WOS:000765648800001,
Author = {Schwaller, Philippe and Vaucher, Alain C. and Laplaza, Ruben and Bunne,
   Charlotte and Krause, Andreas and Corminboeuf, Clemence and Laino,
   Teodoro},
Title = {Machine intelligence for chemical reaction space},
Journal = {WILEY INTERDISCIPLINARY REVIEWS-COMPUTATIONAL MOLECULAR SCIENCE},
Year = {2022},
Volume = {12},
Number = {5},
Month = {SEP},
Abstract = {Discovering new reactions, optimizing their performance, and extending
   the synthetically accessible chemical space are critical drivers for
   major technological advances and more sustainable processes. The current
   wave of machine intelligence is revolutionizing all data-rich
   disciplines. Machine intelligence has emerged as a potential
   game-changer for chemical reaction space exploration and the synthesis
   of novel molecules and materials. Herein, we will address the recent
   development of data-driven technologies for chemical reaction tasks,
   including forward reaction prediction, retrosynthesis, reaction
   optimization, catalysts design, inference of experimental procedures,
   and reaction classification. Accurate predictions of chemical reactivity
   are changing the R\&D processes and, at the same time, promoting an
   accelerated discovery scheme both in academia and across chemical and
   pharmaceutical industries. This work will help to clarify the key
   contributions in the fields and the open challenges that remain to be
   addressed. This article is categorized under: Data Science > Artificial
   Intelligence/Machine Learning Data Science > Computer Algorithms and
   Programming Data Science > Chemoinformatics},
Publisher = {WILEY},
Address = {111 RIVER ST, HOBOKEN 07030-5774, NJ USA},
Type = {Review},
Language = {English},
Affiliation = {Schwaller, P (Corresponding Author), IBM Res Europe, CH-8803 Ruschlikon, Switzerland.
   Schwaller, Philippe; Vaucher, Alain C.; Laino, Teodoro, IBM Res Europe, CH-8803 Ruschlikon, Switzerland.
   Schwaller, Philippe; Vaucher, Alain C.; Laplaza, Ruben; Bunne, Charlotte; Krause, Andreas; Corminboeuf, Clemence; Laino, Teodoro, Natl Ctr Competence Res Catalysis NCCR Catalysis, Zurich, Switzerland.
   Laplaza, Ruben; Corminboeuf, Clemence, Ecole Polytech Fed Lausanne EPFL, Inst Chem Sci \& Engn, Lausanne, Switzerland.
   Bunne, Charlotte; Krause, Andreas, Swiss Fed Inst Technol, Dept Comp Sci, Zurich, Switzerland.},
DOI = {10.1002/wcms.1604},
EarlyAccessDate = {MAR 2022},
Article-Number = {e1604},
ISSN = {1759-0876},
EISSN = {1759-0884},
Keywords = {artificial intelligence; chemical reactions; computer-assisted synthesis
   planning; data-driven approaches; machine intelligence},
Keywords-Plus = {REACTION PREDICTION; ORGANIC-CHEMISTRY; MOLECULAR DESIGN;
   NEURAL-NETWORKS; KNOWLEDGE-BASE; LINE NOTATION; COMPUTER; OPTIMIZATION;
   RETROSYNTHESIS; REPRESENTATION},
Research-Areas = {Chemistry; Mathematical \& Computational Biology},
Web-of-Science-Categories  = {Chemistry, Multidisciplinary; Mathematical \& Computational Biology},
Author-Email = {philippe.schwaller@epfl.ch},
Affiliations = {Swiss Federal Institutes of Technology Domain; Ecole Polytechnique
   Federale de Lausanne; Swiss Federal Institutes of Technology Domain; ETH
   Zurich},
ResearcherID-Numbers = {Schwaller, Philippe/ABG-4328-2021
   Laplaza, Rubén/L-9562-2019
   LAPLAZA, Rubén/A-9529-2019
   },
ORCID-Numbers = {Schwaller, Philippe/0000-0003-3046-6576
   LAPLAZA, Rubén/0000-0001-6315-4398
   Krause, Andreas/0000-0001-7260-9673
   Vaucher, Alain/0000-0001-7554-0288
   Bunne, Charlotte/0000-0003-1431-103X},
Funding-Acknowledgement = {Schweizerischer Nationalfonds zur Forderung der Wissenschaftlichen
   Forschung {[}180544]},
Funding-Text = {Schweizerischer Nationalfonds zur Forderung der Wissenschaftlichen
   Forschung, Grant/Award Number: 180544},
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Times-Cited = {9},
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Journal-ISO = {Wiley Interdiscip. Rev.-Comput. Mol. Sci.},
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}

@article{ WOS:000702393800004,
Author = {Snaphaan, Thom and Hardyns, Wim},
Title = {Utilizing geo-referenced imagery for systematic social observation of
   neighborhood disorder},
Journal = {COMPUTERS ENVIRONMENT AND URBAN SYSTEMS},
Year = {2021},
Volume = {90},
Month = {NOV},
Abstract = {Research methods in social science take advantage from broader trends
   such as digitalization and increasing computational power, however, this
   is an evolving explorative search. The main purpose of this article is
   to describe the methodological innovations in the collection and
   processing of geo-referenced imagery for the observation of neighborhood
   disorder. In this narrative review, attention is paid to advances in
   both the data sources and the data processing methods used. Neighborhood
   disorder is traditionally measured by means of survey methods and
   (systematic) (social) observations, but these methods have specific
   shortcomings, such as respectively the subjective measurement that does
   not deliver a valid measure of actual prevalence of disorderly phenomena
   and the intensive use of resources in terms of time and money. This has
   repercussions for (the interpretation of) the results based on these
   data. Today, scholars have innovative data sources and cutting-edge data
   processing methods at their disposal that can meet (some of) these
   shortcomings, but which have not yet been fully explored. In this
   article, the evolutions in the use of geo-referenced imagery for the
   observation of neighborhood disorder from the last 25 years are
   described with a focus on the empirical opportunities, and the
   methodological challenges and prospects. We conclude by outlining the
   road ahead: promising avenues for future research to exploit the full
   potential of `big primary data'.},
Publisher = {ELSEVIER SCI LTD},
Address = {THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND},
Type = {Review},
Language = {English},
Affiliation = {Snaphaan, T (Corresponding Author), Univ Ghent, Dept Criminol Criminal Law \& Social Law, Univ Str 4, B-9000 Ghent, Belgium.
   Snaphaan, Thom; Hardyns, Wim, Univ Ghent, Dept Criminol Criminal Law \& Social Law, Univ Str 4, B-9000 Ghent, Belgium.},
DOI = {10.1016/j.compenvurbsys.2021.101691},
EarlyAccessDate = {AUG 2021},
Article-Number = {101691},
ISSN = {0198-9715},
EISSN = {1873-7587},
Keywords = {Artificial intelligence; Big data; Convolutional neural networks;
   Geo-referenced imagery; Neighborhood disorder; Systematic social
   observation},
Keywords-Plus = {GOOGLE STREET VIEW; BIG DATA; PHYSICAL DISORDER; BROKEN WINDOWS;
   CRIMINOLOGY; CRIME; REPLICATION; VALIDATION; ECOMETRICS; ERROR},
Research-Areas = {Computer Science; Engineering; Environmental Sciences \& Ecology;
   Geography; Operations Research \& Management Science; Public
   Administration},
Web-of-Science-Categories  = {Computer Science, Interdisciplinary Applications; Engineering,
   Environmental; Environmental Studies; Geography; Operations Research \&
   Management Science; Regional \& Urban Planning},
Author-Email = {Thom.Snaphaan@UGent.be},
Affiliations = {Ghent University},
ResearcherID-Numbers = {Snaphaan, Thom/HPB-7575-2023},
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Number-of-Cited-References = {88},
Times-Cited = {2},
Usage-Count-Last-180-days = {0},
Usage-Count-Since-2013 = {9},
Journal-ISO = {Comput. Environ. Urban Syst.},
Doc-Delivery-Number = {UZ7PN},
Web-of-Science-Index = {Social Science Citation Index (SSCI)},
Unique-ID = {WOS:000702393800004},
DA = {2023-04-22},
}

@article{ WOS:000687767000007,
Author = {An, Li and Grimm, Volker and Sullivan, Abigail and Turner, II, B. L. and
   Malleson, Nicolas and Heppenstall, Alison and Vincenot, Christian and
   Robinson, Derek and Ye, Xinyue and Liu, Jianguo and Lindkvist, Emilie
   and Tang, Wenwu},
Title = {Challenges, tasks, and opportunities in modeling agent-based complex
   systems},
Journal = {ECOLOGICAL MODELLING},
Year = {2021},
Volume = {457},
Month = {OCT 1},
Abstract = {Humanity is facing many grand challenges at unprecedented rates, nearly
   everywhere, and at all levels. Yet virtually all these challenges can be
   traced back to the decision and behavior of autonomous agents that
   constitute the complex systems under such challenges. Agent-based
   modeling has been developed and employed to address such challenges for
   a few decades with great achievements and caveats. This article reviews
   the advances of ABM in social, ecological, and socio-ecological systems,
   compare ABM with other traditional, equation-based models, provide
   guidelines for ABM novice, modelers, and reviewers, and point out the
   challenges and impending tasks that need to be addressed for the ABM
   community. We further point out great opportunities arising from new
   forms of data, data science and artificial intelligence, showing that
   agent behavioral rules can be derived through data mining and machine
   learning. Towards the end, we call for a new science of Agent-based
   Complex Systems (ACS) that can pave an effective way to tackle the grand
   challenges.},
Publisher = {ELSEVIER},
Address = {RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS},
Type = {Review},
Language = {English},
Affiliation = {An, L (Corresponding Author), San Diego State Univ, Ctr Complex Human Environm Syst, 5500 Campanile Dr, San Diego, CA 92182 USA.
   An, L (Corresponding Author), San Diego State Univ, Dept Geog, 5500 Campanile Dr, San Diego, CA 92182 USA.
   An, Li, San Diego State Univ, Ctr Complex Human Environm Syst, 5500 Campanile Dr, San Diego, CA 92182 USA.
   An, Li, San Diego State Univ, Dept Geog, 5500 Campanile Dr, San Diego, CA 92182 USA.
   Grimm, Volker, UFZ Helmholtz Ctr Environm Res, Dept Ecol Modelling, Permoserstr 15, D-04318 Leipzig, Germany.
   Sullivan, Abigail, Indiana Univ, Environm Resilience Inst, 717 E 8th St, Bloomington, IN 47408 USA.
   Turner, B. L., II, Arizona State Univ, Sch Geog Sci \& Urban Planning, POB 875302, Tempe, AZ USA.
   Turner, B. L., II, Arizona State Univ, Sch Sustainabil, POB 875302, Tempe, AZ USA.
   Malleson, Nicolas, Alan Turing Inst, British Lib, 96 Euston Rd, London NW1 2DB, England.
   Heppenstall, Alison, Univ Leeds, Ctr Spatial Anal \& Policy, Sch Geog, Woodhouse Lane, Leeds LS2 9JT, W Yorkshire, England.
   Vincenot, Christian, Kyoto Univ, Dept Social Informat, Sakyo Ku, Yoshida Honmachi, Kyoto 6068501, Japan.
   Robinson, Derek, Univ Waterloo, Dept Geog \& Environm Management, 200 Univ Ave West, Waterloo, ON N2L 3G1, Canada.
   Ye, Xinyue, Texas A\&M Univ, Dept Landscape Architecture \& Urban Planning, College Stn, TX 77843 USA.
   Ye, Xinyue, Texas A\&M Univ, Urban Data Sci Lab, College Stn, TX 77843 USA.
   Liu, Jianguo, Michigan State Univ, Ctr Syst Integrat \& Sustainabil, E Lansing, MI 48823 USA.
   Lindkvist, Emilie, Stockholm Univ, Stockholm Resilience Ctr, Kraftriket 2B, S-10691 Stockholm, Sweden.
   Tang, Wenwu, Univ North Carolina Charlotte, Dept Geog \& Earth Sci, 9201 Univ City Blvd, Charlotte, NC 28223 USA.},
DOI = {10.1016/j.ecolmodel.2021.109685},
EarlyAccessDate = {AUG 2021},
Article-Number = {109685},
ISSN = {0304-3800},
EISSN = {1872-7026},
Keywords = {Agent-based complex systems; Agent-based modelling; Socioecological
   systems; Data science; Artificial intelligence},
Keywords-Plus = {SOCIAL-ECOLOGICAL SYSTEMS; HUMAN-ENVIRONMENT SYSTEM; LAND-USE; COUPLED
   HUMAN; COMPUTATIONAL MODEL; SIMULATION; DYNAMICS; FRAMEWORK; PROTOCOL;
   LESSONS},
Research-Areas = {Environmental Sciences \& Ecology},
Web-of-Science-Categories  = {Ecology},
Author-Email = {lan@sdsu.edu},
Affiliations = {California State University System; San Diego State University;
   California State University System; San Diego State University;
   Helmholtz Association; Helmholtz Center for Environmental Research
   (UFZ); Indiana University System; Indiana University Bloomington;
   Arizona State University; Arizona State University-Tempe; Arizona State
   University; Arizona State University-Tempe; University of Leeds; Kyoto
   University; University of Waterloo; Texas A\&M University System; Texas
   A\&M University College Station; Texas A\&M University System; Texas
   A\&M University College Station; Michigan State University; Stockholm
   University; University of North Carolina; University of North Carolina
   Charlotte},
ResearcherID-Numbers = {Liu, Jianguo/G-5211-2015
   },
ORCID-Numbers = {Liu, Jianguo/0000-0001-6344-0087
   Sullivan, Abigail/0000-0002-2746-859X
   Malleson, Nick/0000-0002-6977-0615},
Funding-Acknowledgement = {National Science Foundation (NSF) through the Method, Measure \&
   Statistics and Geography and Spatial Sciences (BCS) {[}1638446];
   Dynamics of Integrated Socio-Environmental Systems programs {[}BCS
   1826839, DEB 1924111]; European Research Council (ERC) under the
   European Union {[}757455]; ESRC/Alan Turing Joint Fellowship
   {[}ES/R007918/1]; MRC {[}MR/S037578/1, MR/S037578/2] Funding Source:
   UKRI},
Funding-Text = {We are indebted to financial support from the National Science
   Foundation (NSF) through the Method, Measure \& Statistics and Geography
   and Spatial Sciences (BCS \#1638446) and the Dynamics of Integrated
   Socio-Environmental Systems programs (BCS 1826839 and DEB 1924111). We
   thank the participants of the ABM 17 Symposium (sponsored by the above
   NSF grant; http://complexities.org/ABM17/) for input and comments. This
   project has received funding from the European Research Council (ERC)
   under the European Union's Horizon 2020 research and innovation program
   (grant agreement No. 757455) and through an ESRC/Alan Turing Joint
   Fellowship (ES/R007918/1).},
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Number-of-Cited-References = {171},
Times-Cited = {26},
Usage-Count-Last-180-days = {18},
Usage-Count-Since-2013 = {91},
Journal-ISO = {Ecol. Model.},
Doc-Delivery-Number = {UE3BK},
Web-of-Science-Index = {Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)},
Unique-ID = {WOS:000687767000007},
OA = {Bronze},
DA = {2023-04-22},
}

@article{ WOS:000951979400001,
Author = {Alexandre, Tielle and Bernardini, Flavia and Viterbo, Jose and Pantoja,
   Carlos Eduardo},
Title = {Machine Learning Applied to Public Transportation by Bus: A Systematic
   Literature Review},
Journal = {TRANSPORTATION RESEARCH RECORD},
Abstract = {Machine learning (ML) solutions have been proposed to make public
   transportation more attractive. Works that employ ML in bus
   transportation focus on various problems, such as travel time prediction
   or passenger flow prediction. These solutions look to improve elements
   of transportation services, such as the availability of information on
   passengers' travel time and the reliability and regularity of the
   service. An analysis of the solutions proposed in the literature for
   public transportation by bus can reveal opportunities for data
   scientists and transportation professionals, and highlight problems that
   have been only slightly explored. In addition, mapping information about
   modeling these solutions (e.g., types of data produced by devices on the
   transportation network, which can be used in modeling a solution) could
   help beginner data scientists develop public transportation solutions.
   Transportation professionals can benefit from an overview of possible
   transportation solutions to improve transportation problems and direct
   government agency efforts to implement these solutions. This paper
   presents a survey of ML-based solutions for public bus transportation
   and details the modeling of these solutions (e.g., data types, ML
   algorithms). In addition, the problems tackled in the literature are
   categorized into four themes, and the solutions proposed to deal with
   them are schematized, highlighting problems that are little explored.},
Publisher = {SAGE PUBLICATIONS INC},
Address = {2455 TELLER RD, THOUSAND OAKS, CA 91320 USA},
Type = {Review; Early Access},
Language = {English},
Affiliation = {Alexandre, T (Corresponding Author), Fluminense Fed Univ, Inst Comp, Niteroi, Brazil.
   Alexandre, Tielle; Bernardini, Flavia; Viterbo, Jose; Pantoja, Carlos Eduardo, Fluminense Fed Univ, Inst Comp, Niteroi, Brazil.
   Pantoja, Carlos Eduardo, Fed Ctr Technol Educ CEFET RJ, Syst Informat Dept, Rio De Janeiro, Brazil.},
DOI = {10.1177/03611981231155189},
EarlyAccessDate = {MAR 2023},
ISSN = {0361-1981},
EISSN = {2169-4052},
Keywords = {data and data science; artificial intelligence and advanced computing
   applications; machine learning (artificial intelligence); public
   transportation; bus transit systems; buses; innovative public
   transportation services and technologies},
Keywords-Plus = {TRAVEL-TIME PREDICTION},
Research-Areas = {Engineering; Transportation},
Web-of-Science-Categories  = {Engineering, Civil; Transportation; Transportation Science \& Technology},
Author-Email = {tiellesa@id.uff.br},
Affiliations = {Universidade Federal Fluminense; Centro Federal de Educacao Tecnologica
   Celso Suckow da Fonseca (CEFET-RJ)},
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Number-of-Cited-References = {66},
Times-Cited = {0},
Usage-Count-Last-180-days = {1},
Usage-Count-Since-2013 = {1},
Journal-ISO = {Transp. Res. Record},
Doc-Delivery-Number = {A0FU8},
Web-of-Science-Index = {Science Citation Index Expanded (SCI-EXPANDED)},
Unique-ID = {WOS:000951979400001},
DA = {2023-04-22},
}

@article{ WOS:000918519500001,
Author = {Sajno, Elena and Bartolotta, Sabrina and Tuena, Cosimo and Cipresso,
   Pietro and Pedroli, Elisa and Riva, Giuseppe},
Title = {Machine learning in biosignals processing for mental health: A narrative
   review},
Journal = {FRONTIERS IN PSYCHOLOGY},
Year = {2023},
Volume = {13},
Month = {JAN 13},
Abstract = {Machine Learning (ML) offers unique and powerful tools for mental health
   practitioners to improve evidence-based psychological interventions and
   diagnoses. Indeed, by detecting and analyzing different biosignals, it
   is possible to differentiate between typical and atypical functioning
   and to achieve a high level of personalization across all phases of
   mental health care. This narrative review is aimed at presenting a
   comprehensive overview of how ML algorithms can be used to infer the
   psychological states from biosignals. After that, key examples of how
   they can be used in mental health clinical activity and research are
   illustrated. A description of the biosignals typically used to infer
   cognitive and emotional correlates (e.g., EEG and ECG), will be
   provided, alongside their application in Diagnostic Precision Medicine,
   Affective Computing, and brain-computer Interfaces. The contents will
   then focus on challenges and research questions related to ML applied to
   mental health and biosignals analysis, pointing out the advantages and
   possible drawbacks connected to the widespread application of AI in the
   medical/mental health fields. The integration of mental health research
   and ML data science will facilitate the transition to personalized and
   effective medicine, and, to do so, it is important that researchers from
   psychological/ medical disciplines/health care professionals and data
   scientists all share a common background and vision of the current
   research.},
Publisher = {FRONTIERS MEDIA SA},
Address = {AVENUE DU TRIBUNAL FEDERAL 34, LAUSANNE, CH-1015, SWITZERLAND},
Type = {Review},
Language = {English},
Affiliation = {Sajno, E (Corresponding Author), Univ Cattolica Sacro Cuore, Humane Technol Lab, Milan, Italy.
   Sajno, E (Corresponding Author), Univ Pisa, Dept Comp Sci, Pisa, Italy.
   Sajno, Elena; Riva, Giuseppe, Univ Cattolica Sacro Cuore, Humane Technol Lab, Milan, Italy.
   Sajno, Elena, Univ Pisa, Dept Comp Sci, Pisa, Italy.
   Bartolotta, Sabrina, Univ Cattolica Sacro Cuore, ExperienceLab, Milan, Italy.
   Bartolotta, Sabrina, Univ Cattolica Sacro Cuore, Dept Psychol, Milan, Italy.
   Tuena, Cosimo; Cipresso, Pietro; Riva, Giuseppe, IRCCS Ist Auxol Italiano, Appl Technol Neuropsychol Lab, Milan, Italy.
   Cipresso, Pietro, Univ Turin, Dept Psychol, Turin, Italy.
   Pedroli, Elisa, eCampus Univ, Dept Psychol, Novedrate, Italy.},
DOI = {10.3389/fpsyg.2022.1066317},
Article-Number = {1066317},
ISSN = {1664-1078},
Keywords = {biosignals; artificial intelligence; machine learning; mental health;
   neurology; precision medicine; affective computing; brain-computer
   interfaces},
Keywords-Plus = {BRAIN-COMPUTER INTERFACES; COMPUTATIONAL INTELLIGENCE;
   ARTIFICIAL-INTELLIGENCE; EMOTION RECOGNITION; PRECISION MEDICINE;
   GENDER-DIFFERENCES; RACIAL BIAS; EEG; BCI; PSYCHIATRY},
Research-Areas = {Psychology},
Web-of-Science-Categories  = {Psychology, Multidisciplinary},
Author-Email = {elena.sajno@phd.unipi.it},
Affiliations = {Catholic University of the Sacred Heart; University of Pisa; Catholic
   University of the Sacred Heart; Catholic University of the Sacred Heart;
   IRCCS Istituto Auxologico Italiano; University of Turin; Universita
   Ecampus},
ResearcherID-Numbers = {Cipresso, Pietro/G-4676-2011},
ORCID-Numbers = {Cipresso, Pietro/0000-0002-0662-7678},
Funding-Acknowledgement = {Italian Ministry of Health {[}POSTECH: 39C801\_2018]},
Funding-Text = {Funding Research funded by the Italian Ministry of Health (POSTECH:
   39C801\_2018).},
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Times-Cited = {0},
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Journal-ISO = {Front. Psychol.},
Doc-Delivery-Number = {8D8EH},
Web-of-Science-Index = {Social Science Citation Index (SSCI)},
Unique-ID = {WOS:000918519500001},
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DA = {2023-04-22},
}

@article{ WOS:000632740900001,
Author = {Taranto-Vera, Gilda and Galindo-Villardon, Purificacion and
   Merchan-Sanchez-Jara, Javier and Salazar-Pozo, Julio and Moreno-Salazar,
   Alex and Salazar-Villalva, Vanessa},
Title = {Algorithms and software for data mining and machine learning: a critical
   comparative view from a systematic review of the literature},
Journal = {JOURNAL OF SUPERCOMPUTING},
Year = {2021},
Volume = {77},
Number = {10},
Pages = {11481-11513},
Month = {OCT},
Abstract = {Today, a greater generation of information is produced as a consequence
   of the technological development of society. The Internet has
   facilitated the access and extraction of this information, thus pursuing
   the automatic discovery of the knowledge contained within. In this
   context, data mining aims to discover patterns, profiles and trends of a
   large volume of data, for which multiple learning techniques are
   available. The selection of which technique to use depends on the type
   of result desired to obtain and the data that are available, considering
   that the algorithms for these tasks date mostly from the early twentieth
   century and are now the basis of these new technologies. The aim of this
   study is to show the development of these techniques in the field of
   scientific research and to present the evolution of algorithms and
   software for data mining in recent years. To this end, the systematic
   literature review methodology was applied, as it is considered a
   systematic process that identifies, evaluates, and interprets the work
   of researchers in a chosen field. As a result, we present a comparative
   analysis of the most outstanding software: Alteryx, TIBCO Data Science,
   RapidMiner and WEKA, their capacities for data mining processes and a
   description of the algorithms and techniques of machine learning that
   are currently on the rise.},
Publisher = {SPRINGER},
Address = {VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS},
Type = {Review},
Language = {English},
Affiliation = {Taranto-Vera, G (Corresponding Author), Univ Salamanca, Salamanca, Spain.
   Taranto-Vera, Gilda; Galindo-Villardon, Purificacion; Merchan-Sanchez-Jara, Javier; Salazar-Pozo, Julio; Moreno-Salazar, Alex; Salazar-Villalva, Vanessa, Univ Salamanca, Salamanca, Spain.
   Moreno-Salazar, Alex; Salazar-Villalva, Vanessa, Escuela Super Politecn Litoral, Guayaquil, Ecuador.},
DOI = {10.1007/s11227-021-03708-5},
EarlyAccessDate = {MAR 2021},
ISSN = {0920-8542},
EISSN = {1573-0484},
Keywords = {Data mining; Machine learning techniques; Algorithms; Systematic
   literature review; Software tools; Performance evaluation},
Research-Areas = {Computer Science; Engineering},
Web-of-Science-Categories  = {Computer Science, Hardware \& Architecture; Computer Science, Theory \&
   Methods; Engineering, Electrical \& Electronic},
Author-Email = {gilda.taranto@usal.es
   pgalindo@usal.es
   javiermerchan@usal.es
   julio\_salazar@usal.es
   amorenos@usal.es
   vanessa.salazar@usal.es},
Affiliations = {University of Salamanca; Escuela Superior Politecnica del Litoral},
ORCID-Numbers = {Taranto-Vera, Gilda/0000-0002-6012-7818},
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Times-Cited = {6},
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Journal-ISO = {J. Supercomput.},
Doc-Delivery-Number = {UP8WV},
Web-of-Science-Index = {Science Citation Index Expanded (SCI-EXPANDED)},
Unique-ID = {WOS:000632740900001},
DA = {2023-04-22},
}

@article{ WOS:000855653500001,
Author = {Pucheanu, Florin and Colesca, Sofia-Elena and Pacesila, Mihaela and
   Burcea, Gabriel},
Title = {INDUSTRY 4.0, SUSTAINABILITY AND INNOVATION AT THE CROSSROAD: A REVIEW
   ANALISYS},
Journal = {MANAGEMENT RESEARCH AND PRACTICE},
Year = {2022},
Volume = {14},
Number = {3},
Pages = {47-69},
Month = {SEP},
Abstract = {The present article has the goal to provide an overview on the most
   relevant research publications placed at the intersection between the
   study of the Industry 4.0 and the concepts of innovation and
   sustainability. A review of the literature has been carried out and the
   most cited 80 publications identified in Scopus have been analyzed. The
   investigation pointed out that there is an exponential growth of the
   research activity in this field in recent years and that Europe is ahead
   of China and the United States as regards the number of scientific
   publications. The fundamental contribution of this article consists in
   highlighting the most important aspects related to the Industry 4.0 and
   the concepts of innovation and sustainability, by systematizing the
   findings of the publications studied.},
Publisher = {RESEARCH CENTER PUBLIC ADM \& PUBLIC SERVICE},
Address = {CALEA SERBAN VODA 22-24, BUCHAREST, 040211, ROMANIA},
Type = {Review},
Language = {English},
Affiliation = {Pucheanu, F (Corresponding Author), Bucharest Univ Econ Studies, Management Doctoral Sch, Bucharest, Romania.
   Pucheanu, Florin, Bucharest Univ Econ Studies, Management Doctoral Sch, Bucharest, Romania.
   Colesca, Sofia-Elena; Pacesila, Mihaela; Burcea, Gabriel, Bucharest Univ Econ Studies, Bucharest, Romania.},
ISSN = {2067-2462},
Keywords = {Industry 4; sustainalility; innovation},
Keywords-Plus = {BUSINESS MODEL INNOVATION; ARTIFICIAL-INTELLIGENCE; DIGITAL
   SERVITIZATION; SHARING ECONOMY; GIG ECONOMY; BIG DATA; VERTICAL
   INTEGRATION; SECURITY CHALLENGES; TRUST MANAGEMENT; RESEARCH AGENDA},
Research-Areas = {Business \& Economics},
Web-of-Science-Categories  = {Management},
Author-Email = {Florin\_pucheanu@yahoo.com
   sofia.colesca@man.ase.ro
   mihaela.pacesila@man.ase.ro
   stefan.burcea@amp.ase.ro},
Affiliations = {Bucharest University of Economic Studies; Bucharest University of
   Economic Studies},
Funding-Acknowledgement = {Bucharest University of Economic Studies},
Funding-Text = {The paper was supported by the grant <SUB>{''}</SUB>Urban planning
   analysis of Bucharest Municipality from the perspective of sustainable
   development goals/Analiza planificarii urbane la nivelul Municipiului
   Bucuresti din perspectiva obiectivelor de dezvoltare durabila
   (APUDD){''}, funded by Bucharest University of Economic Studies.},
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Number-of-Cited-References = {163},
Times-Cited = {1},
Usage-Count-Last-180-days = {7},
Usage-Count-Since-2013 = {8},
Journal-ISO = {Manag. Res. Pract.},
Doc-Delivery-Number = {4P8PX},
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Unique-ID = {WOS:000855653500001},
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}

@article{ WOS:000862705400001,
Author = {Akhtar, Nadeem and Khan, Nohman and Qayyum, Shazia and Qureshi, Muhammad
   Imran and Hishan, Snail S.},
Title = {Efficacy and pitfalls of digital technologies in healthcare services: A
   systematic review of two decades},
Journal = {FRONTIERS IN PUBLIC HEALTH},
Year = {2022},
Volume = {10},
Month = {SEP 16},
Abstract = {The use of technology in the healthcare sector and its medical
   practices, from patient record maintenance to diagnostics, has
   significantly improved the health care emergency management system. At
   that backdrop, it is crucial to explore the role and challenges of these
   technologies in the healthcare sector. Therefore, this study provides a
   systematic review of the literature on technological developments in the
   healthcare sector and deduces its pros and cons. We curate the published
   studies from the Web of Science and Scopus databases by using PRISMA
   2015 guidelines. After mining the data, we selected only 55 studies for
   the systematic literature review and bibliometric analysis. The study
   explores four significant classifications of technological development
   in healthcare: (a) digital technologies, (b) artificial intelligence,
   (c) blockchain, and (d) the Internet of Things. The novel contribution
   of current study indicate that digital technologies have significantly
   influenced the healthcare services such as the beginning of electronic
   health record, a new era of digital healthcare, while robotic surgeries
   and machine learning algorithms may replace practitioners as future
   technologies. However, a considerable number of studies have criticized
   these technologies in the health sector based on trust, security,
   privacy, and accuracy. The study suggests that future studies, on
   technological development in healthcare services, may take into account
   these issues for sustainable development of the healthcare sector.},
Publisher = {FRONTIERS MEDIA SA},
Address = {AVENUE DU TRIBUNAL FEDERAL 34, LAUSANNE, CH-1015, SWITZERLAND},
Type = {Review},
Language = {English},
Affiliation = {Qureshi, MI (Corresponding Author), Teesside Univ, Int Business Sch, Middlesbrough, England.
   Akhtar, Nadeem, South China Normal Univ, Sch Urban Culture, Foshan, Peoples R China.
   Khan, Nohman, Univ Kuala Lumpur, UniKL Business Sch, Kuala Lumpur, Malaysia.
   Qayyum, Shazia, Univ Punjab, Inst Appl Psychol, Lahore, Pakistan.
   Qureshi, Muhammad Imran, Teesside Univ, Int Business Sch, Middlesbrough, England.
   Hishan, Snail S., Univ Teknol, Azman Hashim Int Business Sch, Kuala Lumpur, Malaysia.
   Hishan, Snail S., THR Project, Brisbane, Qld, Australia.},
DOI = {10.3389/fpubh.2022.869793},
Article-Number = {869793},
EISSN = {2296-2565},
Keywords = {healthcare; digital technologies; artificial intelligence; IoT;
   blockchain; SLR-M},
Keywords-Plus = {INTERNET},
Research-Areas = {Public, Environmental \& Occupational Health},
Web-of-Science-Categories  = {Public, Environmental \& Occupational Health},
Author-Email = {m.qureshi@tees.ac.uk},
Affiliations = {South China Normal University; University of Kuala Lumpur; University of
   Punjab; University of Teesside; Universiti Teknologi Malaysia},
ResearcherID-Numbers = {Qureshi, Muhammad Imran/I-4390-2016
   },
ORCID-Numbers = {Qureshi, Muhammad Imran/0000-0001-8861-0628
   AKHTAR, NADEEM/0000-0002-8283-4353},
Funding-Acknowledgement = {Guangdong Social Science Project;  {[}GD21CSH07]},
Funding-Text = {This work was supported by the Guangdong Social Science Project (Grant
   No. GD21CSH07).},
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Number-of-Cited-References = {49},
Times-Cited = {1},
Usage-Count-Last-180-days = {16},
Usage-Count-Since-2013 = {18},
Journal-ISO = {Front. Public Health},
Doc-Delivery-Number = {5A2EN},
Web-of-Science-Index = {Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)},
Unique-ID = {WOS:000862705400001},
OA = {gold, Green Published},
DA = {2023-04-22},
}

@article{ WOS:000659058000003,
Author = {Contreras-Bravo, Leonardo-Emiro and Tarazona-Bermudez, Giovanny-Mauricio
   and Rodriguez-Molano, Jose-Ignacio},
Title = {Technology and Learning Analytics: A Literature Review},
Journal = {REVISTA CIENTIFICA},
Year = {2021},
Volume = {41},
Number = {2},
Pages = {150-168},
Month = {MAY-AUG},
Abstract = {A study concerning learning analytics is presented. This area consists
   of the collection and analysis of data generated by students and their
   iterations with the purpose of understanding and optimizing learning. By
   using databases, a referential review of the last five years is proposed
   to identify aspects related to the growth of this approach and its
   fields of application in higher education. The volume of related
   research is increasing due to the need to investigate more accurate
   predictive models and new algorithms in the field of data science.},
Publisher = {UNIV DISTRITAL FRANCISCO JOSE DE CALDAS, CENTRO INVEST \& DESARROLLO
   CIENT},
Address = {CARRERA 7 NO 40-53, BOGOTA, 00000, COLOMBIA},
Type = {Review},
Language = {Spanish},
Affiliation = {Contreras-Bravo, LE (Corresponding Author), Univ Dist Francisco Jose de Caldas, Bogota, Colombia.
   Contreras-Bravo, Leonardo-Emiro; Tarazona-Bermudez, Giovanny-Mauricio; Rodriguez-Molano, Jose-Ignacio, Univ Dist Francisco Jose de Caldas, Bogota, Colombia.},
DOI = {10.14483/23448350.17547},
ISSN = {0124-2253},
EISSN = {2344-8350},
Keywords = {analytics; educational research; engineering education; learning
   analytics; machine learning},
Keywords-Plus = {HIGHER-EDUCATION; LOG DATA; STUDENT; MOTIVATION; PERFORMANCE; FRAMEWORK;
   PREDICT; MODEL},
Research-Areas = {Education \& Educational Research},
Web-of-Science-Categories  = {Education, Scientific Disciplines},
Author-Email = {lecontrerasb@udistrital.edu.co
   gtarazona@udistrital.edu.co
   jirodriguezm@udistrital.edu.co},
Affiliations = {Universidad Distrital Francisco Jose de Caldas},
ORCID-Numbers = {contreras bravo, leonardo emiro/0000-0003-4625-8835
   RODRIGUEZ MOLANO, JOSE IGNACIO/0000-0003-2581-277X},
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Number-of-Cited-References = {88},
Times-Cited = {0},
Usage-Count-Last-180-days = {2},
Usage-Count-Since-2013 = {16},
Journal-ISO = {Rev. Cient.},
Doc-Delivery-Number = {SO6CN},
Web-of-Science-Index = {Emerging Sources Citation Index (ESCI)},
Unique-ID = {WOS:000659058000003},
OA = {Green Submitted, gold},
DA = {2023-04-22},
}

@article{ WOS:000569219300007,
Author = {Velmurugan, Devadasan and Pachaiappan, R. and Ramakrishnan,
   Chandrasekaran},
Title = {Recent Trends in Drug Design and Discovery},
Journal = {CURRENT TOPICS IN MEDICINAL CHEMISTRY},
Year = {2020},
Volume = {20},
Number = {19},
Pages = {1761-1770},
Abstract = {Introduction: Structure-based drug design is a wide area of
   identification of selective inhibitors of a target of interest. From the
   time of the availability of three dimensional structure of the drug
   targets, mostly the proteins, many computational methods had emerged to
   address the challenges associated with drug design process.
   Particularly, drug-likeness, druggability of the target protein,
   specificity, off-target binding, etc., are the important factors to
   determine the efficacy of new chemical inhibitors.
   Objective: The aim of the present research was to improve the drug
   design strategies in field of design of novel inhibitors with respect to
   specific target protein in disease pathology. Recent statistical machine
   learning methods applied for structural and chemical data analysis had
   been elaborated in current drug design field.
   Methods: As the size of the biological data shows a continuous growth,
   new computational algorithms and analytical methods are being developed
   with different objectives. It covers a wide area, from protein structure
   prediction to drug toxicity prediction. Moreover, the computational
   methods are available to analyze the structural data of varying types
   and sizes of which, most of the semi-empirical force field and quantum
   mechanics based molecular modeling methods showed a proven accuracy
   towards analysing small structural data sets while statistics based
   methods such as machine learning, QSAR and other specific data analytics
   methods are robust for large scale data analysis.
   Results: In this present study, the background has been reviewed for new
   drug lead development with respect specific drug targets of interest.
   Overall approach of both the extreme methods were also used to
   demonstrate with the plausible outcome.
   Conclusion: In this chapter, we focus on the recent developments in the
   structure-based drug design using advanced molecular modeling techniques
   in conjunction with machine learning and other data analytics methods.
   Natural products based drug discovery is also discussed.},
Publisher = {BENTHAM SCIENCE PUBL LTD},
Address = {EXECUTIVE STE Y-2, PO BOX 7917, SAIF ZONE, 1200 BR SHARJAH, U ARAB
   EMIRATES},
Type = {Review},
Language = {English},
Affiliation = {Velmurugan, D (Corresponding Author), Univ Madras, CAS Crystallog \& Biophys, Guindy Campus, Chennai 600025, Tamil Nadu, India.
   Ramakrishnan, C (Corresponding Author), Indian Inst Technol IIT Madras, Bhupat \& Jyoti Mehta Sch Biosci, Dept Biotechnol, Prot Bioinformat Lab, Chennai 600036, Tamil Nadu, India.
   Velmurugan, Devadasan, Univ Madras, CAS Crystallog \& Biophys, Guindy Campus, Chennai 600025, Tamil Nadu, India.
   Pachaiappan, R., SRM Inst Sci \& Technol, Dept Biotechnol, Kattankulathur 603203, Tamil Nadu, India.
   Ramakrishnan, Chandrasekaran, Indian Inst Technol IIT Madras, Bhupat \& Jyoti Mehta Sch Biosci, Dept Biotechnol, Chennai 600036, Tamil Nadu, India.},
DOI = {10.2174/1568026620666200622150003},
ISSN = {1568-0266},
EISSN = {1873-5294},
Keywords = {Structure-based drug design; SBDD; Machine learning; QSAR; Data
   analytics; Data science},
Keywords-Plus = {GROMOS FORCE-FIELD; MOLECULAR-DYNAMICS; CONFORMATIONAL ENERGIES; SCORING
   FUNCTIONS; SIMULATION; PROTEINS; FANGCHINOLINE; LIGANDS; SYSTEMS; FOLD},
Research-Areas = {Pharmacology \& Pharmacy},
Web-of-Science-Categories  = {Chemistry, Medicinal},
Author-Email = {shirai2011@gmail.com
   ramki.rpcr@gmail.com},
Affiliations = {University of Madras; SRM Institute of Science \& Technology Chennai;
   Indian Institute of Technology System (IIT System); Indian Institute of
   Technology (IIT) - Madras},
ResearcherID-Numbers = {BIOTECH, SRMITE/AAW-8572-2020
   Chandrasekaran, Ramakrishnan/AAP-6328-2021
   },
ORCID-Numbers = {Chandrasekaran, Ramakrishnan/0000-0003-4622-2496
   Raman, Pachaiappan/0000-0003-2739-4882},
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Number-of-Cited-References = {55},
Times-Cited = {10},
Usage-Count-Last-180-days = {1},
Usage-Count-Since-2013 = {34},
Journal-ISO = {Curr. Top. Med. Chem.},
Doc-Delivery-Number = {NO1BB},
Web-of-Science-Index = {Science Citation Index Expanded (SCI-EXPANDED)},
Unique-ID = {WOS:000569219300007},
DA = {2023-04-22},
}

@article{ WOS:000685394200001,
Author = {Subrahmanya, Sri Venkat Gunturi and Shetty, Dasharathraj K. and Patil,
   Vathsala and Hameed, B. M. Zeeshan and Paul, Rahul and Smriti, Komal and
   Naik, Nithesh and Somani, Bhaskar K.},
Title = {The role of data science in healthcare advancements: applications,
   benefits, and future prospects},
Journal = {IRISH JOURNAL OF MEDICAL SCIENCE},
Year = {2022},
Volume = {191},
Number = {4},
Pages = {1473-1483},
Month = {AUG},
Abstract = {Data science is an interdisciplinary field that extracts knowledge and
   insights from many structural and unstructured data, using scientific
   methods, data mining techniques, machine-learning algorithms, and big
   data. The healthcare industry generates large datasets of useful
   information on patient demography, treatment plans, results of medical
   examinations, insurance, etc. The data collected from the Internet of
   Things (IoT) devices attract the attention of data scientists. Data
   science provides aid to process, manage, analyze, and assimilate the
   large quantities of fragmented, structured, and unstructured data
   created by healthcare systems. This data requires effective management
   and analysis to acquire factual results. The process of data cleansing,
   data mining, data preparation, and data analysis used in healthcare
   applications is reviewed and discussed in the article. The article
   provides an insight into the status and prospects of big data analytics
   in healthcare, highlights the advantages, describes the frameworks and
   techniques used, briefs about the challenges faced currently, and
   discusses viable solutions. Data science and big data analytics can
   provide practical insights and aid in the decision-making of strategic
   decisions concerning the health system. It helps build a comprehensive
   view of patients, consumers, and clinicians. Data-driven decision-making
   opens up new possibilities to boost healthcare quality.},
Publisher = {SPRINGER LONDON LTD},
Address = {236 GRAYS INN RD, 6TH FLOOR, LONDON WC1X 8HL, ENGLAND},
Type = {Review},
Language = {English},
Affiliation = {Patil, V (Corresponding Author), Manipal Acad Higher Educ, Manipal Coll Dent Sci, Dept Oral Med \& Radiol, Manipal, Karnataka, India.
   Subrahmanya, Sri Venkat Gunturi, Manipal Acad Higher Educ, Manipal Inst Technol, Dept Elect \& Elect Engn, Manipal, Karnataka, India.
   Shetty, Dasharathraj K., Manipal Acad Higher Educ, Manipal Inst Technol, Dept Humanities \& Management, Manipal, Karnataka, India.
   Patil, Vathsala; Smriti, Komal, Manipal Acad Higher Educ, Manipal Coll Dent Sci, Dept Oral Med \& Radiol, Manipal, Karnataka, India.
   Hameed, B. M. Zeeshan, Father Muller Med Coll, Dept Urol, Mangalore, Karnataka, India.
   Paul, Rahul, Massachusetts Gen Hosp, Dept Radiat Oncol, Boston, MA 02114 USA.
   Naik, Nithesh, Manipal Acad Higher Educ, Manipal Inst Technol, Dept Mech \& Mfg Engn, Manipal, Karnataka, India.
   Somani, Bhaskar K., Univ Hosp Southampton NHS Trust, Dept Urol, Southampton, Hants, England.},
DOI = {10.1007/s11845-021-02730-z},
EarlyAccessDate = {AUG 2021},
ISSN = {0021-1265},
EISSN = {1863-4362},
Keywords = {Big data; Data analytics; Data mining; Healthcare; Healthcare
   informatics},
Keywords-Plus = {BIG DATA; MENTAL-DISORDERS; PLATFORMS; FRAMEWORK; SERVICES; RECORDS;
   SECURE},
Research-Areas = {General \& Internal Medicine},
Web-of-Science-Categories  = {Medicine, General \& Internal},
Author-Email = {drvathsala19@gmail.com},
Affiliations = {Manipal Academy of Higher Education (MAHE); Manipal Academy of Higher
   Education (MAHE); Manipal Academy of Higher Education (MAHE); Father
   Muller Medical College; Harvard University; Massachusetts General
   Hospital; Manipal Academy of Higher Education (MAHE)},
ResearcherID-Numbers = {Naik, Nithesh/W-5086-2019
   SHETTY, DASHARATHRAJ KODU/AAV-3291-2020
   },
ORCID-Numbers = {Naik, Nithesh/0000-0003-0356-7697
   SHETTY, DASHARATHRAJ KODU/0000-0002-5021-4029
   Somani, Bhaskar/0000-0002-6248-6478
   Hameed, BM Zeeshan/0000-0002-2904-351X},
Funding-Acknowledgement = {Manipal Academy of Higher Education, Manipal},
Funding-Text = {Open access funding provided by Manipal Academy of Higher Education,
   Manipal.},
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Number-of-Cited-References = {52},
Times-Cited = {6},
Usage-Count-Last-180-days = {11},
Usage-Count-Since-2013 = {37},
Journal-ISO = {Irish J. Med. Sci.},
Doc-Delivery-Number = {3E5LQ},
Web-of-Science-Index = {Science Citation Index Expanded (SCI-EXPANDED)},
Unique-ID = {WOS:000685394200001},
OA = {hybrid, Green Published},
DA = {2023-04-22},
}

@article{ WOS:000481516700001,
Author = {Emmert-Streib, Frank and Moutari, Salisou and Dehmer, Matthias},
Title = {A comprehensive survey of error measures for evaluating binary decision
   making in data science},
Journal = {WILEY INTERDISCIPLINARY REVIEWS-DATA MINING AND KNOWLEDGE DISCOVERY},
Year = {2019},
Volume = {9},
Number = {5},
Month = {SEP-OCT},
Abstract = {Binary decision making is a topic of great interest for many fields,
   including biomedical science, economics, management, politics, medicine,
   natural science and social science, and much effort has been spent for
   developing novel computational methods to address problems arising in
   the aforementioned fields. However, in order to evaluate the
   effectiveness of any prediction method for binary decision making, the
   choice of the most appropriate error measures is of paramount
   importance. Due to the variety of error measures available, the
   evaluation process of binary decision making can be a complex task. The
   main objective of this study is to provide a comprehensive survey of
   error measures for evaluating the outcome of binary decision making
   applicable to many data-driven fields. This article is categorized
   under: Fundamental Concepts of Data and Knowledge > Key Design Issues in
   Data Mining Technologies > Prediction Algorithmic Development >
   Statistics},
Publisher = {WILEY PERIODICALS, INC},
Address = {ONE MONTGOMERY ST, SUITE 1200, SAN FRANCISCO, CA 94104 USA},
Type = {Review},
Language = {English},
Affiliation = {Emmert-Streib, F (Corresponding Author), Tampere Univ, Fac Informat Technol \& Commun Sci, Predict Soc \& Data Analyt Lab, Tampere, Finland.
   Emmert-Streib, Frank, Tampere Univ, Fac Informat Technol \& Commun Sci, Predict Soc \& Data Analyt Lab, Tampere, Finland.
   Moutari, Salisou, Queens Univ Belfast, Sch Math \& Phys, Math Sci Res Ctr, Belfast, Antrim, North Ireland.
   Dehmer, Matthias, Univ Appl Sci Upper Austria, Fac Management, Inst Intelligent Prod, Steyr Campus, Wels, Austria.
   Dehmer, Matthias, UMIT Hlth \& Life Sci Univ, Dept Biomed Comp Sci \& Mechatron, Hall In Tirol, Austria.
   Dehmer, Matthias, Nankai Univ, Coll Comp \& Control Engn, Tianjin, Peoples R China.},
DOI = {10.1002/widm.1303},
Article-Number = {e1303},
ISSN = {1942-4787},
EISSN = {1942-4795},
Keywords = {classification; data science; decision making; error measures; machine
   learning; statistics},
Keywords-Plus = {SECONDARY STRUCTURE; PREDICTION; PERFORMANCE; AGREEMENT; AREA},
Research-Areas = {Computer Science},
Web-of-Science-Categories  = {Computer Science, Artificial Intelligence; Computer Science, Theory \&
   Methods},
Author-Email = {frank.emmert-streib@tut.fi},
Affiliations = {Tampere University; Queens University Belfast; Nankai University},
ResearcherID-Numbers = {Emmert-Streib, Frank/AAF-2878-2020},
ORCID-Numbers = {Emmert-Streib, Frank/0000-0003-0745-5641},
Funding-Acknowledgement = {Austrian Science Funds {[}P30031]},
Funding-Text = {Austrian Science Funds, Grant/Award Number: P30031},
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Number-of-Cited-References = {33},
Times-Cited = {19},
Usage-Count-Last-180-days = {1},
Usage-Count-Since-2013 = {7},
Journal-ISO = {Wiley Interdiscip. Rev.-Data Mining Knowl. Discov.},
Doc-Delivery-Number = {IR5YY},
Web-of-Science-Index = {Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)},
Unique-ID = {WOS:000481516700001},
OA = {Green Published, hybrid},
DA = {2023-04-22},
}

@article{ WOS:000618081800008,
Author = {Shi, Yao and Prieto, Paloma L. and Zepel, Tara and Grunert, Shad and
   Hein, Jason E.},
Title = {Automated Experimentation Powers Data Science in Chemistry},
Journal = {ACCOUNTS OF CHEMICAL RESEARCH},
Year = {2021},
Volume = {54},
Number = {3},
Pages = {546-555},
Month = {FEB 2},
Abstract = {Data science has revolutionized chemical research and continues to break
   down barriers with new interdisciplinary studies. The introduction of
   computational models and machine learning (ML) algorithms in combination
   with automation and traditional experimental techniques has enabled
   scientific advancement across nearly every discipline of chemistry, from
   materials discovery, to process optimization, to synthesis planning.
   However, predictive tools powered by data science are only as good as
   their data sets and, currently, many of the data sets used to train
   models suffer from several limitations, including being sparse, limited
   in scope and requiring human curation. Likewise, computational data
   faces limitations in terms of accurate modeling of nonideal systems and
   can suffer from low translation fidelity from simulation to real
   conditions. The lack of diverse data and the need to be able to test it
   experimentally reduces both the accuracy and scope of the predictive
   models derived from data science. This Account contextualizes the need
   for more complex and diverse experimental data and highlights how the
   seamless integration of robotics, machine learning, and data-rich
   monitoring techniques can be used to access it with minimal human labor.
   We propose three broad categories of data in chemistry: data on
   fundamental properties, data on reaction outcomes, and data on reaction
   mechanics. We highlight flexible, automated platforms that can be
   deployed to acquire and leverage these data. The first platform combines
   solid- and liquid-dosing modules with computer vision to automate
   solubility screening, thereby gathering fundamental data that are
   necessary for almost every experimental design. Using computer vision
   offers the additional benefit of creating a visual record, which can be
   referenced and used to further interrogate and gain insight on the data
   collected. The second platform iteratively tests reaction variables
   proposed by a ML algorithm in a closed-loop fashion. Experimental data
   related to reaction outcomes are fed back into the algorithm to drive
   the discovery and optimization of new materials and chemical processes.
   The third platform uses automated process analytical technology to
   gather real-time data related to reaction kinetics. This system allows
   the researcher to directly interrogate the reaction mechanisms in
   granular detail to determine exactly how and why a reaction proceeds,
   thereby enabling reaction optimization and deployment.},
Publisher = {AMER CHEMICAL SOC},
Address = {1155 16TH ST, NW, WASHINGTON, DC 20036 USA},
Type = {Review},
Language = {English},
Affiliation = {Hein, JE (Corresponding Author), Univ British Columbia, Dept Chem, Vancouver, BC V6T 1Z3, Canada.
   Shi, Yao; Prieto, Paloma L.; Zepel, Tara; Grunert, Shad; Hein, Jason E., Univ British Columbia, Dept Chem, Vancouver, BC V6T 1Z3, Canada.},
DOI = {10.1021/acs.accounts.0c00736},
EarlyAccessDate = {JAN 2021},
ISSN = {0001-4842},
EISSN = {1520-4898},
Keywords-Plus = {HIGH-THROUGHPUT EXPERIMENTATION; ORGANIC-SYNTHESIS; PHYSICOCHEMICAL
   PROPERTIES; ORGANOMETALLIC CHEMISTRY; DISCOVERY; PREDICTION;
   OPTIMIZATION; INTELLIGENCE; REACTIVITY; PROGRESS},
Research-Areas = {Chemistry},
Web-of-Science-Categories  = {Chemistry, Multidisciplinary},
Author-Email = {jhein@chem.ubc.ca},
Affiliations = {University of British Columbia},
ORCID-Numbers = {Hein, Jason/0000-0002-4345-3005
   Prieto, Paloma/0000-0002-7700-2085
   SHI, YAO/0000-0002-1471-961X},
Funding-Acknowledgement = {Merck Co.; Canada Foundation for Innovation {[}CFI-35883]; Natural
   Sciences and Engineering Research Council of Canada
   {[}RGPIN-2016-24613]; Natural Resources Canada {[}EIP-MAT-001]; Defense
   Advanced Research Project Agency (DARPA) {[}HR0011920027]},
Funding-Text = {The authors gratefully acknowledge Mettler-Toledo Autochem for the
   generous donation of process analytical equipment enabling these studies
   (EasyMax). This research was further supported by Merck \& Co., the
   Canada Foundation for Innovation (CFI-35883), the Natural Sciences and
   Engineering Research Council of Canada (RGPIN-2016-24613), and Natural
   Resources Canada (EIP-MAT-001). The authors are also grateful to the
   Defense Advanced Research Project Agency (DARPA) for financial support
   under the Accelerated Molecular Discovery Program (Cooperative Agreement
   No. HR0011920027). The views, opinions, and/or findings expressed are
   those of the authors and should not be interpreted as representing the
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@article{ WOS:000833423300009,
Author = {Lopez-Vargas, Ascension and Ledezma-Espino, Agapito and
   Sanchis-de-Miguel, Araceli},
Title = {Methods, data sources and applications of the Artificial Intelligence in
   the Energy Poverty context: A review},
Journal = {ENERGY AND BUILDINGS},
Year = {2022},
Volume = {268},
Month = {AUG 1},
Abstract = {Energy Poverty (EP) is a widespread problem in Europe. EP detection is
   hampered by a lack of data and global metrics. Recently, innovative
   approaches using Artificial Intelligent (AI) techniques have been
   increasingly applied for the EP alleviation. In this work, studies
   focused on the application of AI on EP were studied. It was identified
   that there is not a high number of works that apply AI to fight against
   EP (considering this problem as a multidimensional phenomenon).
   Artificial Neural Networks-based algorithms and Decision Trees were the
   most used algorithms in the reviewed literature focused on EP
   alleviation. However, several AI applications focused on partial aspects
   of the EP or on areas intimately related to EP (low-income, high-energy
   price and low-energy efficiency of buildings) that allow the
   characterization of the problem in an efficient way have been published
   in recent years; the last 7 years published literature have been
   reviewed in this work. It was found that Neural Networks algorithms were
   the most used models for low-income, energy price and poor energy
   efficiency characterizations. Support Vector Machines-based algorithms
   were the most popular AI method applied on energy consumption related
   problems. Deep learning was the most popular technique for detecting
   energy billing irregularities and unpaid energy bills. (C) 2022 Elsevier
   B.V. All rights reserved.},
Publisher = {ELSEVIER SCIENCE SA},
Address = {PO BOX 564, 1001 LAUSANNE, SWITZERLAND},
Type = {Review},
Language = {English},
Affiliation = {Lopez-Vargas, A (Corresponding Author), Univ Carlos III Madrid, Comp Sci \& Engn Dept, Control Learning \& Optimizat Grp, Madrid, Spain.
   Lopez-Vargas, Ascension; Ledezma-Espino, Agapito; Sanchis-de-Miguel, Araceli, Univ Carlos III Madrid, Comp Sci \& Engn Dept, Control Learning \& Optimizat Grp, Madrid, Spain.},
DOI = {10.1016/j.enbuild.2022.112233},
EarlyAccessDate = {JUN 2022},
Article-Number = {112233},
ISSN = {0378-7788},
EISSN = {1872-6178},
Keywords = {Energy poverty; Fuel poverty; Artificial intelligence},
Keywords-Plus = {SUPPORT VECTOR REGRESSION; NATURAL-GAS CONSUMPTION; NEURAL-NETWORK;
   ELECTRICITY PRICE; THERMAL COMFORT; LOAD PROFILES; HYBRID MODEL;
   DATA-DRIVEN; PREDICTION; ALGORITHM},
Research-Areas = {Construction \& Building Technology; Energy \& Fuels; Engineering},
Web-of-Science-Categories  = {Construction \& Building Technology; Energy \& Fuels; Engineering, Civil},
Affiliations = {Universidad Carlos III de Madrid},
Funding-Acknowledgement = {European Commission {[}UIA04-212]; Agencia Estatal de Investigacion
   (AEI) {[}RTI2018-096036-B-C22, PID2019-104793RB-C31]; PEAVAUTO-CM-UC3M
   (Government of Spain); Region of Madrid's Excellence Program
   {[}EPUC3M17]},
Funding-Text = {The authors acknowledge funding from the European Commission through
   Urban Innovative Actions of the EPIU Getafe Project under Grant
   UIA04-212. Agapito Ledezma Espino acknowledges funding from the Agencia
   Estatal de Investigacion (AEI) under Grant
   RTI2018-096036-B-C22/AEI/10.13039/501100011033. Araceli Sanchis
   acknowledges funding from the Agencia Estatal de Investigacion (AEI)
   under Grant PID2019-104793RB-C31/AEI/10.1 3039/501100011033. The work of
   Araceli Sanchis de Miguel was supported by PEAVAUTO-CM-UC3M (Government
   of Spain) and EPUC3M17 (Region of Madrid's Excellence Program).},
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Number-of-Cited-References = {173},
Times-Cited = {0},
Usage-Count-Last-180-days = {11},
Usage-Count-Since-2013 = {13},
Journal-ISO = {Energy Build.},
Doc-Delivery-Number = {3J5GM},
Web-of-Science-Index = {Science Citation Index Expanded (SCI-EXPANDED)},
Unique-ID = {WOS:000833423300009},
DA = {2023-04-22},
}

@article{ WOS:000559363400001,
Author = {Smith, Daniel G. A. and Altarawy, Doaa and Burns, Lori A. and Welborn,
   Matthew and Naden, Levi N. and Ward, Logan and Ellis, Sam and Pritchard,
   Benjamin P. and Crawford, T. Daniel},
Title = {TheMolSSIQCArchiveproject: An open-source platform to compute, organize,
   and share quantum chemistry data},
Journal = {WILEY INTERDISCIPLINARY REVIEWS-COMPUTATIONAL MOLECULAR SCIENCE},
Year = {2021},
Volume = {11},
Number = {2},
Month = {MAR},
Abstract = {The Molecular Sciences Software Institute's (MolSSI) Quantum Chemistry
   Archive (QCArchive) project is an umbrella name that covers both a
   central server hosted by MolSSI for community data and the Python-based
   software infrastructure that powers automated computation and storage of
   quantum chemistry (QC) results. The MolSSI-hosted central server
   provides the computational molecular sciences community a location to
   freely access tens of millions of QC computations for machine learning,
   methodology assessment, force-field fitting, and more through a Python
   interface. Facile, user-friendly mining of the centrally archived
   quantum chemical data also can be achieved through web applications
   found at . The software infrastructure can be used as a standalone
   platform to compute, structure, and distribute hundreds of millions of
   QC computations for individuals or groups of researchers at any scale.
   The QCArchiveInfrastructureis open-source (BSD-3C), code repositories
   can be found at , and releases can be downloaded via PyPI and Conda.
   This article is categorized under: Electronic Structure Theory > Ab
   Initio Electronic Structure Methods Software > Quantum Chemistry Data
   Science > Computer Algorithms and Programming},
Publisher = {WILEY},
Address = {111 RIVER ST, HOBOKEN 07030-5774, NJ USA},
Type = {Review},
Language = {English},
Affiliation = {Smith, DGA (Corresponding Author), Mol Sci Software Inst, Blacksburg, VA 24060 USA.
   Smith, Daniel G. A.; Altarawy, Doaa; Welborn, Matthew; Naden, Levi N.; Ellis, Sam; Pritchard, Benjamin P.; Crawford, T. Daniel, Mol Sci Software Inst, Blacksburg, VA 24060 USA.
   Altarawy, Doaa, Alexandria Univ, Dept Comp \& Syst Engn, Alexandria, Egypt.
   Burns, Lori A., Georgia Inst Technol, Sch Chem \& Biochem, Ctr Computat Mol Sci \& Technol, Atlanta, GA 30332 USA.
   Ward, Logan, Argonne Natl Lab, Data Sci \& Learning Div, Lemont, IL USA.
   Crawford, T. Daniel, Virginia Tech, Dept Chem, Blacksburg, VA USA.},
DOI = {10.1002/wcms.1491},
EarlyAccessDate = {JUL 2020},
Article-Number = {e1491},
ISSN = {1759-0876},
EISSN = {1759-0884},
Keywords = {databases; density functional theory; machine learning; high-throughput
   computing; quantum chemistry},
Keywords-Plus = {BENCHMARK DATABASE; FORCE-FIELD},
Research-Areas = {Chemistry; Mathematical \& Computational Biology},
Web-of-Science-Categories  = {Chemistry, Multidisciplinary; Mathematical \& Computational Biology},
Author-Email = {dgasmith@vt.edu},
Affiliations = {Egyptian Knowledge Bank (EKB); Alexandria University; University System
   of Georgia; Georgia Institute of Technology; United States Department of
   Energy (DOE); Argonne National Laboratory; Virginia Polytechnic
   Institute \& State University},
ResearcherID-Numbers = {Ward, Logan/HRA-8888-2023
   Ward, Logan/I-9526-2019
   Crawford, Thomas/A-9271-2017},
ORCID-Numbers = {Ward, Logan/0000-0002-1323-5939
   Crawford, Thomas/0000-0002-7961-7016},
Funding-Acknowledgement = {Division of Advanced Cyberinfrastructure {[}1449723, 1547580]; Office of
   Science {[}17-SC-20-SC]; Open Force Field Consortium},
Funding-Text = {Division of Advanced Cyberinfrastructure, Grant/Award Numbers: 1449723,
   1547580; Office of Science, Grant/Award Number: 17-SC-20-SC; Open Force
   Field Consortium},
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Number-of-Cited-References = {72},
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Usage-Count-Last-180-days = {1},
Usage-Count-Since-2013 = {22},
Journal-ISO = {Wiley Interdiscip. Rev.-Comput. Mol. Sci.},
Doc-Delivery-Number = {QL6HO},
Web-of-Science-Index = {Science Citation Index Expanded (SCI-EXPANDED)},
Unique-ID = {WOS:000559363400001},
OA = {Green Submitted},
DA = {2023-04-22},
}

@article{ WOS:000534727600001,
Author = {Outeiral, Carlos and Strahm, Martin and Shi, Jiye and Morris, Garrett M.
   and Benjamin, Simon C. and Deane, Charlotte M.},
Title = {The prospects of quantum computing in computational molecular biology},
Journal = {WILEY INTERDISCIPLINARY REVIEWS-COMPUTATIONAL MOLECULAR SCIENCE},
Year = {2021},
Volume = {11},
Number = {1},
Month = {JAN},
Abstract = {Quantum computers can in principle solve certain problems exponentially
   more quickly than their classical counterparts. We have not yet reached
   the advent of useful quantum computation, but when we do, it will affect
   nearly all scientific disciplines. In this review, we examine how
   current quantum algorithms could revolutionize computational biology and
   bioinformatics. There are potential benefits across the entire field,
   from the ability to process vast amounts of information and run machine
   learning algorithms far more efficiently, to algorithms for quantum
   simulation that are poised to improve computational calculations in drug
   discovery, to quantum algorithms for optimization that may advance
   fields from protein structure prediction to network analysis. However,
   these exciting prospects are susceptible to ``hype,{''} and it is also
   important to recognize the caveats and challenges in this new
   technology. Our aim is to introduce the promise and limitations of
   emerging quantum computing technologies in the areas of computational
   molecular biology and bioinformatics.
   This article is categorized under:
   Structure and Mechanism > Computational Biochemistry and Biophysics
   Data Science > Computer Algorithms and Programming
   Electronic Structure Theory > Ab Initio Electronic Structure Methods},
Publisher = {WILEY},
Address = {111 RIVER ST, HOBOKEN 07030-5774, NJ USA},
Type = {Review},
Language = {English},
Affiliation = {Deane, CM (Corresponding Author), Univ Oxford, Dept Stat, 24-29 St Giles, Oxford OX1 3LB, England.
   Outeiral, Carlos; Morris, Garrett M.; Deane, Charlotte M., Univ Oxford, Dept Stat, 24-29 St Giles, Oxford OX1 3LB, England.
   Outeiral, Carlos; Benjamin, Simon C., Univ Oxford, Dept Mat, Oxford, England.
   Strahm, Martin, F Hoffmann La Roche, Pharma Res \& Early Dev, Basel, Switzerland.
   Shi, Jiye, UCB Pharma, Comp Aided Drug Design, Slough, Berks, England.},
DOI = {10.1002/wcms.1481},
EarlyAccessDate = {MAY 2020},
Article-Number = {e1481},
ISSN = {1759-0876},
EISSN = {1759-0884},
Keywords = {ab initio simulations; machine learning; optimization; protein folding;
   quantum computing},
Keywords-Plus = {PROTEIN-STRUCTURE PREDICTION; HP MODEL; SIMULATION; ALGORITHM; DESIGN;
   APPROXIMATION; INFORMATION; GENERATION; CHEMISTRY; ALIGNMENT},
Research-Areas = {Chemistry; Mathematical \& Computational Biology},
Web-of-Science-Categories  = {Chemistry, Multidisciplinary; Mathematical \& Computational Biology},
Author-Email = {deane@stats.ox.ac.uk},
Affiliations = {University of Oxford; University of Oxford; Roche Holding; UCB Pharma SA},
ResearcherID-Numbers = {Morris, Garrett Matthew/F-2465-2019
   Shi, Jiye/G-2745-2012
   Benjamin, Simon/A-8673-2008
   Outeiral Rubiera, Carlos/L-3025-2018},
ORCID-Numbers = {Morris, Garrett Matthew/0000-0003-1731-8405
   Shi, Jiye/0000-0002-9628-8680
   Deane, Charlotte/0000-0003-1388-2252
   Benjamin, Simon/0000-0002-7766-5348
   Outeiral Rubiera, Carlos/0000-0003-1408-5554},
Funding-Acknowledgement = {Engineering and Physical Sciences Research Council {[}EP/L016044/1,
   EP/M013243/1, EP/S024093/1, EP/T001062/1]; EPSRC {[}EP/T001062/1,
   EP/M013243/1] Funding Source: UKRI},
Funding-Text = {Engineering and Physical Sciences Research Council, Grant/Award Numbers:
   EP/L016044/1, EP/M013243/1, EP/S024093/1, EP/T001062/1},
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Times-Cited = {48},
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@article{ WOS:000945122100001,
Author = {Guarda, Fernando G. K. and Hammerschmitt, Bruno K. and Capeletti,
   Marcelo B. and Neto, Nelson K. and dos Santos, Laura L. C. and Prade,
   Lucio R. and Abaide, Alzenira},
Title = {Non-Hardware-Based Non-Technical Losses Detection Methods: A Review},
Journal = {ENERGIES},
Year = {2023},
Volume = {16},
Number = {4},
Month = {FEB},
Abstract = {Non-Technical Losses (NTL) represent a serious concern for electric
   companies. These losses are responsible for revenue losses, as well as
   reduced system reliability. Part of the revenue loss is charged to legal
   consumers, thus, causing social imbalance. NTL methods have been
   developed in order to reduce the impact in physical distribution systems
   and legal consumers. These methods can be classified as hardware-based
   and non-hardware-based. Hardware-based methods need an entirely new
   system infrastructure to be implemented, resulting in high investment
   and increased cost for energy companies, thus hampering implementation
   in poorer nations. With this in mind, this paper performs a review of
   non-hardware-based NTL detection methods. These methods use distribution
   systems and consumers' data to detect abnormal energy consumption. They
   can be classified as network-based, which use network technical
   parameters to search for energy losses, data-based methods, which use
   data science and machine learning, and hybrid methods, which combine
   both. This paper focuses on reviewing non-hardware-based NTL detection
   methods, presenting a NTL detection methods overview and a literature
   search and analysis.},
Publisher = {MDPI},
Address = {ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND},
Type = {Review},
Language = {English},
Affiliation = {Neto, NK (Corresponding Author), Univ Fed Santa Maria, Acad Coordinat, BR-96503205 Cachoeira do Sul, Brazil.
   Guarda, Fernando G. K.; Abaide, Alzenira, Univ Fed Santa Maria, Santa Maria Tech \& Ind Sch, BR-97105900 Santa Maria, Brazil.
   Hammerschmitt, Bruno K.; Capeletti, Marcelo B., Univ Fed Santa Maria, Grad Program Elect Engn, BR-97105900 Santa Maria, Brazil.
   Neto, Nelson K.; dos Santos, Laura L. C., Univ Fed Santa Maria, Acad Coordinat, BR-96503205 Cachoeira do Sul, Brazil.
   Prade, Lucio R., Univ Vale Sinos, Polytech Sch, BR-93022750 Sao Leopoldo, Brazil.},
DOI = {10.3390/en16042054},
Article-Number = {2054},
EISSN = {1996-1073},
Keywords = {Non-Technical Losses; machine learning; non-hardware-based methods;
   distribution systems; artificial intelligence},
Keywords-Plus = {ENERGY THEFT; POWER DISTRIBUTION; ELECTRICITY THEFT; SMART GRIDS;
   ALGORITHM; METERS},
Research-Areas = {Energy \& Fuels},
Web-of-Science-Categories  = {Energy \& Fuels},
Author-Email = {nelson.knak@ufsm.br},
Affiliations = {Universidade Federal de Santa Maria (UFSM); Universidade Federal de
   Santa Maria (UFSM); Universidade Federal de Santa Maria (UFSM)},
Funding-Acknowledgement = {State Electric Energy Company; Equatorial Energia Group (ANEEL R\&D
   Program through the CEEE/EQUATORIAL/UFSM) {[}5000003849]; National
   Institute of Science and Technology in Distributed Generation Systems
   (INCTGD); National Council for Scientific and Technological Development
   (CNPq) {[}465640/2014-1]; Coordination for the Improvement of Higher
   Education Personnel (CAPES) {[}23038.000776/2017-54]; Research Support
   Foundation of the State of Rio Grande do South (FAPERGS)
   {[}17/2551-0000517-1]; Federal University of Santa Maria (UFSM)},
Funding-Text = {This research was funded by the State Electric Energy Company and the
   Equatorial Energia Group (ANEEL R\&D Program through the
   CEEE/EQUATORIAL/UFSM project no. 5000003849), National Institute of
   Science and Technology in Distributed Generation Systems (INCTGD),
   National Council for Scientific and Technological Development (CNPq-no.
   465640/2014-1), Coordination for the Improvement of Higher Education
   Personnel (CAPES-no. 23038.000776/2017-54), Research Support Foundation
   of the State of Rio Grande do South (FAPERGS-no. 17/2551-0000517-1) and
   Federal University of Santa Maria (UFSM), Brazilian Institutions. The
   APC was funded by State Electric Energy Company and the Equatorial
   Energia Group (ANEEL R\&D Program through the CEEE/EQUATORIAL/UFSM
   project no. 5000003849).},
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Number-of-Cited-References = {68},
Times-Cited = {0},
Usage-Count-Last-180-days = {0},
Usage-Count-Since-2013 = {0},
Journal-ISO = {Energies},
Doc-Delivery-Number = {9Q7EE},
Web-of-Science-Index = {Science Citation Index Expanded (SCI-EXPANDED)},
Unique-ID = {WOS:000945122100001},
OA = {gold},
DA = {2023-04-22},
}

@article{ WOS:000728484000001,
Author = {Lee, Stuart and Cook, Dianne and da Silva, Natalia and Laa, Ursula and
   Spyrison, Nicholas and Wang, Earo and Zhang, H. Sherry},
Title = {The state-of-the-art on tours for dynamic visualization of
   high-dimensional data},
Journal = {WILEY INTERDISCIPLINARY REVIEWS-COMPUTATIONAL STATISTICS},
Year = {2022},
Volume = {14},
Number = {4},
Month = {JUL},
Abstract = {This article discusses a high-dimensional visualization technique called
   the tour, which can be used to view data in more than three dimensions.
   We review the theory and history behind the technique, as well as modern
   software developments and applications of the tour that are being found
   across the sciences and machine learning. This article is categorized
   under: Statistical and Graphical Methods of Data Analysis > Analysis of
   High Dimensional Data Statistical and Graphical Methods of Data Analysis
   > Statistical Graphics and Visualization Statistical Learning and
   Exploratory Methods of the Data Sciences > Exploratory Data Analysis},
Publisher = {WILEY},
Address = {111 RIVER ST, HOBOKEN 07030-5774, NJ USA},
Type = {Review},
Language = {English},
Affiliation = {Cook, D (Corresponding Author), Monash Univ, Dept Econometr \& Business Stat, Melbourne, Vic, Australia.
   Lee, Stuart; Cook, Dianne; Zhang, H. Sherry, Monash Univ, Dept Econometr \& Business Stat, Melbourne, Vic, Australia.
   da Silva, Natalia, Univ Republica, Inst Estadist IESTA, Montevideo, Uruguay.
   Laa, Ursula, Univ Nat Resources \& Life Sci, Inst Stat, Vienna, Austria.
   Spyrison, Nicholas, Monash Univ, Fac Informat Technol, Melbourne, Vic, Australia.
   Wang, Earo, Univ Auckland, Dept Stat, Auckland, New Zealand.},
DOI = {10.1002/wics.1573},
EarlyAccessDate = {DEC 2021},
Article-Number = {e1573},
ISSN = {1939-0068},
Keywords = {data science; data visualization; exploratory data analysis;
   high-dimensional data; tours},
Keywords-Plus = {PROJECTION PURSUIT; GRAND-TOUR; XGOBI; MULTIVARIATE; ALGORITHM; GGOBI},
Research-Areas = {Mathematics},
Web-of-Science-Categories  = {Statistics \& Probability},
Author-Email = {dicook@monash.edu},
Affiliations = {Monash University; Universidad de la Republica, Uruguay; University of
   Natural Resources \& Life Sciences, Vienna; Monash University;
   University of Auckland},
ORCID-Numbers = {Spyrison, Nicholas/0000-0002-8417-0212
   Lee, Stuart/0000-0003-1179-8436
   da Silva, Natalia/0000-0002-6031-7451
   Zhang, H. Sherry/0000-0002-7122-1463
   Cook, Dianne/0000-0002-3813-7155
   Laa, Ursula/0000-0002-0249-6439},
Funding-Acknowledgement = {Australian Research Council},
Funding-Text = {The authors gratefully acknowledge the support of the Australian
   Research Council. The paper was written in rmarkdown (Xie et al., 2018)
   using knitr (Xie, 2017). The source material for this paper is available
   at https://github.com/dicook/wiley-isghdd},
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Number-of-Cited-References = {79},
Times-Cited = {0},
Usage-Count-Last-180-days = {1},
Usage-Count-Since-2013 = {9},
Journal-ISO = {Wiley Interdiscip. Rev.-Comput. Stat.},
Doc-Delivery-Number = {2R9SO},
Web-of-Science-Index = {Science Citation Index Expanded (SCI-EXPANDED)},
Unique-ID = {WOS:000728484000001},
OA = {Bronze},
DA = {2023-04-22},
}

@article{ WOS:000675381100001,
Author = {Cordeiro, Joao V.},
Title = {Digital Technologies and Data Science as Health Enablers: An Outline of
   Appealing Promises and Compelling Ethical, Legal, and Social Challenges},
Journal = {FRONTIERS IN MEDICINE},
Year = {2021},
Volume = {8},
Month = {JUL 8},
Abstract = {Digital technologies and data science have laid down the promise to
   revolutionize healthcare by transforming the way health and disease are
   analyzed and managed in the future. Digital health applications in
   healthcare include telemedicine, electronic health records, wearable,
   implantable, injectable and ingestible digital medical devices, health
   mobile apps as well as the application of artificial intelligence and
   machine learning algorithms to medical and public health prognosis and
   decision-making. As is often the case with technological advancement,
   progress in digital health raises compelling ethical, legal, and social
   implications (ELSI). This article aims to succinctly map relevant ELSI
   of the digital health field. The issues of patient autonomy; assessment,
   value attribution, and validation of health innovation; equity and
   trustworthiness in healthcare; professional roles and skills and data
   protection and security are highlighted against the backdrop of the
   risks of dehumanization of care, the limitations of machine
   learning-based decision-making and, ultimately, the future contours of
   human interaction in medicine and public health. The running theme to
   this article is the underlying tension between the promises of digital
   health and its many challenges, which is heightened by the contrasting
   pace of scientific progress and the timed responses provided by law and
   ethics. Digital applications can prove to be valuable allies for human
   skills in medicine and public health. Similarly, ethics and the law can
   be interpreted and perceived as more than obstacles, but also promoters
   of fairness, inclusiveness, creativity and innovation in health.},
Publisher = {FRONTIERS MEDIA SA},
Address = {AVENUE DU TRIBUNAL FEDERAL 34, LAUSANNE, CH-1015, SWITZERLAND},
Type = {Review},
Language = {English},
Affiliation = {Cordeiro, JV (Corresponding Author), Univ Nova Lisboa, NOVA Natl Sch Publ Hlth, Publ Hlth Res Ctr, Lisbon, Portugal.
   Cordeiro, JV (Corresponding Author), Univ Nova Lisboa, Comprehens Hlth Res Ctr, Lisbon, Portugal.
   Cordeiro, JV (Corresponding Author), Ctr Interdisciplinar Ciencias Sociais, Lisbon, Portugal.
   Cordeiro, Joao V., Univ Nova Lisboa, NOVA Natl Sch Publ Hlth, Publ Hlth Res Ctr, Lisbon, Portugal.
   Cordeiro, Joao V., Univ Nova Lisboa, Comprehens Hlth Res Ctr, Lisbon, Portugal.
   Cordeiro, Joao V., Ctr Interdisciplinar Ciencias Sociais, Lisbon, Portugal.},
DOI = {10.3389/fmed.2021.647897},
Article-Number = {647897},
EISSN = {2296-858X},
Keywords = {digital health; ethics; law; artificial intelligence; telemedicine; big
   data; patient-doctor relationship},
Keywords-Plus = {ARTIFICIAL-INTELLIGENCE; BIG DATA; MEDICINE; CARE; PHYSICIAN; QUALITY;
   JUSTICE; ACCESS; ROBOT; STOP},
Research-Areas = {General \& Internal Medicine},
Web-of-Science-Categories  = {Medicine, General \& Internal},
Author-Email = {joao.cordeiro@ensp.unl.pt},
Affiliations = {Universidade Nova de Lisboa; Universidade Nova de Lisboa},
ResearcherID-Numbers = {Cordeiro, Joao/M-6122-2013},
ORCID-Numbers = {Cordeiro, Joao/0000-0003-4605-1615},
Funding-Acknowledgement = {Fundacao Ciencia e Tecnologia, IP national support through CHRC
   {[}UIDP/04923/2020]},
Funding-Text = {This present publication was funded by Fundacao Ciencia e Tecnologia, IP
   national support through CHRC (UIDP/04923/2020).},
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@article{ WOS:000897358000001,
Author = {Sethi, Yashendra and Patel, Neil and Kaka, Nirja and Desai, Ami and
   Kaiwan, Oroshay and Sheth, Mili and Sharma, Rupal and Huang, Helen and
   Chopra, Hitesh and Khandaker, Mayeen Uddin and Lashin, Maha M. A. and
   Hamd, Zuhal Y. Y. and Bin Emran, Talha},
Title = {Artificial Intelligence in Pediatric Cardiology: A Scoping Review},
Journal = {JOURNAL OF CLINICAL MEDICINE},
Year = {2022},
Volume = {11},
Number = {23},
Month = {DEC},
Abstract = {The evolution of AI and data science has aided in mechanizing several
   aspects of medical care requiring critical thinking: diagnosis, risk
   stratification, and management, thus mitigating the burden of physicians
   and reducing the likelihood of human error. AI modalities have expanded
   feet to the specialty of pediatric cardiology as well. We conducted a
   scoping review searching the Scopus, Embase, and PubMed databases
   covering the recent literature between 2002-2022. We found that the use
   of neural networks and machine learning has significantly improved the
   diagnostic value of cardiac magnetic resonance imaging, echocardiograms,
   computer tomography scans, and electrocardiographs, thus augmenting the
   clinicians' diagnostic accuracy of pediatric heart diseases. The use of
   AI-based prediction algorithms in pediatric cardiac surgeries improves
   postoperative outcomes and prognosis to a great extent. Risk
   stratification and the prediction of treatment outcomes are feasible
   using the key clinical findings of each CHD with appropriate
   computational algorithms. Notably, AI can revolutionize prenatal
   prediction as well as the diagnosis of CHD using the EMR (electronic
   medical records) data on maternal risk factors. The use of AI in the
   diagnostics, risk stratification, and management of CHD in the near
   future is a promising possibility with current advancements in machine
   learning and neural networks. However, the challenges posed by the
   dearth of appropriate algorithms and their nascent nature, limited
   physician training, fear of over-mechanization, and apprehension of
   missing the `human touch' limit the acceptability. Still, AI proposes to
   aid the clinician tomorrow with precision cardiology, paving a way for
   extremely efficient human-error-free health care.},
Publisher = {MDPI},
Address = {ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND},
Type = {Review},
Language = {English},
Affiliation = {Kaiwan, O (Corresponding Author), PearResearch, Dehra Dun 248001, India.
   Kaiwan, O (Corresponding Author), Northeast Ohio Med Univ, Dept Med, Rootstown, OH 44272 USA.
   Hamd, ZY (Corresponding Author), Princess Nourah bint Abdulrahman Univ, Coll Hlth \& Rehabil Sci, Dept Radiol Sci, PO 84428, Riyadh 11671, Saudi Arabia.
   Bin Emran, T (Corresponding Author), BGC Trust Univ Bangladesh, Dept Pharm, Chittagong 4381, Bangladesh.
   Bin Emran, T (Corresponding Author), Daffodil Int Univ, Fac Allied Hlth Sci, Dept Pharm, Dhaka 1207, Bangladesh.
   Sethi, Yashendra; Patel, Neil; Kaka, Nirja; Kaiwan, Oroshay, PearResearch, Dehra Dun 248001, India.
   Sethi, Yashendra, Govt Doon Med Coll, Dept Med, Dehra Dun 248001, India.
   Patel, Neil; Kaka, Nirja, GMERS Med Coll, Dept Med, Himmatnagar 383001, India.
   Desai, Ami, SMIMER Med Coll, Dept Med, Surat 395010, India.
   Kaiwan, Oroshay, Northeast Ohio Med Univ, Dept Med, Rootstown, OH 44272 USA.
   Sheth, Mili, GMERS Gandhinagar, Dept Med, Gandhinagar 382012, India.
   Sharma, Rupal, Govt Med Coll, Dept Med, Nagpur 440003, India.
   Huang, Helen, Royal Coll Surgeons Ireland, Fac Med \& Hlth Sci, Dublin D02Y N77, Ireland.
   Chopra, Hitesh, Chitkara Univ, Chitkara Coll Pharm, Rajpura 140401, India.
   Khandaker, Mayeen Uddin, Sunway Univ, Ctr Appl Phys \& Radiat Technol, Sch Engn \& Technol, Bandar Sunway 47500, Malaysia.
   Lashin, Maha M. A., Princess Nourah bint Abdulrahman Univ, Coll Engn, Dept Biomed Engn, PO 84428, Riyadh 11671, Saudi Arabia.
   Hamd, Zuhal Y. Y., Princess Nourah bint Abdulrahman Univ, Coll Hlth \& Rehabil Sci, Dept Radiol Sci, PO 84428, Riyadh 11671, Saudi Arabia.
   Bin Emran, Talha, BGC Trust Univ Bangladesh, Dept Pharm, Chittagong 4381, Bangladesh.
   Bin Emran, Talha, Daffodil Int Univ, Fac Allied Hlth Sci, Dept Pharm, Dhaka 1207, Bangladesh.},
DOI = {10.3390/jcm11237072},
Article-Number = {7072},
EISSN = {2077-0383},
Keywords = {artificial intelligence; pediatric cardiology; pediatric cardiac
   surgery; machine learning; congenital heart diseases},
Keywords-Plus = {HEART MURMURS; SOUND; ECHOCARDIOGRAPHY; CLASSIFICATION; NAVIGATION;
   CHILDREN; BURDEN},
Research-Areas = {General \& Internal Medicine},
Web-of-Science-Categories  = {Medicine, General \& Internal},
Author-Email = {okaiwan@neomed.edu
   zyhamd@pnu.edu.sa
   talhabmb@bgctub.ac.bd},
Affiliations = {Northeast Ohio Medical University (NEOMED); Royal College of Surgeons -
   Ireland; Chitkara University, Punjab; Sunway University; Princess Nourah
   bint Abdulrahman University; Princess Nourah bint Abdulrahman
   University; Daffodil International University},
ResearcherID-Numbers = {Emran, Talha Bin/P-9184-2016
   Huang, Helen/HTO-4807-2023
   Hamd, Zuhal/HLH-6448-2023
   Chopra, Hitesh/AAA-6925-2021
   Khandaker, Mayeen Uddin/F-5376-2011
   },
ORCID-Numbers = {Emran, Talha Bin/0000-0003-3188-2272
   Chopra, Hitesh/0000-0001-8867-7603
   Khandaker, Mayeen Uddin/0000-0003-3772-294X
   Hamd, Zuhal/0000-0003-2895-2284
   Kaka, Nirja/0000-0003-4379-9265
   Sheth, Mili/0000-0002-1946-5364
   Sethi, Yashendra/0000-0003-0345-3876},
Funding-Acknowledgement = {Princess Nourah bint Abdulrahman University},
Funding-Text = {This research was funded by the Deanship of Scientific Research at
   Princess Nourah bint Abdulrahman University through the Fast-track
   Research Funding Program.},
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Number-of-Cited-References = {112},
Times-Cited = {1},
Usage-Count-Last-180-days = {2},
Usage-Count-Since-2013 = {2},
Journal-ISO = {J. Clin. Med.},
Doc-Delivery-Number = {6Y8SL},
Web-of-Science-Index = {Science Citation Index Expanded (SCI-EXPANDED)},
Unique-ID = {WOS:000897358000001},
OA = {gold, Green Published},
DA = {2023-04-22},
}

@article{ WOS:000548338000001,
Author = {Ghahramani, Ali and Galicia, Parson and Lehrer, David and Varghese,
   Zubin and Wang, Zhe and Pandit, Yogesh},
Title = {Artificial Intelligence for Efficient Thermal Comfort Systems:
   Requirements, Current Applications and Future Directions},
Journal = {FRONTIERS IN BUILT ENVIRONMENT},
Year = {2020},
Volume = {6},
Month = {APR 28},
Abstract = {In buildings, one or a combination of systems (e.g., central HVAC
   system, ceiling fan, desk fan, personal heater, and foot warmer) are
   often responsible for providing thermal comfort to the occupants. While
   thermal comfort has been shown to differ from person to person and vary
   over time, these systems are often operated based on prefixed setpoints
   and schedule of operations or at the request/routine of each individual.
   This leads to occupants' discomfort and energy wastes. To enable the
   improvements in both comfort and energy efficiency autonomously, in this
   paper, we describe the necessity of an integrated system of sensors
   (e.g., wearable sensors/infrared sensors), infrastructure for enabling
   system interoperability, learning and control algorithms, and actuators
   (e.g., HVAC system setpoints, ceiling fans) to work under a governing
   central intelligent system. To assist readers with little to no exposure
   to artificial intelligence (AI), we describe the fundamentals of an
   intelligent entity (rational agent) and components of its
   problem-solving process (i.e., search algorithms, logic inference, and
   machine learning) and provide examples from the literature. We then
   discuss the current application of intelligent personal thermal comfort
   systems in buildings based on a comprehensive review of the literature.
   We finally describe future directions for enabling application of fully
   automated systems to provide comfort in an efficient manner. It is
   apparent that improvements in all aspects of an intelligent system are
   be needed to better ascertain the correct combination of systems to
   activate and for how long to increase the overall efficiency of the
   system and improve comfort.},
Publisher = {FRONTIERS MEDIA SA},
Address = {AVENUE DU TRIBUNAL FEDERAL 34, LAUSANNE, CH-1015, SWITZERLAND},
Type = {Review},
Language = {English},
Affiliation = {Ghahramani, A (Corresponding Author), Natl Univ Singapore, Dept Bldg, Singapore, Singapore.
   Ghahramani, A (Corresponding Author), Univ Calif Berkeley, Ctr Built Environm, Berkeley, CA 94720 USA.
   Ghahramani, Ali, Natl Univ Singapore, Dept Bldg, Singapore, Singapore.
   Ghahramani, Ali; Galicia, Parson; Lehrer, David, Univ Calif Berkeley, Ctr Built Environm, Berkeley, CA 94720 USA.
   Varghese, Zubin; Pandit, Yogesh, Ingersoll Rand Engn \& Technol Ctr, Bengaluru, India.
   Wang, Zhe, Lawrence Berkeley Natl Lab, Bldg Technol \& Urban Syst Div, Berkeley, CA USA.},
DOI = {10.3389/fbuil.2020.00049},
EISSN = {2297-3362},
Keywords = {machine learning; personal thermal comfort; data mining; human building
   interactions; buildings energy efficiency; intelligent personal comfort
   systems},
Keywords-Plus = {ENERGY-USE BEHAVIORS; OFFICE ENVIRONMENT; HVAC OPERATIONS; TEMPERATURE;
   MANAGEMENT; BUILDINGS; ADAPTATION; OCCUPANTS; BODY; SKIN},
Research-Areas = {Construction \& Building Technology; Engineering},
Web-of-Science-Categories  = {Construction \& Building Technology; Engineering, Civil},
Author-Email = {ghahramani@nus.edu.sg},
Affiliations = {National University of Singapore; University of California System;
   University of California Berkeley; United States Department of Energy
   (DOE); Lawrence Berkeley National Laboratory},
ORCID-Numbers = {Ghahramani, Ali/0000-0003-2043-7261
   Wang, Zhe/0000-0002-2231-1606},
Funding-Acknowledgement = {Ingersoll rand Inc.},
Funding-Text = {This material is based upon work supported by the Ingersoll rand Inc.
   There was no specific award number for this industry/academic effort.},
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Number-of-Cited-References = {93},
Times-Cited = {19},
Usage-Count-Last-180-days = {4},
Usage-Count-Since-2013 = {33},
Journal-ISO = {Front. Built Environ.},
Doc-Delivery-Number = {MJ8KX},
Web-of-Science-Index = {Emerging Sources Citation Index (ESCI)},
Unique-ID = {WOS:000548338000001},
OA = {gold, Green Published},
DA = {2023-04-22},
}

@article{ WOS:000811565100001,
Author = {Grzybowski, Bartosz A. and Badowski, Tomasz and Molga, Karol and
   Szymkuc, Sara},
Title = {Network search algorithms and scoring functions for advanced-level
   computerized synthesis planning},
Journal = {WILEY INTERDISCIPLINARY REVIEWS-COMPUTATIONAL MOLECULAR SCIENCE},
Year = {2023},
Volume = {13},
Number = {1},
Month = {JAN},
Abstract = {In 2020, a ``hybrid{''} expert-AI computer program called Chematica
   (a.k.a. Synthia) was shown to autonomously plan multistep syntheses of
   complex natural products, which remain outside the reach of purely
   data-driven AI programs. The ability to plan at this level of chemical
   sophistication has been attributed mainly to the superior quality of
   Chematica's reactions rules. However, rules alone are not sufficient for
   advanced synthetic planning which also requires appropriately crafted
   algorithms with which to intelligently navigate the enormous networks of
   synthetic possibilities, score the synthetic positions encountered, and
   rank the pathways identified. Chematica's algorithms are distinct from
   pret-a-porter algorithmic solutions and are product of multiple rounds
   of improvements, against target structures of increasing complexity.
   Since descriptions of these improvements have been scattered among
   several of our prior publications, the aim of the current Review is to
   narrate the development process in a more comprehensive manner. This
   article is categorized under: Data Science > Computer Algorithms and
   Programming Data Science > Artificial Intelligence/Machine Learning
   Quantum Computing > Algorithms},
Publisher = {WILEY},
Address = {111 RIVER ST, HOBOKEN 07030-5774, NJ USA},
Type = {Review},
Language = {English},
Affiliation = {Grzybowski, BA (Corresponding Author), Ulsan Natl Inst Sci \& Technol UNIST, Dept Chem, 50 UNIST Gil, Ulsan 44919, South Korea.
   Grzybowski, Bartosz A.; Badowski, Tomasz; Molga, Karol; Szymkuc, Sara, Polish Acad Sci, Inst Organ Chem, Warsaw, Poland.
   Grzybowski, Bartosz A., Inst Basic Sci IBS, Ctr Soft \& Living Matter, Ulsan, South Korea.
   Grzybowski, Bartosz A., Ulsan Natl Inst Sci \& Technol UNIST, Dept Chem, 50 UNIST Gil, Ulsan 44919, South Korea.},
DOI = {10.1002/wcms.1630},
EarlyAccessDate = {JUN 2022},
Article-Number = {e1630},
ISSN = {1759-0876},
EISSN = {1759-0884},
Keywords = {artificial intelligence; Chematica; expert systems; networks; synthesis},
Keywords-Plus = {MACHINE; DESIGN; DISCOVERY; PATHWAYS},
Research-Areas = {Chemistry; Mathematical \& Computational Biology},
Web-of-Science-Categories  = {Chemistry, Multidisciplinary; Mathematical \& Computational Biology},
Author-Email = {nanogrzybowski@gmail.com},
Affiliations = {Polish Academy of Sciences; Institute of Organic Chemistry of the Polish
   Academy of Sciences; Institute for Basic Science - Korea (IBS); Ulsan
   National Institute of Science \& Technology (UNIST)},
Funding-Acknowledgement = {US DARPA under the Make-It Award {[}69461-CH-DRP \#W911NF1610384];
   Institute for Basic Science Korea {[}IBSR020-D1]},
Funding-Text = {Development of Chematica was partly supported by US DARPA under the
   Make-It Award, 69461-CH-DRP \#W911NF1610384. B.A.G. acknowledges
   personal support from the Institute for Basic Science Korea, project
   code IBSR020-D1.},
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Number-of-Cited-References = {67},
Times-Cited = {2},
Usage-Count-Last-180-days = {9},
Usage-Count-Since-2013 = {15},
Journal-ISO = {Wiley Interdiscip. Rev.-Comput. Mol. Sci.},
Doc-Delivery-Number = {8A9TB},
Web-of-Science-Index = {Science Citation Index Expanded (SCI-EXPANDED)},
Unique-ID = {WOS:000811565100001},
OA = {Green Published},
DA = {2023-04-22},
}

@article{ WOS:000544821500001,
Author = {Wei, Jingwei and Jiang, Hanyu and Gu, Dongsheng and Niu, Meng and Fu,
   Fangfang and Han, Yuqi and Song, Bin and Tian, Jie},
Title = {Radiomics in liver diseases: Current progress and future opportunities},
Journal = {LIVER INTERNATIONAL},
Year = {2020},
Volume = {40},
Number = {9},
Pages = {2050-2063},
Month = {SEP},
Abstract = {Liver diseases, a wide spectrum of pathologies from inflammation to
   neoplasm, have become an increasingly significant health problem
   worldwide. Noninvasive imaging plays a critical role in the clinical
   workflow of liver diseases, but conventional imaging assessment may
   provide limited information. Accurate detection, characterization and
   monitoring remain challenging. With progress in quantitative imaging
   analysis techniques, radiomics emerged as an efficient tool that shows
   promise to aid in personalized diagnosis and treatment decision-making.
   Radiomics could reflect the heterogeneity of liver lesions via
   extracting high-throughput and high-dimensional features from
   multi-modality imaging. Machine learning algorithms are then used to
   construct clinical target-oriented imaging biomarkers to assist disease
   management. Here, we review the methodological process in liver disease
   radiomics studies in a stepwise fashion from data acquisition and
   curation, region of interest segmentation, liver-specific feature
   extraction, to task-oriented modelling. Furthermore, the applications of
   radiomics in liver diseases are outlined in aspects of diagnosis and
   staging, evaluation of liver tumour biological behaviours, and prognosis
   according to different disease type. Finally, we discuss the current
   limitations of radiomics in liver disease studies and explore its future
   opportunities.},
Publisher = {WILEY},
Address = {111 RIVER ST, HOBOKEN 07030-5774, NJ USA},
Type = {Review},
Language = {English},
Affiliation = {Tian, J (Corresponding Author), Chinese Acad Sci, Inst Automat, Key Lab Mol Imaging, Beijing 100190, Peoples R China.
   Wei, Jingwei; Gu, Dongsheng; Han, Yuqi; Tian, Jie, Chinese Acad Sci, Inst Automat, Key Lab Mol Imaging, Beijing 100190, Peoples R China.
   Wei, Jingwei; Gu, Dongsheng; Han, Yuqi; Tian, Jie, Beijing Key Lab Mol Imaging, Beijing, Peoples R China.
   Jiang, Hanyu; Song, Bin, Sichuan Univ, West China Hosp, Dept Radiol, Chengdu, Peoples R China.
   Niu, Meng, China Med Univ, Affiliated Hosp 1, Dept Intervent Radiol, Shenyang, Peoples R China.
   Fu, Fangfang, Henan Prov Peoples Hosp, Dept Med Imaging, Zhengzhou, Henan, Peoples R China.
   Fu, Fangfang, Zhengzhou Univ, Peoples Hosp, Dept Med Imaging, Zhengzhou, Henan, Peoples R China.
   Tian, Jie, Beihang Univ, Sch Med, Beijing Adv Innovat Ctr Big Data Based Precis Med, Beijing, Peoples R China.
   Tian, Jie, Xidian Univ, Sch Life Sci \& Technol, Minist Educ, Engn Res Ctr Mol \& Neuro Imaging, Xian, Shaanxi, Peoples R China.},
DOI = {10.1111/liv.14555},
EarlyAccessDate = {JUL 2020},
ISSN = {1478-3223},
EISSN = {1478-3231},
Keywords = {data science; liver diseases; machine learning; precision medicine;
   radiologic technology},
Keywords-Plus = {HEPATOCELLULAR-CARCINOMA; PREOPERATIVE PREDICTION; TEXTURE ANALYSIS;
   EARLY RECURRENCE; MICROVASCULAR INVASION; COMPUTED-TOMOGRAPHY; CT;
   NOMOGRAM; FEATURES; MRI},
Research-Areas = {Gastroenterology \& Hepatology},
Web-of-Science-Categories  = {Gastroenterology \& Hepatology},
Author-Email = {tian@ieee.org},
Affiliations = {Chinese Academy of Sciences; Institute of Automation, CAS; Sichuan
   University; China Medical University; Zhengzhou University; Zhengzhou
   University; Beihang University; Xidian University},
ResearcherID-Numbers = {Song, Bin/AAD-3670-2020
   wei, jing/GXA-1455-2022
   Han, Yu/GZA-9220-2022
   },
ORCID-Numbers = {Jiang, Hanyu/0000-0002-7726-1618
   Song, Bin/0000-0002-7269-2101},
Funding-Acknowledgement = {Beijing Municipal Science \& Technology Commission {[}171100000117023,
   Z161100002616022]; National Natural Science Foundation of China
   {[}881930053, 1227901, 81527805]; Ministry of Science and Technology of
   China {[}2017YFA0205200]; Chinese Academy of Sciences {[}GJJSTD20170004,
   QYZDJ-SSW-JSC005]},
Funding-Text = {Beijing Municipal Science \& Technology Commission, Grant/Award Number:
   171100000117023 and Z161100002616022; National Natural Science
   Foundation of China, Grant/Award Number: 881930053, 1227901 and
   81527805; Ministry of Science and Technology of China, Grant/Award
   Number: 2017YFA0205200; Chinese Academy of Sciences, Grant/Award Number:
   GJJSTD20170004 and QYZDJ-SSW-JSC005},
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Number-of-Cited-References = {105},
Times-Cited = {40},
Usage-Count-Last-180-days = {8},
Usage-Count-Since-2013 = {28},
Journal-ISO = {Liver Int.},
Doc-Delivery-Number = {NG4WC},
Web-of-Science-Index = {Science Citation Index Expanded (SCI-EXPANDED)},
Unique-ID = {WOS:000544821500001},
OA = {Green Published},
DA = {2023-04-22},
}

@article{ WOS:000601904100001,
Author = {Tonkovic, Petar and Kalajdziski, Slobodan and Zdravevski, Eftim and
   Lameski, Petre and Corizzo, Roberto and Pires, Ivan Miguel and Garcia,
   Nuno M. and Loncar-Turukalo, Tatjana and Trajkovik, Vladimir},
Title = {Literature on Applied Machine Learning in Metagenomic Classification: A
   Scoping Review},
Journal = {BIOLOGY-BASEL},
Year = {2020},
Volume = {9},
Number = {12},
Month = {DEC},
Abstract = {Simple Summary
   Technological advancements have led to modern DNA sequencing methods,
   capable of generating large amounts of data describing the
   microorganisms that live in samples taken from the environment.
   Metagenomics, the field that studies the different genomes within these
   samples, is becoming increasingly popular, as it has many real-world
   applications, such as the discovery of new antibiotics, personalized
   medicine, forensics, and many more. From a computer science point of
   view, it is interesting to see how these large volumes of data can be
   processed efficiently to accurately identify (classify) the
   microorganisms from the input DNA data. This scoping review aims to give
   an insight into the existing state of the art computational methods for
   processing metagenomic data through the prism of machine learning, data
   science, and big data. We provide an overview of the state of the art
   metagenomic classification methods, as well as the challenges
   researchers face when tackling this complex problem. The end goal of
   this review is to help researchers be up to date with current trends, as
   well as identify opportunities for further research and improvements.
   Applied machine learning in bioinformatics is growing as computer
   science slowly invades all research spheres. With the arrival of modern
   next-generation DNA sequencing algorithms, metagenomics is becoming an
   increasingly interesting research field as it finds countless practical
   applications exploiting the vast amounts of generated data. This study
   aims to scope the scientific literature in the field of metagenomic
   classification in the time interval 2008-2019 and provide an
   evolutionary timeline of data processing and machine learning in this
   field. This study follows the scoping review methodology and PRISMA
   guidelines to identify and process the available literature. Natural
   Language Processing (NLP) is deployed to ensure efficient and exhaustive
   search of the literary corpus of three large digital libraries: IEEE,
   PubMed, and Springer. The search is based on keywords and properties
   looked up using the digital libraries' search engines. The scoping
   review results reveal an increasing number of research papers related to
   metagenomic classification over the past decade. The research is mainly
   focused on metagenomic classifiers, identifying scope specific metrics
   for model evaluation, data set sanitization, and dimensionality
   reduction. Out of all of these subproblems, data preprocessing is the
   least researched with considerable potential for improvement.},
Publisher = {MDPI},
Address = {ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND},
Type = {Review},
Language = {English},
Affiliation = {Tonkovic, P (Corresponding Author), St Cyril \& Methodius Univ, Fac Comp Sci \& Engn, Skopje 1000, North Macedonia.
   Tonkovic, Petar; Kalajdziski, Slobodan; Zdravevski, Eftim; Lameski, Petre; Trajkovik, Vladimir, St Cyril \& Methodius Univ, Fac Comp Sci \& Engn, Skopje 1000, North Macedonia.
   Corizzo, Roberto, Amer Univ, Dept Comp Sci, Washington, DC 20016 USA.
   Pires, Ivan Miguel; Garcia, Nuno M., Univ Beira Interior, Inst Telecomunicacoes, P-6200001 Covilha, Portugal.
   Pires, Ivan Miguel, Polytech Inst Viseu, Comp Sci Dept, P-3504510 Viseu, Portugal.
   Pires, Ivan Miguel, Polytech Inst Viseu, Sch Hlth, Hlth Sci Res Unit Nursing, P-3504510 Viseu, Portugal.
   Loncar-Turukalo, Tatjana, Univ Novi Sad, Fac Tech Sci, Novi Sad 21102, Serbia.},
DOI = {10.3390/biology9120453},
Article-Number = {453},
EISSN = {2079-7737},
Keywords = {metagenomics; scoping review; classification; data preprocessing},
Research-Areas = {Life Sciences \& Biomedicine - Other Topics},
Web-of-Science-Categories  = {Biology},
Author-Email = {petar.tonkovikj@students.finki.ukim.mk
   slobodan.kalajdziski@finki.ukim.mk
   eftim.zdravevski@finki.ukim.mk
   petre.lameski@finki.ukim.mk
   rcorizzo@american.edu
   impires@it.ubi.pt
   ngarcia@di.ubi.pt
   turukalo@uns.ac.rs
   vladimir.trajkovik@finki.ukim.mk},
Affiliations = {Saints Cyril \& Methodius University of Skopje; American University;
   Universidade da Beira Interior; Instituto Politecnico de Viseu;
   Instituto Politecnico de Viseu; University of Novi Sad},
ResearcherID-Numbers = {Zdravevski, Eftim/K-5276-2014
   Pires, Ivan/V-3573-2017
   Lameski, Petre/P-8389-2014
   },
ORCID-Numbers = {Zdravevski, Eftim/0000-0001-7664-0168
   Pires, Ivan/0000-0002-3394-6762
   Garcia, Nuno/0000-0002-3195-3168
   Kalajdziski, Slobodan/0000-0003-3373-8637
   Corizzo, Roberto/0000-0001-8366-6059
   TONKOVIC, PETAR/0000-0002-4593-219X
   Loncar-Turukalo, Tatjana/0000-0002-3582-8073
   Trajkovik, Vladimir/0000-0001-8103-8059},
Funding-Acknowledgement = {FCT/MEC; FEDER-PT2020 partnership agreement {[}UIDB/50008/2020];
   National Funds through the FCT-Foundation for Science and Technology,
   I.P. {[}UIDB/00742/2020]},
Funding-Text = {This work was partially funded by FCT/MEC through national funds and
   co-funded by the FEDER-PT2020 partnership agreement under the project
   UIDB/50008/2020 (Este trabalho e financiado pela FCT/MEC atraves de
   fundos nacionais e cofinanciado pelo FEDER, no ambito do Acordo de
   Parceria PT2020 no ambito do projeto UIDB/50008/2020). This work was
   partially funded by National Funds through the FCT-Foundation for
   Science and Technology, I.P., within the scope of the project
   UIDB/00742/2020.},
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Number-of-Cited-References = {74},
Times-Cited = {9},
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Journal-ISO = {Biology-Basel},
Doc-Delivery-Number = {PJ6WB},
Web-of-Science-Index = {Science Citation Index Expanded (SCI-EXPANDED)},
Unique-ID = {WOS:000601904100001},
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@article{ WOS:000711970600033,
Author = {Rabbani, A. and Fernando, A. M. and Shams, R. and Singh, A. and
   Mostaghimi, P. and Babaei, M.},
Title = {Review of Data Science Trends and Issues in Porous Media Research With a
   Focus on Image-Based Techniques},
Journal = {WATER RESOURCES RESEARCH},
Year = {2021},
Volume = {57},
Number = {10},
Month = {OCT},
Abstract = {Data science as a flourishing interdisciplinary domain of computer and
   mathematical sciences is playing an important role in guiding the porous
   material research streams. In the present narrative review, we have
   examined recent trends and issues in data-driven methods used in the
   image-based porous material research studies relevant to water resources
   researchers and scientists. Initially, the recent trends in porous
   material data-related issues have been investigated through search
   engine queries in terms of data source, data storage hub, programing
   languages, and software packages. Subsequent to a diligent analysis of
   the existing trends, a review of the common concepts of porous material
   research and data science are presented through six categories
   comprising big data, data regression, classification, image
   segmentation, geometry reconstruction, and image data resolution. We
   provide: (a) a focus on image-based and pore scale methods which has not
   been presented previously, (b) a detailed search engine research for
   trend investigation, and (c) practical examples and comparison of data
   storage in porous media image-based research. By reading this review
   article, an overall image of the active and popular interdisciplinary
   research domains can be obtained. Readers will also be informed of the
   latest data-driven efforts and recommended research directions for
   tackling the image-based porous material problems relevant to water
   resources research. We concluded that porous material image
   reconstruction and resolution improvement techniques are unique means to
   reveal unprecedented details of micro-structures that may have been
   missed in a medium quality tomography image.},
Publisher = {AMER GEOPHYSICAL UNION},
Address = {2000 FLORIDA AVE NW, WASHINGTON, DC 20009 USA},
Type = {Review},
Language = {English},
Affiliation = {Babaei, M (Corresponding Author), Univ Manchester, Dept Chem Engn \& Analyt Sci, Manchester, Lancs, England.
   Rabbani, A.; Babaei, M., Univ Manchester, Dept Chem Engn \& Analyt Sci, Manchester, Lancs, England.
   Fernando, A. M., Monash Univ, Sch Engn, Discipline Chem Engn, Subang Jaya, Selangor, Malaysia.
   Shams, R., Sharif Univ Technol, Chem \& Petr Engn Dept, Tehran, Iran.
   Singh, A.; Mostaghimi, P., Univ New South Wales, Sch Minerals \& Energy Resources Engn, Sydney, NSW, Australia.},
DOI = {10.1029/2020WR029472},
Article-Number = {e2020WR029472},
ISSN = {0043-1397},
EISSN = {1944-7973},
Keywords = {data science; porous materials; machine learning; neural networks},
Keywords-Plus = {ARTIFICIAL NEURAL-NETWORK; PORE-SPACE RECONSTRUCTION; SUPPORT-VECTOR
   REGRESSION; RAY COMPUTED-TOMOGRAPHY; 2-PHASE FLOW; UNCERTAINTY
   QUANTIFICATION; HETEROGENEOUS RESERVOIR; PERMEABILITY PREDICTION;
   DIFFUSION-COEFFICIENT; EFFICIENT ALGORITHMS},
Research-Areas = {Environmental Sciences \& Ecology; Marine \& Freshwater Biology; Water
   Resources},
Web-of-Science-Categories  = {Environmental Sciences; Limnology; Water Resources},
Author-Email = {masoud.babaei@manchester.ac.uk},
Affiliations = {University of Manchester; Monash University; Monash University Sunway;
   Sharif University of Technology; University of New South Wales Sydney},
ResearcherID-Numbers = {Fernando, Ashane/I-1691-2019
   Shams, Reza/V-2230-2018
   },
ORCID-Numbers = {Fernando, Ashane/0000-0002-4659-7207
   Shams, Reza/0000-0003-1565-806X
   Singh, Ankita/0000-0003-2768-5559
   Babaei, Masoud/0000-0002-4201-3489
   Rabbani, Arash/0000-0001-5181-7318},
Funding-Acknowledgement = {University of Manchester; Royal Society
   {[}IEC\textbackslash{}NSFC\textbackslash{}170002]},
Funding-Text = {We thank the University of Manchester for the President's Doctoral
   Scholarship Award 2018 granted to Arash Rabbani to carry out part of
   this research. Also Masoud Babaei acknowledges Royal Society grant no.
   IEC\textbackslash{}NSFC\textbackslash{}170002 for partially supporting
   this research.},
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@article{ WOS:000592240100001,
Author = {Ward, Owen G. and Huang, Zhen and Davison, Andrew and Zheng, Tian},
Title = {Next waves in veridical network embedding},
Journal = {STATISTICAL ANALYSIS AND DATA MINING},
Year = {2021},
Volume = {14},
Number = {1},
Pages = {5-17},
Month = {FEB},
Abstract = {Embedding nodes of a large network into a metric (e.g., Euclidean) space
   has become an area of active research in statistical machine learning,
   which has found applications in natural and social sciences. Generally,
   a representation of a network object is learned in a Euclidean geometry
   and is then used for subsequent tasks regarding the nodes and/or edges
   of the network, such as community detection, node classification and
   link prediction. Network embedding algorithms have been proposed in
   multiple disciplines, often with domain-specific notations and details.
   In addition, different measures and tools have been adopted to evaluate
   and compare the methods proposed under different settings, often
   dependent of the downstream tasks. As a result, it is challenging to
   study these algorithms in the literature systematically. Motivated by
   the recently proposed PCS framework for Veridical Data Science, we
   propose a framework for network embedding algorithms and discuss how the
   principles of predictability, computability, and stability (PCS) apply
   in this context. The utilization of this framework in network embedding
   holds the potential to motivate and point to new directions for future
   research.},
Publisher = {WILEY},
Address = {111 RIVER ST, HOBOKEN 07030-5774, NJ USA},
Type = {Review},
Language = {English},
Affiliation = {Zheng, T (Corresponding Author), Columbia Univ, Dept Stat, New York, NY 10027 USA.
   Ward, Owen G.; Huang, Zhen; Davison, Andrew; Zheng, Tian, Columbia Univ, Dept Stat, New York, NY 10027 USA.
   Zheng, Tian, Columbia Univ, Data Sci Inst, New York, NY USA.},
DOI = {10.1002/sam.11486},
EarlyAccessDate = {NOV 2020},
ISSN = {1932-1864},
EISSN = {1932-1872},
Keywords = {feature engineering; latent variable models; network embedding;
   representational learning; veridical data science},
Keywords-Plus = {PREDICTION; MODELS; CONSISTENCY; INFERENCE; GRAPHS},
Research-Areas = {Computer Science; Mathematics},
Web-of-Science-Categories  = {Computer Science, Artificial Intelligence; Computer Science,
   Interdisciplinary Applications; Statistics \& Probability},
Author-Email = {tian.zheng@columbia.edu},
Affiliations = {Columbia University; Columbia University},
ORCID-Numbers = {Zheng, Tian/0000-0003-4889-0391
   Ward, Owen G./0000-0002-9678-3542},
Funding-Acknowledgement = {National Science Foundation Division of Computing and Communication
   Foundations {[}1740833]; Division of Information and Intelligent Systems
   {[}1741191]},
Funding-Text = {National Science Foundation Division of Computing and Communication
   Foundations, Grant/Award Number: 1740833; Division of Information and
   Intelligent Systems, Grant/Award Number: 1741191},
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Number-of-Cited-References = {57},
Times-Cited = {1},
Usage-Count-Last-180-days = {0},
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Web-of-Science-Index = {Science Citation Index Expanded (SCI-EXPANDED)},
Unique-ID = {WOS:000592240100001},
OA = {Green Submitted, Bronze},
DA = {2023-04-22},
}

@article{ WOS:000659467900001,
Author = {Scala, Enrico and Vallati, Mauro},
Title = {Effective grounding for hybrid planning problems represented in PDDL},
Journal = {KNOWLEDGE ENGINEERING REVIEW},
Year = {2021},
Volume = {36},
Month = {JUN 10},
Abstract = {Automated planning is the field of Artificial Intelligence (AI) that
   focuses on identifying sequences of actions allowing to reach a goal
   state from a given initial state. The need of using such techniques in
   real-world applications has brought popular languages for expressing
   automated planning problems to provide direct support for continuous and
   discrete state variables, along with changes that can be either
   instantaneous or durative. PDDL+ (Planning Domain Definition Language +)
   models support the encoding of such representations, but the resulting
   planning problems are notoriously difficult for AI planners to cope with
   due to non-linear dependencies arising from the variables and infinite
   search spaces. This difficulty is exacerbated by the potentially huge
   fully ground representations used by modern planners in order to
   effectively explore the search space, which can make some problems
   impossible to tackle.
   This paper investigates two grounding techniques for PDDL+ problems,
   both aimed at reducing the size of the full ground representation by
   reasoning over the lifted, more abstract problem structure. The first
   method extends the simple mechanism of invariant analysis to limit the
   groundings of operators upfront. The second method proposes to tackle
   the grounding process through a PDDL+ to classical planning abstraction;
   this allows us to leverage the amount of research done in the classical
   planning area. Our empirical analysis studies the effect of these novel
   approaches over both real-world hybrid applications and synthetic PDDL+
   problems took from standard benchmarks of the planning community; our
   results reveal that not only the techniques improve the running time of
   previous grounding mechanisms but also let the planner extend the reach
   to problems that were not solvable before.},
Publisher = {CAMBRIDGE UNIV PRESS},
Address = {32 AVENUE OF THE AMERICAS, NEW YORK, NY 10013-2473 USA},
Type = {Review},
Language = {English},
Affiliation = {Scala, E (Corresponding Author), Univ Brescia, Dept Informat Engn, Via Branze 38, I-25123 Brescia, Italy.
   Scala, Enrico, Univ Brescia, Dept Informat Engn, Via Branze 38, I-25123 Brescia, Italy.
   Vallati, Mauro, Univ Huddersfield, Sch Comp \& Engn, Huddersfield HD1 3DH, W Yorkshire, England.},
DOI = {10.1017/S0269888921000072},
Article-Number = {e9},
ISSN = {0269-8889},
EISSN = {1469-8005},
Keywords-Plus = {URBAN TRAFFIC MANAGEMENT; COMPETITION; DOMAINS},
Research-Areas = {Computer Science},
Web-of-Science-Categories  = {Computer Science, Artificial Intelligence},
Author-Email = {enricos83@gmail.com
   m.vallati@hud.ac.uk},
Affiliations = {University of Brescia; University of Huddersfield},
ResearcherID-Numbers = {Scala, Enrico/AAF-5974-2019
   },
ORCID-Numbers = {Scala, Enrico/0000-0003-2274-875X
   Vallati, Mauro/0000-0002-8429-3570},
Funding-Acknowledgement = {UKRI Future Leaders Fellowship {[}MR/T041196/1]; UKRI {[}MR/T041196/1]
   Funding Source: UKRI},
Funding-Text = {Mauro Vallati was supported by a UKRI Future Leaders Fellowship {[}grant
   number MR/T041196/1].},
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Number-of-Cited-References = {56},
Times-Cited = {1},
Usage-Count-Last-180-days = {2},
Usage-Count-Since-2013 = {2},
Journal-ISO = {Knowl. Eng. Rev.},
Doc-Delivery-Number = {SP1XY},
Web-of-Science-Index = {Science Citation Index Expanded (SCI-EXPANDED)},
Unique-ID = {WOS:000659467900001},
OA = {hybrid},
DA = {2023-04-22},
}

@article{ WOS:000701260600016,
Author = {Parkinson, C. and Matthams, C. and Foley, K. and Spezi, E.},
Title = {Artificial intelligence in radiation oncology: A review of its current
   status and potential application for the radiotherapy workforce},
Journal = {RADIOGRAPHY},
Year = {2021},
Volume = {27},
Number = {S1, S1},
Pages = {S63-S68},
Abstract = {Objective: Radiation oncology is a continually evolving speciality. With
   the development of new imaging modalities and advanced imaging
   processing techniques, there is an increasing amount of data available
   to practitioners. In this narrative review, Artificial Intelligence (AI)
   is used as a reference to machine learning, and its potential, along
   with current problems in the field of radiation oncology, are considered
   from a technical position.
   Key Findings: AI has the potential to harness the availability of data
   for improving patient outcomes, reducing toxicity, and easing clinical
   burdens. However, problems including the requirement of complexity of
   data, undefined core outcomes and limited generalisability are apparent.
   Conclusion: This original review highlights considerations for the
   radiotherapy workforce, particularly therapeutic radiographers, as there
   will be an increasing requirement for their familiarity with AI due to
   their unique position as the interface between imaging technology and
   patients.
   Implications for practice: Collaboration between AI experts and the
   radiotherapy workforce are required to overcome current issues before
   clinical adoption. The development of educational resources and
   standardised reporting of AI studies may help facilitate this. (C) 2021
   Published by Elsevier Ltd on behalf of The College of Radiographers.},
Publisher = {ELSEVIER SCI LTD},
Address = {THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND},
Type = {Review},
Language = {English},
Affiliation = {Parkinson, C (Corresponding Author), 10 Min Y Nant, Pencoed CF35 6YP, Wales.
   Parkinson, C.; Spezi, E., Cardiff Univ, Sch Engn, Cardiff, Wales.
   Matthams, C.; Foley, K., Velindre Canc Ctr, Cardiff, Wales.},
DOI = {10.1016/j.radi.2021.07.012},
EarlyAccessDate = {SEP 2021},
ISSN = {1078-8174},
EISSN = {1532-2831},
Keywords = {Artificial intelligence; Advanced image processing; Radiation oncology;
   Radiography; Machine learning; Data science},
Keywords-Plus = {TARGET VOLUME DELINEATION; ADAPTIVE RADIOTHERAPY; LEARNING ALGORITHM;
   ONE CYCLE; PET; SEGMENTATION; VALIDATION; TOXICITY; IMAGES; ORGANS},
Research-Areas = {Radiology, Nuclear Medicine \& Medical Imaging},
Web-of-Science-Categories  = {Radiology, Nuclear Medicine \& Medical Imaging},
Author-Email = {catherine.matthams@wales.nhs.uk},
Affiliations = {Cardiff University; Velindre Cancer Centre},
ResearcherID-Numbers = {Spezi, Emiliano/A-8917-2008
   },
ORCID-Numbers = {Spezi, Emiliano/0000-0002-1452-8813
   Foley, Kieran/0000-0002-1299-1759},
Funding-Acknowledgement = {Cancer Research Wales},
Funding-Text = {This work was funded by Cancer Research Wales.},
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Journal-ISO = {Radiography},
Doc-Delivery-Number = {UY0ZC},
Web-of-Science-Index = {Emerging Sources Citation Index (ESCI)},
Unique-ID = {WOS:000701260600016},
OA = {Green Accepted},
DA = {2023-04-22},
}

@article{ WOS:000861490200001,
Author = {Bradley, William and Kim, Jinhyeun and Kilwein, Zachary and Blakely,
   Logan and Eydenberg, Michael and Jalvin, Jordan and Laird, Carl and
   Boukouvala, Fani},
Title = {Perspectives on the integration between first-principles and data-driven
   modeling},
Journal = {COMPUTERS \& CHEMICAL ENGINEERING},
Year = {2022},
Volume = {166},
Month = {OCT},
Abstract = {Efficiently embedding and/or integrating mechanistic information with
   data-driven models is essential if it is desired to simultaneously take
   advantage of both engineering principles and data-science. The
   opportunity for hybridization occurs in many scenarios, such as the
   development of a faster model of an accurate high-fidelity computer
   model; the correction of a mechanistic model that does not fully-capture
   the physical phenomena of the system; or the integration of a
   data-driven component approximating an unknown correlation within a
   mechanistic model. At the same time, different techniques have been
   proposed and applied in different litera-tures to achieve this
   hybridization, such as hybrid modeling, physics-informed Machine
   Learning (ML) and model calibration. In this paper we review the
   methods, challenges, applications and algorithms of these three research
   areas and discuss them in the context of the different hybridization
   scenarios. Moreover, we provide a comprehensive comparison of the
   hybridization techniques with respect to their differences and
   similarities, as well as advantages and limitations and future
   perspectives. Finally, we apply and illustrate hybrid modeling,
   physics-informed ML and model calibration via a chemical reactor case
   study.},
Publisher = {PERGAMON-ELSEVIER SCIENCE LTD},
Address = {THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND},
Type = {Review},
Language = {English},
Affiliation = {Boukouvala, F (Corresponding Author), Georgia Inst Technol, Sch Chem \& Biomol Eng, Atlanta, GA 30332 USA.
   Bradley, William; Kim, Jinhyeun; Kilwein, Zachary; Boukouvala, Fani, Georgia Inst Technol, Sch Chem \& Biomol Eng, Atlanta, GA 30332 USA.
   Blakely, Logan; Eydenberg, Michael; Jalvin, Jordan; Laird, Carl, Sandia Natl Labs, Albuquerque, NM 87185 USA.},
DOI = {10.1016/j.compchemeng.2022.107898},
EarlyAccessDate = {SEP 2022},
Article-Number = {107898},
ISSN = {0098-1354},
EISSN = {1873-4375},
Keywords = {Hybrid modeling; Model calibration; Physics -informed machine learning;
   Dynamical systems; Process systems design; operations},
Keywords-Plus = {ARTIFICIAL NEURAL-NETWORKS; COMBINING 1ST PRINCIPLES; GAUSSIAN PROCESS;
   BAYESIAN CALIBRATION; VARIABLE SELECTION; HYBRID MODELS; DEVELOPMENT
   STRATEGY; CHEMICAL-PROCESSES; INVERSE PROBLEMS; CO2 ADSORPTION},
Research-Areas = {Computer Science; Engineering},
Web-of-Science-Categories  = {Computer Science, Interdisciplinary Applications; Engineering, Chemical},
Author-Email = {fani.boukouvala@chbe.gatech.edu},
Affiliations = {University System of Georgia; Georgia Institute of Technology; United
   States Department of Energy (DOE); Sandia National Laboratories},
Funding-Acknowledgement = {Georgia Tech start-up grant; RAPID/NNMI Grant {[}GR10002225]; NSF CBET
   {[}1336386, 1944678]},
Funding-Text = {The authors gratefully acknowledge funding received from RAPID/NNMI
   Grant \#GR10002225, Georgia Tech start-up grant and NSF CBET grants
   (1336386 and 1944678).},
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Number-of-Cited-References = {217},
Times-Cited = {3},
Usage-Count-Last-180-days = {25},
Usage-Count-Since-2013 = {28},
Journal-ISO = {Comput. Chem. Eng.},
Doc-Delivery-Number = {4Y4IE},
Web-of-Science-Index = {Science Citation Index Expanded (SCI-EXPANDED)},
Unique-ID = {WOS:000861490200001},
DA = {2023-04-22},
}

@article{ WOS:000793846600001,
Author = {Islam, Muhammad Nazrul and Mustafina, Sumaiya Nuha and Mahmud, Tahasin
   and Khan, Nafiz Imtiaz},
Title = {Machine learning to predict pregnancy outcomes: a systematic review,
   synthesizing framework and future research agenda},
Journal = {BMC PREGNANCY AND CHILDBIRTH},
Year = {2022},
Volume = {22},
Number = {1},
Month = {APR 22},
Abstract = {Machine Learning (ML) has been widely used in predicting the mode of
   childbirth and assessing the potential maternal risks during pregnancy.
   The primary aim of this review study is to explore current research and
   development perspectives that utilizes the ML techniques to predict the
   optimal mode of childbirth and to detect various complications during
   childbirth. A total of 26 articles (published between 2000 and 2020)
   from an initial set of 241 articles were selected and reviewed following
   a Systematic Literature Review (SLR) approach. As outcomes, this review
   study highlighted the objectives or focuses of the recent studies
   conducted on pregnancy outcomes using ML; explored the adopted ML
   algorithms along with their performances; and provided a synthesized
   view of features used, types of features, data sources and its
   characteristics. Besides, the review investigated and depicted how the
   objectives of the prior studies have changed with time being; and the
   association among the objectives of the studies, uses of algorithms, and
   the features. The study also delineated future research opportunities to
   facilitate the existing initiatives for reducing maternal complacent and
   mortality rates, such as: utilizing unsupervised and deep learning
   algorithms for prediction, revealing the unknown reasons of maternal
   complications, developing usable and useful ML-based clinical decision
   support systems to be used by the expecting mothers and health
   professionals, enhancing dataset and its accessibility, and exploring
   the potentiality of surgical robotic tools. Finally, the findings of
   this review study contributed to the development of a conceptual
   framework for advancing the ML-based maternal healthcare system. All
   together, this review will provide a state-of-the-art paradigm of
   ML-based maternal healthcare that will aid in clinical decision-making,
   anticipating pregnancy problems and delivery mode, and medical diagnosis
   and treatment.},
Publisher = {BMC},
Address = {CAMPUS, 4 CRINAN ST, LONDON N1 9XW, ENGLAND},
Type = {Review},
Language = {English},
Affiliation = {Islam, MN (Corresponding Author), Mil Inst Sci \& Technol, Dept Comp Sci \& Engn, Dhaka 1216, Bangladesh.
   Islam, Muhammad Nazrul; Mustafina, Sumaiya Nuha; Mahmud, Tahasin; Khan, Nafiz Imtiaz, Mil Inst Sci \& Technol, Dept Comp Sci \& Engn, Dhaka 1216, Bangladesh.},
DOI = {10.1186/s12884-022-04594-2},
Article-Number = {348},
EISSN = {1471-2393},
Keywords = {Machine learning; Literature review; Pregnancy; Childbirth; Data
   science; Neural network; Supervised learning},
Keywords-Plus = {CESAREAN-SECTION; PRETERM BIRTH; DELIVERY; RISK; MORTALITY},
Research-Areas = {Obstetrics \& Gynecology},
Web-of-Science-Categories  = {Obstetrics \& Gynecology},
Author-Email = {nazrul@cse.mist.ac.bd},
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Number-of-Cited-References = {67},
Times-Cited = {4},
Usage-Count-Last-180-days = {0},
Usage-Count-Since-2013 = {3},
Journal-ISO = {BMC Pregnancy Childbirth},
Doc-Delivery-Number = {1D5NH},
Web-of-Science-Index = {Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)},
Unique-ID = {WOS:000793846600001},
OA = {gold, Green Published},
DA = {2023-04-22},
}

@article{ WOS:000782571900008,
Author = {Weisser, Jana and Pohl, Teresa and Heinzinger, Michael and Ivleva,
   Natalia P. and Hofmann, Thomas and Glas, Karl},
Title = {The identification of microplastics based on vibrational spectroscopy
   data- A critical review of data analysis routines},
Journal = {TRAC-TRENDS IN ANALYTICAL CHEMISTRY},
Year = {2022},
Volume = {148},
Month = {MAR},
Abstract = {With worldwide aims to monitor microplastics (MP) in the environment,
   food and drinking water, there is a growing need for fast, reliable and
   high-throughput analysis methods. While on the instrumental side,
   spectroscopic techniques are used widely as they proved suitable for
   identifying even micron-range plastic particles, there is a gap to fill
   on the data analysis side. Vibrational spectra of MP are highly complex,
   and often, large data sets need to be evaluated. Methods range from
   classical library search to complex artificial intelligence models, each
   of which has its strengths and weaknesses. This critical review
   discusses the accuracy, robustness and expenditure of data analysis
   routines proposed for identification of MP using vibrational spectra.
   Programs provided by the scientific community dedicated to MP analysis
   are introduced. Thereby, this review aims to provide guidance for
   everyone who wants to set up or enhance a data analysis routine for
   vibrational spectra of MP.(c) 2022 Elsevier B.V. All rights reserved.},
Publisher = {ELSEVIER SCI LTD},
Address = {THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND},
Type = {Review},
Language = {English},
Affiliation = {Weisser, J (Corresponding Author), Tech Univ Munich, Chair Food Chem \& Mol Sensory Sci, Lise Meitner Str 34, D-85354 Freising Weihenstephan, Germany.
   Weisser, Jana; Pohl, Teresa; Hofmann, Thomas; Glas, Karl, Tech Univ Munich, Chair Food Chem \& Mol Sensory Sci, Lise Meitner Str 34, D-85354 Freising Weihenstephan, Germany.
   Heinzinger, Michael, Tech Univ Munich, Chair Bioinformat, Boltzmannstr 3, D-85748 Garching, Germany.
   Ivleva, Natalia P., Tech Univ Munich, Chair Analyt Chem \& Water Chem, Elisabeth Winterhalter Weg 6, D-81377 Munich, Germany.
   Pohl, Teresa, Johann-Strauss-Str 8, Unterhaching, Germany.},
DOI = {10.1016/j.trac.2022.116535},
EarlyAccessDate = {FEB 2022},
Article-Number = {116535},
ISSN = {0165-9936},
EISSN = {1879-3142},
Keywords = {Microplastics; Fourier-transform Infrared spectroscopy; Raman
   spectroscopy; Hyperspectral imaging; Chemometrics; Machine learning;
   Database; Library},
Keywords-Plus = {PLASTIC PARTICLES; SPECTRAL LIBRARIES; FTIR; MICROSPECTROSCOPY;
   MICROSCOPY; SYSTEM; URBAN; TOOL},
Research-Areas = {Chemistry},
Web-of-Science-Categories  = {Chemistry, Analytical},
Author-Email = {jana.weisser@tum.de
   pohl.teresa@gmail.com
   michael.heinzinger@tum.de
   natalia.ivleva@tum.de
   thomas.hofmann@tum.de
   karl.glas@tum.de},
Affiliations = {Technical University of Munich; Technical University of Munich;
   Technical University of Munich},
ResearcherID-Numbers = {Ivleva, Natalia P./AAQ-9987-2020
   Hofmann, Thomas/O-3346-2015
   },
ORCID-Numbers = {Ivleva, Natalia P./0000-0002-7685-5166
   Hofmann, Thomas/0000-0003-4057-7165
   Weisser, Jana/0000-0001-6997-1452
   Heinzinger, Michael/0000-0002-9601-3580},
Funding-Acknowledgement = {Bayerische Forschungsstiftung {[}AZ-1258-16]},
Funding-Text = {This work is part of the research project MiPAq (Microparticles in the
   aquatic environment and in foodstuffs) which was funded by Bayerische
   Forschungsstiftung (Grant number AZ-1258-16) . The authors wish to thank
   Hans Lohninger (Technical University of Vienna) and Benedikt Hufnagl
   (Purency GmbH) for fruitful dis-cussions. The delighting youtube channel
   StatQuest with Josh Starmer is recommended to all readers of this
   article who want to learn about machine learning.},
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Number-of-Cited-References = {108},
Times-Cited = {2},
Usage-Count-Last-180-days = {17},
Usage-Count-Since-2013 = {38},
Journal-ISO = {Trac-Trends Anal. Chem.},
Doc-Delivery-Number = {0N0XS},
Web-of-Science-Index = {Science Citation Index Expanded (SCI-EXPANDED)},
Unique-ID = {WOS:000782571900008},
DA = {2023-04-22},
}

@article{ WOS:000791258600004,
Author = {Lim, Hooi Ren and Khoo, Kuan Shiong and Chia, Wen Yi and Chew, Kit Wayne
   and Ho, Shih-Hsin and Show, Pau Loke},
Title = {Smart microalgae farming with internet-of-things for sustainable
   agriculture},
Journal = {BIOTECHNOLOGY ADVANCES},
Year = {2022},
Volume = {57},
Month = {JUL-AUG},
Abstract = {Agriculture farms such as crop, aquaculture and livestock have begun the
   implementation of Internet of Things (IoT) and artificial intelligence
   (AI) technology in improving their productivity and product quality.
   However, microalgae farming which requires precise monitoring,
   controlling and predicting the growth of microalgae biomass has yet to
   incorporate with IoT and AI technology, as it is still in its infancy
   phase. Particularly, the cultivation stage of microalgae involves many
   essential parameters (i.e. biomass concentration, pH, light intensity,
   temperature and tank level) which require precise monitoring as these
   parameters are important to ensure an effective biomass productivity in
   the microalgae farming. Besides, the conventional practices in the
   current process equipment are still powered by electricity, thus further
   development by integrating IoT into these processes can ease the
   production process. Further to that, many researchers has studied the
   machine learning approach for the identification and classification of
   microalgae. However, there are still limited studies reported on
   applying machine learning for the application of microalgae industry
   such as optimising microalgae cultivation for higher biomass
   productivity. Therefore, the implementation of IoT and AI in microalgae
   farming can contribute to the development of the global microalgae
   industry. The purpose of this current review paper focuses on the
   overview microalgae biomass production process along with the
   implementation of IoT toward the future of smart farming. To bridge the
   gap between the conventional and microalgae smart farming, this paper
   also highlights the insights on the implementation phases of microalgae
   smart farming starting from the infant stage that involves the
   installation and programming of IoT hardware. Then, it is followed by
   the application of machine learning to predict and auto-optimise the
   microalgae smart farming process. Furthermore, the process setup and
   detailed overview of microalgae farming with the integration of IoT have
   been discussed critically. This review paper would provide a new vision
   of microalgae farming for microalgae researchers and bioprocessing
   industries into the digitalisation industrial era.},
Publisher = {PERGAMON-ELSEVIER SCIENCE LTD},
Address = {THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND},
Type = {Review},
Language = {English},
Affiliation = {Ho, SH (Corresponding Author), Harbin Inst Technol, Sch Environm, State Key Lab Urban Water Resource \& Environm, Harbin 150090, Peoples R China.
   Show, PL (Corresponding Author), Univ Nottingham Malaysia, Fac Sci \& Engn, Dept Chem \& Environm Engn, Semenyih 43500, Selangor, Malaysia.
   Khoo, KS (Corresponding Author), UCSI Univ, Fac Appl Sci, UCSI Hts, Kuala Lumpur 56000, Malaysia.
   Lim, Hooi Ren; Ho, Shih-Hsin, Harbin Inst Technol, Sch Environm, State Key Lab Urban Water Resource \& Environm, Harbin 150090, Peoples R China.
   Lim, Hooi Ren; Chia, Wen Yi; Show, Pau Loke, Univ Nottingham Malaysia, Fac Sci \& Engn, Dept Chem \& Environm Engn, Semenyih 43500, Selangor, Malaysia.
   Khoo, Kuan Shiong, UCSI Univ, Fac Appl Sci, UCSI Hts, Kuala Lumpur 56000, Malaysia.
   Chew, Kit Wayne, Xiamen Univ Malaysia, Sch Energy \& Chem Engn, Sepang 43900, Selangor, Malaysia.
   Chew, Kit Wayne, Xiamen Univ, Coll Chem \& Chem Engn, Xiamen 361005, Fujian, Peoples R China.},
DOI = {10.1016/j.biotechadv.2022.107931},
EarlyAccessDate = {MAR 2022},
Article-Number = {107931},
ISSN = {0734-9750},
EISSN = {1873-1899},
Keywords = {Internet of things; Machine learning; Artificial intelligence;
   Microalgae; Smart farming},
Keywords-Plus = {LIPID EXTRACTION; SPIRULINA-PLATENSIS; OPTICAL-PROPERTIES; REMOTE
   ESTIMATION; WASTE-WATER; BIOMASS; GROWTH; CULTIVATION; IOT; OPTIMIZATION},
Research-Areas = {Biotechnology \& Applied Microbiology},
Web-of-Science-Categories  = {Biotechnology \& Applied Microbiology},
Author-Email = {kuanshiong.khoo@ucsiuniversity.edu.my
   kitwayne.chew@xmu.edu.my
   stephen6949@hit.edu.cn
   PauLoke.Show@nottingham.edu.my},
Affiliations = {Harbin Institute of Technology; University of Nottingham Malaysia; UCSI
   University; Xiamen University Malaysia Campus; Xiamen University},
ResearcherID-Numbers = {Khoo, Kuan Shiong/AAT-4901-2020
   Chew, Kit Wayne/R-2427-2019
   Lim, Hooi Ren/HNS-0014-2023
   Pau Loke, Show/A-7953-2015
   Ho, Shih-Hsin/D-6187-2013},
ORCID-Numbers = {Khoo, Kuan Shiong/0000-0002-5369-2675
   Chew, Kit Wayne/0000-0003-2622-6916
   Lim, Hooi Ren/0000-0002-9104-7925
   Pau Loke, Show/0000-0002-0913-5409
   Ho, Shih-Hsin/0000-0002-9884-1080},
Funding-Acknowledgement = {Fundamental Research Grant Scheme, Malaysia
   {[}FRGS/1/2019/STG05/UNIM/02/2]; MyPAIR-PHC-Hibiscus Grant
   {[}MyPAIR/1/2020/STG05/UNIM/1]; UCSI University Research and Innovation
   Grant {[}REIG-FAS-2020/028]},
Funding-Text = {This work was supported by the Fundamental Research Grant Scheme,
   Malaysia {[}FRGS/1/2019/STG05/UNIM/02/2] and MyPAIR-PHC-Hibiscus Grant
   {[}MyPAIR/1/2020/STG05/UNIM/1] . This work was also supported by the
   UCSI University Research and Innovation Grant under project code
   {[}REIG-FAS-2020/028] .},
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Times-Cited = {20},
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Web-of-Science-Index = {Science Citation Index Expanded (SCI-EXPANDED)},
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@article{ WOS:000776158500001,
Author = {Mallow, G. Michael and Hornung, Alexander and Barajas, Juan Nicolas and
   Rudisill, Samuel S. and An, Howard S. and Samartzis, Dino},
Title = {Quantum Computing: The Future of Big Data and Artificial Intelligence in
   Spine},
Journal = {SPINE SURGERY AND RELATED RESEARCH},
Year = {2022},
Volume = {6},
Number = {2},
Pages = {93-98},
Abstract = {With the emergence of big data and more personalized approaches to spine
   care and predictive modeling, data science and deep analytics are taking
   center-stage. Although current techniques in machine learning and
   artificial intelligence have gained attention, their applications remain
   limited by their reliance on traditional analytic platforms. Quantum
   computing has the ability to circumvent such constraints by attending to
   the various complexities of big data while minimizing space and time
   dimensions within computational algorithms. In doing so, quantum
   computing may one day address research and clinical objectives that
   currently cannot be tackled. Understanding quantum computing and its
   potential to improve patient management and outcomes is therefore
   imperative to drive further advancements in the spine field for the next
   several decades.},
Publisher = {JAPANESE SOC SPINE SURGERY \& RELATED RESEARCH},
Address = {1-1-1, HITOTSUBASHI, CHIYODA-KU, PALACE SIDE BLDG, TOKYO, 100-0003,
   JAPAN},
Type = {Review},
Language = {English},
Affiliation = {Samartzis, D (Corresponding Author), Rush Univ, Med Ctr, Dept Orthopaed Surg, Div Spine Surg, Chicago, IL 60612 USA.
   Samartzis, Dino, Rush Univ, Med Ctr, Dept Orthopaed Surg, Div Spine Surg, Chicago, IL 60612 USA.
   Rush Univ, Med Ctr, Int Spine Res \& Innovat Initiat, Chicago, IL 60612 USA.},
DOI = {10.22603/ssrr.2021-0251},
ISSN = {2432-261X},
Keywords = {quantum; computing; spine; disc degeneration; pain; artificial
   intelligence; big data},
Keywords-Plus = {DISK DEGENERATION; MODEL; CARE; ERA},
Research-Areas = {Surgery},
Web-of-Science-Categories  = {Surgery},
Author-Email = {dino\_samartzis@rush.edu},
Affiliations = {Rush University; Rush University},
ResearcherID-Numbers = {Hornung, Al/GQH-4667-2022
   Rudisill, Samuel S/AGE-4530-2022
   },
ORCID-Numbers = {Hornung, Al/0000-0001-7041-3319
   Rudisill, Samuel S/0000-0003-1325-4395
   Samartzis, Dino/0000-0002-7473-1311},
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Number-of-Cited-References = {45},
Times-Cited = {0},
Usage-Count-Last-180-days = {4},
Usage-Count-Since-2013 = {14},
Journal-ISO = {Spine Surg. Relat. Res.},
Doc-Delivery-Number = {0D7GE},
Web-of-Science-Index = {Emerging Sources Citation Index (ESCI)},
Unique-ID = {WOS:000776158500001},
OA = {gold, Green Published},
DA = {2023-04-22},
}

@article{ WOS:000919918500001,
Author = {Iqbal, Umair and Bin Riaz, Muhammad Zain and Barthelemy, Johan and
   Perez, Pascal and Idrees, Muhammad Bilal},
Title = {The last two decades of computer vision technologies in water resource
   management: A bibliometric analysis},
Journal = {WATER AND ENVIRONMENT JOURNAL},
Abstract = {Efficient management of water resources is an important task given the
   significance of water in daily lives and economic growth. Water resource
   management is a specific field of study which deals with the efficient
   management of water resources towards fulfilling the needs of society
   and preventing from water-related disasters. Many activities within this
   domain are getting benefitted with the recent technological
   advancements. Within many others, computer vision-based solutions have
   emerged as disruptive technologies to address complex real-world
   problems within the water resource management domain (e.g., flood
   detection and mapping, satellite-based water bodies monitoring,
   monitoring and inspection of hydraulic structures, blockage detection
   and assessment, drainage inspection and sewer monitoring). However,
   there are still many aspects within the water resource management domain
   which can be explored using computer vision technologies. Therefore, it
   is important to investigate the trends in current research related to
   these technologies to inform the new researchers in this domain. In this
   context, this paper presents the bibliometric analysis of the literature
   from the last two decades where computer vision technologies have been
   used for addressing problems within the water resource management
   domain. The analysis is presented in two categories: (a) performance
   analysis demonstrating highlighted trends in the number of publications,
   number of citations, top contributing countries, top publishing
   journals, top contributing institutions and top publishers and (b)
   science mapping to demonstrate the relation between the bibliographic
   records based on the co-occurrence of keywords, co-authorship analysis,
   co-citation analysis and bibliographic coupling analysis. Bibliographic
   records (i.e., 1059) are exported from the Web of Science (WoS) core
   collection database using a comprehensive query of keywords. VOSviewer
   opensource tool is used to generate the network and overlay maps for the
   science mapping of bibliographic records. Results highlighted important
   trends and valuable insights related to the use of computer vision
   technologies in water resource management. An increasing trend in the
   number of publications and focus on deep learning/artificial
   intelligence (AI)-based approaches has been reported from the analysis.
   Further, flood mapping, crack/fracture detection, coastal flood
   detection, blockage detection and drainage inspections are highlighted
   as active areas of research.},
Publisher = {WILEY},
Address = {111 RIVER ST, HOBOKEN 07030-5774, NJ USA},
Type = {Review; Early Access},
Language = {English},
Affiliation = {Iqbal, U (Corresponding Author), Univ Wollongong, SMART Infrastruct Facil, Wollongong, NSW, Australia.
   Iqbal, Umair, Univ Wollongong, SMART Infrastruct Facil, Wollongong, NSW, Australia.
   Barthelemy, Johan, NVIDIA, Santa Clara, CA USA.
   Perez, Pascal, Univ Melbourne, Australian Urban Res Infrastruct Network AURIN, Melbourne, Vic, Australia.
   Idrees, Muhammad Bilal, Natl Univ Sci \& Technol, Mil Coll Engn MCE, Islamabad, Pakistan.},
DOI = {10.1111/wej.12845},
EarlyAccessDate = {JAN 2023},
ISSN = {1747-6585},
EISSN = {1747-6593},
Keywords = {artificial intelligence; bibliometric analysis; computer vision; deep
   learning; remote sensing; water resources},
Research-Areas = {Environmental Sciences \& Ecology; Marine \& Freshwater Biology; Water
   Resources},
Web-of-Science-Categories  = {Environmental Sciences; Limnology; Water Resources},
Author-Email = {umair@uow.edu.au},
Affiliations = {University of Wollongong; Nvidia Corporation; University of Melbourne;
   National University of Sciences \& Technology - Pakistan},
ORCID-Numbers = {Idrees, Muhammad Bilal/0000-0002-0917-5478},
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Number-of-Cited-References = {55},
Times-Cited = {1},
Usage-Count-Last-180-days = {15},
Usage-Count-Since-2013 = {15},
Journal-ISO = {Water Environ. J.},
Doc-Delivery-Number = {8F8PM},
Web-of-Science-Index = {Science Citation Index Expanded (SCI-EXPANDED)},
Unique-ID = {WOS:000919918500001},
OA = {hybrid},
DA = {2023-04-22},
}

@article{ WOS:000620925900011,
Author = {Pollice, Robert and Gomes, Gabriel dos Passos and Aldeghi, Matteo and
   Hickman, Riley J. and Krenn, Mario and Lavigne, Cyrille and
   Lindner-D'Addario, Michael and Nigam, AkshatKumar and Ser, Cher Tian and
   Yao, Zhenpeng and Aspuru-Guzik, Alan},
Title = {Data-Driven Strategies for Accelerated Materials Design},
Journal = {ACCOUNTS OF CHEMICAL RESEARCH},
Year = {2021},
Volume = {54},
Number = {4},
Pages = {849-860},
Month = {FEB 16},
Abstract = {The ongoing revolution of the natural sciences by the advent of machine
   learning and artificial intelligence sparked significant interest in the
   material science community in recent years. The intrinsically high
   dimensionality of the space of realizable materials makes traditional
   approaches ineffective for large-scale explorations. Modern data science
   and machine learning tools developed for increasingly complicated
   problems are an attractive alternative. An imminent climate catastrophe
   calls for a clean energy transformation by overhauling current
   technologies within only several years of possible action available.
   Tackling this crisis requires the development of new materials at an
   unprecedented pace and scale. For example, organic photovoltaics have
   the potential to replace existing silicon-based materials to a large
   extent and open up new fields of application. In recent years, organic
   light-emitting diodes have emerged as state-of-the-art technology for
   digital screens and portable devices and are enabling new applications
   with flexible displays. Reticular frameworks allow the atom-precise
   synthesis of nanomaterials and promise to revolutionize the field by the
   potential to realize multifunctional nanopartides with applications from
   gas storage, gas separation, and electrochemical energy storage to
   nanomedicine. In the recent decade, significant advances in all these
   fields have been facilitated by the comprehensive application of
   simulation and machine learning for property prediction, property
   optimization, and chemical space exploration enabled by considerable
   advances in computing power and algorithmic efficiency.
   In this Account, we review the most recent contributions of our group in
   this thriving field of machine learning for material science. We start
   with a summary of the most important material classes our group has been
   involved in, focusing on small molecules as organic electronic materials
   and crystalline materials. Specifically, we highlight the data-driven
   approaches we employed to speed up discovery and derive material design
   strategies. Subsequently, our focus lies on the data-driven
   methodologies our group has developed and employed, elaborating on
   high-throughput virtual screening, inverse molecular design, Bayesian
   optimization, and supervised learning. We discuss the general ideas,
   their working principles, and their use cases with examples of
   successful implementations in data-driven material discovery and design
   efforts. Furthermore, we elaborate on potential pitfalls and remaining
   challenges of these methods. Finally, we provide a brief outlook for the
   field as we foresee increasing adaptation and implementation of large
   scale data-driven approaches in material discovery and design campaigns.},
Publisher = {AMER CHEMICAL SOC},
Address = {1155 16TH ST, NW, WASHINGTON, DC 20036 USA},
Type = {Review},
Language = {English},
Affiliation = {Aspuru-Guzik, A (Corresponding Author), Univ Toronto, Dept Chem, Chem Phys Theory Grp, Toronto, ON M5S 3H6, Canada.
   Aspuru-Guzik, A (Corresponding Author), Univ Toronto, Dept Comp Sci, Toronto, ON M5S 3H6, Canada.
   Aspuru-Guzik, A (Corresponding Author), Vector Inst Artificial Intelligence, Toronto, ON M5G 1M1, Canada.
   Aspuru-Guzik, A (Corresponding Author), Canadian Inst Adv Res CIFAR, Toronto, ON M5G, Canada.
   Pollice, Robert; Gomes, Gabriel dos Passos; Aldeghi, Matteo; Hickman, Riley J.; Krenn, Mario; Lavigne, Cyrille; Lindner-D'Addario, Michael; Nigam, AkshatKumar; Ser, Cher Tian; Yao, Zhenpeng; Aspuru-Guzik, Alan, Univ Toronto, Dept Chem, Chem Phys Theory Grp, Toronto, ON M5S 3H6, Canada.
   Pollice, Robert; Gomes, Gabriel dos Passos; Aldeghi, Matteo; Hickman, Riley J.; Krenn, Mario; Lavigne, Cyrille; Lindner-D'Addario, Michael; Nigam, AkshatKumar; Ser, Cher Tian; Yao, Zhenpeng; Aspuru-Guzik, Alan, Univ Toronto, Dept Comp Sci, Toronto, ON M5S 3H6, Canada.
   Aldeghi, Matteo; Krenn, Mario; Aspuru-Guzik, Alan, Vector Inst Artificial Intelligence, Toronto, ON M5G 1M1, Canada.
   Aspuru-Guzik, Alan, Canadian Inst Adv Res CIFAR, Toronto, ON M5G, Canada.},
DOI = {10.1021/acs.accounts.0c00785},
EarlyAccessDate = {FEB 2021},
ISSN = {0001-4842},
EISSN = {1520-4898},
Keywords-Plus = {LIGHT-EMITTING-DIODES; CLEAN ENERGY PROJECT; ORGANIC PHOTOVOLTAICS;
   COMPUTATIONAL DISCOVERY; SELECTION BIAS; MICROARRAY; CANDIDATES;
   BATTERIES},
Research-Areas = {Chemistry},
Web-of-Science-Categories  = {Chemistry, Multidisciplinary},
Author-Email = {aspuru@utoronto.ca},
Affiliations = {University of Toronto; University of Toronto; Canadian Institute for
   Advanced Research (CIFAR)},
ResearcherID-Numbers = {Pollice, Robert/W-1747-2019
   , Gabriel Gomes/HSE-2721-2023
   },
ORCID-Numbers = {Pollice, Robert/0000-0001-8836-6266
   dos Passos Gomes, Gabriel/0000-0002-8235-5969
   Aldeghi, Matteo/0000-0003-0019-8806
   Yao, Zhenpeng/0000-0001-8286-8257},
Funding-Acknowledgement = {Swiss National Science Foundation (SNSF) {[}191127]; Natural Sciences
   and Engineering Research Council of Canada (NSERC); NSERC
   {[}PGSD3-534584-2019]; Austrian Science Fund (FWF) through the Erwin
   Schrodinger fellowship {[}J4309]; Fonds de Recherche Quebec Nature et
   Technologies (FRQNT); Queen Elizabeth II Graduate Scholarship in Science
   and Technology (QEII-GSST); US Department of Energy, Office of Science,
   Office of Basic Energy Sciences {[}DE-FG0217ER16362]; US Department of
   Energy, Office of Science-Chicago {[}DESC0019300]; Defense Advanced
   Research Projects Agency (DARPA) under the Accelerated Molecular
   Discovery Program {[}HR00111920027]; Natural Resources Canada; Canada
   150 Research Chairs program; Department of Navy award
   {[}N00014-191-2134]; Office of Naval Research; United States Government},
Funding-Text = {We thank all our co-workers and collaborators who contributed to the
   projects highlighted in this account. R.P. acknowledges funding through
   a Postdoc.Mobility fellowship by the Swiss National Science Foundation
   (SNSF, Project No. 191127). G.P.G gratefully acknowledges the Natural
   Sciences and Engineering Research Council of Canada (NSERC) for the
   Banting Postdoctoral Fellowship. R.J.H. gratefully acknowledges NSERC
   for provision of the Postgraduate Scholarships-Doctoral Program
   (PGSD3-534584-2019). M.K. acknowledges support from the Austrian Science
   Fund (FWF) through the Erwin Schro<spacing diaeresis>dinger fellowship
   No. J4309. M.L.-D. gratefully acknowledges the Fonds de Recherche Quebec
   Nature et Technologies (FRQNT) for the B1X Master's Scholarship. M.L.-D.
   also acknowledges support from the Queen Elizabeth II Graduate
   Scholarship in Science and Technology (QEII-GSST). Z.Y. was supported as
   part of the Nanoporous Materials Genome Center by the US Department of
   Energy, Office of Science, Office of Basic Energy Sciences under award
   number DE-FG0217ER16362. Z.Y. was also supported by the US Department of
   Energy, Office of Science-Chicago under Award Number DESC0019300. We
   acknowledge the Defense Advanced Research Projects Agency (DARPA) under
   the Accelerated Molecular Discovery Program under Cooperative Agreement
   No. HR00111920027 dated August 1, 2019. The content of the information
   presented in this work does not necessarily reflect the position or the
   policy of the Government. A.A.-G. thanks Anders G. Froseth for his
   generous support. A.A.-G. also acknowledges the generous support of
   Natural Resources Canada and the Canada 150 Research Chairs program. We
   also acknowledge the Department of Navy award (N00014-191-2134) issued
   by the Office of Naval Research. The United States Government has a
   royalty-free license throughout the world in all copyrightable material
   contained herein. Any opinions, findings, and conclusions or
   recommendations expressed in this material are those of the authors and
   do not necessarily reflect the views of the Office of Naval Research.},
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Number-of-Cited-References = {74},
Times-Cited = {93},
Usage-Count-Last-180-days = {58},
Usage-Count-Since-2013 = {205},
Journal-ISO = {Accounts Chem. Res.},
Doc-Delivery-Number = {QL2QK},
Web-of-Science-Index = {Science Citation Index Expanded (SCI-EXPANDED)},
Unique-ID = {WOS:000620925900011},
OA = {Green Published, Green Submitted},
ESI-Highly-Cited-Paper = {Y},
ESI-Hot-Paper = {N},
DA = {2023-04-22},
}

@article{ WOS:000907887000003,
Author = {Herfort, Jonas Dreyoe and Tamborg, Andreas Lindenskov and Meier, Florian
   and Allsopp, Benjamin Brink and Misfeldt, Morten},
Title = {Twenty years of research on technology in mathematics education at
   CERME: a literature review based on a data science approach},
Journal = {EDUCATIONAL STUDIES IN MATHEMATICS},
Year = {2023},
Volume = {112},
Number = {2},
Pages = {309-336},
Month = {FEB},
Abstract = {Mathematics education is like many scientific disciplines witnessing an
   increase in scientific output. Examining and reviewing every paper in an
   area in detail are time-consuming, making comprehensive reviews a
   challenging task. Unsupervised machine learning algorithms like topic
   models have become increasingly popular in recent years. Their ability
   to summarize large amounts of unstructured text into coherent themes or
   topics allows researchers in any field to keep an overview of state of
   the art by creating automated literature reviews. In this article, we
   apply topic modeling in the context of mathematics education and
   showcase the use of this data science approach for creating literature
   reviews by training a model of all papers (n = 336) that have been
   presented in Thematic Working Groups related to technology in the first
   eleven Congresses of the European Society for Research in Mathematics
   Education (CERME). We guide the reader through the stepwise process of
   training a model and give recommendations for best practices and
   decisions that are decisive for the success of such an approach to a
   literature review. We find that research in this period revolved around
   25 distinct topics that can be grouped into four clusters: digital
   tools, teachers and their resources, technology experimentation, and a
   diverse cluster with a strong focus on student activity. Finally, a
   temporal analysis of these topics reveals a correlation between
   technology trends and research focus, allowing for reflection on future
   research in the field.},
Publisher = {SPRINGER},
Address = {VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS},
Type = {Review},
Language = {English},
Affiliation = {Herfort, JD (Corresponding Author), Univ Copenhagen, Copenhagen, Denmark.
   Herfort, Jonas Dreyoe; Tamborg, Andreas Lindenskov; Misfeldt, Morten, Univ Copenhagen, Copenhagen, Denmark.
   Meier, Florian; Allsopp, Benjamin Brink, Aalborg Univ, Copenhagen, Denmark.},
DOI = {10.1007/s10649-022-10202-z},
EarlyAccessDate = {JAN 2023},
ISSN = {0013-1954},
EISSN = {1573-0816},
Keywords = {Topic modeling; Digital technology; Literature review; Educational data
   science},
Keywords-Plus = {TOPIC MODELS},
Research-Areas = {Education \& Educational Research},
Web-of-Science-Categories  = {Education \& Educational Research},
Author-Email = {jonasd@ind.ku.dk},
Affiliations = {University of Copenhagen; Aalborg University},
ORCID-Numbers = {Misfeldt, Morten/0000-0002-6481-4121
   Herfort, Jonas Dreyoe/0000-0002-2359-8442},
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Number-of-Cited-References = {71},
Times-Cited = {1},
Usage-Count-Last-180-days = {5},
Usage-Count-Since-2013 = {5},
Journal-ISO = {Educ. Stud. Math.},
Doc-Delivery-Number = {8F1XG},
Web-of-Science-Index = {Social Science Citation Index (SSCI)},
Unique-ID = {WOS:000907887000003},
DA = {2023-04-22},
}

@article{ WOS:000577675600001,
Author = {Yang, Su-Qing and Ye, Qing and Ding, Jun-Jie and Ming-Zhu Yin and Lu,
   Ai-Ping and Chen, Xiang and Hou, Ting-Jun and Cao, Dong-Sheng},
Title = {Current advances in ligand-based target prediction},
Journal = {WILEY INTERDISCIPLINARY REVIEWS-COMPUTATIONAL MOLECULAR SCIENCE},
Year = {2021},
Volume = {11},
Number = {3},
Month = {MAY},
Abstract = {Target identification for bioactive molecules augments modern drug
   discovery efforts in a range of applications, from the elaboration of
   mode-of-action of drugs to the drug repurposing to even the knowledge of
   side-effects and further optimization. However, the traditional
   labor-intensive and time-consuming experiment methods obstructed the
   development. Driven by massive bioactivity data deposited in
   chemogenomic databases, computational alternatives have been proposed
   and widely developed to expedite the validation process. By screening a
   compound against a protein database, it is possible to identify
   potential target candidates that fit with this specific compound for
   subsequent experimental validation. In particular, ligand-based target
   prediction methods have made tremendous progress in the past decade due
   to their flexibility, relatively low computational cost, and remarkable
   predictive performance, and are still moving forward. In this review, we
   present a comprehensive overview of ligand-based target prediction
   methods including similarity searching, machine learning and algorithm
   stacking, and the strategies to validate these methods. We also discuss
   the strength and weakness of the existing data sources for model
   development and outline the challenges and prospects of ligand-based
   target prediction. It is expected that the topic discussed in this
   review should guide the development and application of ligand-based
   target prediction and be of interest to the audiences for wider
   scientific community. This article is categorized under: Data Science >
   Chemoinformatics},
Publisher = {WILEY},
Address = {111 RIVER ST, HOBOKEN 07030-5774, NJ USA},
Type = {Review},
Language = {English},
Affiliation = {Cao, DS (Corresponding Author), Cent South Univ, Xiangya Sch Pharmaceut Sci, Changsha 410013, Hunan, Peoples R China.
   Hou, TJ (Corresponding Author), Zhejiang Univ, Innovat Inst Artificial Intelligence Med, Coll Pharmaceut Sci, Hangzhou 310058, Zhejiang, Peoples R China.
   Chen, X (Corresponding Author), Cent South Univ, Hunan Engn Res Ctr Skin Hlth \& Dis, Hunan Key Lab Skin Canc \& Psoriasis, Dept Dermatol,Xiangya Hosp, Changsha 410008, Hunan, Peoples R China.
   Cao, DS (Corresponding Author), Hong Kong Baptist Univ, Sch Chinese Med, Inst Adv Translat Med Bone \& Joint Dis, Hong Kong, Peoples R China.
   Yang, Su-Qing; Cao, Dong-Sheng, Cent South Univ, Xiangya Sch Pharmaceut Sci, Changsha 410013, Hunan, Peoples R China.
   Ye, Qing; Hou, Ting-Jun, Zhejiang Univ, Innovat Inst Artificial Intelligence Med, Coll Pharmaceut Sci, Hangzhou 310058, Zhejiang, Peoples R China.
   Ding, Jun-Jie, Beijing Inst Pharmaceut Chem, Beijing, Peoples R China.
   Ming-Zhu Yin; Chen, Xiang, Cent South Univ, Hunan Engn Res Ctr Skin Hlth \& Dis, Hunan Key Lab Skin Canc \& Psoriasis, Dept Dermatol,Xiangya Hosp, Changsha 410008, Hunan, Peoples R China.
   Lu, Ai-Ping; Cao, Dong-Sheng, Hong Kong Baptist Univ, Sch Chinese Med, Inst Adv Translat Med Bone \& Joint Dis, Hong Kong, Peoples R China.},
DOI = {10.1002/wcms.1504},
EarlyAccessDate = {OCT 2020},
Article-Number = {e1504},
ISSN = {1759-0876},
EISSN = {1759-0884},
Keywords = {algorithm stacking; machine learning; proteochemometrics; similarity
   searching; target prediction},
Keywords-Plus = {LARGE-SCALE PREDICTION; FINGERPRINT SIMILARITY SEARCH; WEB SERVER; DRUG
   DISCOVERY; MACROMOLECULAR TARGETS; PROTEIN SEQUENCES; MULTITARGET-QSAR;
   NATURAL-PRODUCTS; IDENTIFICATION; DATABASE},
Research-Areas = {Chemistry; Mathematical \& Computational Biology},
Web-of-Science-Categories  = {Chemistry, Multidisciplinary; Mathematical \& Computational Biology},
Author-Email = {chenxiangck@126.com
   tingjunhou@zju.edu.cn
   oriental-cds@163.com},
Affiliations = {Central South University; Zhejiang University; Central South University;
   Hong Kong Baptist University},
ResearcherID-Numbers = {Hou, Tingjun/C-7492-2011
   },
ORCID-Numbers = {Hou, Tingjun/0000-0001-7227-2580
   Ye, Qing/0000-0003-3927-1919},
Funding-Acknowledgement = {Key R\&D Program of Zhejiang Province {[}2020C03010]; National Science
   Foundation of China {[}81773632]; Zhejiang Provincial Natural Science
   Foundation of China {[}LZ19H300001]; HKBU Strategic Development Fund
   project {[}SDF19-0402-P02]},
Funding-Text = {This work was supported by Key R\&D Program of Zhejiang Province
   (2020C03010), National Science Foundation of China (81773632), Zhejiang
   Provincial Natural Science Foundation of China (LZ19H300001), and HKBU
   Strategic Development Fund project (SDF19-0402-P02). The studies meet
   with the approval of the university's review board.},
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Times-Cited = {10},
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Doc-Delivery-Number = {RI2SX},
Web-of-Science-Index = {Science Citation Index Expanded (SCI-EXPANDED)},
Unique-ID = {WOS:000577675600001},
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@article{ WOS:000426838500001,
Author = {Ramprasad, Rampi and Batra, Rohit and Pilania, Ghanshyam and
   Mannodi-Kanakkithodi, Arun and Kim, Chiho},
Title = {Machine learning in materials informatics: recent applications and
   prospects},
Journal = {NPJ COMPUTATIONAL MATERIALS},
Year = {2017},
Volume = {3},
Month = {DEC 13},
Abstract = {Propelled partly by the Materials Genome Initiative, and partly by the
   algorithmic developments and the resounding successes of data-driven
   efforts in other domains, informatics strategies are beginning to take
   shape within materials science. These approaches lead to surrogate
   machine learning models that enable rapid predictions based purely on
   past data rather than by direct experimentation or by
   computations/simulations in which fundamental equations are explicitly
   solved. Data-centric informatics methods are becoming useful to
   determine material properties that are hard to measure or compute using
   traditional methodsdue to the cost, time or effort involved-but for
   which reliable data either already exists or can be generated for at
   least a subset of the critical cases. Predictions are typically
   interpolative, involving fingerprinting a material numerically first,
   and then following a mapping (established via a learning algorithm)
   between the fingerprint and the property of interest. Fingerprints, also
   referred to as ``descriptors{''}, may be of many types and scales, as
   dictated by the application domain and needs. Predictions may also be
   extrapolative-extending into new materials spaces-provided prediction
   uncertainties are properly taken into account. This article attempts to
   provide an overview of some of the recent successful data-driven
   ``materials informatics{''} strategies undertaken in the last decade,
   with particular emphasis on the fingerprint or descriptor choices. The
   review also identifies some challenges the community is facing and those
   that should be overcome in the near future.},
Publisher = {NATURE PORTFOLIO},
Address = {HEIDELBERGER PLATZ 3, BERLIN, 14197, GERMANY},
Type = {Review},
Language = {English},
Affiliation = {Ramprasad, R (Corresponding Author), Univ Connecticut, Dept Mat Sci \& Engn, 97 North Eagleville Rd,Unit 3136, Storrs, CT 06269 USA.
   Ramprasad, R (Corresponding Author), Univ Connecticut, Inst Mat Sci, 97 North Eagleville Rd,Unit 3136, Storrs, CT 06269 USA.
   Ramprasad, Rampi; Batra, Rohit; Mannodi-Kanakkithodi, Arun; Kim, Chiho, Univ Connecticut, Dept Mat Sci \& Engn, 97 North Eagleville Rd,Unit 3136, Storrs, CT 06269 USA.
   Ramprasad, Rampi; Batra, Rohit; Mannodi-Kanakkithodi, Arun; Kim, Chiho, Univ Connecticut, Inst Mat Sci, 97 North Eagleville Rd,Unit 3136, Storrs, CT 06269 USA.
   Pilania, Ghanshyam, Fritz Haber Inst Max Planck Gesell, Faradayweg 4-6, D-14195 Berlin, Germany.
   Pilania, Ghanshyam, Los Alamos Natl Lab, Mat Sci \& Technol Div, Los Alamos, NM 87545 USA.
   Mannodi-Kanakkithodi, Arun, Lamont Natl Lab, Ctr Nanoscale Mat, 9700 S Cass Ave, Lemont, IL 60439 USA.},
DOI = {10.1038/s41524-017-0056-5},
Article-Number = {54},
EISSN = {2057-3960},
Keywords-Plus = {DATA SCIENCE; ACCELERATED SEARCH; KNOWLEDGE SYSTEMS; DESIGN; FRAMEWORK;
   PREDICTIONS; POTENTIALS; INFERENCE; LINKAGES; STRENGTH},
Research-Areas = {Chemistry; Materials Science},
Web-of-Science-Categories  = {Chemistry, Physical; Materials Science, Multidisciplinary},
Author-Email = {rampi.ramprasad@uconn.edu},
Affiliations = {University of Connecticut; University of Connecticut; Max Planck
   Society; Fritz Haber Institute of the Max Planck Society; United States
   Department of Energy (DOE); Los Alamos National Laboratory},
ResearcherID-Numbers = {Kanakkithodi, Arun Kumar Mannodi/I-8528-2019
   Ramprasad, Rampi/ABE-7556-2020
   Batra, Rohit/ABB-9972-2020},
ORCID-Numbers = {Kanakkithodi, Arun Kumar Mannodi/0000-0003-0780-1583
   Ramprasad, Rampi/0000-0003-4630-1565
   Batra, Rohit/0000-0002-1098-7035},
Funding-Acknowledgement = {Alexander von Humboldt Foundation; Office of Naval Research
   {[}N00014-14-1-0098, N00014-16-1-2580, N00014-10-1-0944]},
Funding-Text = {We acknowledge financial support from several grants from the Office of
   Naval Research that allowed them to explore many applications of machine
   learning within materials science, including N00014-14-1-0098,
   N00014-16-1-2580, and N00014-10-1-0944. Several engaging discussions
   with Kenny Lipkowitz, Huan Tran, and Venkatesh Botu are gratefully
   acknowledged. GP acknowledges the Alexander von Humboldt Foundation.},
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Times-Cited = {750},
Usage-Count-Last-180-days = {85},
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Doc-Delivery-Number = {FY5AM},
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Unique-ID = {WOS:000426838500001},
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@article{ WOS:000499607100001,
Author = {Thompson, Matthew P. and Wei, Yu and Calkin, David E. and O'Connor,
   Christopher D. and Dunn, Christopher J. and Anderson, Nathaniel M. and
   Hogland, John S.},
Title = {Risk Management and Analytics in Wildfire Response},
Journal = {CURRENT FORESTRY REPORTS},
Year = {2019},
Volume = {5},
Number = {4},
Pages = {226-239},
Month = {DEC},
Abstract = {Purpose of Review The objectives of this paper are to briefly review
   basic risk management and analytics concepts, describe their nexus in
   relation to wildfire response, demonstrate real-world application of
   analytics to support response decisions and organizational learning, and
   outline an analytics strategy for the future. Recent Findings Analytics
   can improve decision-making and organizational performance across a
   variety of areas from sports to business to real-time emergency
   response. A lack of robust descriptive analytics on wildfire incident
   response effectiveness is a bottleneck for developing operationally
   relevant and empirically credible predictive and prescriptive analytics
   to inform and guide strategic response decisions. Capitalizing on
   technology such as automated resource tracking and machine learning
   algorithms can help bridge gaps between monitoring, learning, and
   data-driven decision-making. By investing in better collection,
   documentation, archiving, and analysis of operational data on response
   effectiveness, fire management organizations can promote systematic
   learning and provide a better evidence base to support response
   decisions. We describe an analytics management framework that can
   provide structure to help deploy analytics within organizations, and
   provide real-world examples of advanced fire analytics applied in the
   USA. To fully capitalize on the potential of analytics, organizations
   may need to catalyze cultural shifts that cultivate stronger
   appreciation for data-driven decision processes, and develop informed
   skeptics that effectively balance both judgment and analysis in
   decision-making.},
Publisher = {SPRINGER INTERNATIONAL PUBLISHING AG},
Address = {GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND},
Type = {Review},
Language = {English},
Affiliation = {Thompson, MP (Corresponding Author), US Forest Serv, Human Dimens Program, Rocky Mt Res Stn, USDA, Ft Collins, CO 80526 USA.
   Thompson, Matthew P., US Forest Serv, Human Dimens Program, Rocky Mt Res Stn, USDA, Ft Collins, CO 80526 USA.
   Wei, Yu, Colorado State Univ, Dept Forest \& Rangeland Stewardship, Warner Coll Nat Resources, Ft Collins, CO 80523 USA.
   Calkin, David E.; O'Connor, Christopher D.; Anderson, Nathaniel M.; Hogland, John S., US Forest Serv, Human Dimens Program, Rocky Mt Res Stn, USDA, Missoula, MT USA.
   Dunn, Christopher J., Oregon State Univ, Coll Forestry, Dept Forest Engn Resources \& Management, Corvallis, OR 97331 USA.},
DOI = {10.1007/s40725-019-00101-7},
EarlyAccessDate = {NOV 2019},
ISSN = {2198-6436},
Keywords = {Decision-making; Data science; Operations research; Suppression
   effectiveness},
Keywords-Plus = {DECISION-SUPPORT-SYSTEMS; MACHINE-LEARNING-METHODS; FIRE MANAGEMENT; BIG
   DATA; SUPPRESSION EFFECTIVENESS; DISASTER MANAGEMENT; RESEARCH AGENDA;
   INITIAL ATTACK; FOREST; CONTAINMENT},
Research-Areas = {Forestry},
Web-of-Science-Categories  = {Forestry},
Author-Email = {matthew.p.thompson@usda.gov
   yu.wei@colostate.edu
   david.e.calkin@usda.gov
   christopher.d.oconnor@usda.gov
   chris.dunn@oregonstate.edu
   nathaniel.m.anderson@usda.gov
   john.s.hogland@usda.gov},
Affiliations = {United States Department of Agriculture (USDA); United States Forest
   Service; Colorado State University; United States Department of
   Agriculture (USDA); United States Forest Service; Oregon State
   University},
ResearcherID-Numbers = {Hogland, John/AAJ-8051-2021},
Funding-Acknowledgement = {USDA Forest Service through Joint Venture Agreements},
Funding-Text = {Dr. Wei and Dr. Dunn received funding from the USDA Forest Service
   through Joint Venture Agreements.},
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Number-of-Cited-References = {125},
Times-Cited = {30},
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Doc-Delivery-Number = {KJ0EP},
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@article{ WOS:000338810900030,
Author = {Behnia, Arash and Chai, Hwa Kian and Shiotani, Tomoki},
Title = {Advanced structural health monitoring of concrete structures with the
   aid of acoustic emission},
Journal = {CONSTRUCTION AND BUILDING MATERIALS},
Year = {2014},
Volume = {65},
Pages = {282-302},
Month = {AUG 29},
Abstract = {This article gives a comprehensive review of the acoustic emission (AE)
   technique for its applications in concrete structure health monitoring.
   Basic and established condition assessment methods for concrete
   structures are reviewed to configure a firm perception of AE application
   for enhanced performance and reliability. The AE approaches of focus are
   the parametric and signal analysis which can be used to develop damage
   evaluation criteria. Other than recent localization and source
   discrimination methods, applications of pivotal AE parameters such as
   b-value, Ib-value, AE energy, and hit are discussed herein, with
   highlights on the limitation of the individual parameter-based
   approaches when adopted on site. In addition, the introduction of new
   parameters such as sifted b-value, minimum b-value, and Q value is
   discussed as well, followed by a novel recent strategy for AE
   application in conjunction with tomography method to facilitate
   infrastructure assessment. Moreover, the key role of application of
   artificial intelligence methods towards damage mode identification has
   been highlighted. (C) 2014 Elsevier Ltd. All rights reserved.},
Publisher = {ELSEVIER SCI LTD},
Address = {THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND},
Type = {Review},
Language = {English},
Affiliation = {Chai, HK (Corresponding Author), Univ Malaya, Dept Civil Engn, Kuala Lumpur 50603, Malaysia.
   Behnia, Arash; Chai, Hwa Kian, Univ Malaya, Dept Civil Engn, Kuala Lumpur 50603, Malaysia.
   Shiotani, Tomoki, Kyoto Univ, Grad Sch Engn, Dept Urban Management, Kyoto, Japan.},
DOI = {10.1016/j.conbuildmat.2014.04.103},
ISSN = {0950-0618},
EISSN = {1879-0526},
Keywords = {Acoustic emission technique; Concrete structures assessment; Damage
   detection; Structural health monitoring},
Keywords-Plus = {MOMENT TENSOR ANALYSIS; HIGH-PERFORMANCE CONCRETE; B-VALUE ANALYSIS;
   REINFORCED-CONCRETE; PRESTRESSED CONCRETE; DAMAGE ASSESSMENT;
   NONDESTRUCTIVE EVALUATION; CORROSION MECHANISMS; FRACTURE-MECHANICS;
   STRENGTH CONCRETE},
Research-Areas = {Construction \& Building Technology; Engineering; Materials Science},
Web-of-Science-Categories  = {Construction \& Building Technology; Engineering, Civil; Materials
   Science, Multidisciplinary},
Author-Email = {hkchai@um.edu.my},
Affiliations = {Universiti Malaya; Kyoto University},
ResearcherID-Numbers = {Chai, Hwa Kian/AAW-5514-2020},
Funding-Acknowledgement = {Ministry of Higher Education (Malaysia) {[}UM.C/HIR/MOHE/ENG/54]},
Funding-Text = {The Authors would like to acknowledge the Ministry of Higher Education
   (Malaysia) for providing the financial support for this project under
   Grant No. UM.C/HIR/MOHE/ENG/54.},
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Number-of-Cited-References = {137},
Times-Cited = {214},
Usage-Count-Last-180-days = {22},
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Journal-ISO = {Constr. Build. Mater.},
Doc-Delivery-Number = {AL0IP},
Web-of-Science-Index = {Science Citation Index Expanded (SCI-EXPANDED)},
Unique-ID = {WOS:000338810900030},
DA = {2023-04-22},
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@article{ WOS:000946283700004,
Author = {Gui, Jie and Sun, Zhenan and Wen, Yonggang and Tao, Dacheng and Ye,
   Jieping},
Title = {A Review on Generative Adversarial Networks: Algorithms, Theory, and
   Applications},
Journal = {IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING},
Year = {2023},
Volume = {35},
Number = {4},
Pages = {3313-3332},
Month = {APR 1},
Abstract = {Generative adversarial networks (GANs) have recently become a hot
   research topic; however, they have been studied since 2014, and a large
   number of algorithms have been proposed. Nevertheless, few comprehensive
   studies explain the connections among different GAN variants and how
   they have evolved. In this paper, we attempt to provide a review of the
   various GAN methods from the perspectives of algorithms, theory, and
   applications. First, the motivations, mathematical representations, and
   structures of most GAN algorithms are introduced in detail, and we
   compare their commonalities and differences. Second, theoretical issues
   related to GANs are investigated. Finally, typical applications of GANs
   in image processing and computer vision, natural language processing,
   music, speech and audio, the medical field, and data science are
   discussed.},
Publisher = {IEEE COMPUTER SOC},
Address = {10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1314 USA},
Type = {Review},
Language = {English},
Affiliation = {Gui, J (Corresponding Author), Southeast Univ, Sch Cyber Sci \& Engn, Nanjing 211100, Jiangsu, Peoples R China.
   Gui, Jie, Southeast Univ, Sch Cyber Sci \& Engn, Nanjing 211100, Jiangsu, Peoples R China.
   Gui, Jie, Purple Mt Labs, Nanjing 210000, Peoples R China.
   Gui, Jie, Univ Michigan, Dept Computat Med \& Bioinformat, Ann Arbor, MI USA.
   Sun, Zhenan, Chinese Acad Sci, Ctr Res Intelligent Percept \& Comp, Beijing 100190, Peoples R China.
   Wen, Yonggang, Nanyang Technol Univ, Sch Comp Sci \& Engn, Singapore 639798, Singapore.
   Tao, Dacheng, JD Explore Acad, Beijing, Peoples R China.
   Tao, Dacheng, Univ Sydney, Sch Comp Sci, Camperdown, Australia.
   Ye, Jieping, Beike, Beijing 100085, Peoples R China.
   Ye, Jieping, Univ Michigan, Ann Arbor, MI 48109 USA.},
DOI = {10.1109/TKDE.2021.3130191},
ISSN = {1041-4347},
EISSN = {1558-2191},
Keywords = {Generators; Generative adversarial networks; Data models; Linear
   programming; Natural language processing; Machine learning algorithms;
   Inference algorithms; Deep learning; generative adversarial networks;
   algorithm; theory; applications},
Keywords-Plus = {LEARNING ALGORITHM; IMAGE SYNTHESIS; GAN; MANIPULATION; DISTANCE},
Research-Areas = {Computer Science; Engineering},
Web-of-Science-Categories  = {Computer Science, Artificial Intelligence; Computer Science, Information
   Systems; Engineering, Electrical \& Electronic},
Author-Email = {guijie@seu.edu.cn
   znsun@nlpr.ia.ac.cn
   ygwen@ntu.edu.sg
   dacheng.tao@gmail.com
   jieping@gmail.com},
Affiliations = {Southeast University - China; University of Michigan System; University
   of Michigan; Chinese Academy of Sciences; Nanyang Technological
   University \& National Institute of Education (NIE) Singapore; Nanyang
   Technological University; University of Sydney; University of Michigan
   System; University of Michigan},
Funding-Acknowledgement = {National Science Foundation of China {[}62172090, 62172089]; National
   Key R\&D Project of China {[}2021QY2102]; CAAI-Huawei MindSpore Open
   Fund; Alibaba Group through Alibaba Innovative Research Program;
   Fundamental Research Funds for the Central Universities
   {[}2242022R10071]; Grant of the Singapore National Research Foundation
   (NRF); Jiangsu Provincial Double-Innovation Doctor Program
   {[}JSSCBS20210075]},
Funding-Text = {This work was supported in part by the National Science Foundation of
   China underGrants 62172090 and 62172089, in part by the National Key R
   \& D Project of China under Grant 2021QY2102, in part by CAAI-Huawei
   MindSpore Open Fund, in partby Alibaba Group through Alibaba Innovative
   Research Program, in part by the Fundamental Research Funds for the
   Central Universities under Grant 2242022R10071,in part by the Grant of
   the Singapore National Research Foundation (NRF) underSustainable
   Tropical Data Center Testbed (STDCT) Project, and in part by Jiangsu
   Provincial Double-Innovation Doctor Program under Grant JSSCBS20210075.},
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Times-Cited = {154},
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Journal-ISO = {IEEE Trans. Knowl. Data Eng.},
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Unique-ID = {WOS:000946283700004},
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@article{ WOS:000793595700005,
Author = {Serov, Nikita and Vinogradov, Vladimir},
Title = {Artificial intelligence to bring nanomedicine to life},
Journal = {ADVANCED DRUG DELIVERY REVIEWS},
Year = {2022},
Volume = {184},
Month = {MAY},
Abstract = {The technology of drug delivery systems (DDSs) has demonstrated an
   outstanding performance and effectiveness in production of
   pharmaceuticals, as it is proved by many FDA-approved nanomedicines that
   have an enhanced selectivity, manageable drug release kinetics and
   synergistic therapeutic actions. Nonetheless, to date, the rational
   design and high-throughput development of nanomaterial-based DDSs for
   specific purposes is far from a routine practice and is still in its
   infancy, mainly due to the limitations in scientists' capabilities to
   effectively acquire, analyze, manage, and comprehend complex and
   evergrowing sets of experimental data, which is vital to develop DDSs
   with a set of desired functionalities. At the same time, this task is
   feasible for the data-driven approaches, high throughput experimentation
   techniques, process automatization, artificial intelligence (AI)
   technology, and machine learning (ML) approaches, which is referred to
   as The Fourth Paradigm of scientific research. Therefore, an integration
   of these approaches with nanomedicine and nanotechnology can potentially
   accelerate the rational design and high-throughput development of highly
   efficient nanoformulated drugs and smart materials with pre-defined
   functionalities. In this Review, we survey the important results and
   milestones achieved to date in the application of data science, high
   throughput, as well as automatization approaches, combined with AI and
   ML to design and optimize DDSs and related nanomaterials. This
   manuscript mission is not only to reflect the state-of-art in
   data-driven nanomedicine, but also show how recent findings in the
   related fields can transform the nanomedicine's image. We discuss how
   all these results can be used to boost nanomedicine translation to the
   clinic, as well as highlight the future directions for the development,
   data-driven, high throughput experimentation-, and AI-assisted design,
   as well as the production of nanoformulated drugs and smart materials
   with pre-defined properties and behavior. This Review will be of high
   interest to the chemists involved in materials science, nanotechnology,
   and DDSs development for biomedical applications, although the general
   nature of the presented approaches enables knowledge translation to many
   other fields of science.},
Publisher = {ELSEVIER},
Address = {RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS},
Type = {Review},
Language = {English},
Affiliation = {Vinogradov, V (Corresponding Author), ITMO Univ, Int Inst Solut Chem Adv Mat \& Technol, St Petersburg 191002, Russia.
   Serov, Nikita; Vinogradov, Vladimir, ITMO Univ, Int Inst Solut Chem Adv Mat \& Technol, St Petersburg 191002, Russia.},
DOI = {10.1016/j.addr.2022.114194},
EarlyAccessDate = {MAR 2022},
Article-Number = {114194},
ISSN = {0169-409X},
EISSN = {1872-8294},
Keywords = {Nanomedicine; Materials science; Artificial intelligence; Machine
   learning; Data science; Fourth paradigm},
Keywords-Plus = {HIGH-THROUGHPUT SYNTHESIS; GENETIC ALGORITHM; MICROFLUIDIC PLATFORM;
   LIPID NANOPARTICLES; OXIDE NANOPARTICLES; DRUG-RELEASE; COMBINATORIAL;
   PREDICTION; DESIGN; DISCOVERY},
Research-Areas = {Pharmacology \& Pharmacy},
Web-of-Science-Categories  = {Pharmacology \& Pharmacy},
Author-Email = {vinogradov@scamt-itmo.ru},
Affiliations = {ITMO University},
Funding-Acknowledgement = {Russian Science Foundation {[}21-73-10150]; Priority 2030 Federal
   Academic Leadership Program},
Funding-Text = {The work of Vladimir Vinogradov was supported by Russian Science
   Foundation no 21-73-10150. The research was supported by Priority 2030
   Federal Academic Leadership Program. We especially thank Olga Kononova
   for her kind help with the review concept as well as the useful advice.},
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Times-Cited = {8},
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Journal-ISO = {Adv. Drug Deliv. Rev.},
Doc-Delivery-Number = {1D1VN},
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@article{ WOS:000451736300043,
Author = {Mosavi, Amir and Ozturk, Pinar and Chau, Kwok-wing},
Title = {Flood Prediction Using Machine Learning Models: Literature Review},
Journal = {WATER},
Year = {2018},
Volume = {10},
Number = {11},
Month = {NOV},
Abstract = {Floods are among the most destructive natural disasters, which are
   highly complex to model. The research on the advancement of flood
   prediction models contributed to risk reduction, policy suggestion,
   minimization of the loss of human life, and reduction of the property
   damage associated with floods. To mimic the complex mathematical
   expressions of physical processes of floods, during the past two
   decades, machine learning (ML) methods contributed highly in the
   advancement of prediction systems providing better performance and
   cost-effective solutions. Due to the vast benefits and potential of ML,
   its popularity dramatically increased among hydrologists. Researchers
   through introducing novel ML methods and hybridizing of the existing
   ones aim at discovering more accurate and efficient prediction models.
   The main contribution of this paper is to demonstrate the state of the
   art of ML models in flood prediction and to give insight into the most
   suitable models. In this paper, the literature where ML models were
   benchmarked through a qualitative analysis of robustness, accuracy,
   effectiveness, and speed are particularly investigated to provide an
   extensive overview on the various ML algorithms used in the field. The
   performance comparison of ML models presents an in-depth understanding
   of the different techniques within the framework of a comprehensive
   evaluation and discussion. As a result, this paper introduces the most
   promising prediction methods for both long-term and short-term floods.
   Furthermore, the major trends in improving the quality of the flood
   prediction models are investigated. Among them, hybridization, data
   decomposition, algorithm ensemble, and model optimization are reported
   as the most effective strategies for the improvement of ML methods. This
   survey can be used as a guideline for hydrologists as well as climate
   scientists in choosing the proper ML method according to the prediction
   task.},
Publisher = {MDPI},
Address = {ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND},
Type = {Review},
Language = {English},
Affiliation = {Mosavi, A; Ozturk, P (Corresponding Author), Norwegian Univ Sci \& Technol NTNU, Dept Comp Sci IDI, NO-7491 Trondheim, Norway.
   Mosavi, Amir; Ozturk, Pinar, Norwegian Univ Sci \& Technol NTNU, Dept Comp Sci IDI, NO-7491 Trondheim, Norway.
   Chau, Kwok-wing, Hong Kong Polytech Univ, Dept Civil \& Environm Engn, Hong Kong, Hong Kong, Peoples R China.},
DOI = {10.3390/w10111536},
Article-Number = {1536},
EISSN = {2073-4441},
Keywords = {flood prediction; flood forecasting; hydrologic model; rainfall-runoff;
   hybrid \& ensemble machine learning; artificial neural network; support
   vector machine; natural hazards \& disasters; adaptive neuro-fuzzy
   inference system (ANFIS); decision tree; survey; classification and
   regression trees (CART), data science; big data; artificial
   intelligence; soft computing; extreme event management; time series
   prediction},
Keywords-Plus = {ARTIFICIAL NEURAL-NETWORK; SUPPORT VECTOR MACHINE; FUZZY INFERENCE
   SYSTEM; SHALLOW-WATER EQUATIONS; MONTHLY STREAMFLOW PREDICTION;
   RAINFALL-RUNOFF; SHORT-TERM; FREQUENCY-ANALYSIS; INPUT SELECTION;
   FLASH-FLOOD},
Research-Areas = {Environmental Sciences \& Ecology; Water Resources},
Web-of-Science-Categories  = {Environmental Sciences; Water Resources},
Author-Email = {amir.mosavi@ntnu.no
   pinar@ntnu.no
   dr.kwok-wing.chau@polyu.edu.hk},
Affiliations = {Norwegian University of Science \& Technology (NTNU); Hong Kong
   Polytechnic University},
ResearcherID-Numbers = {Chau, Kwok-wing/E-5235-2011
   Mosavi, Amir/I-7440-2018},
ORCID-Numbers = {Chau, Kwok-wing/0000-0001-6457-161X
   Mosavi, Amir/0000-0003-4842-0613},
Funding-Acknowledgement = {Norwegian University of Science and Technology AI Lab; European Research
   Consortium for Informatics and Mathematics (ERCIM)},
Funding-Text = {This research was funded by the Norwegian University of Science and
   Technology AI Lab and the European Research Consortium for Informatics
   and Mathematics (ERCIM).},
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Number-of-Cited-References = {228},
Times-Cited = {500},
Usage-Count-Last-180-days = {162},
Usage-Count-Since-2013 = {584},
Journal-ISO = {Water},
Doc-Delivery-Number = {HC3XM},
Web-of-Science-Index = {Science Citation Index Expanded (SCI-EXPANDED)},
Unique-ID = {WOS:000451736300043},
OA = {Green Submitted, gold},
ESI-Highly-Cited-Paper = {Y},
ESI-Hot-Paper = {N},
DA = {2023-04-22},
}

@article{ WOS:000497555000001,
Author = {Bhattacharya, Tanmoy and Brettin, Thomas and Doroshow, James H. and
   Evrard, Yvonne A. and Greenspan, Emily J. and Gryshuk, Amy L. and Hoang,
   Thuc T. and Lauzon, Carolyn B. Vea and Nissley, Dwight and Penberthy,
   Lynne and Stahlberg, Eric and Stevens, Rick and Streitz, Fred and
   Tourassi, Georgia and Xia, Fangfang and Zaki, George},
Title = {AI Meets Exascale Computing: Advancing Cancer Research With Large-Scale
   High Performance Computing},
Journal = {FRONTIERS IN ONCOLOGY},
Year = {2019},
Volume = {9},
Month = {OCT 2},
Abstract = {The application of data science in cancer research has been boosted by
   major advances in three primary areas: (1) Data: diversity, amount, and
   availability of biomedical data; (2) Advances in Artificial Intelligence
   (AI) and Machine Learning (ML) algorithms that enable learning from
   complex, large-scale data; and (3) Advances in computer architectures
   allowing unprecedented acceleration of simulation and machine learning
   algorithms. These advances help build in silico ML models that can
   provide transformative insights from data including: molecular dynamics
   simulations, next-generation sequencing, omics, imaging, and
   unstructured clinical text documents. Unique challenges persist,
   however, in building ML models related to cancer, including: (1) access,
   sharing, labeling, and integration of multimodal and multi-institutional
   data across different cancer types; (2) developing AI models for cancer
   research capable of scaling on next generation high performance
   computers; and (3) assessing robustness and reliability in the AI
   models. In this paper, we review the National Cancer Institute (NCI)
   -Department of Energy (DOE) collaboration, Joint Design of Advanced
   Computing Solutions for Cancer (JDACS4C), a multi-institution
   collaborative effort focused on advancing computing and data
   technologies to accelerate cancer research on three levels: molecular,
   cellular, and population. This collaboration integrates various types of
   generated data, pre-exascale compute resources, and advances in ML
   models to increase understanding of basic cancer biology, identify
   promising new treatment options, predict outcomes, and eventually
   prescribe specialized treatments for patients with cancer.},
Publisher = {FRONTIERS MEDIA SA},
Address = {AVENUE DU TRIBUNAL FEDERAL 34, LAUSANNE, CH-1015, SWITZERLAND},
Type = {Review},
Language = {English},
Affiliation = {Zaki, G (Corresponding Author), Frederick Natl Lab Canc Res, Biomed Informat \& Data Sci Directorate, Frederick, MD 21701 USA.
   Bhattacharya, Tanmoy, Los Alamos Natl Lab, Theoret Div, Los Alamos, NM USA.
   Brettin, Thomas; Stevens, Rick, Argonne Natl Lab, Comp Environm \& Life Sci Directorate, Lemont, IL USA.
   Doroshow, James H., NCI, Div Canc Treatment \& Diag, Bethesda, MD 20892 USA.
   Evrard, Yvonne A., Frederick Natl Lab Canc Res, Appl Dev \& Res Directorate, Frederick, MD USA.
   Greenspan, Emily J., NCI, Ctr Biomed Informat \& Informat Technol, Bethesda, MD 20892 USA.
   Gryshuk, Amy L., Lawrence Livermore Natl Lab, Phys \& Life Sci Directorate, Livermore, CA 94550 USA.
   Hoang, Thuc T., US DOE, Natl Nucl Secur Adm, Adv Simulat \& Comp, Washington, DC 20585 USA.
   Lauzon, Carolyn B. Vea, US DOE, Off Sci, Adv Sci Comp Res, Washington, DC 20585 USA.
   Nissley, Dwight, Frederick Natl Lab Canc Res, NCI RAS Initiat, Canc Res Technol Program, Frederick, MD USA.
   Penberthy, Lynne, NCI, Div Canc Control \& Populat Sci, Bethesda, MD 20892 USA.
   Stahlberg, Eric; Zaki, George, Frederick Natl Lab Canc Res, Biomed Informat \& Data Sci Directorate, Frederick, MD 21701 USA.
   Stevens, Rick, Univ Chicago, Dept Comp Sci, Chicago, IL 60637 USA.
   Streitz, Fred, Lawrence Livermore Natl Lab, High Performance Comp Innovat Ctr, Livermore, CA 94550 USA.
   Tourassi, Georgia, Oak Ridge Natl Lab, Hlth Data Sci Inst, Oak Ridge, TN USA.
   Xia, Fangfang, Argonne Natl Lab, Data Sci \& Learning Div, Lemont, IL USA.},
DOI = {10.3389/fonc.2019.00984},
Article-Number = {984},
ISSN = {2234-943X},
Keywords = {cancer research; high performance computing; artificial intelligence;
   deep learning; natural language processing; multi-scale modeling;
   precision medicine; uncertainty quantification},
Keywords-Plus = {RESOURCE; DISCOVERY},
Research-Areas = {Oncology},
Web-of-Science-Categories  = {Oncology},
Author-Email = {george.zaki@nih.gov},
Affiliations = {United States Department of Energy (DOE); Los Alamos National
   Laboratory; United States Department of Energy (DOE); Argonne National
   Laboratory; National Institutes of Health (NIH) - USA; NIH National
   Cancer Institute (NCI); National Institutes of Health (NIH) - USA; NIH
   National Cancer Institute (NCI); National Institutes of Health (NIH) -
   USA; NIH National Cancer Institute (NCI); United States Department of
   Energy (DOE); Lawrence Livermore National Laboratory; United States
   Department of Energy (DOE); United States Department of Energy (DOE);
   National Institutes of Health (NIH) - USA; NIH National Cancer Institute
   (NCI); National Institutes of Health (NIH) - USA; NIH National Cancer
   Institute (NCI); National Institutes of Health (NIH) - USA; NIH National
   Cancer Institute (NCI); University of Chicago; United States Department
   of Energy (DOE); Lawrence Livermore National Laboratory; United States
   Department of Energy (DOE); Oak Ridge National Laboratory; United States
   Department of Energy (DOE); Argonne National Laboratory},
ResearcherID-Numbers = {Bhattacharya, Tanmoy/J-8956-2013},
ORCID-Numbers = {Jain, Rajeev/0000-0002-1235-918X
   Bhattacharya, Tanmoy/0000-0002-1060-652X},
Funding-Acknowledgement = {Joint Design of Advanced Computing Solutions for Cancer (JDACS4C)
   program by the U.S. Department of Energy (DOE); National Cancer
   Institute (NCI) of the National Institutes of Health; U.S. Department of
   Energy by Argonne National Laboratory {[}DE-AC02-06-CH11357]; U.S.
   Department of Energy by Lawrence Livermore National Laboratory
   {[}DE-AC52-07NA27344, LLNL-JRNL-773355]; U.S. Department of Energy by
   Los Alamos National Laboratory {[}DE-AC52-06NA25396]; NCI, NIH
   {[}HHSN261200800001E]; Exascale Computing Project {[}17-SC-20-SC]; DOE
   National Nuclear Security Administration's Advanced Simulation and
   Computing (ASC) Program; U.S. Department of Energy
   {[}DE-AC05-00OR22725]; Department of Energy; DOE Office of Science User
   Facility {[}DE-AC05-00OR22725]},
Funding-Text = {This work has been supported in part by the Joint Design of Advanced
   Computing Solutions for Cancer (JDACS4C) program established by the U.S.
   Department of Energy (DOE) and the National Cancer Institute (NCI) of
   the National Institutes of Health. This work was performed under the
   auspices of the U.S. Department of Energy by Argonne National Laboratory
   under Contract DE-AC02-06-CH11357. This work was performed under the
   auspices of the U.S. Department of Energy by Lawrence Livermore National
   Laboratory under Contract DE-AC52-07NA27344. LLNL-JRNL-773355. This work
   was performed under the auspices of the U.S. Department of Energy by Los
   Alamos National Laboratory under Contract DE-AC52-06NA25396. Computing
   support for this work came in part from the Lawrence Livermore National
   Laboratory Institutional Computing Grand Challenge program. This project
   was funded in part with federal funds from the NCI, NIH, under contract
   no. HHSN261200800001E. This research was supported in part by the
   Exascale Computing Project (17-SC-20-SC), a collaborative effort of the
   U.S. Department of Energy Office of Science and the National Nuclear
   Security Administration. This research used resources of the Argonne
   Leadership Computing Facility and the Oak Ridge Leadership Computing
   Facility, which are DOE Office of Science User Facilities. This research
   used resources of the Lawrence Livermore Computing Facility and the Los
   Alamos National Laboratory supported by the DOE National Nuclear
   Security Administration's Advanced Simulation and Computing (ASC)
   Program. This manuscript has been authored in part by UTBattelle, LLC
   under Contract No. DE-AC05-00OR22725 with the U.S. Department of Energy.
   The United States Government retains and the publisher, by accepting the
   article for publication, acknowledges that the United States Government
   retains a nonexclusive, paidup, irrevocable, world-wide license to
   publish or reproduce the published form of the manuscript, or allow
   others to do so, for United States Government purposes. The Department
   of Energy will provide public access to these results of federally
   sponsored research in accordance with the DOE Public Access Plan
   (http://energy.gov/downloads/doepublic-access-plan).This research used
   resources of the Oak Ridge Leadership Computing Facility, which is a DOE
   Office of Science User Facility supported under Contract
   DE-AC05-00OR22725.},
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Number-of-Cited-References = {32},
Times-Cited = {9},
Usage-Count-Last-180-days = {3},
Usage-Count-Since-2013 = {10},
Journal-ISO = {Front. Oncol.},
Doc-Delivery-Number = {JO4MZ},
Web-of-Science-Index = {Science Citation Index Expanded (SCI-EXPANDED)},
Unique-ID = {WOS:000497555000001},
OA = {Green Published, Green Submitted, gold},
DA = {2023-04-22},
}

@article{ WOS:000526398000006,
Author = {Cole, Jacqueline M.},
Title = {A Design-to-Device Pipeline for Data-Driven Materials Discovery},
Journal = {ACCOUNTS OF CHEMICAL RESEARCH},
Year = {2020},
Volume = {53},
Number = {3},
Pages = {599-610},
Month = {MAR},
Abstract = {CONSPECTUS: The world needs new materials to stimulate the chemical
   industry in key sectors of our economy: environment and sustainability,
   information storage, optical telecommunications, and catalysis. Yet,
   nearly all functional materials are still discovered by
   ``trial-and-error{''}, of which the lack of predictability affords a
   major materials bottleneck to technological innovation. The average
   ``molecule-to-market{''} lead time for materials discovery is currently
   20 years. This is far too long for industrial needs, as highlighted by
   the Materials Genome Initiative, which has ambitious targets of up to
   4-fold reductions in average molecule-to-market lead times. Such a large
   step change in progress can only be realistically achieved if one adopts
   an entirely new approach to materials discovery. Fortunately, a
   fundamentally new approach to materials discovery has been emerging,
   whereby data science with artificial intelligence offers a prospective
   solution to speed up these average molecule-to-market lead times.
   This approach is known as data-driven materials discovery. Its broad
   prospects have only recently become a reality, given the timely and
   major advances in ``big data{''}, artificial intelligence, and
   high-performance computing (HPC). Access to massive data sets has been
   stimulated by government-regulated open-access requirements for data and
   literature. Natural-language processing (NLP) and machine-learning (ML)
   tools that can mine data and find patterns therein are becoming
   mainstream. Exascale HPC capabilities that can aid data mining and
   pattern recognition and also generate their own data from calculations
   are now within our grasp. These timely advances present an ideal
   opportunity to develop data-driven materials-discovery strategies to
   systematically design and predict new chemicals for a given device
   application.
   This Account shows how data science can afford materials discovery via a
   four-step ``design-to-device{''} pipeline that entails (1) data
   extraction, (2) data enrichment, (3) material prediction, and (4)
   experimental validation. Massive databases of cognate chemical and
   property information are first forged from ``chemistry-aware{''}
   natural-language-processing tools, such as ChemDataExtractor, and
   enriched using machine-learning methods and high-throughput
   quantum-chemical calculations. New materials for a bespoke application
   can then be predicted by mining these databases with algorithmic
   encodings of relationships between chemical structures and physical
   properties that are known to deliver functional materials. These may
   take the form of classification, enumeration, or machine-learning
   algorithms. A data-mining workflow short-lists these predictions to a
   handful of lead candidate materials that go forward to experimental
   validation. This design-to-device approach is being developed to offer a
   roadmap for the accelerated discovery of new chemicals for functional
   applications. Case studies presented demonstrate its utility for
   photovoltaic, optical, and catalytic applications. While this Account is
   focused on applications in the physical sciences, the generic pipeline
   discussed is readily transferable to other scientific disciplines such
   as biology and medicine.},
Publisher = {AMER CHEMICAL SOC},
Address = {1155 16TH ST, NW, WASHINGTON, DC 20036 USA},
Type = {Review},
Language = {English},
Affiliation = {Cole, JM (Corresponding Author), Univ Cambridge, Dept Phys, Cavendish Lab, Cambridge CB3 0HE, England.
   Cole, JM (Corresponding Author), Univ Cambridge, Dept Chem Engn \& Biotechnol, Cambridge CB3 0HE, England.
   Cole, JM (Corresponding Author), STFC Rutherford Appleton Lab, ISIS Neutron \& Muon Source, Didcot OX11 0QX, Oxon, England.
   Cole, JM (Corresponding Author), Univ Oxford, Math Inst, Oxford OX2 6GG, England.
   Cole, Jacqueline M., Univ Cambridge, Dept Phys, Cavendish Lab, Cambridge CB3 0HE, England.
   Cole, Jacqueline M., Univ Cambridge, Dept Chem Engn \& Biotechnol, Cambridge CB3 0HE, England.
   Cole, Jacqueline M., STFC Rutherford Appleton Lab, ISIS Neutron \& Muon Source, Didcot OX11 0QX, Oxon, England.
   Cole, Jacqueline M., Univ Oxford, Math Inst, Oxford OX2 6GG, England.},
DOI = {10.1021/acs.accounts.9b00470},
ISSN = {0001-4842},
EISSN = {1520-4898},
Research-Areas = {Chemistry},
Web-of-Science-Categories  = {Chemistry, Multidisciplinary},
Author-Email = {jmc61@cam.ac.uk},
Affiliations = {University of Cambridge; University of Cambridge; UK Research \&
   Innovation (UKRI); Science \& Technology Facilities Council (STFC); STFC
   Rutherford Appleton Laboratory; University of Oxford},
ResearcherID-Numbers = {Cole, Jacqueline/C-5991-2008},
Funding-Acknowledgement = {STFC via the ISIS Neutron and Muon Facility},
Funding-Text = {J.M.C. is grateful for the BASF/Royal Academy of Engineering Research
   Chair in Data-Driven Molecular Engineering of Functional Materials,
   which is partly supported by the STFC via the ISIS Neutron and Muon
   Facility.},
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Number-of-Cited-References = {48},
Times-Cited = {43},
Usage-Count-Last-180-days = {11},
Usage-Count-Since-2013 = {79},
Journal-ISO = {Accounts Chem. Res.},
Doc-Delivery-Number = {LE0EP},
Web-of-Science-Index = {Science Citation Index Expanded (SCI-EXPANDED)},
Unique-ID = {WOS:000526398000006},
DA = {2023-04-22},
}

@article{ WOS:000447126900001,
Author = {Kadiyala, Akhil and Kumar, Ashok},
Title = {Applications of python to evaluate the performance of bagging methods},
Journal = {ENVIRONMENTAL PROGRESS \& SUSTAINABLE ENERGY},
Year = {2018},
Volume = {37},
Number = {5},
Pages = {1555-1559},
Month = {SEP-OCT},
Abstract = {The use of ensemble methods for obtaining scalable solutions on complex
   multi-dimensional datasets has increased manifold in the field of
   advanced machine learning and analytics owing to the ensemble method's
   capabilities of combining multiple base estimators to generate a more
   robust estimator than any single estimator with a given algorithm.
   Bagging and boosting are the two widely used ensemble methods. This
   paper presents a step-by-step approach to the applications of python in
   evaluating the performance of three bagging ensemble methods, namely,
   bagging, random forest, and extremely randomized trees for predicting
   the in-bus carbon dioxide concentrations. The bagging ensemble model
   evaluation results from this study were compared with the results
   obtained from a prior study that evaluated the performance of four
   boosting (gradient boosting machine, light gradient boosting machine,
   extreme gradient boosting, adaptive boosting) ensemble methods utilizing
   the same in-bus database. Among the seven ensemble methods, the random
   forest ensemble method provided better results on the basis of
   predictive model evaluation with operational performance measures. The
   readers may adopt the bagging ensemble methods (inclusive of the python
   coding) discussed in this article to successfully address their own data
   science problems. (c) 2018 American Institute of Chemical Engineers
   Environ Prog, 37: 1555-1559, 2018},
Publisher = {WILEY},
Address = {111 RIVER ST, HOBOKEN 07030-5774, NJ USA},
Type = {Review},
Language = {English},
Affiliation = {Kumar, A (Corresponding Author), Univ Toledo, Dept Civil \& Environm Engn, Toledo, OH 43606 USA.
   Kadiyala, Akhil; Kumar, Ashok, Univ Toledo, Dept Civil \& Environm Engn, Toledo, OH 43606 USA.},
DOI = {10.1002/ep.13018},
ISSN = {1944-7442},
EISSN = {1944-7450},
Keywords = {anaconda; python; spyder; scikit-learn; bagging; random forests;
   extremely randomized trees; data science; indoor air quality; biodiesel;
   public transportation buses},
Keywords-Plus = {PUBLIC TRANSPORTATION BUS; AIR-QUALITY; AVAILABLE SOFTWARE},
Research-Areas = {Science \& Technology - Other Topics; Engineering; Environmental
   Sciences \& Ecology},
Web-of-Science-Categories  = {Green \& Sustainable Science \& Technology; Engineering, Environmental;
   Engineering, Chemical; Environmental Sciences},
Author-Email = {akumar@utnet.utoledo.edu},
Affiliations = {University System of Ohio; University of Toledo},
ResearcherID-Numbers = {Kumar, Ashok/AAK-6073-2020},
Funding-Acknowledgement = {United States Department of Transportation and Toledo Area Regional
   Transit Authority (TARTA)},
Funding-Text = {The authors thank the United States Department of Transportation and
   Toledo Area Regional Transit Authority (TARTA) for the alternate fuel
   grant awarded to the Intermodal Transportation Institute of The
   University of Toledo. The authors express their sincere gratitude to the
   TARTA management and the employees for their interest and involvement in
   this project during data collection. The authors do not have any
   financial relationships with the software (anaconda, spyder) and the
   python machine learning packages (scikit-learn, numpy) used in this
   paper. The views expressed in this paper are those of the authors and do
   not represent the views of the funding organizations.},
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   {[}No title captured].
   {[}No title captured].},
Number-of-Cited-References = {26},
Times-Cited = {12},
Usage-Count-Last-180-days = {10},
Usage-Count-Since-2013 = {44},
Journal-ISO = {Environ. Prog. Sustain. Energy},
Doc-Delivery-Number = {GW7DQ},
Web-of-Science-Index = {Science Citation Index Expanded (SCI-EXPANDED)},
Unique-ID = {WOS:000447126900001},
DA = {2023-04-22},
}

@article{ WOS:000412322500002,
Author = {Bzdok, Danilo},
Title = {Classical Statistics and Statistical Learning in Imaging Neuroscience},
Journal = {FRONTIERS IN NEUROSCIENCE},
Year = {2017},
Volume = {11},
Month = {OCT 6},
Abstract = {Brain-imaging research has predominantly generated insight by means of
   classical statistics, including regression-type analyses and
   null-hypothesis testing using t-test and ANOVA. Throughout recent years,
   statistical learning methods enjoy increasing popularity especially for
   applications in rich and complex data, including cross-validated
   out-of-sample prediction using pattern classification and
   sparsity-inducing regression. This concept paper discusses the
   implications of inferential justifications and algorithmic methodologies
   in common data analysis scenarios in neuroimaging. It is retraced how
   classical statistics and statistical learning originated from different
   historical contexts, build on different theoretical foundations, make
   different assumptions, and evaluate different outcome metrics to permit
   differently nuanced conclusions. The present considerations should help
   reduce current confusion between model-driven classical hypothesis
   testing and data-driven learning algorithms for investigating the brain
   with imaging techniques.},
Publisher = {FRONTIERS MEDIA SA},
Address = {AVENUE DU TRIBUNAL FEDERAL 34, LAUSANNE, CH-1015, SWITZERLAND},
Type = {Review},
Language = {English},
Affiliation = {Bzdok, D (Corresponding Author), Rhein Westfal TH Aachen, Dept Psychiat Psychotherapy \& Psychosomat, Med Fac, Aachen, Germany.
   Bzdok, D (Corresponding Author), JARA, Translat Brain Med, Aachen, Germany.
   Bzdok, D (Corresponding Author), INRIA, Parietal Team, Gif Sur Yvette, France.
   Bzdok, Danilo, Rhein Westfal TH Aachen, Dept Psychiat Psychotherapy \& Psychosomat, Med Fac, Aachen, Germany.
   Bzdok, Danilo, JARA, Translat Brain Med, Aachen, Germany.
   Bzdok, Danilo, INRIA, Parietal Team, Gif Sur Yvette, France.},
DOI = {10.3389/fnins.2017.00543},
Article-Number = {543},
EISSN = {1662-453X},
Keywords = {neuroimaging; data science; epistemology; statistical inference; machine
   learning; p-value; Rosetta Stone},
Keywords-Plus = {GENOME-WIDE ASSOCIATION; OBJECT RECOGNITION; VARIABLE SELECTION;
   PERMUTATION TESTS; CIRCULAR ANALYSIS; PATTERN-ANALYSIS; NEURAL-NETWORKS;
   FMRI; INFORMATION; INFERENCE},
Research-Areas = {Neurosciences \& Neurology},
Web-of-Science-Categories  = {Neurosciences},
Author-Email = {danilo.bzdok@rwth-aachen.de},
Affiliations = {RWTH Aachen University; Inria},
Funding-Acknowledgement = {Deutsche Forschungsgemeinschaft (DFG) {[}BZ2/2-1, BZ2/3-1, BZ2/4-1];
   Amazon AWS; German National Merit Foundation; Faculty of Medicine, RWTH
   Aachen; Deutsche Forschungsgemeinschaft (International Research Training
   Group) {[}IRTG2150]},
Funding-Text = {This work was supported by the Deutsche Forschungsgemeinschaft (DFG,
   BZ2/2-1, BZ2/3-1, and BZ2/4-1; International Research Training Group
   IRTG2150), Amazon AWS Research Grant (2016 and 2017), the German
   National Merit Foundation, and the START-Program of the Faculty of
   Medicine, RWTH Aachen.},
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Number-of-Cited-References = {222},
Times-Cited = {64},
Usage-Count-Last-180-days = {2},
Usage-Count-Since-2013 = {35},
Journal-ISO = {Front. Neurosci.},
Doc-Delivery-Number = {FI9JH},
Web-of-Science-Index = {Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)},
Unique-ID = {WOS:000412322500002},
OA = {Green Published, Green Submitted, gold},
DA = {2023-04-22},
}

@article{ WOS:000508190300008,
Author = {Alexander, Francis and Almgren, Ann and Bell, John and Bhattacharjee,
   Amitava and Chen, Jacqueline and Colella, Phil and Daniel, David and
   DeSlippe, Jack and Diachin, Lori and Draeger, Erik and Dubey, Anshu and
   Dunning, Thom and Evans, Thomas and Foster, Ian and Francois, Marianne
   and Germann, Tim and Gordon, Mark and Habib, Salman and Halappanavar,
   Mahantesh and Hamilton, Steven and Hart, William and Huang, Zhenyu
   (Henry) and Hungerford, Aimee and Kasen, Daniel and Kent, Paul R. C. and
   Kolev, Tzanio and Kothe, Douglas B. and Kronfeld, Andreas and Luo, Ye
   and Mackenzie, Paul and McCallen, David and Messer, Bronson and
   Mniszewski, Sue and Oehmen, Chris and Perazzo, Amedeo and Perez, Danny
   and Richards, David and Rider, William J. and Rieben, Rob and Roche,
   Kenneth and Siegel, Andrew and Sprague, Michael and Steefel, Carl and
   Stevens, Rick and Syamlal, Madhava and Taylor, Mark and Turner, John and
   Vay, Jean-Luc and Voter, Artur F. and Windus, Theresa L. and Yelick,
   Katherine},
Title = {Exascale applications: skin in the game},
Journal = {PHILOSOPHICAL TRANSACTIONS OF THE ROYAL SOCIETY A-MATHEMATICAL PHYSICAL
   AND ENGINEERING SCIENCES},
Year = {2020},
Volume = {378},
Number = {2166, SI},
Month = {MAR 6},
Abstract = {As noted in Wikipedia, skin in the game refers to having `incurred risk
   by being involved in achieving a goal', where `skin is a synecdoche for
   the person involved, and game is the metaphor for actions on the field
   of play under discussion'. For exascale applications under development
   in the US Department of Energy Exascale Computing Project, nothing could
   be more apt, with the skin being exascale applications and the game
   being delivering comprehensive science-based computational applications
   that effectively exploit exascale high-performance computing
   technologies to provide breakthrough modelling and simulation and data
   science solutions. These solutions will yield high-confidence insights
   and answers to the most critical problems and challenges for the USA in
   scientific discovery, national security, energy assurance, economic
   competitiveness and advanced healthcare. This article is part of a
   discussion meeting issue `Numerical algorithms for high-performance
   computational science'.},
Publisher = {ROYAL SOC},
Address = {6-9 CARLTON HOUSE TERRACE, LONDON SW1Y 5AG, ENGLAND},
Type = {Review},
Language = {English},
Affiliation = {Kothe, DB (Corresponding Author), Oak Ridge Natl Lab, Oak Ridge, TN 37830 USA.
   Alexander, Francis, Brookhaven Natl Lab, Upton, NY 11973 USA.
   Almgren, Ann; Bell, John; Colella, Phil; DeSlippe, Jack; Kasen, Daniel; McCallen, David; Steefel, Carl; Vay, Jean-Luc; Yelick, Katherine, Lawrence Berkeley Natl Lab, Berkeley, CA USA.
   Bhattacharjee, Amitava, Princeton Plasma Phys Lab, POB 451, Princeton, NJ 08543 USA.
   Chen, Jacqueline; Hart, William; Rider, William J.; Taylor, Mark, Sandia Natl Labs, POB 5800, Albuquerque, NM 87185 USA.
   Daniel, David; Francois, Marianne; Germann, Tim; Hungerford, Aimee; Mniszewski, Sue; Perez, Danny; Voter, Artur F., Los Alamos Natl Lab, Los Alamos, NM USA.
   Diachin, Lori; Draeger, Erik; Kolev, Tzanio; Richards, David; Rieben, Rob, Lawrence Livermore Natl Lab, Livermore, CA 94550 USA.
   Dubey, Anshu; Foster, Ian; Habib, Salman; Luo, Ye; Siegel, Andrew; Stevens, Rick, Argonne Natl Lab, 9700 S Cass Ave, Argonne, IL 60439 USA.
   Dunning, Thom; Halappanavar, Mahantesh; Huang, Zhenyu (Henry); Oehmen, Chris; Roche, Kenneth, Pacific Northwest Natl Lab, Richland, WA 99352 USA.
   Evans, Thomas; Hamilton, Steven; Kent, Paul R. C.; Kothe, Douglas B.; Messer, Bronson; Turner, John, Oak Ridge Natl Lab, Oak Ridge, TN 37830 USA.
   Gordon, Mark; Windus, Theresa L., Ames Lab, Ames, IA USA.
   Kronfeld, Andreas; Mackenzie, Paul, Fermilab Natl Accelerator Lab, POB 500, Batavia, IL 60510 USA.
   Perazzo, Amedeo, SLAC Natl Accelerator Lab, Menlo Pk, CA USA.
   Sprague, Michael, Natl Renewable Energy Lab, Golden, CO USA.
   Syamlal, Madhava, Natl Energy Technol Lab, Morgantown, WV USA.},
DOI = {10.1098/rsta.2019.0056},
Article-Number = {20190056},
ISSN = {1364-503X},
EISSN = {1471-2962},
Keywords = {exascale; high-performance computing; computational science
   applications; numerical algorithms; machine learning; modelling and
   simulation},
Keywords-Plus = {SIMULATIONS; SOLIDIFICATION; IMPLEMENTATION; DYNAMICS; MODEL},
Research-Areas = {Science \& Technology - Other Topics},
Web-of-Science-Categories  = {Multidisciplinary Sciences},
Author-Email = {kothe@ornl.gov},
Affiliations = {United States Department of Energy (DOE); Brookhaven National
   Laboratory; United States Department of Energy (DOE); Lawrence Berkeley
   National Laboratory; Princeton University; United States Department of
   Energy (DOE); Princeton Plasma Physics Laboratory; United States
   Department of Energy (DOE); Sandia National Laboratories; United States
   Department of Energy (DOE); Los Alamos National Laboratory; United
   States Department of Energy (DOE); Lawrence Livermore National
   Laboratory; United States Department of Energy (DOE); Argonne National
   Laboratory; United States Department of Energy (DOE); Pacific Northwest
   National Laboratory; United States Department of Energy (DOE); Oak Ridge
   National Laboratory; United States Department of Energy (DOE); Ames
   National Laboratory; United States Department of Energy (DOE);
   University of Chicago; Fermi National Accelerator Laboratory; Stanford
   University; United States Department of Energy (DOE); SLAC National
   Accelerator Laboratory; United States Department of Energy (DOE);
   National Renewable Energy Laboratory - USA; United States Department of
   Energy (DOE); National Energy Technology Laboratory - USA},
ResearcherID-Numbers = {Steefel, Carl I/B-7758-2010
   Kent, Paul R C/A-6756-2008
   Perez, Danny/AEQ-0157-2022
   Foster, Ian/GNH-1877-2022
   Mniszewski, Susan/AAX-2025-2020
   Germann, Timothy C./ABF-5034-2021
   Messer, Bronson/G-1848-2012
   Evans, Thomas/B-4405-2018
   Turner, John/B-7159-2018
   },
ORCID-Numbers = {Kent, Paul R C/0000-0001-5539-4017
   Perez, Danny/0000-0003-3028-5249
   Foster, Ian/0000-0003-2129-5269
   Germann, Timothy C./0000-0002-6813-238X
   Bell, John/0000-0002-5749-334X
   Messer, Bronson/0000-0002-5358-5415
   Kronfeld, Andreas/0000-0003-2716-1149
   Syamlal, Madhava/0000-0002-9450-0609
   Vay, Jean-Luc/0000-0002-0040-799X
   Evans, Thomas/0000-0001-5743-3788
   Roche, Kenneth/0000-0002-5675-9265
   Turner, John/0000-0003-2521-4091
   Hamilton, Steven/0000-0003-3542-114X
   Windus, Theresa/0000-0001-6065-3167
   Daniel, David/0000-0002-4873-6856
   Almgren, Ann/0000-0003-2103-312X
   Luo, Ye/0000-0002-5117-2385},
Funding-Acknowledgement = {Office of Science of the US Department of Energy {[}DE-AC05-00OR22725];
   DOE Office of Science User Facility {[}DE-AC02-06CH11357]; National
   Energy Research Scientific Computing Center (NERSC), a US Department of
   Energy Office of Science User Facility {[}DE-AC02-05CH11231]; Exascale
   Computing Project {[}17-SC-20-SC]},
Funding-Text = {This research was supported by the Exascale Computing Project (grant no.
   17-SC-20-SC), a collaborative effort of two US DOE organizations (Office
   of Science and the National Nuclear Security Administration) responsible
   for the planning and preparation of a capable exascale ecosystem,
   including software, applications, hardware technology R\&D, and
   integration of these technologies onto DOE HPC systems, in support of
   the nation's exascale computing imperative. This research used resources
   of the Oak Ridge Leadership Computing Facility at the Oak Ridge National
   Laboratory, which is supported by the Office of Science of the US
   Department of Energy under Contract No. DE-AC05-00OR22725. This research
   used resources of the Argonne Leadership Computing Facility, which is a
   DOE Office of Science User Facility supported under Contract
   DE-AC02-06CH11357. This research used resources of the National Energy
   Research Scientific Computing Center (NERSC), a US Department of Energy
   Office of Science User Facility operated under Contract No.
   DE-AC02-05CH11231.},
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Number-of-Cited-References = {62},
Times-Cited = {40},
Usage-Count-Last-180-days = {3},
Usage-Count-Since-2013 = {19},
Journal-ISO = {Philos. Trans. R. Soc. A-Math. Phys. Eng. Sci.},
Doc-Delivery-Number = {KD9OI},
Web-of-Science-Index = {Science Citation Index Expanded (SCI-EXPANDED)},
Unique-ID = {WOS:000508190300008},
OA = {Green Submitted, hybrid, Green Published},
DA = {2023-04-22},
}

@article{ WOS:000619158000001,
Author = {Johansen, Thomas Haugland and Mollersen, Kajsa and Ortega, Samuel and
   Fabelo, Himar and Garcia, Aday and Callico, Gustavo M. and Godtliebsen,
   Fred},
Title = {Recent advances in hyperspectral imaging for melanoma detection},
Journal = {WILEY INTERDISCIPLINARY REVIEWS-COMPUTATIONAL STATISTICS},
Year = {2020},
Volume = {12},
Number = {1},
Month = {JAN-FEB},
Abstract = {Skin cancer is one of the most common types of cancer. Skin cancers are
   classified as nonmelanoma and melanoma, with the first type being the
   most frequent and the second type being the most deadly. The key to
   effective treatment of skin cancer is early detection. With the recent
   increase of computational power, the number of algorithms to detect and
   classify skin lesions has increased. The overall verdict on systems
   based on clinical and dermoscopic images captured with conventional RGB
   (red, green, and blue) cameras is that they do not outperform
   dermatologists. Computer-based systems based on conventional RGB images
   seem to have reached an upper limit in their performance, while emerging
   technologies such as hyperspectral and multispectral imaging might
   possibly improve the results. These types of images can explore spectral
   regions beyond the human eye capabilities. Feature selection and
   dimensionality reduction are crucial parts of extracting salient
   information from this type of data. It is necessary to extend current
   classification methodologies to use all of the spatiospectral
   information, and deep learning models should be explored since they are
   capable of learning robust feature detectors from data. There is a lack
   of large, high-quality datasets of hyperspectral skin lesion images, and
   there is a need for tools that can aid with monitoring the evolution of
   skin lesions over time. To understand the rich information contained in
   hyperspectral images, further research using data science and
   statistical methodologies, such as functional data analysis, scale-space
   theory, machine learning, and so on, are essential.
   This article is categorized under:
   Applications of Computational Statistics > Health and Medical
   Data/Informatics},
Publisher = {WILEY},
Address = {111 RIVER ST, HOBOKEN 07030-5774, NJ USA},
Type = {Review},
Language = {English},
Affiliation = {Johansen, TH (Corresponding Author), UiT Arctic Univ Norway, Dept Math \& Stat, Tromso, Norway.
   Johansen, Thomas Haugland; Godtliebsen, Fred, UiT Arctic Univ Norway, Dept Math \& Stat, Tromso, Norway.
   Mollersen, Kajsa, UiT Arctic Univ Norway, Dept Community Med, Tromso, Norway.
   Ortega, Samuel; Fabelo, Himar; Garcia, Aday; Callico, Gustavo M., Univ Las Palmas Gran Canaria, Inst Appl Microelect, Las Palmas Gran Canaria, Spain.},
DOI = {10.1002/wics.1465},
Article-Number = {e1465},
ISSN = {1939-0068},
Keywords = {hyperspectral; machine learning; melanoma; skin cancer},
Keywords-Plus = {NEURAL-NETWORK; ABCD RULE; SKIN; DIAGNOSIS; CLASSIFICATION; SYSTEM;
   INDEX; DERMATOSCOPY; SPECIFICITY; SENSITIVITY},
Research-Areas = {Mathematics},
Web-of-Science-Categories  = {Statistics \& Probability},
Author-Email = {thomas.h.johansen@uit.no},
Affiliations = {UiT The Arctic University of Tromso; UiT The Arctic University of
   Tromso; Universidad de Las Palmas de Gran Canaria},
ResearcherID-Numbers = {Callico, Gustavo Marrero/L-6036-2014
   Ortega, Samuel/C-3864-2018
   Fabelo, Himar/S-1009-2019
   },
ORCID-Numbers = {Callico, Gustavo Marrero/0000-0002-3784-5504
   Ortega, Samuel/0000-0002-7519-954X
   Fabelo, Himar/0000-0002-9794-490X
   Johansen, Thomas/0000-0003-3572-4706},
Funding-Acknowledgement = {Agencia Canaria de Investigacion, Innovacion y Sociedad de la
   Informacion (ACIISI) {[}ProID2017010164]; Spanish Government; European
   Union (FEDER funds) {[}TEC2017-86722-C4-1-R]; European Social Fund
   (FSE); Tromso Forskningsstiftelse {[}A33020]},
Funding-Text = {Agencia Canaria de Investigacion, Innovacion y Sociedad de la
   Informacion (ACIISI), Grant/Award Number: ProID2017010164; Spanish
   Government and European Union (FEDER funds), Grant/Award Number:
   TEC2017-86722-C4-1-R; European Social Fund (FSE) and Agencia Canaria de
   Investigacion, Innovacion y Sociedad de la Informacion (ACIISI),
   Grant/Award Number: POC 2014-2020, Eje 3 Tema Prioritario 74(85\%);
   Tromso Forskningsstiftelse, Grant/Award Number: A33020},
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Number-of-Cited-References = {77},
Times-Cited = {27},
Usage-Count-Last-180-days = {3},
Usage-Count-Since-2013 = {21},
Journal-ISO = {Wiley Interdiscip. Rev.-Comput. Stat.},
Doc-Delivery-Number = {QI7JG},
Web-of-Science-Index = {Science Citation Index Expanded (SCI-EXPANDED)},
Unique-ID = {WOS:000619158000001},
OA = {Green Accepted},
DA = {2023-04-22},
}

@article{ WOS:000709352300003,
Author = {Santos, Jessica S. and Bernardini, Flavia and Paes, Aline},
Title = {A survey on the use of data and opinion mining in social media to
   political electoral outcomes prediction},
Journal = {SOCIAL NETWORK ANALYSIS AND MINING},
Year = {2021},
Volume = {11},
Number = {1},
Month = {DEC},
Abstract = {Election polls are the de facto mechanisms to predict political
   outcomes. Traditionally, they are conducted with personal interviews and
   questionnaires. This process is costly and time consuming, demanding the
   development of alternative approaches faster and less expensive. On the
   other hand, social media emerge as important tools for people to express
   their opinions about candidates in electoral scenarios. In this context,
   there is an increasing number of election prediction approaches using
   social media and opinion mining, modeling this problem in different
   ways. In this work, we present a survey on approaches to election
   predictions and discuss many possibilities of decisions in the general
   process of constructing solutions to this end, including the quantity of
   collected data, the specific social media used, the collection period,
   the algorithms and prediction approaches adopted, among others aspects.
   Our overview allowed us to identify the main factors that should be
   considered when predicting elections outcomes supported by social media
   content, as well as the main open issues and limitations of the
   approaches found in the literature for data science communities. In
   brief, the main challenges that we have found include but are not
   limited to: labeling data reliably during the short period of electoral
   campaigns, absence of a robust methodology to collect and analyze data,
   non-availability of domain (labeled) datasets, a lack of a pattern to
   evaluate the obtained results and exploration of new machine learning
   algorithms and methods for tackling the peculiarities of this scenario.},
Publisher = {SPRINGER WIEN},
Address = {SACHSENPLATZ 4-6, PO BOX 89, A-1201 WIEN, AUSTRIA},
Type = {Review},
Language = {English},
Affiliation = {Santos, JS (Corresponding Author), Univ Fed Fluminense, Inst Comp, Rio De Janeiro, Brazil.
   Santos, Jessica S.; Bernardini, Flavia; Paes, Aline, Univ Fed Fluminense, Inst Comp, Rio De Janeiro, Brazil.},
DOI = {10.1007/s13278-021-00813-4},
Article-Number = {103},
ISSN = {1869-5450},
EISSN = {1869-5469},
Keywords = {Election prediction; Election forecast; Election outcomes; Social media;
   Sentiment analysis; Machine learning for opinion mining},
Keywords-Plus = {140 CHARACTERS; ELECTION; TWITTER; SENTIMENT; POLLS},
Research-Areas = {Computer Science},
Web-of-Science-Categories  = {Computer Science, Information Systems},
Author-Email = {jessicasoares@id.uff.br
   fcbernardini@ic.uff.br
   alinepaes@ic.uff.br},
Affiliations = {Universidade Federal Fluminense},
Funding-Acknowledgement = {Brazilian Research CNPq APQ Universal {[}421608/2018-8]; FAPERJ
   {[}E26/202.914/2019 (247109)]; Microsoft Research Grant; Coordenacao de
   Aperfeicoamento de Pessoal de Nivel Superior-Brazil (CAPES); CNPq
   {[}311275/2020-6]},
Funding-Text = {This research was supported by the Brazilian Research CNPq APQ Universal
   (Grant 421608/2018-8), CNPq Research Grant 311275/2020-6, FAPERJ
   Research Grant E26/202.914/2019 (247109), Microsoft Research Grant and
   Coordenacao de Aperfeicoamento de Pessoal de Nivel Superior-Brazil
   (CAPES).},
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Number-of-Cited-References = {106},
Times-Cited = {0},
Usage-Count-Last-180-days = {2},
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Journal-ISO = {Soc. Netw. Anal. Min.},
Doc-Delivery-Number = {WJ9JJ},
Web-of-Science-Index = {Emerging Sources Citation Index (ESCI)},
Unique-ID = {WOS:000709352300003},
DA = {2023-04-22},
}

@article{ WOS:000595541100011,
Author = {Jain, Piyush and Coogan, Sean C. P. and Subramanian, Sriram Ganapathi
   and Crowley, Mark and Taylor, Steve and Flannigan, Mike D.},
Title = {A review of machine learning applications in wildfire science and
   management},
Journal = {ENVIRONMENTAL REVIEWS},
Year = {2020},
Volume = {28},
Number = {4},
Pages = {478-505},
Month = {DEC},
Abstract = {Artificial intelligence has been applied in wildfire science and
   management since the 1990s, with early applications including neural
   networks and expert systems. Since then, the field has rapidly
   progressed congruently with the wide adoption of machine learning (ML)
   methods in the environmental sciences. Here, we present a scoping review
   of ML applications in wildfire science and management. Our overall
   objective is to improve awareness of ML methods among wildfire
   researchers and managers, as well as illustrate the diverse and
   challenging range of problems in wildfire science available to ML data
   scientists. To that end, we first present an overview of popular ML
   approaches used in wildfire science to date and then review the use of
   ML in wildfire science as broadly categorized into six problem domains,
   including (i) fuels characterization, fire detection, and mapping; (ii)
   fire weather and climate change; (iii) fire occurrence, susceptibility,
   and risk; (iv) fire behavior prediction; (v) fire effects; and (vi) fire
   management. Furthermore, we discuss the advantages and limitations of
   various ML approaches relating to data size, computational requirements,
   generalizability, and interpretability, as well as identify
   opportunities for future advances in the science and management of
   wildfires within a data science context. In total, to the end of 2019,
   we identified 300 relevant publications in which the most frequently
   used ML methods across problem domains included random forests, MaxEnt,
   artificial neural networks, decision trees, support vector machines, and
   genetic algorithms. As such, there exists opportunities to apply more
   current ML methods - including deep learning and agent-based learning -
   in the wildfire sciences, especially in instances involving very large
   multivariate datasets. We must recognize, however, that despite the
   ability of ML models to learn on their own, expertise in wildfire
   science is necessary to ensure realistic modelling of fire processes
   across multiple scales, while the complexity of some ML methods such as
   deep learning requires a dedicated and sophisticated knowledge of their
   application. Finally, we stress that the wildfire research and
   management communities play an active role in providing relevant,
   high-quality, and freely available wildfire data for use by
   practitioners of ML methods.},
Publisher = {CANADIAN SCIENCE PUBLISHING},
Address = {65 AURIGA DR, SUITE 203, OTTAWA, ON K2E 7W6, CANADA},
Type = {Review},
Language = {English},
Affiliation = {Jain, P (Corresponding Author), Nat Resources Canada, Canadian Forest Serv, Northern Forestry Ctr, Edmonton, AB T6H 3S5, Canada.
   Jain, P (Corresponding Author), Univ Alberta, Renewable Resources, Canadian Partnership Wildland Fire Sci, Edmonton, AB T6G 2H1, Canada.
   Jain, Piyush, Nat Resources Canada, Canadian Forest Serv, Northern Forestry Ctr, Edmonton, AB T6H 3S5, Canada.
   Jain, Piyush; Coogan, Sean C. P.; Flannigan, Mike D., Univ Alberta, Renewable Resources, Canadian Partnership Wildland Fire Sci, Edmonton, AB T6G 2H1, Canada.
   Subramanian, Sriram Ganapathi; Crowley, Mark, Univ Waterloo, Elect \& Comp Engn, Waterloo, ON N2L 3G1, Canada.
   Taylor, Steve, Nat Resources Canada, Canadian Forest Serv, Pacific Forestry Ctr, Victoria, BC V8Z 1M5, Canada.},
DOI = {10.1139/er-2020-0019},
ISSN = {1208-6053},
EISSN = {1181-8700},
Keywords = {machine learning; wildfire science; fire management; wildland fire;
   support vector machine; artificial neural network; decision trees;
   Bayesian networks; reinforcement learning; deep learning},
Keywords-Plus = {FOREST-FIRE SUSCEPTIBILITY; ARTIFICIAL NEURAL-NETWORK; SUPPORT VECTOR
   REGRESSION; BURN SEVERITY ASSESSMENT; LOGISTIC-REGRESSION; POSTFIRE
   REGENERATION; SPATIAL PREDICTION; GENETIC ALGORITHM; FEATURE-SELECTION;
   ADAPTIVE PREDICTION},
Research-Areas = {Environmental Sciences \& Ecology},
Web-of-Science-Categories  = {Environmental Sciences},
Author-Email = {piyush.jain@canada.ca},
Affiliations = {Natural Resources Canada; Canadian Forest Service; University of
   Alberta; University of Waterloo; Natural Resources Canada; Canadian
   Forest Service},
ResearcherID-Numbers = {Flannigan, Michael/G-6996-2015},
ORCID-Numbers = {Crowley, Mark/0000-0003-3921-4762
   Flannigan, Michael/0000-0002-9970-5363},
Funding-Acknowledgement = {Intact Insurance; Western Partnership for Wildland Fire Science},
Funding-Text = {The motivation for this paper arose from the ``Not the New Normal{''}
   B.C. AI Wildfire Symposium held in Vancouver, B.C., on 12 October 2018.
   The authors would also like to thank Intact Insurance and the Western
   Partnership for Wildland Fire Science for their support.},
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@article{ WOS:000705663500007,
Author = {Masood, Adil and Ahmad, Kafeel},
Title = {A review on emerging artificial intelligence (AI) techniques for air
   pollution forecasting: Fundamentals, application and performance},
Journal = {JOURNAL OF CLEANER PRODUCTION},
Year = {2021},
Volume = {322},
Month = {NOV 1},
Abstract = {Accurate air quality forecasting is critical for systematic pollution
   control as well as public health and wellness. Most of the traditional
   forecasting techniques have shown inconsistent predictive accuracy due
   to the non-linear, dynamic and complex nature of air pollutants. In the
   past few years, artificial intelligence (AI)-based methods have become
   the most powerful and forward-looking approaches for air pollution
   forecasting because of their specific features such as organic learning,
   high precision, superior generalization, strong fault tolerance, and
   ease of working with high-dimensional data. This study presents a
   comprehensive overview of the most widely used AI-based techniques for
   air pollution forecasting namely Artificial Neural Networks (ANN), Deep
   Neural Network (DNN), Support vector machine (SVM) and Fuzzy logic
   through a systematic literature review (SLR). In total 90 papers were
   selected which were distributed between 2003 and 2021. The SLR aims to
   classify the literature on AI-based air pollution forecasting from
   various perspectives, such as input parameters, relative frequency of
   application of AI techniques, performance, year of publication, journal
   and geographic distribution and also addresses the corresponding
   research questions related to this domain. The results showed that the
   number of citations and publications have been increasing in recent
   years. The most frequently applied input parameter is the air quality
   and the best performing AI-based technique is the DNN. On the other
   hand, Fuzzy logic, DNN and SVM are the three commonly used AI-based
   techniques for air pollution forecasting. In addition, some
   technological gaps in the literature and the pros and cons associated
   with the different AI techniques, were identified and discussed. This
   review article shows that AI-based techniques have triggered a
   resurgence of interest in air pollution forecasting and offer great
   potential to fundamentally change the way air pollution is forecasted in
   the near future.},
Publisher = {ELSEVIER SCI LTD},
Address = {THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND},
Type = {Review},
Language = {English},
Affiliation = {Masood, A (Corresponding Author), Jamia Millia Islamia, Dept Civil Engn, New Delhi 110025, India.
   Masood, Adil; Ahmad, Kafeel, Jamia Millia Islamia, Dept Civil Engn, New Delhi 110025, India.},
DOI = {10.1016/j.jclepro.2021.129072},
EarlyAccessDate = {SEP 2021},
Article-Number = {129072},
ISSN = {0959-6526},
EISSN = {1879-1786},
Keywords = {Air pollution; Artificial neural networks; Deep neural networks;
   Relative frequency; Fuzzy logic; Support vector machine; Systematic
   literature review},
Keywords-Plus = {NEURAL-NETWORK MODEL; EARLY-WARNING SYSTEM; SUPPORT VECTOR MACHINE;
   FUZZY TIME-SERIES; OZONE CONCENTRATION; URBAN AREA; PM2.5 CONCENTRATION;
   ENSEMBLE MODEL; SO2 CONCENTRATIONS; REGRESSION-MODELS},
Research-Areas = {Science \& Technology - Other Topics; Engineering; Environmental
   Sciences \& Ecology},
Web-of-Science-Categories  = {Green \& Sustainable Science \& Technology; Engineering, Environmental;
   Environmental Sciences},
Author-Email = {adil.engg.cvl@gmail.com
   kahmad2@jmi.ac.in},
Affiliations = {Jamia Millia Islamia},
ResearcherID-Numbers = {Ahmad, Kafeel/ABD-9190-2021},
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@article{ WOS:000836510100013,
Author = {Mora, Javier},
Title = {Projections of data science in cardiac surgery},
Journal = {REVISTA MEDICA CLINICA LAS CONDES},
Year = {2022},
Volume = {33},
Number = {3},
Pages = {294-306},
Month = {MAY-JUN},
Abstract = {Artificial Intelligence (AI) has the current impact that electricity
   had; and data, its raw material, is the new oil of the modern age.
   Finding the value of it in the context of its clinical utility is the
   link that brings computer science closer to health science.
   Decision-making based on proprietary data supported by computational
   algorithms that ``learn{''} to solve a problem with large amounts of
   data is leading personalized and precision medicine to play an
   increasingly relevant role. Surgery is no exception.
   With technologies that seem to be futuristic and distant, and sometimes
   confusing, it is a duty of popular science to publish the state of the
   art of a reality with which we live daily. Knowing the bases of the
   computer since from publications of Alan Turing in 1937, allows us to
   better understand why we are witnessing today a historical moment in
   which a large volume of data converges with a great computational power,
   which has led to the current development of these techniques, that today
   we see transversally in all industries, such as in health.
   That is why this review aims to gather and clarify the most relevant
   concepts of data science in general, linked to its applications and
   development in medicine and cardiac surgery. With this, it is intended
   to achieve an approach, at least theoretical, to the full potential that
   modern techniques in data computation allow.},
Publisher = {ELSEVIER},
Address = {RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS},
Type = {Review},
Language = {Spanish},
Affiliation = {Mora, J (Corresponding Author), Clin Las Condes, Ciencia Datos Clin, Cirugia Cardiovasc ECMO, Santiago, Chile.
   Mora, Javier, Clin Las Condes, Ciencia Datos Clin, Cirugia Cardiovasc ECMO, Santiago, Chile.},
DOI = {10.1016/j.rmclc.2022.05.007},
EarlyAccessDate = {JUN 2022},
EISSN = {0716-8640},
Keywords = {Data Sciences; Machine Learning; Artificial Intelligence; Deep Learning;
   Clinical Decision Support Systems; Cardiac Surgery; Data},
Keywords-Plus = {SUPPORT},
Research-Areas = {General \& Internal Medicine},
Web-of-Science-Categories  = {Medicine, General \& Internal},
Author-Email = {jmorap@clinicalascondes.cl},
Affiliations = {Clinica Las Condes},
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Web-of-Science-Index = {Emerging Sources Citation Index (ESCI)},
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OA = {gold},
DA = {2023-04-22},
}

@article{ WOS:000642903600001,
Author = {Xia, Jun and Li, Zhe and Zeng, Sidong and Zou, Lei and She, Dunxian and
   Cheng, Dandong},
Title = {Perspectives on eco-water security and sustainable development in the
   Yangtze River Basin},
Journal = {GEOSCIENCE LETTERS},
Year = {2021},
Volume = {8},
Number = {1},
Month = {APR 23},
Abstract = {The Yangtze River, the largest river in China, has been facing major
   challenges in massive flooding and eco-environmental health over the
   past decades. Sustainable socioeconomic development in the Yangtze River
   Basin depends on water and ecosystem security. This overview addresses
   eco-water security under the changing environment of the Yangtze River
   Basin. Looking forward to a healthy Yangtze River in the future, there
   are still uncertainties regarding how to assess and wisely manage the
   Yangtze River through a systematic, integrated approach applied to
   multiple dimensions, water, biodiversity, ecological services, and
   resilience, for the sustainable development of ecosystems and human
   beings. The Yangtze Simulator, an integrated river basin model powered
   by artificial intelligence and interdisciplinary science, is introduced
   and discussed, and it will serve as a robust tool for good governance of
   the Yangtze River Basin.},
Publisher = {SPRINGER},
Address = {ONE NEW YORK PLAZA, SUITE 4600, NEW YORK, NY, UNITED STATES},
Type = {Review},
Language = {English},
Affiliation = {Xia, J (Corresponding Author), Wuhan Univ, State Key Lab Water Resources \& Hydro Power Engn, Wuhan 430072, Peoples R China.
   Xia, J (Corresponding Author), Chinese Acad Sci, Inst Geog Sci \& Nat Resources Res, Beijing 100101, Peoples R China.
   Xia, Jun; She, Dunxian, Wuhan Univ, State Key Lab Water Resources \& Hydro Power Engn, Wuhan 430072, Peoples R China.
   Xia, Jun; Zou, Lei, Chinese Acad Sci, Inst Geog Sci \& Nat Resources Res, Beijing 100101, Peoples R China.
   Li, Zhe; Zeng, Sidong, Chinese Acad Sci, Chongqing Inst Green \& Lntelligent Technol, Chongqing 400714, Peoples R China.
   Cheng, Dandong, Northwest Univ, Coll Urban \& Environm Sci, Xian 710127, Peoples R China.},
DOI = {10.1186/s40562-021-00187-7},
Article-Number = {18},
ISSN = {2196-4092},
Keywords = {Eco-water security; Environmental change; Sustainable development;
   Yangtze Simulator; Integrated management},
Research-Areas = {Geology; Meteorology \& Atmospheric Sciences},
Web-of-Science-Categories  = {Geosciences, Multidisciplinary; Meteorology \& Atmospheric Sciences},
Author-Email = {xiajun666@whu.edu.cn},
Affiliations = {Wuhan University; Chinese Academy of Sciences; Institute of Geographic
   Sciences \& Natural Resources Research, CAS; Chinese Academy of
   Sciences; Northwest University Xi'an},
ResearcherID-Numbers = {Li, Zhe/J-4626-2016},
ORCID-Numbers = {Li, Zhe/0000-0003-1743-7906},
Funding-Acknowledgement = {Strategic Priority Research Program of the Chinese Academy of Sciences
   {[}XDA23040304]; National Natural Science Foundation of China
   {[}41890823]},
Funding-Text = {This study was financially supported by the Strategic Priority Research
   Program of the Chinese Academy of Sciences (Grant no. XDA23040304),
   National Natural Science Foundation of China (No. 41890823).},
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Number-of-Cited-References = {55},
Times-Cited = {9},
Usage-Count-Last-180-days = {45},
Usage-Count-Since-2013 = {112},
Journal-ISO = {Geosci. Lett.},
Doc-Delivery-Number = {RR1YX},
Web-of-Science-Index = {Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)},
Unique-ID = {WOS:000642903600001},
OA = {gold},
DA = {2023-04-22},
}

@article{ WOS:000484387600025,
Author = {Parselia, Elisavet and Kontoes, Charalampos and Tsouni, Alexia and
   Hadjichristodoulou, Christos and Kioutsioukis, Ioannis and Magiorkinis,
   Gkikas and Stilianakis, I, Nikolaos},
Title = {Satellite Earth Observation Data in Epidemiological Modeling of Malaria,
   Dengue and West Nile Virus: A Scoping Review},
Journal = {REMOTE SENSING},
Year = {2019},
Volume = {11},
Number = {16},
Month = {AUG},
Abstract = {Earth Observation (EO) data can be leveraged to estimate environmental
   variables that influence the transmission cycle of the pathogens that
   lead to mosquito-borne diseases (MBDs). The aim of this scoping review
   is to examine the state-of-the-art and identify knowledge gaps on the
   latest methods that used satellite EO data in their epidemiological
   models focusing on malaria, dengue and West Nile Virus (WNV). In total,
   43 scientific papers met the inclusion criteria and were considered in
   this review. Researchers have examined a wide variety of methodologies
   ranging from statistical to machine learning algorithms. A number of
   studies used models and EO data that seemed promising and claimed to be
   easily replicated in different geographic contexts, enabling the
   realization of systems on regional and national scales. The need has
   emerged to leverage furthermore new powerful modeling approaches, like
   artificial intelligence and ensemble modeling and explore new and
   enhanced EO sensors towards the analysis of big satellite data, in order
   to develop accurate epidemiological models and contribute to the
   reduction of the burden of MBDs.},
Publisher = {MDPI},
Address = {ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND},
Type = {Review},
Language = {English},
Affiliation = {Parselia, E (Corresponding Author), Natl Observ Athens, Inst Space Applicat \& Remote Sensing, Athens 15236, Greece.
   Parselia, Elisavet; Kontoes, Charalampos; Tsouni, Alexia, Natl Observ Athens, Inst Space Applicat \& Remote Sensing, Athens 15236, Greece.
   Hadjichristodoulou, Christos, Univ Thessaly, Fac Med, Dept Hyg \& Epidemiol, Larisa 41500, Greece.
   Kioutsioukis, Ioannis, Univ Patras, Dept Phys, Rion 26504, Greece.
   Magiorkinis, Gkikas, Univ Athens, Med Sch, Dept Hyg Epidemiol \& Med Stat, Athens 11527, Greece.
   Stilianakis, Nikolaos, I, European Commiss, Joint Res Ctr, I-21027 Ispra, VA, Italy.
   Stilianakis, Nikolaos, I, Univ Erlangen Nurnberg, Dept Biometry \& Epidemiol, D-91054 Erlangen, Germany.},
DOI = {10.3390/rs11161862},
Article-Number = {1862},
EISSN = {2072-4292},
Keywords = {mosquito-borne infectious diseases; Satellite Earth Observation data;
   epidemiological modeling; entomological data; vector-borne diseases;
   Earth Observation for health; malaria; dengue; West Nile Virus; scoping
   review},
Keywords-Plus = {VEGETATION INDEX; AEDES-AEGYPTI; FEVER RISK; TEMPERATURE;
   EVAPOTRANSPIRATION; TRANSMISSION; DYNAMICS; DISTRICT; IMAGERY; URBAN},
Research-Areas = {Environmental Sciences \& Ecology; Geology; Remote Sensing; Imaging
   Science \& Photographic Technology},
Web-of-Science-Categories  = {Environmental Sciences; Geosciences, Multidisciplinary; Remote Sensing;
   Imaging Science \& Photographic Technology},
Author-Email = {eparselia@noa.gr},
Affiliations = {National Observatory of Athens; University of Thessaly; University of
   Patras; National \& Kapodistrian University of Athens; European
   Commission Joint Research Centre; EC JRC ISPRA Site; University of
   Erlangen Nuremberg},
ResearcherID-Numbers = {KONTOES, Charalampos/L-5514-2013
   Kioutsioukis, Ioannis/B-6866-2016
   },
ORCID-Numbers = {Hadjichristodoulou, Christos/0000-0002-4769-8376
   Tsouni, Alexia/0000-0003-2223-1697
   Parselia, Elisavet/0000-0002-2852-1882
   Kioutsioukis, Ioannis/0000-0002-4653-8442},
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Number-of-Cited-References = {101},
Times-Cited = {35},
Usage-Count-Last-180-days = {4},
Usage-Count-Since-2013 = {17},
Journal-ISO = {Remote Sens.},
Doc-Delivery-Number = {IV6OL},
Web-of-Science-Index = {Science Citation Index Expanded (SCI-EXPANDED)},
Unique-ID = {WOS:000484387600025},
OA = {Green Submitted, gold},
DA = {2023-04-22},
}

@article{ WOS:000684736000001,
Author = {McLean, Scott and Read, Gemma J. M. and Thompson, Jason and Baber, Chris
   and Stanton, Neville A. and Salmon, Paul M.},
Title = {The risks associated with Artificial General Intelligence: A systematic
   review},
Journal = {JOURNAL OF EXPERIMENTAL \& THEORETICAL ARTIFICIAL INTELLIGENCE},
Abstract = {Artificial General intelligence (AGI) offers enormous benefits for
   humanity, yet it also poses great risk. The aim of this systematic
   review was to summarise the peer reviewed literature on the risks
   associated with AGI. The review followed the Preferred Reporting Items
   for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Sixteen
   articles were deemed eligible for inclusion. Article types included in
   the review were classified as philosophical discussions, applications of
   modelling techniques, and assessment of current frameworks and processes
   in relation to AGI. The review identified a range of risks associated
   with AGI, including AGI removing itself from the control of human
   owners/managers, being given or developing unsafe goals, development of
   unsafe AGI, AGIs with poor ethics, morals and values; inadequate
   management of AGI, and existential risks. Several limitations of the AGI
   literature base were also identified, including a limited number of peer
   reviewed articles and modelling techniques focused on AGI risk, a lack
   of specific risk research in which domains that AGI may be implemented,
   a lack of specific definitions of the AGI functionality, and a lack of
   standardised AGI terminology. Recommendations to address the identified
   issues with AGI risk research are required to guide AGI design,
   implementation, and management.},
Publisher = {TAYLOR \& FRANCIS LTD},
Address = {2-4 PARK SQUARE, MILTON PARK, ABINGDON OR14 4RN, OXON, ENGLAND},
Type = {Review; Early Access},
Language = {English},
Affiliation = {McLean, S (Corresponding Author), Univ Sunshine Coast, 90 Sippy Downs Dr, Sippy Downs, Qld 4556, Australia.
   McLean, Scott; Read, Gemma J. M.; Thompson, Jason; Stanton, Neville A.; Salmon, Paul M., Univ Sunshine Coast, Ctr Human Factors \& Sociotech Syst, Sippy Downs, Qld, Australia.
   Thompson, Jason, Univ Melbourne, Melbourne Sch Design, Transport Hlth \& Urban Design Thud Res Lab, Parkville, Vic, Australia.
   Baber, Chris, Univ Birmingham, Sch Comp Sci, Birmingham, England.},
DOI = {10.1080/0952813X.2021.1964003},
EarlyAccessDate = {AUG 2021},
ISSN = {0952-813X},
EISSN = {1362-3079},
Keywords = {Artificial General Intelligence; artificial intelligence; risk;
   existential threat; safety},
Keywords-Plus = {SOCIOTECHNICAL SYSTEMS},
Research-Areas = {Computer Science},
Web-of-Science-Categories  = {Computer Science, Artificial Intelligence},
Author-Email = {smclean@usc.edu.au},
Affiliations = {University of the Sunshine Coast; University of Melbourne; University of
   Birmingham},
ResearcherID-Numbers = {Stanton, Neville/ABD-8671-2021
   },
ORCID-Numbers = {McLean, Scott/0000-0002-7269-5847
   Baber, Chris/0000-0002-1830-2272},
Funding-Acknowledgement = {Australian Research Council {[}DP200100399]; Australian Research Council
   {[}DP200100399] Funding Source: Australian Research Council},
Funding-Text = {This work was supported by the Australian Research Council
   {[}DP200100399].},
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Number-of-Cited-References = {51},
Times-Cited = {7},
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@article{ WOS:000632598600007,
Author = {Guerra-Montenegro, Juan and Sanchez-Medina, Javier and Lana, Ibai and
   Sanchez-Rodriguez, David and Alonso-Gonzalez, Itziar and Del Ser, Javier},
Title = {Computational Intelligence in the hospitality industry: A systematic
   literature review and a prospect of challenges},
Journal = {APPLIED SOFT COMPUTING},
Year = {2021},
Volume = {102},
Month = {APR},
Abstract = {This research work presents a detailed survey about Computational
   Intelligence (CI) applied to various Hotel and Travel Industry areas.
   Currently, the hospitality industry's interest in data science is
   growing exponentially because of their expected margin of profit growth.
   In order to provide precise state of the art content, this survey
   analyzes more than 160 research works from which a detailed
   categorization and taxonomy have been produced. We have studied the
   different approaches on the various forecasting methods and subareas
   where CI is currently being used. This research work also shows an
   actual distribution of these research efforts in order to enhance the
   understanding of the reader about this topic and to highlight
   unexploited research niches. A set of guidelines and recommendations for
   future research areas and promising applications are also presented in a
   final section. (C) 2021 Elsevier B.V. All rights reserved.},
Publisher = {ELSEVIER},
Address = {RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS},
Type = {Review},
Language = {English},
Affiliation = {Guerra-Montenegro, J (Corresponding Author), ULPGC, Innovat Ctr Informat Soc CICEI, Las Palmas Gran Canaria, Spain.
   Guerra-Montenegro, Juan; Sanchez-Medina, Javier, ULPGC, Innovat Ctr Informat Soc CICEI, Las Palmas Gran Canaria, Spain.
   Sanchez-Rodriguez, David; Alonso-Gonzalez, Itziar, ULPGC, Inst Technol Dev \& Innovat Commun IDeTIC, Las Palmas Gran Canaria, Spain.
   Lana, Ibai; Del Ser, Javier, TECNALIA, Derio 48160, Bizkaia, Spain.
   Del Ser, Javier, Univ Basque Country, UPV EHU, Bilbao 48013, Spain.},
DOI = {10.1016/j.asoc.2021.107082},
EarlyAccessDate = {JAN 2021},
Article-Number = {107082},
ISSN = {1568-4946},
EISSN = {1872-9681},
Keywords = {Computational Intelligence; Machine Learning; Travel industry;
   Hospitality and tourism management; Predictive analysis; Hotel industry},
Keywords-Plus = {SUPPORT VECTOR REGRESSION; ARTIFICIAL NEURAL-NETWORK; FORECASTING
   TOURISM DEMAND; ONLINE REVIEWS; SENTIMENT CLASSIFICATION; RECOMMENDATION
   SYSTEM; GENETIC ALGORITHMS; TIME-SERIES; BIG DATA; MACHINE},
Research-Areas = {Computer Science},
Web-of-Science-Categories  = {Computer Science, Artificial Intelligence; Computer Science,
   Interdisciplinary Applications},
Author-Email = {juanantonio.montenegro@ulpgc.es},
Affiliations = {Universidad de Las Palmas de Gran Canaria; Universidad de Las Palmas de
   Gran Canaria; University of Basque Country},
ResearcherID-Numbers = {Sanchez-Rodriguez, David/Q-9484-2019
   Alonso-Gonzalez, Itziar/L-9174-2014
   Del Ser, Javier/J-2187-2014},
ORCID-Numbers = {Sanchez-Rodriguez, David/0000-0003-2700-1591
   Guerra-Montenegro, Juan/0000-0002-2309-9993
   Lana, Ibai/0000-0002-2682-6199
   Alonso-Gonzalez, Itziar/0000-0001-8487-2559
   Del Ser, Javier/0000-0002-1260-9775},
Funding-Acknowledgement = {Agencia Canaria de Investigacion, Innovacion y Sociedad de la
   Informacion (ACIISI) de la Consejeria de Economia, Industria, Comercio y
   Conocimiento, Spain; Fondo Social Europeo (FSE) Programa Operativo
   Integrado de Canarias 2014-2020, Spain; Programa de Cooperacion
   Territorial INTERREG V A Spain-Portugal, MAC 2014-2020, Spain
   {[}MAC/3.5b/065]; Basque Government, Spain through the EMAITEK funding
   program, Basque Government, Spain; Basque Government, Spain through the
   ELKARTEK funding program, Basque Government, Spain; Consolidated
   Research Group MATHMODE, Spain by the Department of Education of the
   Basque Government {[}IT1294-19]},
Funding-Text = {Research work co-funded by Agencia Canaria de Investigacion, Innovacion
   y Sociedad de la Informacion (ACIISI) de la Consejeria de Economia,
   Industria, Comercio y Conocimiento, Spain and by Fondo Social Europeo
   (FSE) Programa Operativo Integrado de Canarias 2014-2020, Spain, Eje 3
   Tema Prioritario 74 (85\%). This paper was developed under project
   ``Sistema de vigilancia meteorologica para el seguimiento de riesgos
   medioambientales'', VIMetRi-MAC (ref. MAC/3.5b/065), funded by Programa
   de Cooperacion Territorial INTERREG V A Spain-Portugal, MAC 2014-2020,
   Spain. Ibai Lana and Javier Del Ser also acknowledge funding support
   from the Basque Government, Spain through the EMAITEK and ELKARTEK
   funding programs, Basque Government, Spain. Javier Del Ser receives
   funding support from the Consolidated Research Group MATHMODE, Spain
   (IT1294-19) granted by the Department of Education of the Basque
   Government.},
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Number-of-Cited-References = {203},
Times-Cited = {9},
Usage-Count-Last-180-days = {5},
Usage-Count-Since-2013 = {39},
Journal-ISO = {Appl. Soft. Comput.},
Doc-Delivery-Number = {RC1XN},
Web-of-Science-Index = {Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)},
Unique-ID = {WOS:000632598600007},
OA = {Green Published},
DA = {2023-04-22},
}

@article{ WOS:000867831000001,
Author = {Ahmadi, Najia and Peng, Yuan and Wolfien, Markus and Zoch, Michele and
   Sedlmayr, Martin},
Title = {OMOP CDM Can Facilitate Data-Driven Studies for Cancer Prediction: A
   Systematic Review},
Journal = {INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES},
Year = {2022},
Volume = {23},
Number = {19},
Month = {OCT},
Abstract = {The current generation of sequencing technologies has led to significant
   advances in identifying novel disease-associated mutations and generated
   large amounts of data in a high-throughput manner. Such data in
   conjunction with clinical routine data are proven to be highly useful in
   deriving population-level and patient-level predictions, especially in
   the field of cancer precision medicine. However, data harmonization
   across multiple national and international clinical sites is an
   essential step for the assessment of events and outcomes associated with
   patients, which is currently not adequately addressed. The Observational
   Medical Outcomes Partnership (OMOP) Common Data Model (CDM) is an
   internationally established research data repository introduced by the
   Observational Health Data Science and Informatics (OHDSI) community to
   overcome this issue. To address the needs of cancer research, the
   genomic vocabulary extension was introduced in 2020 to support the
   standardization of subsequent data analysis. In this review, we evaluate
   the current potential of the OMOP CDM to be applicable in cancer
   prediction and how comprehensively the genomic vocabulary extension of
   the OMOP can serve current needs of AI-based predictions. For this, we
   systematically screened the literature for articles that use the OMOP
   CDM in predictive analyses in cancer and investigated the underlying
   predictive models/tools. Interestingly, we found 248 articles, of which
   most use the OMOP for harmonizing their data, but only 5 make use of
   predictive algorithms on OMOP-based data and fulfill our criteria. The
   studies present multicentric investigations, in which the OMOP played an
   essential role in discovering and optimizing machine learning (ML)-based
   models. Ultimately, the use of the OMOP CDM leads to standardized
   data-driven studies for multiple clinical sites and enables a more solid
   basis utilizing, e.g., ML models that can be reused and combined in
   early prediction, diagnosis, and improvement of personalized cancer care
   and biomarker discovery.},
Publisher = {MDPI},
Address = {ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND},
Type = {Review},
Language = {English},
Affiliation = {Ahmadi, N (Corresponding Author), Tech Univ Dresden, Carl Gustav Carus Fac Med, Inst Med Informat \& Biometry, Fetscherstr 74, D-01307 Dresden, Germany.
   Ahmadi, Najia; Peng, Yuan; Wolfien, Markus; Zoch, Michele; Sedlmayr, Martin, Tech Univ Dresden, Carl Gustav Carus Fac Med, Inst Med Informat \& Biometry, Fetscherstr 74, D-01307 Dresden, Germany.},
DOI = {10.3390/ijms231911834},
Article-Number = {11834},
EISSN = {1422-0067},
Keywords = {OHDSI; OMOP CDM; EHR; PLP; prediction; machine learning},
Keywords-Plus = {COMMON DATA MODEL; SNOMED CT; DIAGNOSIS},
Research-Areas = {Biochemistry \& Molecular Biology; Chemistry},
Web-of-Science-Categories  = {Biochemistry \& Molecular Biology; Chemistry, Multidisciplinary},
Author-Email = {najia.ahmadi@tu-dresden.de},
Affiliations = {Technische Universitat Dresden},
ResearcherID-Numbers = {Wolfien, Markus/AAD-2809-2020
   },
ORCID-Numbers = {Wolfien, Markus/0000-0002-1887-4772
   Ahmadi, Najia/0000-0001-9769-5109
   Peng, Yuan/0000-0002-6163-9532
   Zoch, Michele/0000-0002-5577-7760},
Funding-Acknowledgement = {Federal Ministry of Health (BMG) {[}FKZ: ZMI1-2520DAT02C]; German
   Federal Ministry of Education and Research (BMBF) within the Medical
   Informatics Initiative; MIRACUM Consortium {[}FKZ: 01lZZ1801L]},
Funding-Text = {This work is accomplished as part of the SATURN project funded by the
   FederalMinistry of Health (BMG), FKZ: ZMI1-2520DAT02C, and the German
   FederalMinistry of Education and Research (BMBF) within the Medical
   Informatics Initiative; MIRACUM Consortium, FKZ: 01lZZ1801L (Dresden).},
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Number-of-Cited-References = {62},
Times-Cited = {0},
Usage-Count-Last-180-days = {12},
Usage-Count-Since-2013 = {12},
Journal-ISO = {Int. J. Mol. Sci.},
Doc-Delivery-Number = {5H7BT},
Web-of-Science-Index = {Science Citation Index Expanded (SCI-EXPANDED)},
Unique-ID = {WOS:000867831000001},
OA = {Green Published, gold},
DA = {2023-04-22},
}

@article{ WOS:000528826400001,
Author = {Miller, D. Douglas},
Title = {Machine Intelligence in Cardiovascular Medicine},
Journal = {CARDIOLOGY IN REVIEW},
Year = {2020},
Volume = {28},
Number = {2},
Pages = {53-64},
Month = {MAR-APR},
Abstract = {The computer science technology trend called artificial intelligence
   (AI) is not new. Both machine learning and deep learning AI applications
   have recently begun to impact cardiovascular medicine. Scientists
   working in the AI domain have long recognized the importance of data
   quality and provenance to AI algorithm efficiency and accuracy. A
   diverse array of cardiovascular raw data sources of variable
   quality-electronic medical records, radiological picture archiving and
   communication systems, laboratory results, omics, etc.-are available to
   train AI algorithms for predictive modeling of clinical outcomes
   (in-hospital mortality, acute coronary syndrome risk stratification,
   etc.), accelerated image interpretation (edge detection, tissue
   characterization, etc.) and enhanced phenotyping of heterogeneous
   conditions (heart failure with preserved ejection fraction,
   hypertension, etc.). A number of software as medical device narrow AI
   products for cardiac arrhythmia characterization and advanced image
   deconvolution are now Food and Drug Administration approved, and many
   others are in the pipeline. Present and future health professionals
   using AI-infused analytics and wearable devices have 3 critical roles to
   play in their informed development and ethical application in practice:
   (1) medical domain experts providing clinical context to computer and
   data scientists, (2) data stewards assuring the quality, relevance and
   provenance of data inputs, and (3) real-time and post-hoc interpreters
   of AI black box solutions and recommendations to patients. The next wave
   of so-called contextual adaption AI technologies will more closely
   approximate human decision-making, potentially augmenting cardiologists'
   real-time performance in emergency rooms, catheterization laboratories,
   imaging suites, and clinics. However, before such higher order AI
   technologies are adopted in the clinical setting and by healthcare
   systems, regulatory agencies, and industry must jointly develop robust
   AI standards of practice and transparent technology insertion rule sets.},
Publisher = {LIPPINCOTT WILLIAMS \& WILKINS},
Address = {TWO COMMERCE SQ, 2001 MARKET ST, PHILADELPHIA, PA 19103 USA},
Type = {Review},
Language = {English},
Affiliation = {Miller, DD (Corresponding Author), Med Coll Georgia GB 3330, 1120 15th St, Augusta, GA 30912 USA.
   Miller, D. Douglas, Med Coll Georgia, Dept Med Radiol \& Populat Hlth Sci, Augusta, GA USA.},
DOI = {10.1097/CRD.0000000000000294},
ISSN = {1061-5377},
EISSN = {1538-4683},
Keywords = {artificial intelligence; machine learning; artificial neural networks;
   algorithms; data science},
Keywords-Plus = {MYOCARDIAL-PERFUSION SPECT; CORONARY-ARTERY-DISEASE;
   ARTIFICIAL-INTELLIGENCE; HEART-FAILURE; HEALTH-CARE; PREDICTION;
   ISCHEMIA; ETHICS; PCA},
Research-Areas = {Cardiovascular System \& Cardiology},
Web-of-Science-Categories  = {Cardiac \& Cardiovascular Systems},
Author-Email = {ddmiller@augusta.edu},
Affiliations = {University System of Georgia; Augusta University},
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Number-of-Cited-References = {70},
Times-Cited = {18},
Usage-Count-Last-180-days = {2},
Usage-Count-Since-2013 = {40},
Journal-ISO = {Cardiol. Rev.},
Doc-Delivery-Number = {LH5LL},
Web-of-Science-Index = {Science Citation Index Expanded (SCI-EXPANDED)},
Unique-ID = {WOS:000528826400001},
DA = {2023-04-22},
}

@article{ WOS:000482134500120,
Author = {Dresp-Langley, Birgitta and Ekseth, Ole Kristian and Fesl, Jan and
   Gohshi, Seiichi and Kurz, Marc and Sehring, Hans-Werner},
Title = {Occam's Razor for Big Data? On Detecting Quality in Large Unstructured
   Datasets},
Journal = {APPLIED SCIENCES-BASEL},
Year = {2019},
Volume = {9},
Number = {15},
Month = {AUG 1},
Abstract = {Detecting quality in large unstructured datasets requires capacities far
   beyond the limits of human perception and communicability and, as a
   result, there is an emerging trend towards increasingly complex analytic
   solutions in data science to cope with this problem. This new trend
   towards analytic complexity represents a severe challenge for the
   principle of parsimony (Occam's razor) in science. This review article
   combines insight from various domains such as physics, computational
   science, data engineering, and cognitive science to review the specific
   properties of big data. Problems for detecting data quality without
   losing the principle of parsimony are then highlighted on the basis of
   specific examples. Computational building block approaches for data
   clustering can help to deal with large unstructured datasets in
   minimized computation time, and meaning can be extracted rapidly from
   large sets of unstructured image or video data parsimoniously through
   relatively simple unsupervised machine learning algorithms. Why we still
   massively lack in expertise for exploiting big data wisely to extract
   relevant information for specific tasks, recognize patterns and generate
   new information, or simply store and further process large amounts of
   sensor data is then reviewed, and examples illustrating why we need
   subjective views and pragmatic methods to analyze big data contents are
   brought forward. The review concludes on how cultural differences
   between East and West are likely to affect the course of big data
   analytics, and the development of increasingly autonomous artificial
   intelligence (AI) aimed at coping with the big data deluge in the near
   future.},
Publisher = {MDPI},
Address = {ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND},
Type = {Review},
Language = {English},
Affiliation = {Ekseth, OK (Corresponding Author), NTNU Trondheim, N-7491 Trondheim, Norway.
   Sehring, HW (Corresponding Author), Namics, D-20357 Hamburg, Germany.
   Dresp-Langley, Birgitta, Strasbourg Univ, CNRS, UMR 7357, ICube Lab, F-67200 Strasbourg, France.
   Ekseth, Ole Kristian, NTNU Trondheim, N-7491 Trondheim, Norway.
   Fesl, Jan, Univ South Bohemia Czech Republ, Fac Sci, Inst Appl Informat, Ceske Budejovice 37005, Czech Republic.
   Gohshi, Seiichi, Kogakkan Univ, Dept Informat, Ise, Mie 5160016, Japan.
   Kurz, Marc, Univ Appl Sci Upper Austria, Dept Mobil \& Energy, A-4232 Hagenberg, Austria.
   Sehring, Hans-Werner, Namics, D-20357 Hamburg, Germany.},
DOI = {10.3390/app9153065},
Article-Number = {3065},
EISSN = {2076-3417},
Keywords = {big data; non-dimensionality; applied data science; paradigm shift;
   artificial intelligence; principle of parsimony (Occam's razor)},
Keywords-Plus = {RECEPTIVE-FIELDS; ALGORITHM; MODEL; CHALLENGES; RANGE; SEGMENTATION;
   PREDICTION; CLUSTERS},
Research-Areas = {Chemistry; Engineering; Materials Science; Physics},
Web-of-Science-Categories  = {Chemistry, Multidisciplinary; Engineering, Multidisciplinary; Materials
   Science, Multidisciplinary; Physics, Applied},
Author-Email = {oekseth@gmail.com
   hans-werner.sehring@namics.com},
Affiliations = {Centre National de la Recherche Scientifique (CNRS); CNRS - Institute
   for Engineering \& Systems Sciences (INSIS); UDICE-French Research
   Universities; Universites de Strasbourg Etablissements Associes;
   Universite de Strasbourg},
ResearcherID-Numbers = {Dresp-Langley, Birgitta/F-8243-2013},
ORCID-Numbers = {Dresp-Langley, Birgitta/0000-0002-2860-6472},
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Number-of-Cited-References = {143},
Times-Cited = {6},
Usage-Count-Last-180-days = {1},
Usage-Count-Since-2013 = {7},
Journal-ISO = {Appl. Sci.-Basel},
Doc-Delivery-Number = {IS4PB},
Web-of-Science-Index = {Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)},
Unique-ID = {WOS:000482134500120},
OA = {Green Submitted, gold},
DA = {2023-04-22},
}

@article{ WOS:000795550100001,
Author = {Ozdemir, Vural and Springer, Simon},
Title = {Decolonizing Knowledge Upstream: New Ways to Deconstruct and Fight
   Disinformation in an Era of COVID-19, Extreme Digital Transformation,
   and Climate Emergency},
Journal = {OMICS-A JOURNAL OF INTEGRATIVE BIOLOGY},
Year = {2022},
Volume = {26},
Number = {5},
Pages = {247-269},
Month = {MAY 1},
Abstract = {Lies and disinformation have always existed throughout human history.
   However, disinformation has become a ``pandemic within a pandemic{''}
   with convergence of COVID-19 and digital transformation of health care,
   climate emergency, and pervasive human-computer interaction in all
   facets of life. We are living through an era of post-truth. New
   approaches to fight disinformation are urgently needed and of paramount
   importance for systems science and planetary health. In this study, we
   discuss the ways in which extractive and entrenched epistemologies such
   as technocracy and neoliberalism co-produce disinformation. We draw from
   the works of David Collingridge in technology entrenchment and the
   literature on digital health, international affairs, climate emergency,
   degrowth, and decolonializing methodologies. We expand the vocabulary on
   and interventions against disinformation, and propose the following: (1)
   rapid epistemic disobedience as a critical governance tool to resist the
   cultural hegemony of neoliberalism and its master narrative infinite
   growth that is damaging the planetary ecosystems, while creating echo
   chambers overflowing with disinformation, and (2) a two-tiered taxonomy
   of reflexivity, a state of self-cognizance by knowledge actors, for
   example, scientists, engineers, and physicians (type 1 reflexivity), as
   well as by chroniclers of former actors, for example, civil society
   organizations, journalists, social sciences, and humanities scholars
   (type 2 reflexivity). This article takes seriously the role of master
   narratives in quotidian life in production of disinformation and
   ecological breakdown. The infinite growth narrative does not ask
   critical questions such as ``growth in what, at what costs to society
   and environment?,{''} and is a dangerous game of brinkmanship that has
   been testing the planetary ecological boundaries and putting at risk the
   veracity of knowledge. There is a need for scholars and systems
   scientists who break ranks with entrenched narratives that pose
   existential threats to planetary sustainability and are harmful to
   knowledge veracity. Scholars who resist the obvious recklessness and
   juggernaut of the pursuit of neoliberal infinite growth would be rooting
   for living responsibly and in solidarity on a planet with finite
   resources. The interventions proposed in this study, rapid epistemic
   disobedience and the expanded reflexivity taxonomy, can advance
   progressive policies for a good life for all within planetary
   boundaries, and decolonize knowledge from disinformation in ways that
   are necessarily upstream, radical, rapid, and emancipatory.},
Publisher = {MARY ANN LIEBERT, INC},
Address = {140 HUGUENOT STREET, 3RD FL, NEW ROCHELLE, NY 10801 USA},
Type = {Review},
Language = {English},
Affiliation = {Ozdemir, V (Corresponding Author), OMICS Journal Integrat Biol, New Rochelle, NY 10801 USA.
   Springer, S (Corresponding Author), Univ Newcastle, Fac Sci, Ctr Urban, Sch Environm \& Life Sci,Discipline Geog \& Environm, Callaghan, NSW 2308, Australia.
   Ozdemir, Vural, OMICS Journal Integrat Biol, New Rochelle, NY USA.
   Springer, Simon, Univ Newcastle, Fac Sci, Ctr Urban \& Reg Studies, Sch Environm \& Life Sci,Discipline Geog \& Environm, Callaghan, NSW, Australia.
   Ozdemir, Vural, OMICS Journal Integrat Biol, New Rochelle, NY 10801 USA.
   Springer, Simon, Univ Newcastle, Fac Sci, Ctr Urban, Sch Environm \& Life Sci,Discipline Geog \& Environm, Callaghan, NSW 2308, Australia.},
DOI = {10.1089/omi.2022.0041},
ISSN = {1536-2310},
EISSN = {1557-8100},
Keywords = {COVID-19; critical studies of disinformation; neoliberalism; epistemic
   decolonization; prefigurative politics; radical democracy; infinite
   growth master narrative; degrowth; cyber-physical systems},
Keywords-Plus = {ARTIFICIAL-INTELLIGENCE; EPISTEMIC DISOBEDIENCE; HEALTH; NEOLIBERALISM;
   INTERNET; SCIENCE; THINGS; CRISIS},
Research-Areas = {Biotechnology \& Applied Microbiology; Genetics \& Heredity},
Web-of-Science-Categories  = {Biotechnology \& Applied Microbiology; Genetics \& Heredity},
Author-Email = {OJIB@liebertpub.com
   simonspringer@gmail.com},
Affiliations = {University of Newcastle; University of Newcastle},
ORCID-Numbers = {Springer, Simon/0000-0002-0064-7440},
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Number-of-Cited-References = {129},
Times-Cited = {4},
Usage-Count-Last-180-days = {12},
Usage-Count-Since-2013 = {24},
Journal-ISO = {OMICS},
Doc-Delivery-Number = {1G0LZ},
Web-of-Science-Index = {Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)},
Unique-ID = {WOS:000795550100001},
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}

@article{ WOS:000914960100001,
Author = {Iqbal, Umair and Riaz, Muhammad Zain Bin and Zhao, Jiahong and
   Barthelemy, Johan and Perez, Pascal},
Title = {Drones for Flood Monitoring, Mapping and Detection: A Bibliometric
   Review},
Journal = {DRONES},
Year = {2023},
Volume = {7},
Number = {1},
Month = {JAN},
Abstract = {Floods are one of the most often occurring and damaging natural hazards.
   They impact the society on a massive scale and result in significant
   damages. To reduce the impact of floods, society needs to keep
   benefiting from the latest technological innovations. Drones equipped
   with sensors and latest algorithms (e.g., computer vision and deep
   learning) have emerged as a potential platform which may be useful for
   flood monitoring, mapping and detection activities in a more efficient
   way than current practice. To better understand the scope and recent
   trends in the domain of drones for flood management, we performed a
   detailed bibliometric analysis. The intent of performing the
   bibliometric analysis waws to highlight the important research trends,
   co-occurrence relationships and patterns to inform the new researchers
   in this domain. The bibliometric analysis was performed in terms of
   performance analysis (i.e., publication statistics, citations
   statistics, top publishing countries, top publishing journals, top
   publishing institutions, top publishers and top Web of Science (WoS)
   categories) and science mapping (i.e., citations by country, citations
   by journals, keyword co-occurrences, co-authorship, co-citations and
   bibliographic coupling) for a total of 569 records extracted from WoS
   for the duration 2000-2022. The VOSviewer open source tool has been used
   for generating the bibliographic network maps. Subjective discussions of
   the results explain the obtained trends from the bibliometric analysis.
   In the end, a detailed review of top 28 most recent publications was
   performed and subjected to process-driven analysis in the context of
   flood management. The potential active areas of research were also
   identified for future research in regard to the use of drones for flood
   monitoring, mapping and detection activities.},
Publisher = {MDPI},
Address = {ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND},
Type = {Review},
Language = {English},
Affiliation = {Iqbal, U (Corresponding Author), Univ Wollongong, SMART Infrastructure Facil, Wollongong, NSW 2500, Australia.
   Iqbal, Umair; Zhao, Jiahong, Univ Wollongong, SMART Infrastructure Facil, Wollongong, NSW 2500, Australia.
   Barthelemy, Johan, NVIDIA, Santa Clara, CA 95051 USA.
   Perez, Pascal, Univ Melbourne, Australian Urban Res Infrastructure Network AURIN, Melbourne, Vic 3000, Australia.},
DOI = {10.3390/drones7010032},
Article-Number = {32},
EISSN = {2504-446X},
Keywords = {bibliometric analysis; drones; unmanned aerial vehicle (UAV); remote
   sensing; artificial intelligence (AI); floods; computer vision; deep
   learning},
Keywords-Plus = {DAMAGE; UAV; INFORMATION},
Research-Areas = {Remote Sensing},
Web-of-Science-Categories  = {Remote Sensing},
Author-Email = {umair@uow.edu.au},
Affiliations = {University of Wollongong; Nvidia Corporation; University of Melbourne},
ResearcherID-Numbers = {Iqbal, Umair/S-6797-2019
   },
ORCID-Numbers = {Iqbal, Umair/0000-0003-0894-0489
   Zhao, Jiahong/0000-0003-4243-2277},
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@article{ WOS:000647943500002,
Author = {Liu, Ruixue and Dobriban, Edgar and Hou, Zhichao and Qian, Kun},
Title = {Dynamic Load Identification for Mechanical Systems: A Review},
Journal = {ARCHIVES OF COMPUTATIONAL METHODS IN ENGINEERING},
Year = {2022},
Volume = {29},
Number = {2},
Pages = {831-863},
Month = {MAR},
Abstract = {Due to the great challenges of measuring forces directly, identifying
   dynamic loads based on accessible responses is a crucial problem in
   engineering, helping ensure integrity and reliability of mechanical
   structures. Dynamic load identification is a difficult inverse problem
   due to matrix ill-posedness, noise-sensitivity and computational scale,
   especially in uncertain structures. Unexpected inaccurate or non-unique
   solutions may be found if these problems are not well addressed. During
   the past decades, many methods have been proposed to deal with these
   problems. This paper tries to provide a comprehensive review of
   techniques for dynamic load identification, including under
   ill-posedness and uncertain parameter processing; with an emphasis on
   the statistical, data science, machine learning, and artificial
   intelligence aspects. Classical physics-based dynamic load
   identification theories in frequency and time domain are also
   introduced. Research challenges and prospects of dynamic load
   identification in mechanical systems are discussed finally. This review
   may offer guidelines for dynamic load identification in practical
   complex structures, as well as possibilities for further researches.
   Some methods could have broader applicability to other inverse problems.},
Publisher = {SPRINGER},
Address = {VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS},
Type = {Review},
Language = {English},
Affiliation = {Hou, ZC (Corresponding Author), Tsinghua Univ, State Key Lab Automot Safety \& Energy, Beijing 100084, Peoples R China.
   Liu, Ruixue; Hou, Zhichao; Qian, Kun, Tsinghua Univ, State Key Lab Automot Safety \& Energy, Beijing 100084, Peoples R China.
   Dobriban, Edgar, Univ Penn, Dept Stat, Philadelphia, PA 19104 USA.},
DOI = {10.1007/s11831-021-09594-7},
EarlyAccessDate = {MAY 2021},
ISSN = {1134-3060},
EISSN = {1886-1784},
Keywords-Plus = {IMPACT FORCE IDENTIFICATION; VALUE DECOMPOSITION ALGORITHM;
   CONJUGATE-GRADIENT METHOD; FAST BAYESIAN-APPROACH; FREE-VIBRATION DATA;
   ILL-POSED PROBLEM; STRUCTURAL RESPONSE; SPARSE REGULARIZATION; STATE
   ESTIMATION; INVERSE METHOD},
Research-Areas = {Computer Science; Engineering; Mathematics},
Web-of-Science-Categories  = {Computer Science, Interdisciplinary Applications; Engineering,
   Multidisciplinary; Mathematics, Interdisciplinary Applications},
Author-Email = {liuruixue0218@163.com
   dobriban@wharton.upenn.edu
   houzc@mail.tsinghua.edu.cn
   qiankun\_nvh@163.com},
Affiliations = {Tsinghua University; University of Pennsylvania},
ResearcherID-Numbers = {Qian, Kun/Z-1427-2018},
ORCID-Numbers = {Qian, Kun/0000-0002-5830-765X},
Funding-Acknowledgement = {National Natural Science foundation of China {[}51975312]},
Funding-Text = {The authors gratefully acknowledge the support of National Natural
   Science foundation of China (Grant No. 51975312).},
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Number-of-Cited-References = {209},
Times-Cited = {17},
Usage-Count-Last-180-days = {31},
Usage-Count-Since-2013 = {127},
Journal-ISO = {Arch. Comput. Method Eng.},
Doc-Delivery-Number = {ZB5NE},
Web-of-Science-Index = {Science Citation Index Expanded (SCI-EXPANDED)},
Unique-ID = {WOS:000647943500002},
DA = {2023-04-22},
}

@article{ WOS:000585196500001,
Author = {Mosavi, Amirhosein and Faghan, Yaser and Ghamisi, Pedram and Puhong Duan
   and Ardabili, Sina Faizollahzadeh and Salwana, Ely and Band, Shahab S.},
Title = {Comprehensive Review of Deep Reinforcement Learning Methods and
   Applications in Economics},
Journal = {MATHEMATICS},
Year = {2020},
Volume = {8},
Number = {10},
Month = {OCT},
Abstract = {The popularity of deep reinforcement learning (DRL) applications in
   economics has increased exponentially. DRL, through a wide range of
   capabilities from reinforcement learning (RL) to deep learning (DL),
   offers vast opportunities for handling sophisticated dynamic economics
   systems. DRL is characterized by scalability with the potential to be
   applied to high-dimensional problems in conjunction with noisy and
   nonlinear patterns of economic data. In this paper, we initially
   consider a brief review of DL, RL, and deep RL methods in diverse
   applications in economics, providing an in-depth insight into the
   state-of-the-art. Furthermore, the architecture of DRL applied to
   economic applications is investigated in order to highlight the
   complexity, robustness, accuracy, performance, computational tasks, risk
   constraints, and profitability. The survey results indicate that DRL can
   provide better performance and higher efficiency as compared to the
   traditional algorithms while facing real economic problems in the
   presence of risk parameters and the ever-increasing uncertainties.},
Publisher = {MDPI},
Address = {ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND},
Type = {Review},
Language = {English},
Affiliation = {Mosavi, A (Corresponding Author), Ton Duc Thang Univ, Environm Qual Atmospher Sci \& Climate Change Res, Ho Chi Minh City, Vietnam.
   Mosavi, A (Corresponding Author), Ton Duc Thang Univ, Fac Environm \& Labour Safety, Ho Chi Minh City, Vietnam.
   Band, SS (Corresponding Author), Duy Tan Univ, Inst Res \& Dev, Da Nang 550000, Vietnam.
   Band, SS (Corresponding Author), Natl Yunlin Univ Sci \& Technol, Coll Future, Future Technol Res Ctr, 123 Univ Rd,Sect 3, Touliu 64002, Yunlin, Taiwan.
   Mosavi, Amirhosein, Ton Duc Thang Univ, Environm Qual Atmospher Sci \& Climate Change Res, Ho Chi Minh City, Vietnam.
   Mosavi, Amirhosein, Ton Duc Thang Univ, Fac Environm \& Labour Safety, Ho Chi Minh City, Vietnam.
   Faghan, Yaser, Univ Lisbon, Inst Super Econ \& Gestao, P-1200781 Lisbon, Portugal.
   Ghamisi, Pedram, Helmholtz Zentrum Dresden Rossendorf, Chemnitzer Str 40, D-09599 Freiberg, Germany.
   Ghamisi, Pedram, Univ Antwerp, Fac Sci, Dept Phys, Univ Pl 1, B-2610 Antwerp, Belgium.
   Puhong Duan, Hunan Univ, Coll Elect \& Informat Engn, Changsha 410082, Hunan, Peoples R China.
   Ardabili, Sina Faizollahzadeh, Univ Mohaghegh Ardabili, Dept Biosyst Engn, Ardebil 5619911367, Iran.
   Salwana, Ely, Univ Kebangsaan Malaysia, Inst IR4 0, Bangi 43600, Malaysia.
   Band, Shahab S., Duy Tan Univ, Inst Res \& Dev, Da Nang 550000, Vietnam.
   Band, Shahab S., Natl Yunlin Univ Sci \& Technol, Coll Future, Future Technol Res Ctr, 123 Univ Rd,Sect 3, Touliu 64002, Yunlin, Taiwan.},
DOI = {10.3390/math8101640},
Article-Number = {1640},
EISSN = {2227-7390},
Keywords = {economics; deep reinforcement learning; deep learning; machine learning;
   mathematics; applied informatics; big data; survey; literature review;
   explainable artificial intelligence; ensemble; anomaly detection; 5G;
   fraud detection; COVID-19; Prisma; data science; supervised learning},
Keywords-Plus = {NEURAL-NETWORKS; MODEL; PRICE; CLASSIFICATION; FINANCE; STOCK},
Research-Areas = {Mathematics},
Web-of-Science-Categories  = {Mathematics},
Author-Email = {amirhosein.mosavi@tdtu.edu.vn
   yaser.kord@yahoo.com
   pedram.ghamisi@uantwerpen.be
   puhong\_duan@hnu.edu.cn
   Sina.faiz@uma.ac.ir
   elysalwana@ukm.edu.my
   shamshirbandshahaboddin@duytan.edu.vn},
Affiliations = {Ton Duc Thang University; Ton Duc Thang University; Universidade de
   Lisboa; Helmholtz Association; Helmholtz-Zentrum Dresden-Rossendorf
   (HZDR); University of Antwerp; Hunan University; University of Mohaghegh
   Ardabili; Universiti Kebangsaan Malaysia; Duy Tan University; National
   Yunlin University Science \& Technology},
ResearcherID-Numbers = {S.Band, Shahab/AAD-3311-2021
   Mosavi, Amir/I-7440-2018
   Ardabili, Sina Faizollahzadeh/X-8072-2019
   S. Band, Shahab/ABB-2469-2020
   S.Band, Shahab/ABI-7388-2020
   Ghamisi, Pedram/ABD-5419-2021
   Ardabili, Sina/ABE-9690-2021
   },
ORCID-Numbers = {Mosavi, Amir/0000-0003-4842-0613
   Ardabili, Sina Faizollahzadeh/0000-0002-7744-7906
   S. Band, Shahab/0000-0001-6109-1311
   S.Band, Shahab/0000-0002-8963-731X
   Ardabili, Sina/0000-0002-7744-7906
   Ghamisi, Pedram/0000-0003-1203-741X},
Funding-Acknowledgement = {Hungarian State; European Union {[}EFOP-3.6.1-16-2016-00010,
   2017-1.3.1-VKE-2017-00025]; New Szechenyi Plan
   {[}EFOP-3.6.2-16-2017-00016]; European Social Fund},
Funding-Text = {We acknowledge the financial support of this work by the Hungarian State
   and the European Union under the EFOP-3.6.1-16-2016-00010 project and
   the 2017-1.3.1-VKE-2017-00025 project. The research presented in this
   paper was carried out as part of the EFOP-3.6.2-16-2017-00016 project in
   the framework of the New Szechenyi Plan. The completion of this project
   is funded by the European Union and co-financed by the European Social
   Fund. We acknowledge the financial support of this work by the Hungarian
   State and the European Union under the EFOP-3.6.1-16-2016-00010 project.},
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Number-of-Cited-References = {122},
Times-Cited = {46},
Usage-Count-Last-180-days = {9},
Usage-Count-Since-2013 = {82},
Journal-ISO = {Mathematics},
Doc-Delivery-Number = {OL2UD},
Web-of-Science-Index = {Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)},
Unique-ID = {WOS:000585196500001},
OA = {Green Submitted, gold},
DA = {2023-04-22},
}

@article{ WOS:000543117100001,
Author = {Dekker, Izaak and De Jong, Elisabeth M. and Schippers, Michaela C. and
   De Bruijn-Smolders, Monique and Alexiou, Andreas and Giesbers, Bas},
Title = {Optimizing Students' Mental Health and Academic Performance: AI-Enhanced
   Life Crafting},
Journal = {FRONTIERS IN PSYCHOLOGY},
Year = {2020},
Volume = {11},
Month = {JUN 3},
Abstract = {One in three university students experiences mental health problems
   during their study. A similar percentage leaves higher education without
   obtaining the degree for which they enrolled. Research suggests that
   both mental health problems and academic underperformance could be
   caused by students lacking control and purpose while they are adjusting
   to tertiary education. Currently, universities are not designed to cater
   to all the personal needs and mental health problems of large numbers of
   students at the start of their studies. Within the literature aimed at
   preventing mental health problems among students (e.g., anxiety or
   depression), digital forms of therapy recently have been suggested as
   potentially scalable solutions to address these problems. Integrative
   psychological artificial intelligence (AI) in the form of a chatbot, for
   example, shows great potential as an evidence-based solution. At the
   same time, within the literature aimed at improving academic
   performance, the online life-crafting intervention in which students
   write about values and passions, goals, and goal-attainment plans has
   shown to improve the academic performance and retention rates of
   students. Because the life-crafting intervention is delivered through
   the curriculum and doesn't bear the stigma that is associated with
   therapy, it can reach larger populations of students. But life-crafting
   lacks the means for follow-up or the interactiveness that online
   AI-guided therapy can offer. In this narrative review, we propose to
   integrate the current literature on chatbot interventions aimed at the
   mental health of students with research about a life-crafting
   intervention that uses an inclusive curriculum-wide approach. When a
   chatbot asks students to prioritize both academic as well as social and
   health-related goals and provides personalized follow-up coaching, this
   can prevent -often interrelated- academic and mental health problems.
   Right on-time delivery, and personalized follow-up questions enhance the
   effects of both -originally separated- intervention types. Research on
   this new combination of interventions should use design principles that
   increase user-friendliness and monitor the technology acceptance of its
   participants.},
Publisher = {FRONTIERS MEDIA SA},
Address = {AVENUE DU TRIBUNAL FEDERAL 34, LAUSANNE, CH-1015, SWITZERLAND},
Type = {Review},
Language = {English},
Affiliation = {Dekker, I (Corresponding Author), Erasmus Univ, Rotterdam Sch Management, Dept Technol \& Operat Management, Rotterdam, Netherlands.
   Dekker, I (Corresponding Author), Rotterdam Univ Appl Sci, Res Ctr Urban Talent, Rotterdam, Netherlands.
   Dekker, Izaak; De Jong, Elisabeth M.; Schippers, Michaela C.; De Bruijn-Smolders, Monique; Alexiou, Andreas, Erasmus Univ, Rotterdam Sch Management, Dept Technol \& Operat Management, Rotterdam, Netherlands.
   Dekker, Izaak; De Bruijn-Smolders, Monique, Rotterdam Univ Appl Sci, Res Ctr Urban Talent, Rotterdam, Netherlands.
   Alexiou, Andreas, Tilburg Univ, Tilburg Sch Econ \& Management, Dept Management, Tilburg, Netherlands.
   Giesbers, Bas, Erasmus Univ, Rotterdam Sch Management, Informat Management \& Consulting, Rotterdam, Netherlands.},
DOI = {10.3389/fpsyg.2020.01063},
Article-Number = {1063},
ISSN = {1664-1078},
Keywords = {life crafting; chatbot; mental health; academic performance; academic
   success; academic achievement; goal setting; well-being},
Keywords-Plus = {COGNITIVE-BEHAVIOR THERAPY; IMPLEMENTATION INTENTIONS; TECHNOLOGY
   ACCEPTANCE; COLLEGE-STUDENTS; SOMATIC DISORDERS; HIGHER-EDUCATION; USER
   ACCEPTANCE; PERSONAL GOALS; SELF-EFFICACY; PROCRASTINATION},
Research-Areas = {Psychology},
Web-of-Science-Categories  = {Psychology, Multidisciplinary},
Author-Email = {dekker@rsm.nl},
Affiliations = {Erasmus University Rotterdam; Tilburg University; Erasmus University
   Rotterdam},
ResearcherID-Numbers = {Alexiou, Andreas/I-4314-2014
   },
ORCID-Numbers = {Alexiou, Andreas/0000-0002-8425-0132
   Giesbers, Bas/0000-0002-8077-9039
   Dekker, Izaak/0000-0002-6858-4001},
Funding-Acknowledgement = {Dutch Ministry of Education, Culture and Science; Erasmus University},
Funding-Text = {Research funded with a Senior Comenius Fellowship Grant awarded to MS by
   the Dutch Ministry of Education, Culture and Science. Publishing costs
   funded by the Erasmus University Open Access fund.},
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Number-of-Cited-References = {132},
Times-Cited = {28},
Usage-Count-Last-180-days = {24},
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Journal-ISO = {Front. Psychol.},
Doc-Delivery-Number = {MC2IJ},
Web-of-Science-Index = {Social Science Citation Index (SSCI)},
Unique-ID = {WOS:000543117100001},
OA = {Green Published, gold},
DA = {2023-04-22},
}

@article{ WOS:000676162500001,
Author = {Al-Saman, Ahmed and Cheffena, Michael and Elijah, Olakunle and
   Al-Gumaei, Yousef A. and Abdul Rahim, Sharul Kamal and Al-Hadhrami,
   Tawfik},
Title = {Survey of Millimeter-Wave Propagation Measurements and Models in Indoor
   Environments},
Journal = {ELECTRONICS},
Year = {2021},
Volume = {10},
Number = {14},
Month = {JUL},
Abstract = {The millimeter-wave (mmWave) is expected to deliver a huge bandwidth to
   address the future demands for higher data rate transmissions. However,
   one of the major challenges in the mmWave band is the increase in signal
   loss as the operating frequency increases. This has attracted several
   research interests both from academia and the industry for indoor and
   outdoor mmWave operations. This paper focuses on the works that have
   been carried out in the study of the mmWave channel measurement in
   indoor environments. A survey of the measurement techniques, prominent
   path loss models, analysis of path loss and delay spread for mmWave in
   different indoor environments is presented. This covers the mmWave
   frequencies from 28 GHz to 100 GHz that have been considered in the last
   two decades. In addition, the possible future trends for the mmWave
   indoor propagation studies and measurements have been discussed. These
   include the critical indoor environment, the roles of artificial
   intelligence, channel characterization for indoor devices,
   reconfigurable intelligent surfaces, and mmWave for 6G systems. This
   survey can help engineers and researchers to plan, design, and optimize
   reliable 5G wireless indoor networks. It will also motivate the
   researchers and engineering communities towards finding a better outcome
   in the future trends of the mmWave indoor wireless network for 6G
   systems and beyond.},
Publisher = {MDPI},
Address = {ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND},
Type = {Review},
Language = {English},
Affiliation = {Al-Saman, A (Corresponding Author), Norwegian Univ Sci \& Technol NTNU, Dept Mfg \& Civil Engn, Fac Engn, N-2815 Gjovik, Norway.
   Al-Saman, Ahmed; Cheffena, Michael, Norwegian Univ Sci \& Technol NTNU, Dept Mfg \& Civil Engn, Fac Engn, N-2815 Gjovik, Norway.
   Elijah, Olakunle; Abdul Rahim, Sharul Kamal, Univ Teknol Malaysia, Fac Engn, Sch Elect Engn, Wireless Commun Ctr, Johor Baharu 81310, Johor, Malaysia.
   Al-Gumaei, Yousef A., Northumbria Univ Newcastle, Fac Engn \& Enviourement, Dept Comp \& Informat Sci, Newcastle Upon Tyne NE1 8ST, Tyne \& Wear, England.
   Al-Hadhrami, Tawfik, Nottingham Trent Univ, Sch Sci \& Technol, Nottingham NG11 8NS, England.},
DOI = {10.3390/electronics10141653},
Article-Number = {1653},
EISSN = {2079-9292},
Keywords = {millimeter-wave propagation; radio channel; indoor environment; 28 GHz;
   38 GHz; 40 GHz; 60 GHz; 70 GHz; wideband channel; 5G; 6G},
Keywords-Plus = {PATH-LOSS; CHANNEL MODEL; 60 GHZ; RADIO CHANNEL; WIRELESS
   COMMUNICATIONS; DELAY-SPREAD; URBAN ENVIRONMENTS; MIMO; COMMUNICATION;
   SOUNDER},
Research-Areas = {Computer Science; Engineering; Physics},
Web-of-Science-Categories  = {Computer Science, Information Systems; Engineering, Electrical \&
   Electronic; Physics, Applied},
Author-Email = {ahmed.al-saman@ntnu.no
   michael.cheffena@ntnu.no
   elij\_olak@yahoo.com
   yousef.al-gumaei@northumbria.ac.uk
   sharulkamal@utm.my
   tawfik.al-hadhrami@ntu.ac.uk},
Affiliations = {Norwegian University of Science \& Technology (NTNU); Universiti
   Teknologi Malaysia; Northumbria University; Nottingham Trent University},
ResearcherID-Numbers = {AL-GUMAEI, YOUSEF/B-4394-2017
   M. AL-SAMMAN, AHMED/S-5101-2017},
ORCID-Numbers = {AL-GUMAEI, YOUSEF/0000-0001-9920-3091
   Elijah, Olakunle/0000-0002-6498-5780
   Cheffena, Michael/0000-0003-2086-8627
   Al-Hadhrami, Tawfik/0000-0001-7441-604X
   M. AL-SAMMAN, AHMED/0000-0001-5183-7810},
Funding-Acknowledgement = {Manu Lab, NTNU, Gjovik},
Funding-Text = {This work was supported by Manu Lab, NTNU, Gjovik.},
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Number-of-Cited-References = {147},
Times-Cited = {15},
Usage-Count-Last-180-days = {4},
Usage-Count-Since-2013 = {33},
Journal-ISO = {Electronics},
Doc-Delivery-Number = {TN3UB},
Web-of-Science-Index = {Science Citation Index Expanded (SCI-EXPANDED)},
Unique-ID = {WOS:000676162500001},
OA = {Green Accepted, gold},
DA = {2023-04-22},
}

@article{ WOS:000885231700001,
Author = {Sousa, A. S. and Serra, J. and Estevens, C. and Costa, R. and Ribeiro,
   A. J.},
Title = {A quality by design approach in oral extended release drug delivery
   systems: where we are and where we are going?},
Journal = {JOURNAL OF PHARMACEUTICAL INVESTIGATION},
Year = {2023},
Volume = {53},
Number = {2},
Pages = {269-306},
Month = {MAR},
Abstract = {Background Oral extended release (ER) delivery systems have quickly
   gained increasing importance because of their ability to maintain drug
   levels in the blood more consistently, reducing side effects and
   improving patient compliance. The complexity of ER formulation leads to
   additional development challenges in the fulfilment of quality-related
   regulatory requirements. Despite their challenging properties, the
   potential of ER system formulation and process development can be better
   exploited by applying quality by design (QbD) approaches and advanced
   modeling techniques such as machine learning (ML). Area covered This
   review provides a comprehensive overview of QbD concepts applied to oral
   ER delivery systems, clarifying the impact of raw materials and process
   variables on critical quality attributes (CQAs). Moreover, data science
   coupled with ML algorithms is also elucidated in this article as a
   potential tool for predicting and optimizing ER formulation design and
   manufacturing processes. Expert opinion QbD, as a scientific and
   risk-based approach, provides a comprehensive understanding of oral ER
   drug delivery systems improving product quality and reducing
   postapproval changes. Enabling QbD with ML-driven pharmaceutical
   development can provide an opportunity to move toward risk mitigation
   for efficient ER tablet formulation and process development. However,
   there are some barriers to overcome in the way of adopting QbD concepts.
   The key issues are the lack of understanding and the gap between
   industries and regulatory authorities concerning the scientific
   principles and terms beyond QbD, which can create an obstacle during the
   approval process. Furthermore, it is generally believed that the
   resources and time invested in applying QbD tools are not cost-effective
   during constant and continuous improvement. Today, it is time to realize
   that a multidisciplinary understanding of the formulation and
   manufacturing process is as important as achieving the final result.},
Publisher = {SPRINGERNATURE},
Address = {CAMPUS, 4 CRINAN ST, LONDON, N1 9XW, ENGLAND},
Type = {Review},
Language = {English},
Affiliation = {Ribeiro, AJ (Corresponding Author), Univ Coimbra, Fac Farm, P-3000148 Coimbra, Portugal.
   Ribeiro, AJ (Corresponding Author), IBMC, I3S, Rua Alfredo Allen, P-4200135 Porto, Portugal.
   Sousa, A. S.; Ribeiro, A. J., Univ Coimbra, Fac Farm, P-3000148 Coimbra, Portugal.
   Sousa, A. S.; Serra, J.; Estevens, C.; Costa, R., Quinta Cerca, Tecnimede Grp, Pharmaceut Dev, P-2565187 Caixaria, Dois Portos, Portugal.
   Ribeiro, A. J., IBMC, I3S, Rua Alfredo Allen, P-4200135 Porto, Portugal.},
DOI = {10.1007/s40005-022-00603-w},
EarlyAccessDate = {NOV 2022},
ISSN = {2093-5552},
EISSN = {2093-6214},
Keywords = {Design of experiments; Extended release; Machine learning; Multivariate
   data analysis; Process analytical technology; Quality by design},
Keywords-Plus = {NEAR-INFRARED SPECTROSCOPY; CONTINUOUS DIRECT COMPRESSION; ARTIFICIAL
   NEURAL-NETWORK; MATRIX TABLETS; QBD APPROACH; BY-DESIGN; IN-VITRO;
   QUANTITATIVE CHARACTERIZATION; PHARMACEUTICAL DEVELOPMENT; EXCIPIENT
   VARIABILITY},
Research-Areas = {Pharmacology \& Pharmacy},
Web-of-Science-Categories  = {Pharmacology \& Pharmacy},
Author-Email = {aribeiro@ff.uc.pt},
Affiliations = {Universidade de Coimbra; Universidade do Porto; i3S - Instituto de
   Investigacao e Inovacao em Saude, Universidade do Porto},
ResearcherID-Numbers = {Ribeiro, Antonio/K-4072-2013
   },
ORCID-Numbers = {Ribeiro, Antonio/0000-0002-1399-8944
   Costa, Ricardo/0000-0001-8731-4819},
Funding-Acknowledgement = {Drugs R\&D Doctoral Program by FCT (Fundacao para a Ciencia e
   Tecnologia), Portugal; Tecnimede Group {[}PD/BDE/150736/2020]},
Funding-Text = {This work was financially supported by the Drugs R\&D Doctoral Program
   assigned by FCT (Fundacao para a Ciencia e Tecnologia), Portugal and
   Tecnimede Group {[}grant PD/BDE/150736/2020].},
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Number-of-Cited-References = {168},
Times-Cited = {1},
Usage-Count-Last-180-days = {8},
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Journal-ISO = {J. Pharm. Investig.},
Doc-Delivery-Number = {9M0XP},
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@article{ WOS:000745223400001,
Author = {Park, Youngmok and Lee, Chanho and Jung, Ji Ye},
Title = {Digital Healthcare for Airway Diseases from Personal Environmental
   Exposure},
Journal = {YONSEI MEDICAL JOURNAL},
Year = {2022},
Volume = {63},
Number = {S},
Pages = {S1-S13},
Month = {JAN},
Abstract = {Digital technologies have emerged in various dimensions of human life,
   ranging from education to professional services to wellbeing. In
   particular, health products and services have expanded by the use and
   development of artificial intelligence, mobile health applications, and
   wearable electronic devices. Such advancements have enabled accurate and
   updated tracking and modeling of health conditions. For instance,
   digital health technologies are capable of measuring environmental
   pollution and predicting its adverse health effects. Several health
   conditions, including chronic airway diseases such as asthma and chronic
   obstructive pulmonary disease, can be exacerbated by pollution. These
   diseases impose substantial health burdens with high morbidity and
   mortality. Recently, efforts have been made to develop digital
   technologies to alleviate such conditions. Moreover, the COVID-19
   pandemic has facilitated the application of telemedicine and
   telemonitoring for patients with chronic airway diseases. This article
   reviews current trends and studies in digital technology utilization for
   investigating and managing environmental exposure and chronic airway
   diseases. First, we discussed the recent progression of digital
   technologies in general environmental healthcare. Then, we summarized
   the capacity of digital technologies in predicting exacerbation and
   self-management of airway diseases. Concluding these reviews, we
   provided suggestions to improve digital health technologies' abilities
   to reduce the adverse effects of environmental exposure in chronic
   airway diseases, based on personal exposure-response modeling.},
Publisher = {YONSEI UNIV COLL MEDICINE},
Address = {50-1 YONSEI-RO, SEODAEMUN-GU, SEOUL 120-752, SOUTH KOREA},
Type = {Review},
Language = {English},
Affiliation = {Jung, JY (Corresponding Author), Yonsei Univ, Severance Hosp, Dept Internal Med, Div Pulm \& Crit Care Med,Coll Med, 50-1 Yonsei Ro, Seoul 03722, South Korea.
   Park, Youngmok; Jung, Ji Ye, Yonsei Univ, Severance Hosp, Dept Internal Med, Div Pulm \& Crit Care Med,Coll Med, 50-1 Yonsei Ro, Seoul 03722, South Korea.
   Lee, Chanho, Yonsei Univ, Severance Biomed Sci Inst, Yonsei Biomed Res Inst, Coll Med, Seoul, South Korea.},
DOI = {10.3349/ymj.2022.63.S1},
ISSN = {0513-5796},
EISSN = {1976-2437},
Keywords = {Asthma; digital technology; chronic obstructive pulmonary disease;
   environment; wearable electronic devices},
Keywords-Plus = {OBSTRUCTIVE PULMONARY-DISEASE; PARTICULATE MATTER; RESPIRATORY-DISEASES;
   HOSPITAL ADMISSION; NATIONAL-HEALTH; CITIZEN SCIENCE; SHORT-TERM;
   POLLUTION; ASTHMA; URBAN},
Research-Areas = {General \& Internal Medicine},
Web-of-Science-Categories  = {Medicine, General \& Internal},
Author-Email = {stopyes@yuhs.ac},
Affiliations = {Yonsei University; Yonsei University Health System; Yonsei University;
   Yonsei University Health System},
ORCID-Numbers = {Park, Youngmok/0000-0002-5669-1491
   Jung, Ji Ye/0000-0003-1589-4142
   Lee, Chanho/0000-0003-2065-7379},
Funding-Acknowledgement = {Korea Environment Industry \& Technology Institute (KEITI) through the
   Digital Infrastructure Building Project for Monitoring, Surveying and
   Evaluating the Environmental Health - Korean Ministry of Environment
   (MOE) {[}2021003340002]},
Funding-Text = {This work was supported by the Korea Environment Industry \& Technology
   Institute (KEITI) through the Digital Infrastructure Building Project
   for Monitoring, Surveying and Evaluating the Environmental Health,
   funded by Korean Ministry of Environment (MOE) (2021003340002). The
   authors also thank Medical Illustration \& Design, part of the Medical
   Research Support Services of Yonsei University College of Medicine, for
   all of the artistic support related to this work.},
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Number-of-Cited-References = {100},
Times-Cited = {1},
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Journal-ISO = {Yonsei Med. J.},
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}

@article{ WOS:000499066900088,
Author = {Li, Lei and Wang, Yang and Zhang, Wenjun and Yu, Shuili and Wang,
   Xianyun and Gao, Naiyun},
Title = {New advances in fluorescence excitation-emission matrix spectroscopy for
   the characterization of dissolved organic matter in drinking water
   treatment: A review},
Journal = {CHEMICAL ENGINEERING JOURNAL},
Year = {2020},
Volume = {381},
Month = {FEB 1},
Abstract = {Fluorescence technology has been increasingly applied in dissolved
   organic matter (DOM) characterization due to its remarkable sensitivity
   and selectivity. Interest in using fluorescence technology to
   investigate drinking water treatment with a focus on coagulation,
   adsorption, membrane filtration and disinfection is growing.
   Fluorescence excitation-emission matrix (EEM) spectroscopy, also known
   as three-dimensional fluorescence (3D-EEM) or fluorescence
   fingerprinting, is one of the most predominant approaches because of the
   massive amount of data, visual maps and multidimensional information it
   provides. Various EEM map interpretation methods have been developed.
   This paper reviews current predominant 3D-EEM interpretation methods
   (ranging from basic methods, peak picking and fluorescence regional
   integration (FRI) to chemometric methods) and summarizes the latest
   findings and problems related to practical applications. The
   correlations between optical and physicochemical properties, such as
   molecular weight and hydrophilic-hydrophobic properties, are
   investigated. Novel findings on drinking water trains obtained with the
   assistance of these interpretation methods are discussed and broadly
   classified as follows: a) evaluating water treatment performance, b)
   observing DOM behavior, c) monitoring and predicting micropollutants and
   disinfection byproducts (DBPs). The importance of using artificial
   intelligence in fluorescence technology and developing advanced
   real-time sensors, the weaknesses of fluorescence spectroscopy and the
   need for combination with other technologies (e.g., fractionation
   techniques) are highlighted.},
Publisher = {ELSEVIER SCIENCE SA},
Address = {PO BOX 564, 1001 LAUSANNE, SWITZERLAND},
Type = {Review},
Language = {English},
Affiliation = {Yu, SL (Corresponding Author), 308 Mingjing Bldg,1239 Siping Rd, Shanghai 200092, Peoples R China.
   Li, Lei; Wang, Yang; Zhang, Wenjun; Yu, Shuili; Gao, Naiyun, Tongji Univ, State Key Lab Pollut Control \& Resource Reuse, Shanghai 200092, Peoples R China.
   Li, Lei; Yu, Shuili, Shanghai Inst Pollut Control \& Ecol Secur, Shanghai 200092, Peoples R China.
   Wang, Xianyun, Nation Engn Res Ctr Urban Water Resources, Shanghai 200082, Peoples R China.},
DOI = {10.1016/j.cej.2019.122676},
Article-Number = {122676},
ISSN = {1385-8947},
EISSN = {1873-3212},
Keywords = {Dissolved organic matter; EEM; Characterization; Monitoring; Drinking
   water treatment},
Keywords-Plus = {PARALLEL FACTOR-ANALYSIS; DISINFECTION BY-PRODUCT; REGIONAL-INTEGRATION
   ANALYSIS; ACTIVATED CARBON ADSORPTION; MICROCYSTIS-AERUGINOSA; PARAFAC
   COMPONENTS; EEM-PARAFAC; EMERGING CONTAMINANTS; MONITORING TOOL; UV
   ABSORBENCY},
Research-Areas = {Engineering},
Web-of-Science-Categories  = {Engineering, Environmental; Engineering, Chemical},
Author-Email = {ysl@tongji.edu.cn},
Affiliations = {Tongji University},
ResearcherID-Numbers = {LI, LI/GVS-5344-2022
   Li, Li/AEM-3636-2022
   li, li/HII-4157-2022
   },
ORCID-Numbers = {Li, Lei/0000-0003-4368-8637},
Funding-Acknowledgement = {National Natural Science Foundation of China {[}51678420]; national
   major science and technology project of China {[}2017ZX07201005,
   2017ZX07502003-03]; Fundamental Research Funds for the Central
   Universities; Shanghai City Youth Science and Technology Star Project
   {[}19QC1400100]},
Funding-Text = {This work was supported by the National Natural Science Foundation of
   China (51678420), the national major science and technology project of
   China (2017ZX07201005, 2017ZX07502003-03), the Fundamental Research
   Funds for the Central Universities and Shanghai City Youth Science and
   Technology Star Project (No. 19QC1400100).},
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Number-of-Cited-References = {101},
Times-Cited = {107},
Usage-Count-Last-180-days = {150},
Usage-Count-Since-2013 = {790},
Journal-ISO = {Chem. Eng. J.},
Doc-Delivery-Number = {JQ6QF},
Web-of-Science-Index = {Science Citation Index Expanded (SCI-EXPANDED)},
Unique-ID = {WOS:000499066900088},
ESI-Highly-Cited-Paper = {Y},
ESI-Hot-Paper = {N},
DA = {2023-04-22},
}

@article{ WOS:000752493100007,
Author = {Athavale, Harsh Arvind and Athavale, Sunita Arvind and Pathak, Amey
   Subodh and Pathak, Tanmay Subodh},
Title = {How Data is Helping to Fight COVID-19 Pandemic},
Journal = {JOURNAL OF CLINICAL AND DIAGNOSTIC RESEARCH},
Year = {2021},
Volume = {15},
Number = {9},
Pages = {LE01-LE05},
Month = {SEP},
Abstract = {Early in 2020, the COVID-19 pandemic emerged as a global public health
   concern requiring urgent attention, concerted efforts and intervention
   to avoid catastrophe. This necessitated optimal use of fast-emerging
   data to be analysed to draw out inferences that would shape our
   response. World Health Organisation (WHO) called this pandemic an
   infodemic where data played a crucial role. This paper reviews how data
   from varied sources and different types helped delay the outbreak, limit
   the spread, initiate social and public health measures, decide treatment
   regimes, optimise healthcare infrastructure and human resources and
   helped to initiate a multipronged strategy with emerging evidence for
   further course correction as the world progressed through the pandemic.
   The classical mathematical tools, i.e., Susceptible-Infected-Recovered
   (SIR) model and its variants, were the primary analytical techniques
   utilised to analyse such data. However, newer data analytical techniques
   utilising artificial intelligence and machine learning, were also
   extensively used. These techniques have the capability to handle large
   quantities of data and develop prediction models of various emerging
   situations that offer foreknowledge for policymakers and provide
   solutions. Data Science has witnessed a leap in the past few years, and
   the way it helped shape our response to this pandemic is a testimony to
   the promise that it holds for humankind.},
Publisher = {PREMCHAND SHANTIDEVI RESEARCH FOUNDATION},
Address = {71 JAIN COLONY, VEER NAGAR, DELHI, 110 007, INDIA},
Type = {Review},
Language = {English},
Affiliation = {Pathak, TS (Corresponding Author), Int Inst Informat Technol, Dept Elect \& Commun Engn, Hyderabad, Telangana, India.
   Athavale, Harsh Arvind, Manipal Univ, Dept Comp Sci Engn, Jaipur, Rajasthan, India.
   Athavale, Sunita Arvind, All India Inst Med Sci, Dept Anat, Bhopal, Madhya Pradesh, India.
   Pathak, Amey Subodh, RUHS Med Coll, Dept Med, Jaipur, Rajasthan, India.
   Pathak, Tanmay Subodh, Int Inst Informat Technol, Dept Elect \& Commun Engn, Hyderabad, Telangana, India.},
DOI = {10.7860/JCDR/2021/49455.15406},
ISSN = {2249-782X},
EISSN = {0973-709X},
Keywords = {Algorithms; Artificial intelligence; Data analytics; Privacy},
Keywords-Plus = {CHINA},
Research-Areas = {General \& Internal Medicine},
Web-of-Science-Categories  = {Medicine, General \& Internal},
Author-Email = {tanmay.pathak@students.iiit.ac.in},
Affiliations = {Manipal University Jaipur; All India Institute of Medical Sciences
   (AIIMS) Bhopal; International Institute of Information Technology
   Hyderabad},
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Number-of-Cited-References = {47},
Times-Cited = {0},
Usage-Count-Last-180-days = {1},
Usage-Count-Since-2013 = {5},
Journal-ISO = {J. Clin. Diagn. Res.},
Doc-Delivery-Number = {YV1KZ},
Web-of-Science-Index = {Emerging Sources Citation Index (ESCI)},
Unique-ID = {WOS:000752493100007},
OA = {gold},
DA = {2023-04-22},
}

@article{ WOS:000446517900002,
Author = {Yang DianGe and Jiang Kun and Zhao Ding and Yu ChunLei and Cao Zhong and
   Xie ShiChao and Xiao ZhongYang and Jiao XinYu and Wang SiJia and Zhang
   Kai},
Title = {Intelligent and connected vehicles: Current status and future
   perspectives},
Journal = {SCIENCE CHINA-TECHNOLOGICAL SCIENCES},
Year = {2018},
Volume = {61},
Number = {10},
Pages = {1446-1471},
Month = {OCT},
Abstract = {Intelligent connected vehicles (ICVs) are believed to change people's
   life in the near future by making the transportation safer, cleaner and
   more comfortable. Although many prototypes of ICVs have been developed
   to prove the concept of autonomous driving and the feasibility of
   improving traffic efficiency, there still exists a significant gap
   before achieving mass production of high-level ICVs. The objective of
   this study is to present an overview of both the state of the art and
   future perspectives of key technologies that are needed for future ICVs.
   It is a challenging task to review all related works and predict their
   future perspectives, especially for such a complex and interdisciplinary
   area of research. This article is organized to overview the ICV key
   technologies by answering three questions: what are the milestones in
   the history of ICVs; what are the electronic components needed for
   building an ICV platform; and what are the essential algorithms to
   enable intelligent driving? To answer the first question, the article
   has reviewed the history and the development milestones of ICVs. For the
   second question, the recent technology advances in electrical/electronic
   architecture, sensors, and actuators are presented. For the third
   question, the article focuses on the algorithms in decision making, as
   the perception and control algorithm are covered in the development of
   sensors and actuators. To achieve correct decision-making, there exist
   two different approaches: the principle-based approach and data-driven
   approach. The advantages and limitations of both approaches are
   explained and analyzed. Currently automotive engineers are concerned
   more with the vehicle platform technology, whereas the academic
   researchers prefer to focus on theoretical algorithms. However, only by
   incorporating elements from both worlds can we accelerate the production
   of high-level ICVs.},
Publisher = {SCIENCE PRESS},
Address = {16 DONGHUANGCHENGGEN NORTH ST, BEIJING 100717, PEOPLES R CHINA},
Type = {Review},
Language = {English},
Affiliation = {Yang, DG; Jiang, K (Corresponding Author), Tsinghua Univ, Dept Automot Engn, Collaborat Innovat Ctr Intelligent New Energy Veh, State Key Lab Automot Safety \& Energy, Beijing 100084, Peoples R China.
   Zhao, D (Corresponding Author), Univ Michigan, Transportat Res Inst, Ann Arbor, MI 48109 USA.
   Yang DianGe; Jiang Kun; Yu ChunLei; Cao Zhong; Xie ShiChao; Xiao ZhongYang; Jiao XinYu; Wang SiJia; Zhang Kai, Tsinghua Univ, Dept Automot Engn, Collaborat Innovat Ctr Intelligent New Energy Veh, State Key Lab Automot Safety \& Energy, Beijing 100084, Peoples R China.
   Zhao Ding, Univ Michigan, Transportat Res Inst, Ann Arbor, MI 48109 USA.},
DOI = {10.1007/s11431-017-9338-1},
ISSN = {1674-7321},
EISSN = {1869-1900},
Keywords = {intelligent connected vehicle (ICV); autonomous driving; artificial
   intelligence; advanced driver assistance systems (ADAS); electrical;
   electronic architecture (EEA); environmental perception;
   decision-making; ICV computation platform},
Keywords-Plus = {OF-THE-ART; URBAN ENVIRONMENTS; LEARNING ALGORITHM; ROAD; SYSTEM;
   GENERATION; ENTRY; ARCHITECTURE; NETWORKS; TRACKING},
Research-Areas = {Engineering; Materials Science},
Web-of-Science-Categories  = {Engineering, Multidisciplinary; Materials Science, Multidisciplinary},
Author-Email = {ydg@tsinghua.edu.cn
   jiangkun@tsinghua.edu.cn
   zhaoding@umich.edu},
Affiliations = {Tsinghua University; University of Michigan System; University of
   Michigan},
ORCID-Numbers = {Zhao, Ding/0000-0002-9400-8446},
Funding-Acknowledgement = {International Science and Technology Cooperation Program of China
   {[}2016YFE0102200]; National Natural Science Foundation of China
   {[}61773234]; National Key R\&D Program of China {[}2108YFB0105004];
   Beijing Municipal Science and Technology Commission {[}D171100005117001,
   D171100005117002]},
Funding-Text = {This work was supported by the International Science and Technology
   Cooperation Program of China (Grant No. 2016YFE0102200), the National
   Natural Science Foundation of China (Grant No. 61773234), the National
   Key R\&D Program of China(Grant No. 2108YFB0105004), and Beijing
   Municipal Science and Technology Commission (Grant Nos. D171100005117001
   \& D171100005117002).},
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Number-of-Cited-References = {237},
Times-Cited = {66},
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Journal-ISO = {Sci. China-Technol. Sci.},
Doc-Delivery-Number = {GV9YL},
Web-of-Science-Index = {Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)},
Unique-ID = {WOS:000446517900002},
DA = {2023-04-22},
}

@article{ WOS:000925162900002,
Author = {Kocak, Burak and Cuocolo, Renato and dos Santos, Daniel Pinto and
   Stanzione, Arnaldo and Ugga, Lorenzo},
Title = {Must-have Qualities of Clinical Research on Artificial Intelligence and
   Machine Learning},
Journal = {BALKAN MEDICAL JOURNAL},
Year = {2023},
Volume = {40},
Number = {1},
Pages = {3-12},
Month = {JAN},
Abstract = {In the field of computer science, known as artificial intelligence,
   algorithms imitate reasoning tasks that are typically performed by
   humans. The techniques that allow machines to learn and get better at
   tasks such as recognition and prediction, which form the basis of
   clinical practice, are referred to as machine learning, which is a
   subfield of artificial intelligence. The number of artificial
   intelligence -and machine learnings-related publications in clinical
   journals has grown exponentially, driven by recent developments in
   computation and the accessibility of simple tools. However, clinicians
   are often not included in data science teams, which may limit the
   clinical relevance, explanability, workflow compatibility, and quality
   improvement of artificial intelligence solutions. Thus, this results in
   the language barrier between clinicians and artificial intelligence
   developers. Healthcare practitioners sometimes lack a basic
   understanding of artificial intelligence research because the approach
   is difficult for non-specialists to understand. Furthermore, many
   editors and reviewers of medical publications might not be familiar with
   the fundamental ideas behind these technologies, which may prevent
   journals from publishing high-quality artificial intelligence studies
   or, worse still, could allow for the publication of low-quality works.
   In this review, we aim to improve readers' artificial intelligence
   literacy and critical thinking. As a result, we concentrated on what we
   consider the 10 most important qualities of artificial intelligence
   research: valid scientific purpose, high-quality data set, robust
   reference standard, robust input, no information leakage, optimal
   bias-variance tradeoff, proper model evaluation, proven clinical
   utility, transparent reporting, and open science. Before designing a
   study, one should have defined a sound scientific purpose. Then, it
   should be backed by a high-quality data set, robust input, and a solid
   reference standard. The artificial intelligence development pipeline
   should prevent information leakage. For the models, optimal
   bias-variance tradeoff should be achieved, and generalizability
   assessment must be adequately performed. The clinical value of the final
   models must also be established. After the study, thought should be
   given to transparency in publishing the process and results as well as
   open science for sharing data, code, and models. We hope this work may
   improve the artificial intelligence literacy and mindset of the readers.},
Publisher = {GALENOS PUBL HOUSE},
Address = {Kacamak Sokak 21/1, ISTANBUL, Findikzade, TURKEY},
Type = {Review},
Language = {English},
Affiliation = {Kocak, B (Corresponding Author), Univ Hlth Sci Turkey, Basaksehir Cam \& Sakura City Hosp, Clin Radiol, Istanbul, Turkiye.
   Kocak, Burak, Univ Hlth Sci Turkey, Basaksehir Cam \& Sakura City Hosp, Clin Radiol, Istanbul, Turkiye.
   Cuocolo, Renato, Univ Salerno, Dept Med Surg \& Dent, Baronissi, Italy.
   dos Santos, Daniel Pinto, Univ Hosp Cologne, Dept Radiol, Cologne, Germany.
   dos Santos, Daniel Pinto, Univ Hosp Frankfurt, Dept Radiol, Frankfurt, Germany.
   Stanzione, Arnaldo; Ugga, Lorenzo, Univ Naples Federico II, Dept Adv Biomed Sci, Naples, Italy.},
DOI = {10.4274/balkanmedj.galenos.2022.2022-11-51},
ISSN = {2146-3123},
EISSN = {2146-3131},
Keywords-Plus = {CT TEXTURE ANALYSIS; RADIOMICS; AI; STANDARDIZATION; VARIABILITY;
   MEDICINE; FEATURES; QUESTION; PATIENT; SYSTEM},
Research-Areas = {General \& Internal Medicine},
Web-of-Science-Categories  = {Medicine, General \& Internal},
Author-Email = {drburakkocak@gmail.com},
Affiliations = {University of Salerno; University of Cologne; Goethe University
   Frankfurt; Goethe University Frankfurt Hospital; University of Naples
   Federico II},
ResearcherID-Numbers = {Cuocolo, Renato/G-3147-2018
   Kocak, Burak/A-4749-2012},
ORCID-Numbers = {Cuocolo, Renato/0000-0002-1452-1574
   Kocak, Burak/0000-0002-7307-396X},
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Number-of-Cited-References = {115},
Times-Cited = {2},
Usage-Count-Last-180-days = {7},
Usage-Count-Since-2013 = {7},
Journal-ISO = {Balk. Med. J.},
Doc-Delivery-Number = {8N5BK},
Web-of-Science-Index = {Science Citation Index Expanded (SCI-EXPANDED)},
Unique-ID = {WOS:000925162900002},
OA = {Green Published, gold},
DA = {2023-04-22},
}

@article{ WOS:000596522700001,
Author = {Wadoux, Alexandre M. J. -C. and Roman-Dobarco, Mercedes and McBratney,
   Alex B.},
Title = {Perspectives on data-driven soil research},
Journal = {EUROPEAN JOURNAL OF SOIL SCIENCE},
Year = {2021},
Volume = {72},
Number = {4},
Pages = {1675-1689},
Month = {JUL},
Abstract = {Soil is a complex system in which biological, chemical and physical
   interactions take place. The behaviour of these interactions changes in
   spatial scale from the atomic to the global, and in time. To understand
   how this system works, soil scientists usually rely on incremental
   improvements in the knowledge by refinement of theories through
   hypothesis testing and development using carefully designed experiments.
   In the last two decades, the primacy of this knowledge construction
   process has been challenged by the development of large soil databases
   and algorithms such as machine learning. The data-driven research
   approach to soil science, the inference of soil knowledge directly from
   data by using computational tools and modelling techniques, is becoming
   more popular. Despite the wide adoption of a data-driven research
   approach to soil science, there has been little discussion on how a
   research driven by data instead of hypotheses affects scientific
   progress. In this paper, we provide an introductory perspective on
   data-driven soil research by discussing some of the issues and
   opportunities of knowledge discovery from soil data. We show that while
   data-driven soil research may seem revolutionary for some, soil science
   has a long history of exploratory efforts to generate knowledge from
   data. Empirical and factual soil classifications, for example, were data
   driven. We further discuss, with examples, (i) data, databases and the
   logic of data storage for data-driven soil research, (ii) the issues of
   extreme empiricist claims that arise corollary to the increase in the
   use of computational tools, and (iii) the challenge of formulating a
   scientific explanation based on patterns observed in the data and data
   analysis tools. By considering the epistemic challenges of the
   data-driven scientific research in the light of the historical
   literature, we found that there is a continuity of practices, some being
   certainly amplified by recent technological changes, but that the core
   methods of scientific enquiry from data remain essentially unchanged.
   Highlights
   Historical account of data-driven soil science research.
   Describe data to be used for data-driven soil science.
   Discuss conceptual issues and opportunities for data-driven soil
   science.
   Investigate the challenge of formulating an explanation from soil data.},
Publisher = {WILEY},
Address = {111 RIVER ST, HOBOKEN 07030-5774, NJ USA},
Type = {Review},
Language = {English},
Affiliation = {Wadoux, AMJC (Corresponding Author), Univ Sydney, Sydney Inst Agr, Sydney, NSW, Australia.
   Wadoux, AMJC (Corresponding Author), Univ Sydney, Sch Life \& Environm Sci, Sydney, NSW, Australia.
   Wadoux, Alexandre M. J. -C.; Roman-Dobarco, Mercedes; McBratney, Alex B., Univ Sydney, Sydney Inst Agr, Sydney, NSW, Australia.
   Wadoux, Alexandre M. J. -C.; Roman-Dobarco, Mercedes; McBratney, Alex B., Univ Sydney, Sch Life \& Environm Sci, Sydney, NSW, Australia.},
DOI = {10.1111/ejss.13071},
EarlyAccessDate = {DEC 2020},
ISSN = {1351-0754},
EISSN = {1365-2389},
Keywords = {data science; epistemology; knowledge discovery; machine learning;
   pedology; pedometrics; soil science},
Keywords-Plus = {ORGANIC-CARBON; SCIENCE; HISTORY; MODELS; CLASSIFICATION; INFORMATION;
   REGRESSION; PARADIGM; TRENDS; FIELD},
Research-Areas = {Agriculture},
Web-of-Science-Categories  = {Soil Science},
Author-Email = {alexandre.wadoux@sydney.edu.au},
Affiliations = {University of Sydney; University of Sydney},
ResearcherID-Numbers = {Dobarco, Mercedes Román/N-7174-2014
   Wadoux, Alexandre/ABB-8559-2020
   },
ORCID-Numbers = {Dobarco, Mercedes Román/0000-0001-8078-8616
   Wadoux, Alexandre/0000-0001-7325-9716
   McBratney, Alex/0000-0003-0913-2643},
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Number-of-Cited-References = {83},
Times-Cited = {20},
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Doc-Delivery-Number = {TC8EI},
Web-of-Science-Index = {Science Citation Index Expanded (SCI-EXPANDED)},
Unique-ID = {WOS:000596522700001},
DA = {2023-04-22},
}

@article{ WOS:000535696600012,
Author = {Tamiminia, Haifa and Salehi, Bahram and Mahdianpari, Masoud and
   Quackenbush, Lindi and Adeli, Sarina and Brisco, Brian},
Title = {Google Earth Engine for geo-big data applications: A meta-analysis and
   systematic review},
Journal = {ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING},
Year = {2020},
Volume = {164},
Pages = {152-170},
Month = {JUN},
Abstract = {Google Earth Engine (GEE) is a cloud-based geospatial processing
   platform for large-scale environmental monitoring and analysis. The
   free-to-use GEE platform provides access to (1) petabytes of publicly
   available remote sensing imagery and other ready-to-use products with an
   explorer web app; (2) high-speed parallel processing and machine
   learning algorithms using Google's computational infrastructure; and (3)
   a library of Application Programming Interfaces (APIs) with development
   environments that support popular coding languages, such as JavaScript
   and Python. Together these core features enable users to discover,
   analyze and visualize geospatial big data in powerful ways without
   needing access to supercomputers or specialized coding expertise. The
   development of GEE has created much enthusiasm and engagement in the
   remote sensing and geospatial data science fields. Yet after a decade
   since GEE was launched, its impact on remote sensing and geospatial
   science has not been carefully explored. Thus, a systematic review of
   GEE that can provide readers with the ``big picture{''} of the current
   status and general trends in GEE is needed. To this end, the decision
   was taken to perform a meta-analysis investigation of recent
   peer-reviewed GEE articles focusing on several features, including data,
   sensor type, study area, spatial resolution, application, strategy, and
   analytical methods. A total of 349 peer-reviewed articles published in
   146 different journals between 2010 and October 2019 were reviewed.
   Publications and geographical distribution trends showed a broad
   spectrum of applications in environmental analyses at both regional and
   global scales. Remote sensing datasets were used in 90\% of studies
   while 10\% of the articles utilized ready-to-use products for analyses.
   Optical satellite imagery with medium spatial resolution, particularly
   Landsat data with an archive exceeding 40 years, has been used
   extensively. Linear regression and random forest were the most
   frequently used algorithms for satellite imagery processing. Among
   ready-to-use products, the normalized difference vegetation index (NDVI)
   was used in 27\% of studies for vegetation, crop, land cover mapping and
   drought monitoring. The results of this study confirm that GEE has and
   continues to make substantive progress on global challenges involving
   process of geo-big data.},
Publisher = {ELSEVIER},
Address = {RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS},
Type = {Review},
Language = {English},
Affiliation = {Mahdianpari, M (Corresponding Author), Mem Univ Newfoundland, C CORE, St John, NF A1B 3X5, Canada.
   Mahdianpari, M (Corresponding Author), Mem Univ Newfoundland, Dept Elect \& Comp Engn, St John, NF A1B 3X5, Canada.
   Tamiminia, Haifa; Salehi, Bahram; Quackenbush, Lindi; Adeli, Sarina, SUNY Coll Environm Sci \& Forestry ESF, Dept Environm Resources Engn, Syracuse, NY 13210 USA.
   Mahdianpari, Masoud, Mem Univ Newfoundland, C CORE, St John, NF A1B 3X5, Canada.
   Mahdianpari, Masoud, Mem Univ Newfoundland, Dept Elect \& Comp Engn, St John, NF A1B 3X5, Canada.
   Brisco, Brian, Canada Ctr Mapping \& Earth Observat, Ottawa, ON K1S 5K2, Canada.},
DOI = {10.1016/j.isprsjprs.2020.04.001},
ISSN = {0924-2716},
EISSN = {1872-8235},
Keywords = {Google Earth Engine; Geo-big data; Cloud-based platform; Remote sensing;
   Planetary-scale; Geospatial; Machine learning; Environmental monitoring},
Keywords-Plus = {LANDSAT TIME-SERIES; RANDOM FOREST; SAR DATA; SEMANTIC SEGMENTATION;
   CROPLAND EXTENT; NEURAL-NETWORKS; CLASSIFICATION; IMAGERY; SATELLITE;
   COVER},
Research-Areas = {Physical Geography; Geology; Remote Sensing; Imaging Science \&
   Photographic Technology},
Web-of-Science-Categories  = {Geography, Physical; Geosciences, Multidisciplinary; Remote Sensing;
   Imaging Science \& Photographic Technology},
Author-Email = {m.mahdianpari@mun.ca},
Affiliations = {State University of New York (SUNY) System; State University of New York
   (SUNY) College of Environmental Science \& Forestry; Memorial University
   Newfoundland; Memorial University Newfoundland; Natural Resources
   Canada; Strategic Policy \& Results Sector - Natural Resources Canada;
   Canada Centre for Mapping \& Earth Observation (CCMEO)},
ResearcherID-Numbers = {Quackenbush, Lindi J./AAZ-4209-2021
   Tamiminia, Haifa/GWC-1337-2022
   Tamiminia, Haifa/ABI-3508-2020
   },
ORCID-Numbers = {Salehi, Bahram/0000-0002-7742-5475
   Mahdianpari, Masoud/0000-0002-7234-959X},
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Number-of-Cited-References = {106},
Times-Cited = {286},
Usage-Count-Last-180-days = {110},
Usage-Count-Since-2013 = {552},
Journal-ISO = {ISPRS-J. Photogramm. Remote Sens.},
Doc-Delivery-Number = {LR4WG},
Web-of-Science-Index = {Science Citation Index Expanded (SCI-EXPANDED)},
Unique-ID = {WOS:000535696600012},
ESI-Highly-Cited-Paper = {Y},
ESI-Hot-Paper = {N},
DA = {2023-04-22},
}

@article{ WOS:000716864600002,
Author = {Zhang, Xuemeng and Liu, Chao and Chen, Yuexi and Zheng, Guanghong and
   Chen, Yinguang},
Title = {Source separation, transportation, pretreatment, and valorization of
   municipal solid waste: a critical review},
Journal = {ENVIRONMENT DEVELOPMENT AND SUSTAINABILITY},
Year = {2022},
Volume = {24},
Number = {10},
Pages = {11471-11513},
Month = {OCT},
Abstract = {Waste sorting is an effective means of enhancing resource or energy
   recovery from municipal solid waste (MSW). Waste sorting management
   system is not limited to source separation, but also involves at least
   three stages, i.e., collection and transportation (C\&T), pretreatment,
   and resource utilization. This review focuses on the whole process of
   MSW management strategy based on the waste sorting perspective. Firstly,
   as the sources of MSW play an essential role in the means of subsequent
   valorization, the factors affecting the generation of MSW and its
   prediction methods are introduced. Secondly, a detailed comparison of
   approaches to source separation across countries is presented.
   Constructing a top-down management system and incentivizing or
   constraining residents' sorting behavior from the bottom up is believed
   to be a practical approach to promote source separation. Then, the
   current state of C\&T techniques and its network optimization are
   reviewed, facilitated by artificial intelligence (AI) and the Internet
   of Things technologies. Furthermore, the advances in pretreatment
   strategies for enhanced sorting and resource recovery are introduced
   briefly. Finally, appropriate methods to valorize different MSW are
   proposed. It is worth noting that new technologies, such as AI, show
   high application potential in waste management. The sharing of
   (intermediate) products or energy of varying processing units will
   inject vitality into the waste management network and achieve
   sustainable development.
   {[}GRAPHICS]
   .},
Publisher = {SPRINGER},
Address = {VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS},
Type = {Review},
Language = {English},
Affiliation = {Zheng, GH; Chen, YG (Corresponding Author), Tongji Univ, Sch Environm Sci \& Engn, State Key Lab Pollut Control \& Resource Reuse, 1239 Siping Rd, Shanghai 200092, Peoples R China.
   Zhang, Xuemeng; Liu, Chao; Chen, Yuexi; Zheng, Guanghong; Chen, Yinguang, Tongji Univ, Sch Environm Sci \& Engn, State Key Lab Pollut Control \& Resource Reuse, 1239 Siping Rd, Shanghai 200092, Peoples R China.},
DOI = {10.1007/s10668-021-01932-w},
EarlyAccessDate = {NOV 2021},
ISSN = {1387-585X},
EISSN = {1573-2975},
Keywords = {Municipal solid waste (MSW); Waste sorting; Route optimization; Resource
   recovery; Waste management},
Keywords-Plus = {INFORMATION-SYSTEM GIS; FOOD WASTE; ANAEROBIC-DIGESTION; URBAN AREAS;
   ORGANIC FRACTION; GREENHOUSE-GAS; TO-ENERGY; SOCIOECONOMIC-FACTORS;
   THERMAL PRETREATMENT; OPERATING PARAMETERS},
Research-Areas = {Science \& Technology - Other Topics; Environmental Sciences \& Ecology},
Web-of-Science-Categories  = {Green \& Sustainable Science \& Technology; Environmental Sciences},
Author-Email = {zhenggh@tongji.edu.cn
   yinguangchen@tongji.edu.cn},
Affiliations = {Tongji University},
ORCID-Numbers = {Zhang, Xuemeng/0000-0002-0815-5635
   Liu, Chao/0000-0003-1773-3756},
Funding-Acknowledgement = {National Key Research and Development Program Project of China
   {[}2019YFC1906301]},
Funding-Text = {This work was supported by the National Key Research and Development
   Program Project of China (Grant No. 2019YFC1906301).},
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Number-of-Cited-References = {230},
Times-Cited = {4},
Usage-Count-Last-180-days = {21},
Usage-Count-Since-2013 = {110},
Journal-ISO = {Environ. Dev. Sustain.},
Doc-Delivery-Number = {4N4PL},
Web-of-Science-Index = {Science Citation Index Expanded (SCI-EXPANDED)},
Unique-ID = {WOS:000716864600002},
OA = {Bronze, Green Published},
DA = {2023-04-22},
}

@article{ WOS:000697254900003,
Author = {Liu, Jingyue (Jimmy)},
Title = {Advances and Applications of Atomic-Resolution Scanning Transmission
   Electron Microscopy},
Journal = {MICROSCOPY AND MICROANALYSIS},
Year = {2021},
Volume = {27},
Number = {5},
Pages = {943-995},
Month = {OCT},
Abstract = {Although scanning transmission electron microscopy (STEM) images of
   individual heavy atoms were reported 50 years ago, the applications of
   atomic-resolution STEM imaging became wide spread only after the
   practical realization of aberration correctors on field-emission
   STEM/TEM instruments to form sub-Angstrom electron probes. The
   innovative designs and advances of electron optical systems, the
   fundamental understanding of electron-specimen interaction processes,
   and the advances in detector technology all played a major role in
   achieving the goal of atomic-resolution STEM imaging of practical
   materials. It is clear that tremendous advances in computer technology
   and electronics, image acquisition and processing algorithms, image
   simulations, and precision machining synergistically made
   atomic-resolution STEM imaging routinely accessible. It is anticipated
   that further hardware/software development is needed to achieve
   three-dimensional atomic-resolution STEM imaging with single-atom
   chemical sensitivity, even for electron-beam-sensitive materials.
   Artificial intelligence, machine learning, and big-data science are
   expected to significantly enhance the impact of STEM and associated
   techniques on many research fields such as materials science and
   engineering, quantum and nanoscale science, physics and chemistry, and
   biology and medicine. This review focuses on advances of STEM imaging
   from the invention of the field-emission electron gun to the realization
   of aberration-corrected and monochromated atomic-resolution STEM and its
   broad applications.},
Publisher = {CAMBRIDGE UNIV PRESS},
Address = {32 AVENUE OF THE AMERICAS, NEW YORK, NY 10013-2473 USA},
Type = {Review},
Language = {English},
Affiliation = {Liu, JY (Corresponding Author), Arizona State Univ, Dept Phys, Tempe, AZ 85287 USA.
   Liu, Jingyue (Jimmy), Arizona State Univ, Dept Phys, Tempe, AZ 85287 USA.},
DOI = {10.1017/S1431927621012125},
ISSN = {1431-9276},
EISSN = {1435-8115},
Keywords = {atomic resolution; electron microscopy; imaging; materials science;
   nanostructure},
Keywords-Plus = {ENERGY-LOSS SPECTROSCOPY; DIFFERENTIAL PHASE-CONTRAST; THERMAL
   DIFFUSE-SCATTERING; MAXIMUM-LIKELIHOOD-ESTIMATION; DARK-FIELD IMAGES;
   SPHERICAL-ABERRATION; BRIGHT-FIELD; MICRODIFFRACTION PATTERNS;
   SPATIAL-RESOLUTION; LIGHT-ELEMENTS},
Research-Areas = {Materials Science; Microscopy},
Web-of-Science-Categories  = {Materials Science, Multidisciplinary; Microscopy},
Author-Email = {jingyue.liu@asu.edu},
Affiliations = {Arizona State University; Arizona State University-Tempe},
Funding-Acknowledgement = {National Science Foundation {[}1955474 (CHE-1955474)]},
Funding-Text = {This work was supported by the National Science Foundation under Grant
   No. 1955474 (CHE-1955474). The author acknowledges use of the John M.
   Cowley Center for High-resolution Electron Microscopy at Arizona State
   University throughout his research career. The author is grateful to
   Professor David J. Smith of Arizona State University for careful editing
   of the manuscript and for providing relevant references.},
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@article{ WOS:000844331100006,
Author = {Li, Jiaxin and Hong, Danfeng and Gao, Lianru and Yao, Jing and Zheng, Ke
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Title = {Deep learning in multimodal remote sensing data fusion: A comprehensive
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Journal = {INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION},
Year = {2022},
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Month = {AUG},
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Publisher = {ELSEVIER},
Address = {RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS},
Type = {Review},
Language = {English},
Affiliation = {Gao, LR (Corresponding Author), Chinese Acad Sci, Aerosp Informat Res Inst, Key Lab Computat Opt Imaging Technol, Beijing 100094, Peoples R China.
   Li, Jiaxin; Hong, Danfeng; Gao, Lianru; Yao, Jing; Zheng, Ke, Chinese Acad Sci, Aerosp Informat Res Inst, Key Lab Computat Opt Imaging Technol, Beijing 100094, Peoples R China.
   Zhang, Bing; Chanussot, Jocelyn, Chinese Acad Sci, Aerosp Informat Res Inst, Beijing 100094, Peoples R China.
   Zhang, Bing, Univ Chinese Acad Sci, Coll Resources \& Environm, Beijing 100049, Peoples R China.
   Zheng, Ke, Liaocheng Univ, Coll Geog \& Environm, Liaocheng 252059, Peoples R China.
   Chanussot, Jocelyn, Univ Grenoble Alpes, GIPSA Lab, CNRS, Grenoble INP, F-38000 Grenoble, France.},
DOI = {10.1016/j.jag.2022.102926},
EarlyAccessDate = {JUL 2022},
Article-Number = {102926},
ISSN = {1569-8432},
EISSN = {1872-826X},
Keywords = {Artificial intelligence; Data fusion; Deep learning; Multimodal; Remote
   sensing},
Keywords-Plus = {HYPERSPECTRAL IMAGE FUSION; PAN-SHARPENING METHOD; NEURAL-NETWORKS;
   AIRBORNE LIDAR; CLOUD REMOVAL; OPTICAL-DATA; SAR; CLASSIFICATION; NET;
   EXTRACTION},
Research-Areas = {Remote Sensing},
Web-of-Science-Categories  = {Remote Sensing},
Author-Email = {lijiaxin203@mails.ucas.ac.cn
   hongdf@aircas.ac.cn
   gaolr@aircas.ac.cn
   yaojing@aircas.ac.cn
   zhengke@lcu.edu.cn
   zb@radi.ac.cn
   jocelyn@hi.is},
Affiliations = {Chinese Academy of Sciences; Chinese Academy of Sciences; Chinese
   Academy of Sciences; University of Chinese Academy of Sciences, CAS;
   Liaocheng University; UDICE-French Research Universities; Communaute
   Universite Grenoble Alpes; Institut National Polytechnique de Grenoble;
   Universite Grenoble Alpes (UGA); Centre National de la Recherche
   Scientifique (CNRS)},
ORCID-Numbers = {li, jia xin/0000-0002-1237-542X},
Funding-Acknowledgement = {National Key Research and De-velopment Program of China
   {[}2021YFB3900502]; National Natural Science Foundation of China
   {[}42030111]; MIAI@Grenoble Alpes {[}ANR-19-P3IA-0003]; AXA Research
   Fund},
Funding-Text = {This work was supported by the National Key Research and De-velopment
   Program of China {[}2021YFB3900502] ; the National Natural Science
   Foundation of China {[}42030111] ; MIAI@Grenoble Alpes
   {[}ANR-19-P3IA-0003] ; and the AXA Research Fund.},
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Number-of-Cited-References = {210},
Times-Cited = {9},
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Journal-ISO = {Int. J. Appl. Earth Obs. Geoinf.},
Doc-Delivery-Number = {3Z3QA},
Web-of-Science-Index = {Science Citation Index Expanded (SCI-EXPANDED)},
Unique-ID = {WOS:000844331100006},
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@article{ WOS:000635421900001,
Author = {Ossewaarde, Ringo and Filatova, Tatiana and Georgiadou, Yola and
   Hartmann, Andreas and Ozerol, Gul and Pfeffer, Karin and Stegmaier,
   Peter and Torenvlied, Rene and van der Voort, Mascha and Warmink, Jord
   and Borsje, Bas},
Title = {Review article: Towards a context-driven research: a state-of-the-art
   review of resilience research on climate change},
Journal = {NATURAL HAZARDS AND EARTH SYSTEM SCIENCES},
Year = {2021},
Volume = {21},
Number = {3},
Pages = {1119-1133},
Month = {MAR 26},
Abstract = {The twofold aim of this paper is to provide an overview of the current
   state of resilience research with regard to climate change in the social
   sciences and propose a research agenda. Resilience research among social
   scientists is characterized by much more diversity today than a few
   decades ago. Different definitions and understandings of resilience
   appear in publications during the last 10 years. Resilience research
   increasingly bears the mark of social constructivism, a relative
   newcomer compared to the more longstanding tradition of naturalism.
   There are also approaches that are indebted to both ``naturalism{''} and
   ``constructivism{''}, which, of course, come in many varieties. Based on
   our overview of recent scholarship, which is far from being exhaustive,
   we have identified six research avenues that arguably deserve continued
   attention. They combine naturalist and constructivist insights and
   approaches so that human agency, reflexivity, and considerations of
   justice and equity are incorporated into systems thinking research or
   supplement such research. Ultimately, we believe that the overarching
   challenge for future research is to ensure that resilience to climate
   change does not compromise sustainability and considerations of justice
   (including environmental, climate, and energy justice).},
Publisher = {COPERNICUS GESELLSCHAFT MBH},
Address = {BAHNHOFSALLEE 1E, GOTTINGEN, 37081, GERMANY},
Type = {Review},
Language = {English},
Affiliation = {Ossewaarde, R (Corresponding Author), Univ Twente, Dept Publ Adm, Drienerlolaan 5, NL-7522 NB Enschede, Netherlands.
   Ossewaarde, Ringo; Torenvlied, Rene, Univ Twente, Dept Publ Adm, Drienerlolaan 5, NL-7522 NB Enschede, Netherlands.
   Filatova, Tatiana; Ozerol, Gul, Univ Twente, Dept Governance \& Technol Sustainabil, Drienerlolaan 5, NL-7522 NB Enschede, Netherlands.
   Georgiadou, Yola; Pfeffer, Karin, Univ Twente, Dept Urban \& Reg Planning \& Geoinformat Managemen, Hengelosestr 99, NL-7514 AE Enschede, Netherlands.
   Hartmann, Andreas, Univ Twente, Dept Construct Management \& Engn, Drienerlolaan 5, NL-7522 NB Enschede, Netherlands.
   Stegmaier, Peter, Univ Twente, Dept Sci Technol \& Policy Studies, Drienerlolaan 5, NL-7522 NB Enschede, Netherlands.
   van der Voort, Mascha, Univ Twente, Dept Design Prod \& Management, Drienerlolaan 5, NL-7522 NB Enschede, Netherlands.
   Warmink, Jord; Borsje, Bas, Univ Twente, Dept Water Engn \& Management, Drienerlolaan 5, NL-7522 NB Enschede, Netherlands.},
DOI = {10.5194/nhess-21-1119-2021},
ISSN = {1561-8633},
EISSN = {1684-9981},
Keywords-Plus = {ARTIFICIAL-INTELLIGENCE; POLITICAL-ECONOMY; ENERGY JUSTICE; ADAPTATION;
   SYSTEMS; POLICY; SUSTAINABILITY; VULNERABILITY; GOVERNANCE; THINKING},
Research-Areas = {Geology; Meteorology \& Atmospheric Sciences; Water Resources},
Web-of-Science-Categories  = {Geosciences, Multidisciplinary; Meteorology \& Atmospheric Sciences;
   Water Resources},
Author-Email = {m.r.r.ossewaarde@utwente.nl},
Affiliations = {University of Twente; University of Twente; University of Twente;
   University of Twente; University of Twente; University of Twente;
   University of Twente},
ResearcherID-Numbers = {Pfeffer, Karin/E-1408-2017},
ORCID-Numbers = {Ossewaarde, Marinus/0000-0003-3449-1074
   van der Voort, Mascha/0000-0002-4800-7557
   Warmink, Jord/0000-0002-3273-5664
   Pfeffer, Karin/0000-0002-6080-1323},
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Number-of-Cited-References = {145},
Times-Cited = {1},
Usage-Count-Last-180-days = {5},
Usage-Count-Since-2013 = {17},
Journal-ISO = {Nat. Hazards Earth Syst. Sci.},
Doc-Delivery-Number = {RG3DA},
Web-of-Science-Index = {Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)},
Unique-ID = {WOS:000635421900001},
OA = {Green Submitted, gold},
DA = {2023-04-22},
}

@article{ WOS:000595872200006,
Author = {Wagle, Nimisha and Acharya, Tri Dev and Lee, Dong Ha},
Title = {Comprehensive Review on Application of Machine Learning Algorithms for
   Water Quality Parameter Estimation Using Remote Sensing Data},
Journal = {SENSORS AND MATERIALS},
Year = {2020},
Volume = {32},
Number = {11, SI},
Pages = {3879-3892},
Abstract = {Water is an integral aspect of the world necessary for living creatures
   to thrive. Owing to unplanned urbanization, rapid industrialization, and
   uncontrollable human intervention, water quality is gradually degrading.
   This affects not only marine animals but also humans. Thus, the quality
   of water should be examined regularly. Water quality parameters should
   be estimated to monitor water quality. In general, water quality
   parameters are measured by in situ measurements. Although these
   measurements are accurate, they are costly and do not provide real-time
   spatial and temporal changes in water quality. To overcome this
   limitation, water quality parameters can be estimated using machine
   learning (ML) along with remote sensing (RS) data. A combination of ML
   and RS data is a powerful approach for the routine assessment of spatial
   and temporal variations in water quality parameters. In this paper, some
   articles based on this approach are reviewed. By analyzing the
   literature, it was found that the integrated use of RS-based geospatial
   data with ML helps to produce an accurate result. Most of the authors
   used the regression algorithm in the estimation of the water quality
   parameters, with a support vector machine (SVM) regression intensively
   used. The artificial neural network (ANN) algorithm was the most used
   algorithm of ML in many of the studies. The researchers used
   multispectral images for their study. By applying ML to RS data, water
   quality monitoring systems are evolving into real-time artificial
   intelligence (AI)-enabled models that provide valuable recommendations
   and insights to support farmers to make decisions and take action in
   aquaculture.},
Publisher = {MYU, SCIENTIFIC PUBLISHING DIVISION},
Address = {1-23-3-303 SENDAGI, TOKYO, 113-0022, JAPAN},
Type = {Review},
Language = {English},
Affiliation = {Lee, DH (Corresponding Author), Kangwon Natl Univ, Dept Civil Engn, 1 Kangdaehak Gil, Chunchon 24341, South Korea.
   Wagle, Nimisha, Govt Nepal, Survey Dept, Kathmandu 44600, Nepal.
   Acharya, Tri Dev; Lee, Dong Ha, Kangwon Natl Univ, Dept Civil Engn, 1 Kangdaehak Gil, Chunchon 24341, South Korea.
   Acharya, Tri Dev, Beijing Univ Civil Engn \& Architecture, Sch Geomat \& Urban Spatial Informat, 15 Yongyuan Rd, Beijing 102616, Peoples R China.
   Acharya, Tri Dev, Univ Calif Davis, Inst Transportat Studies, 1605 Tilia St, Davis, CA 95616 USA.},
DOI = {10.18494/SAM.2020.2953},
ISSN = {0914-4935},
Keywords = {remote sensing; water quality parameters; machine learning; estimation;
   review},
Keywords-Plus = {CHLOROPHYLL-A CONCENTRATION},
Research-Areas = {Instruments \& Instrumentation; Materials Science},
Web-of-Science-Categories  = {Instruments \& Instrumentation; Materials Science, Multidisciplinary},
Author-Email = {nimisha.wagle@nepal.gov.np
   tridevacharya@kangwon.ac.kr
   geodesy@kangwon.ac.kr},
Affiliations = {Kangwon National University; Beijing University of Civil Engineering \&
   Architecture; University of California System; University of California
   Davis},
ResearcherID-Numbers = {Acharya, Tri Dev/N-3292-2016
   Lee, Dong Ha/J-1319-2019
   Wagle, Nimisha/AAV-9570-2020},
ORCID-Numbers = {Acharya, Tri Dev/0000-0003-0886-4201
   Lee, Dong Ha/0000-0002-6934-1247
   Wagle, Nimisha/0000-0002-5921-0382},
Funding-Acknowledgement = {National Research Foundation of Korea (NRF) - Korean government (MSIT)
   {[}2018R1A2B6009363]},
Funding-Text = {This work was supported by a National Research Foundation of Korea (NRF)
   grant funded by the Korean government (MSIT) (No. 2018R1A2B6009363).},
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Number-of-Cited-References = {76},
Times-Cited = {7},
Usage-Count-Last-180-days = {12},
Usage-Count-Since-2013 = {56},
Journal-ISO = {Sens. Mater.},
Doc-Delivery-Number = {PA8ID},
Web-of-Science-Index = {Science Citation Index Expanded (SCI-EXPANDED)},
Unique-ID = {WOS:000595872200006},
OA = {gold},
DA = {2023-04-22},
}

@article{ WOS:000709466800006,
Author = {Patruno, Lucrezia and Maspero, Davide and Craighero, Francesco and
   Angaroni, Fabrizio and Antoniotti, Marco and Graudenzi, Alex},
Title = {A review of computational strategies for denoising and imputation of
   single-cell transcriptomic data},
Journal = {BRIEFINGS IN BIOINFORMATICS},
Year = {2021},
Volume = {22},
Number = {4},
Month = {JUL},
Abstract = {Motivation: The advancements of single-cell sequencing methods have
   paved the way for the characterization of cellular states at
   unprecedented resolution, revolutionizing the investigation on complex
   biological systems. Yet, single-cell sequencing experiments are hindered
   by several technical issues, which cause output data to be noisy,
   impacting the reliability of downstream analyses. Therefore, a growing
   number of data science methods has been proposed to recover lost or
   corrupted information from single-cell sequencing data. To date,
   however, no quantitative benchmarks have been proposed to evaluate such
   methods. Results: We present a comprehensive analysis of the
   state-of-the-art computational approaches for denoising and imputation
   of single-cell transcriptomic data, comparing their performance in
   different experimental scenarios. In detail, we compared 19 denoising
   and imputation methods, on both simulated and real-world datasets, with
   respect to several performance metrics related to imputation of dropout
   events, recovery of true expression profiles, characterization of cell
   similarity, identification of differentially expressed genes and
   computation time. The effectiveness and scalability of all methods were
   assessed with regard to distinct sequencing protocols, sample size and
   different levels of biological variability and technical noise. As a
   result, we identify a subset of versatile approaches exhibiting solid
   performances on most tests and show that certain algorithmic families
   prove effective on specific tasks but inefficient on others. Finally,
   most methods appear to benefit from the introduction of appropriate
   assumptions on noise distribution of biological processes.},
Publisher = {OXFORD UNIV PRESS},
Address = {GREAT CLARENDON ST, OXFORD OX2 6DP, ENGLAND},
Type = {Review},
Language = {English},
Affiliation = {Antoniotti, M (Corresponding Author), Univ Milano Bicocca, Dept Informat Syst \& Commun, Milan, Italy.
   Graudenzi, A (Corresponding Author), Consiglio Nazl Ric IBFM CNR, Inst Mol Bioimaging \& Physiol, Milan, Italy.
   Craighero, Francesco; Angaroni, Fabrizio; Antoniotti, Marco, Univ Milano Bicocca, Dept Informat Syst \& Commun, Milan, Italy.
   Patruno, Lucrezia; Maspero, Davide, Univ Milano Bicocca, Dept Informat Syst \& Commun, Comp Sci, Milan, Italy.
   Graudenzi, Alex, IBFM CNR, Milan, Italy.},
DOI = {10.1093/bib/bbaa222},
Article-Number = {bbaa222},
ISSN = {1467-5463},
EISSN = {1477-4054},
Keywords = {denoising; imputation; single-cell RNA-sequencing; machine learning},
Keywords-Plus = {RNA-SEQ; GENE-EXPRESSION; HETEROGENEITY; FRAMEWORK; DECOMPOSITION;
   MATRIX},
Research-Areas = {Biochemistry \& Molecular Biology; Mathematical \& Computational Biology},
Web-of-Science-Categories  = {Biochemical Research Methods; Mathematical \& Computational Biology},
Author-Email = {marco.antoniotti@unimib.it
   alex.graudenzi@ibfm.cnr.it},
Affiliations = {University of Milano-Bicocca; University of Milano-Bicocca; Consiglio
   Nazionale delle Ricerche (CNR); Istituto di Bioimmagini e Fisiologia
   Molecolare (IBFM-CNR)},
ResearcherID-Numbers = {Craighero, Francesco/GSD-6810-2022
   Maspero, Davide/AHE-1900-2022
   Graudenzi, Alex/AAY-5241-2020
   },
ORCID-Numbers = {Maspero, Davide/0000-0001-8519-4331
   Graudenzi, Alex/0000-0001-5452-1918
   Patruno, Lucrezia/0000-0002-3721-9984
   Craighero, Francesco/0000-0001-8457-6979
   Antoniotti, Marco/0000-0002-2823-6838},
Funding-Acknowledgement = {Italian node of the Elixir network; SysBioNet project, a Ministero
   dell'Istruzione, dell'Universita e della Ricerca initiative for the
   Italian Roadmap of European Strategy Forum on Research Infrastructures;
   Cancer Research UK; Associazione Italiana per la Ricerca sul Cancro
   (CRUK/AIRC) {[}22790]},
Funding-Text = {This work was supported by the Cancer Research UK and Associazione
   Italiana per la Ricerca sul Cancro (CRUK/AIRC) ``Accelerator Award{''}
   (award \#22790) `Single-cell Cancer Evolution in the Clinic'. Partial
   support was also provided by the Italian node of the Elixir network
   (https://elixir-europe.org/a bout-us/who-we-are/nodes/italy) and the
   SysBioNet project, aMinistero dell'Istruzione, dell'Universita e della
   Ricerca initiative for the Italian Roadmap of European Strategy Forum on
   Research Infrastructures.},
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Number-of-Cited-References = {110},
Times-Cited = {16},
Usage-Count-Last-180-days = {5},
Usage-Count-Since-2013 = {26},
Journal-ISO = {Brief. Bioinform.},
Doc-Delivery-Number = {WK1AV},
Web-of-Science-Index = {Science Citation Index Expanded (SCI-EXPANDED)},
Unique-ID = {WOS:000709466800006},
DA = {2023-04-22},
}

@article{ WOS:000722493300001,
Author = {Sharif, Muhammad Zahid and Di, Nayan and Liu, Fanglin},
Title = {Monitoring honeybees (Apis spp.) (Hymenoptera: Apidae) in climate-smart
   agriculture: A review},
Journal = {APPLIED ENTOMOLOGY AND ZOOLOGY},
Year = {2022},
Volume = {57},
Number = {4},
Pages = {289-303},
Month = {NOV},
Abstract = {Climate change is a major threat to agriculture production among
   small-scale farms worldwide. Climate-smart agriculture (CSA) is one of
   the technologies and strategies to sustain agriculture growth in a
   changing climate. Researchers are finding ways to collect big data,
   which are required to clarify local climate change and its impacts on
   agriculture to pinpoint the farming strategies for the practice of CSA.
   The honeybee (Hymenoptera: Apidae) hives around the world which are
   equipped with digital devices for continuously monitoring the status of
   colonies for precise beekeeping, accumulate huge amounts of data that
   can be used to address some questions about CSA. In this paper, we
   confer an overview of the big beehive data (BBD) and data science and
   identifies their potential applications to support CSA, as well as
   several challenges confronted by this approach. Here, we also outline
   that how can we predict the bee-plant interaction based on monitoring
   dynamics in honey production using novel and technological approaches.
   Numerous approaches including big data analytics, IoT, Wireless sensor
   network (WSN)-based monitoring systems, machine learning, and AI
   algorithms are being considered as a power source to assist in
   delivering novel insights and explication to the problems. We put in
   examples where all these approaches have been employed for monitoring
   and analyzing BBD. Moreover, we predict their role to aid in apiary
   management with the perspective of CSA.},
Publisher = {SPRINGER JAPAN KK},
Address = {SHIROYAMA TRUST TOWER 5F, 4-3-1 TORANOMON, MINATO-KU, TOKYO, 105-6005,
   JAPAN},
Type = {Review},
Language = {English},
Affiliation = {Liu, FL (Corresponding Author), Chinese Acad Sci, Hefei Inst Phys Sci, Anhui Inst Opt \& Fine Mech, 350 Shushanhu Rd, Hefei 230031, Anhui, Peoples R China.
   Sharif, Muhammad Zahid; Di, Nayan; Liu, Fanglin, Chinese Acad Sci, Hefei Inst Phys Sci, Anhui Inst Opt \& Fine Mech, 350 Shushanhu Rd, Hefei 230031, Anhui, Peoples R China.
   Sharif, Muhammad Zahid; Di, Nayan, Univ Sci \& Technol China, Hefei 230026, Anhui, Peoples R China.},
DOI = {10.1007/s13355-021-00765-3},
EarlyAccessDate = {NOV 2021},
ISSN = {0003-6862},
EISSN = {1347-605X},
Keywords = {Big data; Climate-smart agriculture; Honey bees; Real-time analysis;
   Wireless sensor networks},
Keywords-Plus = {WIRELESS SENSOR NETWORK; NECTAR PRODUCTION; INSECT POLLINATORS; BIG
   DATA; BEE; SECRETION; SYSTEM; BEHAVIOR; SCIENCE; STRESS},
Research-Areas = {Entomology},
Web-of-Science-Categories  = {Entomology},
Author-Email = {2011ag4010@uaf.edu.pk
   dny\_yan@126.com
   flliu@ipp.ac.cn},
Affiliations = {Chinese Academy of Sciences; Anhui Institute of Optics \& Fine Mechanics
   (AIOFM), CAS; Hefei Institutes of Physical Science, CAS; Chinese Academy
   of Sciences; University of Science \& Technology of China, CAS},
ORCID-Numbers = {Di, Nayan/0000-0002-0403-2021
   Sharif, Muhammad Zahid/0000-0002-8980-8713},
Funding-Acknowledgement = {Chinese Academy of Sciences (CAS); Academy of the Sciences for
   Developing World (TWAS)},
Funding-Text = {First author is highly thankful to the Chinese Academy of Sciences
   (CAS), and Academy of the Sciences for Developing World (TWAS) for
   providing CAS-TWAS Scholarship grant for doctoral study.},
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Number-of-Cited-References = {116},
Times-Cited = {1},
Usage-Count-Last-180-days = {2},
Usage-Count-Since-2013 = {2},
Journal-ISO = {Appl. Entomol. Zoolog.},
Doc-Delivery-Number = {4U2PO},
Web-of-Science-Index = {Science Citation Index Expanded (SCI-EXPANDED)},
Unique-ID = {WOS:000722493300001},
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}

@article{ WOS:000635675300001,
Author = {Alghushairy, Omar and Alsini, Raed and Soule, Terence and Ma, Xiaogang},
Title = {A Review of Local Outlier Factor Algorithms for Outlier Detection in Big
   Data Streams},
Journal = {BIG DATA AND COGNITIVE COMPUTING},
Year = {2021},
Volume = {5},
Number = {1},
Month = {MAR},
Abstract = {Outlier detection is a statistical procedure that aims to find
   suspicious events or items that are different from the normal form of a
   dataset. It has drawn considerable interest in the field of data mining
   and machine learning. Outlier detection is important in many
   applications, including fraud detection in credit card transactions and
   network intrusion detection. There are two general types of outlier
   detection: global and local. Global outliers fall outside the normal
   range for an entire dataset, whereas local outliers may fall within the
   normal range for the entire dataset, but outside the normal range for
   the surrounding data points. This paper addresses local outlier
   detection. The best-known technique for local outlier detection is the
   Local Outlier Factor (LOF), a density-based technique. There are many
   LOF algorithms for a static data environment; however, these algorithms
   cannot be applied directly to data streams, which are an important type
   of big data. In general, local outlier detection algorithms for data
   streams are still deficient and better algorithms need to be developed
   that can effectively analyze the high velocity of data streams to detect
   local outliers. This paper presents a literature review of local outlier
   detection algorithms in static and stream environments, with an emphasis
   on LOF algorithms. It collects and categorizes existing local outlier
   detection algorithms and analyzes their characteristics. Furthermore,
   the paper discusses the advantages and limitations of those algorithms
   and proposes several promising directions for developing improved local
   outlier detection methods for data streams.},
Publisher = {MDPI},
Address = {ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND},
Type = {Review},
Language = {English},
Affiliation = {Alghushairy, O; Ma, XG (Corresponding Author), Univ Idaho, Dept Comp Sci, Moscow, ID 83844 USA.
   Alghushairy, O (Corresponding Author), Univ Jeddah, Coll Comp Sci \& Engn, Jeddah 23890, Saudi Arabia.
   Alghushairy, Omar; Alsini, Raed; Soule, Terence; Ma, Xiaogang, Univ Idaho, Dept Comp Sci, Moscow, ID 83844 USA.
   Alghushairy, Omar, Univ Jeddah, Coll Comp Sci \& Engn, Jeddah 23890, Saudi Arabia.
   Alsini, Raed, King Abdulaziz Univ, Fac Comp \& Informat Technol, Jeddah 21589, Saudi Arabia.},
DOI = {10.3390/bdcc5010001},
Article-Number = {1},
EISSN = {2504-2289},
Keywords = {outlier detection; data science; local outlier factor; genetic
   algorithm; stream data mining},
Keywords-Plus = {NOVELTY DETECTION; EFFICIENT; CLASSIFICATION},
Research-Areas = {Computer Science},
Web-of-Science-Categories  = {Computer Science, Artificial Intelligence; Computer Science, Information
   Systems; Computer Science, Theory \& Methods},
Author-Email = {algh5752@vandals.uidaho.edu
   alsi1250@vandals.uidaho.edu
   tsoule@uidaho.edu
   max@uidaho.edu},
Affiliations = {Idaho; University of Idaho; University of Jeddah; King Abdulaziz
   University},
ResearcherID-Numbers = {Alsini, Raed/AEG-9852-2022
   Ma, Xiaogang/C-9286-2011
   Alghushairy, Omar/HHC-1859-2022
   },
ORCID-Numbers = {Alsini, Raed/0000-0002-3163-575X
   Ma, Xiaogang/0000-0002-9110-7369
   Alghushairy, Omar/0000-0002-7378-5545},
Funding-Acknowledgement = {National Science Foundation {[}2019609]},
Funding-Text = {This research was partially supported by the National Science Foundation
   under grant No. 2019609.},
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Number-of-Cited-References = {115},
Times-Cited = {35},
Usage-Count-Last-180-days = {11},
Usage-Count-Since-2013 = {33},
Journal-ISO = {Big Data Cogn. Comput.},
Doc-Delivery-Number = {RG6WD},
Web-of-Science-Index = {Emerging Sources Citation Index (ESCI)},
Unique-ID = {WOS:000635675300001},
OA = {gold},
DA = {2023-04-22},
}

@article{ WOS:000712877700007,
Author = {Zuo, Zheming and Watson, Matthew and Budgen, David and Hall, Robert and
   Kennelly, Chris and Al Moubayed, Noura},
Title = {Data Anonymization for Pervasive Health Care: Systematic Literature
   Mapping Study},
Journal = {JMIR MEDICAL INFORMATICS},
Year = {2021},
Volume = {9},
Number = {10},
Month = {OCT},
Abstract = {Background: Data science offers an unparalleled opportunity to identify
   new insights into many aspects of human life with recent advances in
   health care. Using data science in digital health raises significant
   challenges regarding data privacy, transparency, and trustworthiness.
   Recent regulations enforce the need for a clear legal basis for
   collecting, processing, and sharing data, for example, the European
   Union's General Data Protection Regulation (2016) and the United
   Kingdom's Data Protection Act (2018). For health care providers, legal
   use of the electronic health record (EHR) is permitted only in clinical
   care cases. Any other use of the data requires thoughtful considerations
   of the legal context and direct patient consent. Identifiable personal
   and sensitive information must be sufficiently anonymized. Raw data are
   commonly anonymized to be used for research purposes, with risk
   assessment for reidentification and utility. Although health care
   organizations have internal policies defined for information governance,
   there is a significant lack of practical tools and intuitive guidance
   about the use of data for research and modeling. Off-the-shelf data
   anonymization tools are developed frequently, but privacy-related
   functionalities are often incomparable with regard to use in different
   problem domains. In addition, tools to support measuring the risk of the
   anonymized data with regard to reidentification against the usefulness
   of the data exist, but there are question marks over their efficacy.
   Objective: In this systematic literature mapping study, we aim to
   alleviate the aforementioned issues by reviewing the landscape of data
   anonymization for digital health care. Methods: We used Google Scholar,
   Web of Science, Elsevier Scopus, and PubMed to retrieve academic studies
   published in English up to June 2020. Noteworthy gray literature was
   also used to initialize the search. We focused on review questions
   covering 5 bottom-up aspects: basic anonymization operations, privacy
   models, reidentification risk and usability metrics, off-the-shelf
   anonymization tools, and the lawful basis for EHR data anonymization.
   Results: We identified 239 eligible studies, of which 60 were chosen for
   general background information; 16 were selected for 7 basic
   anonymization operations; 104 covered 72 conventional and machine
   learning-based privacy models; four and 19 papers included seven and 15
   metrics, respectively, for measuring the reidentification risk and
   degree of usability; and 36 explored 20 data anonymization software
   tools. In addition, we also evaluated the practical feasibility of
   performing anonymization on EHR data with reference to their usability
   in medical decision-making. Furthermore, we summarized the lawful basis
   for delivering guidance on practical EHR data anonymization.
   Conclusions: This systematic literature mapping study indicates that
   anonymization of EHR data is theoretically achievable; yet, it requires
   more research efforts in practical implementations to balance privacy
   preservation and usability to ensure more reliable health care
   applications.},
Publisher = {JMIR PUBLICATIONS, INC},
Address = {130 QUEENS QUAY East, Unit 1100, TORONTO, ON M5A 0P6, CANADA},
Type = {Review},
Language = {English},
Affiliation = {Al Moubayed, N (Corresponding Author), Univ Durham, Dept Comp Sci, South Rd, Durham DH1 3LE, England.
   Zuo, Zheming; Watson, Matthew; Budgen, David; Al Moubayed, Noura, Univ Durham, Dept Comp Sci, South Rd, Durham DH1 3LE, England.
   Hall, Robert; Kennelly, Chris, Cievert Ltd, Newcastle Upon Tyne, Tyne \& Wear, England.},
DOI = {10.2196/29871},
Article-Number = {e29871},
EISSN = {2291-9694},
Keywords = {healthcare; privacy-preserving; GDPR; DPA 2018; EHR; SLM; data science;
   anonymization; reidentification risk; usability},
Keywords-Plus = {K-ANONYMITY; PRESERVING PRIVACY; ALGORITHM; REIDENTIFICATION;
   ASSOCIATION; INFORMATION; MICRODATA; MODEL},
Research-Areas = {Medical Informatics},
Web-of-Science-Categories  = {Medical Informatics},
Author-Email = {Noura.al-moubayed@durham.ac.uk},
Affiliations = {Durham University},
ResearcherID-Numbers = {Zuo, Zheming/AAT-6193-2020
   },
ORCID-Numbers = {Zuo, Zheming/0000-0003-1576-0865
   Al Moubayed, Noura/0000-0001-8942-355X
   Budgen, David/0000-0001-7143-0241
   Watson, Matthew/0000-0001-6375-3905},
Funding-Acknowledgement = {UK Research and Innovation fund {[}312409]; Cievert Ltd.},
Funding-Text = {This study was sponsored by the UK Research and Innovation fund (project
   312409) and Cievert Ltd.},
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Number-of-Cited-References = {301},
Times-Cited = {2},
Usage-Count-Last-180-days = {3},
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Journal-ISO = {JMIR Med. Inf.},
Doc-Delivery-Number = {WP1CG},
Web-of-Science-Index = {Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)},
Unique-ID = {WOS:000712877700007},
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@article{ WOS:000854961800001,
Author = {Zhang, Yanli and Han, Zhiyu and Li, Xinyi and Zhang, Hongliang and Yuan,
   Xiangyang and Feng, Zhaozhong and Wang, Peng and Mu, Zhaobin and Song,
   Wei and Blake, Donald R. and Ying, Qi and George, Christian and Sheng,
   Guoying and Peng, Ping'an and Wang, Xinming},
Title = {Plants and related carbon cycling under elevated ground-level ozone: A
   mini review},
Journal = {APPLIED GEOCHEMISTRY},
Year = {2022},
Volume = {144},
Month = {SEP},
Abstract = {Plants play a crucial role in global carbon biogeochemical cycling and
   natural terrestrial carbon sinks. Dynamic changes in plant-related
   carbon cycling processes under changing climate and atmospheric
   compositions are hot scientific issues concerning carbon neutrality.
   Ozone, as a damaging oxidant, shows a rising trend near the ground where
   plants grow, directly and indirectly impacting forests and other types
   of vegetation. This review focuses on the effects of elevated
   atmospheric ozone levels on plant-related carbon cycling processes,
   including carbon dioxide (CO2) assimilation, carbon allocation to roots,
   volatile emissions, soil carbon sequestration and litter decomposition.
   Based on previous studies, we propose that field observations,
   especially in situ long-term observations under natural growing
   conditions in well-designed networks with a better representation, are
   needed to deeply understand the effects of elevated ozone on plants.
   Apart from an overwhelming concern about the influence of ozone on crop
   yields, studies on the effects of elevated ozone on forests, especially
   tropical and subtropical forests, should be strengthened in the future.
   Meanwhile, the interactions between ozone and plants should be
   considered in understanding plants' feedback to oxidants through
   emissions of volatiles and other trace gases. Moreover, geochemical
   techniques such as carbon isotopes and molecular markers, along with big
   data and artificial intelligence approaches, can be extensively used to
   decode and constrain the ozone-plant re-lationships, such as those
   between net primary productivity and ozone.},
Publisher = {PERGAMON-ELSEVIER SCIENCE LTD},
Address = {THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND},
Type = {Review},
Language = {English},
Affiliation = {Zhang, YL; Wang, XM (Corresponding Author), Chinese Acad Sci, Guangzhou Inst Geochem, State Key Lab Organ Geochem, Guangzhou 510640, Peoples R China.
   Zhang, YL; Wang, XM (Corresponding Author), Chinese Acad Sci, Guangzhou Inst Geochem, Guangdong Key Lab Environm Protect \& Resources Uti, Guangzhou 510640, Peoples R China.
   Zhang, Yanli; Han, Zhiyu; Li, Xinyi; Mu, Zhaobin; Song, Wei; Sheng, Guoying; Peng, Ping'an; Wang, Xinming, Chinese Acad Sci, Guangzhou Inst Geochem, State Key Lab Organ Geochem, Guangzhou 510640, Peoples R China.
   Zhang, Yanli; Han, Zhiyu; Li, Xinyi; Mu, Zhaobin; Song, Wei; Sheng, Guoying; Peng, Ping'an; Wang, Xinming, Chinese Acad Sci, Guangzhou Inst Geochem, Guangdong Key Lab Environm Protect \& Resources Uti, Guangzhou 510640, Peoples R China.
   Zhang, Yanli; Wang, Xinming, Chinese Acad Sci, Inst Urban Environm, CAS Ctr Excellence Reg Atmospher Environm, Xiamen 361021, Peoples R China.
   Zhang, Hongliang, Fudan Univ, Dept Environm Sci \& Engn, Shanghai 200438, Peoples R China.
   Yuan, Xiangyang, Chinese Acad Sci, Res Ctr Ecoenvironm Sci, State Key Lab Urban \& Reg Ecol, Beijing 100085, Peoples R China.
   Feng, Zhaozhong, Nanjing Univ Informat Sci \& Technol, Inst Ecol, Sch Appl Meteorol, Key Lab Agrometeorol Jiangsu Prov, Nanjing 210044, Peoples R China.
   Wang, Peng, Fudan Univ, Dept Atmospher \& Ocean Sci, Shanghai 200438, Peoples R China.
   Wang, Peng, Fudan Univ, Inst Atmospher Sci, Shanghai 200438, Peoples R China.
   Blake, Donald R., Univ Calif Irvine, Dept Chem, Irvine, CA 92697 USA.
   Ying, Qi, Inst Rech Catalyse \& Environm Lyon IRCELYON, CNRS, UMR5256, F-69626 Villeurbanne, TX, France.
   Zhang, Yanli; Li, Xinyi; Wang, Xinming, Univ Chinese Acad Sci, Beijing 100049, Peoples R China.},
DOI = {10.1016/j.apgeochem.2022.105400},
EarlyAccessDate = {AUG 2022},
Article-Number = {105400},
ISSN = {0883-2927},
EISSN = {1872-9134},
Keywords = {Ozone; Plants; CO2 sequestration; Carbon allocation; Carbon cycling},
Keywords-Plus = {VOLATILE ORGANIC-COMPOUNDS; NET PRIMARY PRODUCTIVITY; BEECH
   FAGUS-SYLVATICA; BOREAL PEATLAND MICROCOSMS; LEAF-LITTER DECOMPOSITION;
   TROPOSPHERIC OZONE; ISOPRENE EMISSION; CLIMATE-CHANGE; TREMBLING ASPEN;
   ROOT BIOMASS},
Research-Areas = {Geochemistry \& Geophysics},
Web-of-Science-Categories  = {Geochemistry \& Geophysics},
Author-Email = {zhang\_yl86@gig.ac.cn
   wangxm@gig.ac.cn},
Affiliations = {Chinese Academy of Sciences; Guangzhou Institute of Geochemistry, CAS;
   Chinese Academy of Sciences; Guangzhou Institute of Geochemistry, CAS;
   Chinese Academy of Sciences; Institute of Urban Environment, CAS; Fudan
   University; Chinese Academy of Sciences; Research Center for
   Eco-Environmental Sciences (RCEES); Nanjing University of Information
   Science \& Technology; Fudan University; Fudan University; University of
   California System; University of California Irvine; Centre National de
   la Recherche Scientifique (CNRS); CNRS - Institute of Chemistry (INC);
   Chinese Academy of Sciences; University of Chinese Academy of Sciences,
   CAS},
ResearcherID-Numbers = {li, xinyi/GWZ-8941-2022
   Li, xinyi/HJG-4670-2022
   li, xin/HHS-9461-2022
   Zhang, Yanli/A-3225-2015},
ORCID-Numbers = {Zhang, Yanli/0000-0003-0614-2096},
Funding-Acknowledgement = {National Natural Science Foundation of China {[}42022023, 41961144029];
   Chinese Academy of Sciences {[}XDA23010303, XDPB1901, XDA23020301,
   QYZDJ-SSW-DQC032]; Youth Innovation Promotion Association of the Chinese
   Academy of Sciences {[}Y2021096]; Hong Kong Research Grants Council
   {[}T24-504/17-N]; Department of Science and Technology of Guangdong
   {[}2020B1111360001, 2020B1212060053]},
Funding-Text = {Acknowledgements This work was supported by the National Natural Science
   Foundation of China (Nos. 42022023 and 41961144029) , the Chinese
   Academy of Sciences (Nos. XDA23010303, XDPB1901, XDA23020301 and
   QYZDJ-SSW-DQC032) , the Youth Innovation Promotion Association of the
   Chinese Academy of Sciences (Y2021096) , the Hong Kong Research Grants
   Council (No. T24-504/17-N) , and the Department of Science and
   Technology of Guangdong (Nos. 2020B1111360001 and 2020B1212060053) .},
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Number-of-Cited-References = {197},
Times-Cited = {2},
Usage-Count-Last-180-days = {40},
Usage-Count-Since-2013 = {50},
Journal-ISO = {Appl. Geochem.},
Doc-Delivery-Number = {4O8RW},
Web-of-Science-Index = {Science Citation Index Expanded (SCI-EXPANDED)},
Unique-ID = {WOS:000854961800001},
DA = {2023-04-22},
}

@article{ WOS:000637734600001,
Author = {Martin-Noguerol, T. and Paulano-Godino, F. and Lopez-Ortega, R. and
   Gorriz, J. M. and Riascos, R. F. and Luna, A.},
Title = {Artificial intelligence in radiology: relevance of collaborative work
   between radiologists and engineers for building a multidisciplinary team},
Journal = {CLINICAL RADIOLOGY},
Year = {2021},
Volume = {76},
Number = {5},
Pages = {317-324},
Month = {MAY},
Abstract = {The use of artificial intelligence (AI) algorithms in the field of
   radiology is becoming more common. Several studies have demonstrated the
   potential utility of machine learning (ML) and deep learning (DL)
   techniques as aids for radiologists to solve specific radiological
   challenges. The decision-making process, the establishment of specific
   clinical or radiological targets, the profile of the different
   professionals involved in the development of AI solutions, and the
   relation with partnerships and stakeholders are only some of the main
   issues that have to be faced and solved prior to starting the
   development of radiological AI solutions. Among all the players in this
   multidisciplinary team, the communication between radiologists and data
   scientists is essential for a successful collaborative work. There are
   specific skills that are inherent to radiological and medical training
   that are critical for identifying anatomical or clinical targets as well
   as for segmenting or labelling lesions. These skills would then have to
   be transferred, explained, and taught to the data science experts to
   facilitate their comprehension and integration into ML or DL algorithms.
   On the other hand, there is a wide range of complex software packages,
   deep neural-network architectures, and data transfer processes for which
   radiologists need the expertise of software engineers and data
   scientists in order to select the optimal manner to analyse and
   post-process this amount of data. This paper offers a summary of the top
   five challenges faced by radiologists and data scientists including tips
   and tricks to build a successful AI team. (C) 2020 The Royal College of
   Radiologists. Published by Elsevier Ltd. All rights reserved.},
Publisher = {W B SAUNDERS CO LTD},
Address = {32 JAMESTOWN RD, LONDON NW1 7BY, ENGLAND},
Type = {Review},
Language = {English},
Affiliation = {Martin-Noguerol, T (Corresponding Author), HT Med, Radiol Dept, MRI Sect, Carmelo Torres 2, Jaen 23007, Spain.
   Martin-Noguerol, T.; Luna, A., HT Med, Radiol Dept, MRI Unit, Jaen, Spain.
   Paulano-Godino, F.; Lopez-Ortega, R., HT Med, Engn Dept, Jaen, Spain.
   Gorriz, J. M., Univ Granada, Dept Signal Theory Telemat \& Commun, Granada, Spain.
   Riascos, R. F., Univ Texas Hlth Sci Ctr Houston, McGovern Med Sch, Dept Neuroradiol, Houston, TX 77030 USA.},
DOI = {10.1016/j.crad.2020.11.113},
ISSN = {0009-9260},
EISSN = {1365-229X},
Keywords-Plus = {DE-IDENTIFICATION; BLACK-BOX; FUTURE; AI},
Research-Areas = {Radiology, Nuclear Medicine \& Medical Imaging},
Web-of-Science-Categories  = {Radiology, Nuclear Medicine \& Medical Imaging},
Author-Email = {t.martin.f@htime.org},
Affiliations = {University of Granada; University of Texas System; University of Texas
   Health Science Center Houston},
ResearcherID-Numbers = {Gorriz, Juan Manuel/C-2385-2012},
ORCID-Numbers = {Gorriz, Juan Manuel/0000-0001-7069-1714},
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Number-of-Cited-References = {78},
Times-Cited = {11},
Usage-Count-Last-180-days = {1},
Usage-Count-Since-2013 = {19},
Journal-ISO = {Clin. Radiol.},
Doc-Delivery-Number = {RJ6UB},
Web-of-Science-Index = {Science Citation Index Expanded (SCI-EXPANDED)},
Unique-ID = {WOS:000637734600001},
DA = {2023-04-22},
}

@article{ WOS:000898610500010,
Author = {Leary, Del and Basran, Parminder S. S.},
Title = {The role of artificial intelligence in veterinary radiation oncology},
Journal = {VETERINARY RADIOLOGY \& ULTRASOUND},
Year = {2022},
Volume = {63},
Number = {1},
Pages = {903-912},
Month = {DEC},
Abstract = {Veterinary radiation oncology regularly deploys sophisticated
   contouring, image registration, and treatment planning optimization
   software for patient care. Over the past decade, advances in computing
   power and the rapid development of neural networks, open-source software
   packages, and data science have been realized and resulted in new
   research and clinical applications of artificial intelligent (AI)
   systems in radiation oncology. These technologies differ from
   conventional software in their level of complexity and ability to learn
   from representative and local data. We provide clinical and research
   application examples of AI in human radiation oncology and their
   potential applications in veterinary medicine throughout the patient's
   care-path: from treatment simulation, deformable registration,
   auto-segmentation, automated treatment planning and plan selection,
   quality assurance, adaptive radiotherapy, and outcomes modeling. These
   technologies have the potential to offer significant time and cost
   savings in the veterinary setting; however, since the range of
   usefulness of these technologies have not been well studied nor
   understood, care must be taken if adopting AI technologies in clinical
   practice. Over the next several years, some practical and realizable
   applications of AI in veterinary radiation oncology include automated
   segmentation of normal tissues and tumor volumes, deformable
   registration, multi-criteria plan optimization, and adaptive
   radiotherapy. Keys in achieving success in adopting AI in veterinary
   radiation oncology include: establishing ``truth-data{''}; data
   harmonization; multi-institutional data and collaborations; standardized
   dose reporting and taxonomy; adopting an open access philosophy, data
   collection and curation; open-source algorithm development; and
   transparent and platform-independent code development.},
Publisher = {WILEY},
Address = {111 RIVER ST, HOBOKEN 07030-5774, NJ USA},
Type = {Review},
Language = {English},
Affiliation = {Leary, D (Corresponding Author), Colorado State Univ, Coll Vet Med \& Biomed Sci, Dept Environm \& Radiol Hlth Sci, Ft Collins, CO 80523 USA.
   Basran, PS (Corresponding Author), Cornell Univ, Coll Vet Med, Dept Clin Sci, Ithaca, NY 14850 USA.
   Leary, Del, Colorado State Univ, Coll Vet Med \& Biomed Sci, Dept Environm \& Radiol Hlth Sci, Ft Collins, CO 80523 USA.
   Basran, Parminder S. S., Cornell Univ, Coll Vet Med, Dept Clin Sci, Ithaca, NY 14850 USA.},
DOI = {10.1111/vru.13162},
ISSN = {1058-8183},
EISSN = {1740-8261},
Keywords = {adaptive radiotherapy; artificial intelligence; deep learning; machine
   learning; radiotherapy},
Keywords-Plus = {NEURAL-NETWORK; TUMOR DELINEATION; INTEROBSERVER AGREEMENT; IMAGE
   REGISTRATION; PROSTATE-CANCER; RADIOTHERAPY; PREDICTION; THERAPY; CT;
   QUALITY},
Research-Areas = {Veterinary Sciences},
Web-of-Science-Categories  = {Veterinary Sciences},
Author-Email = {del.leary@colostate.edu
   psb92@cornell.edu},
Affiliations = {Colorado State University; Cornell University},
ResearcherID-Numbers = {Basran, Parminder/ABH-1117-2020},
ORCID-Numbers = {Basran, Parminder/0000-0002-1573-1549},
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Number-of-Cited-References = {86},
Times-Cited = {1},
Usage-Count-Last-180-days = {3},
Usage-Count-Since-2013 = {3},
Journal-ISO = {Vet. Radiol. Ultrasound},
Doc-Delivery-Number = {7A7CX},
Web-of-Science-Index = {Science Citation Index Expanded (SCI-EXPANDED)},
Unique-ID = {WOS:000898610500010},
DA = {2023-04-22},
}

@article{ WOS:000663301100002,
Author = {Derathe, Arthur and Reche, Fabian and Jannin, Pierre and Moreau-Gaudry,
   Alexandre and Gibaud, Bernard and Voros, Sandrine},
Title = {Explaining a model predicting quality of surgical practice: a first
   presentation to and review by clinical experts},
Journal = {INTERNATIONAL JOURNAL OF COMPUTER ASSISTED RADIOLOGY AND SURGERY},
Year = {2021},
Volume = {16},
Number = {11, SI},
Pages = {2009-2019},
Month = {NOV},
Abstract = {Purpose Surgical Data Science (SDS) is an emerging research domain
   offering data-driven answers to challenges encountered by clinicians
   during training and practice. We previously developed a framework to
   assess quality of practice based on two aspects: exposure of the
   surgical scene (ESS) and the surgeon's profile of practice (SPP). Here,
   we wished to investigate the clinical relevance of the parameters
   learned by this model by (1) interpreting these parameters and
   identifying associated representative video samples and (2) presenting
   this information to surgeons in the form of a video-enhanced
   questionnaire. To our knowledge, this is the first approach in the field
   of SDS for laparoscopy linking the choices made by a machine learning
   model predicting surgical quality to clinical expertise. Method Spatial
   features and quality of practice scores extracted from labeled and
   segmented frames in 30 laparoscopic videos were used to predict the ESS
   and the SPP. The relationships between the inputs and outputs of the
   model were then analyzed and translated into meaningful sentences
   (statements, e.g., ``To optimize the ESS, it is very important to
   correctly handle the spleen{''}). Representative video clips
   illustrating these statements were semi-automatically identified. Eleven
   statements and video clips were used in a survey presented to six
   experienced digestive surgeons to gather their opinions on the
   algorithmic analyses. Results All but one of the surgeons agreed with
   the proposed questionnaire overall. On average, surgeons agreed with
   7/11 statements. Conclusion This proof-of-concept study provides
   preliminary validation of our model which has a high potential for use
   to analyze and understand surgical practices.},
Publisher = {SPRINGER HEIDELBERG},
Address = {TIERGARTENSTRASSE 17, D-69121 HEIDELBERG, GERMANY},
Type = {Review},
Language = {English},
Affiliation = {Voros, S (Corresponding Author), Univ Grenoble Alpes, TIMC IMAG, Grenoble INP, CNRS, F-38000 Grenoble, France.
   Voros, S (Corresponding Author), INSERM, Paris, France.
   Derathe, Arthur; Reche, Fabian; Moreau-Gaudry, Alexandre; Voros, Sandrine, Univ Grenoble Alpes, TIMC IMAG, Grenoble INP, CNRS, F-38000 Grenoble, France.
   Reche, Fabian, Grenoble Univ Hosp, Dept Digest Surg, Grenoble, France.
   Jannin, Pierre; Gibaud, Bernard, Univ Rennes 1, LTSI, UMR S 1099, F-35000 Rennes, France.
   Jannin, Pierre; Gibaud, Bernard; Voros, Sandrine, INSERM, Paris, France.
   Moreau-Gaudry, Alexandre, CHU Grenoble, Clin Invest Ctr, Innovat Technol, Grenoble, France.},
DOI = {10.1007/s11548-021-02422-0},
EarlyAccessDate = {JUN 2021},
ISSN = {1861-6410},
EISSN = {1861-6429},
Keywords = {Surgical skills; Video-based assessment; Explainable artificial
   intelligence},
Keywords-Plus = {SKILL},
Research-Areas = {Engineering; Radiology, Nuclear Medicine \& Medical Imaging; Surgery},
Web-of-Science-Categories  = {Engineering, Biomedical; Radiology, Nuclear Medicine \& Medical Imaging;
   Surgery},
Author-Email = {sandrine.voros@univ-grenoble-alpes.fr},
Affiliations = {UDICE-French Research Universities; Communaute Universite Grenoble
   Alpes; Institut National Polytechnique de Grenoble; Universite Grenoble
   Alpes (UGA); Centre National de la Recherche Scientifique (CNRS); CHU
   Grenoble Alpes; Communaute Universite Grenoble Alpes; UDICE-French
   Research Universities; Universite Grenoble Alpes (UGA); Universite de
   Rennes; Institut National de la Sante et de la Recherche Medicale
   (Inserm); CHU Grenoble Alpes; Communaute Universite Grenoble Alpes;
   UDICE-French Research Universities; Universite Grenoble Alpes (UGA)},
ResearcherID-Numbers = {Voros, Sandrine/M-6138-2014
   Jannin, Pierre/P-9958-2019},
ORCID-Numbers = {Voros, Sandrine/0000-0002-7418-2367
   Jannin, Pierre/0000-0002-7415-071X},
Funding-Acknowledgement = {French government {[}ANR-11-LABX0004]; MIAI @ Grenoble Alpes
   {[}ANR-19-P3IA-0003]},
Funding-Text = {This work was supported by funding from the French government managed by
   the ANR as part of the Investissements d'Avenir Programme (Labex CAMI)
   under reference ANR-11-LABX0004. This work was partially supported by
   MIAI @ Grenoble Alpes, (ANR-19-P3IA-0003). The authors thank the IRT
   b<>com for providing the ``Surgery Workflow Toolbox {[}annotate],{''}
   software used in this study.},
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Number-of-Cited-References = {17},
Times-Cited = {1},
Usage-Count-Last-180-days = {0},
Usage-Count-Since-2013 = {5},
Journal-ISO = {Int. J. Comput. Assist. Radiol. Surg.},
Doc-Delivery-Number = {WW6JI},
Web-of-Science-Index = {Science Citation Index Expanded (SCI-EXPANDED)},
Unique-ID = {WOS:000663301100002},
OA = {Green Submitted},
DA = {2023-04-22},
}

@article{ WOS:000888226600002,
Author = {Shen, Yuning and Borowski, Julia E. and Hardy, Melissa A. and Sarpong,
   Richmond and Doyle, Abigail G. and Cernak, Tim},
Title = {Automation and computer-assisted planning for chemical synthesis},
Journal = {NATURE REVIEWS METHODS PRIMERS},
Year = {2021},
Volume = {1},
Number = {1},
Month = {MAR 18},
Abstract = {The molecules of today - the medicines that cure diseases, the
   agrochemicals that protect our crops, the materials that make life
   convenient - are becoming increasingly sophisticated thanks to
   advancements in chemical synthesis. As tools for synthesis improve,
   molecular architects can be bold and creative in the way they design and
   produce molecules. Several emerging tools at the interface of chemical
   synthesis and data science have come to the forefront in recent years,
   including algorithms for retrosynthesis and reaction prediction, and
   robotics for autonomous or high-throughput synthesis. This Primer covers
   recent additions to the toolbox of the data-savvy organic chemist. There
   is a new movement in retrosynthetic logic, predictive models of
   reactivity and chemistry automata, with considerable recent engagement
   from contributors in diverse fields. The promise of chemical synthesis
   in the information age is to improve the quality of the molecules of
   tomorrow through data-harnessing and automation. This Primer is written
   for organic chemists and data scientists looking to understand the
   software, hardware, data sets and tactics that are commonly used as well
   as the capabilities and limitations of the field. The Primer is split
   into three main components covering retrosynthetic logic, reaction
   prediction and automated synthesis. The former of these topics is about
   distilling the strategy of multistep synthesis to a logic that can be
   taught to a computer. The section on reaction prediction details modern
   tools and models for developing reaction conditions, catalysts and even
   new transformations based on information-rich data sets and statistical
   tools such as machine learning. Finally, we cover recent advances in the
   use of liquid handling robotics and autonomous systems that can
   physically perform experiments in the chemistry laboratory.},
Publisher = {SPRINGERNATURE},
Address = {CAMPUS, 4 CRINAN ST, LONDON, N1 9XW, ENGLAND},
Type = {Review},
Language = {English},
Affiliation = {Cernak, T (Corresponding Author), Univ Michigan, Dept Med Chem, Ann Arbor, MI 48109 USA.
   Doyle, AG (Corresponding Author), Princeton Univ, Dept Chem, Princeton, NJ 08544 USA.
   Sarpong, R (Corresponding Author), Univ Calif Berkeley, Dept Chem, Berkeley, CA 94720 USA.
   Shen, Yuning; Cernak, Tim, Univ Michigan, Dept Med Chem, Ann Arbor, MI 48109 USA.
   Borowski, Julia E.; Doyle, Abigail G., Princeton Univ, Dept Chem, Princeton, NJ 08544 USA.
   Hardy, Melissa A.; Sarpong, Richmond, Univ Calif Berkeley, Dept Chem, Berkeley, CA 94720 USA.},
DOI = {10.1038/s43586-021-00022-5},
Article-Number = {23},
EISSN = {2662-8449},
Keywords-Plus = {HIGH-THROUGHPUT EXPERIMENTATION; LATE-STAGE FUNCTIONALIZATION; MEDICINAL
   CHEMISTS TOOLBOX; TAMING HAZARDOUS CHEMISTRY; DEEP NEURAL-NETWORKS;
   ORGANIC-SYNTHESIS; SYNTHESIS DESIGN; CONTINUOUS-FLOW; DRUG DISCOVERY;
   MACHINE},
Research-Areas = {Science \& Technology - Other Topics},
Web-of-Science-Categories  = {Multidisciplinary Sciences},
Author-Email = {rsarponge@berkeley.edu
   agdoyle@princeton.edu
   tcernak@med.umich.edu},
Affiliations = {University of Michigan System; University of Michigan; Princeton
   University; University of California System; University of California
   Berkeley},
ORCID-Numbers = {Doyle, Abigail/0000-0002-6641-0833
   Borowski, Julia/0000-0002-3635-8720},
Funding-Acknowledgement = {National Science Foundation (NSF) under the Center for Computer Aided
   Synthesis {[}CHE-1925607]; NSF graduate research fellowship program
   {[}DGE-1752814]; University of Michigan College of Pharmacy},
Funding-Text = {A. G.D., R.S., M.A. H. and J. E. B. were supported by the National
   Science Foundation (NSF) under the Center for Computer Aided Synthesis
   (C-CAS) (CHE-1925607). M.A.H. is grateful for funding from the NSF
   graduate research fellowship program (DGE-1752814). Y.S. and T.C. were
   supported by the University of Michigan College of Pharmacy.},
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@article{ WOS:000618346000005,
Author = {Yu, Chaoran and Helwig, Ernest Johann},
Title = {Artificial intelligence in gastric cancer: a translational narrative
   review},
Journal = {ANNALS OF TRANSLATIONAL MEDICINE},
Year = {2021},
Volume = {9},
Number = {3},
Month = {FEB},
Abstract = {Increasing clinical contributions and novel techniques have been made by
   artificial intelligence (AI) during the last decade. The role of AI is
   increasingly recognized in cancer research and clinical application.
   Cancers like gastric cancer, or stomach cancer, arc ideal testing
   grounds to see if early undertakings of applying AI to medicine can
   yield valuable results. There are numerous concepts derived from AI,
   including machine learning (ML) and deep learning (DL). ML is defined as
   the ability to learn data features without being explicitly programmed.
   It arises at the intersection of data science and computer science and
   aims at the efficiency of computing algorithms. In cancer research, ML
   has been increasingly used in predictive prognostic models. DL is
   defined as a subset of ML targeting multilayer computation processes. DL
   is less dependent on the understanding of data features than ML.
   Therefore, the algorithms of DL are much more difficult to interpret
   than ML, even potentially impossible. This review discussed the role of
   AI in the diagnostic, therapeutic and prognostic advances of gastric
   cancer. Models like convolutional neural networks (CNNs) or artificial
   neural networks (ANNs) achieved significant praise in their application.
   There is much more to be fully covered across the clinical
   administration of gastric cancer. Despite growing efforts, adapting AI
   to improving diagnoses for gastric cancer is a worthwhile venture. The
   information yield can revolutionize how we approach gastric cancer
   problems. Though integration might be slow and labored, it can be given
   the ability to enhance diagnosing through visual modalities and augment
   treatment strategies. It can grow to become an invaluable tool for
   physicians. AI not only benefits diagnostic and therapeutic outcomes,
   but also reshapes perspectives over future medical trajectory.},
Publisher = {AME PUBLISHING COMPANY},
Address = {FLAT-RM C 16F, KINGS WING PLAZA 1, NO 3 KWAN ST, SHATIN, HONG KONG
   00000, PEOPLES R CHINA},
Type = {Review},
Language = {English},
Affiliation = {Helwig, EJ (Corresponding Author), Huazhong Univ Sci \& Technol, Tongji Med Coll, Wuhan, Peoples R China.
   Yu, CR (Corresponding Author), Fudan Univ, Shanghai Canc Ctr, Shanghai Med Coll, Dept Oncol, Shanghai 200025, Peoples R China.
   Yu, Chaoran, Fudan Univ, Shanghai Med Coll, Dept Oncol, Shanghai, Peoples R China.
   Yu, Chaoran, Fudan Univ, Shanghai Canc Ctr, Shanghai, Peoples R China.
   Helwig, Ernest Johann, Huazhong Univ Sci \& Technol, Tongji Med Coll, Wuhan, Peoples R China.},
DOI = {10.21037/atm-20-6337},
Article-Number = {269},
ISSN = {2305-5839},
EISSN = {2305-5847},
Keywords = {Artificial intelligence (AI); endoscope; convolutional neural networks
   (CNNs); gastric cancer; genomics},
Keywords-Plus = {NEURAL-NETWORK; SURVIVAL; CLASSIFICATION; EPIDEMIOLOGY; ENDOSCOPY;
   DIAGNOSIS; RESECTION},
Research-Areas = {Oncology; Research \& Experimental Medicine},
Web-of-Science-Categories  = {Oncology; Medicine, Research \& Experimental},
Author-Email = {chaoran\_yu@yeah.net
   ernjohelwig@gmail.com},
Affiliations = {Fudan University; Fudan University; Huazhong University of Science \&
   Technology},
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Journal-ISO = {ANN. TRANSL. MED.},
Doc-Delivery-Number = {QH5WL},
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Unique-ID = {WOS:000618346000005},
OA = {Green Published, gold},
DA = {2023-04-22},
}

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   Cessation: A Systematic Review and Meta-analysis},
Journal = {NICOTINE \& TOBACCO RESEARCH},
Abstract = {Introduction: Conversational agents (CAs; computer programs that use
   artificial intelligence to simulate a conversation with users through
   natural language) have evolved considerably in recent years to support
   healthcare by providing autonomous, interactive, and accessible
   services, making them potentially useful for supporting smoking
   cessation. We performed a systematic review and meta-analysis to provide
   an overarching evaluation of their effectiveness and acceptability to
   inform future development and adoption.
   Aims and Methods: PsycInfo, Web of Science, ACM Digital Library, IEEE
   Xplore, Medline, EMBASE, Communication and Mass Media Complete, and
   CINAHL Complete were searched for studies examining the use of CAs for
   smoking cessation. Data from eligible studies were extracted and used
   for random-effects meta-analyses.
   Results: The search yielded 1245 publications with 13 studies eligible
   for systematic review (total N = 8236) and six studies for
   random-effects meta-analyses. All studies reported positive effects on
   cessation-related outcomes. A meta-analysis with randomized controlled
   trials reporting on abstinence yielded a sample-weighted odds ratio of
   1.66 (95\% CI = 1.33\% to 2.07\%, p <.001), favoring CAs over comparison
   groups. A narrative synthesis of all included studies showed overall
   high acceptability, while some barriers were identified from user
   feedback. Overall, included studies were diverse in design with mixed
   quality, and evidence of publication bias was identified. A lack of
   theoretical foundations was noted, as well as a clear need for
   relational communication in future designs.
   Conclusions: The effectiveness and acceptability of CAs for smoking
   cessation are promising. However, standardization of reporting and
   designing of the agents is warranted for a more comprehensive
   evaluation.
   Implications: This is the first systematic review to provide insight
   into the use of CAs to support smoking cessation. Our findings
   demonstrated initial promise in the effectiveness and user acceptability
   of these agents. We also identified a lack of theoretical and
   methodological limitations to improve future study design and
   intervention delivery.},
Publisher = {OXFORD UNIV PRESS},
Address = {GREAT CLARENDON ST, OXFORD OX2 6DP, ENGLAND},
Type = {Review; Early Access},
Language = {English},
Affiliation = {He, LW (Corresponding Author), Tilburg Univ, Tilburg Sch Humanities \& Digital Sci, Dept Commun \& Cognit, Warandelaan 2, NL-5037 AB Tilburg, Netherlands.
   He, Linwei; Antheunis, Marjolijn L.; Krahmer, Emiel, Tilburg Univ, Tilburg Sch Humanities \& Digital Sci, Dept Commun \& Cognit, Tilburg, Netherlands.
   Balaji, Divyaa, Univ Amsterdam, Amsterdam Sch Commun Res, Amsterdam, Netherlands.
   Wiers, Reinout W., Univ Amsterdam, Dept Psychol, Addict Dev \& Psychopathol ADAPT Lab, Amsterdam, Netherlands.
   Wiers, Reinout W., Univ Amsterdam, Ctr Urban Mental Hlth, Amsterdam, Netherlands.
   He, Linwei, Tilburg Univ, Tilburg Sch Humanities \& Digital Sci, Dept Commun \& Cognit, Warandelaan 2, NL-5037 AB Tilburg, Netherlands.},
DOI = {10.1093/ntr/ntac281},
EarlyAccessDate = {DEC 2022},
ISSN = {1462-2203},
EISSN = {1469-994X},
Keywords-Plus = {INTERVENTIONS; DEPENDENCE; TEXT2QUIT},
Research-Areas = {Substance Abuse; Public, Environmental \& Occupational Health},
Web-of-Science-Categories  = {Substance Abuse; Public, Environmental \& Occupational Health},
Author-Email = {l.he\_1@tilburguniversity.edu},
Affiliations = {Tilburg University; University of Amsterdam; University of Amsterdam;
   University of Amsterdam; Tilburg University},
ORCID-Numbers = {He, Linwei/0000-0002-6593-1661},
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Number-of-Cited-References = {58},
Times-Cited = {0},
Usage-Count-Last-180-days = {1},
Usage-Count-Since-2013 = {1},
Journal-ISO = {Nicotine Tob. Res.},
Doc-Delivery-Number = {8R0SV},
Web-of-Science-Index = {Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)},
Unique-ID = {WOS:000927609900001},
OA = {hybrid},
DA = {2023-04-22},
}

@article{ WOS:000721546400003,
Author = {Rajgor, Amarkumar Dhirajlal and Patel, Shreena and Mcculloch, David and
   Obara, Boguslaw and Bacardit, Jaume and Mcqueen, Andrew and Aboagye,
   Eric and Ali, Tamir and O'Hara, James and Hamilton, David Winston},
Title = {The application of radiomics in laryngeal cancer},
Journal = {BRITISH JOURNAL OF RADIOLOGY},
Year = {2021},
Volume = {94},
Number = {1128},
Month = {DEC 1},
Abstract = {Objectives: Radiomics is the conversion of medical images into
   quantitative high-dimensional data. Laryngeal cancer, one of the most
   common head and neck cancers, has risen globally by 58.7\%. CT, MRI and
   PET are acquired during the diagnostic process providing potential data
   for radiomic analysis and correlation with outcomes.
   This review aims to examine the applications of this technique to
   laryngeal cancer and the future considerations for translation into
   clinical practice.
   Methods: A comprehensive systematic review-informed search of the
   MEDLINE and EMBASE databases was undertaken. Keywords ``laryngeal
   cancer{''} OR ``larynx{''} OR ``larynx cancer{''} OR ``head and neck
   cancer{''} were combined with ``radiomic{''} OR ``signature{''} OR
   `'machine learning{''} OR ``artificial intelligence{''}, Additional
   articles were obtained from bibliographies using the `'snowball
   method{''},
   Results: The included studies (n = 15) demonstrated that radiomic
   features are significantly associated with various clinical outcomes
   (including stage, overall survival, treatment response, progression-free
   survival) and that predictive models incorporating radiomic features are
   superior to those that do not. Two studies demonstrated radiomics could
   improve laryngeal cancer staging whilst 12 studies affirmed its
   predictive capability for clinical outcomes,
   Conclusions: Radiomics has potential for improving multiple aspects of
   laryngeal cancer care; however, the heterogeneous cohorts and lack of
   data on laryngeal cancer exclusively inhibits firm conclusions. Large
   prospective well-designed studies in laryngeal cancer are required to
   progress this field. Furthermore, to implement radiomics into clinical
   practice, a unified research effort is required to standardise radiomics
   practice.
   Advances in knowledge: This review has highlighted the value of
   radiomics in enhancing laryngeal cancer care (including staging,
   prognosis and predicting treatment response).},
Publisher = {BRITISH INST RADIOLOGY},
Address = {48-50 ST JOHN ST, LONDON, ENGLAND},
Type = {Review},
Language = {English},
Affiliation = {Rajgor, AD (Corresponding Author), Newcastle Upon Tyne Hosp NHS Fdn Trust, Otolaryngol Dept, Newcastle Upon Tyne, Tyne \& Wear, England.
   Rajgor, AD (Corresponding Author), Newcastle Univ, Appl Canc Therapeut \& Outcomes, Newcastle Upon Tyne, Tyne \& Wear, England.
   Rajgor, AD (Corresponding Author), Newcastle Univ, Natl Inst Hlth Res, Newcastle Upon Tyne, Tyne \& Wear, England.
   Rajgor, Amarkumar Dhirajlal; O'Hara, James; Hamilton, David Winston, Newcastle Upon Tyne Hosp NHS Fdn Trust, Otolaryngol Dept, Newcastle Upon Tyne, Tyne \& Wear, England.
   Rajgor, Amarkumar Dhirajlal; O'Hara, James; Hamilton, David Winston, Newcastle Univ, Appl Canc Therapeut \& Outcomes, Newcastle Upon Tyne, Tyne \& Wear, England.
   Rajgor, Amarkumar Dhirajlal, Newcastle Univ, Natl Inst Hlth Res, Newcastle Upon Tyne, Tyne \& Wear, England.
   Mcculloch, David; Mcqueen, Andrew; Ali, Tamir, Newcastle Upon Tyne Hosp NHS Fdn Trust, Radiol Dept, Newcastle Upon Tyne, Tyne \& Wear, England.
   Obara, Boguslaw; Bacardit, Jaume, Newcastle Univ, Sch Comp, Urban Sci Bldg, Newcastle Upon Tyne, Tyne \& Wear, England.
   Aboagye, Eric, Imperial Coll London, Canc Imaging Ctr, Dept Surg \& Canc, Hammersmith Hosp, London, England.},
DOI = {10.1259/bjr.20210499},
Article-Number = {20210499},
ISSN = {0007-1285},
EISSN = {1748-880X},
Keywords-Plus = {PROGNOSTIC VALUE; LOCAL-CONTROL; HEAD; PREDICTION; PET; CHEMOTHERAPY;
   SIGNATURE; SURVIVAL},
Research-Areas = {Radiology, Nuclear Medicine \& Medical Imaging},
Web-of-Science-Categories  = {Radiology, Nuclear Medicine \& Medical Imaging},
Author-Email = {amar.rajgor@nhs.net},
Affiliations = {Newcastle Upon Tyne Hospitals NHS Foundation Trust; Newcastle University
   - UK; Newcastle University - UK; Newcastle Upon Tyne Hospitals NHS
   Foundation Trust; Newcastle University - UK; Imperial College London},
ORCID-Numbers = {Rajgor, Amarkumar D/0000-0002-9323-3107
   ABOAGYE, Eric/0000-0003-2276-6771
   Obara, Boguslaw/0000-0003-4084-7778},
Funding-Acknowledgement = {Medical Research Council {[}MR/V037528/1] Funding Source: Medline},
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Number-of-Cited-References = {46},
Times-Cited = {3},
Usage-Count-Last-180-days = {3},
Usage-Count-Since-2013 = {11},
Journal-ISO = {Br. J. Radiol.},
Doc-Delivery-Number = {XB8AR},
Web-of-Science-Index = {Science Citation Index Expanded (SCI-EXPANDED)},
Unique-ID = {WOS:000721546400003},
OA = {Green Published, Green Submitted},
DA = {2023-04-22},
}

@article{ WOS:000478140300001,
Author = {Zimmermann, Albrecht},
Title = {Method evaluation, parameterization, and result validation in
   unsupervised data mining: A critical survey},
Journal = {WILEY INTERDISCIPLINARY REVIEWS-DATA MINING AND KNOWLEDGE DISCOVERY},
Year = {2020},
Volume = {10},
Number = {2},
Month = {MAR},
Abstract = {Machine Learning (ML) and Data Mining (DM) build tools intended to help
   users solve data-related problems that are infeasible for
   ``unaugmented{''} humans. Tools need manuals, however, and in the case
   of ML/DM methods, this means guidance with respect to which technique to
   choose, how to parameterize it, and how to interpret derived results to
   arrive at knowledge about the phenomena underlying the data. While such
   information is available in the literature, it has not yet been
   collected in one place. We survey three types of work for clustering and
   pattern mining: (1) comparisons of existing techniques, (2) evaluations
   of different parameterization options and studies providing guidance for
   setting parameter values, and (3) work comparing mining results with the
   ground truth. We find that although interesting results exist, as a
   whole the body of work on these questions is too limited. In addition,
   we survey recent studies in the field of community detection, as a
   contrasting example. We argue that an objective obstacle for performing
   needed studies is a lack of data and survey the state of available data,
   pointing out certain limitations. As a solution, we propose to augment
   existing data by artificially generated data, review the
   state-of-the-art in data generation in unsupervised mining, and identify
   shortcomings. In more general terms, we call for the development of a
   true ``Data Science{''} that-based on work in other domains, results in
   ML, and existing tools-develops needed data generators and builds up the
   knowledge needed to effectively employ unsupervised mining techniques.
   This article is categorized under:
   Fundamental Concepts of Data and Knowledge > Key Design Issues in Data
   Mining
   Ensemble Methods > Structure Discovery
   Internet > Society and Culture
   Fundamental Concepts of Data and Knowledge > Motivation and Emergence of
   Data Mining},
Publisher = {WILEY PERIODICALS, INC},
Address = {ONE MONTGOMERY ST, SUITE 1200, SAN FRANCISCO, CA 94104 USA},
Type = {Review},
Language = {English},
Affiliation = {Zimmermann, A (Corresponding Author), Normandie Univ, GREYC, CNRS, UNICAEN,ENSICAEN, F-14000 Caen, France.
   Zimmermann, Albrecht, Normandie Univ, GREYC, CNRS, UNICAEN,ENSICAEN, F-14000 Caen, France.},
DOI = {10.1002/widm.1330},
EarlyAccessDate = {JUL 2019},
Article-Number = {e1330},
ISSN = {1942-4787},
EISSN = {1942-4795},
Keywords = {algorithmic comparison; clustering; parameter selection; pattern mining;
   result verification},
Keywords-Plus = {COMMUNITY DETECTION; CLUSTERING ALGORITHMS; PERFORMANCE EVALUATION;
   EFFICIENT ALGORITHM; FREQUENT EPISODES; DISCOVERY; NETWORK; PATTERN;
   NUMBER; CLASSIFICATION},
Research-Areas = {Computer Science},
Web-of-Science-Categories  = {Computer Science, Artificial Intelligence; Computer Science, Theory \&
   Methods},
Author-Email = {albrecht.zimmermann@unicaen.fr},
Affiliations = {Centre National de la Recherche Scientifique (CNRS); Universite de Caen
   Normandie},
ORCID-Numbers = {Zimmermann, Albrecht/0000-0002-8319-7456},
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Times-Cited = {9},
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Journal-ISO = {Wiley Interdiscip. Rev.-Data Mining Knowl. Discov.},
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@article{ WOS:000890911600001,
Author = {Chen, Jimmy S. and Baxter, Sally L.},
Title = {Applications of natural language processing in ophthalmology: present
   and future},
Journal = {FRONTIERS IN MEDICINE},
Year = {2022},
Volume = {9},
Month = {AUG 8},
Abstract = {Advances in technology, including novel ophthalmic imaging devices and
   adoption of the electronic health record (EHR), have resulted in
   significantly increased data available for both clinical use and
   research in ophthalmology. While artificial intelligence (AI) algorithms
   have the potential to utilize these data to transform clinical care,
   current applications of AI in ophthalmology have focused mostly on
   image-based deep learning. Unstructured free-text in the EHR represents
   a tremendous amount of underutilized data in big data analyses and
   predictive AI. Natural language processing (NLP) is a type of AI
   involved in processing human language that can be used to develop
   automated algorithms using these vast quantities of available text data.
   The purpose of this review was to introduce ophthalmologists to NLP by
   (1) reviewing current applications of NLP in ophthalmology and (2)
   exploring potential applications of NLP. We reviewed current literature
   published in Pubmed and Google Scholar for articles related to NLP and
   ophthalmology, and used ancestor search to expand our references.
   Overall, we found 19 published studies of NLP in ophthalmology. The
   majority of these publications (16) focused on extracting specific text
   such as visual acuity from free-text notes for the purposes of
   quantitative analysis. Other applications included: domain embedding,
   predictive modeling, and topic modeling. Future ophthalmic applications
   of NLP may also focus on developing search engines for data within
   free-text notes, cleaning notes, automated question-answering, and
   translating ophthalmology notes for other specialties or for patients,
   especially with a growing interest in open notes. As medicine becomes
   more data-oriented, NLP offers increasing opportunities to augment our
   ability to harness free-text data and drive innovations in healthcare
   delivery and treatment of ophthalmic conditions.},
Publisher = {FRONTIERS MEDIA SA},
Address = {AVENUE DU TRIBUNAL FEDERAL 34, LAUSANNE, CH-1015, SWITZERLAND},
Type = {Review},
Language = {English},
Affiliation = {Baxter, SL (Corresponding Author), Univ Calif San Diego, Viterbi Family Dept Ophthalmol, Div Ophthalmol Informat \& Data Sci, La Jolla, CA 92093 USA.
   Baxter, SL (Corresponding Author), Univ Calif San Diego, Shiley Eye Inst, La Jolla, CA 92093 USA.
   Baxter, SL (Corresponding Author), Univ Calif San Diego, Shiley Eye Inst, La Jolla, CA 92093 USA.
   Chen, Jimmy S.; Baxter, Sally L., Univ Calif San Diego, Viterbi Family Dept Ophthalmol, Div Ophthalmol Informat \& Data Sci, La Jolla, CA 92093 USA.
   Chen, Jimmy S.; Baxter, Sally L., Univ Calif San Diego, Shiley Eye Inst, La Jolla, CA 92093 USA.
   Chen, Jimmy S.; Baxter, Sally L., Univ Calif San Diego, Shiley Eye Inst, La Jolla, CA 92093 USA.},
DOI = {10.3389/fmed.2022.906554},
Article-Number = {906554},
EISSN = {2296-858X},
Keywords = {natural language processing; ophthalmology; artificial intelligence;
   machine learning; big data; informatics; data science},
Keywords-Plus = {ELECTRONIC HEALTH RECORDS; ARTIFICIAL-INTELLIGENCE; BIG DATA; TEXT;
   EXTRACTION; SYSTEM; VALIDATION; ALGORITHM; COVID-19; GLAUCOMA},
Research-Areas = {General \& Internal Medicine},
Web-of-Science-Categories  = {Medicine, General \& Internal},
Author-Email = {S1baxter@health.ucsd.edu},
Affiliations = {University of California System; University of California San Diego;
   University of California System; University of California San Diego;
   University of California System; University of California San Diego},
Funding-Acknowledgement = {NIH {[}DP5OD029610]; Research to Prevent Blindness (New York, NY)},
Funding-Text = {This work was supported by NIH Grant DP5OD029610 (Bethesda, MD, USA) and
   an unrestricted departmental grant from Research to Prevent Blindness
   (New York, NY).},
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@article{ WOS:000539634600001,
Author = {Bayram, Mustafa and Springer, Simon and Garvey, Colin K. and Vural,
   Ozdemir},
Title = {COVID-19 Digital Health Innovation Policy: A Portal to Alternative
   Futures in the Making},
Journal = {OMICS-A JOURNAL OF INTEGRATIVE BIOLOGY},
Year = {2020},
Volume = {24},
Number = {8},
Pages = {460-469},
Month = {AUG 1},
Abstract = {``The pandemic is a portal.{''} In the words of the novelist scholar
   Arundhati Roy, the COVID-19 pandemic is not merely an epic calamity. It
   has opened up a new space, a portal, to rethink everything, for example,
   in how we live, work, produce scientific knowledge, provide health care,
   and relate to others, be they humans or nonhuman animals in planetary
   ecosystems. Meanwhile, as the intensity of the pandemic escalates,
   digital health tools such as the Internet of Things (IoT), biosensors,
   and artificial intelligence (AI) are being deployed to address the twin
   goals of social distancing and health care in a ``no touch{''} emergency
   state. Permanent integration of digital technologies into every aspect
   of post-pandemic civic life-health care, disease tracking, education,
   work, and beyond-is considered by governments and technology actors
   around the world. Although digital transformation of health care and
   industry are in the works, we ought to ensure that digital
   transformation does not degenerate into ``digitalism,{''} which we
   define here as an unchecked and misguided belief on extreme digital
   connectivity without considering the attendant adverse repercussions on
   science, human rights, and everyday practices of democracy. Indeed, the
   current shrinking of the critically informed public policy space amid a
   devastating pandemic raises principled questions on the broader and
   long-term impacts that digital technologies will have on democratic
   governance of planetary health and society. To this end, a wide range of
   uncertainties-technical, biological, temporal, spatial, and political-is
   on the COVID-19 pandemic horizon. This calls for astute and anticipatory
   innovation policies to steer the health sciences and services toward
   democratic ends. In this article, we describe new and critically
   informed approaches to democratize COVID-19 digital health innovation
   policy, especially when the facts are uncertain, the stakes are high,
   and decisions are urgent, as they often are in the course of a pandemic.
   In addition, we introduce a potential remedy to democratize pandemic
   innovation policy, the concept of ``epistemic competence,{''} so as to
   check the frames and framings of the pandemic innovation policy
   juggernaut and the attendant power asymmetries. We suggest that if
   epistemic competence, and attention to not only scientific knowledge but
   also its framing are broadly appreciated, they can help reduce the
   disparity between the enormous technical progress and investments made
   in digital health versus our currently inadequate understanding of the
   societal dimensions of emerging technologies such as AI, IoT, and
   extreme digital connectivity on the planet.},
Publisher = {MARY ANN LIEBERT, INC},
Address = {140 HUGUENOT STREET, 3RD FL, NEW ROCHELLE, NY 10801 USA},
Type = {Review},
Language = {English},
Affiliation = {Springer, S (Corresponding Author), Univ Newcastle, Ctr Urban \& Reg Studies, Discipline Geog \& Environm Studies, Sch Environm \& Life Sci,Fac Sci, Callaghan, NSW 2308, Australia.
   Ozdemir, V (Corresponding Author), OMICS, New Rochelle, NY 10801 USA.
   Bayram, M (Corresponding Author), Gaziantep Univ, Food Engn, Dept Food Engn, Fac Engn, TR-27300 Gaziantep, Turkey.
   Garvey, CK (Corresponding Author), Ctr Int Secur \& Cooperat, Stanford Inst Human Ctr AI, Palo Alto, CA 94305 USA.
   Bayram, Mustafa, Gaziantep Univ, Dept Food Engn, Fac Engn, Gaziantep, Turkey.
   Springer, Simon, Univ Newcastle, Ctr Urban \& Reg Studies, Discipline Geog \& Environm Studies, Sch Environm \& Life Sci,Fac Sci, Callaghan, NSW 2308, Australia.
   Garvey, Colin K., Stanford Univ, Stanford Inst Human Ctr AI, Ctr Int Secur \& Cooperat, Palo Alto, CA 94304 USA.
   Vural, Ozdemir, OMICS, New Rochelle, NY 10801 USA.
   Springer, Simon, Univ Newcastle, Discipline Geog \& Environm Studies, Sch Environm \& Life Sci, Human Geog,Fac Sci, Callaghan, NSW 2308, Australia.},
DOI = {10.1089/omi.2020.0089},
EarlyAccessDate = {JUN 2020},
ISSN = {1536-2310},
EISSN = {1557-8100},
Keywords = {COVID-19; digital health; innovation policy; digital transformation;
   digitalism; critical policy studies; futures; risk and uncertainty},
Keywords-Plus = {PUBLIC ENGAGEMENT; WATER-TEMPLES; SCIENCE; MANAGEMENT; DIVERSITY;
   ETHICS; SPACE},
Research-Areas = {Biotechnology \& Applied Microbiology; Genetics \& Heredity},
Web-of-Science-Categories  = {Biotechnology \& Applied Microbiology; Genetics \& Heredity},
Author-Email = {profdrmusbay@gmail.com
   simonspringer@gmail.com
   colin.k.garvey@gmail.com
   ojib@liebertpub.com},
Affiliations = {Gaziantep University; University of Newcastle; Stanford University;
   University of Newcastle},
ResearcherID-Numbers = {Garvey, Shunryu Colin/Y-2099-2019
   Garvey, Shunryu Colin/AAW-4362-2021
   Boschele, Marco/AAA-6682-2021},
ORCID-Numbers = {Garvey, Shunryu Colin/0000-0002-7346-8873
   Boschele, Marco/0000-0003-2498-0317},
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Number-of-Cited-References = {81},
Times-Cited = {35},
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Journal-ISO = {OMICS},
Doc-Delivery-Number = {MV2CR},
Web-of-Science-Index = {Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI)},
Unique-ID = {WOS:000539634600001},
DA = {2023-04-22},
}

@article{ WOS:000853973600001,
Author = {Amoatey, Patrick and Al-Jabri, Khalifa and Al-Saadi, Saleh},
Title = {Influence of phase change materials on thermal comfort, greenhouse gas
   emissions, and potential indoor air quality issues across different
   climatic regions: A critical review},
Journal = {INTERNATIONAL JOURNAL OF ENERGY RESEARCH},
Year = {2022},
Volume = {46},
Number = {15},
Pages = {22386-22420},
Month = {DEC},
Abstract = {There has been growing interest in applying phase change materials
   (PCMs) in buildings owing to their energy conservation/latent heat
   storage properties and potential to improve thermal comfort. Various
   reviews have extensively discussed the thermophysical properties of PCMs
   and their energy-saving potential in buildings. However, comprehensive
   reviews on the indoor thermal/ personal comfort behavior of PCMs under
   different climates remain limited. Therefore, this study aims to present
   a comprehensive state-of-the-art review of the impact of PCMs on indoor
   thermal comfort levels in buildings located in cities within different
   subclimate zones and their personal cooling effect when integrated with
   clothing (vest). In addition, greenhouse gas (GHG) mitigation potentials
   and indoor air pollutant emission properties of PCM-enhanced buildings
   were also reviewed. Hundreds of published articles of PCMs in PubMed and
   Scopus databases, including a manual search approach, were utilized. The
   results from this state-of-the-art study have shown that incorporating
   PCMs in buildings satisfactorily reduced the indoor air temperature of
   most buildings located in hot climate (BSh, BWh) zones, but very limited
   studies have been performed in the cold (Dfc, BSk) environments. In
   general, there was an improvement in the thermal comfort levels of the
   PCM-enhanced buildings. However, these were mostly assessed using
   indices such as predicted mean vote, predicted percentage of
   dissatisfied, comfort index, and total discomfort change, without any
   comprehensive survey studies (eg, based on sensation votes) using human
   subjects. The majority of personal cooling studies of PCM-integrated
   vests/garments showed good improvement in thermal comfort, especially in
   terms of skin temperature and thermal sensation. However, very few
   studies have shown a considerable reduction in the GHG emissions of
   PCM-enhanced buildings, and the knowledge of the long-term carbon
   dioxide (CO2) reduction capabilities of PCMs is limited. The profiling
   of PCMs revealed the presence of volatile organic compounds. However,
   studies on indoor air pollutant emissions and the potential health
   effects of PCMintegrated buildings are still lacking. The study is
   crucial to motivating green building engineers, indoor environmental
   quality (IEQ) researchers, and epidemiologists to embark on potential
   future research. Innovative technologies such as machine learning,
   artificial intelligence, Internet of Things, uncertainty analysis, and
   optimization can be utilized to predict thermal comfort, IEQ, and GHG
   emissions in PCM-incorporated buildings.},
Publisher = {WILEY-HINDAWI},
Address = {ADAM HOUSE, 3RD FL, 1 FITZROY SQ, LONDON, WIT 5HE, ENGLAND},
Type = {Review},
Language = {English},
Affiliation = {Al-Jabri, K (Corresponding Author), Sultan Qaboos Univ, Coll Engn, Dept Civil \& Architectural Engn, POB 33, Muscat 123, Oman.
   Amoatey, Patrick; Al-Jabri, Khalifa; Al-Saadi, Saleh, Sultan Qaboos Univ, Coll Engn, Dept Civil \& Architectural Engn, POB 33, Muscat 123, Oman.
   Amoatey, Patrick, Univ Queensland, Fac Med, Sch Publ Hlth, Brisbane, Qld, Australia.},
DOI = {10.1002/er.8734},
EarlyAccessDate = {SEP 2022},
ISSN = {0363-907X},
EISSN = {1099-114X},
Keywords = {energy storage; greenhouse gases; indoor air quality; indoor thermal
   comfort; PCMs; personal cooling},
Keywords-Plus = {ENERGY DEMAND REDUCTION; CHANGE MATERIALS PCM; COOLING VEST; OUTDOOR
   TEMPERATURE; ENVIRONMENTAL-QUALITY; MICROENCAPSULATED PCM;
   AMBIENT-TEMPERATURES; PHYSICAL-ACTIVITY; HEAT-EXCHANGER; URBAN PARK},
Research-Areas = {Energy \& Fuels; Nuclear Science \& Technology},
Web-of-Science-Categories  = {Energy \& Fuels; Nuclear Science \& Technology},
Author-Email = {aljabri@squ.edu.om},
Affiliations = {Sultan Qaboos University; University of Queensland},
ResearcherID-Numbers = {Al-Saadi, Saleh/N-6364-2016},
ORCID-Numbers = {Al-Saadi, Saleh/0000-0002-9095-6983},
Funding-Acknowledgement = {Sultan Qaboos University {[}CR/ENG/CAED/18/07]},
Funding-Text = {Sultan Qaboos University, Grant/Award Number: \#CR/ENG/CAED/18/07},
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Number-of-Cited-References = {201},
Times-Cited = {1},
Usage-Count-Last-180-days = {15},
Usage-Count-Since-2013 = {22},
Journal-ISO = {Int. J. Energy Res.},
Doc-Delivery-Number = {7T4UW},
Web-of-Science-Index = {Science Citation Index Expanded (SCI-EXPANDED)},
Unique-ID = {WOS:000853973600001},
DA = {2023-04-22},
}

@article{ WOS:000848700000020,
Author = {Charow, Rebecca and Jeyakumar, Tharshini and Younus, Sarah and
   Dolatabadi, Elham and Salhia, Mohammad and Al-Mouaswas, Dalia and
   Anderson, Melanie and Balakumar, Sarmini and Clare, Megan and Dhalla,
   Azra and Gillan, Caitlin and Haghzare, Shabnam and Jackson, Ethan and
   Lalani, Nadim and Mattson, Jane and Peteanu, Wanda and Tripp, Tim and
   Waldorf, Jacqueline and Williams, Spencer and Tavares, Walter and
   Wiljer, David},
Title = {Artificial Intelligence Education Programs for Health Care
   Professionals: Scoping Review},
Journal = {JMIR MEDICAL EDUCATION},
Year = {2021},
Volume = {7},
Number = {4},
Month = {OCT-DEC},
Abstract = {Background: As the adoption of artificial intelligence (AI) in health
   care increases, it will become increasingly crucial to involve health
   care professionals (HCPs) in developing, validating, and implementing
   AI-enabled technologies. However, because of a lack of AI literacy, most
   HCPs are not adequately prepared for this revolution. This is a
   significant barrier to adopting and implementing AI that will affect
   patients. In addition, the limited existing AI education programs face
   barriers to development and implementation at various levels of medical
   education.
   Objective: With a view to informing future AI education programs for
   HCPs, this scoping review aims to provide an overview of the types of
   current or past AI education programs that pertains to the programs'
   curricular content, modes of delivery, critical implementation factors
   for education delivery, and outcomes used to assess the programs'
   effectiveness.
   Methods: After the creation of a search strategy and keyword searches, a
   2-stage screening process was conducted by 2 independent reviewers to
   determine study eligibility. When consensus was not reached, the
   conflict was resolved by consulting a third reviewer. This process
   consisted of a title and abstract scan and a full-text review. The
   articles were included if they discussed an actual training program or
   educational intervention, or a potential training program or educational
   intervention and the desired content to be covered, focused on AI, and
   were designed or intended for HCPs (at any stage of their career).
   Results: Of the 10,094 unique citations scanned, 41 (0.41\%) studies
   relevant to our eligibility criteria were identified. Among the 41
   included studies, 10 (24\%) described 13 unique programs and 31 (76\%)
   discussed recommended curricular content. The curricular content of the
   unique programs ranged from AI use, AI interpretation, and cultivating
   skills to explain results derived from AI algorithms. The curricular
   topics were categorized into three main domains: cognitive, psychomotor,
   and affective.
   Conclusions: This review provides an overview of the current landscape
   of AI in medical education and highlights the skills and competencies
   required by HCPs to effectively use AI in enhancing the quality of care
   and optimizing patient outcomes. Future education efforts should focus
   on the development of regulatory strategies, a multidisciplinary
   approach to curriculum redesign, a competency-based curriculum, and
   patient-clinician interaction.},
Publisher = {JMIR PUBLICATIONS, INC},
Address = {130 QUEENS QUAY East, Unit 1100, TORONTO, ON M5A 0P6, CANADA},
Type = {Review},
Language = {English},
Affiliation = {Wiljer, D (Corresponding Author), Univ Hlth Network, 190 Elizabeth St,R Fraser Elliott Bldg RFE 3S-441, Toronto, ON M5G 2C4, Canada.
   Charow, Rebecca; Dolatabadi, Elham; Balakumar, Sarmini; Gillan, Caitlin; Tavares, Walter; Wiljer, David, Univ Toronto, Dalla Lana Sch Publ Hlth, Inst Hlth Policy Management \& Evaluat, Toronto, ON, Canada.
   Charow, Rebecca; Jeyakumar, Tharshini; Younus, Sarah; Anderson, Melanie; Gillan, Caitlin; Haghzare, Shabnam; Tripp, Tim; Williams, Spencer; Tavares, Walter; Wiljer, David, Univ Hlth Network, 190 Elizabeth St,R Fraser Elliott Bldg RFE 3S-441, Toronto, ON M5G 2C4, Canada.
   Dolatabadi, Elham; Dhalla, Azra; Haghzare, Shabnam; Jackson, Ethan; Lalani, Nadim, Vector Inst, Toronto, ON, Canada.
   Salhia, Mohammad; Al-Mouaswas, Dalia; Balakumar, Sarmini; Clare, Megan; Mattson, Jane; Peteanu, Wanda; Waldorf, Jacqueline, Univ Hlth Network, Michener Inst Educ, Toronto, ON, Canada.
   Gillan, Caitlin; Tavares, Walter; Wiljer, David, Univ Toronto, Fac Med, Toronto, ON, Canada.
   Haghzare, Shabnam, Univ Toronto, Inst Biomed Engn, Toronto, ON, Canada.
   Tavares, Walter, Wilson Ctr, Toronto, ON, Canada.
   Wiljer, David, Ctr Addict \& Mental Hlth CAMH, CAMH Educ, Toronto, ON, Canada.},
DOI = {10.2196/31043},
Article-Number = {e31043},
ISSN = {2369-3762},
Keywords = {machine learning; deep learning; health care providers; education;
   learning; patient care},
Keywords-Plus = {BIG DATA; DATA SCIENCE; CURRICULUM},
Research-Areas = {Education \& Educational Research},
Web-of-Science-Categories  = {Education, Scientific Disciplines},
Author-Email = {david.wiljer@uhn.ca},
Affiliations = {University of Toronto; University of Toronto; University Health Network
   Toronto; University of Toronto; University Health Network Toronto;
   University of Toronto; University of Toronto; University of Toronto;
   Centre for Addiction \& Mental Health - Canada},
ResearcherID-Numbers = {Anderson, Melanie/J-1145-2016
   },
ORCID-Numbers = {Anderson, Melanie/0000-0001-9786-1656
   Al Mouaswas, Dalia/0000-0002-5620-0832
   Charow, Rebecca/0000-0002-8553-4006
   Younus, Sarah/0000-0002-0728-7628
   Jeyakumar, Tharshini/0000-0002-4482-3637
   Clare, Megan/0000-0002-2778-6002
   Dhalla, Azra/0000-0001-9806-3664},
Funding-Acknowledgement = {Government of Canada's Future Skills Centre},
Funding-Text = {Accelerating the appropriate adoption of artificial intelligence in
   health care through building new knowledge, skills, and capacities in
   the Canadian health care professions is funded by the Government of
   Canada's Future Skills Centre.},
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Number-of-Cited-References = {72},
Times-Cited = {5},
Usage-Count-Last-180-days = {10},
Usage-Count-Since-2013 = {11},
Journal-ISO = {JMIR Med. Educ.},
Doc-Delivery-Number = {4F7OP},
Web-of-Science-Index = {Emerging Sources Citation Index (ESCI)},
Unique-ID = {WOS:000848700000020},
OA = {Green Published, gold},
DA = {2023-04-22},
}

@article{ WOS:000430132800004,
Author = {van der Aalst, Wil M. P.},
Title = {Process discovery from event data: Relating models and logs through
   abstractions},
Journal = {WILEY INTERDISCIPLINARY REVIEWS-DATA MINING AND KNOWLEDGE DISCOVERY},
Year = {2018},
Volume = {8},
Number = {3},
Month = {MAY-JUN},
Abstract = {Event data are collected in logistics, manufacturing, finance, health
   care, customer relationship management, e-learning, e-government, and
   many other domains. The events found in these domains typically refer to
   activities executed by resources at particular times and for a
   particular case (i.e., process instances). Process mining techniques are
   able to exploit such data. In this article, we focus on process
   discovery. However, process mining also includes conformance checking,
   performance analysis, decision mining, organizational mining,
   predictions, recommendations, and so on. These techniques help to
   diagnose problems and improve processes. All process mining techniques
   involve both event data and process models. Therefore, a typical first
   step is to automatically learn a control-flow model from the event data.
   This is very challenging, but in recent years, many powerful discovery
   techniques have been developed. It is not easy to compare these
   techniques since they use different representations and make different
   assumptions. Users often need to resort to trying different algorithms
   in an ad-hoc manner. Developers of new techniques are often trying to
   solve specific instances of a more general problem. Therefore, we aim to
   unify existing approaches by focusing on log and model abstractions.
   These abstractions link observed and modeled behavior: Concrete
   behaviors recorded in event logs are related to possible behaviors
   represented by process models. Hence, such behavioral abstractions
   provide an interface between both of them. We discuss four discovery
   approaches involving three abstractions and different types of process
   models (Petri nets, block-structured models, and declarative models).
   The goal is to provide a comprehensive understanding of process
   discovery and show how to develop new techniques. Examples illustrate
   the different approaches and pointers to software are given. The
   discussion on abstractions and process representations is also presented
   to reflect on the gap between process mining literature and commercial
   process mining tools. This facilitates users to select an appropriate
   process discovery technique. Moreover, structuring the role of internal
   abstractions and representations helps broaden the view and facilitates
   the creation of new discovery approaches. This article is categorized
   under: Algorithmic Development > Spatial and Temporal Data Mining
   Application Areas > Business and Industry Technologies > Machine
   Learning Application Areas > Data Mining Software Tools},
Publisher = {WILEY PERIODICALS, INC},
Address = {ONE MONTGOMERY ST, SUITE 1200, SAN FRANCISCO, CA 94104 USA},
Type = {Review},
Language = {English},
Affiliation = {van der Aalst, WMP (Corresponding Author), Rhein Westfal TH Aachen, Proc \& Data Sci PADS, Aachen, Germany.
   van der Aalst, Wil M. P., Rhein Westfal TH Aachen, Proc \& Data Sci PADS, Aachen, Germany.},
DOI = {10.1002/widm.1244},
Article-Number = {e1244},
ISSN = {1942-4787},
EISSN = {1942-4795},
Keywords = {business process management; data science; process discovery; process
   mining; process modeling},
Keywords-Plus = {MINING PROCESS MODELS; OF-THE-ART},
Research-Areas = {Computer Science},
Web-of-Science-Categories  = {Computer Science, Artificial Intelligence; Computer Science, Theory \&
   Methods},
Author-Email = {wvdaalst@pads.rwth-aachen.de},
Affiliations = {RWTH Aachen University},
ResearcherID-Numbers = {van der Aalst, Wil/G-1248-2011},
ORCID-Numbers = {van der Aalst, Wil/0000-0002-0955-6940},
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Number-of-Cited-References = {56},
Times-Cited = {19},
Usage-Count-Last-180-days = {2},
Usage-Count-Since-2013 = {60},
Journal-ISO = {Wiley Interdiscip. Rev.-Data Mining Knowl. Discov.},
Doc-Delivery-Number = {GC9QG},
Web-of-Science-Index = {Science Citation Index Expanded (SCI-EXPANDED)},
Unique-ID = {WOS:000430132800004},
DA = {2023-04-22},
}