{ "cells": [ { "cell_type": "markdown", "metadata": { "id": "xKD6nEjZ9NVn" }, "source": [ "Tutorial 7 - The EU's Labour Market\n", "\n", "**Table of Contents**\n", "\n", "1. [Introduction](#introduction)\n", "2. [Theoretical framework](#theoretical_framework) \n", "   2.1 [Labour market states](#labour_market_states) \n", "   2.2 [Transition rates](#transition_rates) \n", "   2.3 [Steady state](#steady_state) \n", "   2.4 [Limitations](#limitations) \n", "3. [Data and methodology](#data_and_methodology) \n", "   3.1 [Data](#data) \n", "   3.2 [Technical setup](#technical_setup) \n", "4. [Data handling](#data_handling) \n", "   4.1 [Introduction to pandas](#introduction_to_pandas) \n", "   4.2 [Technical toolkit](#technical_toolkit) \n", "      4.2.1 [Audit](#audit) \n", "      4.2.2 [Cleaning](#cleaning) \n", "      4.2.3 [Transformation](#tranformation) \n", "      4.2.4 [Controlling](#controlling) \n", "   4.3 [Regression datasets](#regression_datasets) \n", "      4.3.1 [GDP growth on unemployment rates of age groups](#gdp_growth_on_unemployment_rates_of_age_groups) \n", "      4.3.2 [GDP growth on unemployment rates of education levels](#gdp_growth_on_unemployment_rates_of_education_levels) \n", "      4.3.3 [Job finding probability on GDP per capita of age groups](#job_finding_probability_on_gdp_per_capita_of_age_groups) \n", "5. [Data visualization](#data_visualization) \n", "   5.1 [Introduction to plotting libraries](#introduction_to_plotting_libraries) \n", "      5.1.1 [Matplotlib](#matplotlib) \n", "      5.1.2 [Seaborn](#seaborn) \n", "   5.2 [Plotting](#plotting) \n", "      5.2.1 [Correlation](#correlation) \n", "      5.2.2 [Divergence](#divergence) \n", "      5.2.3 [Ranking](#ranking) \n", "      5.2.4 [Composition](#composition) \n", "      5.2.5 [Time evolution](#time_evolution) \n", "      5.2.6 [Advanced plots](#advanced_plots) \n", "6. [Statistical analysis](#statistical_analysis) \n", "   6.1 [Introduction to statsmodels](#introduction_to_statsmodels) \n", "   6.2 [Simple linear OLS regression](#simple_linear_ols_regression) \n", "      6.2.1 [GDP growth on unemployment rates of age groups](#gdp_growth_on_unemployment_rates_of_age_groups) \n", "      6.2.2 [GDP growth on unemployment rates of educational attainment levels](#gdp_on_unemployment_rates_on_educational_attainment_levels) \n", "      6.2.3 [Job finding probability on GDP per capita of age groups](#job_finding_probability_on_gdp_per_capita_of_age_groups) \n", "   6.3 [Multiple regression](#multiple_regression) \n", "      6.3.1 [Unemployment rates on transition rates](#unemployment_rates_on_transition_rates) \n", "      6.3.2 [Model performance on transition rates](#model_performance_on_transition_rates) \n", "   6.4 [Discussion](#discussion) \n", "7. [Conclusion](#conclusion) \n", "\n", "\n", "# Introduction\n", "Hello and we welcome you to this tutorial! The aim of this tutorial is to present the features of the European labor market in Python consistently and with commentary. From a programming perspecitve, the main objective of the tutorial is to learn how to prepare and analyse data. This entails several subprocesses such as the data import, cleaning, transformation, visualization and inspection. From an economic point of view, the main objective is to construct a simple but useful model of a labour market while characterizing it's equilibrium. As it will become clear, using Python to implement an economic model allows us to demonstrate features of the model that would otherwise be more difficult to understand. Before starting, let's not forget that the focus of this series of tutorials is mainly to transmit knowledge regarding programming with Python, and not pure economic theory. We invite you have a look at the following agenda, which will provide you with an overview of what you can expect from this tutorial.\n", "\n", "**Structure and content of this tutorial**\n", "\n", "2. **Theoretical framework**: *First things first!* \n", "Setting-up the theoretical background underlying this tutorial. We construct step by step a simple model for a labour market and explain concepts such as transition rates and steady state equilibria \n", "\n", "3. **Data and methodology**: *With great data comes great responsibility!* \n", "A mindful introduction to the database that will be used throughout the tutorial.\n", "\n", "4. **Data handling**: *Let's get to programming!* \n", "The introduction to the always forgotten, but most important programming apsect, the data handling process. This process audits the data and ensures that it is clean and well-transformed, thus ensuring it's integrity and hence its usfulness for statistical analysis.\n", "\n", "5. **Data visualization**: *Gain the ability to build nice and neat plots!* \n", "Multiple visualization methods in Python exist, we present and explain a subset of the most essential data visualizations. This section will equip you with the ability to construct nice and neat plots in an efficient and effective manner. Don't forget as this will also be extremely useful to qualitatively discover the patterns in your data.\n", "\n", "6. **Statistical analysis**: *Let's examine the data patterns on a statistical level!* \n", "After having cleaned and visualized the data, we will perform statistical models on it. More specifically, we will run simple and multiple OLS regressions to tease out causal relationships between indicators of the labour market and macroeconomic variables.\n", "\n", "7. **Summary and discussion**: *We hope that you learned a lot!* \n", "This is the conclusion of our tutorial. This section will provide you with a summary and the main take-aways from what you have learned. We will provide you with additional resources where you can put theory into practice. We thereby hope that this will support you on your learning process and bring you even further with your programming skills.\n", "\n", "We really hope that this tutorial will provide you with a solid economic foundation and a technical toolkit in methods for quantitative analysis with Python.\n", "\n", "Enjoy and have a great journey!\n", "
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\n", " \n", " Figure 1: Opening caricature (xkcd)\n", " \n", "

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\n", "\n", "\n", "# Theoretical framework\n", "The following section provides and explains essential theoretical concepts that are required in order to understand the mechanics of the labour market model. First, this section presents the notion labour market states. Second, we will show how we can model the transition between these states. Third, we will introduce the economic equilibrium of the labour market model, the steady state. Finally, we will present the essential assumptions on which the model is based. This will give you a critical view on the applicability and limitations of the model.\n", "\n", "\n", "## Labour market states\n", "Most countries in the world have established a system and infrastructure to record and approximate the employment and unemployment rates of the labour force as accurately as possible. However, the methodology for calculating these rates often varies among countries. Different definitions of employment and unemployment, as well as different data sources can lead to different results. Here, we will agree on a clear definition of these concepts following the perception of the statistical office of the European Union. Thereby, the labour market model assigns each individual of a population to one of the following three labour market states.\n", "\n", "- **Employed** ($E$): The number of people engaged in productive activities in an economy\n", "- **Unemployed** ($U$): The number of people not engaging in productive activites but available and actively seeking to\n", "- **Inactive** ($I$): The number of people not being part of the labour force\n", "\n", "Each of these states, is expressed by an absolute number and describes a labour market as of a **specific point in time**. For this reason, those indicators are so-called **stock variables**. We can further use those variables, in order to construct more meaningful indicators that characterize a labor market.\n", "\n", "**Employment Rate** \n", "The percentage of employed individuals in relation to the comparable population \n", "\n", "$$ \\frac{E}{E + U + I}\\tag{1} $$\n", "\n", "**Unemployment Rate** \n", "The percentage of unemployed individuals in relation to the comparable labour force \n", "\n", "$$\\frac{U}{E + U}\\tag{2}$$\n", "\n", "**Participation Rate** \n", "The percentage of individuals in the labout force in relation to the comparable population \n", "\n", "$$\\frac{E + U}{E + U + I}\\tag{3}$$\n", "\n", "The employment rate, the unemployment rate and the participation rate are important indicators for understanding a labour market. You have probably noticed that there is always a comparable population or labour force for which this indicator holds. This is because those rates can be expressed either for geographical areas or even for particular age groups or educational attainment levels. Therefore, an important characteristic of the labour market model is that its structure allows for age- and skill-specific segmentation of labour markets and their indicators. This feature will become useful when we will try to understand the distribution of labour market indicators within specific segments. \n", "\n", "Finally, note the difference for the computation labour market rates. While we put into relation employment and participation with the population, we compare unemployment only to the labour force. The next section explains and illustrates the definition of the labour force. For instance, unemployment is defined as having the desire and availability to work while having actively sought work within the past four weeks. This excludes for example prisoners or disabled people, who are not considered unenmployed but rather out of the labor force. For a complete definition of each labor market state we encourage you to visit the '[Main Concepts](https://ec.europa.eu/eurostat/web/lfs/methodology/main-concepts)' section on the webpage of the statistical office. The following figure illustrates the categorization of a population into mutually exclusive and collectively exhaustive labour market states with main characteristics defined correspondingly. \n", "
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\n", " Definition of the labour market states\n", "

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\n", " \n", " Figure 2: Definition of the labour market states (Eurostat)\n", " \n", "

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\n", "\n", "\n", "## Transition rates\n", "\n", "Having introduced a labour market model, which assigns individuals to distinct states, we can further model the transition of individuals between labour market states. For example, if some unempolyed people find employment over time, the unenployment rate will decrease while the employment rate will increase. The flows of people from one state to another are used to compoute the so-called **transition rates**. Those variables are important indicators for understanding the development of a labour market under study. They express the evolvement of a labour market **as of a specific time period**. For this reason, those variables are so-called **flow variables**. Transition rates tell us how likely it is for an individual to move from one state to another. Since there are three possible states a person can belong to ($E$, $U$, $I$), there are in total nine possible transitions that can occurr within a marginal time period (from $t$ to $t+1$).\n", "\n", "|t / t+1|E|U|I|\n", "|:-----:|:--:|:--:|:--:|\n", "|E|EE|EU|EI|\n", "|U|UE|UU|UI|\n", "|I|IE|IU|II|\n", "\n", "For example, the transition rate from unemployment to unemployment ($UE$) of 5% indicates the percentage of people unemployed at the beginning of the period that eventually found employment by the end of the period, thus number of migrating individuals from unemployed to employed as percentage uf unemployed individuals. We are now able to formulate a general formula for computing transition rates.\n", "\n", "$$Transition Rate = \\frac{S_{t}S_{t+1}}{Stock_{S_{t}}}\\tag{4}$$\n", "\n", "For instance, if we would like to compute the transition rate from unemployment to employment, we need to divide the absolute number of transitioning individuals (from unemployment to employment) by total stock of unemployed people in the initial period. In summary, to compute transition rates we need to follow the following computation steps:\n", "\n", "1. We compute the stock of all individuals in each state ($E$, $U$, $I$) at time $t$\n", "2. We compute the stock of all individuals in each state ($E$, $U$, $I$) at time $t + 1$\n", "3. We compute for each combination of states how many individuals migrated from a state ($S_{t}$) to another ($S_{t+1}$). We denote all combinations by $SS$\n", "4. We divide $SS$ by the stock of individuals in the original state \n", "\n", "As you will progress in this tutorial, you will encounter this theoretical knowledge again in section [**4.2. Transition rates**](#transition_rates) as we will compute transitions rates with real-world data using exactly this formula! As we are now able to calculate those variables, we can use transition rates to develop two other useful indicators for a labor market.\n", "\n", "**Rate of Job Finding** \n", "The percentage of individuals entering the employment state \n", "\n", "$$ f = \\frac{UE + IE}{U}\\tag{5} $$\n", "\n", "**Rate of Job Separation** \n", "The percentage of individuals entering the unemployment state\n", "\n", "$$ s = \\frac{EU + EI}{E}\\tag{6} $$\n", "\n", "Intuitively, $f$ describes the (constant) probability for an unemployed individual to find a job and $s$ describes the (constant) probability for an employed person to lose her/his job. These two equations are extremely useful, because we can now describe the dynamics of our labour market model. Lastly, as the dynamics of the transition rates depend on each other, we can identify a determinisic and stable pattern. At this point, we will introduce an equilibrium of the labour market model, the steady state.\n", "\n", "\n", "## Steady state\n", "In economics, a system or a process is said to be in steady state if the variables which define the behavior of the system or the process are unchanging in time. In economic theory, studying steady states is highly important, because if we assume that an economy converges to a steady state given certain factors, then it becomes interesting to evaluate the dynamics and to understand how the economy converges to that steady state. We will see that understanding those dynamics are crucial in order to formulate useful policy recommendations. In this tutorial we will pay particular attention to the steady state of the unemployment rate. The unemployment rate can possess the characteristics of a steady state if and only if the number of individuals entering the unemployment state and the number of individuals exiting the unemployment state are equal. The following figure illustrates a situation where the antagonist flow variables cancel each other out.\n", "\n", "
\n", " The steady state unemployment rate\n", "

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\n", " \n", " Figure 3: The steady state unemployment rate\n", " \n", "

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\n", " \n", "We will now apply the concept of steady state to our labour market model. Formally, a labour market is said to possess an unemployment rate persisting in a steady state if the fraction of unemployed people finding a job ($f \\times U$) and the fraction of employed individuals losing their job ($s \\times E$) is equal. Therefore, the condition for a steady state such that the unemployment rate stays constant from period $t$ to period $t+1$ is given by the following equation.\n", "\n", "$$fU=sE\\tag{7}$$\n", "\n", "Substituting some terms and re-arranging we can easily compute the formula for the steady steate unenmployment rate in two simple steps:\n", "\n", "1. We exploit the fact that $E = N - U$, i.e. the number of employed people equals labor force minus the number of unemployed persons:\n", "\n", "$\n", "\\hspace{12.2cm}\n", "\\begin{align}\n", "fU & = sE \\\\ \n", "& = s(N-U) \\\\ \n", "& = sN - sU\n", "\\end{align}\n", "$\n", "\n", "2. We re-arrange and solve for $\\frac{U}{N}$:\n", "\n", "$$ (f + s)U = sN $$\n", "\n", "The **steady state unemployment rate** is then given by: \n", "\n", "$$ UR_{ss} = \\frac{U}{N} = \\frac{s}{s + f} = \\frac{1}{1 + \\frac{s}{f}}\\tag{8} $$\n", "\n", "In section [**4.3. Steady state**](#steady state) of this tutorial we will focus on computing the steady state unemployment rates with real world data. This will allow us to see the predictive power of our model when put into practice in empirical research. To understand how useful equation $(8)$ is and how this understanding is essential for formulating policy reccomendations, let's look at an example. Assume an economy in which each quarter 20% of unemployed individuals find a job and 2% of employed individuals lose their job, hence:\n", "\n", "- $f$ = 0.2\n", "- $s$ = 0.02\n", "\n", "The steady state unemployment rate is then given by: \n", "\n", "$$ \\frac{s}{s + f} = \\frac{0.02}{0.02 + 0.2} \\approx 0.09, \\text{or} \\; 9\\% $$ \n", "\n", "It becomes clear that a policy aimed at reducing this unemployment rate, will only succeed one of the following options. First, steady state unemployment rate decreases if a the policy manages to increase $f$, i.e. the probability of finding a job. This could be done by investing in education and job placement programs. Second, steady state unemployment rate decreases if the policy manages to decrease $s$, i.e. the probability of losing a job. This could be done by ensuring a slow and sustainable growth of the overall economy, without major recessions. In next and last part of this section, will sensitize on the assumptions underlying the labour market model as well as its practical limitations.\n", "\n", "\n", "## Limitations\n", "The labour market model is build on the assumption that transition can happen within a specified time period (from $t$ to $t+1$). Hence, the rate of job finding and the rate of job separation do not exactly correspond but only approximate the probability to move from one state to another. In fact, the labour market model assumes transitions in discrete time. In reality, an individual can change the state several times within a time period. Hence, individuals move across states in a continuous way and the computed transition rates will not fully correspond to the true probability to move acrosss states. But clearly, under the assumption of the law of large numbers, we can expect computed transition rates to be more accurate the more often they are calculated and therefore they will approximate the instantaneuous probabilities to move across states. Instantaneuos transition probabilities can be seen as mathematical adjustments to the computation of transition rates we saw so far. With instantaneous probabilities, the steady-state unemployment rate can also be rewritten as:\n", "\n", "$$UR_{SS} = \\frac{\\pi^{EN}\\pi^{NU}+\\pi^{NE}\\pi^{EU}+\\pi^{NU}\\pi^{EU}}{(\\pi^{UN}\\pi^{NE}+\\pi^{NU}\\pi^{UE}+\\pi^{NE}\\pi^{UE})+(\\pi^{EN}\\pi^{NU}+\\pi^{NE}\\pi^{EU}+\\pi^{NU}\\pi^{EU})}\\tag{10}$$\n", "\n", "where $\\pi^{SS}$ are the instantaneous probabilities of transitioning from one state to the other. For the purpose of our tutorial, we will make use of transition rates as an approximation for these instantanaeous probabilities, hence we assume that the following equation hold:\n", "\n", "$$s = {\\pi^{EN}\\pi^{NU}+\\pi^{NE}\\pi^{EU}+\\pi^{NU}\\pi^{EU}}\\tag{11}$$\n", "\n", "$$f = {\\pi^{UN}\\pi^{NE}+\\pi^{NU}\\pi^{UE}+\\pi^{NE}\\pi^{UE}}\\tag{12}$$\n", "\n", "Altough this setting allows us to build a model that resembles more closely to reality (were changes happen rather in a continuous way than in discrete time) throughout the turorial we will use the classical transition rates discussed in the previous section, since they are easier to deal with and confer nontheless great explantory power to the model.\n", "\n", "\n", "# Data and methodology\n", "\n", "Having laid down a theoretical foundation of the labour market model, it is now time to devote the attention to the transition from theory to practice. This transition can be seen as successful if we as the researchers are not interrupted within our data analysis. Such an interruption can either happen because the data integrity becomes questionable or because there are technical issues which have not been adressed before starting the programming. Therefore, the following section focuses on providing information about the data source, its provider, and about the data collection process. The latter part of this section is designated to set the stage and provide the technical prerequisites for the programming part.\n", "\n", "\n", "## Data\n", "\n", "**Data source** \n", "In order to compare the different dynamics of countries related to transition rates and unemployment rates, data needs to be collected systematically and in a reliable manner. All data used in this tutorial is extracted from Eurostat which is an adminitrative branch of the European Commission located in Luxembourg. Its responsability is to provide statistical information to the institutions of the European Union and to encourage the harmonisation of statistical methods in order to ease comparison between data. In this section, we will discuss how Eurostat gather data and the degree of relability of its operations. Eurostat publishes its statistical database online for free on its [website](https://ec.europa.eu/eurostat/web/main/data/database).\n", "\n", "**Data collection** \n", "The data that will interest us in this tutorial are the one related to the European labor market. \n", "**The European Labor Force Survey** is a survey conducted by Eurostat in order to find those data. The latter are obtained by interviewing a large sample of individuals directly. This data collection takes place over on a monthly, quarterly and annually basis. \n", "The European Labor Force Survey collects data by four manners: \n", "- Personal visits\n", "- Telephone interviews\n", "- Web interviews\n", "- Self-administered questionnaires (questionnaire that has been designed specifically to be completed by a respondent without intervention of the researchers)\n", "\n", "**Data integrity** \n", "For the sake of this tutorial, there are factors to be considered that could possibly affect the outcome of the analysis:\n", "- Population adjustments are revised at fixed time intervals on the basis of new population censuses\n", "- Reference periods may not have remained the same for a given country due to the transition to a quarterly continuous survey\n", "- Countries may have modified sample designs\n", "- Countries may have modified the content or order of their questionnaire\n", "\n", "Since 1983 however, the statistical office of the European Union has endeavored to establish a greater comparability between the results of successive surveys. This has been achieved mainly through increased harmonisation, greater stability of content and higher frequency of surveys. Furthermore, Eurostat makes considerable efforts to perform a structured approach to data validation. It thereby defines common standards for validation, providing common validation tools or services to be used. Validation rules are jointly designed and agreed upon and the resulting reglement is documented using common cross-domain standards, with clear validation responsibilities assigned to the different groups participating in the production process of European statistics. A more detailed perspective on the data validation process can be gathered on their [website](https://ec.europa.eu/eurostat/web/main/data/data-validation).\n", "\n", "**Metadata of datasets** \n", "As this tutorial will introduce a programmatic access to the datasets, it is not required to download any local files to follow the analysis. In the following sections, the tutorial will use three datasets provided by Eurostat, namely for unemployment rates, for the transitions and for GDP data. Each of these datasets contain observations for different European countries accross sex, age and citizenship. The following table presents the metadata of the datasets that is valid as of the date of submission of this tutorial.\n", "\n", "| | Unemployment rates | Labour market transitions | Real GDP growth rates |\n", "|:------|:----|:----|:-----|\n", "| File | ['lfsq_urgan'](https://ec.europa.eu/eurostat/databrowser/view/lfsq_urgan/default/table?lang=en) | ['lfsi_long_q'](https://ec.europa.eu/eurostat/databrowser/view/lfsi_long_q/default/table?lang=en) | ['tec00115'](https://ec.europa.eu/eurostat/databrowser/view/tec00115/default/table?lang=en) |\n", "| Time coverage |1998Q1 - 2020Q4|2010Q2 - 2020Q4|2009 - 2020|\n", "| Number of values |1,632,066|293,058|949|\n", "| Last data update |13-04-2021|13-04-2021|23-04-2021|\n", "\n", "**Variables:** \n", "- `unit`: Data format, either percentage (PC) or absolute in thousands (THS)\n", "- `sex`: Sex, either male (M), female (F) or total (T)\n", "- `age`: Age group, from 15 to 74 years, in different ranges, not mutually exclusive\n", "- `citizen`: Country of citizenship, e.g. from EU28 countries (EU28_FOR) or total (TOTAL)\n", "- `geo\\time`: ISO alpha two-letter country codes (e.g. CH)\n", "- `na_item`:\n", "- `s_adj`: Flag for whether data is seasonally adjusted (SA) or not (NSA)\n", "- `indic_em`: Employment indicator of transition (e.g. U_E)\n", "\n", "As it will be shown in section [**4.2 Data Import**](#data_import), we will access these three databases through an API. An [API](https://en.wikipedia.org/wiki/API) (application programming interface) allows interactions such as data transmission between multiple software applications. In our case, we will connect to the API of Eurostat, and by interacting with it we will be able to retrieve the three datasets directly here in Python.\n", "\n", "\n", "## Technical setup\n", "\n", "**Package management** \n", "Python is considered a \"batteries included\" language which means that its rich and versatile library is immediately available without the user being forced to download a large amount of packages. At the same time, Python has an active community that contributes to the development of an even bigger set of packages. Many of these packages enhance the simplicity and the computational power of the code. In order to be able to access these sets of powerful modules, it is neccessary to install the packages and load them into the memory. For the installation of a package, one can use the standard package manager of python, named `pip`. The simple terminal or jupyter command\n", "```bash\n", "pip install \n", "```\n", "\n", "will do the work and intialize a number of subprocesses, namely the identification of base requirements, the resolvement of existent environemnt dependencies and finally, the installation of the desired package. For further information about the functionality and features of `pip` one can consult the [website](https://pip.pypa.io/en/stable/cli/pip_install/). For now, let's install some packages that you will need for following this tutorial. If you should have problems in importing other packages in the second cell, you can just add them to the first cell and install them." ] }, { "cell_type": "code", "execution_count": 92, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "EIKpJ9IqPXg5", "outputId": "ea8c46a4-32c0-42f4-ad0a-410f213eb1b3" }, "outputs": [], "source": [ "# You can skip this cell if you have the packages already installed in your environment\n", "pip install eurostat\n", "pip install geopandas\n", "pip install pycountry\n", "pip install squarify" ] }, { "cell_type": "markdown", "metadata": { "id": "xKD6nEjZ9NVn" }, "source": [ "**Module import** \n", "For the sake of this tutorial, the required libraries are imported below. Note that the interpreter will raise a [`ModuleNotFoundError`](https://docs.python.org/3/library/exceptions.html?highlight=modulenotfounderror#ModuleNotFoundError) if a package has not been installed yet. The tutorial will address each library in detail, but for now, we simply run the below cell to set the stage for the analysis." ] }, { "cell_type": "code", "execution_count": 93, "metadata": { "id": "86SVN0Kp945M" }, "outputs": [], "source": [ "import eurostat\n", "import geopandas as gpd \n", "import math\n", "import matplotlib\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import pandas as pd\n", "import pycountry as pyc\n", "import statsmodels.api as sm\n", "import datetime\n", "import seaborn as sns\n", "import matplotlib.lines as mlines\n", "import squarify \n", "import random" ] }, { "cell_type": "markdown", "metadata": { "id": "hvKiba9FqBpc" }, "source": [ "\n", "# Data handling\n", "\n", "The data handling process ensures that the information can be presented in a way that is useful to analyze. This includes the development of policies and procedures to manage data with regards to its storage, audit, cleaning and controlling. Most importantly, the data handling process is an iterative process that has to be adapted to the needs of the current task. The following section is designated to load the datasets and to conduct an extensive amount of inspection that ensures the integrity and hence its usfulness for the analysis. In order to programmatically address the process of data handling, this tutorial introduces one of the most popular and most used python libraries.\n", "\n", "\n", "## Introduction to Pandas\n", "\n", "**Environment** \n", "The [`pandas`](https://pandas.pydata.org/pandas-docs/stable/index.html) library takes its name from an acronym of \"panel data\", which refers to the tabular format of its data structure. The previous tutorials have introduced already built-in data structures such as lists, sets, tuples and dicitionaries. With `pandas`, we introduce two new data structures, namely `pandas.Series` for one-dimensional arrays and the `pandas.DataFrame` for two-dimensional tabular structures. The `pandas` library shares many similarities with `numpy` as it adopts its style of array-based computing. The biggest difference however is that `pandas` is designed for working with tabular and heterogeneous data. The `numpy` library, by contrast, is best suited for working with homogeneous and numerical data. In order to simplify the code that will follow it is helpful to `import pandas as pd` such that we can reference to the library and its modules with a shorter name. Indeed, you can check in the previous section that we imported `pandas` and many other libraries using the `as` command, in order to more easily reference to each library in the future. \n", "\n", "**Data structures** \n", "The `pd.Series` is an object of the `pandas` library designed to represent one-dimensional and heterogeneos data structures. The array is characterized by its name, its values and and index. Similarily, the `pd.DataFrame` is a two-dimensional data structure consisting of a concatenation of `pd.Series`. As the tabular format can be thought of a spreadsheet, the columns must be of the same length, however they can accommodate heterogeneous data types. The `pd.DataFrame` is characterized by its column names, its column values and its row indices. The ability of indexing is crucially important as it enables to filter the data or to access specific data points in order to read them or to mutate them in place. The following figure should summarize and illustrate the anatomy of the introduced data structures related to `pandas`.\n", "\n", "
\n", " Anatomy of a pandas DataFrame\n", "

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\n", " \n", " Figure 4: Anatomy of a pandas DataFrame (Matplotlib)\n", " \n", "

\n", "
\n", "\n", "**Instantation** \n", "The `pd.DataFrame` can be created in two different ways. First, this can be achieved by passing suitable data structure as an argument in the object caller. Suitable data structures are `numpy.arrays`, or a combination of dicitionaries and other data structures such as lists or `pd.Series`. Second, a `pd.DataFrame` can be created by reading suitable data files. An overview of the possible writer functions can be found [here](https://pandas.pydata.org/pandas-docs/stable/user_guide/io.html). The following cells showcase the different ways in which a `pd.DataFrame` can be created by using the object caller. Note that the resulting table will always be the same." ] }, { "cell_type": "code", "execution_count": 94, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 142 }, "id": "8-haouuAqFwU", "outputId": "53095a13-9528-4219-d383-7d5d095e491e" }, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
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" ], "text/plain": [ " Employed Unemployed Inactive\n", "CH 1 2 3\n", "FR 4 5 6\n", "DE 7 8 9" ] }, "execution_count": 94, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Creation of a DataFrame using a two-dimensional numpy.array\n", "\n", "np_array = np.array([[1, 2, 3], \n", " [4, 5, 6], \n", " [7, 8, 9]])\n", "\n", "df = pd.DataFrame(np_array, columns=['Employed', 'Unemployed', 'Inactive'], index=['CH', 'FR', 'DE'])\n", "df" ] }, { "cell_type": "code", "execution_count": 95, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 142 }, "id": "4YWqpukfqH8G", "outputId": "19c268a5-5ef8-4d1e-814e-8e1987059657" }, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
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" ], "text/plain": [ " Employed Unemployed Inactive\n", "CH 1 2 3\n", "FR 4 5 6\n", "DE 7 8 9" ] }, "execution_count": 95, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Creation of a DataFrame using a dictionary of dictionaries\n", "\n", "dic = {'Employed' : {'CH':1, 'FR':4, 'DE':7},\n", " 'Unemployed' : {'CH':2, 'FR':5, 'DE':8},\n", " 'Inactive' : {'CH':3, 'FR':6, 'DE':9}}\n", "\n", "df = pd.DataFrame(dic, index = ['CH', 'FR', 'DE']) \n", "df" ] }, { "cell_type": "code", "execution_count": 96, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 142 }, "id": "tKib1CslqLca", "outputId": "bd01558a-2dcb-434a-d0d7-38b9ea94e664" }, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
EmployedUnemployedInactive
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" ], "text/plain": [ " Employed Unemployed Inactive\n", "CH 1 2 3\n", "FR 4 5 6\n", "DE 7 8 9" ] }, "execution_count": 96, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Creation of a DataFrame using a dictionary of lists\n", "\n", "dic = {'Employed' : [1, 4, 7],\n", " 'Unemployed' : [2, 5, 8],\n", " 'Inactive' : [3, 6, 9]}\n", "\n", "df = pd.DataFrame(dic, index = ['CH', 'FR', 'DE']) \n", "df" ] }, { "cell_type": "markdown", "metadata": { "id": "VxmJvXJM6Y6h" }, "source": [ "**Pandas functionalities**\n", "\n", "Some other useful functions for dataframes in pandas are:\n", "- `df['Employed']`: Selecting a column e.g. column 'Employed'\n", "- `df.iloc`: Accessing the data via index reference\n", "- `df.loc`: Accessing the data via label reference\n", "- `df.drop`: Dropping columns\n", "- `df.T`: Transposing the dataframe\n", "- `df.sort_index`: Sorting the data by axis\n", "- `df.sort_values`: Sorting by specific columns\n", "- `df.mean`: Calculating the mean row or column wise\n", "- `pd.merge`: Merging two dataframes\n", "- `df.append`: Appending dataframes to one another\n", "- `pd.concat`: Concatenation of two dataframes\n", "\n", "The [pandas documentation](https://pandas.pydata.org/pandas-docs/stable/overview.html) provides a very good description of what you can do with dataframes and if there is something that interests you beyond the application of this tutorial, it may very likely to be found there. Having introduced `pandas` as a useful library for data handling, we can start to put theory into practice. In the next step, we will be making queries to the REST API of Eurostat and as a response, we will receive a `pd.DataFrame`.\n", "\n", "\n", "## Technical toolkit\n", "\n", "The statistical office of the European Union offers the access of a data base through the REST API. One python package that enables to access this interface is the [`eurostat`](https://pypi.org/project/eurostat/) library. Its website provides useful documentation of the functionalities of the package. For the sake of this tutorial, we can use the `get_data_df` method by passing as arguments the filename of the dataset. \n", "\n", "The following cells showcase a structured approach to the data handling process. For the sake of a better readability of this notebook, we will insert descriptive comments in-line. The cell bellow calls the functions to store the datasets by specifying the filenames for the unemployment rates, the labour market transitions and the real GDP growth rates. As Eurostat flags missing values with a colon, we can pass the argument `flags=False` to replace those values with a `np.nan` object which in data science represents a common placeholder for missing datapoints. The function returns the corresponding datasets as `pd.DataFrame` and, as you will see, we will store them into the variables `udf`, `tdf` and `gdf`. " ] }, { "cell_type": "code", "execution_count": 97, "metadata": { "id": "DbWWwLFTbX2r" }, "outputs": [], "source": [ "udf = eurostat.get_data_df('lfsq_urgan', flags=False) # Unemployment rates\n", "tdf = eurostat.get_data_df('lfsi_long_q', flags=False) # Labour market transitions\n", "gdf = eurostat.get_data_df('tec00115', flags=False) # Real GDP growth rates\n", "\n", "# Job finding prob inputs\n", "age_UE = eurostat.get_data_df('lfsi_long_e01', flags=False) # Labour market transitions\n", "age_IE = eurostat.get_data_df('lfsi_long_e06', flags=False) # Labour market transitions\n", "age_U = eurostat.get_data_df('une_rt_a', flags=False) # Labour market transitions" ] }, { "cell_type": "markdown", "metadata": { "id": "EcuNAdCNbf0z" }, "source": [ "\n", "### Audit \n", "In order to make a first verfication of the datasets, we will perform an audit. The goal of our audit is to examine the data with regards to its structure and its content. Thereby we can identify how we have to clean and transform the datasets and we can anticipate if we will encounter issues by doing so. The `pandas` library provides us with useful methods to perform a first inspection. Below we will focus on inspecting the unemployment rates. We invite you to do the same with the other datasets." ] }, { "cell_type": "code", "execution_count": 98, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "bhV7DCkMcfbR", "outputId": "804accbd-821a-4891-9346-07002b6de852" }, "outputs": [ { "data": { "text/plain": [ "(34736, 97)" ] }, "execution_count": 98, "metadata": {}, "output_type": "execute_result" } ], "source": [ "udf.shape # Returns the shape: (rows, columns)" ] }, { "cell_type": "code", "execution_count": 99, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "8qft_hNccf9A", "outputId": "1ee6a275-d10e-48ed-c743-f7639c1f669f" }, "outputs": [ { "data": { "text/plain": [ "Index(['unit', 'sex', 'age', 'citizen', 'geo\\time', '2020Q4', '2020Q3',\n", " '2020Q2', '2020Q1', '2019Q4', '2019Q3', '2019Q2', '2019Q1', '2018Q4',\n", " '2018Q3', '2018Q2', '2018Q1', '2017Q4', '2017Q3', '2017Q2', '2017Q1',\n", " '2016Q4', '2016Q3', '2016Q2', '2016Q1', '2015Q4', '2015Q3', '2015Q2',\n", " '2015Q1', '2014Q4', '2014Q3', '2014Q2', '2014Q1', '2013Q4', '2013Q3',\n", " '2013Q2', '2013Q1', '2012Q4', '2012Q3', '2012Q2', '2012Q1', '2011Q4',\n", " '2011Q3', '2011Q2', '2011Q1', '2010Q4', '2010Q3', '2010Q2', '2010Q1',\n", " '2009Q4', '2009Q3', '2009Q2', '2009Q1', '2008Q4', '2008Q3', '2008Q2',\n", " '2008Q1', '2007Q4', '2007Q3', '2007Q2', '2007Q1', '2006Q4', '2006Q3',\n", " '2006Q2', '2006Q1', '2005Q4', '2005Q3', '2005Q2', '2005Q1', '2004Q4',\n", " '2004Q3', '2004Q2', '2004Q1', '2003Q4', '2003Q3', '2003Q2', '2003Q1',\n", " '2002Q4', '2002Q3', '2002Q2', '2002Q1', '2001Q4', '2001Q3', '2001Q2',\n", " '2001Q1', '2000Q4', '2000Q3', '2000Q2', '2000Q1', '1999Q4', '1999Q3',\n", " '1999Q2', '1999Q1', '1998Q4', '1998Q3', '1998Q2', '1998Q1'],\n", " dtype='object')" ] }, "execution_count": 99, "metadata": {}, "output_type": "execute_result" } ], "source": [ "udf.columns # Returns the column names" ] }, { "cell_type": "code", "execution_count": 100, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 578 }, "id": "dM7FJ-QocaVU", "outputId": "f0516611-ef0c-4129-c132-4bdd685595d4" }, "outputs": [ { "data": { "text/html": [ "
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NaN NaN NaN NaN NaN NaN NaN \n", "\n", " 1998Q3 1998Q2 1998Q1 \n", "34726 NaN NaN NaN \n", "34727 NaN NaN NaN \n", "34728 NaN NaN NaN \n", "34729 NaN NaN NaN \n", "34730 NaN NaN NaN \n", "34731 NaN NaN NaN \n", "34732 NaN NaN NaN \n", "34733 NaN NaN NaN \n", "34734 NaN NaN NaN \n", "34735 NaN NaN NaN \n", "\n", "[10 rows x 97 columns]" ] }, "execution_count": 101, "metadata": {}, "output_type": "execute_result" } ], "source": [ "udf.tail(10) # Returns last 10 rows" ] }, { "cell_type": "code", "execution_count": 102, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "QJpKVb4kcenr", "outputId": "f191e0a2-a5dd-41d8-f25f-1e76492e8efe" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "RangeIndex: 34736 entries, 0 to 34735\n", "Data columns (total 97 columns):\n", " # Column Non-Null Count Dtype \n", "--- ------ -------------- ----- \n", " 0 unit 34736 non-null object \n", " 1 sex 34736 non-null object \n", " 2 age 34736 non-null object \n", " 3 citizen 34736 non-null object \n", " 4 geo\\time 34736 non-null object \n", " 5 2020Q4 8424 non-null float64\n", " 6 2020Q3 8912 non-null float64\n", " 7 2020Q2 8686 non-null float64\n", " 8 2020Q1 8505 non-null float64\n", " 9 2019Q4 13764 non-null float64\n", " 10 2019Q3 13683 non-null float64\n", " 11 2019Q2 13790 non-null float64\n", " 12 2019Q1 13875 non-null float64\n", " 13 2018Q4 13883 non-null float64\n", " 14 2018Q3 13831 non-null float64\n", " 15 2018Q2 14077 non-null float64\n", " 16 2018Q1 14466 non-null float64\n", " 17 2017Q4 14236 non-null float64\n", " 18 2017Q3 14133 non-null float64\n", " 19 2017Q2 14219 non-null float64\n", " 20 2017Q1 14705 non-null float64\n", " 21 2016Q4 14560 non-null float64\n", " 22 2016Q3 14756 non-null float64\n", " 23 2016Q2 14833 non-null float64\n", " 24 2016Q1 14979 non-null float64\n", " 25 2015Q4 14703 non-null float64\n", " 26 2015Q3 14700 non-null float64\n", " 27 2015Q2 14966 non-null float64\n", " 28 2015Q1 15146 non-null float64\n", " 29 2014Q4 14678 non-null float64\n", " 30 2014Q3 14797 non-null float64\n", " 31 2014Q2 14889 non-null float64\n", " 32 2014Q1 15270 non-null float64\n", " 33 2013Q4 14976 non-null float64\n", " 34 2013Q3 14726 non-null float64\n", " 35 2013Q2 14954 non-null float64\n", " 36 2013Q1 15064 non-null float64\n", " 37 2012Q4 14623 non-null float64\n", " 38 2012Q3 14391 non-null float64\n", " 39 2012Q2 14526 non-null float64\n", " 40 2012Q1 14509 non-null float64\n", " 41 2011Q4 14251 non-null float64\n", " 42 2011Q3 13966 non-null float64\n", " 43 2011Q2 14231 non-null float64\n", " 44 2011Q1 14179 non-null float64\n", " 45 2010Q4 13900 non-null float64\n", " 46 2010Q3 13644 non-null float64\n", " 47 2010Q2 14001 non-null float64\n", " 48 2010Q1 13920 non-null float64\n", " 49 2009Q4 12927 non-null float64\n", " 50 2009Q3 12826 non-null float64\n", " 51 2009Q2 13350 non-null float64\n", " 52 2009Q1 12633 non-null float64\n", " 53 2008Q4 11780 non-null float64\n", " 54 2008Q3 11487 non-null float64\n", " 55 2008Q2 11965 non-null float64\n", " 56 2008Q1 11395 non-null float64\n", " 57 2007Q4 11001 non-null float64\n", " 58 2007Q3 11030 non-null float64\n", " 59 2007Q2 11710 non-null float64\n", " 60 2007Q1 11242 non-null float64\n", " 61 2006Q4 11464 non-null float64\n", " 62 2006Q3 11371 non-null float64\n", " 63 2006Q2 12033 non-null float64\n", " 64 2006Q1 11662 non-null float64\n", " 65 2005Q4 7806 non-null float64\n", " 66 2005Q3 7716 non-null float64\n", " 67 2005Q2 8231 non-null float64\n", " 68 2005Q1 7931 non-null float64\n", " 69 2004Q4 5839 non-null float64\n", " 70 2004Q3 5811 non-null float64\n", " 71 2004Q2 8329 non-null float64\n", " 72 2004Q1 5635 non-null float64\n", " 73 2003Q4 5519 non-null float64\n", " 74 2003Q3 5509 non-null float64\n", " 75 2003Q2 8232 non-null float64\n", " 76 2003Q1 5571 non-null float64\n", " 77 2002Q4 4998 non-null float64\n", " 78 2002Q3 4912 non-null float64\n", " 79 2002Q2 5908 non-null float64\n", " 80 2002Q1 5310 non-null float64\n", " 81 2001Q4 4603 non-null float64\n", " 82 2001Q3 4205 non-null float64\n", " 83 2001Q2 5482 non-null float64\n", " 84 2001Q1 4659 non-null float64\n", " 85 2000Q4 4274 non-null float64\n", " 86 2000Q3 3918 non-null float64\n", " 87 2000Q2 5660 non-null float64\n", " 88 2000Q1 4490 non-null float64\n", " 89 1999Q4 3337 non-null float64\n", " 90 1999Q3 3042 non-null float64\n", " 91 1999Q2 5186 non-null float64\n", " 92 1999Q1 3204 non-null float64\n", " 93 1998Q4 1796 non-null float64\n", " 94 1998Q3 1500 non-null float64\n", " 95 1998Q2 5192 non-null float64\n", " 96 1998Q1 2088 non-null float64\n", "dtypes: float64(92), object(5)\n", "memory usage: 25.7+ MB\n" ] } ], "source": [ "udf.info() # Prints the summary of the dataset" ] }, { "cell_type": "code", "execution_count": 103, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 346 }, "id": "X8ZpHnQzcduR", "outputId": "4a69f6bb-6b8a-40fc-8de1-899fdcb80313" }, "outputs": [ { "data": { "text/html": [ "
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count8424.0000008912.0000008686.0000008505.00000013764.00000013683.00000013790.00000013875.00000013883.00000013831.000000...5660.0000004490.0000003337.0000003042.0000005186.0000003204.0000001796.0000001500.0000005192.0000002088.000000
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8 rows × 92 columns

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" ], "text/plain": [ " 2020Q4 2020Q3 2020Q2 2020Q1 2019Q4 \\\n", "count 8424.000000 8912.000000 8686.000000 8505.000000 13764.000000 \n", "mean 10.930437 10.969895 10.178920 9.638013 9.865577 \n", "std 8.803644 8.662468 8.597697 7.922562 7.689105 \n", "min 0.900000 0.900000 1.000000 0.900000 0.800000 \n", "25% 5.200000 5.100000 4.600000 4.300000 4.700000 \n", "50% 8.200000 8.200000 7.200000 7.100000 7.400000 \n", "75% 13.700000 14.100000 12.800000 12.600000 12.900000 \n", "max 91.700000 77.100000 73.600000 65.700000 64.800000 \n", "\n", " 2019Q3 2019Q2 2019Q1 2018Q4 2018Q3 \\\n", "count 13683.000000 13790.000000 13875.000000 13883.000000 13831.000000 \n", "mean 9.747607 10.167817 10.958148 10.664446 10.379372 \n", "std 7.400749 7.602340 8.273551 8.028276 7.370775 \n", "min 0.600000 0.800000 0.600000 0.800000 0.800000 \n", "25% 4.700000 4.800000 5.400000 5.100000 5.100000 \n", "50% 7.300000 7.700000 8.400000 8.400000 8.300000 \n", "75% 12.700000 13.500000 14.000000 13.900000 13.700000 \n", "max 68.900000 74.300000 75.300000 75.500000 75.700000 \n", "\n", " ... 2000Q2 2000Q1 1999Q4 1999Q3 1999Q2 \\\n", "count ... 5660.000000 4490.000000 3337.000000 3042.000000 5186.000000 \n", "mean ... 9.710371 11.380356 10.869374 10.367949 10.251851 \n", "std ... 7.620672 8.546312 8.150764 8.235109 7.718976 \n", "min ... 0.400000 0.600000 0.500000 0.400000 0.300000 \n", "25% ... 4.300000 5.400000 5.300000 4.900000 4.900000 \n", "50% ... 7.400000 8.900000 8.300000 7.600000 8.000000 \n", "75% ... 13.500000 15.200000 14.000000 13.000000 13.400000 \n", "max ... 69.600000 84.100000 72.400000 70.500000 61.100000 \n", "\n", " 1999Q1 1998Q4 1998Q3 1998Q2 1998Q1 \n", "count 3204.000000 1796.000000 1500.000000 5192.000000 2088.000000 \n", "mean 12.671785 13.144710 13.532533 10.971206 14.074377 \n", "std 9.705334 8.964836 10.271037 8.557631 9.859074 \n", "min 0.600000 2.300000 1.500000 0.500000 2.600000 \n", "25% 6.100000 7.375000 6.300000 5.400000 6.675000 \n", "50% 9.300000 11.100000 10.150000 8.600000 10.800000 \n", "75% 15.825000 16.300000 17.200000 13.500000 18.925000 \n", "max 68.900000 70.200000 53.100000 62.500000 55.700000 \n", "\n", "[8 rows x 92 columns]" ] }, "execution_count": 103, "metadata": {}, "output_type": "execute_result" } ], "source": [ "udf.describe() # Returns descriptive statistics" ] }, { "cell_type": "markdown", "metadata": { "id": "EBa7Xs4FcmCo" }, "source": [ "\n", "### Cleaning \n", "We should have a clear picture now of how the datasets are structured (for the sake of this tutorial, we have limited the inspection on the unemployment rates). The second step is the data cleaning process. The data cleaning process is neccessary to prepare the datasets for their later visualization and analysis. Thereby, the goal is to fix or remove incorrect, corrupted or unneccessary data that we do not need for our purpose. In the following cells, we will make some changes on all datasets. Please read through carefully the following list that documents what changes are being made. To help you with your understanding and your learning process, please refer also to the in-line comments which should give you a short and clear explanation of what is being done exactly!\n", "\n", "**List of changes** \n", "\n", "| ID | Change | Pandas method used | Description |\n", "|-----------|----------------------------|-----------------------------------------------------------------|-------------------------------------------------------------------------------------------------------|\n", "| 1 | Rename columns | `pd.rename` | Rename the column 'geo\\time' to 'country' |\n", "| 2 | Replace string occurrences | `pd.Series.apply(lambda k: k.replace())` | Replace all `'country'`occurrences of 'EL' (Greece) and 'UK' (United Kingdom) with 'GR' and 'GB' |\n", "| 3 | Remove string occurrences | `df[~df['column'].str.contains('\\|'.join(list_of_occurrences))]`| Remove all observation whose `'country'` columns contains the string 'EU', 'EA', 'Malta' or 'Germany' |" ] }, { "cell_type": "code", "execution_count": 104, "metadata": { "id": "om59tLQ_cugq" }, "outputs": [], "source": [ "# 1. Rename columns: We rename the column 'geo\\time' to 'country' with the rename method\n", "# (A) Note to escape the '\\' character with a second '\\'\n", "# (B) Note to make changes inplace instead of just creating a copy of the dataframe\n", "\n", "udf.rename(columns={'geo\\\\time':'country'}, inplace=True)\n", "tdf.rename(columns={'geo\\\\time':'country'}, inplace=True) \n", "gdf.rename(columns={'geo\\\\time':'country'}, inplace=True)\n", "age_UE.rename(columns={'geo\\\\time':'country'}, inplace=True)\n", "age_IE.rename(columns={'geo\\\\time':'country'}, inplace=True)\n", "age_U.rename(columns={'geo\\\\time':'country'}, inplace=True)" ] }, { "cell_type": "code", "execution_count": 105, "metadata": { "id": "rZfRDoDGcviU" }, "outputs": [], "source": [ "# 2. Replace string occurences: We replace all 'country'occurrences of 'EL' (Greece) and 'UK' (United Kingdom) with 'GR' and 'GB'\n", "# (A) Note we use apply an specified function to the whole column (details on lambda functions coming)\n", "\n", "udf['country'] = udf['country'].apply(lambda k: k.replace('EL', 'GR').replace('UK', 'GB'))\n", "tdf['country'] = tdf['country'].apply(lambda k: k.replace('EL', 'GR').replace('UK', 'GB'))\n", "gdf['country'] = gdf['country'].apply(lambda k: k.replace('EL', 'GR').replace('UK', 'GB'))\n", "age_UE['country'] = age_UE['country'].apply(lambda k: k.replace('EL', 'GR').replace('UK', 'GB'))\n", "age_IE['country'] = age_IE['country'].apply(lambda k: k.replace('EL', 'GR').replace('UK', 'GB'))\n", "age_U['country'] = age_U['country'].apply(lambda k: k.replace('EL', 'GR').replace('UK', 'GB'))" ] }, { "cell_type": "markdown", "metadata": { "id": "4uPnOvN5dF7A" }, "source": [ "**Lambda functions** \n", "Lambda functions are so-called anonymous functions. In programming, anonymous functions is a function definition which is not stored into memory and therefore is not assigned to an identifier. The function is typically only used once, or a limited number of times. The main advantage of the lambda function for our practical purpose is that it is syntactically lighter than defining and using an indexed function. Nevertheless, they fulfill the same role as an indexed function." ] }, { "cell_type": "code", "execution_count": 106, "metadata": { "id": "aNLRfFZFdNSd" }, "outputs": [], "source": [ "# 3. Remove string occurrences: We remove all observation whose 'country' columns contains the string 'EU', 'EA', 'Malta' or 'Germany'\n", "# (A) Note that str.contains checks whether the expression in the brackets occurs (yielding a boolean series)\n", "# (B) Note that in the brackets, the individual strings are concatenated with a logical OR ('|'), this represents a regular expression\n", "# - More on how to construct regular expressions in python here: https://docs.python.org/3/howto/regex.html\n", "# (C) Note before applying the boolean mask to the dataframe, we negate with a logical NOT ('~')\n", "\n", "udf = udf[~udf['country'].str.contains('|'.join(['EU', 'EA', 'Malta', 'Germany']))]\n", "age_UE = age_UE[~age_UE['country'].str.contains('Malta')]\n", "age_U = age_U[~age_U['country'].str.contains('|'.join(['Malta', 'Germany', 'EA19', 'EU15', 'EU27_2020', 'FX', 'EU28', 'Luxembourg', 'Montenegro', 'Serbia']))]" ] }, { "cell_type": "markdown", "metadata": { "id": "XESdxb9jdRxF" }, "source": [ "\n", "### Transformation \n", "After having cleaned our datasets, our third step is to perform data transformation (we hereby introduce a new step in our data handling process). The data transformation process is differs from the data cleaning process, as that it maps data from one raw data format into another which is more useful for the analysis to be performed. In this section, we will prepare the datasets for our analysis. We thereby compute new datasets containing transition rates, job finding probabilities and steady state unemployment rates. If at any point in time, you lose the overview of what is actually being computed, we invite you to refer back to our secion [2. Theoretical framework](#theoretical_framework) which represents the fundament of our calculations. In order to help you with your learning process, we will refer to the relevant sections.\n", "\n", "**Some universal transformations** \n", "With regards to our datasets, Eurostat uses two-letter codes for country identifications. For our practical purposes, we want to substitute these country codes by full country names. To this end, the [`pycountry`](https://pypi.org/project/pycountry/) package provides useful functions to map country codes to their full names. The following change seems a little bit more complicated, but it follows the same logic of a lambda function that we have seen before." ] }, { "cell_type": "code", "execution_count": 107, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "4dt0eczSdU7b", "outputId": "3f9d2935-2b3d-474a-84a5-db84608f943a" }, "outputs": [], "source": [ "# With `pyc.countries.get(alpha_2 = k).name` we pass the two country codes as arguments and get the `name` property\n", "# (A) In case that the abbreviation doesnt match a code, we return the country code specified by Eurostat.\n", "\n", "udf['country'] = udf['country'].apply(lambda k: pyc.countries.get(alpha_2 = k).name if pyc.countries.get(alpha_2 = k) != None else k)\n", "tdf['country'] = tdf['country'].apply(lambda k: pyc.countries.get(alpha_2 = k).name if pyc.countries.get(alpha_2 = k) != None else k) \n", "gdf['country'] = gdf['country'].apply(lambda k: pyc.countries.get(alpha_2 = k).name if pyc.countries.get(alpha_2 = k) != None else k)\n", "age_UE['country'] = age_UE['country'].apply(lambda k: pyc.countries.get(alpha_2 = k).name if pyc.countries.get(alpha_2 = k) != None else k)\n", "age_IE['country'] = age_IE['country'].apply(lambda k: pyc.countries.get(alpha_2 = k).name if pyc.countries.get(alpha_2 = k) != None else k)\n", "age_U['country'] = age_U['country'].apply(lambda k: pyc.countries.get(alpha_2 = k).name if pyc.countries.get(alpha_2 = k) != None else k)" ] }, { "cell_type": "markdown", "metadata": { "id": "x5IixlJVeh84" }, "source": [ "**Calculation of transistion rates** \n", "The following code cells will compute datasets for all nine transition rates. In a first step, we will filter the absolute transitions, drop unneccessary columns and multiply all numbers by factor 1000 as we are dealing with numbers in thousands. " ] }, { "cell_type": "code", "execution_count": 108, "metadata": { "id": "V_xcWUSZR9HO" }, "outputs": [], "source": [ "tdf = tdf[(tdf['unit'] == 'THS_PER') & (tdf['s_adj'] == 'NSA') & (tdf['sex'] == 'T')] # Filtering relevant 'THS_PERC', 'NSA' and 'sex'\n", "tdf = tdf.drop(columns=['unit', 's_adj', 'sex']) # Dropping columns\n", "tdf = tdf.set_index(['indic_em', 'country']) # Setting the right indices ('indic_em' are labels for transitions)\n", "tdf = tdf.applymap(lambda k: k*1000) # Multiply all numbers by factor 1000" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In a second step, we iterate through the labels for each individual transition, and we filter the large dataset by this transition. This will give us the numbers for one particular transition across all countries. We store our results in a dictionary." ] }, { "cell_type": "code", "execution_count": 109, "metadata": { "id": "LXne4q70gDuK" }, "outputs": [], "source": [ "transitions = {}\n", "\n", "for tr in tdf.index.levels[0]:\n", " exec(f\"transitions_{tr} = tdf.loc['{tr}']\") # this is declaration of exec statements\n", " exec(f\"transitions_{tr} = transitions_{tr}.sort_index()\") # sort values by country\n", " exec(f\"transitions_{tr}.name = '{tr}'\") # put name as the tr , 'E_E', 'E_I' etc.\n", " transitions[tr] = eval(f\"transitions_{tr}\") # evaluate all the statements previously declared in string format" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "A short note on the [`exec()`](https://docs.python.org/3/library/functions.html#exec) function. This function executes the Python code block passed as a string or a code object as argument. The string is parsed as a Python statement and will then be executed by the interpreter. If you need more explanation on how this function works, we encourage you to visit [this website](https://docs.python.org/3/library/functions.html#exec).\n", "\n", "Having filtered all transitions separately, we can calulate all transition rates. At this point, we invite you to revisit section [2.2. Transition rates](#transition_rates) in case you would like to refresh you theoretical knowledge (Hint: Transition rates are calculated based on formula 4)." ] }, { "cell_type": "code", "execution_count": 110, "metadata": { "id": "xUGhIrxtgHr3" }, "outputs": [], "source": [ "transition_rates = {} # Initiate dictionary to store transition rates\n", "\n", "states = ['E', 'U', 'I'] # Define the labour market states\n", "\n", "for today in states: # Loop through each state\n", " \n", " stock = tdf.loc[tdf.index.levels[0][tdf.index.levels[0].str.startswith(today)]].groupby('country').sum().sort_values(by='country')\n", " # today_tomorrow -> E_E, E_U, E_I, ... a total of 9 different combinations\n", " for tomorrow in states:\n", " exec(f\"rate_{today}{tomorrow} = (eval(f'transitions_{today}_{tomorrow}')/stock).replace(np.inf, np.nan)\") # replace inf with nan\n", " exec(f\"rate_{today}{tomorrow} = eval('rate_{today}{tomorrow}').rolling(4, axis=1).mean()\") # rolling mean of each 4 columns starting from 1st in each row\n", " exec(f\"rate_{today}{tomorrow}= rate_{today}{tomorrow}[rate_{today}{tomorrow}.columns[::-1]]\") # reversing columns, 1st col becomes last column\n", " # Setting the normal index to periodic index for later timeseries or other purposes, e.g., plotting\n", " exec(f\"rate_{today}{tomorrow}.columns = pd.period_range(start=rate_{today}{tomorrow}.columns[0],end=rate_{today}{tomorrow}.columns[-1], freq='Q', name='Quarterly Frequency')\")\n", " # Assignign a name to the dataframe\n", " exec(f\"rate_{today}{tomorrow}.name = 'rate_{today}{tomorrow}'\")\n", " # Storage into dictionary\n", " exec(f\"transition_rates['rate_{today}{tomorrow}'] = eval(f'rate_{today}{tomorrow}')\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You have made it through the calculations of transition rates! Let's compute job finding probabilities now!" ] }, { "cell_type": "markdown", "metadata": { "id": "KyFmFLUDHsb7" }, "source": [ "**Calculation of job finding probabilities by age** \n", "The following code cells will compute datasets for job finding probabilities by age. At this point you will probably ask yourself why we would like to do this by age. As we have learned in the beginning of this tutorial, we can define a labour market by an age group of interest. The datasets that we will construct now will allow us to study age-related cross-section differences of job finding probabilites. In a first step, we want make some individual adjustment before we compute the probabilities. Please read carefully the in-line comments in order to understand what is happening exactly!" ] }, { "cell_type": "code", "execution_count": 111, "metadata": { "id": "MmTvk9SoHwuO" }, "outputs": [], "source": [ "# Filtering and dropping: We filter columns by the relevant values and drop unneccessary columns \n", "age_UE = age_UE[(age_UE['duration']=='TOTAL') & (age_UE['sex']=='T')].drop(columns=['duration', 'sex', 'unit'])\n", "age_IE = age_IE[(age_IE['indic_em']=='TOTAL') & (age_IE['sex']=='T')].drop(columns=['indic_em', 'sex', 'unit'])\n", "age_U = age_U[(age_U['unit']=='THS_PER') & (age_U['sex']=='T')].drop(columns=['sex', 'unit'])\n", "\n", "# Indexing: We set age and country indices to locate values more easily\n", "age_UE = age_UE.sort_values(by='country').set_index(['age', 'country'])\n", "age_IE = age_IE.sort_values(by='country').set_index(['age', 'country'])\n", "age_U = age_U.sort_values(by='country').set_index(['age', 'country'])\n", "\n", "# Transform: We multiply the age_U data values by 1000 as those are numbers expressed as thousands\n", "age_U = age_U.applymap(lambda k: k*1000)\n", "\n", "# Sorting: Sorting the daframes by indices\n", "age_UE = age_UE.sort_index(axis=1)\n", "age_IE = age_IE.sort_index(axis=1)\n", "age_U = age_U.sort_index(axis=1)\n", "\n", "# Date transformation: We transform the columns which are strings into datetime objects\n", "# For more information about this particular data object, we invite you to follow this website:\n", "# https://docs.python.org/3/library/datetime.html\n", "age_UE.columns = [x.strftime(format = '%Y') for x in pd.to_datetime(age_UE.columns.values, format='%Y')]\n", "age_IE.columns = [x.strftime(format = '%Y') for x in pd.to_datetime(age_IE.columns.values, format='%Y')]\n", "age_U.columns = [x.strftime(format = '%Y') for x in pd.to_datetime(age_U.columns.values, format='%Y')]\n", "\n", "# We define a period of interest that we would like to study\n", "# Note: We remove year 2017 as this column is missing in the dataframe\n", "period = list(range(2011, 2021))\n", "period.remove(2017)\n", "period = [datetime.datetime.strptime(str(x), '%Y') for x in period]\n", "period = [x.strftime('%Y') for x in period]\n", "\n", "# We filter datasets by the time interval which was specified\n", "age_UE = age_UE[period]\n", "age_IE = age_IE[period]\n", "age_U = age_U[period]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now that we have our datasets ready for the computation, we can apply our formula that we have learned in section [2.2. Transition rates](#transition_rates) (Hint: We are applying formula 5). " ] }, { "cell_type": "code", "execution_count": 112, "metadata": { "id": "MmTvk9SoHwuO" }, "outputs": [], "source": [ "# Initiate age groups with filter key\n", "age_groups_jf = {'young':'Y15-24', 'middle':'Y25-54', 'old':'Y55-74'}\n", "\n", "# Initiate a dictionary to store the results\n", "job_finding_probs = {}\n", "\n", "# Loop through each age group\n", "for age_group in age_groups_jf:\n", " \n", " # Applying formula 5\n", " job_finding_prob = (age_UE.loc[age_groups_jf[age_group]] + age_IE.loc[age_groups_jf[age_group]])/age_U.loc[age_groups_jf[age_group]]\n", " job_finding_prob.name = age_group\n", " # Storing results in dictionary\n", " job_finding_probs[age_group] = job_finding_prob" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Great! We have calculated job finding probabilities. Let's look at steady state unemployment rates now!" ] }, { "cell_type": "markdown", "metadata": { "id": "_O_edA4EgH5r" }, "source": [ "**Calculation of steady state unemployment rates** \n", "The following code cells will compute datasets for steady state unemployment rates. Again we will make individual adjustemnts first. Please read carefully the in-line comments in order to understand what is happening exactly!" ] }, { "cell_type": "code", "execution_count": 113, "metadata": { "id": "TVP-Gh4bgICN" }, "outputs": [], "source": [ "ssdf = udf[(udf['sex']=='T') & (udf['citizen']=='TOTAL') & (udf['age']=='Y15-74')].drop(columns = ['sex', 'age', 'citizen']).sort_values(by = 'country').set_index('country')\n", "ssdf = ssdf.rolling(4, axis=1).mean() # Seasonally adjust by taking the rolling mean over 4 quarters\n", "ssdf = ssdf[ssdf.columns[::-1]] # Make order of quarters chronological / reverse the order with 1998Q1 as first col\n", "ssdf.columns=pd.period_range(start=ssdf.columns[0], end=ssdf.columns[-1], freq=\"Q\", name=\"Quarterly Frequency\") #change datetype of quarters" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In a second step, we want to build dataframes for each individual country. We will store the measured unemployment rate and all transition rates into the dataframe. Further, we store each dataset in a dictionary called 'countries'" ] }, { "cell_type": "code", "execution_count": 114, "metadata": { "id": "j4o9mDqUgTWI" }, "outputs": [], "source": [ "# Inititiate dictionary\n", "countries = {}\n", "\n", "# saving data in dict format in countries\n", "for country in ssdf.index:\n", " \n", " df = pd.DataFrame() # create a temporary dataframe for each country\n", " df['Measured Unemployment Rate'] = ssdf.loc[country] # get the country specific all unemployment data and store in col 'Measured Unemployment rate'\n", " df.name = country\n", " \n", " for rate in transition_rates: # get all transition rates for that country in rate columns of df\n", " # this rate columns will start from rate_EE\t to rate_II, all the 9 rates which has been calculated before\n", " if country in transition_rates[rate].index:\n", " df[rate] = transition_rates[rate].loc[country]\n", " if country in transition_rates[rate].index:\n", " countries[country] = df # finally save the df as value of dictionary country" ] }, { "cell_type": "markdown", "metadata": { "id": "N0bDLhFKgOX1" }, "source": [ "Now we are set up for the calculation of the steady state unemployment rates. We invite you to revisit formula 8 in section [2.3. Steady state](#steady_state)." ] }, { "cell_type": "code", "execution_count": 115, "metadata": { "id": "2YW7GMRbgObw" }, "outputs": [], "source": [ "for country in countries:\n", " \n", " df = countries[country]\n", " # coming from the formula previously mentioned\n", " # Compute Steady State Unembployment Rate\n", " aux_E = df['rate_UI']*df['rate_IE'] + df['rate_IU']*df['rate_UE'] + df['rate_IE']*df['rate_UE']\n", " aux_U = df['rate_EI']*df['rate_IU'] + df['rate_IE']*df['rate_EU'] + df['rate_IU']*df['rate_EU']\n", "\n", " df['Steady State Unemployment Rate'] = (aux_U/(aux_E+aux_U))*100" ] }, { "cell_type": "markdown", "metadata": { "id": "C0eB-6W9dVuN" }, "source": [ "\n", "### Controlling \n", "Having transformed the data, we have set the stage for our analysis. Before we start, we have to ensure that the data cleaning and data transformation process was successful. In order to check our datasets, we will perform controlling tasks that check the datasets for their correctness. We follow two sperate methods. We control the data on a quantitative and on a qualitative (i.e. visual) manner. \n", "\n", "**Quantitative tests** \n", "For the quantitative tests, it will be useful to have a function which can be applied to multiple datasets. Therefore, we create a function that checks datasets for the following numerical attributes.\n", "\n", "- Relative numbers: Rates of all types must be numerical values within the interval [0, 1]\n", "- Absolute numbers: Absolute numbers must be numerical values which are non-negative\n", "\n", "In the following cell, we will construct a parametrizable function which will serve our purpose." ] }, { "cell_type": "code", "execution_count": 116, "metadata": { "id": "-GZjjpMzdo5y" }, "outputs": [], "source": [ "# Note: First any() reduces pd.DataFrame to a pd.Series (reduction along axis=1) of booleans, second any() reduces pd.Series to one single boolean\n", "\n", "def check(format, df):\n", " \n", " if format == 'relative':\n", " \n", " if (df > 1).any().any() or (df < 0).any().any(): # Check: Any values outside interval [0, 1]\n", " print(f'Attention: Dataframe {df.name} contains values outside the interval [0, 1]')\n", " return\n", " else:\n", " print(f'Dataframe {df.name} is free from invalid values')\n", " return\n", " \n", " if format == 'absolute':\n", " \n", " if (np.sign(df.iloc[:, 2:]) == -1).any().any(): # Check: Any value negative (np.sign returns -1, 0 and 1 for negative, null and positive values)\n", " print(f'Attention: Dataframe {df.name} contains negative values')\n", " return\n", " else:\n", " print(f'Dataframe {df.name} is free from invalid values')\n", " return" ] }, { "cell_type": "markdown", "metadata": { "id": "emnD371pGJv_" }, "source": [ "We check all absolute transitions for their values:" ] }, { "cell_type": "code", "execution_count": 117, "metadata": { "id": "u0pV4NbPHO2T" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Dataframe E_E is free from invalid values\n", "Dataframe E_I is free from invalid values\n", "Dataframe E_U is free from invalid values\n", "Dataframe I_E is free from invalid values\n", "Dataframe I_I is free from invalid values\n", "Dataframe I_U is free from invalid values\n", "Dataframe U_E is free from invalid values\n", "Dataframe U_I is free from invalid values\n", "Dataframe U_U is free from invalid values\n" ] } ], "source": [ "for df in transitions: \n", " check('absolute', transitions[df])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Next, we check all transition rates for their values:" ] }, { "cell_type": "code", "execution_count": 118, "metadata": { "id": "c38f4vwbHPPs" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Dataframe rate_EE is free from invalid values\n", "Dataframe rate_EU is free from invalid values\n", "Dataframe rate_EI is free from invalid values\n", "Dataframe rate_UE is free from invalid values\n", "Dataframe rate_UU is free from invalid values\n", "Dataframe rate_UI is free from invalid values\n", "Dataframe rate_IE is free from invalid values\n", "Dataframe rate_IU is free from invalid values\n", "Dataframe rate_II is free from invalid values\n" ] } ], "source": [ "for df in transition_rates:\n", " check('relative', transition_rates[df])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Finally, we check our job finding probabilities. Remember, probabilities are within the interval [0, 1]" ] }, { "cell_type": "code", "execution_count": 119, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Dataframe young is free from invalid values\n", "Dataframe middle is free from invalid values\n", "Dataframe old is free from invalid values\n" ] } ], "source": [ "for jfp in job_finding_probs:\n", " check('relative', job_finding_probs[jfp])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Great! Our datasets have passed all quantitative tests. Let's perform some visual tests now." ] }, { "cell_type": "markdown", "metadata": { "id": "Ov_v-FjbdqC6" }, "source": [ "**Visual tests** \n", "In the following, we will perform some basic plotting tests. The goal of those plottings is to mainly adress possible corruptions of our data. Additionally, this will give us some first inspiration for the data visualization section that will follow." ] }, { "cell_type": "code", "execution_count": 120, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 282 }, "id": "1rCIVGgUeeUm", "outputId": "fd8c897a-1bc0-4bb5-fec7-b5901eb1b066" }, "outputs": [ { "data": { "image/png": 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7/CS/vY5jABpZ2Xdw4c95ZP/edR8DAAAAwEZYz5W1T6iq26rq1qo6UFVPmBz/ziTnJLnu2IVjjHuTfCDJ09bx9QEAAAAAllaNMU79Sar+bZLTk9yS5Kwkr0ry3UmekuRJST6Y5DvGGJ+e+pnfSXLuGONZD/KclyW5LEnOPvvsCw4cOHDK41yko0ePZufOnRv2/B+97e4Ne26Y1/nnnvGAx2v9d7nW9bPn53nOtV5jGWz0fAFsHeYLYF7mC2Be5gvYeHv27Dk8xth1vHPrEmu/4Umrdib5ZJL9Sf4sq7H28WOMz0xd8z+SPG6M8W/Wer5du3aNm266ad3HuZkOHTqU3bt3b9jzb8Q/N4cTNbvlwFr/Xa51/fG2MNgO2yBs9HwBbB3mC2Be5gtgXuYL2HhV9aCxdj23QfiaMcbRJH+d5IlJju1je87MZWcluWMjXh8AAAAAYNlsSKytqkdmdRuEzyW5NavB9pKZ8xcluXEjXh8AAAAAYNnsWI8nqarfTHJtkk9ndcXsLyR5dJK3jTFGVV2d5JVVdUuSj2d1T9ujSd6xHq8PAAAAALDs1iXWJjkvyf9McmaSz2d1n9ofGGN8anL+N5I8Kskbkzw2yYeSPHOM8aV1en0AAAAAgKW2LrF2jPHcNc6PJFdOvgAAAAAAmLEhe9YCAAAAAHBi1msbBIB1t7Lv4KKHAAAAALBprKwFAAAAAGhArAUAAAAAaECsBQAAAABoQKwFAAAAAGhArAUAAAAAaECsBQAAAABoQKwFAAAAAGhArAUAAAAAaECsBQAAAABoYMeiBwBsHSv7Di56CAAAAABLy8paAAAAAIAGxFoAAAAAgAbEWgAAAACABsRaAAAAAIAG3GAMWBg3JAMAAAD4OitrAQAAAAAaEGsBAAAAABoQawEAAAAAGrBnLbClze6Le2T/3gWN5OQs+/gBAACA+VlZCwAAAADQgFgLAAAAANCAbRCAbcW2AgAAAEBXVtYCAAAAADRgZS3AAlnpCwAAABxjZS0AAAAAQANiLQAAAABAA2ItAAAAAEADYi0AAAAAQANuMAawjtwwDAAAADhZVtYCAAAAADQg1gIAAAAANCDWAgAAAAA0INYCAAAAADQg1gIAAAAANCDWAgAAAAA0INYCAAAAADSwY9EDANhOVvYdXPQQAAAAgKasrAUAAAAAaMDKWmBbm13pemT/3gWNBAAAANjurKwFAAAAAGhArAUAAAAAaMA2CEvKTYqA9WIrCAAAAOjByloAAAAAgAasrAVoZGXfwbzs/PtyqdXzAAAAsO1YWQsAAAAA0IBYCwAAAADQgG0QAKa42RYAAACwKFbWAgAAAAA0YGUtwEOYXWkLAAAAsFGsrAUAAAAAaECsBQAAAABowDYIABvINgoAAADAvKysBQAAAABowMpagCVyvJW6R/bvXcBIAAAAgPVmZS0AAAAAQANiLQAAAABAA7ZBANjiTvQmZ7PXz26zsNZ5AAAA4ORYWQsAAAAA0ICVtQBLzkpXAAAA2BqsrAUAAAAAaECsBQAAAABowDYIAGyoZdimYRnGCAAAwNZnZS0AAAAAQANW1gKwrmZXqQIAAADzsbIWAAAAAKABsRYAAAAAoAGxFgAAAACgAbEWAAAAAKABsRYAAAAAoAGxFgAAAACgAbEWAAAAAKCBHYseAADra2XfwUUPYenNfoZH9u9d1+sBAADgeKysBQAAAABowMpaAJbKeqxitfoYAACAjqysBQAAAABoQKwFAAAAAGjANggALNRG3Jxru9/w63jbPGy3zwAAAGAZWVkLAAAAANCAlbVNbfdVYcDyWIabda33GJfhPa/F/88AAAD0Y2UtAAAAAEADYi0AAAAAQAO2QQCgFf88fzk+g2UYIwAAwLKxshYAAAAAoAErawF4SFvhZlpbjVWtAAAAW5OVtQAAAAAADYi1AAAAAAAN2AZhSfhnyMB2Zf7bHLZWWJvPCAAA2GhW1gIAAAAANGBlLQCbahlXyi7jmJfdVljFuhXeAwAAsLmsrAUAAAAAaECsBQAAAABowDYIACy1jlsUrDWm2X8OvxnvoePn1M1Gb1tgWwQAAGAtVtYCAAAAADRgZS0ANLeIVbEb/ZpWmZ66lX0H87Lz78ulk8/SZwgAAMvPyloAAAAAgAbEWgAAAACABmyDAACbbL23GFiGm4ctwxhPlK0c1uYzAgCAE2NlLQAAAABAA1bWAgCwlNZ75a6VwAAALJqVtQAAAAAADYi1AAAAAAAN2AYBAFh3W/GGYmvZju8ZAABYX1bWAgAAAAA0YGUtAHDKNnpV6VrPfzI3grISFgAA6MbKWgAAAACABsRaAAAAAIAGbIMAACzcqW5JMPvzJ7MtwmY70fe81ntaxs/gRK31ma33Z3C819uKnytb38q+g3nZ+ffl0sl/0/47BoC+rKwFAAAAAGjAyloAgCWwiJu4bfTqu41eDbzW82/EZ7psK5zXY7zL9p43g89k8fweALCsrKwFAAAAAGhArAUAAAAAaMA2CADAlrfRWwgsg2X8J8Fb8fdts38fluH3fRnGCMvoVP+35X+bAIux6Strq+ryqrq1qr5cVYer6qLNHgMAAAAAQDeburK2qn40yeuSXJ7kTyff/6iqvmeM8enNHAsAsHWtx4rMrbiq80St9w261rp+GT7zE11pdqrveaNvunai5+cZz3qv5ltrDFYrb7zt+J5nLcMq1VOdk5fhfzvL/t/iso9/PfgM1rYdPqPt8B5PxWavrP25JG8dY7x5jPGxMcYVST6X5D9u8jgAAAAAAFrZtFhbVd+U5IIk182cui7J0zZrHAAAAAAAHdUYY3NeqOrbk9yW5OljjA9MHf/FJC8YYzxp5vrLklw2efikJH+7KQPdOGcmuWvRgwCWgvkCmJf5ApiX+QKYl/kCNt53jDG+7XgnNnXP2onZOlzHOZYxxjVJrtmUEW2CqrppjLFr0eMA+jNfAPMyXwDzMl8A8zJfwGJt5p61dyW5P8k5M8fPSnLHJo4DAAAAAKCdTYu1Y4x/TnI4ySUzpy5JcuNmjQMAAAAAoKPN3gbhqiS/W1V/nuSDSX4yybcn+a1NHscibJktHYANZ74A5mW+AOZlvgDmZb6ABdq0G4x97QWrLk/y8iSPS3JzkpdO33AMAAAAAGA72vRYCwAAAADAN9rMG4wBAAAAAPAgxFoAAAAAgAbE2g1WVZdX1a1V9eWqOlxVFy16TMBiVdUrqurDVfWPVfX5qrq2qr535pqqqiur6rNVdW9VHaqqpyxqzEAPVfXzVTWq6g1Tx8wXQJKkqh5XVW+b/Pniy1X1N1X19Knz5gsgVXVaVf3yVKu4tap+pap2TF1jvoAFEWs3UFX9aJLXJfm1JN+f5MYkf1RVj1/owIBF253kTUmeluTiJPcluaGqvmXqmpcneVmSK5I8NcmdSa6vqtM3d6hAF1X1A0l+PMlHZk6ZL4BU1Tcn+WCSSrI3yZOzOi/cOXWZ+QJIkv+a5D8l+ekk353kZyaPXzF1jfkCFsQNxjZQVX0oyUfGGD8+dewTSX5/jPGKB/9JYDupqp1J7k7y7DHGtVVVST6b5A1jjF+dXPOorP4B6T+PMX57caMFFqGqzkjyF1mNtb+Y5OYxxk+ZL4BjqurXkjx9jPFDD3LefAEkSarqPUm+MMZ48dSxtyX51jHGvzNfwGJZWbtBquqbklyQ5LqZU9dldTUdwDGnZ3U+/uLk8XcmOSdT88cY494kH4j5A7ara7L6l73vnzluvgCOeXaSD1XVO6vqzqr6q6o69pc6ifkC+Lo/TbKnqr47Sarqe7L6L/7eOzlvvoAF2rH2JZykM5OcluSOmeN3JHnG5g8HaOx1Sf4qyf+ZPD5n8v1488e5mzQmoImq+vEk35Xkhcc5bb4AjnlCksuTvDbJ/iTfl+T1k3NviPkC+Lr/ntUFI39TVfdntQ396hjjTZPz5gtYILF2483uM1HHOQZsU1V1VZILk1w4xrh/5rT5A7a5qnpSVve+v2iM8c8Pcan5AnhYkpumtlv7y6p6Ylb3oXzD1HXmC+BHk7woyfOT/HVW/3LndVV16xjjLVPXmS9gAWyDsHHuSnJ/vv43UseclW/82ylgG6qq1yZ5XpKLxxifnDp1++S7+QP4waz+a52bq+q+qrovydOTXD759Rcm15kvgM8l+ZuZYx9Lcuzmxv58ARzzmiS/OcY4MMb46Bjjd5Ncla/fYMx8AQsk1m6QyeqXw0kumTl1SZIbN39EQCdV9bqs/k32xWOMW2ZO35rVPyBdMnX9I5NcFPMHbDf/K8n5WV3xcuzrpiQHJr/+eMwXwKoPJnnSzLF/meRTk1/78wVwzL/I6uKyaffn643IfAELZBuEjXVVkt+tqj/P6h+efjLJtyf5rYWOClioqnpjVveefHaSL1bVsb+xPjrGODrGGFV1dZJXVtUtWY0xr0pyNMk7FjBkYEHGGP+Q5B+mj1XVPUn+foxx8+Tx1TFfAKt71d5YVa9M8s4k35/kp5P8fJL48wUw5dok+6rq1qxug/D9SX4uydsT8wUsmli7gcYY76yqb83qpPa4JDcn+ZExxqce+ieBLe7yyff3zRx/dZIrJ7/+jSSPSvLGJI9N8qEkzxxjfGkzBggsFfMFkDHGh6vq2Vnd5/oXknx68v1NU5eZL4AkuSLJL2d1fjgrq9uovDnJL01dY76ABakx7A0NAAAAALBo9qwFAAAAAGhArAUAAAAAaECsBQAAAABoQKwFAAAAAGhArAUAAAAAaECsBQAAAABoQKwFAAAAAGhArAUAAAAAaOD/A9lAnd6Egh4dAAAAAElFTkSuQmCC\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "udf['2020Q4'].hist(bins = 300); # Histogram to check the unemployment rates in 2020Q4" ] }, { "cell_type": "markdown", "metadata": { "id": "CJ5s6775egx3" }, "source": [ "Distribution is right skewed, with positive values only. This corresponds to the values we are expecting to see with unemployment rates." ] }, { "cell_type": "code", "execution_count": 121, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 282 }, "id": "GNiUQHG-ehXh", "outputId": "0a4c6b3d-a4b9-4e48-a392-4370f5c81080" }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "transitions['E_E']['2020Q4'].plot.bar(); # Barplot to check the transitions in 2020Q4" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Absolute transitions seem to be positive, let's do a final check!" ] }, { "cell_type": "code", "execution_count": 122, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 279 }, "id": "BKWYKYuZeqZ-", "outputId": "125613a5-2d1d-4090-8515-7e57e73343a8" }, "outputs": [ { "data": { "image/png": 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "transition_rates['rate_EE']['2019Q4'].plot.area(ylim = (-1,2)); # Areaplot to check transition rates in 2019Q4" ] }, { "cell_type": "markdown", "metadata": { "id": "Qwq_ZkTfOP4d" }, "source": [ "Perfect! It seems that transition rates are within the interval [0, 1]. For now, we can be sure that our computations were performed correctly. As we have learned how to handle real-world data in an appropriate and correct manner, we will now further built upon our data handling skills as we need to prepare our datasets for the regressions. You will have now acquired a technical toolkit that will serve you as we will prepare the datasets for our statistical analysis.\n", "\n", "\n", "## Regression datasets\n", "\n", "The following section prepares the datasets for our statistical analysis. At this point, you probably ask yourself what the substance of our analysis will be. As we have learned, we can define our labour market in terms of segments based on geographical differences as well as differences accross age groups and education. We want to explore those cross-sectional differences. Hence, we will prepare three different datasets on which we will perform simple linear OLS regressions. For our first regression, we regress GDP growth on unemployment rates of different age groups. In our second regression, we regress the same GDP growth on unemployment rates of different educational attainment levels. In our third regression, we regress the job finding probability of different age groups on the country-level GDP. Please note: During this section you will probably ask yourself, why we transform the datasets the way we do it. This will certainly become clearer when you wen through section [6. Statistical analysis](#statistical_analysis). But for methodological purposes we would like to bring this task forward into the data handling section.\n", "\n", "\n", "### GDP growth on unemployment rates of age groups\n", "\n", "**Data import** \n", "With the Eurostat API discussed in section [4.2. Technical toolkit](#technical_toolkit), we can import the GDP growth data:" ] }, { "cell_type": "code", "execution_count": 123, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 419 }, "id": "lyK8wAGguGfV", "outputId": "2996bd60-f779-4afd-dab2-b0f712d9f462" }, "outputs": [ { "data": { "text/html": [ "
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unitna_itemgeo\\time200920102011201220132014201520162017201820192020
0CLV_PCH_PREB1GQAT-3.81.82.90.70.00.71.02.02.42.61.4-6.6
1CLV_PCH_PREB1GQBA-3.00.91.0-0.82.31.23.13.13.23.72.8NaN
2CLV_PCH_PREB1GQBE-2.02.91.70.70.51.62.01.31.61.81.8-6.3
3CLV_PCH_PREB1GQBG-3.40.62.40.40.31.94.03.83.53.13.7-4.2
4CLV_PCH_PREB1GQCH-2.13.31.91.21.82.41.72.01.63.01.1-2.9
................................................
75CLV_PCH_PRE_HABB1GQSE-5.25.12.4-1.30.31.63.40.81.20.80.3-3.5
76CLV_PCH_PRE_HABB1GQSI-8.41.00.7-2.8-1.22.72.13.14.74.12.3-6.2
77CLV_PCH_PRE_HABB1GQSK-5.75.63.51.70.52.54.72.02.83.52.4-4.9
78CLV_PCH_PRE_HABB1GQTR-6.16.89.63.57.13.54.71.96.11.6-0.50.8
79CLV_PCH_PRE_HABB1GQUK-4.81.30.40.81.52.11.60.91.10.60.8NaN
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80 rows × 15 columns

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" ], "text/plain": [ " unit na_item geo\\time 2009 2010 2011 2012 2013 2014 \\\n", "0 CLV_PCH_PRE B1GQ AT -3.8 1.8 2.9 0.7 0.0 0.7 \n", "1 CLV_PCH_PRE B1GQ BA -3.0 0.9 1.0 -0.8 2.3 1.2 \n", "2 CLV_PCH_PRE B1GQ BE -2.0 2.9 1.7 0.7 0.5 1.6 \n", "3 CLV_PCH_PRE B1GQ BG -3.4 0.6 2.4 0.4 0.3 1.9 \n", "4 CLV_PCH_PRE B1GQ CH -2.1 3.3 1.9 1.2 1.8 2.4 \n", ".. ... ... ... ... ... ... ... ... ... \n", "75 CLV_PCH_PRE_HAB B1GQ SE -5.2 5.1 2.4 -1.3 0.3 1.6 \n", "76 CLV_PCH_PRE_HAB B1GQ SI -8.4 1.0 0.7 -2.8 -1.2 2.7 \n", "77 CLV_PCH_PRE_HAB B1GQ SK -5.7 5.6 3.5 1.7 0.5 2.5 \n", "78 CLV_PCH_PRE_HAB B1GQ TR -6.1 6.8 9.6 3.5 7.1 3.5 \n", "79 CLV_PCH_PRE_HAB B1GQ UK -4.8 1.3 0.4 0.8 1.5 2.1 \n", "\n", " 2015 2016 2017 2018 2019 2020 \n", "0 1.0 2.0 2.4 2.6 1.4 -6.6 \n", "1 3.1 3.1 3.2 3.7 2.8 NaN \n", "2 2.0 1.3 1.6 1.8 1.8 -6.3 \n", "3 4.0 3.8 3.5 3.1 3.7 -4.2 \n", "4 1.7 2.0 1.6 3.0 1.1 -2.9 \n", ".. ... ... ... ... ... ... \n", "75 3.4 0.8 1.2 0.8 0.3 -3.5 \n", "76 2.1 3.1 4.7 4.1 2.3 -6.2 \n", "77 4.7 2.0 2.8 3.5 2.4 -4.9 \n", "78 4.7 1.9 6.1 1.6 -0.5 0.8 \n", "79 1.6 0.9 1.1 0.6 0.8 NaN \n", "\n", "[80 rows x 15 columns]" ] }, "execution_count": 123, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Import the GDP-Growth data with the Eurostat API \n", "gdf = eurostat.get_data_df('tec00115', flags=False)\n", "# Print the imported dataframe\n", "gdf" ] }, { "cell_type": "markdown", "metadata": { "id": "CJM8AQkT9IeB" }, "source": [ "**Data transformation** \n", "To run the regression, we need to have the average values of GDP-Growth for each country. For this, we firstly need to only keep the rows that have the unit *CLV_PCH_PRE*, which means only the values which show the percentage change from last year. We then transform the dataframe and finally, we will caluclate the mean GDP-Growth values for each country:" ] }, { "cell_type": "code", "execution_count": 124, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 1000 }, "id": "i_xz86WouPe8", "outputId": "fc928e1c-6a04-4344-ebe0-4b9f69063a45" }, "outputs": [ { "data": { "text/html": [ "
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Average GDP Growth
country
Austria0.425000
Bosnia and Herzegovina1.590909
Belgium0.633333
Bulgaria1.341667
Switzerland1.250000
Cyprus0.550000
Czechia1.166667
Germany0.733333
Denmark0.966667
EA0.233333
EA190.225000
Estonia1.675000
Greece-2.775000
Spain-0.333333
EU27_20200.441667
EU281.072727
Finland0.100000
France0.233333
Croatia-0.408333
Hungary1.350000
Ireland5.083333
Iceland1.191667
Italy-0.966667
Lithuania1.658333
Luxembourg2.116667
Latvia0.633333
Montenegro1.880000
North Macedonia1.725000
Malta3.966667
Netherlands0.583333
Norway1.025000
Poland3.050000
Portugal-0.191667
Romania1.800000
Serbia1.291667
Sweden1.500000
Slovenia0.516667
Slovakia1.641667
Turkey4.633333
United Kingdom1.300000
XK2.975000
\n", "
" ], "text/plain": [ " Average GDP Growth\n", "country \n", "Austria 0.425000\n", "Bosnia and Herzegovina 1.590909\n", "Belgium 0.633333\n", "Bulgaria 1.341667\n", "Switzerland 1.250000\n", "Cyprus 0.550000\n", "Czechia 1.166667\n", "Germany 0.733333\n", "Denmark 0.966667\n", "EA 0.233333\n", "EA19 0.225000\n", "Estonia 1.675000\n", "Greece -2.775000\n", "Spain -0.333333\n", "EU27_2020 0.441667\n", "EU28 1.072727\n", "Finland 0.100000\n", "France 0.233333\n", "Croatia -0.408333\n", "Hungary 1.350000\n", "Ireland 5.083333\n", "Iceland 1.191667\n", "Italy -0.966667\n", "Lithuania 1.658333\n", "Luxembourg 2.116667\n", "Latvia 0.633333\n", "Montenegro 1.880000\n", "North Macedonia 1.725000\n", "Malta 3.966667\n", "Netherlands 0.583333\n", "Norway 1.025000\n", "Poland 3.050000\n", "Portugal -0.191667\n", "Romania 1.800000\n", "Serbia 1.291667\n", "Sweden 1.500000\n", "Slovenia 0.516667\n", "Slovakia 1.641667\n", "Turkey 4.633333\n", "United Kingdom 1.300000\n", "XK 2.975000" ] }, "execution_count": 124, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Only keep the rows with the right unit (percentage change from last year)\n", "gdf = gdf[gdf['unit']=='CLV_PCH_PRE']\n", "\n", "# Rename the geo\\\\time column to country\n", "gdf = gdf.rename(columns={'geo\\\\time':'country'})\n", "\n", "# Change the country values EL and UK to GR and GB\n", "gdf['country'] = gdf['country'].apply(lambda k: k.replace('EL', 'GR').replace('UK', 'GB'))\n", "\n", "# Drop the unneeded column of unit and na_item\n", "gdf = gdf.drop(columns=['unit', 'na_item'])\n", "\n", "# Change the country codes to country names\n", "gdf['country'] = gdf['country'].apply(lambda k: pyc.countries.get(alpha_2 = k).name if pyc.countries.get(alpha_2 = k) != None else k) \n", "\n", "# Set the country names as index\n", "gdf = gdf.set_index(\"country\")\n", "\n", "# Transpose the dataframe to have the countries as column names and years as index\n", "gdf = gdf.T\n", "\n", "# Create pandas series object with all GDP Growth averages for each country\n", "gdp_on_ur = gdf.apply(pd.to_numeric, errors='coerce').mean(axis=0)\n", "\n", "# Convert panda series object to dataframe\n", "gdp_on_ur = gdp_on_ur.to_frame()\n", "\n", "# Rename the column from \"0\" to \"Average GDP Groth\"\n", "gdp_on_ur.columns = [\"Average GDP Growth\"]\n", "gdp_on_ur" ] }, { "cell_type": "markdown", "metadata": { "id": "a5VuxnkFnJZV" }, "source": [ "\n", "With this we now have a dataframe of the dependent variable. Next, we need to create dataframes of the independent variables. For this, we will need the total average unemployment rate and the average unemployment rates of the 15-39 and 40-64 year olds respectively. We store the segmented datasets in a dictionary called 'GDPxURxA_dic'." ] }, { "cell_type": "code", "execution_count": 125, "metadata": { "id": "wkIqwUTfBfac" }, "outputs": [], "source": [ "# Create List with all Age groups\n", "age_groups = {'total': 'Total', 'young': 'Y15-39', 'old': 'Y40-64'}\n", "\n", "# Create variable \"period\" with the timeperiods for which we want to calculate the mean unemployment rates (based on the available GDP-Growth data)\n", "period = pd.period_range(start='2009Q1',end='2019Q4', freq='Q', name='Quarterly Frequency')\n", "\n", "# Create dictionary to store dataframes\n", "GDPxURxA_dic = {}\n", "\n", "# Run for-loop to create all unemployment dataframes\n", "for age_group in age_groups: # Go through every age group\n", "\n", " aux_df = pd.DataFrame() # Create auxilary dataframe\n", " \n", " if age_group == 'total':\n", " aux_df['Measured Unemployment Rate'] = ssdf[period].mean(axis=1) # Calculate mean unemployment rate for total population\n", " \n", " else: \n", " M_UR = udf[(udf['sex']=='T') & (udf['citizen']=='TOTAL') & (udf['age']==age_groups[age_group])].drop(columns = ['sex', 'age', 'citizen', 'unit']).sort_values(by = 'country').set_index('country') #Select unemployment data for young or old age group\n", " M_UR = M_UR.sort_index(axis=1) # Make order of quarters chronological\n", " M_UR.columns = pd.period_range(start = M_UR.columns[0], end=M_UR.columns[-1], freq=\"Q\", name=\"Quarterly Frequency\") # change datetype of quarters\n", " aux_df[\"Measured Unemployment Rate\"] = M_UR[period].mean(axis=1) # Calculate Mean unemployment rate for age group and store in axuliary dataframe\n", " \n", " exec(f\"UR_{age_group} = aux_df\") # Name the respective dataframes\n", " exec(f\"UR_{age_group}.name='{age_group}'\") # Name the respective dataframes\n", "\n", " exec(f\"GDPxURxA_dic['{age_group}'] = UR_{age_group}\") # Store dataframe in dictionary" ] }, { "cell_type": "markdown", "metadata": { "id": "riHScx46LN-7" }, "source": [ "Now that we have dataframes of both the dependent and independent variables, we can merge them together into one dataframe. This can easily be done with the [`pd.merge`](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.merge.html) function from the `pandas` library. We will store our regression datasets in a dictionary called 'GDPxURxA_regdic'." ] }, { "cell_type": "code", "execution_count": 126, "metadata": { "id": "vwbhLcezI5h7" }, "outputs": [], "source": [ "# Create empty dictionary to store all dataframes\n", "GDPxURxA_regdic = {}\n", "\n", "# Iterate through unemployment dictionary generated previously to create dataframes\n", "for age_group in GDPxURxA_dic:\n", " \n", " aux_df = pd.merge(GDPxURxA_dic[age_group], gdp_on_ur, on='country', how='outer').dropna() # Merge UR dataframe with GDP-Growth dataframe and drop all rows which contain NAN values\n", " aux_df.columns = [\"Measured Unemployment Rate\", \"GDP Growth\"] # Rename the columns\n", " GDPxURxA_regdic[age_group] = aux_df # Save the dataframes into the dictionary" ] }, { "cell_type": "markdown", "metadata": { "id": "Bvc-qODLSx6d" }, "source": [ "Well done! We have our first dataset ready. Let's move on!\n", "\n", "### GDP growth and unemployment rates of educational attainment levels\n", "\n", "**Data import** \n", "With the Eurostat API discussed in section [4.2. Technical toolkit](#technical_toolkit), it is very easy to import the data on the unemployment rate for different educational attainment levels:" ] }, { "cell_type": "code", "execution_count": 127, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 609 }, "id": "oldLv1rYH-Ow", "outputId": "09eb8b28-3b86-4f1d-c7d8-2043cabce12d" }, "outputs": [ { "data": { "text/html": [ "
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15583 rows × 97 columns

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" ], "text/plain": [ " unit sex age isced11 geo\\time 2020Q4 2020Q3 2020Q2 2020Q1 \\\n", "0 PC F Y15-19 ED0-2 AT NaN 15.5 13.3 NaN \n", "1 PC F Y15-19 ED0-2 BE NaN 33.0 25.4 NaN \n", "2 PC F Y15-19 ED0-2 BG NaN NaN NaN NaN \n", "3 PC F Y15-19 ED0-2 CH 7.5 15.2 5.6 4.2 \n", "4 PC F Y15-19 ED0-2 CY NaN NaN NaN NaN \n", "... ... .. ... ... ... ... ... ... ... \n", "15578 PC T Y65-69 TOTAL SE NaN NaN NaN NaN \n", "15579 PC T Y65-69 TOTAL SI NaN NaN NaN NaN \n", "15580 PC T Y65-69 TOTAL SK NaN NaN NaN NaN \n", "15581 PC T Y65-69 TOTAL TR 3.7 4.5 3.7 3.3 \n", "15582 PC T Y65-69 TOTAL UK NaN 2.6 1.7 2.7 \n", "\n", " 2019Q4 ... 2000Q2 2000Q1 1999Q4 1999Q3 1999Q2 1999Q1 1998Q4 \\\n", "0 NaN ... NaN 8.9 NaN 8.6 8.4 9.2 NaN \n", "1 29.6 ... 34.6 NaN NaN 32.1 NaN 38.4 NaN \n", "2 NaN ... 49.4 64.7 NaN NaN NaN NaN NaN \n", "3 3.9 ... 4.6 NaN NaN NaN 6.0 NaN NaN \n", "4 NaN ... NaN NaN NaN NaN NaN NaN NaN \n", "... ... ... ... ... ... ... ... ... ... \n", "15578 NaN ... NaN NaN NaN NaN NaN NaN NaN \n", "15579 NaN ... NaN NaN NaN NaN NaN NaN NaN \n", "15580 NaN ... NaN NaN NaN NaN NaN NaN NaN \n", "15581 3.7 ... NaN NaN NaN NaN NaN NaN NaN \n", "15582 2.7 ... NaN NaN NaN 3.3 3.3 NaN NaN \n", "\n", " 1998Q3 1998Q2 1998Q1 \n", "0 NaN NaN 16.2 \n", "1 NaN 39.2 NaN \n", "2 NaN NaN NaN \n", "3 NaN 5.6 NaN \n", "4 NaN NaN NaN \n", "... ... ... ... \n", "15578 NaN NaN NaN \n", "15579 NaN NaN NaN \n", "15580 NaN NaN NaN \n", "15581 NaN NaN NaN \n", "15582 NaN NaN NaN \n", "\n", "[15583 rows x 97 columns]" ] }, "execution_count": 127, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Import the educational attainment level data with the Eurostat library\n", "edf = eurostat.get_data_df('lfsq_urgaed', flags=False)\n", "\n", "# Print the dataframe\n", "edf" ] }, { "cell_type": "markdown", "metadata": { "id": "U4xVQG9N66HQ" }, "source": [ "To understand what the different values of a column mean, the `eurostat` API offers the `get_sdmx_dic` fuction. For a given dataframe and column, the function returns a dictonary that lists all the values of that column and their respective meaning:" ] }, { "cell_type": "code", "execution_count": 128, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "Bv8Dc9zqIDWa", "outputId": "6fcf9a34-8ff8-4318-cce1-f16de6ebf2ed" }, "outputs": [ { "data": { "text/plain": [ "{'ED0-2': 'Less than primary, primary and lower secondary education (levels 0-2)',\n", " 'ED3_4': 'Upper secondary and post-secondary non-tertiary education (levels 3 and 4)',\n", " 'ED5-8': 'Tertiary education (levels 5-8)',\n", " 'NRP': 'No response',\n", " 'TOTAL': 'All ISCED 2011 levels'}" ] }, "execution_count": 128, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Get meaning of isced11 column\n", "eurostat.get_sdmx_dic('lfsq_urgaed', 'ISCED11')" ] }, { "cell_type": "markdown", "metadata": { "id": "yWKXqQlhTAaP" }, "source": [ "Similarly as we had done in section [4.2.3 Transformation](#transformation), we now transform the dataset into a format we can work with. We firstly want to create a dataframe with all genders and the largest possible age group of 15-74 year olds, which has the unemployment rate of all educational attainment levels (except non responses) stored seperately for each country:" ] }, { "cell_type": "code", "execution_count": 129, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 538 }, "id": "-z6vtA1EINBo", "outputId": "f742ce0c-078e-403e-f09a-26b7be4be138" }, "outputs": [ { "data": { "text/html": [ "
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country
AustriaED0-212.013.413.310.410.110.610.911.011.0...5.68.15.84.95.57.5NaNNaNNaN9.3
AustriaED3_45.45.35.04.23.63.74.04.54.3...2.74.23.12.93.24.4NaNNaNNaN4.7
AustriaED5-83.33.43.73.12.83.22.73.42.8...1.42.22.11.81.82.2NaNNaNNaN2.3
AustriaTOTAL5.55.75.74.74.24.44.54.94.6...3.14.73.53.23.54.7NaNNaNNaN5.5
BelgiumED3_46.06.45.64.95.35.45.25.35.8...6.86.87.38.08.38.5NaNNaN9.1NaN
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TurkeyED5-813.314.011.411.812.715.112.413.912.9...NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
United KingdomED3_4NaN5.54.34.33.94.43.84.14.4...5.05.35.25.45.3NaNNaNNaNNaNNaN
United KingdomED0-2NaN6.96.16.66.76.56.46.56.3...8.88.69.09.49.4NaNNaNNaNNaNNaN
United KingdomED5-8NaN3.72.72.62.32.62.62.42.4...2.53.03.23.42.9NaNNaNNaNNaNNaN
United KingdomTOTALNaN4.93.83.93.63.93.73.73.8...5.65.85.96.26.1NaNNaNNaN6.2NaN
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156 rows × 93 columns

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" ], "text/plain": [ " isced11 2020Q4 2020Q3 2020Q2 2020Q1 2019Q4 2019Q3 \\\n", "country \n", "Austria ED0-2 12.0 13.4 13.3 10.4 10.1 10.6 \n", "Austria ED3_4 5.4 5.3 5.0 4.2 3.6 3.7 \n", "Austria ED5-8 3.3 3.4 3.7 3.1 2.8 3.2 \n", "Austria TOTAL 5.5 5.7 5.7 4.7 4.2 4.4 \n", "Belgium ED3_4 6.0 6.4 5.6 4.9 5.3 5.4 \n", "... ... ... ... ... ... ... ... \n", "Turkey ED5-8 13.3 14.0 11.4 11.8 12.7 15.1 \n", "United Kingdom ED3_4 NaN 5.5 4.3 4.3 3.9 4.4 \n", "United Kingdom ED0-2 NaN 6.9 6.1 6.6 6.7 6.5 \n", "United Kingdom ED5-8 NaN 3.7 2.7 2.6 2.3 2.6 \n", "United Kingdom TOTAL NaN 4.9 3.8 3.9 3.6 3.9 \n", "\n", " 2019Q2 2019Q1 2018Q4 ... 2000Q2 2000Q1 1999Q4 1999Q3 \\\n", "country ... \n", "Austria 10.9 11.0 11.0 ... 5.6 8.1 5.8 4.9 \n", "Austria 4.0 4.5 4.3 ... 2.7 4.2 3.1 2.9 \n", "Austria 2.7 3.4 2.8 ... 1.4 2.2 2.1 1.8 \n", "Austria 4.5 4.9 4.6 ... 3.1 4.7 3.5 3.2 \n", "Belgium 5.2 5.3 5.8 ... 6.8 6.8 7.3 8.0 \n", "... ... ... ... ... ... ... ... ... \n", "Turkey 12.4 13.9 12.9 ... NaN NaN NaN NaN \n", "United Kingdom 3.8 4.1 4.4 ... 5.0 5.3 5.2 5.4 \n", "United Kingdom 6.4 6.5 6.3 ... 8.8 8.6 9.0 9.4 \n", "United Kingdom 2.6 2.4 2.4 ... 2.5 3.0 3.2 3.4 \n", "United Kingdom 3.7 3.7 3.8 ... 5.6 5.8 5.9 6.2 \n", "\n", " 1999Q2 1999Q1 1998Q4 1998Q3 1998Q2 1998Q1 \n", "country \n", "Austria 5.5 7.5 NaN NaN NaN 9.3 \n", "Austria 3.2 4.4 NaN NaN NaN 4.7 \n", "Austria 1.8 2.2 NaN NaN NaN 2.3 \n", "Austria 3.5 4.7 NaN NaN NaN 5.5 \n", "Belgium 8.3 8.5 NaN NaN 9.1 NaN \n", "... ... ... ... ... ... ... \n", "Turkey NaN NaN NaN NaN NaN NaN \n", "United Kingdom 5.3 NaN NaN NaN NaN NaN \n", "United Kingdom 9.4 NaN NaN NaN NaN NaN \n", "United Kingdom 2.9 NaN NaN NaN NaN NaN \n", "United Kingdom 6.1 NaN NaN NaN 6.2 NaN \n", "\n", "[156 rows x 93 columns]" ] }, "execution_count": 129, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Only select columns of all genders and of agegroup 15-74\n", "edf = edf[(edf['sex']=='T') & (edf['age']=='Y15-74')]\n", "\n", "# Rename geo\\\\time coulmn to country\n", "edf = edf.rename(columns={'geo\\\\time':'country'})\n", "\n", "# Change the country values EL and UK to GR and GB\n", "edf['country'] = edf['country'].apply(lambda k: k.replace('EL', 'GR').replace('UK', 'GB'))\n", "\n", "# Drop unneeded columns of unit, sex, age\n", "edf = edf.drop(columns=['unit', 'sex', 'age'])\n", "\n", "# Change country code to country name\n", "edf['country'] = edf['country'].apply(lambda k: pyc.countries.get(alpha_2 = k).name if pyc.countries.get(alpha_2 = k) != None else k) \n", "\n", "# Drop all rows with a non response for educational attainment level, set index as country and sort the index alphabetically\n", "edf = edf[~(edf['isced11']=='NRP')].set_index('country').sort_index()\n", "\n", "# Print dataframe\n", "edf" ] }, { "cell_type": "markdown", "metadata": { "id": "koMKlvVcTwAr" }, "source": [ "From this dataframe, we can now create the dataframes we need for the regression. We need a dataframe with the unemployment rate for each educational attainment level, with the mean value for each country. We create the corresponding dataframe for each educational attainment level and store it in the dictionary 'GPDxURxED_dic'." ] }, { "cell_type": "code", "execution_count": 130, "metadata": { "id": "Bl7Zv1VBIQrx" }, "outputs": [], "source": [ "# Create dictionary with education levels\n", "ed_levels = {'primary':'ED0-2', 'secondary':'ED3_4', 'tertiary':'ED5-8', 'total':'TOTAL'}\n", "\n", "# Create variable \"period\" with the timeperiods for which we want to calculate the mean unemployment rates (based on the available GDP-Growth data)\n", "period = pd.period_range(start='2009Q1',end='2019Q4', freq='Q', name='Quarterly Frequency')\n", "\n", "# Create dictionary to store dataframes\n", "GDPxURxED_dic = {}\n", "\n", "# Run for-loop to create all unemployment dataframes\n", "for ed_level in ed_levels: # Go through every education level\n", "\n", " aux_df = pd.DataFrame() #Create auxilary dataframe \n", "\n", " EDUR = edf[edf['isced11']==ed_levels[ed_level]].drop(columns = ['isced11']) # Select unemployment data for education level\n", " EDUR = EDUR.sort_index(axis=1) #make order of quarters chronological\n", " EDUR.columns = pd.period_range(start = EDUR.columns[0], end=M_UR.columns[-1], freq=\"Q\", name=\"Quarterly Frequency\") #change datetype of quarters\n", " aux_df[\"Measured Unemployment Rate\"] = EDUR[period].mean(axis=1) #Calculate Mean unemployment rate for education level and store in axuliary dataframe\n", "\n", " exec(f\"EDUR_{ed_level} = aux_df\") #Name the respective dataframes\n", " exec(f\"EDUR_{ed_level}.name='{ed_level}'\") #Name the respective dataframes\n", "\n", " exec(f\"GDPxURxED_dic['{ed_level}'] = EDUR_{ed_level}\") #Store dataframe in dictionary" ] }, { "cell_type": "markdown", "metadata": { "id": "pjji9Y6ITzXy" }, "source": [ "Now that we have dataframes of both the dependent and independent variables, we can merge them together into one dataframe, and store the corresponding dataframe into the 'GPDxURxED_regdic' dictionary:" ] }, { "cell_type": "code", "execution_count": 131, "metadata": { "id": "NnRHzNH-ITDp" }, "outputs": [], "source": [ "# Create empty dictionary to store all dataframes\n", "GDPxURxED_regdic = {}\n", "\n", "# Iterate through unemployment dictionary generated previously to create dataframes\n", "for ed_level in GDPxURxED_dic:\n", " \n", " aux_df = pd.merge(GDPxURxED_dic[ed_level], gdp_on_ur, on='country', how='outer').dropna() #Merge UR dataframe with GDP-Growth dataframe and drop all rows which contain NaN values\n", " aux_df.columns = [\"Measured Unemployment Rate\", \"GDP Growth\"] #Rename the columns\n", " GDPxURxED_regdic[ed_level] = aux_df #Save the dataframes into the dictionary" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Good job! Let's move on to our third and final dataset!\n", "\n", "\n", "### Job finding probability on GDP per capita of age groups\n", "\n", "**Data import** \n", "With the Eurostat API discussed in section [4.2. Technical toolkit](#technical_toolkit), it is very easy to import the data of GDP per capita for all countries." ] }, { "cell_type": "code", "execution_count": 132, "metadata": { "id": "JO_9F8Y9VvpQ" }, "outputs": [], "source": [ "# Import the dataset\n", "gdp_raw = eurostat.get_data_df('nama_10_pc', flags=False)\n", "\n", "# Filter relevant rows and drop columns\n", "gdp_raw = gdp_raw[(gdp_raw['unit']=='CP_EUR_HAB') & (gdp_raw['na_item']=='B1GQ')].drop(columns=['unit', 'na_item'])\n", "\n", "# Rename column\n", "gdp_raw = gdp_raw.rename(columns={'geo\\\\time':'country'})\n", "\n", "# Substitute country codes\n", "gdp_raw['country'] = gdp_raw['country'].apply(lambda k: k.replace('EL', 'GR').replace('UK', 'GB'))\n", "\n", "# Transform country codes into country names\n", "gdp_raw['country'] = gdp_raw['country'].apply(lambda k: pyc.countries.get(alpha_2 = k).name if pyc.countries.get(alpha_2 = k) != None else k)\n", "\n", "# Omit rows with specific countries and country groups\n", "gdp_pc = gdp_raw[~gdp_raw['country'].str.contains('|'.join(['Malta', 'Germany', 'EA19', 'EU15', 'EU27_2020', 'FX', 'EU28', 'Luxembourg', 'Montenegro', 'Serbia']))]\n", "\n", "# Sort values by column 'country'\n", "gdp_pc = gdp_pc.sort_values(by='country').set_index('country')\n", "\n", "# Define a period to filter the dataset\n", "period = list(range(2011, 2021))\n", "period.remove(2017) # We exclude 2017 because of merging conflicts\n", "period = [datetime.datetime.strptime(str(x), '%Y') for x in period]\n", "period = [x.strftime('%Y') for x in period]\n", "\n", "# We sort index by column axis\n", "gdp_pc = gdp_pc.sort_index(axis=1)\n", "\n", "# Format years from strings to datetime objects \n", "gdp_pc.columns = [x.strftime(format = '%Y') for x in pd.to_datetime(gdp_pc.columns.values, format='%Y')]\n", "\n", "# Calculate the mean GDP\n", "gdp_pc = gdp_pc[period].mean(axis=1).to_frame()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "With this we now have a dataframe of the dependent variable. Next, we need to create dataframes of the independent variables. For this, we will need the job finding probabilities filtered by age groups. We will store the datasets in a dictionary called 'GDPxJFP_dic'." ] }, { "cell_type": "code", "execution_count": 133, "metadata": { "id": "JO_9F8Y9VvpQ" }, "outputs": [], "source": [ "GDPxJFP_dic = {}\n", "\n", "for age_group in age_groups_jf:\n", " frame = job_finding_probs[age_group].mean(axis=1).to_frame()\n", " frame.columns = ['Job Finding Probability']\n", " GDPxJFP_dic[age_group] = frame" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we want to joint our dependent and independent variables. We store our regression datasets into a dictionary called 'GDPxJFP_regdic'." ] }, { "cell_type": "code", "execution_count": 134, "metadata": { "id": "JO_9F8Y9VvpQ" }, "outputs": [], "source": [ "# Create empty dictionary to store all dataframes\n", "GDPxJFP_regdic = {}\n", "\n", "# Iterate through unemployment dictionary generated previously to create dataframes\n", "for age_group in GDPxJFP_dic:\n", " \n", " aux_df = pd.merge(GDPxJFP_dic[age_group], gdp_pc, on='country', how='outer').dropna() #Merge UR dataframe with GDP-Growth dataframe and drop all rows which contain NaN values\n", " aux_df.columns = [\"Job Finding Probability\",\"GDP per capita\"] #Rename the columns\n", " GDPxJFP_regdic[age_group] = aux_df #Save the dataframes into the dictionary" ] }, { "cell_type": "markdown", "metadata": { "id": "7wQcgVKJ4w3k" }, "source": [ "Finally, you made it! We have our datasets ready, let's investigate the data now. We will present some nice and neat functions on how you can give meaning to your data by visualising it in a useful way!\n", "\n", "\n", "# Data visualization\n", "\n", "The following section focuses on data visualization and inspection. In a first step, we will present the most useful and powerful python libraries for creating plots, namely [`Matplotlib`](https://matplotlib.org/) and [Seaborn](https://seaborn.pydata.org/). In a second step, we will try to approach our datasets from different perspectives. Hence, the goal of this section is to provide you with a toolkit to qualitatively assess the data and create an intuition for its underlying patterns. As opposed to section [6. Statistical analysis](#statistical_analysis) which conducts a quantitative analysis, the focus of this section is a qualitative analysis.\n", "\n", "\n", "## Introduction to plotting libraries\n", "\n", "\n", "### Matplotlib\n", "\n", "One of the most popular libraries for data visualization is [`Matplotlib`](https://matplotlib.org/) as its [`pyplot`](https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.html#module-matplotlib.pyplot) module provides users with a powerful and convenient interface for creating appealing data visualizations. It is common practice to `import matplotlib.pyplot as plt` as it simplifies the forthcoming codes. The [`pyplot`](https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.html#module-matplotlib.pyplot) module provides the user with a state-based plotting interface. This means that the state of the visualization is altered by code until being displayed. In the following, we will discuss the most important modules for creating visualization objects and the methods applicable to alter the respective state of the object.\n", "\n", "The following enumeration lists the most important pyplot methods. See on the Matplotlib website the [`complete documentation`](https://matplotlib.org/stable/api/pyplot_summary.html).\n", "\n", "**Methods**\n", "- `title` – set the title of the plot as specified by the string\n", "- `axes` – adds an axes to the current figure\n", "- `figure` – used to control a figure level attributes\n", "- `subplots` – a convenient way to create subplots, in a single call. It returns a tuple of a figure and number of axes\n", "- `xlabel`, `ylabel` – set the label for the axis as specified by the string\n", "- `xticks`, `yticks` – set the current tick locations and labels of the axis\n", "- `legend` – used to make legend of the graph\n", "- `show` – displays the graph\n", "\n", "In order to provide you with a more visual intuition for a visualization object, let's look at the anatomy of a Matplotlib figure and look if you can match the methods with the graph.\n", "\n", "**Anatomy of a Matplotlib figure**\n", "
\n", " \n", "
\n", "

\n", " \n", " Figure 5: Anatomy of a Matplotlib figure (Matplotlib)\n", " \n", "

\n", "
\n", "
\n", "\n", "Okay, you have learned about Matplotlib but what is the difference to Seaborn? \n", "\n", "\n", "### Seaborn\n", "\n", "Seaborn is a data visualization library based on matplotlib. It builds on top of matplotlib and thereby provides a provides a high-level interface for creating sophistical statistical plots. The main advantage of this high-level interface is that the methods used let you focus on the meaning of the plot rather than its construction. Seaborn's webiste officially states: \"If matplotlib 'tries to make easy things easy and hard things possible', seaborn tries to make a well-defined set of hard things easy too\" ([Source](https://medium.com/edureka/python-seaborn-tutorial-646fdddff322)).\n", "\n", "Some advantages that seaborn may bring are:\n", "- Pleasing aesthetics\n", "- Custom color palettes\n", "- Statistically informative plots\n", "- Flexibility of different display options\n", "\n", "Those are probably some very good arguments, why we would choose Seaborn over Matplotlib for qualitative analysis. However let's start with some plotting and let's see how far we can go!\n", "\n", "\n", "## Plotting\n", "\n", "Data can be qualitatively explored in a wide range of different ways. Here are some relationships that we could examine:\n", "\n", "1. **Correlation**: Scatterplots, histograms, correlograms, pairgrids \n", "2. **Deviation**: Diverging bars, area charts\n", "3. **Ranking**: Ordered bar chart, slope charts\n", "4. **Distribution**: Histograms, density plots, box plot, population pyramid\n", "5. **Composition**: Pie charts, Treemaps, bar charts\n", "6. **Change**: Time series, autocorrelation plot, plotting with secondary Y axis\n", "7. **Advanced plots**: Geomaps, 3D plots\n", "\n", "If you manage to have a look at all those, you will master the art of Python visualization. So let's start!\n", "\n", "\n", "### Correlation\n", "\n", "**Scatter plot**\n", "\n", "Scatterplots are mainly used when we want to investigate the co-moving relationship between two variables. If there are multiple classes we may want to visualise each group in a different color. Let's look at the seaborn `jointplot`method first. We want to understand how unemployment rates and age groups correlate." ] }, { "cell_type": "code", "execution_count": 135, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 1000 }, "id": "rMRpcGgw4z7u", "outputId": "11e44a2b-8446-4c95-c1a0-d223cc31c7dc" }, "outputs": [], "source": [ "# Filter relevant rows and drop columns from our unemployment rates dataset\n", "ageplot = udf[(udf['age'].apply(lambda k: len(range(int(k.replace('Y', '').split('-')[0]), int(k.replace('Y', '').split('-')[1]) + 1))==5))]\n", "ageplot = ageplot[ageplot['citizen']=='TOTAL'].drop(columns=['unit', 'citizen'])" ] }, { "cell_type": "code", "execution_count": 136, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 1000 }, "id": "rMRpcGgw4z7u", "outputId": "11e44a2b-8446-4c95-c1a0-d223cc31c7dc" }, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# We want age groups on the x-axis and the mean unemployment rates on the y-axis\n", "sns.jointplot(x=ageplot['age'], y=ageplot.iloc[:,3:].mean(axis=1), height=10);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Seems like we have higher unemployment rates for younger age groups! Let's see how unemployment rates distribute over sex!" ] }, { "cell_type": "code", "execution_count": 137, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 1000 }, "id": "rMRpcGgw4z7u", "outputId": "11e44a2b-8446-4c95-c1a0-d223cc31c7dc" }, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "sns.jointplot(x=ageplot['sex'], y=ageplot.iloc[:,3:].mean(axis=1), height=10);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Seems like the distribution is fairly similar. Note that that we have a skewed distribution as unemployment rates cannot be negative but some can be hugely positive. Now, let's construct a plot that we want to quantitatively asses in the next section. We regress GDP growth on unemployment rates of the 'old' age group." ] }, { "cell_type": "code", "execution_count": 138, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 1000 }, "id": "rMRpcGgw4z7u", "outputId": "11e44a2b-8446-4c95-c1a0-d223cc31c7dc" }, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "sns.jointplot(x = GDPxURxA_regdic['old']['Measured Unemployment Rate'], y = GDPxURxA_regdic['total']['GDP Growth'], kind = 'reg');" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "From a first inspection, there seems to be a negative correlation between GDP growth and unemployment rates." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Pairgrids**\n", "\n", "Pairgrids are a stacked version of scatter plots. The advantage of normal scatterplots is that you can analyse bivariate correlations of a data set at once. This sounds useful, let's have a look at it" ] }, { "cell_type": "code", "execution_count": 139, "metadata": { "id": "lVItmQ015KFU" }, "outputs": [], "source": [ "# We analyse the pairwise correlations of transition rates in Switzerland\n", "scat = countries['Switzerland']" ] }, { "cell_type": "code", "execution_count": 140, "metadata": { "id": "lVItmQ015KFU" }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 140, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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FyMleNjuuLHPxzAREIiYjS5KDnJXzYsoMVjP2jJQERSSEGE16s5G8NQtjypROkTwtN0ERCTFyzG4H2ctj75uMduu46tdIakAIISYka2oS8284j7qt++iobSRj4QxSZhRItdRJyFWQxbzrz+Hwu7tQSpFz0lxc+RmJDktMEkqnI2flPKzJSdRt24cjK43MJTOxpkqtGiEmq+Tpecy++gxqN+3CYLOQvWIuDml+ISYBnV5P3ppF2NKSqd++H2duOpmLZ2JNGT9NCiUBIYSYsBxZqTjOW0UkHJkQYzWL46M3GkguycNdnBPt/V06GRUjzOy0k7VsFhmLZ8i5RIgpwGA2kTa7iNSZBXJdEZOO2WUne8UcMpfOGpfXNElACCEmvPF4chUjT2q3iNEm5xIhpha5rojJbLxe08ZnVEIIIYQQQgghhJhUJAEhhBBCCCGEEEKIUScJCCGEEEIIIYQQQow6SUAIIYQQQgghhBBi1EkCQgghhBBCCCGEEKNOEhBCCCGEEEIIIYQYdWOegFBKfVIpdVAp1aWU2qKUWjvI/Fcrpd5XSnmVUuVKqTv6fL9eKaX185ndZ77PKaX2KKV8SqkqpdRvlVKO0dhHIYQQQgghhBBCxDKM5caUUtcAdwGfBDZ0//uMUmqupmkV/cx/HvAA8FngWWAOcI9Syqdp2m/6zD4PaO413dBrPdcBPwVuAd4ApgH3AhbgYyOzd0KI4epoaKWrw4c1yYE9xZnocMQU09ncjq+tE4vDiiPdnehwhBD9CAVCdNS3EAlHcGS4MVnNiQ5JDELTNDrqW/F7u7C5HdiS5fouJp5IOExHfSvBrgD2tCQsTluiQ5o0xjQBAXwRuE/TtHu6pz+jlDoXuB24s5/5bwCe1DTt7u7pMqXUj4CvKqV+q2ma1mveek3TGgfY7mrgbU3T7u+ePqSU+htwxQntjRDiuEQiEQ6/f4BNf3uBYFcAs9PKyR87j8zZBYkOTUwRdXsqePtPz+Dv8GG0mFjxkbPIWVSCTictE4UYL3ytHez879uUbdgBGmTMzmfZdWfgzHAnOjQxgHAoTNXmfWz558uE/EEsSXZW33YBaSU5iQ5NiCEL+AIceP0Ddj6xkUg4gjMrmVW3nI87Lz3RoU0KY3anpZQyAcuA5/t89TzRBEF/zEBXnzIfkAcU9infrJSqUUq9pJQ6rc93G4DFSqmTu2MpAC4Gnh7eXgghRoKnroW3//wswa4AAH6Pj7f/9AydTe0JjkxMBZ1N7Wy852n8HT4Agl0B3r73WTx1LQmOTAjRW0NpFWVvRJMPAPV7Kil/e1digxLH1F7TzLt/fZ6QPwhAV1snb//5GXxtnQmOTIiha62oY/t/3iQSjgDgqW1h2382EPIHEhzZ5DCWr3rSAD1Q16e8DsgaYJnngEuVUmcrpXRKqZnAl7q/y+7+t4ZoDYorgMuBvcBLSql1R1aiadqDwNeA15VSQaAc2A58tb+NKqVuU0ptVkptbmho6G8WIRJmMhyf3mYPkVA4pszf4ZMblElgIhyfvrYOAp2xue1IKIyv2ZOgiMRYmAjHpojVUFodV1b9/gGCk/AhYLIcn97mdmIrKIO3yUOXXN8ntMlyfA5Vfy/E6nZX4O/o+15cHI9E1DXV+kyrfsqOuAf4P+BxIAC8DTzY/V0YQNO0vZqm/V7TtC2apm3UNO2TRPuL+HLPBpQ6Ffgm0T4nlhJNVKwHvtNvgJr2R03Tlmuatjw9XaraiPFlMhyf1iQ7SqmYMoPFhNlhPeZygU4fTaWVHH5vLy0HayQTPQ6N9+Ozq62DcKePJZevZvEVp+DMSgZA6RSWJHuCoxOjabwfmyJecmFmXFn6rHwMRmNMWWdDC3Xb9lO3bT+dDROzJtN4Pj59LR7qd5ZRu3UfnsMNaJGBbtvp9zxqdloHvb6L8W08H5+jwZoUP05B4clz8Ld5OPzeXhr3VhDo8CYgsslhLPuAaCSaNOhb2yGD+FoRAHT38fBVpdTXupdrAM7o/vrQMbb1DnBtr+nvA//UNO1P3dPblVJ24E9Kqe9qmhYazo4IIY6fv70TvdJY9+mLefsvz+Pv8KHT61hx41nHbNcb7PJz4Pl3qdm8p6dsxgWryTt5Hkra7Ysh6GrvZOdDL9FWXhstUDDrzJP44PG3WXDpGpyZyUTCYbpaPGjhMGa3E4PZlNighZjCMmfnkzErj/q9VQA40pMoWTsfpTuawPYcbmTrn58i5PMDYLCaWfKxC3Fmpw1pG/72ToKdXow2K+Z+HjqmklCXH39rBzqDHnOyC51eh6+5nW1/f5bO7iZqSq9j0UfOJ6Ukt991JGWnsuCyNWx/7E3QQG/Uc9JHzsYmHU2LCcSZ5mT1zedQv/8wBzfuwpmRTFZJNu/f+1TPPJmLpjPzwjUYbZYERjoxjVkCQtO0gFJqC3AW8K9eX50FPDLIsmGgGkAp9SFgo6Zp9cdYZDHRphlH2OiuMdFLmGjtCyHEGGkvr2H/E68R7PRhsJo55ZazCWp6rEn2njfRA/HWt8QkHwAOPPcOKTPyscsIBmIIPIcbjyYfADSoeXcXp3/5KhwZbiLBIDXv7qTmnR2gabgKsyk6ZxXWlKTEBS3EFGZPTeLkW8/HU9NCJBzGmZWCzR2bJKh9v7Qn+QAQ8vmp+2D/kBIQbYcOc+DJ13uuSSUXriNpWm5cDb2pwNfYStkzb9JRXY/S6chZs4isZXNoq6zrST4AaOEIZS+8iyvvgn4TtAazkRmnLyZrTiFdHi/2VBfOjGNf34UYLyKhMM17D3HwubeIBEKYkxyc9vnLMFjMvHfPEzHz1n2wn5wVc0gulg5Wh2usR8H4JXC/Uupd4E3gE0AO8HuA7hEuTtI07Yzu6TTgKuBVoh1SfrR7+tQjK1RKfZ5obYidgAn4MHApsSNcPAl8USm1mWjtiOnA94CnpPaDEGPD39ZB6WOv9Nwohnx+yp56g/kfvQRr6tEHvFAwRP2+ava8tg2dXsesUxeQOT2XUFd8c4tIKEy4u6MrIQbT+yHlCH97J5qm8f5T75CW4aT27e0937WX11C/dS8Fp6+Ykg8kQoy2oD9IXWkVe1/ZhsFiZNa6hWTMyIkZjcbisGGZMfDwd531zf2UDd4Mo6vVQ+ljrxDuvraEfH5KH3uF+R+9eMolHSPhMIff3UFHdfTdnhaJUP3GVhw5GQS6O+vtzdfsIRwIDVhDzGA0klyQMaoxj0cNZbWUvrmDjsY2Zq5dQPacAsz2yf92vP5ADaUbdtDZ3M6sdQvJnlswYYfL9TW2cuDJ13um/W0dVL68ieLz1hAJxj8yhvu5NxWDG9MEhKZpDymlUoFvEO1EcgdwvqZp5d2zZAMlfRa7EfgZ0doKG4H1mqa92+t7E/BzIJfoCBk7gQs0Tes9wsX3ifYz8T2iI2g0Ek1KfH3k9k4IcSwBT2fcA2AkFCbQ3hGTgKjbW8XTP36oZ3r/hp1c8I0PkZSWhN5sjEk42LNSsCRP7SqzYujs6W5QCnp1kJa5sIRNj2ygbl81J52zMG6Zln3l5KxaKFUshRgFNXsqeO5n/+6ZLn1jBxd983oyZ/Rfvb8/2Utm0VxaFVOWtXjGoMsFPJ1xDw+RYIhAe+eUS0CEfH5aSyviyr31zbhy49v75yyfhUn6dIjRVFHPUz94oGf0j/It+1l32/nMWrcgwZGNrsZDdTz1gwcIB6IP5+Vb9rP+9guZsWZegiM7Pl2t8Z1RdzW1ojcZcOal46k62gGnzmjAmja1zhUjZcwbTmuadremaUWappk1TVumadrrvb67SdO0ol7TjZqmrdI0zaFpml3TtDM1TXunz/p+qmnadE3TrJqmpWiatrZP8gFN00Kapn1H07QZ3fPla5r2SU3TJmZPRUJMQAarBZ1BH1uoFEb70ZsYTdPY9eLWmFk0TWP/m7uwpSax6CPn48hJAwUpM/KYd9UZmOxyEySGxpGdxsIbzsGS4kTpFFlLZpKxZBaHNu2L9kViiz+WHLkZ6E3GftYmhDgR4VCY7c9sjinTIhqHNu8b1nqSp+cy/dyT0ZtN6M0mpp93MskD9E/Qm9FqQfV3TZqCyUa9yYS9nyYrlmQnztw05l17JiaXDaXXkbdqPjkr5kitsD7qDxzuST4csfU/b9Llia9BMpnUlVb1JB+O2PrYWxN2tIj+EmtGhxWj1cycy9eTOrsQVPQF2KKPnIc9XZoXHY+xboIhhJiiLCkuis5dQ9l/3+h5A1145klY+rxp0rRI/MLd87sLs1hy84WEuvyY7FZ5MBTDotPrSJtViCs3g0gwhMlpo2rHIQAi4QjNtW3YstPw1jQCYLRbyV65ID5xJoQYGZH48/3A4yv0z2S3UrB2ERkLoxVoLUPsSNKS4qL4nFWUPf1mzzWm6OyVWFKn3htNvclA3tqldNY09tRUdM/Ix56djt5oJHNBCcnF2YSDYcwuOzq9dPw8FJqmoQ37iJ5g+tu9CbzftoxkclYt5PDGbQAovZ5p552CyWnH5LQz/5ozCHT6MJhNUzJZOVIkASGEGBNKKVLnFGHLSCbQ3onJacOakhTzcKeUYt5Zyyjfsr/XgjC9V1U+o9WMcYK2LRTjQ+83HCl5aTjTk/A0tPHBs1uYuXYe005fiSPVhTUtCUuS9NwuxGjQG/QsOP8kDu86WvVf6RTFy2ce1/qGmng4ui0dqXOnYctIJeDpvialJqHTT82EoyM7jXk3XkRXS1t31XI3RuvRByyTY+B+OARkTMvGYDbG1IJYetkarM7J/XPLnJmH3mSIqQWx5NI1WCZoEx29yUT2yQtJnlFA0NuF2e3Amuru9b0Rq7z8OmGSgBBCjBmdXo89IwV7RsqA82TOyuX8O69lz8vvo9PrmH36IjKnSw/DYnQ40pI458tXUvbOHmr3VpE5M5/k6fk4ZMg4IUZd9pwCzvvK1ex+5X2MFhOz1y8ivSR7zLav0+uxZ6Zgzxz4mjSVWJKdWJLl3Hc8UgszufDrH2LfGzvwNLQya/0icuYUJDqsUZdW1L3fr3d3QjkJ9ttgNuLIie/7RIwcSUAIIcYVg9FI7rxCcucVJjoUMUUk56ax7PJTEh2GEFOO0Wwkb2ExeQuLEx2KECcsfVo26dPGLoE2XmSU5JBRIi+KxNBJIy4hhBBCCCGEEEKMOklACCGEEEIIIYQQYtRJAkIIIYQQQgghhBCjbtAEhFLKNMj3OqXUtJELSQghhBBCCCGEEJPNUGpA+JRSGUcmlFIblFK5vb5PB0pHPDIhhOhHwOfH7+1KdBhiipHjTojJR/6uR1awK0BXp/w8xfgRDoXxebxEwpFEhyJ6GcooGKrP9CLAPMg8YgIx6A2EI+Ehz6/X6QmFQ4PP2EthQQEVlZXDWqYgP5/yiorBZxRTQrArQOW2MjY98iaRSJhll6ymcMl0zHbL4AsLcZyCgSBV2w+x6ZE3CHUFWXLJKoqXzZiwY5wLIbqvJ9sPsumRDURCYZZesoqipTPkenKcwsEQVbvK2fzvDXR1+Fh0wUmUnDQLq8ue6NDEFNZU2cAHT79L1faD5C+axqLzVpCSJ8NrjgcjNQynNkLrEQkQjoR56zcPDnn+1Z++dtjbqKis5J2HXxvWMiuvPnXY2xGTV83eKp755aM90y/85gnO/cLllKycncCoxGRXt6+ap3/2r57pl3/3FGd95mJmrpmfwKiEECeidl8Vz/zikZ7pF3/7JOd8/jKmnzwngVFNXPUHanjqxw/1PA289qdnUcC8M5cmNC4xdXW2dvDsLx+htaYZgF0vvU9daTUXf/06bEmSGEs06YRSCDEhlL61M65sxwvvDblaXTgYRAsPvaaPEJFgiNaaJnT62EvlB89sIhQIJigqMRFFgkG0iFQBHi/2b9wdV7b9uc2E5RpxXKp2lYMGOoOe4mXTKVk5i+0vbsUvzTFEgrTVNPckH45oqmigrbYlQRFNTpFwmEhoeLXiYWg1IDRiazj0nRZCiFHXX1VOq8uG0ilCwRCRUBiTtW/rMAj5fHira+isrMTgdOAqmYY5OXksQhYTVDgQoKu+Ac+BMtKsei7+4kVseHgjjeX1QPRYVDrJ34vBhbxeOquq8FbXYEpy4Zw2DZM7KdFhTXmWfq4ntiQHOiV/18fDbLOQWZLFqstXoutsAU1DlzwPJY8LIkF0Bn2/5QbTSFX+n9oi4TD+xkbaSw+gRcI4S6ZhychAbzQOafmhnGkVUKaUaldKtQMOYFuv6f3HH74QQgzN9FVzMJiPnth0eh0LzllGzd4qnv3lIzzyzb+y/bnNdLZ29MyjRSJ4yg7StmcPoc5OumrraHj7XYIez5C3GwwEqTpQTdnOg3h6rVtMHp7WDsp2HqTqQDWhQIiuhgaa3/+AoMdDsLWVSE05qy4/GQClU8xaM4+OpvYERy3Gu0g4TNuefbTv20+osxPv4Roa3nmXYGdnokMb1yLhCDXltezfXkZLw+i8rSxZOSvuelK0bDpNlfWjsr3JLndeISdffjKRmnJC7e2EPB4CFWUE21qPuVxjbRP7t5dRV1mPpkmyQoyMugOHKf/gAMXLZ8SUz16/iKTslLj5I5EItRV17N9eRnOd1JAYikBLC42bthBobSXY7qF56wf4G5uGvPxQ0kAfPf7whBBiZGRMy+byb99A9a4KIpEIuXML0Ov1/Osb9xEJRavNvv6X5wl0BVh2yWoAwr4uOspjOzLVwmEC7R6MTueg2+xo6+S5B1/i6fufQ4to5Jbk8PHv3EzetJyR30GRENVlh/n9//yZ6gOHUTrFx752AyXO+GrYVlOEFVecgsFsZOODr5I+LYszb78Io+WYI1WLKSzk9eI9fDimLBIMEvR0YLRLG+T+BPxB3nx6I/+869+EAiHcaUl86ke3UTKveES3kzEtm8u+dT1l7+4l6A9iS3bw9oOvoWkRrvjOR3CmSy2V4UjNT6ex8TC+PuUdhw5hy8rst8bY7s17+N0376WjrROz1cxH77yeZeuXoB/gzbUQQ9Fc3cjj33+AoC/ArLXzWXHlWvwdPrJn55M9Kw9Tn2t2KBji3Ze28LefPEDAH8SV7OT279/CrCUzBtiCAPDW1MaVeQ4ewpqZMaQaooMmIDRN++vxhSaEECMrvTiL9OKsnuldr7zfk3w44v2n3mXWugU4kp2gUyi9Lq7ttdIPrZrtod2H+O9fn+2Zrj5wmCf//DS3fOsjGE1Dq2Ymxq9gIMiT9z1L9YHoQ6IW0XjrmXcoue7kuHlDoQj73tzZ037U09BG+5VrSc3PiJtXCACldKDTQd/zjzTfGVDVgWru/9nRTrFbG9u49/t/487ffRGne/Ck8XAovZ5tz29Bp9PF9FXQUt0kCYjj0G93TDo9qPiB8prrWvj9//yZjrZobSC/z88937mP3Gk55EqCX5yA5soGgr4AAHvf2IHSKWxJdhacuxx7cvw55PChWu79/t/QItEaOO0tHv747b/wzT99BXe6eyxDn1B0hvh7YL3J2O/fe7/LDzaDUupspZSh17Szz/cWpdTNQ9qaEEKMIEM/bc2MFiN6ffQNSmdnAGdJbBZbb7NhcrmGtP7ayoa4sh3v7qajTZpiTAYdbZ3sfGdXTNnurfvQpWfGlCm9nk6fFtN5lU6vQ6eXN3ViYAa7DdeM6TFlxiQXJtfIPkgPVWtDK7WHavF2eBOy/aFoqomvwltbXkd7y9CbzQ2VXq8j1BWM6yhRb5S/6+OhWR2xyTWl0Ceno/p5IGltbMPTEnsdDYcjNEn19zit9a3UHqrD19G3fsnE19neSe2hWtpGsElj379fLaIR6AoMeL1urm3uST4c0dLQSluzNLM8Flt2Ztzfu6O4qN+/9/4MpQnGM0A2cKRhXLVSarGmaWXd00nAPcCfhxq0EEKMhPRpWdiS7HjbjrapXvWh0zDZzGzfsJ1//+oRCmblcdYVq3CYwORyYk5NxWCzDW39OalxZdMXTMPmkOrTk4HdaaNk4TS2vbmjp0yLaDS0dlGy+mS6GhtROgMGt5vn//BMzLJzz1qKziidWYmBKaVwFBZgcjnpamrG6HBgSU1Fb7GMaRzhcJjdb+/hX7/6Nx0tHeTPyufqL11JTsn4e9Oc3M8bx7TsVBwux4hvKyk7mUXnn8T7/32npyxnTgEp+Wkjvq2pQJmt+J1ZWM2gNI2uiJ6Isf9j3el2YHVYYx6qlU7hTpOaJ0eEgiF2bdzFv//3UTrbOimcV8hVn7+C7GnZiQ5tRFTureTBnz5M7aFaktKSuOaOq5i5bOaQH2AHklaYiTsnhdbDR0fAWHn1qbgGqNXk7qfcmezAkTTy55zJxOR2k756Ff7GRrRIBEtaGqZk95CXH8rdU98j4cSODCGEGCHJOalc8s3rqdpxiI7mdvIXFJM1I5eag7X89Tv3o0U0dr2zl13v7GXuqjlc99UPYXRYh7z+ojmFrLlgFW/+dyMASakuLr/tIsxWafc/GZgsJi675ULK91T0vIFZc95KcotzMKe4MKcc7axq/nknkVKUha+5A0dGEgf2VKB/cwenXLomUeGLCUBvMmHNzMSamTn4zKOk9mAdf/3234h0NwWp3FvJQz//Fx//6a3YnENLxo6VvOm5XHrLhTx+73/RNA2LzcxHv3YDSalDq7U2HHqDgSUXn0zWzFxq9laRVphBzpyCfkdcEoPLKM4GDar3VKBTiuzZ+TFNJntLz03jlm/cyO++dS+hQAidXsf1X7ia7MLE/Z2MNzUHa/nbd//e0zln+c5yHrnrP3zshzdjtY9tEnOktTe389fv3E9Ld42XtsY2/vzN+/jiH75AZsGJNWt0piVxwR1XU7XzEG21LeTOKyR7Zt6A8+cUZXPt567goV8/ihbRMJmNfOwbN5KaFd9ZpYhldidhPs5RneT1jRBiQkvJSyMlL/aNVdPhxrgqdbs27qatqQ2rc+gJiKQUF9d9/krWX3IKfp+fzPwMuShNMoWzCvjGPV+hrrIes9VMdlEWtn6SVO88u4ntG3ZgT7LT3thOJBKhpaGNVRecLFW2xbjWdLixJ/lwRNW+Ktoa28ZdAsJis3DudWeyaM18PK0dpOekkTmK/azYkuyUrJxNycrZo7aNqULpFJnTc8icPrSaNYtOWcC377uT5roWklJdZBVmYhziEH5TQVN1Y9zIIAd3HKS9qX3CJyBa69t6kg9HhAIhmmuaTjgBAeDOTsHdz2gX/TFZTJx22TpmL51Fe3M7qVkpZBVIImy0SQJCCDHp2PupOudIdmA5jou21W6lZP7I9sAuxpfUrJRBE0u503N476WttNa39pQVzClAZ5AOBcX4ZnfHv9F3uO1Y7UNPxo4lk8VE4ayCRIchRplOpyOnKJucosnRpGCk2d3x9zGuVNdx3ceMN1aHBZPFSKArGFPe3z6PBaPJSMGMgWtJiJE31DunhUqppUqppUSbYMzrNb1o9MITQojhyynJZuX5J/VM63Q6rvrCFdKjsThuc0+eS1rO0Zo2jmQHK8876YTbqwox2rKLszn5wqMjuyid4srPX4E7w524oIQQx5QzLZsV5yzvmdbpdFz5hStGpTnSWEvLTePyz14eU3bGdaePSO0HMTEMtQbEc8T2/fB4n+81hBBinLA5bVx42wUsO3MpHa2dpOWlkVUkVerE8csoyOATv/g4NWU1aJEIWcVZpGbHd1IqxHhjc9q44JbzWHrGEjpaOkjLTSWrqP+2+UKI8cGeZOfiT1zE8rOX09neSXpu2qT5u1VKsfj0RWQXZ9FU04wr1UV2cRZmqznRoYkxMpQEhNQ9FkJMODanjZJFJYkOQ0wiyRlukuWtsZiAbN0jvgghJg6by8b0xZPzPsZoNJI3M4+8Y3QQKSavQZtgaJpWPpTPkfmVUncrpQYcw0gp9Uml1EGlVJdSaotSau2xtq+Uulop9b5SyquUKldK3dHn+/VKKa2fz+w+87mUUv+nlDqslPIrpfYrpa4ebP+FEEIIIYQQQghx4kajE8oPAz8HGvt+oZS6BrgL+CSwofvfZ5RSczVNq+hn/vOAB4DPAs8Cc4B7lFI+TdN+02f2eUBzr+mGXusxAs8DLcDVQBWQB/iPcx+FEEIIIYQQQggxDKORgDhWj1xfBO7TNO2e7unPKKXOBW4H7uxn/huAJzVNu7t7ukwp9SPgq0qp32qx49PUa5oWl/To9lEgA1inaVqgu+zQEPZFCDFJdTS20VhWg6+tk5SCDFKLsjCYZQgwMXSaptFcUU9TWQ06g560adm4cwesACiEOAGdzR6aDtbQ2eTBnZdGanEWJmkzPiW11TTReKCGUCBIanE2KYUZ6HQyItFUEQ6GaCqvo/lQHWa7hdRp2bgykxMdlhiGMRuGUyllApYRrR3R2/PA6gEWMwNdfcp8RGsvFBKbRNislDIDu4Dva5r2Sq/vLgXeBH6tlLqEaE2Jh4EfaJoWOwaMEGLS62z28Npvn6Clor6nbPXHzmXa6nkJjEpMNI0HanjhZw8TCYUBMNnMnHnH1aRIT95CjKiudi9v/+U5anb1tPhl2TXrmX3mUpRORqKZSloPN/HCTx/C7/EBoPQ6zvzSlWTOyk9wZGKs1Oyq4NVf/6dnCARHehJnfPEKnBmShJgoxiwBAaQBeqCuT3kdcOYAyzwH3KWUOht4EZgOfKn7u2yiCYgaojUoNgEmorUmXlJKrdc07fXueacBpxNtznEBUAT8FnAAX+67UaXUbcBtAAUFMha1GF/k+DxxLRX1MckHgC0PvUbWnAJsyc5Bl49EInS0dGC0GLHaraMVJoGuADVlNbTWteBKdZE1LRurY/S2NxIm2vHZ2dZJzYHDdLZ1kpqbRlZxFgbjsS+Nne2dhINhDry1syf5ABDw+qncUoo91Yl5FI8LcXwm2rE5lYT8AdprWwj5/NjT3dj7DDXYUt0Yk3wAeP8/G8hbXIJzkI5hI+EILQ0t+Np8NNc0kZThJntaNiaLaaR344RMleMzHArT2daJ2WrGbBu8Bkt7Uzs1B2rw+/ykF6TTUdXQk3wA0MIRtj/1DmnF2ehNJ/5Y0+Xx4qltRul0ODOTMY/za+5YGY3j09/ZhaeumUgogiszGUuSHYBgIEhtWS3NNU04kp1kT8vG5rJFl+noYuu/XosZf7GjoY2mg3U4M5IJBUJ4271YnBZM5vH1Ny6OGssExBF9h+xU/ZQdcQ9QQnTYTyPQTrQPiW8DYQBN0/YCe3sts1EpVUQ0sXAkAaED6oFbNU0LA1uUUqnAr5RSd/RpyoGmaX8E/giwfPlyGWJUjCsT4fgM+/10NbcS8vowOuyYk93oTeOneUPAF9/9S8DbRbjXw+RAWupaeOept9n87GbcGW7O/di5TFtUgk4/stU/w+Ewm5/dxFO/e6qnbN3V6zj9+jPG3Y1zbxPh+Dyis62Tp373FB+88j4QHRrs2q9dy4J1C/udP9AVYM87e3j+vucJdgVYcc5ycpfOoPq90p552uta2Prwq8y/cDWO9KRhxaNpGv7WNgKt7ej0eswpbowO+3Hvn4g1kY7N8SoSDuNvaSXY3oHObMKS4sZgPbEHNH+Hj91Pv0Np99+h2WnjlE9dQkrh0aGTQ/74yqrhQIhwMHTMdTfVNPH2Ext574WtJGe6WXLmUp6+52nWXbmOlRefjF6vP6HYR9JUOD4bqhrY8MgGtr++jaziLM65+Vwys5IItLahMxowpyRjtNt65m+tb+XBH/2Til3RbuL0Rj3XfuUadHodkXCkZz5fi4dwOIz+BB9rPHUtvH3vM7RWRl9QpM/KY/mHz8KRNrxz+WQ00sdnZ1M77z34MrU7DgHgzEph1W0XkJSdyvbXtvHvn/+7Z94V563g3FvOw+qwEg6F6OqVgDoi0OWn7lAdL//jJUrfK6VwbiFnfeRscqbnnGiowxb2B6LnyU4vRrsNc4obvWn83rclwlg2mGokmjToO4htBvG1IgDQor5KtKZCYfey73Z/fegY23oHmNFrugbY1518OGI3YCNaM0OISSMSDhMOJq5lUTgYpGnbbg6/vIH6t7dQ/eLrtJUeQItEBl94jFicVnSG2BvPguWzsDiPfSMdCUd467E3ef3h1/G2ezm8/zD3ff0+Dh84POIxNlU38cyfnokpe/3h16nvU3NDHL/ag7U9yQeIJgCe+M0TtNa39jt/xe4KHvzhP2k+3ISn2cPL/3wFnFaMvdqhpxRmULlpLzU7Dw47Hl9DE1XPvkLdW5uoeeNtql/eQMDTMez1hANBIuHBk2lCDFdn1WGqnnuVuo2bqXn1Leo2biHk69tSdnhaKup7kg8Afo+X9//1GsFeieKkrBSMfRKv2fMK42pK9BYKhnjtwdd489E38Xmi5+tn//QMKy9YyTP3PkNTddMJxS2Gx+/z8/Tv/8ump9+lq6OLQ9sP8Zc7/8zhnWXR4+n1t6l59S0CHZ09y1Ttq+pJPgCEg2Feffg1shYUx6x71hlLevoDCQeP//xXsWlPT/IBoGFvFbU7Dx3XusSxNeyr6kk+AHhqmzm4YQdNh5t48u4nY+bd9Mwmag/WAmBNsjPztEUx3yulcOel8+AP/8n217fT1dHF3nf38tdv3jfg9Xy0REIhmnftofqlN6L3wC+9Qeue/XJN7mM0EhB/J1pTIUZ3549bgLP6fHUW8NaxVqhpWljTtOrudXwI2Khp2rHuwhcTTToc8SYwXSnVe39nAl76Ga1DiIlI0zR8DY3UvPEOVc+9SuveA4R88Vni0RZs89C270BMWdO2XQTah/8gNVo66ltYdtU6UouzMDusTFszj+ScZMKBY79Na29q593/vhtTFolEqC/vN4d6Qro6uwgH4y9YPo93xLc1VfX3s+xs6yTgC/QzN+zdtDeubMfG3eQuiVYDX3zZGup2HAQtenM1HOFQiOZtu2ISdUFPB776oV+igl4vLbtLqXr+FerefBdfY/PgCwkxREGvl4ZNH8SUeWvq8Le0ntB6fS2euLLmgzUEvEcTEM5MN6d99lIyZ+VjdlgpWTuf5decGpeU6K29sZ33nt8SUxYOhaOfYJiuzrG/Pk5lrfWtcefQQFeA1rajvwd/axv+ppae6c7WTvpqrG5kxumLceWkYHU7WHrNqeQvnUGoq4vW0jKqnn+Vmtc24q1voE8F52OKhMPU9mnmA8M/l4uhaTpYE1dWt6eCrk4ffm98LdW2qjpadu0l5PUxfd0CFl6yCovLRnJBOqd9/nJ8/iB1fe7FPM0emqrH9jEv0O6hdVdpTFnzjt0E2uPPc1PZsOoqKaUWAB8n2iziZk3TapRSlwLlmqZtBdA07fZjrOKXwP1KqXeJJgU+AeQAv+9e/4+AkzRNO6N7Og24CniVaIeUH+2ePrVXTJ8nWhtiJ9E+ID5MtNPJK3pt93fAp4n2J/Ebon1AfAe4u2/zCyEmKn9LG9UvvtHzANOw+X0igQDJ82ej1Nh10tVv7YuIRiQ0fvp7daS5eesPT5E5t5D04iwa9lVi1Osw2SzHXM5gMmB322mta40pH42e2N2ZbtwZ7pjsvdlmJiU7dcS3NVWl5qai0+mI9HroL5pfRFJ6/29Vk/p52+rOcFNy8hwMCkpfeo9QVzR5kTmncFixaKEwwY74m+2Qd2gPSVpEo3XvAVp37QMg0Oah83Ad+eeehtkt1YfFidNCYcL++AeDSODEzu22tPi/q/RZ+ZgdR8/H/pZW2rduZtaSLHSrigm1tRJubYLc9AHXazAZsLlsdLTGJr91eh1J6Um4pcO6MWUwGTBZTAS6YhO8pj79NvQ+xjKK4jv0XXTaYnJm55Px1Q+hRcJYXNFmai27S2l8bxsAgdZ2vHX15J99GpbUof2edXo9OQun0dz9pv2IzDmTtz+OREqbnkvZG9tjyrIXFOPOTCa7JJuaA0cTFAajAafdSOPWHYQ6faQtW8iCi1Yx/dRFGIwGTDYztQdrUDqFFol9rBvrkXIi/TUL00AbpLnYVDPkGhDdHUFuAnKJduh4pK5yCfA/Q1mHpmkPAZ8HvgG8D5wCnK9p2pGUY3b3+nq7sXu7bwLzgPWapvV+BWkiOrLGNuCN7nVeoGnao722WwmcTXQUjveJJjz+DHx9KHELMRH4W9vimjm07C4lfILVY4fL5LCj69Pfg9Fhx2gfuC17Z3M7h7eXUf3+fjxjUF0upTiLklMXUrvzIAc3bEdv1DPz7GVxzTL6crgdXPDxC2PKMgoyyJ2RO+IxulJcXPfN68mdGV13en46V33lal7858u89MDLcZl+MXyZhVnc8J0bSOruq6FkcQmrLl3N43c/yb4tpQS6Yh+sZiybgT3p6HGsN+pZe9U6nBlu/B5fT/Ihd/F0suYOMwEBWKbNQGUWYJk2A2N30sCaPrSEU8jrpW3P/th1hsP4W+MqJApxXPQ2K7bc7NhCpTC5YjvujYTCtFTUUbllH/X7qgh0HvsalJyfwdwLTu4ZzcKe5mLRZWsx9OpAzt/ShhaO4K9vwFdRQbCtnZbdpdRX1PHGf97k/u/9nXefeTcmYetKdXHBJy6I2VZmUbRfiQ9/68O4jtF8Q4y8lKwUzrn5nJiykkXF2A2xNf1MvRKmeTPyuOqOq7C5bCidYtH6Ray9ci16vR6zw4LFZcfv8VG3q5zm2nasJTPQ27ofTyIaXb1qU/QW6Aqwd/M+HvzZwzz5h6eo2FMJQP6ymWTMPjqaRu6S6WTNLRqBvZ+6/F4/u9/dwz9//CD//dPTVHXXKEmfkUfhyjk986VOy6Z41TzsLjtXfvkqihYUAZCcmcw1X7wU1RBNSLSWlhHs9KKUwpZkx9TdkWlaXjprr1oXs+2lZy8jPT8+SRnw+WnYX03lln00H6odtC+Z4TDa7egtsUkPg82KQfpziqGGWgFAKfUO8FdN0+5WSnmARZqmlSmllgFPapo29r18jIHly5drmzdvTnQYo0opxVu/eXDI86/+9LXDqtZ2ZBvvPPzasJZZefWpw95Ogo35WGC9j0/PoUpq34xtHmCwWsk//3QMlmO/2R9pvsYmGjZ/gL+pBWtmOmlLF2JJcfc7r6eumQ2/e4KO7loFJruFUz93Oe780R3KMBQI0VHfQjgQwpGRhNlhG3whIBgMUr23mpqDNdhddvJm5pGSnTJqcfo8PjpaPVSVVvPgzx4m1N1MxJ3u5pO/up3UoW07IePUTZTzp6fZQ3NtMy/98xV2bdzVc965+fsfZd6quTHz1lfUU7W3klAgRO7MXHKm56KUIuDtoqO+FaUUjszkY1YN7ysUDFL64nvseGJjT9n8i1aSO78AR04mOsPglRVDXi/l/30x7m109tqTcRSMfIJshCX03CmGLtDmoWn7LjoqqjA6HaQvX4wtKyOmll3lln28fe/TPd2LTztlAQsuW3PMGmbhYAhPfSuhrgCOtKSe3vCPaD9YQd1bm2LKzIWFPPbQRsp79RGw9MwlXPX5KzFZo39/QX+Qqn1V1JbVYHPZSctPIyUrZbijCcnxOUL8Xj9V+yqpO1SPK81FTnEmweoq2g8cxGC2kLZ8Ifac7LiXAW2NbQT9QZLSkjCaj77g8Hu8bH34VSo3R2t+KZ1ixXXrCVUfIhIMkXnyMlwlRXFx7HhrJ3/55n090waTgU/f9SnyZ+YdPZfrFI6M4Z3LE2RcH59bX3mfv3//Hz3TZquZT//fJ8mZlkPIH8BT30okFMaZkYzJ3qvWk7eL5qp6/NWHCdXXonV3Eq4zGCi48KyYzkqP8LZ7qdpbSWN1IylZKeTOzMOZEpsgDfkD7Hr6XfY+fzT+5TecSdGqeSNWW7irsZmGLR/Q1diMNSONtGULsaRMyRpXA/5Ah9MEYx7wdD/lzcDo3X0LIYbEnOJGb7XE1HhIXTJ/zJMPANa0VHJPO4VIMIjObEJvHHgEjNpd5T3JB4BAZxf7X9/Gsg+dMarjuxtMBtx5A1ffHYjRaKRofhFF84tGPiigfH8l5aVV6A16ps0qJLsgE2+Hl4d+/q+e5ANAa0MrNWU1Q01AiGNwpjh597lN7HxrZ0z5S/94CXeWm/27D2EyGSmeXUBWQSYZBfHJMZPNQkpR3z6Wh8ZT28KOJzfGlO18ehO5S2YOKfkAYLDZSF08n4Z3tx4ts9swJ7uPKyYxtTXUNlG2+xC+Th9503IpnlWIXq/DlOQk8+TlpC2ejzIaMJhj3/R1Nrfz3j9fjhnbrGzDdgpOmk36MWqK6Y0G3LkD9wluSUlGbzET7jpaPd9ntMUkHwDee3Erp129npyS6Dsxo9lI8YJiivt0WigSw2wzU7J4OiWLp/eUadmppMydidLrMVj7v19JGmAUitaqxp7kA0Sbom3/7yYWn7eIQF0d5rQUAl0ByvaWc7iilqRkJ8UzC3npHy/FrCcUCFG6dT/5M/NO6FwuYnnbvTx33/MxZX6fn/Kd5eRMy8FgNpE8wMsms81CWk4q1Xt29yQfAFIWzes3+QBgc9mYuWIWM1fMGjCmtprmmOQDwNaHXsWRn05NTSONtc2kZ6UwbXYRdtfx1VqwpKWQc9opRAKBQe+Bp6rhJCBaiDa/ONSnfCkgPbQIkWAml5O8M9bira0n2OnFlp2JJS1x/QXozSb0QxiDua2fnshbyuuIhELjaujOsVC6o4xv3vZDurp7f09JT+Y7v/8qNoslpp+CI/orE8envw4/Q6Ew/7j7Ed56MVqzKCMnjW/99g7yika2wl+gsytuMGotHMHvHV7zKWdhPka7DW9NPUanHVtWBkanVPsUw1N/uJGffPkuDuw+BIDeoOebv/4yi0+eD4DOoEc3QHXikM/fb5MLf8eJdZ5rSnKSe+Y6fDX1BH1e7FmZVNf037xIzosTi9Lpjnu4YX8/HQn7WjqwZGaQvmAm5iQXrzy1gbu++Yee7z/8qasI9TPkdmQIw3CL4dE0LWa41CPC/ZT1x+iwk7N+Nb66BgLtHViz0ofcLHEggY74fpUcWck88+grPHrff3vKrr71Uq782MWYzMd3H6o3GafcPexwDGcUjAeAnyml8ojeKhmUUqcS7X/hb6MRnBBieExJLtyzppO+dCH27Ez0xhMbE3ssZM2LbytfuHLOlDtxh0NhnnzguZ7kA0BzQwtbNmzDnelm3eWnxMxvd9nInpbddzXiOM1cPhOdPvaSuPiMxWze8H7PdP3hRra9u2vEt21PdcUM5QlgdtmwpwyvjbreZMSek0X6soW4Z5bEtc0XYij27yrrST5A9Nx0368eoKM9vpPUvqxuZ1zNMqXT4Uh3n3Bc5iQX7tnTSV+yEFt2JplFmcxaMRO98Wh1/TknzyHtGDUpxOTiyEyOq+SdMTufpIJszO4kGuua+dNP7o/5/l/3PsGaS1fHlOn0OmYsnTHa4U459iQ7Z1x/ekyZwWigaBh9JJlcTpJmTCN92UIcudnoTSfWJMaelhR3b+yaU8B//hpbyf/f9z5OdXn8SB1iZAzn6eQbwH1AOdE/913d/z4A/GDEIxNCTAnp0/OYd9Eqdj/7Llo4QvEpC8hdMn3wBce5SDhC1b6qnmHHZp80m9yZueh0/ed9Q6EQVQer48prKmvR6/WsvXwtqTlpbH5+C3kzcll5/kmky432iCmYlc/tv/gEbz7+Fp1tHay+eDWvPPdWXI/tDYePDukV6ApQsaucve/uxZXmYsbymWQdR9VdR7qbNbdfxKb7X6CzoQ1nVgorbjgLW7IkEMTY87TFJxrqDzfS5fPjGKRKssluYcVHzmbLAy/RfLAWS5KdZdefQVLOyNbGa61v5eC2MtKz05i7cg6d7V5MFhML1y3AMshoRmLycOemseqWC3jvwZfxe3ykzcxl8dXraTrczN7Ne2lvaufG26/khf9uYP+uMgD8XX6sbhs3fecjbHjsTRxuB2suWU3+rLwE783ktOCU+ZitZjY+uRFXWhJrLlk1YMfdNWU17Nu8j87WTmatnEXB7IKYPj9GgjMzmTW3X8ym+1/A1+IhKTcNc5orrs+5SETD109tCTEyhpyA0DQtCFyvlPom0WYXOmCrpmmlx15SCCEGZnZamXPuSRSsmIUW0bClOtEPsd37eFa5p4J77rinp/rh6w+9xi0/u5XCATL/ZouZsy4/jT/88L6Y8hXrFgPRNrCrLjyZk85bgV5/7NE6xPDp9DqmLSimeH4RWkRDp9dx8FAV/Dd2voUnHe2Ucs87e3jwh//smX794de59ee39dtHxGDSZ+Rx+h3X4O/wYXHYMDuH1UmeECOmYHr8g9jpF68lOc09pOXdeems/fSldLV1YrSasbodIxqfr8PHk797gt1v7e4pW3T6Yi797KWYx3jIPZFYOoOevKUzSCnOItQVwJrspLm2hXu+8kd8nqMPj+d85AwqyqoIdAUwmowkpydTMD2PuavmoNPpxnSo8qnG5rSxeP0iFqydf8yfde3BWu758h/p6m7CteGRN7jhOzcy5+Q5/c5/vJRSZM4p4MyvXovf24XVZafN00lyWhItjW0986VmJJN5jGF+xYkZzjCc31JK2TRNK9M07d+apj2saVqpUsqqlPrWaAYphJjclE7hSHfjzEyeFMkHgE3Pbo5p+xgOhdny/JZjLrNy/TKuue0yzBYzDpedT3ztJuYsmhkzjyQfRpdSqqcpxinnrOSKmy/CZDbiSnby6f+5hdmLotV0O9s7eb5P51qdbZ1U7a087m1bnDaSslMl+SASavqcIr7y88+SmpGM3qDnnCtP58IPnYNeP/RWuyabBVd26ognHwAaKhtikg8AH7z8Po1VjQMsISY7W7ITV3YqRouJ8l3lMckHgJ2vbmfluqXkFWfzzV9/ifyS6Bt4vV4vyYcxMtjP+tCOQz3JhyNeuv+lYfeFNFSWJDtJ2amY7BbSs1L5+v9+sef6PmfJLL72qy+QmimdfI+W4dzp/w/we6Bvjy+27u++O1JBCSHERNfVT9U9fz+ds/WWku7mmo9fypmXnYpOp0jNkItfIqVmpHD9p67k3CtPR2/Qk5J+dBitSDhCsE/zDIBgYOTGExciEYwmI6vPWMHcxTMJ+AOkpCdjGEf9CYUH6CxwoHIxtYT6DEUMEOwKcsOnr8PmtOFyS9O28Sjoj7+e+r1dQ+6w8kRNnzeNb/3mDjxtHTiT7NiGODS7OD7D6YRSEddPNwBLiA7FKYQQotuK80+KK1t2zvJBl9PpdKRnpUryYZzQ6XSkZ6fFJB8AnMlO1l61LqZMb9STN3PgoQaFmEjcqUlk5KSPq+QDQFpeGplFmTFlebPzSB3hfibExJQ/pyCuQ+G1V60jKz9Tkg/jWNH8org+stZdfSo259glAmwOK5m56ZJ8GAODXlWUUh6iiQcNKFNK9U5C6AEL0ZoRQgghuhUvKOaGb9/Iaw+/hlLRC2nR/KJEhyVG0OLTF2O2mnn7iY24M92svXIdOdMlASHEaHImO/nQ169j0zPvUrqllNknzWbZOcuxJ8mQswJyZ+TysZ/cwmsPvYanuZ3Vl6xm9gj3IyBGXu6MPG7+ycd4/eHX6GjpYPWlq5m1cnaiwxKjZChp7U8Trf3wZ+DrQFuv7wLAIU3TNo5CbEIIMWGZLCbmrJrD9GXRET2MU2xY0anA4Xaw4rwVLDptETq9bty9KRZissooyOC8W8/nzBuCmKwmaccveuh0OooXFJM/O59IOILJcmLDNoqxodPrmLZwGgVzCuT3NgUMerekadpfAZRSB4G3ukfDEEIIMQSSeJj85EZJiLGn0+kw22TUC9E/g9EAcvmdcOT3NjUMZxjO1478XymVBZj6fF8xgnEJIYQYouaGFir2VxH0h8iblkN2QebgC4njUltVT9WBavRGAwUleaRmJg++kBBTUEtDK+X7qwj6A+QW55BTmJXokIQAus/jZYfR63UUTM+T0Q4mqFAoRNWBw9RW1ZOU7KRgRj72MewzQhy/IScglFIu4NfA1fRJPnSTseGEEGKY6moa2L51NxUHq5g1bzrzF80mKdk15OXrqxv45Vfv5uCeaA7Y7rTxjbu/xLQ5RaMU8dR1cG8FP/jkL/C0dQCQNy2HL//80z0Jn46OTnZ9sJdd2/aRlZPBwmVzycmThy4x/nR2eNn5wV52bds7Ksdq/eEG/vfO33Ng5yEArHYr3/zdlyiZWzxi2xBiKBobmtmxdQ8H9h1k+uxicrKy+Mnn78LTGj2P5xZnc8cvPk12gZyrJ5otr3/Ar/7f79Ai0e4Jz77yNK791OUjnoQ4eKCCbZt30t7ewaJl85gzf4bUbj1Bw2mw+gtgEXAp8ChwM5ALfA740ohHJoQQk1xrcxs/+sZdvPXapp6yj336ej72qeuG3J/Azi17e5IPAJ0eL0/87Vk+/d1bpE+CERQOhXn2wRd7kg8AVWWH2f7Ozp4ExDOPvcTPvv3bnu/nzJ/Bz//wbdIz08Y8XiGO5dknXuYn3/p1z/TMudP5xR+/TWZW+oisf8/W0p7kA4Cv08d//vw0n/vBbRjNcuMuxkZnp5e7f/4XnnrkeQBKZhaxaumynuQDQPXBGra9vVMSEBNMY20T9/zgbz3JB4Dn//0Kq885iTlLZo7Ydg4eqOD26+6guakVAKUU/3vv91m1bvBRzcTAhjMM53nAZzRNew4IA1s0Tfsl8P+Aj49GcEKIqScUCNJaWU/Dvko6m9oGX2ACK9tfHpN8ALjv9w9SWX54yOuoqaiLX+/ucrp8XSccnzgqEAhStrs8rrziQPR3dbiqlrt//peY73bvKKV0z8HRi6mzi+aDNTTur6arvXPUtiMml9rqOn7z03tjyvbt2k/p7rKR20ZlfVzZob3R81I4GKb9cCP1eyvpqG9B0/ob4V2IE3foQGVP8gEgOzeTqrL462v5/qqxDEuMAG+HL+aFwBHtLZ4R3c77m3b0JB8ANE3jD3f9jc4O74huZ6oZzusxN3Dk7qsNSAX2AxuBP41sWEKIqSjg9bP/pS2UvrAFAKPNwskfv5CU4uwERzY6unz+uLJwKEwgMPS+fmctmh5Xdsp5J+NwOU4oNhHLarNwynkn88Cv/x1TvnDlXACCwRDeTl/ccv39jkeCt9nDtn+9Sl33W2ZnVgorbj4PZ5a0ZRbHFgiG8HnjE5T+rpE7VmcsmBZXdsp5J2O1W6h4dxfb//UaWkRDbzKw4ubzyJxbNGLbFuIIT5+XGLu27eWqKy+ifF9lTPmik+ePZVhiBCSnuymamc+hXr9LnV5HVl7GiG6no5/kfmtTK6FgaES3M9UMpwbEAeDIFWU3cK2Kjnt0OdA80oEJISauSDhMxa5y/vubx3niV49yaFsZoeDgD9Xt1Q09yQeAoLeLDx5+lUA/N8uJUF9Rz443d1D6XimelvjM+3AVTssnOSUppmzpSQvIzR96VdBZC6dz3WeuxGSODkV3ynkns/7C1Sccm4i3+qwVrL/4FJROYTQZuOrjlzB78QwAsnLSOefi02Lmt9osFE8vOOY6/T4/5bvK2fbGdir3VREcwt8JQGNpVU/yAcBT20z5xp091VGbDzfxzmNv8dD3HmDLM5toq28d+o6KSS0rJ4PzLj0jpsxiNVM8vXDEtjFzYQkf/txVPeelk89czpz5JTz6k3/hDWnknLWCtDULyDppDu//8yW8I/zWUkxOkUiEmoO1bN+wgwMfHMDrGfgttLfFA3Xt5BUcfYHR3NQKZmLO45fedD7eujZeuf9F6g7WjsFeiOGKhCNU7q7g6d8+yeO/fIRDH5Shef1cdf05FM3MByApxcXHv3wdKW77gOsJBUJUlVaz7Y3tHNpZjt87eNJ14bK5ccP8XnvTZcPqq0vEG04NiPuAhcCrwI+Bp4BPE01ifG6kAxNCTFzVe6u4/86/oEUiAGx7+X2u/95HKF5ccszlfP081LdXNxLo7MJksxDs9KFFIhgdtjEf9/3QzkP84Sv3EOgKADD7pFlc9cWrcKcnDbLkwHLzs7jrLz/kL3f/kx3v72b9WWu4+sZLcDgHvoD25Uiyc9EN57DyjGWEg2HSc1IxmWVYyNGQnpPGLV/9MBffeC56vY6MnHR0+mge32w2c9vnbiQjK51nH3+JaTMKufWzNxwzARH0B9nw2Js8/adngGjb0qu+dCUrzlmOTnfs9wPNB2tROkXW/GKSclJoLq+nfk8ls88P4e/y89gvHuHwvmi14tJ39zB37Xwu/MwlmKwybOFUZzIZufUzHyY9I4WnH3+Z4un53PbZG5g2Y+QSEHannQuuP5uTTluK1+Plvafe5cmfPcLqq9fx+J+epulwEwCuNBcXX38GAY8XW7JzxLYvJqfS90q59+t/IRwKA7Di7GVc+PELcbjja/wFOnzUv7OXL3/mJv770gbe37qLpcvms2rNUmYsmMnFN55LU1Ujb/79FQ5UfwDAe89u5qaf3EJqnvTbM55U76vi/jv/TCQcvafc/soHXP21azn84lYuPm05uktPRQVDNG/Zj3/JTJz9VILQNI0PXv+Af/74oZ5mX2d9+AzWXrIKq8uBztD/WApzF8zkf+/9Pn+462+0NrVyzUcu46wLTh21fZ0qhpSAUEoZgWuBGwE0TXtZKTUbWA6Uapq2ffRCFEJMNDvf2NGTfDji3SffoWhhMeoYD1a2lPiMcnJhJkarieY9Bzn8xhbC/iDpS2aTtnAmpmE8qJ+ILm8XT/3x6Z7kA8Ced/dSta9ySAmIDk8nXq+XlNRkDIbY0+7sedP57i+/QqfHi8vtwjDARfBYdLqRr3Yo+mc0G8kt6r9JUF5BNp/68ke5/ubLsdqsWI7xsN9Y30Rnm5eX/vFyT5mmaTz6f/+heH4xGfnH7gwwbUYeqYVpdB6swLv/AClZ6aSuX4jeZKCxtLon+XDErjd2sPrKtWRNm5zNmcTw5ORncfuXPsqHPno5FpuFSDhMfV1Dv+eo46UAayhEfXk9u1/fgc1lo8Pj7Uk+ALQ3tlN24DBz1i8ckW2KycvT4uHfv3q0J/kA0VGgykvLKZyehyM59lpsdtowO6w0vLKdMwpKOG/pEgKNHnIy0zGaDDiddv75m7/Q1Xm0hqWv3UvtwRpJQIwze97a1ZN8OGLzM5spnJZN4/aj/SwZzEYsSf3fFzYdbuKR//1PTJ8zL/z9JfJzk3FoITJPWoCln1oNRpORVeuWs3DpXILBEO5+5gmHwzQ1NGO123CO0X3pRDekJhiapgWBYkDrVVahadqjknwQQvSl9blQAGiRCIP1NebKS2Xuxat7khQWt4MFV68n0NJO+bMbCHb6iIRC1G3aQfOekeswbTD+Tj81B2viytsajt1JpqZpbH7nfW778Je47Myb+Ol3fk3Foeq4+cxmMylpyceVfBDji1KK5FT3gMmH+tpG/vh/f+OKcz7KZ267k4WXLiWr+GiTm1AghLd98M6tkgvSaPlgN776ZohoeA/XU/f2B4S8XQN26te7t3AhlFK4U5LYtW0vt10fPUf9+H/+j4pDI9Mhn6+xhQOPv0wkFG0r7Uhx0nC4MW6+w2W1GO3WEdmmmLy6Ortoro22+Nbpday/6XT2tVVx60fv4I7Pfo/dO/bFzG91O1h249mY7BY8VY207CinZN1CnJnd/eRoEOnnnCjnyfFnoHvKGeeuwNA9qo7BYmTZR87Bntb/SyGvx4e/nz6ZfJ1+mneXUfPmVsKhgft1sDts/SYfKisO8/Pv381lZ93ErR/6PO+8+R6RSHy8ItZw0tx/BW4F7hilWIQQk8S8dQvY8uxmemcclp27jK76ekxJLgy2/sdoNlrMlKxfTOa8IoI+P7YUF1a3g9p3tsXN27R9P2nzZ2CwWkZtP46wu+0sWLuATc/GjliRUZh5zOVK95TxiRvuIOCP1px48G+P0dbq4bs//ypms1SFn4qeefxFfvOL6AgELc1t/PB7d/E/3/4Std1tj50pTpIz3IOuJ+zzEe7TaWCgzYO/rYO0vDQyirOo79WeecZJs7BbdQQ7OjE65A2NiNq/7yAfv+HLPeeoh//+OK0tbfzgV1874XNUV3MbaBo2o8KVnkTz4SaWLT+ZXRt3x8y3+PQl6PWjk3wN+XwE2trQgiEMTgcml+uYtfDE0GjhMIH2dkIdneiMRozuJAyW0b0Wu1JclCyaxoEPylhw+kLu+csDHDoQHYL6zdc3sXfPAR54/Pdk5Ry9LqfNyOPUO67B19qByW7Bke7u+f270lyccs06Xr7vhZ75zXYLmdNkOM7xZvaauWz67zsxyaGVl64mfXoup37lWvweL2anDUe6e8B1uDOSSM5w09KrPySD0YDDYcYHtO6vIL1uFnq9wpjkwmAdPCkaDAT502//zn8e/C8Au7bv4/aP3MEDj/+e2fNmHO/uTgnDSUDYgeuVUmcBW4CYbkE1TfvsSAYmhJi4cmfnc8MPbmLL05sIBUMsWjsHW2c9TVsOYnC5SF++dMAkhM6gx5WdGlNm6OftmCnJgequKtzRPRSTI2l0Rn4wGA2cfu1ptDW0sm9LKSaLkQtuOZ/8WXnHXO7ggYqeG/sjnn3yZW6+/Tryi3Kx2eSt31TS2tzGg/f/J668qqYGq8OKzWXjQ//vWpKG0KxHbzraz4clIwXlcqJ5OtCbjFiTnVzx1avZ9cYOyrYeYMbSYvILkmnb+h4es4mMlSsxuqS9/WQWCoXpbO3AYrditg7cJ8zB/eVx56gXnn6NT3/5Foqm5Q+6HW+HF4+nkyS3K67Wj767L5rOvWWc9+FTOVRai16nY80lq9n41NtomsaKc1Ywf83c49jDwYV8Ppq2bCXQ2tpTlrZiOdZMaa52onz19TRt2dozbU5LI2XxwlFLQrS1evD7/Fz+ucv4548fwuy29CQfjmisb6b8YFVMAgKiTTv7a94JsOjMpThTXbz/wntkFGay6IzFpOef2PHR5fPj7+zCmezs6SNInJjcWfnc8IOPsuWZTQS6giy/YAUFcwtRSuFIdw+YeAj4A/g8PuxuO64UFzd+6wYe+MmDNFQ24Ex2csnHziZ4KDqKhsFmoauhHl9VFaZkN6lLl8QlIUKhMM0NLZitZpLcTurqGnniX8/GzhMMUVZaLgmIQQwnATEHeK/7/33HV5py9ZUMBgPhcHjwGXvR6/WjvszxbAPggz0fDGv+4XYAqNfpWHn18Dpt0et0w9qOXqcjPMxqTwa9ntAwfl4GvZ7gMapoiSi9QU/hgmLyZufRumMn3urDHPnNhNrbCbS2DZiA6I8jNxNTkoNAd6JB6XVkr1xIV1eA91/YzBN/eRqAS26+gMVrF2JzjPyDfUZ+Oh/59o201LVgNJtIzU4Z9Pi09ZM4cSU5ee6xVyjbV84n7/goM+cdu2NOMXmYzEYysjOoroztad2d5ubSn55HSkYyziF2xGdJSSJt0Sw8EcXLL2xh37YyZi2ZwUUrOshPc5Oam8Yp15zKgmV5dJYdJNIUbUIU8Qfw1dVLAmISq6mo4/l/vsh7r39A4awCLrv1Iorn9N/BpM0efx52OO2YLYN3ZLt7eyl3/+Qv7N62jxVrFnPLFz5Mycyinu+t6Sk48jLpqKqjY9tusp12CtYvw5aVzimXrkHTNFKzUzGYRqbPib4CbW0xyQeAlp27MLmT0EsNtOMW7uqiZcfOmDJ/YyPB9vYRT0CEgiE2b/yAX//oXhpqGzn/8jO47AuX0lzbiE6ni6vq3t/xfCz2JDsL1i9i/qkLR6Rj6/07yvjPH56kqqyak85YxplXnUbmCSY0RPSesmB+EQXzi4a8TPneSp7483/Zv72Mhavmcd6Hz6ZgTgGf/t9P0lbXQqC+Cc+OvYS6E7CZK+birzsMQKClNXo890pAHK6o5cG/PMazj71MZnY6n/p/N1M8sxB3ShJNDbGDQfZ37ydiDfmsr2naaYPPNTil1CeJNuPIBnYCn9c07Y1jzH818DVgJtAA/EbTtJ/1+n498Eo/i87RNG1PP+v7EPAA8F9N0y483v0Ih8O8/LuHhrXM6bdfw8v/+4/hLfP569lw7wNDnv+Uj13H41/5zbC2cclPP01hatGwlvnDx381rPk//ocvsPPR54e1zLzLz+a17/95yPOf+o2bOfTKlsFn7KXotGXsf6G/w6d/088akT+DKUMLhfHV18eVhwOBfuYemCXZxfTLzsRb30wkFMaa5saansx7r33Avd//W898f/reX/nMTz7OkrWLTjj2fuOwWcguHnonfrPmTmfxsvm8v2VHT9mHP3olT//rJVqa2vjq7d/n9w/9jMxs6fBqKrDZbXzqCx/l4x/+ck+iODMrHb83wPZte7nwqrOGvC69yYh9Tgm//exd1FdF29VvfmUr5fsqufP3X8KdmgSRCP66OiJ9hvYMeuPHNReTg7fDx99+8gB7t5YCsOPtXRzceYhv/vmrZOTGd2w6a04JS1YsZOumo83c7vjWp8nOOXbzspqqOr5y63doa40On/nmK5uorqjlrvt/0DO0sMlho/CcNXjrmwl3+bGkJGFNT0Fn0JNRMPoPZVow/mVB2OdDO46XNOKoSDhMxB9/De97nhkJpXsO8v8+8f2eRMOj/3iacDjCbZ++lptuvZo//+HBnnkvu+Z8pg0y7PFARiL5UHOoll987tc9/Qy89O/XaG1o45Zv3XTMWkhi5DXVNvOrL/2W9uZ2AN585h2qDhzmS3d9FofbgcPtIJCbijc3lZC3C0WYQGN9zDEcCRz9fzAY5B9/eoQnH44+x5SXVXHn7d/nj//+BV/91qf5yme+2zPv3IWzpPbDEIxO2nkASqlrgLuATwIbuv99Rik1V9O0in7mP49osuCzwLNEa2Hco5TyaZrW9yl7HtA7BdXQz/qmAT8DBkx4CCFOTMjrxd/UhL+tDUtGOva8PDoOHoqZx+ga/vjJZrcTszv2re0bT70VN9+G/749agmI4crITOMnv/4WO7ftoaa6jlAgzNuvbKGlKdp5ZVN9M4craiQBMYUsW7mI7/38Tg4eKMdoMKCF4aF7H2ParCLOvng9pu4OtYaiqa6lJ/lwREN1I3WVDbhTk1B6PfaCAgKtsX1FWzMzCbS14auvRwuHsGZmYnJF5xcTW1NNU0/y4YhOj5ea8rp+ExDpmWn8+P++wc4P9tJY38T0WcXMWzh70O1UlR/uST4ccehAJTVVdT0JCACT0z5moxX1ZXDEN8mz5eWiH+W+CiY7vcWCJSuTrtq6o4VKYXQ4CLS309XQQNjfFT2vJLnRncCoKof2V8TVcnj6kRf58G1XctPt13HSKcsoP1hFbl4W8xbNwZ7A/m0Ol9fGdXK45bX3uayuiZwBRk4So6O2sq4n+XBE+b5KGqobsBjV0XvU1BQcOfm07dlLqCN2GHiD8+j5o6m+hWf+83LM9+FwhLK95aw/9xTu+9evKd1bRkqqm3kLZ5OVI7VeBjOmCQjgi8B9mqbd0z39GaXUucDtwJ39zH8D8KSmaXd3T5cppX4EfFUp9Vsttqvvek3T4rtX7tY9lOg/ga8DpwFyxy/ECAv7/TTv3EGoPXri76qrxTVrNvbCAjorKtGbzbjnzcGUNPwERH9Ss1L6KUsekXWPlOzcTLJzMzmw9xAfu+wL8VVGHcOrMiomNr1eT1tTO4/+9Wki4UjPiBX5RTnoDcNrL2waoJp87+rzlox0XLNm4NlfhtLpcM2aicFipnHL0U5ivdXVpCxegiU1td/1iYnDaDJiMBoI9Xn7f6whYbNzMget8dBXfw96eoMeq238PNwrgx73vDl4yg4R7urCmpWJPS9HOqE8QTq9Hvfs2bTr9HhrajBYrbjnzwOdjqYtm3tqmPhqakiePx9r5vF36ujoJ3mVlpmK2WLCnZzE6nUnsXrdSce9/pFktsT/jVlsZoymoSeVxcjo73eh0+swmgxx96jWnByc00sAhbe6Gr3FQvL8uZh6vSgzW0ykpidTdzj23bbdacNiMbP0pIUsPUmGEh6OMTsLK6VMwDKgbz3854HVAyxmBrr6lPmAPKBvg8bNSqkapdRLSqn+6sn/ADikadpfhxe5EGKoQt7OnhP7Ee179+AoKiT7tFPJWLsGW3Y2uhF607rm/JNjHsLMVjOrzlk5IuseaflFOVx/6+UxZVfeeBEFxbkJikgkyqpTl+N0OXqSDxarmStvuGjYIwFkFWRy2uXrYsrWX3oK2b1GZzFYLLimTydr/ToyT12Ls6iQrqYm+o6J21FRLlXTJ4GMvHQu+dj5MWULV88ndxhNx4aicFpuXJOhmz51LfmFOSO6nRMRaG2lo/wQ1qwMXDOnEwkFaS8tJTwKTQWmGqPDTsqiBdHr+imrsWakE2xrizuHeA4ePKGmGTPnTmNWr36SlFJ89mu34E4ZvKPesZY/PYeZi6fHlF3xiUtIy5bE7ljLKcpixZnLYsrOu+4sUlJscfeovsOHQdNIWTCP7NPWk3nKaqyZmTGJyuRUN5/92i0xTXVmz5/OjLnSh9fxGssaEGmAHqjrU14HnDnAMs8BdymlzgZeBKYDX+r+Lhs4BNQQrUGxCTARrTXxklJqvaZprwN0L38NsHgogSqlbgNuAygoOL72ZEKMlvF8fGraQP3RasPqdHKops0t4mt/+DIHdhxEKSiZX0z+9GOPTJEoJrOJa26+lCUnLaCmup7MnHRmzisZV28MR8J4Pj7Hi2kzC/nNP37Evl0HCAVDzJgzjZJZRcNej9lq5pKPXcDCVfOoKa8jqzCTaXMKMfd5262UiulMS+uns14tHEYDTrwl9Pg1FY5NnV7H+svWUTirgKoDh0nPSaV4bjEO98iOEGR32rn18zew7uxVNNQ0kVuQxcy5JRiMY12x9hi0CFo4jK+2pqdIb7ON22N8oh2fSq+Pua5rWj/nlUgkLtk5HJk5GXzv1/+PfTsO4PF0UlSSz8y5ffvBHx+SUpO49X9uomznQZpqWyiYkUdx90gNk8FEOj5tThsf+uyVrDxzGfVVjeQUZzFtbhG6sH+AJbTu43ngziNPPnUZd//zJxw6UInTZWfWvOnSfPYEJOJK0fdMpPopO+IeoAR4HDAC7UT7kPg2EAbQNG0vsLfXMhuVUkXAl4HXlVJpwH3AdZqmtQwpQE37I/BHgOXLl0+5ET7E+Daej0+j3YHeZiPs9faUmdPSMFhHr5lBwYw8CmaMz6RDX64kJ8vXLE50GKNqPB+f40lBce6I1H5xJTtZtGYBi9YsGPIylvR0OivKY8ocBYUjVjNpvJoqx6bdaWP+yrnMXzk6w1sekZyaxMlrlw0+Y4KYktygVMwDsKOwCJ1xfFaJn+jH54A/b9OJdcCYlZMxYdrUp2amkJoZ3zR0Mphox6c7LYml6xbHlIX9xuO+RzUajcxbPIt5i2eNdKhT0lgmIBqJJg36NgbLIL5WBADdfTx8VSn1te7lGoAzur8+dIxtvQNc2/3/+URrS7zYKwupA1BKhYB53UkMIcQJ0pvNpCxYiLe2hkBLC5b0DKyZmSfUCZUQYmSZXC5Sly6ls7KSSCiEIz8fU/LkvGkWU5fR5SJ16TI6qyqJdHVhzy/AlDy++giaTIxOZ8zP25aXj1n6lRHjiNyjjh9j9hPXNC2glNoCnAX8q9dXZwGPDLJsGKiGnmE0N2qaFj+231GLiTbNgGjTjL6vhr4PJAOfAg4OcReEEENgdDhImj4DLRyWXvWFGIeUToc5OQWTOxk0TTrlE5OSUgqz240pKUmO8zEgP28xEcg96vgw1imfXwL3K6XeBd4EPgHkAL8H6B7h4iRN087onk4DrgJeJdoh5Ue7p089skKl1OeJ1obYSbQPiA8DlwJXAGia1gns6B2EUqoVMGiaFlMuhBg5cmIXYnxTSkWrTAsxiclxPrbk5y0mArlHTawxTUBomvaQUioV+AbRZhE7gPM1TTvSGDWbaJ8Pvd0I/IxoXxEbgfWapr3b63sT8HMgl+gIGTuBCzRNe3rUdkQIIYQQQgghhBDDMuaNXjRNuxu4e4Dvbuoz3QisGmR9PwV+OswYbhp0JiGEEEIIIYQQQowYaaAlhBBCCCGEEEKIUSfdfgohjltbfQsdLZ3Y3XbcmdK7uBCTWVtDGx0tHmwuG8lZMmqGSDxN02ipbcbn8eFKdeFMdSU6JNFHoCtAS00zmqaRkp2CyWpOdEhCoEUiNNc009XZRVJ6Eo5kZ6JDmlIkASGEOC77t5Ty+C/+jc/jw+q0csmXrmT6shmJDotwKExjdSOBriAp2cnYXfZEhyTEuNFc20xHSweOFCcpw0gaHtp+kP/89GE6Wzsx2y1c/LnLmHHSLHR6qUgpjo+nxUNLXQsWu5W03FR0wxw1IRwKs+etnfz3N08Q8AVwpji5/KtXkz+3cJQiFsPV1tDGK397kR2vfgDArNVzOevmc4b8wsLX6aPpcBN6g5703HQMJnlsEScu6A+y/ZUPKNu6n5S8NIwWEzOWzySrOCvRoU0Z8pcshBi25pomHv3JwwR8fgB8Hh+P/uQhbrnrdlKyEzfut9fjZcNjb/HC/S8QCUfInZHLdXd+iKzCzITFJMR4oGkaezbt5YEf/RNvuxeby8b1X7uO2StmDbpsW0Mrj/74IbztXgD8nV08+tOHueX/bic9P2O0QxeTUFVpNfd/934aDzdhMBm4+PaLWHH2ckwW05DX0VjZwGO/eBQtEgHA0+zhPz//Nzf//DYcKfI2czwoe6+0J/kAsPetXRTOK+Ski4/ZvRsADVUN/Pt/H2H/1gMonWLNJas587ozcMrvVpyg+kN1NNc2U1vXzMYXt6CUYlnZUs7+yFmkZiXuHnYqkVcXQohh8zS29yQfjgj4Anga20943ZFwmEggcFzLVu2r4rn7niMSjt6QVpdW8+LfXyQUDJ1wXEJMZI3Vjdz/3b/3JBG87V7+9t37aaxu7Hf+cCBAJBwGwNPk6Vmu5/tQmPaGttENWoyqEznXnghfh49H7nqUxsNNAIQCIR696z/UlNUMaz1t9a09yYcj2hva8LR4RixWcWJKN+2LK9v95i40TTvmcpqm8c7T77J/64HodERjw3/e5ODOQ6MRpphiOlo9tDS3c3D7QSB6vG1+fgvlO8sHWfLEaOEw4UBg0ON/KpAEhBBi2OxJDvSG2DGU9QY99iTHca9T0zS6Gpqof+tdDr/yBm2lBwj5fMNaR31lQ1zZ7nf20NnaedxxCTEZtDW24++TNPR7/bQ1xiYRgp1eWnbtoeblN2jY9B7+5lZsLhsma+ybaaVT2N3H//cuEkfTNHwNjdS/+Q6HX3mD9v1lhHxdY7b9jtYOKnZXxJU31TQPaz391XKwumxYnbbjjk2MrIJ58c1hihdPQyl1zOW6OrvY8ebOuPIjD4xCnAhHspODO+KPpUO7Ri8B4W9ppWHzVmpefp2WnXsIdkzt+1JJQAghhi0lN5XzP3URqrvNrtLpOO9TF5GSe/wd0wVa26h94y266hsIdXTSsm0nHYcqhpUpTs6Ib1daMDsfq9N63HEJMRk4kuOThgajAUevJEIkHKZ11x7adu8j1NmJr7qGug1vYXeaufAzl/b096B0irNvPY+0/LQx3QcxMgItrdS9sZGuhkZCHZ00f7CDjsqqMdu+1WElPT89rtyVNrwOJNPz0zn9I2dB98Os3qDnos9eijvDPRJhihEwc+VssqZl90ynF2Qwd+38QZczWU0ULyyOK8/MSR7TZJmYnFJyUiiYnR9Xnj8zvmwkBDs6qNuwEW/VYUKdXtr3ltK8YxeRUHhUtjcRSB8QQohh0+l1zD91IVklOXia2nGmukjLS0On1w++8AACbe3QJ9nQXlqGo6gAg3VoCYT8WXksOX0xW19+HwC7y8b5HztvWO2KhZiM0vPSuPILl/OvXzxCJBJBp9Nx5ReuID3v6INgyOujsyL2QTQSDBFq9zB79RxuLfgkbQ2tOFKcpOWnYTAax3o3xAjwt7bFnWs9pQdwFORhsFhGffsOt4OrvnAFf/ranwl0RZuArLtyLTm9HlSHwmgxseKikyleXEJnawdJmW7SciUpNp6k5KRy7XduoLGyAS2ikZ6fPqT+OfR6PWsuXMm+TXtp7W7qNX3RNLJSLQQ7OjBYR/84FZOX1WFj7eVrKX3/AK11rQBMX1xCyeJpo7K9YHsHkUAwpsxXXUNobicm19QcuUcSEEKI46I3GsgsziJzhHoNVv0kL3RGIwyjZ3RXqovLP3sZay5Zjd8XID0vndRsGS5QCL1ez9IzlpI3I4+2xjaS0pLIKMiIGcVCKYXS69HCsW9llF6HTq8nvTCD9ELpdHKiG+hcq4Y5CsWJKFlUwhd//3maapqwOW1kFmViPo7hGY1mI9nTc0YhQjFSHG5HTE2roUrPcHHjZ86jrSOIXq/DaQwTqa9BN2/6KEQppprc6bl85n8/RX1lPXqDnsyiTBwn0Iz4WFQ/o0UpnQ6lpm5DBElACCHGBbM7Cb3FQrjraPVK9/w5GMzDuym1OW0Uz4+vuinEVGcwGsgpySGnpP8HNoPdhnvuLFq27+opMya5MCVNzTc0k5U5xY3ebCbsP9oniHveHPSmsa0plp6f3m9TDCEAjE4HNrMerbYagAhgy8vB4JS+Z8TIcGe4x6TJljHJhSnZTaCltacsafYMDI6pO0y8JCCEEOOC0ekgc+0quhoaCXd1YUlPw5wytLHChRAnTimFo6gAo8tJV0MTRqcDS1rqkJtAiYnB5HSSuW5197nWjyUjDXOyO9FhCRFDZzCQPG82tqwM/C2tmNxJWNJS0EvTLzHBGCwW0lcuw9/YTKDdgyUtBXNKyqCdsU5mkoAQQowbJpcTk0vG+BYiUfQmE7asTGxZmYkORYwiOdeKicBgtWDIy8GeJ81sxMRmtNsx2qdujYe+pm7jEyGEEEIIIYQQQowZSUAIIYQQQgghhBBi1EkCQgghhBBCCCGEEKNO+oAQQowaTdMIdnQQ9HjQ6XQYXC6MNluiwxJCjLCw30/Q4yHs92OwWjG6XOgMcoshIBwMRo8Nnw+92YzR5RrzETfE2IqEQgQ9HkJeLzqTCaPLNewRrYSYiKLXwnbC/oBcC49BfiJCiFETaGujaet7EIkAoDOZSF2yFKNDhtESYrIIB4O0l+7DV1fXU+acPh1HQeGU7uVbgBaJ4K2qwlN2oKfMmp2Na8ZMGc1gEvPV1dK2Z0/PtDklFfe8eZJ4EpNaOBikbd9euurre8pcM2Zgzy+Qa2Ef0gRDCDEqtHCYjkOHepIPAJFAAH9LS+KCEkKMuFBnR0zyAcBTVkbI601QRGK8CHm9eA6WxZT5amoIezsTFJEYbSGfj/bS0pgyf3MToQ5PgiISYmyEOjpikg8A7QcOyLWwH5KAEEKMikgkQrjLF1ce9nclIBohxGjRQuH4wkgELdxPuZhStHAYNC2uPNLfMSMmBS0c7vdvX37nYrLTwqH4wkgErdeLOBElCQghxKjQG43YcnPjys3JKQmIRggxWvQ2G6pPG1eD04nBak1QRGK80FutGOz2mDKd0YhB+gKatPQWC+aU2Ou80unkdy4mPb3NjtLrY8qMLhd6iyVBEY1fkoAQQowaa0YmzmklKIMBvdmMe958TElJiQ5LCDGCjDYbKYsXY3S7UTodlowMkufOQydt/Kc8vclE8vwFmNPSQKfD6HaTsnixJKcmMZ3BgGvmLKxZWSidDqPTRcqSJdL3k5j0otfCJRiTknquhe45c6W/m35IJ5RCiFGjN5txFBVhy84GpdBLL9hCTErmJDepixYRCYXRGY3o+rwFElOX0eEgef4CIsEgOoNBeoSfAox2O+45cwmXTEfp9fIAJqYMs9tN6uLFREJh9EZjXI0IESVXASHEqFJKSfUzIaYAncGIziAPGiKeTq+XpNQUo3Q6DHLtF1OQXAsHJ00whBBCCCGEEEIIMerGPAGhlPqkUuqgUqpLKbVFKbV2kPmvVkq9r5TyKqXKlVJ39Pl+vVJK6+czu9c8tyql3lBKNSulWpVSryilThmtfRRCCCGEEEIIIUSsMW2CoZS6BrgL+CSwofvfZ5RSczVNq+hn/vOAB4DPAs8Cc4B7lFI+TdN+02f2eUBzr+mGXv9fDzwEvAl4gS8AzymlFmuaFjtYsRBi1LU2tFJdWo2vs4uswkxySnLQ6Y+dDw36g1TuqaR0yz7sbgfTl0wnqzhrjCIWQhxLJByhal8VpVtK0Rt0TF86g9wZ0VFwaspqqD1Ui9FsJHd6LilZMhKOgOaaZir3VlK+s5zUnFRmLJtBRkFGosMSE0BLbQsHt5dRW1ZLwdxCiuYX4UgevJNLT2sHh0ur8bR0kJqTQu70XEwW0xhELE5UU00T1fsPEwqEyJ6WRXZx9qDLdLR0cGjnQSp2VpA1LYvihdNIzkweg2jFYMa6D4gvAvdpmnZP9/RnlFLnArcDd/Yz/w3Ak5qm3d09XaaU+hHwVaXUbzUtZnDpek3TGvvbqKZp1/eeVkrdDlwKnAtIAkKIMdRS38Jfv3M/lXsqAdDpddz6o1uYuWzGMZcr3VLK379zf8+0zWXjtp9/nIxCuWEVItEqdlfwp6/cQyQcHe/85X+8zK0/v41gIMQf7vgjoWB0fPSM/HQ+9oObSctNS2S4IsE6WjvY8sIWXvnHyz1lqTmp3Pzjj8kDgjimztZOHvnlvyn7oCxa8OgG1lx+CufcfA4G48CPNV6Pl6d+/ySbX3ivp+yqL17JyvNPQik12mGLE1BfWc89d95Lc030PbPJYuQTP/s4hXMLB1wmFAjx6kOv8tZ/3uwpK1lcwjVfuxZHkozIkmhj1gRDKWUClgHP9/nqeWD1AIuZga4+ZT4gD+h71G1WStUopV5SSp02SDgmwAK0DBq4EGJEVe6t6kk+QPTN6RO/exKvxzvgMr4OHy/8NfbU4W33Ur67fNTiFEIMjaZpbHz8rZ7kA0Rv/ra9to23n3q7J/kAUF/ZwMGdhxIQpRhPGiobYh4MAJoON1G1rypBEYmJoq6i7mjyodtbj71J0+GmYy5Xe7A2JvkA8MTvnux5qBXj1/739sf8ngJdQV55+NWYa0tfjYcb2fj4WzFlB94/QEN5/ajFKYZuLPuASAP0QF2f8jpgoHrUzwGXKqXOVkrplFIzgS91f3ek7k0N0RoUVwCXA3uBl5RS644Ry/eBDuCJ/r5USt2mlNqslNrc0NDQ3yxCJMxEPz697fGJhubaZgJdgQGXiYQj+L3+uPLgMZYRiTHRj08xfFpE6zeB6Gv30t7siStvrW8dg6jiybE5fgQDIYL+YFx5KDDwA8VkJ8fn0ISD4bgyLaL1W96br8MXV+b3+enyxd9biHiJPD6bauOTRHXl9QQD8eeQIyKhMFpEiysPDXKciLGRiFEw+h4Nqp+yI+4B/g94HAgAbwMPdn8XBtA0ba+mab/XNG2LpmkbNU37JNH+Ir7c3wqVUp8DPg5crmlae78BatofNU1brmna8vT09GHsmhCjb6Ifn5lFmXFly85ahjPFOeAy9iQ7p1wZ21+tTq8jf07BiMcnTsxEPz7F8On0OlZdvCqufNHpi5m2oDiuvGjewNVmR5Mcm+OHM9XBglMXxJSZrCaypw3ernuykuNzaNLz00lKT4opm750Bqk5x+5bJi03DaM5dmjE/Fn5JGe4RzrESSmRx+eMpfFNdFeefxJWu3XAZVKyUylZMj2mzJ3uJr1A/rbGg7HsA6KRaNKgb22HDOJrRQDQ3cfDV5VSX+tergE4o/vrQ8fY1jvAtX0Lu5MP3wfO0zTt3eEEL4QYGXkzc7nxf27gsd8+TkdzB8vPXsb6q9ehH2SM+IWnLsRgNLDx8bdwpbo49dr15E7PHaOohRDHUrJ4Oh/6+nW8/q/X0Bv0nHrNeormFZGWm4bX4+WtJzdisVm46OMXUjBbEodTXWZ+JsvPWYHD7WDfpn2k5qRyypVrpWNhMSh3hpuPfPcjvPX4WxzacYh5a+ax7JzlmG2WYy6XWZjJrT/8GI/833+oK69j1opZXPTxC7E5bWMUuTheRXMLufILV/L0vU8T8AVYe/kpLF6/6JjLWOwWLvnMJWx+bjO73txJ0fwiVl26Gne6e2yCFsc0ZgkITdMCSqktwFnAv3p9dRbwyCDLhoFqAKXUh4CNmqYdqxHPYqJNM3oopb4IfBc4X9O0DcPeASHEiDAajSxat5Di+UWE/CFcaa5jdhx1hMPt4KTzT2LR+oXoDHqMJuOgywghxobZZmbBugXMOmkWCoXREv37TM5M5qJPXMi6K9aiNxhISnMlOFIxHuj0OkoWl5BVnMXKC0/GmezEbDMnOiwxQWRNy+aSz15KwBfAYrcMuRPJksUlfOpXt9Pl7cKZ7JQRMCYIi93CqgtXMvfk2YTDYdxp7kFHToNorZdzPnoOp159KiaradAXXWLsjPUoGL8E7ldKvUt0SMxPADnA7wG6R7g4SdO0M7qn04CrgFeJdkj50e7pU4+sUCn1eaK1IXYS7Vzyw0RHuLii1zx3AD/o/m6fUupIit2naVrbaOyoEOLYXCnH9yAy2FsOIUTi9HdDr9frZehN0S97kh17kj3RYYgJSK/XY3UMXAV/IHLMTVxJaUmDz9SHUuq4jhMxusY0AaFp2kNKqVTgG0Q7kdxBtEbCka7ss4GSPovdCPyMaF8RG4H1fZpPmICfA7lER8jYCVygadrTveb5FGAEHuqz7r8CN53gbgkhhBBCCCGEEGIQY10DAk3T7gbuHuC7m/pMNwLxPVvFzvNT4KeDzFM0rCCFEEIIIYQQQggxolS0n0cxEKVUA1A+6IwTUxrRzkHF8ev9M2zUNO3csdz4CB2fchwMbLL8bMb82AQ5f4oeg/2sJuK5cyr8/mUfoybi8TkUk+X3O9X3YyIfn5PhdzcZ9gFGZz8GPDYlATGFKaU2a5q2PNFxTGST4Wc4GfZhtMjPRgxEjo2hm4w/q8m4T33JPk5uk2XfZT8mrsmwz5NhH2Ds92PwLkSFEEIIIYQQQgghTpAkIIQQQgghhBBCCDHqJAExtf0x0QFMApPhZzgZ9mG0yM9GDESOjaGbjD+rybhPfck+Tm6TZd9lPyauybDPk2EfYIz3Q/qAEEIIIYQQQgghxKiTGhBCCCGEEEIIIYQYdZKAEEIIIYQQQgghxKiTBIQQQgghhBBCCCFGnSQghBBCCCGEEEIIMeokASGEEEIIIYQQQohRJwkIIYQQQgghhBBCjDpJQAghhBBCCCGEEGLUSQJCCCGEEEIIIYQQo04SEEIIIYQQQgghhBh1koAQQgghhBBCCCHEqJMEhBBCCCGEEEIIIUadJCCEEEIIIYQQQggx6iQBIYQQQgghhBBCiFEnCQghhBBCCCGEEEKMOklACCGEEEIIIYQQYtRJAmIQ5557rgbIRz5D+Yw5OT7lM8RPQsjxKZ8hfsacHJvyGcZnzMnxKZ9hfMacHJ/yGeJnQJKAGERjY2OiQxBiQHJ8ivFMjk8xXsmxKcYzOT7FeCbHpzhRkoAQQgghhBBCCCHEqJMExBThaevA09aR6DCEEGLCa2/x0NnuTXQYQox7nR4vbc3tiQ5DiDjtrR462jsTHYY4QZFwhNbGNvw+f6JDEcNgSHQAYnR1erxsfm0rj/zpKZSCK2+9mGXrFmNzWBMdmhBCTCjtLR7efmkzT/z1GcxWM1d/4lIWr56P2WJOdGhCjCvBQJBt7+ziwbsfpbO9kwuuO5s1567EnZqU6NDEFNfe6uGdFzfz+F+fwWQxc/UnLmHx6vlYrJZEhyaGqaayjuceeom3nt9E4cx8rv7EpcyYPy3RYYkhkBoQk9zOzXu4+9t/pq6qntrKen7zrT+x+729iQ5LCCEmnPc2fMC9P/47DTVNVJUd5pdfuZt92w4kOiwhxp39Ow/y0y/8HxWlVTTVtfC3Xz3E2y9uTnRYQvD+m9v5U/d5vPrgYX711d9Rur0s0WGJYery+bn/Vw/xzIMv0dbczra3d/KDT/2Cw+W1iQ5NDIEkICa5l/7zelzZK09sSEAkQggxcXX5/Dz74Etx5e+98UECohFifOvvRcczD75IR7s0BRWJE/AHefbhl+PKN726NQHRiBPRUNPIltdjr7++zi6qD9UkKCIxHJKAmOTSslLiytKzUxMQiRBCTFwGg57kjOS4cqlSLkQ8Z5Ijrsyd5sZgNCYgGiGidHpFSro7rjy5nzIxvhlNRswWU1x5f2Vi/JEExCR32iVrMZmP/jGaLSZOOe/kBEYkhBATj8Fo4OIbz0Vv0PeU2V12Fq9ZkMCohBif5iybRVKKq2da6RRX3noxFqv0lyISx2AwcOGHz4k9jzttLF2zMIFRieORkZPGNbdfFlM2Z9ksCqbnJSgiMRzSCeUkN31eMd/98/+jdHsZSilmLJhG0cyCRIclhBATzqxF0/nen+9k3/YyTGYjMxeUkF+Sm+iwhBh38opz+J8/fIV92w/g6/AxfcE0SuYUJTosIZi5sITv/flOSreXYTQZmLmwhPwSeWidaHQ6Haddspb86Xkc2ltOenYaMxZMk1qJE4QkIKaA4lmFFM8qHNYy3nYvfp8fZ7ITg0kOEyHE1NDe7CESDuNKdaHTxVYS1Ol0lMwtpmRucYKiE2LiyC3OJrc4e9S34+v00dXRhd1lx2SV6tfi2I6cx4tnF9Le1I5OL5XBR1skHKGtqR2DUY8z2Tli67U5rCxcOZeFK+eO2DrF2JAnSxFD0zQOfFDGf379GPWV9Sw4ZT7n3HQ2mQWZiQ5NCCFGjd/rZ9uG7fz3j0/j9/k59cp1rLroZJLS5G2KEONV+e5yHvvtE1TtrWLGshlceOv55JTkJDosMc61Nbbx1hMbef2R1zFbLVz48QtYsGY+Zps0ERppLXUtbPjPBjY8/hZ2l52Lb7+IeavmYjRLfzBT2Zin/ZRSn1RKHVRKdSmltiil1g4y/wKl1GtKKZ9Sqlop9S2llOr1fbZS6gGl1B6lVFgpdV8/67hJKaX185FBf/uoK6/jnv/3J2oP1RIJR/jgtW089pvH6fJ2JTo0IYQYNeW7y3nwJw/hafEQ6Arwwt9f5IPXZIQLIcarppom7rnzXip2VxCJRNi7aS/3f/8fdLTISBvi2N5/5X1e/MdLBLqCeFo8/PPHD1K+uyLRYU06mqbx7rObePVfrxMKhGhrbOP+7/2dir2ViQ5NJNiYJiCUUtcAdwE/BJYAbwHPKKX67ZRAKeUCXgDqgBXAZ4E7gC/2ms0MNAI/Bt45xua9QHbvj6Zp8lTdR31lA6FgKKZs35ZSWuvbEhSREEKMvkO74m8+Nz71Dr5OXwKiEUIMprG6CZ8n9u+zvqKe5trmBEUkJgJfh4+3/xv/uLD//f0JiGZy62jt4O2n43/WFXskATHVjXUTjC8C92madk/39GeUUucCtwN39jP/9YAN+IimaT5gh1JqDvBFpdQvtahDRBMTKKWuPMa2NU3TakdqRyYrqyO+UsiMpdNpa2hl54YdJKW5KFpQTFpuWgKiE0KIkdVc28yhHYcIdQW44JbzKN16gH1b9gGQnpuG0STVRIUYjyz2+PsVvUHfbzX6tsY2KnaVU3uwjtwZORTMLcThjh8qVIw/DZUNHNx+kI4WD8ULismblX9C1feNJiOpOanUVzbElLsz3CcYqejLZDaRkplMe2N7TLkz+dh/e952LxW7K6jcU0l6fjqF8wpJzowfBltMXGOWgFBKmYBlwM/7fPU8sHqAxVYBb3QnH454DvgeUAQcHEYIVqVUOaAH3ge+qWna1mEsPyVkT8tm3qq57Ny4CwC7y8bitQv56zfu65knNSeVj/7wZlKyUxIUpRBCnLj2xnb++f0HqC6t7ilb8//Zu+8wt64y8ePfo95G03v1zHjce4ntdKf3AiQkIZAECIGls+zS+VF2l13KLuzCstTQSUIJLZVUJ3bca9zt6b1Xdd3fHxrL1kjTizQz7+d55rF1dO7V0czR1b3vPec9d15CW0MbPe09bL3nSgxGSZUkRCLKLspi882b2PHXN8Jl1z1wbdQNElefi79+7y+8+fqb4bLNt27m+vfcIPPQE1xbfRs/+dSP6W47Pwr33s/fx/JLlk94nwaTgavuvYpT+06HR/ymZqdSvrp80u0Vkcw2Mzc8dAM/+OcfEvAHAMgsyKRkWcmw2wQCAXb8eQcv/OLv4bKS5SXc+7n7cIwSuBCzx0yeWWUQuvhvHlLeDFw9zDY5QF2M+ueeG2sA4gTwEHAQSAI+AryulFqladqpoZWVUg8DDwMUFc2vJSsdyQ7e+rG3sOnmi+jr6iOnJJdffvHnEXXaG9ppONMgAYg4mc/9UyS+2dQ/G6saI4IPALv+tpN7P38fSWlJksxujplNfVOMzmK3cP2D17PikuV0t3WTlptGfnk+eoM+ol5LTUtE8AHgjb+8wfobNpBbOv2rdIyV9M9o9SfrI4IPAM/++BkWrFyA3Wmf8H5LlhXz4f/5EI2VjRhNBvLL82Vk7ygm2j9LVy7gw//zQZqqmjBZTOSX55Oemz5s/Y7GDl7+7UsRZVVHqmiuasKRKkGiuSIet3a0IY9VjLLR6scqH34HmrYD2BHegVLbCY2C+BCD0zeG1P8B8AOA9evXj/l15gpnupOl6aElbTqbO3H3R6fK8Hl8M90sMWi+90+R2GZT//R7/NFlXj9pOWlkFmbGoUViOs2mvinGxpFiZ9GGRSPW8XujP+eapsUsjyfpn9G8bm9UmavPRcAXmNR+lVLkl+eRXy5B5rGaaP/U6XQULCygYGHBmOoHfIGYf1+fV6475pKZDEC0AQFCIxculEX0qIhzmoapzwjbjErTtIBSag+wcKL7mItqztSx8+V91J6pY9PW9Sxfv4TkjGQ2376ZbU9sC9czGA3klAz9swghxOySVZSJ2WbGM+AJl628YmV4LrDH7eH4gVNse3YHDqeDi6/dSPnSUi5YiEmIOanyZA1vvLiH5roWtlyzkWXrFmN32OLdrAnJyM8gJSuFrpaucFlBRQHpecPfhRWJIbcsF51eRzAQDJdtueNinOnOMe+jq72bQ7veZNcr+ylbUsLGy9eSX5I4I19EpNScVBZdtIgTO0+Ey6xJNjILs0bYavJaGlrZt/0Qh3cfZdXGZazespKsXBkVM11mLAChaZpXKbUXuAZ44oKnrgF+P8xmO4B/V0pZLlix4hqgAaiaaFsGl/FcSWhKhgCaapv5f+//DzpaOwF49ekdPPCxe7jt/hvYfNsWrA4bu5/eRXpeBlvfsZWcUglACCFmt8zCLN797+/h5d+8RMOZBlZftYb1164Pzws/tOso//KRb4XrP/XY8/zrTz5P+dIF8WqyENOu5kw9n3/vv9LX0w/Ay397nQ/9v/dy1W2XxbllE5Ocmcw7v/wutv1uG5WHzrJ402I237oFm3N2BlTmk7zyPB762rt58Zcv0NXazaabL2LlFavGvH3AH+Avv36W3//kLwC89uwbPPeHl/jy/32azBwJQCUis9XMzY/cQlZRFodfPUzBogKuuOuKaQ0Y9vX08/1/fZR9rx8C4PXndrHh8jV89CuPYE+S48R0mOkpGN8CfqGU2gW8DjwC5AHfB1BK/RuwUdO0qwbr/xr4IvCoUuqrQAXwKeBLmqaFh/8opVYP/tcJBAcfezVNOzr4/BeBN4BTg3U+TCgA8f5pe6ezTOXJmnDw4Zzf/t8fufiajWTmZnDF269g440bMZgNmMymOLVSCCGmVkFFAW//9D14XB5sTlt4dIPH7QmftJ7j9fg4sOOwBCDEnHb2eFU4+HDOr7/3O9ZdspqUcdx5TiQ5C3K442N34On3YHFY0Ov1o28k4k6n01G6spSCLxfg9/mxjfNisLm+hT/9/KmIssaaZmpO10oAIoGl56Vz3UPXc9nbLsdsM097Muj6qsZw8OGc3a/sp6G6kYXLy6b1teerGQ1AaJr2mFIqHfgckAscAW7UNK16sEouUHZB/W6l1DXAd4E9QCfwTUKBjAsNXc3iFqCa0EoZACmE5i3lAN2D9S/TNG3XlLyxBNffN4Db7SEtPWXYocPBYDC6LBDggjiP3C0QQsxJBpMBgyny61ALgt8XPefU7x957nEwGKSzoxubzYrVFr1MoBCJYri+GgxE93G/P4CmRZ8nzCYGgwFDsqxqMxuZLCZMlvHf/NI0CGrR6QoCgdndl+cDnU6HPXniiUbHI9Y1EEDggvLe3n78Xh+p6Skz0qa5TjfTL6hp2vc0TSvRNM2sado6TdNeveC5BzRNKxlS/7CmaZdpmmbRNC1X07SI0Q+DdVSMn5ILnv+YpmnFg6+ZpWnadYOJKee0YDDInh0H+PCDn+H+Wz/AD7/zC5oaWmLWLV5YiGNIRuHb33kTGRIhFkLMQxabmTvedXNEmU6vY83mFcNuU1fdwHe+9iPecfP7+cT7vsihfUenu5lCTEh9TSP/8x8/DvXVh7/Awb3nV4lYsKgEs8UcUf9t77mN1IyUGW6lEJOTnZ/JNbdfHlGWlplCcXlhnFokElF+SS4VKyJHOixZU0F+cS4+r4/tL+/m/ff9I/ff/kF++aPf0dbaEaeWzh0SCp7DThw9w4ce/Ex47d0f/fevcLs8/MMnH4oaflhQkseX/+9TPP+Hlzl7soarb7uMdZeuRqeb8RiVEEIkhDVbVvKpb32Up377PI5kOzffcy3ly0pj1vV4PHz/P3/Gc399GYD2tk4+eOAYj/7hvyldWDyDrRZiZB6Plx985xc8/eQLQKivHn7Xp3n0D9+hrKKEBYuK+OoPP83TT7xAU20L177lStZsWRnnVgsxfp0d3WhmuOX+6zh58AzZBVmULCnCYJbLH3GeMyWJj37lEbY9u4O9rx1k/aWrueS6TSQlOziw5wgfe+/nwyPCv/O1H6JTinvf/ZY4t3p2k0/gHHb2ZFU4+HDOE7/8C3e/63ayc6OXmCtdXMLDn34XwUAwah1tIYSYb2wOK5uuXMeGS1ejdGrEgGxTQyvP/+2ViDK3y0PVmRoJQIiE0tLYyrN/fimizOP2UHm6hrKKEgAWLi+jfFmpnA+IWa22up5H/+8xnMlJLFpWzuk3dvHbx56kbEkx6Rmp8W6eSCB5xTnc/fAdvOWhWzFccMw7tO8oQwbe89jP/8SNd15DSurszImTCCQAMYdZrNHzj1NSnRhNxmG3UUrJyYYQQlxgLMdEo9GAzW6lv28gojzWcViIeDKajNjsVvp6IxNNWof0VTkfELOdZXAqUU93L7u377+gXI7LIjbDkGOeMzkpqk5aZiomk1xCT4aMr5/DlixfSPGC/Iiyj37mfaRJAhUhhJhSeQU5fPCf3h1Rtnz1EsoXy4oZIrHk5GXx4U+9J6Js6YoKFi6JPb1IiNmqpKyI2+66PqLsrffdQtGCgji1SMw2q9cvJz0zLfxYp9Pxvo++E5tdEvNPhoRvZrHaqnr27jhE9dk6NmxZzfK1iyMidXmFOfznj77K4QPH6GzrYsnKCpauqIhji4UQYu664batFC8o4Pibp8nOzWTF6sVkZWfErHviyGl2btuPx+Nh02XrWLJi4bQvNSbEOdfeciUFRXnhvrp81WKycmL31eE0NbSwf+cRTrx5mpXrlrJq/dKIE3UhpkL1mVp2bz9AY10LF126huWrF2NzjO3iz2a38sjH3sVlV22i8kwtC8qLWLZqkaxQJMaspKyQ//3Vf3Bo75t0dfSQnZtJe0sn1WdqKS6TZKYTpYbOaxGR1q9fr+3ZsyfezYjS1NDKJ979BWorG8Jl//DPD3LXA7cNu9SmmHYz/otP1P4pEk5cDgrSP2M7fuQ0H3rHp/G4vUDojsp//vTLrLlo+BU25jg5ds4y3V09fOnj32DP9oPhslvvvo4PffrdUStozAHSP+OkrrqBj7zzc7Q2t4fLPv2vH+aGO6+KY6sSjvTPGfC33/+db3/1B7hdHgCycjL49s+/Sn5RbpxbltCG7ZsyBWOWOnO8MiL4APCT//ktTfWxl9kUQgiRGF57YWc4+AChJZMf++mT+P3+OLZKiLGrPlMXEXwA+Mvjz1Fb3TDMFkKM38mjZyOCDwD/962f097aGacWifmoraWD73/jZ+HgA0BLUxunjp2NY6tmNxnvOUv5fJEnqqs2LGP1xmU01DfhSHaQlGSPU8uEEEKMJCMnjXc88haUUux57SDHDp+it6efYFBGJIqZFwgEqKmqo7e7j9yCHDKz0kfdxufzRZVpmobfF4hRW4iJ8Xmjg7Iul4dAINTP6uuaaGtpJz09lYLivJlunpgnAoEAHrcnqtznCfXPttYOGuuasCfZKSrJx2CQy+vRyG9olipdWITdYaO/b4Drbr+S9q4Ovv2NHwCw6ZL1fO5fPkZRydiT7AQDQXR6GRAjhBDjNZ7j59nT1Tz2yyd589BxdDodN995LZk56Vx102WYRlihSIjp4Ha5+dPvnuHrX/kuXo+X3PxsvvX9r7Bs5aIRtytaUEBeYQ4NtU3hstUbl8/4cGQ5d5nbyhYXY7aYIkaMvf3B28jMTmf7q7v55w99me6uHhxJdr7yzU9z5TUXRyyXLP1jdgoGgyMuez3TMrPTufvB2/jZ9x4Pl1msZkoXF3PszVP84/u/SG11PQajgY/+88O85Z5bsI8xT8l8JTkgRpHI85yOHznN73/5N7IL0vnO138Y8dz7P/oA7//Yg6Puo7utm2NvHOPgiwcoXFzI2mvWkbMgZ7qaPNfJPDyRqCQHxDQY6Bng1L5T7HpqF2k5aWy8cSOFi4dPSuX3+/naF77N47/6c0T5v/7XZ7ni6otxzN+Ra3LsjJND+4/yjtvfH1G2eGk5P/zNf5KcMvIa95Wnanjyt0+z743DXHr1Rdx4x1UUlMzMXei2+jYOvXyIk3tOsGTTEpZfuoL0vNFHbkyQ9M84evPACX77kz9SW9XArXddx2XXbsbj8XD3Te+lp7s3XM9sNvH40z+mpLSQ2uO17PzrG/S09rDx5osoW1OGLWnOXhDOif6paRo1x2rY9ded9HT0cNHNmyhbXYbVYZ3S15motpYOXnl2O3954jkKS/J4+0N3UFxewIce/DR7d0VOR/vZ7/6bNRtWxqmlCWXYvikjIGaxxcvL+eev/gNf+cw3o5578bnXePCRe0Zcg97v8/Pq46+w4087AKh+s5pDLx/i4W+9j7QcyWQthBAjObztMH/6zpMAVB48y6GXD/LIf76f3LLYd4F7uvt45cUdUeV11Q3zOfgg4qi+tjGq7PjR03S0d44agFiwsIgPf+Y9uF0ebHbrjCXA7u/q5/F/f5y6E7UA1Byt4dS+09z3ufuwJiXGxYqYOstWL+KL3/xHfF4fVnvo73tgz+GI4AOAx+Olsb4ZU1DPjz75Q/yDU5XPHDzDWz7xFtZdu37G2y7Grv50PT/6px8SGJzGdWb/Gd72T3ex5qo1cW5ZSEZWGm+5/2ZuvPMqjCYjBqOBupqGqOADQF1NowQgRpE441vEhBgMBlauWRZVftHFa0fNRN3Z3MnOv+yMKOtp76G5qnlK2yiEEHNNX1cfL//mpYgyn8dH3cm6YbdxOGys3Rh9UlJWUTLVzRNiTGItvVlYnD9q8OEcvV6P3WGb0dW3Wutaw8GHc84eOENbfeuMtUHMLIPREA4+AKSmp2K1RQabDAY9WdkZ1B6rCQcfznnpNy8z0DMwI20VE1N9pCocfDjn5d+8hKvPFacWxWa1W8NLZic5HSxaWhZVJysnc6abNetIAGIO2HTJOjZsPh8hLCkt5I67bxr1hEAphdJF15FlPIUQYmTDHj9jlJ1jMpt46JF7yb7g5GTrdZeycm10EFmImbBoSRnv/eA7wo9tditf+o9/Ii09NY6tGtlwnzGl5JR2vigqyefLX//n8IWgXq/ns1/9GMULCiBG/9Dr9XGaiCjGKtbnV5fgf7fkFCef+crHSHI6wmXveOitLF5aHsdWzQ4yBWMOyC/M5Rvf+xKVp6vx+/2UlBWRlR19V2Oo1JxULn7LJbz62CvhsrScNHIWZE9nc4UQYtazJ9u5+v6r+d03fhcuM9vMFCwaOfnvoqXl/OKP36XqbB1mi4my8mKcY7zbLMRUcyQ5eM8/3M/W6y6ju6uH/MLc0EVcAsssyKRsdRlnDpwJly3etISMgtHPe8TcoJTiqusv5fGnfkRzYyuZWeksKCvCaDJSvLQYk8WE94LElVvfsXUu54CYE0qWl2A0G/F5zq+ws/W+rREjXxLRmvUr+O1ff0BtdQPO5CQWlBVJAsoxkCSUo5jriYB6O3s5vf80R19/k4KFBSzZspSsoqx4N2u2mhOJgMScJEkop4G730XV4SoOvHSQ1OwUVly2krxyWQpuAuTYKcalo6mDE7tOcPbgGcrXLqRifQWp2dM2akP65yxTf6qew68eorutm9VXrqFkeQlm28jTkmexOdM/607WcfjVQ/S297Jq6+rQ3806Z/9u84EkoRSxJaUmsWbrGtZsTYwkL0IIMVtY7FYWb1rC4k1L4t0UIeaVtJw0Nt+6mc23bo53U0QCyl+YT/7C/Hg3Q4xTQUUBBRWJPQJLTA2ZMCeEEEIIIYQQQohpJwEIIYQQQgghhBBCTDsJQAghhBBCCCGEEGLaSQBCCCGEEEIIIYQQ006SUIpRNZ5pZP+L+2k628iaq9eycN1CHCmO0TcUQog5JBgMUnusht3P7MHV62LjTRslS7cQCayvq49Te0+x//l95JTlsmbrGnLLcuPdrHmru62bk7tPcPDlQxQtLWLVFavILpal38XU6Gzq4OiOYxzbcZSF6xay7JLlZOTL8ryJSAIQYkStda386J9/hKt3AIDT+05zzQPXcsXbr0CpuKzsJ4QQcVF/oo4ffvKHBANBAI7tOMo7vng/S7csjXPLhBBDaZrG7qd38/yjzwFwev9p9j67l0f+6xEyCzLj3Lr5x+/z8+rjr7DjTzsAOHvgDAf+vp+Hv/kwKVnTtoSqmCc8A27+8r2/cHzncQDOHjzLkdfe5F1feZfcNE1AEoCYY9rq2zi1/zQttS2UrypjwfIF2Jy2Ce+v6WxjOPhwziu/fZk1V62WLwwhxLxyfPeJcPDhnFefeJXydeWYzKYJ7bOzuZMzh85Sf6qe4qXFlK5YgDPdORXNFSLhaJpG7ck6Tu45ScAfYNGGRRQtKkSnn/oZwd2tXbz8m5ciyly9AzSdbZIARBx0NnWy8y87I8q6Wrpoqmoe8/mkq89F5ZEqTh84TUZ+BgvXlMvfUgDQVtcWDj6cU3+yjtba1nAAorejl7NHKqk6UkVeaS5la8pIy06LR3PnPQlAzCFdrV389Is/o6myCYBXf7eNm957I1fePYnRCsNuJ6MfhBDziy7G8VApJnx87e/u53f/9QeO7xo8afr9NjbfvIlbP3DLhAMaQiSy2hO1fPej/4vf5wfg7798gfd/632UriidhldTsT+bcvoSH4qYv/vx/Dn2PLeXJ7/7p/DjzIIM3vcfD5OaLTfE5r1hvofPlfp9fl787Uu8+vtt4efKVpXxzi+8Q0ZIxIEkoZxDGs42hoMP5zz/i+fpaOqY8D5zS3OxJ9sjyq6890qSM5PHtH0wEMTV70LTtAm3QQghppvH5QlfFA2nYsMi9AZ9RNlld12O0WSc0Gu21LacDz4MeuNvO2mta5vQ/oRIdAdfORTxOQsGg7z+5PaocwS/P4Cr3zWp10rOTObKe6+MKLMn28ktlRwQ8ZCancqmWzdHlBUvKyajMDNqZFksHc2dPP3TZyLKWuvaqD/TMKXtFLNTen561HTIwiWFZBZmAaER4tv++FrE82cOnqG5uhmAwBQcc8TYyQiIOSQQ4+TZ5/UT8I9+YB9ORn4GD33t3Rx+5RBNlU2s2rqa8jXlY7rjV3+2gZef3MaxPSdYc9kqLrlpM9mDBwIhhEgEPR097N92iBd//wppOWnccN81lK8oRaeLjs/nV+Tznq+/l/3P78PV72b9despXlY84df2+wJRZZqmEfBHlwsxFwz0DESX9Q6gBTWUPnReUX2ihucff4mq4zVsvn4jF129nozc9HG/llKK9ddvICU7lYMvHiBnQQ4rLl8pSenixGA0cNnbLiO3NJfD2w6z+qrVnD5axX9+4rssWlvBlXdcRkFZ3rDbBwNBfB5fVHkgxnFUzD8Wm4WbHrmZ8rXlHN95nLLV5SzZvAR7Sugmqt8XQAtG3wwN+IPUnq7jhd+9wulDZ9hw9Tq2XLeRzHyZ2jOdJAAxh+SU5GB1WHH1nY/gbbhuA2k5kxualluaO+47Bp2tXXznU98P38lrqGri9OGzfPBr78OeNPGcFEIIMZXeeH43v/327wGoPV3PkTeO8tkffJKSxUVRdXU6HcVLiyleOvGgw4WyCrNIz0unvaE9XLZg+QIy8sd/sSXEbLDmqjXsemZ3RNnFt10czgHRXNvCNz78HfoHc0/9/n//RHNNC+/85D0YTOM/ZbUn21l1xSpWXbFq8o0Xk+ZMd7Lu2nUs2bKU7332hxzdHRoB1lDVxOEdb/Kp//04acPkg0jNSmHLLZt57cnXw2Vmm5mcEllFQ4SkZqey6ZbNbLplc9RzGXnpLFpfwYk9J8NlKZkp2FPtfPOj/01XWzcAf/rR36g+XsvD/+8BLDbLjLV9vpnxKRhKqQ8opSqVUm6l1F6l1KWj1F+hlHpFKeVSStUrpb6gLrj9rpTKVUr9Wil1XCkVUEo9Osr+7lFKaUqpv07RW0oYmQWZPPL1h1mzdTVZRVnc8ND1XHP/VRiMMx9naqxuihpGfGL/KVrqWme8LUIIEUtPRw/P/OrvEWUBf4Cq49Uz8vrJGU4e/PIDbLl1M1mFmVx59xXc/cm3YXNIkFbMTSVLS3j3vzxIybISChYV8M4vvIPyNWXh5+srG8LBh3Nef/oNWhtlWtJc0lrfGg4+nNPW2E7T4HD4WPQGPVfcdTk3vfdGsouzWHX5Kh75+sOyjKcYE4vdwh0fuoOr7t1KVmEmm27cyLv/9SHamzvDwYdzDrx2iNYLbgyIqTfqlalSau0wT3UDlZqmjXl8v1LqbuDbwAeA1wb/fVoptVTTtJoY9Z3A88CrwAZgEfAo0A98c7CaGWgDvgY8PMrrlwJfB7aNVG82K6go4O3/fDc+jw+r3Rq3dsQKeiilMAyZPy2EEPGi0+sxWaKTPRrNE8vpMBG5C3K444O343F5MNvMMad+CDFXmCxGlm5aSvnqcjRNw2w1Rzwf69zBaDSg18u5w1yi0+tRSkXl/hiaY2eo1OxUtr79Sjbfsgmj2YjBIAO5xdhlFmRww0PXc+XdV2CymtDr9XR1dEfV0+l16A3yXTydxvLb3QPsHvz3wp+TQL9S6l+VUmP9Zvg48KimaT/UNO2YpmkfAhqB9w9T/z7ABrxL07Qjmqb9Hvh34OPnRkFomlaladqHNU17FBg226JSygj8BvgscHaM7Z2VDAZDXIMPAHklOSzbuCSi7LJbLya7SHJACCESgyPZzlseuS2iLCnFwYIlUzPFYqx0eh1Wh1WCD2LeMFlMUcEHgIKyfPJLI/MA3PzgDWTkybSkuSS7MIsr74gcAL14XQV5C8Y23ddqt0rwQUyIUgqrwxoOauaX5lG6bEFEnWvv3kqW5ICYVmP59C4YpjwFWAd8hdCF/zdG2olSyjRYf2i954Atw2y2GdimadqFaUmfHXzNEqBypNcc4l+AKk3TfqaUunLU2mJSHMkO3vWpezmx7xRVx6tZuLKcitXlsrScECKhrNi8jH/8zoc5vONNUjJSWLZxCXklkiVfiHhIy0rlg197mGN7TlBf2cjS9YtZuDJ2Ulgxe5nMRm558AYWrVnIyYOnKV5UxOK1FSTJcohihiWnOXnflx7k+L6T1JysZdHaCipWlsVl+vp8MupvV9O04SbDVgMHlVLdwJcYJQABZAB6YOgEr2bg6mG2yQHqYtQ/99yYAhBKqWuBu4HVY6z/MIPTOYqKohORibHJyEkn48Z0Lr5xU7ybMqdI/xSJbLb1T4vVzNL1i1m6fnG8myKm2Wzrm/NVdkEW2QXzb7TkfOufyenJbLhqHRuuWhfvpogxmMv9MzMvg8w8WR1nJk1FSHkfodEIYzV0DRQVo2y0+rHKY1JKZRDKG/EuTdM6x9RATfuBpmnrNU1bn5kpQ3Dmk0AggKvfHe9mjChR+qfZZEIpNa4fs2n8I2CKi4rG9Rom4/jbZTQYx1W/qHBufflOpdH6p9ftxRtjKTUhpttUHzv9fj+eAc8UtEyIxPluF4nF3e9OiKWZ51v/lOP79JqK8SW5QMsY6rUBAUIjFy6URfSoiHOahqnPCNsMtXywjX+/YPEMHYBSyg8s0zTtxBj3JeawhjONvPbka1S9WcXKy1ex/pp1Mu90BF6fj0NPPj+ubVbefs24X6emtpadj78y5voX3XU5zzz67Lhe4/oHrhvXNtc/cN249i/APeDm5J6TvPTYy+gNerbeu5WFq8tnNOGjEFOl5ngNLz/+Ks3VTWy4fiOrL19JSlZKvJslhJgjOls6OfjyIXY9s5vc0hwuf+vlFC0ujHez5oXqYzW8/PjLtNS0cNGNF7Hq8pUkZyTHu1lzyqQCEEqpbOCrwMuj1dU0zauU2gtcAzxxwVPXAL8fZrMdwL8rpSyaprkvqN8AVI2xmbuBFUPKvgqkAv/A+PJIiDmqo7mTH33mx3QPLsXz/M+fp/5UPe/47L0xE2UJIcbn9P4z/OxLvwg//vFnfsIj33iYhWsWxrFVQoxfc3Uz3//HH+Bxhe6O/eX7f6G3vZsb33ujrNYghJi0gD/AK0+8yrY/vAaEjjnHdh7nI//zIVl2dJo1Vjbx/X/8Pl53aKTmn773Z/o6e7n+oeslF80UGvU3qZQ6rJQ6FOOnGqgllNvhM2N8vW8BDyil3qOUWqKU+jaQB3x/8LX+TSn1wgX1fw0MAI8qpZYrpe4EPgV8S7tg7R6l1Gql1GrACaQNPl4KoGla/+AKGuEfoAvoHXzsHWPb5zWfy0NfSxd9bd20VzbR09SBFhzTLJhZoaW6ORx8OOfojqO0yzrAQkxaMBDk9T+9HlW+74X9cWjN1PK5vXTUtNBZ10pfWw/97T1RS8uJuaXhbGM4+HDOtidfp6ulKz4NmiNc3f30tXYR8PljPq9pGr0tnbRVNuHq7pvh1olEEvD76apvo6O6Ga9raofJa0GN/vb4Hsu7WrrY/ucdEWWeAQ9NVU1xac90Gujopa+tm2AgOK7tPH0u2qub6W5sJziFU1QazzaGgw/nvPL7bXS3Ri/XKSZuLCMgfjdMeQ9wAnhW07Qx/eU1TXtMKZUOfI7QtIgjwI0XJLrMBcouqN+tlLoG+C6hpT87gW8SCmRcaOhZ7C2EkmSWjKVdYmRdda0ceOJlcleUcvzFg/Q2d6I3Glh71+WUbVmGwTL7h1DrY2S71el0o65JLYQYAwUWR/TSwNYYZbNJb2sXe3/zMnUHz4CC4g2LsNjMJGWnsmDLMsyz/P2J2GJlRzeajOhk9MOEBHx+Gg9Xsu+xl/D0DlCwtoLlt24mKSs1ok7NnlPs/OXz+N0+bGlJXPbIzWSU5Y2wZzEXuXsGePOpnRz/+340TSN7SREX3X81zuzU0Tcehau7nzOvHOTE3/eidDqW3ngRCy5ehnmGl7bXGXQYTIao3A+xzlVnK2+/m+pdxzny5+0E/QHKr1jFwq1rsKUmjbptV0Mb23/0DB3Vzej0OlbcsplFW1djslsm3S6DMfo4brKY0Oll9MNUGvW3qWnal4b5+U9N054aa/Dhgv19T9O0Ek3TzJqmrdM07dULnntA07SSIfUPa5p2maZpFk3TcgdfWxtSR8X4idjPkPoPaJp283jaPV95+gZ44ydPY7CYOPvGcXqbQ3k8Az4/u3/1Ap11rXFu4dTIKcmmZFlxRNllb72UdMkBIcSk6XQ6Lr394ojhiwajgdVXrIpjqyaveteJUPABQAs9NifZePOvO2g5URvfxolpk1eeR1puWkTZjQ9dT6rkgJiQrtpWdvzwb3h6BkCDur0nOf70bgL+8yMhuhs7eP3HT+EfvDM50NHL6z9+BlfPQLyaLeKk5XQ9x57fFx6d0HyshtPbDk/JqNzGw5Uce3oXQV+AgMfH4T++RuuJoYvxTb/UrFRuePD6iLKMvAzySufOEtFtZxs48PjL+N1egv4AJ/++j7p9p0bdLuD3c/ivO+moDqUCDAaCHHzyddqrxpoacGT55fmkDglm3fieGyQHxBQbNZQ2uITli5qm+QcfJ2ma1nvB8xbgXk3TfjJ9zRTxMtDeS29jB/mryqjaczrq+d7WLjLLZ/8diKTUJO79zL2c3n+ahjMNlK4spXRFqawDLMQUKVlWwj/81/s5tusEeoOexRsXUVhREO9mTVjA56dm78mo8s76NuzpTur2n6JwXUUcWiamW3pOGu/9t3dzct9p2utaWbiuggXLS+LdrFmrp6kjqqxmzwmW3rwJe7oTgL627qi1z3qbO3F392F12maimSJBtJ1piCqr3XuKZTdsxDyJO+DBQICqHW9GldftP0XB2pnPVbTumnWk5aZxet8pMgoyWbh2IWk5aaNvOEs0H62OKqvacZQFlyzHaB5+xTRPr5uGQ2ejynuaOsgdciNxItLz0nn4a+/h5L5TtDe0U7FuISXLSia9XxFpLFdXTxO50kW9Umq1pmnn/vrJwA8BCUDMQQaLCb3RwEBHL87sVHqaI1cytaU44tSyqZeek0b6DRvj3Qwh5iSdXkfJspI580WuM+jJXlRIR3XkIlDO7FQ6TtdTtGFxnFomZkJWYRZZhVmjVxSjijVs2pbuxHDBCjnWZHtUHYvTJtOc5qGUguglILMqCia9opJOryelIJP2s40R5cl5GZPa70TZkqws27yUZZuXxuX1p5sjK3rKTHJe+qhTn41WE2klOTQfq4koPxesnApZRVlkFcnxfTqNZUKLGuWxmMMcmSmsfOtl1O0/xaKtqyNOCCq2rialcO6vBSyEEEMppSi7ZDmOzPPDMtOKslCAwWwgf3XZ8BsLIcJSi7LIuGA0lNLpWHPX5RHBhZT8DFbetjn8WGfQs/nB68Y0X1zMLVkVBWQvOb8cpTXVweKr16KbgpxdCy5ZjtF2fuUzS4qdvFWlk96viJa9tAjbBUEDg8XEwq1rRs2lY7SYWPOWSyMCl8UbF5G+IGfa2iqmnowvFyNSOkXJRUtIyc/A1dXHlR+5A6/LiyXJSnJeOqYxLFEZ9Pvx94fmaRocdknUJYRIeAGfj8DAACiFwR77uJWSn8G1/3w33Y0dBP0B9AY9fo+X8stW4pB8AGJQwO3G73ajMxgx2G0oJfdxLmRLTWLTg9fTWduKz+XBmZNGSkHkXWejxcTS6zaQt6IUT+8AjoxknHNoOPpcEfT78Q8MgKZhsNnRTcM0Vke6k0vfdzPdDe0EfAGcuWk4pujud2phFlf909vpqmtF6XSkFGTgyEyZkn2LSM7sNK746FvoqmslGAiSnJeBM3dsn+mMBTnc8Pn76G3qxGAxkZyXPqnpN2JiJvN5lwCEGJXBbJxwpmnfwADdx07gagwNabPl55O8uAKDVYZNCiESk6+/n64jb+JubQPAUVxM0sIyDJYYQ8VTk+QurBiWp7OL9n37CLjcKL2e1OXLsOXloiQQH8Ga4sA6ypROg9lIhtzlTFh+l4vuk6cYqA0lbbRkZ5G6bCkG29Tn6LAk2bAsmp7cH0nZqSRNwYoaYnT2jGTsE0zumJSZQpIEh+LG73LRc+o0/TWhhNvj/byPdU2RlUqptUqptYSmYCy74PHsTmMuppW7uSUcfAAYqK/H3TI3Vs4QQsxNA3X14eADQF91Nd6OzhG2ECJawOOh48BBAi43AFogQMfBQ/j6+uLcMiGmnqe9PRx8gND530BD4whbCCFmK097ezj4AOP/vI91BMSzROZ++NOQ5ye/9o2YE3o7e6k+VkPdyTrKVpaS7G6PquNqbsZRXBSH1gkhxMiCPj+upujlvDzt7fR4FSf3nSLgD1K2cgEFFQURS4sKcaGAx4u/vz+q3D8wgCl5Zpd062jq4OzhStoa2ileUkTxkmJsSTISUUydC4O257gam0haUCIjfiaovbGdysNVtDe2U7KsmKLFRVjnSOLVvu5+qt+spvZkLdnF2SxYXkKKjGiYNWJ93gcam3AsKBnTVPuxBCAWTKBdYh7ye/28+JuXePX32wDYmeHk4X+6DYhcYsucnh6H1gkhxOiUQY85LQ1fb29kuc3O9z7+ffq7QxeUeoOe93/zfSxYLl+RIjad0YDObCbo8USU680zO1e5p72Hn3/ll9QeP3+36uaHb+Lyt10mATQxZUwpKQzURy6RacpIl+DDBHW1dvHo//s5DafP/05v/+BtXHrHJXFs1dTw+/y8/PjLvPTbl8Nlizcu4p5P3YMjxoo3IvGYUlOjPu/mjLQx5/kb9ZtH07Tqsfycq6+U+p5SKj5r1sxSwUCQpupmju86Tv2ZBnw+X7ybNCGtda1s++Nr4cc9bT3U1Hejt58/mBidTqzZ2fFonhBCjEophb24CL31/EWiKS2N1nZXOPgAEPAHeO3J7QSDwXg0c0IC/gCNlY0c33WcxsomAv5AvJs0pxmsVtJWrUBdcJHvXFiO0TmzOUMaKxsjgg8Az/7sOToaO4bZYma01rdyYvcJak/U4nF5Rt9AJDRLViamlPMje/R2O46C/Ents7u1m1P7T3H2cCUDPQOTbeKs0nCmMSL4APD0j5+hoym+n9up0FbfxiuPvxpRdnzXCVqqo0cfTqeu1q5w/+rviR6tJoZnycyI8XkvGGGLSNORhPIdwDeA6LEZIqY3d7zJL776KwK+AEqnuOMfbmPjTRsxGie3pvFM83v9aMHI2ThP/Pdf+Kf/+whpTjOgMDjsMRO5CSFEojA5k8jashlfXx9Kp8PocPDyfw+deQi9Hb1o2uyYgRgIBNj34n4e/8YTBANBdHodd3/yLtZuXYNOL3fBp4slM5PsSy/BPzCAzmzC6HCgM8xs/m+vJ/qmht/rj2sA6uyhs/zosz/BMxAKPFxx1+Vcfd9Vc2Z4+XxktNvJ2LAeX28fmqZhdNgnlXC8sbKRn3z+0XCgbPHGRbz1o28hdZ4kiPR7oz+3Xo93TgSO/b5AzOB9rGPVdGmsbOKnX/gp7Q2h/lWxvoK7Pv7WedO/Jmuyn/fpOOuQ9aXGob2xnd/+x+MEfKEDihbU+ON3/0RLdUucWzZ+6XnpFC+NzO2QnJ6MyWHHkpGBJSNdgg9CiFnBYLVizczEkp6O3mxm9RWro+pcfNsW9LNkeHFbXRtPfOt3BAOhk75gIMgT3/wdrfWSFHg6KaUwJjmwZmdhTkmZ8eADQE5xNpYhS9StvGwlqTnxOdHu6+7nif/8fTj4APDy46/QcKZhhK3EbKA3m7FkpGPNzJhU8CEQCPDak69HjNI5vusEZw+fnYpmzgrZxdmYhyx1v+7qdXPiAjk9N43SlZHTF5PSksgqypqR1w8Ggmz/845w8AHg5J6TnDk4f/rXVJjM511ue8RZX1cf7n53RJkW1Ohp7x1mi8Rlc9q4+5N3c/HtF5OalcLaq9fw7n95kJSslHg3TQghJqVkeTEPfOld5C/MI7s4i3s+9XYq1i2Md7PGrLezLxzoPsfv89PbISsyzHWZBZk88vWHWX7JclKzU9l6z5Xc+J4bMJlNcWmPq3eAlpromyzdbT1xaI1IRF6XlzMHoi8G60/Vx6E18ZFdnM0jX3+YpZuWkpqdytX3XcW177wag3Hmg5hTzeqw8raPv41L77yE1KwU1mxdzcNfew9pMxRc8bi9nDl4Oqq89mRtjNpiOsz+XjzLJacn40ix09d1fu6R3qAnNTtlzPsIBoMJk0gquyiL2z5wC9e+8xosNjMGo4G+jl60YBBHuhOlZICMEGL2MVvMLFxdRn5pLma7GXuyI95NijDa90ByhhOTxYTX7Q2XmW1mkjNndjUGMXPOTbUBKFxUyP2fvQ+3y4PdaYvrd7Ej2UHR4kJqhuSlSMtJi0t7vG4v7p4BTDYLFoeM0pysvvZQIMmR7pzwPsw2M8suXsrLj70SUV68rGQyTZsxU3VeXrSkiHd+4R143V5scf7cTrWswkxufeQWrn7H1eHrhank6hnA5/ZiTbZhHBJstdjMLNuyjOYho80lqfTMkQBEnKVkpXD/597Bz7/yS/q7+zHbzNz9j28jszBz1G2rT9by2l+3U3e6gUtu3syKTUtxpk38gD9V9Ho9jmQ77j4XR184wK4nXiXgD7D21s0svWo19pT4nbh7evoJ+gOYnba4DIUVQkyMpml4ukN3683Jjhk/Eas/Ws2rP32Wjro2ileXseXeraSN4Tg93ZrrWtjz0n4OvnaYlRevYMPWNWQXRA9jzcjP4P7P38ev/+23uPpcWB1W7vvMPWTmS87ocwJeH94+F3qTAZPDFu/mTFh7Uwf7tx1i9wt7qVhdzubrNpK3IBeDyYDDFP/vPWuSlbd85E4e/X8/p7O5E71Rz63vu5ncspwZb0t7TQuv//IFag9VklGSzaUPXEve4sIZb8dc4Orp59jLh9jzx9cB2PCWS1h8+UqsSeP/LOl0OjbdeBHVR2uoPFyJUorNt2yidIovEIOBIN6eUK4f8xQElevO1LP96Z2cebOSTdduZNXFK0ib5Chgo9mI0Ty7csKNlU6vm/JVL4LBIHWHq3jlJ8/Q29JN4ZpSyi9bQUZRFpl5oe87pRQbr99A1ZvVnD10FqUUm266iNIVcysAEfD68Pa60FuMmOyJlV8n/t9EgvI15Xzs+x+hp60He7KdjDGcEDZWNfH1D/0XA70uAE4cOMWdj9zKTfdflzAR0oZjNWx79Lnw411PvIo91cHSratnvC0Bn5/241WcfXYnfpeHzJXlFF26GmsCBGyEECPz9g3QtPc4da8fAqUovHQV2WsWzdgXakd9G3/92mP4vX4Aqvefwd3n5pZP3YU5jl/qfT39/PRffsHJg2cAOH34LIdeP8KH/+N9OIacTCulWLppKR/7/kfo6+zDkZZEepzuOCeigbYuqp7fTcepGkxOO+U3biG1vCBiBYvZwOP28Icf/Jkdz+wC4NShM+x+cR//9D8fJS0rceaOF1QU8OH/+SCdzZ1YbFYyCzJmPBmqq3eA5/77T3TUhvKgtFU189evPcZd//YQKbny2Riv2kOV7Pj1S+HH23/5Io50Jws3L53Q/jILMnnoKw/Q3tCO3mggIz8D0xReiLu7+6jfcZimPcfRGQ2UbF1H5opyDJaJTU1qbWjjPz/+XTpbuwA4dfAMDZWN3P2hO+fEtInZor2mhb/9x+PhfEc1+84Q8AU4lm7nhndeh9UWGuWUWZDJQ19+gLbGdvR6HRkFmVPav+Ktv7WTyud20nWmHnOyg/KbLialNC9hvtOmoxW/BGQi3zilZqVSvLR4TMEHgNrTdeHgwzl/+9mzdLR0TUPrJubs7pNRZUdfPBCXDL59jW2c/OMr+AfcoGm0HjxFw66jaLNoCT0h5qvOM3XUvLKfoD9A0Oen+sW9dFXOXMK67saOcPDhnOZT9fS2xTdXT1NNczj4cM7pw2doijG//pz03HSKlxZL8OECAa+Pyud30XGqBgBvTz9HH/s7/c2zb7m71oZ23nh2d0RZS10rDZWNcWrR8JxpToqXFJNdnBWXlVh6W7vDwYdzfG4vXXNgmcN4OP7q4aiyE9uOTGqftiQbhYsKySvNnfKLw9YjZ2gcPA8MeLyceXoHPXUTXway/mxjOPhwzst/fJW2xvZJtlSMR1dDRzj4cE79kSrMJiMttZHfjdYkK4UVBeSV5c2p4IPf4+XMU9vpOhPKmeLp7uPob5+jv6Uzzi07b1xHfKXUCqXU/yilnlZK5Q6W3a6UWnOujqZp79c0TZbgnG4xRjnodCpWcdyk5kWf4KYXZsblRCPWiWTroVN4+1wxagshEklLjGRRbUdmLlu1yR49L9xoMcV9WKwaZtGpRBkFN1t4ewfoPDUk+Zim4Wrvjk+DJkEpFXMtMukT0YwWE/oYd6bNNskDMRGxpqSlF8R/mlosfreXlgOnosq7q5omvM+YHzGl5LM3w8w2c1SZzWmnt7t/3vwtvD399FRH9mUtqOHuSJzxAWO+ElRKXQvsBvKBrcC5cadlwBenvmliJEULC0hKjRxie+u7b0qoIZYl6yqwXTC3y2g1seyatRM6AAQDAXxuz+gVhxFrqLYlLRl9AsyHFUKMzJ6THl2WPfE7+EF/AL/HO3rFQemFmSy8OHIY8cX3X4VzHMmCp0NOcRbLLloSUbZs4xJyimdmKbO5QmcyYnREf0cYrNEnsokuKz+Dy2+9OKIsvzSP/AW5cWrRzAn4/AR8/tErDkrJSWPLvVdGlC3Zuoq0QsmLEkvA78fv8Q37/KJLl2O+IFhrdlijjpuJQmfQYc1IiSq3pCZNeJ8FZflk5EV+V1379q1k5EZ/f4npk16STcGFuRwUVFyzBrfHS3bh1Hw3+tyeqFEWiURvMsb8/jJY47PyUSzjufr6CvBxTdO+p5S6cNzpy8AnprRVCa6rtYvuli5sSTbS82d+3iJATlE2n/z2R9j7yn7qzzax8eq1LF5bMePtGEl6YSZ3/L/7aatuJhgMklGcTTAQ5NSOo5isZjKKs7CP4WDf29BG7fbD9NS1kLNqIdmry7Gmji93gyMvA0deBn0NocE5Sq+j5OoNGCyz7wRTiPkma1U5LQdP4XeFgpBGu4WMpRNLFtVd20LNtoMMtHaSu34xWctLsYySfMzr8lJ20WIKV5Zic9owOyykF2bF/W6KPcnOu/7pXg7teJOju4+xdMNiVmxahj1papN6jZWr30VbXRs6nY6M/IyYd6ISkTnJRtmNWzj++AvhsrSKokkFueLFaDJyy4M3UrpsAQdeO0z5ilJWX7KClMyUeDctpp72nlAeCLuFjPwM9Ab9uPfh9/joPFtPzav7ASi6bA2pZfkYTCOPUOpt68ae7uSqD9yC1WlDp9eRUZwtIyCG0IIaXTVNVL+yH29PP/mblpOxpATzkKCdxWHjiodvwNPvISndiTMzmZS86bn4DgaDtDe0M9DdjzPdSeo4p5TpDAYKLl5J19l6goNBK0uak5SSiQfq0nPS+Ng3/oG9rx6k6lg1665YxdL1iyfUp8XE2VMcXP2Bm2k8UYfP60NvMDDQ088V118EmjapfQ90dNN84BTNh86QXJRNweblJOUmXsDSnOyg7IbNnPjDy+Gy9KULsGclznfaeAIQy4CnYpR3AInzjqZZ9ZvV/Oorv6Svsw+9Uc8t77+FNVevjctQ3ILyfArK88OPPV09dJ6qBk3DmpGKJS3+y6ul5KaFkznVH63hL//2m/Ba9LmLC7nmQ7eRNMJSTQPt3Rz46d/wDbgBOPv33fS3drL4jstjDp0cjiUliSV3XU1/UzsBrw9rZkpCfRCFEMNzZKez6qFb6G/pABT27LQJJZDta+5g/4//Ej7hPP3UDrx9Lsqu2YjSxQ4mdDa089d/f4ye5i4AzHYLt372HowTTFQ2lfxuD2a/j9XLith0yTJMyU4McZoW0t7Yzp++8ySn94Wmyyy/dDk3PnwTKZPMAD9T0hYWsuo9t+Jq78ZgteDISZu1K2GkZqZwyU2bueSmzfFuCr6+AQZaOwm4PVjSnFgz0lCDN23qTtbxqy//ku7WbnR6Hdc9dD0bb9qIeZwjT7qrGzn8y2fDjw//8llWvetG0iuGX82is76Nv/zbY/S2habZWJKs3PrZe7E6Z+fffDr1NrRy4Cd/RRu843viyVcJ+vwUblkRrtPd1MlT3/xdOKeG0WLits/dMy3tCfgDHHr5IE9+50l8Hh/WJBv3fvYeytaUj2s/zoIsVr/nVvpbOtHp9dhz0rCkTHwEBEBuSQ43l8z8ai4ikj01iZJ15ez943b2/OH1cPn6t1zC2ls3YzQb8fa7cLV0EHB7MKc6sWamotMPHywKeH2ceWYnrW9WAjDQ2kX7iRrWPXL7uG+KzoT0xSWsenfoO81ot2DPSceYQCthjOfWfSeh6RdDrQXqpqY5ia2vs4/ffeMJ+jpDS8EFfAGe/M6TNFdNPGnNVHG1dXLy8Wep+turVD21jZOPPcNAS+IkUvIMuNn+qxfCwQeAxuO1tJwZOTFWf0tnOPhwTvOh07g7x5/8zey0k1ZRRObyMhzZ6XG/eymEGDtrejIZSxaQsaRkwqvX9DV1hIMP59RtP4x7cHnPWGoOng0HHwA8/W4OP7eXYJwT2Aa8Ppp2HeH075+n+ultnPj1U7QfPhm+SJhpR149HA4+ABzZdoSTe6MTEScqnV5PUl4mWSvKSSsvmLXBh0Ti6xug+rntnP3Ti1Q/+zonfvsM3VWhpGiuXhd/+u8n6W4NBQCCgSBP//Apms6OP1lm/e5jUWUNe6LLLlS591Q4+ADg7nVx7MUDaJO8QzoX9dS1Rh1XarYdjMih1XCsJiKhp8/tZc8ft+Mfx5SYsWo608Dvv/l7fIPTQVy9Azz2tcfCfWk8bJmpZC4rJX1x8aSDDyKxtFY2s/eP2yPK9v7xdVoqG/H1u6i94Nh08rdP03O2fsT9uTp7w8GHc7x9LvoTKPn/hXQGPUn5mWStLCe1rCDhluEcTwDi18DXlVIFgAYYlFKXA98Afj4djUs0fV19tDdEZ7PtSoCsot1nakMrPAwKeLy0HY5OsBMvPreXrobogIiru3/E7WJFI3V6ffgOihBCjJXOEH3c0BkNI06j66yPzqncVtUcEUyNB3dHN637jkaUNW4/gLtr5pNMBQIBjm4/GlV+YUBCzD8DrR301lwQUNA06l7cia/fRX9PP/Uno0/4Oy8I9o2VMcZUSoN15GkU7TFWimk52xj3wGIi0hmjz8P0ZmPEqLHuGBdhHXWtI+aMmKjOhraov1NfVx89szBprJg+A939UQFFLagx0NWPq7WTnurIlbRqX9qFt29g2P0pnS7mEpbxmIY/F4znt/Y5oBKoBhzAUeBF4DXgX6a+aYnHlmwjOTN6WoMzI/5THdztXdFlbZ1xuxs2lNVpZ+El0cmIUgtGnjtlz0mLSkBXfNkarJNIFCSEmJ8cuemYUyLzPZRduxGzc/icCUWrSqPKFl22Iu4rYARiJNEMLSc39Sf8o9Hr9SxctzCqfMGKieXpEHNDrD7q63cR9PmxJdnILs6Oet6ZMf7RTbnrF0fclFB6Hbmj5MRasD76+cWXr0Q/whDs+Sq5MBvjkLwYpVdviCjLW1wQtd2iS1dgiZHcdbKsdnPUCFZrkg17koxaEuc5M1OwJEX2P1uyHUdmMn5PdFJ7/4AraoTkhaxpSRReujLyNQoyZ2WuoEQw5kn0mqb5gPuUUp8nNO1CB+zXNC1xbrNPM2eak7d+4q388su/xDPgQekU1z5wLdkl0V+iMy15YTGdJ6sjytKWlSXMSAG9Qc+amzbh6XNz+o1jmO1WLrn/KrJKR074Y3HaWXHvNXSebaC/uYPU0jySi3NiRiGFEGIktrRkVj9wI52n63F19JBaXkBK8cjzdfMWF7L5vivZ87vXCPgDLL92HeUXLZ6hFg/PnJyE3myKuMgzOe2YR0moOV1Wb13N8Z3HaTgduqtUsmIBizYsiktbRGIwpyaH1ia84C5kclkhBocVs8HA7R+5nZ9/8ee4el0opbj87svJHeWcIJbkwmzWvvdW2k+GllJNryjEmT9ytvv8pcVcdPfl7P3j6wQDQVbesIGSGEE0AfasVNa852Y6TtXh6R0gvaIIZ2HkeW92eT6XPnAtbzz2Mn6PjyVXrGTx5SuG2ePkpGencN27ruK5n79IMBjEaDZyxwdvJS0/8ZIBivjJLMlm6/tv5vVfvEB3Ywep+elsvPsKMooy8XX2RB2bnKUFGEeYeqfT6yncshJnfhZdlQ04ctJILc3HLIGvCVFjne+mlPoC8A1N0waGlFuBT2qa9uVpaF/crV+/XtuzZ09EWXtDOx1NHdiT7WQWZmIcJdPyTPC5PHQePUPjzkNowSDZ65eRsWIhRntifTD8Hh99HT0YTEYcIySfPMfVM4DBZEiIhG9jMONJJWL1z5milOLQk8+Pa5uVt18z7jm2Sil2Pv7KmOtfdNflPPPos6NXvMD1D1w3rm2uf+C62TZXOC4JT+LZP8fL7/Xhc3kxJ1nRDQlwappGb2s3wWCQpIzkhMlq3tfQQu1Lu3C3dmLPy6Tgio3Y4phct6+zj9a6VnQ6RUZBJvbkMa/GMa+OnfOFFgjSU91A7Us78fUOkLKwmNwtq7Cknh812tHYQVdrF/YkG460pPH0mcm3L6jR29qFpkFSZvJIQ6mlf45RT2s3wUCApAwnesP0LHMe8HjpPFNLe2MnrgEPqXnp5FQUYo7T6j9D+dxe/D4/1pm7ME2I/ul1eQj4AzP5vkelaRptta24egYwO6yk56djMBrQgoPHphd34evtJ7mskLyL1yRE8v45Zti+OZ6jwxeB7wNDJ8jYBp+bkwGIWNLz0kmfpqWFJspoNZO1bikpFcWgaegsZgbae+jv7MeekZwwF/AGs5GUMayJ3N/Zy+nX3+To8/uxpTpY/9ZLyV1SJHOthBBTrvVsI/v+uJ22qiZKNy5m6TVrSc5JDT+vlMKZgKs5WDPTKLx6C1oggMnpiFoWb6Y5Uh04UuMzAkMkHqXXkVxagC0nnaDXj9FhRTd4Udrf3o23343BYsLb0cvun/0dvUnPujsvoXBl6Yys5qJ0Cmd26ugVxZg5Y0xTnmp6s4mMpWU4C0Nz/E0O24ijYl3d/bi7+zDZLNinccp0MBCk8VgNu594FVd3P8uuWUv5lmXY5vgxMeAP0Hi0ht2/34a7u5/l162nbPMSbCkz874HOnrw9LowO23YhkzPVkqRWRQ9GkrpdCQvKMB2TwZBrw+DwzptATMR23h+24pQ8smh1hBailNMQMDrJejzoTOZ0Bsn/4VrSrLj7unn+J+3U7ntMGgaWUuKWfHWy3Ak6DrgsZx45TB7f7cNgL72Hp7+j8e59YvvIKssL84tE0LMJd1NHTz1tcfwDoTmhB55dg89zZ1s/eCtCRO4jcXd3c/J5/ecP84vLWbFWy7HMQUXAMFAgIDbjc5gQG8e35KIYvbwu9xowQAGq3VapzUabdbQrSpCF2lNh86y/zcvoAWDFF62ml2Pvxqu+/dvP8kN/3wXBZI/RAwK+HwEvV50RhP6C0Ycm8Yw4qGjspHdP30Gd1cfBouJ1fdsJXdl6YjLLU5UW2UTT//74+HRkTt/8zLBQJDVt8Z/Odzp1FbZxNNffzx8hfjGr14ETWPFjRun9XU1TaPlWA37fvEc3n43JoeVde+8lqzFRWPeh9FmAdvICWunUsDjJej3oTdb0CXISMp4GfUbRynVq5TqIdS1ziqlei746QeeBR6f7obORe62dppe3U79My/QumM3nq6pyeDbfqaBylcPhec2tRyrpm73iSnZ90zo7+zj6HN7I8q0oEZbAix3KoSYW7oaOsLBh3NqDpyJWKIvEbWdqY88zh+tpm7P8Unv19vTS+vOvdQ/8wKNL72Gq7l1tk03EqMI+v301dbT+OIr1D/7Iu0HDuPrHz77+1Tqa+5kz8+exe/2krYgl6q90SulVO+dN6nFxCg8nV00v76T+mdeoPm1HXg6xr7qnLunnz2PPou7K7TMst/tZe/PnqW3aXpWrms92xh1rDzy3F4GRlntbbZrPlkfdXv6yLN7cfVO7zGlv62bPT99Gm9/aAVAb5+L3T99mv4E/O7WNA1XSyuNL28LXfPt3I23pzfezYqrsYS8Pwh8mNAIiM8CH7rg5z3AJZqm/cNYX1Ap9QGlVKVSyq2U2quUunSU+iuUUq8opVxKqXql1BfUBelvlVK5SqlfK6WOK6UCSqlHY+zjbUqpPUqpLqVUv1LqgFLqXWNt83Tw9fbR/PpOfN2hJdPcrW207tyD3x2dmXW82k9HL23VcOA0Pnd0RupEpDfqMTmiI5KJfDdSCDE7GUzRAwH1RkPC5HkYTtupWMf5M5Na9i7g89Fx8DCuxiYA/P39NL/+Br6emV/aU0wfb1c3bbv2EnB7QNPoq6ym90zljASaBjp60AaXUPS5vZjt0d/1lhnMAyESl9/lomXHLrztoUHW3s4umrfvxD8wtgtbd3c/rs7IizwtqDHQMT0XfkZr9Ggxi8Oa8N8lk2Wyx3jfzul/367OvqjvO7/Li2sw4JRIfL19tLy+E39fKBjlamqhbe8BAt6ZX7UqUYw6BUPTtJ8BKKUqge2Dq2FMiFLqbuDbwAcILd/5AeBppdRSTdNqYtR3As8DrwIbgEXAo0A/8M3BamagDfga8PAwL90OfBU4DviAm4EfK6VaNU17aqLvZzJ8/f1o/sjlXvx9/QQGBjDEWNN6PJILo+c7pZfnxzzRHkrTNDqqmqnbdxK/x0fh+grSF+SiN87c3CiLw8pFd1/Bc//5h3CZPS2JrLLxZ8cWQoiRpBZkkrO4kKbjteGytXdejDNrZueG97V103ysmtaTdWQvKSZrcRH2tOGXG04pzKJ6SFlGed6kjtUBlxt3S1tkoabh6+3HlCzJueYKb4zRln3VtTgXlmGwTn44srunn5YTtTQcqiStJJvc5QtIGsy1YHGeT1DXVd1MxS1bqH+ziuDgkuFGq4niNeWTboOY/fz9AwRc7oiyoMeLr38Ag+18P+pp7KDh0Bm6G9rIX1VOZkUBZocVk92C0WbBNxC5D0vy9CRJzCrPxZbmYKBj8AJYwYa7L48ZZJtLsisKsCbbcZ0b6aFg/VsvwxQjIDOVzElWlF6HNnjsANAZ9JiTRs+F1NfaReObVbSfaSRnWTFZi4qmNVeHr7cvHHg9x9vRScDliphWNJ+MZxnOcBp6pVQOYBryfFQAIYaPA49qmvbDwccfUkpdD7wf+HSM+vcRmjn4Lk3TXMARpdQS4ONKqW9pIVWERmiglHrrMG1/cUjRtwdHQFwKxCUAoYuR70HpdKgpuNDPWJhPWlkuHWcaAbCkOii5ZPmY5nh2Vjfz8reeIOgPAHB222Eu/dAd5CwtnnS7xiN/5QJu+fy9NJ9qwJJkJbsin+QcWWtXCDG1bCl2rnjkJlpON9DT3EnGghyyyvJQuplLLO7pc7Hnl3+n9UQoCFK75yT5axey4f5rhh35lbmogLQFuXRUho7z1jQHJRevmFS7dQYDOpOR4JC7MrG+r8TsFSuvh8FuQ03BHctgIMCJv+/j5POhaZS1e05wdtthLvvIndhSk3DkpLH01i0c/fN2AGpeP8x1n3gLnfXt6I16cioKSIuRNE7MP8OdD+suSBbY39bNq//9B1ydoYv+ml0nWH7bxSy+bj22NCdr7r2K3T99OnyRuuTmzSRlT8+5ZEpuOjd96u00n6rH3eciuzyfzNKRl3meC1Lz0rnps/fQfLIOT7+b7IUFM/K+HVkprHzb5Rx87OXQVESlWHXXFaPmu3P3DPDGj5+mszo0rbt2zwlKLl7GmruuHNON2onQxQgyKINhSo65s9WYf9ODoxH+G7iLIcGHQSP+FpVSJmAd8I0hTz0HbBlms83AtsHgwznPAl8BSoDKURse3Q4FbCU0muKz491+qhidSThKS+g7WxUuS1m+BKNj8hE4e3oyGx+6kd6mDoKBII7s1KjMsMOpP3QmHHw458Tze8msKJjRYWQGo4GcRYXkLCqM+bx3wI3eYEA/TQcLIcTcFwwG8Q14sac6KNu0JG7t6G3uDAcfzqnfd4ol160ntSg75jb29GQ2vmdix/nhGGxW0latoG33vnCZNTcHU/LoSyaL2cOUloIx2RmeAopSpC5fMiWJsPvbujn1wv6Ist7mTnoa2rGlJmEwGVlw2SoyKgrw9LqwpSWRlJ1KwcrSEffr8/ggGIw5zF0kpoDfj9/tx2Q3c8HM6TEzOhykLF1E19HzOcycFeUYk86fJ3fVtYaDD+cce3onRRsqsKcnk7O8hCv+6e0MdPRiTrKSlJOGYRrvOKfkpZOSYKvkzYTUvHRSZ/h96/R6CjcsIbUoG1d3P9YUO0nZaaPebO1pbA8HH86p2v4mC69cTUp+ZlT9gM9PwOfHNIlklSZnErbCfAZqz0+dTFu1HKN9/k43G8/V2zeBVcDtwB+Ah4B84CPAJ8awfQahIMXQTILNwNXDbJMD1MWof+65MQcglFLJQD2hKRsB4B80TXt6mLoPMzido6ho7NlUx0NvNJK6dBH2vFwCbjcGuw1TSvKEDtKxmJNsmCewFm/QH4wu8/ljr38SB67uPmp2n+TMq4ewpzlZetNGMsryZ/RuZbzNRP8UYqJmS//saergzCuHaDh8lsyFBVRcvZaU/Iy4tGXo0MxzgsGRD7wTPc6PxJafS479Evx9/ejMJkwpyegnOS0wUcyWvjndjHY7WVs24u3qRvMHMDqTpizIpAWJmUsieEEfN5gMwwbWhvL7/LQcr+XoUzvxu70svnY9uStL5+Sw9rnUPztrWzj+7B46qpooXFfBgouXkzTOpYx1ej1JZaWYM9IJDLjQWy2YUpIjRkDE6mtaUEMbPHYqnQ5nbjrOMSz/LkaWiP1Tb9STXJBJckF04GA4WqzvVS26XNM02s40cPSpXfS3dVN6yXKKNiyaUKBfbzaTtnIZjqJCgh4PBod93gf2xxOAuAG4R9O0bUqpALBX07THlFKNwPuA341xP0P/8sMt7zlS/Vjlo+kFVgMO4CrgW0qpKk3TXoh6QU37AfADgPXr10e8Tm9XH7XHa2lraCMzP4PCxUU4JpgwSW82Y80e+4dmJuSvLOXk3/eFM6sDVFy9Dr0x/sOENE2j8vU3OfLnHQD0tXTReqqOrf98N2ljPJmZC0bqn0LE22zon54+N7t//jztZ0PTF/rbjtJyspat/3jXpEcRTERSdhrJBRl0153Pv5BZUUBSHJZO1un1WNLTIH1qhikHA0HqTtVRd6oes9VE0aIiMgvj8703G/rmTDHabBhtUz8X3p7hpGTzUqq2vxkus6TYSY5xAdjZ3EnNiVp6O3rJXZBLwaJ8zEOCXR1nG3ntu38KP971s+e46KHrKd64eMrbHm9zpX/2tXbz6rf/iKcvNHj5+LN76G5oZ9O7bxh3MnG9yYg1c/jAcHJeBia7JbwSAsDCrauxp8e+uOtu66b2RC2dLV1kF2VTuKgAq2P0nAEi/v2zpaaFmhM1+Nx+ChYVkF+eh24Cywc7c9NwZKXQ19IVLstbVRY1daOrro1X/usP4VHhh/7wGt4BDytu3TKhm54GiwVDztwLnE7UeAIQKRDOedUNpAOngR3Aj8awfRuhkQdDJwZlET0q4pymYeozwjYxaZoWJNRegAODuSQ+A0QFIIbjcXl47ufPs/1P28Nll955CTe8+/qoL83ZKm1BDpd/9E5OvXQQv9vLwitXkTXMNIiZ5uru5+SQoZ3BQJDuurZ5FYAQQkxOX1tXOPhwzkB7L73NXXEJQFicNja950Zqd52g6Wg1eatKKVxbgWkO3OU9c+gMP/inH4XvgCdnJPPI1x8mS+b5z0l6o4GlN20ipSCDmt0nSC/NpWTzMuwZkUlMu9u6+cVXf0X10fOpVO/+5F1svH5DRL2GQ2ejXuPkC/vIX102rUPpxcT1NHaEgw/nNB6upL+9Z8pHmSVlp3LZR+6k8rUjdNa2UrJ5CXkrSmMOw+/r6uOJb/2OYzvPL1d8y/tu4rK3XjahC1kxc5qqmvjfT3yfvq5Qoku9Qc/7vv4wZaNM3YrFmuLg4kduoeqNY7SerCN/bTkFaxZGBcf6WjpJKcykp6E9vNrGqRf3U375yricJ8w14wlAnAFKgRrgGPB2pdQu4E6gY7SNNU3zKqX2AtcAT1zw1DXA74fZbAfw70opi6Zp7gvqNwBV42h7LDpC0zHGrLW2NSL4ALDtD6+x/tr1FCzMn2RzEoNOryeropDM8nw0DXT6+B2UNU2jr6GVtsOn8Q24yVheTlZFAXX7I9cNn8kVOoQQs5/eYIg59k5vNtDb0ELb4TP4+l1krlxIUmE2BvP0LwHszE5j2S2bWXzDxjmzbJvH5eHZnz0XMfy+u62bs4crJQAxh9nTklh45RpKL105bF9uONMQEXwA+PP3/0LF2oWkXDBU3xTj7rTZYZuyaZfurl66TtfSdbqW5NJ8UhYWYU2dn0Oj/W4vPTWNtB0+hcnpIGN5GY7c8Y9W0pui/+Y6vQ79NJ1PphZmkfL2K9GCQXT64Y+dTVXNEcEHgKd/+izLtiwjcxxD+MXMO7nvVDj4ABDwB3jxNy9SvLhoQokjnbnprLzjEgL+QMxj1EBbF8GebvKKUihdV0pHUzdnX3sTo8U0poT+YnTj+as9CqwEXia05OVfgQ8SupD/yBj38S3gF4OBi9eBR4A84PsASql/AzZqmnbVYP1fA18EHlVKfRWoAD4FfEm7YOKXUmr14H+dQHDwsVfTtKODz38W2AmcJRR0uBG4H/jQON4/Xrc3drkrdvlspnQ64p1Vob+pnWO/Pp+9uPNkNWU3XELjkUoCvtCQKGuKndRiOZEVQoydIyuF8stXcfrlg+GyvFWlmIx6jv3qGbRA6PjSebKa8tuvJH1xyYy1ba4EHwD8Xj/dbT1R5X3d/TFqi7lmpL7siXHe5O534xuyAkvu8hKOP7sH/+D5l9IpFl+7LhREnCS/20PVc2/QfTaUaqy7qoHOk9UsvPMqjJNIODdbdZ6q5uzfXgs/bj10iqXvuAn7OFeNcOZlkFGeR9vphnDZ4us3YJ/GKWVKKdQIwQcIBUSH8nv9oQSnIqH1tPdGlXW1dOHz+Se1ckWsY5Snu48TTzyPtzuU3LS3upHUJaWkFGZScdVarBOcdi8ijemvppQyAm8H3gmhZS2VUouB9cApTdMOj2U/gzkj0oHPAbnAEeBGTdPOhcFzgbIL6ncrpa4BvgvsAToJJcP81pBd7x/y+BZC00VKBh87gP8FCgAXcBx4p6ZpvxlLu89Jz0snLTuVjubOiLKM/Pgnt9GCQVBqypJYJoLuqoaI9X0BWvYf58pPvI2Gw5VYku1kVRSQlJUapxYKIWYjg8nA0hs3krWokI6qZlLy08moyKfrRFU4+HBOw/YDJC/Im5FRENMpHt8R9mQ7F9+2hb98/68R5aUrSmasDSIxZRdnYTAa8Pv84bI1V6wmdcj3eWphFld+4m20nqjF7/OTtaiQtOLxTbnUNA00LerOpbuzJxx8OKe3rgVXR/e8C0D4BtzUv34woizo89Pf2DpiACLWccXqtHHRg9fTerqenoYO0styyCjNi+uIWoCswkwsdgvuC/JFlK8pI02WeE94i9ZX8NJvX4oou/j2i7FO8TRFLRhkoLUzHHw4p+tEJRvuvZakvPgkqp6LxhSA0DTNp5RawAUDVjVNqyE0HWNcNE37HvC9YZ57IEbZYeCyUfY54hmVpmmfBj499lbGlpyRzINfeYBnf/YcZw6epWx1Gde961qcwyS7CQYCoajsNA7X8bvduJtb6a+txeh04igqxJSSPPqGs5ZGcl46aSWTW2M4GAjg6+tD8wcw2GwYrPPrZEOIuSTg8eLr60Pp9Rgd9ogs6cOxOO0UrCmnYE15uKwrRr0YSdZnlaDfj6e9nd7KKpReT9KCEsxpoy9VNhGapkUNg16zdQ0Bn59X//AatiQbN73nRgoTJK+QmFnBQCA0ulIpckpyeN9/vJe//ugpWmtbWbt1DZe+5dKYdzNTCzNJykzC3z8weJd77B9KT1cXfdU1+Pv6sBcWYs3KOr+qy3C7ScDPvKZp+Pr6CHq86K2W6Vm+L9aKEsP8LkLnni3019ZhTB4890w+f+5pT3diT3dG/M3jLbMgk4f//b089eOnqD/VwPJLlrP17iuwzIFcO3Nd8ZIi3vnF+3n6x8/gHnBzxV2Xs/ySZVO2/4Dbjaullf6aWvTJ0UEGTQudM4z1RkSs78LpFvT78fX1owUCGB0O9Al+02Q841Z+BrwX+OQ0tWVWyCvL477P3oerdwBbkg2jOToJUsDrxdPeRn9dHXqzGXtRMabkqVti8xxN0+ivqqHndCgngreri4HGBrIv3oLR4Rhl68SXXJxLvV4XMQoib/NK9JNMPBXweuk5dZbu4ycBMNhtZG7agHlOB26EmJt8vX207t6Ht7MLAHtJEalLF08oqOgsykXp9RGjIPK3rJrVox887e207d4bfuxubiFz86bQShdTyNfXx0B9PZ6uLqzZWVizczBYrSSnO7nq3qvYcP0GDAYDNufUr7wgEpt/YABXcxOullbMqanY8vIwOhyUrizl4X97Dx6XF0eqHf0wJ+ve7h5a3tiNvy80dce5sIzkReXozSOn8fL19tL6xi40f2iUhbezi8DiCpxlZSilsKQ5SS7No/vs+akC9vxMrOmJdS6gBYP01zXQvu8gWiCAzmgk86L1U7qKmtFmIf/i1Zx96vwUDJ3RgCMv+jU0TaOvsoreM6EEod6uLlwNjWRdvDl87ul3uXC3tDDQ1IQp2YktvwBTUvwT9xUvKeKhrzyIu9+NPdmOQXKIzQomi4lVl62kfHUZQX+QpLSp60uaptFXW0fPidA1gdXhxOS04+05P1Uwe+1izClju67y9fXRX1+Ht6s74rtwOvldLjrfPE5/dS0AprRUMtavTojP3HDG88mzA/cNTonYC0RM4tQ07cNT2bBEZjIbMZmH/4JytbTQcyKU6MYHuNvayFi/AZNzahMbBVwues9GZojWfH68fX1gMGMw6Wd1shR7bgZL7r2B1kOn8A+4yFy1iKTCyY18APB2dYeDDwD+/gE6Dx8la/OGMd05FUIkBk3T6KmsCgcfAPqrarBlZ2EoyBv3/uw56Sy59/pQ4tv+ATJXVpBUNPljTrwEAwH6W9rRWywE3OeHHbsaG6c0AOF3u+k4sJ+AJzTHurevF293N6nLloePqc60+ZnYb74L+Hx0nziOp6MDvc2Gp6cXb88x0pavQG+xYLFbRrwDHQwE6Dp2Mhx8AOg5dQZLVga2nJGnYnh7esLBh3N6T5/Fnp+PwWrFYDFTcu0WOk/VhJNQplUUJ9z0C19vH2179oeHIwR9Plp37yVv6+UYbFN3YZNaUcxCi4nWg6cwJTvIXFEWc/qFf8BFb2VVRFnQ58PX04vR4UALBOitrMTVGArs+Pt6cbe0kLF+A4ZpWPp1vMxWM2ZrZPDK5/GhN+hm9I61GD+LzYIWnNohSgG3m97TZ8KPXfW1FF26kv72Pvob20hfWkpyacGY+obf7aL9wH6CF3wX+np7SVm6bFr7lrutIxx8APB2dNJXWUPqiqUJMfoolvFcbS0B9g3+f+i6Jwk4YC0+Al4v/dVVkYWahrene8oDECgV+rmwKD2Hg6+d5M1Xj5C/qIC1N2wgpzR3Ui8T8Hrx9fahdHqMTseMHaCVUiTlZ5GUP7VJJv39A1Fl7tY2Ah7vsAEILRgk4HET9PvQGU3ozZaE/VADlBQXU10zvhlSxUVFVFVXj15xkF6vZ+Xt14zrNfR6/bh/b3qdjovuunxc9a9/4Lpxv8aXvvSlMddXjH8uvUKhjfNQOd5tdEpHIBgYveIsEfC4CXg96PSG0GduyLFH8/lwNUavyOzp6MQ+gQDEdB1zxiM8nWSSx9vWmhYOPL+XqoOVlK8ppXxJHrQN3umd4sC0v78/HHw4x9PWht/lSug7MGJi/C4Xvv4B9EYTxiTHiCtSBFwuPF1d+JNz2L/9FA2nG1l80UKWZnaSXjz6uUnQ68Xd0gKAzmjEWVGK3mxE6XVogcCIiQdjHaOHlllSksjdsIzcDVM3nHuq+V2uqLkQQY8Xv8s95gDEhcdSndkS87hisJhIqygmraJ4xH2FTj1V9DfTYD/wu93h4EO4vT4fvv7+mAEIv8eDr7cfncGAKcmBmsFcEb0dvZzceZz9z+4hvSCTjbduIr+iYMZeX4yN3+en5kg1O/74On6Pj4tu38yCVWWYbeNazDA2pUJ999ypk6YxUH2WzPXrKLpi/fja2ddP0ONBZzRiycoGLXQOFxgYQDfCd6EWCISuMQJ+9CYzevP4gqCejujFKF1NzSQvqUBvTMzlisccgNA07crpbMhcoZSKeXKn1BSf8LlcaJqGs2Ih3cdCoy30SU727DjLoZcOAdBc2cTxHcd48BvvJXWCSXa83b00v7EHd1sHKEhZvJDUJRWzOmeCPsYXtjktFd0wUzu0YAB3eyuuxsFkVUqHo6QMU1JiDdO8UHVNDW0H3xzXNhmrxncCFggEaHj1lXFtk3fZ5ez5zi/Htc36D7+DV77yozHXv/zz72H7f/96XK+x5UP3cvct94y5/o7vbOf/PvF/43qN933zfdx20f3j2uZPO3/Bl9/+1THX/8JvPzeu/ScyX38vfZWn0QYDKpbMHCxZOej057+2lNGINSuT3r7IVRVmax4cb3cPzW/sjTzeLl2EwTK+k6ze9l6e+Jff0tHQBoS+C+pXlLD1puUEutqx5eagaRoBlwsNDYPFOqnRcsMF4xI5SCsmxt3eSeO2NwbzMejIWLsSZ2nx8KMHlUJzZvKHbz9FX2cosVtzZRMdTd3c+MHbYk5jvZDOZMKcnoa7pY30tcvxdrXh8wbw9Xag+T1Rx4QLGZ1OdCYTQe/5FTeciyqmfTj0VDNYos+3dEbj+VwWo/AP9NFbeRotEBoNYk7Pwpqdi84wsQsTvdWKc2FZxEhSvcWCMckZmvseCIQu6oYETWIdDzxdPTS9vgtvVzcoRdryxaQsKp+R+euaprH/2T28+utQcsOmM42c3HmcB7/xXrLGmehUTK+647X8+gs/Cz+uPVrN2z57D4s2LZnQ/gIeD0G/D73RhMFiIbmigq43j4af15lMGCYwlV3pdCijCUtOHu0HjxL0+tCZTBiTnBiHCUAEA37cLU24W5sG96HHsaAco33swXtzSgpD1wmxZGUk9Kju2Ts+P0HpjEaSSssiypTBEJGcZzKCfj/99XW07nyD1h3bAY20Naux5eehsvI5/ErkgiQD3f20VrdM6LW0YJCuE6dDJ8MAGnQdO4W7PTrSNpuYU5JJKlsQfqwzm0lbtWzYKGHA7T4ffADQgvTXVhLwRi/pJISYvKDfT39ddTj4AOBubSLgckXUU0qRVFaC4YKEbNbcHCwZsy+ruRYM0nniTPTxtm38x9v2+tZw8OGcqsNV+GzJZG3ZhN5mo6+qipadb9D6xht0nzwRuss6QQa7HcOQEX62goJZd6EnRhbwemnetS88ilALBGndfQBPV/ew2xisVnr7g+HgwzmHXzlM1wUrig1Hp9eTsmwJjtJifH1dMY4J0SMaw9uazaSuWoGjpBhrTjYpy5dhyYj/qmXjZUxykL5mZXjEq9LrSV+/BqN99OkMwYCf/obacPABwNPegn+E39tolFLYCwtJX7sGW34eyYsryLxoA3qzif66OrqOHcOWFzkCTW+zReUmC/oDdBw+Fgo+AGgaHYeP4e4YvV9Mhd72Hnb84fWIMp/bS0tl04y8vhi7469F31Db9ec3CPjHN+JT0zTc7e207dlN6xtv0LZ/H96eHmz5+aSvW4stPw/nooVkbtqI0TH+RK8Gux1bQQFt+44QHFxSOOj10rx9D77evpjbBFwD4eADhG569tdVExwyfWwk5swMrLnng2YGh4Ok0gUJfRMgcUMjs5glPZ201Wtwt7agN5uxZGROWVJIX28v3cePhx/3VVViyy8gbdVKOps60emih2DrDBOLMwW8PvrrG6PK3R1dOCYwvDlR6M1mUpcvwVFcSNDnw2i3Yxjhizzoi14jWvP7B7/Qp2D4lxAighbwE/S4o8qDPm9UmcnpJOfyiwenLegwJjnQm2Zf0siA18dAjOOtp6MTR8H4ptENt9ydJSUZc2oqrpYWes+en/M6UF+PwWrDUTzy0Ovh6M1m0pYtx9PRgbenG3NaOubU1BGHx4vZJ+D24O3oiir39w3AMBf2OoMBgz06EKXT68a8LKM5JRmdcQG9Z45HPRfr+/kcX083XUcOozOZ0BmN9J49TdDvxVlWntAn5kMpvR5HSRHm9DQCbg96m3XMF0daIEBgoD+qPNaxdDz0ZjO2vFxseeePTe6OdnpOngDAYLOSVFqKr78fkzMZc0YG+iEjOYJeLwON0Rf7vp4+yJ3+EQhKpzCYDPjckb8LnUGOW4nGaI3+TjdZTUNnoY/KPzBA56GDoeVjAX9fH52HD5GxfgO23BxsuZPL+aQ3mzGYrVHLeQd9PnwDLoxJ0deCsY5hwcHpGGMdwWC0WclYvwZfbx9aMIjR4Uj4keoyAmIa6AwGLOnppCxeQtKC0mGH3UyEr2/oIBtwNTcR9PlIyU5ly1svjXguozBzwkPJdEYDlszo5WjMztk/p1dnMGBOTcGalTli8AFCQ7GiyozGCQ9fFEKMTOkN6K3Rn0udKXbAz2C1YM3MwJKeNiuDD3DueBt9EWdKHv/xNqMgk+KVCyLKVl+zlrS80MgQT0d71DYDTY0EAxPPH2Kw2bAXFJC6dBm2nJxRVygQs4/OaIx54asf5UQ3uySXrJLI85BNt28hJTt1zK+tt1hiHxOMw3/efT09QOhC19/fD5qGu6lpxKBFolI6HaZkJ9bszFCehDFeeSm9AUOModz6YY6lk+HrOX9+6m5pobeykoDbjTU7G2OM3A/KZMQcIxnuaOdkUyUpzcmV918VUeZIS5p03jQx9RZvWYr+whVLlOKi2zaPO0dSwOUKBx/CZW53RJLmyTLYbeF8KOcovT7mVCqIfY2ht9qGnVo2HL3JhCU9DWtmRsIHH0BGQMw6sU7qDA4HSq9Hp9ex/uaLyFqQzdm9p8lakE3pmoU4MyY2/UOn15O2rAJXSysBV+jDaS/IjXmSPJfpzRbsBSX019eAFkQZDNgLS0c88RFCTJzOYMCeX0xf9ZnQnTqlsOYURN1Bm0t0ej2pSxfhammb9PHWlmznlg/fTuWBM9SfrKN4xQKKVyzAODiv2mCPvgtjTEqa1asmielnsFrI2rSOhpe3h1eXSF1agTl15HOMpPQk3vrpt3Nm/2mazzZSuqacouUl6Mdxp1mnj3VMyEc/wjQfvSX6OYMjaV6NzNHp9djyCkO/N68HUFizc9Fbxz+8fDRRFz2ahk6vRw1zF1dvMJCxejkNL70eTmKbtKAIS/rYA1OTtfTS5SSlOzm16wSpuWmUr68gLW9+nePOBnkL83nX1x7i5K4T+Lw+Fl20hPyK/HHvRxdjqrXS6YbtoxNhciaRtXEtLTv3hfKgKEXWRWsxOmOPhDdYbFhzC3E11YU+M0YT9vwR8urMEXP73c1BJmcyppRUvF2hOXJKp8NZVhbuqPZkO0u2LGPJlqnJ6GxOTaHwuivx9vSi0+sxJSfNuztbSqfDlJqO3u5A8/tDyZ+m4e6BEOI8g82Os3wxAa8Xpdcn/MozU8GSNnXH25TsVNZct54110Vn8Tanp6O32QgMhOaBK4MRR0HhnP/9ismzZWdSdMNWfH396M0mjMlO9GM4UU7LS5/0hd14jwmmlBQMSU78vaGREEqvJ2nBgnm31KLBasNZtpiA1xP6vZnM0xJsNDqTMTqd4ZEnSq/HUVo64u/bkp5K4fVX4u3pQ2c0YEp2oh8mIfh0sNitLNywiIUbFs3Ya4rxU0qRV1FA3iRXKDHY7diLi+m/YMU356JFU7o8rNLpcJYUYUlLxe9yYbBZMTmThk/WrNdjycgKJXENBNCbTPPiBqcEIGYZvcVC6vLl+Pr60AJ+DDb7lOWXGI7RbhtTsqO5TCmFwWyRlA9CzCCdcX58EV9oJo63RpuN9DVr8feF5osaHI6YQ6SFiMXkTMIUp6mY4zkmGKxW0leuDJ0vBYMY7HaM9qm/8z8b6IzGmHd/p5LBaiV1xcrB40pg8Pc9+vmp0WGfUMI/IcZLZzCQVFKCJSODoMcTSo5qs0958F3pdZhTk0cdHRaurxSGGCO25jIJQCSIrpZOzu47Q9Whs5SsLKVsbTnJWSkx6+rN5nk3CkEIIcTUMVgsw85JjaWnrZuzB85Quf8MhUuLKFu3cMLLOwsxVToa2jm95yT1J+ooX19BycoFJKWfX5FFb7HM6albiWbocSUYCFJ/so6j246gBYIsvWw5+YsKxzX9RoippDMYMaek0lbbysltB2muambRpsUUr1iAPVkCYTNFAhAJwNXn4unv/ZUze08BcHTbERZuqOC2j78Fi2N+RcSEEEIkFq/by0s//zuHXzoIwJuvHqZgSRFv++w9csIm4qa3o5fff+0xmgeXTXzz1cOsv3EjV7/negxGOb1NBPUnavn5p38aTvy395ndvOOrD1C8YsEoWwoxfbpaOvnNl35Bd3MXAG++cojL7tvKpXddJrmQZoj8lhNAR317OPhwzqndJ2lviM5ULoQQQsykjob2cPDhnLpjNbTXtsapRUJAW01LOPhwzt5ndtPZ0BGnFomhDr10MGLVAS2oseep3XFskRDQUtkcDj6cs/2JV+kaUiamjwQgEkBwyJIw52iB2OVCCCHETNGCWszy4b67hJgJsfqfFtTQtNj9Vcy8gDd6aV+/1y9/IxFXsY4dQTl2zCgJQCSA9Lx08hZFZnbNX1xIWn5GnFokhBBChKTlpVO2bmFEWUZhJhkFmXFqkRCQUZhFcnZKRNnSy1aQkjNzyziKka28alVU2fqbNsiKOyKusoqzsTkjEy+vv2njsLn3xNSTSXIJwJZs57aP38mbrxzm9J5TlK9fyLLLV0R9OIQQQoiZZraZuf59N3Hs9Tc5vuMYC1aXsuKKVTjS4rMSghAAyZnJvP0L7+DgC/upOVLN0kuXs3jzEkyW+bVyTiIrWFLIvV95F7v+/AbBQJCNt26iaFlJvJsl5rm0vHTu/cq7OPDcXhpPN7Bi62oWXbRYkqPOIAlAJIj0vAwuu+dKLn7bZfIBEEIIkVBSc9PY8tZLuej2LfIdJRJGZlEWVz94HQF/QPplAjIYjZSuLqNkxQJAQ6eXv5FIDDmluVz3vpsIBoJy7IgDCUAkGPkQCCGESFTyHSUSkfTLxKbTy4xvkXiUUnLsiBM5IgghhBBCCCGEEGLaSQBCCCGEEEIIIYQQ004CEEIIIYQQQgghhJh2EoAQQgghhBBCCCHEtJMAhBBCCCGEEEIIIaadBCCEEEIIIYQQQggx7SQAIYQQQgghhBBCiGknAQghhBBCCCGEEEJMOwlACCGEEEIIIYQQYtrNeABCKfUBpVSlUsqtlNqrlLp0lPorlFKvKKVcSql6pdQXlFLqgudzlVK/VkodV0oFlFKPxtjHe5VS25RSHUqpLqXUS0qpS6bh7QkhhBBCCCGEECKGGQ1AKKXuBr4N/CuwBtgOPK2UKhqmvhN4HmgGNgAfBj4JfPyCamagDfgasHOYl74CeAy4CrgIOAE8q5RaOLl3JIQQQgghhBBCiLEwzPDrfRx4VNO0Hw4+/pBS6nrg/cCnY9S/D7AB79I0zQUcUUotAT6ulPqWFlJFKDCBUuqtsV5U07T7LnyslHo/cDtwPXBq0u9KCCGEEEIIIYQQI5qxERBKKROwDnhuyFPPAVuG2WwzsG0w+HDOs0AeUDKJ5pgAC9A5iX0IIYQQQgghhBBijGZyCkYGoCc0neJCzUDOMNvkDFP/3HMT9VWgD/hzrCeVUg8rpfYopfa0trZO4mWEmHrSP0Uik/4pEpX0TZHIpH+KRCb9U0yleKyCoQ15rGKUjVY/VvmYKKU+ArwPuFPTtJ6YL6hpP9A0bb2maeszMzMn8jJCTBvpnyKRSf8UiUr6pkhk0j9FIpP+KabSTOaAaAMCRI9cyCJ6lMM5TcPUZ4RthjUYfPgqcIOmabvGu70QQgghhBBCCCEmZsZGQGia5gX2AtcMeeoaQqthxLIDuFQpZRlSvwGoGs/rK6U+DvwLcJOmaa+NZ1shhBBCCCGEEEJMzkxPwfgW8IBS6j1KqSVKqW8TSij5fQCl1L8ppV64oP6vgQHgUaXUcqXUncCngG9pmhaegqGUWq2UWg04gbTBx0sveP6ThJbpfAg4qZTKGfxJnt63K4QQQgghhBBCCJjhZTg1TXtMKZUOfA7IBY4AN2qaVj1YJRcou6B+t1LqGuC7wB5Cq1Z8k1Ag40L7hzy+Bajm/EoZ/wAYgceG1PsZ8MDE35EQQgghhBBCCCHGYkYDEACapn0P+N4wzz0Qo+wwcNko+1SjPF8y9hYKIYQQQgghhBBiqsVjFQwhhBBCCCGEEELMMzM+AkLMHgGfH193DwGPB4PDjsmZhFIjDjYRQggRR96eXny9fehMJkzJSehNpng3SYgpEfB48Xb3EPT7MSY5MCU54t0kkWB8ff14e3rRGQyh45/ZHO8miUkKeH14e3oIeryhz70zKd5NElNAAhAipoDXS+ebJ+g8ehIApdeRe/kW7LnZcW6ZEEKIWFwtbTS89DpBvx+A5PIFpK9eJifhYtbzu9y07TtEb1UtADqTkfwrL8GSkRbnlolE4W7voP6l1wl6vADYC/PJ2rAKg9Ua55aJiQp4vLQfPkb3idMAKL2evCsvxpadGeeWicmSKRgiJm9XTzj4AKAFgrS8sRf/gCuOrRJCCBFLwOOlZff+cPABoPt0Je6Orvg1Sogp4m7vDAcfAIJeH237jxDw+eLYKpEogn4/7QePhoMPAP219bjbOuPYKjFZns7ucPABQAsEaNm1D7/bE8dWiakgAQgRk98VHWjwD7jky14IIRJQwOfD29UTVR7rWC7EbBPr5oe7o4Og1x+jtphvAl4f7vaOqHJfX38cWiOmit8d/bn39fQR9Hpj1BaziQQgRExGhz2qzJSSjMFiiUNrhBBCjERvNmGNMSzV5JB58mL2Mzqjz0nseTnozZLjRISOf/b83KhyU4rkC5jNYl2LWDLS0Mu1yKwnAQgRkyk5maxN61B6PQAGh53sTevky14IIRKQ3mgkc91KjMmhE26l05GxbiWmtJT4NkyIKWBJSyV91TLQhRJhm1JTSFuxFJ1BH+eWiUSg0+tJXbYIc3rqYIEibcUSLGmSI2Q2M6ckk7lxTfhaxJjkIGvDGvQmY5xbJiZLklCKmHQGPc7SYqyZ6QS9Pgx2GwarRByFECJRmVNTKLj6cvz9A+gMeoxJSSidrFwkZj+9yUTq0grshXlo/gBGh11uiIgI5mQn+Vdegq+vH2XQY0pyoHRyn3U20xkMJJcvwJadSdDnw2CTa5G5QgIQYlhKKVnuRgghZhGDxYzBIqteiLlH6XSYk53xboZIYHqzSQJTc4xci8xNEhoUQgghhBBCCCHEtJMAhBBCCCGEEEIIIaad0jQt3m1IaEqpVqA63u2YJhlAW7wbMctd+Dts0zTt+pl88Snqn9IPhjdXfjcz3jdBjp8ibLTf1Ww8ds6Hv7+8x5DZ2D/HYq78fef7+5jN/XMu/O3mwnuA6Xkfw/ZNCUDMY0qpPZqmrY93O2azufA7nAvvYbrI70YMR/rG2M3F39VcfE9DyXuc2+bKe5f3MXvNhfc8F94DzPz7kCkYQgghhBBCCCGEmHYSgBBCCCGEEEIIIcS0kwDE/PaDeDdgDpgLv8O58B6mi/xuxHCkb4zdXPxdzcX3NJS8x7ltrrx3eR+z11x4z3PhPcAMvw/JASGEEEIIIYQQQohpJyMghBBCCCGEEEIIMe0kACGEEEIIIYQQQohpJwEIIYQQQgghhBBCTDsJQAghhBBCCCGEEGLaSQBCCCGEEEIIIYQQ004CEEIIIYQQQgghhJh2EoAQQgghhBBCCCHEtJMAhBBCCCGEEEIIIaadBCCEEEIIIYQQQggx7SQAIYQQQgghhBBCiGknAQghhBBCCCGEEEJMOwlACCGEEEIIIYQQYtpJAEIIIYQQQgghhBDTTgIQQgghhBBCCCGEmHYSgBBCCCGEEEIIIcS0kwDEKK6//noNkB/5GcvPjJP+KT9j/IkL6Z/yM8afGSd9U37G8TPjpH/Kzzh+Zpz0T/kZ48+wJAAxira2tng3QYhhSf8UiUz6p0hU0jdFIpP+KRKZ9E8xWRKAEEIIIYQQQgghxLSTAMQEadqII0uEEGMgnyMhhJjf5HtAzDTpc7OX/O3mBkO8GzDbtDW0ceiVQxzdeZxlm5ay4tLlZORnxLtZQswqtSdr2fX0blprW7noxo1UrKvAnmyPd7OEEELMkIazjex5bg91J+tZd81almxcjDPdGe9miTmsqbqZfS/sp/LQWVZduZplm5eQmpUa72aJUQSDQaqP1rDjrzvo7+5ny61bKF9VhtlmjnfTxARJAGIc+rr7+c2/P0bVkSoAKg9XcnTnMR780ruwOW3xbZwQs0RjZSP/+/H/w+PyAHBq/2nu+OBtXHLHJXFumRBCiJnQWt/G/33y/+jr6gfgzMEzXHXPlVz/4PXo9DI4V0y9zpYufvzZn9DR2AHA2cOV1J+s484P34HRbIxz68RIao/X8r2P/y/BQBCA47tO8K7/dz8rL10Z55aJiZKj/Di01bWGgw/nnD10lpa61vg0SIhZqO5UfTj4cM7zv3qBnvaeOLVICCHETGo82xgOPpzzyu+20dHcGacWibmuuaopHHw4Z/eze2hvbI9Ti8RYndhzMhx8OOeF37wUdS4pZg8JQIyDUmpc5UKIaLoYnxedTiefIyGEmCdiHe+VTiFfA2K6xDzHUHIOPxvEGhWll/PGWU0CEOOQWZjJwjXlEWWLNywiqyAzTi0SYvbJX1iA1WGNKLv2ndeQlJYUpxYJIYSYSXlluSRnJEeUXfn2K0jNlvn4Ynpkl2STVRh5vr755s2k56XHqUVirBatr8BgjMwasPXerZgspji1SEyW5IAYB1uSjbd94q0c23mck3tPUrGugqUXLcGaZB19YyEEADkl2bz/G+/jwCsHaatvZ+1VayhbVRbvZgkhhJgh6bnpPPy193Bo22EaztSz8vJVVKxdiE4n98XE9EjJTOHBLz/Ake1vUvVmFcsvXh7zwlYknoKKAj7wn+9n39/30d8zwIbr1rFg+YJ4N0tMgnzqxik5I5kFy0pwpibhSLFjsVuGrRsMBFBKoeQLVcxzQz8L+QvzyV+YP+79tFS30FLTjNFiIrc0VzKmCyHELBEMBFAXDJvOKspixcXLyC3JxpokibznoqF/86ni6nPRVNlEb0cvqTmp5JTkjCmRZFZRFluLsqa0LWL6KaUoXlJE8ZKicW3n8/hoqmqis6mTpLQkckpzsNqn/6axpmlowSA6vX7aX2u2kgDEOB3bcZTf/utvw+vQrr9+Pde/9wZsjvNfngGvF09bG/11tegtFuxFxZiSk2Wukph3Aj4fnvZ2+mtr0BmNOIqKMaWkTCgoV320mp986sf4PD4AChYXcs9n7pEhu0IIkcD8AwMMNDXhbm3BnJqKLS8fo8PBqb0n+cUXf0EwGEout/zS5dz6wdtwpDji3GIxWX6XC3dLMwNNTZicTmwFhZiSpmaapWfAw8u/fYltT2wLl9358bew7tp1cp4twoLBIAdePMAf/+sP4bLL3nYZV963FbN1+pbv9PX10l9fj7erC2t2NtbsHAxWGSk/lNyaH4eulk6e/M6fwsEHgD3P7KH5bFNEPVdLC13HjuLr7cXd2kr7vr34entnurlCxJ2nvY2uN4/g6+nB095O+/59+HrGv9qF1+3l7z97Phx8AKg7Xkvt8dqpbK4QQogpFPD56Dp+jL7Ks/j7+uivraXj8CF6Wjp58jtPhoMPAEe2HaHxTEMcWyumghYI0Ft5lp7Tp/H39THQ0EDH/n34BwamZP8tNS0RwQeAv3z3z7TXy2oW4ryOxnb++r9/iSh79YlXaalpmbbX9LtctB84wEBdHf6+PnrPnKHn1CmCgcC0veZsJQGIcXD3e3D1Rh9A+3vOlwW8XvqrqyIraBrenu5pbp0QiSXo99NXXR1V7m4f/0mCx+Whubo5qryrRZZsE0KIRBVwufB2Rh6nAwMDuPoH6G6NPi/q756ai1QRP363G1djY0RZ0OfD198/zBbj098dvR+fx4e73z0l+xdzg6vPHXHT6pyBGP1nqvj7+wl6IpcGdbe2EJii4NtcIgGIcUjOTKagoiCiTKfXRWTQHS7ng+SBEPOOUqgY899ilY3Gnmxn1dbVUeV55ePPIyGEEGJmKF3sIfEOp5WK9RWRdZUioyBjJpolppNSxFpPdaqmR6TnpUfle0jPSyclK3mYLcR8lJKVQlpuWkSZ0WwkLXf6Vj2Jea2nFAxzHJzP5Kp4HKwOK3d+7E6KlxUD4Ex38o4v3k92cXa4js5oJKk0MqO/MhgwOeXAKOYXnV5P0oLILMVKp8OcljbMFiPsS6dj0y2bWHXFKpRSmG1mbvvQbRQuKpyq5gohhJhieosVa15eRJkpNRVLkoMb33cTZYNLmztSHNzz2XvIWZATj2aKKWSwWnEUl0SU6e12jI6pye2RUZDB/V96J6k5ofxP+Qvzeftn7sGRKkt5i/OSUpO45zP3kFuWC0BqTir3f+md0xrkNNjtGJIik6PbCwowWCXJ7lDqwnwGItr69eu1PXv2RJS5+l30tvdisVtiZuEP+v34urtxtbWiN5uxpGdgnKLkOyKhzXiIM1b/TCRaIIC3pwd3ays6owFzegYm58RXrvB5fHS1dmEwGiT55PjEJfye6P1TJAw5ds5hfo8Hb2cn3s4OjM5kzGmp4RNyz4CH7vZuzBYzyZkJe6NG+uc4BbxevF2deNo7MDocmNLSMNrtU/oavR29uPpcOFId2Ob3KirSP0cw0DtAX2cf1iQrSTMQpPIPDODp6MDX24M5LR1TSgp68/QlvUxww/ZNWQVjAqx264jLuOgMBszp6ZjTp2+YjxCzgdLrMaemYk6dmmCB0WwksyBzSvYlhBBi+hnMZgw5Odhyokc3mG1msmyyLOJcozeZsGZlY83KHr3yBCWlJZGUJjf3xMhsWTcbwwABAABJREFUSbYZDVAZbDYMtnkdEBsTmYIhhBBCCCGEEEKIaScjIBKEz+OjrbaF7tYekjOTySjMjEqyI0QiieyzTjIKs6TPCiGEmDGeAQ+tNS30d/WRkp1KRmEmesP4Ex2L6dPT3kNrdTNokFGUlchTbcQ84u5301rdzECPi7TcNDIKM2TBgBkkAYgEEPAHOPj3fTzz/b+Fy65//82suXadfJGKhBTwBzj4wn6e+d+/hsuuf+Qm1ly3XvqsEEKIaedxedjxh9d47bFXgFCS4zs++VaWXrI8zi0T57TVtfLEv/yG9ro2AFJyUrn78/eRWSTTbkT8uPoGeOkXL7Dvqd0A6A167vr8fZStLY9zy+aPGQ/1KKU+oJSqVEq5lVJ7lVKXjlJ/hVLqFaWUSylVr5T6grpgLR+l1OVKqe1KqfbBOseVUv84wv7uUUppSqm/DldnprXXt/HcD5+OKHvuh0/T0dAepxYJMbL2+jae+8FTEWXP/fBp2uvb4tQiIYQQ80lbTUs4+ACgBYM89T9/pqupI46tEhc6+cbxcPABoKupkyOvHIpji4SAlsrmcPABQjfV/vY/f6KvozeOrZpfZnQEhFLqbuDbwAeA1wb/fVoptVTTtJoY9Z3A88CrwAZgEfAo0A98c7BaH/Ad4DAwAFwM/J9SakDTtO8N2V8p8HVg25S/uUkY6BkgGAhGlAX9AQa6++PUIiFG5orVZwNBXD0DcWqREEKI+aS/K/ocyd3vxtXvJmXmmyNiqDteG1VWc6QaTdO44F6iEDOqr7MvqqyntRv3gBuHJDadETM9AuLjwKOapv1Q07RjmqZ9CGgE3j9M/fsAG/AuTdOOaJr2e+DfgY+fGwWhadpeTdN+q2nam5qmVWqa9kvgWSBiZIVSygj8BvgscHZa3t0EJWckY7ZbIsosdgtOmScnEpQzMxnLkD5rlj4rhBBihqRkp6LTR57GpuSkxVweXcRHxabFUWXLLlsuwQcRV6m5aVFl+YsKcKQ64tCa+WnGAhBKKROwDnhuyFPPAVuG2WwzsE3TNNcFZc8CeUDJMK+zZnB/rwx56l+AKk3Tfja+lk+/1Nw03vaZt5M0+KWZlO7krZ95O6k50R8QIRJBak4abx3SZ98mfVYIIcQMySjM5C2fuhubM7TkXVp+Ond88q3YU+QiIlGUriln/U0bUTodSqdYfe1aFm6MDkoIMZOySrK47RNvCd/8zSrJ4YYP3IrFbo1zy+aPmZyCkQHogeYh5c3A1cNskwPUxah/7rnKc4VKqTogk9B7+pKmad+/4LlrgbuB1WNpqFLqYeBhgKKiorFsMmEBr5eg10fhkkIe+ub76O/uw57sICldhgCJ2Gayf46kZGXptPRZv8uNFgxgsFhQekloOdskSv8UYijpm5FCx9ogBqtlVmZ/1+l1LNq0hJyyXFy9LpLSk7Anz97gw1zsn850J1e/+3rW33QRmqaRmpOGwTS+S4+Az0fQ40VnMqI3maappWI0c6l/GoxGVlyxisIlRXgG3CRlJGNLsk376wY8HoI+P3qLGZ1hfq8DEY93rw15rGKUjVY/VvmlgAPYBPy7UqpS07RfKKUyCOWNuFfTtM4xNVDTfgD8AGD9+vUjtW1S3G3ttO8/hK+nF3NGGmmrVpBUmjtdLyfmiJnqn2ORlJ40ZYGHYCCAq6GJjkNHCHi82AsLSFlSgdFhn5L9i5mRSP1TiAtJ3wwJ+v0MNDTRcehNgl4vjuJCkhcvxGifncfa5MwUkjNT4t2MSZur/dNgNJBRmDmhbT2dnbQfOIK3oxNjSjIZa1ZiTkud4haKsZiL/TMle2b6kqZpuFvbaN9/CH9fP5bsTNJWLMOUPH+ni81kyLsNCBAauXChLKJHRZzTNEx9hm4zmP/hsKZpPwS+Bfy/waeWA7nA35VSfqWUH3gncOPg40UTeTOT5evtp/n1nfh6QhlXPW0dtO7cg9/tiUdzhIg7b1c3rbv2EnB7QNPor6ml59QZtGBw9I2FEEKMiberm7bd+wh6Qsfavqoaes5Uomlz4ppCzBF+l4uWHbvxdoTuHfq6umnevhP/gCS7FrOLr7ePltd34u8LJc51N7fStu8gAa83zi2LnxkLQGia5gX2AtcMeeoaYPswm+0ALlVKWYbUbwCqRng5HWAe/P9uYAWh6Rfnfv5MaCWM1VwwjWMm+fr70Pz+iDJ/X78cWMW85evpiSrrq6kNBSSEEEJMCW9Xd1RZf3UdAbc7Dq0RIjZ//wABV2SfDHq8+PrkPFnMLr7evqibad6OTvyu+XvMnekpGN8CfqGU2gW8DjxCKKHk9wGUUv8GbNQ07arB+r8Gvgg8qpT6KlABfIpQjgdtcJsPEQoinBjc5jLgH4HvAWia1g8cubARSqkuwKBpWkT5TNIZjVFlSqeLWS7EfKAzmaPKDHY7yiB5IIQQYqrozbGOtTbUPJ+TLBKLMhpAKRgyMkdnkvNkMbvE6rPKYEA3j89vZzTrkKZpjwEfBT4HHAAuAW7UNK16sEouUHZB/W5CIx7ygD3Ad4FvEgpknKMntDTngcE6/0AoSPGZaXsjU8DoTCKpdEFEWcryJTLfXcxbptRkTKkp5wuUIm3FMkk6JYQQU8iUloIx+YIlk5UidcVS9HIDRCQQk8NBypLIWdLORQvlPFnMOianE1thfkRZ2qrlszbvzlSY8XC3pmnfY3B0QoznHohRdpjQqIbh9vdfwH+Nsw1RrzPT9EYjKUsXYcvPIeByY7DbMKUky9rIYt4y2mxkbdrw/9m77/i26nPx45+jvSUPeW8ncfZOIIEQVhhhb1oopYsO6J73dtJ521/b295uuuiihVIoUHbLniFhZW878d7W3uf3hxzFiuTYTrz9vF8vt+irc6SvlKMznvP9Pg+R3j4SsRh6uw2Dyzn0ikIIIYZNb7VSsHYV0V5Pcl/rsM/oZGhiclK0WuyzqjHm5xIPBNGazRhcjhlfPUBMPVqjgbzFC7FXlhMPhdHZrDN+nyu/4gmkNRowF5xYZuB4NAqJBBqDQYIWYtJJxOOo0Why+xxBeTedxYzOInWYhRBiLOktFvSWsS87NxkkolHURCLr1BMx+kbz+9bq9Zjd+aPQKyGOOtFz1JOhNRkxmwqGXnCGkADEFKMmEoR7uvHs208iEsZaWoa5pASdyTT0ykKMg4jHg/fAfqIeD8a8fGxVVTN6mJkQQojxp8bjhLu78ezfRyIaxVpRgaWoWAIRY0RNJJLf9769ye+7vBxzcQk6+b7FJJJ+jpqHrapazlEngAQgppio10v3m2+mHnsPHgDAVl0tIyHEhIsFg3S/+QaJaBSAYGsLMb+fvGXLJMGqEEKIcRPxeuh++63UY+++fSiKgq2icgJ7NX1FPB6633oz9di7fz8oCvbKqgnrkxADZZ6jthLzB+QcdQKMaxJKMbi+Lg+Nexrpaes57nIRb2apQn/jYRIzuJasmDxigUBqx35E1OshFgyOWx8S8QTthzto2tdE0Dd+7yuEEGL4/H1+Gvc20tnUiXpMpYPRkLXc6OFGOV8aI9G+zO87cPgw8ZP4vkOBEM37m2lraCMWjQ29ghDHcew5qmJ34k0YaJXta9zJCIhJ4OC2g/z5W3fR296L1WHhus9ex7xT56LJMi9Jo8uM0I3nHCYhjkfRZikppCgo2vHZPoO+IC//6xUev/MJYtEYVQuquPbTV1NYWTgu7y+EEGJozfub+cu3/0prfSsGk4HLb7uM5WcvQ28cvbuQ2e5oagwGkPOlMaFk+76NRhTNiY3O7Wzu5IGfPciOV3ai0WpYf80ZrL9mPXaX7WS7KmaogeeoMYebJ+57jZ2bdqPRaDjj6nWcee2Z2HNk+xoPsheeYL0dvfzh9j/R294LgN8T4A9f/yMdhzuyLm9wONAek+/BMWu2DB0Sk4LOasXoTk+yY6uqQmcen2Rnh3c38vCvH0lFsuu31/Ofvz4tkW0hhJgkgv4g//jx/bTWtwIQCUW45/t/p/lA86i+j8HlSgYcjlAU7LU1UkVhjBicTjTH5Huw19RmvXE2FFVV2fTYZna8shNIjmx8+m/PUL+tfjS6KmYondWKyV2Axmhk2+uH2LlpNwCJRIJn7nmWg9sOTnAPZw7ZC08wT6cHb7c3rS0ejdPd2pP1rq3OYiFv2XIifX0kYlF0dgcaRUOgpQWNXo/ebpcES2LCaPV6nHV1RIuKiAeD6GxW9A4nUb+fmM+PRqdD7xi7bbT9cHtG2/aXtuN/34U43VLSUwghJpqvx0f99vqM9q7mbirnjV5+Br3VSu7SZUS9XtRosrSz3inHgdGgqipRr5eY349Gb0Bvt6O3WslbvpxoX38pbYcDg/3ESg2G/CHefvbtjPaD2w6y6PSFJ9t9MUNp9XocdXUEuvvY9rs/ZDy//8191C0oRWezoR0YvBSjTgIQE8zisGA0GwkHw6k2RVGw5w4+BEhnsaDrL58V6uigbdNm6J8/aS4uJmfhfAlCiAmjMxrRFRwdBRHu7qbj1ddQ43EAjHl55C5djM48+uU2XQWujLayOaWYbFIlRgghJgOz1Ux+SR6dzV1p7Y5c+6i+TywUxrNrD8G2tmSDRoN79SpM+Xmj+j4zUaijg87XtqTOPS0lJbgWzENvsaK3nHxFAYPJQNWCSjoa00cDl9QUn/Rri5lNZzRiLcijakEVbQ3pN60Ki3PoeGUTJrcb16IFUmFwDMkUjAmWV5LHNZ+6Oi3fw0UfuJDCiqHnrMfDYbq3bksdAACCLS1EPJmJKoWYCPFoFG99fSr4ABDu6sqaHGw0lNeVs3DA3RGzzcxF79uI0SwBOSGEmAxsOTau/tTVafke1ly6hpLaklF9n2hf39HgA0AiQc/2HSeVFFEkAzs9b6efewaam4l6vMdZa2S0Oi1nXLUO+4CgVM2iKmoW14zae4iZS6vVcvrlp+PMPzoiqnJuOaWlDtREgmBbG1G5lhpTMgJigimKwuIzFlFYVUBPaw+OPAeFlUXDSsSUiMWIB47J8u/KZ+/bh9m3+WmKZ5Uw+5Q68svcY9R7IbJLxBM07Wlk+3NbiQbDzF1RgyXSSyIYACAeCg/xCifGmefg2k9dzfqr1hEKhikoc5Nfmj8m7yWEEGJ4upo62btpN817mpi9uo6qJTV88pcfp6upC4vDQmFVEWbr6N5tjEcyjzMxnw81FgMZXn3C1FiMeCiU0R4Pj+5xvaS2hI/95DbaD7Wj1esoqirEnjO6o2SC3gAN2+rZ9eIOckvzqFszj8KqolF9D3Hy1ESCpj1NbH9+K7FwlIXrF1M6twzdSeS/K6kt5rYff4S2+lbifh9OswJdrannE2EJVI4lCUBMAlqdlpKaEkpqRhb91xqNmNxuQh3JIWpaq40duzp45YFXAdjxwjZef3wLN37rZpn/LsZV895G/vRfvyMRTwDw1lNvce1nr8DcH4DQ209+iOZgrE6r3CURQohJwtPp4e/f+iud/cm1d7ywjVMuX8vZN507rNGeJ0pvzZzKai4qzEiUKEZGazJizM8j3Jk+hUZnG/3jem5RLrlFuaP+ukdse/ZtHv/VI6nHmx/exLu/+z7y5MbFpNK0p4k//tfvSMSSo2nfeOJ13vn1m6hZWntSr5tblIvL7aTrjTcJtbalPTcW27M4SqZgTGEanQ7n/LkYcpM757g1h00Pb05bpqeli46GtmyrD5uaSJzU+mL8TfS/2fbntqWCDwCoKm8+sw1DXi6uhQswuFwT1jchhBDjp6OhLRV8OOK1h16hp61nxK+lquqwj296h52cJYtQ+qteGPPycM6ZgyZbuWgxbBqdDteC+RhycpKP9Xpyly3FYB/d0QknSk0kUAdMDxmMp8vDs395Oq0t0Oen7UDrIGuIibLrpR2p4AMAqsqrD7xMYsD03hOl0Wpx1dVhzEteSyl6HblLFqN3nFgCVTE8MgJiijPY7eSvWkE8FKKv24+ayNzpJk7wYjTU04d3fz2hrm7s1RVYy4rRW8annKI4MaGubvr2HiTi8eKcVY2lpAidafzv9mQrexmPJ5I7ddmGhBBixkhkuRhUVRVGeGoS6uymb+8BIj4fzlk1WIoLj3t80+h02MrLMeXlkYjH0ZpMaKVk+agw2O3kr15JPBRCo9ONSVLpkYqHwwRa2undewC9zYprdg2m/OOMnlDVrBewJ3rOLMZGuNdDNMu03Xg0xtBhpuHR223krUxeS2m02lSifzF2ZATENKDV6zHY7eSU5rP8ghVpz9ly7bhPYIhjxOuj+ann6d29j1BnNx2vvUnv9j2ocdkxT1bhnj4a//08nv31hDq6aHt5M96DhyakLwvXLwZFSWtbdckaCT4IIcQMU1BRgD0/fRroknOW4SrKGfZrhHt6afz3c3gONBBq76LtpdfwNhwe1ro6iwWD3S7Bh1F25NxzMgQfALyHmmh9cROh9k68Bxpo/PezhLp7B13enufgtGvOSGszmI0UVksOiMki4vHS+O9nqV1QnnFOufrSNWhHcTRTanuW89RxISMgphGdTsdp15xBfkUB255+m9K5ZSw5dxk5IzjIHxHp9WQkCuzdewBnXS0Gx+QYZifShXt6k8m1BujevgtbZRl6y/ieIJTVlXHDN27itYdeJRaNs/qSU6hcMHr13YUQQkwNzgIX7/jajbz1nzdo3HGIBesXUXfKPHSG4Z+Chrp706opAfRs24W9ogydWUrlzXSxYIiebbvS2tR4gnBXD6ZcV9Z1FEVhyYbl2HLsvPHkFtzlBSw/fwXuioKsy4vxF+n1kAhH0PW0cs2nL+et57YTi8RYdfEpVC6unujuiZMgAYgpLOgP0VLfSsAXpKAsn4JSN458J6suOoXlF6w8ucjgMZHGZJOStV1MEoP8m03EP5lWr6N6SS1Vi6pRVdBox26wlafbQ3N9cs5mcVURzlyZtyeEmLkSiQSth9roau3GkWOnpLoYvWFi7/4XVBay4b0XEI/HT+jcRMl6fNOAnJIIAI0CmiznGZrjbyA2l40l5y5j0VmLRyU3SDgUpqW+FW+vD3dxPoUVBVm3XTFM/V9d3B9A4z/AqatKULQ6ihZUoDcdv5JNNBqlpb6Nvq4+cgtzKa4oHNNzUTEyEoCYonx9Pv75m4d56h/PAmC2mfnE9z9CaXEOqGB02VE1GlBBGWIHnI3R5URnsxDzBVJtrvlz0FslK+xkZcx1oTEYSAyocZ67eP6EDo9UNJoTPj9UEwmUAScUiViccK+HRDyB0WlHZzLQeqiNX37ldxzakxyKWzm3gg/d/l4Ky+UOhhBiZtr6yg5+9t93EIvEUBSFa269nLOvXI9hiBP2saAmEoR7vcQjUQwOa9povGP38cdjzMtBY9CTiERTbblL5qMzyeiHmSgRixHu9aLGExhcdnRGI3mL59H20tFE7Bq9DlNecgTwUNvaaAQfgv4QT/zt3zzw22RVDYPJwMe+9yHmr5x70q89UxldTrRmE/FgsuxruKuHvCUL0FnTp0kc++8bjUZ5/sGX+Mv/3oOaUNHqtHzw9vew8qzlaeuEe73Ew/37JuvkmEo0U0gAYopq2H04FXwACPqC/OWH93DdZasJNTRjry7FUFhI4xv7qFm3mPzZpeiMwz/50NsslJx5Gv7GFsLdPVjLS7EUuU8omCHGh9HpoPTcdfgONxP1+rBXlGEunHqlpHobOzj0yg76mjqpWD2XwvlVaPUa2jfvoP2NnaCqWEvcVGxYy+an30gFHwAadh3i9Wff4sIbN0zgJxBCiInR1drN7775R2KR5HQ8VVW556f3U7dsDtXzxncaXDwSpXvHfpqe35K8UHTaqb5oHfGEwuHNu+na20TJslkUL67BMsTIteTx7Qx8h5qI+vzYK8swF0y945s4eVF/gNZN2+h8azcAtrJCys85FWtpCSVnrsVb34jOasFWUUpcVdj/zJs0v7GPvFkllK2qw1GUNyb9atzflAo+AERCEX73rT/x5V9/DucxOVDE8OjtNkrPXoevsYlIrwdbeSnmQndqVImvvZemN/bStqOeogXVlCybhc3toqW+LRV8AIjH4vzuW3+mYk45BaVu4tEY3dv3Ddg32ai+6AwsBWOzbYhMEoCYono7+zLaDu05jGI7CwDvwSZybVY8jR28ese/WP2+jRQvGVm9XKPTgdEpw9mnElOOC1OOa6K7ccK8bT289NP7iQaS+Ue69zcz54LVFNeV0P76jtRy/uYO+g42snPL7ozX2PX6bglACCFmJF+fD2+vL6O9L8s5w1gLdvbQ+MxrqceRPi+NT79GUDFS/8I2ALoPttDT0MrSd5yDbohpIlP9+CZGh6+pIxV8APA1ttG1fT8lpy3FWlqMtbQYSAbA3vzb0zRuTi7bfbCFxtf3cvrHrsKSYxv1fmX7jXW39eDz+CUAcRKMLgdGV+a1SNgX5PU/P0lP/xTcnoOttO88xOoPbKSvqy+jKmAoEMLb46Og1E0oY9/k4/BTm6i94mx0xvGvHDcTyWSYKSq/JDNKV7d0FmqfN/U41NmNvTi53N6nXicejWasI8Rk4mnuTAUfjjj86k4CrZ0Zy3Zv38+y0xdntC/J0iaEEDOBM89JTkF64mlFo5BXdJxyhGMk3JcZCPG3dGB2pg+fbtqyF/8EBEjE1ORvac9o69t/iHgk/RzX3+mh8ZibFMEuD97WrjHpV15x5nl5cVURDslLNSZ87T2p4MMRXfub8LX3kluYi1aXPq3G7rLh6g8EZds3BVo7iQUyy32KsSEBiCmqYnY51952ZeoHVlju5uKrzyDU1JZaxuB04O9IHtSTiVdk+oSY3LIla4qFIhhzMg/g5vwclpy2kBVnLU21rTx7GYvXLhjLLgohxKTlynfywdvfgzMvuc80mo28/8vvprhq/EsLGrLMqTbmOvC3HxNsUECR8xMxTGZ3ZjDNVlaIVn/MoG4l9T/pzWOUFLKstoSbv3ADBmNyJE9OQQ7v+9K7sLtGf7SFGPzfUVEUiisK+eDt78FkSeaIsbtsfOjr70sFYrPlezDmONBNQJ6cmUqmYExRZquJDdeexeK1Cwj5Q7hy7HQ+v5mImhxyZHDaSOiMBHuSIyJmn7sic+csxCTjKM3H6LQQ7jua/HT2hhXYyopwVJXiqW8CQGc1U7ByPhZ3Lu/775u4+KYLURQoKHOnDjhCCDETzVkyiy//9vP0tPdic1opKHNPSCZ+c0Eu7qVz6XgzWR5RY9BTduZq3rjnubTlKk9dgMUtQ9TF8NjKCrGVF+E7nLz7rbdbcS+py0gyac13UnXaQupf2JpqsxfnYS8em9FAeoOe0y9ew+ylswh4AuQV5uByu8bkvQRYC1zk15XTuftoHrDCBVXYClxotBpWnrWc8tnl+Hp9uPKdaaPAzO4c3Mvn0fH6TiCZsLT8nFOlpO84UlRVHXqp0XxDRfkI8FmgGNgOfEJV1eePs/wi4KfAaqAb+BXwDbW/44qirAe+A9QBFqAB+I2qqt8f8BofAG4CFpAc9fEG8GVVVV8Yqr8rV65UN2/ePNRik0I0ECTUnZz3pGq0HHptF7FAhLKVdeTUFKHTT2wZrhlg3M/wptL2OVyeli5a3tqPp7mLkmWzyJ9dhtFmJhYME+ruIxGLYcxxYHTIXYURmJDbi9Nx+xRjQvad01Q8EiXU1Us8HMHgtGPKceBr76V1+0G6D7RQtLAad1055sl9l1i2z0kmFggR6ukjEU9gynFgsGev0Bbs9dGx+zCt2w6SW11M0cIqbMdMUZoGZuz26e/so21nA517GnHXlVM4v3LIhLZHxMJRwj19xEPh1L5JjLpBt80hb4kriuIFhoxSqKo65L+coijXAT8GPgK80P//jyqKMl9V1UNZlncATwLPAatIBhnuBPzAD/oX8wH/B2wFAsBpwK8URQmoqvrz/mXOBO4GXuxf5pPA44qiLFVVde9Q/Z4q9BZzWomrRWVSilBMPY7iPBxZ5lLqzEZspbJNi7Fj0BuIxoafK0ev0xOJRoZeUIgZSmvQYy12p7XZClzMKlgGZy2boF6JqU5nMWEbxmhHs8tGxSnzqDhl3jj0Sow3a76TmnWLqVk38txfOqMeXZFU0pkowxmT/1GGEYAYpk8Bd6qq+usjr60oygXAh4H/yrL8DSRHNbxbVdUgsE1RlHnApxRF+aGatAXYMmCdg4qiXAmsA34OoKrqDQNfVFGUDwOXAxcA0yYAIYQQ4sRFY1H+8sXfDXv5G7713jHsjRBCCCHE9DNkAEJV1TtH440URTEAK4DvH/PUE8DaQVZbAzzfH3w44nHgG0AVcDDL+yzrf72vHac7BsAE9Ayj60IIIYQQQgghhDhJQ1bBUBTlPEVRdAMe24953qQoynBuA+UDWqDtmPY2YLD0zEWDLH/kuYH9aFQUJQxsBn6uquovj9OXb5KcuvFgticVRblFUZTNiqJs7ujoOM7LCDH+ZPsUk5lsn2Kykm1TTGayfYrJTLZPMZqGU4bzUWBgytgmRVFqBjx2Ar9m+I6dzqFkaRtq+Wzt64CVwIeATyiK8q5sL6YoyseBDwJXqqrqyfqGqnqHqqorVVVd6Xa7sy0ixISR7VNMZrJ9islKtk0xmcn2KSYz2T7FaBpODohjM1ieaLbVTiBO5miHAjJHORzROsjyHLuOqqpHpmNsVRSlkOQUjD8NXKY/+PBN4EJVVTeNpPNCCCGEEEIIIYQ4ccMJQIwKVVUjiqJsATYAfx/w1AbgH4Os9jLwXUVRTKqqhgYs3wzUH+ftNIBxYIOiKJ8Cvg5sHE75TZGUiMUIdnbhO9SMzmTEWlaMKXfalTASoyzi8xNsaSPU2Y250I25yI3eYpnobgkhhDgJsXCYYFsngeZWDE4HlpJCjE4pXydOTDwaJdTRhe9wMzqrGVtpCcYc50R3S4iTEvX7CbS0E2zvxFzoxlJUgN4q58ADjVsAot8PgT8pirKJZEnMDwElwC8BFEX5DrBaVdVz+pe/C/gqcKeiKN8E5gBfAG5XVVXtX+ejJJNR7u5f5wzgM/RXwOhf5rPAt4AbgT2KohwZVRFUVbVvjD7rlBWLRPG195KIJ9BrVVqffTH1XM/OvZSdtx5TjmviOigmtVgoTPsrmwm2dQLgOdCAvbqSgtVL0eiy73IGbnM2twuDxZh1OSGEEBNDVVU8+w7S9eb2VJtuj4Wyc85Ab7OmLRvo8RLo8WGwGrG7c1A0Jzp4Vkxn/sYW2l56LfW4d+c+ys9bj2EEQa1EPIGvvYdoKIIl14HZaR16JTGjqaqKr6OPsC+I2WXDmmsfeqVhiocjtL/6BoGW5EB978FD2CpKKTh1BVq9ftTeZ6obbgBisaIo3f3/rQALFEVx9T8edhFVVVXvVhQlD/gSUAxsIzkioaF/kWKgdsDyfYqibAB+RjK5ZA/wA5KBjCO0wHdJVsWIAftJBikGJqG8FdADdx/TpT8ANw+3/zNBsNfHtgdfpv7l5AmGe1YJs9fOIXBgPwBqLEaorVMCEGJQUY83FXw4wnuwgZx5szBm2W6CvT62PfAS9a/sAKBgbjnL33EO9oLMZYUQQkyMmD9A99Zd6W2+AOHevrQAROeBZl6+42FCfX40ei3Lrz+LilVz0erH+56XmMxioTBdb21Pa0tEIoS6eoYdgIiFoxx8aTtv3/c8iVgcS66dtbdcTE5l4Vh0WUwDiXiCxjf2svnP/yYejmKwmVnzgYsomFM2Kq8f8XhTwYcjfIeayJlfhzZPRpAfMZwklJAsfbm5/88CPDDg8WMjeUNVVX+uqmqVqqpGVVVXqKr63IDnblZVteqY5beqqnqGqqomVVWLVVVNjX7of/5HqqouUFXVqqqqU1XV5f3vkRiwTJWqqkqWv5tH0vepwtfro6u5i0g4MuJ12/c0poIPAB37muls7EFnMafa1ER8VPoppic1kRhRe/vuw6ngA0D7rsMcem1X1mUnWiwao6ulG09X1vy1QggxbamqyoBTq6PtiaM5wUPeAJvufJxQnx+ARDTO5j//G09LV9o6Pe099LT1kBjkuCBmAFVFjWfbno6/TUTCUbpauvD2eulr6uTNe54hEUuelwa6vWy+6z9E/KHjvoaYufpau+loaKdwfhUanZaIL8grv32EQI93VF5/wCVqervs69IMJxxdPea9EKMikUiw9/W93Pfj++ls7mLB2vlc9IGNFFYMPxLcsbcxo61tdxO5a6qIBYKgKJgLJPutGJzBaUfvsBP1HN2Zm4sK0NuzD3Fr35O5zTW/uZ+6DSvQGSbPcLWuli7+c9dTvPbYZqxOK5ffdikL1ixAb5w8fRRCiLGit1pwzamld9e+VJvGoMfoOnq3Ouzx4+84ZmarCv4uDzkVhfg9fl57fDNP/PFJEvEEZ11/JmsvXoN9FIdAi6lBZzaRs6COzi1vpdoUrQbjcfKMtR9q59HfPcbWF7aRU5jDJR/YiCXPQWDATYHeQ+2EfQEMVtOY9l9MPc37m/nnzx5k/1v7Kaws4JyrzqDx6TcIewIE+/xYck5+P2Sw2zDkOIn0HN0Pmty5GByyjxtoyBEQqqo2DOfvyPKKovxcUZRhT8sQo6e1vo3ffvH3dDYn7zRsf2kH//zpA4QCw48E51YdW3QE3HPK0Bq02MpKKDv3DIwyhEgch85spviMU3HNn40xx0Xu4nkUrF6GdpBgQl5NcUZbwdzySTVcNxFP8ML9L/LqI5tIJBJ4e7z86Rt/4XCW4IkQQkxHikaDa+5s3CuXYMx14aitovScM9JOrA1WM6Ysc/DNLhsAB946wEO//BfhQJhoOMoTf3iSnZsm54g3MfbsVeUUnroCY14OtsoySs85A1OuK+uykVCEf/3mEd5+fiuqqtLd2s2fvnUXhSvr0pazFeZgGDBqVwgAX5+PP3/7Lva/lZxS3tbQzj9++RAlp8xDbzFiso3ONqMzmyg6bTU5C+ow5rjIWTSPwlNXojUaRuX1p4uxOMO/Efg+ybKbYgz1dPWyd9dBPD0eyqpKiXnDxGPp0yP2bNlLX6cHU8XwIsGFdeW468rp2H0YAFuhi5p1i7AXuFAUBUUz3Fk7YiYzOh3kL12EujiBRqs97rLJba6Mjt3Ji3l7YQ7VaxegKJMnaZm3x8trj2/OaG852ELNouEPEgv4A+zbVU9bcwfuojxmza3GZpeEWUKIqUFvteCqm4VjVjV+X4Cdu+rpbH+LotICZs2txuyyseqm83jpVw8Rj8RAgYWXrMVZkgfAW8+9nfGamx7dxIoNy9EOcawQ04/OZMRRW4WtqhxF0WRNVtrZ3s2+XQfo6/aQV5WPa6+L3vZeIDnyN6oMfD0DK284F6NdAhAiXU9rD2316bkZwoEw4XiCVTedhzX/+NVX4vE4B3Y30HCgEZvDyqy51eQX5GZd1uh0YFy6kMTCeWh0sl/LZiwCEJPnqmEa6+nq4/tf/QXP//sVAOoWzuLm267j9BvW0bKjmf1vJCN8VocFo2n4UTdrvpM179+Ip7UbNZ7AXpiTunMhxEgoioIyjBPKo9tcT3KbK8rB7Jxc25zBZCC3OJemvU1p7bZhZNtWVZXGQ814PD5aD7fzrc//iFg0BsDNH7mOG265ekS/USGEGG2HG5rwef0UlRSQM8gd6IFC4Qh/+tW9/O13/0y1fey/388VN2ykcF4FG/77nfi7vBjtZuxFuej6R7QVZkkOWFRdJMGHGW6wGxUdbV1883P/yxuvbgVAq9Pyqf+6hRf/8hwarYaFZy5Ek2Ni5ccvw6LosObYsUkCa5GF0WJCZ9ARi8TS2ovryilZVDPk+ltefovPf/AbxPvzlqw4dTFf/O4nyC/Mo6/XQ3NjK1abhfLK0tQNNAk+DE5uZ09R+3YdTAUfzt54OoWV+dz23i/w1dt/wI7OelZfthqAKz56Oa4R7oyNNjPuWaUU1JVL8EGMC6PNcnSbm2TBBwCzzcwlH7wY7YCDSdnsUsrnlh93vVAozAP3PsY1F76Pd1x8Cz/54a95/ydvQKtN7nr/8It7OHRApnEIISZGOBxJ7aOuu+gDvPfaj7Nr+94h1zu0vzEt+ADwi/93J40NLSiKgr0wl6L5leSUF6SCDwALT1+IfcA8a5PVxKkXnTpqn0dML3u270sFHwDisTh/+/ODLD57Madct4bf33MvH3jXp3jPOz/B3pZmrO7j38UWM1d+aR4X33JRWtuai0+lZE7pkGWCe3s8/Ogbd6SCDwBbXnmbPTsPsGfXAW5556e47qIPcM0F7+Peux4iFJQkqEOZPJOsxYh4epMJ/vR6HUXlBfz8R79LPffc0y8zf3Edn/zFxymsllJEQoyG2iU1fOynH6XtUBtGs5HSWaXkDBHc27trP1/5zP+kHh/Y28BD9z/OmjNX8sJ/NqGqKl6Pb4x7LoQQ2e3ZtZ8vf/o7qcf799bzjf/+Ab/80/exOwYPBnuy7Lei0Rh+r/+471dcVcStP/owzftbUBMJimuLR5QoW8wsfb2Z21lLYxuLzlrMbe//Aj3dyUR/3V29fPpDX+HuR35DVc3xbwyImUmj0bD6glWUziqlu7Ube66dslmlmK1DT9cJBUK0NLZltKuqyve/8TN29gdtg8EQ3/jvHzB7bg1LVywc9c8wncgIiCmqoroUjUZDcVkh+/YcyHj+6SdfIL88H71eMvQLMRo0Gg1ls0tZcc5yFq5dMGTwAeBwQ0tG2/a3d1E5K3mC5HTZKS7PTPwqhBDjobGhOaNt65s76ezoyrL0USVlRVhtlrS24rJCCksKhnxPd5mbJesXs/SspRJ8EMdVWVOW0XbOReuIxmOp4MMRwWCI5sbW8eqamIKMZiM1i6pZuWEFdSvmYB3GNFqAXHcOZ29cl9amKArOHDuvvJCZH+xwfVNGm0gnAYgpqnpOJd/+2X+jKApllSUZzy9btRiDZFwVYkLluzMrxpSWF9Hd0UPtnEq+84svUVw69Am7EEKMhXx3ZhK1krIiHE5HlqWPKqss5ru//DJVtclg6vwlc/jGjz9PXpZ9nhAnavb8Gm7/38+Rk+dEURTOuvA0bvrQteTmuTAec46r0Whk+xNjwmDQc/Ot13Pm+WtRFIXcfBdf+9/PUlxWSPWsyozlB0tOKY4aiykYfwY8Qy4lTopOp2XtWauYu2gWba2dvPriFra/vRuAwuICrnnnJWikYoUQE6pu3iyue9fl3P2nfwJgNBr48rc+TWV1OXanDYdT6kILISZO3fxZvPPmK7nrzvsAMBgNfPV/PkNe/tAXcotXzucnf/k2nl4fOXlOqegjRp3BoOesC09j0Yp5hENh8gvzMBoNJBIJvvTtT/PVz36XRCI5L/8zX/oINbWZF4NCjIbyqhK++N1P8MFP34TJZCSvP8jw5W99iltv/jzB/rwPV1y3kbkL5kxkV6eEEQUgFEVZBHwQqAXeq6pqi6IolwMNqqq+AaCq6odHvZdiULn5OeTm5/B/v/0O+/fWE4/GqJldRXGpDGsUYqI5cxx87HMf4KIrNtDX46G8qpTq2opJVWJUCDFzOZx2bvvM+7jg0nPo6/FQVllCTZY7eoNxuhw4XccfLSHEyTr2jrJGo+HCS8+mbl4trc3tFBTlUzO7Cr1Bph2LsWM0GSmtKE5rW3nqUv728B0cOtiEw2mndk6V3FwahmEHIBRFOQ94EHgUOBs4krWjFrgZuHyU+yZGwF2Qh7sgb6K7IYQ4ht1hk2REQohJy2aXfZSYegwGA3MXzGbugtkT3RUxw1XXVlIto29GZCRj9L8BfEpV1SuAyID2Z4DVo9kpIYQQQgghhBBCTC8jCUAsAB7J0t4NSLYNIYQQQgghhBBCDGokOSB6gFKg/pj25UDjaHVIjK5gIETDgcP4/UHKKoopLHZPdJeEEAPEojEaDjbS09VLYbGbssoSyREhhBhzzY2tNDe24XDaqKwpz6gqIMR0EI/HOXSwia6ObvIL8lJl7IUYLr/PT8OBRkKhMOVVpTLlfRSMJABxF/D/FEW5FlABnaIo64HvA78fi86Jk9PX4+H3v/wbd/32H0Ayic8P7/g6cxfKfDkhJoNIJMrD/3iC793+M+KxOBarme/85EusOWPlRHdNCDGNvb1lO5/+4Ffp6/Wi0Wj4wMdv5Pp3X4HVZpnorgkxauLxOP959Hm+/rnvE4lEMRoNfO37n+PsC06XQL8Yls72bn7+/d/xr/ueBKCssoT/9/OvUFtXPcE9m9pGEgL8EnAQaABswA7gKeAF4Fuj3zVxsnZt35cKPkDyR/Sj79xBwB8Y0evEozG661s5vHk3HXsaiQRCo91VIcZdoNdHy7Z6Gl/fi6ela0L6cHDfIf7nKz8hHosn++QP8rXPfI/W5vYJ6Y8QYvrr7fHwrS/+iL5eLwCJRIJf/e8f2bvrwIT1KeTx07qjgcNb9tDb1ImqqhPWFzF9HK5vSgUfAMLhCLd/7v9xuL5p0HV8Hb00vbmPprf34+/yjFdXZ6RAt4fmrQdofGMf3vaeie5OVlvf2JkKPgA0NjTzxzv+TrR/mxInZtgjIFRVjQI3KIryZZLTLjTAG6qq7h2rzomT09aSeRHz1pbt9PX6sFiHd5dDVVUObd7N5j8e/fHNOWc58y8+Fb1JhmuKqcnf1cfLv3mEnvo2ALQGHes/fhV5NcVDrDm62ls7Mk60e7r76O7qpaikYFz7IoSYGfp6+ji471BGe3tLxwT0BoK9Pl7705O07WgAQKPTsu62yymoK5+Q/ojpo7O9OxV8OCIUDNPZ0U1FdVnG8r2NHTz3f/cR9gYBMOc6OOOjl+MoklR3o83b1sOLv3gQb1sy8KC3GFn/8SvJqSic4J6lO7C3PqNt8ytv4vP6yclzjXt/pothj4BQFOUriqJYVFU9oKrqvaqq3qOq6l5FUcyKonxlLDspTkxRSeaPePnqRThd6fVpg94gbfVt9HX0ZSzv6+jljbufAUBr1FO7fgmKXkt3QxvxaGxM+i3EWOs60EIiGmfuhaupu2AVVWsXsuPxTcRGOaIdi8bobOygs7EjNcphoMJid8Zc1Lz8HPLyc0a1H0IIcYQr18WsuqqM9sIJCnr2HGpPBR8AErE4b9zzDBF/+mjLRCxOb1MnbbsP4+vsw9vjpbW+FV+vb7y7LCZYNBShq76N9n1NhDyZo3pVVaW7pZs8p4vT16cX6jNbTIPO4T/40vZU8AEg2O2h+a2JGxk0nbXtOpQKPgBEA2EaNu2iq7mL9kPtRMKR46wNwT4f7Xub6D7UTiwydtcjs7JMtTj19BXYHNYxe8+ZYCQ5IL4K/BI49pdu6X/u66PVKTE65i6cxXs+fD1/+NU9JBIJCovdfOwLt2CxmlPLtBxo4f7/vY/GPY1YnVYu//gVzD1lLlqdFoBoMEI8HEXRalhwyRq2PbyJYJ8f5V+vsviytdSdsxyDWUZCiKklFomRU1PC2w++gqqqmF1WFl+yhlgois6gH5X38HR5eO7vz/HKgy8DsPbytZx+1ToceY7UMlW15Xzx25/ku1/5PyKRKDa7ldt/8HlJFiuEGDNOl53//tYn+eyHvkZXZw9arYYPf/o9zJ5XMyH9CfuCGW3eth6ioQgGqwmAWCTKgRe3s/mvT6MzGZh1wSr+fffT9Lb2kleSx9WfuZrKBVXj3HMxEQK9Pt74x/McfGkHAK5yN+tuuQhnSTKoEPIH2fLEFp74/RNEw1GWnDqXpZ9YwE9/9HvMFhO3/+DzlFWWZLxuIh6np6Eto723UaZEjgVPS3fa47y6cjp9Ye7/0I+JRWIsOH0B57/vAvKKM4NFPYc7ePZnD+Dr6AMF5m1YwYKNp2CymzOWPVmLls3jynds5L6/JgtB1syu4Ib3X41ePzrnijPVSAIQCsnkk8daRrIUp5hkHE47773tBs69aD1+X4DS8mLchUd/yEFfkPt/lAw+APj7/Nz1zb9w609vo6Q2uXO25NqxFbhwlrnZ9/w2gn1+ANSEylv3v0jBnDIK52QOYxNiMtNbTez+zxupx8FeP3uffZuKVXNG7T32vLabl+5/MfX4hX+8QGFVESvOW5FqMxgMbLziHBYtm0tPVx8FxfmUlo/vNBAhxMyzcOlc7rzv/2huasfhtFJRXTZhJ9T2wswRX+XLZ2NyHp0q2tfcxaY//weAijXz+ddvHiXUP0Kiq7mLP9/+Z2796a24CmT02HTXsbcpFXwA6D3cwZ5n3mLF9Wei0Wg4vLuRh3/5cOr53a/s4tTL1nDnvf+HzWWlvLI0awJKjVZL5Snz6DrQktZeuqR27D7MDFY4r4L9z76VemytKOSBXx39d9v2/DZyi3M57z3np40UjUVivPXAi8ngA4AKO5/YQtGCSkoXjn5iyNz8HD72X7dwxfUXJatgVJaQK6NUT9qQUzAURfEqiuIhGXw4oCiKZ8CfH3gcuGesOypOjNFoYPbcGpauXJgWfADwdHpo3J1eQVVNqHQ1H03IZ7JbOPV9G8mtKqLncOb8UEnQI6aiqD+c0dZ9qJ1IlvYT9fYzb2e0bX0us02r1VJVW8Gy1Ysk+CCEGDeFJQUsW7WQ2jnVE3o3z1XhZvW7z0+NdiheWM38i05Bqzt6j8zf7U39dxxSwYfU831+ett6x6O7YoJ1HWzNaGveepBoMDlkv3lfc8bz25/fRnllCRVVZcetflG8uIZZZy9D0WjQ6LTMv+gU3JKLZEzk15aw8PLT0Op1GO1megf8xo946+m3CHjTB95H/EHadh7OWNY3hr9/i8VM3YJZLFmxQIIPo2Q4IyBuIzn64XfAF4GBiQIiQL2qqi8P9w0VRfkI8FmgGNgOfEJV1eePs/wi4KfAapIjLX4FfEPtz9zWXwr0O0AdyekgDcBvVFX9/jGvcxXwDaAW2A98UVXV+4fb76nO3z9Hzuo4ekfBZDVhc9ky5k/aXOnzmnIqCoipsOCK0+je10zrtvpU4jxrbno+CSGmAmu+I6Mtp6IgdQI8lHAgTF9HH3qjjpxBklOVzytn3xv70tvmVoy8s8MU9AeJRePYXbYxew8hhBiJ4eyXdHo9lafOI39OGfFIFEuODZ0xfWqnJefo+nq9DkWjoCaODsrV6rSYHdmTa4cDIXo7+jCYDORkGW0hxlfAG8Db5cVkNeF0O0e8fm5VZn6z4gWV6PunA+cUuDKeL6wqwmg2prX5+vxotBostqPD9i0uG0uuPJ1Z6xejKArWPAeKZiQFA8VwGW1m5m5YSfmK2ahxlX3b6jOWKa4tyfh3M1hNFNSV0dSfm8Ne6MJZVYi9LHmT1d/rx9frw+KwYJdrlElryACEqqp/AFAU5SDwUn81jBOiKMp1wI+Bj5As3/kR4FFFUearqpqRkllRFAfwJPAcsIpkkOFOwA/8oH8xH/B/wFaS+SlOA36lKEpAVdWf97/OGuBukrkq7gOuBP6uKMppqqq+eqKfZyoI+IJsef4t7vvNQygaDVe9/2KWn74Ys9WM0+3k8o9fwV++8efUgXzVxlUUVhWl1o9Gomx/YRsP/uxBQr4QFfMrWH3pGnY89BKLLjqVnIrMpFWRcBR/nx+zzYTJMrwLOiHGQtAXJBQIY8+xodMf3d3llLtZdMmpbP3XK6AmD4Sn3HgOxmEEIDoOd/DQzx9k3+v7MFqMXPShi1m8fjGGY6rCLD5zCa8/+XoquaurwMXCdQtH9wOS/I1u3bSDe375AH6vn4tvPJ9Tz1mJMzczyCKEmLmC/iAhf+b+cCxEo1G2bdrJPb/8J94+Pxe9cwNrNqzGlTf4ful4NzRcpW5WXH8Wr9/zDG1bD3DGlafz7L1H711tvGUj+WX5Geu1N7TxwE8e4ODWg5jtZi75yCUsOH0h+lHK9SNGpmV/M//43/to3tuEzWXj8o9fQd0pdWi12mG/RsHsUipX19GwaTcAjuJc5py9NDVMv3x+BZULKmnYnkxsajAb2PDuc1PHaG+fj01PbeGBPzyG0WzgmlsuY8maBRhNyQtdjVaLfRJN5Ql6g4SDYWzj8Lsdb7FojGgCzHYzVQurKJtbTuOu5OgGo8XI2e88K+O3qjPoWXL5afQc7qBgeS079x3m0fufpm7/IS66bgP/+tmDdDZ24nQ7ufoz11C7VKbQTEbKidRaVhSlCEg7284WQMiy3qvA26qqfmBA217gXlVV/yvL8h8GvgsUqqoa7G/7EvBhoEwdpPOKotwHhFVVfUf/47uBXFVVNwxY5t9Ax5FlBrNy5Up18+bNQ320Seu1Z97g+5/5aVrb5/73Y6xYtwSAeCxOW0Mb3c1dWJ1WCquLsNiP3kU4tPMQv/zEL9LWn7Oqjks/dBGOghx0hvSdYcvBFh678wn2bN5N+dwKrvjwJWiARELFVZyL2T688p9T1ODj+sbIVN8+R0M0HKG3uZtIKIKzKAdbTvIkdv/bB3jol/+iraGNJesXc847zsZdfjS5YywSw9PaTSQQwuZ2YTvOifHAde79/t95+9n0qRS3/OCDVC2syli+p7WbtoZkAqvCqsIxufu28/U9fO2W76a1feC/3sW5V505sGnct02Yetunoij85Yu/G/byN3zrvRllVMUJkX3nGDuw9SAP/fIhWutbWbRuMefecA4F5WOX7HbXm3v52ge+m/b7eN/nb+C8a84+4deMR2N42noIe4OYXDY8vT48nX043S6KqovQG9MvVCLBCH/9zl3sfnV3WvuHfvxhKgaMRutr68bX6cXstOAqzkOjzbjjLdvnMIV8QXpbukFVcZXkYRowuiDgDfC7L/w2bYqERqPh1p/dRvEIS2BHAiE8rT3EY3EchTmYnekjd73dXloPthIJRSioKEg79j//6Mv89Mu/SVv+Sz//NItWzx9RH8aaqqocePsAD/7iIdoPd7D0zCWc/Y6zcJdl/G6n5PbZerCVx//4JLs27aRsThmX3HIxrgIXrfWtRMPR5L9b5mdN8XT08qvv/InNz70JwMXv2EDj5gN4u70U1xaz+PSFaBSFujXzKBhwY1WMq0G3zWGH0vpHI/wEuJZjgg/9jhu+VBTFAKwAvn/MU08AawdZbQ3w/JHgQ7/HSU6lqAIOZnmfZf2v97VjXucnxyz6OMnpJdPaf/75bEbbMw+9mApAaHVaSmpLUkknj9XV1JnRtue13ai3XpIRfPD1+fjzN++itT45P6+suohX/voMzf1R6MLZJWy49TLMTgsanXbaRXLF+IhGoqCC3qgn6Amw+Z8v8vYjrwFgdzvY+OlriKFyx+d/nSrN9Nrjm/H3+bnxSzekhvPpDDpys4zgOR5vr5ftL27PaO9s7MwagMgpyh10isZo2fbazoy2R/72b9actxrr9A74CSGGobWhjTs+/2ui4eQA1i1PbsHX6+XdX70pY3jzaNmxZXdGcO6Rv/6bteefcsLl67R6HTkDLkhcxcfft3p7vRnBB4Cuxs5UAKJlTyNvPryJhtf3oSYSrH/fBdStW4hWzk9GrK+th7cf24ynvZem7Q0UzCrhrFs24uyfEuHp9GTkZ0gkEnQ1d404AGGwmMg/zjr2XHvW4feRcJRH//afjPYtz7056QIQrQdbuePzvyHWX/J+02OvEfAFueG/34HBOLWrz/k9fu76n7/S1L89HHj7IHd84Td84hcfZ86K4SUD7/P6U8EHALvDirfbS/WiKmbNq2T7Q6+iqipt2xtY/74LyK8sRNFMyL0YkcVIJjb9AFgCXA6EgHeSzOXQCFw3jPXzSQYpjq1x0wYMFpoqGmT5I8+lKIrSqChKGNgM/FxV1V8O43Wyvq+iKLcoirJZUZTNHR2ZiRenklx35h3X3Czz4wZjzTJvM68kL+vUiq7m7lTwwWg24nLaUsEHAEeBi32v7OS+r/6Rx/73Plp2H5a7hydgOm2fIxGNRKl/Yx8PfeuvPPD1v7D/1V10HW5PBR8AvB0eXrnnWbxd3oy60Dte2Ulve+9J9cFoNqbdSTnCljNxeRdszsyTeWeuA/0EnUDP1O1TTH4zddvsONyeCj4csfu1PfSMYdI2W5Z8DI4cOzr98IfanyyjxUheSWYJP6vLRjQS5cDmPbz05//g6+hj5ZWnUVRXxjO/fpTuLDdexsNU3j4T8QTt+1vobGjD1+Vh6UWr0Rt01G/Zm1rGZDNhzXK8srpOLCB1IjRahZz8zLwTjpzJN2Wx/XB7KvhwxLYXtp30ecyJGs3ts7ulOxV8OCLoC9LZOPzX1ev1aYGYREJFp9cx/5R57HjydVBg9bVn4CzO5cmfPMCLf/43PQOS7IuJNZIAxIXAR1VVfZxkEuItqqr+EPgC8MERvM6xV5yDlfc83vLZ2tcBK4EPAZ9QFOVdJ/q+qqreoarqSlVVV7rdYzdEcTycddm6tGGJBqOBdReuybpsW1MHzz/2Mg/8+TG2bd5JKBSmZFYJC89YlFpGq9Ny6Ucvy3oQMZoNqaGLOUU5aTV+rbl2LE4rr/ztGboOddDw+j7++Y276KjPrLksjm86bZ8j0bq7kYe/ew8tuxtp29/MY/97H96Ovozhss07DmE0Z87vNVlNGUN0R8pit3DxRy5JG70z99S5lMzKPoJopFRVZd+Ogzx6z7958v5nOLS/cch1Fq6ah915NACiaBSufP8lGTkpxstM3T7F5DdTt01jlhsGRosRvXHwIGXL4TaefeRFHvrL4+x8cw/R6MjSf81bUZeWeFLRKFxzy2WYzOOXF8rmtHHpRy9Dqzsa9Fh4xiJKZpXQtqeJR79/L617mmg/0MKrdz9LxeIaFI2SVnFjPE3l7bNtfzNP/uQBmnccorO+jdf+8QL5VYU07Tw6O9vldnHZxy5Puwt9ysWnUDTGw+MDviBvvbqdf/7pEV59egsXXHdOWs4Js9XM8nWLx7QPJyLb79ZsN5/0ecyJGs3t02A2pP0uj8j2mQdTUJrPtR+6LPX42cdeZt11Z5Dov/k0d/1i9r64gz3Pb6OnuYu3HnmNJ3/yAEFPIOvr9Xb3senZ17n/Dw/z2nNv0NctVf7G0khukblIVpiAZCWMPGAf8DLwm0HWGaiTZODi2D1NAZmjE45oHWR5jl1HVdUj0zG2KopSSHIKxp+GeJ1pf/U7e2ENX//tf7H7zb0oikLd0tlU12Vm4u9s7eJ/Pv1jDu4+OmLh49/4IGddfDqX3XYZp1x0CkFfkPzSfAoqsw9bzy/NZ8O7NvD4nY/T09qDY/2S1HNVK2az54X0oeuJWJzO+lYKqmVulhja3ixTH3a/sJ3C2aW07DpakqlkfgWuwhzmrJzDns17Uu2XfugSckdhOkT1ompu/eltdDZ2YLKaKKwuwjZKlSd2vbWXL3/g28RicQCsdgvf+s0XqZozePWM8tpSvvrrz7H7zX0E/SHqls6idl7VqPRHCDH1FVcXM3d1Hbs2HZ2OcMkHLyavOHN0ACSDD7ff+j1aDyfz1yiKwhd++HFOOXPFsN+zvKaUr93xOXa9uY+gP0jdklnUzK86qc9xImqX1ib3102dmG1miqqLsDqtvPb35zKWPfTWAYrqyqS61wlo2t6QMaJ1/yu7WH3d+rS2eafO49af3pbMO+ayUVhViHlAnojRpqoqTz/0PL/+3p9SbWvPXc1XfvlZ9u88iMGgp27JbCpml41ZH05USW0xs5bNSqumddmHLyVnEiXIPFH5pfmcf/N5PPKbR1Nty89dRuEg1xfZaDQazr78DCpmlbF/Rz2FZW7mLKrB19bH2w+9gi3XTs8xo5k6DrbS29KdUTEnGAjx15/fx+P/eCrVtvG6c3n3x68fs2lqM91IAhD7gRrgELATuF5RlE0kK0p0H29FAFVVI4qibAE2AH8f8NQG4B+DrPYy8F1FUUyqqoYGLN8M1B/n7TTAwC3m5f71/t8x7/vSUP2eDmrmVlIzt/K4yxzY3ZAWfAD4/Q/vYvHqBeQV5Awri6xOr+P0y0+jakFlck5fRQG+9l7K6kooqspn9pIyOpp7eeXvLxKPJi+wZJ6lGK5sJTJNNhMFs0pSAQi728Gp167Hkefk+s9ey+E9jURCEWpmF6FTEgQ7OjDYHWhNJ35AURSFwqpCCrOUAjsZsViMB/78aCr4AOD3BtjywlvHDUBA8mS/vKZ0VPsjhDh5ajxOxOsl5g+gNRrQ2+1ojeN7QmvPsXHtp6+hcU8jnm4fBeVuyuYMvr/Yv7M+FXyA5EXcH390N/OX1aWNthpKWU0pZRO8X9JoNBRVF1F0zI2ObBWP9EY9889ZSm5pZjUNcXx6Y3IE7KlXn0ZecXI6Qzii4q5JP04OlXdstLU1dfCnn9yT1vbSvzdx3lVncdE7zxuXPpwoR66Dd3z+Ohr3NOLr9VNQ4aZsEgZKToRWq2XtJWuomFtBZ1Mnznwn5XVlaUnwh8Nqt7BkzUKWrDlaYczhcnDKdetJxBPZ3zvLNLDm+pa04APAI3f/m3MvX0/N3KoR9WkmiQUCRD1eVDWB3u5Abxv+dKqRXP3dCSwGngH+B/gXySSOGuDjw3yNHwJ/6g9cvEhyukQJ8EsARVG+A6xWVfWc/uXvIlk6805FUb4JzCE55eP2IxUwFEX5KMlklEdC+2cAnwF+PuB9fww8pyjKfwH3A1cAZwGnD/vTT3PhYDijzefxJxP+jYDFbmbO8tmwfDYAReW5dL22mXB9MoLrNhk46z3n8O87nsDstMroBzFss0+dz7YntqSCV4pGYfH5K3HXFFO+oCqjCoYz34kz30mwrZ3OzVug/+6MqbCAnEUL0ZkmV4nYRDxBV2tmLLervWcCeiOEGA2B1ja633gz9dhSVopr/jy0hvGdInVkfzgcQX8wo623u49I+ISrsE86tavn8vajr6UfTy5cRdnCqmxVMMQQShdUcNZ7N2BP9JLobALAotdjNlRPaL+ikSihLOe3oWAoy9KTj8vtwuV2TXQ3xoTZZmb2slnMXjZrVF/XaDGy9OJT6G3pprO+jYMDRsLOPm1B1uS12baR47ULiHi9dL76GvFQ8rek0etxn7oag3N4x5lhBSAURdED1wM3Aaiq+pSiKHNJ5lzYq6rq1uG8jqqqdyuKkgd8CSgGtgEbVVU9cuu9GKgdsHyfoigbgJ+RTC7ZQzIZ5g8HvKyWZKnOKiBGcqTGF+gPavS/zkuKolwPfBO4vX+Z61RVfXU4/Z4JymtK0Rv0aQGHcy9fT/5JDlkPtrSQiERSjxORCLmFJs78wIUU15WRM8SdhlB3H/62LhKxBBZ3DpbCXBRFsthOVbFQGH9rF+FeLwaHFWtRHnrL8IZfFswq5sqvvYuGtw6QiMepXDqLgtpiNBoN7kECWbFwmJ5t21LBB4BQWzvRSu+kC0AYjAYuvO5c9n7ljrT2U85cPkE9EkKcjFggSO+29KljgcYmrGVlaPOzT3+YDKrmVKDRatLuIG68bgO50+hCqKC2mCtvv4nexg6sVgMaNY7ZbiAejqAZwTz06URNJPC3dRPs6EFj0GEtysPkGl5yxvzKQnShPrx7j85sTkSj+A4dJmehY8LO29zF+aw+czmbnnk91WaymCirHp8RGGJi6PQ68isKWPee86g9dS5t+5opml1KybwKDFmmVBRXFFJY6qatKZkE85JrzmH1yrnkqiqeQ61YivLQGSYm98ZkFWrvSAUfYOS/92EFIFRVjSqKUs2ApI2qqh4iOR1jRFRV/TnpoxMGPndzlratJEc1DPZ6PwJ+NIz3vRe4d5jdnHEqZ5fztZ9/jj//9O8UlRawas1iDAYDrQdbKa4uzposZjiifX2ZjeEQC85ZOeS6wa5edv3tcSLeZMIYRath7jsuwFE2ukPfxfhIxOK0vradphffSrUVLKuj4qyVw7obqCgKBbUlFIxg6KYaixHPcqdjYFBsMllx+lI++F/v5r47/4XBaOCdH7mKuUuSo4ni8TitB1vpbOrE4rBQXFOStQKGEGJySMRiJLIkb0xEJ+f+54iaukq+8tPP8uef3ENXezfnX30O51x2xqQM/of8IZoPtODp8pBblENRVTEG09AXCoqikF/hJtzYTOOTW1LthSvmUbZ+xYy82PAcamXX3U+kAvbGHAdzr92AaZgVIuKBzOR+0b4+SCRAO37VTwYymY3c/Ml3kF+Yx4tPvErF7DJuvPUayqpKaD/cQWt9KzqDjpKa4mk70mAyaDvURlt9G3qTgZKaEpz541N1xJ7noO70hdSdvvC4y+W6c/ivH36Se37zT/LznKwuy8f/+s5U4sOqC9ZSuLRu7Ds8hUQ9mcl6R/J7H8kUjD8AHyBZelNMM4qisGDFXD7//Y/x6G8f4x8/vC/ZrlF415dvZMkZJ5Yh2FJaSrir+5i24dV79jS0poIPAGo8QfNLb2O98uwTDoiIiRPq6aPppbfT2trf2I170WxsJWOT8VtrMmEqLCTUlp5vVjeCeWrjyZlj58Jrz2XthtVoNRpsA+Zb79q0mzu/8gcSieRdyWVnL+XyWy8btQSYQojRpTWb0Dud6YF4RUFnnZz7nyO0Oi1LT13InIW1hMNhXLnOSRl8CIfCPH33M/z7L/9JtV31iSs59aJT0GiGnkYR7PbQ/HL6AN62LTvJXzgLW/HMygMRC0c4/OzmtNGC4R4PvuaOYQcgzIWFBJrSSytayspQJij4cERJRRHv+8wNXP2+S7BYzZgsJg7tPsyvPnsHIX/yBkVxTRE3f+1m8ksn78ikqap+Rz2/+uwdRELJYGzlvApu/PIN5BaefFLw0VQ1p5xPfONDeA+3su/ef6c9d/ip13BWlWBySYLaI8xFBQSamtLaRvJ7H0kAwgrc0D8lYgvgH/ikqqofG8FriUmqs7GTzY9vTj1WEyr/+NF9VM6twFXgGvHr6XJysdVU46tvQAHstTWY8oc+sCdicYK9mdG1cK8XNRYHCUBMOfFwLO3kJtWeJc9I16F2Og+2oSiQX1NEbtmJBSg0Wi2ueXX0JhKEOjrQGA3kLFyIwTGy6HvAFyASjuLMHZ+hpM5jTvg8XR7u/dE/UsEHgDeeepPVF6xizoo5Y94fIcTIafV6cpcsonfbDsLd3WhNJnIWL0RvnxonsRabGcsYVig4orOhjc6DbWi1GvJrisgpzScUCBH0h3Dk2Ae94dDe0J4WfAB44OcPMmtpLQXlQ2fTT4Sjwz4mTXeJaIxIlvKE0RHkSjDm5eKYOwfv3v2oqoqtqhJz4fH/HWKRGN4+HxabBaN57PKiaHVact3J6hGxaIyn//Z0KvgA0HKglf1v7z/pAISnrYeOA61EgmHyKgrIrypEMwPOV/tae+g40EI0FCGvsjD5ubUaIqEIj/3+iVTwAaBh5yEadhyadAGIUDBM0BeEeDzjuXgkSiIam4BeTV7G3AG/90QCW3UV5qLhj1AfSQBiHnBkElXNMc9l7sHFlOTv82e0GYx6etp7OLTzEGariaKaYuzDKFPVsPsQ9//6IfweP5fceC6Vc8pxFOYP6wKuaXsDcSVzp12wvA6daXyTd4nRYcqxYcxxEO45WltZZzFl3F1p39/Cw9/+K7H+hGd6s4GLv/hO8k+w6oTeZiNvxTLioRCKVoduBHXoY7E4O1/bxd9/fj993R7OuvIMzrh47bgdOCOhCK0HWuls6uC0S9aw78397B1Qksvfl72etRBicjA4HOStWkEiHEbR6SZd7pmJ1ra3iYe//Tfi/Sf3jqIcFl+/nn/+7mGa9zezesNKzrvubAqzBBSyna/EIjGC3swkmiF/kJYDrXi6POQU5FBUU4Qxx47RaSPc50stp7eaMeVMjQDRaNJbzRQsnZM2RRLAWjj8kSBaoxFHbS3WkhJUVUVnNqMcZyRK88EWHv7j47z10lZqFlRz5S2XUJWlYpuny0PrgRbCwTDu8gIKqwpP6kZAJBSh+UBLRntHY2eWpYevr7WHR793D972XiA5svj8z15N+eJjL5mml96Wbh797t34OpPndopG4cLPXUvpwirCgTCt9a0Z6/S0HT+5djQapfVAK90t3dhybBRXF2NxjKxCxkgc2FHPfb96kPpdDXz4i+9Co9OSGFCRzFFVjHEEFYBmgpH+3o817ACEqqpnnVAPxZSSX5KPRqNJ3Wk1mPSce8M5/PZzv0klpJq9cg5XfeoqHHmD30XubO3ifz/9czzdyR3Sj77wG8pnlfKZ//sY9iGGMIX9ITb97RkMZgML1i2mb8d+4pEI7qV15NVNbEZlceL0VgtzrjiLQ89uwVPfgr3UTflZqzJ26rueeSsVfACIBiMcfHXXCQcgADQ6HRrbyA8eDbsa+NFnfp6qb/7gbx9BTcDl779ozEdCJOIJtjyxhYd+9mCqbcX5K4mGo9TvaEBRFNxlM2uYsBBTkVavR6ufeTkFhpKIJ9j2+OZU8AGgZMVsfvy5XxAJJfNkPH3fc/R29HLL196bcYc8rzgPg8mQWhYgp8BFTmFO2nKRcITn7n2eZ+56OtV28Ucu4ZSLT2H2ledw+NnNeBpasZe6qThrFUbHzLvQUBQF9+I5JGJx2t7Yhd5souLs1diKRzYiQFEUdJahLxR9fX5+ffudNOxJltDe9soOGnYd4ku//TzuAe/Z29HL3d/5Gw3bk7PxdXod7/n2e6g+iYt6i93CinOW8/gfnkhrr118cueX7fubU8EHSJavfe3uZymoLcla9nW6aNvTmAo+QHLk9Oa/P0fBrBKsLivLz17Gs/c+l7bOUOVEtz27lb//v7+nHq++aDXnv/cCzGMwIqutsZ0ffvInBPoDl3d89y5u++KNhPfUE+jsJXdOJaWnLUU7A/PCDGW4v/dspNaQSFNUXcjNt787NcJh/TXrefG+F9KyYe/dvIeWfc2DvQQAbYfbU8GHIw7va6KjaegIcywaI9Dro3VPE8//9XmCFidqUQlhnQmdxURXfSv1L++g8fW9+Ls8Q76emDwsBbnMvuwslnzwSqovOgNvt5+DL26jbfdhIsEwakLFmyUy7unoHf/OAof2NKaCD0c8c/9z9HVlSa46yjqbOnnkjofT2rY8vpm5q+diy7Fx01fflVHbXgghpgo1nsDbnr4vDUYiaQEFnUHHrLoKmt7cS/2rO+lt7Eg95y53895vvoe8/rJ6xdXF3PTVd2XcHOk41JEWfAB49NeP0NXUhbUwl9mXJ49Jc64+F+sMy/0wkNFpo/zMlSx+/5UsePcl5NZVotGNZKD0USGPn+atBzn44jY69jQSDacnXu1o7kwFH47w9vpoO5Ser6lpT2Mq+ADJ88NHf/1o1lKxI7HyvBWsvmAVikbBYDJwyYcupnJ+5uiLkQj7M6erBHp8aQG26Sjky/y38Pf4iEViaDQa1l62hqVnL0VRFIwWI1d+/Aoq5pUP+nrdLd08OODGC8CmhzfR3tA2yBonp/VQeyr4AODt8fG9L/wa91mnsOCmS7DPqaZ1dyNdB1uJxzKnZ4gTc2J7FjFtabVaFqydT9mcjxPyh9BoNTx/97MZy/k9mUMfBzJlGeau0Wqylr85lsVpZd45y3jjny8RCYTZ/p83QYFLv3oj7bsO8fxPH0hdFDrL8jn9w5dgzRte3Vkx8bQGHfG4gW33v8D+544mpVx42VrmnreCuWctoXlHeoGdWWvnj3c3AbJG2x15DvTDqNpxskL+UKpG/UAltcV86hefwOmeutu80WgkMsJKJDqtlliWuZmD0Wo0xAfkzBjuOjd8670jWn6kI2FG2q8T+hxaLfERfFcnss6JvkcsNr1PxsXwaQ065p+7jGfvODocXqdPn3p53QcuwffmQTZv2pNcR6/jzE9dRV51Mpn17GWz+Oj/3UbAG8CWY8PqyEzwGciS2yAejRPqv4jVGvRyd7OfoigYs3yHIxH2B3n97mdo3LI31bbs+jOZtX5Jan9pNBnQajXE4+n7NtMx54i+nsxzzY7GDiLBCGbrid8Nzy3K5apPXMlZ15+FVqcht+jkS7y7qwtBIW1S+rxzl2Ge5tWqCmdlViabv2E55v4pE/kl+Vz/mWs5/6bz0Om15BYdfwprKBAiHAhntGf7HY+GY7c5SE4jUVSVl377GD1HAh8KnP7hSymZ5lNqxosEIERWznwnznwn0XCUhacvZOtz6Zmi84dIClhcWciaC1bz8mObUm0bbzyPwvKhkwkqisLcs5aACjv+8wYOt4u1N52N2Wpkz6Zdacv2NXbS09AuAYgpxtvanRZ8ANj+0CuULq2ldFEVp918Hm888BKKRmHFladTPPdotDziCxILhdHbzOhNQwe0TkbN/CoKyty09991UxSFaz58OdYxnIt4hKvAhdPtpK/j6B1Co8VIUVXRlA4+AEQiEd5+7LmhFxxg8QVncPDhp4a9fPVFZ/PoV347ove48Ovv46Uf/XHYy6/9xE38/pYfjOg93nPHp3nwq1krUWd16e0f4anvDr9PAGd//iZ2/+OhEa1Td9UlvPG3R4a9/LLrN/LUL+4eWb8+fN2IlhfTX/nSGtbceA5v/usVdHodlXMrqVs6m91v7sVqt2BBQ9eAaljxaIzd/36dU997AZr+bOv2XDs2l41Qr5dgtweTy46iOXoxmVuci9FiTLuocbqduArSp2qI4wv1+kjE4xidVrTHGRnR19SVFnwAePu+FyiaX4W9P5l5QZmbi2++kAd+e3SU3+pzV1JcnV4lraAyM/fHkjOXjEr1J51eR8EwzkmHK7+qiPM/dRWb/vYMgb4A8zcso2794klZQWY05dcUs+GTV7Lpb88Q9gVZcN4KZp22IG0ZvVE/7O/a5XZRVF1E68GjuSO0ei25JWNToaSkppil6xbz5vNHz0kve99FRHp8R4MPACq8ce+z5FQUoMZiaE0GjLaxPxecriQAIY5Lb9Rz7k0biIaj7Hp1F1anlYs/cgnFs45fStNit3DtrVew6uzldDR3UVxZSNXcSvTDnAdry3Ow4urTmbdhKcHWbvb881lCvT4c5YUsu2Ydb9z7PGoiGWbONuxNTG7RYGZ0W00kiIUiOIvzmH/uMqpWzUFRlFQUHaDnQDO7/vkswS4PjvIC6i5dh71k7IbMFpS5+eQPb+PgjnoC3gAVdeVU1VWM2fsN5MhzcMNXbuT+H91Hy/4W8krzuPITV5I3RgdhIYQYb2aHlYUXrKTm1LkoGg1mh4UPfPXdHNjZQNgfQtebedfT19ZLPJZIBSDC3gDNm3bQ8NybAFSesZTSU+Zj6L84yCvJ46avv5v7f3QfnY2dFNcWc8XHrzxuHitxVCwcpX3bfvY9+gqxUISipbOpOnsFltzs3180lHl8j0dixAfkdtLpdZx7zZnULqympaGN/OI8qudVYrWnX9CVzi7lqk9fzSN3PEzIF2LB6QtYd/W6SVmKXavXUbFsFgWzS4lHY1hctmkffIDkv2XVitkU1ZWNyue2OCxc89lreeAn/+TQzkM43U6u/MSVuEcxWDSQ3WnjXZ+9ntMvWkNXazel1cVUzq2gfVt92nKKRsPcs5ex457/0NfQiinXztzLziCntnRG/DuPNglAiCG5y91c/9/voK+zD4PRMOy7r848J0tPX3zC76soCmoowta7nkDtH6bnOdxGIhajdGktja/vAwWcpTN3zuZUZStwYbCaiAwIHtmLcrDmH922LMcMW/R39PLWHx9NlULyHG5n293/YcUHLsUwhqXiCsvcFJ5gGdCTVTanjPd99/34e/2YHWZskoVZCDENWQbc0c4tzE1VGmrZfpCDz6ePwKxZtwi98ejNjO59jRx8akvq8cGntmDKdVC87GiJ4upF1dzyww8S9ASxuqxY7HLncri8TR3suu/oVNzWN/agsxiZfcGatJEmR9gLctAZ9WnJpPNqirHkpScgtzqsLDxlPgtPGXyKpcFkYMV5K6hdVks0HMXldqX9209GpnEoXTsZjebnLq4t5t3fuhlftw+TzYR9jCvT5OS7yDnDldbmKE5Oyzky5bti1Ww639qDvz2ZpyzU7eXtPz3Gyo9ciW2SlRSdCiQJpRgWg8mAu8w97kO/A119qeDDEb6WLnLL8rHmOzjtQ5eQM0ZRUTF2bPlO1t12OXm1JSgaDUULKlnz/o2YjnNSGOz2ZNRhDnb2Eur1DbLG9GCxW3CXuyX4IISYcfJqSll10wZMDgs6k4GFl66hdGlt2jJtb+7NWK/t7X0ZbTanDXe5W4IPI+Rtzkwe3vbmXiL+7HPyHUW5rPvo5eRUFKBoNJQuq2XVu87FYDnxShAutwt3mXvSBx/E6DFbzbjL3WMefBhMTrmb0z5yKbYCJxqdluL5VangwxGJWJxgtyTDPxEyAkJMavosByyd2Ujp8tlUr1uMUU4kpqy86iLOuO0yIoEwRpsJnfH4iR31lsx8Dxq9Dq1JTkiEEGI6MpgNVK9dQNGCKtREAnOW4d32kny69zWmt42wfKQYnDFLziNLvgvtcZIxu2eVsv4TVxINRjA6LOj0crkhphaNVkvJomryqouIRaIoaoKDBj3xSDRtuWznpmJoMgJCTGq2wlyKls5Oa5tzyWnYi/Ik+DAN6M1GrHmOIYMPANaCHMrWLExrm71xDZbcqZ2QUQghxPGZnVYsOfasc60Ll8xCP6AigsFmpnDxrPHs3rTmKC/AXnp0pKlGp6X2vNXohhiNYLCYksd3CT6IKcxoM2PNdWDJczHnktPSnitZNQ+rTL84IbJXEJOa3mJi1sY1FC2bQ8QfxJLnlB/7DKUzGqg+ZwXu+VWEvQHMOQ5sxXlZ56AKIYSYGWxFeaz44GX4WrtRAGtRLhapjDVqzDkOFt1wHr7WbuLhKNYCF7YiGWEiZp6CRbVY3DkEu/sw2CzYi/PGvBrbdCUBCDHpGaxmcmeVTXQ3xCSgN5vIqSmd6G4IIYSYRCx5Tgk6jCGT04ZJ8hCJGU6r1+EsL8BZnlkeVoyMTMEQQgghhBBCCCHEmJMAhBBCCCGEEEIIIcacBCCEEEIIIYQQQggx5iQAIYQQQgghhBBCiDEnAQgxY6mqippITHQ3ZgT5roUQQpwMNZFAVdWJ7saEUuPxie6CEGKSmkrn2lIFQ8xI4e4ePAfqiXl92KorMRcVoDOZJrpb01K4pxfvgXqiHi+2qgrMxYXyXQshhBiWeDhMsK0d74EGdFYL9ppqTHk5E92tcRXxePE1HCbU0Ym1tARLWTF6q3WiuyWEmATURIJwVzd9+w6QiMZw1FZjKshHq9dPdNcGJQEIMeNE+jy0PvdS6k5CuLsH18J5uOpmT3DPpp+Ip/+7jsWA5HftnFeHa94cFEWZ4N4JIYSY7PyNzXS/uRWAcFc3gcZmis5ah9E1M8puxoJB2l/eRMznByDS00uoq4v8Vcsn9QWGEGJ8hHt6aX3+ZegfIdbR0Yn7lJVYy0omuGeDG/cpGIqifERRlIOKooQURdmiKMq6IZZfpCjKs4qiBBVFaVIU5SvKgCsXRVGuVBTlCUVROhRF8SqK8qqiKJdmeZ2PK4qyq/91GhVF+ZmiKFLUeAaK9PVlDGP07N5HLBCcoB5NX5E+Tyr4cIRnzz7iQfmuhRBCHF8sFKJv9960NjWRINzTOzEdmgBRry8VfDgi2NJGzO8fZA0hxEwSbG1PBR+O6Nu9l8Qx59+TybgGIBRFuQ74MfBtYBnwEvCooigVgyzvAJ4E2oBVwMeAzwKfGrDYeuAp4KL+13wEuH9gYENRlHcC3wO+BcwDbgI29vdFzDRKls1eowG5Iz/qlCzftaJR5LsWQggxNEXJfhyZSceQQT/rDPoOhBCDUjRZ9pFa7aQ+1x7vERCfAu5UVfXXqqruVFX1o0AL8OFBlr8BsADvVlV1m6qq/wC+C3zqyCgIVVU/rqrq/6iquklV1X2qqt4ObAEuH/A6a4FXVFX9k6qq9aqqPgX8EThlTD6lmNQMLicagyGtLWd+HTqz5CUYbQanA63RmNbmmj8Xndk8QT0SQggxVeiMRlzz69LaNHo9xtyZkwNCb7djyHGltVkry9HbJAeEEALMRQUZQQhn3Ww0Wu0E9Who45YDQlEUA7AC+P4xTz1BMkCQzRrgeVVVB47Xfhz4BlAFHBxkPTvQM+DxC8C7FEU5VVXVV/pHXFxKcrSEmGEMdhtFZ6wl0NxC1OfHUlqCKT93ors1LentNgrXrSHQ0krU68NSUozJnTfR3RJCCDFFmEuKKDjtFPyNzegsZiwlxRgc9onu1rjRmYy4V68g2NZOuKsbc2EBJnc+Gp2kcRNCJG+sFq0/DX9TC4lIFGt5yaQP0o7n3isf0JKcTjFQG3DuIOsUAY1Zlj/yXEYAQlGUW4Ey4E9H2lRV/ZuiKHnAc/0jJ3T9z38+25sqinILcAtARUXW2SFiijM4HRicjonuxgmZatvnVP6uxchNte1TzByybU5NWr0eS1EhlqLCie7KmDre9qm3WdHbqqG2eiK6JoTsPycxRVEw5uZM+qDDQOOehBI4toizkqVtqOWztaMoylXA/wNuUFW1YUD7euDLwEeA5cCVwJnA7VnfUFXvUFV1paqqK91u93G6JqaSaCBEb30L3XsOEezqm+junDDZPo+KhcL0HWqja3cDgY6eGV8jfjKQ7VNMVsfbNlVVxd/RQ9fuBvoOtRELhSeol2Kmkn3n+Av2eOjee5jeA81EfIGJ7s6kJtvn1KKqKv725DHNc7iNWCgy0V1KM54jIDqBOMmRCwMVkDkq4ojWQZbn2HX6gw9/Am5SVfXBY9b5JvBXVVV/0/94q6IoVuA3iqJ8XVXVyZsmVIyKsCfAvkdepGfPIQC0Rj0Lb7gAe1nBEGuKySoaCNPwzGZaN+8Ckgl35r9jAzk1pRPcMyHEVNN7sJkdf30yVSGpaEUdlWetQm8xDrGmEGIq8rV2sf0vjxH1hwCwlxVQd+WZmFwzZ3qPmL569jey8+5/o8YTAJSsXkDFmcvQmSbHMW3cRkCoqhohmRxywzFPbSBZDSObl4F1iqKYjlm+Gag/0qAoyrXAn4GbVVW9N8vrWEgGPwaKIymEZwxfS0cq+AAQD0epf2oz8Uh0AnslToa/rSsVfABQ43H2PfQCEb+U+BRCDF/EF2Tfv15IK8/cumU3/rauCeyVEGKsJOJxml7Zlgo+AHgb2+lraJ3AXgkxOsJeP/sefCEVfABo3rQdf1vPcdYaX+M9BeOHwM2KorxfUZR5iqL8GCgBfgmgKMp3FEX5z4Dl7wICwJ2KoixUFOVK4AvAD9X+sdaKolwP/KW//TlFUYr6/wZmFXwIuEVRlOsVRalWFGUDyUSW/5LRDzNDuC+zXravpWtEQ5JUVSURPzaOJY4nEY+P2bSIbIGGcJ+PWHByDTMTQkxusVCYcK8voz3ik2DmZKSO4XFlOpPv7ahENIa3sT2j3d8+eS7QxMyiqmpaEPxkxEKRrFOKJtMxbVxT6Kqqend/MsgvAcXANmDjgHwNxUDtgOX7+oMFPwM2k6xs8QOSgYwjPkTyc/yo/++IZ0nmeYDkFAyVZNChjOR0kIeAL47ahxOTmiXfmdGWN7cSvXV4pTdjwQChrg7iAR8GZy4GVw5ao5TtHEw8HCLc3UnU24fOaseY50ZnGt3Sm+aczGGS9rICDHYp8SmEGD6D3YK9vBDv4fTZoOZcGYo9mcSjESK9PUR6OtGaLJjyC9BZpBTlUBLRCJG+XsLdHWiNJkz5heistonu1oTSGg3kz6+m8YW30tqdFdM70amYnKIBP+HOduKhAIacPAzOHLSGE58qYbCZsRbl4m/tTms3ZTlvnijjnoRSVdWfq6papaqqUVXVFaqqPjfguZtVVa06ZvmtqqqeoaqqSVXVYlVVb1cHhHBVVT1TVVUly9+ZA5aJ9a83W1VVs6qq5aqqfkRVVQl1zhC2EjdV565Go0vWxHVUFFF22uJh1ciNh8N4D+4l0t1BPBQk2NZEoLVp1CKV000iFsPf2ECoo5V4KEi4qx1f/T4S0dEdmWApyGX2ZWegNej7H+dQu3EtOqNhVN9HCDG96YwGai9cg6UgmUFca9Az+7IzsBRIeebJQlVVwh1tBFsOEw8FifR24T2wh1ho8tzRm4xUVSXU3Umg+VDye+vrwXNgD7HgzE64qCgKhUvnkDs7Wc1B0WgoW7cEh+QFE+MsFgriO7CHSG9X8hqjpZFwZ/tJjVbSm03MvmQd5nwXkAy4zbnyTKwFk6dKhhQRFjOCzmSgdM1C8uoqiMdimFz2YV+oxsNB1Fh6rohoXw/xgmJ0ZstYdHdKi0fCxPzetLZEJEwsFMKgH73ggFavo3DJbBwVhcTDUYwOK3qLjEoRQoycrSiPRTdtJOzxozXqMedI6eDJJBGJEOpKHzKvJuLEQ8FRH103nSSiEUIdx+Q1UBPJ722Gn7+Ycx3UXXUmoR4vilaDKceBRjsRxQHFTBYPBVET6Tc0Q13tGPMK0BpPfBSErTifRe/eSMTjR2cyTqrRDyABCDGDKIqCOS9zKsbQ6w1yQFIkh2k2yiDfi6IZm+9LLhSEEKNBbzFJEHOyUkgec4+5KzjY8UYkKYqComhQSRz7xMR0aJLRGvRYC2Wkk5g4WfdhijIqZRIMVjMG6+QM0EqoT4ghaE1mtMfcKTDlF57U/KzpTGswYsjJS2vT2exojZNzJyiEEGJy0xqMmAvTSyxr9Aa0MvrhuDR6A+ai9O9N0eln/OgHISYLrcmC5pjRwebCkml/jSEjIIQYgkavx1ZRQ9TnJRYMYLDZ0VntKBqJ32WjaLVYikrR2xxE/T50Fit6mx2NTnY3QgghTowxJw+twUjE24fWZEJvc0gy6GEwuHLR6PXJ781gxGB3yvcmxCShNRqxVc8m6vMQD4Uw2J0zIkmsXBEIMQxao0kO2COg0Rsw5uRhPGYkhBBCCHEiNDodBqcLg9M10V2ZUjRaLQaHC4PDNdFdEUJkoTOZZ1wuG7mFK4QQQgghhBBCiDEnAQghhBBCCCGEEEKMOQlACCGEEEIIIYQQYsxJAEIIIYQQQgghhBBjTgIQQgghhBBCCCGEGHNSBUOIATqbOgl4AjjyHbjcronujhgn/j4/XS1dGM1G8svy0Wq1E90lIcQklogn6GzqJBQIkVOUg91ln+guCSGmGW+3l572HkxWE/ml+Wik/Pu4SMQTdDR1Eg6EyC3Kxeaa/mUxx5sEIIQA4rE4bz+/lb//4F7CwTCOPAc3fflGqhdVT3TXxBhrOdjCX771V1oOtqDVaTn/5vNYe8kazLaZVRJJCDE84WCY1x57jYd+9TCxaAx3mZt3ffkGSmeVTnTXhBDTROPeJv70jT/T2dSJzqDj0g9ezMoLVmI0GSe6a9NaOBDmlUde5eHfPEI8GqegooB3fekGSmpLJrpr04qE0oQA2hrauOvbfyUcDAPg6fLw52/9hb4uzwT3TIylSDjCo797jJaDLUAyEPXIbx6lcW/TBPdMCDFZNe9v5v6fPkAsGgOgo7GD+3/yAEF/aIJ7JoSYDoK+IPf93310NnUCEIvEuO8n/6Rlf8sE92z6a9zXxIO/eIh4NA5A+6F2HvjFQ4QCsn8fTRKAEALoae8lkUiktfV29OGRAMS05u8LsPu1PRntXc1dE9AbIcRU0NXSndF2cNtB/L2+CeiNEGK68fX6aNhxKKO9uzVz3yNGV0+W73jfG/vw9/knoDfTlwQghAAcuZnzdy0OC1andQJ6I8aLxWambE5ZRrvT7ZyA3gghpgJnfub+oaiqSKZtCSFGhdluwV3uzmh35Mm5yVhz5Dky2kpnlcj+fZRJAEIIoLCykItv2YiiKABo9Vqu++y15BbmTHDPxFgyWoxc+uFLMNuPHlhWXbCS8ixBCSGEACitLWHdFaelHhstRq76xBUSsBZCjAqb08o1n7wKo/lovoczrl5HSW3xBPZqZiidVcraS9ekHpusJq746OVY7JYJ7NX0I0kohQAMJgOnX346s5fPwdvtJbcoJ2v0WUw/lfMq+OQvPk5HYycmi4nCqkLMVtNEd0sIMUlZHBYueO8FLDtnOUFvgLySfNxl+RPdLSHENFK7pJZP/vLjdDV3YbFbKKwsxGiRBJRjzeq0ctH7N7LyvJUEfUHySvJwl8r+fbRJAEKIfnqjnrLZksV8JsorziOvOG+iuyGEmCJMFhOV8yomuhtCiGnMXebGXSY3w8abySr797EmUzCEEEIIIYQQQggx5iQAIYQQQgghhBBCiDGnqKo60X2Y1BRF6QAaJrofYyQf6JzoTkxxA7/DTlVVLxjPNx+l7VO2g8FNl+9m3LdNkP2nSBnqu5qK+86Z8O8vnzFpKm6fwzFd/n1n+ueYytvndPi3mw6fAcbmcwy6bUoAYgZTFGWzqqorJ7ofU9l0+A6nw2cYK/LdiMHItjF80/G7mo6f6VjyGae36fLZ5XNMXdPhM0+HzwDj/zlkCoYQQgghhBBCCCHGnAQghBBCCCGEEEIIMeYkADGz3THRHZgGpsN3OB0+w1iR70YMRraN4ZuO39V0/EzHks84vU2Xzy6fY+qaDp95OnwGGOfPITkghBBCCCGEEEIIMeZkBIQQQgghhBBCCCHGnAQghBBCCCGEEEIIMeYkACGEEEIIIYQQQogxJwEIIYQQQgghhBBCjDkJQAghhBBCCCGEEGLMSQBCCCGEEEIIIYQQY04CEEIIIYQQQgghhBhzEoAQQgghhBBCCCHEmJMAhBBCCCGEEEIIIcacBCCEEEIIIYQQQggx5iQAIYQQQgghhBBCiDEnAQghhBBCCCGEEEKMOQlACCGEEEIIIYQQYsxJAEIIIYQQQgghhBBjTgIQQgghhBBCCCGEGHMSgBjCBRdcoALyJ3/D+Rt3sn3K3zD/JoRsn/I3zL9xJ9um/I3gb9zJ9il/I/gbd7J9yt8w/wYlAYghdHZ2TnQXhBiUbJ9iMpPtU0xWsm2KyUy2TzGZyfYpTpYEIIQQQgghhBBCCDHmJAAhhBBCCCGEEEKIMaeb6A6I6a3jYCtNOxqIR+OULqyisKYYRaMMe/1gVx++lg7UaBxLUR7WojwUZfjri8klGgjjb+0k1NOH0WnHWpyPwWo+qdfsaeqkacchAr0+SuZXUjSrBJ1RP0o9FjNNsKsPf2sniUgsuc8pzEXRaPC099Kyu5Gepk6KZpdSNKcUk90y0d0VQohRkYgnCLR14m/rRms0YCvOx5TjGPb6qqrScaCFxh2HAChbUIm7ukjO2cS4623tpnnnITztfZTMLaNodikGi2nY64c9fvwtnUR8Acz5TqxF+eiMhjHs8cwjAQgxZtoPtHD/7X8mFo4CoNFquPL2m7Dm2tEZdJhsx7/wDHb2svOvjxH1BwFQNBrmXn8+joqiMe+7GH2JWIyWTVtpeWVrqs29eDaV56xGO4Ide6DPj5pQsebY6G3p5oFv3YW/25d88h8vcP4nrmDWqfNGu/tiBgh09rIrbZ+jMPe689G57Ox6biuBPj/1W/ay5Z8vserqday84jQ0WhlIKISY+jwNLez++5OgJnPHGZw25l1//rCDEO0HWtj25OvoDHoa3tjHpnue5YqvvovCWSVj2W0h0ni7PDz6/XvpbuwEBdoWVLH4wlWULapCbxj65lTEH+TAIy/gqW9OtVVuOJXC5XMlmDaKJAAhxsyBTbtTwQeABecuY+ezb7PvpR1YXFbW3ngO5Yuq0eq0Wdfva2hOXQgAqIkEzS+/hbXEPeg6YvIKdntoeXVbWlvH23spWFqHrcQ95Pphf4gDr+3m1XueQ40nWHHFaTgKXUeDD/1e/uvTlM6vxOyQu9NiZLyHWo/Z56g0vvgmlrpadj3zFrFIjLp1C/F1e9nyz5eYvXY+OSV5E9hjIYQ4ebFwhMPPbkkFHwAifT58TR3DCkDEwlHa97fQvPMQkUCY2WvnEw1F2PXcVglAiHHVWd9Gd2MnWr2OU99xJvWb9/Lk//2T0gWVnHLdevIrC4+7frCjOy34AHD42c24asow5djHsuszity6EWMm6A2k/ttZnEssEmP7k68T9ofoaeri4e/dQ8fB1kHXj/qCGW0Rjx81FhuT/oqxlYjG0k5ujohHolmWztS86zBP/fJh/N1eAn1+nr/zCbwdfegM6XHUSCBMPB4flT6LmSUayNznRL0B9r+yE2+nh6AnwJsPb8JZmINOryMh25kQI1ZRXoGiKMP+qyivmOguT3uJWIyoL5DRHguFh7V+675mnvvd43jaegl5g2x9fAtGq5l4VM7XxPg6ck45/+wlvPXwqzTtaCAajlL/+j4e+cG9+Ht8Q6yfuc0mIjEScu0xqmQEhBgzs06Zx47/vAlAxeIa9ryQfvcbFboa2iiaXZp1fWdVCc0vv53WVrB8HjqTcSy6K8aYKceOKc9FqKs31aa3mTHlDm94576Xd2S07X1pB4VzSmna1pBqW7JxNTaJUosT4KgooumYNmddFVvuej6t7dBbB1i8cRV2t2vc+ibEdHG48TCP3fn4sJe/4Obzx7A3AsBgtVCwfC5Nz7+R1m4tyh/W+s07GjLa6l/fy1kfumhU+ifEcOWWF6A36jFaTfi6vGnPedv76GvrwZpjG3R9c54TjV6XvGnWz1lThtE5+Dpi5GQEhBgzRXVlbPzM1bhritDoNFhc1oxlDNbBk8JYS9zMvvwsjDkOdBYT5WeuJLeuciy7LMaQ3mJm9mXrya2rQms04Kwto+7qDRgdw9upOwpcGW3OohxOvf4syhZW4SzK4bSbzmXemYtHuediprAWu5l9xVmY+vc5ZWeuQLVYCHrS7wza8uzMW78Yg0mSUgkhpgf3otmUnrYUndmIOd/FnKvPxVo0vClmFlfmcdyWaye3bHgBDCFGS25ZPpd+6Z1Ys92IUhjyuG3OczH3uvOwlRWiNRpwL5lD5bmr0Q4jf4QYPhkBIYYlFo3h6fJgMOqHfXdZb9RTvXIOpQsqUVUoX1jNw9+7B7V/GH5OSR4FNcWDrq/V68idW4W9sgg1nsBgkzn9U52lIJeaS9YRD4bRGo1oDcPfBdWunsvWx7cQ9ocA0Bn1LDx3OYWzStj4uWuIR+KYbMPPcjxVhAIhAp4AZqsZs/3kKoaI7Px9fsLBMLYcG7l1VdjLi1ATyX1O56F2THYLof4pZVq9jmWXrskaEBNCiKnK6LBSevpSCpbVodFq0ZmHP9q0dH4F1lxbKieTRqth1dXrMI/SeZu320ssEsOeZ0enl0uXmSASjODr82GymLCMMKdX0exSXCW5dDd2sPXxLan2pRedgqs4d8j17WWFzL1mA/FIBJ3FLMmmx4D8isWQupo6+c9fnuLtp9/Cnmfn0lsvZc6qumEngjT0H8TKFlVx5ddvoutQO0arCXdNMc5hnMTrzdPvonIm0+p0aO0j3/XkVxVy5e030XGwhURCxV1dRH5FAQB6g35Y2Y2nmub9zTzyq4c58NYBSmeXcslHLqVivsyHHi2JRIIDb+7nwZ89SFdTF/PWzOO895xPQf92BZBfUcCVX7uRjoOtxGNx3FVF5FcdP4mVEEJMRYqinNDNnpzSfC7/8o20H2whFo6SX1mIu+rkK5ZFI1F2vbqLh3/xL3y9Placv4L1155J7jAuIsXU1VbfxqO/eYQ9r+2hsLKQS267lJrFNSN6DZPVzKqr1lG5fBae9j6cBU7cNcXDLtOuNerRSkn3MSMBCHFcsWiMVx5+FUeeg/XXn0lPazd3ffMubvnhBymvKx/Ra2l1Wopmlw6a80GIoeSW5U+KIZ0Bb4Deth4MZiO5xbloNKMfHff2ePnrN++iq7kLgKa9TfzhK3dy609uk5OvUdLe0M4fvvwH4rFkMskdL+0gHIxw41duxGg5evcvpzSfnNLkdheLxmhvaCcej5NbnItxBHcJhRBiunIV5w7r7rKv10dfRx9mm3nIY1nz3mb++s27Uo9fe+Q19AYDG2/ZKHelp6mgL8g/fngvjbsbAWhraOMPX7qTW396W9rNgcH0dfXh6/ZhdVpxFbioXFI71l0WJ0ACEFNQLBgk6vGQiMXQ22zEw2FCXd3ozWaM+fnobZm5Fk5UX2cfeoOOF+97kWg4SmFVIee993w6DneMOAAhZoZ4JEzU4yUeCqG1WNBZLMR8PkIdnWhNJkz5eejtUzdJZFtDG//4/r007mlEZ9BxwXsvYMX5K9MuWEdDT2tPKvhwRNAbpKu5SwIQo6SzsTMVfDhi/xv76Ovsy3qi4+v18dw9z/LS/S+RSCSYt2YeGz94EXnFk7cUp5pIEOntI9TZiaLRYHTnY3Q6ifr9RL3e5FQTu31K/yaFEOMrHo0S6ekh1NmF3mrFmJeL3mYjFggQ9XpIxOLo7Tb0dgeKoqTWa9zTyN3f+RtdzV0YLUYu/9jlLDh94aDTKlrrMyulvf7kFtZdsw5nvnPMPp+YOL3tPangwxHRcJSupq4hAxAH3z7A3f9zN54uD1anlas/czVzVtWlbYPHnqPq7XYUFMI9PYS6utCZzcnzVJsknRxLEoCYYmKBAF1vvUk8EEBjMGAqKMS7d3/qea3FQsGpq9FZRmfeXV97H8/89ZnU47b6Nna+tINzb9owKq8vppd4NIpnz16CbcmTBkWrxV5TS8/bRyugaIwGCtacOiV37pFwhCd+/wSNe5IHx1gkxr9++S+KaotHPDxwKEaLEa1Om3GBbLZJHojRki1niNluxmDKPuzy4NaDvPCPF1KPd768k5JZpZxz4zlj1seTFe7poeOVTakSuMqeveSfsore7dtIRPtL4Go05C1fjtHpmriOCiGmjEBjE73bj1am0lmt5C5fSu+2rcRDyTxNKAq5S5dhyk0GzP0eP//4/r2pwHo4EOae797DreUFlMwqyfo+1ixz/3MKcyQB8DRmMBkxmAxEQpG09qFyfPW293DXN+/C3+cHkrmd7vrmXdz284/iLnMDmeeoAI7ZswENPW9vTbVpTSbca05Fb5Xcc2NFxi9NMVGPh3ggmQzNVFCArz699FE8ECDi8Yza+/V19Ga01W+rx+KQiyCRKeb3p+3YjfluPPsOpC2TCEeI9PaNd9dGhb/Pz+5NuzLau5u7R/298kvz2fDu9EDf2svXDmsIohieouoi5q+dn9Z28YcvwVWQk3X5g28fzGjb+tzbhAOhMenfyVITCXwH6lPBhyNtwdY2GDhtKJHA33AINZEY/04KIaaUWDBI3+496W1+P7H+u8opqop3/34SsWQ5Q2+Xl7aGtrT1VFWluyV9pN9ApXPKKJl1dNquRqvhwls2SiB+GsstzmXjB9PLty49exmFlcfPvdTX4UkFH46IhqP0tvWmHsePOUcFiAWC9O3andYWD4WIeqbmeepUISMgppj4kTtWgKJoUOOZJ4zZ2k6UPTdzWK67vAB7nmPU3kNMH2o8lvZY0WpRY7Esy8Uz2qYCk9VESW1JagTEEdl+JydLq9NyysWnUj63nJ62Hhx5DkpmlWIwy52f0WJz2bjso5ezauNq/L1+8svyKa4dvDJPcZaqPVULqtAbJ++/ycBjxhGJaBRFk55EOB4KoqoqSsbSQghxlJpQsx7DE4nMtng4hJqIAzrMNjO2HBu+Hl/aMvbcwc8ncwpzuPGrN9Kyv5lQIExBRUHW/bCYPhRFYenZSymsLKCruRtbjo2SWSVDVsKwOC3ojXqi4QHXSRoFW87R0baJeOb5qKLRDHKeKgH5sSQjIKYYvePohU6oqwtLafqOWNFqMThG72KouLaElResPPr+Rj2Xf+wyrI7RyzMhpg+d2YKiOxrXDHd1Yi0vS19IUdA7p2YAy2w1c9GHLkrL97DkrCWUjlFiVaPFSPXiGpZvWMGs5bNHXIpKDM2ea6duVR3LNyynYl7Fcaup1C6rpWLe0Sokznwnp1x66qRNhqZoNNirqzLazUWFxIOBtDZLaSka7fAqGwkhZi6d2YStIj0HmKLVZs0jYy0tQ2tIHi+dbidXfvIqtPqj+5kzrj2DwiGqCrkKXMxbM59l5yyjdHbppN3fitFjMBmoXFDF8g3LmbNyDjbX0FN280ryuOxjl6NokmF0RVG46JaLyC8/mrj82HNUgGjAj+2Y46Si0aRdb4nRJyMgphiD3UHO4iV49u4lEQljqqhEZ7ESaGxCZ7fhqK0Z1WRiVqeVCz+wkRXnrSToDZBXmo+73D1qry+mF53FQt7SZfTt20vM48HgdGIpKUFrNuGrP4TObMYxqxaDc+omj6pcUMWtP7mVzqYuTFYTBZUFWOwSGJgJcotyufGrN9LW0E48FqOgomDQ6RqThTE/j9xlS/HuP4Ci1eCYNQt9jgvX/AV49+9DTSSwVlZicst+XQgxNEWjwT6rFq3JjL+xEb3djr22BoPDTs6ixXj27SURjWItK8NcnH6TbM6qOdz2s4/S3dKN1WWlsKJw1BM4i5lJo9GweP1iiqqL6Ovow5HroKCyAL3+6E2FY89Rjfn52KtrULQ6NAYD/sOH0VmT11IGx9S8UTZVSABiilE0GsxuN0anEzWRQGM0oigKtqpKNBoNyhjcwTLbzFQuqBz11x2JeDRGLBzBYDGnopticjI4neQtWUoiFkOj16PRajE4HVhKS1E0mpO+yxqPxYiFIujNpgm7E5Jf5ia/TC7Ypqp4JEo8EkNvNaVlxx4OW44dW87UuTOi1euxlpZgLiwARUn9/nTFxRj7k8NpjXIBIIQ4vlg4QiIWx2A1J28mzK7FVlWBMuDc01xQgMHlggHnpwNpNBoKKwuHnM8vxInQ6XWU1JZQUps9qSlkP0cFcNTWYK0oH/JaSk0kiARC6IwGtINUbxFDk29uitIY0ucca/WDDxue6jyN7Rx8agvelk4KF9VSesoCLHlT9w76TKDR6dAcM8xtNLZRb3Mn9c+8Tt/hNvLnVlG+dhFWt+ukX1fMDKqq0tfQyoF/bybY3UfxirkUL6/DPIUCCifq2N8jSOBBCDG0RDxBz8FmDj75GhFfgNJTF1K0ZBZGhxVNluO61jB5c+IIAdnPUWHo89RAZy+Nr2ynffsBHKVuqs5agaNUbkadCAlATCK9bT34e/1YXVZchRM3rDcei9PR2EHAEyCnMIecCexLoKOXN373MPFwshzP4Re3EuzxsuCas9EeZ662GB/juc0Gezy8eefDRP3JLNvNm3YQaO9m0Y0XoDfPjAup3vZeutt6MNvMuMvyB62dLrLztXbx5u8fJtFf2rT+qS3EgmFmXbgGjVZDOBim43AHsWic/LJ8bE7JdTNQX0cfvm4vFqeFnKLcie6OEGIceJs6eOvOR1LVdPY/9gpqIkHV+mXDWj8SitDdX3oztzgXwyQ5Xvt6fXQ2daHTa3GXuzFOkn5NR94eL11NXehNetzlbgyTIHGzmkjQ3dxNKBDC6XYOObIxFoqw+6EX6dmXTELe6Wmgt76VlR++Qm6KngA5e50EVFVl/5a9PPDDfxD0BjHbzVz2qauYtXLOuPclEoqy6dFNPPjLh4jH4thcVm6+/d1UL6we974A+Dt6U8GHIzp31BPq9WGd5HOvp7t9m/dkbLO1K2aPeEj7cAU6+1LBhyN661sJ9njQm6d/BLph5yF+/5U78XZ70Wg1bHz/hay9eI3Mnx0Bf1tPKvhwRNOmHZSftphIQuWR3zzK5ic2A1A2p4x3/tf1FFbIUGGA+rcPcN/3/k6gz4/RYuSST1zBnNVzJSGcENOcp6kjrZQvQOPL2yheUYfRdvz8R30dfTz9hyfZ9uzbANStnc+G954/oTfZANoOtfGXb/+Vpr1NAKw6fyUb33chDqnwNupaD7byx2/8OVWC9bTL17LhxnOxT+DIw2g4ytan3uSJ3zxKLBLDVZTDlZ+/Nq3k67GCvd5U8OGIWDBMoKNXAhAnQM4cJoGelm7u+949BL1BAILeIPd97+/HrY08mgKeAC0HWuhp66H9cBv3//SfxPtP0n29fv72vXsyyiaNl4HZko/Q6HUoctI7obpburJss/fQ09o9Zu+ZbbicotGgzdI+WcSiMToOtfffVc8s8zRcAU+Av//wXrzdXiA5JPZfv3qYloMto9XVGSHbfE2dyYBGp+Xg1oOp4ANA455GXnnoFRKJ45fiCgfCtNa30tXchXrMSfp00dfey33fvYdAf431cCDM/d/7O13NnRPcMyHEWNMZM0eb6i2mYeVz2v/63lTwAWD3SzvY8+quUe1fNolEgq6mLtrq2wgHw+nPxRO89ODLqeADwGuPb+bgtoNj3q+ZJhaN8dTfnk4FHwBe/OdLHNp5OOvyiUSCzqZO2hraiIQiWZcZDe31bTzy84eIRZLnZb2tPTzy0wcJegODrqPVarNee2hkJOoJkW9tEvB0eYgE039okWAYb6eH3OK8MX3v5v3N3Pv//k7rwVbMdgsbP3Ah7jI3HY0dqWU6mzrx9vrSaumOF2tRHs7KIvoaWlNtNeesxJwjUeqJ5O3Mts1GxnSbtRa4yKuroGv3oVRbxRlLMB+nhvhE6uvs45m/Ps2mRzahoHDKxadwxnXrceaPPFLu6/XRciAz2NDT1kPVgqpR6O3MYCvJx+LOIdDRk2qbdeEajHYLh3Y2ZCy/Y9MuNrx7A5ZB7vJ1NHbw0M8eYt/re9Eb9Zz3nvNYcd5KTFbTmH2GieDt9hLwpJ+YxWNxPB19uMsLJqhXQojx4CgvxOCwEDmyD1Cg9rzVw5r6uO+1PRltO1/cwapLTh2z0ZJBX5DNj77Gk398klgkRt0pdVx0y8XklyXLMYb8IXZmCYIc3n2YJeuXjEmfZqqAN8DuLZnbQFtDKwvWzs9Y9rVHNvGfP/+HWCTGvDXz2PiBi8grHf1zyt62noy21v0t+Pv8mAepambOc1B11nIO/vvojYqcmlJshTId8URIAGISsDqtaHXa1KgDAK1Oi3UYdW9PRsAb4L4f/oPWg8mL+6A3wH0/uo+zbjyHR3/3WGq53KLcCZsLbbRbmH/N2XgOtxHs9mAvdeMoL5BKGBPM6rRl32adY7fNGqxm6i5bh+dwG/6OPuwl+TjLCtDoRr/yy2jYvWk3r/7rVQBUVF5+8GWKZ5Ww8vyVI34tq9NKQWUB7Q3tae1OScA5IuYcO4vfdQF9h1qJePw4yguwlyUvoMvmlGUsP2f5HEzm7MGEeCzO839/jn2v7wWSQzof/uXDFFUVUbts1th9iAlgcVgwmA1pQUdFo0ypaiBCiBNjdbtY9t5L6DvUStQfwllRiKNseIHHigWVGSMeqpfWjFnwAaBx92Ee/c2jqce7X91NblEeF33wIjRaDUarkTkr5vBy88tp65XOzjwGiJNjsVuoXVLLW8+8ldZeUJG5/RzedZjHf/d46vHOl3eSV5LPBe+/AI1mdEc923Mzj12uolxMNvOg6ygaDWWnLsBR6sbb3Ik5z4mzvBDDcdYRg5Nx7JNAXmkeG2+9BKX/B6ZoNGy87VJyS8Z29IO3y0vzvua0NjWhYrVbUhf4JquJ6z93bdYf63gx59gpXDyLqjOXkze7HL1pYua8xyNhYsEAiWh0Qt5/MsktzWPjbZemb7O3XkJu6dFIcCIaIRYMEI+M3jA6k9NGwcJaqs9aTn5dBfpJfKd564Bhp0dse37rCb2W1Wnl2k9djdmePNApisL57z6PkpriIdac2hKxWHIbCoeHXniYLHkOipfNoXL9MnJqStH1J7OtWVzDonWLUssVlBdw2mVrBs1x4O/zs+2FbRntbccEiaaDnOJcLv7Y5algn6JROP+WjeSVje0xSggxOo7uS0MnNFXM6nZRsmIulWcsxVVVPOzA/5xT5lI062hJRHdFAfPXLRzx+4/Esee1kDz2+j3JKWRarZbTL19LQfnR3FGLz1g8YbnOpjOdXse5N5xD7oCcH6vOX0nlvMqMZZt2N2a0bX3ubQLHmRZxogqqC1l79empx3qTgYs/eim2IW786s0m8uZUUHXmcgoX1WIaxRvF8XAoeY0RO/HpulOJjICYBDRaLQvXL6ZoVgm+Lg+2PAf5pfljntzLZDNhy7Fl5HcorCrgU/+fvfuOc6QuHzj+mfTsJtt7L7fXe6+04+DovYuCCIKKYvsp9o5dUbEBioqCiCAgXXq5O673vr33np75/ZG93GWTvdu9TTbZ3ef9ei1cvpnMPNmdzEye+X6f7+/voaejmwSrGaPHQV9DExjNNO6vofz9vaQUZFC8YiYpIbKYE42qqji7O+mvrUT1eNAYjFgKitHFjf2QlFNxdvdga2nDY7djSkvFlJocsnbCaGm0GmafMYes0uxB+6zvosTV10NfdQVelxNFpyM+vxi9JSGidz1Gqr2mmYr399Fe3UzJqlnkzCrCHMaePgUzCzm642hgW4iT7nAVzy7ms7/7DO2N7cRZ48gsyERnmLiHcLetn76aSjz2ftBoiMvJx5iUgqKJTI+XpPQkrvvCNZx93Vm4nW7S89NIOMnwHmOckezibCp2B44bTkiL3pAgj9uNo7Ude2s7ujgzpvRUDNbRH6cURWH6ihncfv9ddDV3Ykmxkpafhm4CT/8sxEThttvoq63E098Hioa4nDyMSakow6jhMFopOalc/40P0Vbbgterkp6fjmWUN7RUr0preT2H3tqFo8fG1LPmkTk9D61eh6Otg4Tk4PN4dml2QMHmrKIs7vr5nbTUtKIz6MjIT8csd7IjIqckm0/96lO01rWgNxrIKEjHFBd88yglxFCLnCm5aFWV7vIqXH39mNNSMKal4HK4adpfzeF3dmNNT2LKmtmkFmUNOyZTvJlV157J9JUzsXXbSMpKJjU3bVTv83SpXg/Ozg766qvB60VriiM+vwid+eQFXse7iXv1Os5o9Toyi7LIHMEHaLQS0xK54p4r+ft3HsXr8RVaW37pCrJLctBpwH34AL0HO+gFdJZ4ujzx7Hn+AwCaDtRQ/v4+zv/KDSREuZrx6XL39eP1eNDFmU/6Jd1jt9FXVQ747hp4nQ56qytIKJ0ecg7saHH29FL3xru4e49ni7NWL8NaGJluhUPtsx6ng96qo6gDWVzV7aa38iiWojK0JnPIQoBjrbuxndd++iSOXl8RzaYDNcy5dAVzL1kRtuE9c8+ey47Xt9PR6BtrmJqdyuxR3vlJzU4lNcJ1YWKB1+Ohv77al3wA8Hrpr61CazKjD5H483o8uPttKIqCLj7utBNdZouZwhkFw1rWaDZy9g1nUXekzj80oXh2MSnp0auG3VddR9OG4+NT9QkWcs9ejd4y+sSaRqslvSCD9EmQdBYiFrntdrwOF1qzEa1heNMYqh4P/Q21vuQDgOqlv64ardGM3jI2PVstyZaw1hBrq2ri1R8/4b9urd9dwRl3XUJKtpWGN98nMSeXolmFVO711fUxxZtY+6Fzg6Z+TEhJOGmSWYRPYloCiadIzhfOLKRwViFVx/5uFhNnXXcmze9twtnZ5V8uY8Uimqq7+OBv/wOgkSoqNuzj/HtvIDl/+DOiGc1GcmJg2I3b5ksQHuOx99NfX42lqGxYhV6PUVUVd18/qqr6vteMQYJxNKL/TUBE1bQl0/jkA5+ivb6d+MR4MosyMVvM9NU34mg7XqTFkJ7J/r+9HfBaR6+NjpqWMU9A9LV10dPcid5owHoac0p73W766xpo37kHr8uFOTODlHmz0FtDn4y9TifHkg/H2xx43c6YSkA42jsCkg8Ardt3Y85MRzeGw1a8Lpc/+eCnenG0ttBX20TKvNkYEqx4XG56mjqwdfURl2zFmpUc9nF+Q+mobfEnH47Z99JmSlfNxhKmO9iZBZnc/pM7/NWfM4sySZKaDcOiul24+4Jn3vE6HDAoAeHu66fjwEH6KmtQtFoSZ0zFWlx4ygt0r9dLT2MH/R09mBPjsWYmjyg55vF4qN2wjwtvWovT7UGn02Jv66Zhx1FypuUPez3h4uq30botcIiPq7sXR0dnWBIQQojoUFUVe3Mrrdt24Om3oU9MIG3RPIzJp7728nrcuHu6gto9Tjt6IpeAcLtc9DR2YO/uJz41AWtmcth6QDburfInH47Z88IHLFg3AwBPfR3rLp5L7/oFKEYjWVPySIvS3W0xfMmZydz09ZtorGjC5XCSXpCBCRcNnYH7b29DK7uf2xrQ5na4aK9sGlECYjhcNifdjW24bE4sGUlYTqOI+Kl4ncFDTN19vXjdrmEnETwOJz0VVXQdOITq8RBflE/y9Gno4mO3F0VMJiAURfkE8EUgG9gL3KOq6jtDLDsTeACYCSQC9cDjwLdUVQ0afK4oymrgTeCAqqqRHYg2Dmi0GrKLs8kuDhxL7j2huCD4iuiFMtY96turGnnn18/4vzwWrZjJ3CtWY0oY/ofM2dlF65bt/se2pmY69+tJXTQ/5Ic95PSPWi2KJrY+PoP/ZgBehxM8J59GMNwUnQ4UDaiB21VVsDe30LF3P6kL51G56SDbH3sDVVXR6LQs++h68heWjVWUQzSHdxrFpIwkkjKSwrrOyUDRaNEYjEEn5lCfxd7qWvoqfVN6qR4PnXv2Y0iwEpc9dG8yVVWp23GUTQ+/iNfjRVEUFtxwNsUrZg4/CaGC2+Hm0EubA5pnrB95kdGw8HrxuILrrXg9wccFIcT44ertpXnDB6gDn2VXVzctG7eQdfYadKaT10FSNFq0JjMee2DCXaOL3M0Tt8PFkbd2suvpd0H1Tae+8uMXkx2uGgsheikqihJQn8vd0IAJSF8ynyRJPowblmQrU04obtxTFTxdp+r1hk5mhfn+laPXxp5nN3D0bV89L0OckTV3X05qcXhrb4X6LGoMBjQjGG7qaGunc+9+/+O+yhr08fEkTZ8alhgjIeaKUCqKch1wP/ADYAHwPvCioihD9Yt1An8BzgOmAfcAtwHfC7HuZOCvwGthD3yCMSRafV8kB7iam5h53qKAZUwJcSTlhTfbeDIuu5NdT70XcOe6csM+2qubTvKqEOvp7Qtq66tr8N1dDUFjMmFKD/wyE5dXhNYYnWKYQzEmJQadmBOnTUEbYqxdJGkNRuJzAz+uhqQ0eit802fa6htxdPX6kw/gS55s/uur9LZ0jkmMyfnpQUmrORcvJ166Y8YEjV5PfG5hQIbTkJyGdtCYSI/TSV9NcOEqe0vbSdff29LF5r+84r+Lpqoq2x9/g54QU3MNRavTMvP8wGOiotVQMGZJtEC6ODNJU0oC49FoMCbKPi3EeObu6/cnH/xt/Tbc/bYhXnGcRqcjLqfAd1NggD4xGZ05cr2iuhra2PXUu/6Oox6Xhw/+8ir97T1hWX/2jIKgIpizLlqKOXVQjxBFwZQyPocICx9DYkJQrRKD2cDcS1cEtOnNBlIKM8O67Y7qZn/yAcDZ72DHv97GaQtfUWwArdmMIfmEJJmiEJ9bOKIe1raW1qC2vupaPDFcND+2buH6fA54RFXVBwce360oynrgLuDewQurqnoEOHJCU5WiKGcBa0Ks+2F8yQoFuDqMMU84xsQEcteuoX33Ppyd3VgL80jPyyMxN43KDw6QlJdO4eKpYzr8wmVz0F7ZGNRuG+FJTWsM7pqtt1oCEi4n0mh1mDKy0CckobpdaAxGtKbYK1ZkTEki95w1tO/ch6u/n8QpxViLC8a88KOiKBiSUtCazbj7+nD29NF9tAZnZzfgqyfitDmDKnG77U7sPTYsYzBMISEzmbWfv4aqLQfprG2haOl0smaM/e9KDE1nsZJQNhOvw4Gi893F02gDP6ManQ5DYiKunsDhGvpTFF509PbjdgSemFWvir2rD0aQVM2Ylse5n7+ao+/tRWvUU7JyJqnFY1fH50SKRkPSzKlojAa6j1ait1pInTsTQ1L0alIIIUYv1HAyRatFO8wvKHqLlYSyGb5jqVaL1mSKaA8Ie1fwTR5HTz+OXhtxYZhRLaUok/O+dB3lG/bj6O2ndOUsMsrywOtG0Sh0HjyK1mwide5MjClJo96eiB5jUiK5a9fQtmsfru4erCWFJJQWkaTRYrSaKd+wD2t6IkVLp5Mc5huits7gYaDtlY24+h0jHvp9Mhqd3lcYNiUV1e1BazSiMY7sxqEhIfiax5CUGNN1IGIqAaEoigFYBPx00FOvACuHuY4pwHrg2UHtnwCygGuAr4862EnAnJZC9hkr8DpdaE1GFEXBkp5M0dLpw3q92+2hqbqJrvZuktOTyMzPGNUYf0O8mcwZBdQNmlkgfoRfWA1JiZgy07E3tfgaNBpS5s4+6ZhxjVaHJj72Zr04kaIoxGWmYzx7FarHM6Z1H4Ji0WgG7rBo6TpUibO941iQpM6fi9OrQaPTBgwbMVrMYZ2F4lSS89JIzjt110x7v52GykYcdicZeemkZMgdlbGgKAo6kxlOkuxTNBoSppZia2rC6/LVHdEnWDGln/zvak60YIg34eyz+9s0Oi1enQZbnx3zMKd31en1ZM0sJGvm6c9uEk76+DhS584kcWqpL2kTgRlwhBBjS59gJWHqFLoPHb/XljJ/NroR1HY51bE0nOJSfDNenXiTIS7FimnQ+V1VVZpqmmlv7iAh2UpWYRa6YUzvqSgKaSXZpAVNQ60necZUrCWFKBrNsBM0IraZ01PJOXMFXrcHrdHgv1GUv2AK+QumhGUbbpebxuomujt6SM1MISMvnfjU4N6DmdMLMFjC36vY9x3j9JNzpvQ09FYrrh7fDVmNXk9CWSnKGNVVOx2xdnWSBmiBwX3qm4BzT/ZCRVHeBxYCRuBB4CsnPDcH+CawXFVVz6nuciqKcgdwB0BBwfAqok9UGq0WjXnkGTS3y82Glz/grz9+DI/bg8Go5+PfuY0Fa+aediw6g45Zl6ygp7mD7vp2FI3CzAuXjXgqUJ3ZTNriBbi6uvG6XOitVvQJY1MNOhxOtX9q9TqIgZkmAHRmE6kL5+HqKsbrcvp/10ZVZdlH17P5r6/itjsxxJtYftsFIQ/40dTd0cPTf3yOt555F4Dk9CQ+85O7KJg69kUGx4uxPn4ak5PIOvsMXN09KBoNhsQEdHEnv9COT01gxccuZMNDL+Dss6MzGSg8ex6/uPePzFo2gytvv2RcV0ePZvIxlsm5XcSyofZPjU5H4rQy4rIz8djt6OLi0CfG1rTWJ0rISmbJR9ax9R+v43G6MSXEsfyjFwTdYNj1/h5+9/WHcdqdaLUaPvTF61m5fhl6w+gSB7oYGx47UUTz+KnR6SIypTyAy+ni7efe57Ff/guvx4spzsgnvn87U+eWMufyVex5dgOq14s1M5m5V65GH6IXdbTpLRYyVi/H1dWN6vViSLCesidotCmDu0FHk6IoOUAdcMaJRScVRfkmcIOqqkPeelcUJR+wAvOAnwAPqKp6n6IoRmAb8ENVVf82sOy3gKuHU4Ry8eLF6pYtW061mBik9mgd37rlvoBKxWaLmW/9+V7SR1kQyNHTT29rNzqjHktGEtphZMzHyJhfDYz3/bO3pRN7jw1zYnzMJR8Adr6/h/u/8NuAtnmr53DXd27DYIq9k9BJROVKNdb3z+7mDg5tPkBjfRuvP/cena2+atuf+cknmLdq0tcoHkty7JxEFEXhpUdeHvby6285P2jI3hiT/XOEVFWlt7kTR5+duGQLccmBN3laG1r59i0/pK/n+MxdikbhW4/cS/6U6E+NOM7I/jkKlQer+e5HfxRwjElItvKNP32ZpLQEepo6cTucxKclYrLG7qwSMWrIfTM2bpMe1wp48A2VOFEGwb0iAqiqeqxU6j5FUbTAQ4qi/ATfTBozgT8rivLngWU0gKIoihu4UFXVV8L1BiYqr9uDo9eG3mxAN4zsX0dLZ9A0SbZeG13t3aNOQBitcRjlIDAhWNKTxqTmw1Cc/Xa8Lk9Q19BjmmtbgtoO7ThCX0/feEtAiBA8isIjv3qS/p7AYm7NdcF/9+Fw9PSDomC0xF6NGCGEGCuKomDNTB5yos/u9p6A5AP46vB0tHTFRALC3tOPIsfyMeHos6F61BHNZhdOnc2dQQnO7o4eejp7SMlMJjEnNSpxTXQxlYBQVdWpKMpWYB3wrxOeWgf8ewSr0uB7b1p8PSrmDHr+EwPrvAKoPN14J4vuhnYOvrKF+t3lJBdmMvvSFaQUnrzQWnJGMlqdFs8JY/zjE+JJSou9u9xi8vG43DTtr2b3M75ZVcrOmk/RipmYkwK7rGXlBw/vmbl4GvEhCv6I8ceSGM/MJdPZ8vr2gPbMEH/3k3H02anddpj9L36ARqsw6+IV5MwtRW+WJJUQQgyWmJqINclCzwmF/rRaDSlRnrba0WvzHctf2izH8ghzO1w07Klgz7Pv43G6mbZuEflLpmKyjl0tMICUzOSgmiVJaYnjehjmeBCL1Sl+DtyiKMrHFEWZoSjK/UAO8HsARVHuUxTFP42moig3K4pyjaIo0xVFKVEU5VrgPuBJVVUdqqq6VFXdc+IP0Aw4Bh4HlzkVfs4+O1sefZXKjftw9tlp2lfFO7/+D70tXSd9XXZBJrd9/cMYjL6xfOZ4E3d86xbSsofX+8Hr8dJc3sCBN3dRvukA3c2do30rYhxpr2nh8Lt7OPzeXjrqgqcXGq2Oqibe+/2zdNe34ejuZ8+z71P1wYGg5YpmFHL+jef6x9pm5mdw6W0X+fdrMb7pDXouvfUiMgfqyCiKwrnXnEVBWe6I1tO8v4pt/3gNW0cPfa3dfPDIy7QcCZ4eVAghBKRmpXD7t27FPNDDQG/Q89Gv3kzWKW5uRVrT/mq2PfZ6wLG89WjdsF/f3dxJ+aYDHHxrFy3lDUE9gSeq7qYOjm7af/x9e0/9vtvKG9j40Av0Nndi6+xlx7/eon5XxRhEGyi7OItbvnwTOoPvnny8NY47vnUryVHsnTsZxFQPCABVVf+pKEoq8DV8wyf24BsmUTWwSDZQesJL3Pim5yzDN9akCngA+EWkYuxq6aKzuZM4q5nU3DQ02ljM44RHX1s3beUNAW3OPjs9zR1Y0oee3k2r07J07SIKpxXQ095DcnrSiIZe1O+t4qWf/MufkUzMSWH9F64hIcrZcRFeXq83aGaUlvIGnv/B47jsTgAMcUYu/uoNpBZm0tfVR3tDOzqDjvS8dP8JY6TaKhr9c5Qfc+TNnRSvmBkwvMeaZOGK2y9hxflLcdgcZOalS1Z8gklLT+KqG8+jp7sfrUZD28Fa6raXk3jugmEVeVO9Xo6+szuovWbzQXLmlEQi5Jhl67XRVt+GRqMhNTcV4wlTlYX6rAshJq/ZS2fwrT/fS0dLJ9ZkK5n56cM+RqiqSlt9K31d/SSkJpA8winhQx2PvB4vR9/ZFbRszZaDZM8uPuU6u5s6ePEn/6K70Tfrl6JRWP9/15I3u2hEsY03XY3tvPijJ+gZuDGpaDVc+KVryTnF7FANe4KTDUff3EnB4mnoTnKTZ6Tnko6mDrrbuolPjCM1Jy3ovK7X61l14XKmzC2hp6OXlMxk0rJl2EWkxVwCAkBV1d8Cvx3iuVsGPX4MeGyE6/8W8K3Tie3ojqM88aN/0tPeg06v48KPX8ii8xePumpvT0cPPe09xCfEk3iSL/ZjTWvQoWg1qIOyuCc7OByj0WjILsgkuyBzRNt09NnZ9PgbAd2huurbaTnaIAmICaLmYA0bn99ES10ryy9cxrTFU4kfqMNw4I2d/uQDgLPfQfmmg3i1Gh77/j9oLG9E0SisumI1Z157JvFJgd31vF4v7Y3tOO0uUrNSMMYFV8Q2hJhm0ZQQhybE7CEGo56CsuiPSRWR0V7bwvYn3glo66xtJX9uMQnDuKhVNBriQhRQNYdhvvtI6GrrprejF2uyhYQwFn5ta2jj2d88w+EthwGYc8ZcLrj9AoxxRg5vP8L7z20gIcXKyktWUDizMGYr+Ashxk56btpJb0457U7aG9vRaLWk5qSg1fqG9u55ZzdP/eIpXA4XcQlx3PC1GymdVzrkeo5prGxi6/+2UrG7gnlnzWf2qlkkD1xXKhqF+JQEWgns8TDcY3nz0Xp/8gF8NS0++OdbpJdkh7wOmSgaD9X5kw8AqsfL5n+9w4VfykZ/klpZoWpvxaVYQ97U9Xq9VO2t4r1nN9Df08/KS1cwZX4ppriTT4l5dMdRHvv+P+jv7kdv1HPlZ69i9prZ/uL1J+5fGXnpZEe5B85kEpMJiFjV3tjO0/c/TU+7b55Vt8vNs795lpzSXApmnv6UNJX7qnjsvsdorW/Dmmzl+v+7lqmLp8bE3SJLehIzL1zG3uc2+Nty5peSkJ0SsW26nS762oNHxjj6bCGWFuNNfXkDv/3c73EOJBmO7jjK5Z+8lDVXrkH1qnSdcAI/pqupgx2vbaexvBHwndjf/fc7lMwvYfrS45Pj2PvsfPDyZl58+EWcdhdlC8u48tOXkzFoTH9aaTbmZAu2joH9TFGYfdnKk54sxcTktDmD2twOF26ne9jrKF0zl9oth/C4fK/RmQzkL5wathjD5ciOozz2w8fobOkiKSOJG790PaXzT33RPhx739njTz4A7H57F1MWTkFn0vHo9/7hb9/x5k7uvv+T5E+TqWyFEENra2jj+QdfYOdbu9Dqtay94RxWX76KnvYe/vXjf/m7+fd39/PP+x7nk7/5FIlpQ9/A62ju4OGvPkz7wDVGxZ5K6o7UctWnr0Rv1KMoCqVnzqVm2yG8Ll/9Mp3JQN6CsmHFa++xB7X1t3XjcbpgAicg7N39QW19bd24ne6TXlNlzSri4CtbcPb5fm8anZap6xahCTGzXc3BGn77+d/7h7Qc3HyQD3/zZuadMXfI9Xe1dPHP+x6nfyA+l8PFv37yBJlFmWQVZw3sXy+y862daHVazr7+LNZcsRpLktT4GguSgBiBjqYO2uvbgtpb61pOOwHR1drFX7/9N7oGpn/r6ejhz9/8C5/7w2f945JHwtHvoLG6ia6mToxaBXtDK9lzSkkpyTmt7uoarYYpZ84jtTiLroZ24lMTSCnMxBgfucrAcYkWZpwzjx3PbjzeqEBq4ch6UojYVHekzp98OOZ//3ideWfOIyE1galrZtOwvzrg+ZJl03jil08HraupsikgAVFzqIZnHnjW//jwtsO8/vibXH3PlehO6N1gzUzhzHuuor2iEZfNQXJhFsnD+Ly11bfRUtuCMc5IZmEmcTIby7iXlJ2CzqjH7XD527JnFGAZQcHc1OIszvnitbRXNaEoCilFWSQOc8iZy+miqaqZ7rZukjOTyMjPGNbUwqpXpaOqkdotB3HZneQvmU5KSTa6IXrjtdW38edvPIJ94GKvs7mTP3/rL3z2d58hdZTdTT0eD3vf3xfUfnjrIZyuwESOx+WhfHeFJCCEEENSVZXNL29h51u+IREel4dX/voqBTML0Ot0rL52DTq9nopd5VTsLKe3o5futu6gBISts5fmA9U07i7HWJztTz4cs+XlrZx1zZlkFfnufKcWZ3POF6+no6oRRaMhpTBz2MfytOLga9Tp58zHPMQsWxNFxpTsoLYZa+djPsWsFkm5aZz9+Wtpr2zE43KTUphJ0hDXYQc3Hwqqp/H6429QPCOfln3VtB6sIX16AZmzivxTune3d9PbGXgzMyEtgZ6OHtqbOnDYHLTV+2qMedwe/vfoa+RPzWO2TME9JiQBMQJ6o57E9ES6BhVgtCSfflfbrpYuf/LhGLfTTXtD+4gTELZeG6/943Xe+Oeb/nivvusSNv/pBRZ95HyyZp16DFsohngTmTMKyZxx8vFc4aJoFKafMx+vV2X/a9sxJ8az/MazSSuSBMREEKpnj0ajQVEUels66aqoZ/4lyznw1i4UjcLcC5eSOTWf/Bn57H13b8Dr0vMCLwyaqpqD1r37nd1ccMv5QUObrBnJWDOGP260+kAND977kD+bPu+suVz+icvC2o1djL2knFQu+L9r2fiP1+mobaVwURkLL1+JwTyyO1ZJ+RkkjXD2DJfLxabnP+A/DzyDqqpotBpu/PL1zD97/imHKHRUN/Hur57yD4+r3XyQ5XdeQubMotDLN3f6kw/H2HpsdDZ3jjoBodVqKVs4hZpBicOi2UVsfX1H0PIajQy/EEIMzd5rZ8cbOwPa9EY9tu5+/vrzJ3HafQnjhWsXMHXxVGoO1mIZ9EXf43Jz8OXNVL23B4CMpBDX6gpBx9rk/HSS89NHHHNacRbrPnslm/7xOrbufmauXcD0s+ZN+OFm6aXZnPuZy9n0jzdw9NmZtW4hZWuG9yU+ITtlWD2qQw3L0Go0VL67l4o3fLNYNe6poGlvJYtvOR+92Ygl0YLZasY2MM22NcXK4guW8OC9D+Nxe1AUhTVXrgag9rBv2M2RHUclATFGJAExAmm5aZx94zm88IfncdqdKIrCqitXk10SnP0bLrPVjMGk9x9Mj7Ekj7wLUENFgz/5AL7uRi/+43XOv2gpR17bRvrUfLQhxrjHImtaIkuvPZNZ5y1Cp9dhsspczBNFXlkuZosZW+/xITXnffg8rClW6nccoX7rIYwJccw+Yzaq6qVx0z6mLJ/OOTeeQ83+GrrbugGYe/Y88gbdRU0KUT8lpzQHY/zouj86bA6ef+gFf/IBYOebu1i0dhGzVs4c1bpF9GVNy+OCL12Hy+bAlBAX0FsmklqqW/jPb5/x17vxerw88bMnyZuaR3reyS+Am/ZWBtXmOfy/baSV5aPVB/egiE+MQ6PVBNxF0uq0/torozXv7Pns37CfhoGixYWzCpm2bDrxyRYq91b6lzOY9BRPsuKcQoiRMZgNFEzPp6W2xd82/6x5vPTIKwHXy9te285Ft13AskuWk5wV+EW2r7WLqveP37TQudyk56XRUnt8Zq2Vl6wgNSc8BQd1eh1Fi8rImpqHx+UiLsmKMgmSrTq9nuIl08ialo/X7SYu2Rr2pMvUxVN59W//w31Cj7qzrjmDqmfeDViueX8Vvc2dJBdmkpyVzNWfv5rHfvAYbqebeefM47XH3sDj9g2vUVWVd556l/M/cp4/AZFVLDUgxsr4+DYaI+KscZQtKuPaL19HW0MbyRnJZJdmYx1FsbG03DSuuucqHv/RP/0XoRfcev5pDb/obusJauts7kRrNvouVNUQL4phikbBEqOF3MSptTe00VzRhFdVySjMIG3gC1VmYSZ3/exOdr69i7b6NuafPY/Sub5x6Mc+A47ufqrf99210Op1oEJ2aQ533n8XbbWt6E0G0vPT/VN4HZM/LZ/pS6dzYGBKTaPZyEW3X3DKQkWnYu+zU3soeFrFjubgehVifDLGGce8UFhPRw+qN/DA7LQ76evqO2UCQg0xzZmvzbc+W4+NpopGetq7ScpIIq0wncvuupSnf/Mf//KXfeLSU25nuNLz07nlB7fSUtOCRqMhPT+d+MR4LIkWPnbfbWz73zYSUhOYf9Y88kY4zakQYnLR6rScec0ZHNh8kL6uPgCyS7PZ/PKWoGUtKVamLZ0WvBLV/x8A6jft57zLVtJhc1JzqJbZq2YxZcEUWqtbaK1twRRvIrNkdNf0wMANs8l30+xUQy5GI39qHh//8e1sf3Mntp5+Fp6zgIysFCrcIc6DJxSwn7ZsOnc/cDedLZ2oisJrj78ZtOyxpEbe1DymzJ8SsfcgAkkCYoSSM5NPOd2P6vXi7O7G1dWFotdjSExEHx/6LpOiKMw/cx5ZRVl0NHWQkJpAVlEmhtMohpcSohtTdkkWxjgjOWsXoj3NKQuFGKmW6mb+/o2/0jvQW8FkMXPD128goyANvcVC7pQccqfkBL0uMTcNQ7zJX5QIYMq5CzEPDHNKSk8i6SRzMyemJXLDl6+jsbwRu81BZn4G6afRlXKw+MR4ZiybzvZB3ckzwrBuMXklZSSj0+sC7urEJ8aftJDaMVmzizny2raABMaUtQvR6nU4bA7e+9fbbHz6Pf9z6z52AUvOX0TBzAK6WrpISk8kqyhrWPUmhsNtt6Nz9pOeoKC3WNDH+c5hxjgjM5ZOZ8YJtVqEEBObx+XC1d2Nq6cbndmMPiERnXlkX8pzp+Tymd/cTVN1E1q9joy8dHa+uZOqfYFDvTIKMkIO7YxLSyR/8XRqNvtuSKCqtO+tYNXdV2K+aS0AR7ce5vHv/N2f0C2aW8xln7sKqwytjCmtNS0894un0ek06Ix6XvjVf7jx2zeTXJxFR0Wjf7m0slwsJ8yWp9FoSC/IIL0gg67WLhLSEuhu7fY/r9VpKZ1XQsm8ErKLssIypNZtt+Pq6sJt60dvsaJPTESrH91MiRORfCONAEd7O+07d/gfa4xGUhcsHDIJoTPoyCvLHfVdofgEMxfccj6vPf4GTruT1OxUVl64HENKAulS8EuMoYMb9/uTDwD2Xhs7X9vB4mU5JM+cid4SeoiRJSOZlZ+8nOpN++mqa6Fg2UwyZhSiaBQ8LjeOzm5UtwdjUgK6IcboWxItTFkQ3iy2Tq/j3A+dS2tdGzUHa9DqtJz34XVSSE+MSnpeGh/+xs089uPHsfXYsKZY+dBXbxrWnPbJhZms/sxV9Ld2gsdDfGYK1hxfQqyttjUg+QDw+iOvULpwCgXT8iHM+63X6aTrwAEcbce7NluKi7EWFaPEwGxOQoixo6oqtoZ6ug8fnxXHkJhI8py5aI0j62WWmpMaMETiqs9cyd++93daalrQG/Vc8vGLyMoPXSRSZ9Ax/aLlJBVmUrftMGllueQtmop5YJaDvu4+XvrjCwG9ySp3VdBwtEESEDHmyOZD9Ayql7f95W2c9eHzqNt2mKa9VWTNLiJnfimGIXq8JqYlcvPXbuJv3/s73a3dmOJNXPvZq8jOScZojcdgGX0PDo/TSef+fTjb2/1t1uISLEVFci4cRBIQYeZxueg+eiSgzetw4OrqGjIBEQ7Onl66qxrZ9/oOzrn6DBSNBltXH28/+hpXffl6enr6oaef5LSkiMUgxDEtIYpBNte0oqzIw9HeNmQCAiAxL505eemoXtU/ftLVb6Nx025adx4EwJyeQuEFqzGnnPpOcbhkFWZyxw8/RntjO3qTnrTcNLTa8Nw9Doeu9m7cbg8p6UkTvuhVtLU3d6DRakka5UWqRqNh1sqZfO7399Db2UtCasJJe/gMprictH2wA6/LTbvRQNEFa0goysHeEzwtmsftwd4fPE1cOLj6+gKSDwC9lZWYM7Miet4TQsQej81Gz9GjAW3Ori5cvb0jTkAMljsll7t+9DEaD9eg9tlw19VT9fzbFJ63ElOI64G4FCslZ8ylePWcoHoM3e095M0qxN7T7y9UCGALcfwU0dVS3RLU1lTRiCkhnuIz55MyuxizxUz8KWYmK5lTwj0PfNo3PN3touODHRzZuRu9JY7ii88kPmt4M54Mxd3XG5B8AOiprMCUmSnnwkEkARFuXi9elyu42TP8OeVPR3dVA66GZhLSEtj01PE7X6VLp3HocCU//spvURSFq++4lBXrlmBNlHluReRMXzmTvW/vDmibvWoa7t4evElJw1rHiRcLfQ2t/uQDgK2lndbt+8k7e+mYZpXjEuKIi+A4x9Nht9nZ+vZO/v6rJ7H12bjoxvM454o1pKQPf4YPMTwdrV289dy7PPu3lzGaDFz/yStZevZCzKOsMZKSlUJK1qkrgZ/I3tFN1cvv+gtRehxOKl98h2k3XkhSdirGOCOOfod/+aSsFJJGMOvLSKgeT4hGFdUbol0IMaGpqjd0jZpQx4nT4O3spn/LLv9jV3cfbXuOkLNm4ZDJ9xOvJzrbunjrv+/zzF9exGDUs/6qs+k80kTt3ipQFFLzRvclVITf1GXT2PX69oC2eesW0ljXwr8feo4P3thGfmkuH/nsdUxfMPWk60pMS0TrdHD4ibf9ba7efmpf/4DSK89FdxpD4I8ZXBja16iG/DxMdtIfJMy0RiPxecHdW/UJkb1T213VQF91PasvWkzR7CIAUnJSyZ5XyF9//k+6O3roau/m4R8+yt4tB066LltXH037q2jYU0HfCd3ohRiuwjlFnHPLOvQmA1q9jhWXLyc7Ox7V48GYGvxFy9Z58n2uv7ktqK2rsg6PwxmR+MeTI7vL+dVX/0hbUzv9vTb+9cdn2PDq5miHNSFteWs7jz3wFH3dfbQ3d/Dbbz7MwR1HTv3CYXA7XbRXNVG38ygd1c14XCdPWrt6+4MudjwOJ64+GynZKVz/zQ+RNlDMOHdaHld9+drTml1pOHRxcWgGjXHVJyWhNU2+QmxCTHZakxljWmB9JEWrRRemO8B9jYF3w+PzsnBrdNTvPEp3Q3tAEcJQtr61g3/8+kn6uvvoaOnksd8/TdbsAiwpFq760rVkjWJmOxEZBbOLOPej52Mw+64pV169huIFpfzpR3/nvZc24XK4KN9Xyfc/9QtqK+pPuT5nv53kBbOwzJxKyoJZGBIt9De34baNrpegLi4ORRd4LjQkJ6M1je4mxUQkPSAiwJydDQr019SgMRixlpZisEZ2NgdrXgZdh6vo2bGXJYvyWbZ2NilF2fz+l/8KWvbt5zewfO3ikOvpbelkw0Mv0Fnt60JvSornjE9dTmKuFNsTwxeXEM+KK1czY+VM3P390NmE6nJgnTsPw6BkXG9LJxsefIHOmhP3uStIzD1+F8KcGnzn1pqXhcYghX32hEgovvrvNznrktWn7I4ohs9hc/Dqv98Mat/+3k7mrxzdvOEel5uKd/ew419v+RoUWPyhcylaPnPIHj76eDNoFDihCKXGoEc/0Bsjf2YhH77vo9h7bcQlxmOKj9wFkC4ujpT5C+ipKMfV3Y0pPQNLfr4U3hJiEtJotSSWldFnNmNrakRvtWItLglbF3RzxvGaEJbCXNrb7VS/9oZv2zotqz9xGZkzCkK+1ml38upTbwW111Q3cPv9nyA+SXoHx6K4hDiWXb6SGatmoaoqCWmJ1FU2sGfz/oDlnA4n9ZWN5BUHFzk/xu1w0Xiwnr3/3ehrUBTmX7kKvaV9VL0fwHcuTF1wwrkwQ86FQwlLDwhFUe5QFMV4wuNZiqLoTngcryjKd8KxrfFAZzRiLSwibekyUhcuwJSaGvFu4glFuVhyfXe7+msb8TS3YElPIqcweE7bUG3HNO2v9icfAOydfRx9Z3fQVHFCnIqiKCRnp5Jemk/KnLmkLlqMOT0dZVDdhKZ9Vf7kA/j2ufJ39wTsc/E56SSVFfofGxLiyVg0E00M1WCIlpQQ3eozc9LRGyW/HE5avZbMvODpkdNzRt9dt6epgx1PHu8OigrbHnuDnubOIV9jTE6g4JzlviQEoGg1FK5biTHpeF2KuIQ4UnJSI5p8OMaQkEDy7DmkL11G4tSpYbvbKYQYf3RxcSSUlZG+dBnJc+ZiSAxfL2BrbiaJxXkAaJMSqd5yyP+c1+1hyz/+h707dB0HrU5LVn7wcTwzL0OSDzFOURQSM5JIykxGo9VgMBlCTrEeZzl5z7vuxvbjyQcAVWXP8x+QvmQOOvPoz5UB58Kyqeji5FwYSriuUH8H/Ac49i1iAzAfKB94bAG+CnwjTNsbF7SG0WXSRsKYaKX44jOxd3SDqmJMTkAfZ+bMi1fx1n/fwzYwrWGcxcyq9UuHXE9HTXDxwNajDXhcbnRGyeCJ03Oyz0J7dYh97kgdHrcb3UAPB4Mljvy1y8lYOAOv24MxOSEsFYsnglmLp5OamUJbk6/wkU6v4/KPXoRhDI8/k4FOp+OSD53H9vd24XL46vwkpiQwb/noej8AOHptMKjbsNftwdFnG+IVvruMKTNKiMtKw9Xfj8ESjzE5upXbNVotSFJQCIHvC+Noi06GYkiIp+D8lTg6ummpaAp6vr+1G5fNgSlEvSatTstFN65j69s7cQ4M4UxMSWDBqjlhj1NEVkZOGjffcy0P/uCv/rZFa+aRX3ryGQUdIYqMuu1OvISveLecC08tXAmIwX81KcEeBTqzCcug7F3JjEK++6evUHmoBgUomlZAXsnQXZMypxdQ8e6egLb8xVMl+SAiJmtGIZXv7w1oy1881Z98OEZnMqDLlqFAg+UWZfP1332BioPVuOxOCqbmUTQ1dPdTMTpT507h+498lapDNej0OoqmFZy0R9lwxSVb0Rr1eBzHCxgb4k3EJZ986J6i1WBOS8JM0qhjEEKI8UJnMqLLTsduD66VkzolJ2Ty4ZiyOaV875Gv+I7jOh1F08NzHBdjS1EUVq9fTm5xNg1VjSSmJlIyo5DElJMn4uNSEtDotHjdx4uimpMtxEWoRpIITfroTgL5pbmnzAgek16Wy9RzF3L49e2oXpW8hWXkLzp5RVkhRiN9ai5T1y7k8BsD+9yiqbLPjVB2QSbZBZnRDmNSKCzLp7AsuNDwaFgyklh5x8Vs/svL2Lv7MSdbWHbLeuJPcSElhAjNoDfgcgfPSHYyep0ep0sKG48nSXnpLPnweWx/4k3cdieJeWksvO5s9OaT97yIxHFcjD1TnJEZC6Yy4xQzX5woISuFFbdfxOa/vYqz10ZcWgLLbl2PWWYHHFOSgBABTAnxzLlsFcUrZ6OqXuLTEoPuRAsRTqaEeOZcvoriVbLPiclJURSyZhay9ss34OixYUqMk4shIUbB5Xbx2Nf+PKLX3PC9WyMUjYgUrV5H0YqZpJXl4LY5MadYMcbL7DtiaIpGIWduCefeewPOXjvmpHhMCVKnYayFMwFxkaIoXQP/1gDnK4pybHBWUhi3IwZUldey4c0tVByuYuXZS5m3ZBZJYRgDrNFpScge2Zz0QozGZNnnmhpa2LphF9s37mbukpksXjmf7NzgglhiYlNVlf27D/POqxux2+ycef4qZs2fSlyy9ZTDLoQQYjKpOFzNe298QF1VA6vXLmPe4llYBn1htKQlRSc4MW7FpySMqJdhT3cvu7bs493XNpFXlMPKsxZTfEJxdDEy4UxAPDzo8QODHss0CmHUUNfM/93xHRpqfTmeF59+nTs+dzM33X4ViiIlOISINX09/Tzwoz/z5kvvAfDys2+wbM1CvvHTz2OVu92TyoHdh/n0h76C0+nrIv7U31/gZ3/6NotXzItyZEIIETtqKuu455av0dHmu7/5/L//xxe+/Qkuve78KEcmJpvXn3+Hn3379/7H//rLs/z60fvILxq6rp4YWljmhlRVVTOMHykHGkZHD1T4kw/H/PV3T9BYFzyjgBAi+mqq6vzJh2M2vbON6oraKEUkouWd1zb5kw/g6xHx+J/+g9sVXFBNCCEmq0P7yv3Jh2Me/OWjtLW0RykiMRm1NrXx4P1/D2hrb+3kyIGKKEU0/kkNiHHK4/EEtbndHrxeb3i343Zjb2qlu7IandmEtTAPU+rE7yovRsfZ00tfXSO25hbiszOJy8lEHz+5x9h53KE/m2538GdZTGwOe3ChO4fdgcftxtEsx1shxOTgcTqxNbfSU1mDPj4eS2EeppSk48+HuNZ1OV14PNKpWowdr9eLyxlc1NbtDn3TwNXTS199E/2NTcRlZxGfk4neMrmvgQcLSwJCUZTPDfFUF3BQVdV3w7EdcVzJ1CKsiRZ6unr9bVfcdCGZYZ6msL++kcZ3Nvkfdx0qJ++8MzGlJId1O2LicNsdNL2/GXur7w5FX009lsI8MpYtQqufvDnP/KIc5iyaye6t+/xtZTNLKCjJi2JUIhrOWLecJ//6HKp6/CL62o9cirOlNcTx9qyAC3IhhJgoemvqad641f+467DvGtOYlAjAlGnFmONM2Prt/mVuuuMq0jMlMSvGTnpWGjd+7Coe/tXxXhDmeDNTphUHLetxOGnatA1bUwsAfbUN9Odmk7lyCVopsO4Xrm8Ddw/RngQkKoqyAbhEVVXpMxUm+UU5/PLP3+U//3yJw3uPsv6Kc1izdjm6MH7B8zhdtO/eH9CmejzYmlslASGG5Oru8ScfjumtqiV55jS0k/iLVEKSlS9//27+99+3eO+NzSxdvZD1l59NckpitEMTY2zm3Kn8/M/f4YlHnsHWb+faj1zK/EUzaXt3Y8ByqseDvblVEhBCiAnHbXfQvmtfQJvX5cLe1uFPQJRMLeT+v3yPp/7+PFVHa7nkuvNZedZiqXUmxpSiKFx87TpS0pP47xOvUFiaz5U3XUhxWUHQss7uHn/y4Zi+ugZcPT1opUejX1i+raqqGpwCGqAoSgnwKPB94K5wbE/4lM0s4fPfvBO3243BYIjAFlTwhujmpkrXNzE0dcj9Q/ab/KIcbv3UDdx0+9UYjJIJn6z0Bj2Lls9l3uJZoKro9Do8TmfIz87QnychhBjP1GEd36bPKePLP/h0BK91hTi11LRkLrnmPM6/7Cx0Oh0azRBlFIfYp+VcHigsRShPRlXVcuDLwHmR3tZkpNFoInZA1hoMJM+eHtCmaDSYM9Iisj0xMRgSrBiSAqc2is/JQm+VmR6OkeSDANDptP5ea1qDgZTZ0wKel+OtEGKi0plMpAy+xtRqQ/b4iuS1rhAjYTAYhk4+APoEK6bUwF7i5sx0DFaZYvtEYzUguxrIHKNtiTCKz80ie81yOg+XozObSCwrwSjDL8RJ6MwmslYvp7eqhv7GZix5OVjyc9Dq5Uu3ECcTn5vtO94eOoouzjxwvE2KdlhCCBER1sI8tAYDXUcq0FviSZhSjDE5KdphCXHadCYjmSuX0FNVS39DE/G52VgKctEaJYF2orFKQMwFqsZoWyKMtAYDloJc4vNzZMydGDZjohXj3JmkzJkh+40QwyTHWyHEZKI1GrEW5WMpzJNjnpgwDAlWUufMIGX2dNmvhxCuWTCGqqqRCCwCfgL8KRzbEpFTeaiat1/aQOXBas68aBXzls8mKcXXlV4+QOJ0yH4zMg67g33bDvLq029hjjdx7uVnMnXOFLTaiI+WEyHY+x3s3XaAV59+E2uihbWXn8HU2aUn7X4ZDvK5EUJMJpE85rW3dLDtvV289+omps4uZdV5yykozY3Y9sT41FjTxKY3t7H1vZ0sXjOfpWcuJCsvY1TrlHP50MLVA6KVoSvMqcBDwI/CtC0RAfXVjXzjzh/S3dEDwLb3d3HTJ6/m6tsulQ+QEGNk9+b9fO/TP/M/fvP59/jBw19j2twpUYxq8tqxaQ8//Nwv/Y/feO4dfvDnrzN1dmn0ghJCCDEsLpeb//z1RZ599EUAtr+/m9efe4fvP/Q1MnKkvo7w6enq5Tfffog9Ww8AsGvTXra9u5Mv/OhTWBLioxzdxBSu2zhnA+eE+FkMJKmqeqeqqs4wbUtEQOWhan/y4ZgnH36Olsa2KEUkxOTicrp45q8vBLR53B42vbV1iFeISLLbHPz7T88GtLndHnZu3BOliIQQQoxEc10Lzz/+SkBbS0Mb1UdroxSRiEV1lQ3+5MMxOzbuob6qMUoRTXzhmobzrZEsryjKb4FvqKraGo7tiwiSaWOEiC75CMYUmUpLCCHGOTmOi2GR/SRSojWw+ENAwimXEmOmqKyAhOTAKWKu+ujFpGdLFzUhxoLeoOeyD18Y0KbRalh65sIoRTS5mcxGrrr1koA2nU7L/BVzohSREEKIkcjITeei69cFtKVnp1IwJT9KEYlYlFuUzexFgVPCzls2i5zC7ChFNPGN1SwYg0lRgTCrqarjwN4jeDweyqaXUFpWNKLX5xRm8Z3ff5m3X9pAxcEqzrpoFfOWz5H6D0KMUktzGwf3Hqazo5vCknymz5yC3hB6StLZS2bwzQe+6CtCGWfi3CvOoEzqDUTN/OVz+Pqvv8ArT71BQpKVtZefwZSZxaNap6qqHD5YzpGDFZhMRqbPKiMnLytMEQshxOTQ19vPgb2Hqa9tJCMrnRmzy0hIDLyRptfruOzmCymYkse7L29k6uwprD5/udR/EAGsiRY+9c2PsenNbWx7fxeLVs9j6ZkLh1X/weVyc2jfESqOVpOQZGXGrDLSM2X/OpVoJSBEGJUfruLjN3+BpoZmAOItcTz4j18we970U7wyUNHUAoqmFkQiRCEmpbbWdr79pZ/w9usbAF9F5J/+9lusu/CskMubTEYWrJzLgpVzxzBKMRRTnJFFq+exaPW8sK1zx9Y93H7j53A6fGWRSqYU8us/3Ud+oVRlF0KI4XC5XPzzb//hlz/8g7/tI3dczyc+eyvmOFPAsqkZyZx72Zmce9mZYx2mGEey8jO57OYLuOzmC0b0uvfe2sQ9t38Nr9cLwLJVC/nBL74qSYhTkLndJoB339zoTz6ALyv82CNP4fF4ohiVEOLgviP+5AP47n7/4Bv309wk5W8mI7vNzu9/8Yg/+QBQfqSK7VuksKUQQgxXVXktv/npQwFtf/nj41QcrYpSRGIyamvt4Ptf+4U/+QCw6b1t7N97OIpRjQ/SAyLK3DYbXqcLjcmIzmg8rXVUltcEtR05VI7L5aK6so7N722nsb6ZpasWMnfhDOLi40YbthARoaoq7r5+VI8HbZwZrT70UIXxoquzJ6itraUdW7/N/7i+tomtG3dw+EAF8xfPZv7i2aSkJY1hlCJcurp62LV1L1s37qSwOI/FKxeQX5jjf76/305lRfDxurG+OagtmjxOJx6bHY1Oh07OF0KIKHLb7XgcTrQGAzqzr3dDb28fbnfwTbburuBzbnNjC9s27WbfroPMmj+dBUvnkCF3p8UIHDlQwab3ttLZ3s3yMxYze/50jEYDtn4bzY3BN5RC7YcA7v5+vC43WpMJrdEQ6bBjmiQgokRVVexNLbRu3Y7H7kBniSd9yUKMKckjXtcZa1fw5D+eC2i74tqLaGls4+6P3Ov/cDz60JN88ydf4KIr1oVajRBR5XW76a2upWP3XlS3B1NGGinz5mBIsJ76xTGqqCQfrVYb0BvpjLUryMhKB6C9rZNvf/HHbN/suwP++CNPc9NtV/GJz986ZJ0IEZtUVeW5f73Er354/K5cSVkh9z/8PTJzMgBISU3i8msv5He/+HPAa+ctnDWmsZ6Mo7OL1q07cHV2oTHoSV0wj7icLBSNdJgUQowte2sbrZu34+7vR2s2k7Z4PuaMdHLzs8nJy6K+9vg0iYlJCeQNGsrW29PHL37wR1574W1fw19g/WXn8KXv3E28JFfFMBw9VMmdN33Rn1T4yx/+yc/+8G3WrF1OemYq6y48i1eef8O/vEajobgkcDi76vXS39hE27adeB1O9IkJpC2ajzE5aSzfSkyJ1hXFo0B3lLYdE1w9vTRv+ACP3QGAu7eP5o1bcNvsI17XwiVzuffbn8GaYMFoNHD73Tezdv0aDu47EpSZ+82P/0RbS3tY3oMQ4eTo6KR9+y7Ugbsa9uZWug4eRh3HQ4mmzijlVw//gLz8bBRFYd2FZ/L5r34C88BdnIrDVf7kwzGPP/I0tdX10QhXjEJ9bSN/vP9vAW3lh6s4fLAioO3ya9Zz461XodPrSE5J5Ls/u5c5C2aMZahD8jidtG3biauzCwCv00XLpi04uyb16VoIEQXu/n6aN2zG3d8PgMdmo3nDZly9faRnpPKLP3yXBUt89ZJmzJnKb/78Q/LyA2ctqCyvOZ58GPDSM69TXVE3Nm9CjHs7tuwJ6tHwh/v/Sl9PH0ajkbu/+DHWX3IOGo2GrJwM7n/w+0ydOSVgeVd3Dy0bt+AdGH7p6uqmdct2PCcMx5xswt4DQlGUOcDHgVLgo6qqNiiKcjlQparqdgBVVe8K93bHG3d/P+oJY4bAd3B199v8XcyGKyHRyg23XMk556/G4/GSlZOBRqPB6XQFLWvrt+HxeEOsRYjocvf0BrX11zXgmTUDXZw5ChGNnlarZc3Zy5n1zO/p7+snLSMVk+n4UCuXK/gz6vF4cbncYxmmCAOP24MrxDF38N8yOzeLL3ztE9z8sWvQ6/Ux1RXYY3fg7OgManf39U3qOzVCiLHn7uvH6wz8gqa63bj7+9Fb4pkxeyoP/Pk+Oju6SUi0Bs2AAeB2hj6XhjpWCxGKPcSN4b7eftwD36UKi/P47s/u5dP/dzvmODOpacE92V19/aCqgW3dPbjttkk7FCOsPSAURTkP2AzkAucAx741lALfDOe2xjutIXiHU7RatKPodp2ZnUFOXhaaga6yZdNKMA7asW/62NWkZ6ae9jaGw+lw0tncib1/5L05xOSlNQUn3vSJCSj608+T2npsdDR34I7yF/qU1CTyCnICkg8ARaUFZGQFfgFdeeYScvNl7ulY1dPeQ3dbcI+A7NxMLrlmfUCbxRpPSVlh0LI6nY7cvOyYSj4AaPQ6tKbgWkSa06xPJIQQp0tjNMDgqeAVBe0JxyOL1UJeQU7I5ANAQXEuJVOLAtqmzZpCQfH4mXXIdx3Tids98W5MOB0uOps7cfQ7oh3KkOYtmo1WG/h1+ebbryEx6fg+ZzQayCvICZl8AEImGTQGA1rd5B1qG+4eEN8FPqeq6m8VRTmxv8qbwOeHuxJFUT4BfBHIBvYC96iq+s4Qy84EHgBmAolAPfA48C1VVZ0Dy1wJ3AksAEzAPuD7qqo+O6J3F0b6BCtJM6bRuf+gvy11wVx0llPPOTtcZTNKeOBvP+Kvf3yC2qp6rrj+QtZesAZl8AE9jBoqGnjpzy9zcMsh8qflc+mdF5M/LT9i2xMThyE5EVNWBvZGX0E+RaslZc7M0ypEqaoq5bsrePa3z9Jc08zcM+ay9sa1ZOSnhzvsUcnKyeAXD32XJ/7yDDu27uWc9au5+KrziLfI2NRYY+u1sevt3bz0yMt43B7W3nAOC9ctxJpkAUBv0HPrXdeTk5fJC0//j6kzSrnptqspLM6LcuTDpzObSV0wj+aNm/13ayzFhRgSE6IcmRBistFbLKTMm037jt3+tuTZM9GP4Do5JS2Z+371VZ567Hk2vrOVlWcu5vLrLyQpOTESIYeVqqoc3VXOs797jpaaZuadNZ+1N5xNel5sXcecrsbKRl565GUOfHCQvLI8LrnzYgpnFJz6hWNs+uwyfvOXH/LI7x+nraWD62+5nDVrV4xoHfoEKwlTSug+Uu5vS10wd1IXeVbUQV1CRrUyRekFZquqWjmQgJinqmq5oijFwH5VVU85tkBRlOvw1Yj4BPDuwP9vBWaqqlodYvkpwGpgO9AJzAMeBP6iqur/DSxzP9AAvA60AzcB3wDOGiqxcczixYvVLVu2DOftj5jH5cbV3Y3HbkcXF4chwYqi1YZ9O06nC5fDSbw1fMmNUHo7e/nt539PU2WTvy0+IY57fvcZUrJSIrrtGBG5zM4QIrl/RoPb7sDV3Y3X7UZvsZx2AcrGykZ+cdf9Ad0vpy+dzoe/8SGM5ti7m+t2e7DZ7FgscZFKEI75vgkTa//c+/4+/vT1wOKRN3z5OhavWxy0bG9PHyaTEd0oeu9Ei6qquLq7cfX2oTUa0Cckjqpn3jDJsXMSURSFlx55edjLr7/lfEZ6raooCo997c+nXvAEN3zv1qG2I/tnlHjdblzdvbht/ejMZvQJVjS6kR9XPR4P/f124uPN/l7Csa6hooFf3HU/HtfxOlizVs3iQ1+5EYMp4I76uNs/+7r7+MMX/0jdkeP1rswWM/f87tOk5cRWz8BjnA4nLrf7tIuXepxOXN09eBwOdPHxvu9842RfHIUh981wXx114Bt+UTmofSFQO8x1fA54RFXVBwce360oynrgLuDewQurqnoEOHJCU5WiKGcBa05Y5jODXvZtRVEuAi4HTpqAiCStXoc2NfJfzA0GPYYxqKjf1tAekHwA6Ovup6W2dbIkIMQo6UxGdKbRZ/ebq5uDxn4e+OAAnc2dZBZmjnr94abTabFGOEEoRmfX27uC2jb+dxMLzl6AVheYOLaM47+loigYEhMxJMb+HUIhxMSm0ekwpiRhJGlU69Fqx985trm6OSD5ALD3vb10tXSRHmO9OUeqvaE9IPkAvl6GrbWtMZuAMBgNGEZRr0FrMKBNi+wQ+PEk3KmXfwA/URQlD1ABnaIoZwI/Bf56qhcrimIAFgGvDHrqFWDlcAIY6BGxHnjrFIta8SVMRJgYzQY02uBdyhQXe3ecxcRmig/ubGWKN6E3Tt7xdmJ0UnOCLxzSctNCHvOEEEKI0TDFBV/HmK1mdMbx17NuMIPZGJS4BzCGeM9iYgr3ldPXgAqgCrDgq7XwOr6hFN8fxuvTAC3QNKi9Ccg62QsVRXlfURQ7cHhge185ybKfBPKAvw3x/B2KomxRFGVLS0vLMMKOHrfTTX+PLdphAL6L8fNuXhfQtnT9kpi84zyejaf9c6w4+h3YbceLGGWXZDNt8dSAZS696xLpiTMGJur+OXvVLOISjne9NJj0rLx0RURr6gyX1+Olr7sfr8xwdFITdd8UE4PsnxOD2+Wmv6d/1OvJLslmyoLA6Rwvu+tSkjNCFzqMtHDun2m5qZx/y3kBbQvPXUhmUXi/L3jcHvp7+vF65dwYa8JaA8K/UkUpwTfsQgNsV1X18DBflwPUAWecWJtBUZRvAjeoqjr9JK/Nx9erYR7wE+ABVVXvC7HcVfgSD9cPpwhlLI/DO7qnguf/9jKN1U2ccckqlp67iJQoHZiOsfXaqD1cS1t9O4npieSV5WJNPr1x/OPQuBuHN97Z+x3s/WAfz//tZUDh4g+fz6ylMzCajXS1dlF7qJaejl4yCtLJLcvFGKLC/yQhNSDCoKm6ibrD9Xi9XnJLc8guif5sJfWVDbzx1Nvs3riPOctncvaVZ5BTFP24RkiOnZOI1IA4Ndk/x6fKA9W89I9XqT5Uw7LzlrBy/TLSRzGkoLO1i9qDtfR29ZKRn0FeWR4GU1BPznG5f/q+L9TRVt9GYloCeWV5WFPC932h9mg9rz7xBod2HGb+mrmceekqsgrkhugYG5saEIqifAP4qaqq5UD5Ce1m4Iuqqn7nFKtoBTwE93bIILhXRABVVWsG/rlPURQt8JCiKD9RVdU/EPyE5MOHozkDRjjUHq3jJ3f/EqfDN5fxE795it7OXq78+KVR7RJstpgpW1BG2YKohSAmkUM7DvPAVx70P/7NvX/knp99krkrZpGYlkhimoxjF+GTWZBJZgxdwHR39PCHrz9MzVHfWNrXnnyLg9sP84X7P0NCGC/khBBCnFxTTTM/+8yv6Bvo/fDMQ8/TUtfKh//vRgynOfwzKS2RpAl6HeP7vjCFskG9PMKhvbmDX33xd7Q2tgHw8j/+R8W+Su7+0Z3EWyfvzBOxJNzfVL+Jb+jFYHEDz53UwLSZW4F1g55aB7w/gjg0+JIr/gFGiqJci292jVtUVX1yBOuKSbVH6/3Jh2NeeeJ1Olo6oxOQEFHw1jPvBrW989+RHCqEGL+aapr9yYdjao/W01Rz0ny9EEKIMKuraPAnH47Z8PIHtA18CRZjp7G6yZ98OObQjiM018rQplgR7komCr7ik4MtwDf95XD8HPiboigfAO8BdwI5wO8BFEW5D1iqquragcc3A3ZgN+AEFgP3AU+qquoYWOZ6fD0fvgC8rSjKsR4WTlVVhxtXTAk1vZvJbEQTgWk8hYhV8YnBVa2tSaFyoEJMPENN8zkep/8UQojxTG8IPu7q9Tq0cl0+5kKdAxVFkXNjDAlLDwhFUXoURenGl3woVxSl+4SfPuBl4InhrEtV1X8C9+AraLkDWA1cqKpq1cAi2UDpCS9x45ueczOwC19PiweAW09Y5k58yZZfAg0n/Dw10vcaKwqm5pGWHViV/epPXE5y+sTsqiVEKGdcsjKgkrJOr2PVBcujGJEQYyerIJPl5y0JaFt23hKyCk9as1kIIUSY5ZXmkl+WF9B2ya0XkhZiBiURWTnF2cxZPjOg7ewrzyAzPyNKEYnBwpUK+hS+3g9/Ar4KdJ3wnBOoVFV1w3BXpqrqb4HfDvHcLYMePwY8dor1nTXcbY8XGbnpfO4Xn2L/1oO0NbQzfdFUSmeXRDssIcZUyaxi7v3959n7wX4URWHW0ukUTS+MdlhCjAlzvIlrPnkF81fPoWJ/FcUzCimbV4o5xDS0QgghIic5PYlP/uB2Dmw7TENVI9PmT2HKnFI0GpmqeaxZEuL58Jdu5OD2w9QcrqV0dgllc0tPuxaHCL+wJCBUVf0LgKIoFcD7qqq6TvESEQZZBZlS0VVMahqNhpKZRZTMLIp2KEJERXJ6EkvPXczScxdHOxQhhJjUMnLTychNj3YYAkjNTGHl+mWwflm0QxEhhHUwjKqqbx3790CdBcOg56vDuT0hhBBCCCGEEEKMD+GehjMB+DVwLYOSDwOkEosQQgghhBBCCDEJhXtg0s+AecDl+GamuBH4IlALXBfmbQkhhBBCCCGEEGKcCPd8JBcAN6iq+o6iKB5gq6qq/1QUpQH4OPBkmLcnhBBCCCGEEEKIcSDcPSCSgGPTZXYBx+ae2QCsDPO2hBBCCCGEEEIIMU6EOwFxFDg2F+R+4HpFURTgSqA9zNsSQgghhBBCCCHEOBHuBMQjwNyBf/8Q37ALJ/AT4Edh3pYQQgghhBBCCCHGibDVgFAURQ9cD3wYQFXV1xVFmQ4sBg6rqro7XNsSQgghhBBCCCHE+BK2BISqqi5FUYoB9YS2aqA6XNsQQgghhBBCCCHE+BTuIRh/AW4P8zqFEEIIIYQQQggxzoV7Gs544CZFUdYBW4G+E59UVfXTYd6eEEIIIUTYGPUGnG7XsJfXabW4PZ4RbaOwoIDKqqpTLzgKOq0Wj9c7otdoNZoRvUar1eIZ4XvXarWsv+X8ES3vq2c+sm3c8L1bR/waIYQQkRfuBMQMYNvAv0sGPacihBBCCBHDnG4Xr/3yiWEvv/aea+k8cHBE20iaPm2kYY2Yx+vl0S//ckSv+dAP7+HNh58c9vJn3XY1m//w2Ii2seTjN/CPj/982Mvf+IfPUfH8yyPaRvFF53PgX8+O6DXTr7l0RMsLIYQ4PWFNQKiqenY41yeEEEIIIYQQQoiJIdw1IIQQQgghhBBCCCGCSAJCCCGEEEIIIYQQEScJCCGEEEIIIYQQQkScJCCEEEIIIYQQQggRcZKAEEIIIYQQQgghRMRJAkIIIYQQQgghhBARJwkIIYQQQgghhBBCRJwkIIQQQgghhBBCCBFxkoAQQgghhBBCCCFExEkCQgghhBBCCCGEEBEnCQghhBBCCCGEEEJEnC7aAYiJTVVVnB2d2JqaQFEwZ2ZgSEpCUZRohyYmCY/TiaO9A0dLC7r4eEzp6eitlmiHJWKY6vHg6OzE1tSMRqfDlJGBMSkx2mEJIUTMUlUVZ2cX9uZmvF4vcceu9zRyr1OIiSbw8+4hLjNzRJ93SUCIiHK0d9CycROoKgA9R8vJWLkcY3JylCMTk0V/XT2de/f5H2tNFWSsWI4uPi6KUYlY5mjvoGXTB/7HPUeOkr5yhSQhhBBiCM7OLpo3bASvF4Deo+WkL1+GKS01ypEJIcLN2TX4814xos+7pCVFRPVWV/uTDwCoKn21ddELSEwqbpuNroOHAto8djvO7u4oRSRindfjofvo0YA21evF3twcpYiEECL29Tc2+L+MHNNTUYl64jWgEGJCsDU0hvi8V6AOahuKJCBERKkud1Cb1+2KQiRiUlLVkAdDVR3eAVJMTqo7xHHL44lCJEIIMT6o7uBjpOp2Bd6EEkJMCN4Q10mhrp2GIgkIEVGWooKgtvi8/ChEIiYjrdmMtbgooE3R6TBYE6ITkIh5Gq0Wa0lJUHtcRkYUohFCiPEhLic7qM1aXCQ1IISYgEJ93i3FxVIDQsQGY0oKaYsX0V1eDigklJZgTE6KdlhiklAUBUtxEVqTib6aGvQWK5aSIilCKU7KmJ5G6sIF9JSXo9HpsU4pwSDHLSGEGJIxKYm0pUvoKS9H9XixlhRjTEuLdlhCiAgI9Xk3pQ6/3oskIEREaXQ6zFmZGNN9JyGNVhvliMRkozOZsBYXEZ+fh6LRyN0YcUpavZ64nGxMmRkogCLHLSGEOClFq8WckY4pNQUVud4TYiIb7eddEhBiTMiJSESbRieHOzEyctwSQoiRUbRaZKJ1ISaH0/28y61AIYQQQgghhBBCRJwkIIQQQgghhBBCCBFxkoAQQgghhBBCCCFExMmgaCEG9LT3UHu4lu72HtJyUskry8MYZ4x2WCLCPG4PdUfqaKxqwhxvIq8sj+TM5GiHNSE1VDRSf7QejUYhd0ouGQUytaUQQggxmMvpou5IHU1VzVgS48ktyyUpPSnaYU0Knc2d1B6upa+7n6zCTHLLctHp5StzOMlvUwigv6efZ3//HNte2+5vu+wTl7L6ilVoZNaECe3Q1kM8/LU/o3pVAHKm5HDrd24hRZIQYVVzsIbfff4POGwOAOIT4rjzZ3eSUxI8l7QQQggxme19by9/+97f/Y9L55Vw01dvIjE1IYpRTXydLZ389buPUrWvCvBN5/6Rb93MnNVzohzZxBKT36wURfmEoigViqLYFUXZqijKmpMsO1NRlDcURWkaWL5cUZQfKIpiGLTcmQPrOrbMnZF/J2K8aKxsCkg+ADz/0Au01rdFKSIxFvq6+3jmt8/5kw8A9UfqqT1UG8WoJh5VVdnw343+5ANAX3c/e97dE8WohBBCiNjT1drF0w88E9B2dGc59UfqoxTR5FF3uM6ffADf9ct/fvMMPR09UYxq4om5BISiKNcB9wM/ABYA7wMvKopSMMRLnMBfgPOAacA9wG3A905YZzHwwsC6FgD3Ab9WFOWqyLwLMd7Y++xBbW6nG6fNGYVoxFhxOVx0tXYGtdt7bWMfzATm9XpprmkJam+pDW4TQgghJjOnw0VfZ19Qu61Prk0izRbi+0B3Ww8uuysK0UxcMZeAAD4HPKKq6oOqqu5XVfVuoAG4K9TCqqoeUVX1EVVVd6qqWqWq6rPA34ETe03cCdSrqnr3wDofxJe0+EKE34sYJ9Jz04LqPeRPyyclS7rhT2TWFCtL1i8NaFMUhcyirChFNDFptVqWXbg0qH3emXOjEI0QQggRuxLTEpl7RuD5UaPVkCl1kyIuszAzaOj1ovMWkpAmQ1/CKaYSEAPDJhYBrwx66hVg5TDXMQVYD7x1QvOKEOt8GVisKIr+9KIVE0l6fjp3/PBjFEzPR6vTMnv1bK7/v2uJs8ZFOzQRQVqtljOvXsOqy1aiM+hIy0nl1u/eQu6UnGiHNuFMXzqdiz9+EWarGUtSPFfdcyUlc0ujHZYQQggRUwxGPRd89HyWrF+MTq8jsyiT2++7jWypmRRxOaXZ3PaDW0nPT0en17H8wqWce9O5UoQyzGLtt5kGaIGmQe1NwLkne6GiKO8DCwEj8CDwlROezgL+F2KduoFtNgxa1x3AHQAFBUON/BATTdGsIu740e3Y++zEJ1kwGGMzNyX7Z3ilZqdy2Scv5ezrz0Zv1GNJjI92SOPaUPunNcnC2deexcJzFqAoCglSSEuMMTl2ilgm+6c4UXpeOlffcxXnf/g8DGYD8QnRvTaZLPunVqtl+pLp3P2rfJx2JwkpCWh12miHNeHEVA+IE6iDHish2ga7Dl8C4kbgQuBLw1hnqHZUVf2jqqqLVVVdnJ6ePryIxYRgtphJzkyO2eQDyP4ZCVqtluSMJEk+hMGp9s/EtERJPoiokGOniGWyf4rBdHodyZnJUU8+wOTbP+MT4knOSJbkQ4TEWg+IVsCDr8fCiTII7hURQFXVmoF/7lMURQs8pCjKT1RVdQONQ6zTDcg0B0IIIYQQQgghRITFVA8IVVWdwFZg3aCn1uGbwWK4NPiSK8fSVhsIHsKxDtiiqqqUNRVCCCGEEEIIISIs1npAAPwc+JuiKB8A7+GbwSIH+D2Aoij3AUtVVV078PhmwA7sxjcl52J802w+qarqsUnnfw98SlGUXwJ/AFYBtwA3jM1bEkIIIYQQQgghJreYS0CoqvpPRVFSga8B2cAe4EJVVasGFskGTiyd7gbuBcrw1XWoAh4AfnHCOisURblwoO0uoB74tKqq/47w2xFCCCGEEEIIIQQxmIAAUFX1t8Bvh3julkGPHwMeG8Y638JXpFJMYo6eflS3G4M1Ho0UlpkQnH02PE4XBkscWpkmacJSVRVHVx8oYEyIR1GUU79ICCGEGILq9eLo6kPRKBgTLdEOR4iw8jhdOPts6Ax69PHmaIcTQK7WxaTgcbloO1hNxcsbcfU7yJw3hbw18zEnSzX+8Ur1euk4WsfRF97H0dVLyrQCitYuIS4tKdqhiTBz9tpo3HaA2vd2AZB/xnwy50/FEGMnVCGEEOODvauX+o17aNi8H41BR9HaJaTPLkFnNEQ7NCFGrb+lg4r/babjcA3GJCtTLlpJUkluzNy8iakilEJESm9DG4eeehNXnx1UlaYdh2n4YB+q1xvt0MRp6mvqYN/jr+Lo6gWg/WA1Fa9+gMcpdWUnms7yWqrf3IbX5cbrclP12ha6KhqiHZYQQohxqnVPOfWb9qJ6vXjsTo4+/x49tc3RDkuIUXM7XBx9cQMdh30TRDo6e9j32Kv0N3dEObLjJAEhJoX+pvagtuadh3H22qIQjQgHW1snqGpAW8fhGhw9/dEJSERM047DQW0te45GIRIhhBDjndvupGnHoaD2TklsiwnA2d1LV2Xgvqx6vdjauqIUUTBJQIhJIdTYJ1NyAlqDjEIar3RmY1CbPt4sf9MJKD4rJbgtM7hNCCGEOBWNToM5xHBNU7LUgRDjn8agD3mNrDPHzvAiSUCIScGSk058dpr/saLRULRuCTpT8AdUjA/xWSkkT80PaCu9cAVGa3yUIhKRkjG3LOBkqo8zkTazOIoRCSGEGK80Oh15q+aiOaFwtTE5gaSinChGJUR4mBItlF6wIqAtdUYRcZmpUYoomNwqFJOCKcnCjGvX0tfYhsfpJi49Se6gjnOG+DjKLl5Db2Mbbpsdc0piyDvlYvyzZKUy99ZL6G/2DaWKz0zBnJoY5aiEEEKMVwl5Gcy77VL6mzvQ6DTEZ6ZiSrZGOywhwiJ1eiHzbruE/rYu9HFmLFkpGOJM0Q7LTxIQYtIwJVowyTRLE4rBYiZlSl60wxBjIC4tkbg0SToIIYQIj/iMZOIzkqMdhhBhp9HpsOZmYM3NiHYoIckQDCGEEEIIIYQQQkScJCCEEEIIIYQQQggRcYo6aBo7EUhRlC4geA64iSENaI12EOPcib/DVlVV14/lxsO0f8p+MLSJ8rsZ830T5Pgp/E71uxqPx87J8PeX9+gzHvfP4Zgof9/J/j7G8/45Ef52E+E9QGTex5D7ptSAOLUN0bhwHwuKomxRVXVxtOMYz2Lgdzjq/TMG3kPMkt/NqMnxU8Tq72pU+2aMvqewkvcYVRE/dsbwex8ReR9REZb9c5y955AmwnuAsX8fMgTjFCbqxbOYGGT/FLFM9k8Rq2TfFLFM9k8Ry2T/FKMlCQghhBBCCCGEEEJEnCQgJrc/RjuACWAi/A4nwnuIFPndiKHIvjF8E/F3NRHf02DyHie2ifLe5X2MXxPhPU+E9wBj/D6kCKUQQgghhBBCCCEiTnpACCGEEEIIIYQQIuIkASGEEEIIIYQQQoiIkwSEEEIIIYQQQgghIk4SEEIIIYQQQgghhIg4SUAIIYQQQgghhBAi4iQBIYQQQgghhBBCiIiTBIQQQgghhBBCCCEiThIQQgghhBBCCCGEiDhJQAghhBBCCCGEECLiJAEhhBBCCCGEEEKIiJMEhBBCCCGEEEIIISJOEhBCCCGEEEIIIYSIOElACCGEEEIIIYQQIuIkASGEEEIIIYQQQoiIkwSEEEIIIYQQQgghIk4SEEIIIYQQQgghhIg4SUCcwvr161VAfuRnOD9jTvZP+RnmT1TI/ik/w/wZc7Jvys8Ifsac7J/yM4KfMSf7p/wM82dIkoA4hdbW1miHIMSQZP8UsUz2TxGrZN8UsUz2TxHLZP8UoyUJCCGEEEIIIYQQQkScLtoBiInF0e+g7mgdHY0dWFMTyCvLJc4aF+2whk1VVTwuDzqDfDTGu572HuqP1tPb2Utabho5ZTno9fohl3e73Gi0GjQayctGksftAUCr00Y5kpFrrW+l/kg9Xq9KdnEWmYWZ0Q5JRJHX68Xr8aLTx/75oqejl/ojdfR29pKam0bulBz0hqGPh0IIEUkOm4P6o/W0N7RjTbGSW5ZLfEJ8tMPyG0/H9/FIfqsibDweDxtf2MSzv3vO33bGVWtYf8v5GOOMUYxseBorG/ngxc2U7y5n/lnzmLtmLinZKdEOS5yG3s5envr10+x6e7e/7aav3sjCcxYELdvT0cv+TfvZ8N+NZOSls+rylRRMLxjLcCcFh93B0R3lvP3vd9DqNJx59RmUzC0ZNyf3xspG/vilh+hq7QLAFG/irp99nLyyvChHJqKh9lAt7z+3gYbyBpZesJSZK2aSmJoQ7bBC6uvq4z+/+Q873tzpb7vx3htYdO7CKEYlhJisvF4vm1/awtO/+Y+/beWlK7joYxdiijdFL7ABNQdreP+5DTRWNLLswmXMXDGDhJTYPL6PV+Pjyk+MC611rTz/0AsBbW//+x0WnLOAgun5UYpqeDqbO3n4q3+ivbEDgJqDtVQfqOX6L16LwWyIcnRipBoqGgOSDwBP//o/FM8uIjkjOaB9yytb+O8fnwegen81u97Zxad/fTfZJdljFu9kUL6jnIe/+if/44ObD3HXzz5O6bzSKEY1fHvf3+dPPgDY++xs/O8mrronF0VRohiZGGtN1c387gt/wN5nB6D6QA1rmztYf8v5MdmDqqGiISD5AAPHwznFpGQmD/EqIYSIjNa6Np77w38D2t5/dgOL1y2icGZhlKLyaapq4vdf/GPA8f38W85j3YfOlXN9GMXemVKMW/Y+Ox6XJ6jd1tMfhWhGprGqyZ98OGbnWztpbWiLUkRiNELtc/3d/ThtzoC2rtYuXnvs9YA2p91F3dH6iMY32Xg9Xt75z7sBbaqqsu217VGKaOQaq5qC2urLG/B6vFGIRkRTQ3mD/+L0mLf+9TadzZ3RCegU+ntsQW22XhuOfkcUohFCTHaOfjtulzuoPdSxaqzVH60POr6/8fgbdLZ0RiegCUoSECJsUrJSSMtJDWgzxZtIzU0d4hWxQ6sN/igoGgWNRrKd41F6XnpQjYHS+aUkpScFtCkaBZ0uuCOYJsT+IEZBAb0huCeR3jh+xqDPWT07qG3p+iXjspaFGJ1QxwetTosSo+eL9Ly0oP20ZE4xSRlJ0QlICDGpJWcmk1GQEdBmNBtJi4HvCxpt8Dldq9NJ74cwk6tsETbWZCsf/sbNFM8uBiC7OJuP/eCjpOWkRTmyU8sszCS3LCegbcXFK0jLjf3YRbDMokxu+/6tpA4kxGYun8GVn74iqBZJQkoC6289P6AtPjGevCm5YxbrZKDRaFhz5aqAE7hGq2H+WfOiGNXIlM4r4ZI7L8YYZ0Rn0LHuQ+cyc/mMaIcloiCnNIfEtMSAtvM+vC5oeFesyCzMHDgX+46HM5ZO56p7rsIcA2OthRCTjyXJws1fu4mSuSUAZBVl8bEffJT0vPQoRwY5U7JJGFTP5/xbzgu6gSVGR1FVNdoxxLTFixerW7ZsiXYY44q9z05vVx9xFjNxCeNnBozWulYObD5I9f5qpi+dxpT5U4IOQqcw5ulR2T9PrrezF3u/g4QUKwZT6Foetl4bFXsq2f3OblJzUpm1YuZErP8QldT9ifun2+Wm+kANO9/aiU6vY+6aOeRPz4/JMfMn09HcgepVScpIGnexx7Bxd+xsrGxi38Z9NFU1MXv1bErnlMT8+c5/PEy2Sm2jkRl3+6eYVMbt/mnvt9Pb2YvZYo6pGTAaKxvZt2EfzTUtzF41i5K5JeNqRr8YMuS+KUUoRdiZ4k0xUcV2pNJy01idmwaXr4p2KCJMLEkWLEmWky5jtpiZuXyG3M2OMJ1eR8mcYkrmFEc7lFGJ1bvcYmxlFWWSVTS+pmEdzvFQCCHGiinOhCku9r4vZBVlkVWUFe0wJjS5fSOEEEIIIYQQQoiIkwSEEEIIIYQQQgghIk4SEEIIIYQQQgghhIg4SUAIIYQQQgghhBAi4iQBIYQQQgghhBBCiIiTBIQQQgghhBBCCCEiThIQQgghhBBCCCGEiDhJQAghhBBCCCGEECLiJAEhhBBCCCGEEEKIiJMEhBBCCCGEEEIIISIuJhMQiqJ8QlGUCkVR7IqibFUUZc1Jli1SFEUN8bP+hGWuVBTlFUVRWhRF6VEUZZOiKJeOzbsRQgghhBBCCCFEzCUgFEW5Drgf+AGwAHgfeFFRlIJTvHQ9kH3Cz+snPHfmwOOLBtb5AvD0yRIbQgghhBBCCCGECB9dtAMI4XPAI6qqPjjw+O6B3gx3Afee5HVtqqo2hnpCVdXPDGr6tqIoFwGXA++MMl4hhBBCCCGEEEKcQkz1gFAUxQAsAl4Z9NQrwMpTvPwpRVGaFUV5T1GUq4exOSvQcRphxpS+nn46WjrxerzRDiUivN6J+b7EqaleFVVVh7Vsd3s33e09EY5ITHTj9XjjtDtpb+7AaXdGOxQRI9wuN+3Nndj77dEOZVhGcrwXQsSmWDuHOuwO2ps7cTld0Q5FDBJrPSDSAC3QNKi9CTh3iNf0Al8A3gPcwKXAPxVF+Yiqqo+GeoGiKJ8E8oC/DfH8HcAdAAUFpxr5ER1ej5cD2w/xxG+eprWhldUXrWTt1WeSnpMW7dDCwt7bT93uSva/vhNrRiIzzplPRmlOtMOKCeNh/xwNt9NFw4Ea9r6yDY1Ww6zzFpE1NRetPvhw1dfdx+Y3tvPcn14A4LLbLmLR2QuIt8aNddhiwHjcP9trWzj8zh6aDtVRumIGhYvKsKQmRDusYak+XMt/HvwvB7YdYtrCMq68/RLyy/KiHVZMGo/75uloqGzkv399iR3v7KJgaj7XfOJySmYVRzuskFx258Dxfit6k4FZ6xaSWZaLRqeNdmhjbrLsn2J8Gmr/VFWV5qP17P/fdvrae5ixdgG5s4swxpuiFSoAlQeqePL3z1C+p5K5K2Zxya0XkFsi3yNihRJLGWdFUXKAOuAMVVXfOaH9m8ANqqpOH+Z6fgusVlV1bojnrsKXeLheVdVnT7WuxYsXq1u2bBnuWxgzVQer+d7HfoznhJ4PZ12+mhs/dx26CXDi3vfadt778/GOMFqDjsu++SFSCzOjGNUpKWO9wVjdP0ejZlc5L/34X8cbFLjoKzeQMyP4gmzz69v43dceCmj75A/uYNFZ8yMc5bgz5vsmjI/9s6e1i+e++3f62o73oJl25lxW3rIOXYikVyzpbOnkB3f+jNaGNn9bWlYq9/7h8ySnJ0UvsJGTY2eY9PfY+OUXH+DIrnJ/m9li5hsPf4nM/IwoRhZa1bYjvPLzf/sfK4rCxV+7kaxpMZVEk/1TxLKo7p8tFY08951H8bg8/ufPuONCpp0xZ6zD8mttaOO7t/2Ins5ef1velFy++KtPY02yRi2uSWjIfTOmhmAArYAHyBrUnkFwr4iT2QSUDW48Ifnw4eEkH2JZXUVDQPIB4J3n3qezpTM6AYWRrauPHc9sCGjzON20VIQs8SEmEFVV2ffqtkGNcPjdvSGXf/f5DUFt770Q3CbEUDpqWwOSDwCH3t5NT3NndAIagea61oDkA0BrYxvNdS1RikhEW2tjW0DyAcDWa6OxZiSXUGPD4/aw68UPAtpUVaVyy6EoRSSEGKnmw3UByQeAHc9swN5ri1JE0FTTHJB8AKg9UkdrfdsQrxBjLaYSEKqqOoGtwLpBT63DNxvGcM0HGk5sUBTlWuBR4BZVVZ8cRZgxwRQX3LXJmmxFb9RHIZowUxQUTXDSTNHE1O4qIiRU11utNvTfPiM3PbgtL/bu8onYFepYg+K7ExvrjHHGkO0mc+h2MfEZjHp0huCeOyZzdLtDD0Ub4niv0cm5XojxItS5UqPTRPUcagxxDtRoNRhMhihEI0KJxaP8z4FbFEX5mKIoMxRFuR/IAX4PoCjKfYqivHZsYUVRPqIoyo0Dy05TFOULwCeBX5+wzPXA34EvA28ripI18JMylm8snIqm5VMwNT+g7cZ7riExZXyMWz4Zc0Ici64KnCFVbzaQXjK4Y4yYaBRFYda6hQGdthSNwpRVs0Iuv+rCZZhO+BJmijOx/LwlkQ5TTCApeWkk5gSeCmadtwhrRlJ0AhqBrPwM1l17dkDbudeeRVZBTA9VExGUkZvOlXdcEtC28Ix5MTn2WavTMvfCpQFtGq2GokVToxSREGKkMqfmoht083PhFauiWgMipzibFesDjy0X37I+JoehTVYxVQPiGEVRPgH8H5AN7AE+q6rq2wPPPQKcpapq0cDjjwBfAgrxDd84BPzyxAKUiqK8CZwZYlNvqap61sliieVxeK0NbVQcqKK3s5e80lyKphegN0yAHhCAs99Bw4Eajm7cjzUtgeKl00krivmLahknGgYet4fmI3UceW8fGq2G0hUzyZiSg2aIXhC1R+uoPFCNoigUTisgT4qVhiI1IE6is76dqm2HaSlvoHBhGTmzCohPHh/jRHs6e6g8UE1LXSvpOWkUzSgYj2Nc5dgZRrZeG5UHqmmoaiQ5I5ni6QUkxWhNELfLRfOheg6/vxe9Ue873pfmhO6ZFD2yf4pYFvX9s6WikYpNB+hr76F05UyypuVhiHJPvK62bioPVNPW2EZmfgZFMwqlQPnYG3LfjMkERCyRk4AYgaifBIQYgiQgRCyTY6eIZbJ/ilgm+6eIVeOmCKUQQgghhBBCCCEmIElACCGEEEIIIYQQIuIkASGEEEIIIYQQQoiIkwSEEEIIIYQQQgghIk4SEEIIIYQQQgghhIg4SUAIIYQQQgghhBAi4iQBIYQQQgghhBBCiIiTBIQQQgghhBBCCCEiThIQQgghhBBCCCGEiDhdtAMQsc/r9tBV30ZfWxemxHgSs1PRm43RDkuIUbF399FV14rb6SYhKwVrZnK0QxLjWF9bF1317SgahcScVOKSrdEOSYhxw9Fjo6u+FZfNgTUzhYTslGiHJISIMFtnL131rXjdXhJyUrGkJUY7JDFGJAEhTql222E2PfISqL7HMy5YyvTzF6MzGqIbmBCnqb+9h01/eZnWQ7UA6EwGzvzMlaQUZUU5MjEeddW38s5v/oOtoxcAS2Yyq+68hIQs+RIlxKnYuvrY/vgb1O04AoBWr2PN3ZeTXpYX5ciEEJHS29LJ+398nq7aFgCMVjNn3H0lSfnpUY5MjAUZgiFOqrelk62Pve5PPgDsf/EDuhraoxeUEKPUVt7gTz4AuO1O9j6/EY/TFcWoxHhVuXG/P/kA0NvUQf2u8ihGJMT40Vnd5E8+AHhcbrY/8RbOfnsUoxJCRFLT/mp/8gF8vaAOv7kD1euNYlRirEgCQpyUs9+B2+4Manf02KIQjRDh0dvaFdTWWdOCK8S+LsTJqF4vbUfrg9o7KhujEI0Q4489xPVEd30bLpscj4WYqLrqW4Pa2isbcTvdUYhGjDVJQExQjj47Hrdn1OuJS7YQl5YQ0KbRaYlPTRjiFUIc53I4cdljr1dBckFwF7+8hVMxWsxRiEaMZ4pGQ8GSaUHtOfNKoxBNIK/Hi6PPjqqqp15YiAjzejw4+oJ7NVjSk4LacuaVYkyIG4OohBDRkDE1P6itYMk09Kahh3e7nC65UTRBSA2ICaazqYM9b+1i9xs7ySnLZdllK8gqzTnt9ZkS4llx24VseuRleps6MCbEseTmdeN2bLPb4cTr8mCQL5oR5XK4qNxVznv/egevx8OKK1dTsmAKxrjwFy/1OF14nC708WYURRnWa1KKsph75Rr2PrcBj8tN9twSppw1D0UjOdlY5hrokq2PM0U5kkA580rprGul4r29KIpC2dnzyZheENWYmqua2PrCB1TuqmDa8unMO3chqblpUY0p1qmqiqvPhtagR2vQRzucCaWxvIEPnt1I3cEaZp0xh7lnzyNp4DoiuSCDRTetZee/38Ftd5I2NZfZl6xAp5dLVCEmqvSyXGZcuIyDr2zB6/FQsGQ6BYuDk/ngSzxU7argvSffwe10+a8pTfGxdS0Qa1SvF2efHZ0x9s5pitwZObnFixerW7ZsiXYYw+J2uHj+gefY/cYOf5vZaubWn95BSk7qiNbl8XhwO9z+L4yO3n5snX0Y4k3jsrq76vXSWVFP1Rtbcfb0k714BhnzyjAmxIdzM8P79htGsbp/Vuw8yt+/9peAtmu+egPTls8I63a6a5qoen0L/a1dZMydQtbi6ZiTh9c7R/Wq9LZ24XV7iE+1jpuiqg6bA51eh1anHcnLxnzfhPDtn267g7YDVdS8swMUhYIzFpAytQDdSe6UnPa2nG68Xi+GEa7b43bT19KFoijEpyWiGdnfJ6x62rr5y5cfprOxw99WMLuIa756A+bYTL5G/dhp6+imccsBmncdIS4tkcKzF5NQkDnWYU1IHY3t/PkLD9Lf1edvm7lmNpd85nL0Jxx3e1u68DhdxKVYRzTTltfjxeVwYTAbhp2EHqGo759CnMS42D8dNgdanTYgsej1eOlr60L1eIlLSxwy6Vi5s5xHv/ZIQNvV917P9JUzRxz7ZGFr76Zh8z5a9pQTl5FE4VmLSMgf83PakPumpJcnkM6WTna/uTOgzdZjo6W2hYQM39Q2Ot2p/+S1h+t49+l3qTlUy8K1C1hwznxSMlMwWsZvd8iehlb2/uMVGEi4Vb2xFdXrJf+MBZG6YJnU9ry1O6hty/MfULZkGhpteHoZ9DV3sOdvL+IdGGpUt2E3zj4bZRevQjOM/VzRKFgzksISy1joaO5g55u72PzKFnJKsjnz6jPIm3q8SrzH7UFV1Ql517CzooHDz77jf3zoP28x47p1pE4LXy8Dj9tD+e5yXn/sDWy9ds64ajXTl04nzhp83HO73CiKEpAE0up0JGSPLNEbKa21rQHJB4DqPZV0NLRjLsuNUlSxy+t2U/32Dlp2Hgagq8/GnkdfZN7HLiM+Q6bnHQ2P20N3WzdOmyOgfd+7e1lz/VmkF2T42yzpI5+Cr7Gykfef28jRHUeYtWo2S85bRHqeVNEXIlb0dvayb9N+3n3qXRLSEjj7urMpnl2ERqNBo9VgHcYxdv/7e4PaNj27kbKl09DqtDidTvR6vVzPD/C43FS+tpm2/ZUAdFXY2FP7EvNvv4y4tKSoxnbMxLtSncQ0Wg06vTaggEtmSRad3b386DP3o2gULrzxPGYunIphiLu9LXWt/PH//khfdz8ALzz0Ii01LVx9z1XoDON3d+lraPMnH46p37yfrIXTMFjD2gtCACZLcLc4k8WMognfyaG/pcOffDimZfdRCtbMx5w6seaSdrvcvPHPt3jvP+8B0FjRyL6N+/nMb+4mLTeNg7uO8MI/XqGro5cLrl/L3GWziA/xxXm8atx2IKitedfhsCYgqg9U84f/exDV6ztO/P0Hj3Hjvdez6NxF/mX6e/vZs/kALzz+P+LjTVx443lMmz9lWIndsRSyd8yghIk4ztHVR8uuIwFtXreH/pYOSUCcJq/H6zsuPfYqXW3dLLl4MV3lzVTu8M0Oo9VqRp2M7mrt4k9ff4S2+jYAGiubqNpXyS3f+kis9vQRYtLZ+dYunvrV0wDUHann4OZD3P2rT1EwPbgGxFCMIYZamK0m2po72PzmNt59cSNlc0pZe8UZFJYNf70TlaOr1598OMbrctPf0jmxEhCKovxqiKe6gIPAv1RVdQyxzLjT2dJJT0cvCclWEk8jYx8pSRnJrLr2TN569DXAd4e3dM0sfv7l3/mX2blhL1/77eeZszR0t6WmykZ/8uGYLa9u5ZzrzybjhDsV4402RMJFH29CkQvyiJi5ejZb/vsBHpcvGaZoNCy5eKk/O+31eGmrb8Nhd5KSnUzcafSu0Ya406816ifk37SzuZMNz20IaLP32WmobKSzq4fv3PkTvB7f1FUHdxzm7u/ezuoLlkcj1IgwJlpCtIU3cXhw62F/8uGYN594m9mrZmMc6A6+a9M+fvGl48fTre/u4lt//BLT55eFNZbRSstPp6UqnPcAAON8SURBVGTBFMq3H/9SvXD94hEPxQsXe5+d9sZ2tHodaTmpMZcIUXRadEZ90IxPoY4xYniO7q/ku3f+FI/HlyQ+uPMIN951JYaDdThtDlZevYakzCRa61qx99tJykjGMsLPdHNNsz/5cMyR7UdprW8j/4TeYUKI6Ojt6uPNJ94MaPN6vFTtqxxRAmL6ihlU7iyncF4pKNBc3sjKq1bz74ee481n3wWgfH8VG1/bwnf/9BUycyd3LyhFq0Fr0AdNLR9L57RwRTJniPYk4B7g24qinK2qanWYthc1h7Ye5h/3PUZPRw/WFCs33XsDZQtj4+JTo9Ww6IIlZBRmUrWrnII5Rfz3368HLff6f94ZMgER8kudVosSpm7z0WLNScOYaMHR1etvK1q7BL1ZCthEQk5ZLh/50W2UbzuCx+2hdGEZOVN9Xb/tfXY2vrCJFx9+CbfLTeHMAq77wrVkFo5sbFp8Virx2Wn0NRyfyqn43CWYQnxZHe8UrQadXofTE/gFSafXsueDff7kwzHP/vUlFp0xD3P8xLgLmLVgKi27jvh7vGj0OtJnh3eWCWOImg/GOKO/147T7uS/j74c8LzqVdn29s6YS0DEJcRx4acupXpPJQ1H6smfWUD+zEL0xrEvQtVS08JTv36aQ1sPo9VpOeeGs1lz5Wriw1t/Z1RMiRaKzl3Kkf++62+Lz04jPis2htSMR/u2HfQnH45566UNXH/LJSSkJJAzLZet/9vO07/+D067k8yiTG669wZypwx/iJBWG5zIUhQF7Ti/XhFiotBoFQym4Hou+hHW3LKmJ5FWlsNLj76K1+Nl+tLpOLwe3vrvewHLdbV1U1teP+kTEKYkK4XnLKL8pY3+NmteBnGZsTOBQFgSEKqqnj3Uc4qiJAB/B34I3BiO7UVLa10rj3zrLzj6fZ05etp7eORbf+Vzf7iH1BgZ+xuXEMe05dOZtnw6AP974b2gZUwnKe6UXZxFVnEWjRXH57Bfe9NaUsfprBfHmFMTmf2h9XTXNuO2ObDkpGHNmdwHqEhSFIWcslxyQow3rzlUy3O//6//cdW+al7566vc8KXrRzTMx5gQz4xrzqGnthlHdz+W7FQsE/RvmpKZzPm3nBfwe0vPTSOnJIfyIzVBy+uN+rAOd4k2a24Gcz96MT21Lb7HeRlYwvzlsGxhGUazEcfAWHVFUTj3prX+4WqKRsEY6kLKFFuVpY9Jykgi6Zz5zD1nftRi8Hq8vP/cBg5t9dVW8Lg9vPq3/1E4o4AZy8JbkHa00maVYEq20tvQhiEhjoS8jHAXKZ5U9CGGJRmMeuacNRdzvJnqA9X88ydP+J9rqmziqV/9h4/d99FhJ04zCjMpW1jG4W2H/W3LLlpKmtSAECImxFniWH/reTzyzb/628xWM0WzCke0nso9Fbz1r7f9jw98cID55y5Ao9Hg8QYmOiUB6bt+yZhXhjktib7GNoyJFqy56RhjaGhuxPtiqKrarSjKd4EnI72tSOts6fInH46x99npbO6MmQTEYGuvPJN3X97k71qs0Wo485JVIZd1u9x0d/dyxWcup/FoI83VzUxdVEbx7OKwFQ6MJnNq4oSrDTAetda1BrXt27iPns5ekkdYFNKUZMWUNP5mZRmsq6OLnu4+klOTUL0qDTVNaLVacgqzMJmNKIrC0vVLSM9L5+CWQ2QUZDBtYRnJmcnMXjIDo8mA44Tu41fedjGmCda7x5KVhiUrctNI5pXl8slf3MWBDw5g67Mzc/kM8mfk09vVS31NE3q9jktuPp89m/f7X6M36Fi4am7Aemw2O20tHcTFm0lJTYpYvONBf08/u94JLkhbc6Am5hIQOqOepOIckopPf9rq8cZuc9Da0k5cnImUtMBaFy6ni/qqRuw2B5l56SSljOzcOXPRtICEHsDVt1/qTy4MHjoBULm3kp72nmEnICyJ8Vz7has5uuMo1QdqKZlbTOncEgxR6OkjhAht2uLp3PnTj7N/436sKVamL5lGVlHWiNZRsbsiqG3na9u56KbzePYvL/rbcouyyS89/ULLDbVNdLZ2kZyWRFZe4LDz5sZWXC43mVlp46LYt85oILkkl+SS2Cw8PVa/wVZ8wzHGNUtSPBqtJqC7s1avJT4Gu3yrXpXulg5a29q4/tNXUnOoFoPRwMrzljJ1TnDXZVu/nZeffJ1Hf/0EbreHvJIcvvjDT0kxFxF2CanB02TmlOZiPqHIUH9HLx6HE1OSBX0EplqMJds+2M2PvvErKo5U86Vvfoptb+5i70DRxXMuWc1Nn7qG1IwU4qxxzFoxk1krAodPFU8v5FsPfonNb26np6uX5WsXM3VueIcnTBa5ZbnkntBrp66ygV9/60EODMyOcN3tl/HVBz7Plre2Y443seTshZTMKPIvX3Gkmt/8+GHeeX0jOXlZfOk7d7N01YKQXcXDQfWq9Ld3o3q9mFMSYq62ginORMG0fDqbOwPa08dxPaGJovJoNb/58Z94+7UNZOdm8qVv382yNQvRarX0dPXy3N9f5sk/PYvX4yWvOJsv/ujuEV0PFE0r4NsDx6Wu9m6Wn7uYqXOn+J8PNQQnNTtlxMUjUzJTSDk/hSXnLxnR60R4GQ0GnC7XqRc8gUGvx+F0nnrBUSjIL6CmNriX4Mnk5+VTXRPZEeMjjWssYooUg0lP2YIplC2YcuqFh5BVnB3UZkm2cP4N51I8tYDt7++ieFohC1bNIfU0hhmoqsrmt7bzy6//nv5eG/GWOD77/btYfMZ8+vttvP7iO9z/wwfp7enjiusu4ObbryU7T6ZpHo2xSkCsAMrHaFsRk56XzpV3X86/738aVVVRFIUr7r6c9PzI3ZU7HY4+G1Xv76O+q5Mv3fsTVFUlZ+CD0tLbzvT5ZUE9GioOVvHILx7zP64tr+e33/sz3/jNFyZUNX0RffnT8ph75lx2vbULAFO8iUvuvBhTvAmPy0PjnnJ2/etNnL120qfmM/vqM0gY50OAhlJVUctnP/Y1bP12snIyqK9o8icfAF5/7l3mLZvNmReF7rV0TMmMooAvwmL0vF4vrzz1hj/5APDPB5/hi2X5fPT/bgpavr/fxs+//zs2vbMNgPraRj53xzf4y9O/ZuqM8CeEHL02qjbs5dBLm/F6PBSsmMXUdYuJS4mdHkE6g461N53D0V3l9HX1Ab6hLkWziqIb2CRn67fzy/v+yPtvbgagoa6Jz3/8Gzzy9K+ZNnMKR/dV8MSD//EvX1vRwD9+928+d99dGI1DD+EcrHh6IcXTA7taq6pKy8EaOvaUs+TcBWz+33bAt69c87mrsSbHzv4rhs/pcrH/uddG9JoZl6yNUDTH1dTW8NIjL596wROsv+X8CEVz3EjjGouYYtmU+aUUzy6iYk8lANYUK2dcuQZvVz+2bYeYYTShbejAOKiQ9HDVVzfys3t/i8Pu67HV19vPT+99gF88/j3qahv4zpd+5l/2yb//l8TkRO74zM0y7ecohGsWjIVDPJUILAK+DHwzHNuKJq1Oy+L1i8mfnk9nSxdJ6UlkFmVE7O7W6Wo/Ws/+597HPisLdWDqyfraJgBefOZ1Pnb3h8jKGdS1qD64W/zBXYfp7uyRBIQIq4SUBK757FWsvnwVjn4H6Xlp/nnbu+ta2PLISzBwDmk5VMPeZ95jya3r0RkmXrfa2qp6bP12AMqml1C+N7ib4Z6tB06ZgBDhZ+uzs/nt7UHtB3cdYdW6ZUHtzQ2t/uTDMR63h+qKuogkINrL69l/wswoVe/tIT4tkbK1Q52OoyOvLI/PPHA3zdXN6I16soqysCTFXq/ByaSlqdWffDjG4/FSVV7LtJlTaKxrCXrNzo176Onsw5g5/AREKD1NHWx68L94XR5ypuRy3Scvxe1VKV4whayS4LucQgiRkpXCR771YRorm3C73GQWZGDUa3nrJ//EdcLQ+E0PPs+ZX7huxIn49uYOf/LhGHu/nbbmdvbsCJ4G/Ll/v8K1N19GsgzrPm3h6gGxBd9XhlCpoFbgp8Bvw7StqNLr9eSV5ZFXFrtTPLUc9HXrigtRMC0nLwtz3PGu7m6Xm8byBsyKjts/cyMH9pfzziu+qqlFU/OxSBEuEQFx1jhK55YEtfe2dPqTD8c0763E0dWHLj1pTGILp7a6NhorGwBfF8LUQdMgJpwwfKvyaDWrVy6j4lBgN8tpIYZMicgzxZmYs2Qm9VWNAe0l04v8/1ZVlabKJpqrm9DotNx6+3X8+cF/BixvjdAQveYDwd13azcfoHj1HHQxNgY+NTs1ZuskTUbmOBMpqUm0t3UGtCck+i7a00J0YZ42dwqWMNyM6G/twuvyFY3rOFIHR+oAmLl0+ojuJtr7bDSUN9LV0kVSRhLZJdkY40aXHBFCRFdncycN5Q24HC4yCzPJLDo+zMGabA3oIdVyqDYg+QDg7LXR39494gREUmoiOr0O98DU8eArmpuUmkh6iNpTBUW5mOV4MyrhSkAUD9HepapqZ5i2IYbJOtBdPUnVMWf+dHYPZO+0Wg2f//qdJCYdH4O/7/29/PO+f/p7SkxZXMaqtUvY8cFePn7vLRG7eBYiFGOI8b/mVCu6cVgHoqG8gT99+WF/13NLsoWP3ncbWcXHiy8VlxZyxfUX8vTjL1BTVU/KVcnkFmdTV+FLWsxbPpu5y2ZFJf7JTqvVcNH169j1wV4aqn09yBafsYBZi6b5l6ncU8mf7n0Yz8AXqpyyXD780Wv465/+BcBZ562kbHpwoi0crCGGJSXmZ6CJsToQIvakZ6bxxW9/iq/c/X3/uX/NOcuZOsO3r06ZVcy6K87i1affBHwX5x/+9HWY4kZf2NYQosCkzqhHd5LZuQZzOVy8++/3eP3vx7v8r79tPSuvWDUuisMJIYK1NbTx928/6p+FT2/Uc9sPb6NgZugZMwzxJlAUUI/ftVI0GgyncZzKKczmU9+4jd98+yHcbg86nZZPffNj5BRkgQIlZYWUH64CwGg0cMdnbp5whb7HWrim4awKx3pEeKRPK8CSmUz7tqPcctH5dF9+HpgNlE4romzG8YvhzuZOnvn1s/4LEIAjWw5zw9dv5OZ7ricrLwOv243X5UZrMspYp3HC43KhejzoTOPv4JiQl072vFIadh4FfCeTedecHVNTBw3Xzjd2+JMPAL0dvex6a2dAAsKSEM9dn7+VtReeQWtzO7n5WVxwxVqa61rQ6rTkFedIEjCKCkrz+P5DX6W2sgGDQUduUS7WRF+vMEe/nZceetGffACoP1zHlZddTenMYpJSEpk6szRiM2FkTM/HkpFMb3MHAPo4IyVnzEXRKLjtdhStFq0+tnpCiNix5pxl/Onf91NdUUdispVpM0r9M2EkpSTy0c/fyLorz8LWZyenIJP07PDUurJmp1B69nyOvrHD3zbn6jOJTwsuThyKx+WiuaqRN/7xekD7K39+halLR15dXwgRG6r2VvmTD+BLNL726Gvc9M0PodVqUN1utMbj30UsGUnMvGQF+5593/+amZetxDLC2dTAd8NhzfoVlMwooq25g9SMZHILs9FoNOQV5vDLh77Lof3l2G0OSsoKmTJ9qPvuYrjCVQPic0M81QUcVFX13XBsRwyPJSOJFZ+4jO76NrxuDwnZKVgykoOWc9gc2Hr6g9oVVSErLwNbaxttO/fh7OzGWpRH4tRSDFb5MvT/7N13eBvXlfDh30Xvjb1TpHpv7r13x+mJHa+d3pNdb7L7pWfTd1M22XSn2Ynt9OqS2HEcd9myZNmSrE4VimJvANHbfH9AgkQBlEgJJEDyvM/Dx8YFZuYCGgxmztx7TqnS0hqR3j76X36FZCiMe24LrtYmjPbpc/FucdpY8caLaT5/GYlwFEeFB1ft9By63bmnM6etq60rp83jdXHmuatGtVUW6GRfnD5fhRdfRb7jZzxvSdl4KMY1N01+cjVHpTdznO86cpwvw2wzMrB5G4G2/RgddspWLMFaWS7BY5HDZDKxZPkClixfkPd5q93K/KWFn/5ltJiYf9WZVC9rIRYIYSt346opO+k+qqU1wj29DGx+hYDOPurGCWSSxkaD0YL3VwgxNYZ7hnPaett7CfcNE9yxnbg/iKulEffcFoxOO3qjgTkXLKOspZbI8AhWrxNXbfkpjwLUG/Q0ttbT2Jo7xb66rorqOql6UUiFGqv2wTHaPYBbKbUOuEHTtMECbU+chM3rxHaSbNLucjfNS5rZ/8r+bJtOr6O8vpyYP8ChfzyFlszc3RvesYdkOELVuWegK7GkmyIjNjTMoceezg5HG9yyDS2domzFkml1AWJ22KhcMH2CJmNZedkq9ry4Z1TbiktWFKk3otAcHgerLlvFs396dlR7TevU3YG1+ZzZua5aOk3fxpfx78oUnEpFonQ+9jQNV12C2eeZsj4JcTImm5nyuROrTR8dHKLzn8+ApmFvcGFz2QgHjt5AcfqceKtyA4VCiOmhcVFjTtvqy1cz9OKLJEcyo0mHtu0iFY1TceZKdHo9BrMJX0sNIAlspxvdyV9ycpqmzRnjzwvMJZOc8ouF2JYoHIvdwqs+dBOth2vzuivc3Ppf/4K32kOgexhzfRPW2qNf6mD7IRLB0FirE0UWG/aPmgsH4N/ZRjJc+neFEtE4Q+29DOzvJj4N+jse89bM49JbLsVgMmA0G7ns1suZu3pesbslCkSn13HOTeey8rJVKJ3C5rLxuo++nrpTTFCsaRojvcP0tR0i2D884eWT4Qj+PaOrqGjpNDF/4JT6I8TpCA8G6G/rJNA1gJZOn/b64sf8vqW7O3n9h26gsilTzat2bi1v+eytuCskI70Q01XDwgZu+vCrsTgs6HQ61l5zBsvPW5gNPhwR2HeAZDhyWtvS0hqBnkH62zoJDfhPa13i1Ex6th5N0/Yqpf4f8LPJ3paYuKrmKt7ymbcwMjiC2WZGD7z068dpfz6TuLKspZoF5y8kvLcNZTCg08noh1KlM+R+nXVmE0pfkDjjpAkPjbDlj0/T/sJOAMrn1bL2LVfgzDNtaDpxep1c+pbLWH3lGpQCd4UHna60/y3ExJTVlPGaf3sNl73lMgwmA+7yU7sA0tIah17ewwt3P0IylsBoNXPW266mZukE5pnqdOhNJlLHlRLLd1wQYjL1t3Xy7A8fIDYSRmfQs+oNF9N09iL0p5Eg8tj9OJ1IoO/Yx2tvuwBzQwMOrxPbNMwTJIQ4ymwzc+a1ZzL/jPmkkincFW6i3X0cH0LXGQ2o0ziXSqdSHNy4m433PEoqkcRkt3D2O66lamHuCAwxeabqbLgdGPfkGaXU+5RS+5RSUaXURqXUBSd4bbNSSsvzd/Vxr7vo8LqiSqm9Sqn3nMb7KTmDA8NsfP5lnn/mRXq6c2t4n4jZaqa8rhyn10nfro5s8AFgYG83/R1DGOw2ylYuweiUspylyuLzYHCM/vcpX70MQ55yrKWkb2dHNvgA0L+7k/b1uXWXpyOdTofFaeFQdy/rntrAgX0dxe6SKDCD0UBZbdmEgg9dh3pY9/QGNm3Ygn84wEjPEM//9G8kYwkAEpEYz/3krxMaCWG0WSlfvXx0m9uJ2Sd3hcWJhcMRtr68g2eeeJ79e3PLu05ENBDm+bv+Ruxwfql0MsXG+/5BoHPgtNZrLvNicBwNMmjpNN7GWiobqyT4IMQM4qnwUFZThsFgwOxzY/Z6Rj1fvmrZaeU2G+ke4oW7HyZ1uORmPBTl+Z/+jfDQyCmvs31/B888sZ4tL20jFMzNrSdyTdWtkeXAuCplKKXeCHwLeB/w9OH//lUptVjTtPYTLHo18PIxj7P5JpRSc4CHgJ8CbwHOB76nlOrTNO33E3kjAOFAmMBAAIvDgqfCM9HFC+7QwS4++e9fZuPzmbffNKeBb975BVrnN094XX2Ha3Ifq2fnIea+82os3syJbDqdlju5JcjodFB3yXlE+wdJRWNYyr2Yfbml+kpN7+7ci/LOl/ey4Mq1GEyjs/jHo3GGeoYwGA34anwFyW0xmftzwD/Cj797D3f98FcA2OxWvvOzr7D2rJWTsj1RHFpaA8W49sed2/bwvtv+g77ezAXZxVecx4f/9W2kk6lRr0tG40SGQzjKPePuh6OhFsNlFxIbHERvtWIt92G0T13QOJlIMtg9iFIKX7UPvZQELXkjgSA/+d69/PT79wFgtVn5zk+/zBnnrDrJkvnFRkKE+3On/YQGA3ibcu9DRUIRAn0BTFbTCXM4mJwO6i45n9hwAC2VxuiwSW4TIWY4o81G9YVnE+0fIBmOYPF5MZcdPa8dz/lbaDjEyNAINqcNV7mL8NBI5jf7GLGRMBF/6KS58/LZtGEL77/9Pwkeniry5ttfw/v+9a24veOr7DNbFaoKxlhXOW5gDfBVMhf/43EHcJemaT86/PiDh0czvBf42AmWG9A0rXuM594DdGqadiRZ5nal1FnAR4AJBSA62zr5/dd/R1dbF3a3nVd9+CYWnb0IfRETM657akM2+ABwYN9B/vDL+7njY+9Bb5pYGbayOTXse3rrqLbqxU3YKsuIBsIcen4Hbc+8grehkrkXLMXXWFmQ9yAKw+RyYnJN/ABaTGUtNex/9pVRbVWLGkcN19U0jZ49h3j47r+z84VdGM1GrrjtCtZctRarI7eu/HgMtvey56mtDB3spfW8JdQtb8HqLuzF2q7tbdngA0A4FOFz/+9r3P277+CdpPKMYuokowl6dh1k52MvYTAZmH/pKirn1o6ZhTsei/OT792bDT4APP73Z7j2hkvRGfWkjynpqTcbsZzkzm50KMDwrgME2jvxtDbgbm3AVl2BrbqiMG9wAvx9fv5532O88LcXUEpx7qvP44LXXoDTN72OR7PNru1t2eADQCQc4TP/8T/c86fv4Sub+DQ4k8OK1eMgMhwc1a7FYgxsa8PVVIvRnjlm9+zv5g/f/AMHtx/E4rBw4/tfxZILlmAco3xseCTG3ufb6NtziOYzF1K/yojdJyf5QsxkJocd03Gje2PBMJ2vHGDPE1twVLiZd9FyyltyE1Ee3HmQ3371N/Qf7Mfpc/Laf38tFTU+UGpUzjST3YLJMvGy1X5/gC984n+zwQeAX971By698nzOOm/NhNc3mxTqtl8/0Jfnbw/wK+Bh4L9PthKllIlMwOKR4556BDj3JIv/QSnVq5R6Rin1uuOeOyfPOh8G1iqlxr3HhQPhbPABIOQP8csv3Efv/t7xrmJSbHl5e07b+udeordj4v2qXNBA1eKm7GNXTRnN5ywBDXY99hLP3fUIfbsPseuxTTz6td8S6JbCJuL0VC1szN3nzh1duSPSP8Rz9z/Hzhd2AZn60A/d+RCHdp3alAZ/1yCPfu237HpsE327D/HcXY+w67GXcqLiOcv1+9m7eS8duzqIRWInfC1Ab0/usOP9ew/i95/6UD9ROrp3tvPPb/2Rzi37aN+4m3987bf07xsrDg7BYJhNG7bktLftaWfxGy+h+bJVmF02dAY9Z/zLlSesZ54IR9j/16foWvcSoUO9HHpyIx2Pv0AqFi/EW5uwbeu2sf6h9WhpjXQqzdO/e4q2l9qK0hcxfr09uaVkO9o78Q+fWvJSq9vBGbddicF8+NRKKRZeuZrQvnbaH3mW/i27CI2E2bd1H7s37cmOZIsGo/zmv39N997835/gQIDHvvl7tv3tBfr2dPLCfY+x+S/rSB4eRi2EmD32r9/FM3c+RM/Og7Q9vZW/f/U3DLaPvuYJDAb45Rfuo/9g5hg3MjjCH7/1R4LBKAtffxHNl6/GXuFGbzSw7IazCB/szCnvezIj/iC7d+T+zuU79xOjFWoKxiVjtAeA3ZqmBcd4/njlgB7oOa69B7h8jGWCZEYyPAMkgRuBXyulbtM07Z7Dr6kGHs2zTsPhbXYd+4RS6l3AuwAaG48mJQkMBLLBhyO0tMZA1wA1rcUrAXPmOav4468eHNV2xpkruedLv+K1H34NLcvGn8jMXubirLddzUj3EOlUGle1F4vLTrDfz7aHN4x6bTwUZaijD1d16Q/zn0nG2j+nq7H2uWP5e4fZ9vzOnGUP7ek8pcoSwx19xEOjq21se3gDrRcsxTHGXP7Otk5+9qm7GOwZAuC8m87lyluvwOFxjLmduobckoxLViykbAaPfphp++dYUskU2x/ZOKpN0zTaN+6icl7+EoMut4OLLz+PX//iT6PafU4XP/nMzzGajbzpP95Ay8IGHBWeE07piA76ifSODgAH9h0iOjyCvars1N7UKUqlUrz02Es57dvXbWPlpSuntC8nMlv2zYmoa8g9d1m0dD5lZaf+u161sJHLP34L/o4ekiNBwu1dRAYyx03/cIj7v3AvOzdkgsn18+q48I0X8sSvngBg4NAADQsactbpP9RPeHD0qeTep19h8ZVrcddO7f4+WWT/FKWsVPbPaCDE1gefG9WWiicZPNAzalS2v8/PcO9w9rHVaePsG8/l+//+Q2KRGHqDnhvfdR1Laz34t+wgEEvgmd+M2T3+UXten4c1Z65g4/qXR7XX5zn3E6MVqgznE2P8bcoXfFBKfU8pVX6iVR6/SJ62I9vu1zTt65qmPadp2gZN0z4N/BD4j3GsM187mqbdqWnaWk3T1lZUHB3KarFbsOcZon2iC5CpcMbZq7jpdVdnT1bPPHsVcyrr6N7XzW++/luCw+ON/2SY7VbKW2upnF9/9EJQgdLlngwrJbkgptpY++d0lnefO4ZBB1VNudN9vCe4Q3wi+TIoK50a84IvHovz1589nA0+ADzzp2c5eJIRGAsWtfLxz/8rJrMJgPqGGj71xX/HOYEfuOlmJu6fY9HlqTCjO8F0PIPBwM1vfS3LVy/JvFan4/VvuoGRg340TSMejfObr/+OeFo7aZbvsfbVAqRFmTC9Xk/T4twT0rr5p1aSdLLMpn1zvOYvauGTX7wD8+FjVG19NZ/+ykdweU7vGOWs9OBwWhjatI3Y4eCDzmSkeyCcDT4AdOw+RCgQxlWWmUrh8OSfBpfv/APF0TO5GUD2T1HKSmb/VGqMc7jRbVanFZPFlH286vJV/OO+f2RHr6aSKf70g/sJjoQz5eoVE/4BtTts/OdnP0jTnMxvndFk5KOfej8LFs+d4JuafYpVn+stwNfITN04Vj+QIjNi4ViV5I6KOJHngbce87h7jHUmgXGPk/FUerjpw6/ml1+4j/Thutbn3HgOVc3jLvAxKSqry/m3f3sr11xzCR1tXQwe6Oe5P6wDoO9gH0F/6LSDJHafi2U3nM2m3z2VbbN67Hgb5EdSTD57mYfzrl1Lx+5DxMKZH4/mpc00HjN1YyK8DRVYPXYiw0fn7S274WxsY8xXj4xEaHs5d5jdwEkyu1usFl5/y42cfd4aRgJBauqrKa+QEUMzgd6gZ9FVa+l65Wh+ZZ1eR+OaE4/ImdPayPd+9t8cbD9ELBTj8bv/yc7dR6u+RENRRgZHKKs58V1ds9eFrbqccPfRn1H33EbMnuLMiV995Ro2P76ZwEBm6H55XTmLzl5UlL6I8bNYLLzu5hs489xVjPiD1NRVUV5ZmBEFlgovBpslc3IPmN0ODuzOTXTdsaeT8vpyWla0UtNam3dd7tpynNVeRrqPBoEXXLYKRwkkAhdCTB2L08aKV53Lup89nG0zWs2UHXctVlZTxo0ffBW//9rv0DQNo9lIKDC6QoWW1giHYuiBqjOWYjqFSn8Ll8zjrt99m86OHhxOO43NdUXNCzhdFCsAkTfEpGlaXCm1EbgC+O0xT13BxJJFrmT0tIp1wE3HveYKYIOmaYkJrJeFZy/k/d/9AAOdA9g9dqqbq085Cd7p6u3uZ+fWPQwN+GlubaCqsoLffXn0x1TRUDHmHYXxSiVTdLZ1EtXrWHbLpejiSbREksbV83Ce4h1oISbCVumjQdO4/ZNvYrDPj9XloHZ+Pa4JlD48lrPSw2V3vI72F3fj7+ince18qhY2jnlX2eq0MXfVXF55ZnSyzLJxDP3V6/U0t2buDvuHR1j/zCa6DvZQXVfJgqWteLxjv4doKErnnk6GeoZwlbuonVuLPc8IEXHqRgYDdO7pJDgcory+nNrWWozm8aUGqppfz+UffT371m3HYDbSfOZCyppPPvTS5XHiGHTQvucQlctqmXv2fDY+8ALBoSAWuwXnOBLrGW1Wmq8+H//eg4x09OBursPZXDvhxMOFUt1czbu/8R56DnSjdDqqmqrwyO/DtKDT6WhumfiQ6sGBYXZu3UNvVz91jTUsWNqK03X0ZofF42Luay5naPcBon1DeBe1MM/VyeanRie6nrdqLovPWkRFQwU2V/7Eq3afk4s/cBMdL7cxsLeL+lVzqVnUOO5KK+FAmM49nfj7/XirvNTOrcFiL865mxDixOLROJ17OhnoHMDpdVA7r27UjdSG1XMxO6zse34Hzgo3jWvn46kbPbBeKcXyi5ZT1VSVOYcqc/LM/esI+Y/eeFI6hbvSQ9miBpwN1Sec9hiPJ9j1Shv7dh/A6XaycOlcqusyI3PLyn2UlcvNpYkoVgDiRL4B/EIptZ5MXof3ALXADwCUUl8GztQ07bLDj28DEsAmIA3cALwf+M9j1vkD4ANKqW+SmZ5xHnA78OaJdk6v11PTUkNNnmyrExUfCRI+1Em0bwBjXR1xDDg8znFlDR/oG+QLH/0GL71w9ILo41/5V1774Vfzx2//mXQ6jd1t540feT0O9+mNfti1YRf3/Ncvsgn6Ghc38uaP34y7QurLi8KKDQ0TbD9IYiSEs7kBS0UF+sNDg+1VZdiryhjPmIfBniGSsQSeKk92+sPxPHXlOT9YYzGZjVx925V07+1ioCtTZvCCV59Hw/zcucpjiUXj/Oonf+TeHx0NEr76lmt5z7/fhtVmyXl9Kpni+Qee4+GfHo3yn33jOVz1tqswW83j3q4YW3A4yB+/9Ud2PHd0BMLrPvp6Vl++elzL640Gqhc2Ur1wYhdve3cd4MO3fRL/UGa0gMls4o7/fCdP3/ckt3z8ZspqxnciY/Y4qVy9mMrVi8e97eBwMDMqzm3POzIuGYkS6ekldLADs8+Lvb4Ok3t8oyq81V681ROvnCCmXmzw8LE2FMLZ1Iilshy9Kf+xMp/gSIg7v/ELHvr90fRab/vAm7nl3a8dVcXCWu7FWn50n1jocrLo7EVsfy6TPLt5STPLL1pO3RgjH47lrvHhHud341ixSIx/3PMo6/68Ltt25Vuv4vzXno/BWIqnwULMXpqm8dI/NvGn//tTtm3FxSu44f03ZgOUJpuF+pWt1K9sPeG6DEYDdfPqqDucl+mWj7+Zn3/uHqKhKHqjntd9+DV4mmrQ9Dr0Jzmveu6JDXzyg1/JPl6wpJUvfOdjVNWMbyR4PDBCqOMQsYFB7PV1WKsqMdhmbxC05I68mqb9WilVBnwSqAG2AtdqmnZknGsNcPwe90mgicz0jV3A245JQImmafuUUtcC/0umnGcn8CFN0yZUgrOQkpEofc+9QDIUJlFZz5++/Fu69nbjq/bxho+8nrkrW08Yiduzfd+o4APAd77yE378+29wx53/SjgQwVvtxXeCutrjERwOcv/3/jKqOkD7tnY62zolACEKKu4P0P3ks2jJTFbzaE8vvpXLcLWOP4lqLBJj02Mvcf8PHyAairLsgqVc947rqKgfX6DhRGpba3n/t97PQOcAZquJioaKMYMb+Rzcf4j7fvyHUW1/vPchrn31ZSxYmjtfsP9QP3+/+++j2p77yzpWX76K+jxJ2sTEde/tHhV8AHjg+/fTsnwOnsrJu5B+9vEXssEHyOQYWf/Cy9zx4zsoO81j9ons27qPX3/tt/Qd7KOioYI3fuQNzFnanH1eS6cJ7G4jsDsz3Sja209w/0GqLz4Po11G3swUcb+f7iefQUtlyr5Gu3spW70C55zxT2k70NYxKvgAcPcPfsNFV5/LnLljB+R8VT5u+dib6djVQff+bg5sa+fO/7iT13z4NSw5Z/GkBAR623tHBR8AHv3531l09qKiT6EVQow22DXIgz8cnVj/5cdf5szrz2LOBJLq57Ng7QL+7Qf/ir9vGKPFxJYnt/Dbb/weg0nPFbdewdnXnpV3FNbgwDD/96Ufj2rb+Uobu15pG1cAIhkK0/vMcyTDESDz2+psacK7fOkJ80bNZCWZQVDTtO9pmtasaZpZ07Q1mqY9ecxzt2ua1nzM47s1TVusaZpd0zTX4QQp9+RZ5xOapq0+vM45mqb9YIreTl6JkRESgRFUWSW/+s6DdB0uPTXYPchPP/lT+jr6Trh8KBTJaRvxB0kkktTMqaF1RctpBx8gU+4w0JdbjisSzN2+EKcjNuzPBh+O8G/fRTISHWOJXAd3dvDbb/yO6OEKF1ue2soTv3uS1OET7dPlLnPRsmwOdXPrJhR8AIiEo3lLPIXzfJcBYuEY6VQ6dz3B8X8e4sSiodzPMhqMEo9OaGbehHV35h7fOw924/I6Txh4Ph0D3YP89FN30Xcws+2+g3387NN3jUqsmgyFCezZO2q5VDRKXMrGziixweFs8OGI4R27SMZOXlr4iHzHrVQyRSR88uOTTq/jn79+nD999y9s+udLBIdD/OJz99C1t+uky56KWJ7veTqVHlcpZSHE1IpHYyRiub/B0QKd+5TXltG6opXdL+7msV/9k3Q6TTya4MEfPcS+rfvy9ykWZ7B/OKc9PM5roXhgJBt8OGJk7wGSofAYS8x8JRmAmB0yJ5nhhMbQMSeAAPFogoGuwXwLZTW11mM87k7BRVeeS2V1YctROX1OVl62clSb0ikqJfmkmApqYkmJe/bn1pDf9NgmgkMTqwQzGWobqqk9rjRTeaWPuqb807m81d6cHBNWp42yuplRcq4UlNeXozeOvvswd/XcSR/ddf6lZ+a0verN12AaZ+6JUzHUPUT4uARcIX+Ioe6hMZY4qhiVNcTUmug/cX1TDW7v6Kk5LfMbqa0/+YiCwEBgVCUMyAy77jt0fF7ywvDVlmF1jr6rWVZbJtOFhChBnkovDQtHj/I0mo2UF/DcJxKKsvHvL+a079m0J+/ryyt8XPfay0e16fU6mueNczRqvgPsLP9hLVYA4h4g97b6LGJ0OTC6XZhMegym3CGHJ0s01zKvia/9+LPMX9KK1W7lhjdcybvueAtmS2HnhhuMBi65+VLWXLkGnV6Hr8bHWz5z65iZqoU4VWavG2UY/V1wL5yP3pKbH2EszrLcuepVTZUlkTOhrMLLF779/zj34jOwWM2cef4qvvKDT1FZnX96iNPr5OZP3kLrqrkopWhY2MDtX7gNX7UkOiqUquYqbv/CW6lsqkTpFEsvXMoN771x0veX5asX8/GvfJjK6nLcXhfv+4+3cs7Fayd1mzaXLaeUoU6nw+Y8OgfVYLfhmjd6hqPeasHoKk5lDTE5zD4P6rjkje5FCzCYx7/f19RX8dU7P83qs5dhsZq58Ipz+PTXPoLHd/LgndlmxpMnyJevzHkh+Kp93P6F22lY2IBSitaVrbz5kzfj9M7ccshCTFdWh5XX3PFaFp2zCKVTVM+p5vYvvpWKxtxS7KfKZDZSNzf3OqYyT7l3yFwL3fyO1/C6W6/HZrcyZ14j//3DTzN34fimhJhcLgy20UFQ55wmjPb8SXdnA5VvSPBprVCpZcC7yeRpeJumaV1KqZuAA5qmbSroxqbA2rVrtQ0bNkzKuuMjQaI9fWzZfJA/fPf+bPulb76Uy2+5dFwnwcGREOFQFF+Ze1KTKSWTSUYGRjBajKed1HIGm/Jw5mTun8UQGxomeLCD5EgIR1PDhBOjDfUOcd+Xf8XezZlh5AaTgXf/z7toOc15g4UUjcQI+EdwuR1YrCcPrsQiMcKBMBaHBeupZ20vSqh9uuyf4UCYWDiGw+sYdwWMQhgaGCadTlM2BaVZE4kET/3+aR780UPZtuvffR0XvHp0Ir5kJEq0t4/gwQ7MXi/2+tpxJ6E8DXLsnGJHEv4mQ2EcTY1YKsomdKw9IhyOEAyEcHucE7oBsu257fzsU3dlS5qvuHgFr/ngTaddMvxEIqEI0WAUm8s20SCj7J8noZRi+/3/mNAyi264LO+0xEJSSvG3ux4++QuPcfXtV5Vcv07Spxm5f8ZjcULDIcw2MzZn4S/UO3Z18IOP3pmdUl7VVMVbP3cbFfVjj/BOpVIM9g1htlpwTfBaKB4YyRQeGBjEXl+bSUJpnfFJKMfcNwt6xaqUuhL4C/BX4FLgyCfbSqbqxE2F3N50Z3I6MDkdnNlQT8OiZga6B3GXu6mZUz3uH0eH047jFOrWTpTBYMA7icnRhAAwez2YvZ5TXt5b6eXWT91CZ1sXsXCMqqZKqsdRFnEqWaxmLBM4+TVbzSUxgmMms7lsY5b/m0zeMs+UbctoNHLejefSsmwOQ73DeCu91LRU5wSuDVYLjqYGHE2S6HQmO91j7RE2mxXbKWRyX3jGAv71+x+mr6MPu8tGTWstjkkaAXGE1W49nSCuEGIKmcwmTFUTD4qOV/38ej70nQ/Svb8bg9FATUsN3pOUjtbr9VSMMWr1ZEwuJybXglNadiYq9C3zzwN3aJr2PaXUsVmrHgf+vcDbmjGMZiONixppXDTxOtxCiNFcPhcunwwZF+J4ZpuZ5iXNNC8pdk/EbKfT66ibW5t3GLQQQkyFyoYKyWlXJIUOQCwBHsrTPgjIxOVpKjYcIO4PoDPoMXncs3rOkhifVDxObMhPKhLFYLdh9rrRGUqu6q8QJSfmDxAfluOtKE1aOk1s2E8iEERnNmH2ujFMIE+PEELMdKl4gviwn2Q4gsFmxeT1oJ/EafLTUaE/jSGgDth/XPtqoKPA2xJTINI3wKHHnkJLZkp2mTxuai88B+M4p30M9Q0T9Idw+1y4fJLwaTZIJ5MMbdvF0Cs7s23la1fgmdeakwRvOjiSnT0RT1BW7cNik5PtmSYajjLQPYjRZKSirnzSSmGeTKR/gEOPPpUtkWj2uKm58GyMTsm7I0pD6FA3XU+tg8PT0R2NdVScsbLgQYiB7kHCwQjeSg+OkyTlFkKI0zXYM0RoJIyn3I3zNHLRpJNJhnfsYXDLtmxb+apleBbMReml+OQRhQ5A3Ad8VSn1BjI/Twal1EXA14CfFXhbYpKlUykGt27PBh8A4sN+In39Jw1AaJrGK+u389Mv/oLhfj8VdeW889O3M3dZy2R3WxRZ3D8yKvgAMPDiFmzVlZgnP5ldQUVCUZ5+cB2//8GfiUfjLDlzETff8QZqGk9eak5MD93tPdz7jd/wyvrtmCwmXvvuGzn/+nOmfK54OplicPP2bPABIDbsJ9I3IAEIURIS4Qi9L2zKBh8Agu2HcM9twVBTmABEMpFk05Mv8/P/+SWhkTCN8+p52yf/hcZ59QVZvxBCHCudSrN53VZ+9uV7GBkKUt1UxTs/fRtzFjWf0voSgeCo4ANA/0tbsdVWYfZMbonv6aTQoZhPAvuAA4AD2AY8BjwNfLHA2xKTLJ1MEveP5LQngqGTLtvd3sN3PnYnw/1+APoO9fO9T/yIob6T15wX01sqFs9p09Jp0olEEXpzevbvOMAvv/lb4tHMe3pl/Xb+du+jJBPJIvdMFEIymeJv9z3KK+u3AxCPxvnlt37H/h3tU96XzPE2tzp1IhSe8r4IkY+WTJKKRHPaU7FYwbZxaG8XP/jMTwmNZPb79t0d3PWVewmPyPdACFF4XQe6+e7Hf8TIUBCA7gM93PmZuwgM5l7/jEcqnnsOjKaRztc+ixU0AKFpWkLTtFuAecAbgJuBhZqm3appWurES4tSozeZcM5pROl0GKurMdY3YnA5sVSUnXTZga7B7EXbEcP9fgZ7JAAx0xmddnTHZ9a326blXPZDe7ty2jY+/iKBwRH8/X45KZ7mRoZG2PjP3OrQh9o6T7hccDhIYCA3WHA69GYTzpbcRMTWckmfJEqD3mrBWl05ulGB0VW4ETq9h/rQ0qPLDe7ffoCh/uFTXmcqlcLf58+W2xNCzFyJeILhvuGca5Cx9B3qJ5UcfYna09F7yjdMjQ47uuNKGustFgx2mUp2rEKX4fw08DVN0/YCe49ptwIf1TTtc4XcnphcSikcLc30j6R56J5/MNQzzIqLV3D+Kh0n+xo5vU6UUqPqFpvMRuwyl3PGMzkd1Fx0Lr3rXyQRCGLyuqk6a820rHecryRTfWsdO57fzt9/9gjuSg/XvvNaWle1otfrp76D4rRY7Rbq59axc9PuUe2+MUoOxyIxtq/bziM/e5hELMEFr7uAVZevxlmA/DZKKdwtzaTCMQL79qMzGChbuRRzmQQgRGnQG41UrFlB38aXiXT3ordaqDxjVUGHFbu8ud8ld5kLm+PUAtgDXQM88/un2fSPTfiqfVzzrmtpWdGCTidzsYWYabr3dfP3ux+hbVMbDYsaufptV1E3/8TTt/L9flsdVmzOUzvmGB12ai86h571L5Lwj2DyuKk8a/W0vAk3mQp9BP4MmakXx7Mdfk5MM4M9fu750q/o3tdDLBxj/UPrefzXT5x0CHpNczWvftcN2cdKKd7ykTdRWS/lbmYDW1UF9VdcTNP1V1J/+YVYyvJf0JW6OYuaWXrWouxji83M5a+9mAe//wCxcIze/T3c/am76Npz4jvmojRZbBZe996bsNjM2bYlZy5izqKmvK9v39bOb/771wz3DhPyh/jbT/7Gtme35X3tqTA6HVScuZKmG66i8dor8MxvRW8yFmz9Qpwus8dFzYVn03TDlTRccymOhlpUAS/mG+bWcclrLsw+1ut13Paft+Ct8Ex4XclEkn/e90+eu/85YuEYXXu7uOsTP6N7b3fB+iuEKA3B4SC//OJ9bF+3nXg0TtumPdz9qbsZOsnI67o5NVx761XZx0qnuO0/bqaitvyU+2KtLKf+iotouiFzDiwjGXMVOgmlYlR6oqxVZEpximmmt72HdCo9qu2lRzdx6c2X4qsZ+wtlMhu54g2XsGjtAob7himrKaNuTo3cdZhFDBYzWMwnf2EJ81V6eMenb6ej7RCxSIyyKh/3fvYXo4YIa2mN3vZe6hc0FLGn4lS1Lp3Dp37yn3S392C2mqlvrct7FxZg14adOW3PP/gcKy9bidlamH1dp9djkqSTooTpjUb0xskJjNmcNl7z7hs56/K1jPiDVNWVUzun9pTWFegP8NKjo6dYpVNpett7qZ17ausUQpSmwa5B+g72jWoLDgcZ7BzAO8aoRsjciLjuX65i5flL8Q+MUFFbTu2c6tPuj8FsBvP0PgeeTAUJQCilRsgEHjRgr1Lq2CCEHrAAPyjEtmaycCBMOBDC5rJjc5XGUB1znpKDdo8do/nkJx9mq5nWJXMmo1uiRJTiPltoLq+TxWsXAhDyhzDmOfE2FejiUxRHTVM1NU0nP+Fw57kL66v2oTfMjuk3kWCE0HAQq9OG3S3T6cTksDttzF85d9yvDw0HiQQjOLwOLMdUrzGajdjcNoKHk8sdYbbJ8VqImcZkMaF0KieHjOkE3/eRwRHikRgOn5O5y1onu4viGIUaAfEBMqMffgp8AvAf81wc2K9p2roCbWtGOri9nQe/8xf623upaKriug/cSP3C4t9RrW2tpX5BPR07O7Jt173n+oLMeRbTW8eOgzzw7T/T395LeWMl13/wVSWxz04mu9vOte++jns/d082v0l1SzV18+qK3DMxFeatmYfD4yA4nLmgMRgNXPC6CzEYCz2YsPR07j7EQ9/9C91tXXhry7j+gzfStFQCzKJ4NE1j38t7efA7f8HfM0Tdgnqued8NVLfUAJm53de/93p+9aVfZZepX1BPbauMfhBipimvL+eiN13M4/f9M9t2xrVnUtlQmfPadCrFng27eeh79xMcHKF5RQtXvetaKhpzXysmR0HOmjRNuxtAKbUPeFbTtOlXb6+I/L1D/PYL9xEOZDLq9x3o4TdfuI+3fePdePIkwZtK7go3N3/yFjp2dRD2h6lqrqJ2nvx4z3bDvcP85vP3ZvfZ/vZefvP5e3nb/74bT+X0zPcwXvPPmM+7v/EeevZ3Y3VaqZtff8LhfWLmqGqq4p1ffxeduw+RSCSpba2l5vDFzkwWHBzh91/+Ff6+zL2Foc4Bfv25e3nHN9+Lr/bkVZGEmAz9HX38+nP3kjqck+rQzg7+9PXfceuX35ZNeL3onMWHj9c92Nw26ufX464oXNJMIURpMBgNnP+a82lZNoeBrkE8FR7q5tflHfHUu7+X337pl9nREvtf3svDP3yI13/iTXlHfovCK+htG03Tnjjy/0qpasB03PNTX1y9CDRNIzEyQjIUROn1GJ2uE1YAGO4Zzl7IHRH2h/D3Dhc9AAHgqfSURD+OiAZCBPv8GMwmHFWeWXH3sdT4e4dy99lA+PA+O/piPDEyQiIYzJRzdTox2DJTNbS0xkjfMLFAGKvHjuMUkowVg8FooHFxI42Lc0smiukl2DdMZDiE2WXDWelBKXXSZSrqK6iYZcl0/X3D2eDDEfFInKHuoWwAIhWPkwgESMVjGKw2jE4nOoMcmwWkkwkSgRGS0Qh6swWj04n+uDJ1J5KIxgn2DpNOpXBUeDE7MhcIw11D2eDDEf3tfQT6/NkAhNFkpGlJE01L8ieWFULMHDanjbmr53GyCVyDnQO55X437yU4GBwVgEglkoz0DpGMJrCXu7GOY+phKh4jERiR38KTKHQZThfwbeANHBd8OGxWTJSNDw0x8NImODxEW2+zUbZiZfbC63gWpzVn3pLS6bA6pl/Zwsk23NHHs3c+QLDXj1KKBVetYcEVazHbJWI5lawOW559VmE5bp+NDQ8zsOlFSGcSmerMZspWrUZvsdLx4m5e+PnfSSWSGK1mznnnNVQvbp7KtyFmse5X9rPux38lEYmhNxk449YrqF8zTxLl5mG2WdAbDTkXe1Zn5vueSiQI7NpFpOdodQHXvHnYGxrHFdQRM5eWThPq6GCkrS3bZq2twz1v3rhOysNDQbb86WkOPL8DgLKWGs687UqcVd7s/ncsk9UkdzCFECdkdeUeOxxeBybr0UvXeDjK7sdeYtuDz6NpGrYyF+e953q8eaZ0HHH0t7An2+aaNx97Q4P8Fh6n0GdaXwdWADcBUeBm4KNAB/DGAm+rJKWTCQJte7LBB4BUOEw84B9zmbK6Mi56y2Wj2i6+9TJ8dWNXmdDSadKp1Ol3eBpJxhNs/cs6gr2Zz1LTNHb8bQNDB3pOsqQoNF+dj4uP32ffchlldZm7oZqmkUrECR7Ynw0+AKRjMeKDg4z0DLH+roezFzSJSIznfvI3QgNjf0+EKJRQv5/nfpIJPgCk4knW3/0II90nLtc102ip1Lh+R3y1ZVzx9qtGtZ37ugsob8iUKUuGgqOCDwCBtjaSkdGjpMTskwyHGdm7d1RbpPMQyVBoXMv37erIBh8ABvZ2sW/dNjRNo7yxkrXXnzXq9Ve9+zq81aNH4c3G8yUhxNgqm6tYdMHS7GOlU1zz3htwlrmybUMH+3jlgeey+b7CAwFe/v1TJKLxMdebDAZHBR8ARtr2kAzLb+HxCj0m5BrgzZqmPaWUSgEbNU37tVKqC3g38LsCb6/kaKk0qWg0pz0dH3uHNRiNnHHdWTQtacLf58dd6aGyqQpDnmz7mqYR9/sJtR8gFY1iq6/HUlaOvkRLvfS396BLREn5h9Dp9djr6zB7PadUNzwWjNKzI3cWT6hfLlqnmsFoZO11Z9G4tBl/7zDuCjeVzdUYjEYSwSChQx2gFKlIJGfZZCxKOJbKKe8aD0WJ+MPYyyY2P3ewo4+Dm/cRGgrSsHwO1fPrMJqPRrFTsTixgQHC3d0YnQ5sVdUYXZJEdTaL+EPEw7FRbelkishwEHdtGelUivjQMOFDh1AGI7baGuJpRe+eLrp2HKR2USM1C+uxuqZnJQgtnSY2PETwwAG0ZBJHYxPmMh86Q/7qRjq9jhWXraJ6bi3D3UO4yl1UNldhOlxmV0vmubhLp/O3zzLJaJTYwACR7l5MHjfWqkqMjtlTZlVLpUbdkDkinUzmeXWuvj2Hctq6Nu9l0dVnYLFbuOjmS1h07mJGBkfw1fioaK7K3mnUNI348DDBA/tJx+PYGxoxl5ePKiEaGQnTt6+bA5vaKG+qom5xI64SmnIqhJi44a5BOrbuY7h7iMblLVTNr8uWyk4EgyS6Ozj/qsUsv3AJsWiSsvoKKpurRq0j3B/IWW/fzg7ioShGS/4pZOlU7nFNS6fR0jPvt1DTtMx5UmcXWjqFra4Ws8eD0o9vskOhAxAe4MDh//cDZcAeYB3w4wJvqyTpTCZstXUE9+8b1W50usZYIsNsM9OwuImT1RBIjIww8OLG7A+6f/t2tHnzcTSW3nz03rZO4sPDaF37s23hjg7Mc+azd3MHrWcvpOK4L/yJmOxmyufW0rNtdBDCVnbiz1ZMDrPNTMOiRhoWHd33UtEogy+/RCoaRWc0Yq2pybnTZfb60CJazhQOo9WMJc+wuCPS6TQ9uw+x65lXSCdTzD9vKTavgz9//j7C/sw2Nt3/HFd+6FXMO3cJkDlAhjo68G/P3EGLAMH97VSeezZG+/S8eBSnz+KyYbSYRt3JUDpddn5nbGCA/vUbss8FDxzA1DSP3c9uo2puLS/8/ima18zjjNdegE4//aZsxP1+Bjdtyj4e2roF79JlWKvGPh4bLSbqFzRQvyD3V0pvs6H0+szF5mEGhxODZXYPhdfSaYJ792dHAES6ugi1H6Ti7DNPmBdqJtFbLehtNlLH3AFUBsO4339ZSzV7n9oyqq1qUSMGUyaIYHXaaFqWvxpLIhDITAE8fL40vO0V3AsXYa+rI51O073rENsff5lkNEFlaw0vPfAcO57YzNX/9uppG1wUYrYb6ffz4Fd/w3DnIMuvOYOOLfvY+shGGle20LC0meje7dkbxXbAV1aGt7kC3XHltK3e3EBxWUvNCct6Gqx5fgudTgyWmXe8jw8P07vuuezx9chvm6W8fFzLF/rMqQ1oOfz/24E3qUwo+jXAYIG3VZKUUthqa7E3NqJ0OvRmM96lyzC5CnORnBgJ5NxNCB7YTyoWG2OJ4unedQhDfHT9bTSNtH+QvS/s5M+fv5fBjr5xr89oNrHspvOxuI7m0mi9cDleKZtTMhKhUPbAnk4kSCcS2OrqUHo9OpMJz+IlmNxunNVe1t5yWXYkjN6o58zbrsRR7hlz3T27O/njf93D1kdeZNtjL/OnL9zL4ME+woHRAY51v3ycyOEEmalIlMCu3aOeT8cyCYLE7OWo8HDm7VeiN2ZOOJROx9pbLsVZ7SWdShHYM3rIOOk06VCArp0Hefbex5h/wVJeeuB5/D3Tc8pGtD/3uBtsP3DKw9SNNhu+lasygXalMJeX412yBN0EEg3ORMlwmJF9o29GJEMhEiOz5/ijN5nxLV2GuawMlMLodlO2ctWYObGOVzm/gZqlzdnHrpoyWs5fitKdfD51fHg4z/nSAdKJBL17Ovnz5+9lx+Ob2fPcdp699zEWXbKCrh0HGTzYP5G3KIQoIf0HehjuHKT1rIX07etm0wPPs2/jbp74ycM89fNHwXRcrrKBAZLh3NG63sZK5l26KvvY7LSy8vUXYbSOHYAw2u34Vq7C4HQe/S1cvARdnhHt0124qzvn+Dqydx9aOj3GEqMVegTEXcBy4HHgK8ADwAfIBDo+XOBtlSyD1Ypr7rzDSUd0BZ0ekW/qgtLroQSTm8RjCcbKO6rTK2KhKL17u/BNIKO8r7GSy/7fmwj1+TGYjTirfWMOhRJT7/j9M9LVhd5qpWzNGvQm86jvQtPZi/A1VxMNhLF6nThPMux197OvjM5arMGOJ7ZQ1VpLz57ObHMimjjmQkrLO/xXy9MmZpfa5a1c8YlbiAwFsbhsOKu96PT6zL6Tb//QtOz+vX/jHqrm1eVMI5ou8g2RVAbDaSXJMns8+FatQksm0ZlM6MY5DHPGk+MPRqcT77LlpONxlMEwagrEydjLXJz51qsZ6RkinUzhqvZiGe/ohDyjk5RehwbsXrc95/vb/vJequbVkpKpQ0JMW6lE5vvra6ig7Zj8MQAHNrWx/OJFORe/+X77zA4ry151Dk1nLSQRieGo8GAfx4hrs8dD2arVM/+3ME+gQZvAOVHBAhBKKSPwJuBfADRNe0wptRBYC+zWNG3LiZafaZRSkzLkxuhyozMaSScS2TZnS+uESlpNleq5tSSjIWBgVHvCYGOgPXMHTjuF83e7z4XdJ9MuSpHBbsfkdhP3H83LYfaVYbDZcw7COr0ed1057rrxrTud56QwnU6jM45e76obzsLuzeR40FutOFtbCOzek31eGQ2YJAfErKd0Cle1D1f16GS/Or0eZ8scBjaOHt0QSxsIDWbuXKdTaeqXNOGqnFi+klJhKa8gdODAqDsVjqamU8rNcyy90Qgz8E7PqTJYrdgbGwi1H8y26cxmTM7Zd/zR6fXoTnHaidluwdxSM/Hl3J6c4dDOllb0RmPe0T7pVBqr24b3cDJlIcT042uowGg15b+RAKAffelrq6tHP8aILIPZhK9p/FPFs5uYBb+F1ppqgvsPjGpztswZ93lEwQIQmqYllFJzAO2YtnYgN2ugOGVGu52yVauJDQ6QjEaxlFdgdLlIRqIog35CdxYmW9X8Ovr2dKJvmgvBIZRSxA12nvntOgAMZiMVLdVF7qUoJL3JhGfJUmKDgyQCAUxeL2avF6XTkYpGUTo9OtOp7aPzz1/KK4+9dMwRBpZfvRaTxcTGP69jpM/P0itX03LGguzzSinsTY3orVZCBw9idLlwNDbMqiRwYuIsFeWUr13DyP79KL2BqLLw9C+eyD6/6OLl1C1pGpXstNQlozGUUujNJkwuF2Vr1hLt70dLpbBUlGNyTc9gSilTej2ueXMxOp2ED3Vi8nqwN9SPe/qBOD1Gp5OyNWtJBIOQTmOw2TAeng4775zFbP37i6N+T+aft4TK1hqc5fJdEGK68tWV86pP3kzn9oNUzKmmb9/RCk21ixoob6lHi7hHnaPq9PpM9bZorOSupUqV2eul4uyzCO7bTzqdwtncjLls7OqNxyv0FIy7gXeSKb0pJonR6cR4+A5KIhQmsGs34Y5D6G02PIsWYi7zlUS9WaPJSO3iJhKxONBMKpmm/aU2TDYLiy9ZweLLVlEu+RtmHIPViqGuDuoyQxuSkQiBnbsIHmhHZzbjWbwQS3n5hO+2Vs2r41WfuJnND28gnUyz7Oo11C1sxGA2cnVrDelkClOeuXkGiwVHYwP2+rrTvsMrZgedwYC1ugpLVSXpVCZZXfmcalzVPpZdsZq6pU3ZChClLhWLEe7sIrCnDaXT4V4wH2tVFSaXq2C5icTYDFYrzjnNOJoa5fgzxTRNIx2NMbK7jVQkgq2hHqfZjM5my/yefPIWtjy8gVQiydLLV1O7uDHvb4gQYnqpaq2lvKmKxhUt7H1hJ+0vtdG8eh6tZy3E5nMBruw5KhzO17P/AOGDHeit1sy1VHlZSVxLlSql02EpL8sGHSb6WRU6AGEHblFKXQFsBEZlh9M07UMF3t6spqVSBPbsIXywA4C030/f8+upOv9cTO7SieAfuUtoNGfuMMw9ZxE6ORGbFTRNI3SgncCeNiCTmLJ//QYqzzsHs9d7kqVH0xv01C9tpm5JE2iMSkJmMBrAeOLDmZz8i4lSSqE36Klb3Ejtooac/W46iPb1M/zKtuzjwZdepvzMtVgrJfg7leT4M/US/gB961/IDsUO7t0HaQ3P4oWZ35MlTdQtbpxW3+vmpiYOtE9sYLHJZCJ+glLw+Rj0epITTEr7o9/eNaHXw8QuWvQ6HalxJrg7dpmrb79qwstMqF9KR2qC84l1So4Hk01v0FPWUEFZQwVrbjp3zOsOLZ0m0LaX0IHM9yqdSNC3/oWSu5YqVacapCl0AGIR8OLh/2857rnZlXVpCiSjMcIdx9XI1jQSwVBJf2kk+DB7pGMxggdyT5bi/sCEAxBHKKVgepwrihlkOu53WiqVM0cTINLVLQEIMeMlRkZyq2C0t+Nsac5Og5lu3+sD7e0MvrJ9Qsv4liwi1NUxoWXsNfXs+s0D4379/Ddcz1uvfPWEtvGNn/+Cp//v5+N+/fkf+hfWfePuCW3jnDtu43cf/OaElnndt/+Vrfc/O+7XL73hXO75+E8mtI23fOntE3q9OD0nuu5IRaOj8vQAmWupwEhJX0tNdwUNQGiadkkh1ydOTOl0mYSUx0W2Z2zGVTHtKJ0Ondk8KmkqIPPrhJgKOh0Guy1TjvAY+lNMBijEdKIMuae4epMpbxUYIcTspHSZaoVHSshn208yqlacHrkVPY0ZrBY8SxaNajO6XRjdM29ebzQQItQ7RCISK3ZXxAToTCY8ixeOatPbbBg9px5Vjo2EM/tCOHryFwuRR3Q4SKh3iGQscfIXT2NKKRzNo6tb6IxGrFUTz+otRClLp9KEB/yEB/zZ8pomtwuDc3TCYc+SxQUtjS6EmN70ltxrKYPLOWaOpEQkRqh3iFgglPd5MT4S3pnmrFVVVJxzFolAMJPd3OPGMIPubmnpNAO7DrLjT08SHwnjaqhkwasuxFkjZbKmC0t5OZXnnkM8EEBvNGL0uDHax1nH/RiapjHUdogdf3yC6HAQR005C199Ia66iknotZiJUokkfdv2s/uBZ0iEo3jn1jP/+nOxV5zadKDpwOz1UnneucT9w6B0mD3ubBJjIWaC2EiIg89s4eCzmWrvDectp+HcZZidNirWriXu95OKxzE6nZhPI/gthJiZLJWVVJxzNomREfSmw9dSeaoVjXT2s+NPTzJyqA+zy87CV1+Ib27DtMkhU0pkBMQ0pzMYsJSV4ZzThK22ZsaV9wr2DLHl3keIj4QBCBzsZccfH5eRENOI0ukw+7w4m5uw1dWeUvABINw/zOZf/I3ocBCAYFc/r/z6UWLBcCG7K2awYNcA237zj+zomaE9HbQ9sp5UIlnknk0uk9uFo7ERR0O9BB/EjDO4q4P2p15GS6XRUmnan3yJwd2ZOd0Guw1bbQ3O5iYsZT6ZfiGEyKHT67GU+TLnqWNcS8VDEV757WOMHOoDIBYIsfmehwn1DU11d2eEkgxAKKXep5Tap5SKKqU2KqUuGOdy85RSI0qpYJ7nblZKvaSUCiulupVS9yilqgvfe1FIkUE/2nFZj0cO9RPz5/wTixkuMhggnRydlTsyECA2LPuCGJ/wwHBOW//2/dkApxBi+unZvGdcbUIIcapi/hDh3tHBBi2VJjIQKFKPpreSC0Aopd4IfAv4ErAKeBb4q1Kq8STLmYBfAU/mee484BfA3cAS4CZgMXBvIfsuCs9oy51OYrCaMVhkDudsY7RZctp0RoPsC2Lc8h1PrD4XerMkRRViunLW507Dc9VLlRchROEYLCb0ZlNOu9Gee24qTq7kAhDAHcBdmqb9SNO07ZqmfRDoAt57kuX+G9gM/DbPc+cAHZqm/a+mafs0TXsO+DZwViE7LgrPUe2lZu0xSQwVLHjVBVg8jrEXEjOSvdJLwwUrRrXNv+E8rGUzL+mqmBzO2nLKFzVnHyu9jvk3XIDJPnPy5ggx21Qtn4vJdXTItMllo3JZaxF7JISYaaw+FwtuPH9U2d66c5Zir5y5OaQmU0kloTw8imEN8LXjnnoEOPcEy10HXA+sBl6b5yXPAF9SSt0APACUAW8CHipAt8UkMlotzL3qLKpXzCMeimArc2Gv9BW7W6IIDGYTzRetomJhE7GRMFavE3uVL1PHXYhxMDttLLzpQoJnLyEZjWEt9+CQ44kQ05qjysead76KYM9g9rHVJ4FpIURhVS5twVbhITIYwOSw4qguw2iVUbinoqQCEEA5oAd6jmvvAS7Pt4BSqgb4EfAaTdNG8l2MaJq2Tin1ZjJTLqxk3vffgdvGWOe7gHcBNDaecOaHmGSpeIJ4IITBbMBRXZd3GP5sM933z+hQgHgwjNFuxeJ1TSiAYLSa8TTXTGLvxOkq9f3T5LDim1s/5vOpeILoYIB0KoXF48QooyNmjFLfN8Wps/pcpxx0iAwFSIyEMTom/ptUSLJ/ilI2m/ZPLZ0mMugnGYlhdjswuzKjrnUGPa66Cqm+VgClFoA4QjvuscrTdsQ9wPcPT6vISym1GPg/4PPAw0AN8FXgh8C/5Gxc0+4E7gRYu3btWNsVkyw+EqbjqRfp27wbAFtVGXNvuBBruae4HSuy6bx/DrUdpO3PT5CKJ9AZ9LRcdz6+Bc0oXSnOBhOnYjrvn/FgmI6nNtH38i4AbJVeWm+8GNssP+bMFNN53xSTY2h3O3vuf4J0PInOaDj6m1SEIITsn6KUzZb9M5VI0rdlN+2PrkdLpzFYzcx/zWU4G6qK3bUZpdTO+vuBFHB8dYpKckdFHHEp8BmlVFIplQR+AtgPP37X4dd8DFivadpXNU3brGnaw8D7gFuVUg2FfxuiEEYO9WSDDwDhngF6XtqRUxVDTA/RoQBtf3mSVDwBQDqZou2Bp4gM+ovcMyEygof6ssEHgHDvED0bt8sxR4gZKDIYoO3+J0nHM2V404kkbfc/RXRAfpOEmK0ifUMceOS57O9+MhKj7YEnSYQiRe7ZzFJSAQhN0+LARuCK4566gkw1jHyWASuP+fs0EDn8/0cSUtrIBDaOdeSxTCAvUaHOvpy24bZD2QtYMb0kQhFSsfioNi2VJiElEEWJCHX357T593aQjMbzvFoIMZ0lQuGc8wktlSIelN8kIWarWCCU2+YPSgCiwEpxCsY3gF8opdaTSR75HqAW+AGAUurLwJmapl0GoGna1mMXVkqtBdLHtd8P/Egp9V6OTsH4JvCipmntk/t2xKmyVZfntLmba9CbpGTedGS0W9GZDNm7TQBKp8PotJ1gKSGmjq0qNyGlq6kmb+ktIcT0ZrRb0RkNpBPH/CbpdRgd8pskxGxldtlz2kwuO4Y8ZbzFqSupERAAmqb9GvhX4JPAS8D5wLWaph04/JIaYEL1lTRNu4tMec8PAFuB3wG7gVcVos9icjgbqvAtaM4+NvtcVK1ZJPkCpimL10XrdReiDHogE3yYc815WH3uIvdMiAxHXRW+RXOyj81eF9VnLEGnl2OOEDONxeui5boLjv4m6XXMueZ8qaAhxCxmLffScMlaOJwHRm820nrdBZgcEoAopFIcAYGmad8DvjfGc7efZNm7gLvytH8b+Pbp905MFbPTzpxrzqPmzCWkU2ksPhcmuTMxrXnnN7LsrTcSH8lUwbCWuSWgJEqG2WljzlXnUnPGEtLJlBxzhJjBlFL4FjRhK78xU5nJYcXqk98kIWYzvclA9ZpFuOfUkQxHMbkdWL0SlCy0kgxACHGEwWLCUVdZ7G6IAlFKYS3zYC3zFLsrQuRlsJhw1EqJLSFmA6UU1nLPrK+uJYQ4SmcwYK/MnZIpCkfCvEIIIYQQQgghhJh0EoAQQgghhBBCCCHEpJMAhBBCCCGEEEIIISad5IAQYhzSqRSpaJh0MoneZEZvsaCUxO/GoqXTpKIRUok4eoMRvcWK0uuL3S0xjWiaRioWJR2LovQG9BYLOoOU4BVitpJjghBiJkonk6SiEbRUEp3Zgt5sQR2uwjFTSQBCiJNIJ5NE+7qJ9nVn2+yNLZg9kqAmH03TiA0PEO44kG2zVtdhKa9E6SQIIcYnGQwwsn8PaBoAJo8Pa00DeqNccAgxG8kxQQgx06QTCcLdHcSHBjINSuFonovJObNL1MstXCFOIhWLjgo+AIQPHSAVixWpR6UtFYsRPtQ+qi3SfYhULFqkHonpJp1IEOo4kL3QAIgPD5KOhovYKyFEscgxQQgxE6Wi4aPBBwBNI9xxgHQiUbxOTQEJQAhxEloymduWSqGlc9sFaKnkqJPEI9J5Pkch8tHSKdKJeE677ENCzE5yTBBCzET5jmHpRJx0OlWE3kwdCUAIcRI6kwlQx7WZ0RlNxelQidMZTajj5+UqHTqjuTgdEtOOMhgx2J057Xqz7ENCzEZjHRN0JjkmCCGmL32eY5jB7pzx+W0kACHESegtVhxNrSh9JmWKzmzB0dgy4w8Op0pvMuFoajkcuMmcODqbW+XiUYybTq/HVtuA3moDQOn02Bua0VtsRe6ZEKIYxjomGKxyTBBCTF96qw17Q3M2R5reasNW24huhidulySUQpyEUgqT24PeuggtlUJnNErw4SSMdieu1kWkk4lMtnKTjBYRE2Ow2nDOmU86EUfp9BLAEmKWk2OCEGKmUTodZm85BpsTLZ1CZzShM8z8y/OZ/w6FKJB8w6TE2HRGIzrJTi5Og85gmBU/xEKI8ZFjghBiJpptAVWZgiGEEEIIIYQQQohJJwEIIYQQQgghhBBCTDoJQAghhBBCCCGEEGLSSQBCCCGEEEIIIYQQk04CEEIIIYQQQgghhJh0EoAQQgghhBBCCCHEpJMAhBBCCCGEEEIIISadBCCEEEIIIYQQQggx6SQAIWatdCpFKh4vdjdmBfmsxWRLxxOkk8lid0MIMUlS8TjpZKrY3RBCiJKUSiRJxRPF7sa4GIrdASGmmqZpxAYGGd6+i2Q4jLO5CXtDHQabtdhdm5Gi/YP4d+wiEQzhmNOIo6FePmtRMKlYjHBXN4FdbSijEc+iBVgrylB6fbG7JoQogGQkSqijk5G9+9BbLXgWLcBSXoZSqthdE0KIokunUkT7+hnevhMtkcQ1fy62mmr0ZlOxuzYmGQEhZp24P0D3U+uI9vaRDIYY2rqNkX0H0DSt2F2bceLDfnqeepZITy/JUIjhrdsJ7Nkrn7UomHBXDwMbXyYxEiQ+OETvM88RGxoudreEEAUSPHCQoc1bSQZDxPoG6HlqHfHh4WJ3SwghSkLm3Od54oPDJEaCDGx8iXB3T7G7dUISgBCzTtwfgHR6VFtgTxupSLRIPZq54oERtOM+65G2fSTDkSL1SMwkqUSCwO69Oe2Rnt4i9EYIUWjJSJSRPcd9xzWN2JC/OB0SQogSE+7OPecZ2d1GKlG601IlACFmHZ0+d7fXGY2gk+GchabyfNbKYEDJZy0KQOl06MzGnHadMbdNCDH9KL0OZcydLayTKVZCCAGQd6qFzmQq6XNtCUCIWcfk8aC3js5B4F26GIPFUqQezVwmjxuD3T6qzbtsMQar5IAQp0+n1+NZMH90m9GAtaqiSD0SQhSS3mTCu3Tx6DaLGZPPW6QeCSFEabFUVqAMowO17oXzSjpQK0koxaxjdNipuuAcYv39JMNRLBVlmOVkZlIY7XaqzjuLaP8AyXAES3kZ5jL5rEXhWCrKqL74fKJ9/SiDAWtFGSa3u9jdEkIUiLWqguoLzyXaN4DOYsJSXo7J6Sh2t4QQoiSYPW5qLj6PSG8/WiqFpaIcs9dT7G6dkAQgxKxkcjrkBGaKGJ0OjPJZi0midDosZT4sZb5id0UIMQl0BgOWinIsFeXF7ooQQpQkk9s9rW6+yBQMIYQQQgghhBBCTDoJQAghhBBCCCGEEGLSSQBCCCGEEEIIIYQQk05pmlbsPpQ0pVQfcKDY/Zgk5UB/sTsxzR37GfZrmnb1VG68QPun7AdjmymfzZTvmyDHT5F1ss9qOh47Z8O/v7zHjOm4f47HTPn3ne3vYzrvnzPh324mvAeYnPcx5r4pAYhZTCm1QdO0tcXux3Q2Ez7DmfAeJot8NmIssm+M30z8rGbiezqevMeZbaa8d3kf09dMeM8z4T3A1L8PmYIhhBBCCCGEEEKISScBCCGEEEIIIYQQQkw6CUDMbncWuwMzwEz4DGfCe5gs8tmIsci+MX4z8bOaie/pePIeZ7aZ8t7lfUxfM+E9z4T3AFP8PiQHhBBCCCGEEEIIISadjIAQQgghhBBCCCHEpJMAhBBCCCGEEEIIISadBCCEEEIIIYQQQggx6SQAIYQQQgghhBBCiEknAQghhBBCCCGEEEJMOglACCGEEEIIIYQQYtJJAEIIIYQQQgghhBCTTgIQQgghhBBCCCGEmHQSgBBCCCGEEEIIIcSkkwCEEEIIIYQQQgghJp0EIIQQQgghhBBCCDHpJAAhhBBCCCGEEEKISScBCCGEEEIIIYQQQkw6CUAIIYQQQgghhBBi0kkAQgghhBBCCCGEEJNOAhAncfXVV2uA/MnfeP6mnOyf8jfOv6KQ/VP+xvk35WTflL8J/E052T/lbwJ/U072T/kb59+YJABxEv39/cXughBjkv1TlDLZP0Wpkn1TlDLZP0Upk/1TnC4JQAghhBBCCCGEEGLSGYrdATHzpaJREsEQAAaHHYPFUuQeidkmEQqRDIXRGQ0YHU50Rjn0iRNLRiIkgiGUTmF0ONGbTcXukpiGNE0jGQyRjITRmUwYHQ50Bjn+iJkpFYuRCAYhrWFwODBY5XxPCJFLfgXFpEoEg/RvfJHkSBAAo9tF2aqVGB2OIvdMzBaxgUH6XtiAlkwC4GhuwjV/HnqTXFCK/OKBEfpf2EAqEgHA7PPhW7kcg81W5J6J6Sba18/Aho1o6TQArvlzcba0SBBCzDiJUIjBTS8THx4GwGC3U752NUans7gdE0KUHJmCISZVuLMrG3wASPgDRHp6i9gjMZuk4nEGt27NBh8AgvsPkAgEitgrUco0TSPU3p4NPgDEBgeJ9g8UsVdiOkpGogy+vDkbfAAI7NpDYmSkiL0SYnJE+/qzwQeAZChEsONQ8TokhChZEoAQkyqW56Q9NiAn8mJqpBOJUQGwI1LRWBF6I6YDLZkiNjCY057w+4vQGzGdpRMJ0rHcY00qGi1Cb4SYXPGhody2/gG0VKoIvRFClDIJQIhJZa2pzm2rqipCT8RspDOZMPu8Oe0Gm7UIvRHTgc5owFqde4wy+XxF6I2YzvRmEwa7PaddpvKImchSXp7TZq2pRun1ReiNEKKUSQBCTCpLVeWoIIStthZLZUUReyRmE73RiGfJYgz2wyf8Oh2eJYsxulzF7Zgoabb6OswVR0+mHU2NmMskACEmRm8241u5HP3hxMtKr8e3YrnkQBIzkrm8DFtDffaxpaoSW21NEXskhChVkgVJTCqjzYZvxXKSc1sBhcFuk+RbYkqZ3G4qzz2HZCSCzmDAYLejlCp2t0QJM9rtlK1eRTIURukUBrsdndzFE6fA7PVSdf65JCNRdEY5/oiZy2C14l2yGGdzM6Cht9nQG43F7pYQogTJlaCYdDqDAZPbXexuiFlMbzajN5uL3Q0xjeiNRvQeOW6J06e3WLKjIISYyTLnezLCUAhxYjIFQwghhBBCCCGEEJNOAhBCCCGEEEIIIYSYdBKAEEIIIYQQQgghxKSTAIQQQgghhBBCCCEmnQQghBBCCCGEEEIIMekkACGEEEIIIYQQQohJJwEIIYQQQgghhBBCTDoJQAghhBBCCCGEEGLSlWQAQin1PqXUPqVUVCm1USl1wQlee7FS6s9KqS6lVFgptVkp9bY8r7vo8LqiSqm9Sqn3TO67EEIIIYQQQgghxBElF4BQSr0R+BbwJWAV8CzwV6VU4xiLnAtsAV4HLAW+D9yplLr5mHXOAR46vK5VwJeBbyulXjtZ70MIIYQQQgghhBBHGYrdgTzuAO7SNO1Hhx9/UCl1NfBe4GPHv1jTtC8d1/R9pdQlwGuB+w63vQfo1DTtg4cfb1dKnQV8BPh9od+AEEIIIYQQQgghRiupERBKKROwBnjkuKceITPSYbxcwNAxj8/Js86HgbVKKeNE+ymEEEIIIYQQQoiJKakABFAO6IGe49p7gOrxrEApdT1wGXDnMc3VY6zTcHibx6/jXUqpDUqpDX19fePsuhBTQ/ZPUcpk/xSlSvZNUcpk/xSlTPZPUUilFoA4QjvuscrTlkMpdR6ZaRcf0jRt/TjWma8dTdPu1DRtraZpaysqKsbZZSGmhuyfopTJ/ilKleybopTJ/ilKmeyfopBKLQDRD6TIHe1QSe4IhlGUUucDfwU+rWna9497unuMdSaBgVPurRBCCCGEEEIIIcalpAIQmqbFgY3AFcc9dQWZChZ5KaUuJBN8+C9N076Z5yXrgMvzrHODpmmJU+6wEEIIIYQQQgghxqWkAhCHfQO4XSn1DqXUIqXUt4Ba4AcASqkvK6X+ceTFSqmLyQQffgDcq5SqPvx37PigHwD1SqlvHl7nO4Dbga9NyTsSQgghhBBCCCFmuZIrw6lp2q+VUmXAJ4EaYCtwraZpBw6/pAZoPWaR2wEbmZKaHzmm/QDQfHid+5RS1wL/S6acZyeZPBFSglMIIYQQQgghhJgCJReAANA07XvA98Z47vY8j2/P99rjXvcEsPr0eyems1ggRDqZxOSyozeU5O4vJigeDJOKJTA5behNUlV3ptI0jehwEAWYPQ6UUiddRoiJSMUTxEfC6M1GTA5bsbsjhJhk6VSKmD+E0uuwuB3F7o4Qs4ZcgYlZIZVI0r9tH/seeZ5kNE7F0hYaL1qN1ecqdtfEKdLSaQb3HKTtwWeJj4TxtNbTcuVZ2Co8xe6aKLB4MEzXC9s5tG4LAPXnr6B6zUJMdmuReyZminDfEHsfWc9wWwcmp43Wa8/FN68BpSvFmapCiNMVGw7S8exmul/cgc5ooOmSNVQun4fBYip214SY8eSXVcwKwc5+dv/5SZKRGGgafVva6Hz+FbR0uthdE6co1DPI9l//g/hIGIDhtg72PvIcqbjklZ1phvZ0cPCpl0gnU6STKdoff5HhvZ3F7paYIVLxRDb4ABAfCbP9N/8g1DNY5J4JISZL79Y2ujZsR0trpGIJ9v7tOUY6eovdLSFmBQlAiFkh1Jt7Itm3ZQ/xYKQIvRGFEBnwg6aNahtuO0QsEC5Sj8Rk6d28J6et/5W9ReiJmIniI+Fs8CFL0zLHGCHEjJOMxul9eXdO+/D+riL0RojZRwIQYlbIN1Tb4nOjN0vOgOnKYLXktJkcNgzybzrj2GvKctuqfUXoiZiJ9CZD3pwP+Y4xQojpT2fQ552uafU5p74zQsxCEoAQs4KztgJn3dHKrEqvo/nSNaQiEVLxeBF7NvNpmkYiGCQ27C/oZ22v9lG2sOlog1K0XHsOJqckj5tpKpfPw2A7ejFotFspXzwn+zidTBLzB4gHRmRalZgwk9NO67XnwjGJTX0LGrFXSZCrVKQSCWLDfhIjQbTjRr7NFslwmNiwn2Q0WuyuTHs6g57681aMSlxtLXPhbq4pYq+EmD0kCaWYFcweBwtffxmhngFSsQRGm4mRndsZDocx+byUr1mBySUJKQstnUwSau9gcPMraKkUJo+b8rWrMLlP/7M22a20Xnse1WsWkozEsPjccsEwQzmqfKx46/WEeodAgb3Sl00gmwiGGNz8CpGublAK9/y5uOa1oDebi9xrMZ145zWw4u03Eh30Y7CasVeVYXJIktNSEA+MMLhpM9H+AZROh2fJQhzNTbOm6pGmaUS6e+jf8BLpeByDzUr5GWuwlMvv3elw1lWw/O03EO4dQmfQY68qw+KRShhCTAUJQIhZw+yyY3bZifT10/Pks9n2+OAQQ6/soOKM1eikNGdBxYf9DGzaPOrx4NZtVJ51BjqD/rTXb3JYMTnqT3s9ovRZy9xYy9yj2jRNI3igPRN8yDTg37kbk8+DvVbuZInx0+l1OGvLcdaWF7sr4hhaOo1/1x6i/QPZx0NbtmFyu7FWVZxk6ZkhMRKk77kN2dFdyXCEvuc3UHPpBRisEiQ7HfYKL/YKb7G7IcSsI1MwxKyTDOUmKYx09ZCKyVSMQkuEQjlt0e5eUrFYEXojZpp0IkGoI7caRqxfqhcIMROkYjEind057fHASBF6UxzJcDhnalkqGiUZkakYQojpSQIQYtbJNzTb5HahM86O4ZxTSW/OTeJmdDnRGWWkiTh9OoMBsy/37pXRJYnEhJgJlMGIyePOaTfYZs+d/3znLMqgnzVTUIQQM48EIMSsY/K6sdXVZh8rvR7fiqXyYz4JzB439oa67GOl11O2chl6k6mIvRIzhdLpcM1rRWc+uj+ZfV4sFblVM4QQ04/eaMC7dBHqmOmR1uoqzD5P8To1xYxOB54lC0e1la1agcFuL1KPhBDi9MhtSDHrGCwWylYtx9XaTDqRxOB0YHJK4qHJoLeY8a1chrOlmXQ8gcFhxyR3p0UBmT1uai65gEQgiNLrMDqdUj5RiBnE7PNSe9mFJEaCKIMBk8uF3jx7gtg6gwHX3BaslRUko1GMNhtGlxN1TNUWIYSYTiQAIWYlvdmEvkKSjU0FvcmEvlzuSIvJY7TbMcrdQCFmLKPDgdExe28UHJluJrV9hBAzgUzBEEIIIYQQQgghxKSTAIQQQgghhBBCCCEmnQQghBBCCCGEEEIIMekkACGEEEIIIYQQQohJJwEIIYQQQgghhBBCTDoJQAghhBBCCCGEEGLSSQBCCCGEEEIIIYQQk04CEEIIIYQQQgghhJh0EoAQQgghhBBCCCHEpJMAhBBCCCGEEEIIISadodgdEGK6SCcTaKkUymBEp9cXuzslL51KoiWTKL0BnUEONWLitFSKdDKB0unRGY3F7o4QosjkmHBqtHSKdCKB0unQGU3F7o4QYpaTqwIhTkLTNJKhEUIdB0jHYxjsTmy1DRistmJ3rWQlwyFCne2kwiF0Zgv2+iaMdmexuyWmkVQ0Qqi7g2TAjzIasdc2YXS5UEoG7gkxG6WiEcLdh0gEhuWYMAHJaJRIzyES/iGUwYitrhGT043SyecmhCiOghx9lFKvGc9fIbYlxFRLxaKM7NtNOh4DyAQjDu4jnUwWuWelKZ2IEzzQRiocyjyORQnu20MqFi1yz8R0oaVShLoywQcALZEgeGAPqUikyD0TQhTDkWNCIjCceSzHhHHR0mmivV0k/EOZx8kEoQNtJKPhIvdMiIzGhkaUUuP+a2xonPRtnOp2xPgVagTE78bxGg2Qceti2knHYqBpo9pS0QjpRFymFuSRisdJJ+Kj2rR0ilQ8ht5sKVKvxHSSTiZIjvhz2lOxKAabvQg9EkIUkxwTTk06mSA+PJjTnopGMdocReiREKMd7DjI3+56eNyvv/r2qyZ9G6e6HTF+Bbl60jRNxnGJGUsZ8sTNdDoZvjgGpdeDUjlBG51egjVifJROhzIY0ZKJ0e2yDwkxKymdXo4Jp0DpdOhMpuwIziPk91gIUUxyBSXESegtVkze8lFtttoGdCZzkXpU2vRmC9bqulFtlooqGf0gxk1nNGGvGz380eh0Y7Bai9QjIUQx6YxGOSacAt3hnA+gsm16uwO9TXJYCSGKpyAhUKXUHWM85Qd2apr2dCG2I0Qx6PQGbDV1mD1e0skkOrMZg8WKUurkC89CSinMvnIMVntmmorRiN5qy4yMEGKcjC43rrmLSMWiKL0Bg9Uq2duFmMXkmHBqjA4XrrkLD39uegxWm3xuQoiiKtQYrA+O0e4B3EqpdcANmqblTkQTYhrQGYzonO5id2Pa0OkN6BxS9UKcOqV0GGx2md8thADkmHCqlFLyuQkhSkqhckDMGes5pVQLcA/wReC9hdjeTJGMRIgNDpEMhTG6nJi9XvRmiUqL2SsRDBIbGCIVi2P2eTB5Pehk5IQQUyaVSBAfGiY+7EdvtWIu82KU4dozTmIkSGxwiFQ8jtnrxex1yyg1UXJS0RixoSESgRGMTgcmnxeDRaZzCjHdTXoWGk3T9iql/h/ws8ne1nSSiscZfHkr4UNd2Tb3wvl4Fs2X5IZiVkoEQ/Q8/RzJ0NHyYBXnnIG9tqaIvRJi9tA0jVB7B4Mvbcm2mX1eKs4+A4NVTvpnisRIkO6n1o0qYVl57pnYaqqL2CshRksnkwzv2MVI275sm6O5Ee/yJeiNxiL2TAhxuqbqSrcdqBrvi5VS71NK7VNKRZVSG5VSF5zgtRal1F1Kqc1KqYRS6vExXnezUuolpVRYKdWtlLpHKVW0X9tEYGRU8AHAv3M3iWCwSD0SorjiQ8Ojgg8AQ5u3kYrFxlhCCFFIyXCYoa3bRrXFBodI+ANF6pGYDLGh4VHBB4ChLdtJxeNjLCHE1EsEQ6OCDwDB/e0kR+Q8WYjpbqoCEMuBA+N5oVLqjcC3gC8Bq4Bngb8qpRrHWEQPRIHvAA+Osc7zgF8AdwNLgJuAxcC9434HBZZOpXIbNQ0tlZ76zghRAtKpZG5bPCbfCSGmiJZKoyVzf5vyfTfF9JVOJHLaUvG4HGtFScl3LIIxzp+FENNKQQIQSinfGH9zlFKvIxNQ+NU4V3cHcJemaT/SNG27pmkfBLoYI3+EpmkhTdPeo2nanUDHGOs8B+jQNO1/NU3bp2nac8C3gbMm9EYLyOhwoDOPLuNo8now2GWurZidTC4XHFdZxNkyB70M/RZiShjsNqx1o6c8Kb0eo1MSys4kJk9uQmVXa7NMsxElxeCwYXCMTpxpsFkxOiSZphDTXaFyQPQD2hjPacCPgf8+2UqUUiZgDfC14556BDj3NPr3DPAlpdQNwANAGfAm4KHTWOdpMdptVJ13FsPbdxEbHMRaXYV7/lz0ptJLQpkIhYkHRtDp9ZjcTvTHBU6EOF46lSIRGCEZiWKwWTG5nCfNbWLyeqg672yGXtlOKhLBMacZR3OjlDsVs0oxj7c6vR7f0sUErFZCBw9hcjnxLFmIySUBiJnEfOyxNhrD2dKMo6lh3MvHAyMkgiH0JhNGjwu9YdLTiYkZLBEMER8JojMYMLld6E2Z/A4Gi4XKs8/Av3M3kd4+LOXleBbOw2C1FrnHQojTVahfjUvGaA8AuzVNG++ErXIyUyp6jmvvAS4/xb6hado6pdSbyUy5sJJ5338Hbsv3eqXUu4B3ATQ2jjXz48RSiQSpaBSl02Gw2fJeRJm9HirOXE06kURvNpVk8snY0DCH/vkMqUgUAHt9DRVrV2KUkRpFU4j9czKlUykCbQfo27ApE37UKarOXou1qgyDxTrmfq6UwlpVgcnnQUul0JvNEnyYhkp9/yxl0cFhOh8/7nh7xsoprUJhdNjxLV+Ce/5cdEYDuglcXCYjEdKJBHqzuSQD1bJvZiidDmt1JaYyL1oqNaGqAuGePjoffxYtmZmW4108H++SBSe9eZKKxUjFYuhMRgwWuYDMZ6btn6l4jFQ0hs5gwDDGMSw6METnP5/J5npyzmmkfNWy7Ggck9tF2ZqVpOMJdCajVMUqopm2f4riKsgVr6ZpT4zxtylf8EEp9T2lVPmJVnn8Innaxk0ptRj4P+DzZEZYXA1UAz/Mu3FNu1PTtLWapq2tqKiY8PaSoRCDm1+mf/3z9D3/HMED+0nlmXMJZA7MVktJBh/SqRSD23ZlT4YBQh1dRPsHi9grcbr752RLBEaOBh8A0hq96zcROtjByN69J00qqTcaMVgsEnyYpkp9/yxV6VSKoW07c4+3fVN/vFVKYbBaxh180DSNSG8vfevX0//Cevo3vEBseHhyO3kKZN8c7cixdryS0Ri9z23MBh8AhrbtIjbkP+FysaGhzH7xwnr6168n2t+Hpp3yKd2MNZP2z3ggQP+GjfS/sJ6+9c8T7u5GS4/OMZJKJul/eeuoc4KRfe1EB4ZGvU6n12eORxJ8KKqZtH+K4ivWVe9bAFee9n4gRSY4cKxKckdFTMTHgPWapn1V07TNmqY9DLwPuFUpNf5xh+OgpdME29tJHDn50jRG2tpIBKZfFvF0Ikm0tz+nPSYZ0cUJJCPRnHChlkyipSF4YD9x/4lPVoWYjdKJJNG+gZz2+DQ43iZDIYa2bkFLZgLtqWiU4Ve2SgWbGSYdj5MIhnLaj6+oMeq5aJShrVuy+0I6kWBwyxaS4fCYy4jpLR2PM7x9G6lI5t9YS6UYfmVrTpU3LZ4glueG1vHVsIQQM0+xAhB5b21qmhYHNgJXHPfUFWSqYZwqG5nAxrGOPC7obdZUIkG0rzenPTkNy2vqTUbsxyUkA7D4PFPfGTFtGOy2nBE9OrOJI1GJeAneGRWi2PQmI7Y8x1vzNDjepiIROO6OdioalQDEDKO3mDF5PTntBvvYSQFTsRjp48t7ptMnDFqI6S0Vj+c95z3+31xnMmGrqcp5ndElSSaFmOlKb9w/fAO4XSn1DqXUIqXUt4Ba4AcASqkvK6X+cewCSqnFSqmVZHJIOJRSKw8/PuJ+4FVKqfcqpVoOl+X8P+BFTdPaC9l5ncGA0ZU7uGOyM/lHI1F2btvDti27GAnk3qE4FUqnw7NgLpZy3+EG8Cyah6XMV5D1i5nJ5HRSdd6Z6IyZ4dt6s4nyFUuI9nYDmQows0kqlWJfWztbNm2jvwjD6UXp8Q8HeGXzDnbv2Esslrk4Uzod3gWtR4+vR4635WVF7On46PLM/1cGAzqjsQi9ERN15Bi1edM2+ntzR+EcoTeZqDprdbZal9LpqDhjJWZvblWNI3RGQ94pppmgtJiJdEZj3mPC8f/mOoMe37LFR6uyKIVv6UIsvvGfY3Z2dLN50zYOtneeVp+FEFOr5FIXa5r2a6VUGfBJoAbYClyradqBwy+pAVqPW+whoOmYx5sO/1cdXuddSikn8AHg64Af+CfwH4Xuv06vx9nSStzvz86TNJeVYXSN/QN9unq7+/nu//6UP/7qQQDOuWAtn/jCHTQ21532uk1uJ7WXnEdiJIjS6TG6HDIPT5yQ0imcjXWYvW6SoRBxv59wZztaMonR5c57B22mikai/PG3f+XrX/ge8VicuoZqvv79z7F42YJid00Uyb49B/jkR77Clk3b0Ol03PqO1/O299yMt8yDye2i9tLDx1u9HqNzehxvDXY7zpYWRvbuzTQohWfhIslWPw1Eo1H+/Nu/8dXPf5d4LE5NXRXf+OHnWTLGMcpS5qXhqktIhELojSaMLscJ8/XorTbcixYx/Mor2Tbn3LkYbHKXe6bSm814Fi1mcPPL2ZFR9sbGvDcfzB4XdZdfQGIklKmC4XSg9OO7N7ruqQ385wc/x/CQH4fTzhe+/jEuvuI8dCWYU00IMVrJBSAANE37HvC9MZ67PU9b8zjW+W3g26fbt/EwuVxUnHEmyXAYpddhsDsmtbzmC89tygYfIHNQfujPj/KeD+ct8jFhepMJvYx6EBNkcjoyf24XljJfpiKM3V6S2fEny87tbXz5U9/MPj50sJvPffzr/Oier+N0S2nD2SaZTHLvz37Plk3bAEin09x9569Ze9YKLrr8PGB6Hm91BgP2hkbMvjJS8TgGi+WEw/JF6di1fS9f/OT/Zh93Herhc//5VX503//i8uQ/RhmslmyVgpNRSmGtrMJod5CMRtGbTBjs9mkRWBOnzlxWRsWZZ5GMRDLJTh2OMZPaGsxmDBM8Lzh0sIuPvv+zBPwjAARHQvznBz/Hb/76E+a0SoUGIUqdhAknicFmw1Jejtnrm9TgA8D6dZty2v7xtyeJHpNNXYhiMVitme+Czzergg+QCTgcb9vmnQwMDE99Z0TRBfxBnvhHbjqj7Vt3F6E3haUzGDC53VgrKjA6nSVZ2UnkOnSwK6dt+yu7GRgo3HQxpdNhdDqxVlRgcrsnVNpVTE9KKYwOR+bf3OMp+L95X09/NvhwRCwWp+vQ6eSrF0JMlWKdIdwDlH5q72li+crFOW1nnbcas2V2XewJUWoqqnLn7ze3NOCW0Q+zksNhY/UZy3PaW+c3T31nhAAqq3Irojc01eH25CtUJkRp8JZ5sR43Ckev11NRWfo5c4QQkxCAUEotU0p9Ryn1V6VUzeG2m5RSq468RtO092qallvfUZySs89fw5qzVmQfN81p4NVvvO6E8zKFEJNvweK5vPU9b84+tjtsfPorH8Vb5ilep0TRmMwm3vbem0dd9F165fksX72kiL0Ss9n8Ra28/X23ZB/b7FY++z8fxVfmLWKvhDixxuY6/uur/4nhcLJrnU7HJ77wbzS3NBS5Z0KI8SjomCil1JXAX4C/ApcCRzJQtQK3AzcVcnsio66hhm98/3Ps3XOAVCpFc2tj3rsa01W4f5iBXQcZ6eynbEEj3jm1mByS3Gw2Cg/4GdzTQeBgL7659XhbajGXcMkul8vBuz98G1dcezH+4QANTbU0NtcXu1uzWqhnkP6d7YT7hylf2IxnTjXGSa5SdKwFi+dyz5++z7697VgsZlrmNuMeY669EJPN6XLwzg/eyuXXXMjwUID6xlqa5kyvY1Q6lWako5feV/YBULlkDq6GSpkGVEAjXQP079hPzB+mYkkz7sYqDEWsZKKU4vJrLmTugjl0d/ZSUVVGS2sTRpNU3hFiOij0RLzPA3domvY9pdSxk7MeB/69wNua9fbuOsA/HnqKna+0ccX1F7L23JWUVcysuxZRf4jN9z5CuHcIgO5Nu2i6aBVzLluLbpyZksXMEAuG2fabxwh09AKZfaH2zMXMu/Yc9MbSnVNss1lZumIhAF0dPfzuF/fz7D9f4MzzV3HB5WdT11hT5B7OHuEBP5vuepB4IAxA18adzLv+PBrOWTql/aioKqOvq58Hfvd3IuEoN7zhSpatXozFKtPmxNSz2awsWb5wwssd3N/JU48+x4ZnX+KCy8/m7IvWUlNXOQk9PLHAwR42/eR+tHSm4kLHui2sevsNeJrl2FoIwZ5BNv34LySjmZLBnS9sY8kbL6Nq+dwp2f7u7Xt55C9PcKCtnatvupQ1567A7XFhMBiYO38Oc+fPmZJ+CCEKp9Bn7UvIlMQ83iAwvdJ6l7hD7V3c8bZPM9g/DMD6p17kX977Bt76gTehPy67dH/vINFojIqqMszTrPZ2qGcgG3w4ov3pl6lZPR9buac4nRJFEe4dygYfjuh8YRv1Zy3GUV368z5HAiG+9tnv88LTmaSxG559maf+8Txf+s7H0YCRwAi+Mi92h624HZ3Bgl0D2eDDEfsefYGKxc1Y3Lkl4k5FJBJloG8Qm82Krzx/QHj75l186F8+QSqVBuDxh5/lv3/wKc65eG1B+iDEeBzZV602K2Vj7KtjGewf4r/u+Cq7tmVKr2549mUuunIzH/vyh7HZp3aEYueGHdngA4CW1ujauFMCEAUSONibDT4csffRDfjm1mO0Te7osf1tB/nwbZ8kGAgB8NyTL/KBj72dN9x244TWE/CP4B8O4PG6cboKc6wXQpy6Qt9CHgLq8rSvBjoKvK1ZrW3ngWzw4Yhf/eSP9Bzqyz6OxxP8469Pceur3s/rLn8bX/jYNzi4/9AU9/T0HHtScWybpuW2i5ktnWdfQGPa7Asd+w9lgw9HHNzXydaXd/CuN93Bay97G//+rk+za3tbkXo482npdE5bOpUu2D60b88BPvGhL/GaS9/KW1/7YZ594gVSqVTO65569Pls8OGIX9/1ZxKJZEH6IcTJ7G9r51P/+pXMvvqaD/HMP9eTTObuq2M5sLcjG3w44olH1tFxoLPQXT2pVDz3e5NKJKa8HzNVOs8xLJ1M5T0/K7Td2/dmgw9H3PXdX9HfMzDudWx9aQfvu/U/ee1lb+ODt3+MbZt3FrqbQogJKnQA4j7gq0qpekADDEqpi4CvAT8v8LbESex4ZTcf++AXGOgbJJ1O8/Bf/sld3/8VyWl0kmuv9GI87m5Kzer5KL2eWDBSpF6JQklE44QGAyTjJz9ZtFd4sHhHz5UvX9SE1eeerO5NuutffwUf/9AX2d92EIAX12/h03f8N8OD/iL3bGZyVJehN4+eI9xw3jL0BagYFA6F+frnf8DT/3wegK5DPfz7uz5N287941pecgaLqRIJR/nml+7kyX+sA6C7s5ePvOcztO3cN+51KKZ2h40GQoSHg3mDhbVn5E4fqV2zaCq6NSu46itRx015bbpo5UlzccUjMUIDgdM658y3n00kwfqR4/CubZnA/rbNu/jIez5LT1ffSZYUQkymQgcgPgnsAw4ADmAb8BjwNPDFAm9rVpu7oDkn38Ob3v5qquoqso8PtOUOOvnb/f+kv69w9b0nm9XnYuVbr6PurMU4a8tpveosbLWV/PWzP+fR//4VnZv3kprAXRtROgb2d/PUd/7EXz99N+t+9BDDh05cGMfidrD81qtpOG8ZjtpyWq48g3nXnovBPD2STjXMqePM81eNarM5rETC0VFte3cfoKtz9FQTURj2Si+r3n491avm46wrp+mS1Qx1+3nmu39icH/3aa27t3uA9c+8OKotlUrTnmfU2QWXn4X+uBP6N95+E8YSzmUiZo6+nn6efeKFUW2pVJoD+8Y/ULWxpZ75i1tGtV105TnUN9UWpI9HJCIx9q/bxiNfvI+HP/cLdvztBSL+0XfEPc3VLL/tGryttXhb61hx2zW4m6sK2o/ZzFlbzup33EDlshZcDZUset0lVC1tPeEy/W2dPPmtP/DXz9zN+rsext81/hELx5q7aA6O4xJN3/a+N1Kep8R1Pp0Huxk6LqDf3ztI58HTO94LIU5PQc92NE1LALcopT5FZtqFDtikadruQm5HQG1jNV//yX/xj4eeYte2Ni6/7kLOOG/lqPwPrjyZ1esaqrFO8py9QrN47My5bA06o5EdD29g24OPAxDq8/P09//CpR99I+UthZnrmUqliEcTWO3T6zOaTtLJJOHBADv+tp7+PZnhul1b9hHq83PxHa/D4ho7B4Kjysfca84hnUwVPPFkLBxDb9Rny3oVmsNp547Pvpdn/7medY9v4MzzV9GyoDnndWaLGbt95uaBiEfjoDIlKYvBVVdJy9VOtvzxaTY/uIFEJAbAU9/5M5f/vzdhL3eTjERJp9KYJpCPw2az4PW5c052nXlySyxaPp//+/kXefAP/yAcCnPD669i2Wq5YzvdJJNJkrEklmn2e3Ek58NA/+j8Si73+Kux+Mo9fOYbH+WpR59j47qXOf+yszj7orUFz//Q39bF+rsfyT7e8udnMdkttF64PNumNxopn9+IrzUzA1h3XB6sk0klk6QiMfQWc0knNJ4KmqaRCEXQGfQYDo8MU0rhbqzGVV9JOq2hN5z48w10D/Hk//2RZCwzsrFj424iQ0Eu+MBNmGwTG23W3NrAt+7+An+//wn27znI1a++lDXnLD/5goc5XQ6UUqNGzuj1urzH5VIVDUUxmA0YDLN73xQzS6HLcH4a+JqmaXuBvce0W4GPapr2uUJub7Zrmd9Ey/ymMZ9fuHQuq85YyqYXtgKgN+i545Pvxe1xTVUXT0sqkcS/t4P2xzeSjieoWruY2HFzAdHAf6i/IAGIzrYunvnT0+x7ZT/LL1zO2ivXUl5b+skNp5Nw3yCHnn4Z//5OPJU+am++hBd/+xSpRJJA9yChAf8JAxCQORkq5EliYCDA5qe2sO6B5yivLeOSN15M0+KmCQ3zHK/a+iped+sNvO7WGwAIjoR47c3X8/v7Hsi+5t8+/m7qm2Ze8rRoOMquDbv4568fR2/Qc+nNlzJv5VyMRRjBEuodZt8zr4xqiwUjhIdGiA0Mc/CJDaRiCarOWEzFkrmYnCcPRFTWVPAfn/0AH//wl7InuxdfdR7zFrbkvFav17NszWKWrVlcmDckplz7jnYe/80T9Bzo4Yyrz2TlRcvxVHqK3a1xqagq46Of/QAf++AXsvvqRZefw/xFufvqiTQ013LzO17Dze94zWR0E4CuV3KnhbQ9uYWmsxbljH6baOABINw7SMczLxPY34mzoYr6C1ZhH+fd9ZkmFgjRt3k3PS9ux2iz0HDxWtxzarOfq9LpGE/xsZHewWzw4YiBvV2EBwKYbBVjLDW2eYtamDfBffOIppZ63vaBm/nJt+/Ntr3zQ7fSOA1KzQ71DvHS45t54W8vUDOnmotefxGNCxuK3S0hCqLQ4bTPAD8Awse12w4/JwGIKVRVXcEXvvlxdm1vIxgI0dzawNyF06dcUaizj91//Gf2cccTG6k6cxkH7RbioaPD1k0FGNEx2D3Ijz72YwIDAQD+/otHObSnk7d84mbMUhqvIBLhCLv/9ATRgWEAgge7iQ35mX/JcrY/8iJKp5vyuuKapvH8X9fzt589DED3vm52rN/Jh77zAerm5sunW1gOp5333HEbl159Pv29g9Q11jB/UQu6GVi/fs+mNu7+r19kH//k4z/lPV99F/NWz5vyvhgsJpROl5OUUk+a3X98LPu44/GN6Ax6atYuGdd6L7z8HH72+29xYF8HHp+bBYta8ZV5Ctl1UQK69/fwg4/cSezw6Jn7f3A/IwN+rn3ntTlVqErVBZeexc9+/3+07+vA7XMxf1HrmFVbislelpvjx1HpRmc4/WNkIhRm15/+SWww87s/vOcg4Z4BFt96Pebjhv3PBn1bdnPocKLkZDjKrt89yuK3XIezfmKlVY15curojYac/DtTwWwxc8tbX8MZ56ykp7OP6tpK5i1uwWQq7ambqWSKx3/zJE//8WkAeg70sH39Dj78nQ9S1STTi8T0V+gAhCKTfPJ4q8iU4hRTrKKqjIoJRPMT8QTxWBy7s/g/vv79udm0A23tVC9qpH3DLgDctWX4mk6/7nhve282+HDEtnXbGOgcoLa1sHNaZ6vo0Eg2+HBEIhjB5cwEkJZcfxaOKb6DGBgI8PhvnhjVlkwk6dzbNSUBCAC3x8UZ547ODREJR9HpFOYCJEcsBelUmqf/9HRO+4uPbSpKAMJR6WHJdWex9f512bb5l68m3J07T7l34w7c85rQm02YLScOkBlNRhYvX8Di5QsK3mdROrr2dWWDD0c89adnOPdV51JWMz3unmf21fksXj5/zNfE4wmS8SS2kyQbnEzVi5vY6bIRPVw+V280sODyNac02uF40aGRbPDhiPhImOhQYNYFIOKhML0v7shpD3X3TzgA4a4to371XDpe3JNtW/6a83FUFCdhtMPlYPWZywmNhDBbzdNiKsNw7zDrjvl9gsw00e793RKAEDNCQb6FSqkRMoEHDdirlDo2CKEHLGRGRohJkE6lSEWi6Ax69JZTHw2wa0sbf7jrAQ7uPcTlN13E+VedTUV18U6mjHlOekwuO43nr6ZqcRMGiwlfU2XeOyQTlW9Iv06nO+lcRzE+6VQqk+VfKTgui7mrtpyL/vU1eJuqpvzz1ul1mKwmoqHRiSAnKw/EyYQCITY+8zJ/vPshLFYzr3vHjSw/YzHGEr9bc1IKrHmmMViLdGGjN+iZe8kKylprCPX7sXqceJsq8e9uz3mtwW7lF9/9Le17O3n9O17FkjULp12ySC2dJhmJZKYvWa2TMr1oNsl3fDCajAW5KC4Fmqax/aVd/O4nf6G3q59rX385Z19+Br5yz5T3xV1bxiX//nqGDvaSTqbw1FfgqZ/YMP5kJIKWTqO3WEb9G+mM+Y+r+ul+vD0FOoMBo91KIjS6upjhBEHwdDJJKhrLOfc0O6yseuMlNJ+zmKg/jKPSg7epsmjHnZ5DvTz+4DM88dCzzF00h1f9y7W0LmouSl/GS2fQYTAZcpKsz/YcJWLmKNSe/AEyox9+CnwCODYLVxzYr2naunwLitOTCAYZ3radaG8fOrMZ79IlWKsqURMcwt3e1sGn3/1loofv6tz9zV8x1D/MbR9+0ylfFPZ19rN9w07aXtnHwtXzWLh6Ad4Kz7iXdzfVjvpBVDpF7TkrcNVV4Kmb+DzCE6lurqJ5SRP7XzmQbbvwdRdQJjkgTlsiFCKwczeJYJCqVfPpefFoDe6K5fMom1uPvkgJCZ1eJ9e941p++ZVfZdvc5W7q5x0d/XBwzyE2P7uFgd5hVl+wnLnLWrFMMJHWeG1at4VvfPz72cef/8DX+MKPPs7StdM7QaFOp+OCm85j61NbSR+e9mAwGlhx0Yqi9clks1C1sJFoOMaeLW38/Yd/4dyLV+Qcc5JVPh7+5q9Jp9Jse3EnX/zxJ1i8evqMcEhGIozs209w/wGUUrjmtWJvaCzad24mqJ1bS1mtj4HOowM7r33b1XinSQ6Ik9m7Yz+ffvdXsuUT7/zvnxMOR3jd226cku13t/ew9bltdLR1svTsxSxYOZfGtRP/zmmpFOGeHoa3biMdj2OprsKzcCFGR2Z0g8XnoubMJXStP5oPpmLFfKxl0yNPViEZzCYaLl7Dzt8+mr1JYHI7cNSW5319YuTwuWff4XPPZUuxVlZkzz2tbjvWZSfO2zDcN8yOTbvZvnEncxY1seTMRVSMsb1TFY/F+eX3/8DjDz4DQOeBbl58djP/84vPUttYXdBtFZK30ss1b72aP333z9m28tpyaguUcF2IYitIAELTtLsBlFL7gGcPV8MQkyydSjG8YyfR3kw943QsxsDGF6k8/1zMHs+E1tW+pyMbfDjioV/9nevedAVVdROf4jAyHORnX76HHRszUyWeuv9Zzr/uHG654w3jzqlgLfew6OZrCHb1kU6ksFeXYZ+kERlOr5ObP34zbZv20Lm3i5blLcxZOqdod8JnCi2dJrhvP+HOzHQaa1k5c648k1QijaXMg72mvOgXQsvOW4rzf97Jzhd24qn0smDNPCoO32Hr3NfF/3zgm4QOJz99/A9P8q7PvpWzrzyj4P2Ix+I8cN/DOe3PP/7itA9AADQvaeb933ofO9bvwGDQs+CMBdTPL34isJef2cIPP/NTAJ5+4Fluee+rWDC/HrQ0nf1+vns4+ACZO8ObntsyrQIQkd5egnszifw0wL9jFwa7A1tN6Z58l7qyah/v+NLb2f3iHvoP9TN/zXyal46dEHq62bezPRt8OOLPv/grl95wAb6Kyc0TMdgzyP/9x/fpbs+UIn7y/md41duv44bbr0E3ngyIx4gHAgy++FL2cbS7h4BOj2/FMpRej95ooObs5biaa4kM+rF4XDhqytGbZmdwztVUy+JbryXcPYDebMJRW4HFmxuMSSeT2eADHD733LCRqvPPw+QZ34jUeCzOn3/6IE/8ORMYeOr+Z5m3opX3f+lduLzjr8ZyMr2d/Tzx0LOj2oKBEAf3HirpAATAmivWUFZbxu4Xd1NeV8781fPwVfuK3S0hCqLQZTizk6mVUtWA6bjnc8e3CgDikTihQAiLw4J1nGWsUtEo0e6enPZkMDThAES+Id5mqxm9/tR2ka793dngwxFPP7iOy99wCY3zxn/RYS1zYy3AFIvxKKv2UXbNmVOyrdkiFYsR6jiUfRwf6Af68SxdgrN58rI5B4eDJOIJXGWukyaFM9vMLFgznwVrcudC79t+IBt8OOKPd/6FJWcuwukpbBkvnV6H3Z0779g+jgoM04FOr6N5cRPNi0vnQm1kOMgf7vxL9nEynuTub/2et3/iVs64fA3ffv9X6e3sH7VMMefDT5SWShE+2DGqTe/14R8MonNHsUyzksylpLKhksqG088/VIryVaax2qwFnyKXTCQZGRzBZDVhP5xz4WBbZzb4cMSDv3iYc646k8oJTr1IBEM5beGuLtwLF2CwZb7HRpsFT0s9npbiB0OLTafX4aytxFl74v06FYtlgw/HSoSC4w5A9Bzs48m/jA4M7H65ja793aMCEOl0msBAAJ1ej8s38cCEXq/HaDIQP64qx3SY1mhzWll89iIWnz39b0AIcbxCl+F0Ad8G3sBxwYfDZsYEyQLr3NvFgz96kF0bdlM3r5br3nktzUuaT3qAVAYDequFVCQzf91aWwc6I4lokvhICNMEEknOWdBETWMVXe1HAxq3fvANlJ9itDWVSuVtT4/RLqafRDxBf3sfgX4/rnI35Y0VOfusMhgw2O0k/P5R7acy6iEZjhIZHCadTGHxujHnqeOdSCTY8fwO/vzdvzAyFOTs687iotdfeMp3DZLJZE5bIp7M3hEvJIPBwE3/ch2bntlMOp0ZAmu2mDnjwpUF35bI0NJpkvHcf+NkMoXJbOL1b7+RbS/uzJYqtNotrDhr6VR3c0zpRJLokJ9EMILJacfsc43OQ6DTYXS5iA/7UXo9MW8Nf/3lUxzY1s6c5XO48T3XUz/OgHAykaT/YB/+3mGcPicVjZUYT5KUU0xPcxfNwVvuZqj/6HH7Xz70Btx57oafTCoWJzIwTCoWx+x2YvFlLlD7Ovr4+y8e5aXHX8ZX4+PVH7iJeavn5j1HSKfS2albx4tFYvS19xIaCuKp8lLRWJH9DuTL5WCw2VCS2+m06PR69BYzqejoUbO6MUaOBPoD9LX3gqZR3liJu8JNOpXOHlePdWzOA3+/n3X3P8cTv3sSs9XMde+6luXnL8NsM5MIR4gO+EmnUlh8njGThlbVV/CGd93EPd/+bbZtzoImmuZKwEmIYir0+PKvAyuAm4A/AG8D6oAPA/9e4G3NCEF/kHu+eC89+zMX/gd3dnD3Z3/BWz7+ZuatmX/COw4Gsxnv0qX0b9iIraGR3pfaiA5mThhMbgctN16CdZwl4Cpry/nU//07m1/YRtfBHhYvn09ttY9g3zCOCeRtOKKmsYrqpiq6DxwNaCw+YyGVE8ymLEpTKpni5Uc38bcfPJiZL6oUV7/nOlZduWbUPqs3GvEsXkjf8y/A4RNIk8eDye0Ztb7w0Aj+QwNo6RSumrKcfS4+EuLA39cRbO8CwGC10Prqy7BVjg4sHNp1iLs+8/Ps46f/9AxGs4Fr33HtKZW2bF7QiMFkGHWRes1brsA9SXOEF6+cz5d++ileWrcFs9XMijOX0FLiybKmM5fPxTVvuYL7/vfoyanBZKB5YSMAS9Yu4os/+SQvrduCzWFlxVlLmbOgcVL6kozF8XcOEBkKYvM5cdWVYRgjSR5AOpmif+tuDj2xIdOgFE1Xnot34ZxssjelFI6mRiJd3aTcZdzzjT9nq/20vdTGzz51Fx/89gfwnOQYr2ka257ayv3f+iPa4eDYpbdfwRnXn533brmY3uqaa/5/e3cd3+Z1L378c4S2ZZItM1MSJ07jMFOTNGXmldat7Upb1/Hufut27+7d7pih7e66rdsKa9euzEnTJk0bZkbHzAyynt8fkhVLlhPbEdn+vl8vvRIfPXp0zqPz0Pc5wH/+4Rvs+HgPdTX1lM4tYcLUgiF/3tHbS1N5Pa3V9aheB+0nymgrq0RnMJB/5XLMSYm8+vhr7Fi3E4CakzU8/s0/8tDvvkBGXjox8dG0NLa617f4sgXYfMwu0tXRxUfPf8i6p9YAoHQ6rvrqdUxe6Jw21xgXh9mWSFeta3YbpbCWTB63XSz8RR8RQXxJCXWbNrvTIlNSMMUMPC/WldXyzH//nboyZ0uy+FQrN/y/T5GcaaNk/mR2bdjjXjY5M4m03NMzPGxfu523nnwbgO7Obp7636eJS4wlpzCd4298QFu5sxVG7IQ8LDkZ2LvtRCdbiUtLROmcx0CdTsfqq88npzCLXZv3kpWbTsnsySQmS1cGIULJ3wGIi4CbNE1bp5TqBTZrmva0UqoCuAf4p5+/b9Srr6h3Bx/6dLR2ULa/jMS0RJKyB96w9/b0YG9rQ+u1Y4i2kLJoIQ0HTriDDwDdTa3U7z1KxqLpAz4/mPScNGKiItj05DuceO4DTmhgio5k6ReuHvao0/FJ8TzwP3ez7pUN7N20jxlLSpl3wexR1XxZDK6+vI43H3319IwWmsabj75K9pQcbJk27G1tOLq70EdEYrJaSVm0gJ7WVnR6A8a4WAz9RsxuqW7gw9+9REulc0A3c0wkSx68mvis03WutbzaHXwAsHd0UvXJLnJWL0TXL+Bx6vDAqVs/evVjFl+9hDjb8IMG2ROy+OqvHuKtZ96lrqKeZVct5rwFgXsCrjfomTStiEnTgj815Xg1d+UsIi2RvPf8+ySkWFl14wqyJzi7BxmNBiZPn8Dk6YNPVegPvT12Dq3Zzs4XPnSnTb9xOfmLpg7a772zoYlT75++AUDTOPnORqJSbR79tk1xcSQvnM/RPScHTDXcWNNEzYkqYmIizjiDUn15Pa/99iV38AHg3T+/TV5pAWkyTfGYlJWfQVb+yKYiLt9xhA2PnT4/5M6dRFxmKm1llZx8ZyO2ZfPY+cEuj884eh1UH69i+vnT+dIvHmTNv9ZxZM9xFlw4h5nLp/scj6n2RI07+ADOFk2v/upF0gvTiU+xYoiIwDq1hJ6mZhz2HozRMZis8SMqk/AUmZxEyuKF9LS2oTMaMcXGOltFdHdjb29D63VgiIpi/0d73cEHgMbKBnat3cHyW1dyy8M3sPHNT9i8dhuTZkxk8WXzsbrGGOlo6+CjVzYO+N4Dmw+SFG10Bx+i8zKpOlHPqZecgVil07Hw3stJK8l1fyYmPprZS6Yze8nQr4eFEIHl7wBEPNA3jUATkAgcAjYAj/v5u8YEc5QZvUE/cKodvY72poH9F3u7umg6cIDOamfQQmc0kjhjJm3Vzpu3iKw0uiKi6OjoptWhsHf1YBjGE6qq3cepPXC6z3B3aweH125nxk3nD3tmjfS8NK6//yq6u3owS1PdMaW9qW1ANwRHr4P2pjbaVSfNBw86E5XCWjKVyORkTLG+AwCVe467gw8AXS0dHF63gxk3nu9+itE/uNanrbIWR0+PRwDCEjtwvISEZCumET6lVUpRODWfvOIcHL0O6ivrObbzKOZIM2kFacQmjL/R0seaGGsMCy+ex5wVM9Hpgz/1bmd7Jyf3nKCqtpnc1bOp332U5rJatv/zfZInZBLr48kvOLskeU9p67DbsXd0gldTeWN0NJaEWJRSHs2elVLoejqo3byJhGmlGKN9j2vS0dJOj1cfajTN5zlKjG9t9c1s/ts7HnXz2MZ9zLphMZRV0tXUgt1uZ/UdF+CwO7D32Hn/+XXYu+1ERjuP39lFWdzy5Rvp6baf8dqhrV8riT6dbZ10tHYQn2LF3tlJ86GDdNU6b4B1JhOJpdMxxvhvkMPxSul0mOLiMMWdHvPB3tlJ0/79dNU6gwOmhATK9g0c+u3EruNomkZyRhKXffpiLrhpJSaz0WOaTqPJiC3dRtVxz/FAUnJSaGjuoCsrm4gII8akeE69vt39vuZwsPnv77Dy6zcSMUi3DCFE6Pk7AHEYyAdOAHuBG5VSHwNXA/Vn+uB4Zcuwsfr2C3j1j6+506YtmUr10SpKVwyM1nY3N7uDDwCOnh6aDx8iLjeDrs5uNu8uZ+taZ7NGpVPc/shtTF009Ce2jadqB6TVHauit6cXg3n4TdiVUhJ8GINik+IwWyLoaut0p5ktEcTER9F8sN+TLU2jce8ejNHRGKJ8D6bYeHLgYFb1xyrptdsxuPrwWlIG3oTFF2aj95qjPGtSNml5qVQcrQSczS8vvecSImPOreWN3qDn+N4TPPq1x9w3YgXTCrjp6zdgTQ7sqPAiOELRlaC7s5u1z77Pm395y5120W0rsXT10FbTRFd756CfNcVYUAY9Wr/gtSEyAmO07/0sKSuJZdcv5b2n17jTll6zgCitk97OTtpOlRE3YaLHTUCfWFss0dZoWhtO3/AZTAbipe4LL/aOLrrbBtbbni5nN7aYGVP50yN/ob7CeUkYbY1m9W2r2L/5IOmFp6cY1Ol0Z712iE+xotPrPILh8akJxLq6yPU0NbmDDwCO7m5ajh3FOnkK6iyDE4vh62lucgcfAHoaGymcXsCBjfs9lpuypMTjOOPrdzYYDZx/03L2bz7g7gKZkp1CRJSZ333zz+4xQc5bVELetAKqth92f7ajoYWejm4JQAgRxvwdgHgCOA9YA/wAeBl4ANDhHAdCeNHr9Sy8YgGpOSmUHy5Hr9fRWtvMecunYfUxcF5vZ8eAtO7GRhKm5VNZ08zWtaen8dMcGs/9/HmyJmaetY9vn9TiHI56NY3MmTtpWK0oxNhnTU3gum/eyIs/eY6W+hZiEmK4/OFrsFiMdHktq9ntOHoGn5k3dUoOx9bv9kjLnlPsDj4ARKUlkzJnKlWbdoFDIzorFdt5EwbcLCWmJvCZ/76TkwfK6OroIjUnhYzCkTUj7q+zrZOXH3vF4ynw4e2HOXmgTAIQYsSqT1R7BB8A3vrHGq79zIXYP9mP5QwtbMzWWPIuXsLxN9fT29mFwRJJ7kWLMMf6bsVgMps4/6blTJg1gbqyamJizFgjHGjtzm4Z3Y2NaA6HzxuzWFsc13z9Rl74yT9pqm7EEm/hsi9cRUJGYKZFFqNXZHwMcRk2mvo9zFA6hTnCCBnJHD9R6w4+ALQ2tNJQ08Qt37yZmGHOcmDLSuKar9/Ay796kY7mdqzpiVz58DVYXDMU2dsHttDpbnIOXHi22ZHE8Nnb2z3+1hwOUlOjmHnxbLa8vglNg9KV0ymaM2lI68udkssXfv0g5UcqMJoMpOSk8NjXH/cYkHTHB7uYcO9lHp9LmpBJhI8ZpYQQ4cNvAQillBG4EbgNQNO0d5VSk4BZwEFN03b667vGmghLBFMWTiF7UhZtjW1Y4i3EDDLInSFq4EE1wmbDFGOhRxv45KqloYWudu9bwsElFWUw+ZK57HtjE45eB7nzismcIf3RxUC55+Vz50/v8aizPa2toJRH81udyYTebB50PUlFmRRfPJf9b7rq3PzJZE4v9FjGGGkmbe55WCfmofX2Yo6LHnQmDWuKFWuKf4MCXe1dA8ZqAWipbfaxtBBD4z3FKzhnm9CMehbccylR1sFvyJRSxOVnMvHmi+nt6MRgicI0SOuHPlExUUyYUUR7egyNu3dBv/uFiKRkzxk0vGRNzubTP76L1vpWouKiiLUFZ3pkMbqYLBHMvv0CNv3lLRrLajDHRDHjxuVYMxMwzy1h44+fHfCZ8oOniIob/nTDOr2OifOKSc1Po6OlgxhbDJZ+syP56moRkZSEzuDvZ28CwBg9cHtHRRlZdedqZl8yD03TsKZah/VAK70gnXTXODM1ZTU01gzsjqmLMGGyRNDd1klifhrTb1gmM/QIEeb8dhTWNK1HKZUHaP3STuDsjjGmtDe301zXTER0xJBbFgxFTGLsoIGHPsaYGGLy8mk5dhQ0DUNMDNG5uSi9HltmEkqnPAYKy52SO6wLRXNMFJMvnkv2nEloDg1LYix6H4M/jVSvvZe2hlYMJgNREqEe9bzrrMFiwVoylca9e9DsdnQmE9aSqWcc4C7CVedy5p65zim9Dr0lksaKeto6GknMso1oZouRiLZGU7p8Gh+97DkoVkpualC+X4Rea30LmuYg2hrrHpvkXCWkJmCONNPVcTpIHGeLo2DOJBLTbUNahzk2GgZp9TDoZ6xWojIyaT/lHO/HnJhIVOrZ63K0NYboMwRF7D126ivrUUqRkJoQ9PE0RHiwZiWz9KFr6GhsxRhl9giklSyYzLb3tnksX7q8lJa6FuKT40f0fXHJ8cT5+KwxNo7onFxaTxwHTcMYH090ZpbHeFat9S1oDo3oxBif3Y/E0Bnj4ojIm0B3cwuqvQmd0YAlOxuD2YQte3gDmfsSmxjLhJlFHNh80J2mlCIlL5WSBTdj7+whyhqNMXLwBx5CiPDg7zDwn4G7gK/4eb1ho/xwOc/95J9UHK7AEmfhyi9cxaR5k4LWnE9vMhGdm0tESjJarwNNb6Sr14ERSMtL5fZHbuO5nz9PS0MLuVNyueahq4m0DH7z54vS6YgJQLPypupGtv57A3vf205knIXFt68iZ0ahz9GtxeiklCIyORljdDSOnh70ZvMZgw99dPqz17mak9UcWr+HXW9sRut1UHLhLCavKCU+2UpHWwcoRWTU8Or6UOkNepZdt4zWxjZ2fbCLCEsEl95zCVkTZS7xsa6rrZOD6/fw0VNr6O2xU3rpPEpWzcBiHd5Nvy9JmUnc+b1P88yPn6Guop7U3FRu+PJ1Qw4+9Om199LR1kGkJXJIN/16s5nYoiIsmRlomoYhMuqcnwo3Vjey5qk1fPLaxyilWHj1IhZdvWjYzeqFfzgcDtpbOoiIMofkHGuyRGDyce1ROL2IlbesYM3Ta3E4HMxcNYOqY5Ws+fu7XH7/5UxeNAXjGaafHQ69yURMfj6RqaloDgf6yEj0rnV3tnZw4IPdfPzs+zjsvUy/fD5TVpQSFX/u+/V41N3ZxZGN+1n/t3fp7uhi6uqZTL1gJkaL/x40mSPNXHHfFTz/y+c5vP0I0fEWllyzhDX/eI8Vt6wka1KW377rXNi77XR2dGGJjZKglhCD8PdZyQJ8Sim1CtgMeLQv1TTt837+vqBqb253Bx8A2pra+Pv3/sb9v37A3UQsGJROh9ESzaGdh/n3n16j6mQ1Sy5bwPzVc5i6qISsiZl0tXcRa4sl0hIe0146HA52vPYJu9/eCkBbfQuv/+x5rv7ubaTJTdyYM9iAkyPl6HVwavdxtvxrvTtt64sbiE6M5ZBJx1tPvYs5ysziSxcwdd5kIgIQiEjKtPGpb9xEY3UjBpOBBB9jtIixp2J/GWv/+Lr7703Pf4DFaqFk1Uy/rL+wtIAHf/kAbc3txFijsQyzZdipoxW8/cy77Nq4l5K5xay8bjkZ+Wc/H+n0enQ+mkyP1N4Ne/nYNW2ehsa6Z98nLT+N0vNL/fYdYmiqTlaz5sUP2PzeVvKn5HLRLReQMyE8bs5irNGsvu0CZpw/ncPbDrPp9U1UHnFeUz39g6e595f3kTXRf3lVOp3P2V3K955g3RNvuv/++Nn3iU6IoXj5NL9993hSdbCcd373svvvbS9/TERMFDOvWODX70nNTeHah65h+5rtdLR28NEL62lraqPmZA2f+9m9IQ94Ht17nFf+8jonD5Yxd9VsFl+6gKSM4QWUhRgP/B2AKAa2uP6f7/WexijXXNfsDj700Rwa9RX1QQ1AAJw8VMaPHvwlPd3OQfGe+/2/aW1q57r7rvRrt5BzoWkaNUcqOL79CHEpVvat3TFgmfqT1RKAEGfV0dZB2Y6jA9IPfbibrAXFzJ0zhe62TjrrWji29wSTZk4ISD5MESaSs5MDsm4Rno5vPTggbfc728ibNdEvrSAAYhJiRnTh3FTfzO++9TjlR53npbUvfsjBHUf46q++ENQpYnt7ewc0qwfYu2GPBCCCrKO9k7///Fl2bnAO7FtbUcfezfv51mNfJWmYLWsCpW+625d/+9KA9+pO1fk1ADGYwx/vH5C2+91tZE7NI8Ym0ysPV/negb2t9767nSnnlxIR498HEvUV9bz3t3c90hoqG2iqaQppAKLyRBU//sIv6Wh1Dhb/8p9fp6a8lk9/89YRTwUuxFjl1wCEpmnL/bm+cBNhicASZ6HNa+7z4T6x8odTR8rdwYc+7zy3hhXXLsU2yLzxwVZ9uIJ/ffdJenvsFM4rJjoxlvp2zykXzdHh0UJDhDejyUhM8sCxTGJTEzi5YR8V+0660+Z+ajn45+G0EMSlDGzpEpMYy+GN+5iycnpIxzmoPlnjDj70KT9aQdXJ6qAGIPR6PdmTsznpdROSMeHcZ6ARw1NbXusOPvRpaWil4nhV2AQgACKizFhTrTRUNnikR8cH53rKmj7wOinGFstHT69h4S0rZIyqYYr2MdZYdGIsLbXNfg9AWHzUEVOk6Zyn2z5X5ccq3cGHPh+/vZnL77yYtJzxMV6UyWiixz74rGe+6HU6Lrxj9bCWH27XFr1eP6zv6PvMcL5Hr9fT29t79gXP4TMmk4nu7u5hfYfJaKT7DDPRnevyADnZ2Rw7fnxYn5HO98MQnxzPlV+4in987+/uaYDmXT6f1LzgH1iMpoHR1EhLBAaj/y+GKw5XsPWdLVQeqWT6qukUzZxA9BD6SR75eD+9Pc75m49uPsD8m5az/m/vuufstuWmkBzkliNidDJFmCiYM4nD6/fS0ewcut8UaSZvZhGv//Q5j2X3vLmFKUunERnr34uePm1NbRzaeojNb2wiOTuZGatmkO6HqT5FeMouLWDbKxtpa2gFwGg2kj4lhw//+jZx6YmkFqVjDtGgZ0az71O4wcf5IdBmrprJzjU7aK5zzgxjy7BRPG9y0PMx3hmMBvQGPb12z4vacHsCG22N4coHr+Qvj/zFndfS86eT5udrgtbGVg5uPsDWt7aSmp/K9BUzSCtII2/mBHa8vokO1wMlU6SZ5II01j/5LsXLpkkAYpgyirOJSYqjxTVLhd5oIG9WEbve3sryuy5yL9dU28T+j/ezY812sidnM215KSk5KcP6ruTsZJbfvJz3/v4e4Bx76vIHriAhLbTdIn1dlxvNRgzjaDDeHnsPT/2/J4b1mRv/6w5e/86jQ17+wu/czV8/++Nhfcetj3+ZvU/9a1ifKb7xKl79+m+GvPzFP7ifff8a2KrrTCZddRltFWVDXt6Slkn97r3D+o6EKcUce2/zkJfPXT6Txn0DW4idSfykicNaHiQAMWyT5k3i/t88QF15HZZ4C6m5qUSG4Cl+9oQskjOTqC473aLg+vuvIt4W79fvqSmr4fGvPU5Hi/Om79DWQ6y64wKW3bjsrJHBrvZO9/97e3rZ/tonLL7jAgxmI+ZIM7a8VGnqKIYsc3IOF3/1eupOVAOQlJdC9ZHKAcv1dPbgGGYUeji2vbOVV/7wCgCHthxiy1tb+NzP7iU5R7pmjEUJmTZWPnA55XtPgqahlGLT8x/i6HVwdMcR2lo7mLwgNDfaKVkpLLxoLh++dnp2lgUXzSUte3gX9P6QmpfKPT/9HFXHq1A6RUpuSth0BxxPkjKSuPjWC3jpT6+50ybNmEB6XloIc+VbwYxC7v/1A9SdqiMyNpLU3FSi/Bg41jSNT177hLdcYz0c2nqIzW9u4XM//xxJ2Ulc8uVrOb79iHPaaNd+DeCwB+78MVbFpyUw/dK5dLZ04HBo6Aw6try4gbTi091p7D123n9mLRte3ADAke1H2PbONu7+yd3ED2Pgc3OkmSXXLWXi7Ik017dgTbWSkpMS8gEfMwvSyZmUzfF9p1uCXfGZS7CFUcsjIcJFWAYglFL34ZxJIw3YDTykadq6QZaNAH4PzMA5BsWHmqYt87GcCfgWcCuQDlQBP9Y07ZfDyZteryctP420/NCezJPSbXzxJ/ezf+tB6qsamDC9kPzJeX7/nsojle7gQ5+1T61humv2gTMpnFfsHnQSoKWmiag4C/lzhh8pEwIgtTCd1MLTT8gUCr1RT2/P6QvG6ZfNxXKGqQLPRXNdM+/+3bPvaWdbJxVHyiUAMYbFJsXz5i9fdD8tBcials/hnUfZt/kghTMLMZmDP+98pCWCa+69kmmLpnL8QBk5EzIpLMknYpgzH/mLNdWKNdX/MyiJoTMY9Ky8bjl5xTkc3n2M9NxUiqYVEhugY+K50Ol0pOalBqwVaVNNI2v+8Z5HWkdLO5VHKknKTMKaaePj5z7gxLbD7vctiTFYZdDAEYlJjuf9P73pkTa536CeDZUNbHzJcyrrxupGKo9VDSsAAWCOMpM9OWfkmQ0Aa1I8933vsxzYdoiqk9UUTs2nYGp+yAMjQoSjsAtAKKVuAH4B3Ad84Pr3NaXUZE3TBo5yA3qgE/g1cDEQP8iq/wFkAXcDB4EUYFQPQJCSlUxKVoBvenwdN9Vgb3hKnZjBpV+/gS0vbnBNXzeXzKm5fs6gGM9suSlc8a1PsfWlj2iubqTkgpnkzyoK8LcOulOIMSo2OZ7Lv3EjW176iLrjVaRMzKQHxeE128mZkhPSC8x4Wxyzls9g1vIZIcuDCC8x8dGULjqP0kXnhTorIaZ875uuJFOkmSWfXsW+91M5snE/acVZlKyaKS0zRyijOJuLv3wtW/69AaUU0y+fT9qk7NMLKHyeKsfS2TMp3RZWY60IEa7CLgABPAw8oWnaY66/H1RKXQjcC3zDe2FN09qAzwEopc7DRwBCKXUBsBIo0DSt1pV8zO85DyMdja00V9SBBq0OB8cPnQSlKJySR3bh0GedSMtPGzDw5vKbzycuaeCAQ94MRiM5pQVkTM5B0zSMYdYHVYSnrrYOWsrr6G7vIjopnpjUBJTO9yWKUoq0iZkkF1yJw+7AGBHYp9CxibGsuGUFL//udD+/yJhI0gpC2yLq1LEKDu8+SldHF/mTc8mblINOpwtpnsYaW24KJRfNZu1T7/HJ2h00ufo6L7l+qc++v8HQd5zXeh3EpCZg8TEQnBChUn68kkO7j9DZ3kV+cQ75k3LR6XX0dHTTXFFHV3MbUYmxxKQmog/A+FVxSXEsv3k5b/zfG+40S5zFowVrXEoCc69byvRL52EwG+W4eQ6MESbyZk0g87w8FAPHorGmWJl/xXw+dHV1AWerqZTcwHUZc9h7aa6op72uCXNsFLFpNgwRRo7uP8GRPUcxGI0UTskjcwhTFwsh/CesAhCubhIzAe/RRd4EzmUy4SuBT4CHlVK3AR3Aa8A3NU1rPYf1hqXWmiY++eMrNJfXkbpiOr/50d/o6nSOmhoRFcEjf/gK+cW5Q1qXLcPGnT/4DDvX7qDyaCXTzi+lcHrhsJ74GUxhVc1EGOtqaWfn8+s4tfkAADqDjrl3X0Zy/6coPugNBvRBqmal55cSkxDD1ne2kJSVzLTl00I6NWfZkXL+894f0eQa/E+v1/Ot336JyTOlq5O/tXV0klSUjkNBan4qWSW5xCSF5mlpa00jHz/+Ki0VdQCYoiOZf98VxGcmhSQ/QvR36mgF/3Xvj2mobQScU29+6zdfYuLUfA6+vYmDb7kGRVMw49YLyJrl/+OVUopZF84mPsXK9ne3kZqXytSl52Hz0cXCFKKBZMeiwQKyBqOBxdcuITUvjZ3rdpI9MYuSxVOH3f1iOMq3H2bzX94Azfl34YoZ6LOT+K/7foLdNUh6dJyFb//+q+QUyZTwQgRLuN0Z2nB2qajySq/C2YJhpPKBRUAXcA3OVhK/wjkWxLXeCyul7sbZVYPs7DPf+ISjmgMnaS6vIzrZytYt+9zBB4DO9k7WvfbRkAMQgHvMC3uXndpjFZzYuJeIOAtJ+elED6ElhPCv0V4/z6TpVK07+ADgsDvY/sx7LHn4OszR/p3VorWmkZojFXQ2tWHNTsGWm4oh4uxPsqNio5i6ZCpTl0z1a35Gatemfe7gA0Bvby/P/fElCkvyQjIuwVitn3a7nReeeIVNa7eRnpOKOcLEa699wI33XUVneR2aprDlp2INUgCgZv9Jd/ABoLu1g6PrdlB6w3KUPMX1aazWzXC0d+t+d/ABwNHr4J+PvsgD37rjdPABQIMdz6whITf1rC142uqbqT1SQWttMwlZySTmpWCKOvN4J5Y4C9OWTWPasmlnXC4cjIf6GZsYy8wLZjLzgnObK7uxvI7aw+XYu+3Y8lJJyE0Z0HqlrbaJ7c+85w4+ANQeOsWmD7a5gw8ArU1tbP1whwQgzmI81E8RPOEWgOijef2tfKQNh871+Zs1TWsCUEo9ALyhlErRNM0j4KFp2qPAowCzZs06l+8NiZZyZy8TY5SZusPesRyoKa8dkDYUJzbvZ/0fX3f/bc1OZtmDV2JJCL/Brcay0V4/z6TLa8BTgPbaZno6uv0agGira2btb1+iwTWjBsCCz1xI/oIpfvuOYGmqaxqQVl/VQE+3PSQBiLFaP3t7eqmtrAecTcv7VJ+oZte+ClprmzBGmrnga9djDfTYPECzj+N44/Eqent6MZglAOHLWK2b4aipvmVAWl11o8fsWH3snd30tHcCgwcgOprbWP9/r1O196Q7bcb1SyleNXPQLnqjjdTPoWk8VctbP3yGrtYOAJRex4qHryV1UpbHcj0dXdg7uj3S9JEmaivr8FZf3RC4DI8RUj+FP4XbVUot0At4D4mczMBWEcNRAZzqCz649E2kOubCeEkTnAfhprJq5iwY+JR26aULh73O9oYWNj+91iOt4UQ1DSerB/mEEMNn8TFtX3JxDhF+nJoNoOFkjUfwAWDz02tpbxh40RzuSmYXD0hbfcP5WGL8u83GO3OkmdXXLR+QXjQph9Za56mlp6OLk/1G1A+kpIkDT10ZMydgkLF2RBgonjFhQNqFN5xPrDUWncHz0jPKFkvEWWbpaDxV5xF8ANj+woe01DSec17F6FK594Q7+ACg9TrY9cpGervtHstFxkdjSfYMajWfqGbV1UsHrHPWktKA5FUI4VtYBSA0TesGNgOrvN5aBaw/h1V/CKQrpaL7pfWdHY+fw3rDUkJBOhMumgOAqaGVO754A7bURJLSErn3kTtH1De8195Lt88nFz3nnF8h+sRl2Jhx6yqMrv641txUply5EIOfn+Tbu7oHpHW3d9I7Cud/LyzJ50s/vI/03FTiE+O45aHrmbdiVqizNSbNXFLKHV++CWtSPKlZydz91U/RfqDcY5n2huAMK5SYn86E1bOdN3MKsmZPIsPHTZ8QoVA0JZ8v//gBMvLSiU+M4+YHr2X+qtlYkuKZ/ZlLMLuCyjFpicz+9EVEnCVgau8aeK3R223HMQqP2eLcdLZ0DEjraGiht9ezLphjoph522pi0hLcf8+4ZRUzl07nji/fTEKyldSsZL7wP/cw4bzCoORdCOEUjl0wfgr8VSn1Mc7AwedwjtXwewCl1PeBOZqmrej7gFJqMmDCOYZEtFKqFEDTtG2uRf4O/D/gT0qp7+AcA+IXwD81TRvWI/zmumaO7z1OxZFK0grSyJmcTaw1fKZs6uroouJoJW0OKLx6KbZMG9YMGwsvngeoEc8FHmWNJn/hFA6v2+VO0xn0xGUk+innYqypOVFNxaFTaA6N1IJ0UvrN9V5TVsPRncdoaWghd0oO2ZOyMZqN6I0GsmZPIjE/HXtXD5HWaHcwwp/i0m3oDHqPi9eCRSVEjXD/CCVzhIk5589k8syJ2O29xCfKuCyBEpcQy0U3rmT+qjnoDTpq95fx4RtbPZbJnhHoaWCdzDGRTLxwDlmzJ6E5HEQmxmIwnj6ltza2UnHwFM01TVjTEkgrzCAyJvgzT/faezmx/yTHdh8j0hJBXkkeKTmBG/VehAej2cjsZdMpnlGEvbsXvV5H+f5THKk/QGKmjYVfuIbWpjZOHalg0/s7yS7OJntSFhGDjOkQl5qAMdJET78m9elT87Akhs/1lwiOtOJsdr38kUfaxBXTfQ4kas1OYdHnr6azqQ1jlJnOLjtHth8hymDk4e/fizU5js7mDnav3UFkTCTpEzICOiimEMIp7AIQmqY9rZRKBL4FpAG7gIs1TetrqZAGFHh97FUgp9/ffVeEyrXOVqXUSpwDT34CNAAvAF8fTt462zt57f9e4+PXN7nT5l48hyvuuxxzmIygvGvNDl777ekpAvNKC7jyS9ecc5BEbzBQcsk8TJYIjq7fQ0yyldJrFhHvYzRpISqPVPDkN/9EZ5uz1YzRbOSW//k0GRMyqT1Vy6Nfe4z6ytN9Lm9/5FbOW3J6zvqoAF9UxmfaWPGla9n23Ae0VDeQv2AyRctK0Rv8PxVcsETHRZ99IeEX8a76aZqcw5xbV7L71Y3ojAamXbGApKLgTeem0+uITo4fkN7Z1sm7T7zJjne2udMW37SMRdctQW8M7mn/0LZDPPb1P6Jpzi7D0fEW7vvpvRKEGCeiY6Npa2rjpV/8i0OfnB5g+Opv3shrT7xO+aEKd9o1D13Ngsvm+1xPbKqVFQ9fy7Z/fUDDyRqyZ02geNXMgE+9LMJPYn4qS++/gm3/WkdPRw/Fq2eSNXPwwK/JEonJEklLQwtP/egZDm095H5v5S0rOLbxAHWnnGPqpOSlcv23bibOx3FVCOE/YReAANA07bfAbwd57w4fablDWOd+4IJzyVfNyRqP4APAxlc/ZsFl88mcEPrRcxsqG3jnT296pB3ddpjqY1XklQ7/5qS7pZWOymq6m5qJTE0mKimRGdcsofiCWRjNRjnxh6GupmY6KqrpaWsnKj2FCFti0G84APZt2OMOPgD0dPWw5bVPSC/K4OSBMo/gA8BLj75C/rQCouMsQcmfUoqUCZmc/8Wr6enqITImaswMZCaCxxwdyYRl08ieWYRSOszRZx6RP9B6u7vprKmn+lS9R/AB4IOn32fyohKSgjhlbFd7F68/8aY7+ADQ2tjGkZ1HJQAxjtQcr/IIPoAzSN0/+ADwymOvUjxnEtYU30+gYxIszLxyDr09diLjY4lIHH0t1sS5M5iMZM0oJGVSJr29DiJd3XccdjudtfW0lVdiiIwkKi0Zc/zpFoGVRys9gg8Aa55ey6obl1P33AcAVB2tpPJIhQQghAiwsAxAhKseH30Qz5QebPbuHro7ugakd/lIO5uetnbK166np8k5KF/j/sMklk7BOnkiUUG6SRTD093cwqm319Hb6bzxb9x3kJT5s4jNzznLJ/2vsbJxQFpDZT2aw0F3x8DxFzpaOujtsQ9IDzRjhEkCaeKcna3/erC0HC+j5uOtdNsyBrw32L4XSHa7nbamtgHp7T76cIuxq8tHveu1O3ws14V9kDEduptbOPXO+/R2Oq9nGoCUBbOJzRtz44iLIfKegrWtvJLKdRvdf+vNJjJWLcUc52yx1u3jWt3eY0en9xwOz9dMLUII/wqrQSjDnS3Thi3Ts8tBclYytjDphhCXHE/BLM9ByIxmI4mZw89fd2OTO/gAoPR62l1P1kV46qpvdAcf+tRt2429M/gn08mLSwakzVg9G51eT3pB2oAT/qIrFxIrfXmFGDF7ezt125xj9FiiDAMCxUk5KcSnBbdvsyXWwpKrF3mkKaXIn5ob1HyI0LJl2DBFegZ6YxNjMEV4ztgy+4JZWAd58txZ1+AOPvSp274be+fwH7CIsae3q5u6bbsHpHXV1rv/TslJGTAOzoQZhVQdPt0SR+l0JGVL6ywhAk1aQAxDbEIsd3znNtY8s5aDWw4xYWYRy65bSkxCeDQDNEWYuOCzF7IhIZo963aTnJPMik9fQNII5qTXHKefThiSkqlpgx1v7iP1YCMzVs0ivTB4/ZzF0PT/zfo47HZwBH+65uwpOVz20FWs/du7OOy9LLx+CfkznEO3ZBRlcM8P7+b1P79BY1Uj8y+bx8yVM1BKukB4a6isZ8+GPexZv5fCGYVMXVyCLTMp1NkaUyqPVbLt3W2U7TvJtOWlTJwzcVQGwzSHhuZ6emwvL+PK+y5i/cubKT90ioIZRSy+aRmW2OC3Xpu2dBoa8P5z67DEWbjw9gvImpgV9HyI0EnMtHHTd2/j3SfeoupoJZMWTKZwRhH3/Oge3vzLm1Qdr2bmqpnMvWi2x0Cq/Q12ftO0gem+1JbVsGvdLg5uOUTx/GKmLJiMNTXhnMolgqPsQBmb39hEXXkdsy6cTeH0QqK8pubWNIfzeseLo/d0/bClJ3LPD+/m7Sff5uT+MqYtPY9Zq2ay5/2dlO07SVxyPCs/vdpjwGwhRGBIAGKY0vLSuO5L19LZ0klEdMSgJ8tQScywcdHnLmXJTcsxR0VgjhrZ4JimuFh0JiPKYOBoRTtv/30NAEe2H2HL21v53M/uJTmIfYnF2Zni41A6nceFmrW4CH1k8PulR1gimLZiOgUzi0CDaOvpMUh0Oh2FpQXcNfEz9HTbgzbuw2jT2d7Jy79/mb0b9gJwdMcRdn+wizu+dwfRo3C2jnBUX1nPE9/8E811zYDz+Lbw6oVc+JmLRt2ApIaoKOImFdK45wCavZfeowdZfH4RMfdfQqzNisFsPPtKAiAmIYbFVy1ixvnTMRgNIz4nidEtqzibGx+5ha72TqLiLRgMBhLTE7nju7fT3dGNJc5yxiC02er7/GaMPPvMLq0NrTz1g6coP+icMvfojiMc2XaY679+w6CzbojwUHm0kse/+pi7+9ihLYe44sErmHvpPI/lDBERWIsnULtlx+lEnSIiMd5juawJmdzyH5+iq72LqNgodHodaflpzL1yASazkYjo4M8UJMR4JF0wRsBgMBBtjQ674EMfvdFArC1uyBd67S3tVB6rpLG60Z1mio0hY8VizAUFrHthg8fyna2dVBypQIQXszWOjJVLsGSmYYqPJWl2KbGFuSFtWRAdH+0RfOjPHGke88EHe4+dmrIaak7WYB/mGBd1p+rcwYc+5YfKqT5Z488sjmtVx6rcwYc+G17cQENVwyCfGKi7s5uq41XUV9R7DLYYbEqniJ9YgG3meZjiYonOyiCltJiEjOSQBR/6s8RZJPgwzpmjzMTa4jAYTl87mcwmouOjz3qeMlvjyVi5mKgM1/ltzgz0SSlUHq2ktaHljJ+tPlntDj702bdxH3Wn6kZeGBEUpw6eGjB2zbt/e5cWH795TF4WyXNmYIqPJTo/m7i5c2lq7RnweaPZSLQ12t0VVKfXEZsYK8EHIYIoPO+gRdBUHK7guZ/+k/JD5VjiLFzx+SspnleM3qAnIsGKxa58XhhIc/nwo5QiMikR86K5aA4HemPobzrGs6baJt5/9n02/vsjUDDv8vksuW7J0Jv3D7KLyb7nP7625HC2b21ZDa/8/hX2f7IfU4SJ1XeuZsaqmSG70TZGRWGdVERsQS46nR6ll2cMYmxwnt9spC22ojkcHN9zgme/+weaappISEvg2i9fR25Jrs/P6gbZp+VQGv58zU6ldMrnsdsQEUFcUR6G5CS2vrOVt77/B3q6eiieP5mL7roobMZrE0JIC4hxraO1g3/9/HnKDzmfDLQ1tfGP//47Vcer3MvEJ8ez4pYVHp+Lio0iLT8tqHkVQ6fT6yX4EAYOfHKADS+sx+Fw4Oh1sP5fH3Jw04Gzf9DFlm6jxGswz8xJWSRlyRgQ/pKSm0K816B3C69aiDX17IM19tp7WffcB+z/ZD/gbAnx0m9fouxAWSCyOix6o1GCD2JM0un1NNY08+R3n6SppgmA+op6/vafT9JY7bvlki0riaxiz3FHpiyaQmK63JCGu/TC9AEB3RW3rDhjN8TyQxW8+odX3TPU7d2wh49e+shjPAghRGhJC4hxrLm2ecDFsubQqCuvI73g9CCTpSunE2uLY9u720jOTuK8ZdPkJkiIs9ixZvuAtJ3rdjJz9awhfd4cZeaiuy6moLSQfRv3kj+tgOIFxUTH++7SIobPmprA7f99B7vf30XZgTJKlkylaEYRev3Zx39oa2pj5/s7BqRXHauioLQgENkVQgCN1Y10tnnO7tTW1EZjVSPxyQODh9Hx0Vz/1RvY+9EeDm89zKS5k5gwe6J0CRoFUnNT+eyP7mLHe9upK69j+soZ5E/LP+Nnyg+dGpC2c+0Olt6wlBgZP0mIsCABiHEswhJBdHw0rY2tHuneNziWWAtTl0xl6pKpwcyeEKNa9uRsDm877Jk2aXhz1ltTrMy9dC5zL53rz6yJflKyU0i5ZfjTrpkjzaTmpXJs5zGP9Fjb6JtBQ4jRJCo2CqVTaP1meNIb9ER6zYzQX2J6IouuXsyiqxcHI4vCjzIKM8gozBjy8taUgUGotPw0CTgJEUakjeY4FpcUx5VfuAqd7nQ1mHvJXFJyPS/G2xtaOPLhLj783b/Z9+YmWs4yQFtDdSPH956grlwGeBKh43A4qD5ZzfG9JwYMNAhQf7yKbf9cy4bHX6F85xF6Ovw7n/x5S6cRnxTv/tuaamXKkpLBPyDCXndnN+WHyyk7eAoNjdV3XogpwuR+P29qLoaeHmoOnZLmviKsOBwOqstqOL73BE0+joeh1lhWw84XPmT9oy9TtvUgXV4tHPqzZdm48M4LPdIuvucSbJnSpUJA9uQccqbkuP82R5lZcetKTGbTGT7l1Ntjp3r/ST7+8xts+tvb1B4qD+mx3HkdU8OJvSdorg+//VaIkZIWEOPcxLkTue/X91NfXoclPpqU3BSiYk4/RbB329n98kccW78bgPIdRzj+8T6WPHgVkT5mMDi09RB//d6TtDa2EWGJ4MavXs+U+VPcow0LEQw93T1seWcr//rVC/R09WBNjue2R24je5KzH3BjWQ1rfvosvd3OmSnKNh9k7p0XkT17ot/ykJKbwl0/uZuqY1WgICUnxeeTGTE6NNY08fqfXueTNzYBMHleMVc9eCX3//p+yg+eoqO+hd6mVnY+sxalUyx7+Dps/bqyCREqPT09bH1nG8//8l/0dPUQnxzP7d++lezi4bXICpTmynrW/vw5ul1Bh1NbDzH9hmUULiv1ubzRaGTuZfPImZpLc00TccnxpOamDqnrlBj74pPjuflbN1N5tIrurm6Ss5KH3G249nA57//iefffx9bvDtmxvLuzh81vb+aF37yIvdtOQqqV2759K1kTs87+YSHCnNwVjnN6vZ70gnRKFk8lb2qeR/ABoLWmkWMbdnukNZ+qpbliYOuGhuoGd/ABoLOtk79+729Ul8m0gSK4Ko9W8syPn3UPQtVQ3cjTP3qGtmZn3aw9XO4OPvTZ88pHdLcP/tRtJKwpVibNncSkOZMk+DDKHdp60B18ANjz0V62rd2BLTOJ1gMnOfnOFspdg4xqDo0TH+8LVVaF8FB1tIqnf/SM+3jYWN3IP3749IDul6HSeLLGHXzos/vlj+g4Q/5MESayJ2VTsngqWROzMIbBVLMifMQkxFI0s4gpC6YMOfigOTQOvbdtQNrJYQwe7U8VRyv458+ew+66VqmvbOCZn/yT9ub2kORHCH+SAIQ4M00DH1Pb+5rvvqm22R186NPb00vjWbpsCOFvdRX1A9Iqj1XSUu+cO9xX/dUcvuu6EAD7Phl4Ebrz/R309vR69EXv43BIFwwRHuorBx4Pq09U09LQEoLcDOTzeKxpcjgWQab57G7h6O0NQV5877flh8tpCZPAoRDnQgIQ4oyik+PJ8mqWHpNiJS4tceCycRbMkZ6D/CidIiZBBmUTwRVnixuQFp8cT5RrkDJbfjo6o2dz3eKL52CyRAQlf2L0ye3Xp7hP0YwiDCbDwKbiCnLmTApOxoQ4i9jEgefgOFscUbEDu1GGgjUrCUOEZ//84gvnECUz/oggUjodRctLvRIhO0TH8jgf+21iWgJRMZEhyI0Q/iUBCHFGBpORqVcspPT6ZdgK05l8yVwWfO4yIn1cGNgybNzwlevRG5w3dkqnuPL+K0jOkSk7RXCl5aWy8lMr3H8bzUZu+PL1xLqCYdbsZJZ98VpyF0whpTib+XdfQvp5Z57aS4xvxXMmefSZT85KYubKGQDYitJZ/OCVpE/LJ3NmEUu/cA2JeWmhyqoQHtLy0lh12yr330azkRu+cr3PG5xQiE1LZOkXr6Fg6XkkT8xkzh2ryZ1XHOpsiXHINiHDeSw/r/+xPDUkeUnLT+P8m5a7/zZFmLj+S9fJVKJiTJBBKIVP7c3tdHZ0EmuNxZIYS9HyUgqXTUMpdcbPlSyawsN/eIiG6kZiE2JIzknGaJS+mSK4IiwRnH/zckoWTqG1qY3EtASSMj0DYYl5aSTmpaFp2lnrdbiw99hpqW/BFGnCEiZPL8ea5vpmHL0OYhNjPWYISkxP5DP/9WmqTlThcDhIzk4mLtHZ0sZgNJI6OZfUybkhyrUQgzNHmVl+w1KmzCt2Hg9TE4bcLz5YErJTSMhOGdHxuKOtk87WDiyxFkyRZ5/pQAhwdpNrrmtGp9e5H06E07E8MjqSlZ9awdRFU2lvbiMxLTHs9lshRkoCEMKDpmkc3n6EF379ItUnqylZVMLq2y8gJTt5SBcFer2e1NxUUnNDEzEWoo85wjyk0aJHS/ChpqyGt/76NtvWbCcxI5Gr7r+SwukFHjfJYuS62rvY+cFOXn7sVbo7ullyzWLmXzbPoztPtDWaaKs0Cxejz1CPh6E23OPx8b0nePG3L3JyfxkTZhZxyV2XkJ4vrY/EmTXVNrH+pQ28/891mKPMXHr3JUxdWII5ynz2DweROdLsnr1LiLFErlyFh6rjVTz29cepOFpBr72X7Wu288KvX6DTz7MDCCGGrrurm5cfe5XNb2+h195L9fFqHv/mH6k8WhnqrI0Zx/Ye5x//+zQt9S10dXTx1pPOYI8QIjzVVdTx2Dce5/ieEzh6Hez7eD9//a8naW2QQfrEmW17bxtvP/kO3Z3dtNS38I8fPMXxvSdCnS0hxg0JQAgP1SdrsPd4Tk94YPNBGqubQpQjIURTTRO7P/ScDrfX3kvVieoQ5WjsObjl0IC0j17ZSEdbRwhyI4Q4m9pTdXS0eO6f1SeqqfMxe4AQfTpaO/jolY0D0g9tG3gOEEIEhgQghIcIH7MARFgiMEXIOA5ChIrRbHTP4NFfpEVGw/aX+OT4AWm2dBtGkxz7hAhHvq5X9AY9EWHWjF6EF6PJSGL6wJncfJ0DhBCBIQEI4SE9P43J8yd7pF1x3+UkpCaEKEdCiPikeK564AqPtKIZhaQXSF9nfymaXkh8Urz7b4PJwPk3LcdglKGShAhHKdnJzLt0nkfa6jsuwJZhC1GOxGhgMBlYcfMKj2N7fEo8hdMKQpgrIcYXubISHqLjo7nui9dQduk8WhpaSM5KIqMwI9TZEmLcK1lUwoO/vJ/qshosMRYyJ2QQGybT6I0FKdnJ3PuTeyg7dIrebjup+WlkFKSHOltCiEFEWCK46M4LOW9RCY21TSSmJZJRmO6eClyIweROyeHzv3mQyiMV6E0GMgszJHAlRBBJAEIMEJsYy2S5sREirBhNRnKn5JI7JTfUWRmzbBk2uQgVYhSJjrMwcfbEUGdDjDJKKTIK0iXILESISBcMIYQQQgghhBBCBJy0gBBhqbu9k/K9Jzn04W6ik+IonD8ZW25KqLMlgqC3x07VwVMc+GA3er2OokVTSC7MQKeXeKkIjMbyOo5tOkDVoXLyZk0gY2oeFmt0qLMlxJhn7+mh6sApDq7bhcFkpGhRCcmF6SidCnXWhBi2mqOVHP5oL621zRQtnEzapGxMMiiqEANIAEKEpaObDvD+o6+5/9737jYu//YtJGQlhTBXIhgqD5zi1R88BZrz7/1rdnDpt24mdWJmaDMmxqTWumbe+OlzNFc2AHBiyyGmrJ7J3JuWoTfIKVKIQKrYe5LXf/is++99a3dw2bc+RUqRNI0Xo0vdiWpe+e9/0NPZDcDRjftY9rlLKFpUEuKcCRF+5JGiCDsdze1sef5Dj7Sejm5qjlaGKEciWDRNY8/bW9zBh760Qx/uDl2mxJjWUFbrDj702fPWFlqqm0KUIyHGh157Lztf/cQjTet1cGzzgRDlSIiRqzlc7g4+9Nn83Ad0tnSEKEdChC8JQIjwo2loDm1gso80MfY4eh0D0xwD04TwB03zcVzRBkkXQviV5uPY7itNiHDn81Ti0ORcIoQPEoAQYScyzsL0K+d7pBnMRpLyU0OUIxEsSimmrJrhlQhFi6aEJkNizEvITCLa5jnrz8Rl5xGTHB+aDAkxTugNeqZeNMcjTSlF7qwJIcqRECOXVJCG3uTZbW/6VQuIjI0KUY6ECF/SwVWEpbw5kzBHRbJ3zXZik+KYuGwaidnJoc6WCILUiZlc9PXr2fv2NpRex+QVpSQXZoQ6W2KMirbFsvrL13F4/R4qD5ykYP5ksksLMBjl9ChEoKVPzubCr1zLnre3YYwwUrxiOskyNaIYhWw5KVz6Hzexf80OmmuaKF4+jfSS3FBnS4iwJFdYIixFREeSP28SeXMnopSMhj2eGExGMkvyyCzJC3VWxDiRkGkj4folaJomxxshgshgNpI1rYDM8/Jl3xOjXnJBOskF6XIuEeIspAuGCGtyABdCBIscb4QIDdn3xFgi9VmIM5MAhBBCCCGEEEIIIQJOAhBCCCGEEEIIIYQIOCXTw5yZUqoGOB7qfASIDagNdSZGuf7bsFbTtAuD+eV+qp9SDwY3VrZN0OsmyPFTuJ1tW43GY+d4+P2ljE6jsX4OxVj5fcd7OUZz/RwLv91YKAMEphyD1k0JQIxjSqlNmqbNCnU+RrOxsA3HQhkCRbaNGIzUjaEbi9tqLJbJm5RxbBsrZZdyjF5jocxjoQwQ/HJIFwwhhBBCCCGEEEIEnAQghBBCCCGEEEIIEXASgBjfHg11BsaAsbANx0IZAkW2jRiM1I2hG4vbaiyWyZuUcWwbK2WXcoxeY6HMY6EMEORyyBgQQgghhBBCCCGECDhpASGEEEIIIYQQQoiAkwCEEEIIIYQQQgghAk4CEKOUUuo+pdRRpVSnUmqzUmrxWZa/Xim1TSnVrpQ6rpT6itf7y5RSmo/XpH7L3DHIMhGBKmcg+XsbupYxKaX+07XeLqXUCaXU572WuUYptcf1/h6l1FVBLMNUpdRapVSHUuqUUurbSinV7/00pdTflVL7lFK9SqknfKzjLqXUOqVUvVKqUSn1nlJq0UjLECih2DZe67vJtX+87KciCT8JQN1YqpRar5Sqcy2zTyn1ZR/r8du+Hyyh2FaBPtcMp0xKqcmuY1yVa/kjSqn/UUqZvJZb6lpX3zKf87GuoP3+/i6jUupqpdSbSqkapVSLUmqjUupyr/UE9RohAGU863WQa7mQ78fD3S/7fa7I9fu1eqU/MUjZ2/ot4/ffd5i/Ye4g33+h13JB3xf9XY5w3N98lGM4ZY5w1bEdSqkepdQaH8sEff8LQBmCvh8NswzLlFIvKqUqlPNeZodS6k4fywV+H9I0TV6j7AXcAPQAdwHFwK+AViB7kOUvAuzAfUA+cAlQDjzQb5llgAZMBlL7vfT9lrkDaPN6PzXU2yNctqFrueeAj4FVQC4wF1jW7/35rvX8h+t7/8P199wglCEWqASeAUqAa4AW4Ev9lskFfun6rdcDT/hYz9+AB4DpwETg9656URTq3zXU26bfsvlAGfA+8HKot4e8Al43ZgI3AlOAPOAW1z5xX79l/Lbvj4NtdQcBOteMoEyFrvxMA3KAy4Eq4If9lslz5fdXrnXe5fqOa0Lx+weojL8Avg7McS3/CNALLA7G7xakMi7j7NdBId+Ph1v2fp8zAZuBV4BWr/fivH834DDwp0D9viP4DXNdv89qrzyYQrkvBqgcYbW/+aHMFpzXincDLwBrfCwT1P0vQGUI6n40gjJ8E/gesBDndeq9ru13c7D3oYBXUnn5/wVsBB7zSjsIfH+Q5f8O/Msr7UHgJKcHIu3b8W1n+N478DppjdZXgLbhBUDTWbbh08BbXmlvA/8IQhnuBZqByH5p3wJO9ZXBa/mXOcNNdr/lFM6bjgdD/buGw7YBjK7vvx14AglAhNUr0HWj3zLP99+v/bnvj4NtFbBzzXDLNMg6fgps6Pf3/wIHvZZ53GuZoP3+gSjjIMt8DPwkGL9bkH7HZZz9Oijk+/FIyw78DPjTUH4nnDcoGrAgUL/vCI4vua48zTrDOoO+LwaiHIN8LmT7m7/qoGu5X3PmAERYXkMPpQw+lgvofuSn4+AzwHP9/g7KPiRdMEYZ5WwuOBN40+utN4EFg3zMDHR6pXUAmTifBPS3ydU05x2l1HIf64pUzu4HZUqpl5VS04dZhJAL4Da8EvgEeNi1fQ4qpX6plIru95n5Pr73jTN8rz/LMB9Yp2lah9d3p+M8IY6UCYgAGs5hHX4TBtvmv4Fjmqb9eZifEwEWrLrhOi4uANZ6reec9/1gCfG2ggCca0ZYJu91FAIXMrTfdpZSyniWZfz6+wewjL7EMPC4H/BrhCCU8UzXQSHdj0dadqXUJcClwOcHW8bLXcBuTdPWe6X75fc9x9/weaVUtVLqQ6XUtV7vBXVfDGA5fAnJ/ubNH/vfWQR8/wtCGfoEbD/yYxli8axXQdmHJAAx+tgAPc6mg/1V4WzG48sbwJVKqQuUUjql1ATgS6730lz/VuB8enUNcDWwH3hHKbWk33r2A3cCVwA34bwh/1ApVXRuRQq6QG3DfGARziae1+DspnAhzqfgfVKH+b3+LMNg39333kh9D2eTr3+fwzr8KWTbRil1Ac4mcQP6y4mwENC64bqY6AI2Ab/VNO33Q1jPuex7gRTKbRWoc81IytSX3/VKqU6cT5c+wNmUtc9g5Ta4vvNMy/j79w9UGb2XvR9nAP6v/ZKDdY0QqDIO5Too1PvxsMuulEoDHgNu1TSt5WxfoJSKA65zfaY/f/6+I/kNW4EvA9cDFwPvAE8rpW7pt0yw98VAlcNDiPc3byPe/84imPtfoMrgFoT96JzLoJS6FFgBPNovOSj7kGGoC4qwo3n9rXyk9XkMKABexNk8vBln/7Lv4OxThqZp+3HuFH02KKVycR4k33ctswHY4P5CpdYD23B2RRhqVD2c+HUb4gzoaTj7UjUBKKUeAN5QSqVomta3sw7ne/1ZhsGW95U+JEqpLwD3ACs1TWseyToCKKjbRillwxlsulnTtLBoDSIGFai6sRiIBuYB/6uUOqppWv8LRn/u+8ES9G0VhHPNSH6HG3A+gZwG/Aj4GvD9s6zTOz2Yv38gyuhckVLXuN6/UdO04+4vDP41gl/LOJTroHP4Xn8bTh6eBH6nadpHQ1z3LThvbPofuwL1+w65HJqm1QI/6Ze0yXXe/SrOMp5pnd7p/v4NA1EO54rCZ3/z5tdtGKL9L5D7crD2oxGVQSm1EGf38s9rmvbxENbpnX5O204CEKNPLc4bXu8oUzIDo1EAaM7OOV9TSn3T9bkanBEvgGNn+K6NOAcL80nTtF6l1CZgtLWACNQ2rABO9QUfXPa6/s12rbtyON/rzzKc4bsZwff3BR++B1zk4+AVSqHaNiU4W8O8rU4P+q8DUErZgSmuE6wInYDWDU3Tjrr+u1MplYIzQNl38eGvfT9YQrmt8FrWX+eakZSpLw8nXf/do5TSA48rpX6kaZqdwcttB+pcfwfr9w9UGQH3zdBfgds0TTtjq7cAXiMEtIxevK+DQr0fj6Ts5wNLlVKPuP5WgM51XrpP07RHvZa/C2ef8PozZeQcf98R/4ZeNgKf7vd3sPfFQJUDCJv9zZu/yjwUgdr/glGGQO9HIy6Dcs5c9yrwbU3Tfuf1dlD2IemCMcpomtaNcxTjVV5vrcI5Mv+ZPturadop1zpuwjmgSPUZPlKK86baJ+W8yzrvTMuEowBuww+BdOU55sME1799UesNI/leP5VhA7BYeU71swrnbB7HhvP9SqmHcY51cImmaR8M57OBFsJt8wkwFed+0/f6N7DO9f+jvj8mgiXIdUOHc+yY/us5530/WEK8rTz461xzLsd+LzqcD3D0rr83ACt9rHOTpmk9/ZYJ+O8fwDKilLoe5xPaOzRN++fZVhCoa4RAltGHUjzzH9L9eIRl9z4vfRvnGFalwLP9F1RKzcHZOsS72fgA5/L7+vE3LGXg7xO0fTGA5Qib/c2bH8s8FKUEYP8LdBmCsR+NtAyuLi2vAd/VNO3nPhYJzj401NEq5RU+L5xNCLuBz+Kc/uQXOPuU5bje/z7wTr/lbTj7VRXj3Jl/gfPkM6ffMg/hHESxCOf0aN/H2ZTm6n7LPIJz2qB813r+D+fULHMCXeZRsg2jcc6K8axrGy4EdgHP9ltmAc4o4jeASa5/exj5FELDKUMczqjlUzif1l+NsyvJl7zWW+p6vY/zBroUmNzv/a+4vvd6PKcRigv17xrqbeMjH08gs2CE1SsQdQNn08lLcR4/i4DPuJb5Qb9l/Lbvj4NtFbBzzQjKdCvOfryTXPm5HuesHk/1W6Zv2rKfu9b5Wdd39J+2LGi/f4DKeKMrv1/A87ifEIzfLUhlfIizXweFfD8ebtl9fP4OBhmFH+do9wfwPfuTX3/fEfyGtwM3u5adiLNpfjfwxVDuiwEqR1jtb/6ogzin1yzFeX7Y5Pp/aaj2v0CUIdj70Qjq3jKc+8ePvOpVUrD3oYBWUHkF7gXch/OJUhfOCNiSfu89gXMU/r6/bTijVa2uSvW2dyXB2ffsEM6b6nqcT20v9lrmZzif5HcB1TgHZpwf6m0RLtvQtdxEnCPDtuO8uPkNEOO1zLXAPtcOvZd+B9dAlsGVNhXnzXMnzmjrI94HSJwHfO9X/21xbJBlngj1bxrqbeMjD08gAYiwe/m7buC8cNrtOjY0AVtc36HzWo/f9v2xvK0I8LlmOGXC2dJtC9CC8/i/G+fAhZFe61zqWq4LZ2unz/n43qD9/v4uI7AG38e/NcH63YJQxrNeBwX7d/TXfun12TvwEYDAOTZGK/DVQT7n9993mL/h7cAenMeOZpw3gLf4WGfQ90V/lyMc97dzrYMMcu3Y7/2g73/+LkMo9qNh1r0nBqlX3uUM+D6kXCsRQgghhBBCCCGECBgZA0IIIYQQQgghhBABJwEIIYQQQgghhBBCBJwEIIQQQgghhBBCCBFwEoAQQgghhBBCCCFEwEkAQgghhBBCCCGEEAEnAQghhBBCCCGEEEIEnAQghBBCCCGEEEIIEXASgBAope5QSrX6eZ25SiltkNeF/b53sGUi/JkfMTqFsG5+Rym16wyfneXPPInRL8THUb9+rxgbAlQnl7nqn83He8eUUl/u9/dgdfdz/syTGH1CWTfPdB5XSq1RSv3an/kSo1eYHEOv9ef3hwtDqDMgAkspZdI0rTuEWbgQ2O6VVt/v/+1AgfeHNE3rDGSmROiNgropBCB1VYSfMKiTQ3UX8LJXWlMoMiKCYxTVTTGOST0NLWkBMca4ore/U0r9WClVA3yolHpYKbVDKdWmlDqllHpcKRXvWn4Z8CfA0u/pxHdc75mUUv+rlCpzffYTpdTqYWapTtO0Sq9X/x1e8/F+5TlvCBF2RmHdFOOU1FURbsKwTg5Vo4+62xGg7xIhMIrrphhHpJ6GFwlAjE23AApYDNwGOICHgCnAzcAc4FeuZde73msH0lyvH7ve+xOw1PWZqcCfgZeUUtOCUAYxNkndFKOF1FURbqROinAldVOMBlJPw4R0wRibjmqa9qV+f+/t9/9jSqmvAi8qpW7XNK1bKdWEqyVC30JKqQLgJiBX07QTruRfK6VWAvcA9w0xL+8rpRxeaRmapvU1wbSogf2rdmiatmCI6xejy2iqm2J8k7oqwk041cmh+qtS6gmvtPmapu308/eI0BqNdVOMP1JPw4QEIMamzf3/UEqdD3wDKAbiAD1gAlKB8kHWMQNnlHCPUqp/uhl4dxh5uRnwHsyvpd//24FSr/e7hrF+MbqMpropxjepqyLchFOdHKqvAK97pZ3wtaAY1UZj3RTjj9TTMCEBiLGpre8/Sqkc4BXgMeDbQB3OnecfOHeywegADZgN9Hi9N5z+m2Waph06w/vaWd4XY8toqZvNOE9G3uJd/8qT57FvtNRVMX6EQ51sdv0bB9R6vRfPwGNjpdTdcWE01c2mfst567+cGHtGUz0d0yQAMfbNwrkjfVHTtF4ApdSlXst044z69bcVZ4QvVdO09wKeSzEehXPd3A+kKaXSNE2r6Jc+w5WnowH6XhGewrmuivEpVHXyIM5+0zOBw32JSql8nBfU+0ewTjG2hHXd1DStQSlV61runX7LxQKFSB0eL8K6no51EoAY+w7ijNY9pJR6HpiHc1CV/o4BEUqpVTh3rHZN0w4opf4GPKGU+hKwBUgAlgFHNE17fojfn6iUSvVKa+o3Crby8T5ATd8BQYxZ4Vw33wD2Af9QSv0HUAFMB74H/FzTNPuwSipGu3Cuq2J8Ckmd1DStRSn1OPAjpVQXsAPIAv4X2Ais8/pIvI+626ppmvfYT2LsGA1186fA15VS5cAGIBH4fzifSD87wnKL0WU01NOxS9M0eY2hF7AG+LVX2ueBUzibBr0DXI+z+VBuv2V+h/PAqwHfcaUZge8AR3BGASuBfwMzh5CPXNe6fL0+61rmjjMsUxjqbSmv8Vs3XculA0/gPAG1A3uArwHGUG9LeQX2NZrqqus42hrqbSavwL7CpU66Ph8BPIJzALd2nC3CHgVsXssNVne/F+rtKa9xXzf1wIM4b/5agTLgqf75k9fYeo3SeqoB14Z62wXipVwFFEIIIYQQQgghhAgYXagzIIQQQgghhBBCiLFPAhBiRJRSv1dKtQ7y+n2o8yfGL6mbYrSQuirCjdRJEa6kborRQOrp0EgXDDEiSqlkIHaQt5s1TasOZn6E6CN1U4wWUldFuJE6KcKV1E0xGkg9HRoJQAghhBBCCCGEECLgpAuGEEIIIYQQQgghAk4CEEIIIYQQQgghhAg4CUAIIYQQQgghhBAi4CQAIYQQQgghhBBCiICTAIQQQgghhBBCCCEC7v8DksPbKBgGBugAAAAASUVORK5CYII=\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# We create a pairgrid object and plot scatterplots\n", "g = sns.PairGrid(scat, vars=scat.columns[1:-4], hue=\"Measured Unemployment Rate\")\n", "g.map_diag(sns.histplot, bins=10)\n", "g.map_offdiag(sns.scatterplot)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This becomes extremely useful for exploratory analysis of our independent variables. Let's have a look at a last correlation plot.\n", "\n", "**Correlogram**\n", "\n", "A correlorgram does essentially the same as a pairgrid does. The difference however is that it generates a heatmap based on the [pearson correlation coefficient](https://en.wikipedia.org/wiki/Pearson_correlation_coefficient). You can basically view this as a quantiative version of a pairwise correlation plot." ] }, { "cell_type": "code", "execution_count": 141, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 564 }, "id": "NDMcwdXH5mqt", "outputId": "a704157a-ae59-4052-ef8c-fd9998ad11d4" }, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# We define the figure size\n", "plt.figure(figsize=(16, 6))\n", "\n", "\n", "# Define the mask to set the values in the upper triangle to True\n", "mask = np.triu(np.ones_like(scat.corr(), dtype=np.bool))\n", "\n", "# Generate a heatmap object\n", "heatmap = sns.heatmap(scat.corr(), mask=mask, vmin=-1, vmax=1, annot=True, cmap='YlOrRd')\n", "heatmap.set_title('Triangle Correlation Heatmap', fontdict={'fontsize':18}, pad=16);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Wow! Note the scale: Red means positive correlations, yellow means negative correlations. Let's move on to divergence plots now!\n", "\n", "\n", "### Divergence\n", "\n", "**Outlier detection**\n", "\n", "Divergence plots are useful to identify potential outliers from your distribution. It visualizes how far from a standardized mean your outliers are, let's try to build such a plot!" ] }, { "cell_type": "code", "execution_count": 142, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 677 }, "id": "ikQOuORL5lHm", "outputId": "6099d74e-25e8-44e4-afb2-1bc055f7dd1a" }, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# We analyze the transition rates from unemployment to employment (rate_UE)\n", "div_plot = rate_UE.drop('Montenegro').copy()\n", "\n", "x = div_plot.mean(axis=1) # Calculating the historical mean for all countries\n", "div_plot['ue_z'] = (x - x.mean())/x.std() # Standardization\n", "div_plot['colors'] = ['red' if x < 0 else 'green' for x in div_plot['ue_z']] # We color positive and negative deviations\n", "div_plot.sort_values('ue_z', inplace=True)\n", "\n", "# Specifying the figure size\n", "plt.figure(figsize=(15,10), dpi = 80)\n", "# We format the plot\n", "plt.hlines(y = div_plot.index, xmin = 0, xmax = div_plot.ue_z)\n", "\n", "# We plot the observations iteratively\n", "for x, y, tex in zip(div_plot.ue_z, div_plot.index, div_plot.ue_z):\n", " \n", " t = plt.text(x, y, round(tex, 2), horizontalalignment='right' if x < 0 else 'left', \n", " verticalalignment='center', fontdict={'color':'red' if x < 0 else 'green', 'size':14})\n", "\n", " \n", "# Figure annotations \n", "plt.title('Countries UE rate distribution assuming normality', fontdict={'size':20})\n", "plt.yticks(div_plot.index, div_plot.index, fontsize=12)\n", "plt.grid(linestyle='--', alpha=0.5)\n", "plt.xlim(-4, 4)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can identify Iceland as top outlier and Greece as bottom outlier! Let's have a look at an other kind of deviations chart!\n", "\n", "**Area chart**\n", "\n", "An area chart is extremely useful to color time series charts. We can for example color high unemployment periods red and low unemployment rates green. Let's do this for Switzerland!" ] }, { "cell_type": "code", "execution_count": 143, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 639 }, "id": "7CnPsvox5jjS", "outputId": "faa64628-e8a6-46bb-88e8-cffa0dc27274" }, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# We take the unemployment rate of Switzerland and calculate the historical mean\n", "area_plot = countries['Switzerland']['Measured Unemployment Rate'].dropna()\n", "historical_mean = area_plot.mean()\n", "\n", "# We define the figure size\n", "plt.figure(figsize=(16,10))\n", "\n", "# We define an array\n", "arr = np.arange(0,len(area_plot))\n", "\n", "# We fill the plot with colors:\n", "# Red for deviations above\n", "plt.fill_between(arr, historical_mean, area_plot.values, where=area_plot.values >= historical_mean, facecolor='red', interpolate=True, alpha=0.7)\n", "# Green for deviations beneath\n", "plt.fill_between(arr, historical_mean, area_plot.values, where=area_plot.values <= historical_mean, facecolor='green', interpolate=True, alpha=0.7)\n", "\n", "# We can annotate a graph based an x and y coordinates (this requires you to see the plot first, and then annotate in a second step)\n", "plt.annotate('Unemployment rate\\npeaks in 2015', xy=(25,5.01), xytext=(30,5), c='black',\n", " bbox=dict(boxstyle='square', fc='lightgrey'),\n", " arrowprops=dict(facecolor='steelblue', shrink=0.05), fontsize=15, color='white')\n", "\n", "plt.annotate('Unemployment rate\\nhas historical low in mid-2019', xy=(34,4.35), xytext=(15,4.5), c='black',\n", " bbox=dict(boxstyle='square', fc='lightgrey'),\n", " arrowprops=dict(facecolor='steelblue', shrink=0.05), fontsize=15, color='white')\n", "\n", "# Figure annotiations\n", "plt.gca().set_xticks(arr)\n", "plt.gca().set_xticklabels(area_plot.index, rotation=90, fontdict={'horizontalalignment': 'center', 'verticalalignment': 'center_baseline'})\n", "plt.title(\"Unemployment rate in Switzerland over time\", fontsize=22)\n", "plt.ylabel('Unemployment Rate %')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This is is a meaningful plot! Let's continue with rankings!\n", "\n", "\n", "### Ranking\n", "\n", "Ranking plots are useful to examine the data values sorted by magnitude. Let's start with bar charts!\n", "\n", "**Bar charts**\n", "\n", "We want to visualize our previous slope plot, but now with bar charts!" ] }, { "cell_type": "code", "execution_count": 144, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 587 }, "id": "kN9IiEK65f-P", "outputId": "8f7dd871-c6a5-42b2-c3ce-5df37eb71e27" }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Display the mean of each country's unemployment rate between Q2 2010 and Q4 2017\n", "# sort_values(ascending = False) the values are displayed from the highest to the lowest \n", "ax = rate_UE.T.mean().sort_values(ascending=False).plot(kind='bar', figsize=(14,7), fontsize = 14, width=0.75)\n", "plt.title(\"Average UE Rates ({})\".format('Total'), fontsize=25)\n", "plt.xlabel('') # set x label as an empty string for stylistic reason\n", "\n", "# set individual bar lables\n", "for p in ax.patches:\n", " ax.annotate(str(round(p.get_height(),2)), # 2 is number of decimals after the comma displayed.\n", " (p.get_x()+p.get_width()/2., p.get_height()-0.025), # set the location where to display the average UE rate\n", " ha='center', va='center', xytext=(0, 10), # center the text. \n", " textcoords='offset points', \n", " rotation=90) # rotate the number by 90°\n", " \n", "plt.show()\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Great! let's examine slope plots.\n", "\n", "**Slope plot**\n", "\n", "Slope plots are used to investigate a before after comparison between data values over time. For example, we can look at GDP per capita of different countries and compare them between 2010 and 2020." ] }, { "cell_type": "code", "execution_count": 145, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 984 }, "id": "BFSyKTOQ5eNy", "outputId": "1787d1db-8e22-4dba-c999-5819d877c14e" }, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# This is our GDP dataset\n", "slope_plot = gdp_raw.set_index('country').copy()[[2020, 2010]].dropna().sample(10)\n", "\n", "# We want to specify the labels at the endings of the slope\n", "left_label = [str(c) + ', '+ str(y) for c, y in zip(slope_plot.index, slope_plot[2010])]\n", "right_label = [str(c) + ', '+ str(y) for c, y in zip(slope_plot.index, slope_plot[2020])]\n", "klass = ['red' if (y1-y2) < 0 else 'green' for y1, y2 in zip(slope_plot[2010], slope_plot[2020])] # Red if negative slope, green if positive slope\n", "\n", "# Function for drawing a line in a matplotlibplot (this will serve us to draw the slope)\n", "# Source: https://stackoverflow.com/questions/36470343/how-to-draw-a-line-with-matplotlib/36479941\n", "def newline(p1, p2, color='black'):\n", " ax = plt.gca()\n", " l = mlines.Line2D([p1[0],p2[0]], [p1[1],p2[1]], color='red' if p1[1]-p2[1] > 0 else 'green', marker='o', markersize=6)\n", " ax.add_line(l)\n", " return l\n", "\n", "# We define a figure an axes\n", "fig, ax = plt.subplots(1,1,figsize=(14,14), dpi= 80)\n", "\n", "# We format the verical lines\n", "ax.vlines(x=1, ymin=500, ymax=110000, color='black', alpha=0.7, linewidth=1, linestyles='dotted')\n", "ax.vlines(x=3, ymin=500, ymax=110000, color='black', alpha=0.7, linewidth=1, linestyles='dotted')\n", "\n", "# We draw scatter points\n", "ax.scatter(y=slope_plot[2010], x=np.repeat(1, slope_plot.shape[0]), s=10, color='black', alpha=0.7)\n", "ax.scatter(y=slope_plot[2020], x=np.repeat(3, slope_plot.shape[0]), s=10, color='black', alpha=0.7)\n", "\n", "# We draw the line between the scatter points\n", "for p1, p2, c in zip(slope_plot[2010], slope_plot[2020], slope_plot.index):\n", " newline([1,p1], [3,p2])\n", " ax.text(1-0.05, p1, c + ', ' + str(p1), horizontalalignment='right', verticalalignment='center', fontdict={'size':14})\n", " ax.text(3+0.05, p2, c + ', ' + str(p2), horizontalalignment='left', verticalalignment='center', fontdict={'size':14})\n", "\n", "# 'Before' and 'After' Annotations\n", "ax.text(1-0.05, 120000, '2010', horizontalalignment='right', verticalalignment='center', fontdict={'size':18, 'weight':700})\n", "ax.text(3+0.05, 120000, '2020', horizontalalignment='left', verticalalignment='center', fontdict={'size':18, 'weight':700})\n", "\n", "# Figure annotations\n", "ax.set_title(\"GDP Per Capita between 2010 vs 2020\", fontdict={'size':22})\n", "ax.set(xlim=(0,4), ylim=(0,150000), ylabel='GDP Per Capita')\n", "ax.set_xticks([1,3])\n", "ax.set_xticklabels([\"2010\", \"2020\"])\n", "# plt.yticks(np.arange(500, 13000, 2000), fontsize=12)\n", "\n", "# Lighten borders\n", "plt.gca().spines[\"top\"].set_alpha(.0)\n", "plt.gca().spines[\"bottom\"].set_alpha(.0)\n", "plt.gca().spines[\"right\"].set_alpha(.0)\n", "plt.gca().spines[\"left\"].set_alpha(.0)\n", "plt.show()\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Wow! Let's move to our last divergence plot, visualizations of age structures!\n", "\n", "**Age structure**\n", "\n", "Age structures can be useful to examine a metric under investigation across different age groups. The [recent political debate in switzerland](https://www.oecd.org/economy/switzerland-prepare-for-population-ageing-to-maintain-high-living-standards.htm#:~:text=The%20latest%20OECD%20Economic%20Survey,double%20by%202045%20to%2010%25) has had the fiscal effects of an ageing population structure as one of its headlines. Hence, you often see a population pyramid, where the variable under investigation is the total population (and how age structures are distributed in the over all population). For our purposes, we are interested in unemployment rates. Hence, we want to see how high unemployment rates are accross age structure (and sex, as visualizing this is common with such pyramids)." ] }, { "cell_type": "code", "execution_count": 146, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 583 }, "id": "moPd51i75cHf", "outputId": "5b57223d-6092-47fc-de38-226001033fd0" }, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# We slice our unemployment rates data set appropriately\n", "ageplot = udf[(udf['age'].apply(lambda k: len(range(int(k.replace('Y', '').split('-')[0]), int(k.replace('Y', '').split('-')[1]) + 1))==5))]\n", "ageplot = ageplot[ageplot['citizen']=='TOTAL'].drop(columns=['unit', 'citizen'])\n", "\n", "# We specify a figure size\n", "plt.rcParams[\"figure.figsize\"] = (20, 10)\n", "\n", "# We loop through each age structure and plot it seperately\n", "for sex, frame in ageplot[~(ageplot['sex']=='T') & (ageplot['country']=='Switzerland')].groupby('sex'):\n", "\n", " ar = frame[frame['age'].apply(lambda k: len(range(int(k.replace('Y', '').split('-')[0]), int(k.replace('Y', '').split('-')[1]) + 1))==5)]\n", " ar = ar.set_index('age').drop(columns=['country', 'sex'])\n", " ar = ar.mean(axis=1).to_frame()\n", "\n", " # We want to construct a pyramid, hence we plot all male values on the negative (left) side \n", " if sex == 'M':\n", " ar = ar.apply(lambda k: k*-1)\n", " palette = 'Blues'\n", " else:\n", " palette = 'BuPu'\n", " \n", " ar.columns = ['Unemployment rate']\n", " bar_plot = sns.barplot(x='Unemployment rate', y=ar.index, data=ar, lw=0, order=reversed(ar.index), palette=palette)\n", "\n", "# Figure annotations\n", "plt.title(\"\\nUnemployment rates by age groups\\n\", fontsize=22)\n", "plt.xlabel(\"Male Female\")\n", "plt.ylabel(\"Age group\")\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": { "id": "vBSnd-NNIklK" }, "source": [ "Great job! We see unemployment rates are uniformly distributed accross the sex variable (more or less), but there is huge variation accross the age groups! Younger individuals tend to have a higher probability for unemployment than older individuals.\n", "\n", "\n", "### Composition\n", "\n", "Compositions charts are used for the static visualizations. The goal of this type of visualization is to help people comprehend how different components fit together to form a whole. It's simple to concentrate attention on the relevance of each portion in relation to the whole value when using data composition. For example, we can investigate the unemployment rates across age groups with pie charts or tree maps.\n", "\n", "**Pie chart**" ] }, { "cell_type": "code", "execution_count": 147, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 742 }, "id": "dZNzrxrl5agV", "outputId": "45f9bed8-dbca-4116-9d57-45834b0d6e3e" }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Filter the dataframe and drop unrelevant columns\n", "ar = ageplot[(ageplot['sex']=='T') & (ageplot['country']=='Switzerland')]\n", "ar = ar[ar['age'].apply(lambda k: len(range(int(k.replace('Y', '').split('-')[0]), int(k.replace('Y', '').split('-')[1]) + 1))==5)]\n", "ar = ar.drop(columns=['country'])[['age', '2020Q4']].dropna()\n", "\n", "# Define by how much individual slices should explode\n", "explode = 11*[0.05]\n", "\n", "# Plot the pe chart\n", "plt.pie(ar['2020Q4'], labels=ar.age, autopct='%1.1f%%', startangle=90, pctdistance=0.85, explode = explode, colors=[matplotlib.cm.get_cmap('Pastel1')(i/10) for i in range(20)])\n", "# Insert a white circle\n", "centre_circle = plt.Circle((0,0),0.70,fc='white')\n", "fig = plt.gcf()\n", "fig.gca().add_artist(centre_circle)\n", "\n", "# Set axis to equal such that the pie is plotted as a circle\n", "ax.axis('equal')\n", "plt.tight_layout()\n", "plt.title('Unemployment rates in 2020Q4 by age group')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This is great! Let's look at another composition method.\n", "\n", "**Tree map**\n", "\n", "Tree maps are also a great tool to visualize the composition of a whole. The following chart shows unemployment rates based on educational attainment level." ] }, { "cell_type": "code", "execution_count": 148, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 249 }, "id": "BDtAF19o5YOU", "outputId": "eb146e8b-67bd-41e6-b37c-1036f443fe38" }, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Filtering and grouping\n", "tree_plot = edf[~(edf['isced11']=='TOTAL')][['isced11', '2020Q4']].groupby('isced11').mean().reset_index()\n", "\n", "# Defining labels, composition sizes and colors\n", "labels = tree_plot.apply(lambda x: str(x[0]) + \"\\n (\" + str(x[1]) + \")\", axis=1)\n", "sizes = tree_plot['2020Q4'].values.tolist()\n", "colors = [plt.cm.Blues_r(i/float(len(labels))) for i in range(len(labels))]\n", "\n", "# Drawing the plot\n", "plt.figure(figsize=(12,8), dpi= 80)\n", "squarify.plot(sizes=sizes, label=labels, color=colors, alpha=.8)\n", "\n", "# Figure annotations\n", "plt.title('Unemployment rate in 2020Q4 by educational attainment level')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Well done! We see that unemployment is heavily represented in lower educational groups!\n", "\n", "\n", "### Time evolution\n", "\n", "A time series is a collection of data points that have been indexed in chronological sequence. As a result, it's a series of discrete-time data. Manually examining a normal time series with a line chart is a simple way to do so. Other approaches include to investigate serial dependency by using autocorrelation analysis. Also, spectral analysis is used to investigate cyclic activity.\n", "\n", "**Time series with different scales** \n", "For the next plot, we are analysing how GDP per capita and job finding probabilities move together. As we are dealing with two different scales, we also have to include the y-axis of both data ranges." ] }, { "cell_type": "code", "execution_count": 149, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 616 }, "id": "BOaYn8vR5WZK", "outputId": "70284d05-8600-41f5-f1d8-743158c9d63b" }, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# We select the job finding probabilities and the GDP per capita for Switzerland\n", "jf = job_finding_probs['young'].loc['Switzerland'].to_frame()\n", "gd = gdp_raw.set_index('country').loc['Switzerland'].to_frame()\n", "gd['index'] = pd.Series(gd.index).apply(lambda k: str(k)).values\n", "gd = gd.set_index('index')\n", "\n", "# We merge both dataframes\n", "ts_plot = pd.merge(jf, gd, left_index=True, right_index=True)\n", "ts_plot.columns = ['Job Finding Probability', 'GDP Growth']\n", "\n", "# Extracting our datapoints\n", "x = ts_plot.index\n", "y1 = ts_plot['Job Finding Probability']\n", "y2 = ts_plot['GDP Growth']\n", "\n", "# Plot Line1 (Left Y Axis)\n", "fig, ax1 = plt.subplots(1,1,figsize=(16,9), dpi= 80)\n", "ax1.plot(x, y1, color='tab:red')\n", "\n", "# Plot Line2 (Right Y Axis)\n", "ax2 = ax1.twinx() # instantiate a second axes that shares the same x-axis\n", "ax2.plot(x, y2, color='tab:blue')\n", "\n", "# Axis annotations\n", "# ax1 (left Y axis)\n", "ax1.set_xlabel('Year', fontsize=20)\n", "ax1.set_ylabel('Job Finding Probability', color='tab:red', fontsize=20)\n", "ax1.grid(alpha=.4)\n", "\n", "# Axis annotations\n", "# ax2 (right Y axis)\n", "ax2.set_ylabel(\"GDP Growth\", color='tab:blue', fontsize=20)\n", "ax2.set_title(\"Time series job finding probability and GDP growth\", fontsize=22)\n", "fig.tight_layout()\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "From this graph we can guess that as GDP per capita rises, the probability for finding a job increases as well. This seems to be a reasonable relationship. We are going to investigate this correlation in more detail in our statistical analysis.\n", "\n", "**Autocorrelograms**\n", "\n", "The correlation of a signal with a delayed replica of itself as a function of delay is known as autocorrelation or serial correlation. It's the similarity of observations as a function of the time lag between them, to put it another way. Autocorrelation analysis is a mathematical method for detecting recurring patterns, such as the presence of a periodic signal disguised by noise or the identification of a signal's missing fundamental frequency inferred by its harmonic frequencies. We can use statsmodels' `plot_acf` and `plot_pacf` functions to plot autocorrelations and partial autocorrelations." ] }, { "cell_type": "code", "execution_count": 150, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 982 }, "id": "Em9yn_l35UyQ", "outputId": "8a7545c4-7895-4813-f893-677b987a7dd4" }, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "from statsmodels.graphics.tsaplots import plot_acf, plot_pacf\n", "\n", "# Select the data for Norway (as we have many datapoints for this country)\n", "df = gdp_raw.set_index('country').T['Norway'].dropna()\n", "\n", "# Drawing the plots\n", "fig, (ax1, ax2) = plt.subplots(1, 2,figsize=(16,6), dpi= 80)\n", "plot_acf(df.to_list(), ax=ax1, lags=len(df)-1)\n", "plot_pacf(df.to_list(), ax=ax2, lags=len(df)/2-1)\n", "\n", "# Lightening borders\n", "ax1.spines[\"top\"].set_alpha(.3); ax2.spines[\"top\"].set_alpha(.3)\n", "ax1.spines[\"bottom\"].set_alpha(.3); ax2.spines[\"bottom\"].set_alpha(.3)\n", "ax1.spines[\"right\"].set_alpha(.3); ax2.spines[\"right\"].set_alpha(.3)\n", "ax1.spines[\"left\"].set_alpha(.3); ax2.spines[\"left\"].set_alpha(.3)\n", "\n", "# Adjusting font sizes of tick labels\n", "ax1.tick_params(axis='both', labelsize=12)\n", "ax2.tick_params(axis='both', labelsize=12)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You might ask yourself, what those plots will tell you. The autocorrelation shows a serial dependece structure of the GDP per capita level for Norway. It indicates that previous lags contain components that make a subsequent observation to an extent deterministic, hence serial dependence. The partial autocorrelogram indicates that certain lags are particularily meaningful for the determination of a future value. For example, lags of number 1, 16 and 23 have higher explanatory power.\n", "\n", "**Time series across countries** \n", "In the next plot we compare unemployment rates across countries. We will limit the number of displayed countries and we will sample randomly from the datasets." ] }, { "cell_type": "code", "execution_count": 151, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 982 }, "id": "Em9yn_l35UyQ", "outputId": "8a7545c4-7895-4813-f893-677b987a7dd4" }, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Sampling and plotting\n", "for country in random.sample(countries.keys(), 10):\n", " sns.lineplot(y=countries[country]['Measured Unemployment Rate'], x=countries[country].index.astype(str), label=country, linewidth=2.5)\n", "\n", "# Setting axis ranges \n", "plt.ylim(0, 35)\n", "plt.xlim(\"2011Q1\",\"2020Q4\")\n", "\n", "# Figure annotations\n", "plt.ylabel('Unemployment Rate (%)', size = 14) # Name x axis\n", "plt.xlabel('Quaterly Frenquency', size = 14) # Name y axis\n", "plt.title('Unemployment rate in Percent for different countries', weight = 'bold', size = 20)\n", "plt.xticks(rotation=45)\n", "plt.legend()\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Interesting! Can you recognise a collective pattern or can you spot single countries that stand off the crowd? Can you provide an explanation for why some lines move together and some do not?\n", "\n", "**Difference between model and actual unemployment rate** \n", "Our final time series plot is about coloring plot areas. We want to plot the steady state unemployment rate from our labour market model and the measured unemployment rate. If we want to analyze time periods where those two measures deviate from each other, we can do so by plotting negative differences red and positive differences green." ] }, { "cell_type": "code", "execution_count": 152, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 566 }, "id": "CESjDYoT5Rqz", "outputId": "88c35ab8-b413-4307-ef2e-7860a238fcc4" }, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Get the data for switzerland\n", "df = countries['Switzerland'][~countries['Switzerland'].isna().all(axis=1)]\n", "y1 = df['Measured Unemployment Rate'].values\n", "y2 = df['Steady State Unemployment Rate'].values\n", "x = df.index.astype(str)\n", "\n", "# Creating the figure\n", "fig, ax = plt.subplots(1,1)\n", "ax.plot(x, y1, x, y2, color='black')\n", "\n", "# Filling green where measured unemployment rate is below steady state (this is good)\n", "ax.fill_between(x, y1, y2, where=y2 >= y1, facecolor='green', interpolate=True)\n", "\n", "# Filling red where measured unemployment rate is above steady state (this is not good)\n", "ax.fill_between(x, y1, y2, where=y2 <= y1, facecolor='red', interpolate=True)\n", "\n", "# Figure annotations\n", "ax.set_title('Difference between model and actual unemployment rate')\n", "ax.tick_params(labelrotation=45)" ] }, { "cell_type": "markdown", "metadata": { "id": "58IwMKvJIxcZ" }, "source": [ "This looks great! It seems like unemployment rate has been higher than the model would have predicted for most of the time.\n", "\n", "\n", "### Advanced plots\n", "Python is a powerful programming language in which we can create advanced plots. For this tutorial we have decided to show you two which you may find useful, namely Geomaps and 3D-plots.\n", "\n", "**Geomap** \n", "For visualizing Geomaps, we use a python library called `geopandas`. If you want to find out how you can use this library, we highly encourage you to visit [this website](https://geopandas.org/)." ] }, { "cell_type": "code", "execution_count": 153, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 944 }, "id": "q4VN5al75ObH", "outputId": "72ad319a-e73a-41fd-a380-7c0ae5f19beb" }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "from mpl_toolkits.axes_grid1 import make_axes_locatable\n", "\n", "# We get GDP per capita for all countries and convert country names to ISO3\n", "iso3 = gdp_raw.set_index(gdp_raw.country.map(lambda k: pyc.countries.get(name = k).alpha_3 if not pyc.countries.get(name = k) == None else k).rename('ISO3')).mean(axis=1).reset_index()\n", "\n", "# We load from geopandas ('gpd') a world map and select the relevant colunns\n", "world = gpd.read_file(gpd.datasets.get_path('naturalearth_lowres'))\n", "world = world.rename(columns={'iso_a3':'ISO3'}).drop(columns=['pop_est', 'continent', 'name', 'gdp_md_est'])\n", "\n", "# We merge both datasets based on ISO3 codes\n", "df = world.merge(iso3, on='ISO3')\n", "\n", "# We create a figure\n", "fig, ax = plt.subplots(1, 1, figsize = (30, 20))\n", "\n", "# We make legend axis locatable\n", "divider = make_axes_locatable(ax)\n", "cax = divider.append_axes(\"right\", size=\"5%\", pad=0.1)\n", "\n", "# We plot the GDP per capitas\n", "df.plot(cmap='OrRd', legend=True, ax=ax, column=0, cax=cax);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Great job! You will see some empty countries for which we do not have data. Otherwise this looks very good!\n", "\n", "**3D Plots** \n", "Our last plot shows you how you can create a 3D plot. This gives you and idea of how datapoints are located in a three-dimensional parameter space." ] }, { "cell_type": "code", "execution_count": 154, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 524 }, "id": "4bmV2_Is5M2o", "outputId": "212072b9-ddb8-416c-8008-e6dbac9039ae" }, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "from mpl_toolkits.mplot3d import Axes3D\n", "\n", "# Selecting the dataset\n", "df = countries['Switzerland']\n", "\n", "# Creating the 3D figure\n", "fig = plt.figure()\n", "ax = fig.add_subplot(111, projection = '3d')\n", "\n", "# Selecting the datapoints\n", "x = df['rate_EU']\n", "y = df['rate_UE']\n", "z = df['Measured Unemployment Rate']\n", "\n", "# Plotting the datapoints\n", "ax.scatter(x, y, z)\n", "\n", "# Figure annotations\n", "plt.title('Unemployment and transition rates in three-dimensional space')\n", "ax.set_xlabel(\"rate_EU\")\n", "ax.set_ylabel(\"rate_UE\")\n", "ax.set_zlabel(\"Measured Unemployment Rate\")\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": { "id": "0jVFnOzPMzTr" }, "source": [ "Well done! We can see that we have some outliers. Holding rate_UE at a constant high level, we can see that with increasing rate_EU, uneployment rate seems to increase. This is in line with our expectations.\n", "\n", "Congratulations! You have made your way through the data visualization section. Now as you have probably an idea of what patterns you can find in the data, we try to quantify those patterns. Have fun!\n", "\n", "\n", "# Statistical analysis\n", "\n", "While the previous steps have helped us to acquire a rough understanding of the patterns that are observed in the data, the following section will investigate those patterns in quantifiable terms. Specifically, this section will introduce and conduct simple and multiple linear regressions. Thereby, the focus of interest will be the relationship between indicators of the labour market and macroeconomic variables. Additionally, we will pay particular attention to the cross-section of age groups and the educational attainment level. Note please that the following analysis requires you to run all cells in section [**4.3 Regression datasets**](#regression_datasets), as we will analyse those datasets which were prepared for the sake of the following section.\n", "\n", "\n", "## Introduction to statsmodels\n", "\n", "The [`statsmodels`](https://www.statsmodels.org/stable/index.html) library is designed for more statistically-oriented approaches to data analysis, with an emphasis on econometric analyses. It integrates well with the pandas and numpy libraries. It also has built in support for many of the statistical tests to check the quality of the fit and a dedicated set of plotting functions to visualize and diagnose the fit. We `import statsmodel.api as sm` to load the library into our environment. The statsmodels library has a wide range of modules, a very useful address besides the [official website](https://www.statsmodels.org/stable/index.html) can be found [here](https://www.journaldev.com/19119/python-statsmodels-statistics).\n", "\n", "The relationships that we will study cover:\n", "- **Simple linear OLS regression** \n", "  - GDP growth on unemployment rates of age groups \n", "  - GDP growth on unemployment rates of educational attainment levels \n", "  - Job finding probability on GDP per capita \n", "- **Multiple linear OLS regression** \n", "  - Unemployment rates on transition rates \n", "  - Model performance on transition rates \n", "\n", "Have fun!\n", "\n", "\n", "## Simple linear OLS regression\n", "\n", "A simple linear OLS regression creates a regression line that minimzes the squared distance between the predicted values and the actual values. It quantifies the relationship as a scalar response of one dependent variable to a change of one independent variable. This regression model tries to fit a linear line to the data by estimatiing the model parameters such that the mean squared error is minimized. The model can be formulated with the following mathematical expression:\n", "\n", "$$y={\\beta_0 +\\beta_1 x_1+e_i}$$\n", "\n", "In this linear trend line:\n", "- $y$ represents the value of the dependent variable\n", "- $\\beta_0$ represents the intercept on the y-axis of the trend line\n", "- $\\beta_1$ represents the slope coefficient and is the marginal effect of an increase of $x_1$ on y\n", "- $e_i$ represents the random error term which allows for variance deviations that are not explained by the model factors\n", "\n", "With the `statsmodels`package it is very easy to run a simple linear regression. In the following, we will show how to run a simple linear OLS regression by analysing the relationship between the unemployment rate of different age groups with GDP growth.\n", "\n", "\n", "### GDP growth on unemployment rates of age groups\n", "In the following, we will analyse the relationship between the Unemployment rate of a country and its annual GDP growth rate. With this, we now have the dataframes in the right format to conduct a simple linear OLS regression. \n", "\n", "**Model Creation** \n", "Conducting an OLS regression with the statsmodels package is very easy. We first need to create a dataframe **Y** for the dependent variable and a dataframe **X** for the independent variable. Additionally, we will need to add a constant column with ones to the dataframe with the independent variables with the statsmodles method `sm.add_constant`. Otherwise, the regression will be created without an intercept $\\beta_0$ by default. The regression will be initialized by creating and `sm.OLS` object. We then fit the model by calling the OLS object's `fit` method." ] }, { "cell_type": "code", "execution_count": 155, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 232 }, "id": "z3Ec-2vqybuu", "outputId": "66faeebb-505e-4768-9ee8-099cbf84bd20" }, "outputs": [], "source": [ "# Create empty dictionary\n", "regresults_dic = {}\n", "\n", "# Iterate through all age groups\n", "for age_group in GDPxURxA_regdic:\n", "\n", " # Defining the dependent variable\n", " y = GDPxURxA_regdic[age_group][\"GDP Growth\"]\n", "\n", " # Defining the regressors and adding a constant (the intercept b0) with the sm.add_constant method\n", " x = sm.add_constant(GDPxURxA_regdic[age_group]['Measured Unemployment Rate'])\n", "\n", " # Initializing the OLS rergeression\n", " regression = sm.OLS(y, x, missing='drop')\n", "\n", " # Fit the model by calling the OLS object’s fit() method\n", " regresults = regression.fit()\n", "\n", " # Save model to dictionary\n", " regresults_dic[age_group] = regresults" ] }, { "cell_type": "markdown", "metadata": { "id": "bo1Ez3yBPkDD" }, "source": [ "**Model interpretation** \n", "The `statsmodels` library gives many options to analyse the results of the regression. To get an overview of the results, it is advisable to first use the `summary` method, which prints an overview of the regression results:" ] }, { "cell_type": "code", "execution_count": 156, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 164 }, "id": "t6kYz3_gPLAQ", "outputId": "3cce5fc1-fc75-4f59-f28c-3e1f48e92e23" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " OLS Regression Results \n", "==============================================================================\n", "Dep. Variable: GDP Growth R-squared: 0.054\n", "Model: OLS Adj. R-squared: 0.025\n", "Method: Least Squares F-statistic: 1.868\n", "Date: Sun, 16 May 2021 Prob (F-statistic): 0.181\n", "Time: 17:48:01 Log-Likelihood: -61.560\n", "No. Observations: 35 AIC: 127.1\n", "Df Residuals: 33 BIC: 130.2\n", "Df Model: 1 \n", "Covariance Type: nonrobust \n", "==============================================================================================\n", " coef std err t P>|t| [0.025 0.975]\n", "----------------------------------------------------------------------------------------------\n", "const 1.8198 0.527 3.451 0.002 0.747 2.893\n", "Measured Unemployment Rate -0.0667 0.049 -1.367 0.181 -0.166 0.033\n", "==============================================================================\n", "Omnibus: 8.055 Durbin-Watson: 2.176\n", "Prob(Omnibus): 0.018 Jarque-Bera (JB): 6.847\n", "Skew: 0.833 Prob(JB): 0.0326\n", "Kurtosis: 4.386 Cond. No. 23.4\n", "==============================================================================\n", "\n", "Notes:\n", "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n" ] } ], "source": [ "print(regresults_dic['total'].summary())" ] }, { "cell_type": "markdown", "metadata": { "id": "jyqOL5VgQ5aq" }, "source": [ "It is also possible to access different properties of a regression individually. In the following, we will create a dataframe with different properties of the regressions of the different age groups.\n", "\n", "- With `params` you get a list of the different coefficients $\\beta_i$\n", "- With `rsquared` you get the $R^2$\n", "- With `rsquared_adj` you get the Adjusted-$R^2$\n", "- With `pvalues` you get the different p-values of the coefficients\n", "\n", "You can access many more properties of a regression. You can get an overview [here](https://www.statsmodels.org/stable/generated/statsmodels.regression.linear_model.RegressionResults.html#statsmodels.regression.linear_model.RegressionResults)." ] }, { "cell_type": "code", "execution_count": 157, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 49 }, "id": "Fw0-pjkwQ6Eg", "outputId": "354e7850-acf3-4cb4-c9de-829095c9b480" }, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " B0 B1 R-Squared Adjusted R-Squared p-value \\\n", "total 1.819776 -0.066709 0.053568 0.024888 0.180969 \n", "young 1.905412 -0.058017 0.067088 0.038818 0.132977 \n", "old 1.739785 -0.075192 0.045094 0.016158 0.220687 \n", "\n", " Significant (p < 0.05) \n", "total False \n", "young False \n", "old False " ] }, "execution_count": 157, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Create empty dictionary to create the dataframe later on\n", "overview_dic = {}\n", "\n", "# Iterate through all regression results\n", "for age_group in regresults_dic:\n", "\n", " aux = regresults_dic[age_group] # Create dictionary entry for certain age group\n", " overview_dic[age_group] = [aux.params[0], aux.params[1], aux.rsquared, aux.rsquared_adj, aux.pvalues[1]] # Store list with all values that are of importance into the age_group entry\n", "\n", "# Create a dataframe out of the dictionary and transpose the dataframe so we have the agegroup as index\n", "overview_df = pd.DataFrame(overview_dic, index=[\"B0\",\"B1\",\"R-Squared\",\"Adjusted R-Squared\",\"p-value\"]).T\n", "\n", "# Create column that has the value True if B1 if the variable is significant and False if it is not\n", "overview_df[\"Significant (p < 0.05)\"] = overview_df[\"p-value\"].map(lambda x: x<0.05)\n", "\n", "overview_df" ] }, { "cell_type": "markdown", "metadata": { "id": "VykHJLNWu5i2" }, "source": [ "Please note: The analysis was done as of May 2, 2021. You will get different coefficients and p-values as you will always deal with the most recent data from the eurostat API.\n", "\n", "As we can see, GDP-Growth is negatively correlated with the Unemployment Rate across all age groups. This means that with an increasing Unemployment Rate, GDP-Growth decreases. The strongest decrease comes from the older working population: A one percent increase in the Unemployment Rate in that group decreases GDP-Growth by 0.067 percentage points. Additionally, the adjusted-$R^2$ value is largest for the younger working population with 0.025, which means that 2.5% of the variation in GDP-Growth can be explained by the Unemployment in the age group of 15-39 year olds. However, the effect of the Unemployment Rate on GDP-Growth was not significant for any of the age groups.\n", "\n", "To better understand the regressions, we will now plot them next to each other. For this, we will use [`pyplot.subplots`](https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.subplots.html) of the matplotlib, which creates a figure and a grid of subplots with a single call." ] }, { "cell_type": "code", "execution_count": 158, "metadata": { "id": "XXd6ruyrrxIg" }, "outputs": [ { "data": { "image/png": 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vd9ks08c0//9s5rewhHGTzTKtS7mGFXTaOcLmGPkC+M5m+edslhXLd7vrZnRZzq+L834bdBrD38n86TbbftEmftb3tWcsy1xmE85IN/b9Q8s6ru5D3tC/dfPyc63hQ//OzMt8ZhPWSpv4PuFku/2hW3ubl80EUNdFXF81LZsI/Wx1dz9LXcbJek/b7JPd50ebdSs73e2Fs+8Ax8t7TQO4CrrXjwzotFcS9PumH9x4T7WLm4t5zj5Po2Ta0NmnxD5CFz7Phb7XZUGnr9IALILuUUCc7Lvt/kEX7n4G3btAgZPzGQ397vYr9DMyzziv66ArydRz4/o7s0/QaaNHjOvhpLEf6wDcYrO+u8dKwUmawI3r4jpLODkoef+d70Y4AdB5JFugn81HjevtmtKuH5uwKpRHVUo826DkPajQ7nqzWfcSJ+epxL7Bjd8t9D32Gug8yl3GtZALnd7+H/T7bqiLuJR6DdhcQ0+7Ew6ASAD/gX5/yYHOC/kcpvd9m23FQVdC24iz+R8njH37EcDL0O/B5T5//PBTXT+bNm3aq5T6zfx5t9NvyhMfazxcfYYMGZJ+ySWXHHc2PyIiInfKlClpju8AVIMGDfLeeuut3cnJycn33nvvQR8fn6KUlJTNSqnfMjMzNzZr1ixnyJAh6b/88svWjRs3brnhhhuONG7cODczM3OjUuq3devWbX3xxRdTf/31163JycnJjzzyyF8+Pj5FGzdu3GLebq1atQqfeOKJtOTk5OTNmzcn28VvwoQJB2JiYrId31NTUzd169Yt09vbW504cWKjUuq3BQsW/FGrVq3C119/fc+WLVuSFy9enNKsWbOcu+6665Bjvd69e5+IjY09/f3332//6aeftnbv3j0zKCiocMKECQfM+x4WFpb/yiuv7N26devm7du3by4t7NTU1E0+Pj5FTz75ZNqOHTs2r1+/fssrr7yyd9++fZuUUr/169fveI8ePU6sW7du6/bt2zfPnz//j/nz5/9h3ubs2bP/dBzb+vXr5/Xv3//Yr7/+unXJkiU7mjVrljNw4MBj5vMZHBxccOONNx7ZsGHDloULF/4RHBxc8Oijj/7l7Bwb167tde3xHxY//PBTtR+bRKwq4/ouX+Bg8xIGnXmfazNdGQnHDk62FQf9AlvaS9Ei2BQKARjsxrqOz2ewKfCDTcYvdAa0df0Py3AM7RLjT9ss97dlmV+dhOcHXfhQ2j7+BScFG9Bj8KU6Wa8QOlNsr6s4O9mvu6AL8YodQ8t6t0NnVriKew70GKDWeAdAZ1S7c46XWdaNxdkuB0v7PFrade7kuA6BfkkqLfzlsC/4s7v+HkHJDEbHZz8qUPAP4F7jWJcW308BBJdy7m0/ZYyP9ZrbW879Gu/mftWxub5murFePoBH3NyH6dAFpM7Csr2XQGc2POvi3Ds+RdAZqnb3sx8ty34OneFiF85p6Mzc1tAZcHbLHINNQQjcLDCDLlg47sbx7VjG8z3VJoy5ZQnDJswPLeH96OTaeNq0ThiAr93Yv5OwFNgb6++2LDe8lPP5XinHYZmTfXH1Ge3iWv43dCFtma5lN451nTLEbzPsC5kusVn2DthXBlHQaRTbSoTQaQlnaZgjsE9rXFIJ15hCJRf6o5zPD+ieMazn3+7zNgBfN+K7Gvo5YhdGAXTr3obQmc12y+TApsIm7NOgw+D8/p8FUyWDctzDyvXMNNbdaFmmRAGosdx6y3LPlrK/97k4x+9CFw5+5iKuk53Eo0JpTdgX3j4Ifd3ZhVME4HZLGKVt+8ynjL+9S0zrlvm+Bd0i2xqHhqb5gpL3ngU24TxoE04n0/w3bY5RqCWMOsZ083JvmOa/bZl3yiYerW3i8bhlmUU2ywxxcYwiUfI+mgHAu5Rja3fdjHa1TmV9oCtIWbf9tmWZh2yWibcsIzhbMdDxWezG9j+0rGN7HzKWbWgTj0k2y31uWeY0TOlF6F4nrOFsgotKfNCVYq3rTHSy7KWm6/MU9LtYWfaz1GWcrPe0TRztPj+a1qmKdPcnsL9flOmahq4Q7yw+idDvPcWmuxG3D13Mc/Z5GuUo9IeugPmDG+usAdDIJu6jbZa9FSULf83nU6Arf+eXss1jAK4u5fpzfJ5EyYo0xY6PZX13j5VC+Qv9re8d86F7HDFPy4Hrijnh0L97Z3F7xdX1YwqnwnlUbuzvRzbhPVuesCryuwXQBCUr7tl9jsCmwQeqttDf0QuQXXzyYVPhEzoPK8/Na7V7RY43P/xUx8+FVOh/7733HnR8z8vL+y0gIKDwrbfe2q2U+u21117bExUVlVNYWHgmjPz8/N9q165dMGvWrD+dbad9+/YnJ02atN+83X79+jmNl+MzYcKEA15eXiowMLAwICDgTPpr9OjRfzuW6dy5c9bDDz+837zeRx99tCswMLCwsLDwt02bNiUDUMuXL9/umL9z584kLy8vZS30HzVq1N/mcEoLe82aNdsAKEelCOunZcuWpydOnLjf2f4BZwv9X3755b3BwcEFGRkZGx3zv/nmmxQAKjk5OdlxPhs2bJiXl5d3Jowbb7zxSI8ePTKdbcNVob8PiIgq31zoFy07odC1TvuYJxrjEv4I3Tq3NNcBqCUig1T5xye7EbqWeWljbTaBzgCoUsbY6OGWyZucLP4BdHecpYmEHt+zu1Jqm2lb/tAviFFO1vOCruVbnu4OZwDOny0iMgY6E7o0/gDeE5E8pdRc0/T7oFu0lccr0JkOVUJEroV+0Xan653+AL4zxgo95WK5KOiMHmcioMcwH+l2RA0ich/0b9EdNwMIE5GrK/CbOydE5EnYdLXrprnQBUal8QHwooj4KKX+VcqyD8D5/RAAbhWRuUqpFZbp/0bJLnntCHSriWDo3gtcud5FXAKh7y250Pc9O3Wgr5l/uBGv4pEUGQVgTlnXc9M1NtOer+Rt9IKL/Tbuq9/AvftTLQBzRKRQKfWJafpK6IJqhz7QhYcQET8A3Szh9LF872v5bjdsTEWMh27B6Yyza7kytYMuxLDuu51ZcH69+wGYKSItzPc0EWkF3VuMn5P1wqEzA2uCcj0/RCQBumJaLTe2cQ/0sbqjlOV6w/m58IZOO6RAdzdux5EmaKEsY57b+BTOr9NgAF+JSFul1EEny9iqhGfmm9Ctvx1uE5GnLNdfc+iCRwcFfV925U04P7ZjAMRAF7w587SIfKaUsnZ9W6G0phOvuYirAJguIguVUifc2K4nWffhNHQrRoc46Pcds6024WyxmdYVuoKI43+zNKVUpnmCUuq4iOxH8ee2eT1rV+pBItJQKfW3adpFTuIBABARb5S8ho5B9y5hSym1X0T+B+Bq0+QwAJ2hK7ZUR3bX5h7Ld+s5KQJQbMxRpZQSkW3Q9z1n61WUXRf5dufROi0Q+j7rGN7ocpt15pRyn50NXQHKbAB0QfgZxnAOc3D2uE5USv0h4ipJ7FFVke6+AS7ei90hIqOhK/A50wW69XO1JCK1oXtzae3G4r2h3497lvJ+DADvQacfnHkewGNubLMOgC9FZKBS6odSln0art/ppojIp0qpP9zYboWJSH3oYUrM5kJX0D2Ks8N/+EOnS96yCUOg070dXGxqInRhcWmqIt1whoh4oeTQfrnQLdArk8vfrYjUhb6mY90IKxzAN8b19WPlRK9Us+E8HewD4CMR2a6U2gycecec5WIdogvSmA2drcNVnRfat2+f7fjf19cXYWFhBYcPH/YFgA0bNtTav3+/f3BwcIJ5nZycHK8///zTHwAyMzO9HnnkkYjvv/++9pEjR3wLCgokLy/Pq02bNtnmdRISEkp7jgMAmjZtmrtkyZKdOTk5smDBgjrffPNN2PTp0/c75m/dujUoOTm51ltvvXWmrEgphZycHK+0tDTf5OTkQC8vL/Tt2/fM9mJiYvLr169f4rnVpUuX0+bvpYXdvXv30z169MhMSEho27t378xLL700c+TIkcciIiIKAGDs2LGHJ02aFLVixYraffv2zbz++uuP9+nT57R1uwCwffv2gNjY2OywsLAzaewBAwac9PLyQlJSUkB8fHyuEfdsX9+zt+PGjRvnb9y40Z18mRLKNyYAEZFrAl1o3AG6cNOa0dVbRJpapr2B4gX+B6FfpDtAj004CTpR7zAQuoa3WSGAX6BrdV+L4mM4Xw9dG9fsQSOR64rjZfJT6EKG1tAFTP8rZb3S1BGRaBGJEZEBABag+D3Z9gVGRAaj5MvUm9AFTa2gK0Qkm+aFoOTYnHeiZMb6zwD6Qe/fg8b2y5NJ4QPd4uAa6EzPfxjxg4g0hr4uzJZBv6y2gq6p/JVl/psiEmb6bi10+xR6nMmW0NfKYOhuY3+Fzogzs677OHTX3i2hM+NugX4Z/tPF/tkSkVrQrYCt53AS9Hhyl0F3tWfW0ZjvMmjo63oydFd510C3kjMbZoznV5b4RqLk9XUcuhu7DtBdSFoTuVfgbOHQLwCaw77g7WZjnuNTEc1ERLn4DDYvLCLtATxlCSMfuuCrJ/T9oC90i+hDlnWHoWSB/1boa6o99P3miGX+MyISU8o+CPQ19U/oAssnoQtyzIr9pkWkC0pmPP5lLNfOiOduy/y7ROQSN+LyB/S5bA/L2L8AmkIXEP0CfZw6Qxf+mfUVEWeVAuw3KtIIJccvVtAVgP4BfV66Q3dDbN2v0sL2gj4mZvuVUjtslo128bEb99XMcT98A3qs3LbQzxVH4cUDKFngvwb6WHcw5ltffP5jGePYWkhv/n11hc6sN4sVkYbGvtlVCnCE9zD0b3G6Zf5+FP+tNod+FjnjizJey2WwE7pAcBj0tdcK+p53OUpWFuktIt3dCFOgnxFdoc/NKsv8ZtD3BbPnoXv8MPsYOiO6J/RvJsiNbVcHZX5+GJm/76N4gX8K9PMxHrpwYRqKn/fbRcRVobIjLoegfzNtUbJSTogRdgr0Oe+AkpUrolHyfNnxgS686QqdrrEWTIah9AqfxVTCMxPQ16L5GRKB4gWigM5oNvtRKVXaPbEQ+jfeBvo8WQsDL4XuXeR2Y5mHLfO9oXthOaOS0pp2BDr93Af6/My3zA+GTr87OO5L1ut2Okreu9ymlPpRKSXGZ3RZ1jXeX6zX++dKKfNvwi4+h92c1tzJ/86Wt5tuXu9Xm+U/EJFYEQk0xnh/yWYZ83M+CnpYBrONqvSx0BNtpjmr2FMdjLZ8L0LJa9R6TjKcHAfrOWkkItZnS7kZFWNSLJNvE5GbRSRUROoblWC72KxuPrfxNvPtzptZEnSLUDO78/q2aVtLlFLuVPouxvQ7jS7jqq9Dnyu79yzzfeMmoErT3T7Q7yHPQL8Ptoce5satQmHj+TzNMrkA+rneCfp5uQ4VT5fcBH08Flqm/4rix+t16PRQc5S8tyyEfXryGRQv8M+CLkTuBH393Q1dicihPfT7QGl8oJ8nA6CfSwOhC68dlRcftSz/KXSaoBX0cVtjCes9N96nBfpZfxn0e7y1EN0LumGJg7vHqjn09VZWI1C8oPYogG+VUvnQQ2WajXYSxiDoY2i2Azpd08pYLwOlFAhXYbrBrCnOVmRw+M1aUVBEfEp55yvtXlza73YqShb4fwmd79kZOo1pfi74ApglIueq0aUvdMG/+d3FzA+60q9DPADzmN4Z0OnJttD5eX2gf6cfG/OIqAbz8/Mrln8jIigq0lnnRUVFaNWq1en169dvM382b968Zfz48UcAYOzYsU2++eabsClTpuz//vvvU9avX7+tXbt2p/Ly8opViqtVq1ZpFfUBAL6+vio+Pj63S5cuOS+88MKh1q1bZ992221nGigqpWTixIkHzPFJTEzclpycvCUiIiK/+CuYa8HBwcXiVFrYPj4+WLt27c6vv/76j/j4+NNz584Nj4uLi//5558DAWDChAnpKSkpySNGjEjftWtXQP/+/VtNnDgxwm7buk6wfb1B83RfX98S50cpVa4as2zpT0RV4TOl1ATHF6OGujUDoT10F9IwCpD+aZn/T6WUeZ0ko6b4ZNO0e2FqNaWU+ga6paWdJBH5AXosN4cQ6EJfuwwxswVKqVtM30sUJpXDg8bHzkHobrd22sy73/L9LaXUONP3FBFJhh57y6GPiMQrpRwtiqytwjMBDFJKHTe+7zBehly1EHRmH3Q3x47abX9Ad+sL6JaA5kKrZABXmVqTpIjIWuhCpWbGtNrQLx2O1nXW59YbSinz+dsM3YvBFBEJsSxrXjcTwCtKKXOm1W8426rWum5phkB3tWl2n1LqzEuWcf1tRvHWGGNF5BnlOqXyklLKUUCyVUTGo3gmZAD0i6ddKzJnbkfJgq1hpha6m0VkNXQLJ3MPFGOhu/PLAbDXSaLlkFJqbxniUpnuR8kKjcOUUuZCn50A1ojIS9BjFjqMtayXCd0NtOPlNllE/oCuIOPgAz2khavMqSLo8RC3G9+3GJnsV5mWaW9ZxxqXIuiurR2ZDVtEJBH6d+5rWe9HF3EBgBuUUkkAICIPQV8L5hOZC33/PWws8zBK9jrSAWXLnLodJVsNj1dKvWGZ9quIvIaytTSoh5ItfawFRA7WFntmt0F3OevKy0opcwayuXWK9ZztgT5njhrGm0UkHUaGpKEOdCWZt43v1kL/NiJSTyl1FMUrABzC2UpyfaAzVruh+P31OIzWokqpdADpInLcEn5BGX+r5bmWS2U8e5y1ltkK4H8i0hnFCyf+AV05xZWfAYxw3F9F5HqULIhpDyPj17jvW9MiK5VS5mfmzyLSAPa9S1RHZX1+9IJOFznkQ1/H5t/UBqPgc4Rp2r0ovWeJe5RSXwOAiEyB7iXAmnl7o+n+9AD078N8T+8APcSPK28qpc7ck0VkFfS4suZWbLeIyP1u9BrgUKFnJgAopXJFZCZ0xVSHMdBpFgdrob81o9bOf5RSrxj/bxeRodBpErPJSqkPTMuMgC6wcLC28KuMtKadVOh7SB4AiMgt0IUndUzLnLmHOO5PImItVD3uiXSGUcHzc+hWkw4nULJHMGsBOVA8veFg1xKkjotw7MKwC8ccxpfQ58lcQfFKlGwdamXedn2b+c4qIJj9bTPN2qtZtWD8bu6xTP6PUsqabijvOQH0eTlkM728/o3i9wh/GO8xpajQuVVKFYhIBopX1i92Xo3ftqMy0d8ovTeYSmWkK44b6S7rvL02q1RlunuCUspcOJzsdMmSBqBkL4hTTc91iMga6GeP9T3UbUqpQ0ZYJy2zcmyO13FjWWsFs5PWZUX3gnWnZbnblVLmCqZbRaQQuuW+w1jRPeG4ej/+FTr/wtFbjrkSzH0o/m6z1JKXkyIiP0HnCzme7RdBVwZY4mKbJ6G7aj9qfL/fqPhhrvRifob9Bbh3rMrJ2gDmM9N7x8cofl13Ed3LkTW/wJovVATgWlM+VIqIZEM/+1ypqnSDWQObaXbvfE3g+p2vH8r5uzWu6dssy65RSpnTXRuNdIu5wk4MdHrn21K2WxkWK6XM91y7d5crRKS+UuoISuavrVdKmZ8lf0CnvWcaFWNc9bBBRDVYp06dTi9evLhuo0aNCsLDw217eE1MTAy+4YYbjo4ePfo4AJw+fVr27dvnf9FFF9n1QlVmU6dOPdC+ffv4NWvWHO7Tp8/pNm3anE5JSTnTEt6qffv22UVFRVizZk2tSy+99BQA/Pnnn75HjhwpNU+xtLABwMvLCwMGDDg1YMCAUy+99NLBli1btv3kk0/q9ujRYz8AtGjRIv/hhx9Of/jhh9OfeOKJRrNmzWrw6quvHrDZVvYXX3xR79ixY16O1v7Lly8PLioqQvv27Svl2JWIe1UESkQXPGv3p3aF5K5abwPAerG06kXxAn8A6GRtnSkijUTkCRFZISJpInJKRIqM9Uu8+MN5N9ZmVd69v0kS9Bhna6wzRHev2dsy+T6b42TtohUwun02EuqdLPMWmAr8HWaVK/a6IN22OxuUPM/tABRa4l6AswX+DuYuq60t6ZaKyEci8piIDBWRVo7qc0qpLMuy5nVDoQtxZ4jIeBG5UnRLPjhZtzTWfcuBHnPuDCNT4j3Lcg1QetdwZf09ucMa393K0iW3UuokSmYedhGR6tzKtZ/l+6+WAv8zlFInHRlFTn5bC0wF/o51foGuuGFm7VLdaqWpkNTBeg6t5896fn60dhWplNqHkhkHpcUl2VGgZoRxAiVr7H/nKPA32LVGKuv1Zj0vB2DTxaQRp3wX9xA7djVP3K/u6758FG8VcTYCuuKatQvdD0wZbw5foHhrJsB0zoxMV/O1Ijjbe4C50P81m/Wt535VGQo03VWea9ktIhIgIneJyFcislNEskSk0PRssLZGdOfZ/ZY5w9jI2DpqWcYc384omZE1GyW5UxBbXVQ0PeYL4C+bdMYIy3Kl3XuOwZSRbpyXvZZlNlvuT8dQMt3mzvVV7JwZ27J2kR8K97oadqisZ+YMFO8i9wqjAgVEpAWKp89OQI+jXpq5lu92Gd0fWb5b7+tnjmtlpDVdmGWubGncI629K5XrHlLVRCQcuuebHqbJOQCG2hQM2z2X3J3m6vnlrJWHdbr5vpcLndGf5iJcoGTvWJWR+WQX30otLBARR89pzj6lNnIRkdsAfGaJ77ew7+rduk/unhOgktMmSqnZKL1ba7t0QFWc2zPn1binmdN4t1vSldVRVaW7/0b536kB3bOUlfUZl43iFUqrky4oWel3vs3zxPp+HI7Sn9H/Us6HnLOez6tstnkSJSvzlXY+PzMV+DtUSjq4rESkI0pW2DuTHlBKrUPJ3ilG2wRlvcZ+tGl4shBGZQ8ncanKdEOxTdlMq4p3Ple/264o2bOG3bJ2PZuUZV8rwp13F8HZHuK2oXgFtitEZI2IvCwiY0TkH6IbXzne06ukcIqIyicrK8t73bp1geZPSkpKaT0a2xozZkxGvXr1Cq688sqYpUuXBu/YscPv22+/Db7rrruaJCcn+wNA8+bNc5cuXVpn7dq1QevXrw8cMmRIc2sr/4po06ZNXv/+/U9Mnjw5AgCeeOKJA4sXL647fvz4iMTExIDff/894IMPPgi75557mgBAhw4dcnv37p153333NVuxYkWtdevWBY4cOTI6ICDAUQ7kVGlhr1ixotYjjzzSeNWqVUE7d+70+/TTT+scOnTIzzGUwW233dZ0wYIFodu2bfNbt25d4PLly0NjYmJs75FjxozJCAwMLLrhhhuar1+/PvDbb78Nvv/++5sNHDjwuKtKBxXBQn8iqgrWlx+7VhDmTJhIm/nuEJhqtYvINdAvE89Bd73ZBDpR7uoBVFqXzvmwH3OzqnQAkCgidt0k1kPx1kVl4RjLvi5KtqIt0Z29kdl+vBzb+d3FvPKe58am/6ejeHzrQddQ/xd0a9ftAI6IyFuiuxQ3exTFr8VY6BrwrwH4L3TBxh8i8n9GLe6ysHbhk2ZT4AfYd11u2/2P4aSlhSVQ+u/JHdZtOhvSwBpfL1SgJUk52HU/bv5Yh9mw7pe743DVQ8kxvN09Jq7OH2BfyGY9h9bzV97z09DIeHHG7vqzFrAXK7wwMhOtKnq9bXKRUVdW6SiZqV3ee40raTYZfQ5214DdfbUIuqWrq3VLdPEveggDR+H/YeiMG8cLjLNC/9JaXZdHea7lUokeImMr9BAp/4RuDRMM1+8ppT27gbLH1+7eZleA6qr1UFlUtFJGaeuX5/lR3t9OeCmFa/tsfvMu7z2G8lxf7p4zaxrBlUp5ZiqlDqB498leONsC1twlMAB86uT+W9o2rcf1hJGmM3N1XCsjrelMldxDqppxj/oZehgah5PQvRassFnluM006/AszqadcPK/s+XtphdbT+nhbtpDvx9ZC122ARiHksNgHHXyv4NdC3F3lrEOk1RR46F/384+LiuIicjT0AUk5uvuawDXKftu+49bvrt7ToCS57PCjN6HLoN+jzEPIXQSumXuTTarVejcGvd6a8Gm+bw+h7O9CbytlPqvq/CqiapKd29VxXuVKytruiQXuuKsVWWlSypbRdLjpT1PqjrPwU51eoZZW5vvVMV7PwRKDpM0wuZ6taaF7N5fClGyoqZZVaYbzOwqD1XFO5+r362773x2eWml5RlUljKlg41Kq09Y5vWGrvj2LnSvCBkisk5ErD1SEZGHbdiwIbhXr15tzJ8HH3ywTMNxOoSEhBStWbNmR7NmzXJHjhzZon379vF33nln8+PHj/s4Wv6/+eabaeHh4QWXXXZZ3DXXXNOyW7dup7p06WLtKahCHn744UOrV6+u/f3339caOnRo5hdffLFrzZo1IX379m3dp0+f1q+88kqjqKioMwXln3zyyZ7GjRvnDRo0KG7o0KExN910U0bdunXzAwICXBb6lxZ2WFhY4S+//BI8ZMiQlm3bto1//PHHm0yYMOHAvffemwHo4RAeeuihqISEhPirrroqtn79+gWffPKJbZosJCSkaMmSJTtPnjzp3bdv39Y33nhjTOfOnU9++umneyvx0BVT7V6qiajmsxaMKKUKxcnYJZUgEDjTAucTlKxNXprSIvZ3FbSWBIBnlFJPix6P+W7oMcMcGgJYKCLtK5hRYObIfKrqGtJ2GREVdSbjTCmVLiKdoDMoh0FnYlr3qR50V8PXGMfwuLHuT6LHfJ8A3R21tUcBAGgJ3Zq3F4qPK+spdplxlVVYWhOUtfvx6qg6ncPjNtOs9ze7ZSqqyh4ASqkiEdmM4l1VNxGRltaWKsoYC0tEolH2DNKquLfZWQndLalDH+j7nCMDfY1S6qiIbIPuTrSdiNRDybHOq6LQv6qu5Y9QsqeE0rhzTZU1vu4+Hyvres60meZsnFG7HlZKK0A6l/cegY67sxf+4zbTzsW9x+FctQ5zxxsoXhB3u4hMRclCf7d6lLDpqelcHlcrZwWgDtXpeegWEekBXSBu7r78EICrlVLOKhbaPV/suiUuraLRHst6dmHYhVNi+8Z1MgV6+Kta0F3NZzp6thKRCZZVzL0apUKPw20e+qqjiHiV8o7UzWZaWbo2rzJGz2czUbLl6zsA7ndRMXEPiu9XmIj42lT0tZ6TQ1XVOlIptRzAcqMwrx50ZaIjxvv3KOviKH4O7Cq2dwbwk4tNdkDJirLmMM3DB4wVEWvX+VbNTC2xJiilXi9l+ZqkomlHd9MbVZbO9qDSnidVmufgRLV4hhn3r5stk1uW1qIRupD3CgBLXSxzrtNGpR1zs33Q58A8NFQXEQkx99Jo5B043vkuAfBDGeN0rt75rOwqEFmHwXKHu+8u5l6BXhOR36HfQfujZMUuL+iejnqISBOl1KvliBcRVbKFCxfuhYtKWfv37y+W7rZ7d7Eu07Rp04IFCxY4DTM2NjZv3bp11l7jig3pZQ3TmVdfffWAXVf4l1122SlzXIcMGZI5ZMgQu7wTAEBUVFTBypUrz1RsPnjwoM9DDz3UrGXLlmfS3s7e21yF3alTp5zVq1fbDbsMAJgzZ04aXPSmZt1mt27dsn/++We7nlQBnDmfxTg7Ru5goT8RVQfWG5iC7uL0uBvrOlqxXYXimVGAzsyZC505VwBdA9muhrYrVfoip5T6G8BUo8usiaZZcdAF2+ZuG48CyEPxjJZnYd+Fl5WjcOAodO8F5tb+0daFRSQMxccEdZer43UAxbvq+x56PNvSFOvqRimVCd2K5DkRCYQuqI+BzqS6H7rbXgBoCj3W3XTTurugX2buE5G6xrotAVyC4mObXyMiHcxdDZfCeg03FRE/m0obdgVbB93cRmWynosWTpazxrcI9mO0VhcHUHzc2s5urmf323L3mFTF+Ttg2b67cTlciS3oK9N+FL/e3CksKIslKF7oDwD/h5LjiFZEafc2qxLnzGixb61sZL1+foD+nTlauXeCzqBzWGP629ZY7n4Uf/4dxtkx2qs1EWmG4t1lA7pFyUvQLeocL2pfouQ5rmx297aLoFv4mkVX0vb+spnWAvbpFOtvPQv2lQYqynotn4C+Bt35rZ4qfZFzojn0UElm0TbLleVZVmnPTKXUz6LHhu5qTGoK3Vq5vWmxzS4KlKtaZaQ1zwsiMgS6QrG5Ms426HGkrb22mP0B/fsMNU2z68Grnc20RMv/5q6Xm4hIbWNoHkccw1CypWMiXFBKnYLp9yoivaB/N2Y/mJYvEJEfUXw84HAAV8JJ4ZGIRAAYaJl8CMBvruJ2LohIKHSPGwNMkxWAx5RSL5ayeiKKV9DxAtAKpkJvY5gx6/l2eU4qg5H+s7aEtY7XvdkydNX3KDl00QjoyknOjLaZ5moc9JqgqtLdFU2TW59T/tCtha29+ERXcDtVxS59fBX0fbQ0Lp/RpRx36/n8AMBUN7ZZXdIxpbka7vW2Ymc0it+3D6H49WP+H8CZ7vvtGks4nJN0g1HR+1sUH2IqAMCD0HlDlaU873zrzBOc5KWZ3/nsepIpVsHXeI6607OZ1UUoWcEu2mY5a/r0R+h3MMfQdS2gn283QPei6vCEiLxeRQ2jiIjKbPHixSGZmZnenTp1On3w4EHfyZMnR4aFhRW4qihwIWD3/kRUHayyfBcAVyql9jr7QLfojzK1rLBmeGUqpe5WSq1VSu0y1rHLXKsunkHJsZ4fFZEzCX3j5XaNZZlroHsjcHacMgD0cnTtahyvjZYwhhmtfszuquD+2PnR8r0nAF8XcU+DLrQ9U+gvIo3E1G2EUipbKbVZKbVIKfUESo7b29q0brEu1ZRSGUqpX5VSHyul7kTJsdrLMt6v9RoOAFCsdY3xwmwthDwMIKUM26ks1vheJCLmzE8Y194tluU2qLKNt36uWWvyXywiV9stKCLBju6ojd/WWssi1xsv7OZ1uqN4wQwArK5AfJ2xnp9LRCTWEpco6Az3qo5LZbCelwjooTVKEBFfKT4GtjveRMkWxneIyLgyhlMuSqm/ULLL19uMljhmN6Bky4li58y4V5sLLH0BPGD67ngGmNez7ucPSim7Vh7WSkhlaV1TVey65ZyolPqvUmqH8SwohK4IV9U2oGRG3+02y91hM6087FpSWlt7Q0RaAkiwruvkHFfUj5bvtQFcXEp6rBGAsCqKT3kUOz9GmsHaDW4mylYJtLKfmdYCNWuhm1ut/KtCZaQ1q8A5v3eJyHgA81G8wH8l9H66KvB3DOWy0DL5MhGpY5l2veX7nyjeZfV8a7QADLVMs+tqt9h6IlLPqHRWghGndyyT/0LJQly7QuDXjZ7WrGH6Qo/RbW0N/mZlFxIopZ5WSomLz15L3JpAX9/m328OgJvcKPAH9Hm13uus53EgShb2WM9lpRARZ70/QETuRPH9BIC3zV+UUhtRMv3b1bj+7cK8BMA9lslHAXxcemyrteqa7rZ21w5Y0iVGBXhrq+/qIhElh5z5ZylpCgWgtXJveBtnfrR8HwjglIttHgJwiVKqsocfcajsZ9joCqx7jdHwwcF6jV0qIk0t04ai5PvLGec43fAiSlZEfUpErM/GqpKIkkM62DViudtmmvl+cdxmvjXv6T6bZdxh955inaZgVEYTEW+j99GzM5X6Sym1Sin1LkqmO+rCec9DRETnXH5+vkydOjWic+fObYcNGxYTEBBQtHLlypTQ0NALunISW/oTXYCMro1dOWDTQrnKKKXSROQbFG9B8pyIXAQ9HmEadMFHFIAuxnJdoAvKHYln60taqIg8D+Az6HvdQJQcq6raUEplisibAJ40TXZ0U/9v07QZ0F1uOXQEsEZEXoNu2XkauuZ3O+iMniuhj415TLePULzlUF0AK0TkKeguPAei+HADleUDAI/j7ItuLQA/isjL0LWjM6ALGFpBd2l9LXRhQnNjHgA8DF1J4Rvo1pc7oV+a/KBbI1ozPcwFgV8b9QW+hS7c2WvMDwUwCEC8i3VLswi6Zar5helNIzN1OfS5nASgjWW9tz1UUOI4F+bM7Pki8giAX6DHQH0WJbuUm3FuoneGjxv3q32mTOS3oF9qzZnbC0XkFegxWo9Cv6T+A/q31Q5nX7rfRvFa7CHQv60noDPiO6F4zxuArqU/qwz74663UTxTzwv6N/oIdM39WOjrzVqofK7Pj7tmQ19v5spFb4pIWwDzoFs+hEFXBBoL3fX1JncDV0odFhHHuINm042Wmh8a4Z2Ezojvj8r3NvQ5cWgOfc6eh96/PtAZVWbHofffaiWKF/I6Kixl4WyFAHPGkTnzzrG+HetzsoGI3A1dKSMPONMl5rlkl8H6tIhMgy6Y7QzgKZyDQj7jObwYwHWmyZeKyFzo86ugf5fX2K1fDr9AP7fNLUJHikgedC9FGdDP+GkoWVH7vUqKg9VP0NdYB9O02SLSGcA30C2cAqFbEV0M/ZxuA12o7mp83XPpfhE5DWAB9D1nIorvDwB8WkorQavKfmZ+AX2/cIyla76X56LkOLznWmWkNSvTEehemRyuE5El0IXTCsBJpVR6ZW1MRF6HbjlotgL6+VTHpvAeKNl9+3Toyp+O7npDAHxppCmyjLCsvZy8ak4TKqVWW3qFAICXRSQXusV8F5R8rqxXSlkLX64HMElEPoYu3EyDblHYCzptGm1Z/jFlGc9eKbVcRBYBGGKaHANgo3G//gn6OdLeCLOLJcxtAF6zTIOIBKD4mNJ2Y5CGW9KCx1XJYS1KJSLxAJaheGWzHOh043on6c0cpdQhxxel1F4RWQg9xJjDIyLyN3R6Pw46LWq2DzaF/iLSCMXvKdYWnSXSwDbP6A0ish7AV9CVl09Dn89bYKl8DP3bnWONB/Q9cg2Kj8n9qujh1D6CfjesB2AwdK8k1jzEh5Tuhc1hjLGcMy+jeCHSfujxo4Gz73vnWnVNdy+Hbo1rfr98UkSKoFtrN4BOI9kNFeJxSqlcEXkfxSuvjhE9LNVsnO2SOBI63XsV9LUwF/p9vbwc59PRUCASwE8i8hJ044dM6LRzW+je/q6Bfj/4sALbdMWa1u0vIgMB7IIuwC52n3FFROqjZOWTh6DzIuwEQN8bHNeuP3R+ieM+9RGKVzj1gR4u5BHohgndYHPvtnFO0g1KqS0i8iKAxyxxnm+k4T8ztpcD3SNNpQ7XaFzTs1G8QL638Xx8E/rdbhCApy2r/gngf6bvfxhxND8DXhORbGPZfwJ4pJzRvMb43b0P5+8u3ymlHD3DBAL4S0SWQ99zNkOn93Oh7y3Wil5AzekVg4guAEOHDs0cOnSoO70IXVBY6E90YSptLOMElKHApZKMg36pcLy0ekG3ina3e+b/QidMzRkWjxsfh4MoOQRAdfIG9EubuWDsIRH5j6OlmFJqkYh8huLjwXZG2VpYvA9d4GkuaLgYOiPMoQglu2mrEKXUfhGZiOKtTCIAlHVMsGbQ3Vnf78ay5u7rBPpYWTMi7WShDK03lFKnjMKzRThbOBOA4oWAVptKmV9llFJ/icgk6JdThzrQQ2I48x10Jsy5FInS71dhMArulVJJIvIcilee8YPOGHis5KrFLDQ+5ozIttAZqc48rSzjxlcGpdRvIvIqig/50QTApy5We0/pbvmqHaXUIRF5AMW7eRToQo/Sxnp1dxszjRZvU1F83MJ/GJ+q9iZ0YXFP07Q+KH5ftXrASQuXldDPAqt1joJK4366ByW7ZHasb8faTT1QsoXnOR0XVim1U0S2oHilq2tRPJOuEDpjsLxdmZbFZOjMOnNaYgSKdyVagEp4h1JKKRG5H7p7ZXN4d8B1bwLfwnnmbmXE6Q7ogkFHWiQA+nq0uyaro2zo4T3+z8n84yhjN7CV/cxUSuWJyLvQhTVWXyul7MYNPmcqKa1ZmX5G8XtrSxRPo81BxVo+WlkL/AFdmOF0DEYA/WBqXWpKj5jP8SVwPlb6Dyh5PwZ0Zv0vOPt7DIPz83ASzu8dF6F42siZGUopZ+GPAtAYxSsrNEXpBZ9pAK510nK3O0ofb/klFE8vP4OSBSruGIaSvcsEwHWh0yro82b2APTz3fHO6g/gP07WLwBwh6VCiMNncJ0+sUsDW5/RvtAVMYbAtXQAN9jFQymVKCKjoQv/HAWDAj00gHV4AKt/KaWKVSQwFSTZEhFrpeoCD1Q4LKa6pruNZ8UTKF7RzwfGMHemaZWSLqkiTwG4DLpSv8NQlGw9XGmUUhtE5N8ong5oCdfP7Kr0M4Dhpu/1oNMIDnb3GWdGoHjlEwVdkdFppQERWYPiFdtH42yh/7fQBb3mXkFiUfL9NxsuKuCe43TDE9DH0NzCXqALyv9ZBduzehK6kUyxyogoXmnYLB/AXebKdMZveyGK9xAVAWCxZV2Fsr+bnYZOO9j1VuaIjzVfxAd6OLkrSi5ewiqlVFYZ40REROcYu/cnomrBeNn/B9yvbFAI07hYSqn9ACagZJeLDhmo5Jq+lc3I5LW+jDZAydq1t0JnPLvbQjzNsp1c6BeiNPvFUQjdJZk10ybXZtkyUUq9A50h6W7t4HSU7ELNXdOUUqVlJNrJBjDS0mqlVEqpr6G7WXVnbLqVAC5XelxVj1BK/Qe64oQ75/UzAMPK2DLSI5RST0G3MitTbyVG67oRcK8FbQGAR5VSz5c9hm6bBOB5uDeW9nRUUuF5VVFKfQCdeVxl42oppZ6DLrAta0WMdahgRTfjvno1dGvo0pwGcKuLgpXVsB/r0dp60/odANKUUrucxHELSmYmVQe3Q1e0slMIfW2fk5rbSqlt0Blw+U4WOYlKHP7GKDAYAt0LiTu+AHBjVfYQo/RY8gNQeoUrh1zY99jgKUPgPI1xCsAQI81YJlXwzHwb9s8pj3Xtb1GhtGYl+w+q8NlRVZRST0MXzJWWdloC4Bpl0/W9cd++DMXHArZzEMBAY/nyyIZu4e+0O2EjzXoJgFfg/jvBVwC6KKX+LGe8qh2jcK0vXFcCAfT7wDCl1PKqj5VLvwHobjzfbCmlPoOuyLDVzTCPALhF6aHVzhfVMt2tlHofrivI/wH7CmTVglG59VI4r5BaYhXoXlwq6jHoipx26Wk7lbFNZ+ZC9/hRGW61fP/NjV4CrMO1dDF6W3O8/w5HyWEOzaZC95JoZvcMOCfpBqXdDV0R7UAZVi2Cbm3vbvrW2fYzoCsC2lWmtkqHrvRmlyf1KJzHX0Ffv+W5bkYY27VTAGC0UmpTOcIFdM8vVTEMKBERVTIW+hNRtaGUSoFuhT0Eumb9LugM9kLollmboWsL3wkgwhhjyrz+29AZ1d8ay+dBJ0zfAdBBKfXbudiPCnoFJTOBJxnj9QHQNYOVUuOgWyG/AmA9dKWGAujCpL3Qx2AKgK5KqT7WjRiZbx2gW878aWzzMIAvobv7/Bhnu5R2cNlyw11KqdnQXU8+Ct1d6iHoF8c84/810JkbV0Kf579Nq0+DbhnwqrHcLuiM4ELj72boLr67K6XMvTwAuuu626Ez1BOhX6KyoQt30qFf3J4HEGcU4Jdn3xZCt6b6P+iWS4eN8E9BH+dPAAxSSvUvrSXMuaCUegtAC+iX+XXQBU8F0AVw26ELwHsppW5WSpVluAOPUkq9DN0C+kno1hOHoa+vLOhrZiF0ZZqTlvVylFJ3QbdMmAHdpecJ6GOSAX3dvAigpXJv7NeK7EORUmoydMuYV6AzW44ZcTkB3QX3mwDaKaXGK0tXvNWRUcgdDd1i+DvoQopc6PvWHuieOcZD/1bKu41l0MfsGuhCtU3QmdP50N0oHoHOAP8Y+hq4SCnVqwKZH+ZtH1NKXQvd4vMD6G4xs6DPWTr0PetJAM2VUh+5COck9H3dylrIb9cbSWmZqjdAZw4no+Q4qx6hlEqEHj5jDnTmVz50d7ZfAuijlKqKITRcxWchdI9H84x45EFnTL4P3S3pj5W8vW+gnxsPQGfMpkGfG8d9ZyP0/aiHUurGc9G6Rin1C/TYordC9yqQaonTBujjcQuAhkqppU6COueUUt9Bdyv7AXQmfh70dfUBgPblrAzoCLvSnplG2uYLy+R90C3uPK4y0pqVGJe90C3CP4XuCtxZpZxqRyk1Bfp+MgPADuh0Rw70uf4cwNVKqWtcVQJVSv0M3fJyEvRzIB3FnyuTAMQay9lZCp3mXgb9fD2Bs/fZn6CfCTFKqRfc2J88pdTD0M/yR4ywU2E/JNbXSqnrqkN6t7Ippf6Afh7cA91by9/Qx9SRVnwG+piW632iDMZCv+dugk5T5UGfiz+hCxoHA+jmTqULpdSv0Ps0CMXTT9b0ZRGAK5VSrlrB1zjVOd2tlHoIOl27wohLNvQz51no9FNZCj7POaXUQaVUf+gKTB9Ax938/r4degiMB6DTyJMrYZvKqJzdAvo4rUXx94G/oI/nv6AbnjSr6DZdxOUEdG81M6Hfd8o1jKaIdEDJ4YqsBfp27NJoo03xOwL9jJ0MXck2F/pe9h2AK4wK9S0s65e4r5/rdINSai70s+hm6OtqK86my7Kh85V+hk6vjgbQRCl1uVIqtSLbNbadBj0UxWDoip57oPfR8WxdDt1zSAvj3dQujL+gezqdCX09OtZdCKCnKn/jgt+he1B7E2evtyPQv7GuNvfuU0Y8JkAPjbUZ+nmSD30t7IeuLPEggLaqCno5JCKiyidV2FCEiIhqKBEZgZLd0rZ11UqEiIiI6FwxuqT+wDxNKXVOh6ioCBGZAl2BwGGqkblOVOMYY03/iuLDzkwyKmJSDWYMb/Jv06Td0BXRzrsKHURUnIj0RsmKx1cppf7rifjQWSJyCUoOkdNceXjIFKKaJikpaW+HDh2c9ZBBVG0lJSWFd+jQIdpuHlv6ExFdgETkDhF5W0QuMfciICLBIjIKJcem/JUF/kREREQVJyJxAMxdqRcAOKe9WhBVJqO16DUoPhTDv0Xkeg9FiSqJUuolFB965CIA34hIkIeiRESVRESuFJG5xt9Q03R/EfkndM9XZvuge0kgIiKiaoqF/kREF6YQ6C4pfwBwUkSOiMhh6C4D5wCobVo2y1iWiIiIiMpBRC4WkV0ish+6O+OGptmzja5eiWospdRW6OG0Co1JAuAjEenluVhRJRmL4sPbdAMwT0SYp0hUs/lDjwP/XwDHReSoiByCHirkKwBNTMsWABijlMo957EkIiIitzGBTkREXgDCAdRHyefCHwAGVMZ410REREQXsEDocXEjoAtDHbZDj49OVOMZ4xdfDz2u/TMAXgSQ4NFIUYUppfIBDAXwNM6eW8fY0UR0fhAAdaErJfpY5h0EcK1S6rtzHisiIqpxJk6cGNGyZcu2Vb2dyMjIdk8++WTD0pe8sLDQn4jowrQAwATo2tspADKgW+WcML5/Ct1Sp51Sar2H4khERER0PsqBLux/HkBPpdQJD8eHqNIopb5USj1t+liHDaMaSCmVoZR6xnJuN3s6XkRUIWuge/L4HMA2AEegW/RnAdgNYBGAOwC0VEp966lIEhGRvZ9++inQ29u7c6dOnVqVdd1u3brFjRo1Kqoq4uVKWlqaj4+PT6cZM2bUtZt/zz33NGnUqFH7wsJCu9nkBmvNPSIiugAYXci+bnyIiIiIahSl1IcAPvRwNNymlPoRxVv4ExEREXmMUuoogHeMD9UgTFcSEQC8/fbb9UeMGHF40aJF9TZu3BjQqVOnHE/HqTRNmzYt6Nev34k5c+aE33vvvRnmefn5+Vi4cGHdW265Jd3b29tTUazx2NKfiIiIiIiIiIiIiIiIiKgMPv4ltW6355e3a/7o0s7dnl/e7uNfUm1bsVemkydPytdff1333nvvTb/yyiuPvfPOO+HWZVasWFGre/fusYGBgQkhISEde/ToEbt3717foUOHRicmJgbPnTu3voh0FpHOKSkpfkuWLAkRkc4HDx4801g8JSXFT0Q6r169OggACgoKcMMNNzSLjIxsFxAQ0KlZs2bxkydPbliWlvl33HFHemJiYsi2bdv8zNM///zzOkePHvUdO3Zs+qpVq4J69erVMiwsrENwcHBC586d45YvX17LVbgi0vmDDz4IM0+zDgFw9OhR75tvvrlZ3bp1O9SqVSuha9eucY59c8wfPHhw87p163bw9/fv1KRJk3ZTp05t4PbOVQMs9CciIiIiIiIiIiIiIiIictPHv6TWfXbJtmaHs3L9FIDDWbl+zy7Z1qyqC/7nzJkTFhERkXfxxRdnjxo1KmPBggX1cnNzz/QA8vPPPwdeddVVcc2bN89dsWLFjlWrVu0YMmRIRn5+vsycOTOtY8eOp4YNG3Y0NTU1KTU1NalFixZ57my3sLBQIiIi8j/99NM/k5KStkyZMmX/9OnTG7/xxhslKh04M2zYsBP169fPf/fdd4utM3v27PDu3btnxsXF5Z04ccJ7+PDhR1esWJGyZs2a7W3bts0eMmRIS3OFhLIqKirCwIEDYw4ePOi7cOHCnb/++uu2nj17Zg0aNCguNTXVFwAmTpwYsWPHjsCFCxfu3Lx585Z33nlnb5MmTfLLu01PYPf+RERERERERERERERERERuemPFzsjcgqJijatzC4q83lixM3JE92YZztarqA8//LD+jTfeeBQABg0alBUYGFg0b9682qNHjz4OANOmTWsUFxd3et68eamOdczd//v6+qrAwMCiqKiogrJs19/fX73++usHHN/j4uLyNm7cGDR//vy6EyZMSHcnDB8fH9x4443pn3/+efirr756wNvbG/v27fNZvXp16Ntvv70HAK699tosy/7ua9iwYZ0vv/wy1DosgLuWLFkSsn379qDDhw9vCg4OVgAwffr0A999912dWbNm1X3uuef+TktL84+Pjz/dr1+/0479K8+2PIkt/YmIiIiIiIiIiIiIiIiI3HQkK9evLNMrw5YtW/x///334Ntvvz0DALy8vDBkyJCM2bNn13css3Xr1qC+fftmOQ+l/P7973/Xj4+Pbx0WFtYhKCgo4b333mt44MCBMu3vPffck3748GHfL7/8MhQA3n333fCQkJDCESNGHAeA/fv3+wwfPrxZdHR0fEhISMfg4OCEjIwM33379pX7uCYmJgbl5OR4NWjQoGNQUFCC47Nr167A3bt3BxjxOrx06dKwuLi4NmPGjGmydOnS4PJuz1PY0p+IiIiIiIiIiIiIiIiIyE31Q/zzDtsU8NcP8a+yFuIzZswILywsRExMTHvHNKUUAGDXrl2+MTEx+Y7vZeHt7a3MYQFAXl6emJeZNWtW2JQpU5o+9dRTaX379j1Vp06dwtdee63BsmXL6pRlW23atMm7+OKLs2bPnh0+bNiwzE8++ST8uuuuywgMDFQAcPPNNzdPT0/3eeGFF9JiYmJyAwIC1MCBA2Pz8vKcNmQXEVj3Oz8//0z8i4qKpF69evk//PBDinXdsLCwQgC44YYbMvv06ZO8aNGi0JUrV4Zef/31LQcNGnRswYIFe8uyf57Elv5ERERERERERERERERERG4a17/lfn8fryLzNH8fr6Jx/Vvur4rt5efnY/78+fUee+yx/T///PNWx+eXX37ZGhsbm/3OO++EA0B8fPzpNWvWhDgLx9fXt6iwsLDYtIYNGxYAQFpamq9jWmJiYpB5mbVr1wa3b9/+1OOPP36kd+/ep+Pj43P37NnjX559GT16dPry5cvrfPTRR3VSU1P9x44de8Qxb8OGDcF333334ZtuuulEly5dcmrXrl145MgRX1fhhYWFFRw4cODMMmlpaT7p6elnvnfp0uX00aNHfb28vFR8fHyu+RMZGXlmmIPGjRsX3HfffRkLFy7cO3369L2LFi2ql52dLdbtVVcs9CciIiIiIiIiIiIiIiIictOI7s0yplzdJrVBiH+eAGgQ4p835eo2qSO6NyvXuPOl+fzzz+scO3bMZ9y4cUe6du2aY/4MGTIkY968eeGFhYV49NFHD23fvj3o5ptvbvbzzz8HJiUl+b/66qvhO3fu9AOAqKiovE2bNtVKSUnxO3jwoE9hYSHatm2b26hRo7wpU6ZEbN682X/RokWhL774YmPz9mNjY3O3bdsW9MUXX4QmJyf7T5o0qXFiYmK5usAfNWrUsaCgoKIHHnggul27dqe6du2a45gXHR2d89lnn9XbsGFDwKpVq4KGDRt2ka+vr8vuC3r27Jn53nvvNVi9enXQTz/9FHjLLbc09/f3P1Mh45///GdmQkLCycGDB8d88cUXoTt27PBbvnx5rQkTJkQsW7YsGADGjx8fMXfu3DrJycn+GzduDPjqq6/CmjRpkuvogaAmYKE/EREREREREREREREREVEZjOjeLGP9EwOS97xw1Yb1TwxIrqoCfwCYPXt2+MUXX5zVqFGjQuu8W265JePAgQN+X3/9dWjPnj2zFy9e/MfOnTsD+vXr17p3796tFyxYUNfPz08BwGOPPXbI19dXdezYsW1ERESHXbt2+fn7+6uPPvpo9759+/y7devWdurUqRHPPPNMsR4LHnrooSNXXXVVxp133nlRz549W6empvrdfffdf5dnXwIDA9XgwYOPZmZmet96663p5nnvvffe3lOnTnn16tWrzYgRIy4aNWpUemRkpMshE958882/oqKicq+44oq4m266qcXtt99+pG7dumda8Ht5eWH58uU7e/XqlWVUNIgfMWJEi507dwY0bdo0HwD8/f2Lnnnmmchu3bq16devX6uTJ096ffXVV7vKs3+eIuUZ24GIiIiIiIiIiIiIiIiIqKZJSkra26FDh/TSlySqXpKSksI7dOgQbTePLf2JiIiIiIiIiIiIiIiIiIhqKBb6ExERERERERERERERERER1VAs9CciIiIiIiIiIiIiIiIiIqqhWOhPRORhIhItIkpERns6LnT+4fVFREQ1EZ9fRERERFWL6S0SkQ9FZK+by+4VkQ+rNkZERFQRLPQnompHRJ42XjoaOZn/o4jsONfxosonIveX5eXSOPfK9MkWkc0iMl5EyvVME5F445qLLs/6pYQ9WUQGV2D9YCNul1RapIiIqEZi+ujCcb6nj4iIiKorprcuHExvERHR+YiF/kRE5En3AxhdxnUOAhhpfB4DkAPgNQDPlTMO8QCeAhBdzvVdmQxgcAXWD4aO2yWVERkiIiKqEc739BERERGRpzG9RURE5x0fT0eAiIiojDKVUh87vojIOwB2ALhfRKYopQo9FzUiIiIij2D6iIiIiKhqMb1FRETVGlv6E1GNJyKXGF1r3SwiE40xpnJE5GcRSbBZvqWIfCYiR0QkV0SSReQ2F2E+LiJpInJSRL4SkXoi4iMi00TkoIicMsILtoSxV0SWicilIrLBiNNO67Zc7Fe8iCwWkeMictrYnytN80ON6dNt1g0z9u3lytofI5zLROQHEckylvtBRHpalnF0h9dSRN4RkaPGtuaLSD3z8QEQB+Afpu7R9rpzbMyUUjkAEgGEAGhgCr+96LHJ/jSO/RERmSciTU3LjAYwz/j6gykeo03LdBGRJcZ5yBaR9SJydWnxEhEFwB/AraZwfzTNjxKRT0Qk3YjfJhEZZZofDV2LHACeMoXxoTG/mYjMEJEdxnVwXES+EZG2ZTuCRER0PmL6iOkjVM/00U8ikuRkXqKI/Gb67iUi/ycifxjnbb+IvCkitS3r2Y4vK5YxauXsuMWPisgoIw2VK7pr3gE26/cWkV+N45QqIo+IyG1GGNGl7SsREZ3/hOktpreqYXrLWNetdJSTdf1E5EUROWQc6+Ui0sqtg0JERB7Flv5EdD55CIAvgDeNv5MAfCUiMUqpfAAQkdYAfgKQDuBVAMcBXAVgtojUVkq9bgnzEQB5AF4C0AzAeAAfANgPIBa6C6/2AMYAOGTMN7sIwEIAswB8BOAWY1u5SqlPne2IiMQa8cyF7irsJIDbACwRkWFKqS+VUpki8iWAm0TkIaVUgSmIGwD4AZhbWfsjIjcD+BjAD9Dd1nsBuB3AShH5h1LqV8u25kEXWk8B0BLAAwDyAQw35o8HMAPACQDPG9NOOjsmpYgGoKDPp8NlAFoZcf4LQAyAewB0FZF2SqlsAKsBvAXgPgD/ArDdWHedsc//APAdgGToY5ML4EYAi43zsMhFnEYCmA3gFwAzjWl/G+GGQ5/futDX6wEANwGYIyJhSqnpAI5Adzf3HwBfAnBs60/jb1cA/zCm7wUQYezfahFpq5Q65PqQERHRBYLpo7OYPvJ8+uhDADNFpL1SarNjonFuu6D4tTIDwN0AFgOYDt0F7r0ALhaRXo7rtxyGAQgH8C6AbGObX4pIM6VUhhGfDgD+B+AogGehr4+7AJwq5zaJiOj8xvTWWUxveT69BVQsHfUu9NAHC6CPeVcA3wMIKGWbRETkYSz0J6LzSW0A7YyathCRHdCFpQMBLDWWmQ798tBFKXXamPa2iHwOYKqIzFJKmTPzAgBcrJTKM8IMg37ZWQXgUqVUkTG9KYDbRWSCUkqZ1m8JYLhSap6x3EwAvwN4UUQ+c6xv418Agoxt7zDWnQWd2H9dRL421p0D/dIyEMB/TeuPAJCslLK2pCrX/ohILeiXkblKqdGOwETkXQBbjfj2t2xrh1JqhGlZge7ybKxS6oRS6isReQHA3+bu0dzgbRSaA0A9AHdAZxJ/bbw4ObytlHrFvKKIfANgDYAhAD5RSu0WkbXQL1nfK6V+tMT3XegXrgGmY/MW9AvwSzhbEF+CUupjEXkPwG6b/XsUQBMj3BVGuO8YcXteRD5USp0QkYXQhf6bbcL4r1JqgWX/PgawxTgmz4OIiIjpI6aPqlH6CMAXAN6APhePmKaPBFAAo8WbiMRDZ1R/rJQaadr+DgCvA7gVwHuuD4lTLQDEKqWOGGH+CH393Qx9PgFgKnSBQm+lVKqx3AcA/ijnNomI6PzG9NZZTG95OL1VkXSUiLSDLvCfrZS6wzT9XwAec7ZNIqo+IiMj291xxx2Hp06d+ren43IhEpHOs2fP3n3bbbcd88T22b0/EZ1PPnC8YBlWGX8vAs68UAyAzmwMEpFwxwfAt9DdcXWxhPmR44XE4Kg9/KHlBelXY/36lvUPA/jc8cV4CXgPusC3vd1OiIg3gCsAfON4wTLWzQTwDoAoAO2Mycuha0WbE/HRAHqhZK3qiuzPZQDCAHxiOW5BRhz6iIivZVszLN9XAfA24l8RMdCt4I9Aj502Cbr2erFu6kwv0RCRYNFdue2Arn3d2Y3tdIDu7u1jAHVN+1wX+nq5SESalXMfrgbwu6PA34hvHvTLVy0Al5QWgGX/goz9Ow6dGe3O/hER0YWB6SMwfeTg6fSRUuoEgK8ADBcRLyMeAt368H9KqcPGoo6ua1+yBPEOgEzolpHlNd9R4G/EaZMRpuM34Q19bhc7CvyN5dIBOG0ZSUREFzSmt8D0loOn01uoWDrKse7rlumvuRFvIjoHhg4dGt2vX78YZ/MTExO3T5o06Yiz+Z42ceLECBHpLCKdvby8Ojdo0KD9tdde23zXrl3We3mNlJqamnTTTTcd99T22dKfiGoqZTMttdgCSh3TeYioa0xqCUAAPGV87DSwfN9n+X7C+JvmZHoY9IuVw582tacdLYSaAdhkE4f60AW/O2zmbTP+RgNIUkoVGa27x4lIiFIqC7pWtYJ9pmR59yfW+P4/mzAd6kC/+DikWuY7arfVRcWkQXfj5gX9wvU4dNf25pdHx0v1C9BduFq3WceN7Tj2+X0XyzRAyf10RzTsa2Wbz69LIhIA3QptBIDGltnp5YgTERHVfEwfMX1UE9JHc6CHNeoHYAV04UBzI84O0dDnK8W8olIqV0R2w420kgt2cTuGs8ejAYBAADttlmNLfyIiYnqL6a3qnt6KRvnTUc2MdYuleZRSR0TEI61WiahsIiIiCkpfqurl5ORIQECA3TMT0dHROatWrUopKiqSHTt2+I8bNy5q2LBhLTZt2mT3/DkncaosUVFRHj3+bOlPRNWRo3Z0oJP5QaZlzAqdLC/GX8c97zXomsJ2n9VuhlnathzsHiLWZcrCbt050MdqiPH9FgArlVL7bZYt7/44jt1oOD92JyzrunuMyuq0Umq5Uup/SqkZAK6EHl/sX5blPgMwCrqG91DoLucugx6b1Z3nn2OZR+F8n1PsVy23shyb6dDjBn4BPWbe5UactoLPdyKi8xHTR84xfVSz0kffQ4+z6+h2dwSALABfu7F9wL3rCdAt+uxU5BxU9DwREVH1xvSWc0xv1az0ljOlHQNX85kOIqoBIiMj2z355JMNHd9FpPPLL78cfuWVV14UGBiY0KRJk3YzZswoViFpz549vldfffVFoaGhHUNDQztecsklMcnJyf6O+Vu3bvXv379/i/Dw8A6BgYEJbdq0aT1v3rza1u1OnDgx4vrrr48OCQnpeN111zV3FkcfHx9ERUUVREdH519xxRUnb7311vSkpKRaGRkZZ+6Rn376ae22bdu29vf37xQZGdnugQceiMzJyTlzH0pLS/O59NJLYwICAjpFRES0mz59er2WLVu2nThxYoR536dNm1Z/4MCBLQIDAxPGjRsX6U7Yc+bMqRMbG9smICCgU+3atTt27do1Li0tzQcAdu3a5du/f/8WtWvX7hgYGJjQvHnztjNnzgwzb/ODDz448339+vWBPXv2jHWENXTo0OijR4+eeVd19Nzw7LPPNmjQoEH70NDQjsOGDYvOysoqV/4+W/oTUXXkqKkaB2CPeYbR1VgMznaVVha7jb8FSqnl5Y9emcSIiJeldnVL46+zGrlHAJwC0MpmnmPaXscEpdR2EUkEMFJEthnLTKtQrEv60xG3Sj52Fa5Zp5RKFpE5AMaKyKtKqVQRqQP9UvW0UuoZx7JG6/gwaxBOgnbsc1YF9tlZ2Hvh3vl1dXxuhO4eb7x5olGjnC39iYjOP0wfMX3kfgDVOH2klCo0WgbeIyIToCsvLrCMhbsXOmM5DsBmU1z9oHsFWGla9hjsW81Flyd+0K0Ks3H2mjSzm0ZEROcPpreY3nI/gOqb3toL99NRztaNBZBsWrc+3OulgKhGm9l5g0eGTB2zofOGqgz/pZdeinjqqaf+eumll/a//fbb4Q8++GD0gAEDTsbGxuZlZWV59evXL65z584nv//++xQ/Pz/1wgsvNLz88stjU1JStoaEhBRlZmZ6XX755ZnTpk07EBQUVDR37tywUaNGtWjVqtW2hISEM5XhZs6c2XD8+PEHnnrqqYNKuXeb3bdvn8/ixYvreHt7w8dHF1kvXLgwdMyYMRc9//zz+wYMGHBy9+7dfg888ECz3NxcmTlz5l8AMHz48OaHDx/2XbJkSUpQUFDRQw891PTAgQN+dvs+efLk/a+//nqal5dXqWHv27fP584777zo8ccf3z98+PBjmZmZXmvWrAl2hDdmzJhmOTk5smzZspSwsLDCLVu2BDjbt6ysLK+rr766Zfv27U+tXr16+5EjR7zvu+++6OHDh0d/9913jns9fvvtt+CGDRvmL1u27I+9e/f63XrrrRf961//ypk2bdohtw6iCVsCElF1tBy6a6yxxkuV2QjoRPJ/yxqoMUboSgBjRKSpdb6RgK1sDaALZx3bCARwJ/S4Z8l2KyilCgEsA3C1iMSa1g0BcDd0l2jWdedAd5H6CPQLml3X8RWxDHrsscki4m+dWYFjdwolX3rK4yXoimyTjO+Ol1prLeQJKPnsO2X8tcZjA3S3rg+LSG3LPHf32dn+LQGQICL9TOH5AngQwGkAP5YSN0DXXC+2fyJyM3TXckREdP5h+ojpo7KqrukjQJ+bEADvGtv4yDJ/qfF3omX63QBCTfMBYBeAHuZzICKdAPR0My7FGNfa9wCuNY+Xa4ylO7w8YRIRUY3B9BbTW2VVHdNbZUlHOVt3vGX6hFK2SUTV2LBhw47ee++9GfHx8bmvv/76fh8fH7V8+fJgAHj//ffDlFKYP3/+3osvvjg7ISEh55NPPkk9ffq09+eff14bAHr06JH9yCOPHOnWrVt2fHx87osvvnioTZs2p+fNm1fs/tWtW7es55577u/4+Pjcdu3a5TqLz+7duwOCgoISAgMDE5o1a9Zh/fr1ISNHjjwcGhpaBADTpk1rPHbs2EMPPvjg0bZt2+Zec801Wc8+++xfH3/8cf2ioiIkJSX5r127NnTGjBmpAwYMONWzZ8/suXPn7snJySlR5n3NNddkTJw4Mb1NmzZ5rVq1yist7H379vkVFBTILbfcciwuLi6va9euORMnTkxv2rRpAQD89ddffj169DjZo0eP7FatWuUNGzYsc9iwYZl2+zlz5sy62dnZXvPnz9/TrVu37Kuuuurkf/7zn9T//e9/dbZs2XLmmVarVq2iuXPnpnbq1ClnyJAhmVddddWxVatWhZbnXLOlPxFVO8Y4UVMBPAdgrYh8Bd3lZzfo7rHWQ79UlMdYAD8B2Cwis6AT0eEAOkF3j16um6kLOwHMEJEEAH9BvyTGARhlvEw58wR0d11rROQtACcB3AYgCsD1NuOyzQPwKvR4YR8rpU5W5k4opbJEZIyxnSQR+QTAIQBNoF/uTkF3a1ZWv0G/9D4FPV7YSaXUN+WIX4qILAFwu4hMVUodFpEfATxi1GROBdAbwD+gu1Mz2wj9UvaYUSM7G8CvSqk9InI7gO8AbBOR2UY4jQH0gB7nrK0b+zdARB6GPv+HlVIrocd2uwnANyLyBnQXtzcC6A5gglLqhLFfJ0UkBcBNIvKHEfc9SqlfASwGMEpEMgFsAdDRCGM3iIjovMP0EQCmj8oav+qaPoJSaquIbIBu5Z8GS6tJo+XcuwDuNjK7vwcQD51ZnYji1/q7AK4H8D8R+QxApLHcVpT/2n0K+tpfKyJvA8gHcBd067cwVELrQCIiqn6Y3gLA9FZZ41ft0ltlTEdZ190sInON/QkF8AP0EAaXgb1K0gWgqlvce0r79u3P9Krm6+uLsLCwgsOHD/sCwIYNG2rt37/fPzg4OMG8Tk5Ojteff/7pDwCZmZlejzzySMT3339f+8iRI74FBQWSl5fn1aZNG3NvbUhISDgFNzRt2jR3yZIlO3NycmTBggV1vvnmm7Dp06efGRpm69atQcnJybXeeuutRo5pSink5OR4paWl+SYnJwd6eXmhb9++Z7YXExOTX79+/Xzrtrp06XLa/L20sLt37366R48emQkJCW179+6deemll2aOHDnyWERERAEAjB079vCkSZOiVqxYUbtv376Z119//fE+ffqctm4XALZv3x4QGxubHRYWdubZOWDAgJNeXl5ISkoKiI+PzzXinu3r63tmvcaNG+dv3LixljvH0oqF/kRULSmlnheR3QDuBzAZgCOh/CKA55VSeeUM9w8R6QzgSeixxupDJ1q3AXi4MuJusRvAPdA1f9tCv2jdqZSaW0o8U0SkF3S3aA9B7/8mANcopUrUKldKZRgvGUMAuAy7vJRS80VkP4DHoWv4BkEXVv8K4L1yBvs0dMv0idAvuKkAyvySZXgZwDVG3B6Dbok1Hfqlxhd6fLxLoWvun6GUOmC8QD4KYBb0+K+3QReurxWRbtDXy90AakN3+boJOjO4NA8CeNvYz1rQGdorlVLpItITuvD/bmNeCoBblVLWlm63AXgdwCsA/KFfzn41ws6HLui/A/qF9Qroa42IiM5DTB8xfVQO1TF95DAHQGfoAgK7QvR7obtWvhPAVdDX5NsAnlBKncnMUUqtEJFx0Nfqa9CF/cOhr+VLyhCfM5RSm0RkIPTxewq6cOEt6Mz4BNiP50xEROcBpreY3iqH6pjecisd5cSdAP4GcCuAQQB+BjAAutcFIqqB/Pz8ir1viQiKinQ5dFFREVq1anX6s88+K9GQrH79+o6C7iY//vhj7eeffz6tdevWubVq1SoaOXJk87y8vGK9mtSqVctaMcyWr6+vchR4d+nS5dCuXbsCbrvttqiFCxfuBQCllEycOPHALbfccsy6bkRERL67QwcAQHBwcLE4lRa2j48P1q5du3PlypW1vv3229C5c+eGP/fcc5H/+9//Unr06JE9YcKE9GuvvfbEl19+WXvlypWh/fv3b3X//fcfevXVVw9Yw1NKiYi14xfNPN3X17fE+VFK2a9YCinLwSEiIveJyF4AO5RSV5yj7c2DrjnctJRa20REREQewfQROYjI3QDeAdBGKbXd0/Fxh4hMBzAGQDCvJyIiqq6Y3iIiKl1SUtLeDh061LgeLIYOHRqdkZHh88MPP+yymx8ZGdnujjvuODx16tS/AUBEOs+ePXv3bbfddsxumVdeeSX86aefbrJnz57k8PBw23t4bGxsm6uuuur4a6+9dgAATp8+LU2bNm1/ySWXnHAU1Fu368zEiRMjvvnmm7CdO3dudUzbtm2bX/v27eN/+OGHHX369DndqVOnVtHR0TmLFi3aaxdGUlKSf8eOHeNXrFix49JLLz0FAH/++advbGxs+wcffPCgowDebt9LC9uqqKgILVu2bHvllVce/89//rPfOv+JJ55oNGvWrAaHDx/ebN2m49ju27dvs6O1/5IlS0Kuueaa2OTk5C3x8fG5dufT7hhZ9j+8Q4cO0XbzSoxvQERENY8xxuhg6JZSfMEiIiKiCx7TR9XenQASq2uBvzH2sfl7fQAjAazm9URERKQxvUVEdO5lZWV5r1u3LtD8SUlJ8StPWGPGjMmoV69ewZVXXhmzdOnS4B07dvh9++23wXfddVeT5ORkfwBo3rx57tKlS+usXbs2aP369YFDhgwp0cq/Itq0aZPXv3//E5MnT44AgCeeeOLA4sWL644fPz4iMTEx4Pfffw/44IMPwu65554mANChQ4fc3r17Z953333NVqxYUWvdunWBI0eOjA4ICCgSEZct3UsLe8WKFbUeeeSRxqtWrQrauXOn36efflrn0KFDfo6hDG677bamCxYsCN22bZvfunXrApcvXx4aExNj2xPcmDFjMgIDA4tuuOGG5uvXrw/89ttvg++///5mAwcOPO7o6aCysXt/IqIaTESaA+gFYDQAge52lIiIiOiCxfRR9SUitaC7v+0DoAuAmzwbI5f2isjH0EMwRUBXUggGMNWjsSIiIqoGmN4iIvKcDRs2BPfq1auNedrll19+bNmyZSW66C9NSEhI0Zo1a3ZMmDChyciRI1ucPHnSu379+vk9e/bMcrT8f/PNN9NGjx4dfdlll8WFhoYW3n333X/n5uZWaqPyhx9++NDAgQNbff/997WGDh2a+cUXX+x6/vnnG7/77rsNvb29ER0dnTN8+PAzPTN88skne0aPHh09aNCguLp16+Y/9thjB9LS0vwDAgJcFvqXFnZYWFjhL7/8Evz+++83yMrK8m7UqFHehAkTDtx7770ZgG75/9BDD0UdOnTILygoqLBXr15Zb775ZprdtkJCQoqWLFmy88EHH2zat2/f1n5+fkWXXXbZ8ZkzZ9ouXxnYvT8RURU5F92pichoAB8ASAPwf0qpeVW1LSIiIqKKYvrowiYi0dDjyx4H8J5SapJHI+SCiMwG0A9AIwAFAH4D8LRSapVHI0ZERFQKpreIiEpXU7v3J3sHDx70iYqKaj9r1qzdo0ePPu7p+FQlV937s9CfiIiIiIiIiIiIiIiIiC4ILPSv2RYvXhySmZnp3alTp9MHDx70nTx5cuSff/4ZsGvXri2hoaFFno5fVXJV6M/u/emcCQ8PV9HR0Z6OBhEREbmwYcOGdKVUfU/Hg5h2IiIiqgmYdqo+mHYiIiKq/ph2osqQn58vU6dOjTC69C/q2LHjqZUrV6ac7wX+pWGhP50z0dHR+O233zwdDSIiInJBRFI9HQfSmHYiIiKq/ph2qj6YdiIiIqr+mHaiyjB06NDMoUOHbvN0PKobL09HgIiIiIiIiIiIiIiIiIiIiMqHhf5EREREREREREREREREREQ1FAv9iYiIiIiIiIiIiIiIiIiIaigW+hMREREREREREREREREREdVQLPQnIiIiIiIiIiIiIiIiIiKqoVjoT0REREREREREREREREREVEOx0J+IiIiIiIiIiIiIiIiIiMpt4sSJES1btmxb1duJjIxs9+STTzas6u3UNCz0JyIiIiIiIiIiIiIiIiKqAX766adAb2/vzp06dWpV1nW7desWN2rUqKiqiJcraWlpPj4+Pp1mzJhR127+Pffc06RRo0btCwsLz3XUzhss9CciIiIiIiIiIiIiIiIiqgHefvvt+iNGjDi8c+fOgI0bNwZ4Oj7uaNq0aUG/fv1OzJkzJ9w6Lz8/HwsXLqx70003pXt7e3sieucFFvoTEREREREREREREREREZXBx7+k1u32/PJ2zR9d2rnb88vbffxLqm0r9sp08uRJ+frrr+vee++96VdeeeWxd955p0Qh+ooVK2p17949NjAwMCEkJKRjjx49Yvfu3es7dOjQ6MTExOC5c+fWF5HOItI5JSXFb8mSJSEi0vngwYM+jjBSUlL8RKTz6tWrgwCgoKAAN9xwQ7PIyMh2AQEBnZo1axY/efLkhmVpmX/HHXekJyYmhmzbts3PPP3zzz+vc/ToUd+xY8emr1q1KqhXr14tw8LCOgQHByd07tw5bvny5bVchSsinT/44IMw8zTrEABHjx71vvnmm5vVrVu3Q61atRK6du0a59g3x/zBgwc3r1u3bgd/f/9OTZo0aTd16tQGbu9cNcBCfyIiIiIiIiIiIiIiIiIiN338S2rdZ5dsa3Y4K9dPATiclev37JJtzaq64H/OnDlhEREReRdffHH2qFGjMhYsWFAvNzdXHPN//vnnwKuuuiquefPmuStWrNixatWqHUOGDMnIz8+XmTNnpnXs2PHUsGHDjqampialpqYmtWjRIs+d7RYWFkpERET+p59++mdSUtKWKVOm7J8+fXrjN954o0SlA2eGDRt2on79+vnvvvtusXVmz54d3r1798y4uLi8EydOeA8fPvzoihUrUtasWbO9bdu22UOGDGlprpBQVkVFRRg4cGDMwYMHfRcuXLjz119/3dazZ8+sQYMGxaWmpvoCwMSJEyN27NgRuHDhwp2bN2/e8s477+xt0qRJfnm36QnlPkBERERERERERERERERERBeaN1bsjMwtKCrWuDq3oMjrjRU7I0d0b5ZRVdv98MMP6994441HAWDQoEFZgYGBRfPmzas9evTo4wAwbdq0RnFxcafnzZuX6linU6dOOY7/fX19VWBgYFFUVFRBWbbr7++vXn/99QOO73FxcXkbN24Mmj9/ft0JEyakuxOGj48PbrzxxvTPP/88/NVXXz3g7e2Nffv2+axevTr07bff3gMA1157bZZlf/c1bNiwzpdffhl67733luu4LlmyJGT79u1Bhw8f3hQcHKwAYPr06Qe+++67OrNmzar73HPP/Z2WluYfHx9/ul+/fqcd+1eebXkSW/oTEREREREREREREREREbnpSFauX1mmV4YtW7b4//7778G33357BgB4eXlhyJAhGbNnz67vWGbr1q1Bffv2zXIeSvn9+9//rh8fH986LCysQ1BQUMJ7773X8MCBA2Xa33vuuSf98OHDvl9++WUoALz77rvhISEhhSNGjDgOAPv37/cZPnx4s+jo6PiQkJCOwcHBCRkZGb779u0r93FNTEwMysnJ8WrQoEHHoKCgBMdn165dgbt37w4w4nV46dKlYXFxcW3GjBnTZOnSpcHl3Z6nsKU/EREREREREREREREREZGb6of45x22KeCvH+JfZS3EZ8yYEV5YWIiYmJj2jmlKKQDArl27fGNiYvId38vC29tbmcMCgLy8PDEvM2vWrLApU6Y0feqpp9L69u17qk6dOoWvvfZag2XLltUpy7batGmTd/HFF2fNnj07fNiwYZmffPJJ+HXXXZcRGBioAODmm29unp6e7vPCCy+kxcTE5AYEBKiBAwfG5uXlOW3ILiKw7nd+fv6Z+BcVFUm9evXyf/jhhxTrumFhYYUAcMMNN2T26dMnedGiRaErV64Mvf7661sOGjTo2IIFC/aWZf88iS39iYiIiIiIiIiIiIiIiIjcNK5/y/3+Pl5F5mn+Pl5F4/q33F8V28vPz8f8+fPrPfbYY/t//vnnrY7PL7/8sjU2Njb7nXfeCQeA+Pj402vWrAlxFo6vr29RYWFhsWkNGzYsAIC0tDRfx7TExMQg8zJr164Nbt++/anHH3/8SO/evU/Hx8fn7tmzx788+zJ69Oj05cuX1/noo4/qpKam+o8dO/aIY96GDRuC77777sM33XTTiS5duuTUrl278MiRI76uwgsLCys4cODAmWXS0tJ80tPTz3zv0qXL6aNHj/p6eXmp+Pj4XPMnMjLyzDAHjRs3LrjvvvsyFi5cuHf69Ol7Fy1aVC87O1us26uu2NKfiGqUr37fj5e+S8GB49mIqBOISZfHYXBCpKejRURERERlxHQdEREROTBdQERENc2I7s0yAOCNFTsjj2Tl+tUP8c8b17/lfsf0yvb555/XOXbsmM+4ceOONGrUqFip/ZAhQzI+/PDD+i+++OLBRx999FC/fv1a33zzzc3GjRt3OCgoqGjFihUh11xzTWbLli3zoqKi8jZt2lQrJSXFLzQ0tKhBgwYFbdu2zW3UqFHelClTIl5++eW/du3a5f/iiy82Nm8jNjY2d8GCBeFffPFFaOvWrXM/+uijuomJicGhoaHFaxC4YdSoUcf+7//+L+qBBx6Ibteu3amuXbvmOOZFR0fnfPbZZ/V69+596uTJk16PPPJIE19fX5fdF/Ts2TPzvffea9C3b9+T3t7e6rHHHmvi7+9/pkLGP//5z8yEhISTgwcPjnnuuef+at++fc5ff/3lu3Tp0tqXX3555hVXXHFy/PjxEZ07dz7dsWPH7Pz8fPnqq6/CmjRpkuvogaAmYEt/Iqoxvvp9Px5blIz9x7OhAOw/no3HFiXjq9+rpOIcEREREVURpuuIiIjIgekCIiKqqUZ0b5ax/okByXteuGrD+icGJFdVgT8AzJ49O/ziiy/Oshb4A8Att9ySceDAAb+vv/46tGfPntmLFy/+Y+fOnQH9+vVr3bt379YLFiyo6+fnpwDgscceO+Tr66s6duzYNiIiosOuXbv8/P391UcffbR73759/t26dWs7derUiGeeeabYg/ihhx46ctVVV2XceeedF/Xs2bN1amqq39133/13efYlMDBQDR48+GhmZqb3rbfemm6e99577+09deqUV69evdqMGDHiolGjRqVHRka6HDLhzTff/CsqKir3iiuuiLvpppta3H777Ufq1q17pgW/l5cXli9fvrNXr15ZRkWD+BEjRrTYuXNnQNOmTfMBwN/fv+iZZ56J7NatW5t+/fq1OnnypNdXX321qzz75ylSnrEdiMqjS5cu6rfffvN0NKgG6/XCSuw/nl1iemSdQPz06KUeiBER0flHRDYopbp4Oh7EtBOd35iuI6LzBdNO1QfTTjUX0wVERBeO6pJ2SkpK2tuhQ4f00pckql6SkpLCO3ToEG03jy39iajGOGDzAuhqOhERERFVT0zXERERkQPTBUREREQVx0J/KhMReVpElOVzyNPxogtDRJ3AMk0nIiLyNKadiOwxXUdERHaYdrowMV1AREREVHEs9KfySAHQ2PRp59no0IVi0uVxCPT1LjYt0Ncbky6P81CMiIiI3MK0E5EF03VEROQC004XGKYLiIiIiCrOx9MRoBqpQCnFWtZ0zg1OiAQAvPRdCg4cz0ZEnUBMujzuzHQiIqJqimknIgum64iIyAWmnS4wTBcQERERVRwL/ak8LhKR/QDyAPwK4HGl1G67BUVkDIAxABAVFXXuYkjnrcEJkXzpIyKimoZpJyIbTNcREZETTDtdgJguICIiIqoYdu9PZfUrgNEArgRwF4BGANaJSD27hZVSM5VSXZRSXerXr3/uYklERERUPTDtREREROQ+pp2IiIiIiMqBLf2pTJRS35q/i8gvAHYDuBXAqx6JFBEREVE1xbQTERERkfuYdiIiIiIiKh+29KcKUUqdBLAVQEtPx4WIiIioumPaiYiIiMh9TDsREREREbmHhf5UISISAKAVgIOejgsRERFRdce0ExEREZH7mHYiIiIiInIPC/2pTETkZRH5h4g0F5GLASwAUAvAHA9HjYiIiKjaYdqJiIiIyH1MOxERERGVX2RkZLsnn3yyoafjcaESkc4ffPBBmKe27+OpDVON1QTAPADhAI4A+AVAd6VUqkdjRURERFQ9Me1ERERE5D6mnYiIiIicGDp0aHRGRobPDz/8sMtufmJi4vaQkJCicx0vd02cODHitddeawwAIoLw8PD87t27Z7366qt/xcTE5Hs6fhWVmpqaVL9+/UJPbZ+F/lQmSqmbPB0HIiIiopqCaSciIiIi9zHtRERERFR+ERERBZ6OAwDk5ORIQECAspsXHR2ds2rVqpSioiLZsWOH/7hx46KGDRvWYtOmTTs8FafKEhUV5dHjz+79iYiIiIiIiIiIiIiIiIhqMGv3/iLS+eWXXw6/8sorLwoMDExo0qRJuxkzZtQ1r7Nnzx7fq6+++qLQ0NCOoaGhHS+55JKY5ORkf8f8rVu3+vfv379FeHh4h8DAwIQ2bdq0njdvXm3rdidOnBhx/fXXR4eEhHS87rrrmjuLo4+PD6Kiogqio6Pzr7jiipO33nprelJSUq2MjIwzZdaffvpp7bZt27b29/fvFBkZ2e6BBx6IzMnJEcf8tLQ0n0svvTQmICCgU0RERLvp06fXa9myZduJEydGmPd92rRp9QcOHNgiMDAwYdy4cZHuhD1nzpw6sbGxbQICAjrVrl27Y9euXePS0tJ8AGDXrl2+/fv3b1G7du2OgYGBCc2bN287c+bMMPM2zd37r1+/PrBnz56xjrCGDh0affToUW/H/KFDh0b369cv5tlnn23QoEGD9qGhoR2HDRsWnZWVVa7ye7b0JyIiIiIiIiIiIiIiIqIL1szOGzp7YrtjNnTeUJXhv/TSSxFPPfXUXy+99NL+t99+O/zBBx+MHjBgwMnY2Ni8rKwsr379+sV17tz55Pfff5/i5+enXnjhhYaXX355bEpKytaQkJCizMxMr8svvzxz2rRpB4KCgormzp0bNmrUqBatWrXalpCQkOPYzsyZMxuOHz/+wFNPPXVQKfca1O/bt89n8eLFdby9veHjo4usFy5cGDpmzJiLnn/++X0DBgw4uXv3br8HHnigWW5ursycOfMvABg+fHjzw4cP+y5ZsiQlKCio6KGHHmp64MABP7t9nzx58v7XX389zcvLq9Sw9+3b53PnnXde9Pjjj+8fPnz4sczMTK81a9YEO8IbM2ZMs5ycHFm2bFlKWFhY4ZYtWwKc7VtWVpbX1Vdf3bJ9+/anVq9evf3IkSPe9913X/Tw4cOjv/vuuz8dy/3222/BDRs2zF+2bNkfe/fu9bv11lsv+te//pUzbdq0Q24dRBMW+hMRERERERERERERERERnWeGDRt29N57780AgNdff33/7NmzGyxfvjw4NjY24/333w9TSmH+/Pl7vbx04/JPPvkkNTw8vOPnn39e+8477zzWo0eP7B49emQ7wnvxxRcPLVu2rM68efPCEhISDjqmd+vWLeu55577u7T47N69OyAoKChBKYWcnBwvABg9evTh0NDQIgCYNm1a47Fjxx568MEHjwJA27Ztc48fP/7X3Xff3fydd975Kzk52X/t2rWhy5cv39G/f/9TADB37tw9cXFx7a3buuaaazImTpyY7vg+YsSI5q7C3rdvn19BQYHccsstx2JjY/MAoGvXrmcqNvz1119+11xzzTHH8WjVqlWes/2cOXNm3ezsbK/58+fvCQsLKwIApVTqNddcE7tlyxb/+Pj4XACoVatW0dy5c1N9fX3RqVOnnC+++OLYqlWrQgGw0J+IiIiIiIiIiIiIiIiIyF1V3eLeU9q3b3+mwN7X1xdhYWEFhw8f9gWADRs21Nq/f79/cHBwgnmdnJwcrz///NMfADIzM70eeeSRiO+//772kSNHfAsKCiQvL8+rTZs22eZ1EhISTrkTn6ZNm+YuWbJkZ05OjixYsKDON998EzZ9+vT9jvlbt24NSk5OrvXWW281ckxzVBBIS0vzTU5ODvTy8kLfvn3PbC8mJia/fv36+dZtdenS5bT5e2lhd+/e/XSPHj0yExIS2vbu3Tvz0ksvzRw5cuSxiIiIAgAYO3bs4UmTJkWtWLGidt++fTOvv/7643369Dlt3S4AbN++PSA2NjbbUeAPAAMGDDjp5eWFpKSkAEehf0xMTLavr++Z9Ro3bpy/cePGWu4cSysW+hMRERERERERERERERERnWf8/PyK9bUvIigq0uXQRUVFaNWq1enPPvtst3W9+vXrOwq6m/z444+1n3/++bTWrVvn1qpVq2jkyJHN8/LyxLx8rVq1iqxh2PH19VWOAu8uXboc2rVrV8Btt90WtXDhwr0AoJSSiRMnHrjllluOWdeNiIjId3foAAAIDg4uFqfSwvbx8cHatWt3rly5sta3334bOnfu3PDnnnsu8n//+19Kjx49sidMmJB+7bXXnvjyyy9rr1y5MrR///6t7r///kOvvvrqAWt4SikREetkAPocmI+HdZ5Syn7FUrDQn4iIiIiIiIiIiIiIiIjoAtKpU6fTixcvrtuoUaOC8PDwQrtlEhMTg2+44Yajo0ePPg4Ap0+fln379vlfdNFFOXbLl9XUqVMPtG/fPn7NmjWH+/Tpc7pNmzanU1JSzrSEt2rfvn12UVER1qxZU+vSSy89BQB//vmn75EjR3ztljcrLWwA8PLywoABA04NGDDg1EsvvXSwZcuWbT/55JO6PXr02A8ALVq0yH/44YfTH3744fQnnnii0axZsxrYFfq3adMm+4svvqh37NgxL0dr/+XLlwcXFRWhffv2lXLsSsS9KgIlIiIiIiIiIiIiIiIiIqLKk5WV5b1u3bpA8yclJcWvPGGNGTMmo169egVXXnllzNKlS4N37Njh9+233wbfddddTZKTk/0BoHnz5rlLly6ts3bt2qD169cHDhkypEQr/4po06ZNXv/+/U9Mnjw5AgCeeOKJA4sXL647fvz4iMTExIDff/894IMPPgi75557mgBAhw4dcnv37p153333NVuxYkWtdevWBY4cOTI6ICCgSERcdgNQWtgrVqyo9cgjjzRetWpV0M6dO/0+/fTTOocOHfJzDGVw2223NV2wYEHotm3b/NatWxe4fPny0JiYGNsC/DFjxmQEBgYW3XDDDc3Xr18f+O233wbff//9zQYOHHjcVaWDimBLfyIiIiIiIiIiIiIiIiKiam7Dhg3BvXr1amOedvnllx9btmxZiS76SxMSElK0Zs2aHRMmTGgycuTIFidPnvSuX79+fs+ePbMcLf/ffPPNtNGjR0dfdtllcaGhoYV3333337m5uZXaqPzhhx8+NHDgwFbff/99raFDh2Z+8cUXu55//vnG7777bkNvb29ER0fnDB8+PN2x/CeffLJn9OjR0YMGDYqrW7du/mOPPXYgLS3NPyAgwGWhf2lhh4WFFf7yyy/B77//foOsrCzvRo0a5U2YMOHAvffemwHo4RAeeuihqEOHDvkFBQUV9urVK+vNN99Ms9tWSEhI0ZIlS3Y++OCDTfv27dvaz8+v6LLLLjs+c+ZM2+Urg5Rl7AOiiujSpYv67bffPB0NqqG++n0/XvouBQeOZyOiTiAmXR6HwQmRno4WEdF5R0Q2KKW6eDoexLQTnT+YjiOi8xnTTtUH006ewec8ERGVRXVJOyUlJe3t0KFDeulLUk1w8OBBn6ioqPazZs3a7RiG4HyVlJQU3qFDh2i7eWzpT0TV3le/78dji5KRna+HlNl/PBuPLUoGAL5IEhEREVVjTMcRERGdv/icJyIiIk9YvHhxSGZmpnenTp1OHzx40Hfy5MmRYWFhBUOGDMn0dNw8qVK7XyAiqgovfZdy5gXSITu/EC99l+KhGBERERGRO5iOIyIiOn/xOU9ERESekJ+fL1OnTo3o3Llz22HDhsUEBAQUrVy5MiU0NLTI03HzJLb0J6Jq78Dx7DJNJyIiIqLqgek4IiKi8xef80REROQJQ4cOzRw6dOg2T8ejumFLfyKq9iLqBJZpOhERERFVD0zHERERnb/4nCciIiKqPljoT0TV3qTL4xDo611sWqCvNyZdHuehGBERERGRO5iOIyIiOn/xOU9ERERUfbB7fyKq9gYnRALQY8UdOJ6NiDqBmHR53JnpRERERFQ9MR1HRER0/uJznoiIiKj6YKE/EdUIgxMi+dJIREREVAMxHUdERHT+4nOeiIiIqHpg9/5EREREREREREREREREREQ1FAv9iYiIiIiIiIiIiIiIiIiIaigW+hMRERERERERERERERERUblNnDgxomXLlm2rejuRkZHtnnzyyYZVvZ2ahoX+REREREREREREREREREQ1wE8//RTo7e3duVOnTq3Kum63bt3iRo0aFVUV8XIlLS3Nx8fHp9OMGTPq2s2/5557mjRq1Kh9YWHhuY7aeYOF/kRERERERERERERERERENcDbb79df8SIEYd37twZsHHjxgBPx8cdTZs2LejXr9+JOXPmhFvn5efnY+HChXVvuummdG9vb09E77zAQn8iIiIiIiIiIiIiIiIiojL4+JfUut2eX96u+aNLO3d7fnm7j39JtW3FXplOnjwpX3/9dd177703/corrzz2zjvvlChEX7FiRa3u3bvHBgYGJoSEhHTs0aNH7N69e32HDh0anZiYGDx37tz6ItJZRDqnpKT4LVmyJEREOh88eNDHEUZKSoqfiHRevXp1EAAUFBTghhtuaBYZGdkuICCgU7NmzeInT57csCwt8++44470xMTEkG3btvmZp3/++ed1jh496jt27Nj0VatWBfXq1atlWFhYh+Dg4ITOnTvHLV++vJarcEWk8wcffBBmnmYdAuDo0aPeN998c7O6det2qFWrVkLXrl3jHPvmmD948ODmdevW7eDv79+pSZMm7aZOndrA7Z2rBljoT0RERERERERERERERETkpo9/Sa377JJtzQ5n5fopAIezcv2eXbKtWVUX/M+ZMycsIiIi7+KLL84eNWpUxoIFC+rl5uaKY/7PP/8ceNVVV8U1b948d8WKFTtWrVq1Y8iQIRn5+fkyc+bMtI4dO54aNmzY0dTU1KTU1NSkFi1a5Lmz3cLCQomIiMj/9NNP/0xKStoyZcqU/dOnT2/8xhtvlKh04MywYcNO1K9fP//dd98tts7s2bPDu3fvnhkXF5d34sQJ7+HDhx9dsWJFypo1a7a3bds2e8iQIS3NFRLKqqioCAMHDow5ePCg78KFC3f++uuv23r27Jk1aNCguNTUVF8AmDhxYsSOHTsCFy5cuHPz5s1b3nnnnb1NmjTJL+82PaHcB4iIiIiIiIiIiIiIiIiI6ELzxoqdkbkFRcUaV+cWFHm9sWJn5IjuzTKqarsffvhh/RtvvPEoAAwaNCgrMDCwaN68ebVHjx59HACmTZvWKC4u7vS8efNSHet06tQpx/G/r6+vCgwMLIqKiiooy3b9/f3V66+/fsDxPS4uLm/jxo1B8+fPrzthwoR0d8Lw8fHBjTfemP7555+Hv/rqqwe8vb2xb98+n9WrV4e+/fbbewDg2muvzbLs776GDRvW+fLLL0Pvvffech3XJUuWhGzfvj3o8OHDm4KDgxUATJ8+/cB3331XZ9asWXWfe+65v9PS0vzj4+NP9+vX77Rj/8qzLU9iS38iIiIiIiIiIiIiIiIiIjcdycr1K8v0yrBlyxb/33//Pfj222/PAAAvLy8MGTIkY/bs2fUdy2zdujWob9++Wc5DKb9///vf9ePj41uHhYV1CAoKSnjvvfcaHjhwoEz7e88996QfPnzY98svvwwFgHfffTc8JCSkcMSIEccBYP/+/T7Dhw9vFh0dHR8SEtIxODg4ISMjw3ffvn3lPq6JiYlBOTk5Xg0aNOgYFBSU4Pjs2rUrcPfu3QFGvA4vXbo0LC4urs2YMWOaLF26NLi82/MUtvQnIiIiIiIiIiIiIiIiInJT/RD/vMM2Bfz1Q/yrrIX4jBkzwgsLCxETE9PeMU0pBQDYtWuXb0xMTL7je1l4e3src1gAkJeXJ+ZlZs2aFTZlypSmTz31VFrfvn1P1alTp/C1115rsGzZsjpl2VabNm3yLr744qzZs2eHDxs2LPOTTz4Jv+666zICAwMVANx8883N09PTfV544YW0mJiY3ICAADVw4MDYvLw8pw3ZRQTW/c7Pzz8T/6KiIqlXr17+Dz/8kGJdNywsrBAAbrjhhsw+ffokL1q0KHTlypWh119/fctBgwYdW7Bgwd6y7J8nsaU/EREREREREREREREREZGbxvVvud/fx6vIPM3fx6toXP+W+6tie/n5+Zg/f369xx57bP/PP/+81fH55ZdftsbGxma/88474QAQHx9/es2aNSHOwvH19S0qLCwsNq1hw4YFAJCWlubrmJaYmBhkXmbt2rXB7du3P/X4448f6d279+n4+PjcPXv2+JdnX0aPHp2+fPnyOh999FGd1NRU/7Fjxx5xzNuwYUPw3Xffffimm2460aVLl5zatWsXHjlyxNdVeGFhYQUHDhw4s0xaWppPenr6me9dunQ5ffToUV8vLy8VHx+fa/5ERkaeGeagcePGBffdd1/GwoUL906fPn3vokWL6mVnZ4t1e9UVC/2JiIiIiIiIiIiIiIiIiNw0onuzjClXt0ltEOKfJwAahPjnTbm6TeqI7s3KNe58aT7//PM6x44d8xk3btyRrl275pg/Q4YMyZg3b154YWEhHn300UPbt28Puvnmm5v9/PPPgUlJSf6vvvpq+M6dO/0AICoqKm/Tpk21UlJS/A4ePOhTWFiItm3b5jZq1ChvypQpEZs3b/ZftGhR6IsvvtjYvP3Y2Njcbdu2BX3xxRehycnJ/pMmTWqcmJhYri7wR40adSwoKKjogQceiG7Xrt2prl275jjmRUdH53z22Wf1NmzYELBq1aqgYcOGXeTr6+uy+4KePXtmvvfeew1Wr14d9NNPPwXecsstzf39/c9UyPjnP/+ZmZCQcHLw4MExX3zxReiOHTv8li9fXmvChAkRy5YtCwaA8ePHR8ydO7dOcnKy/8aNGwO++uqrsCZNmuQ6eiCoCVjoT0RERERERERERERERERUBiO6N8tY/8SA5D0vXLVh/RMDkquqwB8AZs+eHX7xxRdnNWrUqNA675Zbbsk4cOCA39dffx3as2fP7MWLF/+xc+fOgH79+rXu3bt36wULFtT18/NTAPDYY48d8vX1VR07dmwbERHRYdeuXX7+/v7qo48+2r1v3z7/bt26tZ06dWrEM888U6zHgoceeujIVVddlXHnnXde1LNnz9apqal+d99999/l2ZfAwEA1ePDgo5mZmd633nprunnee++9t/fUqVNevXr1ajNixIiLRo0alR4ZGelyyIQ333zzr6ioqNwrrrgi7qabbmpx++23H6lbt+6ZFvxeXl5Yvnz5zl69emUZFQ3iR4wY0WLnzp0BTZs2zQcAf3//omeeeSayW7dubfr169fq5MmTXl999dWu8uyfp0h5xnYgKo8uXbqo3377zdPRICIiIhdEZINSqoun40FMOxEREdUETDtVH0w7ERERVX/VJe2UlJS0t0OHDumlL0lUvSQlJYV36NAh2m4eW/oTERERERERERERERERERHVUCz0JyIiIiIiIiIiIiIiIiIiqqFY6E9ERERERERERERERERERFRDsdCfiIiIiIiIiIiIiIiIiIiohmKhPxERERERERERERERERFdKAqLiorE05EgKgvjmi1yNp+F/kRERERERERERERERER0oVibmppaJzc311cp5em4EJWqqKhIjhw5UhvAFmfL+JzD+BAREREREREREREREREReUxBQcFdx48fH5uVlTVaKVUXbCRN1V8RgC0FBQV3OluAhf5EREREREREREREREREdEHo3LlzHoDpxofovMCaK0RERERERERERERERERERDUUC/2pQkTkcRFRIvIfT8eFiIiIqLpj2omIiIjIPUw3ERERERG5j4X+VG4i0h3AXQA2ezouRERERNUd005ERERE7mG6iYiIiIiobHw8HQGqmUSkNoBPANwB4EkPR6dG+ur3/XjpuxQcOJ6NiDqBmHR5HAYnRHo6WkRERFQFmHbyLKa7iIiIag6mm85iGoaIiIiI3MWW/lReMwEsUEqt9HREaqKvft+PxxYlY//xbCgA+49n47FFyfjq9/2ejhoRERFVDaadPITpLiIiohqH6SYwDUNEREREZcNCfyozEbkLQAyAKW4sO0ZEfhOR344cOVL1kashXvouBdn5hcWmZecX4qXvUjwUIyIiIqoqTDt5FtNdRERENUdZ0k3G8udt2olpGCIiIiIqCxb6U5mISByAfwG4RSmVV9rySqmZSqkuSqku9evXr/oI1hAHjmeXaToRERHVTEw7eR7TXURERDVDWdNNwPmddmIahoiIiIjKgoX+VFY9AIQD2CIiBSJSAOAfAO41vvt7Nno1Q0SdwDJNJyIiohqLaScPY7qLiIioxmC6yYRpGCIiIiIqCxb6U1l9BaAdgI6mz28APjP+d6sm9oVu0uVxCPT1LjYt0Ncbky6P81CMiIiIqIp8BaadPIrpLiIiohrjKzDddAbTMERERERUFj6ejgDVLEqp4wCOm6eJyCkAGUqpLZ6IU000OCESgB6f7cDxbETUCcSky+POTCciIqLzA9NOnsd0FxERUc3AdFNxTMMQERERUVmw0J/IQwYnRPJFjYiIiOgcYLqLiIiIaiKmYYiIiIjIXSz0pwpTSl3i6TgQERER1RRMOxERERG5h+kmIiIiIiL3eHk6AkRERERERERERERERERERFQ+LPQnIiIiIiIiIiIiIiIiIiKqoVjoT0REREREREREREREREREVEOx0J+IiIiIiIiIiIiIiIiIiKiGYqE/ERERERERERERERERERFRDcVCfyIiIiIiIiIiIiIiIiIiohqKhf5EREREREREREREREREREQ1FAv9iYiIiIiIiIiIiIiIiIiIaigW+hMREREREREREREREREREdVQLPQnIiIiIiIiIiIiIiIiIiKqoVjoT//f3p3H2Vn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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Create the overall figure with fig and the subplots with axs. The amount of subplots is specified here with len(reg_dic), which in our case is 3\n", "fig, axs = plt.subplots(1, len(regresults_dic),figsize=(30,8), sharex=True)\n", "\n", "# Setting the title of the overall figure\n", "fig.suptitle(f\"Linear Regression of Economic Growth and Unemployment '2009Q1-2019Q4' for different Age Groups\",weight = 'bold', size = 25)\n", "\n", "# Set the X- and Y-axis of the overall Figure\n", "fig.text(0.5, 0.04, 'Unemployment Rate (%)', ha='center', va='center', size=15)\n", "fig.text(0.06, 0.5, 'GDP Growth (%)', ha='center', va='center', rotation='vertical', size=15)\n", "\n", "# Create a list with the keys of the dataframes used for the regression in order to access them afterwards more easily\n", "dic_key_list = []\n", "for i in regresults_dic.keys():\n", " dic_key_list.append(i)\n", "\n", "# Create a list with the keys of the age groups and their age range in order to access them afterwards more easily\n", "age_group_list=[]\n", "for i in age_groups.keys():\n", " age_group_list.append(i)\n", "\n", "# For-loop to create the different subplots\n", "for i in range(len(regresults_dic)):\n", "\n", " # Plots the regression line (x-axis: Measured unemploymebt Rate, y-axis: GDP-Growth from regression)\n", " axs[i].plot(GDPxURxA_regdic[dic_key_list[i]][['Measured Unemployment Rate']], regresults_dic[dic_key_list[i]].predict(), '-', color='darkorchid', linewidth=2,label=\"Linear Regression\")\n", "\n", " # Creates scatter plot with axtual values (x-axis: Measured unemploymebt Rate, y-axis: Actual GDP-Growth)\n", " axs[i].scatter(GDPxURxA_regdic[dic_key_list[i]][['Measured Unemployment Rate']],GDPxURxA_regdic[dic_key_list[i]][\"GDP Growth\"], label=\"Actual Values\")\n", "\n", " # Sets title of subplot with the relevant age group\n", " axs[i].set_title(f'Unemployment Rate {age_group_list[i]}')\n", "\n", "# St legend. Since legend is the same for all subplots, we only show it for the first subplot\n", "fig.legend()\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": { "id": "eWfvekv5T82j" }, "source": [ "\n", "### GDP growth on unemployment rates of educational attainment levels\n", "In the following, we will analyse the relationship between the unemployment rate of people with a certain educational attainment level of a country and its GDP-Growth rate. \n", "\n", "**Model creation** \n", "We can conduct the linear regressions and store them in a dictionary:" ] }, { "cell_type": "code", "execution_count": 159, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 232 }, "id": "lJ23RywcIU6B", "outputId": "571f468b-0f1a-4331-9948-0c383a553a12" }, "outputs": [], "source": [ "# Create empty dictionary\n", "regresults_dic = {}\n", "\n", "# Iterate through all education levels\n", "for ed_level in GDPxURxED_regdic:\n", "\n", " # Defining the dependent variable\n", " y = GDPxURxED_regdic[ed_level][\"GDP Growth\"]\n", "\n", " # Defining the regressors and adding a constant (the intercept b0) with the sm.add_constant method\n", " x = sm.add_constant(GDPxURxED_regdic[ed_level]['Measured Unemployment Rate'])\n", "\n", " # Initializing the OLS rergeression\n", " regression = sm.OLS(y, x, missing='drop')\n", "\n", " # Fit the model by calling the OLS object’s fit() method\n", " regresults = regression.fit()\n", "\n", " # Save model to dictionary\n", " regresults_dic[ed_level] = regresults" ] }, { "cell_type": "markdown", "metadata": { "id": "YMeKcz_bqru4" }, "source": [ "**Model interpretation** \n", "With the `summary` method of the `statsmodels` library we can now print an overview of the regression results. As an example, we do this for the unemployment rate of all educational attainment levels:" ] }, { "cell_type": "code", "execution_count": 160, "metadata": { "id": "XW2PG0jaIWTy" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " OLS Regression Results \n", "==============================================================================\n", "Dep. Variable: GDP Growth R-squared: 0.045\n", "Model: OLS Adj. R-squared: 0.019\n", "Method: Least Squares F-statistic: 1.713\n", "Date: Sun, 16 May 2021 Prob (F-statistic): 0.199\n", "Time: 17:48:01 Log-Likelihood: -65.795\n", "No. Observations: 38 AIC: 135.6\n", "Df Residuals: 36 BIC: 138.9\n", "Df Model: 1 \n", "Covariance Type: nonrobust \n", "==============================================================================================\n", " coef std err t P>|t| [0.025 0.975]\n", "----------------------------------------------------------------------------------------------\n", "const 1.7210 0.503 3.420 0.002 0.701 2.742\n", "Measured Unemployment Rate -0.0606 0.046 -1.309 0.199 -0.154 0.033\n", "==============================================================================\n", "Omnibus: 9.835 Durbin-Watson: 2.125\n", "Prob(Omnibus): 0.007 Jarque-Bera (JB): 9.623\n", "Skew: 0.885 Prob(JB): 0.00814\n", "Kurtosis: 4.717 Cond. No. 24.2\n", "==============================================================================\n", "\n", "Notes:\n", "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n" ] } ], "source": [ "#print the regression results for total educational attainment level\n", "print(regresults_dic['total'].summary())" ] }, { "cell_type": "markdown", "metadata": { "id": "gB_8KEBWric6" }, "source": [ "We can also create a dataframe with the most important property of each regression:" ] }, { "cell_type": "code", "execution_count": 161, "metadata": { "id": "o3RgdS-HIW40" }, "outputs": [ { "data": { "text/html": [ "
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B0B1R-SquaredAdjusted R-Squaredp-valueSignificant (p < 0.05)
primary1.443519-0.0189220.009652-0.0178580.557329False
secondary1.589546-0.0458560.0316570.0047590.285197False
tertiary1.499881-0.0599190.0286600.0016780.309587False
total1.721021-0.0605660.0454160.0188990.198923False
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" ], "text/plain": [ " B0 B1 R-Squared Adjusted R-Squared p-value \\\n", "primary 1.443519 -0.018922 0.009652 -0.017858 0.557329 \n", "secondary 1.589546 -0.045856 0.031657 0.004759 0.285197 \n", "tertiary 1.499881 -0.059919 0.028660 0.001678 0.309587 \n", "total 1.721021 -0.060566 0.045416 0.018899 0.198923 \n", "\n", " Significant (p < 0.05) \n", "primary False \n", "secondary False \n", "tertiary False \n", "total False " ] }, "execution_count": 161, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Create empty dictionary to create the dataframe later on\n", "overview_dic = {}\n", "\n", "# Iterate through all regression results\n", "for ed_level in regresults_dic:\n", "\n", " aux = regresults_dic[ed_level] # Create dictionary entry for certain age group\n", " overview_dic[ed_level] = [aux.params[0], aux.params[1], aux.rsquared, aux.rsquared_adj, aux.pvalues[1]] # Store list with all values that are of importance into the education level entry\n", "\n", "# Create a dataframe out of the dictionary and transpose the dataframe so we have the education level as index\n", "overview_df = pd.DataFrame(overview_dic, index=[\"B0\",\"B1\",\"R-Squared\",\"Adjusted R-Squared\",\"p-value\"]).T\n", "\n", "# Create column that has the value True if B1 if the variable is significant and False if it is not\n", "overview_df[\"Significant (p < 0.05)\"] = overview_df[\"p-value\"].map(lambda x: x<0.05)\n", "\n", "overview_df" ] }, { "cell_type": "markdown", "metadata": { "id": "pT3gc__gryIg" }, "source": [ "As we can see, GDP-Growth is negatively correlated with the Unemployment Rate across all levels of educational attainment. This means that with an increasing Unemployment Rate in any of the educational attainment levels, GDP-Growth decreases. The strongest decrease comes from the poulation with a tertairy education level: A one percent increase in the Unemployment Rate in that group decreases GDP-Growth by 0.060 percentage points. Additionally, the adjusted-$R^2$ value is largest for the working population with secondary education attainment level with 0.005, which means that 0.5% of the variation in GDP-Growth can be explained by the Unemployment in this group. However, the effect of the Unemployment Rate for certain educational attainment levels on GDP-Growth was not significant for any of the groups.\n", "\n", "To better understand the regressions, we will now plot them next to each other:" ] }, { "cell_type": "code", "execution_count": 162, "metadata": { "id": "MzSuF3PQIX3K" }, "outputs": [ { "data": { "image/png": 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Create the overall figure with fig and the subplots with axs. The amount of subplots is specified here with len(reg_dic), which in our case is 3\n", "fig, axs = plt.subplots(1, len(GDPxURxED_regdic),figsize=(30,8))\n", "\n", "# Setting the title of the overall figure\n", "fig.suptitle(f\"Linear Regression of Economic Growth and Unemployment '2009Q1-2019Q4' for different Education Levels\",weight = 'bold', size = 25)\n", "\n", "# Set the X- and Y-axis of the overall Figure\n", "fig.text(0.5, 0.04, 'Unemployment Rate (%)', ha='center', va='center', size=15)\n", "fig.text(0.06, 0.5, 'GDP Growth (%)', ha='center', va='center', rotation='vertical', size=15)\n", "\n", "# Create a list with the keys of the dataframes used for the regression in order to access them afterwards more easily\n", "dic_key_list = []\n", "for i in GDPxURxED_regdic.keys():\n", " dic_key_list.append(i)\n", "\n", "# Create a list with the keys of the age groups and their age range in order to access them afterwards more easily\n", "ed_level_list=[]\n", "for i in ed_levels.keys():\n", " ed_level_list.append(i)\n", "\n", "# For-loop to create the different subplots\n", "for i in range(len(GDPxURxED_regdic)):\n", "\n", " # Plots the regression line (x-axis: Measured unemploymebt Rate, y-axis: GDP-Growth from regression)\n", " axs[i].plot(GDPxURxED_regdic[dic_key_list[i]][['Measured Unemployment Rate']], regresults_dic[dic_key_list[i]].predict(), '-', color='darkorchid', linewidth=2,label=\"Linear Regression\")\n", "\n", " # Creates scatter plot with axtual values (x-axis: Measured unemploymebt Rate, y-axis: Actual GDP-Growth)\n", " axs[i].scatter(GDPxURxED_regdic[dic_key_list[i]][['Measured Unemployment Rate']],GDPxURxED_regdic[dic_key_list[i]][\"GDP Growth\"], label=\"Actual Values\")\n", "\n", " # Sets title of subplot with the relevant age group\n", " axs[i].set_title(f'Unemployment Rate {ed_level_list[i]}')\n", "\n", "# St legend. Since legend is the same for all subplots, we only show it for the first subplot\n", "fig.legend()\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "### Job finding probability on GDP per capita of age groups\n", "\n", "In the following, we will analyse the relationship between the job finding probability and the GDP per capita level.\n", "\n", "**Model creation** \n", "We can conduct the linear regressions and store them in a dictionary:" ] }, { "cell_type": "code", "execution_count": 163, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 232 }, "id": "knvXh6m6XTh7", "outputId": "212d6d87-4375-4a8c-cc57-3ebfbdfd0523" }, "outputs": [], "source": [ "# Create empty dictionary\n", "GF_regresults_dic = {}\n", "\n", "# Iterate through all education levels\n", "for age_group in GDPxJFP_regdic:\n", "\n", " # Defining the dependent variable\n", " y = GDPxJFP_regdic[age_group][\"Job Finding Probability\"]\n", "\n", " # Defining the regressors and adding a constant (the intercept b0) with the sm.add_constant method\n", " x = sm.add_constant(GDPxJFP_regdic[age_group]['GDP per capita'])\n", "\n", " # Initializing the OLS rergeression\n", " regression = sm.OLS(y, x, missing='drop')\n", "\n", " # Fit the model by calling the OLS object’s fit() method\n", " regresults = regression.fit()\n", "\n", " # Save model to dictionary\n", " GF_regresults_dic[age_group] = regresults" ] }, { "cell_type": "code", "execution_count": 164, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 232 }, "id": "knvXh6m6XTh7", "outputId": "212d6d87-4375-4a8c-cc57-3ebfbdfd0523" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " OLS Regression Results \n", "===================================================================================\n", "Dep. Variable: Job Finding Probability R-squared: 0.040\n", "Model: OLS Adj. R-squared: 0.005\n", "Method: Least Squares F-statistic: 1.160\n", "Date: Sun, 16 May 2021 Prob (F-statistic): 0.291\n", "Time: 17:48:02 Log-Likelihood: 113.04\n", "No. Observations: 30 AIC: -222.1\n", "Df Residuals: 28 BIC: -219.3\n", "Df Model: 1 \n", "Covariance Type: nonrobust \n", "==================================================================================\n", " coef std err t P>|t| [0.025 0.975]\n", "----------------------------------------------------------------------------------\n", "const 0.0001 0.002 0.073 0.943 -0.004 0.004\n", "GDP per capita 6.276e-08 5.83e-08 1.077 0.291 -5.66e-08 1.82e-07\n", "==============================================================================\n", "Omnibus: 65.834 Durbin-Watson: 2.157\n", "Prob(Omnibus): 0.000 Jarque-Bera (JB): 664.456\n", "Skew: 4.616 Prob(JB): 5.19e-145\n", "Kurtosis: 24.127 Cond. No. 6.06e+04\n", "==============================================================================\n", "\n", "Notes:\n", "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n", "[2] The condition number is large, 6.06e+04. This might indicate that there are\n", "strong multicollinearity or other numerical problems.\n" ] } ], "source": [ "#print the regression results for total educational attainment level\n", "print(GF_regresults_dic['young'].summary())" ] }, { "cell_type": "code", "execution_count": 165, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 232 }, "id": "knvXh6m6XTh7", "outputId": "212d6d87-4375-4a8c-cc57-3ebfbdfd0523" }, "outputs": [ { "data": { "text/html": [ "
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B0B1R-SquaredAdjusted R-Squaredp-valueSignificant (p < 0.05)
young0.0001406.275552e-080.0397690.0054750.290730False
middle-0.0001633.396291e-080.0478130.0138070.245688False
old-0.0003117.541059e-080.0543700.0205970.214956False
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" ], "text/plain": [ " B0 B1 R-Squared Adjusted R-Squared p-value \\\n", "young 0.000140 6.275552e-08 0.039769 0.005475 0.290730 \n", "middle -0.000163 3.396291e-08 0.047813 0.013807 0.245688 \n", "old -0.000311 7.541059e-08 0.054370 0.020597 0.214956 \n", "\n", " Significant (p < 0.05) \n", "young False \n", "middle False \n", "old False " ] }, "execution_count": 165, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Create empty dictionary to create the dataframe later on\n", "GF_overview_dic = {}\n", "\n", "# Iterate through all regression results\n", "for age_group in GF_regresults_dic:\n", "\n", " aux = GF_regresults_dic[age_group] # Create dictionary entry for certain age group\n", " GF_overview_dic[age_group] = [aux.params[0], aux.params[1], aux.rsquared, aux.rsquared_adj, aux.pvalues[1]] # Store list with all values that are of importance into the education level entry\n", "\n", "# Create a dataframe out of the dictionary and transpose the dataframe so we have the education level as index\n", "GF_overview_df = pd.DataFrame(GF_overview_dic, index=[\"B0\",\"B1\",\"R-Squared\",\"Adjusted R-Squared\",\"p-value\"]).T\n", "\n", "# Create column that has the value True if B1 if the variable is significant and False if it is not\n", "GF_overview_df[\"Significant (p < 0.05)\"] = GF_overview_df[\"p-value\"].map(lambda x: x<0.05)\n", "\n", "GF_overview_df" ] }, { "cell_type": "code", "execution_count": 166, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 232 }, "id": "knvXh6m6XTh7", "outputId": "212d6d87-4375-4a8c-cc57-3ebfbdfd0523" }, "outputs": [ { "data": { "image/png": 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1WtvvtVYhUVE9rbYhQKxroKykwNxn31NVeyvprQCWi8gnAP7uWOdvALbY0tcL5lnZ7j7bd2GriIQBmGGb31lEeqrqKtu07wHcqaqrHdvaIiJLYe6DdW3Ta8E8Q9mvrdthhkPxUAADVfU36/1TVt6A/XfuThGZWNCzqq9K8lxSRs9tJc43KMtYvKB7D4Ac2/laAROLucVC9Rz3qXwxWHHzKkoYK0cCuAXmHtkG5t6YAVPB5huYWMY1L8pbXCki1wK4A+aeGwZgB4A5AF5Q1XTb+m+jCHGjl9ilQCJS3To2u4UwsbnHMBF5yHFvcdvWEJh8kTYw8WMSzL31NZiKiAU+y9m2U+x8ncKISDCAN5C/DOMNAHfbKqV4JAL4AsCzItIYLkNSlOR7KyIJMPeLHjC/R+H483f3K5jfxGQvx1LoNeByDa1Q1QsL2w6AD2Aqql0Pk4eYBeBHAK+o6ide0lOsfDGi892MDhs6lMd+R27osKEst//CCy/EPPHEE3teeOGFva+//nr0vffeG9e3b98TTZs2zUxLS/Pr3bt3fIcOHU4sXbo0KSgoSJ977rmal1xySdOkpKTNEREROampqX6XXHJJ6uTJk/eFhYXlzJ49O2r48OGNmjVrtiUhISH3t3DGjBk1x44du++JJ57Y75IN4mr37t0BCxcurOLv74+AAHO7nz9/fuTIkSMbTpo0aXffvn1P7NixI+iee+6pn5GRITNmzNgDAEOGDGlw8ODBwEWLFiWFhYXl3HfffXX37dsX5HbsEyZM2Pvyyy+n+Pn5Fbrt3bt3B9x2220NH3nkkb1Dhgw5lpqa6rdq1arcyo8jR46sn56eLkuWLEmKiorK3rRpk1uFZABAWlqa3xVXXNGkTZs2J1euXPnroUOH/O+66664IUOGxH3xxReeOBnr168Pr1mzZtaSJUv+t2vXrqCbbrqp4bPPPps+efLkAz6dRDtV5Ysvvv4CL5iHG7W/iri+Ol4jHPNHuCwzFObhyjldYYLitl72FQ+TCee2nv21AECEy/oDfFjX8/oAQIDLNp50LLcL5qHHuf7bRTiHF7qs/6TLcr87lvnBy/aCALziwzHuAXCBl200ghlfzm29bJgHnl0FpdnLcd0OU+CW5xw61rsF5kGwoLSnw2TkO9MdApOR6ctnvMSxblOYDBNf1n2osOvcy3kdCFPBpbDtLwNQw8fr70HrM3Hbzl4AsSW4P4y2znVh6X0fQHghn73rq4jpcV5zu7wsVxbneQTMA7LbdvYDaFHMc/xNSc6Jl/Piy3fxVpj7rdvxZADo52VfA+D9/n0I7vfZCws7vz4c01QAqwr4LF3vuTCFHT8XsN6/XD4Dn+/fhZxjt/t4S5flvrDNj3OZPwamu1mv1wnOkWseppDHp+8+TIGv27l827HcNwAegynAddvOVgAxPny33nbMdzvXPl+rLuks6DUCpqKh837a1yXdVZD/O9aniNfjJz6m6ySA63y8rxT3O1gDJpPVbZ0cmBiqwM/Kx2N+12X7Txd1O4Vciwogzsuy3V2WTQEgjuWcy3zjZXuHHMtlFDHtbtf3HTCxn7fP4j4v24oEMNeH62kzgKY+3h8LjQd9OMYAAJPhPQbyvI67fMd8/e7+Avf7i9sxeVpOu20nCy7fNRQQPyL//aeg15O29V72cZ0sAONK8P34xrYt1+9FAet2cknP645l7nNZppVjGYGpYGBfZmExvttFuvYKuAYuLMk9pwj7vsZl3/9wLPOqY34OgEiX74Lzt/WVIqblNZe0XOBYxvkbsN5lO3932c7A0rjmvHxWri/HemURW5Uo38Da7gCUYSzuMs/b6xv4Hgu9bdt+ifIqUPxY+Qq3ZRyvbACPeTnv3ziWnQNT4c3btpYD8C/gvlPQa0Qx7w3jHNs5ANPAJtMx/fICtuEP95jK81oJ01OS6/Xj2Fax83V8PN5bXLa3AoBfcbZX3O8tTB7UDB8+1ywAD3rZb6HXgMs19I0P2/kHgE0FpGmqyzaKnS/GF1/n0+vnn3/eparr7a832q/X8ng501HQa+DAgYcvvPDC497mx8TEZDz22GMpnvcAdPTo0fs97zMzM9eHhIRkv/baaztUdf1LL720s169eunZ2dm528jKylpfuXLlMzNnzvzN237atGlz4oEHHthr32/v3r29psvzGjdu3D4/Pz8NDQ3NDgkJyX3OGzFixO+eZTp06JB2//3377Wv9+67724PDQ3Nzs7OXv/zzz8nAtBly5b96pm/bdu2jX5+fjpu3Lh99mMfPnz47/btFLbtVatWbQGgSUlJv7ilv0mTJqfGjx+/19vxAdBZs2b9pqrrX3zxxV3h4eFnjh49+qNn/meffZYEQBMTExM9n2fNmjUzMzMzc7dx3XXXHeratWuqt31Y167rdc2W/kRUlmbDZNK4iQTwb5juU3OJSD2YB61aPmz/agCVRKS/FlKDuQDXwbSyeqyQ5erAtHwrU9YYhNGOyT97Wfwt5G0p4U0szPiNXVTV3jIkGMCnMLWT3fjBZCgWZxyaaSigNxkRGQlTM7swwQDeFJFMVbW35rkLJuO9OP4FoHYx1y2UiFwF0yrVly54+gD4Qsz4pgW1cKkH4PkC5scAeA7AjT4n1CIid8F8F31xA4AoEbmiBN+5UlFG57kWTOts/wLmLxQz5nKJWySdJTPh/T4cBGCGiDSyf54i0gwmgytf7VhLNID3SjWVf7oH3tMLADeJyGy19dIipinSHABtC1hvPP4cl/RsaOky7Vgh6/wLBd83K+o1P15EtqhqQd0JA6Zw6G8FzI8H8JHVOtG1pfm5QFUPisiHAIbbJt8OU1hgNwB5v2O74D7ET2kIA/CuiPyihbf4Le538AOY1pFuBCaGOlWENOffiOlKur9jcgaAF0uy3aJQ0wPSapjWXR51YFrIbSzKtsSMY17NMbmw+4QvXoVpXe+6WwAvishmVV1iS0sgTCvBgr6DHi1gWs12UtX9hSxbYDzooxkwLdjKUmsAH8LxXOLFLHg/vwEw37Vf1aXFfzkJADBFRBJV1XkfKmtu95KdjvfOVuqe7vVzqaqKyBbk/d451zsfFef8pahqniGGVPW4iOyFuVd5W684admVO1OkEsy9wc5tPPFNLtM6wVTmP+vKKLYqcb5BOcfipaVEeRVeFBYrXwZTIdJbXOvhB2CiiEBVC/usrkPBcdFFMBW7CotzS9NNjvfvq+rvIrIEwJW26SPgfZiTR1Bw3kFPmGE+ClQK+Tq+uNJl2uRSfh7w5Xs7G6Yn0sIEAHheRAJU9dkSp8w3T8N7bAIAY6zYZLptWpnmixFVZGXd4r68tGnT5rTn/8DAQERFRZ05ePBgIABs2LCh0t69e4PDw8MT7Oukp6f7/fbbb8EAkJqa6vfggw/GLF26tPKhQ4cCz5w5I5mZmX4tWrQ4bV8nISHBp7yjunXrZixatGhbenq6zJs3r8pnn30WNXXq1NxebDdv3hyWmJhY6bXXXsstI1JVpKen+6WkpAQmJiaG+vn5oVevXrn7a9y4cVb16tXz5f117NgxT15EYdvu0qXLqa5du6YmJCS07NGjR+pFF12UeuONNx6LiYk5AwCjRo06+MADD9Rbvnx55V69eqVec801x3v27Oma3/Hrr7+GNG3a9HRUVFTu71bfvn1P+Pn5YePGjSGtWrXKsNJ+OjDwz1t57dq1s3788cdKvpxLp2KNCUBE5COBKTRuC/OQ7Hzw7yEidR3TXkHeAv/9MN2EtYXpmvMB5B1TtR/yP/Rkw3TD9yhMF2ddYGqxtoNpMfGtY/l7RcTbA7WH58HxfZgHoOYwDx+uQwwUQRURiRORxlZXYvOQ997smpktIgOQ/yH6VZiC8GYwFSISbfMiAEx3LH8b8hdMfQfTbV5zmFb+GSheZm0AgHUw5ygeJhP5VSvttWGuC7slMF3UNYNppfGJY/6rVtdjHs5M6fdhxgltAnOtDIAZG/MHmMxDO+e6j8B009cEJtNpKExrlt9QRFaG1wzk/wwfgOle9mKYrubs2lnzC9w0zHU9AaZQ5UqY1v12g8W3cYXt6Y1F/uvrOEwrwbYw3U47g91L8WcGwfcw3dy6ZZLfgLxj1JeaMjzPwTDn+SGYa+ISmG7w7RrBVDopMRHRAl5jS2MfMNfO+zDXdneYVhF29WG+O3aTYFoy2P0XJrO9G0ymVlgppc9JYL57f4cphHkcpsavnfPe1x/5u8TeCnP9NoPJ8DqKgjNCSoWIVLYykF9ymb3KZZpdAEwltOth0t0N1li35+A1nwlTQD0e5jx3xp9jqA9H/t/7wtIBmGsqDcCdMPefv8NR8ANzTnzJbCtN98Pcw5zjFO9F3ntcA5jfcLgsO8DqitXuOsf7t1TVea0X5jhMYcVtMPFQO5h4pwvMZ3PctmwQzO96YYrzHbwCJnaw2wHTerINTByXipLfN+oifyH5elX9wz5BRAKs2Mrby2vXez76xmVagss0VyISLGa4nvnIX5BQ2H3CF4EAPoMpiOgIUynQ+RlOcby/C3njoyyYoR46wxTkDYXpFtkjFgVXRvTwGg/6QkSuQP4C/zSYGL8jzPXeFybj+rjLJrbB3I8Hw3Qt3QwmjroEZmxgux4iUmgBB8z5nYW8v4l2QTDn3Fcvw9w/3O6TznvMy7Z5p2AKc+6Cic06wNyHO+LP3h7s7i9CmnKp6oWqKtZrVxFXH+F4nwNzz7JzxohHNX9XzYAZ0squVil8l891IxzvU2EbGsHiPH/O8+Rtus+xuYhEIH/32F+r6u+29/WQP3/RLS1u0/KkpQTXXJGeS8owtiqNfIOzEYu/DN/uPdfD91jofqDU8ircFBQrh8KcF3uB/w/WvpoD6Ir8Q289YQ05URCBuW6HweSd3IX8FYntx1qcuNFnItIO+Ss6ewrR/+uYfqWIVHVM8zQ0cQ6VlApTSbUZzG/kLyjk2amU8nV80c7x/gxcYjERqVNA7FelkH0U+L0VkcHI/wyyGSbfqQ1MnuQhx/ynrOEFzoZAmLy8S2Fi0gdhntfsJlnfE48yyxcjonNTUFBQnmdCEUFOjskyz8nJQbNmzU6tXbt2i/31yy+/bBo7duwhABg1alSdzz77LOqxxx7bu3Tp0qS1a9duad269cnMzMw8z7SVKlXyqVJWYGCgtmrVKqNjx47pzz333IHmzZufvvnmm3MbJqqqjB8/fp89PevWrduSmJi4KSYmJqso2Sfh4eF50lTYtgMCArB69eptn3766f9atWp1avbs2dHx8fGtvvvuu1AAGDdu3OGkpKTEYcOGHd6+fXtInz59mo0fPz7Gbd+m/rJ7/UH79MDAwHyfjxZzqFy29CeisvSBqo7zvBEznqVzbN82MN2iQkTqIP+4hX/XvOMBbxSRyjCFnx6jYTLfAACq+hlMZqebjSLyNUyXbx4RMMGt17GtLfNUdajtves4cEV0L7xnwu+H6SLUOd4rANzteP+aqtrHzEwSkUQA223TeopIK1X1tLBw1uxOBdBfVY9b77damWm+ZOo67YbpXtBTy+1/MF3EAaa7cfvDRiJM13OeH+AkqxXdb/hznOHKMA8dnhbpzt+vVzTv2OS/wPRi8JiVUWVnXzcVputp+wPRepiHPbisW5iBMF3r2d1lb+FqXX+/IG9rmFEi8lQhBT4vqKpn/MbNVqGwPeM0BCbz261VjTe3IH+G0mBbC85fRGQlTKsiew8Uo2C6uksHsMtL8HKgGJnDvirL8/yQquYW2IrIKpjjt+/vRgD/LG7iz7LvAAzzHLOIXIP8Ga5tYBU0Wde88z78lara7xffiRm30a3VRUnlALjS1hp5k5hxy+3jnrdxrOO8l+UAuMp270wSM67wh6WeWuMJEXmikGX2IH8Bk9MJAL1U1T5e13fW33Pqmlcz1vI0uPvJapG53jYtXkRqOgoI3Nykqh9b//8iIuustAQ70jK3kO2UGlU9DOCwiBx3zDrj7R6nqj+KyLf4s0eaIJjCmxcAwMqA7WNbJQemK8+ipm1EAbN/sFrG2yt2+dKKuzjfwWGO99kwQxp4WqQmisgRmELukqjhMs1ZAQ4wrbWcrWHtesO94N5Xbvt0Vupw+pvkH3/VKRtFKyz25meY+Nmzvw1W4dY9tmWai0gHVfVU7HPGlY+oqv3a+VVEfkfeHiuGiMgYW9zopqB40BfO8eCzAFxkdZnosQ1m/PNn7Ata6fJWmLMZwJci0gF5e6j4G0zBYUEWquqttvduv4mXikh1VXUWAORjpfO4iBx2mbergPWcBTZ2G6zvnL1AqYeI+J2tnlJEZBBMJS67f9vuCx6VHe9Pw51by5kqMN1an3dEZDzyj9n9pKqmOaYV9/xV8TEd/jC/T/aYIAvAw4Wkw1tavH2OJVaM55KyjK2KnW9wtmLxYtx7ihILlUZehZuCYuVByNtq+RDM74X9mvteTC8Knl5D/GEqJhZWKWq4qnoqgGwRkRbIWyE2Ny4qTtxYRM6KcJtV9Sfr/4Uw+RuR1vtgmAovrznWuQb58wDGqaonTy3JiuV3oOCC/9LI1/GFM/47an3fnVbb9uP0FKwKIgUo6Hs7yrFsKsy1eNR6nygi/8Of1yNg8p1uh+l6v6z9DjNMmOe++7OIZMNUivSoClNZ3hOPl2W+GBFVMO3btz+1cOHCqrVq1ToTHR3t2rPrunXrwq+99tojI0aMOA4Ap06dkt27dwc3bNjQ7Z5cZBMnTtzXpk2bVqtWrTrYs2fPUy1atDiVlJSU2xLeqU2bNqdzcnKwatWqShdddNFJAPjtt98CDx06VGiDn8K2DQB+fn7o27fvyb59+5584YUX9jdp0qTle++9V7Vr1657AaBRo0ZZ999//+H777//8KOPPlpr5syZNaZMmbLPZV+n586dW+3YsWN+ntb+y5YtC8/JyUGbNm1K5dzlS3tZbJSIyOIM5N0edgtqvQ0Aa50tYJG3wB8A2otIuH2CiNQSkUdFZLmIpIjISRHJsdbP92CLvN0delPm3fvbbIQZ2y5fiy8r86WHY/JdLudpu3NdmFZOnm5c2zvmzXPJuJ1ZrNSbBwZv3fg6P+fWALIdaT+D/A9svWz/O1ufLxaRd0XkYREZJCLNPNXoXDLH7OtGwjygTRORsSJymZjW7/CybmGcx5YOM1ZeLjXdqDtbGdSA94xpj6J+n3zhTO8OW4E/AEBVT8B62LPpKCJl1dLbF2V5nmfZ31gPznMcy7SsQA++r9kzJa0CiCOOZezXTQfk7xJzFvIrqy4sv9L83Y87r3XndX6B4/03LpWl5sO9FejZsBvApdZ3qSAzHZmYdufcNS8ijURkkoisEpH9InLadg9fj/wK+509ClNZy56W/cjfqtGXlrjnglcc72+z/T8QeTNRl6nqbhSDiPQUkddFZIOIHBGRDNvn4OzJxZdYpzS+g1+7FOx9AvMZl4RbSU5Re0coDWWRjlMAhtoK4UvibZfCKLd7dhcgt9efRo55L7jElc6u4f2Rv6cYp4LiwQJZlVZ6OSYvcBT453KrfCAiISJyu4h8IiLbRCRNRLJtx+QcksKX74gvv4kC00tCmRKRdiIyRUS+F5GDIpJuOzZnC9JKKHqcWNx03Qwz5If9u/I5gPvcFi/kfUHTy+P7XyARiZYCehrxcRsTkLegBjAxglsvQvlW93F6oefOivUXIG8r/xyYcbmdleXd9uvrtPL6HMsytipJvkF5x+IlUhp5FQUoSqxcHcBJl/0601bYPrfZCvw9CouLyoSVh3ODY3JuV/lWQbizguUIl00547Z0OHoJUNU9MK32C1Ia+TrFUVb3DNfvrZdrep6twN8kSvV7mEpCdiU9Vl/NsRX4e3iN/SxlmS9GRGdBWlqa/5o1a0Ltr6SkpMJ6MnY1cuTIo9WqVTtz2WWXNV68eHH41q1bgz7//PPw22+/vU5iYmIwADRo0CBj8eLFVVavXh22du3a0IEDBzZwtvIviRYtWmT26dPnjwkTJsQAwKOPPrpv4cKFVceOHRuzbt26kJ9++inkrbfeirrzzjvrAEDbtm0zevTokXrXXXfVX758eaU1a9aE3njjjXEhISGe8h+vCtv28uXLKz344IO1V6xYEbZt27ag999/v8qBAweCPEMZ3HzzzXXnzZsXuWXLlqA1a9aELlu2LLJx48auBfgjR448GhoamnPttdc2WLt2bejnn38efvfdd9fv16/f8YIqHZQEW/oTUVlyPgy51fa334diXeb7QmBq6Z8AABG5EqbAoijjnoQXMj8L7mMQlpW2ANaJSB9Vdbbcroa8rR6LwlP7vSry19rO122Xqh6zaqlXKeJ+fipgXnE/Z3vN/akwD7yeTOpqyN/a94iYcZWfdmQOPARgKf6sld4UjowbEdkG85D0sqoW5QfY2ZVPiqq6jSO+w8u6SV62e0JVnS0LC/s++cKZXm9dtznT6wfznSuoJWVZKqvzfEwd3URbnMcpMBl+JX34Lahr1ZIWjnm4VQ5xXjv268bZ4glw/5zL6rMvanqBvEPCAO73smwR2YX83UOWlXSYylsfAZiuBY/96lHQffOcuubFjN/5Gop2zynsdzbZSwtUZ1qixYyR6db987lkAUxPRp5hjJqKyIWq+g2Aax3LFjnj3ioQnYX8QxwVpLDPACjed9B538h3f1DVHBFJhok/isutW+jixhQl4bbPQlt0u8iBafm+GKbHomJV/HDh6z3bc+8syTksbCzYgu5rhXGLd32uFCGmS90vADQswj59+Y4U9fyWCRF5GmaYg6JktIUjf8W/UiUiTwJw9n7zKUzvZW737eOO96Euy3ib7vb7Vd5eRMH3Za+fl4gEwHRxfqtj1uvwPrTUH8jbCtbX81fgubNakS+C6eLZ4wyAW9R9PO7jPuzT27Ty+hzLKrYqab5BecfiJVUaeRXelHUegxu3z9kZFzkraZSVK5C3Z6EcAO85lvkv8vYG0FFEWjrylJy/TymOFt4ehXXrXlbn3OkQ8lYcqCYiIV5a+xdXQd/bajC9dtkVlG9i7xHLtavnMuAWe/8hIseQt1KK/bMvy3wxIjoLNmzYEN69e3d7b0S45JJLji1ZssQtdilQREREzqpVq7aOGzeuzo033tjoxIkT/tWrV8/q1q1bmqfl/6uvvpoyYsSIuIsvvjg+MjIy+4477vg9IyOjVBuV33///Qf69evXbOnSpZUGDRqUOnfu3O2TJk2q/cYbb9T09/dHXFxc+pAhQ3Ibc7733ns7R4wYEde/f//4qlWrZj388MP7UlJSgkNCQgos9C9s21FRUdnff/99+H/+858aaWlp/rVq1cocN27cvtGjRx8FzHAI9913X70DBw4EhYWFZXfv3j3t1VdfTXHbV0RERM6iRYu23XvvvXV79erVPCgoKOfiiy8+PmPGDNflSwML/YmozKjqEcf7bPEyhkkpCAVM6wqYB5+iFPgDhWeY/V5G3WE+papPikhNmPE3n7LNqwlgvoi08fIQVhyegL6sW1nk686mFORmEqnqYRFpD9Pt62CYByvnMVWDGfrhSuscHrfW/VZE2gAYB9NdsVsXcE1gutjtDuCqUj6O4nDLoHXtbolKxNt3oEy+L6XUxWNhinrt+HqsZXUzL41r/Wy3GJuKP8ezzIFpsXvMah1WFGVx3yxMka95MV2aTkPRMzgLu2bO6vevrKnqGRF5HcCztsm3i8gmmO7lPY7A0cOBj25D0Qr8fVWc76CvLUhLet/YDZO+arZpHUUkwt4Cybq3CgCIyIUAvi7hfp16u0wrrHD7B5gxiAFzftJh7hOlFd/Z+fr9KI3vkbcCRo+S3NdKer28i6IV+Pu6T19/E8vsPiUi/ZC/5zOfVi3ttORu2LRAnYH8rUqnA7i7gN/EncjbK0KUiAS6FL46C0IPlHKBT7myetOZCzMes90E/XN4Lzc7kbfQ320YFMCHylm2tMQD+D/k/f6cgBkCzNni2WM3TAxkz/h1S4uvBdoVWUnzDco7Fi9PZfmbUtx9nkvP4SMc7/0ApPiQxzYCwAO290Xu+aOUFXbOnX5C3jybAAA9YQqsc6lqnOf/wlp4uiir/L7CuD1PRbtMK4yvsbe9B8CKmC9GRJb58+fvArDL2/y9e/cm2t+79SjnXKZu3bpn5s2b53WbTZs2zVyzZs3/HJPzDOHo3KY3U6ZM2efWFf7FF1980p7WgQMHpg4cODDV23bq1at35quvvsrtPWj//v0B9913X/0mTZrkPid4602voG23b98+feXKlW7DLQMA3nnnnRRYw1W7ce6zc+fOp7/77jvnuctlfZ55eDtHvmChPxGdS5w3MoXpgv64D+t6WkFfDsDZ7fYMmG7PDsC0UAhGEcbVs5Tpg52acY4nikhlAONts+JhCrbtXfQeAZCJvLWNn4Z7t39OnpYUR2BqM9tb+8c5FxaRKBRvnMWCztc+AM1t75fCjKVXmDw1i1U1FcAzAJ4RkVCYB5LGMF0i3o0/x7KrC1MwMtW27naYFjN3iRlbuYn1uhBmnHvPw9GVItJWVTf6kD7PsdnVFZEgl0x9t0zo/T7uozQ5Pwtn974ezvTmwBHYnWVldZ6rikgVly6C4xzvFcVr1VkRuH2uDZF3fELA5X5Rjg4gb3rinAtY3TJ6G+OxpI6XUgWOwu6bduV5zV+DvBlUOQCehym4PgxzHI2QvyvwwsSJ+3jTzrQcLkaFivIyA8Dj+HPc1EEwY4nbn8HeK2bLGWf3rsdgxlj+Hn/+1g+F+Z0sa78DqGd7n+86tHomqOecXhRWbwGfAxhmmxwC4F6cneOEiPRA/i7t96iqsztXp/SzVNELcL8PuPUs47nfu2Uk3AHgSx/2VVir8ZJ8Vw/DxH72FqMdfFlRROoD6OqY/A2AF2Ba4XkygT5G0XuAaQgzbrFdnMtyZRknOb//6TCt65fD9BSkMN0uv12GacglIpEwXUr3tU1WAA+r6vOFrL4OwHW2934AmsF2jsWUaLV0We+8ICIxMD1+tLNNzoRpVe9sxeu0Dnm76q4jIpXtvfhYz3POFrmu58+6x32KvL2y7AVwhar+7C0RqnpSzBjg9iEznJ8ZYLr/djuG8lBWsVVJY5SKGIvblUZehTdFiZV/hRm/vDAVIqYUkeoALivm6sNE5CFb/Oy8fut46UXLW96AR6nk6/hgEYABjmkPisgyl+GMiqug68DtmvY138R5rrOR9znKbcjEwoYP8WW/sPIWqzgm57m/lGG+GBHRWbFw4cKI1NRU//bt25/av39/4IQJE2KjoqLOFFRR4K+gVLtfICIqoRWO9wLgMlXd5e0F06K/nq01iDNDI1VV71DV1aq63VrHLbPhXPEUTMa93UMiktvdqPWwtsqxzJUwtZO9naejALqr6jFrG1kAfnRsY7CIOHtIuL2Ex+PmG8f7bgACC0h7Ckwmb+7DoYjUEluVdlU9raq/qOoCVX0UwFuOfTS3rZunizVVPaqqP6jqf1X1NuQfh605fOe8hkMADLdPsAofb3MsdxDeu4gsS870NhQRe4YtrGtvqGO5DVrMMXpLSVme51sc2wlF/sz1LefxuHYbkD/T4xaX5Zxdz5Yn57iyF4lIXce0QThL422WkXPpmnf+zm5S1Ues++hv1n07oZB9uqkKR4aeiNRG/gxO5+d9tjgLAQptpWT1eGQvsAkG8KRjseKOyev8HGar6huqutH2+9nFZb2y4PxMeouIs5B5AErWtb/H8zAVTeyeEJFBpbDtAolIa5hxyp2eK+t9F9HN9hjJ4nbP/gHIHbfX2QXkAAC7C4jNTgHoUJa/hVYFoJWOyVeLiOv9RUSq2N66dTk8XlX/T1W3WseQDVO5tqjczqVzmqLoBZn5en2w7sdunMf3par+U1U3qOpO6/g6uaxX6kSkDsxziT1+TAdwvQ8F/oCpLOAstLnG8b4f8hdcfFSEZJ41qjpCVcXby7m8iLSCqazVzjb5GIB+PhT4A/nPg8DEPHbOIWXc1oOIXAtTYc9+r/4FQJeCCvwL2GaCNcxGQWlJhRmGozycq89tFTEWz1UaeRXF9I3jfTMAMQXsMxmmcNM55GFpKXLcWIhhKH5aayFvLyLOuC0MpvfEXNa93dnziNM3jvdFztfx0X9hehOx6wvgBZd4p9RZ1/Rqx+RrrApVuUSkC/J27Q/kj2OOO97nyWsSkYthrsuiusElZvAa+1n7Kst8MSKisyIrK0smTpwY06FDh5aDBw9uHBISkvPVV18lRUZGlkfvLecMtvQn+gsTkbhCFtnnUtO9zKhqioh8BvNQ6PGMiDQE8CHMQ0IgTEuxjtZyHWEKyj3BtLP1baSITILJpA2AyTR6tMwOooRUNVVEXoVpGejh6ab+n7Zp0wD0sb1vB2CViLwE04rwFMx4b61hHogugzk39syjd5G3ZUhVAMtF5AmYh+B+yDvcQGl5C8Aj+PPBtxKAb0TkRQBrYB76K8M8qPeE6UasFkwrNc845/fDVFL4DKbVwzaYB6ggmN4hnAVWJ2z/f2o9G34Ok6myy5ofCdMaoFUB6xZmAUwrMnvXla9aGdHLYD7LBwC0cKz3einWUi8Kz2cRYpv2kYg8CJMBWQemZUY1x3rTzk7yvCrL8zzZGlP1S5huSZ9A/q5I3cYyPS9Y96CFAK62Tb5IRGbDjCmrMBmPV7qtX07eRd5WggEAllnXcRJMt8EvlUfCStG5dM07f2dbiMhYa/1wmMLC+wvZpzdvWS2Z1sC0YPsn8o8LW17fP+dx1xCRO2C6js8EvA7Z8QryZrrZM2zX+9BCvKD02DMFrxGRFTAxQAxMq50rirntovov8hbS+QNYKiIPANgOU/ngRbcVi0pVN4nI8zC9GngEwPx2LYSJ9zbDFDpGo/hdkdaxYoVwmNZcVwC4Efmvx5UwPTqcS9rCxDpTAKTBZOTf7Vhmq6qut71/DcC/bO8vg/kMXwPwP5iesmpZ274EJgb9DqbAtiy9CuBi2/sgAF+LyHMw95xUmALwfjDDJ3ha3rn1xvOkiEy21ukAc68rTiHMlSLyH5gKO95+E79Q1YNF3K5bmv8hIh/gz54J9lgtMZ3LXiQiN8Fk5leDuVbvKOL+i8wqsF6CvJUQ0mHueWu9PGumq+oBzxtV3SUi85G3wOlBEfkd5vctHub6tNsN90LrWsgb04Y7Fglwpsl537Yqu9q7VnaOfQ0AtRzbOayqRXle8OzrbzCt6ivbJh+FqWyb7OX8nVDV3HFMVXWliKxD3koeL4pIBoD1MM/KzsoXa1U1T6GsiIyDuQfYC9A2wlxL+c6bxXncM2F+/z297gnMUHXjYFq7Xou88RoAvKGqJ122fTack89tFTQWdyqNvIqi8nyenu+sAFhs7XM5TGWNcJjYqStMvBoHM2RPkcc+9kFx40ZvnEM6fQTgwQKW/wjm++8xAqZHEc+8l5D3fjnDahm+Guae/gIKr2RQGvk6hVLVDBEZBWAh8raSvw9AHxF5A+Z+dxzm/uMseC8NrwO4yPY+AuaafhTAbzB5UM5Y9wzMfdHuF+QdJuo2EdkM4CuY70lxn1lrwuTlPQXTC14/5O8F6xj+vAaAss0XIyI6KwYNGpQ6aNCgLeWdjnMNC/2J/toKGz8vAcDPZyEddmNgCmg8D99+MLXrnTXsvfk/5O8K9BHr5bEf+YcAOJe8AvMAY291f5+I/NvTulpVF1iZgNfblukAk/Huq//AVCawd714AUzmnUcO8ndlViKquldExsM8OHnEAJhSxE3Vh8nEdmZku7E/3AjMueroZVm7NOSvne2Vmq4t74DJdPD0phMC89Dszc+FzC8zqrrHKpR51Ta5CgouwPgC5VzoXYbnORvmWn8e+TNIPXYif+bz+WYCzIO+/T46DHm71D6DcyeO/Bwmc9beyrApgE8cy51GyVvZlItz7Jr/GHl/UwOQP4NqP4Dahezb6QxMJtP0Apb5HsC8Im63tDi71QXypzVfayNV/cUqjP+by/q+dHXrzQLk7Wa+NvIXwBbncyiOz2B+K3vZpjWCSaNdad03HoUpjLF3ISsA/m69SoOzlaKb7wBcrfnHHi9vp2AKgwoqEHJWzPk3zLmzf4YXIW8G91mnqp+JyLvI2/q2MoDJ1svuD9t620RkE/JmWF+FvJVAsmEKZaoXMVmnYArc3FreAmb4rIe9zCvIBuQfeusJ6+XRACZTfgGAIbbp4cjflf/Z+P4PRv5eB0JQcMHdCphug+3ugSkQ8jz/BcNck27OALhVVdNd5n0A93utRyzyP/8679uDkb/HMKc5jvc3o3hDKdyMvAX+gKmE/XkB67yD/ON63wLz++h5doyC92fCE3Bv/Xkv8p+Ltsjf0tMuz3Gr6n4RuRd5f9vawBS4uvkV+Xu/OWvO8ee2ihaL51FKeRVF3ecpEbkdJv73FAxXgWnEUBYNGQpTrLjRjYi0g/k+2s0rqNKAVXHEnt9xpYhUtVpzH7Eaxjxtmx/hkr4Cn51KMV+nUKr6fyJyG0w+hf13sp1j/2VlvvWy96TSEvmfN+2eVFXneNDvI2+hfyDy/94pfLw2bE7BVGZZUsAyE1T1tO19meWLERFR+WL3/kR0TrEeXP4G3ysbZMM2Tpaq7gUwDvm7ifQ4iuK3+jorrO6AnYWuNQDc6Zh2E0xhra8tDVIc+8mAyeBNcV8c2TCthJwtlYoz9nAeqjodJsPJ15Ydh2EeOotjsqp+XYz1TgO4UVWLNA6Qqn4K05KlsDEJAVOj+5JybOECVf03TMUJXz7XDwAM1rIfT9sZn+QrUCmj87wHpiDE23fqIICritOaqyJR1S0wrcy8FWSdQNkM/VEsVmurISg4Y3oiTIGKXYnvZWfTuXLNW62DC+qyeTfyDwnii28BTC1g/jYA15yF+48rVd0E08KoONyO6zRMxl9x/Rv5uya2W4azlMltfQevA1BQDft/w3zGpbI/Vb0DpiDYbTx6b3JgWocXVum1MKkwrad6qarPLdXOolEouNXiP1TVXhkSVs9eV8L0rOUrb/FjabsVpvVcUbtovAUmk9pNNsx5Kk6rkGEwcambMwBG+NgVeh7WteRrwcUCuA814bERprePCsFq+d8LpleJgvwBE4cuK/tUVRzW79PFKHiceVjz+1nLl1Va3oL5brlVyrD7AcCFWr7DhZ2zz20VLRb3okR5FcWhqp6x373do53SkL+79VJRwrjR6WbH+ywUPizGIsf7YOTtDXEyCq6E8SXyV3TP9+x0NvN1VPVtAN0B/FTEVX9B/u75i7pvhfn9f9OHxc8AeEhVJ7nMewumtwdvPgUwt+gpxKMwvR14Mw3FrxxRrHwxIiIqPyz0J6JzjqomwdQ2HQiTIb4d5sE2G+ah7BeYB5TbYMZpe8Ox/uswLT4/t5bPhOmufjqAto7uTM9V/0L+ceAesI/TpaqZqjoGpobxvwCshanUcAampu8umHPwGIBOqtrTuRNV/Q2m1vgLMN2SZcIU8nwM80D1X5ja2nZF7a7UlarOgulS7yGYFiAHYB4kM63/V8HUEr8M5nP+3bb6ZJha1lOs5bbDZMJnW39/AfAGzBiU9hapgCmYuAWmp4N1MAVUp2Eeng/D1MqfBCDeyggqzrHNB9AQwD9gHuoOWts/CXOe3wPQX1X7FKP711Knqq/BtMqcCNMV3xGY6ygNphXOmzDjLN5Q1gXe1tiZzq5Uj7gtWxbnWVX/BdPa7GNre5kw36VXALQuy4zSc4l1bhNgWrP9DnMeUmC+N62RfwzHcqWqh2C6EJ8AU4iTAXM//ALApar6BP7s9tmj3L97RXWuXPOq+hBMd+4rYe4T6TCF8i/AtLgpVqGqqo6FKXj8AuZ7nwFTCDQJQHs1Y4+Xp2thWt0mwvzO+mohzDm1m6+qvhQyuLIq7vWDuRYSYc5VGkyG3xiYcVjPWsUWq9CuE8z5+dXa9zGYQpKBqnpPGexzNkwccQNMJupm/Pn7dRomlvgO5r41AkAdVb1EVZN92Tz+/B1MgSmgehcm9oxV1cfUdLN+LtoN8z2cBDPESTrM/fD/APRW1X+6raSqqap6PUyvT9NgCo6Pw8RWJ2FirYWwurtW1RvL9Cj+TNcZVX0Appv352E+0yMw977jALbCxKujHeutg+lu9x2YyiFZML9nHwPoqarOLnd99RNMDwKvwtzrMmF6DPgIJt4uSWWecTDf33UooBtdW2W3kdayp2A+o00wcX8XmO9fhaGq/4OJL+4EsBTms8qCuXbXwVRialzc2Px8p6rfwfRy9ADMs9FhmHvYYev9AwCaWsuVdVqmw4z9/ALMM9kfMN+TvTD3kCEAup4Lz0DAufvcVtFicafSyKso5n4XwXyed8P09rcX5ncwC+Ze/T1MYfZAADWLU0mrCIobN+YSkUDkH7pwdWExpKr+BHPsdiNs87Ot3/GhMJUyT1iv9TDn7jLk78nF9fovYb5OkajqOlVtD9NjzEsw15V9f0dg4pcPAYwF0EpV25ZGZTFVTVfV22Fax0+D+Vz/gLmmPb9VzwNooqquFaStysv9YYbz9MTLf8BU5B2mqgNQeKUpN8dh8u8ehDn+UzD5Yt8AGKSqd1mxg12Z54sREVH5kPz3fCIiIkNEhiF/V+4trdYHRKVORAYhf/fdM6xWnUTFIiI9kL+77stV9f/KIz301yMiy5G3q/SLtHi90BDlssbZdlay6a2q35z91Jx/RORC5G+R10CLNg4zERFRhSIi1WAqUtewTX5BVR8spySRjYg4C3NutnpCIKIi2rhx4662bdv62kMM0Tlj48aN0W3bto1zm8eW/kREf2EicquIvC4iF9p7ERCRcBEZjvzji/3AAn8qbSJSVUTaiMhouHeZ5xynmigPEblMRGZbfyNt04NF5O/IP/7ubngfY5aoVInIAOQdv3MrzvEWekRERER0fhKRyiLylYjcKCK1bNP9RKQTTK849gL/bJiec4iIiOgcF1DeCSAionIVAdON5p0AckTkKEyXttWQv2JYmrUcUWl7HMC9XuZ9rqpfns3EUIUUDDPO4jAAKiLHYLomrIb88e4ZACOt7tGJyoSIjIHpprsKzHVoN9Gli00iIiIiorNBYCqk9gYAEUmDGdKiCoAQl+WfVdXNZy11REREVGxs6U9ERB5+AKIBVEf+34f/AehbxmPeETl9BuD68k4EVTgCoCqAmshf4L8fwFWq+sVZTxX91VQF0Aj5C/znqKqz5wkiIiIiovISAaAW8hf4ZwB4SFUfP/tJIiKiimr8+PExTZo0aVnW+4mNjW39+OOP1yzr/VQ0LPQnIvprmwdgHIBPACQBOArTddsf1vv3AVwHoLWqri2nNNJfQw7MdbcJwCwAfVT1KlVNLd9kUQWxCsAoAB/CjD95CKZFfxqAHQAWALgVQBNV/by8Ekl/WX8AWA/gdgA3lnNaiIiIiOivLQ3ADQDeAPAjgH0AMgGctv7/EsAjABqp6vPllUgiIirYt99+G+rv79+hffv2zYq6bufOneOHDx9eryzSVZCUlJSAgICA9tOmTavqNv/OO++sU6tWrTbZ2dlnO2nnDXbvT0T0F6aqewC8bL2IyoWqjgUwtpyTQRWYqh4BMN16EZU7VX0SwJPlnAw6z6nqLpjeTagMqOo34PklIqLzjKpmA/jAelEFo6qMTYgIAPD6669XHzZs2MEFCxZU+/HHH0Pat2+fXt5pKkzdunXP9O7d+4933nknevTo0Uft87KysjB//vyqQ4cOPezv719eSazw2NKfiIiIiIiIiIiIiIiIiKgI/vt9ctXOk5a1bvDQ4g6dJy1r/d/vk11bsZemEydOyKefflp19OjRhy+77LJj06dPj3Yus3z58kpdunRpGhoamhAREdGua9euTXft2hU4aNCguHXr1oXPnj27uoh0EJEOSUlJQYsWLYoQkQ779+/PbSyelJQUJCIdVq5cGQYAZ86cwbXXXls/Nja2dUhISPv69eu3mjBhQs2itMy/9dZbD69bty5iy5YtQfbpH374YZUjR44Ejho16vCKFSvCunfv3iQqKqpteHh4QocOHeKXLVtWqaDtikiHt956K8o+zTkEwJEjR/xvuOGG+lWrVm1bqVKlhE6dOsV7js0zf8CAAQ2qVq3aNjg4uH2dOnVaT5w4sYbPB3cOYKE/EREREREREREREREREZGP/vt9ctWnF22pfzAtI0gBHEzLCHp60Zb6ZV3w/84770TFxMRkXnDBBaeHDx9+dN68edUyMjJyewL57rvvQi+//PL4Bg0aZCxfvnzrihUrtg4cOPBoVlaWzJgxI6Vdu3YnBw8efCQ5OXljcnLyxkaNGmX6st/s7GyJiYnJev/993/buHHjpscee2zv1KlTa7/yyiv5Kh14M3jw4D+qV6+e9cYbb+RZZ9asWdFdunRJjY+Pz/zjjz/8hwwZcmT58uVJq1at+rVly5anBw4c2MReIaGocnJy0K9fv8b79+8PnD9//rYffvhhS7du3dL69+8fn5ycHAgA48ePj9m6dWvo/Pnzt/3yyy+bpk+fvqtOnTpZxd1neWD3/kREREREREREREREREREPnpl+bbYjDM5eRpXZ5zJ8Xtl+bbYYV3qH/W2Xkm9/fbb1a+77rojANC/f/+00NDQnDlz5lQeMWLEcQCYPHlyrfj4+FNz5sxJ9qxj7/4/MDBQQ0NDc+rVq3emKPsNDg7Wl19+eZ/nfXx8fOaPP/4Y9tFHH1UdN27cYV+2ERAQgOuuu+7whx9+GD1lypR9/v7+2L17d8DKlSsjX3/99Z0AcNVVV6U5jnd3zZo1q3z88ceRzmEBfLVo0aKIX3/9NezgwYM/h4eHKwBMnTp13xdffFFl5syZVZ955pnfU1JSglu1anWqd+/epzzHV5x9lSe29CciIiIiIiIiIiIiIiIi8tGhtIygokwvDZs2bQr+6aefwm+55ZajAODn54eBAwcenTVrVnXPMps3bw7r1atXmvetFN8///nP6q1atWoeFRXVNiwsLOHNN9+suW/fviId75133nn44MGDgR9//HEkALzxxhvRERER2cOGDTsOAHv37g0YMmRI/bi4uFYRERHtwsPDE44ePRq4e/fuYp/XdevWhaWnp/vVqFGjXVhYWILntX379tAdO3aEWOk6uHjx4qj4+PgWI0eOrLN48eLw4u6vvLClPxERERERERERERERERGRj6pHBGcedCngrx4RXGYtxKdNmxadnZ2Nxo0bt/FMU1UAwPbt2wMbN26c5XlfFP7+/mrfFgBkZmaKfZmZM2dGPfbYY3WfeOKJlF69ep2sUqVK9ksvvVRjyZIlVYqyrxYtWmRecMEFabNmzYoePHhw6nvvvRd99dVXHw0NDVUAuOGGGxocPnw44Lnnnktp3LhxRkhIiPbr169pZmam14bsIgLncWdlZeWmPycnR6pVq5b19ddfJznXjYqKygaAa6+9NrVnz56JCxYsiPzqq68ir7nmmib9+/c/Nm/evF1FOb7yxJb+REREREREREREREREREQ+GtOnyd7gAL8c+7TgAL+cMX2a7C2L/WVlZeGjjz6q9vDDD+/97rvvNnte33///eamTZuenj59ejQAtGrV6tSqVasivG0nMDAwJzs7O8+0mjVrngGAlJSUQM+0devWhdmXWb16dXibNm1OPvLII4d69OhxqlWrVhk7d+4MLs6xjBgx4vCyZcuqvPvuu1WSk5ODR40adcgzb8OGDeF33HHHweuvv/6Pjh07pleuXDn70KFDgQVtLyoq6sy+fftyl0lJSQk4fPhw7vuOHTueOnLkSKCfn5+2atUqw/6KjY3NHeagdu3aZ+66666j8+fP3zV16tRdCxYsqHb69Glx7u9cxUJ/IiIiIiIiIiIiIiIiIiIfDetS/+hjV7RIrhERnCkAakQEZz52RYvkYV3qF2vc+cJ8+OGHVY4dOxYwZsyYQ506dUq3vwYOHHh0zpw50dnZ2XjooYcO/Prrr2E33HBD/e+++y5048aNwVOmTInetm1bEADUq1cv8+eff66UlJQUtH///oDs7Gy0bNkyo1atWpmPPfZYzC+//BK8YMGCyOeff762ff9NmzbN2LJlS9jcuXMjExMTgx944IHa69atK1YX+MOHDz8WFhaWc88998S1bt36ZKdOndI98+Li4tI/+OCDahs2bAhZsWJF2ODBgxsGBgYW2H1Bt27dUt98880aK1euDPv2229Dhw4d2iA4ODi3Qsbf//731ISEhBMDBgxoPHfu3MitW7cGLVu2rNK4ceNilixZEg4AY8eOjZk9e3aVxMTE4B9//DHkk08+iapTp06GpweCioCF/kRERERERERERERERERERTCsS/2jax/tm7jzucs3rH20b2JZFfgDwKxZs6IvuOCCtFq1amU75w0dOvTovn37gj799NPIbt26nV64cOH/tm3bFtK7d+/mPXr0aD5v3ryqQUFBCgAPP/zwgcDAQG3Xrl3LmJiYttu3bw8KDg7Wd999d8fu3buDO3fu3HLixIkxTz31VJ4eC+67775Dl19++dHbbrutYbdu3ZonJycH3XHHHb8X51hCQ0N1wIABR1JTU/1vuummw/Z5b7755q6TJ0/6de/evcWwYcMaDh8+/HBsbGyBQya8+uqre+rVq5dx6aWXxl9//fWNbrnllkNVq1bNbcHv5+eHZcuWbevevXuaVdGg1bBhwxpt27YtpG7dulkAEBwcnPPUU0/Fdu7cuUXv3r2bnThxwu+TTz7ZXpzjKy9SnLEdiIiIiIiIiIiIiIiIiIgqmo0bN+5q27bt4cKXJDq3bNy4Mbpt27ZxbvPY0p+IiIiIiIiIiIiIiIiIiKiCYqE/ERERERERERERERERERFRBcVCfyIiIiIiIiIiIiIiIiIiogqKhf5EVKpEZISIqIh0KcN97BKRt0t5m0+KiJb1fs5F1nEuKeVtqohM92G5C61lL7RNc/ssvhGRb0ozjQWkqZKITBeRfVba3j4b+yUior8mxk4VD2OnfGli7ERERGcF46aKh3FTvjQxbiIiojLDQn8icnU2HqS87NcTkLu9SvUh4VzmeQixvTJFZKeIvCQilcs7feVNRMKtc3RhGWz+fgB3AHgLwI0A3iiDfUCMFSJyRERquMyvZD0cbxKRQGvaIyLyie3h0PUh1/b9dXs19jF9A0XkfRH5TUROich2EZkpIjGFrBcgIlusfT3ky76IiM4HjJ3KF2Ongv0VYycRaSYik0XkRxH5Q0QOishXInKxy7qlETs5r0H7K6CA9Rg7EdFfDuOm8sW4qWB/0bgproDvxm2OdUsjbtpVwDayCliPcRMRUQG8PngSEZWz1wB875i2z/obDyDnLKThbO2nIHcD+ANAOIB+AMYC6CQiPVVVC1qxglgJIBRAZiHL9XO8DwfwhPX/N6Wcpt4ANqrqo6W83TxUVUVkJICNAKYAGOZY5EkA9QD0UFXPA88kAAcBrANwuQ+7eRLAb45pB3xM4gwA+wG8D2AXgEYA7gLwdxHpoKopXtYbY6WbiIjOLsZOBmMn4y8fO1kZ1LcCmA8T14TAZK5/KSJ3qqpbJvuTKH7s5OG5Bu2yC1iesRMR0dnHuMlg3GQwbhLxTP8QwCLHst952c2TKH7cNBbmPNtVBTAVwBcFrMe4iYioACz0J6Jz1WpV/cBthqpmnI0EnK39FGK+qnoC5ukiMh/AQAAXIP8DKgBARMJU9dTZSmBJqGoOgHQflivsAa001YApWC81IhKqqqed01U1SUSeBfCUiLyrql9ay7eFeQB6XVXX2FZpqKo7rWV8eQD/QlVdrxMfDFbVbxzHsRDmYW8cgPHOFUSkJsyD8XMAni7mfomIqHgYOxmMncDYyVr8AwBPquoJ23anwVSefEZEZlrn064ksZOH/RosEGMnIqJyw7jJYNwExk2O1X5W1f/6uOtix02q+olzmoiMsv513T/jJiKiwrF7fyLymYj0sLrEPCEiaSKyVEQu8LJ4qIj8W0QOW8vPE5HapZSOPOOe2bpnu0FExlvz00XkOxFJcFn/chHZaC3zPxG5tYz2M0BEEq1lkkTkZnEZO6yIvrL+NrT28Y2IbBWRNtZncxLANGteqIg8Z6UzQ0xXbc+ISLCX471IRDZY6d0mIjc75geJyEQRWS8ix0TktIj8ICJXeUusiFxndbuVLiK/iMjljvn5xlfzsp3c8dVEJA6mBToAPGHr/uttEbnY+v9ql21cbs27wss+LrQ+m+YA/mbb7oXWfJ/OpzV/iYj0FpHvRSQdwD8KOLznAGwB8Lq1Dz+Y7t1+B/CwfUFPgX9RiEiEiPgXdT1ngb817XuY1g8tvKz2HICtAN4r6v6IiM5HjJ0YOzF2Kr/YSVXX2wv8rWmZMK3XomEy3d2Oq1ixU95NSKSVrsIwdiIisjBuYtzEuKl885xs+wjz9jm6LFvSuMluGIA0AJ96mc+4iYioEGzpT0Q+EZFeAJYC2APgGZhKQ3cCWCEif1PVHxyrTAVwEsBEAPUB3AMgXky34L7UoI0QkWjHtOOqeqaAde4DEAjgVevvAwA+EZHGnu7RReQimOBxO4DHYLr5fBZ/BvS+8GU/l8J0JZoEYAJMd2KTi7gfN42sv0ds06oA+BLAApgWTcdFRKz3lwJ4F8APAHoAeBRASwDOB5SGVnpnWssPBTBLRDJU9X1rmUiYz/xDALOsYxpqHftlqursfqs7gGsBvAITtI+0lu2jqiuLewIAHILpgu7fAD62jhMwXYr9AHON3mjNsxtmrettnL5frfX+CdO93STP9GKcz8Yw5/NNmHO129vBqGqmiNwOYDWAxwGkwNSqv1pVU72t56OlMN2lZYnIcgD3qeqW4m5MREJhrrfDLvM6AxgO87mfD90AEhGVCGOnIu+HsRNjp7MVO8UAOIP8XfADpRM7/c/axmkR+czaxh7nQoydiIj+xLipyPth3MS4qazipkdhriWIyM8AHlfVz7xsvtTynESkAYBuAN5x67WAcRMRkW9Y6E9EvpoC4ASALqp6CABE5B2YGpb/gglGnS70dFcmIpsB/AfACJgxNQszw2W5TgDWF7BOZQCtVTXd2udWmCC8H4DF1jIvADgGoJuqHrWWmwcg0Yc0FWU/z8ME+11V9Q9rubkANhVhPwBQVUTOAKgE4BIAo2DGx1plW6YmgHtV9RXPBKtm8aUAnlHVx6zJ00TkdwBjRaSvqi6zbaMJgCGqOsdafwaAnwA8LyIfWF2iHQNQ194FnYi8CuBnAPcj/5hbrQB093QVJqYG+zaYmrndingecqnqSTFdzv0bwC/ObsdEZDaA+0QkSlWPWdMiAFwF4D/eHuJV9XcA/xWRCQB+t2+3GOezEYC/q+pCH49pjYhMhzmPpwEscOvqrAhOAXgbZuy5PwC0h+mOf42IdFTV7cXc7jgAYTAP4bmsB9R/A5ijqt9bNeOJiP7qGDsVbT+MnRg7lXnsJCKNAFwPYKEjQ7k0YqdjMOf4e5iuhHsAuAtAFxFpr6q5BSiMnYiI8mHcVLT9MG5i3FTacVMOTOWOjwHsBdAAJg/oUxG5XlXn2pYtizynodbf2c4ZjJuIiiY2Nrb1rbfeenDixIm/l3da/opEpMOsWbN23HzzzcfKY//s3p+ICiUitQB0gKltecgzXVX3AngfQDcRiXKs9obmHZ/sXQDHAfT3cbeTAFzseCUVss5bnociywrrr6dbslowgei7nocv6zh+Rf6Hh5LsJwZAG2s/uS2IVDWpiPsBgM0wD3K7YLrf+hHApZp3/LQzyP+wegVMzdd/Oab/0/p7uWP6QdgKcq1M0DcB1LGOBaqabXugDhKRqjA1sVfAXB9O69U2NpiV0fk+gK4u10tpegdAEEyNb49BMIXV+R4efFTU87nH14cvm4dgWtALTCuFYlPVuap6s6q+o6qfqOrjAPoCiADwZHG2KSI9rXUXuNTyvgWm9vlDxU81EdH5g7FTkffD2Mlg7FQ0RYqdRCQMwFyYAvlx9nmlETup6lRVvUdV31PV+ao6DqbVXz3n/sDYiYgoF+OmIu+HcZPBuKloCoybVHW3ql6iqtNV9TOrkkcCTCWQF62Cd8+ypZ7nBFPovxfA1y7zGDcR2QwaNCiud+/ejb3NX7du3a8PPPDAIW/zy9v48eNjRKSDiHTw8/PrUKNGjTZXXXVVg+3btweWd9pKQ3Jy8sbrr7/+eHntny39icgXcdbfrS7ztsAEjPVgauV6bLMvpKpnRGQnTLdrvtjkqMXqi2THPo9ZMWlVa1Kc9dftQS4Jvj8cFrYfzzHmOQeW/yF/sF6Qa2HO60kAyaq6z2WZfY4HQsAc6wFVPe5I634ROY4/z4XHb1bNamdaAXM8PwOAiNwGk2nZHOZzz920S7q8HT+Q/3opNaqaJCI/wGSyvmFNHgYgSVXXFXOzcSja+dxR1B2oaqqIJAGo5eVzLhGrNvRqmAcxAICIhMN0xeaRbc9ksS3XCqaLwk0wLSfs8yrDdFf4T3XpupaI6C8qzvrL2Mm3/TB2Mhg7FUFRYicRCQTwEUyGcX9V9doNrm37xY6dbNuYKyL/tLYxwdoGYyciorzirL+Mm3zbD+Mmg3FTERQnz0lVj4vITJhhAeLh/h31LFuSPKeOAJoBeNF5nTBuIiq6mJiYgoaqOWvS09MlJCTEdTiOuLi49BUrViTl5OTI1q1bg8eMGVNv8ODBjX7++Wev95myTlNpqVevXrmef7b0J6KSEi/T3W6e3pYtLdlepovjb0nTVth+ClLUc7BKVZep6ncFBOX5xroqRhoKPScicgPM+GvbYQp+L4OpDf9+cbdZht4B0F1E4qxa8L1R/BrXhXE7pqJ+JmdLMv7MKABM1277ba98D6gi0hCmi7dDMDX+0xyLPAAzzuDHItJYRBrjz0yIata04NI9DCKiCo2xU9G2wdjp7DhvYycR8YNpAXoJgKGq+lURVi9y7ORit2MbjJ2IiHzHuKlo22DcdHact3GTC08llKoFLvXnssWJm4ZZf//rMo9xE1ERxcbGtn788cdret6LSIcXX3wx+rLLLmsYGhqaUKdOndbTpk3L853euXNn4BVXXNEwMjKyXWRkZLsLL7ywcWJiYu53a/PmzcF9+vRpFB0d3TY0NDShRYsWzefMmVPZud/x48fHXHPNNXERERHtrr766gbe0hgQEIB69eqdiYuLy7r00ktP3HTTTYc3btxY6ejRo7ll1u+//37lli1bNg8ODm4fGxvb+p577olNT0/PvSempKQEXHTRRY1DQkLax8TEtJ46dWq1Jk2atBw/fnyM/dgnT55cvV+/fo1CQ0MTxowZE+vLtt95550qTZs2bRESEtK+cuXK7Tp16hSfkpISAADbt28P7NOnT6PKlSu3Cw0NTWjQoEHLGTNmRNn3+dZbb+W+X7t2bWi3bt2aerY1aNCguCNHjvh75nt6bnj66adr1KhRo01kZGS7wYMHx6WlpRWr/J4t/YnIF7usv81c5jWDCbSdrWWaAljqeSMiATA1U1eg/Oy0/rodR9NS3I8nIG7iMs9tWlnYBaCfiFSx1xS2upurjD8/U4/GIuLnqFHrSavneK6DqU08QFVzH65E5GYvaXA7p55tFtq6qhCF1cj7AMBLMA8OGTAPSW4PD77ahaKdz3NVQ5jCe493Aay2vc/z4CgisQCWw4zt1k9VD7pssw6AKAAbXebdb726woxvS0T0V7HL+svYyTeMnQzGTmVjOoDrAdyuqvOLuG6RYicvGiDv58fYiYgor13WX8ZNvmHcZDBuOjsaWn996Sq8yHGTiPjDfPaJquoWGzFuorNqRocNbsOJlLmRGzpsKMvtv/DCCzFPPPHEnhdeeGHv66+/Hn3vvffG9e3b90TTpk0z09LS/Hr37h3foUOHE0uXLk0KCgrS5557ruYll1zSNCkpaXNEREROamqq3yWXXJI6efLkfWFhYTmzZ8+OGj58eKNmzZptSUhIyO0NZsaMGTXHjh2774knnthvu5UXaPfu3QELFy6s4u/vj4AAU2Q9f/78yJEjRzacNGnS7r59+57YsWNH0D333FM/IyNDZsyYsQcAhgwZ0uDgwYOBixYtSgoLC8u577776u7bty/I7dgnTJiw9+WXX07x8/MrdNu7d+8OuO222xo+8sgje4cMGXIsNTXVb9WqVbm9lowcObJ+enq6LFmyJCkqKip706ZNId6OLS0tze+KK65o0qZNm5MrV6789dChQ/533XVX3JAhQ+K++OKL3zzLrV+/PrxmzZpZS5Ys+d+uXbuCbrrppobPPvts+uTJkw/4dBJtWOhPRIVS1QMisgHAcBF5VlUPA7njiA0FsEZVnd1m3SEib9rGWBsOoAqAz89Wup2s4/gJ5jgmecZYE5HmMC1/Sms/+0TkFwA3Wvv5w9pPfGnupxCLANwB0y3aE7bpD1h/FzuWrwETZM8BABEJBXAbzHhaidYyntrmAusByGoFfrWXNHQUka6q+p21bDUAQwB873K9FNVJ66/rOG1W93ef4c8HsJWqmuy2rI+Kej7LlYhUtY8haE3rA6A7gFmeaaq6A166hROR6gCWwYzJ1quA8/dvmPNjVx3ANADvAfgEf3axR0T0l8DYqcj7YexkMHYqZSLyIoDbAfxDVd8sYLnSiJ3ctnELTGa1fSxkxk5ERDaMm4q8H8ZNBuOmUuQljqkJYCSAHaq6rZBlixQ32fQFUAumEoUbxk1EpWDw4MFHRo8efRQAXn755b2zZs2qsWzZsvCmTZse/c9//hOlqvjoo492+fmZxuXvvfdecnR0dLsPP/yw8m233Xasa9eup7t27Zpbcef5558/sGTJkipz5syJSkhI2O+Z3rlz57Rnnnnm98LSs2PHjpCwsLAEVUV6erofAIwYMeJgZGRkDgBMnjy59qhRow7ce++9RwCgZcuWGcePH99zxx13NJg+ffqexMTE4NWrV0cuW7Zsa58+fU4CwOzZs3fGx8e3ce7ryiuvPDp+/PjDnvfDhg1rUNC2d+/eHXTmzBkZOnTosaZNm2YCQKdOnXIrNuzZsyfoyiuvPOY5H82aNcv0dpwzZsyoevr0ab+PPvpoZ1RUVA4AqGrylVde2XTTpk3BrVq1ygCASpUq5cyePTs5MDAQ7du3T587d+6xFStWRAJgoT8RlZnxMAWA31vjOQmAUTBdLN3vZZ2vRWQOTG3re2DGYnu7zFNasH8AWAJgjYi8CSAUwN0wY5W3LcX9PAwTlH4nIv9x7KddKe7Hm8Uwx/m4iNSD6UKrG8wD86cuY9dtAzBNRBIA7IF5cIkHMFxVPQ9eCwEMBLBQRBYCiAUwGia4djt3mwAsEpFXAaTBPChEwpybElHVE9ZYZNeLyP8AHAGwU1V/sC32DoDPrP9vK+Eui3o+y4SI3Ii8YxS2F5EJ1v+zbQ+Zq63Mhl8AHIe55m4F8DuAJ33c3RcwLRResfbT3jbvd1VdCgCquh7Aekc646x/N6nqPB/3R0R0vmHsVDSMnRg7lSoRGQPgPgA/AdgnIsMci3ysqp5M/dKInX4TkQUw39tT+PN4fwUw1bMQYyciIleMm4qGcRPjptL2oojUAfAtgH0weU+3w1R8uMmxbGnETR7DYHqXfN9tJuMmOtvKusV9eWnTpk1ugX1gYCCioqLOHDx4MBAANmzYUGnv3r3B4eHhCfZ10tPT/X777bdgAEhNTfV78MEHY5YuXVr50KFDgWfOnJHMzEy/Fi1a5OnBIyEh4SR8ULdu3YxFixZtS09Pl3nz5lX57LPPoqZOnbrXM3/z5s1hiYmJlV577bVanmmeCgIpKSmBiYmJoX5+fujVq1fu/ho3bpxVvXr1LOe+OnbseMr+vrBtd+nS5VTXrl1TExISWvbo0SP1oosuSr3xxhuPxcTEnAGAUaNGHXzggQfqLV++vHKvXr1Sr7nmmuM9e/Y85dwvAPz6668hTZs2Pe0p8AeAvn37nvDz88PGjRtDPIX+jRs3Ph0YGJi7Xu3atbN+/PHHSr6cSycW+hORN54xTLIBQFVXWrU2JwJ4HKbW7fcArlNVty6U7gUwGKaGagjMw8jdtlrY5UJVl4rIAACTrFcygEcA1EUpPoCp6v+JyGCY8/WstZ+HAbSEe1dvpUpVVUQGwgTbN8A8KOyDOeanXVbZAeBOAC9YadwD4DZVzR2TTFXfEZEaMA/efWHGWRsHoDHcz923AL620tAQ5iHvalX9psQHaNwM4GUA/wIQDPPAZX8AWwLzwFEZQIkeAopxPsvKrQD+ZnvfyXoBpss0T6H/AgD9rVclmFqBbwN4SlX3wjeeQG+My7wVsHWlSEREABg7lXQ/jJ0YO5U2T4XFBLiPs9sAf7bkK43Y6SOYDPprYL7Du2FarT2tqqnFSD8R0fmMcVPJ9sO4iXFTaVsK8xndBVPQ/weANQCedfkOlkbcBBEJAzAAwApV3VPC9BNRAYKCgvL0tS8iyMkx5dA5OTlo1qzZqQ8++CBfzxzVq1f3FHTX+eabbypPmjQppXnz5hmVKlXKufHGGxtkZmaKfflKlSrlOLfhJjAwUD0F3h07djywffv2kJtvvrne/PnzdwGAqsr48eP3DR06NF/PLTExMVm+Dh0AAOHh4XnSVNi2AwICsHr16m1fffVVpc8//zxy9uzZ0c8880zsl19+mdS1a9fT48aNO3zVVVf98fHHH1f+6quvIvv06dPs7rvvPjBlypR9zu2Z27s4JwMwn4H9fDjnqar7ioWQopwcIvrrsFrGTAXQTFWTyjs95wsR+RRAC1U9W+Os/WWJiB9MZuu3qnpdeaeHiIjOb4ydygZjp7OHsRMREZ0tjJvKBuOms4dxE1HFt3Hjxl1t27Y9XPiS55ZBgwbFHT16NODrr7/e7jY/Nja29a233npw4sSJvwOAiHSYNWvWjptvvvmY2zL/+te/op988sk6O3fuTIyOjs5222bTpk1bXH755cdfeumlfQBw6tQpqVu3bpsLL7zwD09BvXO/3owfPz7ms88+i9q2bdtmz7QtW7YEtWnTptXXX3+9tWfPnqfat2/fLC4uLn3BggW73LaxcePG4Hbt2rVavnz51osuuugkAPz222+BTZs2bXPvvffu9xTAux17Ydt2ysnJQZMmTVpedtllx//973/nq9j06KOP1po5c2aNgwcP/uLcp+fc7t69+xdPa/9FixZFXHnllU0TExM3tWrVKsPt83Q7R47jj27btm2c2zw/Xw6KiP6SOgE4DWBneSekIhKRQBEJcExrBlML9qvySdVfTn+Y7uDeKe+EEBHRXwJjpxJg7HROYOxERERnC+OmEmDcdE5g3ERE5SYtLc1/zZo1ofZXUlJSUHG2NXLkyKPVqlU7c9lllzVevHhx+NatW4M+//zz8Ntvv71OYmJiMAA0aNAgY/HixVVWr14dtnbt2tCBAwfma+VfEi1atMjs06fPHxMmTIgBgEcffXTfwoULq44dOzZm3bp1IT/99FPIW2+9FXXnnXfWAYC2bdtm9OjRI/Wuu+6qv3z58kpr1qwJvfHGG+NCQkJyRKTAlu6FbXv58uWVHnzwwdorVqwI27ZtW9D7779f5cCBA0GeoQxuvvnmuvPmzYvcsmVL0Jo1a0KXLVsW2bhx43Rv5zY0NDTn2muvbbB27drQzz//PPzuu++u369fv+Oeng5KG7v3J6I8RGQQgAthupJ6W1UzyzdFFVYszPhy78N0s9YAppus0wD+WZ4JO9+JyAUAWgN4FGZMvyXlmyIiIjqfMXYqNYydygljJyIiOlsYN5Uaxk3lhHETEZ0LNmzYEN69e/cW9mmXXHLJsSVLluTror8wEREROatWrdo6bty4OjfeeGOjEydO+FevXj2rW7duaZ6W/6+++mrKiBEj4i6++OL4yMjI7DvuuOP3jIyMUm1Ufv/99x/o169fs6VLl1YaNGhQ6ty5c7dPmjSp9htvvFHT398fcXFx6UOGDMntmeG9997bOWLEiLj+/fvHV61aNevhhx/el5KSEhwSElJgoX9h246Kisr+/vvvw//zn//USEtL869Vq1bmuHHj9o0ePfooYFr+33ffffUOHDgQFBYWlt29e/e0V199NcVtXxERETmLFi3adu+999bt1atX86CgoJyLL774+IwZM1yXLw3s3p+I8hCRnQAiAHwCYKyqnijfFFVMIlIZwBsAugOoAfPgtQrAo6r6S3mm7XwnIm8DGAbgFwC3qOrP5ZogIiI6rzF2Kh2MncoPYyciIjpbGDeVDsZN5YdxE9H5o6J270/u9u/fH1CvXr02M2fO3DFixIjj5Z2eslRQ9/4s9CciIiIiIiIiIiIiIiKivwQW+ldsCxcujEhNTfVv3779qf379wdOmDAh9rfffgvZvn37psjIyJzyTl9ZKqjQn93701kTHR2tcXFx5Z0MIiIiKsCGDRsOq2r18k4HMXYiIiKqCBg7nTsYOxEREZ37GDtRacjKypKJEyfGWF3657Rr1+7kV199lXS+F/gXhoX+dNbExcVh/fr15Z0MIiIiKoCIJJd3Gshg7ERERHTuY+x07mDsREREdO5j7ESlYdCgQamDBg3aUt7pONf4lXcCiIiIiIiIiIiIiIiIiIiIqHhY6E9ERERERERERERERERERFRBsdCfiIiIiIiIiIiIiIiIiIiogmKhPxERERERERERERERERERUQXFQn8iIiIiIiIiIiIiIiIiIqIKioX+REREREREREREREREREREFRQL/YmIiIiIiIiIiIiIiIiIqNjGjx8f06RJk5ZlvZ/Y2NjWjz/+eM2y3k9Fw0J/IiIiIiIiIiIiIiIiIqIK4Ntvvw319/fv0L59+2ZFXbdz587xw4cPr1cW6SpISkpKQEBAQPtp06ZVdZt/55131qlVq1ab7Ozss5208wYL/YmIiIiIiIiIiIiIiIiIKoDXX3+9+rBhww5u27Yt5Mcffwwp7/T4om7dumd69+79xzvvvBPtnJeVlYX58+dXvf766w/7+/uXR/LOCyz0JyIiIiIiIiIiIiIiIiIqgv9+n1y186RlrRs8tLhD50nLWv/3+2TXVuyl6cSJE/Lpp59WHT169OHLLrvs2PTp0/MVoi9fvrxSly5dmoaGhiZERES069q1a9Ndu3YFDho0KG7dunXhs2fPri4iHUSkQ1JSUtCiRYsiRKTD/v37AzzbSEpKChKRDitXrgwDgDNnzuDaa6+tHxsb2zokJKR9/fr1W02YMKFmUVrm33rrrYfXrVsXsWXLliD79A8//LDKkSNHAkeNGnV4xYoVYd27d28SFRXVNjw8PKFDhw7xy5Ytq1TQdkWkw1tvvRVln+YcAuDIkSP+N9xwQ/2qVau2rVSpUkKnTp3iPcfmmT9gwIAGVatWbRscHNy+Tp06rSdOnFjD54M7B7DQn4iIiIiIiIiIiIiIiIjIR//9Prnq04u21D+YlhGkAA6mZQQ9vWhL/bIu+H/nnXeiYmJiMi+44ILTw4cPPzpv3rxqGRkZ4pn/3XffhV5++eXxDRo0yFi+fPnWFStWbB04cODRrKwsmTFjRkq7du1ODh48+EhycvLG5OTkjY0aNcr0Zb/Z2dkSExOT9f777/+2cePGTY899tjeqVOn1n7llVfyVTrwZvDgwX9Ur14964033sizzqxZs6K7dOmSGh8fn/nHH3/4Dxky5Mjy5cuTVq1a9WvLli1PDxw4sIm9QkJR5eTkoF+/fo33798fOH/+/G0//PDDlm7duqX1798/Pjk5ORAAxo8fH7N169bQ+fPnb/vll182TZ8+fVedOnWyirvP8lDsE0REREREVJGIyGgADwCoDWAzgLGquqqA5VsD+DeAzgCOAngDwNOqqtb82gD+BaA9gCYAZqvqCJftRAJ4BsBgANUApAB4RFXnltrBERERERERERHRWfPK8m2xGWdy8jSuzjiT4/fK8m2xw7rUP1pW+3377berX3fddUcAoH///mmhoaE5c+bMqTxixIjjADB58uRa8fHxp+bMmZPsWad9+/bpnv8DAwM1NDQ0p169emeKst/g4GB9+eWX93nex8fHZ/74449hH330UdVx48Yd9mUbAQEBuO666w5/+OGH0VOmTNnn7++P3bt3B6xcuTLy9ddf3wkAV111VZrjeHfXrFmzyscffxw5evToYp3XRYsWRfz6669hBw8e/Dk8PFwBYOrUqfu++OKLKjNnzqz6zDPP/J6SkhLcqlWrU7179z7lOb7i7Ks8saU/EREREZ33ROQ6AFMBPAsgAcAaAJ+LSD0vy0cCWArgdwCdAIyBqTAw3rZYMIDDAJ4D8IOX7QQC+BKmUsC1AOIBjACws6THRERERERERERE5eNQWkZQUaaXhk2bNgX/9NNP4bfccstRAPDz88PAgQOPzpo1q7pnmc2bN4f16tUrzftWiu+f//xn9VatWjWPiopqGxYWlvDmm2/W3LdvX5GO98477zx88ODBwI8//jgSAN54443oiIiI7GHDhh0HgL179wYMGTKkflxcXKuIiIh24eHhCUePHg3cvXt3sc/runXrwtLT0/1q1KjRLiwsLMHz2r59e+iOHTtCrHQdXLx4cVR8fHyLkSNH1lm8eHF4cfdXXtjSn4iIiIj+CsYDeFtVZ1rv7xGRSwGMAvCwy/JDAYQBuElVTwPYJCLNAYwXkSlq7IKpDAARGexlvzcDqAGgl6p6agjvKo0DIiIiIiIiIiKi8lE9IjjzoEsBf/WI4DJrIT5t2rTo7OxsNG7cuI1nmtUhJbZv3x7YuHHjLM/7ovD391f7tgAgMzNT7MvMnDkz6rHHHqv7xBNPpPTq1etklSpVsl966aUaS5YsqVKUfbVo0SLzggsuSJs1a1b04MGDU997773oq6+++mhoaKgCwA033NDg8OHDAc8991xK48aNM0JCQrRfv35NMzMzvTZkFxE4jzsrKys3/Tk5OVKtWrWsr7/+Osm5blRUVDYAXHvttak9e/ZMXLBgQeRXX30Vec011zTp37//sXnz5u0qyvGVJ7b0JyIiIqLzmogEAegA0+Le7ksA3bys1hXAKqvA3+MLADEA4oqw+wEAvgXwqogcEJEtIvKk1QMAERERERERERFVQGP6NNkbHOCXY58WHOCXM6ZPk71lsb+srCx89NFH1R5++OG933333WbP6/vvv9/ctGnT09OnT48GgFatWp1atWpVhLftBAYG5mRnZ+eZVrNmzTMAkJKSkptftW7dujD7MqtXrw5v06bNyUceeeRQjx49TrVq1Spj586dwcU5lhEjRhxetmxZlXfffbdKcnJy8KhRow555m3YsCH8jjvuOHj99df/0bFjx/TKlStnHzp0qMB8tKioqDP79u3LXSYlJSXg8OHDue87dux46siRI4F+fn7aqlWrDPsrNjY2d5iD2rVrn7nrrruOzp8/f9fUqVN3LViwoNrp06fFub9zFVv6ExERFcMnP+3FC18kYd/x04ipEooHLonHgITY8k4WEbmLBuAP01W/3e8A+npZpxaAPS7Le+b52j1/QwAXAXgfwOUwFQZeAxAO4H7nwiIyEsBIAKhXz3XkASKiComxExEREZHvGDsRnfuGdal/FABeWb4t9lBaRlD1iODMMX2a7PVML20ffvhhlWPHjgWMGTPmUK1atfKU2g8cOPDo22+/Xf3555/f/9BDDx3o3bt38xtuuKH+mDFjDoaFheUsX7484sorr0xt0qRJZr169TJ//vnnSklJSUGRkZE5NWrUONOyZcuMWrVqZT722GMxL7744p7t27cHP//887Xt+2jatGnGvHnzoufOnRvZvHnzjHfffbfqunXrwiMjI/PWIPDB8OHDj/3jH/+od88998S1bt36ZKdOndI98+Li4tI/+OCDaj169Dh54sQJvwcffLBOYGBggd0XdOvWLfXNN9+s0atXrxP+/v768MMP1wkODs6tkPH3v/89NSEh4cSAAQMaP/PMM3vatGmTvmfPnsDFixdXvuSSS1IvvfTSE2PHjo3p0KHDqXbt2p3OysqSTz75JKpOnToZnh4IKgK29CciIiqiT37ai4cXJGLv8dNQAHuPn8bDCxLxyU9lUomTiEqPM0gXl2mFLe82vSB+AA4CuF1VN6jqfACPAxglIvlqCqvqDFXtqKodq1ev7pxNRFQhMXYiIiIi8h1jJ6KKY1iX+kfXPto3cedzl29Y+2jfxLIq8AeAWbNmRV9wwQVpzgJ/ABg6dOjRffv2BX366aeR3bp1O71w4cL/bdu2LaR3797Ne/To0XzevHlVg4KCFAAefvjhA4GBgdquXbuWMTExbbdv3x4UHBys77777o7du3cHd+7cueXEiRNjnnrqqTw3nfvuu+/Q5ZdffvS2225r2K1bt+bJyclBd9xxh7OBjU9CQ0N1wIABR1JTU/1vuummw/Z5b7755q6TJ0/6de/evcWwYcMaDh8+/HBsbGyBQya8+uqre+rVq5dx6aWXxl9//fWNbrnllkNVq1bNbcHv5+eHZcuWbevevXuaVdGg1bBhwxpt27YtpG7dulkAEBwcnPPUU0/Fdu7cuUXv3r2bnThxwu+TTz7ZXpzjKy9SnLEdiIqjY8eOun79+vJOBhFRiXV/7ivsPX463/TYKqH49qGLyiFFRKVHRDaoasfyTkdpsrr3PwXgBlX9yDb9NQCtVPVvLuu8C6Caql5um9YJwFoADVV1p2P5RQAOq+oIx/QVALJUta9tWk8AKwHUUNVD8IKxExGdLxg70fnsfIydKirGTkR0vmDsROezcyV22rhx4662bdseLnxJonPLxo0bo9u2bRvnNo8t/YmIiIpon8uDV0HTiah8qWomgA0ALnbMuhjAGi+rfQegp4iEOJbfB2BXEXb/LYDGImKPu5vCVELgwyUR/SUwdiIiIiLyHWMnIiIqDhb6ExERFVFMldAiTSeic8IUACNE5DYRaS4iUwHEAJgOACIyWUSW25Z/H6Zg/m0RaSUiAwE8BGCK2rrKEpF2ItIOQCSAqtb7FrbtvA6gKoCpIhIvIpcAeArANGWXW0T0F8HYiYiIiMh3jJ2IiKg4WOhPRERURA9cEo/QQP8800ID/fHAJfHllCIiKoyqfghgLIAJAH4G0ANAf1VNthapDaCRbfk/YFr2xwBYD+A1AP+CqTxg95P16gngSuv//7NtJwVAPwAdrP1OBzALwKOld3REROc2xk5EREREvmPsRERExRFQ3gkgIiKqaAYkxAIAXvgiCfuOn0ZMlVA8cEl87nQiOjep6jQA07zMG+EyLRFAr0K2KT7s93sA3XxLJRHR+YexExEREZHvGDsREVFxsNCfiIioGAYkxPJhi4iIiMhHjJ2IiIiIfMfYiYiIiord+xMREREREREREREREREREVVQLPQnIiIiIiIiIiIiIiIiIiKqoFjoT0REREREREREREREREREVEGx0J+IiIiIiIiIiIiIiIiIiKiCYqE/EREREREREREREREREVEFFhsb2/rxxx+vWd7p+KsSkQ5vvfVWVHntP6C8dkxERERERERERERERERERIUbNGhQ3NGjRwO+/vrr7W7z161b92tERETO2U6Xr8aPHx/z0ksv1QYAEUF0dHRWly5d0qZMmbKncePGWeWdvpJKTk7eWL169ezy2j9b+hMRERERERERERERERERVWAxMTFnzoVC//T0dPE2Ly4uLj05OXnjjh07fnn33Xd3bN26NXTw4MGNyjNNpaVevXpnQkNDtaz34w0L/YmIiIiIiIiIiIiIiIiIKjBn9/4i0uHFF1+MvuyyyxqGhoYm1KlTp/W0adOq2tfZuXNn4BVXXNEwMjKyXWRkZLsLL7ywcWJiYrBn/ubNm4P79OnTKDo6um1oaGhCixYtms+ZM6eyc7/jx4+Pueaaa+IiIiLaXX311Q28pTEgIAD16tU7ExcXl3XppZeeuOmmmw5v3Lix0tGjR3PLrN9///3KLVu2bB4cHNw+Nja29T333BNrL7RPSUkJuOiiixqHhIS0j4mJaT116tRqTZo0aTl+/PgY+7FPnjy5er9+/RqFhoYmjBkzJtaXbb/zzjtVmjZt2iIkJKR95cqV23Xq1Ck+JSUlAAC2b98e2KdPn0aVK1duFxoamtCgQYOWM2bMiLLv0969/9q1a0O7devW1LOtQYMGxR05csTfM3/QoEFxvXv3bvz000/XqFGjRpvIyMh2gwcPjktLSytW+T279yciIiIiIiIiIiIiIiKiv6wZHTZ0KI/9jtzQYUNZbv+FF16IeeKJJ/a88MILe19//fXoe++9N65v374nmjZtmpmWlubXu3fv+A4dOpxYunRpUlBQkD733HM1L7nkkqZJSUmbIyIiclJTU/0uueSS1MmTJ+8LCwvLmT17dtTw4cMbNWvWbEtCQkK6Zz8zZsyoOXbs2H1PPPHEflXfGrvv3r07YOHChVX8/f0REGCKrOfPnx85cuTIhpMmTdrdt2/fEzt27Ai655576mdkZMiMGTP2AMCQIUMaHDx4MHDRokVJYWFhOffdd1/dffv2Bbkd+4QJE/a+/PLLKX5+foVue/fu3QG33XZbw0ceeWTvkCFDjqWmpvqtWrUq3LO9kSNH1k9PT5clS5YkRUVFZW/atCnE27GlpaX5XXHFFU3atGlzcuXKlb8eOnTI/6677oobMmRI3BdffPGbZ7n169eH16xZM2vJkiX/27VrV9BNN93U8Nlnn02fPHnyAZ9Oog0L/YmIiIiIiIiIiIiIiIiIzjODBw8+Mnr06KMA8PLLL++dNWtWjWXLloU3bdr06H/+858oVcVHH320y8/PNC5/7733kqOjo9t9+OGHlW+77bZjXbt2Pd21a9fTnu09//zzB5YsWVJlzpw5UQkJCfs90zt37pz2zDPP/F5Yenbs2BESFhaWoKpIT0/3A4ARI0YcjIyMzAGAyZMn1x41atSBe++99wgAtGzZMuP48eN77rjjjgbTp0/fk5iYGLx69erIZcuWbe3Tp89JAJg9e/bO+Pj4Ns59XXnllUfHjx9/2PN+2LBhDQra9u7du4POnDkjQ4cOPda0adNMAOjUqVNuxYY9e/YEXXnllcc856NZs2aZ3o5zxowZVU+fPu330Ucf7YyKisoBAFVNvvLKK5tu2rQpuFWrVhkAUKlSpZzZs2cnBwYGon379ulz5849tmLFikgALPQnIiIiIiIiIiIiIiIiIvJVWbe4Ly9t2rTJLbAPDAxEVFTUmYMHDwYCwIYNGyrt3bs3ODw8PMG+Tnp6ut9vv/0WDACpqal+Dz74YMzSpUsrHzp0KPDMmTOSmZnp16JFi9P2dRISEk76kp66detmLFq0aFt6errMmzevymeffRY1derUvZ75mzdvDktMTKz02muv1fJM81QQSElJCUxMTAz18/NDr169cvfXuHHjrOrVq2c599WxY8dT9veFbbtLly6nunbtmpqQkNCyR48eqRdddFHqjTfeeCwmJuYMAIwaNergAw88UG/58uWVe/XqlXrNNdcc79mz5ynnfgHg119/DWnatOlpT4E/APTt2/eEn58fNm7cGOIp9G/cuPHpwMDA3PVq166d9eOPP1by5Vw6sdCfiIiIiIiIiIiIiIiIiOg8ExQUlKevfRFBTo4ph87JyUGzZs1OffDBBzuc61WvXt1T0F3nm2++qTxp0qSU5s2bZ1SqVCnnxhtvbJCZmSn25StVqpTj3IabwMBA9RR4d+zY8cD27dtDbr755nrz58/fBQCqKuPHj983dOjQY851Y2JisnwdOgAAwsPD86SpsG0HBARg9erV27766qtKn3/+eeTs2bOjn3nmmdgvv/wyqWvXrqfHjRt3+Kqrrvrj448/rvzVV19F9unTp9ndd999YMqUKfuc21NVERHnZADmM7CfD+c8VXVfsRAs9CciIiIiIiIiIiIiIiIi+gtp3779qYULF1atVavWmejo6Gy3ZdatWxd+7bXXHhkxYsRxADh16pTs3r07uGHDhuluyxfVxIkT97Vp06bVqlWrDvbs2fNUixYtTiUlJeW2hHdq06bN6ZycHKxatarSRRdddBIAfvvtt8BDhw4Fui1vV9i2AcDPzw99+/Y92bdv35MvvPDC/iZNmrR87733qnbt2nUvADRq1Cjr/vvvP3z//fcffvTRR2vNnDmzhluhf4sWLU7PnTu32rFjx/w8rf2XLVsWnpOTgzZt2pTKucuX9rLYKBERERERERERERERERERlZ60tDT/NWvWhNpfSUlJQcXZ1siRI49Wq1btzGWXXdZ48eLF4Vu3bg36/PPPw2+//fY6iYmJwQDQoEGDjMWLF1dZvXp12Nq1a0MHDhyYr5V/SbRo0SKzT58+f0yYMCEGAB599NF9CxcurDp27NiYdevWhfz0008hb731VtSdd95ZBwDatm2b0aNHj9S77rqr/vLlyyutWbMm9MYbb4wLCQnJEZECuwEobNvLly+v9OCDD9ZesWJF2LZt24Lef//9KgcOHAjyDGVw88031503b17kli1bgtasWRO6bNmyyMaNG7sW4I8cOfJoaGhozrXXXttg7dq1oZ9//nn43XffXb9fv37HC6p0UBJs6U9EREREREREREREREREdI7bsGFDePfu3VvYp11yySXHlixZkq+L/sJERETkrFq1auu4cePq3HjjjY1OnDjhX7169axu3bqleVr+v/rqqykjRoyIu/jii+MjIyOz77jjjt8zMjJKtVH5/ffff6Bfv37Nli5dWmnQoEGpc+fO3T5p0qTab7zxRk1/f3/ExcWlDxky5LBn+ffee2/niBEj4vr37x9ftWrVrIcffnhfSkpKcEhISIGF/oVtOyoqKvv7778P/89//lMjLS3Nv1atWpnjxo3bN3r06KOAGQ7hvvvuq3fgwIGgsLCw7O7du6e9+uqrKW77ioiIyFm0aNG2e++9t26vXr2aBwUF5Vx88cXHZ8yY4bp8aZCijH1AVBIdO3bU9evXl3cyiIiIqAAiskFVO5Z3OoixExERUUXA2OncwdiJiIjo3HeuxE4bN27c1bZt28OFL0kVwf79+wPq1avXZubMmTs8wxCcrzZu3Bjdtm3bOLd5bOlPRERERERERERERERERETnvIULF0akpqb6t2/f/tT+/fsDJ0yYEBsVFXVm4MCBqeWdtvLEQn8iIiIiIiIiIiIiIiIiIjrnZWVlycSJE2OsLv1z2rVrd/Krr75KioyMzCnvtJWnUh1zgc4uERktIjtFJF1ENohIz0KWby0iK0TktIjsFZHHRURs8/8mImtE5Ii1zFYRud9lO4NEZIuIZFh/ry6L4yMiIiIiIiIiIiIiIiIi8hg0aFDq//73vy2nT5/+6dixYxu//vrr7a1atcoo73SVNxb6V1Aich2AqQCeBZAAYA2Az0WknpflIwEsBfA7gE4AxgB4AMB422InALwCoBeAFgCeAfCUiIy2bacrgA8BvAegnfX3IxG5oBQPj4iIiIiIiIiIiIiIiIiIfMBC/4prPIC3VXWmqv6qqvcA2A9glJflhwIIA3CTqm5S1fkAngcw3tPaX1U3qOoHqrpZVXeq6n8BfAHA3oPAWABfq+oka7+TAHxjTSciIiIiIiIiIiIiIiIiorOIhf4VkIgEAegA4EvHrC8BdPOyWlcAq1T1tG3aFwBiAMR52U+Ctb0Vju049/uFt/2KyEgRWS8i6w8dOuQlaUREREREREREREREREREVBws9K+YogH4w3TVb/c7gFpe1qnlZXnPvFwiskdEMgCsBzBNVaf7sB3X/arqDFXtqKodq1ev7iVpRERERERERERERERERERUHAHlnQAqEXW8F5dphS3vNr0ngHAAXQA8LyI7VXV2CfZLRERERERERERERERERERlgIX+FdNhANnI37q+BvK3wvc44GV5ONdR1Z3Wv4kiUhPAkwA8hf7etuNtv0REREREREREREREREREVEbYvX8FpKqZADYAuNgx62IAa7ys9h2AniIS4lh+H4BdBezOD0CwYztF2S8RERERERERERERERERncfGjx8f06RJk5ZlvZ/Y2NjWjz/+eM2y3k9Fw0L/imsKgBEicpuINBeRqQBiAEwHABGZLCLLbcu/D+AUgLdFpJWIDATwEIApqqrWOveIyBUi0sR63QrgfgD/tW1nKoCLRORhEWkmIg8D6A3g5TI+XiIiIiIiIiIiIiIiIqK/tG+//TbU39+/Q/v27ZsVdd3OnTvHDx8+vF5ZpKsgKSkpAQEBAe2nTZtW1W3+nXfeWadWrVptsrOzz3bSzhss9K+gVPVDAGMBTADwM4AeAPqrarK1SG0AjWzL/wHTIj8GwHoArwH4F0zlAQ9/AM9b21sP4C6YigGP2LazBsD1AG4C8AuA4QCuU9UfSvcIiYiIiIiIiIiIiIiIiMju9ddfrz5s2LCD27ZtC/nxxx9DCl+j/NWtW/dM7969/3jnnXeinfOysrIwf/78qtdff/1hf3//8kjeeYGF/hWYqk5T1ThVDVbVDqq60jZvhKrGOZZPVNVeqhqiqrVV9SlPK39r/suq2lJVK6lqZVVtb+0jx7GdearaTFWDVLW5qi4o84MlIiIiIiIiIqKzRkRGi8hOEUkXkQ0i0rOQ5VuLyAoROS0ie0XkcRER2/y/icgaETliLbNVRO532c4gEdkiIhnW36vL4viIiIiISuq/3ydX7TxpWesGDy3u0HnSstb//T7ZtRV7aTpx4oR8+umnVUePHn34sssuOzZ9+vR8hejLly+v1KVLl6ahoaEJERER7bp27dp0165dgYMGDYpbt25d+OzZs6uLSAcR6ZCUlBS0aNGiCBHpsH///gDPNpKSkoJEpMPKlSvDAODMmTO49tpr68fGxrYOCQlpX79+/VYTJkyoWZSW+bfeeuvhdevWRWzZsiXIPv3DDz+scuTIkcBRo0YdXrFiRVj37t2bREVFtQ0PD0/o0KFD/LJlyyoVtF0R6fDWW29F2ac5hwA4cuSI/w033FC/atWqbStVqpTQqVOneM+xeeYPGDCgQdWqVdsGBwe3r1OnTuuJEyfW8PngzgEs9CciIiIiIiIiIqJcInIdzBCPzwJIALAGwOci4toVrIhEAlgK4HcAnQCMAfAAgPG2xU4AeAVALwAtADwD4CkRGW3bTlcAHwJ4D0A76+9HInJBKR4eERERUYn99/vkqk8v2lL/YFpGkAI4mJYR9PSiLfXLuuD/nXfeiYqJicm84IILTg8fPvzovHnzqmVkZORWtPzuu+9CL7/88vgGDRpkLF++fOuKFSu2Dhw48GhWVpbMmDEjpV27dicHDx58JDk5eWNycvLGRo0aZfqy3+zsbImJicl6//33f9u4ceOmxx57bO/UqVNrv/LKK/kqHXgzePDgP6pXr571xhtv5Fln1qxZ0V26dEmNj4/P/OOPP/yHDBlyZPny5UmrVq36tWXLlqcHDhzYxF4hoahycnLQr1+/xvv37w+cP3/+th9++GFLt27d0vr37x+fnJwcCADjx4+P2bp1a+j8+fO3/fLLL5umT5++q06dOlnF3Wd5KPYJIiIiIiIiIiIiovPSeABvq+pM6/09InIpgFEAHnZZfiiAMAA3qeppAJtEpDmA8SIyRY0NADbY1tkpIgMB9AQwzZo2FsDXqjrJej9JRHpb028ovcMjIiIiKplXlm+LzTiTk6dxdcaZHL9Xlm+LHdal/tGy2u/bb79d/brrrjsCAP37908LDQ3NmTNnTuURI0YcB4DJkyfXio+PPzVnzhzPcOBo3759uuf/wMBADQ0NzalXr96Zouw3ODhYX3755X2e9/Hx8Zk//vhj2EcffVR13Lhxh33ZRkBAAK677rrDH374YfSUKVP2+fv7Y/fu3QErV66MfP3113cCwFVXXZXmON7dNWvWrPLxxx9Hjh49uljnddGiRRG//vpr2MGDB38ODw9XAJg6deq+L774osrMmTOrPvPMM7+npKQEt2rV6lTv3r1PeY6vOPsqT2zpT0RERERERERERAAAEQkC0AHAl45ZXwLo5mW1rgBWWQX+Hl8AiAEQ52U/Cdb2Vji249zvF972KyIjRWS9iKw/dOiQl6QRERERlb5DaRlBRZleGjZt2hT8008/hd9yyy1HAcDPzw8DBw48OmvWrOqeZTZv3hzWq1evNO9bKb5//vOf1Vu1atU8KiqqbVhYWMKbb75Zc9++fUU63jvvvPPwwYMHAz/++ONIAHjjjTeiIyIisocNG3YcAPbu3RswZMiQ+nFxca0iIiLahYeHJxw9ejRw9+7dxT6v69atC0tPT/erUaNGu7CwsATPa/v27aE7duwIsdJ1cPHixVHx8fEtRo4cWWfx4sXhxd1feWFLfyIiIiIiIiIiIvKIBuAP01W/3e8A+npZpxaAPS7Le+bt9EwUkT0AqsPkSz6lqtMd23Hbby23narqDAAzAKBjx47qJW1EREREpa56RHDmQZcC/uoRwWXWQnzatGnR2dnZaNy4cRvPNFUTAm3fvj2wcePGWZ73ReHv76/2bQFAZmam2JeZOXNm1GOPPVb3iSeeSOnVq9fJKlWqZL/00ks1lixZUqUo+2rRokXmBRdckDZr1qzowYMHp7733nvRV1999dHQ0FAFgBtuuKHB4cOHA5577rmUxo0bZ4SEhGi/fv2aZmZmem3ILiJwHndWVlZu+nNycqRatWpZX3/9dZJz3aioqGwAuPbaa1N79uyZuGDBgsivvvoq8pprrmnSv3//Y/PmzdtVlOMrTyz0JyIiIiIiIiIiIidnjrG4TCtsebfpPQGEA+gC4HkR2amqs0uwXyIiIqKzbkyfJnufXrSlvr2L/+AAv5wxfZrsLYv9ZWVl4aOPPqr28MMP77366quP2+cNHz684fTp06NffPHF/a1atTq1atWqCG/bCQwMzMnOzs4zrWbNmmcAICUlJTAmJuYMYFrH25dZvXp1eJs2bU4+8sgjud0r7dy5M7g4xzJixIjD99xzT9y7775bJTk5OXjUqFG529ywYUP4s88+u/v666//w0pTwKFDhwIL2l5UVNSZffv25S6TkpIScPjw4dz3HTt2PDV58uRAPz8/bdGihddKGbVr1z5z1113Hb3rrruOzpw584877rij4enTp5M9FRLOdezen4iIiIiIiIiIiDwOA8hG/tb1NZC/Fb7HAS/Lw7mOqu5U1URVnQlgCoAnfdiOt/0SERERlYthXeoffeyKFsk1IoIzBUCNiODMx65okTysS/1ijTtfmA8//LDKsWPHAsaMGXOoU6dO6fbXwIEDj86ZMyc6OzsbDz300IFff/017IYbbqj/3XffhW7cuDF4ypQp0du2bQsCgHr16mX+/PPPlZKSkoL2798fkJ2djZYtW2bUqlUr87HHHov55ZdfghcsWBD5/PPP17bvv2nTphlbtmwJmzt3bmRiYmLwAw88UHvdunXF6gJ/+PDhx8LCwnLuueeeuNatW5/s1KlTumdeXFxc+gcffFBtw4YNIStWrAgbPHhww8DAwAIL3bt165b65ptv1li5cmXYt99+Gzp06NAGwcHBOZ75f//731MTEhJODBgwoPHcuXMjt27dGrRs2bJK48aNi1myZEk4AIwdOzZm9uzZVRITE4N//PHHkE8++SSqTp06GRWlwB9goT8RERERERERERFZVDUTwAYAFztmXQxgjZfVvgPQU0RCHMvvA7CrgN35AbC3EPuuiPslIiIiKjfDutQ/uvbRvok7n7t8w9pH+yaWVYE/AMyaNSv6ggsuSKtVq1a2c97QoUOP7tu3L+jTTz+N7Nat2+mFCxf+b9u2bSG9e/du3qNHj+bz5s2rGhQUpADw8MMPHwgMDNR27dq1jImJabt9+/ag4OBgfffdd3fs3r07uHPnzi0nTpwY89RTT+XpseC+++47dPnllx+97bbbGnbr1q15cnJy0B133FGsipmhoaE6YMCAI6mpqf433XTTYfu8N998c9fJkyf9unfv3mLYsGENhw8ffjg2NrbAIRNeffXVPfXq1cu49NJL46+//vpGt9xyy6GqVaue8cz38/PDsmXLtnXv3j3NqmjQatiwYY22bdsWUrdu3SwACA4OznnqqadiO3fu3KJ3797NTpw44ffJJ59sL87xlRcpztgORMXRsWNHXb9+fXkng4iIiAogIhtUtWN5p4MYOxEREVUE52vsJCLXAZgNYDSAbwHcCeBWAC1VNVlEJgPorKp9rOUrA0gC8A2AZwA0BfA2gKdU9V/WMvcA2GktBwC9ALwEYJqqPmQt0w3ASgCPAfgYwNUAJgLooao/FJRmxk5ERETnvnMldtq4ceOutm3bHi58SaJzy8aNG6Pbtm0b5zYv4CynhYiIiIiIiIiIiM5hqvqhiFQDMAFAbQCbAPRX1WRrkdoAGtmW/0NELgbwGoD1AI4B+BdM9/3/z96dx8lVlYn//zxZIAkQwpKYBIwBRHYNEFBAQJSIg+OIOCMojmT8KgIKSn4yA+OCKIsOEkUFGZjRIMrIIMg4DhhwAwQEg4CETYQQJBsJELYkZHt+f9RtUlSquqvTVd1dXZ/361Wvrrr33HPOval0n3uee87pMBj4GjARWA08CpwGXFyWz20RcTSlBwfOLNIc1VXAX5IkSWp3Tu8vSZKkthARJ0bEnIhYERF3RcSBXaTfIyJuiojlETEvIr4YEVG2f1xEXBERD0XEmoiY0UV+H4yIjIifN+iUJEmSmiYzL8rMiZm5cWbunZk3l+2bmpkTK9Lfl5kHZeawzByXmWdm2RSjmfnNzNwtMzfJzM0zc6+ijLUV+fwkM3fOzI0yc5fMvKbpJytJkiS1OIP+kiRJGvCKKWovAM4B9qS0Luz1ETGhRvqRwI3AImAf4GTgVGBaWbKNgSXAV4FOR59FxPbAecAtPToRSZIkSZIkSapg0F+SJEntYBowIzMvzcwHM/MkYAFwQo30xwAjgGMzc3ZmXk1pOtppHaP9M/PxzDw5M2cAz9QqOCKGAv8FfA54rGFnJEmSJEmSJEkY9JckSdIAFxEbAXsDN1TsugHYv8Zh+wG3ZObysm0zgfGU1qHtjrOBxzPzsjrqelxEzIqIWYsXL+5mMZIkSZIkSarDmrVr10bXyaT+o/jOrq2136C/JEmSBrqtgcGUpuovtwgYW+OYsTXSd+yrS0S8EzgKOL6e9Jl5SWZOzszJo0ePrrcYSZIkSZIk1e93c+fOHfXyyy8Pzcy+rovUpbVr18bixYs3B2bXSjOkF+sjSZIk9aXKu7iosq2r9NW2VxURWwMzgA9l5rP1HCNJkiRJkqTmWr169ceXLl16wgsvvDA1M7fEQdLq/9YCs1evXv2xWgkM+kuSJGmgWwKsYf0R+mNYfzR/h4U10tPJMZV2B8YBv4x4Zca4QQARsRrYLTMfrjMvSZIkSZIkNcDee++9ErigeEkDgk+uSJIkaUDLzJXAXcCUil1TgNtqHHY7cGBEDKtIPx94vM6i/wDsAUwqe/0MuKV4P6fOfCRJkiRJkiSpJkf6S5IkqR1MBy6PiDuBW4HjgfHAxQARcS6wb2a+o0h/BXAGMCMizgLeAJwGnJlli71FxKTi7UhgbfF5ZWY+kJkvUbHOVkQsBYZkZs31tyRJkiRJkiSpOwz6S5IkacDLzCsjYivg85Sm3J8NHJ6Zc4sk44AdytI/FxFTgAuBWcCzwPmUHh4od3fF5/cAc4GJjT4HSZIkSZIkSarGoL8kSZLaQmZeBFxUY9/UKtvuAw7qIs/oZh3WK0eSJEmSJEmSemJQX1dAkiRJkiRJkiRJkiRtGIP+kiRJkiRJkiRJkiS1KIP+kiRJkiRJkiRJkiS1KIP+kiRJkiRJkiRJkiS1KIP+kiRJkiRJkiRJkiS1KIP+kiRJkiRJkiRJkiS1KIP+kiRJkiRJkiRJkiS1KIP+kiRJkiRJkiRJkiS1KIP+kiRJkiRJkiRJkiS1KIP+kiRJkiRJkiRJkiS1KIP+kiRJkiRJkiRJkiS1KIP+kiRJkiRJkiRJkiS1KIP+kiRJkiRJkiRJkiS1KIP+kiRJkiRJkiRJkiS1KIP+kiRJkiRJkiRJkiS1KIP+kiRJkiRJkiRJkiS1KIP+kiRJkiRJkiRJkiS1KIP+kiRJkiRJkiRJkiS1KIP+kiRJkiRJkiRJkiS1KIP+kiRJkiRJkiRJkiS1KIP+kiRJkiRJkiRJkiS1KIP+kiRJkiRJkiRJkiS1KIP+kiRJkiRJkiRJkiS1KIP+kiRJkiRJkiRJkiS1KIP+kiRJkiRJkiRJkiS1KIP+kiRJkiRJkiRJkiS1KIP+kiRJkiRJkiRJkiS1KIP+kiRJkiRJkiRJkiS1KIP+kiRJkiRJkiRJkiS1KIP+kiRJkiRJkiRJkiS1KIP+LSwiToyIORGxIiLuiogDu0i/R0TcFBHLI2JeRHwxIqJs/5ERcUNELI6IFyLijoj4u4o8pkZEVnkNa9Z5SpIkSZIkSZIkSZKqM+jfoiLiKOAC4BxgT+A24PqImFAj/UjgRmARsA9wMnAqMK0s2cHAr4F3F3leB/y0ysMEy4Bx5a/MXNGYM5MkSZIkSZIkSZIk1WtIX1dAG2waMCMzLy0+nxQR7wJOAE6vkv4YYARwbGYuB2ZHxC7AtIiYniWfrjjmzIh4N3AEcEvZ9szMhY08GUmSJEmSJEmSJElS9znSvwVFxEbA3sANFbtuAPavcdh+wC1FwL/DTGA8MLGT4jYDnq3YNjwi5kbEkxHx84jYs+7KS5IkSZIkSZIkSZIaxqB/a9oaGExpqv5yi4CxNY4ZWyN9x771RMQngW2By8s2Pwx8FHgv8EFgBXBrROxYI4/jImJWRMxavHhxjapJkiRJkiRJkiRJkjaEQf/WlhWfo8q2rtJX205EvB84DzgmM+e+kkHm7Zl5WWbek5m3AEcBjwInVS0w85LMnJyZk0ePHt352UiSJEmSJEmSJEmSusWgf2taAqxh/RH6Y1h/NH+HhTXSU3lMEfC/HPhIZv6ss4pk5hpgFlB1pL8kSZIkSZIkSZIkqXkM+regzFwJ3AVMqdg1BbitxmG3AwdGxLCK9POBxzs2RMQHgB8CUzPzJ13VJSICeCOwoN76S5IkSZIkSZIkSZIaw6B/65oOTI2Ij0XELhFxATAeuBggIs6NiF+Vpb8CWAbMiIjdI+JI4DRgemZmcczRwI+K7TdHxNjitWVHJhFxRkQcFhHbR8Qk4D8pBf0vbvoZS5IkSZIkqVdExIkRMSciVkTEXRFxYBfp94iImyJieUTMi4gvFoNFOvYfGRE3RMTiiHghIu6IiL+ryGNqRGSV17D1S5QkSZLUwaB/i8rMK4HPAJ8H7gHeChyemXOLJOOAHcrSP0dpZP94StPxXwicT+nhgQ7HA0OAb1Iaud/xuqYszSjgEuBB4AZgG+CgzLyzcWcnSZLUeE3ouB4XEVdExEMRsSYiZlTJ4+MRcUtEPBMRSyPiNxHx1iacniRJUsNExFHABcA5wJ6UZpa8PiIm1Eg/EriR0hKS+wAnA6cC08qSHQz8Gnh3ked1wE+rtMmWUerXeuWVmSsac2aSJEnSwDSkryugDZeZFwEX1dg3tcq2+4CDOsnvbXWUeQpwSt2VlCRJ6gfKOq5PBH5X/Lw+InbNzCeqpO/ouL6ZUsf1TsAM4CVKD04CbAwsAb4KHFej6LcBVwK3UurAPgWYGRGTMvORRpybJElSE0wDZmTmpcXnkyLiXcAJwOlV0h8DjACOzczlwOyI2AWYFhHTs+TTFcecGRHvBo4Abinbnpm5sJEnI0mSJA10jvSXJElSO3il4zozH8zMkyjNaHRCjfTlHdezM/Nq4GuUOq4DIDMfz8yTM3MG8Ey1TDLzmMz8TmbenZkPF+W9ALyroWcnSZLUIBGxEbA3pRkey90A7F/jsP2AW4qAf4eZlGacnNhJcZsBz1ZsGx4RcyPiyYj4eUTsWXflJUmSpDZl0F+SJEkDWi93XHdlI2AY63dud9T1uIiYFRGzFi9e3INiJEmSNtjWwGBKU/WXWwSMrXHM2BrpO/atJyI+CWwLXF62+WHgo8B7gQ8CK4BbI2LHGnnYdpIkSZIw6C9JkqSBr1c6rut0FvAi8LNqOzPzksycnJmTR48e3YNiJEmSeiwrPkeVbV2lr7adiHg/cB5wTGbOfSWDzNsz87LMvCczbwGOAh4FTqpaoG0nSZIkCYAhfV0BSZIkqZc0reO6HhHxaeATwKGZ+fyG5CFJktQLlgBrWP9BxzGs/1Bkh4U10lN5TBHwvxz4SGZWfRCyQ2auiYhZQNWR/pIkSZJKHOkvSZKkga6pHdf1KAL+ZwGHZ+ad3T1ekiSpt2TmSuAuYErFrinAbTUOux04MCKGVaSfDzzesSEiPgD8EJiamT/pqi4REcAbgQX11l+SJElqRwb9JUmSNKA1s+O6HhExDTgbeHdm/q47x0qSJPWR6cDUiPhYROwSERcA44GLASLi3Ij4VVn6K4BlwIyI2D0ijgROA6ZnZhbHHA38qNh+c0SMLV5bdmQSEWdExGERsX1ETAL+k1LQ/+Kmn7EkSZLUwpzeX5IkSe1gOnB5RNwJ3AocT0XHNbBvZr6jSH8FcAaljuuzgDdQ6qA+s6PjujhuUvF2JLC2+LwyMx8o9p9KKeD/YeDPEdExe8DyzHyuSecqSZLUI5l5ZURsBXweGAfMpjRj0dwiyThgh7L0z0XEFOBCYBbwLHA+pTZYh+Mp9UV+s3h1uAl4W/F+FHAJpRmXngPuBg5ypiRJkiSpcwb9JUmSNOA1qeMaSh3R5d4DzAUmFp8/CQwFrqxIdxkwdcPPSJIkqbky8yLgohr7plbZdh9wUCf5va2OMk8BTqm7kpIkSZIAg/6SJElqE43uuC7SRBf7J9ZfQ0mSJEmSJEnqvkF9XQFJkiRJkiRJkiRJkrRhDPpLkiRJkiRJkiRJktSiDPpLkiRJkiRJkiRJktSiDPpLkiRJkiRJkiRJktSiDPpLkiRJkiRJkiRJktSiDPpLkiRJkiRJkiRJktSiDPpLkiRJkiRJkiRJktSiDPpLkiRJkiRJkiRJktSiDPpLkiRJkiRJkiRJktSiDPpLkiRJkiRJkiRJktSiDPpLkiRJkiRJkiRJktSiDPpLkiRJkiRJkiRJktSiDPpLkiRJkiRJkiRJktSiDPpLkiRJkiRJkiRJktSiugz6R8TfRoQPB0iSJEmSJEmSJEmS1M/UE8z/H2BeRHwtInZpdoUkSZIkSZIkSZIkSVJ96gn67wBcAnwAmB0Rt0fExyNiZHOrJkmSJEmSJEmSJEmSOtNl0D8zH8/MMzJzO2AK8BfgG8CCiLg8Ig5pdiUlSZIkSZIkSZIkSdL66hnp/4rM/HVm/iPwBuAu4BjglxExJyJOiYghzaikJEmSJEmSJEmSJElaX7eC/hFxcETMAB4GdgcuBN4JXAWcCfyg0RWUJEmSJEmSJEmSJEnVdTkyPyJeBxxbvCYCvwWOA67JzJeLZL+KiNuBHzanmpIkSZIkSZIkSZIkqVI90/E/BswHZgDfy8w5NdLdD9zZoHpJkiRJkiRJkiRJkqQu1BP0fw/wi8xc21mizPwzcEhDaiVJkiRJkiRJkiRJkro0qI40fw+8rtqOiHhdRHyvsVWSJEmSJEmSJEmSJEn1qCfofywwusa+rYv9kiRJkiRJkiRJkiSpl9UT9A8ga+zbHVjcuOpIkiRJkiRJkiRJkqR6Dam2MSI+DXy6+JjAtRHxckWyYcBrgBlNq50kSZIkSZIkSZIkSaqpatAfeAC4mtIo/2nAb4AFFWlWAg8B/9202kmSJEmSJEmSJEmSpJqqBv0z80bgRoCIeAH4j8yc15sVkyRJkiRJkiRJkiRJnas10v8VmXlmb1REkiRJkiRJkiRJkiR1T9Wgf0T8N3B6Zj5avO9MZuZRja+aJEmSJEmSJEmSJEnqTK2R/qOBocX7MUD2TnUkSZIkSZIkSZIkSVK9qgb9M/OQsvdv67XaSJIkSZIkSZIkSZKkug3q6wpIkiRJkiRJkiRJkqQNU3Wkf0Sc2J1MMvOixlRHkiRJkiRJkiRJkiTVq2rQH/hON/JIwKC/JEmSJEmSJEmSJEm9rGrQPzOd9l+SJEmSJEmSJEmSpH7O4L4kSZIkSZIkSZIkSS2qatA/InaNiI3L3nf66t0qS5IkSd0XESdGxJyIWBERd0XEgV2k3yMiboqI5RExLyK+GBFRtn9cRFwREQ9FxJqImFEjn/dHxAMR8XLx830NPjVJkiRJkiRJbazq9P7AbOAtwJ3F+6yRLop9gxtfNUmSJKkxIuIo4ALgROB3xc/rI2LXzHyiSvqRwI3AzcA+wE7ADOAl4Pwi2cbAEuCrwHE1yt0PuBI4A7gGOBK4KiIOyMw7GnV+kiRJkiRJktpXraD/IcADZe8lSZKkVjYNmJGZlxafT4qIdwEnAKdXSX8MMAI4NjOXA7MjYhdgWkRMz5LHgZMBIuLva5T7GeA3mXl28fnsiDik2P7Bnp+WJEmSJEmSpHZXNeifmTdVey9JkiS1mojYCNgb+HrFrhuA/Wscth9wSxHw7zAT+AowEZhTZ/H7Ad+u2DYT+FSdx0uSJEmSJElSpwbVmzAidoqID0fEqcXPnZtZMXWtCevSHhkRN0TE4oh4ISLuiIi/q5KP69JKkqRWsjWl5agWVWxfBIytcczYGuk79tWrVj5V84iI4yJiVkTMWrx4cTeKkSRJkiRJktSuugz6R8TIiLgSuB/4AfCF4ufsiPjvYr1T9bKydWnPAfYEbqO0Lu2EGuk71qVdRGld2pOBUylNddvhYODXwLuLPK8Dflr+MEHZurQ/AiYVP6+KiDc38PQkSZKaISs+R5VtXaWvtr1h5WbmJZk5OTMnjx49upvFSJIkSZIkSWpH9Yz0vwh4J/ARYERmjqRY3xSYUuxX73tlXdrMfDAzTwIWUFqXtprydWlnZ+bVwNcorUsbAJn56cz8ambemZl/ycwzgbuAI8ry+QzFurRFuWcDvy22S5Ik9UdLgDWsP7p+DOuPwu+wsEZ6OjmmO/l0Jw9JkiRJkiRJqqmeoP97gVMz84rMXAGQmSsy80fAPxf71YvK1qW9oWLXhqxLO57SurS1bAY8W5FPZbkza5XrFLWSJKmvZeZKSg8yTqnYNYXSbEnV3A4cGBHDKtLPBx7vRvG3d7NcSZIkSZIkSeqWeoL+L1IaQV7NfOClxlVHdeqVdWkj4pPAtsDldeRTNQ+nqJUkSf3EdGBqRHwsInaJiAsoPfx4MUBEnBsRvypLfwWwDJgREbtHxJHAacD0zHxlav6ImBQRk4CRwJbF513L8rkAeHtEnB4RO0fE6cAhwDebd6qSJEmSJEmS2smQOtJcCHw2In5dPko8IkYAn8Xp/ftS09aljYj3A+cBR2fm3B6WK0mS1Kcy88qI2Ar4PDAOmA0cXtbOGQfsUJb+uYiYQqktPIvSzEfnU3p4oNzdFZ/fA8ylmEkpM2+LiKOBs4AzgUeBozLzjsadnSRJkiRJkqR2VjXoHxH/VrFpR+CvEXEj8BSldUinAMspdYKqdzV1Xdoi4H858JHM/Fmd+bgurSRJ6tcy8yJqPLCamVOrbLsPOKiLPKOz/UWanwA/qa+WkiRJ/UNEnAicSunhyPuBz2TmLZ2k3wP4DrAv8Azw78BXOmZJKmZOOh7YExgGPACcXdn3VPRLfYXSA5mPAp/LzJ829uwkSZKkgaXW9P7/UPFaBbwAvAX4u+LnC8Bq4P3Nr6bKNXNd2oj4APBDYGrRQV0tH9ellSRJkiRJGqAi4ihKyxSdQylIfxtwfURMqJF+JHAjpUEh+wAnU3pgYFpZsoOBXwPvLvK8DvhpRBxYls9+wJXAj4BJxc+rIuLNDTw9SZIkacCpOtI/M7fr7Yqo26YDl0fEncCtlJ6UftW6tMC+mfmOIv0VwBmU1qU9C3gDpXVpzyx74vpoSiP8PwvcHBEdI/pXZuYzxfsLin2nAz8F3kdpXdq3NvNkJUmSJEmS1GumATMy89Li80kR8S7gBOD0KumPAUYAxxbLg86OiF2AaRExPUs+XXHMmRHxbuAIoGMGgc8Av8nMs4vPZ0fEIcX2Dzbm1CRJkqSBp9ZIf/VzmXklpRuezwP3UAq6d7ouLaUR+eMpLclwIeuvS3s8pQdBvgksKHtdU5bPbcDRwLHAn4CP4Lq0kiRJkiRJA0JEbATsDdxQsesGYP8ah+0H3FIE/DvMpNQPNbGT4jYDnq3Ip7LcmZ2UK0mSJIkaI/0rRUQAB1AaHT6scn+xPqp6WaPXpc3Mt9VZruvSSpIkSZIkDUxbA4MpTdVfbhFwaI1jxgJPVknfsW9O5QER8UlgW0qzTpbnU63csVQREccBxwFMmFB15QFJkiSpLXQZ9I+I1wC/AnYFEohiV5YlM+gvSZIkSZIkDRxZ8TmqbOsqfbXtRMT7gfOAo8tmrex2uZl5CXAJwOTJkzurmyRJkjSg1TO9//nAc8BrKTWy30xpWq4vAI9QGv0vSZIkSZIkqfUtAdaw/uj6Maw/Cr/DwhrpqTymCPhfDnwkM39WZz61ypUkSZJEfUH/gykF/hcUnyMzn8jMc4Af4ih/SZIkSZIkaUDIzJXAXcCUil1TgNtqHHY7cGBEDKtIPx94vGNDRHyAUn/i1GL5yGr5dKdcSZIkSdQX9B8FLM7MtcDzrHtKF0oN7v2bUC9JkiRJkiRJfWM6MDUiPhYRu0TEBcB44GKAiDg3In5Vlv4KYBkwIyJ2j4gjgdOA6ZmZxTFHAz8qtt8cEWOL15Zl+VwAvD0iTo+InSPidOAQ4JvNPV1JkiSptdUT9J8DjCve3w8cU7bvPcAzja6UJEmSJEmSpL6RmVcCnwE+D9wDvBU4PDPnFknGATuUpX+O0oj88cAs4EJKM4dOL8v2eGAIpQD+grLXNWX53AYcDRwL/An4CHBUZt7R2DOUJEmSBpYhdaT5P+CdwH8DZwH/ExFPAquACcC/NK96kiRJkiRJknpbZl5EjWU9M3NqlW33AQd1kt/b6iz3J0C1qf8lSZIk1dBl0D8zTy97f31EHAC8DxgG3JiZ1zexfpIkSZIkSZIkSZIkqYZ6Rvq/Smb+AfhDE+oiSZIkSZIkSZIkSZK6oe6gf0S8E9iX0ppdC4A7MvPGZlVMkiRJkiRJkiRJkiR1rsugf0SMB34K7AM8VbzGAF+OiFnA+zJzXlNrKUmSJEmSJEmSJEmS1jOojjSXUBrd/9bMHJuZb8zMscCBwFjg35tZQUmSJEmSJEmSJEmSVF09Qf+3A/+cmbeVb8zMW4HTgEOaUTFJkiRJkiRJkiRJktS5eoL+i4DlNfYtB5Y0rjqSJEmSJEmSJEmSJKle9QT9zwG+HBHblm8sPp8BnN2MikmSJEmSJEmSJEmSpM4NqbYxIv67YtNWwKMR8UfgKWAMsFfx/lDgkmZWUpIkSZIkSZIkSZIkra9q0B8YXfH5keIFMBJYAdxWfN66CfWSJEmSJEmSJEmSJEldqBr0z8xDersikiRJkiRJkiRJkiSpewZ194CIGNqMikiSJEmSJEmSJEmSpO6pK+gfEftHxPUR8QKwIiJeiIjrImK/JtdPkiRJkiRJkiRJkiTVUHV6/3IRMQX4P+Bh4DxgEfAa4O+B30bEuzPzl02tpSRJkiRJkiRJkiRJWk+XQX/gbOBnwD9kZpZt/3JEXA2cAxj0lyRJkiRJkiRJkiSpl9Uzvf8ewKUVAf8OlxT7JUmSJEmSJEmSJElSL6sn6L8U2KHGvtcX+yVJkiRJkiRJkiRJUi+rJ+h/FXBuRHw4IoYBRMSwiPgwpan//7uZFZQkSZIkSZIkSZIkSdUNqSPNvwBbAZcBl0XEi8Cmxb7/KvZLkiRJkiRJkiRJkqRe1mXQPzOXA8dExFeAfYBxwALgD5n5UJPrJ0mSJEmSJEmSJEmSaug06F9M5/8ccFRmXgsY5JckSZIkSZIkSZIkqZ8Y1NnOzFwBPAWs7p3qSJIkSZIkSZIkSZKkenUa9C/8O3ByRAxtdmUkSZIkSZIkSZIkSVL9Op3evzAK2B14PCJ+BSwCsmx/Zua/NKFukiRJkiRJkiRJkiSpE/UE/d8PvFy8P7DK/gQM+kuSJEmSJEmSJEmS1Mu6DPpn5na9URFJkiRJkiRJkiRJktQ9g/q6ApIkSZIkSZIkSZIkacPUM70/ETEG+AywLzAOWADcAXwrMxc1rXaSJEmSJEmSJEmSJKmmLkf6R8QBwCPAJ4AlwK+Kn8cDjxT7JUmSJEmSJEmSJElSL6tnev/vAHcBEzLz6Mw8OTOPBl4H/BH4djMrKEmSJDVCRJwYEXMiYkVE3BURB3aRfo+IuCkilkfEvIj4YkRERZqDi7xWRMRjEXF8lXw+HREPFfk8GREXRsSmjT4/SZIkSZIkSe2pnqD/zsD5mflS+cbMfBH4OrBLMyomSZIkNUpEHAVcAJwD7AncBlwfERNqpB8J3AgsAvYBTgZOBaaVpdkOuK7Ia0/gXODbEfH+sjQfAv4NOJtSu/kjwOFFXSRJkiRJkiSpx4bUkeYBYGyNfeOAhxpXHUmSJKkppgEzMvPS4vNJEfEu4ATg9CrpjwFGAMdm5nJgdkTsAkyLiOmZmZSWu5qfmScVxzwYEW8GPgtcXWzbH/h9Zl5efH48In4AvPJggCRJkiRJkiT1RD0j/U8C/jUijoqIjQEiYuOIOBo4DfhUMysoSZIk9UREbATsDdxQsesGSkH5avYDbikC/h1mAuOBiWVpKvOcCUyOiKHF598BkyLiLUVdJgB/R2mGAEmSJEmSJEnqsXpG+v8PpVFOVwBExItAxxqkK4Cfli9tmpljGlxHSZIkqSe2BgZTmqq/3CLg0BrHjAWerJK+Y9+c4ucvq6QZUpS5IDN/HBFbATdHqdE8BLgc+JdqhUbEccBxABMmVF15QJIkSZIkSZJepZ6g/4VANrsikiRJUpNVtmmjyrau0ldu7zRNRBwMfAE4EbgDeD1wAXAm8MX1Csy8BLgEYPLkybbBJUmSJEmSJHWpy6B/Zn6pF+ohSZIkNcsSYA2lkfnlxrD+6P8OC2ukp+yYWmlWA08Xn88C/isz/6P4fF9EbAL8R0R8OTNX130WkiRJkiRJklTFoL6ugCRJktRMmbkSuAuYUrFrCnBbjcNuBw6MiGEV6ecDj5elqVweYAowKzNXFZ9HUHrgoNwa1s0IIEmSJEmSJEk9YtBfkiRJ7WA6MDUiPhYRu0TEBcB44GKAiDg3In5Vlv4KYBkwIyJ2j4gjgdOA6ZnZMe3+xcC2EfHNIs+PAVOBr5fl87/AcRFxdERsFxFTgK8AP3eUvyRJkiRJkqRG6HJ6f0mSJKnVZeaVEbEV8HlgHDAbODwz5xZJxgE7lKV/rgjQXwjMAp4Fzqf08EBHmjkRcTjwDeAESrMAnJyZV5cVfRaQlAL921JaauB/gc814zwlSZIkSZIktR+D/pIkSWoLmXkRcFGNfVOrbLsPOKiLPG8C9upk/2rgzOIlSZIkSZIkSQ3n9P6SJEmSJEmSJEmSJLUog/6SJEmSJEmSJEmSJLWoLoP+EbE2ItbUeK2OiGci4jcR8b7eqLAkSZIkSZIkSZIkSSoZUkeaacXreeB/gcXAGOA9wGbAfwIHAj+JiGMz84dNqqskSZIkSZIkSZIkSSpTz/T+44FbM3P3zDw9M6dn5mmZuRtwG7BFZh4K/Aj452ZWVq8WESdGxJyIWBERd0XEgV2k3yMiboqI5RExLyK+GBFRtn9cRFwREQ8VMznMqJLH1IjIKq9hTThFSZIkSZIkSZIkSVIn6gn6/xPw/Rr7vg/8Y/H+SmDHRlRKXYuIo4ALgHOAPSk9gHF9REyokX4kcCOwCNgHOBk4ldIsDh02BpYAXwXu6KT4ZcC48ldmrujJ+UiSJEmSJEmSJEmSuq+eoP8QYOca+3Ypy2MlYOC390wDZmTmpZn5YGaeBCwATqiR/hhgBHBsZs7OzKuBrwHTOkb7Z+bjmXlyZs4Anumk7MzMheWvhp2VJEmSJEmSJEmSJKlu9QT9fwycGxHTImLHiBhV/PwspVHmVxTp9gIealZFtU5EbATsDdxQsesGYP8ah+0H3JKZy8u2zaS0fMPEblZheETMjYgnI+LnEbFnN4+XJEmSJEmSJEmSJDXAkDrSfJrSKP6zgPPKtr8MXEppingoTQf/q4bWTrVsDQymNFV/uUXAoTWOGQs8WSV9x745dZb9MPBR4F5gM0rfj1sj4k2Z+Uhl4og4DjgOYMKEqisPSJIkSZIkSZIkSZI2UJcj/TNzZWZ+GtgWeDvwoeLntpn56cxcWaT7bWbOamptVSkrPkeVbV2lr7a9dgaZt2fmZZl5T2beAhwFPAqcVCP9JZk5OTMnjx49ut5iJEmSJEmS1Ici4sSImBMRKyLirog4sIv0e0TETRGxPCLmRcQXO5aULPaPi4grIuKhiFgTETOq5DE1IrLKa1gTTlGSJEkaMOoZ6Q9AZj4D3NTEuqh+S4A1lEbolxvD+qP/OyyskZ5OjulSZq6JiFnAjhuahyRJkiRJkvqPiDgKuAA4Efhd8fP6iNg1M5+okn4kcCNwM7APsBMwA3gJOL9ItjGlPq2vUswKWcMyYIfyDZm5ogenI0mSJA14dQX9i6dpD6I02r/yydrMzO82umKqLTNXRsRdwBTgqrJdU4Craxx2O/C1iBhWdqM0BZgPPL6hdSme2H4jpen+JUmSJEmS1PqmATMy89Li80kR8S7gBOD0KumPAUYAx2bmcmB2ROwCTIuI6VnyOHAyQET8fSdlZ2YubNSJSJIkSe2gy6B/RLwVuIbSOvLVJGDQv/dNBy6PiDuBW4HjgfHAxQARcS6wb2a+o0h/BXAGMCMizgLeAJwGnJmZr0zvHxGTircjgbXF55WZ+UCx/wzg98AjRZqTKQX9T2jamUqSJEmSJKlXRMRGwN7A1yt23QDsX+Ow/YBbioB/h5nAV4CJwJxuVGF4RMwFBgP3AF/IzLu7cbwkSZLUduoZ6f8tSmu2TwEeyMxVza2S6pGZV0bEVsDngXHAbODwzJxbJBlH2VRomflcREwBLgRmAc9Sml5tekXWlTdR7wHmUrpBAxgFXEJpqYDnivQHZeadDTkxSZIkSZIk9aWtKQXcK5eDXAQcWuOYscCTVdJ37Ks36P8w8FFKM0puBnwauDUi3pSZj1QmjojjKJYKmDBhQp1FSJIkSQNPPUH/nYAjM9Pp2/uZzLwIuKjGvqlVtt1HaZmGzvKMLvafApxSfy0lSZIkSZLUgrLic1TZ1lX6attrZ5B5O6UlKksZRNxGabT/SRRLA1Skv4TS4BQmT55cdzmSJEnSQDOojjR/ovREriRJkiRJkqSBbQmwhvX7A8ew/uj/DgtrpKeTY7qUmWsozVi544bmIUmSJLWDeoL+JwCnRMTBza6MJEmSJEmSpL6TmSuBuygt9VluCnBbjcNuBw6MiGEV6ecDj29oXSIigDcCCzY0D0mSJKkd1DO9/43ACODXEbEKeL4yQWaOWe8oSZIkSZIkSa1oOnB5RNwJ3AocD4wHLgaIiHOBfTPzHUX6K4AzgBkRcRbwBuA04MzMfGXa/YiYVLwdCawtPq/MzAeK/WcAvwceKdKcTCnof0LTzlSSJEkaAOoJ+l9IN9bekiRJkiRJktS6MvPKiNgK+DwwDpgNHJ6Zc4sk44AdytI/FxFTKPUjzgKeBc6n9PBAubsrPr8HmAtMLD6PAi6htFTAc0X6gzLzzoacmCRJkjRAdRn0z8wv9UI9JEmSJEmSJPUTmXkRcFGNfVOrbLsPOKiLPKOL/acAp9RfS0mSJEkAg/q6ApIkSZIkSZIkSZIkacNUHekfEf8NnJ6ZjxbvO5WZH2h4zSRJkiRJkiRJkiRJUqdqTe8/GhhavB8DZO9UR5IkSZIkSZIkSZIk1atq0D8zDyl7/7Zeq40kSZIkSZIkSZIkSarboL6ugCRJkiRJkiRJkiRJ2jBVR/pHxBe7k0lmfrkx1ZEkSZIkSZIkSZIkSfWqGvQHTqr4PBwYUbx/Edi0eL+seBn0lyRJkiRJkiRJkiSpl1Wd3j8zR3e8gL8DngI+DIzIzJGUHgD4x2L7e3urspIkSZIkSZIkSZIkaZ1aI/3LfQs4JzOv6NiQmSuAH0XEJsCFwF5Nqp8kSZIkSZIkSZIkSaqh6kj/CrsD82vsmwfs0rjqSJIkSZIkSZIkSZKketUT9P8zMC0iNi7fGBHDgGnAw82omCRJkiRJkiRJkiRJ6lw90/ufBFwHPBkRNwJPAWOAKcAI4G+aVz1JkiRJkiRJkiRJklRLlyP9M/NmYEfg+8A44LDi5/eBHYv9kiRJkiRJkiRJkiSpl9Uz0p/MXAD8c5PrIkmSJEmSJEmSJEmSuqHLkf6SJEmSJEmSJEmSJKl/6nKkf0QMBT4NHAlsCwyrTJOZYxpfNUmSJEmSJEmSJEmS1Jl6pvf/BvAJ4OfAb4CVTa2RJEmSJEmSJEmSJEmqSz1B/38ATsvM85tdGUmSJEmSJEmSJEmSVL9BdaQJ4E/NrogkSZIkSZIkSZIkSeqeeoL+lwIfbHZFJEmSpGaKiBMjYk5ErIiIuyLiwC7S7xERN0XE8oiYFxFfjIioSHNwkdeKiHgsIo6vks/IiPhWRMyPiJcj4i8R8YFGn58kSZIkSZKk9lTP9P6LgGMi4jfAjcDSiv2Zmd9tdMUkSZKkRomIo4ALgBOB3xU/r4+IXTPziSrpR1Jq+94M7APsBMwAXgLOL9JsB1wHfA/4MPBW4KKIWJyZVxdphgI3AM8CHwCeBLYFXm7WuUqSJEmSJElqL/UE/b9Z/JwAHFxlfwIG/SVJktSfTQNmZOalxeeTIuJdwAnA6VXSHwOMAI7NzOXA7IjYBZgWEdMzM4HjgfmZeVJxzIMR8Wbgs8DVxbZ/AsYAB2XmymLb4w0+N0mSJEmSJEltrMvp/TNzUBevwb1RUUmSJGlDRMRGwN6URtyXuwHYv8Zh+wG3FAH/DjOB8cDEsjSVec4EJhcj/AGOAG4Fvh0RCyPigYj4Utl+SZIkSZIkSeqRLoP+kiRJUovbGhhMadmqcouAsTWOGVsjfce+ztIMKcoE2B74B2Ao8G7gC5RmCDi3WqERcVxEzIqIWYsXL651PpIkSZIkSZL0iqrT+0fErsCjmfly8b5TmflAw2smSZIkNVZWfI4q27pKX7m9qzSDgKeAj2fmGuCuiNgK+EZEnFosE7Aus8xLgEsAJk+e3FndJEmSJEmSJAmoEfQHZgNvAe4s3tfqcOzoKHWKf0mSJPVXS4A1rD+qfwzrj9TvsLBGesqOqZVmNfB08XkBsKoI+Hd4EBhBaTYAh/NLkiRJkiRJ6pFaQf9DgAfK3kuSJEktKTNXRsRdwBTgqrJdU4Craxx2O/C1iBiWmSvK0s8HHi9Lc0TFcVOAWZm5qvh8K/ChiBiUmWuLbW8AllF6GEGSJEmSJEmSeqRW0P9Y4AngRUoj+f+YmS/2Wq0kSZKkxpoOXB4Rd1IKxB8PjAcuBoiIc4F9M/MdRforgDOAGRFxFqVA/WnAmWVT8l8MfCoivgn8O3AAMBX4YFm53wU+BVwQEd8BJgJnAhdVTu0vSZIkSZIkSRtiUI3txwKji/e/AXbtnepIkiRJjZeZVwKfAT4P3AO8FTg8M+cWScYBO5Slf47SqP3xwCzgQuB8Sg8PdKSZAxwOHFTk+Tng5My8uizNX4F3AnsXaS4GvleklSRJkiRJkqQeqzXSfwHwtoh4AAhgWESMqJVJZi5rRuUkSZKkRsnMi4CLauybWmXbfZQC+p3leROwVxdpfg/sX3dFJUmSJEmSJKkbao30vwT4KvAcpen9fwO80MlLkiRJkiRJkiRJkiT1sqoj/TPzyxHxf8AuwA+As4BHe7NikiRJkiRJkiRJkiSpc7Wm9ycz7wLuioh3AN8v1iyVJEmSJEmSJEmSJEn9RM2gf4fM/KfeqIgkSZIkSZIkSZIkSeqeQX1dAUmSJEmSJEmSJEmStGEM+kuSJEmSJEmSJEmS1KIM+kuSJEmSJEmSJEmS1KIM+kuSJEmSJEmSJEmS1KKG1JswIkYBuwPjgAXA7Mxc2pxqSZIkSZIkSZIkSZKkrnQZ9I+IIcDZwCeBEWW7lkXERcDnMnNVk+onSZIkSZIkSZIkSZJqqGek/3TgOODLwDXAU8AY4P3AF4BhwMnNqqAkSZIkSZIkSZIkSaqunqD/PwL/mpnTy7Y9A5wdESuAz2PQX5IkSZIkSZIkSZKkXjeojjRrgftr7JsNZOOqI0mSJEmSJEmSJEmS6lVP0P9y4GM19n0c+GHjqiNJkiRJkiRJkiRJkupVdXr/iDix7OPjwN9HxP3Az4CngDHAe4HNgK83uY6SJEmSJEmSJEmSJKmKqkF/4DtVto0HdqmyfTpwQcNqJEmSJEmSJEmSJEmS6lJ1ev/MHNSN1+DerrRKIuLEiJgTESsi4q6IOLCL9HtExE0RsTwi5kXEFyMiyvaPi4grIuKhiFgTETNq5PP+iHggIl4ufr6vwacmSZIkSZIkSZIkSapD1aC/+r+IOIrSDAvnAHsCtwHXR8SEGulHAjcCi4B9gJOBU4FpZck2BpYAXwXuqJHPfsCVwI+AScXPqyLizT0+KUmSJEmSJEmSJElSt9Sa3v9VImIU8AngrcCWwDPALcAlmbm0WZVTp6YBMzLz0uLzSRHxLuAE4PQq6Y8BRgDHZuZyYHZE7AJMi4jpWfI4pYcBiIi/r1HuZ4DfZObZxeezI+KQYvsHe35akiRJkiRJkiRJkqR6dTnSPyJ2AO4DvgxsAjxR/Pwy8Kdiv3pRRGwE7A3cULHrBmD/GoftB9xSBPw7zATGAxO7Ufx+Vcqd2Um5kiRJkiRJajEuKylJkiS1jnqm9/8GsBTYPjPfnpkfzMy3AzsU26c3r3qqYWtgMKWp+sstAsbWOGZsjfQd++pVK5+qeUTEcRExKyJmLV68uBvFSJIkSZIkqS+4rKQkSZLUWuoJ+r8N+GJmzivfWHw+EzikCfVSfbLic1TZ1lX6atsbVm5mXpKZkzNz8ujRo7tZjCRJkiRJkvrAK8tKZuaDmXkSsIDSspLVlC8rOTszrwa+RmlZyQDIzMcz8+TMnEFp6dBqPkOxrGRR7tnAb4vtkiRJkmqoJ+iflEaV1zq+uwFj9dwSYA3rj64fw/qj8DssrJGeTo7pTj7dyUOSJEmSJEn9kMtKSpIkSa2nnqD/b4CvRMTryjcWn78M/KoZFVNtmbkSuAuYUrFrCqXp1qq5HTgwIoZVpJ8PPN6N4m/vZrmSJEmSJElqHS4rKUmSJLWYeoL+n6G05tYjEfH7iPifiLgdeATYiFevzaXeMx2YGhEfi4hdIuICSk9PXwwQEedGRPkDGVcAy4AZEbF7RBwJnAZMz8xXZmuIiEkRMQkYCWxZfN61LJ8LgLdHxOkRsXNEnE5piYdvNu9UJUmSJEmS1MtcVlKSJElqEUO6SpCZj0fEzsBHgX2AccADwPcpre21srlVVDWZeWVEbAV8ntK/yWzg8MycWyQZB+xQlv65iJgCXAjMAp4Fzqf08EC5uys+vweYSzEVW2beFhFHA2cBZwKPAkdl5h2NOztJkiRJkiT1EZeVlCRJklpMl0F/eGU6+YuLl/qJzLwIuKjGvqlVtt0HHNRFntHZ/iLNT4Cf1FdLSZIkSZIktYrMXBkRHctKXlW2awpwdY3Dbge+FhHDMnNFWfoNXVbyvIpyXVZSkiRJ6kTVoH9ELKa+qbcSWAk8AfwU+EZmrmlc9SRJkiRJkiT1sunA5RFxJ3ArcDwVy0oC+2bmO4r0VwBnUFpW8izgDZSWlTyzclnJ4u1IYG3xeWVmPlBsvwC4uVhO8qfA+ygtK/nWJp2nJEmSNCDUGul/IfWvtzWc0jTyXwG2BP61AfWSJEmSJEmS1AdcVlKSJElqLVWD/pn5pe5mFBGfAj6LQX9JkiRJkiSppbmspCRJktQ6BjUwr18DjzUwP0mSJEmSJEmSJEmS1Ila0/t3W7H21tsblZ8kSZIkSZIkSZIkSepcI0f6S5IkSZIkSZIkSZKkXmTQX5IkSZIkSZIkSZKkFmXQX5IkSZIkSZIkSZKkFmXQX5IkSZIkSZIkSZKkFmXQX5IkSZIkSZIkSZKkFmXQX5IkSZIkSZIkSZKkFmXQX5IkSZIkSZIkSZKkFmXQX5IkSW0hIk6MiDkRsSIi7oqIA7tIv0dE3BQRyyNiXkR8MSKiIs3BRV4rIuKxiDi+k/w+GBEZET9v1DlJkiRJkiRJkkF/SZIkDXgRcRRwAXAOsCdwG3B9REyokX4kcCOwCNgHOBk4FZhWlmY74Loirz2Bc4FvR8T7q+S3PXAecEvjzkqSJEmSJEmSDPpLkiSpPUwDZmTmpZn5YGaeBCwATqiR/hhgBHBsZs7OzKuBrwHTykb7Hw/Mz8yTijwvBS4DPlueUUQMBf4L+BzwWMPPTJIkSZIkSVJbM+gvSZKkAS0iNgL2Bm6o2HUDsH+Nw/YDbsnM5WXbZgLjgYllaSrznAlMLgL9Hc4GHs/My+qo63ERMSsiZi1evLir5JIkSZIkSZJk0F+SJEkD3tbAYEpT9ZdbBIytcczYGuk79nWWZkhRJhHxTuAoSrMCdCkzL8nMyZk5efTo0fUcIkmSJEmSJKnNDenrCkiSJEm9JCs+R5VtXaWv3F4zTURsDcwAPpSZz3ajnpIkSZIkSZJUN4P+kiRJGuiWAGtYf1T/GNYfqd9hYY30lB1TK81q4GngAGAc8MuIjmcBSjNtRcRqYLfMfLjus5AkSZIkSZKkKpzeX5IkSQNaZq4E7gKmVOyaAtxW47DbgQMjYlhF+vnA42VpDq2S56zMXAX8AdgDmFT2+hlwS/F+TjdPRZIkSZIkSZLWY9BfkiRJ7WA6MDUiPhYRu0TEBcB44GKAiDg3In5Vlv4KYBkwIyJ2j4gjgdOA6ZnZMaX/xcC2EfHNIs+PAVOBrwNk5kuZObv8BSwFXig+r2z+aUuSJEmSJEka6JzeX5IkSQNeZl4ZEVsBn6c05f5s4PDMnFskGQfsUJb+uYiYAlwIzAKeBc6n9PBAR5o5EXE48A3gBEqzAJycmVf3wilJkiRJkiRJEmDQX5IkSW0iMy8CLqqxb2qVbfcBB3WR503AXt2ow3rlSJIkSZIkSVJPOL2/JEmSJEmSJEmSJEktyqC/JEmSJEmSJEmSJEktyqC/JEmSJEmSJEmSJEktyqC/JEmSJEmSJEmSJEktyqC/JEmSJEmSJEmSJEktyqC/JEmSJEmSJEmSJEktyqC/JEmSJEmSJEmSJEktyqC/JEmSJEmSJEmSJEktyqC/JEmSJEmSJEmSJEktyqC/JEmSJEmSJEmSJEktyqC/JEmSJEmSJEmSJEktyqC/JEmSJEmSJEmSJEktyqC/JEmSJEmSJEmSJEktyqC/JEmSJEmSJEmSJEktyqC/JEmSJEmSJEmSJEktyqC/JEmSJEmSJEmSJEktyqC/JEmSJEmSJEmSJEktyqC/JEmSJEmSJEmSJEktyqC/JEmSJEmSJEmSJEktyqC/JEmSJEmSJEmSJEktyqC/JEmSJEmSJEmSJEktyqC/JEmSJEmSJEmSJEktyqC/JEmSJEmSJEmSJEktyqC/JEmSJEmSJEmSJEktyqC/JEmSJEmSJEmSJEktyqC/JEmSJEmSJEmSJEktyqC/JEmSJEmSJEmSJEktyqC/JEmSJEmSJEmSJEktyqB/C4uIEyNiTkSsiIi7IuLALtLvERE3RcTyiJgXEV+MiKhIc3CR14qIeCwijq/YPzUissprWDPOUZIkSZIkSZIkSZJUm0H/FhURRwEXAOcAewK3AddHxIQa6UcCNwKLgH2Ak4FTgWllabYDrivy2hM4F/h2RLy/IrtlwLjyV2auaNjJSZIkSZIkqU852ESSJElqHQb9W9c0YEZmXpqZD2bmScAC4IQa6Y8BRgDHZubszLwa+BowrewG7HhgfmaeVOR5KXAZ8NmKvDIzF5a/Gn52kiRJkiRJ6hMONpEkqf3k2uTpPy9jzq+fZfXLa/u6OpK6aUhfV0DdFxEbAXsDX6/YdQOwf43D9gNuyczlZdtmAl8BJgJzijQ3VBw3Ezg2IoZm5qpi2/CImAsMBu4BvpCZd9eo63HAcQATJlS9L5QkSZIkSVL/8spgk+LzSRHxLkqDTU6vkr58sMlyYHZE7EJpsMn0zEzKBpsUxzwYEW+mNNjk6rK80gEmkiQ1x6rla1jwxxeZd8fzPPn753n20erP1e36D6N562nGdKRWYtC/NW1NKeC+qGL7IuDQGseMBZ6skr5j35zi5y+rpBlSlLkAeBj4KHAvsBnwaeDWiHhTZj5SWWhmXgJcAjB58uTs6sQkSZIkSZLUd1ppsIkkSVrfU/e9xO8veJKFd7+4wXm87qDNG1gjSb3BoH9rqwyiR5VtXaWv3N5pmsy8Hbj9lZ0Rt1G6ATuJ0tRtkiRJkiRJal0tM9jEGSYlSe1o7arkoWuX8PsLnmT18p5Nwz90k0Fs+5aRbPPmkWz75pGM3HbjBtVSUm8z6N+algBrKN0slRvD+jdkHRbWSE/ZMbXSrAaerpZpZq6JiFnAjl1XW5IkNcLKF9YwdJNBxKDoOrEkSVKbemnxShbMepFnHl3Orn8/mk3HbtTXVWo1/X6wiTNMSpIGqpcWr+Suixfw0LVLGpbnwWe8ju3evgUbbTq4YXlK6j8M+regzFwZEXcBU4CrynZN4dVroJW7HfhaRAzLzBVl6ecDj5elOaLiuCnArLIp1l4lIgJ4I6UnsCVJUg+tXZ0888hyFt77IgvvfZFF97zIS0+t/2d403EbcfT/7M6gwQb+JUlSe1qxdDUL/vgC82eVXrXWpAWY/4cXOOKynXuxdi3NwSaSJPWCeXc+zx0XzGPJQ8sakt/QEYN486e3Zef3bs2gofYXSe3GoH/rmg5cHhF3ArcCxwPjgYsBIuJcYN/MfEeR/grgDGBGRJwFvAE4DTgzMzuehL4Y+FREfBP4d+AAYCrwwY5CI+IM4PfAI8BISk9ZvxE4oVknKknSQLLkwWXcc9lCHrvx2R7ls9Emg4lBDaqUJElSP7Rs8Sru/v4C7r9ycY/zeuOHX9OAGrUHB5tIktQYq19ey/1XPsUdF8xrWJ7j9t6UN5+0LWP22KRheUoaGAz6t6jMvDIitgI+D4wDZgOHZ+bcIsk4YIey9M9FxBTgQmAW8CxwPqWHBzrSzImIw4FvUArizwdOzszyG7pRlKZNGws8B9wNHJSZdzbjPCVJaiUvv7CaB65azD0zFrLqpZ6tqVZu+FZDGDtpU17zpk0Z+6ZN2XqnET6xLUmSWt7LL6zmvh89xR8vXdCQ/IZuMojxkzcrvfbZjC13GO5ySBvOwSaSJNXh+Xkv84cL5/HozJ4N7ii3+4fGsNdHxzFsC0N4kurnb4wWlpkXARfV2De1yrb7gIO6yPMmYK9O9p8CnNKtikqSNABkJn+99XnuvWwhC/74YlPKmHzieHY9crQ3dZIkaUBY/fJaHrx6MbMunt/QByKHbjKIycePZ5cjRzNkmFMfNYODTSRJKslMnrjlOX7/zSd5bu7LDclz+FZDeMtntuX179rSBxQlNYw9ypIkScALC17mvh8+xewfP9WU/EfvNoJJU8fyuoNHMWiwN3R9ISJOBE6l1El9P/CZzLylk/R7AN8B9gWeoTQi7Stlo9WIiIMpdWbvRqnj+t8y8+Ky/R8HPlLsH0Sp4/oLmfm7xp6dJEm9b+2a5C+/eIa7Lp7PC/NXNjTvvT4+jj0+NIaNR9p11VccbCJJaherlq3hTz9cxF3/3pjZhwBee8BI9j1pG7bacUTD8pSkznjnJEmSBrw1q9byyHXPcO9lCxv2VHa5QUODSceOZbejRjN8y6ENz189FxFHARcAJwK/K35eHxG7ZuYTVdKPBG4Ebgb2AXYCZgAvURq1RkRsB1wHfA/4MPBW4KKIWFw2Yu1twJWUpsVdRqkTe2ZETMrMR5pyspIkNUhm8tffPc8fvjuPpx9e3tC8dztqNJOmjmWTMRs1NF9JkqRqnp2znDu/PY+5Nz3XsDz3/H9jedM/jmWjzQY3LE9J2lAG/SVx7d3zOG/mw8xfupzxo4Zz6mE7ccSe2/R1tSSpbkseXMY9ly3ksRsbt35auQkHbc6kY8cydtKmTclfvWIaMCMzLy0+nxQR76I0tezpVdIfA4wAjs3M5cDsiNgFmBYR04vR/scD8zPzpOKYByPizcBngasBMvOY8kwj4gTgCOBdlNaqlSSpTy2890VmfXc+8//wQkPzff27tmSvj41j1HbDGpqvJElSNbk2efTGZ7njgid5adGqhuS52TYb8ZbPbMvEQ0YR4ayNkvo3g/5Sm7v27nmcfs19LF+1BoB5S5dz+jX3ARj4l9QvvPzCah64ajH3XraIlS+uaXj+m7xmKJOmjmWn92zNkOGuCTsQRcRGwN7A1yt23QDsX+Ow/YBbioB/h5nAV4CJwJwizQ0Vx80Ejo2IoZlZrZdhI2AYpXVuq9X1OOA4gAkTJtSomiRJ9Xv2seXMung+c361tKH5vnb/kex93HjG7LFJQ/OVJEmq5eXnV3P39xfypx8salie271jFPt8chtGvc4HFSW1NoP+Ups7b+bDrwT8OyxftYbzZj5s0F9S02Umf731ee69bCEL/vhiU8rY+X1b88YPv4ZRE715a2NbA4OByl6BRcChNY4ZCzxZJX3HvjnFz19WSTOkKLPaYoBnAS8CP6tWaGZeAlwCMHny5KxRN0mSXvHiwpX88T8X8NA1Sxqa7+jdRrDPCduwzVs2c2SbJEnqNUseXMYd33qSeXc2bhaifT45nj0++BoHe0ga0Az6S21u/tLq6zLW2i5J3fHigpX86YeLmP3jp5qS/+jdRvCmY8cy8W2jGDTYzmh1qTKIHlW2dZW+cns9aUo7Ij4NfAI4NDOf77yqkiSVrFi6mj9dvoh7ZixsaL6bT9iYvY8fz/aHbmE7SpIk9Zq1a5I//+/T3HHBk7z8fGNmdNzy9cN586e3Ydv9RvqwoqS2ZdBfanPjRw1nXpUA//hRwzc4z2vvnsd5Mx9m/tLljB81nFMP28lZA6QBaM2qtfzl+me4Z8ZCnpv7csPzHzQkeNOxr2G3o8YwYquhDc9fbWUJsIbSyPxyY1h/9H+HhTXSU3ZMrTSrgafLNxYB/7OAv8nMO+uuuSRpwFu1fA0PXLWYWRfPZ83LjZvkZePNBzP5+PHsfMTWDN7IUW2SJKn3LHt6FX+8dAEPXLW4YXm+4W+3YvLx49l03EYNy1OSBhKD/lKbO/WwnTj9mvteNcX/8KGDOfWwnTYov2vvnveq/OYtXc7p19wHYOBfajFLHlrGPZct5LEbqi493mMTDtqcSceOZeykTZuSv9QhM1dGxF3AFOCqsl1TgKtrHHY78LWIGJaZK8rSzwceL0tzRMVxU4BZmbmqY0NETAO+DByemb/rwalIklrQ2lXJn3/+NLMuns+yJau6PqBeAZOPH8/uR49ho00HNy5fSWozDl6RNsyCu1/gjgvm8dR9LzUkv0FDg7d8elt2eb8PLErShjDoL7W5jpuYRt3cnDfz4Vc9QACwfNUazpv5sDdMUj/y8gureeCqxdx72SJWvtiYqdTKbfKaoUyaOpad3rO166Wpv5gOXB4RdwK3AscD44GLASLiXGDfzHxHkf4K4AxgRkScBbwBOA04MzM7hmFeDHwqIr4J/DtwADAV+GBHoRFxKnA28GHgzxHRMTPA8sx8rjmnKknqTZnJ479ZyqzvzufZx1Z0fUA37HHMGN507FhnPZKkJnHwilTb6hVrufkrc/nLL55pWJ5j9tiEN5+8DeP22qxheUqSSgz6S+KIPbdp2I3M/CpLBXS2XVLjZSb3X7mY2877a9PK2PmIrXnjP76GUROHNa0MqZEy88qI2Ar4PDAOmE1p5P3cIsk4YIey9M9FxBTgQmAW8CxwPqWHBzrSzImIw4FvACdQmgXg5Mwsnz3gk8BQ4MqKKl1G6QEBSVILmD/rBWZ9dz4L73mxofm+4W+3Yq+PjWPkazduaL6SpPo4eEXt7pm/LOdn/+/hhg4I2fUfRrPXx8YxYmsfWpSk3mTQX1JDjR81nHlVAvzjRw3vg9pIA9Mzf1nOjf/8KM/Nfbkp+Y/edQRvOnYsEw8ZxaDB0ZQypL6QmRcBF9XYN7XKtvuAg7rI8yZgr072T+xWJSVJfeKvtz3HLec8wYsLVjY03wkHbc7kT4xn651HNDRfSVJjOHhF7eCBqxbzu68+0dA8d/2H0ez3/23L4KHO7ihJ/YVBf0kNdephO71qWjSA4UMHc+phO/VhraTWsfrltdz57XnM/q+nmpL/oCHBm459DbsdNcZpYiVJUttY/OBL/N8Jj7DyhcYuazR2z02Z/InxjN/HKWolqRU5eEUDwcoX1/Crf32Mv976fEPzfef0HZh48KiG5ilJah6D/pIaqmPqs/NmPsz8pcsZP2o4px62k1OiSYW/3vYcv/jMX8jG9je/yiFfmcjr/2ZLIhylL0mS2sPz815m5il/4dlHVzQ03y12GMbk48cz8W2jiEG2rSRpoHHwilrFX297jutP+ktD89xqp+G864LXs8nojRqarySpbxj0l9RwR+y5jUF+taUXF6zkJ0c/0NB10Cq9/vAtOeCfX8vGm/knXJIktY8Vz67mV597jHl3vNDwvHf9h9G85ZRtGbKx09NKUrtx8Ir6i8zkt2c8ziP/90xD85300bFMPn68yzdKUhswYiBJUh1ybXL7+U8y+8fNmXYfYPiWQ5jybzswds9Nm1aGJElSf7Rq2RqunfpQw0fqA7zu4M1525cmsvFIu0AkSetz8Ip6y0tPreRHf3Nfw/P924vf4FJDkiSD/pIkASy890V+9tGHm1rG3p8Yx57/NI5BQ326WpIktZe1q5MbTn2UJ25+ruF5j951BFP+bQc2HefUtJIkqW/95RfP8OvPzWl4vh/+xRsZMXpow/OVJA0cBv0lSQPequVr+MWn/8KCu15sajnv//EubLXjiKaWIUmS1B9lJr//xpPc96PGz4o0Yuuh/M13Xm87S5Ik9bm1q5NffOYvPHn78w3Nd+f3bc2Bn5tAhANFJEkbxqC/JKnlPfyzJdx05tymlrHPJ8cz6Z/GevMlSZLa1gNXLeZ3X32iKXm//ZzteP1hWzYlb0mSpO547okVXPm++xue7+EX7si2bxnZ8HwlSQKD/pJawLV3z+O8mQ8zf+lyxo8azqmH7eRaa22kWTda5TZ/3cb83X/uxPAtnCZNktQ12yYaqObevJSZpzzalLz3/dQ2TPqnsU3JW5Ikqbvu/M487vn+wobmOXyrIXzgJ7ux8UjDLpKk3udfH0n92rV3z+P0a+5j+ao1AMxbupzTr7kPwM71ASDXJtd98hHm3flCU8t559d3YOIho5pahiSpPdg2UStb/OBL/PTDDzUl752P3JoDT59ADHJWJEmS1PdWLV/D9996T8PznTR1LPueZLtfrcUH16X2YNBfUr923syHX+lU77B81RrOm/mwDZMW8Of/e5rffvHxppax/ZQtOOQrExk8dFBTy5EkCWybqH9bOmcF//33zZkhafw+m/E333o9gzeyzSVJkvqHx3+zlBs+2/hZio6YsTNj9tik4flKfcEH16X2YdBfUr82f+nybm1X73n5+dVcdsi9TS/nvd/fide8cdOmlyNJUj1sm6gvLXt6FT9855+akvem4zbiyB/twrDN7SaQJEn9xzXHPMiSh5Y1PN+pN09io00GNzxfqb/xwXWpfXg3L6lfGz9qOPOqdKKPHzW8D2rTXm4+ay4P/XRJU8vY/p1b8I5ztiPCaWAlSa3BtomaqVnT0HY4+me7M3KbjZuWvyRJUnctf2YVl09p/EONO713Kw7+4sSG5yu1Gh9cl9qHQX9J/dqph+30qumHAIYPHcyph+3Uh7Vqfc1cz7Xc0dfuzsjX2rEsSRo4bJuoJ3Jt8p/73c3a1dmU/N/93R3ZZt+RTclbkqQN5VrSevCaxdxy9hMNz9e2j9Q1H1yX2odBf0n9WsdNoDeH9cu1yfcOuJs1K5vTmdxhz/83ln1O9N9BktRebJuoKz//xJ+ZP+uFpuS9/z+/lt2PGtOUvCVJagbXkm4Pmcmlk//YlLw/etueDNl4UFPyltqBD65L7cOgv9TP+TR06Saw3c65Mw9du4SbvzK36eV89Hd7MmS4N1WSJFWybdLefv+NJ/nTDxc1Je+JbxvFO8/foSl5S5LUF5qxlrR9ZX1j6dwV/PeR9zc8353ftzUHff51Dc9XUokPrkvtw6C/1I/5NHT7WfHcan7w9nubXs6U87Znu7dv0fRyJEmSWs0DVy/md+c0fvpZgBGjh3LM9XsQEU3JX5Kk/qbRa0nbV9Zcd3zrSe69rPEPNx75o13YeucRDc9XUn18cF1qDwb9pX6sGU9Dq2/9+gtz+Mt1zzS1jFETh/EPP9nVzmRJktqUo986N+/O5/m/Ex5pWv5OQStJ0jqNXkvavrKeWbsm+Y99mzMN/8fu3ItBg+2LkiSprxj0l/qxRj8NreZa8tAyrjnmwaaX84Grd2PUxGFNL0eSJLUeR7/Bc39dwZVHNH7q2Q4fvuGNjNhqaNPylyRpIGn0WtL2lXXtqftf4tqPPNTwfN907Gt488nbNjxfSZLUGAb9pX6s0U9Da8Pl2uR7b72bNS9nU8vZ/YNj2P+zr21qGZIkaeBqh9FvLz+/mssOad5ySEdesQtb7+T0s5IkNUKj15K2r6zkV//6GI/OfLbh+R517W5s/loHmkiS1IoM+kv9WKOfhlZtD/3PEm7+8tyml/NPv5vE0OGDm16OJElqTwNh9Nva1cl/vLk5084CvPPrOzDxkFFNy1+SJL1aI9eSbpe+stUr1vK9A+5ueL4xqDQNv0tCSpI08Bj0l/qxRj8N3a6aPRqsw6H/tj3bv2OLppcjSZJUS6uMfvvR4X/ipUWrmpL3Pp8cz54fHdeUvCVJUt8aSH1lT9z6HL84+S8Nz3f/U1/L7kePaXi+kiSpfzPoL/VzjXwaeqD69Rfm8JfrnmlqGSNfuzFH/XQ3n4SWJEn9Wn8Z/dasKWcBtnvHKKb82w5NyVuSJPV/rdRXdu3Uh3jqvpcanu+Hb3gjI7Ya2vB8JUlS6zLoL6lfe+q+l7h26kNNL+cDP9mNUdu5ZpkkSWptvTX67Z4ZC7nz2/MammeHTV4zlGOue2NT8pYkSWqkZs0uOXLbjTn6f3ZveL6SJGngMugvqc/k2uTSfZq3XmuH3Y4azQH/PKHp5UiSJG2Ia++e19AgfSNGv829eSkzT3m0R3l05mO/34tBQ51BSdKGafTvTUnqzJ//92l++6XHG57voV/bnu0PdZlISZLUGAb9JTXN7P96itu+/teml/NPt0xi6IjBTS9HkprFjmupfV1797xXTcc/b+lyTr/mPoCm/h54+pFlXH30g03L/yO/fhPDNvd2U1Lj9dXvTUkD24y33cPKF9Z0nbCbpt40iY02tc9KjWc/giSpkr0wUovq64bdiqWr+cE7Gj99WaWDvvg6dn7v1k0vR5L6ih3XUns7b+bDr/z/77B81RrOm/lwj34HLH9mFZdP+VNPq1fTB67ejVETXRpJUu9r1u9NSQPb8/Ne5sd/N7vh+W6730gO/86ODc9X6oz9CJKkagz6Sy2oNxp2P/vYwyy8+8WG5FVTwMf/sBcRTu0qqX11t+O6rx/6ktRY85cu79b2DqtfXsv39r+7GVUC4PALd2Tbt4xsWv6StKE29PempIHvjm89yb2XLWp4vn/7729g/OTNGp6vtKF8AE6SVI1Bf6kF9bRh9/Sfl3H1B5s3nWuH9//XLmz1hhFNL0eSWll3Oq59ml/qWqs9GDN+1HDmVfn/Pn7z4Vyy911NK/eAf3ktu31gTNPyl6Rmqfl7c9TwPqiN1Ppaqe2UmVw6+Y9Nyfv/3b4ngzca1JS8pUbzAThJUjUG/aUW1FnDLtcmM952D6teWtvUOmx/6BYc+rXtm1qG1OpaqfNEfac7Hdc+zS91rtUejLnqA/dz7KObA5s3PO+Jh4zinV/foeH5SlJfO/WwnV71ux5g+NDBnHrYTn1YK6k19ce205KHlnHNMY0fqDLx7aN453m2jTQw+ACcJKkag/5SH+hJIPDPP3+a028cW3P/pTc25onnqTdNYqNNBzckL6kd9cfOE/VP3em49ml+qXP97cGYW86Zy4NXL2lK3huPHMyxv5nUlLylvuDDkqpXx/fC74vUc33VdvrlaY/x2I3PNjzfI36wM2N226Th+Ur9jQ/ASZKqMegv9bLOAoGH7ziWGQff0/Q6vOPc7djhnVt2WsdDvnOTHShqqoHesdvfAk/qv7rTce3T/FLnevvBmAevWcwtZz/RlLwBPnbnXgwaHE3LX/3TQG8jVePDkuquI/bcxu+G1ADNajutXZX8x1uaMw3/x/+wFzHI9pE6N9DbUz4Ap2Ya6P9/pIHMoL/Uix657mme+sJCTmH0evueum4hM1jY4zI2G78RR/9sdyI27AbIDjc1Q2Vj8ZCdR3P1XfMG9PfMEdnqjno7rn2aX+pcox+Meeq+l7h26kM9rVZNzqykSu3aFvdhSTWTHddSbT1pOy24+wX+92N/bniddv/QGPb//17b8HzVPtqlPeUDcGqGdvn/Iw1UBv2lBnnpqZVc/aEHWfHs6qaW8w9X7coW2zdvRKcdbmq0ao3FH/3+CbIi3UD7njkiW83g0/xS52o9GHPIzqM54Ku/Xu//zYsLV3LFu+9rWn0+cPVujJo4rGn5a+DprC3esX8g/v73YUk1ix3Xalf1PuzS1UPFv/jMX3jilucaXr+jr92dka/duOH5SmDfptQT/v+RWptBf6kOmcm8O17g3h8sZN4dLzStnAP+5bXs9oExTcu/Hna4qZqejI6p1lisDPh3GEjfM0dkq1l8ml/tqN6/Q5UPxkzYdDjH/HRzuG4lx7I5sDkAT121kEsaMMPSu7+7I9vsO7LH+UgdarWFOgKVAzVw6cOSahY7rtWOuvOwy9/uPI6nrlu/TfTUdT1rK2202WCm/nbSBh8v9YR9m83nLDoDl/9/pNZm0F9t79q75/Gtn/2ZifcO5i1zN2laOa87eHPece72/PyBBVUDge/fexsue2g280/r28aSHW6q1NPRMd1pFA6k71lfj8j2BkzSQNHZ36H3vmk8P/qb+1i2ZNWrjikP8PfEIV+ZyI6Hb1VXHf2dq0ao1RYfHDGgA5f94WFJ/x8PTHZcqx1VPuyyzdKhHPuHrXocyK900Bdex85HbL1Bx/o7V41U+X3afPhQli5ftV66gdTn1JecRWdgMzYgtTaD/hrwcm3y7KMrWHjviyy8p/R6ccHKV6U5mp6P0Dryh7uw9S4jukxXLRDYn9Y37w8dbupfejo6plZjMXj1iP+B+D3rqxHZ3oBJGkiu+9YcTvnT6PW2P3XdQi7tYcf1m459DW8+edse5eHvXFWzocGMWm3xyrZYh74KXDY6WNMfHpb0//HAZMe12sXd31vAHy6cDzTu4UeA6W9bxIqhyfChgzn3yD0a8jvR37mqZkPbFtW+T0MHB0MHBavWrut16us+p4H0oIuz6Axsxgak1mbQXy1vxbOrXwnmL7znRZ6a/VJD8992v5FMmjqWcXtvSkQ0JM/KQOABX/11wxtLG9qYrNXh1lHPgdA4Vff0dHRMrcbi+/feht88tLjL71Rv3Rh5AyZJfeOxG5/ll6c9VnP/ZDY8MDN+n804d+KTVQM+24wazq0n773BeXfwd64q9SSYUastft7Mh+sOXDa7TdOsYE1fLl/j/+OBy45r9URf3SNWK/fwN4zlJ0c9sN4glp6a+LZRvPP8HV617YCv/nq9vzmN/J3o71xV6knbotr3adWaZIsRQxmx0ZB+0ccz0B50cRadga2vH8aV1DMG/dWyrr17HlfMeJRDf9f9KfmHbTGEsW/alP+c9wR/HbWKhSNXsXbQuv0BzPnquxtX2S40urHU08ZkZYfbQGucqnt6OjqmJ43F3vruDbTvuDdgkqppVMd1d/N5ccFKfvWvj7HoT419MBPgka1XcNWkpaXGG0Ug/7S3vyrN6rs3qSvgs6HXp7M12Lc77f/sJGlDPQ1m1Ap+1/s9bnabZiAGa2w7DVx2XGtDNfL3ab1tjBcXrOSasx9hxe0rXjVa/6mrFjKjB7MbXbLfEpZsunq97aW+r/UfgKznd2JP2pW2nVSpJ22LWt+npctWcfcX39mwOvbEQGs7OYvOwNeXD+NK6hmD/mpJHTdfr1k2GFg/6D9mj00Y+6ZNGTtpU17zpk0YvuXQqvmc+dW5zF/a92s8Nbqx1OjG5EBrnKp7GjE6ZkMbi7313Rto33FvwKTqIuJE4FRgHHA/8JnMvKWT9HsA3wH2BZ4B/h34SmZmWZqDgenAbsB84N8y8+KKfN4PfAXYAXgU+Fxm/rSBp9alRnVcV8vnzB/P5sWfvMCyXzQ+qP+6gzdn2QeH8a8/m11zevNy1Tr96gn49OT61PqdC6VlbFr9QTKVdCe40YwAcr2By95o0wzEALltp4HNjmttiEb9Pn1VGyNh2CNreOD6J3nqxZ4tUVRu3N6bcviFOzJ46LrRLOV/twZFvHptvUKt33Fd/U7sabvStpMq9aRt0dO/4b0xo8dAazs5i44k9V8G/VtYO3dcd9x8Pb7VGs6Zsu5Gqdrors5sSCPl2rvn8aWf3c/S5aWHBbYYMZQz3rNbj0bKzVu6vKHrm3e3MdlVA3egNU4Hit6aarAvR8f01ndvoH3HvQGT1hcRRwEXACcCvyt+Xh8Ru2bmE1XSjwRuBG4G9gF2AmYALwHnF2m2A64Dvgd8GHgrcFFELM7Mq4s0+wFXAmcA1wBHAldFxAGZeUfTTrhCTzqu16xay+L7lzF/1gvce/WTnPLU6PXSLGPDAv5b7DCM935vZzbadHCn6WJIvKr9VUutzr2uAj7dvT7lf4NHjRi63pqhlfrLg2S9OU1xf1w2p5FrxXYWjGhWALmewGVvtGkGYoDctpNUXTv3O23o79O1q5JFs1/kyduf58nfP8/i+5dxCuu3nbrr57s+x9O7UXefV/l9fHf7nLr6nbgh7UrbTv2rrN6oU3eO7Unboid/w3tr1seB1nZyFh1J6r8M+reodu+4blRnVncbKdfePY9Tr7r3VTcmzy5bxak/ufdV+dWjsmGZ8MpN2DY9bCx1pzFZTwO3vzZOe/uGqD/dgPX0xqS759JXo2N667vXX7/jG8obMKmqacCMzLy0+HxSRLwLOAE4vUr6Y4ARwLGZuRyYHRG7ANMiYnrReX08MD8zTyqOeTAi3gx8Fri62PYZ4DeZeXbx+eyIOKTY/sGGnmEnOms7rV2TPPPn5cyf9cIrr9Ur1lZNP647tw8Bd++6gl+Pfo6Xh67fqVt6WHP9KWWrOWLPbThv5sOdBv1764HJyr/Bzy5bxdDBwajhQ3lu+apqA+k6LaO39OZSNv1x2ZxGrxXbWTCiLwPIvdGmGYgBcttO0vravd+ps9+nK5auZt6dpaD+vDte4MWFK3tc3qR/Gsubjn0Nb7/w5pqj4GNp/fn1pM+pq9+JGzLQxLZT/ymrN+rU3WN70rboyd/w3pr1caC2nWwnSVL/Y9C/dbV1x3UjO7O600g5b+bDVZ9EXrUmu90grNaw7Lj56s5sBdV0pzFZTwO3PzZOe/uGqL/dgPXkxqS/nUtneuu71x+/4z3lDZi0TkRsBOwNfL1i1w3A/jUO2w+4pWg3dZhJadTZRGBOkeaGiuNmAsdGxNDMXFWk+XaVNJ/q5mn0SEfbaZOXB/Ghu7Zg9Evrlj76jxv/uMH53jHhJX4/8SVGvWbjqu2X7U77v4Z15HaWvjcfmKz2N3jVmmSTjYdwzxnv5ICv/rpfPkjWm0vZ9Mdlc5qxVmyt7X0ZQO6NNs1ADZDbdpLW09b9Th2/T4cvhY/dvjVDMl7Z94Or7q07n7WRPLbVyywdvoYntljJQ2Nehqjd93PqYTtxypX3VG0/dact0dM+p85+J3a3T862U/8qqzfq1N1je9q22NC/4b016+NAbTtJkvofg/4tyI7rvgvQddboa1THdSMalt1pTNZTj/7YOO3tG6L+dgPWk+9PfzuXzvTWd68/fsclNdTWwGBgUcX2RcChNY4ZCzxZJX3HvjnFz19WSTOkKHNBkaZauWOrFRoRxwHHAUyYMKFG1bqvo+100AObvCrgX8uWrx/O+MmbMX7yZozda1OGbV66bah8cAw6b4N1tmZrdztya+XV2w9MdvU3uL8+SNabS9n0x2Vzenut2L4KIPdm28l2kjRw2e+07vfp7H9+8lUB/2o2HbsR27x5M7Z9y0i22Wckw7ZY19167d3zuKAb7YIj9tyGWXOf4Ue/f6JHS0A2829xd9s6tp36V1n16kmdNuTYvmhb9Oasj7adJEm9waB/a2r7juu+CtD1Rsd1oxqW9TYm661Hf2uc9vYNUX+7AevJ96e/nUtXeuu719++45KaonLQVOXypvWkr9y+oWmqlpuZlwCXAEyePLmzunVLx++3S9Y8woRnVzN0UDBmr03Z/7CxjN97M0aM7vpBgPJ86m2DnXrYTustjQQwdHB0uyO3mR3C3Tmvrv4G99cHyXqzU7M/LpvTV2vF9gXbNJIaoO37naD0+3Tfr47kl//8GJttsxHbvmUk2+43kjG7b8LgoYPqzgO61y4464g9mPy6LXvUlmjm3+LunpNtp/5VVr16Uqf+eD7VtFobT5Kkrhj0b21t23ENfdOZ1Sod161Yj+7q7RuI/nbD0pN/t/52LpLUC5YAa1i/s3gM63cqd1hYIz1lx9RKsxp4uos0tcptmka1nbqTT0e6L/3sfpYuXwXAFiOGcsZ7dut2XZrdIVzvedXzN7g/Bl17s83XH9uXfbVWrCS1uLbudwIYv/dmfORXb+pRHhvSLuhpW6LZf4u7Uz/bTv2rrN6oU388n2ps40mSBhqD/q3Jjus+0kod161Wj+7q7RuI/nbD0pN/t/52LpLUbJm5MiLuAqYAV5XtmsK69WMr3Q58LSKGZeaKsvTzgcfL0hxRcdwUYFYxPW1HminAeRVpbuv+mbSmRnbi9ocO4VZtO/VmvfvjNeqrtWIlqUXZ79Ti+tPf4v5Ul+6w7bThdeqP51OLbTxJ0kASmQ1/CFa9ICLuAO7NzOPKtv0ZuDozT6+S/gTga8CYjo7riPhX4JPAtpmZEfE14IjM3KnsuEuAPTJzv+LzlcAWmfnOsjQ3AE9n5gc7q/PkyZNz1qxZG37SUoVr757XqzcQvV1eMw2kc5HUWBFxV2ZO7ut6NFpEHAVcDpwI3AocD/w/YLfMnBsR5wL7ZuY7ivSbAw8DvwXOAt4AzADOzMzzizTbAbOBS4F/Bw4ALgI+mJlXF2n2B24GvgD8FHgf8GXgrZl5R2d1tu0kSVL/N4DbTvY7SZKkhhuobSepP3Ckf+uaDlweEXeyruN6PHAxQGXHNXAFcAYwIyI6Oq5Po9Rx3fHkx8XApyLim6zruJ4KlN9UXQDcHBGns67j+hDgrc05Tam23n4adyA9/TuQzkWS6pGZV0bEVsDngXGUgvWHZ+bcIsk4YIey9M9FxBTgQmAW8CxwPqU2WEeaORFxOPAN4ARKswCc3BHwL9LcFhFHU3pw4EzgUeCorgL+kiRJfcx+J0mSJKmFGPRvUXZcS5IkdU9mXkRpJH61fVOrbLsPOKiLPG8C9uoizU+An9RdUUmSpD5mv5MkSZLUWpzeX73GadYkSer/nGat/7DtJElS/2fbqf+w7SRJUv9n20lqnkF9XQFJkiRJkiRJkiRJkrRhDPpLkiRJkiRJkiRJktSiDPpLkiRJkiRJkiRJktSiDPpLkiRJkiRJkiRJktSiDPpLkiRJkiRJkiRJktSiDPpLkiRJkiRJkiRJktSiDPpLkiRJkiRJkiRJktSiDPpLkiRJkiRJkiRJktSiDPpLkiRJkiRJkiRJktSiDPpLkiRJkiRJkiRJktSiDPpLkiRJkiRJkiRJktSiDPpLkiRJkiRJkiRJktSiIjP7ug5qExGxGJjb1/XoZVsDS/q6Ev2A12Edr0WJ12Edr0WJ12Gdvr4Wr8vM0X1Yvgq2ndqa12Edr0WJ12Edr0WJ12Gdvr4Wtp36iTZsO/X1d78/8VqUeB3W8VqUeB3W8Vqs09fXwraT1CQG/aUmiohZmTm5r+vR17wO63gtSrwO63gtSrwO63gt1M78/pd4HdbxWpR4HdbxWpR4HdbxWqhd+d1fx2tR4nVYx2tR4nVYx2uxjtdCGric3l+SJEmSJEmSJEmSpBZl0F+SJEmSJEmSJEmSpBZl0F9qrkv6ugL9hNdhHa9FiddhHa9FiddhHa+F2pnf/xKvwzpeixKvwzpeixKvwzpeC7Urv/vreC1KvA7reC1KvA7reC3W8VpIA1RkZl/XQZIkSZIkSZIkSZIkbQBH+kuSJEmSJEmSJEmS1KIM+kuSJEmSJEmSJEmS1KIM+kuSJEmSJEmSJEmS1KIM+kuFiDgoIn4WEfMiIiNiasX+iIgvRcT8iFgeEb+NiN0q0mwcEd+OiCUR8VKR37YVabaIiMsj4rnidXlEjKpIMyEi/rfIY0lEfCsiNmrWuVeUfXpE/CEino+IxUU9dq9I0y7X4pMR8afiWjwfEbdHxLvL9rfFdagUEf9a/B/5Ttm2trgWxTlmxWth2f62uA5F+eMi4rIo/Z5YEREPRMTBZfvb4lpExONVvhMZEf9X7G+L66D2FLadOsq27bSubNtOVYRtJ9tO2HYqK9u2k9pW2HbqKNu2E7abagnbTbab1tWh7dtOYbtJUndlpi9fvjIBDgfOAf4eWAZMrdj/L8ALwPuB3YH/BuYDm5Wl+W6xbQqwF/Bb4B5gcFma64H7gf2B/Yr3/1u2fzBwX3HsXkVe84Fv99J1mAn8U3GOewA/BRYCW7bhtXgv8DfA64E3AGcDq4A3ttN1qLgmbwHmAPcC32nD78SXgIeAsWWv0W14HUYBjwE/APYFtgPeAezShtdidMX3YU9gLXBsO10HX+35wrZTR/m2ndaVb9tp/Wti28m2E9h2Kr8Wtp18te0L204d5dt2SttNNa6J7SbbTR11GIVtJ7Dd5MuXr26++rwCvnz1xxfwImU3X0AAC4DPlW0bXvxR/UTxeXNgJXBMWZrXFn+IDys+7wIkcEBZmrcW23YqPv9Nccxry9J8GFgBjOyDa7EpsAZ4T7tfi6L8Z4BPtON1KM7nUeDtRSPvO+32naB0Aza7xr52ug7nALd2sr9trkWVc/8csBQY0c7XwVf7vbDtVH4tbDu9+nrYdrLtZNvJtlNn18a2k6+2fGHbqfxa2HZaV7btJttNbd9uKsqz7VT9vG03+fLlq9OX0/tL9dmO0tN0N3RsyMzlwM2UnoAD2BsYWpHmr8CDZWn2o3Rjd1tZ3rcCL1WkebA4tsNMYOOijN62GaWlQJ4tPrfltYiIwRFxNKWb0dtoz+twCfCTzPx1xfZ2uxbbR2k6xjkR8eOI2L7Y3k7X4Qjgjoi4MiKeioh7IuJTERHF/na6Fq8ozv//AT/MzGW06XWQCu38/bfthG2ngm2nEttOtp2qsu0kvUo7f//bvu1kuwmw3dTBdlPJEdh2ehXbTZLqYdBfqs/Y4ueiiu2LyvaNpfRk8pIu0izOzOzYWbx/qiJNZTlLirzH0vsuoDTlz+3F57a6FhGxR0S8CLwMXAy8LzPvo/2uw8cpTTn3hSq72+la3AFMpfSE68eLMm+LiK1or+uwPXAipanWDqP0e+KrwCfL6keVOg7Ea1FuCqWbrv8oPrfrdZCgvb//tp1sO9l2Wse2U4ltp+psO0nrtPP3v23bTrabSmw3vcJ20zq2ndZnu0lSl4b0dQWkFpMVn6PKtkqVaaqlrydNZ9ubIiKmU5rO562ZuaaLugzUa/EwMInSWlLvBy6LiLd1Uo8Bdx0iYidK02odmJkrO0k64K9FZl5f/jkifk/pBuRY4Pc16jHgrgOlhwZnZebpxee7I2JHSjdf3+mkLgPxWpT7OPCHzLyni3oM9OsglWur779tJ8C2k22n8gJsO3Ww7VSdbSdpfW31/bftZLvJdlNZAbabytl2Wp/tJkldcqS/VJ+Fxc/KJ9fGsO4pt4XAYGDrLtKMKZuKqGNqntEVaSrL2br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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Create the overall figure with fig and the subplots with axs. The amount of subplots is specified here with len(reg_dic), which in our case is 3\n", "fig, axs = plt.subplots(1, len(GDPxJFP_regdic),figsize=(30,8))\n", "\n", "# Setting the title of the overall figure\n", "fig.suptitle(f\"Linear Regression of Job Finding Probability and GDP per capita '2011-2020' for different Age Groups\",weight = 'bold', size = 25)\n", "\n", "# Set the X- and Y-axis of the overall Figure\n", "fig.text(0.5, 0.04, 'GDP per capita', ha='center', va='center', size=15)\n", "fig.text(0.06, 0.5, 'Job finding probability', ha='center', va='center', rotation='vertical', size=15)\n", "\n", "# Create a list with the keys of the dataframes used for the regression in order to access them afterwards more easily\n", "GF_dic_key_list = []\n", "for i in GDPxJFP_regdic.keys():\n", " GF_dic_key_list.append(i)\n", "\n", "# Create a list with the keys of the age groups and their age range in order to access them afterwards more easily\n", "GF_age_groups_list=[]\n", "for i in age_groups_jf.keys():\n", " GF_age_groups_list.append(i)\n", "\n", "# For-loop to create the different subplots\n", "for i in range(len(GDPxJFP_regdic)):\n", "\n", " # Plots the regression line (x-axis: Measured unemploymebt Rate, y-axis: GDP-Growth from regression)\n", " axs[i].plot(GDPxJFP_regdic[GF_dic_key_list[i]][['GDP per capita']], GF_regresults_dic[GF_dic_key_list[i]].predict(), '-', color='darkorchid', linewidth=2,label=\"Linear Regression\")\n", "\n", " # Creates scatter plot with axtual values (x-axis: Measured unemploymebt Rate, y-axis: Actual GDP-Growth)\n", " axs[i].scatter(GDPxJFP_regdic[GF_dic_key_list[i]][['GDP per capita']],GDPxJFP_regdic[GF_dic_key_list[i]][\"Job Finding Probability\"], label=\"Actual Values\")\n", "\n", " # Sets title of subplot with the relevant age group\n", " axs[i].set_title(f'Job Finding Probability for {age_groups_jf[GF_dic_key_list[i]]}')\n", "\n", "# St legend. Since legend is the same for all subplots, we only show it for the first subplot\n", "fig.legend()\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": { "id": "HEpMRScWFzJx" }, "source": [ "Higher level of GDP per capita correlates with a higher job finding probability among young workers as high GDP countries offer a wider range of work opportunities. \n", "\n", "\n", "## Multiple linear OLS regression\n", "Oftentimes we are interested in the influence of multiple indipendent variables on a dependent variable. In this case, we can run a multiple linear OLS regression. Like a simple linear OLS regression, the regression line tries to minimze the square distance between the predicted values and the actual values. In this case, the model that the regression tries to fit comes from the following equation:\n", "\n", "$$y={\\beta_0 +\\beta_1 x_1 +\\beta_2 x_2 +\\ ...+ \\beta_k x_k +\\ e_i}$$\n", "\n", "Here we will do the following regressions:\n", "- Regression of unememployment rates with transition rates\n", "- Regression of Model Performance with transition Rates\n", "\n", "\n", "### Unemployment rates on transition rates\n", "According to a paper by Barnichon and Nekarda, the unemployment rate can be forcasted quite reliably with the transition rates of previous periods. The paper can be downloaded [here](https://www.brookings.edu/wp-content/uploads/2012/09/2012b_barnichon.pdf). In the following we will analyse the relationship between the unemployment rate and the transition rates of the previous period by doing an OLS Regression.\n", "\n", "**Preparing the data** \n", "First, we will need to prepare the data for the OLS regression. For this, we will need to shift the transition rates by one period. This can be done with the [shift](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.shift.html) function from pandas:" ] }, { "cell_type": "code", "execution_count": 167, "metadata": { "id": "4jtKTUB7hduP" }, "outputs": [], "source": [ "#Create empty list to save availabe country names\n", "countries_list=[]\n", "\n", "#Get all country names and store them in list\n", "for country in countries.keys():\n", " countries_list.append(country)\n", "\n", "#Create empty dictionary to store dataframe in them later\n", "UR_reg={}\n", "\n", "#iterate through all countries\n", "for country in countries_list:\n", "#Create a copy of the Country dataframe\n", " aux_df=countries[country].copy()\n", "\n", " #Drop the measured unemployment Rate and the Steady State Unemployment Rate since we only need the transition rates for the moment\n", " aux_df=aux_df.drop([\"Measured Unemployment Rate\",\"Steady State Unemployment Rate\"], axis=1)\n", "\n", " #Shifting the transition rates by one Period\n", " aux_df=aux_df.shift(periods=1)\n", "\n", " #Add back the Measured Unemployment Rate\n", " aux_df['Measured Unemployment Rate']=countries[country]['Measured Unemployment Rate']\n", "\n", " #Store dataframe in dictionary\n", " UR_reg[country]=aux_df" ] }, { "cell_type": "markdown", "metadata": { "id": "PKEYTW5HNSUb" }, "source": [ "We can now also change the name of the coulumns to make them clearer:" ] }, { "cell_type": "code", "execution_count": 168, "metadata": { "id": "hi2lOmjQNX-G" }, "outputs": [], "source": [ "#Create empty list for the different states\n", "state=[]\n", "\n", "#Add all possible states into the list\n", "for x in states:\n", " for y in states:\n", " state.append(\"rate_\"+x+y)\n", " \n", "#Create a dictionary to rename the columns of the dataframe \n", "transition_t1={}\n", "\n", "#For each state add an -1 to clarify that we use the transition rate of the previous period\n", "for i in state:\n", " transition_t1[i]=i+\"-1\"\n", "\n", "#ierate through all countries and change the columns\n", "for country in countries_list:\n", " #Rename the transition rates columns\n", " UR_reg[country].rename(columns=transition_t1, inplace=True)" ] }, { "cell_type": "code", "execution_count": 169, "metadata": { "id": "okgR40NO_TwP" }, "outputs": [ { "data": { "text/html": [ "
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rate_EE-1rate_EU-1rate_EI-1rate_UE-1rate_UU-1rate_UI-1rate_IE-1rate_IU-1rate_II-1Measured Unemployment Rate
Quarterly Frequency
1998Q1NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
1998Q2NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
1998Q3NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
1998Q4NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
1999Q1NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
.................................
2019Q40.9567290.0136550.0296160.3055770.4866020.2078210.0749360.0313640.8937004.575
2020Q10.9588540.0133900.0277550.2959890.5085230.1954880.0781510.0327260.8891224.850
2020Q20.9593640.0138820.0267540.3010520.5284350.1705130.0782670.0340680.887665NaN
2020Q3NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
2020Q4NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
\n", "

92 rows × 10 columns

\n", "
" ], "text/plain": [ " rate_EE-1 rate_EU-1 rate_EI-1 rate_UE-1 rate_UU-1 \\\n", "Quarterly Frequency \n", "1998Q1 NaN NaN NaN NaN NaN \n", "1998Q2 NaN NaN NaN NaN NaN \n", "1998Q3 NaN NaN NaN NaN NaN \n", "1998Q4 NaN NaN NaN NaN NaN \n", "1999Q1 NaN NaN NaN NaN NaN \n", "... ... ... ... ... ... \n", "2019Q4 0.956729 0.013655 0.029616 0.305577 0.486602 \n", "2020Q1 0.958854 0.013390 0.027755 0.295989 0.508523 \n", "2020Q2 0.959364 0.013882 0.026754 0.301052 0.528435 \n", "2020Q3 NaN NaN NaN NaN NaN \n", "2020Q4 NaN NaN NaN NaN NaN \n", "\n", " rate_UI-1 rate_IE-1 rate_IU-1 rate_II-1 \\\n", "Quarterly Frequency \n", "1998Q1 NaN NaN NaN NaN \n", "1998Q2 NaN NaN NaN NaN \n", "1998Q3 NaN NaN NaN NaN \n", "1998Q4 NaN NaN NaN NaN \n", "1999Q1 NaN NaN NaN NaN \n", "... ... ... ... ... \n", "2019Q4 0.207821 0.074936 0.031364 0.893700 \n", "2020Q1 0.195488 0.078151 0.032726 0.889122 \n", "2020Q2 0.170513 0.078267 0.034068 0.887665 \n", "2020Q3 NaN NaN NaN NaN \n", "2020Q4 NaN NaN NaN NaN \n", "\n", " Measured Unemployment Rate \n", "Quarterly Frequency \n", "1998Q1 NaN \n", "1998Q2 NaN \n", "1998Q3 NaN \n", "1998Q4 NaN \n", "1999Q1 NaN \n", "... ... \n", "2019Q4 4.575 \n", "2020Q1 4.850 \n", "2020Q2 NaN \n", "2020Q3 NaN \n", "2020Q4 NaN \n", "\n", "[92 rows x 10 columns]" ] }, "execution_count": 169, "metadata": {}, "output_type": "execute_result" } ], "source": [ "UR_reg[\"Switzerland\"]" ] }, { "cell_type": "markdown", "metadata": { "id": "hrgiW4AxNgHV" }, "source": [ "With this dataframe we can now conduct the multiple OLS regression. \n", "\n", "**Creation of the model** \n", "Similarly to the simple linear OLS regression, we will first need to create a dataframe **Y** for the dependent variable and a dataframe **X** for the independent variables. Additionally, we will need to add a constant column with ones to the dataframe with the independent variables with the statsmodles method `sm.add_constant`. Otherwise, the regression will be created without an intercept $\\beta_0$ by default. The regression will be initialized by creating and `sm.OLS` object. We then fit the model by calling the OLS object's `fit` method. We can then print a summary of the model by calling the `summary` attribute of the model." ] }, { "cell_type": "code", "execution_count": 170, "metadata": { "id": "aVtpAi_vN3FO" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " OLS Regression Results \n", "======================================================================================\n", "Dep. Variable: Measured Unemployment Rate R-squared: 0.844\n", "Model: OLS Adj. R-squared: 0.815\n", "Method: Least Squares F-statistic: 28.82\n", "Date: Sun, 16 May 2021 Prob (F-statistic): 1.39e-11\n", "Time: 17:48:03 Log-Likelihood: 42.652\n", "No. Observations: 39 AIC: -71.30\n", "Df Residuals: 32 BIC: -59.66\n", "Df Model: 6 \n", "Covariance Type: nonrobust \n", "==============================================================================\n", " coef std err t P>|t| [0.025 0.975]\n", "------------------------------------------------------------------------------\n", "const 5.1183 0.965 5.304 0.000 3.152 7.084\n", "rate_EU-1 139.6896 21.030 6.642 0.000 96.853 182.526\n", "rate_EI-1 22.6680 12.013 1.887 0.068 -1.802 47.138\n", "rate_UE-1 -8.3902 1.572 -5.337 0.000 -11.592 -5.188\n", "rate_UI-1 -8.0838 1.535 -5.265 0.000 -11.211 -4.956\n", "rate_IE-1 -2.0464 4.518 -0.453 0.654 -11.250 7.158\n", "rate_IU-1 41.5031 6.108 6.795 0.000 29.061 53.945\n", "==============================================================================\n", "Omnibus: 1.799 Durbin-Watson: 2.142\n", "Prob(Omnibus): 0.407 Jarque-Bera (JB): 1.372\n", "Skew: -0.458 Prob(JB): 0.504\n", "Kurtosis: 2.932 Cond. No. 1.65e+03\n", "==============================================================================\n", "\n", "Notes:\n", "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n", "[2] The condition number is large, 1.65e+03. This might indicate that there are\n", "strong multicollinearity or other numerical problems.\n" ] } ], "source": [ "#Create empty dictionary to store reression results\n", "UR_regres={}\n", "\n", "#Iterate through all countries\n", "for country in countries_list:\n", " \n", " #Defining the dependent variable\n", " Y=UR_reg[country][\"Measured Unemployment Rate\"]\n", "\n", " #Defining the regressors and adding a constant (the intercept B0) with the sm.add_constant method\n", " X=sm.add_constant(UR_reg[country][['rate_EU-1','rate_EI-1','rate_UE-1','rate_UI-1','rate_IE-1','rate_IU-1']])\n", "\n", " #Initializing the OLS rergeression\n", " regression = sm.OLS(Y,X, missing='drop')\n", "\n", " #Fit the model by calling the OLS object’s fit() method\n", " regresults = regression.fit()\n", "\n", " #store result in dictionary\n", " UR_regres[country]=regresults\n", "\n", "#Print the regression summary for Switzerland\n", "print(UR_regres[\"Switzerland\"].summary())" ] }, { "cell_type": "markdown", "metadata": { "id": "WtO_BlSQQXt6" }, "source": [ "**Interpretation of the regression** \n", "The outcome of the regression (for Switzerland) is:\n", "\n", "$$Unemployment Rate_{t}={5.1183 +139.6896\\cdot EU_{t-1} + 22.6880\\cdot EI_{t-1} -8.3902\\cdot UE_{t-1} -8.0838\\cdot UI_{t-1} - 2.0464\\cdot IE_{t-1} +41.5031\\cdot IU_{t-1} + e_i}$$\n", "\n", "As we can see, EU, EI and IU increase the Unemployment Rate, while UE, UI and IE decrease the Unemployment Rate for Switzerland. Through the p-value we can check how significant each transition rate is. We can get the p-values either by looking at the summary of the regression above or by calling the `pvalues` attribute of the regression:" ] }, { "cell_type": "code", "execution_count": 171, "metadata": { "id": "fX8zvOtVQsrq" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "('const', 8.215596089921501e-06)\n", "('rate_EU-1', 1.713091089891796e-07)\n", "('rate_UE-1', 7.4435004411936124e-06)\n", "('rate_UI-1', 9.190374011823184e-06)\n", "('rate_IU-1', 1.111276422432336e-07)\n" ] } ], "source": [ "#Setting significane level\n", "significane=0.05\n", "\n", "# For-loop to get all parameters below certain p-value\n", "for item in UR_regres[\"Switzerland\"].pvalues.iteritems(): #Gets all the p-values\n", " if item[1]\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
constrate_EU-1rate_EI-1rate_UE-1rate_UI-1rate_IE-1rate_IU-1R-SquaredAdjusted R-Squared
Austria8.875392106.83528442.698730-13.884424-13.094614-15.08405343.3141200.9501370.940787
Belgium10.479327251.574150-139.882792-12.8428374.525223-14.898509-2.5265320.9605940.901484
Bulgaria14.105001-221.046891284.529851-31.178452-81.138736-218.293471598.3601200.9721790.966218
Croatia-0.69642491.069649124.005737-0.308550-8.159090118.710388163.9287940.9986020.996925
Cyprus2.589941293.576096359.992977-15.642935-28.304033-23.610698134.6213540.9627740.954797
Czechia4.923837393.5164480.107086-9.697002-13.922397-22.00665298.3608430.9958300.995048
Denmark7.804964146.1103637.275155-12.732900-7.776063-17.09686870.9578970.9908510.989136
Estonia11.73680089.30580661.239593-11.798082-25.81650625.947356-19.0981060.9294830.907215
Finland11.654941163.56158220.934645-16.535141-12.908852-39.37380460.1465590.9864930.983961
France10.265118151.49004655.117100-27.121533-17.341235-5.124567127.7528270.9801860.976471
Greece51.993960-653.700902-1018.202294-381.838521-133.3768881183.844166725.2772120.7320740.681838
Hungary8.708572334.97145853.586842-32.446649-27.67849945.256681162.4867200.9864420.983900
Iceland-1.20166335.90618349.040267-11.1339868.05772818.78076745.5855920.9932470.972989
Ireland10.034995-5.723583172.438841-18.723813-28.268628-95.816753227.1430330.9898720.987701
Italy8.120841337.21093398.978918-9.063658-18.425747-106.072151114.7612060.9676290.961560
Latvia7.678254277.710940102.222560-15.277619-21.425123-62.933399102.2312940.9885320.985885
Lithuania22.380467-132.707084264.372077-63.799870-64.579473-175.516990240.7984430.9696880.959584
Luxembourg6.1854740.089051-0.676444-3.2832341.5538200.351864-0.4222751.000000NaN
Montenegro15.2395160.1977730.5580601.8121661.4903660.4631760.430533-infNaN
Netherlands4.920517178.910425102.557086-12.830989-12.33745017.84481552.6611670.9954780.994630
North Macedonia23.641068265.009802-2.471125-71.738768-80.5528308.912374223.0573190.9961010.994987
Norway4.623594153.577287-5.734086-6.845055-5.400521-6.59959160.9208000.9759180.971403
Poland8.943387506.882650-191.129044-7.004338-14.301688-131.50605883.1436960.9955210.994681
Portugal24.508367118.443288-18.054230-61.621162-21.974115-9.98749556.3540230.9962400.995434
Romania5.571059285.870515-6.976953-28.650354-15.48992723.403022243.3069650.9731580.966716
Serbia-8.20468486.51433142.26190318.33892212.482376-28.989027235.7446590.9948420.992908
Slovakia14.430968144.960272109.687926-35.529082-150.531088-105.240354415.7188950.9724580.965573
Slovenia4.976989195.01625245.597161-11.300671-11.925731-26.046018144.6962280.9898820.987985
Spain21.13223999.235380-23.880264-54.028961-20.053641-99.462473186.5332990.9975510.997092
Sweden7.025048310.5837432.154288-6.331858-4.222602-33.0568250.5461570.9664520.960161
Switzerland5.118262139.68958022.668046-8.390191-8.083771-2.04635541.5031440.8438330.814552
Turkey-2.180432140.553580-0.1217053.3039199.373771-33.903835211.7852940.9948550.993752
United Kingdom5.086103233.489991-56.159950-17.646077-6.771571-14.753691191.4732970.9973650.996854
\n", "
" ], "text/plain": [ " const rate_EU-1 rate_EI-1 rate_UE-1 rate_UI-1 \\\n", "Austria 8.875392 106.835284 42.698730 -13.884424 -13.094614 \n", "Belgium 10.479327 251.574150 -139.882792 -12.842837 4.525223 \n", "Bulgaria 14.105001 -221.046891 284.529851 -31.178452 -81.138736 \n", "Croatia -0.696424 91.069649 124.005737 -0.308550 -8.159090 \n", "Cyprus 2.589941 293.576096 359.992977 -15.642935 -28.304033 \n", "Czechia 4.923837 393.516448 0.107086 -9.697002 -13.922397 \n", "Denmark 7.804964 146.110363 7.275155 -12.732900 -7.776063 \n", "Estonia 11.736800 89.305806 61.239593 -11.798082 -25.816506 \n", "Finland 11.654941 163.561582 20.934645 -16.535141 -12.908852 \n", "France 10.265118 151.490046 55.117100 -27.121533 -17.341235 \n", "Greece 51.993960 -653.700902 -1018.202294 -381.838521 -133.376888 \n", "Hungary 8.708572 334.971458 53.586842 -32.446649 -27.678499 \n", "Iceland -1.201663 35.906183 49.040267 -11.133986 8.057728 \n", "Ireland 10.034995 -5.723583 172.438841 -18.723813 -28.268628 \n", "Italy 8.120841 337.210933 98.978918 -9.063658 -18.425747 \n", "Latvia 7.678254 277.710940 102.222560 -15.277619 -21.425123 \n", "Lithuania 22.380467 -132.707084 264.372077 -63.799870 -64.579473 \n", "Luxembourg 6.185474 0.089051 -0.676444 -3.283234 1.553820 \n", "Montenegro 15.239516 0.197773 0.558060 1.812166 1.490366 \n", "Netherlands 4.920517 178.910425 102.557086 -12.830989 -12.337450 \n", "North Macedonia 23.641068 265.009802 -2.471125 -71.738768 -80.552830 \n", "Norway 4.623594 153.577287 -5.734086 -6.845055 -5.400521 \n", "Poland 8.943387 506.882650 -191.129044 -7.004338 -14.301688 \n", "Portugal 24.508367 118.443288 -18.054230 -61.621162 -21.974115 \n", "Romania 5.571059 285.870515 -6.976953 -28.650354 -15.489927 \n", "Serbia -8.204684 86.514331 42.261903 18.338922 12.482376 \n", "Slovakia 14.430968 144.960272 109.687926 -35.529082 -150.531088 \n", "Slovenia 4.976989 195.016252 45.597161 -11.300671 -11.925731 \n", "Spain 21.132239 99.235380 -23.880264 -54.028961 -20.053641 \n", "Sweden 7.025048 310.583743 2.154288 -6.331858 -4.222602 \n", "Switzerland 5.118262 139.689580 22.668046 -8.390191 -8.083771 \n", "Turkey -2.180432 140.553580 -0.121705 3.303919 9.373771 \n", "United Kingdom 5.086103 233.489991 -56.159950 -17.646077 -6.771571 \n", "\n", " rate_IE-1 rate_IU-1 R-Squared Adjusted R-Squared \n", "Austria -15.084053 43.314120 0.950137 0.940787 \n", "Belgium -14.898509 -2.526532 0.960594 0.901484 \n", "Bulgaria -218.293471 598.360120 0.972179 0.966218 \n", "Croatia 118.710388 163.928794 0.998602 0.996925 \n", "Cyprus -23.610698 134.621354 0.962774 0.954797 \n", "Czechia -22.006652 98.360843 0.995830 0.995048 \n", "Denmark -17.096868 70.957897 0.990851 0.989136 \n", "Estonia 25.947356 -19.098106 0.929483 0.907215 \n", "Finland -39.373804 60.146559 0.986493 0.983961 \n", "France -5.124567 127.752827 0.980186 0.976471 \n", "Greece 1183.844166 725.277212 0.732074 0.681838 \n", "Hungary 45.256681 162.486720 0.986442 0.983900 \n", "Iceland 18.780767 45.585592 0.993247 0.972989 \n", "Ireland -95.816753 227.143033 0.989872 0.987701 \n", "Italy -106.072151 114.761206 0.967629 0.961560 \n", "Latvia -62.933399 102.231294 0.988532 0.985885 \n", "Lithuania -175.516990 240.798443 0.969688 0.959584 \n", "Luxembourg 0.351864 -0.422275 1.000000 NaN \n", "Montenegro 0.463176 0.430533 -inf NaN \n", "Netherlands 17.844815 52.661167 0.995478 0.994630 \n", "North Macedonia 8.912374 223.057319 0.996101 0.994987 \n", "Norway -6.599591 60.920800 0.975918 0.971403 \n", "Poland -131.506058 83.143696 0.995521 0.994681 \n", "Portugal -9.987495 56.354023 0.996240 0.995434 \n", "Romania 23.403022 243.306965 0.973158 0.966716 \n", "Serbia -28.989027 235.744659 0.994842 0.992908 \n", "Slovakia -105.240354 415.718895 0.972458 0.965573 \n", "Slovenia -26.046018 144.696228 0.989882 0.987985 \n", "Spain -99.462473 186.533299 0.997551 0.997092 \n", "Sweden -33.056825 0.546157 0.966452 0.960161 \n", "Switzerland -2.046355 41.503144 0.843833 0.814552 \n", "Turkey -33.903835 211.785294 0.994855 0.993752 \n", "United Kingdom -14.753691 191.473297 0.997365 0.996854 " ] }, "execution_count": 172, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#Create empty dictionary to create the dataframe later on\n", "overview_dic={}\n", "\n", "#iterate through all regression results\n", "for country in UR_regres:\n", " aux=UR_regres[country]#create dictionary entry for certain age group\n", " overview_dic[country]=[aux.params[0],aux.params[1],aux.params[2], aux.params[3], aux.params[4], aux.params[5], aux.params[6],aux.rsquared,aux.rsquared_adj] #Store list with all values that are of importance into the agegroup entry\n", "\n", "#Creat list with all coefficient names\n", "cof=UR_regres[\"Switzerland\"].params.index\n", "\n", "#Create a dataframe out of the dictionary and transpose the dataframe so we have the agegroup as index\n", "overview_df=pd.DataFrame(overview_dic,index=[cof[0],cof[1],cof[2],cof[3],cof[4],cof[5],cof[6],\"R-Squared\",\"Adjusted R-Squared\"]).T\n", "\n", "#Print the max and min adjusted r-squared\n", "print(f\"The country for which the Adjusted R-Squared is biggest is {overview_df['Adjusted R-Squared'].idxmax()} with {overview_df['Adjusted R-Squared'].max()}\")\n", "print(f\"The country for which the Adjusted R-Squared is smallest is {overview_df['Adjusted R-Squared'].idxmin()} with {overview_df['Adjusted R-Squared'].min()}\")\n", "\n", "#print the df\n", "overview_df" ] }, { "cell_type": "markdown", "metadata": { "id": "NV7nD_XISL2C" }, "source": [ "We can now see the model coefficients for all countries. Additionally, we can see that the Adjusted $R^2$-Value is quite different for different countries. It is the biggest for Spain with 0.9971 and smallest for Greece with 0.6818. For Switzerland, the Adjusted $R^2$-Value is 0.8146, from which we can infer that the model explains 81.46% of the variation in the Unemployment Rate in Switzerland. However, this value should be treated cautiously. According to the warnings in the summary of the regression:\n", "> The condition number is large, 1.65e+03. This might indicate that there are\n", "strong multicollinearity or other numerical problems.\n", "\n", "Multicollinearity refers to a situation in which more than two explanatory variables in a multiple regression model are highly linearly related. This leads to inaccurate results. In this case, the problem of multicollinearity probably stems from the fact that the individual transition rates influence each other. For instance, if EE rises, it is imperative that EU or EI have to fall. More detailed econometric limitations may be found in a statistics or econometrics course. For instance, you can see lecture on linear regressions [here](https://lectures.quantecon.org/py/ols.html).\n", "\n", "To get a picture of how good the predictions based on the transition rates were, we can plot the results of the OLS regression against the Measured unemployment rate. We can get the predicted values of the regression by calling the `predict` attribute of the regression. We will do this here for the case of Switzerland:" ] }, { "cell_type": "code", "execution_count": 173, "metadata": { "id": "1KCrfopGbDy2" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/Users/gianluca/opt/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:8: SettingWithCopyWarning: \n", "A value is trying to be set on a copy of a slice from a DataFrame.\n", "Try using .loc[row_indexer,col_indexer] = value instead\n", "\n", "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", " \n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "#Select country\n", "country=\"Switzerland\"\n", "\n", "#Drop all rows with nan values\n", "aux_df=UR_reg[country].dropna()\n", "\n", "#add regression results to the right place with the predict method\n", "aux_df[\"regression results\"]=UR_regres[country].predict()\n", "\n", "#Plot the Measured unemployment Rate and the regression results\n", "aux_df['Measured Unemployment Rate'].plot(legend=True)\n", "aux_df['regression results'].plot(legend=True)\n", "\n", "#Create a title for the plot\n", "plt.title(f\"Measured Unemployment Rate vs. regression results, {country}\", fontsize=14)\n", "\n", "#show the plot\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": { "id": "ZOgsoK5LfGZQ" }, "source": [ "\n", "### Model performance on transition rates\n", "In the following, we will do two main things. Firstly, we will calculate the steady state model performance. Secondly, we will look at how each transition rate influences the model performance.\n", "\n", "**Preparing the data** \n", "Before we calculate the model performance, we first need to calculate the mean unemployment rate and transition rate values for each country over all periods:" ] }, { "cell_type": "code", "execution_count": 174, "metadata": { "id": "9WjL61j9DRMm" }, "outputs": [ { "data": { "text/html": [ "
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Measured Unemployment Raterate_EErate_EUrate_EIrate_UErate_UUrate_UIrate_IErate_IUrate_IISteady State Unemployment Rate
Austria4.8720590.9545100.0127610.0327290.2758740.5069550.2171710.0620010.0278890.9101105.127803
Belgium7.5408820.9533090.0100650.0366260.2068300.5175720.2755990.0514670.0261470.9223855.452638
Bulgaria10.2253090.9767710.0087000.0150670.1022700.7965330.1011970.0191940.0128800.9679269.002698
Croatia12.3212330.9665230.0153600.0193260.1469790.7392410.2307300.0212610.0303250.9643718.827952
Cyprus8.8843750.9636130.0225290.0138580.2081640.7234430.0683930.0226630.0224520.95587311.971578
Czechia6.1441010.9823370.0066270.0110360.1971200.6576110.1452700.0176050.0142770.9681184.332546
Denmark5.6594120.9445130.0174560.0380320.3371730.4355150.2273130.0681030.0421640.8897336.334405
Estonia8.8308640.9624510.0151090.0238620.2413940.5535450.2050610.0533170.0378060.9146816.020868
Finland8.3960670.9391380.0175610.0433010.2651720.4717210.2631070.0689890.0523050.8787068.079962
France8.9014490.9604720.0186090.0209190.2174150.5911890.1913960.0271740.0352240.9376029.210156
Greece15.4157300.9770610.0148010.0081380.0537920.9321150.0140920.0056880.0049350.98937725.308979
Hungary7.1079410.9736050.0100170.0163780.1959130.6981020.1059840.0230380.0131240.9638396.219922
Iceland4.2054350.9488630.0166760.0382980.5477500.4524160.2088520.1517400.0695360.8019486.595419
Ireland7.9886900.9633310.0136330.0230360.1565980.6305540.2128480.0426180.0416030.9157799.726803
Italy9.5853930.9564190.0151220.0284590.1440480.4749590.3809930.0302270.0551500.91462310.747803
Latvia10.9831080.9545820.0193210.0260970.1976370.5872980.2238530.0414310.0508970.90767311.038423
Lithuania9.9520270.9714270.0132160.0153570.1571010.7896420.0665020.0268620.0130060.96013110.299465
Luxembourg5.2673910.9603350.0127360.0289130.2689830.5651530.1857880.0521800.0282820.9210704.663819
Montenegro17.5076390.9665060.0129780.0342310.1095570.8247770.1161860.0383260.0297170.93813015.292814
Netherlands4.7401230.9664000.0116750.0219250.2365460.4391090.3243450.0496830.0443000.9060165.283769
North Macedonia27.7622810.9316460.0346300.0336830.1206290.7538430.1365430.0333100.0484440.91824724.479348
Norway3.6225310.9618210.0087500.0294280.2781320.3614890.3603790.0678210.0377240.8944553.677813
Poland10.8641980.9796190.0085740.0118070.1515040.6723860.1761110.0158340.0179590.9662076.591411
Portugal8.7932580.9421050.0227520.0351430.2162670.6044210.1793120.0543380.0419300.90373211.164161
Romania6.5532350.9721650.0047960.0230990.0993100.7474470.1578620.0301720.0144820.9555445.690870
Serbia14.2086540.9259700.0287560.0452750.1918270.5418460.2663270.0569500.0508210.89222913.054313
Slovakia13.2185390.9830210.0072780.0097010.1006790.8696410.0309410.0123660.0082080.9794269.451457
Slovenia6.8311760.9428060.0154190.0417750.2097140.5851390.2051470.0661430.0290200.9048377.540447
Spain15.9592700.9298630.0424210.0277160.1945320.6478900.1575780.0346310.0672750.89809420.108421
Sweden7.0415580.9598760.0144460.0256780.2612740.5547350.1839910.0522050.0511590.8966367.140653
Switzerland4.7079270.9555500.0135130.0309370.3344550.4981210.1674240.0864310.0323670.8812024.590608
Turkey10.3929820.8709950.0368810.0921230.3388330.4008680.2602990.0943460.0343130.87134110.493494
United Kingdom5.6638550.9763560.0080100.0156340.1905670.6778140.1316190.0307920.0226250.9465825.306874
\n", "
" ], "text/plain": [ " Measured Unemployment Rate rate_EE rate_EU rate_EI \\\n", "Austria 4.872059 0.954510 0.012761 0.032729 \n", "Belgium 7.540882 0.953309 0.010065 0.036626 \n", "Bulgaria 10.225309 0.976771 0.008700 0.015067 \n", "Croatia 12.321233 0.966523 0.015360 0.019326 \n", "Cyprus 8.884375 0.963613 0.022529 0.013858 \n", "Czechia 6.144101 0.982337 0.006627 0.011036 \n", "Denmark 5.659412 0.944513 0.017456 0.038032 \n", "Estonia 8.830864 0.962451 0.015109 0.023862 \n", "Finland 8.396067 0.939138 0.017561 0.043301 \n", "France 8.901449 0.960472 0.018609 0.020919 \n", "Greece 15.415730 0.977061 0.014801 0.008138 \n", "Hungary 7.107941 0.973605 0.010017 0.016378 \n", "Iceland 4.205435 0.948863 0.016676 0.038298 \n", "Ireland 7.988690 0.963331 0.013633 0.023036 \n", "Italy 9.585393 0.956419 0.015122 0.028459 \n", "Latvia 10.983108 0.954582 0.019321 0.026097 \n", "Lithuania 9.952027 0.971427 0.013216 0.015357 \n", "Luxembourg 5.267391 0.960335 0.012736 0.028913 \n", "Montenegro 17.507639 0.966506 0.012978 0.034231 \n", "Netherlands 4.740123 0.966400 0.011675 0.021925 \n", "North Macedonia 27.762281 0.931646 0.034630 0.033683 \n", "Norway 3.622531 0.961821 0.008750 0.029428 \n", "Poland 10.864198 0.979619 0.008574 0.011807 \n", "Portugal 8.793258 0.942105 0.022752 0.035143 \n", "Romania 6.553235 0.972165 0.004796 0.023099 \n", "Serbia 14.208654 0.925970 0.028756 0.045275 \n", "Slovakia 13.218539 0.983021 0.007278 0.009701 \n", "Slovenia 6.831176 0.942806 0.015419 0.041775 \n", "Spain 15.959270 0.929863 0.042421 0.027716 \n", "Sweden 7.041558 0.959876 0.014446 0.025678 \n", "Switzerland 4.707927 0.955550 0.013513 0.030937 \n", "Turkey 10.392982 0.870995 0.036881 0.092123 \n", "United Kingdom 5.663855 0.976356 0.008010 0.015634 \n", "\n", " rate_UE rate_UU rate_UI rate_IE rate_IU rate_II \\\n", "Austria 0.275874 0.506955 0.217171 0.062001 0.027889 0.910110 \n", "Belgium 0.206830 0.517572 0.275599 0.051467 0.026147 0.922385 \n", "Bulgaria 0.102270 0.796533 0.101197 0.019194 0.012880 0.967926 \n", "Croatia 0.146979 0.739241 0.230730 0.021261 0.030325 0.964371 \n", "Cyprus 0.208164 0.723443 0.068393 0.022663 0.022452 0.955873 \n", "Czechia 0.197120 0.657611 0.145270 0.017605 0.014277 0.968118 \n", "Denmark 0.337173 0.435515 0.227313 0.068103 0.042164 0.889733 \n", "Estonia 0.241394 0.553545 0.205061 0.053317 0.037806 0.914681 \n", "Finland 0.265172 0.471721 0.263107 0.068989 0.052305 0.878706 \n", "France 0.217415 0.591189 0.191396 0.027174 0.035224 0.937602 \n", "Greece 0.053792 0.932115 0.014092 0.005688 0.004935 0.989377 \n", "Hungary 0.195913 0.698102 0.105984 0.023038 0.013124 0.963839 \n", "Iceland 0.547750 0.452416 0.208852 0.151740 0.069536 0.801948 \n", "Ireland 0.156598 0.630554 0.212848 0.042618 0.041603 0.915779 \n", "Italy 0.144048 0.474959 0.380993 0.030227 0.055150 0.914623 \n", "Latvia 0.197637 0.587298 0.223853 0.041431 0.050897 0.907673 \n", "Lithuania 0.157101 0.789642 0.066502 0.026862 0.013006 0.960131 \n", "Luxembourg 0.268983 0.565153 0.185788 0.052180 0.028282 0.921070 \n", "Montenegro 0.109557 0.824777 0.116186 0.038326 0.029717 0.938130 \n", "Netherlands 0.236546 0.439109 0.324345 0.049683 0.044300 0.906016 \n", "North Macedonia 0.120629 0.753843 0.136543 0.033310 0.048444 0.918247 \n", "Norway 0.278132 0.361489 0.360379 0.067821 0.037724 0.894455 \n", "Poland 0.151504 0.672386 0.176111 0.015834 0.017959 0.966207 \n", "Portugal 0.216267 0.604421 0.179312 0.054338 0.041930 0.903732 \n", "Romania 0.099310 0.747447 0.157862 0.030172 0.014482 0.955544 \n", "Serbia 0.191827 0.541846 0.266327 0.056950 0.050821 0.892229 \n", "Slovakia 0.100679 0.869641 0.030941 0.012366 0.008208 0.979426 \n", "Slovenia 0.209714 0.585139 0.205147 0.066143 0.029020 0.904837 \n", "Spain 0.194532 0.647890 0.157578 0.034631 0.067275 0.898094 \n", "Sweden 0.261274 0.554735 0.183991 0.052205 0.051159 0.896636 \n", "Switzerland 0.334455 0.498121 0.167424 0.086431 0.032367 0.881202 \n", "Turkey 0.338833 0.400868 0.260299 0.094346 0.034313 0.871341 \n", "United Kingdom 0.190567 0.677814 0.131619 0.030792 0.022625 0.946582 \n", "\n", " Steady State Unemployment Rate \n", "Austria 5.127803 \n", "Belgium 5.452638 \n", "Bulgaria 9.002698 \n", "Croatia 8.827952 \n", "Cyprus 11.971578 \n", "Czechia 4.332546 \n", "Denmark 6.334405 \n", "Estonia 6.020868 \n", "Finland 8.079962 \n", "France 9.210156 \n", "Greece 25.308979 \n", "Hungary 6.219922 \n", "Iceland 6.595419 \n", "Ireland 9.726803 \n", "Italy 10.747803 \n", "Latvia 11.038423 \n", "Lithuania 10.299465 \n", "Luxembourg 4.663819 \n", "Montenegro 15.292814 \n", "Netherlands 5.283769 \n", "North Macedonia 24.479348 \n", "Norway 3.677813 \n", "Poland 6.591411 \n", "Portugal 11.164161 \n", "Romania 5.690870 \n", "Serbia 13.054313 \n", "Slovakia 9.451457 \n", "Slovenia 7.540447 \n", "Spain 20.108421 \n", "Sweden 7.140653 \n", "Switzerland 4.590608 \n", "Turkey 10.493494 \n", "United Kingdom 5.306874 " ] }, "execution_count": 174, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#Create empty dictionary to create dataframe\n", "Means_dic={}\n", "\n", "#iterate through all countries\n", "for country in countries:\n", " #create empty list to store all the values for each country\n", " aux=[]\n", " #Iterate throuch all the columns in each country dataframe\n", " for column in countries[country]:\n", " #add the mean of the respective column to the aux list\n", " aux.append(countries[country][column].mean(axis=0))\n", " #add the aux list with all mean values for a country to the dictionary \n", " Means_dic[country]=aux\n", "\n", "#Create Means datafram out of dictionary\n", "Means=pd.DataFrame(Means_dic)\n", "\n", "#Transpose dataframe to have countries as index\n", "Means=Means.T\n", "\n", "#Change the column name to the transition rates\n", "Means.columns=countries[\"Switzerland\"].columns\n", "\n", "#print Means dataframe\n", "Means" ] }, { "cell_type": "markdown", "metadata": { "id": "YMly7L-Tivww" }, "source": [ "To assess the performance of the model, we need to compute the distance between the steady state model and the data. A useful method to compute the distance between the model and the data and tell how accurate our computations were is to use:\n", "​\n", "$$\\text{Distance}=\\log{\\Big(\\frac{\\text{model}}{\\text{data}}\\Big)}^2$$\n", "​\n", "\n", "We use the logarithm because it has the advantage of being magnitude-neutral, giving us the model's relative deviations from the data. The logarithm can be easily calculated with the [`math`](https://docs.python.org/3/library/math.html) module:" ] }, { "cell_type": "code", "execution_count": 175, "metadata": { "id": "HIdaiTTQH7gW" }, "outputs": [ { "data": { "text/html": [ "
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Measured Unemployment Raterate_EErate_EUrate_EIrate_UErate_UUrate_UIrate_IErate_IUrate_IISteady State Unemployment RateDifference
Austria4.8720590.9545100.0127610.0327290.2758740.5069550.2171710.0620010.0278890.9101105.1278030.002617
Belgium7.5408820.9533090.0100650.0366260.2068300.5175720.2755990.0514670.0261470.9223855.4526380.105131
Bulgaria10.2253090.9767710.0087000.0150670.1022700.7965330.1011970.0191940.0128800.9679269.0026980.016216
Croatia12.3212330.9665230.0153600.0193260.1469790.7392410.2307300.0212610.0303250.9643718.8279520.111156
Cyprus8.8843750.9636130.0225290.0138580.2081640.7234430.0683930.0226630.0224520.95587311.9715780.088948
Czechia6.1441010.9823370.0066270.0110360.1971200.6576110.1452700.0176050.0142770.9681184.3325460.122036
Denmark5.6594120.9445130.0174560.0380320.3371730.4355150.2273130.0681030.0421640.8897336.3344050.012696
Estonia8.8308640.9624510.0151090.0238620.2413940.5535450.2050610.0533170.0378060.9146816.0208680.146705
Finland8.3960670.9391380.0175610.0433010.2651720.4717210.2631070.0689890.0523050.8787068.0799620.001473
France8.9014490.9604720.0186090.0209190.2174150.5911890.1913960.0271740.0352240.9376029.2101560.001162
Greece15.4157300.9770610.0148010.0081380.0537920.9321150.0140920.0056880.0049350.98937725.3089790.245789
Hungary7.1079410.9736050.0100170.0163780.1959130.6981020.1059840.0230380.0131240.9638396.2199220.017810
Iceland4.2054350.9488630.0166760.0382980.5477500.4524160.2088520.1517400.0695360.8019486.5954190.202498
Ireland7.9886900.9633310.0136330.0230360.1565980.6305540.2128480.0426180.0416030.9157799.7268030.038753
Italy9.5853930.9564190.0151220.0284590.1440480.4749590.3809930.0302270.0551500.91462310.7478030.013101
Latvia10.9831080.9545820.0193210.0260970.1976370.5872980.2238530.0414310.0508970.90767311.0384230.000025
Lithuania9.9520270.9714270.0132160.0153570.1571010.7896420.0665020.0268620.0130060.96013110.2994650.001178
Luxembourg5.2673910.9603350.0127360.0289130.2689830.5651530.1857880.0521800.0282820.9210704.6638190.014811
Montenegro17.5076390.9665060.0129780.0342310.1095570.8247770.1161860.0383260.0297170.93813015.2928140.018294
Netherlands4.7401230.9664000.0116750.0219250.2365460.4391090.3243450.0496830.0443000.9060165.2837690.011789
North Macedonia27.7622810.9316460.0346300.0336830.1206290.7538430.1365430.0333100.0484440.91824724.4793480.015838
Norway3.6225310.9618210.0087500.0294280.2781320.3614890.3603790.0678210.0377240.8944553.6778130.000229
Poland10.8641980.9796190.0085740.0118070.1515040.6723860.1761110.0158340.0179590.9662076.5914110.249705
Portugal8.7932580.9421050.0227520.0351430.2162670.6044210.1793120.0543380.0419300.90373211.1641610.056989
Romania6.5532350.9721650.0047960.0230990.0993100.7474470.1578620.0301720.0144820.9555445.6908700.019908
Serbia14.2086540.9259700.0287560.0452750.1918270.5418460.2663270.0569500.0508210.89222913.0543130.007180
Slovakia13.2185390.9830210.0072780.0097010.1006790.8696410.0309410.0123660.0082080.9794269.4514570.112528
Slovenia6.8311760.9428060.0154190.0417750.2097140.5851390.2051470.0661430.0290200.9048377.5404470.009758
Spain15.9592700.9298630.0424210.0277160.1945320.6478900.1575780.0346310.0672750.89809420.1084210.053407
Sweden7.0415580.9598760.0144460.0256780.2612740.5547350.1839910.0522050.0511590.8966367.1406530.000195
Switzerland4.7079270.9555500.0135130.0309370.3344550.4981210.1674240.0864310.0323670.8812024.5906080.000637
Turkey10.3929820.8709950.0368810.0921230.3388330.4008680.2602990.0943460.0343130.87134110.4934940.000093
United Kingdom5.6638550.9763560.0080100.0156340.1905670.6778140.1316190.0307920.0226250.9465825.3068740.004238
\n", "
" ], "text/plain": [ " Measured Unemployment Rate rate_EE rate_EU rate_EI \\\n", "Austria 4.872059 0.954510 0.012761 0.032729 \n", "Belgium 7.540882 0.953309 0.010065 0.036626 \n", "Bulgaria 10.225309 0.976771 0.008700 0.015067 \n", "Croatia 12.321233 0.966523 0.015360 0.019326 \n", "Cyprus 8.884375 0.963613 0.022529 0.013858 \n", "Czechia 6.144101 0.982337 0.006627 0.011036 \n", "Denmark 5.659412 0.944513 0.017456 0.038032 \n", "Estonia 8.830864 0.962451 0.015109 0.023862 \n", "Finland 8.396067 0.939138 0.017561 0.043301 \n", "France 8.901449 0.960472 0.018609 0.020919 \n", "Greece 15.415730 0.977061 0.014801 0.008138 \n", "Hungary 7.107941 0.973605 0.010017 0.016378 \n", "Iceland 4.205435 0.948863 0.016676 0.038298 \n", "Ireland 7.988690 0.963331 0.013633 0.023036 \n", "Italy 9.585393 0.956419 0.015122 0.028459 \n", "Latvia 10.983108 0.954582 0.019321 0.026097 \n", "Lithuania 9.952027 0.971427 0.013216 0.015357 \n", "Luxembourg 5.267391 0.960335 0.012736 0.028913 \n", "Montenegro 17.507639 0.966506 0.012978 0.034231 \n", "Netherlands 4.740123 0.966400 0.011675 0.021925 \n", "North Macedonia 27.762281 0.931646 0.034630 0.033683 \n", "Norway 3.622531 0.961821 0.008750 0.029428 \n", "Poland 10.864198 0.979619 0.008574 0.011807 \n", "Portugal 8.793258 0.942105 0.022752 0.035143 \n", "Romania 6.553235 0.972165 0.004796 0.023099 \n", "Serbia 14.208654 0.925970 0.028756 0.045275 \n", "Slovakia 13.218539 0.983021 0.007278 0.009701 \n", "Slovenia 6.831176 0.942806 0.015419 0.041775 \n", "Spain 15.959270 0.929863 0.042421 0.027716 \n", "Sweden 7.041558 0.959876 0.014446 0.025678 \n", "Switzerland 4.707927 0.955550 0.013513 0.030937 \n", "Turkey 10.392982 0.870995 0.036881 0.092123 \n", "United Kingdom 5.663855 0.976356 0.008010 0.015634 \n", "\n", " rate_UE rate_UU rate_UI rate_IE rate_IU rate_II \\\n", "Austria 0.275874 0.506955 0.217171 0.062001 0.027889 0.910110 \n", "Belgium 0.206830 0.517572 0.275599 0.051467 0.026147 0.922385 \n", "Bulgaria 0.102270 0.796533 0.101197 0.019194 0.012880 0.967926 \n", "Croatia 0.146979 0.739241 0.230730 0.021261 0.030325 0.964371 \n", "Cyprus 0.208164 0.723443 0.068393 0.022663 0.022452 0.955873 \n", "Czechia 0.197120 0.657611 0.145270 0.017605 0.014277 0.968118 \n", "Denmark 0.337173 0.435515 0.227313 0.068103 0.042164 0.889733 \n", "Estonia 0.241394 0.553545 0.205061 0.053317 0.037806 0.914681 \n", "Finland 0.265172 0.471721 0.263107 0.068989 0.052305 0.878706 \n", "France 0.217415 0.591189 0.191396 0.027174 0.035224 0.937602 \n", "Greece 0.053792 0.932115 0.014092 0.005688 0.004935 0.989377 \n", "Hungary 0.195913 0.698102 0.105984 0.023038 0.013124 0.963839 \n", "Iceland 0.547750 0.452416 0.208852 0.151740 0.069536 0.801948 \n", "Ireland 0.156598 0.630554 0.212848 0.042618 0.041603 0.915779 \n", "Italy 0.144048 0.474959 0.380993 0.030227 0.055150 0.914623 \n", "Latvia 0.197637 0.587298 0.223853 0.041431 0.050897 0.907673 \n", "Lithuania 0.157101 0.789642 0.066502 0.026862 0.013006 0.960131 \n", "Luxembourg 0.268983 0.565153 0.185788 0.052180 0.028282 0.921070 \n", "Montenegro 0.109557 0.824777 0.116186 0.038326 0.029717 0.938130 \n", "Netherlands 0.236546 0.439109 0.324345 0.049683 0.044300 0.906016 \n", "North Macedonia 0.120629 0.753843 0.136543 0.033310 0.048444 0.918247 \n", "Norway 0.278132 0.361489 0.360379 0.067821 0.037724 0.894455 \n", "Poland 0.151504 0.672386 0.176111 0.015834 0.017959 0.966207 \n", "Portugal 0.216267 0.604421 0.179312 0.054338 0.041930 0.903732 \n", "Romania 0.099310 0.747447 0.157862 0.030172 0.014482 0.955544 \n", "Serbia 0.191827 0.541846 0.266327 0.056950 0.050821 0.892229 \n", "Slovakia 0.100679 0.869641 0.030941 0.012366 0.008208 0.979426 \n", "Slovenia 0.209714 0.585139 0.205147 0.066143 0.029020 0.904837 \n", "Spain 0.194532 0.647890 0.157578 0.034631 0.067275 0.898094 \n", "Sweden 0.261274 0.554735 0.183991 0.052205 0.051159 0.896636 \n", "Switzerland 0.334455 0.498121 0.167424 0.086431 0.032367 0.881202 \n", "Turkey 0.338833 0.400868 0.260299 0.094346 0.034313 0.871341 \n", "United Kingdom 0.190567 0.677814 0.131619 0.030792 0.022625 0.946582 \n", "\n", " Steady State Unemployment Rate Difference \n", "Austria 5.127803 0.002617 \n", "Belgium 5.452638 0.105131 \n", "Bulgaria 9.002698 0.016216 \n", "Croatia 8.827952 0.111156 \n", "Cyprus 11.971578 0.088948 \n", "Czechia 4.332546 0.122036 \n", "Denmark 6.334405 0.012696 \n", "Estonia 6.020868 0.146705 \n", "Finland 8.079962 0.001473 \n", "France 9.210156 0.001162 \n", "Greece 25.308979 0.245789 \n", "Hungary 6.219922 0.017810 \n", "Iceland 6.595419 0.202498 \n", "Ireland 9.726803 0.038753 \n", "Italy 10.747803 0.013101 \n", "Latvia 11.038423 0.000025 \n", "Lithuania 10.299465 0.001178 \n", "Luxembourg 4.663819 0.014811 \n", "Montenegro 15.292814 0.018294 \n", "Netherlands 5.283769 0.011789 \n", "North Macedonia 24.479348 0.015838 \n", "Norway 3.677813 0.000229 \n", "Poland 6.591411 0.249705 \n", "Portugal 11.164161 0.056989 \n", "Romania 5.690870 0.019908 \n", "Serbia 13.054313 0.007180 \n", "Slovakia 9.451457 0.112528 \n", "Slovenia 7.540447 0.009758 \n", "Spain 20.108421 0.053407 \n", "Sweden 7.140653 0.000195 \n", "Switzerland 4.590608 0.000637 \n", "Turkey 10.493494 0.000093 \n", "United Kingdom 5.306874 0.004238 " ] }, "execution_count": 175, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Initiated difference column as float\n", "Means['Difference'] = 0.0\n", "\n", "#iterate through all rows\n", "for index,row in Means.iterrows():\n", " #Get steady state value\n", " model = Means.at[index,'Steady State Unemployment Rate']\n", " #Get actual value\n", " data = Means.at[index,'Measured Unemployment Rate']\n", " #calculate distance and store value in dataframe\n", " Means.at[index,'Difference'] = (math.log(model/data)**2)\n", "\n", "#print dataframe \n", "Means" ] }, { "cell_type": "markdown", "metadata": { "id": "1AXaROXaIVik" }, "source": [ "**Creation of the model** \n", "Similarly to the pervious chapters, we can now create the regression model:" ] }, { "cell_type": "code", "execution_count": 176, "metadata": { "id": "1lQfqxCLINIU" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " OLS Regression Results \n", "==============================================================================\n", "Dep. Variable: Difference R-squared: 0.247\n", "Model: OLS Adj. R-squared: 0.073\n", "Method: Least Squares F-statistic: 1.418\n", "Date: Sun, 16 May 2021 Prob (F-statistic): 0.245\n", "Time: 17:48:04 Log-Likelihood: 45.137\n", "No. Observations: 33 AIC: -76.27\n", "Df Residuals: 26 BIC: -65.80\n", "Df Model: 6 \n", "Covariance Type: nonrobust \n", "==============================================================================\n", " coef std err t P>|t| [0.025 0.975]\n", "------------------------------------------------------------------------------\n", "const 0.0985 0.045 2.170 0.039 0.005 0.192\n", "rate_EU 4.6503 3.394 1.370 0.182 -2.326 11.627\n", "rate_EI -4.5372 2.164 -2.097 0.046 -8.985 -0.090\n", "rate_UE -0.1684 0.349 -0.482 0.634 -0.887 0.550\n", "rate_UI 0.1372 0.280 0.490 0.628 -0.438 0.713\n", "rate_IE 2.2796 1.647 1.384 0.178 -1.106 5.666\n", "rate_IU -2.6810 1.904 -1.408 0.171 -6.595 1.233\n", "==============================================================================\n", "Omnibus: 6.259 Durbin-Watson: 2.609\n", "Prob(Omnibus): 0.044 Jarque-Bera (JB): 4.798\n", "Skew: 0.889 Prob(JB): 0.0908\n", "Kurtosis: 3.575 Cond. No. 378.\n", "==============================================================================\n", "\n", "Notes:\n", "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n" ] } ], "source": [ "#Defining the dependent variable\n", "Y=Means[\"Difference\"]\n", "\n", "#Defining the regressors and adding a constant (the intercept B0) with the sm.add_constant method\n", "X=sm.add_constant(Means[['rate_EU','rate_EI','rate_UE','rate_UI','rate_IE','rate_IU']])\n", "\n", "#Initializing the OLS rergeression\n", "regression = sm.OLS(Y,X, missing='drop')\n", "\n", "#Fit the model by calling the OLS object’s fit() method\n", "regresults = regression.fit()\n", "\n", "#Print the summary of the fit\n", "print(regresults.summary())" ] }, { "cell_type": "markdown", "metadata": { "id": "IPmv7XxgI5zk" }, "source": [ "**Interpretation of OLS Regression** \n", "This OLS regression tests which elements account for the distance between the model and the data. \n", "The outcome of this regression is:\n", "\n", "\n", "$$\\text{difference}={0.0985 +.4.6503\\cdot EU -4.5372\\cdot EI -0.1684\\cdot UE + 0.1372\\cdot UI +2.2796\\cdot IE - 2.6810\\cdot IU + e_i}$$\n", "\n", "\n", "This explains the composition of the difference between the model and the data. The composition is made up of the transition rates and their magintude effects. You can see that EI has the largest effect to decreasing the distance between the model and the data and IE has the largest in increasing the distance. We can also create a dataframe that ranks the magnitude of each transitions rate on the difference:" ] }, { "cell_type": "code", "execution_count": 177, "metadata": { "id": "l2bfYyw0JAl8" }, "outputs": [ { "data": { "text/html": [ "
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0aux
rate_EU4.6502594.650259
rate_EI-4.5371764.537176
rate_IU-2.6809822.680982
rate_IE2.2795852.279585
rate_UE-0.1683900.168390
rate_UI0.1371840.137184
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" ], "text/plain": [ " 0 aux\n", "rate_EU 4.650259 4.650259\n", "rate_EI -4.537176 4.537176\n", "rate_IU -2.680982 2.680982\n", "rate_IE 2.279585 2.279585\n", "rate_UE -0.168390 0.168390\n", "rate_UI 0.137184 0.137184" ] }, "execution_count": 177, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#get coefficents into dataframe\n", "magnitude=pd.DataFrame(regresults.params)\n", "\n", "#Calculate absolute values to make effects comparable/rankable\n", "magnitude[\"aux\"]=abs(magnitude[0])\n", "\n", "#drop intercept\n", "magnitude=magnitude.drop(\"const\",axis=0)\n", "\n", "#Sort the dataframe to get coefficents from most to least important\n", "magnitude.sort_values(by=[\"aux\"], ascending=False) " ] }, { "cell_type": "markdown", "metadata": { "id": "fSC7epqjJM_D" }, "source": [ "As we can see again, EU has the largest effect on the distance between the Measured Unemployment Rate and the Steady State Unemployment Rate, while UI has the smallest effect. We can also check which transition rates have a significnt effect on the distance between the Measured Unemployment Rate and the Steady State Unemployment Rate:" ] }, { "cell_type": "code", "execution_count": 178, "metadata": { "id": "K1GVA1aeJPXc" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "('const', 0.039318055504092446)\n", "('rate_EI', 0.04587714185802691)\n" ] } ], "source": [ "#Setting significane level\n", "significane=0.05\n", "\n", "# For-loop to get all parameters below certain p-value\n", "for item in regresults.pvalues.iteritems(): #Gets all the p-values\n", " if item[1]" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "#Set propertoies of the plots\n", "plt.rc(\"figure\", figsize=(24,12))\n", "plt.rc(\"font\", size=14)\n", "\n", "#Create figure\n", "fig = sm.graphics.plot_partregress_grid(regresults)\n", "\n", "#automatically adjusts subplot params so that the subplots fits in to the figure area\n", "fig.tight_layout(pad=1.0)" ] }, { "cell_type": "markdown", "metadata": { "id": "dvFSWFCW8ELo" }, "source": [ "\n", "## Discussion\n", "\n", "To conclude, in this chapter we conducted five different regressions: Three simple regressions and two multiple regressions. Through this analysis, we were able to identify a significant (p < 0.05) relationship in one of the simple regressions and one of the multiple regressions.\n", "\n", "Firstly, we were able to find out that the GDP per capita of a country has a significant positive effect on the job-finding probability in that country. Simply put, it appears that if a country is doing well economically, as measured by GDP per capita, it is easier for the population in that country to find employment. Secondly, we analysed how transition rates from one period ago (t=-1) affect the unemployment rate in the current period (t=0). From our analysis, it seems that the following transition rates of one period ago affect the unemployment rate significantly: EU, UE, IU, UI. Thus, it seems that all flows from and to the unemployment stock have a significant influence on the unemployment rate in one period, while the flows from the employment stock to the inactive stock do not significantly affect the unemployment rate. In total, the model had an adjusted- 𝑅2 of 0.8146, which means that the model with the transition rates of one period ago explains around 81.46% of the variation in the unemployment rate.\n", "\n", "Furthermore, we conducted three other OLS regressions as well. Firstly, we looked at the relationship of the unemployment rate in different age groups on GDP growth in a country. Secondly, we looked at how the unemployment rate amongst people with a certain educational attainment level affects GDP-Growth. Thirdly, we looked at how the different transition rates affect the unemployment rate. However, none of these regressions yielded significant results, which is why we concluded that, in the case of these regressions, there is no relationship between the dependent and independent variables.\n", "\n", "
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\n", " \n", " Figure 6: Ending caricature (xkcd)\n", " \n", "

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\n", "\n", "\n", "# Conclusion\n", "\n", "To sum up, in this tutorial we first showed you a simple model of the labour market and introduced the concepts of transition rates and the steady-state unemployment rate. We then introduced the datasets we needed for this tutorial and imported the packages necessary to conduct our analysis. After that, we showed how to use the Eurostat API to download the necessary data and presented the basics of how to use the pandas package in python for data handling, which is an essential step in every data-focused application. We then proceeded to show how to visualize the data cleaned in the previous step, using various packages. Finally, we showed how to conduct a simple statistical analysis using the statsmodels library. Specifically, we showed how to conduct OLS regressions in Python and how to present the results from such a regression.\n", "\n", "
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\n", " \n", " Figure 7: Concluding GIF\n", " \n", "

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