{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Analyzing NYC High School Data\n", "\n", "In this project we will use the public data of New York City regarding SAT scores, school demographics, AP school level, survey to parents, students and teachers.\n", "\n", "These are the datasets:\n", "\n", "* [2012 SAT Results](https://data.cityofnewyork.us/Education/2012-SAT-Results/f9bf-2cp4): SAT Scores per school.\n", "* [2010 - 2011 School Attendance and Enrollment Statistics by District](https://data.cityofnewyork.us/Education/2010-2011-School-Attendance-and-Enrollment-Statist/7z8d-msnt): attendance information per district.\n", "* [2010-2011 Class Size - School-level detail](https://data.cityofnewyork.us/Education/2010-2011-Class-Size-School-level-detail/urz7-pzb3): number of alumni per school.\n", "* [2010 AP (College Board) School Level Results](https://data.cityofnewyork.us/Education/2010-AP-College-Board-School-Level-Results/itfs-ms3e): AP Test taking.\n", "* [2005-2010 Graduation Outcomes - School Level](https://data.cityofnewyork.us/Education/2005-2010-Graduation-Outcomes-School-Level/vh2h-md7a): how many students graduate.\n", "* [2006 - 2012 School Demographics and Accountability Snapshot](https://data.cityofnewyork.us/Education/2006-2012-School-Demographics-and-Accountability-S/ihfw-zy9j): group identity and other info of the students.\n", "* [2011 NYC School Survey](https://data.cityofnewyork.us/Education/2011-NYC-School-Survey/mnz3-dyi8): survey for teachers, students and parents.\n", "\n", "SAT exams are recently [under fire](https://www.forbes.com/sites/susanadams/2020/09/30/the-forbes-investigation-how-the-sat-failed-america/#2f98273a53b5). This analysis helps also to understand why the critics are right: the exams have a problem of circularity, it doesn't show that some people are smarter than others, only shows that perhaps if you are good at taking tests and have money for prepare way ahead you would obtain better results.\n", "\n", "An special part is dedicated of the correlation of free lunch program and the sat scores. Lower resources is a predictor of bad scores.\n", "\n", "Is it necessary to override the SAT exams? Yes, because is made by people that have good test scores to measure people that also is good at test scores.\n", "\n", "But, the positive part is that if we totally eliminate this kind of exams we could also loss evidence of the problems of poverty and academic qualifications. Maybe the test must be focused to spot problems of poverty and how to deal with them.\n", "\n", "Also the TEST is designed for people that will work in big companies with bureaucratic works, not for entrepreneurs or people that love art.\n", "\n", "So, let's see the results." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 1. Read in the data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We are going first open the datasets, but as we have very large datasets, we are going to convert all the datasets in one big dictionary:\n", "\n", "1. We are going to put the names of the files in one list.\n", "2. Next we create an empty dictionary where we put the datasets.\n", "3. Finally we loop using the datasets names so we can assign each datasets to the dictionary with the key name" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", "import numpy\n", "import re\n", "import matplotlib.pyplot as plt\n", "import plotly.express as px\n", "import seaborn as sns\n", "import plotly.graph_objects as go\n", "import matplotlib.image as mpimg\n", "from matplotlib import rcParams\n", "\n", "\n", "#List od names of the datasets\n", "data_files = [\n", " \"ap_2010.csv\",\n", " \"class_size.csv\",\n", " \"demographics.csv\",\n", " \"graduation.csv\",\n", " \"hs_directory.csv\",\n", " \"sat_results.csv\"\n", "]\n", "\n", "data = {}\n", "\n", "#Looping to put the datasets in the dictionary\n", "for f in data_files:\n", " d = pd.read_csv(\"schools/{0}\".format(f))\n", " data[f.replace(\".csv\", \"\")] = d" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 2. Read in the surveys" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we will use `concat`to unify all the surveys datasets that we downloaded:\n", "\n", "1. The encoding in this data sets is `windows-1252`, is and old kind of encoding, you can see the historical statistics usage [here](https://w3techs.com/technologies/history_overview/character_encoding/ms/y)\n", "2. In other datasets the school identification code is written \"DBN\", we are going to change the name in the surveys to later concat using the same code.\n", "3. We are going to use only survey responses, removing other info like principal name or school location.\n", "4. Finally we will assign the new survey dataset to the previously created dictionary." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "# With the encoding 'windows-1252' we use a differente delimiter \"\\t\"\n", "all_survey = pd.read_csv(\"schools/survey_all.txt\", delimiter=\"\\t\", encoding='windows-1252')\n", "d75_survey = pd.read_csv(\"schools/survey_d75.txt\", delimiter=\"\\t\", encoding='windows-1252')\n", "survey = pd.concat([all_survey, d75_survey], axis=0)\n", "\n", "survey[\"DBN\"] = survey[\"dbn\"]\n", "\n", "survey_fields = [\n", " \"DBN\", \n", " \"rr_s\", \n", " \"rr_t\", \n", " \"rr_p\", \n", " \"N_s\", \n", " \"N_t\", \n", " \"N_p\", \n", " \"saf_p_11\", \n", " \"com_p_11\", \n", " \"eng_p_11\", \n", " \"aca_p_11\", \n", " \"saf_t_11\", \n", " \"com_t_11\", \n", " \"eng_t_11\", \n", " \"aca_t_11\", \n", " \"saf_s_11\", \n", " \"com_s_11\", \n", " \"eng_s_11\", \n", " \"aca_s_11\", \n", " \"saf_tot_11\", \n", " \"com_tot_11\", \n", " \"eng_tot_11\", \n", " \"aca_tot_11\",\n", "]\n", "survey = survey.loc[:,survey_fields]\n", "#Assign again the dataset to the dictionary\n", "data[\"survey\"] = survey" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 3. Add DBN columns" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "When we explored all of the data sets, we noticed that some of them, like class_size and hs_directory, don't have a DBN column. hs_directory does have a dbn column, though, so we can just rename it\n", "\n", "Some of the datasets don't have a DBN column. For example `class_size`have two columns, \"CSD\" and \"Code\", when we combine both data sets we obtain the DBN.\n", "\n", "So, we need to pad the \"CSD\" column, convert the single number to a 0x format so we can obtain the same number code that in DBN, and once we have that we combine the numbers.\n", "\n", "We will use the `pandas.Series.apply()` method, along with a custom function that:\n", "\n", "1. Takes in a number.\n", "2. Converts the number to a string using the `str()` function.\n", "3. Check the length of the string using the `len()` function.\n", "4. If the string is two digits long, returns the string.\n", "5. If the string is one digit long, adds a 0 to the front of the string, then returns it.\n", "6. You can use the string method `zfill()` to do this." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "data[\"hs_directory\"][\"DBN\"] = data[\"hs_directory\"][\"dbn\"]\n", "\n", "def pad_csd(num):\n", " string_representation = str(num)\n", " if len(string_representation) > 1:\n", " return string_representation\n", " else:\n", " return \"0\" + string_representation\n", " \n", "data[\"class_size\"][\"padded_csd\"] = data[\"class_size\"][\"CSD\"].apply(pad_csd)\n", "data[\"class_size\"][\"DBN\"] = data[\"class_size\"][\"padded_csd\"] + data[\"class_size\"][\"SCHOOL CODE\"]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 4. Convert columns to numeric" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We'll need to convert the SAT Math Avg. Score, SAT Critical Reading Avg. Score, and SAT Writing Avg. Score columns in the sat_results data set from the object (string) data type to a numeric data type. We can use the **`pandas.to_numeric()`** method for the conversion. If we don't convert the values, we won't be able to add the columns together.\n", "\n", "It's important to pass the keyword argument **`errors=\"coerce\"`** when we call **`pandas.to_numeric()`**, so that pandas treats any invalid strings it can't convert to numbers as missing values instead.\n", "\n", "After we perform the conversion, we can use the addition operator (+) to add all three columns together.\n", "\n", "We also want to extract the latitude, 40.8276026690005, and the longitude, -73.90447525699966. Taken together, latitude and longitude make up a pair of coordinates that allows us to pinpoint any location on Earth.\n", "\n", "We can do the extraction with a regular expression. \n", "\n", "This command will return [(40.8276026690005, -73.90447525699966)]. We'll need to process this result further using the string methods **`split()`** and **`replace()`** methods to extract each coordinate." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "cols = ['SAT Math Avg. Score', 'SAT Critical Reading Avg. Score', 'SAT Writing Avg. Score']\n", "for c in cols:\n", " data[\"sat_results\"][c] = pd.to_numeric(data[\"sat_results\"][c], errors=\"coerce\")\n", "\n", "data['sat_results']['sat_score'] = data['sat_results'][cols[0]] + data['sat_results'][cols[1]] + data['sat_results'][cols[2]]\n", "\n", "def find_lat(loc):\n", " coords = re.findall(\"\\(.+, .+\\)\", loc)\n", " lat = coords[0].split(\",\")[0].replace(\"(\", \"\")\n", " return lat\n", "\n", "def find_lon(loc):\n", " coords = re.findall(\"\\(.+, .+\\)\", loc)\n", " lon = coords[0].split(\",\")[1].replace(\")\", \"\").strip()\n", " return lon\n", "\n", "data[\"hs_directory\"][\"lat\"] = data[\"hs_directory\"][\"Location 1\"].apply(find_lat)\n", "data[\"hs_directory\"][\"lon\"] = data[\"hs_directory\"][\"Location 1\"].apply(find_lon)\n", "\n", "data[\"hs_directory\"][\"lat\"] = pd.to_numeric(data[\"hs_directory\"][\"lat\"], errors=\"coerce\")\n", "data[\"hs_directory\"][\"lon\"] = pd.to_numeric(data[\"hs_directory\"][\"lon\"], errors=\"coerce\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 5. Condense datasets" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We'll need to condense these data sets so that each value in the DBN column is unique. If not, we'll run into issues when it comes time to combine the data sets.\n", "\n", "While the main data set we want to analyze, sat_results, has unique DBN values for every high school in New York City, other data sets aren't as clean. A single row in the sat_results data set may match multiple rows in the class_size data set, for example. This situation will create problems, because we don't know which of the multiple entries in the class_size data set we should combine with the single matching entry in sat_results.\n", "\n", "So first we need to:\n", "\n", "+ Create a new variable called class_size, and assign the value of data[\"class_size\"] to it.\n", "+ Filter class_size so the GRADE column only contains the value 09-12. \n", "+ Filter class_size so that the PROGRAM TYPE column only contains the value GEN ED.\n", "+ Display the first five rows of class_size to verify.\n", "+ Group the data with the unique DBN code of each high school and assign to a new column \"class_size\"\n", "+ Apply a pandas bool to select in the column \"demographics\" only the \"schoolyear\" 20112012 so we obtain the most recent year data of the dataset.\n", "+ The \"cohort\" we choose the most recent year that is 2006\n", "+ In the demographic we only select the \"Total cohort\" values." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "class_size = data[\"class_size\"]\n", "class_size = class_size[class_size[\"GRADE \"] == \"09-12\"]\n", "class_size = class_size[class_size[\"PROGRAM TYPE\"] == \"GEN ED\"]\n", "\n", "class_size = class_size.groupby(\"DBN\").agg(numpy.mean)\n", "class_size.reset_index(inplace=True)\n", "data[\"class_size\"] = class_size\n", "\n", "data[\"demographics\"] = data[\"demographics\"][data[\"demographics\"][\"schoolyear\"] == 20112012]\n", "\n", "data[\"graduation\"] = data[\"graduation\"][data[\"graduation\"][\"Cohort\"] == \"2006\"]\n", "data[\"graduation\"] = data[\"graduation\"][data[\"graduation\"][\"Demographic\"] == \"Total Cohort\"]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 6. Convert AP scores to numeric" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "High school students take the AP exams before applying to college. There are several AP exams, each corresponding to a school subject. High school students who earn high scores may receive college credit.\n", "\n", "AP exams have a 1 to 5 scale; 3 or higher is a passing score. Many high school students take AP exams -- particularly those who attend academically challenging institutions. AP exams are much more rare in schools that lack funding or academic rigor.\n", "\n", "It will be interesting to find out whether AP exam scores are correlated with SAT scores across high schools. To determine this, we'll need to convert the AP exam scores in the ap_2010 data set to numeric values first.\n", "\n", "There are three columns we'll need to convert:\n", "\n", "* AP Test Takers (note that there's a trailing space in the column name)\n", "* Total Exams Taken\n", "* Number of Exams with scores 3 4 or 5" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "cols = ['AP Test Takers ', 'Total Exams Taken', 'Number of Exams with scores 3 4 or 5']\n", "\n", "for col in cols:\n", " data[\"ap_2010\"][col] = pd.to_numeric(data[\"ap_2010\"][col], errors=\"coerce\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 7. Combine the datasets\n", "\n", "Both the `ap_2010` and the`graduation` data sets have many missing`DBN` values, so we'll use a `left` join when we merge the`sat_results` data set with them. Because we're using a`left` join, our final dataframe will have all of the same`DBN` values as the original`sat_results` dataframe.\n", "\n", "We'll need to use the pandas`df.merge()` method to merge dataframes. The \"left\" dataframe is the one we call the method on, and the \"right\" dataframe is the one we pass into`df.merge()`.\n", "\n", "Because we're using the`DBN` column to join the dataframes, we'll need to specify the keyword argument`on=\"DBN\"` when calling `pandas.DataFrame.merge()`.\n", "\n", "First, we'll assign`data[\"sat_results\"]` to the variable`combined`. Then, we'll merge all of the other dataframes with`combined`. When we're finished,`combined` will have all of the columns from all of the data sets." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "combined = data[\"sat_results\"]\n", "\n", "combined = combined.merge(data[\"ap_2010\"], on=\"DBN\", how=\"left\")\n", "combined = combined.merge(data[\"graduation\"], on=\"DBN\", how=\"left\")\n", "\n", "to_merge = [\"class_size\", \"demographics\", \"survey\", \"hs_directory\"]\n", "\n", "for m in to_merge:\n", " combined = combined.merge(data[m], on=\"DBN\", how=\"inner\")\n", "\n", "combined = combined.fillna(combined.mean())\n", "combined = combined.fillna(0)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 8. Add a school district column for mapping\n", "\n", "Mapping the statistics out on a school district level might be an interesting way to analyze them. Adding a column to the data set that specifies the school district will help us accomplish this.\n", "\n", "The school district is just the first two characters of the `DBN`. We can apply a function over the `DBN` column of `combined`that pulls out the first two letters." ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "def get_first_two_chars(dbn):\n", " return dbn[0:2]\n", "\n", "combined[\"school_dist\"] = combined[\"DBN\"].apply(get_first_two_chars)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 9. Find correlations" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Once we have the data ready we can start with the correlations, `pandas.corr()`help us to find the correlations, we select only the data correlated with sat scores." ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "jupyter": { "outputs_hidden": false } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "SAT Critical Reading Avg. Score 0.986820\n", "SAT Math Avg. Score 0.972643\n", "SAT Writing Avg. Score 0.987771\n", "sat_score 1.000000\n", "AP Test Takers 0.523140\n", " ... \n", "priority08 NaN\n", "priority09 NaN\n", "priority10 NaN\n", "lat -0.121029\n", "lon -0.132222\n", "Name: sat_score, Length: 67, dtype: float64\n" ] } ], "source": [ "correlations = combined.corr()\n", "correlations = correlations[\"sat_score\"]\n", "print(correlations)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 10. Plotting survey correlations" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Visualize correlations is easier than loom at the table.\n", "\n", "On this part we are going to use [`plotly.express`](https://plotly.com/python/plotly-express/). Pros: the visuals are astonishing, easy to see the values of the data, easy to deploy colors and sizes. Cons: takes more memory, heavier code, takes more time to process. " ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "# Remove DBN since it's a unique identifier, not a useful numerical value for correlation.\n", "survey_fields.remove(\"DBN\")" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/html": [ " \n", " " ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.plotly.v1+json": { "config": { "plotlyServerURL": "https://plot.ly" }, "data": [ { "alignmentgroup": "True", "hovertemplate": "index=%{x}
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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "data = combined.corr()[\"sat_score\"][survey_fields].sort_values()\n", "fig = px.bar(data, y='sat_score', color='sat_score', height=600)\n", "fig" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The top correlations that we obtain are:\n", "\n", "1. `N_s`: Number of student respondents\n", "2. `N_p`: Number of parent respondents\n", "3. `aca_s_11`: Academic expectations score based on student responses\n", "4. `saf_s_11`: Safety and Respect score based on student responses\n", "5. `saf_tot_11`: Safety and Respect total score\n", "\n", "I couldn't find the exact questions that the NYC government asked to the students, teachers and parents, so I assume that the score is 0 to 10, 10 is the highest.\n", "\n", "So, the correlation indicates that more responses of parents and students implies better results in the `sat_score`. But this is really weak, lower than 0.5, so you need take this with a big pinch of salt.\n", "\n", "The most interesting part is the Safety and Respect score, is not clear what is the question, but it is related with: how the students feel about their institution, how the students feel about their borough. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 11. Safety and Respect : scatter plot and maps." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 11.1. Scatter plot: how the data is related\n", "\n", "What we are trying to see is how strong is related the Security and Respect score with the SAT scores of the schools.\n", "\n", "The most interesting part is always the outliers: which schools have low Security and Respect score and also have good sat scores.\n", "\n", "One useful scatter plot library is [`plotly express`](https://plotly.com/python/plotly-express/). The quality of the graphs are good, but the most valuable is that you can see which point is what, so you can easily retrieve data." ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "scrolled": true }, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "config": { "plotlyServerURL": "https://plot.ly" }, "data": [ { "hovertemplate": "%{hovertext}

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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# With plotly express automatic put a degrade color using a column name\n", "# and the name of the school can be deployed directly in the data using hover_name \n", "fig = px.scatter(combined, x=\"saf_s_11\", y=\"sat_score\", color='sat_score', size='saf_s_11'\n", " ,hover_name=\"SCHOOL NAME\")\n", "fig.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As expected we have a graph that shows that schools with greater SAT scores tend to have a better qualification in Security and Respect. But as alway we have some interesting outliers:\n", "\n", "1. [BRONX HIGH SCHOOL OF SCIENCE](https://en.wikipedia.org/wiki/Bronx_High_School_of_Science): is an elite public school with a mean score of 1969, with 8 Nobel prize winner and 2 Pulitzer winner between their ex alumni, but in Security and Respect the results are near the mean of results.\n", "2. [PEACE AND DIVERSITY ACADEMY](https://insideschools.org/school/12X278): near to the previous BRONX HIGH SCHOOL, with a better score in safety and respect, but sat_score 1155.\n", "3. [BENJAMIN N. CARDOZO HIGH SCHOOL](https://en.wikipedia.org/wiki/Benjamin_N._Cardozo_High_School): with above 1500 sat score and 6.5 safety score is a curious example of that security is not necessarily equal to more sat scores.\n", "\n", "The point is to see that this correlation is not clearly strong, and is not clear that if you choose a safer place you will have better sat_scores." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 11.2. Mapping the security score" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We want know to map the relations between the safety score and the security of the boroughs.\n", "\n", "1. We are using `pandas.groupby` and `numpy.mean`aggregation method so we can obtain the mean value of safety score by borough.\n", "2. Next, we are using the `plotly` to map the values in a high resolution map, also using the `mapbox_style` like `scatter_mapbox` and `density_mapbox` to see the results. " ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0 6.768611\n", "1 6.910660\n", "2 6.716667\n", "3 6.885714\n", "4 6.314286\n", "Name: saf_s_11, dtype: float64" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "districts = combined.groupby(\"school_dist\").agg(numpy.mean)\n", "#We need to reset the index so we can later use the results as dataset\n", "districts.reset_index(inplace=True)\n", "districts['saf_s_11'].head()" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "config": { "plotlyServerURL": "https://plot.ly" }, "data": [ { "hovertemplate": "sat_score=%{marker.size}
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saf_s_11=%{z}
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lon=%{lon}", "hovertext": [ "HENRY STREET SCHOOL FOR INTERNATIONAL STUDIES", "UNIVERSITY NEIGHBORHOOD HIGH SCHOOL", "EAST SIDE COMMUNITY SCHOOL", "MARTA VALLE HIGH SCHOOL", "NEW EXPLORATIONS INTO SCIENCE, TECHNOLOGY AND MATH HIGH SCHOOL", "BARD HIGH SCHOOL EARLY COLLEGE", "47 THE AMERICAN SIGN LANGUAGE AND ENGLISH SECONDARY SCHOOL", "FOOD AND FINANCE HIGH SCHOOL", "ESSEX STREET ACADEMY", "HIGH SCHOOL OF HOSPITALITY MANAGEMENT", "PACE HIGH SCHOOL", "URBAN ASSEMBLY SCHOOL OF DESIGN AND CONSTRUCTION, THE", "FACING HISTORY SCHOOL, THE", "URBAN ASSEMBLY ACADEMY OF GOVERNMENT AND LAW, THE", "LOWER MANHATTAN ARTS ACADEMY", "URBAN ASSEMBLY SCHOOL OF BUSINESS FOR YOUNG WOMEN, THE", "GRAMERCY ARTS HIGH SCHOOL", "NYC ISCHOOL", "MANHATTAN BUSINESS ACADEMY", "BUSINESS OF SPORTS SCHOOL", "THE HIGH SCHOOL FOR LANGUAGE AND DIPLOMACY", "HIGH SCHOOL FOR ENVIRONMENTAL STUDIES", "PROFESSIONAL PERFORMING ARTS HIGH SCHOOL", "BARUCH COLLEGE CAMPUS HIGH SCHOOL", "N.Y.C. 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import plotly.express as px\n", "df = combined\n", "fig = px.density_mapbox(df, lat=\"lat\", lon=\"lon\", z=\"saf_s_11\", radius=10,\n", " zoom=9,hover_name=\"SCHOOL NAME\",\n", " mapbox_style=\"stamen-terrain\")\n", "fig.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To understand better the results, we need to compare with the [crime map in NYC](https://maps.nyc.gov/crime/), it is not the same distribution at first glance, but it is also because they evaluate the safety into the school, not only externally. " ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "image/png": 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BldyjWg1HOvT7fcIgmNL4TJ6n2c9WYtFIaRm3PM+JopBZKF9N4oTA9yqVexgG1fi1tufK8xyECFDKIUkTGlGN8KjP2voaa2vrNBpNfN8ljhOktOr7wPfwXJdMKbJcMe/CGz07h/2QTSl9iam1iOME17XEZh4TUBKg0h+mvGNzVdPSMk7GjJnqUhMzb713r6lV+xsMea5Ag/A8ZHE2prryPUa9AQBBZXum8ls5EBTLorXGlRDVAoLA2vvPnFzl6GqXd/VHXLm+xcuX1rj89Fd56cmvU1ta5dj5O7nj/HnuvPPOXfPKM0UUOuSZ9RspNZJKafr9PlEY4nkBV69eodVqMVjfqt7tDUfU6yEuzi4JfVKzF8cxeaZo1upsbW0SeF6lVRw/P+fMFLbxal8nfl5ot7jr/Fm++NVvsLy4SJyMzZvPv/wqn/m3X+T46jI/8WM/zPeffYF//ou/wn/yF36CX/70Z9ja2ma71+fCpcu88HLC4x94H/1+n0tXrnHi2FG6nfaUb4gQgka9hhCQZjlpavt66uln+M3f/jwP3n83H370vTz9gxf48teeIE4SojDktz//JR647y5uO3OKl195jSROqjZfePlVPvbYI3zz29/ld7/4FU6fOM6Fy1f47tPPcv/dd3DuzCl+8PxL/Mq//iw/95f/0twjIQT86m/8Dts7O/z4j/4Q2hi+9dT3+OaTko8+9gjScfY0QxrMjLZqTzFxj+8P+szMnhaC0nxFw837utkTb5XkjzHms8BnD/p8PfTZ6ccMU4VjUprNFq1Wk16vx3A4otmogxAIaaX7/fBNSTSl62IYqyZvBo50kAXvIIC8cNKyl8ki7nNnTnJ0dZF6rTYlJRgsoTxz8jiddhNtNHmWW6QsBasrSzQadZIsJ1U5ge/xsQ89guN6BL7DsZUunutyz523c/bMibEmQ5uKew98qwl41313zUgPggfvu4c7bz9bOSJGx1ZY6LQYxTFGGzzPJYrCgmBaorvU7dBo1Dh320nrjON6tFuNgmBYRHPX+du47fRxPM9FKevApIzhyOoSn/joY3iui0FXDk1C2P3RyuxWOwtLpCZV3qLY073U/0mSMIqtOrVerxOEIaN4RBzHNNuN6jltNIK9pV3Hca0PBJYQpWlyE6Il8Dy3kqBLwlqaHkqwRNclDAKu37jO0mKXxe4ivd4OO70dugudSk1aaqIcx1R2UWuamh6H40i00RjNxHvzGIBCFTujvRBC4PseWhursRAC13WmfAfK/XWKe2EQaL3f+pVmBFGcu9JRcVLlP+lPMKNFKD64joPGoCbPyMRzrutUKtgoiugPB6RaFwzXAaFob3Njk27dx51Qt3uei+e51KKAhXadMyeW6Q2GXLq0zuXNPq89+WVefeoJvnP8NGfuuZe777mHRqNR9S+EIE1TlNbUfLfyFYiTlNWVZYbDIcYY6rWIhUyxudMDIM2tWch33cIhdzeR1sawubnNK6+8zOc//3mCMOQjH/zA1PqJPRD/9O/TZ0UIwSc+8hif/uzn+MJXvk4tCtHa+mNcv7HGcy+9gtKar3/rKba2dnjt9Ut0Wk26C22+88yzbGxucef5s7z4yutcuHKVXq9PEPicOnEU13ULZsTCKE74R//0X2CM4fqNdVZXlrn3rvN87ou/T60W8Z4H38GZU8epRxGf/9JX+c73n+XIyhKO4/DJj32Yei3i7tvPEkYhV6+vAfD4B97P+XNnCAKfX/7132IUx7zy+kVeef0CSmu2e32uXr/BlWs3WFtbn3sktDZ895lnicKQ7zzzLI7j8NrFywS+T28wRArBXjrIgzuLv7kgJjVtB4aDOwy8ZcT/jUAjcNnsD2mFPoNBn0ajQRiGjIZDBsMhjUZjfGkYe4KWMCkpGyNwpUCpaeI/T+WmjZXuM6XItUU8rnRwpYM2BlnabQQEgYvnNXeN3RhNLQqoRYGVboRFmI4jMYDnCfwgIFYW2YeeS9RdqDSpbmCliMQoJIaS4ZMStBCEvketViPPc+IkxpEOcRxXEmktCqlFYTU3ISSNeo1GvTbhXSsrpF9aRMLAJwz8Ge59/LdWC6nVQrIsYzSK0cbay6UULHXb1fpbYlWu/XxEbQneJAGyeyilRGmNVdpIDJp4lBAX3H8YBARBiOM4JElCvz9AC8EgzvAdFyEFWhmks/dFdV2HLMvRSiOlg5pA5rNQenHPqs4Nuxma8vdarUaSJNxY26DdatJqtej3B/R6/YoYj00kAmOsc9MoMXSajV1tOsLBiDLyRGP03hdazsxh7CRoz5ox1rYqpMYtTB1MMErz98tgjJg4C6Jw4MumCPG0puAmGgBEsceFZg1dMR+Ta2mjMgpzX69PnGYHl/onYGfjBrctNec6yUkpicKAKLRMwFKnye3DmM3tAa+8fpULF3/Ak+uXefn5H7C6epzuygpHjx3HdV2SJEEIKywopUnTnDDwcRyX7Z0btNstpJR0mnWGo5gkywpNyRyGmML8YjS9nR6XLl3ke1//EtcuvYobLPKjn/ihfZi/vVd6qn0MC+02/9EPPc6vfPoz/ODCJVrNRsU0OlJyZGWJ82dPY7Th9KnjtFpN3vPO+/nWU0+zsbnFRz74fhr1OhcuXuHl1y5wz53nOHPiGLOaVSkFR1eXyZXi6996ip/81I/Q7bRBCGs2DHyiMLTnRpTETWCw97w0NXqeX7V54tgqYRCw0GkX+MuAsdrQE0dXOH3yBGdPnyTLMur1GvNACoHSioVOizMnT9BqNjh1/BjLS13CwKdf+Gu8KXBzRd1bCAfv4G1B/HWWka9vEtbrGCRRFJLEMXGcEIYhOtQkScJwOKJWi6y9EUOmFa6QM97QYw5YSlkQPjNXMizV0iUjkaqssuELBI7rILEcOQXymQwzmreXeZaDsc5NspA4RPFernJUnhMF9SmEZNV9CXEcFyrmaBfR8Vxrq3Ych1peI0li+v0Bo9GosqsGgV/YBosZFE2U6qwyHGuecDf+bv4JLdcKIC+ccZzC18IYY9flJsKZYL5KHlNIkBKyNGcwHIIxBIGP5/m4nkOWZgyHVh1cr0UoYGsQ4zsOYehZLYyW7OGoXcxrTPB3S9Tjv3me47jOrnUqJfHda2e1CWEYYkYxOzs71OsNGo06a2vrFQGeZDyV1gyTtHJY3bUm1TgNBquOKh0+zcxazxLxSSI6+b0pzFBSyiI8cj8Yn59y3JWt2F5Aa7Zxnbn7PsVkzrYL9h2tLXe764nSHFCajSZlsgPYOo0hiWNUMuDo4srcNZ4EKSWNekS9FrLcabG80ODs+g5XtgdcXLvIs6++AI02p87dydnb76TeqNNsWkdTrTV5Zh09lcpRuSIo/FB8z2Wh1eD6xhZSCjzX3eMOGtI04/q16zz9xB+w+dJT5KLGvQ/eTRCVPiEHQerTmheYxlfnz57h0Ycf4rd+94sMRyMcKVle6nLqxDFev3iZhU6bfn9At9uhFoV84L0P8Q//yS9gindXlrp8+jOfY2Nzi5XFRRYXF3btr+e6fPB978EYw4VLV3j51ddJ04z77r6TP/zuM/zb3/sKR1aWefm1CwjggXvvxvc8nvzu9/nVf/1ZVpcXuXp9jcc/8L6qTddxyFQ+tQZnz5zkyrVrXL56Hd/zyfKMbqdDvVarQnTtETPV8r3zvrt5+bULXLu+xs5ODwqNrHScPZjLN0it93rl348CYU94WxB/ozT5Vg8/DEFIjLD29TRNcF2LVJXSjOIRfuAjhbDqfGPIjcYR4BT5irQxM17lVAjTFNhITiDLSurP8ynPZ1UiuQLmxRbP7mWWZWAK5yspqgMohEAbUIWK1y88ma0kpYjjEXmucF2PIAhwPNfmPChjDCoiniOlQ7PZxPM8siwrDrr1CM8rIm8vYRAEBQGwjpK7vYXnwfxnxmpPiVYalSsbWsbNiX4JpTfufNWyYdAfWR8Jx+6/7/vkec5oNKrWNgh8wigkV5o4zdgZxeRGUwv9PTUOQOVXIKWY8RgXu/4qpfEDn1kobeRmjgq6lLZrtYg4jun3+ygVWY2GkJgZu4aUAt930Wr/xbPMo5gg/gZM6U9Rqo6nGdpZE1ep9tdaYFBoo6eiHex787Ug5bzBEn/rlV06yIp9935fq0rJDc0BbQxSOjY6Y/qlfRqchpdefAFHxax0W1PahZuBEILFbpvFbpsTw5hT69tcvrbBa9e32Xj5abavXcSvtVlePcKJU6dptNvWt8JzyVReqMHHE2/UIkZxguNIfN+du8ZKa0ajEUorrl69Sp7GiM4x6t0u3/vudzl/7nZqzcYBTJe7215dWeK+u+7A91081+G973wH/f6AGxubrCwtcvLYUT762Pt5+gfP0+sPrFf8onU2vO30Se4+f45Go8bq0iLLiwvcduoEJ44d4eyZk/je2BfBdR1WV5Z56IH7OLq6jNaKH/344/zOF36frZ0dbj9zio984H1cvnqNK9dvUK9FvOOeO3nw/ntQSrO1vcPrl65w+ep1XNch8D0c1+WhB+4v8ICi1axz/z130W41OX50lSzLuXTlKoPRiCgMWewu4LguJ44fJSqYgONHV5HSOll+5IPvpxZF1kyTpiwudKhFEXme75E74q2k1n8saoB94W1B/IUjEfUa5WJkStOqhQwHQ+I4rqSqNEsZjkZV7KnBxgsbpTDShovkSuFNecYKq1QypZOkxZgGK+nbj6ryOJYUSTvMOBkL7I12TKGF0EUImOu5lTrNmDJ6H5IsJ8lUFTKmlCKOE/I8xxiN7wc06nVGcUw8GEwhx8lz6TgOURRZKdNYW76VjgxxElsp3wiSJCXPVWEWEPsj4mqG8yThcp5j4qm1mkpAclAo1eaTkqqNSx+hlCVIruvSbNZJs7yK6TdG43kenudZ27O2DF4rCtkcxQziFG0M9cDDxUqis0hWG4OQspBgy4QhM+OrGD4zES0yHr2UEmEMCj12dpu0rUqBIyTNZoNer0+cJEW7lvkoE5uAtbWHnstIzRK4vWGWEZgHWmvEXCbPjt8TonKinc0fMP+9iRZMKajbWTsFoXsjiYmqM6DNuF9jEKbsxyb6UsZMnP+DI8vXXn2VuqPpdloHZHp3Q70WcrYWcub4MrddW+fG1pDXr29x7dIVNi++xMVXXmBh9SjNhSWWlpZotdvUolLtbO+P40gWWg3K6JZd2iRjiEcxly5d4sa1q+g8QWkIsiGvPP0tXvtezuZdD3D8/F0cK8IS92cCpgWGO8/dxspit1KnL3RaPPbIexgMR7SaTTrtFg+/6wFuO3WC9Y1Nut0uSwttlMoJfJ9P/ejHrWkwtILERx57P1JIFrvtKW2h77mcu+0UtSgkzy0TdO+d58hzayL0PJePf/hRrl6/wWA4pNVosLTYpRYFGAM//JEPcunKNYajEUeWl1he6jIYDPnkxz4MUpDEKc1Gg8ceeS/tdosoDHn3O9/B7WdPMxrFNBtNut0OaZZx1x23W5NanHL+3G2cOH6MOM1YXV3hox96lI3NLYQULLTbBIHPKEnmrONbDX9MhH+fq/m2If7uSpc0NxgR4zpjKarfHzIajajV6kRhSH8wwHHdabU51jnPyqG7bVCl1DopAWhjSNV0bKcjJJ6wjlGlzb1SFk/RQvuhVL+W2aY8z92FULXSJErRG6VoDPXQI0lSsiwjSTNcx6HdbpPnmsGwz3AY47gOvmfDCPNcVVyp1hqVxOQqp9Pu0O/3cFwXz3XxPB/fD3AdF89zGA6H9AdDtLHOff5NCfWsBDwGa+e3qjQpBEYp0lwTzjhSzqp5d2XJE9Z/ofIT0BqlNMPhCMd1abfagDWBbO/Y6Icw8AmCaFc/YKWNhUZEbxAzGCY27BNB4Hu7GACniBAZxTFaG8I5nv5AQRSdOfOZIJITz096ApfOjEIIms0GozRDDEcYbdXtnmfNNlprkjQjSXOiORqG+eryadhLStd6L83KWBtWhipOMiOlmn48hul2Jgl8+VuZS0DdRHux1/ilkBilAGk1KoDO8iq3QpwkJGo2FdDBIIlHnFio0WxEB5CY9wedK1aXFjh2bJUzx/tcvb7OpRubXNm8woUfXGInMeTC59ztd/DAO9+FlNBuj4ljMGePS8hzxfrGBt/9zlNceuH7eL3+0EkAACAASURBVNuvc+eygxDr9DZ2aIeC53/vZV5/8S7ueOh9nDh3nuWVVaJoMkR0EjlN7/1St8PiQmfq2eXFBZYXF6rPvu9yZGWJdrNBu91mfX2NWq0GGG6/7eTUu6eO787rQSEwtRsN/OPWa75Wq7G9vc0d504VSZMMvudxpkxCNmW2gij0OXfm5NjEZGwc/vHjR+gPR2S5wnNdFhe7pLliuL2DlIJWu0W73UZrzXa/D8YmCIqiiO1+nyAMCMKAnUG/iNDwWFlZBgxZrugNh+QHiDb6Dxb2wSNvC+JvhCBXmu3Chht4LlIInMKOnaYpaZoQBCFxkhAnic38N9FGaU92Z+z60pEV8SoRXXmwqmcQSFH2a53PnFJynyBck71VNiWlwJgqM9okN2w0xGnOzjBGA81agCsE/f4AIQTtThtXOqRZQm+nhzGGVquJ63lIIchUjphRSWVpRpqkSCkJw5DtnR0C32c4HKK0IQisE5Pn+bQ7Hr2dHlmWIYUNcTs4IhxPOE0ts2LXo9gvMfYlKNu05ntDnuWVE5MqknRUq2JMJS1a9bug1WoRhhG5ytnc2GRUOPp1F6zEVGaJs30JZOG0ZpTAFZJOPcKTDluDETo3dDsSz3GnCJjveyS9lOFwRBSFROE4scsk5JmqkvSMbd6TvgITfiKVVsjsygynjLbx+q4kz3SVsEpKSZYrdgYjjBAstEskPkbgN9fS7A1Kjce/F0yG3U0zVeN9mmUgrPli/EzJIGmjp5iV+YzLbhVn2W+e55hcgytBCIbDEe1WE601cZwU4ZBzmthDa2qzMvbor1/jzH1Lb1jqn+xEKY0XBAghqYU+t58+yl23n6TXH/Lia1f45vde4EvfeoYbLz5N78Zlbjt3B3fccy+1VptWYRbYy7E0SRLW1tZ49YXncK+/wO0txU9+5A6yJOfijT7Hlxq8eGmLZ64/x9c/+yrLd7yHB9/3CKtHjhBFEc1mc8oxcx7c7DxZZtTecSEEw9GIzkKHeYu8V1tGG+J4xCiOOXrkCKPhgP6gT6fdRiuF5weFNm3vdqwJqzSHZlzf3J5iFPIZuz8wFbZYQjovYU8xlXy0u40/djjgWX5T+5gDbwvij4HtnSGJMpxYahFM5OCOwgCV54xGMUEQ0G61uXHjBgQBWoyRiFdlw5tWQ0ohsTmhpuWH0r7vCBffcao0qPadGcK/B1jEp4miyKZ4nZScsHGuW4MR0pEs1kMkWOJTi2g0mqg8Y2trkzRLadQbRLUQgSDJFZ4jMXPC5Uq7M5jKAbDRbFSagkF/yNb2No506HQ6LHTa9Pq9gqAKgsCfmy52z60xhsFgWK3LaDjCdV3qgY8feMSjhCAIKilfG1v/QBe256BIqjP2QC+96MH1PHzPvpskMds7O2htVfwYm1p3PG9R5OcXeL7NsmYKKVdKSSMKkNKwsTOkpSOkmPYk9zyPMAzwfY96vbaLCSrXWSmFFOOUphWxnyD8Y35wzABMqv+NMeSFWcAPrVpTJSnDIpOYMjBMUmrhpET45tz+0i4/H0qVcFlHIJ82w+wTTqcK9W1p7nDdSS995v5/4tu5bVrHwyIBUK7BsR7hzWaL/qA3fUZnm9hnub79rW/iq5hzJ1Zvzd6/awJjJr9yTBTjvhu1iNtPHmVjs8fXnnyady7E+Nef5JkXnuAPP9fi1Dsf5j2PfpBOp0sURfhBWDiSiuLMJ2xv75AnCVIlRKrH4w+cR0iJH/mcPWWT59x7foV7z8P/9bmXuPKDp/ja9iZes835e+7jkUcfRb6BugGToJQmTRLq9VrlyDsbQbI3FKmBs4zhYMDSyjJK5ayvr9Nq2xTatZk0vgcBKSSea/HyqCLw+7RxM2I3R4N304beDKJctTHR2C2c5TcMB2jzbUH8tTEMc83RriVi03ZISRhFqMGQra1tut0FoihkY2OTer1GULOS02RhkZseNGNTaALUZzLEmcLWPkn4BQVDIMbITymNFOAX6rdca1xppT9tNLk2bMcxQkKnHuI5VhXvOA6NeoNev8fO5ha1esTK8jJgawv40sGgUcqQmRwz7wQWnHHlCJgrVCF5NptNWq0mOzs7bGxs0Ol06LQX6Pf79Pt9siyjVo8qAlGGns1KueXfrIheKNWM7miEK8G1Yn4VUQHQ7w9Is4yFhQ71Wn1fW7BNuONUzneu57O4uDStzRFjVbXWijzPSdOMUTxiMNjCGJsIJopCpOPgOy5Z4ZE/ibyszV0VCZvEntoPpdVUIZPSIVQrU5mZdjFjxYi10TYhx+wz2hAGAWma4jo23G84SjDIN61oSjnHyb73U/0bU2THLMIfbRji/qC1QspyvIKyeNTMKLhVTCYKjZRlMgfWZANsbW6T32JbJQN68dWXaYuYY0cXd++1mRUDJsYyHlS1oKXNWggBWlFks7BNYUjzjN5ohGcM7z7X5R13HuXa2oDvvbzG89//HL/x3JN4fpd7H3yIs/ffz9LKCkEYIoRgp9fnwsULXLzwOlJn3H+6y8ry7jDiEk4tSBrbV9Fba1y9XuNG6NK79z7aM8W5DmI2mgSttb23gc8ojnEOcC7HfQjyPLPRA66H63hsrK/hFHi89Dm6FShNSidXl1FKcenGhtUg7jmYyS4OeAb3fGwfhvNADcx8v4vpOOB7twrl4txCE28L4p8rzdFuk8ZMEYkSPM/F910GwxF5ntNq2ZAU6U9UZmMewhsvqKXpY8RY8wLibH4+Z1Vh0rJhQBRRAsJWkhOOIPCsh7nS9mBmhdRlQwuNTeHqe7iOJB6N0FqzsNAhjkfsbG3TbDVpNhuVRKm1YagTXOlSkv1ZEEIgpCzMGC6qcK6JR0kR42oH3Wq10LpkANpFzoSAXq/H9evrYGxlO9dzCfyAIPCr/OlZltnoh8KxLopCfN+3xWg8lyzP6Pf6pDPr12o2WF1dpUzRvLG5SRynlCfTOij6tFstoihiMOizvrE51YalsTYUzXFtDgLfd3EcD8+zoX+NRqNoT9Pr9YpKgwLf85EOZCon8v0J5sEica0Uzj6+D0mcEkbTzGDpjT+bjGYSjAE0GGGl6CpjYWFuisIA13HwfJdGvY7vjRiORiw0JmOSb3b59/+9pFdjYrf38yXSlkLYRFYzZrE9ekCpcRW7Sguip595I1C2lWYZ3YUF8jxFG02c3lrhJ4Dezg75cIuPPXLfLum19NHRuc33MNm3lBLX94rNHBN/nSv80sF4tiaMsaG98XAIwnBy2Yb/HVlucGS5wceBZ166wXdf+T5P/e6zPPnts6zedgfHj5/i6NFjbPZ6fPuJb7Bz4QVq2RoP3L+679wef/dtAKRxzhe+t86FK5e4dvHiLuJ/K4Tf4sQyGsglHu1Qi3annJ6FSWEhz3OyXHH0yBGUUvQHw0pIO4jEP8/cZMOXbUKmsyeOcPHaGqM4mc8AiD0/7DOBgz126w3crOE3+t4baXZ/zfXbgvhLTEH4d/9WHgw/sMlzNre2WFlephaFqFGG1/RxmHPJx58AgTaaJMuRoqLlaNQuxyYARwjLAFQHvIwqsCF1rmed7CrpeCJGNFU5rpRIBLa+kLTFipSm1WrasJatbRYWOoQTufOTwvlQ4likrPRu8m+s1GEZDFVJo7Yam0+aZfieV8ypSKMrJesbm/T6fdqtFt3uos2bjtUepFnGaJSyvr5RFJ2x1cNmcw1Ua+O4hEQ0G625ezkYDdlYX0cpTbvVorNq0zQPhzYrXxCERFFIf9Bje7vH6sryVMy50gqtTBXGuL2zXURE2D3zPY8wigijiCgIabXatFpt8jxle3uHWpqQxjmE0+KPdfos/UGmCeOktmNsOtpd9GbvOP+iD6NxhE2Ra3LLzFVV9VyPXn+EQFIvEi/ZDGhLRRtvFGlMz2H2+f2kQCklnu+SJClKKRqN+X4QxpiivkFiGcIiAyHm4Ahr1sFrdmxKKbIsJwwj1tZuWK3ILRJ/YwxXrlzCl7vvtDEGneU266br4E044Rlj73YSxxgDnhr7BgkpKJNHaK0wRiNwJhiWnO1hjBDQaex2Ir3n3DL3nFthbWvEDy5c55mnX+brX3eI/Q5Oewlfgupfp+aOWFlq7Hp/HvihS60mGK1vc/XGDc7PwWHTc9/7DNjCUbbmhTGGwXDIiblOfbvbMsYQF/lX6rUIpRTXrl8jDAObj+UAUGprsjy30TxzBhrnGXGaviHHz7l98mZr2kuGcerPAfp9AyO5pVf2f/BtQfzH6s/5YUOl+hVHWM/pPKO7uMSFCxcIG7XqNJbvaqNJ4oQ4to6CZV5yUXhjlwfOcRx0lNNuTxMyISUuVKomY2ydcW20TaQjxxXTZqtBlRoAp1AO5mmKLwVRFCGlQ6+3g1+oqifBdxxyYd+J83nxzSXXYqerlCYMrRTrBz6e6zIaxZX6315SSbfbwXVdhsMRm5tb3LixZu3fUY0gCgh9n1pUh+5C1ZtSGaPRwFaui2PyXLHQ6SCEYGPTSupCCKIwpN5qErgO/f6Are0dPM9lsdudUiUPhyMGgyG1Wo16o8F2r8dwMGB1danajxJ5OdLBkVQMwSRBUso6ziVJwtbGJtfSFM91abWaNJstlpaWSS9fYjAasdhuTtuzjUIImzp3noNUluVVchY7P9D5+CxWtt/J/ZjdpSKuUwib0GWYjsNSR2lOkucMhwMcR9JoNMlyzZWr11heWjpw2en9QGk1p8jOZGrhabChg7JwEtzPI90yX1UkgaGSnA+KieY5AVZjErZiXqe4h/3BECeMgDcWguWhuOP0kXH7BvIkA2PwwhBRODxa7hyQEuG4BL4PRqFzBVrjOS7C8wocoJGugzFFmGfRdpymrG1s4QmFmWdNKrzYlro1Ptit8cEH4PVLmzx7YYNXt9a5PoCVCH72Q3fd0hzPLIZc6Q25cvElBoOHK23YPJhc+1lhJ89zW+eiEETcmzhITrVVvG8QNBoNdnZ6GG1YWlk88DzSLGOrNyBTitWFzlwfDZXrmwmxtwRvLuGfaHH6zwH6fQMjeRMH/7Yg/vNssKXUlRdZxQAcz8WrBayvbXD06FHq9Rpb61s0mw3SJKXX6xcFcWy61bAW0Wq38Fy38k4eg2EUj9je2uHSpSs2c14tolGvIctYbMexaUizvCJ2Uy0YQ6oU89KHli6Go1FMs9shqkX0+z1ylbPYnVXTCSQST8wxOdgnptZKuGKqzKtWGlyIopDBYEgYBSRxiu/7lSrX1gOPMMZWo0uShN7WNhtZZqMbpKiSEDmOUyUJWlzs2mxYWI/5ei3CcRyazYYNydvYIFeKwA9YXVmuxpQXXtppmtLrD2g2bMa7Xr9HPBqxtNSt9nlvB7Xp/ZLSOixOhk6lacbOTo/1jU3OnD7N0aPHuHLlMv1+n2azMYXobOEad64UlKbprvrie8PNn7H1JyYiT7SiHoV4UtDv25S/NpZ5wNVr120u+KIOwBtlAObVCTgQCOae4RLKvSxV/tVLfyQoUss6lvlI0pTl5RX6oyGe507HXhvYXd9kt2OW1ppnn32e5WZIozmtxVBa4QWhTVAUj6o8B6W5DWwmOTfwkVVqWQ1akSVJlfjJ8TxUmmG0AiFt2Nkoph3OwzHz1+j4Up3jyw00gn4/Js4UYf3WcmYc69Y4sxHz9Re+wxNfOc3jn/jhA703L/+FNgbP90iSve3987QHSRyjckWjXiPPFb1+nyOry3Pf3wvSTIGBlYXO/KJSAHuVcZ4aIAc+kn8kyX/ey7dob9/tA/jmOBneqq/H24L4a2OIc1vz26q17V9lpmN8BQJXOsRao3XO4uISm5sbDIZDfNdlcXGBMBwjcKVzsjyn1+9XtmxVZCZrNhs0mw2OHLElIPPcMg9Xr92gEG4qqbTo3CJmJlJGMs4j4Pkege/jBD6OI0mSHF9nNDoNavUao+GIOElptdp7SmFISV6sw142HCklEuj1+3Q6giCwJVCDwNq4g8Av1HBjj/bZ/nzfw/c9mhO+RUqrqbz7pR3YYO3+ZT7/IAwYDIa0Cl+AKApJs6xygpqKDcfQHwxoNuo0Gg36fZvHoNVsWhV6sbsadQDJd9op0UZaWELZbrfwRyMuXHidkyetPfX1CxeozXj1zxbAqeZZOPrNj2ufTEKzx9DmwGgYVzZPu7cOmTE0oghfWfNVkqR0u11qRQW3i5eusNBp3wITMg15Nl2pUGl71m2C4N0RHnYdD6ZaLxMw7edX80ZAa5vXwS/8ZzbX1ggbdbY3dsYP7SL8TPc5qYZev8qd5y0BqvBAlhYCgCArCh0FUTTdqIEsTkiL+hX2fctsu46L61gm20gHjcAojdIpW70B2/0RD622ppMkVeu0x9oYg+tI2jWfFvud+/ngeA6OIzBZdqBjuRdhKHNtOI7LcDC02o85MPmuLvJWxCObjtwyDmmFg24FSm2c48w/n3GecXl9iyjwGSUpNtnYnD5uods/Ets67+VbbHC3TLf7LO8F+922W0UZbwvin+c5vuNOe2hjna2MtrZ3rTRZkjIajtBGc/36GkePHmVhYSxFK5XR6+0UqVV15UnsFTZ613VxHQff9+j3B1y9eh2tNbUopNVq0uks0OkskOcZucopk9pIKZGOgyP3Xq40GTEcxfS2tjFFAY96vUa91iAexSRJTD2K8PfibrEIIFfZPislijnJyqmu02lz7dr16gnXdfE9v5i/rPLKVxrWOSfEGDMVoy6E9ZFQWObLCXyyJCWOUxzHtd7BaUYQWITtOJI0yXZ5r29v2RwEYRgV1RmHVaawWbDpjeevzeT4daF2Lc3xZdiUdSAcMhj0qdcbVVhaySBobVX/Su2u8Jgm2S4zzNjeb1XjiPlFWSZhUoExL3ueVlaLVa9FeJ5Lrzfg4qVLdBc6LCx0aTYbrK2t0+v16C52d0W+HAQmn88mPKQ9OU5yNfmM67jIUDIa2lDa2Zz/SivLNOndzF3R4y2NbxZ9KaUYDkd0FzsolVUFl24V8jznpReex1Ujzp64feo3lWtbOMvYCJ0y0yNT+MbY7Jz73E8V5whjioRROesb27zy2iVMknD+aLdorzTPHWBdpEC4ZaKjCTgAP5WnmjiXBN1ljp8+fdOuplX/u4eni2RbnYX2nm0orcnSrHKQlNLmZjAahsMBjXr9ls+rMfacptk44ZUxhkGcsLa5TbtRq4SuYiZzGpn/9a0+c1A29s33GTh4u+Kg/R/gobcF8TfAK1c36DZC2vUaqcrJ4pg0s/aossqa7/t0ugv4roPjjBHj9vYW7XaH9Y1N0iIm1HVdarXI5sp3pK2GFydkRV3wdrtV2f7jOGF9c4u8sPvWGzXCILL2PWWT7OR5TqbzIjVqTlaMDWyp2SgKaLc7tFrjyyOETdQTJ3GVlGe/uzFtV94NNiuasA46nsfOTo+Fha5llIwupDubUWw0smGGQsxPMDLb7p7fGWt/z5KUXCmbWjgI2NzeZqG7gO84c+rPw2AwQAhsPgOVk2YpQVj6Ouw9Hus9bLUMYxuaRarWRl3kC5CldkeBtgxAp9NmfWOTWq1Oo15n0B/itBqVqj/PFXmeVQRuHGWhd6tDb5q1zt6ucsekFBUDlSRJJSVP5o5Ita3vYD2ZPTqdFmmWsb2zQ6/fZ2mxy8rKKnE8YmtzC+k4tIo6Dvup8+fNoYxAKSHTRaXJmZDYEpHXalFRZyKZkuDSNEPl6hYTRO0HYvxHgDbWQOa5ARub6wRRwDA+eMrjErTWbGys45Fw6sTK1G8GA0JWGSBFYYffPbK9SxpPgpQSz3VxpEQpg0BzarVlJyQK9GzM2MtxzHlX4xFCIpCYQuM2M5CbgutLGjUHd5Cwvrlx8xcqKJhhY0iTlDRJxynHtZ6bCVRpTVpoTBzXwZGS4WiEwVbW00WG070zGc7XiytlQwzzXO2y9Q9GMUmasbZlNUDDvTz9dzf7hp85qE7rrSD8B273oKr9g5yhg/T3VoMAPJUy6uekwyHSkQS+Ty2K8Hy/Qqql2jZOEra3N3Ach263W0lti91Fssyqhiwii0mSpCjsYDCFqjjLc5TSNBq1gkmQVa78NE3Z2d5hm50CGUz7JNjsaNBPUhq1iMj36McxvV6PMjyl1mwihbXtpklCFEWFGr7k22D6aB2Ml5RCYFMWWYZjY3OLblfQbDbZ2e7R6bSrdsrMiAK5ty1t3z0ppRibzU46NsJAa02j0STf3mFjbQO3qPPuedZHwHWtZiBOEha7i7iuw+amXZswmCT8+xvKpuu3W9W7lJb4C6xUXxUbklRe9mUehlarzZWrV0kzWwypLOlbheEVkCu1K859rtNpGeY1sULlOglJkbe9NJmA447nleY5mVIErkNYMB6l/TwIbBjgcDji6rXrtJotGo0Gvh9YJmB7G4B6rUYUhXuG4+V5Ph01MTNPp2AcZ4nblBbAc9FFzYkwDKbKNIs57+4HN7M/CqwmZDRKaNStfX44jGm0W2ytbe794h6gtWLt6mVOdhu7zvt4O03FdNwyFGGCBgqHP0mGoDcc4bqCo8sNKqI/793yr7b/jGOdCI3WiCmN2V5G5WkbhRCCZiDwRhu8+v2neeSRR+Y+N2+yWtvIpyRNEUVp4+o2ClEM17K1WVFOuWRAyzOQV/fKYzQaTiVJ2w3zvx8lKXGa4Xu26NAkhIX5obzjN3O23QtuHcuOYerZN0vcPii8Ec6jPGbiYKN4WxB/z3U5dmQc41o6AoG1O8dxTJqmhTOfQBSFImo1izQ8Lyj+egVytMUyBmpQ5HOWlmgbQZblVWxqrz+wBLVo0xaXsRJU6dg2PtAlYjf0RgmZETTrEY6UOK6DjiL6o5jNYUKSbWKEzV7WaDYJfW/CoUrM/B3/f1do2cRTjpC4whYGcV2nynaXpNaGfunylYL4UzEsjuNWDpD7x3AfYI98nzROGI6GdNo2c+Ako5XnOcPRqErn22zW8X2fra0tELZWt1KKNE0n9mmsKp9U4wvJTPw4BfK1p3qS8JfvamFtgWHgs76xzmJ30WY37PWr+Y9z0aviPNj/B37AfmDYXTOiBCFtPfS9EF+eK9a3B6AN9YaNyqjeFQWL5brU6zVrO41jBsMh9VpEFNXw/QClMgZDW+MiCAKiKNqlnrfhWmPJy3UcUAqFVZk6UlZ+GXPnIQQYU63LqLDnyjm22HJV9kMx+xJ+YRm5PFfkma1TEccjpJTEhVr5VkBrzXAwINlZ58S5cdTKblp4i55ZE6CKapOyNBkYwzCO2djaJnTMLuK1F+R5jpAS6TrWh6Cw/Y/HOIO5DXMW035uBg4LTsL3n/sOv/Hrv86Zs+c4f/52arV68cp86pEqmyDM9zzSJCnClyel9jLU1pZsLvEN2L0roz8cxxZfSpKEer0+9f5+a1yG9g2GI2sejaLCNKfpD0eMkhQ1t8Tu7JxuziiJiY/ipkOb/nGKgRXzn9l7bAd5fp8x7PvKHm2KqT83hbcF8QdbrjbPVUVMwBI5x7FR/GEQFLY6URA2B6+Iac/zjF6vP1WTWWtNEARk2uC4LrXQt4fZSTHGFPZqCvV9hs4mbbqlr8C0zTVXit4woR+neK5DnOY0ayG+lOCBdBw830doZf9fFN2ZVdnuJRUJIQhd3yZewXohW2ct6+goiwtiK/nZ7HaD/pBut1vNvWzb+gZYaVcbg7xFL3IhQApQhbDkFaGIZT543/cYK0SsVkApTb/ft2rkqE6/36uqMiakRZjUOKe89YOTNp2odADLuEkhUcwwQqaIu57wzp79HQy1eo2trR18v0+9VrPFfIajgqjZZ8qa9jZJjZnQyEwgjYIBsTTR7OnsV671PMiUYmNnwCjNWGhERIE/R31vCmbXISzCNEsTVZykhAWxdxyXNEvJ0oxer4fjOARBUPldzEYJSCmRRqO0KdK13oyIj1MYO64DCOI4RjrW4e3WbP17IyeBZZjsORiXwN3p9fCCgO1ReqCmJkHlORtrN3B1ytkTE4lyZt6b8N3df+gzYwZbw0N6brXfSmtGw5g4SagHB0OjWik7/6IyJcriiSlkcAvL3Kz5nF0J2RxusP6dz3Pjhad47hurnLnnQVZPHuf06dNT+2bDNe0ddKWD5zuo3GoAosg6x8ZxUtxNietanCYoz7hdnLgoUex5LkrZqptWe1aYwmZCaSfx3WAUEycpSZqitKEehUSBbxm4JGVju3cLhXYOIBrfyrEtfyzp76zob3Z9+QbHeJDn5xz86qs3R7vwtiD+1u5u48lLIqK1tsV7RGnr1YjCkc5xXMQEt7y1tWW9ct1JpzWXRGtGuWKUpyAljcjHcYp41iJJj+s65Llr08aORlV/ozgp1NmWARilGb1hTJor6pFP5Ps4M4i8VF+ViHgvorDX+ZFC4jkCozNsiHlRNraQlKxW0Tqslaq44XC0Z9vlmuRKkRvLTB2cAbDlacemd0Hu2nVKkhjHcdna3in6Ga+757nU63XyPKc/GOD5Hl6B4LWWCFGGwBX2/SwnZyxRSOkQBF7xezWUyhl0Uio0TEjlxcVwPJdE22gIR0qiMKrOlymdtfKcIPDRxuB6TtXJJOMkpU1kY8qO9gCjja3ZPPu9ge3+kN4ooRmF1Issf/PWuYzDFwiCwCsYK+tUFscj0izFcz38wMNzvcJ8kRPHNg+DEGLCVjtGGtpYxtHZN5RyWrQpx6HQuK7VHCmspsQS773Oj9nV1rxHbK4NQZJnZJmiVousT0iaEtQ88mzG4fUAxzXLc65duUIrgCMr3T2fs4z0AWC2zyJjp1MmgNIak9vkw1I6ZNnBfCG0UkjHRUiJyXMQAvkGTHIlBL7LmWMtajWfUW5Y27nKCy+8xFOXXqS2fJIL73gny6vHOHPmNEFg802U1TK9WlQ57ZVOsFEYMIpjmo36DNEfL0yZidH3bY2QJFF4rldpBqYFG3smJpvQhTNfnKQ0opBaGBThnlYbkOyRdfWPFeaeuZtJ438Mg3iT+39bEH8YOywJxyXP0ypTXCgYwQAAIABJREFUXanqh9IhzkCaIlKH2HFwpLWXrhSZ4spne6OYwSixUqTS9IYJoe8STDi0lNEAjlNoGESJ/KUNZYlTskyRG8MwzVFaUw8DWrVgz4Iht2obnYXJvAalRGZEKfXasZW/iQPEx5clZK1TjtjX/j+rkSgvtM0CllUq/TRNCQJJvz+wzwkbAuj7AWEYVBoAz/WIwqKYSWWn11P7WXrkl3udphlaK5toyXEq5z4pCuc6DWMJQ1MFgxaCSa4UWkg8z2Ew6CNli1pUw2ib16FkLLXWRbWxyfMwMXchoQzvml2oSeZ8D8iznH5x5tr1cF+1cNnveP1F5ccyHA7Jspzs/2fvzWIsS847v1+c/dz95laZWXt1V29ssskWF5FDUhQ12gVpLEsDeWwY8MA2jIFt2E8zb34y7Bf7wYBhj+ExLBs2RpqRhBnKkkmJiyiKSzebzd7Yze6u7tqrcr/72SP8EOecu2dmVTeptuEPIDsr895zIuLEiS/i+/7f/5/EZFlachU4jk+SpDlgKiHM++V57lS6qnBYp52TxeZKb8SMcpMmpdYuWC4Kdfrr6/mUolSxgR1hWKZWaJvL95xsWZpwsHuPR9sVfH95CkdnjsRS0RqFWtiLNEn0fMjft+I9aDZqbGys8861Hjd2+lw8s5yXX2VSh6ANU28eUPrU/x5AlIZpUK951GseSiqiMGGjBve7h9y9f8Db/du829jmzoVHWD97AbtS5ea1tyGJaK+us7a9TbXeyHFMOlLa6/eP1ZyI4jhPVVhluk/L/2qbHdrZNcXJ01WObVGteDi2pVO7UbxQoe909j7m2v9fbQ82Dh8M5680baxl6RraXhBT81yMPBRZ7iz1VpQ0TsiylChKMIRJpZpPvhwtP4oSDvsjTEOwUvMZRgmDPNxUOP/Z8jfNI10rG6QFZCKiKCJMM5Rh0qx61E/JV338g5jNLWl5W8MwSDKdWzQRJBlkSouJKKlz2tZUzngxQdKsmZaJSrNyI1XgKWbbADp3nGVZWeNPESpUCokGhZm5Uy5YxcwcLW6aFnEca0U2NNHSJNagLA+aGocJ5D2KJNboX6lSZF6iaecllgKhGfRyWlmZzXRDQBAleK5NtVJlNBwyHA6o1epUKrpkKIx0hClOElAcSzAkMDAM8g3KeMyLsHXRp1lTSjEIIzKlWK36OPZxFRfjDhSOf/KjxcJaRFwKvgrbdrBsi7pbLfnU41hv0PRc1jiT0xEoTVsh1CSlxLJ1hCxLs3KjNv8cT29K6Y1FJiW27QCC4XCEZdsMB8FkFuJU61iapnQ7HZJBh0tXNmdupoGAZTRXLQH8Fcj84m/5w85SXTGhx9TRmhppisrLH9dXWzz56AXevHad77y+c6zzl6mmFRZCXxfBVPTyvZowBF7F4akrazypFHfud7nT6XD76Ih33n2JG6sXUfVV7t+6QdLZp9Vuc+byVVbPXaK9doYwimi1WrptS0wpRTAKsG0by9KHLqWY2EDPPjQ9l6Mc1FeklVCKiufi5ymrMIo56g0J4od1/j8Nx/8QG4wHnMtL73fq7y/40DEHlA+E8xeGoFr1S7CR6FkIw6TqO1Mo6sLcPCoglSRLdQ48DENAkSnY7w0AwUqtiu/ajKIEJaU+FeaMcsv9tx4tx3EI0gyZpNimgeOYeA8YNj/N3wqnG6aJLh3KaUUNoKDvEEIg8jxaARycDq0db4YwME1IVZqf6PINEFIrAubAI4XK/50v9Oh10DQMXM/DcmwMgW6fYdLKJTsLANBwONDPQQhq9dqxjqcIdZcjkqc2nOo4T58mCVmaIqWNZdkl4AoxUQ0gpt+NYRizUqtgCEG1WqXfHzAcaYbBarWKUhDHIcFITWkrLHxKYhxpkfmipYFxIv95ufPvBRECVWI13qtpYqbilBQRRSFhqHAcB9fzaTbqSCnp9wcEQZir9Tma2+IU/AkwlipOckbLAhNRRMiKsttizj7sBiBOEpRU+DVf08tmGVbOpnlS5mDWoiji/t07uCriwtbaTP8kMklL3JDOO8xfWAFkWrNAKaUZM5UkSzIdKTBNhGUhUGRpkgMhTepVi8tnz9BuNnj15kFOlrN4TKSSCENzDQjQJ/73YV7M9ALtcAXntlqc24JnhjFv39rj+uF1dnbvYwwDavRYlwHG3T7v3n6Dd6trrF+4wqVHnyDFxDIE9WajTBUUb1ics4FWXU+Do6WulDlufidpSqc/oN2ok8mM3mBElkfeRmGUR+QihmH4Po4FD+lwj7MHuFhx79m5/EBtelDHf/xlFtkHwvnrkpFxCRQCRmGcK+Itbr0QAlOYmLmyn+d5dPtDekGEUoIz7Rqek19T6XI1hSCMEyque0wpkv5lkqZ0ByGGkviWQRqnBCqcAwG+H5ZmKQpZasCHSYyJQKJ34ZroaD7Mb5hGKYlb7qqZzdNpMw0DZeh0RhTHeQ4+IwpjjezN1w5NyuNpVT3TYpL+VEqp0e2mdgT9vs75B0GgNchtm3q9juPYpRTuMls0hDLTqn6moQWGpJREcUQQRFhWWpLQzDodATkeQpGmEs+xy1B3vV7TG4DhkFqtRq1WodfLiJP41JK6hmEg8rEtwtbANKPbjLmWyShKGYxCBDrMOVkqNdP6U5qO2lQrFSq+TxTH9Lp9RkGgS2MrPs1mEyEUvd6A/mCoMRgVf+G8nf6nXuQLcqsCUzJVCpiXcRZ13foa0306qcRPA1kzNAGVwWAwwLIsBg9R2w9649nvdWm4gnarNm4EGqEvlcJ2PVQOtsvJLyYbBEq3yTBtlJRkcYJEb7hEIeAlBMgsz/PnNMdK0aj5XD6/yTe/e5+DTsDGapVlUTWEQKXZ1Kby/bOZPuX/9KoOTz+2zdOZ5NbugOev9bDMKs9cXqFV9bi1N+DG7tvceeUdvvfG9wmsOrcvX+XKE09x5twFLaDl64NZv9cf/5xTVJdR19k25DYKYwYjDfodhRFSSnzXJU4SesNgaWXHFOZnyqbHtsAxJbNYgblU2kPY5MkdmCodOM6WfeRB2jGZ+f0J2QfC+c+agSAIQ+KKe0LIdGyplHRDjSjfXm1j55NCqZy5yzToDiOSNMO17QU5+/GDzaRkrzMgkxnNRoWKbROGutQwSdMSwHY6m5wwixeFcE4cSDL5Gy1CJOZOFYYhyPLv2rbOwc2WgBXOe1I9a5SX2JimQcX3qVaruK5bfg4gjiOCICSK45wWWZ/4KpUK62u6bK/X6+P5Lq1Wa4LgQ/fRwECKrJzEk3ntZTO7+IycOFlWfJ/ESomimCAIkNKZEuAx8k2dgcZleJ49dVXD0Cp6w+GIfr9Po9Gg2WzS6XQIQy2DfJoyyCIkr/LTo2A+ZTC5kLUaNdLOgM5Qqxm26tW8fPS9vs3FGVY/v1argWXZDIdDDo86mIZBo9mg0WhgWSY7u7v0B0Nq1Qq2vajaYLrtSZyWJ/1FC0+BwZC5wmVReVO8Dye9FnEUgwLHtUmShGEQYHse6ejBT35KKeIwYHS4y1NrTT0H89SfSjLSNMOtVlEyI4njPGU188zyiEep0hfGmuI2jy6SZqRJjOU6TBEECX208zyXjbUV9rsB331zj9/82ercIKgkwzJ0BUUmJSKns304O0U6ce7PAgyD86s+51bOQ441kEpxdbvOxRWXQZTy7sGI12/e4NWvvcwL3/gK1XOXufLYkzz2xJO4fgUhDM6ePctoNASlMQLLT/26LRXXoVb16faH1Co+rUYV27LoD0bI3oBoFuCJjm46tk0Ux+MIX9nt8f1s28J3XUDNO/+i54v3RKezuSiUeIiLPKDNRg3ewyVOsg+k86+5Ft0kJk5SMldO1FHPJ1F0+F9x96BDnEjONPwpFL4Q0Kz61HyX3ijkqD+iPwpozcmXjq/XHYwI4pSNVpWq7+YSuorRKGE0HNFo1PNrv/dZILMMxfEgJ70wi6kTr2b7M8oTu2mYZb63WMiUgiRNCIOIOGc2dByb1ZU2ruuNF/wkpt/va2EkqSlQC2CZ6zjUaxq9PxqFJalPlul65Xq9XoIzJ1oM5LoHE6f/8U/Hj5uarOFXlOVgURQzGoVkmcT3/TxnP2bZ6w2H1KdOItosy6JWqzIcjuj2+qy027RaLfb39+n1+hP8CCenagxhIIzlEZZMauyG7zq4q012jrqMgpAwSfHkZFj4YVaR8fyXmU7RFKHZel0LJ0VRTOeog5SS9fU1NtY3ODw8pNcfUKtWS5XBRfcv2l5GNqb+PoFNQJQOv9DLcBwn9zvL+1SARS3L0iyUYQAoRuHDqfclScKg20EkAy6fv0hR9IFURHGMV/UBRRyEOLY9X1anG4VMdBQjiSK9sbQsyNUjS+e26HEJiOKUnf0eg1HE82/u8JufvjTXTpllmK6DECpf1xeM0XvJ6+b9WLjzUvmgFE7WMEhiLeV9NIi4udfn3ftdbu6P6ISSbqA/V3eP6L1ygxfffo5X/+Ysd7oR0nT4/Oc/zzMf/RgXL12iWq0g5Txd9mQ7DdMgCCM2WnWqVa3AmmUZhhALQdOGEGy2m/gVn6PegMNOTz/Tma7Zlkm7XsWxbHqDwWkG7v3x2Q8Vwj+FvQ9Ov7DTXuID6fzX2g0kcDQIiTNJu1bBnYoAjEPcCri9f0QYZVzZ0mU+Ot9ZhCz1d0zDoOo6xHFKZxjSzJ3E7AKeyYyjQUi7ntdl56c7TXkKw8GQfq9PrSiHOXEDIJb8rC05Bbp5McBqOibmeS5hGOYlbJJed0AQhvp0X6mwtraKnYunZFlGt9uh3++TSU0y4vs+rVazPMEnScpwOKSX8yc4jk29Vi2BcyXyP4qnTjHLurMkujf3mUwuBhzpunZ9co3jmDRN8TyvHJcUSZBINmfERQrBENM0EEaV0XDE/sE+62vrbGxscH/nPnGO5p4uFV3+XI/7WxwleJ6OTFiWSb3iEmcS33NLpHN+lZMHZP7OwORYTofchdAnUc/bYDAYsrOzx8pKm3a7Tb/fp9frkaQ+9RyoOY7GqKm2h0ud8XQUS/M9OCRJosmbnOlKmlkbAwZN0lQyGkUIyyIaPly+t9/rcePdt6hbKVcvbpYA1TAIteMXJvFQpz6Evdjx5y3T1LaeB4ZJGui8tOd7CEMQh1oWPBO6YsXMQ/4SuLt7yDeee4X15hL8iFJIpJbZTlO9mZiNvrwfjkTkp9JZDFAmIc2QhoHMMuIo5sd3O7x8bY837/W4O8iITJ+1tXW2r6yyYQjiJOX+Xof7O/dxwgBx+DahEpgovvK/v8ZzX7nAx372c/zMZz7DlatXaTSb5Ro1WckDcG/3gIrnUs1FtnrDgMNujyherGEihBYrMw2D1WadOEnpD0dzn0vSjP1OP9cuOcXi8n7baVMAf1vXO4V9YJz/7MZ1o93Ad0MOu33uDYe061Waud53YanMuHvQJYgyrmy2S4rJOIpRyirr7QuzLZNqxaG7H7HfG7Banz39w629IzzHoubP12U7joPRMOj3B3S6fRrN+tRnHoz+ND91p5rbfCkngATbNheAtqY/77gOo9GI/f0Dojih1WywsrJdhrTjOObgYJ/haKRfMMel0WjklLzm1Eubphn7BwfUqlXObKxppLPQRD5adVDguh6GEHS6PaRSWu5YFC2b78tpcm9FaH0ZiLGoMjAtk2AUMByO8H1P54wHIVVvusxLp3dF+T/XcVBS0u8P2d3dZX19nZV2m8ODI1bXVjTHeL5ZmJ07J9nkAlTcL5MZh/0Q2zSxTGPuMw9jk2mc48o2azWdytnbPyBJEur1Bp7n0uv16HZ7pbZF0Y7i1D9zN8YbDjHz/Iowv1Z1U0oRhVHJ3jg7fgqt3me7Hn61QhxGBHGMnJPBXXz/ReMQjAYMDnd5dnMFJ9+0RkGAX/FRwiQJRrpMdEmaTqFLObXjd3PHHwACt+JPzGPBP/+zb3H/oMvHHr/Ix566TLvZ4MbdXf76uZeRQY8VZ/GcVTIXzRKax8KcYMubGsr3uvZPOn4pKUKCKt9o3Lh7wDdfvslLt/vcG4J0KpzbPMuvfuYRPvPRJzi7vQ6pJI4jzUMgdXn0WzfvsXvYozcY8eqbN7hx5z6Dgzs8/+U/4JVvfxl3dYsz567wyU/+LE997KOsr6+XY50kKcMwolLxy85ZAuoVH2DhBkBXqlgnO3RVEBYV/f7pOs6p+50qP7+gjVO/+mm3/wPk/BethTXfxTEFh90B+4OAIFNUbJujUUCY6COmYxs8dnZtCnhkWZbOrYnZkxG4lk2z4nLQCzjoBTi2Qbvq06z6HPVHgGCj1Zipyx4/JcuyaDTq9Hp9up1eSchSXRBunv4+zD7gTEr2c+nSlZV5RSwFWCX73cJRK3O4tmUTxQkr7Rabmw2EEATBiF6vR5SHWiu+z5mNjamTehCEGKYGTxY2GAx0uL/eQCHpdDoMh6M8JaAX+/W1Nc6ePcu9+/c47A+pDwPqq63lm5g8CCPQa9Oij4lCec4Ya8jPX0fkYfwaYRgyGgWYpkE3jDnTmi61mq8UUaWGw2A4RMoU1/WmqESzTJZc/bOmcoT/she1YA4s7PZ+lziOWF1tYxlat15zFjw82EvnqGUZzTjOLMtka3OD/f19siyj0WhSrVbpdnvs7R3QbDZKhsAw0PiH6U3A9LtzrAmB7XlkeXrJXiAQEycxNcdBZRlhFGIZBlGWYVsmycLnveim+l1MkoRRt4MTD7hy/nH9l0S3XRkmMtLRBLtMc8ybzCRZmuYERoXjB8vLo0f5a/utH77Bt779fcL+Ec899wL19gqtZoNREJGOejy+ZtKPFr+jMk0xHBtBjvIv8QKn6OqDmgKyEjULWcbBfp8/+tbrfPd6QE/aXLl0iX/rFx/jY09cZmOlqQMGwkCzdKY4jguWfv6rFZ/Vtdb42ujo0ItvvMvXn3uVH711nYNrr3Pw9mu8+50v426cZ+uJZ/n13/gNHn/8Mfa7fT2elln2z/c9hmGk8UmWiWWaxLnWimWanJlQFcykXHjqnxsvJeZ/98AD9x4egDjNNX4Sz3z2ng/Wjw+M819kQmglvzNrbXqjgN3ukO4wwpApddtgtVHHncjdF6dLJ9eXlsKY4dAQWKbBRrvOertOlKQc9gbs94fsdDRhzeXN9jTXdv69qX8ZAqfiMej2iWMNpPN8d2FZ4qLvF23tjyLiJGV9ZXF9sJGC7VoLta5nzbJszmys4/sVRqMhB4dHWKZJtVah3W7N4QV0G5R+GYejEhQXhhFC6Nrm+/fvoZR+YVdX29i2jZSS4TDg3v0dNjbW2N7aZmdnhyCKCI56NDwHx3FmdBEmQ8zHOxKdUjg+FSLyXYTv+7iepN8bUrVM4lGA1xiPZSazmWeij1i27eC5GZ1Oj5WVFdqtJr1uv4wsKamjSsW4j8Fsxz+DOI7LTWCnPySO0xxprxc/pUBlqqziKCiEDVPMbTaWjZOut1cTaa1jximPYKytrdHp9tjd26XZaLK5eYbBcMBRp0O1UikFmQDSJDu2imFZ2yQKE4HpOKWGg+NoJ6pQDAda/MW2bcIwoj8ckSlBPAF2tS2LRALyOJY3fePdnfu8e+3HrNVtzm+ugWSc51cajOjkJ8wFUCFUJlGpVj4wXQ8SLWDjVCZ4PPL//LM/+TrmoMuvfewCmZS8fvuI3btaSe/Sep1PP3GJP/zWW7iOy929AdvrtbK1WZZhGD6kKeYyx/9eLa9YIMsgP7SoJOUHb9zjj773Lm8eST7xsQ/xb3zxU2yvtfF8De5FaclyiY4kpVmK48zwmKjpHxzP5lMffYxPPnOV0SjkrRv3+OYLr/P9V99i//ZN7uwfsXXxEk597MSHo5CKq3kSgjgmSlMUiiSV5abPsS221to6OpffMz2O7W/Sz73nIZ2MNp50sWUn+IdoxEJffRoHvqytD9aGD4jzn+cmn+TZRyiqnsulPOcrEMT9AeE7N7EffwTDdZA5O5SR53z1zl2ipEBMUU+OvZBnW2yvtvSGWWakmcKZKP9atgALBBXHxW6bJHHCaBSUJ6cTe5oDFMMkZhgMqVQcTHtS2CPvMgamKeZChLNtEoz7ZNsW3W6HXr/Pykp7Sg9eO4Lpm0yGtw8OtJKa73v4S4iMdC2+kashmuzu7rHSbnPmzBkODo8YDPrsxBlNJ6bV0tGH4pBrGAZKgjoG41CwMZ7GSgY7wyAVOkCrlGbEazYbWidCghLT4Dyl9InZdmyGnR5CCGr1OodHnbm00mQU4DTlbJOf2esNkUrRqlZwbbvUaZi0YigWRQLGl9LPqqiSmL/38YtF0aZWs0HgOIxGQ7IspdVq49guBwf79AdDtja1DG6apiV470HMADKhMJUGAxqmUeIAslSXVtbrNQxD63iIPH8+aUmxETCsEzYAMOh1SQdHPHp1DduxiYNQg/WESTwc6BMs089ksk+ZzJAoHFenguI4xvVdvbFUCjK9Cd057CCikLNNm08+vs6FrTZKGCRJxo27He4e9GjXfVIpeXe3w3/+P38bzzP5X/+zL+oS4yIEniaYllWuRe+bKaVDaQqN4gdkkvKn37vOv/jeLaxqi3/8H/4yj13YolatYNi6ikPGmrPBEKJMDSq1IGVXvq+CEtCZR/CqVZ+rF7dp1av8zFNX+LPv/Ii4fpaPPPNRXnvlFXZ27nP5yiMIIdjcPMPZjQ2kEERxMqfPkWYZ3f6ItRUb09Dz3XUdqp7LMMeg/GSD+6e98vt4gl/4vdPkgN6fUfhAOP/CKRUOP8uyCfCIIpOSTEkskQuUAG69hrh4FmkaGEBw9z7ZYITIOci97U0S2yJRMZZhYNq2nvi5TZ1KAWGYRbRrwmEubmvhNAvhHsu26Pc04rRarSwPM0pJkmXEqeYyHwUxtYq3eIFKdR5ylk51duHPZEGNm2BZDv3BLu1Wq3T8yyIRUkpGo6As47NtC8dxSrBaMfaTud7JzZPnu5hmi6OjI9I0ZXWljW0ZqE6XbgyiP2Cl1Zg60S5Txhv3TTtmXeqnys3A8dECg34Ys16rYJk6HNzpdKnVq1NOu0Sw52Ni5PcajUb4vk/F9xgOh1Qq08+v2EAsfwbT7Qe4uXeIlIrzGy28cgM24/gn/j0pZDV5nfyqc58zrXm1yZNMCFFKAg+HI/b2xxu3wWDAvfs7rLTb47m/LNeOym8pZn4vSkbOwgowYBRpMhfL1Kf+JMuwXYe4KO+bXevkZDRgPiVwsL/Pwf27tD2Di5urpFGs3xPbLg8BWHqdQCqNdLetkko3S1JUKnNaW0EaRxqsKHRFSTxRfvZ//NnfUBUxv/3ZJ9heb2IKrQBq+TaPXV7j0UurCCH4b8//PHd3e/xvf/4Cr98P+W/++GXuHQ6o2Ab//q98mDN1C9M5rhbiAU2jY/XPE5HKNM349ku3+aPnb7O2dZb/9N/5Nc5urGK7un/60elojJQSTBMjp/ctUnMLbXa6CYFQGo/UHY74xotv4axs87kv/iovv/RDrr/2A1IFt95+E0MlPPL0s4hPfBLP9xaCf6VUdAdDBkHIWrNBo14hSdO5dXrxP/4/ZguVHd//23wgnP+kSSVJlcTGyPPDMj81QaYUIh+JWGaIXHY3ShPcs5u65ltK0ijCrHhIqcg6XcJuH7Nawds+s4Be9nQh3cL0x8aLnFIqJ7epMRgMieOERr2qd/kz1xRC6NyvMAijFBSYeaTBNR2iTC9cIiXXzz453D9rSo1DwnMYAiU1H3wc5SJK49xxvV4jzql1p1Hvs32nzIc6jsPKSpvDww5ZltFqtRCWxcHBIaM4pRIlOLbKZWGnQXHLx1fo8OiC+89aEUGxLJN6vYLMpGZmDAIGgyGe6+nqhwn+AiFyuWPLwqtU6Pa6VCoVVlfXuHvv7hRPuZmD9OafweK3UUrJMIiJ4pT1Vg13CSFUMQxSSoyZhXsZiE8IcnKcZZiBk1YIVabEDKNGEATs7e/Tajap1xs4js3e/iEV31uYrwfIxkoKCBTmhExOoT452RwhBJZtMRgMqNaqOI5NEAQkSUYGY/W2Y5o96fhN00BJxe1bN7n59htcboBMM61a6LiQpsRJjFfxIEl1yR6QjgJM30M4NqA5CjAMDMvSAjtKaYEdqaYc/1e/+wovvfoGX7jS4srZFq7nQJblhEFmTsOtzXNMDvu6HG6lDvtHPTq9EbcTqFW04Jc+Nb9f4WGmnD7oSNX1O0f88XPv4rbW+I//wa9w7swalmPr03px70yWe1Glcj2DTC3VKylvPtt2oWWBq76HVNALM1578QWGe7cwwyOGVhuVJmSICXbI47uZZhl7nS7dXNNiubTv/Hffq3/824ANLrQTovnvVzs/EM6/mFNJTr+rgCTTqlfki68ttMykQANBFGCZRvlzZggQhqagdWwtDCMlNOpY1QqmbYNSZHGCMA1d87t0CE8OuxSn0TIK4Nh4nksQaBBatVZdSB6jgCiNCcKYiu/iuiauqZXvDKE53BWqJCl6EMuyJAcvCU19nGgmtjQH1BT1/6B12yvVSv7CixxfYWsAoDEfcZgbgaLfts3KSpter8/BwQErK6uIFcnhUZcwCDCNSp73Pd7ERNRlSrnvmC9KpRgEEQ3fzWVrBVkmqdWqBEGYK0Wm2DkoU+TPrWAqdG2LYaYRzo7jgmJKZhaWUdguORUrrfGuFFQ9TXtahLcnT/pC5FS6eZtmx2BhX3OUZMEMN78pWdSmydzgOJplWSbVqk7ddDodfN+n0Why5ozN4cE+vX6fRr0+HQGZi1zozYDW21z0kPLNsdTRKddxS+2BREqi9CEEfDLJ22+9ybvX3iZMJbc6GV976TqfA66c2yQu6vSjmCwIMRt1Tajje+Uik2ap3gCaJjIvWbXyEkVdyaLH9v7uEX/61e/waFPyy5+6Qquuy/5UBqgcdMk0jqbmWvzbX/wQjYaHZQj+i9//Fv1BRL8f0PLrD+74Z1O7ijFaduIEXoxzOIr4m9fucH8k+Ee//TnOb67PO370+llvIcHGAAAgAElEQVSsq3o1EDnD4RJg8YJIT2GaOEySoXUPkr1r/J3Ht9k7sHjtTk/jWpTk/jtv8Krv8dSHnynXjUmyrknLpCR4QO6HpSM7t5Sr+ZP1SddYdqm/JTu+Dadv5QfC+ReNNQ1Bzhqqu6C0jKaRL+yFGYaByMPBk/X+mZKIbLxgm4ZBZhoIy8S0bVSSkhx1yYIAf3sTw1l0wjnd4E3mZEUeTiu44qMoIo4TXHc+Zw8KyzSp+Dae72IYAtu0EIBlWiSk2I71wKd+w7CI4xAlFcPBIHdyKq+rFqVyYVHWJ4SYAeUphDBwXUcDp9zlKOnxGOi/27ZFo1Gj0+nS6RzRbDZzZb8hcRhhVrR0ab7vOPZamcxykp/Z0rLJEdQbxd4oJM0kaw03//14tXQ9ByPWeecsV++zTD2uSgGSHBBZpdPpsrGxwcpKKyf9aS1s2+KWjP9mmiZ1x6YzDNk5GlD3XBpVH8S840QwfVI+9j652I7SKYg0F2haLBE8cZ+Z8jyZ52+11oNZztfBcESc6EqRlZVV+v0eR50urWbjmNRRjjPLx1xvYybnkv45iqJy/vUHA9K8UkFmx+X0Z99B/e9rb7/N33zzr7h/5zZCSfZRJMJEvHQNzzLZ3FghDENMw8CuVsAwiOM0pynWl5Gp1PwcpawuuXyzIk7GUYZ//Y3nyQYdfvvnn2ZztTaurBD5xj8HzBUXtkyDR863sW0TxzbZPxwSp3rj881X7/Ebn3JpNa2l6ZSFNjUEeZi/cPwzz0QqSbcf8MrNDo88epmPPH4xD/XPz6viNK3ppvUmOs3khCz0gkYsafYojNg7GhArC1slOCri8vYKH37kLE8faRC171p86VuvcrS/R6dzxOHBAesbG2xubs1cbfK5z6/DtqWJx+I4OXVEYGG7T/MIFuwRHs7xP8SWYdFXTn2Z09/rA+L8NbraEONXQ5/kjNKxTk5gA6YEUwwUSkqkglRJDKlL5DTgTO/wpdLhZ7Pi6+iCWAbcepAHNbv4G3ieS5qmRJHeuTqOPRMBEHn+VSOsrQkCn3JRPwXn/CQqvlig4zhBCDRw0bYxDL08FypcxYZinIOen9qmaWKkGUmSLuTRX2RCCOyc138wGDIcDqlWqpqbPwwhFPgVD0OYJ+b9NQoZCnDbon5HSUovjDCAlbqmC53UINCnYkOHr4UgiWOSOEFZqpRwntzs9Hp9pMyoVKocHB5pNbtTRV6m/24YmihovVljEISa2jdNadV8rAXjWC5zRRTpmLmX5dLCIv/CVMg9H6f55ipGoyCXSZ4/nwu0A/BcFwUcHB5Rq9ZoNBr0ej39HKvV/B3MQZULnp+R/2/RuMRxXJIxSZlhGmJBNc2szToAwVtv/pi3Xn2R6PAu5+sC1/HY6wZ0ByHXTYNvPP86H35km16vx4WLZzl/9gwCwdeee4VPP/M4jZqPTLNysAuNAsuy9MEhzcj0aYJ3bt/npdev8YmLDR49vzJub5F2FPmhI5MIS0cQDQNq1XwTqhSvvX0PyzTwXJur2w18zz6ximKpKcaO3xhHccZ/15oWdw+GHIaKv/fhx6hWvDxSNP9Z/Z4orDyaqm+xhNfimHcgTTPu7h7xxp0DhN9EBgGZ0sx9GysNVpq66iGIdPlnrVZjf2+P/b09Go3Ggise72qNvFxWLloYZi2fOvM+85TPYC448LDn/uWbmeO/MvP5h7j1SaP0AXH+GtQnDKPMO52kQ25OnIy1QK1AAoU8biJlHubVjj9NwXVsrKqvKwJMkywKSTs9nNWV8iV+sMjc/IdN08T3PQYDXTZXhJ0t26II8lqmqfUBmKa4TOJEs5Ed0+9yxJQqGfwKKxZaJxfAKcLYpmkwW3q3fDZpRx7H8UKuhPGknN6lF4AypZTWZzdMqpWq1hmPYxDaycyJ8pSbMJ2qOGnGKqVIsowoTlirV6nlAMVUZtPfVcWmREdV0jTVOcQsw7ZtbNtGofOcpmEwHA6p1xtUfJ8wDKk3Cha8yZf3uHHTVKYykzSrPo5tMggiBmFEzXOwbD1LFwbI89Pn0hN2nq6RQiFyty9mvj9rhS6DACxb57pne6KUrnNP05RKtYIA+v0e9XqdRqNBp9NlNAqoVPw8baLftdkzl5hIK8z2K8symn6lBFwahjkXCVlu+poHB/u88/oreNEBv/DRi5zbaDMchbz45h3e3IvYHcR0OyPeuXfIaDDg6V7G5f0eW6sN3nj3Lv1BwOc//iStiodpaoGlIhVjmJq6O0n0xiCKE77y7ZfwZMAvfvxDuN4EdqfAKBgGQkodgmdx9GW95eOYJufXKzx+tqU1Jx4m3z85mIZY6oyzTHI4iIkxefzi1tL8vcxk6VuEIXT/0cJnpz4l55uN+/tHvH59h25icemxx7l94x28LKNV1xtG2zKJ4pTnXnmLXmqy5le4fetWrgcxdj1pmtLtdtnf2+ORRx9devhJ0+yBT/zvYcQXX/Cn9v333vKTrvCBcP4KpoBMxS502cuysAzNGINvkjghLUKchoFQeqEreKhNx6Dgvo+HI0zfx2rUHgqLs2hHp1HzWho3SXLJ0tTCzuucHcsa91UpXessNSBr3klPm5S6hrmILtj5i6KUypnc6jkVsSh/L6Vuy2kiCroNBratiZKkEDOOafa/4/4rBZWKVuMbBSOqRk3zf/dlLrkMvrekjJD50q/F7dMbJsc0y3kzixGYvLzINzP6nhrQqFQOrBTaKRbSv/W6FsTZ3d2jXq/NPNaTJ4dlmkRRjG0Lar6WPR0E8UQEo9g0zZ/yjyPsWcy1v9iUUgyHI6IowrJsPM/D930UijQvpTONMctcHEf0BwOCUUitpqmb+/0+zWaLer1Gr9fXtNGep5Uh9V3K+02H+ydMFGkBsGyLYDQqN+bxcfXbC+zaW2+SHN3lZx7b4GNPXKJeq3Dz7i7XdzpYHUkSR8QYvLEfYEoI37rPS2/d5vxGg73OkFdev8b+YZfPfuxxHrm4je9Y6Br3cQmwVBpY/Nq127zy2tv8wqOrPHZxfUonRPfLyPPYqkxNLlo4tlbrpErxxFYjp3U+3eKilNZssCapoAVz4L6570lFKkFiUK94S0sKs8L5i/G6K6RcKvg00zhAO+Hrd/d55dodru0M8FYvcP7CBQ7u3mCjXqM1wZz6zu0dXri2Q2I32dndIwpDLl66RH2Cj2MwGPDj13/E/s49Ll26tHSdOrXj///tVPaBcP6oiTB9vpgX+vKlktbsF5a8TMVLKYXKI4YaNwAaDFYsekIILM/FWVspv5sMR1h+ES5j6T2m77/4M76v0wtRGJGkKUmUkAqte29ZJsIcv3xJkqKyDL8yyQ8+3UcN2svIMkkcJ+hwvkWapCWhSpxrl0+aZZnEscz7vkyEY94sy0QllNKty5Tv5mlfNbWs7EuGgwHNlhbpiHspcZLguQ7CMCdC++MTx2mieUIIbNPAsUyiJB2j+CnGc1l/9AnOMIqNU1wq0vm+S7fXzcfRxbJtRkFAtbK8bLNob/Hnyc1W/leiOJmrcFjmwKWS5d8W5miF7tzSGal06VYYRYRhiONotcUkSQiCgCxLy2dpmEae97dwHJtWs8lRp8tgMKJWqzAYKPr9Hq1WS2+MBn19YnbskjdB92Uet1D0XQhBGEZ63BEEYYQSECdpPn9PZ/1ej7vvvs3ZmsljFzdp1qukSYqhFI5tYRkKx7NxiFFGnZWKRc21uLN3xMvv7DDo9Qj29/nyN/bp93p85KmrnNtcY3utRatZCHTpjVtvMOIvvvMyTTPmF569kFPwF/FjNRF2z79U0OhOKjXmz/vO/pAgkTy6WcN2rFMdLDIpubk74J07Xc6sVjm34tOsL94szwz31GSM03ScYphZKosKn2J9VfqXi9X5yiCf7lMYxhiGlkV//fo9Xnp3H1FZoVFv0T08IB11uHjlzNQlnn/tGp3ERTkm927f4Mqjj3Px4iXqtbHzHw2H3L93F8+xNOjwbxtZt/D+y4GCP7VGvM/j8sFw/uXiP15Yil2euTAcunwEsizDsAyMbEyqooTeGZNlU7lcIQReznEOEO3to1ba2LUqx0Kvp+6/+IkIAaYQVCo+aZbpMGyoZXJNU+eaFZrVTOXUlgXGobhHsaBLqQjDkDiOEUJz27uOg2lZ7O3tEwQjnbvMVeOma9IFlmWSplku+XtySqG4v2WZRFEGyKVh6alT9sTPjXqd3d09sjTBsiyMUgteTbAu6i8sEuc4nltf/77IxUqpZiiDFz+TQhtACF3PnSSJRn1LDcI86nTZWF+j3daKf5Zp5WJC83Nh/sA3GX3QUaUoSU/EOBQmpUaPF2MxzTVQzuQcDGmgzPEYFUJLo1FAGEU0Gw18v0IUx3S7XV3BYBol416Up6OMHPRXrVRZW13V8r/9Ae12i8NDTRbVbrUZDodImaHUIpnfRWOt/z0cDGk0mnkfZFned6qcbW4/fuN15PCADz17iVajhswykJJWvcrV7VXuHvQ5DBVmHLNdkTyyVeXZp65w694Bb9w95K+e7+FVKrTNiNs33uXauzc4s7nBL3zmZ/j0s0/pKJwpEKbBW9fv8fbb1/mtD69wbrM5Pf+KNivK8LvI+yUmha0AMsm9gyFxCh8618KyT5aMzjLJvcMRf/Ltd/n+9S6X16p88kqLxy602WxVaNTc+SjExHALQ/MsoBSd/lDPE/OYNGa+U1aKnHbamJrUSimGo1CnSg1dqvvWzXsoNJfG/U5AbFao15okwYCbd9/hTFVw9cLm1G1u3NsnNdqobICVRTiOje04U5UFnudx/sJF6o0G3U6nxA8ZOTZnan79xDcGE2PAZILzp+n4AcR8V9/n+38gnL/Gzuh8r5WHczOl5sSvTmNpqulJTQSGaWKZGgwWy0QD/zKJuaSW2jAtor1DffoHFgJm5uy4vxdOSnNYS9vEsmyyLGM4DHIOdEuT7FR9smws+Vo4yjjWTkopnU4oyHgK6VXT0AjYNEvy7y52etpB6mueVlO+yJkXGwdrIS5i8duo10rBZEY4y0FW858dg/Um773IDEOTrEil9ex1dcCsI17eP5GnMQxDL2K2baNywGIQjgiCEZVKlWqlQq/fpynq5XhP9q243/hnvUwYpqHnoECXs4nx4em4qaThDhqUZxrmlGPXg6fG4X9D5QTBWjQmTVNd2hjFnNlYBwRhGLCzu0+KYLVVp+p7OW8Bec21vm632yNNMlqtJptnznDrzh2iOGJlpcWdu/dpNVsLgWrjU//83wxT13VnUlGpVBiOhigUUZwu4fBfbMPhgB+9/EPOu4qL587gex5pHIFQ1OoVnri8hWsbXLu9w/4RrLZrfOpDV9ncXOHqxS0eu7tLv9/njftrbBj7fO6RBntHXb791m1eeKXOk4+cZ2O9jZSSo+6AP/vmD7hYk/zyJx6deTgTiMry4FAErxbM5zTjsDdECFhr6Iqe45YJpRTdYcyXf3CHF28N+czHn2T3KOBPX9vDe3WHn3vyDJ94YoPNdgXHXrAJF7qaplW1sUl55+4eT1+9uDiVlE9G3SRRIv0NpTAyzcxY9PHt6/eo+C7bG20MQ/DNF99iv9MHBYnTwG1uQDJiePcml9drPHX5ImutaSDfaqtG1gvIlEWKJAqDMgJV2MrqKs1Wi4P9ff7mr75Os9Xgqac/ysbmJnMDd4ogyEP5yPKLEwen093yQW9w6kYe+5H3YRP0gXD+ArBNizBNIMvIULne8wIFrGOsOCmnSi+6vmHleu85DWwmSbMsd/4T4ct8slfObyPzXXAWhAjTzOkwT3tanmsRxRMqaHNBO7ooiul2eyglqVarHB518TyXer2GQhEGIUminYjjOLiuM5ULK06XUkpc1yGMovxkt7glpmUgE0mSxAgxQYF8gpmmSZZJnRNV8xz0y2ZgKd9q6hN/JjXBUNHOqSsIceIJWRhM1Opr8g8zpwx+GCtSS6PRCM/ThFCObbO3f8C5sx6tVpsoiuj1+lM4CiEmNz/z0QnbtkppXCFA5KHCCb+RRzkWj5uSTEHn9UZCb06U0umbLGeJ1GWcBoPBEMuy2N7a0o7s6JBef0iQSTzXxXWdMm0jVaYPr4a+5kq7xcHBIUdHGWtr66yurXKwu8vm5jh8W4AkT7vYGMKgc9SlXqsCikF/gBR6Q/8gp/4ffP/7mPGAZz50nprvIlOd7ipKf6sVj6cfu8jTT1wuBracRTJNObe9znrT5+1DSaR8zp1psd32uHEQIbOEIIrLE+7zL7/J7Vs3+Xd/dovVlQmabqUgzcVyJvc6+QNVSs3l/aXM2B3E2KZgFGbgJFN/Ly+Tz6U4znjj5hF/+doun/vE0/zD3/1lAL738lt878XX+fPXbvPijSP+zU9e4Oq5JhXfnnPsrm3y2NkWNVPyxrt3+PXPfgyT+dI9/f7oSJ5hGoX4H1IpkjjGdpwykjEMI/7VX73A5z/+JI5pEmQW2BUyZeLW15BZQlWN+PyzV/joU1fKJVXlEao4Sfmtn/s4//obz3HQ7SOkvfRANxqNePON1+jt3OToTsi9+7v87u/9gxMxUPP9e0g7Jutx7NfyyGqZvl06v8XCHx/Yika9DzuSD4bzzxdVSxglUM+aqe0/3rQjTPIFsRgYKSXKMEhkSiRTzQCo5nUEJq0IRyUHR2RBiLOxht0c56ceTJZ10d+1Ep/rOtRqVcIwyoFmTXZ3d9nbO9AlgKaBV6ngOvZS5G4BYjRNm2DUWUqaAXpB1qf4lDCMynD2NJp9cX8KoaQ0STGck/kHlFJ0Ot0cJGbS6feJogiJIJHTIX3DHBeRLbuWaY7LITMp9QkyU1gPh9DEsi2qVpUojIiimCiKEELLFLuuZG9vj42NDc6c2aTb69Pt9fFcl0rFX0DBOz0fTGGWgELbEKSKvLzQKCtPlo2zYYqZ1E/BtT8uFy38TJKk9PtDRiPt+Dc3N1FKsbu7SxzHBJkEw2K93Sj1KpRSpEkGKJx8rpimydraKvd39ojjkIrrcWSYDAbDEgSmpAJDIPNmFWuPROWVB+P2mqZBGGrBqq1mi+FoCCjCOBsz+p3S3vzxj/HiIY+e38S3LdI0KTkD5qxw/EqhMonh2PT7I/Z6MUjJuRrUKzaJkeLZBvu9ETfvHbC20uSdG/f4wz/9Ok9tOHzho5emrkkmc8cvphw4hgGGTiUuWvBfun7IKEj5X75+Dde1p564ZRhYhiiBtYMw5vU7Xba2Nvi9X/9CSbH9c598mp/75NP8+de/y1e/9xr/3f/9I7741Bm+8JGzbK3VsMzJDbzAcSyurHu88OPrxHGK47Mw5CSljn4Kw0CkqR5Ty0JmWS7LrAHJ7ZqPNBz+8sXrVI1UaxqYDaRTw/OreHGXSytVnnrkfNGEMop3eNTjBz+6zjCKqVSr7HcDAsOnUmsuZJD0PI8rV58gilPuvHuNaDTi5o0bXL5y5aRpwgMfgxd9fMHvTnb80KpXWW3WSTPJQbfPYDQ6Gbv0Xk7tJy/Xp7YPhPMvzLYsrAdyrmNnnMmMNElxPYcgiTW6WUnMMpysFzDD0MQvxzlKAP/8NklvUBIBTSPKH0zrfZEZhkGl4tPpdLl3/x7bW9tsbGxw9+5dDENQb9SX7nqLCEe/P6CRo2YHwxFbWxvH31MYOLaDaWREUYxla20CfY/j+1PgBor6/2MJaWRGFMdsttukaYxAsdKoE2aKIIpJU/2civD2MhMG9IcRnmPjOjZJmtHpB4RJTLtWWQpCPNbKCKw++UZ5DTJoSmXPc7l3f4f9/T1WV9doNurUaz6Hh0fsHxyUaRbLMnPiJO3YJ2l5i+ejDAGZzsNqpr8iZ7+gWUue8/znxr83DP1cCse/s7ODUpJBpq/lmEwxRUqlch9mzlxT0G63uHtvh0sXL3L27Flu3Lw51SfHtLDEOGJW6C9kKKwCu2Hqcs00TfFzAqGjow6O7yLj405F86aUwgRMlRFGEVEUauzI1EZYlA4HKGlrCw2Pf/kX36EfSQYHO7TWfBxTsbVe4yPna3zpB3f551/6Gt9/9S1u3LrLppPwH/zqx3Gr7jjMn2ZaLEdMh+1LkKkhEEpTDqs00eOcizjd6sacu3COdwZgBLNvl8yvkyGVjmY02mv85hc/xcrKfP37r/78z/LM44/yr772bb7x8pu8eKPLbzy7zd95ahPXK6i8wfYcPnJ5kxfu3OA7L/+YX/zss6hJkKjSc1FvpjWwTmU6FZilKZ5tYzsGaZyQqpQL2xu4RkoifEJMTJkhSXn06qP0jg7ws5ir58/juGNnLpXi1t09/sXXfkDgtHCrDYadiEA0ePrjn+HJD39kikK7MNu22d7eZnt7G6UUBwcH3L97dzzWx663+SaVk1ax/BOLz2QPZKZp0KhVaOZMrqZpsrHSxLEtDjq947/8fuQR3oecwAfK+cNpHH6xgIgpx1+o6gkhEGmMIcyylMfO8/5pHooumO9Ouped13pHRx2SXh/T93HXVsikxF6CG3gQMwyDs2e3GA5H3Lp1k62tTba3t7l9+zZhklI5pvZbl9OFXDh/nkEwyiMEpwuRaXY3TbcaZTGuOw7DL6trN02Tglgoy7JjNya7u/ustNtYlsXB4SGe51CtVlBBRJwkJEmSp0C0ZXI6ByiMsdLdIIjZ6w7xcjUy0zRYb9VxHsbx590qelZswEYjreNebAK2tzY5Oupw+84d2u0mtWqNtbV1pMxI0kSXWiYZSZoRBAMtjrQAy1CQ3+zv7Y/7JiYiXZaFY9s4jk7pFEGBSSepWdhEObag+Rz6/QEg2NzcyhfLfZIkxqvVYdQB02K1XiujRkopkjjJw/3W1PUKLXXXsdnf32NtbZ1Ws5HfQ5tUGYZpo6QGWBa0P9OuWOhSvjieUoYchfEDl2kN+n3IK1qazRpC6EigkdNRIyZiLlLpUlnTKNH4SinOnVnlpfs7rDVrnF9xcdHgkM8/c4laxeNLz93gu8+9iGUafOqCy8baWIq3PDHP7IuVUtzb6yOEYHOtpu+Z5uyLSkEco5RkpVHh2Q89xr/3m5+nUltcNZKmGUlOeYzQVTLLbHt7jX/4O7/Chx69wF9+51V+/69v8MNre/zOZx/hwpYGJ9qWydMXmtTMlG//8Bo//4mnsbzxIaek9rZ1aD+NtYiRBESmSESG444jW9WKyz/6+7/M//Avv0ovs+lS46mnnmEUxgRHOzx1oc4j5zenrh8nKX/yVz9EbFzlFz73BRrNFv/sf/qnpDg88tjjCx3/rAkhWF1d5d6dO1x/910uXb5c/n58M+b828kr3/vhdbU1a1Wa9eqUCqxlmqw06viOzf3DTinE9dO30/XzPTl/IcR1oA8ayKuU+rgQYgX4A+AScB34+0qpowe57vHgqJk9dJYRhtGUmp5vu1oRriCpQeBYNkaWEacZpmWQZllZI3/S/e1mAxSkwyFJEGL57lw7HtaEEFSrFQzD4M6de1y4cIEzG2vs7u1jt1sLIxQaMDiiVqujlOJwf5/1tbUHvq/rOmRSX2tejni+f3oDoPUHfH+x840iTSzjeT5hGKBkpjclCDzLJDQMumFCpZKW4T/TMDVJD0xVK4yCGMuAtWaVLFPYpkHVcxbed9Jmn98kXmv273oTUxyKxk631WqSZRlHRx0G/SErKyt4nofrmLgnN+GE9knSNCGOU0ZBSKfTnaJctUyTdrudYwXGIM4oihkMhyRJQq1ao9VqlTn+wXCE57rsdYf4nsv2ensKn5FlsgQ7TtrkxqvVarGzu8faGjSbLbrdHrdu38bzXExDYz+WQTO0QmJQVre4nkOchBgCojCeIwYaDwYLXyXfMqi6BkakiIOQpt/QKbnyIQKZJuoRwqCU5CRHqg9GPPf6LUy7QbNqcHmzjufo8L3pWnzi6fN85OoWN+51eOGN29zd7y7o1Hy67fnXbvOXP7hJreLza5+4xKPbDU36Y5mgFDLSEQBDZcctYuWYmZ576qXEcW0+96lnePTSOb7+3Zf46+df47/849f4pY9s8juf19K5zZrHRy+1+csf/ZjucMTqhPNPogjLdhCWSRrFmvmRmUeQyXyTredOs+7zn/zeL/E//uFXSOKQH738IpZQPHO+zmMXNrCdCbKeLOPG7T0OI4NPfugZGs0WQggqlQpPPvUh/FM4/rEp3rn2Nnu33uGJj32ST336M+N0wU8c8X+8rbWbNGuVhelYIaDie1zc2mDvqEdvMPxbaKE2I2fJXWbvx8n/55VS+xP//ifAV5VS/7UQ4p/k//7HD3LB00TUlVLILCPIHf+kFR0uHo7IobkaQCjJ0gylljv/eTCtwGo1EPWqJgwKYqIgxF5pEmUpVs6VLmDhDv805vsetVqV3d1d1tfXqVa1OI0QBs6EBkFR1hWEEWfPrpZlfrb9YI+yQJKbhkml4jMcjnBd91haWyEEpmUAFmEYTZ3eCzs4PGTzzAZKZRweHdFsNHLwm8jFPGyGQUAYRiX5jlJqKsSsKzagH+rSoLrvYVtmKU70oLYAHJ1vADQY1DBtZJaUm8iirysrWuJ2OByxf3BQkuQIoR20bVtYlqU5HXJ51KIXpqk3mHIizy2EyE/5NrbtYtsu1WoVWJ1qXxDo+xmGQcX3KdgclVLUaroszzQ1fuPo6FAzPXo+h0FM3XfYWm1N3RMgSZIyVQFjwGMBmCyeQ6vZ4Oatm1w4f4GtrU3u7+xQqVQ0uDRbzBMhDL0hdHJQZJqmuI5Pv9+jDLOeAgc1aV/60pewkhG//tkPU/Wc8SKW5e0WIHK1SDHxkJVS3Ly1y7d/+GNuHUW4ZodHWjE1Tz+zSaCr61o8dmmNq5fWxu2bnV/5Z4Nhwpe+/QZff22HGzt9Wr7FmabDo9uNEiCngGIr1ap6JKM+qmCenOmnynJxnWI8T7FuFOVmW2dW+d1f+zxPPHqJP/3G8/zRC+/wrddu8x/90lNcPdfi41c2+ObbPf7iO6/yu7/0aUzHIo11NFQYJmPvJFIAACAASURBVFmcaNBfzkhZrFuOYxOFEV/73it8+tknadX1garqOzx2aZv02i06Ehwj45Gt82xvrM7gUzKu39lD2hW2trbGY5JlXL5yZala5KRlacqNGze4f+8uQRDgNFa4+vgT0ym+n6DjF0LQqFVxbIsgjBjkUcHCKr5HzfeWlKBPrqsGm6stmo0K+wddgkJq+qdgQgga1SorzfqxuLmfRNj/t4Av5D//PvANHtD5n2TFwhWEEZWKD0w73aUPJUedKyRIHTVYqmQ1YVJKnTJAYQqBSlPC3T0SlZF4+v6eZ2MJo1xgHsSKzzebDe7d3yFJImq1KgcHh2RZChOo3aKsq1rxUVJxeHDI6urq0n6fdN8i/VGtVogiLYIzyRA49x0EhmEihCROklIMRIPZdP7Qshz29vfwPW9qU6JDkxaO6zKIYhwnnruXUoobu4dIpVBZytZqu6znL5z2yf2a+XdewjzpgMbZDYWSafm7brdHc0bQplqt5OFKVbYxTQsGx4Rerw9olLeC3OErDNMkTCWWANs0Qei5lGUZBS7Vsiw8v0LFd/F9vfHw/Qpbm1Zeaz8onX6jXsdxXNI0odM5YjAYgDAYxBnE+oTRarTKsS4szXXRhTgZrOm6LvR1f2zbAfQzMy0DmS0f/KLqpCgnBU0xHGbHy7gus6Df58mWw9XzZ6hUdApBZplONpiGzrfrjo77mWX8xbd+yH//B1/Gth2s9jmeqh7xd5++SNUSmoxn4jRf8n0U/5fnxMkkTGxG41HM//nVl/nWj/ZoWS5nWzVqNYv1lar+XNHBGbxSGCeU2Y7JDUDx8B/A8Y8bmr9HtsVHn7rM+a1VvvfDH/MXf/MC/9WX3uDvfXyLX/rwGc43DP76xdf5zZ97loppIjOJ7fkgpY7GCV29kKIjQrZpEgYxX/3eKxwNh7TqlfKmQgievnqeR8+f4eW3blP1HS5trmCbIqc4FiAEaSa5f9ChtX4Bd4LJ8zOf/Ry1GZXIZZZJyf17dznYucfHP/kpLl5azvh3WlsYKFgSPTCEoFn18VxHMxUqRZAkpY6KY5onKq5Opul82+HcmXWCIKAzGDEMI32AOS568TCRjfw7hhDUKj6NaoU/+b++wo3bd5Z+5b06fwV8RQihgH+qlPp/uHuzIEuu887vd06ud6+9qvcd3WigsRIACVAQSZHUMiLHwwlJY8eMFOEJK2Je5tUP8+Lwg8PPth8m5KexIyZGlkeURNEWKZLgDhD71gvQ3eiteq39brmf44eTmffeqlvV1WhKgfEXgehC1b2ZJ09mnvMt/+///zNgXmt9G0BrfVsIsTMK7UFPmIOpwnzjf5ANz+CBNU4O0orimEqlMvazaWaEhpQ2ugNqqP1QNGpUjh7i1so6Sd+kC/ftmd6ZF2hXJmjlnOrT0zMIYVgJC1riYtOJk5T5uTmiKETmEejggdv9WgKjD6rnuaYdL+8GGNcOaFLHpqUtDCOTuszTy1EYU81rvUmS0mzUt2ADKp6L0prba22ClQ2mfQe3bIOEjV6A41gsTLaQYpOGgzTM8ps5AXa8vjEb/2ZTObWy1ozU6UYdSijeSK01risBAxA03xtQ1iZJgus5JHFK1AvoxylzjSpVz4DBSlQ6JtJJkoT19YDllRW00ggpcB2XKDYLhRQGDGtZFr1el3a7g2VZtCYm6IURhCaqqPgeFWdrTSLL1IhIU5H1AJOhGBaI0toQHoVhgO8P3g0pJFhqSHhpMC9xNHiPVlbXmJ6aBCCMDOp/CCrL/VY1rTXvv/UGnurywuOnTUeKZUB10rZKwOSIw6g0KknIlGbx7ipHjz3Cm2cv88R0xKyraNRtpMhAjOcjHJ0sNXAQ8nP8+U/O8bNzy3xhzxzHpmq8emuF0MpoeMXyOUhtOI5FHKZULLP5S2sTbqAY/2An32Yidp4q01kimJls8lsvPsmJgwv85+/9nL968wbvXbzHMwdb/OXZJT66cpNHj+7Hq1YQUhJHkSG7siVZXAAPNbdX1vnLH/yKIEl49uSRwfzmz/2hPbNo4OjBBQRGnGkEH6TNe7QRpNSm6yP3p9lqcfniRSanp43yZaOxpd23MMuyOHLsOLOzcxw+cmR3rX4jc7V14h5kH1Vas9ExpbPCuay67iYE1NYBDJhOB+cvnUthOowmJuo00ipRnNAPo00MoEPj/rSZDW3YO13b4bU33+bb3/0+U5OtbT/+sJv/S1rrW/kG//dCiAu7/aIQ4k+BPwU4ePDArk9Y9Mh77u571SGXM9UaR1i4jgvaEOiM2uAGSIxsbKGFJhiIDRk+fkWUpKA1XpKg7q1g7VvgYUwIk/7v9wO01tRqdZIkyZn5ikVa0KjXDYArSZlotcZsUp/m3OaLZpMw1KxGBnfry1fUjj3PpdcLqdV800roOqxvGGfIsQ3PfYGEHf5u1XNZmGhya3mdpW6MG6e4QYxC0Q9ipls1nLwWPzy2oq8ddF7D3vmapJQgxiPsDbhuwAJZqVRIU0M9Oxz97zRXxc+DcRmzLIskNnS6k7UKy1mfME4MHa0ly81Ta43lunkPvy4XgkK/wXXrKKVJUyP4FMex4UnwXBr1GpnSZMpoJghgamLrmIMgwLJtEykPLUbD4xeSEb6E4po2X7NEkmbJiKOTZVmZfev3A7I0o15vsL6xMUZJUIz+LMTIiU2tvsulsx/wyFyVg3tmkEJDphG2qdcPZ2wKtHqmspy4yYwnVjDbqOAn67QDQRJE1Ouu6cfPsnIcH15f5S9fvULTdzi6p8VqJ6Yfxnzh5DynD07iVhy+/eML/PT8Mo/PTvLlY3tYaFS42w355eJd3r+6yiOHZmnVPdCFC2CO3aj7BFE6es3Dkrybb8RmG9pXd9oMpJRUfI9jh/fx3/7h7/DdH77Gr97/mBvnlmhKwV//+E0eObwPIS3SOMLKxXyEEIOSRhhz4/YKK31FbDd5++ItHMfmy88/VmZFinfYti1QIw9LOU7blhyYbfLe4hXiKDKcAQLqtRo//vvvk8YhWtq8/OWvsP/AYM1XWUa322VleZl9+/czOzvLzPT07iP+4fnZLQXvdj6X1sahHv6ouJ+qxjD3x/ZrhhQS4ZjSqV9xSZKMKIwJomgL+VW+PO3eEcg/Z5RxNb98811OHjvK5fca237loTZ/rfWt/N97QohvA88Dd4UQe/Kofw9wb5vv/hnwZwDPPvvsUDhReNFbrzrLlImqHNv0qT7ATqe0RoGRus1rvZZlbUr9Dy3qUiKLuihQdqPnmYB2kAM5hGBiegI7X1Q+bc2/MCktqjnpj5TCgK0si063x7Ubt4jjmMdOnuDjS1f4+JMr/JOvfQmtBYu3bvPeh+c5dfw4hw/tLYFsD2pCmD5qzzPRu8oFgca11RVZh36vT71eNyyGSpFlCa1Wi7X1dVzX3fLdwgGYn2xwb62DKwXdKEInCUpp6r5v1shN2YxBBgCEkEaaeRskmcHJbWUOHIzBOJIFAt5E8C6WZSiYNzbaNBr1HdX2hm04gs6yrNQN8DwXS4bEaWYofO1Be+NgMx6KYjVYlslueb6X41NUmQUSiLLvP1UJSZqCtKk6FtVN0VScmmfSkoWe5OY5GOA+tDBlG50xwjQ5GJem3e6QphlWroSnlCLNjCPg+z7tToe5uTmUyuh0OoQ79vXrLTUcrRUfnf0AESzz8pefR0qNbQ2iayEESFCZRsUpxTohpSTVmrfPfsLNjZR7a11OzTg8tb9CzYYkzVBSmmvKWypur/Z5+/Iyn9xex7cEF2+uIgVkSnPx9jqOlCRZzM27XR6dneT3Tuxjf6uGY0meOzDLvV7A2x/fpVXz+Mbnj+F5OTNpDqxc60Y05uSgG2I41bvbF3OXi39RBpifmeSbX3+RQ/sX+Pb3f87tW/dYee8s3/35IV58+hTTjSpexc0ZG828WVKy2u7yy7NXSKvTnDj5OMt3b/HLj+5Qq1zi+TPH+fDidc5euc3Jg/OcPLyX6jDodugeurbNqcN7eOf6ea5fu8axEycAQRhFpFlKt9el3pocYYzMsoxbizd447XXSJVpV/38iy/ubn7GTsaDzd04U9syhz080rAIWCVF67XRFkmSlChOCON4RHzpgc8s8uzFRpuD+/ZuyxEDD7H5CyFqgNRad/Kfvw78j8DfAH8C/M/5v3+9yyNu87MxExnlfP/2zn3mm62QirU21TwtyyJRGs8yU2ue48HCLIRA6MGymSqD4E0TE6GCAQ25tQpuPsnh0grScXAatUGP7a4zFII33/2QX7z+FssrpkFi3555XnzuGarVCu+f/4gwCDl14hgra+ssLa+iNfT7Pf79f/hPzExOMD8/R++jPmvrGzx95jStZqO8ps2o9+1+VywMBbWvYa5iBJQn8jy569gkcUwYRvi+h+e69PsB9Xq9bKnccpV56aDiuXnkp5lt1Ihis5lt1SfY+n0wL4/Saui+jb75m1PUm63InvgVvyyr+L6XX3NCr2fY/+7Ha1CMqQD5gSFGSpIUBNiOJI4N3e0gQTxwaoZLNUV5YTMOQkqLNO8ltyyLTCmCKCaMYiSCyWZ9yxiz1HRU7OTAjLwPwkIKTaYybNvN59iMuN8PzFwVKoH5fU3ShF63RxTFtFpNXNfl3tJdatUqYdZDpbtT8NNa0+/1uX7pAo/un2R+doIsjhFOwTlPjs/QoBVCmhpzp9snCCMcz+PVc9e518uYlTFHp12+9OgsltA0mxWUMpw8BcDu8t02Z68u8fi+Ji+e3k+QpEw1PJIk48Zyjx+9d4NP7qxzsNHkGycPcHCijpM7RPuaNV4+vEB8WfHaudssTFT44hP7QRu5aQtY7cXsyUXCzAWSawLcbyIYygzsaupKk1IyOzXBC0+dYqJR49s/fJ0337vAX/3oNZ5/7CiO2zJ4HQRZZii7e0HEvZUO7V4EXsLa+jpPPf8FLnzwLr/88BJKa97/+DpXO4J+mLB3dnJ0888tTlKu317h1Q8uEaeaxcUbzC0sUPF9VpaXmZicot/v89jjZ5icHIippWlKu91meekeCIlKE44cO8bU5CTONqWBXVkx7Q9YBjVfygGs7maA4qfZ+EdCx5G/GBItw2Lr2Bae51DNPKLICI9FyUAAa7dnLkp6czPTXF28WQYk4+xhIv954Nv54mED/1Fr/XdCiDeA/0sI8a+B68AfPMQ5SisoYu1dbPzZULpeClEKo2xOYVu2Zeou9lbVrYKNbSCpUhxc0Y9isjyirNf8XKXPjEvYNvHqOiiFcGzSXh8sidNojPTcjrNLn1zjP//t3zHZavHk44+SJim37t5jZW0dKSUb7Q79IERIgV/xmJpsobXm7IVLvH/2An/6J/81zXqNN9/9gGs3btFqtjh+5CDVik+31+fO0jJBEDDZarF/7wJCwOraBrfu3OXIoQNcvnod13U5ceTQEDJc5JLAKVobZrACeV2k/4tSheM4VGtVwiCknqt2rfX6iCACpfAdi1rFL1H+shBI0cYRqHguSZZuj4MZ2STFYKHUMPx6SCkNCc2OG7/KhZJE3k45cJBs28pZDY1CnlIurutsSzU9/ExlaVZG5kqZ8oRnW8RxzgUghMnnbdnwxw1yFKync0dJSEGcpHR6Ria34rtUKt6II1FgVjY7UjsthlFknDitKR0G27YJgigH8qkyirMtG9/3qOQsjpnKqFYqrG+skyYZrWaT5XZ3/InGWJZlXLpwHtVd4ekXzpj3thCbkRigWkGVLCVRFHPl5j3euXCNVGlc2+L6WsTtu/eYS1e5fEvwfSvhheNzTE7XeeXtG6x0Y/bP1pmoeXx8cx0QPPfIAp87tUCWZvi+Q6YUcxMd/u6tqxyeaPHfPHmUR2aaWKLAhph++hMzLW62+1y5dIPF5TYaQZZmWPlzmWSaVqtZoq1VLgB03yDg4QJLhBA06lXOnDjEhx9f4+3zVzi6fw9V3zXZUiDOS4lKKRxLsjDd5NFD83y0uEx3aRGtn+TRJ57m7//qIq+eX2RlI6An6iy1+9tqM7R7AT95+wIX7gbE0ufm4iIHDh7i5o3rhGt3iPs9GvUGC3v2juCsLMtidm6e02eeYPHGDXrtdT781U+pTszx+NPPUK9vdWp3Zfna8Km+qjUb3R6zU0XXzPZx9/2ciwILsNMwhBAIy5RjbNvCsW18zyGOU+I0IYqTHTfxzWPXwG984Tn+7P/4c9bWx7Sw5vapN3+t9SfAk2N+vwL81qc97g7nQyuN5eyMqktVZljGlEmRmokXpWRwYUXqBaU2LcKCJDORrtLDNUuNRJJpTTcwNSHPdfA9FztnqlNK4TRqkGUI2zbaALZt0oFhaFQFt/TTD6LvV37xK/r9kH/++7/DscMHkNLi2o2bNBt1wiF8QpYp1tfb3Lh1B6UUr739HlprOt0u3V6Pjy9f5fbdJd545/2SWvTsR5e4fXfJsHsJyYmjh3nx+We4dPU6P/jxzzl+7Ah37y7R7nb52m++xNNnTuN5Ts4GZiKFTCl0kiKlNUJza9s2Tr5ZFoj/8n4kGZoMlSYksWk1apUtPwKExHLMomTbFra9/f0tg6g80tdDc7dlPu8T8RdiRdVqrlOfHyiO4zJdL6XI6X8HXRD3q0NmWVbSs1qWJI4THCkRGpPZ8IxKWlnkyn2Y4fEWAE8znqSkYs4y8zwagqeglFidaNSGpKrNMZIxkUtRg5dDmYVCPCqKorLzozmktW5YEA241nUdMpWZckkak3RiLNum4vt4rkev16PT6ZAqWOv0yizI+JvAUHSmScOAG5fOc3xPnQN7ZkgTowZpZL0LAB5G1EkpVjc6nL9yk3NX75BZPlplLN5ew+vf43OPTNALIt65dJc7awF7r67w7sd36YYpsxNVfMem04/YP1vn9NE5XNcCWxgGUGFx5c46692Yf/nEMZ7dN4MlikXVlCoEAt+xqbkOloCqa96FVClcxyKNMuJEUa94JsVdPKgPnzW+r5l1UrHe7nFraYUDe2b5519/iWa9hs4ytGUif5lnI23PYf/8FK7nsjDT5IOr97h44Ty/+ZXf4uAjZ1i8+B6pqNKanCbu36UfJVvKQnGScm+tw+U7G4RWC4RFt9vlwrlz3Ll1Ay/rg9YcfvwY1U0y2bZtMzU9zenHz+B6Huff/hXZynXurtxGSMnnXvj8A2d6gYeaZw10+sHQ5r/9we7ry+V/tyxJsosN3KynFq5tU82zkHGSkiRpnu3big8YtkwpoiTh5PGj/M5XfoMf/PWfb/vZzxzD3zhParhGut1DkGZZjh/S+cYuUBjOf1vKbaO2recuUtWiTG0XiRugvBkV36Ne9bClRaYH/eeOZePNTJX/b1V8VBSTtDukcTJ28y/sg3Mfcer4UU6dOEaWJlQqHo+fPolSiivXrpcLSJKm3F1a5uPLVwCBnxMB1Ws1fN/HdRxs26Ja8ZBC8NZ7H3Lh0ifs37PAnvk5rl5f5Huv/IwjB/exvLLKOx+eZ9+eBfbv28OPfvYqP331DU6dOIbrOmWt3bZtRJ4qNKpcegSJ63se7XaHKIqpVitliaZe8XBsizRJ6cUJYZLSYrBIUWQTZJHKN/XInVbKIsJVmcoV/Qaf1Zt30i33WJPmCHvbtkZIlAy4MsWybeI4KTMbSZISRWnezuaNLQMUzt9gEzbPj1K5XLMURLEh9nEq3uCZlvkzNjTkLMuMulo+plq9htZGXElKaaL+foAGqp5DLXc2imsv6vHD6f5hZkssiZWPLY5j+v0AK5cv9jx/5P2rVCqEYYgQImftk2SZAUamOdthp9PNxxWTKk2QZAQbnW3vgbnwwY9pknDjymVUf4Xnnj2NbdtkSWL655XOy2aUC0NJx2zbCCEJM8F6J0Kt3+bZBYvffnIvSaY5f2uNn5+9zbufLFGxBc+cmEMjuXF3A0sKju+dYHaiUi46GugFMa+8f4P5is9Lh+awc6Ck1oPSY3H+NDOtnXXfQZOzhkqLTpiSaUG96AZQ+fOpdcHsmzsSm57Nrb8Y/xbkAY0oQ0oxlAEzjt9bZy9x/uod/snLn+PxRw7gSEESxyAElgSppRmKELiuxeH9VeZmWqx3+rx58xqWbfPks8+ysnQb4Xjs33+QWxfW6QaxKW0Mbf5BGHPz3jqp9NGYbI1Hyr0bl1HCJsIFNLPze8am8m3bpjUxwcGDB7n8/uscmW+Sas0759/jyLHjubrfP66ZdU7lDJvw0LV+KbC1JL0P02XhmFlCIKTEcxxqFbMmpJmiH7r0o4gwiscyCGqtCaIIx7b56m++RLOxPWvkZ27z352Dt/WViLIEgZk0S9jYlsgZ/thCyLDZwRiuSxtnwWxqulhItWkFy9KMIAxxpGCiUTU97FoT56AniaCQ7t6CkLcs07Kkt6MVFgwHrEIYL84R5nqVHtJ0HxqvEIJnn3yM73zvh/zuV17GsiQfHD5PlCR88fPPMTM5wXf//sdMNJv8zldeZt+eec5euMi//w//kSvXFylQqP/V732NiVaTlbV1Prr4ydiordjszYOY5eA0WS5CWV6aqdfrht0PqPguvueZsk2nRxQlJGGE5TjEYQRopGYIFFjOwJg5Gtyv4r/BizncbrO9ZVlmmM2Uolar5I6E2SyjKEZrSOIUZZsWO611DnCj/J7W3lgQZJKmOO4ozzmY9i/PdemHkXn58wh2cEGjY1R5eUvlDJWO7ZTMibZlEcQJUWw0Fpo5O6R5FszzW0gVD5+goHbNJ7Cci34/QErJ5OQEQRAaquIsxbZTKpUK1WqVIAwNfoEwz3wYUqZKxSdNU1bXDJWpV6uQRLur8Q/fj421FS6efZ/DMzWO7JsjSxJsxynr5MMbfzFfrVqVvbNTaH2dXq/HxvI9npqzeOFIk5mGS71V45EjM/iW5LtvXuPATJ1vvngMx7L4+PoycaY4vn/anKMQLlKaj66u8NHiOn94+giNIQ6KwglWSgyBHTVa5FmjIQeuFyRYjsmOaWXIyIQQ6Gx0ex/Fw2ylHNf5Mbc806JQlxzKa4tiPhUXr93hJ2+dY2ayxcvPnsLONxKlgTQrgctCmFRzAXi2pGRmok527S5KKaanp3nksSdwHYfWxCR3Lp+jF0QkSYafP+dhFHNraY2rt5dRwkKgsUmpEaCRrGuH6fm9uWR2SHtjA9cz5SJ3yPG2LMswAAqYm2qyZ3aSCzff4+OPztOcmMAf4g3Y2bYJGh4w66Ix77PnOpt+u9O6tPP+5Vg2SiVs7oEpzBqisC6wNuXf8q4pz3WoJC7dXki72zeA302WZYqNbpeq7+P7W4nYCvvMbf7jbDh62c4KmtIUsJTGsSzT1iI29aPmpYAyMs9BWrZllWIlxXcElBGY1pooSwiiBM+18RybWBkkdpm+ZDMi3pzP8j2k56LSjDQIkY6NNbQ4F8M7eeIoFy5+wpVriyzMzxCnAXfuLBmWN6XHPlhbW7JACEmapoYCuFY1aXulCeOYfhAShHmvb74B1WoVJlpGUKRRqxqSmm3muogmhcpI4hStzSKntMppZysIIdnY6CCGJJkLUEvY7bOx3sZv1ml3eji2syPz12ZkfJF+VVs86J03/uKeF9oEjmPjeV55vChn4CqiZ8e2zLORpnkEIA07ZJbR6wW4rlNyIRRzojJVtqCag+ULApJGxSNOMsIkpZZmBieCYahLdZZ/fHTOo8iAKKWURHGEFAbVHSYJhvnRo5aD74bb89I0HWG91LqQWMY8q6XDY8ofExM1+v0e6+vtoQyZWcl8z2VqcpJOp0MQBog4xh5q3yx4/l3XMQBRVbiou1trozBg8ZOLWNEaX/z8aSwhyITIOfOLnvvNR9J0+wFLax0iLVjd6DCbrfCFo/McnXGpVNyiJYSnTyzw1qW7/OaZ/cw0q1QrDnsKDv980wbQQhCECT9+7xqeEvzGkYWx4y/udZwaUSMhTcCBUkZWIMto+jYCzWonMClyu5D93XkuChQ+mPZLAXk3S/FcFLM6cH7LX2Oe7cW7K/zVK6+ztNHnj7/xG0zmRDW6ICEpdighkLaFNUzCBfiOg0QThiHVapXTjz0OebYMabG60SOKY2oVj34YcfXWCu9cXOTmagBYCK2oiwBbhWTCUBc/8eRTpGnK2ffeZnV5Cd/zOHj4MPN79uLYQ1LReuAA7Z2b5tF9k7x36SzX5xc4evzErvBe207ydl/b4UHNtqD+dz731oz1wCEv3s/tjmDlBHG7UbL1HRe35ZCkKUl3vLOtlHlHdiq9/Rex+W+1nSco0ylZmlJxvHLShxLDozWnXH9dCBOpOdYw+G/wuTRThHFCpjVN3yNTGRo1MpTN7VHDfxRCoMKQcHkNq1GlOjO96bOa3/3Ky5z/6BJ/8df/D2ceO0mapXx86QpPPvYoB/bvQSBzTXsGHmK+gQ+3dDQadfr9gPfOnucLlafZv3cPH57/mJ+99iYH9+3hwsXL1KpVHjl2hHc/PG8UvoZGXIqnbGNCCGzLxvINkK1IkadpSrPZJIpC0izD9zwMN72Zfc9xCKs+7X7AxkbXyIZWXbwcPTwuK7J5YwNT2tF6t9vL4AU0hEkZUooRkpEsBz+ZurqpNadpShRlOXBP5ZFJBa1UHiGnJRjPcYwDU7DbCWHGbFmyjP4rvocfJrT7fdp5nR6d5V0G+cKhRx2AIAiZnJxASAMmsz03j+CMc9KseFvKWWmWlp0LxbyVGz/mHEppkCrHGxj5ZaU1k5MTpchTvx+wsrLC5EQLz/NpNBrUalXCMKTX6xMEYZ71kWRZhut6xmnMnfDyzhQZ7zF3S2tN2O9z8+pljk75HNg7RxyEeJXKKI5TjHwJgHtrbS5cu0u7F6GDdU7OWhybddl/YD/2JvGFuqM5sNCgWhnjZGoT9aeZ4uqtdc7d2OC5g/NM5JTU458nQTfJaCcZ1arHVKsBSmNLiUozKr6DJzWr3QiNxnbsoUzPaKT+qa3EEejcqc1Ya/f4zk/e5PL123zp+TO8+OQjKLT9TQAAIABJREFU+SWqwenyjIFlW4Y0KXeIoxwEaDAWmiAIqFar5XuyurpKqqEfJSSZ6TQ5e2mR1z+6xUrqIaYOEi0tI4QyMgsZCB2DBd1ul7fffAP6a6ws3UNowzJYb7ZKfQqlTPCglGml9nyHLzxxjKt3Xuft135BqzXB7Px8vsZ+yskb9xCK8R+wpBwSD/t05yuTMoNYwNAQbKr4WEJsI2EvtlkTyQnnzPq/pZ15l0vjf3Gb/3apFYmFYuDl2FjIfPLSnHa1SKsUppRCilySUzPSU7zZkjQhjCJsKfFdm5R0y+cstt6k4bFatSqulKiuoWw19UzK9qOjh/bzb//0j/nb7/2Yn/zidSxb8sTpU5w5fQrXcZienCAIQ6SQNJsN9szNAoYY6NDB/aUj8MyTj/PBuY946/2zLMzP8eJzz9Bs1PnlG29z7qNLHNy7wJ/80beYnZ6i1Wiwb2G+HOPM1AR7FmZH2vq2MyFMxJemGXFcoMRtwrA9uL6hJ9FzHabtBp7n0O6GTFRc6vVa/nCPL4cUEb7Iszgm5fpgObyiXlvU9F3Xzfv6dUkapbXGcRySJBk4jHnEU4BwLEsibRvXdfOoOSWOY5IkKevipkxgjdAWF/Xgmu8QR4b8KPVNl4hSo2RFaZKWoEKlFK7rEcWxyYBLST+M6YUJnufRqFWGrtEsAEls2vuyTJXqiJsXh7ySRNHj77oOtXp1RAioWq3gOA4rq6s4Tkir2TDdHNUatVodpTKiKKTT6aKBKDAlHGHZkA2iEb3DXpclCUF7DSsLeOzYIdIwNlmgIjMntrITmHuiWOuGXLq5xJ07S3zhyBQvHK2zZ9+Emcshh0hKwenD81vb04qoX4OWgjBK+MFbV+gHGX/wxOGxz9GwdYKEtV5Iw7fZP9804F6lSFOFFILJmkO30y6vY8e01AOmpCH/vCjTOIRhxBtnL/LBxRs898RJ/vBrn8f1Pci0oQjPnw+lFJbnIvP3O00z7i6vc+HKLTIFS+tdxJjM2s3FG1hxh7mJeSqey+pGl0/urLMSW9Tn9uK4LstLy2gkgfKoihCzTWt+9eovsG0HrzmNgwCVUWs08PJyYL/fZ3l5mbXlJQSwvN4ljBJeef0c3dRCuBkffvgBL01Omk6BTzNfxZxta5qCIEgA9VoF23Z2WYbenUkh8GzTUaKUItMqL1Vvj0kr1sbRQHL4b6PlAfOH8op2tM/45r/1Lm93MywYUQ4Tlii9XFtapEqRqKyMmjUGCFhEFjtx8he1NyktXNdCS0Bt/WyUpTi5NGsR/Q2blBKvVoVaBZ0popV18By8HF0thODksSM88m+OUNw6A+rQWJbNH/zT3y254b/68ov8zle+CMBjJ4/xv/xP/648z8LsFP/9v/3vhsYPexdm+fqXXtoyj1/8/DO89MIz5fm++qWX+OrQ58bMBpszGgalX2Vjo02WmY0pCELDma9Ha5lSCGqui1MXJTNcfqSx8178qzNGnLsHMSEhS1ROc+vkgMQBTbSQkjROCYKgzGC47iClb7ICgxZH0Hn2wMF1DTtfPzCtjmEQEkUhYRiWMsgqs9FS4nsuXsVno9ujF0a06kOpeQbXKqUkiqJSFrfX7WJbEmlJwl6AQNGsbY1Mi9JXUYZRmSbbpKdXZIviOKHXC3K61fHtVI5jMz83y9raOsvLy3ieS73eyEGSAs+r4PuDa+j1urC2hpID8aAg3VrhLOZ+6e4dPnz7DSY9zaPH9hGFEa5rapR6S8rfbNRpmnLj7jIXb68jLI9jUz7HJ+DFM3tJ+z3sTTXOmakaL505jOdbmwdh/ssJi9bWAz68vsbTeyaZro6n/B621TDiXhBSm/So+zYix/+QI7rnWxXWOkHeEnyf3ephNph8LtfWNnj1nXNUfJd//c2XcV0PctzSsDneKOlWpxfwweVb/PTcbZRTRVoWojJFv9eD2dnyc41GEyktJpt1fN9lrd0lyjT1yVmOHj/B2upqPp5cOwWz8dcJ6VDhxCOP8Myzn0MIQb/fp1avU6lUaLfbnD97lnffeRuhFVUdc+7yDSwhOH+nw8Kpz/H4E08yPT09JIK03Vw8zFyK8ruWJZluNh5w49/9ya2cXMnKywCftpThuw6uYxNuIxp0v6N+xjf/PHLaodZfRjybNgYbWbazCCFwpUApSaqMOIiTb/Y7HTs/Qx4VWrRatZxvYPwmVCb2xPD/6TE/55GA6xBeuYFz6jjSc0tQ1nB9Owj6eQ23TrVq0q4bG+u0JlrAeG9xeOz3q4UPxrsbG/+AF/PouoapynWdnCXLytvLBnkuQy6Slqn++56xzP3e5xo2UdQOmxSSNI3MlLsOBQq/oFH2fd8goRm02TmujSUtwlxZ0BppQRyMp3RQlMZ1nFLoKElMR0EURfR6RnnR9z0QuZKb3HRNm1L+3W6PqalJw16XZniuSzuI6IQJNd+nXqmw+X6kSVbOqyUtLGkcgkKQRwiG9CfMc20724s4me8YdUODIQlYXlkmTU25wnONI1WpVLFth1qtTq1m6ulZltDt9pDtThlFZkIQpxlBEPLx+XO8/8YvOdiSvPzMUeIwwvf8MkItp3moBKfRhGHMlZurnP/kNkm/zYuHqvzT5w9AEpvMxZgIqlLblO4fSslpoNuP+Mm7n9DrZfyrLx3bdi6Gbb0f0glDDk9M4FbccriO59DvR+ybqvPJ9R7tbo9Gq471UBvT9qaBfj/k3OUb3Fxu880vv2CIfJwhTIa0Rl/yoR+7/ZCbyx386X38sz/4I8gzZN4mJ+rQ4cOce2+C9y/fZG6qzsLMJEfmVrl3aYm11RWOHD3Gu2+/BUCCRBtyBnwREeBTr9X55S9+zs3FReI45osvv8yjpx8ruUEsyyLLNLHwUFmPCzeW2KDOP3vxpRGRoPKix83lp60G5I+DlJJmrcrMZHNHZrzxNi54Ga75b/3Gbur7O1nF8/CcaNvN/372Gd/8R01uWjCLhTdItl78ZkINPVRn2S7KH4/EFyXS1yzP27dqjE/diLE/CynxWg28J08Dht9apylWDkITQhBnmREfylPESWJ60B3HodvpMTnZuo/X+OtebXY+nm1becQ6AQhszyPNFMQmla21Is0yLNvOU8w7rYh6B5rNBxixhF6/TxwlVCp+mWpM06ycu36/D+SMj0mS054OoqP76WIbRrzRV8lxjMxytVoplRi73R5RmlGxLeqlszfw/LPUYAx0fu2+X6Hf72FbFq7n0O/2kTqj4ozWIovjKKVGxg152n+TY2xKFirnhd+dE2Y6CGxma0acpbimXj9gZXW9/FytWmFycgLbdmm1Jmi1TK90lhkA6o9eeYVf/PznJN1VTi5U+dzxg4aDQ0q0VeQrx7yHGrI45eriEhcWV0mzlEdnLc7sb2L7DknQx92NXrxBP5qfLQuVKu6tBPz8/BJPLkwwu0Mr7uAQmihTIGGyOnTfzQ6CbUmmakb4am2jz9492tALbsc7/zCOgTab/9Wbd0FY/MbTj+BUBij6TKWGY19nyDHVh41un2u3l+j7gh98/3skccxXv/7bW07j+T6HTjzKtfde48rNFSabNSZbNar+BnEUjeikJMIhoEJV9/AtzWS2wbnXf0SgXZSwqdUbvP7aa3TaHR47c2YwfYCjTcv2XKtKJ21tcULKD45OwUOtdMX6XvFd5ofksB/WNtf8d287r4vF32zboup7hHFMFCfbfH57+4xu/qMXr7Qm0xgPdpNlKiNVo4hH0+U/OnlK67wv1yyslhilqs1UtmXhHDZbWtjSdAT042iLEyCQ2A8h65dtdOhfv4l/9CBeow5CEAdhztJmla1faZrSaDRZWrpHEISbUuf/mLb1AZU589pw8OZ5LlmalYuDlAbVamxnx2WwcT3c660ylc+hOa9p94uxLJs4SfE8r+z9H+48MFTDRYZo4MXD6AutFVva/ob+imVZ1Os1gigm6QeoNKPX62FS527JNRAnGZ5rE4URlXzR63S6VGsV0lTl3RWQjclWpWm2bTalGLfWkKUqZ/ILjYiTtbt5NaC+QcpYSonvezQadZTWrHc6LK91SIOIXv92+b1Ws0G9XsNxXK5cucKbv3qNSx++hZVFXP1Y8Z0fvkrFczl1eA//w7/5w0GrpLUZQQOdXp+PF5f4eHGJuL3BwozD6aMzZEGweypYpcvWPoDVdp+fvXeFIFB86/OHd3WIRGt6mcJ2HeYntjoLAsG+loOOU/pROMgubjfVD/pojyQTDUAu1UZpb7iUBCYjZUQLCodq9GRTzTqnDszyweIG9+7e5Q/+6F+MtOAN22OPPc7S7Vu8f20RSyiW1nt0I4Hd7/Pd7/xNfi2mlJoJF7Ie3/ry87z63kdcu7dGqD1qjQZRFJGmKe+9+w5JEvPFl3+Tx888Qbvd5m/+8i9QUczCdJMPP1omCsPxDsDQHDxsiCOEoFb1mZvYXgHv12mb15CtmYHdB3Rm80/+/7T5b4rwCzIYa1BLzFRGotRY2kN7qOY4DN6CAvQ3uklbtjXoKtpi5hhKmx72dIymecOr7lA+GJf2z69yKL3vTE3gO7bpb85/H0URnufhui5BENLpdgHNzPQU09PT3FtaxvPdHZyWX3eucfh4W49bsNEVD7Jr2Sa1lUfUBTL+Qdi6pCXYpsoyOrJtkgTdbs+kwz0D0gvDqOyfB43KErJM5Gh+n26nNxKJD6SN86seM3QhTcrdcca9TgOpzyAyqnH1ik+zXqXX6xGGUamLYEmI4pROp8Pc7EzefZDRaXfBtkm1wndsqmXv8eB+JElCxd66SAohENL0mAM5QDHGsqTh/d+lw2qcoEE2raAudl3T4rjcDszxk5ThPFzS7rLe7iCAdqfD/HSL+hOPsH+6Rq3i8cnNJS5ev8PH1+/yr/7d/8a+2Ul+/8vP8MWnHsXNuxsQgixJuHTjDrc6Gb7nc2JvnScP1hCpIQMStrW7R92AdyDvoLi93OWNyyucWZji8OT2CmjDdq/dY7kXUq1VmGxu/Y4eKt39A2T6Rw6qMQyNnU6HWsUb1MVzU1qDsMiUKYtsfvfmZyZ45tQhPlp8i0il3Lhxg7fffINarc7vfeMbI593XJc9Bw5y/f1b/OSdSyivydyxM5x56mmOHD3Gj3/0w3JUmTbj2DM3ybe+9nn+5pU3CJdSOt02Clm+SFmWkSYJjutSq9X48m99nR9/5y9449wVnNr++278D2Oe41Cv+tQrflkO/HXadmn/zad5mNNalqRWMdF/sEmN8H72Gd38CzN3uUCOa6Xpp9GWSH+LidxhKJwFTI+8uw01qyUEKs1gLMK9eIlzxjbSkai/0O3aSiI0VN/fdCxTbx3eUMwPXqNefrLfD0pGOcuy6Pd7oBW27bC2ts7MzAwTrSZLSysszM9tPxGbLMsyQxgjRIl47/eDsi/8/mWErW+ewSaEZCoro2gwRDBZnq3w8jR3mqa76iQoxiKRaKF2cK5GbbO6nsp02dNvxpNhYfppoyg2WgRhgm8LUyfPn5kkSUuOh61zMjoHlrSI1f08b8O1IKUh6rAsSbVaRSlFEARlZsTcG6hUqvT7PRzHodGosdoLyUhxfb9sxyvejULSdrt7J4VEi4wwNOlZx3FKZ+h+NgCry6FIZVCuyLKMdrszgvAftiTNKGZman4PtdY0lWSd5x87wLEDC8RJwlq7y/krtzj3yS2uL97if/+/X+GvfvgWf/z7X+TMI4fwXI+NTpeLiytcv71KVfU5s8/n+L6GeZ58b3craJqRI35BCO6sdnn17CJZLPjGqf27XvyX+zHrUcTElM/C5NbM21YOin84K2jPhTBscNujxk0WQGx+di2J5zlInSGzmDu3b7G2tsb6+jpxFOF6A2CpEILp6RkWvTpr6+vsO7CPoyceoVavMzE5UQwIoTMmWENgZNGbjRqnjx/g1sqHSK3oiBqJdvB9g6gPwhDHddFK0e/3sMjINMzu3V+eF4beuvtnxO9rliVpNWtM1AcMeA+3+W8PTv81+xQj5zTlCo9JpU2GdZdCWvAZ3/xHGNvy9LdvSdJMEqaD+EIghxCmEkdakKf5jZRvvlihsMu08+BmSctC3SdtIoTAQuJbDinStGug8beNZHfeRMdGkEO/7Pf71Os1PNej2++RZipHZUt6vR7tdpt6vUG/H9DpdIda5ra3JO/ldT3HOFJ9E61VKtuzQG22zSx6BU9+gZL3/QpB0EcJAzKyLYssTU1bmW1BapwPz9tdmvZBXkghh1Dzea9yweRXROW2ZZcsf9KSRJGhahaC0ikC8mvZrtd7NHuzG8ckyTLTg48ypDD5caQ0TkA/iEjiEKUV1apB33e6XRw7FwnSGlsKbGvUodTapPxtZ2eHKsn5waFgZTTN2PebXyFMac1EMMZ5K2iCLUsSJQmrnd59rx/I070xnm06Hyxb4mSS6Uad508f5dmThwiSlB+9+i7vfHSN//U/fZ8D81N86ysv0A4CLt/tQNThqf02T+ypIjKFtdtMUpblNfnBZ1c6EZdutZmruhyarO/w5YEZSK4mVXBjqcvbF+/w1eeP5n/Mg5TRGYTd+a2f2pIsox+EeK69FfMhBagMYVkG9KrlaDgiRI63cOlGigvnz5fg0zt37nDw0KGR480vLHDyqedZXFxgfmEPM7OzbGxscOHcufIzjlAIpfjdl56kUa+ysrrBT986RxCGWMIpzx9FIUEYlN9Ls4yle3eRpCjLpboJw7HZcdli4n67/+DvWaZyFdEMdyhjtx04b+zRRj77D7bD72CD7FLVd5ls1rmzun7/ecrtM735jwI889qWykjyKKPY9C054EwuiA8EOdGJ1kitDf8+uiRdKSdu7OK9/eRJKXGEQOTMSTvhBD6tpTmHviUtNIokTkBrHNtBWhKtK7TbHXy/wszMDLdu38bPQYHbLYRxnJBlBphmUNGmdbEAu+x2k9288Wuty2jS/F0YIRjXKbstXNc1tLE5StzwAmwVnhkHuCy7BbbY+HtUINvDIMzT0s6IBkEhZZqpDJ1qqlWfNDGZiTQ1m37Ropdf8a7PvZMVHBPdKObOyhqOY1OrVGhWjeNVqeRp/3aHav674p5Vqz5aGfy03JJ9yQWvdij9FI6ZEOD7xhFSOR/BbixLsxIzIKRA51oWmYbVdreknh47I/kfNOB6HtJx6ccpvTCmiEO9qoeHj05TqlnGb7/0DF946hQ/e+scr5+9zJ/95x9i+RVu31tj2g6JW9PEsYuqCEQKOhs8I0bBc1zkPRCzAcF6N+Ti9VXSGL56dMG0/e7CBHBqdoJulPL9y7f4219dJQhjfvv5ozieQ5akOLZFL1L55evtqn4PbIu3l2hUK9TrFXPvchRzkmYst/vsX5gzLc5DZkmJzjKE46D0aDNFYSazmuKJhDDVSDQai/PnznLg4MHBO6lN+efAoUMs7N1rgLuWkZrudbvlBOk85f/4ycM5N77gqy88wd/98l2Wg7wlO9+obWugr+HYNnv3H+CTc1Vq6TrXP7nIc8+/MJJ92HkS7zO5mwCXnV6fLMtoNerUK/4DbfzwDxnRD2w7kp/RMkKurup7NKo+3V64q+fsM735D1umzKJdtH5ZckBeMtx6V7zcxYQVUZal5SaczHYc+7Dd2zqcfkKYKHLnTXPcg3r/FSBNTNpcWoYuVaUputsjSTP82Skcx3jF6xvrzM/NM9Fqsra+zvT0JJLR9G/BC6C1xrbtIQdBj2EkfLBxag1hGOF5LkmS4uWgqzSvlRfdGYZRzDYdDdog4dMxGYBxhBZKDbVpjNjWsRV1fwPeM0I8lm2N1EGlNLKZ5JGsyksEBV+BcVQybNsI+hgRn+2ZG4fHnqlsE5308N9BSAuNYZNLdQq6T903in0S8tJEHylELqFsqHM7nS5JnOI5Nm5ZMtF5oKm2dd7MImH6qotMhkn3K7TSOO7uXv9CylZKQ4gFxgkIk4R+rnC57VNepD4BISV+pUZfCTa6RulNY+6JBpAS17aZ9lya9Spff/Epnjp1lO+88jq/+ug2Wa/Nhp3x07MR52+sMNf02Tdt6GvDOCFKUvqbEniVHELj2hauYxzfiiNpdwPevHyPJNYcbhpeCjMEYTgdLDnkLGw+ps3Te6dxLMkrV27zvbcXiZKEb7540pS+HJtOlCEcC89xh/K/u5rusbZ4e4lv//1rNOo1vvTC4xzeN4fOyX1WN7poBAcXpvP8/uBcVs5WWRLWlF6cIElSbt1d5d2zn2DplJroYYkqiZYkWNy6eZOzH7zP6cfPGIc43zsd1x0BWDq2Q6PRLP8/RZJIj9t3Vzh8YIFWs0Y/iBBCEmHnGgCCubl5Dhw8VAYOlm2zsLCHx575PGdf/wkyaPPeu+/wuedfKNf1h7JNX1dK0wsibMuimitnjrdP57VtV/PfvZMxfo/aLmtsWxb1ik+3H+5qfJ/5zT9VilQbX94shrLswdRs5UR3xqCEi8VxsyDOzrb9DReYiH9zq/b4T+7md6PnNZFVnpXIFNKykPUqdsHxrxS+7xHHMevrazQaRgVrdXWdRr1egtQMq13BUGeXXjhsTd8/6DgNHWeaM99ZdDrdUkQiSRLq9YF0pxDGWdNKobVCSgfbNlF44TyMq6uXKnS7Ml1eVxhGRHFMvZ6L3uSHyIrskGWVFLiZUiNKa8Y5yD+fbQfi22r3T58PhFhcx8J1HIIwohfFtGqVkrXQOGkWWqsh+k6FZwtsoYmiqARPmg1ZlM7JOGBREIREUVxSEBfSwIMx77ywJUmCzh0jSxqJYjOXmm5vCMm+6TDbHdGvVLCcHBeic72KfIUU5Jz+gHBdZiabVHyP1tQ0U5N99i+4HGrZfHJnnfevrlJzHRrVdTzPxrUlNobEplwOhCCJI6JMI4UwpRcsdJqy0emytBHQdFy+c+4Kvm0xX/XwLInrulRtyZMHFpDFtW9qD657DmcWJvEsi59evcMr793GkoLffeE4aM1SO8LzXSZbNeMEP0SYeOvOMn/9w1/x9rkrVHyPhZkWU80a9apPvx9wd2kVreDkoT1bziPy+22TP4PDx11a441zl7lyaxktLKRW2FITM1CULEpFO93Taq3G/gMHOHfuLEVyvqsrvPLmOf5oeoJq1efV9z+mFysyYRnAX/49P1eNLKR+fd/n+KnTdDsdorBPpbKL9s2HMK1zB8DuMVGv7jobthvbruZ//0fh0zkbUgo81y2Jye5nn7nNf3gB01rnvOtmUVZal+jkIioa3vgtaWENRV6bF+TdpbbHAfVGTYhczKPccHa+oePaw8Z9ZvTvxbGVkYN0K0YeOE6I19ZBSiqNOp1uF9u2adTr9IBer0eWZVQqPoXqnWXJXJnOlE0As9DCjkCx7azY+MFoIwCEUUSrNWH4ubUa60UXyIsiiizS0WZzsvPfjZ5n8M3xYxwFV5o6YpqakkLF9/ONrpjLImqVpXNUEP6kOS4hTQfc+OOke7ee36Ded0rPFf/K/D/XtqlXfMIoph8lRnyFQRnFshySJEZKQaVaIcs0URCWTY8DngIQwjgIaZqWwMbCjBRxhJSybNMrhIwG4xOb3rnBz8V9tvJ7U9wPwxCYjqKLd/EIxXFMv9dDZ6kB32qNVejdbzJpS2xls95eoZ/ZzDQrfP5Qg8f3VlndmOLmcqdU63RcC9e2cKTAde2yZg2mfTLNFAhBnBip3Rv31vnVhT79SKHThLdurVFxHSZ9h41+zFzN4fdOH8npvg2mRSnjlA0/1zXX4dG5FlGa8fprS7z+8T2++eJJtFIsLndp1eo065Vdv1+Gqa/LytoavTCiE6aEQZ9L1+7wwcfXefHpU5y/fIN3z11i3/wUp48eoNOPuLnSRkrBkf1jgL9ioG0iNzmJhZBMP0pJpY/SmkRBJiwmp6aYn19g77593L1zpzzcxMQEfqUyMg+O4zAzO8vpxx6nXq+xdG+JK5cvcmNpnQuf3MRxbK7eWiJWNoXkL8DqyjIfvv8+e/bu4cmnnjbDlZJGo8FjTz5NHMfUavfHMj2sJWlKu9tDa0295uM7u2wbfSDbTSa1ANJ+ujMIYfREar5Hpx/c9/Ofuc1/+MLTNAOtsKTAlHINdaTJXBWTOVg5XGt7dbj7n7eIiMdP/nAEqrTOsw+Fk3G/Y+/m/OVPm/7N/08aYI6QEmFZZHFMxXPJVLUEttVrVfrCoNiVVji2g50v3FobvEOm1JB7I7BgqO/+/laWETAYBIEohTkcx2FjYwM3L4eMlB+GEOLD/zqOQxwnZYq9cACK7OToXGyHqB20c5r+fYtqtWqIhUjzzZkS3zBsOq+ZJ1laXocZl31fFbFi4y9wApvb5jZ7/qZEaxwf33Wo+h5BkpKkGU4uslLQ+xbpfM916YUxcWrSyY7jGBBllhrQaWYgaEXvtGljtFAqKxUJKxW/7FxQyig2DnTKt2YLCjOERwJLGkcxSY2UcxprumFYZlJ2a6srK6wv3WbSE8xPNfNrzcG3eutAtNbcvLfGWj/GFxkHpprsm6xwaLbKMydmoYhkh96dwZOiB/38Q0tFlmb85P2Mj655PDIjaHoevSTh5SMLoDXf//gWVzb6LHX7BGlKM5ctHs50DTvMjiVpVRykhjtrIX/31jVmaw6LawH7jszjFYDEfAwm25XQ6fbo9gP6YUS3HxrNhiBieWWDW3eXCJKMOANLKNJU8/yZE/z2F5+mVnF59/xlzn58lfnJJt0gZGW9Q6tRpVkfEyUPTam0LHSWgTSB02SzxrH98yytdVlcCwnwUchyRX3iySfJsox3Xn8VUuPoTc7vZc/+Q8zMzIy04VWqVR5/4gm0Uty+dRuEIBQV3rlwxXCspJnJoqDQCJQQbGxsECcJExOjxDpCCKZnZh7o2XpYi5OUjW6PJEuZbNTxR3gjfh3Ox/jAYPR3BqTu2Ha5rj2oWVIy0aghpWSjuzMQ9zO3+ReWZRlZZhjPkBjEOJu07PN/LWEeWHto49jNhjv+z6i8AAAgAElEQVSIGosIaHyNZUQ+k4JJTe76PA9ihY7AYGEe3USl6+BOT5IFhpPeF4I0Tujndf163XQEhGFImqT42kcO9aoX12m2DA0qKxUC8xEMNmqGz61RulDG07mevcw3XFNThLxLoVoZ0qM231d5dCytgeJccWzXdUpw20DLfly6f+fJTpIEhKRa9ctSgm1bJHFKkialFO3wfU7TrFzcC3EfQ3u786tRlAyyrFAJ3N7xzJRRVRtEzkbeuFmrEG50We/1maxXRxTLVFZwEQjCJDXncewcIOWilG3kOnMHREiBVqZFVEgLlY+r2Ky00igKXgyj6raTFdGu49qDZyavrXcCU1YZPzFse5vu3L5J1lvj4MEm8zOTeQeDGYdAj1IeK00QRly9t0G3F3DEh1bVUBYLKYwk9tiXzwxAACSpad8d+txGP+HacoBUFl89vo+Dkw3udAO+dnwPKlNUHJs3bizxk8s3ybTiuUN7mWvUsITg9uoGnmvTrPi4hVMNVB2LFw/Ostju8X/+4AJH5uvc64Q8Pdks08hr7S73VtZYXm2zut5lZXWd9XbbbPy9Xp55MnwmH1xd4cSxA5w6eoBGrcJko8bnn3yEqYkGLz7zKDfuLHPh8iIV1yaKU/r9gMP7F+5LSSulJEkTZA7I81yHg3tm6fQCbr1xgUz4KGme+431dSYmJ7l65QqLVz9Bxh0kmnt3bhNFMX7FH9n8bdum1WyyurZqMFhC0Mfn5vIqglxThRRfRih8FKaMMjExwd59+3Yc9z+WpWlGtxtgS2vT5l/Y7qL3NL1/uXC7fUMrXQKzH9SKda3ie9i2TbvX3/Hzn7HNfzC5aZohpCwjFimK6H/40xpLSFzLGckc3m9DHkSBUIYEgi3Mf8UmWYxss3PwD5GNMpvJIC1dnGS49F1gALRSpEGAFQQ4M9P0gsAsRhVTu+r1e4ShaR/z8rSvLU3ts7gyBblefVHqEKhcBVGKgsXAmMoG0rfD2YIoMjU7rTVxEuNWJvJxF2PX5phawxCSfjgL4LoOaZaRxIWq3v1mavCsFBtTFEW4nqHwHSCUTR1ZpLkaoMhKp0ZrTZrkrHmFo1nM8TbAveJ8WZaRKTNvBrC0/cJQOAlpmmAJjWPJkhu/UfFo94wiYMWxcUq1tYJd0LRyKYpsxIDASimFYxsnoJBezXrGIbAso0JoaJUL2ddBi9525D6lVkZipI3RlB0aQghSpUnSlG1v0DbvRL/fZ+3ebVqeZu9sC9d1yNIECkiG1mW9HyBLUu6tbnB1qQdxwCMHfCaqxuFEyh1ePrH92ICrd9ssLnfZ16zxG0cXmG/URv7+jccOc3iqybfPXuNvz12nHSQ8vmcK33H58NZdHMvi+OwkB6ea1H0PS0j2t+r8iyeOcGFpg7/96CZ3VgL6UcLMRIONTpdrd1e48MkiH128yvLqGipN8SyN70hcWzDvayZrHpPNCjXP4vKNFZ5/9BD/8ltf3/IMHt4/z8kj+/npGx/y//7iXbTWeJ7PoT2zyG0yeOUhNk2ZZUkmJ+oc2jdH69xV+oGigIoVWbBqtcrC/gNE3VVkpkiU3hbrZFL2TT733PO8aztcv3aVTLrYyqSgPdsCldDXLlrYCMB1PWq10XvwsF0RD3MsrfUObHnjAkNzkmLTL971YvPfTUl4ONCzbGtE2fNBxp0plZfGDRaocp926s/Q5q9HftZaY1sDcJ9j2TnN7wDZL4XAdwx62dlBjnezDW7G4Mkwuu16hOWvSJVLIXLnQw+xw/1/1L15jGVXft/3Oeeub3+1d1V3V+9cmvuQFMmZ4SycVaulsSxZshRBcGAH/sMOYCBAEgRBkH8S+48ETmADsSNFlg3HSzQjjaxtFs1wOAuH+85ustnNXqtrf/tdz8kf5963VL1XVd3kCNQPILuq3n33nHvvuee3fX/f309GoijGc50sVJ8iRO65jNnQhMCt10iDEEcKpmo1Njc2iKKYaqXC9FSddqdLt9NDK0WpVEIK09QoTpP+1cudteMKkiwAaEvZz/em2bWbhT0AqHW7PQ4tVojjEDvrfrez4sC8JMJYtugMVT2Kz3BssyGYZjp7NZzZ/UanWSVBsThKTZqqrMLANWhnlQ6zQur+//O5xHG8J+gnV/x5c5sBHfDuuSptunbZUtLp9ojiBEsOaIZty2KqUkJrTbPdoyc0taxNb5IkJMqwAsaxMQQc10XKPD2A8foz5L4ZXpvmQTDoKJhFNPKfpTUwqPPrMZiaQSQmyYhCxJAezaMW+Tup0oN1WDT94QU3V27Q3rjJ8UMF5uoVyIxIbR5SVnpIf8A4iXnj4grr7Yiam3Dm0BQlx1jsY1HZO5fEGAMgSRTvXN2g1ehwdmm34jfXLLhvcZqZss//8/x5fnhlgxcv3yRViq6GSMN9Wx2ePLbA6dkatVLRGFtC8OjyPPcfmeV3f/QmN1eaKJXylz98mZfevkiv16PiKO6YK3Fsrs7x+TKHZ0uUiu7I5MM45fe//TY3N7Z3zS2Xh+89xaXrq3z/pfM4tsV0vcDp5YWxTY0gN+nNGhms+IEUCx4nluZYe29z5D7EUcT8wgIf/8zn2dzYyLpg9jh06BD1+tTYe+d5HotLSxxaXOTf/ZvfJ2wGSBFTdiWnji5w8doq24EZv1AoUPB9E7HbOeEPKvtDt8aLMHiufsOukSjl+LRjqjRRFBOEEanWlDK8Va4/LDnZkRjNdBk9M/xOHuhStSZRijCJjX7CsNzWSqU9L/8jpPzFhJ+N5M14gigizVD+BcdDCEmUxAcou9t7TCkFUZhZXNmDT7UaKfeRQgzl+z98yRectAblaVKAVmpcB2GzKG2L0pElM98gpKKhFQRsRBHT09OUiiWSOCGOjcKybQPycaRFpFIs5K4acSFA6D7CApGHlmEonDWYUKoUju2xtbU5lkUxB9MNb9pKaZQ2+a3B9ZPl2aOh+zGuKmEUpAaGQGbYo+2D+dAm3J/fT3uA5jdzSrEzBSmkyCIB4z3HQahf9elxB/McxRMorQcdAbNGQUJIHEviDhkXljTtQyWCbrtFJWN57PUCWolms9VFCqiXS/i2iQSYKgiFEBKVKjzPnegpmHKwoL8ZCRi5R0opoig24f0sIhJFcZb+oN8FMK8c6fWC3Zv1HpJn4butJr5MOTw/RbVcRKcpTtYOWaWpCeNnqX9SRRQnnL+2QZRojviC6ZqHbYEWDCm5oc24n53KrRW9i7FzdbPDxdUOM8USZ2Yn87gLIThcLfGPP30//+7FC7x0bY3NTkiMwpGScxsdGsFVPtZs87HDc9R8D9syQDZbazzbplyt8ePXL7C12aTmJXz5/mU+ed8itZLD7v1tNJq4WCux1eqYqxvjuR5dnOP+O0+y2YqQls2Zw1OcOLywt5upFYh84Q/+rHIDEYEtJeVSxUQIh/agWq1GrXbrvPcPPPgQzzz9XZTtcGbe4YmH7mK90cFOBE6hzJ13n+XsPfcMef7DF3vr7r8Q5jJvS+kPnye77ihJCOOYQhY1FYze3vzeBWFEEEaUir5B24u80iqm1YuYrpay+R1kUsa4TdIUV9p7PlIY7ElxlrrMY7WxSimW/D0dmY+Q8s9Fj43aaW02St916UWhKcsSkihN8PfItx5EDHrfQusINVSrPa7OV8jdfxt3DXutvr3K1/LSNJXl8Ptd3oZy4KMLYnScuNHCr5QRvsPm5qZRgBn6vteT+AUfS1r9PPQ4ql3LstCJ8f6BPpp9v8qAOE4Ioohh+M4Aw7DbY9tp2eandl2XNE36JESjB2XH5eV5mVLTWlOulImTxEQVMq0l9c4WT8PjmSiGsdBN4xzPd0niyV5tzgsw3AAIyGqpB69TEMVcX9tkqlYxzVZEFmUZcx+EgFq5SK/TxnG8EY9BS4tKwdS953PNGQrz8rO9QoRCCGzHIckU9gDYOlD8ruuQpoper5s17BlmfBzcve12h+5+7UPHLH2tFOurK5RtQdm1zQ4tBVoKUGq0xbE2hs87l1doxxI3aPDw8QJlqbL3cnxr5dEBh96voet94dx1rqw0efLoIe6Y21+Z+ZbFbz96B08szxKnKdeaPY7Xi1zaavOdizf5xrsrvLfa4ImTS5yeqRDGCanSvHpjgwCbK9dCfvmxI/zc4yfwXTuHB+85pkBwaLbMyzdbpjRWjjYpyw0/07DHRmUg5/3Omit8rUd0P3EUc/XmNi+/v0n16F089sQTTE9Pjz1L/9EeUC+XKxVc3ycIQ965sorQr7HZjqnPHOaBn/rELvbA/RzAiaJNCsP3XLpBuOf+OvydSUMIaRhcr9407auLvsdMrWI4Sfo3UBPEMZ1uj04vxJKSWrnUf1YmlenSWt/CdSwqxcIezsxAcvxQlDkh+1YbkZHCKZOWy3FYKiVziv8aKP+dD2wn0G04L+JaNnFqLtazP5jiHxalFEJDpJL+C2IJYUBJIsMYHIjRb/+Fq7TqGy921jI2LzszOSOHOE5xXYEU1khJownH7l7gwnXwTiwTN9uIzQZzhw+hM089ThLCIKTV7OC4DsWih8VuZa61YUNM+nTJgiSNsxD3+OViSs0iKtUy3V6vf57ci7SsHMMw9B1LTLyXjmMTRZHZ6MXAe+wr/qG5An1efMc25EFawWanQ8l3jcLdJ3wmhOkh4Lg2vW6Am+XKho2sPKcHeux90Jh77GQ59tWNLVKl2G62KXoutmNh2YbgJ07SEYCg1ppWq0W5bJrENBrbFHyPxXptl6EwbOwAB6JJti2LKIwQctTwyM+R8x4IUaDT6Wa91Y0BJIeU59iLnuzE9qXT6dBtbOGTonVWKeIYimmVUfRmgwDw+vlL/NmP3uJm4LDodrnn6ByuBQjTpGii5HNM1S6vv9uOOHetSdV2OFIu4NxC9O6uBaMM7zMBNk5O15j1LL72xmW+fWWTF1a2ODVd5nDJxXIcLq432LJ8/te//RCP3zmHnRjDSw9RMAsY69IJAfM1n+6ldTY2m8zNDkxprTXbjRbrmw3WNrfRWqHihCBKCeIEt6BNpCC3NAff3G2Q5ZExpYkThe0VOXbsGJVKZYD3YNRbneBv7JJ8fR5dXuYzn32Kb33jL+hR4tzVdbqyzD3LJ5ia2p02uG0R4Do28zN1Wp0e61uN0fnsP+XhU5nyUwbX3g1CukHITKVMpBRhHPcxEblT5to2QRBRqYzuDeVigVanh2fbWfOgg81DZ2nWvSuxdN+ZEKnJ0fXTFdAHBE+Sj4TyVyolCMNsE8o2G2lCfLs3HROKPmhzmFubh/FCPGERZs1phBADcpgPIdyfX49SGi3SzMMbXEuhYIg7bMfOQHJZznZHpUN+rjhJSJOBp2rbNuW5aZgzG1bS64Ew4DLXcYmiiF6vR6sZUxiq180BcEor4n4+14R6pZBY1uSl4rke3W6PatV4U6lKsa3Mo1ZpRupjo7TKPOfJij8Xy8q6tO3K5Y6u55wnIFemuXfsOQ6dXohryYw0Z28rOo/+lMu788D9obOY4iQmsBQNSYLKwtZgXsBOGFEp+tjSohMGBFFEseCNoOg73R7HlpcB2G40WVpcGDtfU51gwIl5fn8/6Y+TDgwHlaUvXG9Ay2zbdoa1GNxHyICwtsXc7BRbjRbNdoYiPuBGtr2+hkgjpmo+hZyACtHP++fn0ZjIynefe5WX3rlOoVjhc3d6lCo+QsWm7eakd3AAThj2FLKPNM++dYX3bjR48vD8gbz+CYNAlhoJ4oRIJdx5fI7H75znhQtr/LgdEkYxqe3y9z5zN59+5FQG10nRYUzSDUzppGOjhzb1USdHsDxdQus1bm5sMTNdze4XNBpt/uOffo93L99AaU0v1iAdmC+bEr5Ug5Xl7IYNbWmZz6TBbKg0NWx9lo3j2FQKLmFri6e/+x2+9/R3cV2XX/+N35zY2nffu6Q1b7z2GgClcpl77r2Xt958k0B7VGpTLB1ZplKt7n0Obi1yL4TAtW2mKiWEhrXtgQEw9jwTw4FgC4NrKXjeCMnRRqs9cfxUKXpRRFmPNnrybYvNhoniLM7N9Jfvfh697dhEYYS9Z8t2cw47w/eESWwU/tDlKT7iyl9KC9d1UEqTxGkfcJSKZIB6J1MIUoLSpjnNmGjBZNl/OY1jRTJ5UjE2XHu7orQm0Qq0WWjDIoTALxhlaihq87bEiiQrfzRc67oPOPOKk72/pN0hXN/ErdfwF+bwfQ/Xdeh0urTbHbrdriEJqpRRWhENAbl0ln6wsy5040QIQalUZHu7QbVqUNzra5vMz89m9Lr2UMWGRNoHu4ee5xIE4VCqQYMWQ70ZzLPpZZGG6o7WqkKn2FISpQonTZHWaPh0fxmslzzfjRBjLfEcrJgmsTEALMPiF0amLrzke1m9tyRJFUEUG9Y120Jr01AoNyyTJOqD78ZFZfKSsLGtTvcQ13VJkoQgI+aRUpqKgD2MsPzdi8LYVBpIiTWpSoDJb9eFC+/y3vk3OfHgCXwvB7MaRIk1FLlLleI/fP1bvH5hhWvrHT555xQPnZzFtwWmNeA4azAfHaP4Nbu8/qgX8cxbq4gw4VitRL1we0qNDOB6dbvFaysNZKHAzz92ki8+uMTf+cyZPhZCC8eAQjttQ6ftuQjfxwFEmpBGEWnWRMp2HXTuZWOyH/cfnyUOz/PNZ17g6RfPs76+yvW1Jq1ON9sjE+IwIkjB8n0OzxSxHGNcW3mIPzMYSE3tuJUZedL1SIOANNUIyzhRrj8wAKWUY8Lxu+/2nm+S1jSbDd598YfMnTrLl7780zzxiU8ShSH2Hp0HR+/0wcVzHeoZeFNK061vWPnfimit6YYhNSY7AeMkVYpuEPZxVXGcsLq5TSd73zpByNWbaxyanc4aCU2+i0Lk9PXjycN2iszC/ZaSu7g39sqAfCSUP2SKwSIDaDm4Gbp/JzJVKU0qDSJ8WPnnXtAkVOZBltPwosxBWYkybUB3L9hbB6SMk3HKRAqJbdn00sAoUEuShDFxFOO6bp9D/yDiz83i1U3pnRACnaZIy6JSKZOkCY3tFlEU9zsD5tfmWg5hEJrua/uQAPm+h9KKJImYnqpzc3UdrU25mOt6/dDxB5OBIjZKLOhjCWq13T3VbdumWHTodIPbHH/0O6YUcG/Mg2fZhFnTqfnZKbrdHgXXG4AgpQRhyuaCMKJs+yiVsrXdYGnxEAA3bqwwPTU1dpwwy7d7njexVG+SGBIm+5ZLiPLOj3mIWOnxxD6T7opjW2yur7K13eT/++azvPLuFT79yD08ctcJqpUBKU0UJ/zlD1/lD779HGxtc/rkfXhuzFTJQeYVNmLSSNk7PwbkB/DG+9usNCI+dXKJk9O718qtSKw1r15d5Y2Nbe668xBP3DENWY8Ex3WIO13ckg9Rtj9pTdoNTMrQthGuiywUDSwrTVFRSBoaYirLccCSFCsuPhHfe/4tHEswXZAs1go8fqTO8kyJOxbrNHoxX3vxOj+42DDtoX3PtDFnUAqaxDFJnOKXCiAskiDAcl0cxyGNY9IowfacrPmZiSYdXV7m5KnTO5rp7L7bfRkTmUMIqtUahflljh5d7gPonFvYt3bJ2HGgWiowO1Ub2UeFEExVy2w1J3vqE4fR0O4FtIIeM9UyzX2IcoYlSmJubGyxODvF2nYzix4PJIhM++qZemUE6DxJ7CyNuVfDtlykkDiWRZJhuQ4iHxnl30fUI7LOYboPVBv2NiwJDmT/M6LS1ITKbXsCT/zeorVGK5XlZEQ/Hw971XvfvkLLN9BJSsmAtAb12ZZlZaQ61r4KaNy5yHLUaa9HuLaBihOKRw9juw6lUpEoikiShO3tBqVyCcd26PWCXR3xJp1fa025VKLRaDIzY5i5wjDqe+23UxppyoaM92860okRrvH8lDmRzU7xPBelFZ0gAOGbl+02H1kQhBnbnddn0htHO7oTl1EsFvBsu/+c7YwtT2lFqhKSJGV7u5kpZUPpC+wZzs899lu9p3lvBzO3g303jhOSJOnzJmy3O31P5qBy8fx53LTHb/38p7h87TrPv3GB//s/fYPvHVvg84/fz6MP3IWUkvcuXuc//OkzzPVaPHbXUZ4vTWOH140iVcnetf1aQ5KazWHMMd98+SJxN+CO2RrTxVuLmOyUVy9d47kbW0zP1/nEPYepVAY9LPplYZADc8BzjTrWGuKEuNsFYUiKhO1khoCGJCYJIzQa23b4J7/1BPWig1fy+il8keFfhIDLb66w0YmYn53mzPJhpJCkGU5Ha00UGIyHXzbVI3G3a4yRMMByPbBMtz+VpQxtW7K0uMhTn/8Cb7/5Jn/5rW/y2c99fv+1MsbHEgjuue8+zt5778j7n6cvbkvGjJNHPu0d74MA5qZq1MslNppt0iQhStOREP5eorVmfbM5gehnr+9BEEasrG9RKngEO4iwlg/N4bkHbEFNZkxG8YFS3AarYGFbqo/X2U8+Mso/l35eXZvaWeP764mejtYaaVmUSkXixLBdSccxm+4B6yTBeB65wk2VQou8k5nol0B9GGIMjSyNMQE9HyfJSMmZ7bqEUUQYhgcCeO2UfAyrUMCbnyMZYn7yfc947krRbndot9rGUtd6j7F2Rz0q1QrXrt1gZmaWWrVKt9uhVq/d0jPYeW4hBIWCn9EJg+sZo6TTHoDSpHRMyqFWyQy1YUNSMlWt0A1CojjB3/PeTY7kJEls6u4zS34Sm58aYqFy+sx1giRNaXUDWh2zAZeKRYp+gW7X0DIfOmS8/tXVtT7V6bh1oTJSoVtZi1qD0nnaY+d5d19zjgEABpwH2SFRFGegx7FfHSvvnj9HSYScPX6STz1wki8+fh9Pv/A2z7z0Fr/7te/w3Gvv8rF7T/PSa+dordzgn37hQd5YaRCHvb6yS1WKZbvjjWU9BAIZ8/m5i+tcvNHiyaMLLA41m7od6cUxb642aCvFp88s8LHT8xkxkUlJmekMqbfs575x4DrYrmN4DZKEJIoM5a3rom3XpEC0Qkcxs2WJJcDWCmGNshQ+89p1vvriKhuxx1MPn+GLH38ALbKqnNDkfX3PA8cBlRL2AuM8OBZxGKFVimU7JNpgZuqVEg+dOcL33rnAv/nXv2feN6V44KGPMT09fcv3bC+Df+xfbzOImoPudkZ7c4fEcWwWpmv9IcKsnXacpPuOGcfJgY2FYdFa0w1CemE4EnKfmar1Ff9B76fMAOAHEQNaBt+26fx1Vf75jVHZC2UytXt7nrk4mcIPw4hQpbiOO5Kr3qtm0ng4xls0OX7Z9+QOWtq31/mHJQdhxGnaB2jkVQtJltN3szy7UimucJBiQGRzK+/i8PFCCCzfw3JdEJD2AnrXVygeXUK6LpVKmV4v6Lc3bbc7lMulMaHi0QmIzMuxpCRJTJvebrd7m472kAWfGy1DIb2cote2LWr1CnnL22azRbVayVoam2OjKMZ2bKrl4ljFsVfJ5LDk75/jOLiuO7Hhj5AMShDVoIyy1ekZ3nClqVfLVAs+7XabOEmZn5/vN62Kk4RCwR8zt+H7ISZ+PnbuOiWJU2xbjLQ2Hr7mnecKgnDECNBKZxVOeudX95T3LrzL6vvn+eRd8xR9B89zObwwy1e+8Dg/9eAdfP+FN/n+i2/x6juXCNsd7pmpMOV7JvcNYLvEQYRj5Wj1ccofg+7foSBz+f7rl2m1I+55cJpaYbLxp7UmVAp/QopLa837a9u8t93h2KEq9x2bwnaGxsz0j3lGWSljMjRjMbSLWRJpuUitQSlUHJNGBgNgeS7C8/FcD1RKGoboDH0vbIurqy2efqfBamDxyN3H+eRDd1IuFfpPRlrS4EGEIA0DA+r0vX6VhOM4pmV4hl9JtcaRcHh+CufcNZrBIHJ6UMXzgeUD+FXDRGzmd0YcgOH3tOB7VMslGq1Of9/9sGRXVmJHiMO2ZT/1ehDJDRj3Fpy9vGrJd1wTddhnqI+U8s9rmKWUJDmjmBhPRZqH6vMNLbeoojA2OXEBcRyhtT1UtjJ57OGHkmqF1Hme/yAPK19oBzhSGIKdRCnU0HaqtSZOlSE/cRykNO0toyjKfrcyAGC6bw5+dLzd42OZhSVdB+m7xI0m/vxc39MWQpjeAElKu93B970MqCMQQ8o1m/nQ9Y+2pB387dYMlt3XYL4cBCFBEOI4DuVyqW/YVasVWu0ezUYLEFSr5ezZZaQ2HwCoaQhQNJ7n4fnuLoDcpGsT0qR3NrdbtLo9YgVTlRLVgk8UhqRKMzc7m/UxUKyurlIsFOgGEVEcE6WaOE1ZqJf7Yc1RRsSDzV8KiePkAMLx7o45Vx6NsogtTdAL8TwvS3dk4KOx/RYmy7tvvU7dS7nnzFFKBa8PAC25DqcKh1iYqfPwvaf5o289y5995znKNYvza1uUPRtaPSKpSFJMGlvncxyTapkAitrY6vLypS3umK0yV/Kx5c7vmrPFacqlzRbffvM9fvbBOzhSK+86V5qmbDTbdBPF3bMllqaLYx+CFNJYi9I4D4MjhuY+ZAjojEzH1tpEBKIYpULTK8N1sXxpLMkkJQ5C3ru+xc1GwPGlRT7x4BmWjywgskocp5CtD6GJewEC0+JYZNHLLDaMSE3IX9sCYVm4lsV8vYyjIxwdktoFnvr8Fz7ccryfkMRpSpTE/U58g7U87gUR1Cslur1gt/L/gBCuvb5aLhYouu5tnT5/7/bL+w/vQ7YlsWxr3/D/R0b5p1mr2XyDs6VFklGz7pRc8adaI9LULG5lvGbbsQbK0abPt2zb9thSvTzMnyuIAZmO8bIO6vXfymdSSpw8pZGNr1Su+O1+aNcveDQaraybmGNawHZ7fRa4DyLGCLDw50yOXmtNtLmFU6vieV6/1CsIQhOe1iZkbUmJ73sUizlYa8hoSlNs2yFNkx1j7TWTCUieHRLHCVFkcASFYmEkD2ZZFuVSgc2tLaSQbDXbfWOwJCSOLcamH3YaMVrvJuDoBQEqTQmVKXezvVHO7uHj5e2XoXgAACAASURBVJDr3+tFNDodgjAmRTBVKVEpGMxAmqbUqhUcx0EpQ64TRRGh0kRYWMIA5Tx3tIXszvH227GSxNTU78yJjhdjHGgNrm2hpcyMK4OV2G52CCZynmdhb2HW0ZXLl7mxssLW9ff57NklakXXdAYU+X8mSjdVK1MqGjzGVqPN9rtvM1cpsNmN8F0HrWN6UUK9YCNUit6Z8lB6UNo35vKeefUy642Qrzy8TN3fjQXKfwvjhHPXV/nxjW22ojf59PEFHj+9PHK8Uortbo9OFHHh5javvrfKXcfnmZseRYULKUwkIqMdl7tGGz54FB+ghURqhVAKnaSkGemSdGxwHCzbxvV8Cr5HvVKkXiuZng7ZeYSUaJUSBSFoje15u0nJhMByLJLIdHy0HZOKcCzBfLVIvN2lpTxefeUV3n7rTb7wxS/tCf77ycuONb7j114Qsd1oM1OrjmUfHf6SECZy6Do2YRSPVA4NvjJU5UM/oPOBoN2lon9bjXogL+Mbndv4qGD+rwANvu2Q7lNO/ZFQ/jmXsZWVY+X5C8lkNqRU52gA0Q9LJonC93MLUPSBUWmqiKOYRBhw2E7POY7j/sKRgLTs20ao7xdKHgG/ZAdLIQijCDcrg8mPySmL0zTBcQs4jj1EraonjnFQMWFGL5u3Jul2idtdSkeWsLLudkJIOp0uOk3xfIPc7/VCgjCi4PsUhnisDdBX0m53M5DYXh53/krtnL/Y8Xley29yaJ7n7kLKiuy5VqsVVKrp9Hrm65ag3QuoFAu4cr+lvtszzhsNSSlNrbQeNEHabzMUlqAXGjKQWqVM0XOyazDgSMd1CeMIS0iTsqhVuL7ZZMq38TNwp5CMZZncfa8mfTwZKzPxKyLjNs9ObVkW2+0OrW5v4C3t2n3ykKvmnfPnOPfqS3TabapWzN0nD2NlyknmoL2h71pSUPJduu0Op6oFpktFGr0In5ReYrHV6rEwVUOkCmn142SYTdAg/MWO9aC15oU3r/L0mzdYKHicmq6apjITJIhT3rqxQYLgerPH1966SjdKOHlolqLnIICrqxu8eHOLK+2QrSsNlqYrnFjazYRnFPAAeDdRMqyCVgqU4S4xkRXzHUkW6bEskFZ/X5wue9hC0+wYZjm0ygyvPO8gsRwXncQkGWZD2tbA88e8o9Iy0RyVpigEnV5AtxfgihSPkMbadaJU8N3v/CWWZfHgxx6mXq8fIOo4QVWO+/M+WjVT2aMH7zheKUW7GyClZLZeHelOOliXo3qkWi5hWZYpuU3SfqoXDKVvLmLHv3tMcs8PfMcZg7fZ/6T58VEcZRVeB4v69fWnZe2pxz4Syh+yTW7ognWWx9m7NnvwmWFXs0YAFaMGQMa3nA447vOFrJXu/5xvULdr6d7K1/oGAIBmRPHnn3uua5rTZB3coigcqUYYyAe1T8Gp1UiDAASoJDHhSNuiUPDo9YIsB+Vj2TZxnNDtdglDE4aP4ohiwRBSdLpd6rXqRG6A7Oom/H3UKOh2e6YvvdK4jo3jjKvmGLSyXNtqkaQKW4BQkkSnB6yX3X3/zDWD77smvSSEodHMNhOJHJBSZQas2bQFMs+xK4VrG8IOS8qsSsCw3nm+R5y1FDZNTkLC2PSQH87v34rkyylJkl3Gw62mX8yxgiRRo/XDY06iMZwLb7zyEp3rF6iVPO47tUC16BmvX0rz347vNts9fvDCW4Qba3z6vmUcKXGlQOqURAvCKEELiSbDu5gJZBeaXdBQVEdrzWvvrvD1H12kuR3wt+49Sb3gZYfsfsaJUmwHIVdbAWdma3zi+ALffOcaf3r+GgtrLbxMcW40m1xqBhRsK0sJCio7MAQC0FKgomTg8WttQuxqYCAPK/jc8xdCGoKeoY1f5PxHqcrwPpq6L6m5mitb21y5sc6Zo/MG2+Q66DhGaQyJkHCzkHFKEioQWd8MaQwFaZnUnEoTpG3j+R6Fgk8jSUix+pGcixcusLC42DdoDrBqDv7n/ZTYAQ9OlaIXhJlhLvc9vuC52JZFKU371OVCQJIqNhtNkvQWsA4ThzEf5O27b02GDZ4BpfgtnWFIB06Sj4TyFyJrIqOyRjpkPv0YJTxMbTryu1I4E0qkZN6ZThvCC50OCHOGTmRewlvOD3/wcFicJBPLOTzPpd3pEsUxju1gSYsoig2al4Nt6Ac5RgiBW62gSiaXmXR7qCDELpdwPJfUcwnDkF7PePWVcomOgCAMCdptkiTh0MICcUZCY0Bxt3M3Rr+UlyE6WX3yeIPCGFGdDGVbKfqoJDE97YUgCCPI0L+TPZed4xrPyfM802tAG9rjPMeMBolGZsRDAlO9YQuz1lKtTA8FDCeB79oUiwVc16XXM10WLSEJ04hCwUcKSb1YJExS0qynt+sM6EC1zpjaGAUTTbrHQRhRLPoj17UzIpVvepPuQU7z6rkOlWKxzytvGokYsOqAsVJx/epVWqtXeWh5iuNHFzi+OGs8ELcIxWmU4yFQiKiFiNrEccK1lQ1+8NxrnCo73Hd0AYCiY2OrHpF2CZMJHGU6o7Idel9TpXjh7ev8xfOX2dgI+dKpwzy2PI9nTcbu9KKYS+vbdJXg8WMLfPrUEgXH5oVr6zR7EUmWwpKWw8dPTHGj0aGRhMzUfMqlTPlneAStFPSNQCMqSZDDF5AZdbnSR8gsJ5+dJuNSUCqLagAiN3QQVMsup+aLXH23zYVra9y/1WRhpo6l7awXfGKiAVJi2TZamcinUIo0MUA/KY3nL6SErGGX7/ukSpMIj0Q4oDWSFAvTgtqsxQ++1/2kRGvDyeL0VdpkZ0gIgevYu8h24sT0JukzWH4IUvDcfXuiTJb+qui/Zx8sDT0qHwnlD/QBfsOhfktaYy9jJ9FImqT9RitZ+mzspiiFoYBUliaKTKvUvI4+jhOT5+bg4ZkPCmTrk4AkKe4ElLfMyu7y6IRlWcRDyn8gkycybo5mnN0LJe8JLjBpAOl7WJ4pBzQh/4BOp0Oapnieh+/7NJottBZ4ns9Ws9GvC/+ghlFeWmcUkLtvvetGo0XZ96hXyv3a/DRVRFGESlPc1NQE27a961zDBqVSijA0Hfk8z0NKMbaFbaoVYayQAjx71LCwLIltCZQUWAJKxSKO49BstVhb3yQII3zPpVYtUy6V2Nxq8Mob53jy8UfY3G7w4qtv8JmP/xS+53J9ZY2r11ewLYvDiwsgBO9fvY5WmjMnjzFVrw49Y02r3ePpHz7HZz/xWGYADKTZ6rCyuoZAcvrk8vAdYPh5aUynMCEsVtfWiaI4yysbIGrR94nTlF4Q9u/X+bffZM5XfPqRu1lcmicNTAMuXaizKWe4fKPBwkyNw76NFbXZarZ56bXzdNdWeeKeIxQdw4znOza2Sgk1tIJkN+o8N/i1NmFxIE5SXnnnBn/4zDu8crnJr5xd5ufOHtuXkrsTRrxzcxPXtXlgaRopBI8tz3NypspGO+gbHkEQcmJhiq+99h7vNiKKrkSgSfM0nMag9wGRsVjKrDMilpW9V4Ocrc4YPhUaoUznzLyspO9lCxOiR5hUpJAS23M5s1Thles93l/Z5OK1NebqFQTaMGmm2f6YKITWaGn1881pkpgOoTodagMtUEmK0tDthggshFbYpLg6YLrosX3zGjduXOdEsWgwBhPWzG75sLLm+4vSijBKKHjervH23qMHH1pSUi4ZLv4DNQc6gBQPSMG9lxjwnjogpf3B7/NHQvkPZ/JMiV/2hww1KxmEyM1/2fFDUYDh8qu9HrbpeGQ699m2nYHUDDJSJIZc56AiJoQSb0WSJO3nZYZTHmFiGv4kcdLPaedNfwb19/tTP+499+Hvjl6HU62C45oQolaoXowtBJVKmTCM6HS7BEFApVJlenqKTscwwXVabcqlW6PGHBUzD9NlzpQcFgp+xjk//lq11kSxoa6dq1dJsnvk+54B0oXG0Ot1e0jbxvc8AwmQJmw/3CDItp0+Ba7v+/3PR8rcsjWYpIo0SfF9b0eXP52lraDse0zVaziOy+raJm+ef5cLly7T7XZxHIflI4d59KH7uHLtBv/sX/4eH3/0Iba2G3zvB8/xxMMP4tg23/n+s7z+9nmWFuZ55MH72Wo0+NbTP2RxYZ6pWpWpenUkGtDqdPjuD57lsY/dT6HfQ8B8duPmGn/xne/jOnZf+ZvPRt+dVCksyyJJUv7y6R/S6fXwXNOjoV6vc+bEMY4dPYwUwnAghAHlgsfxu+5k/shRtO2SUMC2bXRpjouXtvnqnz/Npx5/mKWPHSN1a2zHXa5v9rh7usxDmdefpApnKE3Ri1WmLAf5cPqOsAlNJ6ni8so2v/+nL3O1qbDihLOz1T6gNlYqa541un5SpWkGEddbXebLReZKheweCObLBebL5vc0NcaN77lEiaIbK5LEgPJy0JhAoC0T9bGzTnuW4/ZD/Vpp0AqBUfLDaQAhBh1EhcwopOVkpbk4VWSp7vPslTZvvHeDM8sLFIoFENIANh0HpTVpnKCT1HARWDbSNp0F0yQhVWnWetbQjXuOzakj87z6/hq+pfB1zF1H6lQKBZ57d5VXX34JhGR5eRm/UDigcb/zGg5qLBzk2FFJUkW3F2Q0v7eW3stFSknBdSn4HkEY9nlD4uT2ywIHIMT9r2e3M2nWlp2lDT/sfjYfCeWfv9M7dWmaNYKxpdX3gFW2EedYgCRK8ffgth8ZRg/aodr2oDGKIYQwBDJWdoP3U6qDB3V7yjdXOHEyTD5jLj5OUjabHWqlAknW8MhxHMIoIklTan1e9w/Tmt59LieLRiTdHtHGFkiBNzuTha8dNje3ubm6yuz8HLWMW9+yLIIgwPMcnFsKFQ5y/UprwjAkjhMKxcKBzrPZbOI6tumal6YUHMfwlrsulm3T6wWkedtdAUFkQDSuzD3HhGazRbFoGA+HGfwMCM7w0afaALTSxIRmS1mvhJEr0cZTdICp6Tq25dBqt/n2Mz/iuZdeZapW4ejhJRrNFt/70fPMz06bNqJZT+7FuTm+8JlP4nseq2sbPPvCKxxfPswTjz5EHCe89uY5PNfmy089ydzsNCs31+gGIbZlMT1VY2aqzpeeepJyyVRkbGw1aXc6oGFzq0EUR1nrT1NFsbHVoBcE2JbFzHSdYqFAkmpsIYlVysuvv0WtVuXE8hHSVPHjF17h3PkL/Oov/gxzszM0wgjbcvjUZz6HrWLCskNX2XTQOI6LjgSRahjyLAShO822VHQrRT77BQ/rWUHFk/20H2QpFdcn1b2haFxuFChAI6QkTlKu3Gzy589eYHU7xlIWR2o+L71/A8+1KTsOG72AuudS9hx8x8FzLGwpCZKEa60eG4Hic8fHl7UppfrpDyEE22HCajumESpSaeO4Dn3wHqbWQ2QNucieZ26X5WlMKSWWY+1mLczOM5BxITuYnSpx9nCF86sh797Y4tK1NZYWZk15YYZtsYThDEBBmg4IhaRjYdk2lrAN2C9JEAKKnsNP3X+ajWYTISS2XeTXfvZJ3rl0nZcvrrK2ucnLL75AtVJhwfP6EZeRie2b2D+IsXDQY8fcmgN9Ze+DpJRMV8u0OhLbdrCkoNHuZGniD8J7sP/kdm9xJgpuidzRhQ/i8O2Uj4byHxIhBo4/mJ8TlSKzunhzzBAMMEM076+sDaYgSVLcDPAxGNN817Ksfke6Pc5E/lD2l3EvxOBvhp1qEGo3f9NsNNsgTLOGIE0puC5CSJMeGGmM0d9WDjyHg+eOBmIXfbSuk2b5d2lJ0ywkI1YxgEoDrFs8dIi1tVWazTaVSrkfkbkVI8B0HgzwPBfXsXfU6Y9es9aaJFW0g5TlhVlc28q6B+p+qMy2LAoFnyAIDPDOsvD90brvKIyyfHzQV+ame57JjUopTRg1UcSxYRysFH3jHacpYmhNRFFMo9lifm4Gy7KJopjX336H198+z6kTy/zyz3+Z2ekpwihibWOT2ekp3nrnvf5c3r96jd/793/Ag/fexfd//AJb2w2kZXHuwiWazTYXLl5GSMGLr7yB1pr/+Ed/xsbWJqVikb/9lZ+jXqnw+//hD7nr1EmKpSJ/+Gff4o2338GxLaZqVdqdLoUFnzCKuXTlKn/4n7/Byto6xUKBpz71BJ/95OMIpYjVYO08cM9dPPXkE0xP1fnu95/lmWef5613LtAJAr7+Z99mfWMTreHTn3iUY1/8DD98+kd8+5lnWT68iGPbHF4yLIZJqnh/ZYNnfvQiG2trfPnsEc4cnofmOmmq+u2cfZFCqugEkfGu+w7hAHMRpYpLKw3+4DvnePdqk8eOH2W5VuAH76/wgxsNfnhllRnPZStS1BzJqZkKpxZnOT5TY7rosdkJeW+9heXY3LcwaJ2bi1KaKDIVG3nk6XC9xHutNjc2ulxfbXLsUNVEANQgCtBnnxTSkPbsRU18KzL0Ki/WXOYKmtdW17i8ssETA1r/UZFgWQ6WMOH9NI5RwjD8SctGWjZoE4k4dvQQ/+UvPUW3F1KtmnLiO44fpuS8yIZSfOapp5ibX5jAmzGcMrot1X1b38rFkvIW6XjHjyeFoFTwKRUG6bJquUij1WG71SFObp31b/L4DM1hb12Rc21MBlHf+v37SCh/IQSuZRkAjxBEaZJjaEAM0gGWyPvCm9rlMAzx9+jQlRvR+XuX1/CPa6ajtcZ1HbrdHlZhsvc/rhZ8eLy9AFQ7/xbHyQiDk9aaRjcgjBMWZ2rkwWYpJFEUkaYpxZEWjwd52MaT1lqPrDfrQJuRHoRcXZdEG0R33GwipUW9VqVUKqGUZn19jV4QsnhontnZORqNbTY3t6hUygbQti/oZRDqbbc6SGkIh3YD9EZTFUpr1rabVIoeblbtYQkLKUaNHNsyUYA4iul2TV297/v9Zka2YxMEIUIIgqyCAUuQKo3MwsZKGWUURzHlQoFUK6IgYbvTw5KSomNaa7baHQ4dmse2HFZXVymVy1y7cRPPcbjj5AlmpuqAAq2oVUrIHeG8OEloNJskacodp09Q/F6BO08d5/6772R1fYOV1TWklNx79g4uXbnKxctX+B/+8T9gc6vB3MwMvSCg0WwSpwnffvoHPPfiy3zmE49z+sQxfvziq5y7cJHFQ/Osrq3z1a//BbFK+Ud//7e4ePkq/+J3/i0fu+8s9XqF66sb1HNu+Dim1wtoOx3anU6/ZHGmXuPxRx7g6NIi33/2Bf7Nf/wjfuFLnyeIIt57/zLzczM88tC9RJHZNC9fvcb5C+/R7nT5W1/+LCdo414bGMKDR6tp9rqESbb+hpS/QBDGCW+9v84fPPMuV1YDnjp5iL/9wCkAfu6uo7x2Y53fff4dhG3xsaUqK80OL663+d6NBhVbcKTiU7At3t9qUyyWuGN+emhos3by0sxCFuaOooj7Ziqcu7nJ+SvrvP1+mSPTBcP/4HoD8KHWAz18y0o/D5VP+Cg7XdmzqBVdEHokLD3Oe8xF2gZ7YIyACGKB7Thg22bqQmD7BcpCEgUhbsFHC82pI4fYvBbsmXobP+KtyORvHUStWVJQuIWU7a3M0pKS6VqFcqHAtbX1frvu25NBhHPcfEZ1iOj/m/P8S+lOWFK3ftc/Esof6DOnaW2UfKrVSBhHCtEnKxn0JNd7tCQdLBnTCS4lTRPcodzsMKgjSGJ82zSyUVm+cxygb6+1f9D3fLinuiWt/jyCOGFtu83h2RqObdNstPBdF9c1dLmmM9t4Xvm9xtpu92h0TJ22JQS1UoGZMSxmO76J1oYPu9lqEccGZFitVPA8D8syzHStVouNzS2kFMzOTHNjZZV6rWq6ehUKbG1vE8Ux5VJxCJQ5+UZFGYBqr3ri/Lnkhkm70+XE0sLIZ8PlkPnfZIaALpZKlIol1jfWsyiAR7fTRUpBtVYhHgKY5YZnpFLixDQHafcC2hkeYUSKpv/30uICtu2ysrJCnMT4iUIgSbUmSZN+CDvNqhHEHtiipYV5PNfl8OIhjh09jGVZTNVrRvnfdYZysYDve/wv/+z/4tEH7+OuM6dJ4gEZz7uXrnDHqRM89vADHDtivt9sm25nnW6Xty+8x6H5Ob72n79Jzjtx+fpKvwwxt6C//b0f8cIrbxBGEUEQ8vAD93L/3XeysrbOa2+c47vff452t0scx1mKQVMqFvjtX/tlpIo4d/EKURzzje/+gGLB56lPPsGphWnsN99CN9ZNRM51yRVft93iyvUN2tPTdJoBbtE2Rrs29+6Nyxv8p6fPs76V8DfvWeZLdxzpX7MQgvuX5vjffmFu6CkKGkHIqzc2ef7KGu+sbbPWNlUXD0zVRtYWGMKqNE0pZ8ZPr2dAYPcszXK9F/PNi9e5tN4lQWA59ujLf9tevoCdINyxDohmu5fQijS2ZRPHo224d55y9HwgHQtpSdI0JY4jRBJjOy5YBmAtPQ9XSoJuD9uSeI75exgEWXfLIefoJ4/lG+8bD/1iSUnR93c4RnvL8N5+UOC269rM1CqsbzcPjANQjE8VTBpz0jwsKdlJsTXs3E4CcO8lHxnlPxyOdmwbhyzfloXThkPHWpvQque7O84xajX1wUupymrFU6Rt4wy9IVprOlHYb7vq+x69bojwZGbIf/grW2u9a/4qTbm6vsV8vULBNaQQURRRKNQM4E6lY8vnxi+i0UUQxTEFz2G6Usc5cFdAgdIpzVaLUrFIpVLB9KNPaXc7tFuGedCxLWamp/ov3uLiPDdvrhGGIdPTMxxaOMTW9hZb2w0KBZ9SqZjx74++0vnz73V7WJakUilPCC8Ov7SadhCCZQ8BazKQV6pxs0Z++fG2ZVIAvTAkRTEzPcXN1fU+XqFer/Vb8g7uwkBMR16x6/NChu72XJelxSUArl27Zrr1+RWU0Jw4fphX3niTF199g1PHlzl57CgayaVr11leWtz3afQJWZTKct6mu9/x5SP87//zf8/zr7zOv/73XyVJEn72C5/tf69U8Fnb3CIITfi83enSaneolEtIaVEplzi8uMDnP/1xlFI89aknOHV8ud+EKJeTx49y9s7TzM1Mc3hxgcVDC1y9doOv/sk3mJ2e4h/8zZ/nnQsX+af/57/qo/NLxRLolO1rF0iCBKU0jzxwDwg4d+E9nnljgU8fO4s+/yIyWe9vYgApEpXE/PCta7x1aYX5isWZxToPnF4kTlO+89o12l34ytllPnd6aZ+7Z55Zzfd48sQiT55Y5MpWmz9++zKv3tziy6ePDB2r+5iNUoaZ6PUMmNUYvZJTU2Vev+Hz3Ns3mS46/I1P3Ilb+BBK4XYq6gly7WaTVy41ubCR0I0hSFI6vR6l8pBBn89l0pykwJK2MQLihCgMQIPreWAbRsGC43BjZZU7lxd48fyLvPKj73P0zF1MzcxSq9f7EZG/Kpk0ku85TO3rzOw415721WQlWi2XCKOYRrs74L7YQ+eqSMGITSImjLm/mPdKZ3t+rt+G09B/DcP+wATiGiMyA1wNJOcEGD1+0g1NkoQ4jSHLVTuW8baDJM5ASArXGlQL2K5FFEX9lrYf3LzdnadO0xRPDJT/ta0m1YJHteSb2vQ47ufTTfc3Tblc3HXm/UJAeWcqz7Fx9iTd2S1bm9sUikWKpTKtdptGo9EHRxYLBUpzxV3PQArJ4qEFGo0GKys3mJ6eol6rUfB9ms0mm+E2tWplqGRoMNe8pKxYGC1P22ngDHv+URhTcUajBGEYYTvjykQNR3+qFK1Gk9mZWXzfy/Lzs5ObfWSPX0rZLx2TAjxpwFuVSpXpKZMzDoIeq2trJqKQdZGLwpizd5xme7vJH/3Zt/if/sn/wdzsNN1uQBhH/L3f+FXczBBMh/nzdTaQIOs0achl8nu+1Wjwje98n+deeo27Th9HacXszDRxdh0SwVOfeoJ//jv/ln/+O/+WmXqNrUaTrUaTpUPzzM1O8+TjD/PV//xNkjimF4T0woD/8b/5h9zc3DIB6Gw6Z04e5+OPfgy/UKAXhrQ6XYSUFHyX1986j2NbvPDqG2baKgdHacJejylXsSIUvuvw8IP3cfrEMf74L77Nt575MXfPfo6Z2jx2Z3vklr95Yw2lXaal4M5alXfWt/n61Wv8ySsruI6k7jh86ughnjq1v+E0TrbabdY3tzlcKfLI8tzIZ71eD7/gI4QkCIL+usmN0TNzNX7mriN87Y33+fqPLxNECb/62bN4xQ9e1nUQefVGj5dbJbylJe73NXccnabUr1Rgf8U/JEJKLNfBwgENcRCSBgGObWF5Ll//3sv8jc8+ys8/+QDfeuEdXnvmMqGSLBw5xkMPP8qR5WNESnHw/fFDChVkp/Bch2qpNDaVe5C55Ip07MknyHS9ShgndPLo35AS3imtXo/6GMPkoNGGYZFSThjl9u7pR0T5j6chzXPVw61vlVJ9r/kgVmecGtIUIQyXvtKpaZmrFYnK6sh3UL/m3a6iOMHRjJRY3M5DG34wqVJEKu3n+ntRzI3NBpYQzNer/eOSMKLge9i2bTwPwT6MeeMlSlOkAPuARBP59aXK9ETwXY9up0Or1aJYKFCplA1piBB9oOVwqDEfo1arUSgU2dpu0AtCqpUqs7NzdDpttra28X2farXST+P0egFSSI4fO8b7ly9THzrppNSL1hqVJEzVKqPXHEW47rhyQ4GU5vn2UkUQ9JidmeXGjRvk5ZQ6SdhFK5OPrzQ2moIlcBybWrVKpWKeWbfXYWN9AyEk1UoFyxukOFzPbKyffPwRzt51hnPvvsf1Gzep16o8dO89TE/V2Nze4jd/7SsUfY+lxQX+zq/8IqViAQT83Bef4sTRwxQ8j7nZaT7x2MMmD1mv8wtf+hzLh5e4tnKTv/vrf4vHH36Q7WaT3/jVX6Raq3D0yBL/3X/9X/Hqm+dQSjE/N2sAbI7DdL3GL/7MFzl752nOvXuJ6XqNxx5+gF4Ym9JXTJ7453/mCxxZPESiNZuNpqlQcR1m6lV+5Rd+htfefps4Ufyjv//b24nRtwAAIABJREFUvPH2OUq+zdlTh/Gsx0jaG1T1JicXDvP5Tz7CwkyVpVqBX/rsE5y7eJnNzQ3m7dG1eWm7RTNOKBSnueNwif/i/mVcFDfbXV652eD1lU1Oz9b42bNHs+/d2pYYK8X1dsB2lPDwUnUooqjpBYHpGmnZJIkB+/n+KLe9EIKzC1N4tsVXX7/EHz9/lc12wK9/7j7mZ2+z78YB9++tRo/tQOCVpnBsj6NVxWP3n0Eg0DlN7S1uUHn4XguNU/BwAB2nRN0e7VaLf/H//im1coFemOBqKFmSaPUiP/zWBnNHjnP3fQ8wNb9w0NFu67pzMc/GGOGeY1MuFij4u3sPHGyfPihwe1SkEBR9jyhODABQm3ONu45eaBp17SRJut2Aid51YbdvSH1ElH8eCtl9IU5W5jda539wzvI4jolUYmhYhcnfBmk0coxvj+bRhRB4tk2s44xoRh+oM+CBRGtIFNKz2Wi02Wj3mKsWs/rUwT2I4wTX99AYD2p3RcPB3pogSpHCwj1gjWg+hOnqZgB3URxTLBrFn2MnfN/vz2d87krgug4z01MGF7CxQaVSplKp4nkFGo0Gq6tr1Go1PM8liqJ9c3ZJkhDFCUJIXMem0wtJtBriPciMlr7js3tiSmnzTIWgVCob6mR038DLFb+FIM1+TuOEXsfksz3fZ/HQAp7vo5Wm1WqaiAhQqldH+g4MlwoGYUQvTPA8j088+hAaQ5gUJIrVRpO67/OVn/480hIcmpvhF7/0VN/T/Pidp7GLBYPWr1apnS1n6QdNoeDx2MMPmFysbbi8p6dq/NKXP9///uLCHPOzM8Zgk8MbkMZzbe658wx3nzmFlJJmu8v6dgMwK6zRbvPgvXebNRHF/ejIjRsrPH/hbeqyx6fOHqdQqmE5De44pbBWfsw9TsqZIwFsvYCjthHxKr3322xfiOldKHOkXmepVIAoQIS9/pw08K9+fJ4lV1CYmcKWpssnacpipciRqSo/e/cxhBgGre58zlkodMI6Wmt1udrs4ftF7prN+r1rTRzHaKXxSz6gR0L/O9eSEIKTM1V+82OnmXvrCn/82nVefv97PHLnER49Oct9x2colB2iboRbcHeli8ZMd/T3/kCjh75+eZPrSZ2Z43cgkojWxiVW1japlEuA3nucTKIwRsodnCZicMe01gjH4uU3r9CNEpT02W4H2BJ++fOPc2xxlss3N3n+zYtcePdlNIqnvvjThOke4JVJcgv7qW1bHJqdojDUzAjGv+c/yWyEEALXtrClMHn4PcaqlksjOLMPIsOlsB+GfESU//gHOMrOpbONO8Wd0E99p4RhRKqV6bPeR1XtzNmO94iFEDiOQxwnWStdvaue+3ZEaehEMTcbbWzb4th8Hdd2cvOnf1ySJBRlAZ1FLYzXP7xLjLv+Hbn+KKIXhhQ9F7sfNTig0RAEWec+DVohhE2aKpRWB0f9Zt50vV6j0+nRbLYIw4hazRAD9Xpdtra2DLVoqvB2sRaOSjcI2W6ZTmdF38V3HRbnZkbm0u0ZJP6k+fWCHmEUMTNd73s8KqNUjYbC/ikaHae0Ox3SJKFarVAszmJZNkmS0NjeptPpYNk2lUoF27FRY4yO/G5HcWwiElZWGpkh+kOlKfge5UoJxKBd8ghboGNDFKFdhzRrVWxLJ0t9aJRKQWisoY1/NEokTD/xMZ6OwZ9EOI7N2sYWzayHQy7tTre/k/apfOOIxuo1Vt7+MUeqTcqFtwy1dqrRaYLIjLGCNmsHNM986zv86I2b/PSpZeqnFhHXRUZ7m7HbYSJ9r11b53on5ETFo+F4dHXH9JwXAsuyD9yhcC+52Wyz0mgzXfK581C9n4ZLkpRSySjRdrtDIUs/TRpPCsF8pcgvPXCSk/M1fu+N6/zJeymvb26xfDHA61znZ87WOHP2Tmy5x94hIIoSLlzeRGnN9UbIq5c2+ekHl7jr5FzfYAu6MZcbErt6iMNHj9FtbLGxeZUgTtirSGCnvPDmBdBw16kjE3PlQS/izLFFojjmuy+8Q1NUKYkehQxgurbZ5PpWgHZrlEpVw2QXhLdnABxQ5mpV/Ky3x2iO+68AdbhDLNvOSrQndbqEeqXM3JSJDH4wbMTo9f2V1fkLIX4H+DlgVWt9b/a3aeDfA8eBS8CvaK23ss/+W+DvAinwD7XWf367kxvZRLVGZ+QZ0tpfCSuliNMEhdrTGnb6m8nuRWsMADsLg5umQM4t55aGH5wp3tNKY0vJoanqSEpjZP5aZZUHZl5796TfXT4SJjGr221cx6Zc8Ia+f7CFk/PpJ4lBp5suYCnog84lGy17UQ0gzydJU9Y3NqhWKpRKZUMJvLVJEIZUq5U9zxknKY5tMTtVyxjRds+lVJhsmERRnLVHtrFtl07H4Bgcxx5R/GEY0W130EpTKZf5/5l7syBLsvO+73dyz7vfWrurq3pfZ+vZZzAABYCrRFGiIVkK+UmMoIMPVthPjqAc4bCfFKEX+8lhW3qxRIuyghZBSlxEEwRIAQNggOlZunu6p6e36rX25e655/HDybxL1a2lB8OI+SK6qypv3syTmSfPt///xQnVqRCGIY1mkzCMsCyTSq2qRqYJpBjvaYosShcliqBDz+o44iTB1ASmEPSCcBenxPAl2Fk6KIxiwlYbTYJRKyMy+NgkSXYYpjufAYx77jn0sZ5xyu9U/PmRdpaQr66u8fD2TerxFs8fLaOnESQaRBHC0JU3r+e97ZIHTxrcfNxg1rU5WrQQSdwnW8qxIkC9Y797bZGjjkapXMVyHAqap/AV5E4egs8vm22PnuczXyvjGgrFMIqiPuhXr+dhZ5jsB4kmBGXb5LVjU9imwdXVNrcj+HQT1jZ0ihWDuRMRlYqx7zoUpZIHmx43lz3iwKNj1fjT2x5CbHD+5CS6rvFgtcVGYDJ18SQz0zM8ajcYXbpExgw4Xno9X2Ep6Dr3n67R6nlYhsGNe4/RdZ1y0SWKYv7W115hZqqG7Zi88dJ5fnT1DlYcE2Dz+9+7wvGZGiutAFma4pUXX+Xy5ctM1CdINjcIeuFfix6uV4oUXGcPRs3Pc8KfzWDwg3Dfnn8BlHN0TWBlfRMpoVYu4oydWwePJ4ljYC8Hadz39zfEDuP5/yvgfwN+Z2jbPwW+K6X850KIf5r9/dtCiOeAfwQ8D8wBfyGEOC+lPLAvIk2SsaxfuSSJIhQ5LGpcEASqzeKAXa1+mHb8jsp7NSBOSGJFZGIZn7efNKM2RZKK8XgDuahFWBDHKkWh70NOsnN7GCesbbVVMUzBGYE+PozkbZQCQRAE/YmaF/sdpl9/WOKMbctxBuHUVqtFt9ulWqsyMTGB53n4vo/rqjBrGEbYds6al3meQiNBIT+axngDcL8F2w9UH3+lXMb3PRrNFgVXeTJSQpzENJttUplScF1KxRKmaeL7Ps1mK4t6mNSq5T4wUyr3auYZuh2p8oiRaQYprCEAx1UY+Vqjtd8REDlojK4YwpJ2h3ArwZmdRhUt7UT92v9ZK083JU4Un4XQNHrBQPHvly9No5DW2lOSzYecqkLR0dAsE5IEMuY+4gH1bxhE/OeP7vF0pcm3Lp3keK2UKX5FVJSPO05SPni8zlKry985c4wlaSKERlEHyK/vsGBRey+k3TBktRuQajrTZZckY1VUBX06QaDoYQdpvoPPpwlBwTS4PFvnbL3M017Iv7/xBPPoAg9igyuLTb5yyaLg7F43ur2Q7ZbHkakyl47VuHJvC6mZGFFAU7j8yWdd1roxb12Y5v5qm9BZoDI5S7PVZHXpETUbpmqVA73+m3ceMVEt8d61Ozxa3sCPUljrEGLQ9HRSBG43QEPw+391DVtPqZWLCKGA0XSh05MWzW6XW082kYUp6tNHKJSrWLaN0KBerRIEGwT7GCCfV0quqwC3/jrj+c8grmXS1fWxPf9CCOrl0qBWRCpG0u1WNyNJ2glcdpCoa7ZsO6tnOpxhetA6cKAWk1J+XwhxcsfmXwe+kf3+r4G/An472/7vpJQBsCiEuAu8Cfx4v3OkMsWLQ7RUvdx6hso2XIij2pvEoS46iiKiJEVqe7y82dqga/pI7cB+vZeGoUOivKwoU4Lj5KBCk/yjdJ8XJE3T/sscRSo/t1ffu5QqZL3Z7vV7T5MkxTEMKgVHTbRnfGEUxr0yGMIo7tMfa7r+jJNWSRKrPH2eVy8UtAzJLWJrawvHdimXy4qRDMVk2Gq1MS2/P/a80FMmKY3thoqYIPpjMwwDyzb3RHv0sx5l23IUB3ing2Nb/TqD7e0GcdZKV69VcRyXJEnYbmyTJimaoeNaFpZp9hEgk1QRoqjnsPdz9zseSawMIKFpxHFCGIYKwdCxqU/sRpcbFpWPjtF1DatURNoq55nGCUHPRxqHXRQHc07TBKYwMuQwOcJktt+h1lZX2Xhyl2m9w4UjBWXACgFpgsjfCV3l6NF1/uLKIu9eX2LSKbJQKeKaekZcI0fe73YU8e0bD5gx4KunZvn2wyYmKBRGKdH0ZynO2nvHphfS8kPCRBJFGfaHpVAzgyDI0ntWdh8O/94IIXBMA9vQKdkGv35uim8vdVjqVPhwKeD5hQjH1NH00VB1sxdy63GD2VqBuZkS/+jrp0EoKPMbD5t8sJpw5WlEyW7woCkpn5mnUqvTaDQgjpgo2tTKReXx69qQASeJwphHyxvcfbLCw6fr2I7DcsOnGRjE6KSxRoogFYrrIk5UqnWyNs/9xfuUOj5O0iKVEk1GmJj4wqaTGOAlWD2PII7xo5hOp0ulXMYyDYIgGrnGn00kSNGnNN7ZIvz5ZfS7wx1Eh3nshrH3WmgaOrVysf+5EIJKqQhS1c3kSJD7jWfscU2DXreHYZgI8WwG/9hreOZvKJmVUi4DSCmXhRAz2fZjwHtD+z3Jtu0SIcRvAb8FsLCwgCY0NF15A6mUhFGEQKBrGqlUIcJcIe2W0YkQRVGGBbLHDck2G2JUoQ52z483CKeLzFggAwzSNG2sQt59jF3XncEI750rUrCyyvhJ0hRNG7YUB8dV/ewpG802AnBNk7w/q+jYmPsq/nFVRurvIAj7YU+ZpqRJ1m75ORR/njYYttr1zIgwDAM9UG2VW9vbWdES1Gs1ojhBygEPuhCqzxohRgy2nKEsCAI831Phy1KJYVwIUGE6VYRoEEYZR0K10ofNDMIQyzSpVVX/crvdJgwDhK5h2IrQxtC1/rlTKftMlDB47kYGOhWnyhATKG9TpgnFgoNlGURxlIXqbYRgBOVxnIRh1E9xCE1Hy7zSOFJ0wUYYktrWCEz0QEbrRHIa6+G5K0nxgoNBS9IoYnP5MaK1zJm6Rq1oIQwDklQp/DysLQRPVpqkEn7w6TJLjZC4pPPuncd0uj3OTNepF53+PfPihB8urrLW7PJ3L8wxWXQJZRtNqESZpomMTvjzdtsMpOJYzFdc7qxv86StFlJNU3MwR/n8PF01uQih0Eqfn5ng+4/u0SxNsyaKfPigycl2j5JtUC1aFAs2hinohQlL2x69IKZadTk3V4G8sDmK+Gi9y1pa4af31ljTp7g4u0CxWKTdboOm40cDevI8fZPGCVvNDj+6do+VVshao4vnR0RAIgxilNJX2PUaSRhSLJV44403EUJQrdWYP3UemcRcf/8H1E3Fa4EX0MUhwSBNUkzLolgsEUYxQRBAuUyxWCSImgTpgH/lZxMBAnpBSMF9VnTT/WV4Lj3rnNI0RXOsa8Gg3x/1/F3bHMEdATANg0qpiOZ5IMSYaN3hzmmYBlEUIYSFrud64PO9E190wd9emnn3Rin/JfAvAV577TWpaxpGFgZMZIpM1JueJEm/wlgINRE0MQr4M3wT4zgmQSK0g+/IwALbqQjFjp9KVAhaR6IiAPtD1u6dRjANHUOATNNduV4Az/exHBstI19R3TvjxxTGEUEYM1svYw9FI8bnxvYbn+hPojiOsV0XmdW+y2zcz+r15+FlPWM7Gzlblk5RNMUafqDy6RN1Hdt2ECIAoaMJXdV5aFo/v52TXGiaQGgGAo0oionjmCAMaLXaIAS2ZfU7AdIk7efFozDKAFsU3kOv18MyTarVGrZt0W638QIP3TDUc8jGnpLNGSn7hC0jKVfYhdYXhnHWBy1x89CyH+5BKwxCSIYhpOM4zgzfvOtFkiSqYyGOY6V0dY0ojsmf/mj6bPCck4ycJC8gk1KSpCmNVqdvFOwnje1NWiuPmRBdTkw4GGbu9afKCMiO2emG/On7D0nTlMdrXX75/AKdIOLWRoulVo+LWy2ePzLByek6BdNgs+vz/fvLHCsavHnyKAkoBZXEJKiokQomDAyXz2sAuIbOfLVI1bVpBRHdKMFNB87FfsiSh5U8CnB5usRWGmNPHueT3jqP/ABXiyibESWrg07MdsunHUjWmz7VqpsfAIRgo+0TYeLrRRb9MrPnLjJ1ZA7LtqlWq5Sqk2ytN1la3+bUsWl0JIZp0Ol0+eHHd7i23OPEhRc5uWBy/dpVvCBGSrAdFf0S2Vgty+LYsWNcuHSpfw0zs7Mq8rW9xeb9axhRm4IBMo3R0AiBdqvF0tISxYJLyZ4CVNREF1982r/rBUxUU3Q+v2G2U/aK8h4kMqt7Krg2fhgO+v1RusntFy6Ptm2YpkFJuENO1KjeOYwSV7VHUVaka/5MqZDPq/xXhRBHM6//KLCWbX8CLAztNw8sHXSwHMM+99YMoYOmlGMUx/1JmtPxKjtLjEW8i6J4f69/SJR39mz5KU3TMIWiMY2iaAe/9cGSV8DrmiBOU8wdSjFJU3zPo1afIMnC+Hu1NUrodz+4do75vPu6D2sZ5vukqSrESjOgloNx+cdHOeI4UevYPt8XQmDbNpZlsbKyRhD4fd57XRPohtkvOJNygD+QJAlmpkA1oQwydRyTnufT6/UyLGw1bzRNeTpxrKq6y2WVe+71PMIwplyu4Dg2rVaLnu9j2spwGB53mqZESPQMHASZD001ZGpAItLshkMahHS8AJmmOKaRFVAqY7ZYGAAZ5c9npxeSyrQfBcojQXl1fxyr9lW36CJKBVWz4PkknS5mvYZumeQRq+HnASq6lj+1KE5odXtZVf7wg9FGtqVxzOrTx6TtFY4WUqolU0UapMyAiAYn+vCzJd69uYxMUo64Ln/3ueN0gohPVre5s7bNh083edjo8EKrx3y1yINGj412j29dmmOm7BJm7Gm6oZPEymAxXIs4ijMDQM3JZzFGc0M0SRJKjsNEqcimF7LcbHOyVurXPqy1e9imTmXHs4+zNqvxkbTx8//y3CQf3G2gOQ6Tp95GJyHye6x0m3itLbzGJkE3oGK5bLYDzsLgPkrJ9Yfb+GKBVOg4R89z4twlqrU6uq5TqVSYmJ3j4foT7i+tc/zoJHocg67zaGWDjxY3OHH5HS5dfgXDNOkGEbdv3yYMAgqFAqVSiWKxhFtw0XWdo0dHURJz4/z8cy9wKw55/NlVSiLEEBInDTBkRLj1lEef+qRBj/TcOSYm6n9tNfdRHPdxX37Wuo9nkXFrZ/63Y5m4toXnBwNqZyH6haOj0WMl5j71Yoe5LMXwKrMutIgk0dB1rb8+PIt8XuX/H4F/DPzz7Od/GNr+b4UQ/yuq4O8c8NODDialQiPLf+8vgpnS0A29X4CTc9qnqfKGdl5wmqYcxjjUhUJL+jwhk/zF8P2AOKeJHXNN4y1Lgcjy0nGasrNywPd9NN3AMk3a7Ta6pu0KIQ1OovL7Rj+PuJeC3X9MOyVJVVdDjqIoGAXyGXOG3UOTkiRR92awYA6nLIaVnfolZ3QDlXrQBOiG8lbCKFahNtskQRLECUEYYWXh+FRK4ijEcVxq1YoifgoD9WImiRqHrhEGISDRdU312QcBpWIZx3HodDr0ej3cUhHLMvuFjzK7njCKswp1lSdX/1J0VGuLDkhNIFKJ1ARp16cbhpi6oFgsYBo6Pc9Tof4ho3H0mYxGsXamTNIsDWKaRv8YirsiJvIikqw1TxZcjGIByTDrYtJPuaSZYe0HYaZEd3r+o890e3OdrSf3mBQt5utZWFMASYowB15/s+3zFx89pCB0TkyWeWNhhomiw9Gyy8l6iTvTVT5a2uTeRpO/urdE0TJo+zEzjsFbZ46rdFqSYgupkCWbBg3Pp2gZGKbRNwDSVKJpyqg7yAjIuT3SNEEIQdm1KDsmT5sdNjo+JyYqdMKYom1ya20bw9D5yvEBaE0iJXc3WxRNg+mig7PjfdwrwD1ZcDhuRCy2tjn/jZ+nWp8gjiLa7Tbb29s8un+PB7dvYooOnWA3V8RGK4CKurYz5y9w5Ohc/5kbpsnE1DTrE7M82XxAq+tTEwJd03m0vEWoF3nh8is4Wdvr5VdeZXl5mc0gYHp6mlOnzzA9PU2pvFeHTXYNk5O89PrbbDc7tFceKJjvyEf0WlSdhKLRZXPxBt3tdQwNJDrdXg9Mm8mpKfJq9y/CKNjZiZJtHTl6Hj3bOwj9bCPJjzVu7dQ0DSOLXKaZYW0a+kgE9vOdezT6t1NU+lvLlL/qgEvTFE3Ts2ji4c51mFa//wdV3DclhHgC/M8opf97QojfBB4B/wBASnlDCPF7wE0gBv7JYSr91YlUSFUb8l4HHMaDFzzPtYdhRBInI0AVMnfHDhBNaDimNZSz3a2cDiOWbRL4IUJou8Ls+ylZkaUudsLJJokqRKvVJ5BSEoShYrYzjD0sXrWo2YfEHziskZNm+cMoirKQ/c7g9sESJ3HfOxsXgh5XGyGE6BdCWqaJaZo4jk2KKkgTmsB17Wye6LS7PXRNp1oqYJkmrVabza1NqtUKjuNSKLi0Wi2iKAKh4fsBSRwhhMD3fXw/oFQs4RYcOu0O7U6HYlEVsaVxSpQoBSlzHIAoyuB3BVEcq8jM8D3dNX0EhqahawLHdpTSSlIs82CchDRNSeJkBFZWhemzRWbnAiNALxSwiwWC1XWCbg/ddZUDD5lRpepNUikJwoh2p0sQxmO8WQFDr23gedy//SnJ5iInJmOO1FWtBKkcoauVEj66tczdlS7funCcXz4/T8HQUbUGMYau8dxMjeO1Ih882eBPPnvC1dUmLgm/fmmB6aILWTfIMRvWRcoTrczNjQ5FU6dWcDFNo9+Tn1OcqsjObr6I/n3M9jdNA03TKdqSkqERxzGtOMFLUq493WChXubedpdOEDNXUfUn00WHlY7H9+4ucW6ySnHe3KX893uSr06XuLfxhK21VYqlMpZtM2krxRiFIXfv3CZINbY7/s6hszBVxOs1aRs2R+fmKFcqI2mJWr3O7NwCd1ce8GRlk/q5BdIkJogSTNPCtAZtYaVSifn5eVzX5fyFi8wvDAdo2XfpK5fLvPTKq3xy3WBh4ThB4LP++B6u7HC0ZtPtedy6d53vbKzgxxqmZVOanObnf/GXVQfIHhHJsbLPOMY7IKM773+uwyvfvVK/4/bMx6VrGu6u+p3DHWf8d/Yel/L27czpiYjCCKElWS3A4VJXh6n2/6/2+OgX9tj/nwH/7FBnHxKliNMRtL3c0hugkg0u3jCUAaAbA4apNEkOVFECgakb6Jo+hhHw8A9ICIGWkatEcYRtWUMKemfB4ED6+WohCMKYkiP7E9bzPBU2smwajW1SAUGSIIMAS89D3KIf/o3ihCCOKbnujnM8ezRjWPLiKqX89f5k2ssA2akBpYQ4Ur3naqw7xzP+5YqimFKp1K+6z+sPEFl9BEqTaZqG61rohsZ2s8dm1GRqsk61WsHzfba2GjiOR61ao16v02y26HS7dLtdLMvEMk08z6dUKuE4Lu1Ol0azRblUJAgTWp6K6ASxhHRHK48QWZRbYJj6SDdCkg4q0gxdQ+gmBV2F+nRdV8yMMsVx9k8VpVISBCGmtTvtwC5FpySJU3RDQ9MN3Pk5gs1NEBB3umhZK6NpKq8/ihM6fkCj6431pqSmI7LrTpKE+7dvsXz7OueMLkfKSgGjCVXoN5zr90K++/EDXDR+5cI8bhaKVCBKinZV1zQ6ocdKq4vnh9Qdk+mCy89dOIUQ6vbpus6lms2DzU1WixO8v/WEo8UepoCCrSCvbdsgTRPCMAKhIju6pmdpHjXH0jRD7ZMyS+EohS/SFMfUiJCsdXy6QcSPH2/ww6UGqdBJMfg3nywhZMLPnznCj+8vEUQJpybLVN1nS/MdqZYwH62w/OQxM8cWFHFOJpquYxkaScdnectjq+VTK1poWcHh33n9OP/nn98i0F38Xq9vlOdimiZ2oUggDR6tbnN8bgqkxHUtgt4G7XabWm3AWPj2O1/de6AHrBfHT5ygVq/jui6mafJgepq7N65xc+kpZtxDkxF10cU3TLab2zxtbdPrfS0jG3qGMP0+u4ZRhJl52s/Wurx7PTzsmpSL2nd0rYuThDiJSbP0mNAEtvPs/A77pRZ2bN01Xi2r2dB1nSBQjJuFgjukh/aWLw3CH4CxI7cdxwp6VfSV9MBD7BdhpXkxmCRKJekBRo8QCuf+ixBVLGOq3GySYGQFZAOFNz4cLmVKwbZZbXYpuTa2aSJlSrPV4uiRWXLvJxQaEQntXoguBOWCTTEDuoiTlI1WBzSNwo4Faf/34vDRjSRJMbKUi8xTDMZOvIHdx4qiuB8JGf+Sjj+/bdv0ej38DA+7Ui2TovJ9SX5d2mAO2KbBbK3EerPNysYWc9OT6txCKdz1jQ0m6lUqlQqGYdBqtYhj9ZzKlQqWadFqNWm22mi2w+p2s3+PhNAUII+uD+ag0DA0dV7bNLBsA8dU7YWNZoduGPajDDO1MrZlsbW1qZgMNZAyzcKE+0/SMAwzuufBIpeH9vu4EztE13VV42CraIs9OUkaRvSeriAmathV5TXGseK20HWBY5l4fshOGlmRxP0/u50O3/vzP8N7dI368RKt2TkmghjL1DENvb9fkkg+urXK3VWP//LSCZx+jQIkeRRICKIk5erSJj9aXCWKY2qOxa+emePiHZ8sAAAgAElEQVRoudA3RBzH5lStyPPtLXA1lnoVPt3sMOHYmHrcT7WZppGhQ6rWyVRPh9I1KUEQZpTNqr4iCIJ+YV/JstEQ3NvuMrvWwjdc/Oo0sV3BFJIIDdtv8h9XI2xpYGlS1R49Y+T4SaNDT7c4NTGBsSNaU61WOTJ/nLV7HZrtDf7gh4/4tdeOUSxbSODuUoMUDTNscuezm0zOzFLdUWNUqtSYmj/NtXufsNr8mPnJEnEqcU2N5toytVrlUAM+zGVVypX+TidPnaZcrnDr5g0e3P4U1xRUSgW+evYYH9y4x911j9XVVU6XDuY6UOc+eIzr2y2iSkKloFJohzUADlfYd5hjje7T7vZodXoDIDYhsMxnLxj9fI7a6JdyavIoiknS9FBER18a5a8Jsau3P00TNE3fs8XMti16PQ/XsdF0nTTzEhEDmye/RbrQ0YVGIpMxHv94OYwHnUcAPL+HXhjAyo7Ls+e8BGmSUijY1OKYlUaHE9N1ulkI2zAsNjY2sEyDyQw2NkkS2l5As+vT6PqUXIsgikmkZGGqfuBLMHodh5hpYnBtQD9HnKZ5Znv/c0VRiOu6e+Zix1u6gomJGr2eR5LEzMxM9qM5aSrRUVjuA6Q3AUIgTJ3pySqbW00er66jS0BTOXbbtmm1OmiaQalUQtdVTYHjWGiaztZ2A9/vkWo6ve6gz10TGqWCS7VUwLGtrN1S23MeplISS0m1VGBju4ltqQKrVrenyIAMg27XU5XW+7T15crP9/xdaIdJBoI1bgx5EammC7ye318INMvEmJ4i1hVCoxCCRrvDVqszWO3HabOhP7fXVrBEhOeW+NGDDp8t3+SF+SpvXDzGxVOTGJmRkkrJ+naTIEz5pfPHBok0OVgYhRDcXW9wY6WJaxu8vVBnqlTgrfMn+vcxj2zousZXj0+wcvcpy8YE13s9xHKTN2crHC07/YLb3AhwXZdeTz1DhY2h9edgHMeEYdhvlbq/1eHeVptOqnO3o7HRtLGOnUDPwuQhqiYjieskMiUtTyKbq9ze6lJ1TCYLzsg6NfwcRp5ZmvKDB2skJ9/g+NnzuDsidEeOHuW5Fy+ztLREQ79AJ2zyL/7iDjNVh1CzaTZa+O4kie5SLJXHtiBOTEzw6lvvsDg9y9Ljx/z4wQNE7FGsTmb1Abvf9ziO+2uroRsHOdxD8yKbK9mPyakp3v7q1zh1+jRXf/pjHmyvEt5YZGaiyt2NiMDfXccw9rBAftD9jJAkSdjcbiHjhGqlhLln6/ezy8Fr/e6RqYJjo4+vAiC+MICj/c2xvdZQ01S1aEbB3ff78CVS/uYOhRwmCej7ISGpgohisUCcJESB8pbQJUiRBd0lAk0p1WzxE/LwXv+zzKuC6+D7Aa7rjITHR44hJTJTJAKBbhjoker3bzZbHDt2FCEUqt7U1ES/nkHXdWqlAqWCTdcPaLZ7CE0/lOJ/1utQ+6vVwLLMzAtN6L+iB1TbhlG0K1y913jGTeBhch8pJbqQBH6ABMU9PsaqFUIwOVGl2ezS8iOqumBldZ2pyQlV/d/rAmm/DiAIAra3twnCEHQT3/NBMyCNqRQLTE9UMgNxPITwrusBXFNHSEmpUKDT62GXSoRhyNzRo5imSbPZwjQNTNPcdQ/zMDVAp9OlUCyMRE1ykJ8c0GinDBSsRqHgqkJGP8BxbPRSAdcykUg6noefFVQO1oW9n5Oha9y6cZ3XTs3yzb//DmvbTf78x9f4/mf3ubJ4g5cXSnzzldOcPTGRjX904cujXMNpiqurW3y2tsWxgsXpiQqvnZ7Hzq5JIzOk4hgQGBLemHJo9yR3qfPTXodgucXXhODkZFlxfUQRcdZqlTsMpVKxf129Xg8ppTKGNI1PVxu892STu7GDWLjImdl5fuVv/SrT09P823/zf/fH/hu/+V/Tabe5efMGn964Qccp8+7SZ5TtNpcNHTd7N1MJQZqiodoItcxABbj6dJ2lwjSXX38LxxlPWjU3N8ff/NVf44Mr77OysoJfPII7P49lWcSbm7S2tpienuHl19+itIcXXSgUeP6FF7n03PN0Ox1cQyOOAl544QWWllfpxaOdHB9+cIVPb97k/PkLvP3Vrz5LsnPkB6i1aX5hgaNTk3z88Ucs3r7Bp7e3mT5+kTNnzx76yP2o7oH7SZo9DytL/3wxqn//NWl4fMNScGy8IMTzgy9oFPufDwbj22t5TdI0gwE+WL4Uyl/AjsVQkkZRBkIxHhBhuLAjVwa+76PLQQ9wkqQkMiHVU6SlSEHsPWBhf6bxZ+FhzTTxoxjHHF+gl6QpcaJY6FIpieKIQlao5jgOhmGxtrbWX6h2iqHpVAsFqoXCF34Nw6IJjSSO0UyDNEM9M009a5VKd/Wn56JQxSKKmfI6SA4VVdFUTsv3A3q9ANux0J3dOT8hBLVaCaPjs9XxqDs2m1vb2JalajJsB03T+8VhpmnQ8z16ORpZGjNRrTBVKz+DNzGwzi3LYrPZplotKk+z16VYVChfjcY2caIQ+sZ1h+S57iRVGAY7PZo4Z+zbJ2Ll+wFr2038MKJccCm7NmCT9AISYLvn0ep6I/38+/kWUkqu/PhHhM1V3nz7DJO1EnNHp3jludPcXlziT37wIX/50U2uPLrO189P8I2XT7LeCncdI03TEXCuXzw7x7Yf8N79Je5vtHjn3G5DTgUAJKVSiecLLt+/skhl6ix+och3797kvXtPmSkXqbgmLx+p8Atn5kiSJDO+lccTxxG+H2BZZh+SWkpJK4zYcicI69OUbJdarUan0+b2rU8BcF1XUWgD5UqFt97+Cm+9/RWV/vhPf8x3rv2Q79+7BUCiW8S2SyihEvX426eneHFuCgAvSfjBahf70s9x9sKlPUmrDNNk9sgRvvmLv0SxVNwV/v7d3/nXnLtw/lAtxZqmUa4oHoiZ4jSW5ai1MU5HIhSddhs/u8a8LZU9apR2ypgqJkABA/0X3/p7RNHf5s6dexTrkweMdnet0GFD70mS0O72MHUNdyjH/kW0AD6Lo6RpGq5t49qKQE1KiOXe6PtfhOw3vjyqPB7sa7d8KZS/RC18eTg+lWm20Kkqa5HF8sdduJSSOEkI/IBqtbLr8ziOFbtfFB8qD/KziG3oeJ5PqueFR3LESNkpGoKuH6DFISdOnMAPFL79kSMzu5T/z1rE9yySKynLMkmSWLX+YfQX83Ghf5kVqe21SH3e8WtCo1x0SKSk0e4SZbUVljV+6haLNo5lsbLdwhH0CYPy3ttOp4PneVSrZVUrst3AS+QzK/6dNR2maRCnKc1mF8fQ8GOFVOj7Hp1uj1hK3KI5Nl+fS6fdVex+OyTNDK5hOt5d40F5zaahUy8X+4t93GmTRCG9MNoF5LPflYa+x91bn3ChblIvOdiOpeZ0Ijl/ao4zJ45y72+8yre/8x7/4epN/uqTZba2m/z8uVP9Qr8wjEbaVCXwsNFlq+Xx/HSdr5xb2HXeJEn6LZ9SpuiazpmqTUuGbPoJUWWGnqazEnR40O7xyfIW6+0e37p0PEupiH74v1AYZXf801uPuerpuOdf4ERthqdPnnD3zm0++/gK58yQ6pETvPz2N7lw8dLYeRBrBt6Rs0SRTywFseniFArUy0Waizf448/u8qe3l6i5Fg0vpHP8eV594aVduf6douk6xeJuxQ9wdG6OY8fm950348QPAuI4ZGpqkmRtnUanxx/+wbdpNpuUy2UKhQJLT59w5/ZnnL9wMfvWIQz2fbYIISi4BaampvAOZPfbeaRnWRwEXc+n6/nouka54FKrlg7p2I1v23tWyY/h2hZF18YLfjbv/4sYU05XfljsmS+F8k/imCiM0R2lVLyej21bbHU9Wl11U21Lp2RbFG0L21YAJjnoj9fzKJdVSGznS5svgqZuDBX67W395YQ64x/EzkqCUckBHjzPVyHXIWMjjlUhhmkZWZ0A1MtF/E6bel1hu6+urDExPTUUJh2cby/DZ29lNXyN+/p4uz5TSj9BSjNnZM0AYsZxGgy+H8dxn/985z6HG+fotuFCdNvUsQ3VVmaOFNXsKFYTAt2E+ckKW9sNHNelVCqTJCm+3+r36W9sbjE5WWfSmGJrc4tqcVRR7P0yyuw8jJy34wcUXYcgCOl0exxfWEDKhEazSRAnlAou9pgcZR7yj6JYIXbtTH9FUT8FsJ9h4jo2J+dm+0a0l4XCjWqZtuf14YYPKzeuXcUImrz53IuUi67C3JDZNUswhODCyXn++9/8FouPvsL//u/+hBtrHv/q+mOaXsg/fu0ME85w+kfS9kP+8u4ST7aa/PpzC5yeHFSiK44K2Ue8tCxT1cHoOscrLvd7PgtHjjFtSmS4znNTFY6VLP7i0wd89+4qrxypcvHoLHGisBiG00dCCK48WmfRk7SKM3T8lHnT5MXLl6nXqrSeLGI8/gTtQYufvFvl2LF5AJaWnnL23Hl0TadQLPLr3/p7LD19yscffcDG+gYzk5M4rsvK8jLmkbNsmlVSw6QTtUna2wRWlXMXLh5Y4Lnfc/3mz//CUEyaPV/jJEnodbuAilj04pTt7QbT0zOKH2Fov0ajgSliun6L+5+ZVCpVjhw92v/88aNHnDx1at8x7xaJag/dJ0V2kHP/jMWUoKK7jXYXP4yYm5rYGxNlSMQIDOe4E+6nH/Kwe5Zq0wSObanaoCTJ4L6eVQZO4v5GwF7jkv2fUsp+B9xB8qVQ/rph0Gy22G6k2U1MafohKYLZehnL1FVuJYhodH2SVFIwQWReIJaN7AVUCjsDLjILyQswhjHh9wv77Df7DhPKFuiWRRSpvGVepJOmWb5fmNnfKVtb25TLJSqVKs1mQ1Uvj0ze/c/3+ca6cwLt3k/XFX50oVDMPLgQwzQwjXGMfsoI8zx/H0KUZxnnYHx5OByg4ykyltIuyt4xvogQNJoNSsUClUo1g/xtEYaRohIulYnilPX1LSYn6kxNTrC2vsHU5EQf1W/vWzuangrDiK1Orx/OjwOfycyAW1peoe2HaJqObVsYhr7LYMuvMY7jXZ4qQBIPWib3HNFQbUBuPCiimhDNsvA6PfZ0xHZMByklidfl3o2PefX4BPWyi2Pbfcx4xKDVVNMFpmZw9sRR/off+of89v/yO9x43OCPl0Ou/NEH/MKZaf7hy2eo2RYCwXfvrfLB0016nsf11SbzU9u8cHSif+4cjEl5/4psJwwjpoo2ohFAKnGTAIhZbiu+hJePH+GPP1vi//roAf+NW2DSELiuM3JfPl3Z5EdL2yxXFqAyyXPPv8DzL7ygamnKJX60eAuhafjHLvHGO1+lVC7zR3/4h6ytrfYx8wEWjh9n7tgx6vU6W1ubCKFx5OjRQUg9M2Def+9H3Lh6lanpmUOgY+7/TIbhv3vdLov37zE9M8vMrAIhiqKIrY0Nbt36lEcPH/L2O+9QKpfptNsUDI1poDYxSSzpF4vaBLhxD0OGdJ/2uBL0OHnhRc6cO0+r1eL2Z7dGlP+jhw/pdjp8/NGH9Ho93njrLYWT4fmcOXuWhYV5VXRqmhj70a0fdBt+Bs83jhOaXY+p2v6gRZ9vIKNr0vA+QoBtmpRdh/VGi9X1BkldFf8eXkbXg3Hn3X/c+ftPxsZ6OPlSKH9NCI5mlme73aLneRD6kEragddfbCRgC4FmqMr9SMZEcUwUp5SnRgtqklQRvqRkOfk0Xww+h3nZl4O/K4TANnT8OEamiUJ8y5RJjuyXpime7xMnCbP1CfzAo9lsceTIzBgP74sY77N9P6+WHhQtiqG2s9Hx5KyCwKGs7oNlyEtJVaV/zwsJ/ABTE9iWse8tkUCn2cI0lUeTpokiQUHDsiw8X82nWrWM53lsNxrMTE8xMzPJxsYWU1OTh6oiDqOY7Xa3b3PbpknQ6+K6DiW3wMbGBn6c9I2XvTx3ZUCEWPZuBZ9kiHT7GyNDdy57T4IMtS8MQ9YbbcL9CoCGncpsrFeuXMGK27zx3AVcywJtUECrcIwFIuNakGlKEMe8+/511toB/+S/+28JgpDvfuc7/MnjRX66/CF//8I856bL3FzfZnp+FrtY5F6zw7+4cosXKg7fuHiSs1PVfsvu8MUahq546NOUztY6R2KP105MM11SrIuODlvtDu9v+PxP37nKC5NFfuONs0xlJFF+knB1tcF29QiRWyVNUq5du0oYhpw8dYrltQ3chXN0y3Ve/9rX+yHwd772Nb797/9f3vvRjwD64dS3v/IOp8+cYS6LDuiazk7j6c2vfJVXX38TbWdb5+HT2mPlL7/3XVZXVnj9zTcpFAqsrixz88YNNjY2sWyL1954g1OnzyCl5L0f/4hTp09TLhVJkpRWY1uxWoqYQuLxzVfP8PKFEzxZ3eKDzx5z/6Mf8vjhQ1bX1kiShEcPH7Jw/DgCwerKMouLi3Q6HdI05cb161k6VVFkT01NsrG8RKvV5OyZM89+vTtl72DgnhInCc12B12Der9T5mdZO4dlf0dK17XMWBUUXYdyYXxx517f33uczzZ2Tcsh70W/s0zuhO0eki+F8o+iiLW1NSq1KqVSmVKpPBZ8pC8CBIJUpgSBz9ZWk16ni1OvDnKdadrHW5ZS/ZdjAoyX/UIquWUl2Bs6cmh4QmDbFkEQgtjdl61CdD2mpyZJkpiN9U2mpibGeAk7YR4PHuOOkRxy29DY0gTP95mdmSWKVN7VNO19saPDIOq3sB2u7fBwb3fe6tfu9QjCkHKpMICV3UN6zTZCCiYmJoiiiEazScfzB56vEHhxl3IYUK6UKRSLtNsdHNehXquxtbVNtVIZcHHvGFsQRXh+SBDFtHs+ugaxFJQcxa0wNTmF53n0PI8wUfj09VIRe8R7H55TilzH0nbn6eIo6b/Mz7IQ5PPcdizqtTKbjVYf138vyZV74vd4cvczXj85S8l1sBwLoQnSJEUILePNkP0FJk1T1tYb/OWVzzh68jwXLlxASsmpU6f46IMPeP+9H/I7dzYwrz/EqpT41V96na++9hxbzTbf/8k13v/wBnc/uM2LVZtvPn+OoyWnT7iUGwO2rmE5DiKCphcQOxrzpZryNDWdf/BGkbcaXb578x431rb5H797jd+4fIa5msvN1Sb37GkmL77Kq+efY3Nzi6Wlp9iOzczsLPV6nU7nEjc/uU7P8wl8n0KxSH1iAiEEntdDpglJFOMJuPL+T0jTlLPnzo0A9vTvo1AFqmPzrod4hFJKet0uxWJp1/6zR46wvbXF/bt3ebC4SKuxTeh72G6Rl195hbPnzmeYHJKXLl/mz//sz4h6HVaXlvDaDaLGGoW4iy5jbNOgVi5SdB2mamWu33nCe7c+xcTBSHze/avvcfLMWaampnmwuEiz0cjqfaDdbmcdFKrl8eHDh9y6fo3LL73I+fMXDnW921tbhFFEoVDAcZzRdOLn0IOGrpA+R7lSDvfO7L0mHSSi/9M0dAoZnsR+tTmj583X9oP2H79e7oQAFhlTap7yE9r+68aXQvlruobrOrSazX76QggVfjbNHGAmIZWpQvHL9nFdl3KljGXarK+v43kejuP0CSB0oUL9iSH6QCCWZe1hAOwfUsnHtB/m8si3sghFrAYPWd4+lYpgRAJmNm7XdcYuFrvPdfAYf1ZptzoUC0UMw2Bjs4FhW1i2PVap55jpcHA7XH+ke072MeF7BO2eRxAqmGFjpwEy9E5IKfG7HkmaMjU5BULSbDZpez7xUO+tQLWw6YZBo9Gk4BawLAvbckj0mHq9RqPR7Btww4o3lSlBGNHqdAnjBCl0XMtEE4Iw8JmeVsxm29vb9KKYVApMy8S2FXXqIOSfHS9NieN4LEFVHO/kLz9Y8iiMEIIoinAzL6TT9UkSf3+DWh2ADz/4ACtp8+ql81imrvABgDRVz05kJD5SSmQqaXsBf3nlUza9lF/7tV/sz2PbtnnrK29z8blL/OV3v8vV937AS7PTnDs1z7EjU8xO15mdqvPaC+d59/2rfP/aZ9x77zqvTJb5+gtnqVjmgIMglUghEHaBxLDRDLAMnSSOkQbUCy4Fy6Kin+bsWos/uL/B729IjnZ9OlQ4/vbXOff8S1RrdY7NL3Dh4kUs28Y0TQLf5+HiXdYWP8UyBGEQ8M7P/Q1831dKPwx46803kVLy6NEjthoN3v/pe/i+z/Mvvoht28/uX+4XuZKSO7dvc+7CBQoF1TXz+NEjlpeesrW5RZwkbGxsIGSCmXi4IsUxi7iuO7KGTE5NkyQJNz78CdJvY+spzx+tcvncee48WiFOErpeQKngMDNR5aXzgq1mm88er4FMmXM9Gvc/ZmPRIuj6yBhMLW/HhESqe3X//j0eP3qIY1kczyIF+0m73ebhg0WWH94j6LQQukF1+ggXn3+Jqenpz33jDF2jUirsyZi333EOp4D3/75lGtQrJbqeTy/wKdjOob633+cDGb9NCPrQ1UkGAGbaFkZG5X5QndCXQvnrmlL+SZLS6PZwbRvbUheQpqoYwjB1hFDDVYuRpNvtEoYhkxm0a7vdxrZt1S0gFMmFJgSKNVBDkwxRIY6rWB/3QEblsGuxEApGOIrjDJZVbU/ihCAIKA7l02u1qf539jrXXmMbv/1ZlqO8uE4ZJT3PZ3ZmBs9TbWE569fYb0oVtdnJfvfsYxg3ptyLMjFNnTAcwGj2JYuYSSShFxAGIZOTqsVoa2t7RPHblknRcbAtIwvrq7kQBD5BIPD9gGKpgOsUSCsp7U6HJHFGwIqiSMHJJmmatbApjH8D1d/vOgU2NtaU4acbpKmk4Fhj0wh5WC5JUhzTzp6BepZxnBBFUb9F7VnSQEmS9gF9giim0/MIonBI8Y//vpSSOPB5unibyycmqRRdTMsCkXn9Wv+x9OsUojjh8dI6Vz65y8zCKU6dOslgPilM+HK5DJrBzPQEP/faJU4vzGa45BpHpurUykWOTNW4/8J5vvfuB/zZwyc87N7g7fkZXjxxBBknbHkRnihQqNYpxG1cO+hfg4oSpIg0Ybro8rXTJR51Q26XJ6m9/Aqnp2c5euI0lWoVXdexbDuDnFVimCaVSo0InajXo5i1wK2vrRKFIa+9+ipnzpxB0wSVSoWVlRWeLi3xybWrNBrbTE/PUCwVmZs7huO6h5v2+3wupWR7e4tbN2/w6utv8NEHV1hd/Ay/tUEgTeJQItIYm5DT00UmKy631z0ajcbg8EJxOFy4eJG71z9gvqDx9TdeYLpapFpwqBSVp21bOY6IRsGxKbkWx6bKfO3VS5QKDr0gotHy+P9+fB0jTRFyEKQGQYpO6vmEwkIrVnjw4CG1Wn33NWWX/O73/zOtRoO4vU5ZBByfKJGkEY+W7nAzTXj1zXfGYBkotBZxwI3Lr1kp/50PYWd4ffxxDltxP84B1DQNyzQIYh0/iMYof7nj93HvYH7c3Z+nWRtfmtFP5++gRPYhfhGDuo7DOA1fCuUPIiOBEaSaTrHoYptGpuTVgt8PiWch/yRN6HS6SJnSbLaoVqs0Gk38KMTKQkh5nYBAoAutHwaJshzoTqW2+8YfpjyVPffRNEXskhsAioREEEWKQjYI/BHGwv3OOT70v5dn+CxKd7Bvu92mWChgmibbjW0cx8YyzbE0pjnJTI7Gtv8Y9prse71sg40FxwYpaXV7hFFMt+djW2Yfa0AiCXoeYRBRq9UwTYPNzS1aXY9oyOPXdR3XsSlkFehSSirlcj90Hccx7XaHyI4plYoINDqdNgCO4xBEMWkqFZqkptHueX3ceM0yqVTK9HpdoiihUiljhhFBGFFy7SxiMXpdedREH4Eplf12HcUtP65YbK+FSxUfxnGcYQJooAm8INjR4jf++2macPPaxxTSNq8/9zKGrqHputo7u19q3cx+l5JGu8OPP77FhpfyK6++PtLLng/74cOHNNaXeeXCCV6+eIrqjlZGxzQ5tXCEudlJJifr3Pxskfd+8hFr95b5ZH2b8zOT3AsMjIU5RBhTFQklQ40hZ/UMQ9VjbVkWRdPg9bk6TzohE3PHOX3mLLbj7LkYWpbF/PETIL7B2vIT5o6fAmBzYwOk5Ny5s33cimPH5piYqGOaBnfu3uP+ndtsrq8BgoczMxybnx9qm3t2kVLiex5rK0tsLj2kUChy+8Z1pkSTt1+cZ3Fpm/srDaI45Ei9wEunZ6mWCqw2Fllf2c2cfvG551lbforuPeHkkUnK5SKR7zM7UVXzyxjtmtF1jVcunuLiybn+A/SDkDDwefejW4RRzNsvnWMyK6rbanW593STpc0Wsutz79NrTE1N9YsRcxFAHEU8fvSIbqtBmR4vX5zj8rkFJJLa4ipXnyzy6Scl3nj7nR1XcRjgX7XCpKkcYpceXnN2/tz5efbpoR278ds1IbB0Ay8Ox3wq9vh99Lh510uada2o7bkTrPSfJrSRMeS681mdrS+J8ld48B0/pOhY2KbR78nfC9EsiRVYTqFQYHNzi3p9AtMwSOIEzbL6HiEo8gMp1CTSDCODBlWVy7vhUg9+SIf/XKU0iOkXMuXpC13XaTabuM5OC1H088BJmtcpKOhjNdZnGd/hJEeQ8/2A2dlZPK9HkqTYtr0npK2UkjhSikax7O03lr0n+0GiazpFVy3e7Z6P56nWT8s0kKkCFQr8gHK5gus6bDcaNLu9EcUPan51PR9NE7gZyUve+58rYsX055GmKZVqRRVmZvcilip64GaefCIl7ThG1wTVisJPb7VaquU0Y/cq2AOAmZ33Ls2iB/YQLW9e/Kfr+li66r0k/25ujBgYfba7NElHWiaHvkU//ZAkbK+vc//WdS4frTFVVVDIubPUr/PMC/8keH7I3UerXL23xJETZzgzXOg1JFfefx8z7vKVyy8xNzs5MBRVIQ4gM7AUi1eeO83po9NUq2X+j9/9I5a3VljTK5TOXsSZPEL7zg0mCfr49rquKxyKRGH652tFxTIQoer1PwhtUtM0iqUSZ89fYHJ6RkRlzh0AACAASURBVEUqgGajgevYOM6gA8O2bWzb5syZM5RKpaymB7a3m6yvLLO2skIURv16gIOf36jzkKYpm5sbhM0NXOGz+MlPCNtbzC4UefnCcY5N1Th7rInvh9SrRY7PTSFTyfHpCh+tbLC5ucHk5FT/6HEcEUQJ7UCxvoGqQZFJuuvdsy2TcyfmmM3bLzNjz7EtXn3uFD+5foeia/P82QWOzagOjWa7x9xUjSermzxea7DebVPYA4AsSRImJycJvB5p5GOaJqWiQ7moImvNjsftxTs0Lj5HrVY74L7tvo8ylSRxrPgmDrUufv61cy+nRQjF+yEPAfHbV/JpmtnUOTulKgiXDLx3BXYGIuPHOCicf1j5Uih/KSWe7xOmKdOFYQrTvULdMstpuopYJ4sO5K1UaSpVdTqDGzVsRRmGAhFJkhR9yGIaLvxQrHKDm5+dmeFJc7g0gegvSjlNq2UqBkDf95maGmp1kim9ICKKEhKZIKXov4QqiqBj6DqmrkJMhy8IPFi63S6FLMS/udnGdezdOfbsmkFmnOopGjpxkqCJjPhEE4Mg3aEWv/33UbUfOq5rIzRBzwtI4pQ0ihFCI/QDCoUilUqFZrPBdrO9S/GDwgwPwgjLNDLlP/q5aRoYRpFer0ev10NoGuVSKXtOnupBNw0VRdI1XMsgNA2KrkOhUGC7sY1hGKp2pdNDCIFjGWNbvdKMf1vXdbTspQeVQgExgoh3WMlz/fl3c9jRaM9Cv8Hx4zjm/u1bmHGby+cuI1OJ5iivX+a010I913ys69strnxyj3ak8zfffGssvsOjR49ZvHOLN8/McOHUHMXCwNDNdb8QYqADJZSLLm88f5rfm5mFXhlx6kXOvv0NFh88IGlt0k3aPDVcdN1gxjT6VL15oZsXx9xr9LDrx6nV63u+uztF13WmpqaysUlarRZuwR37fk9MTDAxMdG/d61Wm/X1Ne7cvc+Vn76HRHJsfp5a7SD47d1GYafdxibmxeNT2K7Bpl3hzPwUpmFwamGWUwuzGZW0moe+H3JsusZHjxZZWV5mcnKqvz5ev3qVzvYGhoyy7qdUtfEmwaD4OTdsLJOzx4+MDi971wuOzcsXTlIqulSKg0r2arlAtVxgdrJKL7zDxrbGsWPHaLZa7DzQ5uYG5UoFzTBJIkHXC/CDkGqlyES1xNn5aW6v3mfx3j1eee21fe7Z+PuYylQheUr70B78s8jwWr/X8fO13nXG7aDmX5KoDp68Lk2m6sBCyBFnVWR06Hsd54uQL43y7wQhBcdGJyNzQSA0gcZ4KydN010kKULTFI1nmqBpIguPjDLs5Q9R07WsG2CQa81Ds1JK4oxSVhNaP0u0X05+cC27t+dGied5pElKsVTqt3HlWO9hnOCHER1fhTB1QUZTqhGnCX6QgFRseaahMVEuZopl+Ly7FfXo5zt/z+h3Y+X1z80dwfO6JElCobAXMY+yVqMoVvdbhVhIZIIGaFLd89ECt73k8JPY0HRKrouh6XS6Hj3Pz0LjOvV6nZ7Xo93uEI5JbZuWSbng9o2mvZ6jEIJisYiu6zSabTRNUC6XMQydTqeTeeWastKTFMcyqVUr+L6qEs8VQpImJCloyB2KXN1vRZkssSyTXPPlIFDuSIh694u+n9ehlL9OEEVstToZ5vgBNQJxzNbGOk8f3OXS0RpzUzW0PKWSpGpBkoz0m3t+wJ2HS9x8sMrs/GkuXLiw67hBEPDuuz+gqIV89ZWL1CpDudwsbZBHE0glUhfIJMXzA27ee8qZM+cQlRmeu/wKpq3Irvwo5nbLZ73rE6MxkbFZGoZ6h/w45trKNld9k+Nvv8WRo3OkScLG+rqCdLZMCoXiGKCq3TJczLqfGIbBxISqOSqVSrz//gd8/OEHbG5s8PyLLzI5OXVoQ04IoVgKhc7C7AQvXzrF2maDiWo582iVDIfrkzTFD0PiwGfp6VMKhQJSQtjr8OCzmxSFz8kjdWzT/P+5e/Mfu840v+/znv3uVbdWFlncd1KiSO1SS90tjXrU3eOxPR7DRuBfggTjIIDzW2AEAfIfJECAbECCxJ6MHY8nDmZtW+5Wt7ql1kpSFPfiziqSxdrvfs/+5of3nLtU3Vqk7gBynkaLZNW955x3Oe+zfZ/vA1GENE0QGiTKZ6fP9vYrz2z6uyhSTgvCwXEcms0mcZB0uEzkxrXrtJoNZBigy5isY2HoOkEQYpkG4yMFJgoms3dnOHr8OLncRqbLrURKSTAgopH8lt9UYe48JSD6otXp+yplci4kqT4ZS6XfDK2DZzD1/28jFuvlW6P8vUgyknWg89LJjvXTDXUoMEcaQu949Ml8aJqGDNNcyVZ5FbVIMgwBc8PCRnFMLNNwaYy5RWfB9Rtrs00Sx1HSklXDMHWarTa6YSS55Jh6y6XphZRyFqVctjPm7vdj/CCk4Xq0XJ9CxsG2us80WCEMeNqe6EZ6BtfrjSRcp7G2Vk1a0PYaTus3sVIK2jomqRiIZIwmu13cvi5ifSvpBa3FUYQfx4yOjHQa9eTzWRrVFnEY9O2JUj5DMZfF3CG9s+M4FOOYWrWKQHTId+qNBvV6Qxk9UUyxWCCKJJWKImtKcQheLNEkCZajP00TyygJ7+md+Unbz2ayG1NA6XjTCE//VPbsPdmt8W96bgfXst1h0W63uPTFZ9SWnjBy4BDNtkdpuIhAhWo1XQc9iS7FyniZm1/kyxsPiO0iL7z0ysDrPnw4y92Za7z9zG6OHpgi43QN9XQZO7nZJK0QhSEP5xf59Y0nlPee5PmXX+b2nTtc+uA9Ws0mwrSRuSJR2ML1faJIESBJJC0/ZGalxk8eVjFPv8Zz556nVq1SWV1h5uolDNMkly8wMbWH4lBZNdXSdUzTTNI//fM0MjbGk4cPWFhcpDw8jOM4W1a06LrOrl27ePHF57l6/Qa3blzDc12++9bbWNukHnpFCIEnNWYePOHFM0fZMzU++IPJ0i+v1bg0M0vdi1m9e5d7d++ClGRkm8OjOUqOye++doZ8NqO8famwR3EYKSBnz31FusEG54k2FdM0KOUyPKwHLC4t4rkuC0+f0qjX08Aljx7cRQYuOiF65JJzDOotl1qzzZ7JEUZKec4dmeIvPr/H3Owsx0+c+FrPwBZAvq3egZ2C/L6ppHn8MEoUf5q+TbZSnKR41Wd3nur7bci3QvnHUlLK2liG0ckdK8s7JAxCJKqOM8XFRXGMnek5KJO9qusGYRB0w4iJrJ9PIVQO3ff8gcopBQquu/wGGeRt9/8usUgTzxoE+XyOOIppNhpI3WS11sALQizLYGqkiLVJrlfTNJyECCaKY1qej23tvElRf8iqe/0oCmm7HnunxxXATdDTmKe/O6FSVKq0RNuE0EcAZpIuUOkATeWr1qVevomoCEmI6/vYukapWEJKWF5epljMYzs2Zr1FL8t2LuuQta0kvbPxeps9SzabJY5jarVq599CaNTqtaR5UQ7btqlUqkm4vxsOHc7nqDVaGD0lfGm6JI5iZCyxbOX1Swm+HySHwmbGyWblpV0yj7Q6QJX+7MxrjeOYeq3GlS+/gOYKf/WLVUxN5/jRvZQyTgc3IITWeQlabZebD+a5u1hn6uDpDbl+VcEQ8eknH1M2Qt584TS5vndVWZyS5Lqp1x/ENJsuH3x5C5kf543vfY9rM7e48uHPOdZ+ROC2cWOBppvsH8pwqJxPqgZ03DDi2lKFv3xYx9h3mhdefxOhCT79+CMWZu+y2w4QpsnKQszCg1tY+SHsbB4nk6U8Osbu6X2qCVOPcbhnzx7u37jBz95/n+fPnuXo0aObNudJRdNU2HtqaoovvviCazdu8dy55xkdG9t2zyvDzeWLjz9kxPQ5sHv/1ouXXK7t+ixXmmhWHk3GSDQkkBMeP3rpLFOTCeVtGBPHQiXAhOisk4hFEqnTFSGZrn1t3zKXsZmeHOazmev89V/9Ffmsw8OZKziGBsRoCHY5AjMrIYqRsUWj0eL84ip+EPL62ePsnhxh19gwWT1m7sE9Dh858jX7GcgOr8vXkfXL8ts0BtLzTqL6c6RVUb35/kjGHVA6YfS1sD6/qXx7lH8u0+8jJSFx0zQJo5ggDIk8D90wCIOw0zoXSFD8skOuoPTWZqvY9ZZ0XScIw051ACSH1wZWpMGbaqs16lh8YUij0QShOl8hJdVqBSnpdKQq5hyyOwIIqTyfbZg0fY9SnN2GUGLzCEiKjajV6h3U++qaIrjZjJI03bSaJjY1iIAOjXKlWsexLDJZpxM56YSRE+Ni52EsSRAqrgZDCHK5PKZpsrq6gmVZZDIZ1eDJ74YaNSHIZhIMSd+tktdtm/nO5/Pouk69rnoC5HI5ihSJopBsNkur3cJ1XSYmxvq2m6kLhvKZHlZE9bsoAfJ0PUgBqNId29lZM46+GUn2f9oNEBQnftfr7x9vaiikXp7bbrH8eJZje0Y5MrGPL2/c4X/6s3/PqSN7+bvfe4HpqbGu4paKLXPm/hyfXb2PyJZ57rnnkDKmA7FI5nhu7hH3b1zmnXMHOHJgT5Le6H3mLklQOmdRFDH7dJlKK6alB3i+z5XznzK6fId3z+yjqAts00LXFeUvSExTdcesuT6fPK7CoXO89P232X/gIB/98hc8vTfDiGjyB2+9zmi5xPJalccLqzxZrrKw8pSVdsjjGZ27o7t45c3f6RD7tFotJiZ38frbb/PRhx/xaPYRe/fu3bEHL4TgpZde4vLlqzydf0K+UMA0zQ1YDlVlEiiQsq5TXVvDb9Q4NVXiwJ5NPP51YtsmoyWbuFpHd/I0Imglga/PLt/mhegg+/dO9r9lCSRe03UMy0RGMUHgI1ApFAZW7/SMr/c/UmKZBqOlPFrU4tGDO4zlTV4+NMyBqTGytoNEks3YlIdLCvApBHEQcPnmfT65fIsPL17n3dfPks85HJ0a5vzcXZaXl5mcnNxw781OjCiWtL1BKPv1qmDrM2fnerf3OoPSc+m7GaNrGrZtdd6/MIyIVMIUUOeULrRkP2xnAPz/LOefWp6bDdjQNQzdJIp03IQW14wMNESnHCkMu4e+bZgKR9znaaaT1r2HbVuKk75H+aeWWOfZ6AmJ7WDie4lUvDCgnijAQqGA5wdUKmtomsauyZ293OvvqWsalqWz1oyoNluUctnOr3tDeP0/SZ+tf3P7foDr+YyPT1Cv1zv94Ac9g8IGRIpS1bGJpcSPNtLGWon3FIQRumESSIkdS8WEF0fEUmJoGrrYKSq3++y+5xEHIcVCgWw2Q7VaVaQ+o8PEcczc06XO5zO2RSmXQZck3k53LOk/dmLlpy1iG426qgIoFpXidNtUK1WGh4c27Ftd2+jFp9URIBKvPxWxY4Kk7rN3xxBFMZ7rq4hQFNHyQ9weAyi9B4DbVl0jDdPAth0qa6vcuXGFo+M5/uDtl/n+iyf56MI1/u3HX/Hf/I9/yhvPn+QPf/Aqo+UipqFTq7f4amaWJxWXA6dOURgaZq3eUmFLqQ4uXdf543/2f6C7Vc6cPEK3TKk7D92lkJBEFzzfY6nWJLSHsHSbjz/9DPvhNX58dBeFBOyqaVrSM0MmXqGk4QVcXaqzNrybV7/3FocOH2FtdZW7V79kUm/yj37/TcbHyui6xt6sw97dCSe+H7BWrXP74TyfXJ/lo1+8x5tvv0smm+NP/vk/A+A//cf/GWurq3zxy18y/2Qe0zQ7BkDv/9fvozSVY0p4+vQp1UqF0vAwhw8fIduTy65WK1y7coXZhw85dfo0808eEyG4Pl8jPn+Nf/h7bw5e/h7JZx32jA/je4u8cmYPv/5qhmasM5IxOXpgF7VWWxnccZwcZjpxGCogp1AeqWaa6gyMI0LfJ/R9NNHdm6Lv70KxPKb7UKKuFcVkDciJKm+cOMUrZ0+qR/Z9fN9XLczdNqEA07TQDINTx/bTDgJ++vFX/FnjY/7wnVfZPznKpQeLPLh3d6Dy3yKAT5oS3hqbNcip+Sbefvc93Iz4LY6TiqgEm9Z3ZiZrKQBdaOhJejuMIoIw3IJivPuz3zRK8a1Q/oPz6et3ulLcumFgOzZBFEMUEbTaCRYA0BXyXEqZlAFFCjfA4EmKo2jDwRv01EQbWndRus+wuUgpcQO1uJauU6/UyGaz5PN5Wu02jXodx7axbPtr5MLXb2SBY1pMDBV4slql7nqYmoYuBPm8QyapJBh07d4fhWHI6toa42MK4byyuspIosgGkcrEcXdeNxOdbvOkerNNMZfpWLEpDiOWMWEcd8CY20s3NB4EIY7jkM/naLfb1BpNDMtmea2G6/sdZLtp6IyWCh1PS9e1AU2ddo5FcBybttum1WpiGDq5XL6DV7N2kHqRUilpITZyIgiRlIN+DemkEOK4UxoYRRFOxubxan3T71UqFS59eZHZhw8RgC0iJrMx504cww8CRkaH+ds/eJ3TR/bzk199xpVbD/j00g1+9zvnePe7z/PVzEM+vznL2O4DPPPsGSr1xrrnkqw8eUy7toIeCf75n/+c2cfzvPXKc0oBi6QORIgE7KdC1DKIqLVcZmaXKI0cZPehQ3z21/+G3ylb7B/KQRglwNgYKeOkrlnDiyLuVVt8VIHJ117j8JGjVCsV/vxP/4T9Bcl/8nd+SDY3uMbftEzGx8oMDxXZNVbmbz6+yp//X3+ML1THUIB6rcbxEye5euUKn58/z7WrV8nn8pRHRxgZHaVcLlNMeOR7I85SSm7enKHlezi2za2Z6xiGzeTkLrK5XMcDdNsu9XqdRr3Oxc8+JiN8SlGdjG3w2pl1fAGb+B2eH+K6AQenx3npzFHOHNsPgJPpT1EEQYihGR0sh9B1dF0jCkKiwEc3TDTLwnAyHaUgZKzSMklZalrhsz68LoRgrFzkP/8HP8QyTayM3THyfN9H04SiZwaIVIoqDHx0w2B6YozjB6e58WCe//5f/XuwC2iFXRw+uhFEutVEpOXKWyvO9ddII4BbfHTL7/dEmgeJAGOzfidCdtYjjajqmsAURtKvZvtx7DSasZl8i5X/xjCKlLHqbW7bGHryM12guy5uEJLPZFkNlgnCEE3XUZVyMaqBbneR1bUUanl9iZJjmPhR2KEGFuy8/3MsY+YWKyAkcQwFE0qmRb3eoN1udXisgzBQtf5iZwC09ZLWqu8dHcb1Q8I4Iopi5hYqjJVyDOVzpKmKLTcP4DgZGu0WmiY6GIJ+40H0IJ9lpzphfW/49LlSabbbyDimVMxhdnJ3snP9tERrO0kVZ73RxLYsSqUizWaTSqXKyPAQCEGj7SXIdkAzknbK6sURWv8cdNdyZ4YHKKrcMIzIZnNks7nECNKwLYtWq71JG+P+64RhgKbpmxpPcSS3i7aSrkUcq/lPy4bSznOe60O8eROfXVNT6IaOZZrcvXUTIT1yusHh6V0ESeQsDiMO7Z/in+z9u8w8fMRf/OwTfv7pJX7+6RW8ICI7uosT+w9hZ2zaXjfCkHq77/3137BvNM9//Uf/Mf/6vU/5y19+xefXHnD26DTvvHaWPVPjSQWZigaEXkC92ebizENWQovnjh7h408+4UDzCS89uweiGNM00DRBux0kHAiKZ+HWSp0/v7uKOPYyL73yKmEY8q/+xZ+Q91Y4cuwATnb7VJppGuyfnuAfvpPhf/nX71O1RvFiOP3MM8zPP+HoseP8/X/wD/nqq0usLC3TrNdYnLmNd+UKmhAUbIeRkRHMXJaFhQUCz2dlcYFIN7h/9y6PHs0xuXs3h4+d6CiCa1eucPvWDPV6HbfVwIldRnE5cWgP77z25gbFnSz9QDF0jaFSlnKpoByDQd9FpRikqdJMKpoJmqGrczKOCYMQv9FAEyodqumGCv9rgG6qSp4tZxJMRxK6bdyGMgqdXAY7l6PdaGCEMdLQEIaGadiQ3HN8KMepQ7uZmVvGKo1x8ORznHjmDPYG/pNtJgJBEEY8WV5j10gZy9wqdL6VM7cTRTo4cijXGUWBH6Dp2qb6TQjl9ad3Tb9uGsoA2LlK/2bu/7dC+e9EYilVW8p1ZTq2phNlMqwsLpHbu5fJiTEWl1cYGiph6DqhjJGCPqR3yoZmO4NfFFNo+LHqzWxoOsYOw7KPV6qUchajpQLLS8uUSkVMU6fdVh3B0pC6aRj4XkAm882Uf+c5TaNPgZZyGR4n0YDJ4QKWsbOSpma1hhNKWq5HLrfxpQmSaIaeKPFYSqJ1WX8d0Ql1B2HI+HBJVTckG1OR2qjQ3NfdrJVKFdM0yOdzNFstavU6hWIBoSxAGq1W57PjxVxfPXnX41fy9az8xANMmA+zWYd6vUoUxRSKJSzHSZqwbK38oyjGMIwtPXy5bj43v5aiGE65LUBhCGzb4vbsRpY36D+katUq1WoFW4PxrMmJA5N4vqcO2+SaMpYIXePYob38l/v2cOGr6/z5Ly/y+cwTDu87zKGjRwnWwWLiOObOrVuE9SXeffd59u+d4J/+0d/h0eMl/vlffsBPP7vOhev3eeW5Y7zx4in2To7htV3mlyt8evUB99ciDp48x6490wSz/zsvjGfJ6QZ+5CdjUFiT1MBca7k8bARYe49w9s3v4mQcZm7cwHTXeOP0NO++ufNacSFEgrmQGH4Dzyhw9coV3vjudwHI5fO89vp3Op/3XJfHjx/x+NEjKmtrzNy5w8z1a5THxvjeW2/jxpLHc3O8cHiCYnmEuijw/bd/R2F+gFPPPMPkrl1cvvwVd2/eQNM0shmb7790cnPsxyavzcToEBOjz207RhV4Uyh/Idb14tAEhm1i2KYK44cRcRQSh0kdOrKD6xCgStSEqkVXaR3FXyJRZc1ONgNRjNts4+TzOLksbrOFrfdEYYRAt0yErnFgaoy/9/1z/PWnM7SajYENk7aah94feZ7Pw/kF9k6Mdki8vp7s/IAYBBYMw5Awwdxomtbnvad/pmelop5PjAbZrWJDdpta/dbQhwPkW6P8t7PQ4yhChtEGha3pKhdYKhV5+nSeiYlJioUC9XqDXC6LbhoKwBd1DYCUI34QiY0QAi/uqradzL2UkkqjRRjF7B4ZQiSRBV03aTbbSCnJJCx1v63St0Heq2ka7J8YYaXa4NFyldFClnzG2TJUD0pZj0yOqfaptTrU6mR2TaAlyj49eNPa/V7Fk4rRc48ICZoyFtSLoMBiqkCwS9epQonqp2lofv3cLK+sYhoG+XwB1/VoNFT/Bs3UiWRMrd7uhPsdy6RUzHW842Smdjijg6VebyCEhmXZtNou1Wodx1EARse2qVaqW34/CMJu855NnkXTBGEQwTa2WhgpzIRu6GhSU2QvgGVbPF3d/DnSKXXbbZ7Oz7O2vESWkPGczevPHSOOI9AEsR+qNU+JRwB0QXm0TCwl+w8e4sjRo0yMlGm5HpVao5MmC8OQz375C47uHuLH33+lkz7as3uM/+qP/h6XbtzjL3/+Oe/9+kvOX73FM0f2MVIe5vaTNUJnjP2nnuPUuRe4PXMTgGLWUTnRnpRRckYihKDqhjxqRmijo0xO7uKTjz5i5spFymbED996KRn3gPkeoDxabY8vrtwm0k0aMkcKZ/3oV7/i8JGjG96faq3Kvbt3WVtdZfeePTiZDIZpMjo6ytvv/IA909Nc/uAn/PiF/bx/4RbImGazQaFQQE+UwcjoKK9/5w0mJyb55Jfvs9Js8z//6U/5zvPHePXsgDK3Tbbxjs6SHsxFyhgqUkNU0N2XEqSQCFNH/W8TkSRlnwqsqoCDVh/2SBoaZmQQuW10J6OMtl7bP9lfmq7jODbjwznOHZng4sO7LD46wsT03oG3HTTcviSllHiuy+zCEtNT4zjG+m6a67+x4Q6bjXpTiZNIqAJuqj41nWdL7h0EIZquYek9FW0oHFTUe54mj6hL0Xemfr3n3Nk4vjXKv1fWGzxRkmdaz3WeHgwKiZ2l3W5TrVYoFksEQUir2VasdZZBLGOiWFlVvu+TyWQ2fXFMXSeOVR4mZQnc/FklXhixWG2yZ2wITQhcz0/6O2sEQYBh6J2OWx3rz9BxXe8bWqcwaHHTa5dLeWzbZLXeZKXRpuDYFLNOh+AmTXt0xxCj6WqcmVyWWr2FXKmSHRnCD9Wm7W3pq2s6xFHH+9d75qjTfEcI0KRSaqBYi3qeWiLxegCDURRh6aDRXeNqtYZAIfujKMJ12+imgZWxQULgRdQbzeT6BlPj5Y7SieMoCfnv8IAcIFEc0Wy1KA+X0XSB3/AQSAQS3/dIeSeqtSal4mBSkiAIOtSzm5aFxnJb0J/yrhKeCF1X7JSWWhffD7vzsIXcv3+Phfl5LAKO7S7wo5dPEEahMqgjVXon41hFVED9TBf8/NNL3F9q8v0fvsOJEyoXnXVsNKE6wTXbbW5evYwZN/m977/dx1cghEq6nTl+kFOH9nLp5n1+8uFF3v/iBsVCkfH9x/jbv/8HmNk8mqYxuWsKc/oYf3r7PEPmKqN6zN6izbO7J9CBhucxv1LjwnKbhcwo4xNTnP/sE2avfM7Lewp89+Xvbz2XPUtw/9ECP/34K1brLqHUaOpFcn6TplCERFJGNOp1TNMkm5A/AYyNjXPs+Akuf3WJG9evY2qC0WKWcrlMo17n8ue/5vCIrYw6KQilZHFxkXJ5hExiUAuhmrFkczlCYVARw3jS45fnZ/C8kC+u3gXgtbNHObp3F+VyaZMeGtuLjOJO5UkcxwhNH6waeg2BTS8GCJm8z93rdLAcnc9JdNvEbbbRUZwXMoyQJl2MFup+mq4zXCxwav8u7j66yscffcAP3v0xDx/Pc+LUqa7nvM04W60WX144z9yDB7z6xpuAoFzMMzJUTK6xs3D+1xHl6Ued6BtsPG/SyGkvBbpEGfPdyrJ+hb1ZxdXOnnNn4/hWKv/1Y44j1RVvPXiiN8fiuh75fJ5qtUo2m2FoqMTy8gpRFGILkwjl4Qopsbfj+xYam5Zc96HFleExv7LGSCFDJsmZW8JPGAAAIABJREFUR1HYUf7dMfXfz9B12htQ2Zvf6+uIJgQ5xyZjmbheQLPtMb9aRdc0ilmHrGMhiTsh1CiWnfC4ZhiUdo0TyxjX9wi9ACfbD5rSNIEQOkTKAOiNoGhoOIZGFCulrwvFpLiToLbfYwDEMqbtupSHVZ1ypVIlQpLNZBIgUcjymupkJoDpsWEMvXuwqhDxNzssU1lbrZDLZrEsk0q1RhTH5PN54jim0WwyVCqRz+VotVs4trGhDtx1XUzT2FTxp1EU3w82qbLoShCEaJrqYBcEAaZlImNYqzdZq24O8uuV6em9rD59Qjtc5cT0GKVcFiGUpR3HysgjPYvCGKkLLl66wZe3HrP/6Cl27d7dUYBSSmzbYnS4RBQGXL74BWf2jfDaOYXy7ow3VntY18HQTc6dPsT+3WN89OUMH1x9THnXXvJDw7Rcj5++9x4joyO886Pf4+ncsyw+fcLtpUVm5u8xe3eBfVmde/WQ+5GNtusoUweP4TgOD66d59y+IX7vrZew7Z1xX9yfW+BXF29zf9lHIjB0jWxcQSfG0nTcIMLTcvz1X/4F7/7oRz0ofdmp53cch1s3rvPw5lXM9irVhzV+trqI0VpmaHKM9y89xHPK/OHf/kMKxdKGunUhBMPlMq+8+iqu63L54gXiyOPD609pyzwB8IvLcyytNnjn9Wf70lkbZfOzIorCzroJ0YGp7WieBt9i+yv0hbrjSPVU8X0E6wyP5Hl0Q8fQNZaWl7n0YIaHs48ZGRsjXygwvXfvtgZ8pbLGxfNfsPx4joKp8fEvfka7/TovvvB1qYJ3Jp7nEwYBmq5j2dYGUrZeEZronw8UoVV/l9Le6IRAfOMI8c51xrdM+Q+ul1xPDtMbGXBdD8e20HSdWKpGKaura4yOjpLNZnDbigrWtCxFG5zkXwfJ07UqtmF0OrHFcYwXRvhBiBdGhHGMbRjKi068o7VGC03TGMrnkmiWWugwDAaGx3vHYZgGbhDgdLoQrjd8vnnIWhMCLWnV6VgmfhThegH1tsdao41lah2wSe8zCSEQho6QGiIIwPNot11kqYCTz/VtSMMw0Ps2aVq/TSf/r2tdNGskVd5fis1z3EEUYeqwtlolm81iWRa1WpUojrBtG6FphEFIs5Xw7QvBcKmAs66tcKr4v6nX30qun82qiJLruh0jJ0U8C6GRyWRotdpouk6r1cI0rYQOuEkcx+Tym9OUxrFi5LPsjc1/Nors9JswDAMZQ73lslat94cNO5/euHuWFp7SXFtkupxl/9QoMXG3B7xU+V3N0NMKLnw/5Fdf3mSlLXnlmTOMj3fLU0WSm/TcNl9d+IKCbPOjN9/YQDwVJ8hy4hgpBJauUy7m2T0xyr6K5MDBA/hBwOUvLzI/c5XF+RLnXniRUmmIg8dPEAYhTx7e4/bnH/GovoI+McqBY8+SHSozNzfLytwdTk/mePeNZ8luAnYbJEuVBnMLy0yUspw5vJusYzE2VOBf/M1HWGELAwmxoFyeJpcvdPaT63o8nX/CwtMFatUKlbVVhIjJWhrvvn6Cz6/dY/TgBHceL7MU5HjnR+8yNFxWynfAls/n8xw/cZJqrcalixdp6XnaofJSYymxmlU8P7cD43nz/RPFscqvp0m3nTjBX+8Wg0WiyLWkorKVcdxfFpFIEEbceTjPv/zJx8yttTl98gReKAh8n48/+pBzz7/A0tISz545Qy6f32DU+77PytISteVFXnjmBKdOHOX9X37EV198gpAxv/fuO6QAu99UoliRdMVxjGl1uRu2RuX3/y4MI4VFG/xpAAIZEyfGxVaGxeCquG5F0HoQYq98y5T/xgEqVLnsIz5Yj7BMgWi60CkWi6xVKri+r1qxen7SgEZ19rMcW9WrJ2jzXlavrGXScANqbT+9uCpTMRQlo5aQAs2v1rBMhZL1gpCJ4QJ6n3WnbTHp3cUyDYO260Ki/H872I7ezaC6QWmarvJNhk7WsfD8gGar3WcErd9cKQVtZqikSnxQPe+NpAVxevj3b/ze9em/nkhr43YQVWwlOIlcVjXaCYIAwzI74MYgimgm6H7bMinmNqZwdqJMN/WU4ohavUGppA79VrtNGIYqN96TNxRCWfVSxsowsGxarVYSilfe/GYvbtpG2DD0TsSi82QDrP7UCE4b2NRbbSqN9kDFT8/IJHDt8lfMP3lMfW2FuL6ElRvCNvUkFNzdqyIpvVONCTQ+v3iVL+8ucuq5F9k1NbXBaA7DkMWFBe5e+4pnDk3yzPGD/dMr4w4BV5LA74zN0MBvN3k6P8/4+AQ3rl4lCAKCtq9CqI6DjfJ0Heck5eEyYbvBxNQUodS4euUy7tIsJ0ctXj97hGJh51zwYRghgKFchn27yrxw6iC6rnKsrzx7mCMHpvji8m0uzNaT3g5qv7uuy9VLF5i/N0Pk1nF0QVnErHpVDMdgemKI0dJpTNOg7fmsPmlx/+5tSkPDqqnNgEiUrqsKEMt11fygkxIhQYym6TxaWKXdcsk61s6iWb1bO4maIpTRrAmxAQS75ffXSRxFKhXa7Z27uQiV04+jCE23GLRTa40WF67d4ye/vkYgbH74o+9x+sRJPvniAvcfPaVZq3Hh/Hm+8+YbOJu0Zq7Xasw9fEDOMXn+7LOMjY7we+++w/u/+jUzFz7HaDZ444e/Sz6X2+Zc6C8B7Bu3lAS+D4hOGjeNMG4nmhB4YagwZ4kTtJ0xJ4EQiRHFPTTqg55vcEQxCEPqtfrAqqxUvkXKf/COixL+6f5Nr3LWckDnPcM0yOVzuO0WlqFC72GY9DlPyoQ0TXmgfhCiJe1AAXKOjWEYhJECoqUgN13rtv6N4hgviAiiCKFrFLIOznpvJ463YGla97yGge8HChD2tbX/oDkbfA0teRF1TUPGES0Zk88XiOOw775p9CEI0rIqHSMJTwdtj1atgWGZOIVeOtSttboQohtvTHb9YJUlIYZms8VweRhQ3POaYWCYJlJAFMb4fkicpG/GhktYAyM521kam5dwVqt11f3PyVCv19E1nXwu26HhzeZyaAkjl67pnQ5daU7W81RDnY1rqp4pBToqL34w6LRXgqT/Q9q3u+X6VOrNbse+NA/bM94Orgp48uQJ83OPMKTHmKNTLmaxTCMBdMoUCq4MmeT7jVabD764RqA7HDl6nHy+pzFPIrVajWtXrpDVAr730pn+sLRQ6TqhKSNZaCqUqfSQCrMbhNSXnzL78CExcPLFl9k1vR+33WZ1bRUkDA0Pk8lkGN8zjQx8fLfNg3u3WZ67w94hnTeeP8pouTR4iTfZApomOLB7jJxjkbUtCnmVcpFS8sIzhxkq5njw6Cn27ArLy0u0mk1M06RSWWP5yRx5mpw8OsbusWE0CbVmC8NQSnxipIQE3jh3nFLxMZfnZrjQbjCx/wgHDx3uoYHuf7hsNsvv/OAHRFHMz3/2U/WcST58ele5Qw27I+n5WBiEGMn5FEehMuC2U1gbt2zPrxIPfoc8HZrQCKMQDdGJKIHi7Xj0dIXPr9zm/I1ZpDPM3/nxDzl58hiObTNz+w6PniwgkOzbv4/xiUmMDe2xJVEU06jXadQqHJwaZ3xsFMMwmJwY5603XuMz+zzXr18lzli89NLLjIyMbAGAHqxQwzBSY9C0hK58u7H3T5oQAj1p1Rsj2Vb1J1/fSLQ82DDplbRPSKPZJJvNbgn2/hYp/y0GJugD94UJqjKOor4OV6DOMce2adQbnbapYVJXb+lmYq0pvnlIgGeeh6ZpGIZB1tY79xkUzpFSYpsmQRShCXpq2LuiPDojaZm59ahNw6DddndU8z5wYgbK5kZBGIZ4rkcu4yTc9JV16NSkJlh2QWgpqQq2VI2Tkrp3xea31Wbs/k55HMkaxjEEIYQRWi5L2swSKWg1GlgZh4yTYXVtFakJDCstk5OEUUjgKWKbQi5L1rE2uf/2B9P69zfdW57rMTo6qrodeooLwjJNXNfDDwIC30+4/oWKjmQyVKoKU6FpCiyay2UG5PrV31PCFMsyt/TkVLAkTtJHqrTKCwLqrXZSly96LrvOgEj+9DyP8dFR6ivLGL7P0d1lDk9PYJkmWsKuJ+O4g/4WsQRd47Mrt7l6f4Fjz79BoTTc19Uvve7jR3M8uHOD5/bv4uTRff0PH6WGecro123CpWka5WKO6bEST9o+CMGhI0c4dfoZMpkMX108T3XxEQB2tsCufYeYnNrN8vwTFmdvEzdXOTxicmLvLiZGhzdf6k1+rmka5aE8xXymLwqtUkiqjXPGVoDG6toKc7MPyWaz+J5HFIUM5zMc3j3O9MSIiuBEcRIBkp3b7hopYRkaQ4UMN2aXmLvRoLm2woGjxxkeGd0QRbEsi337D3Dj2tXOw0sEPiaOZaEbOyXFSiSJtEVxpEjFosTI31ZxbT2HKnyvQKopXmTLr2saMohRKUF4srDKzIMnzC2ssbDaYLHaQsuUeOH5c7zw/HNkMxmCIODgvn2srFaIFlY488wzm1ArC54+ecKNa1fw3TYCOvMqhGDX5ASvvvISpmlx5+4d3HqDs2fPsWf/vi14BNLpS3WNYuXUNb0DON9+/jb+Xtf0TtkkMu4xAAacn4mfZAwEnm2j+F2PdrtNxnHI53Jbni/fIuUPgwaWhpBT3uM0jJGSnBim0SE5UQtDgiZViG/D0AkCrZMvVddMvW6l6INYlYal3m6q7AZF7tN72NrgqUs5BDKZLDIBdIhNLO0Oel7X+sawlWxe+rl1KAiU4m+3XeJYUijkaLddXNdleHh4w+fUPPRvdMMy0bQcUZJK8TyvD+S2FWZBJOHGOAqJWy1oe6AJ9HweGUe4K2vE2QxhGDIyPEy73Sbwfaxsb6miIK2H1YRQbUq3clO+gTQaDbK5DLZts7K6gmUp6lPDMHAcNQ4/8KnX6zhOhmw2S6lYpNlqE0YhvuclNLeD90fKkrYTQKIQdKolhBC0XI+m6+G6/o7HKQBT17Dx2T1sc3TPKGPloorapGeRjNH1pMZbSmqNFh+ev4GWG2LfocMKF7DuXVhZWWHmxnWywue1s8cp5BOug+RzMpZgaCrsnLwzUlNPJIRgZLjIwd1jLN9bRQCnTp+mNDTErZs3uH/lAsd3qejE3dk51ioVlhcXqT6dZUg0ObF3hEO7xxkuZIjCEP0bGM6apmFZWxleklDqGH6d+3dvs//AQer1Ol4QEdv9TarMvrBsV0ZKBYq5LCPFLDOzyzx4dJPbbpOx6QNMTO2mWBwcsTj3/PN8efEiAJ401PwNWu8ttrtEETbpmg5CKJrZHZwv20piQCjmP1UNsqVogmqjxdyDeR4+WuDxcp1HyzUiYVEeG+fc8T3snd7D3j17yGWzndTW9PRuFleWWVitJq2KBg92dXWVJ4/mmBors2f3VN/4hRBMTozz6isvkCvkufLVVT6rN6g3znHgyGEKhcLAdzAlMUtTaoa+E29/M1HPrQkBukCTkhhBEEeJy7NJpBbxtao7okg5LZ7n4zgZ8oU8Qmj/4YT9N+NIljImDGWCeBboukEcq0MUqVIDHREayJSsJO6EiqIw6nyui3xVB5FlmUQJ45QkQot7ywq/3oK7rgso2lfX85QFt02dvWVZeIk3ux0PQPdXg4Eem0mqrIMgJJdTL1m9Xieby3ZKVKDr/W5WgqglbWqjZgs3irAsm7RF8LbvRhQhW23iegthGgjL6vCEx22XdiwplEoYmkalXse0rA3NUNA00HVkEBJu2Nhf1xDYWLnRbreZ3DVJEAa4rpc0I1HrZyRtYA1fp9FsUqvV0DSB42TQNI1ISuq1esJG1yYIDKweUFAa7octaD83TJkK94dxTL3Z3rR5STqGMAj6wqOB77E4/wjaaxw4MM3kaEl5+amnGsskHJv8XRN8df0ud+YWOP3qW+SLRTRdUx0aE4PFdV0ezc3xdO4BLx2a4tmje7vTmaRu0kBeGuYH+t4nx7EZKmQhekqj0WB0dJRms8XNq1eYsAO+d/YQjm0xnrf49O4Sy/drjDmSMwcmOH5wN4VCjjiMiJJS1PWRid9URoYLaHFILvapLC0QhiH1Wg3XD6g0PZbXaowNF3Ess5Oj792nQlNlk6ahc3h6kpFijuEHT7n15DGz11dpVFaY2LOf0vAwuZxKqei6zpGjx7Adh/knT8jmcszeuUnG3th2fEtJDJMwDLBsRd4kpSJu+o2VP8pwSltTk/Sl77+9Oqvnlyo8WVrl3qMF7j5eYanapjg8yoHjz7J/3172Tu9hapfiZenMW3Ie5LIZ9u7ZzcUrN7h37y4nzpwdOAfZXJbS0DDZbIa903tI3+nej46Uy7z64vM4hsGli5f58ovPqddqHDt5gtHx8Y4BkBp0ikFTvaemZar02ddbAAY7YmqP6LpQeX+ZRLQTYJ7s++T2EM8OkDppeBYEAZZtUyyVOmfZfyDKfzPFr5SRmjRFCKEGrcKrtm0RJTXQUgK6JApCFb7uCd2nLRTTBe615IRQYB9d0wmCoAMyTCMAO114KSW1eoN8LoeU0G61Exa+zcuPekGMURR/DWsvVVqDIgHr8r9SqnB1EGLbFrbtUK1V0XSt78UDOrlltgpvyST8H8cDwX2bStIARNgW2nAJNEEYR4BAlgqIKKZYKlKtVonDECOX2WCZiwQ4FkuJ6/tAZpP774SWuTuHgEq/WBamYbGysjygPl/tJ9tWnQJrtTqVSoXxcYsw2X+lYhEhJNVqHdfzEoIfM4kyqVsaur496IquUav2+wbnu0+klDQaDR7PzTE+MUGr1SKTzfL4/l0aK0/ZNZxlYrRINiV9SsLCUirPPL14o+Xy/qdfEdsFzp49i2baCKE66aWg1oWFBR7cvU05q/Hqc8fI5bp5bAlqnQ0NwiSdkFR4iAT3IZPyxkbbw4sEw7aFH4QsLS5QXZjlh68fZvcu1Qb3lbOOInQKJXt3jTIxUsK2DIUnMHSEjInSvPZvQbGlsm9qHFsExALqays8mntIrVYjimLWak0eL66xZ3wEp2xseAdECqQUCZ5CSoZLBV44mWFseInrDxaYu3eVlYUnTO47zJ59B8knwEInk0FKyZmzZymXR3h8d6aHsGqdDBpusk/ihPoZXSf2vK4z89uYoiR1E8dxcgYknfrimGq9xaOFFVarLa7dfczM7FN8THbv2cMrzxzgyJFDHDt8mGIh33kfNg5K0YjvmdrF1MQYsw8ecOTEKWXIrFvjfL7AcLlMq1lhaXmZqcmJjROCwHFsXn31JTK5LF9dusLl81/QbjV55tw5RsfGMAyjy0Iq6OCdOlfZPsPR87nN81DpdVIgcMrkF8XdVICMJfGAlGTqPKg0YNoWWEUp4ijGsu2kzblKZVdr1f+Qwv5KUiWdhl/iOMKyrL4QcxqaThV36l2HUYRMDIUoijqehyYEuqHAWYp4x+jmshMRQnnscdJZSdGy6h0DIC1j22xtU370XC5Pq92C5HrbMewBWKaJ5wfoerfpz0423ODf9/8wDQlpmkYul8PzPHzfZ2RkeMM3fV+R0mx1W6FrGNkMWtvt+/lWz6vIYwzM8hA6Uin9xOOUcYQbhJRHRwnDgGq1SkE3OrikxInsANjS6/m+v+U9d64L1F6rNxqUR0YJI5UeGRktr1u77gVN06RYKlKpVGi7LXLZArNzc+SyGfL5AsViEUlEo9Gi0WyqMLNpJn0HdkbrHASBiihEETnHwvNMPM/vGgE9h6eUkuXlJT7/7FOmpqZ4PDfH2PgYC3MPmchEnDt6hN1j5Y5hl1KKdrA0cQy6xqXrd7j2cIGX3v5blMvlzg38MAIh8DyPe3dus/zoHm89t5/Txw7Q+zi9uTIpEma5SOEI0vsGfsjCcoV782sY2RKTE7tYXlri1sxNcrLN6R78gJNxePPF033zEvoBURRiaCa6aRH6boIo17+5AbBOEVUqDcIwwvV97s8uE//s35PJZNBliB951Jttas0WhUKmC/hNvGt1pogEHJdoYymxDJ0je3cxPlxi5uE812eXmbtxkcrKCvuPHGN0bBwnAQTalt15sK8T/pVIZBQTegGW44BUOBnLtn8D22iAltYESKGiCpEy8tqezy8+u8rPL9ykHekMl0c5ePxZThw/xplnTzNUKqkSuU5efpP7iIT9T9N45tQxHv3sVyw8mmP3/oMd1tFUGo0Gy8srEHrMPXrC6RPH+96vXkZRgOeePc1QscDnn1/g1s3rNN02z517nrHxcbSks6ydEFj1yk7nbifntZRd5S96jABI000RISrN3asLU+rgtM+KlKopmG1Z5LI5HMdJ0s4+1WqlE6ncTL5Vyj8daEqJmgL2HMfp855lEuroBaqlYugaRsYBJK7rJZvNIvAD3LarIgdCw3U9dNvERoWxFVYASBC2dtIhME4pVRNrfitPt9loJeUk0Gw0yeYyW3r9qQghVC00ftfzZqcbbnv0Z7PZUiCmjEKs12o1CoV8X8OJ3tLE7aIdQtPQLBMRBB1Ky+51Nlq+MooJWi2QEs2xkVqqxtXB6LVcbNvGsSyWlhYxhYbwPIhz0FMeLelWCUgSuts4jZZs/rw7MaJ8P+gAvVZWlslknB7yosFzbOg6xUKRldU1bCtDJuPgBwFLy0uYhkmxmKdULAKqP0HbdTteRf8cb7x+h0ZZ0DFUTcPA1HX8NJTXlw3R2L//AGvPrPDF55+D2+DK/dvUVhY48eZzZG0D3dSTXL/sYCc6njnguj4/+dV5wsww3/3ud/sOjtQLunP7NnP377B3NMfzJw92uxqm+ydV9GGc1PfLbs5fqqhcpdbg9qNl5io+I9MHabdb/OqnP0XXNXJS4iYNUQZWzCRVFWkzGtO2MSwL3/WwLMVt8Y203LqvNJou1UaDR0+XMTSLISNgNGOgE6NLk0bL5cb9x+iGzv6pseQaAjRN7VEpIekIqkjKUq8OivksL50+xIHdY3w585Drs1f5fH6Oo6fPsv/QEYIw5NKXF3n2ubPqLNguApykWyTKaww8X6WVdJ3I9dA0XSnrb6z9B39P05NsfKQ80XqjzZ/8zUccPP4M77z2Cs+ePsm+vdO4rouVACg9z9tiOKLzXwmYlsWeyUnGy0NcvXKJkdFRcqUhZE/UrNVqslapYOoas4+f4LouuZ62yYOefP/+fQwNDXHx8hWuXp9hdWmZ088+x6GjRxkdG/n608NOzpjuO650zdYslFEQ4fsunucTxTGGJjATBzhfMDENq+8MSR3lMAio1WsgBMNDm1TBJPKtUf5xHNNqu0QJUt5xnCRUBYOWUMp4EwWVeswqTxkEAfmEGKJardJstpIogoWIJU23hWH0srN1c3eGrqs2wWGI7wcK3GMamypHz/MplYr4gWpjaaaI/x2KbVm4nt/Jt2+lHHrHu5lIqQygIAjJZrNomka9XsMw9IGGUxCE63rNby4K6GRQWV5laLiEZpnEQaA2td6fNgiaTbylFXTbxrAtohQUBsgwou167J2eptlq4vk+Q6PDne6Kmza3kCo312675PuIdDbOlegrg9uYEpFSUq1WKQ8Pq/B5s8XExGjP/G8+96ZpUCzkefr0KZquUcjnyWQcWq02yyur2Ekorlwu47ptavU6nlehkM+rCEvPPPUaYCr6Q9IPXPFLtFyvq/g3kWefO8uXFy/iN9ewhMQJG/zq04tIv8X3XjvL9O4JzIQ4REuiSypNL/n44nVuzK3x/R//wcD9HQQB927for06z7k3nuHEsW5dvwr3p2slO9Mmo5QuWCCjkEazzbX781x9VEEvTtJqt/nj/+6/xbBMvvPdt3AbDn/8//ycd14/w77pSZx1BEgpz3+K+wl9D8OysW0bz/VUY5zUeFaLP3Ddt5Nqq4UQGoZhUhaSFw6O8J1zxxkbKqFp3chJ8lAIoai8ZZB0Y0s8PEPTupgHAJK6ewRjw0XeeuEkJ/at8ssLM9z49BfcnrnJo/kFLMti9uFDcnGTPRNHtsENpYo/JnB9Fa20bIgCwijAzmQGAwa3kkEaesAlRGpsRTFCQiGfZf++vfz+j9/tfiZJEXRKg1PDM003DbquUOXVuXyeN159ib/56Qdc+OJzXnzpZTLFIWQvvgJFJmToOy+XHhoq8frLLzJcKPDpJ5/z6Qc/p91q8sob31FkYju6Tq9C33iudP+eKvytr9Nh/PR8mvU6tuMwNjaCrpt914vikDAKMDBIeTrCMKTZbNBsNjF0nbHxsW2f/luh/GMZU6vVsbNZctmUsGX9ZPZP3maWk5QqzK1pGqViiSiKabselmkyPj6OlJKW59KqN/A8D8M0yGYUyjyTdVRJRk/YHUDXDXTdIIrCDjBPeTzqM3GsmtPk8gphGUdhh0t7c9m469OXI4149Ix2p1O5bi4kzVaLTCZDJmPTaDaJorgT7k/nNN1YKj+9kUhjo/6VCZe1jWe0aS8skpvejb+6hru4TGbvHuxioRMGNrIZjP3TqnlMLIniqLPRG/UGI2WldJeWlhkaKnZmhzBOPJYBgxMK09FwfXK57KaKugOq6Yxh48WiOCJIWvZWKmsqL67pPUq5f/z9L7Mgm83geR5CiE4Dp5Su1/d9FpeWyOdzFPJ5xkZHVahyZRXTNBgeGuor8/Q8v5NP1XSdMIpZrdZptN0BAMfeZ1LzOXPzBseOHeO216K8eJe2KWjUK/zio4t8efMBL589wXfOnWJ6alSVyUYxsYyo1lv85FcXEJkhXnvttQF7QHL58mUWZu9yct8Ezx4/1D8hpKkdXa2briEk6t9Chb99P+T63Sdcn6tCdgzfD/j1z/+crHDZd+AQb7/zA65fu8rVC5+x/LOLnN4/zu+/9WKHslelJ9S7qZk6RBD5AUG7jZnNYmcFbrONaejouoE0tL40RKrRdnKw750c5b/4j37ESrXO//Av/5rRoTylYh7TVmlBmSxFHMdEcYSuCeysg1zXpjv2VXmooWmItCIpMU7iBERZLhY4fWgPy/VGDAtnAAAgAElEQVRbLKwudwCiUsZoxP2A5o0LrwI5YYQf+qokzrQgjnDbHk7GUfv0ax8h60AmHZtu4/uo+CGUtVPKZ1heWe77va6psmwMI8mA9Fx4i+cSQpBxHPZM7eKl55/l0/OXuXjxAifOnGVouJwMX2Logj2TI7z+ygtks9kd23m2bXP2rGKu/PUnn3Hhyws8XV7mxz/+Mbn81mVyvQ/fHY/s+7eXgHPDMCSfz3UNnvTTnfNXfTcIUmKekNHxMUzDwg88Go0K7XYbzw+wbZvR0RFMw8T3fRrNBu12mzhWFVCGYTAyurPoxbdC+SMlpVJx3Us5OOeyFV1hKr/+7CILyysJXkABOKYmJjh97DBDQ0UK2RyFrPIW226LleVVtCRXlyo1gQJcuZ6nIgMJalvXdaI4wvN8DNPi6dNFPj7/JbV6nf179/Dai+ewzDTP31Ue6Ri6j79xdwogk3FwXVdFFwa8aBvnbt072jNPq6sVLNMkm8nSbLZoNlvksl1F2TvdURxtutm7n+vPvGu6RmlkmGoSEs5MTmAlbUsRAm91DW9pFWdsBGtYkZ/EUoVAZRzjNdsYhk4ul2dxcYGM4ygaUiFo1+uETxYxp8bRNuG9l1LSaLUZLw9tmj9Mfz7I+08V5tpqhfKweu5Ktcbuqcm+vaj+2h+2S++vqiYaSAnl8lDn5+22i+2oNs5FGVOpVFlcVEZAPp8nny/QbDZYWFzCMg3Gx8c6/RC6SP2AxWqTMAjYLvYrpWTm5g0+ef89ZLuJnskjG1X+8YtHGM853Fyq8e9uP+K99z7ksy9v8uzxfRydnsBxbE4fP8R7H57n7nydv/UP/tHA61cqFa5dvQJhkzMnjrN/TxdYlXr9QmikOILefSulxHM9bj18wrW5FUJnGN2yuXP1Elkt4s3XnuORq8CdL7/yKpcvXULEDYby2S6SXErlXSI6P9N0HWELIj/Ab7WwslmcfB6CAN/3iT0VPVHELIqTQRjahplc/54JIRgZKTEyAvmcjRQ6K8trirUx44AUyFhhkTTDxMlmu/MQKDxCFMVYjo1m2TiWTeC2u3gaTRlDYRgzt7DCR5dmeLBYIzbzHD3zPC+/+hqf/Poj5mZnqa7F/N/vf8E/mfxdyqV1REvJu68wSgF2NgtoyuDwA6X4f6Nwf/+9kskaKEIITMtkuJClvQ4LZJiGirjaijY9imNEHG9bBZWKk8lw5vQpwiDi/OWr3J+5weHjJ9Etm1ajiZCSUj7HyWNHt3zGDUNKDuPdu3fx4x/9gGs3bvLeTz/gf7tzjx/+/b/HsWNHtzAUu2eIlArv1W61FZ4qaeRjdEBLWhLNtjfor9RZbbXatFstsrk8Q0MTSCl5uvCUKEHwg6qQGRsdw/c9FheXANXrZHRkBNNUzJ+xlBvwCpvJt0L5a9p29afdgzeto9+07zXwsw8/YWFpmZHyMFnbBiRCSg7vn1bgt8RL0zSBZVpMTU0lZX5dj18Kwc079/izv/i3nDl1gr/749/peJG6ZhAQ8+/e/yX/6//5rzm4b5qx0TK/+OhT/s1fvcc//Sd/xNTkuCKckP2WXjqG/pB+10MVCDJOtlvqIUGuU1r9f5edQ6Dr4Uqq1RqgwIeKzlelO/KbcM37XrBNh8HNlU8+4QzIZBz03mvEMfbEKEY+l3h/ihMfIA5j/CBkz+7deH4b1/MYHi135sXMZogNY8PBpWsCy9Bopy8faaRn/VZev582GpZRpJSyrhsUCkUqlTUyCYXoIEKRQRmIKIoIgpBiSVVNxDKm3XJxMnYneqALnZFyGdd1qdUb1BsNSsUihUKRfL5Ao1Hn0eN5CvkcxWKhc++G2yaOtlf86XN8+MEHaO0modDJzc+wN2tQsEwc2+LM7hGOTwxx9eka7915wvu/usCvi0VC32divEy15TE0tY+zZzf2hpdS8sUX56nOP+R3zh7kbJ/Xn/yReP0yjMDoltIiJaEXsLi0xoVb8zT0Eg0vpjZ7g5PjNvvOvsaFOwuM7d3P7OxDPvzgA6y4xUjW4nuvPtOpoVfZCblBmQlNQ7dMpOfjNRvYGQdMxc2gVi3+f5l7ryhJrvPO83fDpjeV5bvae4NudDfQ8J4EQRL0oiRKwxl5zc5qtA/7tDu7ZzVHZ/ZlpVmjsdKMvEiKEimRogiCIgjCEN4Djfa+urt8pc/wdx9uRGZWdVV3NUjp8DungaqsyIgbN27cz/2//weRjPt6hAQdv5tSSRg/pX5tXj25wkC5gECw2Gzj+yGhF7OFajpWKg0xhsLvdIikYnw0DAPdBN/ziCIHO53CTKWx4r1HjyS6rmOaOoOlLJvGBjk/20TLFNi77xaEENx1z73A87Tmp5Y9jN7gJJIoCAn8gFQ6hUQj6LSJIkk6qcBYa9rj5rIi134XQYhkZrHG8KbxJXucQIXwwyBQPVYcBxlFyP6c9XUur8Xts28/uJ92p83RE2fJZrNk8kWEpjE8Nka13uS9Y8fZt3vX6rpk2UWSvUgXgmwmw8H9tzBYHuDrX/8Gf/3lv+CxT3ySw4cPXye9LHEch2ajiZSqD0h+aAjL7OmmIPCIpGRmahqAdDqtjKA4JdxxHNqtFpZpMjI62k3Nzi8sks/lyBSLNJtNAEaGR3Bcl/mFRQqFPOl0aomiV3vNyqmHleQnQvnfSPrr/1WqTa5aKhVFCkl5697dPP7oQ0yMj3b/5gcB585f4htPPMXFK1ewTYtPfPRhHrjrCJenpviDP/sqswuLrB8f5dMf+zAvvvomFy9fpdlqEYQBhw7s7RoQk5ev8kdf/hqf/fiH+eJPfwaA2bkF/u3v/B5f+Zu/5xd/7nN8+3s/IAwj2p0OlYEym9ev4633jnHs5Fnl5Ry+hS989hNMzy3w27/ze3z28Y/wgx++TL3e5OOPPsSdh29FCMEPnn+JF157E9uyGBmu0Gi0eeCeIxzct4eXX3+bbz75FI7jcvjAHn72M49j2xYdx2VocBDbtmk254kkmNa1yj3xXuH64dDVOBiAWMmplEg/Dam9LPwUxSaN7/t0Wm0GyiWiKOLq1RlKxcKyc2pktqwniJaSYWqatiSqAhACa0Mq9MT3fWq1BpqmMTQ0RKfTpt5oMHZNqVBPlt9/AqY0DB3TMAjjUGsms3K7aNu2GbJtHMehVqtRrdUZKJfI5wuk0imuXp2i0VQ5u1wuy2LT6yvzWn17DIKAkyeOo4UuYbqA4TQRnSYP7t9AMe57IIQgZQoOrauwa6jAybk6z56f4ky1zrtvT9EUFp//F4+teP7FxUUmz51i/XCJfbu3UCr1PFCJ7JX0SaWcBSCDCGlocQQt5K1Tl6iTphOC36qRImC+5jPjzKOV1jM9PcXlM8coBnWG8in+xWcewugHkoYhSUnZSg/GSNkYUYTvKoUrJd3wv9A1hGmhm/R61EuJDHxV2uuosLomFOpaN3SVtoiN61zK4odvneLAri0Udm7BTmdiopuI0HUJAl+BMfveLwGYKRukxO04qsunqXgroi7Rk1J+EgiFBaGgU1tgYKCMH4Tcdc+9vP/eu0i/iet4RPloSXQuMWhMy0QKDekpwLCVWa74kxFdR/r/vJrOWBYBW/7dKJK0Oj5DlUo8xT0DwLQsPM/rVmQklNhJ1OVGdoeu6+imyZ23H6LVdnjvrTeY2LqDQnmAuZmrdByHCxcn2bJxwxLA36r3yLUesmEYbN68kX/9r/8lf/2Nb/Hkt7/F3OUrPPqJx1eMijabTTqOSyabpVgookL3HvV6Dd9XfTtMy8YydSbWbyAMfRqNhuoV4gfImH2xXC7HYGyP6elppITBygCO4zI/vwDAxMQEnu8yNzdHqVgglbKvO2fLeQNWkp945d+ff1csf/6SLmir4cFOnjnHV7/RIZ/NYJgme3Zu4/CBvaQzaXbu2MxHP/wAr735Dv/xv/05dx6+la9+8wks0+Q3fumfce7CJSrlEhNjI+SzGcZGhtm+dROlgvLuHNfj6MnT2KbF5z/1MYRQud3BSplHH7yXbzzxPdodh9nZBd46eoyd27ZwaP8+Cvk8E+Nj3HH4AFemZvjS177F3l07GRooc3HyCn//3R/w8H138uJrb/Hlr/0d68fHOHr8JG+88z67tm9h0/oJXn3zbY6fOsOBfbs4dvoM/+EP/4xf++LPMDo6zB/8yV9SKhb5yMP3xTlpQbVapek4pGybtG2tOFee53eBfispreshWZNSsVTKptVSHQ4TTvv+cyWVHGEQELg+pmmRSqVZXFxQ4f5lwK6g3UFIkJapAITd6xE3w9HjHLhU5EGGseoY47sgeftd16PRaKLrOpVKhU6nzczMLMPDQ0vGEEYh2ioc5lEU4fnKi0w2G8/1lyj+5fm95LNUKkUqlcLzfBYWFqlWa4yPj7NhvSLLaTYbLFaryCDoG/7qO6TvefzwBz8gkhp6p44uBAE60y2X8bLEEIqtUsZYjXw6xa3jFvtGylQdl6+/fYqnL87zxF99hZmZWT7/+Z9aMvann/4BYXuRBx48yM6NY72/SfrCwQIZhL0mJDHzm9NxuDg1y5V6wMC6rUSLVWhMUypbtMIs81EW0+tg1y6yvpThlz7zeBw16dtsI4kgBpGucP9JhEFqmlK48YTLMFIheC/pwqhIW3TTirWzgWlaPcNRhhCo79TrTTIpm8V6iy0bR3nz6GkW6i08KbGBwHEUjbeuq2jDCtGi5Dc7ncZzHAwJmmlyenKKl946waXpRSQakZ1jaPMebrvrblK2TTFt03R9HM/nnvsf4NgPv8v8YhPPVaXElYECbcfhjaNnaDQdHjhyC4OVIp7vY69ieN60rGoAiJUDkGrK8QLlRScU54nomqZ6O0Sqq2oQ86kI81oG+5UvK7BMk9BOce8dt6s9+P2j+BFoumD7+nEeuOculfO/gSTdVq95XvHvlmXx05/5JE8++T1efuZpqotVPv65z5DL5RBCtUCvVWtEUUSxUMSyLBarVer1OkaMBzNNkyAIabcXcT0VVa0MlCkWy9dc1/c95ufnY1peG9fzmF9YIJNOMzo6jGFY+IHH1NQMA+VSDEhcjmXqScIbIFfjh4jlJ17594fHpYyu8fpXWucJoY8C40SIGGCGVH2UZ+fmOXHqHPWmQpfXanX27NzGH3/pa/hhyP133UYmnWbd+CilYoFN69exe9uWOG/cGwv0uP0tyyKKJKZlxAhqxVi/f89OHnv4Afbs3Mblq9NU63VOnDmn2uTKiPnFKkMDZaSEn/upT7B353ZGhof4T//9z+k4HS5MXmFsdIh7jhxmw8Q46ZTNuYuXcV2Xd947jqHrnL1wiTPnL6LrGu8eP8WD9xyhXCqppkG+T9hxMCxr1ZI41aBm9TTKjfaSxADIZjOqDn2FSIKUEhlKAi8giiRDQxVcr0Pb6VAql69dwK6Pt1hFHxlEW9J7QKVGNE0olx8Re1DXH2MSpmu32zSbLdLpDMVikU6nzdz8AkNDg13gnRCCMAqJQonQV46KhGGI03EwDAPTNK7ZUK7dWNQY+ucrAWKVysUlx+dyeTKZLPPz87Q6HRw/vIb0I5Eoimg2m0SA5TYJNQPDqzFsw76xCpae1A+rBlkivqiua+i6xpCh889u280jO9s8+e5pXn7m7/k/33+P+x9+hLvuvpuLFy9x/vRJbt88wLaNI6T72+ZKqRD+uoCwj+ktVLSvYdx6+fl3zuMaA0RtF78+x0AKdDPD3LwPRkg2avC5x+5k/YYRbOva9sZJW2A19FWt0N7KTnZEQ0c39J63H0l8z0eiOEJC38cP/BgoZSoeEF3w7Gsn+I9ffoK0bcdGW4BlGtRbbRq1BrYGumGQyqR6173BuKxUCs9xMXSNS9MLXKj61PQBjFSGrdt3sHvPPnTdwEqlmW/7VDImjuczPr6Ot4ws33zxKLsmyszM1mi0HbwgZK7Wou24LDZa/NJnH8b4MTH43VBWCCgoXnkPdI1KZaD758R5E0KgG0ZMyW52vf/rehbLLxtHZgYqZQ7u30O73eLCpStIoXF1do7vP/M8n3p85ehVIkk7bq3/PV0WzBBCAeceffQRCoU8z7z0Ov/1v/xnPv3Zz1EslmKW1Cyl2BhoNBs4jhs3FeqlvNQ+mEMC7Vab2ZlZAEzTipuUxXT1XUWuUqHFUrmbfvQDj+mZaQLPY2CghG1ZXcWvrtMb91qVfiI/8co/kTBUpAemcePe50IItm7eyKMP3sv46DBJid6VqRm+8/1n6bgejz18H1dn5njn6DEiKbn/ziNMjI7y+tvv8eWvf0s1+YkfgKbpcZtWlzDsYFs2O+I+5N/67vf55GMfRhNQbzR5+rmX2LRxHam4cUQ+lyWfzzIzN8dzL73ChckrfPKxR2h3Opw8fY4w7Hl3lXIZ2zJJp+yuVZdOpWi127TabQI/oFZv4LqeepnieucdWzehazo7tmwin8+i6xqpVIpWq4nj+2QKedLZzIoWouettZ98L0Ugkdd4xIlysyyzm/9f4vkj6bguYRCSz+cUU9pijUw+t3Lvg3wGI/AVapuljogO2IAHWIa+pqZIjuPSbLWQkaRQKJBKp2l32lSrNSoD5W7ZXXKfUVyPqFgX9SVzFwQBjqNwI9ms8jSiSHabTK08l9eGSyMZoYkY4yElYegzPT1DqVQik8lSqVQoBh6L1Rodx8UNwm6ZpBpbyOzMDP/wnSfQW1V1vkwG8+pxPn/7FjKWioYsN8aSZyWligTk0ik2myY/f2Qv+6YW+e77F3jhb77MG6+9ShBGlEyf+w/vYXy40uf1q3UgiRBCR0qloKMgVMx7qNLRl985x7xnMb5tA1cmL5DGIYoiLs+1cCOLAdPlyI51bN48FmNkls2aH8blZKxZSSTHXXO0RtxdMAKhynhF7KHKKMLp+MxXG/zx3z7N+GCBA9s3UM5nGBmssG60QimfJZvLqPV2I6W/ZDwq722aJoHvs3V8iJmay7mqxMwPcO7cec6ePYdt23zs8U+QzQmkNLEtk3yhwCMf/SRnT53ghbdfxW21aLTa1BtNBBG2Ljl+7jLHz06yd3lzpdWk68MkypdueWT/mJccewMJwohas40QBoVCHt3Q8Xy/i72AuIOp6yINQ2EA4gZXy5scXQ8AYBgGQRCwdfMmNE1jw6VLzM0tcvrcBS5cvsLps+fYtmVzfHvxO9xnmMtY8a+M61l6/6ZpcuDAfqxUiu8//yJf+8u/5Mhdd3PbbbdTKBRotVu0mk0Mw6BcLnYBjJGUXRrujG2hxftEOq16UQShIsxK0kyu6+A6rlL6MSC72WxQbzS7300VC0to6ZdLAvZbq+KHn2jl3wfyi2kkgSWEMiuJEICUnL80yT888zz52DobGRqkUMjheh5XpqY5d2mSd46eANTG/ft/+lUGBwrouka73UFGsltC9vIbb2HZJru3b6FSKuK6LuvXjfHxDz/IN554ivOXphislDh24hT1ZosvfO6TlOOSNU1o3cXm+QEzs/OcPneBySvTMSNg72H10K/JfUvuvv0g33n6Of7kK39DpVyi3mhiWSa2ZbF9/yaeeOpZXnztLTZNjPP2+yf44k9/GtNQ9Z/tdgdT18llMktKE5N5klK1q7Ss1b3+pU9EEklFgelGAa22gwBs08S0Tey4RMmyVQe8VMru0lcGXkDgKWZFwzBpNlWrXGs1EiRNwyyX0AyDQPZomQEiIfB1HRGEWOb1ec+DIKTRbBAEIZl0hkxGNVxqNVu0O23K5WJsTV83g9Z7JjIm04j7HyT3l4BFV5floT6fVrNDvljsnkNRS0fUGw0ajSbFYgHbtqkMVPB9j1q9Tsdx8cOIQKqc45uvv8a5k8cYK+YJpCA/c56NOZPBdCqJMxBFYXdsSYQiYceUgJAS09Ap5zLcvsFk+0COFycXefLce1xpS0Z2r8fQlzaXkap0AyFU8x4Rg+aSYzzH5fSlKY5PNSiMbeXq9CxebQZbc2lIk1ZkYQvJpqLBkQPbewqgf5qCuMRtlXD/Wp7X0kegasHDICCMGQsF6h2VhsrN/9m3nkUIyS986mE2jqlokGXbWKah0lAxyE4NVdXwC02sqU2uMDS0SGO0UmT7aIuF1hxzzQae5+E4Du12m+npKbbktlFtd7pkUJWhIXL5PJl8njdee43IqrJl2zZGhwZpNuscf/8oT77wNvt2brp2DoKIKOYWUEaZ6Cn81QyqvijVWsX3A2YXapimTrFQJFktS5uuCeXxR5Eq/4ui7nq89vrL7iMWXdPwpaL/3bxpI6PDQ1y+chXXdZmameXpZ19gbHSETDode9ViaQpppcicUJdJMmtJ3bwf+KTTKQ7sv4XRkRF++PKrvP/eOzidDtt37qJYLJBOp2JOGkU3X293qNabSKnSG5mRQYBuXwxd1zC7e5mg3VZA0mwuh52yqdfrdBwHy1QGhR5jnG5orCyftiXzt7Lov/Vbv3XdE/5TyO///u//1q/8yi+z9EH3biUp2UsIdm4k1Vod2zLj8gcV8sxl0myYGGfDunFF+ysl27dswjJN7jx8K9lMmkajSRCGHLxlL4f272VkqKK8axlRyOXYsG4dpWKBCKWYN21YR6UyQKPRpON0GB8d4qOPPMD2zRtIp1NUa3VGhgdZNzZMqVhQZXYxQcj46DDFYoG9O3dQLhWpVmvcdeRWUratuAk6Hfbt3sH68THGRoZZNzbCxvXrKBZyXJi8wqH9e9m9fSvjYyN0HNU/fvuWTezZuY1cNker1aLpOOQyGdJpewlrYOINRpEKOa3UlhiWeoyJ0o8iiR+GtDsutWYLx/NxXI+2oyhP3SBA09WGGIU9gFK701H55nyeIPBVv+lctmvMKSXU9wIS9wJfqCJdDy32FkQQ4rke9Y6LJgSVYm5F4yUIAmq1Gu1OB9MwyWWz2LaN57nU6w21JrLZVfuky74ayiTMpsJwIY7jIQTLgH2SMIhWZqVbQXzfp+M4FAv5bgfAmdlZ0E0c36fjegS+H3MvGFiWRSqV6ub7oiCgUa/x4osvMjd9ldriPHZ9inzY4TN7NzCQVtGjXjpCI7mh/o1ELPvdMnTyaZvRXIr1OZva4iLVjsPlhQaNVpuUZZDrRxlrQtEu6poC5ek6UeBTq7f43uun6FgDBFLDWbjCgBXhBpJaaKHpJqMZuPeWTUyMDa6w+FBGy43C/St873rHqpytUoa6YSCkMmhd1+fJF97mhbeOc2TPJh6+8wDFkgJWGcm+oyXKPmH7VIo0khEyVBgDGYaqD3uiQJeFk1WUS2KZOq7rceLsJc6dv0SjXseyUzQaDS6cP8+27Tu6aHRN0zAti9rCAguzs0SBy+2HDvChh+5naHCIarXO+QsXuWXbOorZjOKGj8cCqPHGVRJLyhqTzeAGHvCSf6tIx3F5/8xlTl+t8ZEPPUypz6i9BtyYUDHHkURtWf+MFfMKfQ8waVZjmiZmXEJXLOSpN5pMzcwBkk0b1sfzrS1Z42L5HPRfJg6b+55KXfaj8lO2xWClTKPe5NyZs7RaLYaGhqgMVrqGqxeELNTquJ5PFEksw6CQS0qre1E3IVTUQyl+v/tet1ptXKdDJpNVDcH0hMSnB+DrRl+X/VtN/uiP/ohf//Vf/7cr/e2Gnr8Q4g+Bx4EZKeW++LPfAn4VmI0P+1+llN+O//a/AL+Mysj+ppTyyRtd43qSNFtI8j1rkXuOHKLjOD2ktIC0bVMsqhaO5VKRIAgpFfNs27yBfC7Lgb07GRsZxHU9BspFigXVaOPw/r1s2bgeTQgyaVWnmctk1aasa9y2fy9bNkwQRRGlYoG0bZGJN8cjh/ajaxq5XBbTMNi0QSnvMAzJZbPcsnsn5WIB27b47OOPko3BKgOlAh95+D4qA2WmZmY5cfostUYTTRNcuTpNqZBn66YNpFI2t926j4nxMaIwxLYtCrmYXrjdwjaNVXvGJzTKlr2y158sqIRiNpIS1wtwY+rlVtuJyUckIREEkDJN2h2HlKkrxr8wwvcDPF+hkDPptPK62y1s2+6GybvSV4sv6FUHyLaDHreV7bTa1F0PgVSGzQrliY1GE8f1sCyDXCqDbdsEgULaRjLCti1M08SyesxZvfuNf+87XxT3Lk+88ygKSdmJQdX7/s3mW8MwpFqtxfMCURjRiVtVKwIjl44X4HoemXSKTCZLNpNVm2gk0WSE9BzKhTyXLy1Aew47YzKzWGO0kMFOyIJiTERyb6uNsx+vMJBNcWhihIJlcL7h8NrVq3xveoajJ89z6+6tHNy9mXVjw4p0B+IogKp/b7UdXjt2jum2YMOuTVw4fYyJooHn+Lh6hkhYZETA9vESmzeMrjiWKAxjhcXaFT/c2GHtUzJCgDAMfKfD3GKNp185yvrhMo/efZBMVhnqK6W24q+jsD8guuW8sTEQ9Vj8IEa0x89AaBq6blDMZUibgtriHJ1Wk9LgELqus7iwQHVxUV1CSq5eucLIyAjnz57mwtmTDBQy3HlwD7fs2cVgpYJlWezYuYMzp09x+sIU64YrqupCqGhE4m0vVXg3t067EiakW31zEP8YhBHVRlv1vIjLVZO5C2MPP6nUUU3YlNKPYhCw1o0A3ti7TUL/URShGwaZTIax0RF279zOpckrvPveMRr1JocO7mf9uvG+VBWrGjEKTK7OaRiGojDXNJWmAAzTZGx0lLvvsEiZBpemZnjrzTeIZMSGDRvQNF0RGQHlQg5d07EtY8n5PU/xTwRBiJRqPtLpNIZhxF1APXL5HIaxlMq++/NaQvo3AaNYS9j/j4H/APzpss//bynl7/R/IITYA/wssBcYB74nhNghpbw+J2nvDNd8kigYfY3UjULAyHBSarLyJEyM98q5SnF9tmnqrF83ptjePF/lM2VEqZinkFcK2/M82m21wLOZNGCR1zR0TTAwMND1NjVNlcUN95W6SSlJpyxSw4MKcKIJKgPFbm5/88b13fHbtsXISAVDV+yDKdtmoVrD80JGhge590KfRo8AACAASURBVM7bGBocUMdaFhsnxnBdl3ang2GYdDot/DCilMlgxt3keqnafmUnl4bElkkURQRRDygWSUV65LjKI+1/ZrlMikzKJpRxly8AXeD5nmIaS6XiRe4QBiHZQrYvx06cP176sCQgsmmkqRPFTZk6kcSJJCnTopjPdvNgKlQX0m63FVtjKkUmkyaSknang+e5+J6PZVtdjz0BhgoE/bwKK0mi+D3XjXvB929WypCyVyilXE2SRj+GruM4DqpOOE273up6fKFUBkLYVhEV1w9I2zaNRp2LFy8yPzfHpo0buSJ9zFnJ/tER3puc5dvHL3G52WH3yCAbyzlc36fl+hRsk+wqkY6Vxpe2NPatG2aj62EgeeLMFG9W20wvNLg8Pcu+nZvZMjHG6GAJIrVGfM/n+PmrHLvaYmTjdhWFC1qkNJs5TxAYWcwgYDgt2L15jExmKc20jEGEQqAU5ppndO2iaRoy7kePELQ6Lq8dPUuz7fDJ+w+yeWJMtQm+3tVjQ0D0pyRkvIajZDX36JPpdm0TIFXFSiFtMpgzmatC5LukTR0pBOlsjpMnTrAwN8viwgJj4+NMT14gY2rsv2UvO7ZvJZvJEIYhlmlRLBTRDING2+kp+y7BzHXu4Tq59ZXvWcQUzvHPfd8Nw4hmu4Nhqihb/1yrvysSsSQFKqMIzTDQhCCMohVLdZN3UkYre7dJx0zTNMlkM4yPjTJQLjK3UOWdYydoOw5f+KlP941/hWvE3n4YBCotZBgYhqEau8V7XH/ofWx0mCO33Ur2xClOnb3Ii88/z8Ku3azfuAHTtLF1DTuuRtI1jU5c1hdEUbe5nOpzAKahgI+u6+D7PplMhlTKjhv3rDghaxOxlobAa1D+UspnhRCb1njZTwFfkVK6wDkhxGngCPDiGr+/RBQyM560m+hs1ZO1rm51nDKW1fG+54NJ3NVPIIQOmArV6jpEUYBlqXyPH6i81uLiwrJWwb0rKCWvqfxbmLRnDJHxRtBrHRuHIQMfXdMZGRrkyKH9zM4t4AU+5VKR0eHBvhroHstcNqsoJBvNFqmU3W09u/y9SfLW/RbmcklCVf0hMw3lkQX9JWh94gUhlmXSdFyy0sK0TDzPVy9nJkMQKMvXStnX5Eg1RJcPqWugAFgmmmUiAcfxcSIwE8/Jtrphr47j4Mf9FzKZLKmUTcdRQJowUqAi3dC7IJvE+EH2NpnriR+EuK6vGkWZ5jKjI0Bwc2tU13VyMb+/67qEYUQrBgktl1CC4wV4fpNOx2FhbpaTJ09RrVZJ2RZZt87+0RwPbhpm0DY5PtfgB2eucma+xa2jJSQRl+sOI7kUu0YGGC3mSK8BJJmEKLO2RUoTpG2bvRMTVAbLTM5UuTj9Ots2jLF/+0a2bhgnl7a5MrvAm6en0Qqj+BHMXDrLiOUzUwtxjCKRhLzmsXWkyPjIwNILJgs1khBzBNyU179G0XTVjz4KQ9A0FmpNXnj7FOtHShzYtVk1xYGbd5ATQ0DvGQQixkbILqe/Oq8QgrHBEnfs2UQQSs5PL9KuzmHaNnouy/vvvMH87DwAnfnLjI2Ns3/fHrZv3Uw6lVJVTELgei7V6iKh71HMZxT24h9LkhQPsHxvlUjCVTxTTdPiltfq77qhcBcivod+ZrprQtlxamUpJXCva2vSodUyLQbKJe64/TAXLl7m3WPHOXX2PC++8jp33n5oxehnGEVdzoWkGkFhs1QZoq5pGKbZNVhA6aRCocDI0BCnTp/hxLGjTE5Osm3nLg4dOkQl5jiIknNL5dLouoEVp+9Apf083ycIVbQhlbJJp1PXDeH/uOVHAfz9hhDinwOvAf+zlHIRWAe81HfMZPzZNSKE+DXg1wA2bFgff9pbUKpvcYgq6/pgC3qph7OaIbD0c03TSKVsHMftlmHocSOUZNP3PJ9Op43vN5EyS7mccOUrheI4TlfxLg0bii6IRxkRXjcnZpo9UEgkIQwk0pRoumIZKxZyXc5/Lc4bJuN2HIdISvL5PNXqIo7nM1IsdJXR8v1TKf+IVOr61DgK7yBjg18qlKpUGIEgxk0k42i2FaVnKmXj+wFpQ0dEBkEQki3mYlSrBwLslK28INk/84KenyfiSEAcEZCKCrXtePhBQMbSsXStWy4ZRiq9oGs6hUIO3w9ptVpxdUGAZVmkUzadTlwKE4dFNcWcfsM1IlGskn5c122aZvccCWhyLd0bl86t3jUgUqkUbcel2mitenwkJZGEwHGZW1hgevIi9enLiCgiu3CFe/euZyiX4pN7N7NtapFXLs9xer7G3x1dRBcw60bkLYOzCw1uGauwZSDPcDGHdR2K1WQ2pmpN3ry6gF0u85H7b2PbxjHeOXmBt46d492TFzh1/gp37t/FxvEKp65WqUZpJsY38N6br2I5izjCxLUL6Ok8NBdZVzLZuWmU1AosnSKSPcPwH0Hxq9OK2BmP6Lg+Jy9cZb5a5xP33cpgpbiEV+JHvxixMaAvjRAIQbmU57a9W8hn07z6/jnePTeDbugEQUg+n8cyLFKWybaJAfYeOMCmTRt7uKd4bqqLVU6fOYNFwOaJ1QmqVh3bTd+P6Blpfa+Lrmmk0zZh2KHZajGwDIfTK2+N2f5QQO4kGhDGhhj0OEGW75391Thq+KIbrk9C6Idv3c+WzRuRUch7x0/yD99/hpHhQTZv3KBKxAXdPTfBfum66naZnCvhIjAMY8kURVHEYrXG6bNnOXHyFBcvXmBhbo4rly8yPXUF09S5++57KRQKgCAKQ+VwJKDnIIzD/knXWhlXZtmk7LVGDa/n0CaRJRFbnb1HtZJ8UOX/n4Hfjq/228DvAr+0yqhWvLyU8veB3wc4fPjwkmOiKCIIg254bK39z5decvlQVtjU5cr5TyFUgxbP8+O0gwBd63qNqZStPNxmi/n5ebLZLJ7nUakMsLCwSKvVUUxbltUFjCw/v67rZDJpFEWkq7zHpBNgPLYuC1YSEYhUCkTTzCX3U683KJcH8H2far2B0I1VKZN7AJwbo0f7PfBQRkSgyII0QaPVjjmsFW96AixzHJdMOhV7/V4c/hK02y18XzEAaigq0P6nEskIGanwIDHARWgiBn5JHM8jCgLSlkEqBktJVBWIROsC51zXodFsIYQgl88SBSGtZhvfV2E9uay8SIubDa20RpJ0SRRGRGGASj/1jLcEvKnp2k2v0cS78YMATYjrKv5+Sbr7Oa0G3swkutdhyBZMFHuNSPaMltk1XOSlS7N87Z0znF7ssK6iaodPLDicnTvPjsEcRzaPsaGUp5RNL6EFTZ45SEIJb164yiVPcufeHWzdMMbwQJGHbt/H3m0beOWdk7z67mmeeP5Nhip5RHaIfXc+RL3dIS8cIhHRCCwG16+nUa9SScPuzSOMDS/1+hUPh1R7li4+uOJfS7Cv79wLtQavvHuadcMlbt+3Q9Wg/7ikfyyy+x81BE05E8OVMoPlEhMjQwTRq7x7ZpLO1Uk0Oc4dd95BMZfl9sMHKRYLio9AV/1FgiDAcV0uX7nK5KWLbB4bYMP4jTu5/ciShPyXiaHrVAo5omCOer3BQOwQJbX8SWe/RIl33YY4tx6GYRdAeT1nTwJB/A4LITBMpaB931dsh6ZJuVTis5/6ONOzs8zOL/L0sz9k/RfGYy6WqOvxIwSGqUp8wziiqQmxYlc/iaTRbPLq62/y/A9/SLM6R8HW2FgyAQsvdHjrxWfI2jb7bj3EwEAFLTYeHNfF9zw8P0DXBKZpkclkVgWwJ70fYKXU9fUWd3/E+cbvzwdS/lLK6e7lhPgD4Fvxr5PA+r5DJ4Araz9zHHL3fRAahqlfNyd9o/Pc8KgbHGZZprLS4q59hpZ43UqBW5bVrTGfn5+nUqkwUK7guA7NZkM110iluqA7rRvqXmrRJu11lUUYh6GISSBig0P9k4Rh0FVAiYSxpVqtLuJLKGczfdfqSb/Fa1o33uQiGSE0uqRKuVQKPaMRRsqKDYIQ3bKxdEG73VGlipqIm5dotDsdCnkF/vE8DynpKsnEwAmk7AJloiCMkbY93m2VitEwDZ1ULoOm64rMRFdYC01oaLqhOBBqVRzXI53LkEs681kCz/WU8o83Xy8uOUzmf7Vkmoi9BM/zCMMI27K6YbsokiAjwiDCtK7HLqg0wPKXWGERXOqNJinLpNnurPoc+nWIaZls3LIV3w/44ewMo/NnuX28guu5WKbVbUwFkgOjZZ4/abCQtnhkxya2Dg5wcbHOa5eu8OLlRc7MN7htYpAjW9dRyaTJ97Vzjlc5i+02783VyQ+PcsvuLYwOqU1dF4Lx4QE+fv9t3L53K3/8jWc5dmWB3WPDbNq4ia999Uvsq1hMRjZmZZwwkhhOnZ2bSmydGOmF1iEBffS1/+2/895o1iRrPVSo8zeaLS5NzfLAoR2MDJZ+BKOjD5i15PPe/0U3lK1+14RA6AahCJkYGeCzDx6k02lxaa7J9NXL/NznPwMx8UsSeexW7Wga1WqNM2fOELktDu4+sNrAuKn5+4BiGBrFXBrXdajWG93PFfhRdnPrCFXfrqY/MQwEMlDp3RvNf5RE+XSFmUn0Q2IQeZ4HsQJ/9JGH+NMvfZXLV6a4Oj3D+NhoXDkWdZuvhX1APzP29le77tH3j/OdJ7+L5tV55NB2juzdykApRxBGTM3VeO3987z13FNMz8xx+x13kM0XMA3VFjqVSpEv5OO0smB5C+B+UcZRb//7x5IPpPyFEGNSyqvxr58B3ot//ibwJSHEv0cB/rYDr9zMuZWCkliW9gEV/zVnZKXNd61iGAYyRmAHkeyWckkp6XQ6MbWr4nS+evUqhXyBXD7H0NAwzWaTVqtJp6O8/MQI6B9H/7hUqEl5v/WOo8qQYkVtGkbspWoKpd9HSpPNZJiaVg1A/CAkbZkrdnaKIhXu1zQdXbuxp5oofSGEUuyJ4tYk5UIOPwgoFbNxLjxURoepFGHy5ExLcdmHUmKmLKRUxp3QdSIZ4QcBbquDlGCaBvl8nnSMfl+S+1+SA4xpW8MQL/QBl2q1hmValColDLQlLXlN06TdbisvIzZEVuPfXy5h/Ow1TfQZXQrlnYQn5TW7ff95E2Nx6ecd1+fKfBWikKbjXncMYtlvge/jtptYgcNYMcOusSHCMN74UIBRoemcXmhyruVzcPME64oFUkJjx0CJ8XyOE7PzvDk5zRPnF3jr6gL3bJng7s0jZCwTK2aKC6OIF09eZNKHz9y5ny3rR7q3qKIyEoRUnOgb1yGGt/DIY4/z7NNPUaJBRAFXzzAxOsalUyfYlAnZMj5APt9HwZoYeZGM89Wi74b/8XY+TdMI/ZB220WiUS4Wblyrv5qCj0XERoz6t+yIZK0lXpmmwtZCF2jCwLAstm0c43/46Y/we1/6e05Pqd7siUJK2vx2xx//HIRKaQ6V8iu5ifxTKH5QOJzBUh7fD6nVa93PE+4SiUT6QQyKVMaLTOr8k0jAGipmpJRYpoGxzAESsXGUsJq6jsOO7VsZKJeo1ur85de+yS988WfIZ7OkEva8uJTW0HVSK3j7/ddsttp875lncZvz/PLj97Bv2wbslI2ma1gCtuSyDFaKvPD6cd47f4zXNXj4kQ9RLpf78Fz9srri12MDKZKqZHNtcvNG3lpK/b4MPAgMCiEmgf8DeFAIcWt8xfPArwNIKY8KIb4KvA8EwP+4dqS/ElUbfuOQ9NqlPxTywcSMQ9dBEOC6LrZtx+F6h3K5hOeqRiIIqDfqNJoNCqUSuUyGTCZNq9Wm0Wii6Rq5fA7LuJb7vl9qrQ4RkM6kMXUNz/PodJxl3n6IEdeHF4sFDMOg3nbA66xYa57U9UsZYdtLEdaryfWeQRTTG2uaIJe2KebStFsdOo0mpqHoblO24mj3fE+FOS0zjtuFeB0Xx3FAwMjwELreK7trtVo4MXd6EAQEYdTd15K1kaROUNNOsVBQnpEurmEfzGTSuLFi1IS4Jqe4NPS/dM5c1wUBKTvV9QoSxZ9EUGQk+zrGrDZny1W4RMholZjD6hKGIWfPnObN577P5qDKfRvLiq8Bhbdodzq02x18XefJo6dxIsHBsTEqmYSJMCJnmRxeN8q+0WFOzM3zxPEzfOWdM7x46jwfO7Cdg+uGsAyd6UaLZy/MMLHnFnZv30w+t7RXeuCHNJptjp6/ysVqwMbd+3n1jTeoXj3HlsEsZ+Y67Dh4Dy++/ArUrvKhDx1ieLCoeo/HijYx7IR6EDc5Gx9cNE1HN6RquyoEc02XwA+xU2tQ8BErJ1Njq1ch7W8udSGEan5Tzis+kkuL5wC6iqon6gEIQRfRH0noxPihH9++eXNiGBqlQhYpAxqN5jV/T7BbUUz6o8fKNynXE6xN+UMvZdb7oHsRFZE1TTpBgO95/NovfpH/9N/+iFv378GOuTKklLiOg4S44sZY8bVNrhGEIW++/Q4Xzl/go4d3s2l8CDtt9aKr8TtRyGd58K5bKBw9w7NvvcyXT57mU//8i0xs3LiKAbC6dAHWa/7Gzcta0P5fWOHj/36d4/8d8O8+6ID8IMAwb26iVhkJP06rV4WeTTShgGaKz12i6ya1Wr2bp9F1nUK5SKvRpNVsUCwUyOWyZLIZWs0mi/MLWJZFvpDvkuv0nFo1Xi8ISacMdD3BGChlHUmlcAI/xHU89IzSOAmaPQhXtrMSsImUMg5bL09Grn2ektSBGxP7dK665NMpolB1NiuUSlQXFvGDgJGRIUBZ934QoWs+QqpOeJquMzhYwbZTcbVCnVqthkQBCm3bxs6mMcKIatPF8zxKuTTFQnbJppyM3tS0bjRj+drRNA3TMPA8Dy+m600k4Y9IFGjvvDJOVUjsVMJJILsRiS55DgJNv/l1ZpkGA8U8jVYboNsDfCXpooaFYPLSRS6cOEbJb7Alo7G+nO8+F9f1QEIul+Pv3jzGqYbP7etHyfdxOSQGpJQSS0puGaqwZ2iQlycv8dTJS5z+4fscHkzxiUN7ee7901yRFl986HaFzI9L4xAKrOW6HpMzVV46Mc3Axt2kMxn+4A/+Cx89vIXzswH5jfuw8wWqc1O4i1N8/bvPUWs0uPPW3QyU49bFSbh/DdUHq4pcvV5Dxrn2JOWekDdJJG3HoeO4FDI2bx87zd37trIruyHJCKys4GM2v6Sk7scmseGQrGc7V175fuhVEuSyWUbHRnnPznDq4jS37trca2z0Tygy9nzslIWMohWVP6gogBdF1+4+8TsYhCHWDQxAIw7VX6MflgXbUukU7XYHkPyrX/kFLMvCsu1uaiAB7iYh9uVlnf0l0e12m2ee+yFRp8bh3RsZKBUUODlZVAISdrKUbXHHgZ2MDBT4+nde5st/8od88md+np07d6zJAPjgaP+bX4s/UfS+yqKL0MRSrvkPFrL/4KH+Vc8oRDf8Vq83urXejUaTsdFhhKbhOC62aZEZrOA4ik2uUW9QKBQoFkvd/u0z07Pouk4+nyOdstF0vfvgQ98nlUuhL2tbrAkNzdAwdIMgDGi12mSzGRXK1zVCCaZtr7iQpUzANstfruXHrjxn/Z+HkXpdhosFHKdDPpumWBzrHtuxWwRhiKGbcVWEjx+EBL566QqFPMViCSkjarUqi9Uahq6TL5fR9dirj0GBjufEhoVqtbr81UiG6kdR1+Pvf4GSdaTY/RRiP6k8SPVtlEID2W9mS9X9zzQNZVQkEV0pu2VLqxEkXU+SeTQNg8FSgXIhR7XRYG6xsep3nnvmGc6ePUuhWCAMAlqXTrMtaLBztLCEPS0MQ7LZDE4U8vfHLyKlzR0bN5FfAUmcRE5UXX3EPRs2cO+mTfz1e8d5cXKG9558nTYedz/0ABvHh1WFQ4xFQSo8yvRineffu0hpfAsHDtzK//Zv/g2iMc/VmRL2ur186O57+f53v82dWyuMVTbx/Jsn+NK3n+et4+f40J37Obhnm6KiXk72dKMJTH7s/zyIlm7iQjBfrTOzUGWx0aLVcWk1W8zMLzJfbTBXbbHYaOP5IY1WGz3yOH72AlsmRlSXvjgsTxcT8k/jUWuaRiVnUp+f6d1nn0fcjZcI1T1vYnyMbDbHK++e4LF7DlBeI4/DB5JloMVEFmtNnn7lPf7quy8RBBHVWn3VUxhxiR4J45+MeVwMQ2G91iB6zMHQfSpi2ROS6tmZcUlhJpdDCKHSj76PaVlLSnV785WkiHuKv9Nu87W//SZH33ufz92/j0pcubQ0dwv9BGW6prFl4zi/+oVH+avvvszf/eWfcf7WI3zo4x9bE5V6AvS+WUn2lrU+/58o5R/1E8T0yfXAEdeTf7R3IFYAlYGedS7ixZyA94QQqoQjpXLe9UaDWr1OsaiMgEKxiNPpUKvVqVZVjsy2bYUNMHUMbXVSI1Uuo3f7yScVCJ7nkU/Z10yV5/loWmK4XP/eVvt7r6Oh6pNezGVwHBfXdSgWSzSbDarVmrLeTaPLl9/uOLgxmCebSVMsFjFjIqLpmTl0XWNwsILUeveWePEmEBgBbaHFwL9VcnIsveVIKuKe/vmzLMXo53mqsqIZcyGsJu12GyFUt8bEoEiUvmpb/MFQ4cvXsiYE+UxmZeWvGRAFLFYX8TyXuelpkCG21yYvfdaXi0ipAEu9NJLgyy++w9mGzy/euY+MfSMuB2Je8ogoCPjs7u18fNdWfvPvnqGQL/GZR+6kXM5DtyY7IvADrs5WeePkFTpmgYmRcX73//l/IXAwDJ1jk4usSy/y9FNPoVUneexDh9g0MczHH7yNp156lyeee5Pf+/J32LlxhLv27+D+Ow5g34zHmnjlfWQ63eY/yb1JqDfafOVbz3BmcpqWo+pLMimLUj5DpVxg84YJRgcKhFHAX333ZV59/zz3374fOyEe6q6ffxrFH0URk1MLXLys8NRvvv0uR247tEIZmDJIDMNgdGSEPXv28sxTk3zvhXf5qcfuWiWKcoM9dK1b7ApKKQgCWo6PSBUpZ7O4rrPq17to/+55RJwG0lbm+b/OWJc5+0vEcV1kGJLua/GbEAtdUzKY/N7nNoVRxKXJK/z1336Dl158mYNbB/nwkX3k89mVG5Etmz8hoZjL8CuffYjvPPMaL37v74jCgIce+yjpbC8Fd9N5v+tIslx9P+Cd90+wUK1d1xD4iVL+nbazChDrR3n5fvxWu3KWVPlKs6OUbztGa+uWon+E5GGILnez4zjUajVqtRqFgmoKMTraozdttZo0my1cx2G6o+Yim81imisbAsvD1wCpTHqJ558oq4R18IOL2nCS6wRBQLVWY3h4kCDwmJtfYGR4CNM0WFioks5lVSmejEiZBuVSiUwmg+M4TM1NEcmIocEKpmXSaXfQbRMdBSxcorRTFlpTw4tZsmC5lS8RocQJAqpOEycuqUlZJqZlMVjKxxgLYprfIC6t9Gk2W+Ryio1M13SCOPSfNO7JZNLdTanHsdADQSaiOgCq7y7HG6w8jytIrOiXSPz7hx/9CMeOvc/JEydoT13EbNfo6AFTzTZj+QyapikQpRDMNJo8c2GaLUOD7BwZQqefXU275vJCCJwg4Onz53nh9BUe27WeKJKsHyrxi5//KBOjQyq3GQFCbdT1ZptTk7NcrAVsueUw+VKZmcnz+NU5cqPrGF6/hcc+9jHefvZJPnpoKxNjFQxDxzB0Pv7gbdy+bxs/ePkdvv3cWxw79ywvvnOKj9x3mDtu3dUbmJTUag1efu80M/M1qotV2o5Ls+2y2OjQdj1sy2DTaIUHb9/DkUP7ukrl4qUZWk6HY6cvYJoafij45c89wr237aHYBRv2POn5xTqT0wu8feIcb504xyN3H7zO8/vxShAEzM3XeO3oGV555xinp1q0Oy7bdu9lYnxcEWotUf4yDjGr97EyUOa2wwc5efokT738Fh++Zx/lgZVSBjey+j/4PUSRxPNDSsUi/9Nv/Etcb3UPPok4ddNmMqb3jY3sJJK1usTeeYKGjz31pEY/4R8xTRM7nQYpaTebpNJpUuk0TrtNJJZ29esakMkVpGRmZob/+od/xJlj7/PTjxzk8fsOdTt49g1jpaGRaPTk/I89cButlsPpt9/mzvvux06tDXN1I5EosjYZA7lVCbNOEEqe+P6zvPnO+7Ta7VW//xOj/NvtDqm4GUmv/OrmQ/83rotcizFwo2NkrAxMWvOLqgTNMjDiqEW73VEerGXEBEHq83Q6jWladDodms0Wi9UqmtAolYpYpkE2myObzcVepk+r1WJhYbHLAJXQPy6936UgGUXf2n8fSdj/2rm4+TlVoWXHcWNjR2CZKWZmZtA1jXq9gWHoOK5DpVLBcToYukGumMMwDBYWF3Ach0w63VW6AKm0rSiA+16u5J4sXUc3TELXp97sEEVQSNu4QUTbdXE9X5UHLhuz4/k4no8pFBDJMFSfA9+36DgOYSTpdBxyuWxXaei6Rhiqhht6X+1+siEtJ23ql6QFcESowpLXAXRe891IYmoSfxXHJ5PNIlC5f4IAgoCpdofvnJjk0R3rWFfIJpPG37z8LleaIf/7A7eoLovxOcIwJKJ3H91rS0nNdXnl/BQDE6N859wU7WqdI3ft54HbdikinlCCphr3RH7AhakFjl+tMzixg0qlwu/++9+lMXsVBGxbv5nHP/UpXnz+OdbrDrfu2khqWSh6qFzgsx++iwfv2M8PX3+fv33qZU79+bd4/tV3uX3PFnbv2Mpzr7zFU68eY2ZR5Y8zKYuUbZKyTUqlAusyKdodl7NXF5h68iUuTs1zdXaBK3M15qttOk6HthdRyKTYt3Mj64YrpFbqcyElxUKWxx84zMvvnuH4hRkeOtJrS/zjFqfjUKu3mV2sc35qjmNnJjlx7jItX6c4OMTjn/4Ij3/kQ0RAPpfD6XQUIr3rsYqe1xvnykeGBnno/gf4yvQc3372TX7+kw/duHLhA0uyN/ckIdmybJOBgTKV8sp4hX5J9i0h33ik0QAAIABJREFURDedZJomnu/fgPAm8drpAoKTqJRhmmQsC9FHKdzpdDANg3ZcmWWnUopOG3qORt92n1Rx/cP3n+X0iWP86qfu4f5De0hnehHdvmGsdGPXePNCCIYrRY5eukyn06FwQwPnWgnDEM916barEcr5MwwDwzKw40omgHrMGWJZJguLtdVO+ZOh/HvMT/25v6V5mLV6rUuPW0mJr+VEKx2jzpXUmSb1/Y7rMhBvvgmAT9MEfhTh+yGeG4fcdUUvq+sadsruNpZxAp9mQxHgJJEw27Yp5LMUCgUKhSJB6ON0OjQaDRYWFjEMnUwmg2VZiiYyRrKjGctaHq80b705ud6cJhTAiUUZRSFBEHVL36IoYmRYdWNLWlAuz9m1Wm0KhQKaJqjWqiBhoFyKUfNqbFJK1drXsnBd75oue0II0paB66oSx3qjSaPRTMyaFTFZXS9a01lstjAsg0JWlY5msxkM08RxPFLLQuKSGN2PqrRI8ujJJrUS6vrm5NrvRFLS6bj4y/m8+yIBQggW5hdoLsxhNxe4pWhwcGwzs02HgUyqG5XwheRyo0MxbfIHL73F/ZvHOTwxgd3HJphUKSQgq6br8ez5STKVMv/qCx9lcnqBH7z8Nh+57wiZTKa3kQll8F6aWeDUVA0yFYbXbWBmYZGLZ88gdI3N+26jWCrzyosvkHYbBLbspsN6UyC72JnBcoGP3neQuw7t5ltPv8Z3nn+Dd05dJJ97lUajRblc5Dd//jF2bduA3VfamtCtup7P28fO8f/9xbe5Ovc6AHu2rueWHVuZGB1gYqRCZaCAYegxqnuFDVcoxrlyPsfuLRO8e+Is84s1BocGrj12Dc9VSsniYoPZxSpzs/MsNNrMNz3mF+ZpdVzqTYeWF+H4EtcPyeby3Hr3gzx4372sX7cOM27X3Wq349Rhio7TWRUsJoQgm82yYcN6xsbGeOHto/z0R+7BTP8jAP/Esh8SMGWkSoyFUBTQN1JsS94lISAm+dF1Hc9dWvZ6TUov9vDDuC2wneTv4/NFYYjrOGqNA5l0Ogb1KSbUlG1jp1K4rqtaA8dGVXKdKIqYW1jg1VffYN+mEe45sJN0Nr309leT6+Tpc4UcdiF/00ZZwjOiSr4Vj4emq71s+XroN0x0Xefu2w7y7Le/vuq5f0KUf4RpGnieHxMwGH1tHn+UDXb1767s9V5vQ08WV4TneaTTme6Jkjw1qAcQJOaZLhCx5x9JiR/Xc/eHt9KWTaps4jgumq7AfAoj0CRYrGFZihc/k8mSzeaJIsVn3Wq3qTeaKpxtWbRaLSo5m/n5BUDV/mcy6RWY51ZWYAnTYBgGceveqKsgdF3HtCwMS71AYRhQKOQxDJOFBcU/nk6nycRtkXNZ5U0Xinks02SxWuu2rky4Bgyjh8xXYTpDdeNznC4xUrJJFLJpiCJqjRZeGC41rFd6ZEn4PAqRmk612cY0dHJpVTaVtHtejiGIQhmD/MxuiDxRrDeTilrd47/2c00I0hkb6svC/n0/d9ptfKeN5TWxIg9TF4wW0kwUc1i66DIWpoTkNx6+nRcuzvHy6Ys8f+4yr1ya4eC6IQ6Oj1Ho9npQgKKW43B6bpHjM7Pc99C97N+9hT07NnLklm2U81l0U0dI8F2fyFekRCcvzjLbFkzs2kUg4ct/8ecYdoadBw5SKJXVnNZn0QyJ2/E5fvoS+3ZuJGVZIFTeFVTjGU0KUpbNkGny6Q/fQbXR4vnXj9FyAu49vJuf/fh9lPLZJYq/X9Ipix2b1/GpB4+wf8cGRkcHsE1TYWYMHTOmVr1RBEYI1Zjq8fsO8X/9yTd5/rX3+PRj9/X+uEzabYdqvclCtUa1Wmex0WZ2oUa93WF6vkkYRSy2I2U4RxHpQkkB+QZHGRrKsasywLp140yMjzM4WCGVSpFJZ7oORUI563keltXrX7FSKVzyjqQsi8pAmdPvdtaeO/8gkly/D+QWIvHDCENfOw6my5KJegf8ICAVd9NLIh3Qe2NUlZPfrQayYg9fStnl4pdSdllYzZS9BPdjxvX/TlyqnbJtfE+1zTZiwh91roDLV6aoLs7yofv2qgZuov/NFddV8qtJNm0jRES73eqm4VaSfr2UlCQiBHZfyefKzkj/SdSjKRRy5HO5Vcf0E6H8QRDISIVaY27rKCaCMf9/6t47Sq4sv+/73BcrV3d1TuiERk4DTMAMZjBxh7PDXe5yySXFtTItKvhIsqxjU/rLto7/sOVzfI5sS7Z8qEBxTa6W1AZtmk0znAwMcm6kRjfQDTQ6d1V1Vb3sP+57r6oTwuzKHt89i+murvfevffde3/5+w153GV7FGvr0ZQFITYLETz4GX6YYJXNZrFsK0Yqi1z7nu/V41FCxpHU0CWjqZJhz/VcXNulWq1J142uykTB8PupVJJEQmIJOI5LpVKhWCyhqJr8m2lgGGa8yRVFwbJrmEYitOwku6Dve6RDQVwH+BFE2P6y3l3eIxL4iqKiaXWXtxD1xVarWbi2QzIplRHXdalUq7S2FjBC9DHbdmhpaSYIPEzDZLlYxPMky150oMXY+oqCpmuSPEkopFJJlpaKVCpVLMuK3Vq6rpMLGfyKKxUs25FoYRu87ghOWNV0CStsuSFMc6NlLVAbNHBJLOJTrVZlaV+Yxe+5Xiw8Hl6iE85VQ+Lio7QgCKhZDvibw2EsLCxgW1UCTcdVdQJ8TEVBU+uUvSCtnrZcmldGDPZ2N/HR9Ul+eG2KHywscG16gRdHBhhqaUYLx1O0HE7emQYzgRY41KoWuVwasyV0eQcBtmWjKiqqpnFr7C6TSzbNnUMUWlq4PHqV0vIy23bs4C//7u8yefs2V05+REdWsFKxsVyFq9cnGNnSSSLCeGjIxhcEBIoAx6c5neRrXzjK/NIK2wY6+dWXn6Q9ZL3crAkhaGvJ8cVXnyKTkpDbn8ohIwSJVIKBnlbaW/L8+elRdm0bxEcwPTOL5foUi2UWiysslausWB4Vy5MhJ9cjQGCkMqTSKRKtzXR1dtLW2k5HRxutLS1omo5QRIwqp+thKathrEscjcSKacjwlGEYmIaJ4zoY+vpM/iD8nx+eFeVq7ZHX3i/URPyPPNsUgedtXq76oPci6tIOQ9djsp6691HujcjyFaGnwLWs+CzRQobAWEAqjVF84u+4ITuroevoponrODiOE+dG1WpVxscncByLJ3YO1itRVnmvHmei5MBTpo5bLFJeXMbr2ZgLJDpDonJi27ZQNS02PuIcBT+gYlvULBuhKKQSptxfa5qqSq6TzdpnQvgrikBX65aY0ARBIClcXdfFDYhjsA9b148exw4kzjshdnt4iGqqst5VueY6ablqLBeLUug1aJjeGq1bESLWRqPrERquLi14Q9dwnYgkR6L8Ncabo8Micr/XqhVWVsqxVhvB4Pq+h6JIC7epqYnm5maWl4uUSmV5+AgRlt+FLjJBCIij1/vFeq3S8TzsmoVt2fiBTyKRIJ1O4/suxVKJdCq1Ch3LcV103UAIuZFsyyKRMNE0HdfzUTUJ1RuhV7mOrLtVww0tx+KHWexyjiJgpVTCxDT0mJwnfo/hblREnTJUKJImyFssUg18glUHwcbv1XVdFLUOwBRzez/GYVoPXT2aFBJChOGHzU+U+9PTVCsVCMenEtHFBrGVKLkG5LrNJQzSps5x/T6pTIKujI7rObxzc4wrM1l2dbQxUGgil0xwaEsnp6ZnOHbiPBXL40uvHmaop01WEESJgrrO3ZkFbtxdItXax869B6hZNvMz99n/xBM8/exztLW189F775JwivzmF47iuB73Z5dJCB8DiIKVUaw68CQvg4xbS6urq72Zv/6br5JPJWhvyj2SDNc1jUJTwwH3KeRepIimkiaH947wh9/5OX/wnfdRFDXMDwHVTGCYJrqZoqm9QF9zgaamPC2FZnLZHFoI9a3rOqlkkmQiQTKZ2BAn/oF9if4NrU3PdVFDq3Wj8q/Al/vI8z1u37mDKpRfZgL5IzVNVUklTBxnWcbWM5mHmlFr50RRwvNJ0yQWvuty7fpNbNdm365dKCG5muu6eGF4sTEPp1HwRy06F/wIW0ARqMhSQ9uRbKOaLml1I4jf6Xv3uXDxEgMdTbQVcvI9fGrBTzwDhmEQeC6ebcUG1/o5ITT4HDw/iA2qtfMVELAc8pUkTB1/neCPV1Ecit6ofSaEf2SJBYEIExlCF7vv4wcSPKeRAlcosrRHE8o694kQxLHqiHWuMWFtVc1sbNVKAqEgkMx1gRvFRMUqDdInkPz24b2q1SqZTAa9wbXur9mga7O/RRhjTJgmjqLguF6IyKes0nSlpqrGbvcoo1VyTTuyfE9VUIIARVVZqcos9oShsbCwSKFQIJfLUSwuY9sWum6ga7pk+osFfN1j0dhcz8OqSo5pJ1QYDFNaKbqm4fkepVIJRcgYerRoI8x8ORafUrksY7OOR6lq4boeCdOkOZdBUwS+6oeZ9FKAO44bEiFJPALPcyWTnevh+xJYyTB0zIQZhwuAVcK/3sJckQht7QF5HNF78f2AVDrC7q+TkDzK4d34lcc67IVUfFVFwfM9IrAQgFtjN5mamuLu5CTVUhFhV1Fcm8mlCh/fnmFXe56mMLYbIaVFrWI7XJhZprs1xZvPDOP5cH5shlvTZT6cqHFrcZmR1mZ2treS0DS+e/EqV25N8uUGhcJxXDTDZKVqcebKOPdKLluHumhpbWN5eYne7m7aOzpQgoAP3n2Xpdn7tIiAgd52hBB0tzcTeD5GwpAC35f7kSB8Y9HcKiG/uxBs6++Ur8WPEqceUZv/RZuQAC1HDmzjWz/9mDsLVV584TlaWwrkczmMRFJyO5gG6VSKdCoVh7rWJaj9kgqMZAKcTVJLxfHotWEMP5C056qiUK1UGOhuRzUaeRN+OX15cD9VMikT17FZLpZpb2uF6NSMnr9BH2ICm0AqzY4reUtmZ+d494OPuXHjGolUln27duG5Lm643yMmwLXJq7KtH3DjiSxR+WRirxMpAJpGpVLh8ug1PvnkJPfvTvI7rx1YnVwd8Knc/VFLmAZ6ypQgR5vcx/d93BD1UNP0DUK29ZYNExC10DhsbJqmMrClh7a21geeRZ8R4Q9skJymKIokgFECfF/geT5RnXTgB7iBy0arKqqEkfzQq0kUIoEvf5YWohL+IUBq0UGYsemFZWTRS3c9D9uyY3hf13VjvH6oa5j1EbGh8IgUAMUwcByJUR/Vk0exqwCZzQoiTvCQNLAKuqdJxEFdw7JddE3B800qTpVmw8TxPJaXl8lk0mQyGVZWKjiOHZZb1Rd0pFBECICO44S0vaFbXlVIaDIxUdckPe/KSgXPcxFCIZlMhLE7OeqVlRUymRRCCJaLJWqWg6qp+K6/yiPiBwGqiMajxta70vhehMDz9NAFVk80rFZlGZ6m1RG6Gop0GtaDoGZL7G5dE2gbovDVP6tVLXzfxwy1aMuyH5upr/6aH/3EjUIOwQbFyxPj49y8eRPXcSAQKFoSX0+wUCxx6f4iKV3lYDpZD6M0rLOPxu9zv1rjNw4Nc2CkA0NT2dKR5cbUEpcnFpiYXmBupUxnJoeqabiKzvb+Tjpa8nVrA7kGT124wbW7SyTa+knnC7hBQDaX48DBJ7hz+w6XLl/m3vQ0SuAhFIXr43cZGegmEyZKhQMlPoZDxTN2YwYNilt4yPqBL6snI/eyiMWJvE6smvBfShNC0NNR4Ddff47vHrtKb18fRw4/TS6bfbzsbPELytxwzBKn3gYCtND7p/h1hEppEPlYNYvp6RnsWoUD+/shCPAcNzZc6nDDNCRwftrOrW++H+CG7KcS9rtRE37wtYEv82yWiyWmZ2ZYWFzi4vlzXBu9SEr1ub7scv3mGAP9W2RIT31YHkeDodVgAa9WAKIwmY9lWSwuLXHuwiVOnjrF4vRdjh4Y5OCOwboyvQH09+O2ZMJA0zUqtWrDWRjKsrB6zHWk51PTtNXEV+tGKEiHZ+9GzTR0nn3qiRgZdrP2GRL+sNmWEUKsEhRAXHu9kRalCCkkNwOF2awJBIoWuud9Hy9YjbQk3dQO+Xwe1/Ok6zVimdIUvDXuHPUBNd/R50ZIfeu6bqyFQgjlG0LyRmEJed4psWUWC28UDE3DdX3wfDKZNOXyCqVymWw2SxpJqVur1aR12diCesZ8EEh3sqKqmKaJqgo8T3ojVuwKXshBLasNkqveB0C1WqM5LPMpl1ck8UWIMaAoSijwN85SFYh16Fd1QzYINXU3DgM4Tj3eLWk9I7AQeT/LdlgqVwj8gFw6gbnJZorcbLWaBCbxw4Q02efNPAYbtzr4x6Nf4wcBpUp1NbdACDfY1t6BYZrcnZxkaeYetu+h+tCRNBlsSpA26l6KxrBS0XL4+c1pOltSHNzeRTppoKoKw70FettyDHfnOX9zhgvji5ydvkvNFZRcj5RpsFwskUmnCJBrbG5+iQtjd1m2dTraOklmsliuR9IwaG/vwHM9RkevoisBSmDR29nM/MIyAz3tEp+CEMUtsuRDb1okuH3fp1SucnX8Hooi2D3cS9LQG4Kf1E/vyNMjIs9Bw5RFB33jO3vU1xAE4Ad4rodmmLx59BDf++AcN8fGee3lFx+7LOtxHr1ZdyJ3c+SR0jQN17XCaQiFheexsLDIhYuXOHH8OPMzM3h+L+OTM7Tk0uSyaTmFAiphdYumaevi4b9Icz2PucUSN6fmaC4UaGluQnp3xDolKCAg8AMWl5aYm19kcXGR5eVlSqUii4tLzM/Nsbg4j7uyyFNbO+hua+YbPz3BD3/0Fr/9W1+lu7PjU9G7h9klQH0pycRXmLk/w7HjJzhz5jQJYfPywWGe3TtMLp0Ix/FLmSaZxO67VEulmMU08kRH7xKCMOxbH+OjhrHrTe7bof6+h37zMyT8N9OVV38uBUcQKwOPfm82uf/GzxOKsmpyGrPfNV1jpVLFNI3QMvXADfCCCHM6dKmHcefNWiQETcOkWquh69TrX1HQNMkg53l+6AUA8OOyGM/z0TVVlgxWbVQRUKpapPJZstkMxVKJykqFZDJJOp2hWq1i26uTcuQ5rEjsa0NHUVRc1wEh8eZtW9LhBr6Pbugk04kQXTDA9x38IArPSEVJ0+oEPflMalMkPLmoH9U+qit/WojRL0mWZImj4elEGymRMKnWLIqVGuWqRcrUSRiG3PRrsqWjxJqVlWqo5fuxxW+YepgX8gjdi+fyYUJi9Xh9P6Bm2Swsl9eucgB2794DAj45dozl6Tssz81grSwynBMMF1rpK2Ql2FRDTgnA8Qlp9f/2U8O0NqdkqCdspqmxta9Ad2uWkd4ljl+9x8XxBdKWwo1bd3jrPZVnntjFlp4O0gmTizfuUPUVjEyebHMLZiIZE8n4nksymSSTTlASDh0Fk2cPjJBPpXBtR+KfS391PHQhM2QQIWqj47iMT83wgw/OY6qCvrZmEu3NKAFSgETCPAjDBVHIgGAV60mM2d8AgLPKmygaxN2qGK4USJ7rgSJQNI1sJsH2Le1cu3qVubl5Ojs7HoFhNOrVLyZW46Sv8HdV1XAdByOkafZ9r86GB8zOz/Ozt9/h+oXTaALOjI5Ts13am/LsGO5lpL+LwA/45MJ1UoZOT0cLbS15TNOIw2sP7PADtqjv+9yfW+LEpTGmFqu89tpztLa0EBDUpziQyYilcpmZ2Tnm5xcYvz3B5J1J5mdmWCkvI3yHhKHRlDLZ2pph6xP72b9tAE3TuDu3xM9OHmPryDZaXn2JVDK5cWeo5y01mvpytdQFvxACEUile35+gZOnz3Dq5AmymsPrh/dw+MAOfM/Fd5x6wi/8UrxMpgiwyuU6hXnoufHDULIWxvgbBf7jP/bRy+I/Q8L/4W6ch3/3ce+90cre+LtRbXtUI25bNplMJsb3t225WHw/kOA+IUiIHWemr7+vIiBhSmFlOy6madaZosJn2iFyW0Tp67guviPv57iSajYpFBzXJZ1M4AWCas3C0HUy6TQrKxVc1yWRTJDNShKYtQhcUR2078vKgmq1iqLIpDxV1UilUhLfwPOwHBeBi2PZUvCoqsQacBySoXXtujYCWfurqtqqMcWzHLlzH+ogXf13GbpI4vs+ti0xDmo1G9d1JE1rEDC/VMRyPRK6RlJXcWwbz/MwjbpnJdK4Pc+nZlm0tLVSLpZjxUoRkmhEhpo2Ct+s73f975uNqVHxCHA9l8XiSlyTXP+jJyspVlbCPBAXG42SAysrFqO1KqYC+22HnmyKQjYd37NsOXz38gR9bSmO7ttC0thoiwtSSYO9W9sZ2VLg6vgC4/cWGFuocf7SVa6O3+HFwwfZ0d/N5VtT1ESWwcEh2traZIIUEtthemmRuel7jI/dJOeW2TeyjZHBHhQEnlvP0o4Oz8APEIGH4guEkAmKlZrFrbuz2EInpdfZFQMvADU6eAkXjBSs9ehKqAj40nL38evkQ7EgqIcKgug+cbKo/Mf3ZNxcT5j4nofruLzxwhP8sz/+GafOnOX1V19GfSgqm2j499O7/RsFP0gXsGVZ6KE7wHM9ELJySNU0ggA812Ggt53dg10slGtM3Z/npx+f5+l92/gHf+XXUBWFnx27QKlUYdfWXvZs7aO3o4XWQp5MOlnPbVnDUrdqQPGE1Vu1anFudIJjlybYtWsfR488FwtgL/BxbJu5BWnh3564zfmLl5icvI3wLPJJnbZskt2dbbQ1Z2krNNHR0kRroQkzlQLPw7FtvvL6EUZvTfHxsU84cvhpkutYDjdvQRCwjjczkArBwvwCH3z4MZ8cP06T6fMrT+/hiZ0DBJ5M+lWEgmNboKgomhqHQn+RlksnqVYrsSyJQplqSDIUtf83UlzgMyX84VG3zOO7QjZ7xqMvIsdxcV0P23G5OHqDbCbNzpHhOPHMMHSWimWuXh+jt7uT7s52hAi4fus284tLG97X0DWeOrCP85evYjsOTx7Y2wA8I5ibX+TO1D0MQ6e3u4umpiyKIt3j1ZrN+ctXSadTPLFnB6oiaM7nGJ+Y5NatWxTyeQb7e8nlcpRXyiwtLWEYCSzL4erYWNwHgSCVTNDclKcpl5X4+015VEXj3OWr1Ko1+vt60Aydmm2TNE1c22FicgpN1Rge6MNMmiw7NtmszLq2LBvH81gqrdAkRAOy2monIKuP8nXvVf6+/h0F4Rd1XbrIXNelVHZxvYDycgnb9dB1g6QuKwiSiQS2bbMSknoIpGIVUYoauo6p6Sw5NoSelCiTvlKzEAIShh4qdI1rp+5RWs3s9yhrOMByHFaq1VWzEbVqpcKlixewLIuJ8XE0u8JQWxM1KlQXKpyYmufS3Tme7Gnl2W1baE0nSWgqn9y+z7Ln85W9XaQzxsY45A0toavs39rG/sECvqrywfkpvn9inG/96D16u1ppLhRwREBXTy/ZbC6+bml5iXNnzjI9fRfXdaTyBZRKFXRDJwQ8RFUUDF1rACUJJFKg5+FYNsvLZcbvzqES0NuSpRCyFMbTGEnDNUIoygWMXPZCEaiqXg8VNBz6gVdnYWyc/zhsKBSMpCnBampVDMPg8IHt/Mn33+f0ufM8/9zhDbP2691a3UEJFwufTvyvviqioC6VyywuLJLLZsjmcvG6l/lCOvmWJr72hVfQdY3Je3P8k//rT/FcF6dWw1cUUobOfcvi1OWbnLs2QX93G0/tHmbblk6yqaQspdW1BqV8g/6smdf5pRK37y+QaSrw4tEjtLW1ynLDlTLLxRIz9+/z4bHjjI5exbNWaE7p7OttZefWnQz2dFLIpTE0WX0kQiA0z/UIXBeh6wRWjaZsin1be/jBJ2PMzs7SlM89Mq+GaFQCw577vk+xWOLn777P8ePHaU8JXn96F/u3bQHAdRxU30fRDfREAqtaRSdAUbXQo/HpJXM2l6EYCLw4eV3CEEsP9nrZ9zA5txGB2fq/bR63+IwJ/0eb2F9MM3r8i6OkPMt2OHNxlH/xr79OW0uB/+bv/h7btw4gXc5wcfQ6//xffZ0vf/41fu2NV/Fcj29//yd8cua8LANaU3aRSibZu2M73/zuj7h6Y4x/+t/+PkP9vSiqQNN0fv7ex/zk3Q/I53L8xhd+hSNPP4HvBTiux/krV/lf/o9/xeEnD7J/1/Y4Xv4HX/8m12/eorurg//qb/91hgf6aMrnsJNJLMvm1PlL/LN/+W9RVZVEWM/uBwG+H/Dykaf5C7/xRZJBAtNQ+fqffod70zP8rb/6NZ576gB+EDAzu8CP33mfH7/zPvt27eAf/M2/QiqVoFxekYhwEKJnSZhj1/NobcqTTKytUd7MG7D575FA9nwfx3WpVCwsxyHwfVZCcJ5cQm6mplyW0vISTfkCiUSSIPApFousrFTCqgGDXDqNpgp0zaRcLuF6LhC5lmUuwFK5jOv5tDblyKxjn6srAA93+a9urudTqdqbzoZQFFzHYWFhAcdxUF0LpbpEpxGwZ0cfLSmTH16Z4Gfjs1y5v8gruwfZ1VHgTy/dZkdfnhf29aNrj9gn3wdVQSHghT1dPDPSxp9+MMY7V+8xObtMWWumb8dt0s0tZDJSOHd0dPLcCy9w+cI5Ln7yIZlkgv3bBxkdm2S+WGaxWMYLfFrzOQZ72ijkMmRSCcmEqCh4PixXa0xMLzJbtMglNV5/bh+GoYXu+1CANvrBZexmlbWPQNZiN+piaw48ocoSL5nUK//shaVdiqKiJ0LBX63WETKDgGf2j/D2hSnuTE2RTqcx9NVgQxv9JH/7xUy3xjJW23YoFkt8fPwkb731Fr/11a9w+Omn0HUdPwjI53L09vRye/Q0C8Uyfd1tDGzpRFUVUqkEQtfwfTATcq+/cGAbruty/fY03/jhe+SzWQ7uHubJXYPkQgwRVVXijHohFETItBnpYlGlkAyjyT67rsf09H0WFhb55OQpzpw7z9LcNM0pnZH2PAd2HGDHUB88hU2kAAAgAElEQVSFfBZdVVBUFVVT62ymYaJ1DG4UCnjP99m/Y5h3z4xx5ux5urq6aG7Kr50xOeuiIbYfGmURM2oQyLBsrVrl7Xff46OPPqKnyeDzh3exa7Bboq/qUul3LIvAr6GaCcxUErtSRQ1AfRza6Q3s2ISpU55ZpFKpUGhpCSuboj368POwsUX5TWEsrW5SNawdN/REb9Y+Y8Jftl/Msv/lNol8V8P3A5KpZPzZ/OIi//IPv8E/+Ud/X6JArWmKouAr0trIZbMc2LuTXduHaTyl9BBv/sjTB7l6Y4yzFy8zMjSAZE7zuH5rgmJphZWVKnenZ8IXKd36l0evo2s6zz55AJCxwdEbt7g5PoGma0zenWb0xhjtrS3kshLb3vN8CCSa3pbebl5/6XkIYKVa4djJc7z19vuoqsJf/OpXwsHL/whAUVTm5uZ5+4OP+cmff8DObcP8rb/2O6TTyTgjX1Xl4VitVulobWZmsUzVsqjZNqahxyWcn/Y9uK7LSqVGsVyhugZKOPIhaAmDtO2yUiqGh4kWxsQ1mpsLNDWt3Qzy90wmSyJhUiyWmL4/S2tLAVVVMQ2DRGi9bnyo193KG/d747Xseh7lkAxqo5bJZHjhpZcB+N63vsXs0jSaVeGZzgxHhzoxFIV9XS28PzbND65O8C+PjZIUPlVN57UDA5ipR6Abjqzk0PrFlZj2pqHytaODfPHpLfwP3xkl39lD/+BQLPjjMTgOleISOVZobS7w3XdP8ZNPruCisbSwCAQ0NeUppA12D3Xx5M4B+joKKIrgwvU7vH/uJqqRJJvNM9KeoDmbplapxWVcmqYQNFpvAWuE/hpq1U0StIKGl+C7Hp7rEgQhaIwuOeidahVNU1ENncALCDyfV5/eyw/ePc0nn5ymt7uHQnPTugTjXxagztr7Oo4knhobn+CPvvGnzE3fw3FsiqUS5UqFQnMzhqqSSacotLRwvupzb26Rnk4Jua0IQdWy+fj0Fe7MLHLq8g2yCYOdIwPs3bqFmfkFzly+wYVrd3j72AW+/fNPCAKf/dsHJM9I0sTUNAxdJ5UwMHSNpCmV5nTCIBkqE6qA8YkJ/uhPvgE+zNyfJqU4bGnP8/qLe9m3a5iO5rycW1Wts+o1TJsU2oLA9RG6CracC8NMYNsWe3cM0d+Z59gnn3DkyLM05XObGhJrgX0g8sQIPNvlxtg4J06epi0Nn3tqO7uGetENXbJChtfoCRPPsrErFYx0GiOVplYux5Tij/TO13zF931qKxVqZRtNVR4bP2Rtk+HA0PBQBEFDTorn+9iW/VB0y8+k8F/d38eJnq3zD676fSN4zIc16e4P68/V1dwDtyZu80ff/DZ/86/8zoZx7ailUgl2bx/hjVdeiJ9fR9cTHH7yAP/mj/+MU+cu8eu/+joJ0+Dm+B3mFxZpb22hUq1y9/59pmfn6GxrZaVS4dLVGySTCQ7u242qygqA7//4bXw/4C/91q/xre//hGMnz7J9eFBm/cqCEkDWhnZ1tPPmay+G8wJvvvYyv/v3/xEfnTjL137zy6sT44CFxWV++ucf8tbP32PH1kH+9l/9Gq3N+fgbjQqm5weYmk4+nWDRdXBDz4lcjI+v3EX4C8vlCkvlFanERG9B1ckaKrqqYNsWpcUimqpKOuVsFiGCcJ6jckwPz3PxfA/X8eJESkVRMBMGhUILZirF4twcbW2tdLcVwvIlCPyNuQSUByg10TjXJjjK8qjNUf0aV+6+g09wwqpRKi+hKAqGIoFccgmDz+/s48hwJz+8cod/fmqMHqPCxVvTDHRk6WrPosYAZWL1XaM8qNDqJwA0NRaeluOSyaXo6yqQ3L6dTHoDpDABQlGxSXB5cpnJhRVEzWNvskpPj3TBX1taYGzJ5z9OzvDOmev0tORIp1J4QiXwFZTyMu1NPi89/SRqIokKiMDDc1ysai1eK1pU4qWosr9xHkA0YQ2JgGteUhDI0rcI4VIzDISqyXi/Y+PYMslTDT1hQpMJt52dLQz1tXP1+nVK5ZUQLTNShjeoMRePZ/U3CnwZhghRAx2bk6fP85Ofv8PtWzcptLbzwtHnGb0+xvzCMsvFMsmUzPNIJJNs2zrMz95ymb4/S7B7K0IRDPW0Mn5nhvHJGUorNUZ623j9uYPsHtlCOpNiMJtioK+bzx2pcGPiLsfPXeH89dtMz86H2edyHI7rQ2RP+w7g4/iCKIpSs22qNQu/skx/VzNfeW6EbQN97BjqRVNVWYmjrlXSHtwUVSWwbYRh4NdqIBR2DfVy9YNLzMzO0NXZgWmsV26jFbA+zC+VQunuf5fFmSk+98pBdg/1yZytNZwPQghU00BxPWrlMolMBi0E0lIeOcl8dbs3s8jl8Vl2HjxKLrcauXJzufRg2Rdd54fJsATECKrapsZKvX0mhf/q9jjCep3zdPVvjyn4gyCQDG+aSjqdplKV5WDJZIJ9u3cwOTXNW2+/x56d23j+mSc3vY/nerFFGfdFVUgnk2QzKTrb2xga7OfGrQmWiyXM1hYuX71OpVrj8597kZtjE0zfn2Vy6h6d7W0sLi4zfX+Wpw7uJ51KEAQBcwtLfHzyDLu2j/C5F5/n/KVRLl+7ye3Ju2zp6QoZ6zZ2AQkBqWSCdDolOQbWTFOpvML3fvI2P377fbZtHeSvfe036WxvJXKRr52zSBEytRDy1/NxfR+dxrLCR38Pi8UyS6UVCQIiINXgzjYMBdPQSaTTmJqKpq0+FCzLolgshpUOTtw/CWOsxeBOvu+ztLyMoRuSZrm5mbm5eQqFQp1SOXysH/hxCCLwH83l3xhLDYIATVVImwYrlr3x99d+oKrQ8Jzo74oQNJkGv7N/iFe3dvJvTtzgJ2fucXpsntf2dfLqE8M0F1JEcqq+BRpeRGj1o6kQxiL/5IMJPF9QqvmonofPemz5fFMzz7zwIlfbOjh7+jTtM6f4fK/OS1u3hG5XsB2H+ytV3rt5l49mlzg/swi6QWchzb7hLj733NPsHe7DcV1q5TJ66H5VDBPTMMOx+riWje04gCvBrzQV1gpfX9LDRqBgjYBbhq6jpRJhCMHHty2JYiiEpH4VdWHcOMaXn97DH37/OBcuXKC56Qi5XE5m24cKaYQnDzI571HiwnVwKpl7UqlUuH1nkhOnz3Lp8ii3x26CgLaOLr74xV/j0MH9aJrG1PQsVctaR6AVrS2BkAx3isrWLZ2cvXabpbLFoe19/Ne/+xtkk0kUXY1lilAVsvkMT+wd4cCuYWzbplypkU4YlKs2juvieD6WbWPXLCzHxbJtHD+gWrNwbEcCDKmCzpYCg33dZFNJGZN/1JBTfRRyH3kBmmFQq1RJGDIE4bsuz+zbwdsnrnLq1Bl6u3vo6uognsTGud1AAQgCSdj13gcfc/H8BV57Yph9I32YSWOd4I/nRmqcKLaAQJLqOLaNoqgb55/wIO9fwMdnRhFNnRx64QWy+dVhi0fBLNjwrw3XeWGiqvSYPZpY//+B8P//rtmei+t7JHWZEWuF7HmKELQ0N/MXvvwmv//f/8/873/wdbYO9G8q0Wbm5vn6n32Xr//Zd+PP0qkkv/3rv8qvv/k6QsDzzxxibPw2p89f5pXnD3Ph8nVaWwpsHxrEdVwmJu8yNjHJ1qFBRm+MYRg6Tx/cFx4kgh/+9B08z+ONV19A1zSOPvs0d6bucfLsBfp7uxke7EdT11uaQSCt0Lc/+IiZ2XkO7N21qoTSth3+w/d+RLVmUalWJY73mti347hx1YPryiqA+/fvgxBoQQCux9KCw3JoNjWWQCohHG6kMESxuoiu0/NlXDFj6JjZFGZCYqKvFfIAtVqVUknSBlu2Iy1GTcM0jLAyw9jUQ2M7DtlslmKxxNzcLC0trbiu5EjI5XIx3j+Ex2xsAm54uwe2KB7nIlax923WtvQPMDc7y8L0NbJaadPvtadT/P5L+5hdqfKHJ2/wzQ/u8JOz07yws41XDw7Q3Z5H1dWoE0jc2lAYqEp8kPmex70lC5HKY3s2169dp3twO+l0piHZqqGozXdRFyd5vVXnxa1dqw+zIKA3n+E/O7SdA/cW+eh+ictln1kbPr4yxdWpeV49tI2vvHYYM2Hg2Q52zcIPajK5VTdAU1DNBKqJFNyOU682oX7OR2tT1TQ5HmWNleZ5OLYEc9JUTZbPKbLCoFKuYDse6VSCRAMj3ivP7ucbP/qIK1eucvDAAfK5HAiBKsSq8j/XdbFDuO6HHdqSGdRh+v4Mx0+e5sOPPub+vXvkWtpoKTTzq1/8AoMD/Rw8sE+iaxoGlm2RNAzmFxaoVitrxi2xNOZWHPwgQFMUvvr5F2nNZZgrlunqaJdodVG3NuqeqmAkExSSsqrBTKXWn2fRe/WlpRkrS4pYL0QftwnZBz+E2I4+0hMJ7EqVnp52chmT2bl5arU1zH8PsSQc1+XKtRu889679BaSPLVriNaWppj6d10/GpqiKuD6oOsxGNc6izpEKBWbeAVmF4pcuj7F7iNvkMs+mK/i07RIKTdMA9979Nrkz6Twf7Bb+GGukF9evkClXME0TBIJSZ1br7WXXAP9fb3853/pq/yLf/3H/NN//gd87uizbBR0zGbS7Nw2zNbB/vgzXdfZNjwYJ8688MyT/NE3v8PZi1fYu3OE21NTHNq/m0JzEwN93Zw5n2Lq3jSTd+9x5foYmXSKg3t3EfgBjufx03c/pLkpz8hgP5VqlR0jQ+RyOc5evMLBfXsY2NIbP7taszh24gy/83v/AICaZYdZ8Sa/85Uvxsk2AJZtMztv09vdSXM+x6XR63zjWz/g7/2Nv4SmyTK/hYXFGApT0wz6t2xZNX7Pc2nEs45Q7XwvCkYICMtfopbJZMKyvtWJgvLgtKlWi1iWFbpJpSWUbCjHam9rDREAV7+LjcrxokQjVVFoaWlmenqG5eUlcrm8zLQulQmCdDzGh5X8NbaN1qMErVIkmY/vPvQetycmuHXrFmJpkWATptnGPrWlk/zDo3sZXyrx706M8uNz07w3OsvL+/p45Yk+etoyYccaHyuk9a+qCM+jNa0xLzSquU58xeCdt3/Oy0Iwsm1bOGqB57rM3r/PheMfM1y6x9EnBlYnxEWu+bCUbNlxsHyfFuHQIqpUPY97syV+dMynrZDntef2I4SIXZaBCJVAK0I+i7wCktgK35f/X2PpiMCT3v9wz3q+hAVXFMn5roduaNt2uDY2ybd/9hF3phfxA+lMaMplGNrSQ1tWrj3XtrgxNsbE5CSFQhPJhlrzIHzJmqbJ8lPHjWli165b27aZX1jk5JmznDx1ljt3buM6Nl3dvXzpy1/iif176enuQhFiFeAXSCW5o6udi5dHKa9UGgYLiqajJ7O4tZUQQlmuh5eOHFqVMLmpkBRrxFmUFblu4Yb/VcU6ESgas84+RYuBeMIu6rqGW6uiJpL4QcDo9dvYjoMWvc9HQN0LggDHdblxY4x//2ffYnlumr/xF99gaEuXrNx5BEGhqRJOXSZYEuabrJmbh4AmfXz+GkrrFp556SUSqeSm8unRZJekyY4A4Bph4CPMj1XtAff7TAr/jScgOiAf5gpZc1U8oY+TOwBWWL6UTCRwHS/Ex19dYqJrKq++cIQLl67z4Scn+U5Fki2sbZl0mif27eFXXn5+VQ88XzLJCaHQ1trC8MAWLly+yienz1Ot1tg+PEyhOQ8EdHd1MDk1zenzF7l7b5r9u3eSSadwHJd3PzrOcrEMwN/9x/8k3kiRm/vK9ZsMDfSFrnpZfpXPZenf0kcQ+EzdnWZmbp5/+Hd+l5Ghfta685996gm+8PrLLCwu86f/8UdcGr3Kf3zr53zp869RLJVIJExaWuS9a7UqlaolDa8GUgqhKgihxIayrGhoSEjRtdiad10Zt3Icl3JZIhNGfPeKImFPtbA2Np1Ox6iBlmWzEoIa6WHZUiOE7Op1IFZttkYrrqOjjZmZ2ZAVKx9XCUCwAWTm5msqqhKJynpQFCk0PZ9KrYYVr5XN73FrbIwTx4+xfPc2qUqFWu5h9ebE2dgDzVn+8auH+M6lCb55/iZ/9uE1Ltya5smhVg7v7mNLdxNxEoZfnwyha3zpqT7+6GwZXzUZ2jpCa2sbbe1t8TNKy8uMj99iauwG6tQ1XhgqbIio2bhjc7qOGfh0JzUODfbS1ZTiwkyRf3t1jndOXmX/SD9tzbnYmyIQKIaChqwAiJjc/CCQAs80pbQOAlyrhhutEYEkOVIkRLVm6qsOBhHGSG/dvsc33/qAOzNLPHdgO7l0isVSmbmFMm8fOxcLmHLNwp9d4vKlSwz2b1kl/KXHXYThJJmP4bquxMhooJy9dHmU9z48xrWrV3C8gLb2Np5//gj79+xmeGgATdPDTPvV1mO0flVFoau9gzOnTjM3M0u1WiWRTCIQ6IZOKp3m3nyRlZqFGXoulFDxAmKE0F9IQodj/YXvstFRHP6uqCqB66EaOrWVKirwb7/9E05fnWTREhzd07FKyd/0EbHgv8m/++Nvcu/2df6Lr77CjqFedOPh8fC4S6qCZ/nogBEyDa7q/lrFaU0flotlLo/PsufIG2RzUaLi6rMn+vlRjFbP8/FsiWkiywTr55aiiLjIVHbtwUrJZ1L4b9w+3XKrT+h6i+9BrbhcjMk7isUSnu9vUF8qMAyN3/2Lv8mN8Qnuz8xtqF1LWEt/Da2s7IoUJvKaw4cO8I3vfJ8f/uxd0qkUne2tmIZOR1srPV2dnDx7kZm5eTRNY/+enfGw3vr5e2iaxudfPbpqAfm+z4kzF7h45Ro7R4bp6WwHpNdhZHiQv/c3/jK+73Nz4g7/3f/0z/jeT97hiT27V8XrMukUzxzcz86RYWqWxUqlytf/7Lv87L2PGBkeYPf2EfQG9qmFhUX8sEwrCGRs1NAN6T4LrY/YCxD6LmNgl3juAoRQsHwf25EegWwmRVM2jaFJt2oc54w9kT6VSlXW/ht6yLxYP0jryXaRw3Q1ElalUkUPOQwURaG1tYX5hUWEopDLZVleltztQpTk+wyC2O2sqgq5XE7SMjc06bmQNM6e57FUrlCqVKTWTvDQlbhSLnPq+EcU795BcZ3H9mgpQsg8CEXQXcjQ253Hcn1+dP4ep27N88xIBy8dHKSQT0gLWpFqI0KhKaujODWyTSkOHDhIvrl5lWCanZ3h1PFjsLJEIXDpzKc37ENjn4fbcrRmE+iKQj5hoApBf9bGrJUYv7fAhZu3efXwvk3DZxF7oaqqaEZY0+95oGmouqwRBySr5JokTPn6687yqelZjp29zHK5wldeOxx7HeqlpPW9GgQB/+3/9g1mZ+dCJXCzcYblZZqKW7O4e2+ad979gNFr15m7P02+UGDPnr3s2rGdwcF+Ck1NJJOJmNcjetbG9xfs2D7CuYuXuHztOl09XezfuwchBK3NzTx56BA/+dH3uXJrkmey29eF5mISncc0gv6TtE0eL4BAFQR2gAikwjN67RbVmoNlOwwP7+Lzr79KR4MSulGLwoZ3J+/xf//7/8C92zf4O7/xMk9sH5TeO88nEEqIgRF6G6LKl+izyAnZECaMFLrHacdOXcayNJ489GSY4R+OVUgLPvayrJ0LRUg4+TD8GXkndV2P5ZBEaKz3L3jM/n0mhf9/2lK/h9+4Wq2CH6DrJpZlxdn+6+4UGk2F5jx/8y//Bf7H//X/pBYmcHmuGyefzM4t8M3v/pAf/PSdVdcnTJP/8m/9NQb6ugE4/OQBvvndHzI7v8DLRw6Ty2VDF7Ggs62VlqYmbt2ZZLC/j307t+N6LpdGrzNxZ4pD+3bzW196c1UGsjxIFD765DQ3bk2wfWSQqDZX02SZkOv77No2zAuHn+TYqbP87P0PefXoszF3uAhdkEEQoGsq+3dtZ+alI3zvx+/wjW//gN//u79HUz5DECh4noMTLlTTkLS+0VkmkPEzTdMkdK4fUCxK1r+WllzY3/rczC8XsS0nJsEwdR2zQclobEEQUCqWgQA9YUgPNj6e66MpCqqy1sVXx9SOsPw9zyOl1S06VVVpasqzvFxECMK4vylBVUyjDigD2E6NpcXlMBtdatta6FYMDX6EkHTUEk0uiOdkoxbFFZPJJDv37GO+o5Op8TFyzjJZ89E3d3TM31sukzJVXtjTza6+Alcnl3jn/F1+cHqSK1PLPL+jnSe395DOGPGViqLQk4OppEk2aWLqeiOaLul0hrbOTu7eWsEyU9xZKLKjY31MImhwNyc1FSOdCD/08QNoThi82N/Ot+9VuDUVJsRGG6uxCWRCk1BQNF0ejLYNQYDqeaiGhpFKgS/Bg4Qv0LXGuvwgvmelanHu2m1OXpmgr7udV5/dRyq5hplvTds+0MPY7CzF0sY5FzJ3RnLF356c4jvf+xETt24i9ARtbS288cavsHvXDgqFZjJpGUKK16UI3d6hG7fR/R0PXwg62trYtX2E995/n3Nnz9HW2kpXVydGMsGeXdt56/vf5ezlmzy5c3i98BcKAZtXl3wmWvjehRD4jovMsfWZmFmg4ut0dXeTyWQeyLUQ7euZ2Tn++D98i/Gb1/i9Lz/PgR1DJJImvivRNIXWoARF4SkI511Aw/RFK8jzPNRHTGQMgoBiucrp63fZfuAouXw+PA/qRHOBH8QCPCKFi7hcVE2lWpVMpmYDa2REuLYecXRtOPLh7TMp/Ne66eOw1WMpBQERRfDjXOd7HqXSCvl8HkURlEqVGDDDdjw62lo4uH8PQ/1RDF0utl07tvLbv/4Frt0Yo6+3C92QAm7b8EC8udf2T9dD3Pmwc92d7bz0/DMsLxc5/NQBshlpTQmgv6+b5w4fpKuzjZHhQbLZDK7nsLhc5OC+Xbz+ylGa8tmG+ZPjPvzkAYSQCYbVao32thaePLCX4cF+aZl7Loau8+Vf/Ry243Dv/iy27aKpOrt3bKWro43WQlNIpmOTz2U5+uzT2LZDeWWFWxN3OLh/DyBLaUxDJ5vNoqoK9xaKqEDaNFBEHVXPDQmCPM8jk0mvU6xc16Nq2asEv6FvXBcbBAHVqnT5ZnNpKaAa9rHr+7hhElREtNQIFhQlACaTyTXxaoGh66QzGcrlFZSsSjKZIoq5RRn+vu9Tq4X3Uuu0zHW6UBXCBEFDV0mYOpbthCxoa5qigu/Fa6VmWczNzVOr2XiOS1ZTSK8DGtnckhPArfllbher9HSl6GlO0pZPkE600dua4cbUEh9emeabH41z7Posz25r45mdvRgJDQF05xPcKc9xd/I2PYMjGA3u1paWFg4/e4Spnh7Ov/MWY/N3NxT+mqauyuWIuS9CxMeMqbO3M8979+9y7sZdzo6OsX/HUDgHQTy8CI1P0WTf/JD2Wtble9g1S5bj6ga6ohC4rqS9DpHjJJmQ7MPo2B1OnL9GLpPhzecPkk493I28f/sWbtw9x40bN+nv66WlpSDzVcIKg5VKhY+OneDs+YvMz97Hdn22jmxl146d9Pf3UWhuJptJo2payNApw2Cr3lco+EVQB6tp/JtpGuzasZ3bd6a4cVOijL72uc+RyaTJ5vMMbdvBjckbLBTLdCVNWBOHF0KEFm3AupKetW1dXgv8J/EYBJJbIfD9WN9Twj2q6Rpd7S08uWOQhRPXsG0by7LwfElhHIVc6reS+3BhYYHv/uCHjF68wG+/8gSHdg6RDJPhRCOxlFgzpsatJEKl1fVj13qksj+KLPGDgAvXJ3CSzTx99Oiauv5QriF5WzzPx/c8FFUqg0roETDNugGmqBEnxqMJ+UeppvpMCn/Z6hp7NNhHE+ChVhVs/JI2UgQaPyuWy5imgWmaFItFAgRmwpRkL4gQMe8Nsul0CA8rX1rCNHjlhcMc2rebfC4bc3y/+NzTPHlg74Y9VRRBcxjTB5nk8uXPv4btOLS1FGIgDQS0txZ48dmneHLfHrKZjDzUUNk5MsSWni66Qpf+2rGNDPXT1tJMEATkshlA8NUvfZ5kIiHxpV1J2tPb1cFvf/lNBALT1IGA144ewXZs8iFsbzKZJJFIYhg6r798BNfxaC0UEEJQKhWpVCo0NzdjGDrVckVCZRq6JA3SpRBwXZdqSJ+rKBJ+17LsuFpACCHzLcIQiaooZDNJTENft+gj13utZpFImBi6IaEzfT/G9A6QMd5GDiHZD6nFq1FG+EaJv0KQMA1q1Sq1mtTC/QDsSlWyFKpKTDEqcxQifgeJKyCfo8ZMXZ4X4HgBAQqKEoThjlUdW/X8+blZrp38EGV5Fr9apqR5zCabaKsmyZg6uqKw/kCu38/2fX44egdfCzi0rZPO5jQgSJkaW9oytOaTdLekuTq5yLsXpvjW8dvcvF/m2Z09jHTn2NmbY3x0nluj58nmm2np6IozmnXDoNDSAsCt9m7mb0ysn8BwDqN33ZgDEitEQjDQlOG13hw/uDvLn/z4BFMzS4xs6aSzkCeTSaL4AteR+0zR1DDZyUczNKlECAHhWnY9H0PTELqBpngyw9+V/BiqpjKzUOTC9dtULIdDu7cyMtC9Yb/Xtp3DvSTeP8vdu1PMLyyQyaQJgoCVapVb4xNcuXqNYx8f4/atMbzAp69/mNbWVp48eIBcPhfDG0eKp+t6CCFDGKtRA9cL/vhvikJnZyfPHX4a26px8co1MvlmPvfKiyQSSZ555hn++N9d5tK1cdpbmlCiWm9Rvz7w/Y3y1dc8qOHnOFzyGO1hkYWAUNj79bzTxqQ5IVCEDOVl0ilefmYf88UVrt68yjvvf8ArR1+gs71Nzl10y0ASCJXLK/z8z9/n1MmTvLh/kKOHdpBJybMOASII195GHdzgoyjMBI1HxIPnIwgCalWL05cnGNr1DG0dHeGwpWEg12993IqioDYmiYbGYkTrq6yx8tefgzKJe2ZunoXFJQzDYKi/76G5EZ854b8+EeJxtc31isJmP4dPiZ9hWRa27VBobsaq1XAdJ6aljZLnEqZBX3dnDKwDEUgMQoMAACAASURBVL80FJrytDTn4/I7IaCzo/WRtLCoL73dnau8npGLSFVVCk152loKRIAt0hXYEmqL6+dJWvyJEAug/nk2kyIIpJCMFraqKAz09YTXSRrWfDaN45okEgkSIemQ49isVCo0ZbM0NzejKAqOIyFII6IjIQTlagXPc9HUZBj7lktNxm2DEGZXCvxarYZt2yiKQFU1LNtpGL/A1HW0DcpoovrdIJCJeELI8itFCLxAgqb41DdPdPj6gYy4K/GBvPn5FiVtKaoSkgDVsCyJ0hUxPQKk02n00KqL1m2AdOHZto0QCr7noSuS1871RKigbGRhybawsMDy7DTu3CQ4DiuqoFQuc+HuHAlDJaUqtCR0EppGf3uB3qbsqvsdvz3D6GKJp3a2s31LgVTSCAOrAYqATFJnR28T3W0Z2gtprk7Mc3ZinnuLNfb159jZU2B3u8Z79++wODtNpqmZRKoe25fASCbZQiuL3gbhGFYL+o1clYqAnKlztK+VFec+H966zbcXi/R2tNLekqM5k6KQSdDRmmO4v4e0GnpHImVCCFAEqhL6CjypBPieJ+G0dR18ec3iUpkTF64xOjZFT3sLz+zfRiLxcCTEwPPJJhMkdZWlpUXOXbjErYnblCsVZmfnuHvnNpXiAluaTF774hGWylXO3Jzm/LnzPH3oEE1N+XifxfgQsRLgxtZeozs7WquNTQDJhMnw4ADLy8v8+Xvvc/7cOYYHB+jf0sfW4UFSTa0cvzzO0af2oOja6uWlCAIvCBMexcNkWP2hj9s2uyYKecXpN3VLVgq9+sEfqAqB5SJUla6OFt547gArb3/C+RPHWCmWOPLcs2wdCmP4QmDbNjOz83x07BgffvghQ+0Z3nhuH/lsWgp6LcJBkOiNkXc/SgzdrPm+X88vaQwPPKB5ns+1m3eYW7J5dteemEAsujRWggnHvk4xDpWh6OeGz9eeEUEA43fucuzkWSYmp1CEgud7FJryvPnaSw/s52dO+K8W1I0DbXTvPJ4XYNUn666tC4RSaYVMOo2qqhSLxZjTPYjgRKkL3WjDblTaubYUbKPyss3GEf3eiAYXJcnJmKAkgZDxagnVu+nog8b71fsTCSdVUeQKWKOc+IFPZaWK6zrouiEFK4JKtUIlrGjI57KoIaPfcnEZoQjS6VTc36rj4QdgaOoqxUQmrWgIRYIlRdZ/pVIN41+ytj+pa9ghIttaoRGESTJR+ZRpGnGMMxYygcQz9AkaNOeQzMX3JWKciDbc5nPnedH3NVxXlhVKqGKpDESgQaZprgtfyHJGv/69yKJBKhUx7njYTwL5bqJwh6IoNHX1UTYNqCxTUC2SBix5UnlyHA89AN/16ZtZ5qnuAjt6O0jpGks1m5/duEtz3uSpnV205JPyPcQeXAGBj6IKmrIJnt3VxdbOHB1tWS6OzfHTc1PcuFeiuyWDY+VYXpij3bJWCf+480GwcU5M/M6VdYKsfrEU3J3ZFG8MtpMx5rm+VGP+9iTjN13QdBJJk96uNo6ULfYM9dCcS0lhGi9uZIKYphIo8vATfoDnOgRCemccz+fMlTE+OjOKaRgc2jVET1vzgw+TIMB1XPADVFWhtTnH1bszfHLsI0xdUkDjuzSndJ7a18/WLV1sH+xmuVyls+0a/+o77zF69SoDA1vi+YmVUKS7O0Jni0CJFBG6pTcxGJRwn+3Yvo25+XlOnj7LmXMX6O3pZnZ2HtuyuXhjllt37jGytR/WQrxGSkXgrws7/MJtI4s/tvIb8hgigR+539e20PoJAhlSEYpgqK+Lzz+7jw/PXefmlXMUi0scPHiI3btk1dPEnUlOnTnHqRPH6W0yefPZvfSGUMdCEfVyvAjxMwjP8yCQcM6hP0R6khq6H8gQiQj8zfwFuJ5HtWZTrdYor9SYK5Y5d3WKfPcA3d3dRIagEHVkyHWWfChfIkVko8qZePIa2vT9OT765DSXrl5noK+Hnq5OqtUa92ZmHpgbAZ8p4R+6aVe56zeegPU88Jv5mTa2hlc9NbQCy+UVhJAY76VSEQDDNEO3roy5rEdye5RYmNjk583OnFgtjq8JAj90hYVx5jC29bBylY3vvzocoipKCJcrnyvBSmxczyWZTJFOp3Fdl3JFCn4/CMikU2QyGTzPpVQq4vsB2UwmfKa8hxMm0mmaum7eVFUlnUqRSJh1azw8IEqVGhBgahqK4sehuehgjMbl+wG2YxMQkEiud2/JREkVJUquW/Uc1pVUbTZ3ruvI+6gKVq0GQUAqlXzIxoqigxKfXp77Eo/Bq1k4nhcqAnXNHqGihtdF0KrVcolsUzOamUCrzvFEi8vWVoMAKK5YlGoOxarD/OIKN24vMHWpyOGqxYGeTk5OzTJVrvKV/YMMdOQwtTWZxYJICwGkUOlozfBmS5r+thyfXJtm9PYco1PLaOkqyYFyXErX2FzXZXl+li7hUnXcmNa5sSlh+dt6GFPR8B1Bdz7Nm0mdu0tVJqs2U4tllpyAyUqZ0WtVapaNoQgO7Bwgba6x2KO5VAVaeIB6jovve1RqFuevjfPe6ctoms7zB3exZ2QLIhLujbJxlXIuY9EIBU3X2L9jkGRiCs/1SJg66VSC5lyG3s4Whnr/H+reM8iy5Mrv+13/vClvutpUezPtx8/0YAwGwMIsDHeJJUUriVRwQwyK+4FUKBRShBghhYKhCOqDGFpagVxyF1iKcAPMYAbjbfe099WmTJvq8vX8uzb1Ie99pupVdTUWH0YnApiuqntv5s3Mm+fkOf/zP/3SO6WpdOUNXn32MD9642NujU/ILIQVeMJoo1fDdqJ00EDImH/DA77qG5eHju6uPHt272Ji6g6XLl2iK5fhyuUrOI5LqVLn5IXrjG4ZRjdNOefh+KiaStDidv6tSssZJ8pwaiDpQZ70I8XXqfGW+5rGkBqCZlUe2z1KMhHnzPUJzo1N8s6vl7h/f5p8Ps/t8XHuTNxkR2+Kl5/Yy4Gdm1GjNd96YlcU1AjNF/YtMkwEAoIWf1wYskJRCXyv8c1X6zalco1StU6lblOtu1RqLuVqnWLFoSpU/FQ/R489QzqTRmuQFrXrqcZBRY2KJj18Rto94wpjt8e5cXucrSPDfOfrr9LTlcPzfO5NzzRSu9eSL5Dyb27sG70WHi000HC3QQtJhJA14Wt1enq6JcVluUI6nSIWuvw30o+HtQsb7efqa6KToNYCKJNsZvoaB5eHBd1o649UpuC6jmTPUiCbyWCaJq7rUSyVcB0HEFiWSSot3cuVShnbcejK59vQxXXbAQFaSJ+7MiQhaXX1tn4kQ86CiuNRqdYwVTVkeNPxPQ/HCWuYh0rb8zzsuqzkp3dkF4tCL03viSR7ESGAZ2NzV6/b0rOgadQFbS661nZWoYUQDc+B47r4QuCEVbbWAvuZofs6ep/7d+9QX14EVUUNfLqSJjuHMmSSZrhhyfNKqWzzzpVZPrl4h59dm+JOweazuzN05eMc2zlA0tDDc03Yx0Z3V5+8FEXhwLYuDmzr4u3PJ3n30jQ35yr4gpaKd1Cr1yiXStSqFZYX54nbHpdnFhlIJxnMJFBpXV/SmI2+P5QO8Nfwc0zoBtu7NUaFwB/IUnU9bi5XeffuMmN3HnCmK0tPV4bNugwfuZ5POhlvy3eO3kkzDXzb4eKNSX76zklcX/DNF47z+MFdJOIxgrC0cKsJr7TkNAgUDFMaW0EQcHTvFo7v2ybBeuF7qZqsTNdmQCgy937v9k3cvj3O/MKCRPevYXBGeASEBMX6YU6+qqp0oguOvI79fb2Mbt3C5ctX+fHPfknc1HnuuWe5fOkiZ2/e59jkPbaPDGHGrSbmJZrzSCk/bE/a2FYSXhvSd4eTGe2xURbMeh6WCL9DaHD5gUBVJVujokpPnhCwc3Qz/f09DHTneffzK7z91hsI1SCbMDiyY4CvPH2Iob4uOZ5rGRmNgSRci83n0yjzrKAEjRqB1Oo2pVKViu0yu1xhZrHMUtWhUvfxAtDNBFYyi5FPMjQwyNDQELv37G6ENkXjWS2GXwePpvxx7UEPnSKN6+YWlhACdu/YRk9XHkUJKy0qkll2PfkCKf/fRDrzaDe4s6MJjYZdSFesH06yVPwu1WqNfC6PqmosLCxIgJppPiSlZKOGysaNk07PjtzU0TMkMjTAiIqQKK1bFx3+/XBRVRXbrlOpVEBRGejvx/d96nadcrkCSOSpEALLsjA0nXq9TrFUprsrtyqtqGrXpYfA1NEehiqOeqwomKZBTy5FsVyh6vmgyHZ9P8Cr1tB1vcHa5zgSxCV5Eh7u9ZHgRokQl4bHxnY1x3GwYmmCQObmr8YeKCv+2/573/dYKpWp2REz2Rr0m4GPs+JP3f2DaEFAtbSIas+ho8gzS7hJEoYcsukY33p6K4+NZPizj8b54We3UUWdgd44M0tldFWQyyawDJlu2Nj4V5UobhEheG5PL6m4xZ9fdegaGMSyYo1XKywvce3qVQgCPF/wIDB5fWKRwUSJv7R/hIzZzomhaao0YltwJs2mmpt/RIqkKJKuNG9ZHIlbqL7H/fEiF25Pk0zGWVwu4Xge5WqNvaMj9PfmV6H2XddjbPweP3nnJDXb4xsvHJWc7roEvima2pjP6KTXrvxViQ9xbHwhiJkWqq6tGyNulRefeIx/+oPXOX/hEj3d3bL65xp7QQT0k96qAM8LGoWIZNfa71NVlVwux84dO9i1axe+7/PY/j289OILfDSyiZ/+5Cf86Fef8u0XH2f3ji1YltHwIkjUvww7rORD6NCxpqzxyTRd+gL8MJShIMcqMjbXECHCGLwfyDCZkBkipqkhRGQwhgRF4X6cSSd59th+DBXs904xPr3Ipk3b+P1XnyadSjaNsUcxXJTQI6pqaKEXwHc8KrU6Bdvn7vQck3fnmF6u4igxjFSe/MA2tue7ySQSZHM5uvv6iKeSxOKx1fn2aks4b4XSb3Zhpdf34RJ9S47rNoC1ddvhjXc+oCefb8u0WSn/P1L+neP3RNjYlpiiEM14a4QojURRJIpSMyQdZ7Vaw7Is4ok4i4sLBIFPPp9t2aBaY+/NCXpEff5I0hqf98Lyo5qmhoQPfqgA20+Zv4lERpLneRSKJZKJOF1d3XieR7FYoFa3yWbTmKZBqVRBVRSSySSu51IqFUkm4pgdqmvZjjxhJyxj1UfQOoYregMozYp94Z9jMYuYaWDbTpjq4zQWuWm2M1yt955umDesN9zSG3GxCfxAoKk6tuOBYF2MRSfRdEnM4fkBmi/waG4CD5PnX/gSACc/+5S5s8vkkgEJS2uLnwsib4TC6EgX33lS8NrZO2RNjaU6/Ml719nem+SJPZsYGciQiBvEDA2zTfF3+LZ8Ocblukeuq5uh4RESyWa8P5lKk06nOff5SfR6mZwOfnaYqdkHLFfqq5S/pB8NqyO2uP+jUEwEqDUMI8z8kH/3PBfP8xnMp9hcFFyua7xzfpz3P79M4HloKmzqu87XThzjmWP7Gs+s1erce7DAn/zsXZZKdf76t05wZN92EjErLILiSLcySFdw6JJWlRBEqAgCz8P1vFAZmRtW+pE8cWgX6divuHL1GsePHZFKYY2No/XAApL+2fMlKFcPa943xgwamJ2BgT5efflLpFIptmzehOP5PPP0k3iex09+/GP+wy8/4G9/22R061Aj3UxRVQLhg2jPgnmorDAEGmfZFhCdqiobq3sf3i9LLIdGuWmCEuJDfFnZMfpWpNEqT0WRnbF/11Zs1+Pn733OcqmCZRqy4FOn/m5QZKhHULcdFpZKXB2/x9R8hfvLdWKJLJu2HWL76HaGt2whk8+vxroozed0erbS6H/z7ytTADsOVlu4IPqdynB/H5eujnHxyhg7R7eSy6QplirMzy8RM81GPZpO8gVX/u2Kt1XJRYMbiAA3CAgcrzGIqqqCqqJrKpqqryjgIMJDkwRN2Y7DpuFNlCpl6vU6fX29K078rYP+KF/K6nd4NFM0NGKCAFXVwlNRyG626sS2sUXT+rvotGXbLktLS3T39pCIxfGDgJnZmUZc0TB0qtUaCpKNUFU1AkeS+XR3d3V8rh/IWL1prfaeyCla+6Ru207bh6GGsftkMk4sZlGr2dTr9Tbl0Xx2Zy+Q67ohOFNbB0izWhzXbaRoOXYdVVM6sDyuarHt/VRFbXAbKMoaRmOnJdLyO9dxSOBhmWHBGj9o+v/CkrxCQN3x+ZevX+HIcJr/4Q+epFiu87NTU5y7s8S5qcv0pQ12DXdxeOcg20dyGJqKucZJNnBcNMukUHdIdvVjmO2n6lQqxZat23gwcZvC5EVODCRImjXGbZXeVKcUo1ZvXHPOgiDAtmVJXcuyQmPAb1Ajy/x2ix7TYMissJzsYtoWVArQ2x3H1FWuTEwSt85ycI90ybuez4efnOXXn19hvlDlD7//FQ7uHSUWess0VW/UZo8UVxBIZegH0tgWSLY0Kx5bO0a9AXnl6UO8c3GCk5+f5sRzz5LPrS7sIlrGJhJFUTB0TdYm8H00rWn0te5zXbkcmXSKmBWTp9VqDT8QvHjiOUxD59//+z/h7c/O051N09Wdk4UhlSb/ScdCNZ2ksS5FQ/mKFq+kqqkbL+4TemR9V6YZ67qOYugI3ydwJCZGUyVJU4MC3JMMqbKao4IfBBQWCozfm6NQqbOnr+ehOJ6Hd0uGkZaWy5y/NsHnY3dQkr1s2n6AI49188TRIyQzmc4eYYXGXuEHAX7LXKnh70QgGnPdiPVHE/Hw3nX83YF9u5hbXOL1t9/nn/+b/8CObZspl6vcnrrD3l3b133iF075r7aYIiUv8L0mT3rEziZd9DpqYnUeuHze6p8F0m1cLlcYGhrAdW2WFhbo7et9iIt+IzvASoX7aO741vd3PF/mVYexQFVVGvnwG5PVnpII+Ob7AeVymUq1xvDQAJpmUK1WmZufJ5tJk0wmGovctm10XSMWi8mN2XPWdH/74cZt6kYzh36d91ypxMuVWnOZyyNOW8gj2ggjKt2owpdptbcX3eOF4CZdVzfkJWiVeq1OIhFv5GJ3rOG+SlaEG0SA5/ttdLGr1kinJdIS9ikXluiJQdLSUTQNoSjNIjzRfDoevzp9l+mFIt9/fht93UkGe1Ps2tzFYqHOO5em+dXp25z9cIw3z02xbyTP4zv7eGLvZhIpQ57YQp58kPOoqhr3S4LkcHcjzASERmOdwvIS9VKBHr/KM6OjABza1LfmuGiaju9L3IOmSVZFx/XQDQPTMBAhRXMUAopITkASro3EYKxcIKEn2b9jkF1DOQrlKjN3p5h6sMw//Zc/ZMumIS6NTTC7IBkX/8Ff/yb7d2+T1fZWeApFmMmhqiFdcOskhPHgv6j8wTee5+KNf8/rr79Bb28PRw4dJNFSG0AOaMspeoXouhwzz/fbuAJa8RSqomLbNlZMupv9wEcoCi889wyXrl7j5JlPObBjiuPJOLFELHQsh0rHF6BvbF+T8XwQviyso6ghVmHlN7FyCxQr3k4IPMdFgfC0rxDYsoSwoevEE/HVW2X4/fquh/AF43em+cUHpzl7fYp927fw1755AqODF/JRxHE8xm7f4/XPLlMSCfq2HeE73/5durq7mZ9bWKX4GxkL0NhTV4osXLZaZLjj0T22Kw11yzR45YVn2Dm6mY9PnWVubpFcNsXf/ivf45nHj/K//5P/ac1nfWGUf8PtFVpNvu+3lydUFDRdl66dRwBstX6/jdOu47C0uExvXw+aZnD//hT5fF7mBbNePL9TfH1Vixvq11rt2LaDUMHSDfyQJU7QdHO3379e7fDOx0khpJt/ebmIEILNIyOyXafG7Nwcg4N9Ie1os41AyBRDTdPDdL8a6XS6QzvguhJAZbsuhUqFfDqFEbLcrX7/9sW/VKpQai3XuWLuolNQLGY1KuxFcyqNALctg0D2xw3DA61hnBUP7zR6QhoWXV1xfF9W3tNCVrqOHVxDNFWjN5umbMla6bIm/cbWiBCC8fEJKrN36PbqICzwfAlOgpCPX5auLdds/uTDCQ6OJPnmM7ua68r36crH+dYTm1kqlFFUDd3QuTVvc/7OTX52aorDW7t5Ys8Qe3f0ScUgAlRFpVZ2KDoqm1IZNL259hYXFrh4/hzXz56iuzLDseHuh/i0Wk5BqoLnSRpcAMuy0DQVx7FxXY9YPLZGTFSwvTvD+UqRqYJLNWOxabCbr+w8wvF9o7x35goXrk9y4eYZBnpy/N3vf41nju4N2dJavA0hm1zYITTTaBgF7f1VHtVRt6b8vd//Cv/kj/+cd95+l/7eXraPblv9fisPKTQdQKqmoSIpw4E2BRSBAl3XBSEkeVe1GnpSDL7zza9z/vwlPjxzlZ1bhmW5YkUJvUU+iCDMLljjRaO15guCkHhL1TWJpF81RS1KvvV9AnlvlNETBAG6YaIZhtzvbRsBxDop/bYxESiayocnz/Ozdz/nwVKJl586xF969WnSqcTaN25AhBB4vs98oURVSfGdP/hrdOe7GBwcZmlxoYEtalW+rQr8UTBgna7f6P0rqxlKHJLKztGt7Bzd2naYEpG1toZ8IZR/EATUanWABvLVMAxUs1nA5VEHt1WiD9/3fcrlCrVand6+HmJWnJmZGeKxGPHIvbeuPFoHpHJuva+ZYw+r3ydScKZlUiqWCMIPpVMOubx/o0ZIlOYmqNVqlMsVkskUuVwWgHK5xPzCIsNDA6zMqff9AFqMjCCI+mR2aAeWK7XGeDuuH/LYr54/PwgoFiuU6/UGG+JSsZ033dCbbvoo5m8YBolES1W1sL/xeKxR3EfTNGIxS8ZrDb2xWTY9HxtZSxH9r069Lhn9dL1Zq/1R1qOsMmhKpReWH35o60Lw+eenGbtwGm3pPv1DKqm4IcvXBgFKFD8HqpU6/+drV8gbHn/rK0/KjgUCWsoo33pQ4t5ChX1DOb711CiD3QnG7hf52akpfnb2Pm9dus9IPs5Tu3p55ehWUskYU1MFHC1Bb/9gg19cCMHc7CxTt2+ilpfpcQs8M7rnIW/TviFJL5baeGatVkcgGjwRncYCIJ+wGFA97PllrtTr7B3OcuzADvbt3sK+3VukYV93pIJr+KbDzc9bcf5SJFIfRKhlI1UbNcrDP/cNGgebhnv5xonj/Pj98/zqrbf5+te+wuaRTet+v43etBjIkQEbASejda0qKnoIwo0nEpiGEVbxUxgc6Of48aNcPfMhc8slurtzGKba1kjbISI6hEX9iJS+gmQMbA2BdPJYeAGO2wzdhRQpYSVOHUVTEaGHznddAs9FVTUMaz2+EvmsG7fv8dq7n3Ly3DXuzBb47pef5ZsvHP+tKH6EwFAUtg318e7Jm0zfn2brlq0AVKo1crkspqnLPbnDCf9RddPK6/8iTqYoXV1Vmmt4I8/7Qih/VVVXVURrSmdF+UgiBNVajVK5gqEbbNq0iSAIuD99HwXo7u55ZET+RmT1M9cHCtq2Eyr+Mo7jEBH7SNa8v1hfovK4nu/R29uDaVr4vouq6m2Kf6WUyiUMw2jMj8J6BBSEKYHh+zhOA+S18hZNVcllU6TTcebml9tP/KGYhtFAO/vhycE0125bVVSSyQSBCKhUqo1sgJXG1kbG0radRnGeWq1OLG61xfuboJuNTUypVg9jmdItGz5lzevHrl/Dc+o4PsR9j2wyQcwyoheVdQCAuu3x8ZUZLozP8lefGWGoO4ESYUIiC8XzuXLrPvOlGod39rN1OAOqymM7ejgw2k2pWOe9K7P88swEP/hwio+uzbB9IMudhSp3RDdHW0IW165c4fy5s7jz9+ku3KGrO72y6x1EjpPrygplUWUySe1so2kqprlWYR2BHQRcvLfI+cm7XJ1fZqHgQrXM/NKWVVdbMbOpuVrdqprayJJQVqTmrR1zfcj8PsI3+fUXj3H77jSXL5ynr6+PdDpFVy6/5jPWcwhHrJMyBz1ktVRVGV5yHEzLolqpyIJkmsbWzSNc+lxnqVTB9XyMiEo79GRJF35L24EE4kFISmOuoybC2gbNcI6KacUkF66yMuwnDxJ4Ll5oBOuGgfIQT+7iUokf/vxtPr10g+pSice742zti3FvcpLPr97mq88dWWe0Hi4iEAhfAqm7utL05Cxu3b7FK6+8jO9LqnFrJa/EF0hEZOQ+opL4Qij/Vrdg579t9KVWBZuoVmW6mqoqdHd1YVkxbLvG/Pwiuq7T3Z3fkOJf7aZZr88r74tcQ+srf8/zKC6VMHSDgYEB6vVaaAQ8emyotZ9+EFCuVFA1jcHeXun2LxTJpFPMzDwgl83QGkNs65PrE0tFJ1e3QXqzlrQi2T1PMvfpqhby4gfU6g6LxQrVWm3tTqs6BB7ptGRycxy34e6XtdRXexPaGLPCNSB5GjqNeXOddBpaRZHGRjwmGQh1Qw/xBMqK65XGf9eeV3lD3DRBCMpArS6R0aZhkEkmJNAtCKjZNkvFEqVikStnTnL4wH6OfO/b6MJhiz5PvHIDYRcRmU0EvXsRsRzlhSXGLv6CXf0TvHRkGxg6wdAxgq6doOhQnaN44yST1dscf/4VDr/0Mt62nWAmEL4LlXmSi7f4eu8lvvbkZi7fmOHTpTyjR5/lxJYdxDJ5unv6UDUJPiv09fAgHee+W6XXhO8d34/YcRhG9qBoOszfhUsfQbUImW7Y8wT0juArKszdwxq/gDZ/D8+xQ0+OvqLoiZSa7zP2YJEzt6a4OFdgHh1Hs7BSvXRlTAJVo1B1eTCzyEBYUEghcsWGa1BrerEC15PhhEjxN066682b0lwgym8YBghrTQB8/+vP83//2et88smH9Pf38fwzTzWvW+F4WPHjKolIrILQXQ2yVLdt26GxHqdSraKpKoViAU0VsjKj62FXwtoUht5gE5QK32u8Yys1sBK9P0gXvuuHxbkk2E83dMx4nMiCUAAR+AjPCUN1QaPPihKCeHW9MRdruplVvwAAIABJREFU7aFCCM5evM6/+c+/ZubuDMf70nznxQMMpROcmrzPL8buc/HcZQ6MbmLTYO/G5qZ1DsO0aZChFUWVXCLDvTkuzE6j6ybT0/fpyueIuCpURTqJOmHJWoepvbGHBMXWDN9u8D4hPbKPUM0X+MIofynO0jJ6Jt1kgQIe7Wtrv7ZSrlK3bVKpFKlUCtd1mZ+fw7Yd0ukUiUR8XeUdBAG1ao1qrYbremsqYVVViMdipDKpDuVjH/4OUbzJ8zwM3aC3txfHcymWyqSSibZ0ukc18BRFoVIuoyoK6VSKWr1GqVSip7uHer2G7wekUsmO41AoFNENvZFaWKvVqds26bDQz8p3CESA63rtz6jU0HQDw5ehnXK1ir3imlYxdI3Ng70NlCxAzZH8/RFT1nreICEEtWqtQR3cen3LqLSMjxxTIeCDT05xY3yCx48eZvfoFnxfeiM0TWdhuciV62colyvs37OTXdu3UiyV+fX7n1Aodi7zGotZvHLiWU6ePc+DmTmOHjrA5pFhPN/HDwQx06RaqfKTX7yJpqr85e9+k2qtTgk4sGcXO/fs55NzV9A0lfzePrL+LDhFqmqC6/MaZ69fo7+vh2/9wd+mMhojk61AehP+6Cv8+NefsbBU4NUnduMYW/jdv/syerafyfszvPnGKZaLZSzDYPPwAE8cfJKekadQJ97nMf8kO3Z/l8+mFT66eDPEe8hxjMcsuvM5vvTVbxD/na8z8dm7vHnlFM++fJBfj02zsLjE75x4gv57t+TYvvB7TOs53vvsDNV6nWdH8mwzY/iOjd+CxYgwGjXf58bMEmduTXFhrsiSZkE8hdE1xMjwCIohvQOqIphfXOb65AM+Oned777ypDzVA2gKrCi76rshO1sE+Ozkc10V9296nNSogMyGzyDNMGOrdGVTfP1Lj/Pjd87x2mu/BCF4/tmn19T0nX7dpg4UyRCoaloDAK3rBpWqDOulUilcx+H8uUsUiyW6s+Gep6kIP8BzvbA6Ig0PQtT9wHZWtNvESqiahm4ZKKoWKnwBQUDg2o0TvaLKolmGbmBYHfAB0Xt0+IiDIGBy6gHvfnaWd09fpbpY4B8+tYs9PRkShqzqeGi4j8nlCh/fnOLNTy7yt773UsepWCVh+qTcSGU/Fa1ZIVBVFbryaYzFIrOzs/ihp0qyDYYGjKqGBmZzNhSFJvgvWM9s6zAOa27o6y+4lbdFLKgNIjshOh5uIvmCKH8FEQS4i8vUZ+ZJbhtBsyz+IogbP/CpOzbxeIJYPEahWKBaqRKPWWT7ehro7bWkXK5QrVZRVZV0OhMCPtZoy3dxbIe52XkAstlsgx2w3VsQhHnj2qqJs+s29bpNd3cXmqZRmJsjlUqsavdR3f+e5+P7Qejm96lWq6SSSYIgYHFpucWqXdEfW3JV5/N5LMvCcV1s10GBBjBypbiOt2qzcj2fuaVlonrlUS379pmVPxmGwXBvvoFqBiiVyjiOBPLpRifDql2q1dqKFMOND9jpC5f5+OQZTp65yP/4R39IVy7dWCdLS8t8fu4ic/MLpJIJdmzbQrlS4/1PTnF3+kHH53Xnczz/1ONM3rnPp6fP4bgu6VSSRCopSYoQnLl4iV+89S66pvPk8UP0dHdz6fI0+w48xtWJe7z94Wfs2TmKs2dAekSAWCyB4picvngV1/XY9Le+z57nfheu/2e8bS9y/tY0P/zZr9g0NMDvvvIs+Z4XOX3lFm/99M+5NXEnJASReBRd0/npG+/y1Ref4dsvvYwmBHp+mCsffcq7H36G5zcNtSi3PJfN8NSxQzx9+GkWu/spmmk+OfMrxieneOqJo/T3DMK+p1hOD/B//Yt/x62JO3zlS8+SSSURs34jlKWqKo4fMLlU5MzkNGfvzbOoGtjo7DpwiK8d2smBXVt548NzTC3baIHH43u20JNP8cG563xw7iZvnrrK0nKZl57cx7YtQ+0KRci4cqT4Oyqbxom+hWsfUFEJIpx2IEI39l8s9qYoCvu2b2JuqcSbn17lnXfeJZvN8NiBfaxXzS+SVbthdBIQYZEqpPcQoFatYlkW/+oH/4Gx2+PEA5dAKDJDJASJGroeWr5B03iKxoQVjUnt1vx96GHxPafBOKppmiz7HFlKbY9RHvop2rbDpWu3OHl+jPM3pijNL3KkJ8V3XjnIQDKG0dgXBEnT4FB/jruFCmNXxzh/ZSuH9o2uHjAg8LyGQm6kJSqt/VMalpamqvTm0uDPc+fOFEMjI/gIfN9DQ2nw8kdsl1GZ3TYJCX2akJu/2LpZW1p2UBFmwz3C3V8Q5S8tsNimIZl6Yxh45QrO4jKKoWPmsmhrYgI6S7kk4/uWZVGv1XEdh2wuIyuvaWsTnAS+z9LyMkJANpvDNM3wegl8ESIIrSoJwNE0HU1T0XUTKxbH913K5SqlUplkMkk8Lmk9Pc+jWq3J+HHMJJuVpXzltxc0rjFNi3pdusR1XQ+Rzxt969XGUrlcDlPj4mEhIBPDMCgUChi63jF1sFgsSdbDfL5R575arRL4Pul0qkUBN2BBAGsaU83Stc3b2t9JIWmZ9HbnZUpW6xuFiFWtUVuhs0EYeR5UVYKfYKVFvbYhGbmLHcelbts8mJ3jBz/8Mf/N3/g+ySTomoaiqLiuh+04DcCaTHlz8P2Av/6Xv01fWOI2EtMy6cpn2bZlhAtXrjF59z5zC4vszudk2qLnc/naTep1G1V1OX/pKt/62quMXbvG808c59LrH1IoldgxuoVcTIFqHXwX1SkxmE/w5LHD/L8/f4Ofvfkee//OXyHY/Cx+Zgs/+lf/glK5wisvPEM8neXMhav8/K33Gb9zj+1bN/P4kYMMD/RRq9tcuHKNjz47zY9ffwdN0/jWcydQ9Riu51G36zz/1HEO7d9LPBajVq9z9cYtzl++xmenz5OKx/jy809TqbuSItu2pRt620GcVA///Ac/4uqN2zz35DGeP36Q3Mx16gszVGs2Ndfj3lKRUxMPuOMKyuh0DW/hK4f3cGz/DrLZNLPzi/zqk4vMVgKEYpBO6KTiBuP35rg+NUs2lSCfSlCs2bh+kwVTflQSHb+u4m9bAEjlJuT2GbQy/YWg1Q1/hmE7qqK0V7FDGs5PH9pJsVzlvXPjvPfhJ/T39dHXuz4Pe9TNVRIaAEr4b03T8D2Pmfl5Ll+5xqUrVxkcHKA4d58zV26QSVrkMvIb1jVJn4umgboG8VSrPRKW5Jb0w6JZ1joMEUhF+HAl3yqVao279+cYG7/Ljcn73By/T6VQZH8uzlef3EFfKk4ubrXRRUdhtq29XRwqVPjV5APePnlRKv/wtCvClPBWifhfUFsU/oqX1VAY6Mmh+ja3bt9m565dRFwIPpIiXAYAlHBfiKa7XRFrYTud6mE0B/a3aRQ8+vO+EMo/CAFQWkghC6DGLLRMSoIxFGm9eY6LoioYD6lT7Hhyk86kM/i+j+tKlLhlmqznkhdCsLRcQNcNEomkPPE6DsViEdd12pDwERI3Wl+qqpLPZbCsOJpm4Lo2lUoNx7GJxWOIQLKOKQokEomWtqXSaa1nX6/b0iW6siJXR2md9PZr/TCUkEgkiWgy4/EYjmPjODb5llN/lKdaKCzjeX5D8auqSrFYwLYlKr8966C9vdXkQ2vIildKxCx6u7INxd/MLJCAxyjb4WFjsXIc1220RVaetxRF4fylK7zz4Se8+uIJEm0hhNWiqSq7d4yyeXgQVY2qC8q/mYbBvt3b+fDTU0zdm+bB7DwH9u7G8gMKhQK3J6YY3TrC/ekZzl68ynd+56t05fOgqNyamCRmWWzfMkLGm0SpL6EIwdy9CeaXEhzYvY+TZwa4MnaLk5du8tT+43x2cYxrN2+zZ8coh/ftwvMF735yivE793jy6CFefu5xRrtN4l4BX7XYvukEgwN9/MmPfsrP3nyP5586Sq4F3DQ00M+BvbswdI1SqcTIYD+mYfLmex9yc/Iu3/nGq7jBcts4u9l+fvCfXuPcpascPrCXV559ggdXzvD22z9n/s5tbM/HQaHieixUHfRcF//wb36bTCZFPpsil06iqgqTdx8wXXTx7Bp7N/ezd+sAKoLPr45TKpY4sGMT337pCbKZJD25MBQVuTwjQphIMT70O1JAEU2F3bY+wjK4G3lOy+fYIHBZcUsyHuOZw7soV2ucu36ZCxd38MpLL3R6RDMutW6bTTevbdt89MlJTp+/wN2Jcaxkmr/ye9/l3/67/8jHF24x3Jfn8N7tJMM9yXXslqyClnBYWM20NbWtofQiYqhwP1ybwrezQlpcKjJ+Z5rbdx9wZ3qBmZl5SouLWL7Loe4cR7Zvoy9u0p+Ky1LNa4x53NDZ3ZtnYqnCtbFxPjh5mWcO7wZkHYrVtynSw+GH6jys9tccRoEIfBLxGD1pk9nZOZnS2PGtQvBoh32hEwHZatmYot54mLdhwbbdu558IZS/ELC4uISiSPpY0zRQdR0jnZIMUqqK8Dy8UpmgVkekkxIboOuNv7cukEq5gmVZmJZJpVwhyn99WE788vIyiqqSSqXCsr4FHMdF1zUSiURo5bUPboMiN/BZXF5GVTVy2SzxeAJNM7DtOvV6HdeRNKWmaaKHOdNNlkLRUC6+76EZOm7NXXfxtDKlNfivFRUhgkYctVqtSQIV02ygqx3Hka7/dEpyCIQn5lJJooNjVoxUyiQejyNEwPJyAduWZXOjHPpOEhlGuqrgPWLMK/D8hous1atQr9sIIYFM64EMo3dArO19WONOWnbqxj+fe/IYJ8+c5833PmbT0CCP7d3djkFpHQNFtl+p1CiUyk3gJIRsbtDf28PQQD/jk3d5MDtHrVZHVRXuP5ilWqvzzONHiVkW45N3qNVqHHzsAJN3pymWKuzeuY2edAxjeQGcinxwbRm7eAOR2swLzzzBv/vhj3ntrffYvWOUn77xNrbt8NWXTpBPGFy6NcWdew/oyuU4fmgf+3sEicUzUJkDI048vwv12EE+OXmW25N3uDw2zjNHDxA6XNB1nUQijq6qzEzfJ5PJ0tvTJfEwdRtVaSc+EkLw+gef8eHJ0wwPDfCNL79IUFtmbOwyM46LNjBEPhEnmUyQz6TIZVNk8zkO7RttUkEHMn/65tQMni8QqGQTFooQdOXTHNq9lbmFJR7cf8DYzUm+9eVnMAxdfg9+GMsNXbsbzuKRul8SKHUKTQVC4gk28JzWHzoZEwC9+Syb+rs5PTbN3PxCA/fQ2mYDLtbq0eggAunuX1hY5KNPT/LxJycJEDz33HOMjm5j3+5dfP/3vsuf/+hPOXdtkqHeLraODErufaGuEW5QQFthEET737oKP+qqoFyuEQ/5OOYWF5m8N8vU9AJ3788xMztHtVgiiWBLJsbolm66YyZ96QT96QRqqMwkqdva49yTjLO/L8utmw94//PLPHtsrzRSOhUSEiCipRogwX6+1K6NdODwPXtzKc48mG783LkDnQdgJYvlbyKrvasbuitsP3rG+ld/IZS/TPVLEAQ+lUqFalVSelqWJIIAqSCNRIJACxmlwjerT88CEBvsQ1FVnLqNYzvk83ncECVumoZkolpDhJCubt/zyebymKZFobDcIIixLGtdTnchBL4IqKPI+vaFApYVI5FIoOsaum5QpYrvV5HniAAJ1ZEkNNGJ2bJM6vUasViccuh6V5R6Q7GtajfKkgg/RAWlicDVJS1vJpNuAPZc16FWq2KaUpFHHOi2IzfxRDzRAEGWSiVc10WIgFjMCslYwrzodb56RVUh2HjkSdNUNF2jUrcxDD2cWkWW7HVcNF1rGDNrjD4Anuu11UzfmISKesXh6uCBvZiWyQefnOKNdz4kl82sAm+1iud5/PxXb7fxDwB888tfYnTbZnRNZ+vmTVy+foPZuXlm5+bJ57LcmphE1zSOHtyPaZlcuX6TG+Pj7Ni6ldff/QjP8zi4bw9pymAXUITkQS+VqoxNXKO2aPHEK99ky+ZNXLt5mx/+9HXGbo6za/s2juzfjh7Y3L0/Td22Gd28iYHuLPH6XZSFMRTXRqCgOS75PfvZsW0ztyammLhzj6eOHmyOULiuDMOgq7ubhaUC96YfYBgGuUwaIYLGOIpA8Nnp85w8c55CscT2bVvYOjJM/UGJY/s3c3hHD5apEzMNYpZJMm6RiscaHh8RiJYTL0zPL+N5CgYB88tFBroz5DIpju0fRVUE7396nk/PXWXP6Cb27d4q71daXM+/SYy+xWBovTvKBVdWbuqN+HiHtsKxW0kNBWHxGkOyUka4ipXrdqU/r5MakBwpNcYnpzh1+hxnz11kaHiAJ48d4eBj+8llJZfH8SOHOHvuHOMT17gwNkkqGae3Jy/7HQjp/v8teKGDQFAoVbhyY4Krt++RSaUQvseD2XmmZ+ZwS1VyOuyIG/QOZOhNWAykE/SnYlgNkjV5mPH9ILS1V3cs+lXcMhjJp9mSWmLs3jQzC8sM9nWtul7eFD0rPCWL5ncv/EBSPSDDsD25DPatO9TKVdKZNIFolvpVWLs4TySr63c8mls+whT4a4YN1n7uRpf9F0L5C9Ek+KiUy/iBLLMrFaOMS+u6jpFOIlIJhB/S3oKkO20hTqlUKhIcpmsUiyUIAoyHnBprNXk6l0C9GKVSCcdxicdjWB346VeKoijoikYqlcR1XWq1OrVaNXS5S4Wq6xIb4LqSVjgeizWoOyOyEzMWo1yukkikZDU7IQuPqKrWOeIY7TuhCysI/JBC1WsYLqYplXatVqVerzdOcgio1euysFHMIhGXWQWVSrmNiz4ejzfAWW2NriG6pq1PZbti3BLxGOkVdKeBCGT8OPCJG1azlGyH5wkREQ8JTHO9sMPq+9b6SNLJJF998QQPZua4dvM2H3x6mtGtI+1thhwMsr+CmbmFZh5+KOVKlXK1hmVabNsyQn9vDzOz89y5N41pmoxP3qG7K8+2LSOomsKf/WeFc5eucfjAPi5dHcMwdHZv30rSnkCxiwAslWymZsrcub9Aaf4cBx9/hhefe5J//Sd/zq/f/xjP9/nKS8+To4jiaaHRIjBMA01VEK5NUK9D4Mk15dZQEA3GRMdxaeUwuXL9JkKAZZq4rsvd+w+4MnaTvu4uHtuzA8Vv8jMEQvDhZ6dxXQ9N07g3PcPtqTs8tmmA4YSDUi/SSjwETWdlm6gKd6cXsH05ZyoBgz05Ng32kEhYGLrO44/tolKt85M3P+H8tVvsGt2E1uKO/o1EkaVdO23qATRO5m0SIqzXa1NROn69NFLfom+r42C0X9/wNHoey8sF7k8/4M69e1wbu8nde9Ps3rWd5597mgN7dqOFmCE/CIgn4px4/jl+urTIuRt3yaTiHDUNUok4IlL+68lDdJfv+ywXyozffcDVm3e5fGWM2blFDN0ka6okVNiaMOnrSTCcSbApmyAXtzDXqPAYpQdGoc/1FG0umWBbT5bLk4ucvz65tvJvFVWBoMWwECK0FyWWp68ri844C/Pz5HJZVKE0Yv/yulYs1hqDI1r3mY2vyajcb5v3YM218ZtbbA9V/oqijAA/AAaQ38AfCyH+maIoXcCfAVuBCeD3hRBL4T3/PfBfAj7w94UQb6zXRhBInnlV1YjFExiGQbksiW4cRyoiPQTqWZaJ2rJQYwO9CM8DRcG2bWq2Q38uF5Zw9dAcRzK1pUC1TITvS8CZLgtl1Os25UqFVCpFMpkMPQ9VksnEhhT/SokITMrlCrbjICoBvh/HsiwymSzVapV6vUbZq2KaRni6Dgtd+H4jxpZKpfGFrOila3oDYLZeTmwQ8rEHgU+tVscwdVmHvl6nXKmgQON0Wq/bVCoSEZzL5rBtm3K5TK1WBSCZTEqWxUesZBYzdOq207JO198Uk7EY6WS78nccl3qtHgIqVxbjWf08L6y8Jp/ZGJEV1z6adbxl0xCvful5fviT1/j087MUSiWKpZaUPtH0vOi6xkvPP0Vvdz5MjZMemL6+Hmp1B9v16evpZrC/l5vjk0zcuSc5FuYWOLh/DzErxpZNw+SyGS5fG2OpUGR88g6bhgboz6cxp++DK0sr+wJSSZOD2/Isu1CaOMfRZ77JW8ODjE/dZcumIZ46vB916lf46RF681kM3WBufpFCxaaaziPUPGptlkC3MNKbcITK3fsyY6Gvp6et7PDFK9e5NT4lK/xVZOrsYH8vzzxxlKN7t6HVFoAm0U+1WuWrL73AxJ27nLt0ldd//T693/sGw1YPhuugeNVGjLpxwu6gsJcKJRLxGHbgogiVfTs2M9CTb3wfmXSCnu4cxZrDzakZFpbLjXz/1jl65L1xDUWutuTrrxQhaJLktAfs6YAoaXolFCX87oPWw+iaXZZFqjwWFxe4d2+am7fHuXl7guVikUQ8zuGDB3j1pRcY6O9rIaQSoQKFxw7sY25+nvfffZuTlydIxmPs37FZuuYfNk6ddFtY/W6pUOTB3BI3xu9x6dotinOLDFsqu3tTuH5AbzLGplySHT1ZEobe+J5bRyZS+hETKdDyTa/fuYRpsCmbJOZNc+bKbb76/AZIfyRIQj4/IhkS0iZQbMhnkugqzM7OsmXLZnR9PQxWB88EobHQcAytvma90EBE4BSJqqohJ8FvDyi4kZO/B/yREOKMoihp4LSiKG8CfxP4tRDif1MU5R8D/xj4R4qi7AO+D+wHhoC3FEXZJYRY03+haRr1uo3n+8RdB8uKYZomyWSyEaOWcVK1oeB0XW8sDsf38RyXaqXayAevVCqSnUpAULcJTANF1/CrNQkcTMTxCajVbEzTJJPJUKvVKBYLpNOpFYq/UySus0SWXiqVxHItKpUK5XIZ3/eIxeKNZxeLJVnBDrDrTiNHM5fLtVGfNgsZ+W0YA9lWM+avalqDY0DTdFIpCYAKAp+6XSfwfXRdx3W9MLxSwzRN8vk8tm1TLBYIgoBkMtHGiveoErcsliu1h6NNwrH0/aBRojcSz/UIAkEisb7xFbGcSe/JimIpHeZo5Wl/vdO/EAHPPn6Uqbv3ePvDTzh74TKu59HTJUmhZGqVvFnTNJ44cpCR4UFpaCoKnh/guC6VWo2a7ZCMW2waGiCVTHBrYgoA1/PYv3snjudimiZ7d27n1LkLfHLqLJVqlSOP7SPul1DqyyiBdA33d8Xp75Jesqrtca9yn5gGB/fvYXzqLgf37SauC6YufIbfW2Jo+wnyuQxTd6e5PDZO9tAu8oNPoC9PYCtxlMwBbty8w7Ub46RTSfbs2t5WqGV4aICRoUEMw2B6Zpapu/cZGR7i6GN76UqZiMIUxKTyVxSFQwf28nvf/DKTd6eZnp3j1LmL7BzdxstPH6QnaaNUfZTAfej2tWW4j4OFEqev3aHmGZSrdVzPawA6S5Ua0zMLLCwsMZ1JMrNYWK38fzt7pDRW1tuoEWjRYlppb0a3dQgP6CEorVKpygNJqDIicV2XYqnUaLtet5mdnePq1WtcG7tBqVIjm82ye+cODh/cz/49u4nH4+34mJC2WCDxG8898xSVao2TH73PqUu36Mll2LZ54JGGw3FciuUqS4Uy0zML3Bif4tb4PUqLy3Tr8MJglhNb+8lYnQDW0dYgGp6Utp/D15cA3431x9BUehIxRpImNybvUanWSSbWB4WLEIjZ1r9wvlRVRVVkZcFCoUCpVCGZlIRjWkg5/nByN9F4poJMDYw8BkEQZowFobW3KuNMIJQWsGVbU7+tRb0B5S+EmAamw3+XFEW5CgwDvwt8Kbzs/wHeBf5R+Ps/FULYwLiiKDeBJ4BP1mpDURSy2Qw+AZVShdrycsjhLl3RmWwmRMtXKZcrKKqCZVrEYqYECy4sSodMy6TYto0ZM0nkMg0gke+4uHWb2nIRYTsI0yDflcc0TBzHYX5mhkw2g2U1i8M0Y0SN3q47Xq3YHF3XiCcSlEtlSqUyoJBOpzFNi97ejaUuyrzd8DQpZC6yCF2nQfjvIBCYloVmWY1YWVQOVdM00qkUemgQ1eoFFMA0Lbq6unBdWdJX01S6u7s6LOpHszTjcVlZzFsnRh5JEAiWy2UScZNYC5FRFOPXdWNN5R+dElzXayNBWk9a5+YhntrQ66DytZdfYG5hkZNnL1Ct1qAr3/K88AQTCKZn5jo8UNCVz+N4Hq7rs2lokJGhQc5dusLcwiLZTIrdO7ZRrdeJWxZHDu7j41Nn+Pmv3kFVVA7u30O8PAFutfUtGv9KmDqj6QSLXpl0KomCJBaqlCu8ea1AYnmWlw7EOXxgH8uFIu9/cgo/CHjiyGMkN+/E8zym7j/gx794E8/3eO74cXq6cm1tPPfkMU48eRwImLg7zS9//QF37z/g/JXrjHQdIaY0aXlVVeHrX36RjBGwb9sgX37hWX78izd57a13GRro5diOXpJWHWqLbUquk2QzSV566iDLhQq358oUy1UcVyp/23G5dH2ST09fJKk4BI40BIYHuunJZX6b+2NDOoH2WkUEAmUlIFCwuuJdi8Qti0w8Rq1Wa+w1URiyVC4xP7fApavXGh6HhfkFpu7ew3FcurvznDhymGNHDzHY14emaRimufrVI+UjFAIV4ok4XzrxLEuLC4xd+JzLNycZ6u8moRsPdetXa3XKVZsHs4tcuznJ9VtTzM8uIhybvK7wfF+GF3cMkmvJxmo9oKwcTxHuZ42uqkqDUvtRJWWZ7Onv4tLtOS6P3eGJwzvXvjiyOCIPTKsosuaD7VRQVC08cAW4rhvuNUbHPipKK/unfLZM+VttLMhQZdD2c2T8NEM7slpjtKaaWWaiwTUAhPrgNwMXPlLMX1GUrcAR4DOgPzQMEEJMK4oS1fIcBj5tue1u+LuVz/o7wN8B2Lx5RALj0NCzafwgoFatUygUQFVIxOMk4vGQtSqN6zoUCkUWF6uh9SgBSbquhRu7tK40RW38OwgEvhB4lomfy5BMJslkMiG/eI35uQViXoCBXBOB48iFoOtEIKCNp12KZNhwAAAgAElEQVRAhFStVioIIUiENK5y0gXR3LdOXDSp0SJQFKVxTxAEEmy0ThRCUmm6VCpVKpUqhqFLD4QVIxaLYRgG1WoFx3HJZjMIIcIMB+hqUWorZmqjLwzI0rnpmMVytbahRel5Pp4XgCkXfGekbGcDJAgX/uoUw4eAEpXm35ubE7Qaj6qmUS6XyGXTfPXlExRKZS5eud7xo3c9j3/7p/8J01jNmfBHf/hfoRkGtuswONDHlpFhTp29gBBV9uzYRj6XY3p+AYBD+/ei6zrTM7NsGRlmpL8b4+a7UvmvclvIjUAJAmKqrBQYnRTq6HQf/xaZXIa6L/jayydwHIdPT5/jl2+9xzsffko+l5UG7+IyMctk/+6d/NXvfoPz58+zd89e+W6qNHynxm/y+ekzHDt+nP27dzBx5x6nzl5kx+ZhDu/eAjW/MXaapkF5GlyHb796gpvjk5y9cJk33/uIrsyr7OnJYjhl8FfXcug0fS899Rjxc9cZ6Mlimga+H3B76gEfnbxAbXGO3zk8zEzB5k9/+iZ3pmf53u88Ty6dCo07ZdXzfmNZiQpd2WUhOire9cQydSxLrrNqrYoIBNV6natXrvPp6dNMTd6l7sjy2ULITb+np5uvfeUVjh46SDKZQAghs2FCkp+1RFXVsHyxSiaTZueuXUyO3+b65AMO7S2wZQXbafRd2I6LbTssL5e5cO0Wl8YmmJqewymVyauCg/kUR3ZuYndPFkNVWpSXlGYlP4FUcDQAmZLmV3JobGRfrbseTlRzQFHQorofQYDt+aRiJm6lzC8/PNNU/p3mPjrUrdGmqigYmtrAp+S78g0jxfM86rZM+448QpLNUG1774gIKNJPEV+JHJ+g7VpJwS49u0aIA0OJ0hVD5U8zzBjdo4bvEHmE1wsLd5INK39FUVLAfwL+gRCiuE4jnf7QAScj/hj4Y4Bjx441/q6p0n2tpVTiiTj1uk21VqNcKhOPy7i5rhv09EiWPt93KRSKVCoSYJfP50I+dwXH86AuKSdt2w5T2SwGBwdDUInPzOyMjM+Gyk8Jc4Pt+UX8uo3V042RSYVjsLGxCoIg5AcoY1kypNAg+qk5+CEvduSyDgKBUGX8O5lMUa9XicWkmykIAqpVGTpwXU8ugBb3f5SPG4mqSqS/ZVm4nsvSUgEoYMUs0qkU2WyusVH7vk+9Xqe3t7uRJRDOdYfp2+iiUkgmExRrdfyNWqQtl0XcBO0n8/a2I3e/67qY1mqFu9EAZjSGrufhewG5bIbe7i50XaNaqeC6Lsmkwv7dO/jSs0+GYwTxmImiSM9OVz5HrS5L9Xaq2Oe6LqquY7su8ViMkeFBdoxuRYiA/Xt24riyjnm5VmOkv489O0eZmZvn2ccPo9vL+Iv3UII66HrzrUJ3aeD51O0yXnmRRNykuytPPBYjm81x8Ogx3CDAQRKb/P63v86enaN88NlpbtyaYH5hEcMw2L51hCePHuT5J48zMTnBf/yzH/Lf/uHfI5WI0d2VJ2aZLC0XuH1rjGw6wc69j/H08UOcPn+ZTz8/x4HRQXQgn8vQ050nZqiIWhGtPIOW7OFv/N43qVSq3Lk3zdWbtxgw+uh9GB1Ey/Tlsim+9sKxxjxNzy7y9sdnmLh9g5f39/KVx0cplG1+9P41Pn7vfe4+WOQP/4tv0N2VaWSO/DYUv6ZpksI2dNm2dvVRcUGRpBIxenJpxuZqXLk2xuWLF7l6Y5w796bRNY1Dhx7jheeeor+vl2qtTlcuI7k3wkMNCm2x/Ye9aFRUStd1Htu/l6nJCT7/+D3OXB5jqLcLM26FiijA83xqVZtz125x9tIY18anqSwVSePyWF+Wpw5vYUd3hkSYLhx9k9GJNiK3UVTJCqnrDwfurSVCCGzP59T4fa5OzyOEIB0z6U5Kg2W2VOFescrtUpWyF/DZ+TF835dztgZWgfX6smLNtCpqXdc7K02lCU4MQt2CkCHMSCT9so6qyfB1FO6MwohRKDGaUxGIhsKXOTXN037E5GgYIZDc83Acpy3k81Cg+kZOZ4qiGMDPgTeEEP9H+LvrwJfCU/8g8K4QYncI9kMI8b+G170B/M9CiDXd/seOHROffvpxh780Y0CeJ9MAK5UKSli9LZNJo2lG2yQqioLnuZTLZemmRbpCE4kEliXdUb7vUywWKJXKZLMZUqnk6paFwFkqoMVMtHh7Cdm1JBrLYrFEvVYjl88RiyWo1aoUiyU8T6KgjbA0psxfb05QoVAkEILhoWG00NovFAohGFJt1D5XFAXTNBv17UG6qX3fJ5lMtFnfMsXPpVKtUavWMAyD3t5uVFXmRVcqZRYXl9ANna6uPLq2EmTTCFq2/Lz+B+wHPhP3ZvA6lL5sGS0IQzVDPd2kknJulpeLOI6Noqjk87nwVL8iJiZCo8kXD6m21dHsl/8fvpZtuywuLjIw0I+qaiEplIfj2KRSiRDwKP/XCOMpq58jGuE70daS7/k8WFik7jikE3GyqVQjtc31fQrlCsWyBPMN9nYTuA66AjHqmDOnUSc/A7sMRlgIJVL8rsfsQplPxyvoW45x7MvfQ4+nSSaTlKpVHNthuVxpABBjpkE2mSBuWWG/wvQyIZicnOStX/+an7/2Gp7n8vf/uz/ixPPPoSoK9UqJz959i8sXz6J5VY49+xJHn3uJfD6HFki2QUFAEOuWm5NXw588haV5YMTxumUBIhQVfButdBe1PN2WJfAwCYIAzw84eX6M1949xfLMPb68r5evPzmKlZDzX7ddfvnhNV4/NUUh2cf/8od/mW2bBxrfy8PXrZDx8Y52b8u9rfulsvFvotM15UqND05f41///GNMK86tiTuks1m+961v8Ne+/5cwTKNR9VBVVJmXv8b+oykKivJwI0QQGs6ex+enz/HLX7xGKijyX3/vy+S7s3iez43bU3x+8QanL91iYWEJYdsc7c/wpS297O7rItGBFTQIDYbo9BkpsN8QOtR8rhBUXO//o+49gy27rvvO38nn5vBy7JwDGjk0CBIESIEkSIqSaImmJcqW6ImWXa4ale1y1dSUy+X5YHtmNJbt8khWWckSqciMQBAg2I3YDXQAOuf08r333XDyOfNhn3Pfve/dFxqEZuCFKrzX992zzz777L3X2mv913/x7WPv85OpKjNKVig834V4DsuGQXFggC2j/fiey7vnbvEvfuOX2LVtvPcY+AHIUk+CHqKIyA+p1hv89l+9zr7HPs2nPvX0Kop05Ttdfhj7mxJZFvUSlqqmdoQBOszTjz3+BMePH+/5FjaC9peA3wXOJIo/lm8BXwP+9/jnX3d8/seSJP1bBOBvB/Dmevfp7VKX2hutpqkUiwUKhXx8GraYujMlCqSYJvlCnpSZagP+CoUihUJx2T1ETuzc3ByGYTA2NrLWc2OUxfV+04IoRIndbKshN4MwYG52AVlTGRwaRpJgYWEO23bJZLPkc5lYuXhtXEGnKKrG3Nxc25KemZnBdmxyuazg+I9opweGHSlThmG0DYukL52iaRrZrHCtBYGoUR+GAYuLixSLJbLZHPX6ItNTM2iaykAH1ai0YoPr9ezd707Q8G7s9C0aWKLiLBRyzM8vL/zT3VYYhHiuvyKvfs17rPgswvOE4h8ZGUJVdarVCrXaIkNDg+Ryy4sdRT03MimO7QVBwO25eWxn5ek/ub7esqi3rFV1xe2ZOX7n3/87CtPHMHSNf/iz95BVI5JM+kgECwldj7M3q/zFsWmkdB927QKvHv8XBHqeL//K3yOTzZLRZF5+8Xnu3L6DLMm0rBa4TZTAQwtczMBB9yzkegXXsqg4PtL0HCOjo7x65AhHXztCGEGmepmUUyVEo257/Onv/Qfe/clz/PzPHF7aXMMQ33YwMllC10EKQ1B1pMBGm3kHoiguICN3gJjW1woJAOztkxf54++8wsytG5RTEs8cmuDJQ5NtxQ9gGhpffHIfO8b7+KOXz/NP/s1/5iuff4rPf/LBDubIte62iodAav9vjX6vfyCI/7WE7wIyaZM928Z44uBmFmpNHtn1AD86dolqtYqmqbiOg2Ea8S3vYj2tJZFYn7qqsWP7Vq7u3curP3yO7738Boau8fbZK9yYWsCrVtiaNfn17UPct3WUTBfDZg+lJ4l4feLJTFzcPTuwqnO42yMHcGF6nu+cOM/xesDugwf557/wKSZG+qkuNpmZrxFFEYN9BfrKeXw/4PrNaS5c+wbPv3ait/LfcHy8FzvDWv2NHfPB0qFrPenWJXcXm+rEDCRAxfaBr+ugtrpsxO1/GPhl4JQkSe/Gn/0zhNL/hiRJvwZcB74MEEXRe5IkfQN4H5Ep8D+thfRv93+9hdn+Xgxiy2XJ5bKIdD2b2dlZyv39ZFJppu/cxveDdu5qECyVldQ0jcGhIbSNUtECoevgzFWQTYP0+EqDIenD/HyFclkoU9u2qFSqWJ5PqZAnn0t3XbN8ckRRxOzsLCPDQyiKwtTUHWwvoJDPY+gaflwLXZbFBmqaBnLscnMct2syhFFIo9Eik15C/tqWTRhG5PMFUZq1VsV2HGq1xdioKpLL5anVqty5M41h6BSLhfb1nafdlQu/ezw83++wPpdP6mX/jiIatkvT8Rgo5dvXpdOpdprhCuNCVtD1D3KkWMIVuK7LwkKVwcEBFEXj1u1bSJLE2NjIqgtXLFa4m0W6dHGHclnj8vHNW5jHRq+cb3dZikmtIs8n9DzeuVrh+fcrkO6jqWRx5DRhWueX/vZXyeZyRFHEb/32f0BbnCNz5zyerKKHMGICgY/ie+woZ9g+McRQYRAVibOzVa792GI8FyA1p4mMIl/+6tf47ne+Q336CpnmLTKGSstVOHH6LJqq8N8NfpZ8NkXg+Ri6AP55roeRNpcBlGPsTJtRjTbTmoidLo1J58Z/9NgZ/uS517gzNcWoGfALD41z+MAEQ/0rq0qCOA3t2znCPx8u8n/+xZv8wR9/h7OXbvH3v/w0/f2Fu1RGH1CWeX7mFxaZXagyX2kwV2twZ26B+YpIGe0vZhkZKPHI/m1MjAxSKhcZG3yLP3j+bb7zved49tlnNu4mX+Nry+PLyV5RyOUYGRlhrmHzR9/9MRlkhk2JZzcP8fTj2+lLrZb1s/KzzpChoiwpxZXXr9bR7s9bQcCfv3aSH92p0jLz/J1ffJpnnrifdEoHROW9cinXdY2qKgz0F9k+OcAbpy7xP/e4f9ReT6t0A4hkEZdfQejU1c5Snn/71B3RBlp3cjesVhWiG2NxNzVclqbZRvVmL9kI2v8na7Ty1CrX/EvgX67X9oclQRCiqSoZM0WtWkFSVYp5UaM+QhS7WIvoZz3RigWUTJqwB4I9iiIqlSqObTM2NoKq6szOz2FbFi3XJ59JkzIEjDAIQ4KIdhpfZxuOHyADqqrTajXx/QBTU5CiaKnmdDyhfC/Ak/2la+OUQVmWqNcb7YptgiEvbCs7RVUxdF3wIdg2g4P9SMjMzy9QrV6jr6/c9pjUalVmZubahlZSpXAjsthqdbi+pGX7a8eEj3/WYrd3EIYYijDuOqmEOye453l4vr+Ou381WUJT1xYXSadT6LpBpbJAFEYMDQ+sffUqHp9G0+K/fuObjIyOsWPXrt4bZqe9w+oL6pnPfo7f/Y/XMdQcdsMmndMIA5/Idak2HU5dX+TUnIJX2oIdyjiBjJbO8su/+nfb/XnpxRdQpi4gV+eJApcv7ujDUCRKKYN8StRJ0DpSqRquz3fP3qSc0ekv5Gm1FqibfXzzG3+K7DvkMimUIMt42mOokOH6dMj7753l//ojiX/81c+gKjKKqYHnxXHWVZ5OloRbNYxo1FvYrhezzIk/h1HEjZszPHf0XV554zSW3WJLDr7+2DiPHpggl9nYO0/nU3zx8C6uTR/n+p0p5DWrQX7Iij/ZlQUrEP/69/6Kc7cqSJrg+tBVpZ2dcm5mDvvUHaLAI6dHbJ8Y5N1z16guVPnWd7/P4Y89SqmwmtGS9F5axoPRq1tR1+FAkuU2MdHE+DgPP/QQ777yIr++pcTh3ZuRlym1Hi2y2rgpioLn+5DE3O9SwihiptHkD4+c4K16yOEnDvNrP/c0+XziiVv7WXVN5f692zh2/gYXLt9hx9ZuYz4M/LhE87J2Oh5JAgxNJSSiXu9drrvX0ERE+MRzIAiRkASD6RoGwFrtfZjf7yUfCYa/1eTajdtISAz0l0mlOhXm0gmu2WxRrdYYHR0mDH3q9QblvhJGz/Svu7euEtSwrGnImkbouDhzC+j9ZUJFYX5uHsPQGRufIAx9bt66iROA6wdkUiKvP4l/hYEgHtLVlWl+87OzQhlLEgsLFWxksrLSdilD92IU7rWVqFHBheAvUfq6gnFQ0zSymSy+72NZgnVOjmOE/f1lgjCgWlmkUq1SKgovQKFQxLIsarUa1WoNwxDcCwkbXC9pNJpUqo3u1KhVxnz5x7broukauUK2JzjI933CSMT5V6vw1+0lSObJ0mfNpsBfpFIpisUSjUadRqPJ6Ojwhk9ZSXuCWtVhanaewaFhXnn5RywszPPwI4/2jicmz73K3pncPwRamXH+n5cvokQBO4ZyzCzaVH2NwCzT0nM0QxVJURmZGOXTn/lsG+fxxmuvceO157nHuUNpIMuu4RFGi1l0JfGCCUBlUjlS0zT8MORspcbesQxf+8Q2/vMLZ5GqF1ksbkV1aqDrSGaaWafF3qEMH9s1yLmbFV547yz/xx8G/ONffhYQ6bVGkl+9WowEQJHwgpBL16d48/QlUrrO6UvXefO9K9gLC5R0j91lk8cf3cTDB8Yx9I3lVidjGEURl24Lg26xYXPrzjz5TKpnBcufVqJQbPSJx0qW5KXomAS3FxoMj07wd776FSbGRslmTHRNJ4pCWpbF6TPn+NNv/hmXzp2j0nDJFfrY1j+OJEnMzsyRT2faRrcUK2xJEpkdCb5/CdMgfkREKxS+yDPvHj9FlhkfGeHhBx7g0ulTnKvXeNjzNmBYL1tzIEI7oUgzjohE6fJVlH/n9O/0JQZhyK3FBr/76gnO+jpf/cVn+MLTD/bAIa0umqZy396dFH7wGt/9yTH+0dZnu/4uE4ckl7fV+c94POUkEyj+by0FHhIRLHOzJ9d9qAZmL4naAaW7ko+08v/x0Tdp2jafOPwwu7dvaX8uDCufSkXkrE9MTBBFAfPzFaFs1WSRrzw53o10XteeeLJEJEtUb0/jmjp9/X2kzBStVouFhQUacU37tK5TzmcxdS0GIYo4/fINqA0YCyMMI4XrOSCBAaiyHMfPBBI/QeQnBoEsy6iq2kZ+ivaWFl2y+JPPdF3Hsiws26JYyHe4q0Q+arlcJAgDFuarVCpVcjmRWjk0NEQQ+LRaQnFWKlVSKZNcPosidy9wRVE+wDQUktU1THOpjkKn0nZdjwiR1tdGO/e402o6RzAYCo9KoVAgm83iuiLsMTjUv3H3Kh2K33aYnq8QAtt27ODHL/+Is28f5fLFC3z2818kXyj09pb0PDUsfXz48Y9hKhLn3sniTl/m7FQVLzOAlekj8D0Cy2Fo0wSPf+KTpDMZVFUlDEMuX7zI6VMnUYvDXL98m0/dM07JNCAMCMM45h6DRUW2h4/renhB2E4dGhnO8yuf3MkfvHwR364h2xVoBkQpgzB0KWXS7JwsMzqYR1Nlvnv8LP/m9yX+yd/7Ujw2Kx/OsV2qi03uzFaYXaiwUKkxPV/l/Us3OXnmEllDZvtAnkODae69bwsHN/eRyafQVDkmwrn7BfyZw7sopFP8ySsX+ONvv8Rv/OrPMjbUt1rk6oNJ0pYid7uI2ydI4e3L57Js2zzRJt4Sc0IhZYqqj54b0Tc0zkB/P89+7tM8/sjDAGhGD56LCKFo/aWiw51GQXJqD4JApNF14RW6T6ARouz0yMgok9t3cvLNl/Hv0quWIPwT5HpCzpOAkXvV5eg19HXb4fXLt3n+4k2m1Sy/8bXP89h9e+KyuXezNiXyxSx7t47y1qmL3X/0Q1gXjiT2FQnImgqOYxMG4ZJRt4pEYdSTAVIK2cA9f3q52zQ/+Igof3GKhdriIoautxnmPvnEYSIiysUCILVPWs1Wi2KhQKlYQlFkGs0mQRCQz+ep1epoeoCuaciyGBQvjpeHHSjMTlY5VdfRVaVDcaw+iLKmofWVaCxUGRzoR9cN5ufnsSyrrfhVRaGvlO8qIRyGIVEYIWvdi1kAxpK7SnEVQlCVJP1DalvxiTJUFLlN9xsEQQzik2KO+6CdKRCGoSA7MkS2g+/7YjLHyNzEOdIZr5Mkib6+cpsFcGZmBkVRyGTSpNNpMpksQeDTbDaZnZ1HlmRyuSyplDjxtVyXpV1x41ZvzhQEJUmNh86J7HkeSGJc5a6c4PXalrAsm2azSRRBKpWiVEohSTKtVot6fZFiqYiqqHfVV4hwfZ/5xTpejJz+0fPPkW9cQ7er+PXbvPpiisOffJpiqbShcEnnnXfu2oVrW7z3Xg6vbzuNRg0/kgkiBdkw2bxlK4fuu7/LuAjDkNeOvIpy5QRkMoz25UgrCo5ltcdrKU4otRkydV0iWGwwkDJpuOJvpq4iZUpk3WkgAFlB8ppMlE1G+7OYKR3d1Lh/1xDXp2ucvn6LhcVFMobO9Su3qNZbLCxUuD07z8xCnYXFJpbl0bJtXMtGCn0MVfA0FM0UlcUqUV+GrcN5Dt8zga71Sgvb+PuRAF1VOHN9lnkbxrNZVCQiLw7bydIHrwHQ6drv7FGPpoIwJGeqFAsFwiCm6u4Azhm6zqED+/mnv/mPaFkWYRixY/tWDE3UMomiJQUvui2x2ukzikKRYhYbB+3S2ytczkvjKBGXIi8V2bFzF+dPHOPEzRke3jG5VGFx1WFI2DkDJFnuSDkUyjNhE5VkUZ+kZ8gMqFsOZ+7McuTKHc5YEYWxzfzml55mz47JD+SpkSQJVVHYt2OSo6cv8/7FG+zdPiEOT1GIJCurz6JlkUpVkdspi2tJZ4rj8lnqRyFBEMXu/9Xf308rHyQ9+yOh/Kfn5vm/f+cPuXTlKoau84tf+hx7d23n9WPv0Gi0eOSBQ2QzaV45+iZvvXOKIAgo5HN8+QufYf+enXzjr79PtbZIy7KYnpkjm83wlS89y5ZN44RRiO/7bfd4uzyNJPLc/ShCdl2seLPRNBVN03uyOEWRyK+0bAdJUZifXxDthCFN1yeSFVK6Simm8E0u9wOxiOUeVmwURfhB2LbYm80WkawQKWqbEjLJVw38oG0EBEFIOm3ieT4gIcsSrushxSVWE4vbsmx0TUPTdaxWi3qjhe15WH4No9kiZ5qkM2b7BJ+gdqNIJpeTCcN0u1hRo9FEVVVyuSz5fEHQL3surabF4mId0zSo15sbB9TGosoSmqK0Kwp2jpEfcyIoSueJf32xLEvQpkpSnOYpMASOK2oaSEChkBesaHepBFzPp9ZoYTsuruvy3qkTzF95Dw0f2xzAXLxJs1EjDJayGO5GNF3ne9/+Fq2Wha4Z7L7nQa7euMF8pcLY0DBbd+yk0GFUBEHA9atXac7PoGdKhDNX2L9vktDzMHRRnyEpjSqMzag9roahYxoGUhiRivfatKGyZ1Bj+2CBjCG42G0voJg3KRfEO1LiuOZiy2VmMeL3/vwFWo7HYrOF4zi4LZe8GVHIGAzqCn3DBv25EqP9GYo5Q1AgewGnLs3y2985weXpOn/46gXmaov8yqfvw8gsDy0tj9F2xtcFujxRPMn3P/PgNqqNs1y9cJbf+UbAzz/zONs3ja6t2Jb7pNu/dyv91a6NYoxOEAS8f/kGNSsQwGRJrOMudjZEDY0tmybbITxd13E9L/6OIJoRynOpz732EEmSuyoOr6ZslysfSZLIZtJs2bKJ/MAIR25d5r5Nwyim2TUAK2C7Mfh1+TPFrZKQb3m+H3tiu0+mLc/j4vQ8b16d4r2qDcU+PvWxgzzx8AEG+woYukb3O93gGo2EQXPv3h1kv3+Ul998j91bx0VRqyjqMHt6yLJYRFrXqPfg7lhLVqpg0ZgfBASAGme8SH8jhkDnOK3f9kdC+VuWzfjIIPt2buOvn/shrx97l9HhISrVRRbrDebmK1y8fJWT753l8UceYHiwnzePneC//sV3+F//l3/A/HyFU2fP8+XP/wzKwX381Xdf4PjJ9yiXCvT3FTF0rWugO+dpFOfeuH5I6HtxjrcPsmCQUmQZOY69t/Pqo4icodO6NUVm6yQgYXo+mqoKRRYXr2if+oMAKYpQVykL3Go2SWcE14Bw7csoioSqqYR+0DZKAlnBdd32RuG6HglzVGKhJqd+348r+xmiHG/g+ziOgyxL5LIZWpZDo2XjOB5Gq0UqlSKbNttutuR0mLSpaVrsZfBjUGEYYwDS6JpgIbRtCzmKxEILReHi9cRUpLaHxDS7sRBJPrKqbKSs79LfGo0WlmWJvuk6kiyMINdxkGSZlGmKXGFVxfU8DE2L58T6/fWDgIZlU4/rSIRBwOz0NIo1T1nxuCOXqJe3Mz4yTjqT2TBIslOsVou5+XkUQkaGR9izdy8Tm7fw1rFjAksRYzuSZ/d9n/PnzqI0KwQLUxRl2FrOIoVBO889DKN28RmhTMR1rushyTIV16MvVAhsB12Fx7YXyaU01PiUHAQRqiojRwGhI4yaRsvhVsUGT+b99y6ya7LAgf4UWwf7KeZTGJqMoSnoivhpajIpU0PXlsJSqiJx+MAEsiTzo3cu8+LJaSTlDJ+6fzOTIwXkjlh2l0gsLWS5E/+xpPzHR0t89Wf28dLbV3j32iV+/88afOLw/Tx4cCelYo6eIq3y+wYkit8FEai6xrd//A6pbImHHniAXDa7QkmKKbe0zmAJt5OE9ZQ1lP56ny/vndSDx0CSJDRdZ2hokC07tvP+rUtUWzZDMSg5aTq5LAk9dhrpXah1klcT7yGKGmcpCSCvF0Vcnprn2M0ZzlRaWEaGncASa4QAACAASURBVPfv5vGHDjI5Pkh/Kd/Zu2U/NyDxI/aVC+ycGOT4mcvtFNm2Dthgc7IsEW3AgBcZDiL0E4URQTs5d3mYJcKPQgjiao5S7N1do0PLM53Wlv8G3f6KLPPYg/eRz2c5fe4885UqjuOIh5ZgsdHg9vQsuq7z5OGHSadMFEnmX//736VSrSFJEgPlEg/ffwjTNHjj2Elqi6Isb+ImXlt5gKKGhEHsagsjYakFAb7vAR5JKqcelxhWwgg3itA1Tbi9dK0nKEnUgY7ieNxKiz0IQmzLolBaotd1wggzjgckQB3P82NrWrgNbdtpx/rbm3vbkykGzvN80mlRna/ZauIHPrlcBk3TSZsGluvSslwaloPtCe7utGmQSRltwCCIMIOiyESRShhqqKoX0/IG1GqLRBHkclkymSyapuP6Lo1GC9fzCKKEx7v3/q3EbjpxgugeP9f1UDq8GKvL0t8sy6LVapHP5zEMjWbTwvM9JESsW4tdqkhgOy61ZgtDUylms+0UpV6S1CqwHId604pTRyVUTWPX3n28dfsyrYWLqMU+Dt7/KEMjoxgfqEBShKppPPGJJ8G12bRpEyMjIzRtGxQVx3UpdVAxh2GE6zjimTIFlFaFfYUMOtEKT1OnapTj9MFay+L01DxzDYvJIIXvR+gpk8HOuK8Eag/85kA5xxMHxnju+E02DWT5hUc3kUtplLM6pqmt++yyJDFYSvPFhzYxV7M5f3OBoNng9bNT1JouTxwY4d6dIyItt5cN1W5f6v49FlVWmBwp8emHZTYNL/DKe1O8+NJRbk/NcfihA+zYPNpr+Jfdo9fo9bgsivCTNaobSLKoDHdueg4/DFAVdf250BGWaZswdzl/VpzQWVJDK50nUVwvJCSXzbF//35Ovv0mR2/O8Ww208bedH4/kVUNkWV9SMKTYRhydW6BU1NznJlvsCBpjO3cyaEDu9i5dZzx4fKSEfRTYuQS7+U9u7bwzsUjXLo+xbaxAZH6fRftanHl1yAUtO/QSxkveTQUJCJZAAP9nhUgk/ca74fxeEodRcJE+x1v7W8mSgB8RJS/LMsM9JeRZZlCLkt1sdFzoi3xsEdtpZrwuveVSxTyeVRVIZsVceMwimg5Hq7vkzJ0sqneKHUBeJO73IFBrPyDGMEKIo80idtFYYg5PNi2cJVV3lIQBLGF1xv52mpZ+EEQc/gLBeOFEYami1O4LENEu/SvqirtU35yShAnhaViP+K+gkgoYQJ0XRclPvXKsoymivLIKd3DcnVatkvLdnA9D8txMQ2dlGFg6GoX2jgps5t4HjzPw3U9arUamiaUazadRld1giDAchxx6g6CFUaACLsmLunuxeI4Dglh00Y2QMG1IFz62Ww2DkHUCYIAVVHQdS0uUSyoMBtNm5bj0LJsLFkm9EMymRSpHpXIPN9nsdnC9wX7n+MuuQJVVWV8YpLaA49z7ZTO3n0Psmvvvjao7u5FsKPt2LkTKfAZHhwgZRoEUcTo6AjBMv3jui7nz55hcfo2mFkyEjw0MUgQ+OgdnpTE3Z+Mf2JYz7ZsvnPuJildIZPSma3ZjGdMpEhepviW9xLKxQwP7hrm9fdvkTJkdk+uUh8icZnHXrZOcRyPC7cqXJ+poyvC4/XJzSO8dmOWSsNiseXy0O4xsjmjg23x7mR0sEAplyKb0njlxC3efecUjuujKDJbJ5ZVtFut+cSo7up+sh+JTJ4wCFHj8JLVbLFv2zg/fucCbx0/zo5tWzBNfe3+x5ozIflSZLmn43st3bj88+X/TryGnXspkkQqbbJt6xZGN2/nyHtv8dRWt0v5dzKGdsvK3nT1U4KK5XDm1jRvTy9yw/YZ2TTBx/ZsZ9/OTWwaHSCdWpb99CEoPEmSuGfvdswXXuf1E+eYHCqjaN0e4PVElmX8wO9Ok1xxeccJP56eSiSBHNeUWctgpIetuUEP5IchHwnl3zmiUnzijXErEEXkc1kmx0e5cv0m337+JUrFAmfOXeTg3t3kczlRDjcujymuF23JsTvGsUWKWEpXe6af9NqjEyUHtFGcyck94ffWSgVCxyVsn/rFJOg0VsIgjGPwK48uIufcQpJkVEXFslpCwZJgDzoXX4jnCfd+J+o/6U/380g4jkupVELTNBqNujBWYsWffEdVFJSUTMrUMQ0Hx3axXRfLcbHin8VchpSxsrSuJEkYhh5XuXK5MzMHWBi6RjadQlFVTNMQBoZh4LiuKBLium3KWU1RUGPwouu52LaNaZptJZuUVV5PiSaGSKPRxDRNMpk09brw/AjX/xJqOggCXE+QPumqSmgYtCybar2BG/ioxUKbfhnAclxals1ioyXyl1m5YGVZZufuvZipNJs2bf4pFH+3KKpGEOMd1B6FW3zPY25mhvOnT9K6ehZTVdmfUxnJpbBtu2vskvmSbC7CpSlRdXxuWC4f3zfKAzsGUGTA95c9YzyfV+z7ERlDppQzmVm0lin55dJ7E2y5ASevLXDz5gL3Tw5R1kp8dtckGU3ntZszfP+tq7ScgEM7hhjuX6JGvltJpXT2bOrj/LV5Ls5WqNabBBsAc3VJ8k6XuYETD56sitLDZ85f5s5clZn5Kk5lhrePvcNjDz9ENpNdM002EVmWRLEreju+V86stY/KnSnBnSm4EkvFxBRZpr9c5p57DvKXp09yYX6RQ6bRNvTF46/0bK6WABdFEbbvc+HOPKdnq5yaa6D2D3DPvZt5YP8Otm8eI5ddj6Hzp5PR4X42j/Tx9vuX+NKTD2GmuSu9qigyvu2tj/XokMQIUBBjG0YiBXC10IGUXPP/g3wklH/KMGKXL4wODyHJCilT1D9vNvMM9peZHBuh0Why/vJVZmbnUVWVn33qCTRNZfOkKBqYTMxNE2PoqkImnSKbMgjCkJbtYDsemXSvE3hvR1kiiUcgcZMlFZgkIvzZeeTBMqEiUlxkWUZBbp9oJUlaoTjbbYUCrZ/LiRSgRqMJkkgTXL7IkkXq+36c2rU0oZJTf2KsCCS/hq7rsbLzkOPT74onjxd0NpUimxKFlJqOQ9N2aFp2/EwS5hoKzfY83MTj4Hg4rocsS2TSadKmia7rmKaB54kKYb7v4wciNirFpw7FlePrBMJWFKxY/9SfnGIajQaaplEsFqnXF7EsQYus63qbKTD5vgzkMylkRaay2KBl2YRAo2VjahqplEkqdns3Whb1Rgsvxnr0shQTvMLOXbuTu6zZ5x5PQRQJcOOd27cIfR/fF+DNjKFz6NChFQYeiBDHpXNnaE1dR/YdUpUbPPLIXjzP7YojJ8+d9DXxFuGHeEFELmXw8M4hHtk70ja4l3oW/zs5fiYnxrhNFRjMm9y4VmOpVOXyAQLa+e/d46frKsOlDJWpKs/ummA4n0GTJD63e5z+rMGPr07xw3duMFezeWD3MFvHimTT65ygV5GW7VNpubhIFAtZ+gqrxP3XkvbpPGyXU032hYVag7dOnOHHR97CtTyyUsQWU+Hi7Cy3bt9h5/Ztqyr/9q4jJbnowYq/rq7ilynk5PAULqUIJ/nm4lXIPcrMRqTSaQ7s38uPRsd55eo19g4USaWUNdPIVtqDEX4UcWOuypVqnZ9cnqKRyjC2bx8PHdrNfXs2k0mZa/JgfBjSdv3v2MQ3X3qL6YUa6VyqzRmw7vWI7KIEYL0kG4tJiINowioQEgaJt7rXne6+/Q9DPhLKv1jIxcU7Au7Zt5uDeyP6SiUevvcgnueRSpmYKZMvPPMUtXqDRrNJf7lENpMGIp762KMA8ek64pOHH0JWlPjvYOoC2NWwHVIpY1nsfflgrzfwUbtaXRSG+I0mRqlAKIvXHIVhHB+P2nmuqyl/23ZQNZVisYTjOLRaLZCgkDZjg2OpbwmCP4zC+KMoTu9L2P+kdnEfy7Lo6+tDVVUWFxchimLU9/oLzjQNTNMglXKYW6jRsmwMTUVVFLS2Qok3oyjC8Twqi4329UEk0o3kMMKrN6k3mpi6TiadwjANsllB4NNqtbBtu30STadS+IGo2ZCEKza6CGzHxg9CBgf7sG2ber1OoVCIMy6WG1EKhiHcqS3Lpt5qdU2BxZZF03bpK+ZImQYQLbnuVtkA2++6XXnvbhev0ChzszO89fJzaH6Taq1BICnIioYU+mSyOSTNAEUU+EmZJvX6IudOn8aZvoWcyaF5LpoiY7seuUx6/bvGhl0UJ5RJyso0KClm5VvSel1dxjAD+nIGnhvQsDyyCUp/g+5LXZEZLJiciEmI9I45+vD4IJP5DN987yrHz88wU21y+MAY+7cMUMiayD28aWtJJMHoQI7rFZsb12/y5olz3HdwB4PlwtrB1agzRhu1AWSJAe66PlPzNY68c5YfH3mbUdnjULnI3sE8ftDHb52rCB6ANWzCpRO+1EaIJ4ZX23vT2aXl/+5040M7lt8OS8oiY2gtqllVURgZHmbL1q2cfOEMs02LcdOAZQC/3kMU4YchCw2LO80WL525wQ03IDc6wRP37ePThw+RyQgMTOgFRGEId5G980FluL+EDNyYmmdyfHCl8l9F10qSSLeOwuWAv7UPir2+pyCjKBAE4Hd4AXrj/TsNsl7TcnVUx93KR0L5y7IsStb6PilTJ5vLQZTks2siXhvH9kvFnODJj2gjYkulfNfj9/V1F/TRYzrTuuXE7rnOCfDBJ58kSWgD5bYVKwGa3E2w09NVFomiMo1Gnf6BAYIgYHZ2ljAS9ZzThtZ1Wk3aCyNBNrH8b7Isxcx+MVmLIqPreru0sK5raHeZM5vWDfKZDJWgTr1hoSoKuUyahChGpCgGLCzWcb3lhXgESx2xIeDbDrbrosgiVJDP5WPOgAy+L0oyBzGq1jB0UqtyindLMo61Wp3+vn5832dufo58LtdW/MsXkOBNANtxmKsu4nlB1xQQz+IzUwlEtUHTxLJdrMBdcV/bilnydJ1atcrwSHfdh40uyyQ749x7p9Fmz3KgX+Jkq0bGUFlsqbz63LfAyDE4NomWzlAsl5mc3MTtW7e4Mz2F68sYt69R9yyOXp1hS05lLPYaGHHKajI/pCTYT7LhywRB/L6W17uIOoyeCOJjafyZ+J+mawyWckjyHBdvVjm0e1kMfR0xdYXNA1lcP+TqXI3JZSj8kXyGX71vO989e5O3b8/zrSMXWai0eHj/GOVipp05sBEZ6s/x6Qc205fReOnEbf76+68wtVDjC089TLnQu15AGCv6KIw64uWCMU9WVSzb5eL1Kb77o9e5dOkGe9MyX71vNyXTRJYlao6LdvIm1bk5wVexpnQY+7HnsFNZdc4nYXtFS/ZYbJgIhr345BoD3NpgslXuGoYhrufRbDapVKuYKZOqbPDq1Sn+VjEXU0GvYfhGEZbnc6ta5+ilW5yYqyOXBrnnoT186emH6Svnu66Xk8p3YRQDf9YZlrsW8Y7CMOLm1ByyolAsZHvTIK9xb1VRiAKf1Ufu7jouPLMi758eOLPlz9B7zFeY53fVh075SCj/MAqxHYf+/j5URcNxHRZri2iaSiaT7iCQEC54WZJwfR/btlF0HV2R2zt8rwFbcnVKBFGIxsY2jOWKQ+AJuj5ALZVQgMD30GM2P0kSZXQVdeWpPyEdqtUWyeVy6JrOwsI8YQSurDLcV+55Qpek+LkDH99fcuGGYSTi5rGLqtFoUiqLNiqVCooid9Dh3p0Usmkcz2Ox0WKxaaGoChnTFAsrisTJuWGxmppLPg1BhAXCCD908N15ZFkW6YW5DP39/SiKQqVSodFskPWzbSDnWpuOKFBUJ5sRvArT0zOkU+k2X0D8ilaOv+/TbFo9jZZEXNen2mhSyKbRNRXH89pgSsdxuHXjOmdOn0QKfcY3bePajZt85tlnu0hcNqr4wyBgfnaG2+fe4ZF+ifu39XNptsmTewc5daPKVbeKb+aYmp7GD0Mm/C2EYcjxt95k08Qohr6Fm7dvMTMzy7dvNZBrU9w7VOLeTSNsK+dIa6ogsYrvmfRPlWWyhobrBUwtLK7sXNfk791/XVMYH8ihyB63FmwObeCZu65XFYaKaeq2Q3WxN4963jD4yj3b2NaX54WLt3junRvcqrT4xD0TbB0vYRobN2yzWZOPP7iNQj7Fn796kVffeBfDNPjKZx9fGcsHPNshAlRFRdU0SIz7KKLeaHLs1Hn+8vs/pjVf5fHRAl+5d3v7WoCiaaD5PgsLFQHapWMoVywbqf1DjdNQUZbc7slX26d8oiVmufg7gp+k91pfMgGE8vUDAQRuNJtcvXGD48ff5eyZs1RrFcIw4J2rt/n8nkk0Ve3p+o+iCMsPmK03OT+9wAsXblE1smzae4Bf+eKTbJkc7L1+FZmIoG2wfPgxb2EaVSqLvHrsLNsnRti7fXxF9sJ6osgyURS06dWBdo5+FK4ex1+zzdgLsL6sF+5c21m1EflIKH8JidGRUSIi5ufmsW2bQrEQI9N7K3ND09BVFccPBBmKYQiFsQoqVUIcWoIghHX2imRgexqK0lL4U5VErXm/VidyXaShgfh60YdepDSCdc9GkiTy+QKW3aTZatFCYbCYR1uNXCgQDIWapuI4rsAUxIAlz7KRdS3ucEQqRnm7rks2m1kR/92oSJJEMZ/B930aLRtFkdteFNfzmaslm/U68cB4k5NlGTNlkkul8D0X13OYmWkhSzIjIyMUi0WiKGJ2bo7+/r41N51E8QMUiwVqtRpB4JPPl9d2TxK1U/zWk+pig3rTarMzJg80NzvLu2+9jn3rPHr9FicvjNHKj3D+7Fn27t+/brudEoYh83OzvPKDbzPhXefpg9to2D4/c3CEHZN93LNjiN957gy3vT72HLyPTZs3Y7k+58+dQ5MlDFnHDwIGy4OMj45y3z0HeO3Nt3jz0mnefu8a/b7D3r4cj2wbZ7yYJ6UtFfTRFJmhbJrt5TzHLi/wxY8HbaNro6KqCn3lDIYic2mmhwGxjsiKTCprkjZ0au7aJ+OHxgfYUszyvfM3OXp+hpszizx5aJwn7tuCvlbxngiWF1QY7suxa6JM/UZLxP479vFk/Tq2I6oVJoePKCQKfPw41PXGifN876WjaIs1vn5wC4cmBnvePh9ZVBcW8Hx/pcO2h92cuP4lBLZAkuQuRdOm1I3XRpIO29XuKmMYxAREzUaTq9evc+LUad4/e447d26jhj6Tg3me+th+zl4pce7IEWbrFplUasUJNYoi7CDg5M1pvn/6ElO+hDm2ia9//hM8dv/uVe6+JLIkixh4ECJ1HN4+DElSC3949Dgz1Rb/4Gtf6AhZbrQNKQY7ujiO0/aaKbJCGMThFFkiyc74/1o+jOH6SCh/TdOwHZu5uTkMXWdwcGBDm5AkSZiaCpqKbTu0HAezB0I1+SkW0fr9WXtgJWRJFmmGccwxzKRp3Z5G0TT0clEA1xR1hQWe5ALbls3A4ABhGDIzPYdspkhFEvm00f5ep7TTDjuQ/u3e+D5+y0bRNGr1OkNDg8iyzNzcHLquo8Tj+EG4nwEMVaOYyRAGIU1LIMgL2QxzlcV1qC87tqD4hyLLpAw9Rvmm2mCpWq3OjRs3KBTylMtlVFVhdmaOvv6SiP0ve2ee59NoNImiiOHhYVzXplqtUSoVe45fV6/ik1IQbmzBtq3+jqFTNRUzk6OuZtFDhXTzJl6mn9eOHlmh/NfaiKMowmq1eO2Vl8jOvMfPfWySVM4knZcYHMzFQDIwNYWR4UEOHjzAwMAAx46/w60b1yDysbyQdCqFmVLYsXMbP/fFz/GZTz1Jy7J45cjrfOcHL3Dk8rtsG7SYKOY7skQEwDarqzw+OcgfnL7EG6ducvjeTRsal06RZYliRufGbPWurwUR3usrZZnvwI6sMmIMZFN8+cBmJgoZvn32Bn/08gWu36nxtz65l0w+tYZrt/vz4f4cuzb18frFCheuT/GpRw90ZdAEnggpomlEnofnukSIGhgt2+ONkxf4/o9eo+zZ/Prh/YwXsyLODsjx2CbrbaK/n/Mzc9i207UO11uOSly3Q9OWMERhJIwCJc6UWTtqHI9atBSyOH/hEu+ePMWJk6e4eeMGUugy3p/nyx/bxxMP7KNUFPNu39Wb/OabJzlxc5qhfIZsOtXVnhuGvHnlFt87e4M7coadB3fyq/Fpf0MiS0iRwEhFYRTTLW/s0o1IrdrgxddPs2/XOLu2CYa/uwmPC14WwUMShhELCwsAaJqOruvohh4fNlmha/5bkY+E8o+iiJnpGQYGB9C19RDenYijJREoWgPLsvA8D1VTURW148WAxFKlq40QVvQSWZLQ1W4ueNnQKezf1f6O02yRyedEqLQLOR1nCiChKBqe5yBLEk3XY0spj2M7yKqKHAT4zSaKYeLYFp4foKfTqLqG02wRWI4A4WRSqOkUsikY9iRZwjBMgsDDcWwCSaHSbJHPpsln0ihdaXPrrYQEQARuEODHAKdG08KynTVd5vGorvgk4RvolDAK0TSV0dFhpqdn8f2Avr4+ABbmqxiGEVdGVHFdEZd0HJd0Os3AwACua3PnzjS6rrXHutpoUq034nelUcpnCXxRA0HTV+Ip7laGh0d48ulPc/HCZi6cnaA5dY3ByU08+8UvbWAUiOO0Ec1Gg3feOIJ84zifP1RmaEiwmyXPYdVt3rw4zxWnwObSCJlMlsXFOndu36SxWCUMIjKaRl+5yP/4a19rp3EFYUipVORLz36GV149SjFrMlbIYqhKVwZKFEFK03hwcpA/PnWZV05c47FDk3e9iSky9GdVri/2IDbZwIaryjIDxTSXL1fWMVLF52lN45PbRtnRX+Av37/KXx+7wfVKi198Yhd7tq3ial7Rr4i0JpORXM6dv4RlPUE6rkgYhsKzZGSzEAUCN2MaSLKE3bJ58dW3+cHLr7FJl/iVB3YxWhDsnEtV9rpvNVrK8O58g/n5BSYnxoQ3IXEfSt2D1L0vib+4MVZATVI+exgz3Y/WbdheuHyZd955l3dPnub2rVu4rTqjfTl+/on9PHbvbkaGVvIzTA4PMLllklcuvc8j28ZJp8w23MMNQ14+e4XnLk2hDI7w6888wVOP3bP+mC8TSRGHKFFnRerJQHi3IsJ6Ad966SjTlQa/+fd/AYVE8ScAifXvI/BmElEUkM1mKZUKAHFKcYNmc6l0ukgvXlZ98YM/AX8DIIie8pFQ/p7nUS4XN5DDu/7AJBSxjuPge35cwERFkeQ4hrOEpK23HGQZcstJJjYkHadvSWpvWpZl4dfqtKo10iNDyKqK12gShQFyOi2cefGCD0JhyReigMaV64RD/SBJaJKE1LTwI0HJaSoqRKFg+ZNlyMT86nFWgSzL1GqL7bK0c3PzGIpK3XVxvYD5hRqu61HM5zB1bUX/13o+23FpdMTHxak55INMUk1VMDrwG1EU4Tpeu5jP8PAgt+9MwTxxtoJGpVJhZmYWEKedbDZDuVxGURQcx2JqaoZ0OtU+9UuSRMY0qNWb2I6H7Xg0bZu+Qg5DUwl8D9dZn697vadLpdMcuOcQ+w/eQ6vZJJPtDRhrx1g725Og1Why4f1TzJx5g8fHdfZsWzoxvXbiBq+cm8OSMrTUAhMHHmLn/nvIZNOcPHGCK1cuoynC3VvuK/Lf/92/g+/7mCmTMAzaBWDeOXmKxRsXeXSkn0Im1VEEaKlMtKKomKrCp3aM8cNrd3jtxA0evWfirjYwVZGZHMxz/Prsyj92NdM5Cku/q4rEWF+W25dg3rLp2yDgczyf5usP7OLB0X7+/Vvn+Fd/doxfeGiSL35ytdBLj7cagd2wuDY1x56t4wD4riDIkgC7abUpoj3H45XX3uXHb5xkUtf4ud1jjOSXsiqEsb/yrqPZNNyucOv2LXbu3B5XzZO6J0SH+IFAw0fQBmrqWmescv2xmZ2b5+1jx3n9rWNcvHIZp9Fgy0iBzz+yk0fv2cXIYBl5FUZLCdAMnZ998j7+3aVLnLo1R84wyKYMvCji+yfP89L1Cv3bd/HFZx7n/v1bVx/fdUSw20WEXoCyoZj82vcIgpBr1+/wyttnefDAdrZPDMXG0sYVfyKmruF7HplMBlXVcT0HXdcYHBwEJMIwEIRhjSYLCxVM0ySdTq1aynj1Z/kwFP7dt/GRUP6SJJFKpdq/Q7ebegncsP7DJYrYMAzCKIxLlgZEkkQQwUKlztxiAySZwHUYGezbcD/XAlkkfQ2CkOKmMfB8ZE0jcF1Cv1vZyHH4wXMDPEmhPFBEHxnAthzMlIHVspD7SgRhgBZT0vq+j2c7SHG9ARG7E/z6rusK1j7NIAg8PN8nXyygTDWphhG2rFBrtHA9n3IxRza1MXKNIAyoLC5iuU6vJ97wuIlnFqfNIAbcqXHa4nJreXRkmDtT01SrFQqFImNjYyvachyHmZlZgsCnv7+vXYI0aUNTVcYG+7kzN4/tuARBiOsGZMwUkRzSaNnr9ndd06hjImTi9MVVW1o2bzzPY+rmdS4ff4X7i3U+ef8WAKamanz7rZvctHUaue24GPQPjXDowUfIFcscO/Yu598/Bb5gayxmMxx+7GFRtMmykWUF27LaGI9XjryBbzucuT7LSEply0CRjKGT0lS0uF5FEPgYssSX9mzm+Qu3+dYbl9k6UmR+0aIvZzIwkItpj9cekSiKCLwWrUWLVG415S31/F1VJDaXDV5Wdaotl75lxvhq25okSZiqwn1j/fxDWeKf/eBt/suPL3FlqsbXP3cv6XxqWT/i32Pv35aRIp97bDt/8uoVnj96kt1bxgi8gNOXrrN5YoxCKsDxfLwg5OzpG7z81ilOnb1CMfB4dMcIOwYKK7FFPTp6cKwf872bTN+4htVsQKk7GylxyycGmSSJuh6JSz+QBL9HO204ErjjzluFYUir1eLUmbO8dvRN3jtzhurCPP15jc/ct4tPP3qQ/nIBuQfws6fIEo8c2MEfDAxy5Mpt9oyUOTu9wNFLNznVCNhz6ABf+PSj8wsFbgAAIABJREFU7Yp5XeN7FyIhEcmC2yBwPRRdW6eZte9h2Q7f/MGr1N2Af/iVz/b4vkDaLxkCvY0BUV9FRpEl6vU6s7Oz8V4l+FkMQ4+rmabpK5fJ53LUFmvMzy+0icbWNwJ6jdsHNQLu/rqPhPLvhezu/v3u2kuulZExDUMU5AlDyrk0ffk0ju/j+wGWrXFnroaVdSnnM8vc4r3aXfu+lueiJjW4Y4WkmAZKTOwRRiFuXFxHoP7duF0ZRVZQFJkwBMNMyChEfNqOvRhL1L1LXAK+LwrtjI6KNLPp6VnyuTyqrpOdGCMdRSw2LWqNJpbjMjNfxc569BVy656uKrUmluNtCCcB9NyUks+zmRSlfDau1hUDH223C5kvxkJieGiQ6ekZHMdt0x4HQYgfiNLMsiSTSpnk831dsVVIDDSRwjY6UGa2UqPRsqk3m8gKpFOCffCnkk44Q2KgssbyW/YHq9ViYW6GtOSxbTBLtdLkL167zlQjoqIP4OT7IAx45PEnGB+fQNd1XnzhBe5cv4Imw87tW9m7ewe+57Fvz66Y1EnMNz8IyMRFWb7+ta/w/Ngozz33PP/p7FXS715GizwmCxme2D7OPZMj6DE+RpVCfnn/Zn7rzTP8098/ShTBw9sH+OVnDsSkOquLqihsHS2j67e4OrPIntzdedK02HPgBiHTiy229SXhj2RkY0x4z01aQldktvfl2ZLTcFJpfnJ+nrnmW/zSx0UYoItds2MupwyVXeMl7t08z5GTZziyazPbxwf55nNH6Mtn+FuffYL/8pfPU6k0mavU8RuLfHyin6e2bWaimNmwd8RUZEwCZmfnaVlO3I1Y4XfgZro4QZL5hTD2vQ7EeXJ4jYBms8n5i5d4+9g7nDj5PnPz82iRxUN7t/DZv/0JJsYGUJUlhtEN9Tl2YWi6zr37tvHD717mX734Fk01hZ4v8bGnDvH04UNsGhv46ePcEiL2L4OsqsIAUFVYo87GauI4LucuXePM1Sm+9OSD5AoZVgCGuqTDEEhIqGIJo4BMKkUU+gSBh6LIlDoqabquy+Jig/n5ikhfzufp7+snKAZtIyBf6C7rvlGJOvrcVRjoQ0D4d8pHQvl3746rpI19gAdvpzSpKlIQCHY8TSWt60Q6ZFMmhWya+cUmM5UGI31LFaVWu99aMcnA8Uinuz0YnRL6IYHrt1MXnaQwT/y8pmniByG+5xH4IrVMxMNiRjaW+PsT5L/reu0yxL7vEoQBphlPOElCAfIpk6hlUfU8PKBab+D7Pv3FwqrAylbLot5qtQvabEhW8aiZuko2bYrqipIgJgpCEX/vNU6WZSMrCuVyGcuycN0YbYtELpslk0mTpFQuv1zqUMqKrDBYKqLJdRabLWqLTTzHW5sc5gN64Na9pKPdbC7H2KZt3Lh0jr84cQYlmqeilrFyRUJZ5fDjTyAB23ftIvB9Xvrhi8xN30ENfQ4c2s8Tjz9KsVAQ9RoUhcD3SaVS7ToSyfNn0hk+98zTfPzxR3j9zWO88PzzXLp6lWnH4vZ71zh66Ta7hss8uWcLqqygqTKzNYud+/ZguwEnr83w1nu3eHj/GOnU6gaALEv05cS8vzlvs2fbXQxcJOKrAwNZoihioVYFhuN00g5O9fj/qxkAiizRn9LxCjq/9Jk9/PZzZ/iP3zvFpw6N8qlHdpA29aUbdlw3UMrwzINbuDb1Lr/zZ8/xwL4tXD53kQtmmjtTc5w/f5kgCnlq6yjP3n+A0XwaXVFE5Uo2NlUkSWIwpTNXrdC0Wu06HcnfRAhv9dTW5DthFCFFEc1miwsXLvDuyVOcvXSF6TvTRJ7Fzok+vvToAzx0cIcIscX4lg+qoGVN5eEDO/mT775Kpq/ML/3M4zx4aDflYg5dU3umJH8gaRsAEbKmEng+cigjaeIAtNEF2bId3jx5gUCS+PwnHljyFncYkRuVMAgxDR2JiMtXr7F7584uuuykSFjCpVKpVKlWaxSLBUrFIpl0itnZOaJMBnMDBb4iRCiu1WzF2Vy0wZ0bk6W1kQB6ZU3tSlNcLh8R5d856VeJQ/0UFk/n4vI9H2LGugihmIZKWa5MLWB7Hqam9VQqK/u5JAm3fFIbfrUXLSrh+RSKAjxCGBFI0hJvSnxiDXyJSJIIAxEu6KR2jSLhKZFlGc/zaLVajI6OIkkSMzOzFIuFFXz4iqaSL+TQVIWG69H0fOotC9/3GSgV227zzn4uVOt4XkJwscHB7/FVGchl0qRMo92nBFCVMnudECPBVVAqomki5h/E9RFSKbPtKdjIhpaEFUqFHJIsU2u0aDhOtzW98adbkg8yFzuukWWZvsFBHn7yGW5f38W7x48jE7Fz/z0cuu9+0mkRR566fZt3jr1FfX6WwBcx4NmFCrNzcwz09SFLUlz9UkKSZdxWC01fepeSJLXZEp/8+Me4796DnDl3nhdffJF3X3uVW4uLnKnbvHN7jqd2bubI+atMbJ7kn/8Pv8SbJ87zn775HH9+9DKX7lTZNVFmx2iR/lIGVe/eNhRZor8/i6HIXJ7pnavfW4SFnZxudcPg6pzIGFiZsdEb6Nv5rHlN4VLT59DuYf63Ypp/972TfPf4Te7Mt/jCY9sZGsivqLEhyxLlfIqPHxjn91+5yA+ONnm0nOV23Wbq2g0+vX2U4YzJI5NDDGRN1A6Ft9FpEIYRWUPneqvFhcuXUVWVocEB+vuWQo5JuNIPAhRZZCZ1iut6XLh0mTPnznP+wiXu3L6FZzcY7cvyc4/v5sD2cYrFLClDJ2Wu7anZiCTra8/WMfbs3EzOVDiwc5KhvsKqpcl/uhvGIQCEARD5AaHrIWsxa+Y6gx2GEY2Gzbmrd9i9aUwUd1trj+gyCDq+F0UQp/KFUYgfhBiavqLc+NLhQ1B7JwXUarUai4uLDA0N0d/fz9zcHLIsr1rvIzEeLMsmIiKTyVAubzwU3fPR4nBeGEVrGmgfGeXf/SI+fMRjpwHgOi4YOqoiYu+qolDMmFTqTYZLK+N4GxHP89qbdi9ZiulJ6JpBFEVoqors+l33C6MwJuwJ4lORIlyEwRK7WGIMiNO/FoPfHFEdUF+58CVJQjWN/5e7Nw+W7Lrv+z7n7rf37tdv32bfB4PBDDAAsRAESAKgQJG0JEt2EqlcFUmVSkpyUpVK2U65yuWo8k+UuOzEKUeynFCyLIkUKUokJVLcABDENsDMcDCYfd68fV/69Xb7rvnj3O7Xb503wKgE+Vf15r3pvvfcc8495/z274+koWP6AUatLjHtGy7Ti8t05TItiwVAabWMEwOS7Oo9NF/XFpeqmqwp0MwTbuYZ69p2JV+l+UAo4HkuYSQ3YiJhY1nmfWkbUQjlRh3L0MmmkqiqQqkiAwE3d37t8Q9+9W1u0TAMunv7SKTTlGoNVAFnzj5KOiOtT+NjY5x/601qi3O4kTR7Z9MJDgwP0dPVhed7aJqOqmlrcxuGWFsWrpLzZ9smUzNzoJp4Rgoflbof4azUqb5/i4m6x8ufO0dnR5ZnHj1BveFx+cYI56dneXdqkrw+QVcSBosphnsK9BYzFLM2pm2gaQrZpMHofGnbMW+mpqoi/8qnNJaqtXXFZ3Y7q6oQDOTTXJ5axdBVhvpy/MaXHuGv3x3l7Q+mKNWv8eypQY7t6ySdMNZF5tmGxok9HXS/c4Prdxb52XOP0EDgBSFD+TSmqpIydJkutpt+xU0HgS+tylFEd8bm0nyFv/zmt5l4aJzPfPp5OovFTfe21xEJwpDp6VmuXP2AK1duMDo5QW11ma6MxTMn+jmyt4+uQoZsKkEyYd5HoNnuKWGZnD22l/MfjLCwvEpfT5yG/SDtz20kRIxxoKkSEtkPiTTlnpkAdcfhztgk5ZrDi08fkBk9686ldgYPmwAWWlozeK6HbpkIZLnsTCa9bYGxdiGgaaF1nAZzc3N0dXXR1dXNwsJ8Kw6tPdvKcdwW5ksqlcQ0ZeE1123EqcxNlFgFFIGCiPshO9xu/WxaepsWT4RA05S/K8y/yfSbf+9EGw+WraOINz0hFgB0PcJtuISaFqeIQS6dYHJ+mbrrySp2u1zcUut30XR9k/+5nVzPxwtDTNuOS/DG5YI3nHOKUBAquEEcXR+bbZoFJtqhfF3XXUuLW15at0g3oRPGUJKKqpIOAsIgpKTKYjrzK6tk/IBcOknVcSjV6q36BbuibaZK11QK2bQ0n8WaTRRJIWYnHIc1LAPpk/ODIK5pcA/T2YYxK4qCZeh4foDnNWh47ibGsps86V3Ttktv60ND03UymSyPnD0rXRox47904QKjt2/RqK6garIAT85UefShY5w6eZxUjLsgdNHSKMLY5L9jDIJQGJ+YZGZynP6OFPv69nDm2AHeufQB167fgUyWl589C0A6YfL8Eyd5+Pg+ypUa03MLXLx8gwt3xnhvbIbO1CK5pEkhZTLUmSKXMsmYCgtOMwX03vuxXftShKC3kGZiqYQXhugbfLBbtdL+f0VRKOayhBPLrKzUyeVshrsz/My5ffRkLH5weYo/f2uEqcUy5470UG/43J0pcWCgg6HuDJahoegmfemAvnQCM9b0DHX3+POtwl+xH1/W25CHb3fS5lC5xLXZUcZvmSydPr3p/uYecRyHKx9c4+qNm9y+c5e52RmyBpzs7+DQE/voKWbpyKXJppMfusrhbknRVJ4+dYg3Lt3k+sgkewd6JGLodj757V73Vkf7Ntc2934UMzQiZC2Apgtji3tK5RrvXb0NqsqZ4/s3P2vdA7b+UqJt+iBAUTV5oDQa+I1mevLOvEUIWY5bCIV6vcbCwgLFYif5fIGVlWVZsCyOeXIbDaIILMtuuQVct0GtVkcIMM24rkgYAmugT82/QdZuWOs7LRbaxE+JkBbW7ehjxPxh+7d1Ly1i/Xf3isrXNC1+r42WCd3QNFK2yUqlhqlrKOuk6O3iEOSKDsMwfllbUxiGEtc7jEi2WQeiMMTQVOoNl3SiaVYSUuJTFBRVIQyCuIDI+uf6fhBD95q4rovnehTaooh3Oq900yBbzKMTUa451BsuYblCGIXU6w28+y11ug0lbYuEZa3X+sMQdQsUw3ZqIupphk46k8ZzPTzPp+E01lkoNtLGJhVFoAuJG+F7vkTp21amid/xR1H7W/ftvhFVVVtmviiKuPDeu4xeu0xHPs8zZ59hamaWmyNjHDuwh1MPnaCjowBIzATf8zBMaUVqxALolkaY+MOFxUUmJiZIaCFPnDvO2ZOHGOwpsmewm1vHDlL1fHqKeTkNQpBLJ8nnZGBoeU8vDcdldHqJYs7kkaEM08s1ZkoNxhbmCEOfSt1lZL7OH79ynX3dWQ71ZslkbO5lrImiCKfucnt6BQ+Vuu9jGMa6V3Wv2VSEoJhKogrBrckSZ3NynfR2JHn61CC5tM33L43xk2szTC1Vcb2Q0blVBu4scmQgQ6XWYLHsc26wA11Vd830m9a4KFor9NMUbtu1wsOdWTpTNlbkc7u8yq1bd8jlc0ShxBFwGh71uoMfBMzMzHLzxnXqlRU6MgleeGQv/d05ejuydOYzmIa+NjftB8P9auP3WqaRbHO4v4tCJsnN0WkePVmmUMhsHzezRXue68maJNoGUKIdj3SxxqMjiJQ2Ztd8N5H8rFKtc/POBHcmFji+f4hifru0253HGoVScNMME6IIRUgMisDfKNDu1G0JsWzbNnXHYXFxkXw+RyaTbZ3TiiLimAEDVVXxfY9GLAwYpiGRVHUDBFuWnW5WbWx/92t/rq3BewG7fcyY/3a0k8y/+dp77QEpoWmE8cZrak/phMXkwsoW/sbtG/Q8D1Vd2+hbket6BEGAGZu/Pc9FUVRQBAlVZaVSJ52wWi/LD3xURSFE+v6EaJp/2nyDvk8ujh0olUqkUsltTVPtwpAQAqFpGJkUWhSiaRre1CwuEs42DLYAavmQ5HoBjuuiKKaE8wzDVoDXtve4HqqiUi5XsSwZvKgqKqvlckuKvZ/gTyEEihD4QYDnBztkLoh1vz4a3X8jEvOgwbUr76PUyxx/4lEeOXWSPcODDPT3MdjfR2dXZwu9UpY+9mOLk9hizW7uzpWr15mdGufwYCfPnD3B0EA3URRxbN8A+3o7qbru2jQoCvhB61RJJxNomkrKFJwczvPZs4PMlRzmSg5LZYfllQq3Z8vMrjZ45+o0V0cXuZBLksslsQwNTZF506auYuoqhqagqQJTU9EVWFmtMzm/yr60ScXxyDWF6XhYrWCmbWZXEZBPGJhRyOhijbNt32XTFo8f7cEyFN6+Psv7owuMzVewVA07Uvj+1CIeCoPpFJ/Z23tP11KT2a9V0mv2MQ7Ma6um19yPxZRNMWVzd3mVG+Nl3njrLe6Oj6MK8H2XMAiIAl9C3no+fcUkB48dor+rwOHhXizbRETRmiKwQcKTlQZjpDx2NpG3aJdeGdu2OLqvnwsf3GZmYZnBvk5SmrbrTeh7AZrWLFX8IUhI03cUyQpUURDKrKJIBsXNLqxw/sotIqHwmccfAmhZwmj+3IOaNTaEUBCqih+b4+8tAG5ekU0F07ZtqtUqKysrpFISqMzzfBRFQdPUOGarKoO646qpTTyHCInxsvNavLeS8XfE7C/pQaczbEfNYCjXdfE8D93QYyhHCMII7R5SU1OyCoJgR60faKHaWZaN57nUajUy2SyGYeC5Lm4Q4LierFwYRQS+BPNpQnLK/hKjsq2VCrbtBK7bwHEcenraYTXXL4qthiGEQEEhaVvkbJtqENAIQx4c64ea42AYKpahE7GmEW1VZKhppqrXHWzbiiF8K/i+2XLXNAON1o9noz1x84ao1huU684uUAn/FimKmJ6eolapkDH1ViR/f28v3Z2dbRkfrPMvep4ntQhD4j0YusZW+Zae53Hjxk286gpHjx6ir6e47tl2wiKRbotZESDi6muRKqjXG8wurWJrET15m0xa/hwYiDX3msuViRVO7iuiAlMLFeaWa9ycLqEKgSCMBRUFXVXQNYGigKVpGBrUGz4N36fhqfxoZIbudAIBaGGAaZgYmoquSkFOUxQsTcWM87BtXcM2NFKWjh76rNY3gzhpmsLZA110ZGz8MGRqqUZPyubT+/oYX1rFi+DUQDeHctvnZ0ct5ivNrs13AbQJ3vK7jQJA87rBfIqu6RIXR++gBQ4nDg1hGiaaKmGcEYJ8Ksmx/QMM9hU3nUHSKhgzhdaSjw/NphTSSl9rv3OXAsEWpKgqZ4/v471rdxiZnOXQnn4SCXt70/8GCqOoJZzIAvcfzsKmqCqI2AIQ31+pONwYneLuzAInDu7l2IHBVrvyNclnip3GH1sQoiBENQwIAgLfp1ZvEPoRyfROloTt3QCaqpJMJKjV6lQqFXRdxmNJS7CL63pERFimiWmZbcWC5OBk7YadZuReWu7OvPRjx/wfBONfL0Ds7KcxDAPHacSR7WDqGrVGDJqzrelPbjLP99cdyttR018fBD71ej2Gp/VJJhLMlSskTYvlco182pYAJEK0ivc0c/ubKTthFOL7PplMliiKKJfLW0TAb+xzm0Mo/q6ZDw+Q6+rALFeohCGr1fp9JsVsT6qiyKBGobQhyjVrDWx+157n4XkemUwKy4Z6zWmBm9iWtU1Z4vWNNMGP9BgmOghC6o1GrPXfx8g+UtTf/d8cAQvzC3JeVJW5+YVWYOdWhZkURUHVVNyGi67rsgqc67YyIwRQqdYolysoqsLy8gqzU5MUUiaDvUU0TV07kEEWV9nYbVUh8gNEFLGwWGJuoURn2mSwuP4wFEJgJ03OHu7m7OFuAFZWasyt1BibW8VpeLh+gOOH1BoBDdfH90PcMKTWcFmuBFTrHkOdSXRd56dLS3hzi5L5+y6WaWGoGqqQWq2uymqEphAEYUhHwqI3beEFIb5Q8Fxfjku0LbKgCeyT5RNHe1lYrpFB48nhHqKh7jhlVm9Z8mBNwJd/0zqYm2u3uffXRRxFco03cfil+VhBCKlMDOdSnOnJUvKWGR7s4L946XHyG8oYb0vrgnjWfy5A5sa3CwAtgaDpJ267bbcHbQQogsPD/RQyGUanF1hYLtHVmV8vgGy8p72rQuJPKIqCKlRE02rxYfaYIqTVNKaZxSXevzmKZVo8d+4EWrPEcxTFbr4IIkFEU2AQmwSBKGb+QpFZM0GjgVBUZuZXUHSTjs7NQtiWU7UxzkqArmuk0ylqtRrVanVtGIqCYRpYphnju2yuEijb2jhJ9/p/8+Zm8OjfebP//dH693Tvl2ZZJvW6g6ar5NM2y9U6UQRp29ymyl6crub5O/qggVbAhuc1WF2VDMgwdFZXS+TzBXRdQ1MEC1WHbMJCVaTPSIL6hC0m3QQA8YMATdNJJBKxFaFOd0/nvWZk01y0uwFUQydZyGGHIb7ToOYH8qj4CJKYKgTpZIKUbceLO9jy+c3DVBa5cdA0VebPRiHZGIgojOTG3c683f5+fD+gWqtj2ZaMp3AaEIGhabj+Wlnetadvs0J2M/Rt9+H9z5sAisUiuqYQhAHZbKalMYkt2pTujFj7d10M08Q0DBqNBrZts7xc4t2LlxgdvYtpmszMzbM0N8VjR/rp7Mg3FVR56LUWw+Z+NWuvT88tsbJaZn+vRV8hec/x5HIJcrkEh/Zsjmj3XR/fCwjCiFrDp1xvUK17rFTr8oIoYrEsrWWu69HwAup+SKXu43oBXhAxFzSYXalxe3qFrGWwJysxMtSEja0LCII1k29z3cRCuq6AqSmoQdOSFrasKNKt0qz/QWvdSldLM9pa2YYPr1kLNU0jCEPCIIwzVqTp2lJVDnVkuF5yaLgutbq7EfCPtua2WUrR9ibSVkDchu+CSAoHYdR2TdyU/Ketuc2DMy2DI3v6ePfKTaYXltk33LcOhXB9HzZ2SbrdgjBAoc3vv6ttsr2EEIYhc0urzC2tMtTfw6G9fe0PRTQtExGtbClFUdYZxiLkdxJ3xCSKs7IUTWdsdpmOgSEMw9wV8998SdMqJEhtgP9WFCWuChhtwfjFNn/v5v/yo90opf9ZMv/7oSZDsW2Ler2OpesUUkkWVytEQDZhSXM80L4QgzBcV7t9O/I8P5YAdQxDb4GxLC2tUHfqdHYWmZ2dx1TA8QNySQvX9VBi324USYlUVRWJE+B55PMFwjCkXC5j27ZM7XgA86CqKkXLYKHeoIHADz+cE0BRFNK2RUc2jaapUohB+mU3UnPNN1wX3w9Ixya25piiKMJxPcIgRFNVFEW0BKqmabWZTQBgGDqVeoOFlVVSpsFypYamCIllIGStgnXj3uZsabORbH0BbNZe7pvnt90sBJ1dXTSCiCgKOH/hEvv37KGzWCCVlP7CjZtZKALd0HGcBrphoGoaoePg+wHf++Er/Pk3vk7OBBSVmhtwoK/AiYODdOQz8rCJA4eUnSpoKgpRGDAxPUuptIox1IlpfrRjQzO0FlZAMg2dJLd0VWymCNfxCfyASt3jJ1en+cbbI+zrzXFibzflWoOa63NiOL92EkehxMZqruUQ6o5H1fHQY8tQkzE3S7TKPStjhxRVXZdFsTOtv0ZVpFsiDKUQIQUJSBoalgKTpTLzSyv09xS2YeRbfyTuxzQXtyu0uLGtqudFSMuIEDTd8lHzYfGzFFXl1OEhLt0Y4e7kLEf29JO0LcQuqq/KwDkp1G/l8rvHALb9xm14LC2XCYKQPf07KEAiXuORjAUQUTOTQAb5Sa1fWjGCRgNV06jUHCZXajzymdO7fPf3Q01rUtRm6n8wJAStzK570cea+T8I/3+79WuzrzjeGPEXtm1Tq9UxDZ18KkGp5qBrKpmW+T9mB3FwiGXt7OtvpgECJJOJFgQrQDqdolQqYVs99PX1MTk1yepqGdvU8H0PLb5WgzhQxMN1XWzbRtelT7hWq2/w9e9uLnYiq7NIn+9TrTssrKzi+sE2N0XbHti5TJJCJt0qpNQUgNRtDoowDKnX6i0BaSPuwcpqhWrNQVNVVCHwQmmZCIIQQ1Xo7+7EiF0CQgg6cmnsmsHSaokoCnG8CGedv7+d6W49D2KLv7a+8F62y52+38AsWihv8vdX//xbFPJZnnzsUQ7s30vC3gCFzBpAjttoYFoWihCM3B3l69/4Bsd6Uvzci0+hqyqTM/P0dBYYHuhpmUSjOHbkXqQoCrqhoaoqy6t1llZqdOSTH7k64rp52FVTAsPSAR1N1yikTZKWztmDXbz42J6tb4ki8AOEqsryg4pCJm1hWxr1WkCgKthNMz9yLWo7zMn9W6plbIOiaDQx/PO2Qc4QXF1aZGJyhlNH98ZXcs8NKhRFau8f1i21heIo0+diNL2IlitoY3TskT195NIpJmaXmFtcob+3U7qLtupKK/5BoKoahqHssmzvPQbW9nW17rBaqWGZJv2dhV012xR0oyAk9GWZdKEoaIZGFLtqVCEYn16gEaj09w/Et0dbWzk+FEnBcitT/+5o6zlqZhrslh4QPuODpmYwzUdvac283Zzk5u+tJy+RsPE8T0YoqwpOw6Ph+W2+v/j3PT3jMnjN8yScr95WlUtRBJZlkkwkmJqeIooi+vv6MZWIcqUOCMwYvEXXZVSt6/momkY6ncH1XBYWF1owtzv1Yau5uBcpmkYqlSKlKejbwoNuvZF1VaGQTsU4CTJHPyJCUdoD/dbPZa0mzb2p9GaQJAnRm6WYl8iFdc/Dj/36AF4YsVKttoIjm20mEia9XR0UshlZJnpj3++btnvf92rr3s9qMoVXX/mRRAoLpWWp4XrMLyzy16+8ysjdsS1zdpUYGc+Lv6vW6vyrf/vvSER1fvFzn+TQ3gEO7hvg2U+c5sjBYWzblGd6HFvCTlDHrYcIHjl+kKHBPl6/tcS33hljbrGC4/p4fvgRDrAPf50XBNTdAFVRSG1AqIz9SPKnmb2iqS2zf9IyyadswggWK2tR3aJ57w70UY6kVuCqppMQAuHUWSpXCf2A0POJ/LaXg4FRAAAgAElEQVQ13PzZSA9QS1zfOWLNX4CmIDQJr9v+o2oa+4e6qdQbzCyt4vuBFBSCMP69sd9S61J1NTb73b/1ZKev645DuVZDMzSKhcz66zZO00ZXhKJAHDirahpRCF6jgSIUfM9ncn4JL9Lp7+9vuUWaQckflZT7wI7YmjacZkJahiXj333/Pqaa/4M2s9zHk4WsMFivO6Qsg1LNodpoYMaR5hHgByFC3XnqokjmYkugj2bcQFNiE7EfSKLOjY2NMTw8TF9fL5OT01RCE90wWrC7pdIqiqKQzWQIgoB6rYZAkMnsMlDoPiiKRIuRZoudpIIQz/epOQ7V2KS8E+Wy7UBDEb4XI9GpzXLK64WJMAxpNFyJ3hcHA20831RVI5dOYZkG5VqNat3BdZtpfxHLpQqeF9CVz64FsiEFh0I2TTaVoFSRxY2atdF3ORtt1o0HvCY3CO/VSplyuYzbaJDQoFIPSVkWvh9SLldjPPi1W9snSSDdSrVajf/pn/1zRqdm+NUXTjM82L1JE2gGNwH3VeBocaUi/baqyV9dXeGvr84znDd58nAXzxzrIZG2pB2iqUnek3Y7n1tf57gB5ZqLKiBpNE38rX/kGvAD2ZcNiHSFtEV3PsHI5Co3l1YZzH2IvPCPQEJAPpnAVissLldw/YCV1Qpp2yLRxPtQlZbJUrTd2ETAu2/F/8NaCtpIVVR6iwV03WC1WqfqNLCTG2KeNvKeKBYKVGUXVrL762ut3qBSc7B1na6NQZP3uL+JmooiU/tCz0OoKrppEvk+vuNgKWrsDnakC0dTZQ0Cds4Euxc9OAuC3GvNWJRm67uljynzfwArdRPt3jErBQALx3HQFIHrBdQcl4RlEIUhYeBvwnreSK7r4YchtmW1TNJbSWy2bZPNhkxMTDAwMIBh6DQaDvOLHqquQ+BhGQapRBJV1ajX61SrVYqdu8F/vv85vHFnhD/6+rdwXckkE7bFqZPHePjEUbLJJCuVatz3Nf4TRRE9hTxBFJJNJQCJMNhouKiahhLDKG++DxxHAi3Zifb5jDYJAEKAZRhYhk5nLovnB5RrdVarVVzXp1KrU6nVyWdTFDIp1LhkbVPbKmRTpBMW5VqtVd54V/P3NyGHbljeQghS6QwnTpzkRz/4PkHo4aOzXHdJGjJIbH5hgUqlQj6ODiuXK0xOTZMv5Oju7ETXNGbn5rj8wQ0G8yrPnDnWSh1d838306Si7SO1tyFFUXjpqUdIv2izUipz+fodro1M8XuvTfDbX3ubL5weYk9vlnNHB+juzty7wbaxfxhq+AFVJwRhkrKNVvyClM59OW5Ni33c65+RTlvkMzZBEDJf3lze+V7gKB+Fmss6mzTJJAzOX77BP/4XIyTSKX79F1/i8J7+GOSqiRKoEKliTRAQcf+i+zwjH9Bwitk0lqFRrdVxnC1KfW98ThOONgjXshE2zu3GoeyyrxK5M0A3zM1CyA7UVEqEEDJOJggIAh/DslrProWCVE8PqqKQTiWlJS4eb7urrIWouUsXtVCkgLyVjHS/S65Z3r153t7v/R9T5v+3p/m3k2VZhAiWyjUq9UZbZbB7U9VxUABtF2bVVCpJpVzG8xoUix1MT88ShQFKQByMKCEg6/Uaq6urpNPpDxE4sztaXlnl8gfX+cKLz5PLZrh1Z5Sv/8V3WC2t8vkXnmOgq4hAUHfqcbEdW2qEvk8mIaPAw1Dm+CcsE0URNFyZwqdrWsuaEQQhjlPHNC1Mw6ZSqZFJy/t9X35nWYmWFUIiMcp1IeJ0HV0Ihnt6CMOQhVKJUrnKcqnC6mqVfC5NLp2K08MkaZpKPpMmk0yyWq2zXC7f05LxoGjd+bbF8hZCcOjIEW5cv463usBS1cUPA+quFDZfe+MdZmbnefLxR9kzPMjo+Dhf+ca3yGYy/MNf+CJdxSJXr98inU5TadT40ds/5edfehZzYzW+Vp7a/e2xE4eH5R9BBAIeO32Uyel5fv/r3+d7510u19P84LVJ/uTNUWxV4Vhfit7eIpYSIIBcyiaXksGztqGRSxokEgZWYue4mZ0oinwUXPKpWHD0JNMXmkp0j4CnQsoglVAZKVXWfb52mP/NCADNFvvSCfYkDW5NL6A1DOaqDtdvj3JwqHfd+gjDkNAPY0YVuy4iiESbReBvQle6B7m+j3s/FT9VsZZxsJHajaI70vo4oyhqC0xsfr+biQhC+aPp0kIUBKjtltwoIggD0vm1OAIlVgjDKJJCTxShqmpbnM69HyuvW1tfH8WFoLa5Dj7sMv2YMv+/fWri42uahm3Koh5+EBCFEcZGH+MGangBCgLbNltQtlFLLVmPQNj8LpfP4zguyWSSjo6CxB7wPVKpJLYl4xAajQaqqmHb8rBrLwu6NX34U+HZJx+nt6eLO3dHWVopEQQB5UqV85fe5+vf+i7Vag1FVfn000/wmWef4h//z7/F//G//FOGB/p58/x7/P5X/ozf/LVf4fyFy7zyxtssLC5TKOR4/plP8IWXPs3M7Dz/7Lf+d86dfZi3zl/Aabg89fgZ/vtf/0e8cf4C/+EP/5TDh/Zz7cZtypUKzz31BD/zmWdJp5K8+uY7fOMvv0fgB9i2xX/zK/+AUyePkU3YzK+sUm+4LCyvslKuYmkauUwa2zJapjFVVchnkmRSNivlKisrq2x9jG0f1Hi/tKmJLbT/KIo4dPgwP31rHlWBnr5BZmdncXyPMBDcGh2nUMhTdxzev3qDIJL1E9LJJO9cuMgb75ynkM+ghwavX7zO2VNH2b+nr4UTEMbBm6JpUr6f/sfqShTHzghkEOvBfYPcnpzlt37jH2AoCj969yqXb47x9t0pxOIyrusSNjwUXcXARY1kwSpNERJCF2mFyKdN0oYgoWtYtklPzgQhKGZl/EjS1smnLIqFJLZt4DQ8SlUHVQgsDSLXg2ZQX3NqdxhjJmmSTZvMzNZxgqBVEKmJ0PfAytW2kQzwCgnDkKSq8LOHB/j8oQFuL5f57Xeus7y8irKhYiIREMccBW0xHwrxO9ytBecBCQiu7xGEIeNT80zNLLBvqO/eNwnkPtopFq29b9v2df1edD0Pz/exEvZON20iz/NQdQNFUwk8l4gQLYbTJZKgRKWlEla6raR0vJYUpDU0jCI8z4tdu/dOqwOpqTdrxmyEGt+18NDS9pt9+vACxMec+W//QptxJdMzc61gpyZpqkpnZwee61GuVLBtm1w2TXOigiBkZbVMvdFgoLubuaUlas0c4zbSNY1kMomqqAiQ5uVqDdfzGOzrabUXRTC3uEy9ttZGOmEjbJtmwd5ypcryyuq64CghwNANbNtipbQqP1xYituMUBUF206wsLiM63qkUykyGRnk5/shM/NzmKpBZ9d2ka5bzd3uNsm//O3/E0VVWV0tc/TgPk4eO0yxkOfxRx7m6KED5LMZ3jx/gT/4yp/zpZdf4MjBfXz/1Z/wK7/0c/z47fc4deII5y9e5uKVa3zhpc9w6vgR3rl4mXcvvY9lmjx07DCu51IqrfIv/8n/wBvn3+Nr3/wOv/TFlxFCoVavk00l+c1f/WV+cv4id8fGuXrjNpZl8Ob5i/yXP/8FTj90nDfPX+B3//Cr/Jv/9Z9jmiYD3UUqToPFlVVc16PiB1QbLhnbIpdJYbWVFlYVhVw6ief5rFZrW8/fRz0wt5vubdoVQuBHEaoiePSxc5RKJXp6e/nJj19lYXqCd396hQuX3sfzXPxQFoz67g9f5fIHN3j7/EU6sin2FrNcu3mHf/3lP+Nnn3+ST5w91gqKawW2fRgSrKH+BRG6ppLLpBC6xcjEHOcePsTLzz3K5549E+MpSN3Grbssr1YoV2usVuuUynVZXrlWp1pvUG94OK7H6OIywhWUlh2C24sErswSEbqGGTkcKtp86cmDnNjbKSOzwxChRNIipyj3JdQkTJ1COsHcvMN8ud7y+zeZ/4OiIAxlGXHW/LMy6G+tnxnLpMc0mVsqbSnIK4YmBa8gIIy17cAPCZoWgabgoqnbr1cBu9r79wiUG59ZZKVc59BwN71dO7geW/HVEYHn44fSAtSeYgoyx971fPwYW0QoYl2dkyZjFXFevMxMkPtytVrD8wKyicS9xxWT12i02gw8nygIUdqxXGLPxNJyjWMPZTfd3y4EmIZBGIb4fiBrt8SkxWm5qqIQRlELMr15bwvx8D5pa8bfHPf9S3cfc+a/eTBNq6VAMvF/87t/wPjk5LprujqL/Mav/TITU9N875U3OH3yGF946fnWuVCt1fn6N7/LlRu3+O1/8U/4o69/k7fOX9oUaTY8NMDPvfwiB/bvQVEEI2MT/P6ffINqrcav/OKXePT0iVY//+hP/4J3Ll5uBVMJITiwd5hf+MJLHDm4n/MX3+ePv/FtKuU1M6OiqhzYO8zDJ4/yJ1//1qaxFjsK/NKXfobLV2+wWq7w8mc/xaH9ewBYLVf4nS9/hUI2y3/3q7+Msutzb3f+oc+/8BwdhTyz8wu8/ta7vH3hMpZlMTo+yY9+/BZOw6HeaOAHPp7r8dlnn+L/++Ov8bMvfJor127wm7/2K7z6xnl6uors3zNEX283hysVxiammJye5aFjh7FMk08+eY59wwMkLJOvfOMvqdTqQISmaTz39BMMD/azWqmysLjE0koJXde4OzbBN7/7Q/76ldep1urMzi1QrtWkwCUUUrZFwjRYrdZYLVdxPZ/VWp2a65FOWOTSyVb2RRRF1Oqb/b4timDXgUpbT/cO7W7+uLunhyAEhRBVVdmzb5/E8EeCgdR9+dt3pXa6sFJm8o13+On7V+ntyrOnw+If/cwT1Lwn+H+++l3+03fe4MLVW3zq3EMcOzhMKml/ODthq7+ixUh0TaWQSRF6HmPT8zx++nCMibFexbNNg3QmSbPqXTPFqXkINguVNA/JJjJeFEWslCrUHZff+8aPwF9C0TS8MGJ51WGl6mHZNkLXEGF4X7JuJmHSU0hy6dYC1xdLDOZSLevLg6IgFlD0uOLnelqzABqaQkfCYGpxdXMjgrUAMU1F1VQCzyP0gpZrojV/cVqxogh0TZdR++sE2G0UqTAi8gP8wKdZqGjrC+U7N3WNbDKBpak0tkUEjajWHZZWylSqdUrVGkulCqWyLKldrTdYrTrUHFemE7eRrglyqRT5rE1XIUshkyKXSlDIZ+PAXx3P85mYmsdxXQrZJK4vwMoiFA1CH6WxAr6LksgTGVKwC2olgqCBbupyKsKAKNFBlB8kUNZYoRKEfPofdlD3t98n7bDOhqEQRTogyzAHfoCmanhegBoXjkMIFME6YeB+SFGVltVgayTX+9/TH3Pmv3nnivggjpATWa3VGBzo55e++LkWgpNm6HQVi9y5O061WsNpuOsYXhhG1ByHclkGr33+08/xyccfi9sXfPt7r3DpyjVOHDlIf28XmqZRrVa5fWeUkbFxCrksP3r9Lc4+fKLVZq3ukEok+K//q7+PaRjcuD3C91/9Ce9dukKxkMf1PAxd46nHH+UTj54mDCM0TSGRsEklEgz19VJ3Gvxv/9fv8slPPMajp0+SzaTJ57K8/d5PqdbrBEHYlsEUUq3VsS3rPhh/cw63mOlmVG5Mx48cpLeri3K1yvWbd1haWubS+1eZnV/kyKH9PPP4Wa7euM3v/eFX8YOAsw8/xJf/5Ov83h9+lc5iBwN9vRQ7cly/eYe6U0cAlUqNarXGQF8PIDdOJp1CVdVWLEBTZVBUpZXXrsZAK4auY1kmHYU8zz/zBMcPH8JpNHBdl1RbDrwiBIqqkk0lSdkW5ZrUND3PZ6VSpd5wSSVsUgmbat3BD8Pt83hF658HS9s0mU6niYIATVW5c+c2HR1F5ufnqFbKWFaCju5egjDgzu3bJCyLjs5O3vnJjzlzeABL+BiqoKsjh5lM8k9//ed55e0rvPLWJf7gL17hzLG9fPKxk+zdjal2uyAsseai1RSFXC6FqgjmlsvbD1UI1F3iwG+kbCZJFEbYuorwYXy2xIWbs7x9fRY/gidOdEltLQwRaJs7vs1jEwmdQs4miiKmS5utfh+WmsKDLOAiWi6X7VxzEWAogk5TZcRxmZ5dord7C0ueABFKzIwoCFF1DUXTQChrul8U4Tkumq4S+D6BG25uI/6jXQsVyP2mGUYrpmA7cn3ZZmdHnmKxY0vI7TAM+db3f8Lrl27hNDwcz2tp9oamyMBdy8C2DDpTCQxNk9gKQhAGITXHoVStM3W7xPlrY4hIYJkquqaTMDVyqQSmrnB3epGeziKnjuxDmClWrT5GJufYPzhE2rARpUn84lFuTcyhqILhpI6hBIgowG80EIBqZ7k8WeXdy9f51CfOks2k+PKffpPHzzxENm20sB+a71BseJftfvcoklZnVVERQqHuVLl28w6+7zPY30d/b/c6S0177ZbtqF3bb3/eg6CPOfPfzl66foNnMymOHNpPe6qRqqrr7vZ9H89zt5Tu+/s6GRDdRBGcv3iZu2MTPHnuER4/e5pCPosQgtuz89y4c5f9e4Y4ceQgP3jtDSYmpxkc6KW5W0zT5MDeIQzdoLRalmY+TW2lVBm6QU9XkaOH9je9/60gtlw2TbXmoCgKncUChw/sI5tJr9dKxeaXLxfjTnO4nda6+XPHXUO/+3//09ewLJNqrc7S8gpPnTvD8GA/M3PzXLz8AbV6nemZudb1qaTFE2dP8/3X3uDnXn6RdCrBE2dPMzo+yZ9+87t854c/ZrVcoZDPce7sqbX+x+arjYhUIn7PEl1NHlKqqrB/zyB7Bvv5/qtvcHtkjNVKhcfOPMSeygAhEZlkohUMoypSWs6mktiGSaXuUKnVcRounh+wWq0RNDf2bhj833RgVSy06JYFvsvozRtMj40QuhIvorNvkOH9+xkfG2N+bo5MNoum65x+9DFKM2No/gqFTALTMNA0je5cls88eZqDQ7385avnefvyLSbnlnji4aM8cvwAuewOKW47jTP2/ytCYOoqhUyCxeXS+mse0FxpqgoqVBsec3NllksVFp2IxVrIcN6kN6OzPoBxdw9VFIWMbZBNatzdEPTXGsJ9Bv01i26FYRTDca8xhu0ojCJUTWW4I8ePbswzMb2wmfnH0elhIK1BwjRlnnoUEXiyYmhTRq1Ua/yrf/cXdBcLPHR4L2eO7MFKJuJZCTcxdlWINQFCSOAn3/PXvb63r9zmxug0y6tVro5MEngetmVgGNrmsQkII4U7E7NcGZnkFz79OJm0TTGdIpW0YuaooGoKmqqgqWqM3Km01pUfBriej9vwcH2fas1hZnGF+eUyS6sVynWHmSWHUCjsH+5lsK+IoutUfZWvfud1nnrsNC9+4iEIXXw9ze/8yX/guaefoP+RQ2jZLlnEx3PAWSUysyyUZrly8w6PPPwQVlJw8YMbPHTiKPlMhobTQNd0hCJa9UlELDwpqmiL56I1Y1EUMjk7y//9e/9RavkCHMfhiy9+hsfOnOLP/vJ7FHIZTj90nGIhv+FVS/RSER/sMrA7agkXUSRaGR/r19Z6gUSin26/7uBjz/zvTVEUce3mHX773/771gH+zBOP8vDJo+uiKYUi4mIvCq63ZmYSgtgErHD77hh//p0fMNDfw3NPPU5/TxeaptJoeIxNTLGwtMLTTzzKvqEBfvj6W/z47ff4pf6Xae6o2fkF/vXvfBlFKCyXSqTTKQ7sGSKVTMb56CV+8NobXL1xG5DIfZ//7HMcPrgXTTNa6WeqqsiKf57XggQdHZvka9/8LrmsTKNqNBrMLyzRuW7xrG3ZNUvHjk7A1hyGUUjVadDT1cUv/OyLSPxyWeiimM+xd2iAVCpJwra4Oz6JbugcP3yA40cOkk4lcV2fvt5uiCIeO30S0zAY7O/lSy+/wOj4JLVanVQywfBAH3sGB6jV6nzhc5+mu1gkDCMs2+KX/t7LdBRyWJbFFz/3GdLpNKqqMjTQxyeffIyuYpHhgV4+/8Lz3L47huf7pFNJ9u8dwvE8ao5DzWmQsExSCQtd0wBZXUu1JAiGbRrUnAaVep2Gez85/ztM5SZG1/zg/jhg88qz557gx6++guJVqdWlhm2n0nT29FKtVpmZmiCTMDHViKi2gqerdA3tZ27sNjWnQhQJQFYOS5s6xw8Nk0kn+em1O1y4OsK3f3yRsal5zp7Yz+F9Q+gbg8x201cBkRCoCNJJi6mlNgb6AIWkcqXGhfdvMr+4wmLJ49ThYV4+dYRqw+PyTz/g4p0F9vXm2NudYY2z7e7hSUujkDG5NVHBDUOMlj9Vua+gvyZIUxjKQ3o949/cFz8MGS9Vqfs+rh/yzevjuH5IrVJhfn4BONQaiu+5hIEUjlXdQFHXmH4UH/Ba09cfRaiq4IO7M0wvrDAyvchP3vuAfcN99Hd3xMBlKoauk08nyKQS6LqKQAosohnDFMrAUFXXQVH5q59colouo6kqgdegmEuRsnTCwI/dCjHWZIzl0XBcJmaXUBDMLJb46a0x9vZ28olThxjuLZKwLZn+G8+Hom50T8hXGcUpv77vU603qDkuTsOl0fBwGi4C6CxkUMKQ0HXRExqZTJrX37nIZ599ElL9fHB7jLsT0+wZGuTuzAp3xqcoV2vk0kmO7+unr1gkCKdxnAZBDEPuOA3CEAqFAsurZd69dAXHaTDY283xo4c4f/F9ctk0e4cGSCeTjE1OcXd8khNHD1Es5AmCgMsfXOPqjVv8j//tr2IYOuNT0/R0dzI5M8v7V2+QTMhU01MnjtBwPUbGxunI51lcXuGRk8cJAo/zF9+nUq0zNNDL2YdPoiiCG7dHuDM6Trlao7OQ58jBfXR1FBidmOLGnbsMDfRxZ3Qc3/d54szDrZTtrejvHPOPoqhVMKOZ7GLoOrlMprV4jBjrvV2bU4SCom+HrCRYXinxp3/xHVzP44VPPc2+4cFWfv7M7Dy3Ru7ieR7pZIKV1TK5bIY33rnA5194jmRc3McwdHp7utEUhVQqyfjkNGOT0+wZkhCRqqJgWxbZrASk0DUtFki2Hqes0iaZfzOlpV3S2zyWlgFw1xpLU4IszS9TDQJMy+CxMzGedRQhFKUFuev5AQN9PQz19xJGEXZsGag7Ln/wlW8wMTXDuTOn6Cp2oCgCU9U4emAvg309uK6LZZokbAtZ7lLhmXNnZeaCiDANgxc+9TS5bJpMKkXmE4+RSlgIAd3FDrKZtPQ3GgZDA72kMinchottW6iqSq3uyLxfP8D15OFgmwZJ25IBOEJg6M3DT0NRBEulrUzVO3Gubb7b9NH9aaEbaXjvPlw/wG84aIYuzYiKSqW8yuTYXSrlFRLJBAYeStDAXZrAEQNEYcSBPb0oho7fcOMYlgglDNk32EMhm6anWODitTtcuzvJ5Ow8D0/N8/CxA/T37AY3on2IgnKtxvs3R5lfLlOtrrdQPQhqOA2++f03uXj1Doam8rlPnePcqcMc2tdHo+FhKILX3zzP994d5eVze+nt2a5CztZkmRqFTIIwKrNca9CdkvtYUUQLQXInamf6zftkUN/OQkPV9XlvaoGL00t4QcD7s6sUCnlyhSwNz2vFO/iuL11YmirL2cbBc0RrSICuHxKEEZl0giiKMG2LQ8P9TM3Osrevk+mZWd66eBXDMNAUBUVR0fWm6d0kk7LpzElgLkVIrbvR8Dkw1MuxA4OUViuMjM/w8IE+Hjt5mPdvjzHUU2SguwPf81taMEJAAEQRTr3OweFeLNNgtVxmYmaB5ZUK88ur9HTmGeopcmx/P32dsrAZ0IIJbqfmMlI1FdMyKcQZU2soiGvnF/ikVJfHTj/E7/7HrzA+M89AVwevv/0aB/YM0d/TxSuvv8FKuYbn+1y/M0ql1uDpdAfbLdgIwR99/VvYpkUum+bVN8/jBwG3RkZBxKngxYjLV29yd3yCh08cIwwDhFAwDYMoirgzOs7xIwd54uxpsuk0N26P4DQaBFHI3MIilWqN2flF/vL7r9LZUaCz2MHewQG+88NXiaKIYkeBH/74TaIIzpw6zpXrt1qVYW/cGmG1UuXxM6cYHZ/k29/7EQf37kFVVUbGJxifnKZU3t4d97Fl/s2NJf3c630jzaw5RZWFdfp6u/n8i8+1GF4mncY01+cPt5hhtP5FRxG4rsu3v/cjbt8d4+c+/wLHjxyM75fBSTdHRrk9MobreZy/eBnPD3CcBrNzC1y5dpNHTz8EQDaT5qXnn8EyDErlCr/z+3/MtVt3OHxgHyBTo04eO8SzTz0OSLNpIZclisDz1hec8QMfy9BbJqWuYoFzj5xibyxIrKyWGZtYH+jYNtrWX2Eoo2kRsrLdVgeTV62xUqniaxpBm8kyitNearFULICUbVEs5GL88wjLNFhcLiMUwWB/D5966vE2UCN5IGZSCWA9dK+qKqRTCZkqIwRCEXR2yMNbMvkmVj8YhoYRa6ee59NouPhBgKprOK6HHzjr1ocUAuQGcVyPhG1im7JCo0TDWku52Wnu7u+7B0e6YXDs+HHcRoPFxUVKK8vUa1Vmx0eozI1jBQG+oqIiEJGPFjhEpSmSwuXcqUchkrjlmq7JEqW+T+B75DJJzh7fT08hzaUbo1y5Pc5337jM1NwKj57Yx7GDw/dMY22npVKZn1y4ynLVYV9/J77no+kP7kgJ/JB33r+FUBRefOYRnnrkKD2deVRVJQxCPnHmOMuVGu9dukzhgyl+Np+UwVy7JNvQ6cwlEIrg7nKZ7pS9q6C/JvNpr7rZ1Ph3Q5oi6E7aJDSVa7HFpNhZ4KVnz7J3oFsqOPGeVeLA1CAIILYCCkXQcHwmZhe5O7OEYRicOtRPIZdB13U+eeYoX/7GJL35FGeP7WFmYYWF5XJLULk7PcfIxKysjWHoZJJWK64mCAMq9QZCKBzZ08vIxAyB73Fkbx9nHjpMT1cHmaRNLmXLgziuGttEHgz8ANPQef7JR1iYX6RSb3DiwBALK2WqdYe7kzOMTO9P25kAACAASURBVM5xe3yGI3v7OLpvgP7uDlB2EYUcWzK3yliJCLCCEgf2DpJKJnn30hUKn3yS9356hb/3uc/IeCHbxvICdF+jVCpza3SCI4cObvuORyeneOOdC3zxpU+Tz2W5cu0m5396hYHeHj64fovJqRk812NyeoZMKkUyYVOvS9CyI/v38dlPPc2tkTHGJ6cZ6O/hk584R0chj22ZZDJp9g4P0JHPcXd8kpWVEoN9vRw7dICZ+Xlef/s9XnzuaQq5LJc/uM6rb7zDqeNHsC1TWngUlYXFJW6NjHJgzxC1ukOpVCadSnLk4H483+PHb57fsU7Fx4T5R/j+Wgnb5mfNyNNmHW1JEsdYUdWW5l+v15mcmm0x+Gl1niMH9sVBgSHj8UsESCRsih35dZH95y9d4dvfe4ViRwFD1/nplWuAxN9P2Ba37oyiKAqnjh9pCRXZTIooDPnhj9/kzCkZ9e80XO7cHcPQDeqOQ6PhkstmW4eC7weslMpMTs02h8L8whKnThyhWq2hamsHVxSGGIZBEGsVCdumv7ebg/uGiSJYXFpuKxQkWCv9up5cz6dUqRFGIbZpYBkGuq6t87HX6g08VYUoxPMivG32YAQ0fB/HdUkn7NYUZlI2f/8LL6EIhXwus40lI+6pkAKJ53l4ns9mK0XTl7W5kWYbQVOg2bKHa/e5QYBbreG4HinbI2mbGLpOw/Upb5Ha+bdPa2vc8zzGxsYYuX2L0sw4bhAR+A20wMPCJxIqAQoicNEUeOJwN36k0FPM4TmOjA6OU5iE0KXP2PfRdY3h/m46cmmG+7p47cJ1rt6ZYHJ6lqWVMkcP7aGnM7+z5Sh+EXXHZXp+mWI+x0tPn3mgkfIAuqHhhxFDPUU++egJejrXXFyKqtDf08Fnn3qE1Uqd125cZ6gzxenDfa2KgfeihKXT05FC0xXuLJU5N7i+SJZUMtavz7UaEnE/4vry9xOHlTR0HhvslIGSZYcV1yeTsnjh6bMyBc33JLCVZRH6AZHvy4BURZE+f0VQc6tcH5vl/LUJuoo59vbmKeQyqKrCmeP7+Pdfi7gzPs0XX3wSIlitVAnDiHKlxg/efh8UHTcUuH5ExffBDxFRQOjLtMz3b42RSVpcvD6KpinsGezFME0O7Okj9H2iaA2jXkTIGARFYkkkUwn2pxIMdeZRNBXX9VhYLlGt1ZmaW+bmxByjU/NMzS0yOjXPyUPDDPV10ttZkG6ND0Ohj+quksv0cerEEd569xJD/b3U6nVOnzxK3XGYW1hCCAVd07BMkyBW4raiKIqYmpkjjFFaHcdhsK8HPww5emg/I6PjTE7PMjO3QN1p8OjphyQaI7JEtG1bfOlnPsuNW3e4cWeU7/zgNSzT4qXnn8a2LDryOfbtGaLYIeM7ctkMZ04d56Gjh/jh62/ScF1SyQTVWo2h/j7K1SpLKyssr5TQVGkxNk3pGnYaDYSQcO/nzj7MvqEBGm6DH7z2ZqtK6lb0sWD+TcCEVtpPvLE0TUXTNMns12ms8pD3/YDOYoGl5RX++pXXW8e+buj0dneSsOUkz8zO81c/eBWAzmKBzzz7JLlshp6uIlEUceHyFdKpJKlkgtfePN9qp1DI0dNVxPN9zp05xedfeL6l1bqux7e++wPeuXCZarVGIZ9lenaeH/74LRQhJMKdbXH65FF6uztZXFomYVtMTM+0cvqFEGSzaR46fpggCNB1k76eLtKpVKtcsKJIhqqosshPC7deVejsyFPIZeO21s9pFEU0PK+VT+0HAdW6Q8I0SdoWlqGjaSqB61FumTjFPZVbgVgXNOT7AUEQ0LFtUfJmf8KW5hEEAY2GK82UmzTN7TsgtSsFwzRQKkKmhO3iXtfzWPE9Gq6LbZq4QSjNqw+YdpNCuTPJm33fZ+zuHc6//irVmoMVVBCqShDpBMJCES55M6SQsvFDA1URfOrcKYSq4juyUI2qyYJQCFnXXI1UAj8gFAKhqiQTNicPDLKnv4tX3v2A996/wVe/+xMen1vh8dOHGO7r2lRhUQ6yyftlffukbdKZz/DYiYOgKZuu/SjGEl3XMXSNmuO2hOB20jSV4b5Ovvj8OX73jxf42hsjJC2DYwd7dtm+Si5jk7Z1JkrrcR6EIv3+TSG5GfHdVFDWmP5GwfXeAw6jiJlynVuLFcp+hKrrOG7ASqlCLpUgDCMMyyQIQgLXhbh0c8u8Tpzxg0Kg6BL4K1ZKBIJCPkNvdyfjc8ssr1Yp5NLkcxnqjsvo9BL1QEVLZBFCwxQSiSQIA0QY0KhXqflw5dY4s4srzCys0FdM09/TjSoEoee1CZaKDJ6L5yaK/fQoCoFTlxkEmoamqAz0SDP4geEBTh5a5drIBBeujXDl1ig3Ric5tn+Yh4/u5ci+ARL2/aM+CiIIGlhBmXOnH+JHr7/Fd374GkcPH6BYyDA2Ocd3fvAan/3U0+RzWXRdI4zCdXFh7QebrKWSJooiKtUauf+fuvcMsvM67zx/5803d84BjUYjJwIEE0hQpESLkijJtmzZ1thbLmvscZi1PTW7O2XXlsdVs7NVOzU7rq211zNOO+NdeWR7bNmygpVFipkACBAAQaQG0AAanW9Obzr74bz39m10BEl55ecDunFvv+mc854n/Z//k0kRj8cYHhxgqL+PHWOjXL52g4WlLGOjQ0xs34YQEIs5eF7AqbfOkIjHGB0apL+3h5dfO8XN23fwPB9d1ymVy8zOLzYxXIZhkIzH0XWdTEqlYfLFEp3tbTiOzcT4KHfuzvKt772i0qNtGeX8tXAGGLrerJAyI0dyo9a+PxDKX60XPcp/N+pfNw+/aprG0088QrFUWvF5g/VseGiADxx/mEKpRMM7TsRjdLa3c/TgPraNDCGE4MHDB9gxNrLiHCBJJBLYpsVgfz+93Z04jt1ExBumzgeOP0wmnaZSrXLsgQOMDg3Q6AAVi8UYGeynv6+HmGOzbXiQZ548jt9CiSlQi6Ved4nHExiGyXPPfIDtY6Oq0Y2mYZo6hw/swfN8ujramghkx7E58dgxYva9PQbUBuT6Hou5ApVqvakkgyCkWKlGRoDKh7ulIhVva3WnuhDEbJNk3InCnsposx1rQ6UXBGHUklgxYkkZousGjmM32QpXy1rgxaiu1lBrZTVgb/3NN5RQrrmUa+49R6wu8Xu3Omv9Mbi/M96emuLkyy/geFkQAtdwsGSdjF6nJg2EZjIykOH4/u3UPdVDQhgG0nMJwgDTshB6hOCW6tqaYTQ544WuIQyD0HPJJGN8/KkHeXDfOJ/78vd49ewlLt+4ySc/eJxtQz10tKeXN5BGJC5U5DK2ZZJJJ1gsFFfSzTYH5D4H8N6hEpBOONQ8b13iHdPQ2TbYy498+AT/5S+/xl+9fI2+njTt6djG0YtIDAG2FjC5VCGUjeoSoVpHex4ySgM0DFdF0rMeo9vWFH+26vKld27xtWuzxBIJEgkbXdOYz+ZJxW3MmEMYgl+rKbxKtC+2imObxB0bITQQWhNgKFH3emT3KF978Qynz1/lqUcPohkGU3fmeOn8FLezHrZp0x0TZGIauq7h+hIv1CjVE7S1txHzK/R1pbg5Pc+e8WFiMYfAiwhyDD2qNlDPrO5BgAyb6bTAD7AcC0IFJgbl7Ji2SXdXBx3tGcZHBrhw5Sbnr97i1PkrvHH+Cp/52An27xqlPf0umi2FHlZ9np1ju+nv7ebCpav80s/+FLpfwdQFnVHJdTafV8yAthp3w9CJx2OKTAhIJuLYlsn46BBjI8PMzi+QiMeYX8xycP8ebMvkwN5dnLt4mVK5wujQIMlEPFqjaq+9MnmTy9cm2TU+RhAEOI7F3p3jES6sm2s3bvHGmXPEYw6maZCIx6OW5ga7J7azc3yMmbkFMqkkC9ksDx45gO/5dLa34bou2VyOIAiwLBNDV/thPB5D1xXjo2EapJIJZjYYrk2VvxBiGPhToA8IgT+QUv4fQojfBn4emI/+9DellF+JjvkN4LMoCMivSim/ttE1NE1bwwPcXDRN8PjDRxp32vy8YQm1ZZIM9ffQqkQaZRM9XcshxEebpWerM0mt4WoA07BRhgTY3Z2cePwhCEI6O9owD+yNyixkixGjUhbDg30MD/avUBBSqlxeoVCgu7ubIAh4+MFDJBJxdE1DCIllmhzcu2vVs8ccm0cfPNy8z2VRm1W+UKFac9fwjtUGVKrVKa0T8rp3DJpGlWlgRx6IYrZSVqyurZ1XamyatVo9Qp02Nk8LJ9ZA468cj7VLV1aed7lW9l7lf//aRkQY51Yen/cts98859bBl57r8vWvfZVkWKarTTUdqfg+bqDT1xZDJyQVdzi6e5SejgxBGGLGYsgwxK27qgeCoa9Q/AhlkBqGge/5BK6HYdsIy8av1dA0wVBfJ//TZ3+Yv/3Wa3zn1bf4T3/xNR45MM6nPvIEmVSyiQFRFLMC3dAwdZ2E4zCTzaqdYeutxDcZr2WJOxYzETBuPXFMgwcP7KBYqfH//s03+fJLl/nU0/uI2auV5r0ShAH5coW7uSplzyfZglnQNJ0gCJqevrmGEr5fKbkur0zNcS1XJp1JEU9n6M3EOHZwgrZ0AjPmKAxQtbqu4geIOzapmIUQkmoAS4UinR0ZypUaS9kCXhASCIOXT5/n+NE96JrG2cs3mCsH6Pj0xS0eOzzO4T1jAMhQUqu53JqZx/MDutvSdLenKZbKpNrbCN06YSjU/dwThW14/qEMVYVAJKpbXoPdUIscI5W+MHSNwYEehvp7eOTwHk6du8zXXj7LH/31t3j28cN86NFDdLalon10a2MuZAi1PE6yyqc+8jQXr0xy7MAu9NIUI90D/PhzH+Li1RsgQ44d3Es6GaMrEydu6xw7uIuOVAxHD3n86H562pNYYZ1f/Jkf5/WzF1jK5Tm4ZyfjoyN4fkAqmSCVTODYNrt2jDWj1kIo5r9/+k9+nBdeeY0rkzexHYdPf/JjPHbsAQzD4IMnHiOZOEu1WgMJ/T3dHN6/O2Khhfa2DL/8sz/F86+8wfxSjv27J9gxOoph6Hziw09zefIm6VSSh48cJJ1K0dHehmVZPHh4H8l4DF3T6O5q58nHjnH19IvrjtdWPH8f+JdSytNCiBRwSgjxjei735FS/vsVEyDEXuAngX3AAPBNIcROKeX3uYNKS1nfqrVyb01k9OmqMO1qRdnECd5TOteou1zKl3B0QVe8vans1bsh1zhPC2ixkTetVprXqFTK1Ko1kon4igV/P+FkKSV116VYqTZr2N+bKHCcaRpkUgnSyURzoQeBTzy+EsjXUPigNoNKuUoYhti2HUUzBOspw615zjJiMNNXf7Xhcet/fh/6eQvXbJGmRSHZrEdAY0wvvfMOIgxw9Rg3sxVG201O7BuhVvcZG+6lv6sNGao1FviR4pcSt1pRtLENHEjT6JPLpSKo0s3Q8/HqNUzbwozF8Gs1vLqLadv88Ice4cTRffzun32F775xkV3bh3nw4E5ijt2S05fIMMA0dDKpGP6NO6o0TrZW02xtkJqobRoguoitLlpDCcei0VdgXdHANiw++OhBrkzN8PWXX6a/M82JIyOYxr2h+ZXXDkJBLbRw3QrT2SLjXZkoz6/hR7ThhmFESP5NH2dTuTy9wKu3FpiVJh293bSlM2zrSfCRJ4/hJOMgBW6ljGma6yp+UJ5dMmaQtASuF1Io1nA9j2+8fJazkzOIwKWjLcmNu1lmZpfo6+5gqVCj6obYoc9itsxCdplRUGiCWNxm59igGhsg9PxmCN7zfKyIGltK2SxkbTSoESiHwLKsZkfC0POby0AYyquWQaAMRWiWCWaSCR5/6CCD/T188duv8MVvv8GZi9f5tZ9+ju5O1aa7uQY2m4MwwCpOcWLfECf2DUFtBq1wG+mWeHRnP4/sHY1YAD3wa4rlL2my6/gewEWELj/zQwq/cvHNF5gTnfzocz+EbS2v/69863nevnyNufkFnnjkmIr4yuW6+iBQBE9PPf4oTz/xWDRWspGFY6i/l4GPPhOlQtW63z46RBiGlMsVDEOnp7uTT//wRxHNkVbHHn/4KI89dFTNUdPgUAbS7omx5ufD/f387E/+KH/0u7+z7lBtqvyllHeBu9HvRSHERWBwg0M+CXxeSlkHrgshrgIPAa9sdq37l81Ww8bf38/LvFZOHSSOLu4tINhUGosoCAJVzgd4Xp1sNkdPb/eqkOL9KH4/CJhZzK1IL7wXMQydzkyKRMxZkf90XQ/bsVc8j5QS1wvIZpeaFJaO45BMJtbOH6/9FKyeM7Hid8s0aUslFDvfhl351rvee9zFt3x4S/x6E8WPlBQLBV763gu0ixK10EEaDrPZPJ2ZNNuGetB1PUJU+wS+amWKlPjVapQiimhL1wTeLWNFhGYS+AFetY7p2Bgxh8B1cas1rJhNR3uKf/VzP8Iv/Nb/yeTUHQ7sHsOxLcU2JgyCICAIJKZh0pZR0Ye7izmScYdMJrliPaz4SauiV3cVBiG1mku5WqNac1nIFalW68wvLiKB2aUCxUp9bUO2eRJAU5GpX/nMs0zPLPD5Fy7TlrQ5tKsPQ1+jxFdKPNcnl6tQrLik4hbzpSo7utvUtWSAaZpNBLsQDaa+jWd8MxnsbCN+u0S9LAicFJgGnoR8xcVJJKhHil9fgznvXknEY3RkUsyUQvIlhVnQhEaIiYVHf2ea80sFvvvKST7w2IPUpYYesUBamrEqH9w07kLV517hkO6JyEYMf3L5oNYTrMihy1A2bd/Q81d0Im2k2hrXNDWNnaMD/OJPPsfXXnidr7x0hn/x7/6EX/3Mczx4cAdWoyRwk5dPECCq81CdX75JKRGVBbTKwobHrkw1SlJ6jS98+2t09g0yMTER7cuCZCJOT2c7jz14mP17dqJpolnuuTwUknv9XcPQ1RMIIsbLZaY/z2s4VAH1+nJEthVQahgGlm1FUWF1Ht9Xe3GjGkuLeiCoFMbGY3VfOX8hxDbgAeA14Djwz4UQ/x1wEhUdyKIMg1dbDrvNGsaCEOIXgF8AGBkZvp/baD3Le/z+vUndVx3KSn5INzStsM1ESkmlUiGXK2BZJq7rcbc+S0dHO4a+vqey3rkaEoQBt+cWcN2t9KrfXAxDpzOdIhlTuSS1SP2mV6YJrXn9SqVKoVAklCHdXV04Toy5uTkcx1YlZ1t+pq39nSY0bNP8B2vJ+/2ShvVeLBS4cP4tjNCjqCdIU0cPKzyyb4zuzgy6oTdLwGQQqsoQTSP0XEIZYpnWcr/0TUQTAmHoBEIohW/b6JaNLjTq1Rq2Y2PHLDq6Org5s0C9WsczFZOeoetqMw9VCa5jW1RqPv/+P/8tdS/gt3/508wtZgGo1QOqtTqL2Sx+EFAsVihXaxHTYp1StU6xUldhbk+lEwJdB8/F91yVuzQsUunkmq2xJUAYIrTl5xZC8K9/+Sf4+d/6Pf7jV87zm0mHkcF29Ht63kgpuX43x1dPTVEKLVIxrQngcxGcnprj+Fi/Kn8jCmmLtZVlQ2VsdY2PdaW5EVbJ5XK0m0kS8TZ6utqol8toZlTTv5bcYxdnkjH6uzJMLtzlnakKH3h4P/t3DPHmtTv4gNBtpFchW6pTLNdxpY4mAx5/YIKH9o2SSsab520obRn1DNAMxZApI6UiBBCETUdH0OgpsOyVCqHh11yMWAw7EaNerkb0tIrBL5RhRFKkziBlqAwEVOQBIJWK86MfPcG+iRH+77/5Nv/uj7/AiQf38k8/9QypdBxWVQfdOz5i+Xc1SVuak8ZTtUrCsZGVMqKF80EIOPHIMU48ciw6vVyl+BuX3eqW10h7t6a+G3w2vufj+T5exPlSrdaisRaYpolpmti26lgqpaJ/rtVqTX6YYAMncMvKXwiRBP4K+HUpZUEI8fvAv0EN878B/nfg51g/zrryAyn/APgDgKNHj8qV3713C/u9yGbXV4x4klKphGGo7lDFao3UuuC1leL7AcVimdHRUQBu3rxJf1/vivzWVgyJVs+qXKuD3BjdeT9iWyb93R1YhkG5XKFUKiv0c0MJBUEzZAuK4KirqxPHiREEHtlsFtdzSaUS79s9tYplGgz2dOK6Hjfuzq3/hxsFf94tsu/9kJZQd3ZpiS/+zRdwq2X0qGVuKH064wYPHdpBOhVTHr7rEfg+umGimSbS9/Bcr5nnX3HyzTwkoYBrYFCv17ClDaaJrQnqtRp2LMa2gR6uTd6kXnfxgkD1Mo8Ar2EYcndukXMXrzGfL1OrqdLJX/rt36NQUxtOZ1IROnmep+rITUUsY5s6ccemq6uLYVuxzXV3ZGhPJYhH1QPtqTjJdGLlU9y7kUuplEfD6IneFztm8Tu/8Vl+/X/9I37/q+f5F584SF93KgqKqCjL2cl5vvDKTWaqJscfPczJ189ybXYeLwj5/KkLUUgbjm/vwzQNxdUvGik/pfgCKbldrPDW9CIHe9sZbU9t+M5KKelLxdlpZ7kaFDhf1pjyXXozCXLZHMlkrLmJC7kSh65SU/cop5hDRyqGEbrUAovT569xey7qCqobSARGLMlstkihXMX31Z5i6LrywptjFkVk/EAFqYRQSr9xL9FYqN+X96hmCkAo0J9h6/j1Om65jJVI4CST1MtlgjBY5rMPFFhUaAKBBlqrYlX7hKZp7N45xr/67Kf4i68+zwunr3Lu6i3+h5/9BDu3D26YylmZIbwfxb9a0sk4dd/n7vQ04zsnIBoPKYMVOuJeXNj7IY15MnQdBxtIAFGZc93FdVWp9HKVnDquAfw0LQvTMJq9JdaSLSl/IYSJUvyfk1L+NYCUcrbl+z8EvhT99zbQ6soPAdNbuc7y+e7nr99/2ez6Yag8dxFKMh0ZqkGecrW+ZeXfEN93KZXK6NpKUItaWFsbhLrrM5fNUr0Hxf4eMyJR6FP1vq7VaqRSKdLp9Ib34vse8/NzVGs1Yk6M3p7VKYx3L6tvOAhDlgrljQ9bTpltnE14z3dznyLURjg3O8vffuGvMQnIiAoeBsgAK6zx2R99jkTcUX8sQ5AS3bQwLIswCPBqilBEN417Nrqt35nqg2BRd+tYSIRpYRgmXr3OxHAfF965wZ2FHMlkHNMwCAPJjdszvHT6PG9evE6xUmP/th6OH9lDGCjP7svfO0N7JsXPfOJJ2lNJOtvSxOP21mq4Wz02iSrT0MVy06mV1oDa+KIIXKu0JWP8j5/9Yf63P/xLfv/LZ/jljz9Af3cKt+7z/JmbfOn0HYJYJz/6icfYNzHK5Rt3eOnmJF+eWqBz204KuQLfvHCFQ0OdZGxVzdKg/G28m5W6x1S+xqklj4tzN/jVx/cR24BUJQxD6nWXfQPd7Brs4Y07S/z15RnO3Zjl5XNXOH5IUWU3jEIZKs9aEwLWeI8cxyKdcpChRyAdUgmbRw5OcPbKLUI9hh1UeeroLl5/6zLJmIWthxTQWMiXKBTLJJwoWgQQlb1paKpk01f/D4OQxpWllGsurebnAgzHJvR86qUSdjKOlUgQ1Kr4vr+s9KOIgozAf0LTVFRBhgiWn7Ojo41f/MmPc3DXBf7rl1/kX//+n/PJDxzjIyeO0N2xut3uPXe1yfebi22bdKYs7s7PqR4MLd+1bs9rbdX3p8O2vptoUaWX4yyXQ4ZhSBCGTaKmrcpW0P4C+GPgopTyP7R83h/hAQB+BDgf/f5F4M+EEP8BBfibAF7f8h39AEsQKFKIWr2OhGZv+GLVpb9zfcXoeV4UrhGkUkkMQyeVSnB3ZlY1++lXdcmtHaK2Kp5bX634QekLNlhSm1yjI6VKvLLZnOo8mExQKpXIZrNR04iVeexG+C6TSdPWllnhHQSBAsO8N0Og1TiSeL7PYq5IoVzZ4JhVh75v0nI3G15gvW/LpRKX3rnI6ZOvoQm1wZpCMt5lcWdmiZ/9kaeIxewm0Mmte2iGhm6ahL6PX6+jGzq6FZVZvgsvp+G5KSS7ieu62EJDt238Uon9O4b5u2+/xuxCjqGeTqamb/Ly6be5eP0OoRTs37WNjz91jPGRAYRUlQCaEHzzlbO0pVPsGR/GNs2mhymCSEmo3qbL+7PWvKHlEWusr4ZyWrMr4MZUugd2b+czH/8gf/633+Jz37nMh4+McOb6PM9fXKBnYIRPPnucYwd2UKm5fOLpR/j81+Gnn3qQ5546xkunLvJ//eHn+NaFq3z0wE6cyPsXYpleNmGbjCRtOgxJOdXH8xeu8uzB1ZU5QOSpedi2arrk+wHbkjb72xy+vVjghbNTzCwV+dmPP9GMrLVM1Lrz5xg6Hck4NTNJoVRj++gAXek40yXJiSO7QQheOv02lWoNE59AM5heKpItlOjtzChMqiYgVC3GCaXK60elokHdxUDhKZQhEPU/aIx7sFwrL0LlgWq6Kh+slyrYiRi6bVGvVBVwUqiwv6qykc20AkajuVBjZhvzr/Ho4b2MjQzyub/9Fl978TQXJ+/wEx8+zoFdo+j3cksgkYE677rpky1I490wNMjNLzTTEu/6fBsev/Vzr+UUatpy6en9pIy34vkfB34GOCeEOBN99pvATwkhDqPe1hvAPwOQUl4QQvwF8DaqUuBX/mGQ/t+fcEGjPt3zPPyoQ5Op61ixGLahN8vl1vP6c7kc9brb7B+/sLBEZ2c7iUSiiZR/N+VDQRCyWCiQ28DzvZ+zto5gIuaQTDgUikUs2yIWi1EuV6hWK6TTiuyicUyj8VCj3K9V6Td292KlQighEbOVMtjoPjZJuYRhSKVWZzFfoFZ//4l61r4pVg9m87NNwuvrnCSXy3H+rbcgCNCkaqRCUOfZx44Tsy3SmURzLN16PSqRNJF+QOC5zdbVzbFqlJ/c5zO1hm7tmEO9UsNOJrFtm75OVT/+jRdP8fxrZyhVXXTD5NEj+/nAw/sZ7u/G1DVEI/Sr6whTp7s9Q65QhEaIuDFOomUI1tsMV02+WPFj+f5VaLwJoV7nVB9+4gEWveEkOwAAIABJREFU80VeeO0MV776NtVAcPTIAT721MNsG+5F0zQSMZsPP/EATz16AFPXMSKWvOGJCZ6/+g47+nvZ09PexL00PFaAnqTDU2PdfOF6jreNGE8GwQrvX0rZZJGLxWJomsB1XTzfpyvlcGy4k+vlOa7NFpHAq2cv8/DBCd6evM0rb11DF5If+eBDdLSl1/TqbMukLZ3gRt7j1uwCRw/uYN/4MHfO3GRqZoEPPXoQP4BvvHKGgd4eDF2nUK5T8wKErisPPAjRND2i6hXRVEXvsaaB76FZNn6lgg7IMFx2ANQsq1r+RmpAiOWug3UX3YnR8EbCMFwxl829L4o0iEaYuvk3As3U6etp5xd/6jm+/r03+M7Ji/zun32FZx9/gGeOHyKTTiwfExkzQlMU0ELTWk/Vsn7WXDKKzrxS4/zVm5y6OMWdEgwNp6nV6iTi94fHahUZSrhP32er6e93QzQFW0P7v7jO2b6ywTH/Fvi3W7qD90XezYRsHiYtlMq4noeQEkPXSSZimKbVBFxIFMVp0lFAjdZJCMKAXDaPrmt0dHRgWTaVSoVSqXSPgrzPu5YSzw9YzBUoVqorAH/vRRp3Yhg67ekkuq7hex6JRAJN0/B8F11fJsNo3Iumb9SvfHkDqRTLVCs14jEby1DdFTUNAqk6lK2koVx7XFzfp1iqqHn5PgH91nx11n+0+5B7UhZBQK1SxgrKxPSQUGoEuqJT/fATh5v4j8B1EXKZ4z0Mos6Pjbxna+JxlQGwwUaw5jNFnrTvI0wT6brkSjXmFnP0dKT40KOHOX50L4O9HThRsySBpphyABHhFWIxk/lSrUXZi5UX3XDs7seQ3/zvTNPgwMQI337tPDdzPj/yoYd57qljDPZ1NXnPFSOarspHI6++5nrsGR/hm9N3+fbbkwyl99MejyEbhDXR+S1dpz/hMGKH3KqG/NXZ6/z0kR3AsuNgWSaGYSJlSLWqooa6YWBosL0jycd3Sr4wWeTGvMY3z9zklbenqAeSXBgj5he5dOMuR/fFcdbgQonZFl2ZJFfmF7g956IJwUh/F+LcHa4uuuybz4GuUSyrUkARCsquT6GiDBDTNBS+IAjQpAa6QAaSUAYIQ8eyLbx6HVM3FCNoGCgAXwTca4TvW7dTIUTTGfA9Hx2i3PPKtEErfqBBBCSj91qYenOKpacMjbhj8ZGnHmbbUB/ffPkMX3vpNJPTs3zsxFH2TYw0Lq7mMIo2rjLQm7+qe67W6qpNdL7EfLbI/GKWqfkS+dAk0zvGR58+yv4DB4k595fSfT/k3dkZ76/n/w8oK1/87y/wb+MTl0plFQa1bWzLbJZXrAgzSolpGJTv8UCDMCC7lMOybRLxBKZpUqlWKJVL901m5PsKaKUa9ATUPZe656lN5H1S/A2xLYO+LgXyc+tuk8CnWCohpSQejzdBOdDq3a8ey9a5i1kmZV2jVKlRddUG1fAuFFBbUvNDXM+jPZXCNLVm1YMQgrrnUanWKVdr1OpuE4+AUEr09q1bdHd3E4vH37Vl3nymtT58l4GltQ7L53J4nofjOPT19eIt1Ng9kKGzLcUbb09yeOdI87lDP8D3AwzLVv3EXS8CXRoIQ1/5cjQUf6sBsAm3wFpiWiauW282gXr00ARn3rnOjz3zKA8enCCTjGPq+rLHLWWz/KsRRXBMk0qpqm5jK/OxYqC2eMMNRN4mqaTnXz3Dt147j2nqfPbHnuGJo3vp625fu+FJy/vUFnmTmmmzWC2zVKmRdFSZVRh1FVXc9pC2DZ4Y6eIvz01xW0+QrdZI6Dq+77d4+54K5QuaxwkhMDRB3DJIpRM8+tBTdHe2k8/nuH35MlZQJuVIJkb6mqVu94ptm3Sk42iBhycMvLpPd3saHZ+cF+fbb14mX6oQswwWizVCXScUBtOLJWYXcwz3d6vh1zXVKdWXzTp8/CAC/aEiVKZFUKspA0C0RHWi+dMaXnsoI3yKqc7peVFGR6ieLBHKX4X91wA2C4H0gmVFzjKRUMw22b9rG10dGZ5//Rwn377Gf/mb73DiwX08+/gDGEaUVojuSwZRCiNKnzWkXKlz5cY05yenuZuvUZdqXBLpDvp27uLpAwfp6h8gmUgQi8U2XGMQldgJFTmQa6D/v99yLwBxK6/dD5jyX3nH/38B/8rlilL8lo1jWxuW3yUci/m8ohduhLwKuQKmbUdes6BQLOC5Hol4vKUZz+ZSrdWYW8ziB1Kh7ENJ0EIM8X6LbVrYpqrHL5crODEHTRP4vkJrr91+eP2QaxgqOuFKpaZwEhFCOljjaL9UJpQS3w8UKCjaGJGq/7kXBFGHR/XcdbfOnVu3uHHtKuViHqFppDLtIEA3TNra29m5cxeWvTWe8Ib+aeohCU2Snvteh+q4ew+buTvN5YsXKBcL2IkM5XKZmK7R35nm0N7t7BjtpbvRvCZC9xuWjWYY+G4dGaGmtfXWY6R8PdfnP/63b3BgYoRHDk4Q25LBqRq0SF2jVKpQyhaxdZ1MMk7F9SlV6+gIxcrY2FgbZXZNIJ/a9B3LwtsK+YVs/rMxPuveZ40Q+xufWPCVb73Kd16/QD0I+MxHHufQnjHa0olNu+81IgFt6QTEMtwtZHn96hSJPdvpTieiqJ9sIuJ1TaM34bCnK8Wbns3fXZjix/YOY9uKorterxOEQbMRTiPdAuCHkmooMHsGOfTAAyRTaTzXZfLqNSyvwocfPUo6GYvWZOsOr/5vGzrphIMMfcquxivnLpF0HGQY0js0wtLiXaqeYGqxht1lk3DAFxpXZnK0J0ySMYf2TLKJ5m+dByklIury6bkuZiyGYZn4roeUoYr6hRqI5RQAYYh6CSOaWcvCc1UlkmEY0X3TTAs00ygoMCDacjRBQ1tOE0iUwStVd9LBvi6efPggmq7z2luX+er3TnPz7gI/+exjdLSnaCSagzBYLhMVQCiZvD3D6Uu3uL5QRUt0Mrh3hIG+fjp7erEsi5jjkE6nt7x3NCJcQihCtJAwegaxwia/f7nXfVD/X+t8mwEQ15IfKOW/9iBtmHC9n7OvOube60mpWuvWPQ/dMnGc9RT/8rkUcYOkVKlB5KV6dZeuVBrTtCgU8hH/vTIktMiS3iyvHQQB333+e9yYusX4xE5Gt23b4qPJrXt89wxJMsrlh2GI53ukrTSuqzpGmebaJD0bWZzFcoVcqYLreqtohsMwxHNdbNtRHnxk2bc23GmcqvXISrnM7MwMs3enyS3N42hweO8OisUSS7kCrufihZKbs7fJLS4yNDJK38AAjuOwsDCP57p0dnUvG2GRkm/mOFdc/N1an2sfF4aq1/nCzDS6Pkel7hEScvXOHI8c3kVmcLmrnFerqz7uhk7oeSosq2nrK/4W+fxXXuTNa/NMTmeZXSzw9LG9dLan1iy5rNRczrxznSs37+J6ARXXp1Cq4aFhCsnMfJZ8qcr8Uo5KvU47SeUZCtE00po7XLSx65q2xR1oMyXO+udZZ34axs+3Xj7N118+ixOL8elnHuXw7jESCWfjsWuNmgjBA7u28caVGS4WS7w6v0RvV56jtk3aViF8hchXFQCOoXN0sItz529zPQjJ1gMG4hq1Wi1Kj2nL49UiZc9nqliH7jY6O7vQDYO7uSyWrPLkAxOMD/c1ow2tZcAN0YQgbhsYBBQCk1eu5bG0PK4W49FDh4FD1HzBqVOnGN25j2J2gYTtYGiCa/MlMpPTPLR/XFUctRi6DRBfKJev67suummiG7pSytGabPy9kFLl2xvnkcvevWEYai0HQRTuV/PX4AqRUkUcJFHkIYoINjNFQkQYD0W0oxGSScbp6WwjlUyyWKhw+sIVyuUqz554gAMTI2otimVDr1yucvbidc5PLVEWcbbtf4jtO3bS2dFJMpkgFotvUf+sFE1ozZ4QSIkmNAKWu6y+f06seF/P9wOk/Nera9/qZ5vJ6mNE08VT36ucXE2VU9i2ap+54T0pzGrMNslX6pi6wK3VaM+ksSyLeq2GW3exIkOiwXC13uRVKhWqXoDv+wRByKUrV7k1NUVHZxd9fX1out4EDq7/aPfhqbb8nWNbxCPsQrVaxbYsDN2gXCqh6zrWOkC95TFcSX/qui6FkuIHaLQibX5XrzMzM8PtW1NM7NzVLCHM5/Pksll27NwZtcdclsWFBebnZslll8gvLiADl23DA+zZMU5/Xy/1ep1coaDY74KQ2dlZLt+4zeW3i/i+hxOLcWvqFkhJtVLF8z3GxrZH1v0/TIiprb2dnbv3oQvB1OQVDEIIfRaz7op0khd1PDQME4IwQtFHil9bf36llHz71fM8/9YN9h04TD6f46ULt8gVK3zo4X2M9ndhRuHjXLHMhWu3uTA5zc3ZAlXXx7IcrJiDneyms60NGQRY7QMUPChUatRqLiq+G8U17gHtNZrgABC+P0RTaz9o9HNVQEBSKld5/tW3+PpLp+hob+eHnjjK0X3bsTdjzGsq/uXxHR7oZqw3zVK+i7mpMvNVDy8Ioz4HWrPZjxSqHK87YbPNlkyaHbx+Z5GPxExVgmWsrfgB3CCk6IeY8SSGaapoWaGICDx2jvY3e2k0FeSq+1ZNfnrakwSyje2HDoMM6K67DAwNYZomjz7xJFevTdLd08vI6CiJRJIgCJievMjZ63fQBPR1ttHf3dakzW4SeEmlkDVNIwj8iD1OR0hJGAQqrC6iFB6NgISMgmaCwFuuRArDgDBQ4XxdU2klZTSGymFpGJCNyFJDJwiWqwvkMrGQY1uM9Hexa/sQF27MkS1WOHtpkkq9xuxCjqeO7Uc39eYyuXLzLq9fmUVrG2Tv/kPs2bObzo7OTYzp9b9bYSDJButeI364Mgunvatqge/vvvQDpPxbZVUQ9vsoy5NVq9Xw/IBEPIa5hZrJxsS2J+LUgwCCAKkJkgkVWqxUyhiGYm/SxPq5SSklpWqNfL5EzfPJ5/NUqlXKlQpSKq7o6elpFhcWSKVS9A8MkEqncV2XcrlMR0fHex4Fx7Ka9JXlcpW2tgxhqNr16nprfnn13Ki0b6DY/3wfP5SUSyXOvHWOfL7AyOgoXd3dACwuLnJjcpKFuVnm52bxajVS6TQIyC1lyeXzlEtFEskUYRggUC1ulxYXyC7MY5saI/29DPT1MjY6TH9fb/MZ+nqXPefC6DAdnR1896U3uHbpHUIEuVwOzTDIF/IkEgnGto+/53Hbqvi+Ty67RKlcRuomIRpx4ZKJaTy8fzuGuczgF/oBpm2DAL/REEmPQFbrrMkwDLl6c4YvPv8mXf3DPPvM01RrNV569Q0uXLqIF5zj+KEddKQTzCzluTw1x9Xbi+SqPmPjO5kYH8NxHNLpFI5lEU8m0DRdGaSuT35+knKtFhkfa6ccGuH4zo42CAPuzCwyNtwbkQmtI+/iFW/q/pb7aCr+18/xlRdOMtTXzYceP8KRvduj6NxWRDRDuACWbfLg7lEWsgWy8wnuFioUa3XSMQuQUd5bhanRNAhDHhzq4s7dOudLPt2359nelaHDtNdVMIYmiOsaOa+O67qYhkEymcQXJtduz9PVlsa2DPwgwIswIHXXw/V83Oj3xVxJdXgzBfsOHkJKqc4VReuGhofp6e1FItk2tp1EMonv+2iETF6s88qVeTqn82zvy7K9v5PuzjYVKm+JiIGq6JF+AGaUuxeKz5/IAGrlmQcV/g4idrwgCBChUuJNrECU1hNSjV0YKf8mIDr6fnnelztHClSlw2h/j/pUNzh/fR5NCC5PzZPPlcgVK3zy6WMqXQlMz2fJ+SYHJ/YysXsPHR0dCsTYwuB3P+L5Aa7vo2saMctSyr5Fybd6/u+1VHDrsvUX6gdI+Ys1fl/vITZ7wM0HoCW6BwhFnVirY5iKP7kV0R4Eyhtv9PK+9zY1IGWblMoeiUQcTRPU6nVqrkcyEV/lrUspVR7bDzB1Dc/zmV/McuPmFOVyiUIuR7lUolYpo0Ve1szMXc6fPUMmlaJc2MnYxE7y+Rx3bt/msSdOrHjahfl5SpHHblomyWSKWCy2Ya7Tixj7gsCPGnTYFEtFPD/AC+u4oVRlXdGLrQhAAoyoVWzd96m7Htn5BUrVKrMzM5w8eVJVOBSL9Pb3g5TM3J3mxrUrOJbJcG8n0q1QXKxEfQ7qVIsF3jr9OulMG6au41gWUoY4jsW2wR5G+vvYtXMHXZ3rGDzR1KdTKQ7t30s+X+Tk2Qss5Qv4ocqxuq5LKpWmVCzS0dm59np5l0rp3kNmZ2YoFouUikXuTk2Szy7hSYGuQ8bSObZ7iBPH9tEgdfE9D8O0VMtd10WGIbq5BsCvRcIwZDFX5IvfPU0lNPnkM0+xZ/cuNE2jvb2DFzMZ3nzzJLmXL5COO8xmi5RcGB7dzkO7Jti/bw8jQ4NNQy8IAlzPIx6LUa5U2D42wunbV8kVq7h+sC74DNQmd2DPOF96/iRvnLvCYF8nMX0DzEGLp3RfI91ySBhKypUqJ9+6zFdfOEVPZxuf+vBxJrYP3hfpyVrzfWj3Ns5ducVUe4orc9O8OZNlwVV55L64RXfcVuHqICQIfHZ0t5O6cZnbmWFertbILZR43DGJiTX6CwAxQ6fH0bmenaeQy9HZ3U1HVxcdA2O89s4UMcsg5lhU3YCaF+B6PuWaT831qXsBbhBS80JK0iGZWC4dtlvy1U4sxtFjD9HZ2YkZGfmWZdE/PIpmWty9NcVifoGZy/PcXSwxPlBiqCdDd2emGcJXzaQ0wiAg9FWZoIjKIgNfpUBCGTbTUyLaU5tDG6WJDNNsug6ypeyvwbzYOhENMiD1UyJlVLrXYhQ4jsX4YA+2ZeIYOtfmktyZMVnKLfEXX3uJvo4Mxx/cixDguT6m7ZDJtBEilqsdNLElkF4QBPhh2Eyflat19Z44NlgRCZRojQC0LK37jNW/e5zA1g/6AVL+9yObPeBa36/cmkXrokOVfEggZtuqPWqk7EOpGnv4vq/CXLCMeG86wyEIld9r7+8HIF/IU/cDDC/A8HxFSYlaIDXPp+Z5uK6PoeuUi0UuX7nCuTNvsjA/j6kLutvbsIQkNDSKhTyu52HqAulWmLx0gZrrUSgWKBaLIFVDn0aa4vatW1x95wK2beHE4vT0DTCybYz2DSIEtXqdUqVG4LnEYg4gqVarVOoufiiR5aoy1iPPz3M9Zmem6eruwbbsJkD4nStXKRTyXHrnnWZXtCtXLnP1ymUEEsvQ6O1Is2NslInx7ViWCUjcuofnuVyZvBmFF3Xa0imSiTiOY9OWydDb0006ldpwXluDEoZhcuTwAW5NT1OrlgkkVL2AwPfJZpeYunkjUv5rebLrDtW6cu8hlXKZ82+dUZGOhXmSmktb0sEw47Q5BofH+njq4f3NTS9wPTRdEffIIMDzPEzD2DDPL6WkWnN54eTbvHltlo88+yzHH3mo+ffjY6OkU0lS6STfe+lVbizk2DYyxGOHDnDwwD6GBweb52mOpqbCzA1WtvGxbZx5NcbcYoFSuUpH271z0DoIgp1jA3Sk41ycvM1zGzaYEjTaDa+2nBpe+Dppt+jjMFQe/5m3r/J333mNTCrJP/nE0+zY1v+eKz9AVT8c3DXK3FKOc6Uy350ukcxL2uIWB9vqPNSr0WZbaqxQrabbbcGtWBsMjPD6uRfY1+1h63aTp8gPQ+peQNUPKLlqnwncMrOzs3T19OA4Dg8ce5hTrwZ86+yUom+2EyB0DMMCw0aKGLpp4WQSpOMJehyHzrY2tcc0FYcaVNM0eeLJJ1c9WzweZ2z7OMMjo2SXlrh25TKTtya5OnuDPQMpjuweoT2ZIJmIqYZBUvEBBGFA6HsIqTj7ha4rdyAU+H5AEAYrKwGIiH/MiJciUvQKUBgpeoSKnrQGFkOpzi0EMvCbqQgRYQKIEPymaTA22ENXW5o33r7KSU3jlmmjLc7wuS8/z9BAF6MD3XhuHd1II6LqC9fziTuSBuHQRtKIztY8H8vQkVLpDNMwsFuMYSkluqatoNt+N+Dsfwiw+z9S5f9uZPVoyght6vs+5WoVUwh8z8d1QzzPU+hyJJZlEY/HI0UFmmY081xCKDR8NpfDtix03cD16oS+D0IjX6nihSGpmALZVF2XfKmGBExdsFiqMj97l+999zto0iNh6QhN8sCB3Vx45xJzC0tcv3oZCTimjmXpuK7HO2+fI5CCHRMT5HI5CrkcsUSceCxOMpkkdCsEfonF3CJLS0uYlk06o5j31mJFC4KQuYUlbENjcKCv+fxBqBpMuK6L57kg1YtcLpV58XsvcuDgIXbt2dNs9nP79q2m0dRY9AIF/k3HHXo629izcwdPPnF8Tf165PChLc3msq7YWHHX6y6WZWGYBtJ1MWRIuVajUCgQi8fffWIpAgquOLr5X0kQhFy7coVyfhFbBJjSp1wqYvkVejt89vUP84Fj+6IcJsiopNOKxUFKvJrCnmiGvuFG4Hk+l67d5q+/c4aDR47y0Wc/1OSiaEhXVyfPfuhputrbKRZL7Nk9wfaxsTXPF4aKRtgwjGboeGx0BDOeYi5bolCqbKz8I83TlkmSLVZXAT1XSNMFZI1J2GBmGoaKhEq1xlsXr/GXf/8CQuj8zCefYmJsYP1rbiatl41+P7p/nIVsAc0wuTI1yywOZdPGrJTJLBTZ3ZUirokmmr1dSPTiApmOQ+RjGe7kq3Q6Jrqm4wUhc5U6U0WXmarPkhuS9zWc/kyzm5sQgsGhIYJjD3P9WidOPE5Pdw+6YZDJZJpgVdXlTXn4mhDELJO657V4sVtLnRq6QXdPNx0dHdwdGWXq5g3evPwWU3PnODg+wL6xflKJGKahN/P/AkEQBpHRs6zgNF2PnAQNoav9VEZ7iBaB+lQYPGrxG4bN7n8aGlIs4wzCUJVTyjCiAY4iECvWTGQECF0jnY7zxJE92LrBdzyfBdHH/O1JPv+VF/lnn/4h/CBECNX9Utf1CFgdbso8GkrVMCdbLCnQrqZFpFs6jm02y0ZlZLBIbXl9quPDZiXAu5PvT/r7H6Hyf38GotlNrVhqUu/6QoCoY5om8WQSx7ZpdK8KQ+WJ+X5UfyoEuiawTBshNLo6uwhD5SnlC0Ucx6EjHqNQqZEv16jVPTShws5x2yTp2Mxnc1SKeU6+9hrtMYt4PIVbd6nWanR2duDYNoZhkNAUyVA85mAaOvlSCQgoVF2uXblMPrvE3NwcMpTs3L2btvYOfKlTr3oEEnq6VVe47OIihqnK4NYTXdfQNIP5+VnyxTKVWo1qrcrs3WlmZ2cJw5B4PIHQdPL5PHfvTjMwOEi5XMayLO7cvtUM0WlSveSWLkjFbfbs3M5DR48wMrSyyWOLzlwOA24yy1tdAUEYMjE+RqlUYurODMo/C0kkEuzavWfT49e9D7HGXYjlX7JLS1w4e5rx4X6eefxRrt+4wRun3mT+7i36NY3x4d4IOKYQ457rYSdUtUUDJGXa5uoce8sN+UHArdlF/vPffY/u/iE+/akfpb2tbdUDCAHxWIwTjz+24bM2apVBUxS0uo6mCfp6ekim08wt3iKbK7BtqHfN4wUgI174mG1ye6G4Na/nfl5nKRsOH7Way6WrU/zZl77LTK7Gb/zcJ9mzY5j3tEeslX1EMQU+eXQPpy5OcvLaPFfnKlzP+3jzORbLVT442o3jOARBwMM7hrl85jpzNycJUt1czF5kokM1Kbqdr/LqYp2bZjtW7zCpjm4Gu7ro7x9geGRk+dJCMLpt28ZVPi0SSkk9CDBNA38F78jyQ4RBsJxzX/XMAt0wGBoeZnBoiEudXVy5dJHvXrjFlVuzHN09ythAJ8mY0+Tp0DUdw1ANhMLABynRdaPZAwFdw9QVTkCGMurToK20SaK0TxMv0EgzREYGQdg8n4QmdkqB7KJweovytkyDRw5OUKzUeP6SRk9nJ2+8dYW944PkKy4iptORSdPb2c5ynYJs/qYKUJbXbBhK6r5PzfVUusTQiTkWccdWuLCWazeGtVG51JpKkMh7mm9tLsupadH8//r2w/2v+X9kyv/dAAHV3za90BZEcrVWp1ar09vbHfWtFviBF5XvGAS+F9Ha1hS5iq5HoXW18Xqu6qhkmgbtHR3EnBhCCLy6SyKmmPDaknHijkWhUscPAjrjKqQ+t5gjly/wvee/SyU3zyeffYZD+/dw9vzbfOf5F8kkU7RnMuTzBWquy2BfD12dHSwsZtF1g/nFRTpSMRzbRPo1ErZB6PtcfuciQlMM3JoAdIPxnbuIx+OcPPkGjhPjyaeeaq6kezcCyzRZWJjn/PnzTM/OMjk5iV+rkLBUy1NPwpwXUKl71Osu3/z7v+fi+XPohsne/fswAM3QkIGHo2tYjo1pmhzYu4tjDxyip6d71QyJVb80fn3vht5ABAh85/I1/AA8P4wMN31L5383V/c8jzMn30AEHgf27mLvrp08+MBB9uwc5+vf/A5vnDrN9MK3+O9/4sN0pBNIKYkn40g0kGpdWbalNrU1N2rloU/PLvEXf/8qZZHgMx99lm1rtcbe4gPcm6dsbFvK6xJ0dHQwNTdFuVZvbtRrnIQGGjsTj1Ouzm5aybfmOTYRiYroXLh8kz//+xe5MV+mp7ODseGGUfL9iZk6cYfjR/cSc65Rr13lUsViyovTVVxSJW5CUHddYpbFs7sG+dztd7BHDzA1a3G3VKNYdTmd86kN7eHQwycY3zGB/b4xx6n0ZNzUqa7zF9lclng80eQfWE+EEOzes4fde/Zw8e0LnH39VW69fIkDox08tHeM7vak4gNBNacCFQWVQoGT9Yj1M/RUqL6h0EVEbkQom95+dOvqR2NdaaDLiG5Ygq7ryKgvhGwA5wLRgvxcef+aoXFk9zZevXgHvWuAgVqVP/mrb7Nv9wSdO8aoey6apkDOQghFokaIDFT6tFatIyNsU75cxg98kvE4PR1tivxqkzW6Fn5gNeBvjRDT6jOteM82vuz9r/kfeOXFtJy4AAAgAElEQVS/0tq5LxcBKRtxodWTEQQB+Xye7u5uXFcpskQiie8FVCplKhHSPhZzaG/PrCLnadDs5rJZVcce5TallKTTKfL5AplMCttWdLbdGZNQhlSqdWYXcxSLRV5/5WXy89N85KknOLR/D0IIDu/fx+zsHDNz88zNL7J92yiFUpmhgX52T2znzPm3OXPuAoZpEI/F+PVf+nnmFxb5oz/9HJ6v4+Ph6FCq+3hSY9++PXR191AsFCKjR+Vyy6USmiaIJ5Zpdd1qmW989Usszc/jenUcy6QrGWdoYjf7du8k05Zhbn6Bq9euc+7iZWoaVNrSzExPs23bNiYvvkXMNDEMDcNQLVQP7NvD4f376O3paaZN7l9WvhzNRiLrrId7X6VEPIZpms1QXSKVZvv4dt5vJdEwKt947VXaM2nySzFuT88wOjKMYehsH9vGsz/0QeKxOK+dPMn//AdfJG0L8vk8h/fu5Nc+8xF0ITFtC7FB21IpJYvZIi+eusTFW1k+9rGP8PQHTtzPnUZjtP7zq1SNIIg2spHhIW5cOkc2X8b3fMx15lJABE6U4NebHccaodxGPfT6+Xix4seqO5eSet3l1Pkr/M13TjFXNTjy0MPMX3ubSrW+pad/r3JgYpRazeXW9BtMzs0x3htnplxnm2Ur79cQjHWm6L16mZuFLEF7P3935TK1WIrUxBGOPP4Uo+ukXdaSRpRyY1pwRTfuhjIi5vIRLSBDKSVvX7iAZVlM7NxFe3v7ljARe/buY/v2cU6dfINzVy5wbfoU+0e7eWDXKL2dmeZ8hmGo0qBahA0wDKTrg6HKU0PfRwQh0tCiaGpkNERGU2spowgFUiPCBkR8EqamjAY/XC4x1VZ6/dHgKMKvZByQ+AE4MQchCuB55OfnKBZKZAsl2lIJYlF0VxOCmu8zu5hV6QFotsNNJ5O0p+LLkYn3JA1nVGyq1zabn1bQutKT9+cs/cAr/7Wff71NcaWhoAZFNulxNU2goRGEIdlCkXQqhWM7+LpLuVzhzp07ACQScbq6OjEMY90JaLCAdXV1Mj09QyJSorlSkXQyhZSSQqFIMimJxRwFzKq6zC/lyefznDl9ipmbk3zoxCM8+vCxFY/2xGOPcPHSJYIwYKC/j4l4nMvXJvniV75OPB7jw09/gF0T403wW093F7/5L3+N//Qnf4pYWFCpCU116Lpw7hwX3norCqUJuru7efPkSU6fOhlZo0I1KoqY/FJxhyBwefqJR3ngwH7aMmk0oTeHvL+3lwN7d/Pwgw/wx//Pf6U9YbI0X6YtYfLw0Yd4/fQZQin55z//c6RTyS0t4PX/RKz4KRsHNI5lbQ/03k8aVQ6uH9DdN8iRo0fpHxxcddw6d7jOWdeWcqnE2+fOYWmQjDns2rEd2zQIggDDMBnfvp3+vn6OHTvCG6feZG5hkfKVq7x9t8Rv/d7n+V9+5dPLXlmzUHj58o2SttfeusaXX32HB489yCef+8j6t35vPgXUhrqFxxFCQ4YeaBrDQ4M4iRSz2SKL+SJ93euAR6MTd7Zl8HyP189cIhFzKJRr5Etldm4bYO/4MIlEq8e7KuSz9uNISaFY5aVT5/n6q+eR8U5+/Lln0AX8t+uXKFXX8XnfTcBwAzEtg/07Rzh+d4FL16d5ZcmnXLjBk8MdPDS8XG769Hgfn797h84jT3In0UZQr9Lb3nVf3r6Uqmz2nYtvMzwySmdX17o92mUYsjA7z9k3TyNMk0ePP940GlzXZeHWDbK5LJlkknQ6vWGv91axHYfHHn+C/P4DvHXmTU7euMTV6Tc5PN7PoZ2jZJKx5viqcjyl7FWzIBUR0ISmvlOxe0AojoBgOerUaGUsGqWhIVE3x2VlL1DI/GZfgNb5bJnfUIZINEqlHOVcEdu2kLqGIaAnapKULZQJU5KE41D3fBbzRTRNI+04pJJxjAgftb4Rvrx3bR2d3zDgtjT0916R1gduBXU2qtbuR37Alf/9vbVrDaiUkiAMCKTEkBpCSFzPI3RdMt09LCzMU6lWicf+P/LeK8iS7Lzv/J2015vypst1VbtqN23GG4yBHxiCAAiRBMklKVEboeVqn6QIhR70sK+7EfuwK61CChpQS5AACBAEiIGZwfjp6bHtfVeXt7fM9en3IfPaureqeghtDGK/ianqujfzuDx5Pv//wqS70uiKWtfebmYZ2NzcIpHwmXChkCe7voFwHOLxBEJI5PI5DNuvhraxmePe9D3ePX+ewmaGTzxyhqcef3Rbm+FwiNMPnGTy8GFUVUGWZQ6Mj+G67o4403/yh7/HnXvTfOcHP0QIK8gLNrGRsV1fPFxbXWF9bQVNEsjCJhwKI8uCseFhisUSK2tr/PE3f4fBwT5kqbX2KUkyvT09fOOrX+b//i9/hmla/Om//GOSiQQPnT3Nf/qzv8Q0y3hedFfmv5f92rz6nucG54JgLw3cvHWH9fX16gFj2zalUqlaVfGfPMDKlUIQjcX45h/8AS/8/fdxrBKhkE4kEmkws2qaxgMnj3P86CSFYpFz77zPhQsXOX/+PD956yKfe+wEoUi4tqHrtqFjO7z+/g2+88v3eeD0aX7/d/8ZmtYGhrRJs6iu432cEZWyqAfHx4gn0iyuzLCyttGe+Qc0OjxIKhHnL378OpLj4zWYlsHZYweJRcIcmdhXWTRaWeZa0dVbs/zk5bf58PYCoweP8tXf/A0OHTzA+x9eoGzBxlabCpei6XcDfTSJIBGPcurofh6fXuSVi1O8F4pjFlTmb83ytaOj1evC6wvMfXgeqXsQT9ECc3Jz3+3HUMjneeGHf0f27hXmJk7w2LOfoqent+XlpmXx5rk3ydx6H0/IbG1t8pnPPY8QwrdcygqKa5HdWKOQz5Nsjg/Zbc7JJE984mlmx8a4/MF7/PTiFOeuTnNwsINHThygtyPpz8rzqn57bAchC9/XX5/54VFl4FVLXoWZB7j/VaTA+rkG14uqX7P+O6r9r21k2cgVWJhfIF/Ic+r4JJIsYZSyLMzPcry7G0uCQrGMFsQpOI5Dd2cKveK2cNvn/zcz+12PuaYLPloqX7sbPtoe/pgz/x3f2m3UvKCVfHqnkkrluji2QyFfIJFMks/nKJcNBvr7Gnvd9lTam1+LxRIjIyMAZNbXSXd1srWxAUAsFicUCrG5tUk2t8nMvWlef/VVjEKOsYFuPvnMU6011+CzUEgPeq/l7e5UTEcIwfjoCP/2f/lT/q//8mdk1jfQtYqw4KfhWLaDpMgows9aUGSZh06f5IlHH0bTdUrFEiKQ1HdySWVzeX7ysxdZXFrj0MQ4yUQCIQTJRIJ/9cd/SDS6xyI7e9i31a8rucTbv9mR5hYXyWxsgueyMj/LB6bB6YceIRI8t90HuPe+hBCsLswDHs9/5jk6OtJ1mk3lqPdxyyVVIpVM8pnnnub0A8fwgG+9cJ7X3rvKQ0fH+dyTZ0gko7VnIeClty/xD69dYGziMF/54udJp1Lt17lJ8xfbPt/D7F0XZNkHAEp3MLM8zVaufRnpCp0+Ns6/1r+IZdkkolEMy+Q7L7xZ9Qc3LdqObRXyRf7+F2/y2oU7FD2dz375a3z+M58klUziOA7pZBLXdVnNrLeZBDvw2I9oCvA8xgd7+PonH8ZyBfNZm0VJB1fmby/fY180zEvLBtm+IyAEj5w+w6HJo4QbBM7dz7fFhQXK195hzMuzMH2VC+/EOPngo/T0bg+6lGWZoX1DrN2+wKnhFB9OXeAH38vz6c8+TzQWR+AhYWOUi5h1yHt7pco+GxoeobOzi9XVFWbuTfH+1C2uv/AOp/b38sQDvnDn1SHx+T5wp7puiBqjr2r7+ExdCtLmPMv23UfN5FRQ/1ppeoDwgx9nl9aYnlv0i5Ihk5PTqIpChDLvnT9P38Agnb39uEFMi2v5OfoRXfeLEXnejmiaNVN73bTaXNsKZKr5WsO0KBkGqYbqpm0muL21He5pTx9z5n9/1LygluPgeDXpzQNsx8V1HDRNZWlphe6e7r0xqSZyXZdCoUQoMOGVjTKSkAipClp3JxvrWxTLBulUku6uborFAlN3pyhubTHY18Vvf/2ru/bbEOwR+PQqf9XPSTTdI4COZIoHjk4yefgQmqaRLxRYzWRYXcsQCoUYGuxHlmT+8tvfQw+FcIP0rlBI93209f6tFsOswJAWSkV+44ufa/BHxmLRbeNvSXuRfoMJetX/fJJo/2I2r0winkAPhXAKZUKxOH1Dw3R2de3ScYXub2/IwOzsLBIuA329REI1PPnaUb99XdPJFH/0B7/D6L5+PrhynR+en+K7r1zk+GgvDx+f4KGTB7h+d56XP7hDum+Ez33qWcZGR3b2QdZr/vUb5T6mVB8o29Xdye0rvla1GwkhODIx7AcAOj6gVSoRZTNfbG+eb0EXr9/jRy+d48pMhsMnTvHF5z/HkYMHUAOXnCzLdeWg21gQPsK8dyPHdsD1OLR/H//qt1N892fnWKaDfcNjXD73Ctc8ib6nzzASjZFOdzB+4AC63h7prx1NTd0laeZ5ZCTN69OzrE91kD98rCXzlySJ3v4BLDlMOh7ly4928Q9v3+IH3/suqXQn+Y0MsmdjlIqYRoX537/WKIQgEo0yHBllcHAfa4cOc/XSBV69eY1Ldxc5MtLD80+eQiAoly00Va4FvDkuCN+i6IpaBbwKsxWA59QjBdZRU/T79oH513iOx8zCGgVXwkLnyImTRKNhsC3Ucp6t5Vnef+89xg4fZXifDwTlOG4jkxb+eyoFAEOt16E2sJZKnCSqgEC7PXfX87B3RRr81TF++LVj/nt3A9iOsy3H2HNdXMsvrbmyskosFq1iWd/3SDyPXC5HX5/v49vY2CCZ9LVfWch0dqYpFIqsZ9YJh8NsZbdYXVmmv7uD08eOIOS9RZvXXyMa/6x91oLGRofZPzpMIhFHlmUikTBdnZ0cPlCJdpcoGwb7BvvZ2Nys3mdZFpqu72FL+ZCzI0P7eOKRhxq+2fMBt+tljdJBpQRwrQjPTg3Uvjs+eZj5xWXC8TQHJo/T29dXqy/+K+QInudRKBVZWpjjgSMTdZDJ7YdXiUmRZJmujg4+85lP8/nnP8+tW3f58OIFLl68wn976RJ//cIbZLM5Bscm+PJnH+P0qZO75ie36m/PcwluEZLvq11cWmF6Zo71zRy5gkE+XyIW27nUaQW73RMCPah+VypbbGwVyBdKxKLt7y8XDb77k1d4/cPbGFqCb/z2N3nw7Gk6O9MostIgbAohIWSVjY3c/U3yo5Lnp7J6/gAwiyUsB6KpJA8/9jhHJo+CgHgyVWVi7euEtOvCo1goMHPjMt2OzQ+nt8gmhjgyeZJ0Z2erO3zBOx5D1iJcn1nmj7/0FOtbRWaW1ilsZHhgIE5mSydXyFEqVqw3H2H/e1TnJVSV3r5+Uqk0q4eOcOPqZd6busGNqR8BEI2E+OdfeRapEtSHF5j/RTV+xwf4CX47Hnhe63fbCYL9BC21Btt2WFnd5NylW9xZynHg8FGefe6TvP7aqyimhW3nsZBwJJWLl69Sdjw6kglSyQRly6+nUZ+9UKHdEQDbM/72z7x29li2je04hLXtSLC/CqCqdvRrxvz3zvht163TE31yHBfbdohEIkQiYYqG8ZG1/gp4jKrqOI6FZVkUDItMoYzjuiiShCxB2Xa49uEFbl6/ytZ6hv1D/Zw4fhRZEhSLvuVg50O83o+x69SrdOrkcR8dTqrBTUoS4NWC91RV5eTxSRzbrhbucT1vl1QWH8Amm82ztLhCNBapFovZ6Z6dBt4qLq05Gt1PJaqvvrf35xaLRgiHdFa2Cti21bo40kca+XbKLC8hPI/9Y6NNrpr2VIFJBkgmE5TLBieOH+Xgwf0898wnmJmb5+LFy1y6fJV82WZ2boFcoUA6mfyVTqAi0E7NzDIzu8D84hLLKyvMzM3jWQaqXeTU/n7OHj9AJLLXcqei6uqIhDRW1zL8+fd/wb2FVf7ktz61rX+jbHLp5jQvvvUBl++tc+TEA3zly19gaN++arGt5vlIkkQ4GqNkfnRtds/kEQDX+M91em6ZF966woYX5cTBQyiqSjqwKrXOamg/Nss0efvcW+S2tgjHYsRiMZSlKRIhmc1QD5OPPcORk6dJBoWwGskP+pIkmaH94yxffYvNfJHHTh7k4eMupmUhCcHt2WXeurVGIbuJbdt7Dvpr6qr2z2COeihENBZnYytH3vIBdCTP4blHjvtMMLDUVaL66/38Qkh+Wh+iWvXP/7LpQKj23bh+lmWzurbJhZszXJlZoyAn6DnyIE8dO0k4EuH2hQ/Jl4tIUhwv6O/RJ5/g4OEjxONxsvkiZcMiGVgs66mS6npfy7Mr42+cg2n5dVESseYYJLFn18JH2fO/Zswfdptgxc/fzPhd28E2LYQsEYv5gWhG2YIdwMpatA6IwOSfJxkcvuvrG0TCYTJFg1QsgmkazM3PE41FWV1Z5crVq6wszDI22MtDZ04Ri0X9Klm2TblcRlHVqhlzL1Nv95jrPw+1q0VdZ6pSZJmJsVHKpbIP1hHAcjb7qJsbyOXyXLp8hWKpyDe+9uXWo2m4efv3La3QdRJAawNXzXwdJMy0aLv+Mz9syD9wAi17mwTvpxNV+9+jZaUVyUJwb2qKVCJKd3fnLmbeWpmS+mjnCoSppqmoqowiyXR3dTF56BBnz5zmjXPnee+9C2xsbPKFz32GQwd2KU6041o1jcjz+G/f+T6XLnyIMIp4ngOujVUqM9rXyVNnznD04Aid6eR9Wh0EyBKTEyMUSyWu3J6hkM9jlA30IK7F8zzyuSI/fPFN3r42x6Yh+MrXv85DZ8/Q29PdEHXdvDskSSIcDlMyLYyyiR6qS8v9VUb6ez6DxvPBuu7MLfPGpbtkvBiTp88wNj7RhuE3LEbDX45tk8mscenDD9lcX6M4exMts8RK/wRd+w9hbWVYjEToODbJxOQxkqlUNQiz1SQlSeLwkUnuXbvAnbllzh4bR5YkLEvG8SAVj6ArYBklLNO8P+bfZg03Nze5cuki83OzZLc2cZDJyilkHN66NEUyFmGwpxMlYKL1Fcf94MDavhcBtoVr2ci28P1oQbaWkOQG3l8olLk7u8TVuwusZA2yrkakZ5zHTz1ILJ4gEvWZ+Ze/8Vt8/3vf5ejkJENj+8F1iSdTSKpKvlQmpGmk4zE/O6F+ugGzbdT6G88XnxqtBLsz/u0LWylqVE/NTbRqslKUSJbvf3P/GjL/3akSjAH+QSECf4oHaJqKg1+UxrBblR3dKcBL4Ho+7r/juIRCvtmyVCojNJ2wpqArAscWrGcyvPfuu2xtroNlcHh8hNMnj9LZ2YFlWciK4ueeyzKWaWI4DloAOrFb2kZ7tlw/h902g99PSNdxHQdJln0s/jpXSbsW8sUit6fukUwmOHXyeOsu9ybH3Nc39V+L6s8WHTeEtXvk8gWKRhlV06oxGg3X38e425HneTimQWZlheOHx6qMf8d89sDkj6jNpnkAQgjCuk4sGiUajZBOpTh3/h0uXLzM33z3+zz79FM88ehD21pv7GlvnM8DisUC+bVFvvn8E/T1djC7sILreowP9zHc30M0upulqs0YhGDywDBCwGauRLFsMjW3QjwWYTWzyez8MtfuLXBjNsPY4WP87nPPcmBiP8lEoob+1mY9JUkiFo8xvXib//S3P+PASD+PnzpMIl4XdPorMATYpgUebGTzXJ5a4tK9NUwtxZknHqW7t6/F3tqdCsUCU9eusHL+RTopM4zNfDnLJhIrKyuQ6MDsHeHQA2dJpzvaFOeqidFCQDgcwUVQMkxEVdP234WQrqHJEpZh+IBEkQilUomV5WUi0Sjd3RUQrqY902ILra2ucvfObRbm59naXMeybAb2DXHs+AlAkM9lufLuG/zk3Tv0xWd4+Nh+etIJnzlWtGkvUPDdQBSW/N0qSZJv4ncqFoJK0KtfdfTOzBI359aYWy+SNQWJ7mFOHT1ONJEknUr7jLxi4dQjnDpzlvH9+5H0ELIQyIqMbbtEQiFS8SiqLPsp0vWr2nLPtD8sJGnn1MB2pKl+tkHJNIgre8lA8sm2bTyPoGrm/2+Y/86HmSLJAcSif2hYho1j28iyX1feDQ5dT0gUSgaqLFVN17s9OMd2KJXKxOJxhJDI53NomsqWadPfkWBuZpoPLl5mdm6e1bU1FM/m4VPHeOj0A6RSSTxPYNoOsuvDumqqiqpp2I6DYZrIkhSU4txpFLsz9vuhCrv33N1xrgHKpTJLy6t093QTq4AE1Xd5fwHy/5Sbtl+/TXMXWLYPwlRBaGzd987t7IWm701hWwYHx/ejB4LcbuS5rq/JtbDpOY7TwPAi4TD7x0aIRsKkkknOv/c+P37hZywvL/P8Zz9dzQ7xG65vb28TEUBvTze4DgdGBxgd6mW4vxvwiEXDqHvVEtusXTwaJp2MIysKV+4u8O2fvk1YldgsWhTKFuFkik9+/ouceuAkE/tHkSQ5EI7amdB9ikYjPPn4o3znb2d55cosq0WPzZLN6QODjA70oIfUf5rv1COoa+HhunBzbo0r81mUzmHGJw4zODxSiyGpV2v3QOVSmbXFOSKbSzwy2slq1mDa9ZBcC9MwEN0jxAdGKBsGjrtTkaRgqJ7H5sYGsmsy3N9Zc5MFGlE8GiaqSWSKOQr5PHget65fZXnmDqoeorN/mJOnz2x3jTXNyTAM5udmuXHtGqVyiVQqzUMnTtLR2UlvXx+e52EafuGbzbVlrk/dxL10lwcO7GOwJ01ID6wzAXx1RbsWwbNG+PgAlm2jyBJCksnlS8wtrXFvIcP0epmiCJPcd5jR3gHiqQ66urtr73dwnEQjIRzbZWB4BEX302fDYR1cD0XyMVjCwVgqYEMfhSRZqgIetX4B2p9viiwjSxKFskE0FEAZex6G5cM06+r2/es4LqblIMsSykfc27+mzL9Ox206M/2Au5ph0LItTMvCw6/+VM0NFYJwSGUrX0AIiVgkRCy8sx/Tr3Tmuw860x1VIB9PUdAUQUjXsCyL69euk83n8YCQLtHf28O+gX5c18UKsKod148/8IIoe1VVsfH9v6Zp+pCWlflUGXJgEv/vFATiBYFnu5GQBLIsYZpWowXs/nhNrV/aGfp98ksp76z91Qa3/aNwKIyuaSysrnDtyiWWFhfo7e+nr6+//U33OQfheczNzZGMR+nq6tw7o4RqjEWl0FRFALMte1ugmKaqDPT3EQ6HSSTivHnuPK+89galssFnP/0sXZXKjR9xj/T39WE4gkLJwPMgndwp9agNtem6UCyTKxTAcymUDW4sbDJ5cJzx/UN0dXXR3dPF4YMH6Ajyz30UOK8x7bQFhUIhHjpzmq1snvMfXKDgwvk7GbJll7HFDJPjA/R1p+/rmVTJ87AtG1wPSVGYW1jl1twqeUdnYt8ogyOjdabz+2P84MPhlsoGrhriXtFjSSQw+roQwP7xCaam7lI2zEAh2F0w9zyPxcUFFM9goA6LQQ4Yiq6pJKMaS5kcua1NXMdhYeoWIWONmBTl9uU1Yokkh4/sVPfCjwsKhcIMDA4STyTo7OxidGwMWakpUXooxNjEAQp9/bhC4frNC7juDPFo2Gf+FXeX51WXzc+tF342j+txd26FfKGIrCrMr2aZXy9RcFWiXcOMDgzR09tPIpncLtQHDUbDIRRZJh71odcVWUaVJQzDQEgSIU2tHl2SJHCc+2T+wrdSSA3nUrNboP6zxnWsnufCLzFcMk2igQVJEhKGbaGrKk5QUVYOahyUymUQAlXag7u4DX2MmP9Hc8q1m7ckSdi2jWlYeHjImtrA2AQQDqmUPA+Q2SqUsAIQCkWSiIQ0ZEnCsKyahCX5DC8UDiHLMqVyCcdxKLvQlYgiCUFPby+HJidZXV1ldWmJREQhHNKrEb+SJPkBg5btP1DbwQksAJqqYkuSXzfatnECwaZSRavi01PuM3L4vtZzD9dIQqAoMuVy+f+TPm3H8UsWB3Nvrb23p1g0gq6pbG2us7y6SigUpn95kK3RTfbvH0fVdqg3v0eShGBrc4PR/i5URd7V/FcPZ1q/8y3LqlqhHNdtGZwoyzJdnR088uBZErEYr715jldefxPLtnn2qccZHtq3Q8xGexJCMLRvAAuZ5bV1Dk8M4ZoWuWIZw7AoGwalskHJMNE0lX193fclHCyuZLhye5bVzQKaHiae6uDI8ZM88+TjdKS3A864dUyhpXAYTFCWJDrSKT7/6ecY6Ovh2s273Lo3y521Mrdmlsnkyhwf72ekv5NYJIwk79FtETB+z3XZypdYyGxx9fYsi2tbeNEePMSeg0cty6JYLBIOhxugwlVVI9rRxVyij3dKZTrGJzl44DC2adDR3cvUresU55cpHTpEdnMTuaMDVdNwHAfHcfzzIGC4PvRxmfnpO8iu5eObVHQdCWQkPNulMxlFXcuwmVn1v/AcehI6R/b3MfXyJa5cvrQL8xeomsbAvn10dnURi8fbujwURSGZSnFw8iiz9+6SyW5g2rbv26+2VkcueLg+QqkHq+tbfHhzFlcJk/dUOvpHGeztp6e3j1Qq1fbdjeha9byNhUOISK0XI1CufAA1P5XPC9bvvqgl42+6YMe3sPa5IktoqkqhZBAJXIaqIvvp6o6Pz1JJRSwUSxSKJcLhUODiro1fEqKKXbBbDYKPEfMXrSyftDeh7DyxCga47TgoqorSFJHuS4EKiZiCrqjcWVyjbFqoih9UYruun5pkmBhBXXU9wKju6urCdV2yW1kc4ftbooHVIJVO8+DDD3P37l2MQo7RgW6661Jz/Kh7CUWWsGwbXVNxXQ/bssBzkWQFXdMwTBPhONi2S4CBiSQJ1JZmpY+wRi32uQgEj53IcRx/8xUKjI2O1t1c12vlx32aQJs3gI/O6OeI244DCg3aW22GredfyQ1QVb/spiRJJGmd6ucAACAASURBVFMJIqEwiwvz5AoFRkbHUD+i4FlPkgTlQo6O9PieBTM3CHCqzcFfXz2kN+TXt2svEglz8sQx0ukUkUiEN8+9TbFY4OmnnuDQxHhdZPze5zbY34+QZG5Pz5FKxigWDVbXs2wVDXKFEsWSQbZQpDMZ5dOPnWxk/rss41auwM2ZFQwpwpmHjtPT28fwvgHSqTZZC7s9lsqe8/xDOBGP8ehDZ+nu7iKdSnBvbpHpksnbt1ZZ2shzYizL+L5uertSfkGsoPywXC8MeNUfOLaN67hkNvNcurvAxbtLZLIFwEOTixRy2aaI+dYDzmQyZJYX2VzP0NnTR//gvmowmizLaJqOZbtomk4kmaQ7KH41d+s6yvo8lHKsrSyhhSPkcjlUTcU0TMrlMvFEnMF9QyiK7zfe2thgc22FhOeylS8S7kxWXylZknCES19nivTCJosr8xiOhycplEyXjkQUXIuNtRUMw6jGINXT5uaGH6xpGEiSXC2Nu435NyyFX+Nekzz6O1Noisz6Vo580cByHDRFoSsZIxTS/BiAisnfssnkimx5IWLpYYZ6+5k4cJBYPL6jezKka3QkYqiqitl0nrmuX67ddwH6JZhdz215Hu70TGE3xl+hVqbR7STLMmFdo1AqY9oWuuoLNZqiUCyWQBJomopp2Wzmcli2i4OPW6MqMp7nz00JhJlwWEfaxdL1MWL+ewmw8MkLHphl+xuncp8d+EhlScKy/fQ7WZLQVAUhpCrgT/NjsGwHx3FJRUPEIiEKJYNssQyej84U1nUcxyZfMoiH/DK7hmFgWhbIGumwhiSkat1nPAIQDY+xkWHfj9pEiuz79kOaVjX3mqaJZ/uBf5qmBcU5bN8UJiQURUZpVemt5abaw4FfkSPq79hF+i2VDZZXVzEMi8nDh/wPg1uqvL7yo90Q2r4HjR/6cRBWndavtNEYWs+//lMhSSiqyr7hEUbHxrhz+w6xWLR6aG0LCq9JMW0m0Tghz3FwHJNwOLw3X7+fflA9xCrL7rt6JOwgNWs3UlSVsdERvvaVLxGLR3np5deZW1jk61/+AocOHSQSDu/ZAyCEIBIOo4bCXL23zO0FP3JbllWEGkLVdLRwgnxxnXImR2Zjq6mBnduPxyLoms7hsXG+9rXfpLuzM3DpuAixOwpaA9U9mmpMn/BhbI8cPMDwvkHeff8C6WSca7enuLVSZHbtNocXMpwY76e/M4GuqsSiIeLxaEPDHn52kGM5ZIsGr12aYm7LgY4Rkiko5zK+MqCpQc2GOrN/Hdm2zebGBlcvvs/G/BSeWWR5oQdZVRkd2w/4AZazd29TnLlBIh6hcN3l/bk7AGjr83RsLJFJDdLbP0A8kWBm+h5rKyvktjYpFQv0DwwSDkeqoD+GYYKi49gSy5lNkrEwYV2jWllPQFcqzv6+FGu3llm8V8B2PDzX4s7cKgJQnBKZtTX6BwZofllnpqe5c+smm2sraIqMHgoRT3fy8GNPkkjWZYHULUWpVObW1ctodo7xgVFMy+bSnQVmVrMUTYeornBqvJ/D+wfQZKka3Le2vsUHt5fpO/owx0484Gc6tHEBSYEQp8oKqWSMaMQPxtZQqT/k7OCsVxU/rqoRwrfV+96G8ct7Yfy7t1PtOUh/jIQ08qUyuqohhM8nSobp71HPYzOfp2zauK6HZZfIF0pUgN1czxeyZFliQFN35e4fK+a/u6hf+960HdazBZLRsF84xXXJFsooikQ8pFMqlgEPXdfRNc1P8XLB8dyGHiT8Ag+yJNGdivu+KlXByxaJaAqRkA/Uki+UKLoesVgUx3HI5bLEY9EGdDHDtFlZ38JxXb8qoOOb81v5o4SQCOs19CdZlgmFdPKlMqVSmZCuoQS4/qZp+hpsnbm/ptfuvlztVrXZzOUEQXHbV7tG+Xye2Vm/ANLE/rHqfCq/Gplnm3bajanpA8u2q5YIRZGRZWm3Gbfs0IMAxcvB82BgcB8Dg/saLtgWFL5HjbmiKG5sZXGRSSYSuwZN1q97/eFREXIgCHTaQ/qnwBdsOtIpvvblL7C5tcXPf/EK//vMf+Rf/os/ZGx4mFg0SiwWRVaUPa3fgYkJTMukI5VibGyEwYF+ujs76e3uJBaL8cLPf8kvXvgHcvniHlqrUTQSIhpWyOezZDIb9HR3+UKz4yJEHZZ7QK7HNgGoIZGjXdaLEMSiUZ5+8jHGx0fpSMW5PT3H7MIKl2a3uHlvgY6wxIGRPiYnhjk8MYwSBF16gOc42JaFJyRefu86NzMuk2cfY3i/n8p3+9ZNJCGxf2ICXd9udq4Eu62uLHPh3bcRuQVODCSxbZlrK5uUi/m6a8FyXXKOw2BunQNxCbO0BMDhdJx3ymHWk90UywYjySSnz5zl3fPn2FiaRSoXKG6us7S0SE9vL5Ik0dXTgxaOYZh53r1yF8+DE+NDyIHJuAKjOzHci2lbnLs6x3JJwvTgpXdvIHk2il1iaXEhYP7++pbLZSzTpJDdwspvkqSAbpuUN2Etv8m751WOn3iAeMJPsas8Gdu2mbp9m5nrH3JyMMpAV5KrU/NcmstiaGlCHUlWs+u8evEeCI9Dw/2EIyEcy+bGzDKOGuPY8ZM+45ekbc+8Yh7XNZVISCesaciK3ACQU19Bz7JtX2OXJT+wDrculW9vjPz+Gf/OVPHOqIElpViuKTyO66IoCpqqUCwZ5PKlbenKFbM/XpD6J6hbq/b0MWP+uy1m7XtNkdEVmeX1LJGQSsmykCUFq2RBkLIWjUbQNLVaaML3h/gvmxxUiMoGQUidqWTVxKrIMr3pGgCA67rguWiKTDgcwTRNymWDvr5kw9hKZQPL9iM0i4UCjmNTKBYplw0ikXDdla3nKYTv0zY9h7JhonkeahALYFkWpuUDC4kgL90T3s5tNjM0mg7PwIJS+d627QbzXatRFgoFlleWCYfDjI0Ot+uy4eb7tUtUxug6/qZWZAk1iIjdE4ntf6aSCbo7O4lEWvgmmwSF+3mlBf7+2FhdQVVkEsn4LiprpZ/6w8l/joZhEAkKNzmO05AxUH88tRuj4/jgU8gCB/iP//nP6O/r5YHjk3zy2afp6+3d09j+/b/512haDaPAdvzAVFnyD8xQWEfTNXL3AdMLkIxFSYR15nIFNrPZ6p71A618wbPyWTu3R+Po2wgGdTQ0MEDv5zq5fvMO7124xJWbd5idy3FjLsOtuTUymzli0Qi9XSl0TQXbB8TRI2E+vHqHi7NbjJ18hKGx/dV34+ix49snVxG0A8Z/8/o1Lrz9Oim5wCfPHuTASD9vXbiFZeUoFYvk83n/3dY1evsHWO/u4+rKCsXZZT53dBxVUSi6MG1pDE5McuPGDfA8BoeG6eruZatvwNfOxw9x4uQD/nOybe7evs3xEyc492aOxcwSPz93icnRQZ8hBlKuEIJYJMSJiWFMG37x3m0cScWTZFQJNMllYWGeU2fOAlAulbhx/SqLc7MUlqcZisHExD5UReH2zBLXZjPcu3mF2em7HD56kkcffwICl93y0iIXz73G/oTD4ycPYpgmN+Y2UBJ9HD31IH29fczOzPD26y/zk/M3MU2bBw6OUDJM3r0+TarvCIkq49/+zMMhjY5kjGgoXHUTeq6Hg9saWtfz/FiASvAwtcJCeyFlh1LbH5387DPb9VBVQUjTyGzlSMYibOYL2JaDl4NsvhhksbWh+jM3qJy4E33MmP/eqBLR35mMVRdoIJpA1zWWM5vYtuP7PGQpKOfrUTYtCmWDomHheR6dqTCuI7GeL9Lf3UEs1B6QxTTNwK+l4zgOxVIBVVO3Hd6ObVdPaVmWqhUF7yeQRFdVBMIPNAz61TQNRVWxTBOzGvnbtHFFkwBQr3k3wxzX/duybJQgaMmDXUpYehQKRTY2tuhp4cr4VVGld0VTcE3fRLeXLISdKJVMENIUjFJpm4WjVd8NtMeQCtm1Cddh+bejCuiQXIccVqlvLskyruvUUp5ajGvbUDwPy7L46c9f5Pw7H/CbX/oCX/jMJ/nX//bfMzu3gGGUOTAxQSqZJNRifFW0NQK0Nj3U0LbXdOBEwmFkVWNtPbc7BGndOsViYWIRjcJShsy6X/zKv1dCSP7BXTmwbdtuTNsS29trpnaj0DSdE8cmGR8b4We/fJWr0RDLKxlm5xd54e1bXJ9e4ROnDvCJs8cI6Sp6JEzZdHjjgxtI0W6OnniAcLhF/nWdr8jDx6m3TJN33n6LmesXGU/YfPaJB+npSDK/nOHKTIaNsuD8+Xe5cPEyBw8dZmz/fpKpFH1DoxQ7uplaWOL/uLzA/iEfE8HpHOLsI4/y0xde4PJbr3DpvTATR08wPH6QyZNn2Dc0VH1O5XKZt954HTwHxS0j4b/fnvBa7l9ZltAVkF0TDxlFlhgfTIOQuLjkW/ccx+G1l19iceoaYbfMI5PDnDo8TKls8eaFW9yYzeBJCppdQJI81lZXq+fN2uoqr/38x3SrBZ5/8iESkRA/v3SLtbLEgSMT9Pb2+fnt5RKb+SIFVF585wayJLAdlw1D4fET7WGsNVUhFgkTDdUUq2qtgCYBoEKO46LVWVCFJBDe7il+kiQhSb/6TKsKvLflONiODajIEmxm82xmaxaiQml7cLUqSyiywHY9LNttaLNUKhNvgVhYT7+WzB8qsWG+yac75cNd2o6NKsD0IFMoo5RMbNfBtB0UIRMNa3Qlo5imzXKmgBDQnYwRD7cH56gEDoIgFotjGAaFfIHunu2FYYqGWWWsdlAZKqyH0LQac91t60iShK6pASiRD6VrmQGUsKb5gECGgaqqyLKE4/nmUrm5AlWdWbQeTrM2Mf9b27aJxWLYtr2rj7ni7igWCxw/PrnLTFrT/WjWuiKjCD0IrPlI3VV77erspDOdYmphmVs3b3Do8JG9v8h7NF14subn7bdrtwnQRwSoZoBvHQq0/grs817ITz+1uXj5Kj/8x5+yb7Cfb37ja0hC8K3//H/yvb//ET/56Yt869vfYTOb5ZEHz5JMxLcJrpWf9WtSabvy78q1sWiUZCJJObdEsVAiGgu3WaTtH8eiYTzXIbuVpbIbhBBIgBPEvniArMhVYaR1e/dnqgWIRqN8+fnPEotGeevt80TjMZY28txZXmLuxQ+5PrPCpx49Salk8rNzF7EiXZx96qH22SBVt5YvIG1srPPyiz/HzsxwZrSDLz59xt8LHtycXmLTUvAkgeTamKUClm3R3dODrp+lf99INfbn1Vde5sqli4RDYcb7OwhFIqRSSYx5A2djndsXDM584lOMHzhQXR/TNJm6cwfhecRFEUlxcV0dyTYahiwJgYv/PKcWVvnw9hKGksCVFWxJYjNbYmSgG3fRj+e4euUyK7cvMRD1+NJTD9LfnebO3BJvfniDmcUM/pNTINpN/9gED5w65WczlUq8/LOf0OFu8pvPPUhXKsHs0ho3FrN07DtCT/8AiqIwPTXFlUuXEULCchXWvQg/evV9hABD6Wa4TeXNkK6RikWJNVny6nH4PdcDqVH5cRwHhFp9DyUhoJri1/p0+lWb+aE2JtOyKRoGkiRIRH0BU1VVZKp1EP3PFAXLtqsjVGQZSZZJxKPYjkNmc3uxrd0Eml9b5t/8HDzPI7vlayLd6SSmY1Msm6iqTkTXUOtKiTq6f0BHIiFCu+DSG4aJ63roug/EUzYNFFVFqgtQqmyK/p4OZpY2cG0To1wCzyMUBAjC3o+pijRYedCW44Bpomoauq5jWRa2bWOYvrSnyDKyXosQb9CSArbvNbWP52GZVq0qYblMuA3DaX4lhJBQlN1SnGoBB/VB//dlUhcSitLs59tBfNghkLCnu5ND42MsLa8xd2+K4eER3zfZxv++G7XrqlAskdqGuR9EaIjWL6QVgHlIsoxlWUhBEZzqeNoEH3qeLxzOLSzyrW9/B9N2+Q//7t80XPXVL3+BsdER/uqv/5a//pu/Y3Z2nk899zQjQ/uq75DnubgElRKpdeMFwoos++mplfHEY1GS8QRLK7NkC2Wi2zDJ21MyGkbBI18o1OBTgzWRJd9S57kugnYWqMo6VBwg90c3b91mbmEBJAktpNHTlaKjs5P+3l4yKwu88N6UnyXUOc5DDz3CxKFDTcAxXsPhU2HY62trvPbSz3HW7/CbT57g2MRwbfzBLwmPkZFRsvkCmUzGP8QVhXRHB9FYjKk7t9nYWOf4iROszU2RnbnFVCnDt/6rxZFjx9C6h8htrKNEYmxtbrG6vExPn1+OvFwuc+3yJTS7ALoMnkB49nZZqW4dN3NlFrZMpFgXvT3dmMUcTn6aTDbrA+IAczMzpEMev/f8k+i6ysVbs1y4cQ9N1ejv62GhKNHZv5+QpjJ1/SL3rl0i2TPA1toySW+Lr37hcfq6U3iuy1sXbpInyuTEYTqCDCg9FOLg4UOEQmF++YufY3gSstZJzNnwFa9yGT0UIqRrhEIaxUKJkK6RTsRrQEF1JITApRa9X3EDKIpMoVhqCvCr3hQIDdu/koPsrz0H8e7hWs/zLdFb+SKFYglVUYhHw2xsZbE9D9NyaM65ikVDviUgmFdPRxLLdvxiddsyGjxWN3MUDGtHYKiPJfNvnfK3M1WYdCQSRpIEYVkj3EZilyRBOtHeJFLff7FYQlFkotEIZcOgWCiQTqdbjk+TFUKKoGRT1WAMw8S27Wo+8F40XyEgFtERiABK2McCwLII6TpaE25/uVymWCz67oH6VLiqVrf9kPRcX8tTFMWvL1BXBGjbeIJRy7KMpodASGTrTFJtZlH7V8MDvV+vet2Yg3bbZrDvaH0WjI+Nsra+wbkPLnH1ymXOPvgwlmXx3jvnAXxf5R6pJVsSfm5tczVJEAHvFniebxGSKjjnnodpWcRifuCoYQTxIfXOfbG9Ry+4dzWzyl/9zXdYWlrhf/0P/45oNNL4DD04deIYw0OD/D9/+31ee+tt5uZm+fynn+Phhx+mysw8DxdfKJQCjdtxHJBEwJA9H2cdv/hQuruH61cvsbi6QX9vB7uTP5loNIyuCIxiPiiypAXr4OJ6NQGgfRv/dDo4Mc6zTz2B4zj8489eYm55lczSHJquc/PeEr/zzd9l3/4D229s8Rwcx2FpYYHXfvETdHOF3//Mw4wMbC8T/tjJg2Q23mN6a5lTD32C/eMT1e9Wlpf5wfe+i+waSELQN3qAjoERQk6O2YUMU3fv8PXf+V2eeubZtnOShEDXVSRKuF4YycyB5/H4A4dQgpS26l4S4LnQ3xHnyGCSa7PLLOVWsJUoedNiObvJ4PHHAdja3CRqWb6LQFUolss8/dAkK+tZ3rq+TP/oAfaNTfDLn/6YmJtnXyqCxiIHB3XOTj5EZyqO8OD8ldtcXylx8OxDpDtq+6W3r4/evj6Wl/wgRyEEmmdgKjEsr6ZglA2T3s4UnQk/FqudK0ASfryLh4dpWuiaVsV1CIdDfm2GFo+1le9fCgKM7yd1t2xaREM7g8UZjs3qxialsomuKMRCGpGQ5rtzAdO2KRQb42k2tmrnrRIg1SqKghsElSuyQqFYxLAd4rEwhaKxK97/x4z5V8yAO39fz0B8LdklVywG0uFumOoVPtTer135zjAMPM9FUXyN2LZNZCH8XErPw7IdCuUSZdMhrqtEImEfiEEIVFXDdeGlV9/AsW1OP3DcD9bb0zqI6jmjKQqm64Is1wBFgvzwynhDoRCe61IqlbAsK1iDCiP3kLzasekBuL45NxwO47kujm0TieykvQWmaiHQdZ1wWGd1bW1PM2k4MAMGeP8k6n7W/6vJrLCNas9S4OfFH5zYz9r6OtduXmNra4ve3j6uXL7MU594+r5G1DIMwPUDNZsl8abBVz/wq0Ma6IGQWi75WkCtqln73gSwmsnwvR/8A9ev3+Kf/w+/y8TYaEszuQA602n+xz/6Jr88sJ9//NmL/MVff4+p6Xm+9IXPEg6HkYVft8LDP8Q822l8VqJSIMkjHovRmU5hmmZDOeidKbAaRKNEIxHyhSKrmQ36e3twAu2lsr9lWWoMABSNbTS2eX/C5KEDNaZbKpd5/jPPks8XEZLg+s3bCM/FcitTrt+3lbsa+9tYX+eDd94iaq/zW598sCXjx/MD0/p70szPlXAdu+kaD8kz0curgGBxykMFunr70foO8vQzzxFPJJrWokbFYpE7t26xtrJKMhRFMbJ843OP0d+VZmF1w8+S8WqvnyxJ2J5LX0+ap0Ma6cg0565MUVSimHKYkJ2nWPAzOZKpFFtrdlVjLpUNPrg6xfJGAcJd9A2NIDwH2SmT0ARfeuasX6o5GGo2X+R/+7tfYkg64e5RhkbHCNedNUII5mZnefmlF6tCqOyaZImBgL/88z9DAj7x1OMM9TyDvAOoUgUtUJIkSqUyquoz/ma/v6i+YXXnQ9O6tjP11/SYRreT47rkSyXW17eIJ2Kk47G2Acq5XAnL8qP5k4kYqbpUUw9wAv+9qijEoiFKZZOyYRLSNb+arOrn+/vohC65YgnTtAnpOvGESr5QojsVJxaNIEvtY6Xuv0LHf1faVSdu+u0/gK3A31HRemoPLAjEc10M06RUKmGa5o79+Pf7+aC5fB49pBONRiiVypRLRoDpX7s/pPuFIWRNwwxKBuN5HD9xgngyTjIRJ5VKbkMC20mHqcLYUklP82F3pQBQwzBNcrkc+VyOfD5PseDX5o5Eo6iaRqnsF+2oCDJCSEjB/8Lz10NRfb9XqVRC27UITY00VSUSClNqEYCyK+31jN5hcRq/2onxN3boH3yCgb5ennrsYU4c2k9m5hZvv/EaAP39/W3a2K1lXwsZGZ/AsUyWV1ZamvZbTclxXVzPQ9U0XMe38FTQ35pQCrbdm1nf4JU3zvH+B5d58olHeeYTT+7w/Pxnq6oazz39FH/0zd+mv6+XF37xEv/1L/6K5dVVfx7B/qimPgl/HBLBwRrMQ5Zl9HAEFJ3Nrd0sQI0UCevEIyrlYp7lZX+tKoy/GoQVCK5Oi+qcreZWpT0YBuohosOhECNDQxw5dIChwQFOnzyO6zjMzsw0NtiwrLU/LMtia2MdK7vKE8dHGe5vwfiDW4QQzC2tY9ggy7WzwLZtSsUSrlAxQt0YoS48ScEWGiWtk9/+5u8zeeyYf37stNc9F9kzoJilKx1nsLcLSZIY6O7wRaSKnBQwL0n47kLP89gqGXh6gq6eXqKpbiTPJpf1AzIPHZnEUmLcml7BtGyS8RjzmTyLGwUk1XdBLs3P0huT+dIzZ4nHfMtrhXH++LUPyXkRip7Oo09+gmQq3bBGjm1TKhYpFApEIhGeePoZMiKBJTTSqQSRYpGoLHHp4iWWl5cD7JOdz6oK061n3g3Bs6KRj1Taq1gItjP+etdg3UOtP188D1mS6e3sQFMUFlf9QkfNZ8HK+hZbuTy246DraoBBI+rOfF9Q3L+vj9GBHrpTSeJBLFAyGqa3s4NkLEZIUzEsCyEkejrSdCTj5ItFMptZYpEwsWYLYAv6mGn+90eeB/l8Adf1SARBTEIIH3/ftqsmVtetBCsFUdaKgyzJO7oX3EAqVIIX1XYsEKKWOgioikyzHBoJhygbJqZpYlguCg5OVdJv1ET3QrZtI8kyaoDgZVkWruv6teIrjbgexVIJRVHQdT/GwDQMyuUymqYiy0pVKq7g5KuqWn2RlPuIpNc0lVBYZ2NpZc/3bNecoOUKNJi5WzfQLLHvlSpXS5JET1cXp08e5869acytPEXD4o3XX+Ozz39hb41tD4JAEhLhaIqNra2W6TgCn9kTRMe7rottWYSDYNNS2UAPYi529nVDNpfjvQ8v8MuXX2VoaB+//9u/tadnKIID/+iRQ6RSSV58+TV+8dIrrKxk+NpXv8SJo5O+79NyEIHGVBFCZBGUQw6sTrqmo4dCrLVl/q2fUToepTMRYXotw9r6Oq7rNtQvqPyWghznSgZA8/vTsg/R+mOAi5evomoqqWSSVCLhQ6MS+Hrxz5JcroCQZfr7B5oabE2lUonNzCpRxWX/UG/VldNMxZLB9al5lnMmvaOHSKXT1e+MwGV38PARCrkcDz/2GAJBJHADVbIz2h7kXu2X63gotsWZI2NUUKP0gLl4lQjfYK6uBwKPjWyR6YzB0ORpTp15kA/ePc9M5h7W5hqmYTAyMsK76V4u3pljfLibof5OLk2vYeYFZUdibnaG5enbDEWhvzu9bZzFcgkBSJ7D/MI8iXRHg5VxZWWFWzdvkEgmOX78BHdu3QKhoMgy//Of/k+Up2ZwdI3pzCq9vb1Vt6aP/9GIzlhJ9TMNMwiIa4S4tS0/eFWI5nfMXxhJCKQKHkLD97udNX4abCSkI6Aar7W8vkk6EfM/F4JC2SBfrKXruc72EuOVc7oC4e55HtFIiNXNLVY3svUXIgGpeAyBX4tDCEFHIk5iD4wffq2Yf+MbXXnQhmX5DDkwofrMrRKI4/g4xwG0qyxA01UfJYpmxt/Yfi6b9QGCdJ1yuYxtVdK4/JfHcdwq1nI9qariCwm6jqrIbG2tky9UwFB2fiDNZ1YlbqCiddm2XcV7F6Km9UqKRFiWsWybYuD+0IJSvaZl4Xq+CamCB16xQlim2abmfHvmqusayUSCufklisVSHX5Bu3uaPq+a6Vtc3/L2ioRWf/0Op3zLvhuvkySJjnSaU8eP8upb5xG6wsL8fFX7aJT4W2ty2z8TRONxVlfXdxiO/6K7rlsV6oSQMA3f17tzHXD/c8MwuHzlKi/+8hXiiQR/9Hv/rJolsEPH1EtViqIw2N/Plz7/aYb2DfDt7/yAP/+rb/P5557imWefRdTD3Qr8VCiopiMChMIhorE4m4GG2G68zUuoh3SiYR3TKJHZ2PQPsRbmURGsR8U6UrE+bJ/TzvvIw+Nb3/4uS8urfn0ORebk8aOcOXmCeCxG1Tro2EzPzSIkpb0VqHkrB8GJEmxjQqWyyZ25Ja7d9Zl+zpJRE/0MZXp4RgAAIABJREFUjx8imarVMQhHIowfOMDI6CjFYoHOzq6qMLmnrS18a1xnVzdC1UmFohwcGagug6xI25bLDTKRSobFRr6MFArT09dPNBZj/8QBbl96H8UxmJq6y6HDRxgYGmHu8jlyJYOORJSwKuMJQdly8Qp5MIvIEaWlmXtiuI+5q8vIjsHsvXuMjR+oMv9yqcTS4iLZXI4DBw7SNzDAubfeRFFV/uRP/gWJWIz44QN4QtAxMoTh2GTXNwN0O5tYOLC6BoKvFwQxa5rWkJZX+e3XItmuXfggQP+EWKQARKiqYACxcAhNkimYBpEgBqBYMhoUg3aGquYzoCLAVBD8krEYtmNTLBsUymWKht9uMh4lHou0FUKb6WPE/Hdb/OYHBtl8ococ/Yhpr8r4FUVG1fQqgIjneWiahiy1A2mokxAdB9t2iEX9ALpCEJkcCum4np/Gp7QpDiJ5fjGSUEjHE4JkKkkoHA4Qm4I+RM3r1DyC+lVwgrQxKcDcdx0HVfFR/7zAP0swf4RADYL2yobpVwpUlCpAkOXVDKiyLOMEWn/rwJn2z0FRVMKRCK7rkC8W6pj/DkyrpRDd7vpmba6i67dbsdZ3Bg6Ptn1FImFOP3CCcCTMz156FcdyuX3zBiceONW2/Z3G6nge8USC5bl7CES1Ol9FY62UqKlYX4Cqib8CrlQR8rYFNAZ/2rbN5avXePGV1/E8+MoXPsfg4AD1F7ZcpxZxEbIi05FO88iDZ0inknz373/ED378c1Yym/zGl573YYrrlqG+VQ8I6TqJeJzC0grZXIFEE0RutcOmfiVJoGkKtlkms7raUuCp8rWKAOA4eJKAugyA2jzbPyMPj2KhxOpahpWNHLrsoamKb6WpZrbU3jpZkn3o12a/chuLVEjXiafSTBsub1+4xb6eDjwgWyhxY3qJnCmwtASJrhGOj42T7uwikUw1+GElSULXdTRNIxQO7x3PIhhLsVBg6u5drl3+kJRm8tSDJ9BUBS9Ya//SQPMPploxLyuKhK4riCDuR5Ikurp7SPcPk1u8y53btzl46DCJZIoZFGYXM+iKXy69s6sLR1HYWlukN6Hx4NGxlkf4oeF+3ri6gI3DRmaF5cUFIpGIH6fkeWi6ztDQEAcOHiQcifDU008jSzL7+v0sBiHLuLaDIiuUTRujbPoKjKYiKUGqc10lTA/Pj3EQNAymghYaCoUaaznAtmt3p8aJtpPXS5aJWqcguhUcmOB2P8i1XgTYruB6wNqWr/FLQCoRIxGNkCuWKBlmtR5NLBImGYs2CGAVBbkdfYyY/94X33U9ykbZx9UOSlUKIZBlJag2Z2BZNsL2q17JQbBcc3nUGgVHbqBk5vMFQuEwqqZRLpf9ErvBQ7RMCwcfYbAVOZ7vp8xls7i2RU9/d5ApYPoCiSIjPAIEpp3JsWslXR0nyPEMYBu9hvNVBC+7VH2xLduumveVwMRfMbHiedWCR7XD1P8pEDvKYRUYYs+DzPo6PV1d9UvYtKI70fYrGhl2I+26Wm0swO1Iln0o3hNHJ0klkrz61nluXb9KV3cPA4ODezKbNXeYTqeZu3eHXD6HqvoumMqXlRe5YvqvZGyYhuHHc0gSXnUfNgW9Bv++efs2r7z+FrlcnkcffpATxyYbzP1eu+DHNsYLIUnEolGOH50kFArx9z96gZdeeQPbcfj8Zz5FOKyTLxTpTKdrwFLB/opGwnR1drJ27xrrm7km5t9a6we4evMe7166zdJ6jmHDbBNUV/u7IqD67/jubjPP87hy/QZXrt2gI53i1IljVUZn2C4eLvlCgUKxRDJRQ/EUwrdKgEsmk2EgWheE1WZZVU2ju6+f3rEjXJ6f4ub6OuBhWSZKtJ/OoX72jY0TjkSJx+OoLQrl1PoX912tEiCfzzF99xbFtXmSqoWmSNxbXGVmcY1oJMTk/kGSsWiDACBLfslcRdZIRXRC/y917xkkSXre+f3etOWrutqb6Z7u6fFmZ3d2dmc9doElCBAADcgzOkm8EC8YwThJcQqFzoih0IVCp6C+nD5IipB4QUp0AAmQAs3hgMVisQZrZs1477tn2pvyJivdqw+ZVV3VXd09S94H8okAeqeqMvN938x8H/d//g8usw/uYxgm6UyGp888x80rUSamDyCEYO/kFHdvDHN3fh2kT9nysFwX2SgQo87kUJbJPQNdF6m3J4nmN/CkilovcuPiJ+Rz6xw4dISebJbxiQlGw7bAzWsJoFguo+sGdr2G9MENlZphakTNOKYRNOtqGo/N8mhFdDEmpcSyAr4DXd+ZMvvxKs123xsEIH0wzTBt7HrY4Z7cej1kkO7TVIV4NNL1vIVyFatht3rUJGPRQIc09+0Q4BgxjE17gaRQrgYN0baRv0XK//GkyWbWsGwiEROzrX+0EErYZ95GUXV8z0MoAk3XduRJb99sfd+nYVn0ZLNoqkq1Ug5eGE3Fsm2U0MPeTrzQ0Jp58AAFn9HhQZKJZOsmqGpAWOP7ftfQngCQtJgBhaIgw1rV4EEPwj/bRTqb5XsQGACO4wRRAD1IjShhP+gmc2Dn8aHPuf3ejaZrxKIxfN+nUChu/8MussleppmGbEb1/yaht80Bhsc6RAjisSiHDkwjBHzvP/yI61cuMzwy8pjKv3MAvQODOJ7H3QczPJlIoOl6yxJvVlVIKVHDCE0zDdM0Erai/DdkfnGJs5+eZ2FxmcOHDvD8mdPEQ1KQDU+585huq7kVriAwdJ2D+6f5ha99hbd/+gHvvf8R5XKZaCTGvqkJTp96sgOQB5BIxOkf6Oe8A4XyNhz/bRdyHZeb9x7y47PXuP4oj682K2jCrnibDbe2fwdtsL0wa9KdUVDKgDvgnfc/ZGF+gcXVHIlYFDNi4rguhhpAF30Jt+/NEDFNDkzvIxaNBIawqjAyNAS+x5VLFyhWKmSz2RZnfjdRFIVUOsPhE0+RH5toRXSE9Imn0sTjCdKZzDZptU2T3Dqjju/XVleZm3uElJKRkVF6+/rQNI1oNEbfwCBLjx5Q9RXev/wQW9Mp2yC9Og9Xy8QNDYEkHtE5eXCCnlSiFV1Kx6MMpiNcW3rItVqFeDrLaz/zZUzTJJ0JsAmpVIpoIsXywiMaVo26SCEVDcUqMtkf54kD40Q31dxLKVkvlLn7cAlPCoT02TsYZym/wGytSiadJpVOk0wGBli1WuXB/Xuoikq9XgXfx23Uka6DmephavoAo8PDpBIxIoaxBYnvh5UoahenTAiBYzvour5rq+3uX22/L3UzFprnjxp6EJqv12jYHpbjtO68IpqdYAV1yyZidOkBQ7CHZ9NJGo6LQLSwAF6I3YLgOWyld9jw+PPlSivV2E3+zil/3/epNxp40g867HUpZVAUBUJDQNPUoBvcY9xwIaBWs9B1HV3TQzKdIEceNAfyd+0n7/uScqnMyvwcw/1ZxkaGiMUiLdBhgEEIjY3thhSGeNUwxN8kgWmWEXYQ02xzkqYB4LounhtQ+DbLZDwvqEjoxkTXxeHsEF3VWhvm7KN5nnvm9Lae5baftYVRRceXj/uC7fAyhq/XTk2PmuCnoK9BsIlMT+1lZLCPO7MLXL54kWMnTnRwJjyOJNIZerJ93Lx5mwP7pgLuBNMEZJC2kXKjPtf3cd3AMNvN4yuWynz62Xlu3rrDnrERXnzuWQb7N+iVd3iMdv2sWb4nhODwwf3E4zEipsEbb/4E0zBBSJ44cYz4pmMikQg9PRkank+hWN51bS7fus9PPr7OckUyPn2wxX9Rq9dIJVO7Hi9EM+LlI2UzGrDxXNi2zXsfnuX8patBY6yISbVWw9B1Dh3Yz+2796lWa9iez/LqKheuOKyt5zDNoL762OGDjI0Oc/L4EWYXlli4d53lR3GWB4c4cvTYtix/uq7T199PX//nobtu3oVOI8DzXGYezGAYOmN7xrHtoK2uY9tcv3qVhZk7COmxtjTKxNQBUuk0S4uL3Lp5i1LdY+LYESzXIZHN0pfMUCmXuXjxYzSvgUQhZfj09qSCpmGhEkzGIuzpT3F9dpnSuostA+dhcGgD9xBUJFSxLIdKwyU7sRfTcnGqa/Qk0gz1bmAYAt4Kl0+u3GW96jC7lMeTCmP9CfaP9VOpVFiuVsjn1qlVqxjhvlotl7h55QIxxSem+YBElT6aIngwew/fl/T3ZlFkvJUSssOKJtM0QEp8z0cPAdmu61IslohGI0FJc/i73Zpu7Xy/mnPs1BnbiaqpFPOFkKelUwlrmophBHX9juNh2Q6xiLrheIXvpaaqJOMxDNvGdX2EIGR9dVohfUWA2jYQCVgNJ+gwu8M++HdK+Qdtb50wR9pd8UPwQjZD3NuH+rucX/pUqzXS6RSqqlKplFEUgWnoW7z97cJDUvqUy2Wceo2RwX30ZFJoqoojXSQbFKk7gcp83w/yU6G3Llv5460EKDvNrN0AcB0nYCbclBNqH8dj9AlE1VRi4ct0596DtolDOwtdM7ft+36QggjnoIYhbtpCyG1PfOvYzdJeDrPjrGUza755nTfm3E5V2wwta5rO0UOHuPtgjgvnPmP/gQOo8fg2ILOtIoRA03WOHDvBpx++y+VrN3nqxLGWYvc8DyVU/EIIPNfF83wibYRN3c7uOC4Xr1zlk3MXSKdSvPDcM0xP7e38/V8zYNKsgPHD50BRFCb2jPHLv/B1Pvr0PKsrq6yur4flq6GqkhIhAgBpLBZHopAvbqUWbYpl2TyYW+SNDy+Ts1SeePo0o6OjXL1+g6XFZZaXVx9T+YsWnqfVByF8lhuNBlev3+SzC5fwJdQ9iIbrMT42yt7x8aBywHWZW1hieXWN1bU8pVIFVVXIZjIcP3KYZCLBC2dO03PrDpcvXWRxZZW5+UeYZoSpffu2kGv9x5E279WXrCwvs762SqlUwrbqGGaEbG8fDx/O4hTX0TyL+eI61WKBRKaXmft3uXzuM8b2jJLs6WXv5BTpdBrDNLEbDVzfp1pcx7I9Kiuz3JhZAi8gVDINjdGBHnozCRQkaHrL2w+klZymv3+AiurhSYFqxPBLy7iNOg07jtVwiIThbSkla4Uy7156AEIhlU4wPpjGUCS5co26NOgZHCaVybbwDQ3LYnV5Eae4SjypcGJihHg8QiwSQRWC7/3kHHdu3SSVyTCfTDA5vod4PM7CwgJCwNTUFGrYE8O1JWsrqyzOz7O0vEg83cPo2CiZdA+qqvH5XpTNoNIwMbnLKZr7S6VmYYcVBlvuugiIi/SQPbNat0KMlkbTKPR8Pwjxh89JxAj2Dsu2N1IIBMZA3Wrgen7g8KoqlbAUeyfw398Z5R+E+12skOc+sQMpzRbAzpZztSvujZ0z4PCXmGakZWiYIfPSZtnuAQg2JhkaDUYIzgty9IoiOpjdmopsM8Ws63ktBdlkpGrmhFuwvVZOtHMOm6bUaQC4bgvdLxSlFXJtjuNxXgtd00inUqSSSdbXN5DtLW86TFdUqzWWV1dZy+Upl0qtSEY0EiGZSJBKJYnHYi3uAs/zcD0v+O8QnBPgFoKpxGJRsj2ZoDWtorbSF6K1P20YDs2Oee1cgB3ft8JlIvQoAxkfHUXXVOo1i6XFRSYmJzd5/9vd9Oa9EOyZnOLR3EM+vXCFgb5eJo2gK6NQlCASIEQQjQn7wLcbY93Ofv/BDO++9wGe7/Pyi89x7MjhlkEhtj1wJ2ug0zjy/QCK2N4BLZNK88KZZ3j7vZ9SrdZCPvRgrQNe+OB5NAwTPRIjVyhjNWwatkO5UsMOvZJ6wyFXKPPhhZssln1efOUlXnnpBVRVZWFxiZs3b3P56jVGRoaJN9/nbXL/zbG1l6xCMI5qrcZ7H54N5uO5AfYBOLR/OgDlJZM89cQxkvEE127c5JMLl1lZz2O7PqZQMHSdbE8Qnk8lk0QjESwveD5cx+LKpQsMj4w8lvKXUlIpl7Ftm1Q6Hb5fXW9S6zPP8yiXSqyvrRKLxbg8P8fC/By6W8VRTU6fCdgIfSBuqiQjgnJxkUcrC5Rz6+i+hbArlIpFBgYHW2c3IxFOnHwSq16nbtX59P0ad+fnqJTLVB1IxqK8oCpEwqqmmjBb5aYb45Nous5Tp58hX8jjuR73792lYdVxfclivsrNmUWG+lIkohFsx+Xa3TkcNULErTAUV0nFo1y6v4K7ZpMaHOfQiScZHh3DsW1WV1fwXI+F+XlUfKKRKP09KSb3DLXy5WMD97i+lufaubO4rsviwSOkMlkezj1CVQSNhs1Afx/Vao1yqcDMrRss3L9LvphDRJMMDe/h9Z/7GlKmaYUcu9+9Td+JTbpit3u/8VvLdiiU28pgN2V6XNfD9y3s5v4TYj5608nWujebEAG4vkfECEDBdlju3ZSGbZP3PVRVQ9dUYhGDmmWFp/07qfw7vUjX9WjYAUBo99Km4PhmLn/zDexcj41rlEsVEokkiqJQqZRRNRVd03dcwM3jtB0Pz/UQqoaiaUE4Kiw/VEKl1X5YcxOW0qfZ6MV1nCAPHCoqJUSvdnjEzSF184Q3/VNV1Y0cUXPj1nUabYZFa1yCDqW5aUWDPGcqycjIIPdnHrV4AwAq1Sqr6zny6+us5vJcv3mb2Ydz2FYdRRFIBIqikkylGBkapK8vi65rNEJeBNt2sB0H13GwGhYN222Nuacnw/TkBFOTEwwODNCTSQfRkE2RDJ9g/E02NNH2XRNs0yoDkp0zrNaqTOwZYX5ujts3rzM4PEQ8ngh/22VBNq23JFCOTz9zhjdWlrl8/SYDA/2YPWZYfqS0ojAivAftz8JmyeVy/PBHb7GyusaXX3+NY0cOdUQKtpe2AW0578Z3fsiDsSW9IeC5Z06xtrbG0tIiruu08CPB/XHxPZdCsYiiqMwurnHpxn3WihVm55Yplmv4QKlmYzkewkzwzPPP8NILzzE40I/jOBzYN8W16zf46JPP2LNnjCdPHEfXuhDZbPp38zltRpWahqumB++a67pEdI3+vl5+8etfDYhsgD2jowBMTk6wsLRMoVTCdiUN18P3XGzbJh6P43ket+/dD0m1FEw0pLeVrGU7kVKyMD/Po9kZpqanGRweCctHtz/Gtm1m7t/n3Nn3GZ+aRkiJjoPeyONF+llpI7dJxaMc3zfEar7CzQcL1Lw6tutj+SrDI6PUajXcME2YTKW4f/cuA4MD9PX1Mzw+yUK9xHxuDVfoWJ7C4mqBg3uHySZMChWvdWzzGWmufzwRJx5yDwSVRy6lXIq8Vebda/OMpNfYM5CmVm/w6c1HSC2BwKdYaXB3sYTZN0G6p5cTJ59kYHAQq17n5o3rlEol+vsHKJUrVF2FuysWXH2A40n2jQ8QjZg8fXQf8Zl51osVFnM5Lp5dw1FMNCOCImBlfo7JPXuwahUqhRViosHelEJa0XCkxcNrF1l88il6+3q36I5O3bD1JnUabjtbAe1YqUbYo4Mm4G7ToZ4vW/uuFzqaQtRD5d/8jd8C8XUe3rnn+xJsxwPHwyJgrnTcoFX731HPfyPM0gT5eWFJ1HZldpuPf5y8DIRRBdfB9VziIcq3WqsRj8daHfm6H9dpWfkyCL9UqlV86RGPRlEUFd+XYTimTVGx4S1LAqWF9JFeeNNUFTs0dhSh4Lcd12mb7i5+iDBvRQ9kwBhoGkbrGmq42QU13bJ1rY75NmO/MiiZchyHxaVlHMfGdlzuP5jhg4/Pcf7jD+jpH6avr5f9U5NMToyFG6tDLpfnwcM5HszOcuv2ndYaijC9oihBm8oAiGa0cr3zc/Ncu3aDVDrFmdOnePKJY/T39XV0qGuKIpqKfyOq0lQUSpO4ZtNCCiGYmtzL1ORefv/bf8KD+RVya2uYZmR3drXmdcO/2VSSw0eOcf3SeQqFIr3ZIJTqhg2ZApIcY+Nudjl33bJ440dvcv32bZ4/8yynnzpJTybDzlbIJtk2OiVbfP2by56aZz90YJoPz37C3Tt3mV9YpFyuki8UyBeKFIp5SsUSy2vrFEol1qs51r//IZ6ig2JgRmKk0mlG9/QxNDDA8WOH2Tu+p2W46LrOieNHkUj+4Nvf5a++/0N6Mhn2Te5tcXDsNM0m+t8PSYBM0+DYoYO88977NFxIxVW+/MVXW9Ghdrlx6zar6+tkU0mKlRqVWoNarU4uXwjXF/ZPT9KTSXHu4hVc12V8anz7zn5dllzTde7dvsGDe3d49oWXmN5/ADUE3W7HaaBpCsKu8OjONaKqgmyUEUJFIlhfX8f3fXTpsrxe4unD4+wbG6BYrpIrlvGkQBoJBoeGuHX9GsV8DgSceuY5Hs7OcOGTD3ni1DMcO34Cx6qzeLeBZ9Wp1eqUqnU0TSEZ0zFLVqu9d2symxSflJLBoSEymTSlUomVpSXm5x5xef4RNx6uIATYegakxENnrgqxvklOvfAiY3v2oOs6jUaDQi5HuZBjbnaGuQd3kQikEkGLRnhYcZl9/xpff6bBqWPTHJga4cDUCPl8ie/88CP8Yh09oqIqFnXLprq0wu2VGUb7Uzyzf4TDU0dIp+O4jke1Vuf/+vaPuXvnFhOTk0RMswNjswUk+zk8/Z2egUQ0Eob9ne6/EYK4aRKPRnA8l2KluuXCuqbScIISTLVtL3Ncb2v6V2xUFDktemB1433qIn+LlT80FX+j0Wxjq23b6nRz6Pxxb2LzuHKpQjIReHlWwyJok/v5kKHNwHmtWsV3HWIRM7TsJLoaABA3skcyDGmLjly30/T6AdlkPwtZyNg0J2XHENbG/DzPayl413WxG42wSY8Z1OyHZEC24yBEZwKg+UC1i+97RDSNUrHI7/z+H5LPF1nP5VFVDUURRJJZnjtzmtdf+wLDQ4OBhytEK0rQsBqs5XKUyxUajkPECEiJohGTaDSCpqjouoquB6VRjuuwnstz/cYtPrtwiR+8+RbvfXiWV158jhfPPEMqmWiR87Qe9dCo8nwfRRFbsR87LNvPfPFV/ug7f86lCxdIpTPbILY7T6WrCqYaPC/9/X288sorzN69ybsfnEXXVQYHBlAVBV3XwyqL7bWb53l8ev4ib3/wMXv3TvD6qy8zNDiw+8B3lA1DuklOE0RNtvYybxqmQ0MD1G2H3/6dP0AoAcK4aUQFPR6iDAyPkIhNc/rUk4zvGWXP6GgrhL6TGHqAsfiVX/wGv/t7f8S3/uRP+Y1/8l/Q399LO4HVNtNo3c9Go8HC4iLvffQxtlSBwEPu2dJZMZATR49y4ugR1tZzfPjJOe4+eASqTjqVbqXiTp08wbe+8z1cDzw1xlOnn2k5BduNpyVCBEx0qoKmwPzcHNIPjO3xiQmSqdQWA0DXdXr7B/H0BFEnx2gqykLNwYr24QudV77wKm//5Md4dT9okkXgOVoNB08KBkZGmdy3n4czD7j48fuIRpmoqfNWoYiqG6jWOlfOfczg0BAHjx6nWi6w9GiWbFyhLxUnnYhxZHqMu2v3GRgYYDvxfZ9Kucy5T86ytDCP7zRwGnUajovtKUg9RcSvokgXw63gRDLoiSzf+KVvEg3fT9d1uX3zBjPXLyLLK4zqDg3Hpy5VnFg/Tz/7PKZp8sF773D59kNOHdvoxbC0XsITCnFT4ZkjY4wN9nJvbpkrd2bpTSf42stP0ZPduO+6oZExkpw8upeP795i/alTGxHDbZ7P9o//uoaAlJJCpUqjYWPqGo2OvL/E0HRMUyeRiKEqAh9JIhrFdjfK8oQIqsps22G9WCIeiSCcgMCoiaFqiqEHjIh1q9GRXUjs0m3zb43y7wSfbXzWVPyqqhKNRTfdnI1Ny/M9PCkxVK3jXBs3cPvNVkpJvW7R3z+AEIJCvkAsGeu0gtn5HBBgBiTguk5I5BEYIWpbWL3jaNmRxW9VBDRbuwpFafFNNw9WwpA8EHAFdBlS+7o0w6Mb3p7fKgWpVauBMjJNIi2WrOD7FoVKF0+l0bDpyfaSzaRYXlom29vLoYPTTO/bRyqR4H/7P/5vxkZHGRkaIhGPb0SaZdCEyTANUulUEH5vyz8HU9y6vhJJPB5nZGiIE8ePcv7SFT759Bx/9f03+Mm77/OV11/lhTPPYJhmK5QeLE1gcGwQI3UHAW6WoYEBnn/2KX749ocU8jmiIefD5k1bALqmYAiBbugM9PehqgHaWFEEJ049ywc/fZerN+5gGCaDA/1omtb1WW+XxeVlvvunf4HjePzDb/48gwNbkeSbUoiPIRuKvwXwE0GXQT/EVXSmlQRDAwOsrKwyPDTI86efYXLvBCPDg4wOD3VpW/z5JRaLcvLEMX75l77BH/3xn/KH3/4Ov/5r/9lGqqX7NFriOA537j3ge9//AZ4nqNkBMFFVFebnF0gmD7QUTpN0qacngyIE9boVUmQHjbpSqXhg2BA0ypl5+BDHF3zjm98kulOacbMDIEQQHld0VFWy8PAB87MzCCFYW1nmiadOtdrZNkVRFKKxGMlMlmrOYS5Xxor2EU/3US6XeevHb2KXcuiOHUQJfZ/VYoXFQg0tmWVyJMuxY0e59Mn7pJx1POnz7IE9vH31Fg0jg4mG16iztrbK9P6DRBJphBAMZxNMjPajKiox3UBIv2tkoin5XI5333qT4uIDDK9KbzKCHlVZtOrEEgP0jU1RKuSxcsvYeorBif189Wtfb9uLJffv3eX+1QuMRm1eevZJTF3jwwu3uDq7htANJicnKRYLCKvIyERnt8jp8SGW1nIcmBhleKAHRVXYOz7IE4f2cvPeHPfn1ziV3fpcnj46xYXbH3DpwgWy2V76+/tDIiDRdU9ofw82R3c7pWlQb04rC1KxOKlYHNt2yJUDTEzzgenrSRGPRhBCwXaD8j9fsiUKJ4QgEY+RiMewrAblukVaVWnYTofnH9F14rEoQlE6ugF2AJu7yK7KXwgRAd4DzPD3fyql/B+FEFngT4C9wAzw96SU+fCYfwX8GuAB/7WU8o2dr9JtgIGiCLjsJabZnT7S8z2cdsrETRvrTvt8c2GKxRIo3IwJAAAgAElEQVSJRDzcKGyQPobafr2mht15qy1bNkhJTzbL3AOVYqVKX0ihu1kEgiZHXwu16boYIdOb67oBYFBRW5uSULowA3YZkuM4Hah6NVQ4UkoUTcNQ1dbGbzsulUoFTVWDaoAwENE0SmRYFtg0IgLPD545fYozZ54hmwleNlVV0Q2DcrmMABpWHV/6reyNpNPQUdrmsjvUUKCpGpqqMTYywuDAAGeePsUn5y7y048+4o+/++f8yf/3V0zv28tQfz/RaBTD1ImaEfp6s4zvGaUnnQ5SRm0gOyG2Yhua9+LY4UN89NkF3vrRD9kzMcnJp07RPxAYh5oAM2yrmU6nSLah1au1Gmurq0F4dHQP45P7uHP3NhE9eJ6y2R4MvXtNb1OKhSLxuMnSyjKfnb9ET6aHTKZzU9u2rfF2EoaOpAx6nUshWqVC6ibgY1MOTk9z8MA0+6Ym+bmffZ3R4aGQU50NA3G3lFpzlNsMN5lI8PILz7O2ng/C/9k0//g//U92OVkgvpREoibHjxziswtXkUIQNQyef/YZDh8+2BrnW++8x5XrN5memqQnk2ZyYhzdCLpwKgTpOk3TsRsN8qUSFy5fwXE8sn0jJJLJVpqhNYxwj9nJgDty4gnuXbsU4Dw8iR6JMDW9n1Rqa2VDpVLm0oXzlMtlVN2krseQUvLUqad57913cK0Khl1AwwUB9x4uU3N8XNUEPYkWSVAqrJOiyn/+jZf583fOcefhIooi0IVPxCnT0OLUa1Xm5+epVSv0xjWmR3oYGciytF7k7U9v4Osxhoa6UxtLKXnrR29gLd3FkC6gsF6yEDJMp7l1dF3nKz/3Nf74299C6PoWxV/I57l+/mN6lQovPnGQfeMBi98T9QbXZldRVQ1V01haXMDE5cSBvR1j0A2NV589seU56s0kOHNiP842hDYR0yDi17l15SJHjx0nmUy2mEk3G9Gtvdj3g9JBvZuK7NQHWyLAQmAawZ5r2w1ceyP03yoVF2H8V4LreAEfi97dMQj4XoImbEGqa2OeqiLQDY14LGAubDRsXM9DErQBbmyTdoDH8/wbwGtSyooQQgfeF0L8APgl4C0p5W8JIf4l8C+BfyGEOAL8A+AoMAL8WAhxQEq5LdWQ4279Skqo1urYThA+b1rfmy2ZHdgLN8n2Xn+1WmNiYgKAtfUc8UT88VDem68QTrGvrx8UhZXlVYYG+gOl6vvo6gbgr+WFNcfh+RB6qXaj0RGa+ry1qZJ2j122curNmaghS6AQKhFTRRpGgLZ3nC3pE9rGoIeVAbF4rJUf3SyZdOBVWLXqRmgqcCtDhRF22/o8PmvbG6qE+fKB/j6+9OpLPPv0SS5cvsrVK9e5c+8Bc7MPccOaWsdxWp21UukUJ44d4YUzpzm0f5pYLLbBCd6xdoFnHI1F+dlXX+YHP36HlUez3I/HiZsGA329RCIRstmetl70kvX1dSqVAN1b8zbOeOqZMwghuHL7PvlyhVMnjzM2Mtwy6JrrLIRAUVUMXWNkZBhV0zHMCEcOHwrIfLZYKbsr3naRIXo4yC8/HpNcIhEnHo2xtp6j0WiEY2imqR4vJiq2/MdWiceivP7qy3zru3/GzTv3Ose9ObfZCiMFqYOoGWF1dR1DV8moUGh4vH/2U6Yn91LIF6jZFleu32a9VGP9wjV0TaH3ynXisSjFUhnXc8kXSvzeH30HieT+7Bx122NobIIXX365FamZnXlAw2pQqZQpl8uMjo0xvf/AtgbA8RNPcOPyJUCimxF+4Zd+mdQ20ZJkMsUrr77GS698gVq1ipSSd99+m3ffeRthVwEXoaq4aAh8bs+tIIWCpyWY2HeAF19+hdvXrzDvSj64dAdDV3m4VsXRMiA9akYGhEksnuTurZtUF+9xZv8AhydHKFfq3JldZj5XwctkyfT0dFVAN29cx7VqgZEoQfhBKNtTNDxFx/UkKyvLJFIpfvnv/f0t0Q270eC9n7xJtLHOa88fYXJ0oHV/JaBHovSN7Wm7zz7mFsXbNcwJBF6zZnZXZ/FElF/56kt8+42POfvBTzHMCHsnJ0AGe4Suay0uACnBajQoVGoko+Y2yv/xX7xYLArrBTRVoS+dJBk6mXWrQbUWpEw8GbS0Dt+q1pya4vk+8yvrOK5HvbGRQlAVQU8qSSYZRxECQ9dIxWLkygHvhq51d5ibsqvyl8Hb16xZ0MP/SeDngS+En/8e8A7wL8LP/1hK2QAeCCHuAs8AH217jS6fVKtVnEbQm940Ix2/9TwfRXkcj3F3qVZrGGFnNc9zcF0H00zvGhruJslYjHy5RjqTIZ7McOvBDIODAb2vIgSuomK0l3YJBcKaZcdxiMaiSBnUxUfM7lSgn8/fCz3tjgPEpr+BNDkR/mNJZVMTiybHhdKtOmE36eI6ChG0ZI1GInzp1Vd45YXn8H2fcrmK1Wjgei5Wo0G5UuXhw4fcvT/DjVu3+fjT8wwNDvLFL7zEi889TTQSDSMezZa1G0/j/v370HSdN974MZXCOlHTaBmJUkosq8byyipSgu3LltHRLh7w5Oln0Q2DB3dvs/gf3qAnFaenb5DBgT6y6Qz9A31kM+mQ9Mbi3/3u73P95m3++T/7L5me2ruFAW9jTR5f2gGPgTzek9SbzfBoYSkgmhIBqHH7QOLu0skmEYxBCEEmk+al55/nypWr/OCNN/nKl18nIHJpcOPOPWzbZv/UFD3NCIgIkP2u53Hk0AF+5Re/xv/7R9+lvLxOrlTnd/7w2yEmhhZQteFJPHxW8iXIFTAiUUYn9oHvsbCaRwrB3kPHOHb8BImQee7enTt89slZXKuGKm2kUEEoFNaWsW2bo8eOb5ljK/QvBLYL3/ylb7C+tobruqTT6S7pREJbSrQQ9a+8+irf+sM/QOoxXKeGRoOAkkpFKgo+CjLsdWAaBsNjE8yNH+NKbo16qcb4gdM8//IrfPLx2Vbtf6anh9mZ+0R0wUA2RTIe5fq9ed69dI+KkkC1bc5++AHDI6PsnZzsGN7Q0DCariN9B0U6SKHgCxUlTNzp8SwHDx4K8BbZ7JbpXb50ETc/zxfPHGBitL+VzqxbNvlSHakYqJqK53nMPphB9SyS8c056x0MXjXAr2wno6P9fPWlE/z4wgM+eO9tLOsMw8PDJBLxFo7M9XzWSyWKpaBFemqXnPnjSL1h40pJfzJFMpFAIlnPFQKgZZh6sN2gxNmXLrliqcVaGzFNNE1lYTXX1UFOJuIk4lE8z6dcrVFr2JSrdUxdpzedIBGP7chG+1g5fyGECpwDpoH/U0r5sRBiUEq5CCClXBRCNJEio8DZtsPnws82n/PXgV8HGBnt/LpWswKQhBbkzX3f66iPd6UX7Kqh6IqC4/vouwD0NmQjV1MqlxkJm0is5Qsk4oktgJBmTqfesFnLl7Bsm1g0ymh/T8dZTdMgGY9SrtY5/sRJPv3oAz74+FNAsn96H47nBdzSsIHODAFwQThapVarhQ16uivix5mdIAALdtSe7HBgq3Y69Mx3C2nuJkbEpOHYLa9to61NoPh3O3d3lr7OY9rDiaqioIYvcCQSoVyroSoB77eqCE4eO4rruSwsLnHxyjUuXb7Kd7/3F/zVD3/Ey889y+uvvkIiEUOEJEQQIPNLlQoXLl3h2u179A4MYds2juNQLpcolytICQ3PZ3v27EB84NgTTzIyMsKj2VlWVlZYvDvLuUvXUESA8RDA+NgIa2trnL90la995ctMT0+1+A6aHAFb12r3Z6JJLd1B8PSYpnM6meKe/QjPa0utEbwTyucLPrTu6uYxNA25/+rX/zG/+T/9Fn/253/FnrExxsZG+P6bbzEzO4/jOHz5iy5PHDsacgIIDMNg7/geJvaMIYTgN/7Jr/Jb//Z/p+ZIyjWXuKkhhMT1glLbE088QaYnUEzxRAIFyK0sEYmYzD6cRRHBvb965TLFYpFatUotv8pAJsKp555ncu9efN/n8tXrXL5+i/zqMrBV+RPOyZXQ29/PWz/+EfVKheMnn+TQkaMkuil/ET7vYYpMCME//Ef/iMWFRd5/711s10Rzq3iqiSsMfAT7DxzguedfQFFVBgYH+eo3fj7EEW28w69+8Usd71s0EkFNxIkYTVp0EKqKlAaqU+Phhbe5fSnBg4PHef6FFzFCXpBYLAaKSt3sRfh2K52H9JGKSiaZ4ujx4x3vpd1ocOfObaSE3r5+ZvUoi+tFJkZ6iYR0wI+W1jh3cwZL6ji2HUQGFYWB3jR6N0++/YFvBqAeIyUrhODI/nEqNZsPL1zjo2qZM1/4EkeOHsGyHYqVWsAeG9bHRyMm8Ui3jqe7iy8llUqVQrVO3WqgAslElFKtRrFUJRYxGR3sw9A1PM8nX6qQK1dwPY/1QidbpiYE7qZIgBpyMySiQXMk1/PxPJ9KzSIWiTA22Nua807yWMo/DNmfFEJkgO8JIY7t8PNuV9ziLEgpfxv4bYATT5yUEPTHrtctfD9oSFO0bNadOs3bqyiCqKGTikfC7lzBuVRF7Wjo0G3SruthVarUCyVkKkEqGcNtOBi6gaYFD2KjVqdnaGCL4ofgZq4XKjgSDCGp1Wos5xQG2wAmQggGsz2BLTwywvEnn+LWlUvMPJyntzfLYH9f4Om351fDlYlEIkjfQ/o+WpPrvetmvws4Lowk+KHCFbC5nH1baf4sMAI2iHI+b3rZNCPYdafVq1r6suPeNIl4WpHjtnn6fsAg97gvXWuzCdel3rBbPQs0IVrYB4nB5N5xRoaGOHP6FDdu3ubsJ5/x1tvv8tbb7/DF115lfM8o6USCfKnEZ+cvcfXadYAwchP0Hp9bXMT1AyrR3ZR+u/hAurefZLaPI1KiIPFcj0q5zNLyMvl8jsWFec5/eh4hg1Kei5evMj25l97ebKs/g6apHfd9t1VqJ8NpKlqAx22j4EiPSKQd7CjCLI78PI9E17Fu/ncymeQ3/7t/xn//r/8Xfvt3/wBFESTTGaquJG6ofPzZeTKpFEcOBfl8z/MolyuUa9WQoEnj2JFDXLl+g6oHJcsNw6g+UgiuXLmCEAJVSDQBKqArPmY0hmkaFKo1/OUFfCnxPZuBvn5OPXea0089QSwa7eDC8IQgnuye+moXy7Ko12ooUpLt7cWMRGhYFrOzMzy4fx9N05iY2Iuqqi1MSSy+UVMPgnQmQz6Xo6GlSKZS9Pf3M7F3ktE9e1ooeuj+zrR/VioWqVeqVFZzFMtZfM+nZntUGz7okoju8+KBcao1m8+uneXNSpmvfv3nESJoUf6Lv/L3WVpaomFZJFPJluOSTCVJptKtVGnwucVPfvQG1aV7WJhMHjqG7/pETaMFbPNcn2KxxnK+ijk4zdPPPIPv+6wsL+H51e57QJvil36AXxFqFyu0y/MthODp41NETZX3ri2yOPeQ/qFhhKrRsIOUZzwWJZOIo+vt1UGfbwMUBF55NBphYTWHazvMzC8jkfSlU6QSGylHTVOJR0zqVgOrS35+s+IPFs4nbhphtZiCqSutxkXt7/hu8rnQ/lLKghDiHeBngWUhxHDo9Q8DK+HP5oA9bYeNAQs7nVcIqNXqWFYDKSWReIxcuYamqYymEui6iu24lKt1ytUq1VoNRdNJRA2ipkkyqm77AkgZcGYXy1VypQoSUOo2vgTpNBgIObmLxULQxnST1ysE1IoViqUqvqbRG48SMTSWcwXK9QaDdIqiCPqzQdpg7+QUCwuLzCwsMNCfZXhwgOZD1IS/2bbdai9ar1utzl9dFX+Ilm9/HJrZ8+Yxzbxu65jNa73jfQi+9UNQWAvQ9Tl3eUNXcf1OYpSmN9MchAhz1k2imXZR+BwYh03vZSwSadkSzVbOTcCMrunoCZ1IxCSTTnHk8EHu3L3PB2c/5p333sf3Jb7noqgqUdPkySeOcfTIYfKFArPzq1iOS93ZrPJ32xg2vheKQns8R1M1UoZBIpPZYDmU4Dg2xZrLR+eucOHqDXoTEQ4eOMD09D56MpmOhkE7L01Q5qmGkYW/TjCnkM+TTCZafBdN423D6Nrl8dj2B1vTOAADA/388//mn/Lf/ua/JmqYDPkSNZ6i6ghEqcbc4jLDQ0M07AbnLlzm5p17qKrCgX37eOWFZ/nSF17k+s3bxCIgCFrbejJgjdQUiaoKent6SKeTDA0OcPL4UaJmBMtuoKgajhuwfCIhEo2gCS2Ye5jvEEIEZDERk1qlvOsaWNUKp04/y8jISKsZz53797h17QrF9TWklCw9mgUgYOHwyQyM8soXXiUWj6MoCl/9+teRvqRer5PP5cj09NCTzX4u6nKAu3fuYJVWEX6g6NYKZR6tFIml+9g3dYCFWxdIxEyeO36AfLnC9fm5jvujGwYjo6O89847DA4NcejQoVaUoR3LZNXrvPWjN7jw0x+RTScpizg+V+hXLcYHe7AbDiv1AvceLfHprXksJYZTqXDl0mUOHT6EgkdvchsyKxniowS0vL9uq7+NMaCqKnuG+kjdX6VaLpLPF4gmU6iKQsTUScej6Lq66f3qvsbblQI23xFNVRnsyWDZDSIRk3ypSq1hE49H0doONCMGiXgUq+A8lp1hGgHxnKYGnCie5+/Yunc7eRy0fz/ghIo/CnwJ+F+BvwR+Ffit8O9fhIf8JfAtIcS/JQD87Qc+2e06TcWfSMbJVSx8JMlohGjEQBA0LjA0lZ5kDN9xcYSgWqmxWqmxrgS86pqiYmiCRCyGoWv4vqRYqVKu1oNaykScZNSkatlYVoOYrrW8xFKp3Koxtj2XerWOZTu4Mmgg4fg+MU0lGQ9aLyZicXKV7t3MVEWhNxN08hsaGqKwusR6roDruIiw0UsTbKKqKqqmtch2WkDDTQ9BoMT8Lcq8iaBX237cXqrXVbaxiju+f/xDO2RldZVKtUZfmPeTYYvjpjEhxcZfZNCCU0o/oI1lgyhnO5bBLaNpGn0tBdsxqRbosB2ZrmkayUSCaDRKIhZl39QE8wtL1Go16paFqRv09/fS39dLKpni/bOfULPqLUbETtlmhJLd3WsRlNs1yWh0KXn9K18NqjJEsALL83MszD3ks6u3ufdwnkP79zE9NUkqmdiVxtpzvZAdsosx+RhOjW03KBSKrQ6Xm6e3w+w3frdh722S7l6qAkxPTfI//w//is/OX+S9n35IRgi0WIJq3eeT85dZXl5FUQQzc4vkKnXwPUrlKrOPHvKV17/Ib/zarzLz6BHXrt9ibnGRoYER+vuyHNg3SToEpGqaSjwWwzRNHMfBMA0UTUHKWMd4mojcln0qYbCvl4FsDxXbwrKsVs64cyklgqAuf2lxgQMHD6IbBnajwdL8HKYCr3/hBQYHB6hUaziOQ61W49adeywsPeInP36T1770OtFYrJWGjMYC8qTd8Dndbqlt28zP3Mct51Cly1qhjBSQq1pkBkc5cOgwqwuPOH9rjn2jQ0hf4nkbhlDzhE2ukFwuALgGIMagGsiq15mdneHapYuI0jx9CYNbc2vs299LrJHj9ZdPMtCT4p1z15ldzAXpGVuiIVHra9y4cZ2HszPo0mFiuAvfgC830pnNaqHHtTw79tJgX5Cho6RrSquBjlCVIFKp7q7wd7K7mgaAaeroesB/kkpGWV7NU7eCFr3Nd0pVFJKJGKqm4oSspp70qdUbrZRd+z01DINoxAwB2GC7Lk7o6NiuS71hEzGMXY39x/H8h4HfC/P+CvAdKeW/F0J8BHxHCPFrwEPgV4LFkdeEEN8BrgMu8E93QvpDEL7xfZ+GD/VSFct26c8kiUWC1o2e1cApV0CA2ZsFXccEIrqG7bqUKzUqDRchGygCyjULRdWQvo/rugESOJ4gGjXRlIBpz7Xq9PYGnni9XsX3fRbX8uFCSzzXx5M+UqhoqkImGQtuUGgRxk2NXMndtmZbC8PNfX19ROMJCsUKhUKRZCIeRCN8D9/ziUSjIANyH8MwuoZtmoq/w7bbZtNWFEHTMGa733c5rkX+sgucSxCgYXP5AulkokV+IqXk0tXr/Nlf/HsU4NTJE61ymvZBbJT3ESjn5tskN9IU7dfaWTYZLJv+6ft+V6KmoIoBdKGRTqeJJxL09vbi2HZACqQqRMwImqbie15ABdto8OD+PYxIhMnJqYDnffM92LLGny9UiBDBZhqeRwCGGWFwdIzVpUXmZu7z8WcXWV5Z5eTxowwO9G/LGtckdurWNnrrhbtLtVanYTukkqkOQ2PnTWWTR7/TT7sNR4igy96RQ4yNDLO8usr9+zMoqiCeSFKq1rj14GEAllJ1hkdGWV9ZoVSzKNdX+MGP3+HpJ44xNNhPKplkZXWNqYlxYrEYsVgkwCqEZEtSSpywM5wahnk7omaeDHa8MFMnQz2YTqfJ9qRYuf+Ixfl5Jvft6zLXkEdBBO2Mm+cuFovUazXSyTjDw4MM9vcHpVkhW+He8T3MzS/w5jsfcPHiBc4893xrTEE/BaPbMnd8tNmuW15a4uqlS6zm8phCRVEMHq0UWFovkHcMMn0JerK9nDx9ho9+/H0+uXqX1XwR1CxLi0vEEwnOfvABqqYxOTWFZdVZWlygWCiQSqUQCDzXwbFq1HKLeKUcmnSI9o4xajToiXj0JkzGRnq5OTvHncUS6w0TRyr4Xo2YtED6CNcmlRpkff0RiZjZMQvpesFsAqT3Yyj+rXekKZ7n4jkuD+/fJzU4ylODT2MaGpqubrM3dir8Wr2BpqlB050dXobmd2poSOghvbsbKur23+mqSjIWxY8EQAbfl9Tqja4zMXUNTVVaODhFiJBLJjBq86UKQ309OzuAPB7a/zLwZJfP14EvbnPMvwH+zW7nbokI8/lmoPySUZNExGgpWt9xkI6D2gRgCIEKKHpgVRmaRsLxqNQsfOmjCIFPYCEZegwz7JakqgGatGHZxKIRDMMI6k8LRVwUVOkHoX9FaS2oUIINVG+72VLKoFzK9ykXy6Qy3buSeZ5HPJEgkUxRXl9mZW2NsbHhwLLz/JZF37Csloe2QToRLEx7qL/1XbtXLDuNhaBeu4utFf6k9chtYzzs5rAur6zywdlPuHz1OoYeNK2JxmNETJNbt+9Sq9f4ua/8DKeeeoJIxAyIhbY/XfBFm73Rrd78cUQS0NW2E1sIETQw6poLDTcPRVHQlSDi4jV7koebi+e6nL90lcXFJcxonMHhEXp6sqjN6Mzm07b+/TlBElsH1/I0jUgEMxIhEomQTCZ5NDvDo4UVHNvm+LEj7BkbDfAAbfnWVm/zFi/4TmPZfqzlSjVU/p2lrxs8EJsN37/JvMNjwz+GHpAm/YNv/gL/7v/5Q3L5HOMjQ7hSIVeuoehxkokE9XKe3nQU08iwtLLKg7klGrbDay+dYf++KfqyWRLxGIoiApptRQlR635AsCJk691rrl8TSAbQbnGHwSpisSiZTBpVmadULGy7nL4M3lSrYbVIWZLJJPFkisL6Iovz8/T2ZDsoxKORCIl4nDfffo8Hd24zMT7ByOhoqwNex2DY+lFQhWKRz+Uol0tUyhUW5x5SWn6E9ASK9IibKlOj/diOy/pynWgsFrTxHR5lZN8Rrs5cpuYqRNMBG6HrOCwtLeI4LtX8CnalgFKvUq2vImIRUrEopiqIq4LpkRj+oMnHtxdxtQhDacnLR0eY3tOPrijcmF2joacYGRunUiph5x4xmYkhVI0ri1YQZZUKl2fWWCl8zBeePkxPMt7a9zdjhP46YjseLoJyrUq1XEZBoiltqeNNfPiibUO0bBvPc9G0z79PeWHXvW6jD7AoIihaCJW/ss1Wrm4yOlRFRQ/3gGw6Sa3eoFSpkUluw0oZyt8Khj+BCEBVagDiayrFIPcsAyWfTqFsqrlsAtJMw8DQJYamhexlwUuqaYGX3+Q3DpSuR6PRIJvNBpa/6+A4Dj2ZDLoighdRKGFkqXvuvalUDEMnX7WIJuOt/thNcX0vJA/RiESjrLsuhVKpxeLneV4I8gvzkabR5nUH9vtmxd9Sakrbdr45mrvLi9Hk9t/RYu1GJkSg+N//6BM++OhjDE0jmjUplkvk8gUsu0FfJsXP/syrnDh6lJ5MCiEUtiYqulyPTp6/bcfWzdsJa87dZk+EZu4RiSq6v2gd1xailT2QmzzoW3fvceHKDcxED3vH9zI0OkYqmXyMksi/2eYUjGvjvyWgmxH6BocxY3HmZh6wvPAI+/I1GrbNgX1TNNoonAOGwQ0io25Sr1vkCwV0TaO/v2/jWlJStyyq1RqzMw+wLItMOr3RhKhDuin7v64B0P2Y/fumeO0LL/Hd7/0l6/k8hw4cIJ5IsFqsYlkNPMdmYnKMY4cPM/Nojg8+vcD86jrnLl1mYs8YibBczHE9dE1DC9t9O66DJKy0aCl+OhV/h2EaGItCBs9VLBLD0I2A/2CbaUzvP8D9O7eo1+ut9FM0FmNy3z5u1srcuPOAZCrD/ump8PqSRqPBrTt38SVke3qIxmKPrexc12V25gFzD+5hlfMBiMyycCt5dNkgKnQG0iaHJwZJxaPcmVtHj6bIZoN0pxmJcPj4CT5cXcKnzJOnTpPOZKjXgvSm5lawVtc4MTXM6MAImirQNZWooaOErWRjEYPLN2dZr7lUyo8YikqOTT7F6HA/ruui4OM6DuV8DsWpsG8gwdMHRqhaDvcWr1NfX0SXDqu5Buv5PJPDvfQcmnzMMP/Osrpe4tbMHIsredZLVSRawH7q+234gW7vTFs0KASjf168BQTgPoWAjndn7pbwwdu0dUYMA89zsWwnIGVTFRRFRVUD51RRgmhTIhahVK5imTunBf92KH8RcIXLML8hhMBv2NjlchASSSTQttR8bj1HZNNkNwMymm16VVUNFK+UlEsl4vE4qXi0K1hwO1GEQk8qwVqxQq5YJhGNEosYrbHU640AiOF5YQvXNprdNgR2O6FPe9hxq+LfmOeWuW83yG32YV9K1J2Uf3Ojazs4Xyjy2YVLfHruAh0y91kAACAASURBVD2ZNK+98iK92R6qtRr1ukWlVmNsaIDpA/vR1KAu3afTC+8GkGmq/ccqPNvmJ0EZmkSEFR/NBkatuex22iaIrc0EmZtf4NzFq9RdyYEjBxkbH9+2r8Su8jcMBDRPgaqSyvQwMa2hqCoLj2a4cPkasViMPaMjSD9gxFTCksXtNifHcZl9NMdn5y7Q15tlYs8IDcelVqtTrdXI5QuUyhUWF+Yplcv0ZDJt3mloOIbPZOcl/uZGT/spcoUC0UiEYqmE6zqsruZ46ble9k9nuHD1Fo+WVlAFWHWLaMTk2VMnyefzXLx1n/uz89y4dYcnTxxrUftqzVC/G1QBKGGED5qKv/09o0tUKsQwSIjFYkSjJpVyCce2uzb+GR0b48HdO0HfibbNfnBomEI+z92b1/ns4hWK5TKpZALHcVleWeXWvRmEbnL85FP0ZLPd0zquS7FUpJAvoCgKyWQS27a5evEc9eUZeqIaOA4T2TSJvh4qlSq5YpXj+0Y4OrWHa/fneZirkxzZQ19fYPypqkpf/wBHTz1LpVTmwMGDqJpGvV5HSEnEr2PgcHBPH0f3j6O2kUVZDZsHj5Z5tLjK/fkVLBdWl+YZ3tODqQYeu2kaHJsaIX/lIavLD5gaSvPU/nH2jg5QqVm8eGwcy/HRRA+qplC1bFKxaODxN0OS7aVLj/u4+cFeeunmDJfvzlGpWTR8FdVI4ktoOB6a4yG8gB7a130iutGKFrW6gBI0y/F2UgxbZOPl1zWNZDxKxDS6lu12GXaH6IaOIVWshh0YUqGxryoKDdvBcT2K5SqJqImmKuRKlY7Wv5vlb4XyB/AbNk6hhBIx0JOJAJDhBMxWW7XwTrvpRivfzevrul7YujOgz3Qcm3qtRt82zSw2jt96PUURpOIxHDvoWW47LkIkiZoBW165UsN1vaAmvFQiEYsw2N+HIFS+offYbOTTKqsLr9OO6m+WzLU/hDtJMx2w3RLJNqXc/Gnz8+bfzUrzzr37fHb+Ioau8TOvvcKpp55A0/S244P/b6JOd0svdIy3GW5rS00Idj+06fX7fpOGs9mqdyNtsnXu25xTBH0TPCkplct8eu48a4UyE/sPMzI2+jkV/6ar/EfQiU3xgVgiycS+aSTw8P4dPjl3gZ50ip5MptXru4XyF6JjOL7vs7S8xAcffcxPPzxLf2+WdCqO6wVK0fUCT8g0DKKxGM89+zRjoyNtOf+NO9N9C9y42OeOC3RG/vnswiWikQjnLlyiVrPQNY2llRXG94xx/NA01VqNpZU17s48QjMMnjv9FKefOslqrsDiap7zl68xNTlBLEydCMB2HKT0g5a9bdwhHR5/q6Kvs+9E+zDT6STZTIq1mXnK5fIWRjsIsAHDIyOkerIdZCuapjE4NEypWGRx7iG5C1dJJWI4rkelWsWMJpkaGWd8YmLL++66LsVigdzaGsvzD8mtLKBpOvGe/5+69w6SJLvvOz8vM8u77qr2Pe17enr8zizWzDqsxe4C4JIwBIgDBZKiGORJOp4uKIbuQhH3j0KKECVSPIg8GihIEAeaJWEEQwJYg/VYM952j+mZnp72rqq6fKW7PzKzfLWZ3QWHP2Cnursyn8l87/3899eBpukk5qfZ12nleqeyOTqiYXweN5M35kikMoT9XrL5PEvxDHjDdPT2VaQVWnsxEongUlxWaVi1yMz16xi6ipPcuZJIMT2/TGskSNQuQZvO5Hnr1CSLqwkMwOsNY5oCt1spm8hNk71DvSwsx4n6BAd3dzPc24GiSERCPh66awxd01EUBVmRSaUzlkJlGBi2YlSd6lxeTI7C4qRm290BVibZajzFxPVbZPMaCAnJDkROpVMsLi/T69qF12OBfRmGtRcMrFox5RVrlb8u5gtIQmJbxWVrVnu44lk3p8Y7S1UtjV9VNbL5sqLsuHtMIKVlS6iFqUyuFDDYiO4c5l9U0XM5hCJjmiD7vPh83m2aVmqCjBppu/aBaJhmCcoxlU4j2Rj+DiNpzDSajyEaDlLMF8kUiqxvpGkJBtA1jVxRpVgsMj87Sy6VYGywl8F+KwPSNAxcbrflB7Q1VScITWBXv6Ps4y9BwdYC/zQ5XZ0F4XxWXl5i8Lbp31k0UOl0qG40nc4wMXmF5MYGjz78AHcfPYxsF1CqfUR1B+ZWr8+eg/WeN2f3jb51kOtEZXqg/Q4bXd+sdWfc2WyOM+cucvX6DH2jexkZ3W2ZXrdDpQ4/AG7f5FE4s/L6AwyM7Kao6Vw5f4ZYy1k++vCDCEnC0C2scKDKAmAC6/E47504xbvHT5Ir5FmNx7l05SqRUJjx8d0cHB0hFm0l1tpKR0c7bdFWPB6ntLI9hpKU1iieQzT4qdG3zb8UJqSzWU6cOkOhqFrBegE/yUSSd947SWdHO3cdOkgmmyW5sUG6oHPh8nUKxSJPffQhjh7azw9efoOF5VVuzswyOjyET5Ks/WY6uPzOFKoZf+V3teRcCiYBv59oSwRTv8n6+npD5t/Z3c3huz9ixQjVuE0iLS2Mjo0RCkdIp9MU8jlcskzXwAhtbe1EG6DkAawsLXHt8gTzN6dwqSm6Qgpq3uTahVl02YsPw0oRdHnw+iXm1rPoRoar8wl0U3D2+hIe1xrLG0XaukfZ1ddnxbvoOtlsBkVWmJyYZG11hV19/eRzWWYmziAZOqrkQTENLlyZ4ebcMuMjvRzaM0TQ77WUoaCPgN/D1MwSHsnA45IpaCahcMQqFW6C260w0t/FmCzojEasMue2cqOUyp4LMAxCAV8pZqjEyc1q9l9iy8K01o+t6GqGzur6BslMnpWNDDfm1kjmdUs5cax8pkkmnSYZj9PR2UnQ58HjcSEJiYKqks3naYtUxnNVrHvTwDStsW7GopqlA272feU6q6RycaDKa6vFUxPIFVVydj2B2tK/lXRHMH9JklBCAVxhSyra6oFtfqTXsjvrd02zzO8ejwdJklE1jXQqjcvlQlVVNM1C31MUedsvSwgBskx3dztL8Q02UmkK9gtSNY3FxUVuTV/HLcGu7k5CwWAp91wSwkJuq8D7L81AEgjDRDdK5XWABmbzkqZcXsyVz7QuehnLrGmalklecuaAZU4zMUuQqJbLyWrv8rVrzMzOsaunm/37xpFkGQdMyERUFemRJasdwxl7BXeoGosTd7CJ2Xizt+xMVTds/3ZDRrk9i7tzuOi6zsWJSV598x1aOnrYe/AQPq+/5spNWmv41U5t/o40tHUTPp+fkd1jzN2a4d2TZ+jq7EDIEj6Pl56uDop2USlnLeSyWU6eOct7J88Q8Pu59yNH6Ozs5JVX36BQKLJvfIxHHjhGWyxat3bqprrF9zucbfl30yRfKDBx5YotsIMQJoMDfSx43BTzBZZX1pAkwb49oySSG8wvrTK3vMbFKzcI+/08+shDvH38BOvJHItLSwwN9FmuRCfKWojSO3e2VyklcXNTUwWjkvHY8QOJeJyNZJJ8Pm9V57OhgaEevdQhl8tFe0cn7R0VSCFbLBVd15m8cJ5bVy8i8glG+9t48Mg4+YJK6q3zLBXy5CQ/71xdwR/MoygKijeArMi4Okbwuz2s6CpGtoAvHKOzt5+grYkWCgWuXrlCW6yNZCLBysoyqyvLCEOnzdxAmAYpKUxRuElm80gSrMfTrK4nCfq8RCMhPvvMA6zGUyysvIZYi6MWcrjdnZiGCabjVxf0d7chy7IVXW/P2xFPS4q7bp1psiI1fimWdmQLb45gIDAxyOaKLCdSvHvxBtMrGYTbjy4pmKEuCpkUpq6BCZLbS1dXD729feQLRVa1BD6/F7fsQlYkiqpOOpfDZbsxnTNO1Q1UzUBzGfb5LcoaONWxYg2HXsVDGr/roqpZeAaNqME6KSl7wrF62/VCNlnQdwTzrz1IPoAzxWkJwDbxW0VeWltbMUyTTC6LEFYO/+rqGoZhWOAvLZFN/aV1Pmv7D13RCLlCkaIdABRfX+PEe+9QSCe5/+hh+vt2WWMpqxjoqtowV9uR5jRNByzmZtpM1TSpG5u1VwybmVsajtLgcBa2lGrvG1sqNEv3lVHfzZKEresGFy5OkEwmOHxgnL6engrJlJLJqer9Iaw61YaBUUrcr2Zot2PRqZqLY+Izzboc9Oo5b6cby3Uwv7DIj156DR2F3l39pdiFHbZ2OyPY/PpmYxBWRsvI2B5OvLXIN7/39xSLGj6vh1/+4i/Q0R6jaKeQGprG2fMXefud4yiKwnOfeIYH778XTdc5fGAff/Jnf8EPfvQyCwuL/Pynfob2tvZ6mOuK3itjOeq+fH+zZX5xiQsXJ6zAWFtQloDHHnmQq1evc/7CJVoiIZ549BE+9TPPcmXqOv/wwo9ZWk9x/uIlRkeH6d/Vy1ryGplcrjRW0xpwlaZvmjVjqBB6q8iodpXJkoTX68ElSywtLhCPrzM9Pc3efft46OFH6m5XVdWKErcr1zVa/1vVairk82hqHrQCmAbrqRwzi2uM9HXw8Qf28/dvnGY1V6DobWVw3xG6urrp6OzEHwhU9ZfNZHArCh6vB6cGlaapLC0uEl+3kCYBXGgIdEu1EALFLKDixpRkdvd3c3BsgJZwwFI+TCgWNa7PLHLX+CBTL7xNi9/LsYMjVhql7ATigtuOzapKd3N+tAUsoVSn3RmGgaYbJQTQymwsxyplGCbprMqpyWnevjhLwdtC1+hRBoaGUIBcPselS5dYWV5G0zV6O7pp6+xAsuNZVN1ATVVgt5hUlciVwXJDY7k3XbKEx+3CZWedKZKEbhh43e5S6l2tYlfS1M1SF+VgbnvtGYbJejJVF+hvGRUFoqKujcXgLaVLVmRcioUC6pZl67NhYSKL7gjmX02NT5GtrQHNSdM0VE1DtiFSVVUllUwSDofIZrMUbRNJoVgkm80RDAZoZs5pFCnvLGK/xwLxyOVyvPTCCxSzScYG+xkbGSTW2moH/FHy9+uGgbemwlqlGVI3DFyK9RKdoD9TVHBeqpmobpqYmlpGp7IfpfNErc/yvKxypnZlQdMoXVNJSysr3JqbJxIOMtjfZwkrAjDtbAysGIuGPKuiQUHjDILNqeL6mmVRxiVwvq7YTdQwpYqmavNrHYonknzzu38PsoeOrm5S6bQ9vwZ0O4zuNpnjVuRyuxkYGubK5CTrCzfxeH0UVZWLExN0tD9UCgK8MX2T19/8CfFEkqcef5QH778XsDTYvl29/F+/9Zt85Wt/w9vvniCZ3OBLX/w83Z2dKBWMqlZDr0wnrOOg1O6RBvu35t04FAmF+MTTT/GVr/0lXqlIRhPIbpm9Y2OMDA3ygx+9xGtvvI3XLuo0vnsUWZL59vd+QCav8s7xUywsLaMZJonERjkOxRmEZBdxMszygVpaPuV1VGd5kkUZqloIhCyhaUVWVtZRTas+wUYyWfeONE3jxtQUa2urdHV10dbegc/nqxYCtnG+CSHYe+goulBYmL7KrY0s88evMXxjjp9/+n4+/vBdPP/DdzCMAJqmMTg83FDI8AcCKIqNYqdZpnCXy017RwdnTp1CABIaQdIEvG4SagDF1PAYRdxSkY6wn8GednraWq3npVnwyR5Z5r4Do2iGzvdfPYluGgz2tFvIhY4W20CucoQ8EAjJwkYwNK105hmGQTqdY2U9TSqXR5EFoYCPlqCfgN9jWxCsSnmnJ2Z47dI8LQMHeOiee4jGokhCIiIEbp+Hh+67h+e/8Q1uXJ9mz54xxsZ2U1D1OvN5I9IBHB+6CUVTp6jpQL7qOkUIvD4vwYDPqg9QMW3DMKy5mlbgrRBWae1sLo+hG2imhdNf6auX7PXmcUlWqXmXC0WS7BLIErIskG3VTZKtIFajxKfucM2/mkq6BbdzWtYeMlb6jAXkEQqHrOINhTxul8sqlQoYhlWkJdYWs3P5NxldE8afyxdIbKRIp1N8+5vfRMvn8LslujvbS7XYaw9M22NUbsv+n4GNZGVL3E7etkCAYQkApQh1UTb3O4hVtZYLUfNZOQ9HALD6rybDMHj5tTdYWl7m2L0fYdeuXVS6CJzrm8W9lCKn+QD4XgPG7wS1OYN3yvHKwo7hEPWuBuvTerbOriyqKq+++RbLa3F+7gu/RCAY5PrUVE1xpUr/xQ7Xpnkb9zRqosl3siyze2wPby/OIxAYQuatd47THmtj/97dzM8v8O3v/gPTM7f4+NNP8dTjH626XwhBMBDif/+NX+X7g3385d98k9//gz/in33xC+wbG8Ntw4mWx1L2m5efaWWN+/p4i4Z7qqHAKGiLRZmdtxDBTcmFIluC6fd+8CJH7zpId3c316amefmVNwgFAjxw7D52Dw/y4P338MIrb3B9Zh4kGcM0mV9cRi8d2Gap28oD2RIEjGotTVSvX2uRO3O2ZmjBREdYXd8g4A/xqc98tip4Dqw9dGNqiuNvvwV6kcvnTuMLhRnff5Cx8b2lUuXbWRo+v49d/f20tbdz8kSYixcuYOp5sprGzfkVVuIpdNkFpsnVK1d46JHye6621FiWLkWScMkW6IxhWGVlDbWAXxQJSQVMtcAvPvMQf/n910kZbjRkXHqRBw4dZPdAj5UPX6FomJoVRGfoBtGWIDfml8jli2UZz37wTuCgldEE6VyexeU4pilQFIlcoUB8I0MmVyBXVJlZjJMqAG4fQnZZOD+6iqQXafHLtEX8YAoSmRwLKZ3Wgb08+fTTCKwAu/bWcJUpPhQIEPB78brdRAJ+EBKZQpF8oVBdPW8zgbbh+7Ku0UyTdDZH2rYaKLJspXTb7yAcCLCyssobbx9n9/Agdx85SKrCwiBJUim11jRNokF/FWBaNR5M9fmmazqGDewlN0EpdOgOZP4WVUZtVv9tc5Nx7VeaoaPpGrIk4XG5KRaLpDc2iMas3NZgMEAg4LfBgTavCtjM+qAbOreWVgH4u7/5G0ytSMDrQpEFvT09FVC3FlOXFCu/lDqTUJkRC1kqfS0hUHUDTbOwn4Wd12kVQLI3UqlWu1KzIJprFCU/vS191mq6ly5f4dy5C/R1d3LXwX2EI+FS0FRVxk0TC0l505ej8G+H/zWxBQH2xqpgQGC7QISwgoyq1pBZDiDCRDYFqqpy4vRZzly8yr2PPEkgGEQIwcjoaE1/osnP26XbZfyOvab597IsMzI2xsTEJRYXFogGPCDJTF67ykY6xQsvvsKN6Zv83HMf55GH7rfQCaubB2Glr/7Ms08zPDDAf/79P+DL//2P+LVf+SWO3HUIr7d8TwkJ0gTDsBAQ44kk7bEorS0tW852O7LQwtJy2Upjlw/UDZ33TpwC06At1sLSygo/fPFl2tvb2b17mHuOHuTG9E2u3phB8vjYu2eQheuXqxsW5fVqYKAWihSLKguLy+zZs7varVE+cOxAsrKvwDShp6OTe+86zMzsAkKS6hi/aZpcu3qVd998nVjIxxOPPoGqa5w4cYpb168QDIUYHhnd2gVWelbl6/yBAGN7xnG7ZK6cO8G3Xz1JZ2sIWS/iEll277u/qolioVBKRxTCjmgvFjF1jXg8ydUrk1w+8x5RM41HGHzioaN8++V38SoyB0b7ODN5A0038Ps8+DwuJEW2hCZNLwl+kiSBIiG5FP7Fp5/gxIUpm6k51kaBquosriaIb2SYXVpjanaFtXQeQ5IwUdCR0IVVrthAxuMP0NZziNGuTrq7ewgGgxiaRi6XYWVlhYW5OS4nE8iyRLBlFyOjbYzvtyot9nS2NazM9/nPf45cPk8+V8AwTNwemXZf2IIoLhRJ5wsUiipgVggD29m/ja+pjbh3uRTyhQILS8u0t5UDOxVZpiUcIujzkctmyeRyxFqjpUym5EYKxaXg87hJpTO4XAo+r9dyC5smmqaSzmQJh4IUNY1iQzjyMt2RzL90iBuWj0czdTRdR5FkfDYq33Z8xqZpUsgVEFglKXVDJ1csWOb/CnO7ha60dS37Zl1uZLLlcRsGIY/MM08+ysF943b50XI/QljogxJARQ5mJYhP+XoJVdPRsDeYJOFyySUft3OPY7KsZfxWI3VPpe6P+Xyeb3//h+TzeVKZNOlMjqWlZRYXF4lGI9x337MMDg2X767tQjTwa5mVtgFR9bFTssUbS2MwLQau6xZoSzl7oRpOyDCtynnOYK2AyOp2NU1jfnGJl157C9xexsbHy3O4/eHWkDWG229vq7sEpmmQTqVYWloi4HUhSSYg8dZPjpPOpFiLJ3ju2ad5+MH7aY1ESs+zZP2obE0IDuzfy//5f/xr/uPv/D5ryQSJZJLCSpHllVVWlpdYW1tjLZkinkiyML9EvpAn4PPyq//8S9xz5K7tzWgLWfCeI3fxyutvYxg5tLyOrMi2+VOmf9cu7jlyiI10hj/72l/xtb/6W/7Vr/8K7W1t/PIXP89/+C//jUQmzcLCAn6fB6/Ha6dw2ftNEhSKRdbX1pmbm2N9LUlLtLUkkVgBavWaVcVDAqy8a3/Qh8/nJdpdH9iXSad569Uf09Ua4pmPPU5XZweyopBMbHDy/CQL8/P09PRunU1SM4REIkF//wCxo22kNjZwKW7OnXqX6XgGUw6ge1uIxSqAm7Bqbng9HlrswlAAqUyGK5cucObEO4SlAoc7/Dx6/8P0dMbIpHPcNT5MIBBgfKiXietzfPFjx+jtjCELgWEzfUmWkVxSRU8WtYatoMdEOks+X2BhJcn5aze5Mb9OoggFQ0aX3GgiQKC1i6GREYLBEKGQ9V8gEMCzSXptMBKhvauHfQcP160jlyTR0xGrElhryaW4UGUNVVVLAExgF2ZrDduCrVHiNU51PdMuvoVh4e/rpVgEA4/LhUeRyRSKKIqErpug66imiaZZpbmhuhS2VdvDil8I+n2Yus4ff/Uvefn1t5FliVAwyG/9y1/lwPhuvvyVv0A3TBYWl1hbj+P3+fjcz36cJx99gAuTV/na899mcXmFUDBIW7R10xx/uEOZv6prJROJbuhspLKkMnk8Hi9tLUE8Ncy7GRmmYQU8udx4PF5yuRyZVKppGs3tUjpj+XzeeuMNNE3DUGQG+nbh91Vj2zvM39R1hMtVQg5txPjBWiSGYYIscLtcpbzkUnu2W0A39CYIbFB1xDbg3KZpMjM3zze+9R38AR9uWUJRPMRiLRy7726OHDzI/r3jVe1XjrSxTmqWkP0+EJM/lqlQYMco6LqN2lgWCxotc0tZc8ZRjV0gC0GmWOTCpQl00yxhKTi0tfNpu+xcVPz7fqh5f8VikZdefAFMk2y+iCLcnHz7LXKGFe37uc88xzNPPka0tRVd1y1o6pLhu3Gb2UIBE5Pvfu+HfONb36VYtJAwDcNAVmT8gQBt0SgjI8MIyeTMmfPkcvkGLTl91Pcltrjmt3/zN/jdP/hjVC1LOm+57gJehe7uDoaGBlBkGcP4PH/4J3/GV776dX75n32Bl195Ax2J3t5uDF0n6HLh9njIZLP88MUfM7+0zJHDBxno38X5S5O89PIrFPIqHR0x9uweIRIOV7kEgGq/ln3wWxMQeDxuoq0R8rn6Il8Xzp/DLcFzn3iGtrZoKSJ8z+5R1uNJrs/Nc/PmNON79zV8B7VPx/mts6ur9FswFOLIPfcwOjbG8vIyQgi6e3oIhkJ1sRrnz5+js6OT3l27UFWVyxfPMzNxlnalwOHdPTz9UBnFPRDw8dxjHwHTJBqygvoy2bzlanO7ELLcYOVYAtb6+ga3ltYAeOPUZc7eTKAJBeFrpav/CAd7e2lpaSUSiWw/jXYTqhyHW5bpbo/i8dSDLlWSLEuEQgEMwyy5hYSw0GIdqjyXTV23rIyShCJEVTCsAwfs1HDwejxNFUXDsFBf5QqQgJDfh9/jJpsr8NZ7pzh55gK/83//NoP9ffyPrz/P//MnX+WPf/c/IEkSc/Pz/PMvfpZYS4Tn/+ffM3ltCkWRuX7zFl0d7fynf/9b5PIFfue//+mWz+2OY/4WApdhaW42rn7A68HtQHOikdd1FNlHM9Q4h7KZnGXu93jQdJ2CWrTSX7aHzrBt0u3DoLunm/j6Gj4sMIZ6LdyCBdZ1HQc6wsoXpQ4G1wKa0FFckqXR00ALEZSjZBt9j63xOn3VK3oYpsn09E0UReHf/KvfYN/esSrfrSOVQsNYHXus1ay3SjjYzEJjNh5Tg8vsA7mMJ+/ENVQG/jVrxzQsN0U50FGQSG7w+k/e4cS5SQzZg6aqXL18mdGxsYbxEvW0M3ZeNb7bMQNs8qAURWHfvn28+8YrrMWTTE7Oo5sSHW1R/tdf+2WOHjpIKGwhwOmGjmzadSQ2dSdAd1cXLa2ttLdF6epoZ1dvD709XURCIWTFqpVh6AY3b93i9OlzZNPpBq0466/K3lD+NE1Le9I0ax3Zlp3T5y6wsLhIOp1BLRSRkVCFQNVM3jt5Bl3T+ehDxxgeHODnP/0czz//t/y3P0jhD7WSLhRZT97A41bIel3Mzs5x8vRZLl25hmrC4sqrSEKg6SZF083cyjIr8TjP/+23+KUvfQG3q2weL83C/tmwq8o5/mufz0dXZzvnLt0gk05Xmf4nzp/jwNgg4XCwYk9BOByyg4ppvqmqnmD1b5VbSmDthZZolEhra2mslWOfmZnh9IkTJFcXWLh6jlNY50lEUXlgJMqDR8YIR4LWWOzAvFIariQRbg3xmSeP0R6NoLgr0t6EVeRmdT3JWiLN9NwKMysJ4nmTRLpAUhWsSVGOHvsYkXALvX19NlJkZSrcbboDa3UarEJvHe1RPC7X5ucOVDDucgnx2t0gyRKGbhX6kvWysqk7OBrCPhv16piKklvJMOoyZiRJqk/tts+zoqqyuLxCb08Xu0eGEQIee+h+fvTKm6QzGUwTDh0YZ2Swn1hrK91dnSSTKZIbKcBksK+XUNAqA713bJiJK9c3fQZ3HPMHK1DB0EyEYR/qsoSVElq/U5q9Y92wMPz9/gBer5dMJkMhn7ck+y0Wxk4zC4o28x8cGmZqsm8NxAAAIABJREFUagptI9tQk3eadJilVWioGlfaIVXV7HvKgD3NaMuhNrbwWmPRdU6cOYff56avvxdXg41T4RYu+csrte5qS0CDt1TTcaULc7Ox10L0WuZY6gIaJSFBg3LHjfoXtu/sxJlzXLhyg+E9+zh89G7SqRRnz5xmdGys2WC2Yw5oSqLpL++fTMNgfX2dcxcn0As5WkJBfuvf/jv27xvH7/dZpk1bSFJVC2paUZSSL7ERHTl4gP179ti+XCudykldcp6zJAQalg9T1w02knEymUx5mqL6kK/cE+UgVhvnwy6+JYSEkCTGRkdwe9xMTc9YqauGjqyDZkC+qHPm/HkuTkyimybFYpHWtk4uX7xA3pQRWpGg14MSbuHwvr08/+3voao6uP10d3QiKzJrq2tkEgkUr49dA4Pk0ilW43GmpqbYO7638XPWbTHG2pAIIBQMsmd0hLPnL/HeO+/w2JNPlq7XdR2/z4eoiSVKpdK2z9ZDsAIT4LZIlPdHozPi9VdfYWHqAnJ+g5ChcnR8gELRgjceGxhmtK8Lt0tG2AHGyPWYGQJB/652hBDkcwVW1jeYXljmxrwFFpTXJTQhITxBwu3jjHZ1EQgEGblrliNH7y5ZIWoZ4fudt0MS4PW4aIu14N0G46/cwALK2VENyPmu8hqXq7LIlXVKGXZlWtMwS4egaVqgYWCjs5baEFWHpLBrybgUF9HWFt589wQra2u0x6Kcn7hCMOAn4Pdb19igdCW0VwGRcIhUJsPFy1e5PHUDtahy8uzFOstzLd2RzN+juJCFgW7Yvm5kQLb9LCauJoF5lUw7l8vbB5zLktQ0FUkI2+S5OTltbFsI0C1GLcsyxUKBkM9nS3f1Pm+nPUfa0zQNxV1tstc0K9XDZVcirJojFTzIDjwSmyzeit7rKJ/P883v/j3nz1/i4QfvoyXcuDphZb/Wum7cYiPGb/neqT6kNuvAJt2wIodlIZWU3jKIRbVGZoEmOcWbjDoXQOX1uVyOK1PXOX1+ktb2Lg4fvRu3201rNMqxBx9qOv+qQYsGA/6wqUlXxWKR6evXmZ+bQ9cN9u7bT0vIx12HD+LzekFYwV4XLk0wc2uOkaEh9oyNoKkq2WzO3hNKqWqcZEtlsqLgtyFpdV2v0s4r37EkBC7FhRDW+vcHypXETNtnCtY6KL9D68Cj9j0alsVPLxQIBfwc2DNGZ6yNcCTE8ZOnuTBxmWQqg6qbbGR1TDQE1jqeXVwmr5nIbsGTjz9JMBBgfnkFX6iFvoEhdg0O4vF6kWQZtVjk4oULJBJnaO/o4OjRuzl75hR6Nk6HDbojZFGu6GdUCKIO47eHrigyne0xDh/Yy/Gzk9yYmiqV+I22d7C6tm4L++X1MrewwOLyMj5/mFZbW/8gyTRNspkMlycnmLkxxWgInnj0XsIBLz6fpwS241LkaoyMCo3eoWwmz82FFZbXEtyYXyOZ08gZMqpw4QrEiI320N7RSTAUIhSOIMkysmzFZ/QNDFpm9Jo9W0fvcxu5XAqRYBCfy920sUKhSKFQtFLpbH++g91gUGbukiQhyQIFCSSBLCzl0y07AHD17Quw9o8dfGy5Jg1kWcbn81ZfCCX3rappfON7P+QHL7+OJEnsHhnkFz71SU6du8i//0+/R2skwvLqGl/6/KdQZNmKIQDArOBRJt1d7QwP9pHN5vnjr/41rZEwvd1dJJIbmz63O475lzUFSovGVVmAg+ZrpfK95HN5gsEQHo+HdCaNqqqlSO7tj6Xx3x2hwDStanKVlMtmCQWdQJM6g13pb6ZpgiRh2EEglXNz4ucMw8CsqEpY36J1wNaV+6xp0GGelc8uncnw0iuv8/Irr3PkrgN89tPPNZDMa72N9eR8a/kzRUPmWycANDL3V/xi2Iy/smeT8tqoXQPVf5eQMBvCWuqqzvSNG5w5exbZG2T/ocN4PB4rJk8S5bSrbdHW63E7tHODQvVViqLQZxcdWl24xcjgLp5+7KO231Ewv7jI6z95l9m5RcJBP4P9fQjA7fHgMgwMQ0ezC1CVfJyidt1ajF1RlIYlkj0eD263h2y+SDKZxO1ScHu85PN51KJKJpdnaXmJq1M3SiZK+/+loKpSX1JZQDRNCxzn2L0fweVyMTI0wMzsPOvxJG47jSmfL7C2soymFvD5ffz2v/nXRFsivPGTdwBw+/wMjI4SCodLArnH42FoeJhQKER7RwehUIi+/gEmzq1wc3aOcCiAW6oMNqswnYnqxyOEKFXZFAhaKpi51+cjk03Y8xOk0hkuX73GxYkrFA1B/67+ap933QLY/uoyDINsxoqAn705TXxlATW1yi6Pwb17R+jpilqAL7bFosw9rA/d0InH08wsrpJM5UhnsizFU2gopDWJnCbwRzoJD8TY1dZOpDWG1+/H5XKXqtxJNXEAtZDG9bT1/OpPoPrrJUnC5SoHO5umSdGGV08XVArFIqZRLpTmrL2qCGBR/kHUKiqirHjIklW9ULKhiF2OsCM5P0vINcJOJUy787z7d/Xw8889S9Jh0ELQEgnT1dHGr3/pF5hdWCSby9Hb3cWu7i4kSfDpTzyF32eVfAZ4/KH7KRRUujrbME2DJx45RigUJJ3J8Fff/B5tsc1j2+445l9JAuvFyg2ZUnPK5wsgSch2Sp2mWhWQGqMd7fT4dnLrrd+KFSU9UxsbaGqRaGtHVf3zujkJKzBGFgK1wl/ujEISAkWWrEqAktG0tDAV1zftrKJd53MjleLNt9/lhZdfZbBvF5/52U8Sqw2CbBAc2LjvUhUU+8MywdeSo/lJzkaq7ari0/IDmjYDKKMaipr5NJyy7UOUhagqjiQQzMzNcer8JDld5tDRo7RG6/HYd0rNRaWd37+9c9/6o67raKqKxy5aE45E8PkDZNIZZmbnEZJgPR5ncvIKN+aWMQydsdEh2mKtFItFO7fbJBwKIsn2UzLNhjvLEkht14qtNTlaupUeVUDVNE6ePsvMrTm8Hg9DI4Osx5Nomomqa2QyWTLplFU4CMvMWRIAbIFDlgS6ruN2u1BMHQEYQuaNt0+gG9Z4c7k8RVWzKxdah36sNUIuk6Z/sJ9Lk1eR0Umk0ghZYmlluVTnwJmNJAlibW20tLTgcrsRQtA/MMiZ4+/y+ltv0xoOMDRoZbeUgFJKjL/6pei6TjKZYmp6hvbuHkIV1rNQKMT8ygLJVIqNdIr3TpxmZm4RFDcjY+P0Dw7VYQuUXr9pks/lWFtbo9Uu7VvrK06nUszcvMnc7AwSJsVcBjOXwm3k2NMWZGz/KK2RANFIsA6rAfs9ptI5bi2uMrO0zmIiR1qTMGUvkjuM7m8hGGlhqK2dYCiCx+fH7XZbgqPN8N8fNdOuqDu7NrveNK2SzRvpLMVikaKqodrYBZqdMbYlmXU/NBxT2XpV/k+yFdXKyH1JllFkBbcsymqM3bSQBG6Pm5HB/hIYjxPLpGoaLa0RgsEAmq7j83pACLL5PP19PUiSQDMMzELBShO0TL9MXrvOD196DRPQdI18vsBnf+YZvvPXf950yncQ8y+/8RISnh0cUc/4Nj9iM9ksPq8Hl6KQzWYxTANv0yJBdbr0Fu2XLROGCZl8OZfy4vnzmJpKb09X0+j7UrqfHfVZSh+u0SYUOx5ANwwkU9q8BG8D4aDZLHK5HBOTV3j5ldeJRVv49M9+gj4berjBNBv3Z3VaBhmCct62AMkUdQGM1qCqNfjariqtCDJS2S9cIYlXz9FhWpTHUnFAWzYAu9pVKs316Rmm5xYItrTR0hptgk5XarzB0tjcD7SVhtKcGlzf5Pa11VVmZ26SWF/HMC1ktnBLi1WmOhgmnk5x4vwEU9M3SWXzeP0BhNuLXzLo7e4iGo2yurbO9emb+HxeDuwdL3dnH2DOp5Ckkqm/bji2MKbIMpFwiM99+jleeOkV3jt1xopKXlxEkhRcHi+SLIFdVEtHQjMdYdCpDGjvKdPEMARa0UCyj0xTGKRW4wwODtLTuwuP14NAIAtBoVBgZXmJ1ZVlDCT8gSCLS8sIycQUAgmTQj6HbqPFVZIFyVreox6Ph8WlZdTshvXMJFH239pDbHR+ZHN5pmdmSedU7r9nvyX025atoeFhblyZ5LU330Y3TFbjSXr6BujZ1U+svR1/baS7aa3pxfl5FhcXiC/PU8ik8XndyL4Q3mAYSVYwdA1D08jnsiRXFtFTy3S3tdDpVejri9IW8dMS9BONBFBc1em/umaQSKVZWkuytLbBSjJHsgCq7CPY2cdgrAO3L4DsciFJMm6PB7/Ph9tTny/fmJqs/Yb7qfGllbxyO3tK1TTiGykwyy6qbbB7u9lt+nYrxlRdT6B+WKIkCEjIAsy6866Me+B8gqXkOGA/jvslVyjWxz7huNBACAmPy0VnW4yHj32EVDoDCDrbYwwN9JXSCxvRHcT8RdWPVqT/zouH5PN5DF3H4/YAVjCQkEQpgrcxVb7BHfRnmqXqSQCnTp5AaHmuTt1A1zS6uzrxer0lv7TL5SISDuFyuTDsdL+GQ8CGaZQlG3mrQvuvua4RM202C8MwmJ1f4JXX36JYUPnMz32SPWOjJbzqrchZtM6hbdUaMGz/bcV1QiCaapHbe76lTWbPpXR4lzQCUTosMSsKFVW07zyXXDbLxYlJLl2ZIq8axHzbSC+ymykWiyzMzyHLMtForMqnveXN26atr19eWmJ5cYH1lSXy2TRel4V5kUuvs5xcpmgIXMJEU2TW43EKqk57Ryeje8a5cP4sxXSShcUlFEVmYWmFa9dv0NXRzqH9+8COuHeiVHTDXm8VRagqzcW12k8wEOCJRx8mFArx/37lz7n/vrs5e+Y8Q0OD7N+3h3A4hNelkMnmWUsmWVpdJ5lMYRqGLQhY2OQAiiRAElhxPlat9buO3s3A4CDRWAy3A1QDqMUi0Y5OelMphnfvYXV1hUI+R2J9HcWt4Ha7CSkevH7fpudIPp/nypXLJBIJBrtHiMWiFrOXbAG3SSltXddZX49zZeoGwUgrXd09pdcpgI7OLgaGR4kn1gkEgoztP0Rf/wDhSKSh1qwbOjPT01ybOE9+bZ6OsIu+iBdV3SCX2SCTmEU3TRTJik93C5OhsETnrh76utvwe9xEW0L4PDWKh2mt48W1JLcW4ywlsyRzOjlDxhVoI9LdRbAlRqQ1SjAYRG5ktdw2N23ynLejd236dfOLDcOgUNw8p715Jzvdq7X31//JgSvWDQO1/usPnFRNw+NxM75nN5pqlSL2ebxkC8UStHUjuoOYf5kkUR9xuh3SDYN0OoPf70dRXGRzWRDgdtfnyFfSTqP7rXus3PBKk+KtmZuEvC7OnLvA1NR1WltacNv5pqZp4na5aW+LMjw8yOiQHYBkCwaIesYlSVaqSZXSsrWlrCkZhsF6PMH16ZsE/X5ira1WutY25iuorlblDKr07Jz4Anvslv/frGmj8SFaNXbTESrMknvA9gI0TOkTlOOyzIp/nH5SqQwXJyY5fWESXXKxZ98BOjo7q+qrb0bTN24wfe0yIBjff5D+bTH/D5ZWVpa5dmWS5OoyLUE/o2ND9HR14nZZazy9kSSdsypTarrOpcuXKZoykgTp1AaSkMgVNM5PXuPmrVkKxSJFTcfrceN2uSwGb0crAyiSVBK4HH/mptkmQtASifD4Iw/yo5d+zBOPPsKpU+cIBQLc/5GjtLa24Ha7yOfyxJNJVtbWSSYSZLM5Utm8FUyoquiGTks4TDqXp5DPs7q+jmoYjO/dS6QlQrUeaKK43cTa24m1t2PoOqurq2SzWRJra7g8blxuD263m4A/UGcyr6R8PselCxfQNZW2thheBwFRgKmb9lIvrylV01hYXGJ2boGl5VWW15IMje0rCSYOud1u9h86TCKRIBgMEovFbJS96n1hGAa5XJabN65z68oFgkaaw8MR+rtitIQDqJpGPq+SSGfQdAOXnSeuyDJBn4eWcIBQwBFoq9veSGVZWt9gcW2DufUM8RwIf5hARxvdLVEirVFaWqO43c0BcZxn8aHR+w2c+SdHtRN+fw+gqGolV5hkuxAKavqfJsjP7VIumwMhCASsXNtcLofb7bKtAM2pHCiyMyGgqKoVkpVpVcpyuxnfPYqua6ytx8mvr5PP5zExkYTMpcnLTFy5xgP33cPDD9xnMV/DyiW1WqlkhKadF7q1BaTq2wZrydHD22JRDh3Yx4VLk5w6c47do8ONb6jrQDS15lWGB1Sa7iu1f0dbrLu51Kyj2YtSwZVqd4ZZcgNYtzkR/gIhzJJwQEU/uXyei5OTvH3iNCvxNOMHD3Hw0OFNkcNq6fq1qyRXliiqql2edXDb994eVb8LTVW5dvkyiZUlhvu62T8+Rm93J36/H8di6FiECoUCRVWlNRJiaWWNRHKDhZtTqAUrvz+xkSK1sYEsCfr6ehkfsyCMnUAm5/kpslwyO+6EJEni6ScfY1dPD6FQEFVTaW+PEbJz3wN+P7FYlNHhITRNI53JkE5lMEyDTM4yz7e1xUgkkiSSG7z5znvki7pltjfMUjCgRdWDk2SZjk67PO7Q0I7GbRiWf12RZXLZHPF4nGAwZCGCZnJksjlkRQascsMbG2kmrkxx5doNXB4v3bsG6Ovvb9h2W3s7be3tNX8tj71QKLC2ssLywiyzV8/TGzC4Z18f48O9O+QJjjBukskWWN9IE0/nWdvIcXM5SbIocIditI30EevooqU1usU+2E7nW11TG83ThD4Exn87Ct2HI4U0anOr32+nTTvdcAet3GHM//YfvqqqZNJpWmIxJEkinU4jELgUpanWX7tA6njTJgvIqjteLP2sqSrdPd30d8b4wuc+hSQEcwsLpNIZNjZSVq6yLDM7N8/Zcxd5/lvfQVEU2lpb8Pi86LpOa0sLkUi4pOkapmmZ/yu0lm1tyZpz24HaFZJgcKCPTz77FLdm57kwMclz+XzVIVDbdtmVb2vzTpQWFZaKhi7riuuxGVSDNV95PDhjdZh+LfKhVa2Kkh/fMI1ScGCpSWuwaJrO5WtTvHP8NEtrCfKqXhP4tQXZAzN0HUyjFKj44VN1H6lUiuWFebpjYY4eOkB/XxlG1rF0CEyEkPB6vXi9Xh558Bi5XJ7llRUWlpe5eWuWfCxCoaiSzeeRJYnRoQEG+/vKvQqxuTWk2cKr+LskSXzs8UcB6OpsZy0eL/nba5+doii0RCK0RCJ1TXZ1dJDJZFhaXiF19gLp1AahSATXFimtt0tul4u+/n7mbt3i2tQ0Fy5eoqenl/V4gsXFBVZX1pBcbnRdp6BqFIoaqg6BljY6e3q4664jOxIowdb2s1kW52e5PnkBLbnM/k4f9x4cpjNm1UfYyVFoGCa5QoHVRJq5lQ1uLCZY2CgguQL4or30d++irbObcCTSNBi5mpqdBJtd06yN7QgIHyzd3lb9MPb3T7nNHTzOO4z5l7U82By9zyGHOWxspPF4ffg8FiPNZrP4Az4rlatZb9tQdqv7Kv/NNCFbKAf7zc/PEwwEuO/eo7g9bjxuN6Ojwxa4iM2bDMNg7/ge2tvb+O73f8h//fIfMbCrm/6+XazFE9x18ADPPPU4LrerlB7kADrUPqFNx101ZrNkRnfWRcAfYPfoMO8eP8mFS5McPXyw5OczHT86FgOXqJyzWdWJwInUbrzihCSQjFoN3m4LR6go/8GpZV0G1LCqfjmmf7DiQAxnXravv3zE2AKHYbKwuMjLr73JSjyF5PLSEnATDOws1RMgGAqSSSr4fEE8vnqT/2Z77YM41jLpNAKDrs52QqFAaRFu1bbP52Wgv4+B/j7u+8jdCGAtHmd9PY7X46GjvR1F2QYktPNbMyG4iYGgr6+Xd989QTqTIRQK1fm4nRS/yvQwK3VWtwQRl8IjDx5j8vJlVleWaevsrArQ25QaxcVscrnb42F4ZJTJiQnmZ27w7vHTuFwXmJq+STyRRlOLmELC0E2Gx/YwtncvHR2dxNraSlr9Tt61qqqkNpLcnLrGrWsXiMoFDu/p4Mj4EF6vM8fttWghgWok0zmuz69yfHKWnBQk1NbN8Eg/Pp+f3r6+5unA26YPg4mZVT99ED28v/34YfofbrPthmt5k7bE9nu6w5i/RabpREFWTqP5lAp2Tmdvr6UVra2v4Xa7cFehPVUaoJv2vOk11WOyMOYdOnniOLJk0t3VWVFm1qqeJUnlNEOXq4WPHD2Cqeu8+MrrbGykuDkziynDN77zffaN72FoqB+X4rIR1ZqdutZHszS/SnCV2tC7lkiEhx+4j1NnzvF33/qftEVjDA312YzWDrSsNPuW/O3W3EXp3+ZDK1kLmoy/VkABSsAYzgLGTqMxjbLf1ZmLBFWVBWVb5TdNk9W1Vb77wxfYyKpoSOwZHWXf/gNVOdhbkt32yOgY6XTGMt92dGw6j0racpNuk2JtbSiKm0Ryg1wuv62qec3GGGttJdboGdQNs7p10fTC5gDB4XCYQlFjYWmF9rb2UoZLUbVqBBTs9EALhcxe56bJ6locRZEJ2ahwisvDyuoqI0UVtgsDX2eF2pzSqRSvvfqKXQOgwMlT58ipOsgK/QODyIoF8BLw+bn3/mMMjozUCSLbeR+GYaCqRZbm57k6cZHk/HWGojKPHB1noKe5a6ARWaBLOulcgdmlOOenFri1oRHp3s2B3eP0DwxuY0Tbo/Jb39563v6qr1RqPhjGayfNNWzpp2G3a0632fsO13LlnjS3uPKOZP4l/3EVY3MsARVL0TYLr62t0dHRgRCCVGoDQzfwhjzIslKhre9UZ25MZcHECoxySBICBeju7KxGzaIalU5RZFojIR46dh+hcJj4epy7j95FOpvlP//ul/mrv/smv/W//Qa+Viu/c9MRVfi5q8doMclGQDdgIWINDQ7w67/6S/zO736ZP/va1/l3//Y38fn8dnCXxbgrS7eWfPuU30up/HBTZlE11MZ/t9tQVbWknZTbs6QAR4Bw4GBNyrgBUGb8APF4gj/9i78mnVfZd/gouVwO02TL8pbNqLevj2hbWwktcrskKv7dDjV7Pj6/H4/PTzqbI5vLoev6BwuTSpOOm1xYJY6bYNWmsPahrhsgrCj4t985gSQJFpeWGR4csCwPiQQTl69y9dp1NlIpMA0MO1UpFAqgoLGSyKGrBXQkPB4vsqHh97dsPbR6uWTb85IVhba2NpLJJLv37rWsY8EgkUiEjz3zLOFIhLlbt5BNg2hHx9broMFYdEMnk0oxOXGBhakJQmaGx/d3cXTvEH7/9l0GumGg6wbryQzX55a5PLPCbLKIHGpn8PA+Dh26i62P/c0GW087szveLpvbWpnYbju3vzP+ccWDEm25ljcb5/bf1h3J/JtRJRN3mEYyuYHHbZXtdH6PxlpsjPoPawzWOMwK5p/P5VAkgLKfWtd1yoFp1Y14fT7u+8hRC4Ewnaazs51nn3qc57/1HV578x0e++iDhENBSwBoGCFf4Uuv/PsWjN95fi6XwvjYKL/4hc/ylT//Oq+++ibPPvsxHOOG6bB7s/KuMhk2kI+0rQ1TmdNdIZk6z8kwSkFndXOt+LUEuGKalKMJyhflcln+9tvfJZVTyWsGJ44f56mPPY2sKJu6f7Yin8+3o+O0kipjGm7XHBmJxZi5MsGFi5MIIBaLEgwEykKlDUO6k/Fs90QtVTVzgi0r1pWm62QyWTZSKeLrCSauXsPrdXNh4gqFQgETOHX2AucuXbYgtm3AFVUzKRoGpilwu114A1F0jxfJ5SIaMLlx/Tq6rjO0dw+xWIzOrq6tMfBr57KDvR+JRPj0z39u02v6BgaQZQmPLKHpBkV9k9CqSjOtDYR088Z1Ll84S3ZpmgN9LRw7eJiezkbAWo2bNEwT0zC5Ob/Mmcs3mV5OsWG48UY6GPvIOEMjoxVpqJtPvrqbnR+SdcOs+MPt6O9b3bPd/fNhGu1/qvQ+1vJO6J8U868lVdPJ5nL091mRtnNzc4TDwRLW+IdLJqICmnRjI8nRfSPoBqiqjolVw3knWtrTTz3BhUuT/H9//Q1OnDrDZz/1ScbHRvHawUT10fJmza9mQzN/3cjt+1wuhfvuvZvf+8M/5eLlq3zi2afL99r+dMm2PlggWWWpuuQg2SSFspYaacO6jRYn2zUXGqU91rUjbBBfs4xxnc/n+OtvfIfp2XnufeQJbly/wdzcHC++8COe+fgniLRsQ3vccuw22afMdg4bUfNZcXvjthvQvv0HSKc2OH5+guOnzyMrMuFgkN3DA4zvHmFoqB+vpxmIVYPxVB7WTYVEiwqFAvFkkpnZOeYXlrg1N088kcBJ2zR1DV0oGLpOQdOQZBeSkPCGo8wsrtJV0BkeGMHt9lhwuu0dGKbJ9alrFItFHnviyYb9vvziC9x37NgHgCL3wTEFXTdALu/nrZySpmmSTqU48e7brExdpNNv8MSxUfb09xBoVGu+wSCd93N1eo53L95gNl5E90bpHrmHI2N7aO/s3PH8dmKZ2+r+2niQ23nOt2OTrRxvoz3WjHYq/N5p1HTYtzGfO5z5N5fpTNNkbWWFDtsPm0wmEJLA6/VtktN7u2+8Qf9AXrN8/utra8iSye7REbQKNDFZlnC7lKZVo2pH4vd7+Y1/8Su8+ONXePO94/zH//L7DA0O8HOf/DgfOXKoqka1wEKzqxQAmjH+2tE7VgFJYIMQYQOZVBRgQSA7eAtmdeHBcmTE7RwX1WTY9eWtMZnleIMG4y/1a1YLQrph8IOXXuHKzXnu++hT7N6zhz179/GN5/+G9fX1bY5xBySqPrZHFS9hpyvQ5/Px0ceeoPDAQ6yvrbG6sszKyjInJ6c4efY8vT0d/NwnnqWnos77pkOoGIBhmGQyGZKpDeYXl8jl8sQTCSYuT6HpKoZuIkwNAxnVFBQ1A4/PT1t7O9FYjFAojKIotEaj/MP3v8fI6CjRaIz19TU+/4tfItSkimYmk+HWzM02t0J4AAAgAElEQVSmY338yaeaCDM738Plq2tiekxoFqzakEzL7acoEjpWRokznkYtXL18mRNvvoacXeLRw0Pcs3+UUMi3I8P0qUs3ODl5k8UNFaV1F7sO72ZkdJRwRZbEB8HD3t9a3sk7+aB8+zVN1v1xi/uamS5um3460kTjk9H52NkY7nDm31im03WdRHIDxevF6/Gi6yqJRJLu7s4GAXKNZMTbHUd1sw50YiIRRwZ6OjvwejzItnawObCQWSekmJi0t0f5Xz7/GZ5+6jHefOsdXn/rbb7+V8+TTCb4+NONtSSHpIqgt0qAS8mZQQ1j1TSN02fPE/L7Ge3rQQiJWj2rUb63WSE83K6HzbCrxAlZqspNNUwTUZHuVzni2o0rhKBYLPLj197g+OkLjO47xNj4eOm7z37+Fxqmmf2j0PsYgjN+j8dDd08P3T0Wklwum+XK5CRnTh7nhVde5wuffg5V1XG5ZNzuMhxrNptjYXGR6VuzzC8somm6hf2/HieZSiNJFtaEYUKuWERR3KDlKQoXmqphIkCYJeCpfWNj3Hvf/VVj1HWNYbuanfMOasdfSQMDAzZuwuZzbvBN8we11dlXm5pQKwltRQLeO3mCrvY2+vr70Rq06Wj7r//4JZIL1xmKCD7+sQdpj0VwScrWUzBNMpkCk9NzHJ+4yWJaw98xwNj+PYyM7sZrF5/6R13TdV2/X7vD+6T33eRPXXz6gKi2z52N4Q5n/pUineN7tMz9xWKRXb0WJv3CwgKxWLSBxn+7ntrK+6sfqKbpbGSy5HN5ezwm16eu45JdeLweXK7qso8N/fW2H7C26lWpkpQQxKJRfuYTz9Dd1cn3f/AiP371TTweD088+nDlDRb+eEVfTqyr3GwhCBCmoFAscPbcBb78h19hZGSIRx9/vHq820LJuD1p37DdE5IsI0miLj7BxBaO7NuaoQIWCgVOnjnHO6fOM7z3IPcde6BqDo1SDBsM56dD77vPeiHG5/czNDLCxKWL3Lo1z+/94VcwtSIobgQCXdOQZRnTUNFMQV7V0HQTl8eD7HJTyBfJ5XVAr2hVUCio9PUPYugmg0ODZDIZZm/dIpfLEQqFaG1trRuLLCvsP3CQxYUFq5Utnrvicm1R9e02Htiml29nPdd0W/FzNpPh5RdfZHFxgYG+XRQKRSS3m41kkvG9ewGBpqqceO9dpi6dQ0nP8bMPH+Hg7gHcbkukNoTZNEbGMAziiTRvnr3CxVvrFCQ/wWg/+w+PMbp7DMXOXNp+8PJWk3sftJnT/4Np8Kd6+4fX5w6iET6MOWzB/u5w5l8v2aiqSiKeIGpXoUsk4iiKG6/tQ9tpEaDNn3r9izNMk4Kqkc6Xq/ndunmD/UO9tEbCzRmO05sduGPaoD/FQqHsFqiI7rcC4GSOHrmLVCbDD370MidPn2X/3nG6OttLQ6t9vwJBJp8vWRZkySoSVBl7UFSLXL42xZ9+9esM9PfwL3/tVwiHw1jBivYzbJZCaAcC3g4CXHn+FpSsAypTHbxXJgPLCiBRGTdhvY98LseFS5O8/PpPyBsS99z//vzDH+r50WhyO+6w/mIhBG63m+7eXqauXKa7t5dkIkEqmaarq5u1pSWCQSt9Tdd1IpEW0DVS6TRCFEoFStweD/fdf6xUhx6sANafvPkGQ8MjeLxeEokEgWCQzs4uWiKN4ydC4TABG81vy9nsULBs+Lh29Ay3eaFwtO8MAAE7sHJ1ZYVcLothGMzcmmVxcQkhBIFwGJfiolgoMHH+NHpyiUM9AR775FO0hIIosm13q92r9nCKRY35lThnLt/kykKSnAjQt/8YI7v3EAyFke2SsR+Mpv8BMf4PvN0d3v9TZva3311ji/N223vf09zi5juG+SdSGRRFxu/1NMldt0y8qVQav9+Hz+tD01RSqRSdnZbf39og2zPzN04BbPS4azUcCb/Pw0Y6jWGYzNycRpFMRocHtxXcZ5oWPoDbjj5XVRW3I7jU9i/ApSgc3L+PG9MzXJu6wcTlqxbzb0CqZmE8y7KES1YsX37NeIrFIhcnr/A/vvp1An4fX/riL9Bt14vWbX+/ZPdtpfs1lgME2zf5O/H9VhaCYVc0lEvfbn6vjeRH2Q2QzeWYvHKNV958h41sER3B7K1bDA4Nbe+QrL1kC6VwcWEBl8tFOBy2sdl3SI2X8zZo6+3v9njYv/8A0dYo3b29eL1erl+7xsju3fz45Zc4fPgu0uk0iXicTDaDEAJ/IIAiK4zv3cvs7C327dtPa0XRHMCueT9SKt967333oygKHq+3JpXVno4t9G6GoX9bJ5lj/WnW3jZPyLnZWVqj0fpKerXdmSbT169z8p2fIBc3EN4IgZZWMqkUxXQcNyqmITDzWWSjSKGwytk3FpBMnZjb4IEHdjO8qwO/122jTwpM0wDTLLm4BKCrBrNLa5y+MsP0apaiEqZt7G6Ghnfz/7P3pjF2JVli3hd3eVu+3PdMkkkyuZNFVrGqWEtXd3X1Mj093TOjHi0ez8BjQ/bIArwIsAEZFgxYhiDABmRJP7SNpIEh2bI0m2frZXp6merp2otVLLK4M5nMTOa+Z7793SX849778i33vXffkmSWPQeo4st7I845ETciTpwTJ87p7u0j5KYaPhBHVkDtD7EPdGpBa9buhkFA41669fDVexjQUNUKHBjhn86ZxIRS9dsbhkkmkwUh6OpynF3W19fp7OwqE7rBekwIP8t2/dVEEQLdi8MvJUtLi4RUweTxIsEjK48XvfK2e0VIU1WMfL7+hkFAf18vk8ePcvf+A/78p2/T3RUnm8uxs7PLuTOnOHzoELZpYlg2sUh477y8XPAbBjfv3OW3f++PiIR0fulbv8DxY8dKNGaJk5HKM09W4y3QgUrZeiFtx+qhKApe6F/pY73wQyNdX4CVlTWufnKdqYez5Cz4wpe+wifXrvHeO2+TTOxy4eKlxhfMOp+9o6OD27dukcmkOXJkgvHDh92rg2WV2q6R1EemKAq9/f10uhsTVVU5ffYs4UiE48cn6eruZmh4mEwmw/T0Q+LxTvr7+5FS0tPby8jYGLFYrNRqIh28x0+ccO+0S3p6ehDtji8QBBozEDhQ9h0+uvohM9PTnDh5ihMnT/paJ2zbJpVKcuP6dZZnHjAgdjg+0c/6bpLFxSUwTJ47MsTyWob17SRIyfGxfq6cPw6436E7Rlc8RjjkHGc4VyNtd6cs2NhOMLO4xuziOjuJDBmpkQ/30n/8JONHjtHd20skEm3LDYfGYT/V6WbvEuwH6Sba+TSOFbw1yfvdFNReWQ+M8K9+L93x5s7mcpim5SxCQrC9vY2qqg0EyKj8gn435+p5GFu2TTZvAk7EsuX5BSbGR+nr69krWS743aY5GfpkQcMyDAMtVHnu6cW190DTNM6cOsn8/CJvvf0+/9e//x0QAsuy+PFP3uJ/+tv/XcFr3xOs5ZDP57n+6S2+8/0fYBp5fu5nv8oz588S8qEPbl4BF40oF3DSM/9X6yWvzN7ftmvq99MOi3MA1IL1rS0+vXuP67cfkLPg3PlnODwxQUc8zqOHDxHl8RQaAZ+dt/cs1tFBf38/9++uce3qB6wuzXPizPnKhC2+kzTgylHwPK+KyJ9tIdA0rSheu3ScwiQcm5wkFAqhaRqhcJhTp06j6TqRSKSQsa88E10x+UhRvPq64WHbukAWnZVWix9cC9zy3//ed7Etm40NJ9vfxUuxgsWtoooQaKrG9P17hJILfPMXXqUzFiVrGKQzeaSUdMWj7CbTGIaFqirEYxEGeuJ4M0TTvRDZEmxHx88ZBjenHjO7uM5u1mY7B7vpPDnDYmj8EOcvv0h3bx8dsRiqqhWNgTZ2aCBUzeyyghJupR2NjAGfhlbUa4KXlj5DA+f+baVbH8GBEf7VwJY22WwWwzCIxzvQNI10Ok02m6Wnx8uLXS446pvv/czZe3+Xf7C9gpZtk85ksSyLtdVVsqkE51++hKZp1bUi4Vxpk7btnL+rqhNxzhOERewqwk2n6pomvMt7g/39fO6Vl+noiLO8skosGmFtdYUPPr6BrqmEdb0QbQ0fc/+tu/f40Z/9hEw6wxe/8BovXH6Wjlj14DWeid3pDek2o6DDF557wXsqpnixxu86OBZuOHhdK4uLl32fMs4Mw+TRzBx3px7R1TfI8VNnGBwaIhQKMTwyQtT1gm4Oai8aqqoydugQ6+trTN1dYf7xHNF4l0+2Nj8IOINFA2XrIPI2oB1F6YdVVXWvh+2TGlO+kW6Wise87y46IAopefunP2X20SOEdG4xqLrO1vYWI5mMb4Q+0zR5cP8eMrvLpckRDg33+97n7+uKFzbZtSBnmLx97R6biQzzmynWUxZ5qXHy7AU6FZW19XW6h0YZHT9UlminyILZZCfuVWvFVB+EeJUyBXndnvEcHM1+qufNWgyKxnKz9FqZsjV0qgMv/HPZHPlcnnAoRCQScbL3pVLEYtEirbW2oPeD0m8R7EDHtCwy2TzZXB7bslhaXEARNidOHK9Jq6D1KgqqpmGaJpZlFZLpeOQ8AerlsXe4chZzXdc5ND5KZ2ecRCKJqgjeffcdrn16m1g0iqapSOm/IO3uJrh56y4rq6u8ePlZXr7yAl2dnYAXFKgUHNuBcOed643vbipKRpO7g6rd29IJ5OO2S4IXMaimrr/nJeBsnJaWV5iansWUCufOnS+JXS6EaDGIT/3xEovFOD55Al3TSCUTNZLiBIFmZnPwOtVLPTn7ZdNURNm/DYCUkvW1NaanHzJ9+2NCZhoNsBQdG5XlxQUOHTpMZ1cXu7u7ZDNpunt6yeWyPHxwj8f3PuXsSAcvXTxRtS2qWl3o27ZkezfJg8fLbCYyfPRglUTWxBI6phpBCoVYVy8jo6PokZhz46FWhr0mO1H4/GoFS+0y/hvnxijv73xoD64WLAZNsSl8f5ZCE/4RRXBghL8QjpAsblA+b5DN5lzzfgzblqQzKVRNpaMjaJaPQNSBaj4Ae5A3LXZSGSxXmG9ubNDd2UFvT3dVSeY5+AkEqqoW7rdrqoqqKkXHOoLysACFK2sAbnKgvp5uenu6yWQyjlMIFBYQL697ccKb3USCDz/+hKmH04yNjvDC888x0N+3115KtezC5sPTerywrkg3uA54DhN+1pPy4WjZzsahBKd/V1WAEIL19Q0ezy/ycGaW9a1dhkYOMTo6FhBDe2FoeJh4PE4ymfQ3lweGZlaDNmlR/x+DXDbLysoKIDENk/mpOyzMPuCZsU66o11sbu0yu5YkY1gktzQWF+bJpFOsr66QyaTpGxgkm9xlafo2k/1hLp86wthgX126xWCYJqsbO8ytbLK+m+X27BpbaQNDiWKpEdeC4WxnV1dXGT90iMNHjjSdb+JgwdMal+0cy5/VedEa3wdG+MfCOpYlC0FebNsml8siFJVINIaiqKQzaQzDoKu7O7DjUTVvdSklhmWRzZvoqkJIU11Tnj9eKSWGaZF10/hKwMjn6eqoTPPqvUdKLDfzn6ppKEKQNwyEcAR2cYQ+7+ZBtXYpQiBUDVV1hHE24yw6FXq3AMs0WVldZ219nbnH87z/4cfY0ua5Z1/iyJEjRUVd7d7nDN8vK6CzAfB49I8mWOBGOkc2tmWhuEGPKtw6ali2BM49/pu37vDRjdtsJ1OMjB9mZPxQqcd98Qdu+nit6Ky9TnyDWEdHUQz1fYBWTb1PwzmphJHa/Ve7LpXfrwxfMpEgkUyQz+awLJNMOsn0gwcIaYGRI2Js88rxHi6fO8ZgbxfX7j4iHF7FtGzWMwYbc/dYfWSRTSWwpU1yZZZ4SHJhOMzLF0/Q39tVwY4f2LZNKpNjYyfJ1m6K+4/XuTG9gqVGMJQwarQLRUoswwCgs7OTcCRCOBxCIhkaGqoMrdyWsXwAoIz3z14TPMa979G4L07r38+PZnt78kAIf8u2SewkiHd2kskZxCIhstmso9kKQdrNaKYKiEVj6A14w1Y69TnCM5c3SOQybO1kiIV1OiNh9HAITVHQVKXCC9+0bAzDALkXj06oKoaZdf+gnJDj4GdLdF1HVRQMw0BK52/b84grKl970RTuEeiel3w6myeby3F/app4vIN4R4xcLs/8wiJXr13n/oMp0ukM/X29vPriizx78ZlSL3WXnIJzzQ/3jeOh7GX1K+ag1BrhHNv7J9CUOBsfx0NccZsmSzcANSxbQsD84jK37z5AamF6h7oZHjvk42Tn02cNz49aRz51Jpzv6+CTtKJkq6beOpaYBrlpoo5n3ame7rcq+BV3v69lWSQTCR5N3Wd5fpZ8OoE08ximgZnPMdSp0x9TOXt2gounJwrVO2NR3rhynmhI5+H8CovrO06M/p4opmXTEQlxeGSAo4cGiZRZc/xGQi6XJ5HOkkhlmV/f5vajFTYSObT4AN2Hz7CTSJJNpeiIRp0rvaEQmUyGyRMnGBoepre3j3g87h/gqC1jucm+r4Gt4bFcZu4+MIK/blPKvZdasNu33GjfyeD+28yaVAkHQvjnc3n++Hs/4j/71b/CRjKNqgiy2RwdHXEezjzm4exjxkaGuHTuNLruJYApx1JkQK+hwEkgmzdY2dgibzjhedMZi3QmC4pKLBKiMxImFg2judq6ZdtkcjmSRVH9bMsik0rR21d5/CClRFqO1utdvzINA8uyHI/gKnxVmuB9hKprFbFsC9u2WV1f55/8xm8yefwoJ44fZW19g7fe/YCwrnN04jBnT5/kmfPnGBsdKXJ0EiU0JBJV7G0AbJ8OFnjOiGWe+W5Hlz5zNnQIgSLEXlQzTzUt2/cU03AsJjaJVIbrn95mN2vywquvMTQ8Am5Qm6rQsLbvk/1blJeoobpUnWSi9usiBO1bpP0hCPY9PquUrtmQsugUZQt+OzYB3hHb7RvX2F16xEDY4MhoFx2xXjZ3kuykBC9dOM7xQ5W5DU5OjBZ+Xzp9lEunywoEWChNyyKZypI3TRbXtrk1vcjMyjYZqaN19NF/eJIzFy4yMjrKpzeuc+vTT9FUDVsITp0+zdzcHMePT9I3MBD8Gl8LQr+hoVq3QHBG9l/I19fA6wzVOnPZfyzvFdmLWBK4tU2sSYGQ1ioakOaBEP5CCOKdHeiaRjSkkUln0fQwtoTF5VXuPXjoRKq7eB7Lssm5Z2W6ppHP5wmHQ6iqgmXZ5POOQNd0FV1Ti8zNrsZvmGzsJAuCvwRsy7EypDN0xKL0d8UJ6TqpbJat3RQ5z+QvbdLpFOlkAn24qwSFlBLLtLAti3A4jOIKftM0HS1YUXy/myyqD67Q8bFaOILfJqTrDPX30NPbQ29vF1NTD/n009sIASeOT/DVL73BuTOn9mKB1+l/KUEVFDYAlWWUgk+Bx59ngfAeehsA27ZBOrEMigMvFTLB4fgB+FoVpM3OboI333mPu1PT9I0cItYRr3pFq3nwxF31pbLAeTWZWP6Jyj5sRdCmKjwEgX0znUoQ5cltyv+sHIoB+KrVv0FxgGkYbG9v89F7b5Fbm+G54/1cPnOGgZ6uKjUag2rxOJww4ia5vMHGboqffnyPuZVNLCWCrcfJaf109vRx9tw5jh49RjQWAwnPPvsc3V3dHJucJJ1KEevo4LnnX2idT+p/f58sGEXv6uCv8Y0bYqIJCI62vrZbMeeCjuUKeVrJVa3+bQ/UwrvHT601qRE4EMIfAdGoY47ujET48JMbzM0v0t/Xy9rGVsEj3TQtVtfWeffqNaSUnD9zkrff/4hvfu3LDA30MTO3wEfXb2LbNufPnubYkUN0uHEALNsmmcmyvZvwF/xlkEpnsA0TVdfIGyZ59+xOSolhGKwsL6MIm8mjR0rO7vLZHEJRiESjCCHIZ7NYto2maaiaWnOgW0VHCqoo1Uql6z/gnPM7EdjGDk/Q193N13/mK5w4dozZ+XmioTCnTk76exHXIO4IaYEqJLZdep6/F1Mcd4K5fxS32zAKqBVFKQ1JWjbjhHeFy11kFUHhjn46k+X3v/MnPF5awzBt4p1NRtWrBXVXG+Hzq1ap0mMUvxItQbUToXYsxqLwv7JnVG+WT9FAUIXf4kfeBtf5bfNwaopPPnibaG6NLz57nPOTh4kHju1Rm4WCtc078pJuwinLJp3N8cndWd67MUVW6MhoL1ZsnJOnzzIwMMju7i6aru0JfrchQjjBkYDAoY6DwH5r1QX8bdAom6Ld6liupq6XNSz4WK7DTLs3Qm1akxpAeDCEfy6X5zs/+Anf+OqX+N6P3uSPv/9jJo9OMPN4kfnFJcKhEOfPnGR6do7f/He/S97Ic/HsaX7j3/wWD2dmee2l59na2uJ/+Qf/lM+9dJlwKMQ/+Vf/lr/2rW/wyovPYdo227tJsrlcBe29ie/lhheFTHDpfB4jlUQgUN17/JZlkdjdZXFhnqimcezoBLa0sQzHDK+HQoRDIaRtOcl/hCAUDrnx+wW2LM63Vx3KBb9pmo71oCBQJaPDg2iaxvSjOV56/nLBi78UT/V54QcCgao4AYxsvHC//sZEgesQaFlODHKlNNd5SUGPl4JAEe5i6TXHqfHhx9d5OLPA4KEJxg8dduIn1Ge7MTj4CIN9tycmEfYfX8HB1LZ5cO8eAMuL8zyevs9IJMc333iOwyP9RWGhgxiBi0h710ylxJaulcpFYEvHeS+ZyXH/0SJvXbuHpcUw0Aj3HSMcjvHKq59jfW2N0fExBgYGyefz5H3Wk8887PeYCkB3fwwMbcb4BOfGfiE8EMK/GP7srfd57crz/MLXvszgYD+//90f8PGNW+RzeRaXV0kkk/ydv/U3GR0Z4v70LH/77/6vAHznhz/hyKExnr90gUg4zPrmFtMzc4wMD9LT0002l6W8Q7wFZ3Njg4dTU8Q744RCIaanHnL67Fk0TcOyLJaXloh1dDA6Nsb1jz9meuo+HZEQZ44dpifeiZUz0CORQqIa07LIZbOomuZc6XOFYiEoTo1ohh4UJ7O1XW1IVVU096hD03VGh0eIRaLMzM3XwNMcKIpCvXh5AgpamuLk9y1YDKptFkosAEWbG4DtnR1++u77dPb08/kvvlESYa4cin0k9vvcvDYHzZSsL7Yqy4sa/wblohRzsNrN0GoMvARDu7u7/PgHf0Jm5RGqKhG2yXNHBnn9+efp647vJcByoRFuvDG2m8pwd2aB+4+WWd9JkslmMU0bU4tiSwXbtsmLLiZOXSIWi3Hl5VcKVjQvlTJAKBRq8bqnD4/7PJarf8Hg39h/LDfDc3m9up4nZWWDlWyU9pOA9lFrDdOBE/7ZbI7Ozq5CAJxoNOIvBCRo7mKgKAqpbI7t3V3e/fATdE2jIxpjfGSEaCTiWqcrO8m2LL7/J9/j8cwM3VEdL7VA3pLMzjx0rh2659xCUVBVDQWbmCqJ6Qqvf/4V59qXq74a+XzBHyEcDqOqRaF2izReRdQOZ1zOqWWaIIRzS0A61yFVRUEogv7+HpaXlxvo4RahaLxZbrAeoSh7MYCanFPbOzv843/2r7GAtJVE2nbN8nunEAdAVWm4ZKM8izr/7icXzdBqDKSUbG9u8v3v/CFqcoG/+oVnOH5omI5o8+b9clha2+LqrYfcW9hm29AJd/XTPTZJyDTZXl3FNE36BwYYGRlhbXWVl15+hXhnZ9voB4Gn5/wZ/Bu3Ppar1WttLD852q1D+6i1hqmu8BdCRIA/B8Ju+d+VUv7PQoi/C/w6sOYW/TtSyu+6df5H4D/HSRT+30opvx+UoYvnTvPW+x8SjYaJd8R454OP2Nza4blnznJ4bJTB/j7+0W/8H1w8f4Zrn94p1Hvx0nnuP3jIpbOn6Ovv5d7UNMePHiYe7yDleemz112maXLv7h02Vxbpj+u88OwlLpw/ix7S+Mmfv8vM43lyRh7b3hPUIV3l8Og4r7z0PKdOnkARAtsyyecNTNf0HYtGSmPMi71JLRBI4d6Vp9ThzeNOLa4rQWJjSxtFUQvmfy+UsKIoDI8MMf3+x2VY3N/VzooJKJf9CgnX/8ANdKSoSkHjt9wsPQL3ZkBVrxQHcT6f5/6Dh6SyGaYfzZE1bMKd3UTDYUSRlrePOmd9zG6RZjUev1Kej2RlOOng0Ch1GeD8vhGKhb8CDqRaOm02m+HD999G35nlv/ilNxjp7y68Kwk25UOqXmrpuaU13r1+n+nVNPloH0fOvsrlo8fo7etzr70WcSkl6VSqxDJXnEq6/qlxbWhVX62Ft75zaXn52k8ao90Yruao1R7LjX2dKhw8WQNA81CT/eCNCKL554AvSSmTQggdeEsI8T333T+SUv6D4sJCiHPALwPngTHgh0KIU1JKqxoBVVUYHR5CCPjVv/qL/O4f/wk/eed9eru7GezvY3xkmM6ODo4eGedv/qf/Me9cvYa0LX75W9/g7//Df4au63z586+SSmf4wZ+/gy0lZ09NomkqhuGcleN2iWkY7O7uOqltb1zn1MQYv/LX/jKqohTidf+Vv/TzTpx84NNbt/no2g1UVeXypWc4dWKSWEcMISXpdNrpRE2jo2whqSb4PLGvCKXEwa942BbXNQwLIRwHOs/87y1aihAM9nSRy2ZJpdJ0dMRK6hatW0U7AmoKgZINg2cJ9H6z53ho2lbVPOMS5xxVwWcDIB3GdhMJfvrOe1y9cQvVMrAVncmz57ny8iuk02nHpFpPYNUb5w040dQD3wUuwDzzey0qPPiqI6lGovLTVtmpua+8OAtNrW7uN6vAHMR5xS1djaptWWR2d9ldX+D1504x3F89gJfvU79FUErmltZ5+5O7zKxlyUd6OPnsi0yeOk28s7MQbdLz8SmuV13bFz793Fh/7pdcEUX/rwu+CkGwsexPu3wu+CEpFUu1VYLqlAq/fMZy4E1ZYSy3Mhee4vsaFUXN3q2EusJfOtvgpPun7v5Xa9r/IvAfpJQ54JEQYgq4ArxbrUJHLMb/8N/8OtK2UXWdX/nLv4C0bRRFFJUf+s8AACAASURBVKLuaZrK1s4u127dQddUurp6ePOn7zHQ38fI0BDJbJafeePzfOULn3MEhhDO/dxMtsDs+toa1z66ysbaMrYlCSuSF59/jnB5cA+lcDOdSxfOc+bkCUzTQg/pxGIxpG2TyWbRy2JzB4066GXtU4QTN9/TlP3AdnMACCEKDn8eGUVRGD8ygaZpPH48z5kzp8pq+0jPesJqT15UzGVP8NvSrir4iyn7nv8LSKVS3Lp9j2s37xHvG2Z+dg6h5jk/MIim63R2de3dCKjJbLPvG5hhtSROjVWs9tUpvw1R2e+Amroo/+WHS1SW9mGZ8ht/1dit+7w2lZInpmmys7VByMpy8ugZlKB3mPw2nVKytr3L1VvT3JjZJBfq4cyLr3LsxEkiUSdtca2EPMHmb9lkatTt4mlrlo1+y6BjuWTM+W8IAo/lauYy4Vvaj+XqGnCtudDQ56+Hv179cmjANBd0F1wHAp35CyFU4CPgBPBPpZTvCyG+DvzXQohfA64C/72UcgsYB94rqj7vPqsKlm1j5HOEojrbmynGBnoIFeLVU7hRpmsq0VCIq7fvk8nliMdi/K1f/zWkgJ1E0uMVcASPtB3zNIBlmSR2t9lcXWakL87AwAATh8Y5OVmclKdSWCmKgq7rhEIh9FDIiRWQyxUEf9P5zQUIKSi+9V+uiZjuWb8QwskKKCW6phX4UxSF0eFhdF3j4dxcmfBvpynPOfqwXB6EovguvlXxFWkbhmEw+3iBt96/Sqyrj8+9/gZGPs/Mo0dMTT3g1JkzdTOmNc59+bMAwrcV8OZwUFwV8kQ2Na4q6jUglERjSkNNXNXxVH4XKSU59/pqR7h6siTPW78Yg7epNy2L9e0E1+/Pcntuk20jxOixi1y6/ALRqJPGt94RVNPQSNVWNktPa+MQUB4Vly/eEDQb5KlW8K2aUFhr2tBZNdeFuruTBqHGmhQYGqsUSPi7JvtnhRA9wO8LIS4A/xz4ey7Fvwf878Bfr0K9wlIghPgbwN8AGBsfZzdnYiYyeApfyRrmbgDiHTGeu3ieE8ePFs7YO2IxNnZ2Ma2qpwqAo22ur6+jayqnJ49z+dmLRMKRMq2/lHUvCY9wne2kbZPP5wte900LfpwNhrNYKyVPi8EyTecKnRsoSFCaSlRRFPr7etBVhYfTjwpXFINB9YFSIfht2xH8gFDEXrCfOqQ8J8BiltY3N7l19z6GVLj8/AvE43GklJyJRBgYHCSbyRCNxah3llu9CcLnfR1ENfYFLUGRBt9YteaYaKaej0W/NWhIVolCUCwpJXkrmJOnB4ZhsrK5w91Hi9xf3GHHDNE7epqXJ0/TNzhIZ2dXgPnwBCRq03KhCeHXCrl9wPskx3LbG97QuhBw4AfhsR3jJQA05O0vpdwWQrwJ/GzxWb8Q4l8B33b/nAcOF1U7BCz64PqXwL8EuHTpkrRsi1Q2T3dHzFfzE8LxDejuihOJRdjZTWGYJolMBsP0D9pjmgaLi4sY+TzpZIK1xXkmxoY4MXmM3p7uMsFbzBwgKGwoVE1zE/s4UfrqCf7A37cGDsu2saVEUxRsyyqkBC6vE4vG6O/vY35hqUFGyiWTf2Ffwe9X3beNRbH8hRPGeWlpmYXlZYYPHWZgaMjdqQsi0Sijo3uhWJ+ICa5R8CHjT7noaUAegjvr1KVYo1TlG1HjXYMslEAjW1BbgmlRkjSrVv1kOsvj5XUeLqyxtJNjO6cS7z/KhaMn6B8coqvLuS3U9GbI53dTCDxoCEnrEixQ7cBkiiyTTSIJOhar1Q5sO6injPgVCXTE0ABUrdeqWZ+Gp2ets3kI5u0/CBiu4I8CXwH+NyHEqJTSkzjfAm66v/8I+L+FEP8Qx+HvJPBBLRqqqiJUBU1V6YxFnDvjlJqLXV5wHBsgkUrVvC63sb7O7MwMqyvLTkpdadHXFePK888yNDjoZg+0/U3MwhF63nk7uCZ4KdF0va5Z2neAlb2o9w1NV+sXilJwWNTK4oIL4aQJHh4Z4vadqVJ6Qc7My7e23vmeO3o8wQ97gr9k/AUYpIlUkq3tHTLpDGsbm9x7MI2FxsSxyQp/Cd9kJ4EgmCWieag+cYPZTupDteU1OMVaQr1+/frvGi9WAnU1IomqKhXoi6vtJtPMr2wwu7LNwlaWXVMl2j3O8VNHGBgepbe3t0Y0yKC2CP/ftaHxzV4wDhon334yrY+X6pvaYOO9XdPaF4+fAtEugiX+O21AWoKivuW2HsUgmv8o8G/cc38F+G0p5beFEP+nEOJZl4sZ4L8EkFLeEkL8NnAbMIH/qpanPzgLf19XnEQq5wiZwvNi4S4KZVUnHuyeM0AZ3L1zh+XFeRYWFjDyBrGwzsTYMC88e4HjRydQVRXHGU2WmMqLo36ZpomiCFRFwbJtpJR1HYaqN7Cx4rZ0hG7Y9TGQUjpBd3w3KoKxkRE++vhTbt6+y4VzLZyZe/LN2/zYdsHxbmtnl5XVVZAwOjpCvCNWPUmJ294HDx8xNzfH2sY2qUyWnd0E2bzJ+MRxBgYGmmJxT0MuIlT2s3q95ik+fajHxxPgMbDq5AM1i0gE0p2XlQI4nc0xu7TG3MoujzdSZEWU+OAkxwdG6O7to6+/v+0Bd54ctGF8tVL9iQzv5qxZTxYKUrrhmlVrVZXC0v1/sxub9vRXEG//G8BzPs//kxp1/j7w9xthpDceY2s3g7T3BLqUwnfDJIQgHAqRyWYr3q2trXH7xnXSyW1sS9IV7+DYoTEunT/NqROT6LoGkkK+AFmITkNB0HphfPWQ7pglbcdC4BsvPwj4yKpaYBomqnvTwTvSUKoIWkUILp4/x49/8jY/fPOnnD190rk50KR2JgVIV+PPGwZzj+fZTaZY29jk8cIiSJg4coihgT4OHRpjoK8ypDASVtbW+ODqNda3dlD0MHo4Slf/CCPxOGOHDqOHmtPyq2rIdeZD81Ol+UWhvXAAFtDaqlNtqPd9hHQScRU9S6QybOwkmVvd5u7cGlk1TkffUY6MHmZ4bJyurvYk93m60IZvti9m6nbCAdi41oXmO7Gepb+as+DTXpMOTIQ/y7I9XXwvs12V+5hCCDqikQrhn0mnuXPzU6K64OiJo4T0EP19vZw8frQkpa0UZWF23agRbowa55hACBSh7J37lwvfQirbYu6aOXsurWO5gjccDhV48eLm+4GiCC6cP8fRiUPcvnMX25a+9PwD/pSNTgFbW9sI4eRbWFpe4c/f+4DVtQ0sCTaCcCjCTiaPev8h504d59UXL9PZGcfTx9fW1tne2eXeg4csr28xOjHJwNAw3d3dhCMRVFXdHy2tvG1tlYX7uTi1i9EAZzBPEKqsd5XlpMQw8k7kzIhj6dpNZdjeTTG7ss308hbrGYkeH2HixBkOTRylo6OjCY72sw+esvAqI//0tqqtmPefNrSRR68byjX/A7YmHRjhn82bKEKQzOSwLItISC9KCVsKihB0REOsb5U+n52dZW3pMVcuneOVF1+gu2svYEfxpmJPEXcEnpSykMQGKTGNvSh60nW02zOle4Jyb/dWGqsvuDFn75hhr75hGCiqilBU8vk8CFHHjO9wMHnsKNc+ucn2zg6DA/2u38QeH9UFv4NDSkkmm+WDj68RjURIJJPcvHOP9a2Ec+whFGKd3Rw+eoyhoSEW5uf45OZd4pEoFy+eJ94RYzeR5K33PuThzDwgGBw/wskzZ+jq6i4nHqRzWlN2D4rCXpeH/WawNv6n3UWWZZFKJRHYKEJhdXOH2zPL3J9bZdeOonb0M376KBPHjhcc+XzhaTekFhxk3hqA+s14yo08CP3cjjXpCbXjwAj/aDhEZyxCLm+wm8rQF4/R1REtyeJVDOXBcXO5HHdu3WKwp5OXX7hMd1dnyRm+V8cLEwoUhLRdeL9n+lc1zYkRIERZ4BHXP6DM36AkoE3QDydLeZK2c5c+Fos59/ptG13Xq5+tFzVucvI4kXCYH735E37xmz9HLBqt42NSbK9w8CwsLfPBRx87hhAJWcPClAJbOimKz50/z3OXn0dKyeDQEG/+8Af85J33MaTJ565c4b0PrnL7wSOGxifo6+vl5OkzNZPz1IQgfehbxs9xp85sKn7dzonXJK5mE7w0U8/Z8NL2Ngfe/kobI29g2TZT86u8ff0Ba7kIkZ5DnDt/iYljx2qM/yJomv+ndVwSBIp4a4DN/WpNcyeJT24st33D39C6EHDgB13XmmpHY5UOjPDXNZXhXkdTX9lKkDctTMuuKvx1pZT1qQf3yexuM3bsrOM45Al5V6svBwXHe704xC645/uuxcHzdC/XvPfSANsU11abCcxS5Htg5HIFYZnP59Erbhb4uUM5z5+9cI6Lz5zlj779PZ45f44zp066xxx+A6LsmbsJGejtRVVUTMvGNvMYho20IRSO8PyLV7jwzEXA0dbSqRQWkp1Ujrff+YDDY2N8+PGnDB89wauvfR5N0yhEY3+iO/ImzI5+XVoLgranzrpd3Uja7NljY6Z/CdWj+jULDeES5LI5Hs/NEcnl+b237hDuPcTF55/n+IkTJemh92/4iECCZj8z7lWPSy98fz41cH2C/Fnx/0rlrQraj9XKtM2QFmRQNbQutPkDNYpOAiLYWPaglVBq+wZhXcW0rIKXPVDh2F+u+e9sb2NKuHX3Puubm+RNw/Ga97kR4JnqywU/gG3ZjmlRODntFc/sXk7fFfwC50aAk7a3VjIbf1CEQAGMXB6haaiaRjaXQ7g3CwoBdQqcV4IQgnA4wq/96i/T39vLv/jNf8va+oZ7FVKU8i5L8Xj9axgmXd1dbrthxxDkbVA0nddef70g+KWU5LJZNjY26Ovrx5TOs+/8yQ8hFOO1L7xecIwsDEJR+Q0CQb1qrb5vlgevPQHx+4vd4PuMYO9rlJTCt4xolQlZm2wQkFJiWDZ25yEmLr7GG1/7RongD8Ri0zwUWevqwF6ZisnUMojAy3UboNa3rAe+gt+bC7VasHfcWqulAVlooHQNIn5rUhCU+70mNcuDCD6WPTiQwr8jEsaSkDEMzMIGQBZM8raUpNyAIB68+trn+flv/RJZC67duE2iEO7XK1G6TFbrY8u20VQVy3R8EEqC2rAnLIVQUBAoikA0OX2FG1NVSuf2QTQSQbrxBXRN24v3HxD12MgIv/jzXyeZTPG7f/Bt1tbWS24zlLSjqC2GYTibELet2byJtCwQCp9//YtMHD1WqG6aJktLS7zz1lvcv3e30CfzKxtcuvx82eZHNiDmfKCI11rv69WvDsEXkEqxKQM1y4+CdL95ED5q6fKl76vZFpxXjjNrK9+h/JirBnM+fFRroaZp9PUPMHz4KC++9HIh8U4wrI3yUQ7epdFgK25lXsHGCLdhr1QVbyM70cqSwcayP22/IzYfokVFqnFaz/ZS+OUzlmWNMVZJpAVbUqtrTjuOBapUbGgccECFv66p9HVGSaZzbCVSmLYT/jORzvJoeZ2l9R1SqUxFvd6+PvqGx5iamSWVyZSdyzfWq5ZlUc3ZzjunVxSlpnmqGnjvPP6y2SxR19yfTqcJ6bojiKuwLD0efKj8zFe+zMsvXOa9Dz7k42vXSSSSFf4JUDQR3eyFmqaRz+cAUBUJbvz+N//sxzyanvblQgBdqokQGoePn+Tk6dNlC3cbNJrCutTmZTOo1CisbeX0g7Ws2iFEqXxrYuPYYKlAMZ8aoFjoj0BIq4+D7p4efvbnvsGXv/ozDfLjjL+G2uQzhErFUi2ossFvYFju8du+sSwLYjB4TzQ7lv1x1X/SeInatfzGcmPqV5WSjRwZtBUasyTWZv8zrvkDxKMRujsipDJ51ndSZAyDnGliWhIbG9P276n+gQFyOZNUMgO2cw7vNVIAqlBQCKIUyuqZ5QJ8pFr4i9/lcjk0XXfM/ZkMqqbVjSdQj/df+Y9+ifPnTvH/fPt73L3/ANuuHmPJMB0rgxCCUMhJFWwWxVi/eOlZRorC7s7OzPCjP/0+AkkspKKGwqTyJq+8+rk6XHnQ4MwRFT9KsATc79fA22yFZq0G3pNG+JZ1/g2Kw0d7b5i2B+0xVCuKQqzG1T3v+1bfvjYAPhaMlqGpbqimMDTOWyMibw97tbFcn77/WG4Gyuu1NpafHG1aHvpVx3LDeFsbywdW+Ash6InHGOyJY5oWq5sJ8nmTkKZyaKCXwd7KvNsSOHr0KFLRuHn3Hlvb24BACMUR+q4ZXbi/FVHDRUp6mkXlPllCTdNkICOimx3Qtm3C4TCmaTq/3Tvw1fA7VgfbCcRj20gfv4Wuri6+8bNfo7urkz/87p/y6e17vvTNoqMNKWVB8/cGRXdPD0ePHy8EU/n0xg1+/MMfABJNSEIqKNicPv9MjTzo5dAeoSEK/z6x09IqHDRTsr7O6nM6X+PfoFyUa+9B6zVKKziI4qM1X+qN6jN1KbYNU7thv8dyrV6uV6I6jmZ5rtTdPai/fjZs8wlM+0nAQRnLB8bbvxi8gDRCCDoiYVRFZTuZIZXN0hF1tFPDrNRmBbC0uIhtWdy5d5+L504zONDvxqQvdYiQSIQUhXe2G+Gnck9caVaTyHK7bQUf1dsmsW2LXC6PoqqO1iMl2WyWWNQx/dcW/KXXFS0p0cqKCyF45txZXnvlRf70hz/hrbffpbszzrGJCefMT0pMy8KWkpCuF4T/P/4X/5pc3sRWVLAlV668RD6X47vf/mNWV1acoxDbJKJKouEQCEFWapw7f6HirP8gL7LtZ6/97S3BVg39fndzu/Ef8GHxF8DT+0ZFdPeHfJsbduDnRn2EB1L4lyfziYQ0hnrjSNlRSC5jWv6Z/IZHRrh+TSJMm9XVdcZHRujsjFfsqosD/JQ+d8AJ84ufZxXNOPh5Qt8wLaRtoYdCaJqGaVnkslnC4XD1LIPsCX4kKELBlnsXGL0Y/MUCWFVVvvT651lf3+Tqx5+wvrnF17/6Ja68cBnbdaIM6Tq2Lblx6yZvvvUu2WSS3bzAO1F5/HiOSCRKKpUin8+jAGFNJaI7O29VC3Ph0gt0VoRZbWEUP4nFp3C/rYhYw3SLK1TBsY9taSvqanzX2YEE58Et2fZ7ha1CM/wctDZ4EIwv/1Ltlry1ednXHqw6B1uYmH7F2z2W/fC1RKJ+xQMm/P1bK4QouUNv2zZh1Z/13t5ePv/FN7h94xM+uX2Xrq5Ozp85VQjt60u1aANQkPXCE4ISP7c+y7YL4gNAcQdYudZuWRaWZWFaFgInPbCm6whFwTAMTMMgEg6hKKpH2OXJdi0gzmbH0fZlYduhCMUR4gJsJKpPv3XG43zz61+ju7uLDz76hP/wu3/Aw5lZfu5rXyHeEWNnZ5e33v+Qu1PTbGwnyOadWweTkydYmX/M8twjFFXByBtENQVVAVVxQgrbEgypMHnqVOOJhGoN6orn+7FU7An+wr1YHxIFyr4siMrn5XPXH60fhfrs+j1qU7dU8Flp/vK1dIni15VYKelfX8RPG5rh56C1wYPivq6+UfPn3mcst8hLw28D0fbRxMpLNDmWvfZXm+pOker92zr44K1pQm6dhQMm/IO1RgiBrmtoqlqIve+BqmmMjo6STCSYunuLx0vLHBobob+vr4Y53ee3exzgCF0bSzofx7csFAQ1OJsT0xX6AsepKRQKFYSkbduYbl6CUJHgL65foCQ99xbnhxSyQEtRlJJYBeUBHoRQGBwY4Mtf/AJDAwO8/e77vP3Oe6xtbPCFV1/mkxu3mV9eo29kjM9dusLq2hr9/f3kcjm215Y5feIIA339LC2vkEgkOTpxmKHBPm7evsf04yXOnLtAOBz2/0gNCfha0OIOvQ7eim1dhRJU44in6jog/TD7IjgIlvu991VK10RQFlSkbBFt6hy7jZ3yJPX0mrQaZaQJxveONH0U1fqVW2Zk//s6yIyqAXXXJP+xvFekiU1sE2tSIKS1rsMGpHmAhH9jB5uaqhLSHOFfUkKCput09/Sgqiorq2usb27R29uDgn8QHsXdNRfHqVDE3l1sWfT/avxJnCQ8lmUVrgHqmoZwA/V4Dna27QhrRVGcFMGqgijarjrBg4otEUV0i3a1xef+tUBVFfp6e3n20jN0dsZ5/+rHXLt+k5m5xyA0jp48w4Vnn6O7u4eBoSEi0Sg729vcueEENpo8eoTTJ46RzxuEQiFWV1fZ3UkwMDLOiVOnynqhCJ6kgiSpNJs1uxIVD6QaulJl+WK9JPiWvab2HLQNPu0PyH0ZlAmPQPTLWlDYODXY+RW0ZA3TSXAT95MGnwMlB4J+XL/HDY7lyr5vAEELE7mGPh7EDFZBv2pf+hWu8raEr0Z3QM2uYRUTyG1/KzgbYSwgjQMk/IMLfucK3p5w9hurkUjETY5jYLppcUuzBVIYHUI4xvTixUJRFEdzl46Z2zluL11OPFO8E4ffuRqouvfjvX8tN0a/dL0YFUUpRA10Ige6N/arXF3065FyTpTiyII+88yWko5YjIkjh1nf3Ob23Slm5pYYP3yEC5eeY2BgEKBwNNLd3U3/0Agra5vk8jlGR4+AlMzOznHr7j0MoXHpwsUSD/8nKesrQLj/E+XPWka6DzX8S/k+DcqCKPyv4ao1yTaEpMUOr/h2tYRFKz29f9DY8KtSwk+5fJJjeV86LaDgL6NfX8+ujfSprUnFE6juWH56cICEvx/U7rHKS257EOvoQAuFQOZLoteVoy/eJUs3nC84At80HRGrCAWbIk97yZ5AR4JQUFVHmKuKwLalcw3PLSOgIOwVtcyDQAi8/ANSOv4FinfkUKVtJYKfsraVC37bxrZtllfXePhohoXlVQZGxzh69hn6+gdK7vB7oIdCHD9xkusfvsPm1g6Dg2k219f55PoNNraTHDt9gdGxsRq9XwuepCH2oEIzfdCOfvuLvv+LPmg3PK1x2c7v+FkdE63xfYCEv0RKUScT3R4IHE01l8/h1wFSShQhCOshdE2v6UnvgWlZqKrqxvTfM9fb0hHitmVjS7vCYqDqOgL3LN+03DN75+hA07S9GP17LXXbUGr+tXGuHGqKClLUEP9F/aD4d5jn0S+lZGV1jU9u3OTm3QdsJ9P0Do7wxle+6nNevzeYBgYHEYrKo9nHZLJZFhcXWdvcZfzoSY5NnqhWLQC0MMnqHaoGNS02T6RNUA1/rTY0a7ZtAkdL0Ib+8zvGaRt8Rhb5z4w8qsFk4DbUKlQNSR3EbVmTAiAJtCbVKNTSd25tgBwQ4e85sTl/edpqwTTuB0IQi4RIpvxfb6yvk8+mOXxohK6usgA0Poea3t33kK5jmSbCzahn2zbSlCApEeqqrqO673HP8y3LQrgCX3UzA5aQLblVIB0t3T0LUoTAcshgS+lsPorO9Yv7xwPHhiDKBpDElo7vQC5vsLaxycef3ODe1CO2EmmyhkUslyPrXi+sBqqq0tHVw8O5Re5PzxKOxJg8fZZTZ84SCoVK+u6JLVJVT4a88+Z2rJhFR0JtaVdARIVD+hYJe/WftPAo0GsD0eKrmG3Z0H1GoO1juRhve1A1BO1qQ2A07V6TAiCpWUTge624QRL7BQdC+Nu2JG8YTnY81Tmnz+VyhEJ6Ia2tswnY60ABRCI6Tjy6Sg15c2MDI5+nv7eH7iLh72nDjrXeMbkDjnZvWaiRCEY+j8RxKrSlxDQtx4HPDcPradWGaWIZBhJHWEYiYcALHOTSc3n1/AOQFDL1FWv2ElBQsHGu+MnCQur5Kfj1XKXTg21LdnYTWJbJxuY2b779Lo8Xl8kaNqYUhMJhVFVjcWGe7u7uMnx7iCLRKOcuXGRmZoZUKsmRIxNMHDvmCv4yohU8NTmiKzx0AkCJwGmjVaHRxcYPRyM8tSw4C7uHhsjWRFX9QSW0dREr+p7N4G1J5hRXfsJSs11j2RdvPajX1mb6oh392MgY8CnYzFiui6MRaOF7NrMe+iLwhwMi/C22NrcIh8NEYxEMwySbyZBOZ4hEw8RisUIkOg+EEIQ1HaFpSNMowuZ8qc3NDTRF0t0VJxQOF7zsPQ0fKdF13RXIEsMwnZj67mbANJ2/94SdW9cwMFyvfVVRnGA9qlrRpsIGnj3B73noq+4GwZZOcB7L5c2WHo82lk0hB4FfjgHbdvwERJETYy6XZ2d3hw8//oSNzR0WllfIGhZ5W8WwbcLhEGPj45w8eYrhkRHqjeqRsTFGGj7bb0VrbaLOUz0KFzX/bB0aYWw/hcVTVE+akUktsdvwDjA47Id8bRvsJ+FGcLe5E9oxlmso7vsGbaFXG8GBEP6apjMyMszuboLt7V1sN7GMRGLkTbJqDilAV1SKd5MS6AhpJEuEv6PN5zNpBnp7iEWj5PP5gvNd8V6oOEaApmmEdB2QRKKRgo+Ad0XPM+srikJI1+sm3ynfbcoSE750hb/Esp2bCErRDlcCSAtLCGcT4Xk2urkICtYLt7CiCHJ5k1u37/L2+x+yvp3CQhDriHPq1CTJVJL5x4/JZDLk83l0XScai9Xm/ymB75j3ediSldlHy9/Xue23gw9EsI0cldNrWatoAw9BoGhOBBkXBxrqCZED3ZbixYl95DUAYr+xvI99J/GxvLZo4Kw7lvd7TeKACH/TNFAUjZ6eXnp6eh1t2DIxLYt8LkcmkyWdTDve+IpA4nrDu9fj4vqe5i2EYHd3B2kaxDu6iUaiaG7WOkUIRMBodF7iHMMwClaCSCRMU2F9ZakBxnatDQ7DuEcQChrOlsaSNtigqAqKIkpC9zqC33b2A65TYzqd5g+/+wPuP5pFi8a5+OLLTJ48WWK1mH88Rzqd5siRCSLRKI0NrQbKNnM+VwS+VX0eVj5qbars67q7d3rTAsFWfQECPjswUNreoOMiKL5AxepWqXOe61e25T5vZRy0QZz4jeWW8TZY/wmP2+bJ+Y+PJ2bLq231PxjCX1EU5ubmCn9HImGi0SiRSJiOjjidncWx4yWWZaKq1cP1/umf3sGyoY6qaQAAIABJREFUTSfjnSLQVLWgKUvPxG47AlRaVsHBsMQyIJ3dnqbr6K7HfiOw97kl1b7C3tk/7nU9iW2DIgWKrhZuE1TWUxC2TS6fJ5nK8O9/5/dYXt9k8tyzXHn5FVSfY4hDh4+UYylntEZD9mNi1ijY1FpycBeP9tBsM9MHvg+aPCOtWi2gKtWQ1V/Unx8tmaiq0HwqdWuhaRVvGzYkTxoC0RRl/7aKr0Gog/OACH+VQ4ecs2Uv1W06nSWRSJZEzItGIwWnOkNmkIaBbdtYlqOh29JG2pJTp06xtLTE44VFlhaX6OvqBPfaHlAIEuRZAjT3qp6UEsOyHG9/AeFwGFVTSwRwULlU2O8JBSFtSt37ipC5PwzTRFU8Td/x4y8vL6Uknclw8849fvhnf45h5JESspbg5c9/hZOnT1cJYVyD60YWwzqogkFA57FCsWrl26FteMT+AvYXap3bNIqjSsXAgr2oYDNDqFHH0JqGgSYZ8L0B8TTPQD5r5y/l0Cj/+2XpcJ+3aD0NCgdC+JdDOByuuIZmWTa5XI5sNlfYDGiahq5r7KRz7OaMkvKmooIQhEKhQOfb0p1UISEwwA29qxYXaNhq5601Be//4gyCkr0bAMIpI5RSTV8UlUXA8uoa733wIZ/evouI9iBCEItE+PoX3yiJtleFk6ahZIg1g6oKgnqKWm2CT1HbaHSO+ld/4vD0+PXJYdC0ZafxTWxVo2szjSqqE/iEoPEXtRnw3Ue1cY43XO/Jj+aWx3JLi9p+rT2iLvqqY7mJDjlwwr9a8h1VVYjFosRi0ZLnWcNkN7lZUd7I50ln0rz7wVWEEFy8cC4AdacHw+499r3zeieDnyL88vvVwegWV6TAS9cjpcTLm1ss8J37/c5kWlpZJpXO0N3ZRU93F49m53jrg6ssr29z5PRFLr94pUCjPIhQ/RY2Bi3r3U0aHQ4S+K0VQfj301ebMaa0W0ltBF877C71y+7flmi/xlkjBoDWoLa1otVx0SiOirHcZmNKkDJ+Y7mhU53P0uJTBO3cRx444V8NioWbaVmsbe2Qz5vkq8T47e8fYHV5mZnldbJvvUs2l+PK5efwCzzhhektxO8v1tDd0oUQuk35prjpNoXAtpywv0JRChp/MczOzfPehx8xt7BENm8RDmnoqophGKjhKCfPPsOps+d8z/WD89MolDa6jh+JT5XPulmwfNi0uNqV425TmWbqNMt++wPwHfzx4bNyVDzZH2jRWlEAV09vku2q1dpnTGm6zP8f16RSaHxNOpDC34tm512Jc58CAtOy2E6kSaQyBQe9hw/u8+H77yOlJB6Pc+bcOfr6+5k4dpy5mSmyuTy5XN7tkPLpK0toeg54XslypzuJXXDyqW8F2MNN0aZCqEphU1AMP3rzp3x65y47qTzHT54mFI4w/XCKjY0NdF3n2OFjnDx9puSaYVuPE6uCqPFXFbIlKsFneZL5nffUX+2cWnspQvfHKNv8R97T3Nwx2qhN60kuovtOYo9AdVO+Xw+1kaknIosaMFkVwd4sDmbkb64prY9lD0/VsVxtDreBhycLtWw3niCrvyE6kMJfCD+h4fy2bJtkOoNlWezu7HDr5qfMPHpEMpkEIJVKkU2lCIc1LNPENnIcPjbBkcPjVE5nR9JbbnAdpE+8QAlSyIJnvu3dCPBi8FcBr4xzi8AzzSuoinTv99uoblx+KSV/9pO3uXrjNn1D41z+3Dk6O7sQisLo2DgP7t1lbm6WfN4oBCsq7RW/TqzG1f4O7tKFszatCm6aZm8f2hUQZbVizj6yPaehpXiKBVU78PucxzcDdZG0YBBu0Y5cn7Lw+dXIlrcNI7ClzVSdOi0y5987QcrXgz01a3/mSiW1pyfa22NuCTKWg6pcB0z4l7JdzXHdtp3IeIqq8OjRI1LJZMGT37YszGySsYFRhgcHOTw+xvjYCD3dXWWWBArCvBjsUlLOPlKCnyirsccsyvonKu7qC5w0wMLN6PfprTtcu3mX/uFDnL/0LD09PYWrhaFQyLn2GI2yvLTM1IP7nH/mYoC+bA6cEMsKhmmV5CJoFEoU/1plimmrCpqqkjfNAm1dczZYlmVXfCt/TP5EVTdEdCHCo1tK1zWkBMNN+1wNd0jXCGk6trTJGwamG4iq1gRr10JTTQjtD/79qh20XC3viOZI1cXSBqeL9gmV+mO5aeotSb/9EJ0CbwPwJMZyfRr7uTVo51iuvdELejP7gAn/2lyblk02ny80LhyOkE6lQEpUNwSupgg0TfD8pWc4cmiczs444XCoyOouC9n6Ct72LkhwBX0pK54W7wjvvQFbnm3Hmx6W7TgIegK/ZMPhVrdsG2mZPJia5r0PPyEc7+HcMxfp7e0tKa+qKl3d3Zw6fYaBgcGSwD2lIPeQV+3H2v2rCEEkFCKZyRCNhB1LQ4ANgF/SoQDkKqAr3sHjxWXGR4ZIpNLouoZpmKiahqYKDKvehsS/7U52R70Qljkc0jFMi7CukUxn6I7HSzYFpbw7OFVFZXXTcSwd6O3BtHKNNS4wNLvIBvn+reCvg69tKIO0oc3QRvnaPmhHH5R9m4bQldOvookFGmvNr0nNQtU1qXYt2s/PE8bZAKkDJvyrgy0lubzBdiKF5Wpd+Zxz7U9TFCKaACEIh1Qunj/D8aMT9PZ04R23F05hXaEvhBtpj72urLtrFI7+XwgYJMGzFTiDbS9Ov5NqV1Q49HlZ+AzTIrm9zftXr/F4eY1LL7xEX39/hWXCY6qru5uOeLwQpKgKh3VaQc1xE9J1NrZ3WFpf5+zksYJlQtNUQpqGIgS27R1ZKAhFkDMMhFtXSkk6myMWCTsbLFs6txmEcGIxuNp1OKSjqirSlli2RTqbQ1UUcvkc33vrXf7Sl1+nIxYlEgpx7eEjhgf66e/pJqKqjhUFgWVbTvpjVXWugRoG0XAEISCby6MqCiFdw7KdGAqapjI9v0jeMLh4ahLIk0yluf3wEV9++UVU1WmbYVlkc3lCuk5Id4M7SUim0yyurhOLRhgfHkRVFCfGQj5fumloGap8nLprSNAVvoWFyHdF3Q+tM4ig+aycz/pBEN4baFtVdAFwtLRzD9qG5tekZqFxwU/7maiKs9Wx3B4+Gwtb9xTBsmzS2RzJVJrNrU1MwyCRSKAqglhIIawrhDXBS5cv8cqLzxOPd+A68bsgSg7zixXIql0pnXeKK8Q9Fz9ReO2c39vFsfahYCHwBLllWVglAkJgmibXb95hdmGZcEeczc0NlpeW3BTB/oypqoqmV49sGAhqjBtd15ieXyCVyTrBlYBIOEQul2dmcYnbDx+xsLKKEI42Pb+8SkckQkcsyr1HsxiGSTQcJpvLs7ObIJvNMruwxL3pGVY2NpDSRlMVUuk0CyurzC0tg3RM8rqmMb+yysLqOm99fB1NVQmHQkzNzZPOZNA1lZ1EkqnZx0zNPWZje4dwKMTj5RXCuk40HGZje5vF1XUi4RCRcIi5pWWHV8XJo5DKZFjZ2GQ7kSSXy3NzappoJMxuMsmtqUfcmppmN5EkFgnTEY0wu7DE8to6mzs7LK2tk8vniYRCpDM5FlfXyRsGkVCL36MN3+2JQXMrqlu3ZeLtRvgUod2SrpW6DWze9gs+y5+yKTgYY/mAaf7+OyDLtsnk86QyWdLpFPfu3qW/v59EIkFEV3nmzCSr6xtsbm9z5YXLdHd2+t9791GqK8Z0sYW+YLZ3Nfai58giS4LrOGgaeTKZDNlsnlQ6TTabw7JtQrrGQH8/A/19ABimwcLCIjfvPqB/aIyxw0dYW1tlaXGRoeHhFrdkzW+jdVXl8fIKL54/BxI0VSGXN5hdXGZjewcpJWvKNulcjqOjo7x3/RZnjk2wsb3Dm+9/ROg1nXMnjnP15h06Y1HSmQw5N+Xx6tY2R8dGGejp4s70DEtrGxwaHmZscABFCHRNZXZxhefPneH+zBybO7tEQiFyhuFYJLZ2mX68QDaXQyLZ3N4hrIe4fm+Kkb5+OmJRPrx5h0wux69+42ssrq7xcG6Bwb4+VEXBMExGBvpJZTJ8dPsu/V3dZDJZXrxwlvdv3Ma0TGxgaW2DN166jGWa/Pi9q5w8doQjI8OsbmyhKM5tk/uzc6QyWeKxGJrW3JXL9n01fwzN42uPGnZg9PKGGPEr3ACCA9Lo9rFRhKklhK1x9MS79UmfnD0lugdM+PubQvKGSSKdJZXNkUommZ+bY2Z6Gl3T6IyG+Nmf+TK3bt/l3tRDLNdRrWrQm6JjMFlFwwZHsxV+ZnvPac/1/s+bJolEkmQqRSKRZHVjk62tHTa2t1lb38AwDEYG+3nlyvMM9PdhGAbLK6t8+PEN0MJcvvISA4ODGEae5aXlwMF6qkPz9XVNY2F1nW++3udo/aEQt6busbWzy+ljE0yMDnPj3hTX7tznyMgw24kEUko+/PQ2PV2dLK2vc3h0mJmFRS6cOs7Nh4+4cuEcI4P9fHTrLgsrq4Q0laW1DTpiUZ47d4qQprGdTKGpKnMLK3z5lRdQVYWPbt/FOnWSjnAEVVG4duc+iVSKF86fwbRtpucWeDg3T0QLsbq5SSwdIZXOsLG7A0iu3rrD6aMTjsC2bZCS/t4eltY2ePvadU4cHuelZ86zsr7Bg7l5/vq3vkEoHOI3fuv3WVhZQ1c00tkcI/39nDx6mOn5BZKpDOvbO1iWxevPP0dPV5ydZKrQf63MzYp6DSMrP15qFsq9/5tr1QGQgQ7nDcltf/N34B4QB0P+t0/MtqslDeIpY6MVLpr6HvUs9fv1geudBLSZ7gET/g6U+nRJsrk86VSaXDrFzs4OPb19PLh/j1hI49BQN/FYjJdffJ5zZ047d+CFgnRT8nq2/0K/BehARSgV2v4eb3vM2bbN7NxjpqZneTg7SyqdQ+ghFEUj3tUF4RxmfoedRJKtrR0ymSy7iV0++uQGqztJzl26zODQEAChUJgjRyb2zBGtRFFptqqAdC5HZ0eMVDZLSNNYWd9gdGiA/t5uTMsmFNLRFAXDdEz8K+ubzCwt84tvfIE3P7jKezducun0SRY31pFI7s3MMrO4RCqTY7Cvl0Q6A8DnnnsGwzBJZbIAaJrKwuoa44MDDPR1889/6w8QEuIdUWxLksykWdnc5Mb9KcfnQFUIh/sY6u9laW2D5Y11vvrqFX77+z9kaXWDZCbD4dHhgpMfgCoEnbEow329jI8M0dPdxe1Hs5w5PoEQgnzeZLC/l1Q6SyKd5vL504wPDyIQbCWSzCwsEQuHOTY+Sl9PF4lUuuU1obh+yWerdRbVAKX6pStLlPLzhMdgecWgfow+UFq8GqL6ray/Edp73l4FuclObLrva2x0AqxJjZOtUsPn89en4D9u6snT+nj3AdpGzn8sB0V/IIW/YZqYpklI1xFCYJoW6UyGR4+mefunPwWcxmkKZLJ7XtddnXEApBYlkcmBUIjoCjoWSB+nrCo9JChaDqRztl8MUtrYtpNk59/9zu9jS4Ee6+L0uYuMjY/T09uLoihsrK9z49pHbKwsOVpqNEI+b3BnaoaxY6c4dfq0/4IvajDnAxUfu4mBpWsaq+ubdMdj7CRTroOfQm9XJ5lsjkQqjRky2Umk6IjF6Ovupqujgx+8+yGvXX6Wkf4+ltY3WNve4Su//ALWLZtIKMz5yWP0dMZR3TP8T+9PYZgWvV3drG1tA46VxdtUhcMh/t/2zjw4juu+859f9xwYDAACIEAQBAiAAAmC4H2YpG5ZjhRFki3buRwnFa3jXW9teStOpVyJVKnacv5IlXdju5JU5Vhn49gVJ3Hssr1WuFEUSdRpSqRE8RQpSoRI8AB44xzMPW//mAEwM5ie6Z5pYGbI/lah0P3mvd/xfr9+v3d1Pz2us7mvlzeOHuPBXdtobKijuaGB9pbl7N6yEWH+zYQz5y/wyttH8Ptq6F61klpvDfsPHeb+nduSmxPj8xskk3s0EjTU+elb3UEsEae+zsel0WtEYskDnS5cHuUTe3bx3GtvsWNwHbomzASD6KKxZ8sgDf7kGwkXr1ylrWU5wXDEemUbmMqc2QrnstYhMc4h2cQs8C2+cZPctyU2lsafW7JC2Ch02PR1+wVEiqRqGDwLwGjv3xzN/JTMBVqjEmZymOuiGWUt4OnGsLtvYIaWqQ5vbl82K2rFBX+l4Oy5C5wbvsimgX4629sQES5dvMg7hw7h9XpTm++gttbNzu3bkiP9eQqgu/nRvv+Hx+vlvt1b6W5rRsWTJ+tlInmKXvqHc1Ra7wmYM0JCpd7bV7NyKkavXAXRCEQSfObhR2hqbs6Ytl/e0sK9D36coaEhht5/n+dfegM0jbqWFWzbsX2OfKmwRMPAoTxuF5evXGV8apq//dGzaCI8fv9d7Boc4OdHj/PsS68B0NW+kof27CQcjdDR3sKps+fZun4tCEwGg/zyJx4gFImya8sG/u3VA+x7+Q3i8TgDfT1s6V+Lrussa/ATS3uvXtM0ro+Nsa67k2gsxnQwxP0f287Js0O0t7RQX+enf81qjpz+gO/+dB/1/lrWdnWyd+smWpuaGB4Z4atf+E0i0Sgdba1MTE3T3trC1MxMxquKs50MTdNoqPUzE4mwdaCfoeERvvvTfUSiER65ew+tTY1MB2dY2bIcj8fD9bFxVrY2s7qtjdamRmKxGGfOX6C7o73k4G8aFhogW8cwJgdatvO1GfaN+IxnSioRxqN4s5lt5GuEcqyVWOFZLgNb4VvMzFgpH3KxCzt37lRvvXUAgGg0wR9/4y84deYsjz/ycR596H58Ph+TU1OoWByForGhnlgshkrEaW5qRNP0Od11FUfpbr75N9+l1ufjFx64m96uDgSFqAS6ioFKENc8KNFRAlrqQJ1EIjmid0uChEBCJTsBGgl0FUefe61PMR0I8P1/+QnXJ2fYvnsva3r7FqzXp9vj1s2bjI6O4HF76OrpxuutKVgvpc7+5xTEgJ5L12lpXIauz+82jMeTr+G5XS48bjcIRCJRAsEgSkFTQz1KKSYCAdwuFw21PiLRGDcnJnHpOg11tXOzN+HU1wldLp1EQhEIhgiGk7M2uq7TsqweEY3x6WlC4QgN/lpqvF4SiQRTMzO4NB2/rwa320UikSAUjhIIhaiv9eH1uAkEQ4QiEZpSpxuOTU0RjmSe9Fjj8eD3JfcQBMNhpmaCeD1uGvxJOQEmAzOpWQUvsXiM6Zkg/lofHpeLYDhMQilqvV5EE2aCISYDM5ZNYFPMsY40Gna7lZ1585avhEX1WdimVGk0cp7wu7gsS/PlvAStl6m4DYHFFl8ERX79s5/mvRMnclKtqJG/UnD42EnGxyfZONDPhUujnB++xNbNG9A1nTPDwxx89xh3797Oi6++icfj5guf+yxvHz3Om28foa2lhd/5/K/Q2JhsyEPhEC+8+nNu3BqnxuPh7o9t4949OwlHIhw78T7P7X+DqalpNvT38vnPfJIf7/t3NE3nyUcfos5fy4v7X2dscpqdm/pZ370SlUguHQQCM7zx5tuM3hhjy+676O1bm1Of9BpvXr6c5uXLrVdKhtkydyia34iUltOgNxGLx7lyc+HpiPmQnj8UjjCVFggjsRg3xidN0YnH41y9NZ6RNhmYyQisYZLBPhs3JzIDfD4dQpEIoUjmSD0ciXI9MrEg7wTJjXwKCGfxSOpp7UktdVnGll6g5Lw0idwj3uKlsratcH45oYKif6limNRlYU2lMbc62jPBwRINO3pkxdjUZl82Ts3PuxgYFi9mea0EVNx7/i+8+nMG16/lk488SDwW4+y5YULBMLquc+3mTY6eeI9Xfn6QzRvWMXRumK/96V/wwdnz7N6xlYNHjvOjf32Oqelk0Hj32CnC4Qg7twyi6xo/+tfnefG1Nzn23gd846++Q8fKFTz+8IMcOnKCb/3N37Omp5vnX3mDsYlppmdCvPnuceLxOMubG4FkkDp3/gJ//48/4NCRE6zsWsPAwIa8+iiD61z32VhoYMlILe5xtavhLP+M0WIiq4nNAStjLTtqy96AZ10eyVkmfUIpf+n8KUWGniqHOV0W1pS1ustnG2VBDmN6JfeCSiqtclwVw69aPMsuOStq5D907gLvfzjEf/vC5+nv7aV79QdcuDTCmaGP6O9bg65ptLW28JX/8hRer4dwJMJ/vPw6X/3yf0bTNYYvXmZsfJJYLDlC37Z5gEcfupeBdX10r+7g+f2v89bho7S3rWBNVye/8/lfAZXA6/Xw9T//3/zul55iWUMdbx89js9Xg6ZprOleTfOyemKhaT48O8TzL7/OlVvjiMvD7r17C76aly/slmxEm7qAxe3hte9R8bjduNOOKE6oBKFItKSzBUqFfdpJ/o1UBVHIyMU5QTEDNlMj82JRgi+nn7Fh9350M9ztGU8XxcIUbLVbpfpy1tViId95LtVAPx0VE/yVguf2v04wFOYbf/l3aJoQj8URTWNVextdHauA5Ppwnd/P2NhNvB4Pfr8fXRc0QHe55gI/QDgcIRaLz70xEI5EcblcuFx66lOzgohGPJ7A5XLhdrl44K7dHDl5mmg0xmB/H53tK9BUnNFrVzl+6gwRNB565DGalzdTU+NbiprB0KmLeHKtH0ya+av1xzN7DJ1JQdM0murqeOXwu6l7YWVzM72rO5iYnrHh07lmJLaaZykX7QsFtWIbikpYsE6jU1JwM9v8Zy5UzG/tNc/cynY/25rwOUK5ZC3HBpLSfDmbVukHX5daB+af//yBufRnQoqQaf4aS/wrJviPjU9y8PBRfvtXn2RDfx8ejwelFPteeJnLo1d4/+xHc3ljsSiBqemsLqhaoPYHHw3zk30vcOCdo1y8PMrMTJDPPvEIy+rr2P/6W3zzr7/D6vYVvPDaAR66by8uXeOBu3fz4msHGBuf5ImHH6DR7+HU6VMcO3acketjdPb0saqjI3VEb/pKnEGPzYSP5e3tZe/mMelfubNlcSluh6jFrr9k8cksq4kwcuMaB4+e5IGP7SCaiPF/X3qd+3ZuZX1vD411frweT0Zdz35OORKNMhkI0OD3U1vjJZC9AU+RW9YFeufRZy5vWh4rO6zmihbRKKRv1bBeugCKCLi57Fjyrr/ChW3b+JrFz7ovF1fGDMypl/0skdcXjduVdF/OzdiwbC5fLsk2pbdJxT1bYtGXzeicwz4L+OYmt+RtEhUU/K/duMldH9vGvXftYll9PbFEDJRwz55dnBu+iK7pdK1aRWhziFg8xpWrV+hqb2PP9i0Qj6E0GFjXSzwWx+txsWFdH+t6ewAYG5+gv6+HgbW9bB1cj1IJfu+//ieOnHiP8ckpHn/449y3ZwfhcIimxmV0rmqno30ljQ0NvHXobU6cOEEkrmjrWM26/vXo+sJPuhoGbzG4NlM2V5miA78J2jmJZFHL1/BZXCEQEVyaxodXrrG+r4ddmzagUJy7eAWPx02Nx8Wbx04yPHIFgJbGZbQ2N6GAUDDE4No11PpqCMzM8Nxrb/LkJ+6DQDoDAznztX3ZCRb0sR0lBdVFgJm6yJIlT7/PFHK1c8XDpC/nG0QtKGNP5WfOSRTIaHC/0HVNyFVMm2SSRgbMVtPiDqzz87HDlwvJV2y5QnmNyudZOTUd/EVEB94BLiulnhCRZuBfgB7gPPBrSqmxVN5ngC8CceB3lVLPF6Lf1bmKX/vUYzQtawCBqakgsXiCthWttC5vxu1yoes6bStbmZye4dLICBv61/Lkw/eiqRgSS3DP9kEAar069+3aRPL9fyESi6KJ4K/x4FFhpgMBViyrZe+2jSiSH3GZGJ/gyKURLo9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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "rcParams['figure.figsize'] = 12 ,8 \n", "image = mpimg.imread(\"NYC_Crime.png\")\n", "plt.imshow(image)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 12. Group identity issues" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we are going to analyze group identity correlation with `sat_score`. We have 4 groups:\n", "\n", "1. `white_per`\n", "2. `asian_per`\n", "3. `black_per`\n", "4. `hispanic_per`\n", "\n", "To analyze the impact of each group in the sat score we need to categorize the schools regarding their sat score:\n", "\n", "1. We are going to use the `quantile`option to divide the groups.\n", "2. We need to create a function that categorize the `sat_score`.\n", "3. `apply` the function to the schools, and create a new column `school_level`.\n", "4. Plot results using `scatter` and `bar` plots." ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.01 942.72\n", "0.50 1193.00\n", "0.75 1266.50\n", "0.90 1423.00\n", "0.99 1913.80\n", "Name: sat_score, dtype: float64" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#Quantiles help us to divide the data in similar distributed groups \n", "combined.sat_score.quantile([0.01,0.5,0.75,0.9,0.99])" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0 Low: Below 50%\n", "1 Low: Below 50%\n", "2 Low: Below 50%\n", "3 Medium: Above 50%\n", "4 Top: Above 90%\n", "Name: school_level, dtype: object" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#define function for classifying schools based on sat_score quantiles\n", "def level(row):\n", " if row < 942.72:\n", " return 'Lowest: Below 1%'\n", " elif row >= 942.72 and row < 1193:\n", " return 'Low: Below 50%'\n", " elif row >=1193 and row < 1266.5:\n", " return 'Medium: Above 50%'\n", " elif row >=1266.5 and row < 1423:\n", " return 'High: Above 75%'\n", " elif row >=1423 and row < 1913.8:\n", " return 'Top: Above 90%'\n", " else:\n", " return 'Elite: Above 99%'\n", " \n", "\n", "#create new column using the function above\n", "combined['school_level'] = combined['sat_score'].apply(level)\n", "\n", "#view DataFrame \n", "combined['school_level'].head()" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "config": { "plotlyServerURL": "https://plot.ly" }, "data": [ { "alignmentgroup": "True", "hovertemplate": "index=%{x}
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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#explore the correlation between groups and sat_score\n", "group_id = [\"white_per\", \"asian_per\", \"black_per\", \"hispanic_per\"]\n", "group_corr = combined.corr()[\"sat_score\"][group_id].sort_values()\n", "fig = px.bar(group_corr, y='sat_score', color='sat_score', height=600, width=500)\n", "fig" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "`white_per`have a positive correlation with sat_score and `hispanic_per`have a negative correlation. I think is important to be very wary of this kind of correlations, because is useful for a racist perspective and the correlations are not strong.\n", "\n", "Anyway, we are going to explore what are the situation with the `hispanic_per`and comapre the data with the other groups." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 12.1. `hispanic_per` vs `sat_score`" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "config": { "plotlyServerURL": "https://plot.ly" }, "data": [ { "hovertemplate": "%{hovertext}

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"gridcolor": "white", "linecolor": "white", "ticks": "" }, "bgcolor": "#E5ECF6", "caxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" } }, "title": { "x": 0.05 }, "xaxis": { "automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": { "standoff": 15 }, "zerolinecolor": "white", "zerolinewidth": 2 }, "yaxis": { "automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": { "standoff": 15 }, "zerolinecolor": "white", "zerolinewidth": 2 } } }, "title": { "text": "SAT Score vs. Hispanic percentage of alumns" }, "xaxis": { "anchor": "y", "domain": [ 0, 1 ], "title": { "text": "hispanic_per" } }, "yaxis": { "anchor": "x", "domain": [ 0, 1 ], "title": { "text": "sat_score" } } } }, "text/html": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig = px.scatter(combined, x=\"hispanic_per\", y=\"sat_score\",\n", " size=\"sat_score\", color=\"school_level\",\n", " hover_name=\"SCHOOL NAME\", size_max=15, title='SAT Score vs. Hispanic percentage of alumns')\n", "fig.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As we can see, 2 of the worst performing schools are 100% hispanic groups. But almost all the schools in NYC have hispanics in their schools. \n", "\n", "The problem in this case is that if the correlation is clear you need to have not Top schools with high percentage of hispanics and good sat_score, but we have one case: [MANHATTAN CENTER FOR SCIENCE AND MATHEMATICS](https://www.niche.com/k12/manhattan-center-for-science--and--mathematics-new-york-ny/). With 57.4% `hispanic_per`and `sat_score` 1430. \n", "\n", "So, we can discard that this is a racial issue, because we need more info and a dynamic view of the scores, in 2019 hispanic sat_score is [better than in 2011](https://reports.collegeboard.org/archive/sat-suite-program-results/2019/class-2019-results), so the problem is not \"racial\", we have other issues to explore, latter we are going to see another direction." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 12.2. Group identity and school level: visualizing the data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "How many people of different group identity does they have in each school level? To answer the question we will do:\n", "\n", "1. Create a `pivot_table` using the `school_level` as index\n", "2. Create a bar plot that aggregate the values so we can easily see the distribution of groups in each school levels." ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "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", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
school_level asian_per black_per hispanic_per white_per
0Elite: Above 99%46.0250004.1000008.22500041.050000
1High: Above 75%14.52037029.13148140.60370414.888889
2Low: Below 50%4.29322044.19378548.5949152.252542
3Lowest: Below 1%7.75000010.05000078.0250003.675000
4Medium: Above 50%9.81538535.72857146.1835167.496703
5Top: Above 90%31.27272717.37878819.85454530.190909
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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "table = table.reindex([3,2,4,1,5,0])\n", "fig = px.bar(table, x=\"school_level\", y=group_id, title=\"Group percentages vs school level\")\n", "fig.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The group identity distribution show us the following:\n", "\n", "+ The worst performing schools have the most `hispanic_per`: 78.025%\n", "+ The elite schools have the lest `hispanic_per`: 8.225%\n", "+ The best performing schools have more percentage of asians and white.\n", "+ The elite and top schools have the lest percentage of blacks and hispanics.\n", "\n", "The issue here is that the data can mislead us: we can associate better sat schools with some race properties, but this is false. \n", "\n", "Behind the great results are [preparation](https://journals.sagepub.com/doi/abs/10.3102/0002831211425609), [a lot of preparation](https://meridian.allenpress.com/her/article-abstract/76/1/1/31870/Community-Forces-Social-Capital-and-Educational?redirectedFrom=fulltext). The Asian families have an old culture that requires exams to access to government jobs. We don't have any \"racial superiority\". Even the low class members invest in SAT prep exams.\n", "\n", "Also the great proportion of asians in elite schools indicate that these schools have a selection process that help them to have only students with greater chance of high sat scores.\n", "\n", "So, the low sat score problem is not a \"race\" problem, even schools with high scores have a great percentage of black and hispanic people.\n", "\n", "But, if the problem is not \"racial\", what is the problem?." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 12.3. Low `hispanic_per`: how are the groups distributed" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "config": { "plotlyServerURL": "https://plot.ly" }, "data": [ { "alignmentgroup": "True", "hovertemplate": "variable=white_per
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"" } }, "title": { "x": 0.05 }, "xaxis": { "automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": { "standoff": 15 }, "zerolinecolor": "white", "zerolinewidth": 2 }, "yaxis": { "automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": { "standoff": 15 }, "zerolinecolor": "white", "zerolinewidth": 2 } } }, "title": { "text": "Group identity vs school level, hispanic_per < 10%" }, "xaxis": { "anchor": "y", "domain": [ 0, 1 ], "title": { "text": "school_level" } }, "yaxis": { "anchor": "x", "domain": [ 0, 1 ], "title": { "text": "value" } } } }, "text/html": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "low_schools = combined[(combined[\"hispanic_per\"]<10)]\n", "table_low = pd.pivot_table(low_schools, values=group_id, index=['school_level'],\n", " aggfunc=numpy.mean)\n", "table_low.reset_index(inplace=True)\n", "table_low = table_low.reindex([2,3,1,4,0])\n", "fig = px.bar(table_low, x=\"school_level\", y=group_id, title=\"Group identity vs school level, hispanic_per < 10%\")\n", "fig.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If the `hispanic_per`is fewer than 10%, we have some changes in the data:\n", "\n", "+ We don't have lowest level schools.\n", "+ The schools below the top level have a majority of black people.\n", "+ In the top schools we have a greater percentage of asians and white.\n", "+ More presence of black people in low level schools indicates not that the blacks are not smart, only indicates that maybe the problem is more correlated with poverty." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 13. Gender and `sat_score`" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In this part we want to explore the relation between gender and SAT scores, we need to explore first if we have any clear correlation, later the percentages." ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "config": { "plotlyServerURL": "https://plot.ly" }, "data": [ { "alignmentgroup": "True", "hovertemplate": "index=%{x}
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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "gender_id = [\"male_per\", \"female_per\"]\n", "group_corr = combined.corr()[\"sat_score\"][gender_id].sort_values()\n", "fig = px.bar(group_corr, y='sat_score', color='sat_score', height=600, width=500)\n", "fig" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Looking the correlation between gender and sat scores we found: nothing. \n", "\n", "A slightly difference, but nothing strong to have a clear conclusion. Let's see the distribution of `female_per` vs `sat_score`." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 13.1. `sat_score` vs `female_per`" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "config": { "plotlyServerURL": "https://plot.ly" }, "data": [ { "hovertemplate": "%{hovertext}

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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig = px.scatter(combined, x=\"female_per\", y=\"sat_score\",\n", " size=\"sat_score\", color=\"school_level\",\n", " hover_name=\"SCHOOL NAME\", size_max=15, title='SAT Score vs. Female_per')\n", "fig.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Looking through the dataset, we have some interesting stuff:\n", "1. [TOWNSEED HARRIS SCHOOL](https://en.wikipedia.org/wiki/Townsend_Harris_High_School): Have a high sat_score (above 1900) and a high percentage of girls (71.1%).\n", "2. The elite and the lowest level schools have similar percentage of women in their schools.\n", "3. The schools with almost 100% women, have sat_scores between 1127 and 1326.\n", "4. A cluster of TOP schools have more than 60% of women in their schools." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 13.2. `female_per` vs `male_per` and `sat_score`." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To explore the gender differences regarding the school level, we need to do the following:\n", "\n", "1. Define the quantiles to segment the percentage of female and male.\n", "2. Create a function that segment the percentages.\n", "3. Plot the data in bars to see the difference." ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.01 12.092\n", "0.20 43.180\n", "0.50 50.100\n", "0.75 55.900\n", "0.95 73.690\n", "Name: female_per, dtype: float64" ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], "source": [ "combined.female_per.quantile([0.01,0.2,0.5,0.75,0.95])" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0 12% to 50%\n", "1 12% to 50%\n", "2 12% to 50%\n", "3 50% to 60%\n", "4 50% to 60%\n", "Name: fem_level, dtype: object" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#define function for classifying players based on points\n", "def gender(row):\n", " if row < 12:\n", " return '< 12%'\n", " elif row >= 12 and row < 50:\n", " return '12% to 50%'\n", " elif row >=50 and row < 60:\n", " return '50% to 60%'\n", " elif row >=60 and row < 75:\n", " return '60% to 75%'\n", " else:\n", " return '> 75%'\n", " \n", "\n", "#create new column using the function above\n", "combined['fem_level'] = combined['female_per'].apply(gender)\n", "\n", "#view DataFrame \n", "combined['fem_level'].head()" ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "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", "
fem_level sat_score
3< 12%1137.250000
012% to 50%1208.157612
150% to 60%1220.491512
260% to 75%1310.972973
4> 75%1233.832276
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male_level sat_score
3< 12%1243.701824
012% to 50%1241.574497
150% to 60%1211.220043
260% to 75%1195.403528
4> 75%1172.133333
" ], "text/plain": [ "" ] }, "execution_count": 30, "metadata": {}, "output_type": "execute_result" } ], "source": [ "male_table = pd.pivot_table(combined, values=[\"sat_score\"], index=['male_level'],\n", " aggfunc=numpy.mean)\n", "male_table.reset_index(inplace=True)\n", "male_table = male_table.reindex([3,0,1,2,4])\n", "male_table.style.background_gradient(cmap=cm)" ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "scrolled": true }, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "config": { "plotlyServerURL": "https://plot.ly" }, "data": [ { "name": "Male percentage", "type": "bar", "x": [ "< 12%", "12% to 50%", "50% to 60%", "60% to 75%", "> 75%" ], "y": [ 1243.701824212272, 1241.5744974549218, 1211.2200426439233, 1195.4035278154681, 1172.1333333333334 ] }, { "name": "Female percentage", "type": "bar", "x": [ "< 12%", "12% to 50%", "50% to 60%", "60% to 75%", "> 75%" ], "y": [ 1137.25, 1208.1576119402987, 1220.4915119061184, 1310.972972972973, 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig = go.Figure(data=[\n", " go.Bar(name='Male percentage', x=male_table['male_level'], y=male_table['sat_score']),\n", " go.Bar(name='Female percentage', x=fem_table['fem_level'], y=fem_table['sat_score'])\n", "])\n", "# Change the bar mode\n", "fig.update_layout(barmode='group')\n", "fig.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can take some quick conclusions:\n", "\n", "1. We have a progressive tendency that with more percentage of women the schools obtain better sat score.\n", "2. The inverse is with males: more percentage of males diminish sat scores.\n", "3. Another curious result is that schools with lower percentage of women, the school obtain less sat score." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 13.3. Gender vs School levels" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we are going to explore the school levels vs the genders. The main objective is to see how the extremes have the distribution of genders." ] }, { "cell_type": "code", "execution_count": 32, "metadata": {}, "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", " \n", " \n", " \n", " \n", " \n", " \n", "
school_level female_per male_per
3Lowest: Below 1%47.52500052.475000
2Low: Below 50%48.56666751.431638
4Medium: Above 50%52.12967047.870330
1High: Above 75%51.98148148.018519
5Top: Above 90%56.54545543.454545
0Elite: Above 99%43.02500056.975000
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"gridcolor": "white", "linecolor": "white", "ticks": "" } }, "title": { "x": 0.05 }, "xaxis": { "automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": { "standoff": 15 }, "zerolinecolor": "white", "zerolinewidth": 2 }, "yaxis": { "automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": { "standoff": 15 }, "zerolinecolor": "white", "zerolinewidth": 2 } } }, "title": { "text": "Gender percentage vs School Level" }, "xaxis": { "anchor": "y", "domain": [ 0, 1 ], "title": { "text": "school_level" } }, "yaxis": { "anchor": "x", "domain": [ 0, 1 ], "title": { "text": "value" } } } }, "text/html": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "table_gen = table_gen.reindex([3,2,4,1,5,0])\n", "fig = px.bar(table_gen, x=\"school_level\", y=gender_id, title=\"Gender percentage vs School Level\")\n", "fig.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As expected from previous point, `female_per`increase with the `school_level`, we have new observations:\n", "\n", "+ In the lowest level school the male and female percentage is almost the same.\n", "+ The top schools have a mean of more than 56% of women in their schools.\n", "+ In the elite schools the male percentage is greater that the female percentage." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 14. AP Test vs SAT Score" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Another important data point is the AP test takers vs. SAT Score. The [AP Tests](https://en.wikipedia.org/wiki/Advanced_Placement_exams) have a different kind of grade (1 to 5) and is useful for colleges boards to decide if the student is qualified for their studies.\n", "\n", "What we could expect is that a school with more AP test takers have more chance of better SAT scores." ] }, { "cell_type": "code", "execution_count": 34, "metadata": {}, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "config": { "plotlyServerURL": "https://plot.ly" }, "data": [ { "hovertemplate": "%{hovertext}

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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "combined[\"ap_per\"] = combined[\"AP Test Takers \"]/combined[\"total_enrollment\"]\n", "fig = px.scatter(combined, x=\"ap_per\", y=\"sat_score\",\n", " size=\"sat_score\", color=\"school_level\",\n", " hover_name=\"SCHOOL NAME\", size_max=15, title='SAT Score vs. AP Percentage')\n", "fig.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Looking through the data we can say:\n", "\n", "+ Elite and top schools have more percentage of AP test takers than High level schools.\n", "+ Medium and low schools have more percentage of AP test takers than any other schools.\n", "+ Is not clear that AP test takers is correlated with better SAT scores." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 15. Full correlations map" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Visualize all the correlations in a table is really difficult, even segmentation of the data is not enough to fully appreciate all the dataset correlations.\n", "\n", "Here we would explore strong correlation (above 0.7 or below -0.7) and visualize the results using a correlation matrix." ] }, { "cell_type": "code", "execution_count": 35, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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............
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" ], "text/plain": [ " level_0 level_1 \\\n", "0 SAT Critical Reading Avg. Score SAT Math Avg. Score \n", "1 SAT Critical Reading Avg. Score SAT Writing Avg. Score \n", "2 SAT Critical Reading Avg. Score sat_score \n", "3 SAT Math Avg. Score SAT Critical Reading Avg. Score \n", "4 SAT Math Avg. Score SAT Writing Avg. Score \n", ".. ... ... \n", "257 total_students male_num \n", "258 total_students female_num \n", "259 total_students N_s \n", "260 total_students N_t \n", "261 total_students N_p \n", "\n", " 0 \n", "0 0.929221 \n", "1 0.982826 \n", "2 0.986820 \n", "3 0.929221 \n", "4 0.931385 \n", ".. ... \n", "257 0.943833 \n", "258 0.956412 \n", "259 0.942661 \n", "260 0.923733 \n", "261 0.817987 \n", "\n", "[262 rows x 3 columns]" ] }, "execution_count": 35, "metadata": {}, "output_type": "execute_result" }, { "data": { "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(16, 6))\n", "# define the mask to set the values in the upper triangle to True\n", "correlation_A = combined.corr()\n", "correlation_A = correlation_A[(correlation_A>0.7)&(correlation_A<1)]\n", "\n", "\n", "s = correlation_A.stack()\n", "s = s.reset_index()\n", "s" ] }, { "cell_type": "code", "execution_count": 36, "metadata": {}, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "config": { "plotlyServerURL": "https://plot.ly" }, "data": [ { "coloraxis": "coloraxis", "hovertemplate": "level_1: %{x}
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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(16, 6))\n", "# define the mask to set the values in the upper triangle to True\n", "correlation_B = combined.corr()\n", "correlation_B = correlation_B[(correlation_B<-0.6)]\n", "\n", "\n", "s_B = correlation_B.stack()\n", "s_B = s_B.reset_index()\n", "corr_matrix_B = s_B.pivot(\"level_0\", \"level_1\", 0)\n", "fig = px.imshow(corr_matrix_B)\n", "fig.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In the negative side of the correlations we found a very interesting correlation: free lunch. \n", "\n", "Free lunch in 2011 was huge. You need to qualify to had a free lunch in 2011 , and in this year the crisis forced [more families to opt for free lunch](https://www.nytimes.com/2011/11/30/education/surge-in-free-school-lunches-reflects-economic-crisis.html).\n", "\n", "We need to explore further this data." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 15.1. Free Lunch vs. Sat scores" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As in the previous exercises we are going to create groups according the percentage of free lunch students. Next we compare with the sat scores results." ] }, { "cell_type": "code", "execution_count": 38, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.01 18.40\n", "0.20 54.44\n", "0.50 69.60\n", "0.75 77.00\n", "0.95 87.17\n", "Name: frl_percent, dtype: float64" ] }, "execution_count": 38, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#Define the groups to divide the free lunch data\n", "combined.frl_percent.quantile([0.01,0.2,0.5,0.75,0.95])" ] }, { "cell_type": "code", "execution_count": 39, "metadata": {}, "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", "
frl_level sat_score
4frl < 19%1762.666667
0frl 19% to 55%1409.714731
1frl 55% to 70%1210.293112
2frl 70% to 77%1160.910276
3frl 77% to 88%1127.959859
5frl > 88%1100.992351
" ], "text/plain": [ "" ] }, "execution_count": 39, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#define function for classifying sat scores based on free lunch\n", "def frl(row):\n", " if row < 19:\n", " return 'frl < 19%'\n", " elif row >= 19 and row < 55:\n", " return 'frl 19% to 55%'\n", " elif row >=55 and row < 70:\n", " return 'frl 55% to 70%'\n", " elif row >=70 and row < 77:\n", " return 'frl 70% to 77%'\n", " elif row >=77 and row < 88:\n", " return 'frl 77% to 88%'\n", " else:\n", " return 'frl > 88%'\n", " \n", "\n", "#create new column using the function above\n", "combined['frl_level'] = combined['frl_percent'].apply(frl)\n", "\n", "frl_table = pd.pivot_table(combined, values=[\"sat_score\"], index=['frl_level'],\n", " aggfunc=numpy.mean)\n", "frl_table.reset_index(inplace=True)\n", "frl_table = frl_table.reindex([4,0,1,2,3,5])\n", "frl_table.style.background_gradient(cmap=cm)" ] }, { "cell_type": "code", "execution_count": 40, "metadata": { "scrolled": true }, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "config": { "plotlyServerURL": "https://plot.ly" }, "data": [ { "alignmentgroup": "True", "hovertemplate": "frl_level=%{x}
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"xaxis": { "automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": { "standoff": 15 }, "zerolinecolor": "white", "zerolinewidth": 2 }, "yaxis": { "automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": { "standoff": 15 }, "zerolinecolor": "white", "zerolinewidth": 2 } } }, "title": { "text": "Sat score vs Free lunch level" }, "xaxis": { "anchor": "y", "domain": [ 0, 1 ], "title": { "text": "frl_level" } }, "yaxis": { "anchor": "x", "domain": [ 0, 1 ], "title": { "text": "sat_score" } } } }, "text/html": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig = px.bar(frl_table, x=\"frl_level\", y=\"sat_score\", title=\"Sat score vs Free lunch level\")\n", "fig.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can observe:\n", "\n", "+ The schools with less percentage of students with free lunch have **60% better** sat score than the schools with more than 88% of students with free lunch.\n", "+ But the difference between the schools with 55% to 70% of students with free lunch have a **10% better** score compared with schools with 88% or more free lunch students.\n", "\n", "The difference is abysmal, let's explore further the data. " ] }, { "cell_type": "code", "execution_count": 41, "metadata": {}, "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", "
school_level frl_percent
3Lowest: Below 1%80.800000
2Low: Below 50%73.471751
4Medium: Above 50%68.582418
1High: Above 75%56.842593
5Top: Above 90%38.203030
0Elite: Above 99%25.575000
" ], "text/plain": [ "" ] }, "execution_count": 41, "metadata": {}, "output_type": "execute_result" } ], "source": [ "table_frl = pd.pivot_table(combined, values=\"frl_percent\", index=['school_level'],\n", " aggfunc=numpy.mean)\n", "table_frl.reset_index(inplace=True)\n", "table_frl = table_frl.reindex([3,2,4,1,5,0])\n", "table_frl.style.background_gradient(cmap=cm)" ] }, { "cell_type": "code", "execution_count": 42, "metadata": {}, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "config": { "plotlyServerURL": "https://plot.ly" }, "data": [ { "alignmentgroup": "True", "hovertemplate": "school_level=%{x}
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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig = px.bar(table_frl, x=\"school_level\", y=\"frl_percent\", title=\"Free lunch percentage vs School Level\")\n", "fig.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "+ The elite schools have 25.5% of free lunch students, also have less free lunch percentage of students than any other schools. \n", "+ The lowest level schools have more 80.8% percentage of free lunch students.\n", "+ High, medium and low level schools all have more than 50% of free lunch students.\n", "\n", "Now let's group the group identity and the free lunch percentage above 70%. Which groups have more presence with free lunch and have score above 1200? " ] }, { "cell_type": "code", "execution_count": 43, "metadata": {}, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "config": { "plotlyServerURL": "https://plot.ly" }, "data": [ { "alignmentgroup": "True", "hovertemplate": "variable=white_per
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"white", "ticks": "", "title": { "standoff": 15 }, "zerolinecolor": "white", "zerolinewidth": 2 }, "yaxis": { "automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": { "standoff": 15 }, "zerolinecolor": "white", "zerolinewidth": 2 } } }, "title": { "text": "Free lunch schools > 70%, Race vs Level School" }, "xaxis": { "anchor": "y", "domain": [ 0, 1 ], "title": { "text": "school_level" } }, "yaxis": { "anchor": "x", "domain": [ 0, 1 ], "title": { "text": "value" } } } }, "text/html": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "top_frl = combined[(combined[\"frl_percent\"]>70)&(combined[\"sat_score\"]>1266.5)]\n", "table_frl = pd.pivot_table(top_frl, values=group_id, index=['school_level'],\n", " aggfunc=numpy.mean)\n", "table_frl.reset_index(inplace=True)\n", "table_frl = table_frl.reindex([1,0,2])\n", "fig = px.bar(table_frl, x=\"school_level\", y=group_id, title=\"Free lunch schools > 70%, Race vs Level School\")\n", "fig.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Reviewing the data we obtain:\n", "\n", "+ In top schools we have a large percentage of asians, is possible that top schools could help with some perks to asians, like free lunch, to attract students with greater chance to obtain high sat scores.\n", "+ In high level schools we have a greater proportion of hispanics and blacks, but black persons are not in great proportion, is possible that is schools prefer hispanics over blacks to obtain better sat scores? Difficult to say. " ] }, { "cell_type": "code", "execution_count": 44, "metadata": {}, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "config": { "plotlyServerURL": "https://plot.ly" }, "data": [ { "hovertemplate": "%{hovertext}

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"white" }, "hoverlabel": { "align": "left" }, "hovermode": "closest", "mapbox": { "style": "light" }, "paper_bgcolor": "white", "plot_bgcolor": "#E5ECF6", "polar": { "angularaxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" }, "bgcolor": "#E5ECF6", "radialaxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" } }, "scene": { "xaxis": { "backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white" }, "yaxis": { "backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white" }, "zaxis": { "backgroundcolor": "#E5ECF6", "gridcolor": "white", "gridwidth": 2, "linecolor": "white", "showbackground": true, "ticks": "", "zerolinecolor": "white" } }, "shapedefaults": { "line": { "color": "#2a3f5f" } }, "ternary": { "aaxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" }, "baxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" }, "bgcolor": "#E5ECF6", "caxis": { "gridcolor": "white", "linecolor": "white", "ticks": "" } }, "title": { "x": 0.05 }, "xaxis": { "automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": { "standoff": 15 }, "zerolinecolor": "white", "zerolinewidth": 2 }, "yaxis": { "automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": { "standoff": 15 }, "zerolinecolor": "white", "zerolinewidth": 2 } } }, "title": { "text": "SAT Score vs. Free Lunch" }, "xaxis": { "anchor": "y", "domain": [ 0, 1 ], "title": { "text": "frl_percent" } }, "yaxis": { "anchor": "x", "domain": [ 0, 1 ], "title": { "text": "sat_score" } } } }, "text/html": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig = px.scatter(combined, x=\"frl_percent\", y=\"sat_score\",\n", " size=\"sat_score\", color=\"school_level\",\n", " hover_name=\"SCHOOL NAME\", size_max=15, title='SAT Score vs. Free Lunch')\n", "fig.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We find several outliers:\n", "\n", "* [ALL CITY LEADERSHIP SECONDARY SCHOOL](https://www.niche.com/k12/all-city-leadership-secondary-school-brooklyn-ny/#reviews): This school have a high percentage of hispanics, and also have an average SAT score of 1315." ] }, { "cell_type": "code", "execution_count": 45, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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SCHOOL NAMEwhite_perasian_perblack_perhispanic_perfemale_persaf_s_11
361ALL CITY LEADERSHIP SECONDARY SCHOOL1.55.312.979.547.98.9
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" ], "text/plain": [ " SCHOOL NAME white_per asian_per black_per \\\n", "361 ALL CITY LEADERSHIP SECONDARY SCHOOL 1.5 5.3 12.9 \n", "\n", " hispanic_per female_per saf_s_11 \n", "361 79.5 47.9 8.9 " ] }, "execution_count": 45, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#\n", "high_frl = combined[(combined[\"frl_percent\"]>80)&(combined[\"school_level\"]==\"High: Above 75%\")]\n", "groups_a= [\"SCHOOL NAME\",\"white_per\", \"asian_per\", \"black_per\", \"hispanic_per\",\"female_per\", \"saf_s_11\"]\n", "high_frl[groups_a]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Top schools with more than 70% of students with free lunch:\n", "\n", "* [HIGH SCHOOL FOR DUAL LANGUAGE AND ASIAN STUDIES](https://www.niche.com/k12/high-school-for-dual-language--and--asian-studies-new-york-ny/reviews/?rating=Average): It's an school with high percentage of asian students, with sat score average of 1430 \n", "* [MANHATTAN CENTER FOR SCIENCE AND MATHEMATICS](https://www.niche.com/k12/manhattan-center-for-science--and--mathematics-new-york-ny/reviews/?rating=Very-Good): An interesting case because it's a School totally against the odds, with more than 57.4 percent of hispanics and a sat score average of 1423. " ] }, { "cell_type": "code", "execution_count": 46, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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SCHOOL NAMEwhite_perasian_perblack_perhispanic_perfemale_persaf_s_11
46HIGH SCHOOL FOR DUAL LANGUAGE AND ASIAN STUDIES2.389.53.44.048.76.611667
67MANHATTAN CENTER FOR SCIENCE AND MATHEMATICS2.220.119.757.453.66.600000
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" ], "text/plain": [ " SCHOOL NAME white_per asian_per \\\n", "46 HIGH SCHOOL FOR DUAL LANGUAGE AND ASIAN STUDIES 2.3 89.5 \n", "67 MANHATTAN CENTER FOR SCIENCE AND MATHEMATICS 2.2 20.1 \n", "\n", " black_per hispanic_per female_per saf_s_11 \n", "46 3.4 4.0 48.7 6.611667 \n", "67 19.7 57.4 53.6 6.600000 " ] }, "execution_count": 46, "metadata": {}, "output_type": "execute_result" } ], "source": [ "top_frl = combined[(combined[\"frl_percent\"]>70)&(combined[\"school_level\"]==\"Top: Above 90%\")]\n", "top_frl[groups_a]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In the extreme of almost 99.2% students with free lunch:\n", "\n", "* [HIGH SCHOOL FOR EXCELLENCE AND INNOVATION](https://www.niche.com/k12/manhattan-center-for-science--and--mathematics-new-york-ny/reviews/?rating=Very-Good): the vast majority is hispanic and black students, almost all the students with free lunch, but an average sat score 1223." ] }, { "cell_type": "code", "execution_count": 47, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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SCHOOL NAMEwhite_perasian_perblack_perhispanic_perfemale_persaf_s_11
83HIGH SCHOOL FOR EXCELLENCE AND INNOVATION0.00.025.872.547.27.6
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" ], "text/plain": [ " SCHOOL NAME white_per asian_per \\\n", "83 HIGH SCHOOL FOR EXCELLENCE AND INNOVATION 0.0 0.0 \n", "\n", " black_per hispanic_per female_per saf_s_11 \n", "83 25.8 72.5 47.2 7.6 " ] }, "execution_count": 47, "metadata": {}, "output_type": "execute_result" } ], "source": [ "top_frl = combined[(combined[\"SCHOOL NAME\"]==\"HIGH SCHOOL FOR EXCELLENCE AND INNOVATION\")]\n", "top_frl[groups_a]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Conclusions\n", "\n", "* We found a positive but weak correlation between sat scores and security and respect answers in the survey of students. \n", "* Hispanic population have a negative correlation with sat scores. As explained previously this is not a \"racial\" issue, we need to explore more factor and see outliers that have great scores even having more than 70% of hispanic students.\n", "* Female percentage is weak correlated with positive sat scores, anyway the elite schools have mostly male students.\n", "* AP test takers are correlated with sat score results, only that is not enough to indicate a positive correlation with sat scores.\n", "* Free lunch program takers have a strong negative correlation with sat scores. Mostly this could indicate that have lower resources for sat preparation is a better predictor of low scores than race, gender or AP test takers." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Useful links\n", "\n", "* https://towardsdatascience.com/visualizing-nyc-bike-data-on-interactive-and-animated-maps-with-folium-plugins-c2d7645cd19b Dualmaps\n", "* https://www.kaggle.com/daveianhickey/how-to-folium-for-maps-heatmaps-time-analysis Heatmap\n", "* https://towardsdatascience.com/data-101s-spatial-visualizations-and-analysis-in-python-with-folium-39730da2adf heatmap with time\n", "* https://maps.nyc.gov/crime/ NYC Crime\n", "* https://apstudents.collegeboard.org/about-ap-scores/ap-score-scale-table AP scores" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.3" } }, "nbformat": 4, "nbformat_minor": 4 }