{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Model application manual\n", "This notebook describes how to apply the model on different data as presented in the thesis. It will be explained:\n", "\n", "- How to create a data set readable by the model\n", "- How to execute the model itself\n", "- And how to conduct Monte Carlo Simulations with the provided code" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## How to create a readable data set\n", "### Creation of a readable data set\n", "The created model requires an excel file in a specific format. A function was created to create a plain excel file in the right format. The function requires a list entailing the product categories which are treated by the model, a string naming the product category which flows into the reuse part of the model, a list indication the products treated in the reuse part of the model, and the number of considered use cycles in the reuse part. Furthermore, a file name has to be provided. The created file will appear in the \"data_model\" folder." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "from functions import input_file_creator\n", "\n", "input_file_creator(\n", " product_categories=['electronics', 'cars', 'industrial applications'], \n", " category_for_reuse='electronics', products=['shaver', 'phone', 'TV'],\n", " considered_use_cycles=2, file_name = 'example_file_creator')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "All sheets of the file are empty and to be filled by the user." ] }, { "cell_type": "code", "execution_count": 2, "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", "
Product categoriesshare
0electronicsNaN
1carsNaN
2industrial applicationsNaN
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" ], "text/plain": [ " Product categories share\n", "0 electronics NaN\n", "1 cars NaN\n", "2 industrial applications NaN" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import pandas as pd\n", "pd.read_excel('data_model/example_file_creator.xlsx')" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "import os\n", "os.remove('data_model/example_file_creator.xlsx')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To showcase how to input data, an example file will be considered." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Things to consider populating the data frame\n", "A few sheets are presented as examples. In general, common-sense mistakes need to be avoided.\n", "\n", "#### Ensuring mass conservation when splitting the material flow\n", "The following output shows the first sheet, the split of the initial inflow." ] }, { "cell_type": "code", "execution_count": 4, "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", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
Product categoriesshare
0electronics0.412
1mobility batteries0.012
2hydroprocessing catalysts coke0.020
3hydroprocessing catalysts poisoning0.000
4hydroformylation catalysts0.003
5pet precursors catalysts0.017
6dissipative uses0.090
7hard metals0.230
8magnets0.006
9other metallic uses0.210
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" ], "text/plain": [ " Product categories share\n", "0 electronics 0.412\n", "1 mobility batteries 0.012\n", "2 hydroprocessing catalysts coke 0.020\n", "3 hydroprocessing catalysts poisoning 0.000\n", "4 hydroformylation catalysts 0.003\n", "5 pet precursors catalysts 0.017\n", "6 dissipative uses 0.090\n", "7 hard metals 0.230\n", "8 magnets 0.006\n", "9 other metallic uses 0.210" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pd.read_excel('data_model/data_example.xlsx', sheet_name='MaTrace_initial_inflow')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The sum of this column has to equal 1.\n" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "1.0" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pd.read_excel('data_model/data_example.xlsx', \n", " sheet_name='MaTrace_initial_inflow')['share'].sum(axis = 0)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The same holds true for the sheet \"Reuse_inflow_split\"." ] }, { "cell_type": "code", "execution_count": 6, "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", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
Product categoriesshare
0electronics0.412
1mobility batteries0.012
2hydroprocessing catalysts coke0.020
3hydroprocessing catalysts poisoning0.000
4hydroformylation catalysts0.003
5pet precursors catalysts0.017
6dissipative uses0.090
7hard metals0.230
8magnets0.006
9other metallic uses0.210
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" ], "text/plain": [ " Product categories share\n", "0 electronics 0.412\n", "1 mobility batteries 0.012\n", "2 hydroprocessing catalysts coke 0.020\n", "3 hydroprocessing catalysts poisoning 0.000\n", "4 hydroformylation catalysts 0.003\n", "5 pet precursors catalysts 0.017\n", "6 dissipative uses 0.090\n", "7 hard metals 0.230\n", "8 magnets 0.006\n", "9 other metallic uses 0.210" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pd.read_excel('data_model/data_example.xlsx', sheet_name='MaTrace_initial_inflow')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "And as well for the sheet \"MaTrace_D_secondary_material\". In this case, the last two rows need to be excluded since the indicate the export and to production rate." ] }, { "cell_type": "code", "execution_count": 7, "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", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
Product categoriesCo metal or compoundW-Co powder
0electronics0.00000
1mobility batteries0.02100
2hydroprocessing catalysts coke0.01350
3hydroprocessing catalysts poisoning0.01350
4hydroformylation catalysts0.00500
5pet precursors catalysts0.02400
6dissipative uses0.16700
7hard metals0.31301
8magnets0.01700
9other metallic uses0.42600
10export rate0.13300
11to production rate0.86701
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" ], "text/plain": [ " Product categories Co metal or compound W-Co powder\n", "0 electronics 0.0000 0\n", "1 mobility batteries 0.0210 0\n", "2 hydroprocessing catalysts coke 0.0135 0\n", "3 hydroprocessing catalysts poisoning 0.0135 0\n", "4 hydroformylation catalysts 0.0050 0\n", "5 pet precursors catalysts 0.0240 0\n", "6 dissipative uses 0.1670 0\n", "7 hard metals 0.3130 1\n", "8 magnets 0.0170 0\n", "9 other metallic uses 0.4260 0\n", "10 export rate 0.1330 0\n", "11 to production rate 0.8670 1" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pd.read_excel('data_model/data_example.xlsx',\n", " sheet_name='MaTrace_D_secondary_material')" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " Product categories Co metal or compound W-Co powder\n", "0 electronics 0.0000 0\n", "1 mobility batteries 0.0210 0\n", "2 hydroprocessing catalysts coke 0.0135 0\n", "3 hydroprocessing catalysts poisoning 0.0135 0\n", "4 hydroformylation catalysts 0.0050 0\n", "5 pet precursors catalysts 0.0240 0\n", "6 dissipative uses 0.1670 0\n", "7 hard metals 0.3130 1\n", "8 magnets 0.0170 0\n", "9 other metallic uses 0.4260 0\n", "\n", "Sum of columns without export rate and to production rate:\n", "Product categories electronicsmobility batterieshydroprocessing c...\n", "Co metal or compound 1.0\n", "W-Co powder 1\n", "dtype: object\n" ] } ], "source": [ "print(pd.read_excel('data_model/data_example.xlsx', \n", " sheet_name='MaTrace_D_secondary_material').iloc[:-2, :])\n", "print()\n", "print('Sum of columns without export rate and to production rate:')\n", "print(pd.read_excel('data_model/data_example.xlsx', \n", " sheet_name='MaTrace_D_secondary_material').iloc[:-2, :].sum(axis=0))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Furthermore, some transfer coefficients must sum up to 1. In the model one transfer coefficient is sufficient to define a flow which splits into two. However, for the Monte Carlo simulations both are needed.\n", "\n", "The following cell shows the example of the sheet \"MaTrace_end_of_life\":" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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Product categoriesfraction export eol productsfraction collected eol productscollection to recycling ratepostconsumer disposal ratepre-treatment efficiency
0electronics0.20.80.640.360.95
1mobility batteries0.01.01.000.000.85
2hydroprocessing catalysts coke0.01.01.000.000.85
3hydroprocessing catalysts poisoning0.01.00.001.000.00
4hydroformylation catalysts0.01.00.900.100.85
5pet precursors catalysts0.01.00.500.500.85
6dissipative uses0.01.00.001.000.00
7hard metals0.01.00.470.530.77
8magnets0.01.00.990.010.65
9other metallic uses0.01.00.840.160.65
\n", "
" ], "text/plain": [ " Product categories fraction export eol products \\\n", "0 electronics 0.2 \n", "1 mobility batteries 0.0 \n", "2 hydroprocessing catalysts coke 0.0 \n", "3 hydroprocessing catalysts poisoning 0.0 \n", "4 hydroformylation catalysts 0.0 \n", "5 pet precursors catalysts 0.0 \n", "6 dissipative uses 0.0 \n", "7 hard metals 0.0 \n", "8 magnets 0.0 \n", "9 other metallic uses 0.0 \n", "\n", " fraction collected eol products collection to recycling rate \\\n", "0 0.8 0.64 \n", "1 1.0 1.00 \n", "2 1.0 1.00 \n", "3 1.0 0.00 \n", "4 1.0 0.90 \n", "5 1.0 0.50 \n", "6 1.0 0.00 \n", "7 1.0 0.47 \n", "8 1.0 0.99 \n", "9 1.0 0.84 \n", "\n", " postconsumer disposal rate pre-treatment efficiency \n", "0 0.36 0.95 \n", "1 0.00 0.85 \n", "2 0.00 0.85 \n", "3 1.00 0.00 \n", "4 0.10 0.85 \n", "5 0.50 0.85 \n", "6 1.00 0.00 \n", "7 0.53 0.77 \n", "8 0.01 0.65 \n", "9 0.16 0.65 " ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pd.read_excel('data_model/data_example.xlsx', sheet_name='MaTrace_end_of_life')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The column \"fraction export eol products\" and \"fraction collected eol products\" as well as the columns \"collection to recycling rate\" and \"postconsumer disposal\" have to sum up to 1 for every product category.\n", "\n", "Please also consult the system diagram or the excel file data_example.xlsx to find the respective transfer coefficients." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Defining survival curves\n", "The sheet \"MaTrace_in_use_stock\" show examples on how to define survival curves in the model implementation:\n" ] }, { "cell_type": "code", "execution_count": 10, "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", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
Product categoriesdistributionlocationscaleshape
0electronicsnormal14.0517162.09
1mobility batteriesweibull010.6315012.20
2hydroprocessing catalysts cokegamma02.8947422.50
3hydroprocessing catalysts poisoningdefined_distribution_example_dist00.0000000.00
4hydroformylation catalystslognormal12.3157932.50
5pet precursors catalystsnormal00.5789482.50
6dissipative usesweibull014.4351663.50
7hard metalsgamma08.9152351.16
8magnetsgompertz213.9050221.93
9other metallic useslognormal014.6280641.47
\n", "
" ], "text/plain": [ " Product categories distribution \\\n", "0 electronics normal \n", "1 mobility batteries weibull \n", "2 hydroprocessing catalysts coke gamma \n", "3 hydroprocessing catalysts poisoning defined_distribution_example_dist \n", "4 hydroformylation catalysts lognormal \n", "5 pet precursors catalysts normal \n", "6 dissipative uses weibull \n", "7 hard metals gamma \n", "8 magnets gompertz \n", "9 other metallic uses lognormal \n", "\n", " location scale shape \n", "0 1 4.051716 2.09 \n", "1 0 10.631501 2.20 \n", "2 0 2.894742 2.50 \n", "3 0 0.000000 0.00 \n", "4 1 2.315793 2.50 \n", "5 0 0.578948 2.50 \n", "6 0 14.435166 3.50 \n", "7 0 8.915235 1.16 \n", "8 2 13.905022 1.93 \n", "9 0 14.628064 1.47 " ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pd.read_excel('data_model/data_example.xlsx', sheet_name='MaTrace_in_use_stock')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "One can select a distribution via the column \"distribution\". The normal, lognormal, weibull, gamma, and gompertz distribution are preimplemented. The location, scale and shape factors can be set via the corresponding columns. It is also possible to define own distributions.\n", "\n", "An example for this is the distribution for the product category \"hydroprocessing catalysts poisoning\". The string says \"defined_distribution_example_dist\". This works in the following way. A distribution is defined in the file \"defined_distributions.xlsx\" (see next cell)." ] }, { "cell_type": "code", "execution_count": 11, "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", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
example_distexample_dist_2
01.001.00
10.600.70
20.550.55
30.500.50
40.450.45
50.400.40
60.350.35
70.300.30
80.250.20
90.200.20
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" ], "text/plain": [ " example_dist example_dist_2\n", "0 1.00 1.00\n", "1 0.60 0.70\n", "2 0.55 0.55\n", "3 0.50 0.50\n", "4 0.45 0.45\n", "5 0.40 0.40\n", "6 0.35 0.35\n", "7 0.30 0.30\n", "8 0.25 0.20\n", "9 0.20 0.20" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pd.read_excel('data_model/defined_distributions.xlsx').iloc[:10]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The file holds survival curves defined by the user. Only the first 10 lines are shown. It is important that the distribution is defined for a sufficient number of years, meaning at least the number of considered years.\n", "\n", "To use this distribution in the model, one has to fill the column \"distribution\" with \"defined_distribution_column_name\". Hence, when the entry in the column \"distribution\" says \"defined_distribution_example_dist\", the defined distribution \"example_dist\" will be used for this product category. If it says \"defined_distribution_exmaple_dist_2\" the distribution \"example_dist_2\" will be used for the product category." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## How to execute the model iteself\n", "The first step to execute the model is to load the required data. The model receives a dictionary as input which contains the sheets of the mentioned excel file as pandas data frames. The following code cell creates this dictionary." ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [], "source": [ "file_path = 'data_model/data_example.xlsx'\n", "\n", "data_sheets = pd.ExcelFile(file_path).sheet_names\n", "\n", "\n", "\n", "data_dic = {}\n", "\n", "for data_sheet in data_sheets:\n", "\n", " try:\n", " data_dic[data_sheet] = pd.read_excel(file_path, \n", " sheet_name=data_sheet).set_index('Product categories')\n", " except:\n", " data_dic[data_sheet] = pd.read_excel(file_path, \n", " sheet_name=data_sheet).set_index('Products')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Furthermore, the number of years and the start year have to be defined. A pandas dataframe containing the \"defined_distributions.xlsx\" file has to be passed as well." ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "number_of_years = 25\n", "start_year = 2022\n", "defined_distributions_pd = pd.read_excel('data_model/defined_distributions.xlsx')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Optionally, one can decide to print the state to have an indication whether the model is stuck or how long it will still run. One can also define whether the simplified model output shall differentiate between use cycles. Lastly, one can select the number of considered use cycles (default is 3).\n", "\n", "The following code shows the execution of the model:" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Year 1 of 25\n", "Year 2 of 25\n", "Year 3 of 25\n", "Year 4 of 25\n", "Year 5 of 25\n", "Year 6 of 25\n", "Year 7 of 25\n", "Year 8 of 25\n", "Year 9 of 25\n", "Year 10 of 25\n", "Year 11 of 25\n", "Year 12 of 25\n", "Year 13 of 25\n", "Year 14 of 25\n", "Year 15 of 25\n", "Year 16 of 25\n", "Year 17 of 25\n", "Year 18 of 25\n", "Year 19 of 25\n", "Year 20 of 25\n", "Year 21 of 25\n", "Year 22 of 25\n", "Year 23 of 25\n", "Year 24 of 25\n", "Year 25 of 25\n" ] } ], "source": [ "from combined_reuse_matrace_model import evaluate_cohort_combined_model\n", "\n", "matrace_data_dic, reuse_data_dic, graph_data_pd = \\\n", " evaluate_cohort_combined_model(data_dic=data_dic, \n", " n_years=number_of_years, start_year=start_year,\n", " defined_distributions_pd= defined_distributions_pd,\n", " print_state=True, separate_reuse_graph=True, considered_use_cycles=3)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The returned dictionaries \"matrace_data_dic\" and \"reuse_data_dic\" are structured in the same way. The first key takes a string containing the considered year. The second key takes a string indicating a stock or a flow in the year. This will then return a pandas containing the values over product categories or products.\n", "\n", "The following code cell shows the second keys of \"matrace_data_dic\"." ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Index(['U.A use stock', 'U.2 use outflow', 'U.3 hoarding inflow',\n", " 'U.4 no hoarding flow', 'U.B hoarding stock', 'U.5 hoarding outflow',\n", " 'U.6 eol products', 'E.2 exported eol products',\n", " 'E.1 to waste treatment', 'E.3 to pretreatment',\n", " 'E.4 E.5 non-selective collection', 'E.6 to recycling',\n", " 'E.7 pretreatment waste', 'E.12 downcycling', 'recycled w-co powder',\n", " 'co metal compound', 'E.8 recycling waste',\n", " 'E.11 exported recycled materials', 'P.1 total recycled products',\n", " 'P.8 export recycled products', 'U.1 product inflow',\n", " 'P.7 processing waste', 'P.5p downcycled scrap', 'P.4p disposed scrap',\n", " 'P.2 manufacturing waste', 'P.5m downcycled scrap',\n", " 'P.4m disposed scrap', 'P.4 disposed scrap', 'P.5 downcycled scrap'],\n", " dtype='object')" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "matrace_data_dic['0'].keys()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "And the following code cell shows the second keys of \"reuse_data_dic\"." ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Index(['total_use_stock', 'total_hoarding_stock', 'to_disposal_flow',\n", " 'use_stock_1', 'storage_stock_1', 'use_1_to_storage_1_flow',\n", " 'use_1_to_disposal_flow', 'storage_1_to_disposal_flow',\n", " 'use_1_to_use_2_flow', 'storage_1_to_use_2_flow', 'use_stock_2',\n", " 'storage_stock_2', 'use_2_to_storage_2_flow', 'use_2_to_disposal_flow',\n", " 'storage_2_to_disposal_flow', 'use_2_to_use_3_flow',\n", " 'storage_2_to_use_3_flow', 'use_stock_3', 'storage_stock_3',\n", " 'use_3_to_storage_3_flow', 'use_3_to_disposal_flow',\n", " 'storage_3_to_disposal_flow'],\n", " dtype='object')" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "reuse_data_dic['0'].keys()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The following code cell shows the content." ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Products\n", "computer 0.026567\n", "phone 0.045320\n", "fan 0.082400\n", "dish washer 0.185400\n", "e-bikes 0.024720\n", "power tools 0.019502\n", "Name: total_use_stock, dtype: float64" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "reuse_data_dic['0']['total_use_stock']" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Products\n", "electronics 0.38391\n", "mobility batteries 0.012\n", "hydroprocessing catalysts coke 0.02\n", "hydroprocessing catalysts poisoning 0.0\n", "hydroformylation catalysts 0.003\n", "pet precursors catalysts 0.0085\n", "dissipative uses 0.09\n", "hard metals 0.23\n", "magnets 0.006\n", "other metallic uses 0.21\n", "Name: U.A use stock, dtype: object" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "matrace_data_dic['0']['U.A use stock']" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The retuned pandas dataframe \"graph_data_pd\" contains the stocks and the accumulated outflows." ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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Electronics 1st useElectronics 2nd useElectronics 3rd useMobility batteriesHydroprocessing catalysts cokeHydroprocessing catalysts poisoningHydroformylation catalystsPet precursors catalystsDissipative usesHard metalsMagnetsOther metallic usesHoardedExportedDowncycledNon-selective collectionProduction lossesPre-treatment lossesRecycling losses
Year
20220.3775400.0055480.0008210.0121760.0201130.0001130.0030420.0087010.0908670.2314150.0060490.2116410.0059560.0088170.0038400.0106250.0004230.0012040.001109
20230.2811690.0178900.0015930.0125890.0200900.0004210.0031560.0013630.0932210.2197530.0061820.2089730.0459740.0325740.0099010.0344680.0015770.0049410.004165
20240.1997730.0262790.0022350.0129010.0192970.0007280.0021810.0009120.0958380.2076150.0063340.2026970.0672020.0588100.0146610.0615960.0028940.0103180.007728
20250.1428810.0311480.0024430.0129580.0178490.0009090.0019560.0008870.0979350.1952190.0056630.1959000.0753400.0817930.0196190.0868540.0040540.0157080.010885
20260.1060360.0326650.0025450.0127490.0160260.0010100.0018130.0007690.0993120.1827620.0050260.1890720.0757950.1010390.0244310.1095480.0050390.0207810.013581
20270.0830860.0312050.0029170.0122860.0140560.0010630.0016940.0006400.0997780.1704090.0044140.1822730.0738770.1165460.0289500.1296840.0058460.0254690.015808
20280.0682240.0282010.0034530.0116050.0121040.0010800.0015900.0005250.0991930.1583520.0038310.1755930.0687810.1289680.0350740.1484560.0065060.0308230.017643
20290.0574890.0249190.0038290.0107580.0102750.0010730.0014990.0004350.0974460.1467690.0032870.1691400.0627950.1391540.0420080.1664240.0070570.0364560.019188
20300.0475190.0219680.0038840.0098060.0086340.0010560.0014220.0003780.0944780.1358450.0027920.1630600.0579040.1481660.0488510.1841860.0075500.0419280.020573
20310.0411380.0191310.0036910.0087830.0071900.0010190.0013520.0003210.0901270.1254760.0023440.1571970.0535500.1554400.0551730.2014140.0079570.0469710.021727
20320.0343430.0166450.0033860.0077550.0059570.0009740.0012930.0002880.0845050.1158430.0019520.1517420.0500970.1622940.0610080.2191000.0083390.0516670.022811
20330.0282690.0144500.0030540.0067570.0049200.0009240.0012400.0002650.0776820.1068870.0016130.1466160.0468450.1685440.0663820.2370460.0086880.0560160.023804
20340.0228510.0126210.0027270.0058170.0040560.0008610.0011940.0002400.0698640.0985560.0013250.1417690.0438330.1741600.0713300.2550630.0090030.0600310.024700
20350.0180260.0111230.0024230.0049600.0033440.0007880.0011510.0002170.0613780.0908250.0010830.1371830.0408720.1792580.0758870.2729410.0092890.0637380.025513
20360.0149670.0098190.0021530.0041910.0027550.0007030.0011100.0001890.0525920.0836000.0008820.1327650.0377210.1835200.0800800.2900820.0095300.0671350.026206
20370.0123730.0087280.0019250.0035230.0022740.0006150.0010710.0001650.0439780.0769020.0007170.1285590.0346920.1873360.0839490.3063510.0097450.0702680.026828
20380.0101870.0077830.0017400.0029540.0018820.0005380.0010350.0001470.0359440.0707010.0005850.1245500.0318110.1907510.0875270.3213850.0099380.0731570.027384
20390.0083530.0069500.0015940.0024770.0015630.0004720.0010010.0001310.0288140.0649610.0004790.1207200.0291770.1937710.0908420.3348870.0101080.0758220.027878
20400.0068230.0061960.0014750.0020810.0013020.0004160.0009700.0001160.0227920.0596550.0003950.1170600.0267580.1964550.0939220.3467230.0102590.0782840.028318
20410.0055520.0055070.0013740.0017580.0010890.0003680.0009400.0001030.0179390.0547550.0003290.1135630.0245380.1988440.0967900.3568850.0103930.0805630.028710
20420.0045010.0048830.0012810.0014930.0009160.0003270.0009120.0000910.0141880.0502350.0002760.1102200.0225050.2009710.0994680.3654870.0105120.0826750.029060
20430.0036370.0043190.0011910.0012780.0007730.0002910.0008850.0000810.0113820.0460680.0002350.1070220.0206480.2028660.1019730.3727240.0106180.0846360.029373
20440.0029280.0038140.0011010.0011010.0006560.0002580.0008600.0000720.0093240.0422300.0002020.1039620.0189570.2045540.1043230.3788330.0107130.0864600.029652
20450.0023500.0033650.0010110.0009550.0005590.0002300.0008360.0000640.0078140.0386980.0001750.1010320.0174190.2060570.1065310.3840450.0107970.0881580.029901
20460.0018800.0029680.0009230.0008340.0004790.0002040.0008140.0000570.0066850.0354490.0001530.0982270.0160240.2073950.1086100.3885600.0108720.0897430.030124
\n", "
" ], "text/plain": [ " Electronics 1st use Electronics 2nd use Electronics 3rd use \\\n", "Year \n", "2022 0.377540 0.005548 0.000821 \n", "2023 0.281169 0.017890 0.001593 \n", "2024 0.199773 0.026279 0.002235 \n", "2025 0.142881 0.031148 0.002443 \n", "2026 0.106036 0.032665 0.002545 \n", "2027 0.083086 0.031205 0.002917 \n", "2028 0.068224 0.028201 0.003453 \n", "2029 0.057489 0.024919 0.003829 \n", "2030 0.047519 0.021968 0.003884 \n", "2031 0.041138 0.019131 0.003691 \n", "2032 0.034343 0.016645 0.003386 \n", "2033 0.028269 0.014450 0.003054 \n", "2034 0.022851 0.012621 0.002727 \n", "2035 0.018026 0.011123 0.002423 \n", "2036 0.014967 0.009819 0.002153 \n", "2037 0.012373 0.008728 0.001925 \n", "2038 0.010187 0.007783 0.001740 \n", "2039 0.008353 0.006950 0.001594 \n", "2040 0.006823 0.006196 0.001475 \n", "2041 0.005552 0.005507 0.001374 \n", "2042 0.004501 0.004883 0.001281 \n", "2043 0.003637 0.004319 0.001191 \n", "2044 0.002928 0.003814 0.001101 \n", "2045 0.002350 0.003365 0.001011 \n", "2046 0.001880 0.002968 0.000923 \n", "\n", " Mobility batteries Hydroprocessing catalysts coke \\\n", "Year \n", "2022 0.012176 0.020113 \n", "2023 0.012589 0.020090 \n", "2024 0.012901 0.019297 \n", "2025 0.012958 0.017849 \n", "2026 0.012749 0.016026 \n", "2027 0.012286 0.014056 \n", "2028 0.011605 0.012104 \n", "2029 0.010758 0.010275 \n", "2030 0.009806 0.008634 \n", "2031 0.008783 0.007190 \n", "2032 0.007755 0.005957 \n", "2033 0.006757 0.004920 \n", "2034 0.005817 0.004056 \n", "2035 0.004960 0.003344 \n", "2036 0.004191 0.002755 \n", "2037 0.003523 0.002274 \n", "2038 0.002954 0.001882 \n", "2039 0.002477 0.001563 \n", "2040 0.002081 0.001302 \n", "2041 0.001758 0.001089 \n", "2042 0.001493 0.000916 \n", "2043 0.001278 0.000773 \n", "2044 0.001101 0.000656 \n", "2045 0.000955 0.000559 \n", "2046 0.000834 0.000479 \n", "\n", " Hydroprocessing catalysts poisoning Hydroformylation catalysts \\\n", "Year \n", "2022 0.000113 0.003042 \n", "2023 0.000421 0.003156 \n", "2024 0.000728 0.002181 \n", "2025 0.000909 0.001956 \n", "2026 0.001010 0.001813 \n", "2027 0.001063 0.001694 \n", "2028 0.001080 0.001590 \n", "2029 0.001073 0.001499 \n", "2030 0.001056 0.001422 \n", "2031 0.001019 0.001352 \n", "2032 0.000974 0.001293 \n", "2033 0.000924 0.001240 \n", "2034 0.000861 0.001194 \n", "2035 0.000788 0.001151 \n", "2036 0.000703 0.001110 \n", "2037 0.000615 0.001071 \n", "2038 0.000538 0.001035 \n", "2039 0.000472 0.001001 \n", "2040 0.000416 0.000970 \n", "2041 0.000368 0.000940 \n", "2042 0.000327 0.000912 \n", "2043 0.000291 0.000885 \n", "2044 0.000258 0.000860 \n", "2045 0.000230 0.000836 \n", "2046 0.000204 0.000814 \n", "\n", " Pet precursors catalysts Dissipative uses Hard metals Magnets \\\n", "Year \n", "2022 0.008701 0.090867 0.231415 0.006049 \n", "2023 0.001363 0.093221 0.219753 0.006182 \n", "2024 0.000912 0.095838 0.207615 0.006334 \n", "2025 0.000887 0.097935 0.195219 0.005663 \n", "2026 0.000769 0.099312 0.182762 0.005026 \n", "2027 0.000640 0.099778 0.170409 0.004414 \n", "2028 0.000525 0.099193 0.158352 0.003831 \n", "2029 0.000435 0.097446 0.146769 0.003287 \n", "2030 0.000378 0.094478 0.135845 0.002792 \n", "2031 0.000321 0.090127 0.125476 0.002344 \n", "2032 0.000288 0.084505 0.115843 0.001952 \n", "2033 0.000265 0.077682 0.106887 0.001613 \n", "2034 0.000240 0.069864 0.098556 0.001325 \n", "2035 0.000217 0.061378 0.090825 0.001083 \n", "2036 0.000189 0.052592 0.083600 0.000882 \n", "2037 0.000165 0.043978 0.076902 0.000717 \n", "2038 0.000147 0.035944 0.070701 0.000585 \n", "2039 0.000131 0.028814 0.064961 0.000479 \n", "2040 0.000116 0.022792 0.059655 0.000395 \n", "2041 0.000103 0.017939 0.054755 0.000329 \n", "2042 0.000091 0.014188 0.050235 0.000276 \n", "2043 0.000081 0.011382 0.046068 0.000235 \n", "2044 0.000072 0.009324 0.042230 0.000202 \n", "2045 0.000064 0.007814 0.038698 0.000175 \n", "2046 0.000057 0.006685 0.035449 0.000153 \n", "\n", " Other metallic uses Hoarded Exported Downcycled \\\n", "Year \n", "2022 0.211641 0.005956 0.008817 0.003840 \n", "2023 0.208973 0.045974 0.032574 0.009901 \n", "2024 0.202697 0.067202 0.058810 0.014661 \n", "2025 0.195900 0.075340 0.081793 0.019619 \n", "2026 0.189072 0.075795 0.101039 0.024431 \n", "2027 0.182273 0.073877 0.116546 0.028950 \n", "2028 0.175593 0.068781 0.128968 0.035074 \n", "2029 0.169140 0.062795 0.139154 0.042008 \n", "2030 0.163060 0.057904 0.148166 0.048851 \n", "2031 0.157197 0.053550 0.155440 0.055173 \n", "2032 0.151742 0.050097 0.162294 0.061008 \n", "2033 0.146616 0.046845 0.168544 0.066382 \n", "2034 0.141769 0.043833 0.174160 0.071330 \n", "2035 0.137183 0.040872 0.179258 0.075887 \n", "2036 0.132765 0.037721 0.183520 0.080080 \n", "2037 0.128559 0.034692 0.187336 0.083949 \n", "2038 0.124550 0.031811 0.190751 0.087527 \n", "2039 0.120720 0.029177 0.193771 0.090842 \n", "2040 0.117060 0.026758 0.196455 0.093922 \n", "2041 0.113563 0.024538 0.198844 0.096790 \n", "2042 0.110220 0.022505 0.200971 0.099468 \n", "2043 0.107022 0.020648 0.202866 0.101973 \n", "2044 0.103962 0.018957 0.204554 0.104323 \n", "2045 0.101032 0.017419 0.206057 0.106531 \n", "2046 0.098227 0.016024 0.207395 0.108610 \n", "\n", " Non-selective collection Production losses Pre-treatment losses \\\n", "Year \n", "2022 0.010625 0.000423 0.001204 \n", "2023 0.034468 0.001577 0.004941 \n", "2024 0.061596 0.002894 0.010318 \n", "2025 0.086854 0.004054 0.015708 \n", "2026 0.109548 0.005039 0.020781 \n", "2027 0.129684 0.005846 0.025469 \n", "2028 0.148456 0.006506 0.030823 \n", "2029 0.166424 0.007057 0.036456 \n", "2030 0.184186 0.007550 0.041928 \n", "2031 0.201414 0.007957 0.046971 \n", "2032 0.219100 0.008339 0.051667 \n", "2033 0.237046 0.008688 0.056016 \n", "2034 0.255063 0.009003 0.060031 \n", "2035 0.272941 0.009289 0.063738 \n", "2036 0.290082 0.009530 0.067135 \n", "2037 0.306351 0.009745 0.070268 \n", "2038 0.321385 0.009938 0.073157 \n", "2039 0.334887 0.010108 0.075822 \n", "2040 0.346723 0.010259 0.078284 \n", "2041 0.356885 0.010393 0.080563 \n", "2042 0.365487 0.010512 0.082675 \n", "2043 0.372724 0.010618 0.084636 \n", "2044 0.378833 0.010713 0.086460 \n", "2045 0.384045 0.010797 0.088158 \n", "2046 0.388560 0.010872 0.089743 \n", "\n", " Recycling losses \n", "Year \n", "2022 0.001109 \n", "2023 0.004165 \n", "2024 0.007728 \n", "2025 0.010885 \n", "2026 0.013581 \n", "2027 0.015808 \n", "2028 0.017643 \n", "2029 0.019188 \n", "2030 0.020573 \n", "2031 0.021727 \n", "2032 0.022811 \n", "2033 0.023804 \n", "2034 0.024700 \n", "2035 0.025513 \n", "2036 0.026206 \n", "2037 0.026828 \n", "2038 0.027384 \n", "2039 0.027878 \n", "2040 0.028318 \n", "2041 0.028710 \n", "2042 0.029060 \n", "2043 0.029373 \n", "2044 0.029652 \n", "2045 0.029901 \n", "2046 0.030124 " ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "graph_data_pd" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It is easily possible to create a stacked area chart out of it." ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "data": { "image/png": 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "import matplotlib.pyplot as plt\n", "\n", "plt.rcParams[\"figure.figsize\"] = [10, 7]\n", "graph_data_pd.plot.area()\n", "\n", "plt.legend(reversed(plt.legend().legendHandles), reversed(\n", " graph_data_pd.columns), bbox_to_anchor=(1.05, 1))\n", "\n", "plt.ylabel('Stock distribution by product categories and losses in %')\n", "plt.tight_layout()\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## How to conduct Monte Carlo simulations with the provided code\n", "The code to conduct Monte Carlo simulations consists of three files which need to be executed one after another:\n", "\n", "- monte_carlo_simulations.py\n", "- monte_carlo_evaluation.py\n", "- monte_carlo_uncertainty.py\n", "\n", "In the following it will be discussed what each file does and how to use it. To do so, files and the data used in the cobalt case study will be used. Each of the files has the variable \"proof_of_concept\" in the very top. If this is \"True\", an exemplary Monte Carlo simulation and evaluation considering 10 runs will be conducted. If it is set to \"False\" the results as presented in the thesis will be reproduced." ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [], "source": [ "# The folder holding the results of the following demonstration\n", "# needs to be deleted in order to be created and populated.\n", "# This is a necessary step but not relevant in the context of \n", "# the explenation.\n", "import shutil\n", "try:\n", " shutil.rmtree('monte_carlo_results/proof_of_concept/')\n", "except:\n", " print('Dictionary does not excist.')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Executing \"monte_carlo_simulations.py\"\n", "At the start of the file, the user can adjust the settings. Those are the number of runs (\"n_runs\"), the number of the considered years (\"n_years\"), the start year (\"start_year\"), and the number of the considered use cycles (\"considered_use_cycles\").\n", "\n", "Then, the data to be loaded needs to be specified. Firstly, the file entailing the survival curves designed by the user has to be loaded (see above). Secondly, the path to the input data (format as described above) has to be specified. Lastly, the excel file containing the uncertainty rating has to be set.\n", "\n", "This file must entail the same sheet names as the input file. Instead of the data, the colums must hold the uncertainty score (0 to 5, as explained in the main body of the thesis).\n", "\n", "If an input shall not be varied, the column can be either deleted from the file holding the uncertainty scores, or all values can be set to 0. Only numerical inputs can be considered.\n", "\n", "The following cell shows one sheet of the excel holding the input data. The next one shows the uncertainty ratings of those inputs." ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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Product categoriesdistributionlocationscaleshape
0portable batteriesweibull04.0517162.09
1mobility batteriesweibull010.6315012.20
2hydroprocessing catalysts cokeweibull02.8947422.50
3hydroprocessing catalysts poisoningweibull01.5052662.50
4hydroformylation catalystsweibull02.3157932.50
5pet precursors catalystsweibull00.5789482.50
6dissipative usesweibull014.4351663.50
7hard metalsweibull08.9152351.16
8magnetsweibull013.9050221.93
9other metallic usesweibull014.6280641.47
10superalloysweibull017.2825431.74
\n", "
" ], "text/plain": [ " Product categories distribution location scale \\\n", "0 portable batteries weibull 0 4.051716 \n", "1 mobility batteries weibull 0 10.631501 \n", "2 hydroprocessing catalysts coke weibull 0 2.894742 \n", "3 hydroprocessing catalysts poisoning weibull 0 1.505266 \n", "4 hydroformylation catalysts weibull 0 2.315793 \n", "5 pet precursors catalysts weibull 0 0.578948 \n", "6 dissipative uses weibull 0 14.435166 \n", "7 hard metals weibull 0 8.915235 \n", "8 magnets weibull 0 13.905022 \n", "9 other metallic uses weibull 0 14.628064 \n", "10 superalloys weibull 0 17.282543 \n", "\n", " shape \n", "0 2.09 \n", "1 2.20 \n", "2 2.50 \n", "3 2.50 \n", "4 2.50 \n", "5 2.50 \n", "6 3.50 \n", "7 1.16 \n", "8 1.93 \n", "9 1.47 \n", "10 1.74 " ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pd.read_excel('data_cobalt_case_study/data_input_cobalt_extended_data_set.xlsx', sheet_name='MaTrace_in_use_stock')" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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Product categoriesshapescale
0portable batteries00
1mobility batteries14
2hydroprocessing catalysts coke24
3hydroprocessing catalysts poisoning24
4hydroformylation catalysts24
5pet precursors catalysts14
6dissipative uses23
7hard metals12
8magnets12
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10superalloys12
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" ], "text/plain": [ " Product categories shape scale\n", "0 portable batteries 0 0\n", "1 mobility batteries 1 4\n", "2 hydroprocessing catalysts coke 2 4\n", "3 hydroprocessing catalysts poisoning 2 4\n", "4 hydroformylation catalysts 2 4\n", "5 pet precursors catalysts 1 4\n", "6 dissipative uses 2 3\n", "7 hard metals 1 2\n", "8 magnets 1 2\n", "9 other metallic uses 1 2\n", "10 superalloys 1 2" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pd.read_excel(\n", " 'data_cobalt_case_study/data_uncertainty_rating_cobalt_case_study.xlsx',\n", " sheet_name='MaTrace_in_use_stock')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As it can be seen, only the uncertainty scores for the columns \"shape\" and \"scale\" appear. This is intended since only numerical values can be varied by this implementation and the location of the Weibull distribution is a factor which was not considered." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Once the paths and the settings are defined, the script can be run.\n", "The next cell executes the script for 10 runs." ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Creating dictionary for results. Path: monte_carlo_results/proof_of_concept\n", "Importing data\n", "Creating inputs for Monte Carlo simulations based on uncertainty score\n", "Normalize inputs\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "c:\\Users\\rapha\\Desktop\\master_thesis_hand_in\\monte_carlo_simulations.py:154: PerformanceWarning: indexing past lexsort depth may impact performance.\n", " for column in input_pd.loc[:, (\"MaTrace_initial_inflow\", \"share\")].columns:\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Starting runs\n", "Run 1 of 10\n", "Mass balance run 0: True\n", "Run 2 of 10\n", "Mass balance run 1: True\n", "Run 3 of 10\n", "Mass balance run 2: True\n", "Run 4 of 10\n", "Mass balance run 3: True\n", "Run 5 of 10\n", "Mass balance run 4: True\n", "Run 6 of 10\n", "Mass balance run 5: True\n", "Run 7 of 10\n", "Mass balance run 6: True\n", "Run 8 of 10\n", "Mass balance run 7: True\n", "Run 9 of 10\n", "Mass balance run 8: True\n", "Run 10 of 10\n", "Mass balance run 9: True\n", "Total time - seconds: 21, hours: 0.005833333333333334\n", "Time per run: 2.1\n", "Inputs and results are stored.\n" ] } ], "source": [ "%run monte_carlo_simulations.py" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The printed output reflects the steps of the code. After the data is imported, random numbers following the defined distributions are created. Since some split vectors and transfer coefficients will not sum up to 1 anymore (see above) they have to be normalized. Afterwards, the experiments are run. The data is collected in a dictionary. If several thousand runs are executed, the dictionary is dumped in splits to decrease the runtime.\n", "\n", "The results and a file containing the used model inputs can be found in the folder \"monte_carlo_results/proof_of_concept\".\n", "\n", "The following cell shows part of the saved inputs. The rows represent runs." ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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sheetMaTrace_initial_inflow...Reuse_storage_time_3
columnshare...scaleshape
itemportable batteriesmobility batterieshydroprocessing catalysts cokehydroprocessing catalysts poisoninghydroformylation catalystspet precursors catalystsdissipative useshard metalsmagnetsother metallic uses...e-bikespower toolsotherssmartphonesmobile phonestabletslaptopse-bikespower toolsothers
00.1956260.0690500.0498830.0239990.0591670.0047030.0594620.1118020.1522050.103031...2.3608901.2917853.7877811.4320131.6641091.5752161.5211981.7978841.5524581.790137
10.2216890.0311450.0575740.0636520.0497470.1428620.0950380.1952350.0060550.000673...2.1251742.1253484.6872171.6199851.3581891.4471411.4869051.6214131.7690311.507384
20.2039710.0670550.0283440.0348230.2380510.1329330.0132540.0941260.0136340.018468...1.9500792.2443454.6473161.5652511.4020541.3774442.0263061.6991961.3972941.565706
30.4117100.0922290.0398970.0410330.1073870.0193870.0665060.0770420.0149830.001555...1.9751202.1260834.1583801.6632451.3015361.2987861.5302081.4305511.5148071.453668
40.2797810.0260760.0367500.0360710.0158100.0162350.0122340.2529200.0666900.065006...2.4856661.7183792.0798171.3177591.5752141.4135651.5463061.5623071.4520431.174145
50.2827910.0696460.0455120.0803050.0178130.0490540.0526280.1995200.0800290.001548...2.2553361.8217743.5886461.7334701.7474791.4787251.7233771.5770031.5138901.475730
60.3211720.0401060.0778130.0539320.0368620.0586570.0620040.1526090.0248010.059288...1.9751333.3437703.3613171.3457251.5702181.4141501.4098011.4765181.6075881.452492
70.2522500.0325410.0465560.0832940.0961090.0164660.0311110.1445790.0317950.022306...1.7304071.1677254.3270771.6796901.2380381.6005401.5223131.5244021.4673801.247346
80.2675550.0348540.0155550.0016660.0697140.0744320.0651950.2196380.0152090.033571...1.0650563.0330243.5157321.7882271.5065851.4215021.4897971.8324881.2880201.568852
90.3268310.0357600.1331870.0419840.0670350.0040050.0143500.1634880.0584760.040129...1.8111203.2311104.2664481.2819561.7987591.4017111.2003971.5457021.5737161.740299
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10 rows × 541 columns

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" ], "text/plain": [ "sheet MaTrace_initial_inflow \\\n", "column share \n", "item portable batteries mobility batteries \n", "0 0.195626 0.069050 \n", "1 0.221689 0.031145 \n", "2 0.203971 0.067055 \n", "3 0.411710 0.092229 \n", "4 0.279781 0.026076 \n", "5 0.282791 0.069646 \n", "6 0.321172 0.040106 \n", "7 0.252250 0.032541 \n", "8 0.267555 0.034854 \n", "9 0.326831 0.035760 \n", "\n", "sheet \\\n", "column \n", "item hydroprocessing catalysts coke hydroprocessing catalysts poisoning \n", "0 0.049883 0.023999 \n", "1 0.057574 0.063652 \n", "2 0.028344 0.034823 \n", "3 0.039897 0.041033 \n", "4 0.036750 0.036071 \n", "5 0.045512 0.080305 \n", "6 0.077813 0.053932 \n", "7 0.046556 0.083294 \n", "8 0.015555 0.001666 \n", "9 0.133187 0.041984 \n", "\n", "sheet \\\n", "column \n", "item hydroformylation catalysts pet precursors catalysts dissipative uses \n", "0 0.059167 0.004703 0.059462 \n", "1 0.049747 0.142862 0.095038 \n", "2 0.238051 0.132933 0.013254 \n", "3 0.107387 0.019387 0.066506 \n", "4 0.015810 0.016235 0.012234 \n", "5 0.017813 0.049054 0.052628 \n", "6 0.036862 0.058657 0.062004 \n", "7 0.096109 0.016466 0.031111 \n", "8 0.069714 0.074432 0.065195 \n", "9 0.067035 0.004005 0.014350 \n", "\n", "sheet ... Reuse_storage_time_3 \\\n", "column ... scale \n", "item hard metals magnets other metallic uses ... e-bikes \n", "0 0.111802 0.152205 0.103031 ... 2.360890 \n", "1 0.195235 0.006055 0.000673 ... 2.125174 \n", "2 0.094126 0.013634 0.018468 ... 1.950079 \n", "3 0.077042 0.014983 0.001555 ... 1.975120 \n", "4 0.252920 0.066690 0.065006 ... 2.485666 \n", "5 0.199520 0.080029 0.001548 ... 2.255336 \n", "6 0.152609 0.024801 0.059288 ... 1.975133 \n", "7 0.144579 0.031795 0.022306 ... 1.730407 \n", "8 0.219638 0.015209 0.033571 ... 1.065056 \n", "9 0.163488 0.058476 0.040129 ... 1.811120 \n", "\n", "sheet \\\n", "column shape \n", "item power tools others smartphones mobile phones tablets laptops \n", "0 1.291785 3.787781 1.432013 1.664109 1.575216 1.521198 \n", "1 2.125348 4.687217 1.619985 1.358189 1.447141 1.486905 \n", "2 2.244345 4.647316 1.565251 1.402054 1.377444 2.026306 \n", "3 2.126083 4.158380 1.663245 1.301536 1.298786 1.530208 \n", "4 1.718379 2.079817 1.317759 1.575214 1.413565 1.546306 \n", "5 1.821774 3.588646 1.733470 1.747479 1.478725 1.723377 \n", "6 3.343770 3.361317 1.345725 1.570218 1.414150 1.409801 \n", "7 1.167725 4.327077 1.679690 1.238038 1.600540 1.522313 \n", "8 3.033024 3.515732 1.788227 1.506585 1.421502 1.489797 \n", "9 3.231110 4.266448 1.281956 1.798759 1.401711 1.200397 \n", "\n", "sheet \n", "column \n", "item e-bikes power tools others \n", "0 1.797884 1.552458 1.790137 \n", "1 1.621413 1.769031 1.507384 \n", "2 1.699196 1.397294 1.565706 \n", "3 1.430551 1.514807 1.453668 \n", "4 1.562307 1.452043 1.174145 \n", "5 1.577003 1.513890 1.475730 \n", "6 1.476518 1.607588 1.452492 \n", "7 1.524402 1.467380 1.247346 \n", "8 1.832488 1.288020 1.568852 \n", "9 1.545702 1.573716 1.740299 \n", "\n", "[10 rows x 541 columns]" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import pickle\n", "with open('monte_carlo_results/proof_of_concept/inputs.pkl', 'rb') as f:\n", " input_pd = pickle.load(f)\n", "input_pd" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The results are saved in the form of nested dictionaries:" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [], "source": [ "with open('monte_carlo_results/proof_of_concept/results.pkl', 'rb') as f:\n", " results_dic = pickle.load(f)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The fist key represents runs:" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "dict_keys(['0', '1', '2', '3', '4', '5', '6', '7', '8', '9'])" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ "results_dic.keys()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The second part specifies from which part of the model the data is coming from:" ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "dict_keys(['matrace_data_dic', 'reuse_data_dic'])" ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" } ], "source": [ "results_dic['0'].keys()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Since this data structure is hard to handle, the script \"monte_carlo_evaluation.py\" serves the purpose to transfer the data into multidimensional pandas data frames.\n", "### Executing \"monte_carlo_evaluation.py\"\n", "In order to execute this script, one has to adjust the settings in the beginning of the file so they are the same as the used ones in \"mone_carlo_simulations.py\". The path to the results has to be defined. Then the file is ready to be executed." ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Loading file:\n", "Create dictionary:\n", "Start treating data\n", "run 1 of 10\n", "run 2 of 10\n", "run 3 of 10\n", "run 4 of 10\n", "run 5 of 10\n", "run 6 of 10\n", "run 7 of 10\n", "run 8 of 10\n", "run 9 of 10\n", "run 10 of 10\n", "Compact results are stored.\n" ] } ], "source": [ "%run monte_carlo_evaluation.py" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This script creates the multidimensional pandas data frame 'compact_results'. The first key entails the runs, the second the stock or flow, and the third one the product or the product category." ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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20180.1168350.0373690.0128040.0158980.0392730.0013040.0059030.0042840.0404230.008003...0.0002380.0004340.0000240.0000130.0003920.0001750.0001170.0000160.0000330.000074
20190.0901620.0279980.0081450.0112460.0332730.0008640.0054480.0031870.0466280.009983...0.0001290.0002450.0000130.0000080.0002310.0000920.0000660.0000090.0000190.000039
20200.0690890.0201430.0062130.0077280.0270220.0005020.0050070.0024730.0468010.010628...0.0000980.0001950.000010.0000060.0001890.0000690.0000520.0000070.0000150.00003
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20260.0141690.0020590.0014870.0017570.0052480.0001080.0028240.0006860.0154590.004107...0.000060.0001180.0000060.0000040.0001150.0000420.0000320.0000050.0000090.000018
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20330.0017850.000050.0000460.0000780.0003510.0000020.0011520.0001060.0015610.00032...0.0000210.0000430.0000020.0000020.0000430.0000150.0000120.0000020.0000030.000006
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36 rows × 5240 columns

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P.5 downcycled scrap \n", "item ... mobility batteries hydroprocessing catalysts coke \n", "2015 ... 0.0 0.0 \n", "2016 ... 0.000142 0.000282 \n", "2017 ... 0.000313 0.000585 \n", "2018 ... 0.000238 0.000434 \n", "2019 ... 0.000129 0.000245 \n", "2020 ... 0.000098 0.000195 \n", "2021 ... 0.000096 0.000189 \n", "2022 ... 0.000092 0.000181 \n", "2023 ... 0.000086 0.000168 \n", "2024 ... 0.000078 0.000152 \n", "2025 ... 0.000069 0.000135 \n", "2026 ... 0.00006 0.000118 \n", "2027 ... 0.000051 0.000103 \n", "2028 ... 0.000044 0.000089 \n", "2029 ... 0.000038 0.000076 \n", "2030 ... 0.000032 0.000066 \n", "2031 ... 0.000028 0.000057 \n", "2032 ... 0.000024 0.00005 \n", "2033 ... 0.000021 0.000043 \n", "2034 ... 0.000018 0.000038 \n", "2035 ... 0.000016 0.000033 \n", "2036 ... 0.000014 0.000029 \n", "2037 ... 0.000012 0.000025 \n", "2038 ... 0.000011 0.000022 \n", "2039 ... 0.00001 0.000019 \n", "2040 ... 0.000009 0.000017 \n", "2041 ... 0.000008 0.000015 \n", "2042 ... 0.000007 0.000013 \n", "2043 ... 0.000006 0.000011 \n", "2044 ... 0.000005 0.00001 \n", "2045 ... 0.000005 0.000009 \n", "2046 ... 0.000004 0.000008 \n", "2047 ... 0.000004 0.000007 \n", "2048 ... 0.000003 0.000006 \n", "2049 ... 0.000003 0.000005 \n", "2050 ... 0.000002 0.000004 \n", "\n", "run \\\n", "stock_flow \n", "item hydroprocessing catalysts poisoning hydroformylation catalysts \n", "2015 0.0 0.0 \n", "2016 0.000015 0.000009 \n", "2017 0.000032 0.000018 \n", "2018 0.000024 0.000013 \n", "2019 0.000013 0.000008 \n", "2020 0.00001 0.000006 \n", "2021 0.00001 0.000006 \n", "2022 0.00001 0.000006 \n", "2023 0.000009 0.000005 \n", "2024 0.000008 0.000005 \n", "2025 0.000007 0.000004 \n", "2026 0.000006 0.000004 \n", "2027 0.000005 0.000003 \n", "2028 0.000005 0.000003 \n", "2029 0.000004 0.000003 \n", "2030 0.000003 0.000002 \n", "2031 0.000003 0.000002 \n", "2032 0.000003 0.000002 \n", "2033 0.000002 0.000002 \n", "2034 0.000002 0.000001 \n", "2035 0.000002 0.000001 \n", "2036 0.000002 0.000001 \n", "2037 0.000001 0.000001 \n", "2038 0.000001 0.000001 \n", "2039 0.000001 0.000001 \n", "2040 0.000001 0.000001 \n", "2041 0.000001 0.0 \n", "2042 0.000001 0.0 \n", "2043 0.000001 0.0 \n", "2044 0.000001 0.0 \n", "2045 0.0 0.0 \n", "2046 0.0 0.0 \n", "2047 0.0 0.0 \n", "2048 0.0 0.0 \n", "2049 0.0 0.0 \n", "2050 0.0 0.0 \n", "\n", "run \\\n", "stock_flow \n", "item pet precursors catalysts dissipative uses hard metals magnets \n", "2015 0.0 0.0 0.0 0.0 \n", "2016 0.000275 0.0001 0.000076 0.000011 \n", "2017 0.000541 0.000227 0.000158 0.000022 \n", "2018 0.000392 0.000175 0.000117 0.000016 \n", "2019 0.000231 0.000092 0.000066 0.000009 \n", "2020 0.000189 0.000069 0.000052 0.000007 \n", "2021 0.000183 0.000068 0.000051 0.000007 \n", "2022 0.000175 0.000065 0.000049 0.000007 \n", "2023 0.000162 0.000061 0.000045 0.000006 \n", "2024 0.000147 0.000055 0.000041 0.000006 \n", "2025 0.000131 0.000048 0.000036 0.000005 \n", "2026 0.000115 0.000042 0.000032 0.000005 \n", "2027 0.0001 0.000036 0.000028 0.000004 \n", "2028 0.000087 0.000031 0.000024 0.000003 \n", "2029 0.000076 0.000026 0.000021 0.000003 \n", "2030 0.000066 0.000023 0.000018 0.000003 \n", "2031 0.000057 0.000019 0.000015 0.000002 \n", "2032 0.00005 0.000017 0.000013 0.000002 \n", "2033 0.000043 0.000015 0.000012 0.000002 \n", "2034 0.000038 0.000013 0.00001 0.000001 \n", "2035 0.000033 0.000011 0.000009 0.000001 \n", "2036 0.000029 0.00001 0.000008 0.000001 \n", "2037 0.000025 0.000009 0.000007 0.000001 \n", "2038 0.000022 0.000008 0.000006 0.000001 \n", "2039 0.000019 0.000007 0.000005 0.000001 \n", "2040 0.000017 0.000006 0.000005 0.000001 \n", "2041 0.000014 0.000005 0.000004 0.000001 \n", "2042 0.000012 0.000005 0.000003 0.0 \n", "2043 0.000011 0.000004 0.000003 0.0 \n", "2044 0.000009 0.000004 0.000003 0.0 \n", "2045 0.000008 0.000003 0.000002 0.0 \n", "2046 0.000007 0.000003 0.000002 0.0 \n", "2047 0.000006 0.000003 0.000002 0.0 \n", "2048 0.000005 0.000002 0.000002 0.0 \n", "2049 0.000005 0.000002 0.000001 0.0 \n", "2050 0.000004 0.000002 0.000001 0.0 \n", "\n", "run \n", "stock_flow \n", "item other metallic uses superalloys \n", "2015 0.0 0.0 \n", "2016 0.000022 0.000043 \n", "2017 0.000044 0.000096 \n", "2018 0.000033 0.000074 \n", "2019 0.000019 0.000039 \n", "2020 0.000015 0.00003 \n", "2021 0.000015 0.000029 \n", "2022 0.000014 0.000028 \n", "2023 0.000013 0.000026 \n", "2024 0.000012 0.000024 \n", "2025 0.00001 0.000021 \n", "2026 0.000009 0.000018 \n", "2027 0.000008 0.000015 \n", "2028 0.000007 0.000013 \n", "2029 0.000006 0.000011 \n", "2030 0.000005 0.00001 \n", "2031 0.000004 0.000008 \n", "2032 0.000004 0.000007 \n", "2033 0.000003 0.000006 \n", "2034 0.000003 0.000005 \n", "2035 0.000003 0.000005 \n", "2036 0.000002 0.000004 \n", "2037 0.000002 0.000004 \n", "2038 0.000002 0.000003 \n", "2039 0.000001 0.000003 \n", "2040 0.000001 0.000003 \n", "2041 0.000001 0.000002 \n", "2042 0.000001 0.000002 \n", "2043 0.000001 0.000002 \n", "2044 0.000001 0.000002 \n", "2045 0.000001 0.000001 \n", "2046 0.000001 0.000001 \n", "2047 0.000001 0.000001 \n", "2048 0.0 0.000001 \n", "2049 0.0 0.000001 \n", "2050 0.0 0.000001 \n", "\n", "[36 rows x 5240 columns]" ] }, "execution_count": 30, "metadata": {}, "output_type": "execute_result" } ], "source": [ "with open('monte_carlo_results/proof_of_concept/compact_results.pkl', \n", " 'rb') as f:\n", " compact_results = pickle.load(f)\n", "compact_results" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Executing \"monte_carlo_uncertainty.py\"\n", "In order to execute this script, one has to adjust the settings in the beginning of the file so they are the same as the used ones in \"mone_carlo_simulations.py\" and \"monte_carlo_evaluation.py\". The path to the results has to be defined. Then the file is ready to be executed.\n", "\n", "The file calculates for all considered years the spearman correlation and the normalized spearman square correlation between all inputs and the total in-use stock, the total hibernating stock, the total disposal flow, and the total export flow.\n", "\n", "The results are written into the files \"monte_carlo_results\" (results of mentioned stocks and flows over years and runs), \"speaman_results_abs\" (spearman correlation between inputs and outputs over years) and \"spearman_results_normalized\" (normalized spareman square correlation between inputs and outputs over years)." ] }, { "cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Calculate spearman correlation between inputs and:\n", "['total_use_stock', 'total_hoarding_stock', 'to_disposal_flow', 'U.B hoarding stock', 'U.A use stock', 'total_export', 'total_disposal']\n", "Year 1 of 36\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "c:\\Users\\rapha\\.conda\\envs\\master_thesis\\lib\\site-packages\\scipy\\stats\\stats.py:4484: SpearmanRConstantInputWarning: An input array is constant; the correlation coefficient is not defined.\n", " warnings.warn(SpearmanRConstantInputWarning())\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Year 2 of 36\n", "Year 3 of 36\n", "Year 4 of 36\n", "Year 5 of 36\n", "Year 6 of 36\n", "Year 7 of 36\n", "Year 8 of 36\n", "Year 9 of 36\n", "Year 10 of 36\n", "Year 11 of 36\n", "Year 12 of 36\n", "Year 13 of 36\n", "Year 14 of 36\n", "Year 15 of 36\n", "Year 16 of 36\n", "Year 17 of 36\n", "Year 18 of 36\n", "Year 19 of 36\n", "Year 20 of 36\n", "Year 21 of 36\n", "Year 22 of 36\n", "Year 23 of 36\n", "Year 24 of 36\n", "Year 25 of 36\n", "Year 26 of 36\n", "Year 27 of 36\n", "Year 28 of 36\n", "Year 29 of 36\n", "Year 30 of 36\n", "Year 31 of 36\n", "Year 32 of 36\n", "Year 33 of 36\n", "Year 34 of 36\n", "Year 35 of 36\n", "Year 36 of 36\n", "Export Monte Carlo results\n", "Export spearman absolute results\n", "Export spearman normalized results\n" ] } ], "source": [ "%run monte_carlo_uncertainty.py" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The following cell shows a part of the table displaying the normalized square spearmen correlation." ] }, { "cell_type": "code", "execution_count": 32, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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result_itemtotal_use_stock...total_disposal
sheetMaTrace_initial_inflow...Reuse_storage_time_3
columnshare...scaleshape
itemportable batteriesmobility batterieshydroprocessing catalysts cokehydroprocessing catalysts poisoninghydroformylation catalystspet precursors catalystsdissipative useshard metalsmagnetsother metallic uses...e-bikespower toolsotherssmartphonesmobile phonestabletslaptopse-bikespower toolsothers
02.0450290.1142510.0818010.1388890.0331260.1388890.0169010.0126950.0018780.012695...NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
11.9869730.0395610.0628950.0814410.0395610.1795600.0036640.0216130.0006730.000075...0.3914010.3460620.4151170.4395300.1926050.0042720.0196180.2637530.0544940.025198
21.8871360.0126150.0329190.0215730.0126150.1511590.0126150.0269470.0036580.006046...0.2928990.0227491.2299230.0095250.7406300.0095250.0038570.1738820.1077610.028416
31.8871360.0126150.0329190.0215730.0126150.1511590.0126150.0269470.0036580.006046...0.1043740.0476511.4309710.0555800.6039060.0092250.2141610.1409700.0275230.040332
41.8871360.0126150.0329190.0215730.0126150.1511590.0126150.0269470.0036580.006046...0.0007570.0525550.2362010.0370820.3337420.0101750.3337420.0915710.0707170.758888
51.9498610.0819180.0217400.0397930.0091020.1956560.0060930.0036860.0018810.003686...0.0560470.3875660.4798250.3660380.1845950.1421560.3875660.1999720.1292400.324829
61.9498610.0819180.0217400.0397930.0091020.1956560.0060930.0036860.0018810.003686...0.0796540.1229560.2212620.2903110.1229560.3897870.2546160.1756200.1112530.053322
71.9560620.2282740.0218090.0550120.0006790.0924420.0127530.0000750.0000750.012753...0.2454420.0007300.0779730.1948120.0682370.3019140.0993940.1110780.1643040.000081
81.8125080.0634470.0018860.0000750.0091290.1032810.0091290.0036970.0127500.006111...0.3483330.0387040.0639790.2282720.2654840.0387040.1938690.0000880.2851430.285143
91.8871360.0126150.0329190.0215730.0126150.1511590.0126150.0269470.0036580.006046...0.6146130.1356980.0043720.0971570.0750310.0043720.1970800.0471960.4754350.214209
101.7690970.0006630.0059640.0089100.0212810.2068430.0000740.0619280.0000740.008910...0.6146130.1356980.0043720.0971570.0750310.0043720.1970800.0471960.4754350.214209
111.6433120.0018500.0018500.0036270.0539600.1777210.0006660.1013330.0000740.016654...0.6190590.1049620.0720590.1173000.0453260.0144800.2406830.1049620.3188260.117300
121.3315940.0532640.0386510.0035800.0895040.1900400.0211160.2899920.0059180.038651...0.7056110.0927920.1296020.0927920.0191720.0246250.2216270.0818850.3381920.053255
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\n", "

36 rows × 3787 columns

\n", "
" ], "text/plain": [ "result_item total_use_stock \\\n", "sheet MaTrace_initial_inflow \n", "column share \n", "item portable batteries mobility batteries \n", "0 2.045029 0.114251 \n", "1 1.986973 0.039561 \n", "2 1.887136 0.012615 \n", "3 1.887136 0.012615 \n", "4 1.887136 0.012615 \n", "5 1.949861 0.081918 \n", "6 1.949861 0.081918 \n", "7 1.956062 0.228274 \n", "8 1.812508 0.063447 \n", "9 1.887136 0.012615 \n", "10 1.769097 0.000663 \n", "11 1.643312 0.001850 \n", "12 1.331594 0.053264 \n", "13 1.331594 0.053264 \n", "14 1.305751 0.021333 \n", "15 1.049711 0.001853 \n", "16 1.049711 0.001853 \n", "17 1.049711 0.001853 \n", "18 1.200942 0.012583 \n", "19 0.971130 0.033540 \n", "20 1.022361 0.209790 \n", "21 1.079578 0.292661 \n", "22 1.079578 0.292661 \n", "23 0.962534 0.424015 \n", "24 0.893307 0.569097 \n", "25 0.893307 0.569097 \n", "26 0.752894 0.666098 \n", "27 0.752894 0.666098 \n", "28 0.880273 0.697121 \n", "29 0.880273 0.697121 \n", "30 0.880273 0.697121 \n", "31 0.880273 0.697121 \n", "32 0.779623 0.663220 \n", "33 0.779623 0.663220 \n", "34 0.779623 0.663220 \n", "35 0.779623 0.663220 \n", "\n", "result_item \\\n", "sheet \n", "column \n", "item hydroprocessing catalysts coke \n", "0 0.081801 \n", "1 0.062895 \n", "2 0.032919 \n", "3 0.032919 \n", "4 0.032919 \n", "5 0.021740 \n", "6 0.021740 \n", "7 0.021809 \n", "8 0.001886 \n", "9 0.032919 \n", "10 0.005964 \n", "11 0.001850 \n", "12 0.038651 \n", "13 0.038651 \n", "14 0.090426 \n", "15 0.352918 \n", "16 0.352918 \n", "17 0.352918 \n", "18 0.334245 \n", "19 0.549488 \n", "20 0.442806 \n", "21 0.351060 \n", "22 0.351060 \n", "23 0.379993 \n", "24 0.400676 \n", "25 0.400676 \n", "26 0.274632 \n", "27 0.274632 \n", "28 0.136994 \n", "29 0.136994 \n", "30 0.136994 \n", "31 0.136994 \n", "32 0.123532 \n", "33 0.123532 \n", "34 0.123532 \n", "35 0.123532 \n", "\n", "result_item \\\n", "sheet \n", "column \n", "item hydroprocessing catalysts poisoning hydroformylation catalysts \n", "0 0.138889 0.033126 \n", "1 0.081441 0.039561 \n", "2 0.021573 0.012615 \n", "3 0.021573 0.012615 \n", "4 0.021573 0.012615 \n", "5 0.039793 0.009102 \n", "6 0.039793 0.009102 \n", "7 0.055012 0.000679 \n", "8 0.000075 0.009129 \n", "9 0.021573 0.012615 \n", "10 0.008910 0.021281 \n", "11 0.003627 0.053960 \n", "12 0.003580 0.089504 \n", "13 0.003580 0.089504 \n", "14 0.021333 0.039049 \n", "15 0.101480 0.026760 \n", "16 0.101480 0.026760 \n", "17 0.101480 0.026760 \n", "18 0.091212 0.000670 \n", "19 0.182605 0.040232 \n", "20 0.062810 0.054445 \n", "21 0.012461 0.021310 \n", "22 0.012461 0.021310 \n", "23 0.016961 0.092341 \n", "24 0.054812 0.102932 \n", "25 0.054812 0.102932 \n", "26 0.000664 0.311830 \n", "27 0.000664 0.311830 \n", "28 0.003630 0.294067 \n", "29 0.003630 0.294067 \n", "30 0.003630 0.294067 \n", "31 0.003630 0.294067 \n", "32 0.016535 0.176442 \n", "33 0.016535 0.176442 \n", "34 0.016535 0.176442 \n", "35 0.016535 0.176442 \n", "\n", "result_item \\\n", "sheet \n", "column \n", "item pet precursors catalysts dissipative uses hard metals magnets \n", "0 0.138889 0.016901 0.012695 0.001878 \n", "1 0.179560 0.003664 0.021613 0.000673 \n", "2 0.151159 0.012615 0.026947 0.003658 \n", "3 0.151159 0.012615 0.026947 0.003658 \n", "4 0.151159 0.012615 0.026947 0.003658 \n", "5 0.195656 0.006093 0.003686 0.001881 \n", "6 0.195656 0.006093 0.003686 0.001881 \n", "7 0.092442 0.012753 0.000075 0.000075 \n", "8 0.103281 0.009129 0.003697 0.012750 \n", "9 0.151159 0.012615 0.026947 0.003658 \n", "10 0.206843 0.000074 0.061928 0.000074 \n", "11 0.177721 0.000666 0.101333 0.000074 \n", "12 0.190040 0.021116 0.289992 0.005918 \n", "13 0.190040 0.021116 0.289992 0.005918 \n", "14 0.207352 0.080387 0.207352 0.008932 \n", "15 0.071236 0.071236 0.275826 0.001853 \n", "16 0.071236 0.071236 0.275826 0.001853 \n", "17 0.071236 0.071236 0.275826 0.001853 \n", "18 0.054280 0.016753 0.137674 0.001861 \n", "19 0.012853 0.009202 0.093166 0.027455 \n", "20 0.000075 0.001867 0.054445 0.016804 \n", "21 0.001843 0.000074 0.046085 0.001843 \n", "22 0.001843 0.000074 0.046085 0.001843 \n", "23 0.003694 0.000075 0.006106 0.021785 \n", "24 0.006090 0.003684 0.000075 0.000075 \n", "25 0.006090 0.003684 0.000075 0.000075 \n", "26 0.001845 0.001845 0.039043 0.003616 \n", "27 0.001845 0.001845 0.039043 0.003616 \n", "28 0.026747 0.001852 0.062310 0.000074 \n", "29 0.026747 0.001852 0.062310 0.000074 \n", "30 0.026747 0.001852 0.062310 0.000074 \n", "31 0.026747 0.001852 0.062310 0.000074 \n", "32 0.021238 0.012419 0.016535 0.008892 \n", "33 0.021238 0.012419 0.016535 0.008892 \n", "34 0.021238 0.012419 0.016535 0.008892 \n", "35 0.021238 0.012419 0.016535 0.008892 \n", "\n", "result_item ... total_disposal \\\n", "sheet ... Reuse_storage_time_3 \n", "column ... scale \n", "item other metallic uses ... e-bikes power tools \n", "0 0.012695 ... NaN NaN \n", "1 0.000075 ... 0.391401 0.346062 \n", "2 0.006046 ... 0.292899 0.022749 \n", "3 0.006046 ... 0.104374 0.047651 \n", "4 0.006046 ... 0.000757 0.052555 \n", "5 0.003686 ... 0.056047 0.387566 \n", "6 0.003686 ... 0.079654 0.122956 \n", "7 0.012753 ... 0.245442 0.000730 \n", "8 0.006111 ... 0.348333 0.038704 \n", "9 0.006046 ... 0.614613 0.135698 \n", "10 0.008910 ... 0.614613 0.135698 \n", "11 0.016654 ... 0.619059 0.104962 \n", "12 0.038651 ... 0.705611 0.092792 \n", "13 0.038651 ... 0.486022 0.124682 \n", "14 0.062080 ... 0.133836 0.009634 \n", "15 0.012527 ... 0.040197 0.055395 \n", "16 0.012527 ... 0.000075 0.244924 \n", "17 0.012527 ... 0.009178 0.182117 \n", "18 0.000074 ... 0.006119 0.166885 \n", "19 0.012853 ... 0.016798 0.194181 \n", "20 0.125545 ... 0.000075 0.062887 \n", "21 0.177042 ... 0.000075 0.062887 \n", "22 0.177042 ... 0.001882 0.047050 \n", "23 0.262400 ... 0.013193 0.000078 \n", "24 0.126391 ... 0.035755 0.029269 \n", "25 0.126391 ... 0.069717 0.029926 \n", "26 0.239795 ... 0.069717 0.029926 \n", "27 0.239795 ... 0.010066 0.069961 \n", "28 0.224125 ... 0.073177 0.119119 \n", "29 0.224125 ... 0.045704 0.145232 \n", "30 0.224125 ... 0.045704 0.145232 \n", "31 0.224125 ... 0.031193 0.242715 \n", "32 0.238759 ... 0.044414 0.253974 \n", "33 0.238759 ... 0.044414 0.253974 \n", "34 0.238759 ... 0.066528 0.108296 \n", "35 0.238759 ... 0.066528 0.108296 \n", "\n", "result_item \\\n", "sheet \n", "column shape \n", "item others smartphones mobile phones tablets laptops e-bikes \n", "0 NaN NaN NaN NaN NaN NaN \n", "1 0.415117 0.439530 0.192605 0.004272 0.019618 0.263753 \n", "2 1.229923 0.009525 0.740630 0.009525 0.003857 0.173882 \n", "3 1.430971 0.055580 0.603906 0.009225 0.214161 0.140970 \n", "4 0.236201 0.037082 0.333742 0.010175 0.333742 0.091571 \n", "5 0.479825 0.366038 0.184595 0.142156 0.387566 0.199972 \n", "6 0.221262 0.290311 0.122956 0.389787 0.254616 0.175620 \n", "7 0.077973 0.194812 0.068237 0.301914 0.099394 0.111078 \n", "8 0.063979 0.228272 0.265484 0.038704 0.193869 0.000088 \n", "9 0.004372 0.097157 0.075031 0.004372 0.197080 0.047196 \n", "10 0.004372 0.097157 0.075031 0.004372 0.197080 0.047196 \n", "11 0.072059 0.117300 0.045326 0.014480 0.240683 0.104962 \n", "12 0.129602 0.092792 0.019172 0.024625 0.221627 0.081885 \n", "13 0.247971 0.112222 0.013854 0.051234 0.230264 0.124682 \n", "14 0.630646 0.023009 0.023009 0.009634 0.223644 0.086703 \n", "15 1.004931 0.027431 0.012842 0.001900 0.321046 0.017097 \n", "16 1.031938 0.033245 0.012740 0.054955 0.446954 0.012740 \n", "17 1.110530 0.021921 0.006144 0.033450 0.382363 0.033450 \n", "18 1.336367 0.009141 0.033317 0.027273 0.262982 0.039965 \n", "19 1.360613 0.012617 0.039493 0.062786 0.151179 0.032923 \n", "20 1.444752 0.032976 0.102369 0.016825 0.102369 0.000075 \n", "21 1.444752 0.032976 0.102369 0.016825 0.102369 0.000075 \n", "22 1.102184 0.114502 0.166295 0.016938 0.126547 0.047050 \n", "23 1.032383 0.041295 0.056908 0.022560 0.085011 0.056908 \n", "24 0.860146 0.013702 0.023431 0.059105 0.050673 0.068186 \n", "25 0.627457 0.036558 0.069717 0.051811 0.036558 0.183122 \n", "26 0.627457 0.036558 0.069717 0.051811 0.036558 0.183122 \n", "27 0.688879 0.113884 0.090592 0.036686 0.014059 0.113884 \n", "28 0.438626 0.054382 0.019578 0.025146 0.002175 0.119119 \n", "29 0.485978 0.038101 0.072659 0.083027 0.006998 0.190849 \n", "30 0.485978 0.038101 0.072659 0.083027 0.006998 0.190849 \n", "31 0.435574 0.006999 0.105848 0.118290 0.045709 0.261379 \n", "32 0.472267 0.002099 0.114939 0.061206 0.024264 0.052474 \n", "33 0.472267 0.002099 0.114939 0.061206 0.024264 0.052474 \n", "34 0.626600 0.022862 0.076021 0.017799 0.013369 0.006408 \n", "35 0.626600 0.022862 0.076021 0.017799 0.013369 0.006408 \n", "\n", "result_item \n", "sheet \n", "column \n", "item power tools others \n", "0 NaN NaN \n", "1 0.054494 0.025198 \n", "2 0.107761 0.028416 \n", "3 0.027523 0.040332 \n", "4 0.070717 0.758888 \n", "5 0.129240 0.324829 \n", "6 0.111253 0.053322 \n", "7 0.164304 0.000081 \n", "8 0.285143 0.285143 \n", "9 0.475435 0.214209 \n", "10 0.475435 0.214209 \n", "11 0.318826 0.117300 \n", "12 0.338192 0.053255 \n", "13 0.112222 0.023690 \n", "14 0.001990 0.049761 \n", "15 0.001900 0.012842 \n", "16 0.000075 0.016962 \n", "17 0.009178 0.040125 \n", "18 0.033317 0.047217 \n", "19 0.102205 0.039493 \n", "20 0.165181 0.016825 \n", "21 0.165181 0.016825 \n", "22 0.211463 0.012722 \n", "23 0.329816 0.001952 \n", "24 0.456058 0.003973 \n", "25 0.598940 0.010031 \n", "26 0.598940 0.010031 \n", "27 0.545796 0.000749 \n", "28 0.887607 0.010528 \n", "29 0.916575 0.006998 \n", "30 0.916575 0.006998 \n", "31 1.064612 0.002160 \n", "32 0.856461 0.018891 \n", "33 0.856461 0.018891 \n", "34 0.684189 0.160190 \n", "35 0.684189 0.160190 \n", "\n", "[36 rows x 3787 columns]" ] }, "execution_count": 32, "metadata": {}, "output_type": "execute_result" } ], "source": [ "with open(\n", " 'monte_carlo_results/proof_of_concept/spearman_results_normalized.pkl',\n", " 'rb') as f:\n", " spearman_results_normalized_pd= pickle.load(f)\n", "spearman_results_normalized_pd" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Using this data, one can create plots displaying the contribution of an input to the uncertainty of an output. The following graph is not representative since only 10 runs were conducted." ] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0.5, 0, 'Year')" ] }, "execution_count": 33, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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LjpCgOKDVxbqOefFZ47RqZLQJTg4+wcXnGxNhl1O3Kzx35Js8x/RNd0/3TnZsvJNWtQclBfj9La9LfEE2D50iwQeUN2COLvxiL2IaI3o/Tx/+yozer9lQVI1E9yaC0Q1UiqOkB88i5cwfzLpd4cSLj3KCRwFQVY0tm26hr2M/Sa8DUi7NLMOv1wTxSY345EvVyhIv73N1SSnqUQrZFI0aeVEh75TIVQtkC7l1m/EyE7quEwqECJoBgrpFUDUJYGJJHcvVseoaZlXFKgv0qkBUVl4kKJ7AXEI6vo/PdY8A2jQmGeZY/w+Z6O9f1umGR15keORFAHTdYte2e+hN7iVSjSGnFphKPhcChKlO/16KeHnbK4/pTiAgr9gvkay/lhYCUZHIsrPuahL5gmweLFfDXKAXQyR0BjnFM4e/guvOL0KMQJBE91bM0EYcu4NcOkqpqPKSwynabRNNjuFUz5EaPIFdm72cu+s6nDn/LGfOPwtAOJxgz7bX0BPajpkzkaXlE0WqLYimVaJpFbCA2Mv7pJDUQpJSxKFo2RS0ae9avl4kVy7gLOB9mgnpSep2Ha9JPdZM0yQcCBG2QgQ1i5CwCEqToKMTqGuYZZVASUGtiuY0Ffbx8VlbqAKvTTDuXODImYfJnh9dFTNsu8rRkw9xlIcAaGnZyK7N99Bp9qFPqciqC7qCsBSkCZ7m4SoOderUvQpVp0ilXqBUzVEsTZErTFIopPC8tV+su5moqkFrywYSsW5ioVbCVpKAGsHEQrM1RIVp0baGnJFiPcea3HbbbfLgwYPLOkfleIr0J0/OeYxo0TlvH+HAkW/M6tECCCdbibVvQTU2UC23kU8HWejth6J5JNrTCNnP1NAxyvnswn8JIdjWdytbu24l7rRCav3dOcyGp0hcQ+Lo0546V5U4msTRPFxF4igujuLhCA9HuLh4mFIn4OpYNY1ARcUqKWj1dXcb6OPjA9Pen6SOYzoNNXD1hMdYpZ+jpx+mUGxiobFlQFFUdMOiVvWr/DcDRVFJJrpJJnpIxLq47z9+aNnnFEI8J6W8baZ9vodsKbRrvFg6yKGD351xd6Krl3ByMyjdFHNJKgWT9AIC/2fCcxTSI21AG1K9nY4dBUxzkPzkSbJjF+c+WUrO9h/kbP+0eI1F29mz7X66AlswMjqysn7vnBRPoFQFut8L0sfnukHEdCrhMmPF87w48DTZ8/MF9DYfMxQm2rahoXOl55IbvzjnqsdMeJ7bVDGmGQZWJIYZjGJYEVQ9jKKFgCCeF8B1LOyaieuun4BUAehmHVWvoWpVBGWQFVy7iFMvUa/kqZXyVIp5PM8llb5IKn0RzTC5j+UXZHPhC7IGkJ0qxzNPcvyZR2Y9pveGNzExdAOVkebPLxDkJqPAPmAfLX0VQtERqoXTpAZP47lzC6xcfoKnnv/S9FhCYfvWO9nScRMRL4GaY10LNB8fn2sPEdKox20mqoOcGTzAeP+5VbMl0b2JSMsdpMe6KeQar2KvhSTJ3iJmIIVbHyY30U9xarKJloJuBYi1byAQ6UKoLXheENe2qNcMahUDp67ieOAU4ZrKzSqYQGT2/SoYMYkZcDCsOoZZQzdXP9bZF2SLwO1SODLxMKefenrO43r3v52Ji7tWyCoo5QKUcluBrUS7HiCaHCAzcoBCan53nJQep88+xemzT728rbNjK72de2kN9xL2oihZpuMWfJYdEdawow4ZZ4yLkycpVbJ0tW2jJdxDWMQxaiYy64B7jaw5+/jMgDAVnKRkyh3l3MgLXDh2ZFXtUVSNzm03I9lPZiJGZWjpY0opyKUiTAuHzcB9JDbUCMUzCMao5AbJjA7gLiApSgiFSGsHoUQ3utWO57VQLkQp5U2KBUHRj3u9CoGgXtGpV3QghObXIVv7SMDphueHvkv/k4fmPb53/3uYuLhl2e2ajWrJoFragWQ73Xsy4B1n/OwLC/pQv8TY+DnGxq+8A+3s3DYt0kIbfZHWLBSBSGqUzMJ0WZShmcuijIyevuK1qmp0d+6gq3UbyVA3IRFDr+jTtYyamFl7TXAp+BlD4OkeruLiiDp1WcOVdiMhRz7LiCcdLqZOcu7MwQUlRi03wWic1r67yU9tZWrCWPb5KiWTSqkT6ARuwox7RFsKGOYkdmWI7Ph5PNch2rYBK9wJSiv1SpxiLkTdVqm/ysHmR8auL/yg/nk4e+BpvvpnfzTvcUIobLzhvUxc7F3SfIk2jc3tI5TsCGPpCLn00i9KRsAm3jZIbuwAufHmraF2dW5nY+deWkM9BGUEraIhc/Y1kzDQbERAxY1LcjI1XRT4wnNUq81bJ9B1i56unXS2bCUe7EBlfTUFbgQJ2F6NmlOiUi9QruYpVrIUi2kKhTR12+9w4LN4Wnu3E4jeSmq0E3k9FPTzQdMVful/vW7Z5/GD+peAXIC6UFSVnj0/wcTFrobmSLZrbGvvZ2v18ySLj8NLiT46VDb1MRZ+K2PyJkbznUyOC5xFFg2sV3QmBqeXNLt2ZVHEScbPPYezxOaqo2NnGB07c8U2Qw/Q072LjuRmEsFOQjKCVtGReXtNpRc3DeVSfR9TQepyOgVdONjY2LJK3a1QsYsMjZ/k4sDJhrLAFoptVxkYPMzA4OFlm8PH51pF1Q26tt+Gbe8jlwr7y3w+K44vyJaIqut07Xw/k0PtizqvpUNjW9t5ttY+T6LwBKRmPi5QG2Bz7e/YfOm122qRij7AmH4vY9WtjE0GKS6i7UZmPA7cTbDlduJtFymkniczcmFRts9F3a7Qf+EF+i+8cMV2Xbfo7txBZ8sWEsEuQiKKXtUbj4WyJbK8zEumAkRcpx6oU/Ay5CoTlGt5ypU8pXKGQjFNuZxbXht8fHzmRVFVAtHE/AfOgG5YRDtuIju5hdSo/5Xos3r4f31LQDNM2rf9JKnhlgUd39qpsrX1HNuqnyNefHpWETYXqqzSkfsmHXyTGwECUEzuZTT4Rsa8/VyYaF/QMme9qjFxcTOwmY4deXT9NMXUeXKTI8gmFVu9HNuucuHiES5cbF5wbjicoKtjB62xjcSsNoIiglEzkHl30a1HhKkgYwoVvUSmNs5Y+hwXho5RP+/38PTxWYsoqkpb3x7M0C6yk13Uq419nbl1qM5TOWgmujYotEUauIgDrtQZSSXIpFY/Ts5n7eALsgYxAkFaNv0kU6PxOY9r61LZ2nKWrdXPEC8eaEiEzUe4cpztleNsB+7TBBf2fphDhbcxPLiw86dLaNwG3EawxSWSLGKYGTxngnJuiNz40KKSAlaKYjHDmeIznOGZK3cIQWtyAx1tW2mN9hDRWwjIEGpZQRbd6QbvIZuCzJIqDjE8foqxgfPLupzo4+OzdBRVo61vN2ZoF5lUJ7kpHaZWbn4rpLJz0wR76h+fDi/JLGEwDfJbbmIw+B4Gy/sYGtaxa2snrkMoYJgqpiVQ11E4qiehUvKoV9fOe7lQfEHWAFY4Qqz7J8mMz1znRFEFu7fnuMn538smwmZDCElf+mP08THS29/AYeUXOH0+grtAj5HrqGQnYky3P+oD7sCIeUSSZcxABrxJKvnhhooarhhSvlzs7yqEWPPCywyFiXduxgz2gGLi1kYppAfJT6588Usfn9VGUTXaN+9BD+4iN9lJbkpbURGGgA29sCfyKFum/g51qnmJItHyIfaVD7EPcJMWo/H3Mihey2Cqi/REc0IyFFUQjqlEIw6mbmOqNQy1gqmUMShiksf0Mhgyg+mkMZwJTHscw1nBL65mEwdbjVO2tlEyNlNSeyjTQclLUnKilGsWpbJGuSipraG6m36W5TycOfAUX/uzP375dTAaJ9T2fgpToauOFQrs3FbidufPiJYPLatdi6Fi9nEs/O85NriZcqFZni5JJFHBCmVBTiyqZo7PKwhFId6xgVCyD6F0UikmKWQDiBkS1g3LIRzPoelp7OoI+ckLlDJru9WLj08jvFqENbocuRSCUY1dG4bYU/sHYqXnVnz+YmA3g6EfY7C6n6Fha07hYAZVYjGIhipEjSmiyigx7xzR6nHC5eMoNKFB+TWKo0YpWdupBLbT+Wv/d9nnmyvL0hdk83C5IAsnWzFj76OUDVxxjBCwbVuVO7z/Sbz0zEzDrAlcJcCZ5G9wOHUPqbHm3xUoqke0JY9hpnCqw2THz1POLcWnf+0RiMSIdW7GCGzAqbeSn4rh2I2vB1ihOuFYDkWdpF4eITs2QLWYb6LF6xshFDTTRDdMNNNC1c3ph2YilHW0DnOdoKg6mr6ZbGp1RJgQ0NvnsSf4EH1Tf48il5aJ3iw8YTAWeyeD6gNU3RBRbYKYvEDUfpFo5Sim7XvPl4wWgN9d/vexaWUvhBAPAEHgO1LK60pyR9u6UAM/SilrvbJRwNYtNrcrf0NL4YdLnkOaMYbb7uNxbz/tap6d3nnai6fQcgOIJhT3Ur0Ku1L/nV3A8O73c7j+4wyc15q2gue5CtmJOBAHtoHyWpK9NUKxKZBjlLIXyI4O4q2Bgo9LQQgFzTBQdWP6WZv+WVF1VM1AqDqKoqOoOkLoCMVAyhZKhSTlvEW2iSsB04WAp3ucwh7QIdlbJRjJg7K+3+eFIBBIT8XzNDxPxXVUXEfDdVScuoLrTIsuD6g7gAP4pcl8XkU4rrG7Z4Dd5f9LpHJszf2NKLJOd/ZLdPOl1TbFZxlZsCATQvw5UGf62vZR4O3LZdRaI965EbR3Uym8Uqm5b7PLHfr/pS3//SWNXY9v5WTkHr5cuoHPjHVTy11ehPBeADrNOm9tmeCe4BC7OE9H6UWM7DmEbDxosSfzOXr4HLktt3LE+BVOnm9dloDScsGkXOgCuoCbCSRdoi0FdGMSpzqC69QaGtexK+Qnhpc1ji2cbCXavhnN6KZea6WcD+HYCt5ljXYdCY4Na2lFoFywKBes+Q/08VnvCOjsVtiSeJGkPNVQfKguC3RlvoxIr51YIp/rk1mXLIUQfwb8oZQyd+n1F4CfuLT7qJRy38qYODsrsWTZf+g0P/j4wMvu842bJHcEPk5n9msNjScVjXzbbTyj38Enpnbz+FSsoXFaDJu3tExyX2iI3QzQVT6FmT2L8BrzithqkonomxlXb2WiuonxVIBidm17WCSScKxKIFxAUTI4tQlKmWHyqbFFl+7QDINE12as6Eak7KSYjVMtLX+rFB8fn8UhFOjeINgSO8aW4qcIV06utkk+1wJrfMnyy8DnhBDfBP4W+ATwNGABH2u6lWsUSYJ6dYjujYI7w5+iO/MFWKRTx7MSDLXex/fdm/iH0a2MXlj6F326rvPp0W4+TTdwBwARzeEjPRf4IF8nOv7sosbT3Sl6Mp+lh89Ob7CgvGkLE6EHGBc3MlHqZnxCo7bcxVgXgUBcaqweANqBnQAEWjzC8RJmII+QaWrlcQrpYcrZV1KzIq0dRFv7UI1uapVW8ukwhbyg4Idf+fisORRVsGGjx5bwIbYUPkGgNgCT8562rDiRHjKRnQ2dq3p1EpkjiNrauOBIPYgbaKVqtlDWk+TVOFPEmZRRRp0oBVdfbRMXjCYk3XqBLiVLKxni7hShegqrOolSnmjYabESzBvUL4T4APBzwF9LKb++IlYtkJXwkE2eOEP1B3/FxqlPLuo8N9TJ0eSb+VLpBj471o3trWyb1x/vHOc/hL5D58j3l7S0+WpyodsYD7yWCXc3E4U2JsdZdCun1UIP2IQiZSrFILXy2rvAGJZKW7tLe3iCNnGC9spj6G6WydDrmFRuZKLczUTKopRfuxcUH59moeoKGzfabA0epC/3CSy7eX14G0VqAYY6H+CT1fv4++GNSNn4dV1XJO9un+CdkdPst48QTz2PcJofvCa1AKXELkatbUyQZNyLMuqEuVgP018JcbYcIF1fe9fD5UAIydZAlR2hElusIr16ji41RxsZYpTo+tCnVsCGBrIshRAa8Bamo2OeBP4909VDf1dK2bxy60tgJQQZJ78Bn/uZBR+e7bybz/MW/nxwG7U10JT27kSO3299hB2jX1uWD7snDFLRN3JRf4ChwhbGRpR1I9BWE91UaG2TtEdStKunaK88Rqz4LELMHwNTNrcyEX49k2L/JZFmUvZF2qIRq//x9HkVmq7Qu7HGVuspNmU/geGsjbIuxbab+Z7xRv50eC+j1eUJZQhpLu/vGOGtwdPsqb5AKH1k0d4cqQcpJXYzaO7gsNvHw/keHp5KrLhDYD1i6Qqn/vBtyz5Po4LsG8AhprMqW6SUHxRCdAP/BZBSyg8vk70LZq0IMmlGOdH+Dv40fR+PTDXWT2252RKs8sc9T3Nn6ksoleW7yLlKiNHYuxlS7mMo08PEmEcTHXTrDiFAN1WSLZK2aJp29TTt1SdIFJ9E0Lzl35K1g4nQ65nkBibKXaSmDJz6+i1ps1CEAoYhMC2JabiYmo2p1TDVCoYoYYkCJlkML4PppbGcCUx7DKM+hirXaGFjnzWBG+rgcPJt/HX69lW5rrebNj/dcZEHzJNsLz2POXXqimx7qYcoJnZz0dzOoUvi6xFffDXMWhdkR6WUNwghDOBpKeUtl+27SUp5aFmsXQSrLcgqLfv4pvl2/vjiPjL2+mh6ENMd/nDTYd5W+BJ6bmDZ56vpnYxE38OQdwdD6TamJtaXJ0co0NKu0hmfIq6NoFFFo4ZKBU1W0GQZ1SujeSU0WUJ1S2huAc0roDp5/0vfx2cdIVWDsc7X81n7fv52aPOaEjdbgxV+uuMCAWwezPfwyFQcV/pu3maxFgTZXCriY0KIQ4AE/vzyHWtBjK0WUjUZ7HoLf1N8HZ8f7mzauKamsLUnSqg9QDqiYrqCcMXFK9jkslWG02Uq9aV7VHK2xr89eyuquJnf2nSWn3G/Qmjy0NJ/gVkw7TE2p/+OzfwdKFDq28FQ+N0M2TcxMhmlXm3Mi+PYclmWRq2QSme7TUdokE73AB3576C7U5Bt+lQ+Pj5rhErLPh4OvIk/Gb6BgXNrs2TMuXKAP+zftdpm+CwjfqX++bjkIbOjm3gs9iP80fCtnC8v/QMrBGzpCJPsDJOPa7xoSKpz3IwJKen2FDpsCFU8vIJNNlNlJF2mYi9NqP1c9wi/Zn2btrFHEd4aKqg1DxWzj7y1l4K+lQIbKLhtFGoRCiWDQg7q1bnfFyEg2abSmczSqZ2ks/ow8eLTK2S9j4/PUpFCIdtxN98Wr+GxYldDY0zZBs9ko0227NogHtRpiZoYxvrpaiE9KFVsMsU6xdrCV2TWgofMF2TzMHL6OT7zg6f424ubluwe7ooH6OkJU0uanA1IMk3whitS0uUpdNSnhVppvMSJwVxDY/VYNX6l+wxv5BnaJ55AOOt7ua2md1II7KWgb6OgbKTgdlCyIySMCTrlAToK38WwJ1bbTB8fn0VSadnDE8EH+Mux/RwvXN1X2Gd+ArpKa9QkGjawQjpKQMMxFUqGIKvBhDLtJLAUQUJbO0u38+FKSNseLhCQ0OoJYi4EHTDqHqLu4VYdamWHYtkmW6yTq9i+IFsqKyHIvnt8jF/6ZGONZWMBnc0boiitFhdCgmF1Zd7rXbZC7EKJI+enGm6L1GLYfLT7PG9Tn6V78jFEvdhcI33mxQ134RhRjKnTTWmd5eOznnHD3RxOvJm/y9zG91LJ1TaHgK7SEW9stcRxJcOZctPa1s1F2NTYsiGK0WpRNRVyGqS0mR0CHYbCDrPGZmWCjd5pOusHSdQOIuT6iv31hEFR305O305O28SU6CJFCxNehHHHYLQuKLivhLyYErqFylOvv2HZbWtaL0uf+elOBOjpjTKVNDipu4yLl/7qV+4L9ZTuwbYAmzdvpGuowuHTaVxvcfOn6zp/NLCTP2InIe2n+eWeAd6pH6Q39UOUanZ5DL/OkUaYVOsdHFBu5AtTW3n40pdOb6DKT7QN8RrzNNsqRwikjyPk2inQ6+OzXEgzwkDbA/xL5W4+PtKDm1rdIHZFwO7eOPrGEEeCcGoJjqOYTLK1BqGcQ26yTP9YAdtd+veErgq2dkeJdARJRVVO6x7PcuX3kC4EuyzYqufpE0P0OMdorz5JoDa46MLnaxFF1onWjxOtH2fjLMfUlDby5i5y2lay6gbySjuw/IJsLhZSGNYEfgzo4zIBJ6X8L8tq2QJYKx6yrZ1hkhsiDMdUzmtrz5PR4wo2j9U5cipF3VlaILypePxCz0Xeaz3P1vQjKOVVLpe9jpGKRrH1Jo6Zt/D14g6+NN65oNp17abNj7cN84bAGXbWjhJOH0W418BV1MeH6c9FqvN+vibv46+HtpNbAxnsm9vDtPRFORNTGFeW5xpvSdhuK8QLDuVUhf6RAuUFJHIJAX3tYVo7QxTiOi+aksqrhOJmS+EOc5xN8gzd9edJVp9GlWusg/oqoygWr3/d8WWfZ0lLlkKI7wA54Dl4pXCSlPLPZz1phVgtQaYpgh0bYwS6gpwLK4yt0FLkUmnzYNeky/ETkwv6oM+HKjx+pmuMHwsfZmfxANbUqSZYeW1TT2znbPg2Hqzv4ZNjvUzUll4hO6I5vLd9jDeHzrHHPkY8/QLCLjfB2vWJ1AJ4ZgRXD+NoEWpamJoaoiKClESIIkHyMkANHekvBa8pSp7J349tb0ri1FJpi5j0bU0w0qJxbhVutFUk22yFtpKHna4yOFokU6oD0Bmz6OmJYCdNzgZg6lUiUReCW0MOt6mn2FX5GonaMie/XQOsF0F2bC00Ep+JlRRkAV1lx6YYoiPAqSBkl8lzbgrBm6M5LOrkZJCsZ5JxNaYcQcb2aEahh7gHN0x5nD6RIldpXlbl7nCZD7Sf437lCD3pZ1AqqaaNvZ6QCGSwhXqgnZLRSlZr41l3B5+e3MLRFQhADqgub2lNE1Pryz7XaiNRmLJ1Jm2T8ZrBeF2n4q6fjDCftUXQUNm1NUG5I8ARw8UTs69JtukKO8w6jYSjuCiM2jpDNW9R5aH73Oly0hdncAK06Qp3B7LcKJ9hS/GLGN71ef1tlPUiyD4G/C8p5dHlMG4prIQge2gwzZ8fG+K4NXdZiqUSVgU/Gh7m/tL/JGSfm/EYD5Wa1kNF30hR7aKktlMQLRRIUCBMVgY5WQsxVFuYbAtIuDUruXAyzWShuUteQkje2TbJe2Onubn+HNHUCwh3fQsEqei4oXZqVhslvYWsmiRFgjEvxkU7xkAtzNlyiNOlwJpom+Xj4zM/qiLY3RtH2xDicPDq5b5Xc1NI8jb1MXbnP7bkZT9XWOTNG0nre5lQtzBGB0NujIt1ndG6N6fUE8CeINypX2Bv/Xt0lL/rJ/8sgfUiyE4A24B+psP9BNOtk/Y329DFshKC7NuTWX7+2MCyjd+iK/xY8DR3Ff4C0x1f8nieMDge+VW+6NzP2crCPpyGhNuKkDqdYTC1PEtdLYbNBzov8mbzONsKz2JkZxadawk31M54/BaeE3v5eraP76eTS2om7OPj01w0RbB3S4JaT5ALZmNipAaU5/lYW4rgTZEcr69/mo7y9xuaZ7E4IkLWupm0vptxpW9arDkRoorNreIIO8pfJGSfXRFbrgfWiyDbNNN2KeWFJti2JNazIOsxFH488AK35P4STRaaPr5EcDryi/yr91aOLVBjCSm5saaiDRY5PpBZ1pTsW2IF3t/ST0xp7A4zIqpskKO01gYJFAaakvnpRHoYid3MQbmbr2Q28+hUfMlj+vj4NJ+ueIAN2xOcigsml9EZ3WMo/EjgFLcX/w8BZ3D5JlrjCLH6iRULRkpkA32C14Igm/VdFkJEpZR5oPlq4TpmW0DwY/rj7Mv/b5TazEt4mhZHoOI42Yb+sAAEkp2Fv+d3+Hv6wz/NV/lRDpTmvnJJIThkebAjyKatIXrH6xx7Md2Ulk2v5vlchOdzzXOybg5UuDOeZX8gxQ51jG5vlER1ECs/MGuAux3dxFD0Zp7xdvOvU5t4djIKayRpNGio7NycQDNVxoYLXExfv0H6Pj4wXc5h75Ykle4gR0yX/jniu5bKXWGXt4jvsb3wccQiqr2vbwSG3oUqO3DLUcppjfywzeTZPJXc+sriFqpAtzT0gIZmquiWimaqaIaCZiqohoKq8/KzooFirL7onKu5+DeklD8ihOhnOmrx8r9+KaXcshIGzsV68pDdGIT3qN9me/4fZ1znF6hY2n7SJxOc+eEY0pUgIJgIEGqxCCZ0rJiKGREYQQ/VdFCMOqgVpCjienkcN4eUswfpD4fezdfVn+KxwsIz+6JSsD/rMXR6irHs+qvcL4RkX7jM7dEpbrAm6VXSnHC6+UJ6E0fy4dU27wqChsquzQnsDoujr4pZ7HEFfSWJPVHhzMXcsohkH5+1SHciQM+2BCcSCmmxfG77kCp4WzjFa6ofp6X65LLNs9oIoWHo3ShuG04pQjmlkh2sM3Euh11eP63zmo1mmPzGJ7+07PP4lfqXwFIF2T1hh3fKL9Jb/MKM+w29Ey+7mzMPFcmNLL0a/sZb2thwT56KfWjWYyYCb+Dbxs/zg3xwwVmbKpKbywrOQIFTFxtrzeRzNSFz2hNWb7c4tsDEEUPCrroglnWYGC5wYbK0/Ib6+KwguirYuzVJuTvAUcNDzuMNM4Rgg9mYxyyoeLxBP8JN+b/BvAYzEzUtju7uIjcYIn2+Svp8FqfejHz9a4u1IMhW30d3jaALQY8p2KDX6FLydDDO1voPaS/84KpjhdCwlJuYPB7h3KPjSDnWNDsuPj/Jxeeh97bX0nPXFBX76uTY9spDfLDyEO+ybud75kf5diGOPY8wdxEcDErYE2b7jijtI1WOnkkvudDs9cirRdiTAhaTOl8XcMSU0KFCR5xOL8GWksSdqHDuYn5RDXWvNwxNIWioBAwNTfUTNNYahq6S6I1wIqbwtCK5enHmStp1hXcFz3Fn6W8IVvpXzM61jmlsRBa3MHECLhycRLo5psuJ+qxlfEG2CEKqYKMBPVqFLiVDuxyh1e0nbh8nWj+BqDgwR4y6oXfjTu3kzEMF8mMlYPk8G4MHJxg8CH13vY6u2yap2FcHKyaqB3h/9QBvN3bzcPA3+EaxndICWnec0TzO9Bq0bOzmxrTL4NkM47n1t5y5koRNjR2b49Q6LI6ZixdhczGmSMYiQCSAviXAzrogWXCR9nUglgWgClAVPE3gKuCoAluBmiKoCqgKSUWBMpK8gPxq2+yzAOb+bNwUkrxdfZRd+Y+hrnAohabG0Jx9TJzQSZ0r0Uj2k6orJPtCxHoEZryM1Mep2UNzhpzMhUDF0ndRTXUwdLDC5NksMNHQWD6rh79kOQ9n0od4+uzHideOEnQGFn2+EDqWcjPjh4Ocf3J8JVtaXsGWezvouGWMSn32avo1pZVD0V/lG7WbOF9d3Jf5ZlehJ+9SGitxdjjflJ5s65VYQKerJUA4ZkFYoxhQOWl41HyHjI9PwxhC8KZInjfYn6Gz/N0VnVtVI+jePtInLc49MYnnND+GUzMU2nYkaOkzCLY7qMEsjhzCdqZmsSmEwR7yg1H6n8xQnvJbIS2FtbBkuSBBJoRQgQ6u7GW56jnAKyHIJie/x5GjH13UOZoaQ1f6qKZaOfNgluLkwjPkhFDo3HYjmhGdbusiPcBDypd+li//LJFI6U3/LCVISTEzRikzexzEttd20rZ/mEr99KzHSAQD4Z/iB8qP8GjBXHR3gLAHu2sCI1Vj8GK+6UVn1wrtUZO2ZIBg1MQNaeQtwYh2dRuTRtlsKbSqDkcrKtVFNof38blW6DAU3hU4yx3Fv2noprhRVCWIwQ2kT4c4//gETm11EmkiHUHad0Rf9qZ5jkbqRZ0Lz0z4sWBNZF0IMiHErwP/GRiHl7+br5vCsPMJMlUJYqibccstFEd0xl8ske5f/Fq9UBS6d95BrXYTxUywYXuFkLT2DJOfeJzc+Misx+14QxfJvReo1ucu0Jo39/NE4MN8o7SBbIPxYtscQVfeJT9a4uxIAXediQtdFWztihJpC2AHVTKmwpDmUWqyx0theinmDu00+6pfJVF9Bpiu5j0RfAP9+j2c9DZzuBokfT0sR/pc19wckrxdeYSdhX9AlSuzLKkoAUyxj8zZCOceS2E3sbWcz9pmvQiys8CdUsr0chi3FFZakAlhYOp9yEoHpXGDyTNVJl7MsJRlX0XV6N55J5Xyfkq5QLPMBiStPROUM08yNTxLsKuAXW/sJr7rPNX63AGxjohwNPorfNO+k5ML7AAwE1Ep2FUBLVXl4lCBYrWxAPSa47Kcuq6vLURbd5hiTOdFS85bybtRAorg7lCVW8UL7Cx9bsHFJzPWHQyYr+dFdnGkFufCAttl+fisZUwheHMkxxvsT9FevjohajlQFBNT7CPXH+fso5PUS9efCDMCQYKxFqxwAs2MoagRJGFcJ0i9GsCprbP+sGK6bJQQEoGYfo1EiOl94tIx4lIsr2B6yfinfv9Hlt+0JQqyh4E3SSnXXOrWSgiykYEnOPHMP5M6V2f85FTTXMSqptG18x7KxX2U81ZTxpyNZFcau/w0kwMvznyAgN1v7ia+Y5RK/cS84w2H3sVD2o/zYCE8b3bmcmFK6HUVWqoeesmlmqsxMVVpOLmgLWqysSeC22JxLgCpJi07zjiXrnBvYIqbvcfpK34BTS693ElZ38xg4C2cUfZzzO7gVAWc9eWIbBgViGkKMQ1iqktEOESUGhFRIUSJEAVCMkvQyxDw0gTcCSxnDM0rLOlmyqf5qNQW1R9SUQIY3k2MPKczdGiKRoJ0pSuv+b8DVTdo6dmKEUyiaFEgjOMEqdcCVIsmTn2dCa5lQNMVful/vW7Z51mqIPtHYCfwTabbfgEgpfyLZhrZCCsS1H/gKb72Z3/ctPFU3aB75z0U83upFMymjbsQ4u05cA8ydvbwrMd07ErSd6+Cax7GcefORyvpW3km9Ct8q7KF0TUSyxD2oNcVxCsSteRQylYZTVfIlK7sihA2NbZsiKK3WQyHFQbU5b0gbw8I7jEuss/+Hl2lby17E2BXBHFE40vf6wUFD83L+E2VrzMsYzvloV5O/yBFtTBzx5PlItLaQaxjJ0JspFZr7DOmKB6GNYVTvUhm9CyVfLa5RgpBoquXcMt2XGcD2VQcz1nGHlPXAGtBkC2k7MXgpYdx6eHTAJph0rXjPgrZ3UwON/Y2aoaCYSoYhkBXPQzVQxM2urTRvCqaU6FmxulPhWf05GUnYsADtG+7E019gdHTz00nBVzG+Kkpxk+BHuhj5xvbiPSNUKmfnNGekH2ON2T/A69HMBx6Dwf0t/NQqZWpVaxNVlTghCJBB6IadIWBMElP0GNDtCbJBAQv6h7PvlzfqLlf5nFNYY9VZ7s6Rq93kp7qY0TKx2EFux+psowq/XZLPtcOqhpBq9/I4NMweiwNzB4j20yscIRkzy40YxPFfDvlvEWqKVPHgM2gvoaWzRVC4RSeM0Ru8jyF1PiiRwvGkyS6dqJom8hPtVIpG1SGmmGnz0qx4LIXQogI08H8S19faRLrwUOmWwG6tt9HbmoXtfL8LYvMoMpO8zzB8dMopSxKcQo1l0bkUyj1hWUruskOpl7/Ic7bmyjlZ19pDsUrBAJHGT39NK4z+3Gde1rYdI/AMQ7hunO3NvWEwYXwT/CM8gA/LMXJu2vDc7ZcxDSFPZbNNnWMPu8kXbUniNWOLGlMRQlgKruojCeplyG6oYpn9FO3V+YLyMdnLRHQ95If6OT0g5MrEmSv6gatG7djhjdTq3STTYWm45BWECtcJxJPgzdCIX2e7PjQVfXONMOkZeNOzNAWysUOipnQitp4rbEWPGQLWbLcB3wSSF7alAJ+Tkq5/G3R52GtCrJgLEEo3k4gtoVMaht2ZX4hJhTY1lGi57t/gTLVnMr9nmZQeOCDXIjdRmpidsEViNQIR08ydvYp7Ors8Rt6UGfXm9oI9V6kOkfZjJdwRZCzkZ/laXE/jxZD6750Q0xV2BWw2aGOs8k7RXftSWK1F5oytmn0QrmP1GmVwQMp7BmSHVr6ovTcHCbUWcBWzuA4mabM7eOz1tC1JJRvYOCx+qUip8uIECS7NxFObsdxetbk8p4esIkmsijKCAIFx+4hk4oj3bVl53pmvQiyJ4H/JKV8+NLr1wH/VUp5T5PtXDSrJsiEIBRLEIy3YwZbUbQ4nhejXglRKgRw7cUFSHZ1KWw99M8YJ59uouVXUrnj7QxteztDo2LWwtKa4ZLsGCQ39jS5idE5x+u+oYXeu8DWX8B15+84YCtJXox8gCflXTxZNFctGWChRFSF3QGbbeokfd5Jei6Jr2bFKk17wXZTHksw9FyR9MAi68cL6NqTpHOfidWaoe69iOv5S5TzoaohNCWBQhTkysZw+iwAqZM5E+b0wxMLKr4qhELHtv2o+n4KmXhDU7quWPQ12+faY70IssNSyhvn27ZIg/4d8ItMB+8cBX4eCAKfA/qAAeAnpJRzugBWQpCdf+EoB772LRQtgedGqVVDlPMWrrP0D3A4prO7+DiRBz8x77HCsiBgQTCADJh4AQPH0nFMnbqpULdUarogma4TeuIIeDMvFdqb9zF+1wfon4piz1IqQSJp7Urj1F5g/PyxOVuDmGGDHQ+0Et44RsU+wULisapqDyfCH+Bp72ZyXmPdu2wpyDiCjCOXLO5CqmCX5bJdm2SzfJGe2lPEqweaHih+uRfswrOTTS00qWgqG29uoW2ngh4fp+qcabgNy3pDUSw0NYEqouCFkXULp6pTLynU8lDJOBRTNQoTFb+u1DVCIBKjbfOdFLPbKa9wcpTPtYVQQFGn47N/4c9es/zzLVGQfRl4nullS4CfBW6TUr6nQWN6gMeBPVLKihDi88C3gD3AlJTyvwshfhtISCl/a66xVkSQHZrk2393dYPupaAZCjtjI7R9/S9Qqld6l0RbKw+9u5eDkRRZrUZWVMkoFRyx8Fise6ob+fBzMUKPzy7MvFgr6Qd+kX53M8Xc7MuZ4USFQPAkY2efmXM5EyC5KcqW+4OoiTPU6isXTVpT2qjqGyhp3ZSVdopKK0URp0iUnAyRlwGynk7GUSm40Gu67NRS9MnT9NSeJlF9BoVmVuEWGHoHKh14lRjllM7woSJTF1aui6JmKKjGtd+q1vMkdtkXWdcLbZt2YEVuITXWuSrLdZqhkEwIYkqOSO4CemGyoXE8wyLbvo/RcoJibmX/foUA3VIxDIGhg6F56IqDjo3u1dDcClq9jFYvolbyqOUswl5H3VakRLg2wqmDXb/0sw1ODVGvT2936mDXEHYdcUkDCcti16HmhKDMxVIFWQL4A+A+plv5Pgr8/nzeqznG6wGeBm5kus/vV4C/Bv4X8Dop5agQogt4REq5c66x1qMg6+t26H30f6MNnblqX+bNt/L/3HSOCbU5eRMvC7PHDs/q5fI0g+Lrf4YLibuYnCPOTDccEh2DZEefJj85f4xb721tdN/sYutH5k0EWK8IVAyjG8VrxylGqKRVskN1Js7krsvikj4+y4FuBejceju12i7y6fCKzWtYKsm4JEaWcKafQP/z6GdfQHjNbaFU330nuT1vZFzbyOSki2xiHlS8RafVzBObOk3wzLOoqSFEPv2yCPF5hXUhyJYDIcRvAH8MVIDvSSl/RgiRlVLGLzsmI6VMzHDuR4CPAPT29t564cKFZbW1WYKspU1jx8CXCRz4zlX7xIZuPvfuBF+MzlK4dYncU93Ihw9Gpz1mc/x/V+54Oxe3vYOhUWZdeZRIWrtTOJUXGO8/Pud4AJqpsv217cS3Z6m6R1iD9YUXhKF3o7obsAshSpMKmQsVUudzy9JLzgyFae3dh6JuxbYtDHOCenmQzOg5aqU1k+Ts47OsxDs3Em2/nanxjcteuNQKaSSjLjFvilD6LIFzz6ENHFtx4eK2dFG8/Z2kkvsYnTKoVxcu/oQCLa0aLcoU0ckTBI88jDrp171YKGtakAkh/qeU8jeFEF9nhq9nKeW7GjQmAXwJeD+QBb4AfBH43wsRZJezHjxkVkhjtzhG/Ft/e/Wdlaoy+o5b+b09J8iJVyrMtwbaSQS7UYSGIjSEUC89awhFQ6DCpddSqCBUYPpnWR/hxNhDuPLqD/K91Y384gKEWX3HbYze9lMMTARw5yj3Ho5XCARPMHbu2XmXMwEi7SG2vS6G2TEwbw/N1UVgGX3ISg+FYZPhw3lyI8srhMLJNhLd+3DdTWTGE0h5dZq9RBJrKWEFU7j1IbJjZyllp5bVLh+flURRNTq33Ywn9pEdn/Py/zJtHSqbMs8SPPnEvDeIMyHqVdSJhbUsW0k8zaB621vJ9N3NWK2FXOZKr7uqK7S2QIs3QWTkCIHDD6MU/OtBo6x1QXarlPI5IcRrZ9ovpfxhg8a8D3irlPJDl17/HHAX8ADXwJKlbipYARXTkLQoabq+/RcoudTVB27dxD+8w+R7ofNXbN7bcT/PWT9HQTbeTqlXnWJ77TucGP0+zgweqXurG/nwgSjBJ+YWZk7HJlKv+xDn8u3UKrPfqam6S7JjlGr+MJMXFubl69zTwsbbNVSzsaU9obigFfHIYLtpPK/x5sNC6Fj6VtxiB9kLKkOHspSnFt6+pVES3ZuItO6hUt5IocGlmGC0SiiaRrrDFNLn52wofz2hGSbBWBIrFEe3oqh6FEQI6YWw6xa1qolT1xv6AvdZPjxPLChhSlEFvR023Se+inXooRWwbPWxt95Ebv+b8RSdyOBzWIcfQdRXpun69cCaFmSXnfwbUsq/mm/bIoy5E/gn4Hamlyw/DhwEeoH0ZUH9SSnlf5xrrJUQZIMvjPD0l89iqA6GqGN4VXSnhFYroFeyqIUptPwkSnYMNT02/wdE1zn/7pv5z9sOUxOviBxVqOzd9EEecl+HFM0pQtijZtlV/y4nR7+L7V0tfBYqzLxwjMwbP8I5uX3eANRQvEIwfI70xYOUV9B7E0xaxLqChNsMAgkFI+KiBWoIvYQnsjhO6uWyEIoSwFS3YedaSZ+TDB2aWpHAcEVVae3diRXdSTHTvSzZYWbQJhyfQlUmkN7KtpRZFYRAKCGkDOK6QeyaRa1sUq9e+wkN1yNWSGNLaIy2xz6OOtq/2ub4XEOsF0H2vJTylldte0FKefMSDPoDppcsHeAFpktghIHPMy3MBoH3SSnn/EZfCUFW+MEPGPq1X2/KWN7e7fzVm22esq5c10+YSUI9v8ELzpamzPNqupUce5zvc3Lk29Rn+JK+r7qRXzwQI/jk7FmZAFLVKDzwQfpjd5CenCcWTEiSnWmkc5yJ84fn7ASwUlhRk0h7gPRAHm8F2jtZ4QihRDtWuA1F20Qu1eELBR+fBki2avRVjhB98ONXZab7+DSDNS3IhBA/Bfw009mVj122KwK4Uso3NtvQxbJeBJkIBDjy3hv4r70v4L4qHG9r8gYGYh9lzI0saY6F0KEU2O8+yMmRb1Jzr/bk7a938OFT3XQ+fBxZnrvIaPme93Bx05sZHp1/yccI2MRbBylMvkBmdO3FajSMEITiLYRibRjBlksFgqPYtTDlQgC75heb9PFpFCFgQ5ek5/x3CT799dU2x+caZ60Lsk3AZuC/Ab992a4CcESugXS59SDI7Fv38N9en+OYfnWz2Bs3vIcfKu/BZmW/uNuUIje6D3N69OtUnKtjpdrdML86uIM9jwwgxybmHKu++y5Gb34fg+ngrIVmLyfWWkA3XmSy/zlq5fVxp6tbAeKdm7DCnQgljutGprsy5C28ZaiFFIxobAhnaDn7CProOSo776bQuoOsaCGdYUHvs4/PesWwVDbHM7Q//S/oA6veoe8VNA3R3ordkaDYGiQdVymYjcUghuqCHU8OIYf8mM+1wpoWZOuBlRBkZw//kDOf/ycAFG+6EJuQgASBREiBkBIhp7cLmC5Mh+B0p8dfdRy+akxLtdi06dd43Gm42QGWIoiogpAKYcUjqLgEhcOoY/FiZWH/p0mlxC3uDzkz+jXKztXiyJAqv5Dew+ueKKCcODvnWF4kSe51H2DQ2jP/ciagaB7JjgkUpdGCg3U8J0OtPElpapxyPtvgOFcyLb56scI9INqpFBMUstayNxcORjQ2hLK0nP8h1oFvz5puLxUVe+dtlLbcRiG6mSk7QnbKxVvnfUKXG91UCIUVQoZLgDKWnUOzi35M/xpDs8tEHvscSjG78pMLgWhrwelIUmoNk0lqjEY9BkJlTls5TmmTiyrQPR8qgl9I7eWBJ0sox6+uS+mzsqwLQSaEuIvpoq27AQNQgZKUMtpsQxfLSgiyBwcf5Dcf/s2mjdcd7qXU9hucddtnPead0Tw7eBFLFrBkHtPLY3pZDG8Kw0ljeCkUOXPAtkRwJvohvuS+jWMLbG0YFxVuk4/RP/Y18vXcjMe8vbSVn3jeIvjUMXDnro1T3X8/E3vfxWAmsqg6OktBM1yCkTJGoIgisrh2hmpxkuLU+Ky1u3TTmhZfkQ0g2qiWEhQyAVhm8fUSwYhGTzhHy7lHCBz8bsMFJ71QlNqeeylt2E8u0EO6ZM7ZfeFaQygQDGuEApKgWsNyC5iVNGZuFH1yEG3kDGq2sYrqPmscXcfev51Tu8OcjVZYSOu2V+MISb9Z4IQ+SVlZnYLOby9u5f3PmwSeOjZnHK/P8rFeBNlB4CeZrhd2G/BzwDYp5X9qtqGLZb0Jsr0dr+E56wOzlrTQBPxa9BC3Z/+wKfOdi3yQf5Xv5FBpYQIjKOrcJZ5lfOKrTJRnrsa/z+7gI6e66XrkBLI495KjF46Rf+3PcjF0A5MTKyPMZsII2AQjJXSjiBAFpIxSKScoTq2c+HqJQFhjQyRH67kfYh38TtOrfr+El+zEC6xcVfPVQnguysRFhHv9CNDrHaW1hamb+3h6s8OXE+fJKMtfomaluLnexYePd9D2yDFk1S9psZKsG0EmpbxNCHFESrn/0rYnpZT3LIOti2K9CDJVqOzp/Tc8JF836zExTeG3zM+xqfjZJc01E/3hn+bLvJfnFijMNFzuVo9STn+VwfzMS5UtXpBfvbiTGx65iByZv5VSffddTNz0o1zIxuasaXatIQSEohqdwTyt/Y8SePZb84ow0ZIkd2MfL2yGrGFz24hF96k04syAf/fsc/0hBHLXFvr3tvD9DVM8ZA0wQ93kFSWsR4hZLRhqoKHzq06R0eLFWfdvcGL8Wv9Wtj50BjnVUJfCV9A0xIYuqp1xPFWZ9iGKl/6R02WWXn4/xaX3Vl728/pCSFAdD8V2UW0Xpe6g1G1E3YFaHeo21OrIWg0uy/5fL4LsUeCNwD8AY8Ao8G+klI0HQDWJ9SDIpkta/CYvOJtnPWarpfDvvD8mUVve3+VC+H18RbyPZ4sLTyK4TXsRM/dNTqdn/kPVpMJP5Xbx+hMqkWdPIitz39V5VojC636GodgtjI+vf2FmhTRCQQjoDgFZwqpnMYsT6JkR9LHzKGP9KPV54uQ0DXfvVs7tjPFg9xSPzPKF0+6FeEemj1uHDdpPjsP5ayhj1cfnMkQoROmmbRzZofOvrQMMaNlln9NQDOJWC2EzScBIoGkJpBrDVWNURZQCUTJemLQMU5NLK18jpOQ+7TCpsU+SqsyeOBWWBh8d2cMdPxxDXpi7DZIIhfB6Oyl0xRhv0zgXq3M0NMVhc5y6WP/X2uVAkwoRaRL2DJJqhH/5N99e9jmXKsg2AROADvw7IAb8rZRy7ijvFWCtC7KFlLS4N+zwi+Vfx3Bf+VAqiommJREiiBAWEEBKE+mZuJ6B6+q4jo7j6Ni2Qr2uUqsJajVBS4tDa9sT1Gqz9/gcCv0oX1V+kieLC7+o7NWGaCt9m5MTj+PN0v024QX42dQO7jxSw3j+5LyxZvbmfbjxzgXbcDluOEG5fTsFs4Ns1SKXdZralPclNEMhFlOI6hWC9hRmKYWRHUEfH0AdOYtSaaylkujuJHXDBp7dZPO1RD9p5eqAP0MxMDWLQj0/4xh9Tpy3ZTZy46BCy4kR5NBoQ7ZcF2gaIhnHS0Spx0OUYwY1o/kZsj5Lw9YFT3eX+HrkHFUx9zK0JjR64zuIhG8gq29nSiYbmtNDIeMFycnGvF1LISjq3CMf4tTwF6m5s9+4CQkfzOzlLU9V0UcmqW/sINsRYqRVcDpW4VBgktN6egUtv/awVIsDP3tg2efxsyyXwKsFmUAQNWNEzRaCRgJDj6NoMTw1hi1iVESEogyTkWEm3RCemP2i/5OxCX4k9+uIyyqIGEY3J46/kdEF1PeaDUWB226rEo48gm3PHsw8EvoRvqb8LI8V9QWP3aem2Vr/PqdGvz9jkdmX2OTE+cDoZvY9n5k3Q7MZeIEw9Z23U9mwj2JkI3kvSibHgpMKDEslFoOIWiZUnSCQOo81cBT14qmmNBgWlkV1/zZO7wjynfYxDpgzp7sbisG21tvxQndyyNtNWRrs0y7SZh8hlX2G4cLsQnuP3cab093suyCJnRqB8rUTWzMrioKMhXHiYSoxi2JEIxsSpIMOY4E6w0aJC1qOYTW/LpdffF5BExqb4rsIh/eS1Xdyyu2lKJvf7WI16FGz7Kj8K8fGHl5tU65b1rQgE0IcZY6UlZfiyVaTlRBkD40P8BenD1IgTMYLMeUFcZZYN0wXgn8bPcAt2f92xXbL3MaBA/eQzTZHJJum4Pbbc2j6Q7huYdbjxoJv4RvaB3mkYC44R6ldKbDf/SHnxr5J0Z7bS3RzvYv3D3ax9Zlh5ODwIn6DpSGFwNm0l+rWmyklt1LQW8lVDAKGR0QpEqyME5w8i9F/BG2kwWbnmoYSj+LFIjjRILWIRTmsUQgJcgFJ2rQZtao8HBqkKGYWsLqis731drzQXRyWu8l7s/cx7VPTbJHHqRYOcH7q6IxN5H18rgU0RWNTfDeR8D4y6g5Oer2UpLHaZi0rN2oXMKY+yUB2YT2BfZrHWhdkm+YaVEo5+636CrESguzbk1l+/thA08ZLaAq/ZX6KjcUvXrHdsm7i8cf3Uyk332MZDgtuv30S13sQOUu5DICMdTuPWB/mm4VWKgusaxURVW5VDuEUnuVc+oUZm5lfzhvLm3n3uThdT5/DS61cr8uG0TTk1l4mt7Uw2CpJmQ4TZpUxvcyQlmdcKTbkeXlJhLmhOzki98wpwmYjISrsVV9ELz9Pf/oApXmEsc8rHu6Y1YqphhookuCznGiKgRncxpS2g5POJios3Hu/EihAh6HQozt0qUVCojEvdFEG+W4hij3D968iPe7TnmNs9FNkqv4y5EqxpgXZqwbYBGyXUv5ACBEANCnl7C6XFWK9CbLtAcG/c/+QWO3KAHnTvJdHHt5Co+0eNU0jFAwRMoOUa2Wy+ZlribW2Ktx00wVq9UeB2QOuqmoPz0Z+na9UdjJeX3hgVlxUuEE5iVI6wLn0gTljIjSp8M7iNlprjd3xRm2N3rSgZbyCOTiBHJ+cs0H6QhHRKNVdvVzsC3KwvciDoUFySnPSz1/xhN3J4QZF2Kxj43KDNkBL/QijmWcYL62cJ3ItIBDEzDgxq5WA0YJhJBFqHEdNXBaQHWHSC694Zwyf9UVEVdhgSLq1Ch1KlnbGaHUHiNsvEq0dQ5ULLPA4D2nrHj6r/8assbxhUeUu7wecGP5XbG916qNdT6wLQSaE+DDwESAppdwqhNgO/J2U8oHmm7o41pMge02kzs8Xfx3DS12xXdPewsMPtfHqelhCCELBICErRNAIEFItAsIkKA0sV8eqaQSqKmZJoNdeiVPzFMlQX5nnyqdJZ2f2QG3YKNi9+yTV6tx/fK6wOBn9Zb7u3L/gIrMvERJ1blRPY5YPcD71zIydAJpJwgtwe6WTvYUom9IKLWMVrKEUcnR8TqEmNm0gs72D0xsEjyUnedYYbkqskaZoJKxWomYrAbMNJ3ATR7w95GapQdds+tQ0AXEd1DGSClkZJOULreuWrZbCjWaKNtI0UhhWwyXhjZJwzhKrHSPgruzNzOnoh/mn+tu5OEtLtF51ii3lL3J8/LEZ978aXdFpC3YRDXRhmh14WjsVpY28jBEQJYIyj+5lEW4Wx85Qqacp1tJkqunrWvitF0F2CLgDeEZKefOlbUellDc029DFsl4E2U/Hxnhb9t+icHm8j8Dz3sMTj19ZvFPTNN6avJOOiyZiCcpACsnwpirP188wMTVzYP+OHbCp7zmq1RPzjjcUfi/fUX6cHxbMOXxrM2MKhxvVc0Sqz3Eh/RS5WnaRIzROzLO4vdrFvnyUTVMqyVSNdJvJ4c4qP4gOMazOnME4G5rQiFtJIlYrQSOJpieRahxbiVMWMfIySkpGyLjB6fo+Pj4+TUMAOwKCG41JdniH2VT5NkG7f7XNWjKOCPNY7D/zqcLWWcNFbtbOo6Q+yWD+LFEjTkuom5DZiWp0UldbyYtWJrwEY250zmSy2RBSklDLtIgiMQoEyaF7OYSbQXrrKUFIIKWD59XxpI3nTT9caeO4dRyvjuPZOJ6N7dWx3Tp1r46hGOtCkD0jpbxTCPGClPJmIYQGPH+9BPUvRZAZQvCbkSe5MfdnV2wXQqNc+nEOHrwyPsIwDN4WvpO2keYFrkohGdtY43nvHKOpmQu47r/Rpb197lIZL5E1b+WHgY/wjUIb5Qb6J6p47NcGaKk/z+jUMxRnKekwHzW3tiIB7RujW0hGbyJv7KHf6yHtBhu62C2WVl3hNcE0CXIcdzdyuKwtOK7Px+daQQF2BWG/PsFO73l6y9/Gcuaux7WeKRh7+dfAb/O9fHDG/SoeQVGftduLT+NEFMmZ19687PMsVZD9CZBlumXSrwO/Apy4XlonLVaQqcC2gGCXkeU++1/pLn3jiv2KEiQ1+V6OH7/Se2KaJm8P3knL6PIFsY5vqPKC0s/QxNUlF4SAvfs8urpOUa0+z3yu/5rawYHIv+WrlT2MLCLOrFkEsNmmjtImL0DtPJP5U4yVln6hbg200524mZq5j9NyKxPe7DXkmk23oXB/YIxbnO/TXfzqFR5VV1iMB9/IOf1ejnubOVw2ybt+1X6fawtNwJ4A7NdH2e4epLf8rStqNF4vDIbfx//r/RSnK/5N2EoRUAT9r13+evdLFWQC+EXgzUx7jL8L/INcAwXM1oIg6zUVdptFtokL9NrP0VF5BN2budWFpiUY6H8n/f1XvnWWZfEO8w4S4yuTUTTZXeeQMcCFsZlbd3R1KezaPYaUT85ZLgPAEwZnIx/kKfEaHi2GqK6iFychKmxVh4m5/TiVc4zmT82bpRTSw2xK3IQSvIEBsYN+t3WFrJ1mk6lwvzXEzfa36Ch9B7HAGBgPlVTgdZw37+eEt51D1SBp2xdol6MAXYZCt+7QqRbpECla5QhJ5xzx+gksZ8TPslxjKDgoc2SCX09IofFs7D/xz6WbyDn+Z3u5WfOCTAihAEeklPuWy7ilsNKCrE1X2GNV2aYMs9k9TFfl4QW7zw2jh+PH3sjY2JUfrGAgyDu024lNLq0VRyNMddkcsgY5Pzow437LEtx4U4VI5DlqtfPzjldXWjkV+Tke9+7g6aLOWqiQ1a3k2KQMEbL7qVTOMF44T3u4j0B4P+PqLk46PbisbMX2HQHBfcYAN9a+Smvlh00bN23dTb/5ek6xi2O1COXr4BquAB26R7dWpkNkaZWjL2fEReonUOV6in3x8bmaitbLN8O/x1dziUXF78ZUhQ5D0q7VaVFKxCiRY7pg+YSjM2pDyfVvSV5izQuySyd/CvgdKeWaa5y3EoLsWPpFHun/OhuqjxKpH29oDMvcwbPP3kUud+V7HQ6FeLu4nWiqOdlhQp8WFnKRnpJsh8OR8EXOjPQz29/Djh2S3k3nqdefRc5TawygrPVxNPxBHnVu4NACm5pfy+wLwr36GW6ofonEPNmtPj4+q4sQGpqWRFWTQBTPjWDbQWp1i0rZoFhUyeeh2mAicyQC+244QrV6aMHnTATfyCeUX+ZQSdCqK3ToHm1qjRalSAtTJOQ4UXeYqH2ecP0Mupy5/NHl1NQOCsYO8tpmMmoPGdpJyTgpL8S4ozNal9dN7Op6EWQPAbcDzwIv1y6QUr6rmUY2wkoIssnJ73Hk6EcbPt+ybuaxR/dd9cGNhCO8w7uV8NTixZiwVGRUUNWqFN0pUoVhRifPMDE5QCSU5N797yOeSSIX2DboJSpRj7MdGY5PnaVYmrnIaEuLwt59aVT1SRxnYYVdc+aNHAr8NA/Xt3PmGo2J0MR0DFi3btOpFmgnRascJuGcJ1E7QsBZzP2MwDS34tg7cF0dK3CeWu3kgoSwj8/1gUDXW1DVdqRMYttRHKfBZCgpqNUMymWdQkEhl4N83uPVpYiuskAIDKOxOV3XxXFs7ryrimV9C89bWF0hicATJqpcuZI2VbWLurayoRxLQaLgCgtXBHGUAI6YftjCwsGcfhYGNiY2BjYGDjpSGPzJzW9edvuWKsheO9N2KWXz1loaZG0LMgXTuJ+HH+69qsd2LBLl7fathLJzL5WJkIYXllS1Mnk7zWTuIqPjp5nKzNwH8XKCwRj33vgTtOTakJXFCTNPkYz2VjnBxVnjzDQN9t9o09JymGr15ILHTgVey0Hzx3iospHhVUgGWArTFbpdutQS7WJqOh7J7SdunyJSP3VFT9LFouutCLGXTKaLc2eNq7ypwaBg+3aH1tYx4AR1e+aMWR+fawOBrre+IrjqUSqVIMWiSSajkEpJ7BUomWVZFvFwjJgZJqIEiXoBwjWDYEkjmFdQ3Ma8/7YpOdI7zqGhE7S1CW686QWq1SNNtt5nMSiKxetf19gq2GLwY8iWwGyCTNMSaGobiCSuE6VWD1EuWxQLGtmswtSUd5UQA0jEErytehPB3CxiTBGMJgd54eR3KRSX3jbDssLce9P7aCt2I0uLFwzFpMuZlhQnJs9Rqc4cj7OxV2HrlkkU9Tlse3xB40oE46G3UlLaFm0TQFWJMyK2MOi1c94OMFj1Fl0fbTYUYLMl2GkU2CwussE5THvlcSx3tEkzgKKYGMZeSqVNXByMcPHi4jyHPT2CTX1FQqF+arXjc7bE8gFVCaLpPUAn9VqCQjFItaLPukTvszpIKcjlBKmUXFTnEk3TiIYay4hWFIWIFSaqBYkQIFK3CJV1QgUFvbq88aVTXTaPiRNMTk1y9z0VDOObeN51UNB5DbLmBdmlk6/rGLLh4Wc5deqzVCoBCgWdXE4hnfYaujtriSd5a+lGAoVZPuS6wov6cxw6/r2lGT0Dhh7gnpt/nM5qL7KweGHmaJLh3jLH7QuMTM4mTCQ7dsDGjSO43kFct7EaY43giAhT1h2MG/sZEZu54LVxvm4xVPPmzKQzhWB7QLJNy7JZ9NNtH6Kt/NiC4i8Wi2luwXV2Mj7ewtmzgnqTNJRpwvbtHu3tEwjlJPX6tVunaS4EKrrRiaJ049gtlCsRshmTyUmFqSlfeF1rtCVa6Y100l2J0zJqoDnrM1bVUyWntmR4ZuQIbW0eN+x/jmp1+YWBz5WsF0F2XceQnTx5ks997nNLHqc92cpbcjdglmYWYyKg8nz9IU6ffXrJc82FphncffOP0WNvQeYaW2LLtTmcjk9wcvwc9VlUhabB3r0u7e391OrPrZoHx1YSpK07GNdvYFj0MeIlaVfybJJn6a4fJFl9uqnxGKoaRdfapz2nboxaLUS5FKC/XyebXRlR0NmpsGlTGU279mPOpFQoFk3SaZ3xca/hfrA+ax/LstjU0sMG2UpnKkAgf221yiomXZ6MnOHixDB331NE17+F583eD9inuawXQXZdx5A1Q5B1tLTzlqm9GJVZxFhE48mprzI4dGxJ8ywGVdW486YfpVfuRGYaC8awTcnghgLHKv1MTqVmPS4UEuzdWyMWP3UpTmJ9eisUxUTT2lGUFjwvgV0PU60GKRb1S8vUUK2uz9/Nx2etIYSgq7WTXquD7mKU+LiG4q1PL9hCkUIysKXEE6nDJFtq7N13YFExuj6NsxYE2bzFr6SUPxRCdDDtJQN4Vkp5/ZVObpDutk7eOLkHozrzhUQkdB4c/BcmUwONTSAEka4OZHeU0XgFFUHH8RqF0bljuVzX4cnnvsBTQuGOG9/FZnUfcmpxwkyvCbaei7KVG0n11DlhDc9YOqNUkjz7rAHsp6XlRnbtLhAIHKFWO7fY33ZFMYxuBNvJFzoYumgxMjJX5pUvxNYTqqoSDMzcnsZn9dBUjQ2RDnqcBO1jAYyL17YAezVCCjafC9MVvpsD4iI/+L7HvffuRFG/iZTXb+Pv64WFeMh+AvhT4BGmv43uB/5/UsovLrt187DWPWQb2rt5YHwXem2Wi0qbxrdOfYxCfubm3zOhGQahjV1UOgwuRLIcVM+Q5sp4JyEF73DvYMspjfzgwuOJdu+4n13td2GMq+A0JjDKMY8z7WmOpc5QqcxdlLOrSxAKNTaPaUqisQqBQA5VTeG6w9j2wt/HqxGY5hY8dzOZbBuDFwzS6dXLAlUUhZ62LiJ6iGytQKaYnff99JkZwzBoj7fSZiZoccMkChaRKbXhDDmf6xwBIqzhBSWO2tgaueZqiPH5s99He6s8VjlKNFZk9+6nqNZOL8A8FU1/qYZa7OUaarZjoOs1dL2EomSRMoPjTOK6pXnHvB5YCx6yhQiyw8CbXvKKCSHagB9IKZe/gto8rIQg63/xPI8+8kN0oaIrGprQ0FHRUKefPQVNqmiuMv2zI9AcBdUVBPIK6iyBpl6Xwtee/2tq1bk/DFY0itnbTqbV43RgnOfEaWwWfhF4wLuZfWcj5M7O3zj8JSLhFm7b+w46nN5Fe81ewtEkFzeVOFbtZzy9Mg5Vy4LOTki22ITDJQxjChjHtoeuylwSQsc0d2DXN5FKJ+k/r1Aqra6XKxIK05fYwAYnSfuYeVWGV92SFOMuxUCNvFYlJ0tkq9NirdpohcprjGAgSEeslVY9TtIJkciZhDIKQvriy2dxCEtFRgR1vUbJy5MtjzGZGWRk4sy81+2F8Po7P0h7ugvmqZb/UomMIyMnuPfeHKZ1GmQc1wtj20GqVZNy2aBYUMhmIZ+XzPS1LoSYMavYsiCZFMRiklCoTiBQRTfKqEoOyOK4KTxvPYk2gUABoSGEeulnFSFUQEGgwqWfQXlluwhwz92fX37rlijIjkopb7jstQIcvnzbarESgqxyPEX6k81dw6/3eHztqb/EdWcWVrGtmxje7HHUGORFmpPcehd7uHOgg9zJfmb8tM7Cjm13safzXswJAxrslZjqrnMyMMKZ0X48bzW8TpLWVoX2do9IpEYmYzEwQNOyHBtFURR6WrvoNTvozkaW1D6rHvAoxl0KgTp5tUpBlnDk+qrz1giKECREhGQ9SHxKJ1C4tgK9fRaGCKpgKTQUOiAEjmFTFkVy1RTp3BCjE2fI5Zf/RnLX9nu5yXodsjj/TfYrJTKm43V1XScYCBI0AgR0i4BqElAMLGlguTqWo2HUVcyqglkWqDZUopJi1CZv1siJElm7yFQpS66wchnxaxVN0/jd3/3dZZ9nqYLsT4H9wGcubXo/cFRK+R+bamUDrEdBVuqu8I0n/9esoii2vY+PbX+aCsuTXbOfrbx2uI/i0fPIRYijYDDG7ft+hE65GVKNec0qUZezHRmOps5QriysMvW1RjgUZvMcXjAfH5/ZEYFpr1VVL5Orp5jMDTI0dopcbmH1D9ciyUQ3b9r58zA+vyjzVEk1LC8JrOZ5fV1dUoq7FEI2Ob1CVpbI1vKk8xlqtWsj01MIgaZpaKqGqqpoqoqqqNM/KyqmbvLBj/z8StjRuCC7NMB7gfuYjiF7VEr55eaa2BjrTZClO1P84Kl/nHV/bHMv/7j7IEWWP1ZoOxt488Ruqof7cRdZVG3b5tvY2/MaApMmsoFq+64mGeotccK5SKnamDCr1WuzFqpdCwghCAYCBK0gYSNIt966ZC+Yj8/1gjAVZFShalQo2Gkm8xcZHju1oC4l6xFV1XjbXR8lNLL2Ek1qIY+atX687UIKFE+guKA6oLgCxZEo3jydcXSFnj+8d/ntW6KHbDMwKuV0sSYhRADokFIONNvQxbJuBJkCQ4kBnjg4e3JAtHcDn9h3hCyFpc21SDbQxo9kbsR94SL2IuOQrECU2/a+gx5lC0yufAGoSsQjF7fJWhWmZIFUOUM6N4U7U4uEJvBSZl7IChLULYKqRUCYBKSB5WpYtoZVUzHKCkZFXPMp+usOASKuUw1WmKqNkitP+pX61xiedBibPE8qtebqkK8Id9z4LrZU9zZ0o+uzNNaLIDsI3CMvVfYUQhjAE1LK2+c8cQVYCUFWODFK/qsXptf45wm+nBFd4bR5iBeOfnvWQyI9XXzmplOkZLZxQ5dIq4jzntxtKM8PUy8v3mvV13sj+3tfTzAdXHRT82biqZJiwiUbqZHRyqSdPJOFNIXi7EJX0zTCwTBhK0hIDxBULIKYBF2DgK1hVVSskjJrHblmIwIq9YTDeHWAXHmClkgPEb2VgAyillVk3varbCwAEdRwYi55mWYke5bzF56nXG5+BwYfn2ayoWcP93W8t+H6kD6NsRYE2ULWTzR5WZl1KWX9kii7LhgrDfC1Q38MQhCPddKS6CEe6SBitRDUo1giiG7riCrIknuFaBOWymH3UU4efXzW8cMd7XzhxjOrKsYAUjLLP0R/QOJ1Ud5buB3t+TFqpeKCzx8YPMzA4GEMI8ht+97ORn0nTKy810xxBdGURjSl0UsIaAO2Urck+aRNPlhD8xSsmkagqmKWVIyigIX/qs1HAK0aOW2K8xMvcPbUATxvdlGr6xZdHdvoaNlMPNhJWI1h1E1EQa6qGF5VNAEJlZJeZLI8yMDwEcb713adOx+fmRgaPsFXpoZ4xy0fRbs2V2h9ZmEhgmxSCPEuKeXXAIQQ7wZmL8t+rSIl2ewo2ewcDaaFIBnvoiWxgVi4nbH0eYaGT8x6eKitla/edoExufQm4s0iQ55/jDxI9LVB3lu6C+v5CaqFhS+j1utlnnx+ukTdhp493NT3AOFMBFlZXaFgVAWtIwatrI17CRFUqcVtxirnOXHucXLnFx6UbNtVBoeOzdjZIR7vpKt9O8loN5py7ceredJjLH2O/v5DOGf8Bus+K4gQaLre0Kme6+HNkmUPUK3k+dIT/4M33PVvaJvsBM93iV8PLOSK/cvAp4QQ//vS6yHgA8tn0jpGSqYyIwsKPA0mk3znjjEurtGmB3nKfDz0EOH7A7y3fBehQ1NUstlFjTE0fIKh4RNomsEt+95OX2DPdDHE6/HaogCtGlk1xbnRFzh34iByGcpSZLNjZLNjTR/Xx+d6QtV1rHgMLRpChnTqIYWS6ZAzqqTUPKPKFENyclE1IS+nVcT5mf5byJ2Y24v70NMfZ/eO+7kx8BpkyW/Ueq2zkNZJ54C7hBBhpmPOVjbq/BokEIvx0N1TnJPDq23KvBSp8IngwwTuMXlv5W5ih7KUM5lFjeE4dZ499BWe5St0dm7jlq1vIZqPL6j2zrpBTMcsYQlcw8VW6tS8MmWnSKGa5kz/sxTOXX+OZR+f5Sbc0Q7dMaohGrrZk4qkZNhktDITao5hUkwwzzVuiTeVKZnlr/oe4gPtr0d/fBDPmf1aePL0Y4wnz/Om7f9mVcJAfFaOBa9pSClXM8rmmsGKRHji3hKn5MIr568FKtT4VOARzLt1frR2Ny2Hi5RSi19qHRs7y7fGzqKqGjfsegNBK9aQPYZmEdYSWDKIVtOg4CIbLFw7J5eElgyAq08LrapXomznKVYy5EspsrkxsrmxWQv9+vj4NIeXWsdVOwwuRHI8p55lkvV1Lb2cTwYf5vY37+KOA3FKk7PfsE1NDfPFg/+Dt931K4QmQ+B64CdiXnNc+0EmawgjGOTA/TZHWL/BxjVsPms+in67xo/ad9N+pEJxYvE9JF3X4dDx7zXVtkS8i/bWzbREu4larQSUCIZtIErM7O6/XGwZDjVRpeIUKdYy5EqTTGVHSE9dxHH82CQfn9UgmEyibUgylXR5MTjKIXkOW5xZbbOaygFOceb2CD938Q7yR8/OepzrOnzjib9++bUQCppmoOsmmmaiawaaZqJpBpqqo2o6mqqjqQaqomPoFvFQBxE1gWlbiPx1nAS0RmlIkAkhTCnltVG+d4XQLYsjrxM8x4urbUpTsIXD543H0G5TeZtzO1sGLHJnBhbVlqnZZLKjZGZJurCsMB1tW0jGu6lUi2RyI6TSQ7ju2hVbwUQCPRmlNjG1qMQKn7kRQiHYkkANBVbbFJ9XITSVeqvBcLTIYb2fC7zwqgNWx67lJkuBv974ID/V9lqCj43g2vNfl6T0sO0qtt14H9t4vJPujh20RjcSM1ux3CBqSZkOJ7keY31XmXkFmRDin6SUv3DZ6zDwVeCB5TTsWkIzDF58vcnTHGn62JZqsiHQTq8eoVdqZITkm7mTON7KLJ85uHxdexq2wdZtPTyQ3Y16fIJKbm3Ve6pWi1y4eIQLF5v/f9BMgokEckcbh5OjPMVhpJCwEzpIsl32sKGeIFE0MbI29mSO8tTUapu8ZtFMk2B7KyQDlCKQCpa5oE1yhiFK9K+2eT4+V/EZ64fc9KZt3Pd8B8Wx5W8HNVsSkGWF6e7aSXt8E4lAB4ZiLbstzUOgSg3V01BcgbAF1DxkzV3zy7wL8ZANCyH+j5Tyo0KIBPBN4O+X2a41Qz7hwWu3EikoKFNVKuMp6pWFt+xRdZ2BN0R4VDzfsA0hLUhvoI2NWpiNUqG3VmVjKUtvdoT23EUEV7rwfyXRyz9tvoEv516k7q2cB+gcw5yLD6Pfo/EW71a2XgiQf/HCsmQTXksEk0nk9lYOJUd4+iUR9irGmWJcTIHJ9KMF2ApRguykl956K61lCyvn4aYKlCZTi+pVup4JJhLobXHsmEYubDNq5jmnjDAoB5Hi9Gqb5+OzKA5xlrO3BPj50XspvLA6y7PVapHz/c9xnudWZf7lIhiMEgolCQfjBK0YATOCaYSwtCCGHqRnle1baC/L/wHEgFuB/y6l/NJyG7YQVqJS/4ODD/KbD//mFds20s422U13NU68qKNl69QnMld5hRRVZfSNLXxbPbCoOd+R2Mc9lSq9xSk2Tg3RUmosO2881s3Ht97CF/MvUnVXZ4V5C908kNuDdnxy0WUzrmWCySTejlYOJYd5Rp6cUYQtBQ0VTapNHXMt4gqv4dIDPj5rnR+v30f8sQmca6TB91pGM0x+45PLL20aqtR/qaH4SzwL/D+XnqUQ4r1Syn9trpnrh4tMcFFMQIDpRxuwHZJE2cEGeustJEsWQ5EC31afXvC4IS3I76ldvP35by3oeKkaeIEW6mYLFT1BQUvQUhsiPDntjevIjfBbz4/woXA7n9h+O58rnqPsNNbMu1HOM8L52AjaPSpvcm9hx2CY/KmB69JrFmppwdvewguJYZ7h0CsibBniYhxcHOEH7Pr4rGe+aDzOnjf28cChLgrDcxQl97kmmGvJ8p2vev0CoF/aLoHrVpDNxhR5nuYETxuw2ILwuyN9/NnIRXpTD+MFWilEt1HU4uSVOFPEmPAijDoRhuohLlSDnK8EGS0ZULp6rN/bfJKfK/4TWmG6zllrcYJ//8I3+YVgkk/suJvPlM9TtGc4cRlxcPm2eoBvb4a+LZ28MbcP8+QUtQaD1V3bWbOiTrcCWIkoSiSAF9apBOFoZJhnlrBs7ePjc31yQg7Qf+Mov9B1P6WD11aGqc+VLGjJcq2yEkuW5TPf5e+P/zOfLJ6mtkzLfj+b2M+/P/w9dLdOruMufnTyI5wvLy2IMqY7/J++x7l77F8Q9pVesXwgxqd33Mu/VAfJ1fNLmme1CBPgBrmZzdU2kjkdZaJEcXRiznYkS0YIrEgEIxZGRAI4QZVKwCNv1pjSSowrGYZIkVvVxpg+Pj7XKu+x72HTkAmKACGQAlDEtLddXNqmgBSXHoBUmC7xI0GveIhCHTtbpJLNXjdxpgthLSxZzivIhBBtwIeBPi7zqF2eeblarIQg4+Q34HM/w2hiI3+1+Qa+lTmObFI+cMyI8odenNefmW4+fmLjT/Gj595BzVOaMj7A/miRv2n/GhuGvol4ld0lM8Jnd97HJ+rDTNWyTZtztTDR2Se3sK3eQWveRJ2sUB4Zx6kvLLFBMwysRBw1FsKNaFSCkpxZY1IvMCLSDDJODXuZfwsfHx+f5UdDZYNoY4PXSpsTJV6zCFZUtKKDlytTy+QWlcC23lkvguxJ4DHgOeDloJS1ENi/koLsJY713MCftrXxfG72An4L4ebYNv5k4DSd2SGkavL5jn/Hb53fv1RrZ+X9XWP8nvYJQpOHrtpXMYJ8Yedr+LgzxmT12iqjoKGym03sqHfRXghgZBykoWCHFIoBl4xRZlzLclGk1lSTdx8fH5/VJk6ETbQTkeupZp8gKA0CrkHA0zFcFcNR0GyB5ggU20PUXai7eDUbt1rHrdaQnsevf/zzy2/dEgXZISnlTcth2FJZCUFWP/U9jM++76rt399xP3+p17hYXlwjZ0UofCi6l1858l00z8ENdfLb+n/kC2OdzTJ5VoSQ/NHm4/xk7p9QS1fbXVdNvrT7tfyTl2assvjq+z4+Pj4+PuuRgBbg2Z95dtnnWaog+yPgSSnlwlL/VpCVEGTfPT7GEw9+nf+ofYbwxJU1WWzV4DN73sD/rV8kX58/OL3VTPJfawZ390//pxfbbuF9mV/hZDG4LLbPRoth83ebfshto59GOFdXebZVg6/ueg3/SJ6hRQpOHx8fn2uRoBZkR7CLXWqI3dUqu7KjtBYb86pngkn+S0cHR/Lrt43etYalWhz42cWVqGqEpQqyAhACaoDNdJK+lFJGm23oYlkpQfZLn5wWYv+h9ywfcT6FOXVl+6NcMMHf7bqXz+ZOzFoh/+74Tv7rmcO0FicAOL/xvbzz/Hspuc2LF1sst8UK/HXrl+ke/s6M+x1F41s7X8vfq2UGSsMrbJ2Pj4/P6pA04+wKdLALg93lErumLtKb6kdpYma3K1T+fv9b+L+FUzjSr6W32qwLQbaWWWlBBqAKjz/sO85PlP4FLX/ximMvtG7hL3p38FDmxMvbNKHxq5FdfOjwtxFIpKLzja5f59fPzfj/sSp8sHuY3xb/TCB9bMb9nlD47s7X8DHd5mzx4ozH+Pj4+KwWpmrSasYbOlcXGtuMOLtcwe5ilp2pfjpyK1fz61jPDfxOIsRAaWTF5vS5mnUjyC61TNoOvFyLQUr5aNMsbJCVEGTfG8vwl/96nJODV1bhD6kef7b5AG9JfwqlcmUl/YObbuVPYyEyTok/KXjcdHG6Qa4XaOUPAr/FP4+sdoOGqxFC8rt9p/hA5TMY2ZkTFiSCB3fcx8csOFm4sMIW+vj4+EyjKRr7wpu4QwS5IzvOTUPHMGcIv1gvVIwgf77v9Xwuc3S1TbluWReCTAjxi8BvABuAQ8BdwFNSyjc02c5FsxKC7NuTWX756AA391c5dObqeIE2w+avNj3B3eOfRtRfqT8lEVTMEMHa9LZK6z5+pvBveT4XnnfOiKURMjVqjkvV9qjZLt4KOTJ1RfIHm47xvtKn0fOzi64fbr2Hj4UNjuTPr4xhPj4+1y2KUNgV7uVONcLt+SluHTr68rX1WuLxrXfze0blmst2Xw+sF0F2FLgdeFpKeZMQYhfwB1LK9zff1MWxUoLs548NoEjJvaMOB45OzHjc1mCFv+p5kL0jX0K8qoDsxQ3v4EcuvJ+cPX8v9z29MQZ2RphSrvx/seT0w0RgetPPuge6lOgeqJ5E8UAt2Bw5maLuLC3WwVQ8/tvmw7wr/+mXK/7PxJOb7+QT8RhP5U7jrdHK+esZRSj+++pz3SEQbA1v4E4tzh3FPLcNHSFayc1/4jVANpjkD3bdyQ8yx1fblOuK9SLIDkgpbxdCHALulFLW1kopjJUUZC9xf9rjwMHZ4wtuiRX4i7ZvsWn464DgoQ2/wofO3L2guW6/uZMn2xXcJTY37PAEO8ZtjpyYpLZEYRbSXP607znekvk0amlmMQrTjcy/3ncTX3Wn/FiIJbI51MNbtSRvHTlNV3aY0x3bORnr4KShc9IpcLY0jO35BWobwVRNNgba6dMjJLn2m6+vNwwpuLFc4PbhE7QUr+/SO1/d/QD/3R1Z8TZ31yvrRZB9Gfh54DeBNwAZQJdSvr3Jdi6alRBkJ1In+fGTNlOXCZt7CnD46RHcOdYR39Q6Ra9Z4h+HN847R9jU2HxnJwcCzV2XbPNg17jTFGEW0x3+ou9ZXp/6DEpl7lTvQxtv5itt3Xy3uPI9M9crG4KdvNVo462j/ewcOzHnsbaic7ZjO6cS3ZwwTU56ZU6Xhqm46zeGppkoQqHTaqXPTLAJg02Ow+ZSjk2ZEbqyQ03NlPPxWU6Gk738/3u3LbkQuc/8rAtB9qqBXgvEgO9IKRfWj2YZWQlBNjn5PR4691l+3/5NMpeJmlsrgnNPjVC1l3Zx72sLUb4xwaA6/f9gCMG/jzxGh32UstJGWWmhpCQoEaVAiKIMUpAmOU8n76pkHcg53istFGag1RPsnrA5diJFxZ7ryPlpM2z+ctOT3DvxWURt7iWEihHkB1vu5KuWyrO5M01rOXWt0BFo5S1mF2+bGGTf8NKCeT2hMNC2hRPJjZwKhDhNjeosJViuJVSh0K1Y9LkefZUSm3LjbEpfWNcB3j5rE6no2LE+poJ9DCkbOOV2M2qHGhqrRy/wk5m/vyohbCY8ofD/3vAW/qZ02veMLyNrXpAJIRTgiJRy33IZtxRWSpAdOfpRJgIP8PvOr5G7TJTtqSuknx4jV2nsQ3LLrlZe6DWpXFqhtBTBfwp+hS2FTyxqHImgrrZT0boYsl7LF+zXcrZy9f9rixTsmXA4dnxyycKsx6rxlxsf5/bxz12RzDAbo4mNfLX3Br7mpBbd3eBaotVM8qZAD29LjXDTxUNX9Rf18fFZXaQRphzdSsraRL/YwPF6B88WW3k6E2tqn+Hd4TKfbfkHYuNPL+j4U117+J3WxMulhxShENMjJIwICTVAUjFIoJDwIOk6JOs1EvUSiXKBZDlDtJylrhlUjQAVI0BFs6jqJlXNpKrqlFWdqqpRVVUqikJVUagIQRWBLdbPdUoCNSQV6VHFoyIdKp5NxXOoeDUqTo2qW6PuXelTWvOC7NLJnwJ+R0o5uBzGLYWVFGQA48E38Qf1XyHnviLKNjsK3oFJxvMLvyPXVcGNt3fxeOyVWLGQKvhd69P0Fr+4ZJs9VF6M/jKfdx7g9AzCLOkJ9k46HDsxSaW+NGG2JVjlTzY8zi0TX0apZhZ0znO9t/KV1k6+VzhH2Skvaf61jqZotJhx7re6eNvUBLddeM5fMvPxaSJSNci23c6EvqGh2xsXlQHZweFqO0/lWjlaaMzr1Qiq8Pjktke5e+gfEAu4LtQ0i4stvSSLU8TKGVS5tOv39YwrVKpGkIoRpGJY1KwY2z782LLPu1RB9hDTWZbPAi8HBEkp39VMIxthpQUZwFjwrfxB/ZfIXybKOj1B7FCGC5Pzx0u1RU0it7Zzwnjl/Jiq8LvmP9Jd+kZTbZcIXoz+El9w3sypGYRZQsINky7Hjk9SXqIw+//Ye+/wyNKzTvt+T6ocVMqxu9Xq3NNhpid7POOxh7ENzonBBmzjACZ4gWVtDGsMXu/HssBivIDXGGzANtjDONszDjOeHDrnpG51t7JUSpXDCe/3h9RB05JaLZVUUuvc11VXVZ30PipVnfM7z/uESsPk0y0HeCD5rRnLZVyJqegUdc+cxhsNxDhc08phX5BDToqT6a5puySUirARIqoFiWg+IopBVGhEUQjbDlHbJmoViBRyRAppovkU0cwogcK1W2q5uLhcH1aokXPRO/lRYRv/0r+KeFEvt0nz4v2NXfxR/v9M2WPYZZHQfPDHC//5z1eQ3TvVcinlUyWwbV6UQ5AB9AVez58WPkjqClEWkYLVx5Oc6k5Oe6xNLREuvKykRaWu8Mfq56jJPVF64yeQCNrDv8Z/2q/j2BQOqQoJNw07nGsfIZ4sXL3BdaAKh4+taufd9ncIxA/O61jXQ0HzcqJ+E4cqajmkCQ7lBhjMXzs+4+VoQqPJX8MaPcoaqdJayLMmFWfN0AVC+ZWRdu/istSQikaq+mb2Gbv4j9FN/GiostwmlZy1/hzfqP1XKvvKfmldmSwHQbaUKZcgA+gNvIFPFd5Hxr78+XklbD+b49DZq4v63bqjludr1UklLWoNhT8S/5vK/PMLY/wUnAm9n4edn+foFMJMkZKtRYXQQIFTHaOkC/PzOL27vpff8T1GTd8Ts3LHl5r+SAOH69o45A9x2MlyItNNYaJGXFAPsNpXwxo1QKsNa7Ip1iT6aB66gL5EAmelUED3IYpupqrLysMO1NBZcRc/tbbxz/2t9OWNcpu04Agh+ae253lVz/9DrICknCWFK8jmx+IIsp9w+MivT7muO/Bm/jT/K2SvKH+hIrmz22LvsfGaXdOVtGjyKHxC/hmRwoGFM34GzoZ+hUecN3FomhAur4RtOQE9GU5cGMO05/49eUUswX+v/Bnr+76HsHJzPs58MVWDjuo2KnKj1Cxir7rrQWpehqrv5ElxK5/v30BHzsst4TT3RgbY4eml1TlPdfYs+lgHwo0fmRPSCFAMNGDq4XKb4vIyLMXDMf0mvpHcxHcHq5FyfjUZlyvvqu/nM9Zfo6W6y23KymGlCjIhRBT4IrCV8aSI9wOngK8Dq4HzwDullDNGiS+GIDt36EV6Up+gUJw6Lqor+Hb+NPdL5F5Wk+yVQw4DnUlyV5S0uMgar8LH7E8QKp5YMLtny7nQe3hEvpkDmelPfBUSNqcg1ZnidM/0U7LXotWf59ONL3Hn8CMo2eufTrxRcbxRuqvu4VHzFr7Qt4bhWcTDhDSL+2Kj3BnsZ4vaTbN5jkjq9IzFe1cCUqjYwTpyvnoSRi2DopoeJ8Y5s4ITuQiHU0F68t5rH8jFpcw0egs80vA16np/smhjSgR4wlieKKYRIa+FyWoRTJZRjJ4AVdqo0kSVNpo0UaWJMrFMcUwUaSGc8dfi4kPRUH5/4a/JpaxDVgE0SykPz9OgfwGekVJ+UQhhAH7gE8CIlPLPhRAfByqklB+b6TiLIcja97zA7h/8Aw337cNxps6k7Ay+gz/NPUT+ZaJMkxJLTBY6G3yC/2r+Pn7z3JxtUhQPulaDIiuQZhAr4yGfUMgM2XhCChXrB8mZR6/rmJ3Bd7Bfe4AX8tV0F6afXmxyBGtGbPrOJegemVuGZEiz+NSqI/x87vsYublV4xbFJMIueym8OWOFGmmP3sM3szv4176mkqXTt/rz3F8RZ6evH69YGlOvC4kjFXrsCKfzEY5nwhxLBzCdlelVcVk4dFVQE/FSEfbiDerg0yh4FQpzbPbgs6D74CDD6Wufw/6ubQ+v7/v7q1ryzRapeclHWhn1tpBUIiQIMSoDDDsBBk0/A5aP3oKPrpyH7oJ3xf5+vLrCyU+/bsHHmW9Q/5PAGwGN8ebiceApKeXvzdGYMHAIaJVXDC6EOAXcJ6XsE0LUA09KKTfMdKzFEmTf/cvPsO3NDSi1j0+73YXgQ/xZ7h1XibIr2eqH383/Dl57+t6QAIriw9AaEXYEp+CnmNbJjUJ60CTRmyM1eO2YorpNMVbfA0V1P45zfdOEI947OOX9efbZ69mX0SlO8x3ZYCrUDBU5e3aUkcziiqOQZvG2mn5+LnCGLcXDhIcPIpZ4MdB8bCOHAq/gq4mtfHewptzmuLi4TCAEVAU9xCIeAiED1adj+hTShiCuQVw4OKK0QiXmCFrPZDh67trlgt5QE+evxWfREx3TbiONANlwG3Hvas6JRo4U6nkhVcXusRC2LF39tBuV5SLIDkgpdwohPsC4d+xPhBCHpZTb5mjMDuALwHFgO7AP+CjQI6WMXrHdqJSyYor9PwR8CKClpeWWCxdmV2JhrlwUZAB3/kaYnPPStNueC72bT2feRmGKz/TmgOS3sr+Ox5l5qk5V/PQ/dws9h0szpeev8LLhwQr0qhMUzeuPmzKVCi4E38IR5U5ezFdN6T3TpOSmooJvoMCpjhEyhcWPbQqoDm+t7ePBwBm2mkeIDB9EmPOrcSaNAPnQGka9TfSqDcSd8ZgjiQAkclJZV4GUgBBXdCQYX2ZKjUdGV/PCaGRe9ri4uIxTFfJQE/MRiHiwAxpJryCtMrc6ZAKGhCRfBseQkJK7E3Bob98143RrPCaPNH+DxqHnyIbX0u9ZzVnZyOFCHc8nKjmYCq7YmLtSsFwE2RHg54B/Af5ISrlnnoJsF/AicLeU8iUhxGeBJPDbsxFkV7KYHjIAb8hg+3uHKRSnD7TsCP0Kn868eZJX6c6gzYfTH0SXM5dNEEIjfeIe2p8qfWChEIL199cR2xQnZ869Tc+w9y5OeV/PPns9+zPaVd4zr4SbcgKlL8uJ82MU59lDc674VJu31Azw2uBZtppHqBg+gJiir+bLRddZq4aj+Up2Jyo4lfGXwXKojXhpbg6TixnkVEEsY2OO5OnuT89qisPF5UahJuylJubDFzGwAhoJj6Bbh2QZK8dXaAo1uqRKLeJX5pYJOWp72Ju57LVabwnsA8P0jJQv6WmlsxQEmTaL/f8M+BHw7IQYawXa52FPN9AtpbzoavpP4OPAgBCi/oopyyUXnZxPFel6cjW1dw8yXSvP1tS/8kchlc9k3kBRSu4LFXl/6tdQ5bW9NXb/vbQ/1VtqswGQUnLq8T54HGo33s2aewRFbd91T2dW5p/nrvzz3AWYSozzobdyULmTpzMxRiyHvIA9fglrfYRafexMQ6Enw8nOMWaYzS05OVvla30NfI0G4B48isObawd5deA8o47/suhK+sdvB8qIoSmsawrjq/HTHVI4r0kcj8ItnhGCIseRaB3HYj6stT4abEFzHvREkZHBLBfimRmb3LtcRlME1WEvFWEP/pCB0N1pnKWGUKDoUxn1KHRrDp0CJreIWbjvelAV1OiCas2kUskRE0kqGCHiDBC2uwla5wkWz6CaWShBeObByH/j79J3kHUkpzVJYFeMmzsL7D/pJjytVMqVZfkM8AEp5SkhxKeAi70qhq8I6o9JKf/bTMdZnCzLM3z3Lz+GVbwcULn9bQ2IqunjyQDawx9gj7iHX0x+EGUWfdj13H3s+deBedt7PfiiHjY+GEOvPknRnJ8QlEKjM/A29mmv4elsFfGXNV2vcgQbkw6JzhTtfW71+oYKHw1NIbIxg+MeiV9X2OVPc5M4TmvuUaKFg5O2N5UKevyvpV27jSNWE0ezKgUp8UtoKwoiaZvccJ4LfSlS+ZVbvygWNKgKewmGDTS/huVVSRmCYR0GhINd4jggl4VBAGFVIaxBWHUIKzYhpUBQ5AmSI0CKgEzhl6N4nRH89hCanFu9PkWa+MxODGe4tH/ELBj13so/qB+fVLD7jgyc3t0/7+4pLtfHUvCQTSvIhBB/wvjtSFpK+dclNmgH42UvDKADeB+gAN8AWhi/KXqHlPLqCqtXsBiCrONgnH0/+B6dh785afmdHwmSs0vTiNQr7uTFz4+V5FhzQQjBuvvqqNyUoiAP4zjzq9bvoNIbeBP79Z/jqVwt/cXJ4qzJEbSO2sS7UqSzc7vVTBeseffhXEy8usK65ghGjY/OoMKwDjcHimxTzrCu8ATVuZ9dV6NxW/jpC7yWdu1OjtmrOJjVyDkSISWrbIX6PCjzqB23XJAC8oZgVBP0qc5VcUAeIWjwCBq0IrVqkhqGqHS6idkdeK1BFtLj4nL9CGw81hCG3Y/C8vl9zwdb+Hgs8pf8e6Lu0rexxVYIHR3lbH+6rLatJJa6IPvViZc5KeU3Fsq4+bBYguzRzx+houoJ+toPXlrui3i46ZcH5+1Z8unbeOnzDo41y5OPEAQiFfgjVRj+GKoeRRLGKgbIZ32ouoPff5ahC3vIJseu2x5v2EPbfVWEWi6WzphfDJhE0Bf4BQ7or+OpXD09xdLElKlIVlsKtTmJkjAZimfojGdKOi1aETBorPbjq/BSCGqkda4I12fizWUFIAQvu76Pv3EEXNBhQ8Bhh9bFRusZ6jOPosrSxYvYwsuA/zWc1e/mmLOGw1kPGefGb2KuIqg1BA26Sa2aopZhKmUPMauDcPEkAfPMdQldF5dycS70bj5bePul2QVDwu0DFnsOLe7MSdCjURE0CAUMvH4N1auBtsw8y854woSUgCORUk48g3Tk+EOC40icifeqIvjuh+9acNOWXGHYUrGYgszjNymm/o3cFSKnaUc11Xe8gJRz8/J4jVYO/kuY/Mv6R2qGh3B1A95gJaoRRYgIthWgkA+QTXpw7GvHvgghqawfwjaPM3D2EI59/dNY4foga18ZwlPTSb545rr3n4p+/4McMH6BZ/JNXJih3tlc8EtYaypE0jbWWIGegfSse3PWRb3UVvkxoh4yAZUeAwYmeo4qwFqfoEnLYwgbAwtt4lnHRMdCpzjxuogmi2jk0WUBTRYwZIq67M8wrpFh6+LiUn4UxYemRlFECOEEkJYXp2Bg5TWKGSgkHXIJi8xwgWJubuEB/govbQ9myZkHJy3Paqv5V/+f80zqciHWm/OCvr2DjM6ztJBXV4gFPYQDBj6/hubTkB4V0xBkdEFShSFFkl1m2qtU+BTBuXu3L/g4882yXA/8AbCKK5IApJT3l9LIubCYggygsmGInuP/Bld8Zjvf2YCsmDmebCoMvYZT31xFoneyS1ozPFSu/hUS8dD8DL9yLJ9JtKqL1NABRnvnViakZn0FLXcYKOHT8/YKXmTYdzdZpXpO+xbUGL3KejplAx1mkI48mFN8l2sdQUsBvEmL9HCO7niGWMhDZaUPJWKQ9KlcMCSJK7K26g2FzZ4s65ROWqz91LtiysVlySCEjqHVoBBDFkOYGQ/5MYViZm43eFIK8kmT7EiRVDyHOccwirmw9Q0NeJv3YtmXM/Algpein+ILyZsulVCqdqD5dIbjF8amPdalpJWIB39AR/g1il6VpAGDGgyXMTN1ObBcBNkh4POM1wu7NK8mpdxXSiPnwmILMoDqxv10HX1y0jZ3/qafnDX7j0NVQ/Q8uZ3+45ODSIVQaNj8boZ75yZSZkO4KoXH087gub0UMnOLT2i5pZr6nRLbcxTLunZRw8XAFl7GvLcwoN9Mt7KWC04NZ4s+ugvOjBNWYVVhs89kg9rHaucIjbknCZil8Qa6uLhcP0Lo6Fo1KpVIM4SV8ZAbU0gPWoz1ZEn2ZljOMzsvJ1TjZ+vbFHLO5JjkuO9V/F9+mzP5CU+9lNw9Come9KSklbQhGNJgUHGu6gzjMnuWiyDbJ6W8ZUEsmyflEGSK5mCo/8lY/+VaZP4KL1vf3UfRvHb9MCF0EkfupuO5q+MCWra9mcGu1tIYfg0U1SFWN4iVP8pAx1HkHOKNFE1h7d01VG3KUVSOYttLLwDVVGIM+e6gX9tGt1hFt1NBnZJkrTxJU+E5KvK7l1yMkaJ48CibyfbFSMcdAtUCT8RG82WQ6him3b8kP+uliq5VoYkapBnBTHnJDiskegtkR92abksNx5ak+m8swTVbNrymnvCGw5jWZW+8KSJ8J/JXfCsxY0lOlxKwXATZpxivCfYt4FJAzrUyIBeDcggygHBlhuEL/4xtXnZtt+yqpnLX80g5c0yB1ftqjn7v6im/5q2vIt6zszRGXyfeQIFw5QVyY6cZ7j6LY19/dpPmUWm9u4bK9VmKyjFs2y1tcT3oWiWqtYHRM17OvThEMTPztIk/5iXWEiRcZ+CLOejBAsJIYjOEacaRKyRDbRyBrlejUYMshimmvGSHBInePMPnU9f8LF1c5oKiamiGMad9bcvCNq++IfBFPWx/p5+8eH7S8lPhD/PZ3GtJLEKhbUMI/KrAr4BPkfgVibaMpjslkHEUUrYgaUmys8z0Wi6CbKou2FJKuTiunBkolyADqGk5S+eh70xadvND9TjhJ6Y9lpZ5FXu/crUXrX7dDkaHXsWVGXvlQvfYRKqGQXYy1n+C9PD1N//WDIXWu2uJbchhKkddcTYNXmMNdnIV/Uccug7ES1aBQdFUKpqDqHr5v08LjXRgrCeDmXNFl8vcUXUdjz+Exx9E9wbQjACq5kcoPhA+pPTgOB4s08AqGhRyGlZxNnXVpxvPprL2BD0nfzbpxv4irXfXUrVzcrxu2tjIF41Ps+eKCv8+RRCcJJ4cfMLCr1h4MfGJIl4KeMnhIYdH5vDINIbM4JFpdCeJ4SQwnAS6PYrmJFDl0u4JfL04wqCgVFPUaigoleTUGHklSk5EyIkQWQJk8WMKH3+3854Ft8fNspwHwy8c5FsPJyhkJ3scJJJoxY8Z6Dh2eaGAOz/iIWcdvOo4Xnk3L37haqdiZVMr+eIbsE21JPYqqsApYf2pYEUOf6ifYqaD4a5TmIXr+7G64uwyQuh4tU3k+qvp3J1mpLPMLQJcXJY4qqbhDUXw+EMYvjCqHkTVAqD4kY4P2/JimQaFvAerOMfOCxJsqzTn3+slEMlhGC/Rd3r/Vet0v87OX4xS1J/lYvkhB5WcvmZCPI2tmFpti4GieHnVfceuveE8mbcgE0JsBTYD3ovLpJT/WjIL58hiCLLUT3/Kse8dZl9hx1XrfIECubF/JZ++LDKCVT42/WIXpnnZs+TTdvDS/zNxXuZuDsaq0fzvIp+Zm9sbwPCq1MVMKhMnCR14FJFLk7jnl+j2bmJosLQV24XqEK1OoGvdpIZPM9rXOSnj9FqsFHGmKF40tWIibT6ILPpJXPBw7oUh8ik3bqmchKpqCVW1oBn1OE6w3Oa4XIXAtjyYBQ/FvE4xP3cv1HIiVj9MZvinJAZ6rlrXfHM1jXd3ki+eX3zDVhDLQpBNVOy/j3FB9kPgdYz3tXx7ie28bhZLkHX/1m9z5pc+R2fv1XdgVY0DdB/96qRlq++opWL7M0hsvEYbB74UoPCyptCGz0+k4ZdJjQS4XioqNWr1IaLnXsR74McoxalrbRU33srQjjfTma4imy59Ox1voEioIo5dPMvgucNYxdmLDc1QWHV7DZ7A3KbVNJ/AG7VQ/SkcZYii2TfnenCzQVF86FolCiGwAzimFzuvY2YU8ilJfswmM1IgPZhzRdcSwR+OEqldje5rxLaqSI1GVswF3mX5IYSkuqmD/vafUMxN7n2sGQo73lWNHXhuQc9zAhWhGChCR1zxWArhNLPHwbIT133Dv1wE2RFgO3BASrldCFELfFFK+YbSm3p9LKYgs6sa2X3HJ8lNIWyq6nfTffzZyba9pw4lepKT/9lIsn9yjzVF1ahb/x5G+mOzskH3KNRV2lSl2gkdfAyt69R1/Q1S1ci+4u30Nd5N94Ba0inNi2iGTUVtP2b2BIPnjs+pEO18UDSFytVhKpp9BGoEnnAe4UlgMTDJWznlvooHXatGkVGwglg5L8WUSm5UkhoskOjLkR0pXVX9a+ENhqiob0P3NeM4QYRSQFAAmce2szhmDquYxSxkKGbTFLJpbGvl9q8E8PgDROtW4wk24jg1ZJIRcilPuc1ycbluPH6TUOQAPSeeR8rJsyp1m2I07PAgHZCOmHgGxx5/ljZIW+JMvHZsiWOPV6J3LLBNiVV0sIs2dtHBKtpYBRuzYGMVLOQN1G5N92qEav0EKz34KjS8EYEesNG8JsLIIZU0jkxgWiNIaS4bQbZbSnmbEGIf8CogBRyVUm4pvanXx2IKMoD0q97Nbnl1awVVt1Hl10nGLwfsCyGINgUZ7bpapbdseweDXc0zjhut1KnVh6nofAnf3scQxdIEWjqxOkbveTfdxnqG4wtzETe8FtHqXvLJY8QvnLrqpLLYeIIGVa1hIo0GvpjELgiyo5LMoEmiP0s6nr32QRaQQEUV0dq1qEYj2UwNqREf4jrvSFXdxvCaaIaFphfRVJP5tr1aDkgUcpkw6THvdX9mLi5LmUhVGrvwM4Y628ttyg2Pv8JLtCHCQ5/8pwUfayZBNhv//V4hRBT4R8aLw6aB3aUzb/kQ/NlXaf2lXXT0To75sk2VYPUvkB750qWSEVLKqcXYTQ/OKMaEArvEi4Qe+bfSGj+BMtJP5Xf+ikqguPlO4tveSGcqRi5TOnFWzGsMdrUALUQb7ycU6yYzepThrrMlG+N6KKSL9BweoudwWYa/ikhtA6HKVoTWSGasimzKw/AVZenmIitsUyVXosSQ5YgrxVxuNBJDQeANNN3Uy2jXj8mMlb3S1A1LdjRPMVN+7+A1BZmU8iMTLz8vhHgMCEspl8ilbfFp+cFnGLj3M2SSkwVMIh6mactr6Tz8g2n3bdx4GwPdm2e8eGwPtBP63sKIsZdjHH+BxuMvUG94yN79dgbrb6Nn2MAsYY/JXMZDLrMWWEvl6jyBcCep+OHxhIAbHEVV8YUr8IUq8IWbkTSQGq2gkNUp9JXbOhcXl+XAUHcDqvHLtGw7Qc+JqctkuNwYzCrCVQixDVh9cXshRJuU8psLaNeSRUkMcdPQo7zoeeCqulGD3eupXtVO/MLpq/arXrWe0aG7ZpxW2Vw7TOzrf1Nii6+NUiwQ/NlXCfJVVnsDZO56C/G6W0suzjIJL5nEemA9VWuz+AMXkM7cpmKltLCKKYrZBPlMglxybE7dBuaK4fPjC1fgDUTRPWEUPQQEcRw/ZsFHIechn9GxEaRTkL4xE0pdXFwWAdtUGezaSrRxLf7w4MRVxGH8IjTDQzog5ETnA8m4L1kghLj0Gi4mq7389cvXLxccoIB0sjhWFrOQxixkKGRTFNLJJR1ve01BJoT4Z2AbcIzLQSkSWJGCDMD//LdZ99BdtPe9LENSCmxeg+HrnpQlE6mpp1B8LY49/Re7taFI3dc+OWebhNdLfsc6Cj6dyAvHkfm5CR0lnyH0xFcI8ZVJ4qx3xEMxX7qaN+kRP+mRTaU5mABPROIJWHh8eXSjgKrmgCyOncI20xRzCQqZBLlUAkVR0T1eNI8XVfeg6R5U3YuiGgjVQFE845lFwgB0JDrS0bBtD2beSz5jYJkqhSIU3IRKFxeXRSKT8JFJrCq3GcsTAXoI/IaF7rMwPAVUrYCiFhHkUJTyex5n4yG7Q0q5ecEtWWY0fvd/MPBzf0lydPI/MZv0Utv2BrqOfB0AbzCM6n8L2cT0H3VjvaDlGx+7bhtETRVDO1fzwpoi34qcJaWcAKDmtgAf6LmJHS/Fof38dR/3IleKszUeH5m73kq8/lZ6RrwlFWelQVDI6BQyOhCadit9ovSUAxQtwAIWL4HSxcVlARAChDL3SMKFyDx3WZqYRQ2zqJG9XFYVAE0vvydwNoLsBSHEZinl8QW3ZgkyUONB+HzI3OSrtpJJsqXrEV4MvfGq2qjx7kYaN95G/9mDRBvfwdjg5H/8lVRWa6z73sdQrFm4WoRAbmjl3NZKftw4whP+88DBqzYbVDL8z+YD0Aw/l1nPW0+GqXz2ODI792xCUchdntb0+Mjc+RbiDbctUXHm4jI1qq5QGRPEGMabG6Jk/apcSoZiFVHsIsIqIsw8ilVEFPMoxRzCzEMhh1LIIgo5RD4LhQzKFH0hrwepqEivH+kNjD97AjgeHxg+HI8PR/ciJx625kFqBo5qIBWVuaSUWLqfs+naqzrAuKxsZlP24pXA94B+xpuLC8Z7WW5bePNmZjHKXjze+ThHHvkiD37h0JRV6Xvf9T84OVBx1XLdYxOuHGG4t3raY4eiOjtf+AzawIVptxE+L7kd6zmy3uCbNZ2c1eaWaVPh+PjAwAZ2vTSGOHFmTseYCml4ydz1VgYbbi95zJmLy3zxhzSqQkWiuW4CF/bhPfpsyUrIuLjMB7uihoEHP0r7cAzbdM+b5UbTFT78ufsWfJz51iE7A/wecIQrChtJKadXEYvEYgmy//Kz/8L/d+Zm1j58dbUP6fFx8Bf+htHh6wsU9AY0bj35D+hnD161TlREGbqtjedbi3w7fJaUMnUl/rlyT76FXzxVSc0zJ5Gp0kWbO94AmbvfxmDtLnqGdKyie5KZL5quYLkn61khFIhVasS0BOGRdgInnkXrPFFus1xcZsRs2UD3K36dc32G67AtI8tFkD0hpbx/QSybJ4shyJ7ofIKP/uyjAHzpua0Enj541Tb5bffwYvVDs45D0AyF24Yewbv/p1etE9VV/M9f8XHAWPi6CEFp8L74Ju7am0Y9dH3V/6+FE4yQvuvtDFTtpHdQwbbcM821UDVBZaVCjGFC8RP4jzyJGu9GqhpOqAIZiuEEwjj+CI4vhO0NYXsC2LofW/Nha14sxcASOhb6iji3K0jCxUGC3YfwHn0KJZ0ot0kuLnOicNM9dGz9Jfr63BuwcrBcBNnfA1HGpy0vuWqWQtmLxRBkifYf88Z9/4ORwighx8MXv12LONVx1XYD7/gkx+K11zyeUOBW51mCT/771esqovzl+yK85Lm6wexCc2uhgXeer2P1S13IntKKQSdcSerut9NfsY2+AeEG0E7gC2hURUyi+R6CF/bhPfoMouBmGLi4rGQyr3g77XUPMDK0dMsz3IgsF0H2pSkWSynl+0th3HxYDEHGie/z/UNf5A8L41XmN5hVfObLRZyhybFcjmZw9K2fY2hw5h/RzuApKr7/t1ctF5Ewf/v+Kp7xlr9g6usya/n5MyFqXzyLHBkt6bHtWC3JO9/OQHgL/QPgOCtEnAmoiGlUelKER8/iP/0cxtkVW1/ZxcVlBqSiknjwQ5zSd1xVhNxlYVjygkwIoQJ/LqX8g4Uybj4sliDj6+/mQzsf4IWx8Wm912XW8v7Pn0MWJ2f2FDfcygst7582QHNzdZy6hz911XIRCvL/3l/PT/3nSm7+fNCkwjuSG3j1SZ3IS6eQmcy1d7oOnEgVdrhyTvtKbwCruoVipJ5ioIq8HiaPn6ypkc1K8ouQvaSoAo9XxeMVGJqDodoYFNGdPLqVRSum0HJJtMwInhPPo44OLrhNLi4uNw6OL8jw6z/KqUyzm82+wCwFQTZj2QsppS2EuHlhzFpe/PfOdt5S4aFgF3g0cJb179nO3f+8b9I2xqk9bNl8P4dHW67af21Dnrqvfeqq5cLn48vvbeKn/tJlPpYKSzj8e+QE/347hG718O7hndx1zMa75ziUoNqxkhhCSQzNeX+jfT/+adZJjw+rfi1mTQtmRSPFQBUFI0peGd9DwUHFRkgbVVoo0kY4Foq0UGwTxTERtnk5/d4qohazKKkh1LE46sgASrq03kMXFxeXK1Fyaaof+Qyxqkb6HvhtzgyG3ZCPG5jZTFn+FbAOeBi45CJZKTFkFz1kAF/Y/no+lzx6adXfHLuZhu9OzryUisrxd/4dA/2X72YaG2DdN373qlpjwuvlPz6wlkdCpQ2oX2jq7RC/PLCWnQdTqIdPT1kOxMVlqSIMA2dtM/lYEDetbakhsDUFS1ewNYGpCSxNYKpQ1MBUJUUVCqpDQZMUFYecalNQHCwxt2B4TSo05LzUp1RiCZvgcA5jMAED8atmQcqN2bqN1Jb7kZqBrRo4ioaj6NhCxxEqNhoOCra8+BDY9sTDkZdO1VJOnLYlXNQAl5YxvuzK9cvtFD8X0brkPWQTxIBh4MpMyxXZOul9R37CD7fcytl0NwC/v+UAX+7fimf3ZZEmHJv1L36OkQ2/hVlwqKrRWPedP7i68Kuu8833r+OR0PJLy+9TU/xFw0FogI0P1PJQTwsbD46UtL6Zi0spED4f9tpmhlrCnK2R7IuO8qK3h6Ioe9Uel6VEGHhZTpaQsMauZkMhxupsgPq0RlXCITScxzOURPQPzavY9lzQOw4T63BjT6+FoxngDyEDYWx/GOkP43gDOJ4AjsePo/uwdT+O5sHWPNiKAbqn3GZf20O2lFlsDxnA/pabea86jJy4s65yAvzD18PI812Tdht5w0fp0Law89k/Q413Tz6mpvHor23hS7Fj8zYvqAeoMqJUa36qFA9pafNCsh3LWfxA0K1mLe/obmTjoWHEibOLPv4NgabhbFhDX1sFB+oLdHrTNBT81OQNKrMq0bQkmLLwJrJoo2kYGUOmSxvbt1wRwQDW2mbizSHaa232hIfZ4+3Fdr1gLguEJpU51Okfv2585MI6Nj1+5qoEMZfyILxeNh48sPDjzDPL0gv8GrAFLjd/WklZllcKMoBP3fzzPDJ65NL7ncV6PvHFBDKRnLSdVb8are/85OOpKk+8fxufrzrCTESMMNVGhCrNT7UwqJJQY9lUFXNU59JUZ0eoSg7iL159MR7zx/hR6y5+oJocTHZcEo+LyU3FGt7R08SGg8OIk644mw7h91PcvIbONQH21Kb5SeDCdRcCDjkeVlsRmszguHjL6lRkBcoKKGfkKNARM3kxHOeQ3o+ceztDF5dFx+/ofGRgC7c/2Y88333tHVwWjOUiyB4GTgK/BPwZ8G7ghJTyo6U29HoplyBL+KK8cdUqRgqXg7rfkdzAO/7fqZmD3YXguffdzGdrD027iVf18PdmhFsvlObv6om18GjzVn7gJDiT7rr2DgvATcUa3tnTyPoDw1PWcFtJiIoo2c2rOLvK4LmaUZ7yds459sXFxeXGQEj41bEtPPhcDvXI6XKbsyJZLoLsgJRypxDisJRymxBCB360FKr3l0uQAXx/46su1Sa7yB917WT7V/ZMe6i9v7prPPZqGgzF4HOyirs6XpyzuTNxqm4TP6hv5dFCP/25+IKMcS22FWt5R08j6w8M3bDiTHg8EIviRIMUI34yYQ9n6+Gpyji7y1D018XFZfnw8+k23rnPwPfCETdhahFZLoJst5TyNiHE08BHGG8yvltK2Vp6U6+PxRBk6VNPEvz3N0257sM7H+D5sckZkv+wfweVP7rapiPvuY1PN++fdhxNaPy12sCr2p+dn8GzQCLYu+pmflhZx48z50kWS9fP8nrYbFZTbfnmtG/INmjJ+WlIaVQmHELDOTzx5HhmVK7EzaNVFREOQSSEFQmQj3jJBDVSAYVRv0PcYzLgydNrZOhUE4yobrV9FxeX+XFroYFfO1pD5ZOHl1y2543IchFkHwAeAbYBXwKCwCellJ8vtaHXy2IIsh8d6yf64v/m9q4vXrWuq3IVb60wyNuXY348UuXLj65BPXTy0rJTv3gb/33N9GJMEQp/bqzmdSefLKnts8FUDZ5pvY1HQyFezHQzVlz+vQCbrAgbzArWZIM0pg2qEpLwSAHfUAoxNAqGgQwHMMM+igEPuYBKxq+Q9MKY12bEKDJkFBjQsvRqKeIi48Ymubi4lIVWq4LfbF9Dy+MnkKny3DyvBJaFIFvKLJYg+/C/7eMn677Fuq6Hr1r/j9tfx98mJ2dLNtph/uYrBrK3n/Nvu43/tn56MSYQ/KlvHW85fnWj8cVGImivXc+emtXs0xX2ZXsYKYyV26wVS2ugkajmoz3bT8pMl9scFxeXMlLh+Pjtro20XigipERxJDgSISXCufzAkQjHmXjtIOT4MqREGjq2V8f2aFiGiulRKRoKBUNQ0CGvSXK6JKs5ZDWbtGaRVi1MsXy6BChSwZAKPlvF6yh4HBWPLfDYCh5HQbfAsASGDZol0SeeNaFyx//58oLbN18PWS3wP4EGKeXrhBCbgTullP9UelOvj8UUZKpweK71X6jr+cmk9aai884tt10VMH93vpk3dVbPKMYAPhHYxENHf1Ryu0uBRHC2dj17qlex11DZm+2dlMjgUjoEgnXBZnbpUXalk9zSc5xY5nIXg96KFk5VNnM6EOGUKjldHKUz01eWDNrlSIURocVbRYvqo8WGVYUs0cLi1pBymR2momEqKkVVpShUiopCUVEwhUJRCIpCYAooIjCFpAgUkdhz/DU4wKCdp684Rjw/4v6mVihe1cue90wfA14q5lsY9suMT1X+0cT708DXgbILssXElgqvvvArPN+YIjJwOehed0w+OZblVzUx6Yf8nLeL59bPnNX4u6EtPHT40QWzeb4IJG0Dp2gbOMVDE8s6ataxp2YNeyYE2rAr0OaEKlQ2hlq4RQmyK53g5u6jRHLTFyttGO2kYbSTV12xLGsEaK9t41S4htMeg1N2lvZsHxlrZQqNCiNCs7eSVWqAFlvSUsiyKjVM80gn4Vxnuc1zWQaYqkF/tJ6+UA19vjC9Hi99CvTJIn1mir7cEEXHjedyWRhmI8iqpJTfEEL8IYCU0hJiGfkvS0jGUnmw/9f5WVUK3/DlacqdXQd468tqk12LX4/cxPsP/uCq5VIP8I3q3+ZQrpqgahJQLPyKiV8x8Ynxh1cU8WLhoYiHAgYmhiyiyyJV2bMYo+0l+XunonWwndbBdt418b6jei0Hq9dw3OvhuJ3hVLrbPWFNgaZobAmu4hbhZ1dqhJ09xwjm59dM3l/MsL3rENuvWCYRdFe2cCbWTEFR52f0MkCVkvrMKC2u6HIpAbpdpHn4As3DU98cSQTDwWr6onX0BqKkNWNO/rSsovGokuNocn7nAJcbi9kIsowQopKJpm9CiDuA5R/5PUf6CwZvSfwu3wt/Gj15+Uf7uyee48lVq2blMXpvdBu/eeD7Vy0vRtfyG+bv8nhHbF42frCxkw96fkx135MIubA1rlrjZ2mNn+WtE+9NReds7TqOVzRw3OvhmJ3mdLrnhhNpilAI6UEiWoCw5iWsGESETlgKwhIijk3YsgibeWL5DBsHTuErLnyZD4Gc8YLi4uIydwSSqvQgVelBbprnsX4FOFG/mYfr1/CD1FmyK9Sz7XKZ2cSQ3Qx8DtgKHAWqgbdLKcveUGsxY8hezp0VCb4qPomSvVzP6wcb7+PjhZkvuu+quIk/3n+1Z6y/4QHe3PNu+gsGUb+Oz9BIZItki3N3Rt4WTfLH1c9y0+D3EIXyaegrRdoxr4fjy0CkqUKl2V9LmxGlzVFpy6VoSo0QyScJZxOE8kmEG2vi4uJSArKeID9ou5OH1QInUufLbc6KZCnEkM0qy1IIoQEbAAGcklKapTVxbiyGIHv81CC/9qWp/0lvqInzt4U/RhQupyJPVZvsIm+quIlP7//hpAu5FCpPNn2Y97W/AoBYwEC5q4ZuZXwbr4SoM+518VvgtSSaKRGmg1OwMfMWubxFOmtNK+AqDZNPNh/htdnv4hldGlWgTUXnXPVassbcGroWNINBT5C44WVQVRgUDnE7z6CZIl4YxXRm9xUVCOp91bR5KmmTGm35LOsSA6yJd+CxSlzPzMXFxeUaHGu8iYdrW/hh6gw5y61puFgsC0E20cvyI8ArGJ+2fAb4vJSy7FerxRBkj8UT/NPjZ9hzsH/K9b/W2MUfj30SMVGLbKraZAAPVmzhfx34Maq8LJgcXxV/HvivfKG7BQCfoVLzinpO6XP3vDQ5gjVDFufPjhJPXt0T8UNNF/ig8ROqFmE6s1xIBGP+CgbDtQwGosQ9fgZ1D3FVMIJDnVRoKxRoS8Zpi3cQKLi1fVxcXJYWaW+YH7TdwcNKjlMpNwRhoVkuguwbQAr4ysSih4AKKeU7SmrlHFgMQfZofIz3HT3PK3pN9h4ZnHKbT6w+zQcH/uySwPni9tfz2eTRS+vvq9jEXx98Av0Kr02megfvTnyEg8kgAJoiaLunkYPe0kyDqUi25VWMnizHz41g2pOPe3s0yR9XP8PWwe8hCslpjuLi4uLiUm4ON23n4ZomfpRsJ2cvnC/EUAxCeoCQ5iOs+ggpOmGhYSzYiKXHQTAsi8StHPHiGGPF2V3flosgOySl3H6tZeVgMQWZkJI7O4vsPzk05XafXbufN/X8JTC5Ntmd0Q3838NPY1zhMTvd/A7e0vEmMrZyadmOuxt5Mbgwf0PMEWxOOgyeTdA5lJm0rtIw+ZOWQzyY+f6Smc50cXFxcbmalDfCmepWTFXDVBRMRcNSFEyhjr8XCqaiYAkFUzD+fqJumwMEHEnYtglZRcJmgXAxS6iQIZxPEcqN4TXLPvFVcoqqh6FwLYPBCoZ8YQYNH3FNJ65A/ArhVnBMdr9794LbM986ZAeEEHdIKV+cONjtwHOlNHA5IIVgd4vBLWaMQ2dHrlr/0bM3U73uQ9zV9QV0x+RPxrL8TeU6PnvsuUtiTGpevl7zu3y8fXJ+zq276nlmgcQYwIgieTYq4JYoG80YVQMFTpwZJlOwGS7q/M6ZXcAudkVSvLOygzuUYzSO7kXNTD1N6+Li4uKy+ITyCXZ2LXx7nxsJwy5cquM4E6Y3skgWTc9sPGQnGA/ov/jXtAAnGBfcUkq5bUEtnIHF9JBdxCNh66kMxy6MTbn9Y+u+w8aurwNgKRqaY42/Drfwu/w+3xusnrT9rptqeLZBXxDbZ8IrYUcG8hfSnOyeOgPz3tgob63o4FaOUTeyFyU3tXfQpXQ4vhij0Zs4ra3nhcIq8o7Ga30n2JTbh3fomJvZ6eLi4rIQaD7444V3Qsx3ynLVTOullGWLNiyHIAPwSVh/LMWpnqvnplXh8EzrV2joeezSsuH6V/KW/vfSmfNO2nZ7W4w9rV4cUd7O1S22wuphk8RglvMDaQrW1cH+QkheWzXMmyJnudk5SvXwHjf2bJ5IzUsmtoVzno3ss1p5bLSBF8emv0tr9ed5T+057tOO0TL2ElqqZxGtvTFw/FUkwhvo1FsZlBWuvF1iqEISJkOEFCEnid8aw2OOoRfGUPKjCHvplspxWeYsZUEmhPAD5sUSF0KIDcDrgQtSym8ulLHXQ7kEGUBYCpoOjdExcHXT54Dq8Fzz3xPpf4HdzR/gl87ciy2VSdtsaApzanOIfHm12FXoEtbYguqshLEig4MZukeyvPxroiuSN1QP8gvhM2yxTxHLdKAlz9+wmZvzRQqFYrSN3uBmDjltPJFq4kdDVRQc5do7T8MrY2O8M3aGW51D1LgCeRJSNchH2xj0tdEuVrE318ATo9WczvjLbZrLPKjxmKzy5mj25mgwctRqaarVDDGRxicKXHWimgUaNg3Fc4THTri/oZXMEhdkTwO/JqVsF0K0AbuBrwKbgd1Syj9cKINnSzkFGYwHy1fuH6Fr+OoKyzUekzdX9/KF7qsdjC1VfgZ3VjA292vxohKWgjUFQSRjkR/J0zWQYTRz9Z1qSLO4p2KM24ODbNZ6aba7iGXPoicuIOTy7LYlPRH6qu7kGbmDlzJ1SMaL8Y0X1xiv3I0Ql6YSBSAEV7yXmFLl6dEYg4VrT00HPCotNUGClT4KIQ1HgC9hMjrhvbScqX+vHsXhbbUDvCF4iq2F/QSHDiImpstvdOxALYnQei4YrRwxm3guVcfTo1Fy9tWtoxQBLdUBqqoDCP+N31pqWWI6OEUHq2BTKFhk8xapnEkyZ2FP8/0vBUJI7ogmeU2kj536BdaYZ4gkTqDkro4ZdrkBWeKC7IiU8qaJ158GYlLK3xRCGMC+i+vKSbkFGUCdI/DuHqI/MbvslOqQB/P2avrV5T1Z0mgLmvJgpEzSo3mGx/LEU4Upb1ADmj0u1AKDbNb7WOV0Est0oCfPLznRIBHkq7Zw1Hcb305v5hsD9ZjOwrgxPZrCqpogkSofZkij3yvoVJ1pp7D9EtYXBKGkRTKe5Vx/muIU08swLo7D2tL6bBcCU6ozCt2QV2NVXQhfpZdUSOOM7pBeJjdCLpMRUhJCISwhaAt8tsRjSTRLopgO0p7jOdWRjA3lOD+Yvqo8EMDNkRQPRPvZZVyg1TpLReL4pA4tLjcIS0CQzZRleeU3837gfwNIKYtCiBUzLyWY+WLcr0iab62i8sVBhtMzxzcEPRrGbdV0zSDGKjSFTxj/TFap5AXlfp5KB8kt4F3hXOlRJT0BIKBBXRAI4pVQZwtiFvjzDiJrkU+bjCYLPD6i8Vi8Eth06Ri6IvEoc/Oc1XtM7oqMsMM3wDqljwark3C6AzXVe92B7443Sl/lnTwtd/ClgTZOd/smrffqCm2NEXxhA8dycGyJbdpYloNpORSKDkXTJm/a5Ir2lDF4miJoqQ4Qq/LhRAziPoVzqs3hSeLrsv9tKrKC8Tp1XhVqQng3h2grCirSNtl4jo6+FLmJTg0pSyNlzSaJ+sZBCGiK+ampDSCjBn1+hfOKTfzSZ7xiTls3JFIIkkiSAlAkXNLhApint7NBxyvDrCsKohO/p3N9KbJFm/2JEPsTIWAd8BoAtoQyPFjRT5M+txZqYaXAPalHMcbOzM9ulxuKmTxkXwH6gR7g48AaKWVWCBEFnlopdch640/z8U6NHydnjj1ptQSZ5wdI5KZu2aOrgjX3NHHYM/1Foc5Q+CPlL4nlLlcVMZUYJ0Lv5Rnndl5KayzPib/xQrW1jkK1CcGiRMlamBkLx57bRbJQtOkazFzVKqrSMLk7OsquwBAbtT6anW5i2fMYyXOXAoIlglzVTRzx3ca30pt4ZKBukhdMCGitDRKrD5KIaJwyJIXrcJKpSLyOwA94pUBD0Kc4Cx4vqElJm6lSlbExR/JY03jPbiQUReCNeMiENc55JKNLLCbTZfmiSUmrpVCTcTCHC3T1pRiZIlRjrggh+Z2mc7xP/QHR/hdKdlyXObIEPGQzCTIf8FGgHvhnKeWhieV3AWullP+2QPbOmsUQZPH4jzl05CP8OPpZ/jXROOO2G0yFoed6yRQmiwQhYNvdTbwUmP5OapVH4ePyTwgXpu/ZntHXcjjwqzxpbuHo1WFrKw4hJc2OSl1B4k1bZEcL9MYzU540dUVyezTJTYFRfjpSS3tmshesNuylqSmEWemh3ede2F1cXK6mxRY05iTKSJH+gTSDifxc8ghwpOTKiY831MT5g/BPae59DDHLPrwuJWYpC7LlwGIJssNHfgOAA5GP8zepW7Fm+Mi2FgXdz/aSNy97J3bd3jBemHUaNvoEv2/+Pn7z3KztGvHewT7vu3g8v5quwo3vCbkeahxBUxGCGZtioshAPEvf2OQmvQGPytqmCHq1j+6g4MIyj+lzcXFZPgQk3DzscPJYnFT+cqznTaEMf1r3LDvi30HJj5XPwJWIK8jmx2ILMoDzwV/iz/PvIDXDVNvOvMKZZ7sxbcmt22t5pm76WJ6bA5Lfyv0mHntgzjb2BN7IS/obeDxTxdgKmKaaC2EpWGUKwnmHMa/CKd3GKnP9NxcXl5VN1IGbhmyOHotfiv+E8dCLT7cc5IHkN9GTbmPxRcEVZPOjHIIMYMh3D3/h/B49xenFz21ZgRbP80KLgZzmwv+KkMkHUh9Gl1NXyr9ebOHlRPjDPFy8lzP55ft/dXFxcVlJxBzBlkGTw8fik5KCVOHwBy1neI/8HsHBfSUbTyoa6AEcPYCt+bE0P6bqo6j4KSg+8oqPHF5yeMngxZTLq0TMePmhK0oTXVGmSEzzWlE17njv/7fwts0xhuzfpJS/LIT4qJTyswtq4RwplyADyOhtfNb4XxybYyzXg+Es705+EFWWvpmrg8qp8K/zDevVnM65wszFxcVlOVDnCNr6ihw6Eb+qBMe76vv5qP/HVKdOYOlBTC1AUfGTVwPkhI+s8JGRPlLSR9LxkHB8jNoeRi0Pw6ZB3DQYLuoMFXUyU9ToW+l4dYWTn37dgo8zV0F2HHgd8F3gPl6Wjy+lLHu1vHIKMgBTqeDLwb/jyZTnuo751sgIbxn7dZQFzpmUCE6FP8zD1s9x0hVmLi4uLsuCRluwqqfAoVNDC1oM1+UyS0GQzVQi8fPAY8BGYN/LHgurgpYJujPKryV/mXdFZl8k8FciPbxt7IMLLsZg3A27Mfl5/jj7Nv4k9Chb3a4xLi4uLkueHlXyfItB9f2N7FxfiRvuujKYVpBJKf9WSrmJ8ZIXrVLKNVc8WhfRxiWNgs0bx36d344cmVHdAvxG5CQPjv3Ooth1JQLJ+uQX+cPM2/jT4PfY5gqzJU+zR+GN4SRvjYwS09zS8i4uK5HzquSFNV4a729i29pYuc1xWWBmFdQvhNgO3DPx9mkp5fTFshaRRSkM2/ciJ0++DymvXRCwPfwB/iLzerIvczGrwO+Gd7Mz8b8WyMrrpyP0Hr4p38yBjHvrtRRY5VHY6R1hozzK6txjhIonLq1zhMGFwNvZq72GJzMVbibtPKjUFe7wJdgu91JrHWEORdZdFhAHlZxaQ0qtJSWqSBBljCBjjo8RW2fYUhg2JeYyTkabDxtNhaq0DQpIRWCL8Q4GtgK2mHgoAguJJcAUUARMBSzAQV6qmybF5a+/ZKIv+8suB5Ll9xO5GKSvIiYeEgWB6ox7oFQuPwsEiiNRAa+i8OPXLHxHyHllWQohfgf4EPDNiUVvAb4gpfxcSa2cA4shyE6cOMH+/V+nqfm7WNa1syEH/A/w59ZHGJyoQ2YIwceCP2Zj8vMLaudcuRB8iEd4G/tcYbaotHoVdniG2SAPsyb7GAHz9Kz2s4WXC8F3slt5FU9noiTm2OlgpSCArX64VT/PluJPqMn+ZFHCBVwWlrxaT1ZfRUZrIq3UklSqKOCfo3gQ5PCRxkdGekk5OilHI2UrJG1IWo77jVkB+BTBuXsXvgHRfAXZYeBOKWVm4n0AeEFKua3kll4niyXIvv71r1NfL9i85XGKxZ5r7pMytvDX2qfpLsAf+f6T1emvLaiNpaA78GbO6bfTI+voskOcy6vuxb6ErPUKdnqG2eAcYlXuUQLm2Xkf0xY+zgUfYrdyL09lQqTn2lz5BiOiKdzhT7ODA7RlvonfOl9uk1yWOUWlirxWQ1GrJqdUkVMqyIkopvAwU//Z6cgS4lvZ9Zdu3F3Kz1IQZLPpPixg0g2CzVy+gcucvj5JNvsabrv9JfL5kzNuGyoe4+PWh0h4tlOTfnyRLJwfTZlv08S3Jy3LaKsZ8d7CgLqBPtFEtxPjfNFDb8FZUW2afYrg3mCaDeIMuiyiUUCVRTSKqLKAJouoMo8m86iygHLpdRbVyaE5KfTcKOSuPdb1oMocbal/po1/5p0iSEfoIV4S9/B0OnjVtPmNzkaf4Haji83mEzRmf4BIWNfeycVllhjOEEZxaHz+r0TcKSI8H/kE/5FeT9K9+XVhdoLsS8BLQohvTbx/M/BPC2bREiaRcHj6qdt45b0R8vmXZtzW4wxRk1seYmw6AtZ5AunzNL9suSWCjHlvIa5vpU9ZQ7+sps8O0GuqDBZvDLGmCbgzaPIK8SIbUv+Cnhgtt0kzosk065P/yHr+kYdEiPOhd5AV0XKbteBossiq3I8JZE+D29/VZRmhywT3jn2M27UmHo98gkeSdRRWaGycyzizDeq/GXgF456xp6WUBxbasNmwmFOWVyIEvOpVA5jWjxd07OWILXykjE2M6esZVVcTF3UMyhj9tv+SYFvKp5wdAcl92mG2pr+Mz+ostzkuLi4rhJSxhR/4/is/SIZviJva5cZymbJESrkf2F9io1TG65n1SCl/QQgRA74OrAbOA++UUi5Jt4SU8MQTtdx191tR1W9R6jwUw6jHskZxnNJX8V9oVJkjWthPtLCf1VOst4WfpLGZMX09Y2oLptDnNE6eAH2yhm47TGdRZWgesRhtPsH9Rjs7cl8jkj405+O4uLi4zJVQ8Ri/WHwfr/Hdwzf1j/BUyii3SS6LzKwE2QLxUeAEEJ54/3HgcSnlnwshPj7x/mPlMm42PP9cgB07HiISfQTHKcz7eF7vLfT2bOLYMdA0wbp1UFs3hKa1UyicKYHF5UeVWSoKe6kolNazWVRrGPHsZEjfyIBYRa+sotsKcKGgTNkIvt5QuN/Xw67CN6nJPuFOd7m4uCwJqnLP8KHcM7w+8Gb+Q7zHLU20gihLc3EhRBPwL8BngN+b8JCdAu6TUvYJIeqBJ6WUG2Y6TrmmLF9OW5ugueW7WNbYdR9fUTxo6is4ebKJnp7pvTzRqKCtrUi0ohfbPoJlLUnn4ZIko7cyauwgrq1jRNSyznqepvS33PIHLi4uS57T4Q/wVfP1nMmX5lqtAmFNIaJBWHEIKSYhpUhI5AmQIUAGv0zgl6P4nRG89hCqkynJ2IuDwFE82HixFC+O8GDhwRIebGFgYVx6toSOjYGJhhAGn9r55oW3br5TlkKIVcA6KeVPhRA+QJNSpuZh098A/w0IXbGsVkrZBzAhymqmseVDjNdFo6WlZR4mlI4zZyTp9BvZsvUJisXuWe2j61WY5t3s3x8hlZRwjaiBsTHJ3r06sApoYfVqhaamJD7/OQqF40hpzvvvuFEJmB0EzA6aym2Ii4uLy3WyPvlFPiW+zKHw7/GV/J30FcevFYJxYRVVIaI6RFSTsCgQFFlCZAiSJOCM4nOG8TtxfPYAHrMPwxlCmBLcS8YkFMXLeM5i+bimIBNCfJBxARQD1gJNjPe5fPVcBhRC/AIwKKXcJ4S473r3l1J+AfgCjHvI5mLD9dBAJavrmjnf3zXjdv39kmz2fm6/Yy/5/PFpt/N61jE6egsvHNSxLJhb/Jng/HnJ+fMhYBte73ba1tnUVMdBHMA0Z99b08XFxcVlaSOkxY7EX7BVBEl5tuC1+vDavQjTcoXVDcRsPGS/CdwGvAQgpWyfzns1S+4G3iiEeD3gBcJCiK8AA0KI+iumLAfnMUbJMByF+7vW8eIaH8e7Z66mnkxKnnryFu69N0K+8MIVaxS83lvpvLCeU6dKb2M+Lzl6RAFq0bTXcssteYKh52dVxNbFxcXFZXmgyTQV1yi55LJ8mU3X4oK8opGjEEJjHmmFUso/lFI2SSlXA78IPCGlfA/wXeBXJzb7VeA7cx2j1Ci24K4zzdzWcu3mBMUi/PSnbWjaa1EUP7r2c7Sf/mV+8uOFEWMvx7LgpZe8/OyJ+8lm3oXHs2rhB3VxcXFxcXGZF7PxkD0lhPgE4BNCPAB8BPjeAtjy58A3hBC/BnQC71iAMebFttPVBNbeypO9e5kpGUJK+NkT1Xg8b6NQgGvFhy0EjgP79hkI8Uq2b7eprt5N/gbJ1HRxcXFxcbnRmI0g+xjwAeAI8GHgh8AXSzG4lPJJ4MmJ18PMMS5tMVl7Nox/1V38eGQ3pjnz5H1h/pUw5o2UcPCgCtzJ1q23U99wYMYYNxcXFxcXF5fFZ0ZBJoRQgMNSyq3APy6OSUuf+gte3lB3F48V9pDNLZ8CVkePKhw9egsbN95Mc8tR8vmD5TbJZQoMoxEpNzEUr8G2Ferqe7Dtfdh2stymubi4uLgsEDMKMimlI4Q4JIRokVK6fWSuINav8aaK23gscojRJd7n8OWcPCk4efIm2tq2sab1BPn8nnKbtKJR1QC6vpVUsoVz53wMDEyeDj92rAlNa2LjJoe6ui4sax+2vZzqAi09VDWEptWW2wyXKTGR0kLKIlKaSKeII023tI/LDc9spizrgWNCiN3ApauAlPKNC2bVMiEwqvIL/p08Xn2c3nh/uc25bs6ckZw5s5HVqzfRtu4slnkA21k+Hr/li4LXu55ioY3e3go6OsC+VKN26thEy4KjRxSOHlmFrq9i82ab6poLmOZ+HCe3aJYvVzxGC5K1pJLV9PR4J4owuxXQlxcSXRfoukDTmHiWaNr4s6KMbzMXhBAIMb6/EOPvL7/mZesuvgcEiDmMqRsOVVXHyOcPz8lelxuT2QiyP11wK5YxnqzgQXMzzzT7ONN7rtzmzInxmmatqGorra1QXz+Gx9tBoXAKKa1ym3dDoOs1CLGFkeE62tt10um5l9AzTTh0SAVa8Xha2bzFoqryHIXifq5IiF6xKIoPw1hPsdjE8HCUCxfUieLLF5G4Ymw5IjBNMM2L/8uXP8+Hlx9jqmNO952Zy3dJAbbT2rqDtWvbyRdeohzJXy5Li2sKMinlU4thyHJGNQWvPL+GwFofh7qWb8C8bUN7O7S3R4Gb8flvoa3NproqjqKeolC4UG4Ty46i+FDVIIoSRAg/4EdKH47jwbY8WJaOaeoUixqFgkI+r5BOQzz+ckFQGgoFOLBfA9bh969n8+YiFbEzFAqHVswUj2HUIUQbmXQtfX1+OjvHs4wvs/jt4VxcZkNHh6Sjo436+vVs2tyNZT3j3lStYGZTqf8O4HPAJsBgvBVWRkoZnnHHFYbiCG5trye4zstzXfvLbU5JyGUlRw6PF5yFWmIxQWtrgWi0H0ceXxEdAVQ1gKreSk93E+3t4oqpxdmyeGIgm73YXmsTXu9mPJ5FG7ps2Dbz8ja6uCwF+voc+voaiEYfYvuOYYR4uqxJPELo6HotilKN41Tg2MvpZCJRlCJCKSBEAcghZQ4ps9h2FttOs1S9kbOZsvy/jBdwfRjYBfwKsG4hjVrObGqvwN96B4/378ZxluY/fa6MjEhGRgygBWihsUnQ0pIhEIgjxCiOM4xpxpe9Z0YIHY9nJ0PxtRw7plFchjes+bwkny+3FS4uLtfD2JjDU09W4PW+iZtvyeD1Potplr5pjRAaul6DotTgOBUUi2GyGR/JpM7IiGBkRDJDqc1lj8cDPr/A5wWvV+LxSHw+tdxmza65uJTyjBBClVLawJeEEM8vsF3LmlUdAX6h6S6es46TLxYomsVr1ixbjvR0S3q6/Yw3PL/YEUASDitUVEA4bOEPFPF6cmh6GkVJ4DjDWNbQkgxE93o3k0pu5thx/8tijlxcXFwWj3wenn8ugKo+yM6bi0SjuykUZh+jrGkRVLUaIWLYdhizGCCX85FIXBRcDlJOF/t245/7CgUoFCRjl5YINE3hTW8qn00wO0GWFUIYwEEhxF8AfUBgYc1aOhTIj8dfXqezq6bbw1vYeem9FBJbl1g6WIbE1iSW5mBqElu1MRUHS3EwhY2JzaidYiA1RDK13GpPCZJJSTIJ47PbvolHbNJWgYAgGgVtVrcEV+PxSCKRIv5AGl0fRRDHtHqvuxyEx2imWNzJqVPRK8pN3PgnJBcXl6WPbcPePQZwN1tvuov6+sOY5nk0rRohKsfFlhkkl/OSTukkEgojI/IaRcndpJalymwuh7/M+JX1t4DfBZqBty2kUUuJeLaLwXA7q5Lr5jXtLKRAKwq0IlcUD5mJKmANRZ/DWKXJiC/HkJMgnhlhJDE6Y+um5UAmI8nMu5SWwbjQiwFrAYhEBNXVkmi0iD+QwTDGrhBrKQA0LQbs4vy5Ws5duulc3p+ni4vLjYzg6BHB0SM7gB0zbOeex5Yzs8myvJhal2OFlsB48cA3Yedb5y3K5oKRU6jp9lCDB4gCq7AMSaLSZDSQZ4gkg7kRhkaHb7iYtbmQSEgSCRgXawZQAawBIBQSVMSgu0viflQuLi4uLkuJ2WRZnmMK2S2lbF0Qi5YoLx74JnLnW1idXF/2BA2tKKjsM6jEoI0w0IStSVKVNj3hJIeGTi2rlk6LRSolSaXKbYWLi4uLi8vVzGbKctcVr73AO3h5QNAK4aUD34IlIspejmoJogMa0YEYG/U76FiV5EDi9DKMQXNxcXFxcVl5KNfaQEo5fMWjR0r5N8D9C2/a0uSlA9/ifOjULD658qGagnVnIrx9ZBevabidqorKcpvk4uLi4uLiMgOzmbK8+Yq3CuMes9CCWbQMeOngt5E73sya1EZwlm4QpWILVncEWSW209uS56DdQd/Q8uu56eLi4uLicqMzmynLv7ritQWcB965INYsI3Yf/DbseBNrUpuWtCiD8QzPxgs+GtnCYNM6DmnnudDfVW6zXK6BqqpomkZh5hx2FxcXF5cbgNlkWb5qMQxZqijq9C0jdh/8DmyXrElvXvKi7CI13QYPsJ7R2jUcCXZzuqej3Ca5XEHA72dNrJkWs4qaPg8iD4NNBTr0Qc4OXqC4HNsGuLi4uLhck9lMWf7eTOullH9dOnOWHkJppuWmB+g88pMp1+8+9F3YzrISZQAVAzqvHFjDzlgLx2J9HO9td8tmlInqiipWBxtoTkep6NcQI5OLNtZf8FJPC3fozfQ15ulQ+ukY7MSyrDJZ7OKytNB1HUWZW2CvQKDrOh7dwNB0DNXAUDQMRUcXGgYaOhq6o6A7KrqjopkKuq2gXHdv23EsXXIhOMKJoY5lkRGv63q5TZg1Usple26cbZblrcB3J96/AXgaWDFzXoPdN9G0pUD3saenXL9cRRlAaETljpEmtofrOVeb4ETyPKOJ0XKbdUOjKArNNQ2s1upoGAoR6JvdhUQ1BU3nfTSxhjs9q+ltyHKWPs4PdLliepZEwxEqAxVUaCF8GG4dzSWGQKCioDoKqhRo9vizagsUW6DaCqolUC1QJp5VS8Ay7Nsao5Ztag19zXlO0c35ga6yF/w2DIPqSCVV3gpiToCKrI/wsIaWWl6V/W1dUvA54w/DJq9b5FWLHEVyskDOypOzCmTyWbK5LLY9R2VdYmYjyKqAm6WUKQAhxKeAh6WUH1hIw5YaQ7230LgxT8/J3VOu333ou8jtktb0lvmJMgEioiMzNpiLd5H1JVU2J2NsJsZQQ5Gz/jinBjvcKbIS4fV4WVPdTItVRV2/F/38/NJ09YJg1bkAq2ij6GuluzHDGauXrsGesp/Uy42iKFSEo8T8USrUIFHLTzjjITSqog4urwuLy42Nal+8yVpHNrKWjppRTox1kFiEckWRUJjqUCWVaoSY6SeaMPCPKYjk8v+NqKbAb6r4kypwbe+e6XEwfeX/u2cjyFqAK6/KRWD1glizpBGMxO+iri1P/5nDU26x59D3YDu0ZraAPbuLovCp2FFJkmF6R89y9sJesh0JNM2gbfWtNFdvIiqqUUdAFhZHoFX1GlTRyC1GAz2NWU453XQOdC/K2MsVn9dH0B8gYPgJaD78wkNAevBZGv68TmRQQ0kszA/eyCm0ng3RygbywXV01aU4Z/aTt278ZABFKEQ8ISqUIFHTRzhtEBhVUfvLf3J1cbke/AmFrYlKtogYg01FThv9nOk/N2/vjd/nJxaKUuEJE5NBolkfkREdIy4gXiLjlzl6QcEYK38tq9kIsn8DdgshvsW4g/8twL8sqFVLFOkopBKvonp1gfj5U1Nus+fQ95DbJGuzW68WZQoQ08kZGYZyXVzoP0LPuamPY1lFTp55jpNnngNACIXVq3awuvYmYlod+piGzC7sPLlWvOiF2UA20kZHzRgnE+cYSyYWdNylSDgUpiVSj1/x4Hc8+EwdX1HDm1XxZATqmICxclsJ3rTCujMR1hEptykuLi5zQEhBbZeHWlZxu7+Z800pTmTPEx8ZmnYfTdOIhSuo8IWJKkHClo9wziAwpmGMCnCjUJYFs8my/IwQ4lHgnolF75NSHlhYs5YutqWSzz1IrLHISM+5KbfZe/j7yG2SdezACtmMOXF6R9rpuLCf/Nn0nMaV0uHc+f2cO7//0rLGxk201u+gytuEJ2Ugkwsn0PwJ9dLd21C9yRn/IKcHOjBNc8HGLDcej4f1NWtozVRT1acj4q7XxcXFZfEwsgrrz0RYz3ZG60xOhwZJm1mieoiI9BMueAgmdXxJgUi756fljrhWvIkQYi3QLaUsCCHuA7YB/yqlHFtw667Brl275N69exd0jI6DcR79/JGrlhs+E2F/k8RAz4KOfz1UV61m+7pXUzVWi8wvfJCiZUi6mzJ0K8NkzTyZQpZMLku+sAwjbCdQFIU1dS2sk/XUdfnQLPck5+Li4nKjI3SFxk/fvfDjCLFPSrlrqnWzmbJ8BNglhGgDvgh8D/ga8PrSmbj8KOZ0fKE3E4x9g/TI0piIjw+d56dD/0QoVMW923+JQL9/1rFsc0ErjncCWE1w0nJbkxQCkrzPoWBY5HSTnFIkS5Gckydj5sjkc2Tzc89uKXVWTH1VHRu8zTT3hvB0uCLMxcXFRUR1soEMGWv5hKkoQsGrBPFIL1pRg4yzKA6KUjAbQeZIKS0hxFuBz0opPyeEWDFTlmKGvPhcykMg8nYc69/JJscWz6hrkEoN8f1n/5a62rXc1fY29D6xqOn9qiXwJwT+hML4V8xb8jFsTZKLOKQDJmmjSFLkSNoZErkUY+nErKZSI6EwG6KrWTMcI9StltxGFxcXl+WE8KsUoxaDhU5OXXiR+Lnz5TapJPj9Yapiq4hFG4j4qwjoUbz40S0DkQOZXhp1y2YjyEwhxEPArzBegwxmk0d6g9DiOUhdk0F/99QZjpmEj3DNu7Dtr1LIzC0+bKHoHzjLNwf+gnWtt7Gz9jWIgeVxlzAbVEsQHFYJDquMC77wpPX5oE0m5JD2FklpeZIyS6KYJp3P0BKpd+PCXFxc5o82x/OHZEFnL2aNruBUwggDdPQe4Nzxg3ADls3JZpN0Zo/Q2X11+BGAqmrU1rbyEAs/ZTkTsxFk7wN+HfiMlPKcEGIN8JWFNWvpoFLktdof8Y3QX5FNTa2ik8MBKprexfD5r2Iuwfip9o7dtHfsZufW17LB2IUcvXED8S/iTat40yqV6EAAqLy8crBcVrm4uCxXhHe8RFFKjNKf6OBs1wFSybmFq6iqwab1d7E6to1gNowcW6RzsgAqNVLGGBeGj3H67IuYp5feNWuxsW2LwaEL5Tbj2kH9S5nFCOrnxPfh6++mt+LtfOf0e3BmuKuJ1Y0wcOar2Es481BRVF5xy7toyK1BZpaGm9blaoRXxapwcISDMawuWg06FxcXQBVQoZLzZBjKdnOh/yg9facWzHvU2LiJjc13UkkdIu6UrOOL8Ko4EUirCeLpTk52vEAqNX35jJWMZnj46L89suDjzDeof2Ujxl3SDaP/yV2bbubZo5um3XSkP0b9hnfRc/xryOtoZaPqBhX1q/CFm5CilkI2gtc/ipk/Tfz8cWyzdNXyHcfm6T1fw+sNcu/N76ZiOIYsuhf7ciMMBbsSRuQA5/oPce7kAaQc/79omsHWDfexqmIrvlHfgtefc3FZaYiwRjFoMmYN0j18ivMXDlI8k1u08Xt6TtDTcwKAYLCCLevuo9HfhjGqI3OzCzURhoJTIciqKeLZbrr6j9F7vn1eItITCBKtW41mhK+98VJB2uTTcRKDPVjF5VUc2/WQXYPnT3Sy86e/iG/4GAA/Cf87p0/PHKRe3dxN15GHp/whCKEQqW0gEGtB1erIZytJjQaQztRVgjXdJloTx7HOED9/BDNf2pNErKKBe7b8It4+HVxdtnjoCk6lYIxBLgwe5ez5vdj2tYWWEAob193F2uqbCWZCyIQrzlxucFSB8KlIL9i6g6kUKDg5claabGGMZGaYRGqAXH5uMbyWVSSTWZqVUxVFZUPbXbRWbyeUi14ON9EViCpkjTTD2R66Bk7Q3Xvi0k3cXFB1nWhdC/5IM4hasukYmTFfif6SxUciCUYKeANJVHUM2xwil+wnMdg7pZNjKXjIXEF2DX50rJ9/+M5TPKL/MWpmEEsN84j9FYb6Z75rqWnuoPPwtwnGqghXr0bz1GMWq0iNhLHMuWX0KapDRe0wyLMMdx4hn07N6ThT0diwke1rXkM4GVkyGSc3FJpAVioklGE6h47R3rEby5q/53PNqh1saLyDaLESOez+36ZFFYighuN1MDWTgsxStBfPA+IyOySSbDFFOjdKMh1nLNFPIhVfcoHmFQ2rCFVtwpHROe2vCBvH6iMx2E5qaGDW+9XVteE1AnR2H8Vx5pGkJQSR6nqClatQ9ToK+UqSI0GkXf72QQuPQyBawOdPoaijWMU4uUQ/2cQwv/Wlry/46HMSZEKI7zFDsQQp5RtLY97cWSxB9uF/28c76vr5i/QnEFaepH8n3+j/UwrZmX8Qus/EzC1QQqqQVNSOoinnGOk5QmZspDSHFQpbN95HW+XNeOK6O505B0RIww445NUsiUKceKKLs+f2UjQXVgDU1bWxZfU9VMkGiFuLWuqkrAgQAQ3pA0u3KJAjayVI5UcYSw0wNNLFaKJ/yV3UXZYPqq5T1bIRT2A9qbE6cilPyY7tDxUIROM4Zidj/e1kRodLdmwYP6eHqmoIVDSge+uxzGpSoxHMglvq50o0j+DDn33Vgo8z1xiyv5x4fitQx+XMyoeA8yWzbpnwcH8dm9f8Ae/r+zTh7AEeWPNtfnD8DTOe4xdMjAFIwWh/DIghxc3UbUiiGxcY6do/L3EmpcORE09whCfweoPs3PwgTZ4NKAP2yrnAzwYFRFjH8llkRYqx/CDx0Qv09J8key5ZFpP6+8/Q338GgFCwEq83VBY7FhNH2oyNDWDbpYuzdHEB8IXCxJq2ItRWxgYrSYyoUJr73klkUx6yqSagCbiLytU5AuE4VuECo72nyaVmV5RVUTVCVbX4I3Xo3mqkE6WQC5NO+ilaCsWlUb986eKUvwTSbFonPS2lfOW1lpWDxfSQXeQ/1j3BHV1fBGBP5WfZfaxlQce/XhTVoarhPEMXniKbKF1cRCzWyM71D1JtNSCHl24W6YIggGqNMXWI0Ww//UMd9PafKsmUo4uLy9IhWtdEuGYzxUIzY4Nhxn/85SUUy+ALDGLmLzDScxqrWCBcVY8vXIvmqcJxKshnQ2QSvmljkV2ujaYrfPhz9y34OPPNsqwWQrRKKTsmDrYGqC6lgcuJh868iudau2noeYxdQ/+FwdaHOd+xdFy/jq0w2NWKaqyiZdt54ueenPUd1kyMjPTw+Iv/DMDqlu1saX4loWT4xo03mxBhg3Rz+MwTDHd0ldsiFxeXKRCKQqSmAY8/Mqf9FVVH960mk2wgk/CSX2I/9dRIgNTIGmANwrgX3QO5vCDnlg+74ZiNIPtd4EkhRMfE+9XAhxfMoiWOlILXdf4SL9T24R86xAPmR3i46p8YG1pawsS2VAa71qL6VtOypoPBjqfIp0szlXa+8xDnOw+hKCpbN76KVbGt+Cw/SpJZp2gvSQRQpREXPRw++wRDHZ3ltsjFxeUKVE0jUtuEP9KEotVSLERJjQTJF1Tyy6vCwRxZ3DZ4LovLNQWZlPIxIcQ6YOPEopNSyhXx1Z+OhKnx1rHf5gfBT2Kke3ltxV/ySOr3MJdg8U7bVBnsWofmb6Wl9QwDZ58qWYsnx7E5fPynHOanl5ZVVTbTVLeJqnAzYSWGkfeMl2ZYCm1CpqNaY0jp5fDZnxHvOF9uawDQvT6qWjaj6gGS8XaSg33lNsnFZVHRPV4itc34wo2gVJPPVpAe9ZPNKmSz5bbOxaX0XFOQCSH8wO8Bq6SUHxRCrBNCbJBSfn/hzVu6nEz7+ajv43xO/0MqU09x/7od/OjofeU2a1osU2WwawNGcC21a9vpb3+KYq70Z7Wh4S6Ghif7/FXVoKlhI/VVbcT89QRkGDWtIKdpRbUoVGkMq30c6XiKgXNny2fHFYSraonUbca2VzEajzIavxgPsotoc5FQRRzH6iLR3056xI3QnQuKqhKuricQbUTVQ0jX3bCkEEJFyhi5TJT0qI90SlDC6j4uLkua2UxZfgnYB9w58b4beBhY0YIM4PvxKras+m/8+sCnaBv6LAObt3Dw+NIOrzOLGoNdm/BG11HXdoq+9qdLXmz25dh2kQtdh7nQdXjScr8/gsfwz+mYQlFQVR1dM9BUA1Uz0FQdVdFRVW3itYaqaCgTz6rQQEB7527695RfhCmqSlXzerzhdWRTjaTHfMR7pt42nzbIpxuBRuAOKlfnCYTiWGYnY32nS5rAcaPg8QcI1zTjDdYjlCoKuSipsQD5gkJ+9qWfXFxcXBaF2QiytVLKdwkhHgKQUuaEEOVPPVkk1AoPa2qCnBuceprvf11Yx6Z1v8F9XX/PnSO/SbzlYXo6l/5ddzGvMdi9BW9sPfWVJ+lrfwYzv7hRotlsgmx2/gkHywlvMExl82YUrZWxeDXJhEpyDh9BJuElk2gGmkG5m6o1Wfyh8VT5kZ7TJYsXXBYIQbiqlkBFI7q3DtuOkU2FyKW8pBJQgpwWF5cViaIKKmsUAr7llVFu2SqWpWBaAssSWCaYRYllOjP2oy43sxFkRSGEj4lQQiHEWmDFxJCZuqBve4S1RwRn+6f2nb+3/RU82dbD6u7v8KD4L3wj8jnSy6SljZnTGey+CV/FJuqr+jELZ4ifP7bseoAtVTTDQ6SmkUBFG8XieCr9cH/p72fSY37SY6uAVUj9Hqpac6jq8vgOzgeJIJvwUzRVt86Si8s8CUQ06qpy1PrOU2u9RE3qJ2h2EpZrzJ4A9InHxGSMLTxYWhRLjWCqESw1jKWEsPUocF+5LAVmJ8g+BTwGNAshvgrcDbx3AW1acowpYG8Ls14VnO6Z2vPw8+fewYuNPYQG9/Laus/zrcwHsa2lq8RfzrjHbLw4oRF5JXU1wyDPMdx9lFxyrNzmLXk0w0O4ugFfuA5Vr8SxK8hlguOerIwgk1k8WwSC9OjcpoJdXFxWBqouqKkV1IYHqRWHqcs8TjB/HDKMPwApFIoV68gZsbLaej0IJLqVRTeTKMUUopBEyMvZ/6osoJoDeMyXxS1oPuCzi2vsy5hNluWPhRD7gDsY15sflVIOLbhlS4yUgDNbQmxSBCe6rp4DydgKbx76DX4U/lNqEz/glRt38rOjt1xar2oCr1/F6wOvx8JrFPFqeXxqBq9I4GUUrzOE1x7AsEcZ9L6CC4Wb6ezxX7NFU6lxLIWh3mqgGtRbqduQxDA6SQweIzHQu6i2LDV0j5dwdQPeUC2qXoVtR8lnQqQTHrJZ4WZ/ubisEBRF4A/NrQalbUNukWs4hmMadZVpaj1nqTOfozL5M1Qrf6n7gNR8ZKp30Ott47izmmczDfx0uIrRvtn4bZY21YZJnadAnadItZ6nWstTqeWJqlkiIkeYLAHVZH2Z7ZxNpf7Hgb+SUv7wimVfkFJ+aKGNuxaLUan/0fgY7zt6/tJ7r4QtpzMcOz825fYPVI3wBfMPEYUUQ+FXY1jDeAudGPbcem446PRXvJkLyms4H29gZLC801DBihz+YA+5xEmGOs8g5dIr9VFKPP4Alc2bUY01ZFNVpBMexBKo3u3i4lIequpUNlYdY13qH/EXOq69wzRkPWsZDN7PINsYzDYwGDdKJtICYY2aqgI1/m5qnAPUpB/HW+y+tN7xVpCMbqRTb+OQ1cJTyXqeGq3AXALtg8qFV1c4+enXLfg4c2oufsXOHUAX8ISU8k8nlu2XUt5cckuvk3IIMgCPhO1nshzumDqz7bdbzvF78U9OcpOWiqR/O53+t3E+s4Webg3LLJ8g8viLRCoHsM0u8qlBUsP9C1JKY7GJ1jUTrt5I0WwiMRhFypV7knJxcRmPrVrf2MsG82tUpp5asHFSvpsYDNzLoLOVwXQt8bhK4RrFtj1+lZoai5pAPzUcoTb7MwK5k5fWO74qRqNbOKOtZ0+xmSfG6tifCC7Y37BcWQqCbDa+yDHg1cDfCiG+B7ynhLYtSwoCDrT5uUVVONg+fNX6z3WuYUvbb/Ha7tLPR4ezh9iaPcRWwKoJ0x19FxfsuznfX0l6bHG9Z4WswWB2ItMPULwQrSziC2bQjRRSjmLmhsgmBkmPDOLYS7OKv+7xUtmyEcPXSmqsjlzKS7772vu5uLjcuGiGwtrVGTZoj9I08jXEyOTzl/SEyYVWz+nYQjp4cgOI7BDiilp4odwRQrkjrL04RliQaLiNQd89DDobGUxWA5Ka8BC1ynFqck8TyeyFPJAH6QmRqNjK/qr3stdczWOjDewfDYJbFWdZMBsP2QEp5c6J1+8Ffh+okFI2Lbx5M1MuD9lFVCS3Xyiy/+TUIXXvru+lRs8TUguERIGgUiAgigTI4yOPV+bxyBweJ4fh5NHsLJqVRTXTqJnrL5Q0HL6PU9ovceRMLVZxaU0lCsUhEC7g8adR1QSONUIhO4Rjz61RuXQcbKuAVSxiFfNYxQK2OftjXSrC6rQwNliBY7tNeV1cVjpCQGMLbAi9xNrRL6C/LNREal76a1/Jd6y7+FzPWjLW/PoYhzSLbaE0m/1J2jxjtKjD1Mk4FdYg/lwfeqYPYU1djkhqXjIVm+n0bmC/3cpPEg08PeJ69OfKcvGQff7iCynll4UQR4DfLJVxyxkbwYstBncq1ew7fnXO/Vf7GuZ87NW+PG+o6uNO7wXWW6eIjR1FyV3tjbuSyuST3MWT7Khfy37/Jzh6pgq7jFOaVyIdhfSYj/SYj1L3phcG6AboQqLpDppuo2gOmmajqBaKMv4shIXAIZOOkRnzT1uE1cXFZWURq9HYUHOK9Zl/Ipg7MamwkxQqY7V38CPllfxNzwb6zxolGzdlaTw3GuW50SjQctV6ISTr/Dm2BlNs8CZYo4+Qlh6eSDbzk+EYhbR7I3kjMa2HTAgRllImhRBT5rtKKecWpV5Cyu0hu4iQkrv7LPYeGVxQW26LJnhdtJddegdrCicJjBxHWNNX2c/4NrLP8zGOnanAWUYlOFxcXFxmQtUVKioFleE0lUY3MecEfquHuXTe1u0k0cxLVy3PVO/gac99/J++mzid8ZXAapelzFL3kH0N+AXG2yZJmJRaJoHWklm4zJFC8GyDzj1KLXsOLVxPlt1jEXaPRYBNwM/jURweqBzmNZEutnOGhswJjLF2xETmYyB3klfm3sfO5q3s9fw3TraHl3SVYhcXF5crEWK8XENlNE/MO0ClOE1lfi+R9B4UTChxQ4pixTp2B+/n74Z28EJXpLQHd3G5BtMKMinlL0w8r1k8c5Y3z9RpvFKtY/f+/kUZr+AofD9ezffj1cB40uvt0SR/Uv0Umwa+iyiOV/cL5Y7yqtyvcMuqnezRfp/TZ4I4jivMXFYuwYhGRdRiLh4Vl4VDUSRR3yiVyjkqzQNUpJ9Ht8cmFSudCqnoOP65hULYmo/jobv5x7Fd/KCvak7HcHEpBdMKMiHEjGUtpJT7S2/O0uN6a049Xa1yz6569uztWyCLZualsTCvH3sDjd6f43807+WVo99ETY/bEs4e4NW8h1tab2WP8nu0t3u5Rk6Hi8sNgaII6hphVeQMqwo/pDL1xApqALfMmD4K4xJS0chUbuOUdzs/za/nP/obGR2aRwHTxbmHdnGZkZliyH42w35SSnn/wpg0exYjhuzc0B5ee9xDwr6+4PhXjEr27i5/VXufavPHq07wtsK38Q4fn7RuNHQ3u/kdzpwxXEfBEsAX1KiqMqnyx6lS2qks7Ea3U8R9dxNnM/FMHfEhfdErfC9X/GGNlroEq/SXaE4+jMd0r7rLFSlUslU3ccq7g8fz6/nGQCPxol5us1xuIJZCDNk1y14sZRZDkMXjP+bvukb5fGLDde/baAsqbPCbYJgOouBg5y3yWZNUxmQ0XSRdWLyL64eaOvmw/iixvqcn1b4ZDt/HXvkbdPX4Fr1N00pECIhWalRGM1R5e6iSx6jKPEMg3z6r/dPezcQDryTOFuK5euJDHjJJV6QJBeoaFFqiHawq/pCqxE8RYvme31YyUijkKrfQ7t/BE/kN/PtAM4MFV4C5LBzLRpAJIbYCmwHvxWVSyn8tmYVzZLEE2f6jf8AfGv9G7wLU9vJJqHIEkSuFW94mNZLnXF+KglX6MV9dOcIfxZ5gTe8PEPbkeZukfyeD/nuIyy0MpmpmVSnaZWoUVRAIq4SCNrHAGFVaB1XWAWKpZ8bjYkpI1tNKPHgfcXET8Vwjw2M+rLmVeFtWqCrUV6dYZeyhOfF1vOa1vdLSCDBceSt9epM7Zb/EKAqDFwpr+NpAM3350pWXcHG5FktBkF1z0l0I8SfAfYwLsh8CrwOeBcouyBYLTab5Ze+z/K/iXSU/dk5AlyrpUgEDxpNZNagLYmwK0mYqVKZt8sM5zvemSOXn7wl5fDjG48NvZ33g5/l08wvcOvStSzXOwtkDhLMHaLu4cQQSDbsY9N1D3NnEYLqa+KBKMb+yRZqiCgIhlWDQIejLEdSTBJQhQrKHgHWeYOE0/tyZcQ+NyXi/iwXEX+hgVaGDVRcX6BOPlcA1Mu2kopOu2s5Rz838IL2OhwfqKSTd+k0uLi5Li9lEQb4d2A4ckFK+TwhRC3xxYc1aemxN/A0b/XdzMrd4t9RFAccNB2ICYn5Em49WW6EuK3FGCvT0pRhMzj0y+XTGx7va7yeiv5JPthzlFXI/1SP7UPKT+2xEMnuJZPaybuK9jAgSjbcx6HsFcWcjg6lq4oMCs7A0itCWEo9PpbbWojbQQ6U4RdA8Ny628h0IbLCA1DQ7z7Fgth2oZSy8gU5tDSn8NDFAZbGXQLYLNdU7abrZZWrysQ20B3bx0/wmvjbQTLxzpahTFxeX5cpsBFlOSukIISwhRBgYZAXWIFOwebf6bf47byqbDVIIzmqSs2Eg7IHVHuodWJUTaGNFBvvTdA1nr3saJmFq/P7ZHcAOhHgfr60a5g2Rc9wsj1Ezsh8lN7k1lBCSaOYlopmXWH/RtgqVkdArGPTczaC1nsFEjKG4s6wK0goBFdUadbExarWT1OWfoiL1HCIvx3vFlRipeshH1zHgW8tpVrEn18DjozV0DHthmqYMAc3m1nCS7YFRNniGWSUGqLH7iOS60VNd07ZZudGxgw10Rm/jGXsLX4uv4WSvv9wmubgsayoCOlURL6GQB8VYXh7lS/fCQkyxcOp1ulr+v3E2vSz/HvgE8IuM97FMAwellO9bePNmZrFiyA4f+Y1L7/829A1eSs+vf9lCstoSNPTkOXR6GLsEtcaEkLymcpQ3Rs9xizxG3eg+lOzVbaKmwlYCDIXvZ1C7nYFiK4OjIcaGrCUTt+MNqNTWmNQFuqm191Cb/jGGuTDdFuxAHWPh9VzQWjliNfFsqo6nR6IUnNKdBISQbA1muDmUYKN3BI+48QP9C1LjsbEmnhqpKLcpLi7LCkNTqI14iYQ9eIM6+DRyXoUxHfpUSXaFtcT0KYJz925f8HFKlmUphFgNhKWUh0tk27xYDEHW3b2P06cfQjIeM9Xvf5D/mvvQkp80arQFa/qLHDoRxyxxdf77YqO8OXaOXZygfnQfamb25QSKWjWDoQcYVG8mXmjGtOZWO0gIiUAixMTrS+8nni+9diatU4VFpXKWutzPpmyXcr1II4DpryXvqSapVzIqKohTQa8dobMY4mwuyMlMgJ68Z95jubi4lJ+asIemxjBOpUFKn5tqUSWEChKRNEmO5ukaypArlj4uV1cF9RV+YhVejJBO0aeSNgRxDQaFgxQrTHXNwLIRZEKIbcBqrpjilFJ+s1QGzpXFEGQnTpxgaPgvKRafvLTs3yL/ymPJwIKOWyrqHMG6AZOjJ4bImQsTiL8rkuLV0X5uNrpotTuIJU+ipstfg60UOL4qUuE2+oxVXJB19NhhLhTCnM0HOZX2u7WQXFxucKJ+ndWNYZQqL90BQada+ttxISVNjkJdEbxpm2KiwMBwloFEflYzCl5doSHmJxL1ooV08n6VuA7dqoPliq5ZsRQE2WyyLP8Z2AYcAy5GbUug7IJssTh8aBWbNutIOV5H4I35/8MT4r9TXCpzbzPQr0j66zVitfXcOmRx4vhQyWuf7U2E2JsIAeuA8XrBa/05fq5ykFu93ayX56hJn0JPnLvUZ3OpcaXwapdN7MvW8vRYFR2jXhi99v4uLi43Bn5DZW1TGE+Vj4GQyhnVpv+SqFmYc74UYjzb3gf4FKj2QZuPoAOrLIVo3rnkTXOkJBz1oAQNsn6FAR36FIcx8fJ20zDnzCKXsjCb+aI7pJSbSzWgEKKZ8ZIZdYwLvC9IKT8rhIgBX2fcE3ceeKeUcklcCuNxh5vU+zCtnwAQKRzgLdFBvp6YW++0cjCiSJ6pUQlX13HrsM3p40MkcgtXqOps1sc/ZFfxD6wC7gag0jB5oHKIuwO9bBbnqcufRbNm0SdlWsanIsdvIR3Ga4DK8Yccn64cXzd+chLSQQqFrL+JPmM1p51GDuRreWq0irOjPld4ubisQAxNYW19iGCtn+GQRrvusOeSjnEop6hJK3DMcMZLIoV1aHq5R94VXjcSsxFkLwghNkspj19701lhAb8vpdwvhAgB+4QQPwHeCzwupfxzIcTHgY8DHyvRmPNm//4Gtu/w4TjjAuKB9P/kh+pnSV1nS6VykxSSZ6oU/PfUsGtMcu74EMPp4qKMPVzU+Y++ev6DeuCWRRnTxcVl+SMEVAY9VIQMgkEDza/jeBXyHoWEBnEVksztXFwADlzyLi2v87nLjcVsBNm/MC7K+hn/7grGe1lum8uAUso+oG/idUoIcQJoBN7EeAHai2M+yRISZGNjDvAqxmvjgs/q5KFoO19IrC2rXXMlK+DZCoH3rmpuSUL3iWEGEiuzZMJSQVMEdRU+YlEvnpCB7VdxBOhpi/Ronu7BTEkKA690DE2hIeYj4HPj/5YaQhF4fBqKX8P0KGR0wagOg0LSLaD7qj2unEJ0vUQuy5vZCLJ/Bn4ZOEKJbx8msjZ3Ai8BtRNiDSllnxCiZpp9PgR8CKClpaWU5lyTvXuquPW2ELY9Xgn0rsRn+I7xZQYWoKXSYpEX8FwExO0xVtkK9TmJmigyFM/SGc9QgsoZLi8j6tepjfkIhj2IgE7OrzCkQ68iOTPVNSU43rmBTUEabUF9AfxZm/xonr54lnhq7sWBb2SCHo36Sh/hiBclpJP1Kgwa0Ks6nHQv3ssE9wTksnKYjSDrlFJ+t9QDCyGCwCPAf5FSJsUsM0GklF8AvgDjWZaltmsmMhmJbd0P4jsA6DLBe7wv8VfFWxfTjAVBCsF5TXI+BIQMaDIIyCjri4JI2qYwkqezP81YdgU0SJwjmiII+3VCPh2fV8Pj0dC9KoqhYHpVEh5BrybpV2ByoZDZf417VEmPH/ArUOWHdX5ijqDFFAQzNk6ySHwoS89IdsWI6cqgQW3Mhz/iQQZ00l5Bnw6DimRySWM33sbF5XpQgZiu4Cl/zdRZ40iImxJzGSTdvZzZCLKTQoivAd9jfMoSmF/ZCyGEzrgY++oVxxkQQtRPeMfqGe8IUHZUMfmbuGdPhDvvimFZIwDsTP4lbd5vcCa//P751yIj4LBHgkeByvGLf5MtaMyDnigyGs9xYTCNdYNf+YMejeaaAIGIB0VXcHQFSxcUNIWcAilVklQkKTFeNXl6FuZzGlEkIx4JHgGx8Q4OHlmBdmP/WwCwBfQI6Jm0dAX84S4uc8AjBDFdUKE5RBWLqJInLLKESRJkjKAzTMAeJGD34TO78di9iOLy+z05qGT1VlLGOhLqKkaUBoapYkiGGbC89JsKQ6az5M4UsxFkPsaF2M9dsWzOZS/EuCvsn4ATUsq/vmLVd4FfBf584vk7czl+qWm0Y9RX1dI3NABAPi/J51+Fpj0CgJAW79F/yKfyC98lfinQrUq6A0BAhwYdrwyzxhJE8xItbZFNFOgfzi5aokCpCXknxFfMRzGo0ecVdCk2Q1N6cJfuVHVBjD9cXFxKiy4ENwcsdqmnqbE75nQMRyjkRYS0qCBNiBR+UtJH0jFI2BpjtmDMkuRncbPrVQRVuqBStYmpRSqULBFSRBgh5MQJ2n0ErW785gUMZ2j8an6DRzko2ATNdoJmO/XTbGMLHyljAym9jTG1mbRSz3jb7vIxoyATQqjAkJTyD0o45t1MxKQJIQ5OLPsE40LsG0KIXwM6gXeUcMw5oyB4RWEjjyhxHGf8Arxnt597762jaI5PPK1LfpFbgq9lX2blXQHzAk7oEnQgpEG9BgSokNBoCsI5ByVjkR4t0DecXdBSG9dLxKfTVBPAH/OSD2r0eaFHOMQn1RySuFNcLi4rm1pD4Q7fCNudl1iT/iZGaujaO5UAU0TI6w3k1Hpyag1ZpRJwCNl9BKwuAuYFDHsIFqbm9w2NKnNECweJFg7SDCiKF3hbWW2aUZBJKW0hxM2lHFBK+SzTX+FeXcqxSkUkrnHL+i3s6TwCgGVBKnUvHu/XL23zi/LL7KPs7T2XDKMCRg0JhoDIuDcNglQ540ItlHMgY+FYc/MyXYo5FBOvBZe+VZfej7+Z2Ga8aplQIOdX6TGgV5UMXDqiG1/k4uIyjgpsC0hu1c6yqfAYNbmflcWrpMsEejFBiBOLP7jLojObKcuDQojvAg8DmYsLl0LrpMVk67lqzsSijCbGANi71+D++5spFLsAaMh8n1eHH+LxlLeMVi59hhQYuhjvFC1X2YGlFjng4uJSbmKawh3+JDvYx9r0I3jTPdfeycWlhMxGkMWAYS72xBlnRbVOAlBNwT36Vr7LswA4DgyPvIJg8N8vbfOW4t/wlPg4lnu9d3FxcVlQvIqg0YBGvUCdkiRAdk7HMSjSZj5DXeZRlIQ79+dSPq4pyKSU7jzcBDXdHra0beBY9ykADuxXec0DbRQKZwCoyO/hzdER/jMRK6eZLi4uLsseBagzFBp0i3otTQ3DVMtuKq2zRIrH8ZsdiJyE+XRfm6eFqhqc474S286wdBODFBTFi6J4UBQP45O4ywWJbSex7Zlz3pcis2ku3gR8jvFgfAk8C3xUSnl10eQVwC09DZzzdZHNZQFBf//tVFScubT+wcz/5P9v786jJMvuws5/f/dtsW+5VGbWvnS1elW31N1CQvsCEgJhZMDDCB+MYQzGYGzGxnPG2OBhZg72GI85x9g+8ggLsMGWxzASQrtAarW23ru6q6u7qmvNrMys3GLf3nbnj4hauyorKyuzIpf7OeedF/Fiu3nzRcQv7vb7vPXbNCPTTGYYG03OUtybDDhszTLEBdN7vcFYRBTiKQr+K+T8o1jd9oaZEShYeN4hwvAA8wslTp+yaDZXfwKJQDotZLNCKhWTTMUkEiGuG+A4PrbVRqk2SBOtG8RxjTCsonWIZSVRKomIh0gCEY9ewksPrV20ttGxSxTbxLFNFFpEkUUYqt4WWYSBEARCEIDvg+8Lvq/pdjXhFkgIYlmQzQqZLKRTmkQi7Nevj213sKw2opqgG8S6jtaDP9FW0mX5H4E/4vKsx5/sH/vAehVqI3PbirePPciX2t8B4KUXFR/4vvvodI4CkApO89cKZ/i96t5Vv4YCXCVYAhb9/cUNUP29JaDQ2AJKNBZQjhRT3Y36q8tYqRFHccgLSKuA2TDBeV9RXuUEiO3KE+HupOZuZ4ED+lV2db9JsfMkUjdRmHFzgoWXOEwQ7GdhvsjJUxbt1pXnzu2dR1pDo6FpNC4+j6IXVLlAGije1vOvsBQ3uLz5RRFUKppK5eIRu78lgNzr7m/bNu98xx0r3nWtJCAb0Vr/xyuuf1JE/t46lWfDqccVxFPoK4KcPafTHNi/l1MzZwE4d/ZNjO44eun2d9b+d/7M+QTzwc2/QIcdxV2ez35rgd36JGP+cwx1voMVrb4dvuo9yKnEhzjKfTzdzq6oHMZgpC3hrkTMPqvKbplkIjzKSOe7JP1zcM1Sbr4apurdQ9W+i0VrN/PsYC7OMxMlON9VVDdZovu1JMDBhPAGt8ZBOcke/0lG2o9jNQfWn2VsMiI2nncY39/HwnyJkyeFzlXpfbdWwGJsPCsJyBZE5CeBi6PXf4LeIP9todqZZ9J+jsP+G696P37P0gEmnWmCIODVV2HP3ofpdJ4DwImX+FjqWf519aFL93dFOJCAg06NvTLFRPgyO7rfJuWfft0X7+3Kd4/wcPcID9NrzlxMvI2TiffzYnw3z7RTVE1Lyx1nC+xPCAedBntkhon4OKPdp8l1jyCNlX3Qu/ECI+1vMMI3OHSd231rlKp7H2XnIGW1k5CtnzzbImI8PMpY6y/w2gsDHE9krDelPCwrg5I0otJAEq0T6DhBHHvE8erGOcXaYmE+y6lTQnfwvVbGNraSgOxvAv8G+L/phSTf6h/bNp576QsMv3U3pdnLg/VTVcVbD72Rx6eeBuC1Ew+wa/dzl25/c/Wf8zfzv8UOfY4d/jOUOk9htTqve+47YajzLYY63+Ix4KexmE+9hxPuezgSHeS5lktri6c+upME2OkpDrod9qp5duqTjAXPU2w/idVa32jBjeYYac8x0v7LdX2dzcy289j2IbqdCdqd5KCLY1xDayHwLXzfotMR2m1ot4VmM94S45oMYzkrmWV5DvjIHSjLhvblb/8HPvq9v4ozfXnh0LtO5jixc4yZhVlOn9YcOvQ9dLq9sWVK+7yv8iuDKu4NKSJ2tL7CjtZXeDsQSYKZ1PdzwnkHL0V7eLFtmwkJKzTsKA56AfusRXbrs+wIX2S4/V3czgLcZuxt20VsewxwiOMFgmAOrc030q0QcfG8g4ThHqrVItPnXWZnzbm9eehr9oaxtd0wIBORf7rM47TW+jfXoTwb2p8/87v8lQf+Piz00v+IFt7RvZv/V80RxzEvv3yYAwef5HanMrvuTpSk0LpDrNvEcYc4aqPXIT+GpTvsan6aXXya9wBabOaS7+a0+3Ze0Yd4oZ1mbhuPQXNEGHeFcSdg3Kqzm0nGw5cZ6XyXlH921d3NIg6uM46oUaKoSKeTpVFPUK7YzM/pa8augIimVFKUSpDN+aRTHVy3jrIqaL1AEFwgjgfTArsxCJ63B/R+Go1hLlxIMTl57Wwx88VuGCuV8BIUswUKiSyebJ7hDxoIdUSgQ8I4xI8CgigkCAP8MOjtfR+tN97nwXItZM3rHEsDPwMMAdsuIOt2mvzF6f/E+3Z8DN3qfdLn5m3efPh+njp3hPPnNffc8w66/tdX8eyKROIxJs8d4pVXrp++x3EgkRA8j0ub48S4boztaBw7wkt0SXgXiPVZfH/mlkshOrzUgvY9/WNV72HOJt7DcbmXI90Spzp6y3y1pZQw4cG47TOq6gyzyJCephieJhccJ+W/hupEq2rxcuwhLHsMrYfw/RytVppa1WVxUVha0lz/8+D6Nau1sLioWVyEXuJQB8gCE5fuk88rSiVNPh+SSnURtfUDaa0V5aUk586pa5Yg2CpnqGGsn0QiQSlbIO9lyUuaXJgg03bJVC3cqoLqoEu4fkJbE7ua0NaELsTeoEu0TECmtf7ti5dFJAv8MvDTwH8BfvtGj9vq5hfO8ELp6zxovR36XXv3nRrmtaEi5WqZI0f28YZ7voXWK0uibdsFdPw2XnppmIWF5b9EggCCQFOvX3lUuLxonw149Kb03kUmI+zaFTM01CSZWgB9tp/q6da+rPLd53iw+xwPAj8KdOxdTKa+n5PqIV4KxznZsWhE8YZa4tATYdgRSnZM0fIpqjYF6uSoUIzPUwxPkfNfIRFOscoFvlEqgeOMIzJCGBbpdDI06h5LZZuFeeh2b1TP6xMsVKsx1Sr0zofUurzGxmUCMMO4HqUUQ4USw6ni64OuioLKoEs4GHYoEApu/7o4aqDlgZuMIROREvArwMeA3wfepLUu34mCbRSWnelnpr78gX/s+BOMPLqH8YXdQO8f+07nPj7NE8zNxdz/wLsIgq8s+7wJ7zDlysO88LxLEMB6fKE0Grrf2pbpb/tIJGD3bhgeaZNKLSAyie+fvaXxSYlwirtqn+Au4IP9YxohUCV8a4iuNYRvFelKno7K05EsHUnTJk2bJC0StLVLWztEq0zm7UlEQVrkqZOjTDaeJxPNkg6nSAVn8eL53oKStzFryrKy2PYwIiXiKE8QZGk0k1QrDouLikrlRiGoCQ4Mw7jzkskko/lhhpw8pShDoeGRXbKwplf3OWvcWcuNIfu/gI8CHwce0FpvvjwEa0HG2HP/+zn34pevOvz4U3/ER97+90me78XXI1Me9991Ny9Nvspzz+7kwTd6xPHV0YCIg+u+ldOn9nLy5B37C67S6cCJE3DiRBLYDezGcd7Grl1CoRiQTrXxvDrKWiKO5wmCCytq7RM0bryIGy+SWVnj4EDZdgHLGkYoEsV5Aj9Nu52g0XCoVhXlMnQ6ywVWG6k90DCMQbCs1acUiuN41eOYRIRSvshIusSQylHsJimUXRJlBduqyWRrWa6F7H+m177wa8A/FrkUYQu9Qf2vX+p2i5o7fz8j+yaZP/PKVcf//Nu/y1999B8gF3qD7d80NcHp1CTlcguR9wKfB8BxRgmCt/LC83mq1Y3XehIEcPq0htM2vXFJl8cmiWiGhixKpZhc3ieVbOG6NUQtEUUXCIIFBh2ciDjYdh6lcohkQGeI4yRhmCQIPLpdm07Xod1SNBpCuRz3WyVvZOP9jwzD2BhKhSKHs3vZt1ggs3R7OR5j0cQWaKu3jy2IlUZbEPX3sdJEotFKE4vG9S3ySzbWrGn12mqWG0M2+A7VjUILQfh+UrlZWrXKpcNR5PPFY5/gQ/t+Fl0L+2mV3sgXW9/m6adGeOvbHmF+7jBHjljEMWzGL3qthYWFmIUFuJzWowDsAcC2IZtVKKURAaXk0mURENVL9XRpf2nTKCWstk7CUGg0oVHXtFe8vJde9esZhrF9ZTNZDhf3sr82RGHWhtm1eV6lBRUCoQmujJUtDGsAnaZLceKjdJq/TxxdXn6iWr3Adyt/zmPehyCI2X06xcED+zg5fYYvf+meVb1WOpWm0+0QRWu/zMVaC0Mol69sIbuVgOd2giMTWBmGsX4SiQR3jezjQGuE4WkHWdheQZOkbIJCyGI4TbU5P+jirJwIShRKFCLW1ZeVQpBLx0UEhUJEoSybnQMuugnIbkH5QoFd932Yc0c+c9Xx02efZ+ShveyvvgGA71nYz6Q7je8vv0iV4zgM54cYThQokaHQSpJfcnCXhFhpWoWYWjag6rYp6wZL7QqLtTLB8v1thmEYxio4jsPB0b0cDHYwet7DqmyfIEwyNn4uYME/z6np55g6/fKgi3RH2a7H/fzgYMsw0FffhOYmDzF++E3MHH/2quNPPv9pht42QW4mR7KmeOuhB/l6P60SQCGXZyRTomTlKAUp8jWPdFkh9eu/4VUsZJYsMksWEySAIrAbLZp2NqZeCKi4HSrSZKlTZbFWptPdzguDGoZh3DqlFPt27OYQ40ycT2Gf3B5BmGRtulmfBX+KU+ef5fzpVwddpG3PBGSr0Ki9g9zINLX5qwcSfP7b/54ffes/wprRHDqZw9n/PWTbDrklB2dOYO72X1u0kKpZpGoWO0jQG8/Va2jtZCLKpYBJb5FTS1M0mttzYqxhGFuTUopirkAhmSNvZ8jFSbK+R6bu4K4yVawKBOv01g/CJGvTzXWZ60xycuoZZk+/NugiGdcwAdkqBF2L3NBHaFY+SRRc0S2pNZ97/t/ykXv+LiwF7DuVvqPlSjQsxhsW4+zkUZmgMhpyPlfjTGua2cU1iAaNO8pxeulKTBe1sZ0sF3Qla4Jqbf3gaa1IwaGWKvPK9Hc4deSZQRfHuAkTkK1SbTHDznt+mHNH/ttVx1utGo9Pf4p3Fn4U3RncoHzRQvGCQ/HCEPczRDsbMzPa5Jye58zcJGFoElUPiuM4ZFJpMl6alJMkrRKk8EiFDknfJtG2STQFp656XdS5mFo+pOK1Kes6i50qi9UlE6itkG3bFHMFSsk8BZUhFyXJtly8jsUN8lcZA6I0eA1lgq7bICWbirfIsclvcfa5I4MujnELTEB2G+Ymd7Pr3u9l6uVvXnV8euY4Lxe/yz3qkUEv0XVJsq44UM9ygCyRs5/5cb/XtVmeot6o3/wJjJuyLItMOkMmkSLjpEhdDLQil2TXJtm2SLQUTl3BCqtctJCqWqSqFmN49Lqoe2MJW3lNLedT9dos6QZLnSoLlcVtG2znsjlK6QJFJ0tOJ8l1E2RrvVYVaZgveGMLG7Ipu3McPfsE5585NujSbDrKsrC9wSezNAHZbVqaf5TixBTl6bNXHT/y8ld5LfUU6XSJTLJAMpkj6WXwnDSencK1EjjiYeNgaxsVW0goSAB0Y3R3/SI5KxDGznmMMcGj9Lo2p/M1luI61W6DarNKa+WLe20LyUSSQiZPxk2SspKkLwZavk2iY5NsKtymguadKY9oIV0R0pUE49dM+mjmYxq5gEi2fuuPIKRbNpmKhTUvsIlm5xvGqgkwbLNgzXD01NeZPb22qV8s26YwtodkfhfKGiWKkmv6/OtKC1orYi2gFXGketdjQUe9fXzFPooEQbA3ei5L4+biUCHpD+MmP4nfvjpLdatVo9Wqreo7Yu/uBzm08xFKMoZaiCFcvy/XwpxNYa4ElC4dCzxNKxfRSAU0nC51aVMNm1TbNar12qZYI201MukMQ9kCJSdPXqcotBNkKjaJTZKEV7SQqVhkKre3grhhGOvMEsQSuHJToC2gvzK/lstbLDGamGqwwIsn/5KFU+fWpBjKssnv2Em6sBuxd+C3S9TKaZpNRfMO/cAcpI3Udm4CsjXQqiYYPfhRpo7+5zUbk3J28ghnJ3v9/14izT0Hv5fdxXtIt7Lo8vqPHXK6Qn7eJo8NJOl1lfVo0XSyMY1MRDPh07C6tOjQDDs0/BaNdoNmq3Wjp94Q8rk8Q+kiRTtLIU6Rb3lkyjbuosDioEtnGMaGJRCNCUcXnuCVE99Cr3aR6gGMXxSlKOzYRaq4C8seo9spUS9naLfVLWQ8MdaLCcjWyML50esmIV8L3U6T549+ief5EgA7Rg9y9963MOruxl5U69q9eT2ihWTNIlmzGMEFMq+7T6w03UxMOxXT9kLadkBLdWnpLs2wTcNv0Wy3iOLVtbRZysJ1HFzHxbUcXMvFVTausnF6HcG42sbRFnascCOFHSicQJGuWlhzG+l3kWEYG54tdHb4PH3685z/1uYYpyVKMbTzAKniXQT+OLWlLO2ORXtm0CUzrscEZGvoRknI19qFuZNcmOuNGbAsm8MH38q+kQfI+UVYDDdEViEVXw7awKHXymYYhrG5SNKiUizznaN/SuXEGiWxXEfp4jDF8TeA2kNtcZhGw6ZhlqTcFExAtpZukIT8VjiJJNmhMapz569e4+wGoijk2PFvcOz4N4Be69mb7/oguWoB3dyes+0MwzBul+QdZtyzfOfIn9DtbNzBVLbrMbznbtzUAVqNMRrlFAumBWxTMgHZGruYhLzd+CQ6vnFXoihFbniMdHEC2xslikq0GzmaNY9WS0iWIgqjF4g6rzB35ijRCtecujB3ks/N/S6WZwF5yQAAHVZJREFU5fLm+z/IvtT9yIVoQ7SaGYZhbHgjNqe6L/L0C59F6w2ybtE1iuN7yAwfJop2UZkrUFlUZuzrFmACsnVQvlBg9/0f5tyRPwMgkcmRG9mFl94Bahi/XaBRSdENLLrXLKB/cWRT6FssTE0AE6SG3kVhZJag/Qpzp48SRzdv+Yoinydf+AxP8hl2TtzNQ/u/j2w5i25vzdmRhmEYq3bFQP1jTz6xoofkd0yQKe1Z3Y9dAREbURYiNogFWL29tnuXUWhtofUV+1jRbqRpN13aU6t4XWNDMwHZOpmbvIuJe36aVi1Np+VSq7DqZROCrsX81E5gJ5nRd5EfnsFvvcL8mWMrCs7OT7/K+elXcZwEb77/B9jr3gNzpjvTMIzNTxIWUVHjqy6riY5CHfD8ma+uaKC+sizGDj0M6n6WZkt0N/6QMmMTMQHZOlqaLa75c/odm/mp3cBu0jveTWF4Br/xCnNnXl62ixQgCDp857k/4TvAnl3388a97yO9lB5oiifDMIwVk15+xk6qzULnPGdnXmTyzMvrvoREulBiaM9bqC0dZGnOXdfXMrYvE5DdRLFxmntHFznZGKXb2liBS9B2mJ/cA+whO/Ye8sPTdOvHmD/zyk3HPpybeolzUy/huikefeAH2WXfBeUYgo05ZmLTExBXgafQrqCtmMiKCCUg0D5+3MEPW3T8Bi2/TqtTo9Wq4Llpdu24h+HETpKdVG8NOvMvMrYJcRRxSairCjO1k5w8+wz1Uwt36MWFHfvvw06+kcWZUeYmzVI5xvoyAdlN2J0aY5/6p4ymc5Tf/7OckjdQr2y8pM5+22F+ci+wl9zO95EvTdGuvsz8uePL/nr0/RbffOZTl66XihOMDu+nlJsg5w2TkgxO4CJNjd5gAemgiKcgodAOxLYmUiGh+Pi62wuqgmY/qKrSbJVpNMrUG4vEq1xz7eICwdBbJPjAnoeZKB2mYI3gNGx0zXQ/3xLpdXPhmC/YDccS/GSXpeAC5+aPcvbkC0TH7+z57aUzjO5/C63mYarlJJTv6MtvSEptnveKRrNB52LclAnIVkg1awx9+l9RUhaN9/4kZ0pvY34dxmEVhmxG3TKRuDSiFLWG0L7F5Su6TYe55n5gP4VdHyBXnKJVfomFyddu+til8jRL5enr3pZK5dkxeoDh/C7yqVHSVh4vSqAagm5svaBAsjZBNqQazTNdfo3pC8ep1xeIVjBub710O02OHX+CY1weeFwojHNg90PsyO0nGxdQZdDdbRY8S2+9KBKK2I0JVYAvXTpRk7Zfp9GpUG8uUq1doFKdHej/0NiYhnYfJFV4M4uzE8yfH3xew/WklJBIWyQ8SDgRnvi4UQs3qOO0KziNBazqBezFaaz5KcTvDLrItyTO5InzI8S5YaJsiShVIErmCLwskZMisJKEysPHIYht/LCf+3LARA8gfcNaeeSRR/TTTz+9rq9R/8pXmPrFX7rube1HP8TUXR9makZWPYTBdhQ7hmNGWq+Re/6L2Gdfft19ouIowb4H6O44SDu3k6ZdpO571Goxob/ynwKpbJdMYZLm0kssTp1aXYFvoFAYY+/OBxjN7SOnSjhNG13dPF964iriotCwaszVz3B66nkWlzbpNCYRJsbuwnNSgy7Juot13Au0qhdMkLUNKcsmOzRKqjCGZa/ufBfl4ncPUl3IrnHpBsOyhXTWJp2ISEmLRFAl0ZjDKZ/HXprBXphCyheQTfzdvx4kkeANzz+3/q8j8ozW+pHr3WZayG5D8qnPc9dTn2ffoYeZefRjnFlIE65gDFYm7zCeqlA8/zSpJz6Hai+/jLJVnsMqf5UEXyV/zW3hxEH8vffRGT5AO72DKkXm5mPi6PVvtlbdo1U/BByitK9DJneO+sKLlKfP3sJffX2VyiyVytVTjjKZInt3PshYcT85awSv46ErG2AMlOoNDG4n2yx1pzl34WXOHX/xpuPulGVjOU5vs10s28GyHZRlo6zeXiwHpWxE2f1p7TaIjVyc1t6fzg42aIUWhY4vTnFXxLGC2CLWgo4tLNvHcRcJu9PUFs7SWFxBqnqtmZ45fvv1ZBgbRCKTJVMaI5EZRdlDRFGebjtDs5KgGwjdFbwt1lo6Z5NOrj6oiXXvh7zWvctxrHuX496mY02s+/tI926PwfEUmYwi5YWkdJOEX8Grz+IuTGLPnMCaPWuCrU3KtJDdxHItZNeKRnax8O6f4WRrgs4V3YzKEkZHhBH/HPmjX8F99an1Ki4AcaZA65EPUR5/mAudArWbJCPPFNqksufQcRMdh71Nh8RR0NvikDjsXY7CgDjs7aMgIAz8m87uvJKXSLN31wOMl+6i4I6gtLW6P1I0MTExEbHubZGOiAiJ45AwDonigDAKLu3DsIsfdpicPkanXVv26ROZLPnRvXjpCaJ4hFa9QLvura6sa8hLBWQKVSw1h9+epnrhDO368n+LcTVRimQmh+2ZdF4bjVIWiewQbnIELQXCbo5mLY3fcQZaLhEoDtmUnBr5peOkjj2Oc+7VO14OLWKCrXWyEVrITEB2E7cSkF0UJ9JUPvCz1LN7KM29SPrJP0PVBreMcrDvPuoPfoDFzCEuLNn4a7zMheNFpHJNHK+Bkgqhv0SnPkd9cZags/HHHnipNPkde/EyO9G6F3y1aolBF2vFUrkOqWwFJXO065NUZs9uinpfUyIk0hkSmTxuMoftZbHsDKLSaJ0kihKEfoJu26Hbcri8BLNhvJ7tKIaGYCieJzvzIskjf4mq3qHZncZAbISAzHRZ3sSFURfZvRM9eX7Fj1GdJqU/+x1K61iuW+GcOUrpzFFKwEHbpfum91Pd9xbmGGNxPrztJXyCrkV1PgfkgIlLx60kpIe7JDMtHLeOjssE3UVa1Qs0FudXtKjtbRNBKQtlqV6Xo22TGZogkdmJllFa9QLNaoJaRVa9cO+gtWoJWrUxYAx4ECupyY93UNZ2GFMl+N1ekKVjRacLne6gy2RsNsm0zXDep9ieInPmGdyj30D55kQy7iwTkN3E2ULAr32szK8ff4y9n3kWws39JadCn+STnyP55OcYA6KhcZpv/gHKw/fS0Un8WBEEgh+A3721SQPX02l6dJoeUAT2XDru5TWpXAdRq31+QceC1kLc3+tIoWOINb1jkeLalhAN1Ku97fIzDZYIWI7CdhSWBbYtWEpjqd7f0mxxVRf4Cp6RRsV0xxlbk+0osnlF1vVJR2WStRkSF17Dqs6jV7FSvwSdgXQ/Gsa1TEC2Ag3x+Yd3P8tbf2kXv/wVF/Xi1hkwbS3OkPvSJ8jd4PbYdtH5IeLcEFG2RJzuTR+OEllCN9ObQmyn6EiKduTQ7gitRnTdSQVX0lpoVjdm0JBI26RTkHRCkrqJ51fxmvNY7erlNd00yFUf/rqftUVfuo9cPA5I0EV1W0i3iXR6e9VqIO3GiqaUx4k04cRBwtG9BMUJuukROk6ejqRo+TbNlt5wCxcbxmqJQCZvk0tGpKmRas2RWDiNc+5l7KnjZhyVsSWZgOwWfDsxxXc+DL/68KM88qfH0PXlZ0euhrguzcfuJbKFZK2LXWki5Sq6WutNvbnDVOjD4gzW4gwrHVarRYiHdxGO7SMoTRBkd9BNlug6WTo6QTu0abWg0wpXl5j3eqT3IS4iiJL+5f51oXcMEAXJxOVgK+FXcZvzOOUZnPmz2OdPIN32GhVq7ahOE/fUEdxTR254nzidIxo/SDC6lyA/Rmxtk7e31v0v6P7+4mxZrREdczFIlotT2ojXPdWOsToqCnEunMQ5/dKGfB8axnraJp/Ya0cL/POdz3Hg54v8k+8eIv3482vyvLJznGPv2M3Hd59kyn7pdbfb2mZPlGd3kGPcTzLa8RhqW+SbkGmEJGtdnGoTmZlHN5prUqbVEq2x5iex5idZbl5ibLvgrC4vnMQROo6QKEJWuQL+VqOaNdRrz+G8tv4DUw3DMIy1ZQKyVTpll/np7y3z1+97kI/86Sx6du7Wn8Sy6Dx2H194SPPH+WNoufFiOqHEnLLLnLLLkITXLUjWJxoe8/fytqVhDs8IQ6cW4dQ5iDZe0KJCH0J/1Y8f9NgvwzCMzU6yGSjmCfNpuvkkraxDLaMoJzXziYDZRJuG2njpApcjgKUVClBaUCgEjaUFdfE4gmjpXY4F13Z5w2CLbQKy2/WHhZf5zE8l+M2jjzL2+WdXFPio4SFOv+sgn9h/jlec16/Mfzu0wHe983x3/DyMA2+CfJzk3a3dvHk+y57JLukT59FzZgq3YRjGlmRZSKlIPJynU0zTyLtUs4qlVMxcImDWbTPl1jlnV2lIB9hmy+RcR8JK8KsDLoMJyNZAVXX4uw88x3sP7ufnvhAjr14nLZEI4cP38LU3u3xy6Bi+PHvd53owd4CfaUVkgg5LXoolN0HZsikrWCKmHPuUozbloEHFrxGvIItqVXX4dOYEn84A+4F3wuFgB++qTXDfBYcdp6vYJ86i2+ZNaRiGcRWlUKUi0UiR9lCaesFlISfMpgNqzupajgQhHVikQ4t0oEgFQqILXqDxujFOJ8TuhtjtANXuIu0utDu9KdeuA8Ml/KEsrUKCWtZmMaOZTQVMJVqcdquctsuEUmEla/lYYpGwPJJWgoTlklAOKeWQEJuEWCRFYW2i/gi5Zt+7LIC+tL98j8vHXDXYxYfBBGRr6i9SZ3j8RxT/+Nxj3P+nL6LbbSSXY+add/MHhy/wtHfj2Zn3Zvfxd5oh73zhayt+vVgUlVSJcrrEUjJHOZFi1k1xyrF4LWpxsj1LI7j+eLLjziLHhxZhCLi319U5pPMMRQlKUZJC5FIIPXK+TTa0SPuKdBcSXU2iG+O2I5y2j9X2sWot9IV5CDZXs7ZhGFuDeB6kVjdrWyxFXMrTGcpSL3os5YTZdMhkss2pRI0T9gJdqQLVmz7XndEFZvrb62WdDAe8XYzbacbEZiKMGe+22dGukfI7JMIOyaBDwm+S8ts40eqHjWwpdhJ+cMBFGOzLb3z3B5q/VnyALzTPUvVvnqImlJh/tvdZ7vuFUd5buZdPDr9CXd14kPXd2b38Qlvz3iOP33LZlI4pNRcoNRc4eIP7zBZ2crK0m9cyRU7aipNRk5OtWZph66r7aYEFabKgmqx4OuUVLIQDwQ7u8gvsa6aZaNgMVWOySx28+RrMzqPbm2TWlFK9cRXZDHEmSZj28NMuodtP86R179eX5tKPLekveyEXl75ALl/WoJUQW0Lc30eK3mUFkYKof+zKLVQaOxaybUi1Y5KtCLfZxW50UPUW1OoDn8BhGOtGBCnk0aU8QTFDK+fRyFqU07CQipjx2ky5Tc46NeasBnA7s96v/9nuWR4Ft0jRTlOwPIriUtBCUWsKYUhi1ROKhK5SdJSiLYqOCB2ht6HpENPRER0d0Y5DOnFAJ/bpRD5Jy2PMzTGuEozHwnjgM9ZpMNFYYrwyTbp7bvXVYAyUSZ10M8c+C//1YwSWy+MH3sJnsxm+Xj1OEN9ea9ChzG5+oWvx/uPfuGY9q/WnEWaLu3ittIuT6QKv2YpTUZO5oEa5W8OP1+cX064wz91Bkf2tDDsbLsM1jRXe4b9dCR1PaHpQT8RU3Iiy47PodJm328ypJvOqgd4kLfSuthiPsuyI0owGCYYCj2LXIddVWPEm+SNugxaIRRMLvU1B1L/++n3cC3aJiUXQA89yb1wrUjDrtJl06py1K/iyfMCTtBIcSk9w2EoztMp/pwXkopBC4FP0WxQ6DYrtGoXmIim/ddPHG1uEnYRfm133lzGpk9aAE/m878Q3eB9QTRb40oFH+awT8Vz15C2tDr0/vZO/HXh8/0uPo1Yw/ms9CJrx8iTj5UnecZ3bm16WpXSRSqpA2cuw5KUo2w5lS7EkUNYh5bjLUtik7NdphSv70Jqyq0zZ1d4s0aE1/ZO2LV8iztoVztoVll1jxDA2ubHkCHd7wxzWNofbDe5emmLvwmsovXUW6ja2NxOQrUK+XeHHjn6ZHwOmSnv47O77+Wy0xNnm9A0fszc9wc+FST589OsDC8RWKt2tk+7W2b20sqbvRiLHudJuzmVHOJtIcc4SzkUtznUXWepW1rewW5wStaKJG4axVXiWx8HUOHfbWe4OIg7XFjg8f5J8y3TFGVubCchuIhILa5nbdy2d4+eXzvHzwJFdb+TPRnfzheZZKn5vAOiu1Bg/F2f5oaNfw9Ibby2wtZDp1Lh3+ij3Xue2eiLfD9aGOdcP1s5GLSY3abCmROEoB0fZOGJjKwtHbJz+3haFIxaOKGwUjigchKRYpBDSWkhrTSqOSEUR6SgkFfqkQ5900CHpt0j7bVLdBqluE0tHBMrGdxJ0ba+/d+laLr7t4lsOXcvGVzZdy6arLPz+tg16LBHA1ho7jrG1xtLR5etxhK1jbB1hRb29E0dYUYStI9QWfT9uZk4UMrF0CkufGHRRDOOOMwHZTXw1epgnSv+Sv538EmPTX0WW+RB/cOoFHpx6gV9VDk8ceIyqm+QHj30NO97cCclvR7ZT5b7pKvdd57aml6Vrr26l/lgsQtvFtx18y8W3bHzLIbCcXkBi2ZcCk0AUvlL4oghEsAEvjvDiGDcO8aKovw/wohA39PGiADfs4oU+XtDpX+4OpHXTiUOcboN0d+1TdRmGYRgbgwnIbkIDfzA9wR/wN3hT/kf5J6Pf5I3zn0F1yjd8jBMHvOe1b965Qm5Sva7RQZfCMAzDMAZPDboAm8mz1Qw/cuL7eXPzd/iTnf+QbvHuQRfJMAzDMIwtwARkq1AObH7l5MPcPfPr/J/Dv8X8xHvRYqrSMAzDMIzVMVHEbfr41B4ePfWz/I+J3+XF3R9De7lBF8kwDMMwjE3GjCFbI98u5/mh8ocZcb+PX9/zPN/X+DRu5To5LQEtFtgeKAdtucSWi1YusXKJlUOsbPQqY2WlI+yohRU0UUED/OayExEMwzCM5Wk7SexmiJwMoZMhsFJ0rTShrG5SEvQ+qxVRfx+jdIT0Z/9eub+0xSFKR8TKIXCydK0MHStDS6VpSoq6TlHVKSpxkqUwwUKYZD7wuND1mPVdFnwHWyBhRSRUTFLFJK0YT8UkrBhPot5lFeOqCE9iPBXhqQj7Di9evlYEjQig+9lT0CD945fu07tuWTb/0+CKCpiAbM3N+w6/+NqjiDzCo/k63VjRiiwakUUzVDQjRaTvbMNkwQkYcUOGXZ8hO6Dk+BTsDnnLJy9dsqpDRjrkaJDRTdJxnURUxw2qOH4N1a0igVmx2jCMjUPbCbq5vVQSe5i2d1LWWVaTeUajqGuPapSgHHssBR6Locdc12POd5jzXbqdrdGZ1AWa0db4W9ZawlEmINuqtBaerGyM7stK4FAJHE40V5d8FyBtxexKdNiZ6DLmdhh12ozYLUqqRVGa/WCuTjqq44U13KCK3a0inYppoTMMY1W0WITZXdTSe5m1d3IqHudod4Qn6yVeqKWJGia4MLYOE5AZK9KMFK82U7zaTN3S40Q0Y27ArkSHiUSHcafNiNNiWLUoqhY2qwvWbCLStEjpNkndJBE1ccImTtjAChoov25a9bYpbblgJ4n7W2QlCPtbIAl85eFLgkiWW/LZGIQImylGONYd5ZlGiSerBZpzJugytgcTkBnrSmthpusy03WhemdbDB2l2eH67HB9Rr0uQ3aXouPjrDKptBKNJxGOxLgS4hLjSNTbiLAlwiHEIcYmxJYQW0fYREQoQmwCLEIcAhQBFoG28bHwtU2gLXyt8LHxYwtfWwRaEQGRFiKtCLUQI0RX7CMtRAhR3NvH/dtW0Xuz6cQI9dCiGtqUA5tK6BBshxQFhmFsORsuIBORDwK/A1jA/6O1/q0BF8nYpIJYmOp4THU8IDvo4hiGYRjGDW2otmARsYDfBT4E3Av8hIhcL0WiYRiGYRjGlrHRWsgeA17TWp8CEJH/Avww8PKgCpRLONy/c2MMzjcMwzAMY+0l7MGPKd1oAdlOYPKK61PAW668g4j8LeBv9a82ROTVO1CuYWDhDrzOZmXq5+ZMHS3P1M/NmTpanqmf5Zn6uQn5hTtSR3tvdMNGC8iuNxr3qqHJWuuPAx+/M8XpEZGntdaP3MnX3ExM/dycqaPlmfq5OVNHyzP1szxTPzc36DraUGPI6LWI7b7i+i5gekBlMQzDMAzDuCM2WkD2FHCXiOwXERf4H4DPDLhMhmEYhmEY62pDdVlqrUMR+UXgi/SWvfg9rfXRARcL7nAX6SZk6ufmTB0tz9TPzZk6Wp6pn+WZ+rm5gdaRrCb3l2EYhmEYhrF2NlqXpWEYhmEYxrZjAjLDMAzDMIwBMwHZMkTkgyLyqoi8JiL/y6DLsxGJyBkReVFEnheRpwddnkETkd8TkTkReemKYyUR+bKInOjvi4Ms46DdoI5+Q0TO98+j50XkBwZZxkESkd0i8pcickxEjorIL/ePm/OIZevHnEN9IpIQkSdF5IV+Hf2z/nFzDrFs/Qz0HDJjyG6gn8bpOPABestxPAX8hNZ6YFkDNiIROQM8orU2Cw4CIvJOoAH8gdb6/v6xfwEsaa1/qx/YF7XW/2iQ5RykG9TRbwANrfW/HGTZNgIRGQfGtdbPikgWeAb4K8DfwJxHy9XPj2POIQBERIC01rohIg7wBPDLwEcx59By9fNBBngOmRayG7uUxklr7QMX0zgZxg1prR8Hlq45/MPA7/cv/z69L49t6wZ1ZPRprWe01s/2L9eBY/SymJjziGXrx+jTPY3+Vae/acw5BCxbPwNlArIbu14aJ/Omfz0NfElEnumntTJeb4fWegZ6XybA6IDLs1H9oogc6XdpbsuulGuJyD7gYeC7mPPoda6pHzDn0CUiYonI88Ac8GWttTmHrnCD+oEBnkMmILuxm6ZxMgD4Xq31m4APAX+n3x1lGLfq3wEHgYeAGeC3B1qaDUBEMsB/B/6e1ro26PJsNNepH3MOXUFrHWmtH6KX8eYxEbl/wEXaUG5QPwM9h0xAdmMmjdMKaK2n+/s54E/pdfUaV7vQH/dycfzL3IDLs+ForS/0PyBj4D+wzc+j/riW/w78Z631n/QPm/Oo73r1Y86h69NaV4Cv0RsfZc6ha1xZP4M+h0xAdmMmjdNNiEi6P6gWEUkD3we8tPyjtqXPAD/Vv/xTwKcHWJYN6eKXRN+PsI3Po/6A408Ax7TW/+qKm8x5xI3rx5xDl4nIiIgU+peTwPuBVzDnEHDj+hn0OWRmWS6jP+X1X3M5jdP/MdgSbSwicoBeqxj00nD90XavIxH5Y+DdwDBwAfh14P8DPgXsAc4BP6a13raD2m9QR++m102ggTPAz10c67LdiMjbgW8ALwJx//D/Sm+c1LY/j5apn5/AnEMAiMiD9AbtW/QaXj6ltf7fRGQIcw4tVz9/yADPIROQGYZhGIZhDJjpsjQMwzAMwxgwE5AZhmEYhmEMmAnIDMMwDMMwBswEZIZhGIZhGANmAjLDMAzDMIwBMwGZYRjbhvQ8ISIfuuLYj4vIFwZZLsMwDLPshWEY20o/Rcp/o5cD0QKep7dK98lVPJeltY7WtoSGYWxHJiAzDGPbEZF/ATSBdH+/F3iA3gLHv6G1/nQ/cfUf9u8D8Ita62+JyLvpLWY7Azyktb73zpbeMIytyARkhmFsO/1UX88CPvBZ4KjW+j/106k8Sa/1TAOx1rojIncBf6y1fqQfkP05cL/W+vQgym8YxtZjD7oAhmEYd5rWuiki/xVoAD8O/JCI/IP+zQl6qWWmgX8jIg8BEXD4iqd40gRjhmGsJROQGYaxXcX9TYC/qrV+9cobReQ36OXafCO9CVCdK25u3qEyGoaxTZhZloZhbHdfBH5JRARARB7uH88DM1rrGPjr9CYAGIZhrAsTkBmGsd39JuAAR0Tkpf51gH8L/JSIfIded6VpFTMMY92YQf2GYRiGYRgDZlrIDMMwDMMwBswEZIZhGIZhGANmAjLDMAzDMIwBMwGZYRiGYRjGgJmAzDAMwzAMY8BMQGYYhmEYhjFgJiAzDMMwDMMYsP8fidPVOxyeYa4AAAAASUVORK5CYII=", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "\n", "\n", "graph_pd = spearman_results_normalized_pd.groupby(level=[0,1,2], axis = 1).sum()\n", "\n", "graph_pd = graph_pd['U.A use stock'].copy()\n", "graph_pd.columns = ['_'.join(col) for col in graph_pd.columns]\n", "graph_pd\n", "\n", "\n", "dic_legend = {'n_initial_products_Share': r'$\\eta$: Initial inflow distribution',\n", "'n_use_1_in_use_Weibull scale': r'$S_{Uk}$: Weibull scale',\n", "'n_use_1_in_use_Weibull shape': r'$S_{Uk}$: Weibull shape',\n", "'reuse_split_split': r'$\\delta$: Split portable batteries to products'\n", "}\n", "handles_names_list = []\n", "\n", "fig, ax = plt.subplots()\n", "stacks = ax.stackplot(graph_pd.index, graph_pd.transpose().to_numpy())\n", "\n", "plt.ylabel('Normalized square of Spearman\\'s rank correlation in %')\n", "plt.xlabel('Year')\n", "\n" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3.10.0 ('master_thesis')", "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": "3.10.0" }, "vscode": { "interpreter": { "hash": "c03ddfdafb4c1d92fa49edfc22ae58f400f158d7001cc59884f00fb0326d64ae" } } }, "nbformat": 4, "nbformat_minor": 0 }