From 62b59e7426a938f8bb551d483fbdeef0e40fc3f2 Mon Sep 17 00:00:00 2001
From: Avdhesh-Varshney <114330097+Avdhesh-Varshney@users.noreply.github.com>
Date: Sat, 10 Feb 2024 16:09:32 +0530
Subject: [PATCH] =?UTF-8?q?Cardiovascular=20Disease=E2=9D=A4=EF=B8=8F?=
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 create mode 100644 Cardiovascular Disease Analysis and Prediction/Images/target_correlation.png
 create mode 100644 Cardiovascular Disease Analysis and Prediction/Model/cardiovascular-disease-analysis-and-prediction.ipynb
 create mode 100644 Cardiovascular Disease Analysis and Prediction/README.md
 create mode 100644 Cardiovascular Disease Analysis and Prediction/requirements.txt

diff --git a/Cardiovascular Disease Analysis and Prediction/Dataset/README.md b/Cardiovascular Disease Analysis and Prediction/Dataset/README.md
new file mode 100644
index 000000000..7028f1b8b
--- /dev/null
+++ b/Cardiovascular Disease Analysis and Prediction/Dataset/README.md	
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+# Cardiovascular Disease Analysis and Prediction Model 
+
+The Dataset used here is taken from the Kaggle database website. You can download the file from the link given here, [Cardiovascular Disease Analysis and Prediction](https://www.kaggle.com/datasets/jocelyndumlao/cardiovascular-disease-dataset)
+
+## About the dataset
+
+There are 14 features and 1000 entries in this dataset.
+
+
+- `patiendid` - **Patient Identification Number**
+- `age` - **Age of the patient**
+- `gender` - **Gender of the patient** - (0 => Female, 1 => Male)
+- `chestpain` - **Chest pain type** - (0 => typical angina, 1 => atypical angina, 2 => non-anginal pain, and 3 => asymptomatic)
+- `restingBP` - **Resting Blood Pressure** - (94-200 in mm HG)
+- `serumcholestrol` - **Serum Cholesterol** - (126-564 in mg/dl)
+- `fastingbloodsugar` - **Fasting blood sugar** - (0, 1 > 120 mg/dl) - (0 => false, 1 => true)
+- `restingelectro` - **Resting electrocardiogram results** - (Value 0: normal, Value 1: having ST-T wave abnormality, and Value 2: showing probable or definite left ventricular hypertrophy by Estes' criteria)
+- `maxheartrate` - **Maximum heart rate achieved** - (71-202)
+- `exerciseangia` - **Exercise induced angina** - (0 => no, 1 => yes)
+- `oldpeak` - **Oldpeak=ST** - (0 - 6.2)
+- `slope` - **Slope of the peak exercise ST segment** - (1-upsloping, 2-flat, 3-downsloping)
+- `noofmajorvessels` - **Number of major vessels** - 0, 1, 2, 3
+- `target` - **Classification** - (0 => absence of heart disease, 1 => presence of heart disease)
+
diff --git a/Cardiovascular Disease Analysis and Prediction/Images/age_feature.png b/Cardiovascular Disease Analysis and Prediction/Images/age_feature.png
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diff --git a/Cardiovascular Disease Analysis and Prediction/Model/cardiovascular-disease-analysis-and-prediction.ipynb b/Cardiovascular Disease Analysis and Prediction/Model/cardiovascular-disease-analysis-and-prediction.ipynb
new file mode 100644
index 000000000..4c7f9240e
--- /dev/null
+++ b/Cardiovascular Disease Analysis and Prediction/Model/cardiovascular-disease-analysis-and-prediction.ipynb	
@@ -0,0 +1 @@
+{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.10.13","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kaggle":{"accelerator":"none","dataSources":[{"sourceId":7159329,"sourceType":"datasetVersion","datasetId":4134888}],"dockerImageVersionId":30646,"isInternetEnabled":true,"language":"python","sourceType":"notebook","isGpuEnabled":false}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"code","source":"# This Python 3 environment comes with many helpful analytics libraries installed\n# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n# For example, here's several helpful packages to load\n\nimport numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n\n# Input data files are available in the read-only \"../input/\" directory\n# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n\nimport os\nfor dirname, _, filenames in os.walk('/kaggle/input'):\n    for filename in filenames:\n        print(os.path.join(dirname, filename))\n\n# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","execution":{"iopub.status.busy":"2024-02-09T07:40:34.980029Z","iopub.execute_input":"2024-02-09T07:40:34.980380Z","iopub.status.idle":"2024-02-09T07:40:35.325137Z","shell.execute_reply.started":"2024-02-09T07:40:34.980349Z","shell.execute_reply":"2024-02-09T07:40:35.324057Z"},"trusted":true},"execution_count":1,"outputs":[{"name":"stdout","text":"/kaggle/input/cardiovascular-disease-dataset/Cardiovascular_Disease_Dataset/Cardiovascular_Disease_Dataset.csv\n/kaggle/input/cardiovascular-disease-dataset/Cardiovascular_Disease_Dataset/Cardiovascular_Disease_Dataset_Description.pdf\n","output_type":"stream"}]},{"cell_type":"code","source":"import pandas as pd\nimport numpy as np","metadata":{"execution":{"iopub.status.busy":"2024-02-09T07:40:35.326875Z","iopub.execute_input":"2024-02-09T07:40:35.327527Z","iopub.status.idle":"2024-02-09T07:40:35.332794Z","shell.execute_reply.started":"2024-02-09T07:40:35.327499Z","shell.execute_reply":"2024-02-09T07:40:35.331773Z"},"trusted":true},"execution_count":2,"outputs":[]},{"cell_type":"code","source":"data = pd.read_csv('/kaggle/input/cardiovascular-disease-dataset/Cardiovascular_Disease_Dataset/Cardiovascular_Disease_Dataset.csv')\nprint(data.shape)\ndata.head()","metadata":{"execution":{"iopub.status.busy":"2024-02-09T07:40:35.334098Z","iopub.execute_input":"2024-02-09T07:40:35.334492Z","iopub.status.idle":"2024-02-09T07:40:35.361774Z","shell.execute_reply.started":"2024-02-09T07:40:35.334462Z","shell.execute_reply":"2024-02-09T07:40:35.361006Z"},"trusted":true},"execution_count":3,"outputs":[{"name":"stdout","text":"(1000, 14)\n","output_type":"stream"},{"execution_count":3,"output_type":"execute_result","data":{"text/plain":"   patientid  age  gender  chestpain  restingBP  serumcholestrol  \\\n0     103368   53       1          2        171                0   \n1     119250   40       1          0         94              229   \n2     119372   49       1          2        133              142   \n3     132514   43       1          0        138              295   \n4     146211   31       1          1        199                0   \n\n   fastingbloodsugar  restingrelectro  maxheartrate  exerciseangia  oldpeak  \\\n0                  0                1           147              0      5.3   \n1                  0                1           115              0      3.7   \n2                  0                0           202              1      5.0   \n3                  1                1           153              0      3.2   \n4                  0                2           136              0      5.3   \n\n   slope  noofmajorvessels  target  \n0      3                 3       1  \n1      1                 1       0  \n2      1                 0       0  \n3      2                 2       1  \n4      3                 2       1  ","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>patientid</th>\n      <th>age</th>\n      <th>gender</th>\n      <th>chestpain</th>\n      <th>restingBP</th>\n      <th>serumcholestrol</th>\n      <th>fastingbloodsugar</th>\n      <th>restingrelectro</th>\n      <th>maxheartrate</th>\n      <th>exerciseangia</th>\n      <th>oldpeak</th>\n      <th>slope</th>\n      <th>noofmajorvessels</th>\n      <th>target</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>103368</td>\n      <td>53</td>\n      <td>1</td>\n      <td>2</td>\n      <td>171</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>147</td>\n      <td>0</td>\n      <td>5.3</td>\n      <td>3</td>\n      <td>3</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>119250</td>\n      <td>40</td>\n      <td>1</td>\n      <td>0</td>\n      <td>94</td>\n      <td>229</td>\n      <td>0</td>\n      <td>1</td>\n      <td>115</td>\n      <td>0</td>\n      <td>3.7</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>119372</td>\n      <td>49</td>\n      <td>1</td>\n      <td>2</td>\n      <td>133</td>\n      <td>142</td>\n      <td>0</td>\n      <td>0</td>\n      <td>202</td>\n      <td>1</td>\n      <td>5.0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>132514</td>\n      <td>43</td>\n      <td>1</td>\n      <td>0</td>\n      <td>138</td>\n      <td>295</td>\n      <td>1</td>\n      <td>1</td>\n      <td>153</td>\n      <td>0</td>\n      <td>3.2</td>\n      <td>2</td>\n      <td>2</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>146211</td>\n      <td>31</td>\n      <td>1</td>\n      <td>1</td>\n      <td>199</td>\n      <td>0</td>\n      <td>0</td>\n      <td>2</td>\n      <td>136</td>\n      <td>0</td>\n      <td>5.3</td>\n      <td>3</td>\n      <td>2</td>\n      <td>1</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"markdown","source":"## Performing EDA to analyze dataset","metadata":{}},{"cell_type":"code","source":"data.info()","metadata":{"execution":{"iopub.status.busy":"2024-02-09T07:40:35.363507Z","iopub.execute_input":"2024-02-09T07:40:35.364048Z","iopub.status.idle":"2024-02-09T07:40:35.374659Z","shell.execute_reply.started":"2024-02-09T07:40:35.364006Z","shell.execute_reply":"2024-02-09T07:40:35.373833Z"},"trusted":true},"execution_count":4,"outputs":[{"name":"stdout","text":"<class 'pandas.core.frame.DataFrame'>\nRangeIndex: 1000 entries, 0 to 999\nData columns (total 14 columns):\n #   Column             Non-Null Count  Dtype  \n---  ------             --------------  -----  \n 0   patientid          1000 non-null   int64  \n 1   age                1000 non-null   int64  \n 2   gender             1000 non-null   int64  \n 3   chestpain          1000 non-null   int64  \n 4   restingBP          1000 non-null   int64  \n 5   serumcholestrol    1000 non-null   int64  \n 6   fastingbloodsugar  1000 non-null   int64  \n 7   restingrelectro    1000 non-null   int64  \n 8   maxheartrate       1000 non-null   int64  \n 9   exerciseangia      1000 non-null   int64  \n 10  oldpeak            1000 non-null   float64\n 11  slope              1000 non-null   int64  \n 12  noofmajorvessels   1000 non-null   int64  \n 13  target             1000 non-null   int64  \ndtypes: float64(1), int64(13)\nmemory usage: 109.5 KB\n","output_type":"stream"}]},{"cell_type":"code","source":"data.isnull().sum()","metadata":{"execution":{"iopub.status.busy":"2024-02-09T07:40:35.375768Z","iopub.execute_input":"2024-02-09T07:40:35.376058Z","iopub.status.idle":"2024-02-09T07:40:35.389140Z","shell.execute_reply.started":"2024-02-09T07:40:35.376035Z","shell.execute_reply":"2024-02-09T07:40:35.388108Z"},"trusted":true},"execution_count":5,"outputs":[{"execution_count":5,"output_type":"execute_result","data":{"text/plain":"patientid            0\nage                  0\ngender               0\nchestpain            0\nrestingBP            0\nserumcholestrol      0\nfastingbloodsugar    0\nrestingrelectro      0\nmaxheartrate         0\nexerciseangia        0\noldpeak              0\nslope                0\nnoofmajorvessels     0\ntarget               0\ndtype: int64"},"metadata":{}}]},{"cell_type":"code","source":"data.describe()","metadata":{"execution":{"iopub.status.busy":"2024-02-09T07:40:35.390953Z","iopub.execute_input":"2024-02-09T07:40:35.391984Z","iopub.status.idle":"2024-02-09T07:40:35.433056Z","shell.execute_reply.started":"2024-02-09T07:40:35.391945Z","shell.execute_reply":"2024-02-09T07:40:35.431957Z"},"trusted":true},"execution_count":6,"outputs":[{"execution_count":6,"output_type":"execute_result","data":{"text/plain":"          patientid         age       gender    chestpain    restingBP  \\\ncount  1.000000e+03  1000.00000  1000.000000  1000.000000  1000.000000   \nmean   5.048704e+06    49.24200     0.765000     0.980000   151.747000   \nstd    2.895905e+06    17.86473     0.424211     0.953157    29.965228   \nmin    1.033680e+05    20.00000     0.000000     0.000000    94.000000   \n25%    2.536440e+06    34.00000     1.000000     0.000000   129.000000   \n50%    4.952508e+06    49.00000     1.000000     1.000000   147.000000   \n75%    7.681877e+06    64.25000     1.000000     2.000000   181.000000   \nmax    9.990855e+06    80.00000     1.000000     3.000000   200.000000   \n\n       serumcholestrol  fastingbloodsugar  restingrelectro  maxheartrate  \\\ncount      1000.000000        1000.000000      1000.000000   1000.000000   \nmean        311.447000           0.296000         0.748000    145.477000   \nstd         132.443801           0.456719         0.770123     34.190268   \nmin           0.000000           0.000000         0.000000     71.000000   \n25%         235.750000           0.000000         0.000000    119.750000   \n50%         318.000000           0.000000         1.000000    146.000000   \n75%         404.250000           1.000000         1.000000    175.000000   \nmax         602.000000           1.000000         2.000000    202.000000   \n\n       exerciseangia      oldpeak        slope  noofmajorvessels       target  \ncount    1000.000000  1000.000000  1000.000000       1000.000000  1000.000000  \nmean        0.498000     2.707700     1.540000          1.222000     0.580000  \nstd         0.500246     1.720753     1.003697          0.977585     0.493805  \nmin         0.000000     0.000000     0.000000          0.000000     0.000000  \n25%         0.000000     1.300000     1.000000          0.000000     0.000000  \n50%         0.000000     2.400000     2.000000          1.000000     1.000000  \n75%         1.000000     4.100000     2.000000          2.000000     1.000000  \nmax         1.000000     6.200000     3.000000          3.000000     1.000000  ","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>patientid</th>\n      <th>age</th>\n      <th>gender</th>\n      <th>chestpain</th>\n      <th>restingBP</th>\n      <th>serumcholestrol</th>\n      <th>fastingbloodsugar</th>\n      <th>restingrelectro</th>\n      <th>maxheartrate</th>\n      <th>exerciseangia</th>\n      <th>oldpeak</th>\n      <th>slope</th>\n      <th>noofmajorvessels</th>\n      <th>target</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>count</th>\n      <td>1.000000e+03</td>\n      <td>1000.00000</td>\n      <td>1000.000000</td>\n      <td>1000.000000</td>\n      <td>1000.000000</td>\n      <td>1000.000000</td>\n      <td>1000.000000</td>\n      <td>1000.000000</td>\n      <td>1000.000000</td>\n      <td>1000.000000</td>\n      <td>1000.000000</td>\n      <td>1000.000000</td>\n      <td>1000.000000</td>\n      <td>1000.000000</td>\n    </tr>\n    <tr>\n      <th>mean</th>\n      <td>5.048704e+06</td>\n      <td>49.24200</td>\n      <td>0.765000</td>\n      <td>0.980000</td>\n      <td>151.747000</td>\n      <td>311.447000</td>\n      <td>0.296000</td>\n      <td>0.748000</td>\n      <td>145.477000</td>\n      <td>0.498000</td>\n      <td>2.707700</td>\n      <td>1.540000</td>\n      <td>1.222000</td>\n      <td>0.580000</td>\n    </tr>\n    <tr>\n      <th>std</th>\n      <td>2.895905e+06</td>\n      <td>17.86473</td>\n      <td>0.424211</td>\n      <td>0.953157</td>\n      <td>29.965228</td>\n      <td>132.443801</td>\n      <td>0.456719</td>\n      <td>0.770123</td>\n      <td>34.190268</td>\n      <td>0.500246</td>\n      <td>1.720753</td>\n      <td>1.003697</td>\n      <td>0.977585</td>\n      <td>0.493805</td>\n    </tr>\n    <tr>\n      <th>min</th>\n      <td>1.033680e+05</td>\n      <td>20.00000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>94.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>71.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>25%</th>\n      <td>2.536440e+06</td>\n      <td>34.00000</td>\n      <td>1.000000</td>\n      <td>0.000000</td>\n      <td>129.000000</td>\n      <td>235.750000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>119.750000</td>\n      <td>0.000000</td>\n      <td>1.300000</td>\n      <td>1.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>50%</th>\n      <td>4.952508e+06</td>\n      <td>49.00000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>147.000000</td>\n      <td>318.000000</td>\n      <td>0.000000</td>\n      <td>1.000000</td>\n      <td>146.000000</td>\n      <td>0.000000</td>\n      <td>2.400000</td>\n      <td>2.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n    </tr>\n    <tr>\n      <th>75%</th>\n      <td>7.681877e+06</td>\n      <td>64.25000</td>\n      <td>1.000000</td>\n      <td>2.000000</td>\n      <td>181.000000</td>\n      <td>404.250000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>175.000000</td>\n      <td>1.000000</td>\n      <td>4.100000</td>\n      <td>2.000000</td>\n      <td>2.000000</td>\n      <td>1.000000</td>\n    </tr>\n    <tr>\n      <th>max</th>\n      <td>9.990855e+06</td>\n      <td>80.00000</td>\n      <td>1.000000</td>\n      <td>3.000000</td>\n      <td>200.000000</td>\n      <td>602.000000</td>\n      <td>1.000000</td>\n      <td>2.000000</td>\n      <td>202.000000</td>\n      <td>1.000000</td>\n      <td>6.200000</td>\n      <td>3.000000</td>\n      <td>3.000000</td>\n      <td>1.000000</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"markdown","source":"### Features Explanation\n- `patiendid` - **Patient Identification Number**\n- `age` - **Age of the patient**\n- `gender` - **Gender of the patient** - (0 => Female, 1 => Male)\n- `chestpain` - **Chest pain type** - (0 => typical angina, 1 => atypical angina, 2 => non-anginal pain, and 3 => asymptomatic)\n- `restingBP` - **Resting Blood Pressure** - (94-200 in mm HG)\n- `serumcholestrol` - **Serum Cholesterol** - (126-564 in mg/dl)\n- `fastingbloodsugar` - **Fasting blood sugar** - (0, 1 > 120 mg/dl) - (0 => false, 1 => true)\n- `restingelectro` - **Resting electrocardiogram results** - (Value 0: normal, Value 1: having ST-T wave abnormality, and Value 2: showing probable or definite left ventricular hypertrophy by Estes' criteria)\n- `maxheartrate` - **Maximum heart rate achieved** - (71-202)\n- `exerciseangia` - **Exercise induced angina** - (0 => no, 1 => yes)\n- `oldpeak` - **Oldpeak=ST** - (0 - 6.2)\n- `slope` - **Slope of the peak exercise ST segment** - (1-upsloping, 2-flat, 3-downsloping)\n- `noofmajorvessels` - **Number of major vessels** - 0, 1, 2, 3\n- `target` - **Classification** - (0 => absence of heart disease, 1 => presence of heart disease)","metadata":{}},{"cell_type":"markdown","source":"##### Checking for unique values of all the features","metadata":{}},{"cell_type":"code","source":"def uniqueData(col):\n    print(f'{data[col].value_counts()}\\n')\n\nfor col in data.columns:\n    uniqueData(col)","metadata":{"execution":{"iopub.status.busy":"2024-02-09T07:40:35.434648Z","iopub.execute_input":"2024-02-09T07:40:35.435036Z","iopub.status.idle":"2024-02-09T07:40:35.449871Z","shell.execute_reply.started":"2024-02-09T07:40:35.435007Z","shell.execute_reply":"2024-02-09T07:40:35.448385Z"},"trusted":true},"execution_count":7,"outputs":[{"name":"stdout","text":"patientid\n103368     1\n6865627    1\n6757890    1\n6769686    1\n6781392    1\n          ..\n3336723    1\n3347947    1\n3353178    1\n3374737    1\n9990855    1\nName: count, Length: 1000, dtype: int64\n\nage\n20    28\n58    23\n24    22\n76    22\n73    21\n      ..\n41    11\n75    11\n35    11\n65    11\n49    10\nName: count, Length: 61, dtype: int64\n\ngender\n1    765\n0    235\nName: count, dtype: int64\n\nchestpain\n0    420\n2    312\n1    224\n3     44\nName: count, dtype: int64\n\nrestingBP\n127    26\n130    25\n143    23\n126    23\n125    22\n       ..\n188     4\n95      3\n148     3\n179     3\n146     2\nName: count, Length: 95, dtype: int64\n\nserumcholestrol\n0      53\n268    22\n354    21\n248    19\n336    13\n       ..\n446     1\n166     1\n292     1\n358     1\n434     1\nName: count, Length: 344, dtype: int64\n\nfastingbloodsugar\n0    704\n1    296\nName: count, dtype: int64\n\nrestingrelectro\n0    454\n1    344\n2    202\nName: count, dtype: int64\n\nmaxheartrate\n186    20\n138    19\n145    19\n168    18\n156    17\n       ..\n87      2\n92      2\n100     2\n102     1\n95      1\nName: count, Length: 129, dtype: int64\n\nexerciseangia\n0    502\n1    498\nName: count, dtype: int64\n\noldpeak\n2.4    35\n1.8    31\n3.2    30\n1.9    29\n1.0    28\n       ..\n5.8     7\n4.6     6\n6.2     6\n4.0     3\n3.0     2\nName: count, Length: 63, dtype: int64\n\nslope\n2    322\n1    299\n3    199\n0    180\nName: count, dtype: int64\n\nnoofmajorvessels\n1    344\n0    275\n2    265\n3    116\nName: count, dtype: int64\n\ntarget\n1    580\n0    420\nName: count, dtype: int64\n\n","output_type":"stream"}]},{"cell_type":"markdown","source":"##### Insights from above information\n- `patientid` feature is of no use.\n- `gender`, `chestpain`, `fastingbloodsugar`, `restingrelectro`, `exerciseangia`, `slope`, `noofmajorvessels`, and `target` features have discrete values.\n- `age`, `restingBP`, `serumcholestrol`, `maxheartrate`, and `oldpeak` features have continuous values.","metadata":{}},{"cell_type":"code","source":"data.drop(columns=['patientid'], axis=1, inplace=True)\ndata.head()","metadata":{"execution":{"iopub.status.busy":"2024-02-09T07:40:35.451953Z","iopub.execute_input":"2024-02-09T07:40:35.452287Z","iopub.status.idle":"2024-02-09T07:40:35.470060Z","shell.execute_reply.started":"2024-02-09T07:40:35.452256Z","shell.execute_reply":"2024-02-09T07:40:35.469209Z"},"trusted":true},"execution_count":8,"outputs":[{"execution_count":8,"output_type":"execute_result","data":{"text/plain":"   age  gender  chestpain  restingBP  serumcholestrol  fastingbloodsugar  \\\n0   53       1          2        171                0                  0   \n1   40       1          0         94              229                  0   \n2   49       1          2        133              142                  0   \n3   43       1          0        138              295                  1   \n4   31       1          1        199                0                  0   \n\n   restingrelectro  maxheartrate  exerciseangia  oldpeak  slope  \\\n0                1           147              0      5.3      3   \n1                1           115              0      3.7      1   \n2                0           202              1      5.0      1   \n3                1           153              0      3.2      2   \n4                2           136              0      5.3      3   \n\n   noofmajorvessels  target  \n0                 3       1  \n1                 1       0  \n2                 0       0  \n3                 2       1  \n4                 2       1  ","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>age</th>\n      <th>gender</th>\n      <th>chestpain</th>\n      <th>restingBP</th>\n      <th>serumcholestrol</th>\n      <th>fastingbloodsugar</th>\n      <th>restingrelectro</th>\n      <th>maxheartrate</th>\n      <th>exerciseangia</th>\n      <th>oldpeak</th>\n      <th>slope</th>\n      <th>noofmajorvessels</th>\n      <th>target</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>53</td>\n      <td>1</td>\n      <td>2</td>\n      <td>171</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>147</td>\n      <td>0</td>\n      <td>5.3</td>\n      <td>3</td>\n      <td>3</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>40</td>\n      <td>1</td>\n      <td>0</td>\n      <td>94</td>\n      <td>229</td>\n      <td>0</td>\n      <td>1</td>\n      <td>115</td>\n      <td>0</td>\n      <td>3.7</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>49</td>\n      <td>1</td>\n      <td>2</td>\n      <td>133</td>\n      <td>142</td>\n      <td>0</td>\n      <td>0</td>\n      <td>202</td>\n      <td>1</td>\n      <td>5.0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>43</td>\n      <td>1</td>\n      <td>0</td>\n      <td>138</td>\n      <td>295</td>\n      <td>1</td>\n      <td>1</td>\n      <td>153</td>\n      <td>0</td>\n      <td>3.2</td>\n      <td>2</td>\n      <td>2</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>31</td>\n      <td>1</td>\n      <td>1</td>\n      <td>199</td>\n      <td>0</td>\n      <td>0</td>\n      <td>2</td>\n      <td>136</td>\n      <td>0</td>\n      <td>5.3</td>\n      <td>3</td>\n      <td>2</td>\n      <td>1</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"target = 'target'","metadata":{"execution":{"iopub.status.busy":"2024-02-09T07:40:35.471857Z","iopub.execute_input":"2024-02-09T07:40:35.472223Z","iopub.status.idle":"2024-02-09T07:40:35.480816Z","shell.execute_reply.started":"2024-02-09T07:40:35.472192Z","shell.execute_reply":"2024-02-09T07:40:35.479475Z"},"trusted":true},"execution_count":9,"outputs":[]},{"cell_type":"markdown","source":"### Correlation of other features correspond to target feature","metadata":{}},{"cell_type":"code","source":"correlation = data.corr()\ncorrelation[target].sort_values(ascending=False)","metadata":{"execution":{"iopub.status.busy":"2024-02-09T07:40:35.485883Z","iopub.execute_input":"2024-02-09T07:40:35.486210Z","iopub.status.idle":"2024-02-09T07:40:35.496828Z","shell.execute_reply.started":"2024-02-09T07:40:35.486183Z","shell.execute_reply":"2024-02-09T07:40:35.494965Z"},"trusted":true},"execution_count":10,"outputs":[{"execution_count":10,"output_type":"execute_result","data":{"text/plain":"target               1.000000\nslope                0.797358\nchestpain            0.554228\nnoofmajorvessels     0.489866\nrestingBP            0.482387\nrestingrelectro      0.426837\nfastingbloodsugar    0.303233\nmaxheartrate         0.228343\nserumcholestrol      0.195340\noldpeak              0.098053\ngender               0.015769\nage                  0.008356\nexerciseangia       -0.039874\nName: target, dtype: float64"},"metadata":{}}]},{"cell_type":"markdown","source":"- Visualize the data graphically","metadata":{}},{"cell_type":"code","source":"import seaborn as sns\nimport matplotlib.pyplot as plt\n\ndata2 = data.drop(columns=[target], axis=1)\ndata2.corrwith(data[target]).plot.bar(figsize=(10, 8), title=f'Relation of different features with {target} feature', rot=45, grid=True)\nplt.savefig('target_correlation.png')\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2024-02-09T07:40:35.498043Z","iopub.execute_input":"2024-02-09T07:40:35.498434Z","iopub.status.idle":"2024-02-09T07:40:36.341567Z","shell.execute_reply.started":"2024-02-09T07:40:35.498398Z","shell.execute_reply":"2024-02-09T07:40:36.340189Z"},"trusted":true},"execution_count":11,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 1000x800 with 1 Axes>","image/png":"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"},"metadata":{}}]},{"cell_type":"code","source":"plt.figure(figsize=(10, 8))\nsns.heatmap(correlation, annot=True, cmap='coolwarm', fmt='.2f')\nplt.title('Correlation Matrix Heatmap')\nplt.savefig('correlation_heatmap.png')\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2024-02-09T07:40:36.342643Z","iopub.execute_input":"2024-02-09T07:40:36.342965Z","iopub.status.idle":"2024-02-09T07:40:37.248132Z","shell.execute_reply.started":"2024-02-09T07:40:36.342936Z","shell.execute_reply":"2024-02-09T07:40:37.247004Z"},"trusted":true},"execution_count":12,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 1000x800 with 2 Axes>","image/png":"iVBORw0KGgoAAAANSUhEUgAAA2wAAAMZCAYAAAB8g7cjAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8WgzjOAAAACXBIWXMAAA9hAAAPYQGoP6dpAAEAAElEQVR4nOzdd3xT1f/48VdGmzZN00npADrYZe+9QfYUUHEAgiIOREQRFVFE8aPoR1wIKssPKCioKMhesvfetAUKHXTvpE3y+yOSEpoWsKkt39/7+Xjk8Whuzr33ndObc++5Z1yFxWKxIIQQQgghhBCiwlGWdwBCCCGEEEIIIRyTCpsQQgghhBBCVFBSYRNCCCGEEEKICkoqbEIIIYQQQghRQUmFTQghhBBCCCEqKKmwCSGEEEIIIUQFJRU2IYQQQgghhKigpMImhBBCCCGEEBWUVNiEEEIIIYQQooKSCpsQQvx/btGiRSgUCmJiYpy2zZiYGBQKBYsWLXLaNu93nTt3pnPnzuUdhhBCiPuMVNiEEKIMXLp0iXHjxhEREYGbmxt6vZ527doxZ84ccnNzyzs8p1m2bBmffvppeYdhZ9SoUSgUCvR6vcO8vnDhAgqFAoVCwezZs+95+9evX+ftt9/m6NGjToj2n1MoFDz//PMOP7tZCT948GCZ7b+i5IMQQvxfpy7vAIQQ4v+aNWvWMGzYMDQaDU888QT169fHaDSyc+dOXnnlFU6dOsX8+fPLO0ynWLZsGSdPnmTixIl2y0NDQ8nNzcXFxaVc4lKr1eTk5PD7778zfPhwu8+WLl2Km5sbeXl5/2jb169f55133iEsLIzGjRvf9XobNmz4R/urqP5pPgghhLg3UmETQggnio6O5uGHHyY0NJQtW7YQFBRk++y5557j4sWLrFmzptT7sVgs5OXl4e7uXuSzvLw8XF1dUSrLrxOFQqHAzc2t3Pav0Who164dP/zwQ5EK27Jly+jbty8rV678V2LJyclBq9Xi6ur6r+xPCCHE/y3SJVIIIZzoww8/JCsri++++86usnZTjRo1ePHFF23vCwoKePfdd6levToajYawsDBef/11DAaD3XphYWH069eP9evX07x5c9zd3Zk3bx7btm1DoVDw448/8uabbxISEoJWqyUjIwOAffv20atXL7y8vNBqtXTq1Ildu3bd8Xv89ttv9O3bl+DgYDQaDdWrV+fdd9/FZDLZ0nTu3Jk1a9Zw+fJlWxfDsLAwoPgxbFu2bKFDhw54eHjg7e3NwIEDOXPmjF2at99+G4VCwcWLFxk1ahTe3t54eXkxevRocnJy7hj7TSNGjODPP/8kLS3NtuzAgQNcuHCBESNGFEmfkpLC5MmTadCgATqdDr1eT+/evTl27JgtzbZt22jRogUAo0ePtn3vm9+zc+fO1K9fn0OHDtGxY0e0Wi2vv/667bNbx7CNHDkSNze3It+/Z8+e+Pj4cP369bv+rnfr7NmzDB06FF9fX9zc3GjevDmrV68us3w4fvw4nTp1QqvVUqNGDX7++WcAtm/fTqtWrXB3d6d27dps2rTJLobLly/z7LPPUrt2bdzd3fHz82PYsGFFxlne7Pq5Y8cOxo0bh5+fH3q9nieeeILU1FQn554QQpQPaWETQggn+v3334mIiKBt27Z3lX7s2LEsXryYoUOH8vLLL7Nv3z5mzZrFmTNn+OWXX+zSnjt3jkceeYRx48bx1FNPUbt2bdtn7777Lq6urkyePBmDwYCrqytbtmyhd+/eNGvWjOnTp6NUKlm4cCFdu3blr7/+omXLlsXGtWjRInQ6HZMmTUKn07FlyxbeeustMjIy+OijjwB44403SE9PJzY2lv/+978A6HS6Yre5adMmevfuTUREBG+//Ta5ubl8/vnntGvXjsOHD9sqezcNHz6c8PBwZs2axeHDh/n2228JCAjgP//5z13l7ZAhQ3jmmWdYtWoVTz75JGBtXatTpw5NmzYtkj4qKopff/2VYcOGER4eTkJCAvPmzaNTp06cPn2a4OBg6taty4wZM3jrrbd4+umn6dChA4Dd/zs5OZnevXvz8MMP89hjj1G5cmWH8c2ZM4ctW7YwcuRI9uzZg0qlYt68eWzYsIHvv/+e4ODgO37HvLw8kpKSiizPysoqsuzUqVO0a9eOkJAQXnvtNTw8PFixYgWDBg1i5cqVDB482Kn5kJqaSr9+/Xj44YcZNmwYc+fO5eGHH2bp0qVMnDiRZ555hhEjRvDRRx8xdOhQrl69iqenJ2CtWO/evZuHH36YKlWqEBMTw9y5c+ncuTOnT59Gq9Xafbfnn38eb29v3n77bc6dO8fcuXO5fPmy7YaGEELc1yxCCCGcIj093QJYBg4ceFfpjx49agEsY8eOtVs+efJkC2DZsmWLbVloaKgFsKxbt84u7datWy2AJSIiwpKTk2NbbjabLTVr1rT07NnTYjabbctzcnIs4eHhlh49etiWLVy40AJYoqOj7dLdbty4cRatVmvJy8uzLevbt68lNDS0SNro6GgLYFm4cKFtWePGjS0BAQGW5ORk27Jjx45ZlEql5YknnrAtmz59ugWwPPnkk3bbHDx4sMXPz6/Ivm43cuRIi4eHh8VisViGDh1q6datm8VisVhMJpMlMDDQ8s4779ji++ijj2zr5eXlWUwmU5HvodFoLDNmzLAtO3DgQJHvdlOnTp0sgOXrr792+FmnTp3slq1fv94CWGbOnGmJioqy6HQ6y6BBg+74HS0WiwW44+vAgQO29N26dbM0aNDA7v9nNpstbdu2tdSsWbNM8mHZsmW2ZWfPnrUAFqVSadm7d2+RPLh1O46Ovz179lgAy5IlS2zLbh67zZo1sxiNRtvyDz/80AJYfvvtt+KyTwgh7hvSJVIIIZzkZjfEm60Ed7J27VoAJk2aZLf85ZdfBigy1i08PJyePXs63NbIkSPtxrMdPXrU1vUvOTmZpKQkkpKSyM7Oplu3buzYsQOz2VxsbLduKzMzk6SkJDp06EBOTg5nz569q+93q7i4OI4ePcqoUaPw9fW1LW/YsCE9evSw5cWtnnnmGbv3HTp0IDk52ZbPd2PEiBFs27aN+Ph4tmzZQnx8vMPukGAd93Zz3J/JZCI5ORmdTkft2rU5fPjwXe9To9EwevTou0r7wAMPMG7cOGbMmMGQIUNwc3Nj3rx5d72vgQMHsnHjxiKvV155xS5dSkoKW7ZsYfjw4bb/Z1JSEsnJyfTs2ZMLFy5w7do1W/zOyAedTsfDDz9se1+7dm28vb2pW7curVq1si2/+XdUVJRt2a3HX35+PsnJydSoUQNvb2+HMTz99NN2E9yMHz8etVrt8LgSQoj7jXSJFEIIJ9Hr9YC1gnM3Ll++jFKppEaNGnbLAwMD8fb25vLly3bLw8PDi93W7Z9duHABsFbkipOeno6Pj4/Dz06dOsWbb77Jli1bilSQ0tPTi91mcW5+l1u7cd5Ut25d1q9fT3Z2Nh4eHrbl1apVs0t3M9bU1FRbXt9Jnz598PT0ZPny5Rw9epQWLVpQo0YNh8+cM5vNzJkzh6+++oro6Gi78Xp+fn53tT+AkJCQe5pgZPbs2fz2228cPXqUZcuWERAQcNfrVqlShe7duxdZHhsba/f+4sWLWCwWpk2bxrRp0xxuKzExkZCQEKflQ5UqVYp0R/Ty8qJq1apFlgF2Y85yc3OZNWsWCxcu5Nq1a1gsFttnjo6/mjVr2r3X6XQEBQU59dmCQghRXqTCJoQQTqLX6wkODubkyZP3tN7djrFxNCNkcZ/dbD376KOPip1yvbjxZmlpaXTq1Am9Xs+MGTOoXr06bm5uHD58mClTppTYMudMKpXK4fJbL97vRKPRMGTIEBYvXkxUVBRvv/12sWnff/99pk2bxpNPPsm7776Lr68vSqWSiRMn3tN3Lun/5MiRI0dITEwE4MSJEzzyyCP3tP7duBn/5MmTi22lvXnjwFn5UNz/727+ry+88AILFy5k4sSJtGnTBi8vLxQKBQ8//PC/dvwJIURFIRU2IYRwon79+jF//nz27NlDmzZtSkwbGhqK2WzmwoUL1K1b17Y8ISGBtLQ0QkND/3Ec1atXB6yVSEctMCXZtm0bycnJrFq1io4dO9qWR0dHF0l7t5XNm9/l3LlzRT47e/Ys/v7+dq1rzjRixAgWLFiAUqm066J3u59//pkuXbrw3Xff2S1PS0vD39/f9t6Zk1hkZ2czevRoIiMjadu2LR9++CGDBw+2zcDoLBEREQC4uLjc8Xgoj3xwFMPIkSP5+OOPbcvy8vLsZvy81YULF+jSpYvtfVZWFnFxcfTp06fMYhRCiH+LjGETQggnevXVV/Hw8GDs2LEkJCQU+fzSpUvMmTMHwHYx+emnn9ql+eSTTwDo27fvP46jWbNmVK9endmzZzucMfDGjRvFrnuzBeTWFg+j0chXX31VJK2Hh8dddZEMCgqicePGLF682O6i++TJk2zYsKFML6y7dOnCu+++yxdffEFgYGCx6VQqVZHWu59++sk2tuummxXL4ioP92LKlClcuXKFxYsX88knnxAWFsbIkSOLPNahtAICAujcuTPz5s0jLi6uyOe3Hg/lkQ+3cxTD559/btc981bz588nPz/f9n7u3LkUFBTQu3dvp8cmhBD/NmlhE0IIJ6pevTrLli3joYceom7dujzxxBPUr18fo9HI7t27+emnnxg1ahQAjRo1YuTIkcyfP9/WDXH//v0sXryYQYMG2bUY3CulUsm3335L7969qVevHqNHjyYkJIRr166xdetW9Ho9v//+u8N127Zti4+PDyNHjmTChAkoFAq+//57h10RmzVrxvLly5k0aRItWrRAp9PRv39/h9v96KOP6N27N23atGHMmDG2af29vLxK7KpYWkqlkjfffPOO6fr168eMGTMYPXo0bdu25cSJEyxdutTWOnVT9erV8fb25uuvv8bT0xMPDw9atWpV4hhDR7Zs2cJXX33F9OnTbY8ZWLhwIZ07d2batGl8+OGH97S9O/nyyy9p3749DRo04KmnniIiIoKEhAT27NlDbGys7Tlr/3Y+ONKvXz++//57vLy8iIyMZM+ePWzatKnYMXRGo5Fu3boxfPhwzp07x1dffUX79u0ZMGBAqWMRQohyV27zUwohxP9h58+ftzz11FOWsLAwi6urq8XT09PSrl07y+eff243rXp+fr7lnXfesYSHh1tcXFwsVatWtUydOtUujcVinda/b9++RfZzc1r/n376yWEcR44csQwZMsTi5+dn0Wg0ltDQUMvw4cMtmzdvtqVxNK3/rl27LK1bt7a4u7tbgoODLa+++qpt+vWtW7fa0mVlZVlGjBhh8fb2tgC2Kf4dTetvsVgsmzZtsrRr187i7u5u0ev1lv79+1tOnz5tl+bmtP43btywW+4oTkdunda/OMVN6//yyy9bgoKCLO7u7pZ27dpZ9uzZ43A6/t9++80SGRlpUavVdt+zU6dOlnr16jnc563bycjIsISGhlqaNm1qyc/Pt0v30ksvWZRKpWXPnj0lfgfA8txzzzn87GZe3Tqtv8VisVy6dMnyxBNPWAIDAy0uLi6WkJAQS79+/Sw///zzv5IPxR3Ht3+X1NRUy+jRoy3+/v4WnU5n6dmzp+Xs2bOW0NBQy8iRI4t8z+3bt1uefvppi4+Pj0Wn01keffRRu8dHCCHE/UxhsdzD6G0hhBBCiApi0aJFjB49mgMHDtC8efPyDkcIIcqEjGETQgghhBBCiApKKmxCCCGEEEIIUUFJhU0IIYQQQgghKiipsAkhhBDivjRq1CgsFouMXxNC/Ct27NhB//79CQ4ORqFQ8Ouvv95xnW3bttG0aVM0Gg01atRg0aJF97xfqbAJIYQQQgghxB1kZ2fTqFEjvvzyy7tKHx0dTd++fenSpQtHjx5l4sSJjB07lvXr19/TfmWWSCGEEEIIIYS4BwqFgl9++YVBgwYVm2bKlCmsWbOGkydP2pY9/PDDpKWlsW7durvel7SwCSGEEEIIIf6/ZDAYyMjIsHsZDAanbHvPnj10797dblnPnj3Zs2fPPW1H7ZRoxP/X1rjULu8QSmXR83d/h6Mi8gv0Lu8Q/rHUGxnlHUKpePt7lncIpaJW37/37Fzd7u/TV8zZuPIO4f9bapf7+9hRu6jKO4RS8fDSlncIpZJvKCjvEP6xxTMCyzuEYpXnteSBNx7hnXfesVs2ffp03n777VJvOz4+nsqVK9stq1y5MhkZGeTm5uLu7n5X27m/Sy0hhBBCCCGE+IemTp3KpEmT7JZpNJpyisYxqbAJIYQQQgghyo3CRVFu+9ZoNGVWQQsMDCQhIcFuWUJCAnq9/q5b10DGsAkhhBBCCCGE07Vp04bNmzfbLdu4cSNt2rS5p+1IhU0IIYQQQggh7iArK4ujR49y9OhRwDpt/9GjR7ly5Qpg7V75xBNP2NI/88wzREVF8eqrr3L27Fm++uorVqxYwUsvvXRP+5UukUIIIYQQQohyo1SXX5fIe3Hw4EG6dOlie39z7NvIkSNZtGgRcXFxtsobQHh4OGvWrOGll15izpw5VKlShW+//ZaePXve036lwiaEEEIIIYQQd9C5c2dKeoT1okWLHK5z5MiRUu1XKmxCCCGEEEKIcqNwkVFaJZHcEUIIIYQQQogKSlrYhBBCCCGEEOXmfhnDVl6khU0IIYQQQgghKiipsAkhhBBCCCFEBSVdIoUQQgghhBDlRuEiXSJLIi1sQgghhBBCCFFBSQubEEIIIYQQotzIpCMlkxY2IYQQQgghhKigpMImhBBCCCGEEBWUdIkUQgghhBBClBuZdKRkUmET/xrf9s2JeHkMXk3r4xYcwMEHnyVh9eaS1+nYksjZr6GLrEne1TguzppL7JJf7NKEjh9BxKQxaAIrkXH8LKcmvkv6gRNl+VV4qJc33dp44uGm5GyMgW9+SiI+qaDEdXq282RAVy+8PVVcvm5kwapkLl4xAqDTKhney5tGtd3x91aTkW1m/4lslv+ZSk6exWlxd2qi4YFWGvQeSmITTSzflENMnKnY9E1ruzCggzt+XkoSU838si2Hk1GOv+eIB7R0bKJhxeYcthw0OC3m2w3r6UW3Vjo83JWcizbw7aqUO+b9A2119O/8d97HGVn4SwqXrlrz3sNdyfCeXjSs5Y6/j4qMLDMHTuawfH0auU7M+85NNfRo5YbX33n/48bsO+b9wI5aa96nmFi1LZeTUfkO047oqaVTEzdWbMpmcxnlfcfGrnRv4YbeQ8G1GyZWbM7lcnzx8Tep5UK/dm62Y+e3Hbmcii78Pz3eS0vr+q5265yOzufLldllEn+7Bi50beKCp1bB9SQzq3YYuJJoLjZ9o+oqerfW4Oup4Ea6mT92GzlzufD7/vd5ncP1Vu8ysPWI4/9TaT3S15fubb3wcFdyNiqPecsTibtR8r56d/RiUDcfvPUqYq4Z+fanRC5cdnyMTBsfTNN6Hsyaf539x537f7ifYwfnl/kATw/zo0Etd3z1KvKMFs5F5/G/P1K5nuj842foA150banDw13BuRgjC365c7nZo42O/p30eHmquBJnZNFvqXbl5rAHvGhQyw1/b2u5efBULis2OLfc7NLMjZ6t3fHSKbmaUMAPG7KJvl583M3quDKokxZ/bxUJKSZWbsnmxKXC/BzQQUuLSFd89SoKTBYuxxfwy7acErdZGt1aaundzuPv+PP535pMoq4V//9tUU/DkK6ef8dfwIoNmRy/UHjMNKuroWsLLWHBLui0SqZ9lcSV+LKJXVQs0iVS/GtUHloyjp/j5IR37iq9e1gVWqyeR/K2fexsPpDozxfTYN5M/Hu0t6UJGtabuh9N5cLML9nZcjCZx8/Sas13uFbyLauvwcCuXvTuqGf+T8lM/fQ6BoOZN58JxKWEAbNtG3swcpAfP61PY8rH17l83cgb4wLR66w/QR+9Ch+9miWrU5j04TW+XHaDxnW0jH+4ktPiblbHhaFd3fljVx7vL8ogNtHEC8N1eGodxx0RomLMAA92HTfw3qIMjl4w8swQHcH+RYuNxjVdCA9WkZZZ/AWwMwzooqd3ez3frkzhjc/iyTNaeP2pAFxKuPXUppGWJwb4snJjGq99Gsfl60ZefyrAlve+Xip89Cq+/yOVybPj+Gp5Mo3quPPMcD+nxd28jitDu2pZszOX9xamE5tYwISHPEvIezVjB+rYdczAzIXpHL2Qz/gHdQT7q4qkbVzLhYhgNallmPdNa7swpLM7a/fk8cH3mcQmmnh+qAe6YuIPD1Yxup+WPSeNzFqSyfGL+Tw9yIOg246dU9H5TP0q3fZa8EdOmcTfuIaaQe1dWX/AyMfLc7iebGbcAHd07o7jDwtU8nhPN/adzmf28hxORpl4so8bgb6F8b+1INvu9cPmPMwWC8cvlc3F0+DuPvTt5M28HxOZMvsqBqOZt54LKbHcaddUx+jB/iz/M4WX/3OVmGsG3nouBC9d0eOofxdvnHeZ/X8ndiibMh8gKtbIVz8kMfGDa8ycF49CAdOeCUTp5IaG/p096dXOk+9WpTDt8wQMRjOvjSm53GzdSMvj/X1YuSmd1+fEcTkun9fGBKD3KDxneetVLP0jjVc+jufrFck0qu3GuKHOKzdb1HVleHcPfv8rhxnfpXE10cTEh/XFlpvVQ9Q8PdiTnccMzPg2jSPnjTw3TE9wpcJjJj7FxLL12Uz/JpX/LEknOd3MS4/oiy3LSqNlfTce6eXJb9uymP51ElfjC5j8hA+eHo4vvWtUdWH8UG92HM7hrblJHD5j4MVHfAgJKPxHaVwVnL9iZMWGTKfHW96UakW5ve4HUmG7j61bt4727dvj7e2Nn58f/fr149KlS7bPd+/eTePGjXFzc6N58+b8+uuvKBQKjh49aktz8uRJevfujU6no3Llyjz++OMkJSWVSbw31u/g/PRPSfht012lD336YXKjYznz6n/IOhvF5a+WEr9yPeEvjrKlCZ84mqvfrSB28SqyzlzixLPTMeXkUXXUg2XyHQD6dtKzckMaB0/mcCUuny+W3cBHr6JFA22x6/TrrGfznky27c8iNiGf+T8lYzRa6NrKE4Cr8fl8vCiRQ6dySUgu4OTFPH5Ym0KzelqUTvqVdm/hxq5jBvacMBKXbGbZ+hzy86FtA1eH6bs2c+NUVD4b9xuITzbz+195XEkw0bmpm106b52Ch3poWfBHNiZzWV42QZ8OnqzalM7BU7lcicvnyx+T8NGraVG/+Lzv20nP5n2ZbDuQzbWEfL5dmYIx30KXFtYWkqvx+XyyJInDp615f+piHsv/TKNZpBPzvqUbO48Z2P133i9dl4MxH9o21DhM3625hlNR+WzYn0d8spnVf+VyJd5E52b26b11Ch7u7sF3v5dt3ndrrmH3CSN7TxqJTzbz48ZcjPnQpr7jY6dLUw2nowvYdMBAQoqZP3blcTXBRKfG9vEXFEBGjsX2yjWUzXfo3NiFPafy2X+mgIRUCz9tNWAssNCqruMr1o6NXDh7xcTWI/kkplr4c5+R2BtmOjR0saXJzLHYveqHq7kYayI5o2y+Q78u3vy0PoX9J7K5fN3InCUJ+HqpaNXIo9h1BnT1YePuDLbszSA23sjXPyZiMFro1kZvly4sxJUBXb354n8JErsDZVHmA2zak8mZqDxupBYQHWvkh7Wp+PuoqeTr3M5Pvdvr+WVzOodO53IlPp+vlifjo1fRvF4J5WYHT7bsy2L7wWyuJRbw3aoUjPlmOv9dbsYm5PPp90kcPpNLYkoBpy4ZWL4ujaaR7k4rN3u0cuevo3nsOm4gLsnE/9ZmYSyw0L6Rm8P03Vu6c/JSPuv35hKXbOK37Tlcji+ga/PC9PtPGTgTk09SmpnrSSaWb8xG66akSoDzO5z1aqtl+6Ec/jqSy/UbJhb9noEx30LHpu4O0z/QWsuJiwb+3JVDXJKJVVuyiInLp3urwv/T7mN5/LYtm1NRRofbEP93SYXtPpadnc2kSZM4ePAgmzdvRqlUMnjwYMxmMxkZGfTv358GDRpw+PBh3n33XaZMmWK3flpaGl27dqVJkyYcPHiQdevWkZCQwPDhw8vpG9nzbt2YpC177Jbd2LgTn9aNAVC4uODVtB5Jm3cXJrBYSNqyG+/WTcokpgA/NT56NSfO59mW5eRZuHjZQO0wxxffahVEVNFw/HzurWFy/EIutUIdrwOgdVOSm2fG7ISGE5USqgWqOHO58O6/BTgTk09EiOMTVUSImrOX7VsLTkfnExFSeLdSAYzq58HGfXnEJZVt61qA7995f6EwH3PzLFy8YqBmMfmoUkFEiKvd/8tigRMX8opdB8oo72MKu8FYgLMl5X2wmrMx9t1mrHlfmF4BjO6vY8P+XOKSiu+aWFoqJVStrLI7FizA2SsFRAQ7jj88WM25246dMzEFhN+WvmZVNR88q+etJz15uLs7Hm7Ov9OpUkKVACXnrxbmkQW4EGsiNLBoaw1AWKDKLj3AuSsmQgMdnzJ17goiQ1XsO1M2rWuV/dT4eqk5drawBTInz8yFmDxqhzm+eFWroHpVDcfOFa5jscDxcznUDi9cx9VFwaRRgXyz4gZpmc4/ju7n2OHfK/M1rgq6tPIkITmf5DTnHUcBvtYeBCcvFMafm2fh0tWSy83wEFdOXrQvN09eyKNmqOObNABad+eWm6FBak5H25ebZ6LziahS/DnrTLR9ReZUVD7VQ1wcplcpoWMTN3LyzMQmOPe3q1JBWJALpy4VxmOxwKlLRmpUcRxPjaquRSpiJy8aqVHVcfr/axQqRbm97gcyhu0+9uCD9q1ICxYsoFKlSpw+fZqdO3eiUCj45ptvcHNzIzIykmvXrvHUU0/Z0n/xxRc0adKE999/324bVatW5fz589SqVavIPg0GAwaD/RiCfIsZF4Xz6/6ayv4YEuxb+wwJSbh4eaJ00+Di44VSrcaQmHxbmmQ8akc4PR4Ab0/rBV5alv3FQVqWyfbZ7Tw9VKhUCtJvu6BIzzQREuC4IPb0UDL0AR827XFOtwedVoFKqSAj2/5MmpljIdDPcdx6Dwfpsy22LjEAD7R2w2yGLYfKbszaTTfzN/22rn/pJeS9/mbeZxXN++Di8l6rZEgPLzbtzXJC1IV5n5lt3/KSkW0m0M9xDHqdkgwH6b1uyfueN/O+DMcLgrUyYo3/9mPBTGAxLQF6DwUZOfbpM3LM6D0KT4yno/M5esFIcroZf28VAzq48eyDHsxeloXFiY1UHjfjz7XfaGaOhQBvx+WWp1ZBZs7t6c3otY6/b8s6avLyKbPukN56635vL0PSMk22z27nqXNc7qRlFBBSufCO/ZMPVuJsdB77T5TN2MH7OXYo+zL/gXaePN7fFzeNkmsJRt6dG0+BE+ueXjfLTQdloLen4+NfX1z8WeYSy83B3bzYvM9Z5abS4TmrpHLTS6d0mN7rti6IDWu48PRgPa4u1u/0ybIMsnKd2zLuqVVa8/C2eNKzTQRVclzp9dIpycgqen7z0knbipAK233twoULvPXWW+zbt4+kpCTMf9/WunLlCufOnaNhw4a4uRXejWzZsqXd+seOHWPr1q3odEUHz1+6dMlhhW3WrFm88479GLRHFL48qvJ3xleqcNo39WDc8MLvNuubsut2c5O7RsHUpyoTm2BkxbrUMt/fP1WtsoquzTS8vzijTLbfvokHTw0tHIv4wXeJZbKfW7lrFEwZG0BsQj4/b0gr8/39U9Uqq+ja3I33FqWXdyj/2KFzhXfOryeZuXbDxIyn9NSqqubclftrEH3LSBcOn8932oV2x+aePPNIgO39e3OvO2fDt2nRwIMGtdx5+YMrTtvm/Rw7/Ptl/s5DWRw/l4uPXs2ALnomjQzgzc/iyC/4ZxWIdk20jB1SWG5+uPCGs0ItlrtGwatPVuJaQj4rN1b8Muns5XxmfJuKzl1JhyZujBviyfsL04rcpBGiIpEK232sf//+hIaG8s033xAcHIzZbKZ+/foYjXfXtzkrK4v+/fvzn//8p8hnQUFBDteZOnUqkyZNslu2xbfZvQd/FwwJSWgq21cENZX9yU/PxJxnwJiUirmgAE2A321p/DDEO2cc3sFTOVycfc32Xv334FRvnYq0jMKrM2+dipjrjvM9M9uEyWSx3em8ycvTfhsAbhoFb4wLJNdg4aMFiZic1MswK8eCyXyzdaxwn57aoncwb8q4rTUNwPOWVrcaVdV4eih4f7yX7XOVUsHQLu50a67hja9LV5E7eDqHC58Uth7dHODv5am06/7kVULeZ9zMe93d5f3UpwLIyzPz8SLn572nh323C72HssjdV1vcWfatUbenr/l33s961tv2uUqpYGhXLV1buPHGXOddNGXl3oz/tmPHo2groC3+bAt6rf2xo9cWnx4gOd1MZo6ZSt5KzjnxGjz7Zvy3TTDiqVWQUcwFWmaOpcjEBp5apcP0EUFKKvsoWbLOeZXM/SeyOB9T2B2t8NhXkXprueOpIjrWcQtrZpbjcsdbryYtwxprg1ruBPq78L+PqtuleXVsEGcu5TJtzjXu1f0cO/z7ZX5OnoWcvALikwq4cDmPhe+F0rKBll1H/lmr4aHTuVy8Em97b8t/nf2kUF6eKmKuO56tMKO4+HXKIl1P3TQKXhsTQK7BwidLbjix3DTfcs4qVFK5mZ5lvqv0xnxITDWTmGom6noW7433oX1jN/7cnYuzZOaYrXl4WzxeHqoivUTs4r+tNc1LpyI9q2yHG1QUyvuka2J5kQrbfSo5OZlz587xzTff0KFDBwB27txp+7x27dr873//w2AwoNFY+6kfOHDAbhtNmzZl5cqVhIWFoVbf3aGg0Whs27upLLpDAqTtPUql3h3tlvl3a0vq3qMAWPLzST98Cv+ubQofD6BQ4NelDZe/+p9TYsgzWIg32F+IpWYUUL+Wm+1k7a5RUCNUw/rdjrsvFpggKtZAg1puHDiZczNMGtR0Z93OwkqNu0bBm88Ekl9g4T/fJvzjO6yOmMxwJd5EnVA1xy5YT9IKoE6YC9sO5TlcJ+paAXVC1XZd7uqGuRB1zXrC3nfSWGSc1YThnuw9ZWTPidJ308szWMhzkPcNarpx+e8LDXeNghrVNGwspuuoyQRR14w0qOnGwVPWk7FCAfVruLF+V+E67hoFrz9VmXyThQ8X3iDfiQ08N/O+bpiLfd6HurD1cDF5f72AOmEudlP01w1TE3XNGtjek0bOxNgHOeEhT/adNLDbCXl/e/xXE0zUrqbm+MXC+GtXU7P9iON9RV8voHaomq2HCz+vE6oucepsb50CD/eiXYicEX9soplaVVWcjDbZ4q9ZRcXO444vWGPiTdSqomLHscLPa1VVcTm+aGytIl24mmjierLz4raWO/axpaQX0LC2lphrf5c7bkpqhrmxbqfjynmBCS5dNdCwttY2zb1CYa3o/LnDus6qDals2m1/Y2XOG6EsXHmDAyf/WYXhfo69MP5/p8x3RKGgxNkn7yb+ouWmifo13bgcV1huVq+qYeMex90XTSaIvmakfg37crNeDTc27C5cx12j4LWxARQUWJi9yPnl5uW4AuqGuXD0vDXfb56zth4s/pxVN9yVTQcKP48Md+FSCdPow9957uTKgskEMXH5REa4cviswbafyAhXNu13PBvuxatGIiNc2bCn8PN61V25eLVsHhMi7i/SMfY+5ePjg5+fH/Pnz+fixYts2bLFruVrxIgRmM1mnn76ac6cOcP69euZPXs2AAqFtWB67rnnSElJ4ZFHHuHAgQNcunSJ9evXM3r0aEwm5w/gVnlo0Teqg75RHQC04VXQN6qDW1Vra17tmZNotLCwte/y/B/RhlelzqxX8KgdQegzIwga1pvoOYtsaaI/XUjVMcMJeXwQujoR1P/ybdQe7lxdvMrp8d+0ZnsGD/bwpnk9LdWCXHj+0UqkZpg4cKKwkH1rfCC92hfOBvbHtgy6tfakUwsdIQEuPDXUD42rgq37rCf8m5U1jauSuT8moXVT4u2pwttT5bQpnjcdyKN9Iw2t67sS6KfkkZ5aXF1g9wnryXBUXy2DOhZ2od1yKI964S50b6Ghsq+Sfu3cCA1Use3vSkZ2noXrSWa7l8lsISPbTEJK2dwRXPtXJoO7edEs0p2qgS4894g/qRkFtosigDfHBdCzXWHer9meQddWnnRs7kFIgJqxQ3zRuCrYdsB64eGuUfDG05XRuCqYtyIZdzcFXp5KvDyVKJyV9/vt835ETy2urrD7uPVEPqqfB4M6Fc4ctvmgwZr3Ld2sed/endAgNdv+HitozXuT3ass837zQQPtGrrSqp4LlX2VPNzDHY2LteII8ERvLQM6FB47Ww8biAxT06259djp09aNaoEqth+1xq9xgcGd3AgLUuGrV1K7mppxgzy4kWouUhF1hm1H82kd6UKLOmoCfBQM7azBVa2wTRIyoruGvm0Kx5XsOJZPnWoqOjd2IcBbQc+WrlQNUPLXbRU8jQs0qqFm76myv6D6Y2saw3r50qKBB9WCXXnx8cqkpJvYd6ywcvLOCyH07ljY4r16Syo92urp0sqTKpVdGPdQAG4aJZv3WisNaZkmrsQZ7V4AN1ILSEx23v/hfo4dyqbMD/BTM6ibFxFVXPH3VlErTMPLowIw5ls4fMa5j7f4c2cGg7oWlpvjH/IjNcPEwVOF+3njqQAeaFs4PGLNX5l0aamjYzMPggPUPDnYB42rku0HC8vNqWMDcHNVMO+nFNw1Crx0Srx0zis3N+7LpWMTN9o20BDkp+Kx3h5oXBTsOm49Bz3ZX8eQzoVjGjftz6VehAsPtHIn0E/FgA5awoLUbPm7gufqAoM7a4kIVuOrVxIaqGJUPx0+nkoOnnH+WOB1u3Po1ExLu8ZuBPmrGNlPj8ZVwV+HrZXgp4d4Max7YZ5v2JtDgxoaerXVEuSvYlAXHeHBLmzaV/h/8nBXUC1QbXtUQaC/mmqB6v8T49wUSkW5ve4H0sJ2n1Iqlfz4449MmDCB+vXrU7t2bT777DM6d+4MgF6v5/fff2f8+PE0btyYBg0a8NZbbzFixAjbuLbg4GB27drFlClTeOCBBzAYDISGhtKrVy+UzpqX9xZezerTZvP3tveRs18H4OqSVRwfMxVNUCXcqxZ2xcyNieXAgHFEfjyVsBeeIC82nhPj3iRpY2FLYtxPf+JayZda0ydYH5x97Az7+43FeNtEJM7025Z03FwVjBvuh9ZdydloA+/Ni7drEavsr8bTo7A7ye6j2eh1Sh7qdfMhsAbem5dg6+oQXkVDrb9nTPvizap2+3t2xlVupJb+AuTQ2Xw8tbn0b+9me3D25yuybP32ffVKu8keoq6Z+O73bAZ0cGdgR3cSU818vSqL62U8G2RJVm/NQOOq4Omh1rw/F53HrG8S7e7sVvZzwdOj8OS751gOel0qw3t64+1p7cY069vEW/Le1TZb2mdTQ+z29/x7sdxILf3Ni4Nnjei0CgZ0cLfl/WfLM0vI+wK+XZ3FwI5aBnV0JzHVxNyVWVwvw9kgS3L4nPXY6dfOHU+t9cHZX/6cbYvf57b4o6+bWLgmh/7t3ejf3o0baWbm/5ptm0nUbIFgfxWt6rnirlGQnmXhTEw+f+zKc+qECzcdvViAzl1Br5aufz/428y833NtEw34eCqxWAqP65h4M99vyKNPa2tF7kaamQVr84i/rTLctJYaBXD4QtmPuftlUypuGgXjHwnAw13JmUt5vPvVNbtyJ9DfBf0t3X93Hc5Cr1PxcF8/fDxVRF8zMuPLa0Umk5DYS1YWZX5+voW6EW707eSFzt3a1fBMVB5vzokrMvFEaf2+LRONq5KxD/qidVNyLsbAB9/dXm7ax7/3WA56DyVDHyh88PcH3xWWm2EhheXmnNeC7fb3wqxrJDmh3DxwxojOI5uBnbToPawPzv70xwxb12o/L5VduXPpWgHf/JrJ4M5aBnfWkphi4sufMrh+wxqL2QxBfiraDvVE564kO9dMdFwB/1mSXiZl6/6Teei1SoZ09cRLp+RKfD6zv0+1DSvw9VJx69NYLl7N5+uf03iwmydDu3uSkFzAnB9SuZZY+I9qUtuNp4YU3th4brg3AL9szeLXrc6Z8EVUTAqLxZnzcYmKbOnSpYwePZr09HTc3R0/B+SfWONS22nbKg+Lnl9X3iGUil+gd3mH8I+l3iibCUv+Ld7+nndOVIGp1ffvXVlXt/v7fmPM2bjyDuH/W+qSnhh9H1C7OJ6d8n7h4VX889/uB/mG+2tSpFstnhFY3iEUa1eTspkP4W60O3Ko3PZ9t+7vUkuUaMmSJURERBASEsKxY8eYMmUKw4cPd2plTQghhBBCiNJQqO7fG4j/Bqmw/R8WHx/PW2+9RXx8PEFBQQwbNoz33nuvvMMSQgghhBBC3CWpsP0f9uqrr/Lqq6+WdxhCCCGEEEIUS6b1L5m0PwohhBBCCCFEBSUtbEIIIYQQQohyc79Mr19epIVNCCGEEEIIISooqbAJIYQQQgghRAUlXSKFEEIIIYQQ5UYmHSmZtLAJIYQQQgghRAUlLWxCCCGEEEKIcqOQFrYSSQubEEIIIYQQQlRQUmETQgghhBBCiApKukQKIYQQQgghyo1CKW1IJZHcEUIIIYQQQogKSlrYhBBCCCGEEOVGoZRJR0oiLWxCCCGEEEIIUUFJC5sQQgghhBCi3MiDs0smLWxCCCGEEEIIUUFJC5sotUXPryvvEEpl1Be9yjuEUln97p7yDuEfy0hKL+8QSuXauZjyDqFUareMLO8Q/rHYSwnlHUKphEeGlHcIpXLp5NXyDuEfe2l8cHmHUCoffXalvEMoFZ23R3mHUCoqlbR1iH+fVNiEEEIIIYQQ5UYmHSmZ3CYQQgghhBBCiApKWtiEEEIIIYQQ5UYenF0yyR0hhBBCCCGEqKCkwiaEEEIIIYQQFZR0iRRCCCGEEEKUG5l0pGTSwiaEEEIIIYQQFZS0sAkhhBBCCCHKjVIlLWwlkRY2IYQQQgghhKigpMImhBBCCCGEEBWUdIkUQgghhBBClBuZdKRk0sImhBBCCCGEEBWUtLAJIYQQQgghyo1CKW1IJZHcEUIIIYQQQogKSlrYhBBCCCGEEOVGxrCVTFrYhBBCCCGEEKKCkgrb/1GjRo1i0KBB5R2GEEIIIYQQohSkS6QoFw/18qZbG0883JScjTHwzU9JxCcVlLhOz3aeDOjqhbenisvXjSxYlczFK0YAdFolw3t506i2O/7eajKyzew/kc3yP1PJybM4JWbf9s2JeHkMXk3r4xYcwMEHnyVh9eaS1+nYksjZr6GLrEne1TguzppL7JJf7NKEjh9BxKQxaAIrkXH8LKcmvkv6gRNOifl2nZtq6NHKDS8PJbGJJn7cmE1MnKnY9E1ruzCwoxY/LyWJKSZWbcvlZFS+w7Qjemrp1MSNFZuy2XzQUCbxAzw+uDK9OvnioVVx+kI2Xyy5xvUEY4nr9Ovmx9DelfDxUhN1JY+5/7vG+ehcAAL8XVg8u67D9d778jI7D6Q7Nf4xj4bR/4FAPD3UnDiTweyvLhAbl1ts+kb1vBgxpCq1q+vw99Mw9b2T/LU32fa5SqXg6cfCaN3cl+BAd7KzCzh4LJW5i6NJTik5X+5Fl+Zu9GqjxUun5GpCAcvWZRF9vfjfbPO6rgzq7IG/t4qEFBM/b87mxEVrPColDO7iQYMarlTyVpFrMHM6Op+Vm7NJyzI7LebbjejnR4/23ni4KzkblcvcZQnE3XB8PN/Up5M3g3r44qNXERNrYP7yRC5czrN9PvOlqjSopbVbZ92ONOb+kOC0uNs3cKFrUxc8tQquJ5lZucPAlYTi86lRDRV9Wmvw9VRwI83M77uNnLls/zuv7KOgf1sN1UNUKJWQkGJmwdo80rKcU17e7tH+/jzQwQcPdyVnLuXy1bI44hLvkPedfRjSwxcfLzXRsQbm/RjPhZjCvH9/UjUa1PawW+fP7al8tSzeqbFv/XM5639dQnpaMlXDavHI2FcJr1nfYdrDezezduUCEuOuYjIVEBBUjQcGPEabzv1saVb/+DUHdm0gJSketdqF0Op1GTTiOSJqNXBq3Le6X/O/czMNPVu528qdHzbkEBNXfLnTrI4rAztp8fdSkpBiYuXWHE5eKvye/Tu40yJSg6+nkgKThcvxBfy6PbfEsqw0urZwp1fbv8vN+AKW/plZcrkZqWFwl7/LzWQTP23Ksi83u3rQsIaGSj5/l5tRRn7eVLbl5r9FukSWTFrYhEMWi4WCgrIpwAZ29aJ3Rz3zf0pm6qfXMRjMvPlMIC7q4n+sbRt7MHKQHz+tT2PKx9e5fN3IG+MC0eush7CPXoWPXs2S1SlM+vAaXy67QeM6WsY/XMlpcas8tGQcP8fJCe/cVXr3sCq0WD2P5G372Nl8INGfL6bBvJn492hvSxM0rDd1P5rKhZlfsrPlYDKPn6XVmu9wreTrtLhval7HlaFdtazZmct7C9OJTSxgwkOeeGod53tEiJqxA3XsOmZg5sJ0jl7IZ/yDOoL9VUXSNq7lQkSwmtTMsj1pDOtTiQE9/Pl88TUmzrhInsHMzJfDcXEp/tjp2NKLpx8OYumvCbww/QLRV3OZOTkcL0/r90hKzmfEi6ftXt+viicn18TB45lOjf/RB6sytF8Is7+6wNOTj5CbZ+KTGQ1wLSF+dzcVF6Oz+OTrCw4/d9MoqVXdk8XLr/DkxEO8MesU1UK0/OdNxxeU/0SLSA0P9dCxekc273yTytWEAl4a4VXssVO9ipqnh+j562ge73yTypFzBp4friekkjXPXV0UVAtU8/tfObzzbSpf/pRBoJ+KFx7SOy3m2w15wJe+XXyYuyyBVz68Qp7BzNsTqpRY7rRv5smTD1Zi+ZokJr1/mehYA29PqGI7dm5a/1caI6dctL0W/XLDaXE3qalmUAdX1u03MvvHHK4lmXlmgDs6d8dxhwUqeaKnG3tP5TP7xxxORJkY09eNQN/C072fXsGEB7UkpJr5YlUuHy7LYf0BIwXF37splQd7+tGvqy9fLY1j8gcx5BnMzJhQreS8b+7J2KEB/LAmiYnvRRMdm8eMCdWK5P26v1J5/JXzttfCVYlOjf3AzvWsWPgJ/Yc/zbTZy6gSVpNPZzxHRlqKw/QeOi/6PDiGqR8sYvp/l9Ou6wAWffEOJ4/stqWpHBzKI2On8PZ/V/DqewvwqxTMpzOeIzM91amx33S/5n/zuq4M7+bB7ztzeXdBOrGJJiY+XPw5q3qImqcG6dh5NI8Z36Vz9LyR54Z6ElypMOaEZBM/rM/m7W/T+PD7DJLTzUx82BNdMdssjRb1NDz0gI7V27N5Z14KVxMKmPSYd4nl5rgH9fx1JI+356Vw5JyBFx72sis3QwNd+H1HNu/MT+GL5ekE+qmZ8IiX02MXFY9U2MpYZmYmjz76KB4eHgQFBfHf//6Xzp07M3HiRAAMBgOTJ08mJCQEDw8PWrVqxbZt22zrL1q0CG9vb9avX0/dunXR6XT06tWLuLg4WxqTycSkSZPw9vbGz8+PV199FYvF/i6p2Wxm1qxZhIeH4+7uTqNGjfj5559tn2/btg2FQsGff/5Js2bN0Gg07Ny5s0zypG8nPSs3pHHwZA5X4vL5YtkNfPQqWjTQFrtOv856Nu/JZNv+LGIT8pn/UzJGo4WurTwBuBqfz8eLEjl0KpeE5AJOXszjh7UpNKunxVkzxd5Yv4Pz0z8l4bdNd5U+9OmHyY2O5cyr/yHrbBSXv1pK/Mr1hL84ypYmfOJorn63gtjFq8g6c4kTz07HlJNH1VEPOifoW3Rv6cbOYwZ2nzASl2xm6bocjPnQtqHGYfpuzTWcispnw/484pPNrP4rlyvxJjo3s0/vrVPwcHcPvvs9G5O5bO7O3zToAX9+XJ3A3iMZxMTmMfubq/j5uNC2afEX+oN7VuLP7Sls3JnKlesGPl98DYPRwgMdrZViswVS0wvsXm2befHXgXTyDM6tgA4bEMKSFZfZuS+ZSzHZzPzvWfx8NXRo7V/sOnsPpfDN/2LYcUur2q2yc0y89NZxtuy8wdVruZw6l8kn8y5Sp6YnlSs5/t/eqwdau7PjSB67jhmISzLx/ZosjPkW2jd2c5i+e0t3Tl40sn5PLnFJJn7dlsPluAK6tnAHINdg4ZOl6Rw8bSAh2UTUtQKW/plFWLALvvqyOS317+rDT38ms/94FpevGfh0UTy+XmpaN9YVu87Abj5s2JXO5j0ZXI03MveHBAxGM93b2F8gGfLNpGWYbK/cPOcdN50bu7DnVD77zxSQkGrhp60GjAUWWkU67iDTqbELZy+b2Hokn4RUC3/uMxJ7w0yHhi62NH3buHL6cgG/7zZyLclMcoaFU9EmsnLL5vc7oJsvK9Ymse9YFjHXDPx34XV8vdW0buxZ7DqDuvuxfmcam3enczXOyFdL4zEYzfRo622XzmC0lFneA2z8fSkdegymXbeBBFeN4LFxb+CqcWPXlt8cpq9dvzlNW3clqEoEAYFV6d5vBFVCa3LxzFFbmlYdexPZqBWVAqsQUq06w0dPIjcni9jL550a+033a/73aOnGX0cN7D5uLXf+92c2xgJo16iYc1YLN05dymfDvjzik038tiOXK/EFdG1WWE7tP23kTEw+SWlmrieZWLEpB62bkioBRW9EllbP1lp2HM5l59E8rieZWPJHJsZ8Cx2auDv+vq20nLxoZN3uHOKSTPyyNdtabra0XhvlGix8/L80Dpw2EP93ufm/PzPLtNz8NymUinJ73Q/u//9wBTdp0iR27drF6tWr2bhxI3/99ReHDx+2ff7888+zZ88efvzxR44fP86wYcPo1asXFy4U3k3Pyclh9uzZfP/99+zYsYMrV64wefJk2+cff/wxixYtYsGCBezcuZOUlBR++cW+292sWbNYsmQJX3/9NadOneKll17iscceY/v27XbpXnvtNT744APOnDlDw4YNnZ4fAX5qfPRqTpwv7FaRk2fh4mUDtcMcF8JqFURU0XD8fGG3MYsFjl/IpVZo8RekWjcluXlmzOXUU8C7dWOStuyxW3Zj4058WjcGQOHiglfTeiRtLrzzisVC0pbdeLdu4tRYVEqoFqjiTExh1xALcDYmn4gQxxd+EcFqzsbYd5k5HW2fXgGM7q9jw37rhXlZCqzkiq+3C0dOZ9mW5eSaOXcphzrVPRyuo1YpqBnmztFb1rFY4OipTOpWd3yDoEaoO9VD3Vm/w/Ed9H8quLIb/r4aDhwtvIuenWPi9PkM6tdxbsuSTqvCbLaQmVX6VnKVEkKD1JyJLuxeacF6LFSv4uJwnepVXDgdbX/snIoyFpsewN1NgdlicVoX5ltV9nfB10vNsbM5tmU5eWbOR+dRO9zxxZNaBdWrudmtY7HAsbM51I6wr6h2aqHn+4+q89m0MB4f6F9ii+m9UCmhSoCS81cLf1sW4PxVE2GBji8wwwJVdukBzl4xERZkPd0rgMgwNTfSzDwzwI13x2h5aZg7DSKcf8EKhXl/9Ey2bZk173OpE1F83teo5saxW9axWODo2Wxq37ZO55Z6ln5cky/eCueJQZXQOCnvAQry87l86Qx1G7ayLVMqldRt2IpL547fcX2LxcKZ4/uIvx5Drcimxe5jx4ZVuGt1VAmr5bTYb7pf899W7sTYlztnoo1UD3FcjkSEqDkdc3u5U/w5TqWEjk005OSZiU1w7vlLpYTQYDWno24rN0soB6tXdbFLD3DykpEaVYofvaTVlF25KSoWGcNWhjIzM1m8eDHLli2jW7duACxcuJDg4GAArly5wsKFC7ly5Ypt2eTJk1m3bh0LFy7k/fffByA/P5+vv/6a6tWrA9ZK3owZM2z7+fTTT5k6dSpDhgwB4Ouvv2b9+vW2zw0GA++//z6bNm2iTZs2AERERLBz507mzZtHp06dbGlnzJhBjx49yipL8P67O0Valn3hmJZlsn12O08PFSqVgvRM+3XSM02EBDgu+Dw9lAx9wIdNe5zbpe1eaCr7Y0hIsltmSEjCxcsTpZsGFx8vlGo1hsTk29Ik41E7wqmx6LQKVEoFmdn2hXpGtplAP8d5qNcpyXCQ3suj8D5Pz9ZumM2wpQzHrN3k42UtrlLT7SshqRkFts9up/e0HjuO1qkS5Lh1qGdHH65cy+PMxRyHn/9Tvj6u1n2n2V9QpKYZbZ85g6uLgvGjIti0I5Gc3NJfhHhqlaiUCjJuGyORkW0myN/xseOlU5KRfVv6LDN6D8f3CNUqGNrNg/0nDeQZnX/h4aP/u9zJsD8O0jILbJ/dTq+zHjtF1skwUaVy4f9rx4EMbiTnk5JeQFiIhicGVyKksisfzL9e6rg93P/+3ebY50lmjoXKPo7z0lPrKL0Zvdb6G9FpFbi5KujWzJW1e438vttInVAVo/u48eWqXC5dd+4dLh+9db9pGbeV+Rmm4n+3OrX1d5tZdJ0qgYU36bYfyCAxOZ+UtALCqmgYNSSAkEBXZn19zSmxZ2WmYTab0Hvbd1HXe/sSfy2m2PVysjN59aleFOTno1AqefTp14hs3NouzbGDO/jmk6kYDXl4+fjz0vS5eOp9nBL3re7X/L95zip6DrIQ6Oe4UuilU5J5e7mTbcZLZ/9baVjDhacGeeLqAulZFv77Q4bTW5dt5aaDeIL8Hed7seWmznEZpVbB0O469p0om3JTVCxSYStDUVFR5Ofn07JlS9syLy8vateuDcCJEycwmUzUqmV/V81gMODn52d7r9VqbZU1gKCgIBITrf3E09PTiYuLo1WrwjuAarWa5s2b27pFXrx4kZycnCIVMaPRSJMm9i05zZs3L/E7GQwGDAb7i3NTgQGV2nFLV/umHowbXtjda9Y3zhuIXxx3jYKpT1UmNsHIinVlMyZAQLXKKro2d+O9Rc6dlOOmLm28eWFkiO399P/GlMl+buXqoqBzGx9+WF3647RHpwBeea7wt/3qjLKZSOZWKpWCGVMiQQGzv3I85q2iUSlh/FA9CuD7tVl3TH83OrXwZPyIQNv7d7+Kdcp2Hdmws/D4v3zdSEqGiZkTqxLo70J8UsmTOpQHxd/XuiejCth+1BrftSQz4YEq2jVw4dL10t186dRSz3OPBtnez/jiaqm2V5L1f6XZ/r583UBqegHvTQol0D+xXPPezd2Dtz7+gby8XM4e38+KhZ9QqXIVatcvPL/Wqd+Ctz7+gcyMNP7a9AvzPp7C6x8sKVI5vFeS/3d29nI+M75Lw9NdSYfGGsYN9uT9RelFbnRUZColjB/mhUIB368pvxvTzqRw1viV/6OkwlaOsrKyUKlUHDp0CJXK/g6KTlc4rsLFxf4utkKhKDJG7U77AVizZg0hISF2n2k09hUtDw/HXctumjVrFu+8Yz/pRt1WE6jXZqLD9AdP5XBxduHdNvXfg5y9dSq7O37eOhUx1x3PaJeZbcJkshQZ7OzlqSpy19BNo+CNcYHkGix8tCARUzlOnGRISEJT2X5skqayP/npmZjzDBiTUjEXFKAJ8LstjR+GePuWudLKyrFgMlvw9LC/M6n3UJKe7TiTrC0ixaevWVWNp4eCWc962z5XKRUM7aqlaws33phbuorc3iMZnL1U2Mp1c4C8j5farsXMR6/m0pW8IusDZGRaj53b7yT76NWkphe9oGjfwguNq4LNu0pf0d+5P5nT5w/a3ru6/D1BjrcLyamFx7qPtysXo0pfUVGpFLw7JZLAADcmvHHMKa1rYG2dMZkttgl+btJ7KEkvZmaydAetaXoHd49VSnjmQT1+Xio++j7NaXeJ9x/P4lxMjO39zWPHW68m9dZyx9M6+50jGVnWY8dbb3/seOtVpGYU39X05uyjQZVKX2HLzv37d3vbJAWeWgUZxVxcZuY4Sq+0pc/OtWAyWYhPsf9fJKSaCQ8qfbfI/ceyOB8dZXtfmPf2+eatVxF1tbi8L7D+bm8r8731qiKt5bc6dzPvA1ydUmHQeXqjVKqKTDCSkZaC3tuvmLWs3SYDgqoBUC28NnGx0axdtcCuwqZxcycgqBoBQdWoXrshbzw3kJ2bf6XPg0+WKub/K/l/85xV9BxUtNXtpvQsM563lzsOyiljPtxINXMj1UzU9QJmPuNN+0Ya/tzj+DzyT9jKzbuI59b4HZabt/VIst7k8sLfS8mHS5xXboqKTaqzZSgiIgIXFxcOHDhgW5aens7589aBxU2aNMFkMpGYmEiNGjXsXoGBgcVt1o6XlxdBQUHs27fPtqygoIBDhw7Z3kdGRqLRaLhy5UqR/VStWvWevtPUqVNJT0+3e9VpMb7Y9HkGC/FJBbZXbHw+qRkF1K9V2B3NXaOgRqiGczGOTx4FJoiKNdDglnUUCmhQ053zlwvXcdcomPZMIAUmC//5NoH8gvItxNL2HsWvq303GP9ubUndexQAS34+6YdP4d+1TWEChQK/Lm1I23vEqbGYzHAl3kTdsMLKvwKoE+pC1DXHJ+Co6wXUCbO/WVA3TG1Lv/ekkXe/y2DmgsJXaqaZDfvy+Gx56e/45eaZiUs02l5XrhtIScuncWThzQytm5La1bWcvZTtcBsFJgsXYnLt1lEooHGkjjOXinZ57NnRl31HMop0v/1H8eeauBaXZ3tFX8khKcVA80aF3Z607ioia+k5eTajVPu6WVmrEuzOxDePk5HpvBleTWa4HFdA3bDCboAKoG64C5diHV+UXYrNp264/bETGe5ql/5mZa2yr4rZ/0sj24ldknINFuJv5NteV+OMpKQX0LB24bhFdzcltcLdbBeZtyswwaUreXbrKBTQsLaWc1HFX9iFV7GWUykZpT+GTGaITTRTs0rhhbMCqFVVRUy84+3HxJuoWdX+Qrt2VRUxcWbbNq8kmgm4rUtlJW+lU2Z5zTWYibuRb3td+TvvG9UpvBlozXt3zkYVn/cXr+TRsG7hOgoFNKrjwbli1gGIqGrN+5IqFfdC7WKdcv/M8f22ZWazmTPH91O99t2P8bZYzBTkl1yBsZgtFOSX/jEc/1fyv7DcsT9n1Q1z4dI1x3kZdc0+PVjLqeLOcbbtKgpvJjuLyQyXrxdQN+K2cjPCtfhy82o+dcPtu8fXi3DlYmxh/DcrawF+KmZ/79xys7wpVYpye90PpIWtDHl6ejJy5EheeeUVfH19CQgIYPr06SiVShQKBbVq1eLRRx/liSee4OOPP6ZJkybcuHGDzZs307BhQ/r27XtX+3nxxRf54IMPqFmzJnXq1OGTTz4hLS3NLo7Jkyfz0ksvYTabad++Penp6ezatQu9Xs/IkSPv+jtpNJoirXIqtePZ64qzZnsGD/bwJv5GAYkp+TzU24fUDBMHThReQL81PpD9J7JZt9N64f/HtgyeG+HPpatGLl420LeTHo2rgq37rJ+7axS8+UwgGlcln/0vAa2bEu3f9buMLBPOmLxQ5aHFo0Y123tteBX0jepgTEkn72octWdOwi2kMsdGTwHg8vwfCX32UerMeoWri1bi36U1QcN6c2DAONs2oj9dSKMF/yHt0EnSDxwnbMJI1B7uXF28qvQB32bT/jxG9fMgJq6AmLgCujV3w9UVdh+3VnpH9fMgLdPMr9utJ+TNBw1MHuFJ95ZunLhopEWkhtAgNf9bZ/0/ZedZyM6zv2g0mS1kZJtJSCmbps1fNyTxcP8ArsUbSUgy8viQyiSn5rP7cGGFZ9ar4ew+lMHvm63H5S/rb/DyU1W5EJ3LuagcBj3gj0ajZONf9q1oQQGu1K/lwVv/jS6T2AF+Wn2NkQ9V4+r1XOIS8hj7WBjJKQb+2lvYovrpzIbs2JPEqjXWMVDubkpCggoH+gdVdqNGuAeZWQUk3DCgUimY+VoktarrmDLjJEol+HpbL1oysgoocMKNiw17cxkz0JOYuHyirxfQvaU7GhcFu45ZKy5jBnqSmmlm1RZrxXnT/lxefcKbB1q7c/yCkZb1NIQFq1nyd9edm90gQwPVzFmejlKB7U66tVWp1CEX8fuWVIb38SPuhpGEpHxG9PcnJb2AvUcLWzdnvFiFvUezWLs9DYDfNqfy4shALl7J40JMHv27+uCmUbJpj7X1ONDfhY4t9Bw6lUVmlomwKhqeHBrAyfM5XL7mnHGd247mM6K7hquJZq4kmOjU2BVXtYJ9p60XcY/20JCeZeGPPdaL/e1H83lhiDudm7hwOqaApjVdqBqgZPmWwni2HDYyspcbl66buBhrok6omnrhKr5YVfzFeGms3pzCQ338uZ5ozfvHBlYiJa2AvUcLb+zMfKkae45ksmab9Xf566ZkXhoVzMWYPM7H5DKwmy9urko27U4DrHnfqaUXB09mkZltIixEw9jhlTl5PpsYJ+U9QI/+j7Lg8+mE1YgkvGY9Nv2+DKMhl3ZdBwDw3Zxp+PgFMOSxFwBYu3IBYdUjqRRYhYICIycO7WLv9rU8+vRUAAx5uaz5+VsateiEt48/WZlpbP1zBakpiTRrWzbjx+/X/N+4P48n++uIiTP9Xe644eqiYNff56wn++tIzTTzyzbrOWnzgTwmP6anR0s3TlyynrPCgtR8/6e1XHJ1gb5t3Tl2IZ+0LDM6rYIuzdzw8VRy6Izznll50/q9OYwdpCfmegHR1/Lp0VqLxkXBzqPW39nYQdZyc+Vma3wb9+UwZZQPPdu4c+y8kVb13QgLVrP4d+v5TaWEZ4d5ERqkZs4P6SgUilvKTXO59igSZU8qbGXsk08+4ZlnnqFfv37o9XpeffVVrl69ipubtTaxcOFCZs6cycsvv8y1a9fw9/endevW9OvX7w5bLvTyyy8TFxfHyJEjUSqVPPnkkwwePJj09MIuae+++y6VKlVi1qxZREVF4e3tTdOmTXn99ded/p3v5Lct6bi5Khg33A+tu5Kz0Qbemxdv1yJW2V+Np0fhXeLdR7PR65Q81MsHb72KmGsG3puXYOtaEF5FQ60wa55+8aZ9q+GzM65yI7X0d/y8mtWnzebvbe8jZ1vz7uqSVRwfMxVNUCXcqxaOHciNieXAgHFEfjyVsBeeIC82nhPj3iRpY+HjEuJ++hPXSr7Umj7B+uDsY2fY328sxsR7qwTfjYNnjei0CgZ0cEf/94OzP1ueaeu376tXcmtP26hrBXy7OouBHbUM6uhOYqqJuSuzuF7Gs0GW5Ke1N3DTKJkwOgSdVsWp89lM+zia/PzCwIMCNOg9C4u2HfvT8fJU89jgyvh6WbtPTvs4ushkEg908CUpNZ/DJ50zjsqRpSuv4uam4tXna6HzUHPidDovTz+B8Zb4QwLd8dYX3iWuU8OTz2c1tr2fMLYGAGs3x/P+p+eo5OdqeyzAos/tx6C+MPUoR06WfozhgdMGPLUKBnXyQP/3A2z/uyzd1jXp9mPnUmwB3/ySweAuHgzp4kFiiokvVmRw7Yb12PH2VNKktvXGzztP24/Z+XBJGucuO3/8y6oNKbi5Knh2RCAeWuvDg9/5PNau3Ams5Go3wH/noUz0OhUj+vnjo1cRHWvgnc9jbS2wBSYLjepo/67IKUhKLWDPkSxW/Om83++RCwV4uCvo3coVvYeCazfMzFuda5skwUenxGIpvFKLiTezZEMefVtr6NfGlRtpZr5bk2fXBfJElImfthro3tyVIR0V3Eg1s3BtHtFxZXPFt3J9Mm6uCp5/LAgPrZLTF3OZ/tlV+7z3d7HP+4OZeOkSeXRAJXz0KqJiDUz/7Appt+R947paBnSzVqKTUgrYfTiT5Wud2528RfueZGak8tsPc8lIS6ZqeG1enPaFrUtkSlK83dgbgyGXpd/MIjU5ERdXDUEhYYx58V1atO8JWLtLxl+LYc+2P8jKSMPD04uwGvV4deZ3hFSr7jCG0rpf8//gGSOe2hwGdrSes64mFDBneaZt8ixruVP4HS5dK+Db37IY1EnL4M5aElNNfPlzJtf/LnfMZgj0V9GmoRs6dwXZuRZi4gr48Pv0MjmvHThlwFObxaDOHrYHZ/93aVphuemlsruZfCm2gPmrMhjSxYMhXXUkpJj4/Md0+3Kzzt/l5jP25eZ/FqWWSbn5b7pfptcvLwrLvQyGEqWWnZ1NSEgIH3/8MWPGjCnvcJxi2Etl1yLxbxj1Ra/yDqFUVr+7586JKqgrZ8tuMoh/Q2by/T2pTe2WkeUdwj+WdN25j134t4VHhtw5UQV26WTZTWZR1l6ZUO3OiSqwjz67Ut4hlEpgaEB5h1Aqpvzyu2lZWgumV9y8jxp19w0Vzhax6I9y2/fdkha2MnbkyBHOnj1Ly5YtSU9Pt03HP3DgwHKOTAghhBBCCFHRSYXtXzB79mzOnTuHq6srzZo146+//sLf3//OKwohhBBCCPF/nEzrXzKpsJWxJk2a2M3YKIQQQgghhBB3SypsQgghhBBCiHIjk46UTNofhRBCCCGEEKKCkgqbEEIIIYQQQlRQ0iVSCCGEEEIIUW6kS2TJpIVNCCGEEEIIISooaWETQgghhBBClBuZ1r9kkjtCCCGEEEIIUUFJC5sQQgghhBCi3MgYtpJJC5sQQgghhBBCVFBSYRNCCCGEEEKICkq6RAohhBBCCCHKjUw6UjLJHSGEEEIIIYSooKSFTQghhBBCCFF+FDLpSEmkhU0IIYQQQgghKiipsAkhhBBCCCFEBSVdIoUQQgghhBDlRp7DVjKpsIlS8wv0Lu8QSmX1u3vKO4RSGTCtTXmH8I8lbzpX3iGUSnqWpbxDKBUffXlH8M+5ufqVdwilcuKcqbxDKBU3rVt5h/CPrd93f1/6fP1yQXmHUCpHsrzKO4RS6WTaUN4hlEKf8g5A/EP3d6klhBBCCCGEuK/JtP4lk9wRQgghhBBCiApKWtiEEEIIIYQQ5UbGsJVMWtiEEEIIIYQQooKSCpsQQgghhBBCVFDSJVIIIYQQQghRbmTSkZJJ7gghhBBCCCFEBSUtbEIIIYQQQohyI5OOlExa2IQQQgghhBCigpIKmxBCCCGEEEJUUFJhE0IIIYQQQpQbhVJRbq979eWXXxIWFoabmxutWrVi//79Jab/9NNPqV27Nu7u7lStWpWXXnqJvLy8e9qnVNiEEEIIIYQQ4g6WL1/OpEmTmD59OocPH6ZRo0b07NmTxMREh+mXLVvGa6+9xvTp0zlz5gzfffcdy5cv5/XXX7+n/UqFTQghhBBCCFF+lMrye92DTz75hKeeeorRo0cTGRnJ119/jVarZcGCBQ7T7969m3bt2jFixAjCwsJ44IEHeOSRR+7YKlcke+4ptRBCCCGEEEL8H2EwGMjIyLB7GQyGIumMRiOHDh2ie/futmVKpZLu3buzZ88eh9tu27Ythw4dslXQoqKiWLt2LX369LmnGKXCJoQQQgghhCg3CoWi3F6zZs3Cy8vL7jVr1qwiMSYlJWEymahcubLd8sqVKxMfH+/we40YMYIZM2bQvn17XFxcqF69Op07d5YukUIIIYQQQghxN6ZOnUp6errda+rUqU7Z9rZt23j//ff56quvOHz4MKtWrWLNmjW8++6797QdeXD2PYqJiSE8PJwjR47QuHHj8g7nnnXu3JnGjRvz6aeflncoQgghhBBClCuNRoNGo7ljOn9/f1QqFQkJCXbLExISCAwMdLjOtGnTePzxxxk7diwADRo0IDs7m6effpo33ngD5V2OoZMKWwVUlpWqVatW4eLi4vTt3otOTTQ80EqD3kNJbKKJ5ZtyiIkzFZu+aW0XBnRwx89LSWKqmV+25XAyqsBh2hEPaOnYRMOKzTlsOVi0/3FpdW6qoUcrN7z+jv3Hjdl3jH1gR6019hQTq7blcjIq33HsPbV0auLGik3ZbC6D2H3bNyfi5TF4Na2PW3AABx98loTVm0tep2NLIme/hi6yJnlX47g4ay6xS36xSxM6fgQRk8agCaxExvGznJr4LukHTjg9foCDW5eyd8N3ZKXfoHKVOjzwyDRCwhs6THvkrxWc2PMrN65fACCwWj06D55kS28qyGf7b59y8cQO0pKuonHXEV63LV2GvIynd2WH2yyN4zuXcmTrd+RkJuEfXIeOg9+kcqjj2JPjL7Dvz8+4EXuKzNTrtB84lcadRtqlWfxuVzJTrxdZt0G7EXR68C2nx39gy1L2rP+OrPQkKletQ69H3iQkwnH8h3es4Pie37hxzZr3QaH16DL4Jbv023/7nFMH1pKREo9K7fJ3momERDRyeuwAezct5a+1C8hKTyKwah36Pf4GVas7jv/A1hUc2bWahFhr/CFhkfQY9lKR9InXLrF+xcdEnz2A2WQiIKQ6I16Yg7d/sFNjb1lbSdt6SnTukJBiYe1+M9eSLQ7TVvKCro1VBPkp8NEp+POAib1nzHZpQgMUtKunJMhPgV6r4IetBZy96nh7zvJQL2+6tfHEw03J2RgD3/yURHyS43L8pp7tPBnQ1QtvTxWXrxtZsCqZi1eMts+fHuZHg1ru+OpV5BktnIvO439/pHI90XEZ+0+1qqukQ301OneIT7Xwx54CYpMc51eAt4JuTVWE+Cnx8VSwZm8Bu08XPUfcyzZL69c1f7J81WpSUtOoHh7KC+PGULdWzTuut2XHTmZ+9CntWrXg3Ten2JZ37T/UYfqnRz/Ow0MGOi1ugJ0blrHt94VkpicRXK02g0e9TrUajn+3ezf/xMG/VhMfexGAKuGR9HnoRbv0mWlJ/PHDJ5w/vpvcnEwi6jRj8Kg3qBQU6tS4b1qxcSffr9lCcnomNasF88oTQ6hf3fG+ft+xn3fm/2C3zNVFze6FH9nevz1vGX/8dcAuTZsGdfh8yjjnB/8vU9zj5B/lwdXVlWbNmrF582YGDRoEgNlsZvPmzTz//PMO18nJySlSKVOpVABYLHf/m6/4uSOcytfXF09Pz3Lbf7M6Lgzt6s4fu/J4f1EGsYkmXhiuw1Pr+DkYESEqxgzwYNdxA+8tyuDoBSPPDNER7F/00G1c04XwYBVpmWYHWyq95nVcGdpVy5qduby3MJ3YxAImPORZQuxqxg7UseuYgZkL0zl6IZ/xD+oI9lcVjb2WCxHBalLLKHYAlYeWjOPnODnhnbtK7x5WhRar55G8bR87mw8k+vPFNJg3E/8e7W1pgob1pu5HU7kw80t2thxM5vGztFrzHa6VfJ0e/+kDa9n00yw69HuOMW/+QkDVOvw4ZwzZGckO018+t4/Iln159OUljJzyI3rfIH749EkyUq13xvKNecRfOU37fuMZ8+Yqho7/guT4aFZ8Od7psV84spadv31Ai57P8dCkVfgF12b1/LHkZDqOvcCYh5dfVdr0exmtZyWHaYa/9DOj3/7L9hr4jHWGquqNejo9/lP717JxxQd07P8cT721ispVa7Ps07El5P1+6rfsy+OTFzN66o/ofQJZ+t8xtrwH8A0Mo9eIaYx7ZzUjpyzFyy+Epf8dQ3ZmitPjP753LWuX/Yeug57juRkrCaxWm0UfPUVWMfFHnz1Aw9Z9GDN1Ec+89QNefkEs+mgs6SmF8ScnXGH+zEepFBTO2KmLeeG9X+kycDxq1zvfpb0X9cIU9GyuZNsxE/P+KCA+FR7vrsLDzXF6F7WC1CwLmw6byMxxfDHgorZWEtbsK/5mkzMN7OpF74565v+UzNRPr2MwmHnzmUBc1MU//6htYw9GDvLjp/VpTPn4OpevG3ljXCB6XWHZHxVr5Ksfkpj4wTVmzotHoYBpzwTyDx6rVKwG4Ur6tFSz5WgBX67OJz7FwqieLiXkP6RmWlh/sKDY/L/XbZbG1r92MffbxTzxyDDmffoh1cPDmPLWTFLT0ktcLz4hka8XLKFBvbpFPvt5yTd2r1defBaFQkHHtq2dGvuRPX+y+vsPeeDBZ3np/Z8IDq3N/A/GkZnu+Hd78cwBmrTtw/g3F/DCO0vx9gtk3qynbb9bi8XCwk8mkJIYy+jJnzNp1s/4VApm3vtjMOTlODV2gA17j/Dfpb/y1OCe/G/my9SqFswL/5lHSnpmset4uLux7ot3bK/fPy16861twzp2ad57/nGnxy6KN2nSJL755hsWL17MmTNnGD9+PNnZ2YwePRqAJ554wq47Zf/+/Zk7dy4//vgj0dHRbNy4kWnTptG/f39bxe1uSIWtGGazmQ8//JAaNWqg0WioVq0a7733nu3zqKgounTpglarpVGjRkVmh9m5cycdOnSwPSRvwoQJZGdn2z7/6quvqFmzJm5ublSuXJmhQ613rEaNGsX27duZM2eObTBkTEwM27ZtQ6FQsGbNGho2bIibmxutW7fm5MmTtm0mJyfzyCOPEBISglarpUGDBvzwg/3dms6dOzNx4kTb+7CwMN5//32efPJJPD09qVatGvPnz3dmVtrp3sKNXccM7DlhJC7ZzLL1OeTnQ9sGrg7Td23mxqmofDbuNxCfbOb3v/K4kmCic1P7M5u3TsFDPbQs+CMbk7ls7lJ2b+nGzmMGdv8d+9J1ORjzoW1Dxxdo3ZprOBWVz4b9ecQnm1n9Vy5X4k10bmaf3lun4OHuHnz3e9nFDnBj/Q7OT/+UhN823VX60KcfJjc6ljOv/oess1Fc/mop8SvXE/7iKFua8ImjufrdCmIXryLrzCVOPDsdU04eVUc96PT4921cSOP2w2nU7kEqBdegz6PvoHZ149iulQ7TDxr7Mc07P0pg1br4B1Wn7xMzsVjMxJy1/lbdtJ6MeGkhkc374BcYQUhEY3qOmEb85VOkJxdtuSqNo9sXUa/1MCJbPohvYA26DH0HtYsbZ/Y7jr1ytQa0G/AqtZr0RaV23CLurvPFQ1/J9oo5tQ0vv2qEVG/p1NgB9m5cRJMOw2jc3pr3fR97BxdXN47udBz/4Kdm07zLCAKr1cU/KIJ+o6x5H32msJxs0Ko/EZFt8alUlYCQmjzw0GsYcrNIjD3n9Ph3rVtM887DaNZxCAEhNRg46m1cNG4c2r7KYfrh4z+idfcRBIfWpVJwBIPHvIvFbCbqdGH8G3/+lNqNOtLr4VcIDovEr3I16jbtik7v59TY29ZVcuiCmaOXLNxIhz/2msg3QZMajk/f15MtbDhk5mSMhYJi7v9cvG5hy1Fzmbeq3dS3k56VG9I4eDKHK3H5fLHsBj56FS0aaItdp19nPZv3ZLJtfxaxCfnM/ykZo9FC11aFNxw37cnkTFQeN1ILiI418sPaVPx91FTydV7noXb1VRw8Z+bwBTM30iz8tquA/AJoVsvxhda1JAvrDpg4EW2moJj68L1uszR++vV3+vTsTu/uXQmrVpWXnn0ajUbDnxu3FLuOyWTivY/nMGrEQwRXLtrbwNfHx+61e+8BGjeoR3Cgc3sm7FizmNZdh9Ky82ACq9TgwTHTcXF1Y/82x7/bx57/kHYPPEJIWF0qh0Qw/OkZWCxmLpzcC0BS/GUuXzjGg0++RbXqDQgIDufBJ98i32jgyO61To0dYOmf2xjUpQ0DOrUiIiSQqaOH4aZxZfX2fcWuo1CAv7fe9vLzKnqD3cVFbZdG71H87+h+cr88OPuhhx5i9uzZvPXWWzRu3JijR4+ybt0620QkV65cIS4uzpb+zTff5OWXX+bNN98kMjKSMWPG0LNnT+bNm3dP+5UKWzGmTp3KBx98wLRp0zh9+jTLli2zmxXmjTfeYPLkyRw9epRatWrxyCOPUFBg7d5x6dIlevXqxYMPPsjx48dZvnw5O3futDWXHjx4kAkTJjBjxgzOnTvHunXr6NixIwBz5syhTZs2PPXUU8TFxREXF0fVqlVt+33llVf4+OOPOXDgAJUqVaJ///7k51u7f+Tl5dGsWTPWrFnDyZMnefrpp3n88cfv+KyHjz/+mObNm3PkyBGeffZZxo8fz7lzzr9oUimhWqCKM5cLu8FYgDMx+USEOD7BRoSoOXvZvtvM6eh8IkIKT2wKYFQ/DzbuyyMuqWxaqGyxxxR2tbEAZ0uKPVjN2Rj7rjnW2AvTK4DR/XVs2J9LXNK/c7f7bnm3bkzSFvsbETc27sSndWMAFC4ueDWtR9Lm3YUJLBaStuzGu3UTp8ZiKjASd+UU4XXb2pYplErC67YlNurIXW0j35iL2VSAu4dXsWkMOVmgUOCm1Zc65ptMBUYSY09RtZZ97FVqtSE+5qjT9nHu8GrqthqCQuHE5oW/tx13+RThkbfnfRtio47e1TbulPemAiOHdyxH4+5J5Sp1nBG2TUGBkesxp6hRr41tmVKppEZkG65cPHpX28g35GG6JX6z2cy5Y9vxCwxj4Ydjef+5dsx9+yFOH7q7myF3S6WEID8FUXGFFSsLEBVnoWol5/6fy0qAnxofvZoT5/Nsy3LyLFy8bKB2mOObXWoVRFTRcPx8rm2ZxQLHL+RSK9TxOhpXBV1aeZKQnE9yWsldLe+WSgnBfgouXi88r1iAi9fNVPuH+V8W2yxOfn4+5y9G0axRYZdApVJJs8YNOF3COf77H3/G28uLPg90u+M+UlLT2HvwMH163DntvSgoMBIbfZqa9e1/t7Xqt+byhWN3tQ2jIQ9TQQFanfV3W5Bv7U6rdi28QaxUKlGpXYk+d9iJ0UN+QQFno2NpVa+W3b5a1qvJ8YuXi10vN89Ivxdn0HfCO0z65DsuxcYVSXPozEV6PDuNIZPfZ9bCn0jLzHawJVGWnn/+eS5fvozBYGDfvn20atXK9tm2bdtYtGiR7b1arWb69OlcvHiR3Nxcrly5wpdffom3t/c97VPGsDmQmZnJnDlz+OKLLxg50jpupHr16rRv356YmBgAJk+eTN++fQF45513qFevHhcvXqROnTrMmjWLRx991NaSVbNmTT777DM6derE3LlzuXLlCh4eHvTr1w9PT09CQ0Np0sR6gevl5YWrqytardbhAMbp06fTo0cPABYvXkyVKlX45ZdfGD58OCEhIUyePNmW9oUXXmD9+vWsWLGCli2Lv+vep08fnn32WQCmTJnCf//7X7Zu3Urt2rWLpDUYDEWeTWEqMKBS37kbkE6rQKVUkJFtX6nKzLEQ6Of4zqLew0H6bAt6j8J7DQ+0dsNshi2HnD/u66absWdm29+Rzsg2E+jnuAVEr1OS4SC91y2x97wZexmMWSstTWV/DAlJdssMCUm4eHmidNPg4uOFUq3GkJh8W5pkPGpHODWWnKxULGYTHre1Xnh4+pEcF3VX29iycjY6rwC7St+tCvINbFk1m3ot+qJx15U65ptys62xu3vax6719CctMdop+4g6uRlDbiZ1Wgx2yvZudTPvb2858tD7kxR/d/Fv/vljPL0DiIi0z/vzx7ayav7L5Btz8fSqxGOTFqD19HFa7AA5mWmYHcSv8/LjRtzdxb9u+Wz0PgFUr2eNPzsjGWNeDjv++JYeQyfQ86GXuXB8J8s+m8CYqYsIr+OcVk6tBlRKBVm59suzci346++PCpu3p7VsT8uyvyGVlmWyfXY7Tw8VKpWC9Ez7ddIzTYQE2Je3D7Tz5PH+vrhplFxLMPLu3PhiW7buVWH+25fjWbkWKnn/s/vdZbHN4qRnZGI2m/Hxsb9R4uPtzZXYaw7XOXHqDGs3buabObPvah8btmxD6+5Oh7at7pz4HmRnWH+3nl5Ff7eJ1+/ud7tm2cd4+QTYKn0BweH4+Aex9odPGTp2Oq5u7uxYu4T0lHgy0m44Nf60zGxMZjO+t7WQ+Xp5EhOX6HCd0KAApj31MDWrBZOVk8v/1m7lyXc+Y8UHU6js5w1Am4Z16NK8ISEBvsQmJPPlijVM+Gg+C99+EdV9MAZM/HNSYXPgzJkzGAwGunUr/o5Rw4aFd6yCgoIASExMpE6dOhw7dozjx4+zdOlSWxqLxYLZbCY6OpoePXoQGhpKREQEvXr1olevXgwePBit9s7N2m3aFN5t8vX1pXbt2pw5cwawdmN4//33WbFiBdeuXcNoNGIwGO643Vu/i0KhIDAwkMRExwXKrFmzeOcd+zFQzbq9SvMer90x9rJQrbKKrs00vL84o1z2XxrVKqvo2tyN9xaVPJZAlN7uP+dz+sBaHpu8BLVL0ZsLpoJ8Vs17EYvFQu9H726MX0Vyet/PhNbpgM7L+ZOllNautfM5tX8tT7xSNO/D6rTi6bd+IScrlSN//cTKeRN58vUVRSrm5Wn7799wYt+fjJ26GJe/x6fdHChet2lX2vUaBUBwaF2uXDzC/i3LnVZhux+1b+rBuOH+tvezvkkoIXXp7TyUxfFzufjo1QzoomfSyADe/CyO/IJ/p7vn/yU5ObnM+uRzXn7+Gby87q6XwZ8bt9CtcwdcXR0Paygvm3/7hiN7/uTZaYtsv1uV2oWRL81hxfxpTHuqLUqlipr1W1OncQdrE245a1gzjIY1w2zvG9UMZ+irH7Bqy27GD7M+ZLlnm6a2z2tUDaZGtSAGTXqPQ6cv0rJ+rds3eX+RCmeJpMLmgLu7+x3T3DrT4s0uSGaztSUoKyuLcePGMWHChCLrVatWDVdXVw4fPsy2bdvYsGEDb731Fm+//TYHDhy45ybSW3300UfMmTOHTz/9lAYNGuDh4cHEiRMxGo0lrnf7rJEKhcL2XW43depUJk2aZLfs5c/vbrBuVo4Fk/lm61jhLVBPbdFWtJsybmtNA/C8pdWtRlU1nh4K3h9feAdRpVQwtIs73ZpreONr51Tkbsbu6WF/V1vvoSS9uNizzOhLSF/z79hnPettH3tXLV1buPHG3PKtyBkSktBU9rdbpqnsT356JuY8A8akVMwFBWgC/G5L44ch3r5lrrS0Oh8USlWRSS6yM5Px8PIvZi2rvRu+Y/e6+Yx4aaHD7namgnxWzZ9Iesp1Hp202KmtawDuHtbYc2+bYCQnMwmtZ8mx342MlGvEnt9D79Gfl3pbjtzM+9sn6MjOSEJ3h7zfs/47dv35DY+9vIDKVYu22LtqtPhWDsW3cihVqjfmy9d7cmTnz7Tv47wZz7Se3igdxJ+VnnzH+P9au4Ada75h9KsLCKxWGL/W0xulSk1ASHW79JWCI7h83nldq3IMYDJb0N12StK5K8jKc7xOeTt4KoeLswtbb9R/TyzirVORllFY7nvrVMRcd3xuysw2YTJZ8LqtBc7L034bYO1emZNXQHxSARcu57HwvVBaNtCy60jpu4kV5r8Ca8dFK527gqxiJhQpj20Wx0vviVKpJDXV/lySmpaGr493kfTX4+OJT0zkjXc/sC27eXOi+8DhLP76M0KCCnv+HD91mqvXrvPWlElFtlVaHnrr7/b2CUay0pPx9C75d7v1j4VsWf0dz7z+LcGh9uVO1Yh6vPzBKnJzMjEV5KPT+zLnzYepElHPqfF7e3qgUiqLTDCSkp6J311WhtVqFbXDQriaUPz5tEqAP96eHlxNSLr/K2yiRFKddaBmzZq4u7uzeXPJU54Xp2nTppw+fZoaNWoUed28C6VWq+nevTsffvghx48fJyYmhi1brIOAXV1dMZkc9+nYu3ev7e/U1FTOnz9P3brWWZx27drFwIEDeeyxx2jUqBERERGcP3/+H32H4mg0GvR6vd3rbrpDApjMcCXeRJ1Q+zFcdcJciLrmeMxB1LUCu/QAdcNciLpmzZ99J43MXJDBewsLX2mZZjbsN/DZiqx/9iVLiL1u2C0VdaBOaAmxXy+gTph9ZbhumNqWfu9JI+9+l8HMBYWv1EwzG/bl8dny4meR+rek7T2KX1f7Wb/8u7Ulde9RACz5+aQfPoV/18JWXxQK/Lq0IW3v3Y0ru1sqtStB1erZJgwBsJjNxJzZQ5WI4sfL7Vn3DTv/+IpHXvyW4LAGRT6/WVlLTbzMiJcWodU5tzvezdgDqtTj6gX72GMv7CUwrHGpt39m/yrcdX6E1e1U6m05olK7EhRaj5gz9vFHn91LlYjGxa63+89v+euPuYyY+I3DvHfEYjFjyi/5BtO9UqtdCQ6rx6VThWWn2Wzm0um9VKvRuNj1dqz5lq2/zWXk5PlUiahfZJtVwuuTdFuXyqT4GLz9nDelv8kMcckWIoIKb/wogPBABVdvlH+LgCN5BgvxSQW2V2x8PqkZBdSvVThRlLtGQY1QDediHHcFLzBBVKyBBreso1BAg5runL9ccvdxhYISZ5+8FyazdRKX6sGFl0oKoHqwkiv/MP/LYpvFcXFxoVaNCA4fL3zMitls5vCxE0Q6GPJQrUoI333xCd98Ntv2atuyOY0b1OObz2YT4G9/c+7PDVuoVSOC6uFhTo0bbv7GIm0ThtyM/cKpfYTWLP7RH1tWf8emVV/z9GvzqFq9frHp3LWe6PS+3Ii7zNWoU9Rv3tWp8buo1dQJr8L+U4XXYGazmQOnLtCwxt09QsBkNnPxahz+3sVX8BKS00jPyikxzf3ifpl0pLxIC5sDbm5uTJkyhVdffRVXV1fatWvHjRs3OHXqVIndJG+aMmUKrVu35vnnn2fs2LF4eHhw+vRpNm7cyBdffMEff/xBVFQUHTt2xMfHh7Vr12I2m21jxsLCwti3bx8xMTHodDp8fQunSJ8xYwZ+fn5UrlyZN954A39/f9uzIGrWrMnPP//M7t278fHx4ZNPPiEhIYHIyMgyyad/YtOBPEb19eByvImYuAK6NnfD1QV2n7BepI3qqyUt08yvO6y3j7ccyuPlRzzp3kLDiUv5tKjrSmigiqXrrHdPs/MsZOfZn+RMZgsZ2WYSUpw7Acmm/XmM6udBTFwBMXEFdGvuhqsr7D5uvYAY1c/DGvt264CTzQcNTB7hSfeWbpy4aKRFpIbQIDX/W5dzS+z2FfOyih2s0/p71Khme68Nr4K+UR2MKenkXY2j9sxJuIVU5tho6/N2Ls//kdBnH6XOrFe4umgl/l1aEzSsNwcGFLZ+RH+6kEYL/kPaoZOkHzhO2ISRqD3cubrY8SxepdGqx2hWL5xCUGh9gsMbsn/TYvKNuTRsNwSA1QtexdO7Ml2GvAzA7nXz2bH6MwaN+RgvvxCy0q1jFFw1WlzdPDAV5LNy3gTir5zmoefnYTGbbGncPbxQqZ3Xxadxp1Fs+uE1AqrWp3K1hhzbvpgCYy51W1pj37hsCh76ANr2s8ZuKjCSknDJ+rcpn+z0BG5cO4OLqxbvSoUne4vZzNkDv1CnxSCUqrIrzlv3GMVvC16zz3tDLo3+zvtfv5uCp3cA3R60xr/rz2/Y/ttnDH5qNt7+RfPeaMhh55qvqdWoKzrvSuRmpnJg6zIyUhOo27yX0+Nv12skK7+ZSkh4fapENGD3hiUYDbk062gd8/fTvCnofSrTc7i1pWDHH9+wadXnDB8/Gx//EDL/Ht/i6qZF4+YBQPs+T7L8y5cJq92ciMhWnD++k3NHtjFm6mKnxr77jJnB7VRcS7JwLdlCm7pKXNVw5KK1jBjcTkVmjoVNR6zvVUrrs9hu/q3XQqAPGAsg5e/7QK5q8L1laI2PTkGgj4VcI6SXwfwFa7Zn8GAPb+JvFJCYks9DvX1IzTBx4ERh74y3xgey/0Q263Zag/xjWwbPjfDn0lUjFy8b6NtJj8ZVwdZ91s8D/NS0bezB8XO5ZGSZ8PVWM7ibF8Z8C4fPOG+K9l0nTTzYQc21JCWxNyy0rafCVQ2HzlvL7qEd1WRkW9hwyPpepbQ+iw1ApQK9BwT5KjDkW2z5f6dtOtOwQf354L9fULtGderUqsHK39aQl2egV/cuAMz65DP8/fx4auSjuLq6Eh5azW59nYf1eL99eXZODtt37eGZMU84PeabOvYdyY9zX6dqRD2q1WjAjj+/x2jIpWUn6+922VdT8fIJoO8jLwGwZfW3rPvpCx57/kN8KgXbxqVpbvndHtu7Hg+9Dz5+QcRdvcCvi2dRv0VXajds5/T4H+3dmbfnLSMyvCr1qoeybN12cg1G+neyjvd76+ulBPh48fxD/QD45pf1NKgRSpXK/mRl57JkzVbik1IZ1MV64zQnz8A3q9bTtWVD/Lz0xCYk8dmPv1O1sj9tGjp3siZR8UiFrRjTpk1DrVbz1ltvcf36dYKCgnjmmWfuat2GDRuyfft23njjDTp06IDFYqF69eo89NBDAHh7e7Nq1Srefvtt8vLyqFmzJj/88AP16lmb5CdPnszIkSOJjIwkNzeX6OjCu7gffPABL774IhcuXKBx48b8/vvvtla7N998k6ioKHr27IlWq+Xpp59m0KBBpKdXnDFSh87m46nNpX97N9uDsz9fkWV7Xo2vXmnXlTzqmonvfs9mQAd3BnZ0JzHVzNersrheRrNBluTgWSM6rYIBHdxtsX+2PLOE2Av4dnUWAztqGdTRncRUE3NXZnG9nGaD9GpWnzabv7e9j5z9OgBXl6zi+JipaIIq4V41yPZ5bkwsBwaMI/LjqYS98AR5sfGcGPcmSRt32tLE/fQnrpV8qTV9gvXB2cfOsL/fWIy3TUTiDJEt+pCdmcL21Z+RnXGDylXq8vCEb9Hprd1j0lPiUCgK71of3v6jrVJ2qw79nqfjgBfITEvgwjFrq/a379o/7PWxl5cQWtt5g+hrNulDblYK+9d9TnbGDSqF1KX/09/YukRmpl63m90xOyOR5R8XTiByZNsCjmxbQHD1Fgx5rvB/ePXCbjJTr9sqfmWlXss+5GSlsP23z8nKuEHlqnUZMfEbW5fCjGT7+A9t+wFTQT4/z33Rbjsd+z9Hp4EvoFSqSIqL5vjuCeRkpeLu4U1weANGTVlKQMidH+h7rxq27kN2ZiqbV31GZnoSQdXqMuqV+bb405Ptj519W6zHzg+f28ffddBzdBtine23XvMeDBg1nR1/zOeP/72Pf1A4j7wwh7DazZwa+6kYCx4aM10bq6wPWU6x8P1mE9l/d4n08gCLpTDvPd1hfP/Clv129VS0q6ciOt7Mog3WsifYT8HonoWn/14tVICKIxfN/Lrb+eXTb1vScXNVMG64H1p3JWejDbw3L95unFllfzWeHoVdIHcfzUavU/JQLx+89Spirhl4b14C6VnWsj8/30LdCDf6dvJC564kLdPEmag83pwTR0aW884PJ6LNeLgV0K2pGk93iEuxsGhD/i35r7Ar9z218Pygwps9HRqo6dAAouLMfPdn/l1t05m6dGhHWnoGC5f+SGpqGtUjwvjPO2/YukQm3khCqbj3zlZbd+zCYrHQtWP7Oyf+h5q06U12Rgrrf/6CjLQkQkLr8NRr82xdItOS4uzKnd0bl2MqyGfxpy/ZbeeBB5+l59DnAMhIu8Fv339IVnoSep9KNOswgB5D7u7a7l490LoJqRlZfL1yHcnpGdQKDeHzV8fZpuqPT0pFeUv8Gdk5zPx2BcnpGeg9tNQJq8J30ycQEWLthqpUKrhw9Tp/7DxAZnYulXz0tG5Qm2eG9sHV5f6/nFf8g+Pw/ycKy708ZluUm23bttGlSxdSU1NLNc6tLDzzn9TyDqFU7vefwIBpbe6cqIJK3uT8x0f8m9Kz7u9jx+c+7kXj5np/5/2JcxXrMR736vSR2PIO4R+r1cB53VbLw7PtnTvU4d92JKvow7jvJ51MG8o7hH/Ms0Wf8g6hWKnvjS+3ffu8Mbfc9n23pDorhBBCCCGEEBXU/d+GKoQQQgghhLh/3SeTf5QXqbDdJzp37nzfd90TQgghhBBC3BupsAkhhBBCCCHKjUIenF0iyR0hhBBCCCGEqKCkwiaEEEIIIYQQFZR0iRRCCCGEEEKUG4VMOlIiaWETQgghhBBCiApKWtiEEEIIIYQQ5UchbUglkdwRQgghhBBCiApKKmxCCCGEEEIIUUFJl0ghhBBCCCFEuZFJR0omLWxCCCGEEEIIUUFJC5sQQgghhBCi/CilDakkkjtCCCGEEEIIUUFJC5sQQgghhBCi3CgUMoatJNLCJoQQQgghhBAVlFTYhBBCCCGEEKKCki6RotRSb2SUdwilkpGUXt4hlErypnPlHcI/5te9dnmHUCqXl5wq7xBKxUV9/54CCkz3d/eZ/Pz88g6hVIx5xvIO4R+LuZBU3iGUSkL74PIOoVSMBfd3W0GW/v7Nf8/yDqAkMulIiSR3hBBCCCGEEKKCun9vrwohhBBCCCHue/Lg7JJJC5sQQgghhBBCVFBSYRNCCCGEEEKICkq6RAohhBBCCCHKj0LakEoiuSOEEEIIIYQQFZS0sAkhhBBCCCHKj0w6UiJpYRNCCCGEEEKICkpa2IQQQgghhBDlRiFj2EokuSOEEEIIIYQQFZRU2IQQQgghhBCigpIukUIIIYQQQojyI5OOlEha2IQQQgghhBCigpIWNiGEEEIIIUS5USilDakkkjtCCCGEEEIIUUFJhU0IIYQQQgghKiipsJWzmJgYFAoFR48eLe9QhBBCCCGE+PcpFOX3ug/IGLZ/0ahRo0hLS+PXX3+1LatatSpxcXH4+/s7dV9vv/0277zzju29Xq+nYcOGzJw5k06dOtmWh4WFcfnyZQC0Wi21a9dm6tSpDBs2zKnx3G5YTy+6tdLh4a7kXLSBb1elEJ9UUOI6D7TV0b+zF96eKi7HGVn4SwqXrhoB8HBXMrynFw1ruePvoyIjy8yBkzksX59Gbp7F6fE/PrgyvTr54qFVcfpCNl8sucb1BGOJ6/Tr5sfQ3pXw8VITdSWPuf+7xvnoXAAC/F1YPLuuw/Xe+/IyOw+kOyXug1uXsnfDd2Sl36BylTo88Mg0QsIbOkx75K8VnNjzKzeuXwAgsFo9Og+eZEtvKshn+2+fcvHEDtKSrqJx1xFety1dhryMp3dlp8R7k2/75kS8PAavpvVxCw7g4IPPkrB6c8nrdGxJ5OzX0EXWJO9qHBdnzSV2yS92aULHjyBi0hg0gZXIOH6WUxPfJf3ACafGflPzmgra1FGic4eEVFh3yMT1FMdpK+mhU0MlQT4KvHUK1h82sf+c/XHcrIaCZjWVeHtY399Ihx0nzVyKc/7xDnB0x1IObfmO7IwbVAqpQ5eh0wgMdXzsJMVdYM/az0i8eoqMlGt0GjyVpl1G2aUx5mWxe80cLh7fRE5WMgEhkXR+8PVit1lah7YuZd9G67EfUKUODzw8jeBijv2jf63gxN5fSbrl2O80aJItvcmUz45fP+XSycJjP6xuWzoPdv6xD9C6rpIODdTo3CE+xcLvewqITXL8fw7wVtC9qYoQfyU+ngr+2FvA7lOmUm3TGUb086NHe2883JWcjcpl7rIE4m7kl7hOn07eDOrhi49eRUysgfnLE7lwOc/2+cyXqtKgltZunXU70pj7Q4LT43+wuyddWnjg4a7k/GUDC35NIyG5aL7eqkdrD/p21OGlU3ElPp/Fq9OIii38zi5qeLSPF60baXFRwfELBhb+lkZGltlpcW9Ys5LfVy0lPTWFauE1GDVuEjVqRTpMu3/3Nn79aQkJcbGYCgoIDK5K30EP06Frb1uavNwcflg8l4N7d5CZmU5A5WB69h9Gj96DnRbzrfZsXMqOtQvISk8isGodBjzxBlWrO/7d7t+6giM7VxMfa/3dhoRH0nPYS0XSJ167xLrlHxN19gBmk4mAkOo8NmEO3v7BTo//lzXr+fHX30lJTaNGWCgTnh5N3Vo17rje5h27ePfjz2jXqjnvvf6KbXlObh7zlyxj574DZGRmEhQQwJB+vRnYu4fTYxcVi7Sw3QOjseQL8n9CpVIRGBiIWu38unO9evWIi4sjLi6OPXv2ULNmTfr160d6uv3F/4wZM4iLi+PIkSO0aNGChx56iN27dzs9npsGdNHTu72eb1em8MZn8eQZLbz+VAAuJWRBm0Zanhjgy8qNabz2aRyXrxt5/akA9DrrIezrpcJHr+L7P1KZPDuOr5Yn06iOO88M93N6/MP6VGJAD38+X3yNiTMukmcwM/PlcFxcir9L07GlF08/HMTSXxN4YfoFoq/mMnNyOF6eKgCSkvMZ8eJpu9f3q+LJyTVx8HimU+I+fWAtm36aRYd+zzHmzV8IqFqHH+eMITsj2WH6y+f2EdmyL4++vISRU35E7xvED58+SUaq9WIo35hH/JXTtO83njFvrmLo+C9Ijo9mxZfjnRLvrVQeWjKOn+PkhHfunBhwD6tCi9XzSN62j53NBxL9+WIazJuJf4/2tjRBw3pT96OpXJj5JTtbDibz+FlarfkO10q+To8/spqCHk2U7Dhp5pt1JhLSLIzookKrcZxerYbULNhyzExmruOL6Iwc2HLUzLfrTHy73kRMgoWHOiippHd6+Jw7vJYdv8yida/nePSVX/APqcOqr8aQk+n42Ckw5uLlV4X2/V9Gq6/kMM3GH97k8rnd9Hr8Q5547XdC67Rj5ZejyUpz/sX26QNr2fzzLNr3fY4n3/iFylXqsPyzEo798/uIbNGXEZOW8MSUH/H0CeLHOU+Seeuxf/U07fqOZ/QbqxjyjPXY/7kMjv0G4Ur6tFKz+UgBX/6WT1yKhdG9XPBwc5zeRQ0pmRbWHywgI8fxsXOv2yytIQ/40reLD3OXJfDKh1fIM5h5e0IVXNTFl5ntm3ny5IOVWL4miUnvXyY61sDbE6rYysyb1v+VxsgpF22vRb/ccHr8/Trq6NlWx8Jf03jrq0QMRguvPelf4jmrdQN3Hu3rxarNmbz5RSJX4vJ57Ul/9B6Fl12P9fWiSV03PluazLvzk/DRq3jpUeeVP3v+2sT3337Gg488yfufLiQ0vAYfvPUS6WmO7xTpPPUMHj6SGR/N5z+fL6FT9z58Ped9jh3ea0vz/XefcezwXp57eToff/UDvQcMZ9HXn3Bw319Oi/um43vXsmbZf+g2+Dmef3clQdVqs+DDp8hKd/y7jTpzgIZt+vDU64sYP/0HvH2DWPDhWNJTCsuU5IQrfD3zUSoFhfP064t58f1f6TpoPGqXYgrjUtjy126+WrCEUQ89yDeffED18FBeeft9UtNKvgEbl5DI3EX/o2FknSKffbVgCfsPH+WNl55n8RefMHRAH+bMX8CufQedHv+/Tqksv9d94P6Ispx07tyZ559/nokTJ+Lv70/Pnj05efIkvXv3RqfTUblyZR5//HGSkpJs6/z88880aNAAd3d3/Pz86N69O9nZ2bz99tssXryY3377DYVCgUKhYNu2bUW6RG7btg2FQsHmzZtp3rw5Wq2Wtm3bcu7cObvYZs6cSUBAAJ6enowdO5bXXnuNxo0b26VRq9UEBgYSGBhIZGQkM2bMICsri/Pnz9ul8/T0JDAwkFq1avHll1/i7u7O77//XiZ5CtCngyerNqVz8FQuV+Ly+fLHJHz0alrU1xa7Tt9Oejbvy2TbgWyuJeTz7coUjPkWurTQAXA1Pp9PliRx+HQuCckFnLqYx/I/02gWqXX6b3HQA/78uDqBvUcyiInNY/Y3V/HzcaFt0+KvlAf3rMSf21PYuDOVK9cNfL74GgajhQc6Wk/OZgukphfYvdo28+KvA+nkGZxzt3XfxoU0bj+cRu0epFJwDfo8+g5qVzeO7Vrp+HuO/ZjmnR8lsGpd/IOq0/eJmVgsZmLO7gHATevJiJcWEtm8D36BEYRENKbniGn/j737jo6iehs4/t2SbNqm95BKIAQIvffekSqIFQRsCCKKiL1QFEERVJQOiooIKtJ7b6FDCAFCAoQU0ns2294/FjZssgklGwO/937O2XPI7J27T4bJM3vnliH5WhTZ6YkWifmO1K37uPTxXFL+2XFf5QNfeorCuASi3/mSvItXufbDKpLXbiX4jZHGMsETR3FjyR8krFhHXnQs5177GG1BEf4jh1g0doBWYVJOxeo5E6cnLQc2RupQa6BRiPkvrEkZsPO0jqjrerTl3MS/nKjnSpKejDzIyIXdZ3UUa8DP3fLDO07uXkb9NsOo12oIbj6hdBtmOHfOHzF/7ngHNqDDwCmENe2LXG5d5n1NcRGXz2yj/YDJ1AhtjrNHIK37jMfZPZAzB361ePzHdiyjYbthNGg7BHffUHrdPvfPHjIf/4DRc2ja6Rm8/MNx865Jn9Lnvq2SEROXEX7Xud9jxIckX48iO8Oy5367+jIiY3ScvKzjVpaefw5qKNZA09oys+VvpunZEqnl7FVduefOg9ZZWf27uLBmczrHzuZx7aaKucuTcXWS06qRQ7n7DOjqwraD2ew8nMON5GIW/JaCqlhHt9ZOJuVUah1ZOVrjq7DIcr1Td/Rq68Dfu3M5EV3EjWQNC/7IxFkpo2ld23L36d3egd2R+ew7UcDNWxqW/p2FqlhPx2aG65ytQkKnZvas2pjNhavFxCeq+enPTGoHKQj1t7JI3Bv//p0uPZ+gU7d+1AgIZvRr72CtULBn+waz5etGNKF56474+Qfh5VOD3k8MJyCoJjEXzhrLXIo+R4cufagb0QQPLx+69hpIYHAosZcuWCTmu+3fvILmnZ6kWYfBePmFMnDUJ1grbDi+b53Z8k+99hWtuz2Nb2A4nr4hDB7zOXqdjtgLh41ltq2ZS1jDDvQeMRnfoLq4eQVQt0kXHJwsf3N3zT8b6dujK727dSYooAaTXh2DjcKaTTt2l7uPVqtj+tfzGTXiSXy8y/bWn78YQ68uHWkcUQ8fL0/69+xGaHAg0ZevWDx+4dEiGmz3sGLFCqytrTl48CBffPEFXbp0oXHjxhw/fpwtW7aQkpLCsGHDAEhKSmLEiBG8+OKLREdHs2fPHgYPHoxer+ftt99m2LBh9OrVy9jr1aZNm3I/9/3332fOnDkcP34cuVzOiy++aHxv1apVTJ8+nS+//JITJ04QEBDAggULKvw9VCoVy5Ytw9nZmbCwsHLLyeVyrKysqqQ3EcDTVY6Lo5xzlwuN2wqL9Fy5rqJWoPk7XDIZhPhZc+5SyVAYvR7OXS4qdx8AOxsphUU6dBa8fnt7WOPqbMWpC3nGbQWFOmJiC6hT097sPnKZhFpBtpy+ax+9Hk5H5RJe03wjNTTQlpqBtmzdV86YuQek1RSTdD2K4PCSc04ilRIc3oaEq6fuqw51cSE6rQZbe6dyy6gK8kAiwcauCrp5HoBzq0ak7Tpssi11+wFcWjUCQGJlhVOTeqTtvKsnWa8nbdchnFs1tmgsUin4uEJcsmlvR1yKnhoWalxJJFAvQIKVHIsPa9Nqikm5EUVAmOm5ExDWhqS4+zt3StPpNOh1WuRy079fubWCxKsnKxVvaVpNMclmzv2gOm24+YDnvk1F537h7XPf1nLnvkwKvu4SriSWJDE9EJuoI8Dz4c6dqqizIl7uVrg6yTlzscC4raBIx6W4IsKCzTd45DKoGWBjso9eD2cuFhAWYtoN2LG5Iz9/VZN5Hwbx3AB3rCsY6fAwPFwMozeirqiM2wpVemJvFFMroOzNCDBcs4J9rTh/1z56PZyPVRn3CfazQi6XmJRJStWQlqkhtJx6H4RGrSbuSgz1GzYzbpNKpdRv1JzLMefvub9er+f8meMk3bxOnXqNjNtrh0dw4uh+MtJT0ev1RJ09QVLiDRo0blHpmE3i1xSTGB9FaL3WJvHXrNea61dO31cdalUR2ruuWTqdjotn9uLuHcTSWWOY9lpbvv94OFHH7+9G4INQqzXExF6lacMIk/ibNozgQszlcvdbufpPnJ2c6Nu9i9n369cJ4+Cx46SmZ6DX6zl19jw3bibRvHHVDCX/T4k5bBUSc9juoVatWsyaNQsw9Go1btyYGTNmGN9funQp/v7+XLp0iby8PDQaDYMHDyYwMBCAiIiSP1ZbW1tUKhXe3t73/Nzp06cb55q9++679O3bl6KiImxsbJg/fz6jR49m1KhRAHz00Uds27aNvLw8kzrOnTuHg4PhDmZBQQFKpZLVq1fj6Gj+C0VxcTFz5swhOzubLl3MJ4vKcr49nCU717QVlZ2nNb5XmqO9DJlMQnae6e3i7Fwtvp7m70Qq7aQM7u7EjiN5Zt9/WC5Ohj+ZzGzT+XaZORrje6U5Kg3xm9unho/5MUg9O7hw/WYR0VcKzL7/oAryMtHrtNg7mt5FtFe6kZ509b7q2LV2Ng5OniZffO+mUavYtW429Zr3RWFb/p3z/4LCyx1VSprJNlVKGlZOSqQ2CqxcnJDK5ahupZcqk459WIhFY7FTgFQqIa/UXMr8InBXVu5C4ekEo7rLkMugWANr9utIy6lUlWUU5hvOHTul6bljp3QjM+X+zp3SrG0c8AlqzNGtP+DqHYKd0p2YExtIijuNs0eAJcI2unPul47f3tGN9OT7i3/3unuf+3vWzaauhc99OxuQSSXklRoWm1eox8Pp4e63VkWdFXFxNOT1rBzT/JeVqzG+V5qjgyFnltknR0sNr5LGzL7IHFLT1WRkawjyU/D8IA/8vKz5YqHlejmN16zS1588Lc5K88dLaSe9fc0yvc7l5Grx9VAY61Vr9BSUygvZebpyr4UPIicnC51Oi5OL6RBLJ2dXEhOulbtfQX4er40cgEZdjFQqY9Srb5s0xka+PIlF333JuJEDkMlkSCRSxo5/l/D6lr3RVZBriL90z5fS0Y3UxLj7qmPz6tk4ungSWs/wd5ufk05xUQF7/11Mj6ET6DX8LS6dPcCqeRMYM3U5IeGWa3Rm5+Sg0+lwdTa9yePi7MT1BPPn59kLF9m4YzeL535Zbr0TXhrFnO8X8uSLryKTyZBKJLw97iUa1jM/L1H43yEabPfQtGlT47/PnDnD7t27jY2gu8XGxtKjRw+6du1KREQEPXv2pEePHgwdOhQXF5cH/twGDUrulvj4+ABw69YtAgICiImJ4bXXXjMp36JFC3bt2mWyLSwsjPXr1wOQm5vL6tWrefLJJ9m9ezfNmpXcdZsyZQoffPABRUVFODg48MUXX9C3b1+zcalUKlQqlck2rUaFTG6+p6tdY3vGDi25YHyx5Na9fvVKs1VImDLGk4QUNX9uy6pUXZ1bOzP+BT/jzx9/E1+54O6DtZWETq1d+G295efyPKxDmxdyIXITz7690uxYf61Gzbqf3kCv19P7mfubZyZUXlouLNyiRWEFdQOkPNFKysqdWos32qpCr+dmse3X91j0YQckUhmeNeoS1rQvt25EVXdoJg5vWUh05Caeeaucc1+r5q+FhnO/19Pi3O/YXMmrT5fclPz8h4Qq+6xtB0rmAl1LLCYjR8u0if54u1uRnFbxgibladPIltEDnY0/f7XC/Hyp/1U2tnZ88e0KiooKOH/mOL8smYeXty91I5oAsPXfP7kSE8XbH87C3cObi1GnWfbjHFxc3Ylo1Lyaoy+x599FnD2ymbHvrcDK2vB3q9cbGsd1m3ahXe+RAPgGhnP98imO7lpt0QbbgyooKGTGN98xedxLOJdzUx1g3YYtXIi5zIz338HL050zUdHM/Wkpbq4uNGv0P9DLJpRLNNjuwd6+ZJhbXl4e/fv358svy9798PHxQSaTsX37dg4dOsS2bduYP38+77//PkePHiU4OPiBPtfKqqTnSHK7u1b3gGP7rK2tCQ0tWY2ocePG/P3338ydO5dffvnFuH3y5MmMHDnSOC9PUkH38MyZM01WnwSo2/oN6rd502z54xcKuPx1SQPvziRzJ6WUrNySO5ZODjLiE80Pw8zJ16LV6nFyML3r6KSUkZVjetfTRiFh6lhPiop0zFl+C20lh0MeOZXDxdiSXq478bs4yU16zFwc5cReLyqzPxjuqmq1+jI9cC6OcjKzy36paNfcCYW1hJ0HMysX/F3sHFyQSGVlFlnIz03H3qniFUqPbFvCoS0LefrNZXjVKDsJWqtRs27hRLIzEnlm0opq710DQ2+awsv091J4uaPOzkVXpKI4LROdRoPC061UGTdUyaY9c5VVoAKdTo+DjQTD4DMDexvK9Lo9KJ3OsDgJQHKmDh9XKS3CpGyKtNw4YFt7w7lTeoGRgtx07JQPv7qts0cAw974BbWqAFVRHg5OnmxcNhEnN//KhmzizrlfOv78nHQc7nHuH922hMNbFjJi4jI8zZ37WjV/L5xITkYiI960/LlfUARanR4HW9Nzx8FWUu5iNNVR592Onc0jJj7e+POdnOnsKCfzrnztrJQTl6AqvTsAOXmGnOnsaJoznR1lZOaUv5rwnVV3fTwevsF28kIRsTdKbizKZbevWQ4ysu4aGeLkIONakvnPyC3Q3b5mmfbAOSplZN++7mXlarGSS7CzkZj0sjk5mF4bH5ajozNSqYzsTNNh9dlZGTiX6nW7m1Qqxdu3BgBBIbVJvHGNf9aspG5EE4pVKn7/+UcmvTeTJs3bAhAYHMq1q5fZ8NevFm2w2SkN8ZdeYCQ3Jx2lc8V/t/s2LmXvhkWMnrIUn4CSKSB2SmekMjmevjVNynv4hnDtkmWHYjs5OiKVSskotcBIZlY2ri7OZcrfTE4h+VYqU6fNMm6708DsMmgEP//wDe6uriz+5Tc+n/o2rZsZGtA1gwK5cjWe1X9veOwbbJLHZPGP6iKOzgNo0qQJUVFRBAUFERoaavK607CTSCS0bduWTz/9lFOnTmFtbc1ffxmWEre2tkZb3izwBxAWFkZkZKTJttI/l0cmk1FYWGiyzd3dndDQULy9vStsrAFMnTqV7Oxsk1d4i9fKLV+k0pOSrjG+ElLUZOZoiKhVMhTQViEhNEDB5WvmL95aLVy9WWyyj0QC9UNtTPaxVUh4f6wXGi3MWpaKuuKnBNyXwiIdSbeKja/riSoystQ0qlvyxczORkpYTTsuxuabrUOj1XM5vtBkH4kEGtV1IDq27JDHnh1cOXoqx3hhtwSZ3BqfgHrGRRMA9Dod8dGHqRFS/lCWw1sWcWDDD4x4YzG+QRFl3r/TWMu8dY2n31yOncOD9yZXhawjp3Hr0spkm3vXNmQeOQ2AXq0m+2QU7l1K5kcgkeDWuTVZRx5uXlZ5dDrDIiJB3qZ/W8FeEovPN5NIQG7hrC6TW+PlX48bl0zPnRsxh/EJrvwwKCuFHQ5OnhQVZHPt4gFCIrpWus67yeTWeAfUIz7aNP5rFw/jV8G5f2TrIg5u/IHhExbjY+7cv91Yy7h1jRETq+bc1+ogMU1PqE/Jf6oEqOkr5fqthzt3qqLOuxWq9CSnqo2vG0nFZGRraBBWMl/X1kZK7WAbYuIKzdah0ULs9SKTfSQSaBBmR8xV8zfGAIJrGK4RGTkPnzuLivWkpGuNr5u3NGTmaKlXs6R31VYhoaa/NZevm7/JqNVCXKLaZB+JBOrXVBj3ibupRqPRm5TxcZfj7iLnSjn1Pgi5lRXBoWGcP3vCuE2n0xF15ji1wurfdz06vQ612tAw1Wg1aDUapBLTJCOVStFbcrI4IJdb4xtUj9gLJStU6nQ6YqOOEBDaqNz99m5YzK5/FjBq8kJqhJj+nnK5NTWC65OabDqkMi053uJL+ltZyQmrGcLJsyWPidHpdJw4e566YbXKlA+o4cvSeV+xeO6XxlebFk1pHFGPxXO/xNPdHY1Wg0ajRVrqe5pMJjU27oT/XaKH7QGMGzeORYsWMWLECN555x1cXV25cuUKv//+O4sXL+b48ePs3LmTHj164OnpydGjR0lNTSU83PB8raCgILZu3UpMTAxubm44OZU/gb0i48ePZ+zYsTRr1ow2bdqwevVqzp49S0iI6dwbjUZDcnIyUDIk8sKFC0yZMuWhj4FCoUChMB0WJJM/WE/Qpv25DOrqRFKqhlsZGob3ciYzR0Pk+ZLGywcvexJ5vpCtBw1L2m/cm8NrT7kTm1BM7HUVfdo7orCWsCfS0L1gq5Dw/kteWFtJ+G5FGrY2EmxtDEktJ0+HJXPZ39vSeKq/JzeTi0lJK+a5wV6kZ6o5dLJkHNrMd4I5dCKHf3ca7g7+tTWVt8b6czmukJirBQzs4Y5CIWX7ftNj5+NpTf3a9nz0zf2N0X8QLbuPYv2yKfgE1sc3uAHHdqxAXVxIg7aDAVi/9B2Uzl50HvwWAIe2LGTf+nkMHD0HJzc/8rINy2VbK+ywtrFHq1Gz9qcJJF+/wPDXf0Kv0xrL2No7ITOzOuDDktnbYR9aMrfJLrgGjg3rUJyRTdGNJMKmTcLGz4szowzn9rWFvxP42jPUmTmZG8vX4t65FT5P9ibyiZeNdcTNXUbDpV+SdeI82ZFnCZrwAnJ7W26sML8CWWUcidExoJWUpAwJiel6WoRJsZLDmTjDiTmglZTcQsMy/mBYqOTO8vwyKShtJXg56ynWlPSodWko5UqijuwCUMihfpCUIE8Jq/ZYfpW8Jp1HsfWXKXj618c7sAGn9hjOnXotDefOlp/fwcHJi3ZPGM4draaY9ORY47/zslO4lRCNtcIOZw/D/N746P2g1+PiFUxW6nX2/zMLF88Q6rUabPH4W3QbxYblU/AOqo9vUAMid94+99sYPuvfZYZzv9MgQ/yHtyxk/7/zeKK8c1+r5q/b5/6T435CV4Xn/oHzWoZ2kJOQJiUhVU/b+jKs5XDykqFRMrSDnJwCPduOG36WSQ3PYrvzb0c78HGVoFLryci9vzot7d9dmQzr40ZSajEpaWqe7u9ORraGI6dL5hh/9kYNjpzOY9PeLAD+2ZnJGy94c+V6EZfji+jfxQUbhZQdhw09Ft7uVnRo7siJqDxy87QE1VDw4lBPzl8q4NpN8zf/HtaWg3kM7KIkOV1DaoaGod0dycrVcuJCSYNz6mg3jl8oYvthw427zfvzePlJF+Juqom9UUyvtg4orCXsPWG4zhWq9Ow5ns+zfZ3IL9RRUKTnhSecuHRNxZUbD9c7WFrfgU+x4JtphITWIbR2XTb/sxpVUREdu/UD4IevP8PFzYMRLxgeR/H3mpWEhNbBy8cPjVrNqeOHOLB7Cy++angOmJ2dPeH1G7Nq2XdYKxS4e3gTff4U+3Zv5rnREywS893a936BNQun4hdcH/+QCA5uXUmxqpCmHQzPfPvjxyk4unjRa/gkAPZuWMT2tfN56rXZuLj7kZt1++/Wxg6FjeGmeoe+L/Lbd28RHNaMkLotuXT2ABdP7WHseyssHv+TA/oy89sfCAutSXitmvz57yaKilT07tYJgBnffIe7mysvPf80CmtrQgJN5+863O4IuLPdykpOw/p1WbD8F6ytrfH29OD0+Qts3b2PcS8+b/H4/3MS0YdUEdFgewC+vr4cPHiQKVOm0KNHD1QqFYGBgfTq1QupVIqjoyP79u1j7ty55OTkEBgYyJw5c+jd2/DQybFjx7Jnzx6aNWtGXl4eu3fvJigo6IHjeOaZZ7h69Spvv/02RUVFDBs2jJEjR3Ls2DGTclFRUcb5b3Z2dtSsWZMFCxbw/PPV+4e9fncOCmsJLw11w85WSkxcETMX3TLpEfNys0JpX3LRPXymAEeHTIb1dMZZaRg+OXPxLeOk7uAa1sYVI+dN9TP5vNenJ5CaabkvIms2pWKjkDJhlB8OdjKiLuXz4Zw41OqSVqGPpwJHZcmf175j2Tgp5Tw7yAtXJ8PwyQ/nxJWZVN+jvStpmWpOnrfsYikAdZv3IT83g73r55Gfk4pXjXCemrAYB0fD8JLsjCQkdyXMk3t/NzbK7ta+3+t0eGI8uVkpXD5jmDe5+PMBJmWefWslgWEtLRa7U9P6tN75c8nvMvs9AG6sXMfZ0VNR+Hhg6+9jfL8wPoHIJ16m7pypBI1/nqKEZM69/AFp2w8YyySt2Yy1hyu1P55geHD2mWiO9RtD8S3Lz1m5cF2PnUJHxwgpDjaGB2f/ukdL/u3OAkc7ickdUqUtvNS75PxpEy6hTbiU+BQ9P+8ynMt2ChjQSoaDLajUkJKlZ9UeXZnVKC0hrEkfCvMyOLxpHgU5qXjUCGfQq4uxv33u5Gaanjt52bdYNWug8ecTu5ZyYtdSaoS24MkJhv9HVWEuB//9mrysZBT2ztRq2IO2/d5EJrPMkuZ3q9u8DwV5Gey/fe571ghn2ISS+HNKnfun9hnO/b9Knfvt+r1O+/7jyc0sOfeXTjM995+eZNlz/1ycDnsbDd2aylHaQlK6nmVb1eTdPnecHSQmN6SUdjB+UEmDsUMDOR0awNUkHYs3qe+rTktbty0DG2sJrz3tjb2dlOjYQj6dn4BaUxK4t4c1jncNez9wIhdHBxlP93PHxVFGXIKKT+cnGEceaLR6Gtaxu92Qk5CWqeHwqTz+2Gz5v98N+/JQWEsYPcgZOxvDg7O/XJZe6polR2lXcg4dOVeI0kHK0G5KnJSG4ZNfLkszeSj2Lxuz0evhjWfckMvh3CXDg7MtpXX7buRkZ/HnqkVkZWYQGFKLdz/92jgkMi01xeS8VxUVsmzBbNLTb2FtrcC3RiDj3vqY1u27GctMeOczfl+xgO9mf0JeXg4eHt4Mf+5lulXBg7MbtOpDXm4mO9bOIzc7DZ+AcEZNXojy9lDmrHTTv9sjOw1/t6vmvWFST9dB4+g2+HUA6jXrzsBRH7Pn34X8+/MMPHyCeWbCtwSFNcXSurRvQ1ZODst+/cPw4OzgIGZ9PBVXZ2cAUtLSH3gY4Edvv8Gilb8y/ev55OTl4eXhwZhnn+KJXuLB2f/rJHrRj/o/oXv37nh7e/Pzzz/fu7CFDX+7/BWnHgc5aRU/xPJRN2LU4ztu3a1b+Y+YeBycXPloLZDxoDxcH997djaWf87tf+pSrGV7gf5rUcfjqzuEh+bgXP3zbCvjrVFV9ITz/0h81qMxdP5htXY8e+9CjyifOo2qO4RyFf4y496Fqojts+9V22ffr8f3av3/WEFBAT/++CM9e/ZEJpPx22+/sWPHDrZv317doQmCIAiCIAjCg5E+Hs9Dqy6iwfYYkkgkbNq0ienTp1NUVERYWBhr166lW7du995ZEARBEARBEITHhmiwPYZsbW3ZsWNHdYchCIIgCIIgCJUmEYuOVEgcHUEQBEEQBEEQhEeU6GETBEEQBEEQBKH6iDlsFRI9bIIgCIIgCIIgCI8o0WATBEEQBEEQBEF4RIkhkYIgCIIgCIIgVB+x6EiFxNERBEEQBEEQBEF4RIkeNkEQBEEQBEEQqo9ELDpSEdHDJgiCIAiCIAiC8IgSDTZBEARBEARBEIRHlBgSKQiCIAiCIAhC9ZGKPqSKiKMjCIIgCIIgCILwiBI9bIIgCIIgCIIgVB+xrH+FxNERBEEQBEEQBEF4RIkeNkEQBEEQBEEQqo9ULOtfEdHDJgiCIAiCIAiC8IgSPWxCpTm7K6s7hEq5GRNf3SFUSnaevrpDeGjXVkZVdwiV0uT5etUdQqXojjy+x//IGW11h1ApGWkF1R1CpXj6u1d3CA8tKzWnukOolH0Xfas7hErJL9BVdwiVkuraqLpDeGgv16nuCISHJRpsgiAIgiAIgiBUH7HoSIXE0REEQRAEQRAEQXhEiR42QRAEQRAEQRCqj0QsOlIR0cMmCIIgCIIgCILwiBINNkEQBEEQBEEQhEeUGBIpCIIgCIIgCEL1kYo+pIqIoyMIgiAIgiAIgvCIEj1sgiAIgiAIgiBUH7HoSIVED5sgCIIgCIIgCMIjSvSwCYIgCIIgCIJQfcSDsyskjo4gCIIgCIIgCMIjSjTYBEEQBEEQBEEQHlFiSKQgCIIgCIIgCNVHLOtfIXF0BEEQBEEQBEEQHlGih00QBEEQBEEQhOojlvWvkOhhEwRBEARBEARBeESJBpsgCIIgCIIgCMIjSjTYHtAnn3xCo0aNKlXHnj17kEgkZGVlWSQmS3qUYxMEQRAEQRD+B0mk1fd6DIg5bP8DOnXqRKNGjZg7d251h3JfOjVR0L2lDU72UhJuafl9ez7xSdpyyzcJs2JABzvcnKTcytCybk8h56+qzZZ9uqcdHRvb8MeOfHYeV1XVr8DoZ4Lo38Mbpb2cc9E5zP7hMglJheWWb1jPiacH+xNW0wF3NwVTp59n/5F04/symYSXng2iVTNXfL1tyc/XcPxMJgtWxJGeUWyxuM8eWMWp3UsoyE3D3bcOHQZ9gFdgA7Nl05Mvc3TzPFITosjNTKTdgKk06viCSZkVn3chNzOxzL4RbZ+m45CPLBb3Hc1qSWhdR4qDLaRkwpYTWhIzzJf1cISODaT4uEhwdpCw9aSWYzF6kzJNQyU0rSXF2d7wc2o27DuvIzZJb6bGh+farhkhb43GqUl9bHw9OT7kNVLW76x4nw4tqDv7XRzq1qLoRhJXZi4gYeVfJmUCX32akEmjUXh7kHP2IlETPyc78pxFY7/j4LZf2bNhGbnZafgEhDHohfcICDV/7hzZtYYT+9eTfOMKADWC69J7+Bsm5VVF+Wz87RuiTuwiPzcLV08/2vV8ljbdhldJ/C3DpbSvL8fBFpIz9Ww4rCEhzfz/s6ezhK5NZPi5SXFRSth4RMOhC2Vz1IPUWRkdGyvo0VKB4+2cuXpHwT1z5hPtbQ05M1PHX3sKOH9VY7bs0z3s6NBYwR87C9hVRTmzU1MFPVva4uQg5UaKht+2FRCfZD4egKZ1rBnQ0Q53JykpGVrW7i7gfGxJzu/f3pbmdRW4KqVotHquJWv4e28hcYnl11lZw3s507W1EnsbKRfjVSxak0ZyWsWf17Otkie6OOGslHEtsZil69K5cr0kn7/0pBsRtW1xdZRRVKwnJq6IXzZkknjL/PXtYZw/uIrTew05382nDu0GfoBXgPm/24zkyxzbOo+0m4ac3+aJqTRsb5rzf5lhPufXa/00HQZbPuc3DZXQKlyCgw2kZMG2E7pyc767I3SMkOLtCs72Erad1BF5yfTvsUmohCahEpOcfyBKR2ySxUMH4PS+VRzfuYT8nFQ8/OrQeeiH+ASZP/5nD/5B9LG/SUu6DICXfz3a9p9kUl6v13No0zzOH1pDUWEOfsFN6Dr8E1w8g6rmFxAeGY9Hs7IUtdpyyez/C71ej0ZTdRez+9WsjjVDu9ix8UAh05dlk3BLw4ThSpR25iebhvjJGTPAgYNnVExbls3py2peHeKAr7usTNlGta0I8ZWTmaur0t/hmSH+DO3nx+wfLvPS26coLNLy9WcRWFuVP2HW1kbGlbg8vv7xstn3bRRSatdUsmL1dV6ceIL3Z0YR4GfHlx/Ut1jcl09t4sA/X9C85ziGT1qHm28Y6xeOoSA33Wx5TXERTm7+tO73FnZKD7Nlhr35J6M+2W98DXhlKQA1G/a0WNx31A2Q0L2xlH3ndSzaoiUlS8/TnWXYKcyXl8shMw92ndGRW2j+S3ROAew6rWPxFi2Lt2qJT9EzvL0UD0fLxi6ztyPnbAznJ3x6X+Vtg2rQfP1PpO85yoFmA4ibv4KIn6bh3r2dsYzPk70J/2oql6d9z4EWg8g9e5GWG5dg7eFq2eCB04c3s/6XWXQf/BoTp6/BNyCMRV+8TG62+XMn9kIkjdr04ZUPljL+01U4uXmz8IuXyM5IMZZZ//MsYs4eYMRrX/DO7H/p0Os5/l4+nagTuywef0SwlD4t5Ow6reH79WqSM/SM7GmFvY358lZyyMzVs/W4htwC8+fOg9b5sJrWsWJoF1s2HCxixvIcEm5pGT/MoYKcKWP0E/YcPKti+vIcTl8u5pXBDvi6l73cN6plRbCvjKwqzJnNwq0Z1tWefw8U8vnSbBJuaZn4VPk5v6afnLEDHThwuojPlmRz+lIx44Yq8fUoyfkp6Vp+25rPJ4uzmPVzDunZOiY+pcShnDora0AXJ3p3cGThmnSmzk1EpdLxwSveWMnL/7w2jex5YaAba7ZmMWVOItcSi3n/ZW8cHUr+H64mFPPDb2lM/OIm035KRiKBD1/xRmqhX+PK6U0c/PcLmnUfx9CJhpy/YfEYCvLKyfnqIhzd/GnZp/ycP2TCn7zw4X7jq//Yqsv54f4SujWWsP+8niVbddzK0vNUJ2m5Od9KDpl5enaf0ZNXTs7PLdCz+4yOJVt1LN2m41qKnifbSXG3cM4HiDmxib1/zaRV73E8+85fePjVYd0Po8u95iZcOUpY0748OWElIyb9jtLFh3U/vEhuVknejNyxiNN7f6br8E94+q0/sFLYsu6H0WjUVXeD+j8jkVTf6zHwnzXY/vzzTyIiIrC1tcXNzY1u3bqRn58PwOLFiwkPD8fGxoY6derwww8/GPeLj49HIpGwevVqOnbsiI2NDatWrTI7NHHu3LkEBQUZfx45ciQDBw5kxowZeHl54ezszGeffYZGo2Hy5Mm4urpSo0YNli1bZlJPQkICI0aMwNXVFXt7e5o1a8bRo0dNyvz8888EBQXh5OTEU089RW5urvE9lUrFhAkT8PT0xMbGhnbt2hEZGVnh8Tlw4ADt27fH1tYWf39/JkyYYDw+AD/88AO1atXCxsYGLy8vhg4davwd9+7dy7fffotEIkEikRAfH28c2rh582aaNm2KQqHgwIEDDxWbJXVrYcOBMyoOnSsmKV3Hqi0FFKuhTQPzGbhrMwVRV9VsO1ZEcrqO9fsLuZ6spVNT0/LODhKe6mbPkn/z0eosf4f7bk8+4cfKP65x4Gg6sfH5TPvmIm6uCtq3ci93nyMnMlj0Szz7jphP1PkFWt786Cy7DqRy42YhUTG5fP3TFerUUuLlUc7V6QGd3ruceq2epG6LIbh6h9J56KfIrWyIPrbWbHmvgAjaPvEOtRv3RSa3MlvG1sEVe0cP4ys+ag9ObgH41WxhkZjv1ipMyqlYPWfi9KTlwMZIHWoNNAoxn2yTMmDnaR1R1/Voy+mMuJyo50qSnow8yMiF3Wd1FGvAz92yCTx16z4ufTyXlH923Ff5wJeeojAugeh3viTv4lWu/bCK5LVbCX5jpLFM8MRR3FjyBwkr1pEXHcu51z5GW1CE/8ghFo0dYO+mFbTsPJQWnQbhXSOUIaM/xkphQ+TedWbLP/P6LNp2H4FfUDiefiEMe+kz9Hodl88fMZaJv3yaZu0HEFq3Ba4efrTqOgyfgDCux1q+h7BtfRnHY3ScvKwjNUvPPwc1qDXQtHbZGz8AN9P0bInUci5Oh6acc+dB63xY3ZrbcPCMisO3c+avWwtQq6FNhLXZ8l2a2hB1Vc32YyqS03X8u7+I6ylaOjUxbUk6O0gY3t2OpRuqNmd2b2HD/tMqDp1VkZSm5ZfN+RRroG3DcnJ+cxuiYtVsO1pEcrqWf/YVcj1ZQ5emJfEfu1BMdLyatCwdiWla/thRgJ2NlBqelj32d/Tt6MjabVkcP1/A9SQ13/2aioujjOYRduXu06+TIzsP57LnWB4JKWoWrkmnuFhPl5ZKY5kdh3OJvlpEaqaGuIRiftuUibuLHA9Xywx+OrNvOXVbPkmd5kNw9Qql4+BPsbKy4WI5Od/TP4I2/d6hVqOKc76do4fxFR+9B0e3AHxDLJ/zW9aRcDpWz9nbOX9TpB6NBhpWkPN3ndFz4boeTTn3IC4nQmyS4WZeRi7sOaevkpwPcGL3Muq3Hkb9VkNw8wml2/BPkVvbcP6w+ePf54U5NOrwDJ41wnH1rkn3p6eh1+u4EXMYMNx4P7VnJS17vkpog254+NWh13OzyMu+xZWz93dtER5f/0mDLSkpiREjRvDiiy8SHR3Nnj17GDx4MHq9nlWrVvHRRx8xffp0oqOjmTFjBh9++CErVqwwqePdd9/ljTfeIDo6mp497/9Ozq5du0hMTGTfvn18/fXXfPzxx/Tr1w8XFxeOHj3KK6+8wssvv0xCQgIAeXl5dOzYkZs3b7J+/XrOnDnDO++8g05X8tcfGxvL33//zYYNG9iwYQN79+7liy++ML7/zjvvsHbtWlasWMHJkycJDQ2lZ8+eZGSY78ePjY2lV69eDBkyhLNnz7J69WoOHDjA66+/DsDx48eZMGECn332GTExMWzZsoUOHToA8O2339K6dWvGjh1LUlISSUlJ+Pv7mxy3L774gujoaBo0aPDAsVmSTAoB3jKi40t6SPXAxXg1IX7mL1AhvnIuxpv2qF6IMy0vAUb1d2DbsUKS0sofJmQJvl42uLsqiDydadyWX6DlwqUc6tex7C06BzsZOp2e3LzK94xqNcXcSojCv3Yb4zaJVEqN2q1Jjj9d6frvfEbMyfWEtxyMxMJ3rKRS8HGFuGTTL5ZxKXpqWOhCK5FAvQAJVnKqZFjbg3Bu1Yi0XYdNtqVuP4BLq0YASKyscGpSj7Sdh0oK6PWk7TqEc6vGFo1FoynmZtwFatdvbdwmlUqpVb8V1y6fua86ilVFaDUa7BycjNuCajUi6uRusjNS0Ov1XIk6SlpyPLUj2lo0fpkUfN0kXEksyeF64EqijgCPhzt3qqLO8j4nwFtG9LWSHKAHoivKmX5yLl4zzRmGnFnSmJEAI/vZs/1oEUlpVde7JpNCoI+c6PiSYYB6IDqumJp+5hsEIX5yLpTK+VFXy/99ZVLo0FhBQZGOhBTL539PNzkujnLOXSoybiso0nPlmoqwIPONTrkMQmooOHupZJi8Xg9nLxdSO9D8PgprCZ1bKklJV5OeZZmcn3ozihq1THO+X63WpFw7Xen673zG5ZPrqdO8inK+iyHH3y0uRU8NN8vl/Lq3c/5NC+d8raaYlBtRBIaZHv/AsDYkxZ+6rzo0xYVotRps7A15Mzs9gfycVALuqlNhq8Q7qCFJcfdXp/D4+k/msCUlJaHRaBg8eDCBgYEAREREAPDxxx8zZ84cBg8eDEBwcDAXLlzgp59+4oUXSsZOT5w40VjmQbi6ujJv3jykUilhYWHMmjWLgoIC3nvvPQCmTp3KF198wYEDB3jqqaf49ddfSU1NJTIyEldXw9Ci0NBQkzp1Oh3Lly9HqTTcKXvuuefYuXMn06dPJz8/nwULFrB8+XJ69+4NwKJFi9i+fTtLlixh8uTJZWKcOXMmzzzzDBMnTgSgVq1azJs3j44dO7JgwQKuX7+Ovb09/fr1Q6lUEhgYSOPGhi9lTk5OWFtbY2dnh7e3d5m6P/vsM7p37w7wULGVplKpUKlMu961GhUy+b17gRzsJMikEnLzTRNjTr4ObzfzF29HByk5Zso72Zfca+jZygadjiqbf3E3VxfDXe3MLNMvFJlZxcb3LMHaSsKrI0PYse8WBYWV/xJSmJ+JXqfFVulmst1O6U7WrbhK1w9w9fxOVIW51Gk+yCL13c1OAVKphLwi03MhvwjclZW7eHs6wajuMuQyKNbAmv060nIqVWWlKbzcUaWkmWxTpaRh5aREaqPAysUJqVyO6lZ6qTLp2IeFWDSW/NwsdDotDk6m547SyY1bifd37mz8bQ5OLp7UuqvRN2jk+6xZ/DGfv94FqUyORCLhyTGfUjO8mUXjt1OATCopM0Qqr1CPh/PD3bOsijrNuZMzc/JNG1W5BXq83cz3Jjnamymfr8fxrpzZ407OPFG1ObMk/tI5XI93OV+6nRyk5JaKPydfh5OD6XFtEGrF2IFKrK0gO0/PN7/llDsMrjKclYbjnJVnmoez8rTG90pT2suQySRk55ruk52rxc/T9FrXo62S5/q7YqOQcjOlmM8XJJfbq/sgiu7kfIdSOd/Bcjk/LmonqqJc6jSrgpxvbcj5+UWm2/OLwK2S90Y9nGBkN6kx5/95wPI5/841186x9DXXjYyUq/dVx/5/ZuPg5GlsoBXkpBrruJu90o38nLQy+z92pI/lLK3/zH/SYGvYsCFdu3YlIiKCnj170qNHD4YOHYq1tTWxsbGMHj2asWPHGstrNBqcnJxM6mjW7OEu4vXq1UN610ng5eVF/fol84JkMhlubm7cunULgNOnT9O4cWNjY82coKAgY2MNwMfHx7h/bGwsarWatm1L7hJbWVnRokULoqOjzdZ35swZzp49y6pVq4zb9Ho9Op2OuLg4unfvTmBgICEhIfTq1YtevXoxaNAg7OzKH45xx93H7WFiK23mzJl8+qnpPJwmXd+hWbcp97W/pQV4yejSzIbpy7OrpP7uHT2ZPK628ed3PquaBR3uJpNJ+GxKXZDA7B/Mz3l7FF04+ieBddrj4ORV3aE8kLRcWLhFi8IK6gZIeaKVlJU7tdXeaPtfsWv9Ik4f3syrHy7Hyrrkxs6Brau4fuUso976DhcPX65GH+ev5dNwdPGkdkTrCmoUKiPAS0aXpgpmrHi8T/CL19R8tiQLpa2U9o0UvDxIyYzl2eXOObxf7ZrY8/KwkuHtMxelVFC68g6cyONsTCEujnKe6OzIpBc8+WBeEmpN9fby34+Lx/4kIKw99o9Zzk/PhcVbdSisoI6/hP4tpfyyq/pv1N3t2LaFXDy5iWETViK3ssy0COHx9p802GQyGdu3b+fQoUNs27aN+fPn8/777/Pvv/8Chl6eli1bltnnbvb29iY/S6VS9HrThGZuMRIrK9O7WRKJxOy2O0MebW1t7/n7VLT/w8jLy+Pll19mwoQJZd4LCAjA2tqakydPsmfPHrZt28ZHH33EJ598QmRkJM7OzhXWXfq4VdbUqVOZNGmSybZJ8/LLKW0qr0CPVqdHaW96Z9XRXkp2vvnjl5Onw7GC8rX85SjtJcx8zdn4vkwqYWgXO7o0t+H9BZVryB04ls6FS8eNP1tbGRr/Ls5WpGeWDPNxcbbmytW8Sn0WGBprn0+pi7enDRPeP2OR3jUAW3sXJFIZhaUmOxfkpmGnLH/u3f3KybhJwqXD9B41v9J1mVOgAp1Oj4ONBMOgKgN7G8r0uj0onc4wnwEgOVOHj6uUFmFSNkVW7eI1FVGlpKHwMv1/UXi5o87ORVekojgtE51Gg8LTrVQZN1TJlr3Taq90RiqVkVdqgZHc7HQcnSs+d/ZsWMau9Ut4+b3F+AaEGberi4vYvHouL0yaR93GHQHwDQgj8VoMezcus2iDrUAFWp0eB1vTc8fBVkLeQ365r4o6zbmTMw29YyW5QGlXthftjpxSvWkAyrt63UJv58wZr5bcFJVJJQztbEvXZgre/9Fy31pL4i+dw8v2ut2RnadDWSp+R3sp2Xmmv2+xGlIzdaRm6riaqGHaK860a6hg8+FSXTIP6HhUAVdm3zT+LL+9sIizg4ysnJL/A2cHGfGJ5lfwzc3XotXqcSrVA+ekNK0DDMMrC4o0JKdpuHytiGXTA2kRYcfBU/d3XS2PzZ2cX2qBkYI8y+T83MybJFw+TM/nqyjnFxtyfulFfOxtIL/8BZnvi2nO1+PrKqF5bQmbj1vub/fONbcgp/Q1Nx17x4qP//GdS4jcsZAhry/Dw6+Ocbudo4exDgcnT+P2/Nx0PO8q97jSPyaLf1SX/6z/USKR0LZtWz799FNOnTqFtbU1Bw8exNfXl6tXrxIaGmryCg4OrrA+Dw8PkpOTTRptp0+frnScDRo04PTp0w89p6tmzZrG3+0OtVpNZGQkdevWNbtPkyZNuHDhQpljEBoairW1YZidXC6nW7duzJo1i7NnzxIfH8+uXYbV1KytrdGWt6pCJWMrTaFQ4OjoaPK6n+GQAFodXE/WEh5U0uCVAHUCrbh60/yY/auJGuoEmTaQw4PkxvJHzhfz+ZIcpi0teWXm6th2tIh5q3PNVflACgu13EwqMr7irheQlqGiWUMXYxk7Wxl1azty/mLlvujcaazV8LVl4gdnycm13KqeMrk1njXqceNyybwovU5HwuUjeAc1qnT90cfWYevgRlB4x0rXZY5OZ5hQHuRtmtCDvSQWn28mkYC8mkdmZB05jVuXVibb3Lu2IfPIaQD0ajXZJ6Nw73JXw0Yiwa1za7KOWHYug1xujV9wXS5HlSwYotPpuBJ1lMBaDcvdb/e/S9jx14+MnfIT/iGmq51qNRq0Wg2SUs+/MXcjrrK0OkhM11PTt+SzJEBNXynXUx/us6qizvI+53qyljqBpnN26wRVkDNvakzKA4QHWXH1puEacfR8MdOW5jB9WckrK1fHtmMq5v1R+ZtOpeO/lqQpk/PDg6yIvWl+teerN03LA4QHl//7GuuVlDSuKqNIpSc5TWN8JSSryczRUL92ScvBViEhNFBBTLz5IaUaLVxNUBFx1z4SCUTUsuXStYqHoUokVLj65P2Sya3x8KtHwhXTnH/zyhG8AhtVuv6LkYacH1iVOT8TgrxMj0WQl4SEdMvnfJmF16uRya3x8q/H9Uumx//6pcP4BJU/zzhyxyKObPmBQa8uxjsgwuQ9J7ca2Dt6cD2mpE5VYR7J8WfwCbbs3GXh0fOffC05evQoM2bM4Pjx41y/fp1169aRmppKeHg4n376KTNnzmTevHlcunSJc+fOsWzZMr7++usK6+zUqROpqanMmjWL2NhYvv/+ezZv3lzpWEeMGIG3tzcDBw7k4MGDXL16lbVr13L48OF774yhR+vVV19l8uTJbNmyhQsXLjB27FgKCgoYPXq02X2mTJnCoUOHeP311zl9+jSXL1/mn3/+MS46smHDBubNm8fp06e5du0aK1euRKfTERZmuGMdFBTE0aNHiY+PJy0trdzevoeJzdJ2HCuiXUMFrepb4+0m5emedlhbw6GzhovYyH72DOxY0su587iKesFWdGthg5erlH7tbAn0kbPn9tyL/CI9iWlak5dWpycnX0dKRtX0kKxZf5MXhgfQtoUbIYH2fDCpDukZKvYfKenZmDutAYP7+hp/trWREhpsT2iwocfTx8uG0GB74wqQMpmEae/WJSzUgc9mRyOVgquzFa7OVhb5EgLQqONILhxZQ3TkX2SkxLLnz0/QFBcS3sIwN3T7r1M4tGGOsbxh0no0qTej0WrV5GenkHozmqzUayb16nU6Lkb+RZ3mA5HKqq7T/kiMjiY1JTQIluDuCH2aS7GSw5k4w8V7QCspXRqWpDSpFLycDS+ZFJS2ErycwcWhpM4uDaUEeICTvWEuW5eGUoI8JZy7ZtkvBDJ7Oxwb1sGxoeEuqF1wDRwb1sHG3weAsGmTaLjsS2P5awt/xy7YnzozJ2MfFkLgK0/j82Rv4r5dbiwTN3cZ/qOH4ffcQBzqhFD/+0+Q29tyY4X5lRsro2OfFzi6+08i9/1Nys1Y1i39jOKiQpp3NMxd+e2HqWz6/Rtj+V3rF7NlzXyGvfw5Lh6+5GSlkpOViqrI0GtgY+dASHhzNvw6mysXjpF+K4HIvX9xfP966jfravH4D57X0qy2lMahUjycJDzRRo61HE5cMjRihnaQ06NpyTc2mRR8XCX4uEqQycDR3vCzq/L+67SUHZGmOXNETzusreDQOUPvzsi+dgzsUNIw2HWiyJAzmysMObOtDYHeMvacNPQ8GXKmzuRVlTlz+7Ei2jeyoXWEAm83Gc/0tsfaSsLB2zn/xf4ODOpUMrx/Z2QR9UKs6N7CBm83Kf3b2xLkI2fXCUP81lYwqKMtIb5yXB2lBHjLeKGvPS5KKSeiLffMyrtt3JvDkO7ONKtnR4CPFa8/40FmjpbIcwXGMh+96k2vdiUnyIY9OXRtpaRjcwf8PK0YO9QNhbWE3UcNNxI93eQM7OpESA1r3J1l1A5S8NZIT4rVek5GF5SJ4WE07DCS6KNruHj8LzJTYtm37hPUxYXUaW7I+Tt/m8KRTaY5P+1mNGl35fy0m9Fkp5nP+WHNqjbnH72op3FNCRFBEtwcoXczwwIhZ68a8nP/lhI6NSi5PpbN+ZTJ+Z0aSPC/nfM9nAw/B3pCVLzlh6A27TyKc4f+IOroX6Qnx7Ljj09Qqwqp18pw/DevfIf960uO/7HtCzm08Vt6PDMDJzc/8nNSyc9JpVhlyJsSiYTGnZ7n6NYFxJ7bSWpiDFt+fgcHJ09CG3SzePz/ucfowdnff/89QUFB2NjY0LJlS44dO1Zh+aysLMaNG4ePjw8KhYLatWuzadOmB/rM/2RIpKOjI/v27WPu3Lnk5OQQGBjInDlzjAtf2NnZ8dVXXzF58mTs7e2JiIgwLsBRnvDwcH744QdmzJjB559/zpAhQ3j77bdZuHBhpWK1trZm27ZtvPXWW/Tp0weNRkPdunX5/vvv77uOL774Ap1Ox3PPPUdubi7NmjVj69atuLi4mC3foEED9u7dy/vvv0/79u3R6/XUrFmT4cMND5B1dnZm3bp1fPLJJxQVFVGrVi1+++036tWrB8Dbb7/NCy+8QN26dSksLCQurvwJxQ8am6Udv1iMg52EJ9rbGh8CO291rnHegaujlLtvsF+9qWHx+jwGdLBjYAdbbmVqWbA2j8QqXg2yIqvW3sDGRsY7r9fGwV7OuQvZvPXxOYrVJYH7edvi7Fhyl7hOqJL5MxsZf54wxrCQzaadycyYG4OHm7XxsQDL55vO1xw/9TSnzld+jl6txn0ozMvg2Jb5tx/iGU7/lxYZh8fkZiaarPSVn3OL1XNKJpOf2rOUU3uW4luzOYPH/WzcfuPyIXIzE40Nv6py4boeO4WOjhFSw0NUM+HXPVrjpHRHO4lJ74zSFl7qXZLi2oRLaBMuJT5Fz8+7DOePnQIGtJLhYAsqNaRk6Vm1R1dmNcrKcmpan9Y7S45Z3dmGRY9urFzH2dFTUfh4YHu78QZQGJ9A5BMvU3fOVILGP09RQjLnXv6AtO0HjGWS1mzG2sOV2h9PMDw4+0w0x/qNobjUQiSW0Kh1b/JyMtj653fkZqXhG1iHMe/+hNLJcO5kpichuevhUYd3rEarUbNy7psm9XQf/Bo9h44D4NnxX7Hp97n8+v0UCvKycXH3pfewCbSuggdnn4vTYW+joWsTOUpbSMrQs3yb2njuONlLTPKO0g5eH1iyiFD7CDntI+Bqko4lm9X3VaelnLioRmlXSP92NsacOf+PvApyppYl/+bzRHtbBnSw5Vamjh/X5ZFYhatBVuR4dDFKuwIGdDDk/BspGr5dnWtcfMoQf8kvEHtTw+J/8hjY0Y5Bney4lanl+z9zSUw1/M3qdODtLqN1AxscbCXkF+qJT9Iw6+fsKrsu/LMrGxtrCS8Pc8POVsrFOBXTf0o2mWfm5S5HaV/S6D90Oh9HBynDe7ng7Cgj/qaK6T+lGId2qtV6wkNs6NvRCQdbKVm5WqKvFvHBt0nk5Fnm/yq0UR8K8zOI3DqfgtxU3H3D6TemJOfnZZXN+WvmluT8M3uXcmbvUnxDmjPg1ZL8lXD5EHlZicaGX1WJvmEYEtkxQoK9jYSULPh9j478252UTvYS9Jjm/DG9Sv4PWodLaB0O127p+WWX4Zja28ATrQzXEJUabmXBb3t0xFXBVMWwpn0oyMvg0MZ5FOQarrmDX1tsHBKZm5lkMsrg7IHf0WrUbFhiOj2mVe/XadNnPADNu41FXVzI9t8+QlWYg19IUwa/tljMc/sPrV69mkmTJvHjjz/SsmVL5s6dS8+ePYmJicHT07NM+eLiYrp3746npyd//vknfn5+XLt27Z5TmkqT6C09/kT4f+flL6r+kQBVKepg1S8kUpWGv9KhukN4aKXnczxumjxfr7pDqBTdkajqDuGhHTnzeJ876amW6UWpLlpt9c3xrKys1EdodYmH0LZzUHWHUCn5BY/vuQPg7lo1z/v7L7zco7ojKF/h7lX3LlRFbDs/c99lW7ZsSfPmzfnuu+8AwxQBf39/xo8fz7vvvlum/I8//shXX33FxYsXy6yB8SDEGpqCIAiCIAiCIFSfahwSqVKpyMnJMXmVfoQVGHrLTpw4QbduJUNQpVIp3bp1K3fq1Pr162ndujXjxo0zrlQ/Y8aM+1p74m6iwSYIgiAIgiAIwv9LM2fOxMnJyeQ1c+bMMuXS0tLQarV4eZk+ysLLy4vk5GSzdV+9epU///wTrVbLpk2b+PDDD5kzZw7Tpk17oBj/kzlsgiAIgiAIgiAI5lTnsv7mHlmlUFhmXqBOp8PT05OFCxcik8lo2rQpN2/e5KuvvuLjjz++73pEg00QBEEQBEEQhP+XFArFfTXQ3N3dkclkpKSYrlKTkpKCt7e32X18fHywsrIyeb50eHg4ycnJFBcXGx/fdS9iSKQgCIIgCIIgCEIFrK2tadq0KTt37jRu0+l07Ny5k9atW5vdp23btly5csXkkVuXLl3Cx8fnvhtrIBpsgiAIgiAIgiBUp8fkOWyTJk1i0aJFrFixgujoaF599VXy8/MZNWoUAM8//zxTp041ln/11VfJyMjgjTfe4NKlS2zcuJEZM2Ywbty4B/pcMSRSEARBEARBEAThHoYPH05qaiofffQRycnJNGrUiC1bthgXIrl+/TpSaUkj0N/fn61bt/Lmm2/SoEED/Pz8eOONN5gyZcoDfa5osAmCIAiCIAiCUH2qcdGRB/X666/z+uuvm31vz549Zba1bt2aI0eOVOozxZBIQRAEQRAEQRCER5ToYRMEQRAEQRAEofpIRR9SRcTREQRBEARBEARBeESJBpsgCIIgCIIgCMIjSgyJFARBEARBEASh2ugfo0VHqoPoYRMEQRAEQRAEQXhEiR42QRAEQRAEQRCqzwM+wPr/G3F0BEEQBEEQBEEQHlGiwSYIgiAIgiAIgvCIEkMiBUEQBEEQBEGoNnoxJLJCosEmVJpc/nj/kYW1qFvdIVSKi2N1R/DwrOSPdwrSHYmq7hAqRdqqXnWH8NAcf4uu7hAqRa+3q+4QKiXxRlZ1h/DQlM721R1CpXi7V3cElZOeLavuECol2Ku4ukOoBOvqDkB4SI/3tyVBEARBEARBEB5vYln/Cj3eXSOCIAiCIAiCIAj/w0QPmyAIgiAIgiAI1UbMYauYODqCIAiCIAiCIAiPKNFgEwRBEARBEARBeESJIZGCIAiCIAiCIFQfsehIhUQPmyAIgiAIgiAIwiNK9LAJgiAIgiAIglB9xKIjFRJHRxAEQRAEQRAE4RElGmyCIAiCIAiCIAiPKDEkUhAEQRAEQRCEaqMXi45USPSwCYIgCIIgCIIgPKJED5sgCIIgCIIgCNVHLDpSIXF0BEEQBEEQBEEQHlGih00QBEEQBEEQhGqjR8xhq4joYRMEQRAEQRAEQXhEPVCDTa/X89JLL+Hq6opEIuH06dMWD+iTTz6hUaNGFq9XIpHw999/l/t+fHx8lf1Od9uzZw8SiYSsrKwq/RxBEARBEARBEB5/DzQkcsuWLSxfvpw9e/YQEhKCu7t7pT5cIpHw119/MXDgQOO2t99+m/Hjx1eqXuHR1qGRNd2a2+BoL+FmqpY/dhZyLVlbbvnGta3o19YGNycptzJ1/LOvkKg4jfH953rZ0aq+tck+F+LUfL823+Kxd25mQ6/Wdjg5SLmRouHXLXnEJWrKLd8s3JqBnexxd5aRkqHlz535nLtSDIBMCoM62xMRao2Hs4xClY4LcWrW7swnK09n8dgBInet4vDWJeRlp+HlX4deIz7AL6SB2bIn9/3B2cP/kHrzMgA+gfXoPOhNk/J7/5lPVOQmcjKSkcmtbpeZiF9IwyqJ//S+VZzYtYT8nFQ8/OrQeeiHeAeajz8t6TKHN83j1o0ocjJu0nHQVJp0HmlSprgoj0Mbv+XK2R0U5KXj6VeXTkPeK7fOyji47Vf2bFhGbnYaPgFhDHrhPQJCzX/OkV1rOLF/Pck3rgBQI7guvYe/YVJeVZTPxt++IerELvJzs3D19KNdz2dp0224xWN3bdeMkLdG49SkPja+nhwf8hop63dWvE+HFtSd/S4OdWtRdCOJKzMXkLDyL5Myga8+Tcik0Si8Pcg5e5GoiZ+THXnO4vEDXDiyivP7l1KYl4aLdx1a93sfD3/zxz8z5TInd84n/WYUeVmJtOzzLvXavlBu3Wf2LuLEtq+p2+Y5WvV9z+KxtwqX0j5CjoMtJGfo+fewhoQ0vdmyns4SujWR4ecuxUUpYcMRDYeiyubXB6mzsro2t6V3W3ucHKRcT9bwy+Yc4m6Wnzeb11UwuIsD7s4yktM1rNmRx9nLxcb3m4Yr6NzMliAfKxzspHz0YzrXk8uvr7K6NLelV5vbeT9Zw6rNuRXn/boKBnW+nffTtazZkWea97vY0yBUgYfL7bx/tZg/d1Rd3j+6cxWHNt/O+wF16PPMB9QoJ+8f3/sHZw7+w63bed83qB5dh7xZbvl/V3zM8T2r6TViKq17lP838rDO7F/FyV1LKMhNxd23Dh2HlJ/z05Muc2SzIefnZt6k/cCpNO400qSMTqfl6Jb5xBxfT35uGvaOntRtMYjmPV5DUgXLyu/b8hs7/11OTlYafoFhDH1xKkGhEWbLnj66g21/LSIt+QZarQYP7wC69H+BFh36G8vo9Xo2/fE9h3aupTA/l+A6jRg+5kM8fQItHvt/TS8WHanQAx2d2NhYfHx8aNOmDd7e3sjllp8C5+DggJubm8XrFR6MWq2uknqbhFkxuJMtmw4X8cXPuSTc0vL6UHsc7MwnymBfGaP62XH4fDEzV+Zy9oqalwba4+NueupGxamZ+kO28bV0Q4HFY29eV8Hw7g6s35fPp4syuZGi4c2nnVCWE3vNGnJeGuzI/tNFfLook1MxKl4f5oifhwwAaysJAd5y/t1fwKeLM/l+TQ7ebjLGD3e0eOwAUcc2sf2PL+jQfxxjP1qHl38Yv84dQ35Outny12KOUb9FX557ewWjpv6Oo4s3q74ZTU5mirGMq3cQvZ7+kJc/Xc8LU1bh5ObHqm9Gk5+bYfH4Y05uYt9fM2nVaxzPTP4Ld786rPthNAW55uPXFBfi5FaDdv3fws7Rw2yZ7b99wLWYQ/R6bhbPv/svgXXasvb7UeRlpZgt/7BOH97M+l9m0X3wa0ycvgbfgDAWffEyudnmY4+9EEmjNn145YOljP90FU5u3iz84iWyM0riWv/zLGLOHmDEa1/wzux/6dDrOf5ePp2oE7ssGjuAzN6OnLMxnJ/w6X2Vtw2qQfP1P5G+5ygHmg0gbv4KIn6ahnv3dsYyPk/2JvyrqVye9j0HWgwi9+xFWm5cgrWHq8Xjv3p2E8c2fUmjLuN4YtxaXL3D2Lp8LIV55Zw76iKULv406zkJW4eKb0ymJpwjJnI1Lt5hFo8bICJYSp+Wcnae0vD9P2qSMvSM6mWFvY358lZyyMjVs/W4hpwC8w2wB62zMlrUU/BUTyV/78nj45/SuZGi5u1nXVDam8+bof5WvDLUiX0nC/nox3ROXVQx4Sln/DxlxjIKKwmXrqv5Y0ee5QMupXk9BcN7OLB+bz6f/pTBjRQNk551rjDvvzzEkf2nivjkpwxOxagY/5STSd4P9Lbi3335fLowg+9WZ+PtJmfCCKcqif/80U1s/f0LOg0Yx8ufrMPbP4yf54whr5y8H3/xGBGt+jJyygrGfPA7jq7e/DzbNO/fEX1iOwmxZ1A6e1ZJ7JdObmL/3zNp2WscT71tyPn//FhBzlcbcn7bCnL+iZ2LOHfwNzoO+Yjn3t1E2/5vc2LXYs7s+9ni8Z84tIW/Vn5F76Gv8M6Xf+AXWJsfppef9+0dnOg5+CUmTfuFd79aS6vOA1n1w4dEnz5oLLPjn6Xs3fwrw8d+yFszVqFQ2PLD9JdRF6ssHr/waLnvBtvIkSMZP348169fRyKREBQUxJYtW2jXrh3Ozs64ubnRr18/YmNjjfsUFxfz+uuv4+Pjg42NDYGBgcycOROAoKAgAAYNGmSsD8oOiRw5ciQDBw5k9uzZ+Pj44Obmxrhx40waFElJSfTt2xdbW1uCg4P59ddfCQoKYu7cuSa/Q1JSEr1798bW1paQkBD+/PPPCn/nvXv30qJFCxQKBT4+Prz77rtoNCV31VQqFRMmTMDT0xMbGxvatWtHZGSkSR2bNm2idu3a2Nra0rlzZ+Lj403ev3btGv3798fFxQV7e3vq1avHpk2bAFi+fDnOzs4m5f/+++8yd4GmTZuGp6cnSqWSMWPG8O6775ocw8jISLp37467uztOTk507NiRkydPmtQhkUhYsGABTzzxBPb29kyfPr3CY/OwujZTcOhcMUfOF5OcruP37YUUq6F1qR6yOzo3UXAhTsOOSBUpGTo2HCziRoqWjo0UJuU0Gsgp0BtfhSrL3ynu0cqWfaeKOHhGRVKalp835lGs1tOukflvOd1a2HL+SjFbDxeSlKbl7z0FXEvS0KW5LQCFKj1fr8rm+AUVKelart7UsGpzHkG+Vrg6Wv5O05Hty2nc/kkatRuCh28ofZ/9FCtrG04fWGu2/KCxs2nW+Wm8A8Jx9wmh38hp6PU64qIPG8tEtOxPSN02uHj44+lXix7D30VVmMethBiLx39y9zLqtxlGvVZDcPMJpduwT5Fb23D+iPn4vQMb0GHgFMKa9kUuL3t+aYqLuHxmG+0HTKZGaHOcPQJp3Wc8zu6BnDnwq0Vj37tpBS07D6VFp0F41whlyOiPsVLYELl3ndnyz7w+i7bdR+AXFI6nXwjDXvoMvV7H5fNHjGXiL5+mWfsBhNZtgauHH626DsMnIIzrsZbvoUrduo9LH88l5Z8d91U+8KWnKIxLIPqdL8m7eJVrP6wiee1Wgt8YaSwTPHEUN5b8QcKKdeRFx3LutY/RFhThP3KIxeM/f3AFYc2epHbTwbh4htJ2wCfIrWy4dML88feoEUGL3pMJadAXmZlz5w61Kp+9f0ym7cDPUNhWzY2WdvVlRMboOHlZx60sPf8c1FCsgaa1ZWbL30zTsyVSy9mrOrTlDFx40Doro2dre/aeLOTA6SISU7Ws2JBLsVpPh8a2Zst3b2nHuSvFbD5UQFKalnW787mWpKZbCztjmUNni1i/N58LV6v+S2rPVnbsuxN/mpaVt+NvX0H8568Us+V2/H/tzjfk/dvxF6r0zPkli8gLKpJv5/1fNudWWd4/tG05TTs8SeP2Q/D0C6Xf84a8f2q/+bw59OXZtOjyND4B4Xj4hDBglCHvX71w2KRcTmYKm1ZNY8jLXyGTVc36daf2LKN+62HUbTkEN+9QujxpyPkXjpqP3SugAe0GTKF2k77IZOb/bpPiThFSvyvB9Trh6FaDWo16ERDWjpTrZy0e/+4NK2nddQitOg/Cp0ZNho/9CGtrWw7v/sts+Vr1mtOwRVe8a4Tg4e1Ppz7P4htYm9iLhu9rer2ePZt+oefgl2jQvAt+gWE89/oMsjNTORtp+Rt1/zmJtPpej4H7jvLbb7/ls88+o0aNGiQlJREZGUl+fj6TJk3i+PHj7Ny5E6lUyqBBg9DpDN368+bNY/369fzxxx/ExMSwatUqY8PsTsNm2bJlxvrKs3v3bmJjY9m9ezcrVqxg+fLlLF++3Pj+888/T2JiInv27GHt2rUsXLiQW7dulannww8/ZMiQIZw5c4ZnnnmGp556iujoaLOfefPmTfr06UPz5s05c+YMCxYsYMmSJUybNs1Y5p133mHt2rWsWLGCkydPEhoaSs+ePcnIMPQu3Lhxg8GDB9O/f39Onz5tbEzdbdy4cahUKvbt28e5c+f48ssvcXBwuPd/yG2rVq1i+vTpfPnll5w4cYKAgAAWLFhgUiY3N5cXXniBAwcOcOTIEWrVqkWfPn3Izc01KffJJ58waNAgzp07x4svvnjfMdwvmRT8vWRcvFbS6NUDF69rCPE1n/CDfeXEXDMdehIdryG4VPla/nK+eM2Rj15U8lQ3W+xtLDu0QSaFQB850XElw3L0GIZe1qxhZXafmjWsuBBn2lMZdbW43PIAtjYSdHo9BUWWbXBqNcUkXYsiuG4b4zaJVEpweGsSrp6+rzrUxYXotBps7c3fCdZqijm5bzUKWyVeNepYImyTulNuRBEQZhp/QFgbkuJOPVSdOp0GvU6LXG7a+JdbK0i8erKcvR6cRlPMzbgL1K7f2rhNKpVSq34rrl0+c191FKuK0Go02DmUHPugWo2IOrmb7IwU9Ho9V6KOkpYcT+2IthaL/WE5t2pE2i7TL3ip2w/g0qoRABIrK5ya1CNt56GSAno9absO4dyqsUVj0WqKSU+Mwje05PhLpFJ8Q1uTev10peo+/O/n+Id1xC+0zb0LPwSZFHzdJVxJLBkqpwdiE3UEeD5cjquKOsv9LBkE+cq5cPWuvKmvOA+G+luZlAc4d6XivFlVZFIILB0/cKGC+Guaif98bDGhNcpv1NgpqibvazTFJMVHEVKv5PyUSqWE1G3NjSun76sOtaoQbam8r9PpWLfwHdr0Go2nXy2LxnyHVlPMrYQo/Gub5nz/2m1Iin+4nA/gE9yYG5eOkHkrDoDUmxdJvHqCwPAOlY75bhqNmhtXLxAW0cq4TSqVEhbRivhL9877er2emHNHuJUYT2jdpgCk30ogJyuNsAYlddraKQkKjSDuPuoUHm/3fVvEyckJpVKJTCbD29sbgCFDTO+ELl26FA8PDy5cuED9+vW5fv06tWrVol27dkgkEgIDS8bYengYuqudnZ2N9ZXHxcWF7777DplMRp06dejbty87d+5k7NixXLx4kR07dhAZGUmzZs0AWLx4MbVqlU0iTz75JGPGjAHg888/Z/v27cyfP58ffvihTNkffvgBf39/vvvuOyQSCXXq1CExMZEpU6bw0UcfUVhYyIIFC1i+fDm9e/cGYNGiRWzfvp0lS5YwefJkFixYQM2aNZkzZw4AYWFhxkbZHdevX2fIkCFERBjGNIeEhFR4LEqbP38+o0ePZtSoUQB89NFHbNu2jby8kqEiXbp0Mdln4cKFODs7s3fvXvr162fc/vTTTxvrKY9KpUKlMr2rqdWokJX60muOg60EmVRCbr7pOP3cfB3eruZPRUd7CTkFpuVzCnQ43jWc5kKcmtOXi0nP1uHuLOOJ9ja8NsSe2b/mobfQ9U9pJ0UmlZBTao5BTr4OH3fzF24nByk5pX7XnDwdjvbm75PIZTC0qz3HzqsoKrbshbsgLxO9TouDo+lwY3tHd9KS4+6rjp1/zkHp7ElIXdMvp5fO7GbdwrdQFxeidPLg2UlLsVO6WCx2gMJ8Q/x2StP47ZRuZKZcfag6rW0c8AlqzNGtP+DqHYKd0p2YExtIijuNs0eAJcIGID83C51Oi4OTaexKJzduJd7fsd/42xycXDypdVejb9DI91mz+GM+f70LUpkciUTCk2M+pWZ4M4vF/rAUXu6oUtJMtqlS0rByUiK1UWDl4oRULkd1K71UmXTswx4sB96LqiALvU6LrYPp8bd1cCMr9f6OvzlXz24kPfEC/V9dU9kQy2VnAzKphLxC03yQV6jHw+nh7gpXRZ3luZM3s83mTfM9IE4OUrPlnRz++7vgxrxfOo/n6/BxN3/NKjfvO5jvvZTLYGg3B46eq4K8n5tpyD2l8r6D0/3n/e1rbuf9uxp9BzYtQiqT0ar7cxaN925VkfMBmnV9ieKiPH6e2RupRIZOr6V1nzep0+yJyoZsIj/HcOwdnUvlfWc3UirI+4UFuXzwclc0GjVSqZRhoz+gTgPDsc/JMuRLpZlrSU5WWpm6hP8tlcqAly9fZsSIEYSEhODo6GjsPbt+/TpgGM54+vRpwsLCmDBhAtu2bXuoz6lXrx4yWUmy8/HxMfagxcTEIJfLadKkifH90NBQXFzKfmFs3bp1mZ/L62GLjo6mdevWJsMP27ZtS15eHgkJCcTGxqJWq2nbtuRutpWVFS1atDDWGR0dTcuWLSuMYcKECUybNo22bdvy8ccfc/bsg3XLx8TE0KJFC5NtpX9OSUlh7Nix1KpVCycnJxwdHcnLyzP+P91xp8FbkZkzZ+Lk5GTyOrHrmweK2dJOxKg5F6shMU3H2StqFqzLJ8hHTm3/x+cxgzIpvDrUEQnw86aqn5fxoA5uWkjUsU08+dp3yK1MG+dBdVry0kd/Merd36hZvz1rf5pY7ry4R02v52ah1+tZ9GEH5k2K4NTenwlr2hfJIzREYtf6RZw+vJkXJn2LlXXJsT+wdRXXr5xl1FvfMXH6H/R/ZjJ/LZ/GpXOHK6hNsIS8rCSObJhJx2Fflfl7EIT7JZPCq086IZHAzxtz773Df2z/xoWcP7aJp8Z/h9Xt8zwx/jxHt//MwNEzq2SRjqp2+fRmYk78S6/n5vDU2+vo/vQXnNq9lOhj5ocp/tcUNva8+9WfTJ75G/2emsBfK7/iclT5I9D+l+glkmp7PQ4q9Y22f//+BAYGsmjRInx9fdHpdNSvX5/iYsNwgCZNmhAXF8fmzZvZsWMHw4YNo1u3bvecO1aalZVpD4ZEIjEOu3zcjRkzhp49e7Jx40a2bdvGzJkzmTNnDuPHj0cqlaIv1UX0MIuBvPDCC6Snp/Ptt98SGBiIQqGgdevWxv+nO+zt7e9Z19SpU5k0aZLJtnd+KLyvOPIK9Wh1epT2UqBkcoXSXkpOvvk7izn5ehztTL88O9qVXx4gPVtHboEOD2cpMdfLLfZAcgt0aHV6HEvd5XW0L3s3+I5sM71pjmbuvsqk8MoQR9ycZHz1c5bF77IC2Dm4IJHKykw0z89Jw8Gp4kUVDm9dwsHNi3j2raV4+ZddWMFaYYerVyCuXoHUqNmI79/ryakDf9Kuz8sWi9/W3hB/6cnmBbnp2CkffrVaZ48Ahr3xC2pVAaqiPBycPNm4bCJObv6VDdnIXumMVCojr9RE89zsdBydK459z4Zl7Fq/hJffW4xvQMmxVxcXsXn1XF6YNI+6jTsC4BsQRuK1GPZuXEbtiNblVfmfUKWkofAy/d0UXu6os3PRFakoTstEp9Gg8HQrVcYNVbJl7xQr7JyRSGVlFhgpzEvH7h4LipQnPTGKovx0/vm+ZJSJXqclOf440Ud+5YVPzyCVVn4+WEERaHV6HGwlGAbjGTjYSsgtfLg8URV1ludO3izdO2bIm+Yn2GXnle1NqyjPViVj3i+dxx8m75f6fQ036Zxwd5Iya2UV5X2liyH3lMr7edlpODhWfO4f3LyEAxsX8fzkpXjflfevXTpBfm4637xdMnJHp9Oy9fcvObJtBW/Otsxcqgpz/j1ir8iB9bNo2vUlajfpC4C7bxi5mYkc3/ET4S0GVSrmu9k7Go79nV6xO3Kz0sv0ut1NKpXi4W0Y4VEjqA4pN6+y7e/F1KrX3LhfbnY6Ti4li6rkZqfjF2TZaQjCo+ehbyOnp6cTExPDBx98QNeuXQkPDyczM7NMOUdHR4YPH86iRYtYvXo1a9euNc7xsrKyQlverOj7FBYWhkaj4dSpkjHNV65cMRvLkSNHyvwcHh5utt7w8HAOHz5s0mA6ePAgSqWSGjVqULNmTaytrTl4sGT1HrVaTWRkJHXr1jXWcezYsQpjAPD39+eVV15h3bp1vPXWWyxatAgwDBvNzc0lP79kefrSz4kLCwsrM/+v9M8HDx5kwoQJ9OnTh3r16qFQKEhLe7gvRQqFAkdHR5PX/QyHBNDq4EaKlrCAkvsEEiAsQM7VcpZIjkvUEBZoel+hTqC8wiWVnR0k2NtKyM633AVeq4NrSRrCg0qG8UiA8GArYhPMN6JjE9SEB5vebKgbbG1S/k5jzctVxuxfssi38Bcm4+fIrfEJrEf8XQuG6HU64i4eoUZIo3L3O7R5Mfs3LODpiYvwDTK/FHFper0Orbr43gUfgExujZd/PW5cMo3/RsxhfIIrP+fJSmGHg5MnRQXZXLt4gJCIrpWu8w653Bq/4Lpcjir529fpdFyJOkpgrfIff7D73yXs+OtHxk75Cf+Q+ibvaTUatFpNmZ5Aczd5qkPWkdO4dWllss29axsyj5wGQK9Wk30yCvcudzUsJRLcOrcm68jDz08xRya3xs23HomxJcdfr9ORGHsEj4BGD1Wnb83WDJrwDwNfX2d8ufvVp2bDfgx8fZ1FGmtgyDuJaXpCfUr+nyVATV8p12893P9zVdRZ7mdpIT5RQ93gu/KmBOqGWJebN6/cUJuUB6hXs/zyVUmrg2uJGsJDSuX9CuKPvaEmvHT8IdZcSSi5Zt1prHm6yZj9c9XlfbncGp+geiYLhuh0OuKij+Af2qjc/Q5sWszefxfw7FuL8As2zfsN2zzBq5/9wyuf/mV8KZ09adt7NM+9tdhiscvk1njWqMeNy6Vy/qXD+AQ9fM7XFBeV6RmUSGQWz5tyuRX+IXW5dP6ocZtOp+PS+SME1b7/x97odTo0t6+nbp41cHR2J+ZcSZ2FBXnEXzlH8APU+ajSS6TV9nocPHQPm4uLC25ubixcuBAfHx+uX79eZkGNr7/+Gh8fHxo3boxUKmXNmjV4e3sbVz4MCgpi586dtG3bFoVCYXYY473UqVOHbt268dJLL7FgwQKsrKx46623sLW1LfNHuWbNGpo1a0a7du1YtWoVx44dY8mSJWbrfe2115g7dy7jx4/n9ddfJyYmho8//phJkyYhlUqxt7fn1VdfZfLkybi6uhIQEMCsWbMoKChg9OjRALzyyivMmTOHyZMnM2bMGE6cOGGyWArAxIkT6d27N7Vr1yYzM5Pdu3cbG5EtW7bEzs6O9957jwkTJnD06NEy+48fP56xY8fSrFkz2rRpw+rVqzl79qzJXLhatWrx888/06xZM3Jycpg8eTK2tuZXuKpqO4+reL63HddTNMQnaenSVIHCCo6cNySk53vbkZWnY/3+IgB2n1Tx5nAHujZTcP6qmqZ1rAnwlvHrdsOy/Qor6NPGhlOX1OTk6/FwljKwgw2pmTqi4y37XJ5tRwoZPUBJfJKauEQN3VrYorCScPCMIdbRA5Rk5upYt8vQwN5xrJB3nnemRytbzl4upkU9BUG+clbeHvpyZxhkoLecb1dnI5VgnJuXX6hHa+Ebyq26j+Sfpe/iE1gf3+AGHNuxArWqkIZtBwPw95IpKJ096TrkLQAObl7E3n/mMWjsbJzd/cjLTgUMPWrWNvYUqwo4sPFHajfsgoOzB4W5mUTu/pWczBTCm/WybPBAk86j2PrLFDz96+Md2IBTe1agLi6kXktD/Ft+fgcHJy/aPWGIX6spJj051vjvvOwUbiVEY62ww9nDMJ82Pno/6PW4eAWTlXqd/f/MwsUzhHqtBls09o59XuD3H9+jRkg9AmpGsH/zzxQXFdK8o+GO7m8/TMXJ1ZM+T70JwK71i9n653c88/osXDx8yckyHHuFjR0KG3ts7BwICW/Ohl9nY2WtwMXdl6vRkRzfv54nnn3HorGDYVl/+9CSeX12wTVwbFiH4oxsim4kETZtEjZ+XpwZNQWAawt/J/C1Z6gzczI3lq/FvXMrfJ7sTeQTJb2ucXOX0XDpl2SdOE925FmCJryA3N6WGyvMr9xYGfXbvsD+tVNx96uPR40Iog6tRFNcSO2mhuO/d80U7B29aNbTMHpAqykm69btc0erJj/nFumJ0Vgp7HB0C8RKYY+LV22Tz5Bb26Kwcy6zvbIOnNcytIOchDQpCal62taXYS2Hk5cMNzuHdpCTU6Bn23HDzzKp4Vlsd/7taAc+rhJUaj0ZufdXpyVtPZzP2EFOxCWquXpTTY9WdiisJOw/ZcibYwc5kpmj48+dhqHg248W8O5IF3q1tuPMZRUt69sQ7GvF8n9zjHXa20pwc5LhrDR80fJ2MzSQs/N0Fu+J23qkgDEDHYlP1BB3U0332/EfOG0YWTJmoCHvr92Zb4x/ykgXera25cylYlrWtyHIV86K2/HLpPDak04E+sj59rdsJBLJXXlfZ/G836bHSP5a/C5+QfXxC2nA4W0rKFYV0ridIcetW2TI+92fNOTN/RsXsfvveQx92ZD3c+/K+wobe+wcXLBzMP2uJpPJcXByx93HsvNPG3caxfZfp+DlXx+vgAac3rsCTXEhdW/n/G2/vIO9kxdt+5fk/IzbOV+nLSY/O4XUBMPf7Z2cH1yvM5Hbf0Tp4oubdyipN6M5tWcZ9VpafnXazv2e55fv3ycgpB6BoRHs2fQzKlUhrToNBGDld+/h7OrJE09PNPw+fy0moGZd3L380ajVRJ3az7H9Gxg+5gPAMLqsU59n2bruJzx9AnDz9GPD79/h5OJBg+ZdyolC+F/x0A02qVTK77//zoQJE6hfvz5hYWHMmzePTp06GcsolUpmzZrF5cuXkclkNG/enE2bNiGVGpLsnDlzmDRpEosWLcLPz6/Mkvf3a+XKlYwePZoOHTrg7e3NzJkziYqKwsbGdLn1Tz/9lN9//53XXnsNHx8ffvvtN2NvWGl+fn5s2rSJyZMn07BhQ1xdXRk9ejQffPCBscwXX3yBTqfjueeeIzc3l2bNmrF161ZjwzMgIIC1a9fy5ptvMn/+fFq0aMGMGTNMVmDUarWMGzeOhIQEHB0d6dWrF998Y5gT5urqyi+//MLkyZNZtGgRXbt25ZNPPuGll14y7v/MM89w9epV3n77bYqKihg2bBgjR4406dlbsmQJL730Ek2aNMHf358ZM2bw9ttvP9SxrqyTMWqUdoX0a2uL0s7w4Ozv/8wn9/bzglwcpSYLhcQlalm2sYD+7Wzo386G1CwdC//OJynNcFXT6cHXXUbLetbYKiRk5+mJjlez4WARGgt/94i8oEJpJ2FgR3scbz84+5tfs43DM11LxR6boGHRXzkM6mzP4M723MrQ8t0fOdxMNQTmrJTSOMzQO/npS6bPnpq1MouYa5a9o1yvRR8K8jLY+8988nJS8fIP5+mJi4xDInPSE01ucpzY8xtajZo/F7xhUk+H/uPoOGA8UqmMtKQ4zh6aQEFeJrb2zvgGRzByyqoqWTksrEkfCvMyOLxpHgU5qXjUCGfQq4uxvz08JjczyaTHKS/7FqtmDSz5fXYt5cSupdQIbcGTEwzP3FEV5nLw36/Jy0pGYe9MrYY9aNvvTWQyy65I16h1b/JyMtj653fkZqXhG1iHMe/+hPL2sc9MT0IiLTn2h3esRqtRs3Lumyb1dB/8Gj2HjgPg2fFfsen3ufz6/RQK8rJxcfel97AJtK6CB2c7Na1P650lzymqO9vwcOgbK9dxdvRUFD4e2Pr7GN8vjE8g8omXqTtnKkHjn6coIZlzL39A2vYDxjJJazZj7eFK7Y8nGB6cfSaaY/3GUHzL8vMfQxr0oSg/k5M751GYm4arTzg9Ri40PmMtP9v03CnITeWf70sa7ecPLOX8gaV4Bzenz5iVFo+vIufidNjbaOjWVI7SFpLS9SzbqibP0N7B2UFikneUdjB+UEkPT4cGcjo0gKtJOhZvUt9XnZZ0LEqF0j6XQZ0djA/OnvNLpnFouJuTzCT+KzfU/LQ2m8FdHBjS1YGUDC3zfs/i5q2ShN44TMGYgSWrFr72pDMAf+/J4+89JSNSLCEySoXSLo+BneyND87+ZlVWSd53kqErlfcXrsthcGd7BncxxD//92zTvF/ndt5/xTTvf7k80+J5v37LPuTnZrDr7/nkZafiHRDOc5NK8n52qbx/fLch76/+3jTvdxowjs4Dx1s0tnup3aQPhfkZHNk8j/ycVDz8whnw8mLjMPjSOT8/+xa/zR5o/Pnk7qWc3L0Uv5otGDLekL86DvmAI5u+Zc+fn1KQl469oycRbYbTouc4i8fftE0v8nIy2PjH9+RmpeEXVIfX3vvROBQ+My3J5NgXqwr4Y/F0stJTsLJW4OUXzPPjZ9K0TckN0G4DXqRYVchvP31KYUEuIXUa89p7P5rMb35sPSZzyaqLRP8ojJ+xsISEBPz9/dmxYwddu1puaNPjonv37nh7e/Pzz5Z/EKQ542Zn/SefU1WKCv/7oTaW1LnTw4/nr255BY93gvZzt2wv7n9N2qpedYfw0M7/Zn7BqMdFdvbjnXcSb2RVdwgPTfqYfzHs0d38Q6EfF+nZj/fxr+Vr2SH//6UeDct/rmR1yzh34N6FqohrRLtq++z79fgso1eBXbt2kZeXR0REBElJSbzzzjsEBQXRoYNln6vxKCooKODHH3+kZ8+eyGQyfvvtN3bs2MH27durOzRBEARBEARBECrpf6LBplaree+997h69SpKpZI2bdqwatWqMqtL/i+SSCRs2rSJ6dOnU1RURFhYGGvXrqVbt27VHZogCIIgCIIg3NPjsvhHdfmfaLD17NmTnj17VncY1cLW1pYdO3ZUdxiCIAiCIAiCIFSB/4kGmyAIgiAIgiAIjyc9j/fcxqom+h8FQRAEQRAEQRAeUaLBJgiCIAiCIAiC8IgSQyIFQRAEQRAEQag2YtGRiomjIwiCIAiCIAiC8IgSPWyCIAiCIAiCIFSfx/yB9lVN9LAJgiAIgiAIgiA8okQPmyAIgiAIgiAI1UYv+pAqJI6OIAiCIAiCIAjCI0o02ARBEARBEARBEB5RYkikIAiCIAiCIAjVRi8WHamQ6GETBEEQBEEQBEF4RIkeNkEQBEEQBEEQqo14cHbFxNERBEEQBEEQBEF4RIkGmyAIgiAIgiAIwiNKDIkUKs3a5vE+jRJiU6o7hEqxsXar7hAemkb7eE8yPnJGW90hVIrjb9HVHcJDqz8ivLpDqJRDP52t7hAqJS8zv7pDeGjuvi7VHUKl+Ds/vsceIK/QobpDqJRgh6TqDqESAqs7gHLpeby/D1Q10cMmCIIgCIIgCILwiHq8u0YEQRAEQRAEQXisiUVHKiaOjiAIgiAIgiAIwiNKNNgEQRAEQRAEQRAeUWJIpCAIgiAIgiAI1UYvEYuOVET0sAmCIAiCIAiCIDyiRA+bIAiCIAiCIAjVRizrXzHRwyYIgiAIgiAIgvCIEj1sgiAIgiAIgiBUG7Gsf8XE0REEQRAEQRAEQXhEiQabIAiCIAiCIAjCI0oMiRQEQRAEQRAEodqIRUcqJnrYBEEQBEEQBEEQHlGih00QBEEQBEEQhGojFh2pmDg6giAIgiAIgiAIjyjRYBMEQRAEQRAEQXhE/b9rsMXHxyORSDh9+nR1hwLAyJEjGThwYHWHIQiCIAiCIAjVQo+k2l6Pg//pOWwjR44kKyuLv//+27jN39+fpKQk3N3dqy+wKmTud37UtI2woktjK5R2EhLTdKzbp+L6LV255RvWlNG7lQJXpYTUbB0bDhUTfU1rfP+b1x3M7rf+oIrdp9QWjx/g6X5udG/njL2tlItXC1nwawpJqRV/Vp+Ozgzs7oqLo4z4BBULV9/i8rUi4/vT3vQnoradyT5b9mWx4LcUi8V9ZMcq9m9aSl52Gt7+dej33Pv412xgtmzk7j84dXA9KQmXAfALqkv3J98sU/7WzVi2/jGHuIuR6LRaPP1q8vT4b3F297VY3Hec2L2Ko9uXkJedimeNOvR46kN8g83Hf3r/H5w78jdpiYb4vQPq0XHgJGN5rVbNvr/nEnt+H1lpN1DYOhAU3oZOg95C6exl8dhbhktpX1+Ogy0kZ+rZcFhDQprebFlPZwldm8jwc5PiopSw8YiGQxe0Zco9SJ2VdeHIKs7vX0phXhou3nVo3e99PPzNH/vMlMuc3Dmf9JtR5GUl0rLPu9Rr+0K5dZ/Zu4gT276mbpvnaNX3PYvG7dquGSFvjcapSX1sfD05PuQ1UtbvrHifDi2oO/tdHOrWouhGEldmLiBh5V8mZQJffZqQSaNReHuQc/YiURM/JzvynEVjv+NxP3cAhvd2oVtrJXa2UmLiili4Jo3kVE2F+/Rq58gTXZxwdpRx7WYxS9amc+W6yvj+S8PcaRBmi4ujjKJiPZfiivh5fQaJtyyb9zs2VtCjpQJHeykJt7Ss3lFAfFLZY3pHkzArnmhvi5uTlFuZOv7aU8D5q+Z/16d72NGhsYI/dhaw67jKbJnK2rnpD7b8vZLsrHT8g2rxzJh3CKld32zZE4d3sWHtUm4l3UCr1eDlE0DPAc/SplNfs+VXLpjBnm1reerFt+jR/2mLx35y7yoity8hP8eQ87sO+xCfIPN558yBP4g6WpLzvQLq0WHAJJPyer2egxvmcfbgGlSFOfiGNKHHiE9w8QyyeOwAG/5dz7q1a8jMzCA4OISXXx1HWFide+63d+9uvvpyJq1ateaDjz41bl/1y0r279tDamoqcisrQkNr8fzzIwmrE14l8QuPjke6h624uNjidcpkMry9vZHLq7atWhWxW5JaXTUNmXtpFCpnYDtrtkYWM2d1AYnpOl5+whYHW/N3OIK8pTzX04ajF9TMXl3A+ataXuxjg7dryan70dJ8k9dvO4vQ6fWcja34y8DDGtzDlb6dXVjwawqTZ12nSKXjkwk1sJKXf5emXVMlLw7xYPXGNCbNuEZcgopPJtTASSkzKbd1fxYvTLlifC3/K9VicZ89solNv35Jl4HjGPfZWrwDwlj+1VjyctLNlo+7GEmDVn0YPXU5r3z0G05uPiz/agzZGSUNyPSU6yyc9gwePsGMmbqC8dP/pvOAV5FbKywW9x0XIjex88+ZtOs7jhff/wuvGnVYPW80+eXEf+3SUeo278vTk1by/JTfUbr48Pu3L5KbaYhfXVxE8o0LtO37KqPeX8fgV74jPTmOP79/1eKxRwRL6dNCzq7TGr5fryY5Q8/InlbY25gvbyWHzFw9W49ryC0w/yX6QeusjKtnN3Fs05c06jKOJ8atxdU7jK3Lx1KYZ/7Ya9RFKF38adZzErYOFd8cS004R0zkaly8wywfOCCztyPnbAznJ3x678KAbVANmq//ifQ9RznQbABx81cQ8dM03Lu3M5bxebI34V9N5fK07znQYhC5Zy/ScuMSrD1cLR7/437uAAzs6kSfDo4s/CON975JRFWs58NXfCrMmW0a2/PCIDfWbM3kna9uEp9YzAeveuPoUJL7r95Q8f2vqUycmcC0BUkAfPiaD1IL3jBvWseKoV1s2XCwiBnLc0i4pWX8MAeUduY/JMRPxugn7Dl4VsX05TmcvlzMK4Md8HUv+3WrUS0rgn1lZOWWf8Oyso4d2MbqZV/zxPCX+HjOKvyDavP1Z6+Tk5Vhtry90pF+Q1/k/S+W89k3v9OuS3+Wzv+U86cOlSl74sguYi+dw9nVo0piv3h8E3vWzqRN33E8P/UvPPzqsGb+aPJzzeedG5ePEt6sL8MnruSZyb/j6OLDmvkvkptVcs06tn0RJ/f8TPcRn/DM5D+wVtiyZv5oNGrLN5b37d3D4kU/MeLpZ/l2/g8Eh4Tw0YfvkZWVWeF+KSnJLF28iHr1yjaq/fxq8Mqrr/P9DwuZ9dXXeHl68eEHU8nOzrJ4/P81vURaba/HwSMVZadOnXj99deZOHEi7u7u9OzZk/Pnz9O7d28cHBzw8vLiueeeIy0tzbjPn3/+SUREBLa2tri5udGtWzfy8/P55JNPWLFiBf/88w8SiQSJRMKePXvKDIncs2cPEomEnTt30qxZM+zs7GjTpg0xMTEmsU2bNg1PT0+USiVjxozh3XffpVGjRsb37wxtnD59Or6+voSFGb583Lhxg2HDhuHs7IyrqysDBgwgPj6+3GOg0+mYOXMmwcHB2Nra0rBhQ/7880+TMlFRUfTr1w9HR0eUSiXt27cnNjb2nr/z6tWr6dixIzY2NqxatQqdTsdnn31GjRo1UCgUNGrUiC1btlTuP/EeOjWy4nCUmmPRGlIy9azZraJYo6dluPkGdIeGVly8rmX3KTW3MvVsPlpMQqqO9g2sjGVyC/Qmr/rBcq4kaEnPqZq7xf27uLBmczrHzuZx7aaKucuTcXWS06qR+Z4+gAFdXdh2MJudh3O4kVzMgt9SUBXr6NbayaScSq0jK0drfBUWWe5CfnDLCpp1epKmHQbj6RfKgJGfYKWw4cTedWbLD3v1K1p1exrfwHA8fEMYNPpz9DodVy8cNpbZ/udcwhp2oNdTk/ENqoubVwDhTbrg4OhmsbjvOLZjGQ3bDaNB2yG4+4bS65lPkVvbcPbQWrPlB4yeQ9NOz+DlH46bd036PD8NvV5H/EVD/Da2SkZMXEZ4sz64eYfgF9KIHiM+JPl6FNkZiRaNvW19GcdjdJy8rCM1S88/BzWoNdC0tsxs+ZtperZEajkXp0NTzo38B62zMs4fXEFYsyep3XQwLp6htB3wCXIrGy6dMH/ueNSIoEXvyYQ06ItMbl1uvWpVPnv/mEzbgZ+hsHW0eNwAqVv3cenjuaT8s+O+yge+9BSFcQlEv/MleRevcu2HVSSv3UrwGyONZYInjuLGkj9IWLGOvOhYzr32MdqCIvxHDrF4/I/7uQPQt6MTa7dlEXm+gGuJxcz/5RYuTjJaRNiVu0//Tk7sOJTD7qN5JKSoWfhHGqpiPV1aKY1ldhzOJTq2iNQMDXEJxfy+KRMPFzkerpa7IdutuQ0Hz6g4fK6YpHQdv24tQK2GNhHmz+suTW2Iuqpm+zEVyek6/t1fxPUULZ2amLaGnR0kDO9ux9IN+Wh1VdezuXX9L3ToPoj2XZ/Azz+E5195D2uFDft3/mO2fJ36zWjaqgu+/sF4+vjTvf/T1AgK5VL0aZNymem3+HXxV7z05jRksqq5AX581zIatB1GROshuPuE0mPEp1hZ23C+nJzfb9QcGncsyfk9nzXk/Gu3c75er+fErpW06vUqtRp2w7NGHfq8MIu87FtcPnN/+eFB/P3XWnr26k33Hj0JCAhk3OtvoFAo2L5ta7n7aLVaZs/6gmeefQ5vH58y73fq3IVGjZvg7eNDYGAQY156mYKCAuLi4iwev/BoeaQabAArVqzA2tqagwcP8sUXX9ClSxcaN27M8ePH2bJlCykpKQwbNgyApKQkRowYwYsvvkh0dDR79uxh8ODB6PV63n77bYYNG0avXr1ISkoiKSmJNm3alPu577//PnPmzOH48ePI5XJefPFF43urVq1i+vTpfPnll5w4cYKAgAAWLFhQpo6dO3cSExPD9u3b2bBhA2q1mp49e6JUKtm/fz8HDx7EwcGBXr16ldsDN3PmTFauXMmPP/5IVFQUb775Js8++yx79+4F4ObNm3To0AGFQsGuXbs4ceIEL774IhqN5p6/87vvvssbb7xBdHQ0PXv25Ntvv2XOnDnMnj2bs2fP0rNnT5544gkuX778UP939yKTQg1PKZdulHyL0AOXE7QEepv/ohDkLTMpDxBzXUugt/lT18FWQt1AGUejq6Z3zcvdClcnOWcuFhi3FRTpuBRXRFiwrdl95DKoGWBjso9eD2cuFhAWYnoR79jckZ+/qsm8D4N4boA71laWuVWs0RSTGB9FaL3Wxm1SqZTQuq25fuX0fdWhVhWh1WqwtTc0MnU6HTFn9uLmHcSyWWOYMa4tCz4ZzoUTlr/waTXFJF+PIji85HyWSKUE1WnDzaun7i/+4kJ0Wg029k7lllEV5oFEgo0FGw8yKfi6SbiSWNL41gNXEnUEeDzc/29V1FkeraaY9MQofENLzh2JVIpvaGtSr5+uVN2H//0c/7CO+IWWn5v/a86tGpG267DJttTtB3Bp1QgAiZUVTk3qkbbzrh4HvZ60XYdwbtXYorE87ucOgKebHBcnOWcvFRq3FRTpuXxNRe1g8116chmE+CtM9tHr4dylQsKCzO+jsJbQuaWSlDQ16VmWyf8yKQR4y4i+VlKfHoiOVxPiZ76REuIn5+I108+/EKcmxK/kGicBRvazZ/vRIpLSqq53TaNWcy32InUbtjBuk0ql1G3QgtiYew/f1ev1XDh7jOSb1wir28S4XafTsWjuh/Qa8Bx+ATWrJPY7OT8wzDTnB9ZpQ2Lc/eV8ze2cf+ealZ2eQH5OKoF1SupU2CrxCWpI4n1eR+6XWq3mypXLNGpUkhOkUimNGjXm4sXocvf7/bdVODk706Nn7/v6jC2bN2Fvb09wcIhF4q5OYg5bxR65OWy1atVi1qxZgKFXq3HjxsyYMcP4/tKlS/H39+fSpUvk5eWh0WgYPHgwgYGBAERERBjL2traolKp8Pb2vufnTp8+nY4dOwKGhk3fvn0pKirCxsaG+fPnM3r0aEaNGgXARx99xLZt28jLyzOpw97ensWLF2Ntbbjz9ssvv6DT6Vi8eDESieGEWLZsGc7OzuzZs4cePXqY7K9SqZgxYwY7duygdWvDl6OQkBAOHDjATz/9RMeOHfn+++9xcnLi999/x8rK0MtUu3bt+/qdJ06cyODBg40/z549mylTpvDUU08B8OWXX7J7927mzp3L999/f89j9qDsbSXIpBJyC03vJuYW6PF0Nt8AU9pJygzryS3Q4Whn7utZ4wABAABJREFU/tRtUUdOkZoqGw7p4mi46GblmNaflasxvleao4MMmUxSdp8cLTW8Su7S7ovMITVdTUa2hiA/Bc8P8sDPy5ovFla+t6cgNwudTlum58vByY3UpPu7M7dl9WwcXTypWc9wscvPSae4qIB9GxbTfegEeg5/i8tnD/DrvAmMnrqc4Dot7lHjA8Sfl4lep8VOaRq/vaMb6clX76uO3etm4+DkadLou5tGrWLPutnUbd4XhW35vaUPyk4BMqmEvFLnfV6hHo9yzvvqqLM8qoIs9Dottg6mx97WwY2s1Ie/q3v17EbSEy/Q/9U1lQ3RohRe7qhS0ky2qVLSsHJSIrVRYOXihFQuR3UrvVSZdOzDLPul6XE/dwBcbg/7zso1vfGWnavFWWk+ZyrtDTkzu9Q+Wbla/DytTLb1bOfIs0+4YquQcjOlmM9+SCq3Z/FBOdgZrlk5+aaNqtwCPd5u5eR7ezPl8/U42pcc2x6tbNDpYNeJqpmzZvzc23nf0cn0b9fR2Y2km/Hl7leQn8tbY3qjURcjkcp47qV3qdeolfH9zX8tRyaT0a3fiKoKncI7Ob/UNctO6UZGyv3l/L1/zcbeydPYQMvPNkwxsHcsex3Jz0krs39l5OTkoNPpcHZxMdnu7OxCwo0bZveJijrPtq1bmPdd2Q6Bux07eoRZX85ApVLh4urK59O/wMmp/BuRwv+GR67B1rRpU+O/z5w5w+7du3FwKPvlKTY2lh49etC1a1ciIiLo2bMnPXr0YOjQobiU+gO5Hw0alExK9bndDX3r1i0CAgKIiYnhtddeMynfokULdu3aZbItIiLC2Fi7E/+VK1dQKpUm5YqKioiNjS0Tw5UrVygoKKB79+4m24uLi2nc2HCX5vTp07Rv397YWHsQzZo1M/47JyeHxMRE2rZta1Kmbdu2nDlzptw6VCoVKpXpRUajViO3svycpYfRoq4VJy+pLXbB7thcyatPlzR+P/8hwTIVm7HtQLbx39cSi8nI0TJtoj/e7lYkp1XPnMM79v67iHNHNzNm6gqsbs9P0+sNX/jCm3Shba+RAPgGhnP9yimO7Vpt0QZbZR3espDoyE0889ZKs+eqVqvmr4VvoNfr6fX0/c11Eh5eXlYSRzbMpNeLSx6Z3CFYRvumDrw0vGTe4syfkqv08/Yfz+VMTAEujnKe6OzEpFFefDA3EbWm6oYZVkaAl4wuTRXMWJFT3aGUy8bWnk++/g1VUQEXzh7j92Vf4+HtR536zYiPjWb7ht/5eM4q443oR9HRrQu5eGITwyeaz/mPmoKCAr6e/SXjJ0y8Z+OrQcOGzPtuATk5OWzdsokvZ05jzjfzcHZ+8O++wuPjkWuw2dvbG/+dl5dH//79+fLLL8uU8/HxQSaTsX37dg4dOsS2bduYP38+77//PkePHiU4OPiBPvfuBtCdJKTTPdhQhbtjvxN/06ZNWbVqVZmyHh5lJ+ne6bHbuHEjfn5+Ju8pFIaEY2trftjdw8T3MGbOnMmnn5p+oW3Zeyqt+9x7Zbf8Qj1anR5lqQVGlHYScsqZHJ9boC8zuVtpJzVbPsRHipeLlJVbLNe7duxsHjF3zTm8M0ne2VFOZk5Jq9BZKScuwfzd0pw8LVqtHmdH0z83Z0cZmTnlx3opzjAcyMej8g02O6UzUqmszAIjednpODhVvCjE/k1L2bdxEaPeWYp3QMnCEHZKZ6QyOZ5+pkNiPHxDuHbpZKXiLc3OwQWJVEZBqcnm+Tn3jv/otiUc3rKQEROX4Vmj7OpcWq2avxdOJCcjkRFvrrBo7xpAgQq0Ov3thXVKzlsHWwl55Zz31VFneRR2zkiksjILjBTmpWN3jwVFypOeGEVRfjr/fF8y50uv05Icf5zoI7/ywqdnkEqrZj7VvahS0lB4mf5eCi931Nm56IpUFKdlotNoUHi6lSrjhirZsnfpH8dzJ/J8vsnqt/I7OVMpI+uunOmklBF/0/zUgNx8Q84svSiTs1JWpqeuoEhPQZGG5FQNl+OLWD4ziBYN7Dh4Mr/Sv0tegeGaZegdK/lcpV3ZXrQ7ckr1pgEo7+p1C/WXo7SXMOPVki/lMqmEoZ1t6dpMwfs/Wq4hp7yd93OyTf92c7LScXIu/29XKpXi5eMPQEBwGEkJcWxcu4w69Ztx6cIpcrMzmDy2ZNVInU7L6uXfsP3fX/lq4QaLxG57J+eXumYV5KZj71hx3jm2fQlHty1k2ATTnG/vZPjeZbhueBq35+ekm702VIajoyNSqZSsTNMFRrKyMnFxLbs4UXJSEikpKXz26UfGbXduij7Rrxc/LVqKj49h5WUbG1t8ff3w9fWjTp1wxo4ZybatWxg2vOp6PP8L+kf4BsCj4JFrsN2tSZMmrF27lqCgoHJXdZRIJLRt25a2bdvy0UcfERgYyF9//cWkSZOwtrZGq618V0tYWBiRkZE8//zzxm2RkZH3Ff/q1avx9PTE0fHec2Lq1q2LQqHg+vXrxuGZpTVo0IAVK1agVqvN9rLd7+/s6OiIr68vBw8eNPmsgwcP0qJF+T0jU6dOZdKkSSbb3l9yf40JrQ4Sbumo7S/jfJwhRglQq4aMA2fN1xGfrKV2DRn7zpS8X9tfxrXkshfLlnWtuHFLS2K65eYEFKr0FJZarj8jW0ODMDtjA83WRkrtYBu27M8yW4dGC7HXi2gQZsfRM4ZGuUQCDcLs2LTH/D4AwTUMczUycip/Dsvl1vgG1SM26gh1m3YDDDckYi8coVW3Z8rdb9/GxexZ/xMjJy+iRojpilVyuTU1guuTVmpIZVpyPM5ull3SXya3xjugHvHRh6ndyBC/XmeYTN6087Pl7ndk6yIObfqR4W8swScoosz7dxprGbeu8cykldg5WP4OpVYHiel6avpKib5uODclQE1fKUei/4+9+46OqlofPv6dkkzazKSRQoAk9BZAekd6EwRBLKiA2EWuYuFyVRQb3it4FSuiCCgKKIhIkSoivfeekATSe8/094+BCUMmkTLcJL/3+aw1a2Vm9jnnmZ19yj67nJv7396OdVZEpfYkqHYLkmN3E9m8LO+TY3fTrHPFZacytRt0YeRk50kP/lr+Kvpa0bTq+ViVVdYAcncfptbgnk6fBfftSs7uwwDYTCbyDp4guE+XsscDKBQE9e5CwuffuzWWmlh2Sg02Ug3ON6Jy8szENPZ2VNC8NQoaRWrYsN115cRssc8AGdPYm33H7GN/FQqIaezNur/yXC5zhUJBpbNP3giLFRJTLTSNVHPknP08oACaRnmw9UCpy2Xiksw0jVQ7TdHfLMqDuCR73u45buR0vPM5ZfIYLbtPGNl1zL1dJNUeHkQ2aMqpo/to26k3YD/unzq2jz6Dx1z3emxWG+bLM0t37TWE5q2crxE+fGsSXXoNoXvf4W6L/coxP+HMLhpdfcw/s4u2vSo+5u/ZMI/dv3/Jvc99Q1ik8zFfH1QHX10tEs/sIrSufRp8Q0khKfFHaNPTvZUdj8tT7h85cpguXe09maxWK0cOH+auYeXzqU7dunz6+Vynz75ftIDikhKeePJpgoMrnonTZrVV2czf4n+nWlfYnn32WebNm8cDDzzAK6+8QmBgIOfPn2fJkiV8/fXX7N+/n82bNzNgwABCQkLYs2cPGRkZNGtm3xGjoqJYv349Z86cISgo6Kb7+D733HM8/vjjtG/fnq5du7J06VKOHj1K/fqVj1cYO3YsH3zwAXfffbdjNsaEhARWrFjBK6+8Qp06dZzSa7VaXnrpJV544QWsVivdu3cnLy+PHTt2oNPpGDduHJMmTeKTTz7h/vvvZ9q0aej1enbv3k3Hjh1p0qTJDf3ml19+mTfeeIMGDRrQpk0bvv32Ww4fPuyyRfAKjUbjaO27Qu1RWEHq8rYeNvFgPw0X060kpFno1doTT7XCMUnIg/005BXZWLPLfmLfdsTEpJHe3NnGg5PxZu5o7EHdECXL/nA+sWk8oHVDNau2394xAQC/bclhzJAgUjKMpGWaeHBYMNl5ZnYfLsuHt/5Rh92HC1n7Zy4Av27O4R/jwjifWMq5+FKG9QnAS6Nk0y77xUdYsAc9O+g4cKKQgkILUXU0PDo6hONni0lIcs9v6jZoHMvnTSMiuiV16sewc8MijIYS2vUcCcBPc6eiCwhl4Bh7hXzb6nlsWvEJY56eRUBwBAW59v7/nl4+aLzsrbXdhzzK0s9eJKpJe+o378TZo9s5c2grE6ctdEvMV+vYbwKrF0wlLKoltaNasW/zQkzGElp1tY/L/O3bV9D6h3LnyBcBezfIv36bw/CJs9EHRVB4efyCp8YHTy9fezfIuZNJTTzJvc/OxWq1ONJ4++ornd3wRu04bmFUDzVJmUouZdjo2kKFpxoOnLVfxI3uqSa/yMaGA/b3KqX9eVoAKhXofCE8UIHBZCO74PrW6U4tu43jr+XTCI5oSa06MZzYuQizsYTG7exl58+fpuKrC6X9QHvZsZiN5Kbbu31bLCaK8tPJSj6Fh8YHXVAkHhpfAkIbO21D7emNxse/3Oe3SuXrg2/Deo73PtF10LVuijE7j9KLKTR5ZwpeEaEcmTAVgISvlhD5zFiaznyZiwuWE9y7M+H3Dmbf8Ccd67jw0be0nv9vcg8cJ2/fUaImj0Pt683Fha5nzbwVNb3sAKz5M49RA/xJyTCRnmXi/iGB5ORZ2HusbCKmN54NZ8/RIn7/y16J+21rHpPG1iI20cD5RANDe+nReCr4Y4/9OBsSpKbbHX4cOV1MfpGFIL2aEf38MZpsHDxZ7DKOm7FpXynjh/qSkGohPsVMn/ZeeHrAzmP2c9T4oT7kFlhZuc1egdtyoJQXH9DSr4OGY7EmOjTzJDJMxeLf7S1+RaU2ikqdWzItVhv5RVbSst0/AcnA4Q/x9Zw3iGrQjOhGLdm4+gcMpSWOytW8j6cTEFiL0Q8/B8Ca5fOJatCcWmF1MJtMHD24nV1/ruHhJ6cB4Kfzx0/n77QNlUqNPiCY8Igot8bevs8E1i6aSlhkS8IjW7H/j4WYDCW07GI/5q9ZYD/m9xxhP+bv2fAVO1bPYeiE2egCyx/zFQoF7fo8wq51XxAQEok+qA7bf/sYP30IjVr3c2vsACNGjuK/H35Ao0aNaNy4Kb/+uoJSQyn9+g8EYPas/xAUFMT4CRPx9PQkKsq5Z5jv5eFAVz4vLS1h6ZIf6dS5C4EBgeTn57F69W9kZWXSvYfzTaaayGaTFrbKVOsK25UWoKlTpzJgwAAMBgORkZEMGjQIpVKJTqdj27ZtfPTRR+Tn5xMZGcns2bMZPNg+u87jjz/O1q1bad++PYWFhfzxxx9ERUXdcBxjx44lLi6Ol156idLSUsaMGcP48ePZu3dvpcv5+Piwbds2pk6dyj333ENBQQERERH07du3wha3t99+m1q1ajFz5kzi4uLw9/enbdu2/Otf9i6HQUFBbNmyhZdffplevXqhUqlo06aNYyzajfzmyZMnk5eXx4svvkh6ejrNmzdn1apVNGrU6Ibz6HodPm/Gz1vBoI6e6HwVJGVYmftbiWMAfIBWic1WdtKKT7Xy3YZShnTWMLSLJxm5VuavLSX1mhNb28ZqFMDBc7dnspGrrdiQjZengmceDMPXR8mp2BJmfHLJacxEWC1PdH5lrQTbDxSg81Px4F3BBOhUXLhkYMYnlxyD6s0WG62b+lyuyCnIzDGz61Ahy9a5ft7MzWjVeQhFBTlsXjGHgrxMwus1Y/zLXzm6FOZlpaC46nkke7YswWI28eMn/3BaT58Rz9L3nkkAtGjfn+Hj32Db6q9Y/f17BIdH88BzHxPVpB3u1rzDEIoLs/lr1ZzLD1FtxpjJXzu6x+RnO8d/aJs9/l/mTnZaT/e7JtFj2HMU5KRx7oh9HOr8d+52SvPglEVENunkttiPXbDi62Wmb1s1Wm9IybaxYIOJoss36fW+CmxXXcNpfWDSiLIKY48YNT1iIC7FyjfrTNe1Tneq32oIpUU5HNw8h5KCTALDmzFg/FeOZ6wV5TnnfXFBBr9+VjbB0fHt8zm+fT5h0R0Y8tgi9wdYCX27lnTZ/J3jffNZ9mPpxUUrODpxGprwWnjXLZs+uyT+EvuGP0nz2dOIeu4RSi+lcuzJ18jcuN2RJuWndXjWCqTxG5PtD84+coq9dz2GMd19++sVNb3sAKzcnIfGU8mT9wXj663kdFwp73yZ6nTMDA1So/MtO2buPFSEzk/F/UMC8Nepib9k4N0vUx3HTJPJRrMGXgy9U4evt4q8AgunYkt49aNk8gvdV/E5cNqE1qeEYd29HA/O/mRZoWMyrECd0in/45IsfPNbEcN7eHN3T2/Sc6x8uaKQ5Ns4G2RlOnYfQEF+DiuXfEleThZ1oxvzwvRP0Pvbu/RmZ6SivKormqG0lO++ep+crHQ8PTWERUTx+PPv0LH7gIo2cds0bW8/5u9YXXbMHz2p7JhfkJOCQll23Dl8+Zi/ap7zMb/rkEl0u8teIe3Y/3FMhhLW/zAdQ3E+EQ3aMXrS17dlnFvPXneSl5/H998tIicnh/r16/PWW+865lnIyEhHeQMPDVQqVVy6dJHN724kPy8fnU5Lo8ZN+PcHHxIZGeX2+EX1orDZbNVzZG41179/f8LCwvjuu+/+PvH/cS98ev0tbNVR3Imkqg7hljw8/vZVsG+3wpJq92SRG3Iu7va36N5OOt2NT15UXbR8oFlVh3BLds49WtUh3JIzR2rucTO4ds2enOHhYTV3vwU4leLeccL/a72iEqo6hJvWqEFkVYdQofOxVfcsuYYNbmzei6pQrVvYqovi4mK+/PJLBg4ciEql4scff2TTpk1s3LixqkMTQgghhBCiRrNVv0dDVytSYbsOCoWCtWvX8u6771JaWkqTJk1Yvnw5/fq5v8+zEEIIIYQQQlwhFbbr4O3tzaZNm6o6DCGEEEIIIf7PsSGTjlRG2h+FEEIIIYQQopqSFjYhhBBCCCFElZEWtspJC5sQQgghhBBCVFNSYRNCCCGEEEKIakq6RAohhBBCCCGqjHSJrJy0sAkhhBBCCCFENSUtbEIIIYQQQogqIy1slZMWNiGEEEIIIYSopqTCJoQQQgghhBDVlHSJFEIIIYQQQlQZm026RFZGWtiEEEIIIYQQ4jp89tlnREVF4eXlRadOndi7d+91LbdkyRIUCgUjRoy44W1KhU0IIYQQQghRZWwoqux1I5YuXcqUKVN44403OHjwIK1bt2bgwIGkp6dXulx8fDwvvfQSPXr0uKn8kQqbEEIIIYQQQvyNDz/8kMcff5wJEybQvHlzvvzyS3x8fJg/f36Fy1gsFsaOHcuMGTOoX7/+TW1XKmxCCCGEEEKIKlOVLWwGg4H8/Hynl8FgKBej0WjkwIED9OvXz/GZUqmkX79+7Nq1q8Lf9tZbbxESEsLEiRNvOn+kwiaEEEIIIYT4/9LMmTPR6/VOr5kzZ5ZLl5mZicViITQ01Onz0NBQUlNTXa57+/btfPPNN8ybN++WYpRZIsUtiz+dUtUh3JLo5hFVHcItOXbGUtUh3DSTyVTVIdyS7Mziqg7hlthsPlUdwk3bOfdoVYdwS7o+2aqqQ7glKa/9VdUh3DSFsmbPRncy2a+qQ7glKWnmqg7hluz2iKzqEG5aowZVHUH1NG3aNKZMmeL0mUajueX1FhQU8PDDDzNv3jyCg4NvaV1SYRNCCCGEEEJUmRud/MOdNBrNdVXQgoODUalUpKWlOX2elpZGWFhYufSxsbHEx8czbNgwx2dWqxUAtVrNmTNnaNDg+mrR0iVSCCGEEEIIISrh6elJu3bt2Lx5s+Mzq9XK5s2b6dKlS7n0TZs25dixYxw+fNjxGj58OL179+bw4cPUrVv3urctLWxCCCGEEEKIKlNTHpw9ZcoUxo0bR/v27enYsSMfffQRRUVFTJgwAYBHHnmEiIgIZs6ciZeXFy1btnRa3t/fH6Dc539HKmxCCCGEEEII8Tfuu+8+MjIymD59OqmpqbRp04bff//dMRFJYmIiSqX7OzBKhU0IIYQQQgghrsOkSZOYNGmSy++2bt1a6bILFiy4qW1KhU0IIYQQQghRZaxVOOlITSCTjgghhBBCCCFENSUtbEIIIYQQQogqU5XT+tcE0sImhBBCCCGEENWUtLAJIYQQQgghqkxNmda/qkgLmxBCCCGEEEJUU1JhE0IIIYQQQohqSrpECiGEEEIIIaqMTDpSOWlhE0IIIYQQQohqSlrYhBBCCCGEEFVGJh2pnLSwCSGEEEIIIUQ1JRU2IYQQQgghhKimpMJWgQULFuDv71/VYQghhBBCCPF/mg1Flb1qAhnDVk3deeedtGnTho8++sgt64uKiuL555/n+eefd8v6btUDQwPp11WPr7eS03GlzF2aTkqGqdJlBvfUM6JvAP46FfFJRr7+KZ1zCQaXaV9/ujZtW/gy86tk9h4tclvc3WM86NPWA62PguRMK8u3GUhMs1aYvnVDFUM6awjUKsjItfLbTiOnEixOaUIDFAzrqqFBhAqlEtKyrcxfW0puoc1tcV/RsYmSri2U+HlDWraNtXutJGW53k4tPfRpoyI8SEGAn4J1+yzsPuX8WyNDFHRroSQ8SIHOR8GPf5g5fdH9cV/RuZmSHjFq/LwhNdvGb7vMXMp0vb0QfwX92qqICFYSoFWwereZnScs5dLdyDpvRa87NAzopEHnq+RSuoWlm4qJTykfzxVtm3gwvIc3QXol6TlWftlazPE4s8u0Dw7woecdGpZtLmbLftf7xK2qyXkP0KmZkh4tL28rx8bqv4m/b1sVEUH2+NfsNrPzZPn4b2SdNyuwe3vqvzgRfduWeNUOYf+oZ0hbtbnyZXp2pPmsf+LXvBGlF1M4P/MLLi36xSlN5NMPUn/KRDRhtcg/epoTz79N3r5jbo39ij4dvBnU1Qe9n5KLqWYWryvgQrLrsgzQvrmGkb19CfZXkZZl4adNhRw7bwRApYSRfXxp1VBDrQAVJQYrJ+OM/LypiNzCio/Ft6LXHZ707+CFzldh33c3l5CQWsm+29iDYd29yvbdP0s4ccH1732gvzc922j4aUsJWw7cnn330J+L2bfpG4ryM6gV0ZS+Y14nPKqVy7RHdyzjxJ6VZCafAyC0Xgt6DJ/ilN5ms7FjzRyO7fgJQ0k+teu3pf/9bxIQEuX22Ns3VtC12eVzVg6s228hOct12lp6uLOVkvBABf5+Ctbvt7DnjPP+2K2FgqZ1lQTrwGyBixk2Nh+yklXg9tAB2P/HYnat/4bCvAxC6zZl4AOvExHtOu8PblvGsV0rybic92GRLeg9copT+j9XfcLJfWvIz05FpfawpxnxAhH1W9+eHyCqDWlhq2aMRuN1p7XZbJjNFZ/0qquR/QIY2sufuUvSmTrrIgajlenPRuChrvguR7e2fkwYGczSddm8+O+LxCcZmP5sBHo/Vbm0w3r7czsu+e5opGZED09+32tk1pJikjKtPDXcGz9v13FHhSl5ZKAXu0+YmLWkmGNxFiYO9SIssGy3C9IpmDzKh7QcK5+uKOE/PxSzfp8Rc8XXAjetRZSCge2VbD1iYe5qM6k58HA/Fb5ertN7qBXkFNrYdNBCQbHrHPVQ2y9U1+y5DQFfIyZayZBOajYfMvPZryZSsm1MGORRSfyQXWBj/X4z+RXEf6PrvFntmnowuo83q3eU8t6CfC6lW3hujB9aH9dlp36EionDfdlx1MC7C/I5fM7IU/f4UTu4/CG7TSMPomuryC24PRerULPz3rGtjmq2HDbz2SoTqdk2xg+sPP6cy/FXVPZvdJ03S+XrQ/7RMxyfPOO60ntH1aHDqrlkbd3D9vZ3c+GThcTMfYfg/t0dacLvHUyzD6Zx7p3P2N5xJAVHT9NpzTd41gp0b/BAhxYa7hvgx6o/i5gxN5uLaWamPORfYdlvUEfNk6N0/HWolDfnZnPojIHn7tcTUct+rPf0UBAZ5sFv24qY8VU2ny7NIyxIzeQH9G6PHaBdEw9G3enNmp2lvLeogEsZFibf61vxvltbxaPDfNh5zMh7Cws4cs7EUyN9Xe67rRt5EF1bfVv33dMH1rJ1xUy6DHmWh//5CyF1mvLzpxMpKnBd67l4dg9N2w/lvn8s4sGXlqANCOfnTx+lIDfNkWbvxnkc2vod/e9/k7EvL8PD05ufP52I2eTeCmfzSAUD2ir585iVr9ZaSM2xMba3Ch+N6/QeKsgphM2HrRSUuN5vI0MU7D9rZf56C99vtqBSwti+KjzKX0rcshP71rJx2Ux6DHuWx17/hdA6Tfnxo4kU5bvO+4Qze2jRcSgPvbiI8f9cgi4gnB/++yj5OWV5HxQaxcAHpvPEm78x7pUf8A+K4IePHqWoINv9P+B/zGZTVNmrJqgRFbY777yT5557jueff56AgABCQ0OZN28eRUVFTJgwAa1WS8OGDVm3bh0AFouFiRMnEh0djbe3N02aNOHjjz92rK+0tJQWLVrwxBNPOD6LjY1Fq9Uyf/58p22vX7+eZs2a4efnx6BBg0hJSXH6/uuvv6ZZs2Z4eXnRtGlTPv/8c6fvp06dSuPGjfHx8aF+/fq8/vrrmExlLUlvvvkmbdq04euvvyY6OhovLy/Gjx/Pn3/+yccff4xCoUChUBAfH8/WrVtRKBSsW7eOdu3aodFo2L59O7Gxsdx9992Ehobi5+dHhw4d2LRpk1P+JSQk8MILLzjWd8X27dvp0aMH3t7e1K1bl8mTJ1NU5L4WKVfu6u3PT+uz2XusiIRkIx8vSiNQr6JTa98KlxneJ4CNO/PZsjufS6lGvlySjsFoo28XnVO6qAhPhvfx59Pv0ypY0827s40Hu06Y2HvKTFqOjZ/+MGA02+jU3HVDda82HpxOsPDHIRNpOTbW7TFyKcNKj1YejjRDu3hyMsHMbzuNJGVaycq3ceKChcIKTja3omszJQfOWTkcayMjD1bvtmCywB0NXR8GkrNsbDhg5Xi8DXMF1xPnk21sOWy9ra1qV3RvqWLfGSsHz1lJz7Xx6w4zRjO0a+z6TJuUaeP3fRaOxlmxVFCfvNF13qx+HbzYccTArmNGUrKs/LC+GJMJusZ4ukzfp50XJ+JMbNxrIDXLym9/lZKYZuHOts61AX8/Bff192H+6iIs1tv3P6jJeQ/QraWK/Ze3lXF5W6briP/YBWuFN09udJ03K2P9Ns6+8RFpv276+8RA5BP3U3LhEqde+TeFp+NI+HwxqcvXE/2P8Y400c9P4OI3y7i0cAWFp2I59swbWIpLqTt+lFtjBxjY2YdtB0vYfriU5EwLi1YXYDTZ6HGHt8v0/Tv5cPy8kd93FpOSaeGXP4pISDHTp6MPACUGG7O/z2XfSQOpWRbiksx8v66AqNoeBOrcf0nTt72GHUeN7DpuJDXLyo8bSjCaoEtL1/tu73YaTl4ws3GfgdRsK7/tKOVimoVedzjXMvR+Cu7r6823q4uw3L76Gvs3f0tM1zHEdBlFcHhD+t8/Aw9PL47vWu4y/dAJs7mj51hC6jYjKKwBA8e+g81mJfHMLsB+o/jgH4voPOhpGrbuR62IpgwZ9x8K89I5f+T6yuj16tJUycHzNo7E2cjMhzV7rfZzVgPXF9jJ2bDpkJUTCbYKjzs//GHlSJz9HJiWC7/usuLvqyA8yK2hA7Bn47fc0WMMbbqNolbthgx5yJ73h3e4zvuRj8+mfe+xhNVrRnB4A+4aZ8/7+FO7HGladhpG/eZdCahVl1oRjeg/ZhqGkkLSL51x/w8Q1UqNqLABLFy4kODgYPbu3ctzzz3H008/zb333kvXrl05ePAgAwYM4OGHH6a4uBir1UqdOnX46aefOHnyJNOnT+df//oXy5YtA8DLy4vFixezcOFCfv31VywWCw899BD9+/fn0UcfdWyzuLiYWbNm8d1337Ft2zYSExN56aWXHN8vXryY6dOn8+6773Lq1Cnee+89Xn/9dRYuXOhIo9VqWbBgASdPnuTjjz9m3rx5/Pe//3X6befPn2f58uWsWLGCw4cP8/HHH9OlSxcef/xxUlJSSElJoW7duo70//znP3n//fc5deoUrVq1orCwkCFDhrB582YOHTrEoEGDGDZsGImJiQCsWLGCOnXq8NZbbznWB/ZK6qBBgxg1ahRHjx5l6dKlbN++nUmTJrn/H3hZaJCaQL2aI6eLHZ8Vl1o5F19KkyjXt6bVKmhQV8ORM2XL2Gxw9EwxTaLLlvH0UDBlfBjzlmWQW+DeFh+VEuqEKDl7sWy9NuDsRQtRYa4v0KLCVE7pAU4nWogKt+92CqB5lJqMXCtPDffi7Yk+vHCvNzH13X/BqlJCeJCCuJSyi3obEJdio26t6n93SaWE2sEKzieXXdnYgNhkK/VCbi7+27HOirZTL0zFqYSy1nAbcCreRP0I15X9+hFqTic4t56fvGCifkRZ2VAA4+/yZeOeUlIyb98VX03Oe8e2gspv63yylXo3WfZvxzrdxb9zGzK37HL6LGPjdgI6twFA4eGBvm0LMjfvLEtgs5G5ZSf+ne9waywqJUTWVnMyrqzniA04GWekQR0Pl8s0qOvhlB7geKyRhnUqHsHho1FgtdkoLnXvTYsr++7pa/bd0wlm6teuYN+t7WLfjXdOrwAmDPFh495SUrJu375rMRtJu3iCyKZdy7atVFKvaVeS4w5d1zrMxhKsFjNePvYWzLysSxTlZxDZpGydGm8t4VGtSb5wfeu8HkolhAfChVTn/+mFVBt1gt23j2kuF8MSN/dGtZiNpCScILqZc95HNetKUuz15ZPpct57+7puPbaYjRzcthSNt5bQOk3cEndVslbhqyaoMWPYWrduzWuvvQbAtGnTeP/99wkODubxxx8HYPr06XzxxRccPXqUzp07M2NGWfeR6Ohodu3axbJlyxgzZgwAbdq04Z133uGxxx7j/vvvJyEhgdWrVztt02Qy8eWXX9KgQQMAJk2axFtvveX4/o033mD27Nncc889ju2cPHmSuXPnMm7cOABHzGAfR/bSSy+xZMkSXnnlFcfnRqORRYsWUatWLcdnnp6e+Pj4EBYWVi4v3nrrLfr37+94HxgYSOvWZf2X3377bX755RdWrVrFpEmTCAwMRKVSodVqndY3c+ZMxo4d6xjX1qhRI+bMmUOvXr344osv8PIqX4EyGAwYDM5HNovFiErl+m7jtfx19iKXd02FKrfA4vjuWlo/FSqVovwy+WYiQn0c7x8dVYvTF0rZe8z9LYS+3gpUSkW57lEFxTZCA1zf99D6uEpvRedj/51+Pgq8PBX0befJ2t1GfttppGmkiglDvPhsRQmxye47jPhoQKVUUFji/HlhiY1gXfWvsPl4XYnfOT8LS2zU0t/cfafbsU5X/HzsZSe/yPn/WVBsIyzIdeVc5+sifZENnW9ZXAM6e2G1ctvGvVxRk/Meri77Lrblf5Px34Z1uosmNBhDWqbTZ4a0TDz0WpReGjwC9CjVagzpWdekycK3SX23xqL1Ubos+/lFVsKDXR/v9X7K8ukLrehcdH8H+w290f382HPMQKnRvRU2v8vH/fzia+IpthIa6Dp+V/tufpEVnW/ZcXZAJw0WG/xx8PqHQNyMksIcbFYLvlrn5iNfbRDZqXHXtY4/V87CVx/iqPQV5WcA4KNzXqePNoii/Mxyy98sHw0olQqKrqmEF5Xi1nPWwPZKEtPtLW7uVHwl76/JJz9dEFnXmfdbls/Czz+E6OZdnT4/d+QPVsybgslYglZfi7EvzMdH6/7uzKJ6qTEVtlatygZdqlQqgoKCiImJcXwWGhoKQHp6OgCfffYZ8+fPJzExkZKSEoxGI23atHFa54svvsjKlSv59NNPWbduHUFB1xyAfHwclTWA8PBwx/qLioqIjY1l4sSJjkojgNlsRq8vuxuydOlS5syZQ2xsLIWFhZjNZnQ65258kZGRTpW1v9O+fXun94WFhbz55pusWbOGlJQUzGYzJSUljha2ihw5coSjR4+yePFix2c2mw2r1cqFCxdo1qxZuWVmzpzpVBkGaNJhEs06Tna5jZ7ttTz1QIjj/btfJP/t77sZHWJ8iWnszYvvV/6bq5MrPVOPx5n587C9m2xSppXoMBXdYjyITb69F+Ki5qoXqqJPOw3vLcyv6lCEqDIqJTx9rx6FAr5bc5tmjXCzeqEqerfTMHNh9Y93z4avOHNgLfc9vwi1RwUDx2qwIR2UhOgVfLvh9o/BvlE71n3Fib1refjl8nkf2bQTj09fSXFBDof+Wsbyuc/z6L9+Klc5FP+31JgKm4eHc/cJhULh9NmVcVlWq5UlS5bw0ksvMXv2bLp06YJWq+WDDz5gz549TutIT0/n7NmzqFQqzp07x6BBg/52mzab/W5PYWEhAPPmzaNTp05O6VQq+53AXbt2MXbsWGbMmMHAgQPR6/UsWbKE2bNnO6X39a147JYr16Z/6aWX2LhxI7NmzaJhw4Z4e3szevTov53ApLCwkCeffJLJk8tXturVq+dymWnTpjFlyhSnzx6aerHCbew9VsjZ+FLH+ysTi+i1KnLyyw6S/loVFy65rqAUFFqwWGzotc53WP11anLz7V1PYhp7ExbswfcfNHBK88pj4ZyKLeH1j5MqjPF6FJXYsFht5Qaaa30UFU6qUFDsKr3Skb6oxIbFYiM12/lubFqOlehw93aLLDaAxWrD75phI37eCgpLXS9TnRSXXolfAVdNKePnrahwcHlVrNOVwmJ72bG3jpWVea1P+TvxV+Rf05oGoL3qzn3Dumq0vgree7rs5pBKqWB0b2/6ttfw6pfuq8jV5LyHq8t++W0VVrDvVsU63cWQlokmNNjpM01oMKa8AqylBoyZOVjNZjQhQdekCcKQ6r4WErD3KCgr+2V0vkryKpjRMa/QWj69n5L8QueLapUSnh6tJ1iv5D+Lct3eugb2FlOL1YbOx3nf1fkoyS9yvT1X+67Otyx9wzpqtD4K3n2q7MatSqlg1J1e9Gmn4bWv3LfvevsFoFCqyk0wUlSQha8uuIKl7PZt+oa9G77i3ue+pVZEU8fnvjr7zeXi/Cz89GU3Y4sLsgip07Tcem5WsQGsVhu+Xs77mK8XbhnjPai9kkYRChZutFBQ8vfpb5TPlby/ZoKRwvws/P4m73et/4ad675i7JRvCXWRp54aHwJDIgkMiaROgzZ89uoADm//mW5DnnTrb/hfqymTf1SVGlNhuxE7duyga9euPPPMM47PYmNjy6V79NFHiYmJcbSS9evXz2WrkiuhoaHUrl2buLg4xo4d6zLNzp07iYyM5NVXX3V8lpCQcF3r9/T0xFLRqNlr7Nixg/HjxzNy5EjAXhGLj4//2/W1bduWkydP0rBhw+vaDoBGo0Gjcb7bU1l3yFKDjVSD83T92XlmWjXxIT7JXqH09lLSKMqL37e77pNgtkDsRQOtmvg4puhXKOyVtHXb7Mus2JDDpp3OJ7qPX43k2+UZ7Dt+610kLVa4lG6lUR0Vx+Ls+agAGtdV8ddR148jiE+10Kiuij+PlH3fpK6K+BSrY52J6VZCrulSWctfSY6bZw2zWCEly0b9cIVjghAFEB2mYO+Z6t+D22KF5EwbDcOVnEqwx6sAGtRWssvFdOtVtc6KtpOYaqFppJoj50yO7TSN8mDrAde15bgkM00j1U5T9DeL8iAuyR7XnuNGTsc7l7vJY7TsPmFk1zH3tszW5Lx3bCvLRoPaSk4lOm9r96lbiN/N63SX3N2HqTW4p9NnwX27krP7MAA2k4m8gycI7tOl7PEACgVBvbuQ8Pn3bo3FYoWEZDPN6nty6Iz9eK8AmtX3ZMte11fJsRdNNIv2ZOOesu9b1Pfk/KWycWFXKmshQSo+WJhD0W2YpOlK/ImpFppEqjlyvmzfbRKpZutB1/tZXLKZJvXUTl2Vm0aqibv8GIM9J4ycTnDed58b7ceek0Z2HXNvF0mV2pPQui1IPLOLRq37AWCz2icQuaPXQxUut3fjPHb//iWjJ31DWGSM03f6oDr46mqRcGYXIXXt10uGkkJS4o/QpscDbovdaoWUbPs56sylsv9vdJiCfbd4zhrUXknTugoWbbKQe5vmWFOpPQmPbMGFU7tockdZ3sef2kX7PhXn/c7f57Fj7Zc88I9vqB0VU2G6q9lsVszm29u9VlS9/5MVtkaNGrFo0SLWr19PdHQ03333Hfv27SM6OtqR5rPPPmPXrl0cPXqUunXrsmbNGsaOHcvu3bvx9Ly+8VgzZsxg8uTJ6PV6Bg0ahMFgYP/+/eTk5DBlyhQaNWpEYmIiS5YsoUOHDqxZs4Zffvnl71eMfbzbnj17iI+Px8/Pj8DAivsnN2rUiBUrVjBs2DAUCgWvv/46VqvzAS0qKopt27Zx//33o9FoCA4OZurUqXTu3JlJkybx2GOP4evry8mTJ9m4cSOffvrpdcV5M1b/kcu9gwJJyTCRlmXiwaFBZOdZ2HOk7Mg547kIdh8pdFTIVm3JYfLDocQmlnIuvpS7egfgpVGyebe9kpZbYHE50UhGjpn0LPc8+mDrYRMP9tNwMd1KYpqFXm088VQr2HPSvv6x/TXkFdpYvct+4PzzsInn7vHmzjs8OBlvpm0jD+qGKFm6pexEvuWgkXGDvIhNtnD+kv2ivkW0ik9XuP+W385TVkZ2U5GUaSMpy0aXZko81XDovL2sjOymoqDYxqZD9vcqpf25Nlf+1vlAWAAYzZB9uTePpxoCtWXbCPBTEBZgo8QIeW4+EW4/bmF0TzWXMpVcyrDRraUKTzUcPGv/v4/uqSa/2MaG/RZHzCH+Cqf4wwMVGEw2R/x/t0532bSvlPFDfUlItRCfYqZPey88PWDn5Qu08UN9yC2wsnKbvQK35UApLz6gpV8HDcdiTXRo5klkmIrFv9sztajUVm5sh8VqI7/ISlq2+yvgNTnvAXYctzCqh5qky9vq2sK+rQNXx19kY8MBF/GrQOdbPv6/W6e7qHx98G1Y1uPBJ7oOutZNMWbnUXoxhSbvTMErIpQjE6YCkPDVEiKfGUvTmS9zccFygnt3JvzewewbXnb3/cJH39J6/r/JPXCcvH1HiZo8DrWvNxcXrnBr7ADrdxfz2Agd8clmLiSZ6N/ZB42Hgu2H7ce4x0ZoySmwsnyzvWxv3FPM1PEBDOzizZGzRjq19CKqtpqFv9mP9SolPHOvnshwNR//mIdCoXCMDysqsbp9xsXN+w2MG+JDYqqZ+BQLfdpr0HjAruP2fXfcEPu+++tf9n33jwMGptzvR9/2Go7HmWjf1L7v/rDBPmmW633X3jKXluP+fbd93wmsWzSV0HotCY9qxYEtCzEZSmjZ2T72fu3CV/DzD6Xn3S8C9m6QO9fMYej42egDIyjKs49Z89D44Onli0KhoG3vR9j9+xcEhESiD6rDjtUf46cPoeHlSqG77DptZUQXJclZCpKzbHRqqsRDBYfj7Pl3dxclBSWw5bA935TXnLO0PgpCA2wYTfbp/gEGd1ASE6Vg6Z8WDCYcj+EwmHD743Q69Z/AqvlTCY9qSUR0K/ZsWojJWELrbva8//WbV9AGhNLnHnve71z3FX+umsOIx2bjHxxB4eW897yc90ZDMdvXfEnj1n3w869FSWEO+/9YTEFOGs3bDaowjpqipjzAuqr8n6ywPfnkkxw6dIj77rsPhULBAw88wDPPPOOY9v/06dO8/PLLfPPNN47ZFz///HNatWrF66+/zr///e/r2s5jjz2Gj48PH3zwAS+//DK+vr7ExMQ4JvEYPnw4L7zwApMmTcJgMDB06FBef/113nzzzb9d90svvcS4ceNo3rw5JSUlXLhwocK0H374IY8++ihdu3Z1VMTy851bm9566y2efPJJGjRogMFgwGaz0apVK/78809effVVevTogc1mo0GDBtx3333X9ftv1i+bcvDSKHj6gRB8vZWcii3l7c+TMJnLTmJhwR5Og8x3HCxE56fi/qFBBGhVXEgy8tZnSeUmIrmdDp0z4+utYHAnT3S+CpIyrMxdVeLonhHgp8RmKzvhxqdaWbShlKGdNdzVxZOMXCvfrCl16gJ5LM7CT38Y6Nfek3t6KsjIsfLt2lIupLj/xH0i3oavxkqfNirHg4q/22yh6HIjj97XuUuC1hueHlbWLbhbCxXdWqi4kGplweU+/7WDFEwYWHYYGdRBBag4dN7Kyp3u/d8cu2DF18tMv3ZqtN72FsNv15scXTr9/RTYrroO0vrAcyPLbr70bKWmZyuIS7Hy9VrTda3TXQ6cNqH1KWFYdy/Hg7M/WVbomJQmUKd0ij0uycI3vxUxvIc3d/f0Jj3HypcrCkm+jbNBVqYm5/3V2+rb9vK2sm0s2GC6quyXj3/SiLL4e8So6RFjj/+bdabrWqe76Nu1pMvm7xzvm8/6FwAXF63g6MRpaMJr4V033PF9Sfwl9g1/kuazpxH13COUXkrl2JOvkblxuyNNyk/r8KwVSOM3JtsfnH3kFHvvegzjNRORuMO+Ewa0PoWMuNPX8eDs/y7OdXQRDNSruPqJFLGXzHy1Ip97evtyTx8/0rItfLIkj6QM+/HEX6vkjqb2Xh4znnK+kfnvBTmcSXDd4+FmHThjws+nhLu6eTsenP3Jz0Vl+672mn032cL81cUM7+HF3T28yMix8uUvRVW27zZtN4Tigmx2rJ5DcUEGtSKaMfrZrx1dIvNzUlAoynp5HPlrCRaziVVfOw+V6DJkEt2GPgdAx/6PYzKWsOGH6RhK8olo0I5Rz37t9nFuJxPs56w7Wyvx87I/OPuHPyzX7Ldlma/1hieHlJ2PujZX0LW5kvg0G4s22ctPh8b23zquv/Pl76+7LByJc29LbYsO9rz/89c5FOVnEFq3GQ/842tHl8i8bOe8P/CnPe+Xf+mc9z2GTaLX8OdQKlVkpcaxfNcvFBfm4O3rT+2oGMa9sphaEY3cGruofhS2q0u7EDdh5KRzVR3CLYlsEv73iaoxvb7mDgY3map/d8zKZGcW/32iaiww2OfvE1VTihp+M7brk63+PlE1tvy1v6o6hJum8bm+XjTVVds7/Ks6hFuSkuaeXi9VJbpuzW3reLjn36epKjtPVd1EPF2baf8+URWruaVOCCGEEEIIUePJpCOVqzEPzhZCCCGEEEKI/99IC5sQQgghhBCiysikI5WTFjYhhBBCCCGEqKakwiaEEEIIIYQQ1ZR0iRRCCCGEEEJUGavMWV8paWETQgghhBBCiGpKWtiEEEIIIYQQVUYmHamctLAJIYQQQgghRDUlLWxCCCGEEEKIKiMPzq6ctLAJIYQQQgghRDUlFTYhhBBCCCGEqKakS6QQQgghhBCiythkWv9KSQubEEIIIYQQQlRT0sImhBBCCCGEqDJWmda/UtLCJoQQQgghhBDVlFTYhBBCCCGEEKKaki6RQgghhBBCiCojz2GrnMJmk3lZxK0ZOelcVYdwS8wmc1WHcEu8fLyqOoSbZiw1VnUItySkbnBVh3BLTMaaW/YLc4qqOoRbogv0q+oQbsmod3pUdQg37YeXtlR1CLekpp+zarqafN765dNGVR1ChTYdNVTZtvu10lTZtq+XtLAJIYQQQgghqow0H1VOxrAJIYQQQgghRDUlLWxCCCGEEEKIKmOTaf0rJS1sQgghhBBCCFFNSYVNCCGEEEIIIaop6RIphBBCCCGEqDJWmXSkUtLCJoQQQgghhBDVlLSwCSGEEEIIIaqMPDi7ctLCJoQQQgghhBDVlFTYhBBCCCGEEKKaki6RQgghhBBCiCpjk0lHKiUtbEIIIYQQQghRTUkLmxBCCCGEEKLKWJFJRyojLWxCCCGEEEIIUU1JC5sQQgghhBCiysgYtspJC5sQQgghhBBCVFNSYbtJCoWClStXVnUYN2Tr1q0oFApyc3OrOhQhhBBCCCHEdZAukTcpJSWFgICAqg7jhnTt2pWUlBT0en1Vh8IDQwPp11WPr7eS03GlzF2aTkqGqdJlBvfUM6JvAP46FfFJRr7+KZ1zCQaXaV9/ujZtW/gy86tk9h4tcnv8Y4cFM6BHAL7eSk7FlvD5DymkpFce/5A7A7infyABejUXLhmYuySVc/Glju/fm1KPmCa+Tsus+zOHz39IdWvs9w3yp28XLb5eSk7HG5j3UyapmeZKlxnYTcvwPnr8tSoSko3MX5HF+USj4/sn7g0iprE3gToVpUYbZy6U8v3qHJL/Jk9uxoN3BdG/u//lslPCFz+k/W3ZGdLLnxH9AwnQqYi/ZOCrpemcSyjL+3deqEtMYx+nZX7flssXP6a5Le4722kY2MkbvZ+Si2lmftxQTHxKxfnerqknd/fyIVivJC3bwvI/ijkeW/Y7h/XwpkNzDYFaJWaLjYRUMyv/LOFCcuX/y5vVt4M3g7v5ovdTkphq5vt1+VxIqnhbHZpruKePH8H+KlKzzPy0qZCj58rKTLtmGnq39yYq3AM/HyXTv8wiMfX2xH7FfYMD6NdFi4+3kjMXSvnqp0xSMyrf5qDuOnvZ16lISDLyzfIszieWHXeeGBNMqybeBFwu+2cvlPLdqmy3lv0+HbwZ1NXHXnZSzSxeV1Dp/7l9cw0je/sS7K8iLcvCT5sKOXbenvcqJYzs40urhhpqBagoMVg5GWfk501F5BZa3RbzFYHd21P/xYno27bEq3YI+0c9Q9qqzZUv07MjzWf9E7/mjSi9mML5mV9wadEvTmkin36Q+lMmogmrRf7R05x4/m3y9h1ze/xXjOqnpXcHX3y9lZxNMDB/ZS5pWZZKl+nf2ZehPf3Q+6lITDWxcFUucZfKyoWHGsYO0dO5tQ8eKjh6zsC3v+aSfxv+D/cO1NO3kx++3krOXDDw9Yrsvz3uD+jqx7A7Lx/3U4x8+0s2sRft5cjXW8mYgXpaNfYmOEBFfqGVfceLWbo+l5JS9/dtq8nx1/Trnf8Vm00mHamMtLC5YDL9/Yk2LCwMjUbzP4jGfTw9PQkLC0OhqNqdYmS/AIb28mfuknSmzrqIwWhl+rMReKgrjqtbWz8mjAxm6bpsXvz3ReKTDEx/NgK9n6pc2mG9/bmdXaFHDQzirj6BfL44hZfej6fUYOWtyfUqjb97ey2PjQ7hxzWZPP/uBS5cKuWtyfXQa53j//2vHB5++azj9e2KdLfGfncfPYN76vjqpyymfZSMwWDltafCKo29axtfxo0I4qf1uUydnUxCspFXnwxD51d2+Ii7ZOTzHzN5/v0k3pmbikIBrz8VhtLNRe2eAYEM7R3AFz+k8fJ/Eik1WHlzcp3K876dlkdH1WLpmkymvJfAhUsG3pxcp1zer/8rl3FTzzteC37JcFvc7Zt5MqavL79tL+Ht+XlcSrfw/P1atD6u424QoebxEX5sP1zKW9/kcfiskWdHa6ldqyzmtCwLP64v4s2vc/nPd/lk5Vl5/n4tfhWs81Z0bKHh/oFaVm4t5I25WVxMM/HSQwFofV1vq2FdD54arWfbwRKmf5nFodMGJt/vT0RIWfwaDwVnE00s21To9nhdGdFXz5CeOr5alsm//puMwWjj9afCKy/7d/gybmQQP63P4ZUPkohPNvLa09eU/YsGPvshg+dnXuKdL1IAeP2ZcLeV/Q4tNNw3wI9VfxYxY242F9PMTHnIv+KyU0fNk6N0/HWolDfnZnPojIHn7tcTcbnseHooiAzz4LdtRcz4KptPl+YRFqRm8gO350aeyteH/KNnOD55xnWl946qQ4dVc8nauoft7e/mwicLiZn7DsH9uzvShN87mGYfTOPcO5+xveNICo6eptOab/CsFXhbfsNdPf0Y2NWPb1fmMv3zdAxGG/98NBiPSm55d47xZuxQPSs2F/Dap+kkppj456PB6HzLys5DQ/Xc0cyLOYuzePurTAJ0Kl4Y6/7fMLy3jsHddXy9PJtX56RSarTxr8dDKo2/S2sfHhkeyPKNufzzoxQSko386/EQR9kP1KsI0Kn4bnUOL81K4fOlWbRu6s1TY4Ik/qvU9OsdUX3UiAqb1Wpl5syZREdH4+3tTevWrfn555+x2Wz069ePgQMHYrs8WjE7O5s6deowffp0x/Jff/01zZo1w8vLi6ZNm/L55587vouPj0ehULB06VJ69eqFl5cXixcvBmD+/Pm0aNECjUZDeHg4kyZNcix3dZdIo9HIpEmTCA8Px8vLi8jISGbOnOlIm5uby2OPPUatWrXQ6XT06dOHI0eOOL6PjY3l7rvvJjQ0FD8/Pzp06MCmTZuc8iAqKor33nuPRx99FK1WS7169fjqq6+c0uzcuZM2bdrg5eVF+/btWblyJQqFgsOHDwPlu0RmZWXxwAMPEBERgY+PDzExMfz44483+V+6fnf19uen9dnsPVZEQrKRjxelEahX0am1b4XLDO8TwMad+WzZnc+lVCNfLrGfNPt20Tmli4rwZHgffz793n0tI+Vi6RvIsrWZ7DlSSHySgf9+m0ygv5rObbQVLjOiXxDrt+eyeWceF1OMfL44FYPRSv+u/k7pDEYbufkWx6uk1L13Wof20rF8Qy77jxeTmGLi0x8yCNCp6BDjU+Eyd92pY/OuArbuLeRSmomvfsrCaLTRp1PZ7920q4BTcaVk5Ji5cMnIj2tzCA5QUyvQvY34w/oE8NO6LPYeLSQhycBHC1IJ1Kvp3MavwmXu7hvAhh15bN6Vz8VUI1/8mIbBaKVfF+cLVIPJetvyvn9HL/46bGDnUQMpmRa+X1eE0QzdWru+6dO3gxcnYk1s2FNKapaFX7eVkJhqpk87L0eavSeNnIo3kZlrJTnTwrJNxfh4KakTUv6kfqsGdvHlz4MlbD9cSnKGhYWrCzCabPS8w9v17+3kw7HzRtbtLCYl08KKP4pISDHRr2NZOdt5tJRVfxZxMs71XWN3G9pLz/INuew7XkxCspFPvk8nQK+iYyVlf9idejbtzOePPZfL/rJMDEYbfTpfU/ZjS8nItpf9JWtzqOXGsj+wsw/bruR9poVFl/O+RyV5f/y8kd8v5/0vfxSRkGKmz+W8LzHYmP19LvtOGkjNshCXZOb7dQVE1fYgUOf+S4KM9ds4+8ZHpP266e8TA5FP3E/JhUuceuXfFJ6OI+HzxaQuX0/0P8Y70kQ/P4GL3yzj0sIVFJ6K5dgzb2ApLqXu+FFujx9gUDc/Vv5RwIFTpVxMNfPFshz8tSraNXf9PwAY3MOPP/YVse1AMUnpZuavzMVgtNGrvf3/4K1RcGd7XxavyeNknJH4ZBNzf86hcZSGhnU93Br/kB5aVmzKY/+JEhJTTHy2JJMAnZoOLSsu+0N76di8p4Ct+4pISjPx9fJsjCYbvTvYj7UXU018uCiTgydLSMsyc+J8KUvX5dKuuQ9KNxejmhx/Tb/e+V+y2qruVRPUiArbzJkzWbRoEV9++SUnTpzghRde4KGHHmLbtm0sXLiQffv2MWfOHACeeuopIiIiHBW2xYsXM336dN59911OnTrFe++9x+uvv87ChQudtvHPf/6Tf/zjH5w6dYqBAwfyxRdf8Oyzz/LEE09w7NgxVq1aRcOGDV3GN2fOHFatWsWyZcs4c+YMixcvJioqyvH9vffeS3p6OuvWrePAgQO0bduWvn37kp2dDUBhYSFDhgxh8+bNHDp0iEGDBjFs2DASExOdtjN79mzat2/PoUOHeOaZZ3j66ac5c+YMAPn5+QwbNoyYmBgOHjzI22+/zdSpUyvN19LSUtq1a8eaNWs4fvw4TzzxBA8//DB79+69/n/ODQoNUhOoV3PkdLHjs+JSK+fiS2kS5eVyGbUKGtTVcORM2TI2Gxw9U0yT6LJlPD0UTBkfxrxlGeQWVN5V5abjD/YgUK/m8KmybgfFpVbOXiihaX3XJ2+1ChrW8+LIVcvYbHD4dBFNrlnmzo46Fs9uxKfTo3lkRC00Hu5rLQkJUhOgU3PsbFlXwOJSG+cTDDSJcl1xUKugfh0NR8+WOMV+9FwJjSNdL6PxVNC7k5a0LBNZue7r4nYl768tO2cvlNIkuuK8b1DPy2kZmw2OnC6mSX3n8targ47vPmjAnNejePjuYDzdlPcqJUSGqzkVX9Yd0AacumCkQYTrC7P6EWpOxju39J+IM1E/wnUlQKWEnndoKC61cinNvWVfpYKo2mpOxl0Vvw1OxBlpUMd1/A3rejilBzh2vuL0t1tIkJoAvdqpHBeX2jiXYKBxdMXHnfp1y5f9Y2dLKjxWOcp+pnvKvkoJkdfmPXCykrxv4CLvj8caaVin4gqkj0aB1Waj+DZ0ZbtR/p3bkLlll9NnGRu3E9C5DQAKDw/0bVuQuXlnWQKbjcwtO/HvfIfb46kVYG+JOXG+7MZCicFG7EUjjep5ulxGpYLo2h4cv2oZmw2Oxxocy0RHeKBWK5zSpGSYycwx07CC9d6MkMDLx/1zZeW4pNTG+UQDjSo4hqtUUD/C0+lcYbPBsXOlFS4D4OOlpKTUitWN9xlrcvw1/XpHVC/VfgybwWDgvffeY9OmTXTp0gWA+vXrs337dubOncsPP/zA3LlzeeSRR0hNTWXt2rUcOnQItdr+09544w1mz57NPffcA0B0dDQnT55k7ty5jBs3zrGd559/3pEG4J133uHFF1/kH//4h+OzDh06uIwxMTGRRo0a0b17dxQKBZGRkY7vtm/fzt69e0lPT3d0oZw1axYrV67k559/5oknnqB169a0bt3asczbb7/NL7/8wqpVq5xa9YYMGcIzzzwDwNSpU/nvf//LH3/8QZMmTfjhhx9QKBTMmzcPLy8vmjdvTlJSEo8//niFeRsREcFLL73keP/cc8+xfv16li1bRseOHStc7lb46+z/l7xrDjC5BRbHd9fS+qlQqRTll8k3ExFadoft0VG1OH2hlL3Hbl8f7oDLMebmXxuLhQC96/h1fmpUKgU55eK3UCes7OTx57580rNMZOeaiaqjYfw9IUSEeTLzyyS3xO5/uQtgbuE1cRRaHN9dS+vrOu/zCixEhDhfMA7opuXhYYF4aZQkpRl5+4tUzG48jwToLsef73whnFtgdnx3Ld3lslNumXwLdULLLoq27csnI8tEdp6ZqAgNj4ysRUSoJ+9/lXzLcfv5KFApFeQXOV8M5xfZCAtyXSnU+ykpKLJek96K3s/5Hlurhh48PkKLpwfkFdr474/5FJa496Jb66NEpVSQV1g+nvBg1xeWej+ly/TXxv+/EnCl7Lsoxzda9nNdlP2B3XU8NDwQ78tl/63PU9xS9q/kfb6LshAe7Pp4o/dTlk9faEXnojsV2C8QR/fzY88xA6XGqq+waUKDMaRlOn1mSMvEQ69F6aXBI0CPUq3GkJ51TZosfJvUd3s8V8pH3jXHzbxCC/5a1+VZ66O0l51r94ECC7VraRzrNZnLV5LzCq0Vlsmb4Yi/wDmWvEqO+7orZf/a31xgoXaI6xsFWh8l9/TXs2m3e7s41+T4a/r1jqheqn2F7fz58xQXF9O/f3+nz41GI3fcYb+bdu+99/LLL7/w/vvv88UXX9CoUSMAioqKiI2NZeLEiU4VF7PZXG7ijfbt2zv+Tk9PJzk5mb59+15XjOPHj6d///40adKEQYMGcddddzFgwAAAjhw5QmFhIUFBzv2iS0pKiI2NBewtbG+++SZr1qwhJSUFs9lMSUlJuRa2Vq1aOf5WKBSEhYWRnm4f43TmzBlatWqFl1fZHZi/q3RZLBbee+89li1bRlJSEkajEYPBgI9Pxd0MDAYDBoNzFyaLxYhK5frCrWd7LU89EOJ4/+4Xt34B7EqHGF9iGnvz4vuJf5/4BvTqqOPZseGO9299etGt67/a+r9yHX8nJBvIyTPz7pRIwoLTSc288QkMurf15ckxwY73M+fd3m4T2w8UcvRMCQE6NcN765gyLoTX5qRgMt/cRWCvDlqefjDM8f7tzy+5K9RyNmzPc/ydkGwkO9/CO8/XJSzY46by/n/ldIKJt77JReutpEcbDU+O1PLegjwKiqv+wrsq9WjnxxP3XVX257p34p5r/bW/gCNnii+XfT1TJoTy2kfJN132/1dUSnj6Xj0KBXy3pqCqw6kWurbxZuIIf8f7DxZmVZy4Gup+hy+Pjy4bB/f+N+4dB+2Kt0bB1MdCuJRm4ucNube0rpocf02/3qlq8hy2ylX7Clthof1ux5o1a4iIiHD67kqLVXFxMQcOHEClUnHu3Llyy86bN49OnTo5LatSOd+Z8fUt60/s7V1xv3RX2rZty4ULF1i3bh2bNm1izJgx9OvXj59//pnCwkLCw8PZunVrueX8/f0BeOmll9i4cSOzZs2iYcOGeHt7M3r0aIxG524tHh7Od4YUCgXWW2i7/+CDD/j444/56KOPiImJwdfXl+eff77cdq82c+ZMZsxwHjzepMMkmnWc7DL93mOFnL1qJsQrA231WhU5V7VS+WtVXLjkeixLQaEFi8VWbpIIf53a0XIS09ibsGAPvv+ggVOaVx4L51RsCa9/fHOtVHuPFHL2Qly5+P11KnKuarXx16mIu+g6/vxCMxaLzXGX/+plcvIq7jZ15oK9C0h4iOdNVRr2nyjm/Kyy362+ErufyqmF0N9PRXyy6/95QZHrvNdrVeVaGYtLbRSXmknNNHMuoZRv342kY4wPOw7d3B3AvUcLORMf73hflvfqa8qOusKyk3+57Fx7N/Pa/9+1zl7J+1q3XmErLLZhsdrQXTNBh863fKvbFXmFVrS+ymvSl2+1MpogI8dKRo6VuGQz7zzlT/fWGtbtKsVdCoqtWKy2cq1j9nhcNyPlFZZvTXMV/+2y73iR0yygjrJ/TbnVa+0zsLlSUdn316rKtdQ5yn6GmXPxpSyYGUXHVj7sOHhrd7+v5L3uOsrCFXmF1vLp/ZTkX/O/Uinh6dF6gvVK/rMot1q0roG9NU0TGuz0mSY0GFNeAdZSA8bMHKxmM5qQoGvSBGFIdW6ZuxkHT5YSe7GskqBWXT5n+anIvaqVR++nIiHF9bGhoNhqLzvX7gNalaPlJLfAgodagY+XwqmVTe+nvKUubvtPFnPuw7LjYdk513m9+kqO+/lXyr7f3x/3vTQKpj0eQmmpldkL0rHc4i5ek+Ov6dc7onqr9mPYmjdvjkajITExkYYNGzq96tatC8CLL76IUqlk3bp1zJkzhy1btgAQGhpK7dq1iYuLK7dsdHR0hdvUarVERUWxeXPlUw9fTafTcd999zFv3jyWLl3K8uXLyc7Opm3btqSmpqJWq8vFEBxsPynt2LGD8ePHM3LkSGJiYggLCyP+qgvV69GkSROOHTvm1Pq1b9++SpfZsWMHd999Nw899BCtW7emfv36nD17ttJlpk2bRl5entOrcbunKkxfarCRmmlyvC6mGsnOM9OqSVkrnreXkkZRXpyJd32RabZA7EWD0zIKhf2gdeaCfZkVG3J4YWYiU94vewF8uzyDT25hQG6JwUpKhsnxSkyxx9+66VUVfC8ljaO9OR1X4nIdZgucTyylVbOyZRQKaN3UlzMVLANQv669tbSySl1l7HlvdrwupZrIyTfTsnFZK6y3RkHDSA1n4l2fPMwWiLtkIOaqZRQKiGnkzdkKphi+Ol1lM2H9nRKDjdQMk+N1McV12Wkc7eWo3LqKPzaxtFzZadXEhzNxFVdqouvYf292/q33a7NYISHFTLOoshsuCqBZlAexSa4v+OKSnNMDNIv2IK6SafTB/tvUt5DnrlgsEJ9spnl0WSu6QgHN63sSe8l1/OcvmpzSA7RoUHF6d3NZ9vPMxDQuuxnnrVHQKFLD2QsVH3fiLhqclnEcdyo4Vl2d7lbK/hUWKyQkm2lW/6q8B5pVkvexF000uzbv63ty/lJZ2blSWQsJUjHru1yK3NyN9lbk7j5MUJ/OTp8F9+1Kzu7DANhMJvIOniC4T5eyBAoFQb27kLv70C1vv9RoIy3L4nglpZvJybfQokFZ93VvjYIGdT05l+i6wmCxwIVkk9MyCgW0bKBxLHMhyYTZbHNKEx6sJjhA7fTIlBuO32AjLcvseF1Ksx/3Yxpdc9yvp6lwmniLBeKSjE7LKBTQsqGX0zLeGgWvPh6K2QL/+TYDkxuGLNfk+Gv69U5Vs6GosldNUO1b2LRaLS+99BIvvPACVquV7t27k5eXx44dO9DpdAQHBzN//nx27dpF27Ztefnllxk3bhxHjx4lICCAGTNmMHnyZPR6PYMGDcJgMLB//35ycnKYMmVKhdt98803eeqppwgJCWHw4MEUFBSwY8cOnnvuuXJpP/zwQ8LDw7njjjtQKpX89NNPhIWF4e/vT79+/ejSpQsjRozgP//5D40bNyY5OZk1a9YwcuRI2rdvT6NGjVixYgXDhg1DoVDw+uuv33DL2YMPPsirr77KE088wT//+U8SExOZNWsWQIXT+Ddq1Iiff/6ZnTt3EhAQwIcffkhaWhrNmzevcDsajabc4wwq6g5ZkdV/5HLvoEBSMkykZZl4cGgQ2XkW9hwpuxs947kIdh8pZN02e1e1VVtymPxwKLGJpZyLL+Wu3gF4aZRs3p0P2O9WurormZFjJj3Lvc92WrU5m/uGBJOcbiQt08RDd9ciO9fM7sNlXYreeaEeuw4VsGZrDgArN2XxwvjanI8v5Wx8CXf3DcTLU8mmnbkAhAV70Kujnv3HCykoshAVoeGxMaEcP1tEfJL7ZtFb82c+o/r7k5phJj3bxH2DA8jJt7DvWNkA5+lPh7H3WBG/b7f/ntVb83n2wWBiLxo5n2BgaC8dGk8Ff+yxfx8SpKZrG1+Onikhv9BCoL+akX31GE02Dp4qdhnHzfptSw5jhgSRkmHP+weHBZOdZ2b34bJxB2/9ow67Dxey9s9cAH7dnMM/xoVx/nLZGdbHXnY27bKXrbBgD3p20HHgRCEFhRai6mh4dHQIx88Wk+CmvN+4t5RHh/kRn2LhQrKZfh298PRQsOOoff2PDvMjp8DKL1vt+bV5XykvPaSjf0cvjsUa6dBcQ1S4mu/W2fcRTw8Y2tWbI+dM5BZa8fNR0LudFwFaJQdO3fzFXkXW7yri8ZF6LiSbiEsyMaCzDxoPBX8dsl9APD5SR06+lZ832/8PG/cU88/xAQzq4sORcwY6tfQiurYHC37Ld6zT11tBkF7lGAcUFnRlrJD1trTErfkzj1ED/EnJMJGeZeL+IYHk5FnYe1XZf+PZcPYcLeL3v+xx/rY1j0ljaxGbaOB8ooGhvfSXy779d4YEqel2hx9HTheTX2QhSK9mRD9/e9k/6Z6yv353MY+N0BGfbOZCkon+l/N++2H7TYrHRmjJKbCyfLO9bGzcU8zU8QEM7OLNkbNGOrX0Iqq2moWX816lhGfu1RMZrubjH/NQKBSO1t+iEustt5BcS+Xrg2/Deo73PtF10LVuijE7j9KLKTR5ZwpeEaEcmWCfJCvhqyVEPjOWpjNf5uKC5QT37kz4vYPZN/xJxzoufPQtref/m9wDx8nbd5SoyeNQ+3pzceEK9wZ/2e87ChnRR0tqlpmMbDOj++vILbBw4GTZjaJpE4PYf7KUjbvs/4d1fxXy5L0BXEgyEXvRyKBufmg8Ffx5wF4uSgw2tu4v4qGheopKrBSX2hg3XM/ZBAPnL7r3xsbavwoY2VdPSoaZ9Gwz9w3yJyffzL7jZWX0tSdD2He8hPU77Mf1NX/m88z9wcReMhKbaGBID/txf+s+e9n31ih49YlQPD0UfLowE28vBd5e9nKUX2h1a/e2mhx/Tb/eEdVHta+wgX0Sjlq1ajFz5kzi4uLw9/enbdu2TJs2jfvuu48333yTtm3bAjBjxgw2bNjAU089xdKlS3nsscfw8fHhgw8+4OWXX8bX15eYmBief/75Src5btw4SktL+e9//8tLL71EcHAwo0ePdplWq9Xyn//8h3PnzqFSqejQoQNr165FeXlu2LVr1/Lqq68yYcIEMjIyCAsLo2fPnoSGhgL2Ct+jjz5K165dCQ4OZurUqeTn57vcVkV0Oh2//fYbTz/9NG3atCEmJobp06fz4IMPOo1ru9prr71GXFwcAwcOxMfHhyeeeIIRI0aQl5fnMr27/LIpBy+NgqcfCLn84OlS3v48yWm8R1iwh9Mg+R0HC9H5qbh/aBABWhUXkoy89VlSuYG5/wvL12fh5alg0kPh+PooOXm+hDfmXKw0/u37C9D7pTN2eC0CdCriLhl4Y06i46Brttho08yH4X3tB+bMbDM7DxawdO2td/G52q9b8vDyVPDkmCB8vJWcvmDg3bmpTrGHBqvR+pbFvvNwETo/JfcNuvIQTwPvzk1zXFSbTDaa1fdiaC89ft72biun4kp57eMUtz8AdsWGbLw8FTzzYBi+PvaHls/45JJz3tfydM77AwXo/FQ8eFcwATp7V5QZn1xylB2zxUbrpj6XK3IKMnPM7DpUyLJ17hu7sv+UEa1PMXf39Ebna39w9sdLCyi43CUyUKd0PJoEIDbJzNe/FjKilw8j7/QhPcfCZz8XkJxhj9lqhbBgFV1aeeHnraCoxEZ8ipn/fJdHcqb794m9JwxofQsY2dvP8eDs2d/nOCa3CNKrnC5wzl80MXd5Hvf08WNUXz/Ssi3MWZJLUnpZbHc00fDYiLKxxM/c6w/Ayq2FrNzq/oH0KzfnofFU8uR9wY4H2L7z5TVlP0iN7uqyf6jIftwZEoC/Tk38JQPvfpnqKDsmk41mDbwYeqcOX297d7dTsSW8+lGy28r+vhMGtD6FjLjT1/Hg7P8uznV0pw3Uq5ympY69ZOarFfnc09uXe/rY8/6TJXkkXS47/loldzS133Sb8ZTzM7/+vSCHMwnurSzo27Wky+bvHO+bz/oXABcXreDoxGlowmvhXbdsnHBJ/CX2DX+S5rOnEfXcI5ReSuXYk6+RuXG7I03KT+vwrBVI4zcm2x+cfeQUe+96DGO6+/bZq63eVojGU8HEkf74eNkfnP3vb7OcWmRCg9Rofco6Le0+VoLWT8noflr0Wnv3yX9/m+lULr5fk4fNBv8YG4RaDcfO2h+c7W6r/shH46ngidFBjofGz5yXfk38Hmh9y25Q7TpSjM4vhzED/fHX2rsfzvw63XHcj67j6Zhxcc405+Eqk969REaO+45DNTn+mn69879UU6bXryoKm02G+f1ftXjxYiZMmEBeXt4Nj8u7ESMnnfv7RNWY2R39OKqQl4/rCnlNYCx1f2vQ/1JI3eC/T1SNmYw1t+wX5tTs2dF0gRU/O7AmGPVOj6oO4ab98NKWqg7hltT0c1ZNV5PPW7982qiqQ6jQz3v+N2OcXRndqdqPEKsZLWzi+ixatIj69esTERHBkSNHmDp1KmPGjLmtlTUhhBBCCCHE7SMVtv9DUlNTmT59OqmpqYSHh3Pvvffy7rvvVnVYQgghhBBCVEj6+1VOKmz/h7zyyiu88sorVR2GEEIIIYQQwk2kwiaEEEIIIYSoMtLCVrnqP8pOCCGEEEIIIf4/JRU2IYQQQgghhKimpEukEEIIIYQQospYbYqqDqFakxY2IYQQQgghhKimpIVNCCGEEEIIUWVk0pHKSQubEEIIIYQQQlRT0sImhBBCCCGEqDLSwlY5aWETQgghhBBCiGpKKmxCCCGEEEIIUU1Jl0ghhBBCCCFElbFKl8hKSQubEEIIIYQQQlRT0sImhBBCCCGEqDI2eXB2paSFTQghhBBCCCGqKamwCSGEEEIIIUQ1JV0ixS1Te9TsYvTC07WrOoRbsn5Pzc3/+HOZVR3CLcnNyK/qEG6J1t+3qkO4acG1A6o6hFuiUNbs7j8/vLSlqkO4aQ/O6lPVIdyStPVnqjqEW3LmfElVh3BLVCpp67gd5DlslZNSJ4QQQgghhBDVVM29NS+EEEIIIYSo8WRa/8pJC5sQQgghhBBCVFPSwiaEEEIIIYSoMjKGrXLSwiaEEEIIIYQQ1ZRU2IQQQgghhBCimpIukUIIIYQQQogqI10iKyctbEIIIYQQQghRTUkLmxBCCCGEEKLKyLT+lZMWNiGEEEIIIYS4Dp999hlRUVF4eXnRqVMn9u7dW2HaefPm0aNHDwICAggICKBfv36Vpq+IVNiEEEIIIYQQ4m8sXbqUKVOm8MYbb3Dw4EFat27NwIEDSU9Pd5l+69atPPDAA/zxxx/s2rWLunXrMmDAAJKSkm5ou1JhE0IIIYQQQlQZm63qXgaDgfz8fKeXwWBwGeeHH37I448/zoQJE2jevDlffvklPj4+zJ8/32X6xYsX88wzz9CmTRuaNm3K119/jdVqZfPmzTeUP1JhE0IIIYQQQvx/aebMmej1eqfXzJkzy6UzGo0cOHCAfv36OT5TKpX069ePXbt2Xde2iouLMZlMBAYG3lCMMumIEEIIIYQQospYrVW37WnTpjFlyhSnzzQaTbl0mZmZWCwWQkNDnT4PDQ3l9OnT17WtqVOnUrt2badK3/WQCpsQQgghhBDi/0sajcZlBc3d3n//fZYsWcLWrVvx8vK6oWWlwiaEEEIIIYQQlQgODkalUpGWlub0eVpaGmFhYZUuO2vWLN5//302bdpEq1atbnjbMobtf2Dr1q0oFApyc3MrTLNgwQL8/f3/ZzFd8eabb9KmTZv/+XaFEEIIIYSAqp105Hp5enrSrl07pwlDrkwg0qVLlwqX+89//sPbb7/N77//Tvv27W8qf6SFTVSJ+wb507eLFl8vJafjDcz7KZPUTHOlywzspmV4Hz3+WhUJyUbmr8jifKLR8f0T9wYR09ibQJ2KUqONMxdK+X51DsnpJrfF/ce6paxfuYi83CzqRjXmgcdeIbpRS5dpD+7ezNrl80lPuYjFYiYkvB4Dhj9ElzvvcqRZteRL9u3YQHZmKmq1B5ENmjHiwWep3zjGbTFfrVMzJT1aqvHzhtQcG6t3mbmU6fpoFeKvoG9bFRFBSgK0CtbsNrPzpOWW1ukOo/pp6d3BF19vJWcTDMxfmUtaVvm4rta/sy9De/qh91ORmGpi4apc4i6VlQsPNYwdoqdzax88VHD0nIFvf80lv9D9nepratnv08GbQV190PspuZhqZvG6Ai4kVxx3++YaRvb2JdhfRVqWhZ82FXLsvD1mlRJG9vGlVUMNtQJUlBisnIwz8vOmInJvQ54D9LpDw4BOGnS+Si6lW1i6qZj4lIrLTdsmHgzv4U2QXkl6jpVfthZzPM71731wgA8979CwbHMxW/a7nlns1mL3pH8HL3S+Cnvsm0tISK0k9sYeDOvuVRb7nyWcuOA69gf6e9OzjYaftpSw5YD7Y7+iJu63gd3bU//FiejbtsSrdgj7Rz1D2qrKZ3YL7NmR5rP+iV/zRpReTOH8zC+4tOgXpzSRTz9I/SkT0YTVIv/oaU48/zZ5+465JeZrHdy6mD0bv6EoP4OQOk3pd9/r1I5yfXf/8PZlnNi9kozkcwCE1WtBzxFTnNKfObSBw38tITXxBKVFuYz/10pC6za7LbEDdGmuomdrNVpvBSnZNn7dYeRSRsXnl5hoJQM6eBDgpyAz38a6PSbOXCwrD37eMLijB43rqPDSwIUUK7/uMJGVf3vOWZ2bKekRc/n8mG3jt7855/ZrqyIi2H7OXb3bzM4T5feRG1mncL8pU6Ywbtw42rdvT8eOHfnoo48oKipiwoQJADzyyCNEREQ4Ji3597//zfTp0/nhhx+IiooiNTUVAD8/P/z8/K57u9LCJv7n7u6jZ3BPHV/9lMW0j5IxGKy89lQYHmpFhct0bePLuBFB/LQ+l6mzk0lINvLqk2Ho/MqKcNwlI5//mMnz7yfxztxUFAp4/akwlBWv9obs276eZd9+yLAxT/D6rB+oE9WIj956lvzcbJfpff30DBk1kWnvL+CN/y6lW5/hLPh0BscP7XSkCa0dyQOPTeXN/y7jlXfnE1SrNh+99SwFeTnuCfoqMdFKhnRUs+Wwmc9WmUjNtjF+oAe+FXSj9lBDToGN9fvNFBS7Phnc6Dpv1V09/RjY1Y9vV+Yy/fN0DEYb/3w0GI9Kbj11jvFm7FA9KzYX8Nqn6SSmmPjno8HofMvKzkND9dzRzIs5i7N4+6tMAnQqXhh7YzM4XY+aWvY7tNBw3wA/Vv1ZxIy52VxMMzPlIX+0Pq430KCOmidH6fjrUClvzs3m0BkDz92vJ6KWCgBPDwWRYR78tq2IGV9l8+nSPMKC1Ex+QO+egK/RrqkHo/t4s3pHKe8tyOdSuoXnxvhVGH/9CBUTh/uy46iBdxfkc/ickafu8aN2cPlTZptGHkTXVpFbcHsqmu2aeDDqTm/W7CzlvUUFXMqwMPle34pjr63i0WE+7Dxm5L2FBRw5Z+Kpkb4uY2/dyIPo2urbFvsVNXW/Vfn6kH/0DMcnz7iu9N5Rdeiwai5ZW/ewvf3dXPhkITFz3yG4f3dHmvB7B9Psg2mce+cztnccScHR03Ra8w2etdx/vDm1fy1bls+k29BnGf+vXwip05RlcyZSlJ/lMv3Fs3to1mEoD7ywiIdfWYI2MJxlcx6lILes+5fJWEydBm25c8RLbo/3Wq3qq7iriwebD5iZs8JASpaViUM0FZ5fIkOVPNDXk32nLcxZYeBkvIVHBngSGlC2rzwyQEOgTsHCDQY+Xm4gt9DG40M9Ky2LNysmWsmQTmo2HzLz2a8mUrJtTBhU+Tk3+/I5N7+yc+4NrLMmqQktbAD33Xcfs2bNYvr06bRp04bDhw/z+++/OyYiSUxMJCUlxZH+iy++wGg0Mnr0aMLDwx2vWbNm3dB2pcLmJgaDgcmTJxMSEoKXlxfdu3dn3759FaZfsGAB9erVw8fHh5EjR5KV5XwAvdJVce7cudStWxcfHx/GjBlDXl6eU7qvv/6aZs2a4eXlRdOmTfn888+dvp86dSqNGzfGx8eH+vXr8/rrr2MyVXzXPTY2lvr16zNp0iRsN1qKr9PQXjqWb8hl//FiElNMfPpDBgE6FR1ifCpc5q47dWzeVcDWvYVcSjPx1U9ZGI02+nTSOtJs2lXAqbhSMnLMXLhk5Me1OQQHqKkV6J4j8cbfFtOj/0i69b2b2nXr89CTr+Kp8WLHll9dpm/Ssj1tO/chvE59QsLq0u+uB6kT2Yjzpw470nTqOZjmrTtRK6wOEfUaMGbCFEqKC7mUcNYtMV+tW0sV+89YOXjOSkaujV93mDGZoV1jlcv0SZk2ft9n4dgFK+YKboTf6Dpv1aBufqz8o4ADp0q5mGrmi2U5+GtVtGvuXeEyg3v48ce+IrYdKCYp3cz8lbkYjDZ6tbeXN2+Ngjvb+7J4TR4n44zEJ5uY+3MOjaM0NKzr4db4a2rZH9jZh20HS9h+uJTkTAuLVhdgNNnocYfrfO/fyYfj5438vrOYlEwLv/xRREKKmT4d7b+zxGBj9ve57DtpIDXLQlySme/XFRBV24NAnftPS/06eLHjiIFdx4ykZFn5YX0xJhN0jfF0mb5POy9OxJnYuNdAapaV3/4qJTHNwp1tna+K/P0U3Nffh/mri7BYb8/xsm97DTuOGtl13EhqlpUfN5RgNEGXlq5j791Ow8kLZjbuM5CabeW3HaVcTLPQ6w7nAfV6PwX39fXm29VFWG7z7Gw1db/NWL+Ns298RNqvm64rfeQT91Ny4RKnXvk3hafjSPh8ManL1xP9j/GONNHPT+DiN8u4tHAFhadiOfbMG1iKS6k7fpRbYr7avs3f0rrbGFp1HUVweEMGPjADD08vju1a7jL9sEdn07bXWELrNiMorAGDH3oHm81Kwumy6cpbdhpBt6GTiGpWcfcvd+nRSs3e0xb2n7WQnmvjl79MmMzQoYnr41q3lirOXrSy7aiZ9FwbG/abSc600bWFPX2wXkFkqJKV201cyrCRmWdfp4daQZsG7j9ndW+pYt/l82P65fOj8TrOuUfjrFgqOOfe6DrF7TFp0iQSEhIwGAzs2bOHTp06Ob7bunUrCxYscLyPj4/HZrOVe7355ps3tE2psLnJK6+8wvLly1m4cCEHDx6kYcOGDBw4kOzs8q0ve/bsYeLEiUyaNInDhw/Tu3dv3nnnnXLpzp8/z7Jly/jtt9/4/fffOXToEM8884zj+8WLFzN9+nTeffddTp06xXvvvcfrr7/OwoULHWm0Wi0LFizg5MmTfPzxx8ybN4///ve/Ln/D0aNH6d69Ow8++CCffvopCoWbbs9fJSRITYBOzbGzpY7PikttnE8w0CTK9Qw9ahXUr6Ph6NkSx2c2Gxw9V0LjSNfLaDwV9O6kJS3LRFZu5d3NrofZZCIh9hTNWpXtlEqlkmatOhF75ujfLm+z2Th1dA+pyfE0bt62wm1s27ACbx8/6kQ1vuWYr6ZSQu0gBeeTy67MbMD5ZCv1at3c//l2rLMytQJUBOhUnDhf1m2rxGAj9qKRRvVcX7yqVBBd24PjVy1js8HxWINjmegID9RqhVOalAwzmTlmGlaw3ptRU8u+SgmRtdWcjCvrgmkDTsYZaVDH9YVxg7oeTukBjscaaVin4gqkj0aB1WajuNS9FR+VEuqFqTiVUJYXNuBUvIn6Ea7jqR+h5nSCc96dvGCifkTZRZECGH+XLxv3lJKSeXtqPFdiP31N7KcTzNSvXUHstV3EHu+cXgFMGOLDxr2lpGTd3tpaTd9vb4R/5zZkbnF+FlPGxu0EdG4DgMLDA33bFmRuLutlgc1G5pad+He+w62xWMxGUhNPENm0q+MzhVJJVNOuJMUduq51mIwlWC1mvHxvT8t3ZVRKiAhWcO5SWc3FBpxPslAv1PWla2SokvNJzjWds5fK0qsvL2a6avewAWaLjagw914Oq5RQO7j8+TE22Uq9kFs457p5ndWJ1VZ1r5pAxrC5QVFREV988QULFixg8ODBAMybN4+NGzfyzTff0KFDB6f0H3/8MYMGDeKVV14BoHHjxuzcuZPff//dKV1paSmLFi0iIiICgE8++YShQ4cye/ZswsLCeOONN5g9ezb33HMPANHR0Zw8eZK5c+cybtw4AF577TXH+qKionjppZdYsmSJY9tX7Ny5k7vuuotXX32VF1980Y2548xfa7/gyS10PqjmFloc311L66tCpVKQV+C8TF6BhYgQ5wvGAd20PDwsEC+NkqQ0I29/kVph69CNKCzIxWq1oPN37rai8w8kNSm+wuWKiwp45fFBmE0mFEolY5/4J83bdHZKc2T/NuZ9OA2joRR9QDAvvPEFWl3ArQd9FR8NqJQKCkucj0yFJTZq+d/ciep2rLMyV8pH3jVlJ6/Qgr/W9fa0Pkp72blmTEt+gYXatTSO9ZrM5SsKeYXWCsvkzaipZV/ro0SlVJBfdE0eFlkJD3Z9CtH7KcunL7Si83P9O9UqGN3Pjz3HDJQa3Xv29PNRuIy/oNhGWJDreHS+LtIX2Zy64w3o7IXVym0d9+XnfTn24mvysthKaAWtp65izy+yovMtu6Ab0EmDxQZ/HDReu7jb1fT99kZoQoMxpGU6fWZIy8RDr0XppcEjQI9SrcaQnnVNmix8m9R3ayzFhTnYrBZ8dUFOn/vogshKi7uudfz5yyz89CFEXVXp+1/x8bpyfnH+vKCS84uft4ICF+m13vayn55rI6fAyuCOalb8ZcJohu4xavz9lOh83HvcKYvfxflRf5Pn3NuwTlFzSIXNDWJjYzGZTHTr1s3xmYeHBx07duTUqVPlKmynTp1i5MiRTp916dKlXIWtXr16jsralTRWq5UzZ86g1WqJjY1l4sSJPP744440ZrMZvb7sbtjSpUuZM2cOsbGxFBYWYjab0el0TttJTEykf//+vPvuuzz//POV/laDwYDB4HyBYjEbUKld3+3v3taXJ8cEO97PnJfmMp27bD9QyNEzJQTo1AzvrWPKuBBem5OCyVw1t1C8vH2ZPvtHSktLOH10L8u+/ZBaoXVo0rJslqCmLTswffaPFOTn8temX5g7eyr/en9Rucrh/2+6tvFm4gh/x/sPFroed1Fd/f9e9q+XSglP36tHoYDv1hRUdTjXpV6oij7tNLy3ML+qQ7lh9UJV9G6nYebC25PXNX2/FXa713/Fqf1reeCFRag9bv/zqf4XrDb4bqOR0T09eXO8NxarjfNJVk4nWqj57VPi/zqpsNVQhYWFgL0l7+q+swAqlf3u4q5duxg7diwzZsxg4MCB6PV6lixZwuzZs53S16pVi9q1a/Pjjz/y6KOPlqvQXW3mzJnMmOE8ALtZp8m06PK8y/T7TxRzflaS47368uQK/n4qcvPL7rj6+6mIT3Z9t7egyILFYkN/zV1TvdZ5HWDvYlZcaiY108y5hFK+fTeSjjE+7DhUVOFvuh5+Wn+USlW5CUbyc7PR+QdVsJS922RIeD0A6kU3IeXSBdaumO9UYdN4eRMSXo+Q8Ho0aNKKV5+9m+2bVzJk1KO3FPPVig1gsdrw81Zg70Rx+Xd5KyisYHBzVazzagdPlhJ7Md3xXq2ylx29n/MED3o/FQkprsdlFhRb7WXHz/nuo06rcrRa5RZY8FAr8PFSON2t1/spyS24+Saq/ytlv6DYisXq3LoEoPNVlmsBuSKv0Fo+vZ+S/GtaWVRKeHq0nmC9kv8synV76xpAYbHtqvjLtq/1Kd8SdUV+Ufnfq72q5aphXTVaXwXvPV12c0ylVDC6tzd922t49Uv3VOQKSy7H7uMcu85HSX6R67xyFbvOtyx9wzpqtD4K3n2q7DivUioYdacXfdppeO2rW4u9pu+3t8KQlokmNNjpM01oMKa8AqylBoyZOVjNZjQhQdekCcKQ6twyd6t8/AJQKFXlJhgpzs/CVxdcwVJ2ezZ+w+71X3HfP74lpE5Tt8Z1vYpLr5xfnD/XeisqnASrsMSG1lX6q1qkkjJtfLzCgJeHvettUSk8O0LDpQz3dg0ui7/8+bGg5CbPubdhndXJ7Zo34fpU/yq7tKG6QYMGDfD09GTHjh2Oz0wmE/v27aN58+bl0jdr1ow9e/Y4fbZ79+5y6RITE0lOTnZKo1QqadKkCaGhodSuXZu4uDgaNmzo9IqOjgbs3RwjIyN59dVXad++PY0aNSIhIaHcdry9vVm9ejVeXl4MHDiQgoKK77xOmzaNvLw8p1fTDk9XmL7UYCM10+x4XUo1kZNvpmXjssH73hoFDSM1nIl33bXIbIG4SwZirlpGoYCYRt6cTai8O5JCQaUz8F0vtYd9yv1TR/c6PrNarZw6upcGTa7/AYg2mxVzJZO+ANisNswm93ZVslghOctGg9plu7wCaFBbSWIlUyT/r9d5tVKjjbQsi+OVlG4mJ99CiwZld3u9NQoa1PXkXKLr/LJY4EKyyWkZhQJaNtA4lrmQZMJstjmlCQ9WExygdpo6/4bj/z9S9i1WSEg206x+2bggBdCsviexl1yX5diLJppFO48jalHfk/OXygaPXKmshQSpmPVdLkW36YLDYoXEVAtNI53HcDWN8iAuyfUYv7gks1N6gGZRHsRdHh+z57iRd+bn8+63Za/cAisb9hqYs6zQ7bE3uSb2JpFq4ip4pEJcspkm9Zxjb3pV+j0njLy7oID3Fpa9cgusbNxn4JOfbj32mr7f3orc3YcJ6uPc5T24b1dydh8GwGYykXfwBMF9rpqwQ6EgqHcXcndf37iy66VSexJWrwUJZ8rG1NmsVuLP7CKifsXj5fZsmMfOtZ9z76SvCY+8PY+XuR4Wq71y1fCacaMNa6tITHNduUpIs9IgwvnmVqMIpcv0pSZ7ZS1Ip6BOsIKT8e6t5FuskJxpo2G4i/Nj+i2cc928TlFzSAubG/j6+vL000/z8ssvExgYSL169fjPf/5DcXExEydO5MiRI07pJ0+eTLdu3Zg1axZ3330369evL9cdEsDLy4tx48Yxa9Ys8vPzmTx5MmPGjHE8TX3GjBlMnjwZvV7PoEGDMBgM7N+/n5ycHKZMmUKjRo1ITExkyZIldOjQgTVr1vDLL7+U286V37BmzRoGDx7M4MGD+f33310+H0Kj0aDROHePUKlvrMvLmj/zGdXfn9QMM+nZJu4bHEBOvoV9x4odaaY/HcbeY0X8vt1eeVy9NZ9nHwwm9qKR8wkGhvbSofFU8Mce+/chQWq6tvHl6JkS8gstBPqrGdlXj9Fk4+CpYpdx3Kj+w8Yy/5M3iGrYnOhGLdj02w8YDSV06zMcgG8+fp2AoBDueeg5ANYun09Ug+bUCquD2Wzk2IEd7P5zLWOfmAaAobSENT9/TesOvfAPCKawIJc/1i0jJzuddl37uyXmq+04bmFUDzVJmUouZdjo2kKFpxoOnLWfqEb3VJNfZGPDAft7ldL+XBiw34nU+UJ4oAKDyUZ2wfWt091+31HIiD5aUrPMZGSbGd1fR26BhQMnywYuTJsYxP6TpWzcZW9ZWvdXIU/eG8CFJBOxF40M6uaHxlPBnwfs5aLEYGPr/iIeGqqnqMRKcamNccP1nE0wcP6i+55jBjW37K/fXcxjI3TEJ5u5kGSif2cfNB4Kth+25/tjI7TkFFhZvtme5xv3FDN1fAADu3hz5KyRTi29iKqtZuFv9tYblRKeuVdPZLiaj3/MQ6FQOMZYFZVY3T5r4aZ9pYwf6ktCqoX4FDN92nvh6QE7j9kv7McP9SG3wMrKbfYJYbYcKOXFB7T066DhWKyJDs08iQxTsfh3++8rKrVRdM3YKYvVRn6RlbRs9wa/eb+BcUN8SEw1E59ioU97DRoP2HXcHvu4IfbYf/3LHvsfBwxMud+Pvu01HI8z0b6pPfYfNhRXEru9ZS4t5/ZMQFJT91uVrw++Des53vtE10HXuinG7DxKL6bQ5J0peEWEcmTCVAASvlpC5DNjaTrzZS4uWE5w786E3zuYfcOfdKzjwkff0nr+v8k9cJy8fUeJmjwOta83FxeucEvMV+vQdwJrFk4lrF5LwqNasX/LQkyGEmK62Me9r17wClr/UHqNsI9b373+K7avnsOwCbPRB0VQmJcBgKfGB08vXwBKinLJz06hMM/eipqddgEAX10wfvpabo3/r6NmxtzpwaUMK5cyrHSPUePhAfvP2m8+jLnTg/wiG7/vs7/fcdzCk8M86RGj5nSihdYNVUTUUrL8r7KbWzHRSopKIbfQRliggmFdPTiRYOVckvvL/vbjFkb3VHPp8vmxW0v7+fHg1efcYhsb9rs45ypB51P+nPt366zJqrSBrQaQCpubvP/++1itVh5++GEKCgpo374969evJyCg/OQRnTt3Zt68ebzxxhtMnz6dfv368dprr/H22287pWvYsCH33HMPQ4YMITs7m7vuustp2v7HHnsMHx8fPvjgA15++WV8fX2JiYlxjEMbPnw4L7zwApMmTcJgMDB06FBef/31CqcS9fPzY926dQwcOJChQ4eydu1afH193ZZHV/y6JQ8vTwVPjgnCx1vJ6QsG3p2b6jTWJjRYjda37E7ZzsNF6PyU3DcoAH+divgkA+/OTXN0yTKZbDSr78XQXnr8vO1dYk7FlfLaxylue4hqh+4DKcjP4dcfvyA/N4u60U34x+ufOrpEZmemolCW3fkyGEpYPG8mOVnpeHhqCI+IYuI/3qZD94GAvbtkalI8u7aupjA/F1+tnqiGLXjlnW+IqNfALTFf7dgFK75eZvq2VaP1hpRsGws2mCi6PGmh3lfhdMDU+sCkEWWtJD1i1PSIgbgUK9+sM13XOt1t9bZCNJ4KJo70x8fL/gDef3+b5TTrV2iQGq1P2f9h97EStH5KRvfTotfau2H9+9tMp3Lx/Zo8bDb4x9gg1Go4dtb+AF53q6llf98JA1qfQkbc6et4cPZ/F+c6utkF6lVOM23FXjLz1Yp87untyz19/EjLtvDJkjySMuwXFf5aJXc0td/4mfGU81jNfy/I4UyCeyvKB06b0PqUMKy7l+PB2Z8sK3R0rQrUKZ3KflyShW9+K2J4D2/u7ulNeo6VL1cUknybZoOsNPYzJvx8Srirm7fjwdmf/FxUFrv2mtiTLcxfXczwHl7c3cOLjBwrX/5SVCWxX1FT91t9u5Z02fyd433zWf8C4OKiFRydOA1NeC2864Y7vi+Jv8S+4U/SfPY0op57hNJLqRx78jUyN253pEn5aR2etQJp/MZk+4Ozj5xi712PYUx3/1i/Zu2HUFyYzfbVcy4/OLsZY5772tElMj87BYWiLM8PbVuCxWxi5bzJTuvpNnQS3e+y34g8f3QLaxdNc3y36psXyqVxl6NxFny9YUB7ezfe5Cwb89caHBOR+Ps5n7MS0qz8uNnIwA4eDOqoJjPPxqINRtJyyhJpfRTc1UVt70ZYbOPgOQubD976bLquXDk/9mt3+fyYZePb9SYKS13Hr/WB50aWnXN7tlLTs5X9nPv1WtN1rVP836WwVW2nUVGBN998k5UrV3L48OGqDuVv3fvChaoO4ZY891hIVYdwS9bvqbn3XeLPuXfcxv+asbRqul65i9bf/Tdk/lc8vdz7jLz/NYW7nmpeRfKzb21sZFV6cFafqg7hlqStP1PVIdySM+dL/j5RNaZS1dzRRO9NrL4TyHyypuqqI88Nrf7H45p7pSeEEEIIIYSo8axV1wmgRqi5twmEEEIIIYQQ4v84qbBVU2+++WaN6A4phBBCCCHErbDZqu5VE0iFTQghhBBCCCGqKRnDJoQQQgghhKgy1hrS0lVVpIVNCCGEEEIIIaopqbAJIYQQQgghRDUlXSKFEEIIIYQQVaamTP5RVaSFTQghhBBCCCGqKWlhE0IIIYQQQlQZW5XOOqKowm1fH2lhE0IIIYQQQohqSipsQgghhBBCCFFNSZdIIYQQQgghRJWR57BVTlrYhBBCCCGEEKKakhY2IYQQQgghRJWRaf0rJy1sQgghhBBCCFFNSQubEEIIIYQQospYZRBbpaSFTQghhBBCCCGqKamwCSGEEEIIIUQ1JV0ixS1Te6iqOoRb8sGcxKoO4ZZ8+aK5qkO4aWnda1d1CLdk2+maHX9YcFVHcPPq+hdVdQi35GSyX1WHcEs2/Z5X1SHctLT1Z6o6hFsSOrBJVYdwS0bu/6qqQ7gl5p8WVHUIt+Dbqg6gQjLpSOWkhU0IIYQQQgghqilpYRNCCCGEEEJUGWlhq5y0sAkhhBBCCCFENSUVNiGEEEIIIYSopqRLpBBCCCGEEKLKWKVPZKWkhU0IIYQQQgghqilpYRNCCCGEEEJUGZu1qiOo3qSFTQghhBBCCCGqKWlhE0IIIYQQQlQZm4xhq5S0sAkhhBBCCCFENSUVNiGEEEIIIYSopqRLpBBCCCGEEKLKWGXSkUpJC5sQQgghhBBCVFPSwiaEEEIIIYSoMjLpSOWkhU0IIYQQQgghqimpsAkhhBBCCCFENSVdImuI8ePHk5uby8qVK6s6FCGEEEIIIdzGKj0iKyUVNlElRg/Q06ejH77eCs7EG5n/SzapmeZKl+nfxY9hvXTotSoSU4ws+DWH2ItGAHy9ldw7QE9MYy+C/VXkF1rZf6KEZRtyKSl1/1Fg7LBgBvQIwNdbyanYEj7/IYWUdFOlywy5M4B7+gcSoFdz4ZKBuUtSORdf6vj+vSn1iGni67TMuj9z+PyHVLfFvXLNOpauWEV2Ti4NoiN57smJNGvc6G+X27JtO+988BHdOnXg7demOj7vM2y0y/RPTHiY+++5221xX7FhzXJ+W7GYvJxs6kU3ZPyTU2jYuLnLtHt3bmXlT4tIS7mExWwmrHZdho64nx59BjvSlJYU8+PCL9i/exsFBXmEhNZm4LB76T94pNtjP75jMYf//IbigkyCwpvSfcRrhNZr5TJtduo59q6fQ2bSCQpykuk6fBqte4xzSvP9e30oyEkut2yLLg/S857pbo9/z+bF7Fz3DYV5mYTWa8qQsa9Rp77r+Pf/uYwjO34lPekcALWjWtB31AsVpv9t4Rvs37qUQQ9Mo8uAcS7T3KrNa5fx+8pF5OVmUTeqEWMfe4X6jVu6THtg1xZWL59PespFLBYzoeH1GHj3Q3S9c6jL9Iu+eI+tG5Zz/6MvMmDYg26P/dCfi9m36RuK8jOoFdGUvmNeJzzKdV4e3bGME3tWkplsz/vQei3oMXyKU3qbzcaONXM4tuMnDCX51K7flv73v0lASJTbY7/i3oF6+nbyw9dbyZkLBr5e8ffH/AFd/Rh2px5/rYqEFCPf/pLtdMwfM1BPq8beBAfYj/n7jhezdL37j/kHty5mz0Z7/ofUaUq/+16ndgX5f3j7Mk7sXknG5fwPq9eCniOmOKU/c2gDh/9aQmriCUqLchn/r5WE1m3m1pgBAru3p/6LE9G3bYlX7RD2j3qGtFWbK1+mZ0eaz/onfs0bUXoxhfMzv+DSol+c0kQ+/SD1p0xEE1aL/KOnOfH82+TtO+b2+AF+/v0Pvv9tA9m5eTSMrMOLjz5Ai4bRLtOu3rqTdz5f4PSZp4eabYs/d7yft2wVm3buIy0rBw+1mib16/HU/SNo2aj+bYnfq1MffLoPRumnx5yaSOHqxZiTLlSY3rtLf7w69kblH4S1uBDD8X0UbfwZzOabXqf4v0EqbOJ/btidWgZ10/LF0iwyss3cO1DPPyeG8PLsZEwVnL87t/bh4WEBfLMim/OJBgb30PHPiSG8+EEy+UVWAnQq/HUqFq/O5VKaiVoBKibeE0iALoiPvs90a/yjBgZxV59APlqQTFqmibHDa/HW5Ho882YcJrPrC4Xu7bU8NjqEz35I5eyFEob3DeStyfV46o1Y8gosjnS//5XD4lUZjvcGo/suPP74awdffL2Q5599gmaNG7F81RqmTn+HhV/OIcBfX+FyqWnpfDl/ETEtyl9Q/LxontP7PQcOMWvOF/Ts2tltcV+x669NfPf1HCY++zING7dg3aqlvD/9BWZ/+SN6/8By6f20OkaOGUftOpGo1WoO7tvBlx+/h84/gNZt7fF9980cThw9wLMvvkGtkHCOHtrD/C9mExAYTPtOPdwW+/nDa9nx2/v0GvUmIfVac/Svhaz++jEeeGUdPn5B5dKbTaXogurSoPUgdq563+U6R03+GZu1rOxkp57jt3mP0qD1QLfFfcXxPWtZv+R9hj3yJhH1W7N740K+m/0Yz81ch5+ufPzxp/cS03kodRvegdpDw/a18/hu1kSefXc1uoBQp7SnDmzkUuwRtP4hbo/7ir3bN7D02w95+Kl/Ub9xSzb+9gMfvjWJ9z5dgc5F2fHV6rhr9KOER0SjVqs5sv8v5n8yA50+gJZ3dHVKe2D3FmLPHsM/sNZtif30gbVsXTGTfvfPIDyqNQf/WMjPn07k0Td+x1dbPu8vnt1D0/ZDiYhui8rDk70bv+bnTx9l/Gtr0Prb837vxnkc2vodgx9+H31wHbb/9jE/fzqRCa+vRe2hcftvGN5bx+DuOj5fkkl6tpkxA/351+P243dFx/wurX14ZHggXy/P4lyikSE9tPzr8RBe+E8y+YVWAvUqAnQqvludQ1KaieAANY+NCiRAr+K/i9x3zD+1fy1bls9kwAMzqB3dmv1bFrJszkQef/N3fF2U/Ytn99Csw1D61W+L2sOT3Ru+ZtmcR5k4vSz/TcZi6jRoS9O2g/l98Wtui/VaKl8f8o+e4eKC5bT/+bO/Te8dVYcOq+aS+NUSDj/yEkF9uhAz9x1KUzLI3LgdgPB7B9Psg2kcf/YNcvceIXryODqt+YatLQZhzMh2a/wbd+7j40U/MfXxsbRoFM2SNZt5/t2PWfrRWwTqdS6X8fX2YtnHb1/1icLp+3q1Q3nx0QeICK2FwWjixzWb+Mc7H/HzJ+8SoNO6NX5Ny474Db6fglWLMF+Mw7trf/TjXyT7o2nYigrKp2/VGd8B91Lwy3xMiedQBYehvWciAEXrltzUOmsSmzSxVUrGsFUzP//8MzExMXh7exMUFES/fv0oKioql85gMDB58mRCQkLw8vKie/fu7Nu3z/H91q1bUSgUrFmzhlatWuHl5UXnzp05fvy403q2b99Ojx498Pb2pm7dukyePNnl9txpcHcdv2zO48DJEhJTTXy+NIsAnYr2LXwqXGZoDy1b9hTy5/4iktLNfLMiG6PJyp0d/AC4lGbio+8yOXiqhPRsMydiDSz9PZe2zb1RurmUD+8byLK1mew5Ukh8koH/fptMoL+azm0qPtiP6BfE+u25bN6Zx8UUI58vTsVgtNK/q79TOoPRRm6+xfEqKXXfg0l+WvkbQwb2Y3C/PkTVq8sLzzyBRqNh3cYtFS5jsVh4d/bHjH/wPmqHhpb7PjAgwOm1c/c+2sS0oHZY+bS3as3KJfQZOJw7+91FnXrRTHzmFTw1GrZuXO0yffOYtnTo0ouIulGEhtdh8PD7qBfVgDMnjzrSnD11jJ59htA8pi21QsPpO2gEkdENiT170q2xH9m2gOad7qVph1EEhjak1z0z8PDw4vTe5S7Th9SNoetdr9CozVBUag+Xabz9AvHR1XK84k9tRRdUj9r1O7o1doCdGxbQrue93NFjFCERDbnrkRl4eHpx6C/X8Y9+chYd+zxIeL1m1Aqvz90T3sFmsxJ3cpdTuvycNNYufodRT36ASnX77h+uX/U9PfuPpEff4UTUrc8jT/0LT40Xf23+1WX6pi3b065zH2rXjSYkvC79hz1InaiGnD112CldTlY6P3z9AU+88M5ti3//5m+J6TqGmC6jCA5vSP/77Xl/fJfrvB86YTZ39BxLSN1mBIU1YOBYe94nnrHnvc1m4+Afi+g86Gkatu5HrYimDBn3Hwrz0jl/ZNNt+Q1DemhZsSmP/SdKSEwx8dmSTAJ0ajq0rOSY30vH5j0FbN1XRFKaia+XZ2M02eh9+Zh/MdXEh4syOXiyhLQsMyfOl7J0XS7tmvu49Zi/b/O3tO42hlZd7fk/8AF7/h+rIP+HPTqbtr3GEno5/wc/ZM//hNNlZb9lpxF0GzqJqGZd3BeoCxnrt3H2jY9I+/X6/q+RT9xPyYVLnHrl3xSejiPh88WkLl9P9D/GO9JEPz+Bi98s49LCFRSeiuXYM29gKS6l7vhRbo//x9Ububtvd+7q3Y3oOrWZ+vhYvDw9Wf3HjgqXUSgUBPnrr3o5V+wGdu9Ex1bNiQitRf26tXn+kXspKinlfMIlt8fv3W0Apfu3YTi4HUtGMoWrFmEzGfFq5/pmoEe9hpgSz2E4uhtrbham8ycwHN2DR53om16n+L9DKmzVSEpKCg888ACPPvoop06dYuvWrdxzzz0upzp95ZVXWL58OQsXLuTgwYM0bNiQgQMHkp3tfIfr5ZdfZvbs2ezbt49atWoxbNgwTCZ7173Y2FgGDRrEqFGjOHr0KEuXLmX79u1MmjTptv3GkED7XdHj58q6ApaU2oi9aKBRpOs7uyoVREd4cvx82TI2Gxw/V0qjSM8Kt+XjraSk1OrWhzGGBnsQqFdz+FRZpba41MrZCyU0re/tchm1ChrW8+LIVcvYbHD4dBFNrlnmzo46Fs9uxKfTo3lkRC00HoprV3dTTCYTZ8/H0a51WbccpVJJuzYxnDxzpsLlvlvyM/56PUMG9P3bbWTn5LJ7/0GG9P/7tDfKbDJx4fwZWrZu7/hMqVTSsk0Hzp05XsmSdjabjeNH9pOSlEjTFm0cnzduFsOBPX+RnZWBzWbjxNEDpCRfpNUd7qv0WMxGMpJOUKdRWcuMQqkkolEX0hIOu20b5w6uommHe1Ao3FNmrjCbjaTEn6B+i7L4lUol9Zt34eL5w9e1DpOhBIvFjLdvWUuu1WplxVev0HXQREIi/r5b7s0ym0wkxJ6meeuy/6lSqaR5q47Envn7blw2m42TR/eSmpRAk+ZtHZ9brVbmffQ6g+5+mIh6DW5L7BazkbSLJ4hs6lx26jXtSnLcoetah9lYgtVixsvHnvd5WZcoys8gsknZOjXeWsKjWpN84frWeSNCAtUE6NQcO1fi+Kyk1Mb5xMqP+fUjPDl21vmYf+xcaYXLAPh4ufeYbzEbSU0sn/9RTbuSdJ35b7qS/74V92KoLvw7tyFzi/NNlYyN2wno3AYAhYcH+rYtyNy8syyBzUbmlp34d77DrbGYzGbOxCXSIaasZ4dSqaRDTDOOnY2rcLmSUgMjnvknw5+eysv/+Yy4i+W7jV+9jZWb/sLPx5tGkXX+H3v3Hd5U9fhx/J2ke6Wle0A3hULZW/YeypIhogwBERSQDSJLUUABAZE9ZAmigLKH7D1klFFW2VAKdO82yf39EUgJTQtCIOn3d17P00d7c+7tp+H25p57llHzo1Bg4RNAdvSF3G2SRE70RSyLhhjOc/saFj4BWPhqK2hyF3esipch+0rkKx+zMJEk030VBqJLpBmJiYlBpVLRtm1b/P39AYiIiMhTLi0tjTlz5vDrr7/SrJl2PM6CBQvYuXMnixYtYujQobqyY8eOpVGjRgAsXboUPz8/1q9fT4cOHZg4cSKdO3fmyy+/BCA0NJSZM2dSp04d5syZg42NTZ6fnZWVRVZWlt42tSoLhcXLdaNROioASEpV621PSlHj7Gj4+YGTvQKFQqbXdVB7DA0+HoZbHxzt5LRpoGTXsdSXyvWyXJy0fzKJyfpZEpPVuCgN/zk5OVigUMhISMm7j59X7vu270QyD+NyiE9UEeBnTbe2Hvh6WTFx7r3Xzp2UnIJGo8HFRf+mwcXZmdt3DR//3IUotuzcxYIZU17qZ+zYvRc7W1tq1aj62nmfl5yciEajRumi331N6VyE+3dv5btfeloqfbu1QpWTjVyuoHufIXqVsW69B7Fg1mQ+79YKhUKBTCanV78RlCxtvJuPzLQEJI0a2+e6Pto5uJH40DjjDm5c2EVWZgolKhl/7F16SgIajTpP10cHpRuPH7xc/p1/TMXR2UOv0ndwywLkCgXVGn1s1LzPS0nRnjtOSv38Ts6uxNy7me9+6WkpDO7ZDFVONjK5go8/HUGpcrldfbeu/xWFQkHDdzu9qehkpGrPnee7Pto7uhL/IP+b1mft+2sK9koPXaUjLVnb5druuX9PO0dX0pKN230cwPnpNT9FvxaVlKrWvfY83TXfwOdEQdf8to2U/HPUeNf89Kfv//PvlZMrcbEv+f6vn4KD0oOAEjVeXNjErD3dyIrVPweyYh9jqXREbmONpYsSuYUFWQ/jnisTh32YcceAJSanotZoKPJcC5mLsyM378cY3Mffx5NRfboS4u9Hano6KzfspNfXk1g1bTweri66cgf/jWT09AVkZmfj5qxk5tcDcTZyd0i5nSMyhQJNarLedk1qEpZuXgb3yYo8itzOAedeX4EMZAoLMo7tJn3f5lc+pvC/Q1TYzEjZsmVp0KABERERNGnShMaNG9OuXTtcXFz0ykVHR5OTk8M777yj22ZpaUmVKlWIiorSK1u9em6XiyJFihAWFqYrc/bsWSIjI1m5cqWujCRJaDQabty4QcmSeccsTZw4kfHjx+ttK1XjSyLeGWjwd3qnvB092+beZP+w5JHBcsZkay1j2Cfu3IvNYe3OpNc6Vp0qTnze2Vv3/Tez7rxuvHxtP5Co+/9b97NISFLx3SB/vNwe8uBxwROaGFt6egYTp/3M4C8+Q5nPWIHnbd25mwZ1a2FllX+r59tmY2vHpBlLycxM5/zZk6xYNBNPLx/CI7QtJds3/sm1yxcYMvoH3Ny9uHThDEvmasewRZSrbOL0L+/S8T8pFlYLe6Xxu6K+rgOb53P++Ba6DV+G5ZPxUfdvnufYzuX0HrfW6C2CxmJja8+4aavIykznYuRxVi+ZhruXLyVKV+JmdBQ7N61m7NSVZpsf4NiO+Vz+dwsdv1z2RsamGVKzvD292uVe8yctevjGf6attYzhPT24G5vDnzsS3/jPe1lHt88n6uQWOg18e+///2cRxYOJKJ7b2l2meDAfDBzL+p376f1B7iRYFUuFsezH0SQlp/L3rgOM+mkei74fme+4uLfFMjAMuzrvkrpxOTl3r6Mo4oFDiw+xS0kkfe9Gk2YTTE9U2MyIQqFg586dHD58mB07dvDzzz8zatQojh079kZ+XmpqKr1796Z///55XitWrJjBfUaOHMmgQYP0tvUcF5vvz/j3YgbXbufOcmhpob25UTooSHzmiavSUcHN+4YrJclpatRqSdc6p9vHQU7ic61WNtYyRvTwICNLYtqyR6hfs2vM8bOpXLmR+yT1aX5nJwUJybmj5Z2dFFy/k5Vnf4DkVBVqtYTLc/mdnRQkJOU/S9rlG9ouRN4eVq9dYVM6OSKXy0lI0K/AJiQmUsTFOU/5+w8e8ODhQ0Z9mzvhxdOuuQ1bdWDp3Jn4euc+0Yu8cJE79+4zZvigPMcyBicnZ+RyBUkJ+l1+kxLjcXbJO2nEU3K5HC8fbVeXgKDi3L9zi7//WEZ4RAWys7JYvXwug76aSIXK2ocf/oEh3Lp+lU3rfzNahc3G3gWZXEFGqv5T6fTUx9g5ur328VMS7nH36hGadPn5tY9liJ2jC3K5gtRk/fypSY9xcCo4/6Gtizi4eQFdhi7Gq2iYbvutK/+SlhLHT0Pq67ZpNGq2r57M0R1LGTgl/3GV/5Wjo/bcSU7Sz5+cGIfSOf/8crkcT++iABQLDCPm7g02r11CidKVuHLxNClJ8QztlTtrpEaj5vdff2Lnxt/4cb7hcZX/la2D9txJS9HPnpYSh/0L3vsT/yzi+I75tO+3BHffErrt9k7ayVHSk+NwUOZO9JKeEoeHX4k8x/mvTl5M5+q03Guh7prvqH+9VjoouHk/2+AxdNd8h+eu+Y6KPL0bbKxljOzlQWamhqm/Pnzta/6z7J6+/8+d++nJL37/j+1cxNHt8+k4YIlR3te3ISv2Mdae+r+XtacbOUkpaDKzyH6cgEalwtrD9bkyrmQ9MG7rrLOTAwq5nPhE/dakhMQUXAuYJOtZFhYWFA8syt0H+g8NbG2sKerlQVEvD0oXD6Jd/6/ZuPsQXds0y+dI/50mPQVJrUbuoF8JlDso87SQPWXfoC2ZZw6T+e9+ANSxd0mzssaxVVfS9216pWMWJhox6UiBxBg2MyOTyXjnnXcYP348p0+fxsrKivXr9afUDQ4OxsrKikOHcgfe5uTkcOLECcLD9ac4P3r0qO7/ExISuHLliq7lrEKFCly8eJGQkJA8X/m1klhbW+Pk5KT3VVB3yMwsidg4le7rbmwOCclqSofmdre0tZYRXNSaq7cMV3jUarhxL5vSIbn7yGRQKsSGq7dyP/BtrWWM7OmBSi0x5ddH+c4+9l9kZGmIeZSj+7odk018koqyJXKn37e1kVM80JZL1zMMHkOlhmu3MylTMncfmQzKlrDncj77AAQV1f6+BVXqXpalpSXFQ4I4FZk7Zkej0XDq7DnCw8LylC/m58uiWdNYMHOK7qtGlUqUiyjFgplT8HDT/8DeumM3xUOCCA4MeO2shlhYWhIYEsb5yH/18l84e5LQMMNTsxuikTS6MZwqtQq1SoVcpn8ZlMvlSEYc+KiwsMLdtxR3r+WODZE0Gu5dO4qnf7nXPv6lE+uwdXDFv2Sd1z6WIRYWVngHlNKbMESj0XAj6ihFQ8rlu9/BLQvZt3EOHw1egG+gftfusjVa0uebv/ls/Hrdl6OzB+8068HHgxcaN7+lJf7BJYiKzJ2USaPREHXuBMFhebuc50fSSKienDs16jRn/E+rGTftN92XcxF3mrb6mEFjZxktu8LCCs+ipXQThmhzaCcQ8QnKv9vu8Z0LOLJ1Nu9/vhAvf/3fUenqh72TO7eeOWZWRioxN8/iE/j6XYENX/NVRDx3zQ8pVvA1//q9bL19ZDIoHWKjt4+ttYxRvTxRqbW9N4xxzX+WwsIKr2Kl9N4rSaPh5uUj+Bbw/h/bsYDDW2bT/ouFePu//DlmaolHz+BaX3+GX7cGNUg4egYAKSeHpFMXcKv/zGQpMhmu9aqTeNS44x+fTrl/4vwl3TaNRsOJ81FEFH+57pdqjYbo2/dwdSm4gidJGrJzjNyLRa1Gdf8mVkHP3JPJZFgGlSTnzjXD+1ha5R1QJT3zWfQqxxT+Z4gWNjNy7Ngxdu3aRePGjfHw8ODYsWM8evSIkiVLEhmZO7Odvb09ffr0YejQoRQpUoRixYrxww8/kJ6eTo8ePfSO+c033+Dq6oqnpyejRo3Czc2N1q1bAzB8+HCqVavGF198Qc+ePbG3t+fixYvs3LmTWbOMd9PxvK0Hk2ldX8mDxyoexqto31hJQrKakxfSdWVG9fLgxIV0dhzWjkfYfCCFPh1cuX43m2t3smhW0xFrKzn7Tmpff1pZs7aSMXVVHLbWMmyttU92k9M0Rh1UumFXPB2bu3H/YTaxj3P4qJU78Ykqjp7JnVJ3wsBiHDmdwua9CQD89U8cA7v5cO1mJlduZtCqQRFsrOT8czgRAC83S+pUUXLyfCopaWoCfK3p2cGT81fSuHnP8E3Nf9W+9XtM+mkWYSHBlCgewtq/N5OZmUXThvUAmDhtJm6urvTq2hkrKysC/fVbWR3stRXO57enpaez79ARPuvRxSg589Oi9QfM+WkCQSElCCkezta/fycrM5M6Dd8FYPa0b3BxdadT1z4A/PXHMoJCSuDp7YsqJ4fTJw9zcM82PumjHeNpZ2dPydLlWblkFlbW1ri5exF1/jT792zl4x55W51fR9na3dj9+wjc/UrjWbQMkQeWkpOdQYnKbQHYtWo49koPqjUfDGgnO0iIjdb+vzqHtKRYHt+LwtLaDqWbv+64kkbDpRPrCavUGvkbnGWxRuNurF84At+A0vgGleHIjqVkZ2VQvqY2/7oFw3F09qBRe23+A5sXsOevmbTrPQVnN19SkrRdoa2s7bC2scfOwQU7B/2u3gqFBQ5KN9y8jb8eUpOWH7Fw5lgCgksSGFqanZt+Iyszg5oNWgKwYMYYXIq40+7jfgBsXruYgOBw3L38UOXkEHnqIEf2bebj3iMBcHByxsHJOU9+pYsb3r4BRs1eqUF3ti4bjmex0ngHlOHf3UvJycqgdDXte79l6TAcnD2p3Ur73h/bMZ/Dm2fSottUlEV8SXvy3lta22FlY49MJqNCvS4c3TYHFw9/lK5+HNo0AwelByFlGxo1+1NbDqTQpoGSmEfaa37Hps4kJKs4cT73mv91bw9OnM9g+yHtdXTzvmT6fuBG9N1som9n0byWE9ZWMvaeyL3mj/rUEytLGbOWPsbWRoatzZNrfqrxrvmVG3Rn89LheD15/08+ef8jqmvf/02/DsPR2ZM6rbXv/9Ht8zm4aSbvdZ+K0tWX1GfOfSsb7TU0Iy2R5PgYUpO0LT/xsdqxoPZObjgojbc8hMLeDvuQ3Ou1XaAfTmVLkB2fROadGMImDMLG15Oz3bVra96avxr/vp0pMXEod35di1u9ani3b8aJlr11x7gxfQllF08m8d/zJJ2IJKB/VyzsbbmzdJ3Rcj/V6d1GfPvLEkoG+RMeEsjvW/4hMyubFnW1PSLGz1qMexFn+n6o/bdY9OcmSocG4uflQUpaOis37ODBo3haNagJaCck+XXdFmpVKouri5KklFT+3LaHR/GJNKheKd8cryrj0A4c3+9Jzv2bqO5ex7ZGY2RW1mT+q10iwfH9nmiSE7XrrAHZl89gW6MJqphbui6R9g3akHX5rK4i96JjFmaGJtgTcokKmxlxcnJi//79TJ8+neTkZPz9/Zk6dSrNmjXj999/1ys7adIkNBoNH3/8MSkpKVSqVInt27fnGe82adIkBgwYwNWrVylXrhwbN27UtZ6VKVOGffv2MWrUKGrVqoUkSQQHB9OxY8c3+ntu3JuCtZWcnu8Xwc5GzuWbWUxa9FDv6ainqwWO9rndYY6eTcfJXk67xk8WUb2fzaRFD0lK1T59CvC10s0eNmOEj97P6zfxHo8T9LvRvI612+OwsZLxxUfe2NvJuXgtg7Ez7+itweblZonTM915Dp5MQenwkM4t3XFxUnD9bhZjZ97WdRFSqSXKlbSjZQMXbKzlPI5XcfhUCr9vMV43k3q13iExKZklK1eTkJBIcFAAk8eP0nWJfPjocZ7WppexZ/8hJEmifu2aRstqSPVaDUlOSuTPlQtITIjHPyiUEeOn6bpEPn4Ui+yZ/FmZGSyZM4W4uIdYWVnj4+fP54PHUr1W7k1p/2HfsHrpHGZNGUdqajLu7l50/Lg3DY28cHZIueZkpMVzYvvPpKc8ws2nJO/2XKDrEpmaeF9vLFRa8kP+mJ6b4ey+xZzdtxifoMq06rNct/3u1cOkJt7XVfzelNJVm5OWEs/uv34mNekRXsVK8vGgBTgotfmT4vTzn9yzCrUqh99/GaB3nLqtPqde635vNKshVWo2JiU5gb9WzyUpIY6igcUZOOZnlM7aluL4Rw+QP5M/KzOT5fMnkfDk3PHyDaDXlxOoUrPxW89eomJz0lPiObRpJukpj3D3LUm7zxfquuQlJ8TonfdnD6xGrcphw0L9hw7Vm3/BOy20732VRr3Iyc5gx29jyMpIxje4Iu9/vvCNjbPasCcZaysZn7Zzxc5WzuUbmUxc8Pw13xJH+9yHU0fOpuPkkECHJs44O2q7T05cmHvND/TLvebPHOmr9/O++O4uj4x0zS9ZqTnpqfEc3DTzycLZJenQ75n3P17//T+9X/v+/7VA//1/p8UX1HxX+/5fi9zNlmUjda9tWDQwTxljUFYsTfVdudeL8ClfAXBn2Toie4zE2tsd26K5Y7Qzbt7lRMvehE8dSUC/LmTefcC53l/r1mADiPljK1buRSg+tr924eyzURx/tyfZz01EYgyNalQmMTmFBWs2EJeYTGiAHz991V83Vf+Dx/F6152U1DQmzltOXGIyjvZ2lAgqxvwJwwn0094TyOVybt5/wJapR0hMSUXpaE/J4ADmjh9GUFEfgxleR9b548jsHbFv0Fq7yHXMbZKWTkNK03ZflDu76rWope/dCBLYN2yL3MkFTVoK2ZfOkPbP2pc+pvC/SyaJKu3/pL1791KvXj0SEhJwdnZ+oz+r07Dbb/T4b1pq0ptdd+5NmzvYyP2A3qJYjP8h+Tbtv5T/+LnCwOv1h9CZTFHnwv13e/G+g6kjvJZ/tuU/O6u5a9Lc/8WFzJhnk7zd2AuTGifnmzrCa1H98aupI7wy9wlLTB0hX8Pn5z9E5E2b/KnhZZnMiWhhEwRBEARBEATBZCQjThj0v0hMOiIIgiAIgiAIgmCmRAvb/6i6deuKAZyCIAiCIAiC2dOIe9YCiRY2QRAEQRAEQRAEMyVa2ARBEARBEARBMBnRK6xgooVNEARBEARBEATBTIkKmyAIgiAIgiAIgpkSXSIFQRAEQRAEQTAZjUZ0iSyIaGETBEEQBEEQBEEwU6KFTRAEQRAEQRAEkxFzjhRMtLAJgiAIgiAIgiCYKVFhEwRBEARBEARBMFOiS6QgCIIgCIIgCCYjiUlHCiRa2ARBEARBEARBEMyUaGETBEEQBEEQBMFkNGLWkQKJFjZBEARBEARBEAQzJSpsgiAIgiAIgiAIZkp0iRQEQRAEQRAEwWTEpCMFExU24bXZK+1MHeG1ODjbmzrCazmdqjR1hFeWrSrcjfxp6RpTR3gtcUkKU0d4ZakZDqaO8FpiYlWmjvD/1uVrGaaO8FranJxv6giv5XClT00d4bVUivzN1BGE/4dEhU0QBEEQBEEQBJMRLWwFK9yPtwVBEARBEARBEP6HiRY2QRAEQRAEQRBMRjSwFUy0sAmCIAiCIAiCIJgpUWETBEEQBEEQBEEwU6JLpCAIgiAIgiAIJiMmHSmYaGETBEEQBEEQBEEwU6KFTRAEQRAEQRAEk5Ek0cJWENHCJgiCIAiCIAiCYKZEhU0QBEEQBEEQBMFMiS6RgiAIgiAIgiCYjEZMOlIg0cImCIIgCIIgCIJgpkQLmyAIgiAIgiAIJiMmHSmYaGETBEEQBEEQBEEwU6KFTRAEQRAEQRAEkxELZxdMtLAJgiAIgiAIgiCYKbOvsD148IBGjRphb2+Ps7PzG/1ZdevW5csvv3yjP8PUbt68iUwm48yZM6aOIgiCIAiCIAjCC5h9l8iffvqJmJgYzpw5g1KpfKM/a926dVhaWr7RnyFAvYo2NKlmi9JBzp1YFat2pHHjvirf8hVLWNG6jh1uzgpi49Ws3Z3Guegc3esta9lROdyKIk4KVGqJWw9UrN+bXuAxX1XditY0qfps9nRuxhScvVUdO9yUcm32Pemcfyb7e7VsqRxuTRFHuS77X/sy3kh2gIM7fmPvxiWkJD3Gp1gYbbp9RbGQMgbLHt31BycPbODB3WsA+AWG07zjAL3yKYmP2bRqGlciD5ORnkJQiYq06TYKd2//N5L/yM6V7N+ymNSkx3gVLUHLLqMoGmw4//E9azh9cAMP7l4FwDcwnCbtB+Yp//BeNNt+n8r1SyfQqNV4+AbzUf8ZOLv5GDV7xRAZ1UrKcLCB2ETY8a+G+/GGy7o5QZ0IOV5FwNlexo5TGk5c0e8uUiFERoUQGc722u8fJcHBCxqiY4waW+fsgZWc2r2I9JRHuPmUoM77o/HyN/zex8Vc5ejWmTy8c4GUhHvUaj2S8nW76ZXRaNQc2/Yzl09uIC3lMfZOHoRXaUPlxn2RyWRGz39q30pO7FxEWvIjPPxK0KDDaLwDDOc/e3ANF479xeP72nPHs1gparcapFdekiQObZpJ5KE/yMpIxieoAo07jcPFI8Do2SsVl1GjpBwHW4hNgK0n1dyPM1zWXQl1y8jxLiLD2UHG9pNqjl3WP3feKSWjRFE5bk6gUsOdRxK7TmuISzF6dJ32TZQ0qOqAva2cyzeyWLgungePC77ONa7hwHt1lTg7KrgVk82S9fFE38kGwN5WTocmSsoUt8XNRUFyqoYT59P5fXsiGZnG7VpVPVxB7bIWONrKiImX+PtQNncf5f8zIgLlNK5siYuDjMfJEluP5XD5jkb3uoMtNKtiSXE/BTbWcCNGw9+HcohLfjNdwv7ctocVG3cQn5hEiL8fgz/pRKmQQINlN+09zITZv+pts7K0YP/K2brvF6zZwD+HTxAbl4ClhQVhQcX47IPWlA4NMmruIjUrETS4B8oKpbHx8eDk+32J3bCr4H1qVyF8yggcwkPJvBPDtYlzuLtsvV4Z/z4fEjSoB9Ze7iRHXuLCl9+SdOKcUbM/tW7LDlav30R8YhLBAcUY0Ksr4cVDXrjfrgOHGT91FjWrVOT7rwbrtscnJjF36SpOnIkkNS2dsqVKMKBXV4r6eL+R/G+T6BJZMLNvYYuOjqZixYqEhobi4eHxRn9WkSJFcHR0fOX91Wo1Go3mxQX/H6tc0ooODe3ZeCCdbxYlcuehmi8/cMLRzvANWrCvBZ+2ceTg2Sy+WZjI6SvZfN7eCR93ha7Mg3g1v21PY+yCBCYvSyIuScPATk445HPMV1WppBUdGtiz8WAG3y5O4u5DNV9+4Fhg9l6tHTh4JpNvFiVx5ko2n7dz1MseG6dm1fY0xi1M5IflycQlafjyA0ejZwc4fWQrG5b/QOP3+zLw+z/w8Q9j/qTepCQZvvO7FnWC8jWa0+frxfQbvxJnVy/mTfyUpPhYQHvDumRaf+If3qX7kJ8ZNPFPXNx9mPd9D7Iy042eP/LoFjb/NpkGbT7ni2/X4l0sjMU/9CI1n/zXo05Qpnpzen31K33GrsK5iDeLf+ipyw8QF3ubuRM64+4dyKdfLWXA939Rv3UfLCytjZq9ZFEZDcvLOHBeYtF2DQ8TJT6oK8cunx9jaQEJqRJ7zkqkZhj+EEtJl9hzVsOi7RoW79BwK1aifU3tTbixXTm1hQN/TaRq08/5YMh63HxL8PfcHqSnGH7vVTkZKF39eOe9wdg5uRss8++uBZw7tIo674/h4xFbeOe9Ify7eyFn9y83ev5LJ7ewd+1EarT4nC4j1+PuW4I/fu5BWj7571w9RslKLej45TI6D12Nk4s3f/z8CSmJuefO8Z0LOLV3OY06jaPz0DVYWdvyx889UOVkGTV7uL+MxhXk7DunYf4WNQ8SJDrXU+R/7iggIRV2ndGQks+54+8h4+QVDYu3q1mxS41CDp0bKLBUGCz+2lrWc6JZTScWro1n1MwHZGZLfNXLA8sCHhlXL2tHl5ZFWLszkRHTY7h1P5uvenng5KC9bSmiVODipGD5pgSGTIlh9u9xlC1hy2cdXI2avUyQgnerW7LrXxUz12URE6ehR3Nr7G0Ml/f3lNOpgRUnLqmZuS6LizfVdGlshadL7jW9S2NrijjJWLojixlrs0hMlejVwqrA9+NV7Tx8ghnL/qBnu3dZOvlrQv2L8uV3M4hPSs53H3tbGzbP/1H3tf6XSXqvF/PxZPAnnVg5ZSzzvhmGt7sbAyZMJyHZuDV+hb0dyZGXOd9//EuVtw3wo/KGecTtPcbBSq248fNSIuZNwK1RTV0Z7/bNKPnjSK5O+IWDVdqQEnmJqpsXYeVexKjZAXYdPMIvi1fQ7YO2LJz2HSEBxRgyfhIJiUkF7hcT+4jZv/5GmfASetslSWLUxKncj33I918NZtFP3+Pp7sagsRPJyMw0en7BvLxWha1u3br079+fYcOGUaRIEby8vBg3bpzu9du3b9OqVSscHBxwcnKiQ4cOxMbG6h1jzpw5BAcHY2VlRVhYGMuX535YBwQEsHbtWpYtW4ZMJqNbt24AyGQy5s2bx7vvvoudnR0lS5bkyJEjXLt2jbp162Jvb0+NGjWIjo7WHSs6OppWrVrh6emJg4MDlStX5p9//snz+zzbJTIhIYEuXbrg4uKCnZ0dzZo14+rVq7rXf/31V5ydndmwYQPh4eFYW1uzcOFCbGxsSExM1Dv2gAEDqF+/vu77gwcPUqtWLWxtbSlatCj9+/cnLS1N9/rs2bMJDQ3FxsYGT09P2rVrp3vtzz//JCIiAltbW1xdXWnYsKHevgsXLqRkyZLY2NhQokQJZs/OfTL2vISEBDp37oy7uzu2traEhoayZMmSfMu/rkZVbTlwJpNDkVnEPFazYksq2SqJmmUNf/o1rGLL+egcth/NICZOzd/70rn1QEX9Srnlj1/IIupmDo8TNdx/rOb3nWnY2cjx8zDup1+jKjYcOJPF4afZt6aRrYJ3yhq+c2pQ2YYL0TnsOJbJgzg1f+/P4PYDFfUrPpP9YrZe9jX/pD/Jbvw7p/2bl1Ktfjuq1G2Dl18I7/cYi6WVDcf3rjNY/qMvfuCdxp3wDSiJp28QHT79BknScPX8UQAeP7jFratnef+TMRQLjsDDJ5D3PxlDTnYWpw9vMXr+A1uXUrlueyrVbounbwitu4/DytqGk/sN5/+g749Ub/ghPv4l8fAJom3Pb5E0GqIvHtGV2fHHdMLK1qZZp6H4BITj6lmM8Ar1cVAa96avagkZZ6IlIm9IPE6GLSckVCooG2S4Yh4TD7vPSly8LaHK5xnQ1fsQHaO9OY9Pgb3nJLJV4Ov2Bir7e5dQunoHwqu+j6tXCPXbj8fCyoaLx9YaLO9ZrAw1Ww2neIUWKBRWBsvE3DhNUOkGBJaqi5OrH6HlmlIsrCaxtyONnv/k7iWUeacDEdXfx807hMadxmNpZcP5w4bzv9t9KuXrdMazaElcvYJp8tEEJEnDrUvac0eSJP7dvYxqTfsQWrYhHn4laN71B1KTHnL17D8Gj/mqqpeQc+qaxNnr2nNn83ENOWooH2z43/l+PPxzWsOFWxJqteFj/rZHw9nrEo+StK29fx/R4Gwvw9u4p71O81qOrPsniZMXMrgdk8Mvqx/j4mRB5dJ2+e7Too4Tu46lsPdEGvdic1i4Np7sHIl6lR0AuPMgh2nLHnPqYgaxcSouXMvk962JVAy3Q27ER9G1ylhw/JKak1fUPEyUWH8ghxwVVA4z/PnyTmkFV+5o2B+p4mGixI6TKu4/lqhRSlveTSnD31POXwdzuPtI4nGS9piWFjLKBRv/ur9q005aNajJu/XeIdDPh+G9OmNjZcWmPYfy3Ucmk+HqrHzmS/8pUJOaValSJhxfT3eCivrwZZf2pGVkcu3WXaNmf7R9P1fGTif275f7m/L/9AMybtwlathkUi9d59bslTxYu53AAd10ZQK/7M6dRWu4u3QdqVHRnOs7FnV6JkW7vW/U7ABr/t7Cu43r0bxBXQKK+jG4Tw9srK3ZvGtfvvuo1Rq+/ekXun/wPj6e+o0Ud+8/4MLlawz+7BNKhgZTzNeHwZ99QlZ2NrsOHMnniIWHRpJM9lUYvPZlbenSpdjb23Ps2DF++OEHvvnmG3bu3IlGo6FVq1bEx8ezb98+du7cyfXr1+nYsaNu3/Xr1zNgwAAGDx7M+fPn6d27N927d2fPnj0AnDhxgqZNm9KhQwdiYmKYMWOGbt9vv/2WLl26cObMGUqUKMGHH35I7969GTlyJCdPnkSSJL744gtd+dTUVJo3b86uXbs4ffo0TZs25b333uP27dv5/m7dunXj5MmTbNiwgSNHjiBJEs2bNycnJ7dLW3p6OpMnT2bhwoVcuHCBzp074+zszNq1uTcCarWa33//nc6dOwPaymPTpk15//33iYyM5Pfff+fgwYO6vCdPnqR///588803XL58mW3btlG7dm0AYmJi6NSpE5988glRUVHs3buXtm3b6tavWLlyJWPGjOG7774jKiqK77//ntGjR7N06VKDv+Po0aO5ePEiW7duJSoqijlz5uDm5vZy//j/kUIO/t4WXLyR+/5JQNSNHIL8DH/4BflaEHUjW2/bhes5BPsa7rqqkEPt8jakZ2q4G2u8boVPs0fdzM2izZ6db5YgXwsu3szR23bheg5BvoZ/V2126yfZ87nTekUqVTZ3b1wktHR13Ta5XE7x0tW4dfXsSx0jOysTtUqFnYO2a7IqR/teWFjl3pDL5XIUFlbcuHzKiOm1+e/fvEBIKf38waWqc/vamZc6Rk5WJmq1Clt7bX6NRsOls/tw8wpg8Q89mdD3HX4Z25ELJ417wy2Xg7cL3IjV/1C4ESvh52qcypVMBuHFZFhawL3Hxv3wUauyeXj3AkWL18j9eXI5RYvXIObm6Vc+rndgee5cOUrCwxsAPLp3ifvX/8W/ZO3XzvwstSqbB7cv4B+mn9+/RA3u33i5/KrsDDTPnDtJcXdJS36Ef4ncY1rbOuIdUJb711/9PXmeXA7eReDGg+fOnQcSfkasmFs/uYRlGLdxEACPIha4OFlw7mqGbltGpsS121mE+ht+2KVQQJCvFeeu5LYaSBKcu5qZ7z4AdjZyMjI1GKuji0KufQBy9W7u9VgCrt1TU8zT8O2Tv6eca/f0r99X7uaWt3iyW84zH08SoFJLBHgZt9NTjkrF5eu3qRxRUrdNLpdTOaIk565cz3e/jMwsWvcdQcs+wxn6wy9cv3O/wJ/x1z8HcLCzJdTfz6j5/yvnauV4vFu/4vJo50FcqpUDQGZpibJCKR7vOpxbQJJ4vPswztXKGzVLTo6KK9E3qFSmtG6bXC6nYtnSXLh8Nd/9lq5Zh4vSiXcb1cvzWvaTe0+rZ4buyOVyLC0siLx42YjpBXP02k0QZcqUYezYsQCEhoYya9Ysdu3S9jE+d+4cN27coGjRogAsW7aMUqVKceLECSpXrsyUKVPo1q0bffv2BWDQoEEcPXqUKVOmUK9ePdzd3bG2tsbW1hYvLy+9n9u9e3c6dOgAwPDhw6levTqjR4+mSZMmgLZFq3v37rryZcuWpWzZsrrvv/32W9avX8+GDRv0KnZPXb16lQ0bNnDo0CFq1NB+KK9cuZKiRYvy119/0b59ewBycnKYPXu23rE/+OADfvvtN3r06AHArl27SExM5P33tU9wJk6cSOfOnXWteaGhocycOZM6deowZ84cbt++jb29Pe+++y6Ojo74+/tTvrz2YhITE4NKpaJt27b4+2vHCUVEROh+9tixY5k6dSpt27YFIDAwkIsXLzJv3jy6du2a5/e8ffs25cuXp1KlSoC2VfNNcbCTo5DLSE7T/zRNTtPg5Wq40qN0kBssr7TX/2ArE2LJp22csLKEpFQN035Lzrcr2atllz3Jrn/M5DQJr3xuupUOclIMZXfIm71Xa8cn2SV+WmXc7ABpyYloNGocn2s5clC68vD+jZc6xubfpqJ08dBV+jx8AnFx82bLqum06zkWKxtb9m9ZRlL8A5ITHxk1f3qKNv/zLV+OTq48esn8W3+fgpOLByGltH/PaclxZGems2/jQhq360/TjoO5EnmQlTP703PkrwSVrGKU7HZWIJfLSHuux0paJri+ZvdFdyV0ayjHQgHZKvjzoIbH+fd0eiUZaQlIGjV2jvrvvZ2jKwmx+d/0vUilBp+SnZnK8onNkMsUaCQ11ZsPpESllq8bWU9G6pP8Tnnzx79k/n3rp2Cv9NBV0NKStOe3/XPHtHdyJS35sRFSP8lo/fTc0b8epGWCm5PxKmxNKsm5/VDb4mZszo7aVqOkFP1rYVKqWvfa85zsFSgUMpJS9Ss+SSlqfDwMf1Y42slp20jJP0dTjZBay84GFHIZqRn621MyJNydDVeuHGxlpBgo72ir/fd6mCiRkKKhWRUL1h3IIVsFNSMscHaQ42Rn3Ot+YnIqao2GIs+1kLk4O3LzvuHBrv4+nozq05UQfz9S09NZuWEnvb6exKpp4/FwddGVO/hvJKOnLyAzOxs3ZyUzvx6Is9OrDykxBmtPN7Ji9f/+smIfY6l0RG5jjaWLErmFBVkP454rE4d9mHHH3yWlpKDWaHBx1p97oYhSye27hivAkRcvsfmfvSz66XuDr/v7+eDp7sb85asZ0rcHNtY2rNm4hUdx8cQlJBg1v2B+jFJhe5a3tzcPHz4kKiqKokWL6iprAOHh4Tg7OxMVFUXlypWJiori008/1dv/nXfe0WtJe5mf6+npCehXXDw9PcnMzCQ5ORknJydSU1MZN24cmzdv1lV6MjIy8m1hi4qKwsLCgqpVq+q2ubq6EhYWRlRUlG6blZVVnvegc+fOVKtWjfv37+Pj48PKlStp0aKFbpbLs2fPEhkZycqVK3X7SJKERqPhxo0bNGrUCH9/f4KCgmjatClNmzalTZs22NnZUbZsWRo0aEBERARNmjShcePGtGvXDhcXF9LS0oiOjqZHjx706tVLd2yVSpXvhC19+vTh/fff59SpUzRu3JjWrVvrKqiGZGVlkZWl/xhWrcpCYWHcMT//1aVbOXyzMAEHWzm1ytvQu60j3y9JJCXd/Ju6L93K4ZtFiTjayqlVzprebRz5/tcks8q+6+8FnD6ylb6jf8XSSvtvrbCwpOvAGayZP5rRvWoglysILV2NEuVqaR+Hm5G9GxcQeXQrvb5aqsv/tFU6vGJ9ajbrBoCPf0luXz3Nsd2/G63C9ibFpcDC7RqsLaFEURnvVZWzYrfxK21vwtUzW7n870aafjyVIl4hPLoXxYH1E3FQelCyShtTx9M5tn0+l/7dQscvlxl9bKM5aF5ZjodSxpIdxmnVr1nenl7tcscDTVr00CjHLYittYzhPT24G5vDnzsS3/jPex0aCZbvzKZdbSvGdbNFrZG4dk/DpdtqjN+Z+b+LKB5MRPFg3fdligfzwcCxrN+5n94ftNJtr1gqjGU/jiYpOZW/dx1g1E/zWPT9SIoo38Ag2v8H0jMymDB9DkP79sTZyfB7aGFhwYThXzJ51gJafPQpiictdlUrlDVYvrARk44U7LXb35+fVVEmk72ViTee/blPZxQztO1pliFDhrB+/Xq+//57Dhw4wJkzZ4iIiCA7W7+73X9la2ubZ0azypUrExwczOrVq8nIyGD9+vW67pCg7Z7Zu3dvzpw5o/s6e/YsV69eJTg4GEdHR06dOsWqVavw9vZmzJgxlC1blsTERBQKBTt37mTr1q2Eh4fz888/ExYWxo0bN0hN1T5ZXLBggd6xz58/z9GjRw3mb9asGbdu3WLgwIHcv3+fBg0aMGTIkHx/34kTJ6JUKvW+zu57cQUbIDVdg1oj4fRc65iTvZykNMPnTFKq5qXKZ+fAwwQN1++rWLo5FY0GapbLZ1T4K0hNl55k1/+3drLP2+r2bHZHQ9lT82Z/9DT7ljTUGqiZz7i4V2Xv5IxcrsgzwUhqUhyOzgV3gd2zaQm7Nyyi98gF+PiH6b1WNKgUgyetY8Kio4yds5dPR84nPSWRIh7G7Rpj56jN//wEIynJL86/f/Ni9m1awCfDFuJdLDe/naMzcoUFHj7BeuXdfYJIijPeVIvp2aDRSHkmKbC3gbQMw/u8LI1GO4btQQLsjZR4mAiVixv3ts/W3gWZXJFngpH0lDjsnF69+/TBDT9QscGnFK/QAjefMEpWbk25ul05+c+8142sx9bhSf7kvPntX5D/+M5FHNsxn/b9FuHhlzsBgL1SO5FK2nPHTEt+8TH/i/Ssp+eO/r+pvQ1GaYVvWklOqK+MZf+o87QKvaqTF9MZNi1G9/W0l4HSUf9aqHRQkJhiuJKYnKZGrZZQOui3wCkdFSQm6+9jYy1jZC8PMjM1TP31IWoj3n6kZ4JaI+Fgq7/d0VaW7wO11AwJR0Pln/n3uvdYYsa6LMYuyeC7FZks3pqNnY2MuBTj3rA6OzmgkMuJT9R/gpOQmIKrs+GHuM+zsLCgeGBR7j7Qr3jb2lhT1MuD0sWDGNWnKwqFgo278x8X9zZkxT7G2lP/78/a042cpBQ0mVlkP05Ao1Jh7eH6XBlXsh4Yr2UcQOnoiEIuzzPBSHxSEkVcnPOUvxcTy4OHjxj53RTqtf2Iem0/YvveAxw6cYp6bT/iXox2/oewkCAWT5/IlpULWb9kNlPGjiA5JRVvzzc7KZ+g75dffiEgIAAbGxuqVq3K8ePHCyz/xx9/UKJECWxsbIiIiGDLlv8+zv+NzRJZsmRJ7ty5w507d3TbLl68SGJiIuHh4boyhw7p/4EfOnRI97oxHTp0iG7dutGmTRsiIiLw8vLi5s2bBeZXqVQcO3ZMty0uLo7Lly+/VL7OnTuzcuVKNm7ciFwup0WLFrrXKlSowMWLFwkJCcnzZfVkPJCFhQUNGzbkhx9+IDIykps3b7J7925AWxl95513GD9+PKdPn8bKyor169fj6emJj48P169fz3PcwEDDU/gCuLu707VrV1asWMH06dOZP39+vmVHjhxJUlKS3lfZOgNe+H4AqDVwK0ZFyYBnKtZAiQBLrt81PN7s+j0VJQP1Jy0ID7Qk+l6OwfK648rAUmG8G9f8spcMyD/L9Xv65QFKBlpy/V7BY+tkMrCwMO5Nt4WFFX6B4boJQ0D7MOPqhWP4h+b/dG73hkX8s24un46YR9Hg0vmWs7VzxMGpCI9ibnHn+gVKV6qfb9lXze8TUIroi/r5oy8cpVhIuXz327dpIbv/nkP3ofPxC9LPr31PSvPogX6XyscPbhp1Sn+NBmISIMBT/980wFPG3Tjj3qDJZNrxP8aksLDCw68Ud67mjg2RNBruXDmCd8Crj/tQZWfmedglkyl0LZ/GorCwwqtYKW5d1s9/6/IRfALzz39sxwKObJ1Nuy8W4uUfofea0tUPeyd3bj9zzKyMVGJunsUnyHhjYTQa7QQ0gV7671Ogl4y7rzlWsWklOSWKyli+S01i2ovLv6zMLInYOJXu625sDgnJKiJCc59Y2FrLCClmzdVbhgfNqdVw/V623j4yGZQOsdHbx9ZaxqhenqjU8MOSR3rjwoxBrdFWrkJ8c/+oZECIj4LbsYZrhrdiNQT76v8RhvrKDZbPzHnaNVqGn5uMizeNO3b56ZT7J85f0m3TaDScOB9FRPGX6wKo1miIvn0PV5eCK3iSpNGNsTKVxKNncK1fTW+bW4MaJBw9A4CUk0PSqQu41c8dC41Mhmu96iQeNd7YUwBLSwuKBwfyb+QF3TaNRsOpyAuUCgvNU76Ynw+/zpjMop8m6r7eqVyB8qXDWfTTRDzcnhvOYG+Hs9KJO/djuBx9nZpVKho1vylIkmSyr//i999/Z9CgQYwdO5ZTp05RtmxZmjRpwsOHhnsTHD58mE6dOtGjRw9Onz5N69atad26NefPn/9PP/eNrcPWsGFDIiIi6Ny5M9OnT0elUtG3b1/q1KmjGy81dOhQOnToQPny5WnYsCEbN25k3bp1eWZvNIbQ0FDWrVvHe++9h0wmY/To0QW2BIaGhtKqVSt69erFvHnzcHR0ZMSIEfj6+tKqVat893uqc+fOjBs3ju+++4527dphbZ3bYjJ8+HCqVavGF198Qc+ePbG3t+fixYvs3LmTWbNmsWnTJq5fv07t2rVxcXFhy5YtaDQawsLCOHbsGLt27aJx48Z4eHhw7NgxHj16RMmS2kHF48ePp3///iiVSpo2bUpWVhYnT54kISGBQYMG5ck5ZswYKlasSKlSpcjKymLTpk26YxlibW2t97sAKCxevpVy57EMPmnpyK0YFTfuq2hYxQZrSxmHIrUDfD55z4HEFA3r9mqnhf/neAZDP1bSuKotkdeyqRJuTYC3Bcu2aFsTrSyhxTt2nL2STWKqBkc7GfUq2eLiKOdklHFH0O88nskn7zlwM0aty25lKeNQZJYue0KKhvVPsu86kcmQj5xoVMWGc9HZVH6SffnWtNzsNWw5ezWHxFQNDnYy6lW0wcVRzr9Rr9fya0jtFl1ZPecrigaVolhIBPu3Lic7K4MqdbTdz36bPRKliwctOg0EYPeGhWz7YxYfffEDLu4+unFp1jZ2WNtoF/86e3Q79k4uuLh6E3PnKn8tnUjpyvUJK/OO0fPXataVP+aPxDewNEWDIji0fRnZWRlUrK3Nv2bucJxcPGnaUXue79u0gJ1rf+aDvlNwcfMl5Ul+q2fy127xCatmDSYwrBJB4VW5EnmQS6f30usrw5P0vKpjlyRaVpMREw/34yWqFNdOEBJ5XftB8V5V7biXvZHa7+VycH/SK0YhB0db8HTWjlNLeDJEp24ZGdExEsnpYGUBpfxl+HvAqr3G71ZSvm53dv42HM+ipfEsVoYz+5aiys4gvKp2rOyOFcOwV3ryznva9YLUqmziH2hn6dWos0lLiuXR3Sgsre1wdteOvQ0sVY8TO+fi6OKD65Mukaf3LqFUVePP1lapfne2LBuOl39pvP3LcHLPUnKyMihdXZt/86/DcHT2pHZrbf5jO+ZzaNNMWnSfilMRX1KfjFmzsrbDysYemUxGxfpdOLJ1Di4e/ihd/Ti4cQYOSg9CyzY0avYjlzS0ri7nfpyM+3ESVUvIsVTAmSfnTqvqclIyYPcZ7eeZXK4d2whPzh07GZ4uEtk5uedOs8pyIgJk/L5PTVYOutbfrBztumzGtuVACm0aKIl5pOJhvIqOTZ1JSFZx4nzu8h9f9/bgxPkMth/STg2/eV8yfT9wI/puNtG3s2heywlrKxl7T2h/CVtrGaM+9cTKUsaspY+xtZFh+6QlMjlVY7Re2QciVXSoa8ndRxruPtJQM8ICS0s4eUVbO+xQ15LkNIltJ7TfHzqvpvd7VtSKsODSbTVlQxT4ustZeyD38ygiUE5aJiSmSngVkfFeDUsu3NJw9Z7xeyd1ercR3/6yhJJB/oSHBPL7ln/IzMqmRV3tNXr8rMW4F3Gm74fav4VFf26idGggfl4epKSls3LDDh48iqdVA+3U+BmZWfy6bgu1KpXF1UVJUkoqf27bw6P4RBpUr2TU7Ap7O+xDium+twv0w6lsCbLjk8i8E0PYhEHY+HpytvtwAG7NX41/386UmDiUO7+uxa1eNbzbN+NEy966Y9yYvoSyiyeT+O95kk5EEtC/Kxb2ttxZani24dfRoVVzJs6YS1hIECVDg/lj41YyMjNp3qAOAN9Nn42baxF6f/wB1lZWBPkX1dvfwV77OfXs9j2HjuLs5ISnuyvRt+7w88Jl1KxSiSrlDa8pKRjftGnT6NWrl26ejLlz57J582YWL17MiBEj8pSfMWMGTZs2ZejQoYB2Do2n9/tz58596Z/7xipsMpmMv//+m379+lG7dm3kcjlNmzbl559/1pVp3bo1M2bMYMqUKQwYMIDAwECWLFlC3bp1jZ5n2rRpfPLJJ9SoUQM3NzeGDx9OcnLBAz2WLFnCgAEDePfdd8nOzqZ27dps2bLlpRbXDgkJoUqVKhw/fpzp06frvVamTBn27dvHqFGjqFWrFpIkERwcrJtB09nZmXXr1jFu3DgyMzMJDQ1l1apVlCpViqioKPbv38/06dNJTk7G39+fqVOn0qxZMwB69uyJnZ0dP/74I0OHDsXe3p6IiAi95QqeZWVlxciRI7l58ya2trbUqlWL1atXv/gNfUUnorJxsE+jVR07nOy1i09PX52s61boqlTofdBG31Ox4K8U2tS1o01dOx7Gq/nlj2TuP9LeVWg04O2qoEY7Rxxs5aRlaLgRo2LysiTuPzbuncfJqGwc7dJpVdtWl33G7ymkPMlexEmu96Qm+p6KhX+n0rrOk+wJan75M0Uvu5ebguplbHCwlZGWIXEzRsUPy42fHaB89WakJcez/c9ZJCc+xte/BL1GzNN1KUx8HKPX4nF45++oVTksnT5Q7ziN3+9Lk3afA5Cc+Ii/l/9AatJjnFzcqVirJY3afmb07ABlqjUnNSWBf9bOJCXpMd7FStJ96HwclU/yx8Ugk+V2Gji6azVqVQ4rZ+q3ADdo8zkN22onGipVqRGtu49l78b5bFz+Pe7egXTuP4OAMOM+rYy6o+0SWSdChr2NjNhEWL1XQ9qTezilvQyJ3HPH0RZ6Ns19Sl+9pIzqJeHWQ4kVu7U3dfY20LKaHAcb7Y32w0RYtVfDDf2VU4yieIXmZKTFc3TrTNKSH+HuW5JWvRdi56h971MS9N/7tKSHrJrSWvf9qT2LObVnMb7BVXi/n3bpljrvf83RLTPY++d40lPjsHfyIKJGR6o0+dzo+UtUak56ajyHNs18snB2Sdp9sVDXfTElIQbZM3PBn9mvPXc2LOivd5wazb/gnXf7AVClUS9ysjLY/tsYstKT8Q2uSLsvFhp9nNvFWxL21hrqltX+W8cmwG971LpJbJT2Mr3rjqMt9G6e+9FeI1xGjXA5N2Mllv2jva5ULq79Xbs20r8F+PuImrPXjV/h37AnGWsrGZ+2c8XOVs7lG5lMXPBQr0XM09USR/vcSs2Rs+k4OSTQoYkzzo4Kbt7PZuLCh7ou5YF+VroZI2eO9NX7eV98d5dHCca5hkZeV2NvC40rWeBop600L96SpZuIxNlBpveZdStWw6pd2TSpbEnTKhY8TpJYtiOb2IRn/o3sZLxb3UI7QUm6xKmranadMnLz4BONalQmMTmFBWs2EJeYTGiAHz991V83Vf+Dx/F61/2U1DQmzltOXGIyjvZ2lAgqxvwJwwn00/Y6kMvl3Lz/gC1Tj5CYkorS0Z6SwQHMHT+MoKLG65kAoKxYmuq7cpd6Cp/yFQB3lq0jssdIrL3dsS2au2B0xs27nGjZm/CpIwno14XMuw841/trHu88qCsT88dWrNyLUHxsf+3C2WejOP5uT7Kfm4jEGBrUrE5iUjKLV/1JfEIiIYH+TBk7giJPuqPGPorTu26+jLiERGYtXkFCUhKuLi40qVuTrh3aGj27KWhMOIbN0PwMhhoosrOz+ffffxk5cqRum1wup2HDhhw5YnhphSNHjuRpMGnSpAl//fXXf8ook4zd/6QQq169Og0aNGDChAmmjlKo9PzOuH2/37bnu2UVNq2avtxYBHOUrXpjvbLfiqg3cHP7Njkr39BKyW+BteHl3QqN+w/ezA3623Lh9D1TR3hlAcUL93ifEVULHi9j7g5X+vTFhcxYpcjfTB3hlXmWNN+ukx+Nyn/5iDctxHI+48frL9A+duxYvbWlAe7fv4+vry+HDx+mevXcrrXDhg1j3759esOonrKysmLp0qV06tRJt2327NmMHz8+z9rUBSncd0tG8rTb4IULFyhVqpSp4wiCIAiCIAiC8BYYmp/h2VY0c/DGukQWJlu3bqVLly60bNmSdu3amTqOIAiCIAiCIPy/Ycpp/Q11fzTEzc0NhUKRp2UsNjY2z3rRT3l5ef2n8vkRLWxox9IlJyezYsWKlxqfJgiCIAiCIAjC/x9WVlZUrFiRXbt26bZpNBp27dql10XyWdWrV9crD7Bz5858y+dHtLAJgiAIgiAIgmAyhWVKjUGDBtG1a1cqVapElSpVmD59OmlpabpZI7t06YKvry8TJ04EYMCAAdSpU4epU6fSokULVq9ezcmTJwtcQssQUWETBEEQBEEQBEF4gY4dO/Lo0SPGjBnDgwcPKFeuHNu2bcPT0xOA27dvI39m1uEaNWrw22+/8fXXX/PVV18RGhrKX3/9RenS+a9ta4iosAmCIAiCIAiCILyEL774gi+++MLga3v37s2zrX379rRv3/61fqaosAmCIAiCIAiCYDKSxvgLx/8vEZOOCIIgCIIgCIIgmCnRwiYIgiAIgiAIgsloTDitf2EgWtgEQRAEQRAEQRDMlGhhEwRBEARBEATBZArLtP6mIlrYBEEQBEEQBEEQzJSosAmCIAiCIAiCIJgp0SVSEARBEARBEASTkcSkIwUSLWyCIAiCIAiCIAhmSrSwCYIgCIIgCIJgMqKFrWCihU0QBEEQBEEQBMFMiQqbIAiCIAiCIAiCmRJdIoXXlpOlMnWE16JQFO7nFnXUO0wd4ZWlOvmYOsJreVSknKkjvJZAz2xTR3hlgQ4xpo7wWo5a+ps6wms5faTwnjuF/Zqv+uNXU0d4LZUifzN1hNdyssyHpo7wylrkXDZ1hHxpJI2pI5i1wn3VEgRBEARBEARB+B8mWtgEQRAEQRAEQTAZMelIwUQLmyAIgiAIgiAIgpkSLWyCIAiCIAiCIJiMaGErmGhhEwRBEARBEARBMFOiwiYIgiAIgiAIgmCmRJdIQRAEQRAEQRBMRpJEl8iCiBY2QRAEQRAEQRAEMyVa2ARBEARBEARBMBmNRiycXRDRwiYIgiAIgiAIgmCmRIVNEARBEARBEATBTIkukYIgCIIgCIIgmIxYh61gooVNEARBEARBEATBTIkWNkEQBEEQBEEQTEaSxKQjBREtbIIgCIIgCIIgCGZKtLAJgiAIgiAIgmAyYgxbwUQLmyAIgiAIgiAIgpkSFTZBEARBEARBEAQzJbpEmkjdunUpV64c06dPN3UU4O3maVDFjmbv2KN0kHMnNocVm1O4fi8n3/KVS1nTtr4jbs4KYuNVrNmRQuTVbN3rFUtaU7+yHQE+ljjYyRk9+zG3H6jeSPb6lW1pWsNOm/2BipVbU7hxP/+fVSncmjb17LXZ49T88U8q565psyvk0Ka+PWVCrHF3UZCRpeHi9Wz+/CeNxNQ3M/h2zc6DLN+8m7ikFEKL+TC0S1tKB/sbLLtx/3HGz1+lt83K0oLDS37UfT9u3m9sOnBCr0z1iBL8PLy38cMD6zdvZ/VfG4lPSCQkwJ/+n3anZPGQF+63a/8hvp06k3eqVuK7r4bqtqdnZDJ/2W8cPHaC5JQUvD08aPtuM1o1a2T07Gf2r+TkrkWkJT/C3bcE9dqNxjugjMGykYfWEHX8Lx7HXAXAs2gp3nlvkF55SZI4vGUm5w//QWZGMr6BFWjQcRwuHgFGzw6wf9sqdm38leTEx/j6h9Huk5EEhEQYLHvm2D/sWL+Axw/uoFarcPcqRv33ulKl9nt6+bes+YXDu9aSkZZCYIlydOw5Gg9vw+fj69q0cQPr1v5BQkI8gYFB9O7zOWFhJV643759e/hx8kSqVavO12PG67avXLGMA/v38ujRIywsLQkJCaVLl26ElShp9Own96zkyPZFpCY9wrNoCZp0Go1voOFz59T+NZw78heP7mvPHS//UtRrM0iv/L4NP3PxxGaS4x+gsLDUlmk9EN+gskbP/lSnFkVoWEOJva2cS9czmff7Q2Ie5X/dB2hWW0nrBi44Oym4eS+bhX885OqtLINlR/fxoUIpeybOv8/xyDSjZq9WUk6tCAscbOFBvMTGIyruPjbcfcvDWUbDCgp83eS4OMrYdFTF4Qvq1zrm67CpWh+7ms2QOyhRPbhN6qaVqO7dyLe8bfVG2FSph8LZFU16KlnnT5C2809QqV75mK9j3ZYdrF6/ifjEJIIDijGgV1fCX+aaf+Aw46fOomaVinz/1WDd9vjEJOYuXcWJM5GkpqVTtlQJBvTqSlEfb6NnL1KzEkGDe6CsUBobHw9Ovt+X2A27Ct6ndhXCp4zAITyUzDsxXJs4h7vL1uuV8e/zIUGDemDt5U5y5CUufPktSSfOGT3/2ya6RBZMtLAVYtnZ2S8uZGaqlLahU1NH/t6byti5j7nzQMWQLi442hs+FUOKWtKnnTP7T6UzZs5jTkVlMaCTC74euc8arK1kXLmdzZodKW80e+VS1nRs7MCGfWmMnxfPnVgVgz5yxtFOZrB8sJ8Fvd934sDpTMbNi+f05Sz6faDE110BgJWlDH8vSzbuT2P8/Hhm/Z6El6sF/Tsp30j+HUdP89PKv+jVpgkrJgymeDEf+k2eR3xS/u+bva0N22aN131tnD4mT5kaZUrolfnui4/fSP7dBw4ze/EyunV8nwXTJhEc6M/Qcd+TkJhU4H4xsQ+Z8+sKyoTnvTmfvXgZx0+dYdTAL1g6axrtWjZnxvzFHDp20qjZL/+7hX3rJ1Kt2ed8NGw97r4lWDe7B+kpcQbL3712jLCKLWjffxmdBq3G0cWbdbM/ISUxVlfmxD8LOLNvOQ06juPDwWuwtLZl3eweqHIM39C+jn8Pb2P9sh9p1u4zhk1eg69/cWZ/15uUJMP57R2UNGn7KYMmrGDEj2upVq81K2ePJurMIV2Zf/5ezL6tv9Gx12gGf78Sa2tbZn/Xm5xs4+ffv28vCxfMo9OHHzHj59kEBgUxZvRXJCYmFLhfbOwDFi9cQKlSpfO85uvrx2d9vuCX2fP54cdpeHp4MvrrkSQlJRo1+4UTW9i5ZiK13vucnqPX4+lXglXTe5CWbPi9v3X5GKWqtOCjwcvoNmI1Ti7e/PbTJyQn5J47rp4BNOk0hk/HbaTrsN9wdvXlt+mfkJYSb9TsT7Vp6EKLOs7MW/2Q4VPukJWtYcznvlhaGL52ArxTwYHubdz4fWs8gyff4ea9LMZ87ovSQZGn7Hv1nHlTt3oRgXKaV7Vg12kVv/ydQ0y8RPemltjbGC5vaQHxKRLbT6pITjec6r8e81VZl66CQ7MPSNvzNwmzx6F6cAdlt8HI7B0Nly9TDfvG7Unfs4H4GV+Rsn4x1hFVsG/U7pWP+Tp2HTzCL4tX0O2Dtiyc9h0hAcUYMn7SS1zzHzH719/yXPMlSWLUxKncj33I918NZtFP3+Pp7sagsRPJyMw0en6FvR3JkZc533/8iwsDtgF+VN4wj7i9xzhYqRU3fl5KxLwJuDWqqSvj3b4ZJX8cydUJv3CwShtSIi9RdfMirNyLGD2/YF5Ehc0EunXrxr59+5gxYwYymQyZTEZ0dDQ9evQgMDAQW1tbwsLCmDFjRp79WrduzXfffYePjw9hYWEAHD58mHLlymFjY0OlSpX466+/kMlknDlzRrfv+fPnadasGQ4ODnh6evLxxx/z+PHjfPPcvHnzjfzuTWvYse/fdA6czuD+IzW/bkwmO0eidgVbg+UbV7Pj3LUsth5KJ+axmnW7U7kZk0PDqna6MofPZvL33jQuXH+zFdgm1ezYfyqDg2cyuf9YzbJNKWTnSNQqbzh7o6p2nL+WzbbD2uzr96RxK0ZF/Sra7BlZElNXJHLiYhYP4tRcv6dixdYUAnwsKeJk/D/NlVv30rpedVrWqUqQrxcju7fHxtqKDfuO5buPTAZuzk66L1dl3g9lS0sLvTJO9nYGjvT6/vh7My0aN6BZw3oEFPNjUJ+e2FhbseWfPfnuo1Zr+G7az3Tv1B5vL888r5+/dJmm9etQPqIU3p4evNekISGB/kRdvWbU7P/uWULp6h0oXe19XL1DaNhxPBZWNpw/stZg+eZdp1Kudmc8/EpSxCuYRh9OQJI03Ll8BNDeeJzeu4yqTfoQUqYh7r4laPrxD6QmPeRa5D9GzQ6wZ9Myqjd4n2r12uDtF0zHXmOwsrLlyJ71BsuHlqpM2SoN8PILwt2rKHWbf4SPf3GiL53S5d+7ZQVN2n5Kmcr18fUP4+Mvvicp4RGRJ3YbPf9f69fSpGkzGjVuQrFi/nz+xQCsra3ZuWN7vvuo1Wqm/DCJzh99jJd33qfvdevVp1z5Cnh5e+PvH0DPT3uTnp7OjRvGbWk4tnMJ5Wt1oNw77+PuE0Lzj8ZjaWXDmUOGz502vaZSqV5nvIqVxM07mHe7as+dm1FHdGVKV32PoPAauLgXxd03lEYdRpKVkcrDu5eNmv2pd+s588f2eI6fS+PW/WxmLIuliFJB1bL2+e7Tsr4LOw8ns/toMncfZDN39UOysiUaVHfSKxfga0XL+s7MWhGbz5FeT83SCk5c1nDqqoaHiRJ/H1KRrYKKxfNWHAHuPZbYdkJN5HUN6rwNa690zFdl+05jMk/uJ+vUQdSP7pO6YRlSTjY2FWsZLG9ZLISc21fJijyKJjGOnGsXyIo8hqVf4Csf83Ws+XsL7zauR/MGdQko6sfgPj2wsbZm8659+e6jVmv49qdf6P7B+/h4eui9dvf+Ay5cvsbgzz6hZGgwxXx9GPzZJ2RlZ7PrwJF8jvjqHm3fz5Wx04n9++Wuyf6ffkDGjbtEDZtM6qXr3Jq9kgdrtxM4oJuuTOCX3bmzaA13l64jNSqac33Hok7PpGi3942e/23TSBqTfRUGosJmAjNmzKB69er06tWLmJgYYmJi8PPzw8/Pjz/++IOLFy8yZswYvvrqK9asWaO3765du7h8+TI7d+5k06ZNJCcn89577xEREcGpU6f49ttvGT58uN4+iYmJ1K9fn/Lly3Py5Em2bdtGbGwsHTp0yDdP0aJFjf57KxQQ4G3JhejcipUkwYXobEL8LA3uE1LUKk9F7Py1bEKKGi7/pijk4O9jwcVnskjAxevZBOeTPbiopV55gPPR2YT45d8T2c5ahkaSSM807vPiHJWKSzfuUrVUcd02uVxOlVKhRF67le9+GZnZvDvgG1r0H8+gaYuIvhuTp8y/Uddo1Hc0bYd8z8Qlf5CYYtzuSAA5OSouR1+nYtncLnhyuZyKZSO4ePlqvvst+/1PnJVKWjSqb/D10iXCOHT8JI/i4rWVoMjz3LkXQ+XyhrubvQq1KpvYOxfwD6uh2yaTy/EPq0HMzdMvdQxVdgZqtQobe23ra1LcXdKSH1HsmWNa2zriFVCWmBsvd8yXpVLlcOf6RcIiqum2yeVywiKqcfPK2RfuL0kSl88d5eH9m4SEVwQg7uFdkhMfE1Ym95i2do4EhERw4yWO+V/k5ORw7dpVypUrr5e/XLnyXLoUle9+q1etROnsTOMmzV7qZ2zbugV7e3sCA4OMkhu0507MrQsEltQ/dwJK1uBe9Mv9O+dkZ6BRq7C1N9xyr1Zlc2r/71jbOuLpF2aU3M/ydLWgiNKCs5fSddvSMzVcvZlJWIDhJiULBQQXtebs5dx9JAkiL6cTFpi7j5WljEHdvFiw5hGJKfnUjl6DQg4+bjKu3c+9oZOA6Psainnk3zr4to9p+AcpsPAJIDv6Qu42SSIn+iKWRQ13Kcy5fQ0LnwAsfLUVNLmLO1bFy5B9JfKVj/mqcnJUXIm+QaUyua3b2mt+aS4UcM1fumYdLkon3m1UL89r2TnaLrhWlrmf2XK5HEsLCyIvvpmHFf+Fc7VyPN6tX3F8tPMgLtXKASCztERZoRSPdx3OLSBJPN59GOdq5RH+t4kxbCagVCqxsrLCzs4OLy8v3fbx43ObzQMDAzly5Ahr1qzRVawA7O3tWbhwIVZWVgDMnTsXmUzGggULsLGxITw8nHv37tGrVy/dPrNmzaJ8+fJ8//33um2LFy+maNGiXLlyheLFixvMY0hWVhZZWfpdltSqLBQW1i/8vR3t5CgUMpLS9J9mJKWp8Xa3MriP0kFO8nPjuZJS1Sgd3u6zBkc7OQq5jOTnsienafB2M/xnpHSQ5y2fqsHJQJce0N6ktGvowLFzWWRmG7fClpiShlqjochzLWRFlI7cjHlocB9/bw9G9/qA0GI+pKZnsGLLHj4ZP5M1k4bj6eoMQPUyJahXqQy+HkW4GxvHL2s20//H+SwZNwCF3Hj/RknJyWg0Goo46990ujgruX33vsF9Ii9eYvM/e1g4fXK+x+3/aXem/jKf9p/0QaFQIJfJGPL5p5QtFW607BlpCUgaNXZOrnrb7RxdiY+9/lLHOPD3FByUHroKWnryI90xnmXv6Epa8mMjpM6VlpyARqPGyVn/Zzk6uxJ7P//WpIz0FL7u3QCVKge5XE6HHl9Toow2f3Kitjufo/K5YypdSU40bv7kJ+eOs4uL3nZnZxfu3rljcJ8LF86zY/s2Zs6aU+Cxjx87yg+TvycrKwuXIkX49rtJKJXG69Kcnqo9d+yfO3ccnFyJe/By587utVNwcPYgMLyG3varZ/ewbsEgcrIzcFS603ngYuwcjd+tytlJe31Meq5ClZii1r32PEcHhfaz4vl9klX4eua24H/yvjuXbmRy/JzxHxIB2NmAQi4jNUP/epyaIeGufLXr25s4piFyO0dkCgWa1GS97ZrUJCzdDH/OZ0UeRW7ngHOvr0AGMoUFGcd2k75v8ysf81UlpaSg1mhwee6aX0T5omv+Xhb99L3B1/39fPB0d2P+8tUM6dsDG2sb1mzcwqO4eOISCu4e/TZYe7qRFat//cuKfYyl0hG5jTWWLkrkFhZkPYx7rkwc9mHGe1AkmCdRYTMjv/zyC4sXL+b27dtkZGSQnZ1NuXLl9MpEREToKmsAly9fpkyZMtjY5D51rFKlit4+Z8+eZc+ePTg4OOT5mdHR0RQvXjzP9vxMnDhRr2IJUKb2YMrVGfLSxxDyUsihT3slMhks3/xmx+K9rDKhAZQJDdB9XzY0kHbDJrFu92H6tG8OQJPqFXSvhxT1IaSYN60Hfce/F69RpfTLn1fGlp6ewfc/zWLo55/i7OSUb7l1m7Zx8fJVvh81DE8PN85eiGL6vMW4FnGhUjnjtbK9juM75nPp1BY69F+GheWLH4yYC2sbe0b8+CdZmelcPneM9ct+xM3Tj9BSlU0drUDp6elMmzKZfv2/fGHlq0zZssycNYfk5GS2b9vC5IkTmPrTTJydXQrc7205tHU+F45v4eOhec8d/xJV6TXmL9JTEjh9YA1r533JJ1/9kady+F/VruTIZ51yu6J9N8fwzfXrqhxhT0RxWwZPuv1Gjv//kWVgGHZ13iV143Jy7l5HUcQDhxYfYpeSSPrejaaOV6D0jAwmTJ/D0L49873mW1hYMGH4l0yetYAWH32K4kmLXdUKb26yHeHliUlHCiYqbGZi9erVDBkyhKlTp1K9enUcHR358ccfOXZMf3yRvX3+ff7zk5qaynvvvcfkyXlbGrwNjM0oyMiRIxk0aJDetr6TXu7JVEq6BrVaQvncBCNKewVJKYb7ECelanB6rjVN6aAg6Q3NopiflHQNao2E03PZnezl+WZJStXkLe8gJzlV/6mxQg592ilxU8r5YVmi0VvXAJwd7VHI5XkmGIlPSsFVmX+F5lkWFgrCAny5E5t/C4ifhxvOjvbciX1s1Aqb0skJuVxO/HODzRMSkyji4pyn/L0HsTx4+IiRE37QbZMk7ftav00nls/+CbciRVi4YhXfjhxC9UraimdwgD/Xrt/k9782Ga3CZmvvgkyuIP25SSLSU+Kwd3IrcN+TuxZx4p/5vP/FEtx9cwfQ2zm5647hoMy9OU5LicPD98UzH/4X9k4uyOUKXavYUymJcXla3Z4ll8tx9yoGgF9ACWLvXWfHXwsJLVVZt19KUhxKF/fcYybF4Rtg3PxOT86dxOeeoCcmJuBSJG+L0oOYGGJjY/lmfO4EO0/PnZbvNmXegsV4e/sAYGNji4+PLz4+vpQoUZJePbuxY/s2OnTsZJTsdg7ac+f5CUZSk+NweMG5c2T7Ig5vnU/nQUvw9Mv7nlpZ21HEw58iHv74BZfjl1GNOXPwT95p/nozvB4/l8qVm7kTODydWETpqCAhOffa5+yo4MZdwxPMpKSqtZ8Vjvq9EZydLEhM1s5WGFHcFi83S1b8GKxXZlhPb6KiMxg9495r/R4A6Zmg1kg42MrgmWlNHGxlpGS82nX6TRzTEE16CpJajdxB//oud1DmaSF7yr5BWzLPHCbz3/0AqGPvkmZljWOrrqTv2/RKx3xVSkdHFHJ5nglG4pPyuebHPLnmfzdFt03z5O+2XtuPWPHLVHy9PQkLCWLx9ImkpqWjUqlwVjrRe+howkJM30KVFfsYa0/9v2trTzdyklLQZGaR/TgBjUqFtYfrc2VcyXpg3J4JgvkRFTYTsbKyQv3MiORDhw5Ro0YN+vbtq9sWHR39wuOEhYWxYsUKsrKysLbWPkE9cUJ/mvUKFSqwdu1aAgICsLAw/E/+fJ78WFtb637OUwqL9HxK61Or4WZMDuFBVpy6pP2glskgPMiKf44bPsa1O9mEB1mx40ju66WCrbh2p+DpoI1NrYFb91WUDLLi9GXtuDQZUDLIit3HMwzuE30nh5KBVuw8lvt6qSArrt3NnR75aWXNw1XBj0sTSDPiB/azLC0sKBHox/ELV6hbSTsOTKPRcOLCVTo8MwNVQdQaDdfuxPBO2fynLY+NSyQpNR0355erBL4sS0sLwoKDOBV5jlrVtC00Go2GfyPP06Z5kzzli/n5sHjmj3rbFq38nYyMTL7o2RUPNzeyc7JRqdTIZfrjRhQKue4G3RgUFlZ4Fi3F7StHCCnbEABJo+H2lSOUq/VRvvud+GcBx7bPpW3fRXgV058+X+nqh72TO7cvH8HDT/vvkZWRyoObZylb0ziVhacsLCwpGhTOlfPHKFulAaB976+cP0qtpi//sySNBlWO9m/H1cMPJ2c3Lp87ht+TClpGeio3r52jZuOORs1v+WTK/bNnz1C9xju6/GfPnOHd91rmKe9XtCizZs/T27Zi2a+kZ2Twae8+uLm559nnKUkjkZNjvGuTwsIKb/9S3Ig6Qlj53HPnZtQRKtXP/9w5vG0Bh7bMpdOARfgEGF564XmSpEGlev2JmzKzJB5k6b8H8UkqyoTZcfOe9vi2NnJCA2zYdtDwbH8qNUTfyaJMmJ1uin6ZTFtJ27pfu8+6HQn8c1i/kjBjlD9L1j7ixHnjdJFUa+D+Y4kQbzlRt7QP5mRAsI+cIxdfbczcmzim4R+kRnX/JlZB4WRHPRnvKJNhGVSSjGP5TC1vaaUdLPisZydkeJVjviJLSwuKBwfyb+QFvWv+qcgLtGneOE/5Yn4+/DpD/6H0wpVrSM/IpH/PLni4Pdet+MnkWHfux3A5+jo9Pmxv1PyvIvHoGdyb1dbb5tagBglHzwAg5eSQdOoCbvWr5y4PIJPhWq86t2aveMtpjU/SFI7JP0xFVNhMJCAggGPHjnHz5k0cHBwIDQ1l2bJlbN++ncDAQJYvX86JEycIDAws8Dgffvgho0aN4tNPP2XEiBHcvn2bKVO0T5hkT25EP//8cxYsWECnTp0YNmwYRYoU4dq1a6xevZqFCxeiUCjy5ClSpAhyI45Bemrb4XR6tVFy434O1+/m0KS6PdZWMg6c0lZqPm2rJCFZu14ZwI6j6Yz8pAhNa9hx9koWVSNsCfSxZMmG3A9qe1sZrkoFzo7avF5PxpQlpWqM2hK3/Wg6PVs7cfO+ihv3cmhUzQ5rSxkHz2iz92ztSEKKhrW7tDcLO4+lM7ybC02q23L2SjZVS9sQ4GPB0o3a7Ao59G2vxN/bghmrkpDJZDjZa//N0jI0qI187ercrC7j5v1GeGBRSgX789u2fWRkZfNenaoAjJm7Eg8XJV90fBeABeu3ExHij5+nG6lpGSzbvIcHjxNoXU87UUR6ZhYL1m2nfpUyuCqduBv7mJmrN1LU043qZYzbSgLQvlULJs6YTVhIMCVDg/lz4xYyM7No1rAuAN//NAs31yJ82uVDrK2sCPIvpre/w5PW6afbLS0tKFs6nDm/rsDKygovD3fOnL/I9j37+fyTLkbNXrFed7atGI5nsdJ4+Zfh1N6l5GRlUKpaWwC2LhuGg7MntVpq1ws6vnM+R7bMpFnXqShdfUl7MmbN0toOK2t7ZDIZ5et24dj2Obh4+OPk6sfhTTNwUHoQUqahUbMD1Hu3Cyt+GUWxoFL4h0Swd8tysrIyqFa3NQDLZn2FcxEPWn74JQA71i+kWHA4bp5FUeXkcOH0AY4f2ETHnl8D2mtT3eYfsX3dPDy8i+Hq4cum1bNQurhTprLhCWJeR+s27/PTtB8JDQ2lePES/P33OjKzMmnYSFvZnzrlB1xdXenWvQdWVlYEBOhfd+2fdCd/uj0zM4PfV6+iarXqFHEpQnJyEps2bSQu7jE1a+nfcL2uqo26s2HxcLwDSuMbWIZj/ywlJzuDsu9oz52/Fw3D0cWT+m21587hrfPZt2EmrXtOxdnNl9Qk7bljZW2HlY092VnpHNw8l+Jl6+Pg7E5GagIn96wkJSGW8IpNjZr9qU17EmnftAgxj3KIjcvhwxauxCepOXY2t2I1vp8vR8+m6ipkG3Yn0P9jT6JvZ3L1Zibv1nPBxlrOrqPa62diitrgRCOPElQ8jDPeOpwHz6tpV9uCu4/l3H0k8U5pBVYWcOqK9me3q21BcrrEjpPa7xVy7VpsT//fyQ68i8jIypGIT3m5YxpLxqEdOL7fk5z7N1HdvY5tjcbIrKzJ/PcgAI7v90STnKhdZw3IvnwG2xpNUMXc0nWJtG/QhqzLZ3UVuRcd05g6tGrOxBlzCQsJomRoMH9s3EpGZibNG9QB4Lvps3FzLULvjz94cs3Xnywt95qfu33PoaM4Oznh6e5K9K07/LxwGTWrVKKKESeaekphb4d9SO7nkF2gH05lS5Adn0TmnRjCJgzCxteTs921E8Xdmr8a/76dKTFxKHd+XYtbvWp4t2/GiZa5rd43pi+h7OLJJP57nqQTkQT074qFvS13lq4zen7BvIgKm4kMGTKErl27Eh4eTkZGBpcuXeL06dN07NgRmUxGp06d6Nu3L1u3bi3wOE5OTmzcuJE+ffpQrlw5IiIiGDNmDB9++KFuXJuPjw+HDh1i+PDhNG7cmKysLPz9/WnatKmuUvZ8nhs3bhAQEGD03/v4+Uyc7OS0re+I0kHO7Qc5TFmeoJuco4hSwbPdmK/dyWHun4m838CRdg0diY1TMWNVAvce5n4glw+zoVfb3LEmn3dwBmD9nlT+2pNqtOwnLmThaJdK67r2uoWzf1qZSHKaZDB79F0V89cl07aePW3rOxAbr+bn1Unce6T9UHZ2lFO+hLa1cvxn+l2zJv+awOVbxm1FbFytPAnJqcxdu424pGSK+/vy87Deuqn6HzxO0GttSk5LZ8LCNcQlJeNkb0eJAD8Wje1PkK92cLlcLuPqnftsOniClLQM3F2cqBYRxmftmmNlafxLS/1aNUhMTmbJb2u0C2cHBvDD2JEUcXYGIPZxHLL/+JBhzJABLFj2G99N+5nk1FQ83d3p+dEHtGxq3IWzwyo2Jz01nsObZ5Ke8gh335K07btQ1yUyJSEGmSw3e+TB1ahVOWxa1F/vONWafUGN5v0AqNywFznZGexcNYasjGR8gyrStu/CNzLOrWKNpqQmx7N5zS+kJD7GN6AEfb+ai5OzNn/C4xjdAyKA7Kx01iz8jsS4WCytrPH0DaRLv4lUrJFbIWjY6hOyszJYNW88GekpBJUoT9+v5mJpZfz8tevUJSk5iRXLl5GQkEBQUBDffPMdLk8mInn06CFy+cvP0CeXK7h79w67vttJclIyTk6OhBYPY/KP0/D3DzBq9lKVm5OeEs++v2eSlvwIz6Il6TRgoa5LZFK8/rnz7z7tubN2rv65U+u9L6jTsh9yuYK4B9dZe2Q96akJ2No74xMQQddhK3H3DTVq9qfW/5OAjbWMPp08sLeVExWdybez75Gjyr1gerlZ6k3IdOhUKk4OCj5o4YqLo4Ib97L55pd7eSYiedPO3dBgb6OiYUULHG0hJk5iyfYcUp/0+nR2kOk1SjnaQb82uePMa5exoHYZuB6jYeGWnJc6prFknT+OzN4R+wattYtcx9wmaek0pDRtpVfu7KrXopa+dyNIYN+wLXInFzRpKWRfOkPaP2tf+pjG1KBmdRKTklm86s8n13x/powdoZt8KvZRnN65/zLiEhKZtXgFCUlJuLq40KRuTbp2aGv07ADKiqWpvmu57vvwKV8BcGfZOiJ7jMTa2x3bornDUjJu3uVEy96ETx1JQL8uZN59wLneX/N4Z25lOOaPrVi5F6H42P7ahbPPRnH83Z5kPzcRifC/RyYZs++PYBZWrlxJ9+7dSUpKwtbW8BphxtR1zIM3/jPeJIWicK9uMaO5cRd5fptSHX1MHeG1bLhdztQRXkug55tdu/BNCnTIu8REYXL0nr+pI7yWdWvyn1rd3JUsX+zFhczYwBufmTrCa9F0/sLUEV7LyTIfmjrCK2uRY/rlC/LTsJPp7mX+WVXJZD/7ZYkWtv8By5YtIygoCF9fX86ePcvw4cPp0KHDW6msCYIgCIIgCILw5ogK2/+ABw8eMGbMGB48eIC3tzft27fnu+++M3UsQRAEQRAEQXghSRKTjhREVNj+BwwbNoxhw4aZOoYgCIIgCIIgCEYmKmyCIAiCIAiCIJiMRiycXaDCPduCIAiCIAiCIAjC/zBRYRMEQRAEQRAEQTBTokukIAiCIAiCIAgmI2nEpCMFES1sgiAIgiAIgiAIZkq0sAmCIAiCIAiCYDKSmHSkQKKFTRAEQRAEQRAEwUyJCpsgCIIgCIIgCIKZEl0iBUEQBEEQBEEwGUkSk44URLSwCYIgCIIgCIIgmCnRwiYIgiAIgiAIgsmISUcKJlrYBEEQBEEQBEEQzJRoYRMEQRAEQRAEwWTEwtkFEy1sgiAIgiAIgiAIZkpU2ARBEARBEARBEMyVJAhmLDMzUxo7dqyUmZlp6iivpDDnL8zZJUnkN7XCnL8wZ5ckkd+UCnN2SRL5TakwZxfePJkkSWJaFsFsJScno1QqSUpKwsnJydRx/rPCnL8wZweR39QKc/7CnB1EflMqzNlB5DelwpxdePNEl0hBEARBEARBEAQzJSpsgiAIgiAIgiAIZkpU2ARBEARBEARBEMyUqLAJZs3a2pqxY8dibW1t6iivpDDnL8zZQeQ3tcKcvzBnB5HflApzdhD5TakwZxfePDHpiCAIgiAIgiAIgpkSLWyCIAiCIAiCIAhmSlTYBEEQBEEQBEEQzJSosAmCIAiCIAiCIJgpUWETBEEQBEEQBEEwU6LCJghGpFar2b9/P4mJiaaOIgiCIJhYRkaGqSMIgvA/QFTYBLN17do1tm/frvvAKwwTmioUCho3bkxCQoKpowiFRHJy8kt/mSuVSsU333zD3bt3TR1FEN66/v37G9yelpZG8+bN33KaV5ednc3ly5dRqVSmjvL/xu3btw3e20iSxO3bt02QSDBXFqYOIAjPi4uLo2PHjuzevRuZTMbVq1cJCgqiR48euLi4MHXqVFNHLFDp0qW5fv06gYGBpo7ySq5evcqePXt4+PAhGo1G77UxY8aYKNWLHT16lI0bN5KdnU2DBg1o2rSpqSO9FGdnZ2QyWYFlJElCJpOhVqvfUqr/xsLCgh9//JEuXbqYOsr/e9nZ2dy4cYPg4GAsLArPR/zJkydZs2YNt2/fJjs7W++1devWmSjVy9m8eTMuLi6MHz9ety0tLa3QXIPS09Pp168fS5cuBeDKlSsEBQXRr18/fH19GTFihIkT/u8KDAwkJiYGDw8Pve3x8fEEBgaa7TVfePsKz9Vc+H9j4MCBWFhYcPv2bUqWLKnb3rFjRwYNGmT2FbYJEyYwZMgQvv32WypWrIi9vb3e605OTiZK9mILFiygT58+uLm54eXlpVeRkMlkZlth+/PPP+nYsSO2trZYWloybdo0Jk+ezJAhQ0wd7YX27Nlj6ghGUb9+ffbt20dAQICpo7yy6Ohopk+fTlRUFADh4eEMGDCA4OBgEyd7scJ807169Wq6dOlCkyZN2LFjB40bN+bKlSvExsbSpk0bU8d7oR07dlCrVi1cXFz48ssvSUlJoUmTJlhYWLB161ZTx3uhkSNHcvbsWfbu3atXyWzYsCHjxo0z63MHYOnSpbi5udGiRQsAhg0bxvz58wkPD2fVqlX4+/ubOGH+nj6Me15qaio2NjYmSCSYK7FwtmB2vLy82L59O2XLlsXR0ZGzZ88SFBTE9evXKVOmDKmpqaaOWCC5PLen8bMXYnNvJQHw9/enb9++DB8+3NRR/pOKFStSuXJlfvnlFxQKBRMnTuTHH38kPj7e1NH+35g7dy7jx4+nc+fOBh9UtGzZ0kTJXs727dtp2bIl5cqV45133gHg0KFDnD17lo0bN9KoUSMTJyzYgAEDOHToENOnT6dp06ZERkYSFBTE33//zbhx4zh9+rSpI+arTJky9O7dm88//1x3zQ8MDKR37954e3vrtVyZq8jISOrVq8fYsWNZtWoV1tbWbN68Oc/fgTny9/fn999/p1q1anqfudeuXaNChQpm3R0bICwsjDlz5lC/fn2OHDlCw4YN+emnn9i0aRMWFhZm2UI7aNAgAGbMmEGvXr2ws7PTvaZWqzl27BgKhYJDhw6ZKqJgZkSFTTA7jo6OnDp1itDQUL0Pj5MnT9KkSRPi4uJMHbFA+/btK/D1OnXqvKUk/52TkxNnzpwhKCjI1FH+EwcHB86cOUNISAig7RZmb2/PvXv38nQ1MXeJiYksWrRI18pTqlQpPvnkE5RKpYmTFezZBxXPM/cHFQDly5enSZMmTJo0SW/7iBEj2LFjB6dOnTJRspdTmG+67e3tuXDhAgEBAbi6urJ3714iIiKIioqifv36xMTEmDriSzly5AiNGjWiatWqbNq0CVtbW1NHeil2dnacP3+eoKAgvXPn7Nmz1K5dm6SkJFNHLJCdnR2XLl2iWLFiDB8+nJiYGJYtW8aFCxeoW7cujx49MnXEPOrVqwdo7xeqV6+OlZWV7jUrKysCAgIYMmQIoaGhpooomBnRJVIwO7Vq1WLZsmV8++23gPZmT6PR8MMPP+gucubMnCtkL9K+fXt27NjBZ599Zuoo/0l6erpeV1MrKytsbGxITU0tVBW2pw8lbG1tqVKlCgDTpk3ju+++Y8eOHVSoUMHECfP3/HjHwiYqKoo1a9bk2f7JJ58wffr0tx/oP3r06JHBcz0tLe2FYyRNzcXFhZSUFAB8fX05f/48ERERJCYmkp6ebuJ0hpUvX97g+2ptbc39+/d1rbSA2Vf2K1WqxObNm+nXrx+Q2zNk4cKFVK9e3ZTRXoqDgwNxcXEUK1aMHTt26FqvbGxszHaWzqdd4bt3786MGTPMeqiEYB5EhU0wOz/88AMNGjTg5MmTZGdnM2zYMC5cuEB8fHyh6R5w4MAB5s2bx/Xr1/njjz/w9fVl+fLlBAYGUrNmTVPHy1dISAijR4/m6NGjREREYGlpqfd6frOhmYOFCxfi4OCg+16lUvHrr7/i5uam22bO+UE7frNly5YsWLBAN2GESqWiZ8+efPnll+zfv9/ECf93ubu7c+bMmTxPtM+cOVMoKv2F+aa7du3a7Ny5k4iICNq3b8+AAQPYvXs3O3fupEGDBqaOZ1Dr1q1NHcFovv/+e5o1a8bFixdRqVTMmDGDixcvcvjw4Rf2GDEHjRo1omfPnpQvX54rV67oZuZ82mprzpYsWQJoZ8WOjo6mdu3a2Nra5ju2Tfj/S3SJFMxSUlISs2bN4uzZs6SmplKhQgU+//xzvL29TR3thdauXcvHH39M586dWb58ORcvXiQoKIhZs2axZcsWtmzZYuqI+SpoZkuZTMb169ffYpqXFxAQ8MIPN3PO/5StrS2nT5+mRIkSetsvXrxIpUqVzLa14am0tDT27dtncKY/c68sf/PNN/z000+MGDGCGjVqANoxbJMnT2bQoEGMHj3axAkLdvDgQZo1a8ZHH33Er7/+Su/evfVuuitWrGjqiPmKj48nMzMTHx8fXW+Kw4cPExoaytdff42Li4upI/7Pi46OZtKkSXqfucOHDyciIsLU0V4oMTGRr7/+mjt37tCnTx/dxCljx47FysqKUaNGmThh/uLj42nfvj179uzRmxX7k08+KRSzYgtvj6iwCYKRlS9fnoEDB9KlSxe98QCnT5+mWbNmPHjwwNQRBTPl6enJ8uXLady4sd727du306VLF2JjY02U7MVOnz5N8+bNSU9PJy0tjSJFivD48WPs7Ozw8PAw+8qyJElMnz6dqVOncv/+fQB8fHwYOnQo/fv3LxRPuwvzTbcg/H/UpUsXHj58yMKFCylZsqTufmH79u0MGjSICxcumDqiYCZEl0jB7ERGRhrcLpPJsLGxoVixYlhbW7/lVC/v8uXL1K5dO892pVJJYmLi2w8kFBodO3akR48eTJkyRa+VZ+jQoXTq1MnE6Qo2cOBA3nvvPebOnYtSqeTo0aNYWlry0UcfMWDAAFPHK5BKpeK3337jww8/ZODAgbrxVI6OjiZO9t8EBwezYMECU8d4KcnJybpxOy+aEMXcx/eo1Wp++umnfNeRKwyz1arVatavX6+3pEWrVq3Mdi2//O4TDClTpswbTPJ6duzYwfbt2/Hz89PbHhoayq1bt0yUSjBH5vmXKPy/Vq5cOd3T7KcNwM8+3ba0tKRjx47MmzfPLNcp8fLy4tq1a3n6zh88eNAsZ18cNGgQ3377Lfb29rrB2vmZNm3aW0r136WkpHDlyhXCwsJwcHDg1KlTTJ8+nYyMDFq3bk3nzp1NHfGFpkyZgkwmo0uXLqhUKkB7vvfp0yfP7IXm5syZM8ybNw+5XI5CoSArK4ugoCB++OEHunbtStu2bU0dMV8WFhZ89tlnupvVwlZRA1AoFAYX4I2Li8PDw8PsZul0cXHR5c1v8fjCsBQKwPjx41m4cCGDBw/m66+/ZtSoUdy8eZO//vrLbNeufNaFCxdo2bIlDx48ICwsDIDJkyfj7u7Oxo0bKV26tIkT5vX0PiG/TmJPXzP38yctLU1vSv+n4uPjzfrBtPD2iQqbYHbWr1/P8OHDGTp0qG6mvOPHjzN16lTGjh2LSqVixIgRfP3110yZMsXEafPq1asXAwYMYPHixchkMu7fv8+RI0cYMmSIWY6DOX36NDk5Obr/z485dwnbv38/7777Lqmpqbi4uLBq1SratWuHr68vCoWCdevWkZ6eTq9evUwdNV9qtZqjR48ybtw4Jk6cSHR0NKBtNTH0gW5uLC0tdVP7e3h46Ba+VyqV3Llzx8TpXqxKlSqcPn3arBfZLUh+N65ZWVl6U4abi927d1OkSBGg8C8ev3LlShYsWECLFi0YN24cnTp1Ijg4mDJlynD06FGzH7/Zs2dPSpUqxcmTJ3XjBRMSEujWrRuffvophw8fNnHCvG7cuGHqCEZR2GfFFt4eMYZNMDtVqlTh22+/pUmTJnrbt2/fzujRozl+/NY05esAAEYsSURBVDh//fUXgwcP1t3UmhNJkvj++++ZOHGibpIIa2trhgwZorsoC8ZVu3ZtQkND+eabb1i8eDHTpk2jT58+fP/99wBMmDCBP//8kzNnzpg26AvY2NgQFRVV4OQv5qpx48Z069aNDz/8kF69ehEZGUn//v1Zvnw5CQkJHDt2zNQRC7RmzRpGjhzJwIEDDS78ba7dqmbOnAlou6R+++23ejOlqtVq9u/fz82bN8164ezCzt7enqioKIoVK4a3tzebN2+mQoUKXL9+nfLly5v9Oma2tracPHmSUqVK6W0/f/48lStXNtup8f8XnD9/ngYNGlChQgV2795Ny5Yt9WbFDg4ONnVEwUyICptgdvKbKe/SpUuUL1+ejIwMbt68SXh4uFnPmpednc21a9dITU0lPDxc70ZKMC5nZ2eOHj1KiRIlyM7OxtbWllOnTlG2bFlAO2Vy+fLldWOTzFWlSpWYPHmy2U5lXpCTJ0+SkpJCvXr1ePjwIV26dNHN9Ld48WLdv4W5MrTwd2HoVvW0cn/r1i38/PxQKBS6154uwPvNN99QtWpVU0V8ocI+bjksLIxly5ZRtWpVatasybvvvsuIESP4/fff6devHw8fPjR1xAKVLVuWn376ifr16+tt3717NwMGDODcuXMmSvbyli9fzty5c7lx4wZHjhzB39+f6dOnExgYSKtWrUwdr0CFeVZs4e0RXSIFs1OiRAkmTZrE/PnzdV15cnJymDRpkq4Sd+/ePTw9PU0Z84WsrKwIDw83dYz/7OTJk/kOnl+3bp2JUhUsOTlZ173KysoKOzs7vXFIjo6OZl25f2rChAm6llhDrTzmPPlCpUqVdP/v4eHBtm3bTJjmvyusXaye5q5Xrx7r1q0rlFPgPztu2RBzH7fcpk0bdu3aRdWqVenXrx8fffQRixYt4vbt2wwcONDU8V5o4sSJ9O/fn3HjxlGtWjUAjh49yjfffMPkyZP1JoUxx2vQnDlzGDNmDF9++SXfffed7uGKs7Mz06dPN/sKm1KpNOulBwTzIFrYBLNz+PBhWrZsiVwu13VDOnfuHGq1mk2bNlGtWjWWL1/OgwcPGDp0qInTav2XCRXMtdIDsHr1arp06UKTJk3YsWMHjRs35sqVK8TGxtKmTRvdIp/mRqFQ8ODBA9zd3QHtTcXZs2d1rQ+xsbH4+PiYbSvJU8+28jx7A2vurTz/C/bv30+NGjXyzIqnUqk4fPiwwZlfBeP4+++/X2rccseOHc1y3PLzjhw5wpEjRwgNDeW9994zdZwXMnTdeX7CL3O+BoWHh/P999/TunVrvaV0zp8/T926dXn8+LGpI+arsLcuC2+PqLAJZiklJYWVK1dy5coVQNvl5MMPPzTb2du6d++u+39Jkli/fj1KpVLX6vDvv/+SmJhI27ZtzbbSA9pxOr179+bzzz/XffAFBgbSu3dvvL29GT9+vKkjGiSXyyldurTuZjsyMpISJUroWmhVKhUXLlwwy5uNZ+3bt6/A1+vUqfOWkvx35cuXN9hK8vTGIyQkhG7dupntQPrCNsuiIXfv3mXDhg0GW8fNeYbXwj5uubB70XXnWeZ4DbK1teXSpUv4+/vrVdiuXr1KmTJlzHoMnlwuL9SzYgtvj+gSKZglR0dHateuTUBAgO7G4+lMYi1btjRlNIOerYQNHz6cDh06MHfuXN14ErVaTd++fc2yO8mzoqOjadGiBaDtWpiWloZMJmPgwIHUr1/fbCtsY8eO1fveUBeY999//23FeWWBgYEULVo0T8VHkiSzn2mxadOmzJkzh4iICF0ryYkTJ4iMjKRbt25cvHiRhg0bsm7dOrPsovS0BeF5cXFxebqmmqNdu3bRsmVLgoKCuHTpEqVLl+bmzZtIkkSFChVMHa9A586dMzg7p7+/v278VLly5YiJiXnb0V5aYR5DZY6VsP8iMDCQM2fO5DmHtm3bRsmSJU2U6uUU9lmxhbdIEgQzEx0dLZUpU0aSyWSSXC7X/ffpl7lzc3OTLl26lGf7pUuXpCJFipgg0cvz9fWVIiMjJUmSpIiICOm3336TJEmSDh8+LDk5OZky2v8Lcrlcio2NzbP98ePHZn/u9+zZU/rmm2/ybP/222+lnj17SpIkSWPGjJEqVqz4tqMVqE2bNlKbNm0kuVwuNW/eXPd9mzZtpJYtW0oBAQFSkyZNTB3zhSpXriyNGTNGkiRJcnBwkKKjo6WUlBSpZcuW0uzZs02crmDlypWTunbtKmVlZem2ZWdnS127dpXKlSsnSZIkHTx4UAoICDBVxALNnj1bcnNzkyZMmCDZ2tpK0dHRkiRJ0pIlS6S6deuaON3LSUhIkKZMmSL16NFD6tGjhzRt2jQpMTHR1LFeyoIFCyRfX19p9erVkr29vbRq1SppwoQJuv83Z5UrV5a2bduWZ/u2bdukypUrS5IkSevXr5eCgoLedjTBzOSdFksQTGzAgAEEBgby8OFD7OzsOH/+PPv27aNSpUrs3bvX1PFeSKVScenSpTzbL126hEajMUGil1e7dm127twJQPv27RkwYAC9evWiU6dOhXLmwsJGyqeVJzU11ey7w6xZs4ZOnTrl2f7BBx+wZs0aADp16sTly5ffdrQCKZVKlEolkiTh6Oio+16pVOLl5cWnn37KihUrTB3zhaKioujSpQugXQg8IyMDBwcH3cQR5uyXX35h06ZN+Pn50bBhQxo2bIifnx+bNm1izpw5AFy/fp2+ffuaOKlhP//8MwsWLGDUqFF6s3RWqlSpUMywePLkSYKDg/npp5+Ij48nPj6eadOmERwczKlTp0wd74V69uzJ5MmT+frrr0lPT+fDDz9kzpw5zJgxgw8++MDU8Qr0v9C6LLwdokukYHaOHDnC7t27cXNzQy6Xo1AoqFmzpm4mK3NfT6h79+706NGD6OhoXReHY8eOMWnSJL2xbuZo1qxZZGZmAjBq1CgsLS05fPgw77//Pl9//bWJ071YYR1HNWjQIECbc/To0XoLZavVao4dO0a5cuVMlO7l2NjYcPjwYUJCQvS2Hz58WFfZ1Gg0ZlfxfNqdOSAggKFDhxaKRcoNsbe313Uf9/b2Jjo6WreuljlPugBQo0YNbty4oTduuX379nrjlj/++GNTRizQjRs3KF++fJ7t1tbWpKWlmSDRfzNw4EBatmzJggULdOOAVSoVPXv25Msvv2T//v0mTvhinTt3pnPnzqSnp5OamppnLKq5+l+ZFVt480SFTTA7arVa9yHt5ubG/fv3CQsLw9/f3+yezhsyZcoUvLy8mDp1qu6pmLe3N0OHDmXw4MEmTlewp1Pjg3Yw9IgRI0yY5r8rrOOonj6EkCSJc+fO6T64QTuWsGzZsgwZMsRU8V5Kv379+Oyzz/j333+pXLkyoH3vFy5cyFdffQVoJ5Ew14pnly5duHfvHqGhoXrbr169iqWlJQEBAaYJ9pKqVavGwYMHKVmyJM2bN2fw4MGcO3eOdevW6aZqN2eOjo589tlnpo7xSgrzGCrQtrA9W1kDbSvtsGHD9JbrMFcZGRlIkoSdnR12dnY8evSI6dOnEx4eTuPGjU0dr0C//PILLVu2xM/Pz+Cs2GDercvC2yNmiRTMTq1atRg8eDCtW7fmww8/JCEhga+//pr58+fz77//cv78eVNHfGlP168x98lGnqVWq1m/fj1RUVGAdsrkVq1a5Znu3Bz16tWLYsWKMXr0aL3tEyZM4NatWyxYsICxY8eyefNmTp48aaKU+evevTszZswoVOfLs1auXMmsWbN0D1bCwsLo168fH374IaC9sXra2mlu6tSpwyeffELXrl31tq9YsYKFCxeafXfs69evk5qaSpkyZUhLS2Pw4MG6hcunTZtmsNuVObl69Sp79uzh4cOHebqOjxkzxkSpXs7ChQsZN24cU6dOpUePHixcuJDo6GgmTpzIwoULzb5bnqenJ8uXL89Tudm+fTtdunQhNjbWRMleTuPGjWnbti2fffYZiYmJhIWFYWVlxePHj5k2bRp9+vQxdcQCFbZZsQUTMeUAOkEwZNu2bdLatWslSZKkq1evSmFhYZJMJpPc3NykXbt2mTjd/7bz589LQUFBkp2dnVS+fHmpfPnykr29vRQQECCdO3fO1PFeyMnJSbp69Wqe7VevXtVNmhIVFSU5ODi87WivJCkpSVq/fr0UFRVl6ij/8xwdHfM9d5RK5dsP9B+oVCpp3759UkJCgqmjvJL58+dLCoVC8vT0lMqWLSuVK1dO91W+fHlTx3spK1askEJCQiSZTCbJZDLJ19dXWrhwoaljvZR+/fpJfn5+0urVq6Xbt29Lt2/fllatWiX5+flJAwYMMHW8F3J1dZXOnz8vSZJ2ApIyZcpIarVaWrNmjVSiRAkTp8tfdna2FBQUJF28eNHUUYRCwPwfmQv/7zy7Fk9ISAiXLl0iPj4eFxcXg+OTzE1sbCxDhgxh165dPHz4ULe2ylPmvJ5Tz549KVWqFCdPnsTFxQWAhIQEunXrxqeffsrhw4dNnLBghXUc1VMdOnSgdu3afPHFF2RkZFCpUiXd1OyrV68uFEsTFFYymYyUlJQ825OSksz6bxa0a8g1btyYqKgonJ2dTR3nP5swYQLfffcdw4cPN3WUV1ZYx1CBthu/TCajS5cuqFQqQLv+V58+fZg0aZKJ071Yenq6rjVqx44dtG3bFrlcTrVq1bh165aJ0+XP0tJSN2ZcEF5EVNiEQuHZsVXmrlu3bty+fZvRo0fj7e1dKCqZT505c0avsgbg4uLCd999pxuXZM4K+ziq/fv3M2rUKEC7Po8kSSQmJrJ06VImTJhg1hW2ZxeANcTcKz21a9dm4sSJrFq1Sm/9xIkTJ1KzZk0Tp3ux0qVLc/36dQIDA00d5T9LSEigffv2po7x2h4+fKjrDiyTyXB3dzdxopdjZWXFjBkzmDhxom5h8uDg4EIzAU9ISAh//fUXbdq0Yfv27QwcOBDQ/nuYe/fyzz//nMmTJ7Nw4cJCMexAMB0xhk0QjMzR0ZEDBw6YbaWgIGXLluWnn36ifv36ett3797NgAEDCsUU1YV5HJWtrS1XrlyhaNGidOnSBR8fHyZNmsTt27cJDw8nNTXV1BHz9ffff+t9n5OTw+nTp1m6dCnjx4+nR48eJkr2ci5evEjt2rVxdnamVq1aABw4cIDk5GR2795N6dKlTZywYNu2bWPkyJF8++23VKxYMc9i3+Z849qjRw8qV65caCcdSUlJoW/fvqxatUo3/k6hUNCxY0d++eUXlEqliRP+b/vzzz/58MMPUavVNGjQgB07dgAwceJE9u/fz9atW02cMH9t2rRh165dODg4EBERkefvdt26dSZKJpgbUWETBCMLDw9n5cqVBqd5Nndbtmxh2LBhjBs3Tjez3NGjR/nmm2+YNGmSXkuDOd8AFlbFixdnwoQJtGjRgsDAQFavXk39+vU5e/YsDRo0MPvp2Q357bff+P333/NU6MzR/fv3mTVrFmfPnsXW1pYyZcrwxRdfFIoWfrk8d1nVZ1s6pSdr+5lzC+fEiROZNm0aLVq0ICIiAktLS73X+/fvb6JkL6djx46cPn2an3/+merVqwPa5WkGDBhAuXLlWL16tYkT5tW2bduXLlsYKg0PHjwgJiaGsmXL6v4Wjh8/jpOTk256fHP0oqV+ni47IgiiwiYIRrZjxw6mTp3KvHnzzH4q8OcZuul7eol49ntzvwHMzs42ONtcsWLFTJTo5cyePZsBAwbg4OBAsWLFOH36NHK5nJ9//pl169axZ88eU0f8z65fv06ZMmXMunXwf8G+ffsKfL1OnTpvKcl/V1A3TplMxvXr199imv/O3t6e7du35+k6e+DAAZo2bWqWa7H9lzVBC1ul4WmreFhYWKFYVkEQXoboMCsIRtaxY0fS09N1YwCef1ocHx9vomQvVhgrBM+6evUqn3zySZ7JUQpDJROgb9++VKlShTt37tCoUSNdBTooKIgJEyaYON1/l5GRwcyZM/H19TV1lJdy4MAB5s2bx/Xr1/njjz/w9fVl+fLlBAYGmv04tsDAQIoWLZpnHKEkSdy5c8dEqV7OjRs3TB3htbi6uhrs9qhUKvXGA5uTZythGRkZaDQaXXe8mzdv8tdff1GyZEm9ScDMlZisSfj/QFTYBMHIpk+fbuoIr+xFN33m3kLVrVs3LCws2LRpU6Gb8OWpSpUqUaZMGW7cuEFwcDAWFha0aNHC1LFe6PlZXCVJIiUlBTs7O1asWGHCZC9n7dq1fPzxx3Tu3JlTp06RlZUFaGeJ/P7779myZYuJExYsMDCQmJiYPLMTxsfHExgYaPYPKwqzr7/+mkGDBrF8+XK8vLwAbRe9oUOH5lkT0hy1atVKbx2zatWqYWlpWWjWMSvMkzWBdgzemjVruH37NtnZ2XqvnTp1ykSpBHMjukQKgqCjUCgM3vTFxcXh4eFh9jd99vb2/Pvvv2Y9ZqEg6enp9OvXj6VLlwJw5coVgoKC6NevH76+vowYMcLECfP36/+1d+dhNaf//8Cf51Ap7VHhkxY1USklNMw0xKQaU3ZjGUMYkSItGCYasvSZys6HomwzGYWGmZLIyFZ0lF17oQVNSEh1fn/063wdJxXifp+8HtfVNXWf/nhejavO633f9+sVESFWsPH5fHTs2BH9+vXj7C7DqywtLeHl5YXJkydDSUkJ6enpMDAwgEAggKOjI4qLi1lHbBSfz0dJSYlEZ8L8/HyYmJhw8ljeq+7cuYPY2NgG37SGhIQwStU8lpaWyMrKwosXL0QPtQoKCiAnJwcjIyOx7+XiG/AOHTrg1KlTMDU1RVhYGDZs2ACBQIDo6Gj4+/vjxo0brCM2SpqbNa1fvx6LFy/GlClTsG3bNkydOhXZ2dlITU2Fu7s7AgMDWUckHEE7bIR8ANnZ2di5cyeys7Oxbt06aGpq4u+//0bXrl1hamrKOt4b1R8dfF1FRQUnuyq+zsTERCobc9RbtGgR0tPTkZSUBAcHB9H6kCFDsGzZMk4XbFOmTGEd4b3cunULtra2EusqKiooLy//+IGaaf78+QDq7nr9/PPPYq3Ya2pqcOHCBc53rE1MTISzszMMDAxw8+ZNmJmZiY60WVlZsY7XpOHDh7OO8F6kdY5ZPR0dHZw7dw7q6uqIi4sTNXn5999/Of93a/Pmzdi2bRvGjx+PiIgI+Pn5wcDAAP7+/py+PkE+PirYCGlhp06dgqOjIwYMGIB//vkHgYGB0NTURHp6OsLDw3HgwAHWESW0hjd9ALBmzRr4+flh5cqVDXab43pny0OHDiEqKgo2NjZihbOpqaloPhJXxcXFQVFRUXTXa9OmTdi+fTtMTEywadMmzu+yaWtrIysrS6JRUHJyMgwMDNiEagaBQACg7mHLlStXICsrK3pNVlYWFhYW8PHxYRWvWRYtWgQfHx8EBARASUkJ0dHR0NTUxMSJE8UeXHDV0qVLWUd4L9I8xwwA5s2bh4kTJ4qaNQ0cOBBA3VHJnj17sg3XhIKCAvTv3x9A3U7hkydPAADff/89bGxssHHjRpbxCJcICSEtysbGRhgcHCwUCoVCRUVFYXZ2tlAoFAovXLgg7NKlC8tobzRw4EDhwIEDhTweT9i/f3/R1wMHDhTa29sLf/zxR+Ht27dZx2wSj8cT8ng8IZ/PF/uoX+M6eXl50b+XV//tXL58WaisrMwyWpPMzMyER48eFQqFQmFGRoZQVlZWuGjRIqGNjY1wypQpjNM1beXKlUITExPh+fPnhUpKSsLTp08L9+zZI+zYsaNw/fr1rOM1acqUKcLHjx+zjvFOFBUVhVlZWUKhUChUVVUVXr16VSgU1v2719XVZZjs0/DHH38IZWRkhHw+X/j111+L1leuXCl0cHBgmKz5UlNThTExMcInT56I1o4cOSJMTk5mmKpp+vr6wrS0NKFQKBT27t1buHXrVqFQKBTGx8cL1dTUWEYjHEM7bIS0sCtXrmDfvn0S65qampw9rlffHXLq1KlYt26dVDxVbYi0d7m0trbG0aNH4eHhAeD/RimEhYWJ5jtxVW5uLkxMTADUNfD49ttvsXLlSqSlpcHJyYlxuqYtXLgQtbW1GDx4MCorK2Fraws5OTn4+PiI/n9w1cuXL7F79254e3tzfsB3Q9q3by+6t9apUydkZ2eLjo5z9Xfm6012GsP1o22jR4/GF198IZpjVm/w4MEYMWIEw2TNJ63Nmuzs7BAbGwtLS0tMnToVXl5eOHDgAC5evPhWs/JI60cFGyEtTFVVFUVFRRKzhQQCAefbm78+b6d+nk337t2lopEHl2dNNcfKlSvh6OiI69evo7q6GuvWrcP169dx9uzZJudssSYrK4vKykoAwPHjxzF58mQAgLq6Oh4/fswyWrPweDwsXrwYvr6+yMrKQkVFBUxMTKCoqMg6WpNkZGTQtWtXzjcFehMbGxskJyejR48ecHJygre3N65cuYKYmBjY2NiwjtegV7sBP3z4ECtWrMDQoUPFBmfHx8dLRZdIoO5IcH2Hy3p9+/ZllObtSHOzpsWLF4veF7i7u0NDQwNnz56Fs7OzVBwHJh8PdYkkpIX5+PjgwoUL+OOPP/DZZ58hLS0NJSUlmDx5MiZPnszp+w6vz7OxsLDg/DybjIwMmJmZgc/nIyMjo9HvNTc3/0ip3l12djZWr16N9PR0VFRUwMrKCgsWLOD8XQxnZ2dUVVVhwIABWL58OXJzc9GlSxccO3YMc+bMwe3bt1lHbNXCw8MRExOD3bt3Q11dnXWct5KTk4OKigqYm5vj6dOn8Pb2xtmzZ2FkZISQkBDo6uqyjtioUaNGYdCgQZgzZ47Y+saNG3H8+HEcOnSITbBPxNy5c3HmzBmsXbsWDg4OyMjIgIGBAQ4fPoxly5aJ7nlykbR3ZiYfDxVshLSwqqoquLu7IyIiAjU1NWjbti2qq6sxceJEREREoE2bNqwjvpG2tjbi4+NhYWGBffv2YenSpUhPT0dkZCS2bdvGyT98fD4fxcXF0NTUBJ/PB4/HQ0O/1qRhcLY0KygowOzZs1FYWAhPT09MmzYNAODl5YWamhqsX7+ecUJJb3PkKCYm5gMmeX/1reVfvnwJXV1d0RDkelxsJ99aKCoq4vLlyzA0NBRbz8rKQq9evTjdVr410NXVFTVrenUkR1ZWFqysrDi9w//q369XScs4DvLx0JFIQlqYrKwstm/fDn9/f1y5cgUVFRWwtLSUmMfDRY8ePRI9nY+Li8OoUaOgoKCAb775Br6+vozTNSw3N1c0eyo3N5dxmrf3Nm8muHy3sGvXrjhy5IjEemhoKIM0zaOiosI6QouR9tby5eXlOHDgALKzs+Hr6wt1dXWkpaVBS0uL80fJNTQ0cPjwYXh7e4utHz58GBoaGoxSfTru378vUfAAwNOnT5t9z/Bje7Uzs7+/v9R2ZiYfDxVshLSw+l/Erzp//jx4PB7atWsHQ0NDuLi4cPLYkjTOs3n1uFR+fj769++Ptm3Ff7VVV1fj7NmznDxapaqq2uSbCuH/n4/HtR1CaS82X7+zKc24fNS6KRkZGRgyZAhUVFSQl5eHGTNmQF1dHTExMSgoKMCuXbtYR2xUQEAApk+fjqSkJPTr1w8AcOHCBcTFxWH79u2M07V+0tisqTWM4yAfFx2JJKSFDRo0CGlpaaipqYGxsTGAukvQbdq0Qffu3XHr1i3weDwkJyeLuupxxebNmzF37lzRPBuBQAA+n48NGzYgJiaG810YpfE+wNs0E+FaU5X6I6jNwcWf/euqq6uRlJSE7OxsTJgwAUpKSrh37x6UlZWlovmItBoyZAisrKwQFBQkdqTt7NmzmDBhAvLy8lhHbNKFCxewfv163LhxAwDQo0cPeHp6igo48uEkJyfD0dERkyZNQkREBGbOnCnWrKl3796sI76RtHdmJh8PFWyEtLC1a9fi9OnT2Llzp+iX8KNHjzB9+nR88cUXmDFjBiZMmIBnz54hPj6ecVpJFy9eRGFhIb7++mvRm9SjR49CVVUVAwYMYJyucXw+HyUlJaIjkvVu374Na2trTt9lkEavFpt5eXlYuHAhpkyZItYpLzIyEqtWrcIPP/zAKmaz5Ofnw8HBAQUFBXjx4oWo09zcuXPx4sULbN26lXXERtXU1CA0NBT79+9HQUGBqE1+PS63lldRUUFaWhq6desmVrDl5+fD2NgYz58/Zx2RcJy0NmsipLmoYCOkhXXp0gUJCQkSu2fXrl2Dvb097t69i7S0NNjb23N2xlBVVZXYPBuuq28ecfjwYTg4OEBOTk70Wk1NDTIyMmBsbIy4uDhWEZutvLwc4eHhoif1pqamcHV15fx9q8GDB2P69OkYP3682Pq+ffuwbds2JCUlsQnWTMOHD4eSkhLCw8OhoaEhKhqSkpIwY8YMZGZmso7YKH9/f4SFhcHb2xtLlizB4sWLkZeXh0OHDsHf3x+enp6sI76RpqYm4uPjYWlpKVawJSQkwNXVFYWFhawjSpD248CEEOnCZx2AkNbm0aNHKC0tlVi/f/++6I+8qqqqxBNwLqisrMS0adOgoKAAU1NTFBQUAAA8PDywevVqxuneTEVFBSoqKhAKhVBSUhJ9raKiAm1tbfz444/Ys2cP65hNunjxIrp164bQ0FCUlZWhrKwMISEh6NatG+e7/J07dw7W1tYS69bW1khJSWGQ6O2cPn0aS5YsEbtLAgB6enq4e/cuo1TNt3fvXmzfvh3e3t5o27Ytxo8fj7CwMPj7++P8+fOs4zXK2dkZv/zyC16+fAmg7g5SQUEBFixYwMlRIkDd73A1NbVGP+q/h3xYQ4YMQUREBJ2gIK0a9x+dEyJlXFxc4OrqiuDgYPTp0wcAkJqaCh8fH1Ent5SUFHz22WcMUzZs0aJFSE9PR1JSktjQziFDhmDZsmWcHUBa3zxCT08PPj4+Ei3NpYWXlxecnZ2xfft20c5mdXU1pk+fjnnz5uGff/5hnPDNdHR0sH37dgQFBYmth4WFQUdHh1Gq5qutrW3wnt2dO3egpKTEINHbKS4uFh3/UlRUxKNHjwAAw4YN4/zw5uDgYIwePRqampp49uwZvvrqKxQXF+Pzzz9HYGAg63gN4vp93k+JqakpFi1ahNmzZ+Obb77BpEmT4OTkBBkZGdbRCGkxdCSSkBZWUVEBLy8v7Nq1C9XV1QCAtm3b4ocffkBoaCjat2+Py5cvAwDn2vZK8zwbAHj27BmEQqGoRXJ+fj4OHjwIExMT2NvbM07XNHl5eQgEAnTv3l1s/fr167C2tkZlZSWjZE3766+/MGrUKBgaGooaLaSkpCAzMxPR0dFwcnJinLBx48aNg4qKCrZt2wYlJSVkZGSgY8eOcHFxQdeuXTnfUdLY2Bi7du1Cv3798MUXX2DYsGFYuHAhoqKi4OHh0eCuP9ecOXNG7A7SkCFDWEdqttePMpuYmGDatGmcP8rcWtTW1uL48ePYt28fDh48iDZt2mD06NGYOHEi55o1EfIuqGAj5AOpqKhATk4OAMDAwEAquswpKCjg6tWrMDAwECvY0tPTYWtrK3pqz1X29vYYOXIk3NzcUF5eDmNjY8jKyuLBgwcICQnBrFmzWEdslJaWFnbv3i1RXMbHx2Py5MkoKSlhlKx57ty5g82bN+PmzZsA6jrlubm5ScUOW2FhIRwcHCAUCpGZmQlra2tkZmaiQ4cO+Oeffxqc88QlCxcuhLKyMn766SdERUVh0qRJ0NPTQ0FBAby8vDh9pLkh5eXlUFVVZR2jWS5evAgHBwe0a9cOffv2BVB3quLZs2c4duwYrKysGCf8tDx//hx//vknAgMDceXKFanoUEtIU6hgI4SI2NraYsyYMfDw8BDtMujr68PDwwOZmZmcb9rRoUMHnDp1CqampggLC8OGDRsgEAgQHR0Nf39/0dNvrvL09MTBgwfx66+/on///gDqdh18fX0xatQorF27lm3AVq66uhpRUVFiuzwTJ06EvLw862hv7dy5czh37hyMjIzw7bffso7TqDVr1kBPTw/jxo0DAIwdOxbR0dHQ1tbGX3/9BQsLC8YJG/fll1/C0NCwwaPMOTk5nD7K3NoUFxfj999/x549e5CWloa+ffty/g4nIc1BBRshRESa59kAdTuEN2/eRNeuXTF27FiYmppi6dKlKCwshLGxMaePFAJ13Tl9fX2xdetW0XFaGRkZzJo1C6tXrxbrfslF0trh8uXLl+jevTuOHDmCHj16sI7zydHX18fevXvRv39/JCQkYOzYsYiKihKNKDh27BjriI2S5qPMrcHjx48RHR2Nffv2ISkpCQYGBpg4cSImTpyIbt26sY5HSIugLpGEEJEvvvgCly9fRnV1NXr27Iljx45BU1MT586d43yxBgCGhoY4dOgQCgsLER8fLzpaWFpaKhWttWVlZbFu3Tr8+++/uHz5Mi5fvoyysjKEhoZyvliT5g6XMjIyrWLW1+7duzFgwAB07twZ+fn5AOrmQh4+fJhxssYVFxeLjs0eOXIEY8eOhb29Pfz8/JCamso4XdOUlZVFHXVfVVhYKBUNa6SdlpYWFi9eDDMzM5w7dw63bt2Cv78/FWukVaGCjRAiplu3bti+fTtSUlJw/fp17NmzR2qGj/r7+8PHxwd6enro27evaIDzsWPHYGlpyThd8ykoKKBnz57o2bOnqIEK19V3uMzLy0NMTAxiYmKQm5uLYcOGYd68eazjNcnd3R1r1qwR7WxKmy1btmD+/PlwcnJCeXm56N6Oqqoq54/SqqmpiWatxcXFiZqNCIVCqbh/NG7cOEybNg1RUVEoLCxEYWEhfv/99wbnEpKWJRQKsX79emRlZSE0NLTB0SKEtAZ0JJIQIqa2thZZWVkoLS1FbW2t2Gu2traMUjVfcXExioqKYGFhAT6/7plUSkoKlJWVJY4scc3Tp0+xevVqJCYmNvjzr29iw0XSfixsxIgRSExMhKKiInr27CkxGiImJoZRsuYxMTHBypUrRQPA6xsGXb16FQMHDsSDBw9YR3yjOXPm4MiRIzAyMoJAIEBeXh4UFRXx+++/IygoiPM7tNJ+lFma1dbWol27drh27RqMjIxYxyHkg6E5bIQQkfPnz2PChAnIz8/H689yeDyeVDzt1tbWRkVFBRISEmBrawt5eXn06dMHPB6PdbQmTZ8+HadOncL333+PTp06SUXmevXHwl4v2KTlWJiqqipnhzQ3R25uboO7yHJycnj69CmDRM0XGhoKPT09FBYWIigoSNRRt6ioCLNnz2acrmn1R5lXrVqF7OxsAHUnFaRld1ya8fl8GBkZ4eHDh1SwkVaNdtgIISK9evXCZ599hoCAgAYLBq43j3j48CHGjh2LkydPgsfjITMzEwYGBnB1dYWamhqCg4NZR2yUqqoqjh49igEDBrCO8taowyVbJiYmWLVqFVxcXMR22DZs2ICdO3dyfpeKkHf1559/IigoCFu2bIGZmRnrOIR8ELTDRggRyczMxIEDB2BoaMg6yjvx8vKCjIwMCgoKxLr9jRs3DvPnz+d8waampgZ1dXXWMd7Jr7/+Ch6Ph8mTJzd4LIx8WPPnz4e7uzueP38OoVCIlJQU/Pbbb1i1ahXCwsJYx5MQGxsLR0dHyMjIIDY2ttHvdXZ2/kipiDSaPHkyKisrYWFhAVlZWYkxHGVlZYySEdJyaIeNECJiZ2cHPz8/ODg4sI7yTrS1tREfHw8LCwuxXYacnByYm5ujoqKCdcRG7dmzB4cPH0ZkZKTUHqeqrKyUymNhlpaWDR5B5fF4aNeuHQwNDTFlyhQMGjSIQbrm2bt3L5YtWyb6+Xfu3BkBAQGYNm0a42SS+Hw+iouLoampKbpr2hBpOYpN2ImMjGz09R9++OEjJSHkw6EdNkI+cRkZGaLPPTw84O3tjeLiYvTs2RMyMjJi32tubv6x472Vp0+fNlgglJWVcfbi/+uFQlZWFrS0tKCnpyfx85eGY20KCgpQU1MTfS4tHBwcsGXLFvTs2RN9+/YFAKSmpiIjIwNTpkzB9evXMWTIEMTExMDFxYVx2obVz56qrKxERUUFNDU1WUd6o1cb6rzeXIeQt0EFGfkUUMFGyCeuV69e4PF4Yk1GXF1dRZ/XvyYNT7q//PJL7Nq1C8uXLwdQl722thZBQUGc3RkZPnw46wgtora2FitWrEBwcLBoJ1NJSQne3t5YvHhxo7soXPDgwQN4e3vj559/FltfsWIF8vPzcezYMSxduhTLly/nbMFWT0FBQaqKZULeV01NDQ4dOoQbN24AAExNTeHs7Iw2bdowTkZIy6AjkYR84uoH7DaHrq7uB0zy/q5duwY7OztYWVnhxIkTcHZ2xrVr11BWVoYzZ87QINUPaNGiRQgPD0dAQICoaUpycjKWLVuGGTNmIDAwkHHCxqmoqODSpUsS9zezsrLQu3dvPHr0CDdv3kSfPn3w5MkTRinfrKSkBD4+PqKREK//aefywxZPT08YGhrC09NTbH3jxo3IysqihjWkUVlZWXBycsLdu3dhbGwMALh16xZ0dHRw9OhR+r1PWgUq2AghIqtWrYKWlpbYDhsA7NixA/fv38eCBQsYJWvay5cv4eDggFWrViEhIQHp6emoqKiAlZUV3N3d0alTJ9YRm5Samora2lr069dPbP3ChQto06YNp4fCdu7cGVu3bpVoEHH48GHMnj0bd+/eZZSsebS0tPDf//4XkydPFlvftWsXfH19UVJSguvXr+Orr77C/fv3GaV8M0dHRxQUFGDOnDkNdnjl8q5gly5dEBsbi969e4utp6WlwdnZGXfu3GGUjEgDJycnCIVC7N27V9S06eHDh5g0aRL4fD6OHj3KOCEh74+ORBJCRP73v/9h3759Euumpqb47rvvOF2wycjIICMjA2pqali8eDHrOO/E3d0dfn5+EgXb3bt3sWbNGly4cIFRsqaVlZU1OJi8e/fuUtGlzcPDA25ubrh06RL69OkDoK6ADgsLw08//QQAiI+PR69evRimfLPk5GScPn2as/ka8/DhwwZHhigrK3N64DfhhlOnTuH8+fNiHXY1NDSwevVqqRyRQkhDuH2pgBDyURUXFze4E9WxY0cUFRUxSPR2Jk2ahPDwcNYx3tn169dhZWUlsW5paYnr168zSNR8FhYW2Lhxo8T6xo0bYWFhwSDR21myZAm2b9+OlJQUeHp6wtPTEykpKdi+fbvoAYCbmxv+/PNPxkkbpqOjI3EMUloYGhoiLi5OYv3vv/+GgYEBg0REmsjJyTV4TLmiogKysrIMEhHS8miHjRAioqOjgzNnzkBfX19s/cyZM+jcuTOjVM1XXV2NHTt24Pjx4+jduzfat28v9npISAijZM0jJyeHkpISiTepRUVFaNuW27+ug4KC8M033+D48eP4/PPPAQDnzp1DYWEh/vrrL8bpmqe+y+KbvD7fiUvWrl2LhQsX4n//+x/09PRYx3kr8+fPx5w5c3D//n3Y2dkBABITExEcHEz310iThg0bhh9//BHh4eGiDq8XLlyAm5sbzfAjrQbdYSOEiAQFBSEoKAj//e9/xd44+fn5wdvbG4sWLWKcsHGNdYLk8Xg4ceLER0zz9saPH4+ioiIcPnxYdESsvLwcw4cPh6amJvbv3884YePu3buHTZs24ebNmwCAHj16YPbs2VJR7NerqqpCaWmpRKv5rl27Mkr0ZmpqamJ31Z4+fYrq6mooKChIjITg+rHULVu2IDAwEPfu3QMA6OnpYdmyZRJ3Cgl5XXl5OX744Qf8+eefon/31dXVcHZ2RkRERIPHbQmRNlSwEUJEhEIhFi5ciPXr16OqqgoA0K5dOyxYsAD+/v6M07V+d+/eha2tLR4+fAhLS0sAwOXLl6GlpYWEhATo6OgwTth6ZWZmwtXVFWfPnhVb5/JIi6YGBr+Kq7OqqqursW/fPgwdOhRaWlq4f/8+5OXloaioyDoakTKZmZliD4te7/hKiDSjgo0QIqGiogI3btyAvLw8jIyMODt0ujV6+vQp9u7di/T0dMjLy8Pc3Bzjx4+X2DHhgleHrjeF60PXBwwYgLZt22LhwoUNdlmUhnt40kpBQQE3btzg/NgQQghhhQo2Qggh74TP50sMXW8IV3eoXtW+fXtcunSpwU6X0iAiIgJTpkyRWK+ursbPP/+MVatWffxQzTRw4EDMmzev1QyRJx/e/PnzsXz5crRv3x7z589v9HsVFRVhamqK0aNH0yBtIrW4fYudEEI+Mffu3UNycnKD96heHyzMWm5uLusILcbExESqW8h7enri6NGj2LZtG9TU1ADUDQ+eMGECHj58yOmCbfbs2fD29sadO3cabBbE9d1Z8vEJBAK8fPlS9HljXrx4gXXr1uGvv/56q2PEhHAJ7bARQghHREREYObMmZCVlYWGhobYsTwej4ecnByG6Vq3EydOYMmSJVi5ciV69uwpcQRVWVmZUbLmyc7OxqRJk1BYWIidO3fi9u3b8PPzw/Dhw7F582ZON17g8yUnDNXv3ErD7izhvosXL2Lw4MF49OgR6yiEvBMq2AghhCN0dHTg5uaGRYsWNfgmlutu3bqFDRs24MaNGwDqLv57eHjA2NiYcbKm1f+8X7+7Jk1FQ21tLebNm4dNmzahTZs2iIyMxPjx41nHalJ+fn6jr9PdNvK+qqqq8Pfff8PFxYV1FELeCRVshBDCERoaGkhJSUG3bt1YR3lr0dHR+O6772BtbS2aw3b+/Hmkpqbi999/x6hRoxgnbNypU6caff2rr776SEne3Z9//olp06bhs88+w+3bt2Fubo5du3ZJ1VgFQt7FnTt3EBsbi4KCAlGH43pcn79JSHNQwUYIIRzh5+cHdXV1LFy4kHWUt9atWzdMnDgRv/zyi9j60qVLsWfPHmRnZzNK9mmYOXMmIiMjERgYiPnz56OkpASurq64cOECtmzZgrFjx7KO2Kjdu3dj69atyM3Nxblz56Crq4u1a9dCX1+fdkVIoxITE+Hs7AwDAwPcvHkTZmZmyMvLg1AohJWVFefnbxLSHFSwEUIIR9TU1GDYsGF49uxZg/eouPykWEFBARkZGRKzjzIzM2FhYYHKykpGyd4sIyMDZmZm4PP5TY4o4HrjCzMzM+zdu1di/MCmTZuwYMECVFRUMErWtC1btsDf3x/z5s1DYGAgrl69CgMDA0RERCAyMhInT55kHZFwWN++feHo6IiAgAAoKSkhPT0dmpqamDhxIhwcHDBr1izWEQl5b1SwEUIIR6xYsQL+/v4wNjaGlpaWRNMRLj8pdnJywpgxYzB16lSx9Z07d+L3339HfHw8o2RvxufzUVxcDE1NzUZHFEjDHbYXL168cV7irVu3OH2P0MTEBCtXrsTw4cNFb7gNDAxw9epVDBw4UKq7d5IPT0lJCZcvX0a3bt2gpqaG5ORkmJqaIj09HS4uLsjLy2MdkZD3Rm39CSGEI4KDg7Fjx44G52lxUWxsrOhzZ2dnLFiwAJcuXYKNjQ2Aujtsf/zxBwICAlhFbFRubi46duwo+lyaNTbcnsvFGlD3s7e0tJRYl5OTw9OnTxkkItKkffv2ontrnTp1QnZ2NkxNTQGAin3SalDBRgghHCEnJ4cBAwawjtFsDQ063rx5MzZv3iy25u7uDjc3t4+Uqvle7T6Yn5+P/v37o21b8T+L1dXVOHv2rFR0Kjxw4AD279/fYOOFtLQ0Rqmapq+vj8uXL0v8jOPi4tCjRw9GqYi0sLGxQXJyMnr06AEnJyd4e3vjypUriImJET08IkTaSV/faEIIaaXmzp2LDRs2sI7RbLW1tc364PpxQgAYNGgQysrKJNYfPXqEQYMGMUj0dtavX4+pU6dCS0sLAoEAffv2hYaGBnJycuDo6Mg6XqPmz58Pd3d3REVFQSgUIiUlBYGBgVi0aBH8/PxYxyMcFxISgn79+gEAAgICMHjwYERFRUFPTw/h4eGM0xHSMugOGyGEcMSIESNw4sQJaGhowNTUVKLpSExMDKNkrR+fz0dJSYnoiGS927dvw9raGo8fP2aUrHm6d++OpUuXYvz48WL3wPz9/VFWVoaNGzeyjtiovXv3YtmyZaJuop07d0ZAQACmTZvGOBkhhLBHBRshhHDE6w07Xrdz586PlOTtrV+/vsF1Ho+Hdu3awdDQELa2tmjTps1HTta4kSNHAgAOHz4MBwcHsbtgNTU1yMjIgLGxMeLi4lhFbBYFBQXcuHEDurq60NTUREJCAiwsLJCZmQkbGxs8fPiQdcRmqaysREVFBTQ1NVlHIVKooqICtbW1YmvKysqM0hDScugOGyGEcASXC7KmhIaG4v79+6isrISamhoA4N9//4WCggIUFRVRWloKAwMDnDx5Ejo6OozT/h8VFRUAgFAohJKSEuTl5UWvycrKwsbGBjNmzGAVr9m0tbVRVlYGXV1ddO3aFefPn4eFhQVyc3Mb7HzJJcuWLYO/vz/4fD4UFBSgoKAAoO44qpubG3777TfGCQmX5ebmYs6cOUhKSsLz589F60KhUCo6vBLSHFSwEUIIeW8rV67Etm3bEBYWhm7dugEAsrKyMHPmTPz4448YMGAAvvvuO3h5eeHAgQOM0/6f+iJZT08PPj4+aN++PeNE78bOzg6xsbGwtLTE1KlTRT/nixcvinYRuSo8PBzHjh3Dnj17YGBgAABISkrC5MmToa2tzTgd4bpJkyZBKBRix44dEuNQCGkt6EgkIYRwhL6+fqNvNnJycj5imrfTrVs3REdHo1evXmLrAoEAo0aNQk5ODs6ePYtRo0ahqKiITchGPHv2DEKhULS7k5+fj4MHD8LExAT29vaM0zWtvsFLfZfLqKgonDlzBkZGRnBzc5O4D8kl//77L2bOnIm4uDgEBwfj9u3bWLduHXx9fREQECDRuZOQVykqKuLSpUucH19ByPug34KEEMIR8+bNE/v65cuXEAgEiIuLg6+vL5tQzVRUVITq6mqJ9erqahQXFwOoayTx5MmTjx2tWVxcXDBy5Ei4ubmhvLwcffv2haysLB48eICQkBDMmjWLdcRG8fl8VFVVIS0tDaWlpZCXl8eQIUMA1LXH//bbbxknfDM1NTXs378fP/30E2bOnIm2bdvi77//xuDBg1lHI1KgT58+KCwspIKNtGpUsBFCCEfMnTu3wfVNmzbh4sWLHznN2xk0aBBmzpyJsLAw0RBkgUCAWbNmwc7ODgBw5coV6Ovrs4z5RmlpaQgNDQVQN89MW1sbAoEA0dHR8Pf353zBFhcXh++//77B5iLScI9nw4YNWLduHcaPH49Lly7B09MT+/btg4WFBetohOPCwsLg5uaGu3fvwszMTGI32dzcnFEyQloOzWEjhBCOc3R0RHR0NOsYjQoPD4e6ujp69+4NOTk5yMnJwdraGurq6qJZSIqKiggODmactGGVlZVQUlICABw7dgwjR44En8+HjY0N8vPzGadrmoeHB8aOHYuioiKpm4Pn4OCAZcuWITIyEnv37oVAIICtrS1sbGwQFBTEOh7huPv37yM7OxtTp05Fnz590KtXL1haWor+S0hrQHfYCCGE44KCgrB582bk5eWxjtKkmzdv4vbt2wAAY2NjqTmmZG5ujunTp2PEiBEwMzNDXFwcPv/8c1y6dAnffPON6FgnVykrK0MgEIgavkiTr7/+GpGRkejcubPY+tGjRzF9+nRO3nkk3GFiYoIePXrAz8+vwaYjurq6jJIR0nLoSCQhhHCEpaWl2JsNoVCI4uJi3L9/H5s3b2aYrPm6d++O7t27s47x1vz9/TFhwgR4eXnBzs4On3/+OYC63TZpeEo/evRoJCUlSWXBlpCQgNOnT8PPzw/Z2dk4cOAAunTpgrKyMuzfv591PMJx+fn5iI2NhaGhIesohHwwVLARQghHDB8+XOxrPp+Pjh07YuDAgZwvgmpqahAREYHExESUlpZKDK89ceIEo2TNM3r0aHzxxRcoKioSuzc1ePBgjBgxgmGy5tm4cSPGjBmD06dPo2fPnhL3eDw9PRkla1p0dDS+//57TJw4EQKBAC9evABQN4dt1apV+PLLLxknJFxmZ2eH9PR0KthIq0ZHIgkhhAOqq6uxb98+DB06FFpaWqzjvLU5c+YgIiIC33zzDTp16iRxLKm+oQfXZWVlITs7G7a2tpCXlxcN3+W68PBwuLm5oV27dtDQ0BDLzOPxOD0SwtLSEl5eXpg8eTKUlJSQnp4OAwMDCAQCODo6cv44KmFr27ZtWLFiBVxdXRt8WOHs7MwoGSEthwo2QgjhCAUFBdy4cUMq71x06NABu3btgpOTE+so7+Thw4cYO3YsTp48CR6Ph8zMTBgYGMDV1RVqamqcbZZST1tbG56enli4cCH4fOnqJ6agoIDr169DT09PrGDLycmBiYkJnj9/zjoi4bDG/r1LQ4dUQppDun6rE0JIK9a3b18IBALWMd6JrKysVB9J8vLygoyMDAoKCkTDswFg3LhxiIuLY5iseaqqqjBu3DipK9aAumIzKytLYj05ORkGBgYMEhFp8npXVGnqkEpIc0nfb3ZCCGmlZs+eDW9vb2zcuBHnzp1DRkaG2AeXeXt7Y926dZDWQxvHjh3DmjVr8J///Eds3cjISCra+v/www+IiopiHeOdzJgxA3PnzsWFCxfA4/Fw79497N27Fz4+Ppyff0fYUFdXx4MHDwAArq6uePLkCeNEhHxY1HSEEEI44rvvvgMg3iCCx+OJ7lFx+WlxcnIyTp48ib///humpqYS90hiYmIYJWuep0+fiu2s1SsrK4OcnByDRG+npqYGQUFBiI+Ph7m5ucTPPyQkhFGypi1cuBC1tbUYPHgwKisrYWtrCzk5Ofj4+MDDw4N1PMJBVVVVePz4MTp06IDIyEisWbNGNEeRkNaI7rARQghHNLWTw+W7bVOnTm309Z07d36kJO/GyckJvXv3xvLly6GkpISMjAzo6uriu+++Q21tLQ4cOMA6YqMGDRr0xtd4PB7nu3QCdW/Cs7KyUFFRARMTEygqKrKORDjq66+/RklJCXr37o3IyEiMGzcO8vLyDX7vjh07PnI6QloeFWyEEEI+edeuXYOdnR2srKxw4sQJODs749q1aygrK8OZM2ekcr4ZIa1VSUkJQkNDkZ2djZiYGAwdOvSNO+EHDx78yOkIaXlUsBFCCIfs3r0bW7duRW5uLs6dOwddXV2sXbsW+vr6cHFxYR2vVXr58iUcHBywatUqJCQkID09HRUVFbCysoK7uzs6derEOiIh5A309fVx8eJFaGhosI5CyAdDd9gIIYQjtmzZAn9/f8ybNw+BgYGiO2uqqqpYu3Yt5wo2KysrJCYmQk1NDZaWlo3OK0tLS/uIyd6OjIwMMjIyoKamhsWLF7OOQwh5C7m5uawjEPLBUcFGCCEcsWHDBmzfvh3Dhw/H6tWrRevW1tbw8fFhmKxhLi4uomNILi4uUjFg+k0mTZqE8PBwsZ87IUQ6nDp1Cr/++itu3LgBADAxMYGvry++/PJLxskIaRl0JJIQQjhCXl4eN2/ehK6urtgA4czMTJibm+PZs2esI7ZaHh4e2LVrF4yMjNC7d2+0b99e7HUud1kk5FO2Z88eTJ06FSNHjsSAAQMAAGfOnMHBgwcRERGBCRMmME5IyPujHTZCCOEIfX19XL58WaIbZFxcHHr06MEoVfMYGBggNTVV4h5JeXk5rKyskJOTwyhZ81y9ehVWVlYAgNu3b4u9Js07h4S0doGBgQgKCoKXl5dozdPTEyEhIVi+fDkVbKRVoIKNEEI4Yv78+XB3d8fz588hFAqRkpKC3377DatWrUJYWBjreI3Ky8trcE7cixcvcOfOHQaJ3s7JkydZRyCEvIOcnBx8++23EuvOzs746aefGCQipOVRwUYIIRwxffp0yMvLY8mSJaisrMSECRPQpUsXrFu3TjRUm2tiY2NFn8fHx0NFRUX0dU1NDRITE6Gvr88iGiHkE6Cjo4PExEQYGhqKrR8/fhw6OjqMUhHSsugOGyGEcMSzZ88gFAqhoKCAyspKXL16FWfOnIGJiQmGDh3KOl6D+Hw+gLpjg6//OZGRkYGenh6Cg4MxbNgwFvEIIa3cli1bMG/ePLi6uqJ///4A6u6wRUREYN26dZg5cybjhIS8PyrYCCGEI+zt7TFy5Ei4ubmhvLwc3bt3h4yMDB48eICQkBDMmjWLdcQ30tfXR2pqKjp06MA6CiHkE3Pw4EEEBweLukT26NEDvr6+nBuFQsi7ooKNEEI4okOHDjh16hRMTU0RFhaGDRs2QCAQIDo6Gv7+/qI3I9KivLwcqqqqrGMQQgghUo3POgAhhJA6lZWVUFJSAgAcO3YMI0eOBJ/Ph42NDfLz8xmna9yaNWsQFRUl+nrMmDFQV1dHly5dkJ6ezjAZIeRTcOnSJezZswd79uyBQCBgHYeQFkUFGyGEcIShoSEOHTqEwsJCxMfHw97eHgBQWloKZWVlxukat3XrVtEF/4SEBBw/fhxxcXFwdHSEr68v43SEkNaqtLQUdnZ26NOnDzw9PeHp6YnevXtj8ODBuH//Put4hLQIKtgIIYQj/P394ePjAz09PfTr1w+ff/45gLrdNktLS8bpGldcXCwq2I4cOYKxY8fC3t4efn5+SE1NZZyOENJaeXh44MmTJ7h27RrKyspQVlaGq1ev4vHjx/D09GQdj5AWQXfYCCGEQ4qLi1FUVAQLCwtRB8aUlBQoKyuje/fujNO9WefOnXHgwAH0798fxsbGWLFiBcaMGYNbt26hT58+ePz4MeuIhJBWSEVFBcePH0efPn3E1lNSUmBvb4/y8nI2wQhpQTSHjRBCOERbWxva2tpia3379mWUpvlGjhyJCRMmwMjICA8fPoSjoyMAQCAQSMxHIoSQllJbWwsZGRmJdRkZGdTW1jJIREjLoyORhBBC3ltoaCjmzJkDExMTJCQkQFFREQBQVFSE2bNnM05HCGmt7OzsMHfuXNy7d0+0dvfuXXh5eWHw4MEMkxHScuhIJCGEEEIIkUqFhYVwdnbGtWvXRPdoCwoK0LNnT8TGxuI///kP44SEvD8q2AghhLSY69evo6CgAFVVVWLrzs7OjBIRQlo7oVCIxMREscHZQ4YMYZyKkJZDBRshhJD3lpOTgxEjRuDKlSvg8Xio/9PC4/EAADU1NSzjEUJascTERCQmJqK0tFTi3tqOHTsYpSKk5dAdNkIIIe9t7ty50NfXR2lpKRQUFHDt2jX8888/sLa2RlJSEut4hJBWKiAgAPb29khMTMSDBw/w77//in0Q0hrQDhshhJD31qFDB5w4cQLm5uZQUVFBSkoKjI2NceLECXh7e0MgELCOSAhphTp16oSgoCB8//33rKMQ8sHQDhshhJD3VlNTAyUlJQB1xVt9xzZdXV3cunWLZTRCSCtWVVWF/v37s45ByAdFBRshhJD3ZmZmhvT0dABAv379EBQUhDNnzuCXX36BgYEB43SEkNZq+vTp2LdvH+sYhHxQNDibEELIO8nIyICZmRn4fD6WLFmCyspKAMAvv/yCYcOG4csvv4SGhgaioqIYJyWEtFbPnz/Htm3bcPz4cZibm0sM0Q4JCWGUjJCWQ3fYCCGEvJM2bdqgqKgImpqaMDAwQGpqKjQ0NESvl5WVQU1NTdQpkhBCWtqgQYPe+BqPx8OJEyc+YhpCPgzaYSOEEPJOVFVVkZubC01NTeTl5Um001ZXV2eUjBDyqTh58iTrCIR8cFSwEUIIeSejRo3CV199hU6dOoHH48Ha2hpt2rRp8HtzcnI+cjpCCCGkdaCCjRBCyDvZtm0bRo4ciaysLHh6emLGjBmiTpGEEEIIaRl0h40QQsh7mzp1KtavX08FGyGEENLCqGAjhBBCCCGEEI6iOWyEEEIIIYQQwlFUsBFCCCGEEEIIR1HBRgghhBBCCCEcRQUbIYQQQgghhHAUFWyEEEIIIYQQwlFUsBFCCCGEEEIIR1HBRgghhBBCCCEc9f8AVgVxuhJIeTUAAAAASUVORK5CYII="},"metadata":{}}]},{"cell_type":"markdown","source":"#### Visualize the different features correspond to target variable","metadata":{}},{"cell_type":"code","source":"def plot(col):\n    plt.figure(figsize=(24, 8))\n    sns.boxplot(x=col, y=target, data=data)\n    plt.savefig(f'{col}_feature.png')\n    plt.show()\n\nfor col in data.columns:\n    plot(col)","metadata":{"execution":{"iopub.status.busy":"2024-02-09T07:40:37.249886Z","iopub.execute_input":"2024-02-09T07:40:37.250351Z","iopub.status.idle":"2024-02-09T07:40:52.345751Z","shell.execute_reply.started":"2024-02-09T07:40:37.250314Z","shell.execute_reply":"2024-02-09T07:40:52.344256Z"},"trusted":true},"execution_count":13,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 2400x800 with 1 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Building the model and train it","metadata":{}},{"cell_type":"code","source":"x = data.drop(columns=[target], axis=1)\ny = data[[target]]\n\nprint(x.shape)\nprint(y.shape)","metadata":{"execution":{"iopub.status.busy":"2024-02-09T07:40:52.347067Z","iopub.execute_input":"2024-02-09T07:40:52.347371Z","iopub.status.idle":"2024-02-09T07:40:52.356338Z","shell.execute_reply.started":"2024-02-09T07:40:52.347345Z","shell.execute_reply":"2024-02-09T07:40:52.354963Z"},"trusted":true},"execution_count":14,"outputs":[{"name":"stdout","text":"(1000, 12)\n(1000, 1)\n","output_type":"stream"}]},{"cell_type":"code","source":"from sklearn.model_selection import train_test_split\nxtrain, xtest, ytrain, ytest = train_test_split(x, y, test_size=0.2, random_state=42)\n\nprint('Training set:', xtrain.shape, ytrain.shape)\nprint('Testing set:', xtest.shape, ytest.shape)","metadata":{"execution":{"iopub.status.busy":"2024-02-09T07:40:52.357264Z","iopub.execute_input":"2024-02-09T07:40:52.357500Z","iopub.status.idle":"2024-02-09T07:40:52.439743Z","shell.execute_reply.started":"2024-02-09T07:40:52.357478Z","shell.execute_reply":"2024-02-09T07:40:52.438499Z"},"trusted":true},"execution_count":15,"outputs":[{"name":"stdout","text":"Training set: (800, 12) (800, 1)\nTesting set: (200, 12) (200, 1)\n","output_type":"stream"}]},{"cell_type":"markdown","source":"#### Building the model","metadata":{}},{"cell_type":"code","source":"from sklearn.linear_model import LinearRegression, Ridge, ElasticNet\nfrom sklearn.tree import DecisionTreeRegressor\nfrom sklearn.ensemble import RandomForestRegressor\nfrom xgboost.sklearn import XGBRegressor\nfrom sklearn.neighbors import KNeighborsRegressor","metadata":{"execution":{"iopub.status.busy":"2024-02-09T07:40:52.441525Z","iopub.execute_input":"2024-02-09T07:40:52.441835Z","iopub.status.idle":"2024-02-09T07:40:52.513050Z","shell.execute_reply.started":"2024-02-09T07:40:52.441813Z","shell.execute_reply":"2024-02-09T07:40:52.512170Z"},"trusted":true},"execution_count":16,"outputs":[]},{"cell_type":"markdown","source":"##### Creating a dataframe which stores accuracies of all the models","metadata":{}},{"cell_type":"code","source":"from sklearn.metrics import mean_squared_error, r2_score\nresults = pd.DataFrame(columns=['Model', 'MSE_train', 'R2_train', 'MSE_test', 'R2_test'])","metadata":{"execution":{"iopub.status.busy":"2024-02-09T07:40:52.514340Z","iopub.execute_input":"2024-02-09T07:40:52.514612Z","iopub.status.idle":"2024-02-09T07:40:52.520550Z","shell.execute_reply.started":"2024-02-09T07:40:52.514589Z","shell.execute_reply":"2024-02-09T07:40:52.519568Z"},"trusted":true},"execution_count":17,"outputs":[]},{"cell_type":"markdown","source":"##### Linear Regression Model","metadata":{}},{"cell_type":"code","source":"lr = LinearRegression()\nlr.fit(xtrain, ytrain)","metadata":{"execution":{"iopub.status.busy":"2024-02-09T07:40:52.521754Z","iopub.execute_input":"2024-02-09T07:40:52.522055Z","iopub.status.idle":"2024-02-09T07:40:52.541968Z","shell.execute_reply.started":"2024-02-09T07:40:52.522032Z","shell.execute_reply":"2024-02-09T07:40:52.541081Z"},"trusted":true},"execution_count":18,"outputs":[{"execution_count":18,"output_type":"execute_result","data":{"text/plain":"LinearRegression()","text/html":"<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div>"},"metadata":{}}]},{"cell_type":"code","source":"ypred_train = lr.predict(xtrain)\nypred_test = lr.predict(xtest)\n\nmse_train = mean_squared_error(ytrain, ypred_train)\nr2_train = r2_score(ytrain, ypred_train)\n\nmse_test = mean_squared_error(ytest, ypred_test)\nr2_test = r2_score(ytest, ypred_test)\n\nresults.loc[len(results)] = ['Linear Regression', mse_train, r2_train, mse_test, r2_test]","metadata":{"execution":{"iopub.status.busy":"2024-02-09T07:40:52.544485Z","iopub.execute_input":"2024-02-09T07:40:52.544786Z","iopub.status.idle":"2024-02-09T07:40:52.574534Z","shell.execute_reply.started":"2024-02-09T07:40:52.544757Z","shell.execute_reply":"2024-02-09T07:40:52.573699Z"},"trusted":true},"execution_count":19,"outputs":[]},{"cell_type":"markdown","source":"##### Ridge Regression Model","metadata":{}},{"cell_type":"code","source":"ridge = Ridge()\nridge.fit(xtrain, ytrain)","metadata":{"execution":{"iopub.status.busy":"2024-02-09T07:40:52.575565Z","iopub.execute_input":"2024-02-09T07:40:52.575824Z","iopub.status.idle":"2024-02-09T07:40:52.595978Z","shell.execute_reply.started":"2024-02-09T07:40:52.575801Z","shell.execute_reply":"2024-02-09T07:40:52.594180Z"},"trusted":true},"execution_count":20,"outputs":[{"execution_count":20,"output_type":"execute_result","data":{"text/plain":"Ridge()","text/html":"<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Ridge()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Ridge</label><div class=\"sk-toggleable__content\"><pre>Ridge()</pre></div></div></div></div></div>"},"metadata":{}}]},{"cell_type":"code","source":"ypred_train = ridge.predict(xtrain)\nypred_test = ridge.predict(xtest)\n\nmse_train = mean_squared_error(ytrain, ypred_train)\nr2_train = r2_score(ytrain, ypred_train)\n\nmse_test = mean_squared_error(ytest, ypred_test)\nr2_test = r2_score(ytest, ypred_test)\n\nresults.loc[len(results)] = ['Ridge Regression', mse_train, r2_train, mse_test, r2_test]","metadata":{"execution":{"iopub.status.busy":"2024-02-09T07:40:52.597286Z","iopub.execute_input":"2024-02-09T07:40:52.597570Z","iopub.status.idle":"2024-02-09T07:40:52.625505Z","shell.execute_reply.started":"2024-02-09T07:40:52.597547Z","shell.execute_reply":"2024-02-09T07:40:52.624480Z"},"trusted":true},"execution_count":21,"outputs":[]},{"cell_type":"markdown","source":"##### Elastic Net Regression Model","metadata":{}},{"cell_type":"code","source":"elastic = ElasticNet()\nelastic.fit(xtrain, ytrain)","metadata":{"execution":{"iopub.status.busy":"2024-02-09T07:40:52.626910Z","iopub.execute_input":"2024-02-09T07:40:52.627187Z","iopub.status.idle":"2024-02-09T07:40:52.647887Z","shell.execute_reply.started":"2024-02-09T07:40:52.627164Z","shell.execute_reply":"2024-02-09T07:40:52.647196Z"},"trusted":true},"execution_count":22,"outputs":[{"execution_count":22,"output_type":"execute_result","data":{"text/plain":"ElasticNet()","text/html":"<style>#sk-container-id-3 {color: black;background-color: white;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>ElasticNet()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">ElasticNet</label><div class=\"sk-toggleable__content\"><pre>ElasticNet()</pre></div></div></div></div></div>"},"metadata":{}}]},{"cell_type":"code","source":"ypred_train = elastic.predict(xtrain)\nypred_test = elastic.predict(xtest)\n\nmse_train = mean_squared_error(ytrain, ypred_train)\nr2_train = r2_score(ytrain, ypred_train)\n\nmse_test = mean_squared_error(ytest, ypred_test)\nr2_test = r2_score(ytest, ypred_test)\n\nresults.loc[len(results)] = ['Elastic Net Regression', mse_train, r2_train, mse_test, r2_test]","metadata":{"execution":{"iopub.status.busy":"2024-02-09T07:40:52.649441Z","iopub.execute_input":"2024-02-09T07:40:52.650506Z","iopub.status.idle":"2024-02-09T07:40:52.685883Z","shell.execute_reply.started":"2024-02-09T07:40:52.650479Z","shell.execute_reply":"2024-02-09T07:40:52.685116Z"},"trusted":true},"execution_count":23,"outputs":[]},{"cell_type":"markdown","source":"##### Decision Tree Regression Model","metadata":{}},{"cell_type":"code","source":"dtr = DecisionTreeRegressor()\ndtr.fit(xtrain, ytrain)","metadata":{"execution":{"iopub.status.busy":"2024-02-09T07:40:52.687012Z","iopub.execute_input":"2024-02-09T07:40:52.688591Z","iopub.status.idle":"2024-02-09T07:40:52.710390Z","shell.execute_reply.started":"2024-02-09T07:40:52.688559Z","shell.execute_reply":"2024-02-09T07:40:52.709568Z"},"trusted":true},"execution_count":24,"outputs":[{"execution_count":24,"output_type":"execute_result","data":{"text/plain":"DecisionTreeRegressor()","text/html":"<style>#sk-container-id-4 {color: black;background-color: white;}#sk-container-id-4 pre{padding: 0;}#sk-container-id-4 div.sk-toggleable {background-color: white;}#sk-container-id-4 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-4 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-4 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-4 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-4 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-4 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-4 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-4 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-4 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-4 div.sk-item {position: relative;z-index: 1;}#sk-container-id-4 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-4 div.sk-item::before, #sk-container-id-4 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-4 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-4 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-4 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-4 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-4 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-4 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-4 div.sk-label-container {text-align: center;}#sk-container-id-4 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-4 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeRegressor()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeRegressor</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeRegressor()</pre></div></div></div></div></div>"},"metadata":{}}]},{"cell_type":"code","source":"ypred_train = dtr.predict(xtrain)\nypred_test = dtr.predict(xtest)\n\nmse_train = mean_squared_error(ytrain, ypred_train)\nr2_train = r2_score(ytrain, ypred_train)\n\nmse_test = mean_squared_error(ytest, ypred_test)\nr2_test = r2_score(ytest, ypred_test)\n\nresults.loc[len(results)] = ['Decision Tree Regression', mse_train, r2_train, mse_test, r2_test]","metadata":{"execution":{"iopub.status.busy":"2024-02-09T07:40:52.711776Z","iopub.execute_input":"2024-02-09T07:40:52.713474Z","iopub.status.idle":"2024-02-09T07:40:52.741961Z","shell.execute_reply.started":"2024-02-09T07:40:52.713440Z","shell.execute_reply":"2024-02-09T07:40:52.741182Z"},"trusted":true},"execution_count":25,"outputs":[]},{"cell_type":"markdown","source":"##### Random Forest Regressor Model","metadata":{}},{"cell_type":"code","source":"rfr = RandomForestRegressor()\nrfr.fit(xtrain, ytrain)","metadata":{"execution":{"iopub.status.busy":"2024-02-09T07:40:52.743286Z","iopub.execute_input":"2024-02-09T07:40:52.744512Z","iopub.status.idle":"2024-02-09T07:40:52.973576Z","shell.execute_reply.started":"2024-02-09T07:40:52.744483Z","shell.execute_reply":"2024-02-09T07:40:52.972324Z"},"trusted":true},"execution_count":26,"outputs":[{"name":"stderr","text":"/tmp/ipykernel_1582/3889765417.py:2: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n  rfr.fit(xtrain, ytrain)\n","output_type":"stream"},{"execution_count":26,"output_type":"execute_result","data":{"text/plain":"RandomForestRegressor()","text/html":"<style>#sk-container-id-5 {color: black;background-color: white;}#sk-container-id-5 pre{padding: 0;}#sk-container-id-5 div.sk-toggleable {background-color: white;}#sk-container-id-5 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-5 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-5 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-5 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-5 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-5 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-5 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-5 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-5 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-5 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-5 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-5 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-5 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-5 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-5 div.sk-item {position: relative;z-index: 1;}#sk-container-id-5 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-5 div.sk-item::before, #sk-container-id-5 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-5 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-5 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-5 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-5 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-5 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-5 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-5 div.sk-label-container {text-align: center;}#sk-container-id-5 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-5 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-5\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestRegressor()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" checked><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestRegressor</label><div class=\"sk-toggleable__content\"><pre>RandomForestRegressor()</pre></div></div></div></div></div>"},"metadata":{}}]},{"cell_type":"code","source":"ypred_train = rfr.predict(xtrain)\nypred_test = rfr.predict(xtest)\n\nmse_train = mean_squared_error(ytrain, ypred_train)\nr2_train = r2_score(ytrain, ypred_train)\n\nmse_test = mean_squared_error(ytest, ypred_test)\nr2_test = r2_score(ytest, ypred_test)\n\nresults.loc[len(results)] = ['Random Forest Regression', mse_train, r2_train, mse_test, r2_test]","metadata":{"execution":{"iopub.status.busy":"2024-02-09T07:40:52.974914Z","iopub.execute_input":"2024-02-09T07:40:52.975182Z","iopub.status.idle":"2024-02-09T07:40:52.997945Z","shell.execute_reply.started":"2024-02-09T07:40:52.975160Z","shell.execute_reply":"2024-02-09T07:40:52.997256Z"},"trusted":true},"execution_count":27,"outputs":[]},{"cell_type":"markdown","source":"##### XG Boost Regressor Model","metadata":{}},{"cell_type":"code","source":"xgb = XGBRegressor()\nxgb.fit(xtrain, ytrain)","metadata":{"execution":{"iopub.status.busy":"2024-02-09T07:40:53.001530Z","iopub.execute_input":"2024-02-09T07:40:53.001965Z","iopub.status.idle":"2024-02-09T07:40:53.089638Z","shell.execute_reply.started":"2024-02-09T07:40:53.001939Z","shell.execute_reply":"2024-02-09T07:40:53.088632Z"},"trusted":true},"execution_count":28,"outputs":[{"execution_count":28,"output_type":"execute_result","data":{"text/plain":"XGBRegressor(base_score=None, booster=None, callbacks=None,\n             colsample_bylevel=None, colsample_bynode=None,\n             colsample_bytree=None, device=None, early_stopping_rounds=None,\n             enable_categorical=False, eval_metric=None, feature_types=None,\n             gamma=None, grow_policy=None, importance_type=None,\n             interaction_constraints=None, learning_rate=None, max_bin=None,\n             max_cat_threshold=None, max_cat_to_onehot=None,\n             max_delta_step=None, max_depth=None, max_leaves=None,\n             min_child_weight=None, missing=nan, monotone_constraints=None,\n             multi_strategy=None, n_estimators=None, n_jobs=None,\n             num_parallel_tree=None, random_state=None, ...)","text/html":"<style>#sk-container-id-6 {color: black;background-color: white;}#sk-container-id-6 pre{padding: 0;}#sk-container-id-6 div.sk-toggleable {background-color: white;}#sk-container-id-6 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-6 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-6 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-6 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-6 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-6 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-6 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-6 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-6 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-6 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-6 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-6 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-6 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-6 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-6 div.sk-item {position: relative;z-index: 1;}#sk-container-id-6 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-6 div.sk-item::before, #sk-container-id-6 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-6 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-6 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-6 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-6 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-6 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-6 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-6 div.sk-label-container {text-align: center;}#sk-container-id-6 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-6 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-6\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>XGBRegressor(base_score=None, booster=None, callbacks=None,\n             colsample_bylevel=None, colsample_bynode=None,\n             colsample_bytree=None, device=None, early_stopping_rounds=None,\n             enable_categorical=False, eval_metric=None, feature_types=None,\n             gamma=None, grow_policy=None, importance_type=None,\n             interaction_constraints=None, learning_rate=None, max_bin=None,\n             max_cat_threshold=None, max_cat_to_onehot=None,\n             max_delta_step=None, max_depth=None, max_leaves=None,\n             min_child_weight=None, missing=nan, monotone_constraints=None,\n             multi_strategy=None, n_estimators=None, n_jobs=None,\n             num_parallel_tree=None, random_state=None, ...)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" checked><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">XGBRegressor</label><div class=\"sk-toggleable__content\"><pre>XGBRegressor(base_score=None, booster=None, callbacks=None,\n             colsample_bylevel=None, colsample_bynode=None,\n             colsample_bytree=None, device=None, early_stopping_rounds=None,\n             enable_categorical=False, eval_metric=None, feature_types=None,\n             gamma=None, grow_policy=None, importance_type=None,\n             interaction_constraints=None, learning_rate=None, max_bin=None,\n             max_cat_threshold=None, max_cat_to_onehot=None,\n             max_delta_step=None, max_depth=None, max_leaves=None,\n             min_child_weight=None, missing=nan, monotone_constraints=None,\n             multi_strategy=None, n_estimators=None, n_jobs=None,\n             num_parallel_tree=None, random_state=None, ...)</pre></div></div></div></div></div>"},"metadata":{}}]},{"cell_type":"code","source":"ypred_train = xgb.predict(xtrain)\nypred_test = xgb.predict(xtest)\n\nmse_train = mean_squared_error(ytrain, ypred_train)\nr2_train = r2_score(ytrain, ypred_train)\n\nmse_test = mean_squared_error(ytest, ypred_test)\nr2_test = r2_score(ytest, ypred_test)\n\nresults.loc[len(results)] = ['XG Boost Regression', mse_train, r2_train, mse_test, r2_test]","metadata":{"execution":{"iopub.status.busy":"2024-02-09T07:40:53.091020Z","iopub.execute_input":"2024-02-09T07:40:53.091507Z","iopub.status.idle":"2024-02-09T07:40:53.113074Z","shell.execute_reply.started":"2024-02-09T07:40:53.091478Z","shell.execute_reply":"2024-02-09T07:40:53.111725Z"},"trusted":true},"execution_count":29,"outputs":[]},{"cell_type":"markdown","source":"##### KNN Regression Model","metadata":{}},{"cell_type":"code","source":"knn = KNeighborsRegressor()\nknn.fit(xtrain, ytrain)","metadata":{"execution":{"iopub.status.busy":"2024-02-09T07:40:53.114487Z","iopub.execute_input":"2024-02-09T07:40:53.114853Z","iopub.status.idle":"2024-02-09T07:40:53.126259Z","shell.execute_reply.started":"2024-02-09T07:40:53.114809Z","shell.execute_reply":"2024-02-09T07:40:53.125246Z"},"trusted":true},"execution_count":30,"outputs":[{"execution_count":30,"output_type":"execute_result","data":{"text/plain":"KNeighborsRegressor()","text/html":"<style>#sk-container-id-7 {color: black;background-color: white;}#sk-container-id-7 pre{padding: 0;}#sk-container-id-7 div.sk-toggleable {background-color: white;}#sk-container-id-7 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-7 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-7 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-7 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-7 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-7 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-7 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-7 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-7 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-7 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-7 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-7 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-7 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-7 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-7 div.sk-item {position: relative;z-index: 1;}#sk-container-id-7 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-7 div.sk-item::before, #sk-container-id-7 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-7 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-7 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-7 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-7 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-7 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-7 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-7 div.sk-label-container {text-align: center;}#sk-container-id-7 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-7 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-7\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KNeighborsRegressor()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" checked><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KNeighborsRegressor</label><div class=\"sk-toggleable__content\"><pre>KNeighborsRegressor()</pre></div></div></div></div></div>"},"metadata":{}}]},{"cell_type":"code","source":"ypred_train = knn.predict(xtrain)\nypred_test = knn.predict(xtest)\n\nmse_train = mean_squared_error(ytrain, ypred_train)\nr2_train = r2_score(ytrain, ypred_train)\n\nmse_test = mean_squared_error(ytest, ypred_test)\nr2_test = r2_score(ytest, ypred_test)\n\nresults.loc[len(results)] = ['KNN Regression', mse_train, r2_train, mse_test, r2_test]","metadata":{"execution":{"iopub.status.busy":"2024-02-09T07:40:53.127546Z","iopub.execute_input":"2024-02-09T07:40:53.127862Z","iopub.status.idle":"2024-02-09T07:40:53.152677Z","shell.execute_reply.started":"2024-02-09T07:40:53.127837Z","shell.execute_reply":"2024-02-09T07:40:53.151576Z"},"trusted":true},"execution_count":31,"outputs":[]},{"cell_type":"markdown","source":"### Let's see the accuracies of different models in the increasing order of 'Testing MSE'","metadata":{}},{"cell_type":"code","source":"results = results.sort_values(by='MSE_test', ascending=True)\nresults","metadata":{"execution":{"iopub.status.busy":"2024-02-09T07:40:53.153988Z","iopub.execute_input":"2024-02-09T07:40:53.154462Z","iopub.status.idle":"2024-02-09T07:40:53.165941Z","shell.execute_reply.started":"2024-02-09T07:40:53.154434Z","shell.execute_reply":"2024-02-09T07:40:53.165095Z"},"trusted":true},"execution_count":32,"outputs":[{"execution_count":32,"output_type":"execute_result","data":{"text/plain":"                      Model  MSE_train  R2_train  MSE_test   R2_test\n4  Random Forest Regression   0.002946  0.987915  0.023432  0.903483\n5       XG Boost Regression   0.000002  0.999993  0.027463  0.886879\n3  Decision Tree Regression   0.000000  1.000000  0.035000  0.855834\n1          Ridge Regression   0.063654  0.738905  0.059288  0.755792\n0         Linear Regression   0.063654  0.738907  0.059305  0.755721\n6            KNN Regression   0.094450  0.612590  0.125600  0.482649\n2    Elastic Net Regression   0.180585  0.259287  0.155394  0.359926","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Model</th>\n      <th>MSE_train</th>\n      <th>R2_train</th>\n      <th>MSE_test</th>\n      <th>R2_test</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>4</th>\n      <td>Random Forest Regression</td>\n      <td>0.002946</td>\n      <td>0.987915</td>\n      <td>0.023432</td>\n      <td>0.903483</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>XG Boost Regression</td>\n      <td>0.000002</td>\n      <td>0.999993</td>\n      <td>0.027463</td>\n      <td>0.886879</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Decision Tree Regression</td>\n      <td>0.000000</td>\n      <td>1.000000</td>\n      <td>0.035000</td>\n      <td>0.855834</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Ridge Regression</td>\n      <td>0.063654</td>\n      <td>0.738905</td>\n      <td>0.059288</td>\n      <td>0.755792</td>\n    </tr>\n    <tr>\n      <th>0</th>\n      <td>Linear Regression</td>\n      <td>0.063654</td>\n      <td>0.738907</td>\n      <td>0.059305</td>\n      <td>0.755721</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>KNN Regression</td>\n      <td>0.094450</td>\n      <td>0.612590</td>\n      <td>0.125600</td>\n      <td>0.482649</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Elastic Net Regression</td>\n      <td>0.180585</td>\n      <td>0.259287</td>\n      <td>0.155394</td>\n      <td>0.359926</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"markdown","source":"- `Random Forest Regression Model` and `XG Boost Regression Model` are best fitted on it.","metadata":{}},{"cell_type":"code","source":"# features = [age, gender, chestpain, restingBP, serumcholestrol, fastingbloodsugar, restingrelectro, maxheartrate, exerciseangia, oldpeak, slope, noofmajorvessels]\ntest = np.array([53,1,2,171,0,0,1,147,0,5.3,3,3]).reshape(1, -1)\nprint(test.shape)","metadata":{"execution":{"iopub.status.busy":"2024-02-09T07:40:53.167074Z","iopub.execute_input":"2024-02-09T07:40:53.167384Z","iopub.status.idle":"2024-02-09T07:40:53.184654Z","shell.execute_reply.started":"2024-02-09T07:40:53.167361Z","shell.execute_reply":"2024-02-09T07:40:53.183330Z"},"trusted":true},"execution_count":33,"outputs":[{"name":"stdout","text":"(1, 12)\n","output_type":"stream"}]},{"cell_type":"code","source":"pred = rfr.predict(test)\npred","metadata":{"execution":{"iopub.status.busy":"2024-02-09T07:40:53.185860Z","iopub.execute_input":"2024-02-09T07:40:53.186716Z","iopub.status.idle":"2024-02-09T07:40:53.205157Z","shell.execute_reply.started":"2024-02-09T07:40:53.186686Z","shell.execute_reply":"2024-02-09T07:40:53.204198Z"},"trusted":true},"execution_count":34,"outputs":[{"name":"stderr","text":"/opt/conda/lib/python3.10/site-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but RandomForestRegressor was fitted with feature names\n  warnings.warn(\n","output_type":"stream"},{"execution_count":34,"output_type":"execute_result","data":{"text/plain":"array([1.])"},"metadata":{}}]}]}
\ No newline at end of file
diff --git a/Cardiovascular Disease Analysis and Prediction/README.md b/Cardiovascular Disease Analysis and Prediction/README.md
new file mode 100644
index 000000000..05d797970
--- /dev/null
+++ b/Cardiovascular Disease Analysis and Prediction/README.md	
@@ -0,0 +1,75 @@
+<h1>Cardiovascular Disease Analysis and Prediction</h1>
+
+**GOAL**
+
+The aim of this project is to analyze and predict the disease based on the given dataset.
+
+**DATASET**
+
+https://www.kaggle.com/datasets/jocelyndumlao/cardiovascular-disease-dataset
+
+**DESCRIPTION**
+
+To analyze the dataset of Cardiovascular Disease and build and train the model on the basis of different features and variables.
+
+There are 14 features and 1000 entries in this dataset.
+
+
+### Visualization and EDA of different attributes:
+
+<img alt="heatmap" src="./Images/correlation_heatmap.png">
+
+<img alt="graph" src="./Images/target_correlation.png">
+
+<img alt="graph" src="./Images/age_feature.png">
+
+<img alt="graph" src="./Images/restingrelectro_feature.png">
+
+
+**MODELS USED**
+
+| Model                       | MSE_train | R2_train | MSE_test  | R2_test   |
+|-----------------------------|-----------|----------|-----------|-----------|
+| Random Forest Regression    | 0.002946  | 0.987915 | 0.023432  | 0.903483  |
+| XG Boost Regression         | 0.000002  | 0.999993 | 0.027463  | 0.886879  |
+| Decision Tree Regression    | 0.000000  | 1.000000 | 0.035000  | 0.855834  |
+| Ridge Regression            | 0.063654  | 0.738905 | 0.059288  | 0.755792  |
+| Linear Regression           | 0.063654  | 0.738907 | 0.059305  | 0.755721  |
+| KNN Regression              | 0.094450  | 0.612590 | 0.125600  | 0.482649  |
+| Elastic Net Regression      | 0.180585  | 0.259287 | 0.155394  | 0.359926  |
+
+
+
+**WHAT I HAD DONE**
+
+* Load the dataset which contains 1000 entries in it and having 14 columns in it.
+* Checked for missing values and cleaned the data accordingly.
+* Analyzed the data, found insights and visualized them accordingly.
+* Plotting heatmap using correlation and checking the relation between different features.
+* Found detailed insights of different columns with target variable using plotting libraries.
+* Train the datasets by different models and saves their accuracies into a dataframe.
+
+
+**LIBRARIES NEEDED**
+
+1. Pandas
+2. Matplotlib
+3. Sklearn
+4. NumPy
+5. XGBoost
+6. Sci-py
+7. Seaborn
+
+
+**CONCLUSION**
+
+- Random Forest and XG Boost Regression models show promising performance with lower MSE and higher R2 values.
+- Decision Tree Regression achieved perfect R2 on the training set but performed poorly on the test set, indicating overfitting.
+
+
+**YOUR NAME**
+
+*Avdhesh Varshney*
+
+[![LinkedIn](https://img.shields.io/badge/linkedin-%230077B5.svg?style=for-the-badge&logo=linkedin&logoColor=white)](https://www.linkedin.com/in/avdhesh-varshney-5314a4233/)  [![GitHub](https://img.shields.io/badge/github-%23121011.svg?style=for-the-badge&logo=github&logoColor=white)](https://github.com/Avdhesh-Varshney)
+
diff --git a/Cardiovascular Disease Analysis and Prediction/requirements.txt b/Cardiovascular Disease Analysis and Prediction/requirements.txt
new file mode 100644
index 000000000..8e5f0f77e
--- /dev/null
+++ b/Cardiovascular Disease Analysis and Prediction/requirements.txt	
@@ -0,0 +1,7 @@
+numpy==1.19.2
+pandas==1.4.3
+matplotlib==3.7.1
+scikit-learn~=1.0.2
+scipy==1.5.0
+seaborn==0.10.1
+xgboost~=1.5.2