Final Project - W3.Solutions().ipynb

{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"Stage 4 Final Project - W3.Solutions().ipynb","provenance":[],"collapsed_sections":[]},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"}},"cells":[{"cell_type":"code","execution_count":null,"metadata":{"id":"LETb5G0LBXu9"},"outputs":[],"source":["import numpy as np\n","import pandas as pd\n","import matplotlib.pyplot as plt\n","import seaborn as sns\n","from sklearn.preprocessing import MinMaxScaler, StandardScaler\n","from scipy import stats"]},{"cell_type":"code","source":["df = pd.read_csv('full_data.csv')"],"metadata":{"id":"Pd5bypgVCIay"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["nums = ['Customer_care_calls', 'Customer_rating', 'Prior_purchases', 'Discount_offered', 'Cost_of_the_Product', 'Weight_in_gms', 'Reached.on.Time_Y.N']\n","cats = ['Mode_of_Shipment', 'Product_importance', 'Gender','Warehouse_block']"],"metadata":{"id":"ZYEM3js0CKuz"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["df2 = df.copy()\n","print(f'Jumlah baris sebelum memfilter outlier: {len(df2)}')\n","\n","filtered_entries1 = np.array([True] * len(df2))\n","\n","for col in nums:\n","    zscore = abs(stats.zscore(df2[col])) # hitung absolute z-scorenya\n","    filtered_entries1 = (zscore < 3) & filtered_entries1 # keep yang kurang dari 3 absolute z-scorenya\n","    \n","df2 = df2[filtered_entries1] # filter, cuma ambil yang z-scorenya dibawah 3\n","\n","print(f'Jumlah baris setelah memfilter outlier: {len(df2)}')"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"WYccnYFzCOBa","executionInfo":{"status":"ok","timestamp":1650781723419,"user_tz":-420,"elapsed":355,"user":{"displayName":"Muhammad Hamzah","userId":"06012328832276120437"}},"outputId":"a9fdd02d-7823-4b09-9118-49b7fa171ec9"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Jumlah baris sebelum memfilter outlier: 10999\n","Jumlah baris setelah memfilter outlier: 10642\n"]}]},{"cell_type":"code","source":["df2['Customer_rating'] = MinMaxScaler().fit_transform(df2['Customer_rating'].values.reshape(len(df2), 1))\n","\n","#Standarisasi :\n","df2['Customer_care_calls'] = StandardScaler().fit_transform(df2['Customer_care_calls'].values.reshape(len(df2), 1))\n","df2['Cost_of_the_Product'] = StandardScaler().fit_transform(df2['Cost_of_the_Product'].values.reshape(len(df2), 1))\n","df2['Prior_purchases'] = StandardScaler().fit_transform(df2['Prior_purchases'].values.reshape(len(df2), 1))\n","df2['Discount_offered'] = StandardScaler().fit_transform(df2['Discount_offered'].values.reshape(len(df2), 1))\n","df2['Weight_in_gms'] = StandardScaler().fit_transform(df2['Weight_in_gms'].values.reshape(len(df2), 1))"],"metadata":{"id":"Pqbjywu3CTmR"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["df2.info()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"HUFPx5AbGl-c","executionInfo":{"status":"ok","timestamp":1650781728405,"user_tz":-420,"elapsed":408,"user":{"displayName":"Muhammad Hamzah","userId":"06012328832276120437"}},"outputId":"f9896fa4-8976-4d9d-b6d0-780e3791b257"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["<class 'pandas.core.frame.DataFrame'>\n","Int64Index: 10642 entries, 0 to 10998\n","Data columns (total 12 columns):\n"," #   Column               Non-Null Count  Dtype  \n","---  ------               --------------  -----  \n"," 0   ID                   10642 non-null  int64  \n"," 1   Warehouse_block      10642 non-null  object \n"," 2   Mode_of_Shipment     10642 non-null  object \n"," 3   Customer_care_calls  10642 non-null  float64\n"," 4   Customer_rating      10642 non-null  float64\n"," 5   Cost_of_the_Product  10642 non-null  float64\n"," 6   Prior_purchases      10642 non-null  float64\n"," 7   Product_importance   10642 non-null  object \n"," 8   Gender               10642 non-null  object \n"," 9   Discount_offered     10642 non-null  float64\n"," 10  Weight_in_gms        10642 non-null  float64\n"," 11  Reached.on.Time_Y.N  10642 non-null  int64  \n","dtypes: float64(6), int64(2), object(4)\n","memory usage: 1.1+ MB\n"]}]},{"cell_type":"code","source":["df2.describe()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":300},"id":"eRIg2eGsCbId","executionInfo":{"status":"ok","timestamp":1650781731354,"user_tz":-420,"elapsed":478,"user":{"displayName":"Muhammad Hamzah","userId":"06012328832276120437"}},"outputId":"a6dae2c4-37b5-4b2e-8df8-86a94921f3a4"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["                 ID  Customer_care_calls  Customer_rating  \\\n","count  10642.000000         1.064200e+04     10642.000000   \n","mean    5570.347773         6.986956e-15         0.497298   \n","std     3159.806013         1.000047e+00         0.353086   \n","min        1.000000        -1.803627e+00         0.000000   \n","25%     2881.250000        -9.304889e-01         0.250000   \n","50%     5604.500000        -5.735048e-02         0.500000   \n","75%     8300.750000         8.157880e-01         0.750000   \n","max    10999.000000         2.562065e+00         1.000000   \n","\n","       Cost_of_the_Product  Prior_purchases  Discount_offered  Weight_in_gms  \\\n","count         1.064200e+04     1.064200e+04      1.064200e+04   1.064200e+04   \n","mean          5.756426e-16    -6.094584e-15      2.935728e-14   1.339529e-17   \n","std           1.000047e+00     1.000047e+00      1.000047e+00   1.000047e+00   \n","min          -2.383302e+00    -1.135516e+00     -7.689960e-01  -1.613343e+00   \n","25%          -8.440579e-01    -3.595969e-01     -5.688870e-01  -1.103516e+00   \n","50%           9.196863e-02    -3.595969e-01     -3.687781e-01   3.204627e-01   \n","75%           8.407899e-01     4.163217e-01     -1.686692e-01   8.642883e-01   \n","max           2.068025e+00     3.519996e+00      3.233183e+00   2.561018e+00   \n","\n","       Reached.on.Time_Y.N  \n","count         10642.000000  \n","mean              0.590303  \n","std               0.491801  \n","min               0.000000  \n","25%               0.000000  \n","50%               1.000000  \n","75%               1.000000  \n","max               1.000000  "],"text/html":["\n","  <div id=\"df-840c3168-71f7-424b-81ac-6a938a122851\">\n","    <div class=\"colab-df-container\">\n","      <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>ID</th>\n","      <th>Customer_care_calls</th>\n","      <th>Customer_rating</th>\n","      <th>Cost_of_the_Product</th>\n","      <th>Prior_purchases</th>\n","      <th>Discount_offered</th>\n","      <th>Weight_in_gms</th>\n","      <th>Reached.on.Time_Y.N</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>count</th>\n","      <td>10642.000000</td>\n","      <td>1.064200e+04</td>\n","      <td>10642.000000</td>\n","      <td>1.064200e+04</td>\n","      <td>1.064200e+04</td>\n","      <td>1.064200e+04</td>\n","      <td>1.064200e+04</td>\n","      <td>10642.000000</td>\n","    </tr>\n","    <tr>\n","      <th>mean</th>\n","      <td>5570.347773</td>\n","      <td>6.986956e-15</td>\n","      <td>0.497298</td>\n","      <td>5.756426e-16</td>\n","      <td>-6.094584e-15</td>\n","      <td>2.935728e-14</td>\n","      <td>1.339529e-17</td>\n","      <td>0.590303</td>\n","    </tr>\n","    <tr>\n","      <th>std</th>\n","      <td>3159.806013</td>\n","      <td>1.000047e+00</td>\n","      <td>0.353086</td>\n","      <td>1.000047e+00</td>\n","      <td>1.000047e+00</td>\n","      <td>1.000047e+00</td>\n","      <td>1.000047e+00</td>\n","      <td>0.491801</td>\n","    </tr>\n","    <tr>\n","      <th>min</th>\n","      <td>1.000000</td>\n","      <td>-1.803627e+00</td>\n","      <td>0.000000</td>\n","      <td>-2.383302e+00</td>\n","      <td>-1.135516e+00</td>\n","      <td>-7.689960e-01</td>\n","      <td>-1.613343e+00</td>\n","      <td>0.000000</td>\n","    </tr>\n","    <tr>\n","      <th>25%</th>\n","      <td>2881.250000</td>\n","      <td>-9.304889e-01</td>\n","      <td>0.250000</td>\n","      <td>-8.440579e-01</td>\n","      <td>-3.595969e-01</td>\n","      <td>-5.688870e-01</td>\n","      <td>-1.103516e+00</td>\n","      <td>0.000000</td>\n","    </tr>\n","    <tr>\n","      <th>50%</th>\n","      <td>5604.500000</td>\n","      <td>-5.735048e-02</td>\n","      <td>0.500000</td>\n","      <td>9.196863e-02</td>\n","      <td>-3.595969e-01</td>\n","      <td>-3.687781e-01</td>\n","      <td>3.204627e-01</td>\n","      <td>1.000000</td>\n","    </tr>\n","    <tr>\n","      <th>75%</th>\n","      <td>8300.750000</td>\n","      <td>8.157880e-01</td>\n","      <td>0.750000</td>\n","      <td>8.407899e-01</td>\n","      <td>4.163217e-01</td>\n","      <td>-1.686692e-01</td>\n","      <td>8.642883e-01</td>\n","      <td>1.000000</td>\n","    </tr>\n","    <tr>\n","      <th>max</th>\n","      <td>10999.000000</td>\n","      <td>2.562065e+00</td>\n","      <td>1.000000</td>\n","      <td>2.068025e+00</td>\n","      <td>3.519996e+00</td>\n","      <td>3.233183e+00</td>\n","      <td>2.561018e+00</td>\n","      <td>1.000000</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" 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np.log(df2['Customer_care_calls'])\n","\n","# df2 = df2.drop(columns=['ID', 'Prior_purchases', 'Discount_offered', 'Weight_in_gms','Cost_of_the_Product', 'Customer_care_calls'])\n","# df2.describe()"],"metadata":{"id":"DoeIxg1kCeJC"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["df2.info()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"QjZqUP4_GqUy","executionInfo":{"status":"ok","timestamp":1650781360298,"user_tz":-420,"elapsed":335,"user":{"displayName":"Muhammad Hamzah","userId":"06012328832276120437"}},"outputId":"f569cdbf-08a8-4d71-8bde-8c2085a4bf2c"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["<class 'pandas.core.frame.DataFrame'>\n","Int64Index: 10642 entries, 0 to 10998\n","Data columns (total 12 columns):\n"," #   Column               Non-Null Count  Dtype  \n","---  ------               --------------  -----  \n"," 0   ID                   10642 non-null  int64  \n"," 1   Warehouse_block      10642 non-null  object \n"," 2   Mode_of_Shipment     10642 non-null  object \n"," 3   Customer_care_calls  10642 non-null  float64\n"," 4   Customer_rating      10642 non-null  float64\n"," 5   Cost_of_the_Product  10642 non-null  float64\n"," 6   Prior_purchases      10642 non-null  float64\n"," 7   Product_importance   10642 non-null  object \n"," 8   Gender               10642 non-null  object \n"," 9   Discount_offered     10642 non-null  float64\n"," 10  Weight_in_gms        10642 non-null  float64\n"," 11  Reached.on.Time_Y.N  10642 non-null  int64  \n","dtypes: float64(6), int64(2), object(4)\n","memory usage: 1.1+ MB\n"]}]},{"cell_type":"code","source":["# Label Encoding\n","mapping_Product_importance = {\n","    'low' : 0,\n","    'medium' : 1,\n","    'high' : 2\n","}\n","\n","mapping_Gender = {\n","    'F' : 0,\n","    'M' : 1\n","}\n","\n","df2['Product_importance'] = df2['Product_importance'].map(mapping_Product_importance)\n","df2['Gender'] = df2['Gender'].map(mapping_Gender)"],"metadata":{"id":"MHpjdOgOC69I"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["for cat in ['Mode_of_Shipment', 'Warehouse_block']:\n","    onehots = pd.get_dummies(df2[cat], prefix=cat)\n","    df2 = df2.join(onehots)"],"metadata":{"id":"GgADyt3mEQv0"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["df2.info()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"ejbt4DWnHYPu","executionInfo":{"status":"ok","timestamp":1650781744196,"user_tz":-420,"elapsed":477,"user":{"displayName":"Muhammad Hamzah","userId":"06012328832276120437"}},"outputId":"447cd38a-7484-4e41-ff19-0d01b544a1f7"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["<class 'pandas.core.frame.DataFrame'>\n","Int64Index: 10642 entries, 0 to 10998\n","Data columns (total 20 columns):\n"," #   Column                   Non-Null Count  Dtype  \n","---  ------                   --------------  -----  \n"," 0   ID                       10642 non-null  int64  \n"," 1   Warehouse_block          10642 non-null  object \n"," 2   Mode_of_Shipment         10642 non-null  object \n"," 3   Customer_care_calls      10642 non-null  float64\n"," 4   Customer_rating          10642 non-null  float64\n"," 5   Cost_of_the_Product      10642 non-null  float64\n"," 6   Prior_purchases          10642 non-null  float64\n"," 7   Product_importance       10642 non-null  int64  \n"," 8   Gender                   10642 non-null  int64  \n"," 9   Discount_offered         10642 non-null  float64\n"," 10  Weight_in_gms            10642 non-null  float64\n"," 11  Reached.on.Time_Y.N      10642 non-null  int64  \n"," 12  Mode_of_Shipment_Flight  10642 non-null  uint8  \n"," 13  Mode_of_Shipment_Road    10642 non-null  uint8  \n"," 14  Mode_of_Shipment_Ship    10642 non-null  uint8  \n"," 15  Warehouse_block_A        10642 non-null  uint8  \n"," 16  Warehouse_block_B        10642 non-null  uint8  \n"," 17  Warehouse_block_C        10642 non-null  uint8  \n"," 18  Warehouse_block_D        10642 non-null  uint8  \n"," 19  Warehouse_block_F        10642 non-null  uint8  \n","dtypes: float64(6), int64(4), object(2), uint8(8)\n","memory usage: 1.4+ MB\n"]}]},{"cell_type":"code","source":["df2 = df2.drop(columns=['Warehouse_block', 'Mode_of_Shipment'])"],"metadata":{"id":"RkAHB5glEVNn"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["df2.describe()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":364},"id":"bhVt5F_5P-Gx","executionInfo":{"status":"ok","timestamp":1650781760952,"user_tz":-420,"elapsed":395,"user":{"displayName":"Muhammad Hamzah","userId":"06012328832276120437"}},"outputId":"90e5f132-5372-4ce2-b799-e9bfb130e1f8"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["                 ID  Customer_care_calls  Customer_rating  \\\n","count  10642.000000         1.064200e+04     10642.000000   \n","mean    5570.347773         6.986956e-15         0.497298   \n","std     3159.806013         1.000047e+00         0.353086   \n","min        1.000000        -1.803627e+00         0.000000   \n","25%     2881.250000        -9.304889e-01         0.250000   \n","50%     5604.500000        -5.735048e-02         0.500000   \n","75%     8300.750000         8.157880e-01         0.750000   \n","max    10999.000000         2.562065e+00         1.000000   \n","\n","       Cost_of_the_Product  Prior_purchases  Product_importance        Gender  \\\n","count         1.064200e+04     1.064200e+04        10642.000000  10642.000000   \n","mean          5.756426e-16    -6.094584e-15            0.596786      0.496711   \n","std           1.000047e+00     1.000047e+00            0.637518      0.500013   \n","min          -2.383302e+00    -1.135516e+00            0.000000      0.000000   \n","25%          -8.440579e-01    -3.595969e-01            0.000000      0.000000   \n","50%           9.196863e-02    -3.595969e-01            1.000000      0.000000   \n","75%           8.407899e-01     4.163217e-01            1.000000      1.000000   \n","max           2.068025e+00     3.519996e+00            2.000000      1.000000   \n","\n","       Discount_offered  Weight_in_gms  Reached.on.Time_Y.N  \\\n","count      1.064200e+04   1.064200e+04         10642.000000   \n","mean       2.935728e-14   1.339529e-17             0.590303   \n","std        1.000047e+00   1.000047e+00             0.491801   \n","min       -7.689960e-01  -1.613343e+00             0.000000   \n","25%       -5.688870e-01  -1.103516e+00             0.000000   \n","50%       -3.687781e-01   3.204627e-01             1.000000   \n","75%       -1.686692e-01   8.642883e-01             1.000000   \n","max        3.233183e+00   2.561018e+00             1.000000   \n","\n","       Mode_of_Shipment_Flight  Mode_of_Shipment_Road  Mode_of_Shipment_Ship  \\\n","count             10642.000000           10642.000000           10642.000000   \n","mean                  0.162375               0.160026               0.677598   \n","std                   0.368812               0.366648               0.467418   \n","min                   0.000000               0.000000               0.000000   \n","25%                   0.000000               0.000000               0.000000   \n","50%                   0.000000               0.000000               1.000000   \n","75%                   0.000000               0.000000               1.000000   \n","max                   1.000000               1.000000               1.000000   \n","\n","       Warehouse_block_A  Warehouse_block_B  Warehouse_block_C  \\\n","count       10642.000000       10642.000000       10642.000000   \n","mean            0.166980           0.167074           0.166416   \n","std             0.372975           0.373059           0.372471   \n","min             0.000000           0.000000           0.000000   \n","25%             0.000000           0.000000           0.000000   \n","50%             0.000000           0.000000           0.000000   \n","75%             0.000000           0.000000           0.000000   \n","max             1.000000           1.000000           1.000000   \n","\n","       Warehouse_block_D  Warehouse_block_F  \n","count       10642.000000       10642.000000  \n","mean            0.166980           0.332550  \n","std             0.372975           0.471149  \n","min             0.000000           0.000000  \n","25%             0.000000           0.000000  \n","50%             0.000000           0.000000  \n","75%             0.000000           1.000000  \n","max             1.000000           1.000000  "],"text/html":["\n","  <div id=\"df-8b8d42e6-c8d9-4189-8d06-7ac5dbb46b93\">\n","    <div class=\"colab-df-container\">\n","      <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: 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<td>1.064200e+04</td>\n","      <td>10642.000000</td>\n","      <td>1.064200e+04</td>\n","      <td>1.064200e+04</td>\n","      <td>10642.000000</td>\n","      <td>10642.000000</td>\n","      <td>1.064200e+04</td>\n","      <td>1.064200e+04</td>\n","      <td>10642.000000</td>\n","      <td>10642.000000</td>\n","      <td>10642.000000</td>\n","      <td>10642.000000</td>\n","      <td>10642.000000</td>\n","      <td>10642.000000</td>\n","      <td>10642.000000</td>\n","      <td>10642.000000</td>\n","      <td>10642.000000</td>\n","    </tr>\n","    <tr>\n","      <th>mean</th>\n","      <td>5570.347773</td>\n","      <td>6.986956e-15</td>\n","      <td>0.497298</td>\n","      <td>5.756426e-16</td>\n","      <td>-6.094584e-15</td>\n","      <td>0.596786</td>\n","      <td>0.496711</td>\n","      <td>2.935728e-14</td>\n","      <td>1.339529e-17</td>\n","      <td>0.590303</td>\n","      <td>0.162375</td>\n","      <td>0.160026</td>\n","      <td>0.677598</td>\n","      <td>0.166980</td>\n","      <td>0.167074</td>\n","      <td>0.166416</td>\n","      <td>0.166980</td>\n","      <td>0.332550</td>\n","    </tr>\n","    <tr>\n","      <th>std</th>\n","      <td>3159.806013</td>\n","      <td>1.000047e+00</td>\n","      <td>0.353086</td>\n","      <td>1.000047e+00</td>\n","      <td>1.000047e+00</td>\n","      <td>0.637518</td>\n","      <td>0.500013</td>\n","      <td>1.000047e+00</td>\n","      <td>1.000047e+00</td>\n","      <td>0.491801</td>\n","      <td>0.368812</td>\n","      <td>0.366648</td>\n","      <td>0.467418</td>\n","      <td>0.372975</td>\n","      <td>0.373059</td>\n","      <td>0.372471</td>\n","      <td>0.372975</td>\n","      <td>0.471149</td>\n","    </tr>\n","    <tr>\n","      <th>min</th>\n","      <td>1.000000</td>\n","      <td>-1.803627e+00</td>\n","      <td>0.000000</td>\n","      <td>-2.383302e+00</td>\n","      <td>-1.135516e+00</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>-7.689960e-01</td>\n","      <td>-1.613343e+00</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","      <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>2881.250000</td>\n","      <td>-9.304889e-01</td>\n","      <td>0.250000</td>\n","      <td>-8.440579e-01</td>\n","      <td>-3.595969e-01</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>-5.688870e-01</td>\n","      <td>-1.103516e+00</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","      <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>50%</th>\n","      <td>5604.500000</td>\n","      <td>-5.735048e-02</td>\n","      <td>0.500000</td>\n","      <td>9.196863e-02</td>\n","      <td>-3.595969e-01</td>\n","      <td>1.000000</td>\n","      <td>0.000000</td>\n","      <td>-3.687781e-01</td>\n","      <td>3.204627e-01</td>\n","      <td>1.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>1.000000</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>75%</th>\n","      <td>8300.750000</td>\n","      <td>8.157880e-01</td>\n","      <td>0.750000</td>\n","      <td>8.407899e-01</td>\n","      <td>4.163217e-01</td>\n","      <td>1.000000</td>\n","      <td>1.000000</td>\n","      <td>-1.686692e-01</td>\n","      <td>8.642883e-01</td>\n","      <td>1.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>1.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>1.000000</td>\n","    </tr>\n","    <tr>\n","      <th>max</th>\n","      <td>10999.000000</td>\n","      <td>2.562065e+00</td>\n","      <td>1.000000</td>\n","      <td>2.068025e+00</td>\n","      <td>3.519996e+00</td>\n","      <td>2.000000</td>\n","      <td>1.000000</td>\n","      <td>3.233183e+00</td>\n","      <td>2.561018e+00</td>\n","      <td>1.000000</td>\n","      <td>1.000000</td>\n","      <td>1.000000</td>\n","      <td>1.000000</td>\n","      <td>1.000000</td>\n","      <td>1.000000</td>\n","      <td>1.000000</td>\n","      <td>1.000000</td>\n","      <td>1.000000</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-8b8d42e6-c8d9-4189-8d06-7ac5dbb46b93')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg 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Hamzah","userId":"06012328832276120437"}},"outputId":"e45d2c1d-1949-408e-e38f-4928b7b701fe"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["<class 'pandas.core.frame.DataFrame'>\n","Int64Index: 10642 entries, 0 to 10998\n","Data columns (total 18 columns):\n"," #   Column                   Non-Null Count  Dtype  \n","---  ------                   --------------  -----  \n"," 0   ID                       10642 non-null  int64  \n"," 1   Customer_care_calls      10642 non-null  float64\n"," 2   Customer_rating          10642 non-null  float64\n"," 3   Cost_of_the_Product      10642 non-null  float64\n"," 4   Prior_purchases          10642 non-null  float64\n"," 5   Product_importance       10642 non-null  int64  \n"," 6   Gender                   10642 non-null  int64  \n"," 7   Discount_offered         10642 non-null  float64\n"," 8   Weight_in_gms            10642 non-null  float64\n"," 9   Reached.on.Time_Y.N      10642 non-null  int64  \n"," 10  Mode_of_Shipment_Flight  10642 non-null  uint8  \n"," 11  Mode_of_Shipment_Road    10642 non-null  uint8  \n"," 12  Mode_of_Shipment_Ship    10642 non-null  uint8  \n"," 13  Warehouse_block_A        10642 non-null  uint8  \n"," 14  Warehouse_block_B        10642 non-null  uint8  \n"," 15  Warehouse_block_C        10642 non-null  uint8  \n"," 16  Warehouse_block_D        10642 non-null  uint8  \n"," 17  Warehouse_block_F        10642 non-null  uint8  \n","dtypes: float64(6), int64(4), uint8(8)\n","memory usage: 1.2 MB\n"]}]},{"cell_type":"code","source":["# #export data clean and transform\n","# df2.to_csv('data_clean.csv', index=False)"],"metadata":{"id":"-GZZpilyFIj_"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["# RandomForest"],"metadata":{"id":"s6hywuQU3ZXN"}},{"cell_type":"code","source":["def model_evaluation(model, pred, xtrain, ytrain, xtest, ytest):\n","    print('Accuracy : %.2f' % accuracy_score(ytest, pred))\n","    print('Precision : %.2f' % precision_score(ytest, pred))\n","    print('Recall : %.2f' % recall_score(ytest, pred))\n","    print('F-1Score : %.2f' % f1_score(ytest, pred))\n","    \n","    fpr, tpr, thresholds = roc_curve(ytest, pred, pos_label=1) \n","    print('ROC AUC : %.2f' % auc(fpr, tpr))\n","    \n","    # Average precision score\n","    average_precision = average_precision_score(ytest, pred)\n","    print('AP : %.2f'% average_precision)"],"metadata":{"id":"D7F7Rdcy3dhT"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# Feature Importance Function\n","def feature_importance(model, x):\n","    feat_importances = pd.Series(model.best_estimator_.feature_importances_, index = x.columns)\n","    ax = feat_importances.nlargest(25).plot(kind = 'barh', figsize = (10, 8))\n","    ax.invert_yaxis()\n","\n","# Best Hyperparameter Function\n","def best_hyperparameter(model, hyperparameters):\n","    for key, value in hyperparameters.items() :\n","        print('Best '+key+':', model.get_params()[key])"],"metadata":{"id":"Sb8M2Sz83nF_"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["X = df2.drop(columns = ['ID','Reached.on.Time_Y.N','Customer_care_calls', 'Customer_rating'])\n","y = df2['Reached.on.Time_Y.N']"],"metadata":{"id":"1-gjK3pS3pI9"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["from sklearn.model_selection import train_test_split \n","from sklearn.ensemble import RandomForestClassifier\n","from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score\n","from sklearn.metrics import roc_curve, auc\n","from sklearn.metrics import precision_recall_curve, plot_precision_recall_curve, average_precision_score\n","from sklearn.metrics import classification_report, confusion_matrix, plot_confusion_matrix\n","from sklearn.model_selection import validation_curve\n","\n","# Evaluation Metrics\n","X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 42)\n","model = RandomForestClassifier(random_state = 42)\n","model.fit(X_train, y_train)\n","prediction = model.predict(X_test)\n","model_evaluation(model, prediction, X_train, y_train, X_test, y_test)\n","param_grid = { \n","    'n_estimators': [200, 700],\n","    'max_features': ['auto', 'sqrt', 'log2']\n","}"],"metadata":{"id":"7ekwDYBR33Sc","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1650290153944,"user_tz":-420,"elapsed":579,"user":{"displayName":"Fajar Ariev","userId":"18314973382710209713"}},"outputId":"bf8a7fdf-1ed7-4475-b104-06bd67940f2b"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Accuracy : 0.65\n","Precision : 0.72\n","Recall : 0.64\n","F-1Score : 0.68\n","ROC AUC : 0.65\n","AP : 0.67\n"]}]},{"cell_type":"code","source":["# Model Validation Accuracy\n","print('train Accuracy : ', model.score(X_train, y_train))\n","print('test Accuracy : ', model.score(X_test, y_test))"],"metadata":{"id":"PLz84fxG34qm","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1650290157073,"user_tz":-420,"elapsed":361,"user":{"displayName":"Fajar Ariev","userId":"18314973382710209713"}},"outputId":"1fc06847-9d28-4471-e950-00d571578a8d"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["train Accuracy :  1.0\n","test Accuracy :  0.6481916392672616\n"]}]},{"cell_type":"code","source":["# Hyperparameter Tuning Random Forest\n","parameter_range = np.arange(1, 10, 1)\n","\n","train_score, test_score = validation_curve(\n","                                RandomForestClassifier(random_state = 42),\n","                                X = X_train, y = y_train, \n","                                param_name = 'n_estimators', \n","                                param_range = parameter_range,\n","                                cv = 5,\n","                                scoring = 'roc_auc') \n","\n","\n","# Calculating mean and standard deviation of training score\n","mean_train_score = np.mean(train_score, axis = 1)\n","std_train_score = np.std(train_score, axis = 1)\n"," \n","# Calculating mean and standard deviation of testing score\n","mean_test_score = np.mean(test_score, axis = 1)\n","std_test_score = np.std(test_score, axis = 1)\n"," \n","# Plot mean accuracy scores for training and testing scores\n","plt.plot(parameter_range, mean_train_score,\n","     label = \"Training Score\", color = 'b')\n","plt.plot(parameter_range, mean_test_score,\n","   label = \"Cross Validation Score\", color = 'g')\n"," \n","# Creating the plot\n","plt.title(\"Validation Curve with RandomForest Classifier\")\n","plt.xlabel(\"n estimator\")\n","plt.ylabel(\"Accuracy\")\n","plt.tight_layout()\n","plt.legend(loc = 'best')\n","plt.show()"],"metadata":{"id":"vnC6D9xR39Sa","colab":{"base_uri":"https://localhost:8080/","height":297},"executionInfo":{"status":"ok","timestamp":1650290163922,"user_tz":-420,"elapsed":3686,"user":{"displayName":"Fajar Ariev","userId":"18314973382710209713"}},"outputId":"cd03a503-7554-44b8-876e-adf1bd3772a8"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 432x288 with 1 Axes>"],"image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":["# Evaluation Metrics\n","model = RandomForestClassifier(random_state = 42, n_estimators = 8, max_depth = 4, min_samples_split = 5, min_samples_leaf = 2)\n","model.fit(X_train, y_train)\n","prediction = model.predict(X_test)\n","model_evaluation(model, prediction, X_train, y_train, X_test, y_test)"],"metadata":{"id":"FQEVMO_L4DLf","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1650290166380,"user_tz":-420,"elapsed":389,"user":{"displayName":"Fajar Ariev","userId":"18314973382710209713"}},"outputId":"41bc8d7e-93c2-4cee-8323-998ff23b314d"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Accuracy : 0.61\n","Precision : 0.62\n","Recall : 0.86\n","F-1Score : 0.72\n","ROC AUC : 0.57\n","AP : 0.61\n"]}]},{"cell_type":"code","source":["# Model Prediction\n","prediction_train = model.predict(X_train)\n","prediction_train"],"metadata":{"id":"ukMw7t-F4FJh","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1650290170513,"user_tz":-420,"elapsed":359,"user":{"displayName":"Fajar Ariev","userId":"18314973382710209713"}},"outputId":"a079ed46-eb3a-4fba-a60f-d2b0c414ef75"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([1, 1, 0, ..., 1, 1, 1])"]},"metadata":{},"execution_count":97}]},{"cell_type":"code","source":["# Model Validation Average Precision\n","print('AP train: ',average_precision_score(y_train, prediction_train))\n","print('AP test: ',average_precision_score(y_test, prediction))"],"metadata":{"id":"Alq9dMZJ4OO4","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1650290173806,"user_tz":-420,"elapsed":377,"user":{"displayName":"Fajar Ariev","userId":"18314973382710209713"}},"outputId":"d516d5f3-9986-42ca-fe7b-d3a07f8662ae"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["AP train:  0.637838502871672\n","AP test:  0.6127122047085208\n"]}]},{"cell_type":"code","source":["# Model Validation Accuracy\n","print('train Accuracy : ', model.score(X_train, y_train))\n","print('test Accuracy : ', model.score(X_test, y_test))"],"metadata":{"id":"5TKwM0xk4PMn","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1650290175869,"user_tz":-420,"elapsed":598,"user":{"displayName":"Fajar Ariev","userId":"18314973382710209713"}},"outputId":"ac44a05a-c4bb-41f3-e2ab-13fbc78ec551"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["train Accuracy :  0.6382003993891695\n","test Accuracy :  0.6120244246124942\n"]}]},{"cell_type":"code","source":["#plt.figsize(10, 8)\n","feat_importances = pd.Series(model.feature_importances_, index=X_train.columns)\n","feat_importances\n","ax = feat_importances.nlargest(25).plot(kind='barh', figsize=(10, 8))\n","ax.invert_yaxis()\n","\n","plt.xlabel('score')\n","plt.ylabel('feature')\n","plt.title('feature importance score')"],"metadata":{"id":"90U5JC-m4SE9","colab":{"base_uri":"https://localhost:8080/","height":530},"executionInfo":{"status":"ok","timestamp":1650290185036,"user_tz":-420,"elapsed":852,"user":{"displayName":"Fajar Ariev","userId":"18314973382710209713"}},"outputId":"ac965afb-f4a8-4a7b-d14f-ad6a28a00121"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["Text(0.5, 1.0, 'feature importance score')"]},"metadata":{},"execution_count":100},{"output_type":"display_data","data":{"text/plain":["<Figure size 720x576 with 1 Axes>"],"image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":["# Split Feature and Label\n","X = df2.drop(columns=['ID','Customer_care_calls','Customer_rating','Gender','Warehouse_block_A','Warehouse_block_B','Warehouse_block_C', 'Warehouse_block_F', 'Mode_of_Shipment_Flight','Mode_of_Shipment_Road','Mode_of_Shipment_Ship', 'Warehouse_block_D','Reached.on.Time_Y.N'])\n","y = df2['Reached.on.Time_Y.N'] # target / label\n","# Evaluation Metrics\n","X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 42)\n","model = RandomForestClassifier(random_state = 42)\n","model.fit(X_train, y_train)\n","prediction = model.predict(X_test)\n","model_evaluation(model, prediction, X_train, y_train, X_test, y_test)\n","param_grid = { \n","    'n_estimators': [200, 700],\n","    'max_features': ['auto', 'sqrt', 'log2']\n","}"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"o-GtNC4p9yLS","executionInfo":{"status":"ok","timestamp":1650290360870,"user_tz":-420,"elapsed":2222,"user":{"displayName":"Fajar Ariev","userId":"18314973382710209713"}},"outputId":"5f819205-1c0c-413e-e663-fd29f046e593"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Accuracy : 0.65\n","Precision : 0.71\n","Recall : 0.66\n","F-1Score : 0.68\n","ROC AUC : 0.65\n","AP : 0.67\n"]}]},{"cell_type":"code","source":["# Model Validation Accuracy\n","print('train Accuracy : ', model.score(X_train, y_train))\n","print('test Accuracy : ', model.score(X_test, y_test))"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"9uH6ExZQ-NBE","executionInfo":{"status":"ok","timestamp":1650290383190,"user_tz":-420,"elapsed":959,"user":{"displayName":"Fajar Ariev","userId":"18314973382710209713"}},"outputId":"0d9b1b86-e5b5-4cea-9b92-fb5aa3608554"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["train Accuracy :  1.0\n","test Accuracy :  0.6486613433536872\n"]}]},{"cell_type":"code","source":["# Hyperparameter Tuning Random Forest\n","parameter_range = np.arange(1, 10, 1)\n","\n","train_score, test_score = validation_curve(\n","                                RandomForestClassifier(random_state = 42),\n","                                X = X_train, y = y_train, \n","                                param_name = 'n_estimators', \n","                                param_range = parameter_range,\n","                                cv = 5,\n","                                scoring = 'roc_auc') \n","\n","\n","# Calculating mean and standard deviation of training score\n","mean_train_score = np.mean(train_score, axis = 1)\n","std_train_score = np.std(train_score, axis = 1)\n"," \n","# Calculating mean and standard deviation of testing score\n","mean_test_score = np.mean(test_score, axis = 1)\n","std_test_score = np.std(test_score, axis = 1)\n"," \n","# Plot mean accuracy scores for training and testing scores\n","plt.plot(parameter_range, mean_train_score,\n","     label = \"Training Score\", color = 'b')\n","plt.plot(parameter_range, mean_test_score,\n","   label = \"Cross Validation Score\", color = 'g')\n"," \n","# Creating the plot\n","plt.title(\"Validation Curve with RandomForest Classifier\")\n","plt.xlabel(\"n estimator\")\n","plt.ylabel(\"Accuracy\")\n","plt.tight_layout()\n","plt.legend(loc = 'best')\n","plt.show()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":297},"id":"2QRH-4hS-NuK","executionInfo":{"status":"ok","timestamp":1650290407382,"user_tz":-420,"elapsed":5758,"user":{"displayName":"Fajar Ariev","userId":"18314973382710209713"}},"outputId":"3f324fb4-af01-4d4c-f2c0-35cd491d8208"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 432x288 with 1 Axes>"],"image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":["# Evaluation Metrics\n","model = RandomForestClassifier(random_state = 42, n_estimators = 8, max_depth = 4, min_samples_split = 5, min_samples_leaf = 2)\n","model.fit(X_train, y_train)\n","prediction = model.predict(X_test)\n","model_evaluation(model, prediction, X_train, y_train, X_test, y_test)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"hYVzYLu1-Wkh","executionInfo":{"status":"ok","timestamp":1650290421789,"user_tz":-420,"elapsed":360,"user":{"displayName":"Fajar Ariev","userId":"18314973382710209713"}},"outputId":"9f816b18-9dea-49f5-ee73-789fc310b1d9"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Accuracy : 0.68\n","Precision : 0.82\n","Recall : 0.56\n","F-1Score : 0.67\n","ROC AUC : 0.70\n","AP : 0.71\n"]}]},{"cell_type":"code","source":["# Model Validation Accuracy\n","print('train Accuracy : ', model.score(X_train, y_train))\n","print('test Accuracy : ', model.score(X_test, y_test))"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"vuD2R2sy-dn5","executionInfo":{"status":"ok","timestamp":1650290470802,"user_tz":-420,"elapsed":365,"user":{"displayName":"Fajar Ariev","userId":"18314973382710209713"}},"outputId":"a6f93364-e9a4-433b-ae48-0079fce57a75"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["train Accuracy :  0.6789615881592858\n","test Accuracy :  0.6754344762799437\n"]}]},{"cell_type":"code","source":["#plt.figsize(10, 8)\n","feat_importances = pd.Series(model.feature_importances_, index=X_train.columns)\n","feat_importances\n","ax = feat_importances.nlargest(25).plot(kind='barh', figsize=(10, 8))\n","ax.invert_yaxis()\n","\n","plt.xlabel('score')\n","plt.ylabel('feature')\n","plt.title('feature importance score')"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":530},"id":"d9AVo5hJ-mwN","executionInfo":{"status":"ok","timestamp":1650290488729,"user_tz":-420,"elapsed":981,"user":{"displayName":"Fajar Ariev","userId":"18314973382710209713"}},"outputId":"7fdf2846-8d9f-492d-f569-ebe15c6facb2"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["Text(0.5, 1.0, 'feature importance score')"]},"metadata":{},"execution_count":107},{"output_type":"display_data","data":{"text/plain":["<Figure size 720x576 with 1 Axes>"],"image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"markdown","source":["# XGBOOST"],"metadata":{"id":"ga4Hr51k4rCq"}},{"cell_type":"code","source":["import warnings\n","warnings.filterwarnings('ignore')\n","\n","from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score\n","from sklearn.metrics import roc_curve, auc\n","\n","def eval_classification(model, pred, xtrain, ytrain, xtest, ytest):\n","    print(\"Accuracy (Test Set): %.2f\" % accuracy_score(ytest, pred))\n","    print(\"Precision (Test Set): %.2f\" % precision_score(ytest, pred))\n","    print(\"Recall (Test Set): %.2f\" % recall_score(ytest, pred))\n","    print(\"F1-Score (Test Set): %.2f\" % f1_score(ytest, pred))\n","    \n","    fpr, tpr, thresholds = roc_curve(ytest, pred, pos_label=1) # pos_label: label yang kita anggap positive\n","    print(\"AUC: %.2f\" % auc(fpr, tpr))\n","\n","def show_feature_importance(model):\n","    feat_importances = pd.Series(model.feature_importances_, index=X.columns)\n","    ax = feat_importances.nlargest(25).plot(kind='barh', figsize=(15, 8))\n","    ax.invert_yaxis()\n","\n","    plt.xlabel('score')\n","    plt.ylabel('feature')\n","    plt.title('feature importance score')\n","\n","def show_best_hyperparameter(model, hyperparameters):\n","    for key, value in hyperparameters.items() :\n","        print('Best '+key+':', model.get_params()[key])"],"metadata":{"id":"TV1AuwyB4wad"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["#data tanpa feature selection\n","# df2 = df1.drop(columns=['ID','Customer_care_calls', 'Customer_rating'])\n","# df1"],"metadata":{"id":"WVL5ey46GQD-"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# Split Feature and Label\n","X = df2.drop(columns=['ID','Customer_care_calls', 'Customer_rating','Reached.on.Time_Y.N'])\n","y = df2['Reached.on.Time_Y.N'] # target / label\n","\n","#Splitting the data into Train and Test\n","from sklearn.model_selection import train_test_split \n","X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 42)\n","\n","\n","from xgboost import XGBClassifier\n","xg = XGBClassifier(random_state=42)\n","xg.fit(X_train, y_train)\n","\n","y_pred = xg.predict(X_test)\n","eval_classification(xg, y_pred, X_train, y_train, X_test, y_test)"],"metadata":{"id":"VCxEhUxrGP_c","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1650289952582,"user_tz":-420,"elapsed":1764,"user":{"displayName":"Fajar Ariev","userId":"18314973382710209713"}},"outputId":"64eb9157-a42a-40da-c4aa-2af204fe0eb1"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Accuracy (Test Set): 0.67\n","Precision (Test Set): 0.87\n","Recall (Test Set): 0.51\n","F1-Score (Test Set): 0.64\n","AUC: 0.70\n"]}]},{"cell_type":"code","source":["print('Train score: ' + str(xg.score(X_train, y_train)))\n","print('Test score:' + str(xg.score(X_test, y_test)))"],"metadata":{"id":"IoEDPZmrGP63","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1650289952583,"user_tz":-420,"elapsed":9,"user":{"displayName":"Fajar Ariev","userId":"18314973382710209713"}},"outputId":"7d3261d8-c796-411f-8b4e-fa265f0699d4"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Train score: 0.6999882532597205\n","Test score:0.6712071395021136\n"]}]},{"cell_type":"code","source":["from sklearn.model_selection import RandomizedSearchCV\n","import numpy as np\n","\n","#Menjadikan ke dalam bentuk dictionary\n","hyperparameters = {\n","                    'max_depth' : [int(x) for x in np.linspace(10, 110, num = 9)],\n","                    'min_child_weight' : [int(x) for x in np.linspace(1, 20, num = 11)],\n","                    'gamma' : [float(x) for x in np.linspace(0, 1, num = 11)],\n","                    'tree_method' : ['auto', 'exact', 'approx', 'hist'],\n","    \n","                    'colsample_bytree' : [float(x) for x in np.linspace(0, 1, num = 11)],\n","                    'eta' : [float(x) for x in np.linspace(0, 1, num = 100)],\n","\n","                    'lambda' : [float(x) for x in np.linspace(0, 1, num = 11)],\n","                    'alpha' : [float(x) for x in np.linspace(0, 1, num = 11)]\n","                    }\n","\n","# Init\n","xg = XGBClassifier(random_state=42)\n","xg_tuned1 = RandomizedSearchCV(xg, hyperparameters, cv=5, random_state=42, scoring='roc_auc')\n","xg_tuned1.fit(X_train,y_train)\n","\n","# Predict & Evaluation\n","y_pred = xg_tuned1.predict(X_test)#Check performa dari model\n","eval_classification(xg_tuned1, y_pred, X_train, y_train, X_test, y_test)"],"metadata":{"id":"UHAzWzfRGPqk","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1650290021716,"user_tz":-420,"elapsed":69138,"user":{"displayName":"Fajar Ariev","userId":"18314973382710209713"}},"outputId":"a229fcb8-f566-44d3-ed27-6a9c137cdd79"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Accuracy (Test Set): 0.64\n","Precision (Test Set): 0.68\n","Recall (Test Set): 0.72\n","F1-Score (Test Set): 0.70\n","AUC: 0.63\n"]}]},{"cell_type":"code","source":["print('Train score: ' + str(xg_tuned1.score(X_train, y_train)))\n","print('Test score:' + str(xg_tuned1.score(X_test, y_test)))"],"metadata":{"id":"ktgb2QlLHf2F","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1650290021717,"user_tz":-420,"elapsed":8,"user":{"displayName":"Fajar Ariev","userId":"18314973382710209713"}},"outputId":"0044d9c8-ddc9-448e-eb96-a00ac54cafb8"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Train score: 0.762938418300392\n","Test score:0.7309610679175896\n"]}]},{"cell_type":"code","source":["show_feature_importance(xg_tuned1.best_estimator_)"],"metadata":{"id":"flWXGLc5HlOS","colab":{"base_uri":"https://localhost:8080/","height":512},"executionInfo":{"status":"ok","timestamp":1650290022594,"user_tz":-420,"elapsed":882,"user":{"displayName":"Fajar Ariev","userId":"18314973382710209713"}},"outputId":"3a748346-6240-4e87-e787-007afda089bf"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 1080x576 with 1 Axes>"],"image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":["#data dengan feature selection\n","# df2=df.copy()\n","# df2 = df2.drop(columns=['ID','Customer_care_calls','Customer_rating','Gender','Product_importance','Warehouse_block_A','Warehouse_block_B','Warehouse_block_C', 'Warehouse_block_F', 'Mode_of_Shipment_Flight','Mode_of_Shipment_Road','Mode_of_Shipment_Ship', 'Cost_of_the_Product'])\n","# df2"],"metadata":{"id":"PUZKwLdFHMJg"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# Split Feature and Label\n","X = df2.drop(columns=['ID','Customer_care_calls','Customer_rating','Gender','Warehouse_block_A','Warehouse_block_B','Warehouse_block_C', 'Warehouse_block_F', 'Mode_of_Shipment_Flight','Mode_of_Shipment_Road','Mode_of_Shipment_Ship', 'Warehouse_block_D','Reached.on.Time_Y.N'])\n","y = df2['Reached.on.Time_Y.N'] # target / label\n","\n","#Splitting the data into Train and Test\n","from sklearn.model_selection import train_test_split \n","X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 42)\n","\n","\n","from xgboost import XGBClassifier\n","xg = XGBClassifier(random_state=42)\n","xg.fit(X_train, y_train)\n","\n","y_pred = xg.predict(X_test)\n","eval_classification(xg, y_pred, X_train, y_train, X_test, y_test)"],"metadata":{"id":"hWuMSpoqHMDj","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1650290022595,"user_tz":-420,"elapsed":14,"user":{"displayName":"Fajar Ariev","userId":"18314973382710209713"}},"outputId":"7dda8a75-01b0-41bb-f486-b84350c46423"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Accuracy (Test Set): 0.68\n","Precision (Test Set): 0.88\n","Recall (Test Set): 0.51\n","F1-Score (Test Set): 0.65\n","AUC: 0.71\n"]}]},{"cell_type":"code","source":["print('Train score: ' + str(xg.score(X_train, y_train)))\n","print('Test score:' + str(xg.score(X_test, y_test)))"],"metadata":{"id":"Q0vHHwiJHL8m","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1650290022596,"user_tz":-420,"elapsed":9,"user":{"displayName":"Fajar Ariev","userId":"18314973382710209713"}},"outputId":"29f00305-a53d-400a-c93c-552674b9af8b"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Train score: 0.6991659814401504\n","Test score:0.6763738844527948\n"]}]},{"cell_type":"code","source":["from sklearn.model_selection import RandomizedSearchCV\n","import numpy as np\n","\n","#Menjadikan ke dalam bentuk dictionary\n","hyperparameters = {\n","                    'max_depth' : [int(x) for x in np.linspace(10, 110, num = 9)],\n","                    'min_child_weight' : [int(x) for x in np.linspace(1, 20, num = 11)],\n","                    'gamma' : [float(x) for x in np.linspace(0, 1, num = 11)],\n","                    'tree_method' : ['auto', 'exact', 'approx', 'hist'],\n","    \n","                    'colsample_bytree' : [float(x) for x in np.linspace(0, 1, num = 11)],\n","                    'eta' : [float(x) for x in np.linspace(0, 1, num = 100)],\n","\n","                    'lambda' : [float(x) for x in np.linspace(0, 1, num = 11)],\n","                    'alpha' : [float(x) for x in np.linspace(0, 1, num = 11)]\n","                    }\n","\n","# Init\n","xg = XGBClassifier(random_state=42)\n","xg_tuned2 = RandomizedSearchCV(xg, hyperparameters, cv=5, random_state=42, scoring='roc_auc')\n","xg_tuned2.fit(X_train,y_train)\n","\n","# Predict & Evaluation\n","y_pred = xg_tuned2.predict(X_test)#Check performa dari model\n","eval_classification(xg_tuned2, y_pred, X_train, y_train, X_test, y_test)"],"metadata":{"id":"gubo6tQ4HL1Y","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1650290061586,"user_tz":-420,"elapsed":38996,"user":{"displayName":"Fajar Ariev","userId":"18314973382710209713"}},"outputId":"84680062-9dcf-4a57-a72b-f8a318b4c1a8"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Accuracy (Test Set): 0.65\n","Precision (Test Set): 0.71\n","Recall (Test Set): 0.67\n","F1-Score (Test Set): 0.69\n","AUC: 0.65\n"]}]},{"cell_type":"code","source":["print('Train score: ' + str(xg_tuned2.score(X_train, y_train)))\n","print('Test score:' + str(xg_tuned2.score(X_test, y_test)))"],"metadata":{"id":"4h--QvvwHLxc","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1650290061587,"user_tz":-420,"elapsed":19,"user":{"displayName":"Fajar Ariev","userId":"18314973382710209713"}},"outputId":"754999da-71ef-48e8-c892-b0be9cb1076d"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Train score: 0.9550895034637741\n","Test score:0.7316234490147534\n"]}]},{"cell_type":"code","source":["show_feature_importance(xg_tuned2.best_estimator_)"],"metadata":{"id":"94b3UDoIHLtl","colab":{"base_uri":"https://localhost:8080/","height":512},"executionInfo":{"status":"ok","timestamp":1650290062184,"user_tz":-420,"elapsed":613,"user":{"displayName":"Fajar Ariev","userId":"18314973382710209713"}},"outputId":"07ddc766-b6fb-4549-9dc5-accf366089ff"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 1080x576 with 1 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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"markdown","source":["#lightGBM\n"],"metadata":{"id":"NfgaOwxlIwWS"}},{"cell_type":"code","source":["import lightgbm as ltb"],"metadata":{"id":"Lau8dz0VIx-T"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["x = df2[['Discount_offered', 'Weight_in_gms', 'Cost_of_the_Product','Prior_purchases']] # feature yang kita gunakan\n","y = df2['Reached.on.Time_Y.N'] # variable target\n","\n","xtrain, xtest, ytrain, ytest = train_test_split(x,y,test_size=0.2,random_state=42)"],"metadata":{"id":"PGQduhlvI2Ea"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["model = ltb.LGBMClassifier(learning_rate=0.09,num_leaves = 3, max_depth=2,random_state=42, num_iterations=132)\n","model.fit(xtrain,ytrain,eval_set=[(xtest,ytest),(xtrain,ytrain)],eval_metric='auc')\n","print(); print(model)\n","    \n","expectedy  = ytest\n","predictedy = model.predict(xtest)"],"metadata":{"id":"BWkqDmbaI3zK","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1650290062572,"user_tz":-420,"elapsed":393,"user":{"displayName":"Fajar Ariev","userId":"18314973382710209713"}},"outputId":"dc4ca3e4-79a9-42f1-c5a2-8173405416fc"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["[1]\ttraining's binary_logloss: 0.657109\ttraining's auc: 0.728153\tvalid_0's binary_logloss: 0.662647\tvalid_0's auc: 0.731431\n","[2]\ttraining's binary_logloss: 0.641362\ttraining's auc: 0.728153\tvalid_0's binary_logloss: 0.646926\tvalid_0's auc: 0.731431\n","[3]\ttraining's binary_logloss: 0.6279\ttraining's auc: 0.728153\tvalid_0's binary_logloss: 0.633469\tvalid_0's auc: 0.731431\n","[4]\ttraining's binary_logloss: 0.616317\ttraining's auc: 0.728153\tvalid_0's binary_logloss: 0.621877\tvalid_0's auc: 0.731431\n","[5]\ttraining's binary_logloss: 0.606294\ttraining's auc: 0.728779\tvalid_0's binary_logloss: 0.611853\tvalid_0's auc: 0.731994\n","[6]\ttraining's binary_logloss: 0.597575\ttraining's auc: 0.728779\tvalid_0's binary_logloss: 0.603108\tvalid_0's auc: 0.731994\n","[7]\ttraining's binary_logloss: 0.589963\ttraining's auc: 0.728779\tvalid_0's binary_logloss: 0.595467\tvalid_0's auc: 0.731994\n","[8]\ttraining's binary_logloss: 0.583287\ttraining's auc: 0.728779\tvalid_0's binary_logloss: 0.588773\tvalid_0's auc: 0.731994\n","[9]\ttraining's binary_logloss: 0.577359\ttraining's auc: 0.738179\tvalid_0's binary_logloss: 0.583118\tvalid_0's auc: 0.736514\n","[10]\ttraining's binary_logloss: 0.572125\ttraining's auc: 0.738221\tvalid_0's binary_logloss: 0.577852\tvalid_0's auc: 0.736162\n","[11]\ttraining's binary_logloss: 0.567449\ttraining's auc: 0.738212\tvalid_0's binary_logloss: 0.573425\tvalid_0's auc: 0.736707\n","[12]\ttraining's binary_logloss: 0.563297\ttraining's auc: 0.738212\tvalid_0's binary_logloss: 0.569215\tvalid_0's auc: 0.736707\n","[13]\ttraining's binary_logloss: 0.559571\ttraining's auc: 0.738202\tvalid_0's binary_logloss: 0.565717\tvalid_0's auc: 0.736743\n","[14]\ttraining's binary_logloss: 0.556242\ttraining's auc: 0.738202\tvalid_0's binary_logloss: 0.562339\tvalid_0's auc: 0.736743\n","[15]\ttraining's binary_logloss: 0.553244\ttraining's auc: 0.738202\tvalid_0's binary_logloss: 0.559552\tvalid_0's auc: 0.736743\n","[16]\ttraining's binary_logloss: 0.550555\ttraining's auc: 0.738202\tvalid_0's binary_logloss: 0.55681\tvalid_0's auc: 0.736743\n","[17]\ttraining's binary_logloss: 0.548125\ttraining's auc: 0.738202\tvalid_0's binary_logloss: 0.554576\tvalid_0's auc: 0.736743\n","[18]\ttraining's binary_logloss: 0.545934\ttraining's auc: 0.739762\tvalid_0's binary_logloss: 0.552462\tvalid_0's auc: 0.734539\n","[19]\ttraining's binary_logloss: 0.543955\ttraining's auc: 0.739762\tvalid_0's binary_logloss: 0.550665\tvalid_0's auc: 0.734539\n","[20]\ttraining's binary_logloss: 0.542156\ttraining's auc: 0.739762\tvalid_0's binary_logloss: 0.548807\tvalid_0's auc: 0.734539\n","[21]\ttraining's binary_logloss: 0.540531\ttraining's auc: 0.739762\tvalid_0's binary_logloss: 0.547353\tvalid_0's auc: 0.734539\n","[22]\ttraining's binary_logloss: 0.539051\ttraining's auc: 0.739762\tvalid_0's binary_logloss: 0.545814\tvalid_0's auc: 0.734539\n","[23]\ttraining's binary_logloss: 0.537711\ttraining's auc: 0.739762\tvalid_0's binary_logloss: 0.544635\tvalid_0's auc: 0.734539\n","[24]\ttraining's binary_logloss: 0.536486\ttraining's auc: 0.739762\tvalid_0's binary_logloss: 0.543353\tvalid_0's auc: 0.734539\n","[25]\ttraining's binary_logloss: 0.535376\ttraining's auc: 0.739762\tvalid_0's binary_logloss: 0.542396\tvalid_0's auc: 0.734539\n","[26]\ttraining's binary_logloss: 0.534356\ttraining's auc: 0.739762\tvalid_0's binary_logloss: 0.54143\tvalid_0's auc: 0.734539\n","[27]\ttraining's binary_logloss: 0.533436\ttraining's auc: 0.740727\tvalid_0's binary_logloss: 0.540652\tvalid_0's auc: 0.733862\n","[28]\ttraining's binary_logloss: 0.532584\ttraining's auc: 0.739762\tvalid_0's binary_logloss: 0.539742\tvalid_0's auc: 0.734539\n","[29]\ttraining's binary_logloss: 0.531815\ttraining's auc: 0.740727\tvalid_0's binary_logloss: 0.539109\tvalid_0's auc: 0.733862\n","[30]\ttraining's binary_logloss: 0.531103\ttraining's auc: 0.739762\tvalid_0's binary_logloss: 0.538341\tvalid_0's auc: 0.734539\n","[31]\ttraining's binary_logloss: 0.53046\ttraining's auc: 0.740727\tvalid_0's binary_logloss: 0.537826\tvalid_0's auc: 0.733862\n","[32]\ttraining's binary_logloss: 0.529862\ttraining's auc: 0.740727\tvalid_0's binary_logloss: 0.537271\tvalid_0's auc: 0.733862\n","[33]\ttraining's binary_logloss: 0.52932\ttraining's auc: 0.741241\tvalid_0's binary_logloss: 0.536746\tvalid_0's auc: 0.734075\n","[34]\ttraining's binary_logloss: 0.52882\ttraining's auc: 0.740228\tvalid_0's binary_logloss: 0.536194\tvalid_0's auc: 0.734705\n","[35]\ttraining's binary_logloss: 0.528351\ttraining's auc: 0.741241\tvalid_0's binary_logloss: 0.535851\tvalid_0's auc: 0.734075\n","[36]\ttraining's binary_logloss: 0.527929\ttraining's auc: 0.740228\tvalid_0's binary_logloss: 0.535379\tvalid_0's auc: 0.734705\n","[37]\ttraining's binary_logloss: 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0.738917\n","[127]\ttraining's binary_logloss: 0.506736\ttraining's auc: 0.755248\tvalid_0's binary_logloss: 0.517163\tvalid_0's auc: 0.738882\n","[128]\ttraining's binary_logloss: 0.506711\ttraining's auc: 0.755316\tvalid_0's binary_logloss: 0.517137\tvalid_0's auc: 0.738959\n","[129]\ttraining's binary_logloss: 0.506635\ttraining's auc: 0.755234\tvalid_0's binary_logloss: 0.517043\tvalid_0's auc: 0.739243\n","[130]\ttraining's binary_logloss: 0.506282\ttraining's auc: 0.755235\tvalid_0's binary_logloss: 0.516706\tvalid_0's auc: 0.739186\n","[131]\ttraining's binary_logloss: 0.505966\ttraining's auc: 0.755299\tvalid_0's binary_logloss: 0.516403\tvalid_0's auc: 0.739141\n","[132]\ttraining's binary_logloss: 0.505949\ttraining's auc: 0.755385\tvalid_0's binary_logloss: 0.516451\tvalid_0's auc: 0.738871\n","\n","LGBMClassifier(learning_rate=0.09, max_depth=2, num_iterations=132,\n","               num_leaves=3, random_state=42)\n"]}]},{"cell_type":"code","source":["from sklearn import metrics\n","print(metrics.classification_report(expectedy, predictedy))\n","print(metrics.confusion_matrix(expectedy, predictedy))"],"metadata":{"id":"7HcAFgLlI9A6","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1650290062572,"user_tz":-420,"elapsed":12,"user":{"displayName":"Fajar Ariev","userId":"18314973382710209713"}},"outputId":"152e9f31-c37c-4a26-e6f5-d2e294ff4487"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["              precision    recall  f1-score   support\n","\n","           0       0.58      0.89      0.70       897\n","           1       0.87      0.52      0.65      1232\n","\n","    accuracy                           0.68      2129\n","   macro avg       0.72      0.71      0.68      2129\n","weighted avg       0.75      0.68      0.67      2129\n","\n","[[802  95]\n"," [589 643]]\n"]}]},{"cell_type":"code","source":["ltb.plot_importance(model)"],"metadata":{"id":"BOTzIXjEJHJh","colab":{"base_uri":"https://localhost:8080/","height":312},"executionInfo":{"status":"ok","timestamp":1650290063246,"user_tz":-420,"elapsed":677,"user":{"displayName":"Fajar Ariev","userId":"18314973382710209713"}},"outputId":"45b9e43a-30af-4cda-af61-1e5be567f35b"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<matplotlib.axes._subplots.AxesSubplot at 0x7fb8675a7290>"]},"metadata":{},"execution_count":46},{"output_type":"display_data","data":{"text/plain":["<Figure size 432x288 with 1 Axes>"],"image/png":"iVBORw0KGgoAAAANSUhEUgAAAdcAAAEWCAYAAAA0MN3QAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAgAElEQVR4nO3debxVVf3/8debIUAR0FBLHBBHFIyENP0aXcuHI6WkqWgljjmk9UtRq69KZmXl2PDVFBRTwhGHslTCrvpFUUGvggN+TSkkRFBBLyLB5fP7Y68Lh8MdDrAv557L+/l4nAd7r7332p91jp7PXWuvs7ciAjMzM8tPu3IHYGZm1tY4uZqZmeXMydXMzCxnTq5mZmY5c3I1MzPLmZOrmZlZzpxczawsJP1Q0qhyx2HWEuTfuZpVHkkzgS2BuoLinSPi3+tY5ykR8bd1i67ySBoJ7BgR3yh3LNY2uOdqVrm+EhFdC15rnVjzIKlDOc+/tio1bmvdnFzN2hBJ3SWNljRH0mxJl0lqn7btIOlRSe9Kmi9prKQeadutwLbAnyTVSjpfUpWkt4rqnynpgLQ8UtLdkm6T9AEwvKnzNxDrSEm3peXekkLSiZJmSXpf0umSPifpRUkLJP224NjhkiZJ+q2khZJelfTlgu1bSXpA0nuSXpd0atF5C+M+HfghcExq+wtpvxMlvSLpQ0lvSPp2QR1Vkt6SdK6kd1J7TyzY3kXSlZL+meL7X0ld0rbPS3oytekFSVVr9WFbq+bkata2jAGWATsCnwUOBE5J2wT8HNgK6AtsA4wEiIhvAv9iZW/4lyWe73DgbqAHMLaZ85dib2An4BjgGuBHwAHA7sDRkr5YtO8/gJ7AJcB4SZulbbcDb6W2HgX8TNKXGol7NPAz4I7U9s+kfd4BhgDdgBOBqyXtWVDHp4DuQC/gZOB3kjZN264ABgL7ApsB5wPLJfUCHgQuS+XnAfdI2nwN3iOrAE6uZpXrvtT7WSDpPklbAocC34uIRRHxDnA1cCxARLweERMiYklEzAOuAr7YePUleSoi7ouI5WRJqNHzl+gnEfFxRDwCLALGRcQ7ETEbeIIsYdd7B7gmIpZGxB3ADOAwSdsA/wVckOqqAUYB32oo7ohY3FAgEfFgRPwjMo8BjwBfKNhlKXBpOv9fgFpgF0ntgJOA70bE7Iioi4gnI2IJ8A3gLxHxl3TuCcCU9L5ZG+JrDWaV64jCyUeS9gI6AnMk1Re3A2al7VsC15IliE3StvfXMYZZBcvbNXX+Es0tWF7cwHrXgvXZseqMzH+S9VS3At6LiA+Ltg1qJO4GSTqErEe8M1k7NgKmFezybkQsK1j/KMXXE+hM1qsuth3wdUlfKSjrCPy9uXissji5mrUds4AlQM+iL/16PwMC6B8R70k6Avhtwfbinw4sIksoAKRrp8XDl4XHNHf+vPWSpIIEuy3wAPBvYDNJmxQk2G2B2QXHFrd1lXVJnYB7yHq790fEUkn3kQ2tN2c+8DGwA/BC0bZZwK0RcepqR1mb4mFhszYiIuaQDV1eKambpHZpElP90O8mZEOXC9O1vxFFVcwF+hSsvwZ0lnSYpI7AfwOd1uH8edsCOEdSR0lfJ7uO/JeImAU8CfxcUmdJe5BdE72tibrmAr3TkC7AJ8jaOg9YlnqxB5YSVBoivwm4Kk2sai9pn5SwbwO+IumgVN45TY7aes2bb62Zk6tZ2/ItssTwMtmQ793Ap9O2HwN7AgvJJtWMLzr258B/p2u450XEQuBMsuuVs8l6sm/RtKbOn7enySY/zQd+ChwVEe+mbcOA3mS92HuBS5r5/e5d6d93JT2XerznAHeSteM4sl5xqc4jG0J+FngP+AXQLiX+w8lmJ88j68mOwN/FbY5vImFmFUfScLIbXuxX7ljMGuK/lszMzHLm5GpmZpYzDwubmZnlzD1XMzOznPl3rkaPHj1ixx13LHcYuVm0aBEbb7xxucPIjdvTurk9rVtLtmfq1KnzI6LBW1c6uRpbbrklU6ZMKXcYuamurqaqqqrcYeTG7Wnd3J7WrSXbI+mfjW3zsLCZmVnOnFzNzMxy5uRqZmaWMydXMzOznDm5mpmZ5czJ1czMLGdOrmZmZjlzcjUzM8uZk6uZmVnOnFzNzMxy5uRqZmaWMydXMzOznDm5mpmZ5czJ1czMLGdOrmZmZjlzcjUzM8uZk6uZmVnOnFzNzMxy5uRqZmaWMydXMzOznDm5mpmZ5czJ1czMLGdOrmZmZjlzcjUzM8uZk6uZmVnOnFzNzMxy5uRqZmaWMydXMzOznDm5mpmZ5czJ1czMLGdOrmZmZjlzcjUzM8uZk6uZmVnOnFzNzMxy5uRqZmaWMydXMzOznDm5mpmZ5UwRUe4YrMy27bNjtDv62nKHkZtz+y/jymkdyh1Gbtye1s3tKa+Zlx/W5Pbq6mqqqqpa5NySpkbEoIa2uedqZmZtxkknncQWW2xBv379VpTddddd7L777rRr144pU6asKB87diwDBgxY8WrXrh01NTW5xOHkamZmbcbw4cN56KGHVinr168f48ePZ/DgwauUH3/88dTU1FBTU8Ott97K9ttvz4ABA3KJo6KTq6SrJX2vYP1hSaMK1q+U9P1Gjr1U0gHN1D9S0nkNlPeQdGYzx24l6e7mW2FmZnkZPHgwm2222Splffv2ZZdddmnyuHHjxnHsscfmFkdFJ1dgErAvgKR2QE9g94Lt+wJPNnRgRFwcEX9by/P2AJpMrhHx74g4ai3rNzOz9eiOO+5g2LBhudVXOVetG/YkcHVa3h2YDnxa0qbAR0BfICQ9BnQF5gPDI2KOpDHAnyPibkmHAlcBi8gSdp+IGJLq3U1SNbAtcE1E/Bq4HNhBUg0wISJGFAcmqXeqv5+k4cBXgY2AHYB7I+L8xhol6WTgAmAB8AKwJCK+k2JeDHwW2AI4CfgWsA/wdEQMl9QeGA0MAgK4KSKubuAcpwGnAfTsuTkX91/WWDgVZ8su2aSMtsLtad3cnvKqrq5ereztt99m0aJFVFdXU1tbu2KfBQsWMHXqVGpra1fZ/+WXXyYimD9/foP1rY2KTq4R8W9JyyRtS9ZLfQroRZZsFgKvkCXfwyNinqRjgJ+SJSUAJHUGfg8Mjog3JY0rOs2uwP7AJsAMSdcBFwL9ImJNBucHkCXFJame30TErOKdJG0FXATsCXwIPEqWYOttmtr3VeAB4L+AU4BnJQ0A2gO9IqJfqq9HQ8FExA3ADZDNFq6k2YHNqbTZjs1xe1o3t6e8Zh5ftXrZzJlsvPHGVFVVrTJbuEePHgwcOJBBg1ad4Hv//fdzyimn5DqruHLewcY9SZZY9yXrffZKywuB2cCBwARJkCWeOUXH7wq8ERFvpvVxpB5d8mBELAGWSHoH2HIt45wYEQsBJL0MbAesllyBvYDHIuK9tO9dwM4F2/8UESFpGjA3Iqal/V4CegOPAX0k/QZ4EHhkLeM1M2vzli9fzp133skTTzyRa71tIbnWX3ftTzYsPAs4F/gAqCbrxe2zDvUvKViuY+3fs7zrWV5U53KgQ0S8L+kzwEHA6cDRFPTUzczasmHDhlFdXc38+fPZeuutGTZsGO+//z5nn3028+bN47DDDmPAgAE8/PDDADz++ONss8029OnTJ9c42kJyfRI4j6z3WQe8l4ZCdwe+DZwtaZ+IeEpSR2DniHip4PgZZD293hExEzimhHN+SDZM3BKeBa5J140/BI4EppV6sKSewH8i4h5JM4DbmjumS8f2zGjmh9iVpLq6usGhokrl9rRubk/rMm7cqlf26oeFhw4d2uD+VVVVTJ48Ofc42kJynUY2S/iPRWVdI+IdSUcBv5bUnay91wArkmtELE4/q3lI0iKy5NakiHhX0iRJ04G/NjShaW1FxGxJPwOeAd4DXiUb4i5VL+DmNHsa4Ad5xWZmZqWp+OSaeqvdisqGFyzXAIOLDltlH+DvEbGrsguzvwOmpH1GFh3Tr2D5uGbimgn0S8tjgDEF24Y0eNBKf4yIGyR1AO4F7mugXSvqb6A9ezZTv5mZtaBK/51rXk5NP6t5CehONnu4nEameKYDb5KSq5mZVYaK77nmIf0OdLXfgpZCUn/g1qLiJRGxdwnHPg10Kir+ZkSsdlcoMzOrHE6u6yj9FGatbkZZSgI2M7PK42FhMzOznDm5mpmZ5czJ1czMLGdOrmZmZjlzcjUzM8uZk6uZmVnOnFzNzMxy5uRqZmaWMydXMzOznDm5mpmZ5czJ1czMLGdOrmZmZjlzcjUzM8uZk6uZmVnOnFzNzMxy5uRqZmaWMydXMzOznDm5mpmZ5czJ1czMLGdOrmZmZjlzcjUzM8uZk6uZmVnOnFzNzMxy5uRqZmaWMydXMzOznDm5mpmZ5czJ1czMLGdOrmZmZjlzcjUzM8tZh3IHYOW3eGkdvS98sNxh5Obc/ssY7va0WuVoz8zLD1uxPGPGDI455pgV62+88QaXXnopvXr1YuTIkbzyyis888wzDBo0aL3GaG2Lk6uZbVB22WUXampqAKirq6NXr14MHTqUjz76iPHjx/Ptb3+7zBFaW9Bqh4Ul1UmqkfSSpBcknSupXdo2SNKvyxzfD9fh2HMkvSJprKROkv6W2npM80ev0Xl6S5qeZ51mbcnEiRPZYYcd2G677ejbty+77LJLuUOyNqI191wXR8QAAElbAH8EugGXRMQUYEo5gwN+CPxsLY89EzggIt6S9HmA+raWQlKHiFi2luc2s+T2229n2LBh5Q7D2qDWnFxXiIh3JJ0GPCtpJPBF4LyIGCLpi8C19bsCgyPiQ0kXAN8AlgN/jYgLJQ0Argc2Av4BnBQR70uqTvVNkdQTmBIRvSUNB76a9t8BuDcizpd0OdBFUg3wUkQc31Dckr4PnJRWR0XENZKuB/oAf5V0G3AqsHmq60igB3AV0BWYDwyPiDkpxhpgP2BcWm9ov4HATemcjzT2nqb38zSAnj035+L+bSdXb9klu67XVrg96666unq1sqVLl3LPPfcwZMiQVbYvWLCAqVOnUltbW1LdtbW1DdZfqdyefFREcgWIiDcktQe2KNp0HnBWREyS1BX4WNIhwOHA3hHxkaTN0r5/AM6OiMckXQpcAnyvmVMPAD4LLAFmSPpNStTfaaq3mZLcicDegICnJT0WEadLOhjYPyLmS3qalX8odARuBQ6PiHlpmPinrEzQn4iIQWm/xxrZ72bgOxHxuKRfNfF+3gDcALBtnx3jymkV859Cs87tvwy3p/UqR3tmHl+1Wtn999/P3nvvzde+9rVVynv06MHAgQNLntBUXV1NVdXq9VcqtycfbeH/2EnAVZLGAuPTUOsBwM0R8RFARLwnqTvQIyIeS8fdAtxVQv0TI2IhgKSXge2AWSUctx9ZT3dROnY88AXg+SaO2QXoB0yQBNAemFOw/Y6m9pPUg6yNj6f9bgUOKSFWsw3OuHHjPCRsLaakCU2SdpDUKS1XpQk5PVo2tNVi6APUAe8UlkfE5cApQBdgkqRd16L6Zax8LzoXbVtSsFxHy/5BIrJh5gHp1T8iDizYvqjE/cysCYsWLWLChAmr9Frvvfdett56a5566ikOO+wwDjrooDJGaJWu1NnC9wB1knYkG0rchmyC0XohaXOya6W/jYgo2rZDREyLiF8AzwK7AhOAEyVtlPbZLPU+35f0hXToN8mGVgFmAgPT8lElhrU0Dc825gngCEkbSdoYGJrKmjKD7PrrPinujpJ2L3W/iFgALJC0X9qvwWvBZhu6jTfemHfffZfu3buvKBs6dChvvfUWS5YsYe7cuTz88MNljNAqXam9sOURsUzSUOA3EfEbSU0Nb+ahfsJQR7Ke5a1kE3iKfU/S/mQTl14im7y0JE1emiLpP8BfyGb3ngBcn5LuG2TXRAGuAO5Mk3xK/XX7DcCLkp5raEJTRDwnaQzwTCoaFRFNvmcR8R9JRwG/TsPYHYBrUrtK3e9E4CZJQRMTmgp16dieGQU/sq901dXVDV5jq1Ruj1nlKTW5LpU0jCw5fSWVNdVrW2cR0b6JbdVAdVo+u5F9LgcuLyqrAT7fwL6vAnsUFP13Kh8DjCnYb0jB8gXABc204Soa+IMgIno31JaCGAc3cExVA21paL+pwGcKis5vKkYzM8tfqcPCJwL7AD+NiDclbU/WkzQzM7MiJfVcI+Ll9LvRbdP6m8AvWjKwSiHpk8DEBjZ9OSLeXd/xmJlZ+ZWUXCV9hey65CeA7dP1zEsj4qstGVwlSAm05LsrmZlZ21fqsPBIYC9gAay43tenhWIyMzOraKUm16X1N1IosDzvYMzMzNqCUmcLvyTpOKC9pJ2Ac4AnWy4sMzOzylVqz/VsYHeyuxX9EVhI8/fkNTMz2yA123NNN8t/MCL2B37U8iGZmZlVtmZ7rhFRByxPdwIyMzOzZpR6zbUWmCZpAitvHk9EnNMiUZmZmVWwUpPr+PQyMzOzZpR6h6ZbWjoQMzOztqLUOzS9CURxeUT4RhJmZmZFSh0WHlSw3Bn4OrBZ/uGYmZlVvpJ+5xoR7xa8ZkfENUDbeQComZlZjkodFt6zYLUdWU+21F6vmZnZBqXUBHllwfIy4E3g6PzDMTMzq3ylJteTI+KNwoL0wHQzMzMrUuq9he8usczMzGyD12TPVdKuZDfs7y7pawWbupHNGjYzM7MizQ0L7wIMAXoAXyko/xA4taWCMjMzq2RNJteIuB+4X9I+EfHUeorJzMysopU6oel5SWeRDRGvGA6OiJNaJCozM7MKVuqEpluBTwEHAY8BW5MNDZuZmVmRUpPrjhFxEbAo3cT/MGDvlgvLzMyscpWaXJemfxdI6gd0B7ZomZDMzMwqW6nXXG+QtClwEfAA0BW4uMWiMjMzq2ClPs91VFp8DPBj5szMzJpQ0rCwpC0ljZb017S+m6STWzY0MzOzylTqNdcxwMPAVmn9NeB7LRGQmZlZpSs1ufaMiDuB5QARsQyoa7GozMzMKlipyXWRpE8CASDp88DCFovKzMysgpU6W/j7ZLOEd5A0CdgcOKrForL1avHSOnpf+GC5w8jNuf2XMbxC2zPz8sPKHYKZ5aDJnqukbQEi4jngi8C+wLeB3SPixZYPz2zD9fHHH7PXXntx8skns/vuu3PJJZcAMHHiRPbcc08GDBjAfvvtx+uvv17mSM2sWHPDwvcVLN8RES9FxPSIWNroEWaWi06dOvHoo48yevRoampqeOihh5g8eTJnnHEGY8eOpaamhuOOO47LLrus3KGaWZHmkqsKllvF71sl1UmqkTRd0l2SNmpkvyfXd2ylktRb0vRyx2GtmyS6du0KwNKlS1m6dCmSkMQHH3wAwMKFC9lqq62aqsbMyqC5a67RyHI5LY6IAQCSxgKnA1fVb5TUISKWRcS+pVZYf0zegbZUvbbhqKur45RTTuHtt9/mrLPOYu+992bUqFEceuihdOnShW7dujF58uRyh2lmRRTReM6UVAcsIuvBdgE+qt8ERER0a/EIV4+pNiK6puXTgT2AO4GfAO8Du0bEzvX7SRLwS+AQsj8QLouIOyRVFR/TwLl6Aw8BU4E9gZeAb0XER5JmAoMiYr6kQcAVEVElaSSwA1lP/19kvwe+npU9/zOAfwN/Bf6X7Dr2bODwiFgs6VTgNOATwOvAN9P5vg5cQvYTqIURMVhSe+ByoAroBPwuIn4v6dPAHUA3sj+gzoiIJ4radlo6Dz17bj7w4mtuXJOPoVXbsgvMXVzuKNZO/17dVyurra0F4KKLLuKcc87h5ptv5thjj2W33Xbj9ttvZ9asWYwYMWJ9h7rWamtrV/TI2wK3p3Vryfbsv//+UyNiUEPbmntYevsWiSgHkjqQJcyHUtGeQL+IeLNo168BA4DPAD2BZyU93swxhXYBTo6ISZJuAs4ErmgmvN2A/VKyvAN4LCKGpmTYFdgU2AkYFhGnSroTOBK4DRgfETemNl4GnAz8huxezgdFxGxJPdJ5TiZLtJ+T1AmYJOmR1OaHI+Kn6ZyrDZ1HxA3ADQDb9tkxrpxW6sTx1u/c/suo1PbMPL5qtbLq6mqqqqp47rnnmD9/PrNnz+bMM88EoE+fPhx88MFUVa1+XGtV3562wu1p3crVnlJ/59qadJFUA0wh6xmOTuXPNJIk9wPGRURdRMwluz/y55o5ptCsiJiUlm9L9TXngYio7zt9CbgOIMVQ//vgNyOiJi1PBXqn5X6SnpA0DTie7AH1AJOAMalnW/9Hz4HAt9L78TTwSbKk/SxwYupF948IP3u3As2bN48FCxYAsHjxYiZMmEDfvn1ZuHAhr732GsCKMjNrXSrxz/sV11zrZSO/LFqLuko5pnjcvH59GSv/OOm8FvUuKViuIxt2h+xWk0dExAuShpMN+RIRp0vam+xZulMlDSQbnj87Ih4urlzS4LTvGElXRcQfSojJWpE5c+Zwwgkn8MEHH9ClSxeOPvpohgwZwo033siRRx5Ju3bt2HTTTbnpppvKHaqZFanE5LqmngC+LekWYDNgMDAC2LXE47eVtE9EPAUcR3adFGAmMJDs2umRTRw/kew66zUFw8JN2QSYI6kjWc91NoCkHSLiaeBpSYcA25Dd7/kMSY9GxFJJO6f9ewJvRcSNabh4T6DR5NqlY3tmtKGbF1RXVzc4vFpp9thjD55//vnVhrWGDh3K0KFDyxeYmTWrEoeF19S9wIvAC8CjwPkR8fYaHD8DOEvSK2TXSq9L5T8GrpU0habvs/xdYP80zDuV7HpsUy4iG+KdBLxaUP4rSdPST3ieTO0ZBbwMPJfKf0/2B1MV8IKk54FjgGtLbKuZmeWg4nqu9TOFi8qqgeqG9otsOvSI9GrymEYsi4hvNHDOJ4DVZhhHxMii9bnA4Q3U269gnysKlq9jZQIvrOdrDdQRwA/Tq9At6WVmZmWwIfRczczM1quK67m2hPTEn4kNbPpyRPRroNzMzKxRTq5ARLxL9ltYMzOzdeZhYTMzs5w5uZqZmeXMydXMzCxnTq5mZmY5c3I1MzPLmZOrmZlZzpxczczMcubkamZmljMnVzMzs5w5uZqZmeXMydXMzCxnTq5mZmY5c3I1MzPLmZOrmZlZzpxczczMcubkamZmljMnVzMzs5w5uZqZmeXMydXMzCxnTq5mZmY5c3I1MzPLmZOrmZlZzpxczczMcubkamZmljMnVzMzs5w5uZqZmeXMydXMzCxnTq5mZmY5c3I1MzPLWYdyB2Dlt3hpHb0vfLDcYeTm3P7LGF6h7Zl5+WErlj/++GMGDx7Mu+++S+fOnTnqqKP48Y9/zMSJExkxYgTLly+na9eujBkzhh133LGMUZtZMfdczVqpTp068eijjzJ69Ghqamp46KGHmDx5MmeccQZjx46lpqaG4447jssuu6zcoZpZkRZNrpI+Jel2Sf+QNFXSXyTtvIZ1/HAdzn+OpFckjW1ke5WkfQvWx0g6am3PV1BPnaQaSdMl3SVpo3Woq1rSoLU4roekM9f2vFZ+kujatSsAS5cuZenSpUhCEh988AEACxcuZKuttipnmGbWgBYbFpYk4F7glog4NpV9BtgSeG0Nqvoh8LO1DONM4ICIeKuR7VVALfDkWtbfmMURMQAgJfbTgavqN0rqEBHLcj5nsR5k7f+fFj6PtaC6ujpOOeUU3n77bc466yz23ntvRo0axaGHHkqXLl3o1q0bkydPLneYZlZEEdEyFUtfAkZGxOCicgG/BA4BArgsIu6Q9GngDqAbWdI/AzgMGAFMA16KiOMbOdf3gZPS6qiIuEbS9alsBnBTRFxddExvYDJQB8wDzgZOBj4ABgGfAs6PiLvT/iOAo4FOwL0RcUkTba+NiK5p+XRgD+BO4CfA+8Cuqey6dK5lwPcj4u+SugA3A58BXgW2As6KiClF9R4FDImI4ZK2BK4H+qQQzgDOAQ5P7Z8QESOKYjwNOA2gZ8/NB158zY2NNafibNkF5i4udxRrp3+v7quV1dbWAnDRRRdxzjnncPPNN3Psscey2267cfvttzNr1ixGjBix2nGtVW1t7YoeeVvg9rRuLdme/ffff2pENDiy2JITmvoBUxso/xowgCx59ASelfQ4cBzwcET8VFJ7YKOIeELSd+p7gQ2RNBA4EdgbEPC0pMci4nRJBwP7R8T84uMiYmZKwLURcUWq62Tg08B+ZAnwAeBuSQcCOwF7pXM8IGlwRDze1BsgqQPZHxEPpaI9gX4R8aakc7Mwor+kXYFH0pD5GcBHEdFX0h7Ac02dI/k18FhEDE3vXVfgwnSuBt+7iLgBuAFg2z47xpXT2s7ctnP7L6NS2zPz+KrVyqqrq6mqquK5555j/vz5zJ49mzPPzEb8+/Tpw8EHH0xV1erHtVb17Wkr3J7WrVztKceEpv2AcRFRFxFzgceAzwHPAidKGgn0j4gP16C+eyNiUUTUAuOBL6xDfPdFxPKIeJlsCBvgwPR6nizZ7UqWbBvTRVINMAX4FzA6lT8TEW8WxH0bQES8CvwT2BkYXFD+IvBiCTF/iawXTHpfF5ZwjLVy8+bNY8GCBQAsXryYCRMm0LdvXxYuXMhrr2VXVurLzKx1ack/718CSp4cFBGPSxpMNhQ8RtJVEfGHFouucUsKllXw788j4vcl1rG4uMeYjYazaB1jKxzD77yOdVkrN2fOHE444QQ++OADunTpwtFHH82QIUO48cYbOfLII2nXrh2bbropN910U7lDNbMiLdlzfRTolK7tAZCGORcAx0hqL2lzsp7aM5K2A+ZGxI3AKLIhVIClkjo2cZ4ngCMkbSRpY2BoKivFh8AmJez3MHCSpPrrnb0kbVHiORrzBHB8qm9nYFuy66P1Q+RI6kd2bbbeXEl9JbUja2e9iWTDyaT3tTult81aqT322IPnn3+e0aNHM336dC6++GIAhg4dyrRp03jhhReorq6mT58+zdRkZutbi/VcIyIkDQWukXQB8DEwE/ge2TXBF8h6YudHxNuSTgBGSFpKNoP3W6mqG4AXJT3X0ISmiHhO0hjgmVQ0KiKeLzHMP5FdUz2cbEJTY215RFJf4KnUA60FvgG8U+J5GvI/wHWSppFNaBoeEUskXQfcLOkV4BVWvZz1rrcAAAnUSURBVG59IfBnsglYU8jeR4DvAjeka8Z1wBkR8ZSkSZKmA38tntBUqEvH9swouHlBpauurm7w2qWZ2frSorM+IuLfZDNsi41Ir8J9bwFuaaCOC4ALmjnPVRT81KWgvHczx73Gqj3DJ4q2dy1Yvha4tqn6GjquoKwaqC5Y/5hsIlbxfouBYxup927g7gbK55LNDC4uP66UeM3MLF++Q5OZmVnOKub3CpI+SXZtsdiXI+LdZo49kWzotNCkiDirHPGYmVnbVjHJNSWsRn/v2syxN5PdmKFVxGNmZm2bh4XNzMxy5uRqZmaWMydXMzOznDm5mpmZ5czJ1czMLGdOrmZmZjlzcjUzM8uZk6uZmVnOnFzNzMxy5uRqZmaWMydXMzOznDm5mpmZ5czJ1czMLGdOrmZmZjlzcjUzM8uZk6uZmVnOnFzNzMxy5uRqZmaWMydXMzOznDm5mpmZ5czJ1czMLGdOrmZmZjlzcjUzM8uZk6uZmVnOnFzNzMxy5uRqZmaWMydXMzOznDm5mpmZ5czJ1czMLGdOrmZmZjlzcjUzM8uZk6uZmVnOnFzNzMxy5uRqZmaWM0VEuWOwMpP0ITCj3HHkqCcwv9xB5Mjtad3cntatJduzXURs3tCGDi10QqssMyJiULmDyIukKW5P6+X2tG5uTz48LGxmZpYzJ1czM7OcObkawA3lDiBnbk/r5va0bm5PDjyhyczMLGfuuZqZmeXMydXMzCxnTq4bOEkHS5oh6XVJF5Y7njUlaRtJf5f0sqSXJH03lW8maYKk/0v/blruWEslqb2k5yX9Oa1vL+np9BndIekT5Y5xTUjqIeluSa9KekXSPhX++fy/9N/adEnjJHWupM9I0k2S3pE0vaCswc9DmV+ndr0oac/yRd6wRtrzq/Tf24uS7pXUo2DbD1J7Zkg6qKXicnLdgElqD/wOOATYDRgmabfyRrXGlgHnRsRuwOeBs1IbLgQmRsROwMS0Xim+C7xSsP4L4OqI2BF4Hzi5LFGtvWuBhyJiV+AzZG2ryM9HUi/gHGBQRPQD2gPHUlmf0Rjg4KKyxj6PQ4Cd0us04Lr1FOOaGMPq7ZkA9IuIPYDXgB8ApO+GY4Hd0zH/k74Hc+fkumHbC3g9It6IiP8AtwOHlzmmNRIRcyLiubT8IdkXdy+ydtySdrsFOKI8Ea4ZSVsDhwGj0rqALwF3p10qpi0AkroDg4HRABHxn4hYQIV+PkkHoIukDsBGwBwq6DOKiMeB94qKG/s8Dgf+EJnJQA9Jn14/kZamofZExCMRsSytTga2TsuHA7dHxJKIeBN4nex7MHdOrhu2XsCsgvW3UllFktQb+CzwNLBlRMxJm94GtixTWGvqGuB8YHla/ySwoOCLotI+o+2BecDNaah7lKSNqdDPJyJmA1cA/yJLqguBqVT2ZwSNfx5t4TviJOCvaXm9tcfJ1doESV2Be4DvRcQHhdsi+71Zq//NmaQhwDsRMbXcseSoA7AncF1EfBZYRNEQcKV8PgDpWuThZH80bAVszOpDkhWtkj6P5kj6Edmlo7Hr+9xOrhu22cA2Betbp7KKIqkjWWIdGxHjU/Hc+uGr9O875YpvDfwX8FVJM8mG6L9Edr2yRxqChMr7jN4C3oqIp9P63WTJthI/H4ADgDcjYl5ELAXGk31ulfwZQeOfR8V+R0gaDgwBjo+VN3RYb+1xct2wPQvslGY6foLsQv8DZY5pjaRrkqOBVyLiqoJNDwAnpOUTgPvXd2xrKiJ+EBFbR0Rvss/i0Yg4Hvg7cFTarSLaUi8i3gZmSdolFX0ZeJkK/HySfwGfl7RR+m+vvj0V+xkljX0eDwDfSrOGPw8sLBg+brUkHUx2eeWrEfFRwaYHgGMldZK0PdlErWdaJIiI8GsDfgGHks2m+wfwo3LHsxbx70c2hPUiUJNeh5Jdq5wI/B/wN2Czcse6hu2qAv6clvukL4DXgbuATuWObw3bMgCYkj6j+4BNK/nzAX4MvApMB24FOlXSZwSMI7tevJRsZOHkxj4PQGS/KPgHMI1slnTZ21BCe14nu7Za/51wfcH+P0rtmQEc0lJx+faHZmZmOfOwsJmZWc6cXM3MzHLm5GpmZpYzJ1czM7OcObmamZnlzMnVrI2TVCeppuDVey3qOKKlHuogaStJdze/Z67nHCDp0PV5TtuwdGh+FzOrcIsjYsA61nEE8GeyGyaURFKHWHm/3UZFxL9ZeQOGFpfupDQAGAT8ZX2d1zYs7rmabYAkDZT0mKSpkh4uuPXdqZKelfSCpHvSnYj2Bb4K/Cr1fHeQVC1pUDqmZ7plI5KGS3pA0qPAREkbp+dtPpNu3L/aU5ck9a5/Fmc6/r70TNGZkr4j6fvp2MmSNkv7VUu6NsUzXdJeqXyzdPyLaf89UvlISbdKmkR244dLgWPS8cdI2kvSU+k8T9bfUSrFM17SQ8qedfrLgrgPlvRceq8mprJm22sbiHLfXcMvv/xq2RdQx8o71dwLdASeBDZP248BbkrLnyw47jLg7LQ8BjiqYFs16W49QE9gZloeTnaXnPo7/PwM+EZa7kF2N7CNi+LrDUwvOP51YBNgc7Knzpyetl1N9mCG+vPfmJYHFxz/G+CStPwloCYtjyR7ek2XgvP8tiCGbkCHtHwAcE/Bfm8A3YHOwD/J7k27OdkdgLZP+5XcXr82jJeHhc3avlWGhSX1A/oBE7Lb49Ke7PZxAP0kXUaWGLoCD6/F+SZERP3zNQ8kexjBeWm9M7Atqz4MvtjfI3s274eSFgJ/SuXTgD0K9hsH2fM8JXWT1IPsdphHpvJHJX1SUre0/wMRsbiRc3YHbpG0E9ntNDsWbJsYEQsBJL0MbEd2C8fHI3smKOvYXmuDnFzNNjwCXoqIfRrYNgY4IiJeSE8VqWqkjmWsvKzUuWjboqJzHRkRM9YgviUFy8sL1pez6ndW8b1bm7uX66Imtv2ELKkPTRO+qhuJp46mvzfXpr3WBvmaq9mGZwawuaR9IHtkn6Td07ZNgDnKHuN3fMExH6Zt9WYCA9NyU5ORHgbOTk+QQdJn1z38FY5Jde5H9rSWhcATpLglVQHzo+j5vklxe7qz8tFjw0s492RgcHqyCvXXgmnZ9loFcXI128BExH/IEuIvJL1Adi1237T5IuBpYBLZk1/q3Q6MSJN0dgCuAM6Q9DzZNdfG/IRsiPVFSS+l9bx8nM5/PdmTUCC7tjpQ0ovA5ax8jFqxvwO71U9oAn4J/DzV1+yIXkTMA04Dxqf38I60qSXbaxXET8Uxs4ojqRo4LyKmlDsWs4a452pmZpYz91zNzMxy5p6rmZlZzpxczczMcubkamZmljMnVzMzs5w5uZqZmeXs/wOfXP9ZoQqcrQAAAABJRU5ErkJggg==\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"markdown","source":["# KNN"],"metadata":{"id":"1VhwSgF3KxXV"}},{"cell_type":"code","source":["# import warnings\n","# warnings.filterwarnings('ignore')\n","\n","# import matplotlib.pyplot as plt\n","# import pandas as pd\n","# import numpy as np\n","# import seaborn as sns"],"metadata":{"id":"x9DJivxCKzmi"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score\n","from sklearn.metrics import roc_curve, auc\n","\n","def eval_classification(model, pred, xtrain, ytrain, xtest, ytest):\n","    print(\"Accuracy (Test Set): %.2f\" % accuracy_score(ytest, pred))\n","    print(\"Precision (Test Set): %.2f\" % precision_score(ytest, pred))\n","    print(\"Recall (Test Set): %.2f\" % recall_score(ytest, pred))\n","    print(\"F1-Score (Test Set): %.2f\" % f1_score(ytest, pred))\n","    \n","    fpr, tpr, thresholds = roc_curve(ytest, pred, pos_label=1) # pos_label: label yang kita anggap positive\n","    print(\"AUC: %.2f\" % auc(fpr, tpr))"],"metadata":{"id":"5baCiyVyK7Ts"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# df = pd.read_csv(\"data_clean_new.csv\")\n","# df.info()"],"metadata":{"id":"Ris68T1zK_cq"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["#import pandas as pd\n","#df = pd.read_csv(\"data_clean_new.csv\")\n","\n","# Split Feature and Label\n","X = df2.drop(columns=['ID', 'Reached.on.Time_Y.N', 'Customer_care_calls', 'Customer_rating'])\n","y = df2['Reached.on.Time_Y.N'] # target / label\n","\n","#Splitting the data into Train and Test\n","from sklearn.model_selection import train_test_split \n","X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 42)"],"metadata":{"id":"7MLcbV2qLDji"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["### Init Model"],"metadata":{"id":"g2qnehI0LQ5w"}},{"cell_type":"code","source":["from sklearn.neighbors import KNeighborsClassifier\n","model = KNeighborsClassifier()\n","model.fit(X_train, y_train)"],"metadata":{"id":"Kq-x-Q2fLR5K","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1650290063249,"user_tz":-420,"elapsed":11,"user":{"displayName":"Fajar Ariev","userId":"18314973382710209713"}},"outputId":"78bc9e93-f88e-48d9-cf47-de7448fcc2c7"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["KNeighborsClassifier()"]},"metadata":{},"execution_count":51}]},{"cell_type":"code","source":["y_pred = model.predict(X_test)\n","eval_classification(model, y_pred, X_train, y_train, X_test, y_test)"],"metadata":{"id":"LeoUx1paLVTL","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1650290063801,"user_tz":-420,"elapsed":560,"user":{"displayName":"Fajar Ariev","userId":"18314973382710209713"}},"outputId":"a6dbad6f-bdb8-41df-a25c-f6f865dffc28"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Accuracy (Test Set): 0.63\n","Precision (Test Set): 0.69\n","Recall (Test Set): 0.64\n","F1-Score (Test Set): 0.67\n","AUC: 0.63\n"]}]},{"cell_type":"markdown","source":["### Tune Hyperparameter with RandomSearch"],"metadata":{"id":"BXL21wKeLdaf"}},{"cell_type":"code","source":["from sklearn.model_selection import RandomizedSearchCV\n","from sklearn.neighbors import KNeighborsClassifier\n","from scipy.stats import uniform\n","\n","# List of hyperparameter\n","n_neighbors = list(range(2,30))\n","p=[1,2]\n","algorithm = ['auto', 'ball_tree', 'kd_tree', 'brute']\n","hyperparameters = dict(n_neighbors=n_neighbors, p=p, algorithm=algorithm)\n","\n","# Init model\n","knn = KNeighborsClassifier()\n","model = RandomizedSearchCV(knn, hyperparameters, cv=5, random_state=42, scoring='roc_auc')\n","\n","# Fit Model & Evaluasi\n","model.fit(X_train, y_train)\n","y_pred = model.predict(X_test)\n","eval_classification(model, y_pred, X_train, y_train, X_test, y_test)"],"metadata":{"id":"qpTw7xVrLZCs","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1650290074233,"user_tz":-420,"elapsed":10434,"user":{"displayName":"Fajar Ariev","userId":"18314973382710209713"}},"outputId":"9628bae5-0734-4b8b-c042-2c800284484b"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Accuracy (Test Set): 0.66\n","Precision (Test Set): 0.78\n","Recall (Test Set): 0.56\n","F1-Score (Test Set): 0.65\n","AUC: 0.67\n"]}]},{"cell_type":"code","source":["print('Best n_neighbors:', model.best_estimator_.get_params()['n_neighbors'])\n","print('Best p:', model.best_estimator_.get_params()['p'])\n","print('Best algorithm:', model.best_estimator_.get_params()['algorithm'])"],"metadata":{"id":"DhtFx1CYLkXi","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1650290074234,"user_tz":-420,"elapsed":15,"user":{"displayName":"Fajar Ariev","userId":"18314973382710209713"}},"outputId":"83546b7d-4cd3-4fdf-d985-39c357411a7a"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Best n_neighbors: 28\n","Best p: 2\n","Best algorithm: brute\n"]}]},{"cell_type":"code","source":["print('Train score: ' + str(model.score(X_train, y_train))) #accuracy\n","print('Test score:' + str(model.score(X_test, y_test))) #accuracy"],"metadata":{"id":"OK-2nPhuLoG6","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1650290076803,"user_tz":-420,"elapsed":2582,"user":{"displayName":"Fajar Ariev","userId":"18314973382710209713"}},"outputId":"05388aac-258e-4703-a3e8-1f8be5868853"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Train score: 0.7781126362708464\n","Test score:0.7169166883840796\n"]}]},{"cell_type":"markdown","source":["# Decision Tree"],"metadata":{"id":"HLpHxMmqMSNs"}},{"cell_type":"markdown","source":["**no drop columns**"],"metadata":{"id":"vD16LqRsMWTY"}},{"cell_type":"code","source":["X = df2.drop(columns=['ID','Reached.on.Time_Y.N'])\n","y = df2['Reached.on.Time_Y.N'] # target / label\n","\n","#Splitting the data into Train and Test\n","from sklearn.model_selection import train_test_split \n","X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 42)"],"metadata":{"id":"N3UFaj9hMNeL"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["from sklearn.tree import DecisionTreeClassifier\n","model = DecisionTreeClassifier(random_state=42)\n","model.fit(X_train,y_train)\n","\n","y_pred = model.predict(X_test)\n","eval_classification(model, y_pred, X_train, y_train, X_test, y_test)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"EOV4pRwWMmLw","executionInfo":{"status":"ok","timestamp":1650290076805,"user_tz":-420,"elapsed":14,"user":{"displayName":"Fajar Ariev","userId":"18314973382710209713"}},"outputId":"af32c0e2-69cd-4b15-adf7-a736a273e023"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Accuracy (Test Set): 0.62\n","Precision (Test Set): 0.67\n","Recall (Test Set): 0.70\n","F1-Score (Test Set): 0.68\n","AUC: 0.61\n"]}]},{"cell_type":"code","source":["print('Train score: ' + str(model.score(X_train, y_train))) #accuracy\n","print('Test score:' + str(model.score(X_test, y_test))) #accuracy"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"BrrL33qsMw0n","executionInfo":{"status":"ok","timestamp":1650290076805,"user_tz":-420,"elapsed":9,"user":{"displayName":"Fajar Ariev","userId":"18314973382710209713"}},"outputId":"bcaaa6f3-ed1c-45b9-87e2-70af2b5fd2f3"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Train score: 1.0\n","Test score:0.6228276186002818\n"]}]},{"cell_type":"markdown","source":["OVERFIT"],"metadata":{"id":"tsYy8f1TM_JQ"}},{"cell_type":"markdown","source":["**Tune Hyperparameter with RandomSearch**"],"metadata":{"id":"evXiQN1nNDxN"}},{"cell_type":"code","source":["from sklearn.model_selection import RandomizedSearchCV\n","from scipy.stats import uniform\n","\n","# List of hyperparameter\n","max_depth = [int(x) for x in np.linspace(1, 110, num = 30)] # Maximum number of levels in tree\n","min_samples_split = [2, 5, 10, 100] # Minimum number of samples required to split a node\n","min_samples_leaf = [1, 2, 4, 10, 20, 50] # Minimum number of samples required at each leaf node\n","max_features = ['auto', 'sqrt'] # Number of features to consider at every split\n","criterion = ['gini', 'entropy']\n","splitter = ['best', 'random']\n","\n","hyperparameters = dict(max_depth=max_depth, \n","                       min_samples_split=min_samples_split, \n","                       min_samples_leaf=min_samples_leaf,\n","                       max_features=max_features,\n","                       criterion=criterion,\n","                       splitter=splitter\n","                      )\n","\n","# Inisialisasi Model\n","dt = DecisionTreeClassifier(random_state=42)\n","model = RandomizedSearchCV(dt, hyperparameters, cv=5, random_state=42, scoring='roc_auc')\n","model.fit(X_train, y_train)\n","\n","# Predict & Evaluation\n","y_pred = model.predict(X_test)#Check performa dari model\n","eval_classification(model, y_pred, X_train, y_train, X_test, y_test)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"tEDIPBjlNAS5","executionInfo":{"status":"ok","timestamp":1650290077725,"user_tz":-420,"elapsed":925,"user":{"displayName":"Fajar Ariev","userId":"18314973382710209713"}},"outputId":"1994d554-a40b-4738-93db-f200c33ed9c9"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Accuracy (Test Set): 0.65\n","Precision (Test Set): 0.72\n","Recall (Test Set): 0.66\n","F1-Score (Test Set): 0.69\n","AUC: 0.65\n"]}]},{"cell_type":"code","source":["print('Best max_depth:', model.best_estimator_.get_params()['max_depth'])\n","print('Best min_samples_split:', model.best_estimator_.get_params()['min_samples_split'])\n","print('Best min_samples_leaf:', model.best_estimator_.get_params()['min_samples_leaf'])\n","print('Best max_features:', model.best_estimator_.get_params()['max_features'])\n","print('Best criterion:', model.best_estimator_.get_params()['criterion'])\n","print('Best splitter:', model.best_estimator_.get_params()['splitter'])"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"2YZNnXNVNSTW","executionInfo":{"status":"ok","timestamp":1650290077725,"user_tz":-420,"elapsed":10,"user":{"displayName":"Fajar Ariev","userId":"18314973382710209713"}},"outputId":"87929e23-a503-40f2-d148-00410f23c250"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Best max_depth: 110\n","Best min_samples_split: 100\n","Best min_samples_leaf: 4\n","Best max_features: sqrt\n","Best criterion: entropy\n","Best splitter: best\n"]}]},{"cell_type":"code","source":["print('Train score: ' + str(model.score(X_train, y_train)))\n","print('Test score:' + str(model.score(X_test, y_test)))"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"wJ0rJZenNUJt","executionInfo":{"status":"ok","timestamp":1650290077726,"user_tz":-420,"elapsed":7,"user":{"displayName":"Fajar Ariev","userId":"18314973382710209713"}},"outputId":"6e4dca83-b654-4027-9a5f-49289b182271"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Train score: 0.8043837970282733\n","Test score:0.7245119011423361\n"]}]},{"cell_type":"code","source":["from sklearn import tree\n","import matplotlib.pyplot as plt\n","fig, ax = plt.subplots(figsize=(10, 10))\n","tree.plot_tree(model.best_estimator_,\n","               feature_names = X.columns.tolist(), \n","               class_names=['0','1'],\n","               filled = True, max_depth=5, fontsize=10)\n","plt.show()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":575},"id":"7Hcz3FSHNdgv","executionInfo":{"status":"ok","timestamp":1650290079829,"user_tz":-420,"elapsed":2107,"user":{"displayName":"Fajar Ariev","userId":"18314973382710209713"}},"outputId":"77051abc-44b2-4379-8a46-2575d70da30a"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure 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Rz/n7xjV0qVeOTwLLM6RTk5RnOoGlMybQwi+/fs/Ro3u2AxAZHkr/tvUI9HFl5ugBqeYa0qkJXQMr0DmgDNNH9NVv7C+EyDrkdrwQQoi3ptFomDVuCMt2ncLR2ZV5k0awYdl8Pvl8FACtgvrStvvnqcZYWFoRNGg0Ny9f4OaV86naxs75iWx29qiqyuhe7di7dV26vEwlhMg8JAkVQggjiI+LZWzvDjwJvo9Op6VT32HUatyKH76dyOE/tpKUkECxshUZPHE2iqLQv01dChUrxdkTh4mPi2XEN4tYMXcqNy6fp1ajlnw6eCzBd28ztHMTCpcozZW//yJf4aKM+GaxQenLE/t/Z8mM8SQnJeGZJx/Dpi7AJpst308exaHft2JqZkb5qrX5bOTkN79AVUVVVRLiYlGdXIiLjsYrT4GXDrG2yUbJ8v7cv3XDoC2bnT0AWo2G5OTkVPueCiGyBklChRDCCI7v24lL9hxMWboBgJioSACad+6prwQ04fOuHP7jN/zrpOy1aWZuwYLNh/hlyWxGdmvFwi2HsXNwpn11H31lpDs3rjD063mUKFeZyUN68Ovy71OtOEaEhfDj7Ml8s+I3rG2ysXLeNH5e9B0fderBgZ2bWP7HGRRFIToywiDmU4f36astPcvS2pq56/emOmZmbs7ACd/ySWB5rKyzkTNfAQaM/3crpg3L5rNj/Uq8S5Sh96jJ2Dk4vfI7G9yxMRfP/IlfjbpUb9D8lf2FEO8XeSZUCCGMIL93cU4e+IP5k0Zy5vhBbO0dADh9ZB89m1alS71ynD68l1tXLujH/LPxe/4ixclb2AcX9xxYWFqSI3c+faUid8+clChXGYC6H7Xj3InDqc574fRxbl+9RJ8WtQiq78f2dSt4dP8O2ewcsLCwYsrQnuzf/qvB6ilAmcrVn7tx/X8TUABNcjIbf1rIoq1HWX/8BgWKFGfF3KkANO3QjZX7L7D4t2O4uHswZ8KwNH1n05ZvZv3xmyQnJXLqsOE5hRDvN1kJFUIII8iVvxALtx7h6J4dLJ72JWX8a9Kux0BmfDGABZsO4u6Zi6UzJpCUmKgfY25hCYCimGDx9G8AE8UErVbztO0/t6n/81lVVcpWqcWYWT/yX/M3HuDU4T3s/W0D65fNZ+aq7anaX2cl9OqFMwB45ckPQM2GLVkxbxoAzm7Z9f0ate3K8KC0r2paWlnhH9CYQ7s2U75q7TSPE0JkfpKECiGEEYQ8eoCdgzN1P2qHrb0DW1cvJSkxAQAHZ1fiYmPYt20D1et/9FrzPrp/l79PHqV42Yr8vnENJctXTtVerHQFZo4ewL1b18mZtwDxcbGEPHyAS/YcJMbHUbFmIMXLVqJdNcOymP+shKaFm4cnt65eIiL0CY4ubvx58A/yFPQGUkpw/lML/sCOja8swRkXG0N8bDQu7jnQaDQc3b2NkhX80xSHEOL9IUmoEEIYwY1L55k3aQQmigmm5mYMnPAddg6ONGr7CV3qlsXZLTveJcu+9ry58xfm1+XfM2VoT/IWKkLTDt1TtTu6uDF82kLG9etEclISAJ8OGoONrS0jurUiKTERVVXpPWrKW12fa3ZPuvQfQd/WAZiZm5PdKzfDpy0AYN6kkVy7cBZFUfDImYfBE/8twdnG35vYmGg0yUkc3LmZacu3YO/ozPBPW5KclISq0+FbqVqqkqRCiKxBNqsXQmR5WXWz+uC7txke1Jwfdhp3P9APmWxWL0T6kReThBBCCCGE0clKqBAiy8uqK6HC+GQlVIj0IyuhQgghhBDC6CQJFUIIIYQQRidJqBBCGEGgj6vRzxl89zYB3k4E1fcDIDEhgR5Nq9A1sAKdA8qw5JvxBmO+HTvQINbdW36hU53SdA4ow7h+nQF4eO82nzasRFB9PzoHlGHjTwtfGc/SGRNo4ZefoPp+BNX34+ief/cl/WnOVNpXL0aHWiU5vm+X/ngbf2+61CtHUH0/ujf+d5umqIgwBnZoSPsaxRnYoSHRkeEpsW5eS/vqxRjWVSosCZHZyRZNQgiRhXnlya/f69PC0pIZK7djk80WTXIyfVrWwq9GXYqVSUlSL509aVC+897Na6yYO40563Zj5+BEeMhjAFzcczB3/V4sLC2Ji43hk7pl8Q9oiGt2z5fG0yqob6qyogC3rl5k9+a1/LDzFKGPgxn4cQN+2nMOU1NTAGau2o6jc+rEeMW8aZStXIOPPxvCirlTWTF3Gj2Hf0Wtxq1wcs3OmoUzEUJkbrISKoQQr+n7yaPY8ON8/eelMyawesEM4mJj+Lx9fT5tWIku9cpxcOdmg7Gnj+xPtUo3c/QAtq1dDsDlc6fo1zqAbo0qM7hjY0IfB6dr3IqiYJPNFgCNJhmNRqOvuKTVapk3cQS9hn+Vaszm1Uv4qFMPfa13J1d3AMwtLLCwTKnilJyUiE7VvXFcB3duoVbjViklSXPlxStPAS7+deKlYw7t2kJgyw4ABLbswMFdht+1ECJzk5VQIYR4TTUbtWT2uCF81KknAHu2rmPaj5uwsLRiwvdryGZnT0RYCJ99VB3/gEaGpTWfQ5OczLdjBjJx4VocXdzYvXktC6eOZdjU71P12/XrKlZ/b7jK55U3P+PmrXrlebRaLd0bVeb+7es069gDn9IVANiwbB7+dRrqKxv9496NqwD0blETnVZLlwGj8KtRF4DHD+7yv67NuX/rOr1GTHzlKmjKeeazY/1KvEuUofeoydg5OBHy6D4+pf30fdxyeBHy6EHKB0VhcMfGKIpC4/ZBNGkfBED4k8f6WJ3dPAh/8viV5xZCZC6ShAohxGsqXNyX8NAnhDx6QERoCHYOjrh75kKTnMzCqaM5c/wQJooJTx4+IOzJI1zcPV45550bV7h55QKDOjQCQKvTPndcQLN2BDRr98axm5qasnjbMaIjIxjVow03Lp/H3tGJvb+tZ+bqnQb9tVot925e49vVO3ny8D59W9dh6fY/9de8dPsJQh49YGT31lSv/1GqOvH/1bRDNzr1G46iKCye/iVzJgwzSLL/a/Yvf+Dm4UV4yGMGdWhEngLelPKrkqqPoiiQhkRfCJG5SBIqhBBvoEbD5uz9bQNhTx5Rs1FLAHb9upqI0BAWbj6Mmbk5bfy9SUpMTDXO1MwUne7fW9f/tKuqSt5CRZm3Yd9Lz/u2K6H/sHNwpHSl6hzft5M8BYtw/9YNPq5eDICE+DjaVy/Gyn3ncfPwoqhveczMzcmRKy+58hXi3q1rFC1VTj+Xa3ZP8hUuxtkTh6jR4MUvBD2boDZq25XhQc2fjvfi8YN7+rYnwff1q6puHl5AymMAVes14eKZE5Tyq4KTm7u+Jn3o42CcXN3SfO1CiMxBngkVQog3UKtRS3ZvXsu+bRuo2TAlmYqNjsTR1Q0zc3NOHd7Hw/t3DMZ5eOXm9tWLJCUmEh0ZwclDe4CUGvCRYSH8ffIokHJ7/uaVCwbjA5q1Y/G2Ywb/pCUBjQh9on/xKDEhnj8P/kHuAt5UqlWfDX/eYs2hy6w5dBkraxtW7jsPQJW6jfnr6P6U8WEh3L15Fc/c+XgcfI/EhJQCANGR4Zz78zC58hcG4KuBQc99pvPZZ1wP7NhIvsI+APgHNGT35rUkJSYSfPdWSpLrW574uFjiYqIBiI+L5cSB38lXOCVR9q/TkO2//ATA9l9+wj+g0SuvXwiRuchKqBBCvIF8hX2Ii43BNbun/tnEOs3aMjyoBV3qlcO7RBlyF/A2GOfumYsajVrQpV5ZcuTKS6FipYCUF32+nLuS774cRGxUFFqthpZd++gTtfQQ+vghEwd1Q6fToup01GjYgsq1G7x0TIXqAZw48Dud6pTGxNSUXsMn4uDkwokDfzD3q2EoKKiotOk2gAJFigNw4+LfuGTPYTDXvEkjuXbhLIqi4JEzD4MnzgJSvsuajVrQOaA0pmZmDBg3E1NTU8JDHjOqexsAtFoNdZq20T+P2r7XYMb27sDWn5fh4ZWbsXN+SrfvSQhhHFK2UwiR5X2oZTuD795meFBzfth50mjnjI2OYsr/ejJu7kqjnfO/Th/Zz5qFM5m8ZH26zy1lO4VIP3I7XgghsihTUxNioiP1m9UbQzY7+wxNQHdvXsuML/pj6+CYYTEIIdJGVkKFEFneh7oSKtKfrIQKkX5kJVQIIbKAfzbMz2i3r12m10fVqVPYIVPEI4TIvOTFJCGEEOnG3tGJfmOnP7dalBBCPEuSUCGEeM9sX7eCNQtnoqCQv2hxRs1Ykqp986olbF61GE1SMl558zNyxhKsrG3Ys3Udy76diImJKdns7Zn18+/cvHKByUO6o0lKRqfqGD9vFTnzFXzj2Jxc3XFydefo7u1ve5lCiCxOklAhhHiP3LxygeWzJzNn3R4cnV2Jiggz6FMtsCmN23UFYNG0sWxd8wMtunzGsu8mMfXHTbh5eOn3C924YiEtP+lNQLN2JCclodNpDeYb27sDd5+W73xWq0/7Edji43S+QiHEh0KSUCGEeI+cOryXGg2a4+jsCoC9o7NBn5uXL7Bo+lhioiKJj42hQrUAAEqUrcSkwd2p2bAF1QKbAlCsjB8/zf6aJ8H3qRbY7LmroLIHpxDiXZAkVAghsphJg7vx1YKfKehTkm1rl+srHg2aOIsLp49zZM92ujeqzIIthwlo2hYf3woc2b2NoZ80Y/DE2ZSpXCPVfLISKoR4FyQJFUKI90iZyjUY1aMNrT/th4OTC1ERYQarofGxMbi4e6BJTmbXxtW4Pa3Dfv/2DXxKV8CndAWO7d3B4wf3iI2OwjN3Plp+0pvHD+5y/eI5wyRUVkKFEO+AJKFCCPEeyVfYh469/0f/NnUxMTGlULFSDJ++MFWfrgNH07NZNRxdXFNqsMfEADBv4nDu3boOqkqZyjUo6FOSlfOmsXPDKszMzHF2y06H3kPfKr7Qxw/p0cSf2JhoTBQTflkym2W7TpPNzv6t5hVCZD2yWb0QIsuTzepFepHN6oVIP7JZvRBCCCGEMDpJQoUQQgghhNFJEiqEEEIIIYxOklAhhBBCCGF0koQKIYQQQgijk7fjhRBZnqWV9cOkxITsGR2HeP9ZWFo9SkyI98joOITICiQJFUKID4SiKJuBpaqqrn+NMU2BHqqqNnh3kQkhPkRyO14IIbI4RVE8n/5ZGjj9msNPAWXSNyIhhJAkVAghsjRFUUyBi4qi5AOyAY8VRXmdVc17gJmiKDneSYBCiA+WJKFCCJGFqaqqBU4CnUhZ1ZwPNHmN8SqyGiqEeAckCRVCiKxvBxAIaIFSwMDXHC9JqBAi3UkSKoQQWd8OUp4H9QNaqqoa95rjTz0dL4QQ6UaSUCGEyPrOACowXFXVK28w/jSyEiqESGeyRZMQQnwAFEUxffp86JuMNQEigHyqqoamb2RCiA+VrIQKIcQH4E0T0KdjdaSshpZWFMVUURQl/SITQnyozDI6ACGEEJmXoijOgBVPk1CgHnABWJqRcQkh3n9yO14I8V6ztrZ+mJAgJTmFISsrq0fx8VJiU4jMSpJQIcR7TVEUVRP1OKPDyNL6DRrGxStXuHnrDvHx8dy9cg4Tk8z/NJeZvTuqqsqjA0JkUpn/V0QIIUSGmjrxS2KiY7lz9x41qlZ5LxJQIUTmJ78kQgghXsrS0pI1yxdjYmJCMZ8iGR2OECKLkNvxQoj3mtyON54LFy9RsEB+LCwsMjqUNJHb8UJkbpKECiHea5KEiheRJFSIzE1uxwshspSBw77g2znf6z/Xb9aa7n0+138ePGI0M2bPe+H4MRMm8/uefS89x5cTv2b6d3MMjkdERDJv4ZKXjn0Q/JDWHbu+tE9WcfPWbSrVDMS7VAXadelGUlKSQZ9du/dSoVodfCtWp0K1Ouzed0Df9vO6XyldqTolK1Rl2Ohx+uPLVqzGI19RyvrXpKx/TRYv+8ko1yOESF+yEiqEeK/9dyV03a+bWbthI6uXLUKn01GxRl3Mzc059Mc2APxr12f6pPFUrFDujc/55cSvsbXNxqB+vVMdv3X7Dk1bd+DMsf2vPWeuomUIvn/vjWMSb0cxt3ykS0qQ7ZyEMCLZrF4IkaVU8ivPoOFfAHD+4iWKFS1C8MNHhIdHYGNjzaUrVynjW5KTp88weMRoYmNjcXF2Zsn8WeTwyE7XnuLtg+MAACAASURBVH1pGFiXFs0a89uO3xkyYjTZstlQ2a8CN27dZtPaFQBcvHSFWg2acffePfr16kHfXt0YMWYC12/eoqx/TWrXrM7XE8YaxPdsorpsxWo2/7aduLh4gu/fo9Li+0b8psSzjgR5yV6zQhiZJKFCiCzFM4cHZmZm3Ll7jyPHTlCxQjnuP3jIkeN/4uBgR3GfoiiKQv8hw9mw+kfcXF35ed2vfDFuIovmfqufJyEhgc8GDGbPto3ky5uHjz/pkeo8l65c5Y+tG4iOicGnTGV6ftqFiV+O4vzFS5w8tCfN8Z459zd/HtiNS66C6fYdCCHE+0CSUCFEllOpQjmOHDvBkWMnGNCnZ0oSeuwEDg52VK5YgctXr3H+4iUCm7YCQKvV4ZHdPdUcl65cJV/ePOTLmweAtq0+YuHS5fr2BvUCsLS0xNLSEnc3Vx49fvJGsdaqXg0HB/s3vFIhhHh/SRIqhMhyKleswJFjJ/j7wkWK+xQll5cXM2bPw97Oli4d2qGqKj5FvPXPib4JS8t/tykyNTVFo9G82TxG3u4o7NR2rDzyY+NZ2KjnfR3h5/Zwa9VoVFVH9qrt8GrQx6CPLjmRa4v7E3P7HObZnCjUcx5WrrkyIFohxJuSt+OFEFlOJb/ybN2xEycnR0xNTXF2diIyMpKjx/+kkl95vAsVJCQklCPHTgCQnJzM+YuXUs3hXaggN2/d5tbtO0DKm9qvYmdrS3RMTPpfUDoKO72d+AdXntumat8skU5Pqk7LzRUjKfr5T/iO30PIsV+Je068jw+swszGgTKTDpEjoBt3fvkqA6IVQrwNWQkVQmQ5JYr5EBIaRtuWLfTHivsUJSYmFlcXFwDWLF/MgKEjiYqKQqPR0u+z7hQr+m81IGtra2Z9M4WGzduSLZsN5cr4vvK8Li7OVParQCm/atQLqPXcF5PS25Mj6wj+YwmqJgnb/KXJ32ESiokpxz4rRI46QYSf+R0TCyu8+ywl8cktwv7aRdSVo9zb8i3eny3k+g+DscnlQ/TVE7j6NSVbrmLc+nk8qk6Lbd5S5O84CRNzS04N9cOlfGPCz+3BxMKKQt1mY2HvxpmxdfD96gAmZuZo4qM5OzZA//lNxNw4jZV7XqzcUh6DcK3QlPDTOwxWbsP+2kmuJgMBcCnXkJsrR6KqKooi24IK8b6QJFQIkeWYmpoSfv9GqmNL5s9K9dm3ZAn2bt9kMPbZfjWr+nP+5GFUVaXvwP9RtnRKIjpmxNBUY57dkumnJfNfGlvePLn1/Tt/3JbOH7dNwxU9X9yDq4Se2ETxYb9iYmbOjeXDCTm6HrfKrdAlxmGbvwy5mw/j9toJPN6/gpyNB+DsG4BTqTq4lGukn0fVJFNy9DZ0yQmcHl4Fn8FrsPYowNVF/Xi090dyBHQDwNTaDt9xf/Dk8FpurR5D0f4/Yu9diYizf+BcJpDQ4xtxLlPfIAF9cnQ9D7Yb7s1q5Z4X788WpjqWFPEQS2dP/WcLpxxE3zxtMDYp/CEWT/sppmaYWtujiQnH3M75jb9PIYRxSRIqhBAvsOiHn1i+ag1JScn4lixO966dMjqkVCIvHiTm1jnOTWgAgC4pAXN7VwAUMwucSgUAkC1PCSIvHHjhPC4VmgAQ//A6lq65sfYoAIB75VY83LNMn4S6+jV72r8Zt1aPTelTtT0Pts/FuUwgjw+uoUDnqQbzu1VsjlvF5ulwxUKIrESSUCGEeIEBfXoyoE/PNxp77vwFunRPvZm9hYUlR/ZsT4/QnlJx829FnhbDDVoUUzP9rWnFxPSlz3uaWtqk8XzP3Op+Ord9ofLc/OkukZcOo6o6bHIWMRj1OiuhFo4eJIY90H9OCg/G0tFwD3kLJw+Swh5g6eyJqtWgjY/CzNYpjdchhMgMJAkVQoh3oEQxn9faL/RNOBStwuVZn+AZ0A1ze1eSY8LRJcRi6ZrzhWNMrWzRJsQ+t83aowCJoXeJf3QT6+z5eHJkHfaFK+rbQ09swqtBH0JPbMKuQFn9cbfKLbm6oA85G/d/7ryvsxJqm8+XhEc3SXhyBwsnD0KOb6RQd8MSqc6+dXlyeC12BcsR+udWHIr4y/OgQrxnJAkVQoh3aOOW3yhUsAA+RbzTfW4bz8Lk+mgoF75pB6qKYmpGvo+/emkS6lKhKTeWDSH498V4f7YgVZuJuRUFP/mGK/N66F9Myl6jo75dExvJmTF1UMwsUiWGrhWbc2fDVP3t+reRcg0TuDijPapOh3uVNth4pXx3d36dim3eUjj71sW9aluuLuzHqeH+mGVzpHCPuW99biGEcUnteCHEe+2/teMzm2fLgP6XRqPBzCxlLcDM3j1Tl+08NdSPEl9se+6LP6F/biHs9A4KdZv1nJHvhyNBXqiqKkupQhiR7BMqhBCvYcXqtVSsUY+y/jXp1X8QWq0WAIcceRk1biJlKtegcq36PHr8mMPHjrP5tx3874svKetfk+s3blKrQTMG/m8UftUD+G7eAv7Yu59yVWpl8FW9uZsrRnF73SRyNh6Q0aEIId4zkoQKIUQaXbx8hZ/Xb+TAri2cPLQHUxNTVq75BYDY2Dj8ypfl1OG9VPWvyKIffqKyXwUaN6jHlPFjOHloDwXy5wMgKSmJY/t28Vm3rgT16sfKpQtfctbMoczXx567Cprv4wmUmXRI/0a9EEKklTwTKoQQabR77wFO/XWGijXqAhAfn4CbW8qWSBYWFjQKTDle1rcUv+/Z98J5WrVIeXby8tVr5M2Tm8KFJIETQnx4JAkVQog0UlWVju3bMHHsKIM2c/N/t0R6VS35bDZp3RIpY5z/uiV5Wn+Bbd5SRjvnnfWTeXL4FzRxkfjNvao/nhhyj2tLB6KJCcMsmyMFP/1Ov5n9kU9z6beEsnT2oki/HwD4e/JHaBNSyqcmR4Vim8+XIn2XGO1ahBBpI0moEEKkUa0aVWnethMDevfA3c2NsLBwomNiyJM71wvHvKyevHehgty+c5dr1288t/1D4lQqAI9an3B6RJVUx2/9PA63yi1x929N5MWD3Fk3Sf8ClImFFaXG7jKYq/iwDfq/L8/phnPpuu82eCHEG5EkVAgh0siniDfjvhhO/Wat0el0mJub8920yS9NQlu3bEbPvoOYPX8ha35cnKrNysqKRXO/pW3nT196Xm1iHFfm9SApPBhVpyNn4/64VmjK3U0zCD+zC11SAnYFy5G/0xQUReH81y3JlrsYUVeOo0uKo2DQt9z/bTZx9y7iUr4JuZv/j4SQu1yc8TG2eUoSe+cc1p6FKRj0HaaW1qnOHfH3Pu5unIaqScLSLQ8Fu87A1Cobt3+ZSPhfO1FMzXDwqUbeNqPf/IuFVPuOPis++Cp5244BwL6IP5dnB6V5Tk18NJGXDlGg6zdvFZsQ4t2QJFQIIV5D6xbNaN3CcD/MyOBb+r9bNGus35LJv6If504c1Lft/u3XVONq16jGnwd3Y2bv/sJzRpzbg4WjB0UHLAdAExcFgEetLuRq8jkAVxf2JfzMLpx9U1b9FFMLSo7eRvCuRVya1ZWSo7dhls2R08Mrk6NuShnOhIfXKdBlOvaFynNtyUAe7VmGZ+C/FaKSo8O4t+VbfAavwdTShvu/zeHBzgV41OpM2Klt+H61H0VR0MRFGn4flw7pS3s+y8TCmhIjNr3wWv/LJpcPYSe3kSPgU8JObUObEENyTBjmts7okhM5O64+iqkpXvX74FwmMNXY8NPbcSjqj5m1XZrPJ4QwHklChRAik7PJWYTbP4/j9tqvcCpVB/vCfgBEXTrMg+3z0CbFo4mNSNnU/WkS6vT03zY5i2DjVRgLx+wAWLrmISnsAaY2Dlg4e2JfqDwAbpWaE/z7klRJaMyNk8QHX+HvSU0BUDXJ2BYoi5m1PSbmllxfOginUnVwKlXHIGaHIv7PvVX+uvK2+oKbK0fx+NDP2BeuiIWTB4qJKZDyxr6lUw4SntzmwtTW2OQsgpV7Xv3YkGMbca/a7q1jEEK8G5KECiFEJmftUYASo7cTcW43dzZ8jUPRKnjV78XNFSMo8cVvWDp7cXfjdHTJifoxJuYWKX8oJihmFvrjiokJqk77z6fUJ/pP2UtVVXHwqfbcakQlRm0l8uJBQv/cysPdSyk2ZG2q9vRaCbVw8sC79yIAtAmxhJ3aipmNAwCWTjkAsHLLg713JWLv/K1PQpOjw4i5eRrvPovSfC4hhHFJEiqEEJlcUvhDzGwdcavUAjMbex7tX6VPOM1sndEmxBL651ZcyjV8vXnD7hN97U/sCpYj5Niv+lXRf9gVKMvNFSP1teS1iXEkhQdj4eiBLikep5K1sStYntPDKhnMnV4rocnRKW/FKyYm3P9tFm5V2gKgiY3AxMIaE3NLkqPDiL52As/6n+nHhZ7cglOpOpiYW711DEKId0OSUCGEyOTi7l/i9toJoCgopubk7zgJMxsH3Ku258zo2lg4uGGb7/W3U7LyKMDDPcu4/sMgrHMUJnuNzqnaze1cKNh1BlcX9EbVJAGQ66OhmFrZcml2V9TkRFRVJU+bMW99jbfXTiDk2AZ0SfGcHFwW96rtydV0EFGXD3Nn3SRQFOwLVyTfx18BKS8sXf9xGIqioKoqng36YONZWD9f6PFNeNbv/dZxCSHeHakdL4R4rxmzdnxsbCxtO3fj/oMHaLU6Rg4dSOsWzRg/eRpbt+0kPiGBSn7lmfftNBRFoVaDZviWLMGhI0eJjY1j6fezmfLNt/x9/iKtmjdj/Ojh3Lp9h4bN23L56jWj1o5PCLnLpW874zt+t9HOmZlJ7XghjE/KdgohRBrt+H03njk8OHV4L2eO7adenZSa7727B3F0307OHNtPfHw8W7bv1I+xsDDn2L5ddO/amebtOjFr+hTOHNvPjytXExoaBqRUThJCiA+NJKFCCJFGxX18+H3PPoaNHseBw0dxcLAHYO+Bg1SqGYhvxers2X+QCxcv68c0blAvZWyxovgU9SaHR3YsLS3JlzcPd++nrHzmyull9Guxcs0lq6BCiAwlSagQQqRR4UIFOLH/d0r4FGX0+EmMnzyNhIQE+gz8Hz8vX8xfR/cR1LkDCQkJ+jGWFpYAmJiY6P/+57NGk/KWuqLIXWAhxIdHXkwSQog0ehD8EGcnRz5u2woHBweW/PgTCQkpb6m7ujgTExPD+o1baN600WvNe+fuvXSJ79hnhVLVXTeGhJC7/DWqBtYe+fVvw4ef28OtVaNRVR3Zq7bDq0Gfl84Rdfkot1aPIfbeRQr3mItLuX+/v9trvyL87B8A+kpRANcWDyDqylFMn25EX7DrDLLlLs797fMIOboeAFWrJT74KuVmnsXEwoq/JzYh/sFVyk4/hbmdc7p/F0KI1yNJqBBCpNG58xcY9sWXmJiYYG5mzuwZX+Po6EBQ546U8qtG9uzulCvj+9rzehcq+F4/F2rllkefgKo6LTdXjMRn0CosnHJwbnwDnHzrpnpz/b8sXLwo0HUGD3bMT3U8/MzvxN45R6mxO9Fpkjj/dUscS9TSV0DK02pUqoQVwCuwF16BvQAI+2snwbsWYm7rBECpsbs4NdQv3a5bCPF2JAkVQog0qlenlv5lpGeNHz2c8aOHGxx/tkRnjar+1Kjqb9B26/YdzMwMf4pv/zIRS2dPPGp1AeDuxumYWmYje42OXJr1Cdq4SHRaDbk/Gopz6XqpxkZeOsyDHfMp2v9HAG6sGIltnpK4V2lDzK2z3FrzJbrEWMxsnSnYdYa+mlJ6iLlxGiv3vFi55QHAtUJTwk/veGkSauWaCwBFSf2EWFzwVewL+6GYmmFqaoZNzqJE/L0H1/JN0hRLyPGNuFYwLLEqhMgcJAkVQohMyLV8E26uHqNPQkNPbKbowBWYmFvi3WcxZtZ2JEeHcW5iY5x866bpuVKdJpmbK0dRpO9SzO1cCDm+kTvrp1Cw6zep+j05up4H2+cZjLdyz4v3Zwtfeo6kiIdYOnvqP1s45SD65uk0XLGhbDl9uLf5G3LU7YkuKZ6oS4ex8Sykb7+zfgr3Ns/Eoag/uVuMwMT832dutYnxRJzbS772E97o3EKId0+SUCGEyEB58+TmzLH9mNm7pzqeLU9xNNEhJIU/JDkmFLNsDlg6e6HTJHNn/WSirxwDRUlpj3qChYP7C87wr4SH14m/f5kL01OqDqk63XPHuVVsjlvF5ulzgW/BsXh1Ym79xd+TmmBu54JdgbL6uvG5WwzH3MEdVZPEjR+Hcn/bXHI1+Vw/NvzMTuwLldPfihdCZD6ShAohRCblXK4RoSe3khz5GJent6BDjq5HEx1KiS+2YWJmzqmhfqlqxgMopmbwTCES9Wm7ioq1Z2FKjNz80vO+zUqohaMHiWEP9J+TwoOxdPR4+YW+RM5G/cnZqD8AVxb0xip7/qfnSXmEQDG3xM2/jcHzpCHHN+Eit+KFyNQkCRVCiEzKtXwTri8bgiYmjGJD1wGgjY/G3M4VEzNzIi8dIjHU8M16Sxcv4h9cQZeciC45gciLB7ErWB5rjwIkx4Tp68XrNMkkPLqBjZd3qvFvsxJqm8+XhEc3SXhyBwsnD0KOb6RQ9zkA3F43Cdt8vriUqZ+muVSdFk1cJOa2zsTevUDc3Ys4BlUHICniERaO2VFVlbDT27HxKqIfp4mLIuryUQp1m/VG1yCEMA5JQoUQIo0ccuQlMviW0c5n4+WNNiEWC0cP/cqfa8XmXPquM3+Nro1t3pJY5yhoMM7S2QuX8o05M7oWlq65yZa7OAAmZhZ49/qemytHo42PQtVpyVHnU4Mk9G0opmbk+3gCF2e0R9XpcK/SRj9/3L2LOJcKMBgTc/MvLs8JQhMbSfiZXdzdOB3f8XtQtcmcn5ySDJta21Ko23cpq7zA1YV9SI4OA1UlW+5i5O84WT9f2KltOBarhqmlTbpdlxAi/UnteCHEe82YtePfZRJqZu9u1Nrx6eV1atBf+KY9PgNXGiGqFzs11I8SX2wz2CdUascLYXxSMUkI8UEaPmY8cxcs1n/+cuLXTP9uDjExMQQ0bkH5qrXxrVidTVu3GYzde+AQTVp9rP/cb9Awlq1YDcDJ02eoWb8pFarVoX6z1gQ/fPTuLyYDKSYmaOOjODPWcIXzvzIyAdUmxXNmbAA6rQakQpUQmYLcjhdCfJBaN2/GwGGj+Kx7EAC/bNjEbxvWYGVlxboVP2Bvb0dIaCj+terTuEFgmrZASk5Opv+Q4WxY/SNurq78vO5Xvhg3kUVzv03Vb+WaX5j+3Zx3cVlGZ+nsRdlpf2Z0GK9kamGt31BfCJE5SBIqhPgglS5VgidPQngQ/JAnISE4OjqQK6cXycnJjPryKw4cPoKJiQn3gx/y6PFjPLK/ekP3y1evcf7iJQKbtgJAq9Xhkd1wC6T2bVrSvk3LVMf+u0WTMf2zEb5nYM8MiwEgPvga15Z8Tuydv8n90f8yPB4hxLslSagQ4oPVollj1v26mYePH9O6ecp2Pit/XseT0FCO7/8dc3NzChQvq68P/w8zM1N0Op3+c0Li0y2QVBWfIt4c+sPwFv6zstJKaHoyy+ZIvvbjCTu9PaNDEUIYgSShQogPVusWzejRdyChoWHs3rYRgMjIKNxdXTE3N2fP/oPcvnPXYFyeXLm4ePkKiYmJxMcnsHvfAfwr+eFdqCAhIaEcOXaCSn7lSU5O5sq16xQrWiTV+IxcCX1yeC0PdnwPgE3OogbbGD3at4JH+1egapKwcs9HwU+/w9TSmtATm7m7aQaKiQmm1vYUH7aeuPuXubZ0IKomCVSVwp8twPrpPp5vwtzeFXN7V8LP/vFW1yiEeD9IEiqE+GAVK1qEmJhYPD1zkMMj5XZ7+zYtaNq6A74Vq1O2dCmKFC5kMC5XTi9aftSEUn7VyJsnN74lU7ZAsrCwYM3yxQwYOpKoqCg0Gi39PutukIRmlLj7l7m35VuKD9+EuZ0zyTHhBn2cy9Yne/WUl67urJ/C44OryFG7K3c3z6TowBVYOuVAExcJwKO9y8lRJwi3is3RaZJQdVqD+a7M70n8w+sGxz3rdsetcqt0vkIhxPtEklAhxAftr6P7Un12dXF54e30Z7dnmjJ+DFPGjzHo41uyBHu3b0rPENNN5KVDuJRrpN+e6HklLePuX+buhq/RxEWhS4zFsVjK5vD2BctxfcnnuJRrjHPZlM3mbQuU5f7W70gKC8a5bP3nroIW7jnf4JgQQoAkoUIIIZ5xfcnnePdZTLZcxXh8cA1Rl48AkL/TFKJvnCL87B+cG1efEqO34VbxI+zylyb87B9cmtmR/J2m4FC0Sqr5ZCVUCPEikoQKIcQHwqGIP5fnBJGjbnfMbVNux/93NVSbEIOFQ3Z0mmRCjm3A4mnd94THt7DLXwa7/GWIOLeHpLAHaOOjsXTLQ446QSSG3ifu3kWDJFRWQoUQLyJJqBBCfCBsvLzxatiP81NaopiYkC13cQoGzUzVJ1ezIZz7qhFmdi7Y5SuNNiEGgNtrJxD/6CaoKg5Fq2CTqxgPts3hyZF1KKZmmDu449Ww71vFlxT5mHPj66ONjwHFhODfF1Jq/F7MrO3eal4hROYkZTuFEO81Y5btTC/LV67hm1lzURSFEsV8WLZw7ntbtjOrkLKdQhifrIQKIYQRnb94iYlTZ3Dg9624urgQFmb4hroQQnwIpHa8EEIY0Z59B2nxURNcXVwAcHY2fENdCCE+BJKECiGEEEIIo5MkVAghjKhm9Sqs27CJ0NAwALkdL4T4YMkzoUIIYUTFihZh+OAB1GrQDFNTE3xLlmDJ/FmvHiiEEFmMvB0vhHivvY9vxz9PrqJlCL5/L6PD+GAp5paPdEkJHhkdhxAfEklChRDvtayShIr0Z2bvLtsuCZGJyTOhQgghhBDC6CQJFUIIIYQQRidJqBBCCCGEMDpJQoUQQgghhNHJFk1CiPealZXVIzN79+wZHYfIfKysrB5ldAxCiBeTt+OFEFmGoihVgK+BbMD/gB2q/MhlWYqimADtgQnAWWC4qqrnMzYqIURaye14IcR7T1EUH0VRNgI/AfOA0qqqbpcENGtTVVWnqupPgDewB9ijKMpiRVFyZnBoQog0kCRUCPHeUhTFS1GURcBeYB9QRFXV5aqq6jI2MmFMqqomqqo6AygMPAbOKIoySVEUxwwOTQjxEpKECiHeO4qiOCiKMpGUW7AhQGFVVb9RVTUhg0MTGUhV1QhVVYcDpQA34IqiKAMVRbHM4NCEEM8hSagQItNSFMVKURTfZz5bKoryOXAFyA74qqo6TFXViAwLUmQ6qqreU1X1U6AmUAO4rChKx6fPkAKgKIrfs5+FEMYnLyYJITItRVFmARZAL6AdKS+g/E3KCyh/Z2Rs4v2hKEpVUl5YsyblhbWdwG5gvaqqszIyNiE+ZLJFkxAiU1IUpR7QFBgAnAQSgc6qqu7P0MDEe0dV1QOKolQGPgK+A+4C3wKLFEX5Q1XVCxkaoBAfKFkJFUJkOoqiuAAXSUkW7IDhpKxayQ+WeCuKopgDQcBo4B5gA5RRVTUpQwMT4gMkz8MIITKj7YAr4ABEA1+SkogK8bbmAL2BB4A7UAz4ISMDEuJDJSuhQohMR1GU2qSsUEUA8UACcEdV1agMDUy8956usnuS8nyoFeABXFdV9WSGBibEB0iSUCGEEEIIYXTyYpLIUFbW1g8TExKk7rd4KUsrq0cJ8fEeGR2HeD9ZW1k9TEhMlN8Z8dasLC0fxSckyG9ROpGVUJGhFEVRL4XI+wDi5Yq4WqCqqpLRcYj3k6IoauKtUxkdhsgCLPOWkd+idCQvJgkhhBBCCKOTJFQIIYQQQhidJKFCCCGEEMLoJAkVQgghhBBGJ0moyFQmjRzMsvnf6T8HtWrIqP499J8nfzGUpXNnvnD8d5PGcnjfHy89x6wp41g8+xuD41GREaxcMv+lYx8FP6DfJ21e2ier+rR1I8rnd6NHu2Yv7JOUmMjnQe2pW74orev6c+/OLX3b9zOnULd8UQL9inFg904Agu/fpVPTABpWLkkj/1L8+L2U8RbGYZ2/HOXrt6V03Va0+2wocfHxbzxXQJtunDz7+pU/IyKjmb/855f2efDoCW17DXnT0N7ImfOX2bbnoFHP+a5cunaTah91xq6wH98s+PGF/T4dNIbCVRpRvn5bytdvy5nzl40Y5YdLklCRqZT2q8TpE0cA0Ol0hIeGcPXyvz/up08coXSFSi8c32/4WCpXr/1G546KjGDVK5LQ7Dk8+W7pmjeaP6NEhIelyzxBfQYyZe7Sl/b5ZcVS7B2d2HniIp179mP6lyMAuHb5Ar9t+JktB/9i0c9bGDe0H1qtFlNTM/437mu2Hj7L6u0HWbF4HtcuSxlv8e5ZW1lyYttqTu9ci4W5OQtWrEvVrtFo3nkMEVHRfL987Uv7eGZ3Y/W8qe88ln9oNBrOXLjM9kyWhMbGxZOUlPza45wdHfhm7FA+79bxlX0njxjAiW2rObFtNaWKeb9JmOI1yT6hIlMpXb4Sk0el/L/+q5fOU6hoMZ48ekhkRDjW1jbcuHIJn5Kl+fuvU0z+YghxsTE4ubgyadYi3D1yMKxPEDXqNiCwSQv27drG5C+GYG2TjTJ+lfk/e3cdFsX2BnD8u7QKoigWdqAg3WmDoKBYYHdcuwMTu7v1qvdii42dqNhiK7ZYKBbdsb8/VtfLb7FAxTif5/GRnZlz5p0VZ8++M3PeJ2EPWbp+OwD374TSpkEdWSauW2/adu3FrHEjeBz2AK8aVjhUr8OQsVMU4nv6OIzuLb0IDL7M1vX+HN0XSGJiIk8ePqBO/QYM9lNs897mNatYPm86+bULUKWqCarq6oyeOpdhvTqhoZGH0GuXefP6FRPnLmPHpjVcPn8GE0sbpixYQXp6OiP6duXG5RCQSGjSsj3tu/f96L6Sk5I4ELiVgDWrKFq8BNOX/JvDfxmwr1aLs8HHPrnN4b2BuYTSwAAAIABJREFU9BoyCoC6DZowflg/pFIph/cGUq+RN2rq6pQsU47S5Spw9eJ5zK3tKFKsOACaWlpU0K9CxPNwKlY2zHG8gvClHK3NuXbrLsdOX2DsrEUU0M7P7fthhOzdSO+Rkwi5FoqKsjLTRg6ghoM1iUlJdBnkx9XQO1SuUI7EpGR5XzqGjry9eRKArXsOsefwCf6eOZaIV2/oNWISDx8/BWD+xOEsXLWeB4+eYu3enNrOtkwZ3l8htrAn4TTq1JdLBwLwD9jJzgNBxCcmcv/hY/p1bUNKahrrtu5GXU2VHf/MR6eANi4+XTA20OfE2RDS0tNZNm0M1mZGvI2KpuvgsTx88pS8GhosmjwSYwN9xs9ewoPHT3n4+BmlShTjdMgVEpOSOHX+MkN6dKBsKT0Gjp1OUnIKeTTUWTbdj8oVyuIfsJNdh46RkJjEg8dPaVi3JpN9+wGwP+gko6cvJD09nUI6Bdi/binxCYn0HzOVG3fuk5qaxsh+3WjgWuOT/zbnL19n1cbtHDp+mhPb/SmqW+ir/m2LFNahSGEd9h458VXthB9DDEKFn0rR4iVQVlEh/OljLp07g7mVHREvnnH5/Bm08mujb2iERCJhgm8/Fq3egk5hXfZs28ScSaOZNG+5vJ/kpCTGDOzJmsDDlCxTjgFdWmfaz4O7t/HffpD4uFjc7Ixo0aEbA0ZP5O6tG2wPuvDF8YZev8q2o+dQU1PH3c6I1l16UlyvlMJ2Ec/DWTRzEluPnCWfphbtG7lSuaqJfH1MVCQb9p3gyN5AerRuzLo9QUyYs5SmdewJvXaZ9PQMXj4PJzD4smz76Kgs47l1/QoBa1Zx4vB+nGq5MnTcNKqamsuPeUCXVlm2899xiPzaBb74uD/m5fNnFNcrCYCKigpa+bWJevuGiOfhmFnayLcrVkKPiOfPMrV9+jiM0GtXMP3PdoLwvaWlpbE/6CSu1R0AuHT9FhcPBFCulB6zl69GIpFwcf8mbt17iEfbnlw/so2lazaTN48GVw9v5VroHWw9sv5/9V8D/KbhbGtBwLKZpKenExefwIShfbhx5z7n92744nhv3LnHud3rSUpOwbBGQyYO7cO5PesZNG4Ga7bsok8nWSyJSUmc37uBE2dD6DpkLJcOBDBu9hLMqlZm8/JZHD11jo4DRsv3HXr3AUc3rySPhgb+ATsJuXaTueOGARATG8eRgBWoqKhwOPgso6cvYOOSGQBcuXmHc7vXoa6mhnHtRvRo1xwNdXV6+E7g0Ka/KVdKj7dR0QBMWfA3NRysWTbdj6joWBy92lDbyZZ8efNkOsa3UdGs27YH/4Cd6BYqSHvvhsz2G4K6uhoArXoO5c6DRwrvTd/OrWndxOOL38v/N3rGQibOW0ZNBxsmDu0j35/w/YhBqPDTMbe249K501w6f5oO3fsS8TycS+dPo6WljYWNPQ/v3eZu6A06NnUHID09Hd2ixTP18eDuLUqWKUfJMuUA8Gjsw0b/FfL1NVzcUVNXR01dnUKFdXnzKiJbsdo710QrvzYAFSobEP7kcZaD0GuXzmPt4EyBgjqALEsYdv+ufH3NuvWRSCToGxpRWLcolQ2NAahUxZBnTx5h7VCNJ48eMn5YP2q4uONY00VhH6sWzWH2xFEM9pvC0LFTUVNXz7S+fKXKXzXA/pHi4+Lo094H34kz0NTKn9vhCH+AxKRkrN2bA+BoY04HHy9Oh1zB2tSIcqX0ADh1/jI92svuAa9SsRyl9Ypx9+Ejgs9epGcHWVtjA32Mq1T67P6CTp9n5azxACgrK6OdX4vI6NivjruGvTVamvnQ0syHtpYm9etUA8CoSkWuhX44p3g3qAuAs60lsXHxREXHcur8ZTYskV3ar+lgw9uoaGJi4wDwqFOdPBoaWe4zOjaOTgPHcC/sMRKJhNTUD7cq1HK0QTu/FgBVKpbn8bPnREbH4GRjIX8fdQrIzpGHTpxh16HjzF62GoDk5BQehz/HoGJ5eX/hEa+o4uxJ3RqObPl7NqVKKBYnWrtw6le/b58zfmgviukWJiUllR6+E5ix5B9G9O36zfcjZCYGocJPx8LWgUvnz3D35nUqGRhRTK8UqxbNQVNLi8Yt2iGVSqlYxZCN+7J/eUVV7cMATVlZOdv3f6n+Z6CnpJTzfiRKSqj959u3REmJtLQ0tAsUZHvQBYKPHmDDP8vYu2NzpswvQINmLUlLS2XTv8s5FxxEoxbtqFbHDRUV2X/zr8mEXgk5x5iBPQDoM3QMtdw9v+g4ihTX4/mzpxQrUZK0tDRiY6IpoFOIosVL8Dz8qXy7F+HPKFpc9gGVmppKnw4+eDZtgatHoy/ajyDk1Pt7Qv9f3rxZD8S+lETyoZhOUnLyJ7bMHjU1VfnPShIJ6u9eK0mUSEtP/xAHmYv6SD5T4yfv/2Uj/2vszMVUt7ciYNlMwp6E49q8S5bxKCsrkZaWnlUXAEilUjYsnk7lCmU/uk3Rwjr4z5vIqo07aNK5P60a16eFVz2KFNaRb/M9MqHFi+gCoK6uRttmDZi9/OMPMQnfjngwSfjpmFvbEXRgD9oFdVBWVqZAQR1ioqO4fOEs5jb2lKtYmcg3r7l0/gwgG8TcvXUjUx/lKlbm6aOH8qez92z/9M3/APk0tYiPi/vmxwNgbG7F+VMniI6KJC0tjQOB276qfeSb10ilGdT1bEzf4WO5efWSwjaFdIvQpc9gAoMv07ZbH/YHbsXNtqp8NoH3mdCs/vz/pXhTSxv5ui8dgALUcvNg+wZZlmP/zi3YOddAIpFQy82DPds2kZKczNNHD3n04B4mFtZIpVJG9u1KBf0qdOjR76veE0H43hxtzNmwfS8Adx484kn4C/TLl8XJ1oKNO/YBcOP2Pa7d+pCBLFJYh9B7D8jIyGDH/qPy5TUdbFi6RnYeSk9PJzomFi3NvMTFx3+X2AN2yWagOHn+Evm1NNHOr5XpeI6dvkChggXIr6Wp0FZLMx9xcQny19GxcegVKwLA6s07P7tvW3MTgs9d5OET2S037y/Hu1SzZ9G/G3hfLvzy9VsKbZWVlfFyq82OVfPYtnIuCYlJ1PbuTNMuA4iOkWWO1y6cKn+A6L9/cnIp/vnLV4BsoLzzwFGq6lfMdl/ClxOZUOGno29oTOSb13g08fnPMiP5Q0gAc1euZ6LvAGJjo0lPS6Nttz5UqlJVvr1GnjyMnjaPLt4e5MmbD2Nzq8/ut6BOIcxt7PF0MsO5tluWDyZlV9HienTrNxRvF0e0CxakXKXKaOX/8svOEc+fMbx3FzKkGQAMGDnhk9tbOzhj7eBMXGwMVy+ez1Hs77XyqMmDu7dJiI+junE5JsxdinMtV+ZN9sPIzJJa7p40bdWBIT3a42ptgHaBgsxavgaASlWq4t6wKfUdTVFWVmb01LkoKysTcuYkOzatRd/QCK8asn+j/iPGU93F/ZvELAg58VfrZvQeOQmLut6oKCuzfPpY1NXV6Na6KV0G+WFSuzFVKpbDwshA3mbi0D406tgPXZ0CWJgYEh8vm/pp5pjB9PCdwD+bdqCspMT8Cb7YWZpib2mGuWsz6tZwyPLBpOzSUFfDpl4LUtPSWDZtDACj+nWj6+CxWLp5k1dDgxUzx2bZtrq9FdMXr8LavTlDenRgYLe2dBo0hsnz/8a9ltNn961bqCALJ43Ep9sgMjIy0C2sw941ixnepwsDx87A0s2HjIwMypYqwfaV8z7aj16xIvj27sywXp0IOp2989iLl69xaNCamLh4lCQSFqxcx+WDm8mvpUmD9r1ZMnU0JYrq0r7vCF69jUIqlWJqqM+CiSOytT/h60jefyMRhNwgkUikt16nfJe+4+PiyKepiVQqZdyQPpQpX/GTT5R/b+/jSUtLo1fbpjRp1R6X+h+fc1P4oEphNaRS6WcuKApC1iQSiTQ57GJuh/HDuPh0YcqI/liaiFkmvjX1shbiXPQNiUyo8NsKWL2C7RtXk5qSgoGxGT7tuny+0Xe0YNp4Th8/THJSEo41XahTr2GuxiMIgiAIuUlkQoVc9T0zoTlx++Y1hvbokGmZmpo6mw6c/Gxbb1dHUlIyP5AwddEq+RPvwtcTmVAhJ36FTOj1W3fp0H9UpmXqamoE7xAPyPxMRCb02xKDUCFX/ayD0P86tGcHZStU+qknUD9xeD8Thw8gIyODpq070LXvEIVtnj15xIg+XXn75hXaBXSYvuQfipWQzek5Y6wvxw7KHljoPnA49Rp5A7Dm70X4L53P44f3OX07XH5P7o8mBqFCTvwKg9Cs7Nh/FP3yZTCoVP7zG+eS/UEnGThuBunp6XT0acTg//vyDvDoaThdh4zl9dtIdLS1WTVnAiWLFwXAd/Ic9h4JJiMjg9rOdswaMxiJREJKSip9x0zh+JkQlCRKjBvck0bu2auG9y2JQei3JZ6OF4TPOLRnJ/duh2a57keU9vuc9PR0xg3ty/KNgew6eYXdWzdmWfpy2pihNPRpxc7jF+k5aASzxo8EIOjAHm5evcy2oAts3H+SlQtnExcbA4CFjT0rt+ylRKkyP/SYBEGAwANBhN59kOW6n+Xc03f0VHb+M58rB7ewcee+LOMdNmkOrRt7ELJvE8P7dmHUtPkAnA65wukLVwjZt5FLBwIIuXKD42dCANnE9kUK6XDj6HauHNqMs63FDz024ccQg1Dhj7Nz01qauTjgVcOK0QN6kP5ubj2LMgWZPXEUDatb4lPXidcvI7h47jRH9+1iup8vXjWsePzwPm0a1GHSiIE0qW2H/9L5nD5+hEY1rfF0Nmd4ny6kvJsbsJZ5Jab7DcPT2ZxmLg48enCPuNhYalvok5oqq4EcFxuT6XV2XL14ntLlKlCqbHnU1NSo18ibw3sDFba7fzsUO+eaANg615Bvc/92KFb2TqioqJA3Xz4qVzXmxOH9ABiamFOydNlsxyYIwgfrtu3GsWEbrN2b08N3gvzco2PoyOjpC7By88HZqy0Rr95wOuQKuw4dY9ikOVi7N+f+oye4+HRh4Njp2Hu2Yv6q9Rw5eRabei2wqOtN18F+JCfLrirpO9bHd/IcLOp649iwDffCHhMbF4++k4f8XBMTG5fpdXacv3ydCmVKUr50SdTUVPH2rEvggSCF7ULvPqCGgzUgm2w/8KCs/K8E2VyqKampJKekkJqWRhFd2Xyg/wbsZEiPjgAoKSlRWKdgtuMUfl5iECr8Ue7fCWXP9gDW7TnG9qALKCsrE7h5HQAJ8fGYWtqy41gIVvZOBKxegYWNPTXdPBjsN5ntQRcoXa4CAKkpKWw5fIZWnbrj26szs5avJfDEJdLT0li/aql8f1r5tQk8cYlWnbszacQgNLW0sHGsxrGDewDYvXUTLvW9UFVVzRRnYMA6vGpYKfzp08GH/xfx/BnF311Wh/clMcMVtqtc1YSDu7YDcHD3duLjYol8+4bKRiacOHKAxIQEIt+85mzwMZ4/e6rQXhCE7Au994CAXQcI2ryS83s3oKyszPp3c3bGJyRiY27MhX0bcbK1YOWGbdhbmuJRpzpThvfj/N4NVCgjq8SWkprG6cC1dG/jTZdBfqxZMIWL+zeRlp4un4cUQFtLk4v7N9G9rQ+Dxs1ASzMf1e0s2XMkGIBNgfvxcqulcO5Zv30P1u7NFf407z5Y4ZjCI15lqmikV7wIzyJeKmxnYqDP9n1HANix/wixcfG8iYzCztKU6vbWlLF2pYxNXVyq2WNQsTxR7ypJ+c1chG39lrToMYSIV29y8vYLPykxCBX+KKePH+XGlUs0c7HHq4YVp48f4UnYQwBU1dSoWbc+AFVNLXj2WLEix3vuXs0AeHjvNiVLl6VcRX0AvJq34cLpD5Wc6jf2efd3cy5fkE2u36x1R7au+xeArev/pXHLtgr9ezZrmeWk8vNWbcz2sQ8ZO5Xzp47TqKY150+doGhxPZSVlXGq6UL1Om60qFeNgV3bYGZli7Kycrb3IwiCoqMnz3HpWigODWSZ0KOnzvHwsezLnpqaKvVry8pvWhgZ8Oip4pfI95p5uAKyyfPLliqBfnnZrTKtm3gQfO7Dfa/eDdwA8GlQl7MXrwHQoXkj/ANkk837B+ykXbMGCv238KqX5UTwGxZPz/axTxnRnxNnQ7Cp14LjZy6iV6wIykrK3At7zK17D3lwZh8Pz+wj6NR5gs9dJC09jafPI7C3NOXs7nXYWpgwbNLsbO9f+HmJKZqEP4pUKsWreWsGjpqosE5VVVVeck9JWZm09I/fc5Unb74v2t9/S/i9/9nC1oFnQx5xNvgYGenp6BsYKbQLDFjHioWzFJaXLldBYSBatLheFiUxSyi0LVq8BPP/lWVK4uPiOBC4TV4p6a8Bvvw1wBeAgV3bULbC52thC4Lw5aRSaN3EkwlDeyusU1VRkZ8fZGWEP176Mt8nymv+V1bnHgcrM/o8ncyx0xdIz8igamXFqkDrt+9h1lLFJ/IrlC2lMBAtUVSXJ+Ev5K+fPX+JXtEiCm1LFNVl09KZAMTFJ7B932EKaGuxYsNWbM2N0cyXF4C6NRw5c/Eqjtbm5M2jgZdbLQCa1KvDPxu3f9FxC78WkQkV/ij21WpyYOc23rySXTKKinzLsycfz3jCp8t5lqtYmWdPHvHowT0Admxai7VDNfn69+VC927bhJmVrXx5Q+/WDOrWlsYt2mXZ79dkQo3NrXj04B5PHz0kJSWFPds2UctNsXxd5JvXZGTIKi4tmzuVJi1l+05PTyfyrexS1+0bV7lz8xqONV0++Z4IgvB1ajnasHXvIV6+fgvISll+KuMJoKmZl9i4rMt66pcvw6Onz7kX9hiAdVv34GxrKV+/OVBWtjNg1wFsLT5MD9eqiQft+g6nbVPFLCh8XSbUyrQq98Ke8PDJM1JSUtkUuB8Pl+oK271+Gyk/90xbtJJ23rI5kkuXKMbxsyGkpaWRmprK8bMhVKlYDolEQv3a1Th25gIgyyL/zDMECNknBqHCH6ViZUP6DvejU7N6NKhmQccm7ryKePHJNvUbebNywUwa1bTm8cP7mdapa2gwaf5y+nVqgaezOUpKSjRv31W+PiYqkgbVLPBftgDfCTPkyz2btiAmOpL6TRTv8fxaKioqjJoyh07N6lPfwQT3hk3lJUznTfbjyLsHkM6ePIa7rRF1bQx58/KlPPOZlppKa4+a1HcwYfSAHkxb/A8qKrKLJP7LFlDduBwR4U9pUM2SkX275TheQfgTGVQqz9iBPajfpgeWbt7Ua92dFy9ff7KNt2ddZi3zx6ZeC+4/epJpnYaGOsumj6Flj6FY1PVGSUlC11ZN5esjo2OwdPNmwar1TB81UL68RUN3IqNj8Xl3uT4nVFRUmDNuKB5te2JSpwlNPVww1JfdNz921mL5A0jHz4RgVKsRVWt68fL1W4b17ARA43p1KF+6JBZ1vbFyb46JgT4edWSD2InD+jBhzlIs3bxZu203U0d8u5Kmws9DzBMq5KpfYZ7Q7KplXokth05nObfmvp1bOLI3kGmL//nxgf2CxDyhQk78qvOEZpe+Y31OBa7J8onyrXsOEXgwiFWzJ+RCZL8+MU/otyXuCRWEH2z8sH6cOLyfpet35HYogiD8QfqNmcr+oJPsWDU/t0MRBEBkQoVc9jtnQoVvR2RChZz40zKhwvcjMqHflrgnVBAEQRAEQfjhxCBUEL6jNg3qcO1SyA/bX2JCAt2aN8TdzggPR1NmjhsuX7dq0RzqO5jQoJoF7RvVzTQrQGdvD6zL69KthVeW/U7w7Y9FGVGxRBB+FS4+XQi5qli+90do3Lkf5q7N5K/9Zi7C0s0ba/fm1GvTg/CIV5m2v3DlBnkrWLN1zyEArty4TbVG7TBzaYqlmzcBgft/aPzCjyMGoYLwm+nQsz97z1xn69HzXDx3muOH9gFgYGzG5kNn2Hn8InU9GzPDz1feplOvAUxdtCrL/q5dCiEmKvKHxC4Iwq9t+77DaObNm2nZgK5tCdm3ifN7N1CvljMT5y6Tr0tPT2fElLnUcbaTL8uTR4MVs8Zz+eBmAv9dyKBxM+VVlITfixiECn+UhPh4ujVvSMPqlng6mbFn2yYAFk6fQNM69ng6mTGqf3fe3yvdpkEdJo8YRJPadtSzN+baxQv0bteMutaGzJk0GoCnj8NwtzNiULe21LM3pk8HHxITEhT2HXz0ID5uzjSuaUPfjs3lc4/OHDdcnqGcOnpojo4vT9682DnXAEBNTQ1DE3NehD8DwM65BnnefTiYWtnw4vkzeTv7arXIp6ml0F96ejrT/YYxaMzkHMUlCH+6+IREGnbog5WbD+auzeTZvYlzl+HQoDXmrs3o7jtefu5x8enCoHEzsPdshUntxly4cgPvbgMxrNGQMTMWAhD2JBzjWo1p13cEJrUb07z7YBISExX2ffD4aao1aicvgRkXLzs/jZgyD9M6TbB082boxJxXJIqLT2Du32vx7d050/L8WprynxMSEjNNpL/wnw14udemSCEd+TL98mWoVK40IJvoXrdQQV69FV+Ef0fi6Xjhj3LiyH6KFCvO0g2yJ9NjY6IBaNW5Bz0HjwRgSPf2HN2/Wz7hu6qaKlsOn8F/6Xx6tGnClsNnKFBQBxerKrT7qy8AD+/dYeLcZVjYOjC8TxfWrVxCp14D5PuNfPOaJTMns2rLPvLmy8fyedP5Z/EcWnbqzsHdO9h75joSiYSY6CiFmM+cCGLKqEEKyzXy5GXD3uMfPdaY6CiO7t9N2669FNZtXvsP1WrX/ez7tfbvRdRy86BIseKf3VYQhI87cOwUJYrqsmPVPACiY2SZve7tfBjRVza3cIf+I9l9+Lh8rkw1VVVOB65l/sp1NO3Sn9O71qKjrY1B9Qb06dQKgDsPwlg6bTQOVmZ0HezHktUBDOj6oRTw67eRTFnwN3vXLiFf3jzMWPwPc/9ew19tvdlx4CjXDm9FIpFkmWkMOnWeweNnKizPm0eDY1v/UVjuN3MR/Tq3Jo+GhsK60dMXsHbrbvJraXJgvSwT+uzFS3buP8qBDcvoemVslu/b+cvXSUlNpUKZkh99b4VflxiECn8UfQMjpo4eyoyxvtRwrY+VvRMAZ4ODWDF/JomJCURHRlKxiqF8EFrLzVPetlIVQ/mArFSZcrx49gQt7QIU1yuFha0DAA2atmT18gWZBqGXL5zl3p1QWtaXfbikpqRgZm2HVn5t1DU0GNG3KzVc61HDtb5CzHbONdgedOGrjjMtLY2BXdvQpktPSpXNXGlk56a13Lgcwuqdhz/ZR8TzcPbt3IL/jkNftW9BEBRVrVyRoRNmMXzyXOrVdsbJxgKAY6cvMHPJvyQkJREZFY1hpQryQej76kNGVSpioF+B4kV0AShXWo+n4RFo59eiVIliOFiZAdCiUT0WrtqQaRB69tI1Qu8+pEaTDgCkpKZiZ2GCtpYmGmpqdBsylnq1nalX60Olt/dqOFhzfu+GLzq+Kzdu8+DxU2aMHkTYE8VKUOMG92Lc4F5MW7iSxf9uYPSA7gwaN4OJw/qgpJT1RdnnL1/RYcAoVswY+9FthF+bGIQKf5RyFfXZeuQsxw/uZc6kMdhXq0nn3oMYN6QPmw+dprheKeZPHUdyUpK8jaqaOgASJSX5z+9fv6/x/N/LS1m9lkqlOFSvzazlaxRiCjhwitPHj7A/cCtr/17Mv9sPZFqfnUzo6AHdKVO+Iu3+6pNp+aljh1kyewqrdx5GTV09y7bvhV67zOOH93G1NgBkDz25Whtw4HzoJ9sJgqBIv3wZzuxex76jwfjNWERNRxsGdmtHn1GTObVzDaVKFGP87CUkJSfL26irqQGgJFFCXU1VvlxJokRa+vtzT+b9ZHXuqe1ky+r5irfUnNyxmiOnzrFtzyEW/7uR/euXZVr/NZnQMxevcvHqTfQd65OWns7LN29x8enCwY3LM23X3Mudhh36MHpAd0Ku3qRNb9m96a8jo9gXFIyysjIN69YkJjYOrw59GTeoJ7YWJlm9pcJvQAxChT9KxPNwChTUoYF3K7S0C7B5zUqSk2UDzoI6hYmPi+NA4FZcPRt/Vb/hTx9z6fwZzK3t2LVlAxa2jpnWm1nZMn5oXx49uEeZ8hVJiI8n4vkzihQrQVJiAtVd3LGwdaCOZWWFvr82Ezpn0mhiY6KZMGdppuU3r15izMCeLN8YSCHdIp/tp4ZrPYJvfigVaFGmoBiACkI2hUe8Qkc7Py0b1Uc7vxarNmwnKVk2R3JhnQLExSewde9hGrvX/qp+Hz97wZmQK9hZmrJxxz4crM0yrbc1N6Hf6KncC3tMxbKliU9I5NmLl5QoqktCYhLuNZ1wsDSlSjXFWvJfkwnt1qYZ3drInogPexJOo0595QPQuw8fy+/xDDx4jMoVygJwJ3iXvH3ngWOoV9uZhnVrkpKSSrNuA2nVuD6N69X5qvdD+LWIQajwR7kTep3pfsNQUlJCRUWVMdMXkF+7AM3adMTT2ZzCRYpiZG711f2Wq6jPuhWLGdGnCxUqG9CiQ+Ya6zqFdZk8/28Gdm1DSoos09HPdyz5NLXo2aYJyclJSKVSho2flqPjexH+lCWzplC+UmUa17IBoFWnHjRr05Hpfr4kxMfRr1MLAIrrlWLx2m2ybTxq8uDubRLi46huXI4Jc5fiXMs1R7EIgvDB9Vt38Z08ByWJEqqqKsyfMJwC2lp0bN4Ic1dviukWwsrE8Kv71S9fliWrN9F1yFgMKpWnW+ummdbrFirI8hl+tO0znOQU2aDXb2BPtDTz0rTLAJKSk5FKYdrIAVl1/02MnDqPOw8eoaQkobRecRZMHPHJ7TfvPkDwuUu8jYxm9eZAAP6eMRbTqopf0oVfm6iYJOSq36Fi0tPHYXRv6UVg8OXcDuW3JSomCTnxu1ZMep9xvHQgILdD+WOIiknflrjTVxAEQRAEQfjhRCZUyFW/QyZU+P5EJlTIid81Eyr8eCIT+m2JTKgzcm+zAAAgAElEQVQgCIIgCILww4lBqPBHy4166E8fh2FaMj9eNT48AOW/dD6eTmZ4OJry75J58uWh1y7jU9cJrxpWNKltx9WL5+XrzgYfw6uGFR6OprT2/PwTtWv+XoSrtQFVCqsR+ea1fHlgwDoaVLPA09mc5u7VuHX9CgDPnz2hbUMX6juY4OFoiv/S+Z+Na8+2TbhaG3y0Br0g/Kl0DB0/v9E3FvYkHO3K9li7NwfgSfgLXJt3xbROE8xcmjJ/5TqFNrOXr0a9rAWv31Uomrn0X6zdm2Pt3hxz12bkKW/F26hobt8Pky+3dm9OYSNn5q1Y+8l4bt17SLVG7dDSt2XWMv9M6+b+vQYzl6aYuzajTW9fkpJkD3BKpVJGT19A1ZpemNRuzIJV6wHZ/Kq6xtXk+39fCjQxKQlr9+ZoVrKRH4Pw8xJPxwtCLihdtrx82qU7odcJWL2CTQdOoaqmRhdvD2q41qNM+YpMHzucnoNHUq2OG8cO7mW6ny+rdx4iJjqKcUN6s3zTLkqULM2bVy8/u08LG3tquNajbUOXTMv1ypRj9c7DaBcoyPFD+xg9oAebDpxEWVmFoeOmUdXUnLjYWJrUtsWhRm0qVjb8aFz1GnlTSLcoKxfmvASgIAg5V75MSfk0Syoqykwd2R9zIwNi4+Kx82xFHWc7DCrJClo8CX/BoeOnKa1XTN5+YLd2DOzWDoBdh44xf8VadApoo1NAW95veno65WzdaFi35idj0SmgzSy/Iew8cDTT8mcvXrLwnw1cObSZPBoatOw5lE2B+2nbrAH+ATt5+jyCa4e3oqSkxMvXb+XtHK3N2L5yXqa+8mhocH7vBvQdFQt/CD8fkQkVfhszxw1n7YrF8tfzp45jxYJZxMfF0b5RXRrXtMHT2ZzDe3YqtD0bfCxT9m7c0L5sXS/7pn798kVae9amcS1bOjWrz8sXz79p3A/u3MLE0oY8efOioqKCtYMzB3dtB2QTT8fFxgCyEqPvqzXt2rIBFw8vSpSUzb33JfN+GpqYU7J0WYXlFjb2aBeQZYRNrWzlteaLFCtOVVNzADS1tKigX4WI5+GfjEsQ/gQjpsxjsf9G+evxs5cwa5k/cfEJ1G3ZDdv6LbGo683OA0EKbY+dvoBXxw9FJPqOnoJ/gOycdPHaTep4d8bOoyX12/Tg+ctX3zTu4kV0MTeSFZ/Q0sxHlQrlePbiwxfYweNnMtm3HxKyvuVx0879eDdwU1h+5OQ5ypcpSZmSJT65/yKFdbAyrYqqimL+Kz09ncSkZNLS0khITKR4UVl1qGVrNzO8Txd5xaQihXUU2gq/LpEJFX4b7l7NmDRiEK06dQdg347N/B2wG3UNDRb4B6CplZ/IN6/xcXOmlrunQmWRrKSmpjLBtx+LVm9Bp7Aue7ZtYs6k0Uyal7kKSGDAOlYsnKXQvnS5CsxbtVFh+X9VMqjK7ImjiXz7Bg2NPBw7tA8jM0sAhk+cQedmHkwbM4yMjAzW7z0GQNj9u6SlptKmQR3i42Jp260XXj5tvuh9+pTNa1ZlWVP+6eMwQq9dwdTS5pNxCcKfoJmHK4PGzaB7Wx8ANu8+yC7/hWioqxGwdCb5tTR5/TYS50bt8HSp/sXnmv5jprF5+Wx0CxUkIHA/Y6YvZNl0v0zbrd++h1lL/RXaVyhbig2Lp3/xMYQ9CefKzdvYmBkBsPNAECWKFsHEUD/L7RMSEzlw7BRzxg1VWBcQuB/vBornjS+lV6wI/bq0oaJDPfJoqFPH2R6XavYAPHj0lM27DrBj/1EK6xRklt8Q+cT3Zy9ew8rNh+JFdZk6oj+G+hWyHYOQO8QgVPhtGJqY8/b1SyKehxP55hX5CxSkuF4pUlNTmTVhFBdOn0BJSYmI5894/TIC3aLFPtvnw3u3uRt6g45N3QHZt3XdoopZP89mLfFs1jJbcVfQN6BLn8F0alqPvHnzYWBkirKyMgDrVy1j2ITp1PVszN7tAYzs241VW/eRlpbGjSsXWbV1P8lJiTR3q4appS3lKmb9AfIlzpwIYsvaVazdHZRpeXxcHH3a++A7cQaaWvk/GZcg/AnMjKrw8s1bwiNe8fpNJAW181OqRDFSU1MZNX0BwecuoiRRIvzFKyJevaFYkcKf7fPOg0fcuHOfeq1lX6LTMzKybNfCqx4tvOrlKP64+ASadx/EjNEDya+lSUJiItMWrmT36oUfbbP70HHsrUzRKaCdaXlKSiq7Dh1n/JDe2Y4nMjqGXQeDuH1iFwXya9Kix1DWbdtNy0b1SU5JQV1dndOBa9m+7zDdhvhxJGAl5kZVuHtyN5r58rL3aDBNuw7gZtCObMcg5A4xCBV+K24NmrA/cCuvX77A3UtWQi5w83oiX79iy+GzqKqqUsu8krxU53sqKipIpRny1ynvasdLpVIqVjFk474Tn9xvTjKhAE1bd6Bp6w4AzJowkmIlSgKwfcNqRkyS9evWsCkj+/0FQLESehQoqEPefPnImy8fVg5O3L5xNduD0Ns3rjKq/18s27CTgjqF5MtTU1Pp08EHz6YtcPVoJF/+sbgE4U/RpL4LW/ccIuLVa5p6yKqLrd++l9dvIjkTuBZVVVX0HevLS3O+p6KiTEbGh6kRk9+tl0qlGFYqz/Ft/35yvznNhKampuLz1yCae9XDy032QOODR08Je/pM/gDT0xcvsfNoRfB2f/lAeFPgAXyyuBS/L+gkZkZVKKpbSGHdlzoSfJaypfTQLSS7LcjLrRanQ67SslF99IoVxcutFgAN69aiy+CxAOTX0pS3d6/pRN+Rk3n9NpLCOj/+YVMh+8QgVPituDdqxqj+3Yl885rVOw8DEBcTjY5uEVRVVTlzIojwJ48U2pUoVZp7t0NJSU4mKSmR0yeOYmHnSLmKlYl881peFz41NZWw+3eoVKVqpvY5yYQCvHn1kkK6RQh/+piDu7azcX8wILsv89zJ49g6VefMiaOUKV8RgNrunowf2o+0tDRSU1K4GnKOdn/J7jNr36guUxetpGhxvS/ad/jTx/Ru78PURasyDWKlUikj+3algn4VOvTol6nNx+IShD9FMw9Xug8bz+vIKA69q5EeHRuHbmEdVFVVCTp1nkfPFO8fL61XnFt3H5CcnEJiUjJHTp7DwcoM/fJlefU2Ul4HPjU1lbsPHytcYs5JJlQqldJt6DiqVCxHv86t5cuNqlTiachh+Wt9x/qcClwjH9BFx8Ry4mwI/8yZoNDnpp378PHMfCl+0b+yB5Z6tGv+RXGVKlGMs5eukZCYSB4NDY6ePIfFuxKmDVxrcOz0ecqV0uP4mRD5pfgXL19TVLcQEomE85evkyGVUqhgga94N4SfgRiECr+VSlWqEh8XS9HievKHZTybtuCvVo3wdDbHyMyS8pUU6w8X1yuFe8MmeDqZo1emLIbGpgCoqakxd+V6JvoOIDY2mvS0NNp266MwCM2pPh18iHr7BhVVVUZPm0d+bdnJdPzsJUwcPoD09DTU1TUYN0v24FUFfQOca7nSsJoFSkpKNG3dEX0DIzIyMnj88D7aBRRv3vdftoAV82fy+uULGlSzpHodNybMXcqi6ROJevuGce8upykrq7Dl8Bkunj3Fjk1r0Tc0kk8n1X/EeKq7uH80LkH4UxjqVyA2PgG9okUoXkT2EE0LL3cad+qHRV1vLI0NqFyhrEK7UiWK0cTDBfO6zShbSg+zd/XQ1dRU2bBoOgPGTiM6Jo609HR6d2z5Te9zPHXhMmu37saoSkV51nPckF6413T6ZLsd+49Sx9mOfHnzZFoen5DI4eCzLJyUuRb87fthOFiaKfTz4uVrHBq0JiYuHiWJhAUr13H54GZszI1p7F4b2/qtUFFRxqxqZTq3aAzA4O4daNdvBPNWrEMzbx6WTBkNwNa9h1i2ZjMqysrk0VBn9fzJX3TvrfBzERWThFz1J1ZM+p615u+EXmfL2n/xnfDlDyh8a2eDj7Fy4WyWrt/+zfoUFZOEnPhTKyblVm15r4592LRkJmpqqj90v//1/9ncb0VUTPq2xBRNgvCDKSsrExsTk2my+m9F38AoVwege7ZtYtyQ3mgXEJfFBCG3KSsrERMbJ896/ijbV87LtQHo+8nqU9PS5NM6CT8vkQkVctWfmAkVvp7IhAo58admQoVvT2RCvy3xNUEQvqH3E+Tntgd3b+Hj5oxxCc2fIh5BEL6v9xPm5zapVEp/v2kYVG+ApZs3l66H5nZIwk9MPJgkCL8h7QI6jJw0m0N7xbx5giD8OPuCTnLv4WNuBu3g3KVr9B4xmeAduT84Fn5OIhMqCNm0feNqGlSzoGF1S4Z0b6+wfpP/CprWsadhdUt6t/cmMSEBkFVy8nQyo2F1S1p7yOa/u3vrBs1cHPCqYUWDahaE3b+bo9gK6RbB2MIKFZXcezBAEITvY82WXVi6eWPl5kOH/iMV1q9YvxWHBq2xcvPB569BJCQmArBl90HMXZth5eZDbe9OANy8cx/Hhm2wdm+OpZs3dx8+zlFsgQeCaN3YA4lEgq2FCVGxsd+8/Kjw+xCZUEHIhru3brB45mQ27D1OwUKFiYp8q7CNi4cX3m1lJ/o5k0azee0q2nTpycIZE/k7YDdFi+sREx0FwIZ/ltO2ay88m7UkJSWFjPR0hf76d2rJw/t3FJa37973m5TsFATh53fzzn0mL/ibY1tWUVinIG+johW28XKrRad3UxyNmbGQVRt30LN9cybOW84u/4XoFStCVHQsIKvN3qtDC1p41SMlJZX0DMVzT6ueQ7nzQHF+5b6dW9O6iUemZeERLylZoqj8tV6xIoS/eCWfxkoQ/ksMQgUhG86cCMKtYRMKFpJVEylQUHFezruhN5g7eQwx0VEkxMfjVNMFAAsbB3x7dcatYVNcPLwAMLOyZensKbx4/gyX+l6UrVBJob/ZK9Z9xyMSBOFXcPTUeZrUqyOfeuj/y2gC3Lh9H7+ZC4mKiSMuPkFeh93e0pTOg8bQtL6LvAqRnYUJUxas4NnzlzR0qyWfDP6/1i6c+h2PSPiTiUGoIHwnvr07s9A/gCpGpmxd78+5k8cAGDtzIVdCznHswB6a1LZjy+EzeDZtgamlDUEH99KteUPGzlyIXbWamfoTmVBBEL5E50Fj2LxsFiaG+vgH7OT4mRAAFk4awblL19h7NBh7j1ac3rWW5g3dsTYzYu+RYBp26M3CSSOo6WCTqb+vyYSWKFqEp+ER8tfPXrykRDGRBRWyJgahgpANds416NW2Ge2796OgTiGiIt8qZEPj42LRLVqc1NRUdm1eT5HiJQB4/PA+ppY2mFracPzwfl48e0JcTDSlypanbddePH/6hNs3rykMQkUmVBCEmg7WNOs2kL6dW1OoYAHeRkUrZEPj4hMoVqQwqamprN+xF72iRQC4/+gJNubG2Jgbsz/oJE/DI4iOjaN86ZL06tCCJ+EvuBZ6V2EQ+jWZUA+X6iz+dyPeDepy7tI1tLU0xaV44aPEIFQQsqFSlar8NWAYbRvURklZGQNjM6YsWJFpm77D/PCu64ROocKYWNoQHye7B2u63zDCHtwDqRS7arWoYmTK8nnT2blpLSqqqhQuUoxu/YfmKL5XES9oWseeuNgYlJSU8F86n92nrqCplT9H/QqCkLsM9SswrGcn6vh0QVlJCbOqVfh75thM24wZ0B0nr7boFiqItZkRcXGyhyJ9J83hXtgTpFIpNR1sMDHUZ8bif1i7bTeqKioU1S3E0J4dcxSfe00n9h0NxqB6Q/Lm0WD5dL8c9Sf83sRk9UKuEpPVC19CTFYv5ISYrF74VsRk9d+WmKJJEARBEARB+OHEIFQQBEEQBEH44cQgVBAEQRAEQfjhxCBUEARBEARB+OHEIFQQBEEQBEH44cTT8UKu0siT50VyUlLRz28p/MnUNTQikhITi+V2HMKvKY+Gxouk5GRxnhFyTENdPSIxKUmci74RMQgV/igSiaQ34As0kkqlZ3M7nl+JRCIpCGwFooFWUqk0PpdDEoQ/hkQiMQBOALZSqfR+bsfzpSQSiQTYDlyXSqUjcjse4eciBqHCH0EikSgDs4E6QH2pVPowl0P6JUkkEjVgGWAEeEql0ue5HJIg/PYkEokKcBpYIZVKl+R2PF9LIpEUAy4DDcWXf+G/xD2hwm9PIpFoIvsmbgg4iAFo9kml0hSgA7ANOCORSIxzOSRB+C1JJBJViUTS4d3LYUAksDQXQ8o2qVT6AugN+EskkrwSicRKIpFY5HZcQu4Tg1Dht/PuJDfh3c96wHEgAnCXSqVRuRrcb0AqMxHZB+NhiURSF0AikbSUSCTWuRudIPw2KgNDJBKJGdAH6CT9hS9dSqXSAOAiMBFwBtrlbkTCz0AMQoXfUUvARCKRmCK7hLUJ6CKVSlNzN6zfi1QqXQ80Bv6VSCTdAA1gTO5GJQi/jQrAQ8AfGAQUfndb0S9JIpEYAkMAbyAvUD53IxJ+BmIQKvxW3t0E3ws4BxwEBkml0im/cgbhZyaVSoMBJ2AAYALYSiQS8eEiCDlXHtAFXgEtkH2ZLpCrEeVMO2RJgY1AD6BS7oYj/AzEIFT43TgAxZENRKchGxTVzd2Qfl8SiaQosizNAsAe2X1rvXM1KEH4PdgD5si+3O0Dqkql0je5G1L2SaXSoUBTwA7IB1SUSCRiDPKHE78Awu9mPrLsgSZQF3gD3MjViH5vb4EQZB+YpYGyQO93T9ELgpB9+YFdQGWpVDr33UOBvzSpVHoGcER2j2sUIM4TfzgxRZPwW5FIJF2AJ8CR3+Gk/St5dyuEPtAK8JNKpRm5HJIgCILwExODUEEQBEEQBOGHU8ntAL6nPHnyvEgSJSGFn5SGhkZEYjZKUaqq53mRliJ+r4UfT11NjeQUcYHhe1NXVyc5OTm3wxAEIPufVV/it86ESiQSaWr673t8wq9NVVmCVCqVfG07iUQinRwc+T1CEoRP8nUqSNL9c7kdxm9Po4INGWlisC/8HJRU1LL1WfVFfX+PTgVBEARBEAThU8QgVBAEQRAEQfjhxCBUEARBEARB+OHEIFQQBEEQBEH44f64QaiqsoS2bVrLX6elpVG8qC4NPT2+qp+K5cvy+vXrbxbX5oAAjKsaUKd2zSzXJyQk0KZ1K8xMjTEzMaJ6NSfi4uIICwvDzMQoyzZ+Y0Zz+NChbxbj1zgWFMSpU6c+uc3t27epXasGlhZmGFc14K9uXQH4959/6NO7V5ZtPOvXIyoq6luH+8VCQkIwMzWmin5F+vXtQ1YP9h0LCqJQQW0sLcywtDBjwvhx3z2u2DcRrB/Tkene5szvWINVg5rx6vG9r+7nxvHdRDy89e0D/I0cWjGF4+vmAxAwsQfXju7I5Yhkbp85xMwW1kz3sSBo9exPbns9aCe+TgV5eusSAGmpKWye1JM5bR2Y286JBxeDFdr4D22hsGzwhFnMX7Ve/tqjfW/+8p0gfz100hzmrlj70TjGzl7K4ZOfftBp/NxlzF6+RmF5VEwsS9ds/mTb8IhXtOg57JPb/CpevHhBi5atqKhfBSsbW+p7NODOnTtf3c+kyVOyHcO8+QswNDKmdZu2Wa4PCjrGqVOn5a87dOzE5i1bsr2/91TUNDC3tMLY1Axvn+YkJCRku6+atepw4ULIV7eLiopi0eIl2d5vTgweMgyDqkaYmlvQuEnTj34G7tu3nyqGValU2YApU6fJlx85chRLaxuMTc1o36EjaWlpAKxdtw5TcwtMzMxxdKrGlStXfsjx/NcfNwjNly8fN25cJzExEYBDBw9SQk8vl6OCVStXsHjpcg4dPprl+vnz5lK0aFEuX7nG5avXWbZ8Baqqqp/s02/sOGrXqfM9wv2sY8eCOHP604PQ/n370Ldvf0IuXubajVB69vp8tcfA3XsoUCDn5ZPfvn2brXa9enZnydLlhN6+y727d9m/b1+W2zk5ORNy8TIhFy8zctTonIT6WVKplNXD21De3InBmy7Re2UQbt1GExf58qv7unliNy/Dbn+HKD8uIz39m/aX/u4E+6tIiMn5TAcZ6ensnDWYDjMC6L/mDFcObfnol4nkhFhOBiyhlKGVfNn5nf8C0M//FJ3mbGP3gpFkZHyoNXD9WCBqefIp9GVvacqZi1dlMWRk8OZtNKF3H8jXn7l4FTsLk4/GPaZ/N2o72nzdwb7zJYPQEkV1Wb8w+4Ou3PA2KlphmVQqpXGTZlSvXp17d25x4dxZJk0cT0TE1/8fnzxlarZjW7xkCQf27WXNav8s1wcdO8ap06ezXJcTefLk4VLIBa5duYyamhpLli7LtD7tB/yfj4qKYvGSrxuEpqSkEB8fn+N9u9SpzbUrl7ly6SKVKlXK8t8wPT2dXn36smdXIDeuXWHDxo3cvHmTjIwM2nfsxPq1a7h25TKlS5fmX//VAJQrW46gI4e5evkSI0cMp9tfPXIc69f64wahAO7u9dizezcAGzasp3nzD9/w3759S5NGXpibmeDoYMfVq7IT7Js3b3Cv64qpcVW6dumcKQO2ds0a7O1ssLQwo/tf3Uj/xIfqhvXr5dlM32FDAZgwfhwnTwbTrUsnhg4ZnGW758+fo/efwXLlypVRV1cHZL983bp2wdS4Ku51XeUD7I4d2rNls+wkXbF8WYYNHYKZqTH2djbcu3dPvk3PHt1xdLBDv2J5jgUF0blTR4yrGtCxQ3v5/g4eOICToz3WVhY0925GXFycvN+xfmOwtrLAzNSYW7duERYWxrKlS5g7ZzaWFmYEnziR9TG9eI5eyZLy18bGxh/WhYdT390Ng8qVGDZ0iHz5+wx0WFgYRoZVaNO6FcZVDfBp1vSz345jYmJYtnQp9nY2zJo545PbZhnv8+fExsRgZ2eHRCKhdZu27Nix/av7+dYeXDyBsooKtl4d5cuKVzKmnKkDDy4G888QH/nyHbMGE7JnHQD7Fvsxu7Udc9s5smfBKB5dO0to8F72LhrNvPbOvHn2kPC711jU1YW57RxZ7duaxBjZN/BlvTzYNW84CzrVZFYrW56EXmTN8DbMaG7JgWUfMmGX9m9kYZfazGvvzLZp/eQDzjEuJdk9fyRz2znx+HrWmbAnoRdZ/Jcrc9s5sbBLbZITYol8/pilPdyZ37E68ztW59G1s+/eg2CW9nDHf2gLZre2IyM9nT0LR7Ggcy3mtnPk7PZVn3wPj62ZI88C7lvsB8C5nf++a+/EmhFtSUn69O/X/7+fn5KelsbN4D34D23BmuGtP7ntl3gSGkKhkuXR0SuLiqoapnUaExq8J8ttDyyfRPVWfVFRU5cvexl2m/IWzgBoFtQlj5Y2z95lSZMT4gjesJCa7QYp9GVnYcKZi9cAuHn3AYb65dHMl4/I6BiSk1O4dS8M86pVuHgtlDotumHfoC0e7Xvz/KXsKlLnwWPZuvcwAPuOnsTEpRn2DdoyYOwMGnXuL99P6L2HuLT8iyo1vFj4z0YARk1byIPHz7DxaIXv5HlZHmvY03As3JoD4L95Fz7dh+DZvg9VazVh+JSs27y3atMOjGo3walRe7r7TqSf33R5zL1HTaFak45UqeHFsTMhdB06HlNXbzoPHgvIzsmdB4/Fwq05lu4tmLdy3Sf3lZSczPod+3Bt2Z0B42YqrD96NAhVVVX51SIAU1NTnJ2dkEqlDB4yDGNTM0zMzNm4aRMgO19Vr1FLnkE8cSKYYb7DSUxMxNzS6qPZTIBZs+dgbGqGsakZc+bK3qe/evTkwYOH1PPwZPacuQptwsLCWLpsOXPmzsPc0ooTJ2TZ9OMngnF0qkaFSpUzZUWnz5iJjZ09puYWjPEb+8n357+cnJy4d/8+QUHHqFa9Jg29GlHV2ISkpCQ6duqMiZk5FlbWHD0aBEBiYiItWrbC0MiYxk2ayj8fAbS0C8p/3rxlCx06dgIgIiKCxk2aYmZhiZmFJadOncZ3+Aju33+AuaUVg4d8OrseGhrKwEFDqGJolK1s9f9zdXVBRUU2rbudnS3Pnj1T2ObcufNUrFCB8uXLo6amho+3Nzt2BvLmzRvU1NTQ19cHwKVOHbZu3QaAg4M9BQsWlPf7NIt+v7fferL6j/H2ac7E8eOo7+HBtWtX6dCho3ygNNZvDGbm5mzZtp2jR47QoX1bQi5eZvy4sTg6OTFy1Gj27N7NqpUrANkvW8CmjRw/cRJVVVV69ezBurVradNW8T94eHg4w32HcvZ8CAULFsTdzZUd27czctRojh49wtRpM7CyslJoB9C+Q0fqubmyZctmatWqTZu27ahUqRIAd+/eZfXa9SxdtpwWPt5s3bKFVq0VP9i0tbW5fOUaq/39Gdi/HzsCdwEQFRlJ8MnTBO7cSSOvBhw7cZKqVf/Gztaay5cvU7JkSSZNmsD+A4fIly8f06dNZc7sWfIMX6HChTl/4SKLFy9i1swZLFv+N127/YWmpiYDBip+cL3Xt29/XOvUwt7egTqurrRv30Ge5bxy5TLnQy6hrq5OVYPK9OzVm1KlSmVqf/v2bZYuX4GjoyOdO3X8H3vnHZdj98fxd0uhZaYSkpGWpqKBECmyo+w9Inuvx+Phsfcms6yMzMwiFFoysoXKKLuhef/+uHQnd9k/67nfr9f90jXOuc653Pd1vtf3fM/3w8oVywu93pkzZ/BZt5bQc2dp3aYtGzdtEf8gg4OCGDFimESZEiVKEHKmoCc3ISGhgNFcsWJFEov40YaFhWJuVhstTS1mzZmLoaFhkffhW3l8NxbtmqZfVCb11XOunj7IcL8LyMjIkP7mFcVV1Khl54x+vaYYN3QDYFE3W1oMnU1VM1uOrZ3B8fWzaOE9EwA5eQW81gVxdsdKNo/1xGtdECVUSzHH3Rxb94Gkvkgi5sQe+q8IRE5egb1zRxB9dCfmzh3JTE9Fx8ACl8HTC21fdlYmWyf3pNM0H3RqmfM29TXyxYpTspQcPRfsQUFRieSHd9g2tTde64TZg4SbMQzddGyakSEAACAASURBVI7SWpW5ELABpZJqeK09SXZmBisHNKN6HUdKa1WWuNaN0GNcO3OIgauPU0yphNgzaVi/BXVadgPg6OrphB/YQr12fSXKF3U/CyM5/i7hBzZzJXgflYzqYNfRi6pmtoDgoVw1sHmh5dynrEFDV7/QYwCvkx6hVj7/JVW1nBYPr0lOOSbcuMSrpwno12sqDikA0KxmROyZQGo3bserpwkk3Ijm1dMEdAwsOLZ2BvYdvSimVEKiPi2NcsjLy/Eg8TGhETHYmBuT8DiJ85GXUVVRxrCmHjIyMgz/ay47V82lXJlS7DxwjCnzVrB6Vr6h/jYjg0ETZ3J82yp0dbTp4j2xwHVu3onjiO8K3qSmYdKkHX092/L36EFcvXmHCweKnu7/kEuxNzm/fwuKxRQwbtyeAV3d0dGS1H1IfJLEzKU+hO3bhErJkjTtPACTWjXEx1++esMp/3UcOH6adv1GErRjDQbVJ2DbqjuXrt0kJyeHxCdJRAZuE85//abQ9sTE3mT99gCOnArFqX5dZo33xsxIn20BBWdYrly9irm5eaF17N6zh0uXLhEdGUFycjJ1bOrhYG+P39ZtODk1YcL4ceTk5JCWloa9vR3Llq8gKiK8yHsUERHJho0bCTt3FpFIhE09W+o72LNy+TKOHDnKyePHKFu2rES5KlWq0K9vH5SVlRk5YjgAPuvX8/jRI0JOB3P9+nXcWrelXdu2HD16jFu3b3M+9BwikQi3Vm04fToEBwf7ItsFgsczMDCQpk2bAhAZFcXlS1Ho6uoyb/4CkJEhJjqK69ev09TZhRuxV1mxchXFS5Tg2pXLxMTEYGFl/dFrAHgPHYaDgwO7d/mTk5NDSkoKM2f8w5WrV4u8d6mpqezY6Y+Pj/DC2717N6ZOmYSKigoAw4aPJPhUsEQ59w4dCjhaPsX69Rvo0KG9xP6ExAQq6rw/Pmlz/sJFypYtS3Z2NuHhEVhaWuC/ezcP4x9KlF/ns55mzZp+dju+F/9JI9TExIS4+3Fs27oVZ+eCD/2zZ8+wY6fwttbQ0ZHnz57x+vVrQkJOs9N/NwDNXVzEbw8nT54gMjICG2srAN6mp1O+fPlCrxt+8SIO9RtQrlw5ADp18iQk5DRurVp9ss2mpqbcvH2XY0ePcuLEcepaWxFyNpTixYujq6uLqalghJhbWHD/flyhdbi/8/h27NSJke8ZXi6uLZCRkcHI2BgNDQ2xR9LAwJD7cXEkxMcTe+0aDvbCYJmVmYm1TV1x+dat2wjXNrdg757dn+xLHt179MCpaVOOBAayf18Aa1evIiJKiElp6NgINTU1AGrVMuD+/fsSRqiOjg62tkKbPD07s3TJYgkjdKj3EHy3bGbxkmWsWbsOOTm5AscbNGxIRGT0Z7f5czAzN+fOvfsoKytz+NAh2rVpReyNW9/1Gt+KUklV5IspsmvmYPRtm6JfT/Lh8zblFelvXomNJHPnTvhN6i4+XsvOGYAKegZo6OqjWlYQ1CitVZlXTxOIiwkl4cYllvV2BCAr4y3KpYTvvqycHEYNWhbZvuQHt1Atq4FOLXNxewGy3qYSsGA0j25dRlZWjuSHd8RldGqZi43MWxeDeHT7KleChXjNt6mvSY6/U6gRejv8FBbNPcVGVglV4bf95G4sx9ZMJz3lFZnpqVSv4/hN9/NK8D62TulJg64jGOwTjGIJlQLHFUuoMGRD4bMG34Pc3FwOLplA+wnLJY5ZuHTm6f2bLOvdEPUKOlQyqoOMrByJty7zPOEerkNm8OLRg0LrtTE3ISwihrDIGLx7eZD4OInQyBjUVJSpZ1Gbm3fvc/XWXVy6CXHeOTm5VChf0Ii5cec+upW00dURDGn3Fk6s27ZHfLxZQ1sUFYuhqFiMcmVK8yT52Vfdg4Z1rVBTUQagVjVdHiQ+KtQIDb90Ffs6ZpRWF55BbZ0bcysuv//NG9kjIyODYc1qlC9TGqOa1YQ6q+tyPz4Re2tz7j1MYNjUOTg3tKOxvaThs2idL5PnrmDm2MH8O84bRcViX9WnM2fO0bGjO3JycmhoaFDfwZ6L4eFYWVrSq09fsrKyaOXWUjxGfLK+s2dp5eZGyZJC+EXrVq0IOXMWMzOzr2qfm1tLZGVlMTAw4MmTJwAcPXacY8eOY24pjJspKancun27SCM0z3sLgie0V88enDsXSh0rK3R1dQE4e/YsXoMGAaCvr0/lSpW4efMmISEhDPYSvnsmJiaYmBgXeo33ORkUzMYNgjEpJyeHmpoaL158PGxGq2IlTIyNWbN6Jfr6ki+MC+Z/+ezbh/wzYyby8vJ4enh8dhkZGRm2+m5h+IiRZGRk0KRJY4lxMCgoGJ/16wkpxEj+f/OfNEIBWrRoyZjRIzl+Mpjnz77ugQZCrE6Xrt34Z8bM79i6wlFWVqZ1mza0btMGWVlZAg8fonWbtuJpeRB+MO9PN7yPjIxMoX/nlZeVlS1Ql6ysLNnZ2cjJydG4cRO2+OUvQHifvDJycnJfHJujpaVFj5496dGzJ6YmRly5cqVAnXn1Fhbn934fCtsGGDpsOKqqqkz/+y+OHAmke/ce1G/QQHzul3hCtbW1SYiPF2/Hx8cXGk+sqqoq/tu5eXMGew0kOTm5UO/B90BDV19sbH2IrJwcovdi+7IzBSlAOXl5Bq05we2IU1wJ2kforjX0Wbzvi66bN50rIyOLXLH8AVRGRpbcnGwQgblzR5r1n1JIWSVkP3gQfg5ntq9ApVR52m84gyg3l8mN8pXkFIrne+pEIhEth82ihnWjL75GHv4zBtJlxhY0qxsTcciPu1GSi3Xy+Jz7Wc2qAa5DZhJxyI8HVy5i0dwDQwdXFBSVgC/zhL58Ei9eKGTdqgcVqhnx6mm+V/51UiJq5TQL1JGZ9oYn92JZPVhYhJny/CmbxnjQdZYfFfXNcB0yQ3zuiv5OlNXR4170WeKvRzOrnUmRsbv13sWFXr15B8MaelTU1GDhOl9UlUvStV0LRIgwqK7LKX+fIu/fp1B87/slJytL9lfGEb9v6MnJyZKd/ZX1FBPi8WVlZcR/C9tC20qpqXLxgC/HQsJY47cL/0PHC3h+ATq5OZOVnc3arXs4FRZB13YtaFq/rnja9X0MDQzYtevzX/ABHBzsORV0goOHDtOjV2+GDfWma5cuX9Hbb+P9Z3leGJtIJGLsmNH069vns+rIiwn9kDxD+Wt5f8x4+/btN9W1c/s2fNavp237Drh36EC3rl2oXDn/pfdLPKE9e/UmKjoaLU0tDh4QniMbNm7i4MFDHD92pNCxTltLm/iH749PCWhraQFQt64Np08JM0ZHjx7j1q18p0hMTAx9+vXn0IF9lClT5utvwFfyn4wJBWF6e+LkKQXiEEFYULLVT5jeORUcTJmyZVFVVcXe3oFtW9/F0h0+LH4rcnRsxO5d/jx9KgSIP3/+nPv37xd6Tas6dQg5fYrk5GRycnLYvn0rDg71P6u9Z8+eFV8zMzOT2NhrVKok6dX5GDt3CLFUO7Zvx+Y9T+ansLax4dy5s+I40tTU1E/GuaioqPDmTeFTUHkcCQwkKysLEFZ+Pnv2rEDc66d48OABoe+C4Ldu9cPWzk7inCpVqjDt7+nEXLmGu3tHli1dgpGBPn6+wv9xnif0w8+HBiiApqYmKqqqhIWFIRKJ2LJ5Ey1bukmc9/jxY/HD9sKFC+Tm5v5ff9x6Fg5kZ2ZyIWCDeN+j21e4d+kc6hV0eBp3g+zMDNLfvOJOxClAiPN7m/oa/bpOuA75h0e33xn/JZTJSBPifZWU1Siuos69S8K9iArcjq6p7Re160rwPlJeJAHCApwXjwv3pn1I2UrVeZ38hIexke/a+4ac7Gzepr5GpYwGsrKyRB3ZXqRhVKOOI+f3+pCTLXy/kh7cJjO98AUC1awaEHHIVxzzmTcdn5GWgkrZCuRkZxF9dOdH21vU/XwfpZKq1G3bB691QTgPmMr9mDAWdLbm8HIhrCXPE1rY58OpeHWNiuJj1q16UlHfnOSHd3ieeJ/srEwuHd9NLVvngtdXVmPSwTuM8Y9hjH8MOgaWYgM0822a+P7cuhiErJw8Grr62LTuxfiAWMb4x9B/+eFC+25jbsKhoDOUUlNFTk6O0upqvHqdwvmoy9iYm1BDtzJJz16KFzBlZWVz7eadAnXUqFqJew8SiItPBGDnwWMfvd8AKiVL8Cb161dJfwwLEwNCLkTx4tVrsrOz2XPk5BeVT37+ktzcXFo3c2Tq8AFEX5VcJFa+bGlG9utGZOA2vHp0ZPfhExg1bldoNgFHx4ZkZGSwes1a8b6YmBhCQs5gb2/Ljh07ycnJISkpidMhZ6hjZcX9+/fR0NCgT+9e9OrZk8goYcZHQUFB/NwtDHs7OwL27SMtLY3U1FT2BgRgb/d5v/vPee4DNHVqwvr1G8RrCxISEsRj6NdiZ2eH71bBUXLz5k0ePHxIzZo1sbe3x2+bEBZx5coVYmIui8toaGgQGxtLbm4ue/fmv8g3cmzIipWrACG+99WrV+/6llLk9Z2cmrBtqx+ng4NQU1OjVZu2NHFqRlxcHCB4QqMiwiU+hU3F+6xbS1REuNgADQw8wpy5cwnYu5sSJSTDYgCsrCy5dfs29+7dIzMzk+07dtDyXdafvHubkZHB7Dlz6ddXCCt68OABbdu7s2nDenGI2o/mP+sJrVixIoMHD5HYP3nKVPr06omZqQklSpTAZ72wanTS5Cl09uhE7W2G2NStR6VKlQAwMDDgr2nTcW7mRG5uLgoKCixesqzAG1Aempqa/DPjXyENk0iEc3MXWrpJGjGFcffOHbwGDUAkEiHKzcW5uQtt2rYt0uAtjBcvXmBmaoKioiJbfAv3ahZGuXLlWOezgc6encjIEDxp06ZN/+iX1sW1Be4d2rFvXwCLFi3Bzl5ymuXYsaMMH+aNopLgCfp31hwqVKggcV5R1KxZkxXLl9G3d09q1TKgX/8BRZ4rJyeHc/PmODdvztOnT786WHzJ0uX07tmd9PR0mjZzppmzMNCverdqsl///uza5c/qlSuQk5enePHibPHbVuib6/dCRkaGLjM3c2DReE75LkK+mBKlNHVwHTITdY2KmDi2YmHXepTSrIxWdWGlckZaCpvHeZKd+RaRSITL4H8AMGnUhj2zh3LOfxWe0zfSfuIK9s4ZTlZGGqW1qtBu3LLPbpeGrj5OfSbgM6wNIlEusnIKuA2fQ6kKlT5ZVl6hGJ2m+bB/wRiyMtJRUCxOr4V7sGndC9+JXYkM3EYN60aFrtgGsGzRlRePH7CkZ30QiSipXpYuMyXT/ADUtGnMo1uXWdbbETl5BWrWbULTfpNp0ns8y/s2pqR6WXQMLMTGeWEUdT+LQquGCW4j5pKV8ZY7kac/eT8+hZy8PC2Hz8ZneFtEuTlYuniiUbUWAMfWzkBb3xQDu8K9rACpL5LxGd4WGVlZVMtq0mHS568CNqqpx7MXr3BvkR+CYFhTj5S0NMqWFmK8ty77l+HT5vL6TQrZOTl4de+EQQ098fnFlZRYPG0MLXt4U7J4cSxMan3yumVKqVPXwgTzZh1pWr8eM8dJPs+/Fu0K5Rk9oDt2rXtQWl2VGlUro6r8+V63xCdP6Tvmb3GGgb9HDvro+fZ1zLGvY87rNylcjLkmcVxGRobdu3YybPgIZs+Zi5KSIlUqV2HB/LnY2dkSGnoeU3MLZGRkmPXvDCpUqMDGTZuYO28+CgoKKJdUZuMGwRPdp3cvapuZY25mVugqd3NzM7p17Yp13XoA9OrZ87On4lu4utDevSP79u9n8cKFRZ7n5NSE2OvXqWf3bjFcSWU2b9pQZCjb5zBwQH8GDvLCxNQMeXl51q9bi6KiIgP696Nnr94YGBlTS18fi/dia2fOmE4Lt9aUK1sWC0sLUt8ZxQsXzKdf/wH4rF+PnJwcy5cupW5dG2zr1cW4tinNmjZjzuzCsy6UKVMG7yGD8R4ymAsXLkpMfX8Ng72HkpGRgVMzYbyxtrZm5fJlJCYm0qdvfw4e2Ie8vDxLFi2kWXMXcnJy6dG9m3gtwpy58zl46CC5ubn079cPR0chFeS06f/w7NkzBg0WMtPIy8tz8XzYN7f3S5ApLM/hn4KMjIwoK+fP7d+XUK1qFcIuhP/fpoR/NHFxcbRq6Up0jKTH6XdBQU4GkUj0xdapjIyMaOaZb0/rI0XKlzLOrhRv73w8r+fXkpKahnLJEohEIrynzKZaFR2G9Pz82Lf/V3uys7PpMGA03dq1wK1p4XmcvzdKenXIzc78IdeSIuVTyMoX+6qx6nP4z3pCpUiRIkXKr4PP9r1s2X2QzKxsahvUoHenNj+1PdMXreHkuQu8zciksZ01LZ0a/NT2SJHyJyL1hP6fqFfXWjx1nceGjZslYlA/5OiRI4wbN6bAPt0quvjv3lNEiV+fmTP+wd+/YExdu3btGTd+wk9q0a+B1BMqsHlcZ148KhhW0mzA1G9aVFQYj+9cZcff/Qvsk1NQZNCan6Mq9jvy//SEfgtXbtym54iCC+AUixUjZPfH88MC2LfpQUZmQa+jz7y/xCvefwY/whP67NkzGjtJZnE4fvTIJ2PY12/YyOIlSwrsq1evHsuWfDz36v+rPVL+v/w/PaFSI1SKlJ+E1AiV8rvxqxqhfxrS6XgpvxL/TyP0P7s6/k8mYO9erl2TDG7/lejdqydaFcoXqXsPQhqPod5D0K9RDTNTEyIjI39gC6X8DH4H7frP0Wh/+fghawa3YHEPBxZ1s+V66FEAXjx6wCRHTRZ3txdUpObkpweLObGbRd1sWdC5LoeXS6a1kvJx9h0NLiAZ+ity9FQoxo3bYdCwDXNWbiz0nPsJj35wq34N9gYE/PLjVs/efdDQ1Ma4dtE5V0UiEUOGDqN6zVrUNjMnMjLqB7bw90NqhP6BBATsJbaIH/OP0Nj9HLp1686BQ4XrrucRePgwt2/dIvbGLVasXI3XoKJXv0v5M/iYdv2voAn/uRrtJzfOw9ixFUPWn6bj1HUEzMsXUSijXUWcXqn1KMGITX31nEPLJtNrYQDDtoTy5vlTboef+mH9+hPYd+wUsbfvFXrsV3ju5eTk4D11NgE+i4g+sp0d+48UajSPmykpiflfICBgH9diYws99iv8/wF079qVwwcPfPScw4cDuX3rNjevX2PVihUMHOT1g1r3eyI1Qn8DitKmV1dVZtLECZib1ca2ng1Pnjzh3LlzHNi/j7FjRmFhbsqdO3do5NiA4cOGYl3HkiWLF3HyxAksLcwwrW1M7149xbGrhenLv3nzhup6uuK8cq9fvy6w/bXYOzhQunTpj56zb18Anbt0RUZGBhsbG169fMmjR/9NL8Hvyse044+s+ptF3exY3rcJb54/LVS7frWXK/sXjWNpr4ac27mS2+GnWNzDgYVd6+E/w0ucfH9WOxMOL5/Mwq71WNanEcnxd8lIe8Ps9rXFuULfpr4usP01fK5Gu4wMZKS+EV83T02qKJ4nxlFWRw/lUkL2imqW9bkS/GXiAX8afnsPY9e6O3VcPRk0Yab4uVfGuD6T5y7HysUDh7Y9eZL8jNCIGA6eCGHcv4up4+rJnfvxNPHoz8i/51PPrStLN2zn5NkLWLfojIVzJ/qO+ZuMDGG6u4aDG+P/XYyFcyfsWnfnTtxD3qSkUrO+G1lZgvHz+k1Kge2v4eKlq+hVrkjVStoUK6ZAe1cn9h+XTM9VlCH9u7HF1xdrm3qYWVjSb8BA8f+filopJkychKm5BXXr2b0bt0LZt/8Ao8eMw8zCkjt37tDQsTFDh4/AytqGRYuXcOLEScwtrTAxNaNn7z7icUtXrzqjx4zFxNQMa5t64nGrarUaBcat97e/FgcHe0qXLvXRcwL276dLF89345Y1L19Jx62PITVCf3He16aPiIxGTk5OnGg9NTUVa2sbIqMuYW/vwLq1a6hXrx6uLVry76w5RERGo6cn5OLLzMzk/IVwBgwcRK+e3fHbup3oS5fJzs5m5coV4uvl6csPHOjFiGFDUVFRwaF+Aw4dPAjA9m3baNW6DQoKCgXa6efri4W5qcTHvX27r+57YkICFd+T6tSuWJGEIrTapfx6PI27IdaOH7IhBBlZOXHS98z0VCoZWuK98Qy6pnW5uG8TlY2tqWXnjPPAaQzZEEIZbUGOLycrE691Qdi06Y3/jIF0+suHoZvOkZuTTdjefBUepZKqDN10jrpt+nBg8TgUS6hQ1cyO6+eEqfCY47sxdGiBnHzB727U0R3i6fH3P74Tu0n0qTCN9ldJkgNMo55jiTq6g5mtDdkwsgMth84WH3v+6AGLeziw2stFLARQVrsqSQ9u8+LRA3Kys7kWcqiAAtJ/jeu37+F/8BhBO9Zy4YAvcnKybH2np56alo61mTEXD/phZ2WKz7a91LUwwaWRPTPHDuHCAV/0Kgsa2plZWZwL2ET/Lu3oM3oaWxb/Q8ThrWRnZ7Pab5f4eqoqykQc3kr/Lu0ZOX0+KsolcbC24HCQoJK148AxWjVtiIJCwYQyWwMCqePqKfHpNGisRJ8SnyRRUTNfIlS7QnkSnyRJnGesX/3bb+BPJjY2lh07dnIm5BRREeHIycnh6yeIvaSmpmJjbU10ZAT29nasWbuOevXq0rKFK7NnzSQqIrzAuHXxfBiDBg6gR6/ebPPzJSY6ipzsbHEyeRDGrZjoKAYNGsCw4SNRUVGhQX0HDh4SXhC3bd9B61atJMYtXz8/zCwsJT7tO7h/dd8TExLRqZg/blXUrkhCQuJX1/enI03R9IvzMW36YsWK4eIqKCKYm1tw/HjRKiMd3v2obty4QRVdXXGi+a5du7Fi+TK8vYcChevL9+zVm3lzZuPWqhUbN65n5ao1EvV7eHri4en5Pbos5Q/hdsSpIrXj5RSKoW/bDADtmqbcuhhcZD0mjYRUPckPblFKszLlKgmrls2dOxG2ey12HYQwjdqNhRee2k3acmDJeAAsXbtw2m8xhg4uhB/yo80YyQTaZk4dMHPq8B16nM+l47uwcPbAvpMX969cYMf0/nhvOodKGQ3G7LpMSbXSJFyPZvN4T4ZuDqW4qjqtRszFb3JPZGRlqWxUh+eJf4ZH7GsIOneRqCvXsW0tvAikv82gXBnBA1WsmALNHQV1NDOjWpw4e77Ietq5NAHg5t37VNHRorquICLSuY0Lq7b4M7iH8Lzr8C7RvnuLpoz+RwiR6NHBjflrNtPSqQGbd+1n+T+S2Tw6uTWjk1uz79FlMf+O82bH/qPftc4fzYmTQURERlHnnTJfeno65csJv/1ixYrh6uoCgIWFOceOnyiyHvf27QFh3NKtUiV/3OrSheUrVjDUWxAo6NTR/d2/HRk+YhQgJNmfM3curdzc2LBhI6tXrZCo39PD44t02KV8f6RG6C/Ox7TpFRQUxEo8n9JtL/GZGruF6cvb2toy2CuOU8HB5OTkYGQkuZjIz9eXefPmSOyvpleN7Tv9P+vaH6KlrU38w4fi7YT4+C+S9ZTyk/mIdrycfP53V0ZWTtCaL4JixQuXqZOgkO9uFRMbAuaN5G7kGUS5OVSoaiBRLOroDkL8lkjsL1OxKp7TCy4eUS2n+UmNdoDwA1voMU/w+lY2qkNWxlvSXj1DuVQ55IsJWtra+qaU1tIl+eEdKuqbUcvOmVp2giLKhYANyMr9dyeqRCIRnm1cmD5KUmlIQV7+vefex/XfSxZX+qzrvS9olld3PcvaeE+ZxamwCHJycjGsqSdRbmtAIAvWbJbYr1dZh63LCirqaGmUI/7RE/F2wuOnaGmUkyhb2L7fDZFIRNcunZk5Q1I57MNx62Ox3p+rDV/4uFWPQYPvExx8qshxy9fPj7nz5kvsr6anJ5a5/lK0tLV4GJ8/bsUnxKOtrfVVdf0X+O8+5X4TvkSbPo+P6ffWrFmT+3FxYh34LVs2Y18/X7++KH35Ll260qWzB9269Si0Xg9Pz0I12L/WAAVo0aIlWzZvQiQSERYWhqqaGpqakgO+lF+Tr9GOf1+7/kPKVqrOi0cPSI4XFnNEHSmoZR9zYrf430qGVuL95s06su2vPlg0L9zjYebUoVC99g8NUOCzNNoB1DW0uRMhxPs9jbtBdmYGJdXLkvIiWRwX+zwhjmfxdymtVQVAfJ/SX78kbM86LF27fvRe/ck0rGfFnsMneZr8HIDnL199ctX4x7Tka1StzP34R9yJE4wDv72Hsa+TL9/of1DIFbvz4DGszfJzOXu2dqH7sEl0bedaaL2d3Jpx4YCvxOdDAxTA0sSA23EPufcwgczMLHYeOIprI0k54+TnLz/az9+BRo4N2bV7zxeNW8of0WavWbMmcffv549bvr44ODiIj2/fsfPdvzuoa2Mt3t+lc2c8u3Sle3fJ0BoQPKGF6bl/rQEK0NLVlc2bfd+NW+dRU5WOWx9D6gn9xfkSbfo8Orh3ZEC/PixdupjtOwoagUpKSqxdt56O7u3Jzs7G0tKKfv3yE3gXpS/fycOTyZMm0rFTp+/Sr84enTh1Kpjk5GSqVKrI5Cl/0bNXrwIa7M7Nm3P48CH0a1SjeIkSrF336cTTUn4dvkY7/kPt+vdRUFSi3fhl+E3qTm5ONhX1zbFulf9SlP7mJYu62SKnoEjHqWvF+02d2nN0zT/i6fpv4XM12pt7TWfPbG/ObF+OjIwM7SYsQ0ZGhrhL5zi2diZy8vLIyMrSauQ8SqgK08z7F47l8Z2rADh2HyUOO/gvUqt6VaYO749r98Hk5opQkJdn4V+jqKxd9GDe3tWJgRNmsHzjdvyWFjQClRQVWT1rEh6Dx5GdnYOFiQF9PPIVmV6+eo1lcw8UiymwaeF08f6Obk2ZOn+leLr+W5CXl2fhlFG06D6EnNxcurVrgUENwbv614JVWBjXwrWxA6fPR3zztX42BgYG/D1tKk2dm4vHraWLF3903OrYoQN9+/dnydKl7Ny+kcdufAAAIABJREFUrcAxJSUlfNauoUPHTu/GLUv69+srPv7ixQtqm5mjqKiI35Z8z7SnRycmTZ4inq7/Vjw8OxN86jTJycnoVNZl6pTJ9OrZg5WrVgPQv19fmjd35lBgINVr1qJEieL4rF37iVr/20iT1UsR8zF9+V3+/uzbF8DGTZJTT1K+Dmmy+u/HrHYmeK0NoqS6pLLK5aAArp05hPukVYWUlPIl/InJ6ms4uHFu70bKllaXOLb78An2Hz/N+nl//dA2SZPVfz66etW5eD600HHLf9cu9u3bz6aNG358w/4gpNrxUn4q3kMGcyTwMPsOSKaikSLlV2bfgtHcCDtO9zk7fnZTpPxmDJs6hyOnQgnwKVyQQMqvzWDvoQQGHuHg/oCf3RQpH0HqCZUi5Sch9YRK+d34Ez2hvyJST6iUXwmpbKeU704jxwaEh4f/0GtOmjgB3co6qKsqSxzbuWMHJkYG1DY2pItn/gKScWPHYGpihKmJETu2SwaLD/UeUmh9Uv7brPZyJf76j5XLW+3lyrxOVuI8o3kLjUK2LWNBZxsWdbNlrbdbgcVZEYe3MrejBXM7WhBxeGtRVUv5P9PEoz8RMT9WMrJF9yFYuXhg1swdr4n5yfhjYm/+0Hb8rjR0bEx4+M+Jn3Vr1bqAdOeo0WOpZWhEbTNz2rRtx8uXwuKyCxcuinOPmppbsGfv3p/S3l8Z6XS8lB+Gi2sLBg7yolbNgsmYb926xaxZMzkVcpZSpUqJV1QeOniQqMhIwiOjycjIoJFjA5o5O6OqqgpAeHg4L15IPYJSfh3cp6ymor5ZgX1aNUwYtPYkxZRKELZnHYeXT8Vjmg9pr19wwmcWXuuCkEGGJb0aYGDrTHFVydhEKX8evktmoKqijEgkotOgsew6dIIOLZwYME4yrZGUX4fde/agrFzQ8dGkcSNmzpiOvLw8Y8aOY+a/s5j170yMjAy5eD4MeXl5Hj16hKm5JS1cXZGXl5peeUg9ob8IqamptHR1wdysdgGv3/S/p2FjbYWpiRH9+/UlL3yikWMDRgwfhnUdS4wNa3Hx4kXat21DrZrVmTxpIgBxcXEYGejTpbMnxoa1cG/fjrQ0yRQmx44exc62LlaW5nTs0J6UFCFNxvhxYzExMsDM1ITRo0ZKlPtSbGxsCk1VsW7tGgYMGESpUsIq4bxk/LGx17B3cEBeXp6SJUtibGzCkUBBNSUnJ4exY0bx76zZEvVJ+fXITE9lw6gOLOpmx8IudcXplE6sn83S3o4s7FKX3bOGir/fq71cObB4PEt7NWS+pzUPYyPZMr4LcztacHS1sHr5xaMHzPeow7a/+jDf0xrfid3IfCv5/b554STL+zmxpGd9fCd2F6eAClwxVeyhPLR00v+t73rm9hRTEnKdVjK04nWSkGf05vkTVLdqQAnVUhRXVae6VQNunD/+f2vH70RqWjqteg3DysUD82Yd2XlAEOL4Z8labFt1w7xZRwaOnyH+vjTx6M+o6YJEZ22nDoTHXMN9wGgMHdsyZZ6QpDwuPhGTJu3pNmwStZ060GnQWNLS30pc+1hIGPXb9cSmZRc8vMaS8i7t08TZSzFt6o5lcw/Gzvh2fXdVFcGQyc7OITMrS5zf8ta9j6cx+1VJTU3FtYUbpuYWGNc2ZfsOIQ572t/TqWNTF+PapvTtP0D8f9bQsTHDho/EytoGAyNjLl4Mp2279tTQN2DipMmAMIbVMjSic5euGBgZ076De6Fj2NGjx6hna4+FVR06uHcUj2Fjx43H0NiE2mbmjBw15pv7mJKSwoIFi5gwflyB/U5OTcSGpY2NtVjZr0SJEuL9b9++LZDPVIqA1Aj9RTgSGIimlhaRUZeIjrlC02aCCsfAQV6Enb9IdMwV0tPTOXjggLhMsWLFOH8hnD79+tO2tRuLly4jOuYKmzZu4NmzZ4CgNNF/wEAuX41FRVWVlSuWF7hucnIyM2ZM58jR41wMj8TC0pKFC+bz7NkzAvbu4dLlq0RFxzB+wkSJNgcHBRUq1WlvV++L+n7r5k1u3bqJg70ttvVsxIamiUltjhwJJC0tjeTkZE4FBxH/LgnwsmVLcW3RUpp/7Tfh5vkTqJbVxHvjGYZuDqWGdSMA6rbtg9fakwzdHEpWZjrXzwaKy8jJK+C1Lghrtx5sHutJy+FzGLrpHBGHt5L6SsgfmfTgFjatezHc9zyKJVQI272uwHVTXz4jaONcei/cw2CfU1TUN+XM9uWkvnrO1dMHGbo5FO+NZ2nYTfIl605kSKFyniv6OxXZT/8Zg1jc3Z4TG+ZQWLz9xQObqWEtqPgIEqAVxcdUy2vzuhAJ0P8iR0+Holm+LBcP+hEZuA2n+kLO4gFd2nN270YiA7eRnpHBoZNnxGWKKShwLmATfTq1oX2/kSz8azSRh7eyZfdBnr0Qpkdv3r1PP892XDq6AxXlkqzaUjCFXfLzl/y7zIdDm5YRtm8z5sa1WOTjx7MXLwk4GkxU4DbCD/kx1qunRJuDQ8MLlfBs0K5Xkf107T4YnTpNUS5ZgjbOgrKYQfWq33z/fgaBR46gqaVJdGQEly9F06ypkNbKa9BALoSFcvlSNG/T0zlw4KC4TLFiClw8H0a/vn1p1aYtS5cs5vKlKDZu2vzeGHaTAf37c+3KZVRUVVm+YmWB6yYnJ/PPjJkcOxpIxMULWFhYMH/BQp49e8begACuxFziUlQkEycUNBwBgoKCC5XttLVzkDgXYNLkqQwfPpQSJYoW0Fi/fgPNmuWn9Dp//gJGJrUxMTVnxfKlUi/oB0jvxi+CkbExo0eNYNzYMbi4uGJnLyQxDg4KYu7c2aSlpfHi+XMMDQ1xbdECANcWLQEwNjLGwMBQbJDpVq3Kw4cPUVdXR0dHB1tbIaG3p2dnli5ZzPAR+QPu+bAwYq9dw8FeOCcrMxNrm7qoqamhqKREn969cHFxFcuDvk+Dhg2JiIz+5r5nZ2dz+9YtTpwMJj4+HscGDkRdukwTJyfCwy9ib1ePcmXLYW1TF1k5ORITE9nlv5MTJ4O/+dpSfgwaVQ04uHQih5dPQd+2Kbq1hReVu5EhnPJdTFZGGumvX6JRRV+sGpT3bwU9AzR09VEtWwGA0lqVefU0geLKaqiV16aKiQ0AZk07cM5/FTBYfN0HVy/yNO4GKwcIL3U52VlUMrRCqaQq8sUU2TVzMPq2TdGvJ5kHUs/cniEbQj67j+5TVqNWTouMtDdsmdCNqMDtmDt3FB+POrKdhOvR9F164CO1SAEwqqnHmBmLmDBrCc6OdthZCSEOp8IimL96M2npb3nx6jUG1avi8i7hu0sjwXAwrKlHrepV0SwvpOypoqNF/KOnqKkqU1FTg3qWtQEh0fzyjdsZ1qez+LoXoi9z/fY9GnboDUBmVjbWZkaoqSijpFiMfmOn09zRjuYN7STa3KCuJRcO+H5RPw9sWMLbjAy6D5tMUGg4je2sWTVL8NT+bhgbGTFy1BjGjB2Hq4sL9vbCPQoKDmbOnHmkpafx/PkLDAwMaNFCGE9avhvLjI2MMDQwEI9hVXV1PxjDhOdFZw8PlixdysgRw8XXDQs7z7XYWOwcBNGVzMxMbGxsUFNTQ0lJiV59+uLq0hxXFxeJNjds2ICoiM9bGxEdHc3du3dYMH8ucXFxhZ7zz4yZyMvLF5ACtbauw5WYS8TGxtK9Ry+cmzVDSenzlLz+C0iN0F+EGjVqcCE8ksOHDjF58kQcHRsxctRoBnsNJOxCODo6Okz7aypv3+ZPHykqCvJ/srKy4r/ztvMkPD90/3+4LRKJaNy4CVv8JBdFhIZd4OSJE+za5c/y5Us5dvxkgePBQUGMeKcv/z4lSpQg5My5z+67dsWK1KljjYKCArq6ulSvUYNbt25hZWXFuPETGDde0Gzu4ulBjeo1iI6K4s7t2+jXEJJ5p6WloV+jGtdv3v7sa0r5sZSrVI3BPqe4EXqUY2v+Qc+iPg4eQwiYN5JBa0+irlGR4+v+JTszQ1wmT95SRkYWuWLFxPtlZGTFMp+S01sfbItEVLNsQKe/1vEhg9ac4HbEKa4E7SN01xr6LN5X4PidyBAOLh4vUU5BqTgDVkpqe6uVE6T5FEuoYNqkHQ9jI8RG6O2LwQRtmk/fpQfE/VItp8m9qHxP3uunCeiaSRo3/0Wq61YmbN8mAoPPMXX+ShrWs2JE3y54T57N2b0b0dHS4O9Fq3mbkf99USymALx7Hr77O2/785+H4GhrzeZF0/mQM7s3EHTuIrsDT7Ji0w6O+BbUIg8ODRfrzr9PCSUlgv0lv395KCkq4trYgQPHT9PYzpqaelWKPPdXpkaNGkRcPM+hw4eZNHkKjo4NGT1qJIO8hnDxfCg6OjpM/WvaF4xhwkKtzxnDmjRuhJ/vFok2nQ89x4kTJ/HfvZtly1Zw4njB321QUDDDR0rOgpQoXoKzZ04X2Bcadp7wiEh09aqTnZ3N06dPaejYmKCTQgjNho2bOHjwEMePHSl02r1WrVooKytz5cpVLC0tJG/gfxSpEfqLkJiYSOnSpfHs3Bl1dXV81q0V/1jLli1LSkoKu3f506btl6m+PHjwgNDQUOrWrcvWrX7Y2hUc5KxtbBgyeBC3b9+mWrVqpKamkpCQgJaWFmlpaTg3b049W1tqVJOcIvpenlA3t1Zs27aV7j16kJyczK2bN6latSo5OTm8fPmSMmXKEBMTw+XLMTRxckJeXp74xMfi8uqqylID9BfndfIjiquUwqypO0rKaoQf2Cw2OEuqlyEjLYUrwQEYNXD7onpfPonn/pULVDaqQ/Qxf7FXNA8dQysC5o8iOf4uZStWJTM9lVdJj1AtW4GsjHT06zpRxdia2R3MJOr+Ek9oTnY2b1NeUVK9DDnZWVw/d4RqloJnJvFmDHvmDKPHPH+US+XrgtewbsTR1X+T/lqYKr51MYim/ad8Uf//VBKfJFFaXRWPVs6oqyqzfnsAbzOElEVlS6uRkprGnsMnaf1uCvtzeZj4mLDIGGzMTdi+/wj1LE0LHK9jaoT3lNnciXuIXhUdUtPSSXzyFM3y5UhLf0uzhrbUtaxNrQatJOr+Ek9oSmoab1LT0CxfluzsbAKDzmJrJbQlT6r0dyNvDOvs6Ym6mjrrfHwkxrBdu3fTtk2bT9RUEGEMC6NuXRv8tm0Tz+zlYWNjjdcQ7yLHsObNnbG1rYde9ZoSdX+JJ3RA/34M6N8PEGJVW7i1EhuggYFHmDN3LsEnTxSYqr937x46OjrIy8tz//59rt+4QZUqRatG/ReRGqG/CFcuX2bMmFHIysoKEmfLVqCurk6v3n0wNTFCo0IFLCytPl3RB9SsWZMVy5fRt3dPatUyoF//AQWOlytXjnU+G+js2YmMd16FadOmo6KiQpvWbrx9+xaRSMScufO/uY9jx4xm21Y/0tLSqFKpIj179WbylKk4NW3KsWNHMTEyQFZOjn9nzaFMmTK8ffuWhvWFqTYVVVU2bNoijaf5TXl85xqHl09GRkYWWXkFWo2cR3EVNaxadGVhl3qolClPxVrmn67oA8pVqk7Y7rXsmjmY8lVqYt26YKyecqmytJ+wnG1Te5OTJXy/m/SZgGIJZTaP8yQ7U/h+uwz+thXJOVkZ+AxvS25OFrk5uVSzrI9VC0Gv+tCyyWSmp+I3qTsA6hoV6TprKyVUS+HYbRRL+wiGlGP30WIJz/86V2/cZty/S5CVlUFBXp7F08agrqpCD3c3zJ07oVG2DBYmBl9cb42qlVm5xZ9+Y6dTq5oufT3bFjherkwp1syeTNehE8nIzAJg6vD+KJcsSft+I3mbkYlIJGLW+KHf1L/U9HTa9R1BRmYWubm51LexEMuI7tgv6WX/Hbh8+Qqjx44Vj2HLly5FXV2d3r16YlzbjAoaGlhZWn5xvTVr1mD5ihX06tMHg1q1xIZgHuXKlWP9urV4dO4iHsP+nvYXKioqtGrdlrcZwm983tz/3yLWwd5DycjIwKmZEEJkbW3NyuXLOHP2LLNmz0FBQQFZWVmWLV1cqLLTfxlpsvo/mLi4OFq1dCU65srPboqUQpAmq/82Xjx6wMbR7gzdHPqzm/Kf4XdOVh8Xn0ib3sOJDNz26ZN/MtJk9QJ5HsfLl759xk3K1yNNVi9FihQpUqRIkSLlj0LqCZUi5Sch9YRK+d34nT2hvxNST6iUXwmpJ1SKFClSpEiRIkXKH4XUCJUiRYoUKVKkSJHyw5Eaob8B6qrKnz7pOxMXF4dKyeJYmOenMFm4cAG1jQ0xNTGis0cncfqNPr17YW5WGzNTE9zbtxNLpt2/fx+nJo0wMzWhkWMD4uPjP3ndbVu3YlrbGDNTE1ycm5GcnAzAtL+mUllHW6zKdPjQIYl2WpibMnBAf3FdjRs1RF1VmfDwz0vBIeXHMaVJxU+f9J158egBkxw1WdzdXrzPf4YX012rs7BL3ULLhGxdyji7UqS+FNRbTvstFisnLexSl/EOZUh7/fHQiF0zB7Oomx2LutniO7GbWDb0wOLx4rrmdrTkr2ZC6pYXjx+wpGd9Fne3Z0Hnupzf6yOua83gFkxpUpH461HfdC9+dcoY1//h14yLT0TdwJ46rp4APEx8gpPHAEybumPWzJ2l6/MXNI2buRiTJu2xbO5Bh/6jePn6jfjY5eu3qN+uJ2bN3LFw7iTOZdqi+xCsXDwwa+aO18SZ5OTk/NgO/kBU1H58loe4uDhKKKtiZiGswH/48CGOjZpgaGyCkUltFi1eIj53p78/Ria1kVNQJDw8QqKuBw8eoKJWirnzPp0VpnOXrugbGGJc25SevfuQlSVkVRCJRAwZOozqNWtR28ycyMj83+yYseMwrm1aQN40r64y5TTw37Xrq+/D74bUCJVSJHp6euI8oAkJCSxbspiwC+FEx1whJyeH7duEh/K8+QuIjLpEVHQMOpUqsXzZUgDGjBpJ585diYqOYeLEyRJ6ux+SnZ3N8GHeHD8RRFR0DMYmJuK6ALyHDiMiMpqIyGicmzeXaGdEZHQBSbfjJ4Kw+IqUIFL+XMpoVymQ+9OieSd6zPMv9NyXT+K5dTEIdY18g9nBYwhDNoQwZEMITftNRtfU9pNplVyG/IP3xjN4bzyLmkZFQnetAcB1yAxxXfXa9cXQQVCPUSlTgQErjzJkQwgDVx8jeMtCXicLcp59luxHu6ZpkdeS8m1UraQtzvUpLy/HrPHeRB/Zzml/H1Zu2UnsrbsAONrVIfLwVsIP+VFdtxJzVmwAhGdYj+FTWPL3WKICt3PUbwUK79LK+S6ZIciQHt5G8vOX7Dp04qf08U9GT6+qOO+nvLw8c+fM5urlGELPnmH5ihVcu3YNACNDQ3bt3IGDvX2h9YwYOQrnZpIqaoXh0akTsVevEBMdxdv0dNauE14aDx8O5Pat29y8fo1VK1YwcJAXAAcPHiIqKpqoiHDCzp1l3vwFvH79GoAtmzfRsoWkOuGfjNQI/cGMHzeW5cuXiben/TWV+fPmkpKSglOTRlhZmmNa25h9AQESZU8FB+P23hd0yGAvNm7YAEBERASODetTx8qC5s2a8ujR99egzs7OJj09nezsbNLS0tDSEhRiVFVVAeHNLz09XawWERt7jYaOQg7EBg0bsn+fZJ/eRyQSIRKJSE1NRSQS8fr1azQ1tb57P6R8XwJXTBUbVgDH1/3Lab8lZKSlsNbbjSU967Owaz2uhRySKHs38gwbRruLtwPmjyLikB8ACdejWe3lwpKeDfAZ3pbXyY8lyn8rHzMiDy6ZgPOAqVCI+gnApeO7qN24baHH3kepZP7vIzsjvVA1lUvH/andRKhLXqGYWFUpOysTUW7u53Tll2Xi7KWs3LxTvP33otUsWLOFlNQ0mnUeiE3LLlg4d2L/sVMSZU+FRdC6d74q29Cpc9jkL8ieRl6OpXGnftRt2RXX7oN59DT5u7Zbs3xZzIz0AVBRLol+NV0SniQB0MTeRpyzuI6pEfGPnwJwPOQ8RvrVMKlVA4AypdSRk5MDQFVFmNHKzs4hMyur0O/Br8jYceNZtjxfHWrqX9OYO28+KSkpNG7SFAurOpiYmhGwb59E2eDgU7RomZ/Y32uINxs2bgIgIiKSBg0bYVnHmmbOLt99zNLU1MTcXBChUFFRoZa+PgkJiYCgXlSzpmTyeoC9AQFUqaKLgcHn5aFt3twZGRkZZGRksLKyEs/4BezfT5cunsjIyGBjY83LVy959OgR12Jjsbe3Q15enpIlS2JsbEzgkSPfoce/J1Ij9AfTvoM7/jvz3e/+O3fQvoM7SkpK+O/aw8XwSI6fCGL0qBF8buaCrKwshnoPZvsOfy5cjKB7j55MmjhB4jw/X1/xtPX7H/f2n1Zh0tbWZtiIkVStUgkdbU1U1dRo4uQkPt6rZw8qalXgxo3rDPIStLtNTGqzZ89uAPbu2cObN2949uxZkdfIS9JvVtuYShW1iI29Rs9evcTHly9bipmpCb179eTFi/wp0Hv37mFpYYZjw/qcCfl8rW8p3weTRm24HLRXvH05aC8mjVojX0yJzjM2M9jnFH0W7+fg0omf/Z3Oyc5i38LRePy9kcE+wVi6eHJ0taSUYtTRHeIp7fc/vhO7fVOfroUcQrWsJprVjQs9nvk2jZvnT2DUoOVn1ec/YxAzWtYk6f4t6rbrW+DYi8cPePHoAXrmDuJ9L5/Es6ibLbPaGFHf0xvVsppf35mfTDuXJvgfOi7e3nXoBO1cmqCkWIwdK2YTtm8zR3xXMGbGoi945mUz/K+5bF36L6H7NtGtXUumzFshcd7WgEDquHpKfDoNGvtFfYiLTyT66g3q1DaUOLbRfz9N6wva5rfiHiAjI4Nr98HYtOzCvFWbCpzr2n0wOnWaolyyBG2+UO3pZ+HeoT07d+bPFuz098e9Q3uUlJTYvWsnERcvcPL4MUaOGvNFY9YQ76Hs3LGN8Avn6dGjGxMmTZY4z9fPDzMLS4lP+w7uhdRaNHFxcURFX8Laus5Hz0tJSWH27LlMmTzxi+oHoU9bfH1p1lTwoCYmJKJTUUd8vKJ2RRISEqltYsKRI0dJS0sjOTmZ4OBTPHz46VC1PxWp/MwPxszMjKSnT0lMTCQpKQn1UqXQ0dEhKyuLiRPGExJyGllZWRISEnjy5AkVKlT4ZJ03btzg6pUrNGvaBICcnBw0K0gOWh6ennh4en5Vu1+8eMH+fQHcunMPdXV1OnZoj++WLXh27gzAOp/15OTk4D1kMDu2b6d7jx7MmjMX78FebNq4AXt7B7S1tcVegcLIyspi1aoVXIyIomrVqngPGcysf2cyfsJE+vUfwISJk5CRkWHK5EmMGjmCtet80NTU5G7cA8qUKUNERATt2rTi0uWrYu+slP8/WjVMSHmRzOvkR6S+SKa4ihrqGhXJyc7iyKq/ibt0DhkZWV4nPSLl+VNUymh8ss6kB7d4cvc6PsNaA5Cbm4NKGcnfgplTB8ycOnzX/mS+TSNo03x6LSg6Luv62UAqG1t/tsJRu/HLyM3JYd+C0cSc2IOlS/7vMOb4bowatET2vd+GukZFvDee5XXyIzaP64xRQzdUSpf/+k79REwNa5L07AWJT5JIfv4CdVUVdLQ0yMrKZvK8FZy5EIWsrAyJT5J4kvyMCuU+rShz8+59rt66i0s3YYozJyeXCuUly3Vya0Ynt2bf1P6U1DQ6DRzL3EnDxd7MPP5d5oO8nJz4GtnZOZwLj+bsno2UKK6Ec5eBmBnp42grGD8HNizhbUYG3YdNJig0nMZ21t/Uth+BmZkZT5Pyx6xS6vlj1viJkwgJCfmqMevK1atihaGixixPDw88PTy+qf0pKSm06+DOgvlzPzkuTP3rb4YOHYKy8pevwxjoNRh7e3vs7e0+ep6TUxMuhodja+9AubLlqGtj/dFx8U9HaoT+BNq2a8+uXf48efxY/Ebn5+tLclISFy5GoKCgQLWqVcQLf/KQk5cn972pubzjIpEIA0NDzpz9uHKMn68v8+bNkdhfTa8a23cWHheXx4njx6lSRZdy5QTt61at2xAaek5shALIycnh7t6RuXNm071HD7S0tNi5S/CEpqSksGf3LtTV1Yu8RnS0EH+qp6cHQPv2HZg9618ANDTyDZdevfvQqqUQlqCoqIiiojB1aWFhQVU9PW7evImlNBb0h2Lc0I3LQftIef4EY0dBfjD66E5SXz7Da10wcvIKzGpnItaLz0NWTq7AdLP4uEhEeV19Bq76uIRh1NEdhPgtkdhfpmJVPKdv/Kq+PE+4x4tH91n0bgHT66RElvSsz6A1J8QG9KXjuz9rKv59ZOXkqN24Daf8FhcwQi+d2I3bcMnfJYBqWU00dGsRdykU44ZuX9WfX4E2zo3Yc/gkj5Of0d5FeFneGhBI8rMXhAZsQkFBnhoObmJ9+Dzk5eXIzc33ruUt8hEhwqC6Lqf8ffgYWwMCWbBms8R+vco6bF327yfbnZWVTcdBY+jo1pRWTRsWOLbJ/wCHg85wePNy8dS6doXy2FmZUba08JxrWt+W6Ks3xEYogJKiIq6NHThw/PRvYYQCtGvbFv9du3n8+DEdOrQHwNdvK8lJSYRfOI+CggK6etUlxiz5j4xZhgYGnDv78ZkrXz+/QhcHVdPTY+eO7Z9sd1ZWFu3au+PRqRNtWrf+5PkXLlxg1+7djBk7npcvXyIrK4uSkhJegwZ+tNxf0/4mOSmJVSuWi/dpaWvxMP6heDs+IR5tbSG8bML4ceI1Ep6du1CjevVPtu1PRWqE/gTad3Cnf78+PEtO5kSQEAf16vUrypUvj4KCAsFBQdy/f1+iXOXKlYmNvUZGRgbp6ekEnTyBra0dNWvWJDkpidDQUOrWrUtWVhY3b97E0LDg1NG3eEJ1KlXiwvkw0tLSKF68OCdPnsDC0hKRSMSdO3eoVq0aIpGI/fv3UVNfiKNKTk6mdOnSyMrKMuvfmXTvka/rbWSgz5Wc62tdAAAgAElEQVRr1wtcQ1tbm9hr10hKSqJcuXIcP34M/Vq1AHj06BGamsKb8t69ezA0NAIgKSmJ0qVLIycnx927d7l96xZVq1b9qj5K+XpMHFuze7Y3qS+f03epELP3NuU1yqXKIievwJ3IEF4+fihRTr2CDk/jbpCdmUFWxlvuRJyiiokNZStVJ/VlMvevXKCyUR1ysrNIfnAbjaq1CpT/f3hCK+gZMvHALfH2rHYmeK0NoqR6mXf9esW96LO4T15VoNxabzfaT1yBWrn8OGaRSMSzhHuUrVgVkUhE7JlAyleqIT7+9P5N0t+8pJJRvpHy6mkCJdRKo6BYnPTXL4mLCcPOfcB37eOPpr1LYwaOn0Hyi5cc2yrct9dvUihXphQKCvIEh4bzIEEyJrCSliaxt++RkZFJekYGQecuUs/ClBq6lUl69pKwyBhszE3Iysrm1r37GNTQK1D+WzyhIpGIfmP/Rl9PF+9eBZ+bR0+FMn/NZo75rfwfe2cdVkXTBfDfpWwsLLA7QLpDQCxARQQpG7v1tbu7u8XCwA5MVBBRsRO7xQ66ud8fi1d5LyAoiq/f/p7nPnB3Z2bPzt3dOXvmzDkULJBftr2RlQlzV20kNi4eFWUlTodepl9nD6JjYomKiaVcaTWSk5M5fPIM5obCArNlGwT3rJ7tc/c6zk3c2rjSrXtP3n94z6kTwoKqiIivY9bJk6cyGbMqcjssTDZmnThxEgtzc2rVqsW79+85e/YcpqYmmY5ZP2MJlUqldOnajdp1ajNo4IBs1QkKPCn7f/yEiRQuXFimgNo1asJ6n7VoaGikq7N6zVqOHj3G8WNHUFD46uHYwtGRJUuX4e7mxvnzoRRVLUq5cuVISUnh8+fPlCxZkuvXr3P9xg3WN270Q+f4NyAqoXlAvXr1iIqKQl1DQ6ZYeXp64dSyOTraWujrG1A7TZH7lgoVKuDi2gad+ppUrlwFHR3B6VpFRYWt23cwcEA/IiIiSElOpm+/AXI39M9gbGyMc2sXjAz0UFJSQltHl65duyGVSuncqYOwuk8qRau+tsyJPfDUKUaPGoFEIsHC0opFi4UFWe/fv8/Qd0hdXZ3RY8Zha22FkrIylSpWYs06HwCGDxvKtWtXkUgkVK5UmaXLhYHsdFAQE8aPRUlZGQUFBZYsXU6JEiVy7bxFskeZqnVIiI2maKlyqKoJ03E6jV1ZP8yD+e3NKF9bl1KVasrVK1amPPVtnZjf3ozi5SqhXqM+ICzO8Zq8nv3zhxEfHUlqSgrmbXrIKaE/y5Zx3jy+eoaYzx+Y1qoedt7DMXRsl2WdW0EHqWFkg0qBQrJtqampfHjxSG56XiqVsmNKT+JjokAqpWx1TZwGz5Htv358F9oNndMtUnn79B7+i0cDEkCKlUcfylbLvXs5L6hbsxpRMbGolylNubRpc/eWTWndbRD6zTzQ06pDrWqV5epVUC+Di31D9Jp5ULmCOjp1hcUkKirKbFkynUETZxMZFU1ySgp9OnrIKaE/Q8ila/juOYRmreqysE0T/+lFUxtzBoyfRUJioswdwEhHk8WTR1C8qCr9Onti3qoDEiQ0tTajmY0Fb95/wKXbPyQkJpGamkoDE326egozBncfPcVUr36uyf0rqFevHlHRUWiofx2zvDw9aNGyFfV1dDHQ16d2bfmFPhUqVMDVpTVa2rpUqVwZXR1tQBiz/LZtof+AQURERpCcnEz/fv1ydcw6cyaEjZs2o6WlKQvbNGXSJOztm7F7zx769R/Iu3fvcGzREh1tbQ4fOphpW6mpqTx4+DDDsaVnr95UqlQJMwth5qSVkxNjx4zG3r4Z/ocPU6NWHQoWLMDa1asBwTprZS1Y1VWLqLJxvY9skdv/I2LaTpEMefLkCU4tHLl6/Waut33wwAEePX5E3779cr3tf9PQ1poZM2f/kdPzYtrO38unV89YP9SNARuzdlv5EV4/us3Fg5tx7Dsl19v+Nyv7OGLfZxLla+v+8mP9m785beeTF+E4dxnE5cNbv184F2nVZSDbls5ERUVZtk1M25lznjx5QvOWTty4djXX27558yZr161nbgbubLlNp87eODjY49I6Z+4+vxIxbafIb0dRUZGIiIh0wepzCwdHx9+igNo1tOHxo0coKyt/v7DIX49EQYH4mMh0wepzi7JV6/4WBXRV3+Z8DH+CouL/r+XkV6GooEhEVLTM6vm72L16XjoFVOTHEMasSJnVMzfR1NT8LQpo23btCQw6Tf58+b9f+C9BtISKiOQRoiVU5L/G32wJ/ZMQLaEifxKiJVTkp/kSFD+vuXPnDhbmphQqkO+PkEfkv8uXoPh5zdun91javTGjbcr8EfKIfJ8vAfPzGqlUyqAJs6lr44yBvSdXbt75fqX/I74Exs9rskrBKfJziHM6Ir+VEiVKMG/+Qvbt3fP9wiIi/wEKqhan+YDp3A7KfGGDiEhGHDkVwoMnz7l1YiehV2/Sb+wMTu9al9diifyLb1Nwnj8fSq/efTh39kxei/VXIFpC/0I2btiArk599HS16dBefqXv6lWrMDE2RE9XmzYurYmNjQVgh58fOvU10dPVxsZayN5y69YtTE2M0NfTQVenPvfv35drLyeULl0aQ0ND0U9TJEdcPrSVBR3MWdDBgm2TusvtD923nsVdbFnQwYJNo9qTGC9c0zdO7GF+O1MWdLBgRW97AN48CmNJ14Ys7GjJgg7mvH/+8KdkK1y8FBXq6KGoJF7TfyKbdh3EwN4TQwdPOv0zTm7/mq17MHfqgKGDJ+69hhEbJ8Sy3Ol/HL2m7hg6eNLQXchydfveQyxadcTI0QsDe08ePH72U7LtPx6EVyt7JBIJxrpafI6MyvX0o/8VNmzciLauHjp6+rTv0FFu/6rVazAyMUVHTx8X1zaycctvxw60tHXQ0dOngbWQherWrVsYm5ihq2+Atq7eT49bmaXgFPl5REvoX8atW7eYNnUyQcEhqKmp8fHjR7kyrZyd6dK1KwBjx4xm7do19OnTl8mTJ3Lw0BE0NDT4/PkzACtXLKdv3/54enmRmJhISkqKXHue7m7cvXdXbvuAAYNo1759Lp+hyP8bbx6FcWL9bHouP0KhYiWJjZT3h63XoDlGLYRUnUdXTubigU2YuXQjwGcmnebupGgpdeKiIgA4v3cdZq7d0W3cJi03u/w17Tu2M++fyQ9cFm690WvmnstnKPKruH3vIdOXrOWU3xrUShTj4+cIuTJOTazxdhfym4+bswyf7Xvp1cGNqYvWsN9nIRplS/M5MgqAVb676N3RHY+WTUlMTCIlg2unbd+R3HssHzOzX2dP2jo7pNsW/uYt5dW/JuLQKFua8LQ89P9P3Lp1iylTp3HmdFCm45ZzKye6dhHSOI8eM5Y1a9fRt09vJk2ewmH/g+nGreUrV9GvXx+8PD0zHbfcPTy5e++e3PaBA/rTvl16401mKTi/hKsS+XFEJfQv4+TJE7R2cUVNTYjFl1Fcs1s3bzJ27Gg+f/5MTHQ0jRoLuW7NzMzx7tQRF9c2tHIWYtiZmJgyfdoUXr58gVMrZ2pkkNnBd+v3M1eIiPwoDy+fRsumpSxYfEapMt88CuPYqsnERUeQGBdDDSPBIlJJy5gdU3qjZetEvQbNAahYz5CTG+YS+Taceg2ao1ZBPrak58SsM/GI/Dc4dfYizs0ayjIYlShWVK7MrXuPGD93GRGR0UTHxtLI0gQAU/36dB06kdb2djg1sQbAWE+LGUvX8fL1G5wa21C9SkW59jYtmvrrTugv5cTJU7i0bp3luHXz5i3GjB3H54jPREfH0DgtwLuZmRmdOnfB1dUF51bCy4SpiTFTp03nxYuXOLdyynDc2rrF9xeekUh2EZXQ/0O8O3dkx649aGtrs97Hh8DAUwAsXbac8+fPc8j/IMaG+py/cAkPT0+MjI3x9z9IC0d7li5bgY2tbbr2REuoSF6zY2ov2k3dRLkaWlzy9+XRlWAAWg2Zx7NbF7l79iiLva3ps+YUOo1dqVDPgDshR/EZ0oZWQ+ZRTd8qXXuiJfT/h65DJ+C3fBb169Rkw44DBJ2/BMDiySMIvXqTQyfPYNqyA2f3rse9RVMMtTU5fPIMLb0HsnjycGzMDNO1lxNLqHqZ0rwIfyP7/vL1W9TLlv4FZ/nfp5N3F3bv9ENbWxuf9RsIDBSyDS5fuoTz50M56O+PgZEJF0PP4enhgbGREQf9D+HQvCXLly7B1jZ92tWcWEKzSsEp8nOISuhfho2NLa6tWzFg4CBKlizJx48f5d4qo6KiKFeuHElJSWzx3Yx6Whqyhw8fYmxsjLGxMYcPH+L58+dERERQtWpV+vbtx/Nnz7h+47qcEipaQkV+JdX0LNk4sh0W7r0pVLQEsZGf5KyhCbHRFFErS0pyEleP+qFaSpgm+/DyMRXrGVCxngF3zx0n4u1LEqIjKaFeGXPX7kS8ecGrh7fklFDREvp3YG1qQJueQ+nv7UnJ4sJ0/L+todExsZQtpUZSUjJb9x1GvUwpAB4+fYGRjiZGOpocDQzhxau3RETFULWiBr07uvE8/DU37z6QU0JzYgl1tLNk2QY/2jRvTOjVmxQtUliWUer/CVsba5xdXBk0cEC2xi1f3y0yJVAYt4wwNjbi8JEj6catfn378OzZc67fuCGnhObEEppZCk6Rn0dUQv8y6tWrx/ARo2ho0wAFRUV0dHRZm5b68gvjJ0zC3NQYtVKlMDIyJipK8HcaPnQI9x/cB6kUG9uGaGtrM2vmDDZv2oiSsjJly5Rl+IiRPyXf69evMTEyIDIyEgUFBRYumM/1m7dRVVX9qXZF/l7KVK2DTYd/WNXHAYmCIuo16+M6amm6Mo26jGRpNzsKFVOjQl19EmKjATi0ZCzvXzwEqZRq+g0oV12TwE3zuXJkO4pKShQuUQbr9oN+Sr6oD29Y3MWWhJgoJAoSzvgtZ+Cms+QvJF7TeU3dmtUY1qsTjTx6oKiogHbdWqyelX5x0riB3bFs3YlSJYpjqF2PqBhhwcvI6Qt58OQ5UqkUGzND6tepwewVG/Dd44+ykhJl1EoytFfHn5KvqbU5h0+FUNfWmYL587Nyxpifau+/Sr169Rg5YjjWtg1RVFREV0eHdWvXpCszccJ4TMwsKKWmhpGxEdFp49bQYcO5/+ABUqkUW1tbtLW1mTFzFps2b0Y5bdwaOWLYT8mXWQpOkZ9HDFYvIpJHiMHqRf5riMHqfw9isHqRPwkxWL2IiIiIiIiIiMhfhaiEioiIiIiIiIiI/HZEJVREREREREREROS3IyqhIiIiIiIiIiIivx1RCRUREREREREREfnt/NWr4wsUKPA6Pj6+zPdLioj8fvLnz/8mLi6ubE7rKecr8Do5UbyuRX4/+VRUSEgUV23/avLly0dCQkJeiyEiAvz4WJUd/molVERAIpFsBMKkUmme5ZOTSCQ1gTNAValUGpVXcoj8HUgkku3AealUOicPZdAEjiNc07F5JYfIr0EikTgCfaVSaZMc1gsHTKVSqXzaJJFcRyKRHAPmSaVS/xzUcQXaSqXSlr9OMpHsIE7H/6VIJBJViUSiKJFIqgHNgCV5KY9UKr2HMGD3zEs5RP67SCSSohKJREEikdQBGgAr8lIeqVR6EwgBuqbJJ5/UXuS/jB5w+QfqXQb0JBJJcYlEIp+sXiTXkEgkEkAXuJLDqpfT6iGRSCrltlwi2UdUQv9eVgJNgOHAEqlUGpHH8gBMAQZJJJKCeS2IyH8SH8AGGAEskEql0XkrDgCTgSESiSQf8CSPZRHJXfTIgXIjkUiqSyQSU74qODMAt18km4hABSBJKpW+ym4FiUTSCngDqEokkqrANYlEIupCeYTY8X8vykBFwBlYLZFIGuexPOksRxKJRFsikfxcLjWR/zeUgaqAPeAjkUjs8lKYtOn4KOAa4A0Uzkt5RHIdXXJmCS0E7AVeISiwzYDAXyCXyFd+xFrdCMFIcxXogODWk5rbgolkD1EJ/XtJAZyAXYA/0CJvxYG0t80pwBCgNmCctxKJ/MdIAVyB7UAAwiCfl1RGeKk6CwxFkE/kL0AikagBxYBH2a0jlUqvAROAPgjPthTg3i8RUOQLObJWp/EPUBfh92kKHM5toUSyj1JeCyDyy1BGmLqMAkYCq/JWHABOAEHAdaAh4qAtkjNUEHxB9YAhUql0XV4KI5VKD0gkkiaAH8L9JvL3oAtc+QEL2VLAHHAHDknFlb+/Gl0gR88BqVQaJ5FIXIBLQH6g068QTCR7iJbQv5cqQDzQRCqVrvxDHobugCVQEmgNiFMgIjmhIhADNMxrBfQLUqn0MmAAPAYU81gckdzjRxa7kPac7QZEAqG5LZSIHD+0eEwqlT4AhgGJQFhuCyWSfcQQTX8pEolEDwiXSqWv81qWb5FIJErARIQFU+elUqlpHosk8h9BIpEYAE+kUun7vJbl36St0m0klUqP5rUsIj+PRCLZChyUSqUbf7C+5A958f9rkUgkZYHbQMkf7Wvxd8p7xOn4v5Q0C80fh1QqTQZGSiSSlwgrG0VEsoVUKr2Y1zJkRtpAJiqg/3EkEomKVCpNRLCwTfrRdkTF5teRtrZAga8uEz/c1+LvlPeIllARERERkf970qzZ4YA2gntFcaCuVCq9mqeCiaRDIpF0A2oAnxB+o8VAzJ84QyLyfX67ElqgQP7X8fEJYspBkZ8if/58b+Li4n9JGrGcoJKvwOskMYVmtlBUzkdKkpiK8P8FMcXnr0dM7ynyX+VLKtDfroRKJBJp0scXv/WYIumppm3Ks+fP81qMv4bFoX9CHoA/nz5GRRl36mNeiyHym5hgXYL4R3+sB8VfQf6qBqTGx+S1GCIiOUYhfyGkUqlE9An9P+TZ8+d8iBWjI+UGJQuKC6JFRERERER+BDFEk4iIiIiIiIiIyG9HVEJFRERERERERER+O6ISKpIj3r55Q7eOXujVrY6tmSFNrM05sHd3rrQdHHQKD+fmudJWbjFv1nQMNGtipF2HE8eO5LU4IiIiIiIifw15roT+M3I8C5atln23b+1Ft36DZd+HjJ7IvCUrf/o4DZu7cPHKtZ9uJzfo3HsgO/cekNseGBxCS/cOP9RmsQo1f1as7yKVSmnn5oypuSWXbz/gRMgFVm/wJTz85S8/dkYkJydnq1xiYiIxMTl33r8TdpvdO7Zx5tIN/Pb6M2RAH1JSvu9L29ekONO8LJjsZsw0T3MCNi8iNVVIDvX09mX8Zg/NsSy5yZF1s3+47qlty5nUxhCfMV1ISkxgUe8WTPOy4NKxnT/U3kRbNZZ7W7G0oynLvS0J2bYYaVpfhd+5wqGFw39Y1tzg9Ka5P1z3/M4VLGlvzK7J3UhOTGDDoFYs97bi5olduSghfH71jKUdzXK1zezw6dVTVve0Y6GnPjsmdCYl6fsr4V+/e0+7fiOoY90S0xZtadmpH/cfPc3xsfcdPUXY/Wyndf8j+LfME+YtJyD4fK4fRyF/Idp17Cz7npycTOnylWjeqnWO2qlSsw7v3+de1CO/nbuoq62HbeNmGe6PjY2lbYdO1Nc3REvPAEsbO6Kjo3ny5ClaegYZ1hk7YRLHA07kmow54VRgECFnz2VZZvykKZSvWh1dIxN0jUwYPnoMADaNmnLxkhDK26FlKz5//pxlO9+W/5ar167hf/jwD55BzkhISMC9bXtq1NXCxLIBT55kfN9WqVmH+vqG6BqZYGhm8d1283xhkpmxATv2HKB/zy6kpqby/uNHIqOiZPvPhl5k9pRx2WorOTkZJaU8P6U/mvj4+B+uG3TqBCoqKnTq2kO2rULFSnTr2QeAlJQUJo4ZQXBQIImJCXh360nHLt0JDjrFzCkTKVGyJGG3b6Gjq8fytRuRSCQEHD3MyKGDKFigIMZm5rJ2Y2JiGD6oH2G3b5GclMTQUWOxb94S340+HNy7m+iYGFJTUth/9GSm8t69E8YmnzUc2Lub9Vt2UF9HN0fne+jAPlq5uJEvXz4qVa5ClWrVuHwxFEPjrJM8KecrwIjNwQBEfXyHzxhv4mOicOg2kkp19ahUVy9HcuQ2R3zm0qTT4O8XzIDTO1bTZ/FeipfR4PGNCwCyc/0RlFQK0GNNEAAxn96xc1I3EmKjsOk0AvXauqjXztlvltuc3jQPy7aDfqjuhT1raT9nF6qlNXhxS+irL+eaHVKTk1H4Bc+zxLgYFJWUUVRW+al2jq8Yj4lLTzQbtubAnEEsdKlDZMSnTMtLpVLcegyhrbMDGxdOA+B62D3evP9IjaqVcnTsfcdOYW9jQZ0aVX/qHHJCSkoKiopZL0TMqsy/ZR43sEeG5X6WQoUKcfP2beLi4ihQoADHAgLQUC/3S46VE9b6rGfl0sVYmGf8wrRg8RJKlynN9fXCvXL33j2UlZWzbHPiuDG5Lmd2ORV0msKFC2FmapJluQF9+zB44IBM9x/8iZnEq9euc/HyFeybNs12nY8fP1KiRIkcH2uNz3qKFSvG/ds32Lrdj+Gjx7B104YMy544cgg1NbVstZvnllBTIwPOXbgEwK07d6lXpxZFChfm0+fPJCQkcOfeA/S0tZg8cx4mDR3QMWtIjwFD+RJaqmFzFwaNGIexrT2Llq/h0tXr2Dq2xsimGfatvXj1+o3sWDv3HsDUzoG6hpYEnxXeQOPj4/HuPQgd84YYNGjCqdNnAFjvu51+Q0fJ6rZ070BgcAgpKSl07j0QHbOG6Jg3ZP7SVQA8fPwEBxcvjGyaYW3vzJ17D7I874DAYIxt7alraMnBI8fl9n/89InWbb3RtbDDvFFzrt+6DUB0dIxMXl0LO3btO5iu3vsPH7Fo3AL/owEZHnfLjj1ZypUVd8JuZ6nIbfJZQxHVogQEn+f46fNsWLeGp08eA3D92hWmzJzH2cs3efL4MefPniE+Pp4Bvbvju2MvJ0Iu8PbN1wyjc2dMxdLahuOnz7H3cADjRw2TWTOvXb2Cz+btGSqgMTExbN6wDvuGVgzs3Z1atesQFHpVJveooYNoYKwn95k/e4ZcW6/CX6JRvrzsu7p6eV7l0OpbpEQpPEYuIHD7SqRSKfcunWbZwDYA3L8czDQvC6Z5WTC9rQXxMcLL17H185jiYco0T3P2LhZewF7cu87szg2Z6mnGyiFexEYKg/38Hg48vS28IUd//sDYlloAnDuwmVVDvVjSz5kJrXXZs1B4WO9dPI6khDimeVngM6ZLpnIHbF7MFHcTpribcHLLUgC2TBvA+5dPWDrAhWPr57F+XFee3r7CNC8L3r14xLOwK8zvbs+M9lYs7tuKiPevZTLumPt9i2ah4qVoPngeF3avRiqV8uRKML7D3QF4cvUMy72tWO5txYouDUiIFfoq2HcByzqZs9zbkuMrJgDw+v4NVvdsxLLOFmwb3Y64KMHK4NO/OeF3hHTgsZ8/MN9NG4Crh3zZNqY9m4a4sMjLgGPLhT4/vmICyYlxLPe2YtfkbpnKfXb7EpZ2NGNpRzPO+S0D4MCcQXx69YTNw9oQ7LuAXVN6EH73Msu9rfj48jHhd6/i09+Rld1s2DSkNVEfXstkPLxoBCu72XJu5/JMy4Xfvcpyb0uWe1sSumd1ZqKl42XYJfbPGcjSjmayPvlRpFIpjy+fpm6DlgBoN3UnMuITn3ZPlH3+zamzF1FSUqKrl4tsW/06NbEw0iXw3EVaeX8dqAeMm8GGHfsBGD1jETqNXTFo5s7wqfM5e+kaB48HMWL6QowcPHn49AXXbt/FyrkjBs3cadNjMJ8iIgFo5NGNIZPmYNaiHdqNXLh47RZuPYZQz6YV4+YslR3Pd48/Fk7tMXLwpPeoKbIZj5KalgybMg9Dew/OXb6RYV/UtGzOqOkLMWnuxU7/46zZuhvzlu0xtPfAvecQYuPiM5S5y5Dx7PI/Lmtj4rwVmDT3Qr+pG3cfPgHg3YdP2LfrhW6TNvQYPokaFo68//j9365ZkyYcPCRYyLZu88O9jats38ePH2nl6oa2gRGmVtZcvyGc14cPH2ji0BxNXQO69OjFt+EbN/luwdjCCl0jE7r37pvljNCWbdtl1sxho0YDMHHKNIJDztKlRy+GjBiZYb1Xr9+goa4u+16rZk3y5csHQEpKKl179kZT14AmDs2Ji4sDoFOXbuzYJShxVWrWYejIUdTXN8TYwooHDx/KyvTs2x9TK2uq1a7HqcAgOnfrQV1tPTp1+XpfHz12HLMGNuibmNHGsy3R0dGydsdNnIy+iRn19Q25c/cuT548ZcXq1cxftBhdIxNOB5/57m+SGd9anCdNnU5tLR0sbezwbNeB2fPmy8r57dqFsYUVtTS1OR18hsTERMZNnMz2HTvRNTJhm9+OTI8hlUo5cfIUXu07YmRu9UNy7tt/gA5tvQBwcW5FwMlT5EaIzzw3G6qXK4uSkhLPXrzkbOglTAz1CQ9/zbkLl1Atoopm3dqoqKjQq2tHRg8dCECHHv04eOQ4jk0bAZCYlMT5E/4kJSVh6+jCrs1rKaVWku279jFm8kxWL54DQHJyCmePH+TQsQAmzZzHkd1bWbZ6PRKJhKtnArhz7wH2rT25fSFza8XVG7cID3/N1RBByfscIcSI7DlwGEvmTKNGtaqcv3iZvkNGcmzv9kzbefrsOWePH+Dh4yc0atGGhpfSW5MmTJ+DjlY9dm5aw8mgM3TqOYBLQUeZMns+RVWLcPWMcPxP35jx37x9RyvPTkwcNRQ7m4wvtFthd7P8PXLCkAF9OH/2DMrKKgQEn+dkwDFu37zB/t3C1GxkZASPHtxHWUUFPQNDmUKnWV+bZ0+fUKhQYSpVrkK16jUAcHX3YsNaQak/GXCMw/77WTJfmA6Nj4/nxfNnAFjb2lE8kze5ulU1qKdZn/lLV1KzVm25/VNm/vj06o+iplEFaWoqUR/fpdsesGkRbYbOppq2CQmx0Sip5OdWyDGuB/kzZF0AKvkLEgvQUJQAACAASURBVBMhxNXcML4HroNnUkPPggMrpuC/egYug6ZnedwX924wfNNplJTzMdHVgAZu3WnZZwKBfquytF4+C7vCuQObGbwuAKRSZnVqSHU9czxGzCfsXAD9lx2gcLGSVNI0IGDTInrO205KchLzx3Wn2+wtFCmuxqVjO9m/bBJtxywByNZULUBx9cqkpqQQ8yl9X53dthj7ATOpqGVCYlpf3T9/jLtn/Omy7BjK+QsSl6aY757Wk2b9ZlBZx5yTa6cS6DODpn2nZXnc1w9u0H1VIErKKixub4SRc1fsuo8jdPfqLK2X4XevcvWQL12WHQOplNW9GlFJxxzHf+byIDSADvP2UbBYScrX0Sdk22I8p28lJTmJ3VN74D5lM4WKqXHzxC5OrJ5My2GLhb5KTqLbyhOkJCfh098xw3J7Z/TBvv9MKmmbcXTZ2Ezli4v8xPVj27l6yJdCxdXQaeZFs77TUVIRBvcdEzrz/pn8C7Npm15oN3HPvN2Ij+QvXFRmqVUtpZ5p2S/cvvcQPU35ezIrPnz6zN6jJ7l+fCcSiYTPkVEUUy2Cg50V9jYWONvbAWDQzJ2544dgZazPhHnLmbJgFbPH/gOAirIyIfs2snjdFly7/0PIvk2UKKpKXRsn+nX25N2HT+w4cIyTfmtRVlai35jpbNl7iLbOjsTExmGoo8mMUQOzlLNE8aKc279ZJrO3eysAxs1Zis/2PfTq4C4n879RKyG0sWKjH/NWbWT59DFMWbgSa1NDhvbqxNHAEHy2781Wv7m3cWHS1Ok42jfj+s2bdOrQnuAzIYJMk6ago63Nbr9tnDh5ig7eXbkSeo4JU6ZhbmbG2FEjOHjoMGt91gMQducO23fsJPhkAMrKyvTqN4DNW7bSPk0h+Zbw8FcMHzWGi2eDKV68OE0cmrNn337GjhrByVOBzJo+FQP9jGeCOndoRxPHFuzcvQdba2s6tPOiRvXqANx/8ADfDetYtWwJbl7t2Ll7D209PeTaKKpalOuXLrBh02YGDh4qG4s+f/5MSOBJ9h04SEuXNgSfPE69unUxMrfk6rVrlNfQYMr0mRzzP0ChQoWYMXsOcxcsYuyoEcJvo1aSS+dCWLpiJbPnLWD18qV079KFwoULZWnlBJi/aDGbt2wFYPqUSTRp1CjDchcuXmLXnj1cvXCOpKQk9E3M0dP7avRJTk7mfHAQ/ocPM3HKVI4dOsiEsaO5ePkKi+dnPK6Fh7/CZ+MmNvluoW6d2nTq0J4N69bI9lvZNiIqOkqu3qxpU7FraJtu28vwcCqkjeFKSkoUVVXlw4cPchZPiURCE8cWSCQSunl7061LZ7Iiz5VQABMjfc6ev8jZ0IsM6NWN8FevOBt6iaJFimBmLPiCnDodwuyFy4iNi+PT58/Uq11TpoS2aSUsZrl7/yG3wu7S1Fm4OFNSUihX5msyGydHwRdFT7s+T58JwdrPnA+ld1ehk2rXrE7FCuW59zBzX6OqlSvy+Okz+g8bjX2jhjSybUB0dAxnQy/i3unr9EpiQtaDrotTcxQUFKhRrSpVKlfizv2H6fafOXeB7esFX1gbK3M+fvxEZGQUAYHBbF69RFaueLFiACQlJdPEyZ2FsyZjZZ71dPGPUrtOXQ7s+erPNmv+Yj68f09DCyNAeNuaPmcBto2apKsXHHSKfGmDHoCiouL3/TmlUnx8/ahRs1a6zZcunKdgoUKZVvPZvJ1N69fSwcMFZxc33Nu2p0LFr1N9o4YOIjjwlFy9Vq5uDBg8LN22cuoavHzxNbFCePgLyqlrZC13Dqiqbcyu+SMxbNoGbevmFC9TmLuhpzBp7oVK/oIAFCpagrjoCOKiIqihJ/jXGDt4sGbE932Haxk2oEDhosK5VKnFx1fPKV6m/HdqwcNr59C2diRfAaGfdWya8/DqWSrU0s60zpun93n1KIzFfZwASE1Noaja13tPr5Ezp3euyaz6d6mgaczRpaPRsnOljqUjqqUL8+hSIDrNPFFO66sCqsWJj44kPjqCyjqCa4d2Ew/8xnf6bvtV9KzIX1gVgFKVahHx+gVFS3+/r57dOEdtSwdU0vqqtqUjz66fpVyN+pnW+fDsPm8fh7HxH2cApKkpFC75ta/q2bTKslx8VATx0RFU0hamNbUbu/HgvPxsStT7Vyzw0KOGsR3uUzdneD4u49Z+9xzzkqJFCpM/Xz66D5uIva0l9raWcmUiIqP5HBmFlbE+AG2dHfHq8/VedrATXsjr1apOnRpVKVdaGDQrV9Dgxas3hFy8ypWbYZg7tQcgLj6eUiWLA8KzqlXT9INxRrg4Npb9f+veQ8bPWUZEZBTRsXE0ssx6uvYLLZsIx9HVqsOeI8IsT8jFa2xbPguAxg3MKF5UNVtt1dfS4snTp2zZtp1mTdI/j8+EhLBjiy8AtjbWfPjwkcjISE4HB7Nz6xYAHJo1pXhxoQ8CTp7i0pUrGJkLfR8XF0/pUqUyPO6FS5ewtrKkVNp+T3d3goKDcWrx/QWnOtraPAy7xdHjARw/cRIjcytCAk9QIH8BqlSujI628PzR09XhydNnGbbhkWbx9XBrw6ChX2dfHO2bIZFI0KpXjzKlS6OlqQlAvTp1ePL0GS9ehnP7zh0sbBoCkJiYhImxkay+c0vB4q+vq8vuPdl7EfjC96bjv3Dm7FlaODqSP39+8ufPj6NDet/Zb2XI7Py/JfTCRcytbfHu1JGggKMZTo8HnTiWzbPIPqdPHEdDQ523b9/S2KE5tWvVxMoyc9/QP0IJNTM25OyFi9y8fQfNOrWooFGOeUtWolqkMB083YiPj6fvkFGcC/CnQnl1Jk6fQ3z811RlBQsKg5AUKXVr1yT46L4Mj5Mvn+ADJShBWS8wUVJSJDX1q6k5Pi01WvFixbgUdJSjJ06x0mcTfnsOMHfqeIoVLcqloKPZPmchTXHm33OKkpIiujpaHD0RmKUSWrf2jy9gsrK2ZfK40axduYzO3XoCEBcXK9tva9eYtauWY2lti7KyMg/u38tSaatRqzbPnj7h8aOHVKlajV1+W2X7bOwas2rZYmbMXYhEIuH61SvZ8um0sWuMjV1jPn74gN/WzbR1bUUJNTUWLF1JxUqVc2QJbebQnG6d2tKr30Bevwrn0YMH6BkYfb/iv3j/8jESBQWKlCjF6ydfLdGNOwyinnkTbp85ytyuTei9MOcLVhQVFWVTIkkJ6f19lb5R/CUKiqSkZG8h148glUopW6U2g9fKK0OATKn+Hp/Cn6CgqEih4qV4//SebLuF1wBqmDbm/rljrO3bjLYzM596ygwFRSWkUmHRU3Liv/pKOX1fpf7KvgJKV66N99KMnxdf+iqzcvFR2cvQVah4aVqPWckV/01sHelF/SZu1LdzpVDxrwpETiyhm4a0JvrjO9Rr6dB8yALioyNkfquR78K/K0+dGlXZdShjNyElRSXZ4j2A+LSXeCUlJYJ3r+dkSCi7DgWwbON2jmxenq3z/0I+FeG5r6Agkf3/5XtycgpSqRQvZ0cmD+0jVzd/PpXv+oECFCpQQPZ/1yET8Fsxm/p1arJhx36Czl/KkZyKCgok58L119zBgSEjRnHy6CE+fPjxTGVSqZT2Xl5MmyzvYpHbFC5cGGenljg7tURBQQH/w0do7eQkG7tBeObFxWW8tuHbcfTb/79M6ysoKKRrS0FBgeTkZBQVFWlka4PvxvUZtpsT3eFX8eUcsmXEAeprabJ6+VLW+mzAycWNDu3a4ubaGlXVry8yObGEaqir8/zFC8qX1yA5OZmIyEhKliwpV1dDQ5gVKV26NE4tWhB68WKWSmie+4SC4BfqfySAEsWLoaioSInixfkcEcm5C5cxNTKQKYBqJYsTHR3Drn3+GbZTq3o13n/4wNlQ4aZPSkr67vSzuYkxvn7C4H/vwSOev3hJrerVqFSxAtdu3CI1NZXnL8K5cOkqIPhcpqam4tzCgQkjh3Dl+g1UVYtQuWIFduwRVrxLpVKu3byd5XF37j1AamoqDx8/4fGTp9Sqnt7B3sLUiC1+gq9LYHAIJUuWQFW1CHbWlixb/fVG+TIdL5FIWL1oDnfvP2DWgiVkhoeLU5ZyZYVEImHjtl2EBAehW6cadpYm9O7aiXGThanOdp26UKt2XWzMDDA3qM8/fXuSksXNkj9/fuYtXo67c3NsTA1QK1Vatm/wiNEkJyVhaaSDmb4W0yZmPuWYESVKlqR7734Enr/M6PGTszWQ/JvadevR0tkVMz1NXFvaM3PeIlk7bk4OvAr//sAb9ek9W6cPpEGbbnIvGu9ePEKjej0adRhIpbq6vHlyj9rGNpzbv5nEeEG5j4n4SIHCRSlYpBgPrgjTaaH+26ihK9zUJcpV5HmY4Od49UT23tAVlZRISU7KdH91HVOuBx4kMT6WhLgYrp06QDWdrK3rZSrVIPrzex5dDwWEKeVXD8OyJc8XYj6/58DcQRi26iLXVx9fPqZM1bpYePZHvZYu75/dp5q+NVcP+ZKU1ldxkZ/IX1iVAkWK8fT6WQCuH90msxgWK1uB8HtChIzbgRm/qP6b7/VVpfqm3An2Jyk+lsS4GO6cPkjF+ln3lVqF6sR8/sDzW1/76u1j+b7KrFz+IkXJX7goz64LK3OvH/fL8DgKiorUsWqO5/RteEzbQlJcLOv6ObJ1VFviowWfSZdxa+mxJkjuk9FUfNtZO+mxJogWQ4UXwyq6FtwOFK65a4e3ypX/NzZmhiQmJrJ6y9eXrRth9wkOvUJFjbKEPXhMQkIinyOjOBkiLE6JjoklIiqapjYWzBr9DzfC7gNQpFBBomKE372oamGKF1UlOFS4D3x3H8TSKPuL/2zMjNh9KIC37wVF7ePnCJ6+fJXt+v8mOiaGsqXUSEpKZuveQ7Lt38qcXUz1tdl5ULBWHTt9Tubrmh06d2jP2FEjZFa/L1iYm7N56zZAWOGtplYSVVVVLC0s8N0muJAdOnKET58E95aGNtbs3L2Ht2/fAoJP6dNMLHFGBvoEng7m/fv3pKSksHX7dhpYyluvM+JMyFnZMRMTEwkLC6NSxYrZPl+AbTuEl9NtfjswNc6+wcDEyJAzZ8/J/EhjYmK4d/9+lnWKFClMVFR0juTLCnNTUw74+xMfH090dDQH/b+/6r1IkSJER8krkSCMrx3atSUw4CjrVq/g4eNH6BmbpYucEHTiGFdCz8l9/q2AAjR3dGD9JsHlZMeu3dhaN5B7TsfExBCVJk9MTAzHAgLQrFc3y3P4IyyhWnVr8/7DR9y/UZA069YmJiYGtZKC7593e090zO0oU7oU+roZTwuqqKiw1WclA4ePJSIykpTkFPr28KZenVoZlgfo6d2e3v+MRMe8IUpKSqxZMpd8+fJhbmxI5UoVqG9qQ+2a1dHVFm7kl69e06XPINlb+5Qxgsl/w8pF9PlnBFPnLCA5KZk2zi3Q1sy88yuU18DUzpGoqCiWzJ1O/vz50+0fO2wQXfsORtfCjoIFCrB26TwARv7Tn75DR6Fj1hAFRQXGDB1Iq+b2gPCGtGnVElp5dqZw4cL09Jafsi3wzRv7j1C2XDlWb9iS4T4FBQXGTJzCmIlT0m23sLLGwspa9n3mvEWy/xs2bsr5xvIr+woUKMDcxfIWD892HfFs1zFHMusb5tx6+YV/ho3kn2HyjvTb9hzMoLTAl4U/KclJKCoqYWjvhq2nvJXl5JZl3L90GomCAuWq1qauWSOUVfLx4t51ZnawRlFJhXrmjWjRaxztxi9j6/SBJMbHoaZembZjhReNhm37sXZkR87s8aGeeRO5Y2SEuVNHpnqaUaGWNh0nyS9qqVBbB2MHT2Z1FB5EZi3bZzkVD6CkrEKX6Rvwmz2M+OhIUlKSsfHoSblqdbKs92XhT2pKEgqKStRv1AbTNr3lyp3bsZwnV4S+Kl25NtWN7VBSycfrBzdZ2d0WRSUVapg0omHXMTgNX8qBuYNISoijeLnKtBwu+FqaufXBb0JnLu9fTw2TxnLHyAi95h1Y3tmCcjW1cR4tHyquXE1tdJp6sKqH4Oen59Auy6l4AEVlFdpM8OHQouEkxESSmpKMsUsPSlepk+1yLYctZt/MvoCEaoY23z0P1VLqWLUfjGW7f3hy5XS2zv172HUfz46JXTixZirlamh9t7xEImHb8tkMmTSHOSvWkz9fPipplGP2mH+ooF4WFwc79Jq6UbmCOjp1hWd2VEwsrt0GEZ+QiFQqlflmujo2ptfIKSxdvxXfJTNZPXs8fUdPIzYunioVNVg5M3sRVUCw0I7/pyeOHfqQmpqKsrIS8ycMo5LGj60oHzewJ5bOHSlVohiGOpoyxfPfMmeHUf260r7/KHz3+GOsW5+ypUpSpFD2ZhXKl9egX+9ectvHjx6Jd/eeaBsYUbBgQXxWC9f1uFEj8GzfEU1dP0xNjKlYoQIAdevUYdL4sTRxbJHWP8osnj+PSpXkFcRy5coxbfJEbJs0QyqVYt+sKS2bO2ZL3oePHtGrX3+kUimpqVLsmzWhdSunTBXejPj0+TPaBkbky5cP3w0+2a5XqlQp1q1agWf7jiSkGb0mjR9HzRo1Mq3T3MEeVw8v9h04wMK5c7C0MM+0bHYwNNCnuYMD2gbGaS4D9SiqWjTLOjYNrJgxe44Q/mnIYNxcXTIsV6N6daZPnsTk8eNkC9ZyinfHDrTv3IUadbUoUaI4WzYIxrDw8Fd07dmLg3t38+bNW5zdhBfY5OQUPNza0LRx1s9aSW6sbsoJEolEmvTxxfcLivwylEuUF3PH5xIlCyqyODR706P/7/QxKsq4Uz8+LSjyZzPBukS6VfHFW40l/tHFPJTov09CQiKKigooKSlx7vJ1+o2ZTuhBX9n+/FUNSI3PeQzkv5EqNetwIeR0tkMD/YlER0dTuHBhYmNjaWDXmBVLFqGnm7dh6n4VCvkLIZVKJX+EJVREREREREQkPc/DX+PVdzipqVJUlJVZOnXU9yuJ/Gfp3qsPt+/cIT4+gfZtPf9aBfRbRCX0FzJtzkJ2/CszkktLR0b80++XH/vG7TA69uifbls+FRVCjstnahL5/yT680cW9W4ht73vkn0ULpbzYMZ/M7ERH9kwSN6fuv3cPRQsKvbV/wNtegzmyfP0fuBThvWlkdWviUYCUL1KRc4f8P1+wTzAxLKBbOr6CxvWrpbzQf03R44dY/io9EHmq1SuzK7t3/crzorH93Lmg56bTJk+kx270i8sdXF2ZtTwnGXH25wDF4K/BXE6/v+QXz0df3DfHqrVqEntOlk7JOclAUcPM2LIQFJTUmjb0VsuPBPA82dP6dujCx/ev6N48RIsW7NBFut0/OjhHDvsz53bt8Tp+GwiTsf/3YjT8b8fcTpe5L/Kl+n4P2J1/J/O3oOHuX3n3vcL5iFHjp+knpEVtfXNmTl/cYZlnj5/QWMnt18ui/+Bvdy7k3F0gOzme/+VpKSkMHRgX7bvOUjI5Zvs8tvKnTB5eceOGIKbZ1tOh15l8IjRTBonLFA6eugg169eJvCcfC7f/wrXTh3g1aM7eS3Gf4Y7pw/y7smf3V8Pzh9ncTsjFnrqE7x5foZlIt68YP2AFqzo0oBlnS24f05Yef3w4klWdrNhWSdzVnaz4fHlrwH6bwTsZFknc5Z1tmDTEBdiP3/4LefzO/kv5KI/GhiCVkNn6to4MWuZT16Lkyfs2bef22F5Z/HMDoePHqW2lg416moxfdbsDMs8ffoMu6b2aBsYYdOoKS9efM3EN2zUaLT0DNDSM0iXBenEyVPom5ihpWdAR++uf8RYmhuISmg22Ot/hLC7GSuhf8KFkJKSQr+ho9m/fSPXz55k6869GSrNw8ZMoq1bxqvnsmL7lk3YWZrQwFiPQX16yFK2VSylyuRxo7Ey1qVxAzPevnlD6LkQDh/cz7iRw2hgrMfjRw9p0cSWkUMGYmtuxIolCwk8GYC1iT4Whtr07e4tm9LRqV2V8aOGYWGojZ2lCY8ePiAqKgrdOtVIShLC5ERGRqb7/iNcvhhKlWrVqFylKioqKrRycePQAfmQPXfvhGFlLawQt2xgIytz904YZuZWKP2CvN6/i+uBB3n9OGOlKquwWv+v3An2592TjMO9pf4B/ZWakoL/gqF4zdhO7/VnuXliZ4ZKc9DG2dS1caL76kBcxq7m4LzBABQsWhKPqb70XHcGp+FL2D1ViAOcmpzM4UUj6DBvHz3XBlOmWj1Cd6/6ref2O9h3LHMl9E95xvcfN4O96xZy9Ygf2/cf+eOV5l/B3n37uR2W8XPrT/md+vQfhP/e3dy6eomt2/0yVJqHjBhJOy9Prl0MZczI4YwcI4QgPHjoMFeuXOVK6DnOnQ5kzvwFREZGkpqaSscu3diycT03Ll+kYsWKrN+4+Xef3i/h/1IJ3bx9J6Z2DuhbNabnwGEypapYhZqMmTwDPctGmDdqzpu37wg5f5EDh44yfNwU9K0a8/DxE7l89ScCgzFo0AQd84Z06fOPTKmqrm3C8HGT0TFviKmdAw8ePSYqKpoaOqbfKFVR6b7/CKGXrlKtSmWqVq6EiooKbs4t2X9IPhB22N372FjmLIzE3Tth7NmxnUMnThN4/jIKior4bRUu/piYGAyMjAk6fwUzC0s2rFuNkYkZTR2aM2HqDALPX6ZK1WqAEPftxJlQvLv3ok+3zqzeuIXgC9dITklm3aqvoZiKqBYl+MI1uvToxaghAylSpAjmlg04ekgIibTbbyuOLVuhrKycTk6/rZszzAnf0dOVf/Mq/CUaGhVk39U1NDLMCa+pVZ8De4VYrQf27iY6KoqPHz6gqVWfgGNHiI3NWcy/X0nooW3M6mjDNC8LtkzrT2raNT2ogTr7lk5kmqc5szs3JPLDWx5dP8+N0/7sWThGlvf9S373Ge0bcGrbMu6GnmJ6WwumeJiyaVJvkhKFa3psSy32LBzDFA9TZnW04d3zh8THRDGupZYsnmZcdGS6738i149uZ1UPO5Z7W7F/zkBZf01tWoGA1ZNZ7m3J6p6NiP74luc3z3M35BDHlo+T5X7/d473R5cCBetiJ3P2zuhDclp/zXfT5tjycSzrZM6qHnZ8fPGIhNgoFrjryPonISYy3fcf4eWdS5TQqEJx9cooKqtQz9aZO2cOyReUSEiIEeL4xcdEUkStLADlatSniJoQkqhUlTokJcSRnJiAFClIpSTGxyKVSkmIiZLV+ZPJKhf82NlLMLT3wMq5I2/efcgwr3sjj24MnijknF/ss5UTZ0IxdvREv6kb3YZOICEtmH5Ny+aMnL4A/aZuWDi15+GT50RFx1DLqgVJSYJSFBkVne77j3Dh2i2qVapA1YrlUVFRxtWxMfuPBf58R+UxmeWkL1KyNKPGjkfH0BhTK2vevHlDyNlz7Dvoz9ARo9A1MuHhw0fYNGrKgMFDMDSzYMHiJQScOImesSn19Q3p3K2HbCzOKK98VFQUVWvVTWfg+Pb7jxB64SLVq1WlatUqwljs6sLe/fLrMG6H3cHW2hoAG+sG7D1wMG17GJYWFigpKVGoUCG0NDU5fPQYHz58QEVFRRYyqlFDW3bt2fPDcv5J/N8poWF37+O3ez9Bh/ZwKegoioqK+KYFhY+JicXYQI/Lp49haWbMmg2+mBkb4NisMdMnjOJS0FGqVakMfM1X37NLB7x7D8R3zTKungkgOSWZ5Ws3yo5XVFWVq2cC6NWlE/+MHE+RIoWxMjfF/6iQOWTbrr04OTaTU6p8/Xahb9VY7uPWoZvcOYW/ekX5b2LaaaiX5eUr+WDL9TXrsPtAxoH+MyPo5AmuXrmMnYUxDYz1CDp1gqePHwNCXNYm9kIMOG1dPZ4/fZJpO61c2gDw4N5dKlWuQvUaQuYmd6/2hAR/nfpr3cY97a8HF0KFYNztOnnju9EHAN+N6zOME+rq7kXg+ctyHx/fjAN5Z4cJU2dx5nQg1ib6hAQHUU5dA0VFRWzsGmPXpBnNbDLPAvE7ef34LpeP7WLQ6qOM2ByMgoIiFw4LQacT42KoomXICN8zVNcxI2TPeqrWN0bL0h6nfpMYsTmYUuWFRAkpSYkM2xCIlUtXNk7sRacpPozacpbU5GSCv0m5mb+wKqO2nMXKtRs75o4gf6EiVNe35GbwEQAuHduJtk0LFJWU5WT9E3j39C63Tu6m8+JD9FgThIKCIjfSAr4nxcdQvq4BPdacppK2GZcPbKCCpjG1zJrRqMcEeqwJooRGFeBrjncjpy7snd4bl3Fr6LnuDKkpKVzc+zUdZr5CqvRcdwajVl04vHgk+QoWobKOOffPCi+KN0/soraVo1x/XT/mx3JvK7nP9rHy8X+j3r1CtdTX7GSqpdSJeif/DLDuOIwbx7Yz16UevsPcaNZvhlyZsMB9lKuhjZJKPhSVlHEYOJtlnc2Z27ou757eRde+3Q/0+u/jzoPHslzwoQd9UVRQZEta0PiY2DiMdbW44L8FCyNd1m7bjam+Ng52Vkwb3o/Qg75UqyT4fScmJRGybyM92rnSdegENi2cxqXD20hOSWHl5q/TpKpFCnPp8DZ6tG/D4ElzKFK4EFbG+hw6GQzA9gNHcWpig7Jy+pmTLXsOYeTgKffx6CW/mCX89VvKl/ua2lWjXGnC37zN9b77nXybk/5K6DkUFRVlOdZjYmIwMTbi6oXzWFqYs2qtD2amJrRwsGfmtClcCT1HtWrCcysxMYkLIcH07tGdTl27s3XTBq5fukBKcjLLVn612n/JK9+7R3cGDh5KkSJFsLaylMXN3Lp9B62cWsiNxZu3bEXXyETu4+rhJXdOL8PDKV/+a3rc8hoavAyXvw+1tTTZtVdI9LB77z6ioqL48OED2lpaHDl6jNjYWN6/f8+pwCCev3iBmpoaycnJXLwkuIDt2L2b5y/+jrU1/935xB/kRFAwl6/dwKShAwDx8fGUVhNST6moqODQJC3otHZ9jp8KyrQdWb76B4+oROjEIwAAIABJREFUXKkiNdMyHrV3d2XZGh/69+wCgFtrId+re+uWDB41HoDO7TyYs2gZLR2ast53O8vnywcu9nR1xtPVORfO+CszJo6h/7DROaojRYp72/aMnThVbp+ysrIsY4KiomKWqeYKFsw83/u3ZJR2zdjUnOfP+hAcdIqUlBTq1JNffem3dTOL582R216lWjU5RbScugYvXz6XfQ9/+TLD9KLl1NXZsHUnIMRv279nF0WLFQO+BrEvWTDnmZhym7sXAnl25yozOwhBy5MS4iiclppRSVkFTQshGUCFOjrcOX8y03b0GgnX25un9ympXokylaoDQq76oB2rsPEQAl8bNHGR/d05bwQgBLQ/vnEB2taOnNu/Gc9RC3/BmeYOjy8FEX7vGqu6C3mikxPjKVRMiC2oqKxCTVMh6H+5mto8ungq03a+5Hh///wBxcpVomQFob+0m7hzYc8aTFyFKW2thq0B0GzYmiNLhBA7ug7tCNm6iNqWDlw95EvzwfI+nPUbuVK/kbwl/2e4GbAT7aYemLn14fmtUHZP7UGvdSFIFAR7xNvHYRxfOYG2s4TrPiU5iYv71tF9VSDF1StzaMEwgjfPw6r94FyVKzc5GRKaaS54FRVlWf55Xc06BASfz7QdF8dGANx79JTK5dWpUbUSIOSmX7HRj76dPQFo01y4XtyaN2XoZCGpSCe3lsxduYEWja3ZuGN/hqGVPJya4eHUTG77/wtZ5aRXUVHB0V7oG31dXY4FnMi0HTcX4f66e+8eVSpXllkL27f1YumKlQzoKyQKySivvHenjsyaOw+nFs3x2biRlUvl11N4ebjj5SGfQexnmDV9Gn0HDmL9xk1YWpijoaGOoqIijRvZceHSZcytbSmlpoapsRGKiopIJBK2bFzPoCHDSEhMoFHDhj+UBfBP5P9OCZVKpbRzd2HK2BFy+5SVlb5RqhRITsl8BfmXfPXfIyOlytzEkL5DRhIYHEJKagqadWvL1fP128WcRfIZg6pXqcy29ekzt6iXK8eLb9LMvQx/jUY5+Wwf6uXK4rdhNcolysvtywwra1vatWlFzz4DKFW6NJ8+fiQ6OooKFStlWqdw4SKy1F1y8tesxbOnT/7H3lmHVbF1cfglRaQklFQBgxJBSmxMVFQajGt3d3ddxe5W7EBFLDABBSyMa7copRIC0of4/jh6FA/WZyvv8/jIxJ4dZ2b2mrX3Xj8ePXyAgWFldu/YSp16DUTH/ffsZsiI0fjv2YWNbS3Rfq/2/9CrS0dGjCk+Tp6Hdwc8vMW/TIvD0sqGRw8e8CTqMVraOvjv2cWajVvFzktKTKSsqiqSkpIsmjubDp26AsJ5P6kpKagWo5v7MygsLMSuVTva9p8idkxS+u2HgqSklGjYuTg+V98dxO9pwxq12D1nOPcunaGwIB9tw183MkIhhdRo7k2TXuJSsJJS36G9ikjbCf+uUL0WRxaNJOpKGAUFBZQzEG+va8f9iNi5VGy/qo4+ntOKalwramiRlvB2SklaQhyKGuLvgCtHttLBR/hRpmdqS15uDpmpSZQpq0Hai1h2TeyE89gVIm/vswfXRXkCmDo4E7a9+EVPvwof04KXkX7nHS/58Xd8mc9Ulyv6jhf+X9vagsGT5hB6LpL8/HxMq1UWS7djfyAL124R229YUZcdK4o6JrQ1yxET/1y0HRv/Au3y5d5P+lvxMU369x0cH5unXqbM/+/gqFPbnv6DhxASepr8/HzMTE3F0m3bsZN5C8Xv+cqGhvjtKDovU0dbm5h3PJQxsbHoaBfTF2trsXeXUHkwPT2dffsDUHnt4Bg/ZpQotFOHTl2oWkV479jXsuP0KeFCwmPHT3D/wYPPqvevzl83HN+ofl32HTjMi4REAJJfvuRJ9Mfd2ooKZXiVXnwYjGqVDXjyNJoHj4RD1Ft376Ve7bfGk5+/cDHLbv8D1LKxEu3/x8udf3oNpHP74lert/dw5dLpY2L/3jdAAWxq1uDBo8c8fvKU3Nxcdu0LwMmxqdh5b3TvvwQjYxPGTZ6Ge2tH6tla4ObUnGfPPq6r7OrhxbJF82lYy4rHjx4WOSYnJ8fS1evp1sGLujY1kJSQpEuP3qLjKSkvqWdrwerlS5nhs0C0392rPakpL3HzbPdF5S8OaWlp5ixYgkebFthbCvXhjUyEL59/p00WLUAKPxOCXQ1jbM2NSHjxnGGv5TsFAgGtmjbAvubH4+H9KKrZNODqqQBeJScAQr355PiPS92VklcgO7N43ePyFauQHP+UhGjhb3chcBeVa76denD5hDAe3qXj+9Cv/lYS1baVN74Te1DL6fM+Bn4WBjXrczv0ABkvhe2VlfaSlGfRH00jK69ATlbx7aWuV5mUZ09JjhEuFLl2bLdIrx7g5il/0f96pjai/ebNvNg7oxcWLdoXe13zph7F6rq/b4AC6FSrSVLMI17GPyFfkMvNU/uoVltcDle5nC6PLwlHeBKe3CUvNwd5FXWyX6WyfazQMK9Q/e37S0ldi4Sou2SkCN+XDyODUa9Y9aNt9bP5f7TgP6brXtWgIk9i4ngYJbxHtu8/Qj27t9r0ew4JDQO/w8ews3wr2drBtRVdhkygk7t4LF4QekIvHN4u9u99AxTA2tyEB1HRPI6OJTdXgN+hYzg1qf/ROv3qfIkm/RsUFD/s4KhWtSpRT56I9N+3bt9B/Xpv31sf0pX/p0N7OnTpSpdOxU8z6dDOu1h99fcNUBBKb95/8JDHj6OEfbHfHto4tRI7LzExUdQX/+szj66dhF77/Px8kpKE0SeuXb/OtRs3aNZEODr7pp1ycnLwmb+A3j26f6Slfh/+Ok+oiVFVpo4bRQu39iId3CU+M6io92HvoKdLW/oOGcWyNRvY5bu6yDE5OTnWLVuAd9c+5OXlYW1pQe+ub2/mlympWNZtQqlSpdi69q2rv52HC5Nm+eD9erj+a5CWlmaxz3RauXcgP7+ALh28MDUWai9PmTUXK8satG7RjNCwCCZMn/3F13dx98LFXdxYfpqQJvq7jYs7bVyEw7R29nU4e/mG6NiBo0WHUho4NCbk3KVi8xo4ZARTZoiX8fzZMFq7uImGw7+Wpo4taerYUmz/2ElTRX+/W6d3kZOTE9XvVxiO1zIwwqnPBJYNdKGwsAApaWk8R85HVUtc2/kNVs3c2DFrEKG7VtF99uYix2RKydFx4nLWj+1Mfn4+FU1qUte1m+h4ZloKs9rXRlpGlq4z3s4VtWnuyaFVM7Bq/uURGH4kGpWMcOg+ji0j3F63lwwtB/ugoqn3wTRmjVw4OG8IF/auwWOqb5Fj0qXkaDt6GX5TulKQn4e2kSXWbbqKjmelp7CyW12kZUrhNvHtHDXzJh4Er59F9UZuX10nSWlpWg72YetIdwoL8rFo0UGkRR+8YRba1SypVqcFzfpN5+C8IZzbsxKQwHnMMiQkJLjgv5bk2MeEbppL6Ka5APwzby+K6lo06DwK30GtkJSWQaW8Hm3HLP/q8n5P/h8t+I/pusuVKsUan8m0HzCavLx8rMxN6Nn+7W+WkpqGdQtvSsnKsnnxTNF+77aOTJm/Es82zb+6TtLS0iyaMpLWnQeSX5BPZ482mFQ1/Orr/ky+RJP+Dd4e7vTqN4ClK1bit72oESgnJ8eGNavwbN9R2BdbWdGnZw/R8Q/pynfw9mLilGmi4fqvQVpamqWL5uPYui35+fl07dwJUxPhKMekqdOxtqpJG6dWhJw+w7iJk5GQkKBe3TosXyycxiEQCKjfWKi1rqSkyJaN60VRWOYuXMThI0EUFBTQp1cPGjk0/Ory/gqUBKv/jlSuUYtzp46griauqLI34BAHAo+xadWPnzv3q2rHWxgZcDLsAmrvaf+OHjaIk8eC2Ol/SLSg6Vfhb9OOn9S2OqM2haCgIj4V4crJ/Vw7fYTOU8W99fB3Bqtf5FWDXqtPIV9Me90KCeBueCAu48Wn3fyO/I3B6qvWa01EwBbUVcU/jvcdOcHBE6FsXDD9u+VfEqz+8/iYrvyeff4cOHiIzRvXF5OyhO9FiXb8T2Tw6AkcPRHMgV3i84H+Zq7eKT7u3ZwFv+4ilxKE7J47kltnj9N34Z5Pn1wCRxaP5sH5E3SYs+tnF6WE78DQKT4cDY0gYP3in12UEj7CwKHDCTp6jMMB+z59cgnfhRJP6F/Ir+oJ/R352zyhX8Pf6An9m/gbPaE/mxJPaAm/KyWynT+Bxq3dibzy3w/P09S2vijO6JsFWb8qbZo34sqlH9txzZg8gepVKlJBQ6nI/oiw0zjYW1NOUZYD/kU9fBoKMjR4Z3HC38qiPq14cuv3lS/90fgObk3cnSs/NM+tI91Z1b0eK7rYc2j+MNGK/5sh+1nRxZ6pDmrFlin1eQyzHPWKXaFfwluatuvFpWvFyxR/L1p3GYhNy69fpPm34tDUURRz80fT1s2D6jWtRdt+e/dhZmmNVGmFImWKinqCvIqaKC5pnwGDfkZxvzslw/F/AZtWL8XassbPLsYvS/NWTvTo0x9b82pF9uvqVWDZmg0sWywef7R06dKEnr/8SyxMKqGEj+ExZQOlyihRWFiI3+TO3ArZj1ljN8rpG+M5bTOH5g8rNt3R5eOpYtf4B5e2hM9h29J/UVJUQM7A+tMnl/DLsG9/AArvhZQyMzVh767t9OkvbmQaGuhz5bVoy5/KX22EZmRk0q5bH2Li4inIL2DciMF4urZhhs9CDh09QXZWNrVsrVi5cA4SEhI0bu2ORXUzws5dIDMjkw0rF+GzaBk3bt3Bw6UN08aPIuppNE4eHbGsUZ2r/93AxKgqG1cuRl6+aMy546dCmTp7Pjm5uRhWqsi6ZQtQUCjDuKmzOBR4HClpKZo6NMBn+sSf1DpCMjIy6N7Ri7i4WPLz8xkxZjwu7l7MnTWdoCOHyM7OwtbOngXLViEhIUGb5o2oXsOCs+FhZGZmsGKtL4vmzeb2zRs4u3kyfsp0nj6JwqNtSywsa/Lf1SsYGZuwYt0msdirwSeOMXvGVHJzcqhkYMDS1RtQUFBg6sSxBB0+iLS0NA6NmzLt37lfVcd345G+S4WKlQCQlPx9BgxysjLYMK4LKc/jKCjIx7H7SKyauhG4bg7XzwQiyMlG39yWdmMXIyEhwaI+rdCras6DqxHkZmfSacoqjvkuIO7hLWo2caV134kkxT1hxWA39IwsiL77H1oGRnSaslosTubtcyc5vOZf8gQ5aOjo03HSCkrJKxCwbDLXzxQjIfkLkJuVwZ6p3UhLiKMgP5/6nUZg1siV0E0+3Is4iiAnCz0zW5yGL0RCQgLfwa3RrFKdp9fOIcjOwHncSsK2LeLFo1uYOrjQqMd4UuKfsnWUB9rVahB/7z80KhnhMm4lMu+118OLpwjZOJs8QS6q2pVoO3oZsvIKnFg9lbsRgUhKSWNo7UCzfl+3sKVUGaGHvyA/j3yBQBTMUqNitQ+muXPmMCpaFb8gduyvQ0ZmFh0GjCH22QvyC/IZO6AHHk7NmLlkLUdOniErJ5taNWuwfOY4JCQkaNquFxYm1Qi/eJWMrCzWz5vK3JW+3Lj7AHenpkwd3o+omDjadBmIpZkxV2/ewaSKAevnT0O+tFyRvI+fOceMRavJyc3FoIIua3wmo1BGnglzlnLo5GmkpaRoUq8Ws8cN+ao6KikqfFX6X42MjAy8OvxDTKywn5kwdgxeHu5Mm/kvh44cISsrC/tatVi9fCkSEhI4NHXEwsKcsPAIMjIy2bR+LbPnzuP6jZt4ursxY+pkoqKe0KKNM1aWFly++h+mJsZsWr9WrJ85dvwEU2bMJCcnB0MDAzasWYWCggJjJkzk4KEjSEtL0bRJY+bN/ver6piens7CxUtZvWIpXh3eRtAxNhKPE/438fv0rt+BoyeD0dIsz+Uzx7kacZLmTRoC0K9nF86dPMzViJNkZWdz+OgJURpZWRnOnzpCz64dcevYnSU+M7kafpLN23eTlPwSgLv3H9KnW2eunw9BUVGRVeuLxvVLTEpm1vwlHPXfycWQIKwszVm0Yg1JyS8JOBzEf2dPcSXsBONGiH8ZhZwJL1bOs17zD4d66jFgGFb1mzFz7iK+dA7wyeNBaGppc/r8FcIjr9G4qTD2YI8+/TkZdp7wyGtkZWdx9MhbfVwZWVlOhV+ga4/edPR0wWfhMsIir7Fj6yaSX8dAe3DvLt169eXclZsoKiqxYc3KIvkmJSYyf84s9h0+RvDZSCxqWrNyyUKSk5I4fGA/EZeuc+bCVYaPFg9efyY0uFgdecdvKLOZnZ1Nozq2nz7xB3Pr7AmU1TUZuz2c8TvPYWIvjDFX36MnozaFMH7nOQQ52dwICxKlkZKRYfTmUOq6dmP1iPZ4jprPuB3nOH94O+kpwjmcz5/cp557DybuvohcGSVO71lXJN/0lCSCNsxj4PIAxmw5QwVjS05tX056SjL/hRxi/M4PK9P8TB5cOImCmiZ91p+hn28ElW2F7WXr0pOeq0/SzzeCvJxs7p09KkojJSNLrzWnsGrTlZ3jO9JyiA99N4ZzNWg7manC9kqKvo91227033yeUmUUubi/6MrbzJQkTm+Zzz/z/em9NgStapac9VtBZmoyd8IO08/3LH03hFH/H3FlosdXzhQr57m+/4dDAW0d6cY856rIyitg0uDjYeFyM9MJ37GYhp3F5SN/B46FRqBVXoOLR3ZwOWg3zeoLY7b27eRJeMBmLgftJis7myOnzojSyMrIEHFgCz3bu+HReziLpo3mctAutu49RNLLFEConNS7ozv/Hd+DokIZVm8tqsSWmJzC7GXrObJlBecObqNmdWMWr99G0ssUAo4Fc+XobiIDdzJmgHh8x5CzkcVKeDZ07yZ27hucOosH4/9dCTp2HC0tLa5ePM/1y5E4NhPGuR7QtzcXws9w/XIk2VlZHDry9mNWVlaWixFh9O7ZHWcPT5YtWsD1yxfZtHWrKNbm3Xv36Nu7F7f+u4yioiIrVheN3JGYmMjM2T4cP3KIS+cisKppyYLFS0lKSmJ/wEFuXInkv8gLTBgzWqzMwSGhxcp51mnYqNg6Tpw6jWFDBiFf+vM/7B5HPaGmnT0NmzTnTFj4Z6f7nfirPaFmJsaMmjidsVNm0qp5E+ra2wEQciaCeUtWkpmVxcuUFEyNqoqCvzs5CmN4VTcxxqRaVbQ0hXq++pUqEh0bh4qyEno62tSpJQxK3cHTlWVrNjBsYB9RvucjL3P77j3qt3AGQJArwM6mJspKipQqVYqeA0fQqnljkYTouzSsV4dLp499dh03r16KjrYWr16l49m5F1t37f2iNjIxrc6kMSOZMmEMzVu0wv61xNqZ08EsXTCPrKxMXiYnY2RsimMroZRpi9f/G5uaYWRiguZr9aZK+gbExkSjrKKCjq4edvZ1APBo14E1K5YyYMhwUb6RF85x984tkcReriAXG9taKCkrIycnx6A+PWjWopVIu/5d6jVwIPT8953vc/XOY7R1dH654XhtQ1P8F09g/9JJmNV1pLKlsAO+d+kMJ7YsJjc7i8y0l2gZGFG9nlAWr3r9lq/TmqBlYISyuiYAatqVSHkRQ2kFZcqW18WwhtBjbOPoSeiu1dDx7UfS4+sXefb4Dgt6CA2h/Lxc9M1sKK2ghIysHNtm/JodZnkDE46tmMjx1VOoat+ciub2gNDQi9i5BEF2FlmvUtCoZCQK/v7m//IGJpSrVA1FNWF7ldWuRNqLWOQUlFEqpyMK+m7e1JPze9dQ23ugKN+YW5EkRN1lwwDhb5Cfl4uuqQ1yZZSQli3FAZ+BVLVvLpIQfRd9y3r0Wf9hSeHi6Dh3L3k52eyb2YvHV05jaO3wwXNDfOdQy6MvsvK/p7fNrFplRs9axPjZS2jRqB51bS0BCD0byYI1m8nMyuZlahomVQxp1VgY8L3V68DvptUqY1zFAK1ywlA+lfR0iIl/jrKSIrpa5altbQFAO+eWrNi0k6E933q0Lly5zp0Hj3DwEBqZuQIBdpbVUVZUQK5UKXqPnkbLRvVE77R3aWhvzYXD27+onoc2LftjhuOrm5kyYvRYRo+fgFOLFtSrK+wbgkNPM3fBQjIzM0l++RITE2NatxK+r9q0EgaBr25qiqmxMVqv+xmDSvpEx8SgoqyCnq4udWoLn+mO7bxZumIlI4a+9UKfu3CRW3fuUNdBOO0kN1dALTtblJWVkZMrRffefXFq2UIkIfouDg0bfPZQ+dX//uPRo8csnOtDVNSTz0qjpaXJk/t3UFNT49LlK7h4eHHjSiRKSkqfTvwb8VcboVUrG3AhJJDA46eYNNOHRvXrMmJQXwaOHM+5k0fQ09Vm2uz5ZGfniNKUKiULgKSkhOjvN9t5r6XFJIrI9L2n2odQrqxJw/psXSce9PnsiUOcOh3G3oAjrFjny/GA3UWOh5wJZ/j4qWLp5EuX5szRALH9byTDFBUV8HZ35uLlL1sUUblKVYIjIjl+9Aizpk6ifsNGDBw2klFDBnAy7AI6unrMmTGVnOxsURpZ2VKAcBi71Ou/32x/uI2KbhcWFtKwURPWbhJ/MR8/fY7TwSc54L+XdatXEBB4osjxM6HBTBg1XCxdaXl5goLDvqj+H0JbR1xr/legfMXKjN4cys2I4xxaNYNqNg1o8s9gdvsMZ9SmEMqW1+Xwmn8R5Ly9p6VlhPexhKQk0jJvfy8JScm3cnnv38Tvb1OIkZ0DXWdsECvTCN9T3LsYyrmD4tKoPxs1vcr0XhvC/XPHCV4/E/2a9anjPYgji0bSc/VJlMvpCofMc9/e32/aSEJCEql320tCkoL8z7y/KcTQuiFuk4p6lAF6rDzB48unuRUawAX/dXReWPS5fnzlDEeXiY8AyMiVpvvyo2L7ReUuJUe1Oi25Gxb4USM09vYlboUe4PiqKWSnpwrvC1k5bF17fjDNr0QVg4qcO7iVoJBwpixYiUNtG4b37sTgSXMID9iMnrYm0xetJvudZ6CU7Dvvddn33+vChVyf85s2qmPHliWzxMoU5r+J4IgL7As8ycotuzm6rWhs2JCzkYyasUAsnXxpOUL2iD9TfxpVq1Th0rlwjgQdZeKUaTRyaMio4UPpP3gIF8PD0NPTZcr0me/1xe/0M6Xe72c+8zcrLKRpIwe2bxFXITsfdpqTwcHs2bef5StXcfJo0SlFwSGhDBsl7iGVl5cnPKSoQMvZ8xeIvHwZ/arG5OXn8eJFAg5NHQk+HiSW/t36vamXVU1LDA0MuHf/AdZWf9aC2L/aCI2Lf4ZqWRU6eLqhoqzMhi07RC8mdbWypKdnsO/AEVzbiCvrfIynMbGcvXAJe1srduzxp45d0WFbO+uaDBo5ngePHlPZQJ+MjExi45+hrVmezKwsWjRtTG07G6pa1ha79pd4QvPy8khJTUNdTRWBQMCRoydo1FD8K/xjxMfFUVZVFc92HVFWVmGL73qRwamqpk56ejoH9u+ljfOXqb7ERD/l4vmz2NjZs3fXDmrVLjpUbm1bi1FDB4o05jMyMoiPi0VTS5uszEyaOrbEzr4ONU3FNZm/tyc05eVLSsvLF3nx/SqkJMRTRqksti28KK2gzNmAzQheG1BllNXIyUzn6qkALBp9mVLXy2fRPLp2AQNzWyKP+mFoUXQebSUzG3b7jCAh+iEaeobkZGWQ8iIeFQ1NcrOzMK3T7JvV8VvyKjGe0oplMW/miZyCMpcPbyEvV/gOkFdWIzcznVuhBzBpULz04odIfR5D9M0L6Jnacv3EHvSq2xU5rmtizZFFI0mOeYSqrgG5WRm8SoxHUU0TQU4WVWo1Rc/MjiXtLcWu/SWe0NzMdHKy0lFU06QgL4/7545Robr9R9N0XXpE9HfIxtnIli7z2xigAHHPE1BVUaK9c0tUFBXZuHs/2Tm5AKirqpCekYl/0ElcHL9s0VV03DPOXb5GrZrm7DoQJPKKvsHWojqDJ83hYVQ0hpX0yMjMIu7ZC7TKa5CZlY2jQ13srSwwbij+7H2JJzQ9I5NXGZkib+2fQFxcPKqqZenYvh0qKiqs3+hL9ut+Rl1djfT0dPb6++Pm4vJF130aHc3Zc+exr2XH9l27RV7RN9SytWHA4KE8ePiQyoaGZGRkEBsXh7aWFpmZmbR0dKSOvT2GxuISzV/iCe3bqyd9ewmfoaioJ7R2dfuoAQqQkJCAqqoqUlJSPHr0mPsPH2CgX+mz8vud+KuN0Bu37jB68gwkJSWFkmHzZqGirEz3Tu2xqNOE8uU0sPo/VpVXq2LIyvW+9Bo4HONqVejdrVOR4xrqaqxfvpCOPQeQ89ronTZ+FIoKZXDt2I3s7BwKCwuZO2PyV9UvJyeXlu4dEAgEFOQX0KhBXXp0as+A4WM/+xq3b15n8vjRSEpIIi0jw7zFy1FWUeGfLj2oa21OufKaWNb88iGhylWrsW71Cgb26UE1I2O69uxT5Li6hgbL1mygZ+cO5L42CsZNmoaCgiIdPV3IycmmsLCQ6bPnfXHe7zNl/Gj27NpBZmYmZpUr8E+X7oyeMJnLkRfp5O1GaspLjh45xOwZU4m4dJ17d28zbGDfX3LBUtyDm+xfOknopZOWxmv0AuQVVajdtjOz2tVCSa08FUy+/Eu6fMUqnNmzlm0z+qOpX416bkXntSmWVafjpBVsnNCdPIGww3fqMwG5MgqsGdEOQW5OcZf96Tx/dIvjqya/bi8ZWg2dh5yiMjVbdWJl1zooqJZDx0jcEPwUanpVuOi/ngNzBqJRqRo2bYvO7Sujoo7zmOXsnd6TPIGwbRp1H4+svAI7x3cUel4LC2nWb8ZX1S83O5Od4zqQJ8ihsKCASpb1RJKit88cInDxaDJTk9g+1hvNymZ0nPtl03V+RW7efcDYfxcL3+vS0iyZPgYVJUW6ejtT09GL8hpqWFU3/eLrVjWoyKotfvQePQ3jyvr06lBUnlZDrSxr506h0+Dx5OSsD+xhAAAgAElEQVQKn4Epw/uioFAGj17DyM7JpbCwkDnjh35V/TIys3DvOUyUx5/A9Zs3GDV2vKgvXrFkMSoqKvTo2pXqNW3QLF8eGyurL75utapVWbFqNd1798XE2EhkCL5BQ0ODjWtX075TF1FfPH3KZBQVFHD28CI7W9jPzJ/zdYuSPoZ/wAEGDRtOQkIiTi6uWJibE3ToAKfDwpk8bQYyMtJISkqycukSVFXF1Rd/d0qC1X9jop5G4+zdhasRJ392UT7Izw5W//RJFO3c2hAeee2nleFb8TcEq0+Ke8KqYV6M3/l1oUL+lmD1KfFP2T7Wm36+ET+7KD+UPzlYfVRMHK49hnA5aPenT/6BlASr/zBvPI7XL/8Z9+CfRkmw+hJKKKGEEkoooYQSfholntC/kJ/tCf2T+Bs8od+Kv8UT+rfyJ3tCf1VKPKEl/K6UeEJLKKGEEkoooYQSSvhplBihJfxfvK+z/iN4+iQKHdUyRTTbLYwMqGtTgwZ2NYsEj58zYyqmhnqiQPXHg96u+F04dzbWZlWxrWHMqeMfDmnzhsLCQmZMnoCtuRG1LE1ZvWKpaH8JJZRQQgkllPD/UWKEvoOKXtUfnmfU02gUtQ2xqi8MYRMdE0eTNh6Y13Kghn0jlqx6G0fw6vWb1GnaGqv6zbBr1JILl4QxP1+mpOD+T3cs6zbBvkkrbty688Pr8aOoZGAoFn4pIPAkoecvcyr8QpH9fQcOIfT8ZULPX6apozDM1p3bt/Dfs4vwS9fxCzjCyCEDyM//+NSE7Vt8iY2N5tzVW5y7chNXdy8AThz9NaUoP8SwBto/PM+kuCcMrVeefzt8O7WqH8EsR70fnmdK/FNmNtNmVff6on1n/Vawoos9K7rUZu+0HuTlCMPW7JvRi2X/2LKiS20C5gwgP0/w0Ws/u3+d9f2asaKLPSu71eXGqX2iYwE+A1nVvR4ru9Vl96TO5GamC/PevZzlnWuxsltdNg9zJuVZNADJsY9Z1b3+T2mjz0XN7MtC0X0LomLiUDGug22r9oAwpFOz9r2xaOaBZXNPlm3cITp37L+LMW/ihnULbzz7jCAl7ZXomM+KjZg4OFO9sSvHT58V7U9Je0W7fqMwb+JGjabunLv8+y/s/BCKauV+eJ5RUU+QV1HD8rWMc3R0DI2atcDUwgozS2sWL3sb19tv7z7MLK2RKq1A5KW3/dGFi5Ei5SQLGzv8Aw58Mt+OnbtiVN2C6jWt6darDwKB8FkuLCxk0LARVDGpTg1rWy5fEfb3V//7j9oNHDCztKaGtS27/PYUuZaali579vl/kzb5EZQYob8AhpUqiWJ/SktL4TN9EtfOBRN27ACr1m/i1p17AIydPJOJo4Zy6fQxpowdztgpMwGYvWApNcxMuRJ2go0rFjNs3JeFdpo6cSzrVq0Qbc+ZMZVli+aTnp6Oc8umONhbU9emBkcOigfDDzsdQjvX1qLtUUMHsn2LLwBXL1+idTMHGtW2wb2NI8/i47+oXN+DwEMHcHH3olSpUlSspI++oSGXIy98NM3GtasZOXaiKCSTRrlyomuV8GnUdfQZu+3biAT86ZTVriSKAZqWEMeFvWvoufoU/XwjKCjIFxmP1Zt40H/zefpuDCcvJ5vLh7d89LoycqVxHreSfr5n6ejjx9Fl48l+JZzL7Nh/Jn3Wn6HvhjCUy+tywV/44atZxZxeq0/Rd0MYxg3acGK18L2iqqP/xYpNfwsGFXVE8T6lpaWZM24oV4/5cXrvRlZt8eP2/UcANKprx+WgXUQG7qRKpQrMXbERgNv3H+F36BhXgnZzwHcpgybNFn0kD582j6YNanPtxF4uHt6BUWX9n1PJPxhDA31R7E9paSnmzZnFzauXOHs6mBWr1nDr9m0AzExN2LtrO/XrFv24NjM14WJEGFcunCPwwH76DBgoEmj5EO3beXH72hWuXbpIdlYW6zb6AhB49CgPHjzg3s1rrF6+jH6DhEpP8vLybFq/lhtXIgk8EMDQkaNISRFKy27dtJE2rb4srvnP5o81QsdNncWKdb6i7Wmz57Ng6SrS0zNo5uyFTUNHLOo05sAR8eHY0LAI2np3Fm0PGjWeTduFoTkuXb1GIyc3bB1a0NKtA/HPnn/TcmtplqdmjeqAUOXIqGoV4uKfAUK1h7RXQi9FatortF9Lht6+ex+H+kKZM6OqlXnyNIbnLxI+O08XN08C9r3VQd6/zw8Xd0/k5OTYvHMvwWcjCQg8yaSxIz97CFogEDBm+GA2btvNqYiLdOjUlZlTJoid57dzW7E6713ae3xWPhISEri3dqRRbRs2rS+qC7xu1XLq2VowsHd3Ul6+BCA+LhYdXV3ROdrausTHxX40j6jHD/Hfs5tGdWzxbNuShw/ui671swhYNplQv7Wi7cNr/uXE1iXkZKazpF9rZv9Tj5nt7LkWelgs7b1LZ1g51FO0vXvuCM4d2gbA09tXWNS7JXM61WfZQBdSE599/8p8Z06snioyrEAYgD1i51JyM9PZPMyZ1T0bsrJrHe6EHRFLG3UljO1jvEXbRxaN4mqg0MiIu3sV38FOrOnlwNaRbrxK+vZtVZCfR15ONgV5eQiys1B8LalapVZTJCQkkJCQQNu4JmkJcR+9jppeZdR0DQFQVNeiTFl1MlITAShVRji1prCwEEFOtkgNS9+yHjJyQp1rXRPrT+bxvZgwZymrNr8NjTR90WoWrt1CekYmjh36Uqt1B6wcvTh4PEQsbei5SFy6v5VpHDJ5Dpv3HATg8vXbNPHuhX2bjjh1HkD8i8RvWm6tcupYmhkBoKhQBqPKlYh99gKApvVqIS0tDNNta1mdmNf7Dx4PxcOpGaVKyaKvp4NhRT0u/neT1LR0wi5coaunMNC9rKwMKkqK37S834sxEyayfNVq0faU6TOZt3AR6enpNHFsiVWt2phb2RBw8JBY2pDQ07R2eSuEMmDIMHw3Cz+4Ll2+QsMmzbG2r4OjUxviv7GTQ0tLi5qWwhjBioqKGBtVIzZW+AwYGxlRrar4yKm8vLzod83OzhFTaCqOlo6OomfZxsaamBhhvxJw8DD/dGiPhIQEtexsSUlJJT4+nqpVqlClslCkRVtbi3IaGiQkftt790fyxwar93Bpw/Bxk+nXowsAe/Yf4vCebcjJlWLP5nUoKSmSmJRM3Watad2i2WfdLAKBgCGjJ7Jv2wY01NXYve8AE2f4sG7Z/CLnbffbx/ylq8TSV9avxK5Na8T2f4iop9FcvXYDWyvhgzB/1hRauXdg9KTpFBQWcDpI6Jk0NzPB/2Agde3tuHDpCk+iY4iJi6d8OY3PysfcwpKEhBfEx8WRlJiAikpZdHT1EAgEzJg8nrPhZ5CUkCQ+LpYXz59TXlPzk9d8cO8ut2/dwM3ptZZ4QX6x6Ty8O+Dh3eFzm0SMwydOo62jQ8KLF7i1bk6VakbUrlufrj37MGLsBCQkJJg1dRITx4xg6er1/1ceuTk5yMnJcSr8Agf372NQnx4cPhH6f5f5W1CzqSt7F4ylgYcw+PKVk/70X7IPaVk5evpso7SCEukpSczr1pjq9Vt+1v2dnyfAb94oes3bgWJZdS4d38vBldPpOLGovOzFoN2c2LJELL2Gnj49Zn/cI/czMG3kwtFl47B16QHAzZD9dJy7B2lZObymb6ZUGSUyU5JY168Z1eq0+Oy2ClwyGu+Z2yijos6NU/s4tW4GbUcvK3LeteN+ROxcKpZeVUcfz2niUoHvoqShjb3XABZ6miNTSg5DGwcMbRqJlePasd04Dvz8YNqxty+RL8hFVfutJy1gdn/unz+BRsVqNO83XSzNlcNbqWzb5LPz+Ja4OzVlxPQF9Okk/HDae+QEB32XIldKlt2r5qKkqEBicgr13brg1KTBZ77L8xg2dS5+q+ejoVYWv0PHmDxvOWt8io4i7dgfyMK14ve0YUVddqzw+ew6RMXEcfXmXWwtxJV3NvkdwN2pKQBxz19ga1lddExHsxxxz15QWq4UGqoq9Bw1leu372FpZsz8SSM+O/+fiZe7G0NHjKZ/n96AcCg76GAAcnJy7Nu9EyUlJRITE7Gv70Abp1af3RcPGjac/X670NDQYJffHsZPnsqGNUX73W07djJv4SKx9JUNDfHbse2z6xAV9YQrV//Dztbmk+eev3CR7r378uTpUzZvWCcySj+FQCBg6/YdLJo3F4C4uDj03nGY6OpoExsXj5aWlmjfhYuR5OYKMDQw+Oy6/Gr8sUaopbkZCQlJxMU/IyEpGRUVZfR0tREIBEyYMZszEeeRlJQkNv4Zz18koFn+03NQ7t5/yM3bd3F0bQdAfn4+WuXLi53X3sOV9h6uX1X+9PQMPDv3Yv6sKSi9/uJdvXEz82ZOxrVNK/z8D9Jr0AiO+u9k1OD+DB07Gav6zTAzMcLC3AwpKakvyq+tizsH9u/lxbNnOLsLX/Z+O7eRlJjAqfCLyMjIYGFkQE5OdpF00tLSFBQUiLbfSHoWFhZiZGzK0ZDwj+brt3MbyxbOF9uvb2iI73a/YlIU5Y2Gu0a5crRq7czlyIvUrlufcu/8Lp269aCdm1B2UUtbh9iYtyHC4uJi0NL+uA68lo4uTm2FcnFObV0Y2Ke76Fo/C71qNXj1MoGUhHjSXyYir6hC2fK65OcJOLhyGg+uRCAhIUlqQjyvkl6gpC5+n77P8yf3iX90m2UDnAEoKMhHuZh0No6e2Dh6iu3/VdGqYk7GywReJcaTkZJEaUUVlMsJ2+rk2hk8uSZsq1eJ8WQkv0BB7dNtlfT0Pi8e32bLcOFzXliQX2w686YemDf9PK/++2S9SuFueCCDd15BTkEZv8lduXZsN+bN3rb94YUjqGhuT0Xzj0txvuFV0jP8Z/XFecxyJN5R/Go7ZjkF+fkELhnNjWB/LFu8/TC8dmw3cXev0GWxuKfqR2BhakRCUjJxzxNITH6JirIietqaCAR5TJq3nLALV5CUlCTuWQLPE5PQ1Pi0nOW9R1HcvPeQVp36A8J3uWYxMpjtnFvQzrnFV5U/PSOTdv1GMW/icJQUFYocm718PdLSUrRr+/E88vLyuXLzLgumjMLWwozh0+Yxd5XvV5XrR2FpYcGLhATi4uJJSEygrIoKenq6CAQCxk2awpmwMGFfHBfH8+fP0fwMJ8fde/e4cfMWzVoJp4Ll5+ejVUy6Du286dDOW2z/l5Ceno57u/YsnOeDktKnF+Ta2dpw40okt+/coUuPXrRo3gw5OblPpus3aAj16tahXt06n1Wu+Ph4OnXrge+6Nb+ket/n8scaoQBubVux98Bhnr9IwMNFeLNu9/MnMTGZC8GByMjIULlGLZFe/Buk3jOssrOFxwspxMSoKmHHPj4X8Gs9oQKBAM/OvWjn7oJL67fzO7bs2MPCf4Vx+Nydneg9eCQASkqKrF++QFjGwkKqWNhjULHCJ/N5Fxd3T4b0701yUiIHjgYD8CotDXWNcsjIyHAmNJjop0/E0ulVqMjdO7fJyckhOyuL0yGnsKtdh8pVq5GUmCDShxcIBDy8fw8jk6JyeV/jCc3IyKCgoABFRUUyMjIIPnmckWOFQ/7P4uPRfP3FePjAfoxf59uiVWt6de1Iv0FDeRYfx6MHD6hpLVxV79yyKSvW+ooM2ze0bN2WsNBgKlbSJ/xMKIaVhcMwjq1as2nDWn4Wlo2duXoqgLSk59RsKjSGLgbtJv1lIqM3hyIlLcOkttVF2vFvkJKSprDw7f0tyHn74aCpb8SIDSc+mu/v5gkFMGnYlluhB0hPfoGpg/CD4vpxPzJTE+m1JhgpaRkWedUQ6ca/QfK9tsp73ZaFQLlKRnRfceyj+X6NJ/TRpRBUtCpQRkVoHBnXdyL65gWRERriO4fMlCRaT1/48cq/Jicjje1jvGnUfTy6puIeHUkpKcwauRK+Y4nICH0UGcKZrfPpsvgQ0rKlPiuf74Fryyb4B57kWUISHq2Eizh3BASSmJzC2QNbkZGRpmq91iKN+DdIS733Ln99vLAQTKoYELp340fz/VpPqECQh3e/UXi3ccTZsagXe/OegwSeCiNw60qR90+7fDli4t5O8Yp99gJtzXLoaJVDR7OcyJPq4tiYeb+JEQrg7urCHn9/nj17jqeHcHh9246dJCYmEnk2HBkZGfSrGov62je87+TIfsfJYWpiTERo8Efz/VpPqEAgwN27Pe29vXB1bvvJ89/F2MgIhTJluHHzFtZWH5dKnjpjFomJiaxe/nbxmra2NtHvOExiYuPQ0Rb2aWlpaTi5uDFj6mRq2dmKXe934o82Qj1c2tBnyCiSkpM5eVC4giw1LQ0NDTVkZGQIORPOk2jxwPkVdXW5ffc+OTk5ZGVnE3w6nDq1bKlW2ZDEpCTOXriEva0VAoGAew8eYWpcrUj6r/GEFhYW0nPQCIyqVmZo/15Fjmlrlud0+Fka1K1N8OlwKhsKh9NSUlORL10aWVlZ1m/eTt3adiLvaTNnLzauWCS6eT+EkYkp6emv0NLWERlv7l7tae/elro2NbCwtKJKNSOxdDq6eji7eVDX2pwKlSpRvYYFALKysmzctpuxI4aQlpZKXl4effoPEjNCv4aEF8/p5C18oeXl5eHm2Y7GzRwBmDJhNDeu/YeEhAQVKlQUfRQYmZjS1tWD2jXNkJKWxmfhUqSkpCgoKODxwweULUabd8jw0fTu2pGVyxZTpowCi1cIPyTerLj/WVg1cWX7rEGkpyQxZLVwPmNWehoKZTWQkpbhXuRpkuOfiqVT1dLj2eO7CHJzEORkce9iKIYW9pSvWIX0lEQeXbuAgbkt+XkCXjx5gJahcZH0v5snFMDUwYWD84aQmZpMl8XCOYHZGWnIqwjb6vGVM6Q+jxZLp6ypS0LUXfJyc8jLyebx5dNUqF4Ldb3KZKQkEX3zAnqmwrZKin5AOf2ibfU1nlDlcrrE3opEkJ2JdKnSPL58Gq1qwufr8qHNPLx4ik4L9hfxaMbevsQF/3W4jFtZ5Fr5glx2TexEjWZemDR825kWFhbyMvYxqroGFBYWcjc8EPUKVQCIv3+NQwuG0cHHjzJlP29qz/fCo1VT+o2bSWJyCsd3Cp+/tFfpaKiVRUZGmpCzkTyNFZ8TWEFHk9sPHpOTk0tWTg7BERepbW1BVYOKJCS/5Nzla9SqaY5AkMf9x08wqWpYJP3XeEILCwvpPWYaRob6DO7RscixY6ERLFizmeM71iBf+q2XzKlJfToPmcDg7h2Ie5HAg6hobGqYIiUlha5Wee49iqKqQSWCIy5gXMWAoE+MNP0qeHm40avfABITkwg5HgS86Ys1kJGRITgklCdPxd9VFStU4NbtO8K+OCuLU8Eh1K1tT7WqVUlISOTsufPY17IT9sX372NqYlIk/dd4QgsLC+nRuy9GRtUYNnjQZ6V5/DgKPT1dpKWlefLkKXfu3aPSa4dQE8eWbFq/Dh2dolFK1m3w5diJE5wIPFzEo9nGqRXLV67C29OD8xcuoqyshJaWFrm5ubh6evNPh/a4u7r8X3X7lfijjVBT42q8Sk9HW0sTrdeLeNp7uOLcrgsWdRpjZVEDoyqVxdLp6Wrj7uyERZ3GVKpQAQtzoeEkKyvLTt81DB0zidS0NPLz8hnYp7uYEfo1hJ+/yLZdezEzMRKFbZoxcTQtmjZm5WIfho2dTF5eHnKlSrFy4RwAbt99QPf+Q5CQkMDEqCprlswDoKCggIePolAtq/JZeYdd/K/Itpq6+geH058mpIn+njJzDlNmzhE7p3oNCw4Vs1jgW1FJ34DT568Ue2zV+s0fTDd89DiGjx5XZN/d27do7exK6dKlxc5XVlFhp7/4UOTnzF36nmgZGpOdmY5KOW2UXy9YsXH0ZNUwL2a2s6eCsSXlK4lPni9bXpeajZ2Z1a4WatoV0a1mDoC0jCw9Zm/Gb95ostPTyM/Pw6FdXzEj9HeknL4xuZnpKKlroagmbCvzJh7sGNeOlV3roF3NQmR8vYtyOV1MHZxZ2bUOKloV0KwibCspGVk8p/oSuHQMORlpFOTnYefeR8wI/Rp0TawxbtCG1T0dkJSSQquKOVZOwgWThxYMR0VTj/X9hHOujes70aDzKFKfxyBdSnzo72bwfp78F0FmajJXg4TeFucxyylvaMr+2f3IyXgl9IRXNqPVUOH74/jKyeRmZeA3uauwLcrr0m7W9m9Wvy/BpKohr9Iz0NbUQOv1sLl32xa49RyKlaMXNaubUM2wklg6PW1N3Fs1oaajF5X0tLEwEb6rZWVl2LF8DsOmziPtVTp5+fkM6NpOzAj9GiIi/2O7/xHMqlUWhW2aNqIfjg51GTLFh5xcgWg6gK2FGctmjsOkqiFurZpg0dwDaSkpFk8dJZpatXDKSLoMmUiuQIB+BR3W+Ewu1kv7K2JqYsKrV6/Q0dYWzWns4O1FGzcPzK1ssK5ZE6Nq4v2onp4uHu6uVK9pg36lilha1ACEfbHfjq0MHjaS1LRU8vLyGTygv5gR+jWER5xly/YdVDczFYVtmjltCi0dHfEPOMCgYcNJSEjEycUVC3Nzgg4dICwigjnzFiAjI42kpCTLFy9CXV2dgoICHjx8hKpqWbF8+g4cRMUKFajdwAEAl7ZtmTR+LC0dm3Mk6ChVTKojL1+aDWuEi7t279nL6bBwkpKT2bRlKyCM4GJRo8Y3q/uPpES28ycT9TQaZ+8uXI04+c2vfePWHXy37WLezKKT7X9X2c7YmGgcHeqiqqomFiv0Z1Ei2/lxkuKesGqYF+N3niuR7fwEKfFP2T7Wm36+Ef/3NY6tnESNZl6UN/x2Iw4fYpajHuOC3nqP/3bZzqiYOFx7DOFy0O5Pn/yNKJHt/HZERT2htasb1y9/+3v2xs2bbNi0mQU+4s6ab03XHr1o1bLFL+8lLZHt/EWQkpQiNS1N5PX8lpiZGIkZoL8zOrp6XL//5JcxQEv4NJJSUmSlp/12wep/BhJSkuRkpBUJVv+lNOs77bsboG+C1Suo/twh+l8NKUlJUl9liLyeJfxeSElJkZqaJvJ6fkvMTE1/iAHasXNXQs+EfdZCqF+FEk/oN2ba7PkolCnDsIF9fmo5CgsLGTp2EkHHT1G6dGnWL18oij/6q3hC58yYShkFBQYMGf5Ty3Hv7h0G9u7OtauXGT9lxheV52/whB5e8y+l5MvQpOPnzYv6EH+DJzRk42xkS5ehtvfAn1qOxCf3CJgzgPj712jUffwPKc/f4gmdvmg1CmXkGdrzn59ajsLCQkob2pR4Qj/AlOkzUVAow4ihQz598neksLCQwcNHEhh0FHn50mxcu1oUf/Rv5o0n9I+eE/o3E3TiFA8ePuZ2ZBjnIy8zYPhYIk78nBArvzply6ry77xFxSpClVDC70hppbI4DppdbAD+Ev4MPhX+roRfg3eVj85fuEi/QUM4d+bnxpn+lSgxQr+CLTv3sGDZKiQkJKhuasymVUXD1qzbtI11m7eTm5tLZf1K+K5agrx8afbsP8QMn4VISkmirKRE8OG93Lx9lx4Dh5GbK6CgoIDdm9ZQxfD/D0B74MgxOnq7C9UWbKxITUsj/tlz0QKtH83ObZtZvniBcPGUWXWxhUObN6xl04Z1CAS56BsYsnL9ZuTl5QnY54fPrOlISUmhpKTMoeMh3Ll1kwG9uyPIzaWgsADf7X4YVhZfVPK5aJQrh0a5chwPKumwzx/ewcltS5FAAu0qpnSeWjSkWPh+X8L9fckXCFDX06fz1DXIyslz+YQ/gevmICkphZyCEkPXBBL/8DZbp/cjTyAoEuboT+G/ozuJ2LUMCQkJyhuY4jK+aFi2S4c2cfngZvLzclHV0cdl3Cpk5OS5GbKfUF8fJKWkKFVGia5LDvPi8W0C5gwkX5BLYWEBntM2iRSO/h/KlNWgTFkN7p/9eAipEj7O1n2HWLR2KxISEpgZVWbjgqKB/Nfv9GfDDn9yBQIMK+qyYcF05EvLsffICWYuWYOUpBRKigqc3LWWW/ce0mvUVHIFeRQUFLBzhQ+V9b8slN67HPzJghm/Epu3bmP+osVISEhgbmbG5o1FhUnWrt/I2g0bhH2xoSGbN6xDXl4ev737mDZzFlJSUigrKRN68hg3b92iW68+5ObmUlBQwJ6d20UKRf8PH1I+ejfo/N9MiRH6f3Lz9l3+nb+Y00EBqKupkvxaFvJdXFq3pEdnYby9STN92LB1BwN6dWPG3IUc3rMVHW0tUlKFQ7lrfLcwsHd32nu4kpubK9ILfpf23fpy98FDsf1D+vXiH2/3Ivvi4p+h+04oCB1tLWLjn/0UI/TOrZssmDOLwFNhqKmr8zJZfEjWqa0rnboJ1X9mTpnI1k0b6NV3AHP/nYFfQCDaOjqkvtbH3bhuNb37D8TDu8MH26r7P948uHdPbH/fQUPw7tDpG9fwzyD+4W2CNsxl+PrjKKiokZEq/jvVaNiGOs5dADi4cjoRAVto6NWbwPU+9F+yD5Vy2mS+Ev5OZ/ZtoKF3X2wcPckT5DKkzp8zh/DF49uc3jKf7suCkFdRIytN/Pk3rtdatJr91LqZXD6yFTvXXpzeNJeOc/egpKEt0m+PPOCLnVtvzJt6kC/IpaBA/J7eM7UbiU8fiO239+xHjeZfF5C7BHFu3XvI7GUbCNmzAXVVFZJTxKfdODd3oLu3cAHI5Pkr8N29n36dvZm1ZC0HfZeho1mOlLRXAKzdvpf+XdrRzrkFubkC8ov5jTsOHMu9R+LxmAd1b09HV6ci++Kefb4085/MzVu3mDnbh/CQk6irq5NcTP/i6tyGnt2FUR4mTJ7Ket9NDOzXl+mz/iXo4AF0dLRF+uur1q5nUP9+dGjn/cH+xbtjJ+4W078MHTSQTh2Lxr3+HOWjv5kSI/T/JPhMOG5tnVBXE8aVVC0rHnrh5u07TJo5l5TUVDIyMmnaqAEAte1s6N5/GO7OTqJg9LVsrJg9fymxcQkVBiYAACAASURBVPE4O7Uo1gu6fcNKsX2/A6dDg2nj4o6aujC0SnGxOG/fusGsqZNITU0hIz2dRk2EC7XsatVmQO+uOLt64NRWGHvVxq4WC3z+JS42Fqe2LsV6Qddv2fkda/RncjfyNJaNnVFQUQOgjLL47xT/6BaHVs4gMz2V3Mx0jGo1BsDA3I4t0/pSs4kLNRoK1an0q9tydOM8Up7HUsOhzY+ryA/g8ZUzmDRsi/zrtiqtJP78v3h8m+D1M8lOTyU3K0MkualnZkfA7P6YODhjXE8ooqFnasOZrfNJS4jDuL5TsV5Q98kbvmONSnifkLMXcW3ZGHVVYYg7VRVlsXNu3nvIlPkrSU17RXpmFk3rCRe12FvVoOfIKbi1aopzc2HoHTtLc+as2EDss+c4N29UrBd069LPl18tQcipkFDcXV1Qf92/qBbTv9y4eYuJU6aRkppCenoGzZoKJWhr29vTtWcvPNzccHUWvqPs7WyZNWcuMbGxuDq3LdYLunPrh0MAlvBllBih35Hu/YexZ+t6apiZsGn7bkLDzwKwYsFszkdeJvDYSewcWnA+OJB27i7YWlly5NhJ2nh1YsWCOTjULyrf9SWeUG0tTWJi40TbsXHx6Gh9Wg7tZzGgVze27NqHmXkNtm/xJfz1nJn5S1cSeeE8x4OO0KiODafCL+Lu1R4rGzuOBR3B28WJ+UtXUr9hUTWSEk/o92Hr1L70nLsd3arVOXdoG/cvhQHQbuwiom5EciP8KD6dGzBqUyg2jh5UMrPiZvgxVg51/8SV/zwCZvfHa8ZWNCubcTVwO1FXhXP4nIYvIOZWJPfPHWNNLwd6rQmmehN3dIytuH/uGNtHe+E0fAH6NYuuki/xhP569Bw5Fb/V8zA3rsrmPQc5ff4SAMtmjuPC1RsEBodh3+Yfzh7YgndbR2wszAgKDqNtt8EsmzkOh9pFlau+xBOqrfnnjCx8b7r27I2/305qmJvju3kLoafPALBq2RLOX7jI4cAgrO3rEnk2jPbeXtjZ2HA4KIhWbV1ZtWwJjRwaFrnel3hCP6Z8VEKJEfp/41CvDh6dejCkXy/UVMuS/PKlmDf0VXo6WuXLIRAI2OHnj7a20Ah8+DgKO+ua2FnXJOhkCNGxcaSmpWFQqSIDe3cnOiaOazdvixmhX+IJbd2iGSvWbsTLtS3nIy+jpKT40+aD1m/gQCdvN/oNGoqqmhovk5PFvKHp6a8or6mFQCBgz67tIl32x48eYm1rh7WtHSeOBREbE01aWiqV9A3o3W8gsdFPuXXjupgRWuIJ/XKqWddnzagONGo/AAUVVTJSk8W8odmZ6Sira5KfJ+Bi0G5UNIRTPhJiHlHJzJpKZtbcijhOyosYsjNSUdfRp6FXH5KfRXNq27KfUa3vgr5lPXZN7IS9Rz/klVXJSnsp5g3NyUxHUa08+XkCrp/wQ1Fd2FbJsY/RNbFG18SaB+dPkvYilpyMNMpqV8LOrTepz2N4/vCmmBFa4gn9sTS0t8Gzz0gGd++AWlnhcPz73tD0jAw0NdQRCPLYGRCItmY5AB4+icHWwgxbCzOOhUQQE/+c1FfpGFTQoX8Xb6LjnnHjzn0xI/RLPKFOjRuwfof/11f0N6dRwwa4erZj2OCBqKmpkZycLOYNfZWejpamJgKBgO07d6GjLXwWHz58hJ2tDXa2NgQdO0Z0TAypqWkYGOgzqH8/nkZHc+3GDTEj9Es8oR9SPipBSIkR+n9ialyNMcMG0djJHUkpSSzMzdiwvKiG85RxI6nTtDXq6qrYWlnyKl0YSmPM5Bncf/gYCsGhfh1qmJkwd/Fytu3ah7SMNJrlNBgzbMBXla9F00YEHj+FkVVdSpeWY92yBV91va/ByMSUoaPG0rq5A1JSUlSvYcHyNUU1m8dOnEqzBvaoqWtgZWNLerpwHtXkcaN49PABhYWF1G/YCDPzGiye78Pu/7F31mFVPF0AfpcLXDBAsVsMEAQBEUTEbkUsxMbuzs/u7sDu7m7FJhQVUVRERbE7kM79/lhEkBBUQP3t+zw+cnfqzD17d2Zn5pyzfQtqqmrkzZePwcNH/ZJ8b16/ppaNJYGBX1BRUWGF0yLcPG+jpaX1S/X+bRQoaUC9zsNY1KshKioKCuuXo8OEhC8+tj3HMLdzTbLlzE2xshUIDwkC4MDicbx99ghEEX2LahQqbczpTQvwOLYThaoaWrnyZkaX0o28ugZUaT+EDQNtUVEoyF+qHE1HLU2Qp0bX0azpXYcsOXJTyMCciNjv6vSKCXx87ocoiuiaVyVfKSNcty3i1umdqCjUyKaTlyrth/ySfEEf3rCqZ03CQwIRBBUu71lB343uKLP+t+7pX8FQryT/69uFOm16oFBRYFJWnzVzJibIM2Fwb6o070QenRxYmBoRGBwCwOgZi3jo/xQRkRrWlpQz0GPuio1sO3AMNVVV8uXJxYg+nX9Jvvo1Kv8403+AsoaGjP7fcKrXqYdCocDMxIT1axIaVE6eMA6rKtXJkyc3lhYWBAVK48uI0WN48FAaX2rWqI5JuXLMmjuPLdt2oKamSv58+Rg9YvgvyZdc5CMZCdlP6H+QP8VP6L/Af8FP6O/iv+An9L+MUysjPrx5meDav+gn9E9Cjpgk87ci+wmVkZGRkflt9Nt5O8HnSdUTG4jIyMjIxEcO2ykjIyMjIyMjI5PhyJNQGRkZGRkZGRmZDEeehMrIyMjIyMjIyGQ48iRURkZGRkZGRkYmw8lw63hNTY3XYWHhmeOwUgYApVJJeHh4ZovxT6CqriQqQv4uU4NCTUl0pPxd/VdQqqsTHhGR2WL808jPcpm/FQ0NjTehoaH5M3wSKgOCIBQBPERRTLXHWkEQVIEAoKAoiv+cTyBBEJoCw0RRtEnndnIA/kBpURTl4Mu/CUEQSgHOoigWT0MZdeAzkFsUxZD0ki2zEATBAegpimKtdG4nF+AHFBdF8XN6tiWTEEEQDgCHRVFcm87t9ADqiaLYIj3b+a8hCMI0IFIUxYlpKNMJqCOKYrsf5ZX5MfJ2fOZQHriRlgKiKEYBtwCTdJEo8+kKpOuDHCB2kD4EtE/vtv5j/Mw9HQH4AMbpIlHmk1H39AfgJNA2vduS+YYgCPmBasCuDGhuB1BLEIR/K+pD5lMe8ExjGU/ALB1k+U8iT0IzEEEQ6giCYMTP3fjElin/e6XKPARBKCEIgp0gCIUAG2B3BjW9FugmSPSNXWWW+QkEQWggCEIZfv6evsG/dU+XFgShoSAIxQBzYF8GNb0G6BYrQz9BEBQZ1O5/DkEQHARBKAB0BPaKohiY3m2KovgF2A84CoJQUBCElund5r+MIAiDBEEQ+Lnnlg9QXBCErL9fsv8e8iQ0YzEFeiO9RXkKgrBCEATDNJT/pwZswADohfQw3w3EpPeEUBCELIALoA5UBOYCyvRs8x/HHOiBdE/fEARhjSAIpdNQ/p96sUJa1e0OdAa2AyrpPSGMHQzPATqCIFQAFiA/29MTB6SX5q7AWkEQsqd3g7FtrI1tsypgn95t/uP0B2og/U6yCoKQ6h0LURQjgTv8u7uSGYr8oMpYTgL1kAZdC6RJ6cM0lP/XtgFeA/mBLoAz0humZTq3ORBJD5uRXggiRVGU4979PPHv6aqAPtKZ29TyL9/T5wFf0r9/w4HDSPd0H+Bz7EApkz68BioDkUAF4EoGtHkVKAcIQKVYGWR+npNAO+AmsJe06/Bfe25lGrJhUgYSu/z/CsgOBAKWoig+TUN5JfCJf8SQQxCEwoAXknFKNuB/oihuTOc2FcAywAooAbwWRTEtK3cy8Yj9Pt8BCiAYqCCK4suUSyUonwV4D+SIPSP6VyMIQknAFeme1gEGiqK4PZ3bVAVWIQ2KJYBnoigapWeb/2UEQRgHtATeAEWRDIb807nNEkgTJ38gH7BdFMUZ6dnmv4wgCE2A+UAocB3oJKZhMiQIQi/AQhTFrukk4n8GeSU0A4m9yW8BWQDHtExAY8uHA/f4dww53iIN1PmBruk9AQUQRTEa6QjAfkADiEnvNv9lYr9Pb6QXq7ZpmYDGlg8BHgNpOZbyJ/MGyAsUAtqn9wQU4owWuwLHgayAvLKQvgQgPYNzATbpPQEFEEXxEdLqa57YtmUvCL/GWaA40nOrd1omoLH8a8eIMg3ZICPj2QI8FkXx1E+W/3ouNCO2gNIVURQjBEF4iuTG5mQGtisCEwVByAbIK0a/zlbgliiK53+y/NcHutdvkyiTEEUxSBCE50AHURQvZGC7IjA69p4unlHt/kd5CdwFqoqiGJRRjYqi+FYQhCpIz/5XGdXuv4goioGCIBwHJv/krqI3oC8IgjJ2cUjmJ5G34/8yBEHoC5iIotgjs2WRkfkdCIIwBNAVRbF/ZssiIyMjkxoEQfAGOoqi+DNeQWRikbfj/z48gfKCIKgIglAvs4WRkfkNfL2nFYIg1M1sYWRkZGRSwdfnVklBEPQyW5i/FXkS+hcRG93h6/m5SsDszJVIRubXEARhBnAfyd1JFWBq5kokIyMjkzyCIJgIgtCNb8eIxgLpGhXtXybBdvx/Ma67HHv3x6grlUT849/RvxTXXI5nn3r+dr0r1dUIj/hveGNSKtUJD//rHSikO/KYJpMcX+O1Z7Yc8UkwCRUEQYx8+ygTxcl41PKWIDRSNpBOCU01FZ4F/NsDXRFtNYac/JDZYvwW5tfLxVw32Xg2NQyzzsHQv1jv8+rlItTnfGaLkSFoGlQn/PntzBbjj0dZ2IjoGNnWIz05f/48bVq3IiAggIKFCvHwoV9mi5QqFCoCoigKmS1HfOTteBkZGRkZGRmZVFK9enUGDRpMREQEFhYWmS3OX408CZWRkZGRkZGRSQPDR4zAzMyMunVl++BfQfYTKiMjIyMjIyOTBlRUVLh67Xpmi/HXI6+EysjIyMjIyMjIZDg/nIS+fvOOdj0GoG9RHcvadjRu05n7fmk3Xjp47BR3fR/8lJAZzevXr+nQrg2G+qWwtqxA08aNeHD/fprrOXTwAD5376aDhJlPsZxK6tmYU8vKlF6OrQkNSTroRNM6VTJYstTz7Ik/taxMk0w7v2IMnvtWxH3eO9qeUwsGxn2+sHIc1/cu+2UZdg234/X9G79cz4/48uENW8Z1YYa9KQs6V2PN0Ja8e/owzfWc2Tjvp2W4tGsFs9tYsnVi9yTTH3pewt/7WyCwHVN7c/PswZ9u7yvDbXSY39GGOe0qsWlMRyLCkg+Qcm7FGK7H0/ue0facjKf38yvHcS0FvbtunMETz/MpyuO2eRZXdzsluh4WFIDX4bUplg368IpDUzqlmOf1uw90GDIJw7ptsW7Rg6Y9/seDx89SLJMUs1duSXOZryzdvBfTRo50Gp60x62LHjdwv/HNyKj7qBnsO3k+ze28fvue9n2GUaZyfawaOGDXoTf3H/mnuZ6DJ87gc//vMC5JK0MGD2bRwoVxn+vXr0f3bt3iPg8bOpQF8+cnW37C+PE4Ozun2MakiROZN3duouufP39m+bKUn5MvX76kZUv7FPP8Kzx+/JhKVhXRK12K1q1bERGRtLeHW7duUdm6EsZGZTEpZ0xYWBgAY8eMoVjRImhlz5Yg/5DBgylvZkp5M1PK6OuhkzNHuvfld5HiJFQURew79aKqdUV8r57Hw/kQU8eM4O2792lu6ODx0/jcz9hJaHR0dJrLiKJIK/vmVK1ajbu+D3HzuMbkqdN58/ZNmus6fPAgPj4ZOwn9mT6nRFRUVJLXNTQ1OelynTOXvVBTV2PzupVJljtw+tIvt/Wr/Ey9BctW5OVdDwDEmBhCAz7y4cm9uPSXPh4UMEzdgfSY6PTpV2oRRZGNI9tTorwNo/Z4MXj9BRr2Gk/gx7dpruvMpuQHqx/htm8tPRbtp93E1Umm+3m64O/t8dP1J4eaUpMhG10YvtUdhZo67vvXJZu3UCr0XjAFvVfuOIpi5av/lJzhQQF4HU5eNoBsuQpgN25DsumiKNKq/ziqWppy99Q23PauYvKQ7rz58CnN8sxe9fOT0FXbD3Bk7Vw2zBmbZPpFDy8u3/g1S3dRFHHoNpCqlSy453qCy8d3MXXUQN6+S7u3g8Mnz2b4JDSjntXWlSvj5u4GQExMDB/ev+fu3Ttx6e7ublSytk623kmTJ1O7du2fkunz588sX57yJLRgwYLs3r0nxTwlihdFoSL89f9KlSyBh4cHfn5+7N61C00NZZL5zExNuHz5Mnfv3uX27dtkzaKJQkVgxozpPH/+nODg4AT5Fy1ayM2bN7l58yYPHjwgICAgyXpB8oKU1D9NDeXrn1LyL5LimdDzLu6oqanSs1O7uGsmRgYAXHC9zPxlqzm4VXpzHzByAuamxnRsbc/oKbM4cvIMCoWCOtWr0LRRPY6cdOaS+xWmz1/KrnXLCAwKpu+IsYSEhFKyeDFWL5pFzhza1GraBlNjQ1wuXyUkJJR1TnOZvWg5t318adnUlsmjhgKwdfcBnNZsICIiEsvypjjNnoxCoSBHcSO6O7bhzEVXFs+chI1V4gHj6o2bDBkzheAkVu8unD+Hmpoa3Xv2irtWzsQEgIsXzrNw/jz2HTwMwKAB/TA3r0CHjp0YO3okR48cRlWhSq06dWjStDlHjxzi0qULzJo+je279hAYGEj/vr0JDQlBt2RJVq5eS86cOalbqwYmpqa4ubgQHBLMmnUbmTN7Jndue2Pf0oGJk6XVhO1bt7DUaQmRERFYWFqyyGkZCoWC3Dmy07V7D86dOcOCxU5UtrFJ1K9rV68ybMggQoKDUVcqOX7KmY8fPtClkyMhwcEAzF+0hErW1ly8cJ5JE8aTM2dOfH3vJarreywr2eBzxxv3SxeYM20C2jly4nffl4ued9EvmAPfl58RRZFp40ZyzvkkggADho3GroVDkmW+59kTfzq0sMXY1IzbN73QK2PIwpXr0cyShUrGpTh6/jI6uXJz0/MaU8f9j91HzzB/xmSePPbjqf9jChYuysSZ8xg1uC9P/aVV/OnznciXvyAx0dGM6N8zUZsFDS04v2IMAO+f3CN38TIEf3xDWOBnVJWafHx6n3ylTHDfModHV04QFR5GQUNLag+cjyAI7BpuR54SRry8cxn96i0oYlKZ8yvHERkajKa2DvWGOpEtl+Su7f7Fg5xZMpzw4C/UHbyIwsaViIoI48ziYbx+4IWKQpVqPaZQ1LQKd05t4/V9L2r1k+IU7B/Xhgr2fSlkVIlTCwbw5n7i8OsPr19ERVUV62ZdvvWvtDEgDeRHlo7nnrszgiBQu9MwTGs358v712we15nwkECio6JpMXwePm6niAwPZX5HG/LpGiQ7mbyw3QmPI1sBqGjXgaqt+rBn9mA+vvRnzZCWWNq2o2rrvgnKfHz1hMsH1iOoKPA8uZOmg6X+PfZy5eKOpQR+fEOjPpMxqdkEgHNbF3PzzH6iI8MxqmZLvW6jk7s9E6BrUolXD+/w0PMSJ1dPT1Lv51Kh9zcPvDi/chwRsfqsH6vPE3P7UqJiPfSq2PHI4zTnV45FTSMrhcpaEvDqCc2mbAfgw1Nfdg63I/Dtc8o360n5pj25tG4yAa/82dS7GsXKV6da90mJ5At4/ZT949vQaZUrt09tS/zdX7mBmqoq3Vs3ibtWrkwpQNL16LkrOHXxCoIg8L9eHWjZsCav3korp4HBwURFRbNowhBOXHAnNCyCis26YlBKN9nJ5KINu9i09xgAnewb0b9jS/pPnMfj569o2uN/ODZvyIBOLROUefLiFWt2HkJFRcGOw6eZN0ZaaXa5dovFG3bz5v1Hpg3rSfN61QGYv3YHe0+cS9T2eTcP1NRU6dGh1be+GpaRvgc3Dxas3MCBjdIEaOCYaZiblMXRoSljpi/gyOlzqCoU1K5mTdMGtTly6hwXL19jxuKV7Fi1gKDgEPqNnExIaCglihVh1bwp5MyhTR37TpgYGeDqcZ3gkFDWLZzObKc13L73gJZ29Zk0YgAA2/YeZum6rURERmJhVo4l08eiUCjQ0bOgW3sHzl5yZ9G0sVS2LJ+oX9e8vBk6YSbBIaEo1dU5sXMtHz59psvAUQSHhAKwcOpoKlUw44KbB5PmOpFDWwvfh4+5df5Qovqsra0ZOmQwAHfu3KGskRGvXr3i06dPZMmSBR8fH8qXL8/169cZNnQIQUFB5Mqdm/XrN1CgQAE6d+5Eo0a22Nvbc+zYMYYNHULWrFmxtq7Mo8ePOHz4CAB3796lZo3qPH36lIEDB9F/wABGjRqJn58f5c1MqV27DrPnzEkkn7+/P3aNbbnlfZsNGzZw+PAhQkJCeOTnR9OmzZg1ezZPnj4j9L5LkvegzO9BU88mU3zEpzgJvX3vPuVNjNJU4YePnzh47BS33aRB7XPAF3Joa2FbrzaN6tagReOGAJhVa8CiGROpal2RiTMXMGXuIuZPHQ+AupoaV04fYvGq9bRw7MkV50Po5NBG37IGA3t24e37D+w+eISLR3ajpqZGvxHj2LbnIB1aNSc4JARLc1PmTB6TpHwRERG06z6ArasXY2FmglreEgnS79y+jVn5xA+GFPv84QOHDh7g5m0fqc+fP5MjRw4a2drRoFEjmreQthoszEyYv2gxVapWY/LE8UybMom586VtEnV1dVyvXMVp8SJatmiK25Vr6OjoYKhfiv4DB/Pu7Vv27N7FuYsuqKmpMbBfH3Zs20q7Do4EBwdjYVmRWXOS3iqNiIigQ7vWbN66gwoWFnz58gVNTU3y5M3L0ROn0NDQ4OGDB3Rs3xbXK1cB8LrhyXUvb4rr6qKplvyCeVRUFOecT1K9lhRt8fbNGzi7e1G0uG6CfMcP7eeu901OuV7n44f32NaoRMXKVVIsEx+/B77McVqJhVVlhvbtxsY1K+g1YEiKenlwz4e9Jy+gqalJ705tsapchTVb9xAdHU1wUBABnz/x2O8BTms3s31TwhWobLkKoKJQ5cvb57y660EBAwuCPrzipc9VlFm1yF3cEIWaOqZ23ajUfrjUx9m9eHTlJCWt6gMQExVBO6ezREdFsmtYY5pM3EKWHLnxPb8f1w3TqDd0CQBidDTtljjzyOM0l7fMxn7WfrwOrQVBoONKFz4+vc/e0fZ0Xpf8KuG7R94EvX9Fx1WuzK+XK0Ha60c+FC6T9LED7/OHePnAm6GbXAgO+MCirjUpYWqN56nd6FesRe1Ow4iJjiYiLIQSpta47l3NkI3JDwbP73lx9eg2BqxxBlFkcffalDStjP2IBfhedqa302Gy5siVqJxOgWJYNe2MMks2qreVQsh7HNnMlw9v6LviBG+f3Gf9iDaY1GyC75WzvH/mx8C1ZxFFkfUj2uB3w5WSZpWTlQsgOioKX/fT6FtJqzrPfW8myhNf7y/velAwCb0jCJxZOpKmsfq8d34/LhumUT9WnwBREWGcXjSE1vOOoJ2/GEdmJDyC8PHZAxxmHyQiNIj1XStiYtuFKl3G897fB8flF1LsR0rcefAYs7JJRxA8cPoit3we4nFgLe8/BWDj0AubCibsPOpMHRsL/terA9HR0YSEhWNToRwrtu3nyv7kjwd43vFl877jXNy5HFEUqdqqD1UsTFkycSinLnlwYuMCciexLVisUAG6tbIjaxZNBndpDcDGvUd5/e4DZ7cuwffRU+z7jqZ5veo4u17F78lzXHatIIthjYR9vfcAM2PDNH0/Hz595uCJM3hfOJxwfKpbg4a1qtHcVnqOmdduxoIpo6layYJJc5yYumA58yaNBKTxyf3YLpas2Yx91wG4H9uFTg5tDCo3YEB3R2l8OnyC8wc2o6amRv/RU9i+/wjt7ZsQHBKKpZkxs8cPT1K+iIhI2vUZztZlc6hgasyXwCA0NZTkza3DsW2r0dBQ8uDRExz7Dcf92C4Abnj74HlmP7pFC7Nmy+5EdRYsWBBVVVWePn2Km5sbVlaVePniBe7u7mhra2NsbIwgCAwc0J/9Bw6SJ08edu7cydgxY1i77ttzMSwsjN69enL+wkV0dXVp27ZNgnZ8fe9x5uw5AgMDMSijT6/evZkxYyZ3bt/G80bil+PkuOnlxXXPGyiVSgzK6NOvf/9Ul5X5+/jt1vHaWtlRKpV0H/Q/GtWpSaO6NRPlCfjyhYAvX6hqXRGADq2a07pbv7h023rSIGFsoI+hfmkK5MsLgG6xIjx78QpXj2t43ryNVd2mgPTjyJtbGtgUCgXNbesnK5/vw0fkz5cXCzOT39NhQFtbGw2lBr26d6VBI1saNrJNlCcgIIDPAZ+pUrUaAO07dKRda4e49Ea2dgAYGRtjaFiWAgUKAKCrW4Lnz57h5uqCp+d1bKwsAQgNCyVPXul7USgUNGveIln57vv6kj9/ASrE+jPT0tICIDg4mMED+nPrphcKhYIHD76de61gYUlx3eQnhWGhodSzMQekldDWjl24fsUdU3OLJCeTHpddsbNvhUKhIE/efFhVrsJNz2tkz66VbJn4FCxcBAsraZLR3KEd61Y6/XASWqdhYzQ1NQFwu3iOhSvXA9L3paWtTcDnTxQppkvZcklP0AoaWvLyrgcv73pg3rwPgR9e8equB+pZtShYVtLDs5uXuLZ7CZHhoYQFfiJXsTJxk1C9as0A+PT8IR+e+LB3lKSjmJhosul8e+ksZSPdL/lKmxDwRjq79+LOZcyaSBMXnaJ6aOUtwqfnyW8XaucvTsDrJ5xd+r8Uv5PveXzrMmZ1WqCiUJBdJy8lTK155uNJEYPy7Jrej+ioSIyqNqKQXrnU1XfTHaNqjVBqZgXAuJotj266U0j/535vRlUboaKiQn7dMgR9egfAfY+z3Pc4y4JO0ktMeEgw75/7JTsJ/bp6C9JKqGXjDvh7X6GoYXn8PBNPqL/Xe9CHV7y864EyVu9f9bknVp9iTDRZdRIuInx89oAcBYqjnb8YAGWqN8f72Ka49BKWdVBVV6KqriRLjtyEfEr7BFFPKgAAIABJREFU0Yi04nbdG4dGtVAoFOTLrUOVCiZcv32PCkZl6Dl2FpFRUTSuZYOJQelU12dXuwpZs0i/sSZ1quB6/Ramhqkr/z2Na9mgoqKCQanivH0vHR9wdr2Ks+tVrJp3+0Hp1KGdPRsaSnV6DhtHw1rVaFi7eqI8AV8CCfgSSNVK0vOyfUs72vYaGpduW1cqY2Sgh4FeSQrkywOAbrHCPH/5GlcPT25438W6kTTBDg0LJ28uHSD2Wd2wTrLy3fd7TIG8ualgKu1UfD37FxwSyqCx07h5xxeFQoUHj57ElbEwNUK3aGEAnC+6JVlvJWtr3NzccHd3Y/DgIbx48QJ3Nze0tbWxtq6Mr68vt2/fpl5dSbbo6Gjyx45BX7l37x4lSpRAN3ZcaN26DatXr4pLb9iwEUqlEqVSSd68eXnzJu1H2ABq1qyFtrY2AAaGhjx58uQHJWT+ZlKchJbVL82+w8eTTFMoFMTEfIs0FBYbJkxVVRX3k/s5e8mNvYePs2zdZk7v25omoZRKdUBygfD176+fo6KjEEWRDq2aM23siERlNZRKFApFmtqLj2HZsuzftzfJNIVCNUGfw2MPC6uqqnLJ/Qrnzp5h/949rFi2lBOnz6SpXaVSCXztszLuuoqKClFRUp/bd3BkyrQZicpqaGj8VJ+XLFpA3nx58fD0IiYmhhzZNOPSsmbNmmLZr2dCvydLlpTLJUVqygiCkOTn+DoJDw9LkEczFfWqx/uuv6dgWWky8t7fh1zFDciWpxDX9y5FPUt2jOq2JSoijLNOI2i35AzZ8xbCbfOsBOEy1TSyANI2aK5iZWiz8GSS7SjUvt7vCsSYlM+PCgpVEL/dg9ERUp81suegw/IL+F87i9ehNQnK5C9hwK1zaTPwKWlWmT7LjuHjdoqd0/pQtXVfKjRo8+OCvxmF2jf9fI3uJooiNR2HUKlp51TV8fVM6PeoayR9fxSKp/fcxQ3IHk/vZeu2jdNn22T0mRri90tQURDzm84HGpYqzv6TaVtJtbEw4fTmxZw4f5keo2cyoKMD7ZpmvO9Dpbpa3N8iX3UNw3u0o1srOzQNqifIb6hfiv3HTidZl6qqKjHxogaFxxufXI/s4KzLZfYfO8XyDds5uSvlc7iJ5Yz9vQpC3N9fP8c9q+3tmDpqcKKyGkr1n3pWL169ibx5cnHt9F5iYmLQKmkel5Yly7fndvwIiPGpbF0Zdzc3bnt7Y2RkRJEiRVgwfx7ZtbTo1KkzoihStmxZXN3c0yzbV+KPWwqF4qfP+P+uer7n0OmLlNYtgkGplBc8MpNTFy8zbNoioqNj6NTSluE9OyTK8+TFa3qNmsH7T5/JqZ2ddXPHUzh/Xi5c9mTE9MVx+XwfPWXTgonY1anKObdrjJ69jJiYGLJm0WT1rDGULFY4I7uWLCkaJtWoYk14RASrN22Pu3brjg8ulz0oVqQQPvcfEh4ezueAL5y7JL2BBQUFE/AlkAa1azBvylhu3fEBIHu2rAQGSWcPtbW0yKGtjctlaXtx6+79VLW2TLXQNatYs+/w8TgDqY+fPvPk2YtUldUvVYLXb95y9UbirTiA6jVqEh4eztp4b3jet27h4nKJosWK4eNzV+rz58+cO3c2ts9BBAQEUL9BQ2bPW4D3LanubNmzERQYKPVZW5ucOXLi4iIZ6mzbuhmbqlVT3ecaNWuxf99e3r6VVkw+fvyY6jdEPX19Xr9+xbWr0lZ7YGAgUVFRBAQEkL9AAVRUVNi2ZfNvPygfn4qVbDi8bzfR0dF8eP+OK24umJqnPtLEi2dPue4hPSAP7N4etypapGgxvL08ATh2aH+y5StXq8nmtZLVc3R0NF8CAn7YZkFDSx5fOYVGthyoKBRoauUkPOgLr3yuUdDQMm7CqaGtQ0RoEA8uJT6PBaBTuBQhnz/w8q70/UdHRfLeP+WztoWNKuFzVjqs/+n5Q768e07OwqXQzleUt363EWNiCHz7gtf3pb6HBnxAjIlBr4pdorpKmVclKjKCywc2xF17+fA2j7zcKGFSCS/n/cRERxP06T2PvNwoYmjOx1dPya6TF6smHbFs7MiL2K1rhaoq0VHJh3DVNa3EnYtHiQgLITw0GO8LRylhUinFvn5FmSUb4SGBP8ynX7EWHke2EB4SBEDAu5cEfnyXqjZSQ0FDSx59p/ewoC+8jNW7TuFShP5AnzkLl+LzK38CXj8FwPfCgR+2q54lGxGhQb8ke3Wr8oRHRrJ21+G4a96+frhcu0Vl83LsOX6O6Oho3n38jMu1W1QwLsOTF6/JlysnXRxs6WTfiBt3pR0RNVVVIiOTnwBUNi/H4TMuhISGERwSyiHnS1Q2T92KebasWQgKDv1hvjo2Fmzcd5yg4MTn92tUrkh4eESCLWjvu764XLlO0cIFuPfAj/DwCD4HfOGsq+R1ISg4hIDAQBrUqsqcCf/j1l3fWHmyEhj8dXzKTg5tLVyuSC/Z2/YepopVhVT1C6CmjRX7jp7m7XvJQOrjpwCePH+ZqrJ6JXV59fY917y8AQgMCpae1YFB5M+bBxUVFbbuPZzss7pOtaR3AypZW3P06BF0dHSks6k6Onz+/JnL7u5YW1ujr6/Pu3fvcHeXnrGRkZHcuXMnQR36+vo8evQIf39/AHbt2vnD/mTPnp3AwB//pjOCw86X8Hnon2RaehnFpoXo6GgGTZrPwdVzuXFsC7uPOOPz8HGifKNmOdGuaX2uHt7I6L6dGT9XMgquZlWeK4c2cOXQBo5vWkwWTSW1baR51YCJc1k/dzxXDm2gVeM6zFy2MUP7lhIproQKgsCeDSsYOnYKc5esRENDSbEihZg3dRxFChXE3q4RplXrU7xoEUyNpLM5gcHBNHfsQVhYOCIicyZJZzMdmtrSe+honFZvZOfapaxbMjfOMKlEsaKsWTw71UIb6pdm0qihNHDoSExMDGpqaiyeOYliRQr9sKy6ujpbVy9m0OhJhIaGJUoXBIGde/YxfOhg5s2djYaGBsWKFWfOvAUUKVKEFvYtMTc1pnhxXUxMzaQ+BwbSsnlTwsPCEEUx7mxmS4fW9O3dg2VOS9i2czer122IM0wqXqIEq9ak/g3cwNCQCZOm0LhBvbg+L1jsRLFixVLV581bdzBk0ADCQkPR0NTk2MnT9OzVhzYO9mzbvJk69er9cPXzV6jfuCnXPS5Tt7I5ggCjJ80gb778+N33TVX5kqX12bh6OcP69qC0vgGOXSVjokEjxzG8Xw/mTJ1AJZtqyZafNGs+/xvYmx2b16NQKJg+34m8+Qokmx8gd3FDQr98oEyNb0cdcusaEBkWhKa2dPzDuEEHNvW0IWvOvOTXM0uyHoWaOo3HrefcslGEB39BjI7CrFkvchcvk2zbJo27cGbxMDb2tEFFoUq9oU6oqispWLYi2vmLsaF7JXSK6pG3lLTNHfT+FSfn90eMt1L/FUEQ6DRjCwcXjeLcloWoKjXImb8oTQbNQNekEv63rzLP0QZBELDtOxmtXPm4emwb57cuQaGqijJLNlqPWw5ARbtOzOtQmUL6JkkaJhXWN6VCw7Ys6lorNn+HVG/Fl7VpwKYxjty5dCzOMCkp9CvW5K2/L0t6SOf3lJpZaTNhFdl18qSqnR+RlN7zxOo9S6zeG49bz9llo4gI/kJMdBTlv9OnmlKT2v3nsHdMS9Q0siZ7b8RHU0uHQoYV2dCjMroWtZM0TPoRgiCwc8kUhs9wYt6a7Wioq1OsUH7mjOpHZXNjrty8g2XTrgiCwLRhPcmfJxdbDpxgwdodqKmpkjWLJmtnSkZeXVo2xqJpF0wN9ZI0TDIrq0f7ZvWp4iAZcXayb5TqrfhG1a1pO2gCR866xBkmJUXtyhbc83tC9TZ9E6UJgsCuNYsYNnEWc5evRUOppFjhgsydOJIiBQvQwrYeZrWaUrxoIUzLSroJDArGvkt/wsLDEUWYPUHaTXNoUp/eIyaydN1Wtq+cz9qF0+MMk3SLFWH1vCmp6heAgV5JJo3oT6O2PeKe1YumjqFY4YI/LKuursbWZXMYPG4GoWFhaGpocHzHGno6tqZ1j0Fs3XOIutVt4o5AfE+Xti3oOzLxfWNsbMz79+9p06Zt3DUjY2OCgoLInTs3ALt272HQwAEEBAQQFRXFgIGDKFu2bFx+TU1NnJYuo2GD+mTNmpUKFX68iJArVy6sK1emnLER9es3SNIw6WfZfvAkSzftITIyEgsTQxZNHCoZ6prWoa+jPcfPuaGhoWT38pk8evqCo2dduHTVi1nLNrLdaRq9Rs+gnEFp3K/fomWj2pgYlGbUrKVERUdjblyGxZOGoVRXR7+GPS0a1OTUxctoKJVsnD+BvLl0sLDriPfJ7aipqfIlKBhLu05xn3+Gq7d8KFmsMLpFpXlMy0a1OeLskmjl9t5Df2aNks7JVrMqj0OfUYnq2n/iHHWrWpFFUwOQfitfYl+yvgQGUyBv7p+SMT0Q4i/fC4IgRr5Nuw/Qvxm1vCUIjUw8cMt8Q1NNhWcBya9+pTfPnvjTqVVTzlxO/eH2tFJEW40hJ9Pu2uVPZH69XMx1+5zZYvwVDLPOwdB00ntEaBDqmtkQRZEzTsPJWagk5s17/9Y25tXLRajP+d9a55+KpkF1wp//mlun/wLKwkZExyS9Lf+rBAUFkS2bdE/369uX0qVLM2hw4mMHvxuFipDAOv7eQ3/GzFnGDqfpqKmpMnDiXCxNytKuWQM09WzYs2ImjWraMHr2MrSyZWFkn050/980GtSwpnl9ycCtbvt+GJQqzqKJwwgLD8eoThuOb1xIad2idB0+BdOy+vTv5IB+DXu6ODTmf707snX/cfYeP8e+VbPpMXI6trVssKtTlbU7DnL/8dO4yeFXth86xcI1ib1YlChWmO1LEvrQ3XfiHKcvXmH5dMkAbtuBE3jcvMvCCQltHzoOmYiFiSH9Ojpw4OQF2vQfw/MrR8mVUzsuT33HAQzo3IqGNaSVcZerN2nVdxQaSiVa2bJyYfdKtLIlXHTS1LNBFMWE594yADlsp4yMjEw64H18M3dO7yA6KoK8JctRrmHHzBZJRuaXWLN6NZs2bSQiIgJTMzN69Ezs3i4jOOd+Hc87vti0kAzWQsPDyaOTE5C8F3ydfJmV1ees69Vk67FvKO3Y3H/0lOKFC1BatygA7Zs1YMXWffTvJBkPOzSSjKUdbOswYobkBaNTS1sWrNmGXZ2qbNp3jGVTExuFtrGrSxu7ur+jy3HM+F8/Bk+ez5Z9x6lsYULBfHlQKL6drHz19j13fB9Rx6Zi3LUlG3ayf/UcLE3KMn/NNv43fUncZDez+acnofYde/H4acJIITPG/Y+6NVN/FvNvw8G+OU8eJzxHMnXGTOrUzXhDg7Ty6eMHWifxg91x6FS6roLKpJ3ggI+sHJD4/GnPxYfIqq2TYlmPI1tw2b0iwbXixlY0H5Y44kpGyJNemDfv/dMrn+8e3+X47IRlFWrqtFuctCFOevLhUwANuyT2RHFs3fwEqy9JsWnfcZZuTuiIvJKZMQvHD/qtMv6NtOw6AP/vbBmmjR5C3eopuxrLTAYNHvzTK5/e3t50dExoaKNUKnG/fCWZEskjiiLtmzZgyrBeidLU1FTjGa6qEJWCrUMWzaSPNXxPfMNYAelva/NyDJo4j4tXPImOjqGsXolE5dKyElowXx6ev/7mJePF63cUypf4iFHBfLnZuVTycRwUHMKBkxfIoZU9Ln3v8bPY1akSdyzg3cdPeN97iKWJdLTCvmFNmnQdlqp+ZwT/9CR0z8ZvA93BY6coXVIXQ/2fcx/yt7Brz74f5jl08AClS+thYJg2H3vpTU6dXEla3KeWE0cOUqJUafTK/Fn9Si8euh0lZ6GS5CqW/NnS9CKrtk6K/kJTwtK2PZa27dNc7vaFI+QuWor8uon7+yvy/Ink0TXEcfkFHrgdRSeTdPyVXDm1U/QXmhKOzRvg2LxBqvMfcr5E6eJFMChV/Kfa+5vYvXYxB0+cQa9EcQz0Sma2OOmOsbFxiv5CDxw4gJ6eHoapGJdqWJvTsvco+nduRd5cOfn4+QuBwSEUK5Q/2TKSMVzSIXv1ShTlyYtX+D15Tslihdl28CRVLL+57tt97AzDe3Zgz7EzVDT7dk62bdP6dBw6iVF9OiVZb1pWQisYl+Gh/zP8n72kYL487D7qzIb5ExLle//xMzo5tFBRUWHOys10tG+UIH3XEWemDP02Oc+plZ0vgcE8ePyU0rpFOet6Df2SP7YlySh+GDv+XyEzwob+qaQUTvRPsBL8WU4ePciDez5Jpv3N/UqOh27H+PD0/o8z/iPcvnSUN4+TNmSL/gf1C+CXgo4zOxxsenD4jAs+fv6ZLUaGkVK40H/xmZUSBw8e4O7d1IW5Niily4RB3WnceTAWjTti23kQr38QTrxlo1osWLMdqyadefQ04Qq0hlLJqhmjaTdgHBVsHVFREejepmlc+ucvgVg07ojTxt3MHj0g7npru7p8DgjEwfbnwprGR1VVlQXjh9C46xBMG7SjRcOaGJaWVlcnL1rDkTPSS/ZFjxuUq9cW47qtefv+E//r7RhXx5Pnr3j+6m2CCbSqqipLp46gTf+xWDbuyLaDJ5gxos8vy/u7+GsNk1IK29m/RyeOnjqLpqYG+zauxM//KU3bd0NbKzta2bOza90yegweiYmRAUtWbfjrDZNSCufZt/8Ajh89ioamJrv3HeCRnx8tmjZGS1sbbS1ttu/aQ68e3ShnYoK7qystW7fGxMSUUSOGExUVhXmFCmzauCFTDJP27dzKuhVOREZGYGZuybT5TigUCvQL5qBLr/6cOXkUDQ1N1m7fx5PHfnRyaIqWljbZtbVYuWkXw/v3oKyxCR7urjSxb01ZYxOmjh1BVHQUJuUrMH3+UpRKJZWMS/H86ZNMN0y6e2YXNw6sIiYqkvxlzKnVbw4qCgVLmhTFrGkPHl05hapSgyYTtxDw0p/949ugzKqFMqsWjcdt4NSCgeQpYcSNAyv/CsOk6yd24rJ7JdFRERQ1rEDzYfNQUSgYXasQVVr24q7bCdSUmnSetY0PLx6zdlgrNLJpoZlNC8dpm9k1ox+FShvz+NZlTGu3oJBeOQ4vGUtMdDRFDMxoMXw+qupKpjU3xqRWM+65O6Om1KDdpDVky5mHeY6V+fTqaboZJiXFVx1HR0VSIJ6OFzcpSvnvdPz5pT8HxrdBPVbHduM2cHLBQPKWMOLFncuUqd6CC6vH/9GGSdsPnWLpln2SBXM5QxaNHyQ9m8zr07eDPcfPu6OhVGf30mk8evaSFr1HoZUtK9rZs7J90WR6jZ1NuTKlcPf0xvPO/T/eMCmlcJ39urbnmPMFNDU02LNuMY+ePKNZx75oaWVHO3s2dqxaQK9h4ylXtgxuVz1xaNIQk7JlGDllLlFR0VQwLcuS6eNRKtXRs6pLC9t6nDzngqaGko1Os8mXJxfmdZrz5NmLdDNMSgtbtmzBacliIiIisLSsyNJl0riklT0bAwYM5OjRI2hqarL/wEH8/Pywa2yLtrY22tra7N6zl+7dumJiYoqrqwvXr1/PtLCd+jXscd27htw6iaN/7TtxjiPOLqybOy4TJPu9ZJZh0l+5Eupz/2Fc2M7r546iUKiwbY/kjDs4JISK5mZ4nj9GFStL1m7ZibWlObb1ajNzwkiunztKSV1pKToiIvMsvn8X93x84sJ5Xrl+A4VCwY5tUnCA4OBgLCta4eHphU2VKqxfu5pK1tY0srVj+szZXLl+gxIlpW2gyIgIXK9cpVfvvnTv2pnN23ZwzesWUVHp5zs0JR74+nB43272n7rISZfrqCgU7N8lna0JCQ6mvEVFTrl6UtG6Cts2rqVCRWvqNLRlzJSZnHS5TvESUr8iIiI4duEKHbv3ZkifrizbsA1ndy+io6Li/Ib+CXx46sv9CwdoveA4HZZfQEVFhXtnJf+HkWHBFDCogOOKixQ2tsb7+CYKlrWkZKX6VO0+iQ7LL5CjoOTGIyYqIjO7kWre+PvidWYf/VaeZMhGFylm/CkpDGFEaDBFjSowdJMrJUytuXJwI8WNK1K2SgNs+05hyEYXcheW+hsVGcmgdeep3KI7O6b2ocOU9Qzb4kZMdBRu8baQNbJqMWyLG5Xtu3Nw4Sg0smanpJlNhvb5w1NffGN17Lj8AoKKCj5J6LhQrI4Lxeq4WvdJOMbTcXRUBO2dzmJq1zVD5U8r9/yesOf4Oc5tdeLK/rUoVFTYcdgZgOCQMCxNDPE4sBabCias332ESmZGNKphzfThvbiyfy0lYl3VREZG4bpnVUpN/RH4PPCLC9d59dReFAoVtu+X4qoHh4RiWd6Ea6f3YVPRnHXb9lKpghm2dWswc8xQrp7aS8niklFMRGQk7sd20btjG7oPHsOW5XPxPLOfqKhoVm7+5ptTWysbnmf207tzG4ZNnEn2bFmpVin1vpfTEx8fH3bt2sklF1c8b0gR+bZu/TYuVbSy4obXTapUqcqa1auxtramsZ0ds2bPwfOGFyVLfnt+e1y9lpldSZbBkxcwbu4KRvWVDQ5/hb/yTOjZS27Jhu1UV1ePCxVa3sQI5wvJvz05NLVl5Ya0RXP60zh39kyy4TzV1dXjQoialTfnrHPyRg32Dq0AKcRn8eK6lNaT4k+3d3Rk29bN6dmFJHG9cJZbXp7Y1rACICw0jNx5vvWrdn3pHIyxaXkunXNOtp7GzSXrxkcPfClSrDglSkn9sm/jyMbVy+nWJ3kfhRnJ0xsXefPAi239pW2dqIhQNHNIh9IVauqUqCgZluUrZcKTG+eTrUevWjNuHlmf7vL+Kg+uXeCF700WdZXcpUSGh5Etp+S7TqGmjmFlKfRpYX1T7l89l2w9prWl0Kjvnj5Ap0BR8hQtBUCFhm1x3buaqq2kbSezOvZx/x9aJPnBrGjnyLVjiY0G0ouvOt4aT8dZfkLH+rHhYD8+e5i+Av8i5y5fx/POfWwcJAvq0LAI8uSSVpPU1dRoWF0KYmBWVo+zbslPNOwb1Eg27U/inMuVZMN1qqur0ai25Me4fDlDzlxMPjJRy8bSvX//0WOKFy2MXoniALRv2YQVG7YzoJtk3OPQpCEArZo0ZPhEya9u5zbN2bTrx4ER0puzZ87gef06FS2lSXFoaCh5441LtrbSuFTe3Bzn08mPSw6tWqW/sD/A99yeJK8vGJ/+rqn+C/yVk9CUwnaqqX5nGZfCSl6WZBz+/k2kFM5TTU0t3neh+MF3kX6O6n8GURRp2aYDIydOS5Sm+n2/UjgblyVLlnST8fciYlinNVW6jE+UoqL41l9BoUjxLODXUKF/PKJIhQZtaNg78cF7hWq8/qqk3N/kQm8mIv4mU2zduuWsUi3u70BEpGwqdKyiUCD+AzoWRWjftB5ThvRIlKampki9BXMWjXST8XeSUrjOBOOSiiLF/ibniP57Elhsx/5tbVE+LSKnG6Io4ujYkekzUjEupXCvp2cAFZk/g79yO/5nwnbGDxv6L/Ez4TzjhxP9Hj19fZ488cfvobTKsm3Llt8rcCqpXK0mRw/u4/07qV+fPn7k+dMf9CtbdoKCku5XidL6PH/6hMd+Ur/27tyClU2V3yv0L1DUtCoPLh0m5LMUfjL0yye+vHmWYhl1zWxEpCLM5Z9IqQrVuHXuYFy4zZAvn/j46mmKZVIK65mnaGk+vX7G++fSmfbrJ3ZQ0vSbq5ubzlJIVy/nfRQzypwty2KmVbmfRh2rpaBjnSKlfruMv5MaVuXZf/ICbz98AuDj5y88efE6xTKpDef5J/Iz4Trjhwv9Hr0Sujx59oKHj6XfxffhQ/ccOgHA7kMnqGieuqhkGUXNWrXYu3dPmsal7Nn+nBCfqaVu+35c9045BPPvJDAohIp2neL+FbZsxLBpiwDYvO8YRSraxqWtjxe690/mr5yExg/baVatAfVbOvLqzdsUyzg0tWX+0tVUqGmL3+PUxVz/G4gfztPCzATb+nV5/epVimVaOrRmwfy5WFUozyO/hJaZGhoarFqzjnatHahgWg4VlQw/pwyAXhlDho+dRLtmDahjbUa7pvV5+yblftm1cGDl4vnUt6mA/6PE/Zq3dA29O7amdiVTVFRUaN8lcxwtJ0WuYmWo3HE0e0fZs6lXFfaOakHQx5QHbP3qzbm2x4nNfarz+WXiGMN/Mvl1y1C/x1hWD27GvA7WrBzYlMAPb1IsY1q7Bee3LWF+xyq8f56wv2pKDVqNWcqmMR2Z294aQVChUrMucekhgZ+Z18GaS7tW0GRg4tWZjOCrjveMsmdjryrsSYWOy1RvztU9TmxKQseq6n/2CqFBqeJMGNiVxt2GYdGkC7Zdh/H6XcpGYC0b1mTBuh1YNe+WyIL5Tyd+uE7z2s1o2LY7r9+8S7GMQ5P6zF+xHst69vj5J3wJ09BQsmr+VNr2GkL5Ws1QURHo0eHb9vSngC+Y126G07otzJmQ2FF6ZmJoaMjkKVOpX68upiblqFe3Dq9+MC61at2aeXPnYF7eDD+/pD0G/NfJni1LXHz4K4c2ULRQPprW/RauukXDmnFpnR0aZ6KkqeevtY7/XchhO39MZoftTG/+FOv434UctjMh05obM2jdebLmyJUoLT3DdmYEctjO/yZ6VnVxO7aT3LFRguKTnmE7M4vvw3YmR3BIKO0HjufF67dEx8Qwsk8nWjaqxXSn9Rw960pYeDhWZkY4TRmBIAjUbd8PE0M93K7dJDgkjDWzxzJn5Wbu3H+EfcOaTBzcgyfPX2HXdShmRvp43bmPQWld1s4eSxZNDeq278eM//XD3LgMzi4eTFm8loiISHSLFmTVjNFky5qFsXOWc/SsK6qqCmpVtmDmyH6/5Tt58PgpDToO4sGFvQiCwOZ9x7jufS9RmM/UIoftlJGRkclA9s8fntkiyMj8VgaNnZ7ZImQqpy5doUDe3OxfPQeAgMCDR1mwAAAgAElEQVQgAHq1b8Hofp0B6DJsCsfOudKopuQdQ11NFdd9a3HauIuWfUbitm8tOjm0MKzViv6dYg12Hz9l+fSRWJuXo+eo6azcto/BXdvGtfv+42dmLtvIsQ0LyZpFk7mrtrB4/U56tmvOIeeL3DyxDUEQ+Pwl8XGDC5c9GTF9caLrmpoanN+ZvAeX3UfPYN+wZoKzwQdPXcD12k1KFS/C7NH9KVIgX1q/wgznr9yOl5H5nbh7/9lWxjK/xph93kmugjYbMicTpJGR+XXuXz6V5CrowqmjM0GaPwcjvRKccbvKmDnLcLl6E+3s2QC4cMWTKvbdqWDryPnL17n74NvRlq+TUSO9khiW0qVA3two1dXRLVKQ56+kY36FC+TF2rwcAG3s6uF2zTtBux4373DvoT81W/emol0ntu4/wdOXr9HOnhUNdXV6jZ7BgZMXyKKR+AhNNavyCbbYv/5LaQIK0iQ0vpP8hjUqc+/cbq4e3kityhXo/r/ERr1/IvJKqIyMjIyMjMxfT2ndorjvX8fJC+5MWriaGpXMGdK9LYMmzsNl3xqKFMjH1MVrCQ//5k9Zqa4OgIqKEPf3189fvRjEX22UPidsVxRFalauwKYFkxLJdGnvas65XWf/yXOs2LqXE5sSrnr+zEroLZ8HREVHUd7oWzjfXDm14/7u3LIxY2YvT7Lsn4Y8CZWRkfnPsaxvIxr3m5rZYsjI/DR17Dsxc9wwzE2MMluUP4aXb96jkyM7bZrUQ1srG+t3HSEsdsKZO2cOgoJD2H/yPM3qVU9Tvc9evuHyjdtYmRmx8/DpuFXRr1ialmXQpPlxseeDQ0J5+eYdBfLmJiQsnPrVK1HJ3BjDWg6J6v66EpoWdh1xxqFRnQTXXr19T4G8kq/lI2dc/qj48CkhT0JlZGRkZGRk/nru3Pdj9OxlqAgCqqqqLJ40jBxa2ens0BjzRh2k0KbGBmmuV0+3KCu37qPXqBmUKVWcHm2bJUjPo5OT1TPH4Dh4IhGRkhHvhEHdyZY1Cy17jyI8IgJRFJk1sv9v6efe42c5sHpugmvLNu3h6FkXVBUKcubQYvXMMb+lrfTmr5+EBgeH0KZ7P56/fE1MTAyjh/TDoaktU+cu5sipM4SFhWNlUZ7lc6chCAK1mrbB1NgQl8tXCQn5O/3RBQcH075NK148f050TDQjR4+lpUMrpk+dzNEjRwgLC8XKqhJOy1dKFoC1amBiaoqbiwvBIcGsWbeRObNncue2N/YtHZg4eSpP/P2xs22AmZk5Xl6eGBiWZe36jX+Es/eQ4GB6d2rDq5fPiY6OYeDw0di1cGDhrKmcPn6EsLAwKlhaMXPRcgRBoGWjWpQtZ4qHmwuhISEsWLGOpfNnc+/ubRo3b8mIcZN59sSfDi1sMTY14/ZNr8zuYgIiw4I5Mq0rge9eIsZEY9V2GPrVm+G+ZQ6PrpwgKjyMgoaW1B44H0EQ2DXcjrwljXlx253IsJDMFv+nCA8NZvPYzgS8e0FMdAx1Og/HtHZzTq2bxV2XE0SGh1Hc2BL7/y1EEASW9W1EIb1yPL7pTkRoCK3HreDs5vm89ruLSa3mNOg5lo+vnrB6sD2Fy5jywvcm+XTL0Gb8CtT/AGfvkWHBHJ7WlaB3L4mJ1XGZWB37xdNxnVgd7/xOxw2GL+PKzoWZ3Y0UCQ4Jpf3gSbx4847o6GhG9nakZcOaTF+6kaPn3aRns5kRTpOGSs8px4GYGJTG7fotgkPDWDNzNHNWbZUslf+CqEnBISG07TWUF6/eEB0Tw+iBPWlp14BpC5Zz1Pk8oWHhWJmbsmzWBARBoI59J0yMDHD1uE5wSCjrFk5nttMabt97QEu7+kwaMQD/Zy9o3L4X5Y0NuXH7LoZ6pVi3aDpZNBM6tD99wZUp85YRHpF5YXuDg4Np1cpBGpeioxkzdhytWrViyuTJHDlymNDQUCpVsmbFSmlcqlmjOqamZri4XCI4OJgNGzcxc+YMbnt74+DQiilTp+Lv70/DBvVTLUOdKhWpU6ViousTB/dg4uDEQRNObXGK+7tqxfJUrVg+UdqT569QVVWwfm7iIBPxy1evZI7rvjWJ8rjsXZ1q+VPL13C/8ZkyrBdThvX67W2lN3+9YdLJsxcokD8fnueP4XXxBPVqVgWgT1dHLp86iNfFE4SGhnH01Nm4Mupqalw5fYjuHdsmV+0fzamTJyhQoAAenl5c9/Kmbj3pR9qrTz9cL3tw3cub0NAwjh09EldGXV0d1ytX6da9Jy1bNGXhYieue3mzedNGPnyQXNTc9/WlR6/eeHnfRSt7dlauWJYp/fue884nyZe/AKdcPTlz2YvqtaXwhh279+Ho+cucuexFWFgozieOxpVRV1Pn2IUrtO/Sna5tWzB13mKcL3uxe9smPn2U+uv3wBfHbr04d9U7yXYzi8dXz5BVJz+OKy7ScZUrxS1qAWBq1412S87QcZUrURGhPLpyMq6Miqoa7ZzOUq5R58wS+5fwveyMdu78DN3kyvCt7uhbSX22se/BoHXnGL7Vncjw/7d333FVlX8Axz8HuO6BiAoqigsURHGmZuXK3AP3yDTTbJnmKjO1cvQrU7OstJzl3mauVMS999bcCwUUAQWBzu+Pc1leB5e74ft+vXwVcM/5Pl+ec+/5cs55nucRp3auT97G2SUbA2ZupXabXsz+tCtBgyYw+M/dHFg7n5jICEBbzrNOUG+GLthHjtx52bV8xlPjW9ul/ZvJo+/jntN3UipVH3f/cTM9n9LHzi46uv+0hcrNe7FydHcafvCtrZqfLht37MOzcEH2rZzBwb9m0/gVbWnhft3asnPJNA7+NZtHsXGs3ZqyhGU2nY6dS6fzTqdWdPjgcyZ/MYCDf83ij5XrnxXGbmwM3knRIoU58M9yDm9eSeN62oCX93p2Zdffizi8eSWxsbH8vSkkeZtsOh271y6mT/eOtO/dnx/GjuDw5pX8sXgV4fe0adbO/XuJd9/qxLGtf5Evb25+nbMwTdywiHt8M2U66xb+xt71hsWJtaxfv56inkU5fOQox46foEkT7bz0wYcfsnfffo4dP8Gj2EesWZP2vLRv/wHefbcfbdu05qefpnLs+AnmzJmdfF46e/asTfIR1uHwRWhFP182h+zgs6++YceefeTPlw+ArTt2U6dJWwJfa8LWHbs5dfZc8jYt3tBGlAVU8LVJm01VsWIAmzdv4vPPhrFjx3by59ceSA7ZGswrdWpRPbASW7du4dSpk8nbNG/RSts2IAA/P388PT3Jnj07pUqV5vo1bdWW4l5e1HlZW2WmS7fu7Nq508qZPV15/4ps37qZcSM/Y++uHeTT57t7+1ZaNqhDo9qB7Ny2lXOnTyVv83ozbW3i8n4B+Jb3o4iHlm8J71LcvK7lW7S4FzVqvWwY0MYKlfLj6uGtbPt9NNeP7yZ7bu2YvnZ0O/P7v86cd+ty9ch2wq+krNRRpnZTANxLGX+ryR54lvHn3P5g1kwdxcUju8iZR+vjCwe38cM7DZnQvQ4XDm4n9GJKzv6vaDl7lPGjSKny5HP3wCVbdtyKluR+6HUAXIsUT16es+obHbl09NlrdluTeyk/rjyjj+fp+/jake2EPaOPC5YsT56CHjZpe3pVLFeazbsO8PmEaew4cCxlpPK+w7zS6T2qt+rF1r2HOXUh9UjlOtq2PqXxK+uNZ+GC2kjl4kVtkoMx/CuUY/P23QwfO5Edew+SP19eAEJ27aNuiy5UbdiWrTv3cfpsymwcLRrXA6BiBR8q+JTBs0ghsmfPRqmSxbl+U1vIwKuoR/JynF2CWrJr/+E0cfceOsrpc/9Sr82b1GjczgqZPl1AQACbNv3Dp8OGsX17ynkpODiY2rVeonKlAIK3bOHUyZTzUstWqc5L/innpdKlS3NNf17y8vKyfjKplCzuycG//7BpGzIzhy9CfcqUZt+mv6hYoTwjx09kzIQpxMbG8dGwkSyaMZUjIevp3b0TsbFxydtkz540Gs4x0y/n48PufQepWDGAL0d+wbgxXxEbG8uAjz5g/qIlHDhyjLd7v0NcbGzyNtmzZwe0nJP+P+nrhARt7V7DEYC2WS3pSaXL+rA2ZB/l/Svy3ZiRTP7fGGJjY/l80EdMm7uITbuP0PWt3sTFpeSbLVtKvtnSma+9KFC8LN1+Csa9lB8754xj95/fkfA4li0/DaXliNm8NW0HAU17kPA45Zh21mnHtKI45jFdqERZBs7ehmcZP9ZPH8PGmf8jPi6W5RMG89bYOQz+cxcvtepB/OOUPnbRaf2qODkl/3/S1/89Y21ue+lzt+Jl6f6UPt78RB8nPqOPU+drr8qV8mL3st+o6FOKL3+Ywbipc4iNi2PAV5OZP/lLDqyexdsdmj8xUlkHJI1U1iV/31YrtxnDp7Q3e9YtoWL5coz+9kfGTvqF2Ng4+n/+NQumT+TQ5hW83bUdsXGpzkVJI7OVJ0ZmK8qzP5efiKuq0PDV2uzfuIz9G5dZJrl08PHx4cDBQ1QMCGDkFyP4+ivtvPThB++zeMlSjh47zjvv9CHWxPOSyFwc84yVys3boeTKmZNuHdow6IM+HD52MvlN7u7mRnR0DMvX2P+tHGPcvHmTXLly0aVbdwYOGszhw4eT39ju7u5ER0ezYrnxH0bXrl5lz27tStGiBfOTr4ra2u1bN8mZKxdBnbrRr/8gjh89nFxgFyjoTkx0NH+vWm70fm9cu8rBffZxZSy16PBb6HLkxK9hR2q0/5A7F44mF5w58rvx+FE057evtnErzSvy7i102XNSrUkn6nXrz42zR0nQF5y5XQsS9zCaY8HG53w/9DqXj+8D4PA/S/HWXxW1tdR9XL39h4Sm6uOc+j4+5+B9fPNOGLlyZqdLq8YMfLsTh0+fSzVSOb9+pHLIC/biOG7evkOunDno2q4lA9/ryeETp1PORQUKEB3zkOVr/zF6v1dv3GLPQe259UUr/qZOzappfv5S1Urs3n84eY15W0k6L3Xv3p1Bg4dw6PAhg/PSsmVLjd7v1avmycs98PUXv8jMrly/RYGABrzUqmea7ycmJlKrdS+C+g412OaTryenq637j55KXie+Zsu3WLUx5b307mfjKFGrBdWav5lmm8/+NxXvOq2YNGN+xhKyAIcfmHTi1FmGfTkeJycndDoXfvr2a1zz56P3m50JfLUJRQoXolpggK2baVYnTxxn+LChODk54aLTMeWnn3F1daVX73eoFhhAkSIeVKtWw+j9+vj6Mu2Xn+nXtzflK/jR9933LNB64505eYKxI4dp+broGDfxJ/K7utLlrd40qhVI4SJFqFy1mtH7LVPOlzm//cLgDwwfWLelsEun2fb7KBTFCScXFxp+NIEcefIT0PRN5r5bl9wFCuPhU8XWzTSr2/+eYs3UL1CcnHBy1tFuyERy5nXlpVZvMaFbbfIWLIJXBeNzLlSiHLuW/c7icR9SpJQvdYJ6W6D1xgu7dJqQVH3cKFUfz8kkfXzy3EWGf/crTk76kcqjBmojlds3p1qrXhRxd6NaQPkX78hBnDhzns/GTEg+F/047gtc8+fj7a7tqdKoDR6F3KmegemUfMqU4tfZC+g76AsqlCvDuz06pfl5oYJu/DZpLD0+HJLmqrK1HT9+nGFDh+jz1zH1519wdXXlnXf6UCmgIh4eHlSvYfx5ydfX16GfCy1dopjBFEw/zVmCb5mSREWnHUh68PgZ7kcarqr0NP4+pdm5/HdcXFy4dSeMl1r1pHmDl3FxceHNoGb0696Od4amnYZu/LAPyJ3TcMJ8W5K142XteACuXL5MUJuWHDxiOEgnM64df+3KZXp2asPmPdoVBq/8Olk7PpOJuHWFGYM7M2Tes692y9rxjiMrrh1/+doN2vb8gMObV6Z7m8y0dvzly5dp1bIFJ0+eTLN2/IjvfqG4Z2H6ddeegR0zZQa5c+ekT+c2dHjvM+4/iCI+IYFRA/rQstErgHYlNOzIP2zbe4jJMxayfLo2sG/AlxOpFlCeN4OacejEGYaN/4mYhw8pWMCV6d8MT557MyOuXL9F0LtD0zxTev32HfoMHcuw93owZdai5HYkJibSrOcAZn8/ioDGXQg7kv6r5pev3eS1ju/y7/YVuLi4PDN26t9V6mVHQdaOF0IIIYR4ofbNGzJk7JTkInTZumBWz/yeHNmzsejnceTLk5uwiPu81vFdWjSsm67nSuPjE/jk68ks+WU8hdwKsOTvzYyeNJ1p49Muhbpg9UYm/254O7t0yeIs+PHFC2AMGTuFsUPfIzom7VXQX/5cRvMGdY0qevcdPUm/z8Zz9WYoM74dkVyAOhLHa7GwiJLe3k+9CppZeZX0Tr4KKjInN8+Sz70KKoS98/YqZtRV0MzG29ubY8dP4PzEwLRAPx/uht/jZmgYYRH3cM2fFy/PIsTHJzDy+2nsPHAUJ0XhZuhdQsMi8ChU8IWxzl26yqlzF2nRcyAAif/999TturRqTJdWjTOUz9rgnRQu6ErViuXZtvdQ8vdvhoaxfF0wG//80aj91azsz6G1f3LmwmXeGTaWN16rRY7s9j9oMTUpQoUQQgjhUIKa1mfFhmBC70bQvlkDABb+tZGwiPvsWj4Dnc4F3/rtDZ6TdXZ25r//Uh7BS/q5qqpUKFeKkMXTnhvXlCuhuw8eZ83mnawP2UNc3GMeRMfQa/BXdGzeiItXb+D/emcAHj6Kxb9RJ05uWvTiXwRQvqw3eXLn5OS5Sw73nLXDj45/kqt31lpH1901r62bYFW+RV1t3QS78WPrErZugsUNb1jM1k2wuilZoF/dq6V/FRxH4uZj/MAbU12+doP8ZarZdI7QJ+XTzwlrSe2bNWDJ35tZsSGYoCbailqRUdEUKlgAnc6FkD2HuHrjtsF2JYp5cPrfy8Q9fsz9B1EE7zkIaEtzhkXcZ89h7bnj+PgETp03HCPTpVVj9q6ebfAvPbfivx7cj3+3r+Bs8FLmThpNvVrVmDVhJE3r1+HyrtWcDV7K2eCl5MqZI7kAXbUxhC8m/Gqwr8vXbiZPY3Xlxm3OXrxCyWL2PXfw08iVUCGEEMKBlfb2sukcobbgV6400TEPKVqkUPJzlJ1bNqZdv2FUb9GDqhXL41u6pMF2Xp5FaNe0AdWa98C7uCeVK5QDIFs2HfOnjGHQmMk8iIomITGRD9/qiF+50lbN60kXr90gX57cBt/fdfAYE6b/ic7FBScnJ34YNQh3N+0iTY+Bo9i+7whh9+5T5pW2fNG/Nz07tLB209PFrq+EDv/6f/w8Y27y1199O5mJU38jOjqGxu26UaNhSwJfa8LqdYajyEJ27qF1t5TpWPp/Ooo5C7U5yg4ePU6D1p2p2aiV5ZMwwojhn/Lrz1OTvx7z1WgmTZxAdHQ0TRs3onaNalQPrMRfq1cZbLstZCtBrVsmfz2g/4f8MWc2AIcOHuT1BvWoU7M6LZs14datW5ZOJV3GjxrO7N9SlgadOP4rfp0ykZjoaDq3bEzTV2rQqHYgG/42nC9x9/YQenZsnfz1iMH9WTxvDgDHDh+kfbMGNHu1Jt3aNiP0tn3k+zzbZ3zJkdUp6w7v+uN/HFjyE48fRbNkWBv+/KA+c96ty4Vdaw22vXZ0Byu+6GLN5mbI3z+PZmeqdZQ3/D6erfN/JO5hNL9+1IpJPV9lQvc6nNj2t8G2Fw5tZ8bglKlpln8/hP1/zwPg+pkj/Px+Myb1eo3pA4J4EGZ49cNWts34ksNP9Ov+VP36hxH9uvmnoZzYqN0GDD1/hEWDWxpsYwsjvp/Gr/NWJH895qdZTJq5kOiYhzTt9Qm1g/pQvVUv/tq8w2DbbfsOE9Tv0+SvB3w9mT9WrAPg0MmzvP7mx9Rp15eW7wyxfCJP+HzcJH6ZvSD566+/n8rEX2cRHfOQNzr15qUmHajasC2rN2wx2DZk1z7avPV+8tcffz6WuYu1ZzsPHTtJo3Y9qdW0I8279eVW6F3LJ2OCzz79lJ+nppyXvhw9mu8naOel1xs1pHq1qlSuFMCqVYbnpa1bt9KyZUrx89GHHzJ79mwADh48SP16r1GjejWaNHkjQ+elA2vmsuGPlOco3d1cCVk8jQNr5jL9m+EcWT+PksU9AdKMNh839H1O/LOQNbMmsWjqON4MagZAZb9ybJo/lX1/zeHQ2j95u5PlaoRXX6qaPDL+Sanbeuz0BXp2NCwgu7ZpwqG1f7J39Wx2r5xJq9dfTf7Z3ElfcmnnKqJOhfDv9hV2W4CCnRehHdq0YOnqlA/npavX0qFNc3LkyM7S2b+yf/NfbFo+n6GjxpF6qqnniY+PZ8BnX7JoxlT2bbKvyaDbd+jEsqUpa/8uW7qE9h06kSNHDhYtXc7u/QdZv2kLnw4dbFS+nwzoz/xFS9i17wA9evZi9BefG7xuwfx5vFStisG/Lp06mC2/J7UM6sCaFSmTF69ZsZRWQR3IniMHv81byrrt+1m8ZhNffz7UqHxHDh3AtLmLWLttH5269+Tbr78weN2KxfN5o2615H+25vtaW85uS/kQP7dtJb6vtcUlWw5ajZxL96nBdPx2FSHTR6b7d2FvAhu25ejmlGLl6JaVBDbUcuz5zZ8MnL2N9376i79+HJHuHBMT4lkxcSg9xs5l4KwQarbozrppXxu87tCGxUx8q67BP0vzfa0t51L169kn+vXNqcF0MLJfExPi2Tz1U1qOmGWpZhulfdMGLFu/NfnrZeu30r5pA22k8o9fs3v5b6yfM4lPv/3ZiPdxAp+MmcL8H75k17Lp9AhqaqHWP1uHVk1Y9teG5K+XrtlAh1ZNyJE9G0t+/4G965ewcclMhn31nVGfTwO/GMeC6RPZs24xPTu1ZdS3Pxi8bsHyNdRo3M7gX+e+A82WX3p17NSJJUsWJ3+9ZMliOnbSzkvLlq/gwMFDbN4SzJDBg4z6PXzc/yMWL1nK/gMH6dXrbUZ8bnhemjdvHlWrBJotF2tzcnYiMirGYLJ6Y8yaMJJCbgXM0p7P/jeVBas3kjtnTrPszxzs+nZ8lQB/7oaFc/N2KHfDInDNnx+vYkWJj49nxNgJbN+9DycnJ27cvk3onTA8ihR64T7PXrjIyTPnaNKhhxUyME5glSrcvXuHmzdvEnb3Lq6uBfDy8tIKqxHD2bl9O05OTty8cYPQ0FA8PF78/Me5s2c5dfIELZpoo/kSExPx8PQ0eF2Xrt3o0rXbU/eRU2eZv1UqVq5C+N273L51k4iwu+R3daVocS3f/301gr27tHxv37rB3TuhFC7y4nz/PX+Ws6dP0rWN9sxZYmIihYsY5tu2Y1fadkyZJ80rv87gNdZUuGwlHt2/S3T4LR7dDydHHlfyFi5GYkI8O2aN4caJ3SiKE9Hht3h47w653YrYtL0ZUcy3MtH3woi8e4uY+2HkyuuKa5HiJCbEs/bXr7l0ZCeKkxORd28RFXGHfAVfnOOdK+e5ffE00we0AeC/xP+eul3VNzpS9Y2OBt8fXMeyzxgXKVuJh/p+fajv13yp+vV6Bvr13vULhF85zdLP7OMZwEC/ctyNuMfNO2GERdzHNV9evDwLayOVJ/3GzgPHcHJSuBkaZtxI5fOXaNF7EACJidafyzmwYgXuhIdz8/YdwiIiKJA/H15FPYmPj+eLb35gx94D2ufx7TuE3g3HIx1T65z79zInz16gWZc+gJaXRxHD7boEtaBLkH1cvapSpQp37mjnpbt371KgQMp56fPhw9m+fZt2HjbivHT27FlOnDjBG421lYGedV7q1q0b3bp1Mxgd7yi8PItwYZvxq/lZyvhhHzB+2Ae2bkYadl2EArRr2Yxlf60j9M5dOrRpDsD8ZasIC49g36bV6HQ6ylZ7Jc16vGA4Ai7p56qq4udbjh3rtOdndIVt+7zHk4LatWfFsqWEht6mfQftpLlw/jzCwsLYte8AOp0O37Kl0qwLD+Ds7JJ2xJ/+56qqUsHPn5Adu54bd8H8eUz+foLB90uXLWtqSs/VvE071q5axp3QUFoGaVddVyyeT3hYGGtD9qHT6agdUNYwXxfnZ+brU96PVZsMb/2ltmLxfH6d8r2ZszFNuVdac277ah5G3MHnNa2oOrNlCY8iw+n20xacXXT83iMwzZrxAE7OLqiqYyy4UKlBa44FryIq4g6VG7YFtKuUMffDGDArBGcXHWODApKX7Ezi/ESOKT9X8ShVno9+e/7Ezoc2LGbr/ClmzSW9fPT9GhNxB199v57esoSHkeF01/frb0/pV+XJnONTPsMKlixP18kb+P6NFxd01hD0Rj1WbAghNCyC9k21QSIL1/xDWEQku5ZO10YqN+xE3OOnjVROuXqWPFIZqFDWm5CFKY/r5KxQz+J5PKld8zdY/vdGQu+G0b6l9oftghV/ExYRwZ51i9HpdPjUamxw/nFxcXkir1TnH5+ybFs977lxFyxfw8RfDa90l/EuwcLpk0xNy2jt23dg2dKl3L59m44dtcdi5s2bx92wu+w/cBCdTkfpUt5p1oWHpN9DqvNwqs9pf39/du56/hRq8+bN4/sJ35k3mXR41oTu1qaqKoPG/MCGkN3kypmD6d8Mp4q/r03bZG52X4R2aNOcfp8MJzwigs2rFgIQ+SCKQu4F0el0bN2xmyvXbhhsV9KrGKfPXSAuLo5HsXEEb9/Fyy9Vx7dsacLCI9i9/xC1a1Q12M7W2nfoxPv9+hIeHsbGzVsBiIyMpFChQuh0OkK2BnP1yhWD7UqULMnp06e0fB89Ijh4C3VerouPry9hYXfZs3s3tWrXJj4+nvPnzuHn759me1tcCQXtlvyw/v2ICA9nydrNAEQ9iMRdn++ubVu5ftUw3+JeJTl/9jRxcXHEPnrEzpBgatR+mTLlfAkPC+Pgvt1Uq6nle/HCOXwrpM3X3q6Egnbr9p/JA3j0IIKO32mPisTFRJHL1R1nFx1Xj2znQeg1g+3yFfYi4upZEsxY0/EAABwdSURBVB7HGRRv9iawYRBLvvmYmMhw3p+qPfsZG/2APAW0HC8c3Ma924Y5FvDwIvTSGRIexxEf94jzB0IoVakWhUqUI/p+GJeP78M7oCaJCfHcvXoBj9IV0mxvqyuhoPXrxskDiE3Vr4/T2a/hqfr16uFtFPN/CbfiZXl0P5ybp/ZbvO3p1b5pfd4fOYHwe5FsnKvdXo6MiqFQQVdtpPLew1y9GWqwXYmiKSOVH8XGEbznEHWqBeDj7UXYvfvsOXySWlX8iY9PsHZKgHZL/r2howiLuM+mpbMB/fmnoP78s3MfV67fNNiuRHFPzpz/l7i4xzyKjWXLzr3UqVkVnzKluBsewZ6DR6hVLVD7PL54BT/ftH/s29OVUNBuyb/btw9hYWEEb9XWKH8QGUnhQoXR6XQEBwdz5SnnpZIlS3L6VMp5acuWzbxcty6+vr7cvXuX3bt3U1t/Xjp37hz+T5yXHP1KqKk2hOzh38vXOPHPQvYdPUn/URPYvvS3F2/oQOy+CPUv70NUTDRFPT3wLFIYgK7tWtOmex8CX2tCtcoBlC9XxmA7r2JFad+qOYGvNsG7hBeBFf0AyJYtGwtnTGXg518S+SB9a7Rak5+/P9FRURQtWgxP/e2Jzl270a5NK6oHVqJqter4ljecB8zLy4t27TtQLTAAb+9SVA7U1p3Oli0b8xcuYdDAj3kQGUlCYgIffvSxQRFqK74V/ImOjsKjaFGKeGj5tu3YlV6d2tCodiCVqlSjrI9hvkWLe9GiTXsa1QrEq6Q3/pW054ayZcvGtLkLGTlsIFEPIklMSKT3ex8ZFKH2yN27PI8fRZOnoCd5Cmq3tCo0aM/KUV2Z825dPMoF4uZVzmC7vIWL4fNqG+a+W5f8HvY9vY9H6QrEPYwmfyFP8rlrOVZ9oyMzh3RmQvc6FC8fSOGSPgbbuRYpTuWGbZnQvTZuniUp5lMJABddNnqMncPKScOIjXnAf4mJvNLxPYMi1JbcvcsT/0S/lk/Vr0We0a/59P06R9+vhcsGAOCsy0bLL2ax5efPrJrH8/iVK6UfqeyOZ2Ht6mznlo1o995wqrfqRdWKvviWNjw2vTwL065Jfaq17GU4Unnylwwa+yMPomNISEi0aj5J/HzLEhX9kGIehfHUP+7VJagFQT0/pGrDtlSr7I9v2VIG23kV9aRdizeo0rAN3iWKEeivfYZly6Zj4fRJfDJyPJEPokhITOSj3m8aFKH2xt/fn6ioKIoVSzkvde3WjdatWlK5UgDVqlen/DPOSx06dKRSQEW8S5UisErKeWnxkqUM+Lg/kZGRJCQk0P/jAQZFqDXMW7GOyTMXogAVfcsyc0LaMQQzF61mxqLVxMfHU7pkcWZ+9wW5cuZg2botjPtpFs5OTuTLm4dN86dy6vxF+n46nvj4eP5TVRb8OIay3l4Zbtuazdvp2rYJiqLwUmBFIqOiuXUnzKSlRO2NrB0va8e/UGZcO/5JsnZ81iRrxzuOrLh2fHo8ub58Zlo7Pomzk5Jm7XhzOXX+Ip0+GE7wwl9xd3Ml4v4D3FzzpbkdH34vkoIF8gMwetJ0Chd04/0e7aneogerfv+eYh6FuP8gCtd8eRn41SRqBvrTpVVjHj+OJ/G//8iZI+0KRt0/Hsn5S1cN2tK/Vye6tU07AC+o71AG9e3Gy9UrA9C0x8eMGfKeRSakl7XjhRBCCGEUZ2cnHjyIokbjdllurlBTbd19iKAm9ZPn13RzzWfwmlPnLzJ60m9ERkUT/fARr9etCUDtqgH0/XQs7Zo2oHXj1wB4qYo/3/4ylxu379Cm8WtPvQr65w9fWTAjxyNFqBBCCOGgvIp68u/+zbZuRqbV59NxLJ46jkoVyvHH8rVs23sYgB+/GsK+oydZv3U3dYJ6s2v5DDq3bEzNyv6s27qLNn2G8NNXQ6hXO+0UgMZcCS1axJ3rt+8kf30j9A5FnzKbgiOTIlQw5qvR5M6Th4GfDLZ1U6xm4vivyJU7D/36f2LrpggL2PD7eLLnykO9rh/ZuilCmMXX308ld+5cfNKvl62bYhVfjh5Nnjx5GDTYcuelerWr0umD4fTv1ZmCBfIn345PLTr6IR6F3YmPT2Dh6o0U1T8bfPHqDWpW9qdmZX82hOzh+q07PIiKppRXUT7o0YFrN0M5fvZfgyLUmCuhzRvU5dc/l9GxeSP2HT1Jvjx5MtXzoCBFqBBCCCGyIL9ypRnW7y0ad/8QZycnKvv58Nv/0k6aP3LAO7zaoS/uBVypUdmP6JiHgDbx+79XrqOqKvVqV6NShbJMmP4nC1ZtQOfiQpFCbgztZ9p85E3q1WZDyG78G3UiV84cTBs/3KT92SMpQrOYeX/MZfLE71EUhYoBlZg5Z26an8/8/Tdm/P4b8Y8fU7psWWbOnvuMPTmOpQv+YNqPE1EUhQr+AfwwfU6an8+f/fszthSO4MC6BYTM/xEUBc8y/nQdNT3Nz/esmsOeVbNJTHiMe/HSdBk5jWw5cnF0y0obtdh0J/9ZyIFlU1/8wkxi3soNL35RJvLn0lVM+nU2iqIQUMGHWVO+SfPzGfOWMmP+Eh4/jqeMdwlmTRlPrpw5WbbGMX9Pc+fOZeL3E7R8K1Vi7tw/rBa7e1BTuj+xIteI/ilLfvft2pa+XdsabLdo6jiD7w15902GvPum2dqmKAqTRw8y2/7sUZYuQk+eOWfrJljVqZMn+Wb8WIK37cTd3Z2IiAiD17RuG8Tb72ireYweOYLZM2dYu5lmdfb0SaZ8N56V/2zDraA7956Sc9NWbRn28Xs2aJ35hV0+Y+smWNXti6fZNHsCH03bSG7Xgjx8cM/gNQH1WlKr9VsArJs2hn1//UHdDu/yz8ynr9ts78Iun2Hvgol0nrSOXzoaTmeV2Zw6f4lvfrVeUWJrp85eYPwP0wlZ9SfubgWIuBdp8Jo2zRrRu1t7AEZ9O4VZC5bzwdvdGDvpV2s312QnT55k3Ngx7Ni565nnJZF5ZekiNHjH81dryGy2Bm8hqF173N21Z0rc3NwMXnPq5AlGj/yCyPv3iY6J5vXXG1u7mWa1a1swzdu0w62glnOBp+R85tRJazfLYq4d3WbrJljVhYPbqFy/Dbldtbkpc+UzXGP59sVTrJ8+lkdRkcQ9isb3pYYAeFd6idsXT1m1veZw9eg2fF5pRa789rFakqVt3XuYoCb1+Hban7ZuilUE79xLuxaNcdevF+6mnx4otZNnzjP6ux+5HxlF9MOHvP5aHQBq1wjk5NnzVm2vqYK3bKF9+w7PPS+JzMtyS+EIh9Sndy8m/fAjB44c4/MRI4mNjXvxRg5u0Hu9X/wi4bAWjnmftp98y+A/d9H47WHJq0q1H2r95Q+FMId3PhnB5DHDObR5BSMGvpe83OnUb0bZuGVCGCdLF6H169a2dROsql79BixftpTwcG1y7qfd9oiOisLD05P4+HgWLphv7SaaXZ1X6/P3ymXci9Byftrt+Oho+1s5K6O8Kr9q6yZYVdlqr3I0eCUxkVq/Pu12fNzDaPIW9CAxIZ5DG5ckfz/s+iWrtdOcSlR+lXPbV/PoQda4bVnvpSosX7/V1s2wmvovv8SyNRsJv6ctOPG02/HR0TF4FC5EfHw8C1asSf7+v5cNp/6xd/UbNGDp0iXPPS+JzCtL3473L5/5n6dKzc/fn2GfDqdxw3o4OzlTObAKv82cleY1I0d/xasv18LdvRA1atYkOiraRq01D98K/nw0+FPaN2uIs7MT/pUCmfTLzDSvGfz5aD4flDmm8nH3Nv9KGvbMo3QFGr41iJ/fb46TsxPFfCrRecQvaV7TpM/nTOnTkDyu7pTwr0bcQ+2YXjP1i6ft0u65e5fnpS4DWTS4pa2bYhV+5UoxrF93+nz2zYtfnAn4+Zbl0/59adS+J85OTgRWrMDvk8amec2oIR9St2VXCrkVoEaVSkTHxADw2ZjvbdFkk/j7+/PZ8M+pX+81nJ2dCaxShVmzZtu6WcJKZNlOWbbzhWTZTsciy3amnyzb6Thk2c70yYzLdpb2LsGVq9ds3YxMLUf2bKGPYuM8rB03S18JFUIIIYR9u+iAjxnYI2cnxSbrwz9Pln4mVAghhBBC2IYUoUIIIYQQwuqkCBVCCCGEEFYnRagQQgghhLC6NKPjc+bMcTs2Nq6IDdtjddmzZycuLvNPyG6KbNmz8ziT/46cddlJjM8cObpky07C48yRi6U5er9nz6Yj7nHmnrkiSfbs2ZInZRfPJuc08Sw5cuQIffTokdVHwD9PmiJUpJ+iKI2Akaqqpnt2cEVRdEAkUFhVVYeagFNRFAW4B/ioqnrHiO0WAX+pqupwa+4pijIT2KeqaroXZFYUpSPQRVXVtpZrmWUoitIcGKCq6utGbJMDiADcVFWNtVjjLEBRFGe092NxVVXTPaeVoigrgXmqqi554YvtjKIo84BNqqrOeuGLU7Z5E2iuqmpny7XMMhRFCQLeVlW1hRHb5AVuA66qqjpUha8oSjbgPlBIVdUYI7ZbB/yiqupqizXOQhRFWQosV1U13aurKIryDvCKqqpvWa5lIj3kdnzGVQEOGbOB/gPtBFDZIi2yrFJAlDEFqN4hoKoF2mMNRvcxWSxffeF5HqhokRZZlg9w25gCVC9L9TFZLF9VVaOAa4AjrvzgB1wypgDVy1J9jGPnm6lIEZpxVTH+wAc4jPamcTRZKl9FUbIDvsBxIze9CLgqilLQ/K2yuCzVx2SxfBVFyQ14A6eM3PQsUExRlHxmb5TlZak+JovlqyhKAaAw2h/CxjgJlFEUJaf5WyWMIUVoxlVFe+May1H/AstovoeBKoqiONqxVhH4V1XVR8ZspKrqf8ARHPADHTmm0+sQUFX/iIojqQycMvYWs6qqCWh/jDniHRw5ptPHUfMNBI6pqppozEaqqsah/XEVYJFWiXRztMLALuifGSoOnM7A5o76Zs/ILQ9UVb0LPEC7ne9IMpSvnsP1saIoboAbcCEDmztcvnoZ7eObgAoUM29zLC6rHdOeQDYgI8vtOFy+ehnt40tAXkVRCpm5PZaWpY7pzEiK0IypDJzQXyEw1nHAR3+71yHor/hUI+Nvdke81ZPR21rgmPlWAY7or+Qa6yhQUVEUh1kGWH9MZ+iqkaqN5nTEPs6Kx/QhNWOjbw8DgY50B0c/0K4y2p0Yo8gxLWzFYd5gdibDB75+IMcFHGsgR1H9f29kcHtH/IvTlA+3LJWvfiDHdRxrIEdGB9olyVJ9TBbLV1XVCLRZH8qYtUWWldGBdkmyVB/jmPlmOlKEZkwVMvbcTRJHO/irAIczeEUBHCxf/RW9ALQrfBlxBijuYAM5suQxbcL2DpWvCQPtkpwAyjrYQI4s1cdksXxNGGiX5Cjgr586UdiIFKEZY8pfX6B9UDjMmx0z5etAAzl8gRuqqj7IyMb6xzQcbSouOaaN42j5+pOBgXZJ9AM5zuFYAzmyWh9ntXwrkYGBdkn001hdQZvWStiIFKFG0k/OXQ6tyMioQzjWsyimfrgl3cYv+txX2Q9T8wUH6mP9QDsvMjbQLonD5Ktnah9fxLEGcmS1Y9oNKEjGBtolcZh89Uzt47OAh6Io+c3UHkvLUsd0ZiVFqPECgPMmrg5zBAhwoIEcGZ32A0h+6N2RbvWYlK+eI+VbGTiZwYF2SRxmKi5TBiUlccCBHFntmK4CHM3gQLskDjMVl76NJt2O109zdAxt2iNHkNWO6UzJ7k8YdsiUKSEAxxrIoSiKO5Af7cqPKRzpzW5yH5PF8lVVNRzHGcjhifbZd93E/WSpPiaL5auq6i0gHu0ugb3zBmJMGGiXJEv1MY6Vb6YkRajxzHELABzn+ZukQUmmXFEAB8lXfyXP1Af8QXtco5yDDOTIasd0VTI+dU9qDpGv/o5LJTIwdc8TjuE4Azmy5DFthv04RL76gXbl0Y5JUxwBKuuntxI2IEWo8cz1ZneUZ1GyWr6lgfuqqoaZspNUK3I4wlRcWa2Ps1q+Jg20S6KqajTaQI4KZmmVZWW1Ps5q+Zo00C6Jqqr3gLtAWbO0ShhNilAj6K8A+JPxqXtSc5TbAOa4Kgja7fx8DjCQw1z5ggP0sZkG2iWx+3z1zNXHZwFPBxjIkdWO6Txot9DPmGF3dp+vnrn6+CRQWlGUXGbYlyVlqWM6M5MiNB0URSmuKMo/aJf/rwGxphRT+uXkkgZyuCqKstdMTTUbRVHWKYrijf4vbH2bM7ovN7Tl846g5TxfUZRqZmmomSiK0l9RlPdIydddUZRsGdyXs6IohUmZmqqzoiijzdhckymKUlpRlL/RrtSeBxLNdExX1f/udpipqWajKMomRVGKY55juiDgjDbvZqCiKEsVRbGrq96KogxRFKU3KfkWyuitdEVRXJ44pt9SFGW4OdtrKkVRyiuKshL9QDvtW4q7CftLfUx7KooSbKammo2iKCGKohRBv6Kdice0O9pytGeASoqirFYUxcdMTTULRVFGKIrSnZRjunBGB/gqiqLT55zUx30URRlkzvaKF5MiNH1uo73J66P91fQj8IUJ+1sNNAPuAd30/7U3oUAQ2vrYJYC9JowS7QssRPvd1QRaAv+ao5FmdAloj/bhdg7YT8b/Oi6HVnBf1e+jPaYP7DK360Bd4BW0fpkGDDVhfxv0+0oAOgPhpjbQAu4C7QBXtLkBt5uwrw+BP9B+d7WBN4DLJrbP3FIf0/8CB8n4PJ9+aLleIuWYtrf38FWgIVAHra2zgAEm7C8YraDNBXRAO37sTTjQEe1cXhPYbMK+PgFmoP3uXgbqof1O7UnqY/oyWgGZ0QG+ldHeExf0++uA/R3TmZ5i+rP5WYOiKEuAfGhv+upA9Yw+Y6UoSgCwBe2hahX4W1XVSeZqqzkoitIZ7USbE60Q7aSqakgG95Ud2Ib2Bi8GOKuqWtdcbTUH/S28W0As2uMWx1RV/cSE/Y0AmqLdNooDKqiqetscbTUXRVFWo/XvHbR21tQ/95eRfVUBNqKtXpIILFVV9WdztdUcFEV5C3gbyI62bGcbVVV3Z3BfOYBdaHOreqLN2tTQXG01B0VRXNGKiP/Q/ijararqZybs72vgNbRjRQVK6WdFsBuKomwAXNAuHFQEaquq+jCD+6oJrEErUh4Df6iqOsNcbTUHRVH6ov3Rp0N79reZqqoHMrivXMBetKv7hYHHqqo2M1dbzUF/t+Y8Wh8fATapqjrahP19i3Y+D0T7HRYz9dlpYSRVVeVfOv4B7wAPgPtAgBn21wMI0++zgq3ze0r7CgKP0K6IDjXD/krq830IjLB1fs9o4060gnEHoDNxX07AWiAGOG3r3J7Rxg+AKLQr8SYfg/r3yB0gGiht6/ye0j5P/fEXCnxshv2VRvujNAYYYuv8ntHGg2h/WG0BXEzclzPwj/5z4Yitc3tGGz/RH38RQFkz7O8D/TEdAxS3dX5PaV9JfdvuAP3MsD8f/edBNNDf1vk9o40n9O/jDWgXNEzZlwsQov/c32Pr3LLiP7kdn34bgLzAZ6qqZnT95WSqqs5Fu+WrwzwP0JuVql3hiAJuAt+ZYX9X0ArvnMBWU/dnIUn90EnN4FJwSVRtSqs30a4YnTe1YRayHsgDDFRV1ZTVkpLMQD/ASVVVe3v8AFWb9zEW7TbeFDPs7yLQB+12rSm3QS3pDNox2FU1bTECVG0y8676/Z0zQ9ssYSOQG60gM2W1pCQ/o71/41VVNXVeWbPTf64movXzNDPs7xzaHbDcaL9Le5R07HXTH5MZpn9PdMa+j+lMTW7HG0FRlOaqqv5txv3lAF5WVdUuT2CKotRFW5s3woz7bA6sVe3wwNPf6imnquouM+6zKhCmqqq9PVsFWOSYzgXUUlV1i7n2aU6KoryKtpJOpBn3adbfoTnpB62UVFV1nxn3WR24qarqTXPt05wscEznBaqpqrrVXPs0J0VR6gP71Qw+SvOMfdrzMV0cKKKq6kEz7rMWcElV1VBz7VOkjxShQgghhBDC6uR2vBBCCCGEsLoMza9lz3Q63e2EhIQi1o7r7OxMYqJJj6dIXDuOa8vYElfiZrbYElfiZsbYz+Pi4hIaHx/vYet22JtMdzteURR19OjRVo87evRoJG7mjWvL2BJX4ma22BJX4mbG2M8zevRoVFXN6FzbmZbcjhdCCCGEEFYnRagQQgghhLA6KUKFEEIIIYTVSREqhBBCCCGsTopQIYQQQghhdVKECiGEEEIIq5MiVAghhBBCWJ0UoUIIIYQQwuqkCBVCCCGEEFYnRagQQgghhLA6KUKFEEIIIYTVSREqhBBCCCGsTopQIYQQQghhdVKECiGEEEIIq8vyReiiRYuIiIhI9+tjYmL4448/JK7EtdvYElfiWiKuLWNLXIlribi2ji2yeBF6584dVFXFzc0t3dvkzp2bvHnzcvXqVYkrce0utsSVuJaIa8vYElfiWiKurWMLTZYuQo8dO4avr6/R25UvX55jx45JXIlrd7ElrsS1RFxbxpa4EtcScW0dW2iydBF67do1ihYtavR2RYsWNemvIImbuePaMrbElbiWiGvL2BJX4loirq1jC02WLkKjoqLIlSuX0dvlzp2bqKgoiStx7S62xJW4lohry9gSV+JaIq6tYwtNli5CdTodCQkJRm+XkJCAi4uLxJW4dhdb4kpcS8S1ZWyJK3EtEdfWsYUmSxeh7u7uRo2KSxIeHk7hwoUlrsS1u9gSV+JaIq4tY0tciWuJuLaOLTRZugj18fHh8uXLyV+vWrWKGzduALB//372798PwI0bN1i1alXy6y5duoSPj4/Elbh2F1viSlxLxLVlbIkrcS0R19axhSZLF6F+fn5cvHiR//77D4DWrVtTrFgxAGrUqEGNGjUAKFasGK1bt07e7uzZs1SqVEniSly7iy1xJa4l4toytsSVuJaIa+vYQpOli1CdTkf9+vV58OBBureJiYmhdu3a5MyZU+JKXLuLLXElriXi2jK2xJW4lohr69hCk+WfrC1btqxRr8+dOzcVKlSQuBLXbmNLXIlribi2jC1xJa4l4to6tsjiV0KFEEIIIYRtSBEqhBBCCCGsTopQIYQQQghhdVKECiGEEEIIq5MiVAghhBBCWJ0UoUIIIYQQwuqkCBVCCCGEEFYnRagQQgghhLA6KUKFEEIIIYTVSREqhBBCCCGsTopQIYQQQghhdVKECiGEEEIIq5MiVAghhBBCWJ0UoUIIIYQQwuqkCBVCCCGEEFanqKpq6zaYlU6nu52QkFDE2nGdnZ1JTEy0dliJmwViS1yJm9liS1yJmxljP4+Li0tofHy8h63bYW8yXREqhBBCCCHsn9yOF0IIIYQQVidFqBBCCCGEsDopQoUQQgghhNVJESqEEEIIIaxOilAhhBBCCGF1UoQKIYQQQgirkyJUCCGEEEJYnRShQgghhBDC6qQIFUIIIYQQVidFqBBCCCGEsDopQoUQQgghhNVJESqEEEIIIaxOilAhhBBCCGF1UoQKIYQQQgirkyJUCCGEEEJYnRShQgghhBDC6qQIFUIIIYQQVidFqBBCCCGEsDopQoUQQgghhNVJESqEEEIIIaxOilAhhBBCCGF1UoQKIYQQQgirkyJUCCGEEEJY3f8BWWkkYP1oU6sAAAAASUVORK5CYII=\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":["feat_importances = pd.Series(model.best_estimator_.feature_importances_, index=X.columns)\n","ax = feat_importances.nlargest(25).plot(kind='barh', figsize=(10, 8))\n","ax.invert_yaxis()\n","\n","plt.xlabel('score')\n","plt.ylabel('feature')\n","plt.title('feature importance score')"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":530},"id":"8bcOWIjWNkWo","executionInfo":{"status":"ok","timestamp":1650290080810,"user_tz":-420,"elapsed":995,"user":{"displayName":"Fajar Ariev","userId":"18314973382710209713"}},"outputId":"b925f98b-dec0-40e3-d319-cd5c50174744"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["Text(0.5, 1.0, 'feature importance score')"]},"metadata":{},"execution_count":63},{"output_type":"display_data","data":{"text/plain":["<Figure size 720x576 with 1 Axes>"],"image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":["df2.info()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"FzgZ1SG_NnwP","executionInfo":{"status":"ok","timestamp":1650290080811,"user_tz":-420,"elapsed":24,"user":{"displayName":"Fajar Ariev","userId":"18314973382710209713"}},"outputId":"800f906c-6815-4b29-bea0-03ac90102f0b"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["<class 'pandas.core.frame.DataFrame'>\n","Int64Index: 10642 entries, 0 to 10998\n","Data columns (total 18 columns):\n"," #   Column                   Non-Null Count  Dtype  \n","---  ------                   --------------  -----  \n"," 0   ID                       10642 non-null  int64  \n"," 1   Customer_care_calls      10642 non-null  float64\n"," 2   Customer_rating          10642 non-null  float64\n"," 3   Cost_of_the_Product      10642 non-null  float64\n"," 4   Prior_purchases          10642 non-null  float64\n"," 5   Product_importance       10642 non-null  int64  \n"," 6   Gender                   10642 non-null  int64  \n"," 7   Discount_offered         10642 non-null  float64\n"," 8   Weight_in_gms            10642 non-null  float64\n"," 9   Reached.on.Time_Y.N      10642 non-null  int64  \n"," 10  Mode_of_Shipment_Flight  10642 non-null  uint8  \n"," 11  Mode_of_Shipment_Road    10642 non-null  uint8  \n"," 12  Mode_of_Shipment_Ship    10642 non-null  uint8  \n"," 13  Warehouse_block_A        10642 non-null  uint8  \n"," 14  Warehouse_block_B        10642 non-null  uint8  \n"," 15  Warehouse_block_C        10642 non-null  uint8  \n"," 16  Warehouse_block_D        10642 non-null  uint8  \n"," 17  Warehouse_block_F        10642 non-null  uint8  \n","dtypes: float64(6), int64(4), uint8(8)\n","memory usage: 1.2 MB\n"]}]},{"cell_type":"markdown","source":["Fitur yang akan digunakan :\n","*   Discount_offered, (6)\n","*   Weight_in_gms, (7)\n","*   Prior_purchases, (3)\n","*   Cost_of_the_Product (2)"],"metadata":{"id":"fAWyeP4bNruV"}},{"cell_type":"markdown","source":["**with feature selection**"],"metadata":{"id":"J_NeKLUgN0p4"}},{"cell_type":"code","source":["X = df2.drop(columns=['ID','Reached.on.Time_Y.N','Customer_care_calls','Customer_rating','Product_importance','Gender','Mode_of_Shipment_Flight','Mode_of_Shipment_Road','Mode_of_Shipment_Ship','Warehouse_block_A','Warehouse_block_B','Warehouse_block_C','Warehouse_block_D','Warehouse_block_F'])\n","y = df2['Reached.on.Time_Y.N'] # target / label\n","\n","#Splitting the data into Train and Test\n","from sklearn.model_selection import train_test_split \n","X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 42)\n"],"metadata":{"id":"Hgt-hPQRNqVH"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["from sklearn.tree import DecisionTreeClassifier\n","model = DecisionTreeClassifier(random_state=42)\n","model.fit(X_train,y_train)\n","\n","y_pred = model.predict(X_test)\n","eval_classification(model, y_pred, X_train, y_train, X_test, y_test)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"I7rWcQfwOUoD","executionInfo":{"status":"ok","timestamp":1650290080813,"user_tz":-420,"elapsed":21,"user":{"displayName":"Fajar Ariev","userId":"18314973382710209713"}},"outputId":"a54f2f96-bd0b-4a0e-b277-442c39108a3d"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Accuracy (Test Set): 0.63\n","Precision (Test Set): 0.67\n","Recall (Test Set): 0.70\n","F1-Score (Test Set): 0.69\n","AUC: 0.62\n"]}]},{"cell_type":"code","source":["print('Train score: ' + str(model.score(X_train, y_train))) #accuracy\n","print('Test score:' + str(model.score(X_test, y_test))) #accuracy"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"7-ZR-C-BOWSS","executionInfo":{"status":"ok","timestamp":1650290080814,"user_tz":-420,"elapsed":20,"user":{"displayName":"Fajar Ariev","userId":"18314973382710209713"}},"outputId":"989055d5-eb18-4f9e-8961-2b680895b256"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Train score: 1.0\n","Test score:0.6317519962423673\n"]}]},{"cell_type":"markdown","source":["Overfit again"],"metadata":{"id":"_uLqNTA-OZAd"}},{"cell_type":"code","source":["from sklearn.model_selection import RandomizedSearchCV\n","from scipy.stats import uniform\n","\n","# List of hyperparameter\n","max_depth = [int(x) for x in np.linspace(1, 110, num = 30)] # Maximum number of levels in tree\n","min_samples_split = [2, 5, 10, 100] # Minimum number of samples required to split a node\n","min_samples_leaf = [1, 2, 4, 10, 20, 50] # Minimum number of samples required at each leaf node\n","max_features = ['auto', 'sqrt'] # Number of features to consider at every split\n","criterion = ['gini', 'entropy']\n","splitter = ['best', 'random']\n","\n","hyperparameters = dict(max_depth=max_depth, \n","                       min_samples_split=min_samples_split, \n","                       min_samples_leaf=min_samples_leaf,\n","                       max_features=max_features,\n","                       criterion=criterion,\n","                       splitter=splitter\n","                      )\n","\n","# Inisialisasi Model\n","dt = DecisionTreeClassifier(random_state=42)\n","model = RandomizedSearchCV(dt, hyperparameters, cv=5, random_state=42, scoring='roc_auc')\n","model.fit(X_train, y_train)\n","\n","# Predict & Evaluation\n","y_pred = model.predict(X_test)#Check performa dari model\n","eval_classification(model, y_pred, X_train, y_train, X_test, y_test)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"0sK1HKhZOYNJ","executionInfo":{"status":"ok","timestamp":1650290080816,"user_tz":-420,"elapsed":20,"user":{"displayName":"Fajar Ariev","userId":"18314973382710209713"}},"outputId":"8e312d15-1cb3-4e7e-c44c-e7302d1fe594"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Accuracy (Test Set): 0.65\n","Precision (Test Set): 0.72\n","Recall (Test Set): 0.66\n","F1-Score (Test Set): 0.69\n","AUC: 0.65\n"]}]},{"cell_type":"code","source":["print('Best max_depth:', model.best_estimator_.get_params()['max_depth'])\n","print('Best min_samples_split:', model.best_estimator_.get_params()['min_samples_split'])\n","print('Best min_samples_leaf:', model.best_estimator_.get_params()['min_samples_leaf'])\n","print('Best max_features:', model.best_estimator_.get_params()['max_features'])\n","print('Best criterion:', model.best_estimator_.get_params()['criterion'])\n","print('Best splitter:', model.best_estimator_.get_params()['splitter'])"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"Ol4Ms4kOOcgl","executionInfo":{"status":"ok","timestamp":1650290080816,"user_tz":-420,"elapsed":17,"user":{"displayName":"Fajar Ariev","userId":"18314973382710209713"}},"outputId":"d44d5089-f5f3-4dbc-9540-f6feba9f7d0a"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Best max_depth: 94\n","Best min_samples_split: 100\n","Best min_samples_leaf: 1\n","Best max_features: sqrt\n","Best criterion: entropy\n","Best splitter: best\n"]}]},{"cell_type":"code","source":["print('Train score: ' + str(model.score(X_train, y_train)))\n","print('Test score:' + str(model.score(X_test, y_test)))"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"gHySMhoSOeNi","executionInfo":{"status":"ok","timestamp":1650290082152,"user_tz":-420,"elapsed":1350,"user":{"displayName":"Fajar Ariev","userId":"18314973382710209713"}},"outputId":"c0242a7e-62b7-45a9-8da3-83e19b858bcf"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Train score: 0.8224513456254665\n","Test score:0.7291105633496937\n"]}]},{"cell_type":"code","source":["from sklearn import tree\n","import matplotlib.pyplot as plt\n","fig, ax = plt.subplots(figsize=(10, 10))\n","tree.plot_tree(model.best_estimator_,\n","               feature_names = X.columns.tolist(), \n","               class_names=['0','1'],\n","               filled = True, max_depth=5, fontsize=10)\n","plt.show()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":575},"id":"Este6kiIOhL9","executionInfo":{"status":"ok","timestamp":1650290082153,"user_tz":-420,"elapsed":9,"user":{"displayName":"Fajar Ariev","userId":"18314973382710209713"}},"outputId":"59278bf3-60c0-4b9e-81e7-61ed6fd07cd3"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 720x720 with 1 Axes>"],"image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"markdown","source":["# Logistic Regression"],"metadata":{"id":"28KUy66JOkRj"}},{"cell_type":"code","source":["X = df2.drop(columns=['ID','Reached.on.Time_Y.N'])\n","y = df2['Reached.on.Time_Y.N'] # target / label\n","\n","#Splitting the data into Train and Test\n","from sklearn.model_selection import train_test_split \n","X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 42)"],"metadata":{"id":"e0CtmFUvOmbd"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["from sklearn.linear_model import LogisticRegression\n","model = LogisticRegression(random_state=42)\n","model.fit(X_train, y_train)\n","y_pred = model.predict(X_test)\n","eval_classification(model, y_pred, X_train, y_train, X_test, y_test)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"7fzm593UOrKw","executionInfo":{"status":"ok","timestamp":1650290082893,"user_tz":-420,"elapsed":745,"user":{"displayName":"Fajar Ariev","userId":"18314973382710209713"}},"outputId":"aeffe322-955b-486d-fe75-981454e8a32c"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Accuracy (Test Set): 0.64\n","Precision (Test Set): 0.69\n","Recall (Test Set): 0.67\n","F1-Score (Test Set): 0.68\n","AUC: 0.63\n"]}]},{"cell_type":"code","source":["print('Train score: ' + str(model.score(X_train, y_train))) #accuracy\n","print('Test score:' + str(model.score(X_test, y_test))) #accuracy"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"0FkOxGbmOtJ7","executionInfo":{"status":"ok","timestamp":1650290082895,"user_tz":-420,"elapsed":9,"user":{"displayName":"Fajar Ariev","userId":"18314973382710209713"}},"outputId":"8ca14e55-145c-439d-8d32-832796d24d85"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Train score: 0.6315047574298133\n","Test score:0.6378581493658995\n"]}]},{"cell_type":"markdown","source":["**try with Hyperparameter**"],"metadata":{"id":"LiAMwBINOvyA"}},{"cell_type":"code","source":["from sklearn.model_selection import RandomizedSearchCV, GridSearchCV\n","\n","# List Hyperparameters yang akan diuji\n","penalty = ['l2','l1','elasticnet']\n","C = [0.0001, 0.001, 0.002] # Inverse of regularization strength; smaller values specify stronger regularization.\n","solver = ['newton-cg', 'lbfgs', 'liblinear', 'sag', 'saga']\n","hyperparameters = dict(penalty=penalty, C=C , solver=solver)\n","\n","# Inisiasi model\n","logres = LogisticRegression(random_state=42) # Init Logres dengan Gridsearch, cross validation = 5\n","model = RandomizedSearchCV(logres, hyperparameters, cv=5, random_state=42, scoring='roc_auc')\n","\n","# Fitting Model & Evaluation\n","model.fit(X_train, y_train)\n","y_pred = model.predict(X_test)\n","eval_classification(model, y_pred, X_train, y_train, X_test, y_test)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"MSOo_wyxOy0m","executionInfo":{"status":"ok","timestamp":1650290083885,"user_tz":-420,"elapsed":994,"user":{"displayName":"Fajar Ariev","userId":"18314973382710209713"}},"outputId":"e3bddbf2-5065-4b6a-abc0-c3d2440c27ad"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Accuracy (Test Set): 0.58\n","Precision (Test Set): 0.58\n","Recall (Test Set): 1.00\n","F1-Score (Test Set): 0.73\n","AUC: 0.50\n"]}]},{"cell_type":"code","source":["print('Best algorithm:', model.best_estimator_.get_params()['penalty'])\n","print('Best C:', model.best_estimator_.get_params()['C'])\n","print('Best solver:', model.best_estimator_.get_params()['solver'])"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"0EhFD9tsO02T","executionInfo":{"status":"ok","timestamp":1650290083885,"user_tz":-420,"elapsed":28,"user":{"displayName":"Fajar Ariev","userId":"18314973382710209713"}},"outputId":"24651e78-91b6-47c0-bf74-f82b5042ead0"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Best algorithm: l2\n","Best C: 0.0001\n","Best solver: saga\n"]}]},{"cell_type":"code","source":["print('Train score: ' + str(model.score(X_train, y_train))) #accuracy\n","print('Test score:' + str(model.score(X_test, y_test))) #accuracy"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"KSoLhgKWO3cF","executionInfo":{"status":"ok","timestamp":1650290083886,"user_tz":-420,"elapsed":25,"user":{"displayName":"Fajar Ariev","userId":"18314973382710209713"}},"outputId":"476ea5f4-da4c-4b30-c132-6edc4fd4a31b"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Train score: 0.7186105448546587\n","Test score:0.7225917198743286\n"]}]},{"cell_type":"code","source":["feat_importances = pd.Series(model.best_estimator_.coef_[0], index=X.columns)\n","ax = feat_importances.nlargest(25).plot(kind='barh', figsize=(10, 8))\n","ax.invert_yaxis()\n","\n","plt.xlabel('score')\n","plt.ylabel('feature')\n","plt.title('feature importance score')"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":530},"id":"-Fr5G4lsO54_","executionInfo":{"status":"ok","timestamp":1650290083887,"user_tz":-420,"elapsed":24,"user":{"displayName":"Fajar Ariev","userId":"18314973382710209713"}},"outputId":"baa78203-25cc-4e6b-9542-e90d46585c2e"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["Text(0.5, 1.0, 'feature importance score')"]},"metadata":{},"execution_count":78},{"output_type":"display_data","data":{"text/plain":["<Figure size 720x576 with 1 Axes>"],"image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":["df2.info()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"FwF_lRDdO71E","executionInfo":{"status":"ok","timestamp":1650290083887,"user_tz":-420,"elapsed":21,"user":{"displayName":"Fajar Ariev","userId":"18314973382710209713"}},"outputId":"04ad24ab-91bb-4d01-8eb8-f98fab8f0a2c"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["<class 'pandas.core.frame.DataFrame'>\n","Int64Index: 10642 entries, 0 to 10998\n","Data columns (total 18 columns):\n"," #   Column                   Non-Null Count  Dtype  \n","---  ------                   --------------  -----  \n"," 0   ID                       10642 non-null  int64  \n"," 1   Customer_care_calls      10642 non-null  float64\n"," 2   Customer_rating          10642 non-null  float64\n"," 3   Cost_of_the_Product      10642 non-null  float64\n"," 4   Prior_purchases          10642 non-null  float64\n"," 5   Product_importance       10642 non-null  int64  \n"," 6   Gender                   10642 non-null  int64  \n"," 7   Discount_offered         10642 non-null  float64\n"," 8   Weight_in_gms            10642 non-null  float64\n"," 9   Reached.on.Time_Y.N      10642 non-null  int64  \n"," 10  Mode_of_Shipment_Flight  10642 non-null  uint8  \n"," 11  Mode_of_Shipment_Road    10642 non-null  uint8  \n"," 12  Mode_of_Shipment_Ship    10642 non-null  uint8  \n"," 13  Warehouse_block_A        10642 non-null  uint8  \n"," 14  Warehouse_block_B        10642 non-null  uint8  \n"," 15  Warehouse_block_C        10642 non-null  uint8  \n"," 16  Warehouse_block_D        10642 non-null  uint8  \n"," 17  Warehouse_block_F        10642 non-null  uint8  \n","dtypes: float64(6), int64(4), uint8(8)\n","memory usage: 1.2 MB\n"]}]},{"cell_type":"markdown","source":["Fitur yang akan digunakan :\n","*   Discount_offered, (6)\n","*   Weight_in_gms, (7)\n","*   Prior_purchases, (3)\n","*   Cost_of_the_Product (2)"],"metadata":{"id":"ArG6CdK0O-Xh"}},{"cell_type":"markdown","source":["**with feature selection**"],"metadata":{"id":"5n82rIt2PHvl"}},{"cell_type":"code","source":["X = df2.drop(columns=['ID','Reached.on.Time_Y.N','Customer_care_calls','Customer_rating','Product_importance','Gender','Mode_of_Shipment_Flight','Mode_of_Shipment_Road','Mode_of_Shipment_Ship','Warehouse_block_A','Warehouse_block_B','Warehouse_block_C','Warehouse_block_D','Warehouse_block_F'])\n","y = df2['Reached.on.Time_Y.N'] # target / label\n","\n","#Splitting the data into Train and Test\n","from sklearn.model_selection import train_test_split \n","X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 42)"],"metadata":{"id":"UzUhJ17qPBfE"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["from sklearn.linear_model import LogisticRegression\n","model = LogisticRegression(random_state=42)\n","model.fit(X_train, y_train)\n","y_pred = model.predict(X_test)\n","eval_classification(model, y_pred, X_train, y_train, X_test, y_test)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"PX079RB8PNGL","executionInfo":{"status":"ok","timestamp":1650290083888,"user_tz":-420,"elapsed":20,"user":{"displayName":"Fajar Ariev","userId":"18314973382710209713"}},"outputId":"2abcc25f-3241-4d6c-cb6b-98bc7da7116f"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Accuracy (Test Set): 0.63\n","Precision (Test Set): 0.68\n","Recall (Test Set): 0.67\n","F1-Score (Test Set): 0.68\n","AUC: 0.62\n"]}]},{"cell_type":"code","source":["print('Train score: ' + str(model.score(X_train, y_train))) #accuracy\n","print('Test score:' + str(model.score(X_test, y_test))) #accuracy"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"UbmHJ4u0PPPk","executionInfo":{"status":"ok","timestamp":1650290083889,"user_tz":-420,"elapsed":18,"user":{"displayName":"Fajar Ariev","userId":"18314973382710209713"}},"outputId":"f870aecf-dd54-4b96-8419-6073cb889296"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Train score: 0.6292728767766944\n","Test score:0.628933771723814\n"]}]},{"cell_type":"code","source":["from sklearn.model_selection import RandomizedSearchCV, GridSearchCV\n","\n","# List Hyperparameters yang akan diuji\n","penalty = ['l2','l1','elasticnet']\n","C = [0.0001, 0.001, 0.002] # Inverse of regularization strength; smaller values specify stronger regularization.\n","solver = ['newton-cg', 'lbfgs', 'liblinear', 'sag', 'saga']\n","hyperparameters = dict(penalty=penalty, C=C , solver=solver)\n","\n","# Inisiasi model\n","logres = LogisticRegression(random_state=42) # Init Logres dengan Gridsearch, cross validation = 5\n","model = RandomizedSearchCV(logres, hyperparameters, cv=5, random_state=42, scoring='roc_auc')\n","\n","# Fitting Model & Evaluation\n","model.fit(X_train, y_train)\n","y_pred = model.predict(X_test)\n","eval_classification(model, y_pred, X_train, y_train, X_test, y_test)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"fxJ8U3ETPRvc","executionInfo":{"status":"ok","timestamp":1650290084680,"user_tz":-420,"elapsed":808,"user":{"displayName":"Fajar Ariev","userId":"18314973382710209713"}},"outputId":"4f17c5da-2562-4b02-d176-675adcee3b44"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Accuracy (Test Set): 0.58\n","Precision (Test Set): 0.58\n","Recall (Test Set): 1.00\n","F1-Score (Test Set): 0.73\n","AUC: 0.50\n"]}]},{"cell_type":"code","source":["print('Best algorithm:', model.best_estimator_.get_params()['penalty'])\n","print('Best C:', model.best_estimator_.get_params()['C'])\n","print('Best solver:', model.best_estimator_.get_params()['solver'])"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"lh8WNoVTPTtj","executionInfo":{"status":"ok","timestamp":1650290084680,"user_tz":-420,"elapsed":18,"user":{"displayName":"Fajar Ariev","userId":"18314973382710209713"}},"outputId":"457e11fc-4071-4205-8e60-a6af0b25ed2f"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Best algorithm: l2\n","Best C: 0.0001\n","Best solver: sag\n"]}]},{"cell_type":"code","source":["print('Train score: ' + str(model.score(X_train, y_train))) #accuracy\n","print('Test score:' + str(model.score(X_test, y_test))) #accuracy"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"gaadx2zgPVj-","executionInfo":{"status":"ok","timestamp":1650290084680,"user_tz":-420,"elapsed":14,"user":{"displayName":"Fajar Ariev","userId":"18314973382710209713"}},"outputId":"94421a0d-d21f-4690-c5c3-91a4f96a6c95"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Train score: 0.7191197467999759\n","Test score:0.7187685502902894\n"]}]},{"cell_type":"markdown","source":["### KESIMPULAN"],"metadata":{"id":"ji8Yn39Zicc3"}},{"cell_type":"markdown","source":["Berdasarkan hasil dari tiap algoritma model dan diskusi kelompok, kami memutuskan untuk menggunakan model **Desicion Tree**\n","\n","Karena model tersebutmemiliki kombinasi **Skor AUC dan Recall** yang cukup balance dan lebih bisa diinterpretasikan hasilnya\n"],"metadata":{"id":"ASvUg2_CiQeA"}},{"cell_type":"markdown","source":["## Benefit Analysis"],"metadata":{"id":"Eiml5f4NzSCa"}},{"cell_type":"code","source":["df.info()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"JCQbwKd21Vrr","executionInfo":{"status":"ok","timestamp":1650724268956,"user_tz":-420,"elapsed":394,"user":{"displayName":"Muhammad Hamzah","userId":"06012328832276120437"}},"outputId":"5c3db261-30e0-438b-b61b-2ae742257083"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["<class 'pandas.core.frame.DataFrame'>\n","RangeIndex: 10999 entries, 0 to 10998\n","Data columns (total 12 columns):\n"," #   Column               Non-Null Count  Dtype \n","---  ------               --------------  ----- \n"," 0   ID                   10999 non-null  int64 \n"," 1   Warehouse_block      10999 non-null  object\n"," 2   Mode_of_Shipment     10999 non-null  object\n"," 3   Customer_care_calls  10999 non-null  int64 \n"," 4   Customer_rating      10999 non-null  int64 \n"," 5   Cost_of_the_Product  10999 non-null  int64 \n"," 6   Prior_purchases      10999 non-null  int64 \n"," 7   Product_importance   10999 non-null  object\n"," 8   Gender               10999 non-null  object\n"," 9   Discount_offered     10999 non-null  int64 \n"," 10  Weight_in_gms        10999 non-null  int64 \n"," 11  Reached.on.Time_Y.N  10999 non-null  int64 \n","dtypes: int64(8), object(4)\n","memory usage: 1.0+ MB\n"]}]},{"cell_type":"code","source":["#On Time Rate Growth Calculation \n","Total = df['ID'].count()\n","Late =  df[df['Reached.on.Time_Y.N']==1]['ID'].count()\n","OnTime =  df[df['Reached.on.Time_Y.N']==0]['ID'].count()\n","PredRate = 0.65\n","PredLate = round(Late*PredRate)\n","PredOnTime = Late-PredLate\n","\n","print('----- Existing -----')\n","print('\\t\\t\\t','count', 'percentage')\n","print('Delivery : \\t\\t', Total)\n","print('Late : \\t\\t\\t', Late, ',', round(Late/Total*100,1),'%')\n","print('On Time : \\t\\t', OnTime, ',', round(OnTime/Total*100,1),'%')\n","print()\n","print('----- After Modeling -----')\n","print('\\t\\t\\t','count', 'percentage')\n","print('Delivery : \\t\\t', Total)\n","print('Late : \\t\\t\\t', Late, ',', round(Late/Total*100,1),'%')\n","print('  Predicted Late : \\t', round(PredLate), ',', round(PredLate/Late*100,1),'%')\n","print('  Predicted On Time : \\t', round(PredOnTime), ',', round(PredOnTime/Late*100,1),'%')\n","print('Late After Pred : \\t', Late-PredLate, ',', round((Late-PredLate)/Total*100,1),'%')\n","print('On Time : \\t\\t', OnTime, ',', round(OnTime/Total*100,1),'%')\n","print('On Time After Pred : \\t', OnTime+PredLate, ',', round((OnTime+PredLate)/Total*100,1),'%')\n","print('On Time Growth rate : \\t', round(((OnTime+PredLate)/(OnTime)-1)*100,1), '%')"],"metadata":{"id":"5otkNk4DiiKa","executionInfo":{"status":"ok","timestamp":1650783050888,"user_tz":-420,"elapsed":5,"user":{"displayName":"Muhammad Hamzah","userId":"06012328832276120437"}},"colab":{"base_uri":"https://localhost:8080/"},"outputId":"9b473fba-0ebd-42dd-dfd9-9bf98566cc65"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["----- Existing -----\n","\t\t\t count percentage\n","Delivery : \t\t 10999\n","Late : \t\t\t 6563 , 59.7 %\n","On Time : \t\t 4436 , 40.3 %\n","\n","----- After Modeling -----\n","\t\t\t count percentage\n","Delivery : \t\t 10999\n","Late : \t\t\t 6563 , 59.7 %\n","  Predicted Late : \t 4266 , 65.0 %\n","  Predicted On Time : \t 2297 , 35.0 %\n","Late After Pred : \t 2297 , 20.9 %\n","On Time : \t\t 4436 , 40.3 %\n","On Time After Pred : \t 8702 , 79.1 %\n","On Time Growth rate : \t 96.2 %\n"]}]},{"cell_type":"markdown","source":["On time rate potentially increase by **96.2%** from the previous **40.3% to become 79.1%** after action based on predictive modeling"],"metadata":{"id":"ex_tFDey1s89"}},{"cell_type":"code","source":["#Potential Revenue Loss Saved Calculation\n","TotalCost = df['Cost_of_the_Product'].sum()\n","TotalDiscount = df['Discount_offered'].sum()\n","TotalRevenue = TotalCost - TotalDiscount\n","AvgRevenue = round(TotalRevenue / Total, 2)\n","\n","print('Total Cost : \\t\\t', '{:,}'.format(TotalCost))\n","print('Total Discount : \\t', '{:,}'.format(TotalDiscount))\n","print('Total Revenue : \\t', '{:,}'.format(TotalRevenue))\n","print('Average Revenue : \\t', '{:,}'.format(AvgRevenue))\n","print()\n","print('Potential Revenue')\n","print('Late : \\t\\t\\t', '{:,.2f}'.format(AvgRevenue * Late))\n","print('Predicted Late : \\t', '{:,.2f}'.format(AvgRevenue * PredLate))\n","print()\n","print('*Nominal in Dollar $')"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"V7VofW9n1kOD","executionInfo":{"status":"ok","timestamp":1650783779844,"user_tz":-420,"elapsed":493,"user":{"displayName":"Muhammad Hamzah","userId":"06012328832276120437"}},"outputId":"1741b472-9b0d-4dfb-99fc-2f6a8b420dbf"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Total Cost : \t\t 2,311,955\n","Total Discount : \t 147,092\n","Total Revenue : \t 2,164,863\n","Average Revenue : \t 196.82\n","\n","Potential Revenue\n","Late : \t\t\t 1,291,729.66\n","Predicted Late : \t 839,634.12\n","\n","*Nominal in Dollar $\n"]}]},{"cell_type":"markdown","source":["Possibility of revenue loss potentially saved by **$839,634.12** after action based on predictive modeling"],"metadata":{"id":"EsxQHWQs2IKE"}},{"cell_type":"code","source":["# Filtering customers late & give 5 rating\n","df[(df['Reached.on.Time_Y.N']==1) & (df['Customer_rating']==5)]"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":488},"id":"91qdo-052CoQ","executionInfo":{"status":"ok","timestamp":1650724552213,"user_tz":-420,"elapsed":399,"user":{"displayName":"Muhammad Hamzah","userId":"06012328832276120437"}},"outputId":"8314a02c-75bd-404a-f0cd-2b000b37981c"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["          ID Warehouse_block Mode_of_Shipment  Customer_care_calls  \\\n","1          2               F           Flight                    4   \n","11        12               F           Flight                    4   \n","12        13               D           Flight                    3   \n","17        18               F             Ship                    5   \n","18        19               D             Ship                    5   \n","...      ...             ...              ...                  ...   \n","10900  10901               C             Ship                    5   \n","10903  10904               F             Ship                    3   \n","10910  10911               A             Ship                    5   \n","10963  10964               F           Flight                    4   \n","10986  10987               D             Ship                    4   \n","\n","       Customer_rating  Cost_of_the_Product  Prior_purchases  \\\n","1                    5                  216                2   \n","11                   5                  232                3   \n","12                   5                  198                3   \n","17                   5                  227                3   \n","18                   5                  239                3   \n","...                ...                  ...              ...   \n","10900                5                  196                4   \n","10903                5                  241                4   \n","10910                5                  264                8   \n","10963                5                  220               10   \n","10986                5                  255                5   \n","\n","      Product_importance Gender  Discount_offered  Weight_in_gms  \\\n","1                    low      M                59           3088   \n","11                medium      F                32           3253   \n","12                medium      F                 1           3667   \n","17                medium      M                36           3952   \n","18                  high      M                18           2495   \n","...                  ...    ...               ...            ...   \n","10900             medium      M                 8           4604   \n","10903                low      F                 9           5526   \n","10910               high      F                10           5759   \n","10963             medium      M                 8           4100   \n","10986             medium      M                 5           1458   \n","\n","       Reached.on.Time_Y.N  \n","1                        1  \n","11                       1  \n","12                       1  \n","17                       1  \n","18                       1  \n","...                    ...  \n","10900                    1  \n","10903                    1  \n","10910                    1  \n","10963                    1  \n","10986                    1  \n","\n","[1317 rows x 12 columns]"],"text/html":["\n","  <div id=\"df-509b85b0-db8c-4c17-96f3-926578fe31ed\">\n","    <div class=\"colab-df-container\">\n","      <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: 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<td>F</td>\n","      <td>Flight</td>\n","      <td>4</td>\n","      <td>5</td>\n","      <td>232</td>\n","      <td>3</td>\n","      <td>medium</td>\n","      <td>F</td>\n","      <td>32</td>\n","      <td>3253</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>12</th>\n","      <td>13</td>\n","      <td>D</td>\n","      <td>Flight</td>\n","      <td>3</td>\n","      <td>5</td>\n","      <td>198</td>\n","      <td>3</td>\n","      <td>medium</td>\n","      <td>F</td>\n","      <td>1</td>\n","      <td>3667</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>17</th>\n","      <td>18</td>\n","      <td>F</td>\n","      <td>Ship</td>\n","      <td>5</td>\n","      <td>5</td>\n","      <td>227</td>\n","      <td>3</td>\n","      <td>medium</td>\n","      <td>M</td>\n","      <td>36</td>\n","      <td>3952</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>18</th>\n","      <td>19</td>\n","      <td>D</td>\n","      <td>Ship</td>\n","      <td>5</td>\n","      <td>5</td>\n","      <td>239</td>\n","      <td>3</td>\n","      <td>high</td>\n","      <td>M</td>\n","      <td>18</td>\n","      <td>2495</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>...</th>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","    </tr>\n","    <tr>\n","      <th>10900</th>\n","      <td>10901</td>\n","      <td>C</td>\n","      <td>Ship</td>\n","      <td>5</td>\n","      <td>5</td>\n","      <td>196</td>\n","      <td>4</td>\n","      <td>medium</td>\n","      <td>M</td>\n","      <td>8</td>\n","      <td>4604</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>10903</th>\n","      <td>10904</td>\n","      <td>F</td>\n","      <td>Ship</td>\n","      <td>3</td>\n","      <td>5</td>\n","      <td>241</td>\n","      <td>4</td>\n","      <td>low</td>\n","      <td>F</td>\n","      <td>9</td>\n","      <td>5526</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>10910</th>\n","      <td>10911</td>\n","      <td>A</td>\n","      <td>Ship</td>\n","      <td>5</td>\n","      <td>5</td>\n","      <td>264</td>\n","      <td>8</td>\n","      <td>high</td>\n","      <td>F</td>\n","      <td>10</td>\n","      <td>5759</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>10963</th>\n","      <td>10964</td>\n","      <td>F</td>\n","      <td>Flight</td>\n","      <td>4</td>\n","      <td>5</td>\n","      <td>220</td>\n","      <td>10</td>\n","      <td>medium</td>\n","      <td>M</td>\n","      <td>8</td>\n","      <td>4100</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>10986</th>\n","      <td>10987</td>\n","      <td>D</td>\n","      <td>Ship</td>\n","      <td>4</td>\n","      <td>5</td>\n","      <td>255</td>\n","      <td>5</td>\n","      <td>medium</td>\n","      <td>M</td>\n","      <td>5</td>\n","      <td>1458</td>\n","      <td>1</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>1317 rows × 12 columns</p>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-509b85b0-db8c-4c17-96f3-926578fe31ed')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 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Hamzah","userId":"06012328832276120437"}},"outputId":"e3078960-cadf-4704-b756-f019d199ab50"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["          ID Warehouse_block Mode_of_Shipment  Customer_care_calls  \\\n","0          1               D           Flight                    4   \n","1          2               F           Flight                    4   \n","2          3               A           Flight                    2   \n","3          4               B           Flight                    3   \n","4          5               C           Flight                    2   \n","...      ...             ...              ...                  ...   \n","10985  10986               F             Ship                    5   \n","10986  10987               D             Ship                    4   \n","10987  10988               F             Ship                    5   \n","10991  10992               F             Ship                    5   \n","10994  10995               A             Ship                    4   \n","\n","       Customer_rating  Cost_of_the_Product  Prior_purchases  \\\n","0                    2                  177                3   \n","1                    5                  216                2   \n","2                    2                  183                4   \n","3                    3                  176                4   \n","4                    2                  184                3   \n","...                ...                  ...              ...   \n","10985                1                  229                5   \n","10986                5                  255                5   \n","10987                3                  242                5   \n","10991                2                  261                5   \n","10994                1                  252                5   \n","\n","      Product_importance Gender  Discount_offered  Weight_in_gms  \\\n","0                    low      F                44           1233   \n","1                    low      M                59           3088   \n","2                    low      M                48           3374   \n","3                 medium      M                10           1177   \n","4                 medium      F                46           2484   \n","...                  ...    ...               ...            ...   \n","10985                low      F                10           1040   \n","10986             medium      M                 5           1458   \n","10987                low      F                10           1846   \n","10991             medium      M                 9           1227   \n","10994             medium      F                 1           1538   \n","\n","       Reached.on.Time_Y.N  \n","0                        1  \n","1                        1  \n","2                        1  \n","3                        1  \n","4                        1  \n","...                    ...  \n","10985                    1  \n","10986                    1  \n","10987                    1  \n","10991                    1  \n","10994                    1  \n","\n","[6563 rows x 12 columns]"],"text/html":["\n","  <div id=\"df-15db3f07-7f3e-4255-a07c-0568b3e7f644\">\n","    <div class=\"colab-df-container\">\n","      <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>ID</th>\n","      <th>Warehouse_block</th>\n","      <th>Mode_of_Shipment</th>\n","      <th>Customer_care_calls</th>\n","      <th>Customer_rating</th>\n","      <th>Cost_of_the_Product</th>\n","      <th>Prior_purchases</th>\n","      <th>Product_importance</th>\n","      <th>Gender</th>\n","      <th>Discount_offered</th>\n","      <th>Weight_in_gms</th>\n","      <th>Reached.on.Time_Y.N</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>1</td>\n","      <td>D</td>\n","      <td>Flight</td>\n","      <td>4</td>\n","      <td>2</td>\n","      <td>177</td>\n","      <td>3</td>\n","      <td>low</td>\n","      <td>F</td>\n","      <td>44</td>\n","      <td>1233</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>2</td>\n","      <td>F</td>\n","      <td>Flight</td>\n","      <td>4</td>\n","      <td>5</td>\n","      <td>216</td>\n","      <td>2</td>\n","      <td>low</td>\n","      <td>M</td>\n","      <td>59</td>\n","      <td>3088</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>3</td>\n","      <td>A</td>\n","      <td>Flight</td>\n","      <td>2</td>\n","      <td>2</td>\n","      <td>183</td>\n","      <td>4</td>\n","      <td>low</td>\n","      <td>M</td>\n","      <td>48</td>\n","      <td>3374</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>4</td>\n","      <td>B</td>\n","      <td>Flight</td>\n","      <td>3</td>\n","      <td>3</td>\n","      <td>176</td>\n","      <td>4</td>\n","      <td>medium</td>\n","      <td>M</td>\n","      <td>10</td>\n","      <td>1177</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>5</td>\n","      <td>C</td>\n","      <td>Flight</td>\n","      <td>2</td>\n","      <td>2</td>\n","      <td>184</td>\n","      <td>3</td>\n","      <td>medium</td>\n","      <td>F</td>\n","      <td>46</td>\n","      <td>2484</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>...</th>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","    </tr>\n","    <tr>\n","      <th>10985</th>\n","      <td>10986</td>\n","      <td>F</td>\n","      <td>Ship</td>\n","      <td>5</td>\n","      <td>1</td>\n","      <td>229</td>\n","      <td>5</td>\n","      <td>low</td>\n","      <td>F</td>\n","      <td>10</td>\n","      <td>1040</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>10986</th>\n","      <td>10987</td>\n","      <td>D</td>\n","      <td>Ship</td>\n","      <td>4</td>\n","      <td>5</td>\n","      <td>255</td>\n","      <td>5</td>\n","      <td>medium</td>\n","      <td>M</td>\n","      <td>5</td>\n","      <td>1458</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>10987</th>\n","      <td>10988</td>\n","      <td>F</td>\n","      <td>Ship</td>\n","      <td>5</td>\n","      <td>3</td>\n","      <td>242</td>\n","      <td>5</td>\n","      <td>low</td>\n","      <td>F</td>\n","      <td>10</td>\n","      <td>1846</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>10991</th>\n","      <td>10992</td>\n","      <td>F</td>\n","      <td>Ship</td>\n","      <td>5</td>\n","      <td>2</td>\n","      <td>261</td>\n","      <td>5</td>\n","      <td>medium</td>\n","      <td>M</td>\n","      <td>9</td>\n","      <td>1227</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>10994</th>\n","      <td>10995</td>\n","      <td>A</td>\n","      <td>Ship</td>\n","      <td>4</td>\n","      <td>1</td>\n","      <td>252</td>\n","      <td>5</td>\n","      <td>medium</td>\n","      <td>F</td>\n","      <td>1</td>\n","      <td>1538</td>\n","      <td>1</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>6563 rows × 12 columns</p>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-15db3f07-7f3e-4255-a07c-0568b3e7f644')\"\n","              title=\"Convert this dataframe to an interactive 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