Exp6-Prediction-Batch 1-1 week.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "from pandas import DataFrame\n",
    "from pandas import Series\n",
    "from pandas import concat\n",
    "from pandas import read_csv\n",
    "from pandas import datetime\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense\n",
    "from keras.layers import LSTM\n",
    "from math import sqrt\n",
    "from matplotlib import pyplot\n",
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Hardcode all variables\n",
    "batch_size_exp = 1\n",
    "epoch_exp = 7\n",
    "neurons_exp = 10\n",
    "predict_values_exp = 168\n",
    "lag_exp=24"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# frame a sequence as a supervised learning problem\n",
    "def timeseries_to_supervised(data, lag=1):\n",
    "    df = DataFrame(data)\n",
    "    columns = [df.shift(i) for i in range(1, lag+1)]\n",
    "    columns.append(df)\n",
    "    df = concat(columns, axis=1)\n",
    "    df.fillna(0, inplace=True)\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create a differenced series\n",
    "def difference(dataset, interval=1):\n",
    "    diff = list()\n",
    "    for i in range(interval, len(dataset)):\n",
    "        value = dataset[i] - dataset[i - interval]\n",
    "        diff.append(value)\n",
    "    return Series(diff)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# invert differenced value\n",
    "def inverse_difference(history, yhat, interval=1):\n",
    "    return yhat + history[-interval]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# scale train and test data to [-1, 1]\n",
    "def scale(train, test):\n",
    "    # fit scaler\n",
    "    scaler = MinMaxScaler(feature_range=(-1, 1))\n",
    "    scaler = scaler.fit(train)\n",
    "    # transform train\n",
    "    train = train.reshape(train.shape[0], train.shape[1])\n",
    "    train_scaled = scaler.transform(train)\n",
    "    # transform test\n",
    "    test = test.reshape(test.shape[0], test.shape[1])\n",
    "    test_scaled = scaler.transform(test)\n",
    "    return scaler, train_scaled, test_scaled"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# inverse scaling for a forecasted value\n",
    "def invert_scale(scaler, X, value):\n",
    "    new_row = [x for x in X] + [value]\n",
    "    array = np.array(new_row)\n",
    "    array = array.reshape(1, len(array))\n",
    "    inverted = scaler.inverse_transform(array)\n",
    "    return inverted[0, -1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# fit an LSTM network to training data\n",
    "def fit_lstm(train, batch_size, nb_epoch, neurons):\n",
    "    X, y = train[:, 0:-1], train[:, -1]\n",
    "    X = X.reshape(X.shape[0], 1, X.shape[1])\n",
    "    model = Sequential()\n",
    "    model.add(LSTM(neurons, batch_input_shape=(batch_size, X.shape[1], X.shape[2]), stateful=True))\n",
    "    model.add(Dense(1))\n",
    "    model.compile(loss='mean_squared_error', optimizer='adam')\n",
    "    for i in range(nb_epoch):\n",
    "        model.fit(X, y, epochs=1, batch_size=batch_size, verbose=1, shuffle=False)\n",
    "        model.reset_states()\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# make a one-step forecast\n",
    "def forecast_lstm(model, batch_size, X):\n",
    "    X = X.reshape(1, 1, len(X))\n",
    "    #print(X)\n",
    "    yhat = model.predict(X, batch_size=1)\n",
    "    return yhat[0,0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Air temperature | ('C)</th>\n",
       "      <th>Pressure | (atm)</th>\n",
       "      <th>Wind speed | (m/s)</th>\n",
       "      <th>Wind direction | (deg)</th>\n",
       "      <th>Power generated by system | (kW)</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DateTime</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2007-01-01 00:00:00</th>\n",
       "      <td>10.926</td>\n",
       "      <td>0.979103</td>\n",
       "      <td>9.014</td>\n",
       "      <td>229</td>\n",
       "      <td>33688.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-01-01 01:00:00</th>\n",
       "      <td>9.919</td>\n",
       "      <td>0.979566</td>\n",
       "      <td>9.428</td>\n",
       "      <td>232</td>\n",
       "      <td>37261.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-01-01 02:00:00</th>\n",
       "      <td>8.567</td>\n",
       "      <td>0.979937</td>\n",
       "      <td>8.700</td>\n",
       "      <td>236</td>\n",
       "      <td>30502.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-01-01 03:00:00</th>\n",
       "      <td>7.877</td>\n",
       "      <td>0.980053</td>\n",
       "      <td>8.481</td>\n",
       "      <td>247</td>\n",
       "      <td>28419.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-01-01 04:00:00</th>\n",
       "      <td>7.259</td>\n",
       "      <td>0.979867</td>\n",
       "      <td>8.383</td>\n",
       "      <td>256</td>\n",
       "      <td>27370.3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Air temperature | ('C)  Pressure | (atm)  \\\n",
       "DateTime                                                        \n",
       "2007-01-01 00:00:00                  10.926          0.979103   \n",
       "2007-01-01 01:00:00                   9.919          0.979566   \n",
       "2007-01-01 02:00:00                   8.567          0.979937   \n",
       "2007-01-01 03:00:00                   7.877          0.980053   \n",
       "2007-01-01 04:00:00                   7.259          0.979867   \n",
       "\n",
       "                     Wind speed | (m/s)  Wind direction | (deg)  \\\n",
       "DateTime                                                          \n",
       "2007-01-01 00:00:00               9.014                     229   \n",
       "2007-01-01 01:00:00               9.428                     232   \n",
       "2007-01-01 02:00:00               8.700                     236   \n",
       "2007-01-01 03:00:00               8.481                     247   \n",
       "2007-01-01 04:00:00               8.383                     256   \n",
       "\n",
       "                     Power generated by system | (kW)  \n",
       "DateTime                                               \n",
       "2007-01-01 00:00:00                           33688.1  \n",
       "2007-01-01 01:00:00                           37261.9  \n",
       "2007-01-01 02:00:00                           30502.9  \n",
       "2007-01-01 03:00:00                           28419.2  \n",
       "2007-01-01 04:00:00                           27370.3  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "''' Loading data '''\n",
    "import pandas as pd\n",
    "series = pd.read_excel('AL_WIND_07_12.xlsx',index_col=\"DateTime\")\n",
    "series.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Power generated by system | (kW)</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DateTime</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2007-01-01 00:00:00</th>\n",
       "      <td>33688.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-01-01 01:00:00</th>\n",
       "      <td>37261.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-01-01 02:00:00</th>\n",
       "      <td>30502.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-01-01 03:00:00</th>\n",
       "      <td>28419.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-01-01 04:00:00</th>\n",
       "      <td>27370.3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Power generated by system | (kW)\n",
       "DateTime                                             \n",
       "2007-01-01 00:00:00                           33688.1\n",
       "2007-01-01 01:00:00                           37261.9\n",
       "2007-01-01 02:00:00                           30502.9\n",
       "2007-01-01 03:00:00                           28419.2\n",
       "2007-01-01 04:00:00                           27370.3"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''Drop all the features as we will not be having any in production'''\n",
    "del series['Air temperature | (\\'C)']\n",
    "del series['Pressure | (atm)']\n",
    "del series['Wind speed | (m/s)']\n",
    "del series['Wind direction | (deg)']\n",
    "series.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Power generated by system | (kW)</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DateTime</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2012-12-31 09:00:00</th>\n",
       "      <td>6110.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-12-31 10:00:00</th>\n",
       "      <td>6251.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-12-31 11:00:00</th>\n",
       "      <td>6899.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-12-31 12:00:00</th>\n",
       "      <td>4514.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-12-31 13:00:00</th>\n",
       "      <td>1561.25</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Power generated by system | (kW)\n",
       "DateTime                                             \n",
       "2012-12-31 09:00:00                           6110.57\n",
       "2012-12-31 10:00:00                           6251.34\n",
       "2012-12-31 11:00:00                           6899.17\n",
       "2012-12-31 12:00:00                           4514.49\n",
       "2012-12-31 13:00:00                           1561.25"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for i in range(0,10):\n",
    "  series = series[:-1]\n",
    "series.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# transform data to be stationary\n",
    "raw_values = series.values\n",
    "diff_values = difference(raw_values, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# transform data to be supervised learning\n",
    "supervised = timeseries_to_supervised(diff_values, lag_exp)\n",
    "supervised_values = supervised.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# split data into train and test-sets\n",
    "train, test = supervised_values[0:-predict_values_exp], supervised_values[-predict_values_exp:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\envs\\tensorflow\\lib\\site-packages\\sklearn\\utils\\validation.py:475: DataConversionWarning: Data with input dtype object was converted to float64 by MinMaxScaler.\n",
      "  warnings.warn(msg, DataConversionWarning)\n"
     ]
    }
   ],
   "source": [
    "# transform the scale of the data\n",
    "scaler, train_scaled, test_scaled = scale(train, test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1\n",
      "52381/52381 [==============================] - 101s 2ms/step - loss: 0.0140\n",
      "Epoch 1/1\n",
      "52381/52381 [==============================] - 102s 2ms/step - loss: 0.0137\n",
      "Epoch 1/1\n",
      "52381/52381 [==============================] - 116s 2ms/step - loss: 0.0135\n",
      "Epoch 1/1\n",
      "52381/52381 [==============================] - 122s 2ms/step - loss: 0.0134\n",
      "Epoch 1/1\n",
      "52381/52381 [==============================] - 121s 2ms/step - loss: 0.0133\n",
      "Epoch 1/1\n",
      "52381/52381 [==============================] - 130s 2ms/step - loss: 0.0132\n",
      "Epoch 1/1\n",
      "52381/52381 [==============================] - 130s 2ms/step - loss: 0.0132\n"
     ]
    }
   ],
   "source": [
    "# fit the model\n",
    "lstm_model = fit_lstm(train_scaled, batch_size_exp, epoch_exp, neurons_exp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Hour=1, Predicted=17841.948934, Expected=9201.150000\n",
      "Hour=2, Predicted=6441.120420, Expected=4013.650000\n",
      "Hour=3, Predicted=2340.184228, Expected=3423.460000\n",
      "Hour=4, Predicted=3068.701197, Expected=5700.400000\n",
      "Hour=5, Predicted=6286.316442, Expected=17196.400000\n",
      "Hour=6, Predicted=17343.648290, Expected=9121.700000\n",
      "Hour=7, Predicted=9578.989250, Expected=11736.800000\n",
      "Hour=8, Predicted=12553.364989, Expected=11387.100000\n",
      "Hour=9, Predicted=12610.965824, Expected=11611.200000\n",
      "Hour=10, Predicted=13554.336265, Expected=15168.500000\n",
      "Hour=11, Predicted=16988.785875, Expected=17830.000000\n",
      "Hour=12, Predicted=18980.447890, Expected=14867.700000\n",
      "Hour=13, Predicted=15729.742489, Expected=6515.390000\n",
      "Hour=14, Predicted=7408.790366, Expected=4546.470000\n",
      "Hour=15, Predicted=5922.226777, Expected=7461.770000\n",
      "Hour=16, Predicted=9404.120866, Expected=5648.590000\n",
      "Hour=17, Predicted=7959.425088, Expected=9508.370000\n",
      "Hour=18, Predicted=11599.898438, Expected=0.000000\n",
      "Hour=19, Predicted=1343.166306, Expected=8662.060000\n",
      "Hour=20, Predicted=9169.745441, Expected=33313.400000\n",
      "Hour=21, Predicted=32770.467194, Expected=18847.400000\n",
      "Hour=22, Predicted=17417.780502, Expected=21983.700000\n",
      "Hour=23, Predicted=20752.853917, Expected=27665.600000\n",
      "Hour=24, Predicted=26593.833472, Expected=25627.700000\n",
      "Hour=25, Predicted=24534.588100, Expected=15047.900000\n",
      "Hour=26, Predicted=14051.018007, Expected=6001.030000\n",
      "Hour=27, Predicted=5376.162849, Expected=2831.900000\n",
      "Hour=28, Predicted=2692.105090, Expected=3438.420000\n",
      "Hour=29, Predicted=3774.172791, Expected=2174.210000\n",
      "Hour=30, Predicted=2875.484112, Expected=3697.180000\n",
      "Hour=31, Predicted=4751.972235, Expected=3698.550000\n",
      "Hour=32, Predicted=5152.587835, Expected=2191.970000\n",
      "Hour=33, Predicted=4046.318255, Expected=1260.400000\n",
      "Hour=34, Predicted=3416.080893, Expected=0.000000\n",
      "Hour=35, Predicted=2210.103630, Expected=0.000000\n",
      "Hour=36, Predicted=2016.746364, Expected=0.000000\n",
      "Hour=37, Predicted=1728.971037, Expected=6011.980000\n",
      "Hour=38, Predicted=7461.959602, Expected=15081.800000\n",
      "Hour=39, Predicted=16325.347326, Expected=18350.300000\n",
      "Hour=40, Predicted=19408.932279, Expected=22881.600000\n",
      "Hour=41, Predicted=23700.018269, Expected=29675.600000\n",
      "Hour=42, Predicted=30158.612127, Expected=15974.300000\n",
      "Hour=43, Predicted=16070.720874, Expected=13353.300000\n",
      "Hour=44, Predicted=13062.220535, Expected=8196.580000\n",
      "Hour=45, Predicted=7554.705744, Expected=8520.750000\n",
      "Hour=46, Predicted=7627.185484, Expected=11346.600000\n",
      "Hour=47, Predicted=10405.259039, Expected=12161.000000\n",
      "Hour=48, Predicted=11340.302026, Expected=18754.700000\n",
      "Hour=49, Predicted=18158.609096, Expected=13102.700000\n",
      "Hour=50, Predicted=12801.775293, Expected=5068.350000\n",
      "Hour=51, Predicted=5120.392994, Expected=3092.510000\n",
      "Hour=52, Predicted=3517.207809, Expected=9189.220000\n",
      "Hour=53, Predicted=9967.155030, Expected=19598.200000\n",
      "Hour=54, Predicted=20686.986443, Expected=18850.800000\n",
      "Hour=55, Predicted=20209.491502, Expected=26651.500000\n",
      "Hour=56, Predicted=28242.644394, Expected=41222.500000\n",
      "Hour=57, Predicted=42993.904403, Expected=44307.600000\n",
      "Hour=58, Predicted=46179.992013, Expected=45091.100000\n",
      "Hour=59, Predicted=46962.716411, Expected=54367.300000\n",
      "Hour=60, Predicted=56133.859342, Expected=55807.500000\n",
      "Hour=61, Predicted=57383.156243, Expected=56061.100000\n",
      "Hour=62, Predicted=57382.077978, Expected=56544.300000\n",
      "Hour=63, Predicted=57565.350505, Expected=56349.800000\n",
      "Hour=64, Predicted=57034.381075, Expected=56311.400000\n",
      "Hour=65, Predicted=56631.620400, Expected=56247.900000\n",
      "Hour=66, Predicted=56201.958901, Expected=56152.000000\n",
      "Hour=67, Predicted=55784.768503, Expected=22555.500000\n",
      "Hour=68, Predicted=21955.245952, Expected=53470.800000\n",
      "Hour=69, Predicted=52751.162298, Expected=56660.300000\n",
      "Hour=70, Predicted=55940.762105, Expected=52066.200000\n",
      "Hour=71, Predicted=51460.292429, Expected=57112.800000\n",
      "Hour=72, Predicted=56712.786376, Expected=57622.700000\n",
      "Hour=73, Predicted=57498.728587, Expected=57467.300000\n",
      "Hour=74, Predicted=57663.573657, Expected=53316.500000\n",
      "Hour=75, Predicted=53849.672780, Expected=56056.300000\n",
      "Hour=76, Predicted=56914.893409, Expected=58362.600000\n",
      "Hour=77, Predicted=59510.077948, Expected=58434.400000\n",
      "Hour=78, Predicted=59817.052692, Expected=58554.200000\n",
      "Hour=79, Predicted=60110.051971, Expected=58694.800000\n",
      "Hour=80, Predicted=60358.617036, Expected=58575.200000\n",
      "Hour=81, Predicted=60279.644679, Expected=53010.700000\n",
      "Hour=82, Predicted=54687.078719, Expected=47433.300000\n",
      "Hour=83, Predicted=49013.532802, Expected=42583.800000\n",
      "Hour=84, Predicted=44003.128849, Expected=37533.400000\n",
      "Hour=85, Predicted=38732.752324, Expected=30802.900000\n",
      "Hour=86, Predicted=31730.805961, Expected=22118.300000\n",
      "Hour=87, Predicted=22734.577978, Expected=19872.700000\n",
      "Hour=88, Predicted=20154.663648, Expected=20219.700000\n",
      "Hour=89, Predicted=20174.508683, Expected=22311.600000\n",
      "Hour=90, Predicted=21988.093960, Expected=16743.400000\n",
      "Hour=91, Predicted=16229.364986, Expected=11397.100000\n",
      "Hour=92, Predicted=10803.277526, Expected=7571.350000\n",
      "Hour=93, Predicted=7009.341886, Expected=7530.230000\n",
      "Hour=94, Predicted=7094.841699, Expected=5941.390000\n",
      "Hour=95, Predicted=5706.688911, Expected=6121.930000\n",
      "Hour=96, Predicted=6143.163854, Expected=1487.690000\n",
      "Hour=97, Predicted=1798.550062, Expected=0.000000\n",
      "Hour=98, Predicted=611.343468, Expected=725.355000\n",
      "Hour=99, Predicted=1625.584946, Expected=314.976000\n",
      "Hour=100, Predicted=1471.829747, Expected=0.000000\n",
      "Hour=101, Predicted=1364.582196, Expected=0.000000\n",
      "Hour=102, Predicted=1512.325926, Expected=0.000000\n",
      "Hour=103, Predicted=1594.587962, Expected=0.000000\n",
      "Hour=104, Predicted=1610.360943, Expected=0.000000\n",
      "Hour=105, Predicted=1561.587034, Expected=0.000000\n",
      "Hour=106, Predicted=1452.136999, Expected=0.000000\n",
      "Hour=107, Predicted=1287.136644, Expected=0.000000\n",
      "Hour=108, Predicted=1072.658727, Expected=0.000000\n",
      "Hour=109, Predicted=816.639283, Expected=1279.770000\n",
      "Hour=110, Predicted=1810.681136, Expected=2610.950000\n",
      "Hour=111, Predicted=2844.673374, Expected=2893.040000\n",
      "Hour=112, Predicted=2845.906005, Expected=680.289000\n",
      "Hour=113, Predicted=404.282229, Expected=1316.470000\n",
      "Hour=114, Predicted=893.571538, Expected=2730.490000\n",
      "Hour=115, Predicted=2258.534943, Expected=8771.750000\n",
      "Hour=116, Predicted=8347.371568, Expected=11282.000000\n",
      "Hour=117, Predicted=10987.148050, Expected=13774.400000\n",
      "Hour=118, Predicted=13675.441329, Expected=19978.700000\n",
      "Hour=119, Predicted=20121.776064, Expected=23318.400000\n",
      "Hour=120, Predicted=23728.107063, Expected=12301.800000\n",
      "Hour=121, Predicted=12982.170221, Expected=5331.850000\n",
      "Hour=122, Predicted=6267.711899, Expected=1479.590000\n",
      "Hour=123, Predicted=2638.758101, Expected=0.000000\n",
      "Hour=124, Predicted=1336.664723, Expected=1699.950000\n",
      "Hour=125, Predicted=3158.858205, Expected=4932.380000\n",
      "Hour=126, Predicted=6453.154982, Expected=10551.200000\n",
      "Hour=127, Predicted=12072.292468, Expected=11277.200000\n",
      "Hour=128, Predicted=12738.844956, Expected=20258.100000\n",
      "Hour=129, Predicted=21604.572205, Expected=27235.300000\n",
      "Hour=130, Predicted=28416.310674, Expected=29297.100000\n",
      "Hour=131, Predicted=30269.094726, Expected=50140.200000\n",
      "Hour=132, Predicted=50868.832233, Expected=28258.100000\n",
      "Hour=133, Predicted=28722.326853, Expected=0.000000\n",
      "Hour=134, Predicted=197.963345, Expected=3868.210000\n",
      "Hour=135, Predicted=3823.572728, Expected=0.000000\n",
      "Hour=136, Predicted=-232.715856, Expected=0.000000\n",
      "Hour=137, Predicted=-343.100695, Expected=0.000000\n",
      "Hour=138, Predicted=-366.147287, Expected=4135.800000\n",
      "Hour=139, Predicted=3829.837863, Expected=13607.200000\n",
      "Hour=140, Predicted=13434.293639, Expected=24571.700000\n",
      "Hour=141, Predicted=24588.985634, Expected=32106.600000\n",
      "Hour=142, Predicted=32352.003526, Expected=36286.000000\n",
      "Hour=143, Predicted=36777.750656, Expected=32040.800000\n",
      "Hour=144, Predicted=32778.286898, Expected=27413.500000\n",
      "Hour=145, Predicted=28378.838228, Expected=20005.800000\n",
      "Hour=146, Predicted=21166.226514, Expected=19870.600000\n",
      "Hour=147, Predicted=21181.841627, Expected=23296.700000\n",
      "Hour=148, Predicted=24706.767247, Expected=36275.400000\n",
      "Hour=149, Predicted=37728.269534, Expected=41119.700000\n",
      "Hour=150, Predicted=42558.645037, Expected=45831.200000\n",
      "Hour=151, Predicted=47201.504706, Expected=38451.600000\n",
      "Hour=152, Predicted=39702.625860, Expected=38107.700000\n",
      "Hour=153, Predicted=39194.372827, Expected=36325.100000\n",
      "Hour=154, Predicted=37209.677490, Expected=37641.300000\n",
      "Hour=155, Predicted=38296.070777, Expected=36876.500000\n",
      "Hour=156, Predicted=37287.961811, Expected=35723.600000\n",
      "Hour=157, Predicted=35897.403051, Expected=28221.500000\n",
      "Hour=158, Predicted=28186.527948, Expected=22650.000000\n",
      "Hour=159, Predicted=22460.793861, Expected=14845.100000\n",
      "Hour=160, Predicted=14574.453350, Expected=11449.700000\n",
      "Hour=161, Predicted=11176.099124, Expected=11637.200000\n",
      "Hour=162, Predicted=11435.209192, Expected=6465.350000\n",
      "Hour=163, Predicted=6399.082731, Expected=5802.110000\n",
      "Hour=164, Predicted=5919.990545, Expected=6110.570000\n",
      "Hour=165, Predicted=6443.250184, Expected=6251.340000\n",
      "Hour=166, Predicted=6811.653171, Expected=6899.170000\n",
      "Hour=167, Predicted=7682.912755, Expected=4514.490000\n",
      "Hour=168, Predicted=5501.911748, Expected=1561.250000\n"
     ]
    }
   ],
   "source": [
    "# walk-forward validation on the test data\n",
    "predictions = list()\n",
    "expectations = list()\n",
    "predictions_plot = list()\n",
    "expectations_plot = list()\n",
    "test_pred = list()\n",
    "for i in range(len(test_scaled)):\n",
    "    # make one-step forecast\n",
    "    X, y = test_scaled[i, 0:-1], test_scaled[i, -1]\n",
    "    yhat = forecast_lstm(lstm_model, 1, X)#batch_size_exp to 1\n",
    "    '''# Start Debug prints\n",
    "    print(\"X: %\", X)\n",
    "    print(\"yhat: %\", yhat)\n",
    "    # End Debug prints'''\n",
    "    # Replacing value in test scaled with the predicted value.\n",
    "    test_pred = [yhat] + test_pred \n",
    "    if len(test_pred) > lag_exp+1:\n",
    "        test_pred = test_pred[:-1]\n",
    "    if i+1<len(test_scaled):\n",
    "        if i+1 > lag_exp+1:\n",
    "            test_scaled[i+1] = test_pred\n",
    "        else:\n",
    "            test_scaled[i+1] = np.concatenate((test_pred, test_scaled[i+1, i+1:]),axis=0)\n",
    "\n",
    "    # invert scaling\n",
    "    yhat = invert_scale(scaler, X, yhat)\n",
    "    # invert differencing\n",
    "    yhat = inverse_difference(raw_values, yhat, len(test_scaled)+1-i)\n",
    "    # store forecast\n",
    "    expected = raw_values[len(train) + i + 1]\n",
    "    predictions_plot.append(yhat)\n",
    "    expectations_plot.append(expected)\n",
    "    if expected != 0:\n",
    "        predictions.append(yhat)\n",
    "        expectations.append(expected)\n",
    "    print('Hour=%d, Predicted=%f, Expected=%f' % (i+1, yhat, expected))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Absolute Percent Error:  44.742541420265795\n"
     ]
    }
   ],
   "source": [
    "expectations = np.array(expectations)\n",
    "predictions = np.array(predictions)\n",
    "print(\"Mean Absolute Percent Error: \", (np.mean(np.abs((expectations - predictions) / expectations))*100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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FqfNgWRZdXV0UFhZO2zNVmBIREZkpySQ88Y924WlI6gchC7qPQ+dBsBL2qaJKqFkHR7bBwV/A5R+C6/4CiiqyvfJpcfJMiM9tPcT/7jrFvOIAf/OaRoIFXpra+2nq6OOZQ81ct/uLrPE+TdhXhnfRetj0DqheAzVr4cnPwfGnxn2PHce7OdTex92/t94+sfuH4C2Axttn5GtaWVMC2EHoG+oz/L1UN0K4B/raoKx2RtYgIiIiU1dfX09zczOdnZ25XsqsV1hYSH19ho1gzpEKUyIiIjPld1+AJ++xQ7G9fvvcUOezBWV1sPo1ULsRajdBeb3dZdPXBo9/Cp7+Mrz4Pbj+r2Hze8A7O75tnw1F+dLjR7j/6eN4PHDn9cv50CuXU1boH76prx2+/zdYLTv4auBdPFf7h3z9vZelP2j59dD0CPQ02382GXz32ROUFPh4/cY6SMRgz49h1a0zVsxrqCkF4HC7S2GqapV97DyowpSIiEge8fv9LFu2LNfLkAxmx0+4IiIis82JZ+Gxf4A1d8Bbvj21sa7ShXDHl+GyD8LDfw2/+DN47j648RPQcAt48nfvks6+CLf8+5OcDUV5y6WL+dObVrKwfFSrd9te+O7bYPAM5m3/xUv7lrBzf/vY3IelV9vHY0/BxreNea/ugSgP7mnlbVsWU1zgg6ZHIXQaNoy9d7osnRck4PW4B6AP7cx30C6siYiIiMi48vcnWxERkdkqdAZ+/F47fPv2L5571lDtRnjPg/DW/4L4IHzv9+FLW+DZr0LEpTCSY88fO8OZgSjffO9lfPbNG8YWpQ49BF+/GawkvO9haHw9m5dU0h2K8fLpgfR7a9ZCQbnrON//7GwmGk/yjsuX2Cd2/8Aeh1xx0wx8ZTaf18PFVcUcdt2ZrwqK5tmFKRERERGZkApTIiIi08my4Gcfhv4OePM3obD8/J5nDKy5He58Ht70dbvw8tBfwr80wkN3QddL07PuabL/VC9ej+HyZfPGXnz6K/C9t8OCBvjA43bhDbh0aSVg50Wl8Xhh6ZUZC1OWZfHdZ0+weUkFjbVlEI/YRa/G14MvMO1f10gNNaUcbu/PfNEYu2uqQ4UpERERkclQYUpERGQ6Pf0lOPww3PxpWLTZ9ba2njDN3aHJb7frC8D6N8MHHoM/egxW3QbP3wdfvBS++/v2uNs0b90LdrHo2n9+nNP9kUndf6C1l+VVxRT6vekXuo/BI38Fq14D730oLX9peVUJZYU+dp44O/aBS6+2d9nra0s7/fTRLo6eHuAdly+1Txz7rb2b3+rXTeXLOycrq0toOTvIQCSe+Ybq1U6o/fT/fYiIiIhcaJQxJSIiMl1OPg/bPmkXRy7/UMZbmrtDfPGxI/x4ZzOJpMX84gAbF1ewob6cjfX2cX5JwfjvU7/F/u/mT8HzX4ftX4dvvQbqNsNVH7F3pJumoPSf7Gzm5JlBth87w63rJg7z3t/ay2WZuqVad9vH6/4MAsG0Sx6P4ZIllewc3TEFwzlTx38H635v6PR3nj1BWaGP121w1nToIfAHYdl1k/q6zkcqAP1IRz8bF2cKQF8N4bN211xpzYyvR0RERGQ2U2FKRERkOoTOwI/fZ++0d8eXxuRKdfSG+fKvjvC9504C8K4rl3LxgmJebO7hxZNn+dWhjqEGm5ICH0UBL0V+L8GAl0LnWFLgo7zIT3mRn4qgfSyb9x6qXv8uVrT+nPl7/hPvj98LFUvgijvhkndCQck5f0mWZfGrgx0A7G7umbAwdTYUpbUnzJrasrEXO/YDBqoaM7720qWV/Nu2w/SGY+m799VuhECJPc7nFKY6+yJs3dfGO69YandmWZZdmLr4evAXndPXOhUra+w/08PtfS6FqdTOfAdUmBIRERGZgApTIiIi0+GBj0DfKXjfI3YOlOPMQJSv/volvv30MeIJi7dsWcxHXr2Cugq7gPKHzn39kTh7W3rY3XyWtp4Ig7EEg9E4g7EEoWiCwWiCEwMhegZjnA3FGIwlRi1gKR7+gdf4d/Gh3l+w/uGPE972GQqv/IC9u1/pwil/SYfb+znVEwZgT0vPhPfvb+0FsDOfRmvfC/MuHtMtlbJ5SSWWBbtOnOW6lVXDF7w+WHy53THl+NGOk8QSFn+QCj1v2wO9zfCquyb5lZ2fpfOLCfg8NHW45Eylim+dh+DiV2VlTSIiIiKzlQpTIiIi5yvSBwcfhKv/hETdpew+0c1vm07zm6bT7DzRTcKyeMOmRXzsxgaWzi/O+IiSAh9XXDyfKy6eP7m3jCfoGYzROxjjdH+U9t4wHb0R2nuX89Xe1xI99gxvifyUm37zr/C7L8KGt8KVH7Hzjybpcadb6tWrq9lxvBvLsjCpTjDLgrbdQwHmYAefg1thar+9y56LjYvL8Rg70yqtMAWw9Cp4/FMw0IUVnMf3nzvJZcvmsaLaHqnj0EOAgZW3TPprOx9ej2F5VYn7znwl1VBYAR0HsrIeERERkdlMhSkREZHz1dsKwDeOFPP53z1Kz2AMY2BdXTkfuO5ifu+SRUO5RNOlwOelutRLdWkhK6rHXr/7oSB3/nYZh//vSnjmK/DCd+CF/4aGm+HaP4MlV0z4Hr862MHaujJubKzh8YMdnDwzyJL5TsfT4Ufge2+zg8yXXgXAgdY+qkoLqCodlZEVHYAzR+3imIvSQj8ra0rZeSJDztRF19jHE7/jxZJrOXEmxP+5fsXw9UO/hPpX2AWhLFlZU8L2YxnWCvYYZ3Wj3TElIiIiIuPSrnwiIiLnadd+uzNmx5kCbllbwxfffgk7/vYmfv6Ra/j4raunvSg1GQGfh2giiTXvYnjtv8Cf7oPr/wZadsK3Xw/J0aOA6XpCMXac6ObVq6vZUF8OwO6WEbvmHfqFfTzxzNCpA629mbulOg8C1rgdU2DnTO06cZZkctRudnWXgK8Qjv+OB188hd9ruGWtM5rY0wKtu+xdCrNoZU0pLWcH6Xfbma9qlZ0xpZ35RERERMalwpSIiMh56I/E+d/f7gDg3/7oVv75zRt5/cY65hUHcrquAp/9LT6aSNoniufDK/8Srvo/kIhCPDLu63/d1EkiaXH96mpW1pQS8HrY0+zkTFkWND1qf3xqp/0+8SRNHX001mYowrXvs4/Va8Z9z81LKumLxMdmN/kKoP4VWMd+yy/2tHJdQxXlQScg/fDD9nHVa8Z99nRbUW0HoDe5jfNVrYbBbhjozOKqRERERGYfFaZERETOwz0PH6Rg0M5iClQsyvFqhqUKU5F4Mv2C1xmzS4xfmPrVwQ7mFQfYWF9BwOehsbaU3anCVNtu6Gu1d8trsQtTL3X2E0tYmXfka98P/iBULhv3PS9daofG7zjuMs7XtoeBni5et3HE7oCHH7afm9oJL0tWOl1w7gHoTpZX58EsrUhERERkdlJhSkRE5Bw9f+wM9z9znFfVJewiTUH2R/bcBFIdU6MLUz6nk2ucjqlE0uKJQx28cmUVXo8ddr6+vpy9LT32mN3hrfaNl30Aelugr50Dzo58GQtTHfvszCXP+D92LJ0fZH5xIHPO1NKrMFhc4W/ixsYa+1ykH47+2u6WSoWyZ8mSeUEKfJ7xO6YAOlSYEhERERmPClMiIiLnIBxL8PH/2c2iiiK2zItA6cJcLylNwOtSmEp1TI1TmNp18izdoRjXrx4OE9+wqIK+SJxjXQPQ9AjUbYaVt9oXT+1k/6leCnweli0YteugZdmjfBOM8QEYY7hkSSU7M3RMJeq2EMPHm+Yfo7TQGeM7+iu78yvL+VIwcmc+l46p0oVQWK6OKREREZEJqDAlIiJyDr74eBNHOwf4p99bj2+gHUprJ35RFrl3TKVG+aKur/3VwQ68HsMrG6qGzq13AtAPHX0ZmrfDyltg4QYwXmjZwYG2XlYtLMXnHfWjRX8HhLqgZt2k1n3p0kqOnh7gzED6+p5tDrEreTGXmRGFnkMPQWHFpHYYnAkra0rcO6aMsbumtDOfiIiIyLhUmBIREZmifad6+I9fH+XNl9ZzbUOVnbeUbx1To8PPU7wTj/L96lAHly6pHA4YBxqqSyjweYgc3ApY0HAzBIJQ3YjVYndMNS7MlC+11z7WTNwxBbB5SQUAL4wa53twdys7WEvF2X32CF8yYedLNdwMXn+mR824hppSTvWE6QvHMt+Q2plPRERERFypMCUiIjIF8USSj//PbiqDAf72tY32qFpfW/51TLmN8vnGDz9v7w2z71Rv2hgfgM/rYW1dGVWtv4biaqjdZF9YtBmrZSfdoShr6jLlS+23j9VrJ7XuDfUV+DwmLQA9nkjy8N424ouvxFgJOPksND9vd2LlYIwvZeIA9EZ7jQOns7gqERERkdlFhSkREZEpuO+3L7O3pZdP3bGWimAABrvtIk++FaZcd+VLdUxlHuX71UF7h8FXjypMAWxaVMK6we0kG24aDjKv24wn3M0S00Fjxh359kHJQiieP6l1FwW8rKkrSwtA/91LXZwZiLJqyw326ODx38GhX4LHDytumNRzZ8LKmhKAcQLQnZ0CO9Q1JSIiIuJGhSkREZFJ6gvH+LdHD3PL2hpuW+8Uovra7GO+jvJNsWPq8YMdLKooGiq6jHRd0THKzQDt1dcNn1y0GYCN5iVW12bYlbB9H9RMrlsqZfOSSl482UPcGUN8cPcpSgp8XLtuGdRtguNP2flSF11jB4znSH2lvTOfawB6daN9VAC6iIiIiCsVpkRERCap5ewgkXiSOzYtGj7Z12of86xjqsDnBTJlTKV25RvbMRWJJ/jtkdNcv7oKY8yY6xsHnyFmeXnOs3H4ZPUaoibA1cETlBWOynpKxO3w70nmS6VsXlrJYCzBwbY+onF7jO/mNTUU+r2w9Cp7lO/0YVj1mik9d7p5PYYV1SXuo3yltVBQpgB0ERERkXGoMCUiIjJJ7b12l1F1acHwyTztmCpIjfLFEukXxumYeu7lM4SiCa5fNXaMD6Ci5Ql2spoXOkYUu7x+mswyLvUdHfuCMy/Z7zPJHflSLl1aCcCO4938pqmT3nCc1210Cn9LrwHLef9Vt07puTNhZU3pBDvzrVLHlIiIiMg4VJgSERGZpPbeMAA1ZYXDJ4c6pvKrMOW6K1+qMJVhV77HD3ZQ4PNw1fIFYx949iSmYz+Hyq5id/PZodOhaJznohdxUfSI3SE1UmpHvuqpdUzVlReysKyQnSe6eXB3K+VFfq5ZUWVfXHIFYKBmPVQsmdJzZ0JDTQmtPWF6XXfmW63ClIiIiMg4VJgSERGZpA6nMFU1umOqsAL8RTlaVWauu/Klws8TY0f5fnWwgyuXz6co4B37wKZHABhY8mr2neodyn861NbHruRy/MkwnB41sta+3w4rT4WAT5Ixhs1LK3j26Bke3d/OLWtrhgptFFXAlXfCNR+b0jNnyspqZ2c+t5ypqtUw0AkDXVlclYiIiMjsocKUiIjIJHX0RagI+u2so5S+1rzLl4JJhJ+P6pg62tnPsa5Qxt34ADi8FSovom7FBiLx5FCu0oHWPnZby+17Wnakv6Z9HyxoGH7PKdi8pJK23jD9kTiv21CXfvGWz8D6N0/5mTNhZU2qMOUyzle92j6qa0pEREQkIxWmREREJqm9N5yeLwV2x1SejfHBOKN8qfDzUR1Tz718BoBrG6rGPiw2CC8/CQ23sGGxnf+0p7kHgP2tPXQFFmEVlEHLzvTXdUx9R76UzU7O1LziAFctn39Oz8iG+soiivxe9535qlSYEhERERmPClMiIiKT1N4bSc+XAqcwNZs6ppxRvlEdU/0ROx9qQUlg7MNe/g3EB2HlzSydF6S00MfuFjtn6kBrH6trKzCLNqd3TIV74eyJKedLpaytK6OkwMfrN9Ti8+bvjyueoZ35XDqmyhZBoFSFKREREREX+fuTnoiISJ7p6A1TXTqiMJVMQn+edkw5xZzImIypzLvype4r8GXKl9oK/iAsvQaPx7B+UTl7mntIJi0OtvbSWFsKdZuhYz/E7BwuOg7YxynuyJdS4PPyi49ew8dvW31Or8+mhpoSDmtnPhEREZFzosKUiIjIJCSTFp39EarLRozyhbogGc/PjinX8HO/fYynj/JFYgkA/F6Tfr9l2cHnF78K/HZRbn1LhFCQAAAgAElEQVR9OQda+3ips5+BaILG2jJYtNn+s2jbY78utSNfzbl1TAEsnV9MMOA759dnS0N1Ke29EXoGx9mZr0OFKREREZFMVJgSERGZhO5QlFjCoiZtR75W+5iHHVMejyHg9YztmDLG7poa3TGVSFLg82DMqMJU5yF7JK/h5qFTGxZVEE0k+dmuFgDW1JXBokvti6lxvo79UFAG5Yun9evKRytrSgA44jbOV70aBjogdCaLqxIRERGZHVSYEhERmYT2XruQk5Yx1ddmH/OwYwrsnKkxHVNg75I3KmMqErMLU2OkOqCWXDl0akN9OQA/2t6Mxzg705XVQclCOOUEoLfvs/OlRhe6LkCpnfkmDkA/lKUViYiIiMweKkyJiIhMQnufnZ1UnVaYyt+OKXAKU4nE2AvewNjCVDxJgT9DvlTcyYwKBIdO1VcWURH009EX4eKqEgpTr1u02d6Zz7Kgff8578g32yyqSO3M59IxVbXKPnYeyN6iRERERGYJFaZEREQmodPpmKpOG+VzOqZKanKwookFvON0TCVGZUzFE0O5VGlShSnfcEHOGDsAHWBNbdnwvYs2Q1eTPcYX6TmvfKnZxOMxNNSU0OTWMVW+GAIl6pgSERERyUCFKRERkUlo7011TI0sTJ2C4ALwBXK0qvG5jvJl6JiKxpMU+DP8WJAqYPkK0k6nxvkaRxam6jbbx13ftY/Vc6NjCuwA9HF35luwcninQhEREREZosKUiIjIJLT3hakM+inwjRh362vL23wpSI3yuXVMZRjl840zyjeiYwpgY30FAGvrRhamLrGPL37PPs6RjimwA9A7+iL0hFx25qtuVMeUiIiISAYqTImIiExCe28kPfgc7IypPM2XgnFG+bwBiI8e5UsSyBR+nuqs8qZ3hd3QWMNX//BSrlmxYPhkcB7MuxhCXfb4WmH5+X4Js8ZQALrbznxVq6C/DQa7s7gqERERkfynwpSIiMgkdPSGqSpNH2ezO6byuDDl8xBxzZgavStfIvOufPEweAvG7K7n9RhuWbsQj2fUrnupcb45EnyesqK6BGCcAHTtzCciIiKSiQpTIiIik9DRN6pjKhGH/g4oq8vdoiZQ4JoxVTCmYyqaSLoUpqJjxvjGtcgpTFXPnTE+sHfmCwa87gHoQ4Wpg9lblIiIiMgsoMKUiIjIBJJJyylMjeiYGugArAuoY2qcjKlRwefjqr/MPtZunMJKZz+Px9BQXUKT2yhf+WLwB6FDhSkRERGRkVSYEhERmUDXQJRE0krvmOprtY95HH7u2jHlG9sxFYm7jfJFplaYWvwKePeD0Pj6Ka529muoKeWwW8eUx2PnTKljSkRERCSNClMiIiITaO+1d6arHpkx1ddmH/O8YyrjrnzewJiOKfdRvil2TAEsuxY8GbqvLnAra0ro7ItwNhTNfEPVahWmREREREZRYUpERGQCnX12Ead6lnVMue7K5ysY3m3PEYklKfBn+LEgMcWMqTmsIbUzn2vO1Cr7fzeDZ7O4KhGRcxDuhd5TuV6FiMwRKkyJiIhMINUxlT7K1wbGA8VVOVrVxAKu4ecBu+A0QiQ+TRlTc9jKocKU2858jfbx9OEsrUhE5Bw9cTd8+/Zcr0JE5ggVpkRERCbQ3mt3F1WVjBzla4WSmrweWXMd5cvUMRVPEHDLmPKqMDUZdeWFFAe8NLkWplbZx44D2VuUiMi56Gu1d54VEckCFaZEREQm0N4XZl5xIL1w09eW1/lSAAGv16VjqiCtY8qyLKLxacyYmqOMMawYLwC9Yin4iqDzUHYXJiIyVdEBiA3kehUiMkfMaGHKGHPMGLPHGLPLGLPdOTfPGPOoMabJOVY6540x5gvGmCPGmN3GmM0jnvNu5/4mY8y7R5y/1Hn+Eee1Zia/HhERmZs6eiPpwefgFKbyN18Kxhnl8wXSOqbiSYukxTi78iljarJWVpfQ1OHSMeXxQNVK6FTHlIjkuVgIkvExO7iKiMyEbHRMXW9Z1ibLsrY4n98FPGZZVgPwmPM5wG1Ag/PfB4F7wS5kAZ8ALgcuAz6RKmY593xwxOtunfkvR0RE5pqOvnB6vhTYYw553jFV4IzyWZaVfsFbAMkYJO2iVcQpXrmO8qljatJW1pRyuj/KmYHxduZTx5SI5Ie9LT387qXTYy9Enc5PdU2JSBbkYpTvDuDbzsffBt4w4vz9lu0ZoMIYUwvcAjxqWdYZy7K6gUeBW51rZZZlPW3ZP3HfP+JZIiIi06a9N0xN2YjiTDwCoa5Z0TEFw4WnIb6AfUzYXVORWAJgnPBzdUxNVkNNCcA4OVOrobfF3vFKRCTHPv9YE//w8/1jL0RD9jE2mN0FicicNNOFKQvYaozZYYz5oHOuxrKsVgDnWO2cXwScHPHaZufceOebM5wfwxjzQWPMdmPM9s7OzvP8kkREZC5JJC06+yJUl44ozvS328dZ0DEFjA1AT4WZO+N8qevuo3yBGVvjhWZoZ74Ol5ypqtX2UV1TIpIH+sNx+iPxsReiTqdUqkAlIjKDZrowdbVlWZuxx/TuNMZcN869mfKhrHM4P/akZX3NsqwtlmVtqarK3229RUQk/3QNREhapHdM9bXZx1nSMTUmZyo1mucEoEdiTmHKn+HHgoQypqaitryQ0gKfe8dUdaowdTB7ixIRcRGKJRiMJsZe0CifiGTRjBamLMs65Rw7gJ9iZ0S1O2N4OMfUPqTNwOIRL68HTk1wvj7DeRERkWnT0Wt3FVWPzJjqa7WPed4xFfC6FKa8TgeU0zE1lDHlzTTKp4ypqbB35ivhsFthqmKpXehTYUpE8kAoEieUqTAVczql1DElIlkwY4UpY0yxMaY09TFwM7AXeABI7az3buB/nY8fAN7l7M53BdDjjPo9AtxsjKl0Qs9vBh5xrvUZY65wduN714hniYiITIv23jBAevj5BdYxlbo+ZpTPspQxdQ5WVpfS1O4yyufxwoIGFaZEJC+EogkGYwmSyRGDJ4nY0PcHdUyJSDb4ZvDZNcBP7ZoRPuC7lmU9bIx5HvihMeb9wAngLc79vwReAxwBQsB7ASzLOmOM+RTwvHPfP1iWdcb5+MPAt4Ai4CHnPxERkWnTnuqYKh05ytcKHj8UzcvRqiYn4JoxNbpjygk/Hz3Kl4yDlRzOpJJJaagp4QfbT9LVH2F+SYY/u6pGOPF09hcmIjJKKGrnS4XjCYIB5/8aRkcUoxR+LiJZMGOFKcuyjgIbM5zvAm7IcN4C7nR51jeAb2Q4vx1Yd96LFRERcdHRZ3dMVZWOypgqXQieXGxuO3muo3xDHVPpo3xjduVzClca5ZuahlQAens/V2YsTK2CPT+ESB8UlGZ5dSIiw1JjfKGoS2FKo3wikgX5/RO1iIhIjrX3RlhQEsDvHfEts6817/OlYLhjKuJWmIo74edOx1Rg9CjfUGFKo3xTsbKmBIAjHW4B6I32sUPjfCKSO4mkNfT9IS0APa1jSqN8IjLzVJgSEREZR0dvmOrSUYWZVMdUnkt1QI0NP0/vmHLNmIrb3WLqmJqahWX2znyH3XKmatbax/a92VuUiMgoqTE+++MRhamYOqZEJLtUmBIRERlHe1+Y6rJRhZm+Viity82CpmC4Y2rUjktjOqZUmJpOxhgaJtqZr6AM2vZkd2EiIiOMLEaNLFKld0ypMCUiM0+FKRERkXG090aoGdkxFQ1BuGeWdEy5ZEylws9TGVMx+/qYUb7UrkwqTE3ZyppSmjpcOqaMgYXrVZgSkZwaWZhKH+UbUYxSYUpEskCFKRERERfxRJKu/gg1ZaN25AMorc3NoqbAdVe+oY6pUbvyjQk/T3VMKWNqqhpqSjkzEOV0fyTzDQvXQ/s+SCYzXxcRmWEDEZdRvuiIorpG+UQkC1SYEhERcdE1ECVpQXXZiMJMX5t9nAUdU6678g11TI0a5fO7hZ+rY2qqUgHoruN8NevsHJful7O4KhGRYYOxRMaPFX4uItmmwpSIiIiL9l67Y6i6dJZ3TLnuypfqmJogY8qrwtRUNVSXAtDkFoC+cL19bNudpRWJiKRzHeVLje8VVapjSkSyQoUpERERF+29duGmZrZ2TLmN8g3typfeMZXqsBoST2VMaZRvqmrKCigt9Ll3TFWtBo8P2rQzn4jkRihtlG9k+LlTUC+ugthgllclInORClMiIiIuOvrsjqH0wlQr+IqgsDxHq5o8944pZ5TP6ZiKxpMEfB6MMen3aVe+c2aMGT8A3V8IC1YqAF1EciZtV77YqPBz44GieRrlE5GsUGFKRETERXtvBGNgQUlg+GRfm90tNbqIk4dSHVCRMRlTqVG+sHM9MXaMD0ZkTKlj6lysrCmhqb0Py7Iy36Cd+UQkh0Z2SaXvyjcAgRIIBDXKJyJZocKUiIiIi47eMPOLC/CNHHHra5sV+VIwnBk1tjA1Nvw8c2Eq1TEVGHtNJtRQXUp3KEbneDvz9Z2Cga7sLkxEhOGOKY8ZtStfbAACxeAPDudNiYjMIBWmREREXLT3hqkpGzXG1tc6K/KlwB4nC3g9Y0f5PB7w+IfDz2NJCnzesQ9IqGPqfKyutQPQD7SOszMfQLu6pkQk+wacYtS84kB6YSo6YBel/MH0HfpkdrIsaNoGifjE94rkiApTIiIiLjr6Iun5UpY1qzqmwM6ZGlOYAjs3yumYiibcOqYiw/fKlK2ts3PI9p3qyXzD0M58KkyJSPYNRuMU+b0EAz4G08LPnY6pQFDh5xeClh3wnTfBwQdzvRIRVypMiYiIuGjvjVBdOqIo077PHnEorcndoqYo4PMQTSTGXvAGRnRMJYaC0tMMjfKpY+pclBf5WTyviH0tvZlvKF4ApXXamU9EcmIgmqC4wEsw4B3bMRUoBn+xRvkuBM3b7eOZo7ldh8g4VJgSERHJIJZI0jUQoTrVMdW0Db55G5TUQOPrc7u4Kcg4ygdOx5RTmHLNmHI6przKmDpXa2vL3TumABauU8eUiOTEYDRBUcBLUcDLYCxDYSrgjPK5beAgs0PrLvt49kRu1yEyDhWmREREMjjdH8GyoKY0AM/8B3z3LVC5FD7wOMy7ONfLmzTXUT5vAOLOKF/cJWMqHrF38JsFOxDmq7V1ZRzrCtEXjmW+YeF6OH1ouAgoIpIlA5E4xQHf2I6pWMjpmCoCrOHuWZmdTr1gH1WYkjymwpSIiEgGHb0RfMR55eF/goc/DqteA+97BMrrc720KbFH+TJ1TBWO6JhKUOB36ZjSGN95WbfIzplyDUBfuB6Sceg8mMVViYjAYMzpmPL7MoSfO6N8AFGN881akX44fdj+WIUpyWMqTImIiGTQdbqNb/s/S/3R78M1fwpv/S/7N8izjPso33DHlPsoX1jB5+dpbV0ZAHtb3ALQN9hHjfOJSJaN7JhKDz/vHx7lA+VMzWZte8BKwvwG6DmpsUzJWypMiYiIjBbu5dJH38oWzyF6bv0i3PhJ8MzOb5kBn4dIxlG+9IypzOHnERWmzlN1WSELSgrYd8olAL1ymd2VoMKUiGRZyMmYCo7JmArZRSm/ClOzXipfas3t9i+b+jtyux4RF7Pzp2wREZGZdPgRykPHuTP+MYpf8c5cr+a8FLgVpnwFE2dMJVSYmg7rFpW5B6B7PFCzVjvziUjWhaIJigNeCv0jMqYScfvf/kDJcJdwdCB3i5Tzc+oFKK2F+lfYn/eczO16RFyoMCUiIjJa0yP0eyvYU3Q5Pu/s/lY5bvj5yIwp144pZUydr7V1ZRzp6Cc8siNhpIXrnXELjViISPbYHVOpUT7n36eYU4QaCj9HHVOzRDyR5Mc7mkkkR3wvObULajdBxRL787PHc7M4kQnM7p+2RUREplsyAUe28WLhFqrKi3K9mvNW4FaY8hUM7QTnPsqnjKnpsLaunHjS4nC7WwD6Ooj0KJhWRLJqMBqn2Bnliyct+3tFqjvKHxwOP48N5m6RMmmPHezgz3/0Ik8dOW2fSAWf110C5Yvtc/o+I3lKhSkREZGRmrfDYDdPspma0tnfLeS6K583AAkn/DzmFn4esbOo5LykAtBdc6ZSAejtGucTkeywLItQLEEw4KUo4AOwu6ZShalAyXD4uUb5ZoU9zfbI+IkzTodb227AgrpNUFgGRZUqTEneUmFKRERkpKZHwHh5eHAt1WUXQGHKdVe+4Y6paMIlY0odU9NiybwgpYU+95yp6jVgPApAF5GsCceSWBYEC+xRPoBQLD6iMKXw89lmj7P7a3O30+F2ygk+r91kHyuWqDAleUuFKRERkZEObyW5+HKOh/zUlM3+oox7xlQBJKLEE0kSSUsZUzPIGMOa2jL2trh0TAWCMG+5ClMikjUD0TgAQWeUD+zMqeHCVPFwYUodU3nPsiz2DhWmnELiqRegtA5Ka+zPVZiSPKbClIiISEpPC7Tv4cyiVwFQfSGP8vkCEI8M7diXOWNKu/JNl7V15Rxs600PpR0pFYAuIpIFqbDzIr+XIr93+FyqO2rkKJ86pvJeW2+YrgF7PP9kqmOqdZc9xpdSsRTOntRGG5KXVJgSERFJadoKwEeeryYY8HLZsnk5XtD5C3i97h1T8cjQtcwdUxrlmy5r68oIx5Ic7ezPfMPC9fZuSYNns7swEZmTUh1TxQU+gk7GlN0x5fwb5R85yqfw83yXypdaVVNKS3cIIn1wuskOPk+pWALxQRg4naNVirhTYUpERMTRvuMBWqwqjnsW8+M/vooV1SW5XtJ5cx3l8wUgMdwxVeDPlDGljqnpsm5ROTCZAPR9WVqRiMxloVTHVMBL0dAoXzx9lM/jtce5NcqX9/ae6sVj4KY1NZzujxI++QJgDedLgXbmk7ymwpSIiMx5lmXx1cf2UXrqKV4suoyffeQa1jg7qc12Bc4oX3L0CJmTMRWJxYfuGyOhjKnpsryqmAKfZygDZIyF6+yjduYTkSwIRezCVHFgOPzc3pUvNcpXbB/9QY3yzQJ7W3pYUV1CQ439C7W+o8/bF9JG+ZbYx7PHs7w6kYmpMCUiInNaJJ7gz370Ik899r8ETYQb73jXBZEtlZLKjhqTM+UL2Oej4bT70qhjatr4vB5WLyx175gqqYHiKmd7bxGRmRVyDT93RvlGFqaiKkzluz0tPaxbVE59pT1+mWh5AcoWQUn18E0V6piS/KXClIiIzFmhaJx33vcsP9nZwv9degzLV0RgxStzvaxpVeBWmPLaBadYJOzcN2qUz7LsjCmvClPTZe2icvad6sHKFDxrjALQRSRrUqN8wZGjfDEn/Nx4hrtlA0GIaZQvn7X3hunsi7B+UTmL5xUBUNS5Oz1fCqCwHAorVJiSvKTClIiIzFm/3NPG88e6+dybN7Ap/Cxm2XXgL8r1sqbVUMfU6JwppxMqFrVDbceM8iXjYCU1yjeN1taV0RuO09ztEiRcsw46DkIilt2FicicM1yY8g3tyheOJuw8KX+xXSwHZ5RP4ef5LDUivm5ROVUlBczzhSkPHU/Pl0qpWKLClOQlFaZERGTOeuxAOzVlBbxpaQi6j8HKm3O9pGkX8E5QmAq7jPLFI2n3yflbW5cKQHfLmdpg53qdbsriqkRkLhoa5Svwjt2VLzXGB/bHGuXLa3taejAG1tSWYYzhlaWn7AujO6ZAhSnJWypMiYjInBSJJ3jycCc3NNZgmrbaJxtuye2iZoBrx1RqlC+aGuVzK0ypY2q6rF5YitdjxtmZTwHoIpIdQx1Tfi9ejyHg8xCKxe0iVCA4fKNfo3z5bm9LD8urSigusAuMlxU4hae6TB1TS6HnpD2uL5JHVJgSEZE56ZmjZxiIJripsQYOPwLVa4aDQS8gE4Wfx2N2AWpMxlQ8nHafnL9Cv5cVVSXuhan5K8Djh/Z92V2YiMw5oWiCgM+Dz+mqDQa8zq58A+kdU/4idUzlub0tvawbsZPwGl6ilflQvGDszRVL7ByxUFcWVygyMRWmRERkTtq2v50iv5crF/nhxNPQcOGN8cE4o3xOx1QilTHlH90xlSpMqWNqOq2tK3Mf5fP6oWq1ClMiMuNC0fjQbnxgd06Fogm7OypQMnxjoFgZU3mssy9CW2+YdYvKh84tjTTxYuJi+iPxsS8Y2pnveJZWKDI5KkyJiMicY1kWjx1o55qGBRSe+LUd9H2hFqacjqmIS8ZUqmMqVcAakoim3SfTY01dGe29ETr7IplvqFkDHfuzuygRmXNC0QTFTrYUQNHIjim/Rvlmi1Tw+fpUYSrcQ8XgCfYkl9HcnaHTrWKJfVTOlOQZFaZERGTO2d/ay6mesD3G17TV3kJ58eW5XtaMSI3oReKJ9Atee0QvmRrlU8dUVkwYgF69BnpbYLA7i6sSkbkmFI1TNLJjKuCzA9FHj/IFghrly2N7nMLU2lRhqvVF+7y1jOYzGTrdylMdUypMSX5RYUpEROacxw50YAxcv2qBXZhafgN4fRO/cBZyDT93OqGSsVT4+eiMKaejx6uMqem0xskBcc2ZqkkFoKtrSkRmTiiaSBvlKwo4o3zR0KiMqSDEByGZzPAUybW9LT1cvKCYEif4nFO7ANiTXMbJTB1TRRX2L+NUmJI8o8KUiIjMOdsOtLNpcQVVA00w0HnBjvHB8G57YzOm7IJTYij8XB1T2VBe5GfJvCD7XQtTa+yjxvlEZAaFIumFqWDAy2AsAdH+sYUpsAOzJe/sbelJy5fi1AtY5YsJ+ytp7nbJBqtYosKU5B0VpkREZE5p7w2zu7mHGxtroK/VPrlgZW4XNYPcd+WzO6Yst4ypeCpjSoWp6ba2roy9bqN8pbVQWKEAdBGZUaFYnOCIjKlgqmMqNqpjKvWxAtDzTld/hFM94eF8KYDWXZi6TdRXFmXOmAKoWApnT2ZnkSKTpMKUiIjMKY8d6ACwC1NhpzhQWD7OK2a3iXblsxIRAl4PHo9Jvz7UMaXw8+m2blE5x7tC9IZjYy8aY4/zqTAlIjNodMdUkd9HJBK1/+33Z+qYUgB6vhnOl7JHxBk8C2eOQt0lLJ4X5GSmjCkY7piyrCytVGRiKkyJiMic8tiBduori1hZUzI3ClOuGVP2KJ8Vi4wd44PhjCkVpqZdKmfqwHjjfB0H9H8aRGTGjM6YCga8WFGn+DQ6/BwUgJ6HUlmF60YFn1M7UcfUErvQGDqThVWKTM6kClPGmEpjzFpjzMXGGBWzRERkVhqMJvjtkdPc2FiDMQbCZ+0LhWW5XdgMch3lczqmiIeH7kmjjqkZs6bWKUy1uhSmqtdAtE8ZICIyYwai6aN8RQEvJtUVlSpGgTKm8tie5h4umh+krNBvnxgqTG2kvrKI3nCcnsEMnblDO/Mdz85CRSbBtchkjCk3xvy1MWYP8AzwVeCHwHFjzI+MMddna5EiIiLT4bdHThOJJ+0xPrA7pnxFF3TxZcKOqXg0c8dUItUxpYyp6VZdWsD84gAHWvsy31Cz1j5qnE9EZoBlWQyO3pXP78WXcEa/AiXDN6cKU1GN8uWbPaODz9t2Q9kiKF7A4kr77y1j11TFEvuoX35IHhmv++nHwEngWsuyVlmWdY1lWVssy1oM3A3cYYx5f1ZWKSIiMg0eO9BOaYGPy5bNs08MnrW3Tr6ApTKmIi4ZUyYRocDvHf0yjfLNIGMMjbVl7HftmGq0jx0qTInI9IsmksSTFsUF6eHnQZx/9zON8in8PK90D0RpOTuYXphqfRFqNwJQP1SYyvD3psKU5CGf2wXLsm4a59oOYMeMrEhERGQGJJMW2w50cN2qquHRtXDPBZ0vBQx1Q40pTPlShSmXjqnUKJ9XhamZ0FhbyrefPk48kcQ3ekfEglJ716T2/blZnIhc0AajCcDukkqxC1POv/v+kaN8qV351DGVT1I7uw7tyBcdgNNNsPb3AFg8rwiAk2cydEwVVUBBOfRoZz7JH+ON8v3MGPMXxpirjTGBbC5KRERkuu1u6eF0f4SbUmN8MCcKU8YYAl7P2FE+jxeMFxIRl4wpdUzNpMbaMqLxJC+fdvk/ezVrNconIjMi5BSmigtGjPIFfARNqmNqxCifws/zUmpHvnV1zs8w7fsAa6hjqrzIT0mBL3PHFAzvzCeSJ8Yb5bsPqAQ+A7QZY35njLnHGPNGY0zNOK8TERHJO9v2t+P1GF61qmr45BwoTIGdMzWmMAXgK8STdOuYitjdUsbM/ALnoNTOfK7jfDVroesIxMJZXJWIzAWhaBywi1EpaR1TCj/Pe3tbelg8r4jy4Ojg8w2A/UupCXfmU2FK8ohrYcqyrActy/pry7JeBVQBHwXagXuAU9lZnoiIyPTYdqCdLUsrqQiOaAKeS4WpRGLsBV8ATyJGgc8lY0rB5zNmeVUJAa9nnJypNWAl4PSh7C5MRC54Qx1TI8PPA16KTaowNSJjSoWpvLS3pXd4jA/swlRwvh1+7qivDE7cMWVZM7xSkckZr2MKY8wCY8zt2F1TnwPeDGwD3peFtYmIiEyL5u4QB9v6hnfjSwmfhcILO/wcyDzKB+AtwJN0G+ULa4xvBvm9HlZUl0xiZz7lTInI9BqIOBlTIwpTQb+XIjKM8vmLAKNRvjzSE4px4kxobPD5wg1pXc52x9QgVqbiU8USiPbDYHcWViwyMdfwc2NME9AD/A/wCPBpy7L6s7UwERGR6fLiSTuL4crl84dPWtbc6pjKOMoXwBOJuY/yqTA1o9bUlfHrw52ZL85bbo9Samc+EZlmgzF7lC+YNsrnozhT+Lkx9ufqmMobqeDzoXypeBQ6DsCVd6bdt3hekP5InLOhGJXFoyKjKxbbx7PHIThvppcsMqHxOqa+AbQAbwI+ALzXGLPFGJOh319ERCR/nR2MArCgZEShJdoPVnLuFKYSmTumvG4ZUwkVpmZaY20ZnX0ROvsiYy96fVC1SgHoIjLtUh1To0f5ikwEC+N0SY3gL7J3fZO8sDcVfJ7qmOo8AMnYUL5USn2l/feYcZyvYol9VM6U5AaeO3IAACAASURBVInxMqb+ybKsOyzL2gJ8EujDLlDtMcb8OkvrExEROW+9g/Zvh8uL/MMnw/YPdnOiMOU2yucrwJtUxlSuNNaWAnBgvAB0jfKJyDQbjGYY5Qt4KSZM3Fs0dtOLgDqm8sm+U73UlRcyL9UF1brbPi7cmHbfcGEqw9/dUGHq5EwtU2RKxs2YAjDGXAxcBlwOXIEdhO4SiCAiIpJ/esMx/F5DoX/Et725VJjyeYhkzJgK4LOiypjKkTW19s584xam+ttgoCuLqxKRC92AsytfcYZd+WLe4NgX+ItVmMoj+071sKZuVL5UoATmXZx2X32l/Xd5MlNhqrACCsrUMSV5w7UwZYz5qTGmFXgIuAHYAbzdsqwqy7Jel60FioiInK+ewRhlhX7MyN8CD561j0UXfvh5gVthyleAzxonY8qrwtRMqggGqCsvHH9nPlDOlIhMq1CGjqlCv5egiRD1Fo19QSCo8PM8EYrGOXp6gHWLyoZPtu2GhevBk/69vLzIT1mhL/MonzHDO/OJ5IHxOqa+CawHVluW9V7Lsu6zLGs/gDFm0j+pGmO8xpgXjDEPOp8vM8Y8a4xpMsb8wBgTSD3T+fyIc/2iEc/4K+f8IWPMLSPO3+qcO2KMuWtKX7mIiMwZvYOx9DE+mHMdU5l35QvYhSm/ws9zpbG2bPyOKdA4n4hMq1A0jtdj0n4pUeDzUEyEqMkwwq3w87xxoLUXy4K1qY6pZALa9kLtxoz311cGOXnG5e9OhSnJI+NlTD1gWdZp4OsjzxtjSoBfTuE9/gQ4MOLzzwL/ZllWA9ANvN85/36g27KsFcC/OfdhjFkD/D6wFrgV+IpT7PICXwZuA9YAb3fuFRERSdMzGKN0DhemClwKU0lvAD9uGVNhZUxlQWNtGS91DhCOJcZeLKmB4Hx1TInItApFEwT93rQuYmMMJZ4oYZOhY8ofVPh5nth3yv5Fxto6p2Oq6yWIDcDCDRnvXzyvKHPHFAwXpixrJpYqMiUTZkwBLcaYewGMMZXAVuC/J/NwY0w98FrgPudzA7wa+LFzy7eBNzgf3+F8jnP9Buf+O4DvW5YVsSzrZeAIdubVZcARy7KOWpYVBb7v3CsiIpKmNxwfp2Pqwh/lc9uVL+kJECDukjGljqlsWFNXRiJpcaSjf+xFY+xxPu3MJyLTKBRJECwY+wuJEk+YcKaOqUAQYi7FDcmqfS29VAb91JY7f09tTvD5OB1Tzd2DWJmKT/8/e+8dHVlWnnv/duUqSaUqqdXdaqklTc90Tx6mZwYPQ47GOIDBGGPAATBgTDR3fTbYvsZhOV1/GGxsAwZ/gMEYY65tBi4XMJgMk3Niema6FTtKqipJlav298c+p+IpqRRKqvD+1tI66kra3VKrznn28zxvZAKyK5BabtFqBaF5NhSmtNb/E0gopT6MEaXep7X+eJOv/wHgtwD7bHgYiGmt89af54Ax6/MxYNb6mnkgbj2+dHvNcxrdXodS6o1KqTuUUnecP3++yaULgiAI3cJKKkc44Km+0Ram/OH6J3QZjabyFV0+/GQbdExJ+flucLlVgP7QwjpxvnOPQNEhiikIgrAFkrkCIZ+n7vY+MqRwivJJ+Xm78MBCnKvGBstut9P3mD7IkUsdH384GiSVK7C4lq2/c/CwOUqcT2gD1is/f5n9AdyGmch3N6Ct29ZFKfXTwDmt9Z2VNzs8VG9w32Zvr79R63/QWt+gtb5hZGRknVULgiAI3Ug8lSNc55iKgW8A3PUn591Go46pgsuHT+Wdo3yFrAhTu8DkUIiQz924AP3AlSamETu1q+sSBKF7SWbyhHz1v/dDKs0aDr/3fRLlawey+SKPnl3hikMVG2qn74MDV4Db6/gcezKfY5wvMmGOC3ft9FIFYdOs55j6mYqPn8aIUt6KP2/E04AXK6VOYWJ2z8U4qCJKKfsqYBxYsD6fAw4DWPcPAkuVt9c8p9HtgiAIglBCa00i3aD8vAf6paBxlC+vvPjINYjyScfUbuByKS49ONC4AH2/XYAucT5BEHaGZLbgKEwFdZqkdhCmvEFxTLUBJ86tkCvocvG51nD63ob9UgDjQ6YzbG7Z4fu37xhEp+BLvwmffTVcONGCVQtCc6xXfv7adT5et9ELa63fo7Ue11pPYcrL/1tr/Wrgm8DLrYf9CvAF6/ObrT9j3f/f2oRhbwZeaU3tuwg4inFw3Q4ctab8+ayvcfMm//6CIAhCl5POFckVNOFADwtTbncDx5QXP/kGUT7pmNotrhgN89DphHMHyP7LACWT+QRB2DEaRfkCZFhxFKb6jIu2kK+/T9g17OLzq2zHVHzWuL8b9EtB2TE1u+TgmPKF4M0/gOf8HjzxLfi7G41ItXJ2p5cuCBuyXpTv96yy80b3P9eK622W3wbepZR6DNMhZU/9+0dg2Lr9XcC7AbTWDwKfAx4CvgK8RWtdsHqo3gp8FTP173PWYwVBEAShRDyVA+hpx5Tf6yKTr5/6llM+fOQad0y5RZjaDS4fDbOSzjMfc7pw6DM72jKZTxCEHcIxylcs4NcZVooNonwgrqk95sH5OH0+N1PDfeaG0/ea4zrCVL/fQzTkdXZMgXmPedb/A2+/B578erjrn+BvjsM3/0wK74VdZb1ijfuBLyml0sBdwHkggHEsXQt8HfjTZr6I1vpbwLesz5/ATNSrfUwa+PkGz/8T4E8cbv8y8OVm1iAIgiD0Jom0EabCwdry8xiEx/dgRbuPz+0iV9AUixqXq1zRmMeLz8kxVciDLkqUb5ewC9AfPr1S2t2u4sCVEuUTBGHHSGYLBGuFKatDaqXoq3+Ct0KYCnT/wJB25cGFBJePhsvv46fvA+Uy01vXYTwaYtapY6qS/hH4yb+EG38dvvFH8O0/h8wK/ERTl/uCsG3Wi/J9QWv9NODXgQcBN5AAPg38mNb6N7XWMuJOEARBaGtsx1RdlC8Vh2BkD1a0+9gdUrU9UznlxaU0fldNzC+fNkeJ8u0Klx0cQCka90wduBKWnpDda0EQdoRkNk9fbZTPckPF806OKcuhIwXoe0axqHnodIKrxiqc3mfug32Xlh1tDTg8FGzsmKpl+GJ4xSfhSa+COz8OyaVtrFoQmme98nMAtNYntNaf0Fr/mdb6A1rrr2qt5cxIEARB6AgSEuUrOaKchCmAgKumNySfMUdxTO0KfX4PU8N9PLTQqAD9CuNgO//I7i5MEISuxLH83BKdEgWH6W5eU6AtUb694+TiGslsoWYi370w2rj43GY8GmJ+OeXcY9iIp73DfL9v/cgWVisIm2dDYUoQBEEQOplylK/iZLtYhEyiZ4SpkmOqpgA9iyVMqVphynZMOUQ6hJZw+egAD59ZxzEFcO7h3VuQIAhdSaGoyeSL9eXnljC1nPfWCxhe2zElwtReYRefX2kLU6vnYOX0uv1SNoejQTL5IudXMs1/wf2XwaU/Bbd9BDKrW1myIGwKEaYEQRCEriaedHBMZRKA7h1hyr2+MOWvFaYK4pjabS4/GGZ6MclqxmHqVWQSULB8areXJQhCl5HMmt8xjRxTqzpApnaKq5Sf7zkPLsTxuV0c3T9gbjh9nzkebM4xBXD/fHxzX/TpvwmpZVOILggtRoQpQRAEoatJpM1J+ECgYnc4bZ2c9Yow1cAxldHm38RPoyifdEztFnY84xGnnimPDwbHRZgSBGHbJLNmQmvI7yxMpbSfdK5miqtXhKm95sH5BMcO9pfezzljTeQ7ePWGzz12cACf28XrP3kHL/377/OpW6aJJbMbf9HDT4apZ8AP/xbyGzx++oewdHLj1xSEBmwoTCmlLlJK/ZVS6t+VUjfbH7uxOEEQBEHYLvFUjpDPjddd8ZaXjpljoLfLz7PWcF6/ylU/oRTlE8fUblGezNcgzhedEmFKEIRtUxKmah1TOSNMrREoPaaET6J8e4nWmgcX4lw5WrGZdvpe877QxBCXsUiQ7/32c/idn7yMZKbA//zPB/ixP/kGb/70nXz9obPrd089/Z2QmIf7P9f4MY9+DT7xk/CPL4DFx5v/iwlCBc04pv4TOAV8EHhfxYcgCIIgtD2JVM65+Bx6xzHVIMqX0ebfxdfIMeWWjqndYnQwwGDQy0OnV5wfEJ2S3WhBELbNWsaO8jl3TCXx1wtTpfJzmcq3F5yOp1lO5rhqrLL4/L6m+qVs9ocDvPGZF/OVdz6DL73t6bzmKZPcdnKJX/unO/jod59o/MSLn2figt/7gOnnrOXM/fD511pDOjT8089CfH4TfztBMDQjTKW11n+jtf6m1vrb9kfLVyYIgiAIO0AinSMc6G1hyu81O+OZfPXFRtqK8nkRx9Reo5TiitHw+o6ptXMyrl0QhG2RyjVwTNnClA6QqhOm7CifDGbfC+zi8ysOWecs6Tgsn2yqX6oWpRRXjQ3y+z9zBbf8zvN41rER/u6bj5cGxTg8wXRNLZ6AR75UfV9iAf75FeZc6tWfh9f8b9NJ9emXQXJp02sTeptmhKm/Vkq9Vyl1k1LqOvuj5SsTBEEQhB0gnsoRDtbsDPeYMGU7pmoLbdPaXJh4da0wZXVJiDC1q1w+GuaRMwkKRYdYRXTKHJend3VNgiB0Fxs5pkyUr8ZFW4ryiTC+FzwwH0cpM70VMC4lgNFrt/W6XreL3/qJS4mncnzsO+u4pq54CQwdge+9n0Qqa96jMqvwmV8ww2Re9a8QHoVD18KrPmvcvf/8csg0cAALggPNCFNXA28A/pxyjO//beWiBEEQBGGnSKTyEuVrVH5eNBcm7mJNqWnJMSXl57vJ5aMDpHNFTi06XPxFLzJH6ZkSBGEbpBp2TCXRKNL4SNaWn7t9oNxSfr5HPLiQ4Mi+vrKYGJs1x+Ej237tKw8N8lPXjPKx753kwmrG+UEuNzztHbBwF//jLz7IKz70XTL/+qtw9gH4+U9UF7BPPd3ctnAPfPbV5WoAQdiAZoSplwJHtNbP0lo/x/p4bqsXJgiCIAg7gWOULxUDFPjDjs/pNvwNhKmUJUxRqBWmZCrfXnDZQfPz+OgZh13mkmPq1K6tRxCE7sPuj+pzcEwVvSFA1Uf5lDJxPik/3xMeWohz1VjFRlrKiskFh3bk9d/1gmOkcwU+9K3GxeX5q36BJdcQr9P/wUvO/j3+J/6LuZv+CI6+oP7Bl/0kvOTv4OS34X//GhQL9Y8RhBqaEabuBXpjbJEgCILQdZgon4NjKhAGVzNvg51Po6l8KSvKV3JI2Yhjak+Y3Gd6XKaXHC7+QkPgGxBhShCEbWHH9IJ1HVOrpS6puvJzAF9Iys/3gKW1LAvxNFceqthISy2Dcu3Y5trFI/28/PpxPnXLNAsx5x6xD31vjg9nXshN6n5+2fV/+Rf3i3nedy7hC/c0KDq/9hfhhX8KD98MX3nPjqxT6G6aOSM/ADyilPqqUupm+6PVCxMEQRCE7VIsalYz+QbCVG/E+KDxVL6SYypf45gq2I4p6ZjaTcIBL0N9PqYXHYQppYxrSoQpQRC2Qckx5a8VppKlLqlUbccUGNFKys93nQcXTPXAlYcqHVPLEIjs6Oba2593FDT8zTdO1N13z2yMD3zjBLHLXwP9B+Hyn+HH3/ERnnQ4wjs+ew9//n8fce5GvOktcOOvw20fgce/uWNrFboTz8YP4b0tX4UgCIIgtICVdB6tIRxwKD/vJWGqQZQvWbCjfDUdEBLl2zMmh0NMO3VMAUQn4UL9RYMgCEKzrFnCVMDjMJXP1w80ckz1SZRvD7An8lU5ppJLxkW7g4xHQ7zqxgk+dcs0b3zmEY6MmJ+FtUyed372bg6GA/zuz90I7rvBG2RYKT79+hv5gy8+yIe//TiPnl3hA6+8tr464fl/AI99HW5+G7z5B8atLggObCizaq2/DZwCvNbntwN3tXhdgiAIgrBt7PHHjuXngd5JqTeK8iULdpSvVpiyonxuEaZ2m8mhkLNjCoxjKjYNxaLz/YIgCBuQyuYJet24XKr6jtwaLssx5ShMeXs4ynfyOzBz65586Qfm44xFgkRCvvKNqWUIRnf8a73lOZfgc7t4/9fLGyB/9MWHmF5K8leveJI5l/KFjIMXc27xpy+9mj/+2av49qPnef0nbq/bAMMbhJ/9MCTm4Wu/t+NrFrqHDYUppdQbgM8DH7FuGgP+s5WLEgRBEISdIJ4ywlTPR/kaOKbWbGFKys/bhsnhPhbiKTJ5hwvDoYuMaLh6ZvcXJghCV7CWLdTH+ACyayh/H36Pi1TtVD4wAkOvOqZufht8+X/syZd+aCFR7ZaClglTIwN+Xvf0Kb547wIPLST4ygOn+dc7ZvmNZ1/MjUeGGz7vl54yyV+94kncfmqZP/zig/UPOPxkuOmtcNcnjXtKEBxoJpj6FuBpQAJAa30C2N/KRQmCIAjCTpBINXJMxXrKMWVP5cvURvlKHVMOUT63v7QrKuwek8MhtIa5ZYcuF5nMJwjCNkllC/XF52CifN4QIZ+7VJBeha8Pcj0oTK0tmt+5Zx+EdGJ3v3Qmz8nFteqJfGCm8u3QRL5a3viMiwkHPPzBzQ/y7n+/n2vGB3nn849t+LyXXDvGm551hH++dYbP3DpT/4Dn/C7sOwY3v91sDgpCDc0IUxmtdWkrVSnlARzazQRBEAShvbCjfHWdB73mmHI7C1Or+XUcU1J8vidMDpsojWPPVPQicxRhShCELbKWydPnc6gZtjqmQj4PqaxDXNgb6nph6uSFNc6v1GzULNxtjroIc7fv6noePp1AaxwcU7GWOKYABkNe3vSsi7nt1BKZXJEP/MK1eN3Nlaz/1gsv41nHRnjvzQ9w+6ml6ju9ARPpWzkNX/2dFqxc6HSa+Sn7tlLqd4CgUuoFwL8BX2ztsgRBEARh+5SjfBUn4YW8GYvdQ8KUUgqf21U/la+gKOJy7pjy+BB2n8lhM67dsWdq8DCgRJgSBGHLpHLrOKZ8fQR9blI5J8dUqKujfFprXvOxW3nvzQ9U3zF/J6BAuWB2d3umfnR2BYDLRiuEqUIOMomWCVMAr33aFE+7ZJg//7mrSyXozeB2Kf7mF48zHg3x5k/fyUKsxvk7fj087Z1w96fh0a/t8KqFTqcZYerdwHngfuBNwJe11r/b0lUJgiAIwg6QSJmT66ooX8ay4veQMAWmZ6pWmMrkC+SV13kqnzim9oThPh/9fo+zMOXxweC4CFOCIGyZho6pXBJ8dpTPqWOqu6N887EU87EUt59aRuuKcND8nTByKRy8GmZ+uKtrWl4zbuaR/oq+x1TMHHd4Kl8lIZ+Hf/61p/CSa8c2/dzBoJeP/vL1pHNF3vSpO0nX9pU9+90wcjl88e2mK0sQLJoRpt6mtf6o1vrntdYv11p/VCn1jpavTBAEQRC2SSKdw6WoPglPWyd1wd7pmAJLmCpUnyBm80UjTOVronyFjBSf7xFKKSaGQs5RPjA9UyJMCYKwRZJOHVPFgiVM9RP0NhKmgsZVpbuz0eWuGXNucH4lw7zt9NHaCFNj18PETTB3h3Es7RLLyRx9PndpgAlQFnNa6JjaLpfsH+D9v3At98/Hec+/318t9Hn88NIPweo5+Pof7t0ihbajGWHqVxxu+9UdXocgCIIg7DjxVI5w0Fs9Ftvebew1x5RDlC+TL1Jw+cQx1WZM7Qs5O6YAopMiTAmCsGWS2QJ9tcKU7YSyys9TTsKULwS6sKvCzG5y13TZvXO3JVIRm4HkBRi7Dg7faP6dzty3a2uKJXNEQjWx+pTV3dTmm2svuOIA73rBMf7j7nk+dct09Z2HjsOlL4JT39ubxQltSUNhSin1i0qpLwIXKaVurvj4FrC4aysUBEEQhC2SSOWci8+h94QpxyhfkYKTYyqfBrd0TO0VE0N9zC4nKRQdnAnRKVg929VdL4IgtA7jmKqJ8tm/T3x9hHwe56l8XjOYgVwDN2eHc/fMMtdPRgl4Xdw1Y4lU83ea49j1MPEU8/nM7vVMxZLZ+qnCJcdU66J8O8Vbn3MJVx4K86X7TtffGZ2C+FzXOvCEzeMQMC7xA+A0sA94X8XtK8DuScWCIAiCsEUS6Xx18Tn0tjBVqBamsvkihYA4ptqNqeEQuYJmIZbi8FCo+k57Ml9sGvZfvvuLEwSho0lm8/WOqeyqOfr6Tfl5I8cUGBGrjWNkWyGdK/DgQoI3PPMIbpcqO6bm7wS3H/ZfaTr+IpOmZ+qm39iVdcVSOaJ9jYSp9v8euFyKYwcGuO3kUv2dg4chn4K1C9A/svuLE9qOho4prfW01vpbwPOB72qtv40RqsYB1eh5giAIgtAuxFO5+t3GkjDV3jb4ncbndpHJ1ZefF10+h6l80jG1l0xYk/lmlhxcUbYwtXRyF1ckCEI3oLUmlSsQahTls8vPawurAbyh6sd2EffNxckXNddNRDk+EeGhhQSZfAHm74LRa8pTaidugplbds3ls5zMEgnWuJeTdpSv/YUpgPFokNPxFLmajTEih80xPrP7ixLakmY6pr4DBJRSY8A3gNcCn2jlogRBEARhJ5AoXxm/t9oxVShqcgVN0e2DgkOUTxxTe8bUsInMnHIqQI9OmaP0TAmCsEnSuSJaQ8hfG+Wzftf4+gg2nMoXqn5sF2FH945PRDh+OEq2UOTBuSU4fY+J8dlM3Ahr52DpiV1ZVzyZIxJycEwpd8ecw4xHgxQ1nImnq+8YtISp2OzuL0poS5oRppTWOgm8DPig1vqlwBWtXZYgCIIgbB9nx1TMnNT5+vZmUXuEz+0iU9ExZfdNNXZMScfUXnEwHMDncTHjVIAeGgLfgAhTgiBsmjWrO6rOMWVH+bx9BL1uE/Ou7bizo3y5VItXufvcNb3M5HCIff1+rpswbupTD91p3GFVwtRN5jjb+p4prTWxVANhKhgB1RkBpvGo+bmZXa55Pys5pkSYEgxNCVNKqZuAVwP/x7ptvW4qQRAEQWgLEmkzla+KdNzsNHbISd1OUVt+bn+uxTHVdrhciomhkLNjSinjmhJhShCETWJ3RwW9tcJUZfm5ua+uAL1Ly8+11tw1E+P6CRON2x8OMBYJkpq+3TygUpjad6mpAZj5YcvXtZLJUyjq+ihfaqljYnwAY5EgAPPLNYJmIGI2WcQxJVg0I0y9A3gP8B9a6weVUkeAb7Z2WYIgCIKwPTL5AulckXDAofy8QyzwO4m/RpjK5K2ohttnhKhKClnpmNpjJodCTDs5pgCikyJMCYKwaWzHVN+6UT5zX10BemX5eRcxt5ziwmqG45Nlsef4RIT+C/eac4WhI+UHu1xw+EbTM9Vi4skcQAPHVPtP5LMZjQRQyvw7V6GUcU2JY0qwaEaYWtJav1hr/RcAWusntNZvb/G6BEEQBGFbJFLmBNyx/DzYW8XnUD+VL1NyTPkhL46pdmNyuI+ZpSTaqWQ3OmWm8hWL9fcJgiA0wO6OCtaVn5eFqZDXXfXYEl1afm73S9kRPoDjE1GO5h4lc+B4vbt64ilw4VFYW2zpupaT5n05Eqp1TC13lGPK73FzYCBQL0yB6ZkSx5Rg0Yww9WGl1G1Kqd9QSvXembwgCILQkSTSZrexYZSvx/C5ax1T1uceHxQcOqbc0jG1l0wOh0hmC5xfzdTfGZ0y4uHq2V1flyAInUsyY8SmPl9jx5Qd5UvVTubr0vLzO6eXCfncXHpgoHTb9aM+jqlZZoOX1z9hl3qmYpZjKlrrmEp2ljAFpgB9rrZjCizHlEzlEwwbClNa66cDrwEOA3copT6jlPrxlq9MEARBELZBPNVAmErFelOYahTl84hjqh2ZHDYXgY5xvuhF5ihxPkEQNkGyYfm5JTZ5giU3VZ1jqkvLz++aWeZJ4xE87vJl8RXqFB5V5O7CkfonHDpuNm5a3DMVS60T5Qt1TpQPbGGqgWMqHYd0YvcXJbQdzTim0Fo/Cvwe8NvAs4C/Vko9opR6WSsXJwiCIAhbJWELUwFxTEHjKJ/yBKodU4U86KIIU3vM5LApGnYWpqbMUYQpQRA2gS02OQpT3j5wuQg16pjqwihfMpvn4dMrXDdZHQrynbkbgK/Fxuqf5A0YcarFPVMxK8o3WFl+XshBdqUDHVMhziTS5As18XOZzCdUsKEwpZS6Rin1fuBh4LnAz2itL7c+f3+L1ycIgiAIWyKRtjumpPwcwOd2k6mIZmRytjDlM9E9G7sIXcrP95SxSBC3SzHtNJkvMgEoWD656+sSBKFzKQtTDlE+X591X4OpfG4vuLxdFeW7by5Ooai5frJG6Jm/k5jvIN894yJXK6aA6ZlauLul7rGYU/l5yvRhdZ4wFaRQ1JxJ1AxaGZwwR+mZEmjOMfW3wF3Ak7TWb9Fa3wWgtV7AuKgEQRAEoe1wjPLlM5BPmTHFPYbfW+2Ysj83jqmKKJ8tUokwtaf4PC4ORQLOjimPDwbHxTElCMKmKEX5/LXl58lSVC/YqGMKzGO6yDFlF58fP1wvTCX3PYl0rsgjp1fqnzhxExRzRpxqEcvJLP1+D96KiGGnClNj0SDgMJlPHFNCBc10TD1Ta/0prXVKKRVVSl1Tcd+nWrs8QRAEQdgajlE+u8egJx1TLnIFTbFoprzZ7imX1y+OqTZlarjP2TEFJs4nwpQgCJug5JjyOkT5fP3mvkYdU2Dift0kTE0vc2RfH9G+irjc2gWITRM68mMA3D27XP/EwzeaYwt7puLJnHO/FHScMDUeNaJnnTDVt9/0dcWkAF1oLsr3LaVUWCk1BNwLfFwp9VetX5ogCIIgbJ1EOofP4yJQeQKejpljDzqmfB7zlm87peyOKbfXD7oAResixO6bko6pPWdiKMT0UoOLwOikCFOCIGyKtWwen8dVVfQNQHa11CEV8pqYn6Mw5QtBtjuEKa01d83EOD5R65a6C4DBi29k/4Cfu2di9U8ODcG+S1vaMxVLOQhTySVz7DBh6lDEnE/UTeZzuYz7VxxTAs1F+Qa11gngZcDHI70NvQAAIABJREFUtdbXA89v7bIEoTcoFjUv+dvv8dUHz+z1UgSh60ikcgzWTuRLx82xBx1T/gbClMtrCVC2a0qifG3D1HAfsWSOuNU1UkV0ClbPds1FoiAIrSeVLdQXn4P5PWJ1TJWifLUdU2DEqy5xTE0vJllay9YVnzN/JygX6tBxjk9EuHvGwTEFpmdq9lYoOnRQ7QDLySyRyuJzKDumOmwqn9/j5kDY33gyn3RMCTQnTHmUUqPAK4AvtXg9gtBTJHMF7p2Lc8+sw26MIAjbIpHKEw7UFp/bjqneE6ZKjilLkLKPHp8lQNlOKTvK5xZhaq+ZGDYOhuklhzhf9CJzjE3v4ooEQehkktkCfbXF51BVfu51K9wu1SDKF+qa8nO7X8qp+JyRy8Dfz/GJKKcWkyyuZupfYOIms9l1/pGWrK+bonxg4nx1jikwPVPimBJoTpj6I+CrwGNa69uVUkeAE61dliD0BvYoXrsLRxCEnSO+nmMq2INRPne1MJXJm98/7pJjyipAF8dU2zA1bC4UTzkVoEenzFHifIIgNEkymy85oqrIlYUppRQhr7txlK9LHFN3zSzT7/dwdP9A+UatjTA1dh0Axw+bcwXHDeSJ1vZMLSezDsLUEig3+MMt+ZqtZDwabOCYmjDu31y6/j6hp2im/PzftNbXaK1/w/rzE1rrn2v90gSh+0lb5cNxJ2Hq1PchcXqXVyQI3UMinaueyAc9HeWrdUyVOqZ8ljBVqI3yScfUXjMxZBxTM04F6CJMCYKwSZINo3xlYQpMnC/VyDGVcxAXOpC7pmNceziC26XKNy6fMuLP2PUAXDNu7nfsmYpeBP0HWtIzVSxq4qkc0ZBDlC8YBaWcn9jGjEWCnImnyRdqoo/2ZL7E/O4vSmgrmnFMCYLQImxhKpF2yPH/yy/CD/92l1ckCN1DIpWrnsgHkJIon90xVYryeS1nVJ1jSoSpvSboM70cjo6p0LCZoiXClCAITZLMrNMxZZWfg5nMl8w5Oab6uiLKt5rJ88iZBNdNOPRLQUmYCvrcXD464DyZTykznW/21h1f30omT1FT7/pOLnVkjA9MlC9f1JxdqYlFDlrClEzm63lEmBKEPSTVyDGlNWTikFzcg1UJQnfQMMrn9vWk6GJH+TK5cpTP41LlKF9tx5RE+dqCyaE+ZpyEKaXMjv3Syd1flCAIHUkylydU2zFVLFpRvv7STUGfp6vLz++bjVHUcLyuX+ouc36w/4rSTccPR7lnJkahqOtfaPwG0/O3trPn67Gk2SiKODmmOqz43GY8GgRgrnbSrO2Ykp6pnmdDYUop5SCrC4KwE9g26ZVaYcq+MLRjR4IgbAqtNYl0nnCwtvw8btxSHWiD3y5+r3k7zxbM751MrmhcVLYAJVP52pLJ4RCnnKJ8ANFJcUwJgtA0jo4pW2iqiPKFfO7S5mkV3lBXTAK1i8+vO+xQfD76JHCXN7WOT0RYyxY4cW6l/oUsZ1XJabVDxKxJrFGn8vOOdUxZwlRtz1R4DJRLJvMJTTmmHlNK/aVS6oqNHyoIwmZIW1GaOseUnd9PJ3Z5RYLQHaxlCxSK2tkxFei94nOocExVdEz5PS7jIIOyIFUQYaqdmBwOcW4lQ9LJvRCdMrv1LRpXLghCd+HYMVUSpmqifOuVn2sH91AHcddMjEv29zNYK/xceLTKLQVw3YQRghx7pkavNaLKDgtTyyXHVPcIU4ciDYQptxcGRsUxJTQlTF0DPAp8TCl1i1LqjUqpzhsFIAhtSGkqXzqHrnyTt08SxDElCFvCnnRZ1zFlO6Z6kNry82y+iN/jLgtQdVG+3os7tiOT1mS+mdr4AxhhKp82E40EQRA2YC3rEOXLrppjZZTPu075Obr8PtGh3DsbK03cK5FdM8XndrTMYnI4RDTk5a5ph54pfz+MXLbjwpS9Ye0Y5Qt2ZpQv4HWzf8DP3LLDe9ngYXFMCU1N5VvRWn9Ua/1U4LeA9wKnlVKfVEpd0vIVCkIXY5ef5wq62jJt26QzIkwJwlZIpC1hqs4xFetZYcpfN5WvgN/rAnej8nNxTLUDk8PGxTDt1DMVvcgcJc4nCMIGaK1JOTmm7HPO2vJzR8dUX/VzOpDVTJ7FtSxHRvqr77CFkcGJqpuVUlx7OMJ9cw3OyceuN8LUDrrI7ChfpPIcJp81ImKHOqbAxPnmYw5THSOHIS7l571OUx1TSqkXK6X+A/hr4H3AEeCLwJdbvD5B6GrSFWJUIlUR0xDHlCBsi7h1Uucc5etNYap2Kl8mXzTxPo+1I1vrmHKLMNUOTA6ZC8Fpp56p6JQ5LksBuiAI65MtFMkXNX3+WseU9bulomMq6PM4C1NeE8ci17mT+eatKJndeVTCjpINjtc95+qxQU6cW3GOVI9db5xWO/h72I7yVZ3DpCzHVrBz6wjGoqH6KB8Yx1RiAYoOP3NCz9BMlO8E8BLgL7XWx7XWf6W1Pqu1/jzwldYuTxC6m0qXVFXPlN0xlVmR7hBB2AKJtDl5lChfGbtjKlvZMeV1lSN7pfJzyzkljqm2YDDkJRLyOjum7Auo+NzuLkoQhI7DjuYFvbWOqfooX8jnbjyVDzraMWVHycYaCVM1UT6Aq8cjFDU8fNqh+7VUgH7Xjq0xlswxEPDgcVdcqtvCVIdO5QMjBi7EUvUTDiOHoZiHldN7szChLWiqY0pr/Xqt9Q9q79Bav70FaxKEnqFSmLKjR0DZMaWL5RMGQRCaxu6Yqtpt1NoIUx2827gdGnZM2eXnBTvKlzZuqR6cXNiuTA6FnDumvAHTNyIn84IgbMCaJUz1+ZssP88VqvtPoeyqynWyMNXAMRWbBZfHFHHXcM242dByjPPtvwI8wR3tmYols87F59DxUb58UXM2UdNRZscnpWeqp2lGmNqvlPqiUuqCUuqcUuoLSqkjLV+ZIPQA6VzZDWVHj4CyYwokzicIW8B2IIaDFZGFfNqIL73qmKqL8hVM75TtjCo5pjJSfN5mTA73ccopygfmIiohwpQgCOtjO6CCdeXn9VG+gNeN1uUpriVsx1QHC1PzsRR+j4uR/hpXcHwWwofA5a57zoFwgJEBP/fPO5yTuz1w6FqYu2PH1hhL5YjWFZ8vmWNHC1Pm56cuzme71GQyX0/TjDD1GeBzwEHgEPBvwL+0clGC0CukGzqmKn5hZxxsw4IgrIv9/2mgMspni7w9LkxlchUdU56K8vNKx5TH5/QSwh4xORxifjlVcrtVER4Vx5QgCBuylrEcU3Xl55Yw5S0LU3ZBel3PVBeUn88tJxmLBlG1ruDYrOk6asA1Y4Pcv14B+ul7oZBzvn+TxJK5+o7MkmOqs6N8QP1kPjuWHpMC9F6mGWFKaa0/pbXOWx+fBnZu7IAg9DCpbAG3y7wxVndMVfzCFseUIGyaeCrHgN9T+v8FQCpmjj0qTPlrHFMmyldRfm47pgpZcUy1GZPDfRQ1ztOMBkSYEgRhY2yRKdhImPI5CVM1PVNdUH4+t5xiLBKsvyM+t64wddXYII+dX2Ut41SAfp0ZIHL2wR1Zo4ny1TqmOj/KZ/+71zmmfH0QGhbHVI/TUJhSSg0ppYaAbyql3q2UmlJKTSqlfgv4P7u3REHoXtK5AsN95o2neipfZZRPHFOCsFkSqTxhp4l80LPClF1+nqksP/e4KxxTFVP5pPi8rZgcNvEHx8l8A6Oweg4KDhdLgiAIFqmc+R0RahTl85Y7puy4X6rWMdUF5efzy6lSpKxEIQcrC47F5zbXjA+iNTzkWIB+g/XiOxPnM1G+mnOY5JLpwPIP7MjX2AsCXjf7+v2lyYhVDB6Wjqkex7POfXdinFH2dvObKu7TwB+3alGC0CukcgX6/WYkrzimBGHnSKRz6whTnbvbuB2UUvjcrvJUvlzBivJZ/072ND7pmGo7JobMRdSs08l8eBTQsHoWBsd2d2GCIHQMDaN8uTUjOLnKfoWQd4MoX4d2TCWzeRbXsvXF54kFM3BoHcfU1WPlAvQnT9XE6SITENpnJvM9eXtrLBQ18VSOiFOULxjt+MEk49EgczGHn5/IYTj/o91fkNA2NBSmtNYX7eZCBKEXSecKBLxuBoNe56l8IB1TgrAF4qkc4UDNW1yPO6bA9ExlqxxTLnOS6/ZXO6bc0jHVTuzr9+N2Kc7G0/V32hOkVs6IMCUIQkNS60X5KmJ8sE7HVIeXn883mshnR8jWcUztDwc4EPZz/1ys/k6lTM/UDkzmW0nn0BrnKF8Hx/hsxqNB5xL5wQk48XUzQbnDxTdhazTTMbUllFIBpdRtSql7lVIPKqX+0Lr9IqXUrUqpE0qpf1VK+azb/dafH7Pun6p4rfdYt/9IKfXCitt/wrrtMaXUu1v1dxGEVpHKFQj63AwEPDWOqRS4rJ2StMMboCAI65JIOTmmertjCixhqmAuNLJ2lA9MdE8cU22L26XYP+DnTO2IbagQphZ2d1GCIHQUa1ZfVF9dlC9ZFeODsnhVOaQH6Pgo31yskTA1Z47rOKYArh6LOIsqAOM3GMfPNis4lq0p3ZHaKF9qqUuEqRALsRSFYk1ldeQw5FOQXNybhQl7TsuEKSADPFdr/STgWuAnlFJPAf4CeL/W+iiwDLzeevzrgWWt9SXA+63HoZS6AnglcCXwE8DfK6XcSik38HfAi4ArgF+0HisIHUM6VyTgdRnHVG2ULzBoLg4lytd75FLwiFT5bYdEymGiTUmYCu/+gtqEqihfvojfa50GuH0VjqmMdEy1IQfCAc6s55hKSAG6IAiNaVh+vnyq7n3R7qGqc0y5XObctEPLz+dKjqmajim728ieDteAa8YHeeLCGquNCtDRsHD3ttYYS5pNonpharmjJ/LZjEeD5Aqacys172e2KCiT+XqWlglT2rBq/dFrfWjgucDnrds/Cfys9flLrD9j3f88ZeZ4vgT4rNY6o7U+CTwG/Jj18ZjW+gmtdRb4rPVYQegYUtkCQa+bcNBb75jyhow4JeXnvcd9/wqffZU5WRS2RCKdJxxw6JjyBHtadLGjfMWiJlsolgrRqx1TaXFMtSGjgwFnx1TfCCi3TOYTBGFdktk8bpcqTWgFYPqHMHsLXPPKqsc2nMoH5vw059B31wHMLSfxuV2M9NecB8RnzO9Sr8O0vgquHjMF6A86uaYOXWeO24zzxVK2Y6o2yhfrEsdUg8l8doxSJvP1LOtN5btuvY9mXtxyNt0DnAP+C3gciGmt7d9yc4BdiDAGzAJY98eB4crba57T6HandbxRKXWHUuqO8+fPN7N0QdgVKjumVtKVU/mS5s0xMCiOqV7kwglzXBM781bIF4qsZvIOjqk4BCN7s6g2wedxkckXyRaMa6qxY0o6ptqNho4plwsGDoowJQjCuiSzBUJeN6qyv+dbfwp9++GG11U91nZVpWqjfGD6qDo0yje/nOJQJIDLVdNhFJvdMMYHcJVVgO4Y5wsNwdCR7QtTtmOq9hwm2S1RPluYqvkZKjmmRJjqVdabyvc+6xgAbgDuxUzouwa4FXj6Ri+utS4A1yqlIsB/AJc7Pcw6OrWc6XVudxLVtMNtaK3/AfgHgBtuuMHxMYKwF6RzxjE1EHByTAXNWFgpP+9aMvkCs0tJLtlfM/p38XFzTC/v/qK6AFvkDQcdys97uF8KylG+jBXnq+6Yqig/F8dU2zE6GGA1k2clnWOg1g04MCrClCAI65LMFAj5K2J8p74PJ78DL/xT8FVH2xqWn4PlmOrcKF9djA9Mx9R+p8vUakYG/BwaDHDfXINN47Eb4NR3t7XGmNUxFa10TOUz5t881PnC1FjE/PvP1zqmglHw9Ytjqodp6JjSWj9Ha/0cYBq4Tmt9g9b6euA4Jk7XNFrrGPAt4ClARCllXy2MA3Zb5xxwGMC6fxBYqry95jmNbheEjiFV4ZhazeTJWy6G6iifOKa6lc/cOsNP/vX36rsKlixhKiXF91vBnnDpGOXrcWHK73WRLRTJ5M3FRinS4faVhalCtqfjju3KwUEjFp51ivOFR6VjShCEdUnmCqXuKAC+9WfQf6DOLQUQ8KwnTAU71jE1t5xiLFIT19PaCFORiaZe46qxQR5oVIA+dr3ZJEhs/ZLULj+vGuBinw92gWMq6HOzr99XH+VTyrimxDHVszTTMXWZ1vp++w9a6wcwZebropQasZxSKKWCwPOBh4FvAi+3HvYrwBesz2+2/ox1/39rrbV1+yutqX0XAUeB24DbgaPWlD8fpiD95ib+PoLQNthT+WxnRynOl0ua3atAWDqmuphHz66QLRRZXsuWbywWyt1SMpFxSyRS5v9RXZQvFet5YcrnNlG+TM6I4D5PRcdUQRxT7czBsPmenIln6u8cGIWVM7u8IkEQOolkJl9yQnHyu8bZ8/TfdOxVcrkUAa+LlFPHlK/PnKd2GOlcgQurmfqJfGsXzDS4JqJ8UC5AtzfBqhi73hy3EeeLJ7OEAx7clXHD1JI5doEwBTAWDdULU2B6puJSft6rNCNMPayU+phS6tlKqWcppT6KEZg2YhT4plLqPoyI9F9a6y8Bvw28Syn1GKZD6h+tx/8jMGzd/i7g3QBa6weBzwEPAV8B3qK1Llg9VG8Fvmqt53PWYwWhI9BaW1P53KUL6FKcLysdU73A9KI5sauKccZnjWMFxDG1Rex/z7BTx1SvC1NW+XmpY6rkmKosP88YB5XQVtiOqdNxh5P5gVHIxCHbmfEaQRBaz0raEqa0ttxSB+H6X234+JDPs06Ur/OEqfmYNZFvqEaYsoWQSHPC1NXjpqvS0TV18GpwebclTMVSOaJ9tcXnVrVDF0zlA9MzVdcxBeKY6nHW65iyeS3wZuAd1p+/A3xooydpre/DxP5qb38CM1Gv9vY08PMNXutPgD9xuP3LwJc3WosgtCN2x0vA6ypFjkq7L7mkeeP3h6VjqouxhSm7TwAo90uBOKa2SCnK59gx1dvl536Pi8UKx1RVx5T98yaOqbbkQHidKN/AqDmunIHhi3dxVYIgdALzsRR3zizzuqdNmV6p6e/Di/7XulPogl43KSdhyheCWOcJU7ZDp65jKj5njk06pq62CtAfmI/z1Iv3Vd/pDcDBq2Duji2vczmZqy8+LwlT3eGYGo8G+dqDZygWdXURfeSwORfJrIB/oPELCF3Jho4pSzD6MPBurfVLtdbvt24TBGEb2G/2Qa+bwVCNY8ouPw8MmovEnPyX6zay+WLJ+VDlmFp6whzdPnFMbRH737Mqyqe1OKawHFNOHVMeyzFVyIMuijDVhgS8bqIhL6edJvOFLWFqG70mgiB0Lx//3kkAfvWpU8YtNXAIrvuVdZ8T8rkbOKY6M8pnO3TqOqZsh87geFOvM9TnYywSXKcA/XpYuMdUM2yBeDJLJFTjmEp2V5RvPBoiV9CcW6mJptvioC0WCj3FhsKUUurFwD2YGB1KqWuVUtLlJAjbJJ0vC1Mlx1TK7piqKD8HcU11IXPLSYrWjNBYqqJjavFxc9I3dEQcU1skkXIoP8+ugS6IMFU3la+i/LyQMUI4SPl5m3JwMLiBY0oK0AVBqCaeyvEvt83w09eMMrZ0K8z8EJ7xLuPuWYeQz00y16j8vPNiw/PLKTwuVXKflojPmmlwmxB9rt6oAD27AhdObGmdy8kckVADx1SoS6J8ljg4X+u8swvoJc7XkzTTMfVeTPQuBqC1vgeYauGaBKEnsB1TdR1TWltRvmD5IloK0LuO6aXym3G1Y+pxI0oFo+KY2iKJdA6PS5VLXqEs8vW6MGV3TNnClLfSMZUpT+YTYaotORj2OzumRJgSBKEBn71thrVsgTc8/SLjlgqPwXW/vOHzgj53g/LzzuyYmltOcSgSrC4VByOCDB42U+Ga5OrxQU4tJoknnQrQbzDH+a3F+WLJrEOUbwlcHiOgdQF2AX1dAXrJMSUF6L1IM8JUXmst7cuCsMOkcmVhyu7CSaRzUMgZZ0eVMCX/BbuNmcUKYaq2Y2r4iFV8L8LUVoincoSDXlTlSab9f0iEKTL5QoVjyhLv3D5Tui+OqbamoWPKP2CclgkRpgRBKJPNF/n490/x1IuHuSp9J8zeatxSTfyOD/k8pXPVKjxB816hdQtW3DrmlpP1MT4wjqkmi89trhm3eqYWHM7Phy8xHbFbKEAvFDWJdL4+ypdaNhuWmxDP2pmxRsJU/wFzPiKOqZ6kGWHqAaXUqwC3UuqoUuqDwA9avC5B6HrSVvlw0Ocm6HXjdSvjnLF3oezyczDTloSu4tTiGiGfm5EBf9kxVchDbBqGLjYl3Sn5vm+FRCpPOOBQfA4Q7O3yc5/bbUX5zMWGz1PjmCrYjinpmGpHDoYDXFjNlr5/JZQyPVPimBIEoYIv3bfAmUSaNzzzCNzyIeOWOv5LTT032LBjynp/KGTr72tj5mOpklOnivhs0/1SNlcdMsLU/U5xPpcLDh3fkjBlnw86Rvm6ZCIfGNFzuM9XP5nP5TI/o3ERpnqRZoSptwFXAhngM0Cc8oQ+QRC2SNp2THlcKKUIB7ymGydn7R5UdkyJY6rrmFlMMjEUIhL0lqfyxaahmDdTtYIRcUxtkXgqV118DuVYpC329ih+ryk/z9Z1TPktx5RE+dqZ0UFzQXgukam/c0CEKUEQymit+eh3T3J0fz/PPjYC5x6CqWc0/fs91Ggqn71xkUvV39emZPIFziYy9RP5MqtG9GlyIp9NtM/H4aEg9zcqQN9/BVx4bNOusljSiH1Rp/LzLik+txmPBpldcvgZihwWx1SP0oww9VNa69/VWj/Z+vg94MWtXpggdDulqXxWD85g0FvvmApYF9HSMdV1TC8ZYar0fYfyRD7bMZVJbHmqSy+TSJsoXxX2BbvdxdOj+NwucgVdimeUonwen9UxZcXE3CJMtSMHLGHqTKMCdBGmBEGw+P5jizx8OsEbnnEElc+YqZ1DFzX9/IDXXdpErcIWtvIOAnmbshAzvzPHah1TtjPHLt3eBNeMRbhvvsEGYnQScmuQXNzUay5bG5WDdY6pWNcJU1P7+nji/Gr9HdEp07cq9BzNCFPvafI2QRA2gX1hGPSaC8OBoJdEOl8hTEnHVLdSLGpmlpJMDoeIhCqEqUXrjdh2TIF877dAIuUgTMXnwOU1/QU9jB3dW03nq/6M2w/FHOSkY6qdsR1TZ5wK0MOjsHKm43pfBEFoDf/w3SfY1+/nJccPGUc2GqLNC1N+j6vUR1iFxxJ38p3jmJq3uozqony2M2eTjimAq8YGmV1KlVxOVUQmzXF5elOvGU81cEyllrtmIp/N0f39LMTTrKRrCuRHLjOC3trmRD2h82koTCmlXmT1SY0ppf6m4uMTgMOIBkEQNkO6ovwcKh1TFVE+Xz8ol3HObEA8lWM1I/81O4GzK2my+SKTw32EqxxTj4NvAPpGjGMKyiOChaaJp/KEAw7CVHjU9Bf0MHZ0z/5dUYryeayT4MyK9WfpmGpH7DHnjsLUwKiJYyaXdnlVgiC0Gw+fTvCdR8/z2qdNGWfs0klzxxYcU7pW7O5Ax5TdZVQnTJUcU5sXpuwCdMeeqeiUOS6f3NRr2tUOjlP5uswxdfTAAACPnatxTe271Bwv/GiXVyTsNeudoS8AdwBp4M6Kj5uBF7Z+aYLQ3dQKU+GAh5WqKF/QFNr6w025Zn7tk7fz+k/c3rL1CjvHtDWRb3I4RCToq3ZMDR8x3/eSY0p6pjaLifLVlJ8n5re0I9pt2A6plXSNMGVH92wRXISptiQc8BDyuRtH+QBWFnZ3UYIgtB0f++5Jgl43r77RiqjZAskmHVNFDflirTBlvT/kHX4PtSlzyyncLsXBcM17W3wWXJ4tuanXLUC3o4GxzTmm7ChfVfl5Lm2uDbpseMsxS5g6cbZGmBqxhKnzj+zyioS9xtPoDq31vcC9SqnPaK1zjR4nCMLWKEX56jqmLNeT19rVCQxuKEwVi5r75+Okc0XunF7m+snu2lXpNqYX1wCYHOpjMBhjNZMnVyjiXXocDl1nHhSQKN9WSOcKZPPF+vLz+CxM3LQ3i2ojfG4jRCXSOdwuhcddMZUPyj9vHp/Ds4W9RilzYdXQMQUmznfw6t1dmCAIbcPZRJqb753n1TdOErEjYUsnjQu/b1/Tr2NvnKZzBbzuCi+DPZUv1znC1HwsxcFwoPyeZxObNVPgXO5Nv+ZgyMvkcMi5AN3fD6F9m4/yJbNmyGql69veoOyiqXwAE0Mh/B4Xj55dqb5jcNz8rJ4Xx1Sv0UymYUop9Xml1ENKqSfsj5avTBC6nHTO5PYDlmMhHPSSSOfQ2YryczAF6BuUn8/HUqXX+4fvSGFguzO9mMTjUhyKBEq7YonVNYjNmH4pKO+MpcQxtRkSlvus6qSuWDClr+GxPVpV+1DpmPJVnqC7JcrXKRwcDDg7psKWMJUQx5Qg9DKf+MEpCkXN655W4Y5aPmncUko1/Tp+r3mPqOuZ6kjHVLI+xgdm02oLxec2lx8M86NaYcUmOrklx9Rg0IvLVfF9suPZXRblc7sUF4/082htlE8p2HdMHFM9SDPC1MeBD2F6pZ4D/BPwqVYuShB6gVSugNdddiwMBr3kCpps2voFXXJMRTZ0zZw4Z94Un3rxMF976KzzlAuhbZheSjIWDeJxu0rOnrWzj4Mumol8UOGYEmFqMySsEs0qx9TqWSjmzS5cj1MWpnKliw6g7JgqRfmk/LxdaeiY6j9ojjKZTxB6mi/ff5pnX7qfieFQ+calkzA0tanXCXjKjqkqSuXnndQxlWI8Gqq/Iz63rZj/4aEg88up+h4uMAXom3RMxVI55+Jz6DphCuDYgX5OOAl7I5fC+Ud3f0HCntKMMBXUWn8DUFrraa31HwDPbe2yBKH7SWULJZs0lB0emaSJeeHrM0d/eMPyczuf/cc/exVet4uPfW9zZYvC7jKzmGRiyJwg2QJK9txj5k5xTG0Lu6+raipffN4cpWNP+w9IAAAgAElEQVSq5JJaSefL/VJQdkyVonzimGpXDg4GOJtIU6zrffGZ6IgIU4LQs+QLReaWU1w+OlC+sVgwzp2hI5t6rcaOKbv8vDOm8mXzRc4k0ozVOqYKOfP7chubVuPREJl8kQurDpP5opNG+CoW6u9rQCyZra8isIWpLpvKB6YA/XQ8XdpULDFyqelLlDqLnqIZYSqtlHIBJ5RSb1VKvRTY3+J1CULXk8kXCFYIU/YbUSZp7RxsomPqxLlV9g/4uXikn5+7bozP3znH+ZXO2cnqNaYX15i0djIHrSifXrQimLZjyhs0hdTimNoUiZTpaAsHKioU7ak74pjCb/3OMcJURadGqWPKEsHd0jHVrhwcDJAvai6sOfyOD49CQoQpQehVTsfTFIq6tPkFmHhvIbup4nOg9B5R75iyo3ydcZ55Jp5Ga4eJfIl541TfwkQ+m8ND5jVnral/VUQmoZjbVLw6lsxVF5+DmcgHXeqYalSAfpk5imuqp2hGmHonEALeDlwPvAb4lVYuShB6gTrHlDVFLJu2HFO2VbqJjqkTZ1c4eqAfgF97xhFyhSL/9MNT1heKyfjwNiKWzJJI55kaNo44W5B0Lz9hRMjKHbFgRBxTm8QxyhefM8dB6ZiyHVOrmXwp1gdUTOWTjql2x54qdTbucFE4MCqOKUHoYWaWjEAyMdRXvtGeyDe0SWGqkWPK21kdU3OWaFQnTMXsTautC1N2PHBu2cE9Fp20vk7zcb5YKttzUT6gPs6375g5XpAC9F5iXWFKKeUGXqG1XtVaz2mtX6u1/jmt9S27tD5B6FpSOWfHVC69Zi4KXdZ/z8CgifIVi04vg9aaE+dWObrf7DpcPNLPCy4/wKdumSaZzcN/vhk+/qKGzxd2l+lF+6TRnMxErO97IHHKuKUqi0kDEXFMbRLbKTjUV3FiF58zkdjA4B6tqn2o6pjySMdUJ3Jw0FwUno47XAiJMCUIPU3pHKO2Xwo27ZiyO6YyuQbl5x0ylc8WjcYjNR1T9qbVNsrPxyJB62s4OKaiU+a4fKrp14ut5ZyjfC6vmVTXZYxHQwS8Lk7UFqBHp8yGmRSg9xTrClNa6wJwvVKbGOEgCEJTpHNFAr76jqlCZq0c4wNzQY2GrPPUj4V4mmS2wCX7y29Yb3rWEWLJHJ+7fRYW7jG/2E98tSV/D2FznFo0jrhJyzFldyH1r02X+6VsxDG1aeZjKfp87uoTu8S8xPgsbDEqV9DOwlQ6YU4G5W2/bbGFqbNOk/kGRmHtvOlOEQSh55hZSuJ1q5KzEjCOKZd30++DtmMqna+N8tkdUx0iTMVSuFT5d2cJO+a/jYm9fX4PQ30+Z8fU4GFQrqYL0HOFIiuZfL1jKrlk3FJd+L5cmsxX65hyua3JfOKY6iWaifLdDXxBKfVLSqmX2R+tXpggdDupXIFAxYWhfSFdyCbBW7GrY7s8GvRM2fbXoxXC1PWTQ1w/GeUz333QlAcC/OCDO7h6YavM1DimvG4XQ/4iA5kz5X4pG3FMbZqFWIqxaJCq/ZT4rAhTFpXxPZ9T+XkmLm6pNmdfnx+PS3HaaTJfeNQcV87s7qIEQWgLZpeSjEdDuF0V74FLTxhXkMvd+IkO2JsX9Y4peypfhwhTy0kOhgPV73kAsRno21+OJm6Rw9Egs0sOjim314heTUb57OEt9R1Ty10Z47M5dmCgvmMKrMl84pjqJZoRpoaARcwkvp+xPn66lYsShF4gnSsQrHBMDVhlzTqbrHZMbSBMPWbZX48eGKi6/Y3PPEIwbhVqTz4dpr8Pc3fu0OqFrTK9lGT/gL/qe3+5fwmFFsfUDjAfS3EoUtMjEZ/b1o5oN2F3TAHO5eeZFRGm2hyXS3EgHOBMI8cUiDAlCD3KzFKyuvgcTJRvk/1SQKkHNVPrmHJ7QLk7SJhK1U/kA7NptY3ic5vxaIh5J8cUmAL0Jh1TseQ6wlQXTuSzOXqgnzOJdEmYKzFyqekBy67tzcKEXWdDYcrqlar9eN1uLE4QuplUtrpjyuN20e/3QC5VI0yFzbFBAfqJs6sM9/mqO3WAF1x+gJvCiwDoH/9j8A/CD8U1tdfMLCZLE/lsjnnPmU/EMbVt5pdTpc4HwPx/Si6KY8qicsfY7+SYSiek+LwDOBD2c8bJMVUSppqfAiUIQvcwvbhWLUxpbTqONtkvBes4psCcp3bIVL755VSppLyK+Ny2is9txqNB5mIpikVdf2d0chOOqSwAEafy8252TFkduY+dq4nzjVwKaLhwYvcXJewJGwpTSqljSqlvKKUesP58jVLq91q/NEHobtL5amEKzIh7lU+Bt2KaykZRvnMrVf1SNi6X4sVjK+S0m1vWRuGG18JDX9hUCaOw80wvrVVPywEudlvuhuEj1Q8ORoxQIMX1TZHM5llO5qodU/F5c9yBk89uoEqY8jo4pnRBHFMdwOhgcH3HVEIK0AWh14gncyTS+WphKrlkhlpswzFV1zEF5n0i18Al1EbkC0XOJNL1E/m0NsLUjjimgmTzRc6vOgh1kUkzkKKJovjlNcsx5VR+3s3ClJX4eLQ2zjdymTlKz1TP0EyU76PAe4AcgNb6PuCVrVyUIPQCqWyx+sIQU4Ttzicdys8pT8uqoDSR74DzpI5L3aeZVaP8f7fMw41vMtbrWz60Y38HYXOksgXOJjJM1TimJjhDXA3Un3gEIoA2vT/ChizErMk7lSegdrnpoET5oNolVRnrw+13/lxoSw6EA5yJp9G6Zoc+NGxKjmUynyD0HDNWz9HhSmFqeWsT+WADx5Qn0BGOqdPxNIWirnZSgxkSkU/vjGPK+vd2nsw3aY72ucg6xKwoW135eZcLU+PRIEGvu74AfegIuDxwQYSpXqEZYSqktb6t5rZ8KxYjCL1EOufgmAp68RTSNeXnEesJ9eLEuZUMK+k8R/cP1N0H4F46QbzvIh4/vwrhQ3D1z8NdnzI7aMKuY580TtQIU2OFBWYYrX9C0PreS89UU9hTcapOQO1x0BLlA2odU5VT+SpOhMUx1faMDgZIZgsk0jWnYy6XcU2JMCUIPYd9jlFVF7BkCVPbcUzlnBxTgY7omJovbVjVRPli9qbV9oWpw9ZmmONkvuiUOTbRMxVLmijfYGXHVC4NuWRXC1Mul+KS/f2lztwSbq+puBDHVM/QjDB1QSl1MaABlFIvB+SMRxC2iSk/r/4vOBj04immm+6YsqdYHHWI8pHPwtJJEv1HuLBi7Wo99a2QW4M7P74jfwdhc0wvmgLHyeHqKN9Ibo7HCwfqn1ASJUWYaoaFmDlJroryJeYBBQOH9mZRbUZ1+XkDx5R0TLU9B6yx52cd43wHRZgShB5kesmcYzg7pqY2/Xoel8KlIJNv5Jhqf2HKFovqony2g2kHonxjEdsx5SBMRSzHVOzUhq8TS+ZwKRjwe8o3ppbNsYvLz8EUoNc5pkAm8/UYzQhTbwE+AlymlJoH3gm8uaWrEoQuJ1coki9qAp7ajikvvmKmWphye42DykGcOGEVBdZO5ANg6XHQBdKRS0ik82TzRThwJVz8PLj1Ix1hwe42SruZlSeN2STh7DkeLxyo35UUx9SmmI8l8VgTy0rEZ82FusfX+Ik9hFKqJE5VTeVzi2Oqkxi1hKnTTgXo4VHpmBKEHmR2Kclwn88M0rFZOmlclF6HqXQboJQi4HXXT+UD8z7REcJUEqVgNFKz4RLfOcdU0OdmX7+P2SWHKF//AbPx00S/ayyVJRLy4XKp8o22MNXFjikwPVNnExmHyXyXwdITcs3SIzQzle8JrfXzgRHgMq3107XWp1q+MkHoYlKWABH0VQtTg0EvPp2pjvKB6ZlyiPKdOLdKJORlX7/DRbdlfS0MHwVgcc12Tb0NVs/C/f+2zb+FsFmmF5MMBDzVo4Ct3cxT+mBpVHAJcUxtioVYmoODAdyVJ3XxOQhLv1QldpyvMtaHy2W6iUCEqQ7goCW+nm00mW/lzC6vSBCEvWZmKVntlgJzjrGFfikbv8dFutFUviYKvfea+eUU+wf81RsxYKJ8voHygKFtMh4NOTumXC6ITDQV5VtO5hyKz63qja4Xpkzy40Sta2rkUtBFWHx8D1Yl7DbNTOXzK6VeBbwD+E2l1O8rpX6/9UsThO7FdsYE6jqmPATIUKyN0gQGHcvPHzu7ytH9/Sil6u7jwqMA+A5cCsDiqsmuc+TZcOBq+MEHzVQSYdeYXkoyORyq/n5Zb7Yn9cH6nSJxTG2K+eVUdYwPrHHQ0i9ViS1IVUX5oCxIiTDV9uwPm++Ro2NqYBSyK5BxiEUIgtC1zFjnGFUsndxSv5RN5zumUvX9UmAcU5HD4HT+vAXGo0Hn8nMwBeixjYWpeDJXvXEJFY6pLo/y7W80mc9cw0icrzdoJsr3BeAlmMLztYoPQRC2SDprdp9qhamIX+FXeTLKQZiqcUxprXn03AqXNCg+5/yPYHCCaMTsspTG2CplXFPnH4HHvr79v4zQNDOLa3X9UiwZYco4prLV94ljalPMx1KMVwpTWkN8XoSpGspRvppTADvOJx1TbY/f42a4z8cZx44pa5CCuKYEoWfIFYosxNJM1FQFsHqmNY6pDpnKNxdL1vdLgRGmdiDGZzMeDTEfS1EsOmz4RiabdEyZKF8VaxfMscsdU2ORBpP5hi8B5ZIC9B6hGWFqXGv9C1rr/6W1fp/90fKVCUIXU4ry1QhTUZ95809R41gIhOvKzxfXssSSOeficzDjVUeOlWJ+pQJ0gKteZsqgb/n7bfwthM2QLxSZW05V90sBLD5OLrCPVUL1jilv0MSrxDG1IflCkTOJdLVjKrkE+ZQIUzWIY6o7ODgY4EzcIToStoSpxMLuLkgQhD1jIZaiUNQ1xeenzHEbjim/p5FjKmDeX9uYYlFzOpauntRrs8Nu6vFokFxBc3bFYbMgOmk2GB0qOSqJOUX55m43olS4uwe4uFyKowf6S925JbxBI+xdEGGqF2hGmPqBUurqlq9EEHqIdKljqvq/YNRrRn8ndc2OiYNjqjSR74CDMFUswoXHYN+l7Os3F5mLaxVuHLcXjv04nLl/O38NYRMsxNLki9rRZl+0djNjtcKUUibOJ46pDTm7kqFQ1IxV7oyWyk1FmKqkLEzVdG7Yjim3CFOdwMFwgDMJB8dCyTElBeiC0Cs4DlexJ/JtK8rXuY6pWCpHvqjZP1DznlbImYhcv8M05C1iC4LrTubbwDUVT+WqHVNawxPfhoueCS534yd2CUf3D5SubaoYuUwcUz1CM8LU04E7lVI/Ukrdp5S6Xyl1X6sXJgjdjO2Yqp3KN+gxwkSdMOUP13VMPWZP5HOK8sVnzE7WyDFCPjcBr6vaMQXQtx+Si1B02AkTdhx7jPPEUH2UTw1fAkCiVpgCE+cTx9SGzFsng1U7o/E5cxRhqgrbKeX3imOqk2nomBJhShB6DluYmqjc/Fp6why3FeVr4JjyBtq+Y2rRqrAY6q95T0summPf8I59LTsu6NgzFZ0yx3V6prL5IquZfHXH1OLjkJgz3bA9wLED/ZxbyRCvHQQ0cilcOAGF/N4sTNg1PBs/hBe1fBWC0GOUhKmaqXxhS5haKTThmDq3yoDfw4Gww0XkhRPmuO9SlFLs6/dXO6YA+vebSRfJJegf2fpfRmiK6UVrN7PypDG7Biun8YxcgktRH+UDcUw1yULMXKBXRfkS8+a4gz0S3cDGUT7pmOoEDoYDLCdzpHOF6r5Cf7/ZzEiIMCUIvcLMYhKf28WBgYrf30snzfljaOvF2X6vi5W0gyDgCbT9VL4L1tCffX0NeptC+3bsa9mbYnNLDpsF0Y0dU/b5X7RSmHrim+Z40bN2ZI3tzrEDVgH6uRWePFXxMztyGRRzJpq675K9WZywK2zomNJaTwOHgedanyebeZ4gCI1JZ507pgbc5k10tViTMQ+EoZCtOgk4cXaVSw40mMhnW173HQNguN/PhdVax5T1hrx2bot/C2EzzCwl8XlcpTHvQGk307XvYsJBL7HaXSIQx1STzMecHFOz5uQ5tHO7ot2AXX7uqys/F2Gqkzg4aL5PZx0L0A+KY0oQeoiZpSTjQ0FcropzwuWT23JLge2Ycorytf9UvsU1c947XOuYWjtvjn07tykb8LoZGfAz6+SYCkbBP1ju/HLAHn4zWBnle+JbMDgBQ0d2bJ3tjF1NUleAPmKuZWQyX/ezocCklHov8NvAe6ybvMCnW7koQeh20nlnYarfZYSJeL7GzBgYtJ5Ydk2dOLe6fvF5aLhkUx7p95V2jkr07TfHVRGmdoPpxTUOR2tOGhfNRD6GLiYS9IpjahvMx1IM9/kIVroQ43MQ/v/Ze/PoSM7zvPdXvW/obqAbywAYzI4ZDneRokhKXLVRsizJlmwlsWVbPrbjWN5yozg+ceLYvjnXSW6sOIqvEzteriXrWnIkW2JkbZQo7qJEUuRwnRWYBTu6G0Dve90/vqpGd1U1gJnpFfP9zsEpTK81je6ur573eZ93omXjoHcLTTOmZCtfX6ELU0sbTSbzSWFKImlO/Bz84NPd3ouWcTGRNQ9XScxeVb4UiIypQskq/NwrXCw9HAeR0DoFIgGDY6rWytc6xxSIdj7LjCmAwaktW/n0jNFa+Hm1AuefhIP3XTNrmImwF7/Lbs6Zikph6lphJ86nHwHeD2QAVFVdAJrMp5dIJDshVxTVJ49BmHKrorqzUTIIU+5GYWotUySWLljnSwGsnobo0do/o1aOqYAmTOmVI0lbuRDPsj9iyJdaPQkoMHSQkNdpDj8H4ZjaZpKLRGRMjRsn77R46s5uwd2slU8PP5fCVF+wRxemLB1TeyC11OE9kkj6iCf/AB7+lZ4P8N4JqqpyMZ5lql6YqpSFa7idjinoaddULF1EUWDQZ2zla71jCmDvoK+5MBXet2Urn+6Yr+3r4kti7Xfw/pbuYy+jKAqHRwfMjin3gIhkkAHou56dCFNFVVVVQAVQFMW/ze0lEsk26BlTRseUUhIHtDWjMKU7prQA9LOroppw2Goin6oKx5RufUVUixKZItWqunk7/YAsham2o6oqFxPZxlBSgLnnYeQ6cAcIbumY2hCTFiVNmV/PmUdCb8xLYcoC6ZjaHYwGt3BMBTXHlPzekEjMqCrMPiF+1/OG+pj1bIlUoVybDAcIUapabo1jyir8XG/57mFhL54uMOhzYbcZHEeZGCh2UfhrIZODXhbWc1Tq19o6g/th/aJ471mwprXy1cLPZx4T22skX0pneiTAaavJfNFpcW4j2dXsRJj6W0VR/gQIK4ry88C3gP/Z3t2SSHY3+Vr4ueEjqAlT8WKzVj7R0qXbXC1b+TIxMQbX4JiqVNVGR44nJBwSspWv7aymC2SLlUabvarC/PMweTsAYZ+LjWzRfGdPWITUF1Pm6ySAEP4W1g2OqUpJnJhLYcpE84wp3TElM6b6gQGPk4DbwWKzVr5qebNlRSK5Rvn8cxf51b95sfHCtVkh3MCuKM7VJvLVrzHWZsW2BY6pfMlC4HZqx4lSE4dQDxBPF4kYg88BsjERd2FrbWTy5KCPclW1drGG94lp2U3W3OtWwtToDdfccKIjowFi6QJrxoFNw8dEN4gstuxqdhJ+/p+BLwBfBI4Cv62q6n9r945JJLuZfKmComyeINYoicVFwuSYCmp3FI6pMyspfC474yGDQwQ2KwoNjinhgGho51MU4ZraBYuyXudibSJfneE0MSMExAkhTIW8juaOKZAB6Fuwni2RLVaYGKyfyLcAqFKYsmD7qXzSMdUvjAbdTcLP94htaqGzOySR9BifefYCX391CbXeqTLz+Obvu8AxpQtTjWsMTZi6SseUe1vHVO+28sUzBXO+FIi/eYvzpUA4pgDmEhYB6IP7xbZJztQr80mGB9wE3A4h9l383jXVxqdzRJvMd2bF4JoaPiqEvY2LXdgrSafYkVSsquojqqr+S1VVP6Gq6iPt3imJZLeTK1bwOu3miXqaMLWaN7TYGMLPz66kOTwSaAzS1qlN5Kt3TIkDs3ky37B0THWAC5ow1dDKN/ec2E6+GYCw18VGrtTYbgmbVnMZgN4U64l8c2IrhSkTNWHK2WQqn10KU/3CnpC3uWMKICkD0CXXLrF0gVfnkxQrVVKF8uYVs09sOkR3QXFOF6b2DtUdA9dmxXf5wPhVPbbHYadUUc3taf3QypcpmifygRCm2jCtV2+ltMyZGtwnthY5U9WqytNnY9xzJCrOCy4+C5XCNSlMTWvClHky3zGxXT3d4T2SdJKdTOVLKYqSNPxcUhTl7xVFuTbmV0okLSZfrpjypYDNVr68QXBya44pLWPqzLIQpiyJnQanv+GEfLjmmDJYYwMjkJHCVLs5H89gUzaraYAQplwDogoEhLxOqiqki+XGO0vH1LZYClPJebENSmHKiJ4tZXJsOmT4eb8xGvRYO6aCumNKClOSa5cnz2yKTnF9/aPnSx15l/j3LlgDXYxniQbc+Fx1bvvErBBDrrJdTS9gmFxTNWGqt1v5olatfJnVlgefA4yHxWtiKUyFp8R2/bzpqtcXkyQyRe45orm4Zh4DmwOm7mr5PvY64yEPAbeDMyZhSk7muxbYybfVJ4F/CUwAk8AnEBlTnwP+on27JpHsXnLFqmkiHwClLGXFQSJvqEy5/CKoMb9BMl9iKZlvPpEvdhqiRxrGy+oVo7jJMTWyK2zsvc6r8xscHgk0hk3PPQcTt4JNXBbScgU2soZ2PumY2pZ5bRHY0Mqn54eEJrqwR73NpmPK8B2kO6VkxlTfsCfkYSVVMLsZAqPimJGY6c6OSSQ9wBOnN9c3iYy2/ll5Q2QMHX2P+K7bJY6pqSFDtMPa+avOlwLwaMcLU85UbSpfbzqmiuUqG7kSQ36LQku2Pa18boed0aCbS2sWrXxOr/hetnBMPaEJqG89XCdMTd4B7iYF6F2MoigctgpA9w6K109O5tvV7ESYekhV1T9RVTWlqmpSVdU/Bd6rqurngcE2759EsivJlyp4jG00AKUcZZuHZL7UmIegKCJnKp/k7MoWwecgbK7DRxsuCnud2G2KRStfVCzKmkwJkVw9qqry8twGN03WTX8pZmH5tVobHwjHFGDOmZKOqW2ZX8/hddoZ1ENDQbTyeYeEqCtpwOOwoyhWGVMy/LzfGA15qFRV83e73QkH74PXvyS/3yXXJNWqyhOnV2vu8ppjfFbLlzpwr5az2f/FuYuJbGO+lKoKx9RV5kvBZgHD5JhyakJYj4af61PuTBlT5aKIxWiDYwpEAPqclTAFIgB97bzp4idPx7huT5CRAQ9kE7B44pps49M5NBxgNpYxXzF8VDqmdjk7EaaqiqL8uKIoNu3nx+uuk6sdieQKyJcqeF3WjqmK3UOpopIrGRYBnhDkNzirT+QbtRCmCmlIzomxqnXYbApDfhexlEUrX6Uo3ThtZGEjTzxT5KbJ0OaFiyfExKydCFPSMbUtYiKfpzGzbWNO5ks14cO3T/KHH7kFp7GVr+aYsmh9kPQke4JCRLTMmbrpI2I8+cVnd/RYZ1fSLFk9jkTSh7y+mCSeKfIjtwrXbK2Vb/YJ4SQKT20W5/qYYrnK4kaulm8EiP9TKdMax5SzPx1TulgfNQpT+qTSNmRMAewd9Fq38oForTSEn2eLZZ6/kOBevY3v/JOAek0LUxODXpZTeUoVw3suOg3xM93ZKUlH2Ikw9RPAR4EVYFn7/ScVRfECv9zGfZNIdi25UgWPwzpjquoQi4tkzpA1pAlTp5dTuB02Jgd95vvHtFBAg2MKIBpwE89YtPIBpPt7YdbLvHxJCEoNjik9+FybyAebI4LXja18Lr/IGpCOqabMr+eYMH4eNualMNWEibCXD9xi0eIoHVN9x1hI/K0sBaVj7wOnD17+/LaPkytW+LH/8Qy/8/Brrd5FiaQrPH5arGs+WBOmClApw/mnhJsQxBqozwfAzK/nqKowVS9M6S28rXBMOZo4pnp8Kl8iozumDK18Wc0h14ZWPhCOqcWNPGWjqALCMbUxL96HGt+bTVCqqLytPl/KFYCJN7Vl//qBybAXVbU4roWnhNtNm1Au2X1sK0ypqjqjquoPq6oaVVV1WPv9rKqqOVVVn+rETkoku41cU8dUDlWzR5ucM+4gFJKcWUlzaDiA3Woiny5MGRxTIKpGq6bwc83K3OcVw17mxNwGTrvCdXvqMsHmnxcLlMCmlbypY0pRhGtKOqaasrCeYyJsEFOkY+ryqTmmZPh5v7BHE6bOrabNV7oDQpx67e+3dTV88QdzrGVLvLFkseDPxEV7iUTSRzx+epXrx4NMhL0MuB3EM0VYOiGGyBy4V9xoF7Ty6RP5GoWpWbFtoWOqYHJM9bYwpTvkhozh5/p6t22tfF4qVdXaxTq4H9SK6GzQePJ0DLfDxpv3D4kLZh6D/W8T7djXKOPaIBt9sE0NfU23MYdkd3J1oxokEskVkS81Dz/X+/aTeWNLVwg1v8Hri0mmrdr4QIQC2hwwZB6YGQ24LcLPdWGqvyuGvcwr8+scHRswBJ8/39DGBxD2isXTes4gHoLImZKOKUvypQqxdLFxIl9+AwobUpi6XPSgVafM5eoXIgE3b5oK84UX5qgaA9ABbv6IELVPf6PpY1SrKn/xlDiRvZjIkivWOSMqZfjLh+Dvfr7Vuy6RtI1UvsQPLqxx37RY40QCLiFMzT4hbrD/HrHdBTmbujC1L1InTK3NAopoHbtK9LVL3hgv0ePCVK2Vzxh+ntFb+drnmIImk/n0v0ddAPqTZ1a548CQOCdYvyjcbgfvb8u+9Qv6IJt542sY2iu2UpjatUhhSiLpAvlSBa+VMFXMorjEQc08nS1EKbPOaqrA2440qfTETgtRyqLSEg24iKULjaHqspWvrVSrFsHnyQVIzpuEKY/ThstuMzumQDqmtkCvqDVO5JsX26CcyHdZ3PBh+MefBz5lxIsAACAASURBVH97sjck7eGn7trPbCzD0+csnB8H7heTjLZo5/vOqRVmYhkeun4MVTW4r17+vDiuLJ5o/Y5LJG3imXNxylWVe2vClFaYm3kcRo6LfE0QxblqSRQz+pRLiSxuh43h+pa1xKwozLTA/VpzTJUNjimnLkz1ZsZUPFPEYVMIeh2NV9QcU+0RpvZq0xEtA9DDmjCl5UwtbuQ4s5LmnlobnxbMf/D+tuxbv6A7gc2OKV2YutThPZJ0ii2FKYuwc4lE0gJyxWZT+bLYNWGqmWNKUeCBo02EqdVTlm18IBZm+VKVTH013DcEik06ptrE+XiGVL7MzfXB53PPi+3k7Q23VRSFkM9pFiRBOqa2YEFbuIyH6oSppCZM6YsYyc7wBOHoQ93eC8ll8p4bxxjyu/jMd81jyLE7hOB4+htN2/H+7MlZ9oQ8/Po7jwBwejklrigX4fH/IH7PrMp2Pknf8PjpVQJuB2+aEsPDh/wuUumMGASgt/FBnWu8f9v5LsQz7B3yYauPd1ibFW1jLWBbx1SPTuWLpwtEAq7GoSggMqYU++ZgmRazJ+RFUZo4poIT4rk1x9RTZ8T77h692DzzmCgkDB9ry771Cx6nnWjAXVvf1QiMgs0phaldzJbClKqqVWTAuUTScnLNHFOlHA63aKOxyphyVzLcNjlgDnMEqJTEYsQi+BxEKx/Q2M5nsws7c5+Hf/Yqr8yLKqwp+NzugrEbTbcPeZ3SMXWZ6FbvRseUtmiRrXySawC3w85H3ryXb72xbK4wg2jnq5bg9S+Zrnp1foPvzsT5mbv3c2g4gNOucGZFc0y9+BnRWnLHL4h/r55q4/9CImkNqqryxOlV7joUweUQpznRgIux9KtQzsGB+zZvHOj/OIOLiVxjvhSIVqfwVEse3+1o4piy9/ZUvkSmSMTYxgdCZPdFwNaepiGXw8ZY0MMlK8eU3SHWJZpj6skzMaIBN8fGBkQ76ezj4v1pFNOuQSYGvebjmc0GwXHZyreL2cmn8hFFUT6hKMpeRVGG9J+275lEsovJlyp4moSfOzxCmDJO5UspYuHx0JEm+VKJGaiWIWotTEW0kbkxY85UYKSvq4W9zIlLG3icNo6M1P3N5p6HPTdbWuzDzYQp6Zhqyvx6DpsCY8G68PONOVGVHBjr3o5JJB3kJ94yhQr8zfcumq8cu0lU4E+Y2/n+/KlZ/C47/+iOKZx2GwejAc4sp6CUhyf+M+x9C9z1cXHj1ZPt/U9IJC1gJpZhbi1Xy5cCiPjd3FA8garYYN/dmzf29/cAGFVVuZTINgpTlTKkl2FgT0ueQ89DNTmmbDYhTpV70zEVSxdr694GMvG2BZ/rTA56rR1TIHKm1s5Trao8dTbGvUeiwtW18rp4Hx68v6371i9MhD3WhZbwlBSmdjE7EaZ+Fvg48ATwgvbzfDt3SiLZzVSrKoVyFY/DOvzc5vIRcDtMAsUbWhfF/fssDrSwWc0etm7l0/MHYsbJfP5oX1cLe5mX59a5fjyEw6591VZKsPAiTNxuefuQ18m6VSufJywyMPo4oLVdzK/nGAt6Nl9jEBlTwXHhCJRIrgEmB328/dgIn3vuIkWjs0FR4KaPwKVnN6d1IUZx/+8TC/z4m/fWpoIeHg1wejkNL/wlpBbggd+C0BQ4fZtTXyWSHubxU0Jkqhemhvwu7lJepTJ6syj06PS5MLWWLZEulBuFqcwKqFUItkaYauqYAtHO16OOqXimQMQ4kQ9EK1+bcxT3DvrMwd06g/th/gWq/3E/j5c/yn86/W74vSj8d00wPXif9f2uMSbCXhbWc425uKA5zmQr325lW2FKVdUDFj/mkV8SiWRH5Mui6uRt4pjC6SPocZgypp5fFl/Oh4IV8/0AYpow1SRjKloTpoyT+UZkK18bKFeqvLqwwU31+VIrr4vq4mRzYaqpY0qtQCHVpr3tX+bXco1tfCCqabKNT3KN8ZN37iOWLvK1VxfNV974Y2L7yv+qXfRX3z1PVVX52N2bI+WnRwaIrSVQn/gDMbns4H3CGRE9Ih1Tkr7giTOrHIz62Vsn1ox4ytyqnCW1567GG/s0gaJPXeMX4hmARmEqqX3+B8Zb8hzuZo4pEM7vHp3KF08XrWMvMqsdcUwtbuQoVSzEvDf/PNz2Md6Ivpu/rdxP4Zafhbt/Be79Dfjg/5BrF43xsJd8qSqmadYTmhRFk0rZ+o6SvmZbYUpRFJ+iKP9GUZQ/1f59RFGU97V/1ySS3Um+JA5UpoypalWIFk4fQYNAkS9V+P6i+BJW8knrB149LcKeXdaj3oe0ylHc6JgKjPRttbCXObuaJl+qNgpTc8+JrWEin07It0XGFMicKQsWNnKMh43C1CW5uJNcc9x7ZJh9ER9//axFCHp4rxCaTnwOVJVMocxnn73Au68fY6puzPz0aICfsn0TJbsKD/6bzftHj4pjjETSw+RLFZ6didem8ekcyL6CU6mwFHlL4x3sTvAO9W1x7mJC5BjVf4ZJLYhtJxxTTo9o++0xcsUK2WKleSufrz0T+XQmB31UVVhct3ht9twE7/skv8/P8r+iv4T/h38f3vHv4MHfglv+cVv3q5+Y0NZ1pgD00KRwBKYsCjCSvmcnrXx/CRQBvSl7Dvj3bdsjiWSXk9OqTqapfHrVyekl6HWSrBMovjebYLWkZegUmghTy682DT4HEcgY8jotHFPDUMpCIW19R8kV8fIlq+Dz58Xr3SSUNOR1ki6UzVU2vfVA5kxtMv8C1b/9afZtvFBbwABC4E0uiOk3Esk1hM2m8JNv2cdz59d4Y9HiOHHTRyBxDuZ/wBd/MEcyX+bn7mk0wE8Pwj91fIWlkXtg6s7NK4aPQnJOujYlPc1z5xPkS9WGNj6AscT3Kap2LvhvMt/JP9y3xblLmjC1d7CNjildmLJ0THl60jEVz4h1btQYfl4uQGGjI44pgDmrAHSEcPb8+TXuOdJegayf0Z3wppZIfdqyzJnalexEmDqkqup/AkoAqqrmADkuQCK5QnJFXZgyOKZK2gHM6TO1dD36xjJFx4D4R37D/KDJRdEmtv+eLZ87EnBZC1PQtwuzXuXE3DoDbgcHInUOtrnnhVuqycSVsJbzkjS6pjya60o6pjY5+Q/YXv8Sf+389/zU6V8WY8BB5GtUS9IxJbkm+bHbJ3E7bHzGyjV1/P3g8FA98Tf8+VOz3DoV5rZ9gw032X/mrxhU0nxt+Gcb76sXPWTOlKSHefzUKi67jbccbJzRFFx8hhfVI6zmLU57/MN928p3MZFlZMDdGA2RWgCbo2Xii6IouB22vsqY0jsDhowZU9m42LY7Y0prrWwWgP692TjFSpV7jrRXIOtn9IKjKQC9JkzJnKndyE6EqaKiKF5ABVAU5RCw7beQNsXvO4qivKEoymuKovyadvmQoiiPKIpyRtsOapcriqJ8SlGUs4qivKwoypvqHuuntdufURTlp+suv01RlFe0+3xKUeR8TUnvo/fpm1r5asKUl6DHSSovWvdUVeVbb6xw/IB2om3VynfuUbE9/PYtnzsacJvDzwMjYiuFqZbyyvwGN06GsNm0r6VsAuJnmuZLgWjlA8ztfB7pmDKRWaXkifC7pY8ymJ2Fv3g3fOZH4Y3/La7XFy8SyTVE2Ofi/TeP86UX5005hXhCcPQ9lE98gfl4kp97myEuNJvA/uz/w9POO3kyY/j8DB8TW33IhkTSgzxxZpU7Dgzhczk2L8yt4Vh+mWcq15vXP6ANgOnP9c9F40Q+EIXKwJjIhmsRHqd9C2Gq96by6Y4pUyufLkC2uZVvLOTBpjR3TD15JobLYeOOA3LIfTNCXid+l91CmNLc8FKY2pXs5Fvrd4CvA3sVRfks8G3gN3ZwvzLwL1RVvQ64E/i4oijHgd8Evq2q6hHtsX5Tu/17gCPazy8A/x2EkAX8O+AtwB3Av9PFLO02v1B3v4d2sF8SSVfJl5o5prQvX6e3wTF1ejnN/HqOu45rAbVWjqlz34bAKIzesOVzDwfczR1TfZqx0IsUyhXeWExyY32+1PwPxLbJRD6AsFcsokzClFdmTJnIxMi6ovxl5T1c+sln4J2/JyYefvUT4nrpmJJco3z0rn1kixX+7oXGVodSpcqJwXfjKq7xm4Gv8lD1cXjxr+H5v4Tv/Sl85dehkOLxiV/g9LKhZW/wANicUpiS9CwL6zlOL6e5d9ogOpx/GgWVl103kzAGKYOWs9mf659LiZxZmEottCxfSsftsG0Rft57jildgIwaw891AbLNrXxOu409IS+XmjimnjyzylsODJnPAyQ1FEVhPOw1t/K5/GJogWzl25U4truBqqrfVBTlBYS4pAC/pqrqtp5XVVUXgUXt95SiKG8AE8AHgPu1m/0V8Bjwr7TLP62KuZDPKooSVhRlj3bbR1RVTQAoivII8JCiKI8BQVVVv6td/mngg8DXdvQ/l0i6hJ4xZZrKV9fKF/Q6SBfKlCtVvn1yGYD7rxuHb/vNGVPVinBMTb+naYuYTiTgsg4/h75dmPUiJxdTlCoqNzfkSz0HKDDxpqb3C2qtfOvSMbU96RWSdvG67BmOwOSvwe0/C9/7EyFQRY90eQclku5w02SYm/eG+cyzF/jpu/fzxmKKL/5gji+/NM962sd3PYP8XPlz8KXPme98+88y4L2JuZOnyRTK+N3aMtHugMhhKUxJepYnzwjR4b7pkcYrLjwNDi8LnuN4MxYiin9YFPzKRXBYhGX3KJWqylIybx7+kVradDi2iKaOKae3J91m+jrX5JiqtfK1P9tpYtDLyaUUZ1dS7B3y4XaINf/SRp7Ty2k+9CZZPNuOiUEvCxsW4l5oEtalY2o3sq0wpSjKZ4AngCdVVb2iWcGKouwHbgW+B4xqohWqqi4qiqIfQSaA+nfZnHbZVpfPWVxu9fy/gHBWMTVlHToskXSKplP5DI4pgFS+zKNvrHDDRJCxkEe0YhhdMwsvQm5t2zY+ENWjjVyJYrmKSwu0rFma+zRjoRd5eV4PPq93TD0PI8fBPdD0fqFmGVPuAVDs1m65a5XMKnHlKGGfc/Pk2T0A936iu/slkfQAH71zH5/4Xyd48A8eZzaWwWlXePuxUT50202EJ78P+bhwQNm1H/13T4gjr4liyLnVdOPwhuFpWHqlS/8jiWRrnjkXZ2TAzfRooPGK1VMwPE2IQPNWPoBsDIKtCQzvBPFMgUpVZTRocAUlF+HQgy19ri0dUz04lS+RKeBx2hpbOqHOMdV+YerY2ACf/u4F3vHJJ7ApQmTZH/Gjp87IfKntmQh7OXHJoiAb2gvxc53fIUnb2VaYQkzlexvw3xRFOQi8BDyhqup/3ckTKIoSAL4I/LqqqsktYqCsrlCv4HLzhar6p8CfAtx+++2Wt5FIOkWuaSuf5phy+Ql6hEBxPp7hBxfX+OUHNfeHJ2gWJ85+G1B2tBDRq0eJTFEIXSAqhJ6wbOVrIS9fWmfI79qcFletiuDz4x/Y8n5hLWNqPWsQphTFWpS8lsnEWPbc3jiRTyKRAPC+m/bwx985i9/t4Hfffz3vv3mcwfog4OBI0/vqJ/anl43C1DGR4VbKizHxEkkPcXYlzfHxIKbzjMQ5GH8T0aKLU0sWUyXrB8D0kTC1vCHcX6PBus9iIQXFFAy0uJXPuVX4ee8JU/F0kYhxIh+IAqzNselCbyP/9n3H+ZFbJzgfzzAby3I+ltF+z3BkJMCxseZFSolgPOxlLVsiWyw3ioyhSZh5DFR1204RSX+xk1a+RxVFeRx4M/AA8IvA9cC2wpSiKE6EKPVZVVX/Trt4WVGUPZpbag+gnw3PAfVpm5PAgnb5/YbLH9Mun7S4vUTS0+RrU/kMEW8WjqmHTyxQVeEd12knEZ6QOfz87LdEe5hv+xBFvd8+li5sClPQ1xkLvcjLcxvcNBnaXCAnzglRaYvgc9h0TJkypkDkTF2DrXwvz63z9VeX+PgDhzedUcUMlDJcsgcYH5bClERixOO08+gn7r+i+04N+XDZbZwx5kxFp0GtQvwsjG2dZyiRdBJVVZmNZcxh0uUirF+EG3+MoaTLOmOqTycTLyWFINQgTCUXxbbFApvHYW/imOpNYSqWKRI1tvGB+Bv7Ih0RM5x2G7dODXLrVOPkU5FYg1lAlZiYHBTru4X1HIdH6oS80F4opkWh3tt+kVHSObYNP1cU5dvA08BHgFPAm1VV3bZ5WZuQ9+fAG6qqfrLuqocBfbLeTwNfrrv8p7TpfHcCG1rL3zeAdymKMqiFnr8L+IZ2XUpRlDu15/qpuseSSHqWXNOpfLow5atlDT380gLDA25uGNdawjyhRsdUbk20iB1+x46eWxemVk0B6COQ7q9FWa+SLZY5s5JqdBrMPS+2k2/e8r5Ouw2/y252TIGo8F2DjqnPP3eJP37sHD/6x89wPpYRF2onELNZr3RMSSQtxmG3cXDYbw5Ar03mu6JUB4mkbSwl82SLFQ4OG9r41i8IMXXoEBG/m7VsiXLF4PypDYDprzXQsiZMNRQZU1p9/pp3TBWIGIPPQWRMtTn4fDsURZGi1A7R89PmjAHo+nAbOZlv17GTqXwvA0XgBuAm4AZFUXZyJvBW4KPAg4qivKT9vBf4D8A7FUU5A7xT+zfAV4EZ4CzwP4FfAtBCz/9P4Dnt5/f0IHTgnwF/pt3nHDL4XNIH5JuFnxe1k+46x1Q8U+TBoyPYbNpBzB1sDD+feUwsunYsTIkKkjkAfVg6plrEawtJqircXJ8vdf5JISpGp7e9f/1ExgauUcdULF1g0OdkOZXn/X/0FI+dWqnloS2UB2oVNYlE0jqmRwc4vZxuvDByGBQbxE53Z6ckkibMror108Gov/EKPYcmcqi2/klkDeufPnVMLSfz2BSI1Lfodtox5fT05FS+RKbY+LroZGLCMSXpC/TC48K6QfwMaQ1WcjLfrmMnrXz/HGpZUR9DZE6NARZSdMP9nsI6BwrAlNKsTeP7eJPH+gvgLywufx4hmEkkfUMtY8rRPPw86N38aD54XV0WiNExdfZb4rLx5pPe6qlv5WvAP9x3i7JeRQ9qvFEXpqoVOP0NOPIusG0/Gjjkc7GRs2g38IRFS8I1Rixd5NhYkP/04Zv4+U8/z8f+3+f4o9uW+SEgpobME4kkEslVMz0a4OETC42T+ZweGNwvHVOSnuOc5qY9OGwQphKaMDV0iKE1cVxNZIqMDNS5jNwDYHf33RpoOZlneMCNw17nMag5psZa+lxbOqZKuZ7K+lFVlXi6yFCzVr6J2zq/U5IrYjTowW5TmF/PNl5Rc0xJYWq3sZNWvl9WFOXziNDzDyIEove0e8ckkh1x9ltiPHwfkStVcDlsmy4oHT383OmrOaZcdhtvO1w3PcQTFBlTqip+zj4KBx8Qo7x3gM9lx+O0Ebdq5ctv9GTlq994eW6DPSHP5sJ37nkx7Wf6oR3dP+R1SMdUHbF0geiAm71DPv7ul+7mfTeN88SLrwMQV4OylU8iaQNHRkWex5kVg2sqehRWpWNK0lvMrmbwOu2MDhhC+ePnRPHON1Qb/mJyjCuKVpzrr8nES8kCY0HD/ze5CO4QuPzWd7pCPA47hXKTqXyoULFYs3SJVKFMsVIlahV+no13ZCKfpDXYbQpjQQ/zxlY+/7AQk6/BYu1uZyetfF7gk8AxVVXfrqrq76qq+mib90si2Z5KCR7+NfjGb4lJJH1CoVQ150uBqDopNrC78DrtOGwKdx6KbFarQSywqiVx25U3RHXssMmA2BRFUYgG3OaRyYH+tLL3Iq/Mi+DzGqe/JqbA7LDdsmkrn54xpXZnsOh6tmi9MG0zsVSh1oLhczn41D+6hQ8d1U4wCDIhW/kkkpYzrQlT5pypoyL8vFLuwl5JJNbMxNIciPrNBb/EOa0FVakdR0yOcejLOIPljTwjRmEqtQjB1uZLgXBM5UtWjint+NtDOVO68BgxOqbKBRGFIYWpvmJi0Gtu5bPZIDQhHVO7kG2FKVVV/28gD/yi5p66uf27JZHsgFe+AMk5IdTMPN7tvdkxuWLFPJEPhNjk9IEWjPgv3nWUX33wcONt3EGxLSSFWwzg0M6FKYBIwG3Ryqe1C6b7a2HWa2zkSszGMo3B56e+Bvvu3vHkkLDXZR1+7g1DtbyZRdZhfviPnuKTj3TWKZErVsgUK7UWVBDi6h3DVcrOAL/67hutcyQkEslVMTXkw+2wmMw3fFQcc9dmu7NjEokFs7EMB4xtfADxGRg6BMCQ5qBpOpmvzwpzy6m8hWNqoeXB5wBuh52C5VQ+7djcU8KUWN+aws91R5xPClP9xETYy/x6znxFaFIKU7uQnbTy/SrwWWBE+/lrRVF+pd07JpFsSbUKT/8hjBwXYs2Zb3Z7j3ZMrlRp4pjKCmFK45/df4jb9xtGH3s0J05+A859W/z/QxOX9fzDAZeFY0oTpvpsYdZrvDIn8r9qjqnEjMhjmd5593PIt4VjCroymW8jW+JSIsdLFzv73LqAahr7nFnBMTDCxx84LKfbSCRtwG5TODQcMAegDx8V29VTnd8picSCQrnCpUSWQ8bg81JeTO2KCGEq7HViUyxa+aDvWvnypQrr2RKjQYP4klpsefA5aI6pZhlT0FvClCY8mopW+vpWOqb6iomwl6Vk3jxNMzQlp/LtQnbSyvdzwFtUVf1tVVV/G7gT+Pn27pZEsg2nvy5O+N/2f8DB++HMI11rcbpc8qUKnmatfM5t2pJ0cSK5ABeegUMPXvbzRy0dU9qBWgpTV8XL80K4uWlC+zud+rrYHt1ZvhSIVr5CuWqegKM7rrqQM3UhIVxaZ415M21mU5gyVj5Xuz7yWSLZ7UyPBsyfeX2yqAxAl/QIlxJZqipmx9TaeUCtOaZsNoUhv5t4xqKVzx8Vx5U+WUeuJMX/YbTeMVUpQ3q5LY4pj8NOsVxFNb4++pq11EPCVLNWvqwmPMq1Q18xMeilUlVZThk+t6FJSC1B2UJolvQtOxGmFKD+DKlC82l7Ekn7UVV46pMQnoLrf0RMO0stwMrr3d6zHZErVfC6tndMWeLRWvlOfRUqxR3nFtUTCbhIZIpUq3ULDNnK1xK+N5Pg4LCfkE+E13P6azB8DIYO7vgx9OB7k2uqi46p83ERzB/PFM3B+Wvn21Zp1p19ZmEqJheXEkmbOTI6wPx6jnShLk/KPQDBSYjJAHRJb3BuVZvIFw00XqFP5ItsHn8jflcTx9SIWFPVTz3uYZaSQggaC9UJU5kVUKtty5gCzJP5eriVb8jkmIqLrWzl6yv0ycumAPTQJKBuTqKU7Ap2Ikz9JfA9RVF+R1GU3wGeBf68rXslkWzFhWdg7jm4+1fFNDpdnOmTdr58qYLH0UyY2s4xpbWIvf5lIWJN3XXZzx8NuKlUVdbrhQ+XD1wB6Zi6CnLFCt+diXPftCaY5NbFe/Xo5Q0xDWuililnqouOqfOxzVwr05Su/+8j8NVPtOV5a46pAemYkkg6zZERcaJvzpmalo4pSc8wqx2fTI6puCZMaY4pEIW5eLOMKeibdj5dmGpwTCUXxXagDa182pq1YAxAr4Wf985E53imyIDHUdvnGrKVry/RJy8vGHOmQpNiK3OmdhU7CT//JPAxIAGsAR9TVfUP271jki3YmIcz3+oby3EryBUrm1Xbp/6LqHjc8hPi38E9MHajeE36gHyp2sQxldveMaWHn6eXYf/bwOnZ+vYW6IGQJueLf1g6pq6CZ2fiFMtVHjiquc/OfkuElV9GvhT0qmMqUwvsbxCmKiWInYHFE2153phm3W7IiqhWtJHPUpiSSNqJPpnvjCln6pj43FctMmckkg4zs5omGnAT9Dgbr0icA1+kYfBIJOA2r32g7+IMVqyEKd050gbHlH78zxsn89YcUxbh1F0ili6YXdYgWvlszs0Cr6QvGA+L97gpAD08JbbrMmdqN9FUmFIUxaMoyq8rivJHwJuBP1ZV9b+qqvpi53ZPYsmrX4TPfggKqe1vu0v41c+9yE/82fdg6RU4+wjc+YvC5aNz+J1w8bt9YcPeOvx8h44puKI2PtgMkl41Ls4CI32zKOtFHju1gtdp544DWmD9qa8JAXXy9st6nLBX/H3Ws4aqbjczpuJZbp4ME3A7OFvvnti4BGoFErNtmRYYSxcY8DgaM9myCdGuIIUpiaSt7NUm8502Oqai0+J4JYNnJT3AbCzDQWPwOQjHVJ1bCrRWvi0dU/2xBlrayONx2gh6HJsXppbEtqOOKT38vHccU4lM0dzGB+Jv64uAHJjSV/hcDob8LrMwpYf8S8fUrmIrx9RfAbcDrwDvAf5zR/ZIsj16ZSfbH5bjqyWWLvDoyRVOXFon8+gfiJazN/9c442OvEucIM881pV9vBxyxUqtX7+BnYSfO71g0xYih95+Rc8/rFWSTJP5+nBcci/x2OlV7joUESJKpSQE1Ol3g81ChNyCpo4p1wAotq44pi7EMxyI+jk8YpjSldBHxqttmdIVSxdr79ca0o4vkXQEu00Rn3lj+64+mU/mTEl6gJnVDAeNbXwghKmIWZhK5csUjM6f2mTi/nCNL6cKjAU9jVNpkwtifdiGok1Tx5SzB6fypYvmiXwgMqZkQasvGQ97zBlTTq/4e8oCya5iK2HquKqqP6mq6p8AHwbu7dA+SbZDD+7Tg/x2OV99ZZFKVWWvsoz39Jfh9o+Bd7DxRpNvFm6iPsiZym/pmNqmlU9RxP8zvM+04NopspWv9czGMlyIZ7n/qLbo0d17l5kvBVsIUzab+Nt32DGVypeIpYvsi/g5MhJobOVbm938vQ3DB1atLPm6MKWfSEgkkrYxPTpgkTF1TGxlzpSky2xkS8QzRQ4YHVPFrGhtMzqmtONJwuia8kXEtk8yppY38owEDVEOqUUIjIm1QovZ1jHVS1P5MoXa37mBzCr4I53fIclVMxH2mh1TIHKmpDC1q9jq26t2XbWmIgAAIABJREFUVqSqanmL20k6jX4A7bRjqpSDT70JzjzS0af98ksLHBsb4F8FH6GCDe78uPlGdgccfKAvsreaC1O5xvbEZkzdBbf+5BXbkcNeJ3abUguWrhEYEdk9Fflxv1weOyUEvfunNbHk1NfB7hLvyctkwONAUSyEKRA5Ux12TF3QJvLtj/iYHh0gli6wpi/qE7NiYerwwHLrhalYukB0wDhZR3dMycqnRNJujowGWNzIk8zXfR/5hkSBrA0uSYnkcpiJiULJwWHjRL4ZsY00TsSNaFEGpsl8dqcoePaJa3wpmWfMKEwlF9qSLwWbjimT06zHpvJVqiqJTLEWWdFAVk7z7Vcmwj4W1nOoxvO70F7ZyrfL2EqYullRlKT2kwJu0n9XFCXZqR2UWOBvb2UnkSly6+99k+fOJxqvSC6IMMkLz7Tlea24lMjywoU1PnLczUOlb/PFyj2sKkPWNz7yLkgviRyqHkVVVXKlSmNmjs5Ows8B/tFn4b7fuOJ9sNkUhqxGJvuHAVWIU5LL4rFTqxyM+pmK+IQweuqrcOA+cAe2v7MBm00h6HFaC1PecMcdU+fjIjtqf9TP4VHx/zm7qrmm1s7D4H7R2tMGx1QstYVjSi4wJZK2Mz2yRQC6FKYkXaY2kc/omEqYJ/LB5iCNpjlTfSBMqarKcjLPWMjCMTXQHmFKd0zlm07l6w1haj1bpKrSvJXPJyMA+pHxsIdssWKeVq0LUz1uSJDsnKbClKqqdlVVg9rPgKqqjrrfg53cSYkBX3szps7HM6xlS7x00XACrAthHbRNPnxCTBn50dI/YK8W+ZPy+/jWG8vWN9bDwM921tF1ORQrVaoq5ql8qrqz8PMWEQ24zY6pPgv/7BVyxQrPzsS5T2/ji50WLW5HH7rixwz7nOYDMHTVMbUv4quNj6+FISdmYfAAjFzfcmGqUK6QzJethSmbY3NKoUQiaRv6ZL6zK8Z2vmmInZInBJKuMrOawW5TmBoyFPXimjBlzJhqFmUAmjDV+618G7kShXKVkQHDsTG5uBkI3WL6xTGlt2gOGdcNpTwUUzKbsk+ZHBTnRqZ2vtCkOHfKrXVhryTtoPWNyJL24/KL1pl2OaY0J43pC0AXLDo4mvN/n1jg9n2DhM59BQ49SHnwIN98bcn6xgOjsOfmjrcaXg56tcnkmKoUxaSxjglTLnP4eZ+Ff/YKz87EKZSr3H9Ub+P7mthOX7kwFfL2kGMqlmFkwI3P5WA85MXnsgv3hKoKx9TQARg9Dunllube6Y4+kzCVXhHifBtyNCQSSSOTg148Tlvj0AMQjqn8hvjcSyRdYjaWYWrIh8thOB4kzoF/BNwDDRc3beWDvsnZXE4KUa3BMVVICeGl044pZ285pvR1bdTomNIL+VKY6kvGw02EqfBesV2/2OE9krQLubLvRxRFnJi1qeUqoY2pX9wwfAFkO+uYOrmU5ORSin9yVIXEOZTD7+Ddx8d4+mycVN7ipB3g8Dvh0vd7Vj3Pl0S1yWOcylcUdvQdtfK1AGvHlCaspKVj6nJ47NQKHqeNtxzQWkxPfQ3GbhKVnCsk5HWy3iMZU+fjGfZHRJuEzaZwZCTA2ZW0WMCXMppj6jpx4xa6pvT3pykrIiNzIiSSTiE+8wObLkmd6LTYynY+SRc5t5o2t/EBxGcsB8QMuB047Upft/ItJYUINFqfMZVcFNs2OabcjiaOKZtDTAvukfDzeEasG0zh5/rfVbby9SUTujBlnMynr7NlztSuQQpT/Yo/0taMKYCFdcOBRv9iTy1CpYkw1EIefmkBu03hXZ43xAWHHuRd149RrFR5/HSTxcORd4FagXPfafv+XQm5ojiom8LPS9qXbUcdU4XGIMGA3srX+xXDXuKx06vcfSgqXHCZGMx9/4qm8dUT8jpJWgpT2lS+DrbPnI9n2R/dFEwP6yep+kS+Ia2VD9ojTBnbFTKrsuopkXSQ6dEBTi01m8wnhSlJd6hWVc7HMxy0EqYS5yyFKUVRiPjdzVv58utQthCteojlDbE2bwg/T4nYi3Y5pnSXv8kxpSiig6NHHFO6Ey5iKmhphXxZ1OpLhvwuPE4bC6ZWPs0xJYWpXYMUpvoVX7RtGVObwpSxlU97PrUqgtDbiKqqPHxigbcdjhKYewIGxmH4KLftGyTid/GN15q0D0zeLlwlZ7/V1v27UvLl7YSpzjimIgE3+VKVbLGu+uUOgt3dFxXDXmE2luFCPMv9er7UmW+Kz0cLhKn1rMXi2BuGakn01HeATKHMaqrAvsjmwv/IaICVVIHs0llxweABGBgTn7tWClMp8f8ftqp86m2nEomk7RwdE5/5tXqXycCYOGbEpDAl6Q6LyTz5UpUDwwZhqpASLaZDZmEKhGhh7ZjS81t7ewDMsuaYGgnWHRt1x1TbWvmaOKZAE6YshL4uEE8XUBQY9DWb5iuLWv2IoiiMh73mVj5fRATwdzD7WNJepDDVr/ijLc1zqUevOMQzxVrrGdDo0Grzl8APLq4zt5bjAzePwszjcOgBUBTsNoV3Hh/lOydXrA+QNjscfrvImapWzdd3Gd0x5TGGn+tCQwfDz4HGdj5F0TIWpDC1Ux47Jdxl909rQsmFZ8SBcs8tV/W4YZ+TZL5sHo2rB353KGdKDz7fXy9MaQHo6wunhIU/PCXeO6PXw3LrhKnVWiuflWNKVj0lkk5xdEzMuzlV386nKGIap3RMSbrEjDYd9mDUMP02MSO2Fo4pEO4LS2GqT3I2l5J5Bn3OWu4TsOmYCnbYMQWaMJUzX94F4pkigz4XdpvSeIXMmOp7JsJes2FCUUQ7nxSmdg1SmOpX2uqY2hQrFjfq7LmZVXHSDW0PQH/4pXncDhsPDS0Ja/WhB2vXvev6UdKFMt8910SYO/xOsbBYermt+3gl5PSMKUe3HVOimmQOQB/u+UVZL/HYqVUORv1MRbS/W3IewvvEwfIqCHmdVKoq6UK58QqvJkx1KGfqfFxkn+2LbL4v9SldxZVzEJwEh1aZHDkOK2+0rM0wli7gd9kbJ1gWM0LElYtLiaRjHBsTn3nLdr6VN7qwRxKJcCwDHDQ6pvSJfE0cU9HAFq180POu8eVkoTFfCoRjyh0Sw5HawNaOKXcPOaaKRIzB5yAK6zancHlK+pIJK8cUaMKUbOXbLUhhql/xR8QJWrH1LT2JbKkWzr1Y/yWQjYupd9DWL4FypcpXXl7kHdeN4rv0hLjwwH216+8+FMXvsjdv5zv8DrHtwel8Ba3a5G3qmOqMMDVs5ZgCEYDeB1NpeoF8qcKzM3HuO1rn3mnRuGbdhr60Ycht0AOH556/6ufYCbowtb8uw2MiLKZ0OTYuwND+zRuPXCemArWochVLF63zpWAzqF8ikbSdkQE3Ia+Tk0ZhauS4KJBJl62kC8ysZvC77IwYjxMJXZg6aHm/iN/VfCoftC2/tVUsJ/NmYSq12Da3FIghCC67zdox5fRuFle7TDxTMOdLgTY0JXrVRUNJ95gIe4mlDZ08IIWpXYYUpvoVfbJEG1xTiUyB43tEVaFBnc6sii8A/whstG8059Pn4sQzRd5/y7gIMR+7aTOYG2Epvv/oCI+8vkylauHOCAzD+K1wtveEKd0x1f3wc7GQMy3OAv0xlaYX+O5MnEK5yv1H60SS1EJLMh7uOiScid94banxipHjonXu1Fev+jl2woVYlmjATcDtqF1msykcHgkQzF0S+VI6o1oAeova+WKpgkUbn27Hl618EkmnUBSFo2MWk/naMI1TItkpM7EMB4b9KEaxIT4jckld1oW+oYCLXKlCtmhwJOtO3B5fAy0l843B5yByX9uUL6Xjdtj6wzFlXDeAOFeSTuu+ZmJQnB+Z2vnCUyJTrkcmQ0quDilM9Su1A2gbhKl0kePjQpiqtfJVq5tj2sN729rK9+WX5hnwOLh/vwcufU/kSxl41/WjxNIFXrq0Zv0g+++B+ReganEQ7SK1jCmn4aPXYcfUkF9v5TM6pjRhqoNT3/qVx0+t4nHaeMuBIXFBMQv5jZZULScHfdyxf4i/f3G+MWdKUeDoDwnBtpC+6ufZjvPxDPsj5vfkDRE7weqGmMino0/patFJaixdIGqsfOpuPrnAlEg6yrGxAU4vpRq/j0aOi61s55N0gZnVtDlfCppO5NOJ+psU5vQBMD3sGi9XqsTSBUaDBvEl1Rq39la4nXYKZauMKW/PTOWLpQtNWvlWNwv6kr5kPCyEKVM7X2hSbJPzHd4jSTuQwlS/UnNMJVr6sPlShUyxwljQQzTg3lSm8+ugVsTztjFoLl+q8M3XlnnvDXtwzz0L1XJDvpTOA8dGcNqV5u184Slx3x6rfDWfytfZ8HOXw0bI67Ru5auWIddE8JPU+M6pFe46GKmFgpJq7VScD9w6zrnVDK8tJBuvOPZDUCnAuUdb8jxbcSGebZjIp3PrgHh/5AJTmxd6wyJzqoXClKnyWWvlk44piaSTTI8OkCqUG08KAiPgHZKOKUnHyZcqzK/nOBC1yFSKn2vaxgebGZumAHR9AEwPt/KtpguoKoyG6hxTlbJwjHTAMWVqowLNMdV9YapYrpLMl4n4LRxTemFd0rdMhJs4pnRhSrbz7QqkMNWv+NvTypfQDtRDfjcTYc/mIrS+hSa0V3wBtMFV8+jJFdKFMh+4ZVyceDs8sPdO0+2CHid3HYryjdeWzJPLoO6LqrcU9OZT+Tobfg7ayGRTK58+laa3BL1eYzaW4UI8a2jja60w9UM37sFpV/jSi4b38NRd4B1seztfrlhhKZm3dEwdc4vBA+dVQ9bT6PGWuCfKlSpr2ZL1RD6QC0yJpMPoAeinjZP5RlrzmZdILocL8SyqahF8nlsX6+ItHFN6waN+0E8Nf7Sn1z/LSbHPDa18mVVQq23NmALh9Ld2THl6Qphay4r1rGXGVDYundZ9zljIg02B+bVmwpSczLcbkMJUv6JPx2txZWdTmHKxJ+TdbOWrjVqNCDdSOd+WqtLDLy0wMuDmLQcjMPMd2PdWcHosb/vu60e5EM9yetmipSk4IbbJ3lLQ802n8nXWMQUiZ2rVqpUPetrK3gs8dkq8Pg/UC1NJTZhqkZ0+7HNx3/QID59YaMxSsztg+iE4/XVRKW0TFxLm4HOdvYjsqzdyhoXeyHVifHyldFXPrX8PDRsXmJlV0W7R5DtBIpG0h2lNmDIHoF/X0mmcEslOmI2JdZ+plS+x9UQ+oNbqZZpKDJtxBj2KPhClIfw8tSC2A21u5XPYKVg5ppyensiY0jsATBEApRwU05vnTZK+xGm3MRr0MGd0TAUnAEU6pnYJUpjqVzwhMfq0TY6pSMDFeNjLwnpOOJLqnQqhveL3FgeglytVnj4b4x3HR7Gn5iF22rKNT+edx0dRFIuAaNgUpnrNMVWqYLcpOO2GsE7dMeXo3An3cMBtbuWrOaakMLUV359NMDXkY6reTVRbHLauavnBW8dZSRV4dibeeMXR94p2y4vfbdlzGTkfE2LpfotWvnB+noQ6wBtrhpPRkeuhWoL42at67tXaAtPCMSWrnhJJxwl6nEyEvZyyEqaKKXlSIOko51ZF4eSA0TEVnxHbLR1TWitfU2Gqd1v5lpMWwlStKNbmVr6tHFM9EDyt/z3NEQByaMpuYUI7L23A4YbAqHRM7RKkMNWvKIpQ/9vomBoPe8gWKyRzZYMwpdkmWxyA/vL8BqlCmbceiopwZ7AMPtcZGfBw694wj7xukTPlGxIHyx4Lw8uXqniddvMUmVJWtPHZOveRHB5ws5ps4pjq4YVZL7C4kWefscUtuQiuAHiCLXued1w3SsDtMLfzHXpQhLSe/IeWPZeRC3Gx8J+yaOWzrc2w4hjnzIrBrdiiKV16JTtqHAOeWRU5aBKJpONMjwYshCkZgC7pPLOxDCMDjRNjAc0xpTROjDXgcznwOu3WrXyBYVGY61EH4HIyj8OmNAZ812IE2uuY8jjsPZ0xFdf+nkPG8PPa+YssavU742Evc8ZWPhDnpW0cyiXpHFKY6mf8UdE33UL0MMghn6txAkJGex5fREzlg5ar00+fEWLIXYciIl8qMLq56G3CLXsHObuSNudMKYpwTfWYMJUrVcwT+UA4pjrYxgei4pYqlMkU6trBvEOg2GUr3zasJPOMDBjcbanWj2v2OO08dMMYX391qXFB6A4I0fbUP7RtAX0+nmHI7yLkdZqvTJwn7d/LGWMb7fBR8f5ZvkphKtXMMSVHPksk3eLoWJBzq2lKlTrXxEhrp3FKJDthZjVtzpcCEXwemty23XvIb5GxCaI4VylCIWm+rgdYSuYZGXBjs9UVN5MLYHO03RHU3DHVG1P59L9n1Bh+rp8nScdU33NszwBzazlWUob3W2hSunZ3CVKY6mfa4JhayxSx2xRCXmdNmFpYz4mKgycMdqfYugZa/iXw9LkYx/cEGfI6YOYxOPiAEJi2YDzsIVfSXF1GguM918qXL1Y2p7jVU8p1NPgcYCwkDt66NRwQji1/VLbybUG1qrKSKtRevxrJxbZY6T94ywSpQplHTxr+JkffC+sXYfnVlj8niFY+q+BzykVIzlEN72d+PUe6Xth0uCFy+KrdE02zItIrcnEpkXSJY2MDlCoqs7HM5oXeQeHUkI4pSQeZjWU4YMyXAuGY2mIin0404CJmnMoHPe8aX0kWGAkai2KLEBhru+O+9x1TRRw2haDX4KLTHVMyY6rvufeI+Hw+fdbw+QxrQ7namLsq6QxSmOpn/NGWZ0zFM0UGfU5sNoVxbRzt4kZOPI/uVFAU8SXQQttkrljhBxfWeduRKCydgFxiy3wpnTFtHxc2mlg7kwst28dWkC9X8FoKU9nOO6Y0x8+yqZ1vBNK9G/7ZbeKZIuWq2pjxAGJx2AYr/V2HIowMuPl7Yzvf0fcACpxsz3S+C/GMZb4U6xdBreIZOQzAOat2vpXXruq5Y+kCboetsU2jWtEm60hhSiLpBtOjIgDdMmdKOqYkHWItU2QtW+JQM8fUFvlSOpGAm7gxYxM217k9GoC+lMw3TuQDsc5tc74UbOGYcnqFy6xqcV0HiacLRAIuc1SGzJjaNRzfEyTid/HEacO578RtUCnA/Avd2TFJy5DCVD/ji2622LWIRKZQ68+OBtw47Qrz69oEvvov9dBkS8PPn7+QoFipcrfexgdw8P5t77cnJMQcfVJJA8EJIRZULSo8XSJXrOB1WQhTxS4IUyFdmDK8dj0+Lrnb6K9XQytftSrea21YHNptCj988ziPnVphPVtX4Q2MwN47RDtfi8mXKixs5NlnJUytzQIQmRItPA3j4wFGr4e181DMcKXE0kWiAXfjAjObANTNgH6JRNJRDo34sdsUa2Fq9VRPHWslu5cZzbF3wDgxNpuA/Lpw7W7DkN9Vy1RtQM8w7NE4g+WNfK0gWyO12PIYASs8DjuFklUrn+Ye77JrKp4uEjG28YEorNtd4B7o/E5JWorNpvC2I1GePBOjWj+t+sB9oNg2zx8lfYsUpvoZXwQKG6K1pkUkMsWaMGWzKewJeYVjypjtEmqtY+rps3GcdoU7DgyJ4PPRG2BgdNv7jYe3ckxNgFqBlMXUvi6RK1XwOJo5pjrbyqc7fkzCVGBEtvJtgf56NSwOszGoltsWPvrBWyYoVVS++orhvXz0vbB4ouWhj5cS2kS+qMV7MiGEqdF9x3DZbZw1Oab0MOSTV/z8sXTBOvgcZMaURNIl3A47B6N+TloFoFcKte8GiaSdzKyKY87BYUMrX/yc2A7txDElMqZM+aS1Vr7eK85lCmVShTIjQasYgfYGn4NwTOXLVq182lqo28JUplibuNhAJiYK+dtEg0j6g3uODBNLFxqPQ74hGH+TFKZ2AVKY6mf8Wr90CwPQ43XCFMCekGczY8pXd0IY3isqU4WUxaNcPs+ci3Hr3kF8FODis1tO46tnOODGpsDiehPHFPRUO1++VMVj5ZjqQvh5wO3A77KzZCVMpZZ7dipNt9FbH0frF4f6e6xNdvobJoIcHPbzpZcM7XzH3ie2p77W0ufTM2QsW/nWZsHpxxEc4+Cwf4vJfFfezreaKjBsXGDqYqm040skXWN6bMDskmzRNE6JZCfMxjI4bAqTg4Y1U0ITpnbQyhf1uylWqqQKhkwaPYeoBzOmakWx+la+QgqKqc44ppzNHFO6MGXRGtlB4plC47RCHTk0ZVdxzxHxt3zijEE8PvQgzD8PubUu7JWkVUhhqp/RhaIW5kytGYSpibCXpbWMOdslpE/mu/oA9I1siVfmN7j7cAQuPAPVkgg+3wEOu43RoIfFZq18AMnemdSQL1XwNp3K11nHFIh2vhVjxlRwQlS/5Ze7JcvJPIpimBjX5nHNiqLwI7dM8P3ZhJiSqRM9DNFpOPmVlj7fhbjmmLISphKzMLgfFIXDIwHOrBhOUgcPiCk9VxGGrLfyNVDLiZCtfBJJtzg2OsDFRLZxmuvwUUCRAeiSjnBuNc1UxIfTblhLxc+Jdp7wvm0fQ1/nJoyT+RwuMeCnBx1Tm0WxOmEqqa09OuGYcgjHlMllVhOmLDoXOkg8XSRiXDeA+FtKYWrXMBr0cGxsgCethCm1CrNPdGfHJC1BClP9TC2ksTXCVKWqsp4rMVTXo70n7CGfigOquZUPWtJC9N2ZGKoKbz0cFTZMuxv23b3j+4+FPKLd0EhIE6Z6aDJfrtRsKl/nW/lABKCbWvn0yluyd163XmI5mdfy1+q+PtvsmAL4wC3i/fzwSwYH4NH3woWnWyokno9nCPuchHxO85VrszB0ABBhyJcSObLFupNUm02MkF++MsdUpaqSyBQshCnZyieRdJujYyKnpcE15fILsVo6piRtJlss8/TZOLdNDZqvXHldiFIOC9eMAb3lK56xcPn0aJyBvlZrEKZS2npgYKztz+9x2lFVKFWMwpSeMdU9x1SuWCFbrFi38mVj0mm9y7jnSJTnZtfIFetaSydvB3dQtvP1OVKY6mdqjqnWtPKtZYuoKg1W2PGwl7C6If7hN7TyQUsC0J8+G8fnsnOLehJ+8Gk4eN9ltbWNh7zW4eeesBB7ekhgEY6p3mjlAyHqmVr5ak6zxY7vTz+wnMw3tvGBcEwptra6eaYiPt40FebLVu181TKceaRlz3UhnrUOPq9WRbD54H4AjoyIjI9zK4ag85Hrr9g9sZYtUlXFOO8G0itgc4jPtUQi6QqWwhSInCnpmJK0mW+8tkS6UOZDt002XpFPwtlvweF37Ohx9MJHzOiYAiFi9GAr35JVvmWyvW7tetwOccpoypnS165dzJhaTQlRLGoVfq5nTEl2DfccGaZYqfLsbN35r90JB+6Fs4/KKJI+RgpT/UyLHVP6hJL6Vr7xkJeIktSer67iEBgDm7MlrXxPn4vxT8aXcP7Nj0FgFH74U5d1/z0hDwsbObO9WFGEyNJDwlSu2Mwx1Z1WvpGgm5VkofG1C0rH1FYsJQuMDhjHNS+K967d0dbn/uCtE5xcSnG2vn1u4jbx3CdbN51vNpbhQMTi/ZheEotPzTF1ZFQIU6Z2vtHjouJ8Bd9NMW2Et2X4uS8qHFkSiaQr7B304XPZLQLQr4P42a7nzEh2N198YZ69Q17u2D/UeMXJfxDHpht/bEePU2vls5zM15uTiZeTefwuOwF33Toj1X63to4uTJlypnTHVKl7wtRKSpuWbCwaFjOiI0E6rXcVdxwYwu2w8eRpwxrz0APCMKEPQpD0HXKF3894BwGlZRlTlsJU2EsETZiqrzjYbKJV7ipb+ZY28gRjJ/iN1X8t7NM/85XLPsCOhTzkS1XWsyXzlaGJnmrly5eqW7Tydd4xNTrgoVipslb/2gVGhfunh0Lje4mVZJ5R07jmhY6Ej951UASzvjK/sXmhzQbTD4lqcQtOCgvlCgsbOWvHlD51a1AIU/sifpx2pXkA+hW088VS4nvIMmMqIO34Ekk3sdkUjowOcMpKmFIrEDvTnR2T7HoW1nM8fS7Gj946ic1mmLD26hcgPAV779jRY+nr3Hja4pjpH+5ZYcq09kgugjsk2mnbjFtbu+ZLBseUo/uOqRXNMTViLBrqU7k70Ooo6Rwep507DgxZBKC/XWxlO1/fIoWpfsZmFyMy2+mYCnuIKHorn+GkMLQXNq5OmHr9+e/wadfvi2rGT3/ligIcx8PioGgdgD7ZM86fSlWlWKmaW/kqJRH43gXHlG4Jb8iZsjuFOJWSwpSRYrlKPFM0O6ZSSx0JHz0Q9eOy23hj0XBSeOyHoJgWwwOukkuJHKoK+6MW78c1TZjSHFNOu419ET/nTMLU9WJ7Ba09NceUVcaUzImQSLrO0dGAdSsfyHY+Sdv4+xfnUVX40JsMbXzpVTj3Hbjhw8IpvwM8TjsDbkfzVr7cmlib9RDLVm7t1GJH3FJQ55gqN3FMdVOYSjZxTKWXxTYw2uE9krSbe48Mc3YlLSbH6wwdEIXTc9/u3o5JrgopTPU7vmjLHFNxTZiqz5ga8DjZ48igoggRrJ7Q3qtzTC28yJ1P/xxJZQDHx/5hM6z8MtHFFcsA9OC4EA16YIGhV5m8LsPHriQmoHXFMaUdxC0D0KVjyoRuFzdlTCU745hy2G0cGQ3wxmKy8YqJ28T2CgPH67kQF3lRTR1Tin1z+AFicqdJFA6MgHcIVq7AMaUJU8MmYWpFClMSSQ9wdCxILF2sfVYBiBwWGXAyAF3SBlRV5QsvzHHHgSGmjG3mr39JuPVu/PBlPWYk4LJu5asNgOmtNdDSRr4xXwqEMNWBtQdQc/ubHVP6VL7uCVPLqQIOm8KQz5BNKR1Tu5Z7p8V68Kkzxna+B2H2SShbfLYlPY8UpvodfxQyrQk/18fmDvobv9j3ujOkbUHh0KonvFccFHf64a9WIb8B6xfh3KOon/4g61Uff3Lgv2Ib3Lv9/ZswHtrCMRWaANTNg1MXyWkHc1MrX0kT1LoLS9RMAAAgAElEQVQiTFk4pkAIejL83ERtXHP94rCUg/x6x6qW1+0JmvNd/FEh2qxevVvhfFwIpfuthKm1WfG5t29O6xsPexorViCq1iPXwerpy37+1XQBl91G0GvI68rIyToSSS9wTAtAb2jnc7ggckQ6piRt4QcX15mNZfiwMfQc4JUvCMfe6PWX9ZhDfpf1VD7NEVxzCPcA1arKSirf6AiaeRxWT11xUfdyaeqYcurCVPfy5VaSBYYH3OYWz5pjSgpTu43p0QAjA24eN7XzPQilDMx9vzs7JrkqpDDV7/giLcyYKhD0OHDaG98Wo440CSVkvkNoL0L02aKq9Hf/FP7LjfD7U/B7Q/AfpuAPb4TP/Ahlh58fL/wW1x274ar2e3jAjd2mNHFMaYuYHmjn08eamoUpzTHVgYwAI8MDumPKsKAIjvdctbAX0O3iDXZ6/XXqwFQcECeFq6lCo1sBYPhYS04Kz8cyBD0OBn1O85WJ2Vq+lM54yEs8UzRXUcNTV9TqG0sViQRcKPUtGbUAUylMSSTdZnrUQpgCIUZLx5SkDXzhhTm8TjvvvdFQAFq7AJeevWy3FEAk4CZu1co3dFBsEzNXsKftYS1bpFRRGQt6RDH4m/8WPv0BsVZ76z/vyD7oa9eCcSqf7pgqWazBO8RKKs+IcWAKiKK0zall8kp2E4qicM+RYZ4+G6NSrRvgdOBe4eyXOVN9iRSm+h1/tHUZU9lSQ76UTlRJsloJmO8Q1lxOzdr54ufg5c/B4D645R/Dfb8B7/6/4P1/BD/+Gf7uzZ9lTh3mrYcjV7XfdpvC6IB7C8cUPSFM6QdzU8ZUFx1TboedIb+rNoa4RnAcChtQSFvf8RpFf50aWvlSmrOsg44pgJPGnKmR60T19CrH5J6PZ9gf9TcKQzprs5vVZI2mGW8hzVF5mW20sXTBOl8KpDAlkfQAwwNuIn6XhTB1HNYvyOOGpKXkSxW+8vIC77lhrHEiHcCrXxTbGz502Y8bDbhqERYNDIyD3d1TwpS+9jjEAvz5O+CZT8FtPwO/8DhED3dkH5pP5eu+Y2o1VWDYmL8FwjEVGJHTfHcp905HWc+WeLV+IJAnKIYgnJU5U/2I/KT2O76oCGmsVra/7TYkMgVLYSpcXWepMmB2ROg5M81cESe/IrYf/GN4z3+EB/413PVxeNNH4fj7efRihYmwl6mhqw/93hP2srhuFX6uuVh6YDJfrigO5s2Fqc6Hn4No51sxZUxpr1tKtvPVs5ws4LQrjZ8TveWxQzkPehvNySVDztTIdSIA/SoHElyIZ63zpXLr4rvG6JjShClTO194L6jVyxaFY+kCkYDheygthan/n73zDm/jvu//6wBiECQAEtykSInUopZjyxreI3Y8kjh2bGfYaZwmTbPTJm2apyu/pEnbpGm6krZZzR52Yid2HMeJ95CXZMtLg5IokpJIiQPcAEgABHC/P753JIA7UBIHBvl9PY+eszFPFIi7e3/f7/dHIskn1te6OWQoQNemcfoPZ3+HJEuWhw/2EwjHuMUsxrf/V7BiB5SvOuvXrShxMByKEounCS0Wi3i94fyJ8g2MhbnN+hiXPH6zWAx+18/ghv8Ee/bOG0/rmMrxVD5D8TkIx5QsPl+yXLJGTIt/+ohJnK/3tQUzbkiyhxSmCp2SSkCFieF5v9RQMIqvxPjF7oqNMqx6jBeeHs2NlMkxdeh3UHuOiPSkEU+oPN8xxCVrKs2dGWdJrddpHuVzesHuzgvHVOaOqdyVn4Nw/5hG+UDG+dIYGA9T7Xamfmb1KGuWhKmKUgfVbgcH0wvQq7SLwnnE+X74bBcnhiemxa8U0iby6dSXiZPSk+nfD97TOCozMBSMZnZMlUphSiLJB9bVuGnvD5BIjlDowpSM80kWkF/t7aHe6+TCljR3ff9B6N8PW94xp9ddUV5MPKGau+19zXklTK165jN82fY9ovXb4aPPwYa3Zn0fdMdUOKNjKjfCVDSWYNhsWjJAcEAWny9hKkodbG7wsMusAB0VOp/MxW5J5oEUpgodl3agnph/AfpwKJoykQ+A+BSOqTGGVA+n0h1JNqdYiTBzaAT6oXsPtJofPA+cGmM8HOOiecb4dOq9TnrHwqhmMSZvA4z1LMj7zIfMU/lyF+UDqPU4zaN8IIWpNPrGwyYT+XrBXirsw1mitc5jEuVrFds5CFOJhMqXHjjIF357kGs21vCBi5uND9JP0tMcU/qUIKNjShOkz8LBpaoqQyEZ5ZNI8p3WWjcT0Tg9I0m/9+WroKhYFqBLFoz+8TC72v3cvHWFsdh6/z2gWGDTTXN6bd2t3z08YbzT1yIWY+YZjV8QJoZpPnk/P4tdheWO+7JWG5BORseUxQJWe86EKb/Wt2nqmApKx9RS57K1Vbx8YoRAOKk2ov48cJbJnqkCRApThU6JsDHOtwBdVVVGJqKGiXy6E2sID6fMHEneRhjr5jevnuSJwwMzlugjvwfUjKs6zx4VQtpFqyvntd86dd5iIrEEIxMmfTZ5UuSd0TEVDYltjqJ81R4ng8FIqp1dd//MVmy/DOkfNxvXfCprbimdDXVujg4EmUr+NysuF/txlheF4ak4H/vZy3zvmS7++KJVfPOPzqfYbjU+UHdMpUUmHEVWqtwOY5T2dI5KE8Ymp5iKq1SmR/lCA2LrWpjvC4lEMj/Wm0WKLVaoWi8dU5IF495XTpJQ4eataZPnVFVM42u5QnQIzYFGTZg6kUmYmpqYmeqWS7Ro7B77Tuy2otM8ePHI6JgC4Zqayo0wpVdRGMrPY1GxaC8dU0uaS9dWEdNSONNYrOK7oePx/BCXJWeMFKYKHf1CbZ452kAkxlRcNTqmNKfCMCZRPgDvCqaGTvDnd73K+3/wIhd95XG+/Ps2Qq/dJy5gqzdOP1RVVboGQ/zyxW7u3tvN+hr39FS4+VKniQXmk/kashfli0Xhya+YRivDGaN8uXVM1XgcqOrMqhMgegucZXkh6OUTA+MRqtPt4uO9WV/B3FDrIRpP0OkPpd5R1Qr+MxemhoIRbv/uCzx0sI/PvXUjX3jbJqzpq9I6w11QUg0O4yCE+rJio3A97ag8ccb7o08aNHwvhAbB4Z0ZSy2RSHLK2oyT+TZKx5RkQVBVlXv29nD+ynJaqtKOOz0viaL9Ocb4QBy3iiwKx82EKd0ZnA9xPu2YPubOTsl5JhyZHFMARY6cOaYGAppjKv3cTF/Qko6pJc35K8tx2a3sah9EVVVOjU7yh/19PBTeCIFePvutXxCNmYipkrwkd9K7ZGFYIMfUsDYy11B+rglTMWeFebl4WSNK24NYlARfuflcHj7Yz127DvAXtl3c57qB8ReOo6qwp2uYPceG8WsHkHKXjc/fsGle+5xMnT4ZbDTMpnpv6p3eFSJnHotCkbHcfUHpfAKe/DKUaZMIk5iO8mXsmMqNY6rWIw7m/eMR6rxJ4pinQQpTSYQiMQKRGDWedMdUL6y8OKv70lo341ZYn9wHVb0RXvq+GIZgMXE9JdE1GOL9P9hD71iYb75nK9dtPo24NnLM0C+lU+91cji9CBmEo/IsHFP+gPgeMo3ylUi3lESSL5Q6imj0FRt/76s3wGs/F4szLl9udk6yJHi9Z4yjA0H++e1bjHfuu1tMzstQF3EmWC0KK8qLMzimdGGqE1ZeOOf3WBD8h5nEibXMpPw9izhndUwV52wq37RjKj3KF9DcbtIxtaSxF1m4sKWCX7/cw4P7eqcnba6w1HGtHdw9T3Ok/3o2N2jXhpMjontq7CRc8DE5sTHPkMJUoaN3TIXm1zGl/yL70iM0WneVzVNtGuVTvY0UqVGuabLyzu2NvHN7I+MvdeJ4IMYu605+9ZsDgLhwvXh1BdubfexY5WNNdemClJ7rnNYxhSoEhPKVC/aepnQ9Lbb6Sk0Sk9FMwlTup/KBiKml4KmTwlQS+s+n1pt08pNIiM9Vlh1Tq6tKsVkV2noD3Hhu0h3VrRCbFCJSxeqMzw9GYrzjW8+RUOHnf3oB568sn/0Ng37oPwBrrzG9u76smCcP+1FVNfX3uqwJel8947+X7pgyF6Zkv5REkk+01no4cCp9Oqjmkh5og1XZFewlS4t79vbgKLLwlnPSjq/xGBy4F9ZdO+9ux0afy7xjqqwJFKsQpnKMOtBGByuo9ubmHFGnyGrBalFmcUyZnH9ngYFABIuCMfER7BNb6Zha8ty2o4nBYIR1NW62rPCyucHLxjoP0f/9L64YfI2BtmfgyOvQ8Ric3CsmRgM0XQArtuV25yUpSGGq0LHaxOS5+TqmNGEqU5TPVV5Dm9940OmKVdAC3JrkMPYc+wO4Kvm3v/gQHx2cwFFknc7yLxaVpQ6KLIr5dJXpIu+TWRCmnhLboIkwpa0y5V+UL5MwVQ+9r+dgj/ITfXJhyuSXiUFIxMBdn9V9sVktrKl2p/a7wMxkPv+hWYWpx9r6GQxG+fmf7jwzUerHbxOf0+1/YvqQ+rJiJqfijE5MpfbUlTXCoQeEgHcGq1IzwlT6CaZ/1r+PRCLJPttWlvPIwX4GAuGZGE3yZD4pTEnmSCQW5/7XTnHNplq8xbbUO489LRb/5hHj02nyufjdvl7jHVabOH6N5DbKNxSMYDm+j0PxzWxtKsvpvoBwTUUydUzlzDEVoaLUQZE17RwjoAlT0jG15Ll6Yw1XbzQKkIm1V3HpyLfhmdsBBRrOh8v+Siyg3P0+6Nsnhak8Q/rXlgKuynl3TI1owlS5K12YGgTFiqe8mt5R49S7h7qFtnlRlbbiFIvAkYdh/fVgsbKm2r3oohQIS3aNx2kuTHk1+/PYIvdMTQxD337x36bClFhl0gskp5maEJb000SvFouKEjtWi2IUptz1QpiMRXOyX/lG/7RdPEmY0h1lOZiSs6HWTVtvujC1XmxP0/Hy4L5eqt0OLmg+zVRMXZQa7oL3/BIad5g+rF5zLJ5M76HzNkI8esYFsoPBCFaLYvI95J9zwa1EIlkcdraI748Xu0ZmbvTUiz442TMlmQePtQ0wNjnFreebxNf23QMOT0YH79mwssLF6MQUY5Mmg3PKm3PqmHq1e5Tbv/4HyhPDrN20jXdsa8zZvug4bFbCZo4pmzOHHVNhY/E5aOcdinRbL2MsOz/M/c4b+brvb+GznfCnj8GVfwsbbxTfIf37c72LkjSkMLUUKKmct2NKj/JVGKZh+cFVQX15ybQjQicWT/CLdvHfrpB2gX5sF0QDsOGGee3PXKjzOjNE+ZIcU4vJsWcAVWTtTaJ8kak4TpvFOPJ4ajJnbikAi0Wh2u2gbyxttctTD6gzduhlzkyUL0mYCmgrrVl2TAFsqPPQPx6ZdjsCItbgbZz1ojAUifHkYT/Xb641fhZTHjg4I0rd/gtovizjQ+u1jjfDgISyJrEdO7OeqcFAFF+JPXW/EnERKZYnlxJJXrGp3oPLbmV3V1KVgKII15QUpiTz4Fd7e6jxOLhkTVq34FQY2n4rzjEXYBhGk7Zwahrn87XkpPxcVVV++sJx3vGt52ihB4A3bL0g6/thxqyOqVxN5QtEMgtTrgrhfpMsTypW8+yaz/DDsa2oxUnpAEWBms3CMSXJK6QwtRRwVcy7Y2o4FMFps+Cyp6U7Q4NQUjXtiEjumXrm6CDHQjamikpnLjzbHgBbCTRfPq/9mQt1ZcXmjimHW6zgLrowtUv0RDVfmtExZYjxgXBM5ahfSqfG42QgYBLlAzF1TkL/eIQSu5VSR9LvSA4dU8kF6ClUbxBRvgw8fmiASCzBm7fMss+hQfjRDTOiVMvsv8+6MGX4/fNqK7yjZzaZbzAYMfZLTQwDqhSmJJI8w2a1cP7KcvZ0pU2hrdkkTvjjsdzsmKSg8QciPHnEz9vPW2GcEtv+METGYcutC/JeuqPfvAC9BcKjplOWF4vJaJy/vPs1/v6+/Vy8ppJ/u0I7Hla1Zm0fZkM4pjJF+XIjTPWPR4xDaUCUn8sY37Kntc7NcCiaOnkcoHaL6E5NyIl9+YQUppYCrooFcUxVlJisOEwMQklFkiNi5sDzm1dP4XEWYfU1wViP+OU+/CCsvTonY92FY8oYNwTA27D4Ub6up6HpQiHomAlT0bix+Bw0YSp3jimAGo/DvGMKFl/QKxD6x8PmE/kUC5RkP2bWWitKX9t606ZiVbXC4JGMF4UP7uulstTBtlUZJmZNi1KdcPtdpxWlQMRB7UUWE8eUJkydqWMqGDH2S+nuQylMSSR5x85mH4f6AtN1AACsvEg4p/tkR6Hk7PnNqyeJJ1RuPb/BeOe+u8WxYFVmB+/Z0DSrMKVN5stSz1QsnuDd33mee185yaeuXsv337cd19hRsdjrzX2MD0QVRWQqU/l59oWpWDzBUCiTY6pPFp9Lps+VD6WfK9duhmgw5z1yklQWTZhSFOX7iqIMKIqyP+k2n6IojyiK0q5ty7XbFUVRvq4oylFFUV5XFGVr0nPepz2+XVGU9yXdfr6iKPu053xdWcgRb4VGSaWIupgJMmfIcEhEaAxo07DSozoT0RgPHejjLefUYdFHwp/cK6yz8xjfOx/qvE6isURqtEnH0wDjPYv35oF+4VJpvlSIFBNDBmEgHEtkEKYmc+6YqvU46Ut3u+jCVEA6pmAWYaq0BqzZnyNR5XZQWWrnUHrPVPUG0etk0o0xEY3xxOEBrt9ca1yJBiEu/+Ttmij1C2i54oz2xWJRqPM6jR1TDjc4y8T3wxkwGIxSZTaRD6QwJZHkIdM9U8eSXCUrtdLz48/mYI8khYyqqtyzt4c3NJaxptqdemd4HI48BJtuXrBjrttpw1di5/hQBscUZC3Od2woxGs9Y/z9WzbyqavXiUi7vw2q1uXNSPt8c0wNhaKoKlRJx5QkA6214nvE0Mlas1lsZc9UXrGY33Q/BK5Lu+2vgcdUVV0LPKb9P8D1wFrtz4eAb4IQsoDPAzuBHcDndTFLe8yHkp6X/l7LB1elmAwWHp3zS4yEoqnTtHRCotulosSO3WqZjvI9crCfiWicm85tEK6IsRNw6LdgKVqQQsq5UOfNECcC4ZjSY1eLwbFdYtt8mVbSrApxKonJ6GxRvtw6pqo9TsbDMSajSSthzjLRl7WYP7cCoj8QpsaTJpqM94I7+zE+nQ11Hg71pa0C6VOx/MaOlycO+QlPJbh+S4aTte7dwuXw5q+dsSilU+8tNjqmQPt+OL0wpaoq/mCEyvSVT32wgxSmJJK845wVXhxFFnYnx/k8deKi/pgUpiRnx4FT4xzqC3DrVhO31KEHIB5ZkGl8yTT6XOYdU+WrxDZLBehH+oOAcCFO4z88M203D3BmckzZcjOVb0CblmxwTCUSwm0tHVPLnvISO7Uep/m5smKVPVN5xqIJU6qqPg2kB7NvBH6k/fePgJuSbv+xKngBKFMUpQ64FnhEVdVhVVVHgEeA67T7PKqqPq+K3NaPk15r+VGilUPOo2dKRPnShKlYBCJj4KoUjogy53SU775XTlLvdbJ9lU9YjMNjYlLKqkuhODcjbeu0HixTYcrToE2YW6QDZ9fToseq9g0z08PSJpGFtfJzA1OTYM99xxSQGudTFOGaksIUqqqa9xgEemecZTmgtdbN4f4AsXjSCmblekAxLR9+cH8vFSV2dmaaxnfgXrHyuensv07rM3W8eZvOyDEViMSIxhLGKJ8eiy2pND5JIpHkFEeRlfOayow9UysvhhPPieEFEskZcs/eHuxWCze8weS4uu9uKFu54OPdm3wu8yifrVgMNsmSY6q9P4iiwOqqUnHD5Kg4x9Cn7eYBszqmpkwWphYZ/ZzVcG42MSQW7KVjSgJsqDOZYm0rhsq1M9PUJXlBtr2hNaqq9gJoW72YpQFIvnLp0W6b7fYek9uXJy7tgm0ePVOmUb5pp4J4/Tqvk97RSYaCEZ5uH+TG8xqE1VjvkRk/Ca1vmfM+zJe6Ml2YMpvMp308Fqsv6dgu0athLZrpG0qbzBeeilNsz8/y81ozYQqkMKUxOjFFNJYwnvyMn8qpY6q11kM0luDYUGjmRrsLylcahKnJaJzH2wa4NmOMLw4HfwNr3yQieGdJQ5mT/vEwU/G0k1bdMXWaqPFgQIjGhvLz0IBwYiZPVJFIJHnDjuYKDpwaYzw8M7WXVZeIBav+A7nbMUlBEY0luP+1U1y9sZoyl8kCRedTovR8gZs7VvpcnBydTF3g0fE1Z88xNRCgsdw1c57oPyy21fnjmMrcMZUjx1Qgg2NKnyYtHVMSoLXOQ4c/SDRdVK3dIqN8eUZ+hJbB7CijzuF28xdXlA8pivKSoigv+f3+Oe5iHlOiuR9CcxOmwlNxJqJxozA1kSpM1ZeJqM4Dr/cST6gixgeppYw5FKYqSxzYrErmKB8sjsgy2i1OXJq1Ms5px1TqZ21yKlP5+WTOo3x6RK0/kHZi4amHgBSm+gMmq3JTkyI+m4OJfDob6jIUoFdvNEzme+rIAJNTcd6SaRrfiRfEydymt89pX+rKikmoJuKmt1EUTE6OzPr8waDohjMIUyPHxGss4xpBiSSf2dnsI6HC3uNJv+OyZ0pyljxxeIDhUJRbz19hvPPAfaDGFzzGB8IxFU+oKcN9pvE1Z60c+Wh/kHU1pTM36HH8PHJMOW1WInnUMaVPkzacNwS0xIIUpiSIdMFUXKVzMJh6R81msXCaxcmbktnJtjDVr8Xw0La6paQHSB45sQI4dZrbV5jcboqqqt9RVXWbqqrbqqqWYE/JPB1Telm4IcqXVjpc7y2mPxDh1y/30FrrZr1WKDctTDWcn9NYk8WiUOMRri4DumNqMSbzJfdLwYwwleaYmpyK48hXYUqLQfani3ruOtGjtMzHqerF8CkdU7rI6c7dZ351dQlFFsVoUa5qhaGjEJsZBPC7fX34Suyp/RXJHLhXdIqtvXZO+6IPSDAIw2c4mW8wmMExNdQBFavntE8SiWTx2dpUTpFFSY3zlTVCWRMceyZ3OyYpKH61t4fKUgeXrTU5T993t7iIXAT3UOOsk/laRC1DJGi8bwGZiifoHAymFr77Dws3vbdpUd/7bJjVMaXGIT5lvG8RGQhE8GlTgVPQqzTcUpiSJC/ipp0r124RW+nszRuyLUzdD+iT9d4H/Cbp9ju06XwXAGNa1O8h4BpFUcq10vNrgIe0+wKKolygTeO7I+m1lh/THVPzE6YM5ed6Z5UuTJUVE0+ovNYzxtvPS0pOltZAxVo4771zev+FpN5bzKlMHVNw9pP5EnHo3mOIIXX4g1zwz49xfCgEXbug2CdcKgD2UnGBH0wVpiJTmaby5T7K53YUUWyzmkT5GiAxNa+Y6FJAL9hMcUzp0wpz6JhyFFlZXVVqXuqYiAlxCuGKfLytn2s31VBkNfna12N8664BR6nx/jOgQYvSGgrQy7ST6tP0TE0LU+6k7yFVFW5EnxSmJJJ8pdhu5ZwVXnZ3pvVcrrwEjj+37Bc2JKdnKBjh8UMDvP28euMxauQY9OyBzbcsynuvrJhFmCpv1vZhcV1Tx4cmmIqrqY6pgTaozJ+JfABOmyWDY0pbUMqya2pgPGyM8UFSlE92TEmgubIEu9XCofR0gS5MyQL0vGHRvu0URbkTeB5YryhKj6IofwJ8BXiToijtwJu0/wd4EOgEjgLfBT4GoKrqMPAl4EXtzxe12wA+Cvyf9pwO4PeL9XfJR0KR2Mz/2IrBVmKYAnemDJ3OMeUSUcF67cJTUeBt5ya5RCwW+ORLsO39c3r/haTW65x2t6Rgd4mOmrON8u27B773JnjtzpSbH2vrp288zP6eMVF83nzpzMmDokBplUGYyucon6Io1Hgc9BmEKU10WeY9U7pgV53imNKEqRw6pkCUOh5KXwXSV5UHDgLw1BE/oWicN2eK8R1/Tjj85hjjg5mpmCfThSl9tXf0xKzPHwxEUBTwJXeLBAdEDFA6piSSvGZnSwWv94ylTnZddTFMDhtixRJJOve/dopYQuUWsxjf/l+J7SIJUzUeJ3arhePDIeOdvhaxXeQC9PZ+ccG8Nt0xlUf9UiAWw8KmU/m0c9ipLAtTgQjV6d2fIKJ8Dk/OBwtJ8gOb1cLamlLa0hdxS6tFL7DsmcobFnMq322qqtapqmpTVXWFqqrfU1V1SFXVq1RVXatth7XHqqqqflxV1dWqqm5RVfWlpNf5vqqqa7Q/P0i6/SVVVTdrz/mENp1vWfDtpzo45x8eTj04lFTMwzElnArG8nM/WGzg9AIzUZ2dzb7pi9B8o65MCFOJhMnHwbPi7KN8Rx8V2z/8TYrQpEcWJgfahQtr1aWpzyutMUb5oiZT+RJxscKUY8cUiJMz3Rk0jR7NXObCVN94mHKXDUdRkrCod2/l0DEFotTx1FiY0YmZ2B4Va0GxTF8Q/n5fL2UuGxe0zDKNz+aCtdfMeT9KHEV4i21Gx5TLJ177NFE+fzCKz2VPXS0f7hBb6ZiSSPKaHc0+YgmVl0/IninJ2XPP3h42N3horfUY79x3DzReIIZ6LAJWi8KK8mK6TaN8mmNqkQvQ2wfERL411ckT+U7lVb8U5KNjKpLZMSX7pSRJtNZ6jIu4IFxTfa9nf4ckpuSPP1RyxjRqRY1HB5Iy767KOcethoK6Yyrty31iUMQEtdLhJp+LRl8xf3zRqjm9Tzao8ziJxhMMJ1+k63jqzy7Kp6rQ+SSs2CHidg/+FQCJhDotTJWeel48tvny1OeWVKcIWaqqEo6ZOKZ0MdGVQTDIIjUe53TJ9zS6G2iZF6D3j0dMJvL1itjmHCbYLSStWtdbSpzP5hRizkAb4ak4j7YNcO3GWmxmMb54DNruh3XXgr1kXvtSX1ZMb3qBrKKIHrrTOKZODIdYUZ4meA9pwlRFy7z2SyKRLC7bVpZjUWB3cs9U+SoRB5c9U5JZaOsd58CpcW7ZauKW6j8gnL9bbl3UfWj0ucyjfE6vOD9b5Cjfkf60iXyDR8S2Kv8cU9dej6gAACAASURBVLGEapxgWKQdu7M4mS+RUBkMZhCmAv3gljE+yQwb6twMBCIMBdM+o7WbhTsxy/1oEnOkMFWA6BeiKSVuJZVzdkyNTESxWhTczqLUO0KDM/1ViGkcuz77Rq7bnFuHyGzU6QXMZtNVvA1n55gaOChcT+e/Dy7/LBy8Dw79jsP9AcbDIkpZM7RHZNgr16Y+Ny3KF4klUFVw2tOEKd1FkjzZMEfoMcgU82FpNSjWZe+YGgiEjcJU4JQoh88xG7VSR2OcrxUG2nimfZBgJMb1WzKcpB1/Vrgj5xHj02kocxqjfCCKkE/jmOr0h2ipSuu3Gu4Qrs08Kn+VSCRG3E4bm+rTeqYURbimjj9r6GmUSHR+tbcHm1XhxnMbjHfuu0ecg2y8aVH3YWWFixNDJsIUiDjfIjumjg4EWVud1i8FeemYAoyuqWnHlMnxf5EYnogSS6jSMSU5I3Q3pqGTtfYciEdnxGBJTpHCVAGysqIEp82SOiLeVTnnjqnhUJRylx2LJW0ce2hwuvi8UKjXIoanxjJM5pscFp1OZ0LHE2LbcgVc/CkxEeZ3f8krR44BUFVqpzn4suiXSh9lX1It/j3iQsDSY5cGx9S0MGWyUphlqt0OIrEE45NJ/WUWq1h10vuUlil9Y+HUiXwgfiY5jvEBVLkd+ErsxoNt1QYY6eLh147hLbZx8ZpK8xc4cK/oqFvzpnnvS31ZsTHKB5pjKrMwNRGN0TsWpqUyzbE11CFcF9Yi0+dJJJL8YWezj1e6R4nE0nqmQn4YbM/djknymt/v7+PyddXGOglVhf33wOorxWLfItLkczEejqVG4nXKm2H42KK9dyyeoNMfYm1NWr9UUTGULU58ca7odQaGnqkibeEui44pvfvTsGioqtIxJTHQWmdi6gBxbQeyAD1PkMJUAWK1KKyvcXOoL9kxVSGEkDmsSg4Fo8bicxAnk64MF7N5Sq1XHKBMC9CnJ/Odofun80nR1eNdAVYbvO0bEOynae9XqPc6ub52jLLECDRfZnxuaTWgTouFuthT4ki7wNYv1sty75jSD+7GAvR6GD/Lbq4lRCyeYDAYodbgmOrNefE5iOL61lq34WCbqNoAaoLOw6/ypo01s8f41l+3ICWhdd5ixsMxgsnDGUB8vieHIWpSLotwSwGsrk53THXK4nOJpEDY0ewjGkvwWvfYzI0rLxHb4zLOJzESjMQ4OTrJeU1lxjt7XhQR8M2LG+MDEeWDDJP5fC1iEXGRRJdjQxNE44lUx5S/DaryayIfgKMog2PKpgtT2euYGgiIf4/q9EXDSEA4t6RjSpJEZamDKrfDuIhbsUYIq1KYygvy6xtPcsa01npo6x2fiV25KsUBIcOF32wMh6LGlSooSMdURYkdu9Vi7pjyasLU2Bn0TMUiIn6w+sqZ2xq2ol7wcS4Z/x23Vx9nu6pNccgoTAHBfmBG7DGIG2M9YnKIVjCfS3RRrz9dmHLXCRFmmTIUipJQSZ38kkiIn0keOKYANtR5ONwfIJ5Q2X9yjH9+sI333i8uDlcnTvBHF2RYdT22S4inCxDjg5nJnb0ZJ/OZu6Y6/KIvr6UqyTGlqkKYksXnEklBsH2VD4A9XUnu7YrV4gLxmCxAlxjp0hYlDG5ZgH13iwvG1rcs+n40zSpMNQMqjBxflPc+OiAulNelO6byrF8KRKUHzOKYyuJUPr82rKfanXZerZ13S2FKkk5rbZqpA4Qjv3qDFKbyBClMFSitdW5GJqbwaysG011QcyhAHw5F8ZWmCVPRCZgKCSdWAWGxKNR4HfN3TPW8KArPW65Iufn4OX/OsUQN7xv6d7ZM7KZHrSTqNum/KdGEKW0yny5M1XnThanuvOiXAqhxZxCmPA3LumNK/yyl2MUnBiERywvHFIiDbXgqwWVffYK3fuMZfvBsFyX160koRXzpYivnNpqsRoOI8dlLYc3VC7IfDVrHm6FnSncEZuiZ6vSHUBRYVZF0cRLoFb+DsvhcIikIykvstNa6UwvQFQVWXiR7piSmdA6KRQmDWxag43FY/UZwmkzqW2BmF6a0Y9AiFaAf6dd/BtrxLzwmXOp51i8FszimcjCVb0Ab1lOV3jEV6BNbtxSmJKlsqPNwpD9oLO+v2Qz9++UxKg+QwlSBskErPG7TLYl65C509j1TwxNiTHsKusBVYI4pEHEi0/LzaWHqDBxTHU+Iws1Vl6TcvLtnkr+JfRD3RDerRp7jufgm/OkTHiDJMeUHoE9zcNWYClO575eCGTu0UZiqg2gQwiZjVpcB/WZuN12oyxPH1I5mH25HEU0+F1++eQsv/t3VfOf9F2GpXIt96LD5k+JT0PZbWH892IrNH3OW1GvC1Kn03z9dfM0wma9zMERDWfH0aiwwM5FPOqYkkoJhR7OPvcdHmEo+8V95sRCaF7lAWlJ4dAwEsSiifDyFWBSGu6B6Y1b2o8RRRGWp3bwAvbxZbBfp89s+EKTRV4zLrlU9+LUS5ur8dUwZhSl9Kl/2hKn+8QjeYlvqeQMkOaZkx5QkldZaN9FYgq7BtHRR7TkiPbCM0yH5ghSmCpTpEfF6r8wcHVOxeILRiSljlC9UuMJUvddpHuWzOcXY3zOZzNf5BKzYZojY7e4a5kjxeajn3QHA84mNDKQLOWCM8o1FKLFbcZt1TOVBvxSIE44yl43+8TSh7Wy7uZYY/ZorMaX8XD945YljamVFCfv+4Vru/NAF3LajiTJdaNYm85nS9bTofVqgGB+IAn2LgrEA3V0LlqJZHFNB84l8IDumJJICYmdzBRPROPtPJvVM6Qs8x5/LzU5J8paOwRCNPtd0qfY0I8dAjRsnHi8ijT6XuWOqpBLsbiGULQLt/QHWVifH+PJzIh/MOKaMUb7cOKZMJ/JJx5QkA/pkvjbDZD69AH1/lvdIko4UpgqUMpedOq9zpsTNpUXuQmcnTI1MTAFQkR7l01+nwMrPAWq9xfSPh0kkTCyZnobTF3lPjsCpVwwxPoA9XcNsX+VDufaf6Dv/r/hDYvt0AWMK9lKxghTSHFPjk9R4nSjJ0/siAQiP5o1jCkScz7T8HJZtAXr/WBirRaGi1ESYyhPHVEaqN8LocfPuuQP3ipPt1Vct2NsVWS3UekyEYYtVfM5NHFOqqtI1GDKfyGd1gCd/fj8kEsnsbG8uB8SxcpqqVnGOclz2TElS6RgIsjp9UQJmRrdXZE+YWplJmFIU0TO1CI6pmYl8ycXn+TmRD8CRyTFly75jaiAQMRafAwT7xLmDM0OFgWTZsqa6lCKLMmPq0KnZJLb9WeiZikVkZHAW5AzuAiZlEtccHVPDITEa1+CYmo7yFZ4wVV/mZCquMhiKGEsRM1wcp9C1C9QEtFyZcvPJ0Ul6Rib5wMXN4PRgufwzTD77mLkwpShivHFQdEz1joVN+qW0SGGedEyBiBoaHGBuTXxZphbX/vEwVaUOrJYkUXG8FxTLTJdYvlLVKrYv/C/YSoRDamJI/Gl/FDa8dWaazgJRX1ZsdEyB+JyblJ/3jYeZiMbNJ/L5mvNuKpFEIslMtdtJS1UJD+7vo6G8GJfdSrGtiA3V2ynp3EU0EjNOp5UsSxIJsShxyRqT88yhdrGtXJO1/Wnyubj/tVNMxRPGKba+Zug/sODveXxYn8iX5JgaaBNOMYs18xNzxGkdU1ksPx8Yj7Cj2We8I9Av3FLJC8ESCWAvsrCmutQ4mc/pFULwYhegH3sG7rwdLvoEXP7ZxX2vAkWe8RcwrXUeOvxBorGEcOhYHWftmMooTGlOn0IUpvQuoIwF6CZT+Y4Nhvjibw8SicWh80nx81yxLeUxL2orwPqBsKJUxJb8ZlE+EBNBtPLz/rEwtZ60Hh/9Ij2fhCm3wxjl04WpZRrl6xsPp8b4AAKnxL+vNc8vsOreACjw+D/CQ38Du/4NDt4PA4eg/jzY+ZGFf8uyYmPHFEBZk2mUr2NAuLlWmzmmZL+URFJwXLm+mte6R/nEz1/hAz98idu++wL/2V6NLdDDu//1lwyZ9TJKlh0nRyeJxBLmxeeDR8UxNosTixt9LhIqnBwxWVjxtYipfPHYgr5nu1Z8vi7dMZWH/VIwW8eUtsCVJceUqqr4Z3NMyX4pSQZSTB3J1G5Z3Cjf4T/AT2+ByJhYLJ4y+Z6RSMdUIdNa62YqrtI5GBS5WVeFcEKcBbMKU0VOIdAUGMkFzOekp4AqVov43Ind0LRz+uYfPNvFj54/zqpKF3d0PiE6May2lKfu7hrG7SiaLp7X412mjikQbpqRLuIJlf5AhFpv2gFUv0jPk44pgFqvE38wQjyhzjiE9G6uZSpMDYxHjMWsw8eE0JLvlK+EP3tZ2IZdPnB4F92BVF/m5KH9IkprSXaZeRtF90MsCkUz3zf6VKaUjqlEQkxAWrsw0wIlEkn2+Ls3b+D9F69iIhrX/sSw9pfAwz9mffh1vvbwFr5885Zc76Ykx3T4te/+9EUJEI6pLMb4IHUy36r0fSpvhsSUGJ5TvmrB3rO9Xzg3puOM4XHxHnnYLwWzOKasdkARMaUsMDoxRTSeMKYiQCQVsthNJiksWus83PfqKUYnojN9rCCEqUO/E9UXdpPvpPnw+i/h3o9A3TlwyV/AL98L++6GrXcs7PssAaRjqoDRBZJDvZolsaRiDo4pcRAxClNDovi8AK2wemSuz6wA/bz3is6aBz4lppIh7OQPHRAl5b967DkRIUqL8QHs6Rpi26rylEhXtXsWYUqL8g1pQk+tN80xNdYtCqFL86egsdrjJJ5QjSvanvplK0z1B8LUeNJOfgYP5+2JowFfixBki8uzEotrKCsmGk8wGEr7DJU1AqphKmanP0SJ3ZrqShs/KVZepWNKIik4LBaFFeUu1tW4ObexjItWV7LzgsvAWcZ7arq568UTohw9GhKF0id2w2B7rndbkmU6/Zpb1tQxdSSrMT4QQ0QA854pX4vYLnABevtAkBXlxTPxVr1bq6rAHFOKIhazY9lxgejn3RnLz/PovFqSX0xfO6fH+Wo2A2rmgUFzZc934dcfgpUXwR33w4YbxHvt/o7smjJBClMFTHNlCXarhbY+zZLoqjzrjqkhzTFV7jJxTOmF6gWGr8SOvchCr1mUz1EKb/kaDByE574OwOsnx+gbD3PbjkZaJ18Wj2u5IuVpg8EIHf4QO5pTfybVbgf9s0X5JoboHRGrgrXp4sZYj4gW5lGPQI12kDcUoLvrRXxtmRGeijM6MZUqmoSGxO+H3t8kSaHeO+NYTEGPrKb1THX4gzRXlaQOBhg6KrZyIp9EsjSwWGDlRZw7/gRPOf6C1f+3Hv65Hr5+Lnz/Gvjv7XDihVzvpSSLdPiDeJxFVJgtjE6OZN0xVe12YC+ynEaYWtgC9CP9AdYmC3MD+TuRD8BhE5eNkXTHFIieqSw5pgYC4vzCIExNhUUqQkb5JBnYkD7VXqdWc/H2vb4wb6Sq8PTX4MHPwLrr4D13g9MjRNwdHxJF63JSrQEpTBUwNqtW4jbtmKqcU8eUt9hmLHqcGBSOqQJEURTqvE5OmQlTAOuvhw1vg6e+CsOd/GF/H0UWhb++bgNv9xxhAB8hT+oF8UvHUvuldKrdzlmifFWAyrBfCDqG8vPR7ryLg9Vq+2jomVqmjqkB7eeQ4pgaPCy2lfl54phr6srEz8pQgK5HVtN6pjr9IeNUpuEOsZWOKYlk6bD9gyiN21Hrt/LzqSvYv/HTcOP/wu13i++H+z4KURNRQLIk6fSHWF1dmrooAUnF5+uyuj8Wi0JjeTEnhkw+g+460eM6snCOqVg8QedgiHU1ScXn/kPCebSAccGFRI/yGRxTICbzZaljSj83q05f8A2K9ANu6ZiSmFPlduArsRsdU2VNou5inj1Tqqry4J4D3PWl2+HxL8GWd8K7fjIzuRJgyztEimHPt+f1XksRKUwVOK11bg6lOKbOvmPKsFoFQuAqwOJznVqP0zzKp3P9V8FiQ33gL/jDvlNcuLoCr9PKtsTr7Ipv4ofPH095+O6uYZw2C1saUos4qz2O6aiegVIxsS0wKASdWrOpfN70EqzcogswBheYp158trI4cSUf6NdW5VKEKb8mTOXpimauaZjueEv7/fOsAJQUx9RkNM7J0UlaKtOEqaFOMS5bL96XSCSFz5qr4I7f0PjBn3N/7Sf4QPslBDe+C9ZdAzf+j3CjPPbFXO+lJEt0+IPG736YiXVmOcoHIs5n6piyWMRkvgWM8p0YniAaS7Am2THlP5S3E/kA7FYLimLSMQXCMZWlc8T+TI4pXZiSjilJBhRFMS9AVxSo3Qz9cxSmVJXAoSd48d9v5arfXc67Ew/yW+db4e3fNnQWY3eJfqm2B0ynVS9npDBV4Gyo9dA/HhEl5iUVEA2e1YFhOBSlPF2YUlURVSpgYao+02QwHU8dXP15lM4neMPoI1y3uRb6XqcoMsJwzcV8+6kOxianph++p2uYrU3l2ItSf2Wq3Q4SKuZThkqEMDU50ofNquBLjkvGp0Q0Lo8m8gFUapMGTYUpgEBv9ncqh+iTHQ3ClL0070TFfMFbbMNltxp//4rs4K5NcUx1DYqOkZaqtKLJ4Q4RnchCJ5ZEIskuFovCF962iYFAhG88rokQzZeJeMPub4qR2pIlTSA8xUAgwurqDMXnVrsY355lmnwuuocnUM26X8oXVphqH9An8iU7pg7nbb8UiIt6R5HF3DFVlF3HVKmjaKabSyfQJ7bSMSWZhdZaD4f7A0ZTQc1m4ZhKmHy+MxH0w7P/xcS/n4f7rptoHX+OIw03cd/Ou/jk6O3s7w2YP2/7BwEVXvrenP8eSxF51l/gtNYlZWX16J2+YnAGDIeixuLzaEgcXAo0ygfiQH9ydJIj/Rm+EAC2fYBe92Y+Z/sp1zTbofMJAC699lbGwzG+94w4ARkPT3Gwd9wQ44MZG7FpnE9zTMXG+6jxOFMnlAV6QU3knbhhtShUmfVm6c6VZSZM6T+HlH4wfUWzAAcDZANFUTRh2MSx6G2E0RPT/zszkS/t4mSoAypaFnM3JRJJDjmvqZxbz1/B95/polObzsbVXxAX//d9DCLBXO6eZJGZLj5Pj3GDcEz5WnLiGmr0uQhEYoxMTBnv9LUIV98CFRbrE/mmHVOTI2LhJs/d2E6bdZaOqewIU/5AxLz4XDqmJGfAOSu8hKcS/L/f7GcymvRZrt0CUyF4/a7Ti1PhMXjk86j/sQke+X8cGLPxVeef0/2BV9jyof/jysuvxlFk4RcvZnBElTXB+jfD3h/BVHaGBhQCUpgqcFprxXSBtr6ANlEA6H31jJ8/ZBbl01ccXIXrmHr39kZcdivferIj84MsVr7IhylXglQ9/0/Q+SRUb6R17VrevKWW7z/TxUgoyt5jI6iqsV8KZmzEehFjCpowRXDAWHyuWzfL8ssxBcId1GfomGoQ22XWMzUQiOAosuApTlqVGzwii89Pg+h4MznQlqUJU9rFSXPyaO54DEaOyX4piWSJ89nr1uMosvLFBw4Kh4q9BG76X/Ed8ejnZ39yPJadnZQsCvqixOr0RQkQwlRldovPdZp8LiDTZL5mMXVugc6D2geCNJQlTeTrfFJsV126IK+/WDiKLISnzBxTzuw5pgJhqjJN5FMsBZ34kCw+bzmnjg9e0szPdp/ghv9+hgOnxsQd668X5/f3fRS+fRkc/n2KED0RjbG/e4jX7/0aE187B579T/6gXsjVka/y++0/4s/+8vNsWilEUa/Lxpu31HHfqydTxa9kdn4EJodh3z2L/VcuGKQwVeBUuR1UltqFY6r2HFHO2L3njJ6rqiojZo6prqfEdsX2Bd7b7FFeYuf2HU385rVTdJudYADHh0L83l/B/pV3wCs/EfGBlisB+PTV6whFY3z76U52dw1jsyqc11hueI1px1S6kAMi7lVUjC08aN4vBXkX5QMhTA0YonyaY2r8ZPZ3KIf0jYWp8ThnylnD4+JnkOcrmrmmIVOU1tsofn4JcZDu9Aep9zpx2ZOEv7FuSEzJiXwSyRKn2u3kU1ev5cnDfh4/NEAoEuMltZUDK98DL/4ff/O1b3Dl154kENbcK6oKHY/Dj94G/1y3oLEqSXbpGAhhtSg0+dKEqfiUKBjP8kQ+nZUVswhTdeeKbc+ZnWOfjiP9QdbWJDnG2h8FZxk0nL8gr79YOG1WIjGTC22bM4tT+SKpFQs6wX5Ro5GnHV2S/MBmtfD3b93IT/5kB+OTU9z0P8/y3ac7SRRXwEefg7d/R1Tj3PluAv99OT/88fe4+MuP8bEvfAXHdy/lnNe+xOvROj5g/xp3rvg7/uFPbuH/3bARpy31c/eu7Y0EwjEe3JchbbLqEqjeKErQF8iJWehIYWoJsKHOI6YLFNmhYSt07z6j542HY8QSqlGYan9EZPtztGK1UHzw0hYsCnznafPxvg8dEM6wijd/TlgqEzFouQKAtTVubnxDPT98rotHDvZxzooyiu3GA11Vqe6YMjkYKwpqaTXOyJDRMTWmuUbyLMoHwu3SMzKZmr12eITQNr68onx9Y+HUf7vBI2IrJ/LNSn1ZMYPBiLEgVf8901yZnYNiKlMKciKfRLJsuOPCVayuKuFjP3uZzV94iFu/9Ty3HL6KY9Tz6Yn/wj/o59XjQ7D/1/Cdy+EnbxfltPEoHNuV692XzJHOwSBNPpeht5OR4+IYkaPzz8ZyIUyZLmjWnwd2N3Q9Pe/3iSdUOvzBmX4pVYWjj8LqK8FaNPuTc8ysjqksRJJUVaV/PJw5yif7pSRnyKVrq/jDpy7jyvXV/NODbbz3+7s5OR7luZKr+FzjD/iS5aOMDZ7kjzv/gvvjH+GH9n+l3lNE97X/x7l//wzf/9s/5ccf2MHFa8wdejubfTRXlmSO8ykK7Pww9O2DE88v4t+0cJDC1BKgtdbNkf4AsXgCGnfAqVfP6OAwHIoCpApTU2HhmFp7TcF36NR6ndyydQW/eKnbNGr3h/19bG7wsKKmEm76Jqy+SqjXGn9+9Tqm4iod/hDbVxljfAD2IgvlLpuxk0kj7qqiXB01d0y5KlPHh+YJO5p9BCMxXjkxMnOjooieqWXmmOocDKbGzPyHxFY6pmalXpvMp5fHT1PWJLZj3aiqSsdAkJbK9H4pTUiWjimJZMljL7Lwr+94A1dvrOHPr1rLd+/YxhN/cz0rP/BDqtQhvmX7D7bcdzXc837Rf/m2b8CnD4LTCz0v5Xr3JXOkYyBkHuMb0srwc+SYKrZbqXI7OD4UMt5pLYKVFy2IMGWYyNe/H4J9sOZN837txSajY6rIkRXHVCASIzyVoNqTIcpXKoUpyZnjK7Hz7feez1du3sLLx0e5+CuPc/v/7eaeV/rpW/NO9t30ONFrv0pF/Rq47l8o+dRLNF74Dpz20wvIiqLwru2N7Dk2TIc/Q2/ilncKp+Tuby/w36wwkcLUEqC11kMkluDY0AQ0XiBiMKdO3zPVpWX8U3Lax5+FqQlYd+1i7W5W+fDlq4nFE3z/mWMpt/ePh3n5xCjXbdIKElddAu/9tRjhqdFcWcKtW4WjaadJv5ROtdtp7pgCJu0+qpQx6rxpAtRod172S4FYQSiyKDx2aCD1Dk/9sio/H5uYYjAYTZ0a5D8s4rLlq3K2X4VAvSbEGnqm9OjqaDcDgQihaJyW9PLb4Q7hzpMnlxLJsmBrUzn/c/tWPnX1Ot60sYY6bzFK006Uiz7JJdYDjCZc8M6fwMf3iBHbNqeIO53cm+tdl8yBeEKlayhk/O6HJFfymuzuVBJNPpd5lA/E9MihozA2v0U6fTDPtGPq6KNiu+aqeb1uNsjsmMrOVD69OqPanSHKJ88dJGeJoii8e0cTv/uzS/jw5S188z1beflzb+J/bt/K9eetxH7hh+EDv4cLPiLSSWfBzVsbKLIo/DKTa8ruEse1tt/O1LwsY6QwtQSYnszXNy4cU3BGcb679nTjK7Gnlnq3PyzsuEnOoUKmubKEN2+p46cvHGdscmbKysNajO+6zbNP7vjMtev5xJVruGhNRcbHVHscGYWpcWs5lcoYtd60lZ2xnryM8QF4i21sW1XOE2bC1DIqP+/QJ8ZVJp08+w+LiIHsL5gV3TFl6JnSxdixE9OrR6YT+XzNBe/YlEgk8+SqL/CllT/kPXwZNr4t9Xu3YRsMHJTT+wqQkyOTRGOJzMXnJVVQbOz0zBYrfS66hzOkDpovE9t5xkiPDojP7bRjqv1RMRHMnf/T5GZ3TGVBmNISEAbHVCIOIX9B/Awl+UlLVSl/c/0Grt9SZ1rfMheq3U6u2lDNPXt7iMYyTPrb/kFAhRe/tyDvWchIYWoJsKa6FKtF4VBvQEyi8K0+bQF679gkjx0a4B3bVuAoSvrla39YHHjzMGI2Vz56xWqCkRg/feH49G1/ONDH6qoS1lS7Z31uldvBZ65dn/ozMnmMP0OUb0Qpx0eAWrdt5kZVFQXP3qaz+4tkkataazjUF+DkaNLJmadB2KSjJhb3JUiHduKY0oHkPyRjfGeAHl09NZp2cm8vgWIfjHZPT+QzdUzJfimJRGKxULP6HE6OhaerB6ZZsQ3UxFlNIZbkBzOLEiaOqaGjOYvx6TT6XJwamzQXX2o2C9FsnnG+I/0BGsqKKXUUiaEq3S/Amqvn9ZrZIqNjypZjx1TIL74TpGNKkme8e3sTQ6Eoj7X1mz+gfCVc/CmoPze7O5aHSGFqCeAosrK6qkQ4pgCaLhCOqVka/u/a001CVXnPjpUzNw51wHCn6JdaQmyq93LF+iq+90wXk9E4I6EoL3QOn9YtdaZUu534gxEx7jqN/oQHi6JSbQnM3Dg5IuKSeeqYAriytRqAx5NdU6uvBDUuxqdmC1WFEy/Ag58VK6lZpMMfwmZVaCzXRNrohBhjXtWa1f0oRJw2K5WlDqMwBcI1NSaEqWKblbrkcvn4lCi/lf1SEokE2NzgBWDfybHUO/TJg4BI7AAAIABJREFUZbJnquDQhanVplG+9pzG+AA21LlRVdh/ctx4p8UiEgVdT89rilZ7f3DGLdX1lCh8L4B+KRDXHBkdU1M5dExpQ1WkY0qSb1y2roo6r5O7MsX5AK7+PGy8MXs7ladIYWqJ0Frroa1XEz8ad8DEoBCZTJiKJ7jrxRNctraKpoqZTiXaHxbbtYVxcDwbPn7lGoZDUX7x4gkebesnnlC5blPdgrx2jcfBVFxlZGLKcF9vTDiybJODMzeOahP58rRjCmB1VQlNPldqnK/pInDXw757Fn8HpsLw6s/FFKbvXytGqf70FggNnv65C0SnP8iqihKKrNrX5FA7oELluqztQyHTUF7M4f6A8Y7qjXBiN0P9J2iuLMFiSYrsjZ4Q4qd0TEkkEmaEqf3pwlRJpej6OymFqUKjwx+i3GUzToSeGBbnrjl2TOnDbvZ0DZs/oPly4XofOTan15+ZyKcJU0cfFZOP9SqOPMdhsxAxiyQVOSEeWfSx9wPjEZw2C25HWvl0UHOjlEphSpJfWC0K79jWyNPt/tQkisSAFKaWCK11bk6OTjIenoLGneLGDD1Tj7X10z8e4Y8uWJl6x5GHoHL9kix23r7Kx/ZV5Xzn6U5++3ovDWXFbG7wLMhr63Zis8l/xyLaiUcoSeDRy+3y2DGlKApvbK3m2aODTEa1lTGLBTbfLE6iJjKcsM2X8VPw+D/Cf2yC+z4qJry89T/gfQ+Ik45f/FFWpr6AWNVN6T/yHxZb6Zg6I966pY5XToymTncEuPQzEAtzTe+3zfulQDqmJBIJAB6njebKEl7vGTXe2bANemQBeqHR6Q9mjvFBzhd/KkodrKkuZU/XkPkD9J6pOcb5Ov1BIrEEa2vcQsRpfxRaLger7fRPzgMcRdYM5eea+3mRz9EGAhGq3U6U9B5KXZhyyyifJP94x/nimu/ul2ZxTUmkMLVU2FArRJbDfQEhLjm9GYWpn75wgnqvkzdqcS1AFIgef3ZJuqV0PnbFGk6NhXn6iJ9rN9UaD2pzRLcT67n3ZDpCmiMt6J+5cUz7UsrjjimAN7ZWE4kleL4zyaW0+RYx9bHttwv/hn374evnwdNfEyuHd/wGPvYCbPsANF8KN30TTjwPv/3zRV+Rm4onOD40kRo18B8GSxH4Whb1vZcKt+1swlts43+f7Ei9o3INsR0f5i3xx7nQeTz1vmHtsdIxJZFINLY0eM1jVSu2QeDUshrKsRTo8IcyF5+DGDCSY3Y0+3jp2AjxhMm5RuU60WM0R2HqqSPifPDClgpxXjHeUzD9UgBOmyVDlE8XphbXETIQCFOTHuMDCOiOKSlMSfKPRp+LS9ZU8ssXu82/VySAFKaWDNOT+XrHhbNlxQ44YRSmugZDPHN0kNt2NGFNjtB0PQ3x6JLrl0rmivVVbKgTAt5C9UsBVLvFAbLfpAD9UFDrJwomFd6Ndouxui6f4fH5xM4WHy67NbVnqv48IczsX4Q435NfBqsDPrkXbrsTWq5Incy2+Wa48u/htTvhmX9f+PdP4sTwBLGEmiZMHRJ/97McFbtcKXUU8b6LVvHIwX7a0yJ9XRs/jl/1cn33f0AiaeV1qENEGkoqs7y3EokkX9nS4OXk6CSDwbTFn4ZtYit7pgqGsckpBoORDI6pdrDYoGyl8b4ss7PZRyASo63XRBBVFOGammPP1BOHB1hbXUqjzwVHHxE3FpAw5SiyEjF1TGli0WI7psYjxuJzgGAfOMtm9kMiyTPevb2JU2NhdrX7T//gZYoUppYItR4n3mIbbX16z9RO8LfBZKr9/WcvHKfIovCuHWn9Ru0Pgd0NTRdmaY+zj6IofO6tG7jx3HrOX7lwo4hnonypB+PJaJzesJUpi0NMC9EZ6xb9Ugvk2FosHEVWLllTyeNtAzPF7ooCm2+Frl0zRZMLQd9+OPQAXPCR2WNcl30GtrwTHvsiHPzNwr1/GvrEuNSJfIflRL6z5I8vWkWxzco3n0p1TR0dU/hq7F34Rl6Dfb+cuWO4Q/z75/nvhkQiyR5bVmQoQK/dIoQM2TNVMHServjc1wzWIuN9WWZH8+l6pi4TFQ16xP8MCUZi7Okanh4ww9FHoWpDXlc7pOO0WYjGEyTSXR/6NO9Fnsw3EIhQ5TZzTPXJ4nNJXvOmjTWUuWz85lXp8s2EFKaWCIqi0Frrnlnd0UsUk1YSw1Nx7t7bw7WbalNXG1QV2h+B1VcseTfIRasr+a93n5fqFpsnxXYrbkcR/jRhqm88DChEnVUQTO6Y6i6Yk5A3tlZzaiycWmK95VZAhQP3LtwbPfUvwilzwUdnf5yiwNu+IRyBv/4wnHx54fYhiZlx1lrcIBYVwwRkv9RZ4Sux8+4djdz/6il6Riamb+8cDHFP/DLidefBI5+HiPb5GuqQMT6JRJLCpnrhdN7fkyZM2ZxCnJI9UwWDvuhj6BcEIUzluPhcp85bTJPPNbswBXBs11m97rNHB5mKq1yxvkqr0HgO1haOWwrEoiVgLEDXnUqLOJlvIhojGIkZJ/KBSCbIGJ8kj7EXWbiqtYbH2vqZipu4DiVSmFpKbKjzcLgvIFYxGs4HxQrdL0zf/8DrvYxNTvGenWndRgMHYfzkko7xLTZVHoeh/Lx3TOTsE66q1CjfWA9483ciXzL6qt5jbUnCWtV6qNmycNP5+g9A2/2w8yNQfAZONpsT3v1zKK2CO2+D4a6F2Y8kOgaCVLkdeJxaGelwh5gWVykdU2fLn17agqLAd5+emRLaMRCkxuPC+uZ/Ffb7Xf8mxL+xbll8LpFIUnA7bbRUlfB6umMKRM/UqVcgYdJ5I8k7OvxBiiwKTT5X6h3xmFj8yYN+KZ0dzT72HBuecYwnU74Kypqg66mzes0nDw9Q6ihi20ofHHtGVGgUUIwPhGMKMPZMFS2+Y6p3TLx2jVmUL9AvHVOSvOfaTTWMh2O80JlhuMIyRwpTS4g3NHqZiMb55J2vMBAtgtrNKQXoP9t9nJaqEi5cXZH6xPaHxXbN0i0+X2yq3Q5D+bneOWVxV89E+aYmxX8XiDBV43GyucHDE8k9UwBbbhHxiYUQhZ76FxEjPZ1bKpnSKrjtF+Ln+a1LYe+PFrQQvcMfTC1n9R8SWxnlO2vqy4q56dwG7nqxe7ojpmMwJFbMG7fDOe+G5/8HOh4HNSEdUxKJxIAoQDcRphq2wVQIBtqyv1OSs6bDH6SpwoXNmnb5MXpcDFbJM2FqOBSddlAbWHWZqDVInJnzQVVVnjzs55I1ldiLLKJfylZScBUaumPKMJlPc0ydHBxJf8qCsU9zTep9sdOoqljkko4pSZ5z2boqim1WHj7Qf/oHL0OkMLWEuOGcev7yTet45GA/V//bUxyxb0Tt2QvxGAdOjfHKiVHes3OlcRrdkYeh9hzw1OVmx5cANR6noWNKX9mxl9XNRPnGToptWWEIUwBvXF/NyydGGAlFZ27cfIvY7v9V5icOtsPv/xpCg4a7To5OcqhvnM6De+Dgbxjc9Me0jRXRPTxh8kIZqNkIH9kF9efCb/8MfvaOBZnOpKoqHf5Qajmr/wig5NVJcyHxkStWE40n+MGzXaiqqo0L14S/q78gemLu/6T4f+mYkkgkaWxp8NI7FjZE5lmhFaDLnqmCoNMfMu+XGjoqtnkS5QNRgA6we7Y4X3gU+ved0esd7g/QOxbmytaqmQqN5ssKrqw7s2NKuJj+9pcvMpx8vgjQ+RT81xtgcn6i1csnRiixW1lf6069Y2JYuM+kY0qS5zhtVi5fV8XDB/uMPW0SKUwtJYqsFj551Vp+/6lL2VDn4b/bK1CmQpw49BI/feEETpuFW7emdRtNjghXlYzxzYtqt4jyJVu++8bCeJxF2Dw1MDEkrOpjJ8SdBdIxBfDGDTUk1JkRx4CwsDfuzCxMBQfgpzfD7m/CD9+SUpR+/2unuORfHue6/9zFwTs/R0At5urnN3P9f+3i0q8+wS9f7D7znStfCXfcD9f/Kxx/Fv73Anjtrnm5p4ZDUcYmp4wT+cpXzpR7Ss6K1VWlXLeplh8/f5xjQxMEwjFaKrWfr6dOlNqHNPHW15K7HZVIJHnJlgZRgG5wTflaRARcTubLe2LxBMeGMghTg0fENo8Wf5p8Lmo8jll6pi4V266nz+j1njgkzqGuWF8t+hRHjxdcvxTM4piyCWHKkojy4rG0n9n+X8HIMdGpNQ/2Hh/h3KYyY09s7ytiW71xXq8vkWSDazfX0D8e4bWe0dM/eJkhhaklyOqqUu760AVcc92NAPzwrjv51cs93HBOPV6XLfXBHU+I7hwpTM2LareT8FSCQCQ2fVvfWJg6bzGUVAEqTAyKfikomCgfwDkNXipL7TyeHufbfCsMHOTUkb38am/PjCg3NSm6n4J+uO5fYLQbfvBmGOthbHKKL/72AJvrvfzkBjdvse6mv/UOvvJHl/OtP9rKtpXl/NODbcax4LNhscDOD8FHnhHTbe79MNz1HlOn1pnQoU/kS4nyHZbF5/PkY1esIRCO8aUHDgJpEw8v/DiUN0OxD1y+HO2hRCLJVzY1eFEUeD29AF1RRKfmSVmAnu/0jEwyFVczF5/n2fe/oijsaK5gd2eGnilPvXB4nakwdXiAjXUeajxOMY0PCq5fCsBRZO6YmlLE8CQnUaOYp/+MTrzAXAlFYrT1jnN+k0kXafeLgPZdIJHkOW9cX0ORReEhGeczIIWpJYqiKLz10u3ES+t4S3k3qPC+i1YZH9j+iFht1O3wkjmhTwgZGJ8pfewbD1PjdUKpNhY4OCBEGsUiTmgKBItF4fJ11Tx5eIBY0hSJ+IYbSWDh/p9+nb+8+zW+/+wx0bXw6w+Ji4RbvgsXfATee6/o1frB9Xzv/scZDkX58s1buPTUD1DsJay58a+5bnMd122u4yu3bGEiGuOffzeHvpCK1fD+B+GafxInfT9/lyjUPksM46zjMREzkP1S82LLCi+Xrq2cFjhbKpMuTooccNud4jMjkUgkaZQ6imipLGHfSZMV5oZtomMqEjDeJ8kbOtKPrckMHYXKdVneo9Ozo9lH33iY7uFJ8wc0XyZcQPGpWV9nbHKKvcdHRIwPRL9UxVpRol5gOG3mU/nah8XCrMsylSpMjXbDiNZHmtR7e7a81j1KQoWtK02EqZ49wi3l9Bjvk0jyDK/LxoWrK3j4QJ+56L2MkcLUUkZRsDbt5HylnX3/cA2bNSv8NImEODiuuRos1tzs4xKhyq0LUzNOn76xMHUe50wZY2hAOKbcdWC1mb1M3nLVhmrGwzFePiEuCg73Bbj5J0d5Jr6Jm+27uWJdJV/5fRsD9/61mLB3zT/ChhvEk5t2wvvuJzY5zm0HP8Knz1PYbOuFA/fCjg+lrJCuqXbz4ctW8+tXTvLc0Tk4nixWuOgTcPN3ROfIw3931i/R4Q/iKLLQUKbF9kaPQzwiJ/ItAB+9QvRHpfx8dao3FOTqsUQiyQ7nrChjX6bJfKhiOp8kb+k0cyPrDLZD5Zos79HpmemZyjBBq/kyiAbh1Kuzvs4z7YPEEypXrq+G6ISYyFegxzuH1jEVnkp1TL1ySoh3F60s5cCpMQJhTaw7tktsV18lfkdjZ+GIT+LlE6Kf6rx0x1QiAT17xTAViaRAuGZjDZ2DIY4OZBiusEyRwtRSp3EnjJ3AMZFmF0wkxDS0kF/G+BaAam10rV6APhVP4A9GhGOqRFshC/phrLugYnw6l6yt1Gynffzno0d46zd20T08gXvbu6iO9fKNyxJ8sPhJqvd9m+jWD4hoVhLx2nP5tPMfsStxPn7sz+DBz4DNBRd+wvBen3jjGpp8Lv7+vv3Gcs0zZdNN4rX3fAdev/usntrhD9FcWYJF7zCYnsgno3zz5cKWCrY2ldFa55n5+UokEskZsLnBS/94JMWZDMzEd2TPVF7T4Q9SUWKnzGVPvWNyVCzc5VHxuc6aqlLKXbbMPVOr9J6pp2Z9nScOD+AttnFuYxns/QHEwrDxxgXe2+zg1DqmImkdUy/2iOE159Y5SahML2TS9TS4KmDb+0VB+WlEvEzsPT7C2upSvMVpC7uDhyEyBit2zOl1JZJc8KaNoqj/oQN9p3nk8kIKU0udpp1im2yfnRyFu26Dp74CW94BG2/Kzb4tIaajfIGwto2gqlCXEuXr14Spwik+1/E4bWxf5eN7z3Txn4+28+YtdTzy6cs475r3/v/27ju+6vr64/jr3ExCSCCQQBghYSpLEAEHMkRBFLe4V+uu2mpb6+qvta21tta6W6sWR8W996gC4sAwBEGQTQCBBAg7kPn5/fG5IZsREm5u8n4+Hvdxk++993s/N3z43u8993zOgYhoWky5k98UP8mkkv7cvP0CKiemTvwmi3eyk5g9aiKBiEj/DdrgK6F56yrPFRsVwZ9O78OyDTt4bPKy2g/6+Dt9G+Z3fr5frcSXrt9esf7R+oX+OrnhLTMIN2bGU5cN5slLtHRYRPZPv44+67tK1lRcki+CrjpTdWZLXiFvzf6RW577nJfuupjciZfDG9fCG9eUXd663he03kfL1u+ovr5UaUe+BlT4vFQgYAxKTyKzcjHvUs1bQ9s+e6wzVVLimLxwPcN6JBNZvBO+uB8yhkPno+pp1PVrd8ZUuS8OS0oc01b6wFSnhACRASNz+UbfiGb55z6A1+lIf+dV+19nqqTEMWvlZgZWt4xvVaa/7qTAlISPdomx9O/UUnWmKlFgqrFr1w8im5UduNfNhcdH+Bo8Y++FM5+AyOg97kL2rkVMJLFRgd1L+dZt8QGqdomxEB3vs4O2Z8OWH6Fl+GVMAVwwJI0ebeP5z6VH8OB5A2gdHwPNWvqMu1XTsLa9WHzsQ7z1XQ4vleusl7NtF/d+uJBjurXmuKFDfR2oI6+DoTfW+FzDeyQzrl8qj05ewvINO2o34IgoOPsp//d/6SLYtXWvD8kvKmZVbl6ljnwLIaEDxLSo+YGyzxLjonYvfRUR2Ve9UhOqL4AOvs7U6hkH1JG1qVu5MY8npy7j/Mencfhdn/CbFzM5Z8ktnFX4Lm75VL/8bMWXvgNu1pcw+3n48qF92veuwmIW5Wwr68Za3obF/roB1pgCX2cqa2Pe7vO6KjKG+S9/C6u//fs1W9mwPZ8RPZJ9FveO9XDcb+txxPWruoypRTnbWB98+dElBfTukOizzHKXwdYf/d8oPhmSusLK/a8ztWzDdrbsLKy5vlSzVtC64S0FFdmTMb3bMffHLfy4uYYadk2QAlONXUQUdDjcv2nOeRGePMGnEF/2vu9kZlpOUxfMjJQWsbuX8u0OTCXE+r9x82QfFCwpDMuMKYBTDmvPxzcNZ9ShbSvecPTPoctIuOBlrhjVj6Hd2nDnO9+zKNsXov3zewvILyrhT6f1wcz8N9sn3u1PJPbgd+N6ERMR4P/enFf74oAJqTD+KchdDm9fv9cPLVkb8yhxlTvy/aDC5yIiIdY8JpJuyfHMq6nO1PZ1/kOw7LcP561l+N8ncdd7C8jdUcA1x3ZmWs8XGch83u32BwbnPcDan2bCTXPhxuCl79kw91XfiXcvHvp0MZvzChl3WGrVGzcuhkBkgy0EPiTDZ3bXmDXVZaQ/r/72v9XePHmhb/gxIj0WvnwQup0Q1tk91WVMZS7PpZgIXCASinYxJCOJOau2ULhksr9DxnB/nXak/zyyn+d0s7L8ssDqM6amQ8dB+jwjYWdMb/956hMt59tNgammoNMQn+L+xtW+FsNVU8qW+EmdaZsQs3sp37pgDYzURF97iviUsnX1iWmhGF79SRsCl7wJCe0JBIx/nHsY8TGRXDdxFp8uyOat2Wu4ZngXulTXiWcPUhJiufnEnnyxZANvz1lT+/GlD4Xjfw/z34Jp/9zjXZfmVOoaVFLiv81VfSkRkZDr2zGR737cUvXLig7B5cGqM7Xfsrfu4tbX59KnfSJTfzOSj248lpuLn6RV1odw4j0MPPkKSpzj+W9WVnzggIt8bZ8F7+5x/3NXb+Hfny9j/MCOHNs9ueodNiz2QakG2hTm0NQWxMdE+qVp1el+gi9k/vFvqy0bMGlhDod1TKT1vAmwcxOMvL2eR1y/qsuY+mZ5Lu0TY/0KjaJ8BqcnUVBcwtb5n0KL9r5rMvjPI3kbYOPS/XrOmVmbaBkXVbGbL/i/54aFqi8lYalLcjzdU+K1nK8cBaaagm6j/PXRN8Alb0GLtnu+v9RKxYypncREBsqKNDZPgYJgK+swzZjaVyktYrn/3P4sztnOlc/OIC0pjp+NrF2K9YVDOtOvYyJ/encBW/L23I55j47+ORwyDj7+P8h8wp88llQtrF7azjqj9ORn62oo3NFglxiIiDQlfTsksn5bPtlbK3X2atcHIqJ9N1bZZyUljl+/ModdhcU8cF5/OiXFwZS/wYwJcMyNcOS1dEqKY9QhKbyQubJiQ5LOQ6Fl5xozhQAKikq4+dU5tImP5rfjelU3AMj+vkEWPi8VGRFgYOdWNRdAN4PT/+WX+792RYUlfbk7Cvh21WbGdI2Frx6Bnif7VQxhrHLGlHOO6ctzGZSRhEXGQNFOBqUnEbASmq35yi/jK81mSqtdnamZKzdxeForn3Vf3upgXTl15JMwNaZ3OzJX5LJpR0Goh9IgKDDVFKQPhVtXwei7ICIy1KNptJJbxLC+tMbU1nxSE2PL3kRLC6BDow9MARzbPZmfjehKiYM/nNab2KiIWu0nImDcfUZfNucVcPkz09meX1S7AZnB6f/0mU/v/xr+eSTckwZPnQQf3QHzXoNt61i2fgftE2NpHhP8f7K78LkypkREQq3GAuiRMb6m5moVQN8fz3y9gqmLN3DHyb18pvCMp2Dy3XDYBb6BSNAlR6WzYXsBH8wtt+QkEID+F/qOdJuyqt3/I5OW8MO6bdx9Rt+q3dQApj8JuUvh0FPq9HXVtcEZSSzK3k5uTR8e41PgtH9C9jz49A+7N09dvB7n4Mxdb/jssjDPlgKIjvAfHUszprI25pGzLZ/BGUkQGQtF+STGRTG6zSbiCjf5wFSp1t0htiWs3PfA1Oa8ApbkbK9+Gd/q6WCBss6cImFmTO92FJc4/rdAWVOgwFTTEZsQ6hE0eikJMWzLLyKvoIh1W3bSNiG27MbSwFRsYpP5t7h5TE++uvU4RvZM2fud96BPh0QeOn8A367azGUTMmsfnIpNhGu+gOsy4fTHoP8FUJTvM6he/Snc15NrFl/JjTHv+Iwq58oFplRjSkQk1HqlJhIwmLt6c9UbOx4Ba2dDcS3fI5qYxdnbuOeDHxjZM5mLhqT5JXnv/dI3NDn1oQo1e4Z2a0OXNs155usVFXfS/3zAYM4LVfb//Zot/HPSEs4Y0KFqbUqADUvgk9/5mkv9L6jT11bXhmQkATC9pjpTAD1Gw+CrfcmAxf8DYNIPOXSN20XbBU/7Dtjt+hyE0davQMCIjgyQX+QDU6WZZEMykiAq1tfbAk5J8Mv1CtOOKf9gv5xv1b4XQP92lf+/fnhaDYXPU3qpOY2ErT4dEmifGKvlfEEKTInUkZQWPhCVszWftVt2ldWXAl/8HBpffak9MDPat2xWJ/s6qW8qD9dFcCoQ8EGm/ufDSffClZ/Cbavhqsm4kb8lv7CYc7Y+5TOqHhoAM5/2/3ZxSXXyOkREpPaaRUfQPaVF1Ywp8HWmCvO0nG8fFBSV8IsXZ9M8JpK/nt0Py/oKXrsc2h8O45+uUu8pEDAuPqoz367czHflg4It06DLCPh2ol+WF1RYXMLNr3xHy7hofn9KNUv4iot83dPIGDj14QZfuLpvx0RiIgM1L+crdcIffaDkzWsp3pbDlEXruaPlx1jBDhhx28EZ7EEQExlgV6FfyvfN8lySmkf7jLvI2N1LGQeWzGVFSVu+z2tZ8cFpQ2DDIsjby98yaFbWJiICxmGdEiveUFLiMyQ7ahmfhC8zY3TvdkxdvJ68An2ponVdInUkpUUM4AuJ5mzNp11iuaBMfPDbwiawjK++nNQ3FQOuf+FbLp2QyTM/HUx8TB0cwiKjof0AcuIP5ZQPenHvmGTGx8+Fhe/D8s99UVMREWkQ+nRIZMqiHJxzFWvOdBvl32vf/BlcNclnye5J/jb47M8+KJLc0y/ZTu65146xNSougryNsCMHdqyH7ev9dcEO35G3pMhfioPXgQif6REdDzHxEJPgf45L8ucK8e3qrfzCPz5ZxPy1W3n84oGkbFsAz5/rg0wXvAzRzat9zFkDO3LvRwt59uss/j6+XLBhwEU+qLXicx+kAh6bvJT5a7fy2EUDaRkXXXVnXz7gA4hn/cd3z23gYiIjGJDWcu+BqahY/5oeH8HmF64gMu8chtmb0Hc8pDSekgCxURFlGVMrNjIoPVj/KTKYMVVSTErudF4qGcj25bn071RuvnQqrTOVCT1P3OtzzczaxKGpLYiLrvR/YcNCvzwyjDscigCM7t2Wp79awZSF6xnbt+EfD+uTAlMidSQlwQemFmZvo6C4hHbB34GypXwtO4VgZI3H2L6pPALcUNfBKcoKn6d27ALdB8Ogy/0HikDD7BQkItIU9euYyGuzVrMqdydprePKbohLgvHPwDPj4I1r4NyJPku2OkX58OKFsGJqMMsjr+y2+HY+QNUq3QeIEjr468SOkNDedwJbv9B3k9uw0Gd/bFgM29YBrvrnswAEIoOXKB+UKinywbEaHxPhn6/0uVul+2yctr0hqWutg1bfLNvIvz9fynmDOjE6eTM8dSbEtfLNcZq3rvFxCbFRnHl4B16esZrbTzqUpObBgNMh43wQ8NvnoMsIFq7bxkOfLWZcv1RO7NOu6o7WzYXJ90DvM6Dv2bV6DaEwOKM1j3y2mM15BdUH20q17UXJ6Lto/cHNvNJsCRElhTDi1oM30IMgJjJAfmExa7fsZFXuTi47OsPfUBqYWjuHQP5WljQ/nKzluVw5rEvZgzsc7v8PrJq218CJQQjGAAAXeUlEQVRUUXEJs1dtZvzAar7UXZXpr9WRT8Lc4PQkWsVF8dH36xSYCvUARBqL0qV8c1b5JQYVMqZ2L+VTxtSBGts3lYcpC049/ZNBtIg98ODR0vU7AOiaUu7b4hq+ORYRkdA47pAU/vz+Av760Q88ekGlDmedj4LRf4YPb4Ev7oNhN1fdQUkxvH6lL9p9+mPQ71zYssoHm9YvCF7/AAs/8NlPexKT4Lu2dhnpM47ik/37ffMUfx2fDNEtag6QOeeDYvnbIH+77967YwNsWV3xsioT5r0OLtgVLyLGB8/a9oZ2ff2H89TDfAbwHmzdVcgvX55DWlIcvxvaHP57sl+2d/GbPgi2F5cclc5z01by0vRVXDuiq98YFQt9z4FZz7JxeDa/emURLWKj+MOpvavuoCgfXr/aBxFP/sden68hObF3Ox75bDF//3ghd53ed4/3fT1iLC2Ln+f4iG99RlnrrgdplAdHacZUhfpS4Jdm7trss80Blz6U6YtyKSlxBALB7MaoZn6urtx7namF2dvIKyjm8GoLn2dCs6RG97eVpicyIsC/Lz6CbinxoR5KyCkwJVJHWsVFERVhu+svtCtfY6pVBgy/BfqcFaLRNS5j+6byiMH1z3/Lr1+Zw2MXDazaRng/Lc3ZTlx0BO3KF60XEZEGpVNSHDeM7MZ9nyzi7MNzGHlIpQYbQ672y8Q++zO0H1BxObZzvsD3/LdgzN3B4t1Aq87+0mN0xX0V7oJta4IBoh9h62rfVSy5pw9Ixbc9sPpIZv4LkOjmsLf6zYW7fHZWznzf/S17PiybXFZ4PDLW14jqNBjSjvRFpsvVR3TOcccb81i3dRevX9yVuBfPhMKd8JP39/nDfY+2LTiySxLPTcviqmFdiCgNNgy4EKY/wb8fvZdFhaN45PwBtI6PqbqDyX+BnO/9ksEwq93Yq30Clx6dztNfreCMAR0Y2Ln68e8qLOa+TxbRLflmRnWfig298SCPtP6V1pjKXJ5LfEwkh6YGm/pENYPt2T4wlXwIvXr0YMKcOSzK2cYh7co1/kk70ndkLCrYYzB1VtYmoIbC56um+/pSDbw+mci+GJwRXsfD+qLAlEgdMTNSWsSypHRJWPnAVCDQKNoENyQn9knl1rE7ueu9BTz7dRaXHp1+QPtbtmEHXZPjDzjAJSIi9evq4V15a84afvvmPD755bCK9WfM4JQHfeDmtSvgqik+6ATw2Z98U4uhv4Sjrtv7E0XFQlIXfwm1qFhI7ecv5W3L9suiVn7ju519/Yiv4YT5zJSux0HX43htfQfembOGO0akcNhnl/rMrEve9llX++HSo9K5duIsPl2Qzeje7diSV8jvpziuLkljfNRkzr7h9/RoW02UbeU38OWDMOBi6DGm9n+HEPrV6J58OG8dt70+l3dvOJboyKqZcBO+XM7aLbu4/9wjsS4nh2CU9a98xtTAzq3KApSRMbBrK+QugwEXlXUzXJ5bMTDVaYifp2vnQKeai5fPzNpESosYOraq1Ehn5ya/jLbf+Lp+aSISQurKJ1KHklvE4BxEBIw21X1bKHXq8qEZflnHewuYV12Xpv2wNGc7XZK1dE9EpKGLjgxw9xl9+XHzTh743+Jq7tAczv2v79z10kU+M+jrR2HqfXD4pTDqdwd/0PWlRVvodRqceHdZp9nL3vdd4KKa+WDQM+MY+96RvJb4IFesuMkHDs5/AToO3O+nO6FXW1ITY3n26ywmL8xh9ANTeHfuOnK6jqd78WJ6uKyKDygpgXmvwSuXQUJHn6kWpuJjIvnTaX1YlL2dJ6Yuq3J77o4C/jVpKccfmsKRXWqu1xXuYiIDrN2yk8U52ytmekTGwpaVfnlqxjA6tmpGamIs31QuGp9WWgB92h6fZ+bKTQzs3KrqF4arZ/pr1ZcSaVQUmBKpQ6Wd+VJaxJR9gyT1xsz4+/jDSGoezfXPz2J7fu1are4sKObHzTt9u2MREWnwBmckcd6gTvzni+V8v6aaLyZad4WznoB137Hs3hHw0e3kdx8H4+5v3Mt/oppB+jEw4hb46Yfs+tVS/tD8t7xrIzisWQ6W8wOc8wxkDKvV7iMjAlw4JI0vlmzgsqemkxAbxRs/O4bhZ1/ni1rPnujv6Bws+gj+PQxe/Sk0awnnPguxCXt+ggbu+F5tGdunHQ9+upjlG3ZUuO2hTxezo6CIW05sPB34qhMbFbG7LueQyoEpAAw6H4OZMSg9iczluThXrsh/fIovcbGy5sBUzrZdrMrdycCa6ktZADrsf2BVRBouBaZE6lBpZ74K9aWkXiU1j+ah8wewMjePO96YW/HkZx8t2+CXXyowJSISPm4beyit4qK4/fW5FJdUPPY755i46RAeLj6LLgU/8EVxbw6ffy43vzaPBWu3hmjEB989n63hqY29aHPuw0TeONtnVPUce0D7PG9wGt1T4rlqWBfeuWEofTsm+o5+h5wE370ESyfBhBPh+XOgYDuc+SRc84Wv+dUI3Hlqb2IiAhXOOVZs2MFz07I4d1Aa3atbytiIxASXMEZHBvy/fanSwFRqv901xAZnJJGzLZ+sjXkVd5J2pF96WsM526wsX691QLX1pTIhpTfE6JxNpDEJ+8CUmZ1oZgvNbImZNa5+rBJ2SjvzqYD2wTU4I4mbju/BW7PX8MqM1fv9+GXVdeQTEZEGLTEuiv8b14s5q7fw3LSyJWQ7C4r51StzuOONecxMv4rtZz5H6jVvcsagDN75bg1jH5zKBU9M49MF2VUCWo3J/+Zn8/RXK/jJMemMOrSt3xh14OcnbeJj+OSXw7n9pEOJjYoou2HAxZC3Ef57OmzOgnEPwPXTfS2gQETNOwwzbRNi+c3YQ/hq6UZen/UjAPd+tJCoiAA3Hd89xKOrf6WBqQGdWhITWe7ftXRulcvGK82oylxRaTlfp8GwY71fVlqNWSs3ER0RoE+HShl2JSXw48w91qYSkfAU1sXPzSwCeBQ4AVgNTDezt51z80M7MmmqSpfyKWPq4PvZyG5MW76R3709j/5pLasvvlqDpeu3YwbprRWYEhEJJ6ce1p5XZ67m3o8WMqZ3O/KLirn6vzNZmL2NX4zqzi9GdScQMOKBuzqk8OvRPXkhcxXPfLWCy5+ZQZv4GE7q245x/dpzROdWZW3tw9y6Lbu4+dU59EpN4NaxB2lpWdfjfHAquScMusIvK2ykLhycxhuzVnPXe/Np1TyK9+au5eejupPSBL6YLA1GDqncSaw0Yyq9LDDVLSWepObRZC7P5ZwjOpXdt1NpnanMartCzszaRN+OiRUDX+CLnudvVX0pkUYorANTwGBgiXNuGYCZvQicBigwJSGxeylfEzgxaWgiAsb95/bnpAencv3zs5hw2SCiIvYtKXTB2q10bNWs4je/IiLS4JkZfz69LyfcP4VrnpvJ0vXbCZgx4bJBjOyZUuX+LeOiuXZEV644NoNP5mfzzpw1vDR9Fc9+nUXbhBhO6pvKyX1T6ZQUF4JXUzecg5temk1+UQkPXzCg6of7+hKIgNMeOTjPFWKBgPGXM/tx8kNTufLZmbSJj+GqYQ2ge+NBUJoxNTijUoH3VukQ1xo6H7V7k68z1Ypvlm8ke+uusvvGppMck8iupV+yrcsZFXZTVOKYu3oLlx2TXvXJV2X6604KTIk0NuEemOoArCr3+2pgSIjGIkKHlv5ENpxPaMNZSotY7j+3P5dMyGToXyft12OPO6TqBxgREWn40lrH8Yvju/O3DxfSp0MC/7pw4F7fh6MiApzUN5WT+qayI7+I/y3I5r3v1jLxm5U89eWKgzPwenbv2f1UO7Ee9WzXgquHd+HRSUu58fjuxMeE+8eqfZPQLIroiAAD0lpWvKHfOdD7DIiIqrB5SEZrPvo+myF3f1ph+1NR6bSfM4kx0ytuL1Vj4fNmSZDUNIKAIk2J1aZQcENhZuOBMc65K4K/XwwMds7dUOl+VwFXAaSlpQ3Mysqqsi+RuvL5ovUc3bU1kfuYrSN175tlG3d3jNlXx3RrTWct5RMRCUvFJY4pi3I4umubA8p+3barkM8XbWDLzsI6HN3Bl5oYy4ieyVhj7kDYABQWlzB9eS5HdmndaJaB7k3ujgKyNu6ovjB5NfIKinjvu7UUFlf8zNl7yeMctvhhZvf4OTuatScvNpUdzdqzM6YNsbHRnNKvfdVz6UcG+8ysC1+uo1cjInXNzGY6547Y78eFeWDqKOBO59yY4O+3ATjn/lLTY4444gg3Y8aMgzRCERERERERqSD7e3j2dNiRU3G7RUCLdr5GWUSMz8CKjIGIaFgxFY77LQy7OTRjFpG9qm1gKtxzTqcD3c0sA/gROA+4ILRDEhERERERkRq17Q03L4b87bBldfCyyl9vXQNFu6C4AIoLoTjfX3ceCr1OD/XIRaQehHVgyjlXZGbXAx8BEcAE59z3IR6WiIiIiIiI7E1MPKQc4i8i0mSFdWAKwDn3PvB+qMchIiIiIiIiIiL7R9WZRUREREREREQkJBSYEhERERERERGRkFBgSkREREREREREQkKBKRERERERERERCQkFpkREREREREREJCQUmBIRERERERERkZBQYEpEREREREREREJCgSkREREREREREQkJBaZERERERERERCQkFJgSEREREREREZGQUGBKRERERERERERCQoEpEREREREREREJCQWmREREREREREQkJBSYEhERERERERGRkFBgSkREREREREREQkKBKRERERERERERCQkFpkREREREREREJCTMORfqMRxUZrYeyAr1OOpAG2BDqAchjZbml9QnzS+pT5pfUp80v6Q+aX5JfdL8kvpSfm51ds4l7+8OmlxgqrEwsxnOuSNCPQ5pnDS/pD5pfkl90vyS+qT5JfVJ80vqk+aX1Je6mFtayiciIiIiIiIiIiGhwJSIiIiIiIiIiISEAlPh6/FQD0AaNc0vqU+aX1KfNL+kPml+SX3S/JL6pPkl9eWA55ZqTImIiIiIiIiISEgoY0pEREREREREREJCgSkREREREREREQkJBabCkJmdaGYLzWyJmd0a6vFIeDOzTmY2ycwWmNn3ZvaL4PY7zexHM5sdvJwU6rFKeDKzFWY2NziPZgS3JZnZJ2a2OHjdKtTjlPBiZj3LHZ9mm9lWM7tRxy45EGY2wcxyzGxeuW3VHq/Meyh4PvadmR0eupFLQ1fD3LrXzH4Izp83zKxlcHu6me0sdxx7LHQjl3BQw/yq8f3QzG4LHrsWmtmY0IxawkUN8+ulcnNrhZnNDm6v1fFLNabCjJlFAIuAE4DVwHTgfOfc/JAOTMKWmaUCqc65WWbWApgJnA6cA2x3zv09pAOUsGdmK4AjnHMbym37G5DrnLsnGGBv5Zy7JVRjlPAWfG/8ERgC/AQdu6SWzGwYsB141jnXJ7it2uNV8EPeDcBJ+Ln3oHNuSKjGLg1bDXNrNPCZc67IzP4KEJxb6cC7pfcT2Zsa5tedVPN+aGa9gBeAwUB74H9AD+dc8UEdtISN6uZXpdvvA7Y45/5Y2+OXMqbCz2BgiXNumXOuAHgROC3EY5Iw5pxb65ybFfx5G7AA6BDaUUkTcBrwTPDnZ/DBUJHaGgUsdc5lhXogEt6cc58DuZU213S8Og1/ku6cc9OAlsEve0SqqG5uOec+ds4VBX+dBnQ86AOTRqGGY1dNTgNedM7lO+eWA0vwnzFFqrWn+WVmhk9oeOFAnkOBqfDTAVhV7vfVKIggdSQY4R4AfBPcdH0wvXyCllrJAXDAx2Y208yuCm5r65xbCz44CqSEbHTSGJxHxRMiHbukLtV0vNI5mdSlnwIflPs9w8y+NbMpZnZsqAYlYa+690Mdu6QuHQtkO+cWl9u238cvBabCj1WzTesx5YCZWTzwGnCjc24r8C+gK9AfWAvcF8LhSXg7xjl3ODAWuC6YDixSJ8wsGjgVeCW4SccuOVh0TiZ1wszuAIqAicFNa4E059wA4JfA82aWEKrxSdiq6f1Qxy6pS+dT8cvBWh2/FJgKP6uBTuV+7wisCdFYpJEwsyh8UGqic+51AOdctnOu2DlXAjyBUnyllpxza4LXOcAb+LmUXbrkJXidE7oRSpgbC8xyzmWDjl1SL2o6XumcTA6YmV0KjAMudMHiv8ElVhuDP88ElgI9QjdKCUd7eD/UsUvqhJlFAmcCL5Vuq+3xS4Gp8DMd6G5mGcFvic8D3g7xmCSMBdcF/wdY4Jz7R7nt5etknAHMq/xYkb0xs+bBovqYWXNgNH4uvQ1cGrzbpcBboRmhNAIVvqnTsUvqQU3Hq7eBS4Ld+Y7EF35dG4oBSngysxOBW4BTnXN55bYnB5s6YGZdgO7AstCMUsLVHt4P3wbOM7MYM8vAz6/Mgz0+aRSOB35wzq0u3VDb41dkvQ1R6kWwa8f1wEdABDDBOfd9iIcl4e0Y4GJgbmmbT+B24Hwz649P7V0BXB2a4UmYawu84eOfRALPO+c+NLPpwMtmdjmwEhgfwjFKmDKzOHyX2vLHp7/p2CW1ZWYvACOANma2Gvg9cA/VH6/ex3fkWwLk4TtCilSrhrl1GxADfBJ8n5zmnLsGGAb80cyKgGLgGufcvha2liaohvk1orr3Q+fc92b2MjAfv4T0OnXkkz2pbn455/5D1RqfUMvjlwUzRkVERERERERERA4qLeUTEREREREREZGQUGBKRERERERERERCQoEpEREREREREREJCQWmREREREREREQkJBSYEhERERERERGRkFBgSkRERJokM3Nmdl+5339tZnfW0b6fNrOz62Jfe3me8Wa2wMwmVdo+wszere/nFxERETlQCkyJiIhIU5UPnGlmbUI9kPLMLGI/7n458DPn3Mj6Gk95ZhZ5MJ5HREREmg4FpkRERKSpKgIeB26qfEPljCcz2x68HmFmU8zsZTNbZGb3mNmFZpZpZnPNrGu53RxvZlOD9xsXfHyEmd1rZtPN7Dszu7rcfieZ2fPA3GrGc35w//PM7K/Bbb8DhgKPmdm91by+eDN71cx+MLOJZmbBx40ys2+D+5tgZjHB7StKg3RmdoSZTQ7+fKeZPW5mHwPPmlnv4OudHXwN3ffz7y4iIiKym771EhERkabsUeA7M/vbfjzmMOBQIBdYBjzpnBtsZr8AbgBuDN4vHRgOdAUmmVk34BJgi3NuUDAg9GUw4AMwGOjjnFte/snMrD3wV2AgsAn42MxOd8790cyOA37tnJtRzTgHAL2BNcCXwDFmNgN4GhjlnFtkZs8C1wIP7OU1DwSGOud2mtnDwIPOuYlmFg3sT4aXiIiISAXKmBIREZEmyzm3FXgW+Pl+PGy6c26tcy4fWAqUBpbm4oNRpV52zpU45xbjA1iHAKOBS8xsNvAN0BoozTjKrByUChoETHbOrXfOFQETgWH7MM5M59xq51wJMDs4tp7AcufcouB9ntnHfb3tnNsZ/Plr4HYzuwXoXG67iIiIyH5TYEpERESaugfwtZqal9tWRPA8KbgELrrcbfnlfi4p93sJFbPRXaXncYABNzjn+gcvGc650sDWjhrGZ/v6QiopP87i4Nj2tK/drxmIrXTb7rE5554HTgV2Ah8Fs7ZEREREakWBKREREWnSnHO5wMv44FSpFfjlawCnAVG12PV4MwsE6051ARYCHwHXmlkUgJn1MLPme9oJPrNquJm1CRZGPx+YUovxAPwApAeXFQJcXG5fKyh7zWfVtAMz6wIsc849BLwN9KvlWEREREQUmBIREREB7gPKd+d7Ah8MygSGUHM2054sxAd9PgCucc7tAp4E5gOzzGwe8G/2UvPTObcWuA2YBMwBZjnn3qrFeAiO4SfAK2Y2F5/l9Vjw5j8AD5rZVHyGVU3OBeYFlyMegl8KKSIiIlIr5lzlLHMREREREREREZH6p4wpEREREREREREJCQWmREREREREREQkJBSYEhERERERERGRkFBgSkREREREREREQkKBKRERERERERERCQkFpkREREREREREJCQUmBIRERERERERkZD4fy9pxLYT2QxsAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1440x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# line plot of observed vs predicted\n",
    "pyplot.figure(figsize=(20,8))\n",
    "pyplot.plot(expectations_plot, label=\"True\")\n",
    "pyplot.plot(predictions_plot, label=\"Predicted\")\n",
    "pyplot.legend(loc='upper right')\n",
    "pyplot.xlabel(\"Number of hours\")\n",
    "pyplot.ylabel(\"Power generated by system (kW)\")\n",
    "pyplot.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}