RUL Prediction using LSTM Lookback=1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Run to Failure degradation simulation of NASA Turbo Jet Engine Fans"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# <a id='index'>Index</a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "+ <a href='#data_analysis'>1. Data Analysis</a>\n",
    "    + <a href='#info_about_data'>1.1 Info about data:</a>\n",
    "+ <a href='#noise_removal'>2. Noise removal and Normalization</a>\n",
    "+ <a href='#training_and_validation'>3. Training LSTM Model to predict RUL</a>\n",
    "+ <a href='#testing_var'>4. Testing VAR</a>\n",
    "+ <a href='#health_score'>5 Health Score Assignment</a>\n",
    "+ <a href='#pred_analysis'>6. Analysing Prediction</a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import math\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import cm\n",
    "\n",
    "import statsmodels.api as sm\n",
    "from statsmodels.tsa.stattools import acf, pacf\n",
    "from statsmodels.tsa.stattools import adfuller\n",
    "from statsmodels.tsa.arima_model import ARIMA\n",
    "from statsmodels.tsa.vector_ar.var_model import VAR\n",
    "\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.model_selection import train_test_split, cross_val_score\n",
    "from sklearn.preprocessing import QuantileTransformer , PowerTransformer\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.metrics import mean_absolute_error\n",
    "from sklearn.metrics import r2_score\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.model_selection import KFold\n",
    "\n",
    "from keras import optimizers\n",
    "from keras.models import Sequential\n",
    "from keras.layers import TimeDistributed, Flatten\n",
    "from keras.layers.core import Dense, Dropout, Activation\n",
    "from keras.layers.recurrent import LSTM\n",
    "from sklearn.metrics import mean_squared_error\n",
    "\n",
    "import keras\n",
    "from keras.models import Sequential,Input,Model\n",
    "from keras.layers import Dense, Dropout, Flatten\n",
    "from keras.layers import Conv2D, MaxPooling2D\n",
    "from keras.layers.normalization import BatchNormalization\n",
    "from keras.layers.advanced_activations import LeakyReLU\n",
    "\n",
    "import warnings \n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "%matplotlib inline\n",
    "cmap = cm.get_cmap('Spectral') # Colour map (there are many others)\n",
    "\n",
    "from sklearn.metrics import mean_absolute_error\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.metrics import explained_variance_score\n",
    "from sklearn.metrics import r2_score\n",
    "\n",
    "from keras.models import load_model\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# <a id='data_analysis'>1. Data analysis</a>\n",
    "<a href='#index'>Go back to Index</a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
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       "      <th>25</th>\n",
       "      <th>26</th>\n",
       "      <th>27</th>\n",
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       "      <td>-0.0007</td>\n",
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       "      <td>100.0</td>\n",
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       "      <td>641.82</td>\n",
       "      <td>1589.70</td>\n",
       "      <td>1400.60</td>\n",
       "      <td>14.62</td>\n",
       "      <td>...</td>\n",
       "      <td>8138.62</td>\n",
       "      <td>8.4195</td>\n",
       "      <td>0.03</td>\n",
       "      <td>392</td>\n",
       "      <td>2388</td>\n",
       "      <td>100.0</td>\n",
       "      <td>39.06</td>\n",
       "      <td>23.4190</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0019</td>\n",
       "      <td>-0.0003</td>\n",
       "      <td>100.0</td>\n",
       "      <td>518.67</td>\n",
       "      <td>642.15</td>\n",
       "      <td>1591.82</td>\n",
       "      <td>1403.14</td>\n",
       "      <td>14.62</td>\n",
       "      <td>...</td>\n",
       "      <td>8131.49</td>\n",
       "      <td>8.4318</td>\n",
       "      <td>0.03</td>\n",
       "      <td>392</td>\n",
       "      <td>2388</td>\n",
       "      <td>100.0</td>\n",
       "      <td>39.00</td>\n",
       "      <td>23.4236</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
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       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>-0.0043</td>\n",
       "      <td>0.0003</td>\n",
       "      <td>100.0</td>\n",
       "      <td>518.67</td>\n",
       "      <td>642.35</td>\n",
       "      <td>1587.99</td>\n",
       "      <td>1404.20</td>\n",
       "      <td>14.62</td>\n",
       "      <td>...</td>\n",
       "      <td>8133.23</td>\n",
       "      <td>8.4178</td>\n",
       "      <td>0.03</td>\n",
       "      <td>390</td>\n",
       "      <td>2388</td>\n",
       "      <td>100.0</td>\n",
       "      <td>38.95</td>\n",
       "      <td>23.3442</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0.0007</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>100.0</td>\n",
       "      <td>518.67</td>\n",
       "      <td>642.35</td>\n",
       "      <td>1582.79</td>\n",
       "      <td>1401.87</td>\n",
       "      <td>14.62</td>\n",
       "      <td>...</td>\n",
       "      <td>8133.83</td>\n",
       "      <td>8.3682</td>\n",
       "      <td>0.03</td>\n",
       "      <td>392</td>\n",
       "      <td>2388</td>\n",
       "      <td>100.0</td>\n",
       "      <td>38.88</td>\n",
       "      <td>23.3739</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>-0.0002</td>\n",
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       "      <td>642.37</td>\n",
       "      <td>1582.85</td>\n",
       "      <td>1406.22</td>\n",
       "      <td>14.62</td>\n",
       "      <td>...</td>\n",
       "      <td>8133.80</td>\n",
       "      <td>8.4294</td>\n",
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       "      <td>100.0</td>\n",
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       "      <td>23.4044</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "   0   1       2       3      4       5       6        7        8      9   \\\n",
       "0   1   1 -0.0007 -0.0004  100.0  518.67  641.82  1589.70  1400.60  14.62   \n",
       "1   1   2  0.0019 -0.0003  100.0  518.67  642.15  1591.82  1403.14  14.62   \n",
       "2   1   3 -0.0043  0.0003  100.0  518.67  642.35  1587.99  1404.20  14.62   \n",
       "3   1   4  0.0007  0.0000  100.0  518.67  642.35  1582.79  1401.87  14.62   \n",
       "4   1   5 -0.0019 -0.0002  100.0  518.67  642.37  1582.85  1406.22  14.62   \n",
       "\n",
       "   ...       18      19    20   21    22     23     24       25  26  27  \n",
       "0  ...  8138.62  8.4195  0.03  392  2388  100.0  39.06  23.4190 NaN NaN  \n",
       "1  ...  8131.49  8.4318  0.03  392  2388  100.0  39.00  23.4236 NaN NaN  \n",
       "2  ...  8133.23  8.4178  0.03  390  2388  100.0  38.95  23.3442 NaN NaN  \n",
       "3  ...  8133.83  8.3682  0.03  392  2388  100.0  38.88  23.3739 NaN NaN  \n",
       "4  ...  8133.80  8.4294  0.03  393  2388  100.0  38.90  23.4044 NaN NaN  \n",
       "\n",
       "[5 rows x 28 columns]"
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     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_file = \"train_FD001.txt\" \n",
    "test_file = \"test_FD001.txt\"\n",
    "RUL_file = \"RUL_FD001.txt\"\n",
    "\n",
    "df = pd.read_csv(train_file,sep=\" \",header=None)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Section-0</th>\n",
       "      <th>Section-1</th>\n",
       "      <th>Section-2</th>\n",
       "      <th>Section-3</th>\n",
       "      <th>Section-4</th>\n",
       "      <th>Section-5</th>\n",
       "      <th>Section-6</th>\n",
       "      <th>Section-7</th>\n",
       "      <th>Section-8</th>\n",
       "      <th>Section-9</th>\n",
       "      <th>...</th>\n",
       "      <th>Section-16</th>\n",
       "      <th>Section-17</th>\n",
       "      <th>Section-18</th>\n",
       "      <th>Section-19</th>\n",
       "      <th>Section-20</th>\n",
       "      <th>Section-21</th>\n",
       "      <th>Section-22</th>\n",
       "      <th>Section-23</th>\n",
       "      <th>Section-24</th>\n",
       "      <th>Section-25</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.0007</td>\n",
       "      <td>-0.0004</td>\n",
       "      <td>100.0</td>\n",
       "      <td>518.67</td>\n",
       "      <td>641.82</td>\n",
       "      <td>1589.70</td>\n",
       "      <td>1400.60</td>\n",
       "      <td>14.62</td>\n",
       "      <td>...</td>\n",
       "      <td>521.66</td>\n",
       "      <td>2388.02</td>\n",
       "      <td>8138.62</td>\n",
       "      <td>8.4195</td>\n",
       "      <td>0.03</td>\n",
       "      <td>392</td>\n",
       "      <td>2388</td>\n",
       "      <td>100.0</td>\n",
       "      <td>39.06</td>\n",
       "      <td>23.4190</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0019</td>\n",
       "      <td>-0.0003</td>\n",
       "      <td>100.0</td>\n",
       "      <td>518.67</td>\n",
       "      <td>642.15</td>\n",
       "      <td>1591.82</td>\n",
       "      <td>1403.14</td>\n",
       "      <td>14.62</td>\n",
       "      <td>...</td>\n",
       "      <td>522.28</td>\n",
       "      <td>2388.07</td>\n",
       "      <td>8131.49</td>\n",
       "      <td>8.4318</td>\n",
       "      <td>0.03</td>\n",
       "      <td>392</td>\n",
       "      <td>2388</td>\n",
       "      <td>100.0</td>\n",
       "      <td>39.00</td>\n",
       "      <td>23.4236</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>-0.0043</td>\n",
       "      <td>0.0003</td>\n",
       "      <td>100.0</td>\n",
       "      <td>518.67</td>\n",
       "      <td>642.35</td>\n",
       "      <td>1587.99</td>\n",
       "      <td>1404.20</td>\n",
       "      <td>14.62</td>\n",
       "      <td>...</td>\n",
       "      <td>522.42</td>\n",
       "      <td>2388.03</td>\n",
       "      <td>8133.23</td>\n",
       "      <td>8.4178</td>\n",
       "      <td>0.03</td>\n",
       "      <td>390</td>\n",
       "      <td>2388</td>\n",
       "      <td>100.0</td>\n",
       "      <td>38.95</td>\n",
       "      <td>23.3442</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0.0007</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>100.0</td>\n",
       "      <td>518.67</td>\n",
       "      <td>642.35</td>\n",
       "      <td>1582.79</td>\n",
       "      <td>1401.87</td>\n",
       "      <td>14.62</td>\n",
       "      <td>...</td>\n",
       "      <td>522.86</td>\n",
       "      <td>2388.08</td>\n",
       "      <td>8133.83</td>\n",
       "      <td>8.3682</td>\n",
       "      <td>0.03</td>\n",
       "      <td>392</td>\n",
       "      <td>2388</td>\n",
       "      <td>100.0</td>\n",
       "      <td>38.88</td>\n",
       "      <td>23.3739</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>-0.0019</td>\n",
       "      <td>-0.0002</td>\n",
       "      <td>100.0</td>\n",
       "      <td>518.67</td>\n",
       "      <td>642.37</td>\n",
       "      <td>1582.85</td>\n",
       "      <td>1406.22</td>\n",
       "      <td>14.62</td>\n",
       "      <td>...</td>\n",
       "      <td>522.19</td>\n",
       "      <td>2388.04</td>\n",
       "      <td>8133.80</td>\n",
       "      <td>8.4294</td>\n",
       "      <td>0.03</td>\n",
       "      <td>393</td>\n",
       "      <td>2388</td>\n",
       "      <td>100.0</td>\n",
       "      <td>38.90</td>\n",
       "      <td>23.4044</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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       "<p>5 rows × 26 columns</p>\n",
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      "text/plain": [
       "   Section-0  Section-1  Section-2  Section-3  Section-4  Section-5  \\\n",
       "0          1          1    -0.0007    -0.0004      100.0     518.67   \n",
       "1          1          2     0.0019    -0.0003      100.0     518.67   \n",
       "2          1          3    -0.0043     0.0003      100.0     518.67   \n",
       "3          1          4     0.0007     0.0000      100.0     518.67   \n",
       "4          1          5    -0.0019    -0.0002      100.0     518.67   \n",
       "\n",
       "   Section-6  Section-7  Section-8  Section-9  ...  Section-16  Section-17  \\\n",
       "0     641.82    1589.70    1400.60      14.62  ...      521.66     2388.02   \n",
       "1     642.15    1591.82    1403.14      14.62  ...      522.28     2388.07   \n",
       "2     642.35    1587.99    1404.20      14.62  ...      522.42     2388.03   \n",
       "3     642.35    1582.79    1401.87      14.62  ...      522.86     2388.08   \n",
       "4     642.37    1582.85    1406.22      14.62  ...      522.19     2388.04   \n",
       "\n",
       "   Section-18  Section-19  Section-20  Section-21  Section-22  Section-23  \\\n",
       "0     8138.62      8.4195        0.03         392        2388       100.0   \n",
       "1     8131.49      8.4318        0.03         392        2388       100.0   \n",
       "2     8133.23      8.4178        0.03         390        2388       100.0   \n",
       "3     8133.83      8.3682        0.03         392        2388       100.0   \n",
       "4     8133.80      8.4294        0.03         393        2388       100.0   \n",
       "\n",
       "   Section-24  Section-25  \n",
       "0       39.06     23.4190  \n",
       "1       39.00     23.4236  \n",
       "2       38.95     23.3442  \n",
       "3       38.88     23.3739  \n",
       "4       38.90     23.4044  \n",
       "\n",
       "[5 rows x 26 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#columns = ['unit_number','time_in_cycles','setting_1','setting_2','TRA','T2','T24','T30','T50','P2','P15','P30','Nf',\n",
    "#           'Nc','epr','Ps3 0','phi','NRf','NRc','BPR','farB','htBleed','Nf_dmd','PCNfR_dmd','W31','W32' ]\n",
    "#delete NaN values\n",
    "df.drop(columns=[26,27],inplace=True)\n",
    "columns = [\"Section-{}\".format(i)  for i in range(26)]\n",
    "df.columns = columns\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Dataset statistics  for each parameter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
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       "\n",
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       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Section-0</th>\n",
       "      <th>Section-1</th>\n",
       "      <th>Section-2</th>\n",
       "      <th>Section-3</th>\n",
       "      <th>Section-4</th>\n",
       "      <th>Section-5</th>\n",
       "      <th>Section-6</th>\n",
       "      <th>Section-7</th>\n",
       "      <th>Section-8</th>\n",
       "      <th>Section-9</th>\n",
       "      <th>...</th>\n",
       "      <th>Section-16</th>\n",
       "      <th>Section-17</th>\n",
       "      <th>Section-18</th>\n",
       "      <th>Section-19</th>\n",
       "      <th>Section-20</th>\n",
       "      <th>Section-21</th>\n",
       "      <th>Section-22</th>\n",
       "      <th>Section-23</th>\n",
       "      <th>Section-24</th>\n",
       "      <th>Section-25</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>20631.000000</td>\n",
       "      <td>20631.000000</td>\n",
       "      <td>20631.000000</td>\n",
       "      <td>20631.000000</td>\n",
       "      <td>20631.0</td>\n",
       "      <td>2.063100e+04</td>\n",
       "      <td>20631.000000</td>\n",
       "      <td>20631.000000</td>\n",
       "      <td>20631.000000</td>\n",
       "      <td>2.063100e+04</td>\n",
       "      <td>...</td>\n",
       "      <td>20631.000000</td>\n",
       "      <td>20631.000000</td>\n",
       "      <td>20631.000000</td>\n",
       "      <td>20631.000000</td>\n",
       "      <td>2.063100e+04</td>\n",
       "      <td>20631.000000</td>\n",
       "      <td>20631.0</td>\n",
       "      <td>20631.0</td>\n",
       "      <td>20631.000000</td>\n",
       "      <td>20631.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>51.506568</td>\n",
       "      <td>108.807862</td>\n",
       "      <td>-0.000009</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>100.0</td>\n",
       "      <td>5.186700e+02</td>\n",
       "      <td>642.680934</td>\n",
       "      <td>1590.523119</td>\n",
       "      <td>1408.933782</td>\n",
       "      <td>1.462000e+01</td>\n",
       "      <td>...</td>\n",
       "      <td>521.413470</td>\n",
       "      <td>2388.096152</td>\n",
       "      <td>8143.752722</td>\n",
       "      <td>8.442146</td>\n",
       "      <td>3.000000e-02</td>\n",
       "      <td>393.210654</td>\n",
       "      <td>2388.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>38.816271</td>\n",
       "      <td>23.289705</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>29.227633</td>\n",
       "      <td>68.880990</td>\n",
       "      <td>0.002187</td>\n",
       "      <td>0.000293</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.537152e-11</td>\n",
       "      <td>0.500053</td>\n",
       "      <td>6.131150</td>\n",
       "      <td>9.000605</td>\n",
       "      <td>3.394700e-12</td>\n",
       "      <td>...</td>\n",
       "      <td>0.737553</td>\n",
       "      <td>0.071919</td>\n",
       "      <td>19.076176</td>\n",
       "      <td>0.037505</td>\n",
       "      <td>1.556432e-14</td>\n",
       "      <td>1.548763</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.180746</td>\n",
       "      <td>0.108251</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.008700</td>\n",
       "      <td>-0.000600</td>\n",
       "      <td>100.0</td>\n",
       "      <td>5.186700e+02</td>\n",
       "      <td>641.210000</td>\n",
       "      <td>1571.040000</td>\n",
       "      <td>1382.250000</td>\n",
       "      <td>1.462000e+01</td>\n",
       "      <td>...</td>\n",
       "      <td>518.690000</td>\n",
       "      <td>2387.880000</td>\n",
       "      <td>8099.940000</td>\n",
       "      <td>8.324900</td>\n",
       "      <td>3.000000e-02</td>\n",
       "      <td>388.000000</td>\n",
       "      <td>2388.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>38.140000</td>\n",
       "      <td>22.894200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>26.000000</td>\n",
       "      <td>52.000000</td>\n",
       "      <td>-0.001500</td>\n",
       "      <td>-0.000200</td>\n",
       "      <td>100.0</td>\n",
       "      <td>5.186700e+02</td>\n",
       "      <td>642.325000</td>\n",
       "      <td>1586.260000</td>\n",
       "      <td>1402.360000</td>\n",
       "      <td>1.462000e+01</td>\n",
       "      <td>...</td>\n",
       "      <td>520.960000</td>\n",
       "      <td>2388.040000</td>\n",
       "      <td>8133.245000</td>\n",
       "      <td>8.414900</td>\n",
       "      <td>3.000000e-02</td>\n",
       "      <td>392.000000</td>\n",
       "      <td>2388.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>38.700000</td>\n",
       "      <td>23.221800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>52.000000</td>\n",
       "      <td>104.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>100.0</td>\n",
       "      <td>5.186700e+02</td>\n",
       "      <td>642.640000</td>\n",
       "      <td>1590.100000</td>\n",
       "      <td>1408.040000</td>\n",
       "      <td>1.462000e+01</td>\n",
       "      <td>...</td>\n",
       "      <td>521.480000</td>\n",
       "      <td>2388.090000</td>\n",
       "      <td>8140.540000</td>\n",
       "      <td>8.438900</td>\n",
       "      <td>3.000000e-02</td>\n",
       "      <td>393.000000</td>\n",
       "      <td>2388.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>38.830000</td>\n",
       "      <td>23.297900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>77.000000</td>\n",
       "      <td>156.000000</td>\n",
       "      <td>0.001500</td>\n",
       "      <td>0.000300</td>\n",
       "      <td>100.0</td>\n",
       "      <td>5.186700e+02</td>\n",
       "      <td>643.000000</td>\n",
       "      <td>1594.380000</td>\n",
       "      <td>1414.555000</td>\n",
       "      <td>1.462000e+01</td>\n",
       "      <td>...</td>\n",
       "      <td>521.950000</td>\n",
       "      <td>2388.140000</td>\n",
       "      <td>8148.310000</td>\n",
       "      <td>8.465600</td>\n",
       "      <td>3.000000e-02</td>\n",
       "      <td>394.000000</td>\n",
       "      <td>2388.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>38.950000</td>\n",
       "      <td>23.366800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>100.000000</td>\n",
       "      <td>362.000000</td>\n",
       "      <td>0.008700</td>\n",
       "      <td>0.000600</td>\n",
       "      <td>100.0</td>\n",
       "      <td>5.186700e+02</td>\n",
       "      <td>644.530000</td>\n",
       "      <td>1616.910000</td>\n",
       "      <td>1441.490000</td>\n",
       "      <td>1.462000e+01</td>\n",
       "      <td>...</td>\n",
       "      <td>523.380000</td>\n",
       "      <td>2388.560000</td>\n",
       "      <td>8293.720000</td>\n",
       "      <td>8.584800</td>\n",
       "      <td>3.000000e-02</td>\n",
       "      <td>400.000000</td>\n",
       "      <td>2388.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>39.430000</td>\n",
       "      <td>23.618400</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          Section-0     Section-1     Section-2     Section-3  Section-4  \\\n",
       "count  20631.000000  20631.000000  20631.000000  20631.000000    20631.0   \n",
       "mean      51.506568    108.807862     -0.000009      0.000002      100.0   \n",
       "std       29.227633     68.880990      0.002187      0.000293        0.0   \n",
       "min        1.000000      1.000000     -0.008700     -0.000600      100.0   \n",
       "25%       26.000000     52.000000     -0.001500     -0.000200      100.0   \n",
       "50%       52.000000    104.000000      0.000000      0.000000      100.0   \n",
       "75%       77.000000    156.000000      0.001500      0.000300      100.0   \n",
       "max      100.000000    362.000000      0.008700      0.000600      100.0   \n",
       "\n",
       "          Section-5     Section-6     Section-7     Section-8     Section-9  \\\n",
       "count  2.063100e+04  20631.000000  20631.000000  20631.000000  2.063100e+04   \n",
       "mean   5.186700e+02    642.680934   1590.523119   1408.933782  1.462000e+01   \n",
       "std    6.537152e-11      0.500053      6.131150      9.000605  3.394700e-12   \n",
       "min    5.186700e+02    641.210000   1571.040000   1382.250000  1.462000e+01   \n",
       "25%    5.186700e+02    642.325000   1586.260000   1402.360000  1.462000e+01   \n",
       "50%    5.186700e+02    642.640000   1590.100000   1408.040000  1.462000e+01   \n",
       "75%    5.186700e+02    643.000000   1594.380000   1414.555000  1.462000e+01   \n",
       "max    5.186700e+02    644.530000   1616.910000   1441.490000  1.462000e+01   \n",
       "\n",
       "       ...    Section-16    Section-17    Section-18    Section-19  \\\n",
       "count  ...  20631.000000  20631.000000  20631.000000  20631.000000   \n",
       "mean   ...    521.413470   2388.096152   8143.752722      8.442146   \n",
       "std    ...      0.737553      0.071919     19.076176      0.037505   \n",
       "min    ...    518.690000   2387.880000   8099.940000      8.324900   \n",
       "25%    ...    520.960000   2388.040000   8133.245000      8.414900   \n",
       "50%    ...    521.480000   2388.090000   8140.540000      8.438900   \n",
       "75%    ...    521.950000   2388.140000   8148.310000      8.465600   \n",
       "max    ...    523.380000   2388.560000   8293.720000      8.584800   \n",
       "\n",
       "         Section-20    Section-21  Section-22  Section-23    Section-24  \\\n",
       "count  2.063100e+04  20631.000000     20631.0     20631.0  20631.000000   \n",
       "mean   3.000000e-02    393.210654      2388.0       100.0     38.816271   \n",
       "std    1.556432e-14      1.548763         0.0         0.0      0.180746   \n",
       "min    3.000000e-02    388.000000      2388.0       100.0     38.140000   \n",
       "25%    3.000000e-02    392.000000      2388.0       100.0     38.700000   \n",
       "50%    3.000000e-02    393.000000      2388.0       100.0     38.830000   \n",
       "75%    3.000000e-02    394.000000      2388.0       100.0     38.950000   \n",
       "max    3.000000e-02    400.000000      2388.0       100.0     39.430000   \n",
       "\n",
       "         Section-25  \n",
       "count  20631.000000  \n",
       "mean      23.289705  \n",
       "std        0.108251  \n",
       "min       22.894200  \n",
       "25%       23.221800  \n",
       "50%       23.297900  \n",
       "75%       23.366800  \n",
       "max       23.618400  \n",
       "\n",
       "[8 rows x 26 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <a id='info_about_data'>1.1 Info about data:</a>\n",
    "- Section-0 is MachineID\n",
    "- Section-1 is time in, Cycles\n",
    "- Section-2...4 is Opertional Settings\n",
    "- Section-5...25 is sensor's data \n",
    "\n",
    "\n",
    "- Data Set: FD001\n",
    "- Train trjectories: 100\n",
    "- Test trajectories: 100\n",
    "- Conditions: ONE (Sea Level)\n",
    "- Fault Modes: ONE (HPC Degradation)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Names \n",
    "MachineID_name = [\"Section-0\"]\n",
    "RUL_name = [\"Section-1\"]\n",
    "OS_name = [\"Section-{}\".format(i) for i in range(2,5)]\n",
    "Sensor_name = [\"Section-{}\".format(i) for i in range(5,26)]\n",
    "\n",
    "# Data in pandas DataFrame\n",
    "MachineID_data = df[MachineID_name]\n",
    "RUL_data = df[RUL_name]\n",
    "OS_data = df[OS_name]\n",
    "Sensor_data = df[Sensor_name]\n",
    "\n",
    "# Data in pandas Series\n",
    "MachineID_series = df[\"Section-0\"]\n",
    "RUL_series = df[\"Section-1\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Max Life >>  362\n",
      "Mean Life >>  206.31\n",
      "Min Life >>  128\n"
     ]
    }
   ],
   "source": [
    "grp = RUL_data.groupby(MachineID_series)\n",
    "max_cycles = np.array([max(grp.get_group(i)[\"Section-1\"]) for i in MachineID_series.unique()])\n",
    "print(\"Max Life >> \",max(max_cycles))\n",
    "print(\"Mean Life >> \",np.mean(max_cycles))\n",
    "print(\"Min Life >> \",min(max_cycles))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "0          1\n",
      "1          1\n",
      "2          1\n",
      "3          1\n",
      "4          1\n",
      "        ... \n",
      "20626    100\n",
      "20627    100\n",
      "20628    100\n",
      "20629    100\n",
      "20630    100\n",
      "Name: Section-0, Length: 20631, dtype: int64\n",
      "1\n",
      "0          1\n",
      "1          2\n",
      "2          3\n",
      "3          4\n",
      "4          5\n",
      "        ... \n",
      "20626    196\n",
      "20627    197\n",
      "20628    198\n",
      "20629    199\n",
      "20630    200\n",
      "Name: Section-1, Length: 20631, dtype: int64\n",
      "2\n",
      "0       -0.0007\n",
      "1        0.0019\n",
      "2       -0.0043\n",
      "3        0.0007\n",
      "4       -0.0019\n",
      "          ...  \n",
      "20626   -0.0004\n",
      "20627   -0.0016\n",
      "20628    0.0004\n",
      "20629   -0.0011\n",
      "20630   -0.0032\n",
      "Name: Section-2, Length: 20631, dtype: float64\n",
      "3\n",
      "0       -0.0004\n",
      "1       -0.0003\n",
      "2        0.0003\n",
      "3        0.0000\n",
      "4       -0.0002\n",
      "          ...  \n",
      "20626   -0.0003\n",
      "20627   -0.0005\n",
      "20628    0.0000\n",
      "20629    0.0003\n",
      "20630   -0.0005\n",
      "Name: Section-3, Length: 20631, dtype: float64\n",
      "4\n",
      "0        100.0\n",
      "1        100.0\n",
      "2        100.0\n",
      "3        100.0\n",
      "4        100.0\n",
      "         ...  \n",
      "20626    100.0\n",
      "20627    100.0\n",
      "20628    100.0\n",
      "20629    100.0\n",
      "20630    100.0\n",
      "Name: Section-4, Length: 20631, dtype: float64\n",
      "5\n",
      "0        518.67\n",
      "1        518.67\n",
      "2        518.67\n",
      "3        518.67\n",
      "4        518.67\n",
      "          ...  \n",
      "20626    518.67\n",
      "20627    518.67\n",
      "20628    518.67\n",
      "20629    518.67\n",
      "20630    518.67\n",
      "Name: Section-5, Length: 20631, dtype: float64\n",
      "6\n",
      "0        641.82\n",
      "1        642.15\n",
      "2        642.35\n",
      "3        642.35\n",
      "4        642.37\n",
      "          ...  \n",
      "20626    643.49\n",
      "20627    643.54\n",
      "20628    643.42\n",
      "20629    643.23\n",
      "20630    643.85\n",
      "Name: Section-6, Length: 20631, dtype: float64\n",
      "7\n",
      "0        1589.70\n",
      "1        1591.82\n",
      "2        1587.99\n",
      "3        1582.79\n",
      "4        1582.85\n",
      "          ...   \n",
      "20626    1597.98\n",
      "20627    1604.50\n",
      "20628    1602.46\n",
      "20629    1605.26\n",
      "20630    1600.38\n",
      "Name: Section-7, Length: 20631, dtype: float64\n",
      "8\n",
      "0        1400.60\n",
      "1        1403.14\n",
      "2        1404.20\n",
      "3        1401.87\n",
      "4        1406.22\n",
      "          ...   \n",
      "20626    1428.63\n",
      "20627    1433.58\n",
      "20628    1428.18\n",
      "20629    1426.53\n",
      "20630    1432.14\n",
      "Name: Section-8, Length: 20631, dtype: float64\n",
      "9\n",
      "0        14.62\n",
      "1        14.62\n",
      "2        14.62\n",
      "3        14.62\n",
      "4        14.62\n",
      "         ...  \n",
      "20626    14.62\n",
      "20627    14.62\n",
      "20628    14.62\n",
      "20629    14.62\n",
      "20630    14.62\n",
      "Name: Section-9, Length: 20631, dtype: float64\n",
      "10\n",
      "0        21.61\n",
      "1        21.61\n",
      "2        21.61\n",
      "3        21.61\n",
      "4        21.61\n",
      "         ...  \n",
      "20626    21.61\n",
      "20627    21.61\n",
      "20628    21.61\n",
      "20629    21.61\n",
      "20630    21.61\n",
      "Name: Section-10, Length: 20631, dtype: float64\n",
      "11\n",
      "0        554.36\n",
      "1        553.75\n",
      "2        554.26\n",
      "3        554.45\n",
      "4        554.00\n",
      "          ...  \n",
      "20626    551.43\n",
      "20627    550.86\n",
      "20628    550.94\n",
      "20629    550.68\n",
      "20630    550.79\n",
      "Name: Section-11, Length: 20631, dtype: float64\n",
      "12\n",
      "0        2388.06\n",
      "1        2388.04\n",
      "2        2388.08\n",
      "3        2388.11\n",
      "4        2388.06\n",
      "          ...   \n",
      "20626    2388.19\n",
      "20627    2388.23\n",
      "20628    2388.24\n",
      "20629    2388.25\n",
      "20630    2388.26\n",
      "Name: Section-12, Length: 20631, dtype: float64\n",
      "13\n",
      "0        9046.19\n",
      "1        9044.07\n",
      "2        9052.94\n",
      "3        9049.48\n",
      "4        9055.15\n",
      "          ...   \n",
      "20626    9065.52\n",
      "20627    9065.11\n",
      "20628    9065.90\n",
      "20629    9073.72\n",
      "20630    9061.48\n",
      "Name: Section-13, Length: 20631, dtype: float64\n",
      "14\n",
      "0        1.3\n",
      "1        1.3\n",
      "2        1.3\n",
      "3        1.3\n",
      "4        1.3\n",
      "        ... \n",
      "20626    1.3\n",
      "20627    1.3\n",
      "20628    1.3\n",
      "20629    1.3\n",
      "20630    1.3\n",
      "Name: Section-14, Length: 20631, dtype: float64\n",
      "15\n",
      "0        47.47\n",
      "1        47.49\n",
      "2        47.27\n",
      "3        47.13\n",
      "4        47.28\n",
      "         ...  \n",
      "20626    48.07\n",
      "20627    48.04\n",
      "20628    48.09\n",
      "20629    48.39\n",
      "20630    48.20\n",
      "Name: Section-15, Length: 20631, dtype: float64\n",
      "16\n",
      "0        521.66\n",
      "1        522.28\n",
      "2        522.42\n",
      "3        522.86\n",
      "4        522.19\n",
      "          ...  \n",
      "20626    519.49\n",
      "20627    519.68\n",
      "20628    520.01\n",
      "20629    519.67\n",
      "20630    519.30\n",
      "Name: Section-16, Length: 20631, dtype: float64\n",
      "17\n",
      "0        2388.02\n",
      "1        2388.07\n",
      "2        2388.03\n",
      "3        2388.08\n",
      "4        2388.04\n",
      "          ...   \n",
      "20626    2388.26\n",
      "20627    2388.22\n",
      "20628    2388.24\n",
      "20629    2388.23\n",
      "20630    2388.26\n",
      "Name: Section-17, Length: 20631, dtype: float64\n",
      "18\n",
      "0        8138.62\n",
      "1        8131.49\n",
      "2        8133.23\n",
      "3        8133.83\n",
      "4        8133.80\n",
      "          ...   \n",
      "20626    8137.60\n",
      "20627    8136.50\n",
      "20628    8141.05\n",
      "20629    8139.29\n",
      "20630    8137.33\n",
      "Name: Section-18, Length: 20631, dtype: float64\n",
      "19\n",
      "0        8.4195\n",
      "1        8.4318\n",
      "2        8.4178\n",
      "3        8.3682\n",
      "4        8.4294\n",
      "          ...  \n",
      "20626    8.4956\n",
      "20627    8.5139\n",
      "20628    8.5646\n",
      "20629    8.5389\n",
      "20630    8.5036\n",
      "Name: Section-19, Length: 20631, dtype: float64\n",
      "20\n",
      "0        0.03\n",
      "1        0.03\n",
      "2        0.03\n",
      "3        0.03\n",
      "4        0.03\n",
      "         ... \n",
      "20626    0.03\n",
      "20627    0.03\n",
      "20628    0.03\n",
      "20629    0.03\n",
      "20630    0.03\n",
      "Name: Section-20, Length: 20631, dtype: float64\n",
      "21\n",
      "0        392\n",
      "1        392\n",
      "2        390\n",
      "3        392\n",
      "4        393\n",
      "        ... \n",
      "20626    397\n",
      "20627    395\n",
      "20628    398\n",
      "20629    395\n",
      "20630    396\n",
      "Name: Section-21, Length: 20631, dtype: int64\n",
      "22\n",
      "0        2388\n",
      "1        2388\n",
      "2        2388\n",
      "3        2388\n",
      "4        2388\n",
      "         ... \n",
      "20626    2388\n",
      "20627    2388\n",
      "20628    2388\n",
      "20629    2388\n",
      "20630    2388\n",
      "Name: Section-22, Length: 20631, dtype: int64\n",
      "23\n",
      "0        100.0\n",
      "1        100.0\n",
      "2        100.0\n",
      "3        100.0\n",
      "4        100.0\n",
      "         ...  \n",
      "20626    100.0\n",
      "20627    100.0\n",
      "20628    100.0\n",
      "20629    100.0\n",
      "20630    100.0\n",
      "Name: Section-23, Length: 20631, dtype: float64\n",
      "24\n",
      "0        39.06\n",
      "1        39.00\n",
      "2        38.95\n",
      "3        38.88\n",
      "4        38.90\n",
      "         ...  \n",
      "20626    38.49\n",
      "20627    38.30\n",
      "20628    38.44\n",
      "20629    38.29\n",
      "20630    38.37\n",
      "Name: Section-24, Length: 20631, dtype: float64\n",
      "25\n",
      "0        23.4190\n",
      "1        23.4236\n",
      "2        23.3442\n",
      "3        23.3739\n",
      "4        23.4044\n",
      "          ...   \n",
      "20626    22.9735\n",
      "20627    23.1594\n",
      "20628    22.9333\n",
      "20629    23.0640\n",
      "20630    23.0522\n",
      "Name: Section-25, Length: 20631, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "for i in range(26):\n",
    "    print(str(i))\n",
    "    print(df['Section-'+str(i)])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the above vizulization its clear that \n",
    "- Section-4 (Oprational Setting-3)\n",
    "- Section-5 (Sensor-1)\n",
    "- Section-9  (Sensor-5)\n",
    "- Section-10 (Sensor-6)\n",
    "- Section-14 (Sensor-10)\n",
    "- Section-20 (Sensor-16)\n",
    "- Section-22 (Sensor-18)\n",
    "- Section-23 (Sensor-19)\n",
    "\n",
    "Does not play a vital role in variation of data and there std is also almost 0 so, these sensor data is useless for us hence, we can drop this coloumn data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "#delete columns with constant values that do not carry information about the state of the unit\n",
    "#data = pd.concat([RUL_data,OS_data,Sensor_data], axis=1)\n",
    "df.drop(columns=[\"Section-0\",\n",
    "                \"Section-4\", # Operatinal Setting\n",
    "                \"Section-5\", # Sensor data\n",
    "                \"Section-9\", # Sensor data\n",
    "                \"Section-10\", # Sensor data\n",
    "                \"Section-14\",# Sensor data\n",
    "                \"Section-20\",# Sensor data\n",
    "                \"Section-22\",# Sensor data\n",
    "                \"Section-23\"] , inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Section-1</th>\n",
       "      <th>Section-2</th>\n",
       "      <th>Section-3</th>\n",
       "      <th>Section-6</th>\n",
       "      <th>Section-7</th>\n",
       "      <th>Section-8</th>\n",
       "      <th>Section-11</th>\n",
       "      <th>Section-12</th>\n",
       "      <th>Section-13</th>\n",
       "      <th>Section-15</th>\n",
       "      <th>Section-16</th>\n",
       "      <th>Section-17</th>\n",
       "      <th>Section-18</th>\n",
       "      <th>Section-19</th>\n",
       "      <th>Section-21</th>\n",
       "      <th>Section-24</th>\n",
       "      <th>Section-25</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>-0.0007</td>\n",
       "      <td>-0.0004</td>\n",
       "      <td>641.82</td>\n",
       "      <td>1589.70</td>\n",
       "      <td>1400.60</td>\n",
       "      <td>554.36</td>\n",
       "      <td>2388.06</td>\n",
       "      <td>9046.19</td>\n",
       "      <td>47.47</td>\n",
       "      <td>521.66</td>\n",
       "      <td>2388.02</td>\n",
       "      <td>8138.62</td>\n",
       "      <td>8.4195</td>\n",
       "      <td>392</td>\n",
       "      <td>39.06</td>\n",
       "      <td>23.4190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>0.0019</td>\n",
       "      <td>-0.0003</td>\n",
       "      <td>642.15</td>\n",
       "      <td>1591.82</td>\n",
       "      <td>1403.14</td>\n",
       "      <td>553.75</td>\n",
       "      <td>2388.04</td>\n",
       "      <td>9044.07</td>\n",
       "      <td>47.49</td>\n",
       "      <td>522.28</td>\n",
       "      <td>2388.07</td>\n",
       "      <td>8131.49</td>\n",
       "      <td>8.4318</td>\n",
       "      <td>392</td>\n",
       "      <td>39.00</td>\n",
       "      <td>23.4236</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>-0.0043</td>\n",
       "      <td>0.0003</td>\n",
       "      <td>642.35</td>\n",
       "      <td>1587.99</td>\n",
       "      <td>1404.20</td>\n",
       "      <td>554.26</td>\n",
       "      <td>2388.08</td>\n",
       "      <td>9052.94</td>\n",
       "      <td>47.27</td>\n",
       "      <td>522.42</td>\n",
       "      <td>2388.03</td>\n",
       "      <td>8133.23</td>\n",
       "      <td>8.4178</td>\n",
       "      <td>390</td>\n",
       "      <td>38.95</td>\n",
       "      <td>23.3442</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>0.0007</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>642.35</td>\n",
       "      <td>1582.79</td>\n",
       "      <td>1401.87</td>\n",
       "      <td>554.45</td>\n",
       "      <td>2388.11</td>\n",
       "      <td>9049.48</td>\n",
       "      <td>47.13</td>\n",
       "      <td>522.86</td>\n",
       "      <td>2388.08</td>\n",
       "      <td>8133.83</td>\n",
       "      <td>8.3682</td>\n",
       "      <td>392</td>\n",
       "      <td>38.88</td>\n",
       "      <td>23.3739</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>-0.0019</td>\n",
       "      <td>-0.0002</td>\n",
       "      <td>642.37</td>\n",
       "      <td>1582.85</td>\n",
       "      <td>1406.22</td>\n",
       "      <td>554.00</td>\n",
       "      <td>2388.06</td>\n",
       "      <td>9055.15</td>\n",
       "      <td>47.28</td>\n",
       "      <td>522.19</td>\n",
       "      <td>2388.04</td>\n",
       "      <td>8133.80</td>\n",
       "      <td>8.4294</td>\n",
       "      <td>393</td>\n",
       "      <td>38.90</td>\n",
       "      <td>23.4044</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Section-1  Section-2  Section-3  Section-6  Section-7  Section-8  \\\n",
       "0          1    -0.0007    -0.0004     641.82    1589.70    1400.60   \n",
       "1          2     0.0019    -0.0003     642.15    1591.82    1403.14   \n",
       "2          3    -0.0043     0.0003     642.35    1587.99    1404.20   \n",
       "3          4     0.0007     0.0000     642.35    1582.79    1401.87   \n",
       "4          5    -0.0019    -0.0002     642.37    1582.85    1406.22   \n",
       "\n",
       "   Section-11  Section-12  Section-13  Section-15  Section-16  Section-17  \\\n",
       "0      554.36     2388.06     9046.19       47.47      521.66     2388.02   \n",
       "1      553.75     2388.04     9044.07       47.49      522.28     2388.07   \n",
       "2      554.26     2388.08     9052.94       47.27      522.42     2388.03   \n",
       "3      554.45     2388.11     9049.48       47.13      522.86     2388.08   \n",
       "4      554.00     2388.06     9055.15       47.28      522.19     2388.04   \n",
       "\n",
       "   Section-18  Section-19  Section-21  Section-24  Section-25  \n",
       "0     8138.62      8.4195         392       39.06     23.4190  \n",
       "1     8131.49      8.4318         392       39.00     23.4236  \n",
       "2     8133.23      8.4178         390       38.95     23.3442  \n",
       "3     8133.83      8.3682         392       38.88     23.3739  \n",
       "4     8133.80      8.4294         393       38.90     23.4044  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# <a id='noise_removal'>2. Noise removal and Normalization</a>\n",
    "<a href='#index'>Go back to Index</a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n"
     ]
    }
   ],
   "source": [
    "print(type(df))\n",
    "gen = MinMaxScaler(feature_range=(0, 1))\n",
    "df = gen.fit_transform(df)\n",
    "df = pd.DataFrame(df)\n",
    "#df = df.rolling(20).mean()\n",
    "pt = PowerTransformer()\n",
    "df = pt.fit_transform(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# grouping w.r.t MID (Machine ID)\n",
    "# col_names = df.columns\n",
    "# def grouping(datafile, mid_series):\n",
    "#     data = [x for x in datafile.groupby(mid_series)]\n",
    "#     return data "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-1.84688137, -0.31975365, -1.3916867 , ..., -0.76399955,\n",
       "         1.42665355,  1.24248381],\n",
       "       [-1.82422371,  0.87202066, -1.03174892, ..., -0.76399955,\n",
       "         1.03734696,  1.29226848],\n",
       "       [-1.80166973, -1.9503069 ,  1.01385485, ..., -2.35059786,\n",
       "         0.72414823,  0.465476  ],\n",
       "       ...,\n",
       "       [ 1.25582441,  0.18308976,  0.01311911, ...,  2.66650739,\n",
       "        -1.90974435, -2.75272539],\n",
       "       [ 1.26682846, -0.50209065,  1.01385485, ...,  1.14636389,\n",
       "        -2.49938469, -1.91568423],\n",
       "       [ 1.27779988, -1.45465135, -1.75831429, ...,  1.68933126,\n",
       "        -2.19492018, -1.99819889]])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# <a id='training_and_validation'>3. Training LSTM Model to predict RUL</a>\n",
    "<a href='#index'>Go back to Index</a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>rul</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>192</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>191</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>189</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>188</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   rul\n",
       "0  192\n",
       "1  191\n",
       "2  190\n",
       "3  189\n",
       "4  188"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def RUL_df():\n",
    "    rul_lst = [j  for i in MachineID_series.unique() for j in np.array(grp.get_group(i)[::-1][\"Section-1\"])]\n",
    "    rul_col = pd.DataFrame({\"rul\":rul_lst})\n",
    "    return rul_col\n",
    "\n",
    "RUL_df().head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(20631, 17) (20631, 1)\n"
     ]
    }
   ],
   "source": [
    "X_train = np.array(df)\n",
    "\n",
    "y_train = np.array(RUL_df()).reshape(-1,1)\n",
    "\n",
    "#X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.20) \n",
    "\n",
    "\n",
    "print(X_train.shape,y_train.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[192 287 179 189 269 188 259 150 201 222 240 170 163 180 207 209 276 195\n",
      " 158 234 195 202 168 147 230 199 156 165 163 194 234 191 200 195 181 158\n",
      " 170 194 128 188 216 196 207 192 158 256 214 231 215 198 213 213 195 257\n",
      " 193 275 137 147 231 172 185 180 174 283 153 202 313 199 362 137 208 213\n",
      " 213 166 229 210 154 231 199 185 240 214 293 267 188 278 178 213 217 154\n",
      " 135 341 155 258 283 336 202 156 185 200]\n",
      "20631\n",
      "20631\n"
     ]
    }
   ],
   "source": [
    "print(max_cycles)\n",
    "print(sum(max_cycles))\n",
    "count = 0\n",
    "for cycle_len in max_cycles:\n",
    "    for i in range(1):\n",
    "        y_train[count+i] = 0\n",
    "    count = count + cycle_len\n",
    "print(count)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[  0]\n",
      " [191]\n",
      " [190]\n",
      " ...\n",
      " [  3]\n",
      " [  2]\n",
      " [  1]]\n"
     ]
    }
   ],
   "source": [
    "print(y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(20629, 1, 17) (20629, 1)\n",
      "[[190]\n",
      " [189]\n",
      " [188]\n",
      " ...\n",
      " [  3]\n",
      " [  2]\n",
      " [  1]]\n"
     ]
    }
   ],
   "source": [
    "def create_dataset(X, look_back=1):\n",
    "    data = []\n",
    "    for i in range(len(X)-look_back-1):\n",
    "        data.append(X[i:(i+look_back)])\n",
    "    return np.array(data)\n",
    "\n",
    "\n",
    "X_train = create_dataset(X_train)\n",
    "y_train = y_train[2:]\n",
    "\n",
    "print(X_train.shape,y_train.shape)\n",
    "\n",
    "print(y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "lstm (LSTM)                  (None, 1, 128)            74752     \n",
      "_________________________________________________________________\n",
      "lstm_1 (LSTM)                (None, 64)                49408     \n",
      "_________________________________________________________________\n",
      "dense (Dense)                (None, 16)                1040      \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 1)                 17        \n",
      "=================================================================\n",
      "Total params: 125,217\n",
      "Trainable params: 125,217\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "def build_model(layers):\n",
    "        #d = 0.2\n",
    "        model = Sequential()\n",
    "        model.add(LSTM(128, input_shape=(layers[1], layers[0]), return_sequences=True))\n",
    "        #model.add(Dropout(d))\n",
    "        model.add(LSTM(64, input_shape=(layers[1], layers[0]), return_sequences=False))\n",
    "        #model.add(Dropout(d))\n",
    "        model.add(Dense(16,kernel_initializer='uniform',activation='relu'))        \n",
    "        model.add(Dense(1,kernel_initializer='uniform',activation='relu'))\n",
    "        model.compile(loss='mean_squared_error',optimizer='adam')\n",
    "        return model\n",
    "\n",
    "model = build_model([17,1])\n",
    "print(model.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/75\n",
      "35/35 [==============================] - 2s 57ms/step - loss: 15848.0137 - val_loss: 19185.3477\n",
      "Epoch 2/75\n",
      "35/35 [==============================] - 1s 19ms/step - loss: 15621.4824 - val_loss: 18589.9551\n",
      "Epoch 3/75\n",
      "35/35 [==============================] - 1s 19ms/step - loss: 14848.5371 - val_loss: 17476.4023\n",
      "Epoch 4/75\n",
      "35/35 [==============================] - 1s 18ms/step - loss: 13600.9961 - val_loss: 15937.7979\n",
      "Epoch 5/75\n",
      "35/35 [==============================] - 1s 18ms/step - loss: 11897.4570 - val_loss: 13864.3760\n",
      "Epoch 6/75\n",
      "35/35 [==============================] - 1s 19ms/step - loss: 9786.5342 - val_loss: 11447.0635\n",
      "Epoch 7/75\n",
      "35/35 [==============================] - 1s 20ms/step - loss: 7811.9751 - val_loss: 9442.3291\n",
      "Epoch 8/75\n",
      "35/35 [==============================] - 1s 20ms/step - loss: 6235.2598 - val_loss: 7791.5552\n",
      "Epoch 9/75\n",
      "35/35 [==============================] - 1s 23ms/step - loss: 4942.3198 - val_loss: 6365.0176\n",
      "Epoch 10/75\n",
      "35/35 [==============================] - 1s 20ms/step - loss: 3863.1145 - val_loss: 5231.7954\n",
      "Epoch 11/75\n",
      "35/35 [==============================] - 1s 20ms/step - loss: 3071.1504 - val_loss: 4434.1968\n",
      "Epoch 12/75\n",
      "35/35 [==============================] - 1s 21ms/step - loss: 2550.0669 - val_loss: 3883.3474\n",
      "Epoch 13/75\n",
      "35/35 [==============================] - 1s 20ms/step - loss: 2231.4065 - val_loss: 3544.3093\n",
      "Epoch 14/75\n",
      "35/35 [==============================] - 1s 26ms/step - loss: 2021.0587 - val_loss: 3311.4185\n",
      "Epoch 15/75\n",
      "35/35 [==============================] - 1s 25ms/step - loss: 1863.7025 - val_loss: 3145.4177\n",
      "Epoch 16/75\n",
      "35/35 [==============================] - 1s 22ms/step - loss: 1738.5463 - val_loss: 3038.3367\n",
      "Epoch 17/75\n",
      "35/35 [==============================] - 1s 21ms/step - loss: 1635.6982 - val_loss: 2968.2676\n",
      "Epoch 18/75\n",
      "35/35 [==============================] - 1s 20ms/step - loss: 1550.5405 - val_loss: 2904.2180\n",
      "Epoch 19/75\n",
      "35/35 [==============================] - 1s 22ms/step - loss: 1484.2980 - val_loss: 2874.6785\n",
      "Epoch 20/75\n",
      "35/35 [==============================] - 1s 21ms/step - loss: 1431.8302 - val_loss: 2870.2209\n",
      "Epoch 21/75\n",
      "35/35 [==============================] - 1s 20ms/step - loss: 1393.5696 - val_loss: 2798.7224\n",
      "Epoch 22/75\n",
      "35/35 [==============================] - 1s 19ms/step - loss: 1362.8365 - val_loss: 2812.2341\n",
      "Epoch 23/75\n",
      "35/35 [==============================] - 1s 18ms/step - loss: 1339.5497 - val_loss: 2721.8162\n",
      "Epoch 24/75\n",
      "35/35 [==============================] - 1s 18ms/step - loss: 1320.9473 - val_loss: 2764.3303\n",
      "Epoch 25/75\n",
      "35/35 [==============================] - 1s 19ms/step - loss: 1306.0642 - val_loss: 2698.8660\n",
      "Epoch 26/75\n",
      "35/35 [==============================] - 1s 19ms/step - loss: 1293.4822 - val_loss: 2659.1306\n",
      "Epoch 27/75\n",
      "35/35 [==============================] - 1s 18ms/step - loss: 1284.3638 - val_loss: 2628.6797\n",
      "Epoch 28/75\n",
      "35/35 [==============================] - 1s 17ms/step - loss: 1276.3358 - val_loss: 2714.2644\n",
      "Epoch 29/75\n",
      "35/35 [==============================] - 1s 18ms/step - loss: 1271.2159 - val_loss: 2782.2305\n",
      "Epoch 30/75\n",
      "35/35 [==============================] - 1s 18ms/step - loss: 1272.7096 - val_loss: 2652.5764\n",
      "Epoch 31/75\n",
      "35/35 [==============================] - 1s 18ms/step - loss: 1260.4098 - val_loss: 2700.9673\n",
      "Epoch 32/75\n",
      "35/35 [==============================] - 1s 22ms/step - loss: 1260.2023 - val_loss: 2598.8132\n",
      "Epoch 33/75\n",
      "35/35 [==============================] - 1s 24ms/step - loss: 1254.8069 - val_loss: 2673.1340\n",
      "Epoch 34/75\n",
      "35/35 [==============================] - 1s 22ms/step - loss: 1248.5609 - val_loss: 2770.0073\n",
      "Epoch 35/75\n",
      "35/35 [==============================] - 1s 22ms/step - loss: 1248.7166 - val_loss: 2622.9163\n",
      "Epoch 36/75\n",
      "35/35 [==============================] - 1s 23ms/step - loss: 1241.9731 - val_loss: 2689.6746\n",
      "Epoch 37/75\n",
      "35/35 [==============================] - 1s 22ms/step - loss: 1241.2373 - val_loss: 2692.8879\n",
      "Epoch 38/75\n",
      "35/35 [==============================] - 1s 23ms/step - loss: 1236.7433 - val_loss: 2635.8335\n",
      "Epoch 39/75\n",
      "35/35 [==============================] - 1s 23ms/step - loss: 1234.5621 - val_loss: 2697.2097\n",
      "Epoch 40/75\n",
      "35/35 [==============================] - 1s 24ms/step - loss: 1231.2312 - val_loss: 2771.0850\n",
      "Epoch 41/75\n",
      "35/35 [==============================] - 1s 23ms/step - loss: 1229.8882 - val_loss: 2713.3884\n",
      "Epoch 42/75\n",
      "35/35 [==============================] - 1s 24ms/step - loss: 1232.9622 - val_loss: 2777.5098\n",
      "Epoch 43/75\n",
      "35/35 [==============================] - 1s 22ms/step - loss: 1229.2510 - val_loss: 2726.0640\n",
      "Epoch 44/75\n",
      "35/35 [==============================] - 1s 27ms/step - loss: 1224.5214 - val_loss: 2723.9854\n",
      "Epoch 45/75\n",
      "35/35 [==============================] - 1s 25ms/step - loss: 1221.0967 - val_loss: 2709.0388\n",
      "Epoch 46/75\n",
      "35/35 [==============================] - 1s 26ms/step - loss: 1217.9905 - val_loss: 2724.3679\n",
      "Epoch 47/75\n",
      "35/35 [==============================] - 1s 25ms/step - loss: 1217.2317 - val_loss: 2780.3616\n",
      "Epoch 48/75\n",
      "35/35 [==============================] - 1s 22ms/step - loss: 1216.7878 - val_loss: 2674.7004\n",
      "Epoch 49/75\n",
      "35/35 [==============================] - 1s 18ms/step - loss: 1213.0455 - val_loss: 2759.7651\n",
      "Epoch 50/75\n",
      "35/35 [==============================] - 1s 21ms/step - loss: 1214.8521 - val_loss: 2745.4614\n",
      "Epoch 51/75\n",
      "35/35 [==============================] - 1s 20ms/step - loss: 1208.7351 - val_loss: 2769.3584\n",
      "Epoch 52/75\n",
      "35/35 [==============================] - 1s 19ms/step - loss: 1209.4221 - val_loss: 2760.1560\n",
      "Epoch 53/75\n",
      "35/35 [==============================] - 1s 22ms/step - loss: 1205.8854 - val_loss: 2678.5481\n",
      "Epoch 54/75\n",
      "35/35 [==============================] - 1s 21ms/step - loss: 1205.3961 - val_loss: 2804.0630\n",
      "Epoch 55/75\n",
      "35/35 [==============================] - 1s 22ms/step - loss: 1207.7275 - val_loss: 2913.6753\n",
      "Epoch 56/75\n",
      "35/35 [==============================] - 1s 26ms/step - loss: 1203.1017 - val_loss: 2791.4482\n",
      "Epoch 57/75\n",
      "35/35 [==============================] - 1s 25ms/step - loss: 1201.1904 - val_loss: 2783.1465\n",
      "Epoch 58/75\n",
      "35/35 [==============================] - 1s 25ms/step - loss: 1198.3256 - val_loss: 2725.5225\n",
      "Epoch 59/75\n",
      "35/35 [==============================] - 1s 26ms/step - loss: 1195.1284 - val_loss: 2714.0852\n",
      "Epoch 60/75\n",
      "35/35 [==============================] - 1s 26ms/step - loss: 1195.7639 - val_loss: 2759.2808\n",
      "Epoch 61/75\n",
      "35/35 [==============================] - 1s 25ms/step - loss: 1194.0911 - val_loss: 2812.2375\n",
      "Epoch 62/75\n",
      "35/35 [==============================] - 1s 24ms/step - loss: 1192.2048 - val_loss: 2678.9304\n",
      "Epoch 63/75\n",
      "35/35 [==============================] - 1s 25ms/step - loss: 1190.5607 - val_loss: 2733.3047\n",
      "Epoch 64/75\n",
      "35/35 [==============================] - 1s 24ms/step - loss: 1192.7866 - val_loss: 2898.3994\n",
      "Epoch 65/75\n",
      "35/35 [==============================] - 1s 24ms/step - loss: 1187.2471 - val_loss: 2777.0701\n",
      "Epoch 66/75\n",
      "35/35 [==============================] - 1s 25ms/step - loss: 1186.1155 - val_loss: 2743.2490\n",
      "Epoch 67/75\n",
      "35/35 [==============================] - 1s 26ms/step - loss: 1185.9575 - val_loss: 2711.5862\n",
      "Epoch 68/75\n",
      "35/35 [==============================] - 1s 26ms/step - loss: 1184.1569 - val_loss: 2714.0879\n",
      "Epoch 69/75\n",
      "35/35 [==============================] - 1s 25ms/step - loss: 1180.7889 - val_loss: 2771.0317\n",
      "Epoch 70/75\n",
      "35/35 [==============================] - 1s 25ms/step - loss: 1182.0162 - val_loss: 2801.1912\n",
      "Epoch 71/75\n",
      "35/35 [==============================] - 1s 26ms/step - loss: 1181.1218 - val_loss: 2800.1868\n",
      "Epoch 72/75\n",
      "35/35 [==============================] - 1s 25ms/step - loss: 1179.6730 - val_loss: 2769.4148\n",
      "Epoch 73/75\n",
      "35/35 [==============================] - 1s 23ms/step - loss: 1178.7362 - val_loss: 2803.0942\n",
      "Epoch 74/75\n",
      "35/35 [==============================] - 1s 27ms/step - loss: 1175.7120 - val_loss: 2838.6555\n",
      "Epoch 75/75\n",
      "35/35 [==============================] - 1s 25ms/step - loss: 1173.1670 - val_loss: 2805.8250\n",
      "dict_keys(['loss', 'val_loss'])\n"
     ]
    }
   ],
   "source": [
    "history = model.fit(\n",
    "    X_train,\n",
    "    y_train,\n",
    "    batch_size=512,\n",
    "    epochs=75,\n",
    "    validation_split=0.15,\n",
    "    verbose=1)\n",
    "\n",
    "print(history.history.keys())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# model.save('LSTM_with_lookback_1.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Loss Graph\n",
    "plt.plot(history.epoch, history.history['loss'] , label = \"loss\")\n",
    "plt.plot(history.epoch, history.history['val_loss'] , label = \"val_loss\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mean_squared_error >>  1415.4031449527815\n",
      "root_mean_squared_error >>  37.62184398660945\n",
      "mean_absolute_error >> 25.70032441054944\n"
     ]
    }
   ],
   "source": [
    "y_train_pred = model.predict(X_train)\n",
    "print(\"mean_squared_error >> \", mean_squared_error(y_train,y_train_pred))\n",
    "print(\"root_mean_squared_error >> \", math.sqrt(mean_squared_error(y_train,y_train_pred)))\n",
    "print(\"mean_absolute_error >>\",mean_absolute_error(y_train,y_train_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# LSTM (Lookback = 1) without VAR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(13094, 1, 17) (100, 1)\n",
      "[144, 114, 29, 46, 43, 70, 91, 48, 59, 107, 56, 89, 103, 45, 131, 98, 15, 40, 100, 21, 47, 129, 86, 3, 126, 76, 67, 85, 74, 44, 24, 33, 76, 26, 32, 23, 24, 13, 155, 21, 21, 27, 31, 126, 24, 21, 53, 80, 19, 71, 75, 17, 11, 100, 100, 25, 34, 27, 110, 60, 37, 35, 25, 20, 86, 24, 118, 39, 135, 47, 82, 51, 114, 59, 73, 35, 18, 129, 70, 63, 28, 20, 91, 43, 157, 70, 116, 82, 65, 10, 15, 24, 16, 27, 79, 81, 53, 37, 78, 6]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mean_squared_error >>  1082.23\n",
      "root_mean_squared_error >>  32.897264323952534\n",
      "mean_absolute_error >> 26.39\n",
      "scoring function >> 3819.0804431676456\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import math\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import cm\n",
    "\n",
    "import statsmodels.api as sm\n",
    "from statsmodels.tsa.stattools import acf, pacf\n",
    "from statsmodels.tsa.stattools import adfuller\n",
    "from statsmodels.tsa.arima_model import ARIMA\n",
    "from statsmodels.tsa.vector_ar.var_model import VAR\n",
    "\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.model_selection import train_test_split, cross_val_score\n",
    "from sklearn.preprocessing import QuantileTransformer , PowerTransformer\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.metrics import mean_absolute_error\n",
    "from sklearn.metrics import r2_score\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.model_selection import KFold\n",
    "\n",
    "from keras import optimizers\n",
    "from keras.models import Sequential\n",
    "from keras.layers import TimeDistributed, Flatten\n",
    "from keras.layers.core import Dense, Dropout, Activation\n",
    "from keras.layers.recurrent import LSTM\n",
    "from sklearn.metrics import mean_squared_error\n",
    "\n",
    "import warnings \n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "%matplotlib inline\n",
    "cmap = cm.get_cmap('Spectral') # Colour map (there are many others)\n",
    "\n",
    "from sklearn.metrics import mean_absolute_error\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.metrics import explained_variance_score\n",
    "from sklearn.metrics import r2_score\n",
    "\n",
    "from keras.models import load_model\n",
    "import pickle\n",
    "\n",
    "model = load_model('LSTM_with_lookback_1.h5')\n",
    "\n",
    "train_file = \"train_FD001.txt\" \n",
    "test_file = \"test_FD001.txt\"\n",
    "RUL_file = \"RUL_FD001.txt\"\n",
    "\n",
    "columns = [\"Section-{}\".format(i)  for i in range(26)]\n",
    "\n",
    "df_test = pd.read_csv(test_file, sep=\" \",header=None)\n",
    "df_test.drop(columns=[26,27],inplace=True)\n",
    "df_test.columns = columns\n",
    "df_test.head()\n",
    "\n",
    "df_rul = pd.read_csv(RUL_file, names=['rul'])\n",
    "df_rul.head()\n",
    "\n",
    "RUL_name = [\"Section-1\"]\n",
    "RUL_data = df_test[RUL_name]\n",
    "MachineID_series = df_test[\"Section-0\"]\n",
    "grp = RUL_data.groupby(MachineID_series)\n",
    "max_cycles = np.array([max(grp.get_group(i)[\"Section-1\"]) for i in MachineID_series.unique()])\n",
    "max_cycles[0] = max_cycles[0] - 2\n",
    "\n",
    "df_test.drop(df_test[[\"Section-0\",\n",
    "                \"Section-4\", # Operatinal Setting\n",
    "                \"Section-5\", # Sensor data\n",
    "                \"Section-9\", # Sensor data\n",
    "                \"Section-10\", # Sensor data\n",
    "                \"Section-14\",# Sensor data\n",
    "                \"Section-20\",# Sensor data\n",
    "                \"Section-22\",# Sensor data\n",
    "                \"Section-23\"]], axis=1 , inplace=True)\n",
    "#df_test = df_test.groupby([\"Section-0\"])\n",
    "#print(df_test)\n",
    "\n",
    "gen = MinMaxScaler(feature_range=(0, 1))\n",
    "df_test = gen.fit_transform(df_test)\n",
    "df_test = pd.DataFrame(df_test)\n",
    "#df_test = df_test.rolling(20).mean()\n",
    "pt = PowerTransformer()\n",
    "df_test = pt.fit_transform(df_test)\n",
    "df_test=np.nan_to_num(df_test)\n",
    "\n",
    "X_test = np.array(df_test)\n",
    "\n",
    "y_test = np.array(df_rul)\n",
    "\n",
    "def create_dataset(X, look_back=1):\n",
    "    data = []\n",
    "    for i in range(len(X)-look_back-1):\n",
    "        data.append(X[i:(i+look_back)])\n",
    "    return np.array(data)\n",
    "\n",
    "X_test = create_dataset(X_test)\n",
    "#y_test = y_test[6:]\n",
    "\n",
    "print(X_test.shape,y_test.shape)\n",
    "\n",
    "pred = model.predict(X_test)\n",
    "\n",
    "pred.shape\n",
    "\n",
    "def scoring_function(actual,predicted):\n",
    "    d = []\n",
    "    for i in range(len(predicted)):\n",
    "        d.append((predicted[i] - actual[i]))\n",
    "    scores = []\n",
    "    for i in range(len(d)):\n",
    "        if d[i] >= 0:\n",
    "            scores.append(math.exp(d[i]/10) - 1)\n",
    "        else :\n",
    "            scores.append(math.exp((-1*d[i])/13) - 1)\n",
    "    return sum(scores)\n",
    "\n",
    "final_pred = []\n",
    "count = 0\n",
    "for i in range(100):\n",
    "    j = max_cycles[i] \n",
    "    temp = pred[count+j-1]\n",
    "    count=count+j\n",
    "    final_pred.append(int(temp))\n",
    "\n",
    "print(final_pred)\n",
    "\n",
    "\n",
    "fig = plt.figure(figsize=(18,10))\n",
    "plt.plot(final_pred,color='red', label='prediction')\n",
    "plt.plot(y_test,color='blue', label='y_test')\n",
    "\n",
    "fig.suptitle('RUL Prediction using LSTM with lookack=1', fontsize=35)\n",
    "plt.xlabel(\"Engine Number\", fontsize=35)\n",
    "plt.ylabel(\"Remaining Useful Life\", fontsize=35)\n",
    "\n",
    "plt.legend(loc='upper left')\n",
    "plt.grid()\n",
    "plt.show()\n",
    "\n",
    "print(\"mean_squared_error >> \", mean_squared_error(y_test,final_pred))\n",
    "print(\"root_mean_squared_error >> \", math.sqrt(mean_squared_error(y_test,final_pred)))\n",
    "print(\"mean_absolute_error >>\",mean_absolute_error(y_test,final_pred))\n",
    "print(\"scoring function >>\",scoring_function(y_test,final_pred))\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Remaining Useful Life')"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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45JNPQgrg33///Xogg+d5PPHEEzjxxBN9HrNz504sXLgw6N/pzzXXXNNhH83MzMSLL76I8ePHxzWriJDujIIdhHRTDQ0NuPXWW/XbOTk5VGsdpE8++UT/WlvsBfLdd9/BarVi9uzZOOmkk9Da2orly5fjmGOO0R/z0ksv6YEOq9WKO+64A1OmTPFZ2NXV1eH+++/HkiVLIAgCbr31VixevBh9+vTxeb6ff/7ZJ9AxdepUzJo1yyf7ZM2aNbjpppuwZ8+esF8DzcyZM/XgQlZWFu6//36cfPLJ+vcVRcEnn3yCu+66C5IkYd26dXjrrbdw1VVX4fjjj8fxxx+PkpIS/WQi2DHA7UmShH/961/6tmRmZuK+++7rsE9v3LgRt956K/7++2+0trZixowZWLx4MXr16uX397a0tGD58uUoKCjAAw884FOKI0kSXnjhBTz77LMA1MDP559/jvPOOy/k7Y+2N954Qw90DB06FK+88gry8vJ8HlNSUoLrr78eLS0tqK+vx5tvvokZM2aE9XzReJ0qKiowe/ZsPdAxZswYPProoz5XTnfv3o2ZM2diw4YNerAmmRiNRtx0000+mSd//PEH/vjjD/A8j2HDhqG4uBhHHnkkRo4cGfOr+uGaOHGi/vdatmwZbr755oCP9c7k8e73EYnx48dj7ty5ANRsDX/lQLW1tT4npdpj/dmwYYMeZBg7dmxQDRkjOT5VVVWhf//+HbLHHA4Hbr31VnzzzTcA1NKa6667DgcccECXvzMct9xyix7oMBgMmDVrFi688EKf7IiysjLcfvvtWLduHSRJwl133YVDDz0UQ4YM8fldX3/9tU+g46STTsKcOXN8ykJcLheef/55PWvwxx9/xBNPPOGz1ujKpk2bcO2116K1tRUAcPLJJ+Oxxx7zWzLy5ptv6oGOnJwcPPfcc34v2nzzzTe49dZb4XA4oCgK3n77bb0EztvcuXP1fcpoNOLOO+/E1KlTfV6v77//HjNmzIDD4cDu3bvxxBNPYM6cOUH925588km9PwbHcXjsscd8Pi9jLTs7O+alMoSkGmpQSkg3IYoi6urqsHHjRrz88ss47bTTfJrpzZ07t9NyDKJat26dT/nH5MmTuzxhefDBB3HOOecgKysLPXr0wMUXX4z+/fsDUBfs2gKSYRg8//zzOO+88zos7PLy8vD4449j0qRJANTU5BdeeKHDc3mPvTzttNNw3333dfi7/uMf/8Cbb74ZcQf2H374QZ/WwHEcXnnllQ4LN4ZhcM455/icRL/99ttRH9f7f//3f/oVN4PBgNdff91v8G7YsGFYsGCBHtxoaGjoUJffHsdxePnllzv0HOE4Dv/5z39QXFys3/fjjz9G+k+JCu8ynlmzZnUIdABAcXExbr/9dv22diU9XJG+Ti+//DLsdjsAYNCgQXjllVc6pIgfeOCBeOONNwKWNSSD8847DzfccEOHdHtRFPXjx5VXXoni4mJceOGFePHFF7Fr164Eba1/hx56qP4al5WVdSjH8KaVD4wYMaJD8DVcvXv3xqGHHgpAvdpeWlra4TG//fabfhzJz88HoF4Rr66u7vBY7xKWeJzs5ebm4v333+9QJme1WvHwww8jKytLv699dkq0rF69Wg/SAMBTTz2Fiy++uMN+2b9/f7zxxht6cMPtduPRRx/1eYwkSXjiiSf02yeffDKeeeaZDv0vTCYTZsyYgZkzZ+r3vfXWW3rmT1d27NiBK6+8Ui9hmzBhAp544omAmSEffPCB/vWcOXMCZqeedNJJuO666/Tbq1ev7vCY7du3Y8mSJfrtRx99FBdccEGH12v8+PG4//779duffPJJh5I7f1577TV97cBxHB555JGAF5imTJniM2o9nP9oPDsh0UHBDkKSyIQJEzBw4EC//w0ZMgRHHXUUzj33XDz++OOora0FoF69mDdvXtAjEtONFiQqKSnBfffdh2nTpumN83JycnDDDTd0+vN9+vTp9Grnhx9+qPf/OPHEE7tMr7711lv1xddnn33mUzddUVGBDRs2AFAXU94nsu0ddNBBuPzyyzt9rq54l92cffbZGDlyZMDHXnzxxXrQRZbloBe/wXr//fd9nquztPu8vDyfpoufffaZT1lSe//85z87XOX05t0oNBrZMtGgXRUF1KutgUycOBEzZszAk08+6bOAD0ckr5Msy/jyyy/123fccUfAZok2my2oppmJdP311+Odd97ptNRGFEX8/vvvePLJJ3HyySfj2muv9WkemGjegctADUc3btwY9RIWjXfQ7Ndff+3wfS1IYDAYcMEFF+j3+zuR9Q6uxbpfBwBMnz7db4ARUPdf79K5WAW6vI+JJ510UodSCW8WiwX33XeffnvlypU+F0N+/fVXvRzRaDTinnvu8dskVHPVVVfpTXZFUcR7773X5fbu3bsXV1xxBerr6wGoQamnnnrKp+mvt7q6OgwcOBCHHnooDjjggC7XMN7BVi3Lx9uyZcv04NlRRx3V6e879dRT9Wwci8XSZfPuhQsX4pFHHgEAsCyLBx98UL9wQQhJbhTsIKSbyszMxHnnnYelS5cmRdp9Imkd9jsLEl166aV499139RrirKwsPPPMMwE7/2tGjRrVaW2s9yI+mGZhPXr00KcBuN1uPbgBqJkWmtGjR3e5bWeeeWaXz9cZ70yArhZuVqsVixcvxtq1a/Hzzz9HNW27paXF58rvOeec0+XPnHDCCfrVYEEQ9Eki/nTWDBaAz9Xs9k37EsW7Aejs2bPx888/+82mycrKwr/+9S+cdtppGDp0aETPGcnrtH79er3PRc+ePX1OTPw5+uijA5YeJYt//OMfWLx4MRYtWoTp06d3mY3y/fff4+yzz8bnn38epy3snPfJXqBgh1bCwjBM1APm3kEJfw2HtfuGDRuGsWPH6ve3D3bU1dXpJU+HH354yA0lw9FV0Nr7qntngdZIlJSU6F8Hc0wcMmSIT7DS+zX3/vr444/Xj52BMAzjs67oKnulqqoKV1xxhV6GOG7cODzzzDM+0+Lay8vLwwsvvIAlS5Zg+fLlnQZfAPhkYLZvAg7AJwumq88zhmHw1ltvYdWqVfjtt986LWX94osvcM899wBQAx0PPPBAxJ+9hJD4oZ4dhCSRSZMmwWaz6bclSUJDQwNKSkrQ3NwMQL0Kdtttt+HCCy8Mu0t6OjMYDDj99NPx73//u9PGpJqupnNs3LhR/3rZsmVB9SHwXhxv3bpVX1h7p5oPHjy4y98zYMAAZGZm6vtGKBoaGvSeEACCOlH2nlQRTdu2bdPHJttsNj39vTMcx2H48OH6Anfz5s1648H2utpu7/dcsoxvvuiii/D1119DURTs378fV155JQoKCnDsscfi2GOP1RtpRlMkr5N3sCqYCTkMw2D48OEoLy8PcSvjb/jw4Rg+fDhuu+02VFVV4bfffsOqVauwcuXKDhlOLpcLt912GwoKChI+mWXQoEHo378/ysrKsHXrVuzatavD31grYRk5cmTUg0+jRo1CVlYWmpqasG7dOjidTj3bZ9++fXp2UHFxMYYOHQqe5yGKYodgx08//QRZlgHEp4QFQJflPN5ZS/5OvCNVWVnpc3zuLOvO2xFHHKH3wPD+LNJKBEP5XaNGjfL5eUVR/Ab+W1tbMX36dOzevVu/b8SIEZ0GOoLhcDj0fXfDhg34+eef9e9p+4M376yqYD7PginZWrlyJV588UX9+Xie93ldCCHJj86UCEkiM2bM8Funabfb8cgjj+CDDz6AIAiYN28eysvLg24api0iAf+LhK54L+YCpaQmkneH/faMRiNsNhtyc3MxcOBADBs2LKSmgt612e3Z7XafcoNffvkl+I328E7H9V7cBjtGuHfv3l2m4PqjlUEBahpvLMfJdkVLewbUrIBgu8x7Z774S2vWeJ+k+9PVFcVEKC4uxs0334wnnnhCf8/W1NRg8eLFWLx4MRiGwdChQzFhwgRMmjQpKpk2kbxO4ey78bhCH21FRUWYPHkyJk+eDEA9wfr888/x7rvv6nX/sixj7ty5+PLLLxO+b5188sl4+eWXAajB2Kuvvlr/XmlpqR5wiEUZJM/zOOaYY/Dll1/C7XZjzZo1evabd6ZBcXExrFYrhg4divXr12P79u2oq6vTg3nx7tcBdH7cby/a/YsA32Oi2WwOesqS93vP+5jY/hgbDO/jqyRJaGpq8juNqK6uTs/q0rz00ks49dRTg+7N09LSgm+++QarV6/G33//jT179vgcU7ricrn0fkFA8Megrqxdu9bnttvtxl133YW3336bpqEQ0k1QsIOQbsBms2Hu3Lkwm836pI7XXnsNAIIKeJjNZn0hHs5VKO8T+kB1+IkU7gSQYHTWBDSYpmZd8S4H8M7QCPZ17uoENRDvhXCi/6bei9RQmq56N2713kfbS/QJZ7iuvvpqHHnkkXjllVfw448/6r1hAPUEa+PGjdi4cSOefvppnH322bjzzjsjmg4SyevkfTIV7OjeSBvsJoODDz4YM2bMwGWXXYZ//etfellaWVkZfv/994SPA584cWLAYEcsS1g048eP13u5rFy5Ug92ePfr0K6UFxcXY/369VAUBWvWrMHJJ58MRVH0IHJBQUFQWUPRkOisyWgfE71/X7CNzNs/b2tra6ejl0eNGgWn04ktW7boQYEFCxZ0GRR455138PTTT3eaoXjIIYdg+PDhPpPUvLUPdkf7M+3888/HokWLIMsyVq1ahUWLFmHq1KkBH79u3Tp9PG24ZsyYEfYocUJIGwp2ENKN3Hbbbdi8ebM+nu+1117DQQcdhHPPPbfTn8vPz9dPzBsbG0N+Xu+f6arWN520X1AtX748oivs3gvJzk7evXmfAIfCe8HbWQPMeAg2aNFeuCcEySDYDKuRI0fiueeeQ0tLC3799Vf8/PPP+O2333yaImrjgffu3Yu33norIcEd7wBHsH9DrVFwsti1axdKSkpQW1sLm82GSy+9NOifzcvLw/z583HSSSfp/67NmzcnPNgxdOhQ9O3bF3v37sXGjRtRUVGhX9nXgh0jR46MWZbNuHHjwLIsZFn2yebQgh0jRozQj6PFxcV46aWXAKh9mE4++WRs3LhRzxo47rjj0uZquncQO9xjovfnk/cxNti+RN6/q/3va2/kyJF49dVX8ffff+P888+HLMtYu3YtPvjgA1x44YUBf+6JJ57Q/+aAGvwaOnQoDjvsMBx00EE45JBDMHjwYOTl5WH79u0Bgx3tj/9OpzNq0+lmzpyJq6++GjzPY8GCBQDUSS/HH398h2k2mp07d2LhwoURPe8111xDwQ5CoqB7Xu4iJE2xLItHHnnEp+Tg/vvv96mV9ad379761/v37w/5eXfu3Kl/Ha3RhKkgMzPTZ8RsV3+HrhQUFOhfV1RUBPUz7dOHg5WTk6N/7XA4gspScbvdMUnZ9l7QVVRUBP0c3vuy92uXDCRJ6vT77U8kupKRkYGTTz4Z9957L5YtW4bly5fjjjvuwIABA/THrFq1Ct99911Y2xsp79dfa1LYlaqqqlhtTljWrFmDu+66C0899RSee+65kH++qKgII0aM0G8nS7NbbSqLoij45ptvAACbNm3SS1iiPYXFW15ent4/YcuWLWhoaMDff/+tj5f1bop7xBFH6GWSWkDfewpLoFLFVOR9THQ6nT6ZU53xPiZ6n4i3P8YGw7ufjtFoDJjVkZOTg9deew0ZGRkYPnw4zj//fP17jz32WMDjwaZNm/SsIwC48MILsXLlSnzwwQe49957cfnll+PYY4/Vy5k6awTb/rPY3/ji9txud5dB5/POO0/Phrrpppv017Spqcln+g0hJHlRsIOQbqZXr1647bbb9NsOhwN33HFHpyeI3uMTvRtqBmPv3r0+/R06G8WYbliW1SerAL7d8zuzbNkyrFixAn/99ZfP1W3v37V+/fouf09dXV1YwStArdv2DngE01j1/vvvx8iRI3HaaadFdeLEwIED9YWq3W73adQaiCiKPvvyQQcdFLXtCZd36ntXWQtdje4VBAE7duzAH3/84ff7BxxwAC677DJ8/PHHPk10/Y3tjAfv8gKtQWJXgn1cvHjvQw0NDUG9B9vz7mmULFlw3iUqWrBDa0wayxIWjTaCVpZllJSU+PRB8A52WCwWfeT0tm3bUF9fr/frMBqNCW/4Gk89evTw2X/WrVsX1M9577Pe+7P353awv8v7cf379w+YVWOz2XzK526++WY9KNDS0hKwxHTx4sX6umX06NG45557Ou0d1f6Y6b3mYRgGhx12mH47mM+zN998E8OHD8fEiRP1suD2vPuWZGRkYNasWfrtr7/+GsuXL/f7c1OmTMHWrVsj+s9f/zZCSOgo2EFIN3Teeef5LBJXr14dML0T8B2Junnz5qAXOwCwZMkS/WuTydTleMp04z0y8ZNPPumyrKS8vBw33XQT/vWvf2HSpEk+qd0TJkzQT5g3btyI7du3d/q7li5dGvZ2MwzjM25Pq6sPRFEU/PTTT3A6ndixY4fPJJBIyyYyMjJ8RiZ2ti9rvv32W71Om+O4hJcLAL6p511dPfUeWdze2rVr9aDStdde2+nVR5vNhnHjxum3o9FHJhxHHHGE/u+vrKzsdBQwAPzxxx8+pTjJYNiwYT4ZKs8991xImUwtLS0+walkOVaOGDFCz+5bu3YtGhoa9FG0RxxxRJcjriPlPYL2119/1QNyRqOxw2QLbWSxoihYvny5/nqOGTMm7LKE7tqzx3v/CeaYWFpa6jN1xXv8s/fX33//vc8FDH+00jh/P9+VzMxMnwsyy5cv14Nr3srKyvSvg5kQ0/53tM+eO/LII/Wvu/o8A9TGt4IgoKysLOheR6effrrPemru3LkJO+YSQoLTPT8BCCG45557fK4iPvLIIwFLGsaOHevTS+Kee+7pdHqFZs+ePXjllVf022eddVa3640Qa+eff76+mK6ursZDDz3U6eMfeOABvUlsjx49cMwxx+jfy83N1a+CAsCcOXMCNpStqanB888/H/G2axYtWtTpVJdPP/0U+/btA6CmR3sHebwzGsIdw+hd171gwYKAGQ2A2gzT+3WeMGFC1MewhqN///7618uWLQsYpPj777/x0UcfBfw9hx9+uP7erqur8wk4+uN9FdO7rCWeLBaLPqEEAB566KGAgT9BEPDAAw/Ea9OCxvM8Lr/8cv32jz/+iAcffDCo/iqKouD+++/XU+2PPfbYmI1qDsdJJ50EQM2IevXVV/XSxFhndQDAkCFD9Cv9v/32mx4I8+7XofE+qX7uuef01z6SEpZoHJ8SwfuY+M033wTMIgDUvktz5szRbw8ePNhnfPk///lPfQ3gdrtxzz33dLpfv/766/rnAcMwXfYFa++MM87wycS57777OpSheI+m/euvvzr9fUuXLu3w72//t/T+PPvhhx98pvi0V1JSopdKGY1GvdQrGHfffbfeo6iqqgqPPvpo0D9LCIk/CnYQ0k0dfPDBuOqqq/TbDQ0NAT90OY7D7bffrt/+888/MXXqVPzwww9+FzyCIGDx4sWYOnWqftUiLy8P//vf/6L8r+j++vfvjwsuuEC/vWDBAsyePbvDwq6urg6zZs3Sr6gCwA033OCz4AOAO+64Q7+CuWbNGlx33XUd6o//+usvXHbZZV1enevKuHHj9KuHgiDgyiuv9Mk00SxduhRz587Vb1933XU+JxDeYxrLysrCutI1adIkfXGubYvWQNHb5s2bcckll+jlO9nZ2bjjjjtCfr5Y8L6CvXv3btx9990dmr/+8ssvmDZtWqcZQFarFWeffbZ+e86cOVi6dGmHLANRFPHkk0/6TLY444wzovAvCc+///1vPQ1948aNuO666zr05aiursZ1113XIbssWRpPXn755T4naW+99RbOPfdc/PDDDwH/ZuvXr8cVV1yhXwm3WCxJs09qJk6cqH+tTfRiWdbn/lhhGAb//Oc/AajHBy1o6i/zZdSoUfqJpHfPCO/3VqiicXxKhOLiYp9RuzNmzMB7773X4TN79+7duOKKK1BaWgpAPQ7MmzfP5zEsy2LmzJn67WXLluGGG27oMN7V7XbjmWee8VlLXHHFFRg0aFDI2z9nzhz98626uhqPPPKIz/e9szl+/PFHLFiwoMMxrqGhAU8++SRuvvnmDr+/fePWQw45BGeeeaZ++3//+5/f7MeVK1fihhtu0G9PmzYtpEag/fr1wzXXXKPfXrhwYZeZbISQxKFpLIR0Y9dddx2WLl2qp4N/8sknOPvss/0uIk888UTceOONeOqppwCokweuueYa9OzZE4cccgiKiorgdrtRVVWF0tJSn+Z6WVlZeOmll4JuArlu3TrcfffdQf87cnNzMWPGjKAfn2xmzZqFbdu26QueRYsWYcmSJTjyyCNRUFCAiooKrF+/3uc1nTJlCs4555wOv6tv376YO3cuZs2aBVEU8dNPP2HChAkYO3Ys8vPzsWfPHqxduxayLKNHjx4wGAxd9n8IhGEYPPbYYzj//PNRXl6O6upqXH755TjssMMwaNAgMAyDP/74w6dB7UknnYSLLrrI5/f06dMHBoMBgiCgvr4eU6dOxahRoyCKIh5++OGgtsVoNOLpp5/GRRddhOrqajQ1NeGGG25A3759MWLECBgMBuzYsQOlpaX6gthsNuPhhx9Gr169wvr3R9s//vEPjBkzRr9iuGjRInz//fd648UtW7Zgx44dANQr/zU1NT5p595uvPFGrFixAuXl5XA4HJgxYwYef/xxHHroocjPz0djYyN+//13n4DXzTffHPOShM4UFhbivvvuw0033QRZlvHzzz9jwoQJGDNmDIqKilBdXY2SkhK43W5wHIfMzEyfUqRoWLFiRVDNCTUDBgzAFVdcod82GAyYP38+pk+frvc/2LRpE6655hpkZmZi8ODBKCwshMFgQH19PTZt2uTzfGazGU8//TQOPvjgqPx7okUrV6msrNSviMejhEUzfvz4DqUY/kojTCYTRowYob+HAOCwww6LqDF2NI5PifLQQw/hoosuwo4dO+B2uzF37lw8//zzetnY7t27sW7dOr2kg+M43HXXXT5lgZpTTjkFV111FV599VUAbb2jjjzySPTs2RNNTU1Ys2aNT9bnUUcdFfZnc//+/XHNNddg/vz5AICPPvoIkydP1tcnU6dOxeuvv65npN5777146623MHDgQFgsFpSXl2P9+vV6kNFgMIBlWT2AXF9f3yGjb86cOdi6dSv+/PNP/bj55JNPYvjw4TAYDNi6datPJtyoUaPCuohzzTXXYMmSJdi5cycURcFdd92Fzz77rMPFC0JI4lGwg5BuzGQy4e6778aVV16p3zdnzpyAH7rXXXcdBgwYgPvvv1+/4lpRUdFpf4GxY8di3rx5IY1ULSsr86nH7UqfPn26dbDDaDTijTfewH333YePPvoIsiyjtbXVZ5KAhud5XHnllbjxxhsD/r7JkycjOzsbN998M5qbm+FyufDDDz/4PKawsBDPP/887r777rCDHYBaSrNw4ULcdNNNerBm27ZtfpuEnn/++Zg9e3aHq/BmsxkXXngh3n77bQDAjh079JP6mTNnBhzP196BBx6Ijz76CDNnztTr+vfu3ev33zdgwAA89dRTYV1xjKWnn34a1157rV6GU11d3aHW/MQTT8TDDz/c6WjT7OxsvP322/jvf/+rB0QCvRZWqxUzZswIaVRqrJx66qkQBEGvZXe73fj55599HmO1WvHAAw/gnXfe0ZtVRuskYdOmTSE1Ph0zZoxPsANQ+6AsWLAA7777Lp599lk9E6C5ubnTJsRHHnkk7rnnHp+GscmCYRicdNJJePfdd/X7YjmFpb1jjjlGDzgA6t87UJ+G4uJin2CHd3ZDOKJ1fEqEnJwcfPDBB7jjjjv05rL+jimA+pnw+OOPd9pf45ZbbkHv3r3x+OOPw263QxAEv/2DWJbF1VdfjRtuuCGiQOS1116LJUuWoKysTA8KfP755zCZTMjOzsaLL76I6667Tg/a7tq1y28vn/79++ORRx7BY489pu8bGzZs6BBU1N67t99+u/567d692++ktJNOOgkPPvhgpyN1AzEajZgzZ45e9vb333/jhRde8MkYIYQkBwp2ENLNHXvssTj99NPxxRdfAFA/dF999VVcf/31fh9/yimn4IQTTsDy5cvx008/YfPmzdi/fz8cDgcYhkFmZib69euHESNG4JRTTunQQI74ZzQacd999+Gyyy7DJ598gt9++w379+9Hc3MzLBYLDjzwQBQXF2Pq1KlB9VU47rjjsGzZMrz77rv47rvvUFZWBoZh0Lt3b5x44om47LLLotanokePHliwYAFWrFiBpUuXYt26daipqYGiKOjRoweOPPJIXHDBBT4TN9qbNWsW+vTpg48//hh79+6FIAgoKipCZWVlSCcTPXv2xLvvvouffvoJS5cuxe+//47q6mq43W7k5eVh2LBhmDhxIk477TSfUppkkZeXh4ULF+Lzzz/HF198gc2bN6OxsRH5+fkYMmQIzjnnHEyYMCGo33XggQfi448/xrJly/DVV19hy5YtqKmpgSAIyMnJwYEHHojx48fjzDPPTGhGR3uTJ09GcXEx3n//fXz//ffYv38/nE4nevbsieOOOw5XXHEFevfurV9hBtDpFIZE0Pp3TJkyBd988w1KSkqwbds2lJeXw263g2EYFBYWoqioCGPHjsUpp5ySdIG39k455RQ92MGybEh9CiKVkZGB0aNH6yVXI0eO1MtV2hs7diyeffZZ/XYkJSyaaB2fEiErKwvz58/H+vXr8dlnn2HVqlWorKxEa2srcnJyMGjQIJx00kk488wzg+qpdfHFF+PUU0/FokWL8NNPP2Hnzp1obGyE0WhE//79cdRRR+Hcc8+NSv8fLSigBRTLysrw3HPP4aabbgKg9m1ZsmQJFixYgO+//x67du2Cw+GAxWJBYWEhBg4ciPHjx2PSpEkwGAwYN26cHuxYvHgxpkyZ0uE5MzIyMH/+fKxZs0Z/vaqqqiCKIgoLCzFq1Cice+65OOqooyL6tx111FGYPHmyPpnslVdewWmnnYZDDz00ot9LCIkuRgml1TghhBBCSBRMmDBBz1RZsmQJnSQQQgghJKqS77IYIYQQQroVWZbx6KOPom/fvujXr5/PeEZ/Kisr9SazBoPBZ5INIYQQQkg0ULCDEEIIIRFhWRYff/wxGhsbAaijir1HX7b30ksv6VMlxowZ4zNGmxBCCCEkGmj0LCGEEEIi5p3Ncdttt+lNIL21tLTgsccew4IFC/T7LrvssrhsHyGEEELSC/XsIIQQQkjEdu7cibPPPhutra0A1AkgQ4cORd++fWE0GlFZWYmNGzfCbrfrPzNt2jTMnj07UZtMCCGEkBRGwQ5CCCGERMW6detw8803Y9++fZ0+zmg04r///S+uueaaOG0ZIYQQQtINBTsIIYQQEjUulwtLlizBt99+iy1btqC2thaiKCIzMxMHHXQQjjnmGJx33nlJNS6XEEIIIamHgh2EEEIIIYQQQghJKdSglBBCCCGEEEIIISmFgh2EEEIIIYQQQghJKRTsIIQQQgghhBBCSEqhYAchhBBCCCGEEEJSCgU7CCGEEEIIIYQQklIo2EEIIYQQQgghhJCUQsEOQgghhBBCCCGEpBQKdhBCCCGEEEIIISSlULCDEEIIIYQQQgghKYWCHYQQQgghhBBCCEkpFOwghBBCCCGEEEJISqFgByGEEEIIIYQQQlIKBTsIIYQQQgghhBCSUijYQQghhBBCCCGEkJRCwQ5CCCGEEEIIIYSkFAp2EEIIIYQQQgghJKVQsIMQQgghhBBCCCEphYIdhBBCCCGEEEIISSkU7CCEEEIIIYQQQkhKoWAHIYQQQgghhBBCUgoFOwghhBBCCCGEEJJSKNhBCCGEEEIIIYSQlELBDkIIIYQQQgghhKQUCnYQQgghhBBCCCEkpVCwgxBCCCGEEEIIISmFgh2EEEIIIYQQQghJKRTsIIQQQgghhBBCSEqhYAchhBBCCCGEEEJSCgU7CCGEEEIIIYQQklIo2EEIIYQQQgghhJCUQsEOQgghhBBCCCGEpBQKdhBCCCGEEEIIISSlULCDEEIIIYQQQgghKYWCHYQQQgghhBBCCEkpFOwghBBCCCGEEEJISqFgByGEEEIIIYQQQlIKBTsIIYQQQgghhBCSUvhEb0Ayk2UZdrsdBoMBDMMkenMIIYQQQgghhBACQFEUCIIAm80Glu2Yx0HBjk7Y7XZs27Yt0ZtBCCGEEEIIIYQQPw477DBkZmZ2uJ+CHZ0wGAwA1BfPaDQmeGtCU1paiqFDhyZ6MwiJCdq/SaqjfZykMtq/SaqjfZyksmTav91uN7Zt26aft7dHwY5OaKUrRqMRJpMpwVsTuu64zYQEi/ZvkupoHyepjPZvkupoHyepLNn270AtJ6hBKSGEEEIIIYQQQlIKBTsIIYQQQgghhBCSUqiMhRBCCCGEEEIIiRJBELB37144nc5Eb0rU8TyPLVu2xP15zWYz+vbtG7A/hz8U7CCEEEIIIYQQQqJk7969yMzMRP/+/QP2k+iu7HY7bDZbXJ9TURTU1tZi7969GDBgQNA/R2UshBBCCCGEEEJIlDidTuTn56dcoCNRGIZBfn5+yJkyFOwghBBCCCGEEEKiiAId0RXO60nBDkIIIYQQQgghhKQUCnYQQgghhBBCCCEpbtu2bRg4cCC+/vrrTh+3Z88e3HHHHWE/z8CBA8P+2WjqtsGOlpYWTJo0CXv37gUArFu3DlOnTsXpp5+Om266CW63GwCwZcsWTJkyBRMnTsSdd94JURQTudmEEEIIIYQQQoiP79fuwfR5yzD55s8wfd4yfL92T9Sf4+OPP8Ypp5yChQsXdvq4/fv3Y8+e6D9/vHXLYMeGDRtw4YUXoqysDIAa+Pjvf/+Le++9F1988QUA4KOPPgIA3HLLLbj77rvx9ddfQ1EUfPjhh4nabEIIIYQQQgghxMf3a/dg/qINqK5vhQKgur4V8xdtiGrAQxAE/N///R9uvPFGbNq0Cbt37wYA/Prrr5g8eTLOOOMMXHvttWhpacG8efNQWlqKuXPnoqSkBNOmTdN/z5w5c/DJJ58AAJ588klMnToVEydOxLRp01BTUxO17Y2Gbjl69sMPP8ScOXNw6623AgB++eUXjBw5EoMGDQIAzJ49G5IkYd++fXA6nRg5ciQAYMqUKXjmmWdw0UUXJWrTCSGEEEIIIYSkie/W7MY3q3Z3+pitu+ohiLLPfS5BwjMfrsfXJbsC/txJYw7ECf84MKjt+OGHH9C7d28MGDAAJ554IhYuXIgbbrgBM2fOxGuvvYbDDz8cjz/+OD799FPMnj0b8+fPx5w5c1BSUuL39+3atQt///03PvjgA7Asi1tvvRWff/45pk+fHtT2xEO3DHbcf//9Prd37doFq9WKGTNm4O+//8YRRxyB22+/HZs3b0ZhYaH+uMLCQlRWVsZ7cwkhhBBCCCGEEL/aBzq6uj8cH3/8MSZNmgQAOO200zBz5kxMnDgRPXr0wOGHHw4AuPnmmwEgYIDDW79+/XDbbbdh0aJF2LlzJ9avX48DDwwu8BIv3TLY0Z4kSfj555+xcOFC9O7dG3feeSdefvllHH300T4jahRFCWtkTWlpaTQ3N27Wrl2b6E0gJGZo/yapjvZxkspo/yapjvbx9MbzPOx2OwCg+PB8FB+e3+nj//P4z6hpdHa4vyDbjNmXjer0Z7Xn6UxdXR1+/PFHlJaW4s0334SiKGhqasLy5cshy7L+O5qbm+FwOOB0OiFJEux2O1wuF0RR1B8jiiJcLhdWr16NO+64A5dccgnGjRsHSZLgcrn0xwWzXaFyu90hvbdSIthRUFCAESNG4IADDgAAnHrqqXj33XcxZcoUVFdX64+rqalBUVFRyL9/6NChMJlMUdveeFi7di1Gjx6d6M0gJCZo/yapjvZxkspo/yapjvZxsmXLFthstqAff9npgzF/0Qa4BEm/z2TgcNnpg0P6PYEsXLgQRx11FF599VX9vmeffRY//fQTGhoaUF5ejkMOOQQvv/wyAOCf//wnFEWBzWZDr169sH//fvA8j9bWVqxbtw7jxo1DaWkpxo4di8suuwz19fWYN28eTj75ZH17o7Hd7RmNRowYMUK/7XK5Ok1M6JYNSts79thjsWnTJpSXlwMAVqxYgSFDhqBPnz4wmUx69Oezzz7DuHHjErmphBBCCCGEEEKIbvzoA/Cf80agMNcCBkBhrgX/OW8Exo8+ICq//9NPP+3Qt/Liiy/G1q1b8eijj+LWW2/FGWecge3bt+Oaa67BwQcfjObmZtxyyy049NBDcdxxx+H000/HDTfcgFGj1EyT0047DX/++SfOOOMMXHrppRg6dKg+KTVZpERmR69evXDvvffiX//6F1wuFw4//HDcdtttAIDHHnsMs2fPRktLC4YMGYJLL700wVtLCCGEEEIIIYS0GT/6gKgFN9r7v//7vw735eXlYcOGDQCgT1fR2Gw2LFmyRL9977336l/b7XY9a2PRokV+n2/r1q0Rb3M0dOtgx3fffad/PX78eIwfP77DYwYNGqSPoSWEEEIIIYQQQkjqS4kyFkIIIYQQQgghhBANBTsIIYQQQgghJAVJTrvfrwlJBxTsIIQQQgghhJAUIzbVoqX0B8ii0Pa14Er0ZhESN926ZwchhBBCCCGEEF+S0w771hLU/fA+DDk9UbPsdTA8j4zBxwIGU6I3j5C4oGAHIYQQQgghhKQQzmxD5qiTYMjtiYqF9wMA+l79JDhrVoK3jJD4oTIWQgghhBBCCEkxsqMJNcte1283b1xBZSwkrVCwgxBCCCGEEEJSiFbGwvA8el0yF1ljzkDrrk1QKNiRtFrLNmLPyzdCbKn3+Toa9u7di6FDh+LMM8/EWWedhdNPPx1XXHEFKioqwvp9n3zyCW6//XYAwNVXX43KysqAj33mmWewZs2akH7/wIEDw9qu9ijYQQghhBBCCCEphDPbkDlyAnqcextqvn4VtsFHo9cFs6mMJUm1lm1ExcIHIdTsQ9Xip/Sv639eFLXnKCoqwmeffYbFixfjiy++wMCBA/HII49E/HtfeeUV9OjRI+D3V69eDUmSIn6ecFDPDkIIISRCktMOzmzr8DUhhBCSKKzBBOfuzRCq90CsK4elz2GJ3qS01PzH92je8F2nj3GVb4ciqlk3zt2bAEVRf3bdcgjVewP+XOaIE5A5fHxY21VcXIwnnngCJ5xwAoYPH44tW7bgvffew08//YS33noLsixjyJAhmDNnDkwmExYvXowXXngBVqsVBxxwAKxWKwDghBNOwNtvv43CwkLMnTsXa9euhcFgwPXXXw+3243S0lLMnj0b8+fPh9lsxj333IOGhgaYzWbcddddGDx4MPbu3YtbbrkFDocDI0aMCOvf4w9ldhBCCCERoNF+hBBCkpYkAAAUUUjwhpDOGIv6gTHZAIbRAx1gGBjyesXk+QRBwNdff42RI0cCAMaNG4evv/4adXV1+PDDD/HBBx/gs88+Q35+Pl577TVUVlbisccew4IFC/Dmm2/Cbrd3+J3vvPMOHA4HvvzyS7zxxht47rnncNppp2Ho0KGYN28eBg4ciNtuuw233HILPv30U9x3332YMWMGAOC+++7DlClT8Nlnn+GII46I2r+TMjsIIYSQMHmP9uMzC1D77ds02o8QQkjSUCRR/b/oTvCWpK/M4eO7zL7Qylj0QAcAsBzMBw5B4anXRGU7qqqqcOaZZwIA3G43hg8fjptvvhm//PKLnk1RUlKCXbt2YerUqQDUoMjgwYOxbt06jBo1CgUFBbDb7TjjjDPw22+/+fz+1atXY+rUqWBZFoWFhfjiiy98vm+321FaWopZs2bp9zkcDtTX12PVqlV4/PHHAQCTJ0/G7Nmzo/JvpmAHIYQQEiZttB+fXYjKRQ8BoNF+hBBCkgcFO7qHmmWvQfFk4TC8EYoiA5II+58roxbs0Hp2+GMyqRdoJEnCqaeeqgcb7HY7JEnCypUroXgFYni+YxiB53kwDKPf3rVrF3r1astMkWUZRqPRZxsqKiqQk5MDAPrvZxgGLBudAhQqYyGEEEIiIDuaULv8Lf02jfYjhBCSLNqCHVTGksx6XTQHmUecBNaahcLJ/0PmiAlgrVnocfZNcd2O4uJifPPNN6itrYWiKLjnnnvw1ltvYfTo0Vi/fj0qKyshyzKWLl3a4WePPPJILF26FIqioLa2Fpdccgncbjc4joMkScjMzET//v31YMcvv/yCiy++GABw9NFH4/PPPwcALFu2DC5XdNZRlNlBCCGEhKn9aD/H9rVo3bUJOYKLylgIIYQknBbkoMyO5MZn5KLwlGtQeIqaxZFx+FFRy+gIxaBBg/Cf//wHl112GWRZxuGHH45rrrkGJpMJs2fPxuWXXw6TyYTDDuvY7Paiiy7CvHnzMHnyZADAXXfdhYyMDPzzn//EnDlz8PDDD+PRRx/FPffcg1dffRUGgwFPPvkkGIbB3XffjVtuuQULFy7E0KFDYbNFp9E7o3jnoxAfLpcLpaWlGDp0qJ7a012sXbsWo0ePTvRmEBITtH+TZCILLgi1+1H1+dPofen9gCxFXMZC+zhJZbR/k1SXTPt47XfvoHHlYmSPmYT8k65I9OakjS1btuDwww9P9GbEhN1uj1owIlTtX9euztcps4MQQgiJAGswobHkcwjVe8AAYKlfByGEkCShlbHIlNlB0hD17CCEEEIipMiSz/8JIYSQpCDS6FmSvijYQQghhERIbwAnUbCDEEJI8qBpLIlD3SKiK5zXk4IdhBBCSKS0xaRMV84IIYQkD22cKQU74ovjOAgCrQmiSRAEvyNvO0PBDkIIISRCevkKZXYQQghJIpTZkRg5OTn6mFYSOVmWUVlZiezs7JB+jhqUEkIIIRHSe3Z4FpWEEEJIMmjL7KAsg3gqKCjA3r17sXXr1kRvStS53W4Yjca4P6/NZkNBQUFIP0PBDkIIISRSehkLZXYQQghJHm2ZHRTsiCeWZXHggQcmejNiYu3atRgxYkSiNyMoVMZCCCGERIgyOwghhCSjtmCHK8FbQkj8UbCDEEIIiZAe5JAp2EEIISSJUGYHSWMU7CCEEEIiJWujZynYQQghJHlon0uyQA1KSfqhYAchhBASIUWSfP5PCCGEJAMto0NrVEpIOqFgByGEEBKhttGzlNlBCCEkeSha5iFldpA0RMEOQgghJFL6NBYKdhBCCEkeemaHSMEOkn4o2EEIIYRESO92T5kdhBBCkoj+uaTI9BlF0g4FOwghhJAI6aNnZerZQQghJHl49+qgiSwk3VCwgxBCCIkQ9ewghBCSlCQRYDkAVMpC0g8FOwghhJBIURkLIYSQJKRIIliTVf2agh0kzVCwgxBCCImQXsZCwQ5CCCFJRA12WAAAMpWxkDRDwQ5CCCEkAoqiANSzgxBCSJJRZAlQZLAmm3qbMjtImqFgByGEEBIJ73GzlNlBCCEkSWjZhlTGQtIVBTsIIYSQCChSWzYHZXYQQghJFhTsIOmOgh2EEEJIBLwDHN4j/gghhJBE0kbNsmarz21C0gUFOwghhJBIeJeuUGYHIYSQZCG3y+wQKLODpBcKdhBCCCER8M3soJ4dhBBCkoOe2aEFOyQKdpD0QsEOQgghJALeAQ7v/h2EEEJIIrXv2SFTZgdJM3yiN4AQQrozWVbQaHdBEGUYeBbZNhNYlkn0ZpF4kr2DHZTZQQghJDlofaTaGpRSzw6SXijYQQghYZJlBbsqmjDv9RJU1beiKNeC2dOL0a9nFgU80ohPNodMwQ5CCCHJQc/sMNvU2zSNhaQZKmMhhJAwNdpdeqADAKrqWzHv9RI02l0J3jIST9SzgxBCSDLqmNlBwQ6SXijYkYIkpx0Mw+hfE0JiQxBlPdChqapvhSDKCdoikhDePTtoGgshhJAkoQXgGYMJYFgqYyFph4IdKUZsqkVL6Q/o07OH/rUs0FVmQmLBwLMoyrX43FeUa4GBp0NrOlGoZwchhJBkJHqCHZwBDG+gzA6SdmhFnkIkpx32rSWo+/59WFsqsP/dOWj6fRkUCnYQEhPZNhNmTy/WAx5az45smynBW0biyadnBwU7CCGEJAk9s4PjwfBGCnaQtEMNSlMIZ7Yhc+SJ4DJyULXoAQBA36ufBGfNSvCWEZKaWJZBv55Z+N/5o8CxDHKzzOiVb6PmpGnGJ7ODGpQSQghJEtpnEsNTZgdJT5TZkWLk1mbUrXhPv928cQWVsRASQyzL4N0vt2DW879g+54GCnSkIy2zg+N9szwIIYSQBNJ6dLRldlDPDpJeKLMjhWhlLAzPo9e0eXD8tQqtuzYhR3ABBkqrJyRWREltSFrb2NrFI0kq0pqSsgYz9ewghBCSNNrKWAxgeCNkyuwgaYaCHSlELWOZAFOfgahY8R4OnHorcgQXlbEQEmOipAAAahqdCd4Skgh6mrDBCNA0FkIIIUlCGz3LcDxY6tlB0hAFO1IMazChed0yiOV/gTWYKKODkDjQMjtqGiizIx3JghrskDkjRLcbsqxQORMhhJCE07MNOZ56dpC0RD07UhBnywHjcuip1YSQ2KJgR/qSZQU19c0AgD01LpRXN2FXRRNkWUnwlhFCCEl7XpkdjIF6dpD0Q8GOFMRl5IKBAsnRlOhNISQtiCL17EhXjXYXlvy4HQDgVnhAFjHv9RI02qkxNCGEkMTyGT3LGaEIlNlB0gsFO1IQn5EDAJBaGhK6HYSkC61nR32zS8/yIOlBEGU4HGqvFhd4cFBQVd8KQaT9gBBCSGIpogAwLBiWUzM7JAp2kPRCwY4UxGXkAgCklvoEbwkh6UGQZJiMHBQFqKMmpWnFwLPItqrtr9yKARwjoSjXAgNPH6+EEEISS5FFMJz6GcXwBsrsIGmn267GWlpaMGnSJOzdu9fn/nfffRfTpk3Tb2/ZsgVTpkzBxIkTceedd0IUU38sIKdldtgbErodhKQLUZLRM88KAKihUpa0km0zYeKYPgAAQeFgYBTMnl6MbBs1hyaEEJJYiiiC4Q0AoJaxSNSzg6SXbhns2LBhAy688EKUlZX53L99+3a8/PLLPvfdcsstuPvuu/H1119DURR8+OGHcdzSxOBsOQAAkTI7CIkLUZTRM98GAKhtoMyOdMKyDHJs6kJyxOC+sJlY9OuZRdNYCCGEJJwieWV2GIyQqUEpSTPdMtjx4YcfYs6cOSgqKtLvc7vduPvuu/G///1Pv2/fvn1wOp0YOXIkAGDKlCn46quv4r25cccaTJB5E5WxEBIHiqJAkhX0yFczO6ppIkvaYTyTr37ZVAPIEgU6CCGEJAVFEgFODcizVMZC0hCf6A0Ix/3339/hvscffxznnHMO+vbtq99XVVWFwsJC/XZhYSEqKyvjso2JppgyqEEpIXGgNSfNtplgMXE0kSUNKbIIBWoZC+TUL5UkhBDSTUiCV88OIyCLUGQJDMsleMMIiY9uGexo75dffkF5eTlmzZqFkpIS/X5ZlsEwbVfYFEXxuR2s0tLSqGxnPGWYbGis2IO9a9cmelMIiYm1SbJvuwR16kZlxX7YTAz+KivH2rV05SSdmPftgwEsRLCALGHtmjVAGJ817SXLPk5ILND+TVJdMuzjtppqcIKItWvXwlRRBSuA31evAnhjojeNdHPJsH8HIyWCHUuWLMFff/2FM888Ew6HAzU1Nbjxxhtxyy23oLq6Wn9cTU2NT+lLsIYOHQqTqXs1m/tzw2JYnHU4bPToRG8KIVG3du1ajE6SfbvZ4QYW7Uf/fgeiqqUCrS4xabaNxEdtfSmqd3KQFLUy9IhRI8B40obDlUz7OCHRRvs3SXXJso9X7FgGES4cOno0GuVK1G5bgZHDhoKzZiZ600g3liz7NwC4XK5OExNSItjx4IMP6l+XlJRg/vz5eOqppwAAJpNJ/4N89tlnGDduXIK2Mr5kUwakmh1hZ7MQQoIjimpmB88xyM8xY93W6i5+gqQaRRIhg4XkaYOlSFLEwQ5CCCEkUj4NSj1TWRSRsk9J+kiJYEdnHnvsMcyePRstLS0YMmQILr300kRvUlzIpgwogguK2wnGZEn05hCSsgRJC3awKMixoL7ZCVGSwXPdsv8zCYMii5DA6JkdikR9OwghhCSe0r5nByjYQdJLtw52fPfddx3uKy4uRnFxsX570KBB+Oijj+K5WUlBMWUAUMfPGinYQUjMiFqwg2dRkG2BogD1TS4U5tL7Lm1IEiSF0zM74JnOQgghhCSSIolgPUEOCnaQdESXHlOUbLQBACQ7jZ8lJJbayljUzA4AqKHxs2lFkSU1swOU2UEIISR5KKL36Fk12CGLQiI3iZC4omBHipI9mR00fpaQ2NJGz/oEO2j8bFpRZBGiwkJUOP02IYQQknCy4KdnhyuRW0RIXHXrMhYSmGLyZHa0UGYHIbGklbEYeBYF2WYAQC0FO9KLJEJSGMhQm0FTZgeJhCwraLS7IIgyDDyLbJsJLEuNxglJJ9E6DiiiqAc52spYKLMj0eg4Hz8U7EhRisECsDxECnYQElN6zw6Ogc1igMnIoabBmeCtIvEkS76ZHZCoZwcJjywr2FXRhHmvl6CqvhVFuRbMnl6Mfj2zaCFMSJqI5nHAdxoLBTuSAR3n44vKWFIVw4DLyIFkb0j0lhCS0kSvaSwMw6Ag20I9O9KMLGqjZymzg0Sm0e7SF8AAUFXfinmvl6DRTmnnhKSLaB4H1GCHltlBo2eTAR3n44syO1IYn5FLZSyExJgoenp28GrsuCDHTD070oyW2SGBenaQyAiirC+ANVX1rRA8jZAJIakvmscBn9GzhuSYxpLuJRx0nI8vCnakMM6WA7GxKtGbQUhK0zM7WDXYkZ9twR9/VSdyk0icyaIICSwkhTI7SGQMPIuiXIvPQrgo1wIDT4m4hKSLaB4HfMpYOE+wQ0hcsINKOOg4H2/0qqYwLiOXenYQEmOCFuzQMzssqGtyQpIoQp8uFEnylLF4enbI1LODhCfbZsKsy8egKFed7KSdCGTbTAneMkJIvGTbTJg9vTgqxwFFEoD2mR1S4np2UAmH+vedceERdJyPE8rsSGFcRg5kR7NPVJcQEl2i2NagFFCDHbIC1De79FG0JLXJkgBJYSF5rh9QZod/6Z66HAyWZSDJMq46cxgKcy3IzzbT65SmJKcdnNnW4WuS+liWQb+eWfj3eSNgMvDoXWgL6zigKArg06BU7dkhJzCzg0o4gJrGVry5ZDOuOnMYsjOM6FUQ3t+XBIcyO1IYn5ELQIFkb0z0phCSsrwblALQx89S3470oUgSJDAQFQp2BKKlLs98+kdcOe8bzHz6R+yqaIIsK4netKRTU+/EA2+uwmc/7EBuppkWwGlIbKpFS+kPkEWh7Wshfa58EzXg8epnpZjzysrwjwOe/lF6g1Iu8Q1KtRIOb+lWwvFbaTm27q7HV7+V4d7XSug4H2Pps2elIc6WAwDUpJSQGNKCHQavMhYAqKXxs2lDkUWI4CBrmR3UoLQDSl0OXrNDPRERqBQuLUlOO+xbS1D3/ftwlv2B/e/OQdPvy6BQsCPtiKICtyCpGRph0ALvemYHw4DhjQkNdkSzRKe7WrmxHAf2zMQhfXPQ6hLD/vuS4FBtQwrjMnIBAKK9AelzCCEkvtrKWHyDHdU0fjZ9SBJkpS2zAxL17GiPUpeDpwU7RHpt0hJntiFz1EngMvJQsfABAEDfq58EZ81K8JaReBNlGYqiXlQx8FzIP6+InmCHp3xF/doIRUxczw6tROdfU4bDYuKRl21Gzzxb2mQ2NLa4sPnvWpx34mEwG3nIsgKXIMFspFPyWKHMjhTGZ+QAoMwOQmJJ9KTha8GODIsBRgOHWipjSRuKLKnTWKhnR0CUuhy8Zod6IiJSZkfakh1NqFvxrn67eeMKKmNJQ1rA0+UOL4CuZ3awbSfSDG9I+OhZtyDh3tdKMOv5X1DX6EybQAcAlGyqgKwARw/rDatZ/bu0umjNEEu0ykhhbWUsDQndDkJSmZ7Z4TlpYxgGBdlm1FBmR/qQRd8GpVTG0gGlLgev2e7J7KBgR1rSylgYnkfvS+9H9tjJaN21icpY0pB2DHAJ4QY71MCpb2ZH4oMdFXUO/et0y+5bubEcPfKsGNA7CxaTJ9jhpDVDLFHOTApjeANYSwZldhASQ+0blAJqKQsFO9KIltmhl7HQwqU9LXX5unNGwGzk0KcwA9kZ1H3eH72MRaI67nTEmW3IHDkBxqJ+aPz9axSe9i/kCC4qY0lD2vrCLYQXENCzDDnvzI7ElrEAQHmNXf86nXoTOZwC1m+rxqRjB4BhGD3Y4aDMjpiizI4Ux2XkQrQ3JHozCElZgiSDYQDO66StIMeCmkZqUJo2PMEOsGpNtSJTzw5/WJbBMwvXYdbzv8Bs4inQEUBLq6eMJc2ueJI2rMGE8gX3QKjZB9ZgokBHmtICnuFmdkDyncYCIOENSgGgotYr2JFGx7k1WyohSjKOGtYLAKiMJU4o2JHieFsOZXYQEkOiKPtkdQBAfrYZdU1OSDRWMy0wsgRJYWE0ecb6UWZHQG7Por3Fkdgri8msyU7TWNKdIkuAIkMRqXQlXSmK4pXZEWEZS4fMjsQGO8q9gh3pFNT9dWM5cjNNGNQvDwCojCVOKNiR4riMXOrZQUgMiZLSIdhRkGOBLCtoaKbsjrSgqJkdJrPZc5MyOwJxexa2La2JXWwnsxZt9GwanQQQX9rJaKLLDUjiyLICbSJp+D07fEfPAgDLGyAnOrOjxo5MqxFA+hznXIKEtVsqMXZoLz2r0WpWL5BQGUtsRbVnx549e7Bt2zY0NDTA4XBg2rRpAABBELBr1y4ccsgh0Xw6EgQuQ83sUBQFDEMpw4REmyh1zOzQxs/WNLQiP9vi78dIilAUBawW7DAZAReoZ0cAsqzoC1utVIP4UhTFq2dHepwEkI60kaGJvgJPEsc7syv8aSwBMjuc9kA/EhfltXYc2DMTm/6uTZsMtvVbq+B0S3oJC+CV2UHBjpiKONjR0tKCBQsW4IMPPkBFRYXP97Rgx969e3HGGWdg2LBhuPnmm1FcXBzp05IgcRm5UCQBsssBzmxL9OYQknJESYaB9w0kFngCHDWNTgxMxEaR+FHUhZoMFmaTAbKLoWksAXgvaqmMxT+nW9Lr9CnYkb4os4NIXg2Kwy9jSb6eHaIko6q+FSMPK1KDHWJ6ZEKuLC2HzWLAsEMK9PvayljofR5LEZWxbNq0CWeddRaeeuopVFRUQFEU/T9ve/fuhaIo2LhxI6644go8//zzEW00CR5vywUA6ttBSIwIAXp2AEAtTWRJeXp/DpaDgWchg6WeHQEIXgt2O5Wx+KVldXAsk1a17MSXdkWeMjvSl3ewM+xgh+gns8OQ2GBHdX0rZFnBAT0yAKRHzw5RkrFqUwXGDO7hs140GzkwDJWxxFrYwY7t27fjsssuw759+/QAR3Z2NnJycjo8tra2Vv9almU8++yzWLBgQbhPTULAZeQAoGBHupG8UhSlBKcrpjrJT8+OLJsRRp5FNQU7Up82eYXlwPMsJHA0jSUA77pzKmPxr9nTnDQ300SZHWlMO0lVRHeHC4iJIssK6pudqKp3oL7ZCZkacMeU9/s/7J4dsp/Rs5wBipC4YIfWnPSAokwA6dGIedOOWjQ7BBw1rLfP/dr4WWpQGlthBTtkWcaNN96IlpYWAMCRRx6JDz74ACUlJZg4cWKHx5911ln44IMPMGzYMABqTepjjz2GysrKCDadBIPL0DI7GhK7ISRuxKZatJT+AFkU2r4WqKN7rIiSDJ73PZQyDIP8HAtqafxsytOakTIsBwOnZnZQzw7/vBvRURmLf9rrkpNlpmBHGvMpX0mC44ksK9hV0YSZT/+IK+d9g5lP/4hdFU0U8Igh7+Nl2KNnPb1fGN6rjMVg1DOHEkEbO9tXC3akQWbHrxv3w2TkMGpgYYfvWUw89eyIsbCCHUuWLMH27dvBMAyOPvpovPHGGxg5cmSnPzNy5Ei89957GDduHADA6XRi0aJF4Tw9CYEW7BDtlNmRDiSnHfatJaj7/n207vwD+9+dg6bfl0GhYEfMiJIMnu14KC3ItqCGMjtSnveVMzWzg8pYAvFOxdbKNYivZk95T16mGYJIJ5LpSpHa3h/JUMrSaHdh3uslqKpXP9Oq6lsx7/USNNppbREr3qPr3UJ4AYGADUoTmdlRY4eBZ5GfbQbPsSlfxiLLCn4rrcARA4tgNnZslWk181TGEmNhBTu++eYbAADHcZg3bx54Prg+pzzPY86cOTAY1Ajjr7/+Gs7TkxCwJisY3khlLGmCM9uQOeokFJ5+PSo/fABifTl6nHUTOGtWojctZQmSDJ7vOOkoP8eMmkYKdqQ8vQGc2rNDAktlLAF4L9ipjMU/vYwlSy1jSZYSBhJf3pkdiR4TCqhX37VAh6aqvjUtrsonincQIKoNSjkDFElI2LGlotaOnvlWsCwDA8+m/D60bU896pqcONprCos3KmOJvbCCHaWlpWAYBqNGjUKvXv7/eIH06dMHI0eOhKIo+Pvvv8N5ehIChmHA2XKojCWNyI4m1K1o64nTvHEFlbHEkOinQSkAFOZYUNfo9Lk6Q1KPFthgWB4GjoWoUBlLIG6RenZ0pVkrY8k0AQAdP9KUd7AjGSayGHgWRbm+Y9SLci0w8BHNOSCdiM7oWS3Y0XZRmjUYPd9LzH5VUetAz3x1OmQ6BDtW/lEOnmPwj8E9/X7fajJQGUuMhXWUqqurAwAccMABYT1p3759AUDv+UFii8vIpcyONKGVsTA8j17T7kP22Mlo3bWJylhiSJT8Bzvysy2QZAWNLfTapzJtMcl6l7HQ6Fm/BE9mh9HAwU49O/xqdrhhNnKwekYSpnqKN/HPN9iR+MyObJsJd15RrAc8inItmHX5GGTbTAnestQVlWksAUbPAkhIKYuiKKiotaOXV7AjlXsTKYqClaXlGH5oITIsBr+PsZipZ0esBVd/0o7JZILb7YbLFd4ivrGxEQBgs9nC+nkSGi4jB0LtvkRvBokDzmxDxvDjYSw8EHUr3kWvi+YgR3ClXRmL5LSDM9s6fB0LoiTDbOp4KC3wjJ+taWhFXpY5Zs9PEsyT2cHyPFhPZgf17PDP5cnsyMsyoYVGz/rV7HAjw2rUA6ipfCJAAvO+6p4MwQ6WZZCfbcZVZw5Dz3wrqhta8dWvO/Hv80YmetOCEs81QbRIUltWV9jTWAL07AASkzHU0OyC0y2lTWbHropmlNfYMWX8IQEfYzHxcDgp+B9LYWV29OjRA4qiYOPGjSH/rNvtxpo1a8AwDHr06BHO05MQ8Rm5kOwNid4MEicMFJQvmAPX3q1gWC7tAh3xnkYjigoMfjI7CnLUK2DUpDS16dNYOF7t2aEw+n3El5bZkZdlpjKWAFocArKsRn3CUzqMZSQdeQc4kqGMBQCcbgkPvLkK2/c0oL7Jia9LduPnDfsTvVld6q4T6sQoTGPRA+8+wQ41w0AR4/8aaGNnexWowQ6eS+1gx8o/9oNhgOKh/ktYAMBK01hiLqxgx9ixYwEAe/bswYoVK0L62eeffx5NTU0AgDFjxoTz9CREnC0HcmtL0nxgktjy/jtLjuYEbkn8JWIajSj7L2PRgx3UpDSlaSUrHM+rneUVFqAyFr+0BXtulhktjsQ1yEtmTXY3MqyGtswOmsiSlpKtjAVoK6UwGjicOKYfBvTOwhtLNoU/FjUOvNcEzl2l3WpCnRCFMhZIAsDyYJi2JuqJzOzQxs72zLcCSP3Mjl83lmPwgHzkZgbO7rWYeTicIn0exlBYwY6zzz5bf+PMnj0bO3bsCOrn3njjDbz00kv67cmTJ4fz9CRE2vhZyu5ID7JXtD7d/ubaNJoeZ90Yt2k0gRqUZtmMMPAsahucMXtuknjePTsMvBrskEUKdvgj6GUsZkiyAmeYTfdSWUurG5lUxhJXsqygvtmJqnoH6pudkJOgKWyylbEAbcFKo4EDxzK4+qxhqK5vxaffb0/wlgWmT6ib9G9UfDCvW02o0977PMdEMHpW9ClhAbyDHfHfr8prHGAYoEeed7Aj9T4HZFlBZZ0D1549DFefNbTTY4rFxEOSlZQO+iRaWD07Bg8ejLPOOguffvop6urqcO655+KCCy7Asccei/r6tkaYsixj7969KCkpwQcffIDNmzdDURQwDIMTTzwRw4cPj9o/hATGZeQAAMSWevDZhYndGBJz3k2nJEdjArckMWRHE2qWvaHfbt64ArnjLgBriE0jNTHA6FmGYVCQbaEyllTn6dnB8erVeAksZOrZ4Zfbq4wFUEs2LH763aSzZruATJtRL42jYEdsybKCXRVNmPd6CarqW1GUa8Hs6cXo1zMLLNvxuB4vyTaNBWjLLjAZOQDAsIMLcMzw3vjou79w0pgDkZ9t6ezHE0Z2NKHuu3f127FeE0SL1rPDajaEX8YiCmD4QMGOxGR2FORYYODVfcjAcylXqhfqMUVrRt3qEmE0cPHe3LQQ9syoe+65Rx8h63Q68eabb+Kqq67CN998oz9m2LBhmDhxIu6++25s3rxZv/+ggw7Cgw8+GNmWk6DxWmYHjZ9NCz5lLPb0Cnb4TKO5ZC6yxpwR82k0gaaxAEB+jpnKWFKcntlh8ExjoQalAWknS3qwg5qU+lAUBc0ONzKtBj2ASsGO2Gq0u/STEgCoqm/FvNdL0GhPbJmD9+e4nCSZHXqww+uE7IozhkCWFbz5xeZAP5ZQ3muC3pfO61YT6rQggC2SYIckgmHbBzvUnh1ygnp2aJNYAKjj2lMsoyHUY4rFrP59HE5aN8RK2MEOk8mEN998E+eeey4URdH/A6CXuEiS5PM9RVEwfvx4LFiwABkZGdH5F5AucbYcAKDxs2nCOzUx3TI7OLMNmSMnoMeUmaj5+lVYDzsSvS6YHdOUVUFUAgY7CrItqGmkMpZUpnhldhi00bMU7PDLLWqZHeoV1RYaP+uj1SVCkhVkWNrKWCi1ObYEUdZPSjRV9a0Jf92TsYzFrY+Obvu865FnxVnHHYzv1+7Fn7vqErVpAWlrgoJTrkFDyefIHXdBzNcE0aIFAawWPqLRs1pwQ8MmOLOjp1ewg+fZlMvsCPWYYjGpfx9qUho7YQc7AMBsNmPevHlYvHgxzjvvPPTu3btD4AMAcnJycPLJJ+ONN97Aiy++iJycnEi3m4SAs2UDYCCmWf+GdOUT7EizzA4AYA0m2LeshFC9B+7yHTFf1HSa2ZFtRl1ja1LUgJMY8Uxe4T0NSiWFylgCEUQJPMci06outimzw1ezJ/iTZTNQz444MfAsinJ9yy+Kci0w8BEtjyOWjNNYvHt2eDtvwmHIyzLh1cWlSflZxxpMqPjgfrgry8AaTN0i0AG0vfdtZgNcYfY3UiQhaXp2OJwCGlvcenNSIDUblIZ6TNHKWGj8bOwELJZ99dVXwfM8jjjiiC57awwaNAj33XcfAKCurg61tbVobm6GxWJBTk4OevXqFd2tJiFhOB6sNZMyO9JEugc7gLbXQHa2xPy51GCH/9ruwhwLRElBY4sLuVmBu3GT7kvynIhwhrbMDlCwwy+XIMFoYJGhBTsos8NHs0M9bmV4jZ6lYEdsZdtMmD29uEN9fbYtsf0cFFEAwxmgSEISZXZ0LGMB1AaLl542GE99sA7f/74XJ/zjgERsXqcUSYDs7l5ZlpLnvW81R5bZAc43s6Nt9Gx896uKWgeAtrGzgFrGkmrBjmybCXdcPgYPvLkqqGOKVsZCmR2xEzDY8d5776G8vBzjx4/HCy+84PO9xYsXAwAGDBiAESNG+HwvLy8PeXl50d9SEhE+I5d6dqQJ/SoQx6dtsEOrcZZbYxvsUBTF06A0UM+OtvGzFOxITaKgvt94rwalipR63eWjQRBkGHkOGRZ1sd3SSsEOb8129biVaTWC8zSyo9GzscWyDPr1zMK/zx0Bi9mAnvlWZNtMCW1OCqgn56zZBsnekDSZHe4AmR0AcPzoA/DFLzvx1hebcdSwXknVeFhRZECWoHSzYIfg1aA0/NGznU1jie9+Va6PnW1XxpJiwQ6WZWA187jqzGHoW2RDhtXY6THFYqJgR6wFzNOrq1Nr73Jzczt87/bbb8esWbPw6aefxm7LSFRxGTmU2ZEmtBN9PqsgbYMd2kQaKcaZHbKsoGe+DScMy9fvk5x2/esCT3f6Gho/m7L0zA5j2+hZRe4+ixbv/dX761hwi2pmh8XEg2Uo2NGelumSaaUylnhiWQYvfrIRT7y3FrmZ5oQHOgBPZofRDDBs0mR2uPSeHR2DHSzL4JqzhqEwx4I9lc1JNsZXPR4rolvvsdQd6GUslggblHbI7EhMGUtFjfr54tOglGdT8hjX0irggTdXoaLW0eUxxUoNSmMuYLBD67mhBT1I98Zl5FLPjjShfYDx2YVp16BU01bGEtuTN0lWcO8lg2DetwayKEBsqkVL6Q+QPZ3eC7TMDho/m7JEtyezw2DUMzvQTRbU+v7qZ9+NBbcgw8BzYFkGNosBLY7kOIlLFs2tbZkdWrZYqjXvS1aCKCXVVAhZdIPheDAGY9IEO9rKWPyfOhx2YC6mTx6Ch99ejSvnfYOZT/+IXRVNiQ94eE+26UbZHaJXGYsoKZDCeB399uwwJC6zI9NqhM3SFnxJxTIWAHC6fcc0d4YyO2IvYLCjsLAQiqJg5cqV+Ouvv+K5TSQGOFsOpJYGn8axJDVpWQ18ViFke2Na/s0Vz0i1WAc74HaA37sejT++j9a/12P/u3PQ9Psyfaxdlk09Aa6l8bMpSxTVBYrB2L16dmgjGeu+fx+tO37vsO/GgtvTswMAMixGyuxoRytjybAa9T5AyXQCnsoESU6uwJIkguGNYHhjUpWxMAwCNuRutLvw+IK1yTfG1+t43J1KWURJBsO0NbAMp5RFncbif/SstlaMl4paO3p79esAUrOMBYDeULZ9fxt/zEYKdsRawKK6I488Env37oXb7cbZZ5+NQw45BFlZvh2Mv/vuO/z9999hPznDMHjrrbfC/nkSPD4jF5BFyK0t4KyZid4cEkPaVSBDdqHa3MzdCsZk7eKnUov2IR7rBqWtihErXYdi/KT/oHLRQwCAvlc/qXd7Z1kG+dlmKmNJYZIWXDQY9Gks3SGzgzPbkDnqJPA5Raj88EEAvvtuLKjBDnXxZ7MaKNjRTrNDgMXEwcCzVMYSZ25BRhJUr+gU0Q2GN4DhDJDjfFIaiMvz/mUY/y9U8o7xbTuJlN3d58KDKKqT3rRjpluQQu6FoogCGIvvmp9hWIDjoUjx3a/Kax04vJ9vT0cDz0IUk//zMlRa2ZHJ2PXfi2UZWEwclbHEUMC/wvTp0/HVV1/B6XRCFEVs3brV5/uKoqC6uhrV1dVhPbGiKAEPmCT6uAy194rUUk/BjhSnXQXiswsAAJK9AWy6BTtErWdHbDM7ZEXBkQfbUP9/z+r3NW9cgdxxF4A1qJ23C3IsqKHMjpQliiJYAAbvaSzdpGeH7GhC7Tdv6rfb77vR5hbVBqUAkEllLB00O9z6WF4DTWOJK0FMrmCH7JnGwhqMcT8pDcQlSPr71x9t5KZ3wCMpxvhK3bWMRfEJdoQzflaRRYDreKrH8Ma4BtEEUUZNvQM9R/f1ud/Ac5AVdfIMFyBjqDsKJbMDACwmA2V2xFDAPevQQw/Fiy++iAMPPBCAGpzQ/tN43xfqfyS+uIwcAIBopyalqU4R3QDLtQW47E0J3qL4857GEsvjjRlu8HvXg+F59LpkLrLHTkbrrk0+pQAF2Rbq2ZHCZMFTxmIyqsEOhQUDJekb4WllLNq+m1Xccd+NNkGUYNDKWKxGGj3bTrPDrY/l1TM7UjDFO9loU7WSKrAkCWpmB29IqjKWznoQaGN8i3LVXlVJM8bXp4yl+3wWq2PtWf2EOZwmpYoo6GUr3tg4l0dV1zsgK77NSYG2oG5SlZBFQVtmR7DBDh4OZ3K8z1NRp/k1xcXF+Prrr7Fv3z7U1tbC5XJBlmVcdtllYBgGxx9/PC6//PI4bSqJBGdry+wg8SXLChrtLgiiDAPPxnysnZr+agRnywGAtJzIoteiKjIUtxOMyRKT56ltZfB1eW9ccOr1qPnyefSeNg85R53tUwpQkGNG7R9qV/pk6PJPoksSBfAAjIa20bMAoMgSGDa4hU4icGYbMkdOgLnfEFQtfhI9z7sduUefHeMyFllfuGdYqIylvRaHgEyremKiBTu08ZMkdrQyi2S6wiyLAjjeCIY3xb23QiDq+zfwa6ON8X30f+NQXmPXbyf6c0/pxg1KeY7xKWMJmZ/RswA8QbT47Vf+xs4CXhlsogwY47Y5MRdyZoeZp8yOGAqq+KtPnz7o06dPh/uLioowZsyYqG8UiT5eL2NpSOyGpBlZVrCrognzXi9BVX2rfqUjlgsA2VPry9myAahlLOnG+0NcdraAjVGwQ5QULP5lD46p+AnG+j1QRAF8pu+47vxsC0RJRpPdjZzMxF7hItEnSyIkhYHRM3pWC3ZAkoL8hE0c1mBC87rlEKr3QG5tgSG3Z0yfzy1I+uI2w9Ozg0pa2zTZ3RjQWw028VTGEjfePSWEJAl2KNo0Ft6QNGUs3j13AmFZBnlZZjz53u9ocrjx9E3j47Nxnei2PTskGTwfYWaHn9GzADyNb+O3X+ljZ9s3KNWCuimWwebyZHwGm9lhNVGwI5bCPqJTKUr3whjNYAwmyuyIs0a7Sw90APHpTq6IAljeqPdmScfxs4roBmMwAwCk1tg1KdVSzBlFXYTIro49Qmj8bGqTRBESWL2ppKh4Mju6wUQWoK2e3buuPVbcom9mhywrtMDz0tLa1rODGpTGj/eJlpgkmTSKPo0lecpYXEEEOzQD++WirLwJziR4f3sf27rVNBZR7dmhnTCHPY0lQM+O+GZ2OGAycshtd8FHL2NJtWCHWwLHMgEnF7WnlrEk/r2SqsK67vTnn39GeztIjDEMAy4jFyIFO+IqEd3JtTIWhjOANWekZxmL6AafmQehbn9MJ7JoJyKsrAU7HB0eU5CjBl1qGltxyAE5MdsWkhiyKEJS1CZyBp6FrJexdI+Fi3YiFY8TKu/MDptFPalvaRVgNXe88phuFEVBs0NApk19XTiWActQz454cHtNgxBECUDi90d9GgtvhNKSHJ/hbkEKOi1/UP88yLKCv/Y2YNjBBTHess75NijtPhcdtJ4d2rhutxD6sSBwZkd8g2gVtXb0zLN2yOJL5Z4dwWZ1AFTGEmuJz9UjccPZctKypCGRtO7k3mLdnVwLdgAAZ8tOy8wOWXCD85STyDGcyKIFOxhF/ZDyG+zIVv/+tZTZkZIUyTezQ1K0MpbusXDRru7FI7NDENuuDGd4elPYqW8HAMDhFCHLit6zA1CzO1Ltimcy8g4oiWKSZHZ4prEwfPJMYwmmjEVz2IHq5++fZXWx3KTg+JSxdKPMjnY9O8KaxiIJSZLZYe/QrwNI7cwOcwjBDipjia2AmR2rV6/2uX3kkUcG/F4kvH8viS0+Ixfu6l2J3oy0onUnb9+zI5bdybUrQoAn2JGumR0Z6jx3KQ6ZHdAzOzoGNLIzTOA5BtUU7EhJsiQCYGHk1cwO0atBaXfQltkR+4WWW5Dbgh0W9RhFE1lUzZ4xvBmWti59PM9SGUscuH16diTH+1ZJwmksLkHWswy6kmUzok+hDVt3JT6b2PvYlk7TWBRFBmQpYGaH7IjPayHLCipqHThiYFGH7xlSdOqUS5BgMgRfPEFlLLEV8C8xbdo0Pd2IYRhs3rzZ7/ci0f73ktjiMnIh7dyQ6M1IK1o38munDIfVxCM304xeBbbYTmMR3GAMnswOazbc1btj9lzJSJElQJbilNmhXgXsrGcHyzLIy7agtrH7XFEiwVMkrYyF9Z3G0l0yO+LUs0OSZEiyAqPnSp7Wm6KlNTmuWieaFuzIsnkFOzg25dK7k5HgVcaSDCddiiKrUzR4Y9yvwHcmlMwOABjYLw+//1mV8CbE3iWF3S+zoy3Y4V1uFQz9MyhAZoccpyBafbMTbkHym9nBp3BmR6hlLKIkq+PZ+eSd4tZddRmiVRQlYDNS7XuR/Efih8vIgexyQBZi1xyTdCTJMu57rQSznv8Ff+1tiPkYNnWuevqWsWhj+jhrNsCwkGPYoFT7gGbkwGUsAFCQbUZNY/e5okSCp0iSp4yFA8syUBjOc383CXbomR2xPaHSrp5rCznK7PDV7HkdMtqVsSTDyXeq85nGkgSvt/ae1MpY4nVS2pVQenYAwKB+uWhocaGyzv/nYrzomTEM2816digw8Gz4ZSyezyCG7xjsYOMYRCvXJrF0VsaSJBlV0eJyh/ZesZrU4z5ld8RGwMyO3r17B/yhzr5Hkpc+ftbeADanR4K3Jn14X9Gvi8PVfVl0g/MKdsitLQE7cqci7QOc4Y1gzba49OyAHuzwv5AqyLHgr90NMdsOkjiKLEIGC57zBDFZzwKn2wQ7tJ4dsd1ebZKAydA2ehZoO8lPdy2ezA4t4wVQTwSojCX2BMF7GkviX29FP0k1gOUN3TazY1B/tZT0z131fq/qx4uWtcZaMrrXNBZJhpHnw57GogfN2MT27KioVddgPQusHb5n4NR/WzIEGaMp5AalJvVv1OoSkZ0RuzL3dBXw7Oe7774L+EOdfY8kL86WAwCQWuphoGBH3HgHO+qbY/9Bq4husHxbGQsASI4m8Jl5MX/uZCCLauYSY1CDHfHt2REos8OClY3lCU/nJTEgSZDBtv1dPQvLbtOzw3NiFesyFm2SgMFzsmQx8WBZhspYPJrtHYMdPMcmzSjUVOZdKpQMJ13aSSjLG6DwRkASocgSGDax6e0uQdLL0IJxYM8smI0ctpbVYfwRfWO4ZZ3TjnGcJaObZXbI4D2Nr1mWCb1nhxY081vGEr8gWnmtAyzLoCjXT7AjZctYRORkmoN+vNXcFuwg0UfTWNIIp2V2tDQkdkPSjJbNwTJAXVN8gh3eDUoBpFWTUq2MheVN4MwZsc3s0D6gPYsK2ek/2JGfY4Ygymiy04ldqlFkETLT9lHKcN2tjMWT2RHjVHmtL4J2ssQwDGxmA1poGgsAoLm1YxkLZXbEh0/PjiR4vbX3okMA7J63hyQk9n0iyQpESQkpNZ9jGRx2YC7+3J3YJqVtmR1Z3SuzQ1R7dgBqRlzowQ5PZgfvp0GpwRi3xrcVNXYU5lj0f4s3LdiRDO+7aAo3s4PKWGKDgh1phMvIAQCILYnvjp1OapvUKwkH9MiMU7DDu2dHDgCk1chhnzIWS0ZMe3aIkgwGMqCoH9T+GpQCbeNna2giS+qRJciM16JGz+zoHosWvWdHjIMz2kLdOw0+w2qAncpYAKiZHVYz73NCwHNMyl3xTEZuIbkyO2RPwP695Tvw/vK/AQC799VClhOX5aOXoYVwAgcAA/vlYue+xpBP1KNJm8bCWTK7XWaHNq3EaOB89tNgdJrZwRmhCO649E4sr7X77dcBpHJmR2g9OyyU2RFTCQl2/PDDD5g/fz7mz5+fiKdPW5w1C2BYyuyIs5oGJ0xGDn17ZKI+bpkdWrAjCwDSqkmpHuyIQxmLIMng0LZYCDTWriCHgh0pS5b0pqSA18Ky22V2xDbrSFvM+gQ7LJTZoWl2uJHhVcICaGUsqXUSkIy8T7SS4fVualaD5rUtEgSo75dn3luFRnvimsu7/QQrgzGoXx4kWcH2PQ0x2KogeY7FrCWjm01jUcB5ekGZDFzoPTv0YIf/zA5A0fuNxVJFrR09C/wHO7TgbsoFO0LM7LCaKNgRSwkJdnz33XeYP38+nnvuubB/R0tLCyZNmoS9e/cCABYuXIhJkybhjDPOwKxZs+B2qwu3LVu2YMqUKZg4cSLuvPNOiGL67kgMy4GzZkGizI64qm1sRX6WGflZZtQ1xXaxoiiKb2aH1rPD3hTT500mWhkLwxtjXsYiSQo4tC1AApWx6MEOGj+bemQJik8Ziyezo5t0l49fzw719TB41fyrwQ4q7QLURq1ZVt+TEp7KWOLCu2eHKCa+R4roVtcJIjgIinrC1NBoT+gJob/MrGAM7KeWT2/dVRf1bQqWIgkAy4E1WbtZsKOtjMVo4MLu2eF/9Kx6rNHWS7HS0iqg2SGgV37Hfh0AZXZoLDSNJaa6ZRnLhg0bcOGFF6KsrAwAsHPnTrz22mv44IMP8Pnnn0OWZbz33nsAgFtuuQV33303vv76ayiKgg8//DCBW554XEZuWpU0JIPaRifysy3IzTKj1SXGNnIri4Ai6x9kjMkKhjOkVWaH7GcaS6xSNUVJBs+0fUgHalCanWECxzKopfGzKYdRfDM7WK67lbFomR2xnsaivk9MPmUsRho96xEwsyPFTgKSkeB1EpkMIzC1ALqocBAU9XjSM5v3CRTGW7iZHdkZJvQqsOHPXYm7yKZIAhiOB2s0Q3E741K6EQ1ag1LAE+wId/Ssn8wOrYl9rMcaV3jGzgaaxpOKwQ5FUULv2UFlLDHVLYMdH374IebMmYOioiIAgNFoxJw5c5CRkQGGYXDYYYdh//792LdvH5xOJ0aOHAkAmDJlCr766qsEbnnicRk51LMjzmqbnMjPMSMvSx0nFctSFj2rwaB+kDEMA9aWnVYBLr2TvUHt2QFFDlheEilRlMFB+5BmAgY7OJZBfraZylhSECNLgN8ylsSfNHVFywQD4pDZIfrJ7LBSGYumxeH2mcQCUBlLvHifaAlJkNlh8xxCMjKtED1lLNdMHoRsW+JGUrYFK0M/bRjYLxd/ltUlLMigSCIYzgDWaFHXA0kyyrcrotjWsyO8MhZPg1K/mR3qsSbWr0W5Z+xsr0BlLCnYoFQQZSgKQszs8AQ7nPR5GAsBR88ms/vvv9/ndp8+fdCnTx8AQF1dHRYsWIAHH3wQVVVVKCws1B9XWFiIysrKkJ+vtLQ0sg1OkLVr13a4z+qUYaiv8vs9En2yoqCmwQGhlUNNpRrkWLn6D/TvEZtFC+NqQQ6APfsrsMPzN84ED+f+PdiTYn/zQPuwcd822ABs+nMb+Poa2ABsWP0bZEt21Ldhz75GGFhPc1KDGUpri9/tslituP6c4bBZDNhTXovmhiq0OvwHRkj3IksCBEbR/+6tLjUFfeeO7XC7/KfuBivmx2lZQq6n50xNZQV2x/D5/ixT9/e/tv2Jhkr1SmNLYyOaHW6sWbMm7Ucy1zW1oneO79+8pbkRTS1Syn5eJ8u/q2x3W5nn3zvLsNZQk8CtAQxV25AB4IzjDoOVFYDvv4OrsRzr1iUugLqnWj2u7SrbCaO7PKSftTItqG924bufViHHFv/TDmtFOQyKgr0VVbACWL+6BIrJ/8l3tEWyj7sFCTU16nrd5bSjWVRC+n189Q5kAti6fQekGt+LbIb9e5EBoHTDOsgZBWFvY1fWblLfW+W7t6GuvGOgTGu6u2v3Xqxdmxrl1g6XuiasqtyPtWubg/45A8dg5+59WLs2dqXX0ZYsx/CudMtgRyCVlZW46qqrcM4556C4uBhr1671WUApihLWgmro0KEwmRIXUQ/H2rVrMXr06A731zX9iYb9G3HEqJEJn9meDhqaXZDlfRhyWH8MP7QA73y3AoW9DsToUbGZOS80VGHPCqD/QYcgc4T69y//6ytI9kYM9LM/dFeB9m8AaGJqUbMRGDbyCLj2Z6KydCkGHzIApp4Dor4dG/Zvgnm7uvAzZedDqNmLI0aO8LmSIssKdlU04fHXS1BV34qiXAtmTy/GoIFZYNn0PsFLBWu/BnijSd8fl/1WAzQA/Q7si6xR4b/nOtvHo0V2taJsmfp1Xk4WDo/h89VLuwHUYdTI4eiRpwaBdjVtx0+bNmHw0BGwmjumWqcLWVbgen8vDurXB6NHH67f/+3mNWh2NcZ8P0iEeOzfwdpUuRmAeqLVq3dfjB59cEK3p2WLC1W/A68v3QZeduHGLKBfn96wHjQiYdvE/1UNfFONIYMHYtjBoZ0cZ/dowNI1P8CQ2Sdma5/OVO3/Fc4mC3ocMhDVW5Zh2OGHwZDbM+bPG+k+Ln+wD31698Lo0YOxdH0JqhscIf0++1YJlWuBwwcPhanXQb7fyxBR+QcweOBhMVkbaX7Zvg45GS4cPfbIgI9hF+5DYVEPjB49OGbbEU81Da3Ax/tx6MEDMHp0v6B/LuP/qpGVk4/Ro0fGbuOiKJmO4S6Xq9PEhG5ZxuLPjh07cMEFF+Dss8/Gv//9bwBAz549UV1drT+mpqZGL31JV1xGLqDIkBzBRxtJ+LQeDfnZaoNSADFtUuo9dlXDpWkZizaNBQDkGE1kESUZJk69MsFZ1ck37UtZGu0uzPMEOgCgqr4V814vSWhnfRI9jCKBYduCW6xBa1Ca/LW33inMSoxrtwVPGYuxXRkLgLTv2+FwiZAV+OnZwaRUeneyEkQZWtxZ208TSXtfOiVGb1Ca6NILffRsiD07AGBAryyYjBy2Jqhvh9q03QDWqK7BukOTUllWIMlKWxmLMYwyFk/fKIZPXBlLRa0DPQM0J9XwPJdSPTtcYb5XLCYerdSgNCZSItjR0tKCK6+8EjfccAOmT5+u39+nTx+YTCY9zeazzz7DuHHjErWZSYHLUDtjp9PJbyLVevpzFORYYLMYYOBZ1MWyZ4ce7Gi7SsrZsiE5GrtNU65IeQd8WEsmAMRsIosoyjB5Ps8CBTsEUdYDHZqq+taU+nBPZywkwCtLjtXee3LiT5q64t2nI9Y9O1yC/9GzANK+b0ezXT1mZdnaTWOhnh1xIYiyXjMvSon/nNQCj4LC6T07Yv3+7Irbz/s3WBzH4tADcvBngiayKJIIcAYwnmCH0g2CHZKsvt5tDUpZ/RgaLG0/SnTPjkBjZzUGPrUaMTs9TUZDaVAKqE1KHdSgNCZSItjx0UcfoaamBm+88QbOPPNMnHnmmXj66acBAI899hgefPBBnHLKKXA4HLj00ksTvLWJxWvBDmpSGhe1DW2ZHQzDIDfLHNsGpdqHm3dmhzUbkEQoAZpnphrZZ/Ss+iErxTCzw+jp2cFa/Ac7DDyLolyLz31FuZaEdtYn0cMqMhiubVHDcd00syPG26tndhj8ZHak+fjZZof67++Q2UGjZ+NCEGWYjDxYlok4s0PyCqxLYQbZvYMdemZHmCNCZVlBfbMTVfUO1Dc79R4JoWobPRve59bAA3Px977GkLMTokISPdNY1M9hOUYNy6NJC7pxbPjTWPSx4gzfYR/QR8/GMNghiBJqG1vRK8AkFo2BZ33GP3d34WZ2WE0GmsYSI926Z8d3330HALj88stx+eWX+33MoEGD8NFHH8Vxq5Ibl5EDgIId8VLb6ATLADkZas+X/CxznDI7fMtYAEByNOplHalMEd1geKM6icacAQCQW2MT7BAkGcYuyliybSbMnl6sl7JoPTsS2VmfRA8LGfApY/EsIrtFsEP0+jq2AQe3IINh1GwFTYZFPU6lexmLFuzIahfsMNDo2bgQRAkGnvVk0oSf2SE21cK+rQQZQ46DIjhh31aCzBETwBpCO9ZrWRyiwkHwTPsK5/2p9Ytq/9nTr2fo/aLCPYHTDOqfh49XbMeOvY04fEBeWL8jXOro2e5VxqIFOXle/TuZDJw+0Sponv1ob60T93+w3mcf6MVpwY7YHXsrah1QlMBjZzUGnk2pTFctKBVyZoeJp4l9MUKXFtMMZ8sBAIgtDQndjnRR2+hETqYZnGeBn5tlSlyww94Ys+dNJlqwA4CatsqwMStjkSQFRtYT7LBpwQ7fDyuWZdCvZxbmXn0UHrz+GNx37dFhLTZJcmIh+6QJd9vMDjG22+sWJBh4zqdJOJWxqJo9wR4t00XDcyyEJCirSHVuUYaBZz0nXeFlHkhOO+xbS1D3/ftoLduA/e/OQdPvy6AIofdm0k5ARbBtZSxhBDui2S8qkp4dgDp+FkBCSlkUUQDD8WA8mR2xGkUfTVqQs/3o2VDKkbXPoMc9gQ6gbR9o8exOcgyD3BXa2Nkugh18igV1I+rZQZkdMUHBjjTDGs1gjBZIdsrsiIfaxlbkZ5v123mZsS1j0T64WEO7MhakUbBD8Ap2MAxYS0ZMy1gMWrBDL2PpGFhhWQYtrQJmPf8LymvtFOhIIRxksF5lLAYDB1Fhu1XPDsZoiXlPALco+TQnBahBqabFk9mRSWUsCSGKMow8p2bShBlc4sw2ZI46CT3OuhFVnzwOsb4cPc66Sc/4C4Ua7GAggfVqUBr6eySa/aLcehlLeMGO3EwzeuRZE9KkVJFEMDzfrTI7tLIOLRPOaOCgKAjpb6cFOyrqfQMaVfWtED2J/bHM6Cv3BDt6FnTeoDTlyljCzOywmnk4XLH7LIxWSVt3FLCMZfXq1TF70qqqqpj9btI1PiOXyljipKbRib5FGfrtvGwz7E4RTrcIszH6VWSK4K9BaQ6ANAp2iG4w3sEesy1mmR2CKCNT69mhlbE4/fdGsZjVv7eDum2nDElWOmR28BwLGWy3yOzQg6Mmq57yHCuCKHeo97eYeE8gMM17dngalGqZLhqeY/WpDBwFSGNG8GR28BwT0RVm2dGEmmVv6LebN65A7rgLwihjcQOcAQDj+X94wQ6tX5R3wCPcflEuQQLDIKJeU4P65aH075qwfz5ciiR6yli6T88OSevZ4TWNBVCDTsEGnLTPoLxcm0/AoyjXAoNJ3SfD7QUTjIpaB8xGTi/jDiTlylj0zI7Q1vixnMYSzZK27ijgX2LatGk+6aYkdXAZOZCojCUu6hpbMeKQtpn0uZnqlYX6Jhd6FcQg2KE3KG37cOGs6kSSdAl2yF5lLADAmjNiOnpWz+wI0LNDYzWpi1YKdqQOtyCAYxSwXsEOA89CBKuP/UtqntIV1mSJ+YQCl59FOsMwyLAYqIylVYDNzOsnNhqeU9dgkiSDY8O7ok665hYl8DwLQwQjMLUyFobn0euSubBvW4PWXZuQI7iAUIMdogB4jilFeVaIIgspjHKYaPaLcgtyhzK0UA3sl4sf1u1FTUMrCnIsXf9AlKg9O3j1NWW5bjGNRcvoMnhldgDqcTQj4E/5UrP1GMy64ijc/8Yq330g24BGIKYZfeU1dvTMt3W5zxi48MvHklEkmR1uUYYoyT69raIhUEnbYzeM089LUlmXZ1uxGFdJQZTE4jJy4SrfkejNSHlOlwi7U0SedxmL5+u6Jid6dTGOKxz+Rs8yHK+WcjjSI9ihCG6wPsEOW8walHoHO1ijGQxvDHjVyOrJ7GiNYZoiiS/B87dked/MDklhY94DIxpkScvssEFsbY7pcwmek6X2MiyGtC9jaba7kWkzdrhfu4ouSnLY5QOka4Iow2riwfNM2GVDnNmGzJETYDloBCo/fhQFp16L3GOmhF3GongyOnrk2+Cu4OBsDT0bQesX9a9zhsNi5JGTaULvgoywruS6BQmmMCexaAb1b+vbcWxOn4h+Vyi0zA6GYcAaLd0is6Njg1L1tXeFMM1G8Uyh6dcrG7MuGwOnW9T3CUZrfBtiZocsKxAczWgUeJgMHDI4AZzFf/ilotaOA3pkdvk7DTyXFCOfo0XP7AijQSkAtLrEDiWNkYpmSVt31OmRKxaBjlj+XhIczpYDyd6Q6M1IebWe3hz52W1XMPKy2oIdseCvQSmg/c3TJNjRroyFNdti17NDlGHwlLEwHA/WZAlYxmI2URlLqnG7PMGCdpkdEljI3aCMRcsEY83WmHblB9QFoL+TpQyrQe9Zka6aHe4OY2eBtnr9dFmQJopaxsLBwIWf2QEArMEEx/a1EKr3QKjdF1agA/BcbfdMeOqRZ4UIDk5HeGsGRVEw7/VVmPX8L9hb1RJ2ynoo5ROBDOidDSPP4s+y+JZRq5kyavCIMZq7R88O0bdnh1YS4RZC7NnBG8CyDH7duB+znv8FC5dvA8syYFgOYNiQenbIsgJ3Yw1aSn9CyYY9cDfUoLn0R0jujllHsqygss7R5SQWABE1Bk5GWmZH+x5VXdGDHTFYI2olbd7CLWnrjgJmdnz77bfx3A4SR3xGLhS3E7K7Va9hJNGnjZAqyGnL7MjNVNNHY9Wk1F9mB6A2KU2bzA7R7TNil7Nkxqxnh5pu6AnecjxYk9Vvg1IA4FgGFhNH3bZTiCB4ggWGjpkdcoyDB9GgePXsiHWPEXW8p7/MDqM+ejVdtTgEZLbr1wG0nehQk9LY0kfPRpDZodGChpH0QlBENxRPsKNnng2CwsMVRmYHoE6E0xoROt3hn1D6K0MLFc+xOOSAHGyN90QWWdT7KrFGc7eYxtK+Z4fW78gdQmYHPFNogLa/vffakzEYQypjERzNsP9ZgqZfFuKY0/6Lls+fhZPnYR10NDijb2lUbaMTgiijV37nzUkBz9SpFArougQJJmPoJV9Ws6fUOQZrxGybCbdfdiQeemt1xCVt3VHAYEefPvFLMSPxxWXkAACklgaweRTsiJXaxo6ZHVk2I3iOiWFmh3pFiGlX383ZsuCu2hWT50w2iugCw+fqt1lPg1JFkcEw0Y1ii5ICg8GT2cFqwY7ACymLyZA0mR2yrKDR7tKb82XbTGnRqCqatMwOjms7UdV6dkjdoIxF0Xt2qJkdiqLErMzULXRsUAqoZSxa1/501eRwo4efk4K2YAdlw8aSIMowGCLr2aHRAoiRTLlQRBEKqx5TeuZbIYCD2xV6zw4AqG5o+zxyRRDsUMtYIi+lGtQvD5//9HfA4GcsKKIAhteCHZZukdnRWc+OYGllLIBaVg34ZhUzvDGkoFyjwKPEdSjGn3Y9qj99GACQddHDqLSzaKmpw6D+uWAYRs0AESQ8eP0xyMk0QZaVTtcWKdeg1C3CHGIJCxDbzA6WZdBsd+OqM4ehV74V2ZmmtFrzRb9DIkl6XIZ6Iii21MOQ1yvBW5O6ahvVRUZ+VltmB8MwyM0yxyzYoTbn7HiFkLNmQ7I3xeQ5k40stC9jyQAUGYqrFYw5un1SBFGGgVFPRBiOB2OyBmxQCqgfZg5n4q/4p3tn7mgR3G6wADjvnh08C1npHtNYFMlrGgsUQBb1dO9oc4uSPmrWm81KPTtaHG6/Ndo8T5kd8eAWZBg4dRpLKGUC/kQls0NyQ/ZkdhTkWFAJDmKYwY6q+rbPI5c7/GNSoGBlqAb2y4X4vYy/9zViYL+8iH9fMLxP+lmjuVv07Gg/elYLNIUSsNIbswJ6Rmlji1tvgMnwxpAyEE0GDmMPyUD958+2befWnyANmYRb5/+KolwLzj7uYAwakI8H31wV9Noi5YIdYQYGvXt2xEJ1gxPzF63HxacMwgUnDYzJcySr9CjWIT54T7CD+nbEVl2jEzYzr/dq0ORlmlHfFN7CpSuK4AZr6Lho5mw5kJ0tMe28nSwUsWODUkDtlh9tkiyDZ9TFB6OXsQQOdqhz1BN/EhyoM3ejPTb7ZaoS3Or7iTN4ZXZwamaH3C0yO7SeHTbP7dhts1uQYQzQoNTuFNK2l5csK2hpFfwGO7SrupGMQyVd07Lb1MyOyHoHaO8pWQz/WKqIAmRGXTcYeBYya/DbFyEY3sGORJexAMCg/mqA489d8evboTUoBdSeHd1pGoveoNRr9GywvIM83ifQ2vqT5Q1QQthPMzgBhv3rwfA8el5yLyyjJ0HavwV9co2YceEo9O2Rifwcqx7oAIJbWxh4NqUCuk63FHJzUqCtib0jRk3stX0glQJLwaLMjjTEZeShaMrNesNKyWkHF+Ur3gSoaWxFvp/xannZZuytik3DTEVyd2hOCgCcLRsAINmbwGflx+S5k4XSbvQsZ1Y7havjZ4ui+lyiKIP3dDUHxwUV7IjVHPVQpHtn7mgRBAEmdMzs6D4NSj2ZHZ7eTWowtOMxKxolT4IoweDnynCm1QhZVtDqEvWa5XhKdDmXGugBMv1kvWijZ4UkOBFI9OsUS6KknsjzHBNxyZBexhJRzw5Bz+zgORYsb4AcZllMdX0rsjOMaHYIcEaU2SGhX68sn/ViOGvHvCwzinIt+LOsDmeOOzjs7QmWIkuAIuvBju5SxiK1y+zQAk2hBTsE/d/tHeiqb3aiMNeilrGEkNnBWTIg9i9GQVE/1HzzBnLPvwdmVgZvy8IJ/8jGCf84EBW19pDXFnyqZXa4I8zsiNEaUQt2hNT3JUVQsCMNKaILYmMNjD0PgthUC/u2EmSOmAA2xFnwpHO1jU6fEhZNbqYJG7fXxOQ5FSFwGQsASI7G1A92tC9jsaiLsVg0KRUlBRwjAywHhmHV3gedBjsMaGiOTaArFFpnbu9FSTp15o4WydOglDe27W8GjoWj25SxiGB4o371z9/CN1olT4Fq/jM8jTlbHELcgx3JUM6lNWf1O40lScpYkuF1iiW3EMXMDs/7PrKeHQJkRl2PGXgWrMEIxRneaOjq+lYU5lohiC0R9ezIshkx7bieaCn9AZkjT4LsaAp77TiwXx7+jFOTUu3voR3jmG5SxiKKatBND3bwkfXsaHWJyMsyoa7JpZdRq8GO0PbTHTsrULh8DgDAamTBmnzHzpqMXMhrC0OKNSh1CxJMxtBPr7XMjliVsTjTOLODVrZpRnLaYd+6CvU/L4LicmD/u3PQ9PsyKAKlr0dbbaPTpzmpJi/bjJZWISbRVbURV8eFB5ehZXak9kQWRVE6ZHawnsyOWIyfFSQZPCO31QObLJBdrVAU/x8mFlNylLFk20yYPb1YH0WWbp25o0UvY/HK7NBGz3aLYIenx48WIPVX5hatkifthLI9rY9HS2v8S+ySoZyr2a6ebGTZknf0bDK8TrEiywokWYGBVzM7hGhldkQS7JDckBn15NbAs+qkCym8Uq+qegcKcywwG7mQTpTbO+fYPlDK1qLu+/fh3L0porXjoH65qK5v1fuaxZIWwNWOcazR0i3KWDr07DCGF+zQejA5XSJ6F6prId9gR2jH3ca6Bv1rf+vJcNYWKdezI8LMjlitEamMhaQNzmxD5qiTwGfmofIjtZty36ufDHsePPFPkmQ0NDuRn90xsyMvU72vrskZ1AzyUCidNCgF0iDYIWkLm7YPVi3NVm6NRWaHDA7ewQ4bAAWK2wnG1HG6gtXMJ8U0FpZl0K9nFq45exhsZgN6FdiQm2lOiau08STqmR1t7zlt9Gz3CHaoac5aqrO/hW+0Sp7cYqCeHepJfiLGzyZDOVezpzmrv+atfJL07EiG1ylWtJNKA8+C59iIX2u9Z0cEF5AUUYTEqe8VnmPBG01gIMHhFGHzM6I44O9RFFQ3tGL0oB4oK2+C0xV+sOPpj//Ef845Aoecno+K9+8DEP7acWA/tW/c1l31OHp4bCcC6msC79GzohuKLHWYWpdMxA5lLNro2RD2z3aZHT3zbNj8d61XsMMQcsarvbFtDSk5mjoMOdDWFo/dMC7okjfe07MjltPA4sklSMjzs/bvCsexMPJs7MtYIsxe644osyMNyY4m1H73rn67eeOKiD6YSUf1zS7ICvz27Mj1lLbEoklp+6wGjd6zw5HiwQ5PnTTbfhoLtJ4dbWRZQX2zE1X1DtQ3OyHLoV81E8X2wQ41wBFo/KzVbEBrkjRjdLpFzHt9FWY9/wtMBo4CHWHQy1h439Gz3SezQ+gys4NjGf0qnSbUkidFUUcR+mtwmMjMDq2cy1u8y7laPEGeLH8NSpOkjCUZXqdYETxXytUylsivMGsTjiLr2eGG5NWg1Gg2wcBIIWdCNNndcLklFOVaYDJwEfXsyLAZ0T+PQd2KBfp94a4dB/TOxp1XjEHPfFvYn71B047DXmUsAJI+u6OtZ4f6uayXsYQ0jUX06tmhBsqyM0z62jOcMhZHc9tUv0AXz1iWQW6mGUW51qAuoiTLcS5aws3sANQ1ImV2RF/3/6QiIVHLWErA8Dx6XzoP2WMno3XXJipjiTJ97Kyf6K52X11z9D9sA2V2MEYLGM6Q+pkdng9uxmt8JmM0AyzncwVDq0Gf+fSPuHLeN5j59I/YVdEU0qJLS3/mIAFsWxkLAMgu/1dLrCYeshJaKmqsVDe0LZzT8cMvGrTMDoOhXWYHWEBO/N+4K4rkCXZomR1+AjQr/9iP/50/KqKSJ20R6290pc2rZ0e8ZdtMuPOKxJZzNXXWs4NLjpOAbJsJd1w+JiXL3rRjn5Fn9SvMkdBHz0ZUxiJARFsZi9liAQ8JNY2hrRmqPdk4hbmeMpYIenZcemJ/yGVr1bXjZQ+EvXaUZQX7qlvwyuKNuOGJ78P67A1FW2ZHWxkLgKRvUqqXsXgCASzLwMizoTUoFdXRs7KswOmWYDZxyMs2+2R2hLqfuu1tF40kR1MnjwyewZPFlCrrEJcghjWNBVBLWWKd2SFEOF67O6IyljTDmW3IHDkBpr4D0fDrpyia/F/kCC4qY4myWs+ixH+DUk+wI8SFSzBkUQDnJ7ODYRhwtuzUz+zQgh1emR0Mw4A123x6djS0+K9Bf+yGcfrfpyuSrH5gcIy/zA7/TUot5rZu2+YwGlhFUw0FOyImeU5sDMZ2mR0KC0XuDpkdbjCcsS2zo10ZS3mNHa/932acfkx/XHXmMPQqsCE7wxjyJA6XoJUKBG5Qam+NfxkLyzLIyjDiqjOHqVMi8ixxnzLS4hDAMPBbnqBf8RQTmwnGsgwMPIurzhyGTKsBPfNtyMtKjbI37dhn4NmoNErUgx0RlbEIkDzBDo5lYbZaIDESqkLM7KhuUD+HCnOtMBv5sDM7ZFnBvHc34u6LBmHgyf3RvPEH5J94WVhrx0D9X0L57A2FNk7bu4wFQNI3KW3foBRQJ7KEFOyQRTA8D5cgQVEAi5FHbqbZp2eHHGLPDsHR1ig3WutJ7TiXKuuQSDI7LCY+Zg1K2zI7kv9CTLRRZkeK0VLze/Q5OGB6IGswoXHlpxCqd4E1mCjQEQN6sMNPg9IsmxEcy6A+RpkdrJ9gB6CWskgtKR7s8KQOty/l4cwZkFvVYMfuiiZU1TkirkHXHsspUttCSusP4vQf7LDGuAFVKKq9/v2JvnLcXUmehbT3NBaeZyF2l8wOsX1mh+/C940lm8BzDCYdexAeeHMVVm+uCKu3i1YqYPKT2WEx8WBZJiFlLIAadH7gzVV45bONCelb02x3w2Y2gPPzvHqD0iR4fzY7BDzwplr2tquiKSUCHUBb/TrPcz69A8Kl9+wIM7NDbbKtZnbwHAOWZWCxWmCAhNqG0E7Qtc+4olwrTEbOZ/xoKNyiBEGUsWdvDcoXzIFQvTvstWO8+7+0TWNRj3FMN8nsaN+zA1CDHSE1KPX0ZNKmcFjMPPKy2oIdbIhlLIqi6GsbxmCKWqZwskydihaXIIWf2WHm4XDF5rOwrWdHarzOoaBgRwrxTs2/7tEfOk0PZHhTyF2YSfBqG1vBc6zfDvtqPaNJD4hEkzp61n+wg7WmfmaHtsBs/xqwZhuk1hZ8/uMO3PjkD2iyuyOuQZc87yvWX8+OAFeNtNGaDmfi33uU2RE5UQt2GHynschg22rFk1hbsKPj6Nk/tldj5cZynDvhUPTIs4JlEMHJUuDMDoZhkGExJKSMBVD7GgCJS+1tdgjI9FPCAiRPGQvgW3q3fU9D4jYkyrzLWNp6B0QQ7JAinMYiiwAUT7BD3R7eaALLKKhtDDzW3J+qegdMRg6ZVgPMRj7s8kmtKaaJ9QRyAvSkCka8+7+0NS3Xyli0nh3JndnRvmcHoE5kCWf0bKsno8dsVIMdjS0uSJIcchmL3SnCoDghsUZwthzIUStjSZ3MDlGSIUpKUmZ2OCmzg6SCUMbDhdOYiASvttGJvOzAVwlzs8yob4pBsENy+5RweONsOWnQs8PTeKvdayDyFuzZU4lXPivFyMMKMbBfTsSjV7Wu/ax3Zod21ShAh3OtjCUZJrJQz47IyZ6eHSzn27NDVFhASf4Fhd6zwxMc1E4MJEnGK4tLUZRnxVnHHQKGYWCKIA3erWd2+F8AZloNCcvsaGxRjxmJ6qPT3Or2O4kFAHhe/fxIhmCH9jdkGeCvFAx2aGUsQGSvd1sZS5iZHZ4AqqBwegBAe382NIQ2Pr26vhVFuRbP+5eDK9L3L6P+fCQlIPEee96W2dHuMzrJMzsESQbHMj7TSUyhlrFIAsDx+hQei4lDXpYJiqKW8oY6era2sRUWxg3FaI1qWXQqlbHo75UwMzus5jj07EiB1zlU1LMjhYSSHsjwBsgRdAvv7mRZQaPdFfRorFDVNjr99uvQ5GWZUVkX2lWaYKiZHf4XzpwtC5KjMWXGe/mjLTDtbqCl3gEDz2Lnvkbs+rsFfVgH/nPeCJxc3M9zNVmt1e+ZZ0VOlinkfUBLLWch6Z3eWXPnPTv0MpYkCHZQZkfkZL3Tf9vCRpvGwnSXMhZrNhjeN7NjWckulJU34fZLj9QDFJE0ONQWgAY/ZSyAOn62JQGjZwGgsUV93kSN42u2u5HpJwMQaLvimejRs0DbFIiD+uakbLCD9zrpsoR57t3WoDS8nh3aRShRacvs0IIdTU2hBjscKMxVP5PMkZSxeN6/RkQe7NBGk86eXgx7q4AeeVbkZ1tiVhbVvkFpd5nGIkqKvj9qjAY2tGOwltnhasvsyM1SX+e6JicKeCMgS0GP4a1tcMLCCGBNVnDWbIiNVcFvSydSaRqL9vcxhdmTzWLiY1LmrChKWxkLNShtc/jhh8f8yRmGwebNm2P+POlCSw/0DngESg9kDaFFdFOJVu6jZcFoVxb69cyK2gdubWMrDuqTHfD7eVlmbN5ZF5Xn0mi1voHKWDhbNiCJkF0OcJ7eEqlG8gQ7nlpUij/qy1CUa8H/zh+FrPxcFDgrMWZsf/2xHMfisQVrccaxA3D5pCEhP5f2waxmdqivOWMwAwwbONjhKWNpjVFNZiiqG1qRm2lCfbMLgpT8J+bJSNaa33ktFHlObVDaPXp2uMHwvE/PjpZWAe9+9SeGHJSPo4f30h9rNrZdIQxVW6mA/wW1zWpAsz0xwY4mT+ZjospYWhwCehdk+P1eMpWxaCe8Qw/Kx+IfdqC2sdVvT6ruRkvpNvBclDI7Ihs9q2UiCArrldnhmVjUFNoFkuqGVhzcNwcA9J4d4VzscOnBDk8gJ4IyFkANeOzY24inF67DK3ecGNP+L9o6t2NmR3KXsYiS7NOvAwBMBj6kfgva6NlWr54dWiPk+iYXCj0ZsIro1nuZdEbL7OAtWeCsWXCVbw96WzqTSpkdWkAx2cpY3KIMraNBMvSAireAZSxagyZFUWL6H4meUNIDGd4IyCKUbrAgj7ZQyn3CoSgKahqdKMgJ/OGRm2VGs8Md3YO7pNb6Bg525KgPS+FSFkeLWj5S2ah+WFTVt+KZhesw6NC+gLsViuL7eht5NuxmTW1lLG09OxiGAWuyBKxptiZJGYuiKKhpaEXvQvUkKxUWGYkgt2t+B6g11hJYMN2hjEULjmrBDlHEB8u2otnhxtVnDvVNoTZyYZex6CdLATM7EtezQ8vsSFQZS5PDjUxboDKW5GlQqr0+Qw7KB5A6fTvc3mUsUTjp0jM7JKHD501wP+/pIeMV7NCajrc6HEHX2zvdIhpb3CjSMzvUEaThBHL0zCxPZociCR2aGYdKOxbE+rNHn4rVYRpLsmd2yD79OgBPZkcYPTu047bF07MDUDM79CB3kIG52iYnLIwbRluGp4ylKax9vD0+hXp2uCIuYzHA5Zb0ni3R4l0aIyTosy6ROs2zoWBE96KlB95z9VFobHEhN9OMXgU2v1FzvUY7yIhuKol1N3B7qwC3ICE/u/MyFgCob3bqi5FIKQGac2o4q5ppIjsagfzeUXnOZCO6PVdpvQ5tVfWtUIw2QJGhuFrBeGW1GA2hza33eS5PEztGkcCwbc/HmqydZHZ4Rs8meBpLY4saaOuVb8Omv2tTYpGRCHoZC+tdxsJBAgtWCe8qajwpktqtn/VcOW5oaMaSn//GSWP66VeENZGUseiZHQGudmVYDGhJwOhZAHqQOxFN2yRZgb01cINSbUJLokfPAmpJzT2XDcch/fPQp9CGPjnhLeaTjU8ZS4SZHYqiePrgqD3RFMGtl00E/Ts8wRK3nzIWnpFQ1+RCj7yu1wzV+iQWdX1n9px8Od2S30bBndFO4AxK23tUdjnBBeg1E4y4lS5o2XeeYxxjUC/+JX1mh9gxsyOU0bOKLAGK7DONxWzikZNpAsN4gh09fHs1daW20YlDOAG8RQ12QJYgO+3gLJkh/Ms6agsydv+TcK0vTiSZHQDQ6paQYYleW01tzWkzh5YdlCoCBju+/fbbeG4HiRKWZZBlM+L6R77DFZMGY8rxh/p9XFuwQwDSLNgRSrlPOPSxs1mBX9e8LPUDt64pesEObRIJG7BnhxrsEO0NUXm+ZMTK6oe2oLR90BTlWsBbFbgBSE67Ph4WUE9Mwz3R1xZpjNLWswPQgh3+G5QaeHUBm+jMjuoGNRjTq0B9LSjYER6tmSDDeZexMGoZC6CWsnDJ2xpLz+zwHDPWbNoHk/FQXHLqoA6PNUehQWmgY2yG1Qh7qwBZVv6fvf8Ok+QqzIXx95yK3T15d0erDQorrRIKSDJIGBDGGBmDifbPBsMFE/w43gsY3e8aG1vYBoONbCPAfL7ftTHGGHC4RmDAZCGBhCQkAVrluKuws2HyTIdK5/z+OHWqq7urqquqq3p6Rvs+j55HO6G7p7vqhPe8YeiVpqu+ssP1ODzGIytgy0LdD2WNCyglhIjA2xFQdjznDBP2I7djzNiN9/23c0Gf/AHYST8DqpUTLDkshIm4oAIz73jok5/UrMFbt8UBRFayw9942l6vjUWDh4WVZjqyw89kkpkdMkPAsj2MZ1xySI+/GiY77CaUav5NriRcSld2dGd2UAVEMzZBZkeUjSU94RwOZm0EmR1i/TFZMwTZsVvsA9Lm980vN1ElNqhZA/Vrh73GaoFkx8aPc4NiUGVHQHa0XIxV8pOJ3ZBz90TNwEIJ5QijjthV2O7du4f5Ok6gQEyOGZiqKXgwQWYaVnY83TBZM/C7v3Ip/uozd3RkdhSVBi7JjpkEZce0VHYUOOgE3tR+yo4tbGMxFYYmgMmpMawtecFnW1u9Hw0ArLUOYDb4+UGUHXJiJswNbCyAJDviT40qhrrh1bMynFSSHaOwmdqMCBaUIWUPIQSciIUOZ17HtTFqaGd2iNd45PgKfvlnzsb0eO/YZegKltfzWf36tbGMVTQwLk6fagUu8NIgbF+0HS9YbA4Da34o60SMsgMANJVs+P3ptepwH7sDazf9C6on7UHjq38HoqqYOP/5m5/skEScQgeuwJTrKUF2LOVaXwXKDkZDyg6f7CBe6sr640uC0N7hKzuMQNmRnbCUGziFteetQatb5Xudd/5Ni+42FkDkdoy8ssPjvWSHnkHZ4bUVLa2G+B2pLJ2eEGQHzbgPWFxuQocNatSC9aRXXwG2DbZflMTXRo9zRcAaMLMjsDpbDoDiDqLlAdtETceRxfrIq06Lxuiuwk5gIOzapid6auXk+XRsZKGU4PPfeQhve+UFmKjpOHl7DVNjxbWxzK+ISTQps2Nb4JssJicESGFjqflMfL2YbvSRhL9QfOcbLoPtEezeMYbJMQOWI7IpWLMzzb4oZUc4s4EaVbjrS7G/VzXLSdvOAnnqt+uEsmMgBJlHStfCxre1cM8FRnQzKAONLY/i+FILnCg4dUcVP/v8fZE/b+pq/jaWwCoQT3YAwHrTGT7ZsW5DVQhcj28Y2TGWQHaoCt3wNhbFrGFu8gKc+rJpHPnsnwIAJn7lz7HsqNi+oa9scMg8FE1rt7Hk3XQxf/6hpj/fONnndzmPW5xC8zdM0nqhEqHsSINjS01QSoK1RtjGkhV2QHbYkL+dROingTqk0/xuZQcgGlk2h7KjO7MjA9khCwioUOQplATkycyEiaXVFog67v9sun3A+toqqM5BzRoUX9nBGoOvJ08oO9oIlB0FrxHl443XdHAuLJTd19dWRnGGoBMYKeya0XF0sYGVmJM4oj19lR31poNb7zmKT/zn3fi9v/keLNsrvHYWaOdyRGFizAD1fZNFQYZMxZEdhCqglXF4W9jGwl0bIBTX3zmHP/vkbZieMEEpCawrXqvTXqKr+ZUdnszs6Dq9T8rsAMrtUU+L40tN6CoN1EdbYZGxEWgrOzoXNlwqPbyN/ZyTIJpkOL5621N46/u/AZtRnLNnLNbGYRr5A0rtfgGlvo1j2PWzjuuhablBq8iwK/lkA81ETPUsIMiOUQgo3TOlYPH6zwT/tu//LlZWslWhjiKcEBE36KaLe+LzlG1neRpZwjYWuRmRG/WKyjMpO7ZNmlD8Da4kO/IQlvL+pV6njWUQaEMK3+1uYwE2i7KD9VbPqhkCSplUdoh2D9NQg5P8mQkTi6tWp529DxzXg9MQ9zs1q4EtuojA+y0VUFqUsqPgNWLbxiI+87IVVaOGXEcYhw8fLuwF7Nq1NYMSNxq7t4nJ8ZEnV3DJObM936fK05fsODgnmOifvnQvPvP1B/DoUyuBnL8ILKw0MTmmJ2aAKJRgatws1sbiL0QkkRX5vLVJeI2ta2Phrg2i6WhYbscJrVLxT9pavcqO3G0scpEWaWNJIju0Dc/smF9uYsd0ZWi+6a0KzjwwkB6yA1Tc+3yEyY6VVXEvLDfFZ+9wirvvO4xtV1qxNpY8p8JAm0SIDSj1lQ3rzeHau2QTy/apCo4uNmAPOSBvrZGc2QGIE/CNlnd7rTq8gz8EUVXs+tUPYP3+76N16B5MXvyyDX1dRSCcJxNsuvIGlEplRyXbiXnUY1iMBOOzVOJOV2lqskNYdNvhHKaf2TGIjYV67cOzwsiO0pUd7U2/BNXN0W9jiQgoNXQ1dbZQmORpdq2HpidMLK+1wKls4ep/nS6stFAhUrlUC/JailhPnlB2tFGasqPVSXZshfc6C3KRHT/90z9diNeHEIJ777134Mc5gV6cPC0u6IeeWIokO57Oyg5Jdrzgkj343DcfxCNPLeO5FxVHui2stBLDSSVmJooNCmorO+IXzkptcktXzzLXBlF1NFoOqmbIWuKftLEuZYem0dynyXJBTLpCKIlRSSQ7KoaKpbWNXWgdX25i+1RlS6Wgbwg8DzxKIOlfD6Nc7e1YYuPi+mG+LhQ4dnwdtrCxuLm8vvL60uMCSkM2lmFCKh9lrsGwT7vk2BPXxgJIG8vGtrEoZg332rtw6ZVvQ+PhOzBzxWtxeG4Rn/r2E/jdX+ldX2wmSIuQprQDQfPahgKyI1B25LGxiMdoeRRjqq/s8NdrUxWK+1PaWI4vNXDe6duCfxsDKTv8uc6zQStjYM31xDkuDSTxOSwbC8K5SroJ1lgr9XkHRWRmh9bOOelnt+OhWvSW5aFitDffMxMmGAfq/luTnuzwlUtGTbR4mTV4J2wsHRhU2REOKC0SkjyZHBOWuK3wXmfBQDYWzvnA/51AOTB1it07xvBQTG5HFvnaVsPBuVXUKhpO3l7DKSeN49Gnit38L6w0sW2qfwL79ETByo4goDQ+I0Cpbn1lB1X1npMMopkAVXqUHcLGklPZ4cYpO2qA5wbtON2omupoKDumqkFI3EZnAmxacDcIIw2DkFBmx4hC9d33Dnyyg1PU9PjGFFNXwHi+RZLleFAoCST13Rir+MqOxpDJDt9GsmNqY8iO1YYNQoCamaDsGJE2lh8deBRz/3w1nPmnQDUDX/zBMdx6zxEwtrnXcY6fjUBDmQaDKjsUmdmRR9kR2FgIND8LiPhK3AmTYD6FssNjHPMrrYDEA8IBpfltLHBaUMamxescUBnRnnuGEFCqqB0ErVB2bAIbS0RmB5COsGpnlaho2m6g7AHabYCrLXHvptkHLIbIDknmKdViDs+2FNnRJ4y7H+QhnQgoLQ7NbhvL0+yAKzfZkYeoIISAEILnPe95uPLKK/HiF78479OfQArs3zuVQHb48rWnYUDpwcMrOO3kCRBCsG/3ZAlkRyvwgCdBhEQVF1Dar3oW2PrKDu74NpaWi4rZ2ZBBzRq8Zndmh5Jb1dBhY6GdNhYAYK3ok6+qqW1oZofrMSyutrB9qgJKCRRKRiITYFOCeWAkStnh34NsdMmOMV0spKtVQcxyquKC06diW6kG2Sw5LovN6wA2LrNjVSo7pjYms2O94WCsoiVmRmkjQnZIlYI80d+/ZwqNlovD85s7t8N2WLDZKiqzg1Z8ZUcuG4v4naZHofrKDjmnT5hi09lv/b240gJjPNLGYuWwsdiOB02l4I4FtTYFAAMrO4ZpYwmHkwIys2PEbSwx1bNAOlI23ELTirCxAMByQ7z3qZQdq81esqMgW7Q2YDDwKMGyPagKjSX2+6FMG4uqkCATZCsQS1mQy8Zy//33p/o5xhjq9TqefPJJ3HTTTfj7v/97LC0tYWFhAR/84Aexfftmz/EebezfO4Xv3PmkUBp0bb6frtWzjHEcOrKKF/3EKQCAfbsn8e3bn8DSaiuYAAaB43pYrdvYllA7KzEzYWJ53Yqc1PKgXxsLACi1KbBWHdxzehYAWwGiStNA03Jx0ky143tKZaw3s0OjA7WxEDCA805lhymel9sNAFM9v1c1NraNRSyW221Bmpr/PXjag3lAlLIjyOwY3dMT4hMxP/f8s/Gzp1yC2tdvgK7y2I132POfFKgZBbFZij/pMnUFCiXDt7FIZYe/KRx6ZkfdTrSwAICqjgYZKZvbArLjFHHC/9ATy9gzO75hr2tQOG772lQHVLoVYWORjS6WF8rs8G0sY7qoIV6t24EcPQrHumpngcHaWCzHg64pYE4LyvgMiGYUltmRNzMrNVynx9or2lg2g7KjK6BUKjtSkR3tFpqm5XYQXzI8f6nBMY10+4D55RbGNTFnUENc37Q6AWdx8AzHLRVQ6ni58zqAdnZQGTaWiqG267WHTOxvNEptY6GUYnx8HOeeey7e9ra34ctf/jJOPfVU3H///XjnO99Z5lOfAID9e8ViJKqCVk6eeWSWmxnHlhpoWh5O2yVqs/btFonSj8SoO8LtHd1NHlGQ4WHbUhAnAbu+ln5BxBjH0loLx5YaWFprdUiIU5Ed1a1dP8uDzA63I7MDEHWA3Zkd+iABpS6DCt/L3JX0DiQpO1TYjrdhpxiydlYuhEeh2nKzgnAPvDucFGhndoywjUWOF7fefxwf/dcfQTWMxPaYwdocWGw4KSCUV2NVbUMyOyglmB4XG8dh21jWGinIjlG5P71OsmPv7BgMXUmsuN8McNxeZUfesZl3Vc/mUs76m9SmS9o2BqoCIKhqYr7vF1Iqx/jwBtcIyMp896+hUXC7BapXhDLCGkwZMbzqWaG8DK+biCYCSkfZSj842eGP5YqKluV12FhkAPViXTxOmn3AwkoTM6b4rOSBTlE2FkLEtb4VssNalhvMlXlRNYs/EGvZIudFG1JWzqhhqNWzMzMzuOaaa8A5x+23347//M//HObTP+1w+u4JUEoirSzkadrG8thhsck/7WSf7NglyI4oK4u7uoD1u28Ac532//c5qQnIjqn+NhZJiKStn5WqlKuuvRFvfd83cNW1N+LQkdWA8GhndiS3sQDF1IWNIphvY2laTk+AFzVr0cqOnBscx+NQiD9hRCg74mS+ZckU0yIgO8LKjhE4Od5s8BgH4SyS7KCBjWV0F29yMdywxeKKKFo7zC8Cg7Q52K4XG04qMVbRhm9jqduYqOnt8MZhV882ncQmFmB0MjvktSHHNUWh2LdrMtYqu1kQJjsGPWGW79EgmR2Ryg5CQDQdVVXM9fN9QkqPS2VHaB2iUAJNpbltLLqmgNktUN0UAZ/2oDaW4Wy6mOvAA+1YN61YBOBspNe/kQGlenobC9x2QGnTdmGGAko1lWKipmPBJzvSZHYsrLQwrXsguhm0jym1CbDGWiFB3FtlHWI5Xu68DomKoRZvY/GVHXqgqBrdtUkZGCrZAQAXXHABnvGMZwAArrvuumE//dMKpq7ilJPG8dCTyz3fo0/TNpaDc6sgBDhlpyA7ahUNO7dVe8gOr1VH/YFbsfidz6J18AAOf/pqrN759b6y1AV/EZLGxjLth0SlJTtW6hbe94lbcWxJPMexpSbe94lbsVIXr6mt7EjO7ACKqQsbRXDX9mWbXuBNlFDMMXjN7oDSwapnlShlhy/xjCM7yupRT4t5n+w4YWMZDI7rCWVPhI0FigwoHd0AaDleNByCqqmBqGrionfwzI7kBeBYRd8QZcdkTYcuN17DVnbUbYz3sQSNQvUsAJAusgMA9p8yhUeeWoE3Aq8vLwTZIT7/wZUdnZkGuTM7FBW2yzsCKomqwVTTKTuOLTUxXtVhdhH+pq7kUmbJDRx3LBDNADWq4NZgNhCFilDYsk/zbcvC/JrbsW762u1HAAweslomXI8FmS0SeTM7ugPbAWFlWVi1AZBUCqSFlSbGNTdY3wBC2QFwsObguT2qomyJdYhlD2ZjAcQasXAbS8uFaahbKgw2C4ZOdgDAGWecAc45HnzwwY14+qcV9u+dwkOPL/fI9Z6ubSwH51awc1utY+CPCilVzBrGLvwp7HjZb+LIv7wf7tIcTnrV7wY2kDgEyo6UAaUAUjeyOC4LJmyJY0vNYNAKqme15MwOYOsqO7hrg/mn6tHKjm4bi9jo55Gzel6MjcXwbSxxyg6Ztt3amHvv+FIDYxUteH+GRXYkWbA2IxyXCWVPgrJjlKtn5dhftzlqpuYrO8qxsQjPfx9lx4bYWET2gZ5hE1Ek1lPYWLQRqJ4FAPjXC3es4DrZv2cKtuPh8aOjXeOZBNv1igsolTYWowIQmq961g/UZIx35NwQRYdBPFDSPlSJw/GlBmZnetcghqbkzuwwVCLaznSzsIBPfQhzD/McWF4naXDcXwaMciOL60bZWMS/U7Wx+JlMnKqw7N6q2ulxA4trFoimB8G6cWCMY3G1hZriBEQeUOzhmaaOiF1vQIy8siOwsYzu2qQMbAjZsb4uWMCVla254Rol7N87hbWG3bNJBlVyT8abGQcPrwYWFol9uycxt1BHvWuhzZrrWLz+M8G/1w5cn8rGYugKamb/7N+pMQOEAAspyQ5NpZid7lzAzE5XggUa92y/Yi3+thZMPODVl1M952YDd2wwIjaa3coOmdnBeXtCHcS/6HgMKhUTRifZkWxjqRobrezorCTUBmikSYt+FqzNCNvxQBFNdhB19Ktnmb/AXbN8G4uqJZ5ED2JjcRyWGFAKCGVHfcjVs6t1CxM1vb2JGKKNxfMY6i03VWbHKMi7KWt/NnKTGA4p3axwXBZIuxVaTGYHUXUQVc+t7JDqzA5lh6YDzMXUuIHFFMqOcF6HhKGr+Wxojoeq5hP7uimqWwdsYwGGs8ElXdXwAGDW/Dl61JUdsW0s/d8zeS3a/o+GMzsAYGbSxNJqS4z7fZQdq3UbrsdhwoYSJjtkBlxj8Ay4LWNjKUDZUSkhxL5pC7JDXlPDbh7baAyd7FhfX8ftt98OQGR4nEC5OHPvFADgoSeWOr5OCMk9GW9WtCwXcwt1nN5FdpyxewoA8NjhNvnmtepoPPQDEFXFrjf9GSYvfwWah+7pSw7NrzSxfdLs6HSPg6JQTI4ZqetnJ2sG3vm6SwLCY3a6gve85bKgKpI5dmJeByAWKkTVt7SNxSNiUq8aXQGllRoADhaS3+oDJMK7LoO0wWYiO8yNzuxoBBYWQMjkyz5d62fB2oxwXGFj6l5IA4AiMztGuI1FerrXLS5sLH0yOwIbi5Un4DBFZkdVw3pzuPNRt7JjmKddUsUyvgkyO4Rlrz1eybHt5G01VE11U4eUhm0slMqgxMFsLETVxIl5joBS7jpBdXWHssMnI2cmK4EVMfL3OcfxpUZHXoeEaeRTdtiOh5rih1NqJqhRLUQVMYwNrgqGXSdNdqybfvFnLwAw6jaW3syOPAGlNhOPUTE6N+AzEyaW1iwQRe+r8JZKIgNWsL4Bwodng68nVWVr2GmFsiNX0WmAqqmhWbDyt1fZsfnf6ywY7BPJiEajgauuugqrq6sghODCCy8c5tM/LXHayRNQFYqHn1jG8y7a3fE9ovUf5LYSHj+6Bs4RNLFInLG7HVJ6/hmiDlkxa6icdgH0Hadi/e4bMfOiN2LKsfraWBZXWqksLBIzE2bqzA7b9fCPX74Xb3vlBZgeN3DStioma0ZQFcldB7Qf2UGI6Ebfom0szLXhQgzmlYjMDgBgrfXgdCKQLTseUMlWxet6HDr1lQmhDS+hikh7j/E0VzfYxjK/3MQ5p7WJZm0Ii4x+FqzNCNvxfBtL7zRKVL+NhY2wssPfmNUdoGaqII4G7ibZWMTflCvg0PUwpcVXZQIioLTedMBYfP1tkXA9hvWmg8maDlWhoJSk2kQUhTU/jHUsRfXsRpMdtuNBC5MdftMUpQRn7pnqOUzZTHBcD2OV9mcwCLkUVnZQVQdz89hYnGBM0cLKDkUHc2xsnzRxeD6+GW696aBle0GdchimruZuU6pW/QwI3QTRK4VUt6qqUrp1jHsuzIqJt73yAkzWdJy8owZj6TEcwYjbWDzWoewBQsqOFKSsJK5brniM7vyW6XETHuPgfUhuoG3PVr1WtI2lALJjy9hYClJ2FG5jafnVs4FVb4QPYkpALrLjYx/7WOqfZYyh2Wzi6NGjuPHGG1GvtwfpV7/61Xme/gQyQFMVnLZrIqaRRXtaVc+2m1gmO74+PWFietzoqZ91lo/h6L9+AOYpzwDVDKDPYh0QDPh5+7alfk1ZyI4HH1/C/YeW8A//eQ9W6hb+5f0v6/h+WP6ahKLqwkYR3LHh+sNaVGYHALBmHZgSX5MnZ7mUHV5I2dG14aVGZSSVHS3LxVrD6Tj1G0Zmh7RghQmPsAVrM8J2GRRwEKV3YaP49+Eo21jkxszhCqoVDYT3a2PJH1Dar3oWEMoOxsV9UctIPObBWl3MfRNjYlw3NDpUae9aXbzXE5ugetayPWik/bmHmzj2753CF258BI7r9bUqjSLCbSzAYOMh9xyAUJ/wHlzZoYbeT6rp4J6DbZMVHHhkIfb3jy2Kz6bb8goIdZa87rPAcjxUfMumUHZUYsn8LBgG0Q7PAfQK/uyTt0FXKf7vn78cdnO0bSyccz+gNLqNJVVmhz/3SLdsT0CpH6LPiJrCni0+a+o0O8gOWhkDQIqzsWwFsmNEMztadmcby1Z4r7MgN9mRRqbfDc558HsveMEL8MIXvjDP059ARuzfO4Ub7nyy58SMak8vG8vBuRWYuoKTZnpPPKJCSuVmNa03VYY4yUrZNJgeN/BIRFtOFO55ZAGEAFdcvBv/8s0Hsd50MBbaFAiyI3nhDAg23l1bTP0aNws48wDmwuZiounJ7Ki0lR0S0quf53RJkB29AaWAsLIwK/r0rbKBmR3dtbOAsLGUTbxM1gy85y2XBVaWbgvWZoTjMCjE6yG6AIBKZccokx1+ZocLBVVDBbGS21gkWZGL7HBTBJT6Y9lawx4K2bHib/omx8SYqanKUOv41ppS2ZH8t2ojYGOxHA96mOxohcmOabgex2OHV3GWn+GxmdBNdgyq7JAHDkQ1cmd28MDG0tnGwhwL2yZN1JsOWpbbc1oPICCUd0SQHaauYH45n42lovj5VLoBqlfE62ReUEOaB8PY4HLPBSftQw3bEfWpAApRp5QBxjg4R6yNJdV6RSo7/CG90p3ZMS7eA5coKWwsLSiEg9uNjjYWQhXQ6nhxAaUnMjsA+G0slleYytFxPZG5YihBTl3eFsLNitzHapzzzP9JvPrVr8a1115byB9wAv1x1t4pNFouDs931kM93TI7Ds2t4dSTJyIHj327J/H40bWOSUQu6NKSHSt1C67HO/IQ+mFm0sTKupWquu+exxZw+smTgQ3n+FLn60pLdtDq5JbM7JDXss18G4sRbWPxQo0s8iQyV0Cpy6ArYlyLJjuirxtpB9gIskN6vcMS52GcrlFKcNJMFW975QX4y7dfgWvefgVO3Rl9L24W2K4HBRw0QdmBkW5jEddfoOxQ9URlB6UEhq7kDijV+5z613wrwbAaWVbWxWmmJNx0rXxJfRjrvo1lIkX17EafwlmOBxUeOMT92lE/6+eCPZyStB812EUqO0LqSqHsyNfGwgMbS7iNRQRJylr7uGDz48tS2RERUJqzjcV2PJhU3PdUr4QaxwYjC4aR2cE9JyA7ADG+UN1//SOq7HD94O5usiOL3S5QdnjSxtKV2eFfRw5X+u4DFlZamJ1QAM47lB2ACCktysay0eNcEShK2QHkCwOPglxrVgwVmhKybj+NkEvZkdV+oqoqarUaTjvtNFxxxRXYtWtXnqc9gZw4c287MX3P7Hjw9acT2cE5x8G5FfzkhdHX3hm7p4LGiP3++yVP5tOSHe3a2fTKjpkJE4wDy+tWYtaH6zHcf2gJL372KcGp/PGlJk7f1bbkiFOl/mSHOiYyO8JKq60AKRmWZIfMxpCgZoKyI8eJrusxkdnBxUK047mMauxCkFLip20PP7NDKju2D9nGAgh7wp998jacf8Y2fOC3nlf685WNpIDSTaHs8Md+D1RkdigawDxwzmIbnUxdyef5dz1oKapnAQytkWV1XdpYxJg5bBvLqm9j6ZvZMQrKDt/GwvUaiL3eMSfumK5goqbjoceXgZ/cuNeYF67rdVisBrENcdcBUcTnKTI78ik7ZGaHGlZ2hGwsgLAW7N4x1vP7x5ea0DUlkkQTmR3ZxyTLYTCIT3ZoIrMD8JURld7XkBa6ppRu0eKuC4b251tvOpja5ls4RpXs8N+TbrIDEONUKrLDFZaqpiUeqzezQ5C8Nuuv7JhfaeLkcQI0AGp2kmhKdRKsEBuL0tOIuNnAGIftFKPsAARJ0b2OzQNJcFYNtR3CvAVUNFkQS3Y88sgj2L59OyYnJ3u+94EPfKDUF3UCxWLv7BgMXcHDTyzjhZfuDb4uyI7NPbikxeJqC2sNp6eJRWJfKKS0TXZIZUczFTGwGJAd2QJKAWBpNZnsePSpFVi2h/P3bQ9ObI5FKjv6D4y0OgkwF6xVhzLAQmXUIDdvrSB9PC6zI0R2DKDscD0GjXLAQ0dAKSDIDnct3lddNVU0N0jZQUgnITesyjdpldkKpzdAO6BUEhthqP7X2CiTHZ4DTjUARLSxSILGdUBi8okGqa7sd9olbSxDU3bUO5Ud2hDCEsNYb9igpF1FHQdBdvANJadlQCmvTAL2egeRSwjB/r1Tm1bZ4bgsOO0EBhsPuReysWgGeHO9z29EPIbrgCkyQDvcxiIOpwJlR0z97DG/iSXqWhHKrGzXOOdiA6cTGVBqtJUdA9pANCXdxn0QcM8FCys7Gg7I7BhAlZG1sUhyU1MiPkNNTVc961fuNu32qX4YuqZgrKLBYjSVsuMZ0/DJji5lR20C9rFDfV9PPwzSgjQqkIdmRSk7irIXy8eRhJemDpfYHwXEHrW84x3vwOWXX47f/d3fHebrOYESoCgU+3ZN9oSUEjVfgNZmRBBOuquXvAOAk2aqqJpqR0hp4Etmbt+0aqAd4pRV2QGgb0jp3X4g2Xn7ZjA5ZkBTKY53tVukqZ4FQgnaW8zKIk/RWh6FptKe8EuiGQBV4YWUHfK02ckx8HuhNpZIG0srXhFUNdWNyexYamJ63Ow4MdJUBe4QsgoCsmOLTLK2r+ygEQQj1cTXPGd0yWTu2uA0lG+j9A9VNXNsljzG4Xq8b3jleFXaWIYzJ62s2yAEGK9JZcdwyY7Vho2xqt7XyiVP912PJ/5cmbAcoewgehVQ1J48ojP3TuHxI6tobVCd9iCw3c4gyEFsQx02FlUHz9nGwv36dLWjjUXaWKSyI8bG4mciRUG2sTCW/lqS74VBxFhGNbNtAxnQxiLe67LJDgcewjYWG4QQUN0cXWWHT3YoEcoOXaOpxinuuSCqFtyT3WQHIKwsLa8/2bG40sT2irhmFKPbxlJMu5+mKpue7JCqR7OANhagQLKj1XkNiPf66WVjiSU75ubmAABKhB/5nHPOwbnnnov3vve9pb2wEygW+0+ZwiNPrXRkQxD16dPGcnBODManxig7KCU4fVdnSGl4QZe0cZWYX2mBUoKp8SwBpeJnW7bbkSUR/n8AuPexBezaXsP0uAlKCbZPVXqVHZ4NqmUgO7ZYI4sk7poe7QknBfza3UoNLPTe6kEbS/aB3/EYNBoTUGpWOxoLulE1tA2pnp1fbnaEkwLDtbEAgONtjUnWcTxBdkTMkaokO0ZYOcddF4yI11kztcCKlbTwzWNjkYsqvU/zTqDsGJKNZaVuYayiQ/HJBl1Thhratt5wMN4nnBRAoDrIY2VhjGNprYVjSw0srbUybXLDsH2yg2p6ZB7RWXunwTh6Gs02AxyX9dpYBgooFXPwIG0sjLY3JRLSxlIxVFRNFQvL0UTD8aUmZiNC2IH2JizLfCc31jra1bOFKTtUWvo9xz0XXmirI5VjRK+MbPWsJDajbCy6lm4M5m6nssPUI8iOcRMNhySO+U3LRb3lYsYUn1NvZsckWGt9YMvmsBSmZUJ+LoPbWMS8UNQasVvdo2+RfJQsiF19tFqC8WTs6fWGbFXs3zMF2/Hw+NG14GvkadTGcvDwKnZMVzraS7pxxu5JHJxbhecvCMMEB0/YuEosrDQxPW4Ei+c0mJ4wcPL2Gp65W8X63TeAuQ7c1QXx/364GWMc9z62gGeEKm1npytB/kLwGtMqO6pbU9khr+WmQyJPMQAxUYczO4LO8RxqA9f1bSyIIDv0KrjdEg0xEaiYKhobcAp6fLmB7dMbQ3a0LPFebBX5pO0yKIS1w0hD0FQVjANslMkOz4bny7tFQKlUdiTVz2a3scjPu1/1rKErUBUyNBvL6rqNqfH2eKmlPDEtCmu+sqMfpOog6wZcZlBdde2NeOv7voGrrr0Rh46s5iI8LNuDDhdUM0CNKnjXif6ZmzSk1PMYGOOFVs9K0pCqRu7MDhal7AgpcbdNViIDSi3Hw/K61UNoS2SpLg0/JgBo8Gt1Vb0wZcdw2lgcuF2ZHQBAdRN8xJUd3dWzgPgMUweUKhqaLReq0qt0BcT6s+4g0c4uFcuTup/ZEmFjAQCvsYZBoKkbX7E9KOTnYmi54jADlGVjCSs7tso6LC1iyY6pqSlwzvGjH/0Irrv5pIkn0In9fiXcwyErC30aBZQenFvBaTGqDol9uydh2R4OHxeb4fDpVRplx8JKK5OFBRDM/W+/Yj/cx27H4nc+i8ZDt+Pwp6/G6p1fD5Lcnzi6hrWG00V2VCPaWNIFlAbKjvUtSna4BFUjmtSi5linskPLr+xw+yg7gPjF4EbYWDjnOL7c2jBlR2OLZXY4rq/siMzsoPBA4Y3w3MldBx5RoVACXaVtsiPhNefx/AfKjj4BpYQQjFX0oWZ2TISqj4dtY1lr2IF1JwmBsiPjfbNSt4KqZ0BUkr7vE7cGWSVZYDlMKDv0aGXHzISJbZOmCCndRJBjUTizo7Dq2ZyHSdxzAxKyJ7PDc8A5x7ZJM9iEhhHVthWGVHZkuYflBk7lIsuHEAJihAJKB0DZcw/nHPBcuLyzjQUQrTIja2OJuC4l9LTZQp4Doqho2R4qRjTRPDNhom4jkZSTdqlx1Sc7umwstCBb9DBa4cpGUcqO0m0sGt0yCtu0iKWf9u3bh/n5eRw+fBhvetObcOWVV/aElT722GO47rrrBnoBr3rVqwb6/RNIh5O31VA1VTz0xDJefNmpAJ4+bSyO6+HJY+t49jN2Jv6cDCl95KkV7D1pHMyqg1YnwBqrqeSOCyst7JnNHvj5ya8fxGtfdBHOf8VJOPpvHwQA7Pm1v4ZSFeTMPY+JvI4w2bFjqoLFVQuO6wULorQBpeJxydZTdvinXnWHoBJhYwEE2RG270hpfR4presxaMQ/JaW9mR2AIMyiQmCrhlbYRJYWaw0HtuP1kB2qQuExXlinexy2XkApA0WcskOQHSOt7HBtuFBEOCkh7UahPsqOrG0Owclwn8wOAKhVtKCStWysrFvYe1K7nWwYkvow1hoOTgk9fxyklD2rxNtxWUB0SBxbaua6/yzHhQoPimaAxNRqi5DSpcyPvZGQ72m4KUhTKVbr+TM7qD9vSyVG1mBZ7tpg6FV20JDyatukiSeO9p6kH1uUtbNxyo7slZbyBFhjNqguDnPa1a2Dkh0lt7Ew8Xe6vP0+Spsc0c2RtbE4QWZHVECpEtgSksA938ZiuT1NLBIzEyaO9AkolWRHlTrwENXGIpUdg60n1a1gYwmUHcW1sRSBlt2t7Hj6BZTGkh2/+Iu/iNtuuw0AcOedd+LOO+/s+D7nHLfddlvwM3lACDlBdgwJlBKcuWcKDz3RXow8XdpYnjy2Do9xnH5ydDipxN6TxqGpFI8+tYKfumQPWKsBdXwb7MZqSmVHExft35759Z22awJnbFewcN0/Bl9bO3A9pq94Lahm4J5HFzAzYeKkkA9XntzML7dw8nbBtAuyo/9JIaEKaHUcbItldsjTiXUHqFSjhzalMgZn4ang35o/KeXpHHddDk2PVnbIk6+42mLRxjLce08qgbZHKDsAWaU72CSdhFZAdmyNEwXb9aAQDiVK2aFQeJyCjbSyQ5x41iri9ctrOGlOyBNQKi1iaRaAY1VteJkd6zbO37eByo66HYSjJiGvjUVTKWanKx2Ex+x0JVLO3g+yelY1DHC3Anf5aM/PnLl3CrfcfQTrTSfRLjpKsCOIuEGUHcx1oMjMDlUHOBMbbiX9+8FdBy6JyOzwH1eGlC6ttuB5rCPE8nhKZUcWG4t8jxQeQXYUYmMp756TKjWH0+D5wjYWd0DrRVmQ2XqR1bO6kkqdJS1VLduNzOsAgOkJE09wFfBccOaBRMz/UkFkwkZDN3t+Rtqi2YAhpcNSmJaJkVV2dLWx6GWTjCOI2FnvFa94BV772teCc97zn0TU97L+dwLDw/69Uzg4txpMLuGTh62MdhNLso1FVShO3TmOR59aBiA2quqkIC+SwiYBMZg0Wm6m2lmJV1y2E+TQnSCqipPf8MeYePbL0Tx0D7hjgXOOex5dwPn7tnWcDs3OiOeRIaWc89TKDkCw8W59OfNrHWXI5Pu6hciAUkBmdrRtLNoAyg7HY1BJfBsLEE92VAyR2THMe68tcY4mO8peaGw1ZYfjiDaW7s8eCCs7Rpjs8Gw4nAaWrzSZHXlsLNIipvWxsQAipHQYbSwe41hr2JgYa5MN+hDJDtdjaFpuKhuLmtPGMlkz8J63XBac8s9OV/Cet1wWVO1mge3bWBTdADVqkZtcWdn+yCbK7ZBjkV5UZke4jcWvb84SUsqZBzAvaA8Jb3YDssN1sH3SBOPA8nrnpvfYUgOUxDfCmTmUHfK0WmEOiCYelygqiKoPrIwoOyhRjmWS7JiZMIPxhW6CgNJIG0vKcSpQdrTc2HrrmQkTjn+txQWMLqy0UKtoIE6zx8ICFNfupykUjPEgM28zwnLEezgo2aGpFKpCClN2NC0X1LerysfPY93ezEhMUXnve9+LV73qVfje976H+fl5WJYYWD//+c+DEIJTTz0VF1988VBe6AkMjv17p+F6HI8dXsVZp0z7k7LwNCLlJnkz4uDcKjSVYtf23oG6G/t2T+H7Bw6DuQ6404I6sQNA/xOMPLWzEjc/sAKluQ2vvPJtmP/a32H7z/06pp/7GijVCRxZqGNhpYXzQhYWANgxJTbTQW6HP1ERNd1CVqlNgjUGrwsbJchF5ZoN7EkMKK2DcwZCaLCYyKXs8BhUEpPZ4S8KkpQdnMP30w4WZpUW8tSvR9mhDJfs8PwFTZYg31GE7bji8484DQuUHQMm1JcJ7jqwuYKqVHYEmR3F2liCgNIUioKxio6njq/3/blBsd6wwTk6Nv7DtLFI9cp4CgVEQHZkrJ6llODUnRP43V+5FJxz7JiuYMdUNZdVzbJdaHBBVR1IsLEAwENPLOOi/TsyP8dGIMjsUAvK7PBcEEWQEtQnJ5hj94Q6Jv0+gIDsCL+uNhnZWT8bPmA5vtTEzGQlUhEA5AsolRtr6lmBsgPwbSAx81taSOtCVqtPWsj302ZhssO3sWgmuDPamR1RAaW6RmGlsCDwUGaHmZDZ4fC2DRpa7/pxYaWJbZOmsHVHXMfUrAGEDtzuF1awKSUqTMtEoOwY0MZCiAjZLzKzo2KowT2mqXRDAvI3En1X2c985jPxzGc+s+Nrn//85wEAl19++Yn62U2E/aHE9LNOmQbR5ElBekXAZsTBwys4Zed4ZGd5N/btnsTXbz2E48cXAQDqhCAZ+k3q0teYh+yYqum47Tu34tn3fBcA4Mw/icrecwGIylmgM68DEBtWQsTiBmhbOFIrO2pTsI88lvm1jjKk73S11a7u6gY1xwBwMKsJxayBUgJVybfJcT0GBf6isWtylsqO7tYCiUqoWmxYZMf8chOqQntOdlVfKj0sskM8lwclRlq7WSCDu+OUHfaoKztcBzYjqPnXYlA9m5jZocD1uCD6UoynQKi6MsUCcHxINpYV/0R8MqTskDaWsjZeYaz5uSRpbCxaThsLIAiP6254GLfcfQTX/u5P4aSZfH+XY9ugRKgLKAT53/0+jVd17NxW7QhBH3VEkR2DKzt88jBYX6UPhJVzmCMzO9QIZYdjY2ZSZL3MLzdxlh8+D4j1QFwTC9AmO7Kos9pkhw2iTQVfp3pl4DYTTaXgXBB5mloG2SHGEtuj0FWKiZqOo36uCTXMgW04ZcFlyZkdqapnPUFONi0XE7VoW9P0hNFWdjg2EHHpLKy0sG3CBGvVoUSQHYRQKNUJeAMenmmhdcigZMFGIcjsGFDZAaBYssN2O9aZunbCxpIaW936sBWxY7qCiZoeJKYHJw9bPKT04Nxq3yYWiTP8kNJDjx8DIBQQRNVTkB3+qXkOG8v0hIlT1HlwIm7HsLTy7kcWMFbReoLsNJVietwM/NhS1UBTZHYAwme55QJK3XZAaZyNRU7WrNn26upaPkmf5zFohAGK2rM5okFmRz3qVwNZ6TAbWeRCuPtkN7CxlJzO3Ul2bP6J1rX9E8IYZYcLBZyNMNnhObAYDe6VgOxIbGORMvj010rbKpAioLSqod5yctWjZsFKXYwVYeJP1xR/41X+tSnJjlTVs/6GJ+89I4ncrJXBYbi+qpdooo0FnEWeiu/fO92RCzbqkJbecDaGpuSvwAw3ogU2lgzrK3nvuWjLzSXCNhZ5qCIPWSSOLTUwG5PXAbRtLFnUWVJFQFwLVG/fL6KVZ8DMDkVucMuZe6RKzWYEhq6gVhHjCwBQrQLu2rH18BuJQNkRZ2NJ8X5x16+etdzYAxVTV4M1YxzJvbDSwvapClirHhzidEOpTRRgY5Hj3Oh9HmlRlLIDEAd2jYJy3cQ10DnGDTOfahSQi+z4wAc+gA984AMbGi66vr6On//5n8eTTz4JALj55pvx8pe/HFdeeSX++q//Ovi5++67D695zWvwsz/7s/iDP/iDp3WNLiHET0xfFv9W28qOrYrlNQtLaxZO6xNOKnHayRMgBDj8lCA7qFEFNSqplR0zOZQdM5Mm9ioL8KZOAQBwq72AufexBZx3+rZI6fGO6Uo7s8PzlR1aSrKjNinsHEMMqPVCWRnh/y8KzCd8XCixk7tQdqCzflZVcm0kHJdDIdGZDf0yO+QGc5iNLMeXmz0WFmD4mR3DeK5hwJX3TsTnr6oUjJNE4mCjwV0blksDFVRYJh+HdsBhds9/v+pZQNhYOEfpEtvVdfE3dmZ2+Pk9Q0ip3zlOoKkU2ybM2LFQfr1qqnjvmy6Ex3KSHf77nzVrJQzm16BTVW+PbRGh3c869yS87ZUX4MhCHUtrrdJJq0ERaWNRB6uela0pJGRjSf37XnsOA7ozO9r352TNgKqQjvpZj3HMLzeDPK8o5KmeDTZFrgUSsrFQ3eybZdYPZc89gY3FIzA0pSMAmRjibxlUnVIGkjI7wgq0RDBpY4knOwDAqPjvQ8R16nkMy2stzCTYWAD/8GzAgNJhKUzLxMgqO1qd14Cont2873Me5CI7Xv3qV+PVr351j71lWPjxj3+M173udTh48CAAoNVq4fd///fx8Y9/HF/5yldw991344YbbgAA/M//+T/xR3/0R/ja174Gzjn+9V//dUNe86jgzL1TePzIKlqW23FSsFVxaE4MwKenVHaYhordO8Zw7IiwjwiyI9qjHIYMcYpLvU7CdE3HXnUR67U9IJoB5ogFzNJaC08dr+MZ+2Yif292uhrkMMiJKk0bCxAOlRpOboe7uoD1u28Ac532/zvp5b1pwF0bUDRwkNhALloRk7XXaucCaBoNGiOyQNhYoskOouoAVRPIjraNZViYX2n2hJMCwyM7WlZ7gb2ZFzQSnh9GFqXs0DaDssN10PLaKqh0mR3ZN0vypC6NjUW2eJRdPyvbDCbHOpUdAEo/8XJXF0AevQVv/rmzsLNiRY6F4fFy75iHU+v3YKqS04Lij21Zs1Y6Xo/tKztUPdjwdI9tjHHsOWkcf/eFA/i1P/smrrr2Rhw6sjrShIddoI1FhIv2ZnZks7G0AzUpJR25RsH96diglGBmwsTCanujvrzWgsd4aTYWuBaoFiI7jCqYNbiNBShPTSXJDstXdoyZQungeSz4W9gIkh1yIxqV2WHoQoHW7xrlnguiamhaXmz1LABUqr7lNuLQc2nNAuPAtskKWKsRS3bQ2uBK4eBa2MRrA8v2QEm0IicrKoZaYPVsZzacpiq51rybGYN/Ij5uueUWfOQjH8Hv/M7v4A1veANe85rXBN9bXV3FX/zFX+CRRx4p5Ln+9V//FVdffTVmZ2cBAHfddRdOPfVU7N27F6qq4uUvfzm++tWv4qmnnkKr1QpImde85jX46le/Wshr2Kw4a+80GAcePbzS4QHdqnhsLl0TSxj7dk9icV5IcalZA9H7yzVliFMejDvzMIiLBX2nOC3xFxD3PipyQ7rzOiRmpys4vtQEYzxYJKUmO/y6sEFDpdLAa9VRf+BWLH7ns2g9fg8Of/pqrN75dfCSyA4gPrNDiVR25LOxuB6DCgZCI8gOQnxFUPR1U3SPej94jAdy1G6UveCUaFrtTfRWkFC6jn/PxSg7PJDYhPtRAHNFG0tvZkcKG0uGEyeplEhTeTpW9cmOZrkk4IpUdoQyM6TNxirx2pRj4fKNn8XzZpdx9DN/jNU7vw53dR4rP/gyVn7wZVhHD6J+//fFeHnoHhz/3B+jeeCbmM5JdlgFKDs8SaZrRrt2tKvJYqVu4YP/eFtgrTy21MT7PnFrqprMjYIbYbFSFQqP8cwkDQ9CwgdoY/HncZcrPRsmGT4uf2bbZAULy+2N+rHF5NpZoC2vz2JpkmM1dzqVHUQ3wQdsMymdaPc/k5ZLYGgqaqHxJbCajmAji6yejQrxTkvKctcFqFCBVBKUBtWa/z5EkB1B8P6EsHJHtbEAKCizQ9ppN+8m3HI8GLpSSOZTxSxQ2WF11g/rJVc+jyIGToj71re+hWuuuSZQWQDoCa56/PHH8YlPfAKf/OQn8cpXvhJ/+Id/iGo1fkDuh/e///0d/z527Bh27Ginf8/OzuLo0aM9X9+xYweOHu3th++Hu+++O/dr3UjccccdPV9rNMUF/u2bD+D5245jHMB999wF7/Dm8dlmwR13L2LMpHj4gfSfocHXsdJcB8aAex96FFXbBWkdw+GI91Pi8cMLqBg08j3vB/3Ju1ADcPeCitM4RePIU3j8jjtw/e3L0BSClWOP4o753jDR5to6XI/hxpt/gKnWYUwAePixg3DX+m8qlKU5TAC4/8e3wy35syeEYPeuC7HjZVM48tk/BQDsfPOHcP9jT6DVyn+q0v1eV48cBuVi3Hny8cdwhzvX+1paa5gC8NgD98JuiMWj69g4dnwh02fHOYfjMljNddicRf7uBBQcP/wEDkV8b2ldTGL3PfAwDKf3dRaNlYYLxjgaq8dwxx2dJ7KPHhGfwT333I/6fPZayrRYWqlDoYDHgLsO3I1jT6Yj5kYVqyvLAIBDTzwJm3d+xgtrLsAVNOv1XGOCxCC/2w+Ttg2HKzh+7DDuuGMVxG5gCsDjjz0Ki0c/7xNz4lr58YF7sXw03bXyyKMiH+e+e+9GRU8em546JjbGP/zxPVg5mo88ToOHH1uCqRH8+Ec/DL725BPivvjRjw9gx2Q5gd2EEOzZsR87XvabOPYffwEAOPkNf4yj//dDcI4/IX5G0TD9U6/Djpf9Jo58ToyXE7/y57j+zoPYPX0w83OurYu/64GHHsUEjuV63fXVZQDAo4ceB9OqmADwwIEfwz3Szj46afcZAdEhcWypiXq9hUcfvCfyccu8vtPg/sfFe/Pgg/dj5Zj4zI8dFZu2226/I8gRSAPiNDEF4Mm5I3jkjjugrB4Vc/ID98FZTreBU5afwgSA40troDA63h+6dgyTAB5+8D44qwBhTTy14AQ/c+Cg+FuOzz2GOxpPxj6HphA8/sThnnkgDo8dWgUFAzwHR44v4qD/fNXVOrT6ykCfYdn3nLpwEOMAFlebcHgT80efAgDcevuPsLP5FMYA3HvXj+BNZt8XpEWe9+eRR4Xy9L5778FT1U6i4shh8b0f3PEjTFTjSYzJVgOrC+Iwa/74EdxxR7RdzvKVLQ/cew+84533771PiH/PP/EgtnOGw/NLeCzi7zGX66hYDdzxg1uBiMOfNHj8SfFcdx24B/NPbc61wRNPLYESXsi41lhbxsqaVchjLa/WMWG4wWMtLKygZbuFPPZGj+FpMRDZ8eEPfxj/+3//bwDJgaVPPfVU8DPXXXcd7rvvPvzTP/0TxsfHY38nCxhjHeSKJFvivp4V559/PgyjvE1AGbjjjjtw6aWXRn7vk9/+GiyM46zzdmDuDuDsM/ahcvqFQ36Fw8E/3fgd7D+lFvteREEZP4Zv3ncLAODCSy/D8SN3wF0+grMSHqP1pa/iGWeehEsvzV7FPH/8TsxDQ72yFxVzCupYFedeeik+dcN3cO7p2/DsZ/1E5O95lSP4yu234uS9Z+IUZmLuVuCsc5+Byinn9X1OZ2k3nrj1U9i3awfGL0z/3uSFs3Icc1/4TPDv5n3fxblXvBY0ouosDaKu72NPfBcrS+KU4qILzsU5p/Xaf5hj4eB3Poq9s9sw5f/+5PduQK2qZ7pGPI8Bn30K41UDBqtG/u6TP5yGWjNxbsT3VtYt4ItfxezOPbj00n2pnzcv7ntsEcARXHrhObj03JM6vld5bAH49vdw+hln4pKzZ0t7DfxLX8XkmIrF1RbO3H82zj412p61WfClG54EmsBp+/Zh/IKua3GpgTu+Q2DqKp6R4boKI2kMHxScczz2NRcuFJx31hm49JI9YHYTB78N7Nm1M7g3ulF9bBG4/rs47fQzcck56a6Vx1YeArCCZ/3EJX2D27bNreKT37weJ+85DZdetDvrn5Ua37r3dsxMouP9dYw54ObbcOZZ5+DMPVOlPbezdARzX7k2+Hf94Tuw6w1/Asi1CSFgVhNz//zHwc/Y938X5577Uuw/7aTuh+sL+l9fB+DipJ27cemlZ+Z6zd/89gOADZx59rlQx2fw5K2fwhmn7MLYee33b2mthdnpSgfhMTtdQa1mRl7HZV7fabGKJwAs4pkXno9dO4Tq7/G1h4Ef34MLL7woViEYBXdtCY9/CzjltH2YuPRS2AtP4cmbgX2n7MXY+en+zuYhA3O3ANXJGZjznXOSsziHJ24CTj9lL8YvuBQ/fPJuPHLkIC655BIQQvz7bBE/9dyfSMxoqH7xGKamt+PSSy9K9ZoOzN2DqiJsvbtP2xeMDQtLd2P16P0DfYa2PgfcdBvOOvtc7NudLlctCxoPExz5AaAYVWyrTOL88/bh89+/FaftOwt7eQ1zd/4bzt53KiqnXVD4cwP5r/EjzUcBLOOSiy/qsNoBwAp/AvjBnTj73POwa/tY7GMcvIFgars47N1/xmm49NLTIn9u/sg6sATs3b0b27rmsacajwBYwOUX7sfyHcApZ56FiWf2/j2rdBHzD92Ai84+M2gwzIzaUeDGBezff3bk2m0z4IYH7sDYIi9kXLvzyQO4/6nHC3ks9oX/wu5ds8E9f//x+3HTvQ8EY0dejMIYLmFZVqIwIbeN5Z//+Z/xt3/7twDEwmnfvn1405vehIsv7t3szc7O4owzzggIkQceeAC/93u/l/epe7Bz504cP348+Pfx48cxOzvb8/X5+fnA+vJ0xv69U3joiaVAFrlV21g8j+HxI2s4bVe2SXTf7imYxG83Mf3MjogwtvDzLK9ZucJJAcCaewSL6klYWrf80K8m6k0Hjx1eibWwAAhS148tNUPVsxkzO4ZkY2k8eBuIqmLXr34Ak5e/As1D9xRuY2GuDUbF4jRusUdUHVDUzsyOHAGlUmpJeXRmByCunTiJbGBjsYaT2THvZ7tE+bmH5ZVtWl5gG9gKmR0sqJ7t3RBpKoXHFWAEk/4BiNfFOVwuGgqAcBtLQmaHkV8GHxW21412ZkfZNharZxMxjMwOr1VH46HbQVQVM2/4ICYvfwVah4TqQamMQ6mMAyBoPPgDMV6+6c8wcdkrwObuw+xYvrOpIgJKeWh+iQtfnqwZeM9bLsOsnws0O13Be95yWU/V9SihHVAaairIaa0IQsJ9G4sk8lmOzA6b0Z68hu6MtW2TJizbQ923Qh5bamC8qvWtMjd0Ndv96zKM6/58FzqcoHoF3GkN1GYi3+s8NtI0kNaipiuyLqRlb73pjHhmh9grRWU/GME41T+zw+XiZ5MyO8bHxf28uta7xl1caUFVKGqKXA/H21iAwdaT8m/dzGsDaWMpAhVD3KdFNJ82LReVsI1FG451eZSQa/acn5/HNddcAwCglOLqq6/GL/3SLwEArr76avzwhz/s+PmLL74YX/rSl/BP//RP+Iu/+Au4rotvf/vb+MEPfoBnPetZA/4JwEUXXYTHHnsMhw4dwp49e/ClL30Jv/ALv4Ddu3fDMIyAffrCF76AK664YuDn2+w4c+8Ubrn7CFquYPS2ahvL4fk6HJelrp2VmKjpmKkwuEQDoYogOxJ8neEQp6zgngPr6GNYG/sJLC62QE4ywRqruO/gIjiPz+sA2hvX40sNcMOvU0tJdhDNBFH1oZAdillDdf9PQN9xKhoP34HpK16LKccKJsiiwF0bHhFDWiWmepYQAsUc68nsyOqNlGnpCrzIzA4AoHoV7srxyO9pqgJNpWgOKbPj+LJYyEQHlJafgs45R8t2A7LD3sQLGgnPTQ4o9UCAEQ0oDYIQobRrmqkCEBpbQQjkDzjUVBrZKNWNgOwoObNjtW7jpJlOK60uN7klBrcpZg3GnnOwffY0fO7Gx/Cm1/aOhYpZw/gzXwRj5z6sP3ArzMt+Aaunvwg/fqKJK7Znf067gIBS7tkA6Wpj6cojopTg1J0T+B+/fDFMXcHsTBWTNSPV575RcCQRF25jUfJtBLpzs/JkogX3paf0kIPtAGHxeEH97HITYxVNVIsn5HVImLqS+f6tqT7Zobfnj3CbCYnZBPdD+W0s4v1sOQRTfhsLANSbDsh28beMYhtLkNkRYaNqt0b1yezwHDAufjYps2NyUqhDGuu9NpeFFdnEItYPSlxmRxB4n389Oayg9DJh2V4htbMAUDVUcN4bLpoVrsfguKxjTRyQjA7rIHq3MnIpOz73uc+h2WyCEIJ3vOMdAdGRBEII3vjGN+Jd73pX8LXPf/7zeZ6+B4Zh4IMf/CD++3//73jpS1+Kffv24SUveQkA4JprrsEHPvABvOQlL0Gj0cAb3/jGQp5zM+PCM3fg93/12VizxIaN2VuT7Dh42G9iyRBOKrGjCjS5n6ruB03GMazzfojT9hzKDvvYE4Dnwp06BUtrFoheAbNbuPexBSiU4OxTp2N/t1bRUDNVHF9qBonvckHUD4QQzL7mXYHCoYwq2DC8xhrm/vlqOEtHQDWjcKID8MkOn79Nkh9TswbWpezIuvmWKgjKvWRlR0KLT9UsLm27H44vN1E11cj3pb3IKO9Eu2V74LwdCLmZE9clmAzyjCA7VFW0sYyqskNuAlyutKtnCQFR1ESyQ4acZdk4Oy4LiIR+MHQRzFh6G0uCssMqObjNXTmGuX++Gqt1O3YspJqB+a/+H7iLh+ERDW//+B1Yred7T+R9PYiyg4SqzUVIJQGzeucMSgmuu+ER/O1/3IXpcXOkiQ6grdDrbmMBcig73E5lR1uJkaV6Vtx7FicJyg5JdojNuqy9P77USGxikTB1BVaGa8FyPNQ08V50Vs/6pNcAZMGwyA6p7AiTqW1lx+gFlEqiLUoNF4xTCZ8h5xzwXDj+Fi9J2THlkx31eu/7ML/SxPZJM1A2J1XPAkWRHaM5Z6ZBocqOghr7ZJh4dxsLsLmJpazIRRd973vfAwBMTU3hLW95S6bffcMb3oB/+Id/wPHjx3sUIFnx7W9/O/j/5zznOfjiF7/Y8zPnnHMO/v3f/32g59lKYIzD0BT83RcOwFqex59MA/OLKxhjfOQXJlnx2NwKFEqwZzbe1xiHaZNhfV2kIVOjCnAG7rRA9N7FhFxs5FF2WHMPAwCU2X3w2GF4RAezm7j7kQWcuWeqb5Xtjukqji01M1fPuqsLcBbnUN33TLirC6g/eCvGL3pR7gyNfpCp7WU2/3DHhuMPaUknGdQcA2t2Vc9mnGA95pMd8IA4sqNPZXHV0IZGdswvN2MXwtoQ5KNywt1KNhbuOQCNbmPRFArGCciokh1Ryg6IjRp346/JPNWzluNBS3naRQjBWFUrVdnBOcdq3cbkWOdYaQypelaOgWY1eb4gmgHu2AO1JXmMByq0LNaFHrgOoIr5hRDqHwBEj22GpsDaJLWGchySp+VAfjl9j7JDy6/ssD2aoOxo21gA0ZjBOcexpSYu3L8D/WDqaqbGIdvxUFUY4HXZWGSbidUAkC+noWwLpbSxNBxxXdZC1dbtNpbRU3Y4HgMhiFyTy8104mfoKwodqexIIjumx3EUQLPRS3YsrLRw5p6p4HCov40lfyOL3IDL8WozwrK9joavQSA/s0EbWRr+73e3sQDl2cdGEbmUHY8//jgIIbj00kuhKNlYLE3T8IxnPAOc81zNKCcwGFbqFt7/D7fi2FITDsRn9/WbHh7peri8ODi3ij2zY7lkWmOKixbTcfDwaqxsVyKo58qh7LDmHgGtjGF8dpf4N1Qwq4mHnlhOtLBI7Jiu4NhSIzjBkAusJMj6w6Xv/isIVUqrgg1DLih4Bv9yVnDXhgsFhq5AScgHoGatQ8miq7Sv/7UbThplhy42BHGKoIqpDi2z4/hyM7J2FhhO5VszIDvEYnkrTLLM/xuibCyKQuFBAfho/p3ydNgNVc8CIrcj2cbiKzsybJYclwWnkWkwVimX7Kg3HXiMB9eihKa1pb1lQr73lT5kB9V0MMcayMvuhD6nvMoOzjmInF/8nC9ixNex6xottb63SMjPOpyNkJdcCuZgfz4ghIIoWsbMDnFtWB7pqWomVAGo0mtjWW2h3nTQtNwgLyUJuqZkzNxhqKq+Za9D2TF4dWvZJ8ySuG04giQwNKEcqzeddjXwCJIdrsugKjQyPDINKStJHoeJ308iO+Q4ZHWRHZyLuvptk2ag4oqrniVGVWShnVB2FKbsqPqf2aAHYnLtVTV6bSxb4dApLXKRHWtrom5samoq15PK33MTTpBOoBw4LgvS0h0uLv5Ws7UlL/qDc6s47eR8Cd8msdHkGh59arnrBKMTjHGcfeo0PvBbzwVjHIxlY6Wtww/DOPkMTPsLlybTwJwWXM9LRXbMTldxfDmbskMxaxi/+MWYffl/x9H/+yG4S3M46VW/W4q1REIuKEpVdrg2bK50DOpRUCpjHTYWXVMyT7ByIUwSA0prviIoerGb18bCGMfSWgvHlhpYWmuluubml+P93MOY+BpbUtnhf3YxhD8ndOSVHS5XOiTORFETA0oVKjZhrQynTbbjpbaxAILsqJcYULri20E2Stnh2GI8eN6lpybev0TVwV07IG7znHiG7XlZPrMwXI9BhU/sadLaGa9a0zWl9PewKDiuyJMJbyrV3DaWTmUHIN6vTMoOaWNhSo+NRT62JDs0VcFETcf8chPHgwDqlJkdVjZlR4WKn6dhssMYnOzQy97g+gqHliuI2rByjFAFRDNG0sbiMQ41pvY4sLEkkR3+3spmvo0lQSEsr1er1Un61JsObMfDtslKkHFGzejrixACpTo5kLJjSwSUFpjZITM2BlV2BDaWcGaH9vSzseQiOyRZEW46yYJDhw51PM4JDA+aSgP23/WVHVOV3lOEzY71poPjS02cliOvAwCo14KrmHjkqZXY9HnGOA4dWcVffOp2vPvjN+Gqj9yIQ0dWUxMezLFgH38cxslnYmZCLCIangLCOTR4OPf0/vVbs9MVMSn5E1XazA7WWMXCtz4V/HvtwPVgpSo7fBtLiWG4zLFhM6VvmJPI7GgrOzSVZh705cYjUdkRc91IVA0t80Qmr7mrrr0Rb33fN3DVtf2vOcvxsLJuY/tUtPIoCOQbgo1FbjCdTbIZioPHOLiv2ohqYwEARhSQUVV2+JsqqulQQlJpouqJyg4gu+ffdjIqO6o61pvljRMr62Kc624JGUYbC2McK8uCaL3qY7ck3r/CxmJBoQSUklw2FrsAZYflMGjE3+yq/ckOYzORHR7rWftouQNKOzM7xP/r2TI7fMKk5UXnNQibWfv+3DZpYmGlhWOL8QHU3TB1NVPmjuV4MBV/rAuRHdLSy2MUPmmQl1hKizCpKzehYeWYaL8bXWVHFLIoO2xPKjvix19CCFwocLrIjvnAnm2CteoguhmpYpRQqhNgRSg7NnFDiFB25A8TDaMoG0szsLH0Nk5tlnG6COTa4Z5++ungnOOOO+5AoxHvSY/CwYMH8aMf/QiEEOzbty/P05/AAAjXw3EQuJziigtmR7oeLg8OzQmGOWsTiwRr1aFXx/Do4XiyY2mthfd94tZAKXNsqYn3feLW1JYg++hBgDMYJ58RkB1rjrgl9+80MV7tr9KQJznr6w1AUUFI/1ta2liIquLkN/xJaVWwYcgFRZmECndtWIx2ZBBEgZpjYK0GOPf92gMElBLuJpAd8YogQCg7sraxrNStzNfcQkLtLDBsG4te+nMNA47rQYF/DcQtAOkIkx3+JkDpsr0RNVnZAcjqygxkh5td2VGmjWVlXWw+J7qUHe0azPKuzZW6hRtvfwwA4IIm3r+S7AAEIZmHjAzbxfK2sVi2C4244CBBPhE1KrF17EZGMmwj4Ti9ZEexyg4jF9lheb3Vs/KxWejxtk1WsLjSCuaD2ZLaWCpUXDsy1BMoRtlR9j0XBDGDBvaCWkg5Rvz63FGD48WTHamUHaxdYQwkB5QCAKMa3K6igrA922s1Yi0sEkptcqB2v61grSi0jcUs1sYSPgTUt8B7nRW5yI4XvOAFAIB6vY6/+Zu/Sf17tm3j3e9+N5gf7ve85z0vz9OfwACQ9XC//+Zn4wO/9Vyoholxk2y5cNKDh8Wgm6eJBRAb1O07ZvDaF5+NpZa4TbxWA47LcNu9R/ChT9+OIwv1YJEhcWypmXoAkeGkxslnQtdEUviK/1zn7em/aAGAHTNiwdFsNFPXzspqw9lXvRML3/pHTF/xWpz82vcMx8ZSorKDuzZaTEHFSFa3KJUxADxYrGsaheN4mfrMAxsLSw4oBeLJjoqRPbMjbEOT6HfNHV9KljgrCgUl5U58PZkdmyTAMA6Oy6D6ZEdUGwsAcKKAjizZIe5D1egkuftldgDiZDGL5z9PZsdaiTaW1XqMskMtX9nhuAxWy4LLKbi//Iq7f6lmBBtbTcmn7JA1upQMouzwoMEDp2pg96BGFdyOt7F4jAf1maMMx+2tXixa2ZGF4OeuDRAK20Ok2pZ2KUW2TZpYWBU2Fl2lPdasKBg5yA6DJGR2DKDsKD2zw3MBQsBBO5UdLalsMwd6/WXB9Vgk2QWEq2fj37PAxuJfR3HESfDzVIVrWx1roHDwPrPqseGkEkp1Al5jgIDSIShMywTnvNg2lsKUHeJe76yelffdaK5PykAusuOXfumXAgvKJz7xCXzsYx/rm7/xwAMP4PWvfz1+9KMfAQBqtRp+8Rd/Mc/Tn8CAoJRgbr6Od3/8JrFRK3EDulF4bG4V41UtUExkAXNtwHMxvX0G/+e6A/hf/9/tAIDDh4/jj//u+/jTv78VP3zgGCihPYFgs9OV1JYga+4RKLUpKOPCrjI9YeKBObGAPGtXumYXeZLTajRTN7EAYiFdv/dm2HOPgqh6qUQHADCn3DYWzjm4Y6PlkhTKDjFpy9wOXVXAuLAmpIUTZHYk2Vj850lQdmRl7cM2NIl+15z0c8cFlAKAqiolkx1iUh2rio3AZj9RsB0PlPjXQMznz0dZ2eETGpreNWYoWl9lh2lk2yxZjpeJ7KhVNTRaTub8o7SQyo7ujSH180jKJDs0lWLCJHB4+/2Iu39F3oOv7FBpLjWUPP0dq+q5yQ5b2lhCdi1q1OIzO9QUbREjAkF2FKzsUNrXFVWzZna4IKoG12MJNpZOZcfKuo3Dx9exY7oSGWjZDdNQ4XosNRllOUyQHX7gqkQxAaXlZnaIxizxmuUmdKyiB8oOalTARlDZ4XkcKo0hO+T9lXQ/ezKrhPRt9AMAKDoodzvWI5LsmJkQNhalH9lRmywmoHQTkKRRcD0etF0WgaJtLJUTAaXZMT4+jj/4gz8IWMC/+Zu/wQtf+EK8613vwp133hn83H/8x3/gwx/+MF73utfhVa96Fe6++26R7E0I3vnOd57I7NhATI8LEsAjWqmn7RsFGU6aZvLvhsxz+MYPj+HYUhMtLhYv37nlIbzuynPwR2+9DP949Utw9qnTgSUIEIvW97zlstSWIOvwwzB2nRm8xpkJA8fXxOCzbzbdY0yNGVAVCruVjewAZNgcD0K8ykRgYynrWvNcABxNj3Yw2FGgpqgilp9z+6Qk/YIrsLEwF4TmVHaYKhyXZVroTdYM/N6bnp3pmptfkWRHPPEnckvK25wEieCmVvpzDQOOy1LYWFRQ8MAuNUqQGzPN7LwmqNpf2SE8/xnaWBwvUybUWEUH50CjVY66Y6VuoWIokQSMrtJSbSyTNQPPOnsGHhFjRtL9S1RRPcs5z21jkYvZiZo+sI0F4U28WU20sQCbg+yw3d5rM7eyI2isCQX+Zrax2CCqJpRjsQGlnZkdAPDAoaXYAOpuGClsEGFIZQfRzY71FFE1QFGDWvk8KPs0n7suuD8+t20sapAJRDRzoMyRsuB4DKoavXallPgNckmZHX72i4u+6yFAjPsa8bC42iZ+FlaamBozoKkUrFUP1jNxUKqT4I6VOwOFUgJSssK0TMjxtShlh6EpoJQMPA8GZEe4elbmvmzS9zoPciepvPzlL8fCwgI+9KEPwfM8zM/P4ytf+QoABAPiH/zBHwQ/H5ZHvfWtb8XrX//6vE99AgVATpIe6e/R3kxgjGN53cKbf/48KAoFYzyzRUduTo/5GZYtv7XGbTawY7qC2el2S8qpOydwzduvCE6IJmtGqudjVgPOwmGMPeN5wev+lSvPAeZ14BvfRE1JNwhRSrBjqgLHskBSZHyEIckR7tixIYtFoew2FkmiNFySKqAUADxf2ZFHShsshFmSsqNPZodvt2m0XEyOpZsgKSWoN2287ZUXYHrCwPKahdmpSuI1d3ypialxI7GCOU9Iaxa0J1yl9OcaBmwnlNkR8/mDKgAD4HnAiAVAyzFf77KxQNHAYzaxEoauYK2e/j62HRacRqbBuK/+WW86GMs4pqXB6rrdUzsrUXaTCKUErmWhUjXxv3/jRagYauycQX0ymnuOIDsGUHZM1HQcXcyWryZhOwwaPCBEplO9Au454K7TE4ptDKnCtwg4LuvJk8l76hmZ2aHqYM21TI9BFF0oOzKQHcvrVmwmUzdkUGHL9lA1+8/7wsbkdOR1SIh69fxkAaUECiWlneZzzwX8w4jAxlLVUW8K5RjVTbir+YoWykRSQCnQf5ySAaUtj6CSYvNNNR0aBNmx96RxAELZMeNfX8yqg+7Ym/wYvjrYa6x2tPakBSEEmrJ51wZyrDULIjsIEWvZIpQdlHSSMMEYtwkI6aIw0ArsV3/1V/HJT34S5557rpCRh/4D0PPv3bt348Mf/jCuuuqqwV/5CQyEad/e4XBlyyg7ZFPF//zIjfi9v7kJH/qn2zO1owSP4y/2jZpQAHBQWFzFTKV3AUIpwfS4idnpKqbHzdTEinXkUQAcxslnBq/7Lz9zB/7q3+4FABw7vpj6de+YrsC1rMyEBfUXqaWpLUIIpK7MBS+hjlOSKE2H9K+elcqOprSxZF+cyzYWwpICSvu0seSsFnvy2Dr+7JO3odFy8f5/uA3fuv2JxJ+fX272XQiXTUC0LBe6SqEoFHrJlplhwHYZFOLfnzHKHvl1PgTlVFbIe143uzI7Uik7Mnr+XS9QT6XBWMUnO0rK7VhZt2KzDXRNKV2RYLdamF/3oFKaOGcQTXw23LF8siO7rUcuZserOhw3vXUhDMvxoBGvg9RIGtuG0WpTFNyIzI687VRtG0tY2aFnml+554CoQvEXtdnttrFsn2yP67MzKZUd/glvmtwdzrmojoYTuYGlRmXg6lZdK2/u4Z4DJskOvZ3Zwbj4+8mAZE1ZcBMCSgE/BDiFsqPp9A8nBUR2k0ZcLHUpOySZxlqN/pkdtUkAGDikdLOqPqXasSgbC+Dnug0YUNqyXJiG2qHKejraWAbuyHnWs56Fz3/+87jzzjtx880346677sLCwgLW19dhmiampqZw7rnn4vLLL8cVV1wBGuNDO4HhwtAU1CoaHK4MZbM7DMQ1VVzz9isC204aMEtIOn75Zc/Ew19ewrGlJmzouOysqcJaa6y5RwAAxslndLzuaX9i/tL19+G/Xfj8VK97x3QF3rwNoo1leg2BsmMInz8PSRu5awe1dYU9viu87U1P6Xta1c7sEJ9zHt9wWNkRF1BKpLIj5qQ8b9r20poFSgmeuX8Hzj5lGl+5+SBe/vx9sZat48sN7JkdT3xMLadMPi2alhvIaVWVdrREbEY4DoMCWT0b38YCtE/ZRgr+YtgwO+9DoqjB9+KQtboyT/UsgNLqZ1fqdmyWk67RINSzLDDXgcOVvnJnokqyw869CbBDNhZAnObXKtnWYDKglKihcEqzTXbITY5EVpvERiLSxpIzO4B7NkDVDlsbzdHGQtR+yo6l4N9yMwqgJ8spDvLkOY0VzfUYOAdU7oDovWufQZUdAKAqJZLfngtOfBtLKKAUEGQq1c2RbGPxPN5X2ZFMdojxuekClUr/bZ5mGNCwgsXVdpjuwkoL55w6A84ZmJWijaUqxgE2SEipquQidUcB8vMoysYC+I19BSg7unNbhtE8NmoophAYwCWXXIJLLrmkqIc7gSFgZsJAi9Eto+zI01QRBXlatevkHbjm7RfAcRkan/0mTMUprLXGOvww1MkdUGqTcJYawetucTER24166tc9O10FYXZHgFwatE8Oh6HsCJEdjg0UTnaIv8GB0t/GUpGZHb6NJYd/UZAdIu8kTtlBCPVPjvrZWLKdYC+ttjA1poNSgp/7ydPw4c/9EHc9PI+L9u/o+VnOOeaXm7j4rNnEx8wbgJgW4QlX3wo2FtcLlB1JmR0AwL3R2/TJmsFKpXPTT1QNrG/1bHZlR7bMjraNpQysrluxLV3DUHbAteGiP9lB/fGZORZUheTaBNghGwsgTrNrlWzzhGX7yg4tHE7ZX9mxGepnHZf1zBcyKyMr+csiLD1E1TNVunNXWEqTlR3t+6JW0QJLQ1zbVjfkOJzm87F84k/ldqSyg+iDKzvKDAXmngNGOpUdtdD4MqWLNhaZJTgqSKqeBQRxk2hj8a+Rpg1Upvtv8xTdgE5ZkNlhOx5W6za2TZrioIqzFMoOaWPJr+xQt4Syo7BtdSE2lobl9oxxesktSKOIEzKLpzGmx020XDKUze4wkKepIgryJF6tjgUWFa1SGyiIqxvW3CMwTj4TQOfrtnyyY6ZKUr/uHVMVqGAdCf9pMExlR3hBxNz0i7+0kNeww5W+bSxE1UEUDZ4MKA1sLNkCSin8zW4CyUSNauzJVyWnjWVpzcKUr/h53jN3Y6yi4b9uPhj5s/Wmg6blYUefU79hZHbICXcrZHaEA0pjlT1S8TGCNha7JRa1ZrVb2aGnUnakJTs453AcL5O0Vzb2lGFj4ZxjpW7HKvSExarkxbYnlB39ckxEgLQYn/MGlEoCd9xXy+QhIGzHg0ZcKFpnQCmw+W0sUW0sMjQz8xgVR3ZksrHINhYeq+wIK3E5B979pmfhA7/1XJw0U01lfTWCzI7+45L8DBVmd9TOSlCjMnDAp1Yi0c5dB6xb2eGPL/WmIxSmnPW17g0bwsYST74YmpJ4L0vrZMNNlyFBVR2GwgIbiyQ9tk2agQJW3vNxkMqOwW0sm3NtEJAdRSo7DBXNAmws3SG10la6WYmlPCid7Lj//vvx6U9/Gn/7t3+Lr33ta7DtrbGx3gqYmTTR9OjIDfR5sdaw8T9++eLc7SgS0sYSTp+mRnz6fFZ4jTW4y0dhnHwGAJHQL1tdGChcruCnLtie+nXPTlehwYXNst3Ow7axSFKgDHJNLgAd3l/ZQQgBNWuhzI58AaVqYGOIfz5qVoPrqRt5bSzL6xamx8W1YWgKfubZp+CWu+c6ktQl0tTOAkOysWwhsqMjoDRG2UECZccokh2CcKxUujM7+gdWm7o4VUyzsfIYB+OAliGzo1aisqNpuXBclpDZQcsP1vRPm/upBNsB0lZu5VWQ2RGysWSFtLFIpQmQnNkhF/ubg+zwekinQNmR8f1mrtNDfIv64GxtLFKhGXWyT0Pkicz6+n//74/x7o/fhHd//HupMsqMUEBpP7TJjoSAUnuwdVGZ8wFnLjxJdviKlrFK2yYn1Sqj1sjietFtPBK6piQrUV2f7LB5qswOouowKMOCv4YIamcnK8GhUD9lB9VNEM2AN4CNRd0CAaWFZnaYKhoF2FgqXTYWObZshhDpojCQ3sbzPHz+85/Hd77zHbzzne/EGWecEXzPdV285z3vwRe+8IWO35mZmcF73/tevPjFLx7kqU+gAMyMm6jbW0PZ0bJc/Nk/3IaTt9fw57/zfDDOM7WjhCFIDdJxkkGNKty1hUJeqzX3MADA2CWUHZSSjlaX+ic+jzGNpX7dO2YqsIiHlpeN7KD+SR0bho3FaYFWJ+CtLZRyvbVtLGpfZQcgrCxBZkcOltvxGBTSp40D4rrhcdWz/iIk62S2vNrCqTvbGRw/95zTcN0Nj+Drtx7Ca198dsfPSrKjf0CpUrqNRZ4ua8M4PS8ZIqDUf7/iyA5ldMkOx7bgcopqpXPTTxSt7+s1Q9Wi/YhFuVnK0sZiaApUhWK9Ufw4seq3yCS1sayVFIwqQZgLj/TPYgoHlGoKzbXotSJsLJkfw7exqHpKsmMTZXZEVbwqsgIzR2ZHt7KDqoZorWFevN0t/BiuA+5nI0QrO9o2lrwZZWYGskN+htSLUXboFTBrsMwLTS2PaOeuC88/0zVDAaWAn9nhV28zpwUFk9EPsgHon9lBsZLQiCUPMOsW79noRoGooo1lKSA7/EOSSRPMEm01Sp/MDgBQqhMD2Vg280FIGcqOotpYui1uhJBNHQabB7mVHUePHsXLX/5y/OEf/iG+9a1v4dFHH+34/l/91V/huuuu62lpWVhYwDve8Q589atfHfjFn8BgmJ4wYTMKL4OntCwwxrG01sKxpQaW1lqZG1T+zxfuxuH5Ol71gjOxfaqSuR2l47VYDVCzCkLatwc14rMXsiIIJ925r/34oVYXxayAu+kXENsnK9CIh4aT7W8dqo3FagVBdmU8X6eNpb8nnZq1ILNDbsQyZXa4HKq0McS1cUB42+NsLPJ1NjNkdnDOfWVHe+G5a8cYnnnWDnzt+wd72hbm0yo7Sp74upUdm/1EwZHKDqrEer2J6l8XJbQPDQrXsuBwBbWu8DqiiM1UuCq+G1naHOTnnCWglBCCsapWirJjZV3MdbHKjiEQcYQ54AljhkRHZoear3rW6bKxtKy8NhYPNJLs6B3bNpONxY6wsRBCctmGZLhox2MFVqR01zL32tdGdGZHW9mRN6OsndmR3sZCPau0NpZSlR2eAw8KFEqC9zOsHAtCxEdM2RGX2SKh97OxBAGlpMfCEAWi6lCJi6U1se6cX5Y2lkqgaO6n7ACElWVQG0uZCtMyYTniPS9S2VE1tUzrwyi0rOhDic1MLOVBbrLjt3/7t/Hoo48Gi6LHH388+N78/Dw+9alPgRACQgi2b9+OK6+8EqeddhoAoQj5kz/5E6ytpe8fP4HisW3ChIONr56Vcsyrrr0Rb33fN3DVtTdmqoy96ceH8fVbD+EXXrg/MqQx8+ux6h0WFgAgRrVAsuNhaDO7YicP6odmpYWuKdCJh/qIkh3ccwDmttO6y8jsyGBjAUT9rNet7MhUPZtS2WHGXzemroCQbDaW9aYD1+OBjUXipT95GuZXWvjBfUc7vj6/3ISqkL6tPsOonpWfi66VqyIZBmyZ2ZFEdI2wssN1LLhQgpBcCXEyzRNzRrK0OcjWHT1jbtJ4VSslpuP/2gABAABJREFUs0Oehk6OxSk7aBDKWBYIc8Fpf0I2UHYMktnhiHBYee/lUnZIsiO0kU+n7Bj9e9xxo5uCVCW7bYi7dlDnLpF1juWuE9hY4jI7wDxw5uXOKMui7JBkJfHs4HrseD16BdxugvP8n7VWYhU591x4nHactlcMFZSIzA5pzRm1Rpa+mR16n4BSf87xQHuaOKJAVA0Kc9G0PDRaDhZWmzB1kX/GUtpYAFE/O4iNpcz8lrJRprIj6fChH5qWC9PofU16iffdKCIX2XH99dfj7rvvFgy4quLXf/3XceWVVwbf//KXvwzX94zt3LkT1113HT7ykY/gK1/5Cl796lcDAJaWlvDFL36xgD/hBPJiesKAw5XUpw5lIU6OuVLvvyk+vtTER//tR9i/dwqvf8k5hbwe1mr0kB3UqILbLfACTmmtuUcCC0sU5AIiCzTiYT0jhyAlt2WTHbKJRaZ1j4SNJVLZka16Nshs6GNjiSM7CCGoGtk8mVJm2k1ePPu8ndg2afYElR5famJmstJX4aQOI6DUDGV2bIJT3yQ4rgeFsPjaWYRsLCOo7PBsGy5XUO1WdgRjQhLZITfO6T3/WZQdgPDVl1E9u+oPktLW0Q29T8tBEVCYC5aiOStQBfg2ljzKDtvfzGfZ4HZDZHa4wesBxLVNVD0yj0iG322WNhYt4gQ9D/mbrOxIS3bYQXtIXBuL/Llw1heQPqPMyNTG4kGBB8Jc0IgGNblO4nb+A4wyVYXcc0TzUWj8oZSgVhHKMTqiyg530DYWn+xwuYJKxEa3G0TVQbkLgGNpzcLCSgvbJk0QQkIZdv3JDlqdGEzZUXJ2WJkoJbPDUMH4YGNpVBsLINZ8Wda8mx25Mju+8Y1vBP9/zTXX4CUveUnH97/5zW8G//+6170O27dvBwBQSnH11Vfjm9/8JtbX1/Htb38br3/96/O8hBMoADOTQtlBmJvaU1o0XI9hec3KJcf0GMdffuYOMMZw1RsuTZwcskDYWDoH9mBStFtQUjDccXDXluCtLQbhpFGgeiVg09OAcw4VHlZb2SYJog6nepZLsqPqkx0lkCvMyabsoOZYO6BUyx7W5HoMOk1DdiTbnyqmlql6dmlNLCqnupQdikLxs5edis98/QEcnl/Hru2iXvf4crNvXgdQspSYc796Vin9uYYF2xFkV9KY2VZ2jF4AtOc4cKCg1mX5CkKEPQdA9HWTpc1Bfs56hoBSxjje8HPnQlcpltZauXKX4rCy3k/ZoZROxFHupqoJp6q0sdh+QGm+6lldbZ9sp7EudMOyXOjE69nIx4V2G1p28ngjwDmHE1OLnMvG4jkdIa5A+DNMRwZwzw1sLLHKDghiRdErHVlfaTPKVIWAUpK6jUUn4ucibSz+15jdDNZIWaGpFKv1kuYDz4XTpewAhJVlveGAaGJNwkZM2dE/syO5IlvOOW5qZYe4rlSI+tnFlRa2Tfpr3pRtLIBQdrDGau4q37IPXcqEJCSyEvtJqIYa+9IEzXbDYxy246Ea8bv6Jn6v8yDX7vCHP/whAOCMM87oITrW19eD7wPAi170oo7vm6aJSy+9FJzznpyPExguZsZNOHzjpNZPHlvD//PR7+LIQiNSjrmw0kIr4dT737/9IO55dAG/8ZoLg81dEWCtXhtLcIIxoJUlCCc9OV7ZQTN218uJbbnFM2WdyFMnVrqyQ/wt0sbCS8iIkQSKzZVUkzs1a2BWw5cDyzaWLMoODjl/JJMdNXDXjt3wVk01k40ljuwAgCsvPxWUEnz1+4eCr6UmO3KeHKeB7TIwjs7Mjk0+ydqBsiOhdlhmdnijt+ljrq/s6FJBBWqUBLVfYGNJkf8gF+RayoBSaWm89nN34n9+9LuZLY39sFK3oas0to5RV8u3sShIR3a0VQHWYDYWTcmkxumG6xPJkWRHxDylKBQKJSOf2eExDh7TFJRHTs+j2liCRp0cyo4+ZAfQmfWVNqOMEAJTT858kLAdDzrE/BRlY2krI/Kvi0ol2l0HLqc9p+1jFU20sfivf9TaWJw+bSxplR0eaKrMDhlWrxEPiystzK80sW3SJ7JadRDdTHUYqlQnRO5MzhwXcd+N9rgRh5YtiOWiiHmgvWbKG1Iq909RRImunbCx9MXCwgIIITjzzN4N2y233ALXdUEIwc6dOzsaWiSk0mNhoZh2ixPIB9NQg0XXMBtZOOf48k2P4e1/dQOOLNRRNZQeOeZVb/gJ/P0X7sbb/+o7eODQYs9j3H9oEZ/52gO44uLdeOGlewt9fcyKtrHI7w0Ca+5hgFDoJ50W+zNENwPrRxrIz87yaCrrjwQdUmZH28biZ3aUaGNRjXQLPqUiyDFmNYI8gezKDn8D1sfGIp4nJqQ0Y9r2sk92TE/0nrJtm6zg8vN34pu3PQ7b8eAxjsWVZt9wUqDcExXZE18NyA5l00pVJRyHQaNItLFI//4oZnZw14ZHlB4SIpDJJ6hRzAwBpTIHJ620dxBLY6rHX7cwMWbEnjoamgLXY/AKIle6wZkItk0iySQ6qmcVktvGYmh0IBuL51sU0io7AKH+GXUbixzvtIh7OF9AaW8bS3YbiwOWqOyQNpbB7gdTV9K1sdgejERlhz+/ZVivdENTypsPuOfCZr3KjrGK3pHZMcjrLwP9bCy6psD1ePw45TngVAFA0rWxyJwYuFhY6VR2eK1GKgsL0D7Qymtl0ZTNuwG3HC+wiBUFuWbKciAWhlxbxtpYRpyQLhK5PplGQ0xwY2O9p+nf/e53g/+//PLLI39/eXkZAKAkLBRPYDgwTBPg5W54GeNYqVtwXAYC4PM3PIz//O5juOScWbz9ly/GzIQJxniPHPNNLzsPf/25O/H/fOx7+OWfOQv/vxftB2vVsWwrcByGv/zNn8DJJ+/IJZdLfL1RNha9GG+nNfcI9B17IhcO4efKwozLUx4HKo4vNfuGUQZQVADlVw9LGwst0cbCHQsMBIYR7cXvBjV9sqNVhzYhPutMyg6XQVph+2V2AOKakjaeMKqmhnqG1onltRZUhaIWc1rz0uecjpvvmsP3fnwYF+3fDtfj2DG9sTaWZtfpgr4FvKK262GKsNjaWQBQfGWHt8GZSFHgrhOcIIfRfXIcBSNLwKH/OUednkchb8NEWqzWbUzFNLEAgKa1VV5KwQtXoP2+dhMHUSBUAVG0wdpYHAZNVaAoFJpKc9lYZFsb7SY7EsKX+8nsRwF2oDoaQmZHCjUj5xzcc8CIuAbj2ljkcw0CQ1dT21gM4l+zWu+6ghh+wOfAyo6yMjtcOIzC0Drv5VpVw/xKs93GMmpkh9sOKPVa9cA6Lf/fCKy30U0bYTtUVDhlNySJVlE5Ds6twGO8reyw6qnCSYH2gZbXWIU2c3Kq3wljU7ex2F6h4aQAAlVOXmVHEtnxdLOx5JrNJycnsbi4iPn5+Z7vhcmO5z73uZG/L+0r09PTeZ7+BAqEUakAjfLIDilLlqd1s9MV/I9fvhjPOH0bfvLCXQFRIeWYYVxw5nZ89F0vxP/+/F14/Mgq3NUFNB78AW5rnYnLzxyDNvdjmHteFPW0ucE5j1Z2+IP9IMoOzjmsuUdQ2/8TiT9HfWVHWt+jPOVxuIJjSw2cdUq6+4oQAqLp4N6QlB3VEqtnXRse0VKFkwKhz7O5Dm16Jygl2apnM2R2APHXTcVQcXw5/TW1tGZheiL+ZPrC/duxe0cN/3XzY9i9Q/yNaZQdmlLexCcX1WEby2afZB2XQaU8+bP3T8tcZ/TIDoQWw2GQFDkjuQJKU9pYZMNEmPBI0zCRFsu+siMO4fweMx1vmgmB/UBL9+BEM8AdO3dwn8zsAMRpfp5FM5PKjq7XTPQK2PpS5O8MI+h1UEjyKCpPJg+5FG1j8TM70sx50npA4pUdknBiA5IdaW0slsP6ZHZIsiD/IVCZDRzcc2BzEqHsEAGlRNEAQnPbLsoA50KxMT1uwF1dQP3BWzH+zBeDNVbF/1/0onbjkR1DdrgOuE+apcrs8C1K28ZVPPTEsvj/UGZH2pw6eaCTW9mxmdtYHK/QcFKgvWbKkusWRhLZoak0k5p5syPXCuLss88G5xx33nknms32IHHXXXfh8OHDAIRq4/nPf37P795000149NFHQQjBOecU055xAvlhVn3PYklkR5Qs+SP/8kOct29bqo18raLhd3/lUvz3V52NxgO3YfV7n8NzZ1fQ/OIHsPbDr8Mr2GvJ7RbAWS/ZoQ/uTXVXjoM1VhPDSQGxiATzUgcbtpUdCo4vZXs/iKqXruwIMjsqNYAqpWR2MNeGh94MgjhIG4usn9UzSvocj0FXhIQ0rbIjCpkzO1ZbPbWzYRBC8JLnnI77Dy3hB/eKGtp0AaVCvj9IxVkc5N9XCdlYHLec5xoWbMcTZEeSssM/TfRGkexgDniElaIdUJrQxmKkD7uUi6m0AaV5GybSYnXdwmRMEwsQCtcsaaMu59nuitI4EFVvV8/msrF4QWCeOM3PYWOJzeyoxdtYNoWyw7exRCk7crzf3Ou1sdAMNhb5M55/BhnVElOUjcXQUmZ2uG0bS5SyI5jfBiE7tHIzO2xGIjM76k0HhBARIj5CAaWuH0R86b5x1B+4FYvf+SxaBw/g8KevxuqdXwd3rOCejhunuOcGZEeaNZEc97fVFDx1XAS3hzM7utfDcWgrOwYgOzbpQUgZyo6qHyBehrJDrMNGe4wuErmUHT/1Uz+Fm2++GWtra3jXu96F9773vWi1WvjDP/xDAGLBfdlll2FycrLj9+688078r//1v4J/d4eXnsDwUa1VgPn0aeFZUZQsuc403Grtxwte+tuY//yfAwAmfuXPscYMbCvs1bY3pb1tLINndlhzjwBIDicF2ico3GoCKeTO7QW0jmNL2V4fUbXSMzukjYVoFVFXWJKyw4GaqokFCCk7/PpZLWPnuOsyaJQDDCARJ+TB8xjyeWKUHRnJjuV1C7PTyQuPFz1rL245cBj7907hA7/1XIxXdTDGE7NM5ILf9VjqMMm06J5wy3yuYcF2GTTCABq/aVU0X9kxgjYWwlwgYsPd3kz1DyhNs3GWi6m0yg5KCU7dOYH3vOUy1JsOTpqpYluK6uS0WKnbsU0sQDtItSyblZxnldTKDl1kdtQoGBehmkqG98J2WbBgTnua3wM3huxIsLEY2uifGsprMyqzI5+Nxe21sagZbCyhUEkgJqBUK8bGYmawsVRoCmXHAIdOZakKOfMAzgTZEdHG4rgMluOBaCaYlY7sCFuy07bfZIUk2W57eA2veO6LoU5sw5F/eT8AYM+v/TWU6gR0bRUAYglF7rmBTTGdskNcV9NVCnkG0WFj2ZEuF09alb3Gaqqf74ZaosK0bFiOW5qyoxQbi7b5g+KzIJey49WvfnVgQbn++uvxghe8AD/7sz+LBx98MPiZN7/5zcH/P/DAA/iFX/gFvP71rw/CTXfv3o2f//mfH/Dln8CgqI2JzVizUQ6zLWXJYeSRJRuagsvPHMPydz4dfM154LswlGIXpe1O8eIDSq25hwGqQp89NfHnAutDytMGSVbUxqq5lB2lt7H4fwfVTVCtHCUJd2w4UIKFfT+EMzsAf+DPcBKZPqA0WRFUNTQ0LTd128TSmhXZxBJGzdTwxpedh//vugN498dvwv/62Hf7NlrI+7GMhUa3jUWe8m/WRQ0gshAUkmxjUXziwHNGL6CUMjew2YQRKDsSxgRVoamrK2WzSdrMDkAQHoeOrOHdH78JtssK20y0bBeW7WEilbKjnGvT8S0hVE+nVKGaITI7lDZBmAW2065WFaGUOWwsUtnRRdDIbCnOesfNzWBjCQJKC7Ox2D3jQZZ690DZQRKUHUpRmR0pA0odD1VVvA9RZIfMvBjEBlKW0k+SR5YX3cYCAOsNG1Q3wZ3+r19asq+69ka89X3fKLwpSkJed+NVDayxioVv/VPwvbUD14M5VrtKOpbscALSLK55KgxJck9V2rbyqXGp7OjNsIsDVXUQvTKYjWWTqj5LyewYMKBUtrFENfJoihIEiD8dkIvsGB8fx4c//GGYpilClUL/AcDrX/96PO95z+v4nXvuuQeA8KPVajVce+21MIxipKknkB9j42IQW11dL+Xxxys63v7aiweWJY8pDrTDPwJRVZz8hj9G5dKfh3f4Ppik2JtVnsB3kx1ENwGQgU4wrLlHYJx0ao/UtRtEdtenfC5JVoyN1zKTHWWRD2HIzA6iGyCqMbAENwrcteEwJbeyQ1eVTF5R1+PQUmV29LexAOmaLTzGsbren+xYqVv4y3++I1OjRZlkR7PbxqJkb78ZNdiuB5XwxDYWqewYxYBSyt3IcYikaJDJUl0pT8+znniFPelFYXVdjHNJyg49FPxXBuymGAvVLJkdbojsyHh/Og4L3su8Nhb4dsqogFKgrdwLYzPYWAKyI8bGkmUslCqCHvVLhnp3mcPhQol9XW3l1WBztqmrKatnGar+gVJkQKmiAVQZTNmh5iPy+qFNdiCyjQUA1psOqF5JpewouylKQr4P5++tov7ArWLN+9/+BJOXvwLNQ/ektrF4RIGuiXDifpCk3ERF/OzMuAGFEnDO/Ay7dGQHIKwsg9hYAJTWhlUmysjsMHUFhAyu7IgivHStvGDgUUTuuPHLLrsM//Ef/4GPfvSjuOWWW9BsNnHmmWfi9a9/PV71qld1/Ozpp58OSik453jBC16A97znPdi7t9i60BPIhwmf7Fhfq5fy+A8/uYxPffk+/NFbL0fFVHNL/5TKGKpnPRvG7KmY/9rfYfqX/ximSqDWehsuBkGg7OhisgNvp51P2cE5gzX3CMaf0Ztj0w0pDY1aREY+tk9WTEzUcOxwVhuLMQQbSxNEM0EI9WXZ5dhYLE6Dqq5+oJoBomjwmr6NRaOZWG7X820s6EN2mOnIjkbL7atKWV23wDj6tu3ksY6VteAEettYVFU2XmxessNxGVTC+mR2SGXH6JEdKrzIkMx2Zkfya05bXdnORchIdgRWmeJUMXJTkpTZIe02ZZEdVssPazbSNWa1A0rFfJlZ2eG2lR0VQ8XyWg4FpxeX2dEe27rnS11TsLRWjjW2KMjxJ8pilTUjhQdWn5jq2TQ2Yf+ec7kCgEe3sWSsso2DaaRT+diOhwkl3sZCCBFkwYABpQB8a0iBm0Wf7Gh5BDPdNpaq+JzqTQdTugmeQkVbdlOUhOuKdcWBx+v46YtfBH3nPsx/9e+w603vx9RzLCjVCRiauFZixynPhccVVFI0sQDtDKEJnweW4aRBhl1KZQcgguhZThtL+FpIqt4dRZSh7CCEoGKopdhYVPXpZWMZqFvt9NNPx1/91V/1/Tld1/GhD30IF110Efbs2TPIU55AwZiYrMEBsL6e356RhJsPzOGRp5axfaqCWiWdxSAO7sKTOPK59wEATOJCrRWZ1iEQZHZEBDIRI96j3A/e+jLgOjBPPb+jSiwKWRPO5cZkamoc680FNFpOajuHDMArE8xuBQulsp6POTYsT4mU68WBmrW2jSVjHaogO6SyI/69JoomrEIJNhZApm0nB4kur4sFc1JAKZCv0UIuLEpRdsTaWDbvqYLteFDAgIS8Fs0nO5g7WjYWxjiUOLLDr8vtJ5PPUl1JCYIaxbSQJ1FFKjtWUik7SraxWOIe1vSUFdmqDrfVCPIb8thY2sqOdARVGJzzYBMeFVAKRBO5xmaysRRQPRtbKUxV0faRQdnhQAHgRis7CrSxpFHeWI4Hk3oAUWLtmuIQqBiyo0jIdZHDlXgbS9PBjF6Bu3I81esssylKQt7jlFJQzcCx//shePVlENLOxOinfOOeCxc0VV4H0L5ut42p+P1fvRjbp0wsrbVQc8VhkDy0SQOlOgF35Vjqnw9DDV0LlU0m/C9D2QEAVSNbrlsYTUtcH1HXgZ4xp26zY2jU2cte9rITRMcIYnJKZBc0SyA7OOe45cAcLty/Y2CiAwCcpSPB/8tNatGQjxsl26NGNZdc011dQP2+72P6p14HY9cZWL/7hsRAWEkMpO1+l0qJaf+zPL6c/jUOo42F263AmkM1o5SMEOZYsHl6GwsA0MpYZ0BpFmWHy6CR/soOwF8Mxlw3WXrUl1bFNdPPxpKn0aJsG4uq0OA5ynyuYcFxGRTC0tlYUrYqDQst24UGL9JKkUXZka7NgUHTlFTNW2EEi/kCN8yrvrJjYixB2eETcWVZMGxf2aGmVnboHTaWrPeMfP8BX42T8YTQdhl0SBtDN9kRH05p6KNPdkhyOyoIVM1Y9dsmO7qUHbLePUMbi1B2xASUFmRjMTRBfPXLRrB9soNq8XXnRI+f39KgPLLDD3zlNLJ6FgDWGw6obqbKRyu7KUpCkh3S7inXS+GQ83BFdhS458DlNPV6SJIdCnfwd184gN/98I246tob8eRTgrTIpOyoTeYOKNWUzXsQUoayAxBrxEGUHaauRCrpBaG7+d7nvBhI2XECmx/VmmBsm43ie8YPzq1ibqGOX/jp5PaRtHBDZIfXWivkMbvRbmPpZbLFpjUbKeS16qI+7MbPYcfLfgtzn/lTEFXF2HnPA7ToSZLo2UK/5MJnZlqw/seXmjh1Zzp7D9F08PXyq2ep1lZ2DHIKFAfPtuBATa1oAURIqRcOKM2o7FB9ZUdSQCkgSbJoci5sY+mHJV+C3s/GIhstrnn7FalT40slOyy3Y9GlbwEbi+14oApLJLqksoOPWEDpet0CJRxKlLogCCjtR3akV3boOU4+jQyNL2kRKDsSNidS2VHWItCxLOgA9JR5ZcLGki+glHPe8f6bOTI7bMeDSnyyI9bG0ju26ZsosyPq+tRUminDiXvRZIf4WroDhbYSwSeFy7Sx6CoY430bsQTZ4QaHFVGQQbV5oZU0H/AgA4XC0DrHaXn4Vm86IClfv5xX3/7LF4NSgplJEztnaqW1sSi+Gk5aoJhVB8ZFMYTehwzmnguH09RKV3lvf/3mR3Bs6QwAwqLzqetuxxsJoGTJ7KhOwGusgnMGQrKN/Zv5IKQsZUfFUH3lb3Z0r73C0FUK1+N9m/q2Ck6QHU9zyNCxVrP4NpbvH5gDIcCzn7GzkMdzlo4ChAKcgTVLUnZYDYCqvXJU+JvWjIy1YtYwfvGLoU5sx9F/F5W5sj4sDm0bS7Y2lu3bxwEgU/0sUbUgbb8sMLsVnAISVQfPmdSdBO46cLiJ6Qw2FsWswV1bBCAWXPUMUkHX49DUtMqOePtTkLadRtmxlk7ZAYiFWT9SJIz2grP4DYqYcNuLAHliWVa95zBguwyKwoCEzA5VVeBxApYQ9rkRaNTFtahFNIJQNZ2yw9CVVKdNtuMFC/MskLLbYm0sFlSFBARjFPoF/w0K1/bJDjNlG4vqt7EENpb0wX2ux8F5+29Km7MShmV70HyyoyegNCF8WZAdo71hadtYIjI71KzKDj+zI8LSSFUdLEUod7A5T1J2+I8/6Jwdro9OJjsYTOJE5nVIUKMykNK2vcEt9p7joQyUWGVH01d2pFxrUUrwt58/gCeOruHPfvO52LV9rNDXDLTJDlWl4m/w247C91nfNhbXgctoqiYWoE3StZqdpI+1vg6MZ1d2gHlgrTqUynjq3wM2b56Xxzgcl8FIaRvKAtnYlwetBLJDKv4cj8FIWMdsFcR+Mh/72MdyPSAhBJqmwTRNbNu2Dbt378bZZ5+NSiXZj34CGwO5qbdbxZ+2f//AHM47fVumTVcSnOUj0HfsgX3s8cB+UDS8Vh3UrEZKNqlR7VCXpIWoD/tU8O+1A9dj+orXgsYoO9o2lrTKDjGpT0+NQ1VIpkYWOoTMDm63QP1JL62kNyuYa8PJY2M5/gQA/yQvwwbHcRlUs38bC5Cc9SKVKM0UzP3ymoWKke1vTAutxMyOlu11KTs27+mNhON6oEYfZYdK4YEGXvxRQaPegAlAi1IXBDL55MWVqStYThFA6bgsMgCyH9qL+eKIotW6jYlavBwfCNtYyrk2XT+zQzezBpRmb2ORm0f5Nxm6Ctdj8DyWqqEBEJspDa7/WmLIjlZ8ZgfnPLOFaViQ708hbSxxmR1A6lBu+Rg2pyAEUCJOWwkhIIrWl4zsB7kpa1kexhPiGCzbg07cyCYWCapX4K7O534tWkB+l2RjQW/1rKIIi8d60wadroA7FjjzEgOnJRZWxPqqLOWSDChVFdpBaoUJJaNfGwtzYTM9CAXvh3b1LAVCvM9JPpeTNaAUALzGamayo8yg9DJh+SrHUpQdphqoerOiYbmx10BAMpakSBk1JJIdRU1SiqLg4osvxute9zq89KUvLeQxT6AgKCo4AKdVrLLj8Pw6Ds6t4m2vPL+Qx+Ocw106itp5z4N97PHAflA0RM1W9OyfJ7ND2liIqmLXm96P+gO3onnoHkw5VryNRdUAqqaWhkpPp6Lr2DZZyajsGEJAqdOCOrnDfz4jMa8kN1wbDpTEU9tuiIDSdvVslsWW6zFf3k2E2ijpeYwqnBg1S1Yby9RYMcRhN9SSMzvCZMdmlqpK2A4D5cltLJpKUedU1FKOEJqNFkxEqwskedM/syOdjcVyPGhadhtLWQGlkwl5HUDYYlXSRsbfvBgpD3/ERtmCSrO3scjNmDy5D5/m1yrpPhPbEcoODtITxhuQHRHzVJAp4LKRXUgnBZSqatY2liQbS7rGs7CNRVVofEZGAQcGZsq2I8vxoKkOqB6/aU3KpEqDYINbUkBplLIDEFaW9YYDcpKYU7ljgcSs/SSalhvM1aWpv0KZHeEWn/CBSd/qWdeBzYz0mR1UAaiKF5x/Er73YxHCOjtdwSsu3wncGp1hFwcZourVV4Btu1P/HrB51wZyrDVTtt9kQcVQUyl/o9DPxgJsvvc6L/reCf0CjJJACBGbVNfF7bffjttvvx3/8i//gmuvvRZTU1O5H/cEigMhBIxowYlTUbjlwBwA4Dnnn1zI43nrS+CuDWPn6Vi/i5Sm7GCt+E7xPJkdilnD+DNfBP2k09B49MeYvuK1mHKsRBuLeK700kru2qL1g1DMTlczKTvKqoINg1mienZprQWLETDHKt4n6DnZlR3mGJjVAGee6BzPsHhxPQYVHERR+5LCRdlYltesVBaWPAgWGSVVz4a9w2VaZoYFx/VA4SW2sagKBQMFRkzZIS2LekRIJiFUEK19NlNpmz0cl+WysagKBSUFkx11q2+YYD8v/KBgtphnjbTKDtUAwKFS8Xqy3J8ycNnwiQd5wtey3dSB4ZbtiYBSResZ54hmAIRGWhjCJ8+bkezQVArGhTw9SmHRjbjqWaBNWKV9DMujka8peDxFG3jO7meDkLBdD7riJCo7SIHVs0VCqtPcCGUHIKws9WbbosOsZuxBl4RUdQDlkx2KQjoqccNrCFWhoJTEf36eC5uRTOshomoYN9CR9cV/+EUsI2MbS00qO7LblctshSsTcp4qq42lmbONpWW7scr6shRVo4rYO2HXrl25H9R1XTSbTTQaDXieuAgkaXLbbbfhzW9+Mz772c/CTDnhn0C54IoGwtxMlaX9cPOBOZyxZxKzM+kHySTIJhZt5uSOytCiwax67MBO9Sq4a4N7bl/rQhhEUTH36asx9dzXCOtKjKKj47k0M4ONxQ4kxjumK7jrof41asFr85UdZcqNudPCukvx/mtvxHNac3ie2cKhI6s4dedEIYQHZx4Ic+HwbAGlsv6XWQ1oGTvHPY8LZUeK6yCJ7FAVCl1TUis79p6UTRaaFmWdrgGienZmsj3WB5PsiHv64+AxDtfjQtmR0MaiqRQuKBRvtEgdSXaY1ej5l6haIAGPg6mrgXQ3CXkDSgkhqesx02J13cZJpyTPR6pCQEl516bnb1Lj3vtuyHpg1beSZLk/bTda2ZGFQLIcDxpxg+DaMAghoGb02FZ29kkR6H5/wlBDrRBKCh9+oOxQIrK+VD1VA1nbxqIEzx8FouoD21jSXgu240E1XNCIfB8JEVDayr2G0JSSchr8MSxO2TFW1fzMDj8jLUUjy8Jy+2dKs7rJzI4EGwsgNtZJAaWWS1JndgBirOGe07E5XrDqILqZyt4jIW0srJ69kaXMdUiZkJ9DmW0see6vZsvFru1xNpbNf+iUBbGj+Le//e2BH9x1XRw6dAh33XUXvvSlL+Gmm24CANx///348z//c1x99dUDP8cJFABFg0ZcLK1ZhZAdCytNPHBoCW/4uXMKeHECMitDm95ZMtnRgFabivyeJEGY1YRSTb/pZM11gLNgEkgDkkEayh07CC7bMV3B4mpLKA9S+LLFyaGQfEb5jQcF5xzMbuGmexdwbGk37IoKnbh43yduwTVvf0EheS7BIhEKqhmVHYD4fHRNyTToO56sHk1DdlTArWZsOnnVTJe2vbxm4cIzd6R+jVlQ5olKt5Rys0pVJeR1QrjXP7ODU9ARCyiV+Uxx6gKian3VKDLsst8CzHa8IGw0K4wc7SFJWKlbmBxLJppF5lh5tanMteFyCtNIN88SnxhXub9xy6DskISNtJTIjU+WsDvLt7EgZm6gejWSlDdKsCEVDcdlIEQQXN1oZwekUzYntrFoBnizvxJVPobtkWRlRyE2lrbKJwm240FlNogWb7uiRgXgTNhAEoJM41B6QGmMsqNmaji62ADRxTqAp1hvLawOT9mhqfE2FkDm4sRXzzo8o7JD0XquKy9B6RwHuTbOo+woU2FaJspUdlQMFR7juSyBiTYWbXOvw7Ii+5FLBqiqijPOOAOvfvWr8fd///f48Ic/DEVRwDnHv/3bv+HJJ58s8+lPICWopkODh8WVYnI7brlbEBM/eUF+dVA3ZBOLOrlDVIamWDzkQaKNRZ4AxNSIxsHz8xqkvC8N5GlJGnDPCZQds9NVMA7ML6cjSmhQZVeS1N5zAeZhqSkWlI6fNL+4VC9skJUTdFYbizI+g9nXvAteq4GZCRO/99r0+TKux6AgLdlRA8BjP880MkXH9bDWcMq3sQwhs0MPpYBvRsj3iPA+bSyKCCjlbLTIDqslQzJjMoMUNVUbC+f9JbB2ThsL4J9cplCPpIHjemi0XEzW+hO6uloe2cFdBw6ST+7DGIjsCAJKxfsfhFJmICBsx4MKL5YIp0Y1MqC0bDtQEXAcBi2UjRHOAbv8rEkRWp1yA56c2aGDZ2hjsfzMjjgUaWPpdy1YDoPK7eQ2lmBdlM/KIjN9ip4PgoBSTvsoO3wbSxplR2iNXBrZ0RFQGm1jAcRmNe41MM+Fy5XUAaWAJNE6x32hdM5GdhBFFWv0HK17ZQall4kylR3yAC+PlaVlJ7SxqKOvvisSpZId3XjJS16C3/iN3wAAeJ6HL3/5y8N8+hOIAdUMaMTD4moxZMf3DxzGntmxQiX3zvIRqJPbQRQVSmWDbCyGtD1kDCltSLJjKvXvUL2SPrPDsYPF6Oy0WHgcT0l2yN8rK7dDnvppFfGeSrJj17SWeHqVBZLscKFmmmy0qVm4K/OgRgUvOm8Mp9Tvhmeny65x3SxkR3xFI+ArO/qcti6vib9xehNmdnRPuOEU8M0IuTgg3APpk9nhcdrXEjJs2D7ZQbXoDaxoe+hvYwFEtV0SHDdfQCmQPhckDVbr4v6Z6KPsAETGRVmLbe7YcKGkliNTVZId/im1mz5DTWZ26IPYWPzq2e4mluD1xdhY+rVFjAIcj7UVHKsLWL/7BjDXgbu6AP3x2/Cyy3anfr9Z38yONDYWP7PDTaHsGNjGIqud4+9f12MiW4s5iTYW4tfK583tKC2zI1B2KDGZHTrqTTsTWbOw0kLNz58qjexg4cyOBBuLrsTfy54LDzSjsqP3OmWtemD3zQKlNgGv8TSysZSp7JAh9la2e54xjqblxYambnaFbVYMlewAgDe+8Y1QVfHh3XbbbcN++hOIgKobQtlRANmxWrdx4JEFPOeCYoJJJdzFI9CmdwKQLRprhT4+ILIfuN2CkhBQCsRvWuOQR9lB9IyZHarM7BAb6+MpG1lIUDVZQkMKEBA2Vz73LMxOV2D7zrl3/fIFfcMCUz+HP0ETTU+9ifBadTQevgNL3/s3uEtzWP7396F54JupTncY4yK4Dsk2Bonguok4AQWASooe9eV18bqKqnHuRln+Tcf14Hp8i9lYGAgYCHjfzA4PFBixNhbHEtdSVL4A4Gd29JHJp904W07+Ng6zwMyOlXXx96RRdmgJXvhBwT0HXv9c+ACSZFB8ZUcWMlL+DW0bSzrrQhi2n9lBY20s0aHdxmZQdrgMmqYEjWmL3/ksWocO4PCnr8b6j76OF140Cydl3k5yZoeRLrPDc0EUDa7H+2d2DKjENFMoO2zHgwIPlHt9q2cBpG6P64bM7Cg6J0e+R3EKh7GqhqblgUkrb4q5f365ie1TFagKLbF6tp3ZIV8TUbQIZUf0OMU5A5gHlyuoZLAQChKtl+zoF9oaBaU6mdPGIlWfoztuRKGt7Mhn2UxCxbc8ZlV2yHE+ztr9dAsoHTrZMTExgXPOOQecczz66KPDfvoTiICqG9ApK4TsuO2eI2CMF052OMtHoE5JsmOsFGWHZPbjlR3JJ/RxCJQdGTI7hI0lC9nhZ3ZMiYXHsZSNLIGyo6T6WWnd0KpVvO2VF+CXrnwGAGD3tF5YG4t87XEL8igoZg1jz3g+drzsN3HkX/4M7tIcKi/+Hdi0/8TuyZMXsNQBpQDA7ARlR5/MjqU1QUZJG0tYcl1EDXNZJyoyeDV8uhDIJzfpJCs2Af6Jbz9lB2gQlDcqcKz4U2hAerf7V88C/TfOjuPlVnAZmlpY5sPKurh/+mV2iOctz8YCz4ZHspMdlPm1pBnuGafLxiLvwSxqGcvxoMETwdoRoGYtJqB09EOIbf/aVMwaxi9+MWZ//ndw5HPvh7s0h5mXvwN/+R8Pph4P5SaRDqjsIKrWoTiJAlG0juDKPEiTqWI5Hgwi7u9EG0vOQyCJ9txTdGZHu40lKiS55ufTNZm4H1MpO1Zb2DZZgaFlCzTPgnD1LPMzO5Tx6V6yI8ZuF/67s1ShErXXHpXHxgKI+tk8yo6NbmNhjGNprYVjSw0srbXAWDplV9ltLEC2rCWgPc7HV8+K17rZVDR5MXSyAwD27t0LAFheXt6Ipz+BLhBNQ0VlWFod/HT/lrvnsH2qgjP3TA3+wnx4rTpYcx3a9EkAxALLa9YHqkWOgsziiGOySbBpzWhjWV8GqAJaydBVrqevnmWuE2z0dU3B1LiRun62dBuLfzLRdBX82Sdvw833zYvnZcVlhARkR4LUNvq1WVi8/jPBv+37v5tK4SInYtrHxiAhFwuxyg5T7dvGIu/NbZNmj+R6/e4bgkVRXpSltpATbvh0QaEECiWbNgXc9i1MABKVPZQSeFBGTtnh+ddKLNmRoo3FSLlxth0vf2aHXlxmx4q0saTJ7ChxIwPPhUcyNCT4p86U+1a9HAGl8t5OY13ohmV70IkHRU/I7EhoYxllZYfrsmATzBqrWPj2PwXfa9x9Ay45Yyr1eNjO7Oh9n8KNZ/0eg6g6XDc5XJxGnMBnRZr8Ftth0H2yI1nZIddF+Q7LyrJQyjFM0XprkwGh7ACApieu1TSqzsWVJrZNmtBLJETl+6CqNFiXKWMz0TaWqNcQkB0ZMzsiFEOs1chFdii1yXyZHRuo+mSM49CRVVx17Y146/u+gauuvRGHjqymIjzKbmMB0Nfq3A1JjsRdA21lx+iO0UViQ8iOWk3cPLZdzgbrBLKBqDqMApQdTcvFnQ8cw09ecHKhNabhJhYAUMwxgLmpuuuzQG5GYwNK+9gR4uA1VqFUJyKbOOKQ18YCCHXHsbQ2Fv/kMI3MNg/kaUkLYmHRcuXCorjPTl4HSopKXwmvVUfjwR+AqCp2vfmDGH/2y8Hm7guCAJMgE/ppahtLn8wOoz/Zsbwm7s1J3WtLrh+/B4c/fTVW7/z6wPeCQgkIKX6RISfcSlf7hAj/25wnCo4jmngAJNpYAICDAny0FhOunULZ0ScTIHV1ZWhDmRVFVs+uZlB2aCUGlBLmgGVSdojXq8j8gUxkR5eyI2UoZRiyjSVW2eGTHd0b+U3RxuIxaGrbxkJUFSf/tz/F5OWvgPXEPXjhRbOp3+9gkxgxHxDNADgD+gQVc88BUVRhr0lSdqjplCJJUKjIBUkivmzHgwHxdyUrO/yAzwGVHWVldqha9Dg3VhFfrzviWu0XCO96DEtrFrZNVmItJEXA89cXCiWBjUUd61V2xCnQ5LXo8YyZHV0VyZwzMCt7GwsAKNUJsMYaeEaiv92CNPy1wUrdwvs+cWugij621MT7PnErVur911Zlt7EA2W0s8udjA0o3gfquSBRvMEqBxcVFAIAZU313AsMFUXXodPDMjjvuPwrHZbi8aAuLT3aoMrOj4leGtuqJk3BWyMmkn42Fx9gR4uDVVzJZWADfB+u5/gIouaawm+yYna7i4Fw6Vl2eHJZtY2n5UtGmRwt/PvlYagZlh2LWMHbRi6DvOAXW4UewetZLQfb9DBYtFf06hORETLkXm3sQRjsALc7GoqFpOYk1nstrFsYqGtTKGMYv+mkoY9M48tk/BQDs+bW/hlKd6Ps6kkAIgaYUT0A0I2wswOYmO2zXays7EtpYAICR0cvsCDJuYjM71L6EbhobC+cczsBtLAXZWOo2KCXBBqff8643yxkPieeA0/T17pLskDaWLJJjqU6RcmUjZahsGJYjlB2xmR1GBWCemINChMhmCCi1HQ+qtLE880XQTzodSzd8Bjtf+x6Y63W882/vxP/4pWemeiw5T0eN33JuZo4NJWEuD5QdFgtOc6OQxmaWBmafAGDLbttYkiplyYCZHWVZFwJlR4wqaawivr7e8jCt6n0Pl5ZWLXCO0pUd4erZlmMBIFDGpiIzO/rZWLKRHZ1ZTdxuAZzlU3ZUJwFwsOZ6pqy6jVR2OC7rsX8fW2qmei2SNCyljcXMZ2NpHzQl21g26zosKzZE2fHEE08AAHbs2LERT38CXSCqqJ5dWhuM7Pj+XXOYHNNx3unbCnplAs7SUQCANiVtLJLsKLZ+VsoEY20sqg5QJUdA6XKmAV+8Bplw3v8zEQvNkLJjuoLjS81E2az0Ji41mf88JQWUOr6yg4mBtemKxWCRthl5GqEZ2YgvRTdw5HPvg7t6HPNrHt7+8TtSSQXbZEfKNhazfxuL6/HESWdpzWrndTRWsHj9PwffWztwfSFKGVWlhZ+oNO3oCVdTlU07yTpuW9mRlNkBAIwoICNGdngJzRFAus1UmupKudnOm9nRbzOWBSvrFiaq6XKCRKVjOdcm5S5YBrKDdpMduZQd4v2Xp/nZqmeZaGNJqJ4FomoxR5/scEKqI6oZOPpvH4Q19yioZuDgEsfcfD2DssOOvZ9oSqtokNnhsqCCMwpRQZJ5YOhqIlkpiK40mR2D2VgoJVBLINrhOfBAYWjRY3StIr6+3rR9JW3y619YFWuZbZOmyOwoaYyQhKZChY2FaAaoUQNrNUT4qI84MjhooeFKoOZKA9plYwnWwzGHf0mQ692sIaUbmdmhqTRoM5SYna6kmr8sx4PiX8dFQ66d+ql/uxG39pJoE0ujO0YXiaGTHffffz8eeughEEJw1llnDfvpTyACVNWhwEOj5WY69QnDcT384L6juOwZJ0MpKHhSwl06AqU2GRAAsgrLK5rsCJQd0Uw2IcSX7Wavns1KdkiPbBorC3fsDvXHjukKbJcFDQTdCHsT//D//AAAcGw+nTcxK6Syo+H7YhuS7ChS2eHkIzuAdnic5i/OnRQLmIDsgJcqoJRoJkBooo0FSJ7MltZamB43O+w3J7/hjzFx+SvQPHRPIZauMtQWcacLmko3rVdUBJSmtLEQBWSEbCyc81C+QFJmR7qA0iQZvKwWzivtNXS1OBtL3cbEWLoA47jgvyKgMAc8xZghEZAMni1sZlnIjq6AUkASSFkyO9zk6tmgjr1XYg+MdmZHt12EOS1wpwXOQ5W0Xro5UaoyoiAVL/3yoLgr21j62Vh6gyTzwEyqLoVvY0mR2UFUPXF+S4My5h7uuWBEiT1tH6uKz6vedPxA+D5kx4r4vrSxlJnZQSkBpQTMaYFouk848I7XqGsUVsR6RSo7MlfP+tkyEl5AduSzsQDInNtBgzyv4ZMdkzUD73nLZQHhMTtdwXveclmq1kDL9kpRdQDtuTazsqOfjWWTt+JlxVBtLJxz/OVf/mXw7+c85znDfPoTiAHRdChcLG4X11rYZYxlfowfPzSPpuUW3sICdDaxACFlR7PYRpaA7Eio2qJGdNVeErz6am5lB7dSKDs8p2MxOivrZ5cbgRpAgjGOuYV64E2c8WX4//md+/HGi55feLWpPC1puGKoqbsUIAVndvgTtF7JXmUrJ3g9Q1iTPHkhPF1mhyDJ4q+bikyFt9yez0tiac3C/j1TUMwalKlZbL/ybZj/2t9h+8t+C9PPefXANhYApdpYuidcXdvMNpZQQGkfZQcnoxVQarsM1M+libPHZcnsSKXsGNDGkmTvSouVdSt11XWZGxnKPaCPLTEMOa5zx4aqmJlsLI7DQAk6Dh/EaX42ZYcKN4Wyo5OU11QKQkad7PBQ821NnHlBsCN3rNAJc/rq2VjyUH6GfQh+7rWVHWpiG4s4gR/0vjD6KKdsx4NO+md2EEL8QPV8NhagHPKbuw48KLFkq7S0rTedVK9/YaWt7NA1JfPmMy28UPUwd2xQX9kBwM/QEPecrimR71lgY+E0UxVqd0CpDOxX8mR2BMqO7I0sG2VxpZTg1J0TeNevXArGOSbHDOzeMZZKDWg5XiYVTdbXVTEUNKxs1rV+NpZ2K97ojtFFYmjKjvX1dfz+7/8+vvvd7wIQeR0vfelLh/X0J5AAomggngOAY3ElnxTx5rsOo2KouGj/9mJfHISNRTaxAAhaTYZtYxHfi67ai31MW5wW5crsQLqEcO50Znbsnh3D7//qs2HqKpbWWvA8hkeeXMY//Oc9eOv7v4Gl1VbgTXS4zxo30nkTs0IsIAjq/jjdsItXdkjiRK9U+vxkLwKyQ0vvXwwCSlk6sgNAoiJIejKT6meX1yxMTYjNWv2e7+HYFz8Cd+U46nd9pxCiAyjHWhKr7FCUVCqaUYTjeG0bS5/Pn1NFbHBHBI2WAw0eGFFjN0rCxtKnjSUN2SFtFAPYWIBiNswr6+mVHVqJbSwKXCBFzo8EoQqgqOCu2ICnVRoAfriopnR8zhUjWw6KZbvQEG9jIUHtaOfBAyHED3od3Xs8rOwIn5gzuxWqQ02p7PASyI5QZkfiY8jMDq+/jQXgfQNP+8HUk6udbYfBQH8bCyCa6liKg5k4aCotvAKTe24i2aFrCjSVYr3hgOiV/jaWZXFdTNR0GFp585f4/KXd1xI2FmmFDTWyGHqbDA5Dkh1U1TKprGVmh3y8ILA/d2ZHdmUHIMmOjZkzKSW47sZH8O6P34R7Hl1IRXQAvrIjxi5VBCqGljmgtNXHxqIq5YTSjypiP53Dhw/nflDGGBzHwcrKCg4fPowDBw7gP//zP7GwsABATIS/9mu/hsnJbBvAEygHRDNAwEHBc9XPeh7DrfccwbPOOylgC4sCdx14qwtBOCnQVnaUYWMhmpFcJ2lUMtlYpGcxs7JDlwnnyc/FOfcXWv6CinE4DsPffeEAji01MTtdwTteewn+8cv34uEnl3Hx2bMYr+qYna6I8CWIz2u6SnN76xNfn90C0Q20HDGBrtsAjGLJDqclrlkjR+Ax0QTZkcW/KG0sQtmR7pRWkB3RSqTAkxlzUtSyXKH68JsknPmnoM+eAkIomgcPpHr+NFBVCscrdpERN+Fq2sYtaAZFp7Jjc9lYGi0XKmGJJI2wsSTfn4amgJDkgNLuNpCsCDd6mBlOKKOwWrcwmaJ2FohvOSgCKvdiN8VxoJoB5kiyI4Oyw2VBCJ1Ev5yGbrRrirNldgDlvo9FIEx2hJWG3G5BVQSBnHY85K4dOxcEmR19bSwOSGW8v7LDv3667atZYegK1urx97nVYWNJVkVR3cwc3B5GWTYWoW6IH3/GKhrqLV/Z0UpWCi+stDAzYYIQUmobi+O1P3/mtHxlR+99ZoQOaDrG2BDZkQVE1f3WIGHPZQPYWESJAMmc2QH4xFcGUrdoSAJwaS39XshyyrOxAGL9lNfGEqc4kYT0Zj10yorYFcRP//RPF1ofGpbcveAFL8Bv/MZvFPbYJzAY5EJGg4vFHCGl9x5cxGrdxk9e0K/HIjuc5aMAeKeyw6gAIKXYWJJUHYBQXLhri6kfUzLbmTM7UiacS9JATmwrdQvv/4fO+qwPf+5OvOctl2HbZAUTNR2McbznLZfhfZ+4FQtLYmB/8aU7U8u8s4DZLVDNDAbeutM+sSgKtn+iZFazKzuoqoM5dihQr//ALxdlhLl9AyqD5/ErGqMQpG3HMPfLfm3m9LgJzjw4i4dROf0CKOMzaDx8B9zVBagTg4cCl5XZIYMRu5+rrNPzsuG46TM7QBTQEbKx1JsOVCRbKcQJX/LCihDSty2l3QaSs3pWa5Mdg8DzGNYaTqraWaBtYynCPhMGYxwqcWObTeJAVEPkCqkkc0CpDCeVyBr66vk1xTQusyM4cY4iO+hIV8/aYWWH06nsUI0pAICTVtmRJrOjn7LDCyk7+thYAIC5zkDSbFNXcHwp6f5NZ2MB/PltQBtLGdWzbkJAKQCMVTWsNxzQsQrc1fnEx1tYbWLbpHgfdK28zCnXZVBo28ZCwjaW0H0WDgEOkx3tyt2s40zbbkUUNTicyVM9S6gCWh3PZWNRVWVDD0IkqbCUoZ1SKDtKJDtMNVV4fhgNy4WuKVASVGKbOTstK/qOlZzzgf6TjwEAlFK86U1vwkc/+lFQuiFFMCcQAXlSUNWy2Vhko0fVVPEHb342Ljl7tvDX5somlpCygxAKatZKsbH0JTvMbDaWgOzIbGORAaXJn0c7bFBMVHH1WbWKhgn/ZFN6E//orZfjfb/5fIAQTJo0tWQvC5jdBDUqQTK0VJL0k/RmgdNqweEUFTPb5A60bSx5lB1ImdkB+IvBmDrPqp/ZEWdjkWqr6QkD7spx8Xq37UHltAsBAM2Dd6V6Df1QCtnRcmEavZYJTSletjws2E76NhZOVRCMzt/ZbInAyUSyI0VmByBk8GlsLHkzO4IQ1AFPUFcbYqxJq+zQVQrGAa/gwGbb9XxLSMYTV199lrWxwnZ6a3/NopUduqxj793olpl9UgTckPIlPM9yJ2xjKSCzo+A2Fkk8ZWlk8UKqBfn/wsaSrMwyiAtQpa+ChOrZFK/d0JTiLZTcdeDy+IBSQNTPpm5jWWlh+6Q4UCnz2vYYD5Qd3TYWL6QO1WNCgNuVuzmUHWivKQdpYwGA2Zf/DrymWKN7fVQzYZSRHZYFbWVHBrKjRGUHYxy/+rLz8CtXno2ltVbqIoGW7QXh93HQNygfZSOQyDgkVVemhaZpOP/88/Hrv/7r+K//+i+8+93vhqbll96dQPGQg9y2cTW1siPc6PGOv7oB/+e6AziyWC+80cNZPgIAHQGlgE86ZBhA04BZjb6SPTGp5yA7MttYZPVsOmVHoM5JWZ9FKcHiagvv/n9vBld0oIAqu8jXZ7dANDMkwSOAohVqY3GtFhyuBgqJLGgHlMqwpvRtLIS52ciOmM+y2sfGsrwu7snpcRPO/FMAAH37Huizp4BWJwqzspRCdthupGdU1zZv9aztelCIGOf6KjtGLLOj3hLKjiSJM1E0gHkdNYdRMPo0e0h5bP42lmKUHat+K9VEBmUHUHxtaqtlQyE8ViURB6rpuWwstuv1qGoMXUHLSv93SVI63sbiz1MRRG6ZUv8iYLteSNnRVhoyuxVsNp3UbSzx1bOpA0pdF/DbWNLaWNLAXV3A+t03gLlO+/8dSyizEj4fWT2b1MQSvKZBA0pLsDVyz4XDSOImtFbR/IDSSqKKlnOOheUmZnxlR5kWLddtZ3b02FhavTaWHrLDzavsENcV8+1WrFUH0c2+Vs3Iv2F1AfbCYdTOelbHNZcGGxVQKiHntCyWfst2c9s1kyD3WR/5lx/iqo98F1ddeyMOHUnXnCgOmpJf00a/18NE7Er9U5/6VK4HJIRAURRUKhXMzMxgx44dJ1QcIw65+NpepamlWyt1K2j0AIR64H2fuBXXvP2KQhs9nKUjIJrZQxYolbFSMjv6kh2mCJpMK3GWnkWa2cYi3sO0Nha5GJX1WfKzSarPkrJuTrWOFO4iwZwWqG6itRraFKlGoTYW17bgQOnLYkeBqDp4fSWQe6exsQSKhMwBpXFtLMnVs9I7OjVuwH7iSQCAtn03CKGonHo+mo8dKERyryq08IT5phVNdqibWD7pOAxyDdH386cK6AgpOxotR1SJJlgpiCr+Ju46iV79ftWVlv/55s0Ckov5LEqEKKzUxf0zmbZ6NrSJkKqrImA1xdyqZN6EiPFSVbNndnSrasRnlv79ZI4NqIitniVUERvdiDwiQx9tsqMjsyMcUBpSdmRqY4kJnqX+PcT6ZXZ4tgij5UgOKO06gU+C16qj/sCtWLzhs1AndmDhm/8IoqoYO+95KdpYGEzi9LWwAGJ+44MoO1SaqVY5FTwHDqeJLRljFQ1PHlvz21hasfPoetOB7TJsCyk7ompfi4DjsY42FtIns6N7zcL94FpNz06qAu3ryms1cllY5DW39N1/xY6f/20c/vTVwTWHPtkvgMwOGwGyYwSUHYPss+LWXmGUEUo/qoh9J5797GcP83WcwAZCTtIzYwoOLKW7wePsEkXfOK7fxNI9AVFzrHhlR6sOdXJH4s8QvQowV5zkpBi4vfoKiFHN4dP2u+v72lh8skPrtKhc8/YrgsXcZM2ItKjIxb9H1EJtJWEwqwWlNtm5iS5Y2eHZNhyuYCIP2REElMqwrxQ2Fp9VF8qOdBMcMSpgrUbkYsrQFFBKEm0shAgZ/uL8U1Bqk1Aq4wCAymkXoH7fzXAWnoK+fU+q1xIHTaVYrRdvY6lEnC5sZvmk7XowFP9kpY+NhSgKFIzOhq/ecqGCQUk6rfUl69x1Ehen/docBlZ2GMW0saysSxtLSmWH3OgWvJmxWpLsyJaNRDQjqENN2w4C+DaESBtLhvfT9cmOhPkrrmlqlANKOef+/CjeH94TUOrbWNIqO1K0sfS3sTjgVDxGYmaHmk4pAgCKWcP4xS+GOjWLo//6AQDAnl/7ayjVieD+ZYxHrg9sx8OY4qUjO/TKYMoOhcaS/XnBXEcElCaMP2MVLWhjAfM6wt7DWPDt3eHMDsa4UOEkEFN54HosyFlgTkuE5qu6CA3tsLHIA5poZUdWskPuA+R1yqx6viYWec1N7sDRf/sggPY1lwYbbXGV4+PiqpX6EGmQzA6vVYfiv8/h/wcG22e1YlS1YWgqHdkxumickFycQLBRnq5QLKaUbqW1SwwKZ+lIRxOLRCmZHWmUHQHDnm5i9+rLuapBCSGi+aWfskPKjEOeWkoJpsdNzE5XMT1uxmZxTPiLfxdK36T4vOBOUyg7LC8YeLmigRVcPStsLNlPYYmqg7l2UMOVXtnBRWp56oDSmiDJIrIQCCGoGmpsQOnSWguTNQOKQmEvPAlt2+7ge5XTZW7H3aleRxLKkDS2bC9ywtU2OIRsEDgug6H6hFc/iS9VQQDwEQkpFW0sXqK6QFpc+uV29LOxSOWOpg2m7BjcxiLGtrTVs3Fe+EFhNcVYrujZyA7RxmKLTUDGgNLu+ThrQKm0NyaR9XGqtVHO7JDvY7uNpTOgVKEElJJsyo5+ZEdfG4sD7s8nSRvowMaScg5ljVUsfOOTwb/XDlwP5liB4iHuM7IcDxXqpbKxUN0MFK95UMYJM0uR2VHz21iCENmYw6WFFXHvyswOoySrGwB4Hg+UPdyxQTXDXw925n7F2fxkZodm5LOxyHGfdW28s4A1VrHwzX8M/i2vuTTYSGsF5xwtW6g0XI+h3kyneM6r7IizmEkMss9qWCIvLQm6tnkPnbLiBNlxAm0LRIWi3nRSLfKkXULeiEl2ibzgnMFdPtbRxCJRirIjTRuLIVtS0uV2eI1VKLWpXK+HaGbf7no5McXJjJOgqRS1igYXaqFKizCYLWwsTdvF1Li/oFD0Qm0s3LWFjSVHZgf1MzsIIX4wVrqA0nYbR3obCxDtbQdEI0tsZseahalxQ5xGzj8FbVtbwaFOnQR1ckchIaWaohR+ohInpfz/s/fnYXZcB5k4/J5T6116ldSybMnyvsVrlMQJBifBLAkhOzMEyI/fRybDwzKZDAPfxwCZyYQxhCw8/r4ZJvxgZhggJARCgISMCVnsmKyOLduxJVnetUvd6r37LrWcc74/Tp26W1XdU3Wr1WpH7/PkiSXdvre6btWpc97zLlta2REw2LGNJXuCo64PNQHdbDTbAWzKsnMjDD2yQzegtL/+VBcqoDTX4jwBK1G95ng1H9lR9kLGjyqyTSenssO0IcL8NhY/GKyedR0TIeNgGu/DuQDhw58vaWTHsEyIzYQae9TuuPB7lR0AooyUHJkdKSGecWZHhrJDcAYIDk7kNa+n7NC3sRDTxK53fgDjr3gjWkcPQnSRHWn3l+fLgFJdGwsELzyPsDag9pyFqo0lQ9lRtSAEENKo4SZlc2l+uV/ZsTGEKIA4s0UIEQeUAoP19anHEI3bdt5xJr5OO5kdw+bDSci65nSwIZYmTQQhB+cCu7ZJkmdR09bfLqDsiC1mX/1LtI8ewKm/eD9WH/liz3kaZZ0lVbUXbCwKo5XXn4f47Gc/iz/+4z8GANx555349V//dXzzm9/EBz/4QXieh9e//vX4lV/5lU0+yvMLitEdj55pS6ttXLRtiMKBEozVbLz7zTfh4u01jNftVLtEUbC1RQgWwJocVHYYlRpYq1FaPaAIA4jQ1yA7shet/WCNFVjTuwodE3WyQ7OAToBcXpuMwmTdhi+MDSM7hN8GsWVA6a5tNZyeb4CTcskVEfrwhTF0YE+CsrEAsjVCN6A0N9nhdnlu65MD/151rdS8DEV2sMYKeHsd9vaOsoMQIq0sT30HgrNCYWIKckel3Mlb2u6CuYWrZ/2Qo2YAYNCwsUT/fp4oOxqtAA7lmddtZ+c4m6AZlv+gVFJFgttYuwHHNrBrew037s5fKd2NlXUPY1Urs4KvG2oBXPYk0Pc8VACYOZUd0sbiwzSJlvIs/rwwuXoWkJPzWiX7fPiBbI8BkJpHAaimqcGNB9uiG5ZrMCrUd2vFdoHegFJAjoe65FKa/QGQ7XHEsDIzO9QzSJEd2cqOnDaWW++Cs/tanP2H/4ZtP/RzmLrjbTCq43DsZQAqo2DwmlRtLDpkB1GB6l4rzijJgw2png1DhLAwNiSzAwA8EY15KcqOxUjZMRVNkuNA8w24voOQw7Zo9P2K+Hwabi0xoLSflOXRuG3nVnb0tbF4DdAde3Ifv7rmajd8H05/4j9j6jU/E19zOjA30caiiL9d22s4cnoVS2seLh1cfvRACCGJwZzKDgLAnJzBjjf8Is586m4Ag3YfZUv/nV+8A/PLLYzXbOyeGdNaZ7X9cGiOnWWWn9N2vuJFpexotVr4nd/5HXz84x/HZz/7WTz88MO477778Ju/+Zv42Mc+hnvvvRcHDhzAAw88sNmHel5BDXLjrryBdNnMg88t4Hf/9DsIGc+0SxRFsBQ1sSTaWOrSFlCSQkDtTGnbWDT9qby5krt2Nv4su6Kf2VGQ7JioO/A41U52zwMhhDx+qwLPZ7Gyg1Gr1M8ToQ8Gs5CFiph2fCy2pn8xDDlMkpPsiCeDKSGljpme2bHWxtSYg2BBhZP2ZnNULrsZvL0Of/aI1rGkYSN2VNopyo6tnAIeBBxqDanTxgKcR8oOL4RFeHZAqdqh1rKxZGR2hErZke++VHLeug389juvg3XsO9oS6CSsNPzYsqeDjdq1DSKVXpEdVxEWaWMZrJ51YrXM8OvRC5isKcYwZUdyQ9n5bGNRi1RTZXZEz1liubGlJU8FZpaNRb6vna3siBaoPBovspUdXZk6GqCWg4Uv/i8EZ4+jfexQvJiKbRAp35EfcNgkO6Q4/oxY8Vost2NDyA4WgAkKx0p/Rtcq8rpuc/matHndwmobk3Un/l420saiMjtii3KPsmN4GwuLrgvHzVcUMFg9O9zWnQZqOTBrkxB+G41DX89l5d6ITRddqHFRKTt0ChvUpk0eZUfr6AGc+evfg1Edx+L9n4z/PsnuQynBWNXGb3zsG3j4yVntdVZLx8byPaTseFGRHYwxcM7RarUQhiHCMES9XsfevXuxZ88emKaJN77xjfjCF76w2Yd6XkFJmmvRfEaX7Dh8dBGObeCyXfkzKXSgyI5kG4scjMrK7VDyQF0bi46yQ3AG1lzLXTuroFPn1iE7irUGTNQdtBndEGWHCH0pzY0kopNR+wsjVqmBqIQF4NQspPAhpi3ltyyEpVmHGjBRWNkhMhpZkgLahBBYXvOi2llJdvQHkbp7bwSAkStoN6R61kveXbAtA5wLLTn9+QY/ZLCpyuzI/v5pbGM5PxZ9zZZqY8mqno0m/kMWU66TbWNRu/r9jSBZ6Jb2hiefhP8Pv4f1x744Eqm9su5pN7EAG7eQCTz5O1g5yQ6Z2VGA7EjJ7AD0rEFewGBBjknZAaW1ZBvLkLaezYSyTCjlCw/aIIYlW0WUjcXU22EWQmRWzwJRo07GM1b9G4vE1lnVs0rFmSf3ijdX5fs3luO/c4dUO/sBgw1NG0uXsqMINorsCJGd2aGUHS0uX5O2uTS/3MK2yc55UNfNRthYVGaHylFTZAdJsbH0j1NqnLHdgpkdoQ8heGTrLkZ2xMe441IEZ4/n+hm56VIs+2VUeF3KDgBaGYbqZ5TtMgsiDLDw5T/D6b/4z5h81VvgnXxG2n3+r9/GxCvflGr3qVUs1CsWZhf1FOVCiNSNpm5sJrF0rvGisrHU63W8973vxetf/3pUKhW8/OUvx9zcHHbs6DRszMzMYHZ2Ntf7HjgwevjfZmD//v1aryOtVUwCWDpzDMBOfPfgM6iEZ4b+3COHZnHRpIHHHnt0pONMg/v0d+ESisefPQ7Qkz3/Zp2ZRR3AgUceAhubGfmzjJVTGAfwwonTCPz080abS5gA8PzTh+A3hkjYvQYmBcephRW8oPlddKPW9EBby5nfo33yadQAHDz8NPixs7k/w2+tohkQNNdWtK8XXRC/gUkAx06fBXAxmmsLAIC1lgeHjP556udp0EaIaqH3c87MoQrgkYe/Ax76mJ2bH/o+R4+twox2PI+eOAkfwz/XWJ3FOIBnDj2BYHFwEek117C4Egx8dtvn8EOO9ZWzOLH8KBzDxnefPgKQoz2vG69vx+x3v47n7PyyU4WF+WV4fljadcC4bDxYXJjD/v29k8jZM2sAgO88vD/3zv9mY3FpBXuFnNQ/fuAgRMaEcHVdTk6f+O6j4JX8pGfZ9+TcwgoMEWJheRXHU97bnD+KMQCHDx0AO72S+l5LCytoeYPXrMKx4yugFHjs0Ue0j48QgksuuQU73rg9lvZO/+yHcfiF42i39asAuzE7v4Lt45b2uTyzJBeRh596BrR1csir9XHq+HHsAnDs5EnMMv3v1Z1fhOt7WFtdxtq6/v3ZavtYXlroef3J4/K6ffSxJ3BmOnsxdHYliJUdTxx6EsJJXrRUltfgtNYHjmtxfmXoeFL29a2LuWU5Bh8/dgT7MYfKqeOwqYmQA63ZUzi2fz9Y6GN27uzwY+QMUwBOz55Nfc6Pc4Hm7GkcS/l3Na84fmYOwA4cPfJC6hyMeNFz9fln4bFJnV8XE6tLoAAWTh6Jj+HorLyfHj9wCGtnBwmNheUVWMLH2eXV1ONWMBeOyzHjwGMITy1pHVM3FheW0c4YS4qg7nsIBcWRF54FaZ5IfI261586ega3AXj2yYMIlgYXfyfOLGGiZsTHd/S0PHdPHDiEldls8jLv77TWaMA1fDzx6H5MADhy/BQCth/V9Tastc58sOnJ43z2+SPYbi10HewJTAhg9sxp7N+/pv25pLWCSQAvPPcM/IaBKcFxan6p0NxVocIsOGdPYP9DDwFU7zm/sryEZqu9KWPDyQV5PSzOnYBpEBx+9ij2j6c/AwFgpSEJ4TOnT2D//vRr31ibQ/W7n4W5fhbenttweCnA9h3XYduPX4/5z/9XiB/6ZUzf+jo8+fwxeN4g4VF3gWeOnMH+/cNVeX7IwQWwOH8G+/enEyRrq8tYb3gjnevNGsPz4kVFdhw+fBif+cxncP/992NsbAy/9mu/hiNHjvTs+BbJeLjxxhvh5NyN2Wzs378f+/bt03ota67i6APAlXsvgfkQQ31iBvv23ZD5M17AMPup/4O3vuaqoa8titmjX4U3OYN9L3/5wL+1XrBw+rG/w7WXX4rK3peM/FnNF0yc+RZw9UtuRuXS9N+HNVdx9J//EJfu2omJIefXnzuGE/cDl11/E+rX630X3Zg78XW0T6zgmozPWSULmH8CuOnWl8Icm879GU+efRLt0xSOaWpfL7oIlmdx/D5gx+69wEMBbrj2cjxw4HHY1XE4wdpIn9d9fT/xRQ5qOYXeb0WcxcJTX8EtL7kB419/DPWxytD3eWbxKRiPy8nTZVdcibGbhn9usDyL49/8X7h89y6M3TL4+m8+9xhOL58Z+OwTc2sATuGm66/E1FMPgM/swb6XvWzg5+fnX4G1x76Ml95yc2GVz5NzT4IffhovfelLS8nBWW/6AE7iyssvxb59V/b828nmc8BjK7jxplswphkceb7gE197AGPEAdaBW257KYxKPfW184efAVaBl1x/HextF+f6nDxjuC7+55e/Aotz7Nh5EbanvHfrmIvTDwPXXnlF3PaThGeXnsLXDx3GLbfelpgx8OiJA3CsVu7fIVxdwKm///P4z96TX8P1r/2pQlkAnAv8QmUW28ZdTE+4WrlSp86uA//4Fey+9DLs21ecPOzHytGTwGngJTe+BBN7r9X+uaX2ESw99w1ctH0SS039cZP/9Snsvvgi7NvXeT6S+hzwtW/h8iuvwUuu2Jb588+dWMahr9wHALh138tTVY9LzeexdORBvPS2W3syg55ZlNfHrbfelpiXshHXty6ePb4M3DuLa6++Cvtu3IW5U99Ee7kGWh2HWa/iun37UP/KVzA+MT70GLnXxJEvApdcehkmU157fP8YrPE6rk/5d//scZz4Z+CiXXsAtHHdtVdj33WDitb48+4Hdl+8K/XzuiFYiBe+IBfnYyZwXfQzY8eWgK/8My697Ersu2HQKvzxB+6H1QoxfcmlmB7yOd6pCZx86JO46rJLUbs6/3d64MwhPPzss6VeD89+WSCEgZtvvAHXXDqV+Jq5pSb+n3/8EqZ27gWeAy7fczHGbh48hubf/yNeev1F2LfvFgCA+/wCcP/XcfkVV+HWa9I324pc4+YXv4wd26dwwzVTOPl14MprrkPt2n1YWHwcq3NPxe/nBQz4zOcxs/Ni7Nt3TfzzL5x8FP4xiuuuvQr7br0k7WMGwBorOPoAcOklu1C7+moc+zJw6VXXYPzW4t/JmrWGsy98GzddvmtAkZqGh44+jmdOn9iUscF+dh7AHF5yw7W4/+BjsCuTQ4/jxNwa8NkzuOaqK7Dvpb2/I2s3QG0Xa9+9D3TnJBaowI6f/E1Ur+q85+qjX0Jw9jgu3T4Fc3w7btyWfN9f/sR3cGJuXeu8LK21AZzCVVdchn37Lk993TefewwnFmcLn+vNHMP74XlepjBha22pDcHXv/51vOpVr8K2bdtg2zbe9ra34cEHH8TZs50d77Nnz2JmZnQlwIsJsUSVBZgad7VsLM+dWAbjAtftTX6IlIFgaRZWQl4HANBogVGejSXK7NC1saTYEbqhJKNFMzvy2VgKZnbUHATCACuxHUVBSVoDRG0/kY0lgFlqZochAiAjQC8LysIlQh+WZg1Xb2aHHrHQqSzOaGNJsLEsrcnvZWrMhT9/ciCvQ6Fy2U0QoY/2yae1jicJlkkhhFRklAHVLpNWPQtsjOd5oxGEHJaysQzJ7KCm/N3DQM9fv9FotkMYgmlldui0sQDplgg/GAzIHIb+JP/Kvh9HcOJQIRsL5wJHz6zij/72cfy7ex7Ar/3//hlHz6yCD7m+N6qNJfSVjSWfl16RPDbh2pk6PFJV9fvIK9F3pmMv8boDSjNtLMnPxLRMgfMBcUCppTI7PBDblZYhFVBq6NkaVcZBFslMzWGZHfI9QqKT2aEfUAoArKl29wnCJBuLl/z9sCAABQfRCShVmR0j2FhCJobem3lAeBhldgy3sawFkZ0pwcbiBwxrTT/RxrIRAaUys4N0bCy2yuyoQQTtuMZcKSL7jyEMZOVu3sD27swOFgUOF83sULCjgFN/Xt/KYubIyikbKrOj4piYGnMj0iAbaiztt0uFqwtYf+IBrD/5TdgzexGuzmP3uz7SQ3QAgFmXm5Th2mLm58xMVTG31NSqd27Fc6/s+cn3ko3lRUV2XHfddfjmN7+JZlNeEPfddx9uueUWvPDCCzh69CgYY/j85z+PO++8c7MP9bxC7NULfEyP6ZEdh49Iuda1e/OrCXQRLp9JJztcRXaUUz/L48E9m+wghgVi2npkR1PK34pmdlC7kpoOrhCTHQWqZwGZoxHA2JiA0ijozY+SzseqFkyDIBBmLr/xMBgiBAqqGbonjrZpwNesnnUMvcwGhaFkR5R/0E80LEdkx4TLwdYWempnu+HufQlA6EgVtGqCXdZEo5VBdtglf9a5hB8wmDkzOwL/fCE7AlAxLLNDv40FQGoji2wDyZdQr5L8t7/+FzD/T/8TD+IWmK/71VwBdworDQ93/8mDmFuSC7C5pRbu/pMHsdLIJk6slEXEqGDRGJs3s0ONUY7BtDM7FCnSn5fiOiqzQ0MKHQWUCkIzs4nSxraNrOccFSqzQ33XPGiDWg6I7XZldhCt862Tm0WsIZkdTP5bqDI7spqDqAEQ/ZwtNQ8xp3aCNZbjxZIKq/WC5GtBLbZzZXaMEFAKIFcmTRaEEABnsno2I7Oj4piglGA9UBXEg8e/sBLVzo53WqE28tpmjMMyaUy8KLIzbnSL8uIIIYmh6izwwUALkB2dzA7dDLthkJszBMGcPtmxmeHlba9DXEyNO3pkR3T+u0m1OHvqgb8EoSbmPvtfsfbdr8gKlj4YY3LDmA0jO6Zl0P9qY/h932qnz726sZWD4vPiRWVj+f7v/34cOnQIb3vb22BZFm666Sa85z3vwR133IH3vOc98DwPr371q/G6171usw/1vAKhBkBNCOZjatzB6fnhBMLho4u4aFs1btgoG6y1Bt5uwEwIJwXkpBgAWMnKDkMjkCktfb4frFEC2RH6mZWiKuhTV2HQj/G6jUCY8WSrTKiHdTsaZlzbhGMZ8EskV4QQMBEWkrgDvWSHZVE0msMXpSETUPMn3YBSQg2Z9J8aUCq/v7YXolbpfJfqYTvmL2AFgL0tWZZquDU4u65E68gB4NVahzQAs4uAqJRwW7czlR1bmOwIOSwSkVJbSNnBGIfvR7u1WcoONekdMiY4Q5UdvFAeCzFtnPnkByBCH48sH8GBUwHe967k50AWgpDHRIfC3FJr6DW3UQGl8VidU4VHYmVHqF3JGATJTThOnoBSn8EiITDk2aJCDPtDu50N3P0eFeqY1DgkAg/EckFtF+HK2ejf9JQdPFZ2ZN1TNngrPT9BKTtYtP+YqewgBMS0tNtYVDipvWMPwqUz4O0GjEp9eFht4AEWcrWx6MyLktD9PChSVT0AzkAgEAojU9lBCEHNtbDa4gChicqOhah2dttEh/TZyDaWIBQwDQoRyM8lZqeNBZBh+kZ1TB6HbQy2sQQymLWWtwrVMGMSTd3LOvPhLFDLgTm1E/78Me2fsUwKxqXKp+yGx2FQJLBrS2XH48/MD/2ZJGWH4dZQv/FOGPVJzP3tRwEM1soqmGPSTjhM2bFzSn7/s4vNWCWd/nvIY0qae7F2I14//ehLd+Afv3Uk871eLHhRkR0A8PM///P4+Z//+Z6/e9WrXoXPfe5zm3REWwPEssEDH9PjLg4+v5D5WiEEnjq6iJuv2pH5ulEQLMkQWWsyWdlBnIp8OLXKUnY0AZBYjpmF/gqwNLDGCkCNwlJAJR/lfjsenPohWABi2oUzFpSyg7CgUJ5NFtQOWZvJYabimHBsAz6nsjY4g8TRBg9B0LGj5AWxupUdFEu6yg6qFrv6Qyh1KqktPlVXvk+z3Ut2LK95MCiB1Yjuh+3pHtzKZTdh+dufBfdb8U5bHihrSVmyxixlx1YmO4KQwaRyckxI9mKeRsRBuAHKqbxoeSFMZUvIuG51qy3VhF+RWv2QNpb89zdvr8e71jWLYb3ggsIyKWamKj2Ex8xUZWhFtVJD+CVfmx2yIx8xHdtYEGrvfKsFUP/5d3NUz/oBh4VsyxPQtdD1k5Ud56NVTY07ttlp4TBqkyBWp+7dNIgWUaOn7LDB14bbWAIY0WdnX6PEtHMoOxTZsRfNpx8CayzDqNSHEl8kbAMWtJ4lxHIBkOLKDqPc54Gq+h6m7ACAetXCelu2ziQdf6zsmOi2sWxs9axp0LgCmXbZWIBeQimp3pkFAUJBUXHzL+/UdcVLsrEAgL19D/wcjSzdKh971PlhTrTjZhWp7FhvBUOfY0nKDkB+T/21slN3vmNgY45WxwBqgK1nB/vOTEuyY26pmZpBo6DmXv3Vs+HqAhpPP4ixW38YvLmK6qn9eP3LLwaL6o5fzHhx/3YXoA1qWhChJDvWmkHmgufscguLqx6u3cC8jjCunU0hOwgFdaulVs8SpzJ08QIAxNYnO4zqhNZ7JkFNMrK660WQXXk3DBN1mdkB6HuAdaEmDi3RTXaY8KJO+zLUHeo9jFGVHUFkY9GY3IaMwzbzVc8CEUnmpyg7oodS0+tdYC6tepgccxAungSokXo/AJLsAGdoHzukfUzdKHvCmU12lEusnEv4AYdJhNZ3b0TKDpYiFT+XaLTDuEVIL7NDz8aStlgKQh4vJvMgXO2Q7TWTFa4vnag5+I3/+xWYmZLj6MxUBe971+2YqGWPFQYl0UK3ZGVHwXwltbPu0Bw2FrWY78tMib+zlJyGbnhBGNUUDyM7euX1CnFmx3lYP6vGnY6yow1qO6BdNhap7Bh+7Oo+yVJX0mE2FpXZIYZndgCIlB05yY6ZS+Wfo0WVYxkgJJ34IiyysVgamR1EbhSlVbcOgyIYyyM7IqXMEGUHIGs911tBj4WpG4rs2D6ZZGPZmMwO0yBxTlGs7HAH7WK2Najs4FHlbkWjCrUfSjEU21jKIDt27EGweHpoBpTCZm6ExMqOKLMD6FiJ05Ck7GDtBprPPARimrjkXR/JrJUlhMKoT2lldgDAnEb9bJKNpbvWvX30AE79xfvReOyLeO0tM1ty0ykvXnTKjgsoBsnoBpjaJm/wxVUPO6eT/XpPRXkd121gXkcQkR1pNhZA5naUltnhNWFo+hOpW9Xqk2fNlcIWFqB7xyx9AiFCv3A4KQCM1WwE0TAgQh8oSBokQR13M5QPgYojbSwep53P01DSZEGF/hn2aGQHD32YmmFNQchhxwGVOcmOjIBSoPOQUlhe9zA15sCfPwFrelfm5zm7rwUxLLSOPDEQgqWDjcrscBNCsuJchC34kA1CJgNqNXadqCUXQExTcr6RaLYDWIhIOp3MDs2A0rTFrBcwWDkDSoFe73LV4PBaxRbLlBL4IcO733wTLr1oDFXX1GpjAZJ3TEeFTpBlEtQYZYEhZEJLgaeO3eojm+KcBg1lhxdwWAiHkx0JizDgPM/sCHttLNz3QKxKtLvfUXaEbHgYYLeyg3OBlYYnQ4xNGl9vxLQzQ3bVvRbwPMoOvTFFZnYQ2NtlWKQKTieEyOdxwv3LGIfBIxJHI7MDkNkeYlQbCyvnWlF5Q5waQ3es6xULjVYAaldSlB0tVBwDVbdz33YsWhuT2WEanUwWdf47drHOnDfp+xNhACbowK6+DogpFd6xsmPEzA4AsHdcCnCGYOF0TLhloexNlzxo+wyESPvf9Hi0Flprx6qKJHSUHZ3zbbg1uJe+BPaOvRCCY+rOd2Ay8FKzp8z6FNh6NtlRq1ioVyzM6pAd/iDZYbg1VK95BYz6ZFzrPvNzH8Wvf+JpfPCXZuBurcLR3ChEdtx1112FP5AQAsMwYNs2xsbGMDMzg6uvvhp33HEHbr311sLvewGjQTK6XnyDL622U8mOw8cWYVsGLrs4f2icLoKlWRj1qcwsBsOtgbVKUna0m0PDSRWoXUHYzO7eBiJlR634OVI7KlnEiiQ7iis7DEpiVYTu5EkXoo/scG0Drm3AY9EEM/Qwqkix3ZTnxixYDU27A0qtmtbiW8orBSAKkB1pNhZHfof9jSxLa21MjbkI5k/C2pFdg0ktB87ua9F64QntY+pG2SFxLS/dN2qb5e7knSswLhAyAYOIoU0sAGCeR5kdjVbQpezIIDtMRX4OITuGhF0GIUO9QK1w9w5XhYapAYo6WFr18Ht//hD+26+9Nt6p04FtGeUTcSwAg5Fb6aeUHRYiaT4TsMxhZIc89v5dbYPKUEP9zA421CLYkdf3Pqe2go2lO7OD2jKgFDyEYIF2Zkd8n1ALR8+sxqG4Skm096LxobYT9W+BRmYHEC1Kcyg7aHWsE4TY6MxdXNtMvBa8gMEhQfRr6T1b08gCHcRkR0lKCcEjYlEjy6xesXB2qQVad1MyO9qYHu/dlDENCkLKv7YZF+BC5md1Akrl/ZdEKjoJpCwPQzBkt9CkgZgWBJNkB7HcXPObNFhdjSw6ZIe5ycoO1zZBCInzCJdWs5Ud6vk30MayPIvZv/kwLvlXH5H3UNZaZmwawcLJocc3M10dyKFKQpqqVrCgx1rTPPgAXnrlzVtSYZsXha7kkydPghAyUIGTtduQ9dp/+qd/wh/8wR/glltuwUc+8hHs2VNet/0F6IGYjmxjUWxmRiPLU0eWcPWeyaG7D6Mgq4lFQSo7yrOxUM0wpqxFazdYYwXW9K7Cx0SiisJMG8uIyg4AsFwXYBtkYyEUjYDAtuQOi2MbaDUj20wJNpZWTHbkq3NUiDM7Ah+2ORYH+2UhZBwVKgCWn+wI15LzcOLMjgQby5W76gieP4Pa9a8a+hmVy27C0gN/GRFt+VRF57KNpTO53VoPWTUpMMG1mniMiFRg5wHZ0fSkLQHIXgR0VxBmYZjnv2hAabeyw6XhSDaIVnQ/5W0mSGo5GBnMByP5FyBqsWMiBGAgjNoasuD32TS64dimVmaHFzBMEAZqZavvSFr1rH3+Kjs65yfK7AjaIJYTN49w35PNYTnaWBoBcPfHB9t/PvreO0EsvepZXWUHzWFj4c1VGNVx2WBHTYRd2QC2bSSqfPyAwyE5lR1OFdwraGMpO7MjVNai4fd9vWqj0QpAUtrvFlZaPXkdQNSEkmAhGRVqo0EGlHoAoUD0nEnN7Og7Z4KF4MQoFO5JLTuqntXf/BsGa9vFAKHwzx4DcMfw15es8smDtsdiq1+88TukkSWtelaRvzrqGHNsGu2jB4a+bud0FSfmhq95kuZerN1A69n9IKaJi3/uQ2g8+Q20jh7Ea1/9Q1tu06kICq1WLcuCZVkwTcmAKeJCCJH6PwBDX/vYY4/hHe94B44f1w+zuYByQEwbggWYGpfsYxrZEYQMz51cwXUbmNcBSBtLloUFkH7C8qpnm9qSPZm9oGtjmSx8TJ06tywbSzAy2WFH+rWy62e53waxXbR9Fvd9O5aJdqh200b/vHZTPvjtomRHdxuLSfWUHaGIbSx5AkonXvHjcUgh67tuK10BpQqcCyyve7jIWgcEj2rcslG5/GYAQEvjwdkPcwMyOygZrGQD5O/7n//vmxHyrfWQVefGIFzruzes84jsaAWdgNKszA5VbaltY8kIKC2S2bG2EB+fS0MtFUIamhmEWxY2YiFDWAhGCkjLY2VHlOugsQD3UwJKAanI0TmnfsBgEzZ0Z5+aNmCYsc9fYSMbK0ZFGAeUUpm5wUJQy43PtQjasExDq/1G3SchjNT2H2o6ECyA4MnnIraxCH1lh76NRZIdhBAY9cnYxgJItWXStSC/e3nv6IZdSwtQURtLyUo/pm8Zq7km1lt+j4WpGwur7Z68DgWZ8VUy2REqsoOAhz6I7cZrptjW3DV3sC06OE7xAKIAqQoAxJCkHPcapeR1AHJ8sKYv0g4ptSLFpG7zVJlQyg4AmKjZIGS4siMOg+67ZxUppbOuMOrT4O0GeIbVDZC5HXNLzQHxQD/aXgjLpD2kqeHWYO24FNt/5N3wTj+LqTvfgfE3/zp+768PXyA70vDEE0/gq1/9Kq688koAkri47bbb8F/+y3/B5z//eTz88MM4ePAgHn74Ydx777348Ic/jFe/+tXxF7R9+3b89//+3/FHf/RH+L3f+z385E/+JFxX3tQLCwv41V/91fJ+wwvQAjEt8MCPPaZpZMdzJ1YQMr6h4aQ88MDWFlObWBRopV5q9azu4C6rZ1uZAw732xCBN1pmR7zLlE6s8BKUHU6lEr9XmRB+G9Ry0fLCeLHh2gaaYUR4lvB57ZY8N5Y7OtlhW1K2POxBEjIOi+YLKA1XF9A+/SwmXvEGhKsLWD/wQM+DTfmBu8mOtaYPzgV2kGUAgL1tONnh7LoSxKmidSS/lSW2sZT04Gt7IVzHjCdr8e8dBthutbC3cRA76lsrNkpNbg1wrSYhM87sON8CSrMXAcQwNciOIcqOgjWS4dpCrIhzSDAS6dCOrVT5jsO2jNIk9TF4AE7yWw5VQKGysehMTBVp2x9QCqgFrp6yw9YIKAWS84i2io1FjcPEdno2GEyDaNpY5HPMcpw4DFdBtf90Wr+S7yn19z7PQ3bo21hUVoBZmwBbX47/zbWTMzu8gMGJyDWd6lkAkTJiRBtLWZkdUWgs1bGxVG2ZhWM6A3MtzgUWV9oDyg5A5naUXavco+zw27HNFpBjMrGcXhtL0vfHQnAN1WESiGXHNhZdpbMOrO17EGiSHZtpY/F8FtszDYNiou5oKTsc2xhwNqhrSYcsNJXFbFhI6XQFns+w2si+95teh7TpOdaTT+P0J96PcOUsqOXg4Ckfp+cb5+UYXTYKkR1CCPzyL/8ynn76aRiGgbvvvht/+Zd/iX/xL/4FrrrqKtTrdRiGgXq9jiuuuAJvetOb8Ed/9Ef4wz/8Q7iui4WFBXzyk5/Eq1/9arzlLW/BBz7wAdx7773YvVtO5p944gncd999pf6iF5AN9fCklGBqzEllMw8flTfjRoaThstzANKbWBSMKKB02OJUB9LGoq/sgOAQQfogqHZP0gKJdEB02lhCv3DtqkKlGn1OmM0q5wUP2qC2JDvUwOvYBpqRsmMYi60DvyW/A6cyoo0lUnYAw3dOJdkRqdU0JhUqBXvpn/8K5tg0Tv3F+7H6yBd7AusUGdRqdybDKgV8ksl7ztp28fDfhxqoXHrDSGSHjnRbB90kV38S+OwnP4DWE1/GzPi5rZYbFd3KDp3MDmVj4edNQOlwGwugMpyyF8ROXGOa1sZSPKDUnNgBYliwESIIORgvNsa3vBCmQQaCOodhI2wshAUQBRYhanw3RJTZobEIUERNkrLGSclp6IcfMNiUae2OU6cK0ZfZcT63sfghByVyQSPibAS3k5Plt2GaVEtFo+6T8bEa3veu2xPbf7pJ9eT3iMiOSNkxPKDUyqXsoNE8xKhNJmR2JNlYWGxjoZrh35LwGpHsKLmNRQVEZ6EeVb0zag/YWFbWPTAusG18cH6xESHGPWRH6A8QTdSp9VioE4+Bh7H1JS+IaUllR7sBoyRlBxA1siyd0dpQK3sekgetPpIgay2k4AUd60s3uNcEMSyt8dMYk+upcEhI6c6okWVYSGnLCxOrh5XtX6mqFRm+Gef6XKMQ2fF3f/d3ePTRRwEAv/Ebv4Gf+Imf0Pq51772tfjt3/5tCCHwzW9+E/fee2/8bxdffDE+/OEPx3/u/rcL2HhIr568AabH3VRlx+GjS5iZrmIqYfAvCzpNLEBUi8VZJumgAyFEbhsLMFi11w1V97bhNpbA1wrhyoIiO0KvZLLDa4HYFbT9zqLXsQw0g/KUHV5bkR3FWl3kuSPggd+1Ezmc7DCJvrLDcGsYu+2HMfPmf4e5v/19hEunsfMt/76HCDMokaoXrzPxVDsKVe8szPHtsdJnGCqX3YRw6QyClTmt1yuUXQfbvbugzsGON/wSznzqboRLp1H54X+D7x4rNjneLKiJJRVca0Jp2dFEekiN67lAsx3CUYqkocqO4ZkABiWwTLoBNpZFGGPTILYLO9pd1mkPSUI34ZYHG2FjMURYiOxQC2UzDigdPjH1smwsKbv5A+8RB5QOX+wmKztodCzn30Q6CHlcd9pRdrhxPoXwW7AMmkvZYVg29l40jt/8f70CH/ylO/Cf/tUrsfeicdnGEmdDJT9jBQsAaiBg0sIwrG1HV9khOANvrsXPGqM+FVfPAnLzIdnGwmETeb3qzi+kDeR8ITvkvaKqv7NQi8iOgNgDx69qZ6cnEmwsG5LZIUld05CKo36iSTYB9rax9JMdhLNc9tqen43sUWXaWICokUVwBAunhr52M9tYlEpDYWrcxaKOsiNhnOVeM84zGgazLsmO4cqOqH52KZvsaHshqgnPPVXooMahOCj+PByjy0YhsuNzn/scAGDXrl34mZ/5mVw/+8Y3vhF79+4FAHzmM5/p+bfbbrsNV111FYQQeOKJYo0CF1AM3Q/PLLLjqSOLuO7Sjc3rCJdnAQxXdlC3DgAj53aIwAMEz2FjiciOjAe72j0ZxcYiWX2S+TmCBfFEqiiqdfl7N9aL+W3TILqUHTHZYRtolEh2BBHZUakWC9MihES7ZH7sufSHLPZ7lB2akwreXMXCl/53/Oe1J+4fULZUXTMOlgKApUjZYa3PauV1KFQuk7kd7SP5cjvKnnC2+3YXeHMVi/d/Iv6zf/hrmKpuXMjxRkDZA6imjUVldvDzILOj0Q5QteW9N8yaIFP5hxMMaZ5/IFow5VR28MADb63BHNsGartxTkXRRUUrslINQ3eGDms3UHHN0hPqKQ8hChDTxDABasKIGiZ0duHUsSfbWPQDSi0Ut7HEmR3nYdJ/ELJ4UaU2S2hPQGlH2TFMOSrifAgblBJ85+AZ/MbHvoHDR5fikEgaWZHSdrZVq5pO+Kz6LJ3nJ2+tAxAwqnIeYtQmwZqrcXaIa5spAaVRG0sOi6wKKC2itC2d7AiVsmP48Stlhw8LIvB6clUWVuTca/tkko1lI5UdBCJoxxY2hf77zEkgXKgItRSnSVDXVZ7NPx3YqpHl7LGhr93sNpZucnx6zMVyRlkDEFm+EpQdwmtpn8NY2bG2lPm6mUjZMaeh7EhUm0TKDh6puC3NOe+LAYVmmk8//TQIIbjtttsKfejNN98MIQQOHTo08G+XXXYZAGB2drbQe19AMXQHXk2Nu4k+tfnlFuZX2rj2sg0OJ108DeJUQStjma+jFblIH7V+Nm+nOE1Jn+9GbGMZhewgBCQlNEtBBKNndtTq8vduNsoJe1XgviI7Oj5IxzbRYp26v1ERROnvbrWYsgPoPOAtTZY7DAWsSNmhs4OiLBzENHHxz96N8Zf9GFpHDw78/hXH7MnsWF7zQCAgVk7nIjusHXtg1CZzW1k2oo2l2mdjkefgdzD+yjeBn34Su6dHu3bPNdS1QcG1iC7Lkb8fPx+UHa0QdTsi6XSUHUMyO4D0Zg/GpPUkb2aH2nU2I2WHyeWCrqgVQkfZobJkmN+K//uV102XqkhgjMMAAwqq8Khlg4ooCFMnsyNQmRTJyg6VZZIFL2AwRyA7DIPCNMh5aWMJwg6poMZhYnUpOwKvy9Y4hOwIe8MwW9Hv212Z3N36lfYexLQRhFyr5U7XxsKa0aZLbGOZAASPSJBosZyU2RFGNhZTX8FL7Epc25sXZasKY2WHDtlRjcgOIf+/+7k8Hyk7tqUoO8qup47JDpPKuV0BGwsRDNCwTiSBmLYknHNk2OnAmt4llUsauR26duKNQGtA2eFgac0Dz7BRZik7tMN9nSqIaYMNsbHUKhbqFUvPxpLw3FP3vfB7yY4LAaUpWF2VEn1KR9uVazYHv7CxMbnADc+DQLfvJfQrO1bW/YEb4KmjciK6kXkdABAszcKa3DlUymmUpOyIU5PzKjsyyQ41yShOdgDSytLvhe5GGdWzY2Py9241yrUUiKiNpUfZYRkIhJIPj67sCD2l7BiR7Aj8eBd0GMsdFLGx3HoXLn7nb8ObPwkIjov+xX8YyHOpuBaaXZkdS2seZqwmEPqwt12i//sQAveyG9F64fFcu2wbQXao791wa6hd/33Y/iPvxvJDn8fUD7wD/qv/Lb52eGXIu5xfUNcGFUzruzfPp8wOL0A1OuThmR2m1mIqtc0hbrvIR3aoamZjbBuo5cIQysayMWSHJOG+LbNkjhyI83Ru2jte6q6tFzAZDluQ7CCWEys78rWxJCs7dGxBfsBgItRSDiaRHfLzy9/9LgM9NhaV2WE7PZkdSvkx7HyL0I8qQlVgrzy33dfs0MwOFoAYZunKjthO22VjARDXz2a2sSDUrp0FOoHqWfOVNGyUjcXUyuyQ301LyHGie3NpYaUFSgkm6oNWrsQmlBHRaWOhcrOon+zos7HYloGQCbCua9QQDLSwjcUCb63lUjprva9hwZrepdXIspkLcM/vz+xwwbjAWjP9XvMDFudXdYP7Le36XkIIjLFphENsLIC0svS3PvWj5bHEzA5V6CD6lB0XbCwp2LZtG4QQePzxxwt9qPq5bdu2Dfzb/Pw8AKBWK+9Gu4DhUMFEADAd1c+qgESFw0cXYZsUl1882gJ+GMLlM0MtLECHnOCjKjtyVER1vy4rjIs1VyK2drQ8Deq44BmZJEr+OgpisqNZLtnB/RaoXZF2huhh4DoG/IjsKMPGwpT3sGAbCxAlkHcrO4Y8ZEPGYYADhGpZGQApkTaq46hcej1W938BjcPfHnhNtU/ZsbTWxhVjUdtMDmUHANSueQW2/ei/QjB/AsBg1W0STM3JvS5kZ32XjaW1htOfeD/8My/AsB38v//40aHhX+cbgi4bCzS++46yY/MXfM12iErJyo60/IesxXYWlGfZHJsGtV0YStlRcFHRPfYkgToVGLVJ7HjDL2L2078X5+l85YmlUm0snh8tHgsS08S0u8iO4QSmHw5RduhkdnjhSMoOYGNyDcqAH3TbWJKUHe1YTj+c7AhATCvenGlHVsTuczw0s6OAskMn7FGRHSqg1KxPyr+PlKfpmR1S2aGT16KgY+9Nw0YFlJr28GtXZXa0mCI7Ose/sNLG9JgDgw5uvG18QKmXoOwYtLEAveMjBQcdQdmh5mVltrEAMrdDx8aymWRH2+8NG52K1kJLa+nzlHRlRwvU1rcCmWPTQzM7AGDndFVL2ZHUxqLWSspCrZSXZbUgnc8oRHbccsstAIBjx47h7/7u73L97D/8wz/g6NGjIITgxhtv7Pk33/dx8OBBEELiXI8LODcgpi174IWIw0cXV3sfWk8dXcKVuye1dh6KQnCGYPns0HBSoJPZMWr9bF4bC4ltLOkLSNZYGcnCEn+WVckkVcqonp2YlOfRa5VNdrRBLKcnGdqxDASQ/12GjYX58sE8Sm4JMW3wrjaWYRMYFVCqm9fRDXvbJbC270bjqQcH/q0/s2N51cNuZ03+XE6yw7n4aoQr8+B+O7HqNgkbouzozuyI7jM1WbNMWroMeKOhrg0imFYbixkF5OnkX2w0Gq0AFaXsGJrZYWspO9JsLFk2iiyEq1LZYY5vA7FdUCavWZ2MiSSkpdIrrH33fpgTO7B4/yc7f/fE/bhsxi3VxqKUHYXJDssB5fo2liBkMA2SuEhzNKtnFZFMtcmOwTr2jcg1KANB2MmTiZUdlhMv7ruVHcPGQ6nK6CwuFXnQq+zIzuzg0aZFt70mC8S0ARZCiOxjYw2l7FCZHfL/Vf2s60g1CesjdLyAwyaBdig20BWofj4oO6Kxy8pBdjRZtAnTpexYXGlj22SyanRjMjtUQKkMTR8gO9xeG4uj1KjRWCWEgAFWmOzovtfLVHYAMrcjXJ7TmIfI7yE8xzkSnAtZPdun7ACQmmEIpGd2cK8ZW951YNSnYsVVFmamqphbamaqdlsJAaVCiI6NJdrY7sx5t9Y8rAgKrVrf9ra3xf/9/ve/X7s55Stf+Qr+43/8j/Gf3/SmN/X8+8c//nEsLkpma9++fUUO7QIKgnZVcE7HZEdnUApCjmdPLOPavRscTrq6APBQS9mhqrHOvY0l+twsZUdjBXREC4v8LDe1elYIDrAwDj8rinoUUOq3R2u16YYQQk4aTAeMi/gB4tomOChAjFKUHdz3wEEKV60B8gEvwm4by7DMjsjGUlAqWrv2lWgfOxTvuilU+2wsy+seLqIroNXxXBXGrN1A85mHsfT1T4OtLyVW3Sah7Alns89CoEjJDtlhbDmvqLo2iGBaIXC2ZSAU9LwgO5peCNfQbWMxtZUdSTvDShXh5FR2hGuLkuRwqqB2JSY7NsLG4p15HtSpon3iqThPZ+KVb0Lr6EFcs6taro3Fl2GfRWvCqeWA5Ago9QKWmpciF7hiuGIhmhDrVc9WEuvYz1dlR8C6MzvkMRPLBTFk+wj3W53sgGFkR6TsUGjFyo7OPd89v0oEC0EMGVCqo+ygsS0m+x7lsY1F2sONmpy/KWWH2sXuv4djZUeOxRpxlAVo88kOnoPsMChB1TXRCJWtqXP88ystbJtIJnw2XNkRJNhYnCoEC2LSrNMgJ48jCDlM8DgYOy+6r2NdC4YurB17AIhYbZoG05AE7bmeG6hxKknZsZzRyJKZ2ZEj5NUc2wa2tjjUejwzXYHnM6w2UixxQiSS/CL042e6GvMUsfS9EFBaaLb+mte8Bj/wAz+Ar33ta/B9H7/6q7+KT33qU3jzm9+Mm266CTMzM3BdF81mE3Nzczhw4ADuvfdefOtb34IQAoQQfN/3fR9+6Id+CABw+vRp3H333bjvvvsASP9SN6FyARuPbk+pIju6Q0qfP7mMIOS47rKNzesIo9pZHbKDOBWA0DhhuChy21hUYntWZkdzBdb0xSMdl/ysSrzb2Y/+YLSiMCwLXJC42aQMyEmdQEjlddXdxgIAQlOGq/M5Icyh+S5ZUDYW3RqukMkJRRFlBwDUrr0dy9/4GzSefgjjt94V/32SjWV6YilXXgfQqXk1x7dh9m8+BADY/a/vGUqYmCVWvjEu4AcMla6JQ3yfslAqaSxaeuPFRiNQyg6uZ2MxDQqG84TsaAVwt2u2CBkWREa1toJrm4k1eLGNIm9A6doizCiZnlguSOQtLt7GwpKD2vw25v7+HvAwxO6f+xAql96Apa99GjNv/RVMBh7+z8NnEYQ8nq+MirYfyhrXovJyywZpy/FSx2YWBDw1L6V7gVuvpC+seeAB1nAVENBbx94dyudY9LzcNQwCHk/01U6zeq4T24Xw253xUCOzo/sZ7CUqO4bZWCJlR6Cr7LDin0OG1YQ1V0DdWny/E9sFsZyOjaXLBqEUDurYbRLCyKXskNeAbmYHazfiDSsaSnKprAVuqHaubb2NoHrFwpovx8bezI42brt2JvFnZGbHuQ4o7brP6nY8n1LjY7PlgxIBYwQbi4KxATYWAPDPHoez68rU1+naicuGIie727s6yo70jaK2Hw4oO4QQ0saSR9kxNiWbcLxmfF8kYWfUyDK72EzMkglCDs7FwHOvY/cnHRuLJqH7YkDh7dDf//3fx8/93M/h4MGDAICHHnoIDz30UObPKMbq5ptvxj333BP//aOPPoqvfOUrsn2CELzjHe/A1VdfXfTQLqAASNdOwUS9DkqkhE+hE066wU0sEdmhY2MhhEayvhGVHXltLNSQE6IhAaXunutHOi4AURtLirIjHN3CoRASE6FfXn6CUr2E1AEgUFFtLNHkSlArNZk+DwTzwUhxVQcQ2VjaDe0arpAJGIQXrnezL7oc5sQONJ96sIfsqLgmml4oH5RcYLXhY6y6AGvbtbk/gzdXsfCVj8d/Xnvifkzd+Y5MDzYhBKZRDgGhfOtJNhb5301YBt1ywVix6kc3oNQgkbJjc0kdIQSaXgibSkUSIdkLKlk9q2NjSff8A53JlC7CtUUYYzLLizouEE3KdNpDkpBWwbfwpf+NYOE0dv3M+2HUxrFw35/Bm31B3h+WAy+QzyE/5Im7dnmhbCyGxk5zEojpgDB5/+iQHV7AYKWoalSYnueHce1mEuRCWu/5EudneU0AnSy28zeglMWLGtVMoH5PasucrI6cXkfZ0TlHStnRTdApG0vaM49H7xG2dDM79JQdrLnaQ3ITQmT9bGRjUddCv61JVc8aORZrcUudhrIjXF1A4+kHMXbrD4M3V9F4+kG88VW7SyO/w8jearl691utYmEtas9RNpZmO0DLC7FtPJnw2RAbiwoopfL+U4G5Cj33WX0yVnaoa03lrum00CSBbKCNxZq6CDDMobkdm9XGosjJ7udFxTFRcYzEdsr454JBZYdgAcBDkDyZHXVJ8rO1hUyyY2Y6qp9dauKaSwfXY2r86c/sYC1piTbqk4M2lu8BsqNw+ML4+Dg+8YlP4Gd/9mdhmqaUrQ/5n2maePe7340/+7M/w/h4ZwA+dkxe/EIIvP3tb8f73ve+0X+zC8iFbmWHQQkmx5wen9rho0vYPllJrOAqE8HyLEBNmGOD4bVJoG5t9MwOrwlQY4BFz/xcu5pqYxGcgTfXRm5ikZ9T6fGQ9nyOkhkXTPjvBiMmWIlkh5LJBZDHVnHk/8fKDsOOE6FHQhiAj0x2WJGNRV/ZYYyg7CCEoHbt7Wi+8N2ea6jqWuBcwA85Vho+qmjDCpu5w0m7a153vfMDsTRfJyPFMqmWTH4Y1AO3x8bSFSQs/CZsyyjls84l1GSc8FDr+yeESNsW31xlR9tn4FzAMbjWTj0xTC2bWVqNaSegNH8bixr7qVUBQlm/XETZEYQcIeMDct71w9/C2mNfxuT3vQWVy26Sn2VXeuyCTp88fFQoG4uR4xnTDWrZICxSdmhldqQrOyop1oV+8Fg5OPyY08Ip06pNNxvdNhYetEFMOw6b7ig79OT0IvT7MjsiG4s3aGNJVTNGbSzyuIbfM8PaXeK3ba7G4aQKRm0SYZ+Npf878pSNpUhmxxCyQz2fFr/6l2g9/1hss3zNLTPlKTsissPWVnbYWPHk962Of0HVzqZkdtiWEe+ilwWV2WEIee30E40dZUcjPgagM061I/WXTjBrEjaS7CDUgL3tkqGNLJsVUJpGEkyNuZlB6p4/mNmh1E15bCzGWNSUtJad2zETKTvmUkJKk+ZeQEdZa45ti+eChkFBKdlyduIiGGmV4LoufvM3fxPvfve78cUvfhH3338/Dhw4gJWVTp2gbdt4yUtegh/4gR/A29/+duzcObhjf+211+K3fuu3cMcdd+CKK64Y5ZAuoCBiWWS0gJ4ad3sSiA8fXdxwVQcgbSzW5Ix2y4Xh1sFbo2d2UKeaS65MnUqqjYU11wAIGLXJkY4LkBMInkZ2RDuvZSg7OLVKqYKN3y86Zh8WAB+uUnZEDwVOzVKUHWAB+Ig2HpVAHj9kM3bhhZBedwq9nf001K57JVa+83k0n3sE9RvuANB5ODXbAZZW27jIkONo3nBSVXVbueIWzH7mI7j4pz+AyVe9VSv3oywpcdLEodtuxttNmCY9L3d9sxBL8jnTsrEAOC9sLCoLxiZ6Sf0ysHr4MafVmBapnhWCg60vdWws0ULLQqhVldqPpElfuDqP+f/z/8DZdRWm7nxH/PfUrsQBm4SQ2H5T1vXZbvswiADVXHz1g1gOEOarnk1rwol38730c8oYB40IOr2A0mih26eytC0js8lgVHAusNLw4mDPiZoDmhDK2g8/6M7s6G29oJYL7nv6bSysV9mhSKQ8bSw8DGCYNsKQo5oRqBu/X7eNJQOsuQqrTyVr1CcRLJ4C0Bmf+wlL3w9hI28bi15AacdmuR2zn/49ANJm+Z8+/Twu3amfTZWF0PfBBeA4enODetXCwpz8bzV3WViRv0dWZgcglaBJzRdFENtYRIAASLexRHPPflLWa7VhA7DsEjI7cizUdWHvuBTtE4czX2NQAkI2IbNDKTuc3meWXAslz8FDxsG4GFB2dKzx+pvDiuRn69mNLLWKhVrFSm1kaSWoaoGOjcUY3wZx+lkIzkCoIYPit9g8rAhKuUNnZmbwzne+E+985zsBAEEQYGlpCa7rYmxsbOgi8rWvfW0Zh3EBI6B/p2B63MXCcmfQP7vUwpt+IN1nVxaCpVktC4sCrdRGz+xoN3IP7NSpgvvJgw1vykVqKW0stgsRtCEEH5CedwLkRic7YFjgXnlkh9ol9WJlR6eNBQA4sUoJKKU8gBhR2UJMBzzwu3ZJ0h+ynAsIAVk9OwLZ4VxyDYzaBBqHvx2THWqS22qHWFrzsDMiO6zt+TI7ABlo6J89huDscYSNJTg7L9P6Ocukpfg3kx64PTYWrwm7RI/2uYIfMhACIJoo6IDDkOTIJkJlwViEARr3CzEsQDOg1A/lhK+7+UNNntKsFElgjRWAMxgR2aF2lR0SFlIHxFaqaCEiOMPcZ/8rBAsx85Z/10NWEtuVAZvRwrW/5WBU+O1oJ60o2WE6QKTsCHSqZ7MCSjWUHV7AYJHk3eUk9C/CFDayjYVzgaNnVnH3nzyIuaUWZqYqeN+7bsfei8aHEh5hl/KF9wVB0uiZq7vDLG0snWupnWBjATUBQlOfeaKnjSWPsmN4QKlxyTU9f2fWJtE+dghAZ/Oh38YS+gEMIkBs/cWaIid1bCzSZvnn8Z/Xnrgft1x+G+ZWy7lWWBCAgcbE3jDUXAvPtAEYnblLrOxIJTs6Y4SmW2YoFNlhcEl29JNNRo9drCtzJbqXW+2I7HCKkqp29P/uSJs5abB27MH6wa9l5ll07LSblNkxoOxw8MKplaQfic97v7KDF1F21JWyQ6N+dqqKuaXk+6zV99xTUMpac1ySKiLwQZzKlpyHFcGGdIhaloWZmRmMj4+XEu51ARsP0ieznB53sRixmXFex2Ubq+wQQiBYOqMVTqpA3XopbSx5JXvUqfZUgHVDSUSN2ui7FEoammRliTM7SiA7iGmD8qC0ianaHfG4HHAV2aEeJIxYQyvIdEB5oLV4y3yPOKB0eGaHsl0YIyo7CDVQveYVaD73SHzPVWNlR4jltbYkO0wb5vj2Qp9hRg9PplFnprCRNhbeWo/VENxrlhpId66ggg0F1//+Odl8G0sjUnaY2soOU7t6FsCA8kJZwfLkXbBoktev7BizWCEbSz/htvytv0f72EFsf927YU3v6nltPM5Gk9R+efioCLwo/b7gIoRaNhDq21j8DBuLm5LT0PPzAZfEGHQDSpMbyhx749pYVhpeTHQAwNxSC3f/yYNYaQx/rgQh6yg7/HZ8rQFyocdzBZQGIEY0f+Kiq3q2c34JIXEQduJ7RG0sAeMwzeFzZh0bixB8ILMDkMoO3lqDYGEq8aUIizw2FkJoZAHSs7Eom+X47W9E6+hB3HHD9tKeBywIEAojsRI0CfWqhfWWrIbuKDsU2ZFePQug1JDtbmUHkKHsiG0svaSsIlV1WmiSQKPruOwmFgV7+x4AgD8/3Mpyri2uLS+ZuOhXuXdDjW39pJraCM2j7KCWI235GmTHzHRluLIjw8YCdIKZt2IrXhFsCNlxAVsPtO/hOTXmYmXdA2McTx1dgmlQXHnJ6EqFLPDmKoTfykl2lJPZUUzZkfxQ56rbvhQbi9otGSQ7eEx2jJ7ZQUwbFmFYWS9H3aGOt8Xlg6O/jSUkepkAWQgZl95WYzSyR9pYgli6npXZoTy1VBTP7FCoXXs7hN9G64XHAcjMDgBoeoFUdtAVWNsuGRommQZ1/eUhO8raUUnaXWDtRryrIMkOY8u1sfghg21SCM60lT2cnD/KDhNMa7wghqVnY3HSPf8AtJolFFTrVCezQ459dYsNzZdIQssLsWt7DTfurqB98hmsPf5V7Pq//gvqN71m4LW0b1faLrmSL/CiiWXhHVcnskAMr4wFosV8iqpGfWfDlB028pAdysLQOwHfyIDSIOQDu5tzSy2t8csPeWxT4b7XEwRJo1Bw7epZ1mlj6f5d+88vMe1Ugl8pO8KQwzJ0lB3y87IazXi7AQiemNkBSCVVf5uHAvNVHW++61XZwbJguDW4l96A7T/ybsz/0//E2E2vwa53vA//60tHMy2kecACHyEMOJbeGF2vWGj7rCcQfn6lhbGqlUrY9oeDlgF1rVE+hOyI7rP+Y/BHHmcU2VFuXoeCvSMiOzRyO851Q4giJ/tJgqkxB812mEgOx8qOfhtLtBFKcwSUAlLdEWrM12amq5hbaibW1CpLWr+NhbXWAMOMyU+VmWeZ9EL1rC6eeeYZHD9+HM1mE2GYbwfrLW95SxmHcAEjol8WOT3hQghged3D4aOLuHL3hJa8chQEy7MAADMH2SEzO9ZHqghk7UYuggWIMjtSlB1M2VhKCigFkqWhiiygGgFyw2DYDiy0sNLwsGNq9BBatbvTZr2KDvVwZsSIQ0yLouXJOsdRlS0qs8OMpM9ZydTxZEQwEGO081657EZQp4rG4QdRu/pl8cNJKjs8XGGuwNmxr/D7x7LIKHlfB2VNMpLbWNZhTswgXJ6LyI76lttRCEIud9NYmMPGQs8DskOO6wbCeBc6C8SQNrNh42razrAisfIElCr5btzGEhEQNUsUsrFwLvDb77wOeP7b8AwLM29+L7xTT8PZdeXAIoLE42w7Ou5ybSwdskN/p7wbxHIAIYOR9TI70ltk4gVuhrLD88OOjUWHHLNdAATcG8zsKLueU8EyKWamKj2Ex8xURYtg6w5wFUEbpMtelL96NojPUSs6pwYlA9csNTOUHVEbi66yg2rYWFgz2nRJJTuW4VYuBpBwLUTP5jzKDvn6ipaNpfX8Y1i8T7aFBQsn4ey8DCvrHoyU5pO84EzZWDSVHaqVyHRiFe3iSjszkF/H9poXYRR2Snk0t+sfp5wKuu+zfrLKiwJKbadoQKk8D7Tk2lkFc3JGXufDlB2bYmMZbGMBOvWzy2seLtrWu2TuKDv6yI4Cyg5AEv06yo6dU1V4PsNqwx+on215ckzo/z14ax2GW4+ffSo/yLa2nsK2CEYiOz71qU/hj/7oj3DmzJlCP08IuUB2nCcYyOwYkzfE3GILzx5fxuu/7/INPwZVO5tX2QHBpRQ158CiUNjGkpLZwRorADVKYceVvDapu17Eafmj21gM24mUHeWEyalFwzqLlB3RotegRHoEhQURJvsgddFqh7AQjhzQSixb7oCBDa1eVQuNUQNKAbmgrF79MjSf+Q4E/4U4s6PZDrG6soYp2oC1LX9ehwK1HBCnCtbIaWMpU9nRbWNpN+BcLCvFlY1lq1We+QGTNpYcZIcgBojYZBtLK1qEiVBv8apew8NMm1hqdaUKKM1lY1kACI3tf2rsq5nFbCwXjRHwZx/D0jf+Cjve8IuY+/v/L4hpon7D9wP9O6ZqnI3JjnJ3bcOI7KAjVkJahOnZWAKWuujv2FjSfzc/4LCUskPjmAmhiaHdKrNjlM2INEzUHPzaz+zDRz+xvyezY6I2nIRWgaaAlHMryx/QXT2rp+zgXdWzald1om5jtdFLREh1TpqNRbaxhLkzOzKUHWlkR30SAMDWl+FOyJ32/mtB7frmJjsygtu74Z1+FiRqQOJRJWaZC1weBAiFPtlRi8gOYbpdbSwtTKfkdQDlNzYBw5Ud8X3WTg4oVcoOxy2o7IiI8Kzq01FAqAFr+26N+tlzb61Qz7B+S8p0RMAtrrZx0bbe86JIwsGA0vyZHYBsZPHnTwx9naqfnV1sDpAdzQwbC610yA5VSGAZxtAGwhcDCs/WP/ShD+FP//RPE2U0F7D10GljkYOlGuT3PzULP+S49hw1sQAE5uSM9s9Qtw4gupFHITtyDkrEqUL47TjRuBussQyjOlHK5C6WByeoIESJNhbLcWAhLJ3saDIDlCDOwwAkCx7AGLn9pRkpO/Ikxiehu4nItmjmTk1MdggmQ+dGRO3a27F+4J/RPnYI1W0ySK7VDoCV0wCKhZN2w6xP5szsKGeS0Wlj6dwbvN2AUR0HsRxwrwV7C3pFZRYCAUL9gFpODBh8c39PpeyggoGYwxcwnbaHMLPaOq26Mq6ezWNjWVuEUZ+Kx1PaRXYsFmhj+e7RdbT9y/EDb/hFzP3t7wOQrQ9JrUTxONtnYwnKIjui52rRsVqNcS5lWl727MyOSI2TUBmsIANK5b/rKgepM1jHHitkwnSlSVFQSvDAoyfw7jffhJ3TVUyNO1ptLJxLK1CnjaXds6gklgvhe4iEHcPHKNZRdqgF00TdweKqtAEb0RsR005sYxGCS6WYUnYYOpkdw9tYmLLT9ilMlbIjbCzFNsMBiX5EdpCcZIckMIYrNr3Tz6Fy2Y1oPv1Q1F5X7gKXsyCysehmdshFPjPs+PgXVtq4/OJ0da66tku1saj5BUtWdgDqPuu1sajxVpGqdtHq2Q22sQDSytI68kTma0yTlGZp0kWqsmNcfgdJuR2pyo6CZIdZnwZbX0osJOjGzojsmFtq4ppLe9dmalzvb3VirXUYlbH4mlLKasvK3uB7saDQbP3xxx/Hn/7pnwKQ6gwhBCYmJnDxxRejVqtdCCXdgiDRhCa2sURs5refkIuu6/ZOb/gxuJfdBOPxr4KaNli7ocUuGxVJdrB2A+bEjtyfKTiDKJTZ0ZE99x8na6yU0sQCdHzrST7YmOwooXrWdl1YhGGupMwO4bcAQtH0Adcxe8YExzbhC2PkzI5WW5IdRRsOFJQkmEf1s1n+RTUZIWJ0ZQcAVK64FcS00XjqQYz/4A0AJIljN6Sly96Wr3a2H0Z9CiyPjcWgmcGFulBhX2oXmQceROiDuvV4siZVJFvrIRsEHI5JgBBbStmhMjsIzyYvFNRrZL11OomcFnapCMM8mR1sbSHOdAE6C62qwXC6gI0FAF52xRgW/+EP4z+vPXE/pu58x8Aigm6wjYX5ynI42iKkYvI4NygLOtWzmTaWgMHMEVAKyA2AJGWHOp6yyQ4A+PaBM/g/3ziCH33lXvybf3Gr1s+oMdzqyuwgfZkdgICFsOf1aeBhEN8vaqExGe22egFDVZEdKQGlas5Fi7SxZGwYxHbavqB0NTdhjRVYJk203BCl7LDyKzvC5bOZr2HNVYTLcxh/6Y+ideSAzBKA/D7WWyXNP8Iwl7KjXrGwa3sNO+/6Gax+49MIGcd733YtnplNP56yQ4yBLstUPLdLIDvcWmxjMY3o+1PKjmC0DTBiu5h526+ifVzWw+rOw/PA3nEp1p94IPO9LWMTlB1eCNOgsX1NQdlYllYHSby0zA7hNwHDzP09GGPTgOBgjVWYkQIrCTumIrIjIaS05YUwKBn4PXhrHebEjgEby1ZU2BZBodn6X//1X8eyxD179uB3f/d38bKXvazsY7uAc4j+NpbJugNCgKNn1rBtwi0lxyEL4eoCvFPPYvo1P4VwdQGNpx/E2C13Dd21Vwx00fpZNbktYmMBAOE1gX6yo7laGtmhrDmJbSzqwTZiQCcgveSl2liCNqjtou2zATmdYxmS7BixjaXpBXAQwhyR7OiWBFtmtqSvE1BaDtlBbReVK29D46kHMf3DPwfTIGh5IarePLhFYE3ny5Lph1GbhHf6Oe3XmyZF0CpH2VFxjHiXVaXHG24tIjsa0Y7C1nrI+iGD2jDR/f4l2TG82WQj0WwHqDhmFISok9kR1bUOaWRxMjI7bJPm2vgI1xbjADsAoJYc+1xarHr22otd0KOy9eGSd30E64e+htbRg5gMvAEbS2wXVMoOtZApiYzrKDuKkh3yeCuGXmZHEKZXzyorYWZAqc9y2ViA3h1nBadL+TNWcsFDsx3EjRnrLf37S03srTizwwPteoYoRZERWQmGV8/6A5kdE5ENuO2zOHiaWk5iQGkcBGxYPYqTLMTPrIx6aJXZ0R9QSi0H1KnGJLhjG6lkR15lh8zsyLaxqOeRs+tKGNWxjo2lxHYuESk7+nfp0zBRt/Hb77wO7NSjGLv5tWBrC9jbOIjq5enrmo2wsTAm5CJVnf80ZUdXXpzMxYmUHX5kfynaUCcEwpV5VK99Ra55eB6oRpbg7HEYe65LfM1mBJS2fZZ4vYzXbFBKcis78m6gAlLZAQBsfTGT7KhXLNQqVmIji5x7mQPPXtZeh33R5fF3qZTVtmmURjKezygU8//ggw8CAAzDwB//8R9fIDpeBOiXRRoGjb1gG21hUVVkS1/7a1DTwam/eD9WH/mi1mI4trG0itXPKoa8SBuL/PnBwYY1lstTdmQFlDLl6yynetYus43Fa4PYFbS8cKC33LENeNyIAxCLotkOYRMGs2DyuEKH6Zb1s5nKjugBTHJUjw5D7dpXgK0twj/9HCqOieU1D9NiCZ67rfikJYJZnwKLqpB1UGZmR29ehyQjaaVL2WFkW4bORwQhz012gBogYnMVLI12gJpr9oQpZiF+HmQspoD0gFIvYHG7kS7CtUW5sxVBLTorRrHMjvseX8A35sax/UfeDQGBqTvfgV3veF+yjaVvnC1711aMuOOqrCQVg2stArwge9Hs2Ga8ME+CHzDYJASIoa1gok5Vkv9d2Ijdb4UTc50NjkZTn+zohOcqZcegjQUAjCgkMotc6ragAB21jFJ2dCuepI0lSdkR/Z1hgXMxsCObhM58LZvsILabqCYy6pNxlpNrG4PNMSr8PK+yw64kNsd1IyY7LroCRmUstrGYZVaRsxBcDO7Sp2Ha5TBPPCbnoE4Vs5/8AFpPfBl7tqXPrTq5PiUGlDIOyyTxQjTp/PeTijIXRx5D6KsNsPxzE9ZuoPn0Q1j6+qch/HaueXgeWHEjS3puh7kJ1bNtP0wkOyglmKw7uZQd3GvGz5Q8MMaiUHnNkNL+Niogmnu5g98/b6/DcGuJyo6ttulUBIVm62fPngUhBK94xStw2WWXlXxIF7AZkIsqEj94ORf49z/1UlgmRcUxwbkY6oUtCsOtYezWu2DUJzH7Nx8CkO6t7getSFVF0frZuCIqZ694h+zoHWyEEOXaWPrk1d3gI06gu0EsWT27XJKyQwQtUNtNHHhd24DnR7tqLCi829lq+zAJL9xwoNDdRGRb2fLJsm0sAFC96mUANdB46kFU3Itwar6BlxkrYGN7hv/wEBj1KQi/De63tB6+ZT342n0kl1J20FjZ0YI9bSBkfEPCCzcKfsAwWYkIulw2ls1uYwlRrVhREKIO2TG87QHo2Fj6LRFByOGk2CiSwL0mhN+Ka2cByEwUasAlxZQdzXaI5x85gBsP3IdL3v37ckcrZYdy0MZS7kKmM1aPZmNxKRuq7GCMg3ORaRtxncHd/G7EmR05ni3UqSBcOt3zdxtRz6lwYk4uki/aVsV6Ow/ZESk7DCpVFTzsGRs7yg6/5/VJ6LagAB37Xmxj6TrHsvUrQdkRvQenJgBPU9nRyZlKA2+upjbCGbXJLmWHOWBDM3jBzA6NgFLv1LOwtl0snwWV8Q1RdoCHYNTQfq5Qt4ZveVfj1W98L+b+5oMAgPGf/hCWAwv1lJ+J66lLDig1DQoRyHOYtJFF3Rp4V5uJbRvx+BsGKrQ+/5zQcGsYu+2HYU3vwplP3Q1Afx6eB+bEdhDLhZ/RyGKZNM79Oldo+wyukzynmxp3EpUdiiQcVHbkt8YDgBmR/TqNLDPTFZw8O7jJm7TBKFgA4bdBK2OdyAKV2WFuvU2nIiik7HCindRdu3aVejAXsHkghICYsm6Qc4GjZ1bxB59+DL/xsW/gd//0Ozh6ZhWcb1wYLWusYPH+T8Z/Xnvi/tRO+m4YcUDpaMoOI2fVVpqyQwRtiNAvpXYWQFyJl1U9O+ruPyAnYiYYVtZGq4NV4H6XjaVf2WEZ8JgKhytOrrSa0WDtlkV2RJkdGZOXeLeBh9oBlcNgVOqoXHYjGoe/japtYHZ+FTvoKujUxaO/d1fyvg5Mg2rJ5Ieh2UdysVak7OjL7ACGe+LPJwRhlNmBHJkd1JCBtpuIZjtA1cmh7FA2loLKDtVaowu1k9VNdhBCQG0XNgkL5ci02iHGrChkc1h4dUSsKBuLImrKypTpNGcV9NJHE1TXGB5Q2m/TSILczc9WdljIV+tNnVpqZsdGkB3HZ9dhGgRX7p7MqezoZHaoOUaPsiNa4CsrR6ayo68RrTugFOgjO6wUZUd0j3HIc6Wl7KAGQE0IlpXZsZq6UDVqk7I1Dmrzoa8mN7Lw5LUvULsCsDBz3PBOPwdn11XyOKpjcWZHqYHVLIQg+s9nyzTwiitrWP7Kn8Z/5x/+Gqar6fdQJ9en3MwOw4iuS8NMfMb021icrlD1mFQtODfhzVXM/9P/iv+sOw/PA0Io7B17EJzNJjs2I7MjzfY0NeZiMZeyo1WoMEGGBxOEGqHyM9NVzC01B9TRLS9EtY+0YS212VSPLXuxjcUyzrmKZjNQiOy45BLZEDA/P1/qwVzA5kLJLFcaHu7+kwdjidTcUgt3/8mDWGmUO+gpKPkcMU1c8q8+golXvgmtowe1FsLErgCEFs/sUMqOkmwsagJRWmYHoVE6fDLZQUy7nNaXaLLWaA6vjdOB8NsgtotWOxzM7LANtLkiO4rbZvzoWO0RbSw0zqvxhis7NsDGAgC1a1+JcOkMLrFXQNfnYRABd0cJyo5aJIvUbGTZOBtLd2aHrM5TZMdW2lXwAwbbkJMLfRuLCYrN/R0bStkR6impOgGl2SRDavVswFMDMpMQri0AQI+NBZCWAgdBocVyywtRNyOyw84e3yWxUomVeqZBQUh5i3S1KC2q7FCLTocOt7GoxVeWssa1zeGZHSTMFahK3d5FmDyGjbOxHJ9dw67tdUzU7Fye85jssIw4C6t7UR/XrYYeTINkKzuUlTQaC+KA0rFBGws1nTgTrec9lLIjWpzrhvoSywYfYmNJJTvqkwgje6Pbp+xgXMASPjgxcz/j4uB2L3nTJFxbBFtfhLPrSvn6Sr0noLS0Ba5gEJpkNBAF3J94DMQ0cfHP/R7GX/Z68NNPSitXCjYys0MEfqqFSG0WqEVud2YHC4tndig7OTFN7P7X9+Sah+eFtX0P/AyywyyxhlgXbZ8N1M4qTI05WE7YCIwzO0qysRDDhFGb0FJ27JyqwvMZVhu9Y0r/3Avo2IiNSr0r3LjLxrIB4/P5hkJkx1133QUhBB566CGsrq6WfUwXsEmQMssAQcgHvGBzS60NG3wMtwb74qux/UfeDWI6md7qgWMmRMr6WgXJDpXZkdfGomTPaWRHScoO+Vluoo1FN2xQB0ou2Vovh+yQyo4KWn4I1+l9ELi2iVYYkR0jNLJ4bXlORm1j6U62HzbhCruUHWWSHdVrXg6A4BrxAi4y5DU0tuvSkd/XrEuyQze3ozQbi99vY+nL7PCb8c7zVvKL+iGHo+x8upNpSjdf2dEKUHOlck/nutWptgSQ2ubgZwRkJoHFyo5esoPaLiwEhWwsLT9EzZSLFZ1dNmq7ccU3IQSWaZRGxI1aE67GZ0fDxhI34WTZWGxzuI0FLFceFHWqUi7dtQAvu9WmGyfm1rBnZx31qo1GK9DOf1JqnR5lR5ddQ2V2CL89VOmWpuwYr6kMDw1lR3RtsIjs0M2ZoKY1tI2FptpYpiC8Jnjgyc2HruMMorwWXiD4vGMHS55HxHkdF0fKjso4hNeEYGGp7VyE51N2GG4N969dDusN/wHm5AyEACo/+PMwa+lz0A3L7DBoVIecfP6pWwMEj20IMrNDnjce9JJveaDs5Be/87dhz1yaax6eF/aOPWCN5TivpR+bEVDq+YMkgcL0uIvlNQ+sT93u+bLBxehvPvFbhet7jfqUVmbHTFQ/2x9S2k6wjqv1Ea3U5bVBzdhS973SxlKI7Pipn/opTE5OotVq4UMf+lDZx3QBmwRVjWaZFDN97SszU5VcNYJ54Z96Gqc/8X4YVdkDnWeApW5t9MyOkmwsHWXHZKHjSQKx3RQbS/G8i4HPUPWrgY92CV5J7reksiOBZXZsA61QLhhHITuCaPdo1HPQvbCzTUOvepYzEFoe2WHWp+DsvhaX+c9gZ0R2TO6+fOT37dhYdJUd5UiJW+1eKSVrd4KAqVOD8NtQa7GyGi/OBYKQwTJzKjsMa9OVHU0vRM2hgOB6yg6NAESFpIBDP2Cxp10H4WqysoPaFVgIwLjIfV222iGqVIZHahE8ttuTweRYJe54sd5FcV6QWNnBhlbPxgGcmQGlw20sNuW5bAxJGwAbldkRhBynF5rYMzOGmmuBC2h7/GMyKFpUAsnKDu63h5K//SRWywvh2Eb8zGsPZHYMhnIrdQiLbCzayg7DSrWxCCGiVrg0G0unftaxem0sXsDgFCQ7SKzsGJyvADKvA4TC3imfbbQyJo+jtV6qsoMWsJl+56lF/LfPPQezOoHHHn8Ws5/8T5kWDhrVe5ZtYzFNAh54qfdePPeM5q7dyg4eFic7APTMvfPOw/NAtW7588khpdamBJSy1KriqXEXXACrfZl2XpDc4FJU2QFIwl9nvrYzIjvmlnrXIK0EO45STynbP7Xs+Nou1T52HqPQ6nV6ehof/vCHYds2/vZv/xb/9t/+Wxw8eLDsY7uAcwxiWuChj4mag/e96/aY8JiZquB977odE7Xy6qf6Ea4tghhW/PDLA8Otj5DZUSygVO4EkYGHeqfbvkxlRwUiYfLQXXk3KtQk3CYMK43iBISC8NuglitZ5oTq2Wak7BjFD+q3ld96VLKjK7PDyg5rCkMBAgEIVkpWSjdq192OyWAON1gnsCJqcGqj99vTSh2gpnZmh9xRGX3y1h9My9vroE4VhBrxveYgajvYQg9aP+BwaER2aJJdhFIYm012tALUnChrRCuzQ8/GAkQBh96gjSUPOc7WFqXqJ6ES1oyCIvMumFteiAoNtC2K1K70VHxbplHaIp1E57F4G0uk7CBs6P2ijjlLWePaRmy5SHwPn8GhOTM73MENgO7q2TJxan4dnAvsnqmjXpXndF0zt0OdP9vq1J+TLsuAWqSIIIeyw+goOSq2mVjJTCwHEFzmPSW8ByMR2aGp7JCbU8m/s/BbAAtTF6uq1pI1lgdUPn7AYZMQwsw/31N2MZHSyOKdfg72jj3xfW5U5XyPt1ZhGRSMi1Ky4YhgEDk3I+pVK64w/nZwHVzhofHkNzN/RuZllBtQalBpYyFpNpZIMaBUyd3KjjgrpUTV6UbA3iFVq/5cspXFMo1zXz2bmdkhr9f+kFIvhSARXism/vLCGJsGWx+u7NgxFZEdi4NkR5qNhVYk2SGt8d1tLGykZsStgEJ3xJ//+Z8DAH7wB38Q//iP/4gvfelL+NKXvoRarYY9e/agVquBUo2QJULwZ3/2Z0UO4QI2ADTaeaCUYO9F4/joe+9EEMpJ60TN2bA2FkB6to2x6UL5E7QyGtkhd/7yTUIJITJ7INXGUh4jTp1KLK/uhgj9Umpngc6C3yQMK+tezBoXBQ9k9WzbZ4nKjkYJyo5Q+a1HVnb0Vs9mSWlDzju79CVPKGrX3o7V/V/EK1/703j4c38LQCoijIJySEBmvhi1iVyZHVzIRod+aWYeDNpYGnFNNIkWE46Q3/1WklAGYSezA4aujcXaVLIjCDn8kKMeDXH5Mjv0lB39i9kgZKhV9MfUcG1xwMICyF12UywDkJLheo73bPsh3GqgHRRHnd7azO5Kx1EghADhATgxQEixe0opO2wyPKA06FrMp6E/p6EfXsBg07w2FrUI61bHbExmx4lZOXnfvXMsnuyvtwLMaPysOn+WSePvm9qDAaVS2VHVy+xQyg5fKjvilqKgt3oWkOpJo2u+EZMd0XTc1FZ2JNtiAJnXAaTPQ1SWE1tfgus4PdeCHzI4CIAiZIeTbO8F5H3gnX4WtWte3jmOWNmxBjM6hwHjcHLkbSTBKNCWVqtYOHZmDUIIPLo6jbfs2A5n/z9h7ObXpv5Mt6qiDDAuYJpDbCx9qmJFdgghYnK6TIvtRsAYmwZxqghSGlnKtDTpIilMX2F6XI4Ji6ttXHFJZyPTC9hAXocIAwimT7L3w6xPgzVWhjan1SsWahVrwMbS8gbn3HFAfHS/ybyfiOywKISIrj1ja7TiFUGhO+J3f/d340Wp+n8hBNbX13H48GGt99hKdYPfK1AyS0BK9KbGRmu5yAOWMtnVAXVrCJdnC/0sbzcKD0oqe6AbrLEC6tZKU1wAkoVVJEo3eODHO0qjQhEGFkavnxWCQ/htMBrVJfa3sdgGPBZVz44QUFqajSUOKPVhD1nghCGHGS1cy55QWJM7seMt/w7esUO49AffhnB1AY2nH8TYLXflTsXvhlmf0ld2GKqFojjZwbkYeODy1nq8I6V2gC2oasetYWNhXCBkAir3UbeNhRgGDGze79iMajmjGAHNzI6ojUXLxjIYdunlDihdhFHfNvD3xHZhMDke5VUHtLwQTjUAGRJOGn+W5YJ33Se2RUuxWIVMwACLqkWLgURtMRYJNTI7OpkUaUiyHnXDCxhswvIFlMYL3c7Gg71RZEdUO7t7Rx2NaDde/f8wdJ8fHjV6de+iy+cJAffbMI165g5zv41FKRndBEUL7cqGQheBrQiTIBJaa9tYzHQby1Cyo6uly7Ev7rkW/MjGQqz8JHvHAjSoRA1X5sBba3ETC9BZfPHmGixzBwBVWz0a2UHBgLzKjopUdqw1AwShwMol34fa85+Dd/p5OLuuSPwZq0tVUQbCUGZ28MDvIeC6ESs7og0+O7IheQGDAQ4Bqv1s2iwQQmBnhJRuXkBp8nlTgcP9IaVJyo5YLV7QxmKMKSJyGebEjszX7pyq9uQrBiFHyPigsqO1DhAaj9HUcjoBpUZnjNbNC9qKKPybCSF6/pf292n/u4DzD6qNZTMQri3CGB+c7OrAcOtxJkBecK+Z28KiQJxqoo2lzHBSQE4iE9tYWBA3iYwKteC3SDjgS8wLNYgyKieA/WFJrm0iiHhWxS4XAfejieYIRADQNQkNA1jGEGUH4zCI/PeyyQ7WbqB97Eksff3T2DZm4dRfvB+rj3xx5DR0oz6ZI7MjIjtG8MuqXcJKT2bHeiyhVOSixeTEYav4ReMsBJovs4MYJgwiwDap3q3Zlt9H1YqOO4+yQ4PsSMp/CHIHlC4kKzssF1SRHXltLO0QjvByKTu6x9myFjIq7FPQ0QhwYjmwSTj0flFkbdaC0bENeMOqZwnLRdon5VhtVPXs8dl1zExV4DpmrPbRbWTprp5VUm7aHVBKCIjtQATtodkB/ZXCbV96+C1TtvkM2FiAOBiw8x5RQGn0TMxlY0lTdkSbI6kBpREJwhorkY0ljOflMrMjAFJsFFnoXAOD85U4nDRqYuk5jtZaTI6WQX4byHftAkC9YqPZDjC/LI+dXn0HiOVg9ZF/Sv0ZZ4jtNS8CxqM2lnbqvKb/PrMtCi/gaHsMBvK10Gwm7B174J89lrgeVPfduVorhkySBG5KQOlUrOwYzOwYaGKJniGFlR3Rc1CvfrbSo+xQuUVJNhbq1mJlIbGceOzo3HdbYx5WFIVm6x/84AfLPo4LOA9ATBsiQUGw0RBCjKzs4K31Qmoh7jVzh5PGnxtVgHWDNZZLzesAJEOc2MaSwf7nhVoAWYRheX00wksdaxgpOyp9zLdjGQjE6MoOpvzWo4a0GiYAAhF6coGTMegHbOOUHYZbg7f7Zdjxhu2Y+8zvAQAu+df3jGyJMupTMhhOA4rsGMUv23ngdr533m7A3i53LJSv2+Ty+wu2SPWsmgyYysaireyQ10kYBDCMjcs9SkMjUnZUYhuLRmaHIgA1bSyKUFHwA64dUCpYCNZYSSS7qe2CRrvXWRkT/WBMWncs4euTHXalZ0e6LBuL54cwCQNGzPihpg1LaCg7wuHKjopjImQCYbS4GjzmqI0ll7JjkOxQi/7SyY65NezeKVUB9ao8Rt3Mjg7ZYcQBpf0LS2rJBjRzaEBpb2aH9P2bIIQM2LvUhkL/M0+9RyDk95DHxsL95DaLYcoOYpig1XGEjSW4tgEu5HmxI4LPJmFq9WnmMcVtLAlkx6lnAcOEvXNv/HeKAOettR5V4SgQgsMAB815v9UqFoQAjs3Kczq9fRr1G74f6we/hm13/Wxiu0bZNha1K88DbzjZEQWUOrYJP2Ro+yFMwnNnlWwW7B17sPbYl8EaK3GGjIJlSmsF5wLGObBWKFKyX4Ws4FgGaq6JpTzKjoJkh1GX6yCd+tmZ6SoeffpsvPZpp5AdrLUGI7rXADneKWt8vMF1gewYxFvf+tayj+MCzgMQ0xopQ6EoeGsdIvRhjhVTdtBKXVZx+e3coUAyS6CojaUC3uytXmaNFdjbdxd6vzSkt7H4INX8ga6JnxFNaqumwMqoyo7oWH3YAPzEzA5fkR0jXG/c9wG7BBsLIXETkR11jqcRZ2EoYJCNITs4F5ismZj73Cfiv1t7/H6Yd75jpHpdozYpPaCcDZW3miVMONXEYdDGEik7lI1FkR2bpHjIi1j+nlPZQaPXBb4Pxz33ZIeysVTNPMqOyMaiQ3Y45kBwm2xj0Vu0SdWRSCS7ie0CoQdA9OQfDEMrugZN7mmT2aSv4ts2KZolNFNJ4iAcmewglgPLD4cSkYo8zFLWOHanLaReGfye/JDDRJiT7BjM7Ci7wheQ4+SJuXXcdOV2AIiVHYrUG4ak6tn+hT2x3agxakhAKRtUdijJu9OXixJndoTJZEeYt40lw8bCh5AdQPRcWF+Gs7OjvlE2ToeEPWoXXaifSVKieqefgzNzWU8OAbUcENMGa67Bmipp0cUi5aWZ38YCAMfOyHM3PeFifN+PYu27X8HaEw9g4uU/NvAztlmujYUpZUfbTyWb+gNKbYvC8xma7VBuxGwRssOKGlmC+eODZEcJdto8UCq3tIBSQKo7lhKUHWPV3jFSjX+6JHs/YmWHBtmxc6oKz2dYbfiYqDvxRlO/QkUqOzpkB7UchFGOhxVtSmylVrwiePEadC4gN2RozbknO8K15NpBXXQ8jPnrZ6WyY4TMjn5lR3O1fBtLNPEaqKwrs40l2nWacDEy2aEWDD7ksfUPvK5txDaWUSwacb5MCVYeYloQgWxj4VFYUxLCLmVH2QGlQXMNjcMPgpgmxn/6Q3Bf+uNoHzsIllLjpwuzPgVAJOa+9KMMG0urPbi7IEnFKLMjut+MiOwo28+/UYh3hGlEdum2sXSRHZsBpbpwjRwkTVw9q9PGMmhj8aNdYh2oSV1yQGkFBAIWWK7MDnUNGiyHjcWuACyMF7B2mTaWEpQdxLJhYbiyQ7eNBUCqlcXzQ5jIG1CaHE4pq01HJ40Uzi634AcMe3bKyXvFMUFIfmWH3WVj6f89lZpyuLKjL7PDD+PnXX+lK02zsbA+ZUcOGwvPCCglpp1JWJi1CbDGcof4ipRTni8zO4os1gg15K5x3zNLCA7vzPM9FhYFWh0Ha63Fi65RyQ41hzVyzgtUq8+xM2sgRIZSOruuhLPrKqw+8k+Jlgqn7MwOJqSqISOglJg2QI2egFIAWGv4MEj+YNbNQtzIkpDbUcY8JA9ikiCL7Bhzz4myg1bHogY9PWUHgNjKkmpjaXVsxICysUTVs98jNpYLZMcFxOgOKD2XYPFkt3hmB9BJHM6D0W0snYe64Ay8uQa6ATYWQAwQA5LsKGenWOVWjDkEKyXZWDwhB9zB6lmzY2MpeL2FTIAKtatWBtmhlB3ZgXoys2NjlB0rgYm/P30xgrt+De//m2P4x9Yt8F/9b7HGR/uOu8PohqGMCWf8wI2yWnjgQbAgllHGZEfYGvmzziXUNWESRRroLeapqcgOvcVY2VDKDmejlB19AaVCCAQhg6UZUNohuxMCSqPdTYcEmYGa/Wh5AQABytraQXFxuGIUfFwW2dH2GEySzxKSeHymAxNsKNkRZ8sMCSgFkHpOvYDBEPmUHcQwQUy7J6AUKD/X4HhkM9g9I1WNlBLU3E5t6DAom6JpUtkaZtoDijdqu+B5qmdNZWPpNDq4dq/FgZhDbCw8yuzI08aSkqkjN12y7Y9GfSqqnlXXghy3PT+EjbCwmrDfDgYAweJpCK8J5+KrBl5vVMakjSWW0492zylS2cid2SFff+T0KibrTkw6je/7UQTzJ9A+dmjgZ4YFmudFEEplBw/81FBy2QRYjW0sitRcaXgwwbcM2WHUJkErdfhnjw3827m2VsQ2lpTMDgCYGncGlR1+mJHZUUzZQQiFWZ9EuDY8s0O1Js4tZZMdrLUeNx8BsoGQxwGl0bneInbiorhAdlxAjM0iO7J29nQwkrJjVBtL1y6W9MmKDVF2AIM+WBEGpSz0gc5ErO7Ih+YoEIrs4Clkh20giCS7aTtTw+AFXO6Wojyyg0c2FiD9IRsyDjvnzr4uLJPim4fm8d6P7cexM2v4+28cx+/85aGRK5+NeqdmUOcYgNEmnK1YEhqRHVF4cFw9G+1M0VAFlG4NZYdaJFkkX2YH7crs2Aw0WkrZEV23OpkdhALU1HoeuH1hlyHjEALamR1ZZLca+xwS5vLGt1XmhODaO2xx3kAgx1nZxlJCZkcQwgIfWYVHLAcmZFNE9udF16mGjaWVYtPxgxAGGGhOMr1/AwAojzRSiJtYZjo7lfWqVSyzw2/HVbPdIFZkY8mp7FDVs0BEAnqDNpZksoMgiNSEusoOatmpZCRrrqSGkyooG4sbkZJKhRJ4HigRMN1iizVZ4dx7DajMqCRlh1GpgzXX4qySURe4XlspPvNndgByl3zbROeaqN1wB6hbSwwqleGg5WZ2mJQDPOxpCOoHdWtgEamoFtvL6x4MMvo4c65ACMHMW/49/IXTANBTMlCGnTYPvDizI6eyI0hQdkQklG4LWBKMsWmwaBMgCzumIrJjmLJjwMZixwoz9ZzYKptORZE6W7/++uvj/yaE4NChQ4n/Ngr63/cCNhfUTN8p2EjInT0SL8ryQt3EPGcji2AhROiPoOyoQYR+3IetbAJGvVyyQ03CpQ+2c454mTYWRXbYwMrcqDYWOdFpcbXDNUh2AATCsAYkvbrwQgE7qvPMI7VOA42S7YcN/EHIoZ5tZe+gTNQcvO9dt+PuP3kQc0stzExV8L533Y6JWjnKjrAxnOwoY8LZb2NRJKQiJdXOVIfs2BoPWbXzYeTN7DA3l+xoepEtQ5F0muSgzATQtbF0cm58jcyIboRrCyCG1SOzVeghO3LaWFwif299G0uUN6CUHSX58ds+g0XCke12xLJhagSUKvLQyVDWJFWjdiPOQ8p5zEnWTscuN8Tx+Ow6xms2JuqdcbFesXJldhiUwKAEPPASd9Cp7SBcm4dpD8vskPcHMSxwLuD5ncptdV8oxAGl/ZkdTD7H1SnKldmRslnAtZQdkxChD5fK30EpO8JosWYVbamzKxB9hJd3+lmZOZOQZ0ar4wjPvFDabr7flvevmdfGUum8fttEZ8ygloP6za/F6sNfQLi+3JMvYVtGqVkHjHG4ZPi8JknZsbruYwxbx8YSri7Anz+O+nW3I1ydR+Pp72DslrtALacTlH7ObSzp52563EHbZ2i2A1RdOe/2/ME2FuErG0sxshCQG1TBwsmhr6tXLNQqVlw/m0R2CM7kpm63jcV2YwufOtcv9syO1G9WTVySfGpZ/3YBWxfEtAHOtIIMywRbW4RRmyg8SCt5PMup7BjZWxd7lNswqhZYMyI7Sld2dD6nGyL0S6+erVkCy+t+oWYbBWVjabJI2dFXPaseDpymT9aGQSo7QghqxnVao4CYDkQYxMqOtIGfcQFHtXFo2hh0QSnB3ovG8dH33okg5LBMiomaM7Kyw6x1etuHoYwJp3rgVt1kZQcQ3XPRDnoZu+fnAuqayNvGo+TU4SbVejdbISyTwhBqYaaZNWJagAb57domhJDfY7eH3da2sSzCGJtOHG/UrrtNgoFckCw0vQ7ZQTTHd9rXJFFaZkekMkmTpeuCWg4MEQxvYwk4CMlWCLhxQGnyOeVBsfDn7kWYQtmNFSfm1rBnZ28wd61iYb2pXz2rxrm0is84oLSqr+xoB72NDo5t9Fhr1PffX7euFJqhGl90MzuM9EB51lxNJBa6oVrjHCbHZ/UdhdE8wyyqeE2wsXinn4O98/LEeaVRGYsyO0pSdnjy/Bo5lR0qswOQ4aTdGH/pj2D1O5/H2ne/gqk73h7//UZkdjhUvl+WqqqbVOxWdkyBwyjQonOuwdoNNJ56EEv//FfY8YZfxKm/+M8gpon6Dd8PWE5p+S268DRsLJNj8rwur3moupLc9EOekNnRAqgxkuLYHJtG+8gTWq/dOVWNMzuS2ljUeNzdxkJNB4IFEJwNVTO/WJA5qmaRGReIjhcflEpglDrQIgjXFhL92rooquzoLMKKB5QCnVTsWNlRdmaH0yuvBiRbCxbGlXejglADoAYqpqwjTJM360DV+bWYfAgMVM9Gfxa0ePuPH4hSQv8UlIUrVnak+BfDkMPOubOfB5QSTI25mJmqYmrMHZnoAOR9TSv1nDaWUdpYendJVJaO0VXd1012bJWHbFw9G2W2aNtYlLKjxJDGPGi0A9RcayBfYBiIYWpXzwKdiZYir7TbWFYXUi2MSm3h5raxhHBJJGfXzexQ46yvMjvKyZrwApnZkTcwsR/EtGHwILIJpc+//IDBMo1Mstp1sjM7+u0ZuqBugrKj5AXh8dn1HgsLIHfldTM7JNkREe6+l9h6EVfP6mR2UAOEGvH1r86ta5s99q4sGwsxTARMfqfayg7LkS10fPDcamV2RCS4HcrxWV0LTF3/brFFs7T39s5V/DMvJOZ1ALJNj7fWYUfPupGVHRHZYdr57jfXNuLn7faJ3jHD3nYJKpfdhLVHvthzvsu2aAWMw47IDpKRmULdWjzvVPOplXUPJmGgW8DGYrg1jN32w9j5ln+Hub/9fYRLp7HzLf8+vmbLym/RRVujjWV6XH4fi6tRAH+gFHSDAaXUqRbeLAQk2cG9Zk87WBpmpitxZkezbwwCAN6Wtr/uzaZulZkdq5m/R5UdX/nKV1J/KOvfLmDrIn4Yhz4wggQrL9jaIsyJmcI/T2wXIBQ8Z0BpR9lR0MZiK7JDPtg7ZMdkofdLg/Juii5lRyyfLUnZAcjv3zXkgLe87sVSvbxQA/R6QGGbdKA6LF4EUysOScoLL+CwwIASM0t4e32osqMns6MkouVcwKhNItQhO4zR5aPKxuL221gqvcoOVU+4VcgONbmJA2o1M1s2XdnRDlF1zS6yQ++6lTvH+mSH2h3zNdpAuhGuLcK55OrkY4jGvpqZs42lS9mhS2bH42yXsoNxAcZGqz9s+yG2oQSyw3JARQghZP2qYSRPpv1weO2vGoOTWlJCxjsqoALKjv5xxrYMbYvJMKyse1hr+gPKjnpVP6B0QNmRsKiMlR0mzaz67W5E688qcqw+G0v3/Kr7PZhUdqjFhpnDxiJ/Bx+ka77GAw8i8IaSHcqOYQdyfFbXQlydqUkS9qNf2eGfPQ4R+ol5HYCqxxUweTkZTkFb9sDlJTsIIahXLKw2/J7MDoWxfT+Kuc98FM3nHkXt6pcBUJkd5T2/QsZhqeD1DCVYr41FXi8r6z5MwrcE2QFIq9X8F/93/Oe1J+7H1J3vALWc0vJbdKGIyn6VRjemImWHqln3YiXXYEDpKBYWADDqkvxn64ug0xdnvnZmuorHnj4LIQRaXghKegkY1pKkmNHXxgLIsUMpycoM2j0fkTpbu+SSS1J/KOvfLmDrIs1TutEI1xbg7r6u8M8TQuTuQF5lR2k2Fvk+vLkCULPw+w39nK4JhNodKjOMilo23Gghv7Lm4+Ltxd5H+C2AmmiGybJA9UBhRC8AMQleIFApoeFAgVg2xLrX1caSktnBOGxV4blF+uwBWT/LGsvDX1fCJKPpyZA+I9ol6yioepUd4cpZGJRsmR0FpVgwiABD/jYWplHjuhGQHmMTgsnFRL7MjuELSKfPEqHIDksjoFQIIcnuIcqOmsWLZ3ZoBsX1j7NqLPAChuoIZIfny+pZs2C7hQKxHBhcjpdBBgEThHyohSirjcWLMkbkZ+ZcMNqDNpYylR2qiWXPTJ+NxbXQ0G5jYTHZwQMvqubuBbUrECyATcUQG0snJFxdn5VY2dFbPRvPrwaUHZIwCVm+gFJFtvdvTik7LR2q7JiU/++vAuhcC0qZmRTcqnVcdiUmDAGZ1wEAzq40ZYf8Ls1QPidGXeAGviQ7rJxkB4BMsqN29cth1Kewuv+fYrLDsQxwLtWwut9bFhjjuW0scRvLugeLbI02FmVjIaaJXe/8ABpPP4zW0YOYDDxpYznHAaXq2q9kZHZMjUdkR6TsUPd2UvWs7jMnDcaYHJPCtSVYQ8iOnVNVtH2G1YaPdpQZ1K0q6WSmdQeURpa6wINtyb/fKptORXH+3xUXcM6gHtr8HJIdPPDAW+swCjaxKBhuLX9mR8IiLA86Nhb50AnXV2DUxkeSryV+jt0rrwa6ZcblKjvsaNd6lEYW7rdBbRctLxxIhQakvJ2QEcmOkGO8hNA/BWJa4IHftdhPUXYoGwvfGBvLRsGoTyE4cXjo68qxsbCeSYOysXSTgNStgs81YVvZnvjzCYFSdoDLaFxNG4sZecfZZgWUtkNU3Y5lTDuzI6eyQ00Y1fepk9nBW+sQLEitHVdjX81kuWwsLT9EheYNKB20sQAlZAgoG8uIZAc1bVAufye1ME77vGGqGie2Hg2eUz+QGSPqM3MdY4KNpczd7+NzcizZvbPPxlK1EIRctiMM+d27ySDhJ2d2xMG4NDsQlodBTDq04t3hlIBSQmWQ6UBmRwhiWAhCBhoFp+ogbXOKNyR5MSw7jFbHAEJBvTUAY/G1oK7/JHuPDvptLN7p50CdKqzpixJfryoxjaAssiMKXnTy3W+cC/zC22+GZVBsn6yAc9FjIyWGiYlXvQXm2DSC5VlYkztx50um8VdfpvADNjLZIYRAyAQsRETjEBuL8FsQvHO9r6x7sCpiS8xLDLeGsVvvQvXaV+DMp+7Gjjf8MqbueFuCjeXckh1Z4+ZY1YJpkAFlh2P1NZ94zZGVHep5yNYXh752pqt+ttUOBzYYleKdJio72rAcec63yqZTUVyonr2AGDSWWZ67SbnKETDHi2d2AJK1PNfKDtJHdvDmSunhpEBnh6VH2VHQU535OaYd7+itrI9GdhDbRTuF7CCEwLEMhDBHCCgVpYT+KdAos0NNgtNCM6WNRQWUnv+TCgWjLmsGh2UtWcbowWCtdtgXkNWQHtYucoDasrbZNMr1PG8kYmVHwYBStglNV0CU2VGxIEJlTdC0sWi2sXQsEZGNJdS3sYRRvV4a2a3GvgpluQJKW16Iuhkt2LWrZwdtLABGDteMA0pLqJ4lgoOCZ1orgoAPVdX0q3F6jjdghWu9lT1NiM7xlZlrcGJ2Da5tDGQq1KPaUJ2Q0jDk8TjHg3ZMbHRDWZocwrLHQhbE95MiCyox2WEiZBysiywhUetXN1QbS8iEdl4H0L051TuusKYiO7KVHYRQGQzflq+PLU2RtbSosoPaFdlSF2VbeKeeg73rytQgcUV2UL8kssOT59fOoezgXODomVX8wV8/ht/42DfwW3/4DRw9swrOe5+XtWtegXBlHsHyHMLVBbgnHsIbbr+klABeRWAqsmOYsgOQliNFdjTaIcwtVD1LLQdgIYKzxxEsnOy5Xs1z3MbSjiqjszLSCCGYHHPjzI50ZUdrZHW3UpuFa8OtxzsV2bHYStxgZC2phjPcQbKDB/73TEBpabP19fV1NJtNMMZyhZdefHG2ROcCzh3SPKUbiWGTXV1QtzZCZseoAaWdzI6y8zqArkrERLKjnMW+fC8bBjqZHUUhhig7APmACGDK1P8C8AMOq4TQPwUVUKqk66kBpUzAovkWu+cDjNqUnIB6TZAMJVNc+TYCy9//vfP2+kCtqJLh2hbZMg9ZtfNBwQEQQLMFyLTkPvmmKTtaysbiA4Z+exExdG0sStmhbCz6AaUsGv/TlB3EtAFCUTFyZna0Q4yZ0aJBM3tAfZYazxXZMepC3fcCGESMrMJTO/k2yVYb+CEbqqoxKIFt0gwbi6q/zPd8UTuawmvF44zTZ+cYBcdn13DJTH1gUaJqQxutoKc2NAkywFVldngxsdENGu2qOyQA42Jgl1+hu/49DjnssrEAvTYo2fqVEFAaZXbkUQfEmR1976dLdgDSysIby7DMPZ0FO5PP/iQSSAcde28L1HLgzx3FxO0/nv76qiQ7SElkh8pGyqPsWGl4ceU7AMwttXD3nzyIj773zjirgbUbaD7zMJa+/mnseMMv4dRfvB/ENPHau36tlLwDRYpZiDI7Ms5/t6rYtjqWLnOL2FgU+tXRCpuh7MgKJ1WYGnOwPKDsGLSxkG2jrWuJUwWxnPj5mIUdU/Iczi42E+fciZlpsbLDi4mlrdKKVxQj3RXPPPMM/sf/+B/453/+Z6ysrOT+eUIIDh06NMohXECJ6AReFV/o5gVblTKttMmuLqhbQ7B0Jt9nKxtLQclZPLGLerVZY3lo3VsRkKjGqtvGonZz8sqMMz/HtEF4gIpjYnW9OOHF/RaoXUF7lcX1o/1wbBOBMCDCfGocBS+QFW1lKTvUjltH2ZFuY7HIxrWxbBTinYLGMmwNsiMYYUel7Yd9aeCNHr8oEFnHBEfV4FuG7FATWioYYGS3XXTDsCXZwTVUEhuBpidtLN2Sex0Q0xzIX0hCv40lT0BpuKbG/xRlByEgtosK8raxMOwwGQi19JUshIDaLniUWVDWjpeS1Y+646p2ei2wbLIjYDFpmwXXMdOVHUpKX0DZAURSbkV2lJnZMbeOG68YnCvUlLJDI7cjYBxWNM5z34uJjW4oAsSOzoNU9A2eU9mkIs9RfwtV932hwr7lc2awepa61dzKDpqyOZWL7KhPIlxfhtttuYlsNkWfrSS2gzXlnIyHqXkdAGBU5HESrwGAIGCjXSuhL8+Hk4PsCEIeEx0Kc0utnntfNYiYkzsx+9e/CwDY+a7fx//nL57Gf/jZl490zEBHxWCK4Xk5qtmMe004Y5Px35tgWyo4PcmiDWwG2RHG920WpsfduOY1Tdkh/NbImR2EEBj1Ka1Q+XrFQq1iSRtLorJjXZInXeMX6SI7Oht8W0NhWxSFbSz33nsv3vrWt+If/uEfsLws5dG6/wPQ898XcH4gvgHOodw6jHf2Rs3sKGZjIXYlsftdB8S0AWqCe00ZtNdchVEbPsEo9Fm222djKWcC3fMZlg0e+JisOyMpO5SNpemFqKSRHZZUdhQPKOVwKC8voNS0IcIgDsbKCijdksqOKHl/WP1sGZOMZt8Dl7XXe2pnge4shnDrkB0hAyEAETxXOG3HxnLuyQ7OZUK7bGPxc40XxLA1Mzt6mz3i6lkdsmN1AYCc2KWBWi5cEqTWpCah5YWoGkFPS4UOiO2CeyqzoxwbS+DlC4ZNPbbo+WyT7HvGD4YHlAKDAZoKvTaWnNWzTmcRpqBabUaVpLe8EPPLrYG8DkBmdgCaZEfAYRlUWrR4mKLskH+ndtnTjl102VhakY1FEb39iicgav1KUnYYRZQdybZjFgWlEw3FqlGbBGssw7E7xBcJR7SxxOqeNrxTUTjpxclNLPHnUBPw1kHI6AtcFik7HFef7LBMipmp3rFiZqoyQD7x5ioWvtRpEGkeeAAvvXKyFBuL2mAwoTayNGws7UbPOGtgayk7iGkB1Iw3DBXMcx1Q6oVayo7JMQdLa5GNJUhucOFeS7sBLAvm2DawteGZHYAMKc1Sdhj9m01dAaUGJTANMtIG11ZAIbJjdnYWv/Vbv4Ww4OTtAslxfmJzbCyLIHZlZI+bamPJc23xdnOkzyWExGFcwm9DhP6G2FiAyAfbE1Cq6snKU3ao3IqJuj1SZofw26CWm8mWO7YBnxvFMztCAZuw0n5/aQcSsKKA1jQbB2Mc5hZUdqjFJFtfznxdGZOMxMyOAWWHvO9qxtYhO+IsBM60m1gAwLLlYqjfW38u0PJkVWnNlZkdeRbcxDRz2lh6lR06u9RsbVHmBmTcS9R24ZAwd/VshYa5x3fa1SSh5MlpljZdML+cMOlusmOYjUWnCad7gdvz811kR+6A0j5rJ9A5j6OqO05G4aT9TSxAd2aHBtkRctiWEdeeJ1l1lDrBjhaeaWOUCP04i0WRfd2ZHfLvO783tZz0zI4wb2ZHso2FN1dhVPWC0s36JFhjBa7VsTQR5iOEWXgTiHYpO7zTz4FWx2GO70j/PQiBUamDt9ZgGXT0+y2yC9o5yI6JmoP3vev2mPCYmargfe+6HRO1znv0N4iMv+KN8I4fxGtvmSlFuRSGURuPUnbo2li6rhkDbEvNS4DBQFugHDttHkgbi56yY7XhI2Q8vl+6bSyCBXJMKFjb3A1jbCpWPg7DzHQlVdnBW4M24m5lByDP9/ds9WwWPvGJT6DVaoEQAtd18e53vxt33XUXdu/ejVqtVnobxQWcG6iH57lsY2FrCyOrOoCoVklwCL+ltaMBANxrFG5iiT/XkUGLqu5tIwJKgcHu+rh61ijXxiLJDieW6hUBD9qgTgWtdohqSmaHaxvwGga4KK7ssEi+xVsWVKuLmuRnBZQqZceWCiiNSLhhskgasfyj7MIOZHa01gfuMzVZqxoh1rZICrgfMtgmheChdhMLAJiWChI898qOZlt+ZtU1IZb9XCGZupkdKv09bmNJ8TInIVxbhDHEwkhsF3Y7yNfG4snq2SJkhxpnldVh1F3bsCwbixqjhtpYuNa5d/vaQhRkoGpRG4vKa+ioLLsVMsrOUQTH56La2Z2DZIeysejUzwaMwTRpvHmQZNdQyg5T+ABourIjDEAiIlcpO9TvG2d2dDeymHasyuy8RwhiWvK4Cik7Bm0sOhYWIHou8BCTlSA+ToN5YLT499Sd2eGdfhbOriuHrglodRystQbLpCPvMKsg6Dx5XpQS7L1oHB99750IQg7LpJioOT05LapBpHbdK3H6L38b237kX6Hy8jfhV/7wEfzS228e6ZgBgHFlYxkePk9jG0sDhJA4BJiCbal5CaDG3H4bS0Q0Z7ROlQnPZz3W2zRMjTkQQgb4J9lYlCpw1M1bADDr02iuL0EIMfT+mZmu4rGnz8K2jEQbi9FPdpj9ZIfxom9jKXRXPPDAA/F/33PPPXjNa15T1vFcwCYizQO6kQjXFksiO+Tgz9rr2gONrIgaUVHi1CTZ0VgGABi1jSE7pI2lS9nByld2qNyKiSkHTx8b7hVMg/BbIErZkRZQahnwBB3BxhK1sZRlY4l8riaUFD8ls4MJmEZkYym487UZoG4NxLDi6zQLpjFaHWzb75AdQgh5Tw4ElMr7tUYDLG4VZYeqrGT5ds/MWNmxGWSHHCdk9WwA5MnsMDoNLllQVdJKJaBqRrWUHesLMCd2Zr6G2i4s0ei0RWig5YVwLB/UyWcr7B5ny1Ik8JJqwtXP2ySMd4GTEIQsJmqy4NpmolqmW9mR9/mSFDjoWNnWQF0cn12DQQl2bR/coMiT2eEHcjGrslmSdmFjG4sIADjpyg4WxItLqWTsNDqo62fAxhK1I8TvEfogRhFlR5qNRd9Oq0jwCdPHSnScBvcRjrCJos4naywjmD+B2rWvGH4clTHw5mq06BrtOuEqCDrnop9SEoeRpr7GcgBCEJw9Du/k05g3duP0fKOUXXH1extcbuJkBUl3bCzyPnMsCj8IYYitldkBANRxMwJKz5WyI8R4bfhaYGpcXh9Lq15iQKkieUetngWkskOEPni7MUBW9GPnVBVtn0mFyoCNZW2g7ZLGGzAdZcdWUdgWRSEby8mTJ0EIwfXXX3+B6HgRIX54FrQWFIHOzp4OlCeNt/RzO0a1sQBdyo5GpOzYILKD2hUIr1vZsQGZHV02ltWGP1C7pgvutwHTRshEKlvu2AbazAB4GFfU5YEfcBgIy7OxRO9DeQBKSaqUNgg5THCA0C1FdsjAq8mhmR3AaA8+IURP5bAIfYCFg5kd0WSgQoMts6MgWxwMCB7myuywrYjs4Oee7GhEZIe0seRUdpiWbHAZ9jpCevIfgjzVs6vDyW5iubAQ5Lax2PBzB8V121jiNpZRF1+RjWVUYlbbxhIwLWWHYxtoeWkBpUWrZ9WOc7eNpTfTpShOzK1j1/ZaovrBNCgqjoH11vDrNQg5bNOA8DNsLFa3siPLxhLE32v/QqO/klm+b0JmB+u0sVglKTuorrIjynKaMNodZQcPwEYgO1ROTvvYIUDwzHBSBVqpg7XWYJp05OcBVxtBG6RwoKYNYrtgzdXScn2ATi4MFcHQvBRlA1UkgW0ZuSvRzxcQu9rTNAggvg+yKrbLRNtjA9kbSZiOyI7FtXaKskP+HqMGlAKd0ga2PtzKMjPd+bwkZUe6jSWqaTaNF72NpRDZwaK05GuvvbbUgykD9913H972trfh9a9/Pe6++24AwDe/+U288Y1vxI/8yI/gnnvu2eQjPH9xrjM7BGdg60vlKDsq0SSrrV8/y73GwCIs9+c6VXCv1SE7NsrG4nRaAoDu6tkybSxWbGNhXMQLpTwQnEEEHkIqB9O06lnXNtFmqv4v//XmBRymKM/G0n3t2ybNtLGYdGuFgCkY9akNJzs8n4GLzveu6qAHMjsiktElwZbZUfAjZYdgYa7MDiWn1lFJlI0eGwvLm9mhp+wAVP5DxwIm7VDZ0wseeDI8bcj4T20XppA2Ft1MppYXwuZ+7h026nTZWFQlX2nKjlFtLJ02liypv1IuDIMkqFLIDsIAauQmdDsWhu6A0vKUHUkWFoVaxdazsYS9yg6S0MaiGlpMMSSgtLt6ti/ksD/LBogyOxKrZ02pGiwhs4M1VvLZWACM01Z8nKbwIejoyo7WkQMAoEV2GNVx8NZ6KTvMcZ7ZBj6jjeo4eHO1NPUX0LnGDOYPJUaJYUmVUKQkcLrJjhI3wM4FqD2Y2UEpgUHPXWhmtxo1C5NjclxQyg5K0POc41HQainKDtWgp5HbsTOF7BBCgLcGA0qJYcpg2GgMtCw6cgvS+Y5CZMdFF10EAGi1WkNeeW5x/PhxvP/978fHPvYxfO5zn8OhQ4fwwAMP4Dd/8zfxsY99DPfeey8OHDjQY8O5gA6IlSyL3CiwxirAWSnKDrWYytPIUo6Npdqn7NigNhar94Ggwg7LJTvkrtNEXQ7oqk88D5TihEWTpUpaQKlldMiOAuRaGIYgEOWTHYEvpbQpk5eQSWXHliQ7arJmcBjMEfybLVW/qMgOVe+cktnhEn/L7Ch0B5TmyexQ14rYhOrZjo2lSBuLXmYHoPIfIgtYwHpC89Kgkub7Jbb9oLYLk/sQQk9lwbiA5zOYwss9vhOrfBtLWcS02o2zhgaUcq3q2Ypjpmd2EFboeInlYOZtvxYTRqzdiBf9o+x+h4zj9HwDu2fS5dz1iqUZUMpgmTR+VtGENhZiyJYIgw9Xdqjz1PJ6A7mTyA5i9lbPCiF62liKKDu6g49FGED4Le1NF7WgGkMrJr4sEYCPcK0qsiNcmYMxNg1zLL1pKT6OypjM7DDI6DaWMAQH2VDlpVGdAGuulKb+AgAW5VNQHiSqjfpBnWpsY7EtAybZmsqOboK5G+Y5tFa0fT1lx5QiOyJlh2P3VtDHyo4yMjuiTQCdRpYdU91kR1dgqt8CBAetDJLE1LLjkObvhYDSQmTHHXfcASEE9u/fH6s8zgd86Utfwo/92I/hoosugmVZuOeee1CpVLB3717s2bMHpmnijW98I77whS9s9qGelyDUAKhxzpQdrKTaWaDTO85aesoOIYS0sYxYESVZaRlQqnIRNgLUcXukfuo7KiuzQr6XA7AQkzX5sFxt5L8O1EIhIPI8pFbP2gZaoRx+eJCPVBFCgAfDu+jzICb6WCAH/gxlhwG+5ULAAMDUVXaMkNmhZPFqd4FFSqsBGWUk03Xhb5nKsziglLFcNhZEKpDNIDsakbKjVrGiRVU+sgOcadnMuvMflN1nGNSO1TBlB7HdeNGpY2WRizYBg+UnO+Q421s9O+okUJRETKtQOZuwVHm3EEKSTZo2lkSyI5ABpUnBncPA1hYRrpyFu+d6hKsLWD/wAHZOWPH7FsXp+QYYF9id0MSiUKtYetWz/cqOlN+Tdl13mQGlcRsL69lVjW0sQW9mR8/8istrlZiWJNJzKDtoQmYHa64CgLaygzpVwDBRRRNtXyqnLAQQRv7vXoEYZnyt66g6AIBWxwDOUC+hilywAAwbazE1quNgjdWuMaK86lnK/cQ65H5Qt9qj7DCV9SzPs+k8QH/4vsIo85A8EEJkNgf2HJNpYKxqYWm1DS9gceNS/F4R2aFbkpCFWNmhMWerV6w4t6h7DFLroaTMD9LVDGWbxjmzDG0WCpEd//Jf/kuYpon5+Xl84hOfKPuYCuPo0aNgjOEXfuEX8OY3vxmf/OQnMTc3hx07OrVXMzMzmJ2d3cSjPL9BTOuctbGoyW5pbSzQt7GI0Ad4GHuMi3+usrEsb1heB9BJrFYybjlhIqUuutWCf9yVw8JygfrZmOyAHHizqmeboWTE85JrXsDiB3tZZI/qtOeBD9ui6QGlIYdBtqiyoz4J3lobuls/ipS41e4lO5SyY0BGSQ0QuwIH/jmrlxsVqrJS8Hw2FrXDKDYhs6PZ6g4ozansMPUVKY5loB0RXUHI41DKLIQx2T1E2WG5oMwDIBKrUvvR8kKY4KCC5bex2BWI0IfgHXVK2ligAyEEoDIESmpjsZGu7FB/b2sHlCZVz3K4Bs99vKqac+nrn4YIfJz6i/dj9ZEvYsJV71v8PJ6Im1iylR3DbCyMCzAuZPZOlNlBU/IRiO2CsmHKDj8mEFt+2LM7nNjGEoWAd35etd5YMQmjDaUY62p36bTC6ZEdhBCYtUlUhSQ7/JDDISFgFic7gA6Z7ey6Uuv1RrTzXDdKyHBiITjZWLKDVsfBmqtd4btlVM9Gygzmx/d65jFE4fhAlNmhlB1bzMZCEqpnATkPGaUVThd+yCEEeixoWZgad7G05sHzw4FsJPV9lGFjoZYD6ta1lB2ADCkFMNCEBwzaiAFFdnQpO7bIPKwoCs3Yr7nmGrznPe/BPffcg4985COglOInf/InYVmbe5MxxvDwww/j4x//OKrVKn7xF38Rruv2yIx0anz6ceDAgbIP9Zxg//79uX9mAhRnT5/E0QI/mxfOscdQBXDwyEmI0yujvZkQmCQEJ488h+fs4cfugmHv234VR54+jBMHDmD3zDY8f+K0tic8fp+zS6jwECunjkJQo9A514FzdgFVwfHIQw8ChoXKieNwqIFHHnmkvM84fQZVACeePwQAeOLQM3CD07new1g5g3EAR06dBTCJY0eeA2meGHjd/NwqfCEfFIce/y7YhD4Bud5isIicJB49cRo+Rj/n6riffeoQWDiOubmFxO/SCxiCdgM+4Rv2XW8U7IVV1AA8+q2vQ1TSJ8O+18LColfo9zs6Jx+ex489D9s/BfvkAdQAHHr2BfBTyz2vnaAmeGsVzba/Jc7l0vIqqo6BVbIEwsJcxzwhCJpra7l/z1HOS6VaxXWXTuODv3QH5hdX4LWaWF9ZwwnN93ROz6IK4LH9D0FY2ZM332ui2ZJqz9OzC+Aa58d5/nFUATzx3DHgaPr9755dREVIRdUjjz6OHRPZ84z51QAukQvKE7PzeC7HOXTmFlAF8Oh3vg1huaAEOHrsJPbv18+C6kYQipiYPXj4KfBjc4XeBwDAGaYgbSzPPPs8anzwnLV9uTiYPXMK+/evDfx7NxbmVxEygQe/8zBMozMnOnFyCTeTEF6Yb4wjhOCSS27Bjh+fxuzffAgAcNHPfQSPHD4GADj81LOwvFM9P6P7/t8+KBULZ08+i9W5ZELAa61icSV73FKKvbnZUzjSeAY1AE88+RSEc3LgteNMoLE8D+AqPHn4aYSrx3pfwDmmBMfpubN4Yf9+LC2vgYyZ8eerucQLR0/E34V7dgEVFmD/ww/JkGu/gUkAJ06dwXrDxJqTb1yZpCbOnDyBF6KfMeefxxiAZ4+fRtjQe58xYoM0F+AHDN95aD/qJMSKn+84uuE4Dvb+2C9h/u9+H2dEDWTu9NC5lTU3izoA0lrEUmu0eVTot8EI3dBnSmWtBaexjO8+KudfR46dSL3fdI/j6ZNywe81VsF5dejP1b0QpHEWp/bvR7u1LoPTARw5dhx+eP4/TxXchWW4fgv7H34Y6LaE8BBnZs9u+Nyg0Zbj89nZ00PHTAAwhI/jpxcwVjHAWdBzfM7zz6AK4LuHnsrVfJaGcdPF/PHnh67HKtUqfu7Hr4NpmnAtgUNPPolWswlz/gU5Hhw7iXC99z3GQo7m2Vkc378fzcY6mh4rdK63wtwNKEh2HD58GK9+9avxwgsv4O///u/xO7/zO/iDP/gD3Hrrrdi7dy9qtRoo1WOo/82/+TdFDiER27dvx6te9SpMT0ulwA/90A/hC1/4AoyuXbizZ89iZmYm1/veeOONcJzRmO5zjf3792Pfvn25f+7YN6sYn5rA9QV+Ni8WVw9jmRq47VU/kFmzpYsjXxvDzGQdOzSOPVieRePwg7j6lT8IozKGxtMP4tZb7sot3V0RZ7HwzFdh+6uo7L0R127QeVsRc1h4+qu49SXXw6hNYH7+UazPuoW+4zSs0iXMP/klvOLWG4DPfwsT0xdh3758IcStowdx+lvA9K5LAazi1ptfgisuGVS8nGo+jxMHDwMArrv6Crh7rtf+jFNn12HhSQDA5Vdfg/oNo58D/+xxnPgWcMXeSzF+OEBtLPncik+dRM214RpVXH0O7pEy0RjjmD34j7jhij1wL06XF09+62swDVro2hJPzgI4i1tuvB7X7p3GCjuNhSeAm1/2ygEp5fH9kxj3KASKfda5hn3//dixvYYxowqA4Jocx/zUFygqrp3r9yw6hgMA5wJHz6zi7j95EHNLLcxMVfBbNYFtMxdpj+2rOIv5w8DNN74EZj3bd3/vYw9ifrmFffv24Z+e+A7G/cbQY59feAxrdgX7bv++zNet8FksPPNVOCTAVddch6t2T2a+/pnjS3DJMwCAvVddi7Gb9M/hKl3E/OEv4+YbroU5vh3OZ85g2/YZ7Nt3o/Z79Lxfw8f9n70fAHDTrS8dScUohMALX6KwCMPuPXuxb9/egdcsrbaBvzmFKy/fi337Ls98v+Prz+H+xw/gJTfejHq1s5P89WcehTsvUKmP5x7jwtUFnPr7v4j/3Hrya7j5ZW8DcASX7L6055jzXN9ffWo/tk8G+L5Xvjz1NY+ceAJPnTya+Z7rTR/461O4fO+l2GOtYPEQcOvLXpFYP3vyu1OgUdD2ZZdfgX037ur5d+63ceSLwCWXXobJfftAvvAl7No53fP5lb89g+ltnetn2T+OxWe/httuvgnUdhGuzuPYfcCll18J4xDHzI7t2LfvpVrnBACOfNXFzLYpbI8+c+1AA2cfBq6/7eWwt+/Weo8zz30JwcnjAIArr74By/cFGJvcVnjsCVcXsP7kNzH1mp9C7fqXofnMQ0PnVu2TYzj1yKcxUzewGFZHeh6c+MJnIAxrQ58py/5xLL7wbbz05hvh/O0stm3fiX37XjLwujzXuG+fBh5YQNUyUN22Y+hccvbo/fDnjuKaffvwxQPfwfqs3Ji6/KprUL/+/H+eKiwHJ7D43Ndx280v6bkP61/6MsYnJjd8bjC72ARwGldfeVnimNqPrz61H4deWES1Vsck/J7jW1w9jOVnKF768ttzb6on4fRTl4B7TVyXcQ7Us/73u57173vX7bju2nE0D7cx9zBw/a37YO+4tOfnTj7xN6B2Bdft24cvPP4gziw0c5/rUeYoZcPzvExhQiGy4y1veUv8RRJCIITA8vJyoeDPMsmO1772tfj1X/91rK6uolar4Wtf+xpe97rX4Y//+I9x9OhR7N69G5///Ofx9re/vbTPfLFBBmidOxuLUZ8qhegApFReJ6CUtRtoPv0wlr7+adjbL8HsZz4KYpqo3/D9QE6yI06fb63HyeYbAfUQ4H4LRm0CIvBBRqiHS0KnfjVEvWJhpYCNRfnd21wOLWkJ145tIIh8tTzn9dZsh7ItAOUFtMaZHYEH27QSq2d5JIE2wLakjUUtWIfldpijZHb02VikZ5QkZuNQuwqr3U4Ngz3fILMoKETI4rYGXXBQ4Bxmdqw0vJjoAIC5pRaaaMHhOSZg0c6UTkhpd0CpF52nYWBrw2tngY7NwCGhVmZHywvhEnnMuTM71DjrqfpZOpJEve2HhWtc+0EIAUwns3pWKRd0MlPcrgDNetdp8gIGmzCQIWqefigbCzFN7HrnB9B8dj9aRw9i7BVvlsc2io1ldg17MsJJAaBesdHymMy+SAn6DOLzQ+OsqLTMDmK7IG35mqTz3cliidpYEnz/sqWoN7ND/qwP2G7Xe5gIWTufjQWqMalzf/I4s0PfUmvUJmAFcvNgZc2DjRDtIdWnaYitTF/7a+x4wy/j9Cc+oDW3UkR4Fd7oOQ08BDQya0aBsgmx5kpU21leGwuYr7XpRp1aT0DpVq2e7R5zu8kOOQ/Z+LlBuy9UfRimxlwsr7axY7IyEGrK/RaoUy2F6ABknpU/P6iM7kbSs/7uP3kQH33vnTBiG8tg1hGxnHgMLOsaPp9R+K5IkqTltQCUdUEo3HLLLXj3u9+Nn/7pn0YQBLjjjjvwUz/1U7jiiivwnve8B57n4dWvfjVe97rXlfq5LybIAK1z1MaytlBKXocCdWtamR2GW4NzyTXY8YZfxJm/+l0AwO5/fY+2z7XnM7sm0xtVOwt0kx2STBAsKC2cM/6MronYRN0pltkRhb61IrLDdZInHY5lwBfKc5yP7Gh5IWyivM7lV89appPoX2Q8qoYDB+jW8sUCncCrYWSHZVI0vWIL88E2lnVQt5pIaFKnCpOfBRcAYxxGjiaCzUDccuGHAM23iOaIWlzOEYKQx5MfBQMheI7rtlNtqZHZ0RV2GQRcKyAzXNUb/0lOsqPtsS6yI39mB9AZZ23LGClY0/MZzJiYHX3MoJYNGyyd7IiOVS+zQ5Edvd+vH5EdefOQDLeGsVvvQuWKWzH7mQ/jonf8R0y+6q3wDXlOvYJBr5wLnJhbx4/cnr3rWo8C+hqtIG4U64ffRXaIVhvEtFM3W6jtgjSkrD1pAR637KjMDo8NLJgcyxjI7AA6rWXd4bVB2Bxa19wPVRevwBorAKGgFf0sMqM+CSNogIBjdXUdO0h6jsnQ93JrGLvth2FN7cw1t1JtEVXSHpnsIIJBbHBIp5rrqdyOUsmOME9AaROcC/zYqy6HuDIEvvx/gK0WUOp0NvK6UUYNsQ7U/amb2TE97sAPOZZW29i1vfc+416rlLwOBRUqLzhLbRdKetbPLbXkuVNteAnjATVthBEZIqtnX9wBpYXuire+9a1lH0dp+Imf+An8xE/8RM/fvepVr8LnPve5TTqirQVi2eesjSVcWxyQVo0C6tbBW8M9d4DcSVm8/5Pxn9eeuB9Td74jt42lh+zYoNpZoDPhV40sPPBKD6LqLG58TNRtrK4XaWORx9dk2coO1zYQRJkdImcbS7MdxLulOkFeOqBxjZ8P2zLiVpFuqAcvFVtT2SEDdAnYkPpZy6SFk7nVeat2BZT2184qULcKk8lFpR9yVM5zsiMIGSyrQBsLAE7oOSU7LJNiZqrSMwkywWHkuF/UIk7neeA6nbBLP2Tx95+FcG0RlctuGvo6VQ3qIOhptkhD0wvjzA5q56ye7RtnbdMYacIta1w7IZSjgliOrJ5NOaYO2aGh7Ii+o/5GllGqZ6nlgK0tIDh7HOHSaVT23gg7mkQXJY3mV1po+yyzdhYA6tXhZIfaKVYBpSRjUU8sB4ieTUnnW7AOUcG4bMGp9C2YXLuXLIuDsKN7qpswyR1QisF2F9ZchVEdy6WWNWpTIBCoEw+NtTXsAGAk2Hp0wZurmP/i/47/rDO3om4NIBQVtEdedFEebjjZQWNlx+rIhKhCfI2FnnZAqQh9HDm5iI9+Yj/G117Ae8aB2RUPl3EBSsvdTN4okD41nYJlGuckoFTNWXTaWABgckyOGXNLTVx2ce+cn3vNUmpnFYyxaUBwsOZqqpU06Vk/M1WR6rX2GohpJxLXxHLicGPLNBLVzC8mFBoRPvjBD5Z9HBdwnqB/p2AjEa4toHLFraW9H63UECwND9RkrXW0XngcxDSx+1/fg7Un7kfr6EFMBl5hGwsA0I1sY3F6Hwgi9EutnQV61Q0TdQcn5vKH8ikbSzM0QAkG0qoVpI2luLIjtrGUVPUb77iFQeqOQsikco2CgYxQzbdZIIYJWh1D2Bim7DAKy0fjiUOXjSUpCRyQu+hGRHYEIUflPD+lfiCVHbKNJSfZcY6VHRM1B+971+2xvHXnlAODcLgV/R3bmPzUsN+4XcoOP2CYTFlsKgjOwNaXcis7kqpS+9FjY8lZLd5tFwRGt7HIGle501yGXZNaNmwSpi4I1bhl57Cx9KtlvCAiOwoSyeaEbL8Ll+eAvYBhUJgGKXweT8zK59Dunem1swDi6sWs+tl+G0vmAtyuAKGejUURff1VlK5txi1FQLeyIyI7WIcIy7LfpKFficuaq/FCXBdGXc5bxmgLjbWoPcsZzcaSd25FCAWt1OGK9kjtXIwLULANVzeojS0ekR2lKDu4ACCA0Eu1VnVDLarv+bOvY24JmDLlNfqn//g0fuWaWzE1Vuw7PNdQc1ux2cqOFBVyP6bH5XcTMjHYxhLZWMqCWZfPR7a2lEp29D/rVWbHRM3BQmsdNKF2FgCI5YL7ysZybixDm4mttz15ARsKYtqx73Mjwb0mhN8u1caim9kRLM0iXJ7DRT/x67Cmd2HqzndgMvBGtrGYG5rZIR9cyiYiwqA0C4eCesCKwMdk3cHB5xdyv4eSfzcCAtcxU61qjmXGyo48mR2cC1x60Th+9kevAr55P0RZuSXUBAiFCPxU/6Ka8FKxNatnAcCsT2rZWIpOMtpeCMuk8aSdtxswMpQdHbLj/H/QBiGDbVG5QMlRPQsAglCQc1g9SynB3ovG8YtvvwWTdQfTNWD5D/9HLiVUTCRqZHY4tgHGBYKQww+G71CzxiogOIwhtbNAh4BwSKBpY+kiO3LuUNNokVeujYWXkswPKGVHG82U+9PLZWNRyo7e69ILGEwRFn6+mOPbAEIRrJyN/26UBeFxVTs7k012KBvLenM42WFbBkTQzlxUEtsFgnbPz3UjJjsMKybhKn0Lpm57F9C9oaBsLNGzj1oImSiY2dGv7Mg3jzFrchE1TltorSuyo5iyQ1mZ6i/5fhjV8VxzK6MyBsdvwR9hh9kPGEzw0jZB0mB0KTsca3qkY1YIQ9k4BSG0bSwA0FhZBTAe2+XmV4NzQhKUhf6cJAXTpIkK27IRZ3ZoKju6SaR+clN4TdAS7exGtD4K1xfh4IrE16hn/Uffe2esDpuoOaCUgLXWB4Lh45+z7C5lB40tfi9WnN+64Qs45yCmHUssNxJh1B1takx2dSEzOxoQIvumbR/5Llb3fyGWz1HLKUR04P/P3p/HXXIVZOL4c86p5S7v3t1vdyfd6YSsgGSFREQDAwOiDKIMXw0IgigojsCgjiKDIsgo6/Dz+xsdHUGNO2RYdRjQSNghkAXSCdlJekkvb/e73q22c873j1Onbt373qWqbtW79X0+n3zS3e/73qr33qo65zznWdBJduT5kOuG3t2MKztyJztiyo6pCQu1pgcu0uXwCK8FMANNf/DgUbJZVD2bVNmhU6f/21/ejlv+RdXjnlhxIVKeYy8QQiJJsGX2fvAH29zGAqjcjiQ2lqyTpaYbdPa8O/13FqhdBRUBGPiWn5xxIcPFiFJopLexMEBuLKFDKcF///s78a/fOoLpsrrX0lgpiKGVV0kCStX3ul4QkkKDyaCgpojU1AGlCRbMowSU6lBOvctom6PZWHRAaV6WQ2rasEn/zI74Yn4Y7FhAaRyux2EgO9lBmAk2OYdgtV2zOwppdHyhjsmKiemJwecTz+zoh0jZwSiE50YWqV6gZgnSdwDI3soOHlpQDDNSb6zL7OiysaxTdoT3lgifJ2mVHbRrviYykB1sYgYAMEkcOE1Fdpil7DaW+HwqzdyKlidhi9ZINpYoI6fg8ZmYJWVfaq7mZ2PhAnZoeUtqYwGA86bVNaOrZ6emKqlJs81EW03X7Ph3c4Sg9DRoueqz6w4b7YfZqRjZ0a3scJv5ZnZMamXH0sDvo5RgdrKE+dkKZidLkYVJZab1JomJaUfPITO0a6bN3dxO2D53xBgbArpBbSx6ssum8gwonQCkgOxiiLvRevwwzD0XwAgH+ZGOGXuwsSJtLFbnJLwQZUcst2JmwoaUQK2R7lqQngNqldHqWvR2wzZjNpaEmR3x1GkdUPpHt9yL1Ub6INVe0Hk1Zh9Jn57wEln8ZKoosOrscGXHKG0s68iORn8bS7gQLZGtvxOlrwfL0JkdaZUdTF03GwgpJRpOgGrZ7NiFTgqSso0FUAtnL0FAKU9BdrRtLP46FUIvtNwAE0YAMCM1ydCt7DANOpqyww8zO3JSoJGwjaXfglCrJ5IsdiJlR9fuqRdwsBGUHQBgTu9BsNJWdtgjLAiPna7hwPzk0ED7to2l/5il3x/DoErZMSCzg1olQEqYfcjYeLio0yfksGS1s2yAHpkd4b3Fw2aybJkdnTaWtEHpet4yRVtwW2rBaZbzk+InPo/yJCzeHC0jx+dgKF55SQgBrUyFmR35BZSaCG1NSWwsobLjtS98EuZny2BEvW+v/4/XYLq6xT2hMeh5gHCdjn83NszG0tkgNwzVkhHdp+vaWNx8bSw6Zy0YQnb0Ax9oY7EhAw9SikgJuBEZKZuFvp/uz/3cz0V/JoTg5ptv7vm1UdD9umNsPjaqjYWv6cluvsoOQPlG+4UiisCDc+wBTF7z/FyOSZip3jMpcn3IdSOyseg2liD/gFIa23XSAW+rDRczk8kHTuE5oFYJjsfXSXrjsC0GAQpJaGJlRzx1WgeUnlwJchsQCTMhfC+UXK9/TT9GdmxfZccMgsYKpJR9Fw+GQTMPei2nTXZIKcGdev+A0pAoLBF/y9eeRTvCJoUUWWwsG092uB6HEBKVUlvqnmYB2w4oTWJjaVsivIDDGrJo05M3NpXExqKefSWavHp2DwsyPY/VIoPEMjtG8+MrlQQHzelZTUwLFuFDA0r7ZSXFURqg7GB2MFL4szEzD+fo96K/j/I+Hl+o4Yan7h/6fRMVdb4DMzu4Vr5QiCE22ohko73JpfY9ZfYNObTNLhtLnzYWTkYhO9r5H8Kpp87soFYZMGxMxsmOHHenk4JVJmHyVrviPUPApusFud5vg8AqU+CNHDM7uIwpOxLYWEJlx54K8IE334jmvQLuF76C8/fObJtwUiAeCt2l7BhhHpIG/YjKfiCEYHaqhIWlZuHKDsIMsOr00A2qfhCtOli/zabItu5Gzx1lQS22tnmz0HfG/q1vfQuEkJ6TYv21UTBosj3G5mGjAkqjyW6f0J0s0De1qp+d7/k97vEHIQMvUQtAUsz/5Ftw9ta/AiEEfEBGwSggzASYEU3CZZB/9WxHQOms+vNqyvpZ4bVArJKSkg9SdoSTQkHNxLapeOq0DiidmZnITbKpPYym3VvZwXVA6TYmO4yJWSCcFLNyb3njSJkdXozsCDyAB/0H23AxWibelq89i1ouMtpYJGWgG0x2NBy1kKqWjI4wxaRoB5QWoexYAihLpIbTu5wVgycmO8osSJ3XAaigRGLZEalsm6Ol1Dths0nalq9+oKZSduiw5G60q1WT21jcLrWM7/ugthhJ2WFM70GwthhVJqp6zvTv41rDw2rdw8G9g5tYAPVZmQZNlNlhGgzSH9zGokm2KutNLnUoO2q9d4dLVlf1bGyMBdoKD45sNpb4fI2HTXSZsseqM5hqtHDSCe1blY1XdtDyJIygCUDCDzhYwgyFOFxfZeTkvRHUC6wyBdFcg2333hxJi4AL2CwMXk9kY1GfkfSamJ0sgdkULpCqcWsrgBgWQOj6NpYNsrE4XgBKSap7b3bSxsJSs4MgkTxQxQF2vvN/NjmXWdkxyEYcz+jT48VWV9iOgoGf7iD/jpRypP/G2JrYqOpZXlsCLU/kNgkEEN3Ug0JKW48fVhVnh56ayzGDtUX4K6cw+yP/D4K1RdTv/RJEyirVpKBWOWo7kYEHklc4Z4jO6tlQ2VFLaWPxHVCzlMjGAiiyI6mNRadOz8+WYZIAUgJv/fln5ibZ1LtkqvJMrssriWwsIki92N0q0OTioNwOk1FwITNlocQ/dxF2uPdXdsRsLFu89qydhRDaWNKSXYSBDMkSyhtNJ6wBLpntmsw0NhYjubJDT/paboCAiwTKjkWwidlEDSWEUBCzhArjiTM7KjSbsgMIn7PhxHt0G0sAg3BQMydlhxEqO/pldqQIKG2rcdq/n5QyCowejeyYB6RAECo4bctIRFR149hptYA/MCScVGOibEYkXy/4MZuP8J2B8w9NhFQM0VvZEd1TRvQedsva1wWUxhYYAKLw3yCcio9iY9HB8lnIDmNiBpPUAdfBvJtAdrDyJKgMYKF/tfIwOKGSim2UsiPn6tkyC+cYiWwsalwVrlJERM0+22wjhhACalciglljo9pYHE9VRqfZfJ8Lczvi97veiMxT2QGoDaphmR29IHwXMvAGBJTa0fdFyo5tEBSfFX3vikH1suPq2Z0LveArWnkT1JZytbAAcRtL/8rU1uOHYZ93SS6WE12ztvyVWzD/4jfixN++A8QwMPGUH05dYZsE1CpHA4IoWNkxE7OxpIFwHVA7zOwYsDNjGhSMEnBiJCbXdOr0r/701Zi49/sgDz+EC/dP5ybZjAJKwwd/9+5SNPCK7avsYGFjEK8vA3sO9vweQ//+XMBOmU3RcgPsmQk9uCHpOCigFABKxNvyOwrtLAQGyQOQtDYWykClM/wbc0Sk7CibkIH6LFIpO6LMjiTVs+p+0Bk/wwNKl1I1cVGrhLLPcTpRGwtHifigdrYMJfWcbQeUjmpjmSN8JEtIHMS0YQxYDLp+8oBSRgmsLptFwAWM0CI4UmbHjFJWBqsLMGfmYRk0It/S4PiCJjuGKzsAda0nU3ZQ+L47sPVCWwnKLEFmR5g30E3w25aBgAtwLsAYjeriRaTsUK+hm8myVc+Gyo6RyI5ZTNFTkK4DmIC9CZkdtKIIrSp1M48HrsdhEpEbuTgIEdlh5JfZUabqdRLZWLS1ORxn22RH8b973qBWaX1AqcEQ8OIX344brGtVGQZt7Y7bWLQyJYuicBDY5BycEw+n/rn2ZtMwZYcbm/Nu7XnYKOj7Cf/UT/1U3x8a9LUxtjeiBS/PPwAzjqC2GNUq5QXNYIpWb2WHcJtwTzyCmWf+ZD7HK1Uxec3zYc6dh1P/+PsAgAOv+1DmZpdhIFapbWPxvdx9qYQygCryYaJigRBgJaWNRfotkOo0HDdAuTR4ALEtpsiOFIG4lBL8z4/fgxezJ/A008rVm6pUTT5Ms/3gL8VugXZAabB9A0pDZUcwwAMalzQm8f7H0XJ51FfPHbVQGZbZUSb+lq+e1fYAixFABEBKEgjUAN1oZUdLKzuMETM7ht+feoer1gzT3YcoC3htEdaeQ8nPxSqh1AoSBpT6sImXeYdNPWfb1bOjVPK5HodFRrOExEFNGyb6B5TGg3SToGSxjvdUna8OSRzNxgIAwcoCcEi9j8u19IrHY6frsEyG+dlki++JsjkwoFS/b6YRtrFY/Tcl9GKywnqTS/HMDsdVC4vuzA6teHJ9jgqjfTM7gnAqnq16Nsz9iMiO9CSfVnbYYUAms4cvtvOGtlVWiZv5nnN9jhLEhig7aGUa0ndQYiI3sqOUwsZCKAOxyjFlR1tptN1A7Mr66llGNiiglCfO6wBUK+Dzr78AN159PqaqFoSQoJREn0Pe2X3G5BxEc02tyVIQWSLc9KV97MrRWs93YRrqnHcy2TFuYxmjA+3d/WJDSnmByg7RR9nROnIfIAXKF12Z2zFFcw1nP//h6O+1w7cVbGNpQQoOiNHS8vuBmBaE74FRgqmqhdV6OhuLDihtucHQ3nLbZAhgpn6/6k0PNs1vAaFBwiYiK1zsd09gIun4NlZ26AYi3ljp+z1mTNmSFp02FkU6DsvsKBF/y3e8a5uNaShyLbWNiTJQbFZmhzliZkdyZceaVnYMyIyQUiJYW0pFdlOzBDtFQKkNbzQbS0gqm2HTQlbrretzWDk+q4hpg0GA9xmfvUCAUgKWUCHQnSnh+jwKfx7JxjK1CwCBv6oaWborWJPi2EINB/ZMJCa1JyrWwIBSna1gEKnG0AE76CSu7OhBLokEbSzr6n2pAcRCufUCNZDq9zMykR1Kicsbq+oQGTZbWHUGVeKiQl14kqVum8oDNEZ2ZCW/dfXsRuRW6E2tCnHg5VDbGXAJm2obSzKyiZaq62ws23EjJm7R1tB24ix22jRoecPnqhpCSBw5tYb33Pxt/PaffA3v/PA3ceTUGoSQ0edAcraxsAk1Tg7aoOoFHio7+tpYtDLId6PNia0eFD8KxmTHGB2gEdtXXG6H5AF4YzV3ZQcxSwBlfTM7Wo8fBjEs2Acuz+V42sZCDAMHXvchTP/gT6B15L7EGRRpQW214xiXz+Z+jJgsdqpqZwgodUBM3cYyeAApWQZ8sFQZMUJI1Fs+bJqfNFyDGBZE4EV+926WO9rdE8G2JTuIVQYx7YHp3mZGSaOUspPs0DsLQzM7toGNRe+YUzXxSmtjATVAsbE99s2Q7NjIzI61BDYW6TYh/cFNGOvOxSrBRpA4s8OUXmY5MY0pO7SyKXNgr6sCSvMKTIye+X3GZ8/nsBPkdWjYlhE1iQC6KjeU0o8wvhBmqmC91QV1nIx2oOMLdRxIEE6qUS2ZaAxqYwnvY1Oq948mCCgtkX7KjjaB2PICELL+um8H94ZqGUI6ctFk4AHMgH55M7WNJZSicz+m7EiWbxIHC0nwXbQOH5tjg4jIg1FsLD6HAQFjg2wsAFCRihgdlbBXyg7dxpLMBk3tSnu+y32AGtuy+IHapYgs0NDzEC6KnRu4XluNOgyrDRfv/ovbo1bAheUW3v0Xt2O14UY5T9TKW9kR5qzV0pEdYxtLJzZtxn7ixAl86lOfwq/8yq9s1imM0QPtCW4xC3YgzAuAzF3ZQQgBLVX7ZnY4Rw6jdODykSZxcbBSFZNXPw8TT/1hsMoUZm+8CTO+W5yNxSxB1JYySdITHyNGdsxMpCc7pOdAMPUQHVQ9C6hdLx/JMzsAoOmqYFIT+StbNNHTL5k64BIEAkTK7Ut2EAJWnRkcUBoOfGlD4vxAQAgZkR08yuzoJ6M0AWZuCxtLFFCqL3k7fgUAAPlhSURBVOmUnz9hDAzq/WFsYyajjdDGUi0bsV3oFGQHZWoXOkEbS7eNZVBApk6WT/P8p1YJFlldV5PaCy2XwzTdzDtsxC5DLOqA0lDlFQxvmOkF1w9ggucWJh0RvH0+E8/nqaoD1yk7woBHYPTxxZyZR7CilB1p6zmFkFiuOXjLTdeomvJQKj4ME5XBmR36mUY1+ZcgoLTMOGqDqmeZqXJiLLbuHHUWQHcji4jZWIhhRc+XLMoO/TqiuQZansikytBZTrtZDT7ZJLIjHCcmyAhkh+uDEgljgD0pL7CqmueVNdnh89S2zziCQKBKOCCTW8ioXelQdhBje85LqFWJwow14psuRdahOl6AiXKy99sPRER0aCwst9Q5etrGUpSyI11IqbYR91N2kCg/yIVp6Tnv1p6HjYKR7oxGo4HPfvaz+Pa3v42VlRX4vg8h1u9eSSkhhIDv+2i1WlheXsbiorqwx2TH1kLE9hVoYwlq6rNPs7OXFKw0ETGacfDGKryFo5h9zs/mejxq2lEYafzPRYDaqiWgSGUHMa1I1TM9YeGxE6uJf1YKDhl44OHEfpiywzYZfI+lUsLU9WKK8GhXKy+0bSy9JX0+FzAQyky3KdkBqNyOwZkd2Vh+vUPcqewgAwd/YpW3h7IjvBYMLTNObWMxwCDgByKxxWBUNB0fhCgFVSMjQUqYkZDsSG5jiWrHp9IFlFrwh9pYpJTwPRdU8uw2FrMUSartuLy3nH4RqGX1eQaUAgB4P2VHOlKmbBsdmR1eTNkxagC2Mb0HzrH7ASiyI2kbi5aK6x3U+dky3v7aG3Bo39RQwkO3sfQjR7xAwGAE4GrMGRQEqZUdNg16B5RyH2BqJ93pI4XXi9/47x5XTyofvhFZJNNmdtBYgxpvrmXeaNFkxy5awwqZyfQao0IHWVdHIDv8MCjWtDfOxmKLJgBzZAtAIARsygGeLKAUUBtueiyXgb9t5yUqe6SrenaD1AYtl2PXdLJnpmlQzM+WOwiP+dmyygDSyo6cq2f1OimzsmNIG4v0XZiVsbKjL+677z788i//Ms6ePZv4Z7pJkO0ot9rp6O6BLwLRZLcAsoOWqj1tLK3HDwMAyhc+LfdjbhR0G4tW3RTRJR9XdkxP2KkyO/QCwSfqGioNIzssBtdhkEFz4PfFoXeODfD822hCebHZR7oeBAI0JDu2oy9Ww5iYgXf2eN+vayl1vxDEflhPdjRAS9WB9aK0VFGZHVu8etaLKTs8IPXuKWEMjIi+laFFoOH4qNgGKCVtgjRlUn88AHEQGCWwDBprY+n/mfOI7E6u7CBWCab0h9pYHI/DhjrfzGSH3W5j0cRB1oWM43EwGeRnYxlGdgQ8cTgpoJ7B9dX25+v6sYDSEcl0Y3oewX1fhRRqx1vnGgyb9/WTin/gzTdidnLwInCiYkJK9Syq9iCn9C6x9MIxdJCyI/yaTYKe961WZQBhA1APsqPbxqJfV28orFN2ZGhjUa+jyY5sDUQ6y8kkAnyTlB2EMkizggp1M1fPem5oT9qggFJAkx3TI9fPBoGETQKA0MTzC2pXIMKxXLWEbb8mFkDZWHROkoa+F4pegLteMHRjTmO6auPtr71hHRE7XbWx5haj7KCVSYAa0SZxUginrmxNfYizeA12u3p2a8/DRkGmGbvnefhP/+k/4cyZM4l/hhASDXJSSpimiR/6oR/KcvgxCgQxkifwZwXPIGNOClqagGitrfv31uOHQe0K7P1Pyv2YG4U22aEluEXYWMwOsqPe8sMJ4vBJmIjIDnUNDVV2WAyeZBAp8mFqoUTZKMDG0l092905zoWAQTLu7G8hsIlZ8JD86wUjo41Fkx2lLrJjEKhdRZk4aGxAxdwo8LWygwh4AJAys4OwtrJjo9B0AlTKOmQ0vY0FUORIUpWfbRntNpYByg5r74Uq02FiDtxpgA25RgC102lID+6QNhbHDVAimuzI2sZShvRdSMH7hhUnhcoQ4LktQmioZiN9xmc/pd2mZBkd76nni1wCSgHAmNkDSIFgbTEiv7wEDU+DpOLDUC2p97ne8nuSHV7A1S6sr8YqMsDuQAgFMW3YxO9bPasX1Y4X9PT9l/rYWNptLKpVLauyIx4oz5urMHedn+rnNbSyAwACWrwqoi9KVUzUndREu0bgt61FRYPaFYAasAJFdoxK2AdcKMWqaSXeCO6wsWzjLDFqVdTcNkaGbpSyw/F4ZMMcBkoJDu2bwgfefGM0L56u2mEbSwsInxl5ghCKvf/xv6Dx0O0AkHjM5K06WHmi77WklR3Cd6Ixw9/BAaWZ7oxPfvKTOHXqFAghkFLiR37kR3D99ddjcnISH/rQh7C2toZnP/vZuPHGG1Gr1XD8+HF88YtfjMiRqakpfOYzn8G+ffty/WXGGB1tH1eRyo5FEMPqK68aBaw8AX/55Lp/bz1+D0qHnropKeN5gVglQATtiqucfOBx0DCkEwBmJtTrrzVc7JoevnDQu6Eq4MxHeUjCdcky4AqWKh9G21iY5MWTHf56ZQfbCTaW6gyE04AIvJ75NXnZWHir3jccKzoXu4wSXcPKFt9R0DseZjhvSEt2EWrAICLzJD4LGi0/WvxlaWPR35+kjQUASjaLBZT2XrQFa4toHf0e5v79z0G0amg8dDsmr3re0EA+YpVgiOE2lpYboER0+GTWNha1EyZ9t71Iz7iQ8VwfFHm2sajXIaL3+Oz6fKCqphuqerYzsyMKKB2RTDen5wEAweoCbLMcvf4wsmOQVHwYJioh2dH0sHdu/ecfBAKWQSOyYZhdgFol2KKPsoN70aK6r42lu40F6Awo5QHAzOhZmz6gtNPGUjr4lFQ/H38dj5RgSQdiE8kOUppEhbYyk4u+q+16xY/PhBCwyiSMQCmJR7axcAGLBIktLIBSRgq3CSnltraxULsMSAHpu1FWTpQdVvCY6SRoDoyDUtJTYSa8Jqhdzt2xEKwtwl8+icpFVyFYW0w8ZopWfeAaq5eyYyfbWDKZh7/85S9Hf37jG9+IP//zP8frXvc63HTTTfjhH/5hSClRq9Xwile8Ar/0S7+E3//938ett96Kn/mZnwEA1Go1/O7v/m4+v8EYuWIj2liCtUWwyblCbEzKxtKZ2eGvnEawsoDyhflVzm4GdLuArpgrRtnRzuyYmlAPQ72AGQZtY3FlqOwoDc/scDhNda1pZQeTQeLE8qTQk3ut3ugOa/K5bCs7tumkAlDKDgB9Q0pNlq2BQpMdlZiyg5WHKDtKVRVQusVtLFGLg87sSKvsMFQby0baWJpOgEp4D2YmO5iZKLMDUAtnXfvZS12g26uWv/xRGBNzOPG378DaXf+SKLOHWmVQGSAIAvABVYTNHJQd+jkr3FbbxpIxuI372i6Rb2YH7fOZ+H66MD+7m+zwOcy8bCwzIdmxshAt+pMsCLVUfH5WfQ5xqfgw6KDBfvWz2saiVYiD2lgAFQpuob+yo9PGsv5970V2UNOONhQiZUfmgFI9X3MhWvWRwtFdQz2rN5fsmEB1hAyntrJjY8ZnVpkC89V8c2QbCxewEKR6VlC7CgiulGghcbYdET1zY1aWjViAcyHhBQLlhMqOQRBuM3MDWD9EY+ZXPgYwI9WYKZzBm02EGQBVmXljG0sf3H+/Cp2anp7G61//+o6vXXvttfg//+f/4Lvf/S5c14Vth75H28Y73/lOnDlzBl/4whfwla98Bd/4xjfwzGc+c8RfYYw8sRGZHby2VEg4KRDaWJwmpBRRVkDrse2f1wG0J2a6Yq6QzA6zs40FAFZqyZQXWhrsSvVY6TX5i8O2GFxBIQMvkZcbaCs7iCjGxgIgmuyvU3bwuLJje04qgLY/mzdWYIYLkjjak4x0kzfHVd/ftrHUYAwJoaR2RZEdG0gCZIG+FlhYPZs2s4UyA8YG21gajo+5qVClwL2wljDtYspIZWPRsVy9AkpZqYrJa54PY3IOpz/+fgDAgdd9KNECLQqLJD48v3+tdaeNJauyI5x4+w6scJc1666t8D2A5fe80M8oOiCzo5LQfw4odZ3TXT2bl41lahcAAn/1DKzqper8EryPWir+5p+5BpbFsHeuEknFh0ErO/rVz3oBh2FQSG1jGUKYU6sE0/X7ZHZ40Rjc8gLMTa8nTiIbix/L7DAsyGYtfI0ws4NnVXaozyioLQFSRA0hWeCbk4C/CJFz8Hca0PIkqsTBalayw9s4GwsQkh31/JQdJglS2SD0M064TYBvXxuLbs5SuR1qM6bdilectUJb+OwUyo5+EG4LJOOY0w/RmDm9B6dveQ+A5GMmb9XDZ3B/ENOG8F2U+zQQ7iRkUnasrq6CEIKrr74aZlef9RVXXAEA4JxHpEgcv/VbvxX9+bOf/WyWw49RIDSrXGwby1Ih4aQAQMtVJYeLJTu3Hr8HbGIW5u4DhRxzo0C0siMiO4pQdtixzA71+qsJlR16t6wldGbH4AlHyWJoBeoRlHT3uNb0UbZZsWRHONnvHmQDLmCEEu/tHFAaKTv6pHtHZEfqgFL1GXYGlA62sVBbtbGMOlEsGl7AQYiyTwEZbCyGCUokgiGZE3mi6XTaWLKQo2kyO+LkZj8rhWiuYfELfxv9vXb4tqiGc+B5RGRHMNDK0nIDlEfO7AgJIrc1so1F5xHl9azSajYqe19Hns9TZnYwcCGjSW5HG8uI50yYCTY5h2B1ITqnpLvflBJ8+DP34pZbH8bsZCkR0QF0Znb0gso0oRBRQOkQZYdVgiX7KTuCmI2lNwGnlR3rMjtC62bUxqJtLBmVHbq2cxRlR2Cp6le5iWQHK0+iSt3M5Df3swUxZwWtTIG6irgamewIJEykt7EAiuzY1gGlMTWdhrkBAaVacdUrbyctpNvMPZwUCMfMW2+O/p50zBym7ADUeBJXduzk6tlMZIfrqjd69+7d67528cUXR39+6KGH1n390KFDeOpTnwopJe67774shx+jQBSt7JBShsqO/MNJAVU9Cyj5lz6ec+RelC982rZv/4mUHdrGUgjZ0dnGAgCr9WTKDp2m7Qg1cJSHDCDKxqK+J2n9bK3pYaJigQg//zaW8P00ECo7erSx7CgbS2MI2ZFyktGMZXZIKcFbSQJKK7Dhb/lgrMgeIHVAbbrJEY2sQcWRyN1otAJUym0bSyayw7BS2Fja98QgGwsxDJz3mj/E9A/+BFpH7ktsYwEAG35Hs0U34pkdZMTMDuGNZmPhQgIZg2H7IbKxiD7KhZTVs1qFpXc4XS+m7Mjh+WrOzCNYOdOuYE1xn6/W3YhwT4p2ZscAGwtrKzvogIBS9XXVAtQrrFnytrLDcYOeIYeWQUFId2ZHnm0s6vjBmsrDoyOQHcJWZAc2kewwqlMokQCBlzzHK4422bFRNpZpICQ73BGtmD4XMGS6eY2uORVOIyLOtiM0SRC3sRgbYGPRY0mazI5+EG4rs5qwH+Jj5v5XvhNT17848ZjJh2R2APpZFLOxbHE78SjI9AlPT09jaWkJvEeC/szMDCYnJ1Gv1/H973+/589fcMEFuO+++3DixIkshx+jQBTdxiJaNUjuF2hj0Q//OoB5+GeOgTdWUTr0A4UcbyOhBwTeLJDsMK1oN7JaMsEoSUx2aGVHIyQwklTPetBkhwckIMXrTR+TJRPELSCg1OxUdvSysVhM6fS366QCCHf/CEVQ7012ZJ1kaBtL2TbUYCwCsPLkwJ+hdhWEIFp8bFXoSs8orDNlZodubQi8jSE7pJQdyg4RyxdIA8KMxGHV8cVerx1qVqpi4qk/DGvPBfAWn8DsjTdhxneT2VjMmLJjwIK55fK2jaU0oo3Fc2BNZG9j8XweKcHyzuxgst9inqdSB5RimRITlTDglAYAS2956gVjeg+cY/dHZEfS91EIidW6h5nJdAvvsm2AEmXh6oUosyPKUhkSjGuWYMjeNjsZ+NG14rhBz0BuQghKFutQdsRDwLUVZtQ2lrayI1v1LADIUngfbibZET4LZFfuWlLwSEm1cTYWeC0w8JGVHZwLmMZoyo4ilAUbgZ7Kjg0IKNX35TDLdRIIrwljdu/IrxMHK1UxefXzUH3Ks3Dy79+J2R/5acw+66VDx0zJA0ivFW3+9gM1LQjfBSEEBqNjZUc39uzZAwA4evRoz68fPHgQAPDwww/3/DoLJ4r1erYH2hjFoWhlRxDWzrKClB1atiVa6tpqPX4PAKB80fbO6wDakltRoI2FGm3ZOqUE0xMWVuvpbCzNgMI06NBdKtsy4MuQ7Eh4vdWaHqYrJDzXYpQdDOr3Xx9QKmCHv9J2JjsIZWCVqf4BpRknGS03gMGIqnYMlVVJlB0AQPxmqmNtNLT8HSKbjYWG14v2lBcN1+fgQrYDSrmX7ZplJmSQsI0lbmPpE5LJm2s4+XfvAKEM1LQTy+61tcRKYGMpER+gLLOkO7KxeE5kY8mya+t4QUSc5tWcpZ95bICyY1jbSRzaq653OD2fw6Yit2erMT2vqmdDkjjpzmHD8cGFjHKjkoIQgmrZjLKduuEHHKaplB3EsIYSOtQqg0mvt7IjVEvpkMN+5L5tGh1qJGK2Q8C19cAPBAhBYrtOdH4hicbXzgIYzcZS2nM+5l/662ChWkCrYzcS1kR4/hnJDk0ibWRAKQBUiZtLZkd6ZUeM7Aj81OPSVgHZpIBSHaqem7Ij54BSQFlNjOo0IDjq99yW6B6P5l/DlB2GHVnqLJOOMzu6ce2110JKie9+97s4ffr0uq8fOnQIUkocPnwYQqx/844cOQIA6/I+xth8RNWzBbWx8JragSha2aEH6tbjh2HM7otq8LYz2soORXbQIgJKDQsQAWS4qJuq2ilsLIrsqPss0eBhmwx+GGaaxIMIAPWWh5mSemzlbWNpLyTUANj94A+CnaHsAJSVhfdRdmT1yrbcIJbXoSarSTI7AID4rYHft9nwfA7TYJGyI+3nz8KxLtggG0vTUedZLcczOzIoO4w0bSzqPTEY7bto0xY8Vk23Ax0PKB1GdtjEB7UrmW2L8WYATRxksVnFa1xz22lmBiQIDAQQPVppvHAxnxSlrrYQ1+Mosfyqco2ZPYAUsPxa9PpJoEOxp1OSHYBqZBnYxsIopNeuuBwEatowhA+fr3+vtSrDjaTwvUkmu0vZoa2iqi60rewwGU19zWpCL4jIjsFKukG44pqrEKyexSXXXotgbRH1e7+UeFzOC6YOWHWzkh1aebdBmR3h+U5QJyeyI21AaczGIoINU7TkDT0PiGftGdsss0O4zdxtLHFY84fgLRxJ9L28pZ63LJGNJayLN9iY7OjGs5/9bAAqhPSNb3zjOsLjqquuAqAqZj/+8Y93fO2uu+7C4cOHQQjB/v37sxx+jAJBCAlD6YpVdgxLCc4KfXMLpw4pOFpHv7ftW1g0aHdAaUHVswA6GllWEttYWiCGhZYnhtbOAmoS6COtssNHWDBRmI1Fch+WQddNXriQsFg4GGzTHRQNNjED3ljp+bWsNpY42cFDsoMlVnZs7KQ6LbxQ2RGRHSkzO5ihlR0bQ3boNopKLKA0CzlKjORjgV7s9QsnBRBdc0Z1Jt15WMlsLI4boMpGk3O3/eMOzAxZExquz2HkmH8BqPFZUBMWOHiPjaS0yo5SV4CmGyo78jpfvclgOIvR6yeBHnPS2lgAoFox+5Idni+U8sx3EtkFiFUCEx6CHvJuTSBGu8N9lB0li3X83sS0VfaPCDoyO9LWzgLtMZA3VhXBl3GRz50GvMfvxvJXb8EMa6WquMwTeteauNlUJVHF9gYrOyaJM3pmRyDBpJ/dxhL42zY4fbOqZ92cMjtkWP9bpI3Imr8QweqZSLUxCIk3m0w7UmQbBs1csb4dkOkTfvazn40rrrgCDz74IA4fPowXvOAFeOELX4j3vve9AIAf/dEfxfvf/34IIfCud70Lp06dwlVXXYWHH34Yf/qnfxq9zg033JDPbzFGrlD1o8VMypW3lIClnOwmRWRjcRpwTz4K6TZ3DNlBOqpnSSEL7ojs8D0Io4SbXnA5KAGWa87Q+j/pOSBWSS16E3ggSxaDJ3WA4vAFlZQS9aaHyXABXVQbi/Q9mOZ6ltsPBCy6Q5Qd1dm+uwRmxhqylhu0a2db2sYyZLANP0sabF1lhxASL/qhi2AaFPXlMFQ7rbLDUIYGntASMiqaYWZBNbKx+Jl2Owkz2zklQ6AtEYMCMtvKjplU59HO7Biu7JhnPmjGcFKgneMgvBasEYLbOpQdOdlYAEAwCyYJovwJDS6kUgj0sRD1gp7k6wW7yuzILw/JCKutjaba5Ei6+63VhGltLAAwUTb7Vs/6XAW4Cs8BGRJOCihFEZM+pBTgQoLFxj8VCGlG12O/Mc+2WEe9b1w9q0MlfS5S53UAnSTaKOGkrFSFuOTZ2D2zH2du+QMAwPkJKy7zBA0znoifUdnBA8Dc4IBSAFPG6DYWLgSY8FIRjcQsASS0jW7jNhZi2ur36JHZUWQtfcvNJ7NDn3eRyg57/hAAwDtzFKWDTx74vTy08dMhmWnEjNlYjJ1tY8n8RPjABz6An/3Zn8Xq6ipc18Xdd98dfW3//v142cteho9+9KMIggB/8id/En1NSrVYMAwDr3zlK0c49TGKQryRI2/w2hJYdbqwwYiYNkANCKeO1mNhXscOCCcFQskqZYDgIKZdSLuMHmi55+KJ1TX897+/EwvLLczPlvH2196AQ/um+hIeerfMcXvX8HXDtlg7syPBDpLjcQRcYtKSHeeaF+KqFsug69tYuIBJJSC3P9lhTMyAN1YhpVjnW2eUgFKSOqyqp42lPETZEe7oUL41A0qFkDhyag3/v3+8CwvLLTx79gm8lAAyZXgjM0Oyw98gZUdoY2krO7xMOQyEmVGjyDBEyo4BizbeWAEIHeol7kZkY0EwtI2lQgNQO3tQI6FMTQK9Fggh4SQwm43FyNvGAkBSExYJEHRZK/Q5Dnr/u9Fdjer5HBbJkeyY2gWAgDQWAVSTKztGsLFUyybOLPcmT4MwwFX6bmJlBwBYCOAHHCy2A6wzOzRRZPfZHS5ZRkcbC42R6lrZEQQidRMLECrMCAWkGCmcVAiJKnGwcOtfRv9Wu+c2GDfeBJaAFMoLOtCaeemVHVLm3340DJoMmjZGr0/nAQeTAUiKgFhCCGipEque3Z7zEkIIqFWC8NrZXW0bS3Fqg0jZkWC+Ogj6vAu1sexVZId7+shQskNnFiaxsejIAnOHkx2Z47YvueQSfPSjH8WznvUsAO1QUo23ve1tePrTn658ieF/GoQQ/M7v/E5HTe0YWwdppMtpEdSWCgsnBdS1xcpV8FYDrccPw5q/MLU/fKtCDQhhxkFBgzkNB9q1Wh3v/ovbsRBOGheWW3j3X9yO1UZ/UkJVb5U6dvgHoWQZ8EO+NUnjQy0Mnata+lyLIztMk62bvERkB7Y/2cEmZgHBIZq1nl/PMvD1srEMz+xQkwNji5Idqw234z5ohNd/3U333hhhZgffsMyOUNkRZXYEGTM7jMQqP012DFIW8MYqWGUytQ2oI6B0YBuLCigdddJJrXK0W2eaLLONJapxzZPsYBZMwtcFCGv1SZrqWX2/OvHqWZJfZgdhJtjkHFA/G55jchsLJcBkNf15DFJ2aBuL9N1E2QjxFqBucknnbWgio1/Vut3LxgJA+i21ccHMzMoOIBasPYIKw2/W0HhAVVxOveK9KF37H+AcvQ/c3VjFHTFMuNIEC9IHVvuBAIOuht+gzI7yBEAoppib6RnRgZCooQmyZDrOwd7+ZAegQkqF254HREHpwfq8nLzg5NTGorNGSIE2Fja5C7RUTZTbkcbGojcae815dxJGujMuvPBCfOQjH8Hx48dx6tSpjq/Zto2bb74Zf/M3f4NPfepTePzxx2FZFq6++mq8/vWvx9Of/vSRTnyM4lCosqO+CGMm33qmbtBSFby+BPf4g5h6+gsLPdZGg1glwKkX0sQCtCdOvutGCzyNheXWwAWw9B0Qs4yWF2BueviArQJKY9WzQ1Bvhgs4U3aca16Iy4vNMCE/jiCQsMgOIjsABPXlnmSgyWjPBoJBcNwAc2GgirKxkKEeVu05ZnxrZnb4gei4D/RkOpAplR2GJvU2KrNDKztibSxZMjuYmZjs0DvbgzIjeGMlE/lMKAMMEzbxo4rjXsiL7CBWCSKsQ7ZNmsnG4nhBLKA0x91xZsJCsO7+jJQdKQJK7a6AUqXsCKKWjzxgzsxDrJ0BIYcSLwhX6x6mqnaHbSQpJsom6i0VANqtfvRDm4/wnEQh6XpzwSZB+P6G5KGUSpXBrIgo6t/GwqIdZCCmngytfsRQY00WZYd+Pek7I5Edq76Bfzp5Hv7d834D7/nfD+Hai6/Cv3v281ETNorbmuoNh5RgZiA7XJ+DaXJxg8ZnEqrUJpsulkdYKHIhYSDMg0o5r6F2VQWUbmMbC6CykmRHZkdop+XFLcD1c6+fKispIhvLCPbJYSCEJA4pVQGlZGj9OomRHWMbSwIcOHAABw4cWPfvjDG85jWvwWte85o8DjPGBoEYdmFtLMHaEkoHn1LIa2vQ0gRaj98Lyf0dk9ehQa0SOIqpnQXaEzFDBpifLXcs9OZnywN3n4TngFrljh3+QbAtBi+FjUUrOyqGtrHkK6/VE3wZeLBMu7eygwkgwLYNAtMwJmYAoG9IqWnQ1F7ZbhsLLVWHVjsSswQJAlNsTbLDNGjHfcCIek/SNokZVrjA2fDMjlgbS5bMjrCNpdfCsRuRsmNgQOlq5rwmapaHBpS23ACW9EbeYaNWOdqts0yWKbjN9QpSdhgWTNLsq+zIktmhF+M6VDXP8cWY3gPn2P0wDZaijcXB9ES2c5ioWAi4hOvzdcGDvt+2sSRpYyGxFqCOHWYRAJBK2RH5/pPZWCJS3W2THUEwgrIjvK9ZNTvZYRoUX//eWXzqa8cAAEdP1fD1753FB958Y+bXzIrMZIfHYZCNVXYASlEz0XIyEaIanAtYJCQ7Us5r2jYWf1tvwlCr3GVjUeNNoW0sbgDLoJlI1TiEq20sxSk7ANXIUrvntp72447zadVBy0nmX3bYDKWeP6Ncw1sdmW0sY+xcFGVjEb4L4dSVrLVA0FIV0ncAylC6oFhiZaPRtrEUpexQk4SKCbz9tTdgflYdT2d2TFf7D8QiDCh1UpAd2saS5HrTyo6yEU5oCrSx9KrhCriAGU2mtu+kAmgrO/rWz45oYxFOI1EuAyEEAbVhyq1JdkxXbfzXn2/fBzMVtZCcmkw3qWHGxtpYGo4PQto2BS25T4to0SCGkzR6sWcNtLGsZCc7rBLKNOjYJe9Gyw1gwsvBxlKKUupNI5u81/WLyeyAYcEiwToyUp9jmjYW/b3x6lkT2SxP/WBMzyNYW0TZJCkCSr1MeR1A27rVbWXhXEBItYOZtI2FhgtP9X63zz1q/YhldvSTwpe6qmf1a+pWBW1jyazsCK+tUQJKp6t26vG+KDi0DJOnt890tB9t4PjMKtOoEmekJouAC1ihsiOLjYU7jcgStV1B7U4bCyEERgaFaRo4XjCyqgOIkx3FKTsARXZIz0Gwembg93GnPtTCArSfRdL3YBqs0HyUzcb2nrGPUQioaUF4+S8+uK6dLZjsmPmhl8J5/F6UL7seskc933aGHgjzzqvQ0JNcwj0c2jeF97/pRpw4U4dp0IHhpAAgvVak7EjigeywsaTI7CgxgRYKIDuYARAaZnasr+HyAwFjp9hYwgUnr6/0/HraSYYQEm/6mWswP1vBcs0Bb9WH1s5qBMyG5W5NsoNSgomKgV98ydNw/p4qzIcEWl/5WuoaV53ZITZM2aGIJ32/Sp41syOW+TFkIm0PqZ6VUobKjmwZSsQqocQ4lgeoAzzXhcGCiBTOCmKVo+aYzDYWl8PUu7W5kh12TxuLfl4NUtZ0g1ICy2QdNhZmB7mGPxszewApsNtsJX4fV+ouLj04k+l4EyHZUW/62DXdvg40eWsaNGwOG76Qj1cex9/vdsWpGTWt9CP4bYv1Vna0auHfR1N2aEXiKDYWSgkO7ZvCB958Y9jyQ4e2rxUFj5QxK1ZT/1xc2bGRyktWmUIFJ0bKO/CDmLIji42lGb5f23heQqwyRNd8pOjQTMfjKPXJ2kmDiOwYcdwZBks3spw+AnNAHIBo1aOw30FoNxC6StkxtrH0x8mTJ3H33XdjYWEBjUajI4g0CX71V3911FMYI2cQw4LsE1w4CoLaIgDAKDCgNFhbhHvq+5h9zstRufha1O/9Eiavel7EYG53kIIDSqOJWOCBUoK5qRL+6//8Gg7uncTbXnP9wJ8VngMYNgIuUS4lUXYY8KEGGpHCxmJTRXbk6SvXULXLStmxbmdQCJh0Zyg7qFUCscoIBik7EtpYdGPJn3/qcNTc81/n11CqJiM7OCvBQjG2uTxwZtnBH/zVt/Cu1z8TF5lEEW0pAzZplNmxMWRHo+VHTSzA6MoOyX0AgydyURtLH2WB9FqQgTeisqM50MYi3RZQGX2HjVolBMsqh8zKGNwWBZQyc6icOA2IMSSgNOWiuRSrRnV9DsMKcq3KNadV/exuo5Eis8PNVDsLxMiOrue3nsgbBoVI2MbSbgHyOxZdMmr9sODUtO+/X0CpgYALcC7AGI3GWO40o9fwR7KxaLJjtCB2SglmJ9OpCoqAx8qwg/SB1a6nlB2SGoU01fUDq0yhLJMTeb0QcAErVIGlVnaUKuBNTZxt33kJtcuQbqd9KYudNg0cL+hrP0sD4RVfPQsA1p6DAAi8hSOoXt5/Pq5sLAnIDq0yC1ylZt7BNpbMn/KZM2fwjne8A1/84hdTExxxjMmOrQdlY8l/pzUIlR1F2Vi400Djwdux/OWPYs+L3oBTt7wXxDAw8ZQfBnYI2aE9gXnXrkavr3MrYpkte+cqOL003EMrPQecqp8vJxhAGCUwDQZOktmmGi1f+Stlth2QJCCGFUr61u8oBIGEGVpoQLfvpEJD1c+u9Pxamh2V7saSheUWzmIR+y/bnejnhVGCDQdCyE3ZSRyGpVU18Z6bKkGeCa89lrJNJCTHBN+4zI5qjHDMnNnBtA1muP1GBzT2s7Hoay2rsoNaJZTIat/cBykl4OtJ5+iZHXoCaxlsYN1tP7heAIuK1CqgYSDaxtKt7PB1QGm6a7NkG+02Fl+AyryVHSHZwRo4m4Ds8HyOphPkbmOJAlypjOrbh6FD2cHjyg41XqnMjgCE9LcPaRLQ9TkqjLYXGGFjAmEmAu5EYcJpQXJQdmwl+EYFtutCCp6KVHa8QGUqpSSiRwWtTKEkHXhedosi5zJ7ZoddAWS4CbON5yXUqkTWQQ3TKNrGwkduYgG0soMkygEaBdQqw5jdOzSklDt1mHP7E7yeOl/pueGcb+faWDJRyfV6Ha985Stx2223QQjRUS+b5r8xtiaIYSdO4E+Dto2lGGUHK1Uxec3zsfcn34KFT3wQwfJJ7P3JX9sxkwAgltmR485bHLo1IE4+7NtVxenFwb33kgeQ3AcPzytJZgegJoicGonaWGpNHxcfmEHp4JNBmAli2MqrmiOIYUEEXs9QQp+HNhbKNnTnqCiwidkBmR3J/ZvdjSUAYEsH0kym7BBGCSXir9up3ipYqoVkx3QJUoTvScpJpZ6ECr5R1bNBpOyQgqvFXRZlh5a5JjjvSsnA215zPX7yORdjuabIqzi0LSSrsoOYJdgk6LAExOEFAhbC+sZR21jscjTxVsqO9Nem63GUWX41rhrUtGEiWHe/aPJjUGZKL5RCm4WUEp7PQUWQq03SmNoFgGCW1hMpO1bqaqNlZjKjsqOilR2dY4p+f2ySPBuBmmq8tYjfUT3bzuyw0Ap3h/uNCd2NN3pDQbRCsmPENpbZH/lpVfE7MZv7eLgZCAx17+r3JylUZocANji3Qs8vmZ/9vY9ndmSxsWgUpfjdCFCrpIJWY2vDom0srrc+xDgLhNsCtcsbMi9M0sginHqyzLS4jcUc21jW4eabb8aRI0eiD/bSSy/Ftddei5mZmdQp9WNsPRRVPRvUFkGscqGJxaK5hrP/8hfR32uHb8PsjTftIBuLmqAVpeyIh3Rq7J2roOEEqDc9TFR6H1cvDHyivt6vhq8btsXAYSRq/zEYwX956ZPgHL0bs895OYRTQ+Ohb+VqU6Lhtd/LvxhwAUb4trewaLDqDLzTj/f8WppJRndjCSBRIR6McjKyQ5pllIivFqspd6U3AstrDkyDYqJsYpkH2ciuSCGxQTYWx48k6ZJnV0K1MzsGkx1CSCyvufjwp9tWpre/9oaOnJ9gVGWHXYYFH67f+z1sOap2Vn3viDYWswTpOZBSwuqR35MErs9hUZH7AoSYNizC4fBOMsnLUD0LtAM0vUCAQv2XJ0FDmAk2OYdpt5bIDrSqyY7MNhZ17jrQWiMig5B8B53GlB1x8lfEAkrdIbvDpYjs6LwPO9pYeDYbS7C2CPf045h9zsshhUBjB9h2A0ONG7xVS/Ws0DaWjR6f9TmaQXayw+ciyvfJYmPR2M5zE2qXASmU5TK8fosmO1pugLmp0dUYwm2BFGxh0bDnL0TzwW+FVrz197mUAqLVSBVQGtlYxmRHJ2699dboz29605vwK7/yK7md0BibD2KYhVTPBmuL4S5PMdA2FmIYOPC6D6F2+Da0jtyHGd/dOTaWojM7+thYAODUUhOX9CE7pB+SHVDnlVTZUbIYAmIksk0998rdoMfuxtJXlU3pxN/+Xu42JW1jsUwGv0f1rAGxrScUcbCJWQSP3t3za4ZBo5aBYZiu2njrq5+B99z8bSwst3D+rAlGBMpTCSeqZhll4oWLia1Hli+uOZidKoEQAimC1HkdQNv2IjfKxtIKcP6ezqajbJkd4WsMUXasNlz8t7/stDK9+y9uxwfefGNEuvD6aMoOapZgwu9rY2m5MbJjxKA4RchLSN/JnNnheBx2QcoOQgT8Ltl8ZhuLpWwsHVW5OZPp5sw8pk7Vkik7amosyFo9q+1b620sYUBpmkUlMyAJC20sMXKJtwNKW64/kNy3o3pf/d6qsUqrMCJlR0qyI7LtfuVj2POiX8HJv8t/PNwMCFMrO9Llxrk+ByMbPz5rZUeWulyNIBCR4kira5OiQ9mxjdtYiKU+d+k50fVrsKKVHcmaA4dBes3Ca2c1VEiphHfmGErnXbLu68JpApBgSZQdHW0s9o62sWT6lLWq46KLLhoTHTsQ1LQKs7EU2cTCSlVMXv08TDz1h8EqU5i98SbM+O4Os7GEyo6Cq2dFl40FAE4vNnHJgZmeP6eVHR4MAMkHENs0EARGIiXRn/7zI3j5867CU39iL07f8h4AwIHXfSjXzzcKKK30UHYEAsYmTKaKgjExA+m1IDxn3cTfTDHJoJSg1vDwiy95Gg7tn4TtrWL1L5FosFUHC5UdI6TZF4nlNQdzoaRecp4p8b5tY9mY37Hh+KiWOlUZ2TI7dEDpYJKml5VpYbnVcQ1FmR0Z71dilWBKv6+NxfEClEhI7OQQUAqo59ooNhaTFEB2WDYkAN4V6qzPMa1CwLYY6qvq/jMzNkIMgzG9B5Mnnkin7MgYlskYRdk21gWU6om8KcP7IUFAKSEklpESV3bEMzucgRlVOssjIjv0GKttLMxSyo6UNhZt2zWm9xQ2Hm4GhBUqO5prqX4uUnZssJVDv98Wz67s4ELCJJ02p6SIq9i289yE2uEz12221TIGLdTemmdmB7U2Rtlh7Q0bWRYe70N2qOdKEmVHlB/kO7CMMgIut2x22qjIZBLUnqorr7wy15MZY2uAMAsQQdufPgBxj+gwv2hQWyosnFSDmnY0+MT/vFNAiq6eJVTtZgXrlR2nl/p/vprscMOd+aQDiG0x+GCJyA7bpLhs3sDirTdH/1Y7fFuiJpekIDEbSy9lB4PYEeGkgFJ2AOgZUpp2knHybAN/8FffQsU2McHC3fUEgy2gGoYMIuBv0frZpTUXs1rqmjI0TyOahIrilR1SSjQdPwo8HEnZEdlYBt+f2soUx/xsuWPhzRuroOXJzBNyapVgIOgbBNjssLGMWj2rg9taI9hYAlgk/8UXCytTuddNdoStIBmUHa4XqPYYohfkeZMd8yjzGrw+FqQ4VurqWpuuZj+HiYrZt43F1LkuSdUPZgk2fARB78wOxwv6NrEAPWwshIIwE2JEZQegbLtFjoebAWmrcYNnUHYYJP9A4GGgYQtOSbSGfGd/+IFIZa/qOH684n07kx2hGk8HQwM6O6xAssMdfO8mhc7s2AgYM/MgZqlvbgcPSdQkmR00Una40fOnyPabzUQmsuPAgQMAAMdJXw81xtZHZGUYMsEN1hZRv/dL4E4z+nO/gVYKDl5fhjFRLNmx09G2sRRDdgDt3AqNatnEZMXEqcX+Mk0ZDlCOCG0sCZPlbYvBk8kCSn/m2QchjtwFYhjY/6p3Y/oHfwKtI/dBFkJ2MHiB6AjL0mTHdt49iSMiO7q67YH0XtlauLCYqLQn8UmVHST0HPutrRmut7TmYNdUO/8i0+cf2UGKV3Z4gUDAZdRKsRGZHdNVG29/7Q0R4aEzO6ar7Yk7b6xkzusA2gSE8HvPOzpsLCMrO8KJt+vAMrLbWEzCc39Wa7Kju7kgWsynJjsYWi7vsLHkTaYbM3tAIVH2hu/Wr9RclCyWOPepFybKZn8bi1Z2JMxGIJYKxvVj966+H6hhwhkihS912VgANceK2lgyZnZ023aLGA83A5rsEM2UZIenAko3muxgFVXxWSEt8IwLRVU9G0BSlnp86VR2bF8bi/49hBsjO1ixDSGOx3OxsQh342wshFBY8xf0JTu0/SutjUXbH7s3+XYKMn3Kz33uc/Hwww/jjjvugO/741DSHYZ2SKUP9PE+64F26Uv/AFadwdJtfz/QL8obq4AUhWZ2nAvYCLJD51bEMax+Vk+8HaEeKUmqZwG1C+lJNjQjxvM5fv9vD+N3Xn0NrnjhRTh595dw4Y//fO42JWpaCAIvCvlTk9BwEAgk2CYEoBUFnZ0Q9GhkSeuVrTU8VEoGDEbhRjLKZAGlWv7pN9Ol728EXJ+j0fIjZUfmzA79MxvQxtIMF3lRG4tWdhRoY6GU4NC+KXzgzTfCD9TCbbpqd8hheWM1c14HoDI7AAB9FnPKxuIDhI78fIzIDr8FyzQQcAkuJFgKea8bkR35zo8MqwQXWLexoCepae0QJVspO4q0sZjTqn62yleHfu9q3c1cO6sxUbbW21jC94fJdMoO1QLkwe9QdrTVUi2XY/dM/2dCdxuL+jkbvK7a6QhTyo5MNpYdaNtlpg1f0kzKDpOKDV/wE8oQGBVMEDeqF04LTXYgQ8veTrGxkB7KDsOg8FvFKA38QIALmaOyI9l8Jw9Y84fQeOAbkFKuC0vPZmNxYWllxw4NKc2k7HjlK1+J6elpLC4u4o//+I/zPqcxNhlJpMvaLzr/E2+Kal7nX/zGvgNtENbOsoJqZ88V0MoU5l/66+tCzvKEzq2IY++uKk4NqJ+VIdnR4mrgSB5QasATbGhAab3lww8ETi21cPJvfgctxynEpqSJHk1waB+8lBJchE0F23hCEYcRKTvWkx2plR2xph6t7EhqY2GhsiNoZQ94KwrLa2Ht7FS4MMqa2REGlEIUP5FoOGoxV41sLO3miLRokx3DSRpKCWYnS5ifrWB2srTO9zuqsiMKHQ36KDucMLPDqoxcARjZWFynPQlMueOlqjALUHaE3vbuzA7X5zANmtpvratnNTkD5B9QaswosmNSJlB21N3MtbMa1bKBerOrejbcdWcyJCoSkh3UKsEiAYJgvbKDGBbcsHq2H3qSHfH3lyllRxYby0607ZomQ0OWEKTO7AhCsmPjx2duVjFBnEzZPgDAuVQ2lpThpEBXG8t2rp4NlRGyw8ZSXECptpXlUj27gQGlgCI7RKsOHq6r4tA2FlaeHPo6NCokcCNl2U6tn81EduzZswfve9/7YJom/uzP/gy/+7u/iyeeeCLvcxtjk9CrfrQX1vlF77kN7qnv9/xeXlsEgEIDSs8FsPIkgtWzKB188lDrUFb0qh7eN1fBwnILQsieP6PZ+EbAQAgSs+W2xeAKOtTGUgsnrtNUHUeUipnU6d9dKzu0V18ICSmxo5QdtDIJUNaH7GAIUlguak0fkxU10eLhzgJLqOzQ31cEcTcqliKyI5yIZVR2RDkvYgOUHY6axFXKnURFtsyOztyPUTCqskMTEIbwe+bJaBtLHpPOuH9cy3uTNInE4XpBIYGJhq3zRLoW84GIiJk0sC0GLiQajt9uY8nbxjK1CxIEM6Q2NAtote5mrp3V6KnsCCfxTCQPKAUU2aFsLDFlR0cby7Dq2dDGEssrib+/gjJImV6Rs1NhMoqGsMEbGZQdZOOVHQAg7AlMUCdzyLYfKjuykIzUsKLfmWzjPLG2dbDbxlIQ2eGqz2pUskMKDuk5GxZQCuhGFvS0sujg4yTKWsJMgLKQ7NAK5rGNpQPPfvaz8Wd/9mf4hV/4Bdxyyy245ZZbsG/fPuzduxeWleyGJYTg5ptvHv6NY2wo9AN3kLUg7hc979V/gPqD34Rz5D5Ur3gmWkfuRfnQD3R8f1vZMSY7soI7DTQfuQPLX70F8y/5z1j49B8VUjVHDHvdZ793VxUBF1hac7B7Zv1iQttYGpyiZBmJd1Zti8HlwwNK6001uZyUagJUONkR7eaqgVbvClKIbR0CFgchFKw6jSCPzI6mh0mt7Gg1lJUg4aJTkx2qMm1rYXlNEYmzU+02lkilkQKEEAhQIEHo86jQi7x1bSyZMjs08T1asKrwHEjfGc3GEpIdNlE1qUa5c3HYCm0sbMS8DqC9y6jbWID08l7X42ClIP/8izCzo1sN5wUidV4H0J7srzW8wgJKCTPhW1OYcxvwfA5jwMJ+pebi0oOzIx1vorI+s0PvujPuIUDC6lkAzC7DJn7HIiCulhqW2aGJ/3hmR9xCw8NpeNrMjp0K06RoSBtBWhtL1H60CeOzPYkJspyaENUIAhFa3rLN42ipAt5Y3ZzfPSf0Cig1CmxjaSs7RrOx6Llv0vlOHoiTHZVLru34GnfqIFY58aYcMW2ImLJjp9pYMt8Zd9xxB/7rf/2vUYCflBInT57EqVOnEv18L6/RGFsDlA1XdrBSFRNPezasPRfAPfUY5m68CcJt4sw//TGcY/dj/yt+F6UDl0ffz2uLAGUjyZjPdSiP7vNhzOzF6Y/9AYBiquaIYa63sYSNLKcWGz3JDm1jqXsUZTv54FGyGBxBO6pue0ErO8pcsdaiNFyilwXE7LKxhBPcINzVo5KDsPyIpc3Gnhf9Clbv/BwARaZp4kG1sSSvIas3PczPhgFjTh20VFHNPglglHUw2dYjOxbX1MRrLtbGkrWNRxC2IWRH09GZHV1tLCNldoymSIlqZ0cJKDU12eHD9XkUwKrRcgLM0ACsNJP5GN3HEmEbC4DUu7aOx8EQ5K+S0MqOrmem5/OImEkDPdlfbbjtgNKcbSwAEJTnMNeoq1yDUu9rUQiJ1YaXg43FhONxZQ8JiRVtQ6FC21iS/Y6K7AiiMQBov/eCqLYIe8DusGVQENKd2REem1AEUj1fBxFA5xJMg6Eh7ChoMSl0G8umKC9LkyMpOwIuYJMAxMyW+0DtqiI7tvFGDLFsAKRT2bEhNpbRyA4Zzls20sbCSlUYU7v7KjuShsMDYSGB70ZjR9ZreKsj09P12LFj+OVf/mWcPHkSADoaC6SUif4bY+ui3cYyeIIbrJzGyb97B1h1CtS0YUzMYs9/+E9gEzM49Y/vhnuybWkJakswJmYTL4DG6A3RqmHxX/8q+nsRVXPUtNZ99vui+tneC1Lht0BMG44vU6Vb2yaDK9jQBHntv7Z9FXBXGNlhWJDch5676oE20BJouXNsLMHaIryzx1G9/AfXWaI0y590V2Wt0baxCKeROK8DAMxyqOzYgmTH8poLgxFMhTWYUmRsYwGUskMWP5FotNQkbr2yIwvZoVtkRiU71H1r5KDssEgQTVTjaLkBytSPgu5GQdw/nsXGEnAVfkdFUFj1LHqRHZlsLKGyo+4VFlAKALwyhzla71A4dKPe8iGExPTEaMefCIkwrQgE2uo8Erggpp14LkItGxaCjmehuqcInEARFYMIfkIIShbrbGMJ319imNHYMlZ2KJhMKTtSkx1emJGzCTYWUp5EhbjwejyXkiDgEiaC1LWzGjqkdDu3sRBCQaxSV/VskWRHaGMZsY1FkzOjNoClhTV/CG5PsqOWav5FTFs1ELKxsmMdbr75ZtTrdRBCIKXEs571LFx//fWYm5uDsY1lVGMoJM3s8M4cAwBYuw9G/2ZMzuK8n/09nPjrt+PkP7wL573qXbD2XICgtjQOJx0R3VVztcO3oXXkPsz4bs42Fguyq/Ztz2wFhKBv/axwHVCrpHzzaciOsHoWgkMK3jcPoRZOWk13FYFdyRTklQRacm5SRchGZIeeKINva1+shr6Wlr/yMex50a/gxN++o8MSFSc7hu0WCyHRaLVtLLxVT5zXAQBWZQI+AOltPbJjac3BzGQpUiFKnjGzA4AkDGQDqmcjZUcOmR2IwqpHs7HkoeyIbCzwey6YNdmRxw4boQzEsCC8FuwMNhZ9flQEuS9AtNS928biB8Pv1V6IlB314mwsACCruzFDm3BdD0Dv58NKTSkER8/sCMmOVlslom0shHupFpXULCkbS4zsktwDMcyIABvm+7dNo4Og0xtKxDCj62qs7FAwDUV2wK1DSpGYlHI9DrZJmR20PAVGJJqNGoDdqX8+4AJlErQbp9IeX4eUbvONGGqVITuUHaywxbd+Ro9uY9HKjo0nO5rf/w5k4HeM7dxJqeywQhuLubMDSjPdGV/72tcAKMb6j//4j/Hc5z4315MaY3MRtbEMqwM9cxSgBsy5/R3/bkzvwf6f/T2c+Jvfwcm/eyfOe80fYOaGF6N2+MsAOuXyYyTHRlXNqdyKzom0aVDsmi7j9FLvEEnpOyCmIjtSKTssAz5Y+BpeX99jrekpO0VjqdD6Yj0JtcJJv5b0abKDimxtHFsNuk3JmJ7H6Vv+EECnJSoNy990AwiJWBtLPdXOgmWZWJIG4LeGf/MGY2nNwa6p9gRU8uyBk5IwkI1QdoRkh74PI2VHhlrDSNkxYkCpVnawiZnMr0G6Mju64XgcJXi5TTrVLqMTEX9plB2OF6jmJojMu7V9z8vsrexwMyo7dE34asPFdEEBpQBAJnaBEQl/dRE4r3cmx2pd/U6j2lj0syie26GfZSRwUy0qiVUGIxI8pnZUCwyrvTs8ZMFkdyk7dGYHYVY0toyVHQqmoQJKISWE00y8cHN9vmkB4prEDZrDq5V7QVfPUivbdT/zrJfBOXo/iGFt6/k1tcsReQAABiMdLUh5ouXm08ayacqOvRcCgsNbfAL23gvb59Oqw5i/IPHrEMOG9B1YoXU72KFkR6an66lTp0AIwTOe8Ywx0bEDESk7+OAJrn/mGMxd5/UcXMy5/dj/ineATe8Bb9bgnX0Ck1c9t7AGkXMFG1E1p3MrurFvV6W/ssNrtZUdKQYP22TwpXrIDrom6mHbB68twihQIaSv/YjsCB/80UR5B9lYVJvSX0V/j1uijBRhVbWGulamqjEbS4qdBdNkcKQJeFuP7Fhec6JwUgDAAPXRMEi6MWRH01GEIwuzVqLMjiw2FkIBZuSX2VHJQdnRh+xoOQEs+LlNOqlVgvScSNmRxsusa2eB/OsgI1VA12eSVdlhhxaMtYYHixZTPQsAZHIPAMBfWej7PSt19fyZHlHZoS1c9Q6yQ1XzCt8N8wGSQV930m9XHsvAB2FGe8E0hOAvWayDLGvbWIzoGatzos51RMoOAKKVvH7W9TiY5JEabSNhhmQHb6Sry9UIAgGLcLAMxGiwtgj31GOYfc7LIdzWtp5fU6sM4bbvM9NgEBLgfVoAR4HrJbt3h0Hbb2kO9sk0aIeUPt55Pk4dNIXNm5h2mFPX2UC405CJ7LBtdUOef/75uZ7MGFsDSdpYAMA7ewzWnoN9v27tOYi9L/0NOEfvw/JXb4HkPk787Tuwdte/DM1oGGPzQAyrZ2Do3rlK38wO6TkgVhmOG6CSYvAoWQy+HL57XGt6mChbCNaKJjvURMmEGgj9LmWHIju2ry9WI26J2v/Kd2Lq+hejdeS+6L5Mk8ytw2P1bip36olqzzRMg6IlLSC2mNgqWFpzMNuh7BjRxrIhmR0+qqX2PThKZgegfOCSj2pjWQUtVUda+BNmQlIWBZR2w3VcmAhyJDvKEG4rFtyWzsYSWULytrEwE1IqO0bHMTMHlOrMDhclFj7nClgwmjOK7OBrZ/p+z0pNPX9GtrFUemR2BAKmQSH9dMqOqLXFi5EdPFR2hGRHeZiNxWLR9wJxG4sVZYkYbBzYD2iyQ73nPKzRTALXD0A3SdlhTiiyIw05E0cgRPjsSmdjadtRPwpjeg9O/t3vbev5tVJ2dGZ2AMXUoSZVZQ3DZik7zLn9IMzsCCmVUiobcRobi9llY0kxzm0nZHoqXHjhhfjud7+buHlljO0FkqCNRXgOgpUFTF45WNljTu9G9Ypnwpjei9MfWy+XH2PrgYb1q93Yt6uKpbVjPVP/heeAlqrZMju0jWXA9VZv+pguU/D6Koyp9J7YpNB+eLNL2cHDJP6douzQlqjqk38IJ//+9zD3734Os896aczGkrxzXZMdk2ULUkoIpwGWJqCUUTjSRCnYWsoOP+CoNf1OG8sINiZJGagsfiLRdPworwOIZXZkPG9imMCQsOph4I2VXJq4SJif0CuglHt60pnPDhuxyhB+28aSStkRJzvyrnElBD4x1pEdvs8zWSHabSweSlWVeVBEkLg5vQdCArLWn+xYrbugBFH+T1bozA5t6QJC5YuhwrBJwtpZoG2fii8gZeCFtbM65HDwgqlkGT3bWAiLB5SOlR1Au40FAHgzjbIjADM2Z3y2JqfhAqlDVTWCQMImQTt8OCG0HdWc3YtTHy2uoW+jQKwyZMwKFGWHBQLIWWym70d7ZLJj49tYAJUrZe452El2eA4geCplrVJyu7E5384kOzKNaC960YsgpcS3v/1tnD59Ou9zGmOTQRO0sXhnjwMArD3DvWGEMizd9rfR34toEBkjPxCjt41l74BGFm1jcbyUmR0xG8ug3Yhay8OekgdAgk3NJX79tNABpUyGyo6gS9kxQhvHVoNqUJoBb6yi+dDtHROkVDaWcPd0smoqqbfgqZQdlBK40gQNtpayY3lNXY9xZQf4CJ9/qOwouo2s6QSRjB8In+PUyKxIIbnYWFbBRmhiic7FLPW1segdwbzkxNQqQbqtbDYWj0c1rkVYQgKY62wsXriYTws92fcDAZuJQs4XAOySjVVRAeqLfb9npe5iasJOVHc9CNVebSyBgGFQRcynDCgFVNaHRjuzI5nv3+6ysdCQVO8IKDXGyg6g28aSnDwIfE3qbrzysjQ5o/7gZCU7fBhEpLouNURzDWf/5S+jv2/n+TW1u20sxTWEOG4AQhA937NCkx1pCNS8YM0fgne6TXbw8PpL18ZSgvDdqGK9CBXNVkAmsuNlL3sZDh06BN/38Za3vAXN5tZL0R8jO5K0sfhnjgIAzAE2FmB9g8j0D/5Eh1x+jK0HYliAFOuk64PIDuk5gFlSIYFDdrniKFlGQhuLj92mWswUamMJJ/pGaGPRkr6otnCHBJTGYc7uh798svPf9CQjQfWsrgWerFgQjgqwTTPYAoBLbDC+tciOpTV1PnMdyo4RbCyUwYBAwIslOxqOj0qHjcUbzT5irK+iTgtFdoyu7KBWCRYJeoeFRjtsOdpYYtWzaVLqXZ/DIMVkdgBAAANUdJEdPo8mrGkQX6jbRBQSTgqoRcWSmABt9Cc7VuvuyBYWALBMBsugHZkdXpjZocK0kx8jyvdYR3aYkTVlmJrRNlmUEQB0tbHogFI2VnYAnWQHT0h2BFxAcm0b2/jx2S6X0BImiJuN7NDzYZKyjWWnza/1M1fDKLAO1fE4ShaLmtayQngtEKtciBpuGKz5Q+CNlSgAXLTU/CutjUX6bqHE0lZApqdCuVzG//gf/wO/8Au/gLvvvhs/+qM/ite+9rW4/vrrceDAAUxOToLScbL0tgVlAKEDH5je2WMgzIQ5u3fgS21Ug8gY+YHElD3xicO+XWq3/vTi+kYW4TuQhg0ph/uX47AtFrWxDMqIqTc9zDK1mDGmdgMDQu5GgZ7o63BBHdYUJVTvIGWHhjm3H63H7+n8twwBpRNlE8FZ5bGm5XRp8B6xwPjWmqD1JDs4BzJWDxNqgBEnCkosCs1WgPN3tyc73dV0aZGPsmMFrHrVSK8BqJ0/G80OS4AGCZVB/Rqd0oJYZQjPiQiENMoOxwsiZQfN0IIzDAExQEXn89LzMwaUxn7GorwwssMKyY6DraW+37NSczE9kc/xJypmRMQC2sZCIbyUmR2mup4oH2BjGSKF729jsaKxZazsUDANZWuUhEI0k5EHXsHk4jCYBkVdlkDd5BkjceiNHppSVbXT5tdK2bE+syNIsOmSFo4XwB6xiQUApNvc8LwODTsKKT2C8kVXQjh6/pXBxmJoUn9nKjsyfdJvfvObAaiA0oWFBZw5cwbve9/7Ur8OIQTf+973spzCGAWCEKJ28wZMcL2FYzB3H0i0y0lNGwh3UuJ/HmNrokPZE1s4zE7asAyKU71sLK4DQdXPlUvpbCzeEGUH5wJNJ8AUFMmiqmeLJTuotrGEyg412MqwjWPnkR31w1+E8N1IRtvhlR2CWhiIyRiFFyo70mR2AEBAbBhbTNmxHJId69pYMu7ASsrANkrZ0ZXZMYq0mzBzJGWHCDwIt5mLjYVZJdhkDatdZIcfcJgyXDBYebaxxANKM2Z2FGAL4cRcp+zISqJRSqJqVJtmr1YeBsukWBJVmO7jKvumB1brHi67oHctbVpUy9a6zI5I2ZEiG0HL00mc7OABqFlN18YSJzsiZYfRVnaMMzsA6PeBgJvVxMoO1+MwEKovN8HGQghBEyVM+hnJjnCjJ0tN9U6aXxOrDIggIuiLtbHwVBtz/SDc1obndWjoRhY3JDt0oC9L08Zi2JCBB0YlCBkrOzrw+c9/PpL+xCVARXuRx9g49Ksf1fDOHkP5gqds4BmNsVHQC34RuIhPvwgh2LtrfSOL5D4gAgRE/Vyq6llreGaHliJPiDqIVS6URdeTUCZCG0vQJjsoJAjkjlR2AECwfCoaPNPYWGoNL2piEeFgmyazAwB8asOQPuQI1a55Y3HNAaUE09X25HEUGwthDIyIQj2xUko0nfVtLKPZWEZrYxGhxDYfG0sZJbrextJ0ApSIek7QUn5kh/AcMEpAKUllY3G84qpnAYBTM8oVAtTn7vk8s/9cL8ZN8CikOW/YJsMSnwCBQLDW28qyUncxPZmTsqNsdmV2cFVl6WRrY6GxQFil7JiB43FQAlhDSCbbYh3KjnZmh9XO7Bi3sQBojz3cqCTO7FC2MU12bM743EQZs/561WsiZLSx7DTovCXhNsGMaZiF2liCkcNJAUB4m6fsYNVpsOpMFFKq75c0yo6obYoHMBmNNvh2GjJraaWU6/7bSnjve9+Lt771rQCAr3/963jxi1+MF7zgBfjQhz60yWe2PUCY2bN+FFAPIr52FubuwXkdY2xPRAG1PUNKqzi92El2iLCSzydqUp8qoNRi8DFY2aHJjjJfC1UdxUEHlIJ7MBhpB5QGEgybO5kqCubsPgCAv9TO7Ujjla01PUxW27WzQPrMDp+qAVeHfW0FLK+5mJ3sDEuUfIS0f2oUruzwAvX6lVhAqQi8kawJStkxuIZ8EIKI7JjJ/Boa1CqhRIJ1bSyOx9tkR17KDrussosCD5ZB0yk7/Hj1bDHKDhZTdnAhISSi+sC00HJuk/DUUvqkYIxiFWrHMVhdr8xzfY6WG+SS2QGokNJ4ZocKYA3VeSnCBCOyQ/QOKC3ZxlDfv20ZCLgA19lPscyOcRtLJyJVoVEBT2hjcePk4iZVwzukDCvIOH6FqqGi7r3tAq2Q0Lkd+p4oguxww8yOUbGZyg4AsPYeapMdTvrNpkjJ7TkwTTa2scTxb//2b3mfR674xje+gU9+8pN4znOeA8dx8La3vQ1/8zd/g/379+OXfumX8KUvfQnPfvazN/s0tzSo2bt+FIg3sYzJjp2IQQG1++Yq+N5ji5BSRhM8GfiYf+mv4/iJRezfXcaTz08+kbSMtrKjX2aHrja1vDUYs8WSHfHf3TRYNMj6XEQ7RzsuoDRUdsTJjjTy0XrTx2RomxCRjSWdsoMbNuCpiQMrJ5dgFomlmtPZxAIAPMj8+ROmyI4ilR3NcHG3Ttkxio3FMKPPNQt4YwVAPsoOYvVuY2m5AUoktLHkldkR7rJKz4Flst6hqH2gbCyKkClC2SGoCStoLwQ1EZOljQVoZ04YCArL7ACAGlNZAsHKAoDO62G1phZ8eZEdExUTx07H3qNAYLqsnmdpWi+0tYCtU3aYcFrJFkz6e1yfo8JoR/WsVs+NlR0K0dhjVJLbWGLKjs0anx1agcUbHXOjxAi0jeVcV3YoolpvoOkcG5/nP2a2UjYH9oNwm4WG5g+DNX8Ia9/+v5CCg7fqIIaV6fkmAhVSOraxxHD++efnfR65YWVlBR/60Ifwy7/8y3jggQdwzz334NChQzh4UC3MX/ziF+Nzn/vcmOwYApXA34fsWFBNLGOyY2diENmxd1cFTSdAveVjMrQuSO4jWD2LXRc9Ge+6rAw8+g2IyecleuBSSqKHrQz62FhCKTJzlmFMPinT75QU8d/dMq1oERFwARbtHO0ssoPaFbDqNPzlU9G/6R2VIMHCfK3pYe+ucJLi1AFCQVLKOgUNB9wtpOxYWnWiBiKNUWw2hBlgpFhlh84oiGd2gI8aUDpaZkeb7JjJ/Boaqo3FX092hDYWCZLJ997zWLFdRstkqeS9jhegbKjPuYjMDkFNGDEbi26NyhJQCsTJjuICSgHAMaYgAfirZ4Au8mulrp7/05M5kR1dyo4gEKgwnaOSfFFJKENADLBYIKzKwbHguEEi26aWyzseR6VkjpUdAxAFJbIyRPOJRD/jenzTx2ePVUC5gHSbICnJfp0Hk9eza7uC2J0KT91QVJSyYzaHZ41wm5ur7Jg/BMl9+EsnIVr11KpaPU+XvlIw7lSyY8dVpvzu7/4u3vKWt2BqSu0gLCwsYM+ePdHX5+fncfr06c06vW2DgWTH2WMgpg1jZn6Dz2qMjUC04O9pYwnrZ0MrC3caaD50B5a/egtKwRq8f3oPanf/S6rqs0G2GUApOygESGsNrGAGncTOJa7s4FzsWBsLABiz+0dQdngR8SWcBmipmnpnixvaq5tdQZA3lruUHVIKQIrMAbUkDCgtVNnhqAVwtcPG4oOOlNkxWhsLzzGzg5glmPA7ajwBtUtXIj5glkeuEtSI/OOek8nGEpEdBdhYBDOjemygnaA/LDuiH/SC3UBQCDmjwUwLLTbZ08aiyY48bSxNx4cQ6nPwA44SDRtyUgSUAgAnFgzZHp9E4IMYhrruEuwOR8qOkKQbZ3b0h87IcUkZvFVPZJF3vKCd2bEJbSyAUqIAAG+upf5ZEj5fz3kbS/jMlZGNJXlQelo4XjKichiE20q9uZMnrPkLAahGFu7UU+V1AG2CTTey7FSyI/dZe71ex/LyMprNJi6//PLo3zNJu1Lilltuwf79+/HMZz4Tn/jEJwAAQoh1Iappz+Pee+/N9Tw3CnfeeWfmn51ouUDLwYkerzHx6H0g5Vncddfdo5zeGFsUbOUEpgA89MD9CJY6FzlLy2rC9/U7DmN1oQJCCA7OXYg9L3oDFm75AwDA/M+/Dw88dgyOk6xdQ0JCgOLE8aP4fo/r7XsP1jBNmwAknlhu4LHwe0a5vgdhhqhzEfwKnDp9BnfeeSe+/3gtmkwdOXYcnijm2JuFijBhnnksek+brvpdv//YEdxp96+KFEKi1vRRWz2LO++8E9UTR8GIkfqzqblqEfDwfYfhn9n8VpaAS6zWPTj1pfbvIgLMAjhx6lTP63ToazabMCBw7333Y20h2WIr7fv46En13h07+n0wR+2KTq6tQNgTPZ/lSVBZrcFoNjLfb+XHHoLNLNx9z+jjqH1mERUAK4tnO87ne0ebKBEfAWG5PReMs8cwCeD+w98BDyycPruY+LVPnFzCPqhn5z333Qdp5rvz1/Q4DOlH53N2TR3rieNHcadxNvXrOWGwMAIXS6s1HC/o2Sq4h1VahXns+8B5P9Txfn73UUV0Hnv8IdTOjD41XVmsQUrg67ffgbJFUW84aNXUQvT7R4/Dd5L/jgYYaOBG5zvju1g4u4QziysQQg69Lp44pjYH7vrOPdg3a6EcNHDBS38dRw9/G6vmWfzeq6/EvYfvAdtx24/ZwAhwthngoAhw17e+AQwJzb3/aDPajHj4ke8jWN743IE1X3149931LfDZA9G/J3lmOHV1Xd73wMMQR88Uc4LbALR+FtMAHnngPvirwOkV9Vx78OFHYLgncj1WreGgtrY82nghJWa9Fk4vruBIQc/MoRABZgjFke9+E8bySQDDn0dxGItHMAnggXvvQeDbWEgxzgHFzcPzRi5kx913341//Md/xLe+9S2cOqWk0PFa2RMnTuCnf/qn8VM/9VN47Wtfi9nZfKrFuvHZz34WZ86cwUte8hKsrq6i2WziiSeeAItVBZ45cwbz8+kUCT/wAz8A295e8rI777wT1113XeafP/nQ/4Vo1XB5j9c48tX/ifKTrsJlI7z+GFsX3sJuHP8mcPGFF2DiyZ2fcdPx8af/97OoTu/Dddddqr7/7HGc+tj/jL7Huf+rePKNNyX2DU7927+BBwb27prF7h7X1INnH8A0fRgAcPHTrkXl4mtGvr4H4bEv2Ni3ew5TT1QwMVXFddddhyNrj4DdrexbF118CSaeurOu/eXWY1g+cRjXPO2poFYJjhsAHz+BffvPjz7nXlB5Kk/g8osP4brrLlbPDezCpSk/m6/eeRo4A1x08DxM/sDmv7dnllsAnsAPXHERrrvuQgDKzvD4vwDnX3AIMxmuvQcf/iqcs4/ikksuw9Mu2T30+7Nc4853TwA4i2uveiouOk8pKY7d+Tcw53bhioz3y5mTt6O59kTm++300S/BnZrL5X5dwxmcffALqNhGx+st86OofceHNTGT23PBOT6BE3f8Iy678ALM3FdHtWwmfu1/vffbmKhRQABXX/eMVB7qJFi6/YswXY6nhufz2IlV4J9P4/LLLsZ1Tzsv9et94Xt34KEnnoBFBXbt3d/zOZwHpr/8RTT5HMriDOpAx/v5/ZWHACzjWT94XS47rsv8KD5/19245LKnYN+uKsg/fQ57ZyeAOnDpFU9F+aIrE7/Wd28rw/I5rrvuOkgp8djnOPadfxDmQhmTVWvodSErp4GvfhMXX3I5rrhwDv7KAhoPfBMXPfMFOGDPwnv0Dsxe93ywlIqTnQr7k6dhTe4B6sCVl18Mc2bvwO9fEUdxmBwGAFz+lKeidODygd9fBL52+1FgGbj0gvNQvUxdD0mf4d/70jeAFnDltU/PRQG3XRGsLeLoV/8XLjx/P6auuQ4nztSBz57GwQsuxHXX5Wub5//7JA6evw/XXfcDmV9DuE08/nng/IsuzjQnyAvH7j6AKnURmIAxs7/nuq0fnOMTOPHtf8ClT7oQ099bQ7U8/HmmUeQ8PC1c1x0oTBiJR67X63jjG9+IV7ziFfjMZz6DU6dO9WxnOX78OM6ePYsPf/jD+PEf/3F84xvfGOWwffGXf/mX+Od//md8+tOfxpve9CY897nPxYc//GE89thjOHLkCDjn+Od//mfceOONhRx/J4EYvdtYeKsOXl+GNW5i2bFo51ast6JUSiYmKxZOLcVsLI/eDWIY2POaD6B07X+Ac+S+VDYW21Se6H62qVrTw76Ser2NCIKipgUReDBi/sVgBweUArGQ0jC3I2n1rA6PjapnnTpoOZ1fGQCgg8mcrZHZsVxTCokOG0tYv5o1s4MaBihEoTLRKLMjZmORgd9uGcoAVT07WmZHHnkdAKIWDel3qn8cTwWUshy9020bSwumSdMFlPocNtOy+vyl6ZJZMAiHFOqctMUma+6DzpSgIijUBmBbBtbIpKqeFZ33wWrdQ9lmuRAdgLKxAO02Lz8QsHVobIo2FgAQ1IIp1evoe0G3sZQTnG/cxsKdBpoPfxvLX70Fwmlg5X+/G63DtwJ9MqvORZgGRYuE+Q0JGllcf/PbWISl7AO8uZr6Z2nYrFSkhWw7QDcf6TYWI4WdNg2EkOoZPWIbi3DVeebVAJYV9rxqZOGtOtjYxtITmcmOWq2Gl7/85bj11luH1s8eP348+vPy8jJ+6Zd+Cd/+9rezHjoVbNvGe97zHrzxjW/Ej//4j+NJT3oSXvjCF27IsbczSJ82Fv/sMQDjcNKdjEGZHYAKKT292G7doFYJu3/sl/Fvj3B8zrka+296O1hlKvHxbMtAgP5kR73pY94K07kLrp4F2nk1cZ9+R0BpxsyGrYw22aFyOxijoGS4V7bWUJ/ZVDWe2ZFusFUnoDM7tgbZsbiqrre5DrIjXOxm/PwpM2AQgWAIgTQKmk6fNpaRMjtMyCAY/o19wBurue1WUm0H6SI7VBuLDyML0dYHUVhe2MaSKrPD47CpAGFmIfZd2RUirQNK7awBpbYBQIIKv9CAUsugWJETgBSgTme2wUrNxXROeR2AamMBgEYYcO37HFaYc0JTtl4IZsEMbUk6rJcYJhwv2YJJEziOF4CVqpi8+t9j/sVvxOmP/SGC5ZMov+BXU42ZOx2mQdGEuteTNLKo6tnNzdSSlmoRExkyO6jQbSzntrJHk5CaRDBZMrJDCInlmoOF5SaWa06U09MPns8hJUYmVvV8ZTMDSgEVUhqsnQVvrKbO7NA5McJXbSw7tXo2M9nxW7/1W3j44YchpcTs7Cx+7dd+DZ/61Kfw0pe+dN33Pu95z8Ov/dqvYXJyEoQQeJ6H3/qt30rs6c+Cl770pXjPe94DAHjmM5+Jz3zmM/j85z+Pt73tbYVnh+wE0D4Bpd4ZRXaYY7JjxyIK6exDPuybq+D0UntRWrvnNix/6R9w8mwDt951KvWkzbZU/Ww/NUit6WHOaIFYpQ0JgiKGGSZTsw5lh0V14OAOJDtmFdkRxEJKDTY8mbsWLiT0woI79VQd7xrMtMAlhfS2BtmhlR1xsgNC7xxmV3awopUdLbWYK8eVHXzE6lk2urLDyFnZQbqeFZrsyFXZYbbD8mwz3Y6X4wWwqChOJRF+npqQ1hNU08waUMpAIUEgC11wWSbDolCTcdrq3AFfrbu5hZMCqo0FUMoOKSV8LmDRkKhIaReRhg0L6nX0uEgNE46brL4y3sYCAKJVx+K//XX0df+Br0CkUEPudJgGQ1OqzygR2RGrniXG5ozPzLbhSSNTQCkTPgIYIOTcDm0hlIGYpfUBpQM2CISQOHJqDb/xR1/GL7z7X/Ebf/RlHDm1NpDw0PdheWRlhyY7NlfZYc0fUn8QHLQ0mepnidFWdlhjZUcnvv3tb+MLX/iCCic8eBCf+tSn8PrXvx5XXHEFTHP94D49PY3Xv/71+NSnPoULLrgAAHDy5El85jOfGe3sxygM/dpYvDNHQawSjKk9PX5qjJ2AQdWzgGpkWVhugoeDSbB6Fsb0brTcAGU7/eBhmwweDIg+SpJ608csbcCY3LUhRCUxbMjAg2m2WW4/ELCMnUt2ULsMVp1Z18iS1MYyWbEgpYRo1cEyKDtMk8GBuWVsLEtrDihBx06zFKPaWEwYpOg2Fh9l2wCjsVDuwBu5ehaibZlIA8kDdU3kRHZomTPhXcoOJ0CZBrlOOmlM2WEaGWwstLgaV/263FPvQ1Q9O4KNxQxVD0UqO2yL4WygyNBusmOlnrOyo6x+j3rLBxcSUgKW1K0XaZUdNmwSvo6uYWYmWi6PLCqDoMkO11c2lsaDt4MYBg687kOoPuPF4CfuT2X93OkwDYpGSHaIhMoOi26uzdQ2GRqylInsoMIH34GK0SygdjlSdiSxsaw2XLz7L27HwrL6mYXlFt79F7djtdH/fnLCNi97Byk7NFLbWLSaJlR2FDk/2UxkIjs+/elPR39+3/veh717B4cHaZx33nn4wz/8w+jvt956a5bDj7EBUGTH+t0878wxWLsPjtUxOxh6stuPfNi3q4qASyytOpCCg9eXYUzuhuPxRLtc3ShpZceAzI5JNGBMDQ91zANtGwuLFhGdyo7N8QQXDXOuu352OMvfQXZ4jpKnZ1B2mAaDI60tY2NZWnUwM2l3kgbaxpJxMk3DHcfAz66SGIaG43dYWABABsFIC1i9U6ozS9Igz9pZoE120MDrsM22vAAleLmSHYSZADMgQmVHGhuL43GYRBRIdoS73hHZEVbPZlB2CCHx9Cfvxe+9NgyaK/D5ZpsMdGoX5l/662CuWsRyR1kiV+ouZibzIzuqZXXdNlpeO9NEEzpp1SuGDZsE8AMRqZwkNRBwkbB6ttvG8jyc98p3wZq/APfP3Aj+3P88trHEYBoUDW4CIOAJMzv0Y2+zxmfLZKhLG7yRnuwwpA9Ozu28Dg1qlaPMDp1BNGge4gciIjo0FpZbA39GKztKGTbn4tDnudnKDjY5h70v+00QZoJN7Y6eqUmgbSzSd2Gaw9W82xWZlR0AcNlll+Gaa65J9bPXXnstnvKUp0BKifvvvz/L4cfYABDDgvS9dTks/tlj47yOHQ5CiJKuD1B2AMCppQZ4fRmQAsbULrScINHErxu2ZcAVg2wsPqqiBrYB4aSAsvEIXyk7gsjGImGHZMdODCgFAGO2k+wwErD89aYPQlQYoHBUfWVazyigvPwtYYJvEbJjueZ2hJMCALSyI+Pnz0J1RTBC/sUwNJ0AlXLMwiI4MGLoZFvplZ6kaZMdM5mP33EuIdlhwu+YlDmOB4sEue+wUasEGWV2JJ8Eeh6HSXhxNpbwM/Ed9czU92laZYeWgP/BX34L7/8rFRy/1ORDPe9ZsWemjLf85CUIVs9i75OvRbC2iPq9XwL3XKzlbGMp2wYoJai32teKzt1IHQRp2rBIgICL6D7wwzLDJL5/OxZQCgDUtCNy41sPLuEP//F76c5nh0OpCgFariZWdthsczcjLJOhxkuZAkqZ8CHoztxESQtitZUdjBJQSgbOQ0yDYn6287k/P1uOLDC9oJUd+WV2bC7ZwWtL8FcWMPucl8OaOw/1e7+U3BZHDYBQRXYwmmqc207IRHacOXMGhBBcdtllmQ76pCc9CQCwupr+oTDGxkBN0iQQ283jjVXwxirMPRds3omNsSFQAbW9Fzd7d6kH++nFJoLVswAAY3oPWgmT6bthm0yRHT3IFSEkWi0HdlCHMTWX+rWzgIaqJstgkY2F7/DMDkApO3hjJbarkiCzo+GhWjLBKIl2EzLZWAyKltxCNpZVpzOvA6O3sURkRx/FVB5otHxUO/I6NEEzio1FKzuykB0rAAA2kZeyQ01qbRJ02EqCVjGTTr3LmDa4zfUDmCjOxqKrbAM3VHboxXxKZUdcAq5VD5/8ytGBEvBR8MNPmQU79h0sf/UW2KaBE3/7Dqzd9S/gXgtCIlcbCyEEE2Wzg+wwpA9i2umzEUJlh+cH0TjlC/UaSayblkFBSJvsiCPgInOLzk6FHntYeSpxQKm9yTZTy6RK2ZHBxmIgAKdjZQegLCE6swMYnh02XbXx6z97XUR4zM+W8fbX3oDpav9nieuGyo7c2lg2z8aibXHLX/kYjOk9OPmP78baXf+S2BZHCAExbYjAg2WyHWtjyfRU0Lv9RsYgICH0wDxmMrcq4iGVenfKGzexnDPol9kCAHtmKqBEKTuC6iIA1ZLiuMvrFohJULIYXEEhelTvNd0AE6QFArkhtbOA/t3dsI1FPat8LmCynU527AMA+EunYO+7KBnZ0fQxGaudBTCCjcUEd5PLL4vEUs3BpRfMdPybtrFkbeNh4XjJ/SKVHX5nzkh4D4+kMNBhmKOQHTkrO2ziw3E5JkNuoyg5MbHKEJ4Du8rg+xxSyqEWTiklHI/DwGj2oUGgYcBm0GVjSdvGEpeAm0S9xmKdFyZl/ur9K5iwLsVzf+KNOH3LewEAB17333HWUfdGnsoOQIWUNpptsoNJP7JCpQEJMz4Cx2krO6QiO5L4/gkhKFksks/H4QcCBhvbguMwDYZGywednEzUbuL6gVJe8hGfdSPANhkWRAm8cSzRcyIOQ46VHRrUKiNYPRP93TTowIBSQoBb/u0h/OJLnobJiom9c1Xsmi6B0v7vf17KDhmSHWmrrPMEK1Uxec3zYc7sxamP/QEA4MDrPpTKFkdNGzLMphrbWGLYvVt55x955JFMB/3ud7/b8TpjbD0QFi5gYgte3cRi7R6THTsdg8gO06DYNVPG6aUmgrVQ2TGlAkqzMOW2xeBJ1rPqtt70MEOb0TE2Arp22Yyx3EEgYOq0951Kdsx21s8mIzu8qIlFtBRRkaV6Vik7rC2h7OBcYLXuYnay28aiMzsyKjtCcp9nsIMkRcMJOpUdweihk3rxMJqNJR9lB2EmJKGwSAA3RhppOTEpxMbSgmUyCKnsbMPgBwJSAgzF2VhIt7IjJGXTKgTiEnBNdlQnKgMl4KNgZsLCMy6uYvHWdhPJ2t23QjaW1NdzzOwAlL1OKTvU72YIP1PbjCY7fKfZrvsV6r1O2uhgm0a0yIojCMbKjm60lR0T4K360O93PQ6LCYDQzMq7UaEyO0oA9yH9dE2TJgLIHZoFlhbUbmd2AMPnIY+dWMMd9y/gq995Ar/9J1/DqaXGQKIDAFp5ZXa4DdUSuEnXXHQezTWc/de/jP5eO3xbqnYnYoah/AaNVII7DZlGtKuvvhpSStx3332pCY/PfvazeOKJJ0AIwZVXXpnl8GNsAGiP+lH/zDHQUhVscmPsBGNsHlRuRf+H5b65qrKxrJ0FtSugdgUtN1tAqSI7jJ7HqzU9zBC1iDamNkjZwXT1bPvBH3AB65xRdoRkBxu8owKE4bHVsBkiyuxIr+ywTApHmh3y1c3CSt2FlMDcdJeNZcQ2Fq3sEAUrOzoyO/joyo7RyI4VENPOTeZLCIFkylIQtwRIX8uJC7CxuK0o+DNJSKm21xRJdmgbC/fUM9MLOChBaoXAdNXG2197g/K4Q533y3/8aQMl4KPgmosmYBz/DohhYN/Pvx9T1/8HOE88iFnUcIP1CKaq+b5fysbiRc9xKryIuEgDff0GTiu6D1yh3uukOVW2xXo2+vhcwGDnduVoN/QCl1YS2lh8DovKTR2bLZOhLsL7MqWVxUAAwcY2FqAzoBQYTnbc+cBpAMBLnn0xAOD46SS2p7wyO1q5jzlp0d3uNP2DP4HWkftStTsR04bwXUXqCwk+ZN63HZHpCftjP/ZjAJRc861vfSuazWS7cd/97nfxO7/zO9Hfn//852c5/BgbgF6hdN6ZozDHTSznBOgAZQegQkpPLzWi2lkAYfVslswOo28bS63pY4ap58tGBpTq6ln94A+4jJQd2KEVcdQqg03MtsmOBG0s9aaPybK2sYyW2eFIC9JrrQtF3mgsraldubmuXebIxpJxB04vfHmGVpOkaLSCjjYW/fweLbND21iytbHkpeqIYNqw4XdYAki4k5p3QCmxShC+CigFkCi3wwn94Ez4hdlYWEh2CE12+AKmyVKPzZQSHNo3hQ+8+Ub8+k1PAwDsn58dujOaFYePNvHpk+dh/uW/h8eWW5h7ziuw7/95K459+f/gFRNfB/vS/4zUQHlgomKh0fKjoGnGvYgoSgOibUNOK7JzeWFmR1I1Y8livTM7AlGYkma7Qtees3JSG4uqnt1MssPWyg4gVSOLlBIWfMgx2QFAqfO0PQQIN10GzEPuuP80Lj4wjUsOzKBkMRxbGK4EitpYRs3s8JqbXjvb3e40e+NN2H/T29PbWMKAUmBw+812RaYn7HOf+1xcddVVkbrjZ37mZ/DFL34Rntd7cXTkyBG8973vxStf+Uo0Go0o3PQFL3jBSCc/RnGIdvNCa4GUEt64ieWcwSAbCwDs21XB0poLf/Us2ORu+IFAwEVmZYcPAwjWt/8oG0sDMOxMWRBZQAwLIvBgMr3AEWpCusMDSgEVUhosnwKgdokHTTIAYK3pYVLbWJy6khFn2MU3GUNLmoAUqSXAeWN5TS0g17Wx6MV+RmWHVoSIgqpnPZ8j4AKVDhtLDsoOTZRkzOzIK68jgllaF1BK/WIyO6ilJt6WoZUdwyeB2l5DZQBaFNlhhzvI4b3i+zw6x7SglGB2soSpkiI4mFWMqgNQDU+f+toxLDoGHMcBNW0Yk3P41q4X4VOtp8N7/G4c//NfQ+vxezvqE9NUKcZR7QoopcLL5K9n4TONu21lRytIr+xw3PWEocrsGJMdcZgGw9xkCRM/8COAlBC+N/AacFxV9byZTWmWSVEX6tpKQtBocCFhEj4mO0JQqwzJ/YhU1MRXL9SbHh44sozrrtgLSgnOn5/AsQTKDn0fJsnbGQThtja9iQXobHeK/zkpiGlF1bMAdqSVJfMn/cEPfhA33XQTFhcX8cgjj+ANb3gDCCGgtP3QftWrXoWjR49iYWEBQDvYdGJiAh/84AdHPPUxikSk7Ahl0LyxAtGqj8mOcwTEsCIffC/o+ll/7SxK51/aDnzKQHaULAZfqoWgDLwOT3Wt6WOGNEEn5jZMUUQNG+AB9Djo+VxJjenOzuwAAHN2H5qP3KX+bDD4AwgvLiQaLT+ysYhWHbRUzfQ5maZSdgBaGrp5uyWLobJjVz8bS8bMDn3diIKUHQ1HTQ47lB08j8yOsI0lg40laKzCnN2b+di9QE0bNvGjXfKACxhSXad5XzfUKkF4MWVHEhuLrhcdsfJ3EFi4YNc2Ftfn0Tlmhd7YSF3LmgI6QLXbzrHa8PEd4xr86mtfgaXb/g7EtFC75zZMXvVcSLeFxkO3Y/Kq56VWZUyUTdSbfqTIIdwDNWdTnze1w/fbbUEa6n1yQrIjaQNZyTL62ljGyo5O7Jou4WU/uAvO0e9i9jkvB2+soPnIHX2vAdfnMCsCBJuXe2HFlR0p6meDQMBCALcgYnS7QSslhOuAVUwYA2wsdz90BkJIPP0KNcYcnJ/EvY+eHXoMx+MwGBn5vtsqZMeooGYJQX05yg7aiY0smT/pAwcO4Oabb8Yll1wCKSWklBBCgHMeTXbvuOMOnD59umO3dt++ffjwhz+MSy65ZPSzH6MwRG0s4QTI1+Gk49rZcwLE6F89CwB756qqqtCpwZhW4aQAMtpYGHypF1Sdi+t608MMa8Cc3rgwY33t66pZrVoxyLmh7OCNFQi3Ge6o9B/0Gi11feiAUu7UM6tvdPUsgIEk20Zgec0BIeubIaQYrY1F/1xRAaVNR92DHZkdOSo7smZ25K3sIFZZBZSGBKvjBigRdW60lLOywy6nJju0RJqI4tpYDNOCkIh82X4gYI0Yctm+VopbdFl9yI6VmovpCRvW/CHs+Yk3wjn+IJa//FG0HrsnqqdN40HXmCib4EKi1gztXNzLFFBqhAsw7jnRjrPDdRtLwoDSPm0swVjZsQ7PumIG7Nh3sPTlf1R1mn//roHXgOtxGERsWhMLENpYMmR2+AGHRQLAKE5RtZ2gCWud2zHIxnLnA6cxUTZx2SFFYB7YO4Gzqw6azuCxyvGCkVUd6hw338aSB7SyQ6sDxzaWLlx88cX4+Mc/jt/+7d/GoUOHACAiPvR/GrOzs3jDG96AT3/607jqqqtGO+sxCkd3G4t35igAwBw3sZwT0LkV/bBvVyXWkrKrTXZkGEBsi8FDqOzoamSpt3zM0ubGkh3hZN+mamLqBTwkO3Rmx85NzjfmwkaWpVNDg8FqTfVZTVXamR2sPJnpuJbB4G4RsmNpzcH0hA3WvQDRxE9GZYeWWGfJvkgCTT51trHkkNkRqfzSkR1ScIhmLffMDmZ12liaIdkhQTKFTw4CMUuACGCF934yG4smO/zCFl+Gqax/OtTZC3gkQe5GUjvIRpAddh/SaKXuRuQiK09i6roXYu9L/jMWPv5+BMsnsfcnfy21NBtoE7ErNaXWIoGbSf3DQmWH8Jx1NpakBL9tsoigi2Os7FiPr3xvGd/wLsXen3oLFj7xwYHXgBASb/qZq3HerA0BlbO1GbBMBhcmJDXSkR2+D0YkMFZ2AGg3aul5gGmwnjYWISTufGAB114+DxZmDB2cV/OP40NyOxyXJ25RGgThtkA2OaA0D5Aws8OKlB07j+wYmdqyLAuvfvWr8epXvxpPPPEE7rnnHiwtLaFWq6FUKmFmZgZPfvKTcdlll42DLbcR2m0sYRjXmWOg5cn8w+bG2JLQuRX9MDNpY95SzLsxtSfyQJZLWWwsRszG0rlzU284mCItGBsUTgq0J/tWWMXo+wKcS5imBJixo59j8fpZ06gkIjsmYmTHaMqOzqDTzcLSmoO57tpZjN7Gou0vIiiG7NC7WZWOgNI8lB2apElHdqgJv8xd2cHsMmziYy3cJW9pssMo5X5v6l07K1SOpLGxEF5cQKnJKDzJYIbksOeLnjaWYG1RWUCufj5Ec22gHUT4o18rw6BbbVyPd+y0rdZd7D/Ufnb0qlKcvfGm1DaWaqhy0jk8CLIpO1hJ7zY7kIF6nxu+BKXJpfAly+gbUGqMyY4OTE9YuO4CC2f/6f8f/Vuva0AIiSOn1vDnnzqMn/BPY4/pQp5aw6F9U4WF7PaDurYJuFlNRXZwJyTixsoOAG1lh/TU+2IaFPXW+rno90+sYqXm4ront22SB/e2yY7LLuhvV8tN2eHuDGUHNWyIwIueQ0nGue2GXPXY559/Ps4///w8X3KMTUK7jSXcOTp7DNaeC3b0Qm+MNoa1sRBCcMGkD/BQ2XFGV3mlXwjaVtvGIrqUHUF9BZTIDaudBdo2FjOm7PADAWbyHW1hAVRmB6DqZw3j0sFkR0N9VvGAUmNmPttxDVU9C6Cjdm4zsLzmYHZq/cQzyr/I2sZStLIjtLFUO6pn88jsyGZj0a0aeZMdRqkMmwSRJUDZWDzInFUdQHvibSEkOxJ4mV0vAIUApCjOxsIo6tKAoZWXPQJKdSXh0pf+Acb0PBb/9a9ADAMTT/lhoMeCf0OUHeECw/MF4p/Wal3ZWOLnTQwD5736v6F+31fROnIfZny353kPwoQmO2oOAAkEbqY2FrNUgQ+1AJOBCRAK11fjXdI5UamPjWWs7FiPay+ahHHkdviGgf2v+n00HvhGz2tgteHi3X9xOxaWWzAmORxO8O6/uB0fePONmO1BWBcJTTZycyJVQKkfNo8UmZWznbDOxtJHYXrn/apy9trL23OO/burYJTg+MLgkFLH4yjZI9r+pIB0WzuC7CBWqOwIyeh+gbDbGZv2hA2CAH/6p3+6WYcfYwgissNXDRnemXETy7kE5eHrT3YAwL6SCwnAmNyFVli3mDmzA+2A0g40loDwGBsFvbizoBaKUWbHJlfbbQSoVQKbmAuVHRTBgEFP++B1QOncc38OrKKUX2nbEzrIDmfzbSxz3U0saFfPZrYx0WIDSpstrewoKLMj5XnzxgoA5G9jCZUd2i7ScgOUiZ+pBWgY9Guasq2gGAbX5zAQqjuKsrEYFB6MaDOiV2YHK1Uxec3zMf/iX8Xpj/3hUDuIDDwQwyp0QyNSdsR2Dh0vQMvlmJ6w2ucdVikGq4uAlNj3st/KZmMJa7GXay4YBCB4pjYWw7IhJIH0VWYHMSylKEqxO9wvs2PcxrIed32/jk+fPA97X/FONL73ddDSZM86TT8QWFgOFaYQCCTFwnJrU2T42qLlm5VUyo7A1cqOjSVntipol43FYL3JjjvuP41LD85gJlYRbzCK/burQxtZHC/dvdsL0nMByB0RUEoMXT2r/u4nGOe2GzblCXvHHXfgJS95Cf7oj/5oMw4/RgLEfdq8tgTpNsd5HecQdPVsdxVsHHvMJmqyDDBjpIDSksXg6YDSrgAy5qyo/2+gskPXReoFi++HZAdE5nDK7QRzbj/8pZMw+0wyNOo6s6NqIVg7C3/5FCqX34BgbRH1e78U5QkkgWWyto1lEzM7uJBYqbk9yQ7k1MYiC7KxRMqODhtLDpkdUUDpYPKzG22yYybzsXuBWiXYCOC4YXZCaGMposGHhgtjTXb0atPohuNxmESTHUUpO4iy/vnt8+plY+GNFSz+299Ef68dvq3vfanJjiLRq41lta5+h9nYokXXJ9oHLsXanZ9D/f6vZzpeO7PDhU2C6LXTwjIZXGkAgQsZqCwWx+Mop9gdti0DARfgXQRyMFZ2rANjBJ/62jHUuAVql7Hytf/dcwfdNCjmZ9W/GxDgYJifLW/K+xmFGLNqujYWTXYUWPm8ndBL2dG96bLW8PDQUVU5242Deydx7PSQzA6PZ1Ihx6HnKTuB7NDPxCibage2sSSeuX/1q1/FV77yFRw5cgScc+zZswfPeMYz8GM/9mMolZIxkisrK3jf+96HT37yk5BSji0RWxjxNhYdTjpWdpw7IIYFSKEWeH0WSlNoYIlXUGv6UfVsJmVHV/VsHJa3CjBsrI0lnPCb4eRYB5Qy7HxlB6DIjsZD34KxV5Ed/Z7Va00PhABleKg/+C0sf/UWzL/kzTjxt+8YKJfveUyDwpUmJAiEu3mZHWt1F0ICs72UHSO3sYTXuCg2s6Oct7LDyKrsUBN+I2dlB7VKoETCd9WiveVy2MTPnBcz+FjhQkqErWSJbCxxsqMgZQej6pnJ2+fVHVDKnQaaoR1k/6vejcb9XxtoB5H+xpEdns+B8FCrdfU5Tk+sPydzeh7m7gNoPXoXZm54cerjaUvX0pqjGi+ATCG2BqNwYQC+AxkYIdmRzvevF1euz1EJlRxcSAghx8qOLpixVoiJPRcAgsNfPAlrvrMNcLpq47d+7hl4719/GwbnCFgZb3/tDZiubjxxoG1kHquAryVXdgjPBQXAMpBwOxEkJA+E29/GcveDCxASePqT19tmD8xP4Pb7TsEP+pOIrhegNDsaSaHJmJ1AdugcIzOmZt5pGPqk/s53voO3v/3tePTRR9d97ZOf/CTe97734V3vehee//znD3wd/b0rKysDd4vH2BrQkzQRePDO6trZMdlxriBuY+q3K1wOVrEsqji91EAr3FUuZSA7DEYRkFDiHyM7pJQoBTVwwwQtTaR+3ayIHvxSZ3YIBIEAIyKyIuxkmHP7IZprKEF9FgGXMI31ZEe96WOibIKVJ1C57BlgEzM4/bE/BAAceN2HUsnODUYV0cHsaJKzGVhcU7tsA20sIyo7MKDOdxQ0HB9lm0XJ9MBmZ3asgDAzmrzmBb1Y5eFks+UGmCV+FCKZ67FCZQcTyZUdrs9hUTVZLCygNLSxIGjba2xzvY2FmCXsfuHr1YRcSsy/5M2DbSwF5wb0qvBdGUB2AEDlkmux+u3PQnit1Oodbelaa3jYy0JlR4YddNOg8KQBM/BCZYelGh1SjHe6otbxeHReesd6rOzoRJzsMMN5p3f22Dqyg1KCo6fW8IsveRr2ffX/wJidx/mbEE4KAIxRMErg0DKk5wwMeI+Dew4o2iqycx36fZDh893oQXbc+cBpTFYsXHJwfQjpgflJCCFx8mwdF+zr/axruaNndkTKjgIUhRsN/dw3NNlxrtlYvvSlL+Hnfu7n8Oijj66rlNWExfLyMt70pjfhE5/4RM/XOHbsGF71qlfhbW97G5aXlwEg2iX88R//8Tx/lzFyBCEUYAZk4MFbOAZWncnkmR1je0JP0kWfBY6UEoazghVRxanFJlqhsqN7wp3oWITEyJW2xNr1OKZQh29Pb6gKTJ8LI/rBz+Fzee4oO8KQ0qqvntf9drNrDS9qYpFeC0u3/X37awPk8r0QhbsxO5GNJWmdZlosR2RHjwXRiG0smiQpKqC02Qo68jqAmLJjhOuWUAYQmr6NpbEKVs3/3tWTYRHKv1thQKlZLkLZocmOMKg7wSTQ8QJUDDU/KlLZ4UkDhLcDSrsXzDLwsfhvN6N+zxdh7z0E98QjOP2/39d3s0lsgI3FNCgIQUcryUpNvbcz/ciOi68FeIDW4/emPh6jJLJ1VYyQgMqg7NDKMxK4KrODGWh5QSopfKTsiP3uehE3VnZ0ooPs2HUeQGikMO7Gg0dX8D9u+Q4McFi2vSlEh4ZlMrRIaMNImNvBw9aRLCTcTgShTLUB9gkoFULirgcXcN0V8x3EvsbBvWpj7NiA+lk3h8wOvSmzE5QdNHwmmmEQt1/Qhsxmou8Ttlar4Td/8zfheW12cvfu3Xjuc5+LF77whbj88ssjebOUEu985ztx/Pjxjtf4xCc+gZ/8yZ/EHXfc0fG9F1xwAT7ykY/ggx/8YHG/2RgjQzdy+GePRez6GOcG2tXDvXcnhFMHAhfLvIrTS00VEmizzBMNraaIh6LWmj5maBOi3L9CrAhEZIeIKTu4AAMfKftgu8CcU/WzFU+Fw/aTNNaaHqYqlpLLP/odEMPAgdd9CNM/+BNoHblvXf7KwGOGk9sgAdmhM0F4cy1TPsggLIVkR08bCw8AQrNXz9JQIVGQjaXh+D3IDh+gLDtBE4IwI5OyI++8DgAgOsDOi5MdPowCbSxUB4EmrJ6dsBShQAtsY/ElA+G6JWZ99Wzr8cOQbhPVK24AAExd96Pwzx6Hc/R7PV9T+h5ogbWzgCK2LZN1ZXaEyo7J3ou90sErQKwSmo/clemY2spSMdQxs+ygG4zClQYId2PKjrQBpep7teUTULWzwFjZ0Q0zDNv1Aw5qWDDn9sE7c6zn9y4sNzE/W1afyyaPz7bJ0IJa/PJGMrJDeOr6Z2OyIwK1K20bS1d22CPHV7Ba9zoqZ+M4MB/Wzw4IKc03s2MnKDvUtWfI0Lq9A5UdfZ/Un/70p7G6ugpCCBhj+I3f+A286lWvAotJeO+77z785m/+Jh599FF4nof/9b/+F971rncBAD7wgQ/gIx/5SIff2zAM/OIv/iLe8IY3wLLGNUtbHcRQjRze2WOYvOq5m306Y2wg2tXDvcmOYG0RAOBa0zi12ICUGIkpZz3IlXrLwyxtgFQvyvy6WaCJHibVQsL1OISQYHLnV88CgBEqO8ruIoD5vo0stZaP6aoFXl9GsHIae//jf4G163zM3ngTZnw3lRKMUQJKgICWIAeQHfE6TVadwdJtf586H2QQltbUxLNXbaEUfCTSQAebElnMrknT8TvCSQGEzRGjLwCIYaVXdtRXC8na0btQ0ldkh+N4KJEArIAdNk2sSN+FwczENpZypOwoiOwwKHwYIDpLpEf1bOPB20GsMsoXXgkAqD7lWVi89a+wdtfnUT701HWvuREBpQBgGWydjaVsG31VgYSZKF94JVqP3pUp622ibGFhuYUy08qO9M8JQgh8mKDcDd8nM3V9ZRTOOlZ2DEVc2QEA5u6D8PuQHWeWmzgwPwm5Emz6+GyZFI2wVDlpSKkIiVRjByya8wKxSpGywwgDSvW9f+f9p0EIcM1le3r+bNk2sHumjON9lB2cC/iByGS5jkOTHWQHfG4kmvO2c+p2Gvo+Yb/5zW9Gf/71X/91vOY1r+kgOgDgqU99Kv7u7/4Ou3btgpQSn/vc5yCEwPvf/358+MMfBoBIzXH99dfj05/+NN785jePiY5tAmJY8JdOQHoOrHETyzmFeGZHLwRrZwEAdHJXTNkxCtmhJqAiruyoO5iirQ1tYgHa7RNUttseAICeIzYWatpgk7tgO+oz7qvsaHiYrFpoPHg71u78XLQI1U0KaUAIgWEw+NQaqOxgpSomrnwO9rzoV7DwiQ8OrdNMi+U1B1NVq/dOKw8y53UAsTaWwjI7AlTKncRG9YpnRjLbUew+W0nZodUWmuzwW8VNOgkzlYXHa8E2KbwEwW2ux6E/hmJtLAyU+6oavkvZIQVH46FvoXLJtdE5UNPG5JX/Do0HvomgvrzuNTciswOAeh9jO4crNbevhUWjcsm1qvHpbO8F7yDoRpaKzuzIYGMBAJ+YoNyDiCk7ylkCSmNkxzizoze6yQ5rz0H4y6fW5WBIKXFmuYU9s2VIHhR2vyWFZTLUhbqWk9bPamWHMc7siECtMmQsoBRQ2WEAcOcDC7jsgtm+GT8AcHB+AscWeis7dP3zqMoOGQWU5q8o3GjoNhadTRXswIDSvk9YHUg6NTWFn/3Zn+37AjMzM3j1q18NQFlf/uEf/gEf+chHIpJjYmIC7373u/HXf/3XeNKTnpTz6Y9RJIhpwT31fQCAteeCId89xk4CGWJjCVbVQriya29EdozClDNLH69tR2isLIERCWt6d+bXzYJullu3XFDJgXOA7AAAc24frNZgsqPe9DBZsdB44HbY51068i6+ZVD4ZLiNhdeWsHTb30V/T5sPMghLa07v2lloZccInz/Vyo6iMjt8VGM2lmBtEc4TD2PmWS8b2e5DDDNV1oiUAry5BpZzEwvQDg0l4e8ShCROEXJiQgioXYbwWrDMTkVCP7geb6sIWDHkAaMEPgxQ4bd3v2MLZuf4AxDNNVSv+MGOn5u89kcBwVH7zr+te82NUnbY1noby0wfC4tG5eJrAQDNR+9OfTxtYynTsCEno12grexQdgnHSzfmlXrYWHQekjlWdnRgPdlxASAF/LNPdHyfaoLj2DNTUWTHpis7GGopyQ5t96T22MaiQe1KO7ODtS1Nq3UXDx3rXTkbx8G9kzi+UIcQ6/OJ9P2XpkmpF9oBpdufpNI5Rky0bZE7DX2fsGfPngUhBFdeeeVQJcYP/dAPRX9+73vfCwCRmuOf/umf8LKXvSyn0x1jI0GYCRn6oseZHecW2gGlvRdHvHYWoAamd+/GmeUmmo4/krKjZJsIYHSQK+7yAgCgPLe+XqxIRBP+wAclQDNSdoxmY9hOMGf3w2wqsqOXjYVzgYYTYBdtwDv16LpFVaZjGhTeELKDN2toPnIniGHgvNe8B1NP/7HU+SCDMJDs4DyyomRBNBEXRdlYAlRCG4u2+yx/5aNg1Wmc+Nt3YO2uf8n8PhFmprKxiGYNkAJsYibT8QZBTy4JV79L2ztdzA4bscoQngMzKdnhc5RYaGMpUCnBYYLKAK6nPpe4DaTxwO0gzETl4ms6fsbadR7KFz4Ntbv/NapS1tgwG0vX+7ha9zA9Mfi4xtQuWPMXZMrtmAjJjhILMzsyKjsCYoEJD5J7ADMRcJlqdzjexhK9ZrhbbYyVHR1oZ3a0lR0AomZAjYVlde/Pz5bD4NjNz+yoB0oNljSgVAaq7twcZ3ZEoFapo3oWUNfCXQ8uQPapnI3jwN5JuB7H2ZX1zW5uTsoO4bZAzNKOmBNq6za4B0rJjqye7fuEbbXURbJnT29fVBznn39+9GfP80AIwWte8xrcfPPN2LdvXw6nOcZmINrhnpwDKyD8bYyti6E2ltWzMKbmsHfXBAIucXyhPhLZYZkMPoyO4+lckIldG0x2UKYqZrkH02RRrS49RzI7ABVSSr06ysTrOfDVW2qBtb/5IACgevn1ox/ToHBhDayerd//dQQrC9j3029T3nspMf+T/zlXG8tsryYWhAGlIyg7CGWQIIVldjSctrKDlaooHXwy9rzoDVj4+PtHtvsQw+ir8uoF3lB+9UICSsPFKgk02aHlxMV4p6lVattYEraxlHTzR4Gy+iAMvPWccEMiJDuklGg+eDvKT7q6Zy3i5HU/imDt7DriQPgeiFH8gqs7oHSl7g6UpGuUL74WzrH7E7U1xaGVHTbRyo6MZAc1YQhVPSuIeq/TKDs02RH/3SNlx5js6EB7gRu+P3P7AcrW5XacWVb3/vxMCRCbHyBumRReIMEqU4mVHfBd+DAigmeMLmVHZGMRuPP+BcxM2Lj4/JmBP39gXjeyrLeyaFvy6G0szR0RTgogeu5L31WbTglI/e2Gvk/YIFAXRJJ8jamp9gSKEIKbbroJb33rWze0LnKM/KGT5K2xquOcg/7s++3mBmtnYUztxr5dKg9greGNRHbYFkMA1pHZIeqqDWSjlR2AIvpE4MMyaDuz4xwjOwBgD13rSXbUmupzmlm6D9b8BTDnzhv9mAaDC9UA1eu6kzzA6tc/Aff04zCm98DacxCNh76NpVv/euRjA6rSbrnm9lV2QAQjKTsAQBIGUoCyww84/ECgUlbXp7+yAMn9keqA4yDMggyS21h4YwUACrGxaGUHDWtX297pYioAqRWzsSQIbnM9DpsWa2MBAEHUZ+2FG1M6oNQ79X0Ea2f7EpDVS58BNjGLtbs+3/HvMvDaO3wFwjZZtLvKhcRaAhsLEFpZBEfrscOpjqczO0o0AEAyq1c4tUAhIL0WePjep1kw9bKxaGXH2MbSCf1+6LGHMBPm3P51jSxnQmXH7qmQ5NgCNhbX56CVqcQBpeAePGmM1T0xEKscPdd1eK/rc9z14Glce8X80Na/g2Ejy7HT60NK88rs2FFkh25DDDxYBj23MjvSIB5cOjk5id/8zd/M42XH2GToScE4nPTcQ5TZMSCg1Jjajb1zbcXPKINHyWLwpNGR2UGaK/AlA6tMZn7drKBhE5FpMDRDZQc5pzI7QrKDrUW7a3HUGj4mSAv28vdRueyGfI5pUDhSTVqFs373tn7vlxGsncXss14KQggIIahecQNaj3032gUaBWsND1zI4jI7AEhCQcHBe3iJR0Gjpa7RaknZTfzFJ+Acu3+kOuA4VGZHchtLscoOCxIEhvQghAQJg0p7qRjyALVKEJ6zrkWkH5w42VGgsoNrZYcmO0JlR+OBbwKEonLpM3r+HGEGJq/+92g9+h34K6ejf99QG0v4TKk3PQiJoQGlAFA6cDmIXUHz0XRWlolQ7WSRAMS0M2/CcareG96sRURTFhtLZxuL+vN4oduJSNkRs1Baey6Ad+Zox/ctLCsSctJWnykxNp/s8HyeWtnhSQNsyAL+XAK119tYvvf9JdSaPp4+JK8DAKYnLExWTBzvoexo21hGVXa0QK1iCPaNBo0KAkJlx5jsGI4bbrgB5fLOYLvOdUz/4EtAmAn7/MtHSvIfY/thUPWsFBxBbQnG1G7smS1Dj9Hl0gjKDpPBk6zjeIa7jDqZ2BSFGDGUwsAyKZquWuSRc0jZYcyoCcVuWuut7Gh5+AHrOAhkLnkdgJIAt2SYFdNFXkjBsfL1T8LaexHKYVghAFQvvwEy8NB89DsjH39pTS2aZ/tmdozWxgIAkjIwiL51vlmhQ3QrJRPLX/4oTt/yXtgHn4zzXvkuWPMXYPbGm7D/prdnt7EwIxXZERSo7CCEQlATNgJ4PgcJQrKjIGUHCf3jP/vCK/CqH3sylmtOz+A7DdfjsDTZUaBSQoSqEd9Vv79WdjQevB3lQ08dSBJPXfN8gBCs3fUvAJT1RfrehrRZ2LHMjpW6It+S2FgIM1C56Eo0wwrapKhW1PtkkwB0hFwErlU6UiCAeg6kUTNaBgUh3W0sobJjTHZ0QFuy4mOPtecggpWFDnXamZUm5mfLUQ7SqGT0qLBjZEfSzA6l7GDjayAGalWUwlPw6H355r0nQQlw9eXDoxUIITgwP4ljp3vYWEJlVZra6F4Q3s5RdoAZYeuYA9NIpmDcbsj97pqZmcn7JcfYBARri3BPfR+zz3k57P1PGinJf4zth0FkB2+sAoLDmNoNg1HsnlEP/DQ1fN2wLUORHTElie2tock2XtUBhLvHgdup7MhhZ3+7gJo2UJ3DHtaH7Gh4uMo8CjI1D2v+UC7HNA0GR/RWdjQe+Cb8pROYedZ/7CC/SgefDFqZQuPBb2JUaLJjV18bSx7KjpDsyHnnpBGSHTO1R7Hy9U9h4mnPRvn8yyJyI0sdcAeYmd7GQg3Q0kT2Yw6ANGxYxFeS8YjsKGbiScwy/FYT/+9H78Zv/L9fwW/80Zdx5NRaX8LD9TksnQ9RYIaADK/FwFXjsmUyeGePw198ApXLBxOQxtQuVC59Omrf/YKqFBYBALlByg4aLfhXaurckyg7AJXbwWtL8BaOJD6eDii1EERy7SwQtP2zmuxIs2AihKBksY6A0nFmR290t7EAOiRfwj97PPq3heUW9syUo6aorVA96/kilbKDcA8ejMiuMUb7WS7cVnQt3P3gAi4/NIfJSrJnlG5k6Ybr5ZXZ0QIpiGDfaBBCQEwbItzgO6cCSrOCjbjzNcbmo53k/zEY03tw8h/ePVKS/xjbD3pHUvSwsQRrqqXDmFKVsPt2KSvLSG0sFoMrWAehVuY1eFb+O8NJQJgJ6asHfytmYzlXlB0AQKb3hjaW9QNfY20Nl5knUb70+tyUN6ZB0RTq/RVuW0kmpcTK1z4Bc9d563IICGWoXvoMNB++Uy3aRsDyUGVHDp8/NcCIyH0y0WwFqBAHU3f/Dcxd+7Hr+T+f6+srG0u6gFJWnSpMlSWZDZsEaDoBTOmq4NeCKgADaqFRq2MhDENcWG7h3X9xO1Yb68dDISQ8n8OkAoSZharSRGirCLSyw2RoPHg7AKB6WW8LSxxT1/0oRHMN9Qe+EZHMRSpRNGzLiIJeV0NlR5LMDgBRu0wrRQWtzuww4UfhtlkgYkRQINXUOe2CyTaNzsyOIGxjGS90OxBldsRsY9aeCwCgI7fjzHIT83OVSHW2FQJKXZ+DVaYhnEai5i0SZnaw8TUQQT/LhdeMyA4vELhuSAtLHAf3TmCt4UXPGI28Mjuk2yxMTbgZoKatAkoZg58giHu7YXx3jbEOrFTF5DXPx96X/GcsfOKDIyf5j7H9oCcNskf1bER2TCuy48pLduFtr7keT3/y3qES736wTQZftgNKpeCYkHX49iaRHaaysZiMRtWzRATnFNnBZvZhD13rabmwTt0LgwhMPeWZuR3PNCiaOrMj1rjQeuQueAuPY+aHXtqz5q16xQ2QXgutx9MFF3ZDKzvm+rWxiAAYoWZOCAnTMvH0y3bBC3im+6QfGi0PN1W/CerWMP+St0QhnnmBGGYqMok3VgrJ69CQpg2b+FituygRH4JZIKSY6YxgFix0Ej0Ly62ehJW2Z6h8iGKJA21j0WSHaVA0Hrgd9vmXwZjaNfTnyxddCWN2H9bu/DxEqOCjG6XsyGBjAQBjcg7W3otS5XZMT9h422uux3mzJgQ1M993krXP0ZehsiPlgsm2OptofD5WdvQCpQSMko7MDnN2H8CMKLfD9TlW6x72zMaUHZs8PmsbizG7D/Mv/XXQME9okA2ccB8+xpkdcWgSQboOJsom3vaa6/GHv/Is3Hj1+Ynv3wNhSGm3usPRbSwjbM4Bymq7Y2wsCOe8vgvTpD1z2rY7xk/YMXpCNNdw9l//Mvr7KEn+Y2w/EEKi3IpuBKuK7GBTuyGExFWXzuPDnz6MX/3AbUMl3v1Qshh8tJUdvLEGRiRkZXb0XyYDqGGFkr5YKKE4dwJKAcCY2Ycq9cBb66WgU4v3Yk1WUD54WW7HMw2KeqDJDrWLLqXE8tc+DmN6Dyae+iM9f6584ZUgVjna1c6KpTUHkxWzfwUgz97GIoTEkVNrOLvm456HTuO3//irme6TfmCPfgVXWUdh3vDTsPc/KZfXjIMwM1pQJIFSdhRHVBKzBJsEWK6FZIdRjKoDAFipApMIULQXXvOz5Z4LVL2QNbABNZjh6wdeqOxwl+GdehTVy5MFBhNCMXXtj8I9/gDcE4+of9sAssOOVc+u1FxQSiKrSRJULr4GzrEHEuWICSHRcnx8+NOH8diRBTxyysl838lYLa+nyY6UC6aSxboCSsfKjn4wjU45PaEM1q7zI2WHbmLZM1OJiNjNV3YwzM9VYJ93CYLVszj//P0I1hYH2sAp9+Dj3JlXJIEOmw6cJgCCD3/6MH77T76Gt//p1xPfvwf36kaWztwOreywzewbF1LKHRVQCqgx9ZwOKG00Gjhx4sTQ/9J+f/fPjbF1oG0seSX5j7E9QcJGkm4Ea2fx/7V33+FtFOYfwL+3NC2v2I6zF0nIJoMMAoGwk5ACgULSUgp0A4VCgAJNWSUQRpuWUkr7K6M0UCBAAqRllSxCIDtkLxInjuN4T+0bvz9Od5ZtSZZlnSRL7+d5eLDkk3SxTqe7997BmCxgzTY0OL14ZtnWqFK8IzEHprFoBwSe+kp1HbLyu/iviI0a6PEHndAoGZfZoU1kYZsqW90v+70oaD6Cb9lBcb2abuI5uEStjEU9kPWc2Atv2UHkTL0q7N+e4QXYzpgA56HNULow1rWuyRu2hAXo2jSWBqcXj7+8CT4J4Bk55s9JKL7qk+hxcCUO+HshZ8oVXX6+UBhOADo1jcXYzA412OFHfbMXZsYPCMZdYbPa1b4jffPU974oz4o7rh+PAyW17ZbVDqR5SIYHDpRAZofkU7ch9uROAIg62AEAjrEzwXACGjZ/CABd6mkRLbPAQZYVSLKChmYfcrNMHY6SDGY7YwKgyHAf29Xhsg1OLxa/slmd2sGIaPYzsX/ugoMdcoxlLCZOv7IMACL17AirbbADUPt2+KvVYId2zFGUZ20pY0n2NBaew8/mDIH72+2o27AcVm8DTi17OGIZOCv7IDHJDdKkGi1jormxEU/+c3NMx5eFuVaYBA6lle2DHSaB69Q+py3F7wEUOa0yO7QyFhPPpWVmR4d7hlWrVmHVqlVRPZmiKJ1anmEY7Nu3L6plSeJwFjscZ12ErFHngrNlI2/GfOT6vVTGkmG0E/62tLGzDMPAL8r6F5EmXIp3JGaBhz9oGouzSj3BFgJ9QRJNK2MxBRrQsVCvJCT7ylEimQt6owkA56xqdb/76DfgFT9KrfHL6gDUg9smiQO4lmBH/ZfvgrPnwnHWhREfaz9zKpz7voSn9ACsA0bF9Pq1DZ6wY2cBtWdHrGUs2udEzFYblAKxfU7arZPoR+WKpZBYAa83T8dlFmNOsBmehxxlGYuiKGpmloGZHaxJzew43eRFNuMzNNjBWdTnfuyW8ZCs+eA5Fu+tOYwPvjiKuxZMwMyJLaPZteZ3HCTDmyVqwRTZ5wVggVKyDaaiAXqQMhqczQH7yOlo3r221XMaSRuR65cUNDR7oy5h0Zj7DANrscN1ZDuyRkQuowv+fjIxInwKH/vnLkSww9rJiQ4WE9+mjIUyO8IJFewwFfaHc+8GyF43qvRghw1KcyAgn/QyFhaLX9+DZQ9MR6EjH5XLnwQA9P3J0rDHz6zsh8hkzkWUaARndsR6fMmyDPoWZuFkRfsyls5+btvSMk/TqWeHXsYS4nOXDqLawyqK0uF/DMPozbiiWV77j6Sm4O79Xe7kT7ol7YS/LSkQ7ADUA5KivNYnGuFSvCMxmzn4weuv565TD14sOUkKdvBmKH6f/u/goU1XyJyDEkuPXpAVgHe1zuxwHtwED8xodAyK6+sJPAuvqJZPyV4XPGWH4T62CzlT5nbYS8A2ZDwYTuhSKUttU+RgB+TYy1i0z4kEBhyjHkjE8jkJJnmcqF33b8h+L7KvexSKNcewJncMbwKiDHbInmZAFg3N7GDNVpjhR32TB1bGD8bAK2zagbdDkFGUZ0N+tgU/mD0SY4YU4I//3o4vd7VkqGonspwiGp/ZETj5lv1eZDFuSKcOwtaJrA5NzuQrUDRvIRhOAGuxGz5mXg92iArqYwh2MCwH66BxcEcxgjb4+8nMiPAqfMyfOyWoVMors+BYptNBCjNNY4maEOIKs6lADSz6qktRVecCywD5OZYUalDKoSDXCg4Sate8od8fqQyck/0QKbOjFSawz2Ulb5eOL/v2zAqR2SHC3MVJLFAUMJwAzpFn+P4yURjeDDlwzOvLtAalnQlIUACDkPTCBPpWtCU21ujBjhy7GYtumaJ/IRXlWbHolinIsXfyal2gQSkCr+etr4ZfYWHLTU7PDq1fiSnQv0E7Qc2kYIdgNqNOtsPkrtbvUyQRrsNbcQgDkGWPb58E7eCWNVshe12o3/guWEsWsidc1uFjWZMV1sHj4Dy4KabvIEVRUNfoQV6EqRCKFHsZk/Y5YTkBHOSYPycasbEGzbvXgrPnoOe198FcuR9XTOsb03NFg+F4KJI/qr+t5GwAAPAGBjs4kwUmRkR9oEEpZ2iwQ5sM4NHvMwscFt0yBcMH5OPZZVuxZd9pAC1lLIkIdrCBBqiK34vRppMAlE6VsGg4mwNiYw3yLlgA3pFv+Jh5rVZelBTUN3mjnsQSzHbGBEjOevgqjkVcLvj7yQQRvNkS8+eONbU8xiupY2Q7O23HLHB69g+g/g0AyuwIJXRmRyDYUVWKyjoX8nOs6t9OTI0GpSYhUMZyeDMYnkevGx5F9pTwZeCKooBX/FTG0oZWHmKBv0vHl/16OlBV54Y7qHTM45O6NIlFbKyB8+Am5F2wAEKPvobvLxOFFcxQRC9MAteqMXC6CLtneO211xK5HoSQFMOG6NmhiH5Izno92MGyDAYUZ+PZO2fAL8oQeBY5dnOn6yG1nh2MIkORREhNNWiS7egR48lgVzG8AFn0QRC0zI7Azj/Gng3dEcMwqFGyUeyu0e9zn9gL2dOMnZ5+6BPlvPtoafPdWbMN3rKD8FWeQN5510ddF2sfPgWuw1vhK/8W5t5ndOq1G50+iJKC/BxjenZonxOufz5Erxc/njomps8J0NJTqXbdmyi84jZUvPM0GJ7HeZfdF9O6RUO/YiqLemPMsOvnrAcAQ8tYBIsNZkZEfaBBKWexG/Za2lVG2dc6ndpq5vHwj6di0Ytf4sl/bsHDP5qqHySyigiGN3bfxQR6diiiD+OEE+Bzi2EqGtCp51C3pc2o++JtFM75Bcr//TgYnkfWyHMBg/p3mNuUseR2MrMDAKyD1RG0riPbYS4O35BX+9w9c8d5qH/+/zBj4hAUF2fH9LnjeQF+hYPASPCIbEzTHCwmvk2DUgkcy3Spf0C6ChXs4HOLwPAm+KtLUVVv1k+CW6axJD+zY/GyHfjbveeh15nTUP7vx5B3/gLknXN1yOxoLZNVYinYEUzLplP8ni4dX/YLTGQpq2rGGX1zAQTKWGLM7Gj57v03Cuf8AqffXGz4/jJRGJMZis+jfu78GdSzY/LkyYlcD0JIiglVxiI2qSe+fFB5CcsyyHN07Sq/xcTDD/UgWBF9UJy1qJdtGGBLzkEAK6iBnkzO7ACAWiUHg3wl+m3XgU1gBDN21fbEmfb4BjsEjkVhng09Lv0RKpY/BaFoALIndpzVobENPRtgWDgPbup0sKOuSb0yE7GMRRKBGMtYAPVzIphMEN0uPPHqZtx27ThcPm1gp5+Hs9hh7jschXN+gcp3nwEA5P3gaby0/hTuGdA75vWLROs/oYhihycUWmaHkWUsvNUKnpHR2OiChfFDsBoX7NAyOxSvp93v7FYBj/70HDz4wgb8+7MDuPmKUXjy1ungP/8CDJdl2DoBAM+z6shKbyOGCeWwnzm305kG2ph5oUcfnP73YwAi9xeIB1MggOzyyvD4pE6XsQAAn5ULU/EQuL7dgbxzr424LMsyyLPxqFdkWLOyYg4s8DwLH3gIkOAWO9+cFFCnsQSXsYiSAp5KWEIKFexgWA5CQV/4qk6gsq4PRgxQG5i3NChNbtDALHDwizKqm2QUFuTDJXNQPn8N/X7xfMjltWwPmTW+V053wnA8GE6A7HV16fiyb091H3yyoqkl2OGTYI4xs0PbX/I5hahYvkR9DYP3l4nC8mb1Al+mTmMhhGSmUKNnxQa1WSUf58ahehkLANnvA+euR71shyPO2QPRYngTIIvQjme1ppKZFuyoZ3IgSB5IriYoigznoc3gB4yDHzwcnRgXGY1chxmP3XAmfNUnkXfBAvS86ldo3v9l1CminM0B64BRMfXtqG1QT2QjNijtQmaHhmF5mFgFRXlWbN1fEdNzKLIEKEqrmnDv/vUYNcC4Ay4twBGqh09bicrsAAB3c3MCgh2hMzs02XYTFv/iHHz/shF4+l9b8cALX+J0ZQPcEhu30cKh8BwLv8Ihv34feEaOqYQFCIyZ//j/9NtGj5nXTjQanOrV+Nys2PbxtjPGw1t2CJK7qcNlZb/6+Q4uReksgWfhVdTPv1tkYYmhyWGonh0ClbCEpJY1tj/pMhX2g6+qFDX1bhTlt83sSHYZi/peauPqfb1GQ6w7De+pwyGX14MdVMbSDmO2ht3nRqt3gRrcLK1saVLq9UkxBSo1sqsRNf/7p37b6P1lojCBaSzhPnfdHe1lCSEhMbwARWy9ExcbA5kd2T3i+lpmEwd/4EBSEb3gfY1oUGywWZJz8KLV25tZdaefiQ1KAaCeyQUA+OvK4S07DKm5DnK/CQAAR5wzO8YPygJ/cifqvngbfE4hTi9/OuLIvlBsw6fAX1MGX/XJTr12bWO0wY6udXFnOA6KLGLimT3xzeGqmEa8+WvL4Tmxt9VocLFsP8YPNjDYoWV2SGIHSwJScz3AsGBtDsPWR+vRYRLVE13BlogylvaZHRpZAf745nZ9cgAj+7GnpCEuo4XD0TINTJILjYoN5j5DO/0cyRgzrzUorW9Wt/3cGK/a2oZEP4JW+/d0ZbSuwLHwyur+3xVjZofZxEOUZEiBcie/KFNmRxgCx4bcP5oK+kFqqoVJ8aIwTw16plKDUgDwBdbbVzwcDG9C8+51IZfXTpIljjI72mJNVigR9rnREHgWvXrYUFrREhB1+8SYApWAtr/8GgzPo/fNSxKyv0wUbRqLiWPgF6W0679Je1lCSEgMb4LcpmeH2Kg2q+Qc8Q12WEwcfIEyFrG+Eqwiwc1ldzotO17aBjsytYyliVMbxPprT8F58GuA5eHscSYAICvOWTc7S5z4yjsURVfdhcr3fg+xrhw9r7q7Uymi9mFq+aXzwNedeu26JvWgKq/DMpYuvv8cD0USMWlET3h8EvYdre3UwxVFRsXKpZD9PvT6/qMwFfVH3oz5EGfeic92de65OkPP7JA6nsgiORvA2bLBMMYdXmilJdmMGlzgDBwBqJexRLjK2HYENw8JzT4YeoWsR7YFA+fdDoYTcCpvgj4OsTO0MfO9b3hM35Z6zV9kcBlLINjhUgNnOTFmdph7nwFTryHgHPn6feEmI2iBKlaIvdyS51l4oX4O3H61Z0tnaY0Rtak9oiTTJJYweJ4N2SjRVNgfANCTq0dhbiCzQ9SCHckePRsIdmjTLHgzbMPORvO+L0MGirWTZCWDRtpHizXb9BH0XdG3yIGTQRNZvD4x5swOzmKHufdQFFz6YzAsl5D9ZaJo+0YTL0NRAMnArMRkoL0sISQkhjfrBxEasbEarC0bbJybMQWXsfhry9X/m5P3BcIIWrBDPSjVG5RmWLCjmc+BDAb+2nI4D2yCddAYNPnVv4Ejzv1UHDYBZw+xo+bTl/X7Opsiymf3gLnPsE6XstQ2eGC3CvrBaijqNJYuZnawPCBLGHtGAXiOxdYDnStlcR3cAn9FCUx5xeADZSKsYMbCF7fDLxp3cNLSsyOaYEe9of06gJYARC6rHgxH28Q2FoxgBhg2Ykp12xHcAiOBN5kMPZG9fGwOmMbTyLtgAaZedU3MUwESPWbe3CazI5aeHYDav6Fwzq3wlh2G7PepE4rC/A20K8RdyuzgWfgC2YcuPxNT3b/2GK2UxS/KNIkljFA9OwBACExk6cXVtzQolVOljKV1MAsAskbPgOxuguvoznbLy3qwgzI72mJNli6XsQBA36IsnKpyQgwEzro6jaV59zqcXv4kTIX9E7K/TBRt32gOZDH70qxJKe1lCSEhsaEalDZWg88ujPtrcRyrN+nSgh2iOTljZwG1WRMACFoZS4ZmdrC8CS4uG86DmyDWV8A+fCqaXOoJb7z7qQzvZQF/cmeXU+rtw6fAd/oo/A2VUT+mtsmD/OzIJ0Jx6dkRyOywmHmMHtID2zoR7FAUBfUb3wWfVwz7yHP0+/2iDJ8ow25gyZe23Ued2ZFlXL8OAGC0zA492GFcZgfDMGBMlohlLG1HcJsYCZNG94t5tHBHJI8TUsk21G9YDj6nCM3vd77kK1n0YIfesyO2v5HkccJzfA/qNiyH5/genFr2cNi/gXZSqQXJYsFzLT07nD7ENNFBz+zwUWZHR0xhegfwOQWQWBN6cfUtZSyB0bNIcoPStj07AMA2+CywVgea96xvt3xLZkf3nuRhBMZkjSlbra1+PR2QZAXl1U4oiqIGO2LIytK4S3bB0m9k2h0LtlzgUz9L6da3g/ayhJCQQjYobayOe78OXeBAxV97Sr1t8NXhSLQr2WZG3fGbOPWqebp9wXWkV4ENfa66HWLtafD5vWEfdjZcHmOCHYcrvHi/vDcKrn+4Syn1WqNG18HNUS0veZxobPbijL65YdPgFUUBZAnoas8OllMbjAKYNKInSiuacbom9Gu25T72Dbzl3yJ32lWteodo74fNYtyBPmvJQtG8hfpJRbi/E5CgzA5BDSrksOrBsJHBDvX1LBHLWIJHcL+06BKYWBk5ObFP/ugIZ7GjqvBs5Mz+JSrfezamkq9k0a5+N7gk2Cy8fruzOIsdWWNnonDOrTj91uKIfwPF3/XMjpwsE86+7iYwnACbw46rzunV6ecwBwIkHl/LCQVldoQWbgQmw7BoMhWgr9ColxK19OxIlTKWlvVmOB5ZI6fDdWhLu7IMhTI7wmLN1oj73Gj166n2jqqud0FyO8GxDApyLBG/w8IRG2vgrzkF66AxXV6vVKNla5sYCnYQQjIIw6vjV4MbFYmNNa3Gzsb19Tgts+MURLAQkhrsUNdFCKT0ZWqw4yeX9AVbX6ZOR7n6LjTv/xK9cgWwLBP35rE8x2Lll6UoVSeXxpwiKuT3gqmof1R9O7TU94sm9sbNF/YOXwogx6lBbSCzAwAmnlkEANh2ILoMlPov3wXnyIdjzAWt7ne61QN9u9W4bZN39IDYUA3Onh25XEBR1MwOAyexAC2ZHTkJKGNRn7/jlGptRGJhjhlQZLCCsScwvRwymte+pt/uLlMBtKvfshx7CYtG8XlQu+Z1/Xa4v0E8MjvG9xVgaVInRd197Ug4yrd3+u+tnQzrmR0iZXaEI4Tp2QEA1chHMVffcockAixnaJ+gaIQqYwGArDHnQxF97b6T9O0nyRkpqYiNU2ZH36Is9CqwY0i+Aufe9bh51jCcO9QWU9mfu2Q3AMA6IP2CHQyvBTsCZSwxNE9PZbSXJYSEpKa1KeqBBADZ44TidcV97KxGiyz76yrRINuRFedpH52hpfQJgR2/iQ0EO7pYxtCdSB4ncGI76jeo01Eq3vsDGrd/igGFZjhsQtybx2oH/fG4omAbPhWe0gMQm+vDLqN1Vq9d+2+Mz65F03uLw6bBa9kY8ZjGon2e+hRmobiHLapSFk/pfnhO7EPu1Cv1rCON0+DMDsnjhOvINtRtWA5/TVnEcgHF64Ii+RPWsyM7UZkdJmvEMpZgSqCpsxYwNYLkcUI6ug0MzyP3hqdhmzS320wFCO6LE2sJC9B6kkyvH/wO2ZNmhf0btPTsiC3YIXmc8B/diobAvtD74RI4v+l82VDbMha/RJkd4YTr2QEAJ/3ZsMMFyaU2nlQkf9InsQDqOjNMUIPSAHPvoeDzituVsujbD09lLG2xcRg9C6jfi7dfORTi0a2oXfdvTC9qQN3y38VU9ucu2Q3Wlg1TzwFdXq9UwwTGcgugzA5CSAbRTqrkQClLy9hZo4IdgZMDWUSdZIt7mURntM3sMAcyOzKpQSlnseOIbRxy5tzZajrKB1uqkGWN/3sj8IEGtXG4omAfPgV8XjFkd6N+n5a26m+oRP3GFSh/4zEokojCOb9Aw4olkdPgtU76XXz/GbYls4NhmMAI2uoOm4HVffkuWFs2HGdd3O53Lrf6fHaDgh2cxY6s0TMC5QJPRPw7iU41LcfozI52DUpNxmZ2MCZL1GMQtdK/tkGpeOIsdpzKmwD/Rffg2f+cxm7H9G4zFYDjWPCcGijNdcR+khc8SQZQAEVB0XfuCPk30DM7Yixj4Sx2uPufEygbUveFeVfc2em/t6VNGQtldoQXLtihKAqOOtVR077qE+p9oj8lsi4ZhoHAc+325wzDIGv0DLhL9ujHUUBwZgcFO9piTTYofq9+oaErVnx5Cnu4USiccxvqVzwVU9mfoihwl+yCdcCopGcQGUHbNwpQL574/RTsIIRkADZwwq9dqdTGzhoe7ABQL9uQFedpH52hBTv4QP2iwGZmGcvQIh7Na17VbzftXoPeuQKyDci60Q76fXG4omAqGoCiq34F19FvIIt++Buq0LxrLeq+WonS53+B2jXLwOcUwHbGRNSueUN/XNhSAC1A0cXMDnA8oMhQFPXfOGlET/j8EvYcrQn7EO/po3B/uwM5k68ImYbfktlh3LapiL5W5QKNYf5OkrMeAAzP7NB6LzgCo2eZLpQnRKMzKdV6/wCDU9NLa7y484VtOHKyDju/begWgQ6Nlu7f1TIWrdTN2n8U/HUVOPXqA5ACAbdg2hXcrmwnuYK/VdmQa2/n0+DbTWOhzI6w+ECwI7iMFgCa3X6UuNVt3V9VCkCblJUa381mgQ0ZvHaMPg+AguZ9G/T79O0yztPt0gFj1kZ+RxdkjmTYgDyMLOZQu2aZfl9ny/78tacgNdXCOnBsl9cnFWllLNoFPsrsIIRkBO2EX5ESE+zgguru6+XkZnZogRceWrAj86axSB4nlJJt7aajjBnoMCQQpZ0AxeNLVva64Cndj7ov3ob72+0of/1RNO78DNZ+I5B3/gL0u/UvKJxzK9zHdqlp8Dc8Csfk8KUASpx6duiPl9TnGz2kB0w8i237w5ey1H/5HhizDTkTLw/5e61Bqd1qXBmLVi7Q55ZnkD1pFjzH94ZMMZb0zI5cQ9ZFw7AcJIYHxygQWVPXg1AdYE1WyP7OZnYYu//STpLdXinmJp/JYtaDHfH7G/W45CbIPg9q1/273e/UbZWJ+T2RPE74vt0ChudRdPOzsEy4Ar7SfZ1Og9eCHVpPB8rsCE/7u4hS62BHVZ0bDYoNMm+BLzjYkSJ9L0wC165nBwAI+b1h7j0UzbtbSlkUvxeSwoDjM+e4IlqsSS1NjEcpy4VjC8CWqpPeim94DI5Jsztd9uc+FujXMTD9+nUALQE3TlGPJ9KtZwd9wgghIWk7v1aZHQwLLivXkNfjTS1XN+ple5LLWNR14QNRboFVAAkpURecKJzFjj3cSAy8dDxMRX2QN2M+cv1e/OaVXeiZH/8eCXrPjjjMd+csdtjPnAY+uwcq3nkaANDnx8/C3HMQLH2H68s5zroIzMCJqF7xJGzn/xC9ps8LXcYiB8pY4jCNRXs+BgIsJh6jzyjAtgMV+AnaH0T5qk/CeeBr5J5zNViLPeRzOj3quhnVs0MrF8gadS44WzZk0Qv78KmoXb0MhbN+1uokoyWzw9gyFgCQWBM4SYScgLGNjCn6yQByYH/JGh3sCDpJFoTudcKsBWfyupjZ0eo5C/oie9LlaNz6MbInXg5zz4H67xS/F4xgjrnPEGexw9l/Gvw9xmFjqYRS9zhcd9WVnc6m0YI8wdNYBMrsCMkUVNYYHBCqrHMBYMDk9QkKdqRGGQugbttte3ZoskbPQM2nL8FXeRymogGQ/V74wbf6LBOV1nQ6Hk1KK50KrHWNKL78F/j0/Y8xfVgWiq97sFOfX3fJLvDZBeDziru8PqlIK2PhFT8AhjI7CCGZQc/sEFuCHbwj37CrqCaTACmwS0qVMhZOVqPcJiYzy1j2HG/ColfUKxpayvjRsnpDAlHxbFAKqHXSwSUqzXvWt0tbZQUzPt3VgKqKanh3fx7+4EfSMju62qBUG5Uo6vdNOrMnyqqcOFXd3G75+q9WgBFMyJl8RdjndLmNL2MJnoxj7TcC/voKNO9ag4oVv2/1b1GDHUxCSiqkQJBD5o3t1wGoPUKiLmMRE1PGEnySbO5mmR16GUsXenaEknfe9WAtdtR89nKr8gfZ5+3SJBYAaPICd76wDaeqmrHyy1Icq+18UNYSGJWqT2ORZDrRDSPc94Ea7AAsRf3hq27J7ECKXIgwC1zYq+JZI6cDDIumQKNSxe+FV+HBs7QNtKV9XuOR2dHL5Aa7YwVWvv0R/ldqR9PW/8L97faoH68oMjzH98IycGzcG7OnCu3iJh845o1H77RUQp8wQkhIIYMdOYWGvZ7ZxMGvqAfBdUnP7FAPnDildRlLJjUoBbQmcS1fen5RhtsrwWFAIEo/uA0zbrAzgksvgktwQqWt1jZ5cVTqDV/pXr2XRlstZSxd/HezWrCj5W86cURgBO3+1iNoWVc9mnevh2P8JREzJZweEWYTl9Da/+yzLkKPy34M16EtqPzgOf3vIzkbwNochpeVAIASCHYoMU7Y6AzWZIUi+qJqlqeV/RlfxtJy0N3dSiG0co6u9uxoi7NmIW/GfHiO74Xr4Cb9fsXv6XJfBO3z1eRSTwa0ZqOdYQpM69CnsVAZS1jhgh1VdW6YeBb2XgMhuxohORsCDUpTI9hhEtiQZSyAmvFmGzIezXu+gKLIUPxe+BTK7AhFm7AVj8wOvmwHAGBdTU8clwrAZBe1m4wTie90CWRPM6yD0rOEBWjJ7GC1MhZqUEoIyQRa3wrZ3zKNhcvuYdjrmU0c/IHKugbZhiyDehBEQxs9q+34+Qzs2QGoV4+DDzabXeq24DCkQal6AhSPL9ngSQ2mov7ImzE/7LSK2kYPyk0DILsa4as8EfL5lDg1KNW3H7klG6J3QRZ6F9jbjaA1H/saYFjkTvlOxOd0efywG5jVEU7OpFnIv/AHcO77ElX/eRGKIkNy1hver0OjaBMMBOMzOzrTLC9RmR3BJ0jdtWdHV0bPhpM94RIIhf1R8/k/9Ulist8L1tS119JOvpsC+0CrufOfOYZhYBY4vUGpKFEZSziRgh2FeVaYivoBAHxVJwA5dRqUqmUs4YOiWaNnQGqqgefEPkh+L/xKYgPV3QUTmLAVbflgJM6Dm1HNFuC0zwaAgWXEee0m40TiLtkFALAOSN9gBzheLVPXMzso2EEIyQDalRJF9EFRZDWzw6DmpABQmGtD/3m/hMJb4OjRA1mc37DX6gjDsADHgw2clAoZWsYi8Cz8UktHfO1A32HA6FmTfnAbn/TJ4NKL4J+DybKCy6cOwKVXqc0/Xcd2hX4yKT49Oyx9h4PhBCiKoo/CBYCJI3pi95HqlsaFzfWwnN6H3KlXdjhBwunxG9avoyO5065C7nnXoXnXatR9sRzZEy8H71ADosH/vniTZQV2RxYAwOZwQJaVDh7RNaygpVRHEezwJ7ZBKdDS36A7kGUF37t0OJ68dTqsZj7u7x3DcuhxyU0Q6yvRuHkVAC2zo2sZQFpQojmQ2aFlp3SWxcS36tlBV/VDE7jQo8gr61wozLPBVNAfAOCrKlUzO1KkyWdHwQ7bsLPBmCxo3r0ess8LL/hWWVpEpZexeF1deh6xqQ7ekwdRkztSvy97zAy0nYwTibtkN4SCvuAdeV1al1TGMAwYwQSWylgIIZlEy25QRB8kZyMgiYYGOy4elQU0ViBn5g/w2A9GoHlv+x4LicTyJj3YwTMyAAZIw/nqkQg8C0UBJFkLdqhfhA57/E+utZO3RF1RkGUFx0834s9v78Q9L+1BjZKDuoM7Qp58xWMai9hYA/exXci7YAGgyGje0zK6ctKZPeETZRw6XgexsQbN+zYgf8Z1cIy/uNVyobjcIuxJCnYAQN551yFv5vdhGzIevsoTyJ54mfpv6GC9Y6W9bwfK1IPgnceacPx0o6EBD71ZXhRXGVtGzyYw2NFNGpRq792f3tqBB174Er9+/gtD3jvboHGwDT0bdRvehdhUp/bs6GoZSxwyOwA1SKIHNSUqYwmHD5fZUe9GYa4VXFYuWGuWGuxIqdGz4RuUAmrg3X7mVDQf+AqSuwl+hafMjhD0MpYujp51HdoMQIHSb6L6vAxgK+rTbjJOOIrkh6d0f9pOYQnGChYwgTJMyuwghGQEvWeH32v42FnJ44R0bBvqNyyH2ZEL7wdL0Lj9006P9osnhjeBCZy4CIwMhuPTtjlVOPr4v8AXn3agn2VAPxWWZcC3KZsxUoPTi8df3oTKOvUEdr+3J7yl+1Hf2D4jQZvGEmsZi9ZDpHb9m+BzClH+xu/QuP1TeMu/RckfbkL+f+/DE7lvYaD/WzTv24C69W+Bzy1G+RuPdfg5UDM7knegzzAMsidcCs/Jg6jbsBwAcGrZw4Z9frX3rdGnfhbrvCwef3kTGpzG7Su0lOqoMjv00bMGNygNnsbSTTI72n7mKuvchr13PS6+EYokonbt61B88czsUN/fWHp2AIFgh0+CLCsQJYVOdMMI1cPJ55dQ3+RFUb4NDMPAVNAP/mot2JEaPTsEPnzPDk3W6BlQvC6IFUfVnh20DbTDavvcLmZ2OA9ugpDfC4WDhgBQmwQzDIOs0TPgqyyBr/J4xMd7yg5B8XszItjBCCY92OGjYEdqe/755zFnzhzMmTMHTz+tjhzcuHEj5s6di0svvRRLly5N8hoS0j1o41cV0Q/J4GAHZ7GjvvdU5Mz+JareewZiXTl6XnV3QqY6hMMIJsiiF7+5eTLO7J8NheUMT5dPNXybA84mZ6CMxaDmsQLPJmS+uyjJqGv06iddAHDI3wtmxg/vqSPtH6A1FI1xGgtnscMx/hL0nLcQle/9HmJdOQrn3ArPiX3IGjkdWSOno9Q+Ep++/wlMhQNQ9J07UPnu01F9DlweP2xJ7G8DAJwlC9kTLkPRlXeiYvkSQz+/flFGZZ0bXkX9N3sUAZV1bkODZFpKdTT143oZC5e4zA5zN8ns0N67YEa9d0J+b+RMng1P6QHknvddMBb1SnGs5VV6ZofbD45lYs7IsJg4eLwiJFn9N1NmR2hatlLwtlFVr247RXnqibBQ2A++qhOB0bOpEewwd1DGAgDWAaNh7jscRfMWQuQsyM4yGVr21x0xvABwfJemsUjuZriP74Ft+BQM6J2DB2+ajId/PBV1TR7YRpzTajJOOO5juwGGhWXA6JjXo7tgBDMg+vDgTZMx8cwi1DV50uaYN632shs3bsSGDRuwYsUKrFy5Env37sWqVavw4IMP4oUXXsB///tf7NmzB+vWrUv2qhKS8rQyFln0GZ7ZAQBFVhHNa1/TbzftXpPUMhaGN8HZ7ML/rdyNL3eUwuWD4enyqUZoU1qil7EYNBbYJBif2VFa0YR7n1uPilqXftAMAIfFYigKoJTvb/cYvUFpFw6oZVcjqj/+h37beeAr5EyZi4LLf4KCy38CZcr38YlyDrgefVDzv3/qy3X0OXB6klvGopHdTaj57FX9tlGfX4FnUZRnhVdRr6x7FAFFeVZDTxpbrjJ2ooxFSOA0lm7SoFR774IZ+d7lnftdFF11J8T6SjhGntul8iptX+j1SfoI2VhYTDy8fknfz9FV/dD0nh1BJSFVgbGzhblq4MpU0A+yxwmxoTplylg66tkBqBmChbN/DrGhGiMvmoWJ/QTDyv66M9ZkjaopdDiuI1sBWYJt6GQ43X784/3d+PXzG3DPn9bjZCMD6+Cz9Mk44bhLdsNcPBicxR7zenQXDG+G2+nEP97fjTt+vxb3/Gl92hzzptVetrCwEPfffz9MJhMEQcCQIUNQUlKCAQMGoF+/fuB5HnPnzsXHH3+c7FUlJOWxQaNnxcZqMIIZrDXLkNeSPE74jm4Fw/PI/t5TsE2aG3ZUaKLIDI/DxypRWecGBwl+hTE8XT7VtO2I3+z2gWOZmOvVO3w9jtVLZuJNlhV8+MVR/OoPa1FR64bNwmHRLVP0k6+s3FwwBQOgnNrX7rF6z44ulrFEGoU78cwi/GzOELgObgbD8yi++ZmII3M1Lndyy1iAzo367aocuxmLbpkCwRK4umu1Y9EtU5Bjj/9kD41exuJPnTKWVtNYukl2gPbeaZ+5ojyroe+doijwBsqrFFnqUnlV8N/bGmNzUkA9Gfb4WoIdlNkRmhCiYbWWFVQY2H5MRWqTUsXvAVIks8MkcPB2MFFM8jjhLtmNug3LUZxnRe3bv0t62W4qYk3WLpWxOA9sAufIhzunX8jyOXboOfpknFBknxveU4fTeuRsMJkVcPxkTULKDBMtNUKhcTJ06FD955KSEnz00Ue44YYbUFhYqN9fVFSEioqKUA8Pa8+ePXFbx0Tatm1bsleBdGeKglwAp0qPg3PWgDPZsX37dkNeimEYwDwEuReNw5J3DuE7UyZh+pWzsP/oCXi9oXe0Rm/f+SwPRWwGoDYoFRUWlfVuOJ0eHD2019DXThWlpYEmkN/sQkG2gKPH62AxMYZtB5Lkx+mKqri8t1abDY7cIjAMB1ES8d7qw1izsxJDe1vwnSl5EBtL4RZteORHE8EwHBRFgvuLvVCOfIVtm79udfAsnD6ELAD7Dx6CdKq+0+vCMAwKCoYj/5qJOHy6Go7B5yL/rMvbbd8vflSJ2VP64ZJrHsCxCMtpREmBT5TRUBufv1msov33xUtOTg6uu/k6VP7rG8y7ZDwcZi927tyhTw2KN8bThFwAJYcPwueNfIXPcuI4rAB27NwFGNjjp97ZMr645Ni3YN1lhr1WPFltrT9zTfWnsKM8ROlYHDAMgz59z0LhFYWoWL4EAFB88zM4cKwUHk/nrhi32rYUMebPm9vZiIZGP7bv+AYAUFZWim3b6mJ6rnRWUa9mSB08fAS89xQA4Jt9DQCAE0f3o6yEAeNzIjewfE1dHUpT4Ji3proBPr+ErVu3gmGYkNsJwzDo02ccCq4oQNW7arl9rNtlOnPIgKvydGzvq+hD7pHt8PYdB87lC1k+5+s5DgpnQsna9+Aa3f57iq86AocsodRnwbEU2LaMls/wes8OTWVd5GPe7nKemVbBDs3hw4fxs5/9DPfddx84jkNJSYn+O0VROt1kcPTo0TCbjbtqZIRt27Zh4sSJyV4N0s0dW21GcUEPeNyVYIv6YqiB29Tub6tx5wtfAgAqmouR1aMnRvfoGXLZRGzfJ/Z+ABvfBADgIEOCmoJtt1sy5rPlEU4BG2sxbPgIDOqdg8/2bEF+DmPYv9+xejUcOVldfn5t6sMjL6lXc4ryrLjj+vGYOm4Apo3pFfY7wDXxApw+vAEjCiywDT5Lv795rxuVO4GRo8fAVNC3S+s2Kmibbrt9Ty3bg9c+O4ZZM0bC4zmJUaNGhVxO09DsBVCGM4b0x8SJQ7q0XvES6d8XL2JjDZwHvkbeBQtgGzQU7qNbcda4i7o8cSMc2etGydo/o19xIXI72DZrGvah8bgJEydNMmRdNHWNHuD90wCA0SNHYMSgfENfL970fXivHoa+jthYg1Mr/6Xfdu//AiNmzI9pWxGWl8MvysjNtse8j9pweAcqGioxYuQoYGU5zhg8CBMn9o/pudLZqapm4L8V6Nd/ICZO7AcAWH9oO3rk+DH57JbPVsnXr0B2NaKwuBcKUuB7+Wj9Iazfsx9jx43H7l07w24n8dwu01XZnvfA8ALOjOF9dR74GhWyiEHnzYXHbkFRnrVVwKMoz4qsnBz4R54D56HNGD5ujJ7NrKn53240cgLGXDg3I96X0iP/g5VrXYIV6Zg3lc4zvV5vxMSEtMuf27ZtG2666SYsXLgQV199NYqLi1FVVaX/vqqqCkVFRUlcQ0K6D4YXAmUsNYb26wDUxl4aoxpgdoZgtqBvDzOK8qzq6FmWNzxdPtW0LWNpcvmQZTXuvRF4NuLYvmiFmvrw3Fs7MGJQfsRgt6XfCIDl4S7Z3er+rk5jidbEM4sgSjJ2fVsd1fJOj3r1MxV6diRK28k2p99cbHgKOGNSP/PRTmMxuoQFaFPG0k0alCZavMurtP1hV3p2mE2ty1ioZ0dooUbPVtW5UZRna7WcqVANhKRKg1JT4DgmUt+O4O0y+3tPwTHZuLK/7ow1WaHEWMbiPLgJrDULlv4jI5bPaZNxXIfbZyi4S/bA3Hd4RgQ6AMBksaLQwSWszDCR0iqzo7y8HLfddhuWLl2KadOmAQDGjRuHY8eO4fjx4+jbty9WrVqFa665JslrSkj3wPAmyF4XpOY6cAYHOyym4GBH8g9cWMEEgZHw7J0z0Pj+JsDFol9xNlg2c8bPtm9Q6mt3sBlPJp6LS8+OWKc+sCYLLH2Gqh3Yg7Q0KDX2K3PU4B4YPTgfDpsJjvwhqGvyIMduDrvNudzqetkyKNihTbYR8nvh9JuPAwD6/mSpoZObGIYFI1iim8YiJmYyRPBJsqmbNChNNM5ih+Osi5A16lxwtmzkzZiPXL835m1F+5vHOnZWfawa7BAl6tkRSdtAOwBU1rkwfEBeq+UsA0Yje+Ll8FWdAKAGEpLZTFL7LEYaP6ttl/zQqbjv/3bi/u9dgd7Tr07q9LlUxJqtEBs613YAUJtEOw9vhX34VDAsBwbAgOJsPHvnDPhFGQLP6t+r1oGjwdlz0bxnHbJGTNOfQ3I1wldxDHnnL4jjvyi1sSYzWNkf8u/U3aVVsOOll16C1+vFkiVL9Pvmz5+PJUuW4Je//CW8Xi/OP/98XH755UlcS0K6D4Y3wV9XAUAxPrMj6AAyKwUyOxjBBEX0Ic9hgYdTIAtCWuz0O0M74NQOzJtcfgzuY9yJXLxGz2pTH9qmrUZzYmEdOBZ1X7wNyd0MTmvIq42eZY39yuRYFjfMGolnl23Vy28W3TIFA8IE2bTMjqwkj55NNNnViOpPXtJvN+1egzyDU8BZkyXKzA4/GN74/VdwsINOmMNjBTMQ2C6Cf46FntnRhQalZhMPUZL1k2Ge3ruQTHxgGksg2CHJCmoa3CjM7d1quawR0+E6sg3WgWPV8rZDm+AwsKStI9oY6I4yFFnBjG+rG3HidBPqPQr6UaCjHbVBaedHz7pL9kDxumA/c0rLc7EM8hyWdssyLIes0eehYctHkNxN4KwO9TmOqyUR1oGZ0ZwUAFjBAsXvC/l36u7SKtixaNEiLFq0KOTvPvjggwSvDSHdHyuY4K9Vm4MZHexondmRAsEO3qRPVlAkMWVG2yVSqDIWI98bgWfh9oodL9gBlgHuuH48nntrR6ugQTTpmNZBY1D3xVvwHN+rHyzp01g4Y6+gNzi9+MMb29p1Q3/2zhkhD0BcgWBHsqexJFLb0oSm3WvgPr4XuX5vl05kO8KarZCjyuzwGj52Fmg9etZMmR0JoQWYujKNSnuvmgNjvAUqYwmp7TSW+iYPRElBUX5LZqHkccJ9dCfqNixH0ZV34tSyh8HwPLJGnmvoviCSaMpYNKKoNr2lbSC0aPe5bTkPbgJjssA6aGxUy2eNnoGGTR/Cuf8rZE+4FADgPrYbjMkKc+8zOv363RUjmKD4vTH1tkx1mXOERAjpNIY3Q3arTTr5nMT17MhKhTIW3gTZ3xLsaNu8KhPwQWUsPr8Er08yPNjRUalJNNbtKMP67WVY/IvpYFmmU+mY5t5ngBEscJfsCgp2JKZnR2fLb5yBMhZ7BmV2xLs0IVqMyQol2syOBJSxMAwDnmMhSjIECnYkRDx6dljMgWCHWw12UGZHaHybEsrKWnW/GFxGyVnsyBo7E5wjHxVvPwnA+JK2jkRTxqLRMiapb0to2j5XUWQwTHR/I0WW4Dq0GbYhE6I+ZjP1HAShoC+a96xvCXaU7IK1/0jDv/NTiZoNpah9p9KsTwl9wgghYQWnYxud2WFKsQalwZkdkEQggzM7RFFGk0v9WxjZT0XgOf1KXles3XYSflFGcQ87ivJsyHNYoi5BYjgBlv4jWjcp1cpYDN4GtPKbYJHKb1oyOzIn2AGoB2XaCU3wz0bKv/AGyKL695Y8zrDLJaqMBQAEXt2mTXTCnBAtPTtiPwHSHusM7E+pBCk0lmXAc4we7KiqVxtVFua23j8qPjdq17yu327avQZyEht9mvnoMzv8gWAHx6XXVfR4Yc3qex1NkFnjOXkQkrMB9jOnRv0YhmGQNfp8eEr3w19fAbGhCmLd6agzQ9KFFuBQ/L4Olux+aC9LCAlLO2hnrQ7Da2BZltEDHqnQg4DhTYAsQZGlDC5jCdRNS5Kedu2wp3Zmx8nKJhwurccFE2MfEWsdOBb+mjKIjTUAgspYDL7KE6lrfChOj9agNPO2zUQSG2vgqzyBrDEzIDbWoHnPurAnVImaxgK0nHzTCXNi8HzXG5SaBfWxemYHXdUPK/j7QMt4KwwKBsd72k48tJSxdPw9JlFmR0SsSX2vO9O3w3lwE8DxsA2Z0KnXyhp9LgCgec8X+oWOTOrXAaiZ3IBaiplu6AiJEBKWdtBudFaHxixwYJjUmC6g1d0rfl8GBztaUon1zA6jR892MdixdvtJMAwwY3yfmJ9DO8hxH98Nx5gLoEjqiQkMDnawLIMBxdl46vbzcLrGCYFnwzYnBdTMDrOJo4NlA2knVHVfvI3CObd22BdAEX1gbTkJWTeeY2Hi2bSrr05VWm8FrRQlFuZAZkeT1rODAlVh8VxLpl9lnQtZVqFVFluyStoi0cZAe/0SOvqmpIk8kWmZHdH27VAUBa6Dm2AbNE5/bLSEnCJY+o9E8571MPcaAs6eA6Gwf6fXuTtjOzFivbvJvKN3QkjUtBN+PruH4a8lywruWjAedqvQ4cjNRNCyWhRRC3YkP9sk0UIFO4zsp2ISuC5ldiiKgrXbTmLcGYXokdO5g51W69FzAFirA+4SNdgBWQJYPiEnlSzLoCDXiiUvr0dVE/Dyby8Nu6zT7YedsjoMpY+6zeuJ0289ASByXwBZ9INLVGYHz1K/jgQS4pDZoZWxNLsDZSwUqAwrOLOjqs7dKqtDE89pO/EQ3KC0o2CHP9CglILVoTGdzOzwnT4GsaEKued+N6bXy554OcAwqP7Pi8gaewFkryupY4wTTc/soDIWQkgm0Ro8GZ3ZIcsKjp9uxIvv7cKvn9+Ae/60HsdPN0KWFUNfN5K2wQ6jx46motbBjgSUsXAs/FHUOodzoKQOFbWuLpWwAADDsLAOGA33sd1QFAWKJBk+iaWtsYNsqG30YM+R6rDLuDxixvXrSAbZ1YjqT1/Rb0fqC6CIvoQ1M+Y5Vh91SYynlbFYu1LGYmo9jYUalIZnEli9r0VVnatVc9JUZe7MNBbq2RFRS8+O6IIdzoNfAwwL+7CzY3o9c59hEBuqkXvuNcieeFnEcsV0pPfsSMMyFtrLEkLCYhIU7GhwevH4y5vajdxscCZvp8sGslpk0QfIUkaXsYiSjCan1qDUwGBH4OBWUWILcq3ZXgqTwGHamF5dXhfrwDGQmmrgry2HIosJ78o+rLcVNguPNdtLwy7j9Phhp2CHoYL7AvS55WlkT5oVsS9Aont2aH11iPHiUcaiZYVoPTuohCE8gWfh96vfB5VhMjtSTWdGz2o9Oyi7J7TO9uxwHtwES/+RMZUxSR4nXIe2oG7DcvA5hTj91pNo3P5pUvu/JJrWly8dAzyZd/ROCIla1tgL0LTzcwiFfSF5nIal9HV25GYi6JkdGdyzo+3oWZ5juzSJoCMCz0JRAElWwHfyapdflLFhZxmmjiqOS7aDdZDat8NTsludxpLg91/gGUwf2xsbvjmFn88TQ6bOuyjYYbjgvgCs2QbGbEfBZT8Ga3WEXD5R01hkWcFPrhwNi5lPibK/TBCXBqXaNBYt2EEnumEJnFrW6PSIcHtFFOamfmZHS88OGeggNkOjZyNjzer7HU3PDrGxBmJdBXKnXR3TsaperpjfC6fffBxA8scYJ1rLNJb0C3bQJ4wQEpLYWAPPyYPIu2ABTAX9DE3p6+zIzURo37Mj84IdDMOA59S66Wa3Hw6bYGjfClMnxva1tf1ABZpc/i6XsGj4vF7gsgvgLtmtvv9JKGO6YGJfuL0ituytCPl7p1uELQUmF6U7bbwtw/EQHHk49eoD8BzfE3LZRAQ7tLK/55fvxH1//iIlyv4yQVwalApaGYuaKUdlLOGpPTskVNWpY2eL8lM/s0MvY4lihLpWokPbQGh6ZkcHwQ6xsQbOA18h74IFgSajsR2ryq5GVH/ykn472WOME41J48wO+oQRQtrRJxCsfwt8TiHK//24oSl9nR25mQgU7FAJPAO/KKPR6UOWgSUs6mu1ZJJ01prtJ5FtN2H88KK4rAvDMLAOHAP38T1QJH/Ce3YAwOjBBSjIsYQtZaHMjsTLGnchuKw81H35bsjfJ6KMJRXL/jJBXBqUmtXHNlFmR4d4Xi1rrKwNBDu6Qc8OjmPBsUx0PTsCDUo5ysgKiTFZAEQuY9GOVWvXvwUhtyfK33gspmPVVBxjnGhsGmd2ZObROyEkopYJBMU4/dZiAMam9GkjN5+9cwb8ogyBZ5Oelq0FO2S/F5DFhJcxpApt/F+zy49sA5uTArEHO5xuPzbvPY3LpgyIa0qwdeAYNO9aA+/po4aPnQ2FZRmcP6EvVq77Fg3NXuRktQ7+OT0ibDSNJaFY3oScKd9B7ef/hKfsECx9hum/UyQRUGTDMztSsewv3cmygkumDMCFk/rBbOIgy0pM30/qqGDA65PAMurJMQlN4Fm4vSKq6tVtvTA39TM7ALVvhzeanh2yDJ5jaHR0GAxvAlguYoNSzmKHddBYFGblouLdZwDEdqyaimOME02bvpiOwQ7ayxJCQlInELys3zY6pY9lGeQ5LCjKsyHPYUl6/bnWoFQJXFXI3MwOVh89m2VwyYTWbLGzJ20bd52CX5TjVsKisQ5U+3b4q08m7f2/YGI/SLKCDTvLWt0vSmofFTuVsSRc9oRLwFqzUN8mu0MR1av1Rgc7UrHsL51pZUPPLtuKB174Eg/8ZUPMZUMMw+ilDtSrITLtu6eyzq1eAMlK7ljZaJkFDn5/x99hflGmbSAChmHAmqyQva6wyyiSH4rPg9o1b+j3xXqsqpUrtv05U7SUsdDoWUJIBqCUvqDMjsAXbaKncaQKIZBK3OTyJSyzI5p652Brt59ErwI7hvXPi+v68I58CAVqACVZ7//AXtkY2Csba7afbHW/1uCQMjsSjzVZkXP2HLgOb4W3okS/XxHVg0Sjy1hSsewvncW7bEgrg6HgVGQmngsEO1wozLUm/QJItEwCG1VmhyhRsKMjrMkC2ecJ+3tfdRncJ/Zm9LFqvDCcADBsWv7t6CiJENIOpfSFCHZkfGaH3/CeHaYYyliq6tzY/W01Flwy3JB0YOvAMfBXnwSS0KBUM3NiX7yyah9OVTejd0EWAMDlEQGAenYkSfak2aj/+n3Ub3wPPa++G4B6lREwPtiRimV/6SzeZUPaRBZqTBmZ1qC0upuMndWYBC7QsyPy+ytKCgU7OsCYbWEblErOBpS/8Rhyz70GvW94LGOPVeOFYRgwggmKP3xwqbuiTxkhJCRK6aNgB6AecLrcfvj8Ehy2xJSxiJ04iVi/4yQUBTg/ziUsGtsZk1A0b6HeLE3yOA15nUhmjO8LhgHWbWvJ7nB6tMwOCnYkA2fNQvbEy+Hc/xX8tacAtKT/srzxGRapVvaXzuJdNqQFO6g5aWQtZSyubtGcVKMGOzr+DpMkudMj1jMNa7JCCVPGUrvu35A9zbANGpfRx6rxxApmKmMhhJBM0TazI1MblAo8i7omNa3RYfQ0FqHzZSxrt5/E8AF5esZD3NepoA/EhmpkT7gEYmONoSOYwynItWLMkAKs2X4SiqL2CXAFgh12a2Zul6kgZ/JcMByP+o0rASSujIUkVrzLhiyU2REVnmfh8oioa/KisBsFO8x6ZkdkfkmmbaADrNkasozFW1GCpp2fI3vi5TAVGHOhIxMxvBmKmH5lLPQpI4SQELQTFsrs4FDbqB5sGB7s6GQZy7FTDSgpb8TMCcYc7EgeJ1yHtqBuw3KwvBmnlj1s6AjmSGZO7IvyaicOnagDADjdahkLZXYkD5+VC8e4C9G0ex3ExuqgBqX0nqST4LKhlxZdgmfvnIEBxdkxZ9NQz47oaNNYgO4ziQXoRM8OUQbH0jYQCWuytitjURQFNZ+9DNZiR9551yVpzdITYzKnZc8O+pQRQkgIDMOC4QTIgbIFhsvMExieY9DoVK9YO+wGl7EE0rqjSQEGgLXbToJjGZx7Vh9D1kcbwdxz3kJUvPMUxLpy9Lzq7qSkyU4b0xsmnsXaQCmLntlBwY6kyp12FQAF9V+9D0XSMjuMDQqSxItn2ZCJprFEJbjMpyi/OwU7uKiyE0VJpoBXB5gQ01hcBzfBc3wv8s+fD85qTEZnpmJ5M2QfBTsIISRjMIIpaBpL5mZ2aIzO7NBOAqLp2SHJCtbtOIkJZxYZOpJQdjWi+uN/6LeNHsEcjt0qYPKoYqzfWQZRkoN6dmTmdpkq+JxCZI0+H007/wexoQoABTtIZFoZC53oRhb83dP9enZEE+xQqGdHB9qWsciiDzWf/xNCYX84xl+SxDVLT4xAZSyEEJJRGN4E2aumUGZuGUvL10SWNXVGz+75tho1DR7MnNDPsPVJtRHMMyf2Q6PThx0HK1umsVgpsyPZcs+5CoroR/1XKwFQGQuJTCtjocyOyEyBHk4MA/TI6T6ZHWaBgzeK7EQaPdsxtUGpW+9V1bh5FcT6ShRccnPSxsGnM3UaS/oFOzLz6J0QQqKgBjsyvEFp0MGY4WUsUfTskGUFDU4v7FYBi26ejHFDCw1bn1QbwTx+eBEcNgFrt51Efo4FJoGjg+UUYOrRB9mTZsHSfwSq3n8OjMkGyeMEZ7Ene9VICjJTZkdUtL9PnsPSrf5W0Wd2yOBo/x0Ra7YCUKD4PZC8HtR9+S5sw86GddDYZK9aWmIFM8Q0DHbQp4wQQsJoVcaSocEOrVu8wLMwC8ZeSdHSlsNldsiyguOnG3HPn9bjrqXr8PeVu1Fe44QsK4atUyqNYBZ4Fuee1Qdf7ylHTYMHdiphSRk5k6+A2FCNvAsWgGHZpEztId2DPo2FTnQj0gLtbcf+pjqTwEYX7BBlGj/cAdakvvey14PatW9AEUX0uOiHSV6r9MUIFig0epYQQjIHy1OwQ7ui5rCZwDDG1hebAq8VrmdHg9OLx1/ehMo6tbSoss6Nx1/ehAZn5pxUzpzQDz5RxqY95TSJJUVIHidcR7ahbsNy8DmFKH/jsaRN7SGpz0zTWKLCB4Lf3WnsLBD96FlRVmj0bAcYsxrs8Bzfg+Zda5AzeQ6E/F5JXqv0xQgmyP72o367u8w8eieEkCgwvAlQ1BNvCnYYf2LdURmLX5T1QIemss4d9ajadHDmwDxMHV2MCyf1R162GXVNHuTYzV2aDkG6RpvaI+T3wuk3HwcA9P3J0qRmAZHUZaZpLB2SZQXDB+ThyVunQxBYyLLSbfZxJoGDKCkdZhyKokwNSjvA2XNRNG8hav73T5j7nBGYfkWMwgrmtMzsyMyjd0IIiULwVIXMDXaoB+ZZBk9iAQCOY8EygC9M8ILnWBTlWVsFPIryrBl1hVRRgGtmDsUzy7aiss6NojwrFt0yBQOKs7vNyUA6kl2NqP7kJf120+41yJsxH6xg3KQg0j1ZzNSzIxKtXFHL4utu+zhT4DvTL3UQ7KCeHR0ScorgrChBztmzYRs6Cc37NsAx7iLarxqE4c1Q/F4oimJ4Jm8i0aeMEELCaDVCMkODHdqVp2x7YsZpCgIXNlNjx8FK3HH9eL2GWzsIzrFnzoFPg9OrBzqAzCzlSTWpNrWHpDbq2RFZdy9XNAemyIhRBDuoZ0d4kscJ17dqeaCQ2xOn33qSygMNxprMABQokj/ZqxJXmXn0TgghUWAEyuzQMzsSNOJU4Fj4Q9Q7HzpRhz+/vRNXXzAEz94xA35JhsCzGVfCQaU8qSfVpvaQ1GYWqGdHJN19H2cSOs7skGUFP71qDPKyLVSKGAZnsSNr7EzwjgJUvPs0ACoPNBoTyJhRfF6AT8wFrkTIzKN3QgiJQqsyFjYzd5fBDUoTwSSw8EutD2pdHj+efX0b8rItuGbm0ISU1KQqgadSnlTECmYgcKAY/DMhbWmjZ6k5ZWjdfR8nBIId4TI7tDKdF9/b1S3LdBJJ8bpRs/pf+m0qDzQWwweCHaIXgCO5KxNH3WPPQQghScBSz46WYEeCylh4vn0n+7+v3I2KGifu+f7EjA50AECO3YxFt0zJ6FIeQrozrYyFShhC6+77OK2MJVxmR3cv00kUKg9MPC2IlG5j0zPz6J0QQqJAZSyJncYCqONng9OV1+84ic+3lOL6S4Zh1OAeCVmHVMayDAYUZ+PZO2fAL2ZmKQ8h3RlldkTW3fdxWhmLKLYOdvhFCVv3V6Ig19Kty3QShcoDE0875k23gFJmHr0TQkgUGC7oBD8Dgx2yrGDkwHw8eet0ZNtNCRn/JwQFOypqXXjhnW9w5oA8LLhkuKGv252wLIM8hyXZq0EIiUGWzYQHb5qM4h426tcQRnfex2XbA+9vvgW1jR5U1rrwydfH8dXuU3B6RPw2kLXSXct0EonKAxOLEdTPXLqNn828o3dCCIkSE/TFmmmZHcka/6cFOyRJxu9f3wZZARZ+fyKN6COEdHuyrMDl8eMf7++mfg1pSJbVkZ3B7+8d149HRa0TU0b3wozxfTD2jAIsumVKu+/W7lKmQ9JXSxmLJ8lrEl+ZdfROCCGdoDcoZVgwLJfclUmwcHXFz945w9ArbgKvjp59+3+HsL+kFgu/NwHFPeyGvR4hhCRKg9OLJ17ZnPD9KkmMBqcXT77a+v197q0deOaX5yE/x6ov153LdEj6ojIWQgjJMFqwI9OyOoDkjP+TZQXfu3Q4OI5Fo9OH+ZcMwwUT+xn2eoQQkkjdfawqiSzc+yvKrft3dOcyHZK+9NGzVMZCCCGZgdUalGZgsCPR4/+0spk/vbVDT+39zc1TEtInhBBCEqG7j1UlkdH7S7qzdJ3GQp8+QggJI5MzOxI9/i9U2cziV2gcHyEkfXT3saokMnp/SXfWktlBPTsIISQj6MEONvN2lYke/0fp3YSQdNfdx6qSyILfX6fTA7vdQu8v6TYYwYyieQvhr6sEAEgeJzhL9++ZRpkdhBAShtasKRMzO4CWuuKiPBvyHBbDp7BoV8M0lP5LCEk3idyvksTT3t+Ksm/p/SXdiuxqhNhQDXOfoRAba9C8Z11alLTQUSQhhITB8IL6/wwNdiQSpf8SQgghhCSe5HHCeXAz6jYsh+J14dSyh9G4/dO0mMxCR/CEEBIGwwdOtCnYYThK7yaEEEIISTzOYodj/CXgc4tQ8faTAIC+P1kKzpad5DXrOsrsIISQMNgMblCaDJTeTQghhBCSeLKrETWfvarfbtq9hspYCCEknWV6zw5CCCGEEJLe1DKWTWB4Hn1/shQ5U78D9/G9VMZCCCHpLJNHzxJCCCGEkPTHWexwnHURskadC86WjbwZ85Hr96ZFGQsdwRNCSBgU7CCEEEIIIemOFcyAYG73c3dHZSyEEBKGVsYCloIdhBBCCCGEdCcU7CCEkDAYjkbPEkIIIYQQ0h1RsIMQQsJgGAZF19wLJpDKJ3mcSV4jQgghhBBCSDQo2EEIIWGIjTUQ6yuRNfJciI01aN6zLi3GcBFCCCGEEJLuKNhBCCEhaGO46jYsB6Dg1LKH0bj907QYw0UIIYQQQki6o0J0QggJgbPY4Rh/CYT8Xjj95uMAgL4/WZoWY7gIIYQQQghJd5TZQQghYciuRlR/8pJ+u2n3GipjIYQQQgghpBugYAchhISglbEwPI++P1mKnKnfgfv4XipjIYQQQgghpBugMhZCCAmBs9jhOOsiZI06F5wtG3kz5iPX76UyFkIIIYQQQroBCnYQQkgYrGAGAmNng38mhBBCCCGEpDYqYyGEEEIIIYQQQkhayZhgx4cffojZs2fj0ksvxeuvv57s1SGEEEIIIYQQQohBMqKMpaKiAkuXLsV7770Hk8mE+fPnY8qUKTjjjDOSvWqEEEIIIYQQQgiJs4zI7Ni4cSOmTp2K3Nxc2Gw2XHbZZfj444+TvVqEEEIIIYQQQggxQEZkdlRWVqKwsFC/XVRUhF27dkX9+D179hixWobbtm1bsleBEMPQ9k3SHW3jJJ3R9k3SHW3jJJ11l+07I4IdsiyDYRj9tqIorW53ZPTo0TCbu9cUhm3btmHixInJXg1CDEHbN0l3tI2TdEbbN0l3tI2TdJZK27fX642YmJARZSzFxcWoqqrSb1dVVaGoqCiJa0QIIYQQQgghhBCjZESw45xzzsFXX32F2tpauN1ufPrpp5gxY0ayV4sQQgghhBBCCCEGyIgylp49e+Kuu+7CjTfeCL/fj2uvvRZjx45N9moRQgghhBBCCCHEABkR7ACAuXPnYu7cucleDUIIIYQQQgghhBgsI8pYCCGEEEIIIYQQkjko2EEIIYQQQgghhJC0QsEOQgghhBBCCCGEpBUKdhBCCCGEEEIIISStULCDEEIIIYQQQgghaYWCHYQQQgghhBBCCEkrFOwghBBCCCGEEEJIWqFgByGEEEIIIYQQQtIKn+wVSGWKogAAfD5fktckNl6vN9mrQIhhaPsm6Y62cZLOaPsm6Y62cZLOUmX71s7TtfP2thgl3G8ImpqacOjQoWSvBiGEEEIIIYQQQkIYNmwYHA5Hu/sp2BGBLMtwOp0QBAEMwyR7dQghhBBCCCGEEAI1o8Pv98Nut4Nl23fooGAHIYQQQgghhBBC0go1KCWEEEIIIYQQQkhaoWAHIYQQQgghhBBC0goFOwghhBBCCCGEEJJWKNhBCCGEEEIIIYSQtELBDkIIIYQQQgghhKQVCnYQQgghhBBCCCEkrVCwgxBCCCGEEEIIIWmFgh1p5sMPP8Ts2bNx6aWX4vXXX0/26hDSZc8//zzmzJmDOXPm4OmnnwYAbNy4EXPnzsWll16KpUuXJnkNCYmPp556Cvfffz8A2sZJ+li9ejXmzZuHWbNm4fHHHwdA2zdJL++//75+nPLUU08BoG2cdH/Nzc244oorcPLkSQDht+n9+/dj3rx5uOyyy/Cb3/wGoigma5VDomBHGqmoqMDSpUvxxhtvYOXKlXjrrbdw5MiRZK8WITHbuHEjNmzYgBUrVmDlypXYu3cvVq1ahQcffBAvvPAC/vvf/2LPnj1Yt25dsleVkC756quvsGLFCgCAx+OhbZykhdLSUjz88MN44YUX8MEHH2Dfvn1Yt24dbd8kbbjdbixevBj/+te/8P7772Pr1q1YvXo1beOkW/vmm2+wYMEClJSUAIh8XHLvvffioYcewieffAJFUfD2228ncc3bo2BHGtm4cSOmTp2K3Nxc2Gw2XHbZZfj444+TvVqExKywsBD3338/TCYTBEHAkCFDUFJSggEDBqBfv37geR5z586l7Zx0a/X19Vi6dCl+/vOfAwB27dpF2zhJC5999hlmz56N4uJiCIKApUuXwmq10vZN0oYkSZBlGW63G6IoQhRFZGVl0TZOurW3334bDz/8MIqKigCEPy4pKyuDx+PBWWedBQCYN29eym3rfLJXgMRPZWUlCgsL9dtFRUXYtWtXEteIkK4ZOnSo/nNJSQk++ugj3HDDDe2284qKimSsHiFx8dBDD+Guu+5CeXk5gND7ctrGSXd0/PhxCIKAn//85ygvL8cFF1yAoUOH0vZN0kZWVhbuvPNOzJo1C1arFWeffTbtw0m3t3jx4la3w23Tbe8vLCxMuW2dMjvSiCzLYBhGv60oSqvbhHRXhw8fxi233IL77rsP/fr1o+2cpI3ly5ejV69emDZtmn4f7ctJupAkCV999RWeeOIJvPXWW9i1axdKS0tp+yZp48CBA3j33XexZs0afPHFF2BZFiUlJbSNk7QS7rikOxyvUGZHGikuLsbWrVv121VVVXr6ESHd1bZt23DHHXfgwQcfxJw5c7B582ZUVVXpv6ftnHRn//3vf1FVVYUrr7wSDQ0NcLlcKCsrA8dx+jK0jZPuqqCgANOmTUN+fj4A4OKLL8bHH39M2zdJGxs2bMC0adPQo0cPAGoa/0svvUTbOEkrxcXFIY+9295fXV2dcts6ZXakkXPOOQdfffUVamtr4Xa78emnn2LGjBnJXi1CYlZeXo7bbrsNzz77LObMmQMAGDduHI4dO4bjx49DkiSsWrWKtnPSbb3yyitYtWoV3n//fdxxxx248MIL8Y9//IO2cZIWZs6ciQ0bNqCxsRGSJOGLL77A5ZdfTts3SRtnnnkmNm7cCJfLBUVRsHr1ajpOIWkn3Dbdp08fmM1mbNu2DYA6mSjVtnXK7EgjPXv2xF133YUbb7wRfr8f1157LcaOHZvs1SIkZi+99BK8Xi+WLFmi3zd//nwsWbIEv/zlL+H1enH++efj8ssvT+JaEhJfZrOZtnGSFsaNG4cf//jH+N73vge/34/p06djwYIFGDx4MG3fJC2ce+652LdvH+bNmwdBEDBmzBj88pe/xPTp02kbJ2kj0nHJs88+i0WLFqG5uRmjRo3CjTfemOS1bY1RFEVJ9koQQgghhBBCCCGExAuVsRBCCCGEEEIIISStULCDEEIIIYQQQgghaYWCHYQQQgghhBBCCEkrFOwghBBCCCGEEEJIWqFgByGEEEIIIYQQQtIKjZ4lhBBC2ti0aVNcxqf16dMHq1evjsMaJcZ7772HBx54AAAwefJk/Otf/0ryGsXfD37wA2zevBkAUFhYiP/+97/Izs6O+vHffvstZs+erd8+ePBg3Ncx2YK3/+62DRNCCCEayuwghBBCSEaqqqrCU089lezVIIQQQogBKLODEEII6cAVV1wBu93e6cfl5eUZsDYknt555x1cccUVmDZtWrJXhRBCCCFxRMEOQgghpAN33XUX+vbtm+zVIAZ56KGH8MEHH8BqtSZ7VQghhBASJ1TGQgghhBAAwLx583Dw4EEcPHgwLft1hHPixAn86U9/SvZqEEIIISSOKNhBCCGEkIzkcDj0n1977TXs2rUriWtDCCGEkHiiYAchhBBCMtKPfvQj5OfnAwAkScJvfvMb+P3+JK8VIYQQQuKBenYQQgghCRQ83vWBBx7ATTfdhJqaGrz55pv4/PPPUVZWBo/Hg8LCQkyZMgXXX389xo4dG9Vzr127Fu+99x527tyJ2tpaOBwODBs2DN/5zndw9dVXg2VZjBkzBj6fL+RI0Y5Gz95///1YsWIFAGDlypUYMWIEDh48iLfffhtffvklKioqwLIs+vTpg/PPPx833HADevbsGdW67927FytWrMDXX3+NiooKeL1e5OfnY/To0bjkkktwxRVXgOO4qJ4rWrm5uVi0aBHuvvtuAMChQ4fwt7/9DbfffnuXnvfCCy9EWVkZADVjZMqUKWGX7WjMa/B78tvf/hY33HADysvL8cYbb2DNmjUoKyuDIAjo06cPZs2ahQULFrTKWNm3bx+WLVuGzZs3o6KiAlarFcOHD8dVV12lbxPRqKurw6uvvorPPvsMZWVlsFgs6N+/Py6++GJcd911UTfjbWxsxPLly7F27VocO3YM9fX1cDgc6N+/P8477zxcf/31KCwsDPv4P//5z3j++ecBAH/5y19w/vnn469//Svee+891NbWorCwEBMmTMCtt96KQYMGRbVOhBBC0hMFOwghhJAkWrduHX7961+jrq6u1f2lpaUoLS3Fu+++i5tuugn3339/2OdoaGjAPffcg/Xr17e6v7a2Fl9//TW+/vprLF++HH/5y1/iuu5/+9vf8Nxzz0EUxVb3B/f9eOKJJzB79uywz+HxePDoo49ixYoVUBSl1e/Ky8tRXl6Ozz77DH//+9/xxz/+EUOHDo3rv2HOnDn48MMPsWbNGgDAiy++iMsvvxxnnHFGXF8nXj755BM8+OCDaG5ubnV/Q0MD9u3bh/fffx8vv/wyevbsiRdffBHPPfccJEnSl/P5fNi8eTM2b96M//3vf3j++ec7DCLt2LEDt912G2pqavT7PB4P6uvrsWvXLrz88st45JFHMGvWrIjP8+GHH+Lxxx9HfX19q/tra2tRW1uLnTt34qWXXsLChQtxww03RPX3uPfee/HRRx/pt0+ePImysjLcc889UT2eEEJI+qJgByGEEJIkO3fuxB/+8Ad4vV7YbDZMmjQJBQUFKC8vx+bNmyFJEhRFwSuvvIL+/fvje9/7XrvncLvduOWWW7Bnzx79vv79+2Ps2LFQFAXbt29HeXk5duzYgZtvvhmyLMdl3V999VWsXLkSAPSr6VarFYcOHcK+ffv0dbv33nsxZMgQDB8+POS633zzzdixY4d+X79+/TB27FiYzWacOHECO3fuhCiKOHLkCObPn49XX30VY8aMicu/QfPoo49iy5YtaG5uht/vx6JFi/DGG29EnfWQKF999RXWrl0LURSRk5ODCRMmIC8vD7t378bhw4cBAEeOHMHixYsxduxYLF26FAAwaNAgjBkzBn6/H19//bUeWFu9ejVee+013HzzzWFfs76+Hj/96U/R2NgIlmUxadIk9O3bF6dPn8aWLVvg9/tRX1+PhQsXgmVZXHbZZSGf59VXX8WTTz6p37bb7Zg4cSIKCwtRX1+Pbdu2ob6+Hi6XC7/73e9QVVWFu+66K+Lf4913322XCQMAkyZNijqjiBBCSPqiYAchhBCSJNoV6Xnz5uHXv/41cnNz9d8dPXoUP/vZz3DixAkAahbFggULwDBMq+dYunSpHugQBAGPPPIIrr32Wv33sizj9ddfx5IlS3Dw4MG4rfvKlSshCALuuecefP/734cgCPrvVq9ejbvvvhtutxuiKOL//u//8Oyzz7Z7jsWLF+uBjvz8fPzud7/DxRdf3GqZsrIyLFq0CBs3bkRzczPuvPNOvP/++61KNbqqZ8+euOeee/DII48AUDMZli1bppeXpIr//e9/AID58+fjvvvug91uBwAoioKnnnoKr7zyCgA1++Ozzz6DzWbD448/jjlz5ujP4Xa78atf/Qpr164FALz++usRgx1OpxMAMGzYMCxdurRVxktpaSl+9atfYc+ePZAkCQ899BAmTZqEHj16tHqOLVu24Omnn9Zv33LLLbjtttuQlZWl3+f3+/HKK69g6dKlkGUZL774IsaMGdNuewimBTp+9KMf4Yc//CGsVis2b97calskhBCSuSjYQQghhHRg6dKl+olltKxWq95rIZKLL7641RVvzeDBg/HEE0/o6fynT5/GoUOHWmVIVFRU4I033tBvL1myBFdccUWr52FZFj/4wQ9gsViwaNGiTv0bOnLvvffihz/8Ybv7L7zwQvz4xz/Gn//8ZwDAF1980W6ZAwcOYPny5QDUv9U///lPDBs2rN1yffr0wd///nfcfPPN2LJlC8rKyrBs2TL84he/iOu/Zf78+fjPf/6DLVu2AFDf8wsvvBB9+/aN6+t01cyZM/Hoo4+2uo9hGNx1111455130NTUBEANcj366KOtAh2A+rdevHgxZsyYAUmSUFpaiqqqqoh9MoqLi/Haa6+168vRr18/vPrqq7jyyitRVlaG+vp6vPrqq1i4cGGr5Z544gm9lOa2227DHXfc0e41BEHAT3/6U+Tk5OChhx4CADzzzDO48MILI2bYfP/738d9992n344UHCGEEJJZUis/kxBCCElBq1atwltvvdWp/959992onvvnP/952N9NmjQJOTk5+u3S0tJWv//000/16SGTJ09uF+gI9t3vfhfjx4+Pap2ikZubiwULFoT9ffBJZ319vX4Srlm2bJn+8/XXXx8y0KERBKHVCfSbb74ZyypHxDAMHn/8cZjNZgCAy+XCww8/HPfX6apbb7015P1ms7lVI9s+ffpg7ty5IZctKChA//799dsVFRURX3PhwoVhG5A6HA786le/0m+/9957rXqvbNu2TS9rKiwsjLi9A8B1112HwYMHAwBKSkqwadOmiMtHykohhBCS2SjYQQghhCSJ3W7H6NGjw/6eYRj07t1bv+1yuVr9XmuqCQDf+c53Ony9a665Joa1DO2ss86CyWQK+/s+ffq0ut123Tdu3Kj/fO6550b1elqZz+nTp/XynngaOHBgq0ksGzZs0KfPpAKbzRaxX4k2RhcAJk6c2K7kKVhwyVTb96bta15++eUR1+vSSy/VS0eqq6tx5MgR/XfB7/PkyZMjbjOAus2fd955+u1IwY6ioiL069cv4vMRQgjJXFTGQgghhHTg888/N6ScoV+/fhFPSAG0Kp8JnqoBoFVT0miadp511lmdW8EIgjMDQmlb9hM8saW2tlYfzQoAb7/9Nj777LNOvf6hQ4c6XIdY3HLLLfjoo4/0bIQlS5bgvPPOQ0FBQdxfq7P69u0bcXvh+ZbDuo4adAb3tWg7BSfYiBEjOgxQWCwWnHHGGdi/fz8AdYywNjVn165d+nKHDh3SS1QiOXToUMif20rViTmEEEJSAwU7CCGEkCSJpg9IcL+C4JNSn8+HhoYG/XZRUVGHz1VcXNzJNQyvo3Vv22cheN2rq6tb/e7TTz/t9Ou3HdUbLzzPY/Hixfjud78LURRRX1+Pxx57DM8995whr9cZwQ09O6KV43RVtFNNCgsL9WBH8HsTPK728OHD+tSYaLUdUxssOzu7U89FCCEks1AZCyGEEJIkHMfF/Ni2J/sWi6XDx1it1phfr62urHvb/h2xiFR60VUjR47ELbfcot/+5JNP9EkoyRScuZEo0WxXQOtty+v16j83Nzd36fUjvc/x3J4JIYSkH8rsIIQQQrqhtiehbrcbNpst4mM8Ho+RqxS14JNUlmWxa9eulBsXevvtt+PTTz9FSUkJAOCRRx7B5MmT4/46sizH/TnjyefzRbVccFAieCxw8Ha6ePHiVmORCSGEECNRZgchhBDSDTkcjla9FDqaqBHtMokQ3BxTluVW/TtShdlsxuOPP673yKiqqsJTTz3V6efpKJjhdDpjWr9Eqaqqimq506dP6z8H9zcJfq/bThMihBBCjETBDkIIIaQbYlkWo0aN0m/v3bu3w8cENzRNpl69erU6Ce5ovKhm5cqVWL9+PY4ePRp1xkFXnH322bj++uv12++88w6+/vrrDh8XXG7idrsjLnvy5MnYVzABDh8+HLGBKaCWJR09elS/HTwCd8SIEfrP0b7P27dvx8cff4zdu3e36ktDCCGEdAYFOwghhJBuKnhE53/+858Ol49mmURgGAZTpkzRb7/11lsdPmbnzp349a9/jZ/85CeYNWuWXl5itHvvvbdVY9ff//73HT4muHlrcMZDKMGjWVNRbW0ttm7dGnGZDz74QJ8UNHDgQPTq1Uv/3dSpU/Wfd+zYgQMHDkR8LlmW8dvf/hZ33nknrr32Wrz44otdWHtCCCGZjIIdhBBCSDd17bXX6r0uvvrqq4hNNNesWYN169YlatU6tGDBAv3nvXv34qWXXgq7rN/vxxNPPKHfHj16NIYNG2bo+mmysrLwyCOP6LejKTsZOHCg/vNHH30UdrlNmzal1HsSzpNPPhk2k6aioqLVpJrg9xUAZsyYgT59+ui3f/Ob37RqYNrWsmXLcOTIEQBqUOzqq6/uyqoTQgjJYBTsIIQQQrqpnj174oc//KF+e+HChfjggw/aLffhhx/irrvuSuSqdWjatGm44IIL9NvPPPMMli5d2u5EuKysDLfddhu++eYbAOoJ8D333JPIVcXMmTMxZ86cqJc///zz9Z83b96MP//5z3rmA6BmL6xatQo///nP47qeRtm7dy9+8YtftBojCwD79+/HjTfeqI+HHTx4cKuyH0Cd2nPvvffqt/fs2YMf/vCHOHbsWKvlfD4fXnnllVZ9Ua688sqEBbUIIYSkH5rGQgghhHRg6dKlrUoTOmPWrFmYNm1anNeoxe23344tW7bgm2++gcfjwb333ovnn39e75uwe/duveSjR48e+glrMsaYtrVkyRJ873vfw9GjR6EoCl588UW88cYbmDRpEnJyclBWVoYdO3bA7/frj7n99tsN/XuGs2jRInz55Zf6iX0ks2fPxl//+lf97/78889jxYoVGDduHGRZxp49e/ReHddccw0+//zzqJ43Gc4880ycOHECGzZswMyZMzFlyhQUFBTgxIkT2L59u96ANScnB0uXLg05DnbWrFnYvXu3nr2zY8cOzJ49G2eddRYGDBiA+vp67N27F5WVlfpjhg0bht/+9reJ+UcSQghJS8k/0iGEEEJS3KpVq2J+7ODBgw09Obdarfj73/+Ou+++G19++SUA4Pjx4zh+/Hir5c455xx8//vfx2233QYArSa5JEteXh7eeustPPDAA3oJTmNjI1avXt1uWYvFgoULF+LGG29M9GoCAPLz8/Hggw/ivvvu63BZk8mEv//97/jpT3+qBzzKysraTZ25/vrr8dBDD+Hzzz83YpXjYvDgwbj77rtx9913o7m5GevXr2+3zKBBg/Dcc89FzMK477770Lt3b/zhD3+A0+mELMvYvn07tm/f3m7ZmTNn4qmnnkJWVlZc/y2EEEIyCwU7CCGEkG4uNzcXL7/8Mj777DN88MEH2L17N2pqamCz2TBy5EjMmzcPc+fOxdq1a/XHpMqJZHZ2Nv7yl7/gm2++wQcffIDNmzejoqICTqcTWVlZGDRoEKZPn47vfve7rRqFJsOVV16JVatWhTzhb2vAgAH48MMPsXz5cnz88cc4fPgwnE4nCgsLMX78eFx//fWYPHlyAta6684//3ysXLkSr7zyCtavX4+KigrY7XYMHToUs2fPbtU7JpIbbrgBs2fPxjvvvIMNGzbg6NGjqK+vhyAIKCoqwoQJE3DVVVe1al5LCCGExIpROponRgghhJC0sHLlSvz6178GAFx22WWtGksSQgghhKQTyuwghBBCuqnVq1fj4MGD6N+/P8aPH4/evXtHXD64ZIAaPxJCCCEknVGwgxBCCOmmTp48iT/+8Y8AgAsvvBB//etfwy576tQpvP/++/rtc845x+jVI4QQQghJGho9SwghhHRT06dP139evXo1/vGPf8Dn87VbbseOHbjxxhvh8XgAAOPGjcOECRMStp6EEEIIIYlGPTsIIYSQbuw3v/kN3nnnHf12fn4+Ro0ahcLCQjQ2NqKkpARHjhzRf5+Xl4c333wTAwcOTMLaEkIIIYQkBgU7CCGEkG5MFEU8/vjjePPNN9HRV/ro0aPx+9//ngIdhBBCCEl7FOwghBBC0sDBgwexYsUKbN26FcePH4fT6QTP8yguLsaYMWMwa9YsXHTRRWAYJtmrSgghhBBiOAp2EEIIIYQQQgghJK1Qg1JCCCGEEEIIIYSkFQp2EEIIIYQQQgghJK1QsIMQQgghhBBCCCFphYIdhBBCCCGEEEIISSsU7CCEEEIIIYQQQkhaoWAHIYQQQgghhBBC0sr/AxbTmKhGghMCAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1296x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df=pd.DataFrame(np.arange(1,101))\n",
    "df['Actual']=y_test\n",
    "df['Predicted']=final_pred\n",
    "df=df.drop([0],axis=1)\n",
    "\n",
    "sns.set_theme(style=\"whitegrid\")\n",
    "a4_dims = (18,10)\n",
    "fig, ax = plt.subplots(figsize=a4_dims)\n",
    "sns.lineplot(data = df,markers=True, dashes=False)\n",
    "\n",
    "fig.suptitle('RUL Prediction using LSTM with lookack=1', fontsize=35)\n",
    "plt.xlabel(\"Engine Number\", fontsize=35)\n",
    "plt.ylabel(\"Remaining Useful Life\", fontsize=35)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Max Life >>  362\n",
      "Mean Life >>  206.31\n",
      "Min Life >>  128\n",
      "mean_squared_error >>  1960.8811887310094\n",
      "root_mean_squared_error >>  44.28183813631735\n",
      "mean_absolute_error >> 28.22695503478032\n"
     ]
    }
   ],
   "source": [
    "# For training results\n",
    "\n",
    "train_file = \"train_FD001.txt\" \n",
    "test_file = \"test_FD001.txt\"\n",
    "RUL_file = \"RUL_FD001.txt\"\n",
    "\n",
    "df = pd.read_csv(train_file,sep=\" \",header=None)\n",
    "\n",
    "#columns = ['unit_number','time_in_cycles','setting_1','setting_2','TRA','T2','T24','T30','T50','P2','P15','P30','Nf',\n",
    "#           'Nc','epr','Ps3 0','phi','NRf','NRc','BPR','farB','htBleed','Nf_dmd','PCNfR_dmd','W31','W32' ]\n",
    "#delete NaN values\n",
    "df.drop(columns=[26,27],inplace=True)\n",
    "columns = [\"Section-{}\".format(i)  for i in range(26)]\n",
    "df.columns = columns\n",
    "\n",
    "\n",
    "\n",
    "# Names \n",
    "MachineID_name = [\"Section-0\"]\n",
    "RUL_name = [\"Section-1\"]\n",
    "OS_name = [\"Section-{}\".format(i) for i in range(2,5)]\n",
    "Sensor_name = [\"Section-{}\".format(i) for i in range(5,26)]\n",
    "\n",
    "# Data in pandas DataFrame\n",
    "MachineID_data = df[MachineID_name]\n",
    "RUL_data = df[RUL_name]\n",
    "OS_data = df[OS_name]\n",
    "Sensor_data = df[Sensor_name]\n",
    "\n",
    "# Data in pandas Series\n",
    "MachineID_series = df[\"Section-0\"]\n",
    "RUL_series = df[\"Section-1\"]\n",
    "\n",
    "grp = RUL_data.groupby(MachineID_series)\n",
    "max_cycles = np.array([max(grp.get_group(i)[\"Section-1\"]) for i in MachineID_series.unique()])\n",
    "print(\"Max Life >> \",max(max_cycles))\n",
    "print(\"Mean Life >> \",np.mean(max_cycles))\n",
    "print(\"Min Life >> \",min(max_cycles))\n",
    "\n",
    "#delete columns with constant values that do not carry information about the state of the unit\n",
    "#data = pd.concat([RUL_data,OS_data,Sensor_data], axis=1)\n",
    "df.drop(columns=[\"Section-0\",\n",
    "                \"Section-4\", # Operatinal Setting\n",
    "                \"Section-5\", # Sensor data\n",
    "                \"Section-9\", # Sensor data\n",
    "                \"Section-10\", # Sensor data\n",
    "                \"Section-14\",# Sensor data\n",
    "                \"Section-20\",# Sensor data\n",
    "                \"Section-22\",# Sensor data\n",
    "                \"Section-23\"] , inplace=True)\n",
    "\n",
    "\n",
    "gen = MinMaxScaler(feature_range=(0, 1))\n",
    "df = gen.fit_transform(df)\n",
    "df = pd.DataFrame(df)\n",
    "#df = df.rolling(20).mean()\n",
    "pt = PowerTransformer()\n",
    "df = pt.fit_transform(df)\n",
    "\n",
    "df=np.nan_to_num(df)\n",
    "\n",
    "\n",
    "def RUL_df():\n",
    "    rul_lst = [j  for i in MachineID_series.unique() for j in np.array(grp.get_group(i)[::-1][\"Section-1\"])]\n",
    "    rul_col = pd.DataFrame({\"rul\":rul_lst})\n",
    "    return rul_col\n",
    "\n",
    "RUL_df().head()\n",
    "\n",
    "X_train = np.array(df)\n",
    "\n",
    "y_train = np.array(RUL_df()).reshape(-1,1)\n",
    "\n",
    "count = 0\n",
    "for cycle_len in max_cycles:\n",
    "    for i in range(6):\n",
    "        y_train[count+i] = 0\n",
    "    count = count + cycle_len\n",
    "\n",
    "\n",
    "X_train = create_dataset(X_train)\n",
    "y_train = y_train[2:]\n",
    "\n",
    "\n",
    "\n",
    "y_train_pred = model.predict(X_train)\n",
    "print(\"mean_squared_error >> \", mean_squared_error(y_train,y_train_pred))\n",
    "print(\"root_mean_squared_error >> \", math.sqrt(mean_squared_error(y_train,y_train_pred)))\n",
    "print(\"mean_absolute_error >>\",mean_absolute_error(y_train,y_train_pred))\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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