SDAE_Code_v1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1.Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import glob\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.read_csv('./Data/data_9_5day.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.utils import shuffle\n",
    "df = shuffle(df).reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>Source.IP</th>\n",
       "      <th>Source.Port</th>\n",
       "      <th>Destination.IP</th>\n",
       "      <th>Destination.Port</th>\n",
       "      <th>Protocol</th>\n",
       "      <th>Timestamp</th>\n",
       "      <th>Flow.Duration</th>\n",
       "      <th>Total.Fwd.Packets</th>\n",
       "      <th>Total.Backward.Packets</th>\n",
       "      <th>Fwd.Packet.Length.Max</th>\n",
       "      <th>...</th>\n",
       "      <th>min_seg_size_forward</th>\n",
       "      <th>Active.Mean</th>\n",
       "      <th>Active.Std</th>\n",
       "      <th>Active.Max</th>\n",
       "      <th>Active.Min</th>\n",
       "      <th>Idle.Mean</th>\n",
       "      <th>Idle.Std</th>\n",
       "      <th>Idle.Max</th>\n",
       "      <th>Idle.Min</th>\n",
       "      <th>ProtocolName</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>180881369</td>\n",
       "      <td>47126</td>\n",
       "      <td>3003188461</td>\n",
       "      <td>443</td>\n",
       "      <td>6</td>\n",
       "      <td>1.494323e+09</td>\n",
       "      <td>33970783</td>\n",
       "      <td>13</td>\n",
       "      <td>8</td>\n",
       "      <td>517</td>\n",
       "      <td>...</td>\n",
       "      <td>32</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>GOOGLE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3232253999</td>\n",
       "      <td>52539</td>\n",
       "      <td>180881158</td>\n",
       "      <td>3128</td>\n",
       "      <td>6</td>\n",
       "      <td>1.494318e+09</td>\n",
       "      <td>7113423</td>\n",
       "      <td>13</td>\n",
       "      <td>11</td>\n",
       "      <td>1418</td>\n",
       "      <td>...</td>\n",
       "      <td>20</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>GOOGLE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3232261637</td>\n",
       "      <td>49543</td>\n",
       "      <td>180881157</td>\n",
       "      <td>3128</td>\n",
       "      <td>6</td>\n",
       "      <td>1.494314e+09</td>\n",
       "      <td>114364374</td>\n",
       "      <td>145</td>\n",
       "      <td>173</td>\n",
       "      <td>220</td>\n",
       "      <td>...</td>\n",
       "      <td>20</td>\n",
       "      <td>5.093378e+06</td>\n",
       "      <td>7.418616e+06</td>\n",
       "      <td>18077732.0</td>\n",
       "      <td>209313.0</td>\n",
       "      <td>1.237077e+07</td>\n",
       "      <td>3.847733e+06</td>\n",
       "      <td>16359072.0</td>\n",
       "      <td>6443215.0</td>\n",
       "      <td>HTTP_PROXY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3232254071</td>\n",
       "      <td>1917</td>\n",
       "      <td>180881156</td>\n",
       "      <td>3128</td>\n",
       "      <td>6</td>\n",
       "      <td>1.494322e+09</td>\n",
       "      <td>3977362</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>...</td>\n",
       "      <td>20</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>HTTP_CONNECT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>180881160</td>\n",
       "      <td>3128</td>\n",
       "      <td>3232273938</td>\n",
       "      <td>54107</td>\n",
       "      <td>6</td>\n",
       "      <td>1.494322e+09</td>\n",
       "      <td>114225707</td>\n",
       "      <td>91</td>\n",
       "      <td>38</td>\n",
       "      <td>1427</td>\n",
       "      <td>...</td>\n",
       "      <td>20</td>\n",
       "      <td>2.955797e+05</td>\n",
       "      <td>2.659303e+05</td>\n",
       "      <td>652026.0</td>\n",
       "      <td>64467.0</td>\n",
       "      <td>1.868151e+07</td>\n",
       "      <td>1.028667e+07</td>\n",
       "      <td>28859303.0</td>\n",
       "      <td>5083478.0</td>\n",
       "      <td>HTTP_PROXY</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 76 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Source.IP  Source.Port  Destination.IP  Destination.Port  Protocol  \\\n",
       "0   180881369        47126      3003188461               443         6   \n",
       "1  3232253999        52539       180881158              3128         6   \n",
       "2  3232261637        49543       180881157              3128         6   \n",
       "3  3232254071         1917       180881156              3128         6   \n",
       "4   180881160         3128      3232273938             54107         6   \n",
       "\n",
       "      Timestamp  Flow.Duration  Total.Fwd.Packets  Total.Backward.Packets  \\\n",
       "0  1.494323e+09       33970783                 13                       8   \n",
       "1  1.494318e+09        7113423                 13                      11   \n",
       "2  1.494314e+09      114364374                145                     173   \n",
       "3  1.494322e+09        3977362                  2                       0   \n",
       "4  1.494322e+09      114225707                 91                      38   \n",
       "\n",
       "   Fwd.Packet.Length.Max  ...  min_seg_size_forward   Active.Mean  \\\n",
       "0                    517  ...                    32  0.000000e+00   \n",
       "1                   1418  ...                    20  0.000000e+00   \n",
       "2                    220  ...                    20  5.093378e+06   \n",
       "3                      6  ...                    20  0.000000e+00   \n",
       "4                   1427  ...                    20  2.955797e+05   \n",
       "\n",
       "     Active.Std  Active.Max  Active.Min     Idle.Mean      Idle.Std  \\\n",
       "0  0.000000e+00         0.0         0.0  0.000000e+00  0.000000e+00   \n",
       "1  0.000000e+00         0.0         0.0  0.000000e+00  0.000000e+00   \n",
       "2  7.418616e+06  18077732.0    209313.0  1.237077e+07  3.847733e+06   \n",
       "3  0.000000e+00         0.0         0.0  0.000000e+00  0.000000e+00   \n",
       "4  2.659303e+05    652026.0     64467.0  1.868151e+07  1.028667e+07   \n",
       "\n",
       "     Idle.Max   Idle.Min  ProtocolName  \n",
       "0         0.0        0.0        GOOGLE  \n",
       "1         0.0        0.0        GOOGLE  \n",
       "2  16359072.0  6443215.0    HTTP_PROXY  \n",
       "3         0.0        0.0  HTTP_CONNECT  \n",
       "4  28859303.0  5083478.0    HTTP_PROXY  \n",
       "\n",
       "[5 rows x 76 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "X=df.iloc[:,:-1]\n",
    "y=df.iloc[:,-1]\n",
    "XU_train,XL,yu_train,yL=train_test_split(X,y,random_state=0,test_size=0.7)# labeled unlabeled split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "from imblearn.over_sampling import RandomOverSampler\n",
    "from imblearn.under_sampling import RandomUnderSampler "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\dhruvjyoti\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    }
   ],
   "source": [
    "XL['ProtocolName']=yL\n",
    "a=XL.groupby('ProtocolName')['ProtocolName'].count().sort_values(ascending=False)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "#oversample\n",
    "b=a.where(a>3000)\n",
    "b=b.fillna(3000,downcast='infer')\n",
    "threshold=dict(zip(b.index,b.values))\n",
    "ros=RandomOverSampler(sampling_strategy=threshold,random_state=0)\n",
    "Xi=XL.iloc[:,:-1]\n",
    "yi=yL\n",
    "X1,y1=ros.fit_resample(Xi,yi)\n",
    "#undersample\n",
    "b=b.where(b<4000)\n",
    "b=b.fillna(4000,downcast='infer')\n",
    "threshold=dict(zip(b.index,b.values))\n",
    "rus=RandomUnderSampler(sampling_strategy=threshold,random_state=0)\n",
    "\n",
    "XL_train,yL_train=rus.fit_resample(X1,y1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "'''\n",
    "Function mask_noise\n",
    "\n",
    "Input: numpy array , fraction_delete(between 0 and 1)\n",
    "\n",
    "Removes a fraction_delete fraction of the input array X randomly\n",
    "\n",
    "Output: numpy array after deletions\n",
    "\n",
    "'''\n",
    "def mask_noise(X,fraction_delete=0.2):\n",
    "    mask=np.random.rand(np.shape(X)[0],np.shape(X)[1])\n",
    "    mask=1*(mask>fraction_delete)\n",
    "    return mask*X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.layers import Input, Dense\n",
    "from keras.models import Model\n",
    "import keras.backend as K\n",
    "import tensorflow as tf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "'''\n",
    "Sparcity Autoencoder's Sparcity Activity Regularization\n",
    "\n",
    "'''\n",
    "def kl_divergence(rho, rho_hat):\n",
    "    return rho * tf.math.log(rho) - rho * tf.math.log(rho_hat+1e-12) + (1 - rho) * tf.math.log(1 - rho) - (1 - rho) * tf.math.log(1 - rho_hat+1e-12)\n",
    "\n",
    "class SparseActivityRegularizer(tf.keras.regularizers.Regularizer):\n",
    "\n",
    "    def __init__(self, p=0.1, sparsityBeta=3):\n",
    "        self.p = p\n",
    "        self.sparsityBeta = sparsityBeta\n",
    "\n",
    "    def __call__(self, x):\n",
    "        regularization = 0            \n",
    "\n",
    "        p_hat = K.mean(x, axis=0)\n",
    "        regularization += self.sparsityBeta * K.sum(kl_divergence(self.p, p_hat))\n",
    "\n",
    "        return regularization\n",
    "\n",
    "    def get_config(self):\n",
    "        return {\"name\": self.__class__.__name__} "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.layers import BatchNormalization\n",
    "from keras.optimizers import RMSprop\n",
    "from keras.optimizers import Adam"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "#X=df.iloc[:,:-1]\n",
    "#y=df.iloc[:,-1]\n",
    "yL_train=pd.get_dummies(yL_train)#One-hotencoding\n",
    "\n",
    "# Split into train,test and val\n",
    "\n",
    "XL_train,XL_val,yL_train,yL_val=train_test_split(XL_train,yL_train,random_state=0,test_size=0.2)\n",
    "#print(len(X_val))\n",
    "XL_val,XL_test,yL_val,yL_test=train_test_split(XL_val,yL_val,random_state=0,test_size=0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "scaler=StandardScaler()\n",
    "#scaling and normalization\n",
    "XU_train=scaler.fit_transform(XU_train)\n",
    "XL_train=scaler.fit_transform(XL_train)\n",
    "XL_val=scaler.transform(XL_val)\n",
    "XL_test=scaler.transform(XL_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "#XU_train,XL_train,yU_train,yL_train=train_test_split(X_train,y_train,random_state=0,test_size=0.5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2. Training the SDAE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "'''\n",
    "Function SDAE\n",
    "Inputs: numpy array X, list layers,list activations,float fraction_delete\n",
    "layers is a list of no. of hidden nodes in each hidden layer\n",
    "activations is a list of activations used in each encoding layer\n",
    "trains each autoencoder layer and outputs all the encoder-layer models.\n",
    "Output: a list of keras models\n",
    "\n",
    "'''\n",
    "\n",
    "\n",
    "def SDAE(X,layers,activations,fraction_delete=0.2):\n",
    "    layers.insert(0,len(X[0]))\n",
    "    #layers.insert(0,len(X_train.iloc[0]))\n",
    "    ec=[]\n",
    "    for i in range(len(layers)-1):\n",
    "        encoding_dim = layers[i+1]\n",
    "\n",
    "    # this is our input placeholder\n",
    "        input_ = Input(shape=(layers[i],))\n",
    "    # define regulariser\n",
    "        regulariser = SparseActivityRegularizer(0.1, 3)\n",
    "        \n",
    "        #bn=BatchNormalization()(input_)\n",
    "\n",
    "    # \"encoded\" is the encoded representation of the input\n",
    "        encoded = Dense(encoding_dim, activation=activations[i],activity_regularizer=regulariser)(input_)\n",
    "    # \"decoded\" is the lossy reconstruction of the input\n",
    "        decoded = Dense(layers[i],activation=None)(encoded)\n",
    "        #bn2 = BatchNormalization()(decoded)\n",
    "\n",
    "    # this model maps an input to its reconstruction\n",
    "        autoencoder = Model(input_, decoded)\n",
    "\n",
    "    # this model maps an input to its encoded representation\n",
    "        encoder = Model(input_, encoded)\n",
    "\n",
    "    # create a placeholder for an encoded (32-dimensional) input\n",
    "        encoded_input = Input(shape=(encoding_dim,))\n",
    "\n",
    "        autoencoder.compile(optimizer=RMSprop(learning_rate=0.001,clipnorm=5), loss='mean_squared_error')\n",
    "        X_noisy=mask_noise(X,fraction_delete)\n",
    "        print('Training Layer ' + str(i+1)+' : ')\n",
    "        autoencoder.fit(X_noisy, X,\n",
    "                epochs=10,\n",
    "                batch_size=128,\n",
    "                shuffle=True)\n",
    "        ec.append(encoder)\n",
    "        X=encoder.predict(X)\n",
    "    print('All Layers Done!')    \n",
    "    return ec        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Layer 1 : \n",
      "Epoch 1/10\n",
      "121358/121358 [==============================] - 2s 16us/step - loss: 1.5314\n",
      "Epoch 2/10\n",
      "121358/121358 [==============================] - 2s 14us/step - loss: 0.6530\n",
      "Epoch 3/10\n",
      "121358/121358 [==============================] - 1s 12us/step - loss: 0.5635\n",
      "Epoch 4/10\n",
      "121358/121358 [==============================] - 1s 12us/step - loss: 0.5027\n",
      "Epoch 5/10\n",
      "121358/121358 [==============================] - 1s 12us/step - loss: 0.4476\n",
      "Epoch 6/10\n",
      "121358/121358 [==============================] - 1s 12us/step - loss: 0.4034\n",
      "Epoch 7/10\n",
      "121358/121358 [==============================] - 1s 12us/step - loss: 0.3691\n",
      "Epoch 8/10\n",
      "121358/121358 [==============================] - 1s 12us/step - loss: 0.3370\n",
      "Epoch 9/10\n",
      "121358/121358 [==============================] - 2s 13us/step - loss: 0.3122\n",
      "Epoch 10/10\n",
      "121358/121358 [==============================] - 1s 12us/step - loss: 0.2892\n",
      "Training Layer 2 : \n",
      "Epoch 1/10\n",
      "121358/121358 [==============================] - 1s 10us/step - loss: 0.1740\n",
      "Epoch 2/10\n",
      "121358/121358 [==============================] - 1s 10us/step - loss: 0.0747\n",
      "Epoch 3/10\n",
      "121358/121358 [==============================] - 1s 10us/step - loss: 0.0672\n",
      "Epoch 4/10\n",
      "121358/121358 [==============================] - 1s 9us/step - loss: 0.0608\n",
      "Epoch 5/10\n",
      "121358/121358 [==============================] - 1s 9us/step - loss: 0.0568\n",
      "Epoch 6/10\n",
      "121358/121358 [==============================] - 1s 10us/step - loss: 0.0522\n",
      "Epoch 7/10\n",
      "121358/121358 [==============================] - 1s 9us/step - loss: 0.0480\n",
      "Epoch 8/10\n",
      "121358/121358 [==============================] - 1s 9us/step - loss: 0.0443\n",
      "Epoch 9/10\n",
      "121358/121358 [==============================] - 1s 10us/step - loss: 0.0416\n",
      "Epoch 10/10\n",
      "121358/121358 [==============================] - 1s 9us/step - loss: 0.0385\n",
      "All Layers Done!\n"
     ]
    }
   ],
   "source": [
    "#XU_train,XL_train,yU_train,yL_train=train_test_split(X_train,y_train,random_state=0,test_size=0.5)\n",
    "#train Sparse Stacked Denoising Autoencoder\n",
    "layers = [50,50]\n",
    "activations=['relu','relu']\n",
    "stacked_layers=SDAE(XU_train,layers,activations)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3.Training the SoftMax classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "'''\n",
    "Performs a Forward Pass through the SDAE\n",
    "\n",
    "'''\n",
    "def SDAE_predict(stacked_layers,X):\n",
    "    for layer in stacked_layers:\n",
    "        X1=layer.predict(X)\n",
    "        X=X1\n",
    "    return X1    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "#forward pass through SDAE\n",
    "XL_train1=SDAE_predict(stacked_layers,XL_train)\n",
    "XL_val1=SDAE_predict(stacked_layers,XL_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras import regularizers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 131546 samples, validate on 16443 samples\n",
      "Epoch 1/40\n",
      "131546/131546 [==============================] - 3s 21us/step - loss: 2.3818 - accuracy: 0.4191 - val_loss: 1.9706 - val_accuracy: 0.4960\n",
      "Epoch 2/40\n",
      "131546/131546 [==============================] - 3s 21us/step - loss: 1.9281 - accuracy: 0.5005 - val_loss: 1.8533 - val_accuracy: 0.5212\n",
      "Epoch 3/40\n",
      "131546/131546 [==============================] - 3s 24us/step - loss: 1.8568 - accuracy: 0.5139 - val_loss: 1.8031 - val_accuracy: 0.5379\n",
      "Epoch 4/40\n",
      "131546/131546 [==============================] - 3s 20us/step - loss: 1.8241 - accuracy: 0.5229 - val_loss: 1.7728 - val_accuracy: 0.5464\n",
      "Epoch 5/40\n",
      "131546/131546 [==============================] - 3s 20us/step - loss: 1.8090 - accuracy: 0.5272 - val_loss: 1.7580 - val_accuracy: 0.5494\n",
      "Epoch 6/40\n",
      "131546/131546 [==============================] - 3s 19us/step - loss: 1.7971 - accuracy: 0.5321 - val_loss: 1.7545 - val_accuracy: 0.5476\n",
      "Epoch 7/40\n",
      "131546/131546 [==============================] - 3s 22us/step - loss: 1.7942 - accuracy: 0.5335 - val_loss: 1.7470 - val_accuracy: 0.5604\n",
      "Epoch 8/40\n",
      "131546/131546 [==============================] - 3s 20us/step - loss: 1.7922 - accuracy: 0.5340 - val_loss: 1.7495 - val_accuracy: 0.5518\n",
      "Epoch 9/40\n",
      "131546/131546 [==============================] - 3s 21us/step - loss: 1.7938 - accuracy: 0.5339 - val_loss: 1.7426 - val_accuracy: 0.5579\n",
      "Epoch 10/40\n",
      "131546/131546 [==============================] - 3s 21us/step - loss: 1.7952 - accuracy: 0.5350 - val_loss: 1.7499 - val_accuracy: 0.5624\n",
      "Epoch 11/40\n",
      "131546/131546 [==============================] - 3s 20us/step - loss: 1.7986 - accuracy: 0.5368 - val_loss: 1.7496 - val_accuracy: 0.5616\n",
      "Epoch 12/40\n",
      "131546/131546 [==============================] - 3s 21us/step - loss: 1.8051 - accuracy: 0.5383 - val_loss: 1.7566 - val_accuracy: 0.5631\n",
      "Epoch 13/40\n",
      "131546/131546 [==============================] - 3s 20us/step - loss: 1.8092 - accuracy: 0.5383 - val_loss: 1.7627 - val_accuracy: 0.5565\n",
      "Epoch 14/40\n",
      "131546/131546 [==============================] - 3s 21us/step - loss: 1.8153 - accuracy: 0.5368 - val_loss: 1.7746 - val_accuracy: 0.5587\n",
      "Epoch 15/40\n",
      "131546/131546 [==============================] - 3s 20us/step - loss: 1.8235 - accuracy: 0.5380 - val_loss: 1.7768 - val_accuracy: 0.5639\n",
      "Epoch 16/40\n",
      "131546/131546 [==============================] - 3s 21us/step - loss: 1.8313 - accuracy: 0.5381 - val_loss: 1.7787 - val_accuracy: 0.5641\n",
      "Epoch 17/40\n",
      "131546/131546 [==============================] - 3s 20us/step - loss: 1.8383 - accuracy: 0.5393 - val_loss: 1.7856 - val_accuracy: 0.5650\n",
      "Epoch 18/40\n",
      "131546/131546 [==============================] - 3s 21us/step - loss: 1.8467 - accuracy: 0.5389 - val_loss: 1.7936 - val_accuracy: 0.5635\n",
      "Epoch 19/40\n",
      "131546/131546 [==============================] - 3s 20us/step - loss: 1.8536 - accuracy: 0.5390 - val_loss: 1.8026 - val_accuracy: 0.5604\n",
      "Epoch 20/40\n",
      "131546/131546 [==============================] - 3s 21us/step - loss: 1.8622 - accuracy: 0.5387 - val_loss: 1.8140 - val_accuracy: 0.5612\n",
      "Epoch 21/40\n",
      "131546/131546 [==============================] - 3s 21us/step - loss: 1.8703 - accuracy: 0.5383 - val_loss: 1.8233 - val_accuracy: 0.5580\n",
      "Epoch 22/40\n",
      "131546/131546 [==============================] - 3s 21us/step - loss: 1.8790 - accuracy: 0.5397 - val_loss: 1.8306 - val_accuracy: 0.5609\n",
      "Epoch 23/40\n",
      "131546/131546 [==============================] - 3s 21us/step - loss: 1.8867 - accuracy: 0.5389 - val_loss: 1.8354 - val_accuracy: 0.5602\n",
      "Epoch 24/40\n",
      "131546/131546 [==============================] - 3s 20us/step - loss: 1.8946 - accuracy: 0.5396 - val_loss: 1.8466 - val_accuracy: 0.5628\n",
      "Epoch 25/40\n",
      "131546/131546 [==============================] - 3s 21us/step - loss: 1.9012 - accuracy: 0.5389 - val_loss: 1.8528 - val_accuracy: 0.5584\n",
      "Epoch 26/40\n",
      "131546/131546 [==============================] - 3s 20us/step - loss: 1.9128 - accuracy: 0.5380 - val_loss: 1.8590 - val_accuracy: 0.5621\n",
      "Epoch 27/40\n",
      "131546/131546 [==============================] - 3s 21us/step - loss: 1.9190 - accuracy: 0.5394 - val_loss: 1.8682 - val_accuracy: 0.5680\n",
      "Epoch 28/40\n",
      "131546/131546 [==============================] - 3s 20us/step - loss: 1.9262 - accuracy: 0.5399 - val_loss: 1.8757 - val_accuracy: 0.5643\n",
      "Epoch 29/40\n",
      "131546/131546 [==============================] - 3s 21us/step - loss: 1.9339 - accuracy: 0.5400 - val_loss: 1.8858 - val_accuracy: 0.5605\n",
      "Epoch 30/40\n",
      "131546/131546 [==============================] - 3s 20us/step - loss: 1.9435 - accuracy: 0.5396 - val_loss: 1.8973 - val_accuracy: 0.5621\n",
      "Epoch 31/40\n",
      "131546/131546 [==============================] - 3s 21us/step - loss: 1.9511 - accuracy: 0.5392 - val_loss: 1.8903 - val_accuracy: 0.5643\n",
      "Epoch 32/40\n",
      "131546/131546 [==============================] - 3s 21us/step - loss: 1.9573 - accuracy: 0.5386 - val_loss: 1.9102 - val_accuracy: 0.5635\n",
      "Epoch 33/40\n",
      "131546/131546 [==============================] - 3s 21us/step - loss: 1.9651 - accuracy: 0.5387 - val_loss: 1.9094 - val_accuracy: 0.5604\n",
      "Epoch 34/40\n",
      "131546/131546 [==============================] - 3s 21us/step - loss: 1.9713 - accuracy: 0.5396 - val_loss: 1.9208 - val_accuracy: 0.5667\n",
      "Epoch 35/40\n",
      "131546/131546 [==============================] - 3s 20us/step - loss: 1.9775 - accuracy: 0.5395 - val_loss: 1.9252 - val_accuracy: 0.5573\n",
      "Epoch 36/40\n",
      "131546/131546 [==============================] - 3s 22us/step - loss: 1.9876 - accuracy: 0.5381 - val_loss: 1.9352 - val_accuracy: 0.5619\n",
      "Epoch 37/40\n",
      "131546/131546 [==============================] - 3s 20us/step - loss: 1.9930 - accuracy: 0.5390 - val_loss: 1.9381 - val_accuracy: 0.5583\n",
      "Epoch 38/40\n",
      "131546/131546 [==============================] - 3s 21us/step - loss: 1.9995 - accuracy: 0.5378 - val_loss: 1.9432 - val_accuracy: 0.5584\n",
      "Epoch 39/40\n",
      "131546/131546 [==============================] - 3s 20us/step - loss: 2.0038 - accuracy: 0.5382 - val_loss: 1.9461 - val_accuracy: 0.5623\n",
      "Epoch 40/40\n",
      "131546/131546 [==============================] - 3s 21us/step - loss: 2.0105 - accuracy: 0.5378 - val_loss: 1.9553 - val_accuracy: 0.5619\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.callbacks.History at 0x2068fb3cf60>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Train Classifier separately\n",
    "input1 = Input(shape=(layers[-1],))\n",
    "bn1=BatchNormalization()(input1)\n",
    "#L0 = Dense(100,activation='tanh')(bn1) #if one wishes to include another layer, just uncomment\n",
    "L1 = Dense(len(yL_train.columns),activation='softmax',kernel_regularizer=regularizers.l2(0.0001) )(bn1)\n",
    "classifier=Model(input1,L1)\n",
    "classifier.compile(optimizer='rmsprop',loss='categorical_crossentropy',metrics=['accuracy'])\n",
    "classifier.fit(XL_train1,yL_train,\n",
    "                epochs=40,\n",
    "                batch_size=64,\n",
    "                shuffle=True,\n",
    "                validation_data=(XL_val1, yL_val))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4. Fine Tuning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras import regularizers\n",
    "from keras.models import load_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tensor(\"model_2/dense_1/Relu:0\", shape=(None, 50), dtype=float32)\n",
      "[<tf.Tensor 'dense_1_1/weight_regularizer/add:0' shape=() dtype=float32>]\n",
      "Tensor(\"model_4/dense_3/Relu:0\", shape=(None, 50), dtype=float32)\n",
      "[<tf.Tensor 'dense_3_1/weight_regularizer/add:0' shape=() dtype=float32>]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\dhruvjyoti\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\keras\\engine\\saving.py:341: UserWarning: No training configuration found in save file: the model was *not* compiled. Compile it manually.\n",
      "  warnings.warn('No training configuration found in save file: '\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[<tf.Tensor 'dense_5_1/weight_regularizer/add:0' shape=() dtype=float32>]\n"
     ]
    }
   ],
   "source": [
    "# change regularisation settings\n",
    "a=[]\n",
    "input2=Input(shape=(len(XL_train[0]),))\n",
    "a.append(input2)\n",
    "for i in range(len(stacked_layers)):\n",
    "    print(stacked_layers[i](a[i]))\n",
    "    encoder=stacked_layers[i]\n",
    "    encoder.layers[-1].kernel_regularizer=regularizers.l2(0.0001) #set or reset weight regularizer \n",
    "    encoder.layers[-1].activity_regularizer=None #remove Sparse activity regularizer\n",
    "    encoder.save('temp.h5')\n",
    "    encoder=load_model('temp.h5')\n",
    "    print(encoder.losses)\n",
    "    a.append(encoder(a[i]))\n",
    "classifier.layers[-1].kernel_regularizer=regularizers.l2(0.0001)#set or reset weight regularizer \n",
    "classifier.save('temp.h5')\n",
    "classifier=load_model('temp.h5')\n",
    "print(classifier.losses)\n",
    "a.append(classifier(a[-1]))    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "model=Model(input2,a[-1])#final fine tuned model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(optimizer='rmsprop',loss='categorical_crossentropy',metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 131546 samples, validate on 16443 samples\n",
      "Epoch 1/50\n",
      "131546/131546 [==============================] - 2s 12us/step - loss: 1.9004 - accuracy: 0.5669 - val_loss: 1.8611 - val_accuracy: 0.5728\n",
      "Epoch 2/50\n",
      "131546/131546 [==============================] - 1s 11us/step - loss: 1.6892 - accuracy: 0.6152 - val_loss: 1.7495 - val_accuracy: 0.5972\n",
      "Epoch 3/50\n",
      "131546/131546 [==============================] - 1s 11us/step - loss: 1.5866 - accuracy: 0.6387 - val_loss: 1.5829 - val_accuracy: 0.6395\n",
      "Epoch 4/50\n",
      "131546/131546 [==============================] - 2s 12us/step - loss: 1.5140 - accuracy: 0.6567 - val_loss: 1.5211 - val_accuracy: 0.6472\n",
      "Epoch 5/50\n",
      "131546/131546 [==============================] - 1s 11us/step - loss: 1.4566 - accuracy: 0.6677 - val_loss: 1.5677 - val_accuracy: 0.6414\n",
      "Epoch 6/50\n",
      "131546/131546 [==============================] - 2s 11us/step - loss: 1.4113 - accuracy: 0.6786 - val_loss: 1.4556 - val_accuracy: 0.6627\n",
      "Epoch 7/50\n",
      "131546/131546 [==============================] - 1s 11us/step - loss: 1.3757 - accuracy: 0.6836 - val_loss: 1.4818 - val_accuracy: 0.6631\n",
      "Epoch 8/50\n",
      "131546/131546 [==============================] - 1s 11us/step - loss: 1.3460 - accuracy: 0.6903 - val_loss: 1.3773 - val_accuracy: 0.6852\n",
      "Epoch 9/50\n",
      "131546/131546 [==============================] - 2s 12us/step - loss: 1.3186 - accuracy: 0.6965 - val_loss: 1.3496 - val_accuracy: 0.6871\n",
      "Epoch 10/50\n",
      "131546/131546 [==============================] - 1s 11us/step - loss: 1.2942 - accuracy: 0.7016 - val_loss: 1.2966 - val_accuracy: 0.6987\n",
      "Epoch 11/50\n",
      "131546/131546 [==============================] - 1s 11us/step - loss: 1.2722 - accuracy: 0.7051 - val_loss: 1.3125 - val_accuracy: 0.6934\n",
      "Epoch 12/50\n",
      "131546/131546 [==============================] - 1s 11us/step - loss: 1.2515 - accuracy: 0.7080 - val_loss: 1.2773 - val_accuracy: 0.6963\n",
      "Epoch 13/50\n",
      "131546/131546 [==============================] - 1s 11us/step - loss: 1.2331 - accuracy: 0.7107 - val_loss: 1.2715 - val_accuracy: 0.7091\n",
      "Epoch 14/50\n",
      "131546/131546 [==============================] - 1s 11us/step - loss: 1.2184 - accuracy: 0.7139 - val_loss: 1.2773 - val_accuracy: 0.7050\n",
      "Epoch 15/50\n",
      "131546/131546 [==============================] - 1s 11us/step - loss: 1.2023 - accuracy: 0.7171 - val_loss: 1.2304 - val_accuracy: 0.7079\n",
      "Epoch 16/50\n",
      "131546/131546 [==============================] - 2s 12us/step - loss: 1.1880 - accuracy: 0.7202 - val_loss: 1.2158 - val_accuracy: 0.7168\n",
      "Epoch 17/50\n",
      "131546/131546 [==============================] - 1s 11us/step - loss: 1.1724 - accuracy: 0.7238 - val_loss: 1.2800 - val_accuracy: 0.6969\n",
      "Epoch 18/50\n",
      "131546/131546 [==============================] - 1s 11us/step - loss: 1.1589 - accuracy: 0.7260 - val_loss: 1.1743 - val_accuracy: 0.7237\n",
      "Epoch 19/50\n",
      "131546/131546 [==============================] - 2s 11us/step - loss: 1.1453 - accuracy: 0.7288 - val_loss: 1.1791 - val_accuracy: 0.7253\n",
      "Epoch 20/50\n",
      "131546/131546 [==============================] - 1s 11us/step - loss: 1.1305 - accuracy: 0.7310 - val_loss: 1.1745 - val_accuracy: 0.7221\n",
      "Epoch 21/50\n",
      "131546/131546 [==============================] - 2s 11us/step - loss: 1.1183 - accuracy: 0.7334 - val_loss: 1.1997 - val_accuracy: 0.7167\n",
      "Epoch 22/50\n",
      "131546/131546 [==============================] - 1s 11us/step - loss: 1.1067 - accuracy: 0.7348 - val_loss: 1.1195 - val_accuracy: 0.7329\n",
      "Epoch 23/50\n",
      "131546/131546 [==============================] - 2s 11us/step - loss: 1.0962 - accuracy: 0.7366 - val_loss: 1.1295 - val_accuracy: 0.7325\n",
      "Epoch 24/50\n",
      "131546/131546 [==============================] - 1s 11us/step - loss: 1.0839 - accuracy: 0.7402 - val_loss: 1.1360 - val_accuracy: 0.7282\n",
      "Epoch 25/50\n",
      "131546/131546 [==============================] - 1s 11us/step - loss: 1.0739 - accuracy: 0.7402 - val_loss: 1.0907 - val_accuracy: 0.7393\n",
      "Epoch 26/50\n",
      "131546/131546 [==============================] - 1s 11us/step - loss: 1.0633 - accuracy: 0.7437 - val_loss: 1.1329 - val_accuracy: 0.7264\n",
      "Epoch 27/50\n",
      "131546/131546 [==============================] - 1s 11us/step - loss: 1.0542 - accuracy: 0.7453 - val_loss: 1.0935 - val_accuracy: 0.7315\n",
      "Epoch 28/50\n",
      "131546/131546 [==============================] - 1s 11us/step - loss: 1.0450 - accuracy: 0.7460 - val_loss: 1.0954 - val_accuracy: 0.7331\n",
      "Epoch 29/50\n",
      "131546/131546 [==============================] - 2s 12us/step - loss: 1.0384 - accuracy: 0.7477 - val_loss: 1.0526 - val_accuracy: 0.7451\n",
      "Epoch 30/50\n",
      "131546/131546 [==============================] - 1s 11us/step - loss: 1.0280 - accuracy: 0.7507 - val_loss: 1.0789 - val_accuracy: 0.7371\n",
      "Epoch 31/50\n",
      "131546/131546 [==============================] - 1s 11us/step - loss: 1.0206 - accuracy: 0.7514 - val_loss: 1.0574 - val_accuracy: 0.7382\n",
      "Epoch 32/50\n",
      "131546/131546 [==============================] - 2s 13us/step - loss: 1.0136 - accuracy: 0.7525 - val_loss: 1.0322 - val_accuracy: 0.7480\n",
      "Epoch 33/50\n",
      "131546/131546 [==============================] - 2s 13us/step - loss: 1.0031 - accuracy: 0.7546 - val_loss: 1.0563 - val_accuracy: 0.7443\n",
      "Epoch 34/50\n",
      "131546/131546 [==============================] - 2s 13us/step - loss: 0.9987 - accuracy: 0.7555 - val_loss: 1.0338 - val_accuracy: 0.7475\n",
      "Epoch 35/50\n",
      "131546/131546 [==============================] - 2s 16us/step - loss: 0.9901 - accuracy: 0.7557 - val_loss: 1.0474 - val_accuracy: 0.7406\n",
      "Epoch 36/50\n",
      "131546/131546 [==============================] - 1s 11us/step - loss: 0.9850 - accuracy: 0.7569 - val_loss: 1.0174 - val_accuracy: 0.7484\n",
      "Epoch 37/50\n",
      "131546/131546 [==============================] - 2s 13us/step - loss: 0.9768 - accuracy: 0.7596 - val_loss: 1.0066 - val_accuracy: 0.7544\n",
      "Epoch 38/50\n",
      "131546/131546 [==============================] - 2s 12us/step - loss: 0.9701 - accuracy: 0.7611 - val_loss: 0.9921 - val_accuracy: 0.7524\n",
      "Epoch 39/50\n",
      "131546/131546 [==============================] - 2s 12us/step - loss: 0.9653 - accuracy: 0.7625 - val_loss: 1.0068 - val_accuracy: 0.7473\n",
      "Epoch 40/50\n",
      "131546/131546 [==============================] - 2s 12us/step - loss: 0.9585 - accuracy: 0.7619 - val_loss: 0.9934 - val_accuracy: 0.7460\n",
      "Epoch 41/50\n",
      "131546/131546 [==============================] - 2s 12us/step - loss: 0.9531 - accuracy: 0.7636 - val_loss: 0.9769 - val_accuracy: 0.7623\n",
      "Epoch 42/50\n",
      "131546/131546 [==============================] - 2s 12us/step - loss: 0.9474 - accuracy: 0.7646 - val_loss: 0.9796 - val_accuracy: 0.7573\n",
      "Epoch 43/50\n",
      "131546/131546 [==============================] - 2s 12us/step - loss: 0.9423 - accuracy: 0.7649 - val_loss: 0.9752 - val_accuracy: 0.7560\n",
      "Epoch 44/50\n",
      "131546/131546 [==============================] - 2s 12us/step - loss: 0.9371 - accuracy: 0.7646 - val_loss: 0.9883 - val_accuracy: 0.7517\n",
      "Epoch 45/50\n",
      "131546/131546 [==============================] - 2s 12us/step - loss: 0.9313 - accuracy: 0.7668 - val_loss: 0.9713 - val_accuracy: 0.7573\n",
      "Epoch 46/50\n",
      "131546/131546 [==============================] - 1s 11us/step - loss: 0.9266 - accuracy: 0.7674 - val_loss: 0.9534 - val_accuracy: 0.7615\n",
      "Epoch 47/50\n",
      "131546/131546 [==============================] - 1s 11us/step - loss: 0.9218 - accuracy: 0.7691 - val_loss: 0.9610 - val_accuracy: 0.7626\n",
      "Epoch 48/50\n",
      "131546/131546 [==============================] - 2s 12us/step - loss: 0.9173 - accuracy: 0.7698 - val_loss: 0.9484 - val_accuracy: 0.7617\n",
      "Epoch 49/50\n",
      "131546/131546 [==============================] - 2s 12us/step - loss: 0.9123 - accuracy: 0.7699 - val_loss: 0.9656 - val_accuracy: 0.7530\n",
      "Epoch 50/50\n",
      "131546/131546 [==============================] - 2s 11us/step - loss: 0.9073 - accuracy: 0.7707 - val_loss: 0.9529 - val_accuracy: 0.7595\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.callbacks.History at 0x20690304d68>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#fine tune\n",
    "model.fit(XL_train,yL_train,\n",
    "                epochs=50,\n",
    "                batch_size=256,\n",
    "                shuffle=True,\n",
    "                validation_data=(XL_val, yL_val))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 5. Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1.78892712e-09, 6.48167848e-12, 7.75055076e-10, ...,\n",
       "        2.32765340e-09, 2.32134227e-07, 1.03883202e-07],\n",
       "       [4.23985557e-06, 1.38816079e-08, 4.24314184e-09, ...,\n",
       "        3.04522746e-06, 2.20475886e-05, 5.69362019e-04],\n",
       "       [1.51185282e-02, 1.16384726e-04, 5.50670266e-05, ...,\n",
       "        3.78914028e-02, 4.86062914e-02, 1.18606212e-02],\n",
       "       ...,\n",
       "       [3.14814663e-08, 3.76504577e-05, 4.39647696e-09, ...,\n",
       "        3.19572426e-02, 5.49352137e-07, 1.00038135e-02],\n",
       "       [6.27197907e-04, 1.58488483e-03, 4.83004925e-08, ...,\n",
       "        1.16681054e-04, 7.53008237e-07, 5.42599191e-05],\n",
       "       [1.61502423e-04, 1.54664530e-08, 1.01122730e-13, ...,\n",
       "        1.90077626e-04, 3.18546663e-04, 2.96439953e-06]], dtype=float32)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred=model.predict(XL_test)\n",
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred1=y_pred.argmax(axis=1)\n",
    "y_pred1=pd.get_dummies(y_pred1)\n",
    "y_pred1.columns=yL_test.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_test1 = yL_test.columns[np.where(yL_test!=0)[1]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.metrics import f1_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "16444"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(y_pred1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['RTMP', 'SIMET', 'NETFLIX', 'AMAZON', 'MICROSOFT', 'APPLE_ITUNES',\n",
       "       'WHATSAPP', 'CLOUDFLARE', 'TEAMVIEWER', 'UBUNTUONE',\n",
       "       ...\n",
       "       'NTP', 'SSL', 'TEAMVIEWER', 'STARCRAFT', 'CONTENT_FLASH', 'H323',\n",
       "       'MQTT', 'WHATSAPP', 'OFFICE_365', 'HTTP_DOWNLOAD'],\n",
       "      dtype='object', length=16444)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred1=y_pred1.columns[np.where(y_pred1!=0)[1]]\n",
    "y_pred1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "cm=confusion_matrix(y_test1,y_pred1,labels=yL_test.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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g1NBBQenVCZzw6lT+nbi2nPJqUx3I4rlJr+F2uxnx9P/a1qZXNGD0YwW/HpzKv1N9gRPYdG3Z0m87lf9L6tZhxGODGPDoUNLT04mrFMuEp58KSq9Oty1PPPwAY1+axE3d/kV4eBjNrmxKv57dgsarTeVqUz9gU7l6s3vvfqrGVfJbx6b821SvbHp+s6lcnXrWtOVeQAksOirUd1yB+BrjXwUR2Q4MN8Ys9VpXEdgNnAK6GWPWicj7AMaYu0WkFRAPDCFzSoMaxphUr/QDyfzo2GngciB7ZPN1Y8zrIlILeBFoQGYd3gEMMcb86tHZBbQyxuzyLMcB3wGDjTHTfMnfwnJxAT/ZXfaZQEs6h9uBeWhcDjU3TlyXQTT59V+KtBRndCMK/S1AJS/02go8KcnO6EY58GaAE/0AOFOvwoJ3TroLhk39dkZ64DVtqgNnEgOvWbxU4DUVBbS+2oRN/YBN2HQ/bFMdKBET4g8FOfNQWEzQBx0nZZwMinOnAdoQQgO0FjXuNnWaoY4GaO1Cr63AowFaDdA6hU39tgZoA6+pAS/FKbS+2oNN/YBN2HQ/bFMd0ABtjmiA1nd0igNFURRFURRFURRFURRFUQJKWFCEPu1A/3ykKIqiKIqiKIqiKIqiKIpSROgI2hDCiekI0obcFXBNgIjn3w+4Zvr6BQHXDG99Z8A1AX1l2iZ0KgK7SE/Nf5+CEup1ICKyqB34jmOvNQb9m1vO4tRUL0684u9U92rTdAROoK9324NT09vZdO8aVayoHSi+EurTETgxfQ7YVa42eVUUP9EAraIoiqIoiqIoiqIoiqIoAUVD7L6jZaUoiqIoiqIoiqIoiqIoilJEaIBWURRFURRFURRFURRFURSliNApDpTzWLdhIy9Nfo2UlBSkXl3Gjx5BqVIFm1csrNejuA/sxr16EQCuFu0J+0dbiCqGe8/PZMx4BSpUJrzPY16JwnBVrUX6m0/j3vqpT8d5dsIkPl69hpiYMgDUrlmDV54dVyCvADM2bGHWxq0Ui4ygTqVyjOjchrIliwNw8Pgpuk+czQdD7uaiUiUKrJ1FIMr1Qmhm4Xa7GTZqDJfUq0vfXj391nPKq03lql7t0ARYtXY9k958hzCXi5iYMox78nFqVKsalF6d0g10GwCBa7O9salclyxbwdvTZ+ByuSgeHc2Tjw2mYf3LgtKrU+X6/twFzJi3kOhixahTuyajHhtMWU998JfVa9czdNRTfLPhE7+1bKpX6lW9hnp75YTu4o9W8O6sOeeW/0hM4tChw6xfvpgK5csFjU8nddWrXV4hsP0g2NUO2FQHQpEwm+YoL2Jcbqcmig8hRKQBsB243Riz0LNuHVANSCTzkxRhwFhjzDwR6Q1MAPZ4JIoD64EHPGnWGWNq5XAcN7At2+plxpgnfTJ6+mSeJ/vYseO0v70bs999i1o1a/DCxMkkJZ0mfvjjuaY57yNhcdUJu3MArlpCxrKZuFcvwtX4H4R17EX6i0PgTBJh/Z7Avfsn3P+Zf55OWOe+EFOOjHdfAHz7SNidve/l8UcH0rTRFfnuCzl/JGzzT3sYNmsFsx++i7iypfnwq52s2fEzr/TuyJIvv+PVlZ+y/9gpNj01IMcArS8fCStMuRaFZha//PobY559nm+3f8fAAf39Ds445dWmclWvQaSZz8eMkpPP8vcb2rNk1jRqVq/GtFlz+fSLr3jzlRdyT5TPR8JsOldQiDbAxw9YFLTNzu+jS0FVrvncS/26aze9+g9g0czpxFaswPqNmxg9/jnWLf8w90Q+3MwGzbXlw0fCPv9qC4+NHsu8d94grlIsi5d/zJr1G5n0XB5Beh8/vLVrz17uHfgoR38/xjcb1+S9s031qgg01WsIeHWivYJ826ygKtcCfDA0NS2Nnvc+wG233Ey3LrfmvmN43h/MDKr8F4Gmei2kpo/3WAXqByHfD2/95e9bnNItEaORyBwYElE26IOOL6adCIpzp1McBIZ7gPnAfdnW9zPGNDbGNALuAqaLSIxn24eebY2B+kAjoG9+B8pK4/XzLTjrAxs/30zDy+tTq2YNALp37cLSFR/jaxA/rEV73JtW4t6y8dw61zXXkbF6EZxOBLebjNmv4t6crdOoczmuJs3JmP2qz15TUlLYaX5k6nsz6XBHDwYOfYIDBxN8Tp/Fd/sO0eySmsSVLQ3A9Q3rsfa7Xzlw/BSf7PiZt/p3KbBmdvwt1wulmcXMeQvoelsnbrqhjd9a4JxXm8pVvdqhCZCekY7b7eaPxEQAkk6foVhU3gHYovLqlG6g2wAIXJvtjU3lGhUVybiRw4mtWAGABvUv4+jR30lJ9T1IcKG8OlWu333/A/+4+iriKsUC0LZ1S9Zs2OR3GZw5k8zQEfEMG/SwXzpZ2FSv1Kt6DfX2ykndLN6aNoNyF12Ud3DWB2zKv3q1y2ug+0Gwqx2wqQ4oSn5ogNZPRCQS6AGMAJqKSJ2c9jPGbCdzNG3dHLalAxuABg5azZeEhEPnHpwA4mJjSUxMIikpyaf0GfNex/3V+vPWuWKrQumyhD34FOFPvkpY+x5wJvG8fcI730PG0umQfMZnr4eOHOXvV1/JIw/058O5M2jU8HIeGPxYgRvMK2pW5vOf9rD/2CkAPvhiB6np6USGhzGpTydqxRb+NaYs/C3XC6WZxahhQ+nQ7ia/dbJwyqtN5ape7dAEKFmiBGOeGEK3vgNo3q4TM+cvZMjAAX5p2nSuIPBtAASuzfbGpnKtVqUKra5tDmROH/HMSxO5ruW1REXmPeKqKLw6Va6NGlzO5199zX5PYH7R0uWkpqZy4uRJv3RHjX+OO7vcitT70+1VobCpXqlX9Rrq7ZWTugDHTpzg3ZlzGD7oIb+1bMq/erXLa6D7QbCrHbCpDoQqYRb8goVg8mIr7YHdxpgfgcVA/5x2EpEbPf81OWwrD7QFPsvvYCKyNdvvxvzS+EqGOwNXDq8mhIX79ophjoSH47qsCRlvP0P6s49AidKEdez1v+0XXwalYnB/ua5AstWrVuGtSRO4pG4dXC4Xfe/uwZ59+9l34GCBdK66uBoPtm3GQ+8uoeuEGYSFuYgpEU2kP3nOhhPl6si5cginvNpUrurVDk0A8/MvTJk6jeXzZrBxxRLu79OLgY8/6Vcg0aZz5RSBarO9sbFcT585w8OPD2fP3r2MG+X/CzA2XVtXNWnEg/few/8NHU7nXn1xuVyUjSlDZEThH/ZmzltIRHg4t3fq4Jc3b2yqV+pVvYZ6e+WkLsC8RUto07I51f2chx7syr96tcerE/2gNza0AzbVAUXJDw3Q+k8fYLbn/3OBPiKS9T7sVE8QdQfwJHCHMSZr+GhHz7ZtwDrgAy+dXMlhioOVgcpI5bg4Dh85em750OEjxJQpQ4nixQsvevJY5ke/ks9AehruL9fiqv2/ycXDrryWjM2f5DsfVnZ++OlnFi9bcd46txsiIwr23buk5BSuqlONhYPvZv6gnlx3eeYA6JgS0QXSyQsnytWRc+UQTnm1qVzVqx2aABs/20zTRg3PfRSsR9fO/PTLbxz3Y5SfTefKKQLVZntjW7keOJhAt979CA8LY/qbr1GmdGm/9MCuaysx6TR/a9qYD2a8w6Lpb3N9y2sB/PpI2AcfLWP7zu/p1L0X/R8aRPLZs3Tq3otDR44UWtOmeqVe1Wuot1dO6gIsX7WGzh3a+60DduVfvdrj1Yl+MAtb2gGb6oCi5IcGaP1ARGKBdsBgEdkFTAUuAjp7dsmag7aBMaaFMcZ78tWsOWgbGWMaGmNGG2OKdEKT5s2uYdv2HezanfntsjkLFtGmVQu/NDO+2URY0+YQmRmzdl3xd9y7fzy33VW3IW6T/btn+RPmcvH0Cy+zd/8BAGbNX4TUq3Peawi+cPhUIr1fm0di8lkA3li9mfZNLs3xr2WFxYlydULTKZzyalO5qlc7NAHqXyp8uWUrR38/BsDq9RuoVqUy5cqWDTqvNrUDgWqzvbGpXBOTkri7/wDaXteal599mujowPwR0KZr6/CRo9x9/0ASEzNfDfz3O+/Rvu31fvW3C6a/w0fzZrJk9nTenDSB6GLFWDJ7OpUqViy0pk31Sr2q11Bvr5zUPXnqFHv27qNJo4Z+a4Fd+Vev9nh1oh8Eu9oBm+qAouSHSyc5LjwiMhi43hjTzmtdPNDKsxhvjFmXQ7reQCtjTO8cttUC1hljauWwzW2MKfyTzOmT+Z7s9Rs28dLkKaSmpVGjWlWeGxtP2ZiYXPdPG3LXn9aF3f0o7oO7ca9eBK4wXO3uJOzKFhAWhnvPL2TMnnxuvtnwlxeSPqY/nPj9PI2I59/PNztLln/MW9PeJz09nbhKsTw9cjhVKsflun/6+gU5rp+54Rtmb9pKhttN09pVGdH5OqKj/vfKZf1BL7HpqQFcVKrEn9KGt74zX59Q8HItKk1vho0aQ726dfL/grsPOOXVpnJVr0Gi6cPX5mfOW8iM+YuIjIwgpkwZRg19lHp1Ls49QUT+HxGz6Vxl4XMb4OMXhgvaZhOW/ytkQVOu+dxLvfHONF557Q0uqXv+NPXTXp/CRWVz0fUxcBkU15YP1xXAjHkLmTl/ERkZGVzZ+ApGDR1EdHSx3BP4UAey2HfgIB3u6JH/16ttqldFpKle/+JenWivwKc2K2jKNT3/Dx19+933DH5yNKsWz/PNRHj+07UETf6LSFO9FkLTx3ssKEA/CODKe5zeX/6+xSndEjGBG+X1F2JYZNmgDzo+m3oiKM6dBmj9QES2A8ONMUu91lUEdgOngG6FDND+Cpz2Wr3BGNNORNxA9uGmPxtjbvfJsA8B2oKSU4A2EPgSoC0ouQVo/cHXAK2iKEGCj4GkAuFDgPYvTQEeHgpEAYJzRY4T91IBfJPDcZy4rsCZOmBTvVIUJ3Dq2c+mNsuHAG2B8SFAqygFxql7rHwCtIXTtKgNcAoN0OaIBmh9p/CTvykYY/70zosx5gjw56GW5+8zDZiWy7Zd5DL1hF+jZxVFURRFURRFURRFURRFCTo0QKsoiqIoiqIoiqIoiqIoSkDRD1/5jgZoFb+IeO49R3SnVZWAa/bebwKuqSiKZYT6dAROoK+M62t9el0pij2EensFOh2BYg96j6UoIYUGsxVFURRFURRFURRFURRFUYoIHUGrKIqiKIqiKIqiKIqiKEpAcembGz6jI2gVRVEURVEURVEURVEURVGKCB1Bq5zHug0beWnya6SkpCD16jJ+9AhKlSrll+bij1bw7qw555b/SEzi0KHDrF++mArly+WZ9uI7bqfBQw+CG9LOnGbz48P5Y9dumk14gXING5B2+jQ/zZjND29OBaDaTW259vVXSdq7/5zG8na3kJaY5JPXZydM4uPVa4iJKQNA7Zo1eOXZcQXN8p9wolyd0FSv6tUmr6Gef/WqXtWrPZrqVb2qV/Ua6vlXr+pVvSpK3rjcbndRe1AuFKdP5nmyjx07TvvbuzH73beoVbMGL0ycTFLSaeKHP557ovTUAllITUuj570PcNstN9Oty6257jet2mWUqVuHm5YtYWmLNpw5dIiqN1xPs5dfIGHDJjLS0/jsoUG4wsO5btZ0fpj6DvtWrqLp6BGkJiay/aVX/qTpy0fC7ux9L48/OpCmja7wLUM+TNxeqHItAk31ql5t8hrq+Vev6lW92qOpXtWrelWvoZ5/9apeQ8JriRh9lz8HRkZdFPRBx7Epx4Pi3AXNFAci0kBE3CLSxWvdOhH5WUS2isg2EdkuInd4tvUWkWOebVtFxIjImyISISK1RGRXLsdxe6XJ+j2dj7d4EYn3Wm4vIps8nnaIyFgRCfPy3CoXnRtFZLOIfC8i34nIyyJSwrMtR88i4vb820pEEr3KwnjSB+xPOBs/30zDy+tTq2YNALp37cLSFR8TyCD+W9NmUO6ii/IMzmaRkZLCpwMf5cyhQwD8/s1WileKpULTxvw6Zx7ujAwyUlPZt3IVtTp1BCD2b1dTuUVzOm5cS7sVS6n0j2Y+e0tJSWGn+ZGp782kwx09GDj0CQ4cTChcRr1wolydOlfqVb3a4jXU869e1at6tUdTvapX9apeQz3/6lW9qldFyZ+gCdAC9wDzgfuyre9njGlsjGkE3ArNsz0AACAASURBVAVMF5EYz7YPPdsaA/WBRkDf/A6Ulcbr96SvJkXkJuBVoI/H09We447JJ10b4A1ggDHmMqAJEAksERFfo/VfeZVFA6AC8G9fvedHQsIh4irFnluOi40lMTGJpCTfpgfIj2MnTvDuzDkMH/SQT/sn7tnLvv+sOrd89fin2LtiJYe//IqLu92BKyKCiJIlqdnpForHVQLg7PFjmHfe48Pmrfl6zDhaz5xGiSqVfTreoSNH+fvVV/LIA/35cO4MGjW8nAcGP+Z3I+xEuTp1rtSrerXFa6jnX72qV/Vqj6Z6Va/qVb2Gev7Vq3pVr4qSP0ERoBWRSKAHMAJoKiJ1ctrPGLMdSATq5rAtHdhAZuDSSZ4EnjbG/Og57hngAWB9PulGAvHGmC2edCnAo2QGlpsX1IQxJhUYCnQTkbIFTZ8TGe6MHL+wFxae/2v8vjBv0RLatGxO9WpVC5QuokQJWr33NmUurs2nAx/hqydHgdtNxw1ruG7WexxYu56MlBQA1vbsw+4lSwE4/Plmjmz+kiqtW/l0nOpVq/DWpAlcUrcOLpeLvnf3YM++/ew7cLBAfrPjRLk6da7Uq3q1xWuo598pXfWqXkPda6jn3yld9apeQ91rqOffKV31ql5t8hqqhLmC/xcsBEWAFmgP7PYEPRcD/XPaSURu9Pz3T5OJikh5oC3wWX4Hy2GKgxvzS+NFE2CL9wpjzD5jzOp80l0NfJEtXarH79UFOL53+gTgOHBJYdJnp3JcHIePHD23fOjwEWLKlKFE8eKBkGf5qjV07tC+QGlKVqvKzf9Zhjs9nY9vuY2Uk6eILF2ar0aNYUmzFvyn0+3gcnHq19+IiilDw8GPnC/gcpGR6ts8uT/89DOLl604b53bDZER/n1Lz4lydepcqVf1aovXUM+/elWv6tUeTfWqXtWreg31/KtX9apeFSV/giVA2weY7fn/XKCPiER5lqd6gqg7yBy9eocxJtGzrWPWnKzAOuADL51cyWGKg5UF8JoBJBdg/yzcQE6RvijPtozsGzxTH+T3fr0bOFMIP3+iebNr2LZ9B7t27wFgzoJFtGnVIhDSnDx1ij1799GkUUOf00SUKslNyxaze+ky1t/Tn/TkzGKXe3rTZPgwAKIrVuSSXj34dcFCUv9I5NJ+91Cz4y0AlLuiIRWubML+1Wt8Ol6Yy8XTL7zM3v0HAJg1fxFSr855rzYUBifK1alzpV7Vqy1eQz3/6lW9qld7NNWrelWv6jXU869e1at6VZT8cRX1JMciEgvsAw4DaYALqALcDdxP5rQA63JI1xtoZYzpncO2WsA6Y0ytHLa5jTEFGsSc9YEwY0y8iKwBphljpnttvwQYYYzpJSLrcvIsIp8A84wxb3itiwJ+BboDW4E9xpiLvLZXAnYaY8p7PjwWb4xplW37bqCcMeZ0vhk5fTLfk71+wyZemjyF1LQ0alSrynNj4ykbE5N7gnTfRqd++933DH5yNKsWz/Np/2nVLqPhoIdpMuIJTnz3/XnbPrnrbq559mlKX1wbXC62T5jIr/MWAFC+SSOuef4ZIkuVwp2WzhfDR5CwYRMAvff/aeD1n1iy/GPemvY+6enpxFWK5emRw6lSOS73BGG+veJQ4HItIk31ql5t8hrq+Vev6lW92qOpXtWrelWvoZ5/9ape//JeS8QE0cvywcOYYhcF/ZfVRp89HhTnLhgCtIOB640x7bzWxQOtPIvBFqC9CZgC3GSM+UlESgFzgK3GmBF5BGibkzm6t5MxZotn3t3JZM6ne4Mxxi0iXwDPGWMWetKMBBoYY+7MHqAVkWLAW8BpY8z9PmXEhwBtgfExQFtQplW7LOCavgRoC4yPAVpFURRFURRFURRFUf6iaIA2RzRA6zv+Ta4ZGHoDw7OtmwI8BpzyQ7eGiCR6LW/ICgKLyNZs+/5sjLndF1FjzMci8iQwV0TCgUhgPjDGa7cVIpLutVzfGLNRRHoBE0WknCfdcjIDtlkVtifwbxEZRebUB98CD3rpXOXlPQJYQ+aHwhRFURRFURRFURRFURRFsZAiH0GrXEB0BG3ANXUEraIoiqIoiqIoiqKEODqCNkd0BK3vBMMI2qBARGYCl+ew6UNjzKgL7UdRFEVRFEVRFEVRFEVRbCXMFRSxTyvQAK0HY0yPovZgJQ6NwO594KeAa35SK6f4u3+02b0z4JqKoiiKZTjRF9p0M5ue5oxuuN6mKoriAKHeZttEqJ8rd4Yzuq4wZ3QVRfELvTIVRVEURVEURVEURVEURVGKCB2aoCiKoiiKoiiKoiiKoihKQNFRob6jZaUoiqIoiqIoiqIoiqIoilJE6Aha5TzWbdjIS5NfIyUlBalXl/GjR1CqVCm/NN+fu4AZ8xYSXawYdWrXZNRjgykbU8Zvr0uWreDt6TNwuVwUj47myccG07D+ZT6ljbu9MzUeHAC4ST99hh+Hj+SPbd9Stc+/qNKzO+HR0Zza9i3fPzIEd0oKEWXLIs+MpaRcQlh0NLtenkTC/IU+e3WiXJ3QVK/q1SavoZ5/9WqXV3/6rAvt1alyNT//wrjnJ/BHYiJh4eE8NfwxGlx2adB5dSr/AG63m2GjxnBJvbr07dXTbz2brgGbvELgzxXYVa625N8pXSfabJvyb5NX7V9hxpz5zF6wCJfLRfVqVRk38gnKlysXlF5tKlcn7wcUJSd0BK1yjmPHjvPE6LFMfuFZVi5eQPVqVXlx0hS/ND//agtvTZ/Je69NZMmsabT4ZzNGjX/eb6+/7trNCxMnM/XViSyZM4MB/fowcMjjPqUtUacOdeNHsLVbD75o3ZZdL0/kimlTqdi+HdX79eGbLt34vHlrwotHU+P+ewGoP/llkg8c5IvrbuSbLt24ZPxTFKtc2afjOVGuTmiqV/Vqk9dQz796tcurP33WhfbqVLmeSU6m74OP0K9XDxbPeo8H+vZmyIj4oPPqVP4Bfvn1N/513wOsXL0mIHo2XQM2eYXAnyuwq1xtyb9Tuk602Tbl3yav2r/Cjp0/8M77M5nz7lt8NH8WtWpUZ+JrbwalV5vK1cn7gVAjzBX8v2AhqAK0ItJARNwi0sVr3ToR+VlEtorINhHZLiJ3eLb1FpFjnm1bRcSIyJsiEiEitURkVy7HcXulyfo9nY+3eM+voVeaYyKyx/P/zZ79dolIrWxp14lIK69j98tne47eRKSGiCwVkW9FZIeIzBOR2AIVch5s/HwzDS+vT62aNQDo3rULS1d8jNuPr2d+9/0P/OPqq4irlGmzbeuWrNmwiZTUVL+8RkVFMm7kcGIrVgCgQf3LOHr0d590M1LO8v2jQ0k5dBiAU1u3ERVbkSo9u7Pn32+QduIEuN38MGQYCfMWElG2LOVaXstvL04A4OzBg3x5YwdSTxz3yasT5eqEpnpVrzZ5DfX8q1e7vPrTZ11or06V66bPN1O9WlVaNv8HAG1aXssrz47zS9Om/APMnLeArrd14qYb2vitBXZdAzZ5hcCfK7CrXG3Jv1O6TrTZNuXfJq/av0KD+peycvECSpcuxdmzZzl0+AhlY2L80rSpDtjkVVHyI9imOLgHmA/cB3i/P97PGLMOQEQaAl+KyErPtg+NMb0928KBT4G+wErywBjTuDAGjTHbgcae400D1hljphVQ5hkRWWmM2VtAb28A040xsz3HfwJ4HehcwOPnSELCoXOBVIC42FgSE5NISkoq9FD+Rg0u5/25C9h/MIGqleNYtHQ5qampnDh5ktgKFQrttVqVKlSrUgXIfAXrmZcmcl3La4mKjMw3bfLefSTv3Xduud5Tozm6chUlL6lHZIUKNJ47g6hKlTi5+Qt+GjOOUiKkHDpMjQH3Ub5Na8Kiotgz5XUO/fqrT16dKFcnNNWrerXJa6jnX73a5dWfPutCe3WqXH/bvZeK5csx/Knx/PDTz5QpXYqhDz1YaD2nvDqVf4BRw4YCsOmzzX7pZGHTNWCTVwj8uQK7ytWW/Dul60SbbVP+bfKq/WsmkZERrF67nifHjicqMoqHBtzrl55NdcAmr4qSH0EzglZEIoEewAigqYjUyWk/T4A0Eaibw7Z0YAPQwEGrgeAVYGoh0sUBJbyWX/X8AkKGOwOX68/ju8PCwwuteVWTRjx47z3839DhdO7VF5fLRdmYMkRG+NdpZnH6zBkefnw4e/buZdyoJwuUNqxEcRq8/QYlatfm+0eG4IqMpHzLFmzvez9f3nAzEWXLUmf4MFyRERSvVZO0P/7g6/a3sqP/A9QbF0/pKxr6dBwnytUJTad01at6tUXTKV31ql6z8KfPygmbyjUtLY31mz7jzs6dWPT+O/S843b6PzyYlJSUQmvalH8nsOkasMmrU9hUrk5gYx0IZJttU/5t8ppFKPevWVzfuiWb16xk4H196fvgI2RkZBRay6Y6YJPXUCXMgl+wEExe2gO7jTE/AouB/jntJCI3ev5rcthWHmgLfJbfwXKYRuDG/NIEkOeA8tmnOvDB2xPA8yKyT0TeI7PM1gfKVOW4OA4fOXpu+dDhI8SUKUOJ4sULrZmYdJq/NW3MBzPeYdH0t7m+5bUAAflI2IGDCXTr3Y/wsDCmv/kaZUqX9jltsapVuGrZh5CezpbbupJ26hRnExI4vGw56YmJuFNTSViwiJirmnI24RAAB2fNBeDMb7s4sflLyjRt4tOxnChXJzTVq3q1yWuo51+92uUV/OuzcsOmco2tWIE6tWvRqMHlAFzfqgXp6Rns3X8gqLw6lX8nsOkasMmrU9hUrk5gWx0IdJttU/5t8grav+7es5evvtl6brlLpw4cOJjAyVN/BJ1Xm8rVpvZV+esQTAHaPsBsz//nAn1EJMqzPNUTqNwBPAncYYxJ9GzrmDU/LbAO+MBLJ1eMMY2z/fKcEqEA5PSnKpf3emNMGtCbzKkOqvvqzRjzMVAV6AccAZ7n/Kkg/KJ5s2vYtn0Hu3bvAWDOgkW0adXCL83DR45y9/0DSUxMAuDf77xH+7bX5/jXqIKQmJTE3f0H0Pa61rz87NNER0f7nDa8ZEmuXLyAI8uWs6P/A2QkJ2d6XbqMSp06EObRqtjuRk5t3Ubynr2c2vYtlbt1BSCqYgVirr6SU1u3+XQ8J8rVCU31ql5t8hrq+Vevdnn1p8/KC5vKtcU/mrHvwAF2fP8DAF9u+QaXC6pV8e2DmxfKq1P5dwKbrgGbvDqFTeXqBDbVASfabJvyb5NX7V/hyNHfGfTESI4dPwHA0hUrqVfnYi4qW/h5aG2qAzZ5VZT8cAXDJMeeD13tAw4DaWQGNKsAdwP3A/FZc9BmS9cbaJU1B222bbXInB+2Vg7b3MaYAkUIRSQewBgT77VuGtnmoBWRr4G+xpitXuu+JzOovN372CLyJNACKJaVx9y8iUg5YKQx5lGvdTHAQaCmMeZIvpk4fTLfk71+wyZemjyF1LQ0alSrynNj4/OeZDwt/1cTZ8xbyMz5i8jIyODKxlcwauggoqOL5Z0oPO8pEN54ZxqvvPYGl9Q9fyaMaa9PybUz+qRW5qidmg//H3WeeIxEz0NiFls630n1vr2pdGtHCA/nj2+388Pgx0lPTKRY1SrIc+MpXrMGrrAw9r4xlf3TZ9Bm98688+GhwOVaRJrqVb3a5DXU869eg8hrPvdShemz8PEPmUFRrulpPul+ueUbnp84hTNnzhAVFcXwIY9wVeNGuScIz/9TCUGR/wIybNQY6tWtQ99ePf3WCppr4C/mNYtAniuwq1zBjvwXSreI2uygyX8RaRZKN9T7V7dv0xTMmr+QWfMWEh4eTmzFCowaNpTqVavknsCV/zi9oKkDRaRZKN0SMf6NQvuL8kLx8kUfdMyHoWd+D4pzFywB2sHA9caYdl7r4oFWnkWbArTPA9HAw8YYt4i0BGYAtY0xadkCtBHA50BD4MZ8ArThwI/AGGPMdM+6ZsAsoK5n/t288SFAW2B8CNAWinwCtIUhK0AbSHwN0CqKoih/YZy4l/LzTZMLio8B2gLjQ4BWURSlwIR6m20ToX6ufAzQFhgfArRKIdAAbY5ogNZ3guXOtzcwPNu6KcBjwCk/dGuISKLX8oasILCIbM2278/GmNv9OFYWY4FJwA4RcQPHgE6eaQ3OwxOw7Q187b0+N28icjMwQUTGAqeBA0AHn4KziqIoiqIoiqIoiqIoiqIEHUExgla5QOgI2oBr6ghaRVEUJeRH+OgIWkVRbCLU22ybCPVzpSNo7UJH0OaIjqD1Hb3z9UJEZgI5RfE+NMaMutB+FEVRFEVRFEVRFEVRFMVGwoIi9GkHOoI2lHBiBG2I80nN+o7o6shcRVEURVEURVEURbEEHUGbIy+VCP4RtINPB8cIWh3briiKoiiKoiiKoiiKoiiKUkToFAeKoiiKoiiKoiiKoiiKogQUHRXqO1pWiqIoiqIoiqIoiqIoiqIoRYSOoFXOY92Gjbw0+TVSUlKQenUZP3oEpUqVCjrNYPQad3tnajw4AHCTfvoMPw4fyR/bvqVqn39RpWd3wqOjObXtW75/ZAjulBQiypZFnhlLSbmEsOhodr08iYT5Cy+I1wutq17Vqy2a6lW9qlf1Gur5V6/qVb3ao6le1at6tcurouSFjqBVznHs2HGeGD2WyS88y8rFC6herSovTpoSdJrB6LVEnTrUjR/B1m49+KJ1W3a9PJErpk2lYvt2VO/Xh2+6dOPz5q0JLx5NjfvvBaD+5JdJPnCQL667kW+6dOOS8U9RrHJlx71eaF31ql5t0VSv6lW9qtdQz796Va/q1R5N9ape1atdXkOVMFfw/4KFIg/QikgZEZkiIjtEZKuIrBWRpp5tbs+/UzzbdopIiuf/W0Wkj4j0FpFjXut2iMhPInKrJ20rEVnn+f9oEVkjIi7PsktElorIQB98PiIi33v5fMBrW28ROSoilbzW1RKRXZ7/x4tIfA6a3t7y08iez6zfNQUq8DzY+PlmGl5en1o1awDQvWsXlq74GLe78B/dc0IzGL1mpJzl+0eHknLoMACntm4jKrYiVXp2Z8+/3yDtxAlwu/lhyDAS5i0komxZyrW8lt9enADA2YMH+fLGDqSeOO641wutq17Vqy2a6lW9qlf1Gur5V6/qVb3ao6le1at6tcurouRHkQZoRSQMWA4cAxobYxoDTwErRKR81n7GmAc9224GDhhjGnt+73p2+dBrXQNgKPBGDoccBxQHHvUsDwHSjTGT8/EZD3QEWnn0bwB6iMhIr91KA68XJP85kJ+Gdz6zfpv9POY5EhIOEVcp9txyXGwsiYlJJCUlBZVmMHpN3ruP31d9cm653lOjObpyFcWrVyeyQgUaz53B39at4uLHBpN66iQlatci5dBhagy4jyuXLebqVcspc0UDMs4kO+71QuuqV/Vqi6Z6Va/qVb2Gev7Vq3pVr/Zoqlf1ql7t8qoo+VHUI2hbAzWA0caYNABjzFqgDxDuh25NMoO+52GMSQd6Ak+ISA/gfuCevIREpASZAd97jDGHPDpHgHuBxzzbARYC9UTkLj98B0Kj0GS4M3C5/jy+Oyy88KfCCU2ndAOhGVaiOA3efoMStWvz/SNDcEVGUr5lC7b3vZ8vb7iZiLJlqTN8GK7ICIrXqknaH3/wdftb2dH/AeqNi6f0FQ0vmNcLpate1astmk7pqlf1ql7t8Rrq+XdKV72q11D3Gur5d0pXvapXm7yGKmG4gv4XLBR1gLYJsNUYk+G90hiz3BhzuAA6HT2v+/8qIgnAlUCnnHY0xvwCjARmAP2NMX8K5GbjciDJGLMrm85O4CxwqWdVCtAbmOA9TUEByU+jY7bpDQI2ehagclwch48cPbd86PARYsqUoUTx4kGlGaxei1WtwlXLPoT0dLbc1pW0U6c4m5DA4WXLSU9MxJ2aSsKCRcRc1ZSzCYcAODhrLgBnftvFic1fUqZpkwvi9ULqqlf1aoumelWv6lW9hnr+1at6Va/2aKpX9ape7fKqKPlR1AHaDMC3d7rz5kPPFAiNgZ+AH40xP+ax/7XAIaCdD9puICKXbZGe7QAYY74C3sGPqQ7y0cg+xUHA5p8FaN7sGrZt38Gu3XsAmLNgEW1atQg6zWD0Gl6yJFcuXsCRZcvZ0f8BMpIzq/Xhpcuo1KkDYdHRAFRsdyOntm4jec9eTm37lsrdugIQVbECMVdfyamt2xz3eqF11at6tUVTvapX9apeQz3/6lW9qld7NNWrelWvdnlVlPxwFeUkxyLSgsxgZD1jjNtr/XhgFbDGGOPyWl8LWGeMqeW1rjeZc8P29izXBnYA1xtjPhORVkC8MaaVZ3sf4EHgVuBroLsxZk0eHkuQGcy9yhhjvNZfDnwOVALuyPIgIlHAFmAWmSN0a2V9IMwYE59N+5w373zkonFePgvF6ZP5nuz1Gzbx0uQppKalUaNaVZ4bG0/ZmJhCH9IpzWDx+knN+gDUfPj/qPPEYyR+/8N527d0vpPqfXtT6daOEB7OH99u54fBj5OemEixqlWQ58ZTvGYNXGFh7H1jKvunzwCgze6dAffqK8FQrupVvRaVpnpVr+pVvYZ6/tWrelWv9miqV/WqXoPIa4mY4HlXPoiYUqpC0H9Z7cHEo0Fx7oo6QOsCNgH/AcYaY9JF5EZgGpmjYRMKGqD1rHsWuA64BmjJ/4KgAvwXaGGMMSJyKzAZuMIYczwPn8OAW4DOxpjDIhILLAZWGGPG5hAkvgpYDxwpTIA2F40/5bPA+BCgVQpGVoA20PgSoFUURVEURVEURVEUJQjQAG2OaIDWd4p0igPPqNmOQB1gh4h8CzwO3Jz1Qa5C8gxQGzj3sS0RKQbMAUZmjYQ1xiwmMzic55QExphnyZyzdrWI7AA+AWYaY8bmsv9XwCvZVg8XkUSvX498jpmTRvY5aLeKSK+8dBRFURRFURRFURRFURRFCV6KdAStcoHREbQBR0fQKoqiKIqiKIqiKEqIoyNoc+TfFoygHRAkI2hz+/hVSCEixYHPctk8yhjz4YX0o9iDU4HUpdUvDbhmhz3fB1wTAFdQtGVFhzvDGV1XUX/DsQBkpAdeMyw88JqKotiFE4MInGqztc1SbEHvW/S+xSbSUgKv6dS50jrgDE7cC4T686viFyLyFHA74AbeNsZMEJHrgQlAcWCuMWaEZ9/GwFSgDJnTrd5vjEnLTVsDtIAx5gyZc94qiqIoiqIoiqIoiqIoiqKcQ0Rakvm9qyuASGCniHwCvEPm96/2AstEpJ0xZgWZU6X2M8Z8LiJvA/cC/85NXwO0iqIoiqIoiqIoiqIoiqKEHCJSFiibw6YTxpgTWQvGmPUi0toYkyYiVcmMqZYFfjLG/ObRmgF0FZGdQHFjzOee5NOAMeQRoLXoXRRFURRFURRFURRFURRFUWwgzBX8P+AR4Lccfo9kz48xJlVExgA7gU+AKsBBr10OAtXyWJ97WRWyjJW/KOs2bKTDHXdx462389DQYSQmJgZE1+128/jIeN6ePiMgehA6Xqt27UKL9atpsW4V/1zxITGNG3Hlu2/RYt2qc7+bfjNcPWMapeSS89a33LCGDr8fJO6Wm30+3pJlK+h4Zw86detJt9792L7T/7lrnTpXTug65XXGnPm0v707t3S9iwGPDuX3Y8f81rSpXLNYvXY9Ta5tExAtJ30Guh2w7VwFWzuYF7Z4takO2OTViT4ri0C2V1mEctuiXu3yqvctmdhw32JTvXJCd9Xa9XTo/i863dWbXgMeYs++/QFwmkmg+wGbytWm+zan7gWcbFuUoOMVoHYOv1dy2tkYMxqoCFQHLiFzPtosXEAGmfHWnNbnihUBWhEpIyJTRGSHiGwVkbUi0lREWonIulzSlPKk+VlEtonIBhFp47V9moj0zpYmXkTiPf9f50m71XPcTz0T/5LD9qzfg3l58qRrICJuEemSbX12vV9FZLWIVMrjeCsLXpq5c+zYcZ4YPZbJLzzLysULqF6tKi9OmuK37i+//sa/7nuAlavXBMBlJqHitWTdOtSPH8nmO+7iv61u4KeXXuHq997m6z738t9WN/DfVjew7dEhpJ48xfbHhpNofjy3/r+tbuDI2vXsW7CIhI+W+3S8X3ft5oWJk5n66kSWzJnBgH59GDjkcX+y79i5ckLXKa87dv7AO+/PZM67b/HR/FnUqlGdia+9GZRendIF2LVnL8+9Mjkgk/076TPQ7YBt5yrY2sG8sMWrTXXAJq9O9FlZBLK9yiKU2xb1apdXvW/JxIb7FpvqlRO6yclnGTpqLK8+/zRLZk3jumv/ybgXc4ynFJhA9wM2latN921O3Qs42bYowYcx5oQxZlcOvxPe+4nIpZ4Pf2GMOQ0sAloBlb12iwMOAPtyWZ8rQR+gFZEwYDlwDGhsjGkMPAWsAMrnksYFLAVSgPrGmEbAQ8D7ItKqAIfvZ4xpbIxp4Ek/V0Tq57A96+fLFXsPMB+4L4/jNQbqAqeAQXkc78YC5CVfNn6+mYaX16dWzRoAdO/ahaUrPsbtZ6c0c94Cut7WiZtuCNxfH0PFa8bZs2x7ZDBnDx0G4MTWbRSLrYgrMhIAV2QkTaZM4rsnR5F84Pxrvdzfr6Fyx1vYXoAOKioqknEjhxNbsQIADepfxtGjv5OSmuqzRnacOldO6DrltUH9S1m5eAGlS5fi7NmzHDp8hLIxMX5p2lSuAGfOJDN0RDzDBj3sl04WTvmEwLcDtp2rYGsH88IWrzbVAZu8OtFnQeDbqyxCuW1Rr3Z51fsWe+5bbKpXTuimZ6Tjdrv5wzOyMen0GYpFRfnlE5zpB2wqV5vu25y6F3DyWSPUCMMV9L8CcDHwlogUE5EooBPwBiAiUldEwoG7gBXGmN1Asoj805P2bjLjmHmUVfDTGqgBjDbGpAEYY9YCfYDwXNK0BGoCg4wxKZ403wDjgJGFMWGM+QqYC/QrTHoAEYkEegAjgKYiUieP3UsCFcgMTF8Qj8wqUQAAIABJREFUEhIOEVcp9txyXGwsiYlJJCUl+aU7athQOrS7yV975xEqXs/s3cfhVZ+cW7583BgSPv4Pbk+HU6Nnd5ITEkhY9ufrvP6Ykfzw9LOk/eH7qxjVqlSh1bXNgczXT555aSLXtbyWKE9AuDA4da6c0HXKK0BkZASr166nRbuOfLllK507tfdLz6ZyBRg1/jnu7HIrUq+uXzpZOHmuAt0OWHeugqwdzAtbvNpUB2zy6kSfBYFvr87phnDbol7t8gp632LLfYtN9coJ3ZIlSjDmiSF06zuA5u06MXP+QoYMHOCXT3CmH7CpXG26b3PqXsDJ9lWxF2PMcmAZ8A3wNfCpMWYO0BtYSOa8tD8ACzxJegAvi8gPQClgUl76Ec7YDihNgK3GmPPmajDGLM9jNOzVwFfGmOx/3vgv8KwfXnYAt3gtTxWRrOjXH8aYa/NJ3x7YbYz5UUQWA/0B7+GNU0UkCYglMzA7B3g5l+MBzDfGPF2YjOREhjsDl+vPfz0IC88tDl50hJrX8BLFafzqRIpXrcLnXe86t/7i+/vz7aChf9r/oquvIqp8efYvWFQoz6fPnGHY6KdISDjE1CkTC6WRhVPnygldp+vV9a1bcn3rlsxbtJi+Dz7CqiULCAsr3N/JbCrXmfMWEhEezu2dOrDvwMH8E/hAqLUBF1LXCULdq011wCavWQSyz3KivXIKm86VerXLaxZ63xL89y021SsndM3PvzBl6jSWz5tBjWpVmT5nPgMff5IlM6fleCxfcKofsKlcbbpvyyKQ9wJgZxkoFwZjTDwQn23dJ0CjHPbdBvzNV20bRtBmAMkFTOMm5+BzFP+bpDenyXnzm7TXDZzxWvaeciC/4Cxkjvqd7fn/XKCPZ1i0t14joAtQDvggawRwDsdrHMjgLEDluDgOHzl6bvnQ4SPElClDieLFA3mYgBBKXotXrco/VyzFnZ7Op51uJ+3UKQDKNGyAKyKC3zd99qc0VW7rxL658ws1Z9KBgwl0692P8LAwpr/5GmVKly6whjdOnSsndJ3yunvPXr76Zuu55S6dOnDgYAInT/0RdF6d0P3go2Vs3/k9nbr3ov9Dg0g+e5ZO3Xtx6MiRoPLpFDadK6cIda821QGbvELg+ywn2iunsOlcqVe7vOp9iz33LTbVKyd0N362maaNGlKjWlUAenTtzE+//MbxkycLrelUP2BTudp03waBvxcA+8ogmAlzBf8vWLAhQPsVmdMBnFdsIjIecp0sYjNwlWdKAW+aefQAjgNls22P9azPjSvIHLJcYEQkFmgHDBaRXcBU4CKgc/Z9jTGfkjn0eZaIXLBRzs2bXcO27TvYtXsPAHMWLKJNqxYX6vAFIlS8hpcqSbMPF5Lw0XK23DuAjOT//a2i/D+acXTDxhzTlf/H3zn635y35UViUhJ39x9A2+ta8/KzTxMdHV1gjew4da6c0HXK65GjvzPoiZEcO545x/jSFSupV+diLipb+PncbCrXBdPf4aN5M1kyezpvTppAdLFiLJk9nUoVKwaVT6ew6Vw5Rah7takO2OTViT7LifbKKWw6V+rVLq9632LPfYtN9coJ3fqXCl9u2crR3zNnBVy9fgPVqlSmXNnsj/m+41Q/YFO52nTf5sS9ANhVBspfBxumONgAHAZGi8hYY0y6iNxI5mjUbTklMMZsEJHvgFdE5BFjTKqIXEnm3K/dPbt9AowSkbeMMUkiEkfm9AWv5qQpIn8Dbidz+oTCcDfwiTGmnZdmPHA/mVMZZGcCmR8Suw+4IJ8LLF+uHM/Ej+ShocNITUujRrWqPDc2/kIcusCEitfa/e6hRPVqxLVvR1z7c1WHz267g5J1anNm774c05W8+GJO791bYK8z587nwMEEVq1dx6q1/8/eecdHVaV/+Jk0WiDUECAUEThKUUBdl5UmiGIBVARBFEFYFVwQRaT3IoiwIoIFlCJNQKQjRYrg/mCXXUBY9KBi6CkQBBICafP7YybZIQYSMvckc5n38ZOP3DLfec65JXfe3Dl3e+b8uR/PyPNFualtZSLXlOu9Devzao9udH25N4GBgYSXK8uMqe/6pKtdji27eIJsKxBXO+0DdnI18TvLTthpW4mrvVzlusV67NR+O7k2uu8eejzfmRde7UNwcBBhJUow8713vHY1gZ361S7HFZi7FrBTHwi3Dg47PIVOKVUW11is9wIpwFmgPxCGq9DqOezAAq31q0qpIsB4XOO+puEa03Wke2yIjNzBuAbtTXf/TNJaL3Yv2w5EAgm4hja4DAzXWm/1WD5Ka709i2vz7JyAB4AhWus1HuuWA44BDYGPs+YppboA7wM1gFUePp4011r/fv3e8+DyBd/f2AIAayrfYXlmm+M/Wp4JQB7Hd7plcN5oVBQvcNjhCw5u0tOszwyQ8Z0Ewe8xcY1q6pwt5yzBLsh1i1y32InU5JzXuVlMbSvZB8xg4lrA1OfXomF+/sE4ez4vUc7n61AvXYzziW1niwKtYBFSoLUNUqC1EfJBRz7oCIJgBinQCoL1yHWLXLfYCSnQClKgtT1SoM09NvpNKgiCIAiCIAiCIAiCIAiCcGthhzFoBUEQBEEQBEEQBEEQBEGwEQE+cW+qPZACrSD4IG1O/GR55qQyt1meCTDw3G9Gcm2Dnb7SZwr5SpcgCCYw8RVEh5yvBD9HrlvkusVOBIUUtMGtiZ2GDfD34fQEv0J+QwuCIAiCIAiCIAiCIAiCIBQQUqAVBEEQBEEQBEEQBEEQBEEoIGSIA0EQBEEQBEEQBEEQBEEQLCUAGaYit8gdtMIfcDqdDBw+is/mL/DpzO07d9Gm43M88uQz9B0wiISEBJ/MNJXrTWbDV3vS49//4KW93/P00gUULVc2c1nxyEr0/vUQRcqUzpxX6vbqPLd5LT3+83+8sHMzpWvVzDfX/M415WqXY8BUrl0yTeWKq7iKq31c/b39pnLFVVzt5ArWX7vZqf3iKq6r1m2g7bNdaNfpeTp168nBwz9aYCr9Kgg3Qgq0wjX8evQ3XnylNxu3bPXpzPj48wweOZbpkyeyceVyKkdW4r0PZvhcpi+6lm9wN3/q9ze+ePARPr/3Ac7/epQmI4YAUOe5Z3lu81qKV6x4zWuemPsJ+2bP4bOGjfh+7CSeXDQ3X1zzO9eUq12OAVO5dskUV3EVV3H19/aLq7iKqwurr93s1H5xFdejUceYPG06sz+cxqolC+jVszt93hrok6526ldByAnLC7RKqRJKqRlKqUNKqf1KqW1KqYbuZaHuZb8opQ4opXYqpVp6vHauUuo7pZTDY1439/x67rz9Sql4pdRx97/3uNeLUkod9lhnv1Jqjre5N2jndnc7PN/vNaVUc6XU9hu8rq5SyqmUap9lfhWl1Bql1A/uvluqlAr3dM2y/g3fJ68sXLqcDk+1o3WrljmvXICZu3bvoV6d2lSrWgWAzh3as2bDNzi9eCKliUxfdI3Zd4BP695L8sVLBBYqRGjFCiTFxxNaIYKabR9jaZtnrlk/tGIFytSqxY9LVwBwdNMWQkKLUb7+XcZd8zvXlKtdjgFTuXbJFFdxFVdx9ff2i6u4iqsLq6/d7NR+cRXXkJBgxg0fQrj7W5Z1a9/J2bPnSE5J8TlXO/WrvxLg8P0fX8HSAq1SKgBYD8QD9bXW9YExwAalVBlgDZAM1NZa3w30Bb5QSjX3iLnfPf8atNYHtdYZmauBEe7p+z1WeyxjHfdPd4tyr0fPLO+Xmz+pvAQsA17JMv8TYJHW+i6tdV1gH/BxLvIsZcSgAbR5tLXPZ0ZHxxBRPjxzOiI8nISERBITE30q01dd01NTqdnmMXr/cojKjRtxcP4iEs5Es7LTi5z/5ddr1i0RWYmEM2fA45fRpVOnKV6pYtZYI675mWvK1S7HgKlcu2SKq7iKq7j6e/vFVVzF1YXV1252ar+4imtkxYo0b9IYcA318c6UabRo1oSQ4GCfc7VTvwpCTlh9B+2DQBVgpNY6FUBrvQ3oDrQEqgJvaq2T3cv2AeOA4R4Z7wHDlFI1LHYzlZtrlFLBQBdgGNBQKXW7x+IIoKjH9IfuHyEb0p3pOBx//FNHQGCgT2WayrUi8+c165leuSbfj3uXjmuWQzZ5AI6AAMj6h0KHg/S0tHxzza9cU64m8Pd+9ff2m8oVV3H1d1d/b7+pXHEVVzu5msBO7RdXcc3gclISrw8cwvETJxg3YqjXedKvgnBjrC7QNgD2a63TPWdqrdfjKs7u1VpnLfV8B9znMf0zMB743HNIglyyPsuQA5530HqTez1me7zXzlys/zhwTGt9BFgJvOyxbDDwrlLqpFJqnnvdHR7L23q2DZhtURtsSYWICGLjzmZOx8TGEVaiBEWLFPGpTF90LVn9Nir95X83iP8wbwElqlSmcKmS2a5/8cRJikWUv2ZeaIUILp06bdw1v3NNuZrA3/vV39svruIqrvbJFFdxFVd7uZrATu0XV3EFOH0mmk7dehIYEMD8T2dSonhxr/JA+tVfCbDBj69gtUs6cOU6y5xAUDbzQ/jj/XkfAA6yGZIgB7IOcTDHotzr4TnEQZNcrN8dWOz+95dAd6VUCIDW+hugEtATiAPeBb7yeO1qz7a51/NbGje6nwMHDxF17DgAS5avoGXzpj6X6YuuoRXK027+ZxQpUxqA2p06cPa/P3Il/ny26186dZrzvx7lzg5PA3DbQy1wpqcTd+iwcdf8zjXlagJ/71d/b7+4iqu42idTXMVVXO3lagI7tV9cxTUhMZEXXu7Fwy0e5O8Tx1O4cGGvPUH6VRBywmHlIMdKqabA50BNzztllVITgCTgr8DtWusUj2WvAU9rrVu6H4S1XWs9VylVE/gHrrFZI7XW3Txek7mex7wooLnWOiobrzzn3qCt24FRWuvtWeY3d89vnmV+OHASiAVScRWKKwIvAJuA4VrrNzzWDwPO4Lrz+HF32zxds32fG3L5Qq439qARo6lZ43Z6dH0+1/H5nblj5/dMmT6DlNRUqkRWYtLYUZQMC/O5TF9xnVTmtsx/1/9rdxq+0pP01FQSzkSzud8ALrh/+QAMTIrng8gaJJ2LB6DU7dVpPfN9ipQpQ+qVK2z82xvE7P/Bte653yx3zS2+0K83gx2OAVO5dskUV3EVV3H19/aLq7iK6/+w8trNTu0X11vcNYca0Cefz+X9mZ9Qq8bt18yf+/EMSpW8Tu51hsvz2rWAMvOUWzTMhx435TssKhnu809We+73WJ/YdlYXaB3A97gKjmO11mlKqUeAuUB99/+PAv201ilKqXuAtUBnrfX2rAVSpVQ/YBKw2KoC7c3m3qCt27m5Am1/4CGt9aMe80YBzXGNz3sEGK21nu9e1ghYBNTAVcTN1wKtcOvhWaC1ktwUaAVBEARBEARBEAQfwMIaUCa5LNDe0kiBNlsW26BA29lHCrTZDTmQZ7TWTqVUW+DvwCGlVApwFtfQAzFKqadxjQN7SCmVBsQDz2ctcnrwAdD+JhTWK6WSPaYva63/YkHuzdJEKZXgMb0AeAAYkmW9GcDbQE3gMWCqUmoscBk4DbRxF7kNqgqCIAiCIAiCIAiCIAiCUFBYeget4OPIHbR+jdxBKwiCIAiCIAiC4OfIHbRmkDtos0XuoM09lt5Be6uhlNoGlMpm0cda64/z20cQBEEQBEEQBEEQBEEQ7ECAFO9zjdxB60/IHbSCAZZVqmV5ZodTRyzPFARBEARBEARB8Huc6dZnOgKsz7QbcgdttnxZqrzP16GePR/jE9tOjiJBEARBEARBEARBEARBEIQCQgq0giAIgiAIgiAIgiAIgiAIBYSMQSsIgiAIgiAIgiAIgiAIgqX4xNgBNkEKtMI1bN+5iynTZ5KcnIyqWYMJI4cRGhrqc5ni6nuuVTq2R/V5DZxOUpOS2D9wKOd/OEjDye9Q7i+NADiz+Vt+GDEagOCSJWnw7gRKqFoEFi7Mj1Pf5/iXy/PFNb8zxdU+meIqruIqrv7efnEVV3G1T6a4iqup3AVLlrF4+QocDgeVIysxbvhgypQu7ZOudupXQbgR8pCwfEQpVQ04Ahx2zyoC/AMY5P73b8DDWuvNHq+JApprraOUUs8Ag3EV1gOA+VrrybkWyOEhYfHx53n8mU4snjOLalWrMHnadBITLzNqyMBcv0V+ZIqrb7kuq1SL0Bq303zN12xp/hBXYmKJaNWSe6a+y6EJ71KtU0d2PNUBR0AALTauRX8wg5Or1vDAovlcPHKEg6PGUaRiBR7+fjubHmhO0ukzuXpI2K3er+Ka/5niKq7iKq7+3n5xFVdxtU+muIprnnNzeEjYocM/0XfAIFYtWUDx4qFM+vsHJCZeZsywQdd/US4eEnbL96s8JCxbltrgIWEd5SFhfstprXV9rXV94A4gGsi4bTAFmKWUKp71RUqpSsAUXAXcu4FGQCelVFurxHbt3kO9OrWpVrUKAJ07tGfNhm/wpohvIlNcfc81/Woye19/kysxsQCc33eAwuHhBBYKIbBoUQILFSKgUAgBISGkXb1KcMmSlG/elMOTpgCQdPoM3z70KMnnfzfumt+Z4mqfTHEVV3EVV39vv7iKq7jaJ1NcxdVUbt3ad7Bx5XKKFw/l6tWrxMTGUTIszCtPU6526ld/xWGDH19BCrQFiNbaCYwE6gIlgNPAZlyF2KyUBYKBou7XJgAv8r+7cb0mOjqGiPLhmdMR4eEkJCSSmJjoU5ni6nuul0+cIHrTlszpu8eP5vSGjRydv5CU33/nicP7afPjQRKO/saZbzYRWv02kmJiqdX7VR78Zg0tt26k1N13kZaUZNw1vzPF1T6Z4iqu4iqu/t5+cRVXcbVPpriKq8nc4OAgtmzbQdNH2/Kv/+zn6XaPe5UH0q+CkBNSoC1gtNbJwM9Aa/es/sAjSqlWWdY7AKwCjiql/qmUmgQEaq1/scol3ZmOw/HHvx8EBAb6VKapXHH1PjewaFH+PGcWobfdxt6+b1Jn4FtcPXeO1bXqsrZufUJKlaLWa68SEBxEaLWqpFy6xLbWbdjd4xXuHj+GknfflW+u+ZVpKtffXf29/aZyxVVc/d3V39tvKldcxdXfXf29/aZyxdVcLsBDDzZjz9aN9HmlBz1e60d6+o2HRsgJ6VdBuDFSoPUNnEASgNb6IvBXshnqQGvdC6gGfARUBXYrpZ62SqJCRASxcWczp2Ni4wgrUYKiRYr4VKa4+qZrkchKtNi4Fmd6OtvbPk3KxYtUavM4vy1YjDMlhdSLl4ha/CXlmjQm6UwMAFGLFgOQ+FsUZ3fvofQ9DfLFNT8zxdU+meIqruIqrv7efnEVV3G1T6a4iqup3GPHT7B33/7M6fbt2nD6TDQXLl7yOVc79au/UtDDF8gQB0KuUUqFAApYnzFPa72JLEMdKKUeV0o9q7U+pbWeo7XuBPQFeljl0rjR/Rw4eIioY8cBWLJ8BS2bN/W5THH1Pdeg0GI0X/M1p9asY0+PV0i/cgWA8wd+oPJTrmGSHUFBVHz0EeL/9W8uHz/O+f0HqNr5WQAKlStH2T/dx/l9B4y75nemuNonU1zFVVzF1d/bL67iKq72yRRXcTWVG3f2HG8OHk68+/kgazZspObt1SlV0rtxaP29XwUhJxwyyHH+oZSqBmzXWldzTwcA44EGwKtZlpUADgIRuAq4twGfAS201lFKKQfwIXBeaz0sVwKXL+S4sXfs/J4p02eQkppKlchKTBo7yusBwU1kiqvvuC6rVIs73uhL3aGDuHD4x2uz2j1Dg8nvULJeXZxpacR+t4sDw0fhTEmhSGQlGk6eSLGqVXAEBPDzx59ydO4XAHQ4dcSIa0Fliqt9MsVVXMVVXP29/eIqruJqn0xxFdc85TpzHqpg0bKvWLT0KwIDAwkvV5YRgwZQuVLF67/Akbt7/27pfi0a5ks3Y/oMy0uV9/mi4zPnY3xi20mBNh9xF2iP8L8HewUC+4DXgTA8CrTu9R8GNgK3uYuyLwIDcD0sDPeyAVrrq7kSyEWBVhBulmWValmemdsCrSAIgiAIgiAIgnAT5KJAe9PkskB7SyMF2mz5qnSEz9eh2sdH+8S2kwKtPyEFWsEAUqAVBEEQBEEQBEGwCVKgNYMUaLNFCrS5R44iQRAEQRAEQRAEQRAEQRCEAiKooAUEQbA3Ju52PVL/HsszAWrt22t9qMMn/tgm5Jb0NOszAwKtzzSFiTsmTPQpQGBwzuv4Cia+jSTnFnvh73cj+Xv7BXth6veWCex0jSFYT0ruRjK8aYILmck1gZFvfPv8DZ23FHJFm3vkykcQBEEQBEEQBEEQBEEQBKGAkAKtIAiCIAiCIAiCIAiCIAhCASFDHAiCIAiCIAiCIAiCIAiCYClyV2jukQKtcA3bd+5iyvSZJCcno2rWYMLIYYSGhvpcZgZOp5NBI0ZTq2YNenR93us8U6526ldTuXndVsXbtqN0z544nU6cSVeIHTeGlKgoyk+YSEj16hAQwMWvV3B+1qcAFLn/z5R7eyCO4CDSr1wlbtwYrvzwQ67fb9W6DXw2fwEOh4MihQsz9O3+1Kt95023Nyt22gesPq7AXq4ZbNm2gwEjxrBv57eW5NmlXxcsWcbi5StwOBxUjqzEuOGDKVO6tFeZK9duYM6iJZnTlxISiYmJZcf6lZQtk/dsO50H5dxin/OAKU8Tx5adtpWJ9ptytcu+CvbaB+zkOnHqB3yzZSthYSUAuK1qFd6fOM5rV7D++sLftxXY59iyMtPpdDJo9ARq1ahOj+c7k5aWxsRpM9j5f3tIS0vjpS6d6Nz+Sa987dKvpq6xTP3eEoTr4VfFbKVUNaVUslJqv/tHK6XmKKXKZ7PsB6VUlFJqtMfrQ5VSM5RSvyilDiildiqlWnosn6uUOu6R8aNS6m/uZd2UUvHu+QeUUoeVUs9m8evnfs0h93q9PZaNVEptVUo53NMOpdQapVQfq/onPv48g0eOZfrkiWxcuZzKkZV474MZPpeZwa9Hf+PFV3qzcctWS/JMudqpX03l5nVbBd92G+XeHsjJHi9xvF1b4j+aQcUPZ1Km3xukRp/h2BOPcbz9U5Ts/ByF6zeA4GAqvD+NmGFDOda2DfEzZxDx7nu5fr+jUceYPG06sz+cxqolC+jVszt93hp4s839A3baB6w+rsBerhlEHT/BpPenW/ZgArv066HDP/H5FwtZMmcWa5ctolqVykyb+anXrk8+8SirFs1j1aJ5LJ//GeXKlGb42296VZy103lQzi32OQ+Y8jRxbNlpW5k6t9jpGLDL7wFTuXZyBdj3w0GmvjOGVYvns2rxfMuKs1ZfX8i2ss+xZWXmr79F8WLvfmzcuj1z3pKvVxN1/ARrF89j+dxZzFuyjB/+ezjPvnbpV1PXWKZ+bwnCjfCrAq2b01rr+lrr+sAdQDSwPOsyrfVdwF+At5RSd7oLo2uAZKC21vpuoC/whVKquUf+CI/85sBEpVTGn29Wu5fdDbQAZimlwgCUUqOAtkBzrXVdoBXQRSk13P3acUAR4A339FtAmtZ6ulUds2v3HurVqU21qlUA6NyhPWs2fIPTiwsIE5kZLFy6nA5PtaN1q5Y5r5wLTLnaqV9N5eZ1WzmTk4kZNoS0uDgArhw8SFDZspydNIm4SRMBCCoXjiMkhPRLlyAlhaNNHuDqj66LkeDKlUn7/fdcv19ISDDjhg8hvFxZAOrWvpOzZ8+RnJJyU95ZsdM+YPVxBfZyBUhKusKAYaMY9ObrlmXapV/r1r6DjSuXU7x4KFevXiUmNo6SYWFWKQMwa+4CSpcqRScv7+qw03lQzi32OQ+Y8jRxbNlpW5k6t9jpGLDL7wFTuXZyTU5O5rA+wux5C2nTsQt9Bgzm9JlorzzBzPWFv28rsM+xZWXmwuVf06HdE7Ru2Txz3pbt3/H0E48RFBREWIniPN6qJas3bMqzr1361dQ1Vn5cEwtCVvyxQJuJ1toJjATqAiWyWaUC4AAuAc2AqsCbWutk9+v34SqcDs/mtWitY4CfgNrZLC4OJABXlFJFgQHAS+7XoLWOA/4KvK2UKqq1TgOeBwYrpboArwIv5aXd1yM6OoaI8uGZ0xHh4SQkJJKYmOhTmRmMGDSANo+29jonA1OudupXU7l53Vapp06RuH175nS5wUNJ2LoVZ0oypKURMXkKVdet5/I/95D821H3i1IJLFOG6jt3UXbgIOJn5/4vnZEVK9K8SWPA9ZWed6ZMo0WzJoQEB9+0uyd22gesPq7AXq4AIyZM4tn2T6Jq1rAu00b9GhwcxJZtO2j6aFv+9Z/9PN3ucW9VM4n//XfmLFzCkDf7ep1lp/OgnFvscx4wed1i9bFlt21l4txip2PATr8H7NSvJnJj4s7y5/vuoV/vl1n95QLurleH3v3f9rqQaOL6wt+3Fdjn2LIyc8SAN2jTutU1887ExFLhmvxyRMfG5dnXLv1q6hoLzF4T+xMOh+//+Ap+XaAFcBdbfwZaAxXdQwv8pJQ6i6v4+pTW+iRwH7DXXdT15Dv3sj+glLobqAH82z2rrTv/EPBfYLbW+ipQB0jUWkdlcTsMXMV1py9a619xFYMXAC9rreO9a/21pDvTcWSzdwYEBvpUpilMudqpX311ezmKFKHCtOkEV61KzNDBmfOjB/Tn1/vvIzCsJGX+9r/RPtLOneNok8ac6NiBiHcmEVyt2k293+WkJF4fOITjJ04wbsRQr/3ttA+YwE6uC5d+RVBgIM+0a1PQKjlisl8ferAZe7ZupM8rPejxWj/S09O9zgRYumIVLZs1pnJkJa+z7Hge9Odzi13OA6Y9rTy27LitrD632OkYMIGd9gE7uVauVJFZH0ylVo3bcTgc9HihC8dPnuLk6TN5zjR1feHv28oUdurXDJzp6dcUmpw4CQjwrXKPna6xMjB1TSwI2eFbR2zB4QSScA9xgOuO1y9w9c9mj3Wye6haiHv7XJpTAAAgAElEQVRZBmPcRdiDwKe4CqlR7mUZQxzUxXU3bmelVOcbZAMEZ8lvAsQAj95cE3OmQkQEsXFnM6djYuMIK1GCokWK+FSmKUy52qlffXF7BVWoQJUvl+JMT+PkC11Iv3SJoo2bEBju+uur8/JlLq1dQ6HadQgIDSW01f/+mnz18H+5+tNPFKqlcv1+p89E06lbTwIDApj/6UxKFC/udRvstA+YwE6uX69dx8HDP9Kuc1de7vsmV65epV3nrsTE5f0OBFOY6Ndjx0+wd9/+zOn27dpw+kw0Fy5e8so1g/Wbt/J0G2vuPrDbedDfzy12OQ+Y8jRxbNlpW5k6t9jpGDCBnfYBO7n+9PMvrFy34Zp5TicEB+X9+dqmri/8fVuZwk79+r/88sTGncucjo07R0R4OUuyrcJO11imr4kFITv8vkCrlAoBFLA+Y57WOh3XkAOVcI31CrAHuFcplfVe+UbAXo/pjDFo62mt79daL83ufbXWZ4C1wAPAYSBYKXVNFUkpVQfXNtLu6e5u13uBF5RSLfLQ5OvSuNH9HDh4iKhjxwFYsnwFLZs39blMU5hytVO/+tr2chQrRuSChVzatInoN/rhvHoVgOKPPpZ5x6wjOITijz3G5d3/hzM9nfITJlK4YUMAQmrUJKR6da4cOJCr90tITOSFl3vxcIsH+fvE8RQuXNiSdthpHzCBnVyXz/+ctUsXsmrxfD79YCqFCxVi1eL5lC/nWxe4YKZf486e483Bw4k/7xq7ec2GjdS8vTqlSno/5taFixc5fuIkDe6u53UW2Os8KOcW+5wHTHmaOLbstK1MnVvsdAyYwE77gJ1cAxwOxk/+OydOnQZg0bIVqJq3X/PV7JvF1PWFv28rU9ipXzNo2bQxX61ZR2pqKhcvXWLd5m95qHkTy/KtwE7XWCavif0Nhw3+8xXy/mfAWwClVAAwGtgNpHku01qnKqXeApYppeZrrXcqpf4LvK+U6qe1TlFK3QMMAzrn4b0L4SrOfqy1vqyUGg98ppR6Wmsdq5QKB2YB77qXK2Ai0FRrfVIp9QowTyl1l9b6vBfdkEmZ0qV5Z9Rw+g4YREpqKlUiKzFp7CifyzSFKVc79auvba+Sz79AcMVKhLZqdc2dsSdf7Er4yFFUXev6u0rC5k38Pm8uOJ2c7t2L8KHDICgIZ3IyZ/q/QWpM7h7ssPDLZZw+E83mbdvZvG175vy5H8/w6pexnfYBE9jJ1U6Y6Nd7G9bn1R7d6PpybwIDAwkvV5YZU9+1xPfYiVOUK1vGqzuQPLHTeVDOLfY5D5jyNHFs2WlbmTq32OkYMIGd9gE7udaqcTvD3n6TXm8MIC0tjYjy4UwdP8ZrVxP4+7YyhZ36NYPO7Z/k+KnTtOvSnZTUVJ59qi1/atjAsnwrsNM1lslrYkG4Hg5vBzu3E0qpasARXHesAgQC+4DXgTBgu9a6WpbXbAKOaa3/qpQqAowHHsdV0I0HRmqtv3WvO9edMTeb9+4GTAWO4xqyoBCwBejnvmMXpdSrQG9cd806cRVvZ7iLubuBj7TWn3pkfgaEaq2fzVUHXL7gPxtbsDVH6t9jJLfWvr05r3Sz+NKo4kLOpKflvM7NEuB7Y6ddF6eBcbNM9ClAoPcPd8g3TFxLybnFXpg4thw2+qKbv7dfsBemfm+ZwE7XGIL1pFw1kxtcyEyuCYzUqwyVRYqVkou3bFhdpoLP16HanjvjE9vOrwq0fo8UaAWbIAVawRhSoLU+Uwq0UqAVpEDp7+0X7IUUaAW7IAVaKdDeAqyxQYG2jY8UaOXKRxAEQRAEQRAEQRAEQRAEoYCQAq0gCIIgCIIgCIIgCIIgCEIB4dcPCRMEwTeptf/fRnK3VatteeaDUYdzXknwHfz9q4ImvjIcKH/rleEIBL//Or6/t1+wF/5+LWAn/H0IIVNDEdhpyC8j28tG+8AtgPR27pGrKUEQBEEQBEEQBEEQBEEQhAJCCrSCIAiCIAiCIAiCIAiCIAgFhAxxIAiCIAiCIAiCIAiCIAiCpQTIGAe5Ru6gFa5h+85dtOn4HI88+Qx9BwwiISHBJzNN5YqrC6fTycDho/hs/gJL8nytX8s/8zT3bdvMvVs30XDdKorffRcAFbu/yL1bvuFPu7Zz58wPcISEXPO6wlUq01gfylw/P1zzO9Nk7q2+X+V3pqlccRVXf3f19/abyhVXcfV3V39vv6ncVes20PbZLrTr9DyduvXk4OEfLTCVfs1gy7YdNGjS0rI86VdBuD4Op4mBt30cpVQ14AiQ9ek+s7TWM5RSQcAJYLnWuo/H6woBU4FmQDrwO9Bfa/0vpdSHQITW+hmP9R8GPgbeAEa7Z9cAooEE4Det9VPudcsAp4ChWusp7nkOYAuwU2s9yj2vNLAH6AScA7ZrravlquGXL9xwY8fHn+fxZzqxeM4sqlWtwuRp00lMvMyoIQNzFZ9fmeJqzhXg16O/MXriu/xw8L/06fUyPbo+71WeL/Xrtmq1KXL77TRYuYy9LVuTHBtL6ZYtUJMn8vPwkVQfPJD/PPEkqRcuUOezT7i07wDHp88AIKBQIep/9SXF6tRm/5PPcOnAD0DuHhJmp33AVO6tvF+Jq7iK663j6u/tF1dxFVf7ZPqFaw61iqNRx+j6ci9WLJxPeLmy7Nj1PSMnTGL7+tXXf1EuHjp1y/drLh8SFnX8BH/t8wZnz8Wzb9fWG6+ci4eE3fL9WjRM7hXNhvVlK/h80fGxs2d8Ytv58x20p7XW9bP8zHAvewz4J9BRKVXU4zX9cPVZPa31XcBgYLVSKhgYBNyjlGoLoJQqBnwEvKS1XpXxHsBeoKd7+imP7C7AauBld2EWrbUTeBHorZT6k3v+PGCm1tryx9zv2r2HenVqU61qFQA6d2jPmg3f4E0R30SmuJpzBVi4dDkdnmpH61bW/KXU1/rVmXwV/cYAkmNjAbh04AAh4eWo2KUzJz76hNTffwenkyMDBhG97KvM19WcOJ4zS5aSci4+31zzO9Nk7q2+X4mruIrrreHq7+0XV3EVV/tkiiuEhAQzbvgQwsuVBaBu7Ts5e/YcySkpPudqp34FSEq6woBhoxj05ute5Xgi/SoIN8afC7Q3ojvwNa4ibSeP+RFACBAMoLX+3r1uoNY6Afgr8KG7ODsGWK213n4T7zkTSAYezJiptT4J9AG+APoDDuD9vDbsRkRHxxBRPjxzOiI8nISERBITE30qU1zNuQKMGDSANo+29jonA1/r1ysnTnJuy7eZ0zXGjOTsxs0UrlyZkLJluWvJAu7bvplqA/qTevECABW6dCYgOJgzCxblq2t+Z5rMvdX3K3EVV3G9NVz9vf3iKq7iap9McYXIihVp3qQx4BpK650p02jRrAkhwcE+52qnfgUYMWESz7Z/ElWzhlc5nki/+icOG/znK/jzQ8IqKqX2Z5n3Aq7hB1oBLwGpuIqjn7uXTwPWAXFKqe3At8A8rfUVAK31FqXURmAOcCfwp9yIKKXuxlX83Ql8CbwCZH6HQGv9pVLqKWAoUNN9Z63lpDvTcWTzlY+AwJy/rpCfmaZyxdUMvtqvAUWLcOcH71OoYkV+6NSFezatp1Szphzs2p30q1e5c/r7VB88iOily6j44gvsa/d0gbnmV6bJXKuxU/vFVVzF1T6u/t5+U7niKq7+7urv7TeZC3A5KYlBI8cQHR3D7BnTvM7z935duPQrggIDeaZdG06ePuON3jX4e78KQk748x202Q1xcBB4HvhWa30eWAXcpZRqAKC1jgLq4irg7gG6AvuVUiU9cvsDDwN9tNZJuXR5CViqtU7DVaB9UilVPmOhUioUqA9cBf6c9ybfmAoREcTGnc2cjomNI6xECYoWKeJTmeJqztUEvtivhSpV5J51q3GmpbH/6Q6kXrxIcnQ0cevWk5aQgDMlhZjlKwi7tyERHTsQVLw4Ddet5t6tmygUUZ7aH31ImUda5YtrfmaazLUaO7VfXMVVXO3j6u/tF1dxFVf7ZIqri9NnounUrSeBAQHM/3QmJYoX9yoPpF+/XruOg4d/pF3nrrzc902uXL1Ku85diYmL8zlXO/WrIOSEPxdor0c34C9KqSjgIK6Hgb0CoJSaAFTUWv9Taz1Ba30vcBpXwRYArfVFXA8Pi8rNmymlQoDncI13GwVsdi/q7rHaDGAjruLxbKVUOAZo3Oh+Dhw8RNSx4wAsWb6Cls2b+lymuJpzNYGv9WtgsWI0WLmcuHXrOfxKb9KvXAEgds06wtu1IaBwYQDKPvoIF/cf4JfhI9nTqAl7WzzM3hYPczU6hsO9/sa5jZtv9DaWuOZ3pslcq7FT+8VVXMXVPq7+3n5xFVdxtU+muEJCYiIvvNyLh1s8yN8njqew+zreW/y9X5fP/5y1SxeyavF8Pv1gKoULFWLV4vmUL1fO51zt1K/+isMGP76Cwx8HOVZKVQO2a62rZZl/D64CaaWMu1+VUs2BNUBF4EMgCeirtU5WSmUMS9BGa/2TR04U0Nx9x23W994OjMoYm1Yp1R4Yp7W+02OdbsAI4HZcxdtBwH1a6ytKqfeBGlrrJ67Xjuty+UKOG3vHzu+ZMn0GKampVImsxKSxoygZFpar+PzMFFdzrhkMGjGamjVup0fX573O8pV+3VatNlX6/o3qg98m4cefrlm2v/2zRPboRni7tjgCA7n0w0H0WwNJS0i4Zr0/793Nf3u8zKUDPwDwYNRhI64FlWkyF27N/UpcxVVcby1Xf2+/uIqruNon85Z3zaFW8cnnc3l/5ifUqnH7NfPnfjyDUiWvk5vN19YtcS2gzDzlpqflOvvk6TO06diFfbu23njFgNx99f+W7teiYb5U6/MZvilb0eeLjq3PnvaJbefPBdojQNbKyj+AJK11/yzr/xuYBSwEpuAawiAR1wO9xmqtV2RZP4rcF2jXAhu11tM91gnBdQfueGAU0FJr/YN7WWHg37iKxRuwuEArCLcy26rVtjwztwVaQRAEQRAEQRBuAhO1ilwWaG9pbqJAm2tyWaC9pZECbbZIgTb3+GWB1m+RAq3g50iBVhAEQRAEQRBsghRozSAFWjNIgTZbNpbz/QLtI3G+UaCVMWgFQRAEQRAEQRAEQRAEQRAKCCnQCoIgCIIgCIIgCIIgCIIgFBBBBS0g5CN2+opI8hXrM0OseaqnYF9MDEdwpP49lmcC1Nr/byO5gmA1zoTzRnIdoaWsD01Ntj7TFEEhBW1Q8KSlGgg19C27wGAzuYJgNaaGt7PR18adl+Itz3QUL215poCt9isjONPN5Pr7cAQyzGe+4udH8U0hd9AKgiAIgiAIgiAIgiAIgiAUEFKgFQRBEARBEARBEARBEARBKCBkiANBEARBEARBEARBEARBECwlQAY5yDVSoBWuYdW6DXw2fwEOh4MihQsz9O3+1Kt9p1eZ23fuYsr0mSQnJ6Nq1mDCyGGEhobmKcvpdDJozARq3V6dHs935lJCAkPHTeLoseOkp6fz5OOteblrF698nU4ng0aMplbNGvTo+rxXWRlY2QeeiKv1rt5kFm/bjtI9e+J0OnEmXSF23BhSoqIoP2EiIdWrQ0AAF79ewflZn17zuqDISKquWMnJl7px9dChfHG9ESa2lYlcU+33tf2qIHKt2FarN37LZ4uX4XA4KFy4EENf7029O2plLu8zdAzlypZmxBt/y7OnqfZv3raDDz79nACHg7CwEowbOpAqkZV8LhNkf9W//Mq4d6dyKSGBgMBAxgx5m7p33uFV5sq1G5izaEnm9KWERGJiYtmxfiVly+R9nEl/31biah9XE58HTLlambt607d8tni56/dWoUIMfb0X9e6oxZ/bdCSiXNnM9Xp0eoY2D7coMM/8yBVXe7kuWLKMxctX4HA4qBxZiXHDB1OmtHfjIvt7v5o6DwrCjZAhDgyglKqmlEpWSu3P8vOaUsrpMX1YKTVbKVU4y+unKKXilFKFPOZ9qJRanmW9h5VSR5VSxa3wPhp1jMnTpjP7w2msWrKAXj270+etgV5lxsefZ/DIsUyfPJGNK5dTObIS730wI09Zv/4WxYuv9WPj1h2Z86Z98hnlw8uxdvE8ls/9lCUrVrHvYO4LXH94j6O/8eIrvdm4ZWueM7JiZR94Iq7Wu3qTGXzbbZR7eyAne7zE8XZtif9oBhU/nEmZfm+QGn2GY088xvH2T1Gy83MUrt8g83WOkBAqTJ6CI/jmHjBjp21lItdU+31tvyqIXCu21dHjJ5g8czaz3hvPyjkf0avrc/QdOiZz+eyFS9l7IO/najDX/itXrjJgxFg+fHc8qxbNpUWTBxj33vs+lwmyvyZduUKP1/rRs2sXVi6aR+8e3Xhr2CivXZ984lFWLZrHqkXzWD7/M8qVKc3wt9/0qjjr79tKXO3jauLzgClXK3Mzf29NHsfKz2fSq2tn+g4by9HjJwgrXpyVn8/M/MlLcdbX2y+u9nU9dPgnPv9iIUvmzGLtskVUq1KZaTM/zfmFBeBql341dR4UhJyQAq05Tmut62f5mQGQMQ3UAUoDL2W8SCkVBHQE/gG098gbBNyjlGrrXq8Y8BHwktb6khXCISHBjBs+hHD3X4jr1r6Ts2fPkZySkufMXbv3UK9ObapVrQJA5w7tWbPhG5x5eHLiwuVf06HdE7Ru2Txz3tA3+zKwb28A4s6eIzk5meLF8v7XsoVLl9PhqXa0btUyzxlZsbIPPBFX6129yXQmJxMzbAhpcXEAXDl4kKCyZTk7aRJxkyYCEFQuHEdICOmX/nfIho8czcWvV5B2/ny+ud4IE9vKRK6p9vvaflUQuVZsq5DgYMYO7Ed42TIA1L2jFmfjz5OcksKefQfY+c+9dHryca88TbU/LT0Np9PJpYQEABIvJ1EoJMTnMkH21+9376FyZCWaNf4LAC2bNeH9ieO88szKrLkLKF2qFJ3aP+lVjr9vK3G1j6uJzwOmXK3Mvd7vrX/u+4HAgAC6/K0/bbu9yoy5C0lLSyswz/zIFVd7udatfQcbVy6nePFQrl69SkxsHCXDwrzK9Pd+NXUe9FccNvjxFWSIg4IlGCgKxHjMexz4FZgPvA4sAtBaJyil/gp8rpT6FhgDrNZab7dKJrJiRSIrVgRcX299Z8o0WjRrQshN3tnnSXR0DBHlwzOnI8LDSUhIJDEx8aa/djBiwBsAfL/nX5nzHA4HQUFBvDVyLBu37qBVsybcVrVynn1HDBrgeo//25PnjKxY2QeeiKv1rt5kpp46ReqpU5nT5QYPJWHrVpwpya6syVMIbd2ahM2bSP7tKAAlOnTEERzEhaVfUvrVXvnmeiNMbCsTuaba72v7VUHkWrGtIitEEFkhAnD9Ppk4/RMefODPnL9wkQnTPmLWe+P5cvX6POeDufYXK1qU0YPfolOPXpQMK0F6ejqLZ3/klauJTJD99bdjJyhXpjRDxkzgp59/oUTxUAb0fS3PjlmJ//135ixcwoovPvM6y9+3lbjax9XE5wFTrlbm/uH31oeu31sBAQE0urcB/V95idTUVF4ZOILQokV5seNTBeKZH7niai9XgODgILZs28HQsRMICQ6hb6+/epXn7/1q6jwoCDkhd9Cao2I2QxzUA8iYBk4DFYBvPV7XHVgKrAfqK6VqZyzQWm8BNgJzgIeBISbELycl8frAIRw/cYJxI4Z6lZXuTMfh+OPfJAICA73Kzcp7o4eze+NqLly8yIzP5lma7S351QdW4O+uVmQ6ihShwrTpBFetSszQwZnzowf059f77yMwrCRl/taHQrXrULJTZ2JGDC8wVztjqv2+ul/lZ66VXE66Qr8R4zl+6jSj+veh/6h3GNzn1cw7lLzBVPv1L78yY/Zc1i9dwK4Nq3i1e1f6DBzq1Z0YJjJB9tfU1FR2fP9/PPt0O1Z88TnPd3yGl1/vT3JysjeqmSxdsYqWzRpT2YKxgv19W4mrvVzB2s8DYJ9+vZx0hX4jx3P81BnGvd2Pjm0eZXi/3hQtUpgSxUPp1vFpNu/8R4F7mswVV3u5ZvDQg83Ys3UjfV7pQY/X+pGenp7nLOlXF1afBwUhJ6RAa47shjg4CNcMcRAObAe+BFBKheMqvC7TWicBa4BXsuT2d6/Tx72OtdJnounUrSeBAQHM/3QmJYp7N7xthYgIYuPOZk7HxMYRVqIERYsU8VYVgJ27/0mMO79Y0aI8/vBDHNZHLMm2CtN9YCX+7uptZlCFClT5cinO9DROvtCF9EuXKNq4CYHhrr/qOi9f5tLaNRSqXYcSTz5JQGgolb9cSpVVqwkKD6fCe1Mp1iJ3Xyu307Yygan2++J+ld+5VnE6JpbOvfoRGBDAvA/e5dip05w8c4aJH37Ck9178eWqdWz49juGTfx7nvJNtX/X/+2h4d31Mh/g1aXD0/z862+cv3DBpzJB9tfwcmW5/bZq3F23DgAPNW9KWlo6J06d9so1g/Wbt/J0G++G4sjA37eVuNrL1erPA2CPfj0dE0vn3m8QGBDIvGmTKFE8lFUbt6B/PZq5jhMnwUE3X/ixQ/vF1Z6ux46fYO++/ZnT7du14fSZaC5czPsoiNKvZs6DgpATUqAtQLTW6cDnwAPuWc/jGgLjX0qpKOAhoKtSqojHay4CvwNRVvskJCbywsu9eLjFg/x94ngKFy6c84tyoHGj+zlw8BBRx44DsGT5Clo2b+p1bgYbtmxlxuw5OJ1OkpOT2bBlK3++t6Fl+VZgug+sxN9dvcl0FCtG5IKFXNq0ieg3+uG8ehWA4o8+Rpm/9XGtExxC8cce4/Lu/yNuwniiHmnF8XZtOd6uLamxsZx5600St357o7exxPVWwFT7fW2/KohcK0i4fJmufQbQqlljpo4eQuFChWhQtzbbv1rIyjkfsXLORzzb7nEebdmUcYPeyNN7mGp/7TsU//rPfs6eiwdgy46dRFasQOmSJX0qE2R/bfqXRpw8fZpDP/4EwL/+sw+HAyIrVvDa98LFixw/cZIGd9fzOgtkW4mrfVxNfB4A3+/XhMuX6dr3bVo1fYCpowZTuJDrWc0/Hz3GB599QVpaGleuXmXhitU82qJZgXnmR6642ss17uw53hw8nPjzvwOwZsNGat5enVIl8z4Orb/3q6nzoL/icPj+j68gY9AWPC2B/7j/3Q3oprXOuKM2APgZeBaYa1pk4ZfLOH0mms3btrN52/bM+XM/npHnE3yZ0qV5Z9Rw+g4YREpqKlUiKzFp7ChrhIFBr7/GyIlTaPNcNwAeataErs8+Y1m+FZjuAyvxd1dvMks+/wLBFSsR2qoVoa1aZc4/+WJXwkeOoupa13ibCZs38fu8uV55eut6K2Cq/b62XxVErhUs/Go1p2Ni2fLd92z57vvM+XPen0SpsBKWvIep9je67x56PN+ZF17tQ3BwEGElSjDzvXd8LhNkfy1Xtgwz3pvI6InvkZSUREhICNMnv0Mhd2HFG46dOEW5smUIDrLmUtnft5W42sfVxOcBU65W5i5c4f69tfMfbPEYwuCTSWOY9tl82nbrRWpqKo882IQOT7QuMM/8yBVXe7ne27A+r/boRteXexMYGEh4ubLMmPquT7rapV9NnQcFIScc3o5/JvwRpVQ14AhwOMui74A+wAH3tBO4ALwKhOIa0qCK1jrz8YBKqT5AF631nz3mRQHNtdZRNyWW+Lv1G9vUnxuSr1ifGSJ/+RKs50j9e4zk1tr/byO5gmA1zoTzRnIdoaWsD021ZmzSfCEopKANCp60VAOhhq57A+XBIYJNMPXZz5duQcoB56V4yzMdxUtbnikIOPM+juwNcfj5F6lNnQeLlbTPiTAf2RpeyeeLji1iT/nEtpMCrT8hBVrrMwW/Rwq0gr8jBVpDSIFWCrSCYAIp0EqBVrAPUqA1gxRo85VtNijQPugjBVo/PzIFQRAEQRAEQRAEQRAEQRAKDinQCoIgCIIgCIIgCIIgCIIgFBDykDB/wkZfPZLhCAS7YGoogrkValie2e3ML5ZnCoKRoQhMIcMG2ItAuUwVBMux0+cBQ8hwBIJt8PehCEwh58F8xYH0d26RI14QBEEQBEEQBEEQBEEQBKGAkAKtIAiCIAiCIAiCIAiCIAhCASHfHRMEQRAEQRAEQRAEQRAEwVICZISDXCMFWuEatu/cxZTpM0lOTkbVrMGEkcMIDQ31uUxxFVd/ca3e8Rnq9n0NnJCadJk9A4dwKeoYjaZOpnS9uqRevszPCxbz06ezAYhs/TBNPv6QxBOnMjPWP/oEqQmJxl1vhUxxFVdxFVd/b7+4iqu42idTXMVVXO3lKgg3wuF0OgvaId9RSlUDjgCHsyyaBQwALgPJQAhwChiktf63x+v7Aa8AaUAq8KnWeqZ7WTfgPaCO1jrG4/22a62rKaVGAWitR7mX1QE2AX2A/9zAK8YjN9H92vLAD0BrrfW+HBt++cINN3Z8/Hkef6YTi+fMolrVKkyeNp3ExMuMGjIwx+j8zBRXcfUH17kValCixu20XreKNU1bkhQTQ6VWD9Ho75OJ3vk96Wmp/F/fN3EEBtJi0Xx+mv05JzdupuHIYaQkJHBwyvt/yMzNQ8Ls0q++tK3EVVzF9dZx9ff2i6u4iqt9MsVVXMXVx1yLhsm9otnwXflIny86No056RPbzp/HoD2tta6f5WeGe9lj7unawPvARqVUWQB3gbUt0FxrXRdoBXRRSg33yC4OfJyTgFLqTmAD0FtrveJGXlrr5cBeYKxHxIfAjFwVZ3PBrt17qFenNtWqVgGgc4f2rNnwDd4U8U1kiqu4+otrenIy/+jzBkkxMQCc27efIuXDKduwPkeXLMWZnk56SgonN26mWru2AIT/6T4qNG1M213beHTDGsr/pVG+uN4KmeIqruIqrv7efnEVV3G1T6a4iqu42svVX3HY4MdX8OcCba7QWq8D/gk8p5QqiusO25cy7o7VWscBfwXedi8H+AqoqZR67ngM/FIAACAASURBVHq5SqlawHqgl9Z6VS51euMqBjdQSrUBqgET8tCsbImOjiGifHjmdER4OAkJiSQm5v6r0fmRKa7i6i+uCcdPcHLT5szp+yaM4cSGjcT+ay/VO3XEERREULFiVG33BEUiygNw9Xw8+vN5rG78IP8ePY4HF86laMUKxl1vhUxxFVdxFVd/b7+4iqu42idTXMVVXO3lKgg54c9j0FZUSu3PMu+F66x7CLgDqAMkaq2jPBdqrQ8rpa661wHX8AjdgLVKqW+zyasBbAV+cReAc/TSWh/UWscqpd4EZgJhQHutdep1W3iTpDvTcTj++PeDgMBAn8o0lSuu4uqrrkFFi9L4o+kUq1SRze2fBeDecaNpu3MrSbGxnN62g/A/3QfAtue7Z74udvce4vb8i4oPNueXhYvzxdXOmaZyxVVcxdU+rv7eflO54iqu/u7q7+03lSuu4monV0HICX++gza7oQQOXmddJ5Dk/v/1itrB7uUAaK33Ap+T/VAHT+Maw7akUqrvzXhprRcCV4ClWusfc9PQ3FIhIoLYuLOZ0zGxcYSVKEHRIkV8KlNcxdWfXItFVuKxTetwpqXxzRNPkXzhIsHFi7N3xGhWNWrKpnbPgMPBxaO/ERJWgnr9+10b4HCQnpKSL652zxRXcRVXcfX39ouruIqrfTLFVVzF1V6ugpAT/lygvRnuwvXgrsNAsFJKeS50P+grANBZXjcKqAlkHergffeds88DY5RSd9+kzzEg6iZfkyONG93PgYOHiDp2HIAly1fQsnlTn8sUV3H1F9eg0GK0XreSY2vWseOll0m7cgUA9VI3GgwZBEDhcuWo1bULR5d/RcqlBO7o+RJV2z4BQOm76lH2ngac2rLVuOutkCmu4iqu4urv7RdXcRVX+2SKq7iKq71c/ZWCHl/WTmPQOvxxkGOlVDVgu9a6WjbLonA9ACzKPd0GmAXU1FpfUkoNAp4AnnYPORAOrAQ2aK3HKqW6uV/fzf36e4EdQJzWupr7IWNorUe5l/cHegL3AOHX88riONe93tybavjlCzlu7B07v2fK9BmkpKZSJbISk8aOomRY2E29TX5kiqu43uqucyvUoN6br9Ng2GB+/++1N8t/+9wL3D9xPMWr3wYOBwenTuPo0uUAlGlwN/e/+w7BoaE4U9P455BhRO/8HoBuZ34x4norZYqruIqruPp7+8VVXMXVPpniKq7i6kOuRcN8qdbnM+wqH+nzRcfGMSd9Ytv5c4H2CK47Yj35DmgLXMY1jizAWaC/1vqAx+tfxfXArgBcwxp8rLWe4V7WDY8CrXveeKDLdQq0Dtzj0QLjr+elte7rkTcXQwVaQRB8g7kValiemdsCrSAIgiAIgiAIgnATSIE2W6RAm3v8skDrt0iBVhBsgxRoBUEQBEEQBEEQbIIUaLPl+/KVfb4O9UDMCZ/YdjIGrSAIgiAIgiAIgiAIgiAIQgERVNACgpAtJu7sTk+zPjNQDiG/JzU553XygIm7XS88ZmZg+7D13xnJFWyCoWOAoBDrM025msBE+wXBFCausQICrc8UBDDzOcMUDp+4qUsoKNJSzeSa+Axrp+tBQfBRpLokCIIgCIIgCIIgCIIgCIKlyN+Zco8McSAIgiAIgiAIgiAIgiAIglBASIFWEARBEARBEARBEARBEAShgJAhDoRr2L5zF1OmzyQ5ORlVswYTRg4jNDTU61yn08mgEaOpVbMGPbo+b4EprFq3gc/mL8DhcFCkcGGGvt2ferXv9CpT//Ir496dyqWEBAICAxkz5G3q3nmH164m+tXUtrLTPmAi01T7N2/bwQeffk6Aw0FYWAnGDR1IlchKBeYa3OpxCj37IuDEeeUKV6ZPIu3nnyjcuz9Bf3oAAgNJ/nI+yauXARDUqBlFBo/FGXsmMyOhT3dIumzcNT8zM7B635LjFb74cjkLln5F4UKFuP22qox4uz8lw0p4lWmn49VE+8Fex5aJ/cpErpxbzOROnPoB32zZSph7v7+tahXenzjOJ13t1K92Ol7t1K8mPmeYyAR77VfiasbVxGdYU652uh40eT3gT8hdobnnlu0rpVQ1pVSyUmp/lp/K7uVTlFJxSqlCWV5XQik1Qyl1yL3+NqVUwxwyX3Mvd3rMO6yUmq2UKuyRfZ9SaqtS6ohS6r9KqTlKqbIey4OVUuOUUj8rpX5QSv1TKdXRY/lcpVQ3j+lmSqkzSqkHrOiz+PjzDB45lumTJ7Jx5XIqR1bivQ9meJ3769HfePGV3mzcstUCSxdHo44xedp0Zn84jVVLFtCrZ3f6vDXQq8ykK1fo8Vo/enbtwspF8+jdoxtvDRvltauJfjW1rey0D5jINNX+K1euMmDEWD58dzyrFs2lRZMHGPfe+wXmGlC5KoVffYPEt3uT0PNZrn4xi6JjphLS5hkCIquS0L09Ca88R8gzXQi8oy4AgXXvJvnLeST0fDbzJ7fFWTsdA2D9viXHK+ze+x9mzV/IvJnTWLVoLk0faMSICe96lWmn49VE+8Fex5aJ/cpErpxbzOXu++EgU98Zw6rF81m1eL4lxVk7HQN2crXTNZaJXBOfM0xkgr32K3E142riM6wpVztdD5q8HhCE63HLFmjdnNZa18/yc0IpFQR0BP4BtM9YWSkVAKwH4oH6Wuv6wBhgg1KqzA0yM4/UjHlAHaA08JI7uzawGpigta4F1AM0sN2jiDsLqAE01FrfBXQCxiilXsjaMKVUE2Ah8JTW+nsrOmvX7j3Uq1ObalWrANC5Q3vWbPgGp5dPOl24dDkdnmpH61YtrdAEICQkmHHDhxBezlXfrlv7Ts6ePUdySkqeM7/fvYfKkZVo1vgvALRs1sSSDw8m+tXUtrLTPmAi01T709LTcDqdXEpIACDxchKFQrx7Iqk3rs6UFJImj8YZf9blpw/jKF2W4OYPk7xhFaSlQcIlUrZ+Q3CrxwEIqnM3QQ3/ROjspRT7YA6BdzXMF9f8zMzA6n1Ljlf4748/8Zf77iWifDgADz/YjK07v/fqnG2n49VE+8Fex5aJ/cpErpxbzOQmJydzWB9h9ryFtOnYhT4DBnP6TLRXnqZc7dSvdjpe7dSvJj5nmMgEe+1X4mrG1cRnWFOudroeNHk9IAjX41Yv0F6Px4FfgfnAqx7zHwSqACO11qkAWuttQHcg8CbfIxgoCsS4p98GPtFab3HnpmutJwKXgQ5KqdtwFYt7aK0vudc5CrwJjPIMVkr9BVgAPKm13n2TXtclOjom82QJEBEeTkJCIomJiV7ljhg0gDaPtvZW7xoiK1akeZPGgOsrWO9MmUaLZk0ICQ7Oc+Zvx05QrkxphoyZwNMvvET3114nLS3Na1cT/WpqW9lpHzCRaar9xYoWZfTgt+jUoxeNH23HwmVf8VafXgXm6ow+TerunZnThV97i9R/bMdRpuz/s3fe4VFUbxu+N5uE0AICgVBF2lG6iAWlCVgRQRABKYL4o0oRDFKkdxGUqtJBQgcJHUE6nyAoKAgcKzWk0RMS0vb7YzcxCYEku3Ngh5z7unLBzuw885w+8+6ZM9jC/7thTgwPxcOviP2YG9e5vX4VkR+8Q8ycaeQa/QUWv8JppQ33ej81kzC6bun2CtUqV+LgkZ+56AjIrN2wmbi4OK5dv+60ppnaq4r0g7nalop6pUJX9y1qdEPDI3ju6afo26ML61csoVqVSvToP8DlG10ztQEzeTXTNZYKXRX3GSo0wVz1SntV41XFPawqr2a6HlR5PZDdsJjgz1142NegLSaEOJbic6CUchL2gOtK7LNlFwghKkopTwJPAseklIkpRaSUm8G+xEE6mgDtpZTHHd9J2lcCuAj84Pj8tOOcadnr2BcDnJJSpm3xe4EyQogCjs/PAlOBdVLKIxllQFZItCVisdxZPT2sWY1N3z9uRUczcPgoQkJCmTtzqkta8fHx7DnwI4u/mUG1ypXYsXsvXfr0Z9eGtXi7MHtKRb6qKisz1gEjUZV++dffzJy7kM0rl1CqRHEWL19Fr0+GEBS4MN3z3TevPjnJNXAUlsL+RA3oQZ6vAiHVvbIFEu0XeLeG9UvemnD8KAm//4rnU7WI2xp0f7zeB01V6PYKNZ+sRs//vc+HAYOxeFho0aQx+fP54uXp/I2pmdqrivSDblsqMFP6zdS3lCxejDnTpiR/7ty+LbPmLuBC8CVKFi/mtK6Z2oCZvKrATPmahJH3Gao0zVSvtFc1XlXcw6ryaqbrQTP1r5qHh4d9Bm3a5QgmCSEKAy8Dq6SU0cAGoKvj+4nYA6VZ0ayeFJyFVEscFAZ2Ayscu2ykHxD3duy72/6k3iopbNISaAY0FEK8mYHXLFHU35+w8Ijkz6Fh4eTz9SVXzpxGnsYwgi+F0LrjB1g9PFg8exa+efO6pFfYrxBlHytNtcqVAGhUvy4JCYmcvxjskq6KfFVVVmarA0ajKv37fzxEjWpVkl8y1LZlc/78+1+uuvBrsateLYX9yTNjEbbERKL6fgCRN7GFXcJS0C/5Ox6F/EgMD4U8ecnRtnMaAQskxN8Xr/dLUxW6vUJk1C2eqVGd75bMZ+3ieTSqVwfApZdCmKm9qkg/6LalAjOl30x9y+k//2Ldpi2pttls4OXp2lwRM7UBM3lVgZnyFYy/z1ClaaZ6pb2q8ariHlaVVzNdD5qpf9U8PDzsAdr0aId9FvNhIcQZoBHQQQiREzgC1BBCpPqpRAgxTgjxYlZO4piFOx9IeoHXIaBWOl+t5TjvT0AFIcQjafY/D/wjpbzq+DzYsezC+8A8IYRrr5VOQe1az/Lr8ROcOXsOgOWr19Kwfl2j5A0lMiqK9l2683KDF/liwlh8fHwyPigD6j5fiwvBwZw4dRqAw78cxWKBEsWKuqSrIl9VlZWZ6oAKVKW/4uOCw78cI+LyFQB27NlHiWJFKZA//4PxmjMXeb6cR9y+H4ge9QnE3gYgbv9uvF9vBlYr5MmLV4NXidu/C25F4d2sNZ517WvReZR7HOvjlYn/KXPLX5upDahAt1cIC4+gfbdeREbaHxL5av4iGr/cyOkZqWCu9qoi/aDblgrMlH4z9S0eFgtjJ32RHDBYumotonzZVI+PuotXM+Wrrq9qdFXcZ6jQBHPVK+1VjVcV97CqvJrpetBM/avm4eFhX+IgPToCHaWUKyD5xWB/Aq2ARUAYMFwIMVpKmSCEeAX7kghTgaz+XNIQ+MXx//HAASHETinldkcQeAj2dWpXSSljhBBLsAddO0gpI4UQZYEppF6DNhZASvm9EGI5sEQI0TDtsgzOULBAAcaPGErvgIHExcdTqkRxJo4ekeFxD4LAFasIvhTC9l272b5rd/L2hV/P5JH8+ZzS9CtUkJmfT2DkhM+Jjo7G29ub6ZPGkyNHDpe8qshXVWVlpjqgAlXpr/X0U3Ru14b23Xrh5eVJPl9fZn0+/oF5zfFWayxFiuJVpwFedRokb48K6I5H8RLkmbsKvDyJ3bCahF9/BuDWp33w6T0Qn049ICGeWyMHYLt+TbnX+6mpCt1eoUzpUnR5rx0tO3UhMTGRp6pXZVhAv4wPvAdmaq8q0g+6banATOk3U99SoVxZPh3Qj+4fBZCQkIB/kcJMGTvKLb2aKV91fVWjq+I+Q4UmmKteaa9qvKq4h1Xl1UzXg2bqX90dVyckZCcsD+tb6Bzrxe6WUpZOsa0m9iUNSkkp41Js7wW0lVI+J4QoBHwB1ATigAigv5TyqEPzD+BkmtPtlVL2FkLYgF8d22zAdaCblPK04zzVgYnYX0TmCewHBkgpwx37rcAgoC2QgH25hUkpgskLHWla6PicE3sAeImUcmyGmXLrunkKW0W9THT9hV93YM2Ov3FoUhEfq0bX07W3xqfH9dfV/Oqbb/NeJboak2CiNqDMqwpUpF+jUYWKaywPvc6fRhFmuv/VgY3sTSaXE8syKu5hzXQ9qIpc+XSDTYefipZy+073mUvnMlV2QojhwDuOj5uklAOEEAuA2kDS+6RGSim/c8T/5gK+2N8t1U1Kec9G/dAGaDXpoAO0xmvqAK3GRBcjOkCrUYKJ2oAO0Go0itABWo2ZMNP9rw7QZm90gNZc10M6QJsuD0uAVgjRCBgJvIh9QuZWYAYwCnhZSnkpzfdPAB9IKQ8KIeYBR6SUX93rHDq6pNFoNBqNRqPRaDQajUaj0WgM5SGKWl/C/nR9LIAQ4hT2p+NLAfMd74f6DnsQtySQU0p50HHsQsd2HaDVaDQajUaj0Wg0Go1Go9FoNJqUCCHyA+m9EfialPIagJTy9xTfL499qYM6QH2gB/YlTjcCnYET2AO6SVwCSmTkQwdoNe6Jisd59HIEGhWY6LEbVUsRbCj5uOGaTc6fNlxTowgTtQFTedVozIRejkBjJvSyARqzYKb7V32NpTE3fYHh6WwfCYxIuUEIUQnYBARIKSXwVop904EO2N9blXJpBwuQmJEJE7V4jUaj0Wg0Go1Go9FoNBqNRmMGTPKT2JfYlyFIy7WUH4QQLwBrgL5SyuVCiCpABSnlGsdXLEAccAEomuJQfyA4IxM6QKvRaDQajUaj0Wg0Go1Go9Fosh2OZQyu3es7QoiSwDqglZRyp2OzBfhSCLETiAS6AIuklGeFEDFCiBeklAeA9sCWjHzoAK1Go9FoNBqNRqPRaDQajUaj0aTPx4APMEUIkbTta2A8cADwAtZIKZc59rUF5gghfIFfgGkZncBis9ky+o7mYeHW9QwLe/e+/UyePovY2FhE+XKMG/4pefLkcem0KjS1V+1Ve3Ufr0lr0BZv2YKyH3YHm42E6GhODBpKuT4fkvux0snfzfVoKS4f+JHD7TrilT8/lSeMJa8ojzVnTv6cMpULK1cDmVuD1l3S/6A0tVftVXs1j6b2qr1qr9prdk+/9qq9PvRec+UzydP895efiz3q9kHHp4LPukXZ6QBtdiKDAO2VK1dp/HZrli2YQ+lHSzFp6nSiom4xYvAnTp9Shab2qr1qr+7ldUPJx8ldrizPB61hb4OXuR0aRuFGDag6+TN2VKuZ/L18T1aj5oK5HHi9KTHBwTy9ZCGRf/7JqZFj8SlWlHr7drKnTgNigi9lGKB1p/Q/CE3tVXvVXs2jqb1qr9qr9prd06+9aq/ZwqsO0KaLDtBmHo8HbeB+IYQoLYSIFUIcS/F3XAjxt+P/IY6/pH0FhRC7hRB/OT7/6vj+Oxmcp74QIjLFMVII8YUQIk+a7zURQtiEEE+l2NYpxfljhRAnHf+fmeI7lR3HtTA6j/YfPESVShUp/WgpANq0bMGGLVtxJYivQlN71V61V/fzmnj7Nr/27c/t0DAArh37lRyF/bB4eQFg8fLiyZnT+H3IMGKCg/HKnx+/+nX547MpAMQEX2L/y42Ju3rPpX9c9vkgdLVX7VV7NY/X7J5+7VV71V7No6m9aq/aq7m8ajQZkW0CtA6CpZTVU/xVkVKWlVJWx752xNcp9l12HPOB43M14F1gsRAiXwbnOZLimMpAIeCrNN/pBKwCuiZtkFIuSDo/9je8ve743DPFce+nPc4oQkJC8S9SOPmzf+HCREZGERUV5Vaa2qv2qr26n9fo8xcI2/5D8udKY0YSsvV7bHFxAJRq14aYkBBCNtnXRs9dpjQxoWGU6dGVFzYHUeeHreSrWoWE6GilPh+ErvaqvWqv5vGa3dOvvWqv2qt5NLVX7VV7NZfX7IqHxf3/3IXsFqB1CSnlcexvZiuXhWPigACgtRAiP4AQohDQABgAvONYNDhDhBBe2Bca/hSoIYQom7UU3JtEWyIWy52108NqdStNVbraq/aqvbqua82Vk6fmzyb3Y6X5tU//5O1lunXhz8lfJn+2eHqRu/SjxN+8yYHXm/LzB92oNGYk+apVvS8+76eu9qq9aq/m8Zrd069KV3vVXrO71+yeflW62qv2aiavGk1GZLcAbbE0SxwEZOVgIcQrjv/KrBwnpQwBrgIVHJvaAd9LKc8AR7AHXTNDY+CslPIPYB3QJSs+MqKovz9h4RHJn0PDwsnn60uunDndSlN71V61V/f0mrN4cV7YsgFbQgL/1/Rt4m/cAMC3SmUsnp5cPvBj8ndjQkIAOLd0OQC3/j3DlUM/kb9GdeU+77eu9qq9aq/m8Zrd06+9aq/aq3k0tVftVXs1l1eNJiOyW4A27RIHkzJxzFxHMPcEMAR4R0oZ6cS5bUDSs7sdgWWO/68AumVSo1Oa4zoJIbyd8JIutWs9y6/HT3Dm7DkAlq9eS8P6dd1OU3vVXrVX9/NqzZObWuvXELJxM7/8rzuJMTHJ+wo+X4uIfftTfT/63HmuHfuNkq3ty3p7+xXikWdqcu3Yr0p9Pghd7VV71V7N4zW7p1971V61V/Noaq/aq/ZqLq/ZFYuHxe3/3AVLdlnkWAhRGtgtpSx9l/0jAKSUI1Js2w2MkFLuzsJ56juOqZ9iWxHgLFAAeBw4DFwEEgErUAx4QUp5MMUxZ4D6jlm2CCEKAxeAMCAesDiOay+lXJ4pc7euZ1jYe/YdYPL0mcTFx1OqRHEmjh5B/nwZLbl7/zW1V+1Ve3UfrxtKPk65vr14fPAn3Dh5KtW+H996BzEogNuhYamWOAD7jNvKn40jd+lHwcODf7+ew9lF3wLQ5Pxpw31mloe5rLRX7VV7fTCa2qv2qr1qr9k9/dqr9vrQe82Vz30ifW7EsZKl3T7oWP38GbcoOx2g/W//CDA+QCuEyAHMAW5JKbsJIaYDnlLK7imOWeg4d8cU286QOkDbH2gkpXwtjef6KYPB9yQTAVqNRqPJKhtKPm64ZmYCtBqNRqPRaDQajUbjFugAbbroAG3myW5LHNwvaiatcwv8DFwD+jiWI2gDzErz/SnYXxb2yD00O6Zz3EzgGSGE8dERjUaj0Wg0Go1Go9FoNBqNRqOcbDODVoOeQavRaJSgZ9BqNBqNRqPRaDSabI2eQZsuv5Zy/xm01c65xwxazwdtwIwIIVoBg9LbJ6XM3CvINRqNRqPRaDQajUaj0Wg0Gk22R8+gzU6omEGrqv5Y3OIHDI0mYxIT1Oh6WNXomoS4j1or0fUMGGO4psX/McM1lZW/gj7bdj3UcE0AS35/wzVtkVcN1wSw5PI1XjSb9wGAmmsMVdcXKsYCM9WB7J5+M2FLVKNrMc/Kebaoa4ZrKhkHwFT5qoTs3reouteOiTReM2de4zUBEuKM17wdbbwmQKGSOoiRDnoGbebRM2g1Go1Go9FoNBqNRqPRaDQajaHouXeZJ5v/JKfRaDQajUaj0Wg0Go1Go9FoNA8OHaDVaDQajUaj0Wg0Go1Go9FoNJoHhF7iQJOK3fv2M3n6LGJjYxHlyzFu+KfkyZPHJc2gTVuYt3gJFouFnD4+DBnQnyoVnzDEr81mY+CwkVQoX47OHdq5raaKfFWhaTavKspKle6EKdPYumMn+fLZ1yh77NFSfDnBtfVQzZSvRni1duyP7eIZErevwdplCJbCRf/bWcgf2x/HSZg1EgoXw9qhL5bc+bDdjiZhwecQeiFD/R0/HmbA5K/4ZfV8EhISGf31Ag4fPw1A3ZrVGdD5XSxOPqOjovyTMLq8jOqzl3y3keVBW7BYLJQs5s/ojz/E28uLIZOm8++5CyTabDR7pQH/a9PCaa9GtYH1235g3rJVWCwWfHxyMKRPD6o8XiF5f68ho/ArVIBhH33otFdVdcBMfbZZrjFUpT+JHbv2EDBsFEf3/eCylpnKSrcBc3ldsnwVy1avtffhJYozZuggChYo4JZejRsLdjBv6WosFvDx8WFI3x6ULlGcIRMm8+/Z8/Zx69WX+F+7Vk75VJGnYK56pbJ/NbJvBXPlq5Fjoc1mY+CYiVQoW4bO777DtRs3GDFpKqf+/ItcPj40b/wq7Vu+5bRXo69b123cwoKly5M/34yMIjQ0jD2b11GoYNbaV9C2HcxbuhILFnL65GBI355UrFCOCdO/Yd+hwyQkJPB+m5a0eauJy76zE87eP2VH9AxaAxBCeAohhgghTgkhTgohpBBisBDCIoQYIYSwCSFqpTnmSyGELc22Jo7vPpVm+24hRH3H/5UtsHzlylUGDR/N9EkT2LZuNSVLFOfzaTNd0vznzFkmTZ3O3BlTCVq+hO4fdKLXx58Y4vfvf/7lva492LZjpyF6qjRV5KsKTbN5VVFWKnWP/nacKeNHEbRsMUHLFrt8U2qmfHXZq39JrB+Nx1KjdvKmhNljiR/zIfFjPiTh22lwK5KEZXZN6/sDSNyzmfiRXUncsATPrkMyPMWZi5f4bN7S5JcxBO3cx78XLrF+5kTWzRjP4ROn2Lb/UNYSngKjyz8Jo8vLqD77hPyL+SvWsWzGZ2xYMINHSxRj6vxAps4PxN+vIBsWzGDVV5NZFrSFo7+fdsqrUW3gn3PnmTRrLnM+H8u6BV/RvcO79B4yKnn/3MCVHPn1hFMeU6KiDpipzzbLNYaq9Cdx5tx5Jn453ZAXv5iprEC3ATN5PXHyNPO/DWT5gjlsXLWU0qVKMnXWbLf0avhYMHks6xZ+Tff33qX34JFMnbsQfz8/Nnw7h1VzprNs3UaOnjiZZX0VeQrmqlcq+1cj+1YwV74aORb+feYs7/X6mG279iVvGz91Frly+rA5cD4r5sxg78Gf2HXgR+f0FdxnNHvjNYKWLiJo6SJWL56HX8ECDB3QL8vB2X/OnmfSzNnMnTyeoEXf0P29tvQaMoLlQRs5c/4CG7+dy+q5M1m0ci2/nXTu2lWjyQgdoDWGWcAzQC0pZUXgaaAh0MOx/wLwdtKXhRAWoF46Op2AVUBXpW7vwv6Dh6hSqSKlHy0FQJuWLdiwZSs2FwY6b28vxgwdTGG/QgBUrvgEERGXiY1z/W2MgStX0/Ktprz6UkOXtVRqqshXFZpm86qirFTpxsbGclL+wdxFgTR5py29AgYRfCnEJU0z5aurXj3qv0Hi/m3Yft53l2GOvAAAIABJREFU506rJ9aO/UlYORuuRkD+glj8S2I7sgcA2+9HIEdOKFn2rvrRMbcZ8PksBn7w36/4iYmJRMfcJjYujti4eOLi4/H29sp0mlOiovyTMLq8jOqzK4tybFvyNXnz5OZ2bCyhEZfJ75uXIb3+x4Du7wMQfuUKcXFx5M2dyymvRrUBby8vRn/Sl8KFCtq9P16BiCtXiY2L49DRX9n30xFaN2vslMckVNUBM/XZZrnGUJV+gOjoGAI+HcHAfn1c1gJzlZVuA+byWrni42xbt5q8efNw+/ZtQsPCyZ8vn0ua7p6v9rHgoxRjQXkirlxlQI//MaBnFwDCLyeNW7mz7FNFnoK56pUqXaP7VjBXvho5FgauCaLlm6/zaoO6ydt+P/0nTV99CavVireXF/Wff5Ztu/Y65VXV/VsScxYuocAjj9C6RbMsH+vt7cWYgf3+6wOeqEDE5ats3bmX5o1fwdPTSj7fvDRuVJ/123YYbV2jAXSA1mWEECWAdkBHKeU1ACnlDaAnkHTlGQQ0TXFYHeDHNDqFgAbAAOAdIYSvYut3EBISin+Rwsmf/QsXJjIyiqioKKc1SxQrRv069llvNpuN8ZOn0qBeHby9nAt0pGTYwACavPaqyzqqNVXkqwpNs3lVUVaqdEPDI3ju6afo26ML61csoVqVSvToP8ClCzIz5aurXhOXf4Xt8O5093m88Aq265exHfs/ACyP+MH1y6lmUNiuRWB5pNBd9YfPmEer1xpS4bFSydvealQP3zy5qdehJ3Xa96BU0SI0ePapu2rcCxXln4TR5WVkn+3l6cmO/Qep17ITR377neavNcJiseBptRIwdjJNOvXi6eqVeaxkcae8GtUGShT1p/7zzwL2NE+Y/g0vvvAcV6/fYNzUr5g09BM8PFy7XFJVB8zUZ5vlGkNV+gGGjZtIqxbNEOXLuawF5ior3QbM5RXAy8uTHbv2UPe1Nzn8yzGaN3Xthyp3z9d0x4Laz+Ht7Y2np5WAURNo0qELT1evymOlSjjl1eg8BXPVK2XXrgb3rWCufDVyLBzWvzdNXk4dPK1a6XGCtm4nLj6eqFvRbNu1j/CIK055VXX/BnDl2jUWBC5ncL/eTh1v7wOeAxz5OO1rGtSuRfjlKxQt7Jf8PX8/P0LCIgzxnF2wWNz/z13QAVrXeQY4KaW8mnKjlPK0lHKN42ME8I8Q4mnH51bAijQ67YDvpZRngCNAW3WW0yfRlpju+iAeVqvL2reio+nzyWDOnT/PmGEZP278MKEiX1WVlZm8momSxYsxZ9oUKpQri8VioXP7tpy7cJELwZec1jRTvqr06tGoGYmbl/23wWJJ//G2xMR0j1+6cTtWqwctXq6favvMpWsokM+X/YFfs2fRDK7fjGL+2k1OeVRR/qoxqs9uVPs5DgYF8uF7bfhgwHASHeUwaUh/fgxawvUbkcxcnHY4zBxG16tb0TH0HTaWcxeDGdG/F/1HjGdQr27JMylcQVUdMFOfbZZrDFU+A1euwdNq5e2mxq1bZ6ay0m3AXF6TaPRiPQ7t3Eavrp3p3LNvch/uDGbJ11vR0fQdOoZzF4IZ80m/5O2Thg3kx42ruX7zJjMXBjrt18g8BXPVKxW6KvpWMFe+JqHqfntgr+5YLBbeeq8rPQcO44VnnsLLy/1eZbRybRAN69WmZAnnfvhP4lZ0NH2GjubchYuMGdgfW5pys2HDw6rDaBo16JplDMnRACHE20KIY0KI40KIwym+sxJ4WwhhBZ4H0j6r2xFIijKsALop9JsuRf39CQv/79eg0LBw8vn6kitnTpd0gy+F0LrjB1g9PFg8exa+efO6atVUqMhXVWVlJq9m4vSff7Fu05ZU22w2+wxDZzFTvirzWrIseFix/XE8eZPtSjjkS73mlCV/QWzX0v+l+7sf9nDiz39o9uEgug7/jJjYWJp9OIj1u/bT/KV6eHt5kjd3Lpo1rMOh37K+5hyoKX+VGNFnn70YzM/H/8uvFq81Ijg0nK27DxAacRmA3Dlz0rhhXU7++bdTPo2sV8GhYbTp3herhweLpn3G2YvBXLh0iQkzvqFZp+6sCNrElh/28umEL5zyqqoOmKnPNss1hiqf323cxPGTp2japgNdevcj5vZtmrbpQGh4uNt5VaGr24C5vJ49d54jR48lf27RtAnBl0K4fuOm23k1dCwICaNNt4+wWj1YNH0SvnnzsO/Qkf/GrVw5adzoRU7KP7OsrSJPwVz1SoWuir5VlVeV1+4q77cjo6II6NmFjYHzWDhtEjabjVIuBkFVsHn7Tpo3cW1WenBIKK279bHn44zJ+ObNQ9EihQlz9AEAYRGX8fe7+5N5Go0r6ACt6xwBKiYtSSClXC2lrA40AfxSfO877Msc1Af2SimTfy4VQtQAqgBThRBngGFAZSHEc/cjAUnUrvUsvx4/wZmz5wBYvnotDevXzeCoexMZFUX7Lt15ucGLfDFhLD4+PkZYNRUq8lWFptm8mgkPi4Wxk77g/MVgAJauWosoXzbVY05ZxUz5qsqrR4Uq2OSvqTdei8AWHoylpn2Zb0vFGvYowMUz6Wqs+mIMG2Z9xroZ4/lm5AB8vL1ZN2M8T1USbN1/EIC4+Hh2HfqF6o879+icivJXhVF9dvjlq/QbNYmr128AsGHHHsqXLsWBI0eZuWg5NpuN2Ng4tu7ez3NPVnXqHEbVq8hbt+jQK4CX6tVmysjB+OTIwZOVK7J7TSDrFnzFugVf0appY15rWJcxAz9yyquqOmCmPtss1xiq0r968Xw2rgwkaNliZk+bgk+OHAQtW0wRP7+MD77PXlXo6jZgLq/hEZfpN2goV65eA2DDlm2UL1uGR/I7v2aqu+erfSz4mJfqvcCUkUPwyZEDgK079zBz/reOcSuWrTv38NxT1bOsryJPwVz1SoWuir5VlVdV+ar6fnv5ug1Mm7MQgIgrV1i1YQtvvNTA0HO4yvUbNzh3/gJPVqvitEZk1C3a9+rPy/Vq88WoT5P7gIa1n2fNpq3Exydw42Ykm3bsplHdF4yyni2wWCxu/+cuuOf0HRMhpTwnhPgWWCSE6CSlvCaE8ATeABJSfO+yEOIsMBr4OI1MJ2C2lLJ70gYhxELss2gPqk5DEgULFGD8iKH0DhhIXHw8pUoUZ+LoES5pBq5YRfClELbv2s32XbuTty/8eqbLFyRmQUW+qtA0m1czUaFcWT4d0I/uHwWQkJCAf5HCTBk7KuMD74GZ8lWZ18LFsF0OvWNzwtyJWNv1wfJ6a2zxscR/MzbLb/Ud+L/2jP5qIa917Y/Vw4PnqlWmcwvnHp9TUf6qMKrPrlm1Et3ataRD38FYrVYKFyrAzDGD8c2bhxFTvuLN93sB0KjOc3RwMl+NqleBa9YTHBrGjr0H2LH3QPL2BV9O5JF8xiwHr6oOmKnPNss1hu5b1ejqNmAurzVrVKdb54506NLD3of7FWLmlM/c0qtxY0FQ+mPB1M8YPWU6b3awvyisUd0X6NDyrSzrq8hTMFe9yu79q6r0q77f7tL+XQaMGs8bbTtjw0bvD96jasXHXdY1krPnL+JXqKBLT2UErllHcEgY2/ccYPue//qAeV9M4NzFSzR9rwtx8fG0atqYZ56sZoRtjeYOLEa8pCS7I4TwAPphXzfWCuQFdgPjgXcBpJQjhBCdgeHAo1JKmxDCBuQAgoEXpZTHU2hWxR6cLY599u0IKeVuxzEpVxI/K6WslCmjt64bX9iq6o8b/Yqh0dyTxISMv+MMHu63luz9JO6j1kp0PQPGGK5p8X/McE1l5a+gz7ZdvzNIbgSW/P6Ga9oir2b8JSew5FLwXs9s3gcAaq4xVF1fqBgLzFQHsnv6zYTNtTVP74rFPA9m2qKuGa6pZBwAU+WrErJ736LqXjsm0njNnIqWMUyIM17zdrTxmgCFSuogRjr8XqaM2wcdK/3zj1uUnQ7QZid0gFajMR4doFWCDtDqAK0O0GbvPgDQAVoz1YHsnn4zoQO0OkBrJrJ736IDtDpA+xCgA7SZRy9xoNFoNBqNRqPRaDQajUaj0WgMRc+9yzw6QKtxjew+e1D3NmpQ9WtxfKzxmqq8epukDSjCc8gkJbqJW5Yarmlt3cdwTWV9oIo+20Szeyw+uRUJmycPNNkcZU8+6TZgGpTVATWySoi7bbxmQrzxmgCe3mp0zUJ2v9dSMXsUUNJgTTS+2G7dMFwTzNUNatwTfTWl0Wg0Go1Go9FoNBqNRqPRaDQPCD2DVqPRaDQajUaj0Wg0Go1Go9EYikd2nwmfBfQMWo1Go9FoNBqNRqPRaDQajUajeUDoGbSaVOzet5/J02cRGxuLKF+OccM/JU+ePC5pyr/+ZsxnU7gZGYmH1cqowQOo/MTjLnsN2rSFeYuXYLFYyOnjw5AB/alS8Qm30wQ1+apCMwmbzcbAYSOpUL4cnTu0c1lPlVcjy8tmszFw5DgqlCtD53ZtSEhIYMLUmez78RAJCQm837Y1bVo0y7rmqHFUKGvXvBkZyZAxE/nn7DkSExNp1vhVunRo65RfUJevRpe/Ct0dBw4xYOI0flkfmLztUlgErXoPIuibyTySL+O3KdtsNgZvO0L5Qvl4v2aF/3Ru3qLN0l1816ERj+TMAcChc2F8tvc4CYmJ5PfJwcAXq/K4X/5MnSNlvUo+R2go77zfnaDA+RTIn7HO3XDnPnvJ2g0sX78FC1CyeFFGf9yLgo/Y03opLJxWPfoTNHc6j+TP57RXVW3g2xWrWbJyDT45clD2sUcZNqA/+TNRp+6FHl/U9C0q8lXl+AqwY9ceAoaN4ui+HwzRM0u+6jZgLq8Tpkxj646d5HP0fY89WoovJ4xxWVdFfTVKc8l3G1ketBmLxULJYkUZ/fGH5M6Vk1Fffs3x039gs9mo+oRgWN9u+OTIkWV9FWMLmKvPVqG7ZPkqlq1eay+3EsUZM3QQBQsUcFnXTO11+649TJs9Hw+LhXz5fBkz5BNKlSjulJbNZmPgmIlUKPsYnd9tRUJCAqOmTOPw0d8AqFfrGQZ82A2LE7MiVY0DRvdX9vuMqfyyfikJCQlM/GYh+44cs98Tvt2U1k1ecdmzRpMeegZtGoQQnkKIIUKIU0KIk0IIKYQYLISwOPY/LYTYKYT4QwjxuxBigRCiUIrjvYQQY4QQfwohfhNC/CSEeCfNOR4TQqwUQvzt0N8vhHghxf50V9gWQpxxeDqW4m+BUWm/cuUqg4aPZvqkCWxbt5qSJYrz+bSZLmlGx8TQuWdfPujQlnVLF9Gjc0c+/nSEy17/OXOWSVOnM3fGVIKWL6H7B53o9fEnbqcJavJVhWYSf//zL+917cG2HTsN0VPl1cjy+vvfM7zXoy/bdu5O3rb8u/WcOXeejcsWsXrhHBYtX8Vvv5/MmmbPvmzbuSd529Rv5lGksJ9DczbL1wZx9PgJpzyrylejy1+F7pkLwXw2ezGk6CnXbd9Nu/5DCbt8JXN+Lt/g/dX7+P6Pi6m2B508S4cVewiLiknedvN2HH02HOTjulVY1+ElhjWqTr+Nh4iNv/cLt/6rV3tSbV+3aSttu/QiLDwiU17vhjv32SfkX8xf8R3LZkxiw8JZPFq8GFPnLwFg3bYfaNd7IGERmSuru6GqDRw88gtzFgeyaNZUgpYupO4LtRg27jOXNPX4oqZvUZGvKsdXgDPnzjPxy+mGvUzFLPmq24C5vAIc/e04U8aPImjZYoKWLTYkOKuivhqlmWrcWjCTR0sUZer8JXy9ZCUJCQkEzZtO0LzpxMTGMjtwVZb1VYwtYK4+W4XuiZOnmf9tIMsXzGHjqqWULlWSqbNmu6xrpvYaE3ObgGGjmfHZWIKWLqRBnRcY8/mXTmn9feYs7/Xqz7Zde5O3BW3dzr9nz7Ph27kELZ7DT0d/Y+uuPfdQSR9V4wAY21/Z7zMWJd9nrNj0PWcuXGLDnC9ZNeMzFn23kd9O/2mI7+yCxeL+f+6CDtDeySzgGaCWlLIi8DTQEOghhKgIrAfGSSkrAFUACewWQvg4jp8DlANqSCmrAq2BUUKI9gBCiILAfmCblLKslFIAAcAqIUSRTPh7XUpZPcVfJ6MSvv/gIapUqkjpR0sB0KZlCzZs2YrNhZuIAwcPUbJEcerVfh6AhvXqGHKB5+3txZihgynsZ4+NV674BBERl4mNc/5Nlyo0QU2+qtBMInDlalq+1ZRXX2roshao82pkeQWu/o6WTd/g1Yb1k7ft2L2X5m+8jqenJ/l889L4pYas3/K9S5pD+vXmk949AAiPuExsbCx5czv3q7mqfDW6/I3WjY65zYCJ0xjYtWPyttCIK/xw4Cfmjh+aaZ1lx/6mReXSvFKhRPK2sMhofvgrmDktaqf67tmrkeTJ4UWtUoUBKFPAlzzeXhy7dO8AY3p1IDQ8gh179jFv2uRMe70b7txnVxbl2BY4m7x5cnP7diyhEZfJ75uX0IjL/LD/IHMnjXLaYxKq2sDvp07z/NM18S9iL++XX6zHzn0H9Pjihn2LinxVOb5GR8cQ8OkIBvbr47JWEmbJV90GzOU1NjaWk/IP5i4KpMk7bekVMIjgSyEuaYKa+mqUZmVRjm1LvrGPW7GxhEZcIb+vLzWrVqJb+1Z4eHhgtVqpWK4MF0PDs6yvYmwBc/XZKnQrV3ycbetWkzdvHm7fvk1oWDj58zn/ZE4SZmqvCYkJ2Gw2bkZGAhB1K5oc3t5OaQWuWUfLNxvzaoN6KfQTiY6JITYujtjYOOLi45zSVzUOGNlf2e8zpqa6z9hx4BDNX2mAp9VKvrx5eL3+C6z/IesBao0mM+glDlIghCgBtAOKSymvAUgpbwghegKVgAHAN1LKHY59icAEIURzoKUQYj/QAvCXUkY5vvOPEKIfMB34FugKHJBSzks6r5TyRyFEfyD3/UpreoSEhCZfNAD4Fy5MZGQUUVFRTj968e/Z8/gVLMDgUeM4/edf+ObNQ0Dvni57LVGsGCWKFQPsj2GMnzyVBvXq4O3l5VaaoCZfVWgmMWxgAAAHfjzkkk4SqrwaWV7DAj4C4MChn5K3XQoNo2gq337Iv/52QvNw8jaLxYKnpycfDx/Ntp17eKleHR57tGSW/YK6fDW6/I3WHf7l17Rq/BIVyjyavK1IoQJMHzEgSzqfNnwSgP87G5a8rXCenEx7s9Yd3y39SB6i4+I5cCaUF0oX4XjIFf66fIPwFLNs0yO9OlDErxAzPhubJa93w937bC9PT3bs+5FPJ03H29uT3u+3pUihgkwfPcQpvbSoagPVKlfi2xWruXgphOJF/Vm7YTNxcXFcu36dwoUKZSyQDnp8UdO3qMhXpePruIm0atEMUb6cSzqpNE2Sr7oNmMtraHgEzz39FH17dKF82TLM+zaQHv0H8F3gIqcea05CRX01UtPL05Md+5PGLS96d2pL6RLFkvdfDAlj0Zr1jOqf9XFRxdgC5uqzVel6eXmyY9cehoweh7eXN727/89lTTO119y5cjFy0Me07tyd/Pl8SUxMZNncr5zSGtbf/gPigZ+OJG9r/vorbN25h7pN3yE+IYHaz9SkgeOH/Kygahwwsr+y32e8TIUypZO3XQq/jL9fweTP/oUK8sc/Z13yrNHcDT2DNjXPACellFdTbpRSnpZSrsE+m/andI7b69hXEziVFJxNs7+MEKIA8JzjcyqklMuklP9kwuPmNEscGDaDNtGWmG4n5mG1Oq0ZHx/PngM/0qp5U9Z+O59277xNlz79iY2NdcVqMreio+nzyWDOnT/PmGHG3PQbrakiX1VoqkK1VxV1AMCWmJjqcQcbNjw8jOkyPx85lIPb1nP9xg1mzlvklIaZ6oBRLF2/FavVSotXjZ3NkRF5cngx/c1azP7pNG8t3kHQyXM8W9IPL4Pqg7OYoc9uVKcWB9cv5cOO7/JBwDASExOd9pYWVW2g5pPV6Pm/9/kwYDDNO3TGYrGQP58vXp6u3UCAHl9UYWS+qkp/4Mo1eFqtvN20iUs69xN9jWWcpipdVV5LFi/GnGlTqFCuLBaLhc7t23LuwkUuBF9ySdcMNKpdi4NBS/nwvXf5YMB/49YJ+Rft+nxC22aNebHWM1nWVTW2mLHPVkGjF+txaOc2enXtTOeefV2+3jBTe5V//c3MuQvZvHIJ+7cE0a1TB3p9MsSQJz8AZsxfTIH8+TiwcQ17163g2o2bzF+60mk9o8cBo/qrpeu3YLV63HGfkZiYutxsYNg9YXbBYrG4/Z+7oGvWnST3ZEKItx1B0ONCiMOOfenNOvZ27Lvb/qSR15bmX4QQix3n+EsI8XEm/KVd4sCwNWiL+vunWhMxNCycfL6+5MqZ02nNwn6FKPtYaapVrgRAo/p1SUhI5PzFYFftEnwphNYdP8Dq4cHi2bPwzZvXLTVV5KsKTVWo9KqivJIo6l+EsPDLyZ/Dwi/jX9jPJc19B38i1JEXuXPlovHLjTgp/3DSn3nqgFF89/0uTvzxF8269qfrkLHExMbSrGt/Ql1cyzQjEm02cnl7suidenzXoRGfNqjOmauRlHrkgT704NZ99tkLwfz82+/Jn1u89hLBoeFcvxnptLe0qGoDkVG3eKZGdb5bMp+1i+fRqF4dAJdf5KLHFzUYna+q0v/dxk0cP3mKpm060KV3P2Ju36Zpmw6Ehmf9Uen7gb7GMr4OmMnr6T//Yt2mLam22Wz2GaYPK2cvBvPz8ZTjVqPkcWvTzr10DhhKv/+9R7d279xD5e6oGlvM1mcbzdlz5zly9Fjy5xZNmxB8KYTrN266pGum9rr/x0PUqFYl+aVgbVs258+//+Xq9esu6Saxffc+WrzxGt5eXuTNk4e3XnuZQ78cy/jAdFAxDhjVX/13n9GPrkPGOO4z+uHvVzDVOy7CLl9JNaNWozESHaBNzRGgohDCF0BKuVpKWR1oAvgBh4A7n3+1bzuCfXZtBSHEI2n2Pw/845iZexhIfiGYlLKD4xxLAONeEewEtWs9y6/HT3Dm7DkAlq9eS8P6dV3SrPt8LS4EB3Pi1GkADv9yFIsFShQr6pJuZFQU7bt05+UGL/LFhLH4+PhkfNAD0AQ1+apCUxWqvKoqryQa1q3Nmg2biI+P58bNm2za/gON6tdxSXPLjp3MnLsAm81GbGwsW3bs5LmaNZzSMlMdMIpVMyayYc6XrPtmMt+MHYKPtzfrvplMkUKuv6n3XliAbmsPcCLE/nDFFnmeHJ4eiEKur3HmCu7cZ4dfuUK/UZ9x9Zr95mDDjt2Uf6wUjxjwtuokVLWBsPAI2nfrRWSk/WGYr+YvovHLjVz6dV2PL2pQka+q0r968Xw2rgwkaNliZk+bgk+OHAQtW0wRP9d++FOBvsZSUwfM5NXDYmHspC+Sf5xbumotonzZVI9nP2yEX75Kv1GTkoNaG3bsoXzpUhz9/TRjp89m7qRRNGlU32l9FWMLmKvPVkF4xGX6DRrKlavXANiwZRvly5bhkfyuXaOZqb1WfFxw+JdjRDiCiDv27KNEsaIUyJ/fZW2AiqI8WxwvUo6Lj2fn/v+jWqUnsqyjahwwqr9aNeMzNsyZyrpvpvDN2E8d9xlTaPTCs6zZtpP4hARuREaxedd+Gj7/rCHeNZq0PLw/gzqBlPKcEOJbYJEQopOU8poQwhN4A0gAxgMHhBA7pZTbhRAWYAiQC1glpYwRQiwB5gkhOkgpI4UQZYEpwAjHab4CfhFCdAQWSSltjpeD1cL+8rAHRsECBRg/Yii9AwYSFx9PqRLFmTh6hEuafoUKMvPzCYyc8DnR0dF4e3szfdJ4cuTI4ZJu4IpVBF8KYfuu3WzftTt5+8KvZzo9IKvQBDX5qkJTFaq8qiqvJNq0aMa5i8E0bduJuPh4Wr31Js/UeNIlzYF9ejJ8wmSavNsRgEb16tCh1dtOaZmpDpgdi8XCpNefYdj2n4lLTMQvtw/T36z1wB+Hcec+u2bVynRr34oOfQdhtVopXKgAM8d86pK3tKhqA2VKl6LLe+1o2akLiYmJPFW9KsMC+rmkqccXNajIVzOlXxX6GktNHTCT1wrlyvLpgH50/yiAhIQE/IsUZspY11/u6M7UrFqJbu3eoUPfwSnGrSF88MkIbDYbQydNT/5ujcpPMKxv9yzpqxhbQPdZNWtUp1vnjnTo0sNebn6FmDnlM5d1zdReaz39FJ3btaF9t154eXmSz9eXWZ+Pd1k3iUF9ejB68jRebf0eVqsHtZ6qwQftWmdZR9U4oLq/atPkVc4Hh9Csaz/i4uJ5542XeKZaJcP0swMWPS0001iMWpvkYUEI4QH0A9oCViAvsBsYL6X8QwhRHZgIlMIe4N4PDJBShjuOtwKDHMcnADHAJCnlihTnKIE92FvDcY5Y7DNopzmCvDYg5Tq2Z6WUlYQQZ4Bbju8ncUtKmblVum9dN76wE+INlwTAwyTrJrnReiUPFar6pXhj1j5OhSqv3sbOCDYbtojzSnQTtyw1XNPa2rg3sifjpaj8FfTZtkg1S0xY8imYqaWiDwCwur4+7R3o8UVN/6oqXxMTjNdUcS1kput+3QbUoKKugnmu3QHbtVDDNS150j5AaRCe3mp0zYLNuLXrkzFTtEjVdUucAl0fRUt/KagDtitq1tO2lKqkB650+PPxcm5/8VH+9F9uUXY6QJud0AFa49E3D2rQAVodoNUBWuM1QQdodYDWXOgArfGaZrru121ADTpAqwO0ZkIHaNXo6gCt4ZqgA7R3QwdoM49e4kCj0Wg0Go1Go9FoNBqNRqPRGMqDXhbOTJjo5yONRqPRaDQajUaj0Wg0Go1Go3m40DNoNa6h4rETwL40r8HoX25MhKKnILxcezlduqh6VDCbY3nEX4mu9e2ehmve6tzCcM1cizcZrgmA1fhh35LL9Zfy3TdM9AiuBkWPtiqqA2apW/paSKPrAJb8RR60BU1mMdNyBCpQtMSF7dYNwzUtOfMYrmkXNn5NwuxKAAAgAElEQVR8tfgWMlxTozGCbN7jaTQajUaj0Wg0Go1Go9FoNBrNg0PPoNVoNBqNRqPRaDQajUaj0Wg0hqIf3Mg8egatRqPRaDQajUaj0Wg0Go1Go9E8IPQMWk0qdu/bz+Tps4iNjUWUL8e44Z+SJ49r68l8u2I1S1auwSdHDso+9ijDBvQnfz5fl70GbdrCvMVLsFgs5PTxYciA/lSp+IRLmirSr0pXe4Uly1exbPVaLBYLJUsUZ8zQQRQsUMAtvQLs2LWHgGGjOLrvB0P0snsdWLdxCwuWLk/+fDMyitDQMPZsXkehglmrBzabjYFjJlKh7GN0frcVvQeP4OzFi8n7LwSH8PSTVfn6s7GZ0vP+cAiJ5/4hfv0y8PDAq/NHWCtWByDh6EHiFs/EUqI0OfoM/+8gDw88Hi3L7UmDM+3b3cvKZrMxcOQ4KpQrQ+d2bUhISGDC1Jns+/EQCQkJvN+2NW1aNHMLr2kxur2qGLPA/evA/dAFY8vLTOnXXrVXfY1lnnzN7unXXt3f6/rvdzJv+RosFgs+OXIwpHdXKpYvy+gvv+Lwr8cBqPvc0wzo3hmLk9Mi3T1fbTYbA0eNo0JZ+7VrTMxtRk76guMnT2Gz2ahaqSLDAz7Cx0fBu0002R49g1aTzJUrVxk0fDTTJ01g27rVlCxRnM+nzXRJ8+CRX5izOJBFs6YStHQhdV+oxbBxn7ns9Z8zZ5k0dTpzZ0wlaPkSun/QiV4ff+KSpor0q9LVXuHEydPM/zaQ5QvmsHHVUkqXKsnUWbPd0ivAmXPnmfjldLAZ8wI0XQeg2RuvEbR0EUFLF7F68Tz8ChZg6IB+WQ7O/n3mLO/16s+2XXuTt00bN4KgRXMIWjSH0Z/0xzdPbob375OhlqX4o+QYPhXrc/WTt1nrvoJHsVLE9H+PmI874lGxOtZaL2K7cIaYgE7Jfwm/HiZ+33YSDu29+wlS4O5l9fe/Z3ivR1+27dydvG35d+s5c+48G5ctYvXCOSxavorffj/5wL2mxej2qmLMAvevA/dDF4wtLzOlX3vVXvU1lnnyNbunX3t1f6//nLvApK/mMWfSaNbNm0H3Dq3pPXQsQd/v5N/zF1i/YBbr5s/k8LHjbNu9/4F6VaX5979neK9nX7bt3JO87auFi0lISGB94ALWBy7g9u3bfLNoiUuesxsWi8Xt/9yFBxagFUJ4CiGGCCFOCSFOCiGkEGKwEMLi2P+0EGKnEOIPIcTvQogFQohCKY73EkKMEUL8KYT4TQjxkxDinTTneEwIsVII8bdDf78Q4oUU+9O9khdCnHF4Opbib0EG6RkhhLAJIWql2f5l2vMIIZo4vvvUPc77qxDiZyHEi459OYQQM4UQJxzp3SuEePreuZw19h88RJVKFSn9aCkA2rRswYYtW7G5cMPz+6nTPP90TfyLFAbg5RfrsXPfAWLj4lzy6u3txZihgynsZ68SlSs+QUTEZZd0VaRfla72CpUrPs62davJmzcPt2/fJjQsnPz5XHubvCqv0dExBHw6goH9Mg7wZRZdB1IzZ+ESCjzyCK2dmI0ZuGYdLd9szKsN6t2xLzYujoFjJjK4b0+KOvqxe+H5anPif9hIwo+7/tvo4QE+OcHTC7y8sXh6QWxsquM8nqiKtVZ9YmdPyrRvdy+rwNXf0bLpG7zasH7yth2799L8jdfx9PQkn29eGr/UkPVbvn/gXlOior2qGLPA/evA/dA1urzMlH7tVXvV11jmydfsnn7t1f29ent5MXpAHwo7JjpUFuWJuHKV2Lg4omNiiI2LIzY2jrj4eLy9vR+oV1Wa6V27Pl29Gt3f74CHhwdWq5UnRHmCQ0Kc9qvR3IsHOYN2FvAMUEtKWRF4GmgI9BBCVATWA+OklBWAKoAEdgshfBzHzwHKATWklFWB1sAoIUR7ACFEQWA/sE1KWVZKKYAAYJUQokgm/L0upaye4q9TJo65ALyd9MERbL7zjh86AauArvc4bzVgBLDUsb0v9vKq4kjvIGC9EMIrE74yRUhIaHIgFcC/cGEiI6OIiopyWrNa5UocPPIzFy/ZO7G1GzYTFxfHtevXXfJaolgx6tepDdgfQxg/eSoN6tXB28v57FCRflW62qsdLy9PduzaQ93X3uTwL8do3rSxS3qqvA4bN5FWLZohypdzSSclug78x5Vr11gQuJzB/Xo7dfyw/n1o8nLDdPet3rCZwoUK8lK9OpnSipv3BQn7t6falrB7C0TeJOfsdeScE0RiyAUSfj6Q6jte7XsSt3Q2RN/KtG93L6thAR/R5NWXUm27FBqWKtDtX9iPkLDwB+41JSraq4oxC9y/DtwPXaPLy0zp1161V32NZZ58ze7p117d32uJokWoX+sZwH6tMmHmHF584VlaNn4F37x5qdeiA3Wat6NU8aI0eOHZB+pVleawgI9o8krqa9fazz3DY6VKAnDxUgiLlq/i1YYvOu1Xo7kXDyRAK4QoAbQDOkoprwFIKW8APYEQYADwjZRyh2NfopRyAnALaCmEeAxoAXSWUt50fOcfoB/2oCbYg58HpJTzks4rpfwR6A/kVpS0IKBpis91gB9TfsExC7gB9jS+I4S412KsuwB/R7DZH/AGvACklAewB3qtRplPtCWmO73bw+r8KWo+WY2e/3ufDwMG07yDfa2a/Pl88fI0Jq58KzqaPp8M5tz584wZNsQlLRXpV6Wrvf5HoxfrcWjnNnp17Uznnn1JTEx0WkuF18CVa/C0Wnm7aROnNdJD14H/WLk2iIb1alOyRHFD9FKyaMUaunds55KGV8tO2G5cJfqDJkR3fQtLHl88m7RO3u8hKmPxzX9HYDcjzFhWtsTEVG9ytWHDw8O5SxEztdckjByzwFx1wCzlZab0a6/aa3a/xlKlaxZNVbraa/b2eis6hr7Dx3PuYjBjAvowc+FSCuTzZf+6QPasXsz1G5HMX7HWLbyq0kyPE6ckbbt+SLuWzXmx9vOGaj/sWCzu/+cuPKgZtM8AJ6WUV1NulFKellKuwT6b9qd0jtvr2FcTOCWlTPuzyF6gjBCiAPCc43MqpJTLHMHcjNicZomDzMygjQD+SbH0QCtgRZrvtAO+l1KeAY4Abe+h9y7wp5TyMjAVe5rChRBBQojewI9SyphM+MoURf39CQuPSP4cGhZOPl9fcuXM6bRmZNQtnqlRne+WzGft4nk0csxCM+IlYcGXQmjd8QOsHh4snj0L37x5XdJTkX5VutornD13niNHjyV/btG0CcGXQrh+46Zbef1u4yaOnzxF0zYd6NK7HzG3b9O0TQdCw52bNajSqypdVV6T2Lx9J82buDazJz1Oyj+JT0jgmSeruaRjfbYe8Ts3QXw83IoifvcWrJWf/G//8w2J37M1y+tnmrGsivoXISz8cvLnsPDL+Bf2c1LLPO0VjB+zwFx1wCzlZab0a6/aa3a/xlKlaxZN7VV7NVo3ODSMNj37Y7V6sOjLCfjmzcP2ff9H89dfxtvLi7x5ctPs1YYcOvrrA/eqUjMtm77/gfd796N/z65069jeMF2NJi0PcomD5DtRIcTbjiDocSHEYcc+z3SO8Xbsu9v+pGmZtjT/IoRY7DjHX0KIjzPhL+0SB/dcgzYFK4G3hRBW4HlgX5r9HYFljv+vALql2Z8UGD4JNAfeAXAEdCsDLwGHgA7AMSFE/kz6ypDatZ7l1+MnOHP2HADLV6+lYf26LmmGhUfQvlsvIiPtsfSv5i+i8cuNXF6IOTIqivZduvNygxf5YsJYfHx8Mj4oA1SkX5Wu9grhEZfpN2goV65eA2DDlm2UL1uGR/I7v0aaCq+rF89n48pAgpYtZva0KfjkyEHQssUU8XMuKKXSqypdVV4Brt+4wbnzF3iyWhVD9FLy07Ffee6pJ13urxL//QPr8w3sH6xWrE/XJuGP/16MZa1YncTjP2dZ12xlBdCwbm3WbNhEfHw8N27eZNP2H2hUP3PLR9wPr6raq4oxC8xVB8xSXmZKv/aqvWb3ayxVumbR1F61VyN1I2/dokOfgbxU93mmDB+IT44cAFQsX5atu+whjbj4eHYdOET1io8/UK+qNVOyc98BxkyZyrxpk+9Y/kCjMRqLkS9pySxCiFLAKaCoY2mDpO2lgd3AD0CYlHJQmuMOA9OAPcBJoGTKWbhCiMbANCllWSHEUOAJKeW7aTRGAEgpRwghbFLKO+68hRBngPqOoGhm0zTC8d/pwAHsyzW8KaXsk3QeIUQN4DBwEUjEvjxBMeAFKeXBe51XCDEOmCmlvJhi2wHgSynlqkyZvHU9w8Les+8Ak6fPJC4+nlIlijNx9Ih7vxQgPvbu+xwsWbmGwFVrSUxM5KnqVRkW0A8fnxz3Psh67yUQvpm/kC9nfUOFcmVTbV/49cy7XzxmIsiS5fRnEhW6D7VXW+YeoVu6ag1LV67BarVS2K8QwwYGULJ4sbsfYMn4N6kse01MyJRXgAvBl2jyTluO7t+Z8Zc9Mn4s56GuAwmZe3nSb7+fov+Q4WxftzJzRmJv33XXwDETKV+mNJ3fbQXAyM+n4lewAD063fuX8ltdW92xzbvnYBLP/0v8+mWQxxfvD/rh8Vh5SEwk4fgR4hbPtM+oBXIu2U5M73exXflv1l+uxZsylRy3KKu4u+cpwMCRYylftgyd27UhPj6eidNm8X+HDhMXH0+rt96kc7s26R/olcE44YxXVe01g75F1ZgFblIHVOmqKK/s3rdqr9qrma6xMolb5OsD0tRetVdndG03Iu7Y9s2SFUyd9y0VypROtX3BlHGM/vIrTv31N1YPD56rUZ0BPTrfsY6+xbcQmcEt8jX23g8hDxw1jvJlHqNzuza80rIt12/cSPXjb42qlRk+oN+dB+Yv4kYPy7sPZ6uI+x90zCKPHr8zLvggeCABWgAhxNdAEaCTlPKaEMIT+2zSj4BXsAc520kptztetjUEaAM8JaWMcRxfGOggpYwUQpQFNgNjpJTfOtZ6/QUYBiySUtocLwdbDOyXUo5WEaB1BH63AXmBj6WU/5ciQDsd8JRSdk9x3ELHcR0zCNAuAqKB3lLKWCGEP/bZuU2klKczZTITAdosk4kArVNkEKB1CndaXERzbzJ585BlMnHzkGWyEEDIEpkIIjzUZDJAm2XuEaB1lvQCtK6S2QCtW5BBgNZpMhGgzTKq2quKvkWPWWrKK7v3rRqNma6xNBqNEtIL0LpKZgO0bkEGAVqn0QHadNEB2syT3jIB94se2F/qtcuxHEBe7LNnX5NS/iWEeAWYKISYht3nfuzBy6TW1BMYBBwWQiQAMcAwKeUKACllhBDieWA8EOA4RyywBPssXACEEJEpPJ2VUlZy/H+zECJl9PGWlDKzq0GvBIaT4gVh4v/ZO/M4G6s/jr/v3JmxDrLvKcsphLZfKUIoLVLJFknIGknIEsaulCzRYosMQhhCIkYo2qzJKcrO2JcZZsbM3N8fc2eMaZiZe59j7mO+79frvmbueZ7ncz/POc/5Pueee55zlAokoYM55ZJ/Y4HNSqm30tB8A/gQ+EspFek+l3fS3TkrCIIgCIIgCIIgCIIgCILPkWkjaIVMQEbQWq8pmMFOoztkBK0ZZASt5ZrGkBG0MoLWFDKCVhCsx05tLEEQjCAjaGUE7c3kYBXfH0FbeoeMoLUdSqkxJCzSlZJftdbtb7YfQRAEQRAEQRAEQRAEQRDsjYygzUqYGEErCIIgWM6oAncY0e13+l8juoIgCIIgCIKQpcmZ1ydGYfoaMoI2/cizKIIgCIIgCIIgCIIgCIIgCJmETHEgCIIgCIIgCIIgCIIgCIKlOGRdhXQjI2gFQRAEQRAEQRAEQRAEQRAyCemgFa4hbMNGGjZ9mSeff4nuvfsSERHhk5qmdMWreBWv9vGaVc7//k7taf/bj7T/dRON588mZ6Grq+QGlSzBG/t2kaNA/qS0YvffyytrV9J283ra/bKRSs2b3DSvN1tXvIpXu2ia0hWv4lW82sdrVj9/U7riVbzayasg3AhZJMwwSqkyQJjWukyKdBcJU0xMBh4FXMAorfUc9/bBQFP37su11n3c6Z2BNwAHsBzoo7VOXyGmsUjYmTNneeal5sydMYUyt5dmzPiJREZeIrj/O+mSv1ma4lW8ilfxequff+IiYUXvrcoLc2cy/X81ib5wkcdHDSUwd26+7daTyi83o+bAvuQrczvjSpbj8ukzAHT5awcrOnZj/7r1BJUozms/ruPLx5/i7L5/0lwk7FbPV/EqXjNDU7yKV/EqXrP6+YtX8ZolvMoiYalyuNpdPt/pWHLbHp8oOxlBm7m0AvJorSsDdYCJSqkgpVQ94AngXqAacL9S6gWl1B1AT+B/wD3AI0B9q8xs3LyFeypVpMztpQFo0aQxy1Z+ized+CY0xat4Fa/iNauc//Gt2/ms8gNEX7iIM1s2gooX4/KZM+QuVpQKzz3NVw1fumZ/Z7ZsbBzxPvvXrQfg4pGjXDp1mqASxY17vdm64lW82kVTvIpX8Spes/r5i1fxKl4FIW2kgzYT0VrPBF5xvy0OxABXgGPA21rrGK31FeBPoLTW+l+gotY6EsgH5AXOWeXn+PFwihYpnPS+aOHCREREEhkZ6VOa4lW8ilfxmpXOPz42lvINn+aNvbsoVaM6O2bNIeLYcRY1f5Uze/dds29cdDQ7Zs5Oel+t7asEBuXm6M+/3hSvN1NXvIpXu2iKV/EqXsVrVj9/8SpexasgpI1/ZhvIIhRXSm1LbYPWOlYpNRVoTcIUB1HAH4nblVLlSZjq4FH3/leUUq8DHwA/A6nqekK8Kz7VFfb8nE6f0jSlK17Fq3i1j9esdv5/L1vB+GUrqPpaa5otW8inle6HNH7Bf7jXmzzYpSNfNWpCbFTUTfN6s3TFq3i1i6YpXfEqXsWrfbxm9fM3pStexaudvGZVUslG4TrICNqbw1GtdbXkr+QbtdbtgWJAY6XUE4npSqlKwGqgt9b672T7TwEKAMeBYKtMFitalBMnTyW9Dz9xkrx58pAzRw6f0hSv4lW8itescv633XkHJR95KOn9jpmzyVu6FDluy3fdY5yBgTSaOYWKTRozq/aTnNj5x3X3tdLrzdYVr+LVLpriVbyKV/Ga1c9fvIpX8SoIaSMdtJmIUup+9whZtNangZVAFfe2R4Hvgb7uqRBQSpVyp6O1jgXmJe5vBTWqP8T2nbvYf+AgAPMWLqJu7cd8TlO8ilfxKl6zyvnnKlaERrOmkaNAfgAqNW/CyT/+5PKZs9c9puGMzwgMCuLLOg04f/DQTfN6s3XFq3i1i6Z4Fa/iVbxm9fMXr+JVvApC2jhkkmOzKKXKAGFa6zIp0l1AVxIW+WoM5AI2AR2Bw8DvQDOt9dpkx1QGviFh4bDzwHRAa61Hp8vMpfNpFvb6DZv4cOIkrsTGUrpkCd4bFky+vHnTJX8zNcWreBWv4vVWPv9RBe5I+v/e11/j/o7tiY+NJeLYcVb16M15d2MRoN/lM4wrWY7Lp89Q4qEHaR22itN//U3s5avTGqx7dwj/rllLv9P/Wu41vfhCvopX8ZpZmuJVvIpX8ZrVz1+8itdb3mvOvPIwfyocvf9un+90LP7bnz5RdtJBa5g0Omj9gUlATSAOmKS1/kwpNR5oCyRf/eVTrfWnSqmOwJtALLAB6OFeSCxt0tFBKwiCIGQ+yTtorSQ9HbSCIAiCIAiCIGQQ6aBNFemgTT/SQZuVkA5aQRAEWyAdtIIgCIIgCIJgI6SDNlWkgzb9+Ge2AUEQBEEQBEEQBEEQBEEQbi0cPtH1aQ+kgzYrEROV9j4ZJSCb9ZpgphbHx1mv6ee0XhPgSrT1mqbKysgofEM/sjkMrItool4BBGa3XtNEHTCFqboVG2O9pn+g5ZKmRrr+WvYeyzUf+Gur5ZoAOA00UUzV1wDrrwEj8cpuxKVvBqcMYacnxwzEFmP3ARPtNlN1wBVvvabUV3thk7aAAMTFWq/pZ6i+2ikOmLgXmuqFk5gtZCHkyhQEQRAEQRAEQRAEQRAEQcgkpINWEARBEARBEARBEARBEAQhk5ApDgRBEARBEARBEARBEARBsBQ/mYQ23UgHrYDL5aLv0JFUKHsn7Vq1ICoqmiFjPmLn7j9xuVxUqVSRwb3fInt2z+YwDV2+kmmzZuNwOMiRPTsD+rzNPRXvts77oCFUKF+Odq1bWaIJsGbdenoPGsrWDd9bohe2YSMfTpxMTEwMqnw5Rg5+l9y5c3uk5XK56DtkJBXKJZRXXFwco8dPYsNPW4iLi6Nty+a0aPy8T3hNxNQ1MHveAuYuXITD4aBUyRIMH9iPAvnze61r1XVlum6ZKKtErK4DpnRNxIDV69Yz4fPp+Dkc5M2bh+ED3qF0yRJe65qKV57q5m/8AkU7dwSXi/jLlzk4MJhL23ckbS877TOuHA/n4IBBAAQ9Up2Swe/icPoTe/YshwYN4fLuP9P9eXrvPoa/P5aLERH4OZ0M7d+Hynfflf4TTQXLY2uy+noxIoIBw9/jnwMHiY+P5/lnGtChdUuPvZqKVybigKnYYrXukm9WMmPOvKT3FyMiCQ8/wfoVSyhYwPO8NRUDTOja6T5gpzrg6+2L5NilvtrNq93qq9XXlp3KykT7AszEATvlq6nvbybioJ1itiDcCJniwBBKqTJKqf2ppLuUUouVUtuUUnuVUhHu/7cppZol+/+4+5X4voBSKsx9zDal1Hal1E6lVFNvfO77dz+vdu3BqrXrk9I++WIWcXFxLA2ZwdKQGURHR/PZzNke6f+z/wBjxk9k6sfjCZ03m87tX6Nbr3e8sXzV+z//8mrHLqxas9YSvUT2HzzEe+MmWjZ5+pkzZ+k3eBgTx4xm1ZKFlCpZgg8mTPJIa9+/+3m1Sw9WrQ1LSpu3eCn7Dx7im7kzWfjFFGbOW8COP3ZnutdETF0Du3bvYfqXIcybMYVvFsyhTOlSjJ/8ude6Vl1XpuuWibJKxOo6YErXRAyIioqm96BhfPz+CELnfMHjNR9l+AfjvNY1Fa881c1W9k5KDhzA3y+3Znf9pzg2biJlp32WtL1ol07kfuh/Se+dQUGUnfY5h4eOZHfdJznYdwBlP5uMIzB9i6JcjoqiXdcetG/dkiVzZtKlXRt6vRucIc8psTy2pqiv4z+bRpHChdyx9XPmLQpl685dHumbilcm4oCp2GJC9/lnnyJ0zkxC58xk4axpFCqQn4F9enrVOWsqBpjQtdN9wE51wNfbF8mxU321k1e71Verry07lZWJ9gWYiQN2yldT399MxEE7xWxBSAvpoM0EtNYvaK2rAe2BX7XW1dyvrxL/Bz4FPk227bT78Pbu91WBl4FZSqm8nnoJWbiYJo2epUHd2klpD1arSue2rfHz88PpdHK3Ks/R48c90g8MDGD4wP4ULlQQgMoV7+bUqdPEXPF+ZeaQ+Qtp8kIjGtSv67VWIpcvR9H73WD69nzTMs2Nm7dwT6WKlLm9NAAtmjRm2cpvcXnwpSe18loT9gMvPvs0/v7+5M0TxDP167J05XeZ7jURU9dA5Yp3sWrJQoKCchMdHU34iZPky+txVUjCquvKdN0yUVZgpg6Y0jURA+Li43C5XFyMiAAg8tJlsqWzE/JGmPDqja4rOoYDb/fhyokTAERu30FAoUI4AgIIqv4weerU4uSsqz8eZLvzDuIuXuDixk0ARO3dR1xEBLnvvy9dn7dp8xZKlSxBrRqPAFC3Vk3GjR6eIc8pMR1bB/TszjvduwBw8tRpYmJiCMrl2WgUU/HKRBwwFVtM6SYy5YvZ5L/tNpp78QQJmIsBJnTtdB+wUx3w9fZFcuxUX+3k1U71Fay/tuxUVibaF2AmDtgpX019fzMRB+0Us7MqDofvv3wFmeLAxmitdyqlIoBywG+eaAzq/RYAm7b8kpRW4+Gro6aOHDvOzHkLGNavt0ceSxYvTsnixYGERwRGfTiex2vVJDAgwCO95Azqm+Bp009bvNZK0hz5Hs0aP48qX84yzePHwylapHDS+6KFCxMREUlkZGSGHz25Wl4/J6UdCz9BsWv0C6H37st0r4mYvAYCAvxZs249A4aNJDAgkO6dX/da06rrynTdMlFWYKYOmNI1EQNy5czJkH69aN6uM/ny5iE+Pp65Uz/xWteEV290Yw4fJubw4aT3pYYM5Nx3a/DPn59Sw4L5++XWFHrl6uP8Ufv+wS9nTvLUqsmF9RvIWbUK2VUFApJdgzfi3wOHKFQgP/2HjmTP33vJE5Sb3t27ZshzSszE1qv11eFw4O/vT6/Bw1i1dj31a9XkjttLeezXRLwyEQdMxRZTugBnzp1jRsg8Fn05zSsdMBcDTOja7T5glzpgyquJ+4Cd6qudvNqpvoL115adyspE+yIRq+OAnfLV1Pc3U+1hu8RsQUgLGUFrluLJpijYppTaZqW4UupJ97/aSt1Edv2padnxDVo1eZE67l8lPeXS5cu8+U5/Dh46xPBBAyxyaC0h87/G3+nkpUYNLdWNd8XjSOVnGT+n0xJ9V3z8Nb/6uHDh5+dZ1Tbp1dQ1UK9OLbasXUW3ju1o17UH8fHxlmmbwoq6ZaKsTNUBU7om0Hv3MWnqF6yYP5uNK0Pp9Fprur0zwLJRfr6GX44c3Pn5J2QrU4aDfftz5ycTOTR4aNLI2kTiIyLY99rrFOv+BhXXfEuBJo25uPFH4tM5kiI2Npb1m36i2YuNWPTldFo1fYkOb75NTEyMx95Nx9ZEPhgykM2rlnL+wgUmTZvplZbV8cpEHpjKV5PlNX9RKHVr1aCUBfPEmooBJnTtdB9IxA51IBE7tC/sVF/t5NUu9dUUdiorE+2L5FgZB+yUr4nY4Tt8InaI2YKQFtJBa5ajyaYoSJy6wFumujt7dwEDgKZa6wgLdK9h+Xff07Z7T97u2pFObV7xSuvoseM0b9Mep58fsz6fTJ6gIItcWsvib5azc/efNP2R6xEAACAASURBVGrRmg7dexIVHU2jFq0JP3nSK91iRYty4uSppPfhJ06SN08ecubI4a1lt34RTpw8nfT+xMnTFC1cyEMtM15NXAMHDh7i161Xf/No3KghR48d5/yFi15rm8SqumWirEzVAVO6Jtj40xbuq3pP0kIgLZu8yN/7/uXs+fOZ7Mx6AksU565liyEuDv1SM7KVKUO20qUpFTyQiqtXUqh1S25r1JDbP3gPHA7iIiPRjZuxu14DDr07mOx33kH0v/vT9VmFCxWk7B1lqFq5EgD1aj9GXFw8h44c9di/6di6YfPPhLv1c+XMyTNP1GO3/ssjLVPxykQemMpXk+W1YvVaXmz4jNc6YC4GmNC1033ATnXATu0LO9VXO3m1S301hZ3KykT7AszEATvlK9jnO7ydYnZWxeFw+PzLV5AOWvuROAdtZa31Y1pry2etXrthE8PHjmfahA9p+GR9r7QiIiN5pUNnnni8Dh+NHkH27Nktcmk9C2dN55v5IYTOncXnE8aSPVs2QufOokghzzo7E6lR/SG279zF/gMHAZi3cBF1az9mhWUA6j5Wg6+XLSc2NpYLFy+yfPX31Ktd02e8mroGTp46Tc9+Azlz9hwAy1auonzZO7ktn/dzDpnCyrploqxM1QFTuiaoeJfil9+3cer0GQDWrN9AyeLFyJ8vXyY7sxa/XLlQX8/n7Ipv+afzG7iioon87Xd2PPAwu+s/xe76T3FyVghnQ5dxoNc74HJRfvZMclatAsBtzzUkPjqay7v/TNfnPfZIdQ4fPcquP/cA8MvvW3E4oGTxYh6fg+nYunLNWiZNnYHL5SImJoaVa9by8APpm3M3JabilYk8MJWvpnTPX7jAwUOHubfqPV5rgbkYYELXTvcBO9UBO7Uv7FRf7eTVLvXVFHYqKxPtCzATB+yUr3b6Dm+nmC0IaSFz0Ar/4b0Jk3G5XLw74v2ktPuqVGZwn54Z1gr5agFHjx1n9bowVq8LS0r/4tNJWSZoFsifn1HBA+neuy9XYmMpXbIE7w0Ltky/RePnOXjkKI1avsaV2FiavfAc/7vvXp/xauoaeOC+anRq14bWHbrgdDopXKggk8a+n+ZxmYmVdcv0dZVVqf7g/bRr1YJXOnUjIMCfvHnyMPmDUZlty3IKt21DYMkS3PbUk9z21JNJ6bppC+LcDdyU/NO1G2XGjMYRGMiV8BPsfS3983sVKliASR+MZsjoD7h8+TKBgYFMHDOKbNmyeXwOputA3ze7Mnj0hzR8uQ0A9WrVpHWzlzzSMhWvTOSBqXw1pXvg0BEKFSxAgL81TVpTMcCErp3uA3aqA3ZqX9ipvtrJa1avr3YqKxPtCzATB+yUr3b6Dm+nmC0IaeG4VefVy2yUUmWAMK11mRTpLq21w/1/bSBYa107leODAbTWwcnSwtz7h3lk6ly49YUd4N3N77qYGGYeH2e9pp+heaOuRFuvaaqsjMQQQ3HJYeChgZgo6zUBAg38Um2iDpjCVN2KtWZOsmvw936F95vFr2WtGWWYnAf+2mq5JgBOA78hm6qvAQauARPxym7EebdadKrYqd1rIraYug+YaLeZqgMuA/MSSn21F1m8LWAr4mKt1/RwrY40sVMcMHEvNPWYuJ1ids68vvOsvA9x6uFKPt/4Krj5D58oO+mgzUpIB631mtJBKx200kFrBumgNYJ00EoHra2QDlrrNaWD1l5f9gUzZPG2gK2QDlozSAet9ZogHbTXQTpo04+NooggCIIgCIIgCIIgCIIgCMKthcxBKwiCIAiCIAiCIAiCIAiCpThMja6+BZEO2qyEiUemTTyKD2Yebzb1OIsJTE1HYAITAdfnH4JIholHcAEwUV8NPNIHZh7vNpWvJh6TstGjVw/8vc1yzdjB6V8sLCP4D5tuuaYr+pLlmgAOp4F7ltPU4902eqwxzsDj+CbaQmCfqRMMTR/junDKck1HnoKWayYI26g9aAJT01yYmprIBDIdQdbGTjHATtOTYaYt4IpIffFab3Dk8q2FzgQhERtFJ0EQBEEQBEEQBEEQBEEQhFsLGUErCIIgCIIgCIIgCIIgCIKlyAwH6UdG0AqCIAiCIAiCIAiCIAiCIGQSMoJWuIawDRv5cOJkYmJiUOXLMXLwu+TOndsjLZfLRd8hI6lQ7k7atWpBXFwco8dPYsNPW4iLi6Nty+a0aPy8x1713n0Mf38sFyMi8HM6Gdq/D5XvvstjPYDZ8xYwd+EiHA4HpUqWYPjAfhTIn98rTbA2X01qmtR1uVz0HTSECuXL0a51K6/1AEKXr2TarNk4HA5yZM/OgD5vc0/Fu73Wtcqry+Wi7/D3qFD2Ttq93JRzFy4QPGY8f/69l5zZs/PiMw14pckLHutbXl+HjUrw2rL5NdveeOddChcqyKBePTz2OnrsBL5ds5a8efMAcMftpRk3erjHegBLvlnJjDnzkt5fjIgkPPwE61csoWCBjNXblPEqkWPh4TRt25nQkOnkz5fPY692ii1WlJVf8zdwHT+IK2wpAM6hM+Dc6aTt8WGhuH7fAEVK4mzSyT0vqIv45SG4dPrnybXq/Jd+9z3T5i7E4XCQPVs2BrzZmXvuqsDDDZtStNDVeTDbNX+Jhk88nmF9K6/VlJi4BkzFVqu8ulwu+g4dmRCvWrUgKiqaIWM+YufuP3G5XFSpVJHBvd8ie3bP5nM3dR/09XuWCd2l361l2ryvr9at7h2pWL4sw8Z9wi/bdwLw2MMP0qdzO48XEcnqbSxTXgHWrFtP70FD2brhe0v0wMz1apd8tdN1BfYpKxPfCU15tVt9NaFrVZvYdNstEVP5KgipISNohSTOnDlLv8HDmDhmNKuWLKRUyRJ8MGGSR1r7/t3Pq116sGptWFLavMVL2X/wEN/MncnCL6Ywc94Cdvyx2yP9y1FRtOvag/atW7Jkzky6tGtDr3eDPdJKZNfuPUz/MoR5M6bwzYI5lCldivGTP/dKE6zNV5OaJnX3/fMvr3bswqo1a73WSuSf/QcYM34iUz8eT+i82XRu/xrder3jta5VXvftP8Cr3Xqxat2GpLRR4yeTM0d2VoRM56spH/PD5p9Zt+knj/Qtr69vvMWqtev/s23Kl3P4dfsOj3STs3XHTsaOGkro3FmEzp3ldecswPPPPkXonJmEzpnJwlnTKFQgPwP79Mxwh9fVeHXt+S9Z/i0tO3TjxEnvFr+xU2wBL8uqcAn8OgXjqFL9alqh4nApgrixvZJert8T6oXzxdeJ/3ltQvpXk/F7pWe6F3S06vz/OXiIMZOnMmXMcJZMn0zn1i3o/u4w/jl4iLxBQSyZPjnp5WkD36prNSUmrgFTsdUqr/v+3c+rXa+tr598MYu4uDiWhsxgacgMoqOj+Wzm7Ez1mRJfv2eZ0P3n4GHGfDKNKWOGsWTax3Ru3ZzuA0cQ+t1a/j10mKUzJrNk+iR+2baTVWEbPfqMrN7GMuUVYP/BQ7w3bqKlC+CZuF7tkq92uq7APmVl4juhKa92q68mdK1qE9+MthuYy1dBuB43pYNWKVVGKbU/lXSXUmqxUmqbUmqvUirC/f82pVSzZP8fd78S3xdQSoW5j9mmlNqulNqplGqaDi/FlFKzlVJ/uI/7Ril1Z7LtRdzb9ymldiilViml7k22PUwpNTuFZrBSKjgD2/cmO5dtSqlVyfZ9WCn1vdvbLqXUZKVUDvc5Xzc/0lsWN2Lj5i3cU6kiZW4vDUCLJo1ZtvJbXB4EpJCFi2nS6Fka1K2dlLYm7AdefPZp/P39yZsniGfq12Xpyu888rpp8xZKlSxBrRqPAFC3Vk2vO3wqV7yLVUsWEhSUm+joaMJPnCRfXu9XeLQyX01qmtQNmb+QJi80okH9ul7pJCcwMIDhA/tT2P0LaeWKd3Pq1GlirlzxStcqryFfh9Lkuadp8PhjSWl/7PmbRg3q43Q6CQwIoPYjD7Fq3Q8e6VtaX79eQpPnnqHB47WvSd/y21Y2bP6Z5i808shjIjExMezWfzF1ZggNm7akW+9+HD123CvNlEz5Yjb5b7uN5h6Myk8tXoWfPMWa9RuYNuFDr73ZKbZ4W1Z+jz6Fa8saXDuu/vDgKKMgPh5n12E43x6Lo36Tqyso+/lBjlwJ/2fLAbHpr79WnX9gQADD3ulB4YIJt9LKd1Xg1Jmz/Lx1B04/P1q+8TbPtenEpC9CiIvzfjVlb67VlJi4BkzFVqu8plZfH6xWlc5tW+Pn54fT6eRuVZ6jxz2LMabug75+zzKhGxgQwLA+b1LY/UNEZVWeU2fOEnPlCpejooi5coWYmCtciY0lMDDQo8/I6m0sU14vX46i97vB9O35plc6KTFxvdolX+10XYF9ysrEd0JTXu1WX03oWtUmvhltN1P5mhVxOBw+//IVMn2KA631CwBKqdpAsNa6drLNX7m3Bbv3DU7coJQCaK+1DnO/vwf4RSm1Smt9PrXPUkrlAtYDHwCvaK1dSqmWwGql1F0k5Mc6YHqy7fWB75RSNbTW2i3VRCm1QGsdep3TSmt7ku8U/qoAi4HntdZblFL+wETgc631K0C16+WHFRw/Hk7RIoWT3hctXJiIiEgiIyMz/OjFoN5vAbBpy89JacfCT1DsGv1C6L37PPL674FDFCqQn/5DR7Ln773kCcpN7+5dPdJKTkCAP2vWrWfAsJEEBgTSvfPrXmtama8mNU3qDurbG4BNP23xWCMlJYsXp2Tx4kDCI1ijPhzP47VqEhgQ4JWuVV4Hvd09QefnX5PSqlS6i9BvV3NflcrExFxh1boNBPh7FoYtra/uqQs2bbnqNfzkKUZ8NJGp48bw1eKlHnlMrvXwg/fTo0sHype9k2lfhtDl7T4sDplpyQ3xzLlzzAiZx6Ivp3l0/NV49UtSWpFCBfn4/RFee0vELrHF27KKXzwVAIeqdjXRz4nr7x3EL58Nfk6c7QcQH3UJ14blxC2airNzMDzWEHLnIX72RxAfny6vVp1/yWJFKVmsKJAQS0Z//Bl1Hn0YPz8/qj9wL293bEtsbCwd3xlE7pw5ebWp59OSeHutpsTENWAqtlrlNbX6WuPh/yX9f+TYcWbOW8Cwfr0z1WdKfP2eZUK3ZLEilCxWBHDXrUlTqPPoQzR55knWbPiJWo1bExsXx6MP3svjjz7k0Wdk9TaWsXbbyPdo1vh5VPlyHmukqmvgerVLvtrpugL7lJWp74R2ugaM1VdDula0iW9G283U+QvCjbhlpjjQWu8EIoAb1aDmwAmt9edaa5f7uBDgHSAb0My9/YNk21cDM4A+yXSGA5OVUtd7NjGt7dejNzBNa73F/dmxbm9LMqjjEfGu+FS/gPs5nZbou+Ljr1nBz4ULv3Q+ypqS2NhY1m/6iWYvNmLRl9Np1fQlOrz5NjExMV77rFenFlvWrqJbx3a069qD+HR2FlwPE/lqqqxMXwMmuHT5Mm++05+Dhw4xfNCAzLZzQ/p264zD4eCFVzvSte8gHv3f/QQEeNZBa7KsrsTG8vagofTr8UbSL9PeUKpEcaZMGEuFcmVxOBy0e6UlBw8f4fDRY15rA8xfFErdWjUoVbKEJXqmsENsMVFWri1riF88DWKiIeoS8euX4XfPQ+AfgPOVnsTP/Zi4YR2ImzQQv5c6Qr70XXNWn/+ly1H0GDyCg0eOMbxPD5o2fIqBPbqQM0d28gTlpk3TF1m94UePtBOx+lo1GQesjq034/6y609Ny45v0KrJi9Rxj6bKKKZ92umeZRUJdWsUB48cZXjvN5n0xRzy583DxiUhrF84i/MXIpj+1SKPtLN6G8uEZsj8r/F3OnmpUUOPNW4mdslXO11XpjDh1dR3QrtcA6bqq+k4YFWb2FTbzW5xULh1uJkdtMVTPNaf/lVA0oFS6kn3v/oGu90L/J4yUWu9UGsdATwI/Pyfo+AH97ZENgALgAnX+Zy0tk9NkReJLfT/+NNaX9Baf329E7KSYkWLXjPXYviJk+TNk4ecOXJYpF+EEyevLhJz4uRpihYu5JFW4UIFKXtHGapWrgRAvdqPERcXz6EjRz32d+DgIX7devWybNyoIUePHef8hYsea4KZfDVVVqavAas5euw4zdu0x+nnx6zPJ5MnKCizLd2QiMhIenftwDch0/hiwhhcLhelPeyoMVlWu/7cw6EjRxk9fhKNXmnHvMVLWbFmLQNGvO+R3p6/97Jk+cpr0lwuPB49nJIVq9fyYsNnLNEygZ1ii4myctxfC4rdniwBXHFxULQ0BGTD9edvCekH/4bjh3CULp8uXSvP/2j4CVp0eQunn5OZ498jT1BuQletQe/7J2kfFy4C/L37wmv1tWoqDpiIrabvL8u/+5623XvydteOdGrzisc6Jn3a7Z5lBUfDT9Ci69s4nX7MHDeaPEG5Wb3hR158+gkCAwIIyp2L5xvUZcvW7R7pZ/U2lgnNxd8sZ+fuP2nUojUduvckKjqaRi1aE37ypMeaJrFLvtrpujKFCa8mvhOa8mqn+mpK18o2scm2m93ioK/j8PP9l69wM60c1VpXS/6yQDOxo3MXMABo6u5ovR7xQNQNtrtIfdqHQPe25PQHHlJKXW9yxhttb58iLxKfo03Ln1FqVH+I7Tt3sf/AQQDmLVxE3dqPpXFU+qn7WA2+Xrac2NhYLly8yPLV31Ovdk2PtB57pDqHjx5l1597APjl9604HFCyeDGP/Z08dZqe/QZy5uw5AJatXEX5sndyWz7v5oo0ka+mysr0NWAlEZGRvNKhM088XoePRo8ge/bsmW0pTeYtWcaEKV8AcOrMGRYsW8mz9T2buN5kWd17T2XWL11I6JfTCP1yGs1feI6n6z3OiAF90j44FfwcDkaM+SipsTxnwSJU+bLXPOblKecvXODgocPcW/Uer7VMYafYYqKsHEVL4degeULrxz8wYZ7abZvg1DHIkRPKqIQdCxSBIqVwHfk3XbpWnX/EpUu07t6H+o89ytjgfmTPlg2Av/85wIRpXxIXF0dUdDQhi5by1OO1MqyfiIlr1cQ1YCq2moxZazdsYvjY8Uyb8CENn6zvlZYpn3a8Z3lLxKVLtH6zL/Ufe4Sxg/sm1a2K5cvyrXsBzSuxsazbtIVqFT1bcT2rt7FMaC6cNZ1v5ocQOncWn08YS/Zs2QidO4sihTwbVGEau+Srna4rU5jwauI7oSmvdqqvpnStahObbrvZLQ4Ktw6ZPgetl6Q6l+sN+BVokzJRKTUV+AjYAnRJ5bjq7mOT0FpfUkq1BeYBXwNnMrL9Bv4eAFYk85YHCAEaa629f37/BhTIn59RwQPp3rsvV2JjKV2yBO8NC7ZMv0Xj5zl45CiNWr7GldhYmr3wHP+77960D0yFQgULMOmD0QwZ/QGXL18mMDCQiWNGkc0dnD3hgfuq0aldG1p36ILT6aRwoYJMGuvZiMHkmMhXU2Vl+hqwkpCvFnD02HFWrwtj9bqwpPQvPp3kdceXKTq88jJ9ho7i2ZbtcOGie/tXqeLhl1I7lVWFcmV5t09POr/Vm7i4OIoWKczYEUMt0T5w6AiFChawbDSuCewUW0yUVfx38/F7sT3OXmPB6Y9r+4+4tqxJ2DbjfZyN2kJAAMTHEb/wUzgdni5dq84/ZNFSjoafYM2GH1mT7DG4z94byvhps3iuTWdiY2N5sk5NmjzbIMP6iZi4Vk1cA6Ziq8mY9d6EybhcLt5NNsr/viqVGdynp8/4tOM9y1tCFi1z162fWLPh6sKBM8aOZNi4T3jqlQ44/fx4+L5qtGvxkkefkdXbWHZqC5jCLvlqp+vKFCa8mvhOaMqrncrKFFa1iW9W200QbjYOb1cNTA9KqTJAmNa6TIp0l9ba4f6/Nv9dJCxxv2D4zyJhYe79wzLgIwfwBzBCaz3NnfYaCaNvK7l3+5mETtXR7kXCngDmADW01ntSfq5SahzQGRiltQ7O6PYU/u4BVgPPaa1/VkoFAJOBHFrrVjfKj3Rx6bz1hX0l2nJJAPwMzJvk4Xy3N8SXxsPfSpiKSyZWaLzs3WPq1yWHgUdfoy9brwkQ4Nnq2zfE5d38rNcl3rOVXG+Iv4HzNxVbDJx/7GDvFzxLDf9h0y3XdF1Mz2+lGceR00B9dXq3eNR1MRFfTa1+G2PgoaJAQyNX7ZSvBnBdOJX2ThnEkaeg5ZoCZu6DYKbtLghxsdZrOn33R/3/YKq+msDQPcsVcc5yTUcuQz+M5s5vnxv3TeT8Y1XMdzp6Sd4fdvhE2dkoOnmP1vqyUqoe8JFSqicJ0xb8AzyhtY4GUEo9DowBtFIqHjgI1Nda77mObH/gRhPKpbZ9qlIq5VQMtbXWO5VSrYDxSqmcJEytsAbonv6zFARBEARBEARBEARBEATBLtyUEbSCjyAjaK3XlBG0ZpARtDKCVkbQWq8JMoJWRtDaa6SnjKC1XtMQMoLWRsgIWsFOyAjazHaQfmQErYygvQ4ygjb92Cg6pQ+lVDOgX2rbLFqYTBAEQRAEQRAEQRAEQRCEG+HnE32ftkBG0GYlTIygFQRBEAQDrCjt2QJ6N+Kpbess1wRw5PdutWhBEARBEATB5uTMKz2RqXC+dlWf74fKG7bdJ8pOns8WBEEQBEEQBEEQBEEQBEHIJG65KQ4EQRAEQRAEQRAEQRAEQchkbDSnfmYjI2gFQRAEQRAEQRAEQRAEQRAyCRlBK1xD2IaNfDhxMjExMajy5Rg5+F1y587tc5riVbyKV/Ga1c8/q3gt3qQxd3btDC4XcZcvs7v/QO7s/ga57iiTtE+O20tz5sef+K1Vm6tppUvx6Per+KVJC85v237Dz5i9ZDnzlq7E4XBQqnhRhvXsSoHb8jEndAULV6wmKiaGSuXLMqJXNwIDA27q+WeGblb3mtXPX7yKV/FqH03xKl7Fq728CsKNkBG0QhJnzpyl3+BhTBwzmlVLFlKqZAk+mDDJ5zTFq3gVr+I1q59/VvGaq1xZ7goeyC/NXmZjnfrsHTuO+76Yxta2r7OxTn021qnPzp69iD1/gT/69E86zi9bNqp+8jF+AYFpfsauv/Yyff4S5k54j2XTJnJ7ieKMnzGH7zb8xOwly5k+ZijfTJtIVEwMX3wdelPPPzN0s7rXrH7+4lW8ilf7aIpX8Spe7eVVENLCqw5apVQZpdT+VNJdSqnFSqltSqm9SqkI9//blFLNkv1/3P1KfF9AKRXmPmabUmq7UmqnUqppGj5qJ/uM7UoprZT6SCmVO9k+RZRSs5VS+5RSO5RSq5RS97q3tVRKLUm2b2X3ObRMljZKKRWslGqjlDqllCqSWj649wm+js/ySqlQ9/ntUkp9rZS6I8U+/kqpY0qpiSnSg5Pl1Xb38W/cKF8yysbNW7inUkXK3F4agBZNGrNs5be4XJ4vumdCU7yKV/EqXrP6+WcVr/HR0ezs8TbR4ScAOL9tO9kKF8IRkDCK1REQQJWPJ7B7wCCijh5NOq7SeyM5Mm8+MWfOpOmtcoVyrJr1CUG5cxEdE0P4qdPkyxNE6HfreO2lRuTLE4Sfnx9DenSmUb06N/X8M0M3q3vN6ucvXsWreLWPpngVr+LVXl6zKg6Hw+dfvoKxEbRa6xe01tWA9sCvWutq7tdXif8DnwKfJtt22n14e/f7qsDLwCylVN40PvLXZMdUBgoCnwAopXIA64BtQDmtdRXgA+A7pZQC1gKPJNN6EvjO/TeRmsBq9/9Bbu/pxt2huw6Yr7Uup7WuDCwGNimlCiXb9WngZ6CpUipnCplPk51jHaCjUqpdRnzciOPHwylapHDS+6KFCxMREUlkZKRPaYpX8SpexWtWP/+s4vXyocOcXP190vu7hw0h/NvvcF25AkCpVi2IPn6c8BUrk/Yp2eplHAEBHPoyJN3+Avz9WbNxM7WateXXHX/wYoO67D98hNPnztO+bzDPte/OxzPnEpQ7V7o1k+Nr+Speb66meBWv4lW8ZvXzF6/iVbwKQtr4/BQHWuudQARQLgPHXAF6A82VUvmAZsAJrfUHWmuXe5/VwAygj9b6GHBKKVXBLfEkMBCorZRyKKWyAxWALe7tXwPllVIvZ+BUOgNrtNZJ3xi11rOBjUCnZPu9RkLH7c9A8xuc40lgGNA1Ax5uSLwrPtVfD/ycTp/SNKUrXsWreLWP16x+/qZ0fdWrM2cO7p32OTnvKMPOHm8npd/RqQN7x45Lep+nyj3c3qY1u3q9k2GP9Wo8zObFs3nj1Ra07xtMbFwcP/62jXED+7Dwkw85dzGCcdNnZ1gXfDdfb5amKV27aJrSFa/iVbzax2tWP39TuuJVvNrJqyCkhRUdtMWTTVGwTSm1zQLNJJRSiaNYdUaO01ofB86S0LH6IAkdnin5wb0NEkbRPuoebXuH1vpn4F+gKvAQ8KPWOta9bwzQBhibfKqDNEjTg3skbX0gFPgK6JiG5i7grnR+fpoUK1qUEydPJb0PP3GSvHnykDNHDp/SFK/iVbyK16x+/lnJa/YSJai+Yhmu+Di2PP8SsRcuAJDnnso4nP6c2fRT0r4lmjbBPyg3j6xYSo11q8letAhVP/2Ywg2euK7+gSPH+G3n7qT3jRvU5Wj4SQIDA6lfszq5c+UkMCCA5+rVZtvuPZ6cvk/mq3i9eZriVbyKV/Ga1c9fvIpX8ZqF8XP4/iudKKUGK6X+cL/ed6fVc0+j+rdSaniyfasppX5VSv2llJqqlPJPM6s8yuBrOZpsioLEqQu8Zaq7s3cXMABoqrWO8EDHBVx2/00tMwLd2wC+Bx4FapHQaQoJUxrUJmF6g++SH6i1/hWYTvqnOkiPh1bA91rrsyR00lZJnCf3BpqX0/n5aVKj+kNs37mL/QcOAjBv4SLq1n7M5zTFq3gVr+I1q59/VvHqzJ2Lh0O/5vjyFWx7vTPxUVFJ2/I/Up3TGzdes/+f7w5i/UM1khYQizoezvZOb3Di2+9SSidx8vQZeg7/gLPnEzp+l32/nvJlStP0ITWLfgAAIABJREFUmSf4NmwTUdHRuFwuvt+0mcqqvCen73P5Kl5vrqZ4Fa/iVbxm9fMXr+JVvAp2RylVD3gCuBeoBtyvlGpBQr9gI+Bu4EGl1FPuQ2YDb2itKwAO4PW0PsPhzSTHSqkyQJjWukyKdJfW2uH+vzYQrLWuncrxwQBa6+BkaWHu/cMy4OM/n+Ee2XoAyA80BrporaunOG4MkF9r3U4pdRuwHlgDbNFaf6WU+h/wllvjDa3130qpNkBtrXUbpVQg8DswB+igtS6T2jm5P2sgUFFr3SJF+gJgh9Z6mFJqO1AcSJzYpBDwpda603XyqpnbV810ZdSl82kW9voNm/hw4iSuxMZSumQJ3hsWTL68aU3/e/M1xat4Fa/iNauf/63udUXpuyj7Zjcq9H+Hi7v/vGbblhebUqFvb6LCT7Av2RQHKan9+89sbfs657dtB+CpbetS3W/u0pXMCV2B0+mkcIH8DOrekWKFC/JJyAJWrttAXHw8FcuXZehbXcidK+X08ODIX8zy808vt/I1cCtpilfxKl7Fa1Y/f/EqXm95rznz+s5qUz7EhXr3+fzKag8eirwNyJfKpnNa63MASqlKQJDWerP7/cfASeAxrXVdd9orJKwXNQRYq7Uu606vCQzRWj9+Ix+3ZAetUiobMAW45O7czEbC9ALzgNFaa5dS6gkSOlZraK33uI/bQsICYDW11qeVUn7Ab4DTvbAYyTto3e8fIKFj92QaHbQFSFikrJ977lmUUq2B0SRMo1CahBG7JbTWl5Od1zISOm3fTq6rlCoGfAuM0lrPS1dGpaODVhAEQRB8gRWlLZvBJ4nrddB6S3o6aAVBEARBEIRbGOmgTZUL9e/3+X6oBw9GDAEGp7JpSMq+PQClVHlgEzARUFrrVu70ekAft9YYrXUNd3o5YIV7NO118flFwjLAA8nmwP0NOAe8CaC1jgYeB8oDWim1B+gF1E/snHUTBkRqrU+7j4sH9gKbr/eh7qkOUg7f6a+Uikj2aunWrAk8r5Tao5T6C3iehA7ikyQsDjYjsXPWrR0G/AW0dCd1cp/jVmAl8Fm6O2cFQRAEQRAEQRAEQRAEQUjOOOCOVF7/eVTPPZJ2NdAb+IerU5ZCwlQG8ST0taaWfkO8GkEr2AwZQSsIgiDYBBlBKwiCIAiCINgGGUGbKnYYQZtn9W/pKjul1KPA10APrfU8pVQtYKDWup57e/IpDr7XWpdzp6drioM0VxHzFdzzrfZLbZtFC5MJnhAfZ0bXYZPB3Q6JwUYw9cORifKKikx7H0/Inst6TVNeA7NbLumKPGe5JoAjwHqvRsrKUB1wHd6T9k4ZJSi/9ZrA0wet9xobnObc/B7hPyi964VmAD+n9Zp2w0Q9MHXfjrtivaYzwHpNU8TGWK/pH2i9JoArzQEsGccu7VYwc/5grzwQ7ENWj612qq8mygrMtAVM5Svez3t7K+LwuzX6TJRSpYAlQDOt9Vp38paETaoc8C/wMjBda31AKRWllHpUa70JeIWEp+BviG06aLXWXwFfZbYPQRAEQRAEQRAEQRAEQRCyDL2A7MBYpVRi2qdAGxJG1WYHVgAL3dtaAlOUUnmA34EJaX2AbTpoBUEQBEEQBEEQBEEQBEEQbiZa6zdxr3OVClVT2X878L+MfIZ00AqCIAiCIAiCIAiCIAiCYC0yLWS6kQ5a4T+4XC76DhpChfLlaNe6lWW6a9atp/egoWzd8L0leqHLVzJt1mwcDgc5smdnQJ+3uafi3T6nCRC2YSMfTpxMTEwMqnw5Rg5+l9y5c/ucpt28migvK726XC76DhtNhbJ30q5lMwBCvl7CwqUriIqOptJdFRjZvzeBgZ7Ns2cnr4l4GweWfreWaXMX4nA4yJ4tGwPe7MSUkPkcPHIsaZ/Dx47zYNV7+GR0cLp17VRWybGqDoyeNpdVm34mb1CCpztKFGXUm68z9NNZ7Pz7H1wuF1UqlGVQp9Zkz5a+PJi9+Bvmha7A4XBQqngxhvV6g1w5czB03Kfs3PNXgubdikE9OpE9W7YMewZr8tWvaVdcxw/i+mEZAM7BU+H8maTt8euX4tq6Ecfd9+PXrCucO5W0Le6TQRAdlaHPs/peaKeYbULX1+8DiSz5ZiUz5sxLen8xIpLw8BOsX7GEggU8n8fZVFmB9e3BL79ayOz5X5M9WzbK3nE7g/q8Tb68ebzWNZEHs+ctYO7CRQnxq2QJhg/sR4H83s+3baf6aioPTHzPsEu+2im2gj3KylRsNeHVlCbYp76aKi8r7y8ul4u+Q0ZSodydtGvVIin9WHg4Tdt2JjRkOvnz5fPYqyDcCJnNXbiGff/8y6sdu7Bqzdq0d84A+w8e4r1xEy2b5Puf/QcYM34iUz8eT+i82XRu/xrder3jc5oAZ86cpd/gYUwcM5pVSxZSqmQJPpgwyec07ebVRHlZ6XXf/gO82u1tVq37ISntu7AfmL1gMTMmfMDyOTOIjo7mi3kLb6Bya3hNxNs48M/Bw4yZPJUpY4azZPokOrduTvd3hzNh2LssmT6JJdMnMax3d/Lkzs2gt7qmW9dOZZUcK+vA1j1/82HvLiwZP4wl44fxUZ+ufLpgKXHxcYROGE7ohBFExcTw+cJv0qW3S+9l+leLmfvxGJbNmMTtJYsxfvpsPp09n7i4OEKnTSR02sQEzZAFHnn2Ol8Ll8CvwyAcVR66mlaoGFyKIG5cn6SXa+tGABy3VyB+/bJrtmW0c9bqe6GdYrYJXV+/DyTn+WefInTOTELnzGThrGkUKpCfgX16evWF1JRXsL49uPnX35kyK4SZk8cTOucLHnu0OoNGvu+1rok82LV7D9O/DGHejCl8s2AOZUqXYvzkz33Sq6lrwFQemPieYZd8tVNsBfuUlYnYasqr1Fcz5WXl/WXfv/t5tUsPVq1df036kuXf0rJDN06cPHWdIwXBGmzRQauUKqOU2p9KuksptVgptU0ptVcpFeH+f5tSqlmy/4+7X4nvCyilwtzHbFNKbVdK7VRKNU3DR+1kn7FVKbVHKbVQKRXk3r5fKbU72efsd2/PlUyjh1LqT6XULvc+XZJtG6yUWquUcrjfO5RSy5RS3ZRSvZRSW5RSfsn2r6yUOqGUKul9LicQMn8hTV5oRIP6da2S5PLlKHq/G0zfntebriPjBAYGMHxgfwoXKghA5Yp3c+rUaWKueL56pAlNgI2bt3BPpYqUub00AC2aNGbZym9xefEF3YSm3byaKC8rvYYsXEKThk/T4PFaSWlLVn5H25ebki9vHvz8/BjSpyeNGtS/5b2CNXEgMCCAYe/0oHDBhEZc5bsqcOrM2aQyj7lyhb4jP6Rftw4UK1Io3bp2KqvkWFUHYq5c4c9/DjJt0QoadhtAt1ETOXryNA9UUnRq2gg/Pz+cTj8q3nk7R06kr2FaWZVj1ezPCMqdi+iYGMJPnSFfnjw8UKUSnV5p5tZ0UrHcnRwJP5nhcwfv89Wv+pO4fv4e147NSWmO2xXEx+PsPATnW2Nw1Guc9FiWo4zCUa4Szh7v4+w8BO7I2ChNE/dCO8VsE7q+fh+4HlO+mE3+226jeePnvdIx6dXq9uAff+7hkQcfoGiRwgA8UacWazds8sk2VuWKd7FqyUKCgnITHR1N+ImT5Mvr/crcdqqvpvLAxPcMu+SrnWIr2KeskmNVbAV7XQN2qq/Jsaq8rLy/hCxcTJNGz9Kgbu2ktPCTp1izfgPTJnzolc+sjMPP4fMvX8EWHbQ3Qmv9gta6GtAe+FVrXc39+irxfxJWVvs02bbT7sPbu99XBV4GZiml0opmiZ9xr9b6Lnda/2Tbn072uRWA0kBrAKVUMPAcUFtrXRmoD7RUSg10HzscyAG85X7fC4jTWk8EPnKnveHW8gOmAr201oczkGU3ZFDf3jR8qoFVcgmaI9+jWePnUeXLWaZZsnhxatesASQ8hjDqw/E8XqsmgQEBPqUJcPx4eNINA6Bo4cJEREQSGRnpU5p282qivKz0OqjXmzR8st41afsPHub0mbO069GHhq3aMXHqFwQFefZYk528gjVxoGSxItSunjDPusvlYvTHn1Pn0YeSyvzr5asoXLAA9R97NGPebFRWybGqDpw4fY6Hq9zNm60as3TCcKqpsnQdPo5Hq1XmjhJFAThy4hQzl31Hgxrpn+c+wN+fNRt/olaTNvy6YxcvPlWPGg/exx2lSiRoHj/BzK+X0qB2xsorEW/zNT50Oq5tm65N9HPi2ruTuKkjiftkMH4VquJ49CkAXJEXcW1enTB6duUcnK17Qd70j/gwcS+0U8w2oevr94HUOHPuHDNC5tG/Z3evtUx6tbo9WLVyJTb/+htHjh0HYNGyFVy5coVz5897pWsqDwIC/Fmzbj2PPfUcv/y+jRcbPeOVHtirvoKZPDDxPcMu+Wqn2Ar2KatErIytYK9rAOxTXxOxsrysvL8M6v0WDVMMyChSqCAfvz+CO24v5bVXQUgL23fQWoXWeicQAWT0m1MYUPk62/IBeYEzSqmcQG+grdY63P2ZJ4HXgT5KqZxa6zigFdBPKdUS6AS0de8bB7wGDFRKlQC6Ase01rMy6PemEjL/a/ydTl5q1NCI/qXLl3nznf4cPHSI4YMG+KRmvCseRyoTY/s5nT6laUrXlNdErCwv015jY2PZ9MtvjB8xmK9nfMr5ixf56NNpHmnZyavVceDS5Sh6DB7JwSNHGd6nR1L6F/OX0Kl1ixscmX7sVFbe1oGSRQvx+eC3qXB7SRwOB21feIqDx09wJDxhtOyuvf/Squ8IWj5djzoPVsuQdr0a1dkcOoc3Xn2Z9n0GER8fn6Cp99LqzXdo+fwz1KmeocVNkzCRr66fvyc+dAZciYaoS8T/sBy/ygn+4r/8ENfOLQk77te4DvyFo3yVdOmauhfaKWabrAd2ug/MXxRK3Vo1KFWyhNdapr1ayQP3VqXr6215o3d/XmzdDofDQb68eQjw9+5HcJN5UK9OLbasXUW3ju1o17VHUvzyFDvV10SszgMT2CVf7RhbrcakVytjK9jrGkjEDvU1ESvLy9T9RRAyAzt10BZPNnXANqXUNivFlVJPuv/VGTgmFwkjYn9KlrxCKbVDKRUOrAA+BuYDlYBIrfX+5Bpa691ANHCX+/0+YCAwG+igtT6TYt9xwOdAd6BjBk4xU1j8zXJ27v6TRi1a06F7T6Kio2nUojXhJz17pDU5R48dp3mb9jj9/Jj1+WTyBAX5pGaxokWvma8m/MRJ8ubJQ84cOXxK025ewfryMukVoHDBAjxRuya5c+UiMCCA556sx7Zdf9zyXq2MA0fDT9CiS0+cfn7MHP8eedyjWnf/tZe4uDj+V+0ejzymxC5lZUUd0P8eJHTdtSNJXYC/v5PlP2ym3aAx9Hy1KZ2apr9z8cCRo/y282p+NX6qHkfDT3L+YgTL1/5Au94D6fn6q3RqdcOZhW6IiXx13FcTipZOlgCuuFjInhNHnRdS7AzExaVL19S90E4x25Su3e4DK1av5cWG3o9sAvNerSQi8hL/u68ai2dPZ9GsadSrVRPA60XCTOTBgYOH+HXr1a8ZjRs15Oix45y/cNHnvJq6BkzlgQnskq92i60mMOnVytgK9roG7FRfE7GyvEzdXwQhM7BTB+3RZFMUJE4h4C1T3Z29u4ABQFOtdUQaxzyQrIP4ZxI6dMcm2/601roK0AUoBCzQWrtwf9+9jmaAe3siNYFw4KlU9n0PKA+M0FqfSMNrprNw1nS+mR9C6NxZfD5hLNmzZSN07iyKFEr//JCpEREZySsdOvPE43X4aPQIsmfP7rVXE5oANao/xPadu9h/4CAA8xYuom7tx3xO025eTZSXKa+JPPl4LVZ+H0ZUVDQul4s1P2zinrvvSvtAm3u1Kg5EXLpE6+7vUP+xRxkb3I/s2bIlbftl204evq9qqiMTPMEOZWVVHXD4+THi89kcPp7QWTh35VrU7SXZ/c8BRkyZzdQhvWlYq3qGNE+ePkvPoWM46368bNma9ZQvU5qtf+xhxMTPmTpmKA3r1fbIbyIm8tVRpDR+TzRLmHfWPwC/Rxrg2v4TRF/G75EncVR2LyhWvAyOUuVw6fT9VmzqXminmG1C1273gfMXLnDw0GHurWrND0mm7wNWcuLkKV7p1I2IiIRHeT+ZPpNnnqjndcw2kQcnT52mZ7+BnDl7DoBlK1dRvuyd3JbPuzkd7VRfTeWBCeySr3aKraYw5dXq2Ar2ugbsVF/B+vIydX8RLMTh8P2Xj3C9DsOsQnutdVgGj/lVa107rZ201l8rpZ4ApgNPA7uBAKWU0lonjdJVSlUioaNcu9+/BijgAeA3pdQKrfXaZLqxSqmjwP4M+r6lCPlqAUePHWf1ujBWrwtLSv/i00ke34xMaAIUyJ+fUcED6d67L1diYyldsgTvDQv2WM+Upt28migvU14TefnFRpy/cJEXX+tIXHw8lVR5+nbvfMt7tYqQRcs4Gn6CNRt+ZM2GH5PSZ3w0igOHj1KiaBHLPssOZWVVHahwe0ne7dCKzsM/Ii4+nqIF8vNh7y60HfQ+LhcM/Hh60r733V2eQZ1ap6n5QJVKdGrVlNY9+uN0OilcMD+Thg+g/TvBuFwuBo6ZeFWz8t0M6pHxvDWRr/FrFuD3fDucPT8EpxPXjs24fv4egLiZ7+Ns1BaeaALx8cSHjINLmTs6xU4x24Su3e4DBw4doVDBAgT4W9P8Nn0fsJI7y5Smw6utaPJaB+Lj47m/WhUG9e7pta6JPHjgvmp0ateG1h26JMSvQgWZNNazFcFNezV1DZjKAxPYJV/tFFtNYcqr1bEV7HUN2Km+gvXlZer+IgiZgcPKVWlNoZQqA4RprcukSHdprR3u/2sDwal1nroX50JrHZwsLcy9f1gGfFz3M9zb95OwANh+9/vCwF6ghdZ6uVKqL/As8KLW+oR7+xJgpdZ6mFJKAT8Aj2mttVLqeWAiUEVrfdYb7wBcOm99Ycen7/HODOOwyeBuH/q15ZbCVFwyUV5R1iwc8x+y57Je05TXQGtGmyfHFXnOck0AR4D1Xo2UlaE64Dq8x3rRoPQvlJURHPms63BPJDb4dcs1AfwHfWq9qJ/vzR940zFRD0zdt+Myvlp0mjhtNH9ebIz1mv6B1msCuAzMy2iXdiuYOX+wVx4I9iGrx1Y71VcTZQVm2gKm8jVvEekcSIWIZx7y+U7H3Mu3+ETZyZ3UIO4pCN4Dxiil/LXWo0mYW3aNe1qF74EQd+dsNmAeMDBxhK3WegnwHWDgm58gCIIgCIIgCIIgCIIgGMLP4fsvH8EWI2gFi5ARtNYjI2jNICNoZQStjKC1XhMZQSsjaG2GjKC1XtMUMoLWek1T2GlEniBk9dhqp/oqI2hlBO11iGj4sM93OuZettknyi6rz0H7H5RSzYB+qW2zaGEyQRAEQRAEQRAEQRAEQRAEQEbQZi1MjKC1E1n9F1hBiIs1o+uU3/psg4kRA6auK1Oj5wywrkxFyzXr7N9tuaYgGEPaWIIgCEJWJ2denxiF6WtEPlfd5/uhci39ySfKTp5FEQRBEARBEARBEARBEARByCSkg1YQBEEQBEEQBEEQBEEQBCGTkOdSBUEQBEEQBEEQBEEQBEGwFj+fmD3AFkgHrXANYRs28uHEycTExKDKl2Pk4HfJnTu3z2ma0F3yzUpmzJmX9P5iRCTh4SdYv2IJBQt4t0p5Vs5X8Wo/r3rvPoa/P5aLERH4OZ0M7d+Hynff5XNepazM6M6et4C5CxfhcDgoVbIEwwf2+z97Zx4nc/3H8efs5dxdibXudX7KkaMUckVKKcotQik/KlGIkJtIlKSSWwgtWXJFruUX5ZdbfSqs2zpW2LVrr/n9sbPb0GZ3Z+a7+52d9/PxmAfzPV7z/HyvmXnvZz5f7i3s3DUQYNPW7Xzy5Vy8LBYCAwMYN2wwZUqVdCrTbNu1WLs2lHm9D1arleTYWP4Y+h43DhykxEvdKdGlM15583Lj4EF+6z8Qa/zfd73PW6Y0D21az4EOL3DjwMFscc3uTHdy9fT2G5Ern7HEVVyl/eLqXq4AVquVISNGU7lSRXp26+p0Hsh2FYS7kStuEqaUsmqtLUqpEOAE8ITWepPd/AigidY6QinVDniXlOK0F7BQaz1ZKfUkMMm2SkXgAhANnNBaP2/LuRc4CwzTWk+5w6EWMB6oZJt0HHhLa3001RE4YJtnAQoBG4DXtNZJd8tXSs0HugGltdZn7aavAmpqrUMytaEyuElYVNRVWrbrxNfzZhFStgyTp00nJuYmo4YOzlR8dmU6nJuFG1gkJCbS9dXXeP6Zp+nU9rl/XzATN7DI9dtVXN3HNRM3c4qNi6N56/aMf+9dGjeoz+ZtO/hw+mdsWLH031fK4CZhpml/DuWayjWDm4QdPvobbw4aQtjSRfj7F2TSR58QE3OTMcOH/PtKmTiu4uJuUbd5S8KWzKds6VLMX7KM//60ly8/nvzvK2VwkzAzbdetIVXIV6ECtVZ9w95mLYi/eJHCzZqiJk/kj/dGUv7dwfzyzHMkXrtG1TkzubHvAKemzwDAK08eaq5YRoGqVdj/XLu0Am1mbhKW64/XXJLpEa7yGUtcxTXbM8VVXI3MPXb8BKMnfsDBQ0fo26eXSwq0uX67yk3C0iXmufqmLzoWWPVfU+y73DgGbQIwSynlf+cMpVRJYAopBdwaQD2gk1KqldZ6o9a6pta6JrAXeMX2/Hm7iC7AaqCXUspil1sJ+B74VGtdSWtdCfgMWKeUypO6XGq+7bWrA08BT2SUb+Ms0MbuNQOA2lncNndl5+49VK9ahZCyZQDo3L4ta9ZvwJkivhGZRuamMmv+Igrfc8/dvzhkEk/fruLqXq67du+hdKmSNG5QH4BmjRvy8cRxTmW6U/s93bValfvYuCoUf/+C3Lp1i8iLlygUGOiUJ0BSchJWq5Ub0dEAxNyMJY/f3QuwGWG27WqNv4V+axDxFy8CcOPAAfyCilKiS2dOfz6TxL/+AquV3wcN4cI3K9LWqzRxPOeXLifhSlS2uWZ3pju5enr7jcxNRT5jiasnunp6+8XVvVwBFi8Ppf3zrWnRvJlTOfbIdvVQLBbzP0xCbizQngM2kVKIvZMigC+QH0BrHQ10BzLuppLCS6QUXuOBx+ymDwLmaa3XpU7QWocB7wP/KBTbueQH7L+R/Vs+wAqgnd3z54DvMumdKS5ciCS4WFDa8+CgIKKjY4iJiTFVppG5AFF//cW8xUsZ+vabTmeBbFdxdS/XEydPU/TewgwdM4E2L77MS6/3IykpyalMd2q/p7sC+Pr6sHnrdho91Yqff9lPm9YtncoDKJA/P6PfHUinnn1o8FRrFn+zgoF9+ziVabbtGnf6DFc2/5D2vOKYkVzeuIm8pUvjV6QIDyxdRJ1tmwgZNIDE69cAKN6lM16+vpxftCRbXbM7051cPb39RuaCfMYSV8919fT2i6t7uQKMGDKIZ59q4VTGnch2FYS7kxsLtAADgCeVUs3tJ2qtDwBhwHGl1E9KqUmAt9b6z4wClVI1gGAgHFgG/Mdudl1gx53raK1naq0v22XsV0odUUpdAuYDb2qt92QiH2A/EKSUKmZ73gFYnpF3Vki2JmNJ568HXt7epso0Mhdg+cowmjVuQGknx0ZMxdO3q7i6l2tiYiLbd/1IxzatWfnVXLp2aEevfgOItxsvM6u4U/s93TWVxx9rzJ4tG+n7n570fL0/ycl3HxohI/Sfx5gxez7rli9i5/ower/Ujb6DhznVC8Gs29Urfz6qzp5JvpBy6LcGYvH15Z7GjTjySm/2Nn8a30KFKP/uEApWr0aJ7i+iBzn+EzxPP17dJdOoXHdyTUU+Y4mrKzONynWXTKNyxVVcjUK2qyDcnVxZoNVaXwdeJZ2hDrTWfYAQ4HOgLLBbKdXmHyH/5GVguW282GXAc3bFUoC0b5lKqc22YuwJpVRHu9euqbWuCkwAAkkpFmc2H1J60bZRSt0DBAARmfDONMWDg7l4Ka2eTOTFSwQGBJA/Xz5TZRqZC7Bu0xbaPOt8j7FUPH27iqt7uQYVLUKFciHUqFYVgMebNCIpKZnTZ8+ZylX2lTG5J0+dZu++/WnP27Z+lnPnL3Dt+g2nXHf+uIfaNaqn3RSsS/s2/HHsBFevXXM404zbNU/JEjy4djXWpCT2t2lP4vXrxF+4wKW160iKjsaakEBk6EoCH6pNcIf2+Pj7U3vtah7a8j15gotR5fNPuffJ5hm+jitcszPTnVw9vf1G5oJ8xhJXz3X19PaLq3u5GoVsV0G4O7myQAugtf6eO4Y6UEq1VEp11Fqf1VrP01p3At4Eet4tSynlB7wAdLDdcCz1BmQv2f79GXjU7rUft41lux34xxmstf6IlJuQTc5kfirfkDLMwfPAt3dzdoQG9R7hwKHDRJw8BcDS0JU0a9LIdJlG5l67fp1Tp89Qq0Z1p7NS8fTtKq7u5dqofj3OnDvH4V9/A+DnX/ZhsUCpEsVN5Sr7ypjcS5ev8Pa77xF19S8A1qzfSKUK5bmnkHPj0Fa5T/HzL/u5bBtndfP2cEqVKE7hQoUczjTbdvUuUIBaq0K5tHYdR//zGslxcQBcXLOWoNbP4pU3LwBFnnqS6/sP8Od7I9lTryF7mz7B3qZPcOtCJEf7vMGVjZvu9jIucc3uTHdy9fT2G5krn7HE1ZNdPb394uperkYh29UzsXiZ/2EWLLlhkGOllFVrbVFKhQDbtNYhtukBwCFShg5QQDlgDtBUax1huxHXp8BVrfVwu7xtwCit9Tbb87bAOK31/XbL9ABGABWAiqQMTfBy6ji0SqnypPR4naq1/irV0W79qsAvQB2gUgb582ztmq+UOgLcIKVQ62Pf3gy5eS3Dnb09fBdTps8gITGRMqVKMmnsKKdvEGNEpkO5mbjD8MEjvzJg2Eg2rcrk6BGqWahZAAAgAElEQVSZuMOwQ645lCmuudw1KTFTuT//so8Pps0gNjYWPz8/hg7sz0M1a/z7Ct4+rnfNBLl6XxmVa814qIIl36xgyfIVeHt7E1S0CCOGDKJ0yRL/vkImj6vFy1ew6JuV+Pr6EBgQwIhBb1GpQvl/X8En45uImWW7bg2pQpk336D8u+8QbfvjRir723akVM8eBLVuhcXbmxsHD6EHDibJdsO0VOru3c2Rnr24ceAgAI9FZG54/Fx9vOaizFzvKp+xxFVccyRTXMXVyFyAISNGU6liBXp26+qSvFy9XfMHmuduUybiZttHTV90zL9ilyn2Xa4u0NrmPQFsBMrZirLdSbmpV+qnvo3AIK31Lbt1tnF7gfY7YKPWerrdMn6kDDHwstZ6g1KqCilDF1QE/IC/gNnAHK110p0FWlvGLFKGW7h1t3ygE38XaMcCTbTWDdNr713JRIE2V5OJLw9ZJpNfHgTBFGSykJZlMlGgFUxCJgq0Wcao4yoTBVqzsDWkisszM1ugFQRTIJ+xBEEQBE9HCrTpIgXazJMrCrRCJpECresz5cuD4E5IgVaQAq0hSIFW8HjkM5YgCILg6UiBNl1utmtg+jpU/tCdpth3JhptQRAEQRAEQRAEQRAEQRAEwbOQAq0gCIIgCIIgCIIgCIIgCEIOIb9LFTwH+amc4OnIUASCEbcpdaOhCIzCiOEI1pe5P+OFHOCpU78akit4OPIZSxAEQRCEdLB4mWL0ALdAetAKgiAIgiAIgiAIgiAIgiDkEFKgFQRBEARBEARBEARBEARByCHk966CIAiCIAiCIAiCIAiCILgWiwxxkFmkQCvcxrbwnUyZ/hnx8fGoShWZMHI4BQsWNF2muIqruIqrp7dfXD3HtUT7NpR7vQ9YISk2lqNDh1PhzTfIX65c2jL5ypYm6r+7+aVrDwo3qM99o97D4uNDclwcR999j2v79meLa3bnukumuIqruIqrp7dfXMVVXAXh7lisVmtOOwjZxc1rd93ZUVFXadmuE1/Pm0VI2TJMnjadmJibjBo62OGXNCJTXMVVXMXV09svrrnfNfUmYQUqVuDhsFD+2/RJbkVepOjjTan64SS21ayTtmxgrRrUmjuL3S2f49alSzx28H/s7fAC1w8dpugTj3PfmJGE120IZHyTsNy+XXMiU1zFVVzF1dPbL67i6hGu+QOlq2g6xHZsZPqiY75lO0yx72QM2n9BKWW1/RuilIpXSu1XSu1TSh1RSm1SSpXKYP1RSqlRds9bKqV2KaUOKKUOK6XGKqW8bPPmK6WSlVIl78hYpZSKsP2/h1IqyuaxXymllVJfKqVc1gt65+49VK9ahZCyZQDo3L4ta9ZvwJkivhGZ4iqu4iqunt5+cfUc1+RbtzjcfyC3Ii8CcG3/AfIEFcXi6wuAxdeXBz6dxq/DRhB37hzWhAS2Vq/N9UOHAchftiwJUVezxTW7c90lU1zFVVzF1dPbL67iKq4ejJfF/A+TIAXazHFOa11Ta11La10VOAhMzuzKSqkWwKfAS1rrGkAdoAYw2m6xs0Abu3UCgNp3RK22edQEqtgyejrSoPS4cCGS4GJBac+Dg4KIjo4hJibGVJniKq7iKq6e3n5x9RzX2NNnuLTph7Tn940dxcUN32NNSACgdNfOxF2IJHLdhrRlrImJ+BUtwmOH/sd9o4ZzfPpn2eKa3bnukimu4iqu4urp7RdXcRVXQcgYKdA6xlagWhaWHwaM11r/DqC1jgVeA7bbLbMCaGf3/Dngu38L1FonAeFZ9LgrydZkLOkM4Ozl7W2qTKNyxVVcxdV9XD29/Ubliqt5Xb3z56PmnJkUKBfCof4D06aH9H6VY1M//sfy8Zcus7X6g/z4VCsemD6V/BXKZ5trduW6S6ZRueIqruLqPq6e3n6jcsVVXN3JVRAyQgq0WUQp5UtKIfXHLKxWC/jFfoLW+ozWerPdpP1AkFKqmO15B2D5XTzuBZ7IosddKR4czMVLl9OeR168RGBAAPnz5TNVpriKq7iKq6e3X1w9yzVvyZLUXbcaa3Iye55rT+L16wAEVK+GxduHqF1/fxTw8fen2NMt0p5fP3iI60eO4n//fdnimp257pIpruIqruLq6e0XV3EVV0HIGCnQZo4SqWO/kjK8gQUYkoX1k4G4TCy3AmijlLoHCAAi7pjfyuZxANgGfAt8nQWPu9Kg3iMcOHSYiJOnAFgaupJmTRqZLlNcxVVcxdXT2y+unuPqXbAAj4SFErl2PQde7UNy3N8fJwrXr8uVnbtuW96anET1T6ZS6OGUm4gVVJUpWLEi1/63z3DX7M51l0xxFVdxFVdPb7+4iqu4ei4Wi8X0D7NgkUGO00cpZdVaW5RSIcA2rXVIFtcfBaC1HqWU2gLM11ovtJtfGRiute6mlJpPSsF1HzAVWAwEklKA3aa1DlFK9QCaaK17ONyom9cy3Nnbw3cxZfoMEhITKVOqJJPGjqJQYKDDL2lUpriKq7iKq6e3X1xzt+v6MvcDUL7fG1QeOpgbR3+7bf5PbTpQacggbkVGcmzqtNvmFa5fFzV6BF4+viTH30KPe5+o8JRC7lOnfnW5a2Yxw3bNqUxxFVdxFVdPb7+4imuud80faJ5Kn4mIe6GJ6YuOeZdsM8W+kwLtv+DiAm0LYAbQQmv9h1KqILAU2K+1Hp5aoNVaz1dKHQFukDKMgg/ZXKAVBEEQBCHnSS3QuprMFGgFQRAEQRCELCIF2nSRAm3mkSEOsgGt9QZSbhS2zDY8wU/A/4AR6Sy+EkjQWp/JRkVBEARBEARBEARBEARBcB1eFvM/TIL0oPUkpAetIAiCILgF0oNWEARBEATBjZAetOkS1/Ux09eh8i7aaop955PTAu6MUmoxUDWdWau11un1jhUEQRAEQcgQowqpo+8JcXnmyKsRLs8UBEEQBEEQBE9CCrROoLXuktMOgiAIgiAIgiAIgiAIgmA6LKbonOoWyBi0giAIgiAIgiAIgiAIgiAIOYQUaAVBEARBEARBEARBEARBEHIIGeJAuI1t4TuZMv0z4uPjUZUqMmHkcAoWLGi6THEVV3EVV09vv7iKqzO5dfq8ykO9XgIrRB0/wZo+/bh56TIAAaVK0nPH93xRpyGxV6IAqNyyBc/N/oxrp8+kZcxr+jTx0dGGu+aGTHEVV3EVV09vv7iKq7h6JhYZ4iDTSA9aIY2oqKu8O3Is0ydPZOOqUEqXKsmHn8wwXaa4iqu4iqunt19cxdWZ3OK1alC//xvMbdyCz2vXJ+rPYzQdNQyAB7p0pMcPawkoWeK2dUrXfZj/fvQpMx9ulPbISnHWXbar2faVuIqruOYOV09vv7iKq7gKQsZIgfYOlFJW278hSql4pdR+pdQ+pdQRpdQmpVSpTGQ8qZTao5T61bbeR0qp/Hbz2yml/qeUOqCUOqSUGmQ3b5tS6k/b66bO72Cb10MpFWWbl/rY6Kq279y9h+pVqxBStgwAndu3Zc36DVitVlNliqu4iqu4enr7xVVcnck9v+8A06s+yK3r1/HOkwf/EiW4eSWKgsWDua9VSxY90/Yf65Sq+zDlHmvIf34Op8cP6yjToH62uOaGTHEVV3EVV09vv7iKq7gKQsZIgfbunNNa19Ra19JaVwUOApPvtoJSqhkwE+ijtb4fqAX4AmFKKYtSqiQwBXhCa10DqAd0Ukq1sot5xfa6NYAXgIVKqUDbvNW2eamPJ13V2AsXIgkuFpT2PDgoiOjoGGJiYkyVKa7iKq7i6untF1dxdTY3OTER1epp3j5+hLIN6rF/4WKiz19gecduRP1x7B/Lx0ZFsffLecys05Af3htDx+Vf4X9HL1ujXN09U1zFVVzF1dPbL67iKq4ejJfF/A+TIAXarLEVqJbBMu8Bo7TWvwBoreOBt4AqQAOgCCkF2/y2+dFAd+BoemFa60NANFDRBf53JdmanO74IF7e3qbKNCpXXMVVXN3H1dPbb1SuuHqWq169jsklK7J93CS6frcC7jJG2PKO3fj129UAnP7vbk7v/okKzZpkm6s7ZxqVK67iKq7u4+rp7TcqV1zF1Z1cBSEjpECbSZRSvkA74McMFq0D/GQ/QWudYFuvjtb6ABAGHFdK/aSUmgR4a63//JfXTe0hq23/trpjiIPHHGzSPygeHMxF2w1CACIvXiIwIID8+fKZKlNcxVVcxdXT2y+u4upM7j0VylG6ft205/vmLyKwTGny3VMo3eXzBAbQ4J23b5tmsVhISkg03DU3ZIqruIqruHp6+8VVXMVVEDJGCrR3p0RqIZSU4Q0swJAM1rECPulM97PNQ2vdBwgBPgfKAruVUm3slp1te93DwDCgg62nLfxziIOtjjbuThrUe4QDhw4TcfIUAEtDV9KsSSPTZYqruIqruHp6+8VVXJ3J9Q8Opt1Xs8l3b2EAqnduz8UjvxIbdTXd5eNvRFOnd0/uf+5ZAIJrVKfkQ7X58/vNhrvmhkxxFVdxFVdPb7+4iqu4ei4Wi8X0D7NgkUGOb0cpZdVaW5RSIcA2rXVIFtf/AViutZ5pN80POA50BgKAglrrZXbzXwWe01q3VEptI2WIhG3pZPcAmmite2SxWSncvJbhzt4evosp02eQkJhImVIlmTR2FIUCAzNaLdszxVVcxVVcPb394iqujuSOvicEgId6vUyd3q+QnJjIjfMXWNdvIH9FnEpbbuStq3xQogKxV6IAKF67Jk99NIk8/v4kJyaycdBQIrbvTFn2aoQhrrkpU1zFVVzF1dPbL67imutd8weap9JnIuJfam76oqPfvE2m2HdSoL0DFxRoGwBfA6211r/YhkaYTsoYss2BJsAcoKnWOkIpZQE+Ba5qrYfndIFWEARBEITcS2qB1pVktkArCIIgCIKQa5ECbbpIgTbzyBAHLkZrvRPoBkxTSh0BjgBxpBRsrbYhCUYD3ymlNPAbkASMzSlnQRAEQRAEQRAEQRAEQRByBulB60lID1pBEARB8GikB60gCIIgCIIBSA/adInv+YTp61B+c743xb5L72ZWQgYopRYDVdOZtVprPSK7fQRBEARBEARBEARBEARBcE+kQOsAWusuOe0gCIIgCIKQVYzo7TqsUIjLMwHG/xVhSK4gCIIgCIIgmA0p0AqCIAiCIAiCIAiCIAiC4Fosphg9wC2Qm4QJgiAIgiAIgiAIgiAIgiDkEFKgFQRBEARBEARBEARBEARByCFkiAPhNraF72TK9M+Ij49HVarIhJHDKViwoOkyxVVcxVVcPb394iquZnSt2+dVHv7PS2CFqOMn+LZ3P2IuXQYgsFRJeod/z/SHGnLzShQA5Ro34OkPxuHl48PNK1GsHTiUCwcPZ4trdmaKq7iKq7h6evvFVVzF1TOxeMkQB5nFYrVac9rBtCilmgCjtNZN0plXDTgEtNNar7CbXgaYAZQlpYfyUeANrfVFpdQeIA9QGCgInLKt9iIwAGgKRNm9zFqt9TCllBX4Xmv9pN3rFAHOA+O11qMy1aCb1+66s6OirtKyXSe+njeLkLJlmDxtOjExNxk1dHCm4rMrU1zFVVzF1dPbL67iaibX1JuElahVgxeWLWT6Qw25df06LSaOIY+/P2Gvv0XNrh1p9t67FC5XlvHFK3DzShR5AgIY9McBlnTqzvGtOyiiKtF1xWKm125AUnx8hjcJM0v7cypXXMVVXN3H1dPbL67i6hGu+QOlEpkOCb1amL7o6PvlBlPsOxniwHFeBr4B/nPH9JnAEq31A1rrasA+4AsArfUjWuuawAhgtda6pu1xyLbuCLtpNbXWw+xyKyulCts9bwtcdWWDdu7eQ/WqVQgpWwaAzu3bsmb9Bpwp4huRKa7iKq7i6untF1dxNaPruX0HmFrlQW5dv45PnjwElCzBzago/IsHU6VVS+Y/0/a25YtULE/ctesc37oDgMv6D25dv0GZunUMd83OTHEVV3EVV09vv7iKq7gKQsZIgdYBlFK+QBdgOFBbKVXBbnYwkN/u+ae2h7OEAa3tnrcHvnVBbhoXLkQSXCwo7XlwUBDR0THExMSYKlNcxVVcxdXT2y+u4mpW1+TERO5v9TTvnDhCuQb1+GXBYm6cv8CSDt248sex25a9/Mcx/Arkp+LjjwFQ8sFaBFW5D//iwdniml2Z4iqu4iqunt5+cRVXcfVgLBbzP0yCFGgdoyVwUmv9O7AK6GU3713gA6XUGaXUAtuy2zOZO0Yptd/u4W83bznQDkApVcw27bxTrbiDZGsylnQOTi9vb1NlGpUrruIqru7j6untNypXXMXVFZm/rl7HhBIV+WHsJHp8tyLdPIBbN26wqF1XGg9+mzf2hlOrayeObw0nKT4+21yzI9OoXHEVV3F1H1dPb79RueIqru7kKggZIQVax3gJ+Nr2/2XAS0opPwCt9QagJPAKcAn4AFiRXkg63DnEwQ27eT8CSikVSErv2VAXtOM2igcHc9F2Iw+AyIuXCAwIIH++fKbKFFdxFVdx9fT2i6u4mtG1cIVylK1fN+35/+YvolDZ0uS9p1C6y1ssFuJjYpjT/Fk+fagh3701mCKVK3Dl2AnDXbMzU1zFVVzF1dPbL67iKq6CkDFSoM0iSqkg4ClggFIqApgN3AO0UUoVVkp9pLWO01pv0FoPBKoDTyilijrzulprK7AGaEXK+LOZLfpmmgb1HuHAocNEnEy5d9nS0JU0a9LIdJniKq7iKq6e3n5xFVczuvoHB9Nx0Wzy35syZH6NF9oTeeRXYqPSHzLfarXSPWwZJWvXBKB6++dJjLvFhYOHDXfNzkxxFVdxFVdPb7+4iqu4ejBeFvM/TIJFBjn+d5RSTYBRWusmdtMGAI9rrZ+ymzYKaAI0A34HRmutF9rm1QOWABW11km2aT2AJlrrHnYZ84FtWuv56XhYtdYWW9ZHwF9a6xa210VrPSpTDbp5LcOdvT18F1OmzyAhMZEypUoyaewoCgUGZio+OzPFVVzFVVw9vf3iKq5mcR1WKCTt/w/3epm6fV4hOTGR6+cusKbfQK5GnEqbPz7+KuOLV+DmlSgAQhrWp+WU9/H28+XG+UhWvdafqydOpiz7V4TLXTNDbt5X4iqu4pq7MsVVXMXVRK75A81T6TMRCX2eNn3R0ffzdabYd1KgvQu2Au0PQKzd5PPA21rrNXbLFQVOArUBKzAVqAbcBM4B/bTWh+2W74FjBVoLEAGM0VrPMaJAKwiCIAiCkBXsC7SuJDMFWkEQBEEQBFMgBdp0kQJt5pECrSchBVpBEARBEFyMFGgFQRAEQfB4pECbLlKgzTw+OS0gCIIgCIIgCIIgCIIgCELuwmIxRe3TLZCbhAmCIAiCIAiCIAiCIAiCIOQQ0oPWkzBiOIvEW67PBEg2wNWIv9z45XV9Jhizr4z6y5U7DZNixDa4ed31mQD5A1yfmWDQ+eqbx5hcI0hOcn2ml7frM43CgPZbb1xxeSaAJTDI9aFJCa7PBIg34NzKV9D1mQZh1FAEyQd3uDzTq9qjLs8EsMZGuzzTUsD5G5xkG+50bU2Md32mj5/rMw3CGvOXIbmWAoUMyTUC6/XLLs+0BBRxeaYAJMS5PtPXoO9vRmDU9ywDvhNZY665PBPAkiefAaHST1EwJ1KgFQRBEARBEARBEARBEATBtXjJEAeZRf50IAiCIAiCIAiCIAiCIAiCkENIgVYQBEEQBEEQBEEQBEEQBCGHkCEOhNsIW7ueOQsXYbFYyJc3L8PeGUD1Kvc7lGW1WhkyegKVK5anZ9fOadPPR0bS4eU+hC2eS+FCWRuvymq1MmTs+1SuUJ6eXTrdNu+NwcMJKlqEEQP7O+Y6ZkJKbtfO3IiOZti4SRw/eYrk5GSea9mCXt26ZDk3lW3hO5ky/TPi4+NRlSoyYeRwChZ0bnxBV+6rO7FarQwZMZrKlSrSs1tXp/OMcjUq1xXtD9u4mTlLlmPBQr68eRjW/3WqVK7IxOkzCd/zM0lJSbzcuT2dn3/WYU9XHld3nq9JSUlMnDaD8B/3pLh26UTnts857Jr2Gi48row4r+zZvHU7g0aMYV/4D05nGeVqRO7EqZ+wYfMWAgNTxkEuV7YMH08cl+WcRSvXsHT1eixA6ZLFGTuwL4UC/Jn02RzCf/ol5bjq+DydWj/tlK+rj6tV361n3pKlac9vRMcQGXmR7etWUeTewo75jZtE5Qrl6PlCR94cOoqTZ8+mzT9z7gJ1aj3AFx+Md8jXiGPA7Mfr76fPM27Rt0TfjMPLy4vRPdpRsmhhRi9YwW+nzpEvjx9tGtaha/MGTvk6ew1YvXEzc5Z8g8UCefPmZVj/16hULoQxU6Zz6FeN1WrlgSr3MWJAX/LmcWwsbyOvg64+t1Jx5bUVjNkGXy0LZdHyFeTNk4cK5coy4p0BFAp0fmx4M5+vKcdr6G3Ha0ipkgybOIUTJ0+TbLXyXIvmvNq1Y467GpW7+vstzFm6AovFQt48eRj25n+oUqkCYz/+nJ8PHAKgUd06vNOnp8N3JTfivDLzcZUertoGRnzXtMedtquZvxNlx3vhpq3b+eTLuXhZLAQGBjBu2GDKlCrpUJY9+s9jjPtgKjeio/Hy9mbM0Heodv99Tud6HEbdCycX4vY9aJVSIUopq1Jq5h3Ta9qm91BKbVNKNbFNtyil3lZK7bc9flFKdbJbL0IpddRufoRSKlQpVcBumf5KqV+VUodty7xmN8+ilBqtlDqolDqglPpJKdXCbr6vUmqcUuoP2zI/KaU62M0fpZS6YPf6+5VS85RSL9k9j7dznOGqbXk84iSTp01n9qfTCFu6iD6vvETfgYMdyjp2IoLur/Vn45btt01ftXYDXXr15eKlrA/Of+xEBN3feOsfmQCzvlrC3gMHHXd9/XbXaTPnUCyoKN99vYDQ+V+ydGUY+w4ddig/Kuoq744cy/TJE9m4KpTSpUry4SfO7TZX7qs7OXb8BN3/8xobN29xSZ5RrkbluqL9x0+eZvKML5k95X3CFsykT/cu9B02iqVh3xFx+gzffTWb0NkzWLB8JQeP/ubQa7jyuPr7fN2WNm3pt6uJOHXadg7MYsHSbzh45KhD+eD648qI88qeiFOnmfTxdJfcnMEoV6Ny9x08xNT3xxD29ULCvl7oUHH2sP6Tucu+5etPJ7Nm/meULVmCaXMXsWzNBiLOnGXNvBl8M/MjFoSu5uCv2mFXVx9XAM898xRhSxYQtmQBoQvnUPTewrz3ztsOFWePRZyke98BbNz6982uPpkwirAFswhbMIuxgwcQULAAIwf0c8jViGPA7Mdr7K14ek7+kp5PP8bKsW/Tp/XjDJq5mIlLwsifNw/fvT+IpSP6suPgb2zd7/g1y9lrwPFTp5n82SxmTZnAqvkz6dP9Bd4cOpovFiwhKSmJsAUzCVswk7hbt/jyq68deg0jr4NGnFvg2msrGLMNdu/9hVkLF7Pgs2mELZlPo0frMWLCB6Z0dVVmyvE6m1lTxrNq/hdpx+u02fMJLlqUNV/N4ptZ0/l61XfsO+zYeWX2a8vxU2eY/PkcZk0ey6o5n9KnWyfefG88Yd9v4cTpM6ye9xmr5s7g5/2H2Lhtp0OuRpxXZj6u0sNV28CI75r2uNN2NfV3omx4L4yLu8WgEWP59IPxhC2ZT9OGjzLuw48ddk4lNi6Onq/355VuXVi1ZAGv9ezBwOGjnM4VhLvh9gVaG1eAFkop+9u9dgQupbPseKA50FhrXRNoBYxXSj1ut8zTWuuatvmVgTJAN0gpoNrWaaK1rmbL6qKUes+2bgfgQaC21roG0BX4SimVejvqWUBF2/wHgE7AGKXUi3av/0Xq69seL2mt59k5nbNzfD3rmyt9/Px8GffeUIKKptyFtFqV+7l8+QrxCVm/6/Xi0G9p3/oZWjRrkjYt8tJlNm8PZ84nUxzyW7xiFe1btaRF0ya3Td/zv32E7/6JTs+3diw3Hddhb7/J4DdT6u6XLl8hPj4e/wKO/XVz5+49VK9ahZCyZQDo3L4ta9ZvwOrElxNX7qs7Wbw8lPbPt6ZF82ZOZ4FxrkbluqL9fn6+jBvyNkFF7k1xu78yl69cZcOWHbRp+SQ+Pt4EBvjT8vEmrN642aHXcOVxld45sHnbDto88zQ+Pj4prs2bsXr99w65guuPKyPOq1RiY+MYNHwUQ952rGh2J0a5GpEbHx/PUf07sxcs5tkOXeg76F3Onb+Q5ZxqqiIbF3+Jf8EC3LoVT+TlKxQK8Gdz+I+0eap5yjngX5CnmzZk9aZtDvu6+ri6k1nzF1H4nnvo5GDv8b/ftxr/Y158QgJDxk1iaP/XKV4sKJ21M8aIY8Dsx+uuw79TJuheGtdI6RnUtFZVPnrtRY5EnKF1/dp4e3nh5+ND4xr38/3Pjv3h1hXXAD9fX8YOtnsfuK8yl6Ou8lDN6vTu3gUvLy+8vb2pUrkiZy9cdOg1jLwOGnFuufraCsZsgyO//kb9Og8RbDsvn3isMVvCdzn9+cLM52vK8fqW3fFaictRV3nntVd55/VeAFy6EkVCQgL+BQrcLcpwV6Ny/Xx9GftOP4Jsf4yrplK2QXxCArFxccQnJBAfn0BCYiJ+fn4OuRpxXpn5uEoPV20DI75r2uNO29XU34my4b0wKTkJq9XKjehoAGJuxpLHwXPUnl2791C6VEkaN6gPQLPGDR3qtCAIWSG3DHEQDewHGgFbbdOeAG6rfCilCgL9gQe01tcAtNZnbD1ob/5LdiEgEIhSSuUHBgFVtdaRtvUvKaVeBfYopaYAwYA3kAdI1Fr/rpRqByQopcoBbYFgrXWMbf3jSqm3genAVy7YFg5TqkQJSpUoAaT8nOH9KdNo2rghfr6+Wc4aMegtAHbt+TltWrGiRfjUwZ9wAmlDF+zaszdtWuSly4z/aDqzP57Msm9XO5abjqvFYsHHx4eBI8eycct2mjduSLmypR3Kv3AhMu1DPkBwUBDR0VaoQyEAACAASURBVDHExMQ4/JMWV+6rOxkxZBAAu37c43QWGOdqVK4r2l+qeDCligf/7fbJFzRtUI/fj0dQPKho2nLBRYui/zzh0Gu48rj6+xz4KW3a+ciLtxWNgoOKov885pAruP64MuK8SmXEhEl0bPscqlJFZzUB41yNyI28dJm6dR6k/2u9qFShPHO+WsxrA97h28ULsvxzTl8fHzaH/8jwydPx8/PhzZe7sGnHjwTbvkAABBctwu/HIxxyBdcfV/ZE/fUX8xYvZeVXcxzOGGHrGbvrp73/mBe6Zh1BRe6leeOGDucbcQyY/XiNuHCJIoH+DJuzHH3qHP758zGwY0seqFCWsP/+Qq1K5YhPTGTT3oP4eHtnHJgOrrgG3Pk+MHH6TB5rUI8GDz+UtszZC5EsWL6SMe+85dBrGHodNODccvW1FYzZBjWqVeWrZaGcPX+BksWDWblmHQkJCfx17RpBRYpkHJCNrq7KTP94rZtWiBw0ZiIbt4XzeMNHKVemVI66GpVbqngxShUvBti2wYxZPPboI7Rv+SSbw3+kcdtuJCYl8WidWjR99BGHXI04r8x8XKWHq7aBEd817XGn7epO34mMeC8skD8/o98dSKeefSgUGEBycjJfz/7cYedUTpw8TdF7CzN0zAR+++NPAvwLMuhNl/WN8yxkiINMk1t60AIsB9oBKKXqAAeB+DuWuQ9I0Fr/aT9Ra/2z1vqI3aR1tuEHIoF1wKe2/KpAjNY64o71jwK3bPkLgQDgklJqo1JqcMoi+irwEPBranHWjh1AeaVU6u8ne98xxIHK8tZwgpuxsfQbPJRTp08zbsSw7HzpLJGQmMiAEWN4t/8baX+VczUfjn6P3RtXc+36dWbMWeBQRrI1Od3ChpeDXxztcZd9Bca5mnkb3IyNpd97Yzl15izjhgzAesexYMWKl7djl2EjjysAa3Lybe+lVqx4eZnnLcOo9i9evgIfb2/atXZ8bOA7McrViNzSJUsw65OpVK5YAYvFQs8Xu3DqzFnOnDvvUN7jDeuxe/US3ujxAq8MGvEPZ7MdV/YsXxlGs8YNKO2CMczSY8GyFfTp4dz4g0YcA2Y/XhOTkthx8Dc6NHmE0NH96dr8UXpPncNb7Z7CArQZMZU3ps2nftXK+Ppk3dnV14CbsbH0T30fGPx22vTDv/1O19feokvb1jz2aF2Hso1+H3AlRlxbwZht8FCtGrz+6su8MWgobbqljDVaKDAAXx/nih3ucL6mHK/jOHXm3G3H6+QRQ/jxu1Cu3bjBjPmLTeFqVO7N2Dj6j3yfU2fPMW5QP2bMX0LhwAB2rlrM9tCFXLsezdxlK51ydiXucFy5I+64Xc3+ncio90L95zFmzJ7PuuWL2Lk+jN4vdaPv4GFO90xOTExk+64f6dimNSu/mkvXDu3o1W8A8fF3lpgEwXWY81uRY6wGnlJKeZEyvMGydJZJJqWQmhFP24YfeA0oCnyjtbYCVv6917EvYNVaX9VaPwo8AmwEWgBHlFLl77J+6ie+1KvInUMcOD5AXxY5d/4CnXq8greXFwu//IwAf//seuksc/jX3zh99hwTp82g9Ys9WfrtatZt3sKw8c6PExa++ycibWMXFcifn5ZPPM5R/btDWcWDg28bByny4iUCAwLIny+fU47utK+McjXzNjh3IZJOvfuluH06hQD/ghQvFsTFy1fSlrl4+cptvQmzglHH1d/5xbh4yc710hWC7Xr/5jRGtf/b79Zy6OivtO7cjV5vvk3crVu07tyNyEvpjZiTs65G5P72x5+sWrv+tmlWa0pv2Kxw8sw5/nfw7797tn2qOeciL1GsyL1cvGJ/DkQ5fA4YzbpNW2jzbEtDso/qP0hMSuLhWjWcyjHiGDD78RpUKIDyxYOoUaEsAM1qVyMpOZnj5y8ysOMzrJkwiHmD/4MVKBOU9WPLldeAcxcu0rl3f7y9vVkw/UMC/FN6SK3dvJWebw3h7d6v0LvbC1nOTcXo9wFXYsS1FYzZBtExN3m4dk2+XTSXlQvn8Litl7uzNwkz+/macry+hbe3FwumTybAvyDhe/YSafvcUiB/Plo+/hhH9R857mpU7rnIi3R+fUDKNvh4IgH+BdkU/l/aPP0Efr6++BcswHMtmrFn3wGnnF2J2Y8rd8Xdtqu5vxMZ+16488c91K5RPe2mYF3at+GPYye4eu2aU95BRYtQoVwINapVBeDxJo1ISkrm9NlzTuUKwt3INQVarXU0cABoADTljuENbPwK5FdKlbGfqJTqpJT6x4BYWusVwAZgrm3SUcD3zh6tSqmqpGxLbbsBWQ2t9SGt9VSt9WO2jLbAT0BlpdQ9d7xUfeC4rZdtjhEdE8OLvfrwRNPH+GjiePLmzZuTOhlSq3o1tq8OJeyrOYR9NYdOz7fi6cebMn7YO05nr9+8hRmz52G1WomPj2f95i3Ufai2Q1kN6j3CgUOHiTh5CoCloStp1qSRU37utK+McjXzNoiOucmLfQfwROMGfDRmeNodSZs1qM+KtRtITEzi+o1o1m7exuONHnXoNYw4ruxp1qgBK9asJTExkes3brB20w883sTxn2K7GqPaH7pwLt8tX0zY1wv58pOp5M2Th7CvF1KsqOPFaaNcjcj1slgYP/mjtA+fS75ZiapU4baf5GWGS1FRvD3mA67+lfLheM3mbVQqV4bmjeqzYt2mtHNg3ZYdNGvgWI8JI7l2/TqnTp+hVo3qhuT/tP8AdR+s5fBdwFMx4hgw+/Ha8IH7OHs5iiMnzgDw82/HsABbfjnC9JUbALh87Qah2/fwTL1aWc531TUg+uZNuvUdQPPGDZg6elja+8CWnT8y/uPPmP3R+zz7RNMs+9lj9PuAKzHi2grGbIOLly7zYu++REen/ODt87kLaPnE47n6fE05XgfSvPGjtx2vG7ZsZ8bcr9I+D2/Ysp26D9bMUVejcqNv3qRbvyE0b1SfqSOHpG2DKpUqsGFrOJDy672tu/ZQs4p57uJu5uPKnXGn7Wrq70TZ8F5Y5T7Fz7/s5/KVKAA2bw+nVIniFC5UyKncRvXrcebcOQ7/mnJD559/2YfFAqVKFHcq1yOxWMz/MAm5ZQzaVJYDE4G9WuvEO0cG0FrHKqU+BT5XSnXWWl9XSoUAE4De/5L5HvCnUqql1nqtUmo8MEcp1UZrfdF2869ZwAda65tKqUBgrFLqBa11tFIqAKgAzNdan1JKLbKt3802vwIwFRjl8q2RRRYv+4Zz5y+waes2Nm3dljZ9/hczuKdQYM6J5QBD+r3OyIlTePaFHgA83rgh3Tq2cyjr3sKFeX/Ue7w5aAgJiYmUKVWSSWNHOeXnTvvKKFczb4PFK1Zx7sJFNm3fxabtu9Kmz/loIqfOnqd1914kJCbSsXVLh3vQGXFc2dO57XOcOnuO1l1eSnF9vhUP1856scMojG6/KzHK1YjcyhUrMPydt+nz1iCSkpIILhbE1PFjspzz0APV6P1iR7r1fxdvb2+CihRmxrjhBBctyumz53nulb4kJCTQ4dmneLimMUVQZzh5+ixFi9yb5Z7DWckvGVzM6RwjjgGzH69FCwUw/c0ejFm4gpu34vHz8eGTN3twX+niDP7ya54dOhmrFfo+/yTVy5fJONAgFq8I41zkRTbv2MnmHX/f8T02Lg4rVt6bODVtWu3qVRkx4M0sv4Y7XQeNwohtUD6kDL26d6X9S71ITk7mwZoPMGLQ2xmvmAOursr8+3jdxeYdf39umTftA8ZOnU6rbik3Cnu80aN0a/98jroalbt45ZqUbRD+I5vDf0ybPm/qBMZ+/DlPvdgLby8v6tauSc/Ojn0nMAIzH1fujDttV3N/JzL+vbBenQfp2bUzL/bui6+vD4EBAXz24ftOuxctci8zPpzI6IkfEhsbi5+fH9Mnv08eW5FZEIzA4oq7MeYktgLrNq11iO0mYBeBZ7XWPyil5gPbgB7AKK31NqWUNzACaA8kAEnARK31clteBNDEfpxZpdQwoAspNxdLVEr1JmX4Ay9ShiX4Qms9w7asDzDelh9HyrAKn2qtv7DN9wbeteUl2ZaZrLVeZps/CkBrPeoubf6HY6aI+cv1OzsxMyNGOECyAcelEX8Z8TPoL5RGnJdG/WXIna4hRmyDm9ddnwmQ37mfUqZLgkHnq68bfVBJTnJ9ppcbjclmQPutN65kvJADWAKz1ms3UyQ5d0fjfyXegHMrn3M3YskNJB/c4fJMr2qO/WohI6yx0S7PtBQw1x9c74o7XVsTDRg/0Mf5O4ZnF9aYvwzJtRRwrrdadmK9fjnjhbKIJcCcw/W4PQlxrs/0NU8P0wwx6nuWAd+JrDHODSnwb1jyGDC0hsWgH5L732uerpgmIrFfa9MXDHymhZli37l9gVbIAlKgdX2mFGilQCsFWinQSoHW5ZkgBVop0EqBVgq0UqA1AinQSoHWrZACrTG5UqB1fSZIgfZfSHzredMXDHw++tYU+y7XjEErCIIgCIIgCIIgCIIgCILgbuS2MWiFu2FE70Gj/gJpQE8Ut+qNZKKBqjPEnVyNwIierkbhTj1djcKdersagQHtN6Snq1F4+xqTm8+gXA/H6wHX35RmfvGKLs8E6HH+T0Ny3QZ3ura6UW9XI3Cnnq5GIb1d3Qh36u1qBG70PcutfvUhCCZFetAKgiAIgiAIgiAIgiAIgiDkENKDVhAEQRAEQRAEQRAEQRAE1+JGPcFzGulBKwiCIAiCIAiCIAiCIAiCkENID1rhNraF72TK9M+Ij49HVarIhJHDKVjQubFbXZlptVoZMm4SlSuUo+cLHUlKSmLM1E/4ed9BABrXe5h33uiNxcG/0hjRfqNyxVVcPd3V09svruIqrs5llu/Qjmpvvg5WSIy9yZ7BQ7kRcZJ6UydTuHo1Em/e5I9FX/Pbl7NvW69g2TI8u30z3z/fniv7DmSLa3Zmiqu4iqv7ZIqruIqre7kKwt2QHrRCGlFRV3l35FimT57IxlWhlC5Vkg8/mWGazGMRJ+nedwAbt+5Imxa2YRMnTp5mzVezCVs4i5/2HWTD1u057mp0rriKq6e7enr7xVVcxdW5zICKFXho7Cg2te3E6oaPcWDyRzz21XwenjCOhJgYVj38KGubtaBU82aUerJ52nreefLQ8MvP8fLN2o2mzNZ+cRVXcXX/THEVV3F1L1ePxWIx/8MkSIHWhSilQpRSVqXUzDum17RN72F7/ppS6qBS6pBSap9SqrfdsnuUUvuVUqeUUlG2/+9XSlW3+3+Ubf5+pdQeV/nv3L2H6lWrEFK2DACd27dlzfoNWK1WU2QuXrGK9q1a0qJp47RpScnJxMbFEZ+QQHx8AgmJCeTxc+zuvEa036hccRVXT3f19PaLq7iKq3OZyfHx/LfvW8RGRgJwZd9+8hULokjtmhxfuhxrcjLJCQmc2biJkNat0tZ7ZMok/lzyNbeuXMk21+zMFFdxFVf3yRRXcRVX93IVhIyQAq3ruQK0UEp5203rCFwCUEoNBzoBzbTW1YEngBeVUkMBtNaPaK1rAiOA1VrrmrbHodT/A6uBEbbnj7hK/MKFSIKLBaU9Dw4KIjo6hpiYGFNkjhjQj2efaHbbtDZPP0mAvz+NWnegQat2lC1VkqYN6ue4q9G54iqunu7q6e0XV3EVV+cyo0+d5sz3m9Ke15kwhtPrN3Lx572U79QBi48PPgUKULb1M+QLLgZApW5d8fLx4Y8Fi7LVNTszxVVcxdV9MsVVXMXVvVwFISOkQOt6ooF9QCO7aU8Am4F8wGDgVa31JQDbv68Cg5VS+bLZ9TaSrcnpjt3q5e2dztI5l2nPp3MXUrhQILu+W8GOVcv46/oN5i5Z7lCWUa7utF3FVVzdxdXT229UrriKq6e5+uTPT5MFcwgoX47/9u3P3mEjwGqlVfgWmi5ZwLmt20mOj6dwjQdQL3fnx7cG5ZhrdmQalSuu4urprp7efqNyxVVc3cnVY8np4QtkiAOPZznQDkApVQc4CMSTUqBN1lpr+4W11kdt8+/PZs/bKB4czMVLl9OeR168RGBAAPnzOV43NiLTnk3bwmn7zFP4+friX7Agzz/1BHt+2W8qV3faruIqru7i6untF1dxFVfnMwuUKsnT36/FmpTEhmeeJ/7adXz9/dk7YjRh9Rrxfet2YLFw/fgJKnbqgK+/Py2/X0er8K3kKx5Mo1lfUPqpJ7PFNbsyxVVcxdV9MsVVXMXVvVwFISOkQGsMq4GnlFJepAxvsMw23QL826Alecnh/dGg3iMcOHSYiJOnAFgaupJmTRplsFb2Z9pTRVVi/ZZtACQkJrJl53+pUdWxOrdRru60XcVVXN3F1dPbL67iKq7OZfoULECLtas4uWYt21/uRVJcHADq5R7UGjoEgLxFi1K5WxeOh67gp3eH8+2DdVnd8DFWN3yM2PMX2PFqb06v32i4a3Zmiqu4iqv7ZIqruIqre7kKQkZYZJBj16GUCgG2aa1DlFLfAh8BHwMPA7OBPcAHQB2t9W9KqQBSes5WBH4G7tVa37Rl9QCaaK17pPM6822vMz9LgjevZbizt4fvYsr0GSQkJlKmVEkmjR1FocDALL2MSzJjo/911pBxk6hUPoSeL3Tk6rVrjJ3yCUd//xNvby/qPVibd/r2xs/X958r5itojGsmMM12FVdxzUWunt5+cRVXcc165vziFQGo/nY/ag1/l7+O/Hrb/B9eeJFHJo7Hv3w5sFg4NHUax5eH/iOn3cH/sbX7y1zZdwCAHuf/dLlrZsjN+0pcxTW3uXp6+8VVXHO9a/5A8/xW3kQkvtPB9EVHnw+Wm2LfSYHWhdxRoO0M9AUOa617pRZVgZLAk0BboBrwKZAArNJaj7LL6kEOFGhNw10KtA6TiQKtIAiCIAi5l9QCravJTIFWEARBEIRcjBRo00UKtJlHhjgwjjVATf4e3gAArfV4Usao3QpMB5KA00ANpVTp7JYUBEEQBEEQBEEQBEEQBCHnkB60JkEpVReI0FpfMOxFpAet6zMFQRAEQXAbpAetIAiCIAiGID1o0yVxcEfT16F8Ji0zxb7zyWkBIQWt9e6cdhAEQRAEQRAEQRAEQRAEIXuRAq0nYU12fabFoFEyfP1cn3kr1vWZefK5PhPca195OglxxuT65nV9ZmK86zMBfAw4X436dYcR28A3j+szjSI5yfWZRlxbwZhfPRh0DlgN+NWHxb+wyzPdDgOOrR7n/nB5JkDSunkuz/R++iWXZxpGwi3XZxp1bfX0z1hJCcbkeqdzg16zEm/AZzejjleLKTp15RxGfB709G0Kxnx/MeL7AEBSousz3el6JXgUbvRpQhAEQRAEQRAEQRAEQRAEIXchPWgFQRAEQRAEQRAEQRAEQXAt0ms900gPWkEQBEEQBEEQBEEQBEEQhBxCetAKt7Fo6Td8HboSi8VC6VIlGffeu9xb2Llx8LaF72TK9M+Ij49HVarIhJHDKVjQ+bEFv1oWyqLlK8ibJw8VypVlxDsDKBQY4FCW1WplyNj3qVyhPD27dLpt3huDhxNUtAgjBvZ32NWIbWDEvjLK1ahjwOyuVquVIaMnULlieXp27Zw2/XxkJB1e7kPY4rkULlTIFK72bNq6nU++nIuXxUJgYADjhg2mTKmSTmUa4Rq2dj1zFi7CYrGQL29ehr0zgOpV7nco6859lZSUxMRpMwj/cQ9JSUm83KUTnds+57CrO50DqWzeup1BI8awL/wHhzOsVitDxk2icoVy9HyhI28OHcXJs2fT5p85d4E6tR7giw/GO5Rv9veX1d//wJyvQ7FYLOTNk4dh/fpQ/b7KLPl2DaHfbSDuVjxVVUXGD34LPz/Hxm4z+3XQiFyj37NdeW1Z9OMhluw+RB4fHyoE3cPwZxsCMGb1Dn47f5l8vr48/+B9dK1X3aF8I68BVquVISNGU7lSRXp26+pcjptdXz39M9aq79Yzb8nStOc3omOIjLzI9nWrKHKv49vBLa4tYyakXFu6diYu7hajJ3/EoaO/YrVaeaBqFUYOeou8eR0bb9aV1xZ73OW4SsVV15ZUZLu6dpsa/f3FiOurK+sCqRh1XAnC3ZAetNmEUipEKWVVSs28Y3pN2/QeSqkHlFJblFIHlFJHlFKzlVIFbMuNUkr9rpTKZ7duE6XUNlc5Hj76G3O/WszSebP47pslhJQpzbTPvnQqMyrqKu+OHMv0yRPZuCqU0qVK8uEnM5x23b33F2YtXMyCz6YRtmQ+jR6tx4gJHziUdexEBN3feIuNW7b/Y96sr5aw98BBp1yN2AZG7CujXI06BszueuxEBN1f6/+P42rV2g106dWXi5cum8bVnri4WwwaMZZPPxhP2JL5NG34KOM+/Nh0rscjTjJ52nRmfzqNsKWL6PPKS/QdONihrL/31ba0aUu/XU3EqdN89/UCQufPYsHSbzh45KhD+e50DqQSceo0kz6e7tTNOY5FnKR73wFs3LojbdonE0YRtmAWYQtmMXbwAAIKFmDkgH4O5Zv9/eX4qdNM/mw2syaPY9Xcz+jTrTNvDh/L99t3smjFauZ+NJHvFs4k7lY885d/65Cr2a+DRuQa/Z7tymvLnuNnmbNjH3NfbsW3fTvQqHIZRq3azqR1u8jv58uafp34uncbwn8/xbbfIrKcb+Q14NjxE3T/z2ts3LzFuRw3vL7KZyx47pmnCFuygLAlCwhdOIei9xbmvXfedqo46xbXltdv/9z2+fyFJCUlsXrxPFYvnsetW7eYuWCRQ56uvLbY407HFbju2pKKbFfXblOjv78YcX11ZV0gFaOOK4/FYjH/wyRIgTZ7uQK0UEp5203rCFyy/X8ZMExrXQOoDiQAY+2WLQtMMEquWpX72LgqFH//gty6dYvIi5coFBjoVObO3XuoXrUKIWXLANC5fVvWrN+A1ck7ch759Tfq13mI4GJBADzxWGO2hO8iPiHrd6ZdvGIV7Vu1pEXTJrdN3/O/fYTv/olOz7d2ytWIbWDEvjLK1ahjwOyui0O/pX3rZ2jRrEnatMhLl9m8PZw5n0xx2NEIV3uSkpOwWq3ciE65K33MzVjyONizz0hXPz9fxr03lKCiRQCoVuV+Ll++4tg1IJ19tXnbDto88zQ+Pj4EBvjTsnkzVq//3iFXdzoHAGJj4xg0fBRD3nascJrK39fWxv+YF5+QwJBxkxja/3WK267jWcXs7y9+vr6MHdyfoCL3AlDtvspcjrpK6HcbealTGwoF+OPl5cXogX1p/WQzh1zNfh00Itfo92xXXluOnL1EvQqlCA5M6R31eNXybP0tgkNnLtGqZmW8vbzw8/GmsSrL94ePZznfqH0FsHh5KO2fb02L5o4dm2k5bnh9lc9YtzNr/iIK33MPnZzo5QxucG1J51itU7MGfV7uhtf/2TvzMBvL/4+/zhxjGcNQlrGP0F0i0mpJSvWtfKVIpUVKoURSSiqEpEQhfpWKRCQUJbQS2ssa3a3WMfZtxjJm5vz+mDPzHdOYOWbOM3Me5/26rnNdc57lPa/7fp55zjmfuc/9RETg9Xo529QjPiEhX57BvLZkxW3nVbCuLRmoX4Pbp05/fnHi+hrMukAGTp1X4tTAGFPWGLPWGBPnfz7JGPOHMWal/3Gjf3ljY8xP/oGWbxhj8pzBQAXawiURWAG0zLLsauBz/8+xQBSAtTYNeAaYmWXb14FbjDEtnBKMjCzG518toeW11/PjLytp365NgfISErZnXiwBYitVIjExiaSkpALlNmpwDt/99DNbt6W/SZrz0SccO3aMffv3n3TWwEf70PY/Vx23bPvOXTz70jhefOZpvBEF+zNxqg+CfazAGVen2h/qrgP7PUzba44/rypXrMArLzxL7Vo18u3ohGtWSkdF8cwTj3Jr1/tpcW07pr0/m0d73R9yrtWrVqXVpemXQp/Px3OjxnDFZZdSPDLypLNyOlbbtu84rnAYW6kiCTt2Zt81INz0NwAwcPjz3NLhBky9ugXLeeQh2l6d84eFWR99QqUKp3PVZZfmOz/UX1+qV4mlVdOLgfRzdMQrr3F580vYmrCd3Xv3c++jT3J9lx688tZUyuTz642hfh10Itfp1+xgXlvOrVGJ7/7eyta9BwH44OffOJaaRuOalZm38neOpaaSdPQYn/36FzsPHjrpfKeOFcDA/v1oe+01Bc9x6fU13N9jZbBn3z4mTZvBgL69C5wV8teWfg//69rS4pKLqF0z/T3b1m0JvD3jfa5pfXm+PIN5bcmK286rYF1bMlC/BrdPnf78AsG/vgazLpCBU+eVcD/GmIuBZcCZWRZfALS01jb2PzK+GjcVeNBaeybgAe7LK18F2sJnJnATgDHmQmA1kOxf9zAwz199fx0431r7XZZ9dwMPAG9lneog2Fx5+WV8/+UienXvSteefUhLS8t3VpovDU8OQ8YjvN4ctg6cC85rRM/77uHBfgNo37krHo+HcjFliSxW8IvmsZQUHhk4hCf6PJg58qkgONUHENxjBc64OtV+N7k6gVOu9s+/GP/GZD6ZOZVlC+bS4+7O9Hr8yQL9d9/Jfj10+DAPPT6ATZs3M2zgkwXOy8CXlnbct118+IjIZ+HHTX8D02bOppjXy03t2hZELU/efm8293cp2Bxpbnl9OXT4CH0GPcumrdsY9lgfUlJS+OanX3j5mQHMmjiOfQcP8vLEyfnKdtN10KncYL9mZxCMa8sFcVXpecUF9J62kI7jZ6XP612qBI9e0xQP0OGV9+k1bQFN69Yg0nvy1xc3vWZlxQ3XVwjv91gZzJwzl9aXtaBGAeehB/ddW7Kydr3l9u4PckfH9lzeolmBsoL9vsWN55UTqF/dRTCvr07WBZz6nBF2RESE/iNw7gN6AvEAxpgooCbpNbrVxphnjDERxphaQKks9bzJQMc8u+pkTERQmAdca4yJIH16g/cyVlhrJ5M+ivYx0qc3mGyMOW7yBjuzXAAAIABJREFUR2vth8BPODDVwcZNm/lpxcrM5x3atSV+WwL7DxzMd2aV2Njj5qrZvmMnMWXLElWqYPXlxKRDXNSkMR9MfYs5U97kSv8orIJOBg6wdv1vbN4az4gx42l3Z1dmfDCPTz7/kiefzd9cNk70gRPHyilXp84BN7k6gVOuy779niaNGmbeFOz2ju35469/2FuA/0I75Rq/LYFbu9yLNyKCKa9PoGyZMgXKy0qV2Mrs2Lk78/mOnbuJrVQxn1nu+Rv44OP5rFm3nnadOtOtd1+OHD1Ku06d2b4zf6PbcmKd/YOU1FQuOq9RgXLc8PoSv30HnR54GG+El7fHPE/ZMtFUrHA6V7VsTnTp0hSPjOT6q69g5a/r8+XqpuugU7nBfs2G4F1bko4mc0Htqsx+sCPv97yJK86O8y8/xiPXNGXeQ7fy1j3X4/P5qHn6yX/F002vWVkJ9eur3mP9j08++5L2bQs+ehjcd23JYP6nX3BP77480rM7PbrcWaAsJ963uPG8CjbqV/fgxPXVqbqAk58zROhhjCnnv29U9sdxd8Sz1t5rrV2aZVEs8CVwD3AJcCnQFagKbMuy3Tagel4eKtAWMtbaRGAV0AK4gv9NbxBpjHnaWnvQWvuBtbYn0Jych0H3Ir24G9SpDnbu2k3fJ55mz959AHy0YBH16pxB+XL5nxemRdOLWbVmLRs2bgJgxqw5tG7VMo+98mbHzl3c2aMXiYnpXwn5v7feps3VV+b4X8mT5byGDVgybxZz33mTue+8ya03Xs91V17Bs08+lq88J/rAiWPllKtT54CbXJ3AKdf6Zxl+/GUlu3bvAeDzJUupXrVKge7W6oRrYlISd3a7n6uvuJyXRjxLyZIlC5SXndYtWzD7o/mkpKRw4OBB5n/2BVe2yt/X8d30NzBrylt8PHMac6dP4fWxoylZogRzp0+hcsX8FU9y4oeVq7jk/PMKfL0O9deXxEOH6Nz7Ma5q2ZzRg5+gZIn0u37/p1ULFn71NUeOHsXn8/HF0m9pcNaZeaTljJuug07lBvs1O5jXlh0HkujyxlwSj6R/Ueq1xb/Q5tx6zPzxV175/EcAdiUeYvZP62nTqN5J57vpNSsroX591XusdPYfOMCmzVs4r1HDoOS57doC8OXS5QwbPYY3x4761/QHJ4tT71vcdl4FG/Wru3Di+upEXcDpzxkiJOkD/JPDo09uO1lr/7bW3mit3WatPQSMA64jvdaa9SuoHiDPoeJ5TlIrHGEmMAL4yVqbYowBKA08ZIxZbq3NuAXjeaTPWXsc1trdxpgHSB99+22wpC5o0pgeXbvQudsDeL1eKlWswPjRBbsD4umnncZzg5+md7/+HEtJoWb1ajw/dHCBXc+Iq0m3u+6g493dSEtL4/zG5zKwX98C5zqBE33gxLFyytWpc8BNrk7glGvTC8+n6x2duLNHLyIjixFTtiwTXnwu5Fynvfc+8dsS+OyrxXz21eLM5ZNfHV/gD9EAnTrcwKat8bS7/W6OpaRwy43Xc1GT8/KV5aa/gcJg4+atVIutXOCcUH99mTZnHvHbd/D50m/4fOk3mcsnvTSC/QcO0uHeXqSmpVL/zLoM6ZnnlFQ54qbroFvO12BeW2pXLM+9Lc/j1ldnk+bz0aRWFZ5qeympaWk8/v4XXD9mBj7gwSsvomH1k79Znlv6NDuhfn3Ve6x0Nm7eSsUKpxNZLDgfF914bXl+7AR8Ph9PZRmR3+TcBgx6LB+vCQ69b3HbeRVs1K/uwonrqxN1Aac/Z4QdQRhEVwi8TPo0BNnZl9tOxpiGwJnW2tn+RR7Svw2/BaiSZdNY/NMi5IYnmHf5FCfGf4e3xdbaOGNMNLADaGut/cIYMxlYDKwHXgBqkD4vrSV9UuHNxpjBANbawVkypwHVrLWtApJI2hv8g+1xaBB2SnLe25wsqanBzyzh0FdSfAWb5yxHnDpW4c6xI87kRjrwn1on/q4AihUPfqZTr01O9EFkieBnOkWaA9fBo4eDnwlQKn83zsoVh/4GfIcTg57pKXNa0DNdhxPnVnFnRsGkLpgc9EzvdXcHPdMxjh0NfqZT19Zwf4+V6tBdyL0uunlOsgPv3Zw6X91R2HAOJ94PhnufgjOfX5z4PACQmhL8TKeuV6XL6eTKgZSn7gj5omOxYVNP6tgZYzYArYCywHzgXCAR+Ah421o73RizFuhurV3uv8fUH9bakbl6nLy6yA/W2g1AnP/nRCAqy7ouWTa97AT7D85h2e1BVBRCCCGEEEIIIYQQQuSBtXa1MeY5YDkQCcy21k73r74dmGiMKQv8AozNK08FWiGEEEIIIYQQQgghRHA5BUetW2vjsvw8AZiQwzargItOJtdF38cRQgghhBBCCCGEEEKIUwuNoA0n3DQ/lhNz2LjpbHfTsQp3nJgr1imcmhvKCZz6T6ub5ot1gghv8DOdmCvWKRz6G9B8sQ7h1DzvDuDEfLGvVa4T9Mzu2/8KeibgrmtruL/HctNcsU7h0FzUwgFOwZF3IYE+vwgRkoT5OxQhhBBCCCGEEEIIIYQoOtw0plAIIYQQQgghhBBCCOEGNBI+YDSCVgghhBBCCCGEEEIIIYoIjaAVx7F46TJGjZtAcnIypl5dhg96iujogs0v6ESmXOUqV7mGe/vlKle5uiezoLnndOtK/XvvBp+PA/9sYMmDD3N0zx6aDh9C9ataE+H1smrsBNa/NRmAsnXOoNX4MZQ8/TSOJSbxVfcH2Pf7n4XiWpiZcpWrm1zDvf1ylatchcgdj8/nK2oHV2GMiQP+AV631nbPsrwxsAK4G/gFeBk4nfQi+LfAQ9baJGNMCWA0cBmQBuwDHrHW/ujPKQ0MBdoAR4D9wCBr7Vf+9YuBwdbaxSctf2h/rgd7z569tLnpVqZPmkhcrZqMHDOOpKRDDB7w+En/Kicz5SpXuco13NsvV7nK1T2Z+c3NuElYhcaNuHrqJGY1u4zkAwe55NlniIyOZveatdS65moW3nIHkWWiueGLhXzV7QF2/ryCG7/6lDUTXuPP92dT46rWXDJsMO9ffGlANwk71ftVrnI9VTLlKle5hphrVIy+y58DqYO7hHzR0Tt4ckgcO01xkD92A9cYY7LeDvsWYKf/5/eAJ621jYCGwDHSi64AfUjv94bW2nOBJ4B5xphIY4wH+BCIBBr4938ImGqMudTpRi377nsanlOfuFo1AejUsQMfLVhIQYr4TmTKVa5ylWu4t1+ucpWrezILmrtr5SpmNL6I5AMH8ZYoQekqVTi6Zw+127bBTp2OLzWV5H37+WvWB9S7pSNRVWIpd2Y9/pw1B4DNn31BZOnSVGh0ruOuhZkpV7m6yTXc2y9XucpViLxRgTZ/JJI+WrZllmVXA5/7f44FogCstWnAM8DMLOuKk16ExVq7nPRRt16gOWCAvtbaY/71K4BngYHONSedhITtxFaulPk8tlIlEhOTSEpKCqlMucpVrnIN9/bLVa5ydU9mMHLTUlKI+++13G5XU6V5U36bOp3S1auRuHVr5jZJ8fFEV6tKdPVqJCUkQJYPkYnx8ZSuVrVQXAsrU65ydZNruLdfrnKVqxB5ozlo889M4CbgK2PMhcBqIGNY9MOkj4qNB74C5lpr5/vXjQHmAzv90xV8AbxtrT1ijLkIWJFRnM3CEmCEo60B0nxpeHK4w16E15vD1kWX6VSuXOUqV/e4hnv7ncqVq1zD3TWU27/h4wVs+HgBZ3W5kzYfzCQtNfW4IiweD2mpqXgiIo5fDng8HnypqYXmWhiZTuXKVa5uyXQqV65ylatzuWFJDv0ockYjaPPPPOBaY0wE6dMbvJexwlo7mfSRso+RPr3BZGPMy/51G4AGwFXA90BnYKUxphzpBd6cxsyXohCOVZXYWHbs3JX5fPuOncSULUtUqVIhlSlXucpVruHefrnKVa7uySxobtkzahPb9OLM53bKNKJr1iApfhtRVWIzl5eOjSVp6zYSN28hqnLl4zKiYmNJ3BrvuGthZspVrm5yDff2y1WuchUib1SgzSfW2kRgFdACuIL/TW8QaYx52lp70Fr7gbW2J+lTF9wHYIwZDlS11v5grR1urb0AiCe9YPsDcL4xJtK/bUX/vLSXAD873aYWTS9m1Zq1bNi4CYAZs+bQulXLPPYq/Ey5ylWucg339stVrnJ1T2ZBc6NiK9N60kRKnn4aAHVvuYm969bzz7yPOevO2/F4vRSPKUudm25kw8efkBS/jQN//0OdDjcCUL315fjS0tjz6zrHXQszU65ydZNruLdfrnKVqxB549EkxyeHMSYOWGytjTPGdAJ6AWuttd2MMZOBlcBTwM3W2i/9+9wC9LLWtjDGvA0cBnpba5ONMbHAUqAtYIFFwG/AI6TfIKw9UAe4zVr7hX9ahMHW2sUnLX9of54He8nS5YwaN55jKSnUrF6N54cOplxMzEn/Kqcz5SpXuco13NsvV7nK1T2Z+cl9rXKdzJ/rd72bc7rdQ1pKCoe2JbDskcdJ3LKVps8+Q7UrWuGNLM66SW+zeux4AMrWOYPLxr1EydNPI/XIUb7u3Zddq1bTfftfjrgWVaZc5eom13Bvv1zlesq7RsXou/w5kDrknpAvOnoHvhUSx04F2pMkW4E2GtgBtPUXTycDi4H1wAtADSCZ9MLrg9bazcaYMsAo0m8qluRfP9RaO8efHwU8B1znX7eX9KkPviG98LsIuBjIOpFYfWvtpjzlAyjQCiGEEEKIoidrgTZYBFqgFUIIIcRJogJtjqhAGzgq0LoA/zy31wHzrbX5P2Aq0AohhBBCuAIVaIUQQggXoQJtjqhAGzjFilpA5I21Ng34uKg9hBBCCCGEEEIIIYQICE9I1D5dgQq0QoQiqSnBz/Tqz10I0lLz3uZkifAGP1MIEfY4Mdp1TnUT9EyA9lusI7lCOILeC4Q3TnzOAnd91ko+EvzM4iWDnylEmBFR1AJCCCGEEEIIIYQQQggRrrjo3zxCCCGEEEIIIYQQQghXEKFxoYGinhJCCCGEEEIIIYQQQogiQiNoxXEsXrqMUeMmkJycjKlXl+GDniI6OjrkMuUK9s+/GPbCaA4mJhLh9TJkwGM0OPuskHR1U7/K1T2uTrV/xOixLPz8S2JiygJQu1ZNXh4xLCRd3dSvcpWrW1zDpf01OnagXq8HwOcj9fBhVvV/in2r19D4heeo0LwpAAmffcHagc8ct1+t2ztRtc21fHtb50JzLexcuco1g8+/WkK/gUNYsfSLAme5qf1ydeazlhv61efz0X/IcM6scwZd7+jEkSNHeWbkS6xZtx6fz8e559RnUL+HKVmyRJG7OpnpZK4QJ0IjaEUme/bs5YlBQxk3cgSLPpxFjerVeHHs+JDLlCscPnKErj37cG/n2/nw3bd5oGsXHn1qcEi6uqlf5eoeV6faD7Bi9RpGPzeEudOnMHf6lAIXZ8P9WMlVrm5yDZf2R9etQ8NnBvJNx058edmV/Pbiy1wy5U1q3tKR6Lp1+Lx5K7649AoqNmtKtXZtAYgsV47Go57n3OFDT/qOzOHSr3I9NVwz2LBpM8+/PA58vgJnuan9cnXms5Yb+vWvfzZwV88+LPpySeay/5s8hdTUVOZNm8S8aZM4evQor709tchdncx0MleI3Cj0Aq0xJs4Y4zPGvJZteWP/8i7GmHONMV8aY1YZY341xrxhjCkdQHY9Y8xcY8yfxpi1xpjZxpjaWdZvMMYMy7bPZGNMlyzr1xljVmZ5TMrjdw42xiT4t13l/70PnmD9SmPMemPMGmNMc//6Ln6HusaYvcaYktny7zLGzPH3W3I2t5XGmJ559UugLPvuexqeU5+4WjUB6NSxAx8tWIivAG9KnMiUKyz/7ntqVK/GZS2aAdD6sksLXERyytVN/SpX97g61f7k5GTW2d954+1ptL35dnr1e4L4bQkFygz3YyVXubrJNVzan3Y0mV8eeoQj23cAsG/lKkpWqoS3eHGKlY7CW6IEESWK4ykeSeqR9Lt9V7/heo4kbP/XiFqnXQs7V65yBTh8+Aj9nhpM/74PFSgnAze1X67OfNZyQ79Om/UBHdv9l2tat8pcdmHjRtx/T2ciIiLwer2cbeoRn5C/98ZuOgecyg1LPJ7Qf4QIRTWCdjdwjTHGm2XZLcBO/8/vAU9aaxsBDYFjwNDcAo0xlYGvgJnW2rrW2gbAB8ByY0zFLJs+bIw5P5eo66y1jbM87g6gPa/6t20EXA50N8Z0zWF9Y2vt2cCbwOisAdbaP4E1QJts2Z392wPEZ3NrbK0N2r9xEhK2E1u5Uubz2EqVSExMIikpKaQy5Qr/bNxMxdNPY8CQ4bS/8x7u7vkQqampBfJ0ytVN/SpX97g61f7tO3dxyYXn0+eBbsx7byqNGp7DA488VqA3Y+F+rOQqVze5hkv7D23eTMJnn2c+bzhsMNsWfso/70zj2L59XPvrCq5bt5qkf/4hYdFnAPwzeQq/jRxNanJyoboWdq5c5QowcPjz3NLhBky9ugXKycBN7ZerM5+13NCvA/s9TNv/XHXcshaXXETtmjUA2LotgbdnvM81rS8vclcnM53MFSI3iqpAmwisAFpmWXY1kPFOMRaIArDWpgHPADPzyLwf+NxaOy1jgbV2KrAM6JFlu+HAZGNM8YI04ERYa3eSXkzOcWSrMSYCqAHsyWH1JOC2LNtWB84EFgbf9N+k+dLw5PDfgwivN4etiy7TqVw3uaakpLBk+bfc0r4dc955iztuvoluDz1Ccj4+NGUl3PtVru5xdar9NapVZeLY0ZxZtw4ej4eud97Opi1b2RK/Ld+Z4X6snMqVq1zdkulUbjAyvVFRXDRpItFn1OaX3n05+/FHObprN/NNQxY0OI/i5cpTt2ePvIMKwbWwcuUq12kzZ1PM6+Um//QewcBN7ZerM5+13NSvObF2veX27g9yR8f2XO4fWXyyuOkcKKx+FSIrRTkH7UzgJgBjzIXAaiDjivcwMM8Y84cx5nXgfGvtd3nkXQj8kMPyr/3rMpgG/AUMPEHOJ9mmEAhkBG121gJZZxDv4c/aCGwkvfh8Tw77zQRaGmNi/M/vAN6x1mb8u65qDlMcNMyHX45UiY1lx85dmc+379hJTNmyRJUqFVKZcoVKFStQp3YcjRqcA8CVrVqSmprG5q3xIefqpn6Vq3tcnWr/b3/8yYfzFxy3zOeDyGL5v6dmuB8rucrVTa7h1P5S1apx2cKP8KWm8vX1HTh24ABV/3sdG6bNwHfsGCkHD7Jxxkwqtmieb8dguRZmrlzl+sHH81mzbj3tOnWmW+++HDl6lHadOrN95868dy5ET6dy5erMZy039Wt25n/6Bff07ssjPbvTo8ud+c5x0zlQGP0aNhT19AWa4iAg5gHX+keU3kL6tAYAWGsnkz6K9jHSpzeYbIx5OY88H5DTJ+ji/nVZ6QHcd4KpDrJPcZDrHLS5uBzO8vxVa21j4GLgIPCdtfZfw7GstUnAR0B7/6I7SB9Vm0FOUxysyYdfjrRoejGr1qxlw8ZNAMyYNYfWrVrmsVfhZ8oVWjZrypb4eNau/w2AH39ZgccD1atWCTlXN/WrXN3j6lT7Izwenh35UuYb8Hffn4OpV+e4rziFiqub+lWucnWLa7i0v1h0aVp+NIf4jz/hx3t7kOafZ3bfqjVUv+F6ADzFilHlmqvZ89PPBfIsqGth58pVrrOmvMXHM6cxd/oUXh87mpIlSjB3+hQqV6yY986F6OlUrlyd+azlpn7NypdLlzNs9BjeHDvqX9MfnCxuOgec7lchcsJT2JMcG2PigMXW2jhjzAfAS8DLwEXAG8ByINZaOzTLPmcCK6y1J7xRmDHmaaC+tbZTtuXvA6uttUONMRuAVtbaDcaY24H+pM/7+qm1dnLW9SfRnsEA1trBWZbdAjxorb00+3pjTDNgEXCutfYf/w3KWllru/jXtwAGAY8DL1lrL8veb4G6/YtD+/M82EuWLmfUuPEcS0mhZvVqPD90MOViYvLardAzT3nX1JQ8M3/8ZQUvjBnP4cOHKV68OAMe7cMFjRudeAdvYCMAT+l+lesp5ZqvzLS85w+b+8lCJk5+h9TUVGIrV+LZpwdQtUrsiXeIyPurTuF+rOQqVze5nsrtn1PdAHBmn16c82R/9q9bf9z6ZTd0pNELwynXsAG+tDR2LFnKmoHP4Dt2LHObmp1uodr1/+XbTv8bRdV+iw26a6CEQr/K1WWuAbwXyGBL/Dba3nw7K5Z9mfuGei/gHtcAPmeBM5+1QqZfk4/kmtd/yHDqnVGbrnd04j8db2f/gQPH/YOiybkNGPRY3+N3Kl6SQAiJc8Cp3KiY0BmKGUKkPtc95O+s5n3itZA4dkVdoO0E9ALWWmu7GWMmAyuBp4CbrbVf+ve5BehlrW2RS+7p/n2f8M89izGmMzACaGSt3Zm9AGuM+RC4FugerAKtMaYK6XPGPmetnXGCAu4MwGut7Zi9QOtfvw74BlhqrX07e78F6vYvAijQihAhwDcOJ0WABVohTmlO4kNZwATwoUwIIUKBjAJtsAmkQCtEyKD3AuGNE5+zwF2ftfIo0OaLAAu0pzQq0OZI6ogeIV+H8vZ/NSSOXVFfRT4C3gSezrJsH9AGeMEY8wbp89JaoNO/d/8f1trdxphLgReNMU+RPn3DWqCF/8ZdOdED+DXbsk+MMVln/z5krc1rFuwexpgbSJ/awAO8Zq2dkcv2TwDr/aNlc+Id0kfQ9s62vKoxZmW2ZV9ba7NvJ4QQQgghhBBCCCGEcAGFPoJWFCEaQeseNIJWCGfQqBkhRBijEbRCoPcC4Y5G0GoErVNoBG2OaARt4LjoKgLGmIeBu3JYFW+tvc7B3zsSyGlG7J+stfc69XuFEEIIIYQQQgghhHAlERFFbeAaNII2nNAIWiGEEEIIEWTm1Tgr6JnXb/4t6JlCCCGEY2gEbY6kvvBAyNehvI9NCIljp1K2EEIIIYQQQgghhBBCFBGumuJACCGEEEIIIYQQQgjhAjwhMTjVFWgErRBCCCGEEEIIIYQQQhQRGkErjmPx0mWMGjeB5ORkTL26DB/0FNHR0SGXKVe5ylWu4d5+ucpVru7JDCfX6h07UOfB+8HnI/XwYdY88TT1HnqQ0rXjMreJqlWT3cu/5Yc7uhBtzqTR6JEUKx0FPh/rhgxn51eLC8W1MDPlKtdwb79c5SpXIXJHNwnLAWNMHPAPcLW19rMsyzcArYDfgXXZdpsIHAIe8j+vD/wJJAPLgZEn2K8tMBRYbK2dnM2jC9DKWtvFGDMJ8Fhru/jXlfDnDrHWzguoYXncJGzPnr20uelWpk+aSFytmowcM46kpEMMHvB4QPGFlSlXucpVruHefrnKVa7uyQwH14ybhJWuW4fmc2ez5IqrObp9B5WuvIJzR73A540uyNy23HmNuGDSGyy7rh1H4uNpNnc2m997n83vzqBswwY0nzebhXXr03bDr0XSB6HUr3I9dVzDvf1ylWtYuOomYTmSOrJnyBcdvf3Gh8Sx0xQHJ+YYMNEYUyaHdfHW2sbZHuOttZMyngPxwHX+5z1z2W9zgD69gUuNMe39z0cDywMuzgbAsu++p+E59YmrVROATh078NGChRSkiO9EplzlKle5hnv75SpXubonM5xc044eZWWfRzi6fQcA+1auomSlingiIwHwREZy3vixrH1yIEfi49OXeb0ULxcDQLHoaNKOHC0U18LMlKtcw739cpWrXMMYjyf0HyGCCrQnJh74DBhV1CIA1tqDwJ3ABGPMPUBT4LFg/o6EhO3EVq6U+Ty2UiUSE5NISkoKqUy5ylWucg339stVrnJ1T2Y4uR7evIUdn32R+bzBsGdIWPgpvmPHAKh1RyeOJCSQMH9B5jZrHnuCun16c9Wan2k25z1W9+uPLzXVcdfCzJSrXMO9/XKVq1yFyBvNQZs7jwBrjDFXZZ3qAKhqjFmZbds7rbVr8sjLvt80a+3IQGWstd8YYyYDrwP1rbWBDzEIgDRfGp4c/nsQ4fWGVKZTuXKVq1zd4xru7XcqV65yDXfXcG9/sHK9UaU475UxlKxWle863pa5/Iwe3VjVt9//MkuU4Pw3X2Plgw+x/dPPKX9BEy6aNoW9v2R/m+2ca2FkOpUrV/e4hnv7ncqVq1zd5CpEXmgEbS5Yaw8A9/HvqQ5ymqogr+JsTvsFXJwFMMZ4gWbALuDqk9k3EKrExrJj567M59t37CSmbFmiSpUKqUy5ylWucg339stVrnJ1T2a4uZaqVo0WCz7Cl5rKN+1uIuXAAQDKNmyAp1gxdi//NnPbMmefhbdUKbZ/+jkAe3/6hYO/Wcqff16huBZWplzlGu7tl6tc5RrGRESE/iNECB2TEMVa+ymhM9XBQGAv0BoYYow5O5jhLZpezKo1a9mwcRMAM2bNoXWrliGXKVe5ylWu4d5+ucpVru7JDCdXb3Rpms2bzbaPP+Hn++4n7ciRzHUVmjVl19Jlx22f9Pc/RJYtQ/kL028iFhVXizLmTPavWeu4a2FmylWu4d5+ucpVrkLkjUeTHP8bY0wcsNhaG+d/XhZYA8QCJuu6XDI2AK2stRtyysy27WT/usnZlnfxZ3QxxrQE3gUaW2t3GWP6AJ2BS6y1yQE17ND+PA/2kqXLGTVuPMdSUqhZvRrPDx1MuZiYgOILM1OucpWrXMO9/XKVq1zdk3mqu86rcRYAdfv04uwBj3Ng3frj1n9z482c9UQ/jmzfwR+jXj5u3ektmlF/8NN4S5TAl5KCHTmahE8Wcv3m3xxxLapMuco13NsvV7me8q5RMaFzt6kQInV075AvOnr7jg2JY6cCbQ7kVEw1xlwNLAJqA78D67Lt9rW1tneW7TdwcgXaW4GULIuvBeoArYC+wErgPmvtIv8+HtJH9v5srX08oIYFUKAVQgghhBDiZMgo0AaTQAu0QgghREigAm1daji8AAAgAElEQVSOpL70UMjXobwPjwmJY6cCbTihAq0QQgghhAgyKtAKIYQIe1SgzREVaANHc9AKIYQQQgghhBBCCCFEEVGsqAWEEEIIIYQQQgghhBCnGJ6QGJzqClSgFUIIIYQQQuQbJ6Yj6FumZtAzAUYf3ORIrhBCCCFEQdAUB0IIIYQQQgghhBBCCFFEaAStEEIIIYQQQgghhBAiuGiKg4DRCFohhBBCCCGEEEIIIYQoIjSCVhzH4qXLGDVuAsnJyZh6dRk+6Cmio6NDLlOucpWrXMO9/XKVq1zdkynXgmW2eOA+mvXois/nY/ff/zCzW2+OHkyk/SsvUvPC8/F4PGz84SfmPPgox44cIap8eW4c+wKVzz6LyFIl+fy5F/l56nuF4lrYuXJ1j2u4t1+ucpWrELnj8fl8Re0QVhhj4oB/gKuttZ9lWb4B+BWoBhQH6gLr/KvHAD5gNJBxZ4NSwBLgAWttSkC//ND+XA/2nj17aXPTrUyfNJG4WjUZOWYcSUmHGDzg8cAaV0iZcpWrXOUa7u2Xq1zl6p5MueYvM+MmYdWbNKbL+1N48bwWHDlwgLYvDKNkmWgSd+6iXM3qzLj7fvB4uP2diez64y8WDh7OPR9OZ8f63/n4iUHEVKtKv1XfMrJRU/Zvjc/zJmGner/K9dTJlKtc5RpirlEx+i5/DqSOeyTki47eXqNC4thpioOi4Rgw0RhTJtvyntbaxsB1QLy1trH/Mcm/fl7GMqA+0AjoGiypZd99T8Nz6hNXK/0NcaeOHfhowUIKUsR3IlOucpWrXMO9/XKVq1zdkynXgmVu+WUlw00Tjhw4QLESJYipVoWk3Xv4a+k3fP7sSHw+H760NLauWE35WjWIKl8ec+XlLBoyAoD9W+MZ0/QKDu3Z67hrYefK1T2u4d5+ucpVrkLkjQq0RUM88BkwKr8B1tpUYCnQIFhSCQnbia1cKfN5bKVKJCYmkZSUFFKZcpWrXOUa7u2Xq1zl6p5MuRY8My0lhQbt2jBw03rqXNqMHyZP5ffPvmTnH38BUL5mDVo+dD+rZn1Ihbq1ObBtO5c93JNeXy/i4e8XU61JY44dPlworoWZK1f3uIZ7++UqV7kKkTcq0BYdjwD/McZclZ+djTGnA1cD3wZLKM2XhieHO+xFeL0hlelUrlzlKlf3uIZ7+53Klatcw9013NvvVG4wMtfOnc/AymewaMgIui/4IDOvepPGPLhkAcvGT2Td/EVEREZy+hlxHD1wkHEt/8M7t93DDaOGU71J40JzLaxcubrHNdzb71SuXOXqJlch8kIF2iLCWnsAuI+cpzo4EdcbY1YaY1YBi4EPgOnBcqoSG8uOnbsyn2/fsZOYsmWJKlUqpDLlKle5yjXc2y9XucrVPZlyLVhmhTpnULv5JZnPv3/rHcrXqkGp8uVofEsHeiz6kPkDBvPFiPQvph2ITwDgh8nTANj119/8vfw7al50vuOuhZ0rV/e4hnv75SpXuYYxHk/oP0IEFWiLEGvtp5zcVAcZc9A2stY2tNYOstYGbRKUFk0vZtWatWzYmH7zhBmz5tC6VcuQy5SrXOUq13Bvv1zlKlf3ZMq1YJllqlTmznffovTppwFw/u03k7B2HXHNLubGl5/n1Wtu5JfpszK337NhI5t/XsmFnTsBEF2pIrWbXsTmn1Y47lrYuXJ1j2u4t1+ucpWrEHnj0STHhYsxJg5YbK2N8z8vC6wBYgFjrd2QfRv/dl2AVtbaLvn+5Yf253mwlyxdzqhx4zmWkkLN6tV4fuhgysXE5PtXOpUpV7nKVa7h3n65ylWu7smU68ln9i1TM/PnZj260vz+e0lLSeHAtgRmP/go3T6ZTdRp5dm/NT5zu3+++Z45vR6lXI3qdHjlRU6vHYcnIoKvx/4f376efs/d0Qc3Bd01UEKhX+V6amXKVa5yDSHXqJjQGYoZQqS+8mjIFx29D74YEsdOBdpC5gTF16uBRUDtoi7QCiGEEEIIUdRkLdAGk0AKtEIIIcRJowJtjqSO7xfydShvz5EhcexUoA0nVKAVQgghhBAuQAVaIYQQrkIF2hxRgTZwNAetEEIIIYQQQgghhBBCFBHFilpAiBxJSw1+ZoQ3+JlCABw+GPzMUmWCnwmQeiz4md7I4Gc6RbhfW5xoPzhz91OPi/6H7FS/uuncchO+tOBnuul8dQlOjXT9+8ILHck948cfHcl1BCe+QRlCd8HOEyeuAaDrgBCpKcHP9Kpk5Xp0bQwY9ZQQQhQEJ4qzTuFEcVYIN30odxMqzjqDU4UZEdaEfXFWqAAhhBPFWSHCDL2SCCGEEEIIIYQQQgghRBGh8eJCCCGEEEIIIYQQQojgEqFv2wWKRtAKIYQQQgghhBBCCCFEEaERtOI4Fi9dxqhxE0hOTsbUq8vwQU8RHR0dcplZ+fyrJfQbOIQVS78ocJZTrm7rV5/PR/+Bz3Bmvbp07XxHyGaGer/6fD76D3ueM+ucQdfbbmbfgQMMHjmG9X/8SVTJkrRvcw13drwxJFwz+PDjBUx6d0bm84OJSWzfvoMln3xIhdNPCylXp/4GRowey8LPvyQmpiwAtWvV5OURw0LS1YlcJ9oPMHXG+0yfNQePx0ON6tUY9vQTnH5a/s8pcFe/ZhDM1yxw19+WW1ydOFedcnXTsYLQei8Q/d/ribnnXvD58B0+zK7nhnHszz+o8NQgSjQ8Fzwejq5exa5hz+A7epTIOnWoOHgYnqgo8PnY89KLHF6+rFBcCztz7vwFvDllKh6Ph1IlS/LkY4/QsP7ZBcp0ytWp3HB/zZKrXMGZa7b98y+GvTCag4mJRHi9DBnwGA3OPqtAmW5qvxC54fGdQhPFG2OigeeB/wBJwAFgsLX2C2PMZOAKYE+WXeZba580xviAVdniugOl/Pu38ucbYCQQB3iANUBva+0uY0wXYDSQ/Zaz3a213+fifD/woD9vPvCYtdbn/12vAeWBBOBWa+1eY8xdwAhge9Y2BNA9cGh/rgd7z569tLnpVqZPmkhcrZqMHDOOpKRDDB7weEDxQc0M8I7YGzZt5r5eD7Nr9x5WLPsy943zuOGKE+13KtcpV4C//v6HZ0a8wOo1v9Lr/m5BeTFyIjNk+vUENwn7a8NGnnlxLKvX/Uave++i62038/jQEXi9XoY+3pfUtDR69h/Ibe2v5/LmTf8dUKpM8F1P8iZhx1JSuOO+B7jxv9dxa4cbTryhNzL4rnng5LXlli738fjDvWjS6NzAZNx0bXGi/QHcJGztut/o3a8/c2dMpUyZaJ5/aSxJSYcY8lT/XHJz/5KP2/oVgvualW/XIsgMKdc8bhKWr3MViuR8ddOxgtB5L/D3hRcSGVebKpPfYetNN5K6ayelLr2MioOe4eC8DykWW5WdTz4OHg+Vnn+RYxs3sPeVsVSZ9A6J8z7k4AezKX7W2VSdPJUNzS+C1NSAbhIWMudAHp/9/t6wkc7d7mfOtClUqliBJcuWM2j48yz+ZF7uMnm8FoTU+erEdcBNr1lFkClXd7lCPq7ZAdwk7PCRI1zVriPPPv0El7VoxueLv+bFcRNYOHvGiXfy5j6mMGTaDxAVo+/y50DqawNCvujo7T48JI7dKTPFgTHGA3wEJAP1rbWNgN7AO8aYVv7NBlprG2d5ZBY2sy1vnL2oaoypCnwFTLTWngucC6wFPsiy2by8crJl1gb6AhcBDYFmwFX+tswDRvjbsQLIeEdwAdA3pzYUlGXffU/Dc+oTV6smAJ06duCjBQspSBHficwMDh8+Qr+nBtO/70MFzgLnXN3Wr9NmzqLjje245qrWBc5yMjPU+3Xa7Ll0vP46rrmiZeayX3/7g3bXXIXX66V4ZCStml3Moq++LnLXEzFx8lROK18+9+JsAIT6scpKcnIy6+zvvPH2NNrefDu9+j1B/LaEAmW66driRPsBGtQ/i0UfzqJMmWiOHj3K9h07KRcTU6BMN/UrBP81C9z1t+UWVyfOVadc3XSsILTeC/iSk9k58ClSd+0E4Oiva/BWqMCRn35k32sT0ouYaWkcXb+OYlWrpe/k9RJRNv1ciChdmrSjRwvFtbAzixePZNjTA6hUsQIADeqfza5du0k+dnL/5C0MV6dyw/01S65yBWeu2cu/+54a1atxWYtmALS+7NICf0vLTe0XIi9OmQItcBlQi/TiZTKAtXYFMAx4Ogj59wNfWms/8mf7SB+tO8EYk6+pIqy1/5BeTE4CygExwD6gCZBkrV3o33Q4MN7/84XAXcaYNcaYqcaY8vluUTYSErYTW7lS5vPYSpVITEwiKSkppDIzGDj8eW7pcAOmXt0CZ4Fzrq7r1/79aHvtNQXOcToz1Pt14CO9aXv18S/o555zFnMXfsaxlBSSDh1m0VdL2blrzwkSCs81J/bs28ekaTMY0Ld3gbNC/VhlZfvOXVxy4fn0eaAb896bSqOG5/DAI48V6E2em64tTrQ/g8jIYnz+1RJaXns9P/6ykvbt2hQoz039CsF/zQJ3/W25yTXY5yq4q/2O/Q2E0HuBlPitHP56cebz0x97gqSvvuTwN8s5tnEDAMWqVCXmzrtIWrQAgN3DnqHcfd2p+cXXVHlzMruGDobUwEbPF8S1sDOrV61Kq0tbAOlf731u1BiuuOxSikfm/m2ZonB1MjecX7PkKldw5pr9z8bNVDz9NAYMGU77O+/h7p4PkXoS19GccFP7hciLU6lAeyHwk79wmpWv/esAhhhjVmZ5ZH6HONvyl3LIPw/4JesCa22qtXa6tTZjPP/12XJOOHo2S8YxY8x9wN/ANmAlUBdIMMa8aYz5Bfg/ING/yzZgKOkjeDcDr+T1OwIlzZeGJ4evJ0V48/6aZWFmAkybOZtiXi83tWtboJysOOXqpn51E27s1/697sfj8XDjXd3p2X8gzS86n8jI/E0F7rTrzDlzaX1ZC2pUr1bgLDcdqxrVqjJx7GjOrFsHj8dD1ztvZ9OWrWyJ35bvTDddW5xof1auvPwyvv9yEb26d6Vrzz6kpeX+NdPccFO/OvGaBe7623KTKwT3XAV3td9N7zEK6uopVYpKo8cQWbMWuwb+70tpxeufQ9V33uXAu9M4tGQxnuLFqfTiy+x88nE2tW5JfOfbqThoCN7Y2EJzLazMDA4dPsxDjw9g0+bNDBtY8C/sufF8DdfXLLnK1SlSUlJYsvxbbmnfjjnvvMUdN99Et4ceITk5Od+Zbmq/EHlxKhVofeR807Pi/nXw7ykOMiePzLb84Rxy0oAjeThkn+Lg4kDErbUTgdNJn2t2sL8drYD/s9Y2Ib14O9q/7Y3W2uX+QvQLwLWB/I5AqBIby46duzKfb9+xk5iyZYkqVSqkMgE++Hg+a9atp12nznTr3ZcjR4/SrlNntu/cGXKubupXN+HGfk1MSqJfz258PO1NJo8dic/no2Y+C6BOu37y2Ze0b1vwUWPgrmP12x9/8uH8Bcct8/kgslj+76nppmuLE+0H2LhpMz+tWJn5vEO7tsRvS2D/gZzncA4EN/WrE69ZTrm6qV+dyHTiXHXK1U3HyikK4uqtUoWq096D1DS23X0naQfTj3Hpa9tQ5Y1J7H7pRfZNfBWAyHpn4ilVkkNLFgNwdPUqkv/8g5LnNioU18LMBIjflsCtXe7FGxHBlNcnULZM7vPiB4Kbztdwf82Sq1ydolLFCtSpHUejBucAcGWrlqSmprF5a3y+M93U/rDF4wn9R4hwKhVovwcuMMZk//5NU+CnIOT/RPr8r5kYYyKMMXOMMZXzE2iMqWGMaQ7gH4U7g/SRsQnAH9baDO/pwEXGmBhjTNbisQfIezbuAGnR9GJWrVnLho3p9zmbMWsOrVu1zGOvws8EmDXlLT6eOY2506fw+tjRlCxRgrnTp1C5YsWQc3VTv7oJN/brjA8/YuzEyQDs2rOH9z9awH+vuiJfWU667j9wgE2bt3Beo4ZByXPTsYrweHh25EuZbxTffX8Opl6d4746FSquTuQ60X6Anbt20/eJp9mzdx8AHy1YRL06Z1C+XP7n9HNTvzrxmuWUq5v61YlMJ85Vp1zddKycIr+unqjSVJ00laTPPmVHv4fx+eeTjWp1ORWeeIpt991D0vyPM7dP2bSRiOgylGh8HgDFatSgeJ26HF2/znHXws5MTErizm73c/UVl/PSiGcpWbJkgfIycNP5Gu6vWXKVq1O0bNaULfHxrF3/GwA//rICjweqV62S70w3tV+IvPAE84YyRY0xZgHpo037+KcOOB/4GOgEdAEWW2sn57Cfz1r7r7K5/+Zig621rYwxscAq4G5r7Sf+G3kNBK6y1rYwxnQBWllru5yEbwO/X2NgP/AWYIExwF/AtdbaVcaYx4EG/jZsAW6w1n5vjBkIVLPWdg/oFx7an+fBXrJ0OaPGjedYSgo1q1fj+aGDCzwpfr4yA7wjNsCW+G20vfn2oNwR24n2O5XrlGsG/Qc+Q726dYJyl2WnMkOiXw/nPpqi/7DnqXdGbbredjOJSYd4bMhzbNoSjw8f3e7sRLtrrsp5x1J5j1Y5adfUwG7wsfrX9Tzy5CA++3BmQNvjzXteupA4VhDQtWXuJwuZOPkdUlNTia1ciWefHkDVKrl8jdVN1xYn2h/gf53ffX82786cjdfrpVLFCgzs348a1armkpv3/5Dd1K8ZBPM1K1+uRZQZMq553L0d8nGuQpGdr246VhkU9XuBvy+8kHL3dqd87z4k//H7cesiSpUiokxZUnZsz1x2ZMUv7B72DCUvupjTHnmMiOLF8aWmsnfCKxz68nMAzvjxR0dcHcnM47Pfa29N5uUJr3Fm3TrHLZ/86vjcC5QBvBaEzPnqxHXATa9ZRZQpV3e5ZhDwNTs1sHFjP/6yghfGjOfw4cMUL16cAY/24YLGuXwbwZv3t7hCov0AUTGhMxQzhEh9/cmQLzp6uz0bEsfuVCvQlgKeBdoAqcAeYJC19gtjzGQKUKD1P28CjAQqAV7S56TtY63d5S/QjgY2ZYsZba2dkotzd+Ah0kfCLuV/xeWLgXFAadKLsndaa3cYYy4lvYBbCvgd6Gyt3Z937xBQgTZkOIkPuwET4IddIU6KPAq0+SaAAu1JE2CB9qQJoEAbMoT7tcWJ9jv1taAAPuyGDOF+XrmJAAoz+cJN52uY8/eFF+a90UkSaIE2JHDqs18IfUU0T5y4DugaIMKdAAu0J00ABdqQQQXaHEl94+mQr0N57x0aEsfulCrQijxQgTb4mUKoQKsCrZuuLSrQOkO4n1duQgXasEcFWhVoVaAVwgFUoFWB9gSoQBs4Ljrb3Ykxpg4w+wSr780yz6wQQgghhBBCCCGEECLMUIHWYay1f5E+x6w4GTRySLgFJ0a6OoWbRro6RbhfW8K9/U6hfnUPGuUW9jgx2rVH6RpBzwR4NWlz8EPdNNLVKXQdECL4uGmkqyhc9LoTMHp1EkIIIYQQQgghhBBCiCJCBVohhBBCCCGEEEIIIYQoIjQOXQghhBBCCCGEEEIIEVw0rUzAqEArjmPx0mWMGjeB5ORkTL26DB/0FNHR0SGXKVe5ylWu4d5+ucpVru7JlGv4uLbq2Y2W93fF5/Ox669/mHpfL44dTabzm69Q+awziYiI4Nu33+XTF14G4MxWl9Jh5FC8kZEkHz7CzN6PseHHnwvFtbBzw9013NsvV7nKVYjc8fh8vqJ2OGUwxsQBvwPr/IsigLLA20AFoDlQHKibZZsxgA+YBNxmrZ2eJa8P8BJQ278oI9vnz4kH7rbWbglI8ND+XA/2nj17aXPTrUyfNJG4WjUZOWYcSUmHGDzg8YDiCytTrnKVq1zDvf1ylatc3ZMp11PfNeMmYTWbNKb77HcY2qg5Rw4coMPIYZQoE03K0WR8aWm8/3B/ikdFMejX73mjU1c2/byC57b8xrj/3Mjmlatp2OYaOox6lsFnnQ/kfZOwU71fTyXXcG+/XOUaFq5RMbobVg6kvjU45IuO3nsGh8Sx01jj4BNvrW3sf5wLNAMeBV6x1jYGrsu2zST/fluAm7JltQf25ZB9nrX2HGA1MDJY4su++56G59QnrlZNADp17MBHCxZSkCK+E5lylatc5Rru7ZerXOXqnky5ho/rpl9W8nS98zhy4ADFSpSgXLWqJO3ew8yHHmP2o08CEFMllmIlSnBk/35Sjx2jfzXD5pWrAahwRhxJu/cUimth54a7a7i3X65ylWsY4/GE/iNEUIHWeaoAHuBgHtstAS4wxpQGMMbU9O+zP5d9vgIaBEMSICFhO7GVK2U+j61UicTEJJKSkkIqU65ylatcw739cpWrXN2TKdfwck1LSaFRuzaM2PIb9Vo249tJ09KXp6Zy9zsTGbj2O35fvIwE+0fm9mUqVWTElt9oP3Jo5tQHheFamLnh7hru7ZerXOUqRN6oQBt8qhpjVhpjfjPG7AKGATcGMA1BCrCI9BG2ALcAM0+0sTEmkvQRt98GwRmANF8anhz+exDh9YZUplO5cpWrXN3jGu7tdypXrnINd9dwb79TueHmumrufB6tWJuPBz9Hr0VzMvMm3Xkfj1aoTdRp5WkzsH/m9gd37KR/9bN4oemVdJ40gUr16haaa2HlhrtruLffqVy5ytVNrkLkhQq0wSfeP5VBfeAd0vv4swD3ncn/pjm4Afgw2/qM4u9K0qc38AD9CRJVYmPZsXNX5vPtO3YSU7YsUaVKhVSmXOUqV7mGe/vlKle5uidTruHjWrHOGdRpfknm8+VvvcPptWrSpOONxFSJBeBoUhI/TX+fmk0aUbJsWRrf8N/M7TevWMXWVWup1rC+466FnRvuruHefrnKVa5hTERE6D9ChNAxOcWw1qYB/YBqpM9BGwhfARcaYxoAu6y12ac3yDp37dnW2rustYFPVJUHLZpezKo1a9mwcRMAM2bNoXWrliGXKVe5ylWu4d5+ucpVru7JlGv4uMZUqcy9MyZR+vTTALjo9luIX7uO+ldfQZtB6WMqihUvzvk3t8d++TW+1FTufGs8dZpdDECV+mdR+awz+ef7nxx3LezccHcN9/bLVa5yFSJvPJrkOHgYY+KAxdbauCzLrgHeB+pZaxNOsE0XoJW1tosx5jWgIek3FXvXGLMBaOXf9Lj9TppD+/M82EuWLmfUuPEcS0mhZvVqPD90MOViYvL9K53KlKtc5SrXcG+/XOUqV/dkyvXUdu1Rukbmzy17dOWynveRlpLC/vgEpvd8hEN793Hbqy9TtcHZAKz84GM+HvQsPp+Pei2b0+HFZ/FGFiPlaDIfPjEY+9XXALyatLlI2u9Ubri7hnv75SrXU941KiZ07jYVQqS+PTTki47eu54OiWOnAm0Qyan46l/+KbDRWntfAAXa1sBHQCVrbWJhF2iFEEIIIYQQgZO1QBtMAinQCiGECBFUoM0RFWgDRwXacEIFWiGEEEIIIYKKCrRCCCFUoM2Z1CnDQr4O5e38VEgcO81BK4QQQgghhBBCCCGEEEWECrRCCCGEEEIIIYQQQghRRBQragEhhBBCCCGEcCtOTUXwaNlaQc988cDGoGcKIYQQJ8SjcaGBop4SQgghhBBCCCGEEEKIIkIFWiGEEEIIIYQQQgghhCgiNMWBEEIIIYQQQgghhBAiuER4itrANahAK45j8dJljBo3geTkZEy9ugwf9BTR0dEhlylXucpVruHefrnKVa7uyZSrXAua2/yB+2ja/R7w+dj19wZmde/NkYOJtB/3IjUvbAIeD5t++Jk5vR4l5cgR6v/3Gm596//Yu2lLZsaEVtcWimthZrrJNdzbL1e5ylWI3PH4fL6idnA9xpibgCdIL3hHAFOstSONMZOBxdbaycaYxcB5QCVr7dEs+64E9llrWxljBgM9gIRsv6ItMBaoDUQDscCf/nWPW2sXBSR6aH+uB3vPnr20uelWpk+aSFytmowcM46kpEMMHvB4QPGFlSlXucpVruHefrnKVa7uyZSrXPObm3GTsGpNGnHXzHcY3aQFRw4c4L8vDKVEdDRJu3ZRrkYN3rvnfvB4uG3K6+z6828WDR7Otc8O5OjBRL4cMfq4zEBuEnaq9+upkilXuco1xFyjYjRUNAdSpz0X8kVH7+1PhMSx0xy0BcQYUw0YBVxtrW0ENAVuNcZcn8PmB4Crs+xrgKrZtnnVWts422OztfZGa21j4F7gpyzrAivOBsCy776n4Tn1iatVE4BOHTvw0YKFFKSI70SmXOUqV7mGe/vlKle5uidTrnItaO7WX1Yx4qwmHDlwgGIlShBTtQqH9uzh76Xf8Pnwkfh8PnxpaWxduZryNWsAENf0Yupe3pK+Py/jgcWfcMalzQrFtTAz3eQa7u2Xq1zlGsZ4IkL/ESKEjol7qQBEAlEA1tpE4C5gXQ7bzgZuyvL8FmCW04KBkpCwndjKlTKfx1aqRGJiEklJSSGVKVe5ylWu4d5+ucpVru7JlKtcg5GblpLCOde34emN6zjj0mb8OHkav3/2Fbv++AuA8jVrcGnv+1k1+0MADu3ew3evvcXo81vwyZNDuGvWVGKqZR8X4oxrYWW6yTXc2y9XucpViLxRgbaAWGtXAXOBv40xPxhjnge81to/c9h8AdDKGBPpf/5f4ONs2/QwxqzM8vjAOfvjSfOl4fH8e2R3hNcbUplO5cpVrnJ1j2u4t9+pXLnKNdxdw739TuXKNTi5v86bz6DYOnw6ZAT3fTInM69ak0Y8sPgTlk+YyPr56V+ue7vjnayeMw+ADcu/Y+O3P3DmlZcXmmthZDqV65ZMp3LlKle5OpcrRG6oQBsErLX3A3HA/wG1gO+MMe1z2PQosBS40hjTAPgbOJRtm+xTHNzooPpxVImNZcfOXZnPt+/YSUzZskSVKhVSmXKVq1zlGu7tl6tc5eqeTLnKtaC5p9epTVzzSzKf/zBpKuVr1aBU+XI0vrk93Rd+yCcDnsmcb7ZkTAxX9O97fIgHUlbb/9cAACAASURBVI8dc9y1MDPd5Bru7ZerXOUaxng8of8IEVSgLSDGmDbGmFustVuttZOstbcCvYGuJ9jlfdKnObgZeK+wPAOhRdOLWbVmLRs2bgJgxqw5tG7VMuQy5SrX/2fvvOOjKPo//r4cgRBCwkMJoQhIG6UIdlGECIg8oqIoAiJFQaRIEUF671IUECx0pImABEFAkSKgYPkBgsA8AoYWEqpAAmmX+/2RSwgxkuRuN7k13/frda/kZnc/+5nZ2dnZ783OilfxmtfzL17Fq3i1jqZ4Fa+e6gaWCuHVJXPxL1YUgPteeZnIg4ep8OjDPP/BRD797wvsXX5z1rS4a9d4rGsnar6Q/EqM0rXvodyD93Nk02bTveakppW85vX8i1fxKl4FIXNsMsmxZyilngDmAg201uFKKRvwIXAZKAts01ovUEptA0YAPwKHgYvA48AjwAitdahSagSA1nrEbfYXmrJ+ts1ev5Lpwd6+YxdTZswkITGRcmXLMHH0CIoEBWV7V2ZrilfxKl7Fa17Pv3gVr+LVOpriVby6o9s3sHzq/3XefJ3HunbCkejg6tmzfNmjH53Wr8S/6H+4cuZs6nrhP+zmy579KHt/bZ6f9h5+AQE4Eh2s7TuIY9t2MPnqCVO85pamlbzm9fyLV/H6r/fqH+Q9QzG9CMey97w+6Ghv/a5XHDsJ0BqAUqo90I/kl4UBbHJ9/4R0AVqt9Tal1GcAWuu2aQOurgBtFyAy3S76aq03u/aVun62jWYhQCsIgiAIgiAIQu6TNkBrFFkN0AqCIAjZRAK0GSIB2qwjAdq8hARoBUEQBEEQBMESSIBWEATBQkiANkMcyyd7fRzK3qqvVxw7mYNWEARBEARBEARBEARBEAQhl5AArSAIgiAIgiAIgiAIgiAIQi6RL7cNCDmIGdNZ2EwaCZ4QZ7ymGV7z5TdeU7AWjkRzdO0mNM9JDuM1AXzs5uiagSPBeE27b+breAsm1AFnzF+GawLYChczXjQx3nhNMOf6ksfrFWBOudpMGptgpT6WGVipbbXQsTJjOgLHovGGawLY2w00RdcUYmOM1/QrZLymYE4/24w+tlmYNR2lM8l4TbPuB8zou8k9fM7iY6H+TC4jI2gFQRAEQRAEQRAEQRAEQRByCQnQCoIgCIIgCIIgCIIgCIIg5BIWGt8vCIIgCIIgCIIgCIIgCIIlsNKUTVlAKRUI/AA8o7UOV0o1AqYCBYHPtdZDXOvVBuYAgcD3QBet9W3nbZEArXALYes3MHfRYmw2GwX9/Bj87jvUrHa3x7pOp5MBw0ZStUplOrZ71TOdkeOoWrkiHV9tjcPhYMK0mez4cQ8Oh4PX27Si9YvPu63/7dbtTP90Hj42G0FBgYwZ3J9yZcu4rZfCth07mTJjFvHx8agqlRk3fAgBAQFep2mmrlF1IC1WKld99Bhj3pvKtehofOx2Rg16lxp33+WVXgE2b91Ov2Gj2LvjO0P0wPg6YEb+16zbwPyly1O/X4uOISrqHNu/XkPxYkU90rbKOZCCp3Vg7TdbmLtsJTabDb8CBRjcqwuzl6zg5JmzqeucPhvJg7Vq8tGEEW77NKNcjb4WmFmvrNQOTpg6nY2btxAUFAjAneXL8cGEMR5pLl7+BctWrsZms3FH2TKMGTqQYkW9r0zBnD6WVfoCVjsHzOoPe+P56nQ6GbTpV6oUD+L1B6oQm+hg9JZ9HIi8jNMJ95T6D0Mb1MYvn53wy9EM/fb/uHwjnkK++Rjf5H4qFi2cY17N1nU6nQwYPYGqlSrSsU1LAJasWsPKtV8TGxdH9buqMm5QP/Lnd2/+Sm/Pf054BeOv22b0sVOwQt8VzGuzwPh7AjP6bZ99vpLFK1bhV6AAle4sz7B336GIq6/hCWaeB4I1UUo9DMwGqrq+FwTmAfWBU8B6pdR/tdYbgMVAJ631bqXUXOAN4KPb6csUB26ilHpJKfWrUmq/UuqAUqqfKz1cKVUhzXr9lFK/K6XKK6VOK6WeSaezTCk1WSlVQSkVr5Tap5Ta69rmW6VUWdd625RSR13LUz6bjMzT8fATTJo2gzkfTiNs+WK6dnqNHn37e6x77PiftH+zG5s2b/FM589w2nfrzaYt21LTln+5lvCTp1i3bCErF8xm4fIv+O33Q27px8bG0W/YaD58byxhSxfQ4PHHGDP5A488A1y6dJmBw0czY9IENq1ZyR1lyzB5+kyv0zRT16g6kBYrleuN2Fg6du9Np3ZtWLN0Id06dqDvkBFe6RUg/OQpJn4ww9AXExhdB8zK//PP/JewpQsJW7qQlYvmUqJYUYa+28fjAIJVzoEUPK0Dx0+eZtKsOcyeNIY182bStV0reg4Zw/TRQ1gzbyZr5s1kdL+eBAYEMOzt7m77NKNczbgWmFWvrNQOAuz97QBTx48ibNkiwpYt8jg4e/DQEeZ9toTl82ez7oulVCh3B9NmfeqRpln5N6OPZaW+gJXOAbP6w954vh67eJXXV+3kmz8iUtM+2aNxJDlZ07Yha9o2JC4xidk/aQD6b/iFlvfcybr2jehe5y56r9uDM4vXCW+vr8fCT9C+xzts2vp9ato3275n8RdfMn/6ZNYvnU9cXBwLlq/MVZ85oWvmdcDo67YZfewUrNJ3NavNAuPvCczot+3+5f+YvWgJC2dNI2zpAuo9Vodh497zWNfM80DwPpRSRVwxufSfIulWfQPoDqRcOB8C/tBa/+kaHbsYaKGUKg8U1Frvdq23AGiRmQ8J0LqBUqoMMAVorLWuBdQBWimlnku33ttAW+AJrfUJ4E1gplIqwLX8aaAWMMS1SYTWurbW+l6tdXXgN2BSGslOruUpn6eMzFf+/L6MGTqI4BLFAahR7W4uXLhIfIJnb+ZdsmIlLV5oRpMnG3qms/JLWjR7hiYNQ1PTNm/7nubPPE2+fPkICixM0ycbsnbDN27pO5IcOJ1OrkVHAxBz/QYF3PyFPC07d++hZvVqVChfDoDWLV7kqw0bs9yhzSlNM3WNqgNpsVK57tq9hzvKlqF+3UcBaFj/cY8DE2Z5vXEjln5DRjCgTy+PdNJjdB0wK/9pmb1gMUX/8x9aeTAqPwWrnANgTB3I7+vL6P69CS6eHHypcVdVLly6nHo9iU9IYMC4KQzs0ZlSJUu4vR8zytWsa0EKRtYrK7WD8fHxHNL/Y87CJTz7cht69BtIxNlIjzRrVLuLTWtWUrhwAHFxcUSdO0+RoCCPNM3Kvxl9LKv1BVLw9nPArP6wN56vy/b/yYs1KvBU1ZtPCDxQthhdHlb42GzYfWzcXSKIiKvXiYq+wfHL13g6eewI9e4M4XqCg8PnruSIV7N1l6xcQ4tnn6ZJg/qpaWs2fMPrr7xMkaBAfHx8GPluH5o1eTJXfeaErpltgNHXbTP62ClYpe9qVptlxj2BGf223w8f4dEHHyCkZDAAjZ+oz5Ydu7yyzc6z2Hy8/wO9gT8z+PROmxWtdSet9Y40SaWBs2m+nwXK3ib9tsgUB+5RHPAF/IGLWutopVR7IDZlBaVUT6Ad0EBrfQFAa71eKfU9MEopNRSYAbTUWscqpTLaz1ZgvLlZuUnZ0qUpW7o0kPzowfgp02hQ/3Hy+/p6pDtsQD8Adv24xzOdfm8n6+z5KTXtbNQ5SrkaY4CQ4BLoo8fc0i/k78/IgX1p1bErRYICSUpKYtmc245AzxKRkVGpF4xkj8FER8cQExPj9iMSZmiaqWtUHUiLlcr1zxOnKFGsKINGjePIH0cJLBxAv57ujxo00+uwcRNp+eLzqCqVPfL3N12D64BZ+U/h0l9/MX/JclZ/NtdjLbDOOQDG1IGypUpStlRJIPl6MuHDT3nisYdTryer1m8iuHgxnqz3mNv7AHPK1axrARhfr6zUDkadv8AjD95P726dqVKpInM/W0K3d97lyyULsXkwN5mvbz42b93O4NHjyO+bn55d33BbC8zLvxl9LKv1BcAa54BZ/WFvPF+HNKgFwA8nzqWmPVa+ZOr/Z65eZ9HeY4xsVJvIazcILuSHT5rzNSTAj8joG1QrmX6AkfFezdYd1jc5CLXrp19S08JPnubi3Zfp2Ptdzl24yAO1atLvrTdz1WdO6JrZBhh93Tajj52CVfqupt3Dm3BPYEa/rVaN6nz2+UrOnI2kTKkQVn/1NQkJCfx15QrBxYu7rWv2vYbgdXxA8ijX9PyVyXY+QNqovQ1Iuk16pmJCNtFa7wfCgONKqZ+UUhMBu9b6qGuVbiQf4Okpwdk09AJeBuYDi7XWv5ABSilf4CXgxzTJc9JNcTDYwGylcv3GDXr1H8TJU6cYM8yUXRiGMynpljmnnTjx8XGvWuujx5g5ZwFfr1jMzg1hdHmtHT36D/b4V7IkZ1KGN58+drtXaZqpawZWKtfExES27/qRls2bsfqzebz68kt07vUO8fHxbmua4XXJilXks9t5qdmzbmvkFGbX1RWrw2hYvy53GDAHtVlYoQ5cvxFL7+HjOHkmgjHv3vwBesGKNXRp19qQfRiNWdcCML5eWakdvKNMaWZPn0rVypWw2Wx0bNuGk6fPcDribOYbZ0KjJ+qzZ8smerzZkY7de5OUlGn/9x8xu20xso9lxb6AFc6BFIzuD1vpfAX4PeoybVd8zyu1KxJasRRJTuff3vPidII9iz+wWLG+JiYmsuvnX5k2djir5n/MlWvXeP9j935csFL+rXQ/YEYf2yysdH2x0j3BA/fWovsbr/NWv0E0b9cRm81GkaBAfPN5FqC20nkgeI7W+i+tdXgGn8wCtKeBUmm+h5A8/cE/pd8WCdC6ida6K1CB5El+ywO7lVLNXYufAJ4BJimlyqXb7hIwGLgXSP/8RemU4CvJ0xvYgAFplqef4mCs0fmKOBtJqw6dsPv4sOjTWQQWzvrE/7lBqZCSnDt/MfX7ufMXCQl273HZnT/u4b5aNVNfBNOmRXP+OPYnl69k7dGtf/YYwrnzN+P0UefOExQYiH/Bgl6laaauGVipXINLFKfSnRWoVaM6AI1C6+FwJHHqTKZtdI56/XLdeg4cOkyz1u3o3LMPsXFxNGvdjqjz593WNAuz6+rX326h+bNNDdEyC2+vAxFR52jdrQ92Hx8WTptIYOHk0QaH/ncUh8PBQ7Vruu3TTMy6FoDx9cpK7eCRP46yZv2GW9KcTvDN5/7DXCdOnuKXvftSv7/Y7FkizkZy5eo1tzXNbFuM7mNZsS9ghXMAzOkPW+l8/VqfpuOqXfSpW503H0p+yq9UYX/Ox8Td8mPVuZhYShbO2r6sWF+DixejcejjBBQqRH5fX557qhH7Dv7uVT6tVK/MwIw+tllY6fpipXuC6JjrPHRfbb5cPI/Vi+bSqP7jAB6/JMxK54HX42Pz/o/77AGUUqqyUsoOvAJscE1xGquUSnlcsC2w4Z9EUovKEyd5FaVUU6VUS631Ga31fK11K6An0NG1Shut9dfAJ8BS14FKywngjNY6/cQoEWmCr3drrdu7Aro5QnRMDG07d6Vxgyd4f8JY/Pz8cmrXbtOwXl1WfbWexMRErl67xvpvv6NR6ONuaVW7S/Hz/+3jwsXkIt+8fQdlS5eiaJHMH9u6HXXrPMz+AwcJP3ESgOUrV9MwtJ7XaZqpawZWKtd6j9bhdEQEBw8fAeDn/9uLzQZlS5fKZMuc9bpy0TzWrVhC2LJFfDp9Kn4FChC2bBElS7g/R6hZmFlXr1y9yslTp7m3lncGEFPw5joQff067Xr258l6jzF1xED8ChRIXfbzvgM8cl8tjx5rNxOzrgVm1CsrtYM+NhtjJ72fetO89IvVqCqVbnl8MLucv3CRPgOHculy8uCGrzZsokqlivyniPvz0JqVfzP6WFbrC1jlHDCrP2yV83XrsbOM27qfOc0f45m77khNDylckHJBhdjwvzMA7AyPwsdmo2rxrAVBrFZfAZ5qUJ8N320jNjY5ML35+13UvPsur/JplXplFmb0sc3CStcXK90TnDt/gbZdehAdHQPAR/MW0rRxI4/7mVY6D4TcQ2sdC3QAVgGHgCNAytsk2wDvK6WOAAHA9Mz0ZA5a97gOzFBK7dFahyulbEBtYC9QHUh5pmIEsBsYBgzPDaPZYcnnXxBxNpJvt27j263bUtMXfDzTo5sdM2n94vOcPBNBszavkZCYSMsXnuOh++51S6vOg/fT8dXWtO3SA1/ffAQFBjJrsudTABcrWpTxI4bSs98AEhITKVe2DBNHj/A6TTN1zcBK5VqieDFmTp7AyAmTuXHjBvnz52fGpPEUSBO08havVsHM/J84dYYSxYt5NLIvJ/DmOrBk9VdERJ1j844f2Lzjh9T0+e+P58TpCMqElLzN1rmLWdcCM+qVldrBqpUrMeTdPnR9ux8Oh4OQksFMHTvKI80H7qtNl44daNe5G3a7neASxZk51bM3N5uVfzP6WFbrC1jlHDCrP2yV83XSjoM4gaGb96am3Ve6KEMb1GbS0w8yfPNePt5zhAJ2O+83feiWOWlz2quZugCvNG/GlavXaP7amziSkqiuqjCgZ1ev8mmVemUWZvSxzcJK1xcrUbFCOTq3f5UWr3UmKSmJ+2vfw7B+fTzWtdJ5IOQ8WusKaf7/DqiVwTr7gYeyo2uTt9C5h+ulYP1IflkYwCbXdw2Eaq3DXevdDfwMPK21/t6VFgqM0FqHptGrAGxLe6DT7W8byW99i063KDQL82IkE/OX8QfbrBFQCXHGa5rhNZ9xb/YWLIoj0RxduwnBwSSH8ZoAPhaai8nh2RtdM8Tu2RxXOYoJdcAZk7VLUHaxFS5mvGiiSXPSmXF9yeP1CjCnXG0mPTxmRn/aS0eZZ4iV2tY8fqwci8x5/7C93UBTdE0hNsZ4Tb9CxmsK5vSzzehjm4VZsRqn+3O1/yNm3Q+Y0Xcz6x7eP8g6F4McxLF6mtcHHe3Ne3nFsbNQ6+RdaK0XAgszWFQh3XqHSR7OnDZtGxCaLi08/bbplof+0zJBEARBEARBEARBEARB8CrM+nH8X4iUlCAIgiAIgiAIgiAIgiAIQi4hI2jzEhZ6/Apf75s3SBAyxEqPSVlpKgKzsNJj42ZgQh2w5fc3XNM0ZFoac5C2xVp9LDOwUtuax4+VWVMRbK1QzXDNJ8IPGa4JyHQEVsJK/WwzMKu9ir1uvGbBwsZrgnnTPAiCF5LHWzxBEARBEARBEARBEARBEAwnj/8wmh1kigNBEARBEARBEARBEARBEIRcQgK0giAIgiAIgiAIgiAIgiAIuYRMcSDcwrYdO5kyYxbx8fGoKpUZN3wIAQEBXqcpXsWreBWveT3/VvDqdDoZMHo8VStVpGObVrcse6v/EIJLFGdY395e4dVsXfFqHa95Pf/iVbx6olvypeaU694Vp9NJ0o0b/DFoKNf2/0bp19pTuk1rfPz8uPbbbxzp3RdnfDxFHnuUSsOH4OPri+NGLH8MHsq1vftyxOu/QVO8ilejdZ1OJwPGTEzuu73yMn9dvcqISdM4/MdR/P38aN60CW1bvOA9XkeOo2rlinR8tTUOh4MJ02ay48c9OBwOXm/TitYvPu8VXvM0NhkXmlWkpNKhlHpJKfWrUmq/UuqAUqqfK32bUuqoUmpfmmUvp9muilIqzLXOQaXUKqXUna5lr7m226eUildKHXL9P9O13On6W8G1fF+6T3ml1Gml1DPpvC5TSk02Ku+XLl1m4PDRzJg0gU1rVnJH2TJMnj7T6zTFq3gVr+I1r+ffCl6P/RlO+7feZtOW7X9bNvuzpfyy/zev8Wq2rni1jte8nn/xKl490S1YqRKVhg9hf8s2/NKgMeFTp1Fj/hyKN/0vZTu+xr6XWvHT40/g4+fHHW++gc3Xl+qffoTu04+fn3iSE+9Po9rM6Tni9d+gKV7Fq9G6x8JP0L5HXzZt3ZGaNn7aLPwL+vH1knl8PvtDvt/9E1t3/Zj7Xv8Mp3233mzasi01bfmXawk/eYp1yxaycsFsFi7/gt9+d+9lg2YdL0G4HRKgTYNSqgwwBWista4F1AFaKaWec63SSWtd27XsFWCRUipIKVUS2Aqs0FpX1lrXAL4EdimlSmit57u2qw1EAE+7vnfPwEZEyrppPieAN4GZSqkAl9engVrAEKPyv3P3HmpWr0aF8uUAaN3iRb7asBGnB29ONENTvIpX8Spe83r+reB1yao1tHiuKU0ahN6SvufXvezY/ROtXmjmtk+jvZqtK16t4zWv51+8ildPdJ3xcei3+xF/7hwA1/bvJ39wCUq3ac2pjz4h8a+/wOnkf/0GEPnFKpwJCfxQ636iD/4OgF/5ciRcvpwjXv8NmuJVvBqtu2RVGC2ee5omDeqlpv1+5A+aNXkSu91Ofl9fQh99mE1bv899ryu/pEWzZ2jSMDQ1bfO272n+zNPky5ePoMDCNH2yIWs3fJPrXgUhq0iA9laKA76AP4DWOhpoD/ztZxet9QEgGqgMdAU2a62XpFm+GNgJdDHCmNZ6PfA9MEopVQiYAbTTWscaoQ8QGRlFSMng1O8hwcFER8cQExPjVZriVbyKV/Ga1/NvBa/D+vbm2aeevCUt6vwFxr4/g8kjh2L38awLklfLVbxaT1O8ite84jX21Gkubv4u9XvlUcO5sOlb/O64g/zFi3PP8sU8uO1bKvR7h8SrVwBwJibiW6I4j+7/hcrDh3Dyw1k54vXfoClexavRusPe6cmzjRveknZP9bsI2/gtCYmJxFy/waatOzh/4VLue+33Ns82ubWfeTbqHKVu0S9B5Lnzue41z+Nj8/6PlyAB2jRorfcDYcBxpdRPSqmJgF1rfTT9ukqpp1I2Ax4EfspA8nvXsuxQOt30Bv3SLOsFvAzMBxZrrX/JpvZtSXImYbP9vXL62O1epWmWrngVr+LVOl7zev7N0jXLK0BCYiLvDBvFwN5vEVy8mMd6Uq7i1SqaZumKV/HqrV59/AtSfc4nFKxwJ/rtvth8fflP/Xr83qkLvzz5NL5FilBx4IDU9RPOX+CHWg/w69PPcde0qRSsWDHHvFpZ0yxd8Spe0zKgR1dsNhsvtH+T7gOG8dhD9+Pr696rjMz26kxKIq28Eyc+bg4IMNurIGSEBGjTobXuClQAPgLKA7uVUs1di+e4gqYHgcHAy65Rtk4yfuFaftey7JB+ioNJabxdcu33XmBMNnUzpVRICOfOX0j9HnXuPEGBgfgXLOhVmuJVvIpX8ZrX8281rwAHDx/h1JkIJkybSbO2HVn+5Vq+3ryFwWPf8yqvVipX8WoNTfEqXvOS1wJlSnP/+rU4HQ72NW9B4tWrxEdGcn791ziio3EmJBC1cjVBD9yHvXBhij/dJHXb6AMHif79EAHV7soRr1bXFK/i1UzdFKJjYujXvTPrlsxlwfRJOJ1OypUt45VeS4WU5Nz5i6nfz52/SEhwCTe1zPUqCBkhAdo0KKWaKqVaaq3PuOaNbQX0BDq6VkmZg7aG1rqe1nqLK30PyfPVpqcOYOgoV+AEcEZrnWCwLnXrPMz+AwcJP3ESgOUrV9MwtF4mW+W8pngVr+JVvOb1/FvNK8C9NWuwfe1Kwj6bS9hnc2n1wnM83agBYwe/61VerVSu4tUamuJVvOYVr/ZChbh3zUrOr/+aQ292Iyk2eSa2c1+tJ7jZs/j4+QFQ/L9PcXXffkhycNcHUwh66AEA/FVV/KtU5uqve033+m/QFK/i1UzdFJav+YrpsxcAcOHSJb74agPPPNnALS2zvTasV5dVX60nMTGRq9eusf7b72gU+rhXes1T2Hy8/+Ml2GSS45sopZ4A5gINtNbhSikb8CFwGagLjNBab8tgu2LAPmCga+5ZlFLtgAlALa31+TTrhgOhWuvwNGlOrbVNKVUB2Ka1rnAbj6EuH6HZzuD1K5ke7O07djFlxkwSEhMpV7YME0ePoEhQULZ3ZbameBWv4lW85vX8e43XuBu31RswajxVKt1JxzatbkmfMXs+l69cYVjf3hlvWCDzEQr/6nIVr/8qTfEqXv/tXrdWqEa5nm9RceC7RB8+csuyfS+2pGzHDgQ3ew6b3c613w6g+/bHER1NkTqPUGnEUGy+vjjj4jg2dgJ/7dwFwBPhWXv7ulXK1VuOlXgVr9y4dlu9AWMmUqXinXR85WWiY67z7qjxnDwdgRMnndu2plm6uV8BKFjYHK8Jcbf3OnIsVSpVpOOrrUlMTGTi9Fn8sOdnEhITafnCc3R8tfXfN/ItYI5X/yDvmczUi3Cs+8Trg472Z970imMnAdp0KKXaA/1IflkYwCbX9038Q4DWtV0FYDJQg+SRyQeBvlrr4+nWC8eLA7SCIAiCkC0yCdC6TRYCtIIgCIJ3sLVCNcM1sxqgFQQhm2QSoHWLLAZos00mAVq3yGKANttIgDZDJECbdSRAm5eQAK0gCIJgNBKgFQRByPNIgFYQLIQEaI3XBAnQ/gOO9Z96fRzK3rSzVxw775lsQRAEQRAEQRAEQRAEQRAEIY8hAVpBEARBEARBEARBEARBEIRcIl9uGxAEQRAEwcLIVASCIAh5HjOmI/ikZCXDNQHejDpmiq4gWAazpiMwA7OmIxAEL0QCtIIgCIIgCIIgCIIgCIIgGIuPPLifVaSkBEEQBEEQBEEQBEEQBEEQcgkJ0AqCIAiCIAiCIAiCIAiCIOQSMsWBcAvbduxkyoxZxMfHo6pUZtzwIQQEBHidpngVr+JVvOb1/ItX8SperaMpXsWrePVMs3rnjlTr9Bo4nVz9M5ztb71N3KVL1Bk3irJPNsTHbmf/9FkcnrcAgMBKFQmdOQ2/YkVJiI5h65vd+Ot/R3PEa05qilfxKl6t5TVPYrPltgPLYHM6nbntIU+hlHoJGEhycNwHWKS1nqSUKgfMBMq70g8Bb2mtzymlOgChWusOaXRCgRFa69As7/z6ldse7EuXLtP0pVYsmz+bCuXLMWnaDGJirjNiUP/sZNF0TfEqXsWreM3rB+wq3gAAIABJREFU+Rev4lW8WkdTvIpX8eqeZspLworXrkXjxfNZ+Wh94q9e45GxI/ENCODigYOUb9KYjS1fxbdwAM9/t5Gtnbtx/te9vLD1Gw7M+oSjX6zijicb8siYEXzx8ONA5i8J85b855aueBWv4tVNXf8giURmgGPjXK8POtqbdPSKYydTHOQgSqkywBSgsda6FlAHaKWUeg74BFiqtb5Ha10D2At8nJP+du7eQ83q1ahQvhwArVu8yFcbNuJJEN8MTfEqXsWreM3r+Rev4lW8WkdTvIpX8eqZ5oV9+1le+yHir17DXqAAhUqVIu7SJe58til68TKcDgfxf13h2MovqdKyBf6lQihStQpHV64G4NS33+FbqBDFa91jutec1BSv4lW8WsurIGSGBGhzluKAL+APoLWOBtqTPFo2JCXdxYeuT44RGRlFSMng1O8hwcFER8cQExPjVZriVbyKV/Ga1/MvXsWreLWOpngVr+LVc82kxEQqPPNf2ujfKPVYHY4sXkahsmWIPnMmdZ2YiAgCypQmoGwZYiIjIU0gJToigkJlSueI15zSFK/iVbxay2uexebj/R8vQeagzUG01vuVUmHAcaXUXmAryaNmjyqlBgJLlFIjge+ADcAXaTZ/Tim1L833AOC0kf6SnEnYMpgfxMdu9ypNs3TFq3gVr9bxmtfzb5aueBWved1rXs+/WbriVbwaoRm+bgPh6zZwV4e2NP1yBUkOxy1BWGw2khwObD4+t6YDNpsNp8ORY15zQtMsXfEqXsWrebqCcDu8J1ScR9BadwUqAB+RPN/sbqVUc631RqAM0Ak4D7wHrEqz6Vqtde2Uj2s9QykVEsK58xdSv0edO09QYCD+BQt6laZ4Fa/iVbzm9fyLV/EqXq2jKV7Fq3j1TDOw4p2E1Hk49btetISAcncQE3EW/1IhqemFQkKIOXOW6FOn8S9Z8hYN/5AQos9EmO41JzXFq3gVr9byKgiZIQHaHEQp1VQp1VJrfUZrPV9r3QroCbyhlHpfax2rtd6ote4L1AQaK6VK5JS/unUeZv+Bg4SfOAnA8pWraRhaz+s0xat4Fa/iNa/nX7yKV/FqHU3xKl7Fq2ea/iElaTh/Nn7FigJQueVLXD50mD/XruOutm2w2e3kDwqk0ksvEL7ua2IiznL1+J9UevEFAMo2fAJnUhKXfj9kutec1BSv4lW8WstrnsVm8/6Pl2CTSY5zDqXUE8BcoIHWOlwpZSN5ntmrwMvASK31Ite6dYClQGWgLRCqte6QRisUGKG1Ds2ygetXMj3Y23fsYsqMmSQkJlKubBkmjh5BkaCgLO8ipzTFq3gVr+I1r+dfvIpX8WodTfEqXsVr9jU/KVkp9f9qHV+jeufXSUpM5PrZSHa+05/o02eoM3YkZRqEYvfNz6H5C/lt+kwAAitVpP6M9/ErVhRHbBzf9+zDhf2/AfBm1DFL5D83dcWreBWvbuj6B3lPpM+LcHyzwOuDjvbGHbzi2EmANodRSrUH+pH8sjCATa7vFYCpQA3gOhAB9NJaH1RKdSCHArSCIAiCIAiCIAi5TdoArZFkJUArCIKQbSRAmyESoM06EqDNS0iAVhAEQRAEQRAECyABWkEQLIUEaDPEsXmR18eh7I3aecWxkzloBUEQBEEQBEEQBEEQBEEQcgkJ0AqCIAiCIAiCIAiCIAiCIOQS+XLbgJCDxMcar2k3pwo5Y2MM17T52A3XpGCA8ZoASQ7jNc3Iv1mYUVcBfPMbr5kYb7wmgK+f8ZpmTWljxpsvTSpX5/UrhmvaAksYrmkaZrQtNpN+6/WiN6pmStwN4zULFDRe02okmHAtMKNtBetct826DpjRZvsWMF4TzCkDS7VX183RLeBvuKRZUxEkjutuuGa+gTMM1wTMu8YajRltIJhzfTXr/s0MHInm6JrRDvgHGq8J5vQFEky6f/P3/MVkQt5GArSCIAiCIAiCIAiCIAiCIBiLlX7EzGUs8pOcIAiCIAiCIAiCIAiCIAjCvw8J0AqCIAiCIAiCIAiCIAiCIOQSMsWBgNPpZMCocVStVJGOr7YmNjaOkZPe58ChwzidTu6pXo3h/d7Gz8+9ucD00WOMeW8q16Kj8bHbGTXoXWrcfVe2ddZu2szcpV9gs4Gfnx+De3ejyp0VGDVlBgcO62Sv1e5i2Ds98CuQPa9Op5MBYyZStdKddHylJQ6Hg1FTp/Pz3t8AqF/nId59qws2D4bnO51OBgwbSdUqlenY7lW3ddKzeet2+g0bxd4d3xmmaYZXozTT19dr0dEMHjOR4ydOkpSUxPNNm9C5XRu39Rcv/4JlK1djs9m4o2wZxgwdSLGiRd33OnIcVSsne03hbFQUL7/elbAl8yhapIjbXrft2MmUGbOIj49HVanMuOFDCAjwbF6tsPUbmLtoMTabjYJ+fgx+9x1qVrvbI02zvH67dTvTP52Hj81GUFAgYwb3p1zZMtnWWfvNFuYuX53cthQowOCeXbijdAgjp87k8NHj+Pv58cJ/G9H2xec88mvGeWVGuaZgdNtiRt0yK/9GHiun08mA0eOT26w2rW5Z9lb/IQSXKM6wvr3d1jejDMwqV6N0rdi2psXIc8ssr0aer+mPl8PhYMK0mez4cQ8Oh4PX27Si9YvPu+01r18LjdRMbq8mJPeHXe3Vw089R0jwzbnWO7ZpxXNNnsx1r0bq+rz4Js7IUzh3fX1reutecO0ySesWAWC78258/tsGfHxwXo8m6evFEHkyy/sxso+ZFm+vV+kxqg1Mf//Wc9AITpw5k7r8dEQkD957Dx+/N9azfRjYdzOrXI263w7btJm5S1dgw0ZBvwIM7t2dalUrM2HGJ+zY83Nym926Ba1feNZtr4a3WQb2B3Ii/3kSq8yl7QVISeUSSqmXlFK/KqX2K6UOKKX6udLLKaW+Ukr9ppQ6qJRaoZQKdi0boZQaYaSPY3+G0757bzZt2Z6a9tGCRTgcDtYumc/aJfOJi4vjk4WL3dK/ERtLx+696dSuDWuWLqRbxw70HTIi2zrHT55i0qzZzJ4yjjULPqFr+1foOWgkHy9cisPhIGzhJ4Qt/ITYuDg+/WxZtrSPhZ+gfY932LT1+9S0sI3f8ueJU3z12RzCFs3mp72/sXHr9tuoZLKP43/S/s1ubNq8xW2NjAg/eYqJH8ww9IUXZng1SjOj+jrtk7mUDC7BumULWbngU5avDmPvgYNu6R88dIR5ny1h+fzZrPtiKRXK3cG0WZ+677XbrV4B1qzfSJvOPTh3/oJbuilcunSZgcNHM2PSBDatWckdZcswefpMjzSPh59g0rQZzPlwGmHLF9O102v06NvfI02zvMbGxtFv2Gg+fG8sYUsX0ODxxxgz+YNs6xw/eZpJH81j9qRRrJn7IV3btaLn0LGM/3A2/gX9WL/wI5Z/NIUde35l6w8/ue3XjPPKjHJNwei2xYy6ZVb+jTxWx/4Mp/1bb/+tHQCY/dlSftn/m0f6ZpSBWeVqlK4V29a0GHlumeXVyPP15vHalpq2/Mu1hJ885bpuz2bh8i/47fdDbunn9WuhkZrH/jxB+7f63HJuHT9xkiKBgYR9Njf1425w1ivblhKl8Xl9ILbqD/5tka1uU2wV1M2EAgXxeaU3SRuX4fhwEElr52Nv2SPLL0w2so+ZFm+vV+kxqg3M6P5t+rgRhC2cTdjC2Yzu/w6BAYUY/k4v9/dhcN/NrHI17H77xCkmzfyUOVPGE7bwE7q2b0OPwSNYHraO8FOnWffZHFbOmcnCFav57dARt7wa22YZ2x/IifwLQmZIgDYXUEqVAaYAjbXWtYA6QCul1HPAJ8BSrfU9WusawF7gY7O8LFn5JS2aPUOThqGpaQ/WrkXX19vh4+OD3W7nblWFiMhIt/R37d7DHWXLUL/uowA0rP84H0wYk22d/L6+jO7fh+DixQCocVdVLly6zAO1a9KlfZtUr9WqVuZM5LlsaS9ZtYYWzzWlSYP6qWmOpCRuxMYSn5BAfHwCCYkJFMifP9u+U/exYiUtXmhGkycbuq2Rnhs3Yuk3ZAQD+rjf8cgIM7wapZlRfR3cpyf9e3YD4PyFi8THx1O4kHu/wtaodheb1qykcOEA4uLiiDp3niJB7r2NMyOvUecvsHn7DuZOn+KWZlp27t5DzerVqFC+HACtW7zIVxs24vSgw5s/vy9jhg4iuERxAGpUu5sLFy4Sn5DgdV4dSQ6cTifXoqMBiLl+w61zNL+vL6Pf7UlwseQRLDVUFS5cuszBI//jucYNsNvt5Pf1pX6dB9m0fafbfs04r8woVzCnbTGjbpmVfyOP1c3rS+gt6Xt+3cuO3T/R6oVmHumbUQZmlatRulZsW1Mw+twyy6uR52tGx2vztu9p/szT5MuXj6DAwjR9siFrN3zjlte8fi00UnPJqi//1h/ee+B3fHx8eOXNHjzb5nU+nLsQh8OR616N0vV5uBHOX7bhPHjrD7C2O+/GVvUenD+lCcwVC4HY6ziP/578/cJZiLuBrVyVLPk0so+ZFm+vV2kxsg3M6P4thfiEBAaMmcig3t0pVTLY/X0Y3Hczq1wNu9/O78uYAWnut++uyoWLl9m45XuaN32KfPnsyW12o1DWbtrslldD2yyD+wM5kX9ByAyZ4iB3KA74Av7ARa11tFKqPRALhLjSU/gQ+PvPugYxrN/bAOza83NqWt1HHkr9/8zZSBYu/4LRA/u5pf/niVOUKFaUQaPGceSPowQWDqBfz+7Z1ilbKoSypUKA5EcZJsz4hCfq1qHuQw/c9BoZxcIVqxn17tvZ0h7m+mV110+/pKY1f/opNm7ZTr1mL5PocFD3oQdo4LroucOwAcnlt+vHPW5r/E1z3ERavvg8qkplwzTBJK8GaWZUX202G/ny5aPv8NFs2rKdJ+s/zp3l73B7H76++di8dTuDR48jv29+enZ9wzCvJUsU50MPHrNKS2RkFCFpOp0hwcFER8cQExPj9mNCZUuXpmzp0kDyeTZ+yjQa1H+c/L6+Xue1kL8/Iwf2pVXHrhQJCiQpKYllcz7Ktk7ZUiUpW6ok4GpbZs7miccepnAhf9Z+s4X7alYjPj6Bb7bvIl8+9y+ZZpxXZpQrmNO2mFG3TMu/gccqZeqCXXtuXl+izl9g7PszmPPBJD7/cq1H+maUgVnlapSuFdvWFIw+t8zyauT5evN43QyAnY06d0vQJCS4BProMbe85vVroZGaN9urm+eWI9HBow/ezzvdO5OYmEjnPgMJKORPh1YtctWrUbqpUxdUrnkzsXARfJ5+FcfCSfg81OBm+sVIyF8AW+UaOI8ehDIVIbgMFM7649NG9THT4u31Ki1GtoEZ3b+lsPKrrwkuXown6z/u2T4M7ruZVa5m3W+Pn/4xDerW4X/HwymVZpqTkBIl0Ef/dMuroW2Wwf2BnMh/nsWDaSLzGjKCNhfQWu8HwoDjSqmflFITAbvW+igwEHhPKXVaKbUQaAq4/2y9Bxw8rGnz5lu82qI5T7gZnExMTGT7rh9p2bwZqz+bx6svv0TnXu8QHx/vlt71GzfoPXQ0J0+fYUz/Pje9Hvkfr3Z7mzYvNuOJxx5xSzstH85bRNEiQexat4rv13zOX1evMW/pCo91jWLJilXks9t5qZnMf5PC5JFD2b1pLVeuXmXm3IUeaTV6oj57tmyix5sd6di9N0lJSQa5NI4kZ1KGcyL72O0ea1+/cYNe/Qdx8tQpxgwb7LGeGV710WPMnLOAr1csZueGMLq81o4e/Qe7PQrh+o1Yeg8fz8kzZxnTryf9u3XCZrPRvFNP3hoymkcfuBdfDwK0ZmBGuZrdthhZt8w8B8wiITGRd4aNYmDvt1JHaHiCGWVgVrla5XiZ5dOMc8vsMjX6WpCCMynplns1J058fNy7Jcnr10Kz68DLzz/D0L698C9YkMDChXmtdQs2u/k0iSXaFh879pe7k7RhCUT/deuyuBs4lryPT/1m2LuPxad2XZzHD0FiYrZ2YXQf0yr1KifvXRZ+voquHYx734dRmHUOmHG/3SvlfnvAOzjT+XbixMfufW22UZiZf0HIDKlZuYTWuitQAfgIKA/sVko111pvBMoAnYDzwHvAqpz2t/6b73i9Zx/e6f4mXTq0dVsnuERxKt1ZgVo1qgPQKLQeDkcSp85EZFsrIvIcrbv0xm63s3DGZAILJ//Ktn7zVjq+PYA+XTrRpd0rbntNy7fbdvDiM/8lv68vhQMCeOG/jdnzf/sM0TaCL9et58ChwzRr3Y7OPfsQGxdHs9btiDp/Pret5Tg7dv9ElGuOoUL+/jRt3IhD+n9uaZ04eYpf9t48zi82e5aIs5FcuXrNEK9GUiok5Ja5laLOnScoMBD/ggU90o04G0mrDp2w+/iw6NNZBBYu7KlVU7zu/HEP99WqmfpSsDYtmvPHsT+5fOVKtrUios7Runvf5Lblg/EEFg4g+vp1+nZ5na8WzGL+1HE4nU7Kly3ltl8zMKNczWxbjK5bZp0DZnLw8BFOnYlgwrSZNGvbkeVfruXrzVsYPPY9t/TMKAOzytUqx8ssn2acW2aWqRnXghRKhZTk3PmLqd/Pnb94y0uosqeVt6+FZp9XazZ8w5E/bo5uduIkn5uBFEu0LWXuhKLB+Py3DfbuY7E92ABbzUfweb5T8giw+Dgcc8fimDmYpPWLsBUPwXkpKkvSZvUxrVKvcure5ZD+g0SHg4furWWorhGYdQ4Ye78dRasuvZLbvg+nEFg4gFIlgzl3IU2bfeEiIa4pYLKLt/cFzM6/IGSGBGhzAaVUU6VUS631Ga31fK11K6An8IZS6n2tdazWeqPWui9QE2islHKv5+oGW3bsYszUacydPoVnn3LvRQAp1Hu0DqcjIjh4OHki7Z//by82G5Qtnb1AR/T167Tr8Q5P1q/L1JGD8StQINnrzh8Z+8Es5rw/nmcbN8hEJetUU1XY4HqhRUJiIlt2/kCt6p6/vdcoVi6ax7oVSwhbtohPp0/Fr0ABwpYtomSJHKsmXsOGzVuYOWc+TqeT+Ph4NmzewiMP3OeW1vkLF+kzcCiXLiePmvhqwyaqVKrIf4p4PkeY0dSt8zD7Dxwk/ETy24OXr1xNw9B6HmlGx8TQtnNXGjd4gvcnjMXPz88Iq6Z4rXaX4uf/28eFi5cA2Lx9B2VLl8r2m9ujr1+nXa8BPFnvUaYO75/atiwP28D0eckvR7xw6TIr12/imTRzXHkDZpSrWW2LGXXLjPybzb01a7B97crUl+20euE5nm7UgLGD33VLz4wyMKtcrXK8zPJpxrllllezrgUpNKxXl1VfrScxMZGr166x/tvvaBTq3qPIef1aaPZ59cexP5k+ex4Oh4PY2DiWfPElTzdyr79tibbl1FEck3rhmDkYx8zBOH/egvPAbpLWzAGnE3u7vlD6TgBsNR6GxASIPJklabP6mFapVzl17/LTvv08cv+9GY7SzG3MOgcMu9+OuU7bHu/QuH5d3h81JLVP3LDuo6xav5HERAdXr0WzfvM2GtV7zC2v3twXyIn851lsPt7/8RK863nNvMN1YIZSao/WOlwpZQNqA/uAl5VSe7XWi1zrVgOigEs5ZW7i9Fk4nU6GpBnRc989NRj+bp/bbJUxJYoXY+bkCYycMJkbN26QP39+ZkwaTwFXg5dVlqwKIyLqHJu/38nm728+WnUjNhYnToZOmHrTa83qDHunZ7a9pmVgr26MnjKdJq3aY7f7UOf+++j0aiuPNAVzGNCrO8MnTOHZVzoA0Kj+47Rr+ZJbWg/cV5suHTvQrnM37HY7wSWKM3OqeyPbzKZY0aKMHzGUnv0GkJCYSLmyZZg4eoRHmks+/4KIs5F8u3Ub327dlpq+4OOZHt1AmOG1zoP30/HV1rTt0gNf33wEBQYya/L4bOssWb2OiKjzbN7xA5t3/JCaPnPsMMbN+IRnO3TD6XTS8/VXqXl3VY88G40Z5WoWZtQtK+XfLMwoA7PK1SrHyyo+wTyvZl0LUmj94vOcPBNBszavkZCYSMsXnuOh++51SyuvXwvNrq9vdWrPqMnTeLbN6yQmJtKkYSgtmjX1Kq85ec46VszC/nxHsOfDGf0XjiXvZ3lbs/qYVqxXZnLi1BnKhJTMbRsZYla5Gne/vYaIyHN8u30X327flZo+9/0JnDxzlmbtOye32c2auj1C2ZvrVk7kXxAyw2bEW2mF7ON6KVg/kl8WBrDJ9b0CMBWoQXIgNwLopbU+qJQaAQwC0k4o86bWekmWdvpXlPEH225OjN8ZG2O4ps3HhLltCnr2wpB/JMm9N+TeFjPybxbxsebo+uY3XjPRvfmdMsXX2BFLAJjV3psxSsGkcnVez/4UCJlhC7TQyHUz2hazfnX2wtEv/0jcDeM1C3jH4365SoIJ1wIz2lawznXbrOuAGW22b/aCC1nGjDKwVHt13RzdAv6Zr+MlJI7L/guUMiPfwBmGawJeNbLrtpjRBoI511ez7t/MwJG9OY6zjBntgH+g8ZpgTl8gwaT7t+J3WOhikHM4ti33+qCjPbSVVxw7GUGbS2itFwIZvclIk/xisIy2GQGMMM+VIAiCIAiCIAiCIAiCIBiAj1fEPi2BRX6SEwRBEARBEARBEARBEARB+PchUxzkJa5fkYMtCIIgCEZjlcfbBUEQBFOmuXi/RCXDNQHevnDcFF1BEEzAP0iGimaA4/vPvT4OZa/X0iuOnUxxIAiCIAiCIAiCIAiCIAiCsVhlLm0vQEpKEARBEARBEARBEARBEAQhl5AArSAIgiAIgiAIgiAIgiAIQi4hAVrhFrbt2MmzL7/CU8+/RM9+A4iOjvZKTbN0xat4Fa/W8ZrX82+Wrng1Txdg89bt3Pt4Q8P08nq5WkXTLF3xKl7Fqzm6Yes38FzLNjRr9SqtOnTiwKHDWd62VpdOtPtlF21/3slzKxZTsERxbD4+1H9vLO337ua1Az9zT6cOqesX+E8Rmsz7mDY/bqX93t3c3frlbHnN68dKvIpXK3kVhNshAVohlUuXLjNw+GhmTJrApjUruaNsGSZPn+l1muJVvIpX8ZrX8y9ereU1hfCTp5j4wQzDXlCT18vVKpriVbyKV2t5PR5+gknTZjDnw2mELV9M106v0aNv/yxtG3xvLe7v1Z3lDZrw2YN1uXz0GI8OG0jNTh34T5VKLHrgMZY+3oh7u3eh5AP3AfDUpx8SfeYsS+o8wapnmhM6eTwBZUpnaX95/ViJV/FqJa95FpvN+z9egtcFaJVSLymlflVK7VdKHVBK9XOll1NKfaWU+k0pdVAptUIpFexaNkIpNSIb+whVSsUppaqnS3em+b+kUmqxUuqYa5+blFL3ZlG/qVJqlysPB5VSo5VSPq5lC5RSJ5VS+9J97EqpDkqpS2nSDiql/lBKPe/ado8r/WS69WpmNe+3Y+fuPdSsXo0K5csB0LrFi3y1YSNOD24kzdAUr+JVvIrXvJ5/8WotrwA3bsTSb8gIBvTp5ZFOWvJ6uVpFU7yKV/FqLa/58/syZugggksUB6BGtbu5cOEi8QkJmW57bu9+FtR8kPir17AXKEBA6VLEXrpM5Web8vuipTgdDuL+uoJeuZq7W7WgwH+KUL5BKLvHvQdA9JkIltVvTOyly1nymtePlXgVr1byKgiZ4VUBWqVUGWAK0FhrXQuoA7RSSj0HfAIs1Vrfo7WuAewFPvZwlwuUUvYMfBQEtgL7gMpa63uAycA3SimVSR6aAB8Cr7ny8CBQCxiZZrVhWuva6T4O17K1adJqAP1Izjta64e11rWBYenWO+BJIaQQGRlFSMng1O8hwcFER8cQExPjVZriVbyKV/Ga1/MvXq3lFWDYuIm0fPF5VJXKHumkJa+Xq1U0xat4Fa/W8lq2dGlCH68LgNPpZPyUaTSo/zj5fX2ztH1SYiKVnn2aN/44QNm6j/L7oqUULluGa6cjUteJPhNBQJnSFKlUkZjIKO7r2Y2W333NKzu/I7j2PSTeuJGlfeX1YyVexauVvApCZnhVgBYoDvgC/gBa62igPXAICElJd/Gh6+MuPwKXgYyeV2kJnNNaT9ZaO11evgXmA+9mojsYGKu1/p9ruxtAN2C7mz7LA5fc3DZbJDmTsGUwvNvH/rcYdq5qmqUrXsWreLWO17yef7N0xas5uktWrCKf3c5LzZ71xNrfyOvlahVNs3TFq3gVr+bpAly/cYNe/Qdx8tQpxgwbnK1tj331NR+Xq8qPYyfSfO0X2HxsQJqRdzYbTocDe758BN1Zgfir1/i84dOsb9eJ0PfGEHxvrSztJ68fK/EqXq3kNc9i8/H+j5fgPU4ArfV+IAw4rpT6SSk1EbBrrY8CA4H3lFKnlVILgaa4H/RMoRPwdvqpDkge9fpTBut/71p2O+4F/i9tgtb6tNZ6c5qkUemmN0g7mclzrrTjSqlI4H6gWZZy4yGlQkI4d/5C6veoc+cJCgzEv2BBr9IUr+JVvIrXvJ5/8Wotr1+uW8+BQ4dp1rodnXv2ITYujmat2xF1/rzXebVSuVpFU7yKV/FqLa8AEWcjadWhE3YfHxZ9OovAwoWztF1QxTspXefh1O+/L1xC4XJ3EB1xlkKlQlLTA0qFEH0mguizkcnrfbYUgCvH/+TMD3sIcc1Pmxl5/ViJV/FqJa+CkBleFaAF0Fp3BSoAH5E8enS3Uqq51nojUIbkoOp54D1glYf7OknyiNf0Ux04gXwZbJKfW376zJAkIDaTddJPcdA9zbK1rmkMagN/AP9LGY1rNnXrPMz+AwcJP3ESgOUrV9MwtJ7XaYpX8SpexWtez794tZbXlYvmsW7FEsKWLeLT6VPxK1CAsGWLKFmihNd5tVK5WkVTvIpX8Wotr9ExMbTt3JXGDZ7g/Qlj8fPzy/K2ASEleXrRHPyKFQXgrlYtuPj7YY6GradGu1ew2e0UCAqk6kvNOfrV11w9cZKovfuo9mr3yEoyAAAgAElEQVQrAPyDS1D6kYeI+r99WdpfXj9W4lW8WsmrIGSGzZsmOVZKNQUCtNafp0l7A2gOHNFav50mPQg4S3IQtzuA1npEFvcTCozQWoe6vn8DbCN5agKbUqot0E1rXSfddpOAolrrjrfR3gIs0FovSpNWFRiitW6nlFoAbNNaL8hg2w5AqNa6g+v7ncBBoJHW+sd/Wi/LXL+S6cHevmMXU2bMJCExkXJlyzBx9AiKBAVlazc5oSlexat4Fa95Pf/i1Yu8Jjn+eVk6Tkec5dmX27B355bbr+iTtUfo/tXl+i/SFK/iVbx6kddM7n8/mbeAD2Z9QtXKlW5JX/DxTP5TJGPd90vcXPeeN16jVueOJDkSiTkbyZbe73Lt9BnqjR9F+Qah+OT35cDchfw6LfkhysJly9Dgg/cIqlABfHzYO/NjDsxdCMDbF44bn/8s4DXHSryKVyt59Q/6+5wIAkk/fOk9Qcd/wOfRF7zi2HlbgPYJYC7QQGsdrpSykTzP7FXgZWBkSuBTKVUHWApUBoaCRwHacsCvQHFXgLYAyVMcLAcmaK2dSqnGrv3V1VofuY12E2Am0ERr/YdSKsCls09rPSQ7AVpX2gSgAfBwyny4ZgZoBUEQBEHIJtkI0GaZLAZoBUEQhGxiwv1v2gCtkWQlQCsIgpcgAdoMkQBt1vGqKQ601luBkcA6pZQGjgAOYATwNNBSKXVCKXUYGAM8q7VOuSsapJSKTvNpk439ngQGpfkeR3JQtAqglVJHgL7Ak7cLzrq23UjytAmfK6X2kxzo/RUYlma19HPQ7lNKVfsHyfHAncArWc2PIAiCIAiCIAiCIAiCIAjWwKtG0AomIyNoBUEQBMF4ZAStIAiCdZARtIIgmIGMoM2QpB/DvD4O5VOnmVccu4xehPWvQCnVEhiY0TLXS7g80V4CVM9g0Vqt9bAM0gVBEARBEARBEARBEARBEP6GjKDNS8gIWkEQBEEQBEEQBEswu2RlwzXfiDpquKYgCMgI2n9ARtBmnX/tCFpBEARBEARBEARBEARBEHIJm1e9+sqrkZISBEEQBEEQBEEQBEEQBEHIJSRAKwiCIAiCIAiCIAiCIAiCkEvIFAfCLWzbsZMpM2YRHx+PqlKZccOHEBAQ4HWa4lW8ilfxmtfzL17Fq3i1jqZ4Fa/iVbx6olm55Uvc06s7OCHxxnV+6DeIa+EneOz9SRS7pwaJMdf53+Jl/P7JHABKPf4Yj4wbhS2fnbhLl/mx/xAuHfw9R7zmtK54Fa9W8ioIt+Nf8ZIwpdRLwECSA84+wCLgN2Cia5XKQCQQDfyptX7BtV0x4AwwWGs9JY3eAqABcMmVVAi4CHTQWh9xrdMUGAQEAHbgS2C41jopK9u7NFYBVbTW96RJCwXWAelnb28PLHT9X86Vl0tAnNb64SwVVCYvCbt06TJNX2rFsvmzqVC+HJOmzSAm5jojBvXPknxOaYpX8SpexWtez794Fa/i1Tqa4lW8ilfx6q7m7JKVCapSiWe+DmN13YbciIrijsaNqPvBJCJ27MKZmMiOHn2w2e08uWwRh+fM4+wPu2l98P/Y3PZ1IrbvIKhqZRov/4xVj9QnKT4+05eE5fVjJV7Fq9u68pKwDEn6aZ3XBx19HnrGK46d5ac4UEqVAaYAjbXWtYA6QCuggNa6tta6NvAL0Mn1/YU0m7cB1gKdlVLpD8iwlO211lWA3cBI1z6bAB8Cr7n2+SBQK2V5Ztu7NIoD9wLRSqlH0+37lzTbpnz2p8nP2jT6WQvOZoGdu/dQs3o1KpQvB0DrFi/y1YaNeBLEN0NTvIpX8Spe83r+xat4Fa/W0RSv4lW8ildPNP+fvTOPs6n+//jzzjCWjLEbO5FPWaJSUrbSLmkTWqRI2qikEBIqUkq++vYtChGJShQiS6hUvyhSnwpjibGNMAtjZu7vj3tnusZgzNwz7nFfz8fjPjjb6z4/Z7vnvufcz0k/nMrXjz5Bys6dAOz+aQ3FKlag/IWN+XPaDLwZGWQcOcLWBQupdfNNxNQ+m9QDB9i+bDkA+//4iyMHDlLxkiaOuxZ0rlzl6iZXIU6G6wu0QDmgMFAcwFqbiO9u0/W5WPY+4E0gFbjieDMZY6KASvx7R+yzwAvW2j/875kCPAwsy+Xy4CsOfw3MAnrmwtVx4uN3EluxQtZwbIUKJCYmkZSUFFKZcpWrXOUa7u2Xq1zl6p5MucpVrnLNT2bilq1sXbAwa/jSl4ay+YsF7Pr+R87pfAeeQoUodNZZ1Gp/I8UrVmT/XxsodFZxqlzZGoByFzam9HmG4rEVHXct6Fy5ytVNrkKcDNf3QWut/dkYMxvYaIxZDSwBPrDWnvC3G8aYRkAssBz4EHgQWBwwy1BjzBNAWeAQvi4MhvmnXQD0zuaxDdiWy+XBVxweAKwFhhljHrfWZhZwmxhj1gTMO8paO/VE7QkGGd4MPJ5j7+yOiIwMqUyncuUqV7m6xzXc2+9UrlzlGu6u4d5+p3LlKtdwdw1GZqHixWn11ljOqlqZ+bd0BKDpC89z68rFpOzcxbYly6jY9GKOHExkYed7aTJ4AE2HP0f8yu/Y/vUK0lOPFJhrQeXKVa5ucg1bPGfCfaEFwxmxpqy1DwE1gf8CNYDvjDG3nmSx+4EZ1tp0fAXam40xgX9WHOzvvqANEAUssNYe8E/LwFd0PRHHXd4Y0xioCiy01m4FVuO76zeT7F0cOF6cBagUG8uu3Xuyhnfu2k1MyZIUL1YspDLlKle5yjXc2y9XucrVPZlylatc5ZrfzLOqVuGmRZ/jTU/n8xtuIXX/AQpHR/P9oOeZ1bQlX9x0Ox6PhwMbNoHHw5HEJD6/4WY+vuwKvunbn5g6tTmwcVOBuBZkrlzl6iZXIU6G6wu0xpi2xpiO1tq/rbXvWWs7Ab2AbidYJgq4E7jDGBMHZP5m5L7s81prLfAMMNkYE+Mf/SNwVCc+xpi6xpjJuVz+fqAI8Kf//c/FdwfvaaV5s6b8vHYdcZu3ADB95se0ad0y5DLlKle5yjXc2y9XucrVPZlylatc5ZqfzMIlzuLGLz4l7rPPWXxfD9IP+e4TOq9bVy56th8AxcqXx3S5i78+mgVeL9fNmka5CxoBcPatN5N26BAJ63513LWgc+UqVze5CnEyPG7v5NgYcwUwAbjSWhvnf9jXf4B91tqB/nmWAkOstUv9w7cBw6215wXkdAUGA7WB94Cl1tqJAdO/BVZYa/v6HxI2DrjOWvunMaYEMB1YY60daIyZeLzl8fVfux1oa61d5Z9WEtgKtPfPPsRa2/oEbT4mP1ck7z/pxl62fCWvjh3HkbQ0qletwshhQygVE3OyxQo8U65ylatcw739cpWrXN2TKVe5ylWuecl8p2IdGvXpTZNB/dn3629HTfuy0z00e/kFSp5dC4/Hw5pXx/DXhzMBiL38MpqNHE5kVGGS43eyvFcfDsZtBuCBnSfsCTDPrrkhVNarXOXqSG7xmGP7RBBk/Dgv5IuOEU2uD4lt5/oCLYAx5l6gL76HhQEsAPpaaw/7py/l6ALtXHxdDowNyIgC4vDd3dqJYwuslwNfAfWstRuNMZ2Ap4FI//t+BDxvrc04ToE2c/nBQEdr7UXZ2vAqUAV4i9NYoBVCCCGEEEIIcfp5p2KdoGfmpkArhMgDKtDmiAq0ueeMKNCKXKICrRBCCCGEEEK4AhVohXARKtDmiAq0uafQ6RYQQgghhBBCCCGEEEKcYXhc/+irAkNrSgghhBBCCCGEEEIIIU4TuoNWCCGEEEIIIYQIMZzojmBYmVpBzxyUsCnomUIIEW6oQCuEEEIIIYQQQgghhAgunpDo3tUVqIsDIYQQQgghhBBCCCGEOE2oQCuEEEIIIYQQQgghhBCnCRVoxVEsXb6CdnfcybU3306vvv1ITEwMyUyncuUqV7m6xzXc2+9UrlzlGu6u4d5+p3LlKtdwdw3F9jfp2Z2eP33Dg/+3kjs+mkLx8uWyppWsWoXeG9ZRrGyZY5YrVbM6T23fQKULGxeYa0FmOpUrV7kKcSI8Xq/3dDucEGPMOOByIAqoA6z3TxoDvApsybbIg9baVf5lGwBrgduttbMCMpcCFwAVrLWHA8avAf4BugM/AJWstYcCpt8LtLfW3mqM8VprPcaYmsAfAV6ZvAOUAspbax/3L38jMAdobq1d6R83DVgA1AR6AvHZctoBtYG5QGYv8ZFACWCktfat4627Y0jef8KNnZCwj7a3d2Lae+9Qs0Z1Ro0ZS1JSMkMGPJPrtyiITLnKVa5yDff2y1WucnVPplzlKle5hlL7Mx8SFntBIzpMn8TbF7fg8IGDXPXSUKKiS/DFo09y/l0daTmoH6Vr1uCVKnVI2ZuQtXxkkSLcM/9TKjasz+RrbmLHT2ty9ZCwM329ylWuFI9RZ6s5kLF6UWgXHYGIC64KiW0X8nfQWmsfsdY2Bm4AtltrG/uHvcBnmcMBr1UBi98PfAQ8mEP0AeCazAFjjAEq+9/zL3yF3bbZlukCTMgha3sOHuOAxfiKy5lcC3zp/zeT5sBC///fyiFnq3/ajwHjGgK3AGONMSVzWm95YcV3q2hYvx41a1QHoHOH25gzbz75KeI7kSlXucpVruHefrnKVa7uyZSrXOUq11Bsf/zqnxlXvwmHDxwkskgRoqtUIiUhgRKVYjHtbuCDG2/Pcbnrx4zi5/enkRxQtHXatSAz5SpXJ3OFOBEhX6DNK8aYwsBdwEDgQmNM7WyzzAICP3U6AjMDht8D7gzIqwrUBeafgsYPwNnGmLP8w238Ptf6M2sBB6y1f59CZiY1gSTg8EnmyzXx8TuJrVghazi2QgUSE5NISkoKqUy5ylWucg339stVrnJ1T6Zc5SpXuYZq+zPS0jDtbuDxDeuofnkzfp70AYk74vmo070k/LXhmPkb33cPkYULsfrdyQXuWlCZcpWrk7lCnIhCp1sgn9zk75Ygk8PW2qb+/7cFNltr/zDGfAr0AALvR58HvG2MKWytPQLcCAwB6vmnzwBeNsbEWGv3A3cD71tr03PwqJzNA+Aea+1aY8y3wCXGmDhgr7X2B2NMeWNMWaAl/949C9DTGHNzwPAma+0t/v838b/HWUBpYClwdWAXDfklw5uBx3Psnd0RkZEhlelUrlzlKlf3uIZ7+53Klatcw9013NvvVK5c5RrurqHcfjvnC+ycL7jg/i7cOXcm/6l3EeRwl2Bs4/O5qHtXJl1142lzLYhMp3LlKtewJSIkeg9wBW6/gzZ7FwdNA6bdB0zz//9D4D5jTFTA9MPAcuAqf1+1G4HkzInW2iR8/cXe6h91N767anMipy4O1vqnfYWvm4PM7g3A1/VBC//ry4Cc7F0c3BIw7Ud/1w4N/Mv/Y6394YRr5xSpFBvLrt17soZ37tpNTMmSFC9WLKQy5SpXuco13NsvV7nK1T2ZcpWrXOUaiu0vfXYtql3279fnNROnEFO9GsVKl8px/vPv6kSRktHct3Q+D6xaRnSlWG6Z+D/qtr3OcdeCzJSrXJ3MFeJEuL1AmyPGmArA9UAf/52r4/HddXprtlk/wtfNwR34irjZeRe40xhzIb67X//Mg85ioBm+/m4zi7FfAk39r2WnEua/Y/YBoJ0x5o48+ByX5s2a8vPadcRt9j13bfrMj2nTumXIZcpVrnKVa7i3X65ylat7MuUqV7nKNRTbX6JSRW6dPIFiZcsA0LBzB3b/+hspCftynP/LvgN4s+ElvNO0Fe80bcXBHfF80vVB/vg8dz0Ahst6latchcgrHrd0cmyMqQkstdbW9A93BVpba7vmMG8f4Cpr7fUB44b4529tjFmKrzuDb4HfgL347ma9FBhirW0dsNx64BtgubV2UsB4r7XWk90rBxcP8AtQBDjXWpvh795gMb6i75UBflhrh+SQ0ToHr57AAMBYa1Nyeu9jSN5/0o29bPlKXh07jiNpaVSvWoWRw4ZQKiYmV/EFmSlXucpVruHefrnKVa7uyZSrXOUq11Bp/7AytbL+f9ED99GkZ3cy0tI4uCOe+Y/35Z+4LVnTBx1K4JUqdUjJ4YFgj9k1zOzclR0/rWFQwiZHXE9Xplzlmqfc4jH6LX8OZPyyJOSLjhHnXxES287tBdrRwJZss44G+gIDrLVzApYvD2wGLgTewlfwXGqMeR/AWnvPcQqh/fH1XVvZWpscMD6wQPsHsD6bx9fW2l7+eT8EIqy1HQKW/wH4xFr7on94CNATiM+W8xSQloNXIXyF36nW2heOs9qOJhcFWiGEEEIIIYQQZyaBBdpgkdsCrRBnNCrQ5ogKtLnHNQVaEQRUoBVCCCGEEEKIsEUFWiEcQgXaHFGBNvcUOt0CQgghhBBCCCGEEEKIMwxPSNQ+XYEKtEIIIYQQQgghRBjgxN2uz5euGfRMgOf2xTmSK4QQoUjE6RYQQgghhBBCCCGEEEKIcEV30AohhBBCCCGEEEIIIYKLR/eF5hatKSGEEEIIIYQQQgghhDhN6A5acRRLl6/g1bFvkpqaijmnDi8+N5ASJUqEXKZc5SpXuYZ7++UqV7m6J1OucpWrXMOl/Rc/9ABNetwHXkjYuIk5D/UmefceAEpWrUK3r7/krYtbkLI3AYC6ba/j5vFvsn/rtqyM9668gdTERMddCzpXrnIV4kR4vF7v6XYIG4wx44DLgSigDrDeP2kMsAJ4BagPHAIs8JS1dpN/2TggGUj1L1MK+BG411qblCuB5P0n3NgJCftoe3snpr33DjVrVGfUmLEkJSUzZMAzuW5jQWTKVa5ylWu4t1+ucpWrezLlKle5yvVMb3/mQ8IqXdCIO6ZP5q2LW3D4wAGuHjGUItHRzH3kCc6/qyOtB/endM0avFy5dlaBts2wwRw+mMiKl0cfk3uyh4Sd6etVri5zLR7jydebnqFk/Lo85IuOEfVbhMS2UxcHBYi19hFrbWPgBmC7tbaxf/gLYAkww1pbx1rbAPgEWGmMKR8QcUPAMnWB6kCXYPmt+G4VDevXo2aN6gB07nAbc+bNJz9FfCcy5SpXuco13NsvV7nK1T2ZcpWrXOUaLu3fsfpnxta/iMMHDhBZpAjRlSuTvDeBEpViOfemtky58bZjlql66SXUuqIFD/6wnK5ffUH15pcViGtB58pVrkKcDBVoQ4OHgEXW2qmZI6y1U/DdVdvzOMuUAmKAhGBJxMfvJLZihazh2AoVSExMIikpdzfoFlSmXOUqV7mGe/vlKle5uidTrnKVq1zDqf0ZaWmYm27gyY2/UqN5M9ZMnkrijnhmdOxCwp8bjpk/JSGBH99+j/9d3IKvBg2l44z3ia5SuUBcCzJXrnIV4mSoQBsaXAx8n8P4r/3TMvnCGPOLMWYnvrtu/wPMCJZEhjcDj+fYO7sjIiNDKtOpXLnKVa7ucQ339juVK1e5hrtruLffqVy5yjXcXcOt/fazLxhVpQ7Lho/k7rmzIIe8TGZ07MJvn3wGwNZvvmPrd99Tu03rAnMtqFy5ylWIk6ECbWjgJecHtkX5p2Vyg7X2fOBhoDzwkbU2aPfYV4qNZZe/A3eAnbt2E1OyJMWLFQupTLnKVa5yDff2y1WucnVPplzlKle5hkv7S9euRbXLLs0aXj1xCjHVq1GsdKkc5y8SU5LmTz951DiPx0P6kTTHXQs6V65yDVs8EaH/ChFCxyS8WQU0y2F8M3wPAjsKa+0sYD7wbjAlmjdrys9r1xG3eQsA02d+TJvWLUMuU65ylatcw739cpWrXN2TKVe5ylWu4dL+6NhYbn9/PMXKlgGgYecO7Pr1N1IS9uU4f+rBRC7u2Y3zbm4HQGyjhlRpciF/fbnIcdeCzpWrXIU4GR51clzwGGNqAkuttTX9w2WBNUB/f9+zGGO6ACOARtba3caYOKC1tTbOP70C8BfQ2Vr7ea7eOHn/STf2suUreXXsOI6kpVG9ahVGDhtCqZiYU2tgAWTKVa5ylWu4t1+ucpWrezLlKle5yvVMbv/zpWtm/b9Jj/u5uGd3MtLSOLgjni96P8U/cVuypj93eB8vV65Nyl7fo1QqXdiY618bSZHoaDLS0ljQdwBxy1b45t0XF3TX3BIK61WuLnMtHnP8vjzCmIz1K0O+6BhR7/KQ2HYq0J4GshdoA8a9AjTAd2fzOuApa+1G//Q4Agq0/nHPAncB51trT/47kFwUaIUQQgghhBBCiNwSWKANJrkp0AoRMqhAmyMZv38b8nWoiHObhcS2U4E2nFCBVgghhBBCCCFEEFGBVghUoD0OKtDmHvVBK4QQQgghhBBCCCGEEKeJQqdbQAghhBBCCCGEEEIIcWbh8YTEzamuQAVaET440Z2HUyebjPTgZ0ZEBj9TOLOtwJnt5c0IfiaAx4EfY6SlBj8T8Cb9E/RMT4kyQc8k0qGPZwf2VyfWKYAnumzwQ48cDn6mUxQucroNco9T3WW56YLeLZ/bTm0rJ46tqKLBzxSQfMCZ3GLRwc906BzgyLXAWaWCnukmnOqKIGPjz0HPjDi7UdAzHcOpa3dHvhc78+NsrwPnLE+xEkHPFOGDMWYJUAE44h/1IBANjAaKAR9aawfmJVsFWiGEEEIIIYQQQgghhDgOxhgPUBeoYa1N848rBligFbAV+NwYc721dt6p5qtAK4QQQgghhBBCCCGECC4O3V0dTIwxpYCcfgrxj7U28GcXxv/vl8aYssA7wFrgT2vtJn/WFKADcMoF2tBfU0IIIYQQQgghhBBCCBF8Hgc25fB6PNt8pYGvgFuANkBPoDqwI2CeHUDVvEjoDlpxFEuXr+DVsW+SmpqKOacOLz43kBIl8t9Hi9frpd/g56l7Th26dbk7CKbO5M7+fB4TJk/B4/FQrGhRnn26Dw3rnZfvXKfWK8CiJcvoO3goq5d/FZQ8J1ydar+b9tcRo99g/qLFxMSUBKBWjeq8PmJ4vjKdav+U6R8xbebHeDweqlWtwvBB/SlbJn/9rDrhunDJMt54+10iPB5iYkoy/NlnqF61Sp6ypnw8h+mfzcMDVKtSiWFPPUbZ0r4/ou7YtZuOD/dh9vixlC4Vk6d8+9cGhr88moOJiURERjJ0wNM0OO/cPGUFEsrnls++XMyEaTPxeDwULVKEZ3v35J2pM9jy97/XL9t2xHNxo4b8d8SQPL1HMNvv9Xrp9/yL1K1zNt3u7kx6ejojxoxj+berSE9P5/67OtH5tptDJjcTt5yznfp8BWfO2U5dt0BwP7edOgcEc3t5vV76DX2RurV9x8DBxESeHT6SjZu3kJGRwc1tr6NHl7vy7Oqma4FQPl5nL1jEhA9m4MFDsaJFePbxR6hXtw4jxv6P5at+8J2vOneg8y3t8uwa6tfZny1YxIQPZuLxQNGiRXn28YepWbUKz454lU2bt5Lh9XLzdVfzwN0d8+wabvtVTgRjHYycOIMF3/4fMSWKA1CzciwZGRlsid+VNc+2XXu5uF5d3uz/aJ7ew03r1Ylr90yC+ZkVzHOA73j96KjjteG5vpsbd+zcRccHezF74v/yfO0Oznx/EyHL68DEHMYf1Wm5tfZb4NvMYWPMBGAosCJgNg+Qpw6kVaAFjDHjgMuBKKAOsN7//2r4+pZI8M/3I7DPWnu1f9gAC6y1NY0xcUBr4C58tzMDNAIyezb/CGgC1AJKALHAX/5pzwDN8FXf47PptQNqA3MD5o/0Z4y01r6V3/ZnkpCwj/7PDWPae+9Qs0Z1Ro0ZyytvjGPIgGfylbth4yaeH/Eyv6z9lbrn1AmSbfBzN8ZtZtSYsXw8dTIVypdj2YqVPPbUMyz94rN85Tq1XgHitmxl5Otjg9bRuxOuTrXfbfvr6l/WMvqloVzY6Pyg5DnV/nXrf+fd96cye/oUoqNLMPK1Nxjz5tsMHdgvpFwPHTpM38HDmP3BRGpUq8rEDz5k+Cuv8/bro045a539i3c//ITZE8YSXeIsRr45gTHvTmFon0f5dMFXjH3vA3btSciza8qhQ3R75HFeGNSfVs0vY9HSr3lq4BDmz5qe50wI7XPLxi3bGPXmeGaN/w8VypVh2bff02vgcJbMnJw1z9rfLL0Hv8jgJx7J03sEs/0bNsXx/Muv8cuv66lb52wApn/yGXFbtjJ32iSSklPo2K0n9c+ty/n165323Ezccs526vMVnDlnO/U5AMH93HbqHBDM7bVhUxzPj3qNX379jbq1fcfAmP9NoGKF8rwxYhjJKSnc2PleLr6gERc0bHDK+W66Fgjl43Xj5q2MGvc2H7/7XyqUK8uyb1bx2LNDeODuTsRt3cbc98eTlJxMxwd7Ud+cw/n1Tv0PjKF+nb1xy1bf59aEcb518O339BrwPG1aXkZs+fK8MXywb3+9pwdNGjfkgganfs4Ot/0qJ4K1DlbbDbz6xANccG7OGWv/2kTvUW8x6IE785TvpvXqxLV7JsH8zArmOcB3vL7DrAlv+o/XVfQa8DxLPv6AT+ctZOy7k9m1Z2++nYP9/S1sccFDX/3dGJz0CZLGmOZAEWtt5l8sPEAcUClgtlhge1481MUBYK19xFrbGLgB2G6tbWytrQd8g69wijGmnH92Y4wp7v9/C2BhtqwX/Ms39g839r9esNbe4h/fHfgxYNoC/+JvBYzLfG31TwucvyG+W6rHGmNKBms9rPhuFQ3r16NmjeoAdO5wG3PmzcebzxPy1Bkz6XBLe667uk0wNB3LjYoqzPBBA6hQ3repG9Q7jz179pJ65MhJljwxTq3XlJRD9B04hH5P9s5XTiBOuDrVfjftr6mpqay3fzB+0lTa3XEXj/Xtz/Yd2f8Wc2o41f4G9c5lwacziY4uweHDh9m5azelYvL+l2enXNMz0vF6vRxMTAQgKTmFIlFRecpqYOqwYOrbRJc4i8OHU9m5Zy+lSkazc89evlrxHeNHDc2zJ8DK71ZRrWoVWjW/DIA2rVoE5a/voXxuiSpcmGHPPE6Fcr67NxqcWyssv/wAACAASURBVJc9CfuyzqepR47Q78VX6f9YDypVLJ+n9whm+6fO/IQO7W/kujats8YtWvo1t954A4UKFSKmZDRtr27DZ/O+DIncTNxyznbq8xWcOWc7dd0S7M9tp84BwdxeOR0Dzz7Zi2d6PQzA7j17SU1NJfqsvN1B5qZrgVA+XqOiCjO835NUKFcWgAbn1WXP3n3MX/w1t7a9lkKFIn3nq6ta89mCRXlyDfXrbN/n1hP/roNzz2FPwj6efvgBnn6kBwC79yZw5MgRos86K0+u4bZf5UQw1kHqkSP8tmkLEz5dwE1PDKHXy/9l++69AdPT6D/2Pfrf35FK5fJ2F6mb1qsT1+4Q/M+sYJ4DfMdrwDnLf525PX4XXy1fyfhXX8q3rxPf38QZQSlglDGmqDEmGrgXGICvTljHGBMJ3Eke+p8F3UF7Mr7Cd2ft58A1wGJ8d9W2wrfCW+C7s/V0UBNIAg4HKzA+fiexFStkDcdWqEBiYhJJSUn5+unF4H59AVj57ap8OzqZW7VyZapWrgz4fnrz0qtjuLJVC6IKF85XrmPr9cWRdLztZkwQ7+5xwtWp9rtpf925ew+XXnwRjz/cg3Nqn82E96fycJ+n+WTqJDx5/IuiU+0HKFy4EIuWLOPZYS8SVTiKXg89kK88J1zPKl6c5/s/RaduD1EqpiQZGRlMG//fPDsWLlSIRcu/ZeCosURFFaLX/XdRsVxZxg57Ns+ZmWzavJXyZcswYOiL/P7nX5SMLkHfXnm7azSQUD63VK1UkaqVKgK+8+mI/7zNFZc3zTqfzvp8ARXKleXqlpfn+T2C2f7BfZ8AYOWq77PG7di5i0pH5ZfH/rUhJHIzccs526nPV3DmnO3YdUuQP7edOgcEc3v9ewz8kDXO4/FQqFAhnnpuGAsWL+PqVi2oVaNanlzddC0Qysdr1UqxVK0UC/i3+RtvcWXzZvyxMY5KFf79I1ps+fLYvzblyTXUr7Ozr4MRY//HFc0vJcr/x9++Q0ewYOlyrmpxObWq56lrwbDbr3IiGOtgV8I/NG1wLr3uvJlzqlXm3dlf8ujIccwaNQiPx8Osr1ZQvnQprm56YZ7fw23rNdjX7hD8z6xgngNyPl6bUTm2AmNfHBIUXye+vwn3Y62da4xpCqzG98v2cdbab40xXYFZQFHgC2BmXvJ1B+2JWYyvQAtwLbAA+NL/f/zTgtPxp4+expg1Aa9PAqY18Y/70xizB7gbuNpaG7QCbYY3I8eTTURkZLDewhUkp6TQ+5kBbNm6leGD81+ccWK9Tp0xi0KRkdzePu/9gOWEE65O7Vdu2l+rVanMO2+Mpm6d2ng8Hrrdcxdbtv3Ntu07Tr7wcXC6/Vdd0YpVixfw2IPd6PbI42Rk5KkbHcAZV/vXBsaNn8gXM6awYt5set7XhceeeTZfdyFc1aIZ3332AY92vZPufQfnq82BpKWlsWzlt3S8tT0fv/8ud99xOz169yE1NTVfuW44tySnHOLx515ky9/bGf70v33sT5zxKT27dM5XttPHgDcj46hfZHnxEhGR/8umYOa66ZwNwf98dRNOfG47fQw4vb1eeX4Q3y34jP0HDjBuwqQ8ZbjpWsANx2tySgq9Bw1jy7a/Gd6vD95s+V68RETm7zwY6tfZySkpPD5oOFu2bWf4M09mjR81uB/fzp3J/oMHGTdxap59g40b9qtgU7Vied4e2Ju61avg8Xi4v/01bInfzd+79gAwae5CHrq9bb7ew43rNZjX7k5914TgngN8x6v/nBVwvAYDJ76/iTMDa+0ga+151tq61tox/nFfWWsbWWuNtfYJa22evpSqQHtifgLqGGOigOb4Ov5dCLQ2xlQFEjL7pw0S2bs4uCVg2o/+7hEa4Csc/2Ot/SHnmLxRKTaWXbv3ZA3v3LWbmJIlKV6sWDDfJqTZviOeTl27ExkRweS336RkdHS+M51Yr5/M/Zy163+jfecu9Oj1JIcOH6Z95y7s3L075Fyd2q/ctL/+/udffPr50b9y8Hp9d23mFafav3nLVn5cvSZr+Lb27di+I579Bw6GlOuKb1dxYaOGWQ8Fu6vDrfy5YRP79u8/5azN27bzf7/8mjV82/VXs33nbvYfTMyzXyAVypejdq2aNGpQH4CrWrckPT2DrX/nqWuiLEL93LJ95y46P/wkkRERTBozkpLRvrtD1v/xF+np6VzSuGGePcH5c0Cl2IrsCvjJ5K7de4mtkLfuGJzKddM524nPVzfhxOe2k8eAk9tr+Xffs9PvfVbx4rS95irW2z/ylOWma4FQP163x++kU8/evm3+n1cpGV2CShUrHNWP4649e4ktX+4EKSd5jxC/zt4ev4vOPZ8gMjKCSWNHUTK6BMtX/chO/zo4q3gx2l51Bevtn/n2Dhahvl85gY3bxuyl3x41zuv1UqhQJOs3biE9PYOL69fN13u4ab06ce3u1HfNYJ4DfMfr40RGRjJp7CtZ15nBwonvb2GLJyL0XyFC6JiEINbaDOB74B7gD2vtYWvtNnzr7Tqy9T9bQE6HgQeAdsaYO4KZ3bxZU35eu464zVsAmD7zY9q0bhnMtwhpEpOSuKfHQ1xz5RW8NuIFihYtGpRcJ9brzMnvMnfGVGZPm8zbb4ymaJEizJ42mYrl81c8cMLVqf3KTftrhMfDC6NeyyrIffDRx5hzah/1M6dTxan2796zlyf7DyJhn6+P9DnzFnBO7bPz9QRUJ1zrnWv44ac17Nnr+xvZomXLqVq5EmVKlTrlrN0JCTw59GX2/eMr7s5ZtJRzalWndExwuvhueVkztm3fzrrffgfgh59W4/FA1cqVTrLkiQnlc0ticjJdej3D1S0vZ/SQ/hQtUiRr2g9r1nLphY3y/fMwp88BbVo2Z9acz0lLS+PAwYN8vvArrmrdIqRy3XLOdurz1U048bnt1DHg9Paat2gx48a/h9frJTU1lXmLFnNpk7z9FNlN1wKhfLwmJiVzz2N9uKZVc14bOjDrnN2m+WXM+nw+aWnpHDiYyOeLlnJVHrumCfXr7MTkZLo89hRXt7qc0c8/m7UO5i9exrh338/aX+cvXsalFzUOinswCOX9yik8ER5efHc623b6ioXTFizF1KhKbNky/LD+D5o2PDckrzHcdO3uxGdWMM8BvuO1D1e3an7U8RpMnPj+JsTJUPn/5HwFPAmMDxi3BOgNPHo6hKy1+40xzwGvGGPmWGtTgpFbtkwZXhoyiF59+3EkLY3qVaswctiQYES7gqkffsT2HfEsXLKUhUuWZo2f+Na4fH3AuWm9OuHqVPvdtF7r1qnNwKef5KEn+pKenk5sxQqMfiF/D55yqv1NLmxMz25d6dLjYSIjI6lQvhzjRr8ccq7NLr6Ibnd35p6ej1G4cCFiSpbkzVfy9kCAJuc3oOc9HenyeH9fm8uVYdzwgfnyC6R8ubKMe2UEz494hZSUFKKiohg76iWK5PNiMpSPgakfz2H7zl0sWv4Ni5Z/kzX+vddeYvO27VSJrZjv93C6/Z1vu5ktf2+n/V33cSQtjY633MQlF14QUrluOWc79fka7jh1DDi9vfr1foTnRrxKuzu7AnBVqxZ06Xh7nrJC+TyYnVA+XqfO+pTt8btYuGwlC5etzBo/4bURbPl7B+3v7eE7X7VvyyUXNMqTa6hfZ0+dNdv3ufX1ShZ9/e86eG/MywwbPZabuvgeFHZVy8vp0uGW48UUOKG8XzlF3epVeLZbZx566T9kZGRQsWxpXnnC1+fq5h07qVKhbL7fw03r1YlrdycI5jng3+N1BYu+XpE1/r0xo4J2g4UT39+EOBmeYDyN8UzBGFMTWGqtrRkw7jxgPdDAWvurf1xb4EOgjLU21T8uDmhtrY0LWNZrrT3mz3fGmNbAEGtt64BxQ4CeQPZHAz4FpOUwfyHgF2CqtfaFXDUweX94b2wn9nWnOgjPSA9+ZkRo9Bt1xuHEtgJntpc3OH2qHoMTPwtJy1//rMfDm/RP0DM9JfL2hOATEunQ308d2F+dWKcAnuj8f8E6hiNB67bdeQoH/24Qx3DqWtJND+Fwy+e2U9vKiWMrKvzutC4Qkg84k1vMgW5LHDoHOHItcNap/4JHnJyMjT8HPTPi7Lz9keG04NS1uyPfi535cbbXgXOWp1hwu0PIokQZF124FBzeDT+FfB3KU/vCkNh2KtCGEyrQBj9TBVqhAq0KtCrQBj0TVKBVgRYVaFWgDX6mUIEWFWjdhAq0KtCqQOt+VKDNPeqDVgghhBBCCCGEEEIIIU4T6oNWCCGEEEIIIYQQQggRXCJ0X2huURcH4US4d3EghBBCCCGEECJsGVSqpiO5w/6JcyRXuIjiMSHxM/lQw7tpTcjXoTy1GofEtlMpWwghhBBCCCGEEEIIIU4T6uJACCGEEEIIIYQQQggRXNz00NfTjO6gFUIIIYQQQgghhBBCiNOECrTiKJYuX0G7O+7k2ptvp1fffiQmJoZkplO5cpWrXN3jGu7tdypXrnINd9dwb79TuXKVa7i7hnv7ncrNT2bThx7gsTXf8Ojqb7hz1lTOKl8ua1rJqlXou+lXipctkzWu/HmG7kvm8fAPX/Pw98uoc/WVBeZa0LlydS5XiOPhyoeEGWNqAkuttTWzjfcC9wGjgS2ABygMPG+t/dA/TxzQ2lobF7DcUmCItXapP+MBa+347NOBy4EO/tGNgJ/9//8IWOnPaH08T2PMOcArQH3gEGCBp6y1mwLcplhrBwZkTPRnTPRPTwZSA5q92lp73wlXWCYneUhYQsI+2t7eiWnvvUPNGtUZNWYsSUnJDBnwTK7iCypTrnKVq1zDvf1ylatc3ZMpV7nKVa7h3v5Qcs18SFjlCxrR6cPJjGvSgsMHDnDtiKEUiY7ms0eeoPHdHblyUH9K16rBS5Vqk7w3AYD7F85hzZTp/DRpKpUaN+T+hXN5KfZsMtLTT/qQsDN9vcoVPSTsOHg3rw35oqOnRsOQ2HZn6h20n1lrG1trGwFXAu8YY2JOYfmXjDHVso+01r7gz23sH27sf71wskBjTEVgCTDDWlvHWtsA+ARYaYwpHzDrE8aYi04QdUPA+zbOdXE2F6z4bhUN69ejZo3qAHTucBtz5s0nP0V8JzLlKle5yjXc2y9XucrVPZlylatc5Rru7Q9F1+2rf+b1ehdx+MABChUpQskqlUlOSCC6Uizn3dSWSTfedswynshIipYuBUBUiWjSDh0qENeCzpWrc7lCnIgztUAbSDSQiO+O1dzyOjD+pHOdGg8Bi6y1UzNHWGunACuAngHzvQhMNMZEBfn9T0p8/E5iK1bIGo6tUIHExCSSkpJCKlOucpWrXMO9/XKVq1zdkylXucpVruHe/lB1zUhL47ybbuCpTb9Ss3kzfpo0lYM74pl2Rxf2/rnhmPnn9u5Ly6ef4KmN6+g6/xPmPNaHjPT0AnEtyFy5OpcrxIk4Uwu0Nxlj1hhj1gG/AuOttYdPYfmRQFljTPcgOl0MfJ/D+K/90zKZCmwABh8n5wt/2zJfQbuDNsObgSeHJ+xFREaGVKZTuXKVq1zd4xru7XcqV65yDXfXcG+/U7lylWu4u4Z7+53KDUbmb599wYjKdVg8bCT3zp2VYx5AoSJF6Dh1Ah93f4RXzm7AhCvbctO41yhZtUqBuRZUrlydyw1LPJ7Qf4UIbi3QZmQfYYzxAJn3m2d2cdAAqAF0NsZ0Pt6y+PqqzRpvrU0DunKcrg5y65Qt1wsUymGeqADvTHoCDxynq4PsXRy8l0u/k1IpNpZdu/dkDe/ctZuYkiUpXqxYSGXKVa5ylWu4t1+ucpWrezLlKle5yjXc2x+KrmVq16L6ZZdmDf80cQqlalTL6sIgOxXqn0fhYsX544sFAGz7/kd2rf+dapecqHfC4LgWdK5cncsV4kS4tUC7D8jep2wF//ijsNbuAObie8BX5rLZz7rHLGutXcepdXVwstxVQLMclmsG/JjtveOBJ4GJ+Aq4BULzZk35ee064jZvAWD6zI9p07plyGXKVa5ylWu4t1+ucpWrezLlKle5yjXc2x+KrtGxsdwxZTzFy5YBoNGdHdj162+kJBxTUgAgYcNGisSUpNqllwBQ+uyalD/PsH3NL467FnSuXJ3LFeJEeNzaybEx5ntgpLV2ln94ENAAmAe0ttZ29Y8vAiwH3rLWvmuMeRkoCvS21nqNMa2AKUAta22aMcZrrfX4ly0EfAc0BK611i4NeP+s+fzDkcAmoIO1dpUxJgL4H5BgrX3GGFMWWAP09/c9izGmCzACaGSt3W2MifO7x/mnfwpcDzxorZ2Yffopk7z/pBt72fKVvDp2HEfS0qhetQojhw2hVMypPF+tYDLlKle5yjXc2y9XucrVPZlylatc5Rru7Q8V10Glamb9/+Ie99P0oe5kpKVxcHs8c3o/xT9xW7KmD0vdx0uVapO8NwGAWq2ac81Lz1O4aFEy0tJYMnwkv332hW/ef+KC7ppbQmG9yhUoHhM6v5UPIbxb1oV80dFTvUFIbDs3F2jrAv8FyuG7y/QX4BHgRmA0sAVf1wFFgEXA49baDGNMNPAGcIl/eoJ/2k/+3OyF1wbA/3GSAq1/3KXAa0Ax/2sx0Mdam+yfXhN4BV8hOQJYBzxlrd3onx7H0QXaWHx96PYJKNAmA6kBb5tsrb0sVystFwVaIYQQQgghhBDiTCSwQBtMclOgFWc4KtDmiHfLryFfh/JUrx8S2861BVqRB1SgFUIIIYQQQggRpqhAKxxDBdocUYE297i1D1ohhBBCCCGEEEIIIYRwPYVOt4AQQgghhBBCCCGE0zh1p+tLZWsFPbP/3k1BzxSiwPGExM2prkB30AohhBBCCCGEEEIIIcRpQgVaIYQQQgghhBBCCCGEOE2oiwMhhBBCCCGEEEIIIURwURcHuUZ30IqjWLp8Be3uuJNrb76dXn37kZiYGJKZTuXKVa5ydY9ruLffqVy5yjXcXcO9/U7lylWu4e4a7u13KjcUXS/q2Z3u//cN3X9cyW0zplC8fLmsadFVq/DohnUUK1vmmOXO73IXt8/8oEBdCzLTqVw3uQpxIjxer/d0O5xRGGNqAkuttTWzjfcCVwBzgb8AL1AMWAfcZ609aIyJA6ZYawcGLDfRnzfRPz0ZSAU8QBrwlLV2Sa7kkvefcGMnJOyj7e2dmPbeO9SsUZ1RY8aSlJTMkAHP5Cq+oDLlKle5yjXc2y9XucrVPZlylatc5Rru7Q8H18yHhMVe0Ihbpk3i3UtacPjAQa58aShRJUow/7EnaXBnR1oM6kepmjV4vWodUvYmAFC0dClaPT+I+p1uZ+vyb/jots5A7h4SdqavV1e5Fo/RraI54N32W8gXHT1VzwuJbac7aAueH621ja21F1hrz/WPGxAw/QljzEUnWP4G//KNgCHAqf+J7Tis+G4VDevXo2aN6gB07nAbc+bNJz9FfCcy5SpXuco13NsvV7nK1T2ZcpWrXOUa7u0PJ9f41T/zvwZNOHzgIJFFihBduRIpCQmUqBRL3Ztu4MN2tx+zzHm33UzijngW9x9coK4FmSnXcMbjgldooALt6Wcp0CBg+EVgojEmKhfLLgFijTFlgyESH7+T2IoVsoZjK1QgMTGJpKSkkMqUq1zlKtdwb79c5SpX92TKVa5ylWu4tz/cXDPS0jin3Q08+tc6qjVvxi+TPyBxRzwfd7qXhL82HDP/6vETWfnSKNIPpxa4a0FlylWIk6MC7WnEGHMWcBPwbcDoqcAGIDd/PrsT+NNauzcYPhneDDw5dOAcERkZUplO5cpVrnJ1j2u4t9+pXLnKNdxdw739TuXKVa7h7hru7XcqN5Rd/5zzBWOqncPy4S/Tcc5Mxx6UFG7rtSAyncwV4kSoQBt8MrKPMMZ48PU5C9DEGLPGGLMG+B6wwOhsi/QEHjhOVwdf+JdfD9wK3BEs8UqxsezavSdreOeu3cSULEnxYsVCKlOucpWrXMO9/XKVq1zdkylXucpVruHe/nByLX12Lape1jRr+JdJU4ipXo1ipUvly8kJ14LMlGsY4/GE/itEUIE2+OwDYrKNq+AfD//2QdvYWlvfWvuYtfZQ4MzW2njgSWAikL2rg8w+aOtZa6+z1q4JlnjzZk35ee064jZvAWD6zI9p07plyGXKVa5ylWu4t1+ucpWrezLlKle5yjXc2x9OrmdVqkj7yRMoVrYMAPU7dWD3r7+RkrDvJEsWvGtBZspViJPjUSfHwccY8z0w0lo7yz88CF8/s/8FhlhrWx9nuTigtbU2zj/8KXA98KC1dmL26adM8v6Tbuxly1fy6thxHElLo3rVKowcNoRSMdnrzaeGE5lylatc5Rru7ZerXOXqnky5ylWucg339p/pri+VrZX1/wseuI+LHuxORloaiTviWfB4X/b7C30A/VMSeL1qHVL2JhyV0fDuzpx7y018dFtn33x7Nznieroyz3jX4jGhcytmCOH924Z80dFTxYTEtlOB1gGMMXXxFWPL4bsD9hfgEXxF2lMp0MYCvwJ9CqpAK4QQQgghhBBCiNwTWKANFrkt0IoQQQXaHFGBNveoQBtOqEArhBBCCCGEEEIEFRVohQq0OePd7oICbeXQKNCqD1ohhBBCCCGEEEIIIYQ4TahAK4QQQgghhBBCCCGEEKeJQqdbQIgcSUsNfqbHgb9HROoQcoSMdGdyndgHPCHxa4jc4c1wJteJ9eoUTnTr46Z9wAlSEp3JLVbCmVwn0H4lwh0nPrcjIoOfKQQ4d53pBDoOXIMT3REkd2kb9EyA4pPmBj/UTdctqYecyS2e/weTnZm4aN84zbjoW7UQQgghhBBCCCGEEEKcWahAK4QQQgghhBBCCCGEEKcJ/T5bCCGEEEIIIYQQQggRXNzU/cVpRgVacRRLl6/g1bFvkpqaijmnDi8+N5ASJfLfD6DX66Xf4Oepe04dunW5Owim8P6HM5kyYxZFixShdq0aDH66D6ViSuYr0/61geEvj+ZgYiIRkZEMHfA0Dc47N9+uTqxXp7aVm1xHjH6D+YsWE+Pf7rVqVOf1EcPzlTn783lMmDwFj8dDsaJFefbpPjSsd16+Xd20XqdM/4hpMz/G4/FQrWoVhg/qT9kyZULO1an2O7EPuOl4heCds71eL/2Gj6Ru7Vp0u7Mj6enpDB39Bj+s/gWAVs0u4elHe+LJ44Wbm9arm84t4Mzntltc3bRfuck1k0VLltF38FBWL/8qKHnhvl7l6q791YnMcN9WTm7/UPx8iXr0WTK2bCTts2kQEUHhbk8QWa8xAOmrv+PI5HF4qtakSO/n/l0oIoKIGrU5PGoA6au+ztX7OHXdEurXF16vl35DX6Ru7bPpdndnDh06zPOjXmPt+t/wer2cX78ez/V9gqJFiwTFXYhAzrguDowxC40xNwcMv2KMSTTGRAWM226MiTPGtM627ERjTNeA4ULGmB3GmLEB48oaY9b4X/H+V+ZwWWNMdWPMHGPML8aYdcaYGcaYCtne5zFjTKoxJjbbeK8/Z7V/+ZXGmIYBblsC3us3Y8yjwVpvAAkJ++j/3DDGjhrBgk9nUq1qFV55Y1y+czds3MS9Dz7MgkWLg2Dp47sff+KdyVOZ9OYYZn8wkZaXN2Pwiy/nKzPl0CG6PfI43bvcxacfTOLhbl15auCQfLs6sV6d2lZucgVY/ctaRr80lNnTJjN72uR8F2c3xm1m1JixjP/PGGZPn8JD3e/jsaeeybenm9bruvW/8+77U5n+3jvM/egDalavxpg33w45V6fa78Q+4KbjFYJ3zt4Qt5l7H+vDgiX/fhGYPX8hmzZvZc7745k9+R2+X/0L85csy1O+m9arm84t4Mzntltc3bRfuck1k7gtWxn5+tigPVgv3NerXN21vzqRGe7bysntH2qfL54qNSjy3BgiL22dNS6y5bVEVK7OoT73cuiprkTUa0xksyvwbovjUN/7sl7pP/9A2vKFuS7OOnXdEurXFxs2xXHvI4+zYPG/16b/nTiZ9PR0Ppv6Hp9NfY/Dhw/zv0lTgqUvxFGccQVaYDFwecDwVcC3QHMAY0wdIAmIy0XWDcD3wB3GmOIA1tq91trG1trGwFvAW5nD1tq9wP+AD6y151trGwCr/fMFch/wKXB/9jf051xgrT0fmA4EVkYGB7x3a2CEMSb/f8bys+K7VTSsX4+aNaoD0LnDbcyZNx9vPi8gps6YSYdb2nPd1W2CoQnAr7/9zmUXNyG2oq/2fc0VrVi8fCWpR47kOXPld6uoVrUKrZpfBkCbVi3yXfADZ9arU9vKTa6pqamst38wftJU2t1xF4/17c/2HfH5yoyKKszwQQOoUL4cAA3qnceePXvztV+Bu9Zrg3rnsuDTmURHl+Dw4cPs3LWbUjH5eyKpm9rvxD7gpuMVgnfOnjrrUzrc1JbrrmyVNS49I4OUQ4dIPXKE1NQjHEk7QpGoqBOkHB83rVc3nVvAmc9tt7i6ab9ykytASsoh+g4cQr8ne+crJ5BwX69yddf+qmPAPa4Qep8vha67lbSv5pL+7ZJ/R0ZEQNFiUKgwFI7CU6gwpKYetVzEeecT2aw1qW+PyrWrU9ctoX59MXXmJ3RofyPXtWmdNe7ixo146P4uREREEBkZyXnmHLbH5+87Z9jh8YT+K0Q4Ewu0X+Ev0BpjKgOHgZnAtf7pLYAvc5l1H/AJviJtp1wuEwsUDxj+j/+F3+l8oAwwEnjAGHOibbAEaJDTBGvtTuB3oF4uvU5KfPzOrIInQGyFCiQmJpGUlJSv3MH9+tLu+uvyq3cUjRrU57sf/4+//QW5j+d8wZEjR/hn//48Z27avJXyZcswYOiL3HrP/dz3SG/S09Pz7erEenVqW7nJdefuPVx68UU8/nAPPvtwCo0a1ufhPk/n64KsauXKtG7RHPD9vOWlV8dwZasWRBUunC9XN61XgMKFC7FoyTJaXn8TP/y0hlvbvP5aXQAAIABJREFUt81Xnpva78Q+4KbjFYJ3zh7cpzftrjn6AvzWG66lZHQ0LdvfQfObbqdG1Spc6f+j2KnipvXqpnMLOPO57RZXN+1XbnIFGPziSDredjPmnDr5ygkk3NerXN21v+oYcI8rhN7ny5EJr5G+YuFR49KXzoPEgxR7+1OKvTObjPhtpP/fyqPmKXzPIxz54G1ISc61q1PXLaF+fTG47xO0u/bqo8Y1v/QSalWvBsDfO+KZNP0jrmtzRf6khTgOZ2KB9v+A2saYoviKsl/6X5kF2pZA5pltfECXAWuAmzJDjDHlgauB2cCHwIO5fP/+wMvGmG3GmElAWyDw95v3AzOstf8HpAV4HYUxxoOvKPztcaY3Aur42xsUMrwZOfYDGBEZGay3CBpNLmjEIw/cz6N9B3Brl254PB5KxZSkcKG8f2ikpaWxbOW3dLy1PR+//y5333E7PXr3ITXbXyFPFSfWq1Pbyk2u1apU5p03RlO3Tm08Hg/d7rmLLdv+Ztv2HfnKBUhOSaH3MwPYsnUrwwc/m+88N63XTK66ohWrFi/gsQe70e2Rx8nIyMhzlhvbH8x9wE3Hq9P8593JlCkVw8q5s/j60w/558BB3v1gRp6y3Lhe3XBucQq3uLppv3KT69QZsygUGcnt7dvlR+0Ywn29ytU9+6uOAXe5OoETroU73If3wD5Surcj5cFb8JQoSaF2/95XFmEa4ClZ6pjCbm4J9nWLExTUPrDuN8tdDz7K3R1u5Yo83lwgxMk44wq01tp04DugCf4CrbV2E1DcGFMaaIavGwSA7gHdEzQGPguIuhv4ylq7D1+R9nxjzAW5eP/5QBWgO7AbeBmYBWCMKQzcBUzzzz4D6Bm4fECxeD2+u2N7BEwe6p++Fl/XBz2stXG5WC25olJsLLt278ka3rlrNzElS1K8WLFgvUXQSExK5pILG/PJlHf5ePIErmrVAiBfDwmrUL4ctWvVpFGD+gBc1bol6ekZbP17e75cnVivTm0rN7n+/udffPr5vKPGeb1QuFD+nn24fUc8nbp2JzIigslvv0nJ6Oh85YG71uvmLVv5cfWarOHb2rdj+4549h84GFKuTp6vgr0PuOl4dZqFS5dz243XE1W4MNElSnDL9dew6qc1J18wB9y2Xt1ybnEKt7i6ab9yk+sncz9n7frfaN+5Cz16Pcmhw4dp37kLO3fvDjlXN61Xubpnf9Ux4C5XJ3DCNbJpK9IWfw5paZCcRNrSeUQ2+LdkEXlZG9KWzc9Tn8dOXLc4QUHsA59/+RX393qSPo88SM+u9wQtN3zwuOAVGpxxBVo/mf3QXsK/d6AuAtoDe6y1B3KR0RW4zBgTB6wFMjjJXbTGmDLGmNestYestfOttU8BDYFr/HfktgNKAZ/4c7sCbY0xVTMzAgrG51lrb81WgM3sg7ahtbaptTZvtx0dh+bNmvLz2nXEbd4CwPSZH9OmdctgvkXQ2LV7D/f0fIzERN9PF/777iTaXnNVnp8EDtDysmZs276ddb/9DsAPP63G44GqlSvly9WJ9erUtnKTa4THwwujXssqoH/w0ceYc2of9ROXUyUxKYl7ejzENVdewWsjXqBo0aL59gR3rdfde/byZP9BJOz7B4A58xZwTu2zKV0q7/3Quqn9TuwDbjpenaaeOYd5i5cCcCQtjcUrvqFR/bx1pe6m9eqmc4tTuMXVTfuVm1xnTn6XuTOmMnvaZN5+YzRFixRh9rTJVCxfPuRc3bRe5eqe/VXHgLtcncAJ14xNfxB52ZW+gchIIi9uTvof67OmR9ZrTMbaU//BrVPXLU7g9D6wePlKho8ew4Q3Xj2m+wMhgk3+bjULXb7C1y3BWmttmn/cQmAovv5oT4gx5iKgGlDFWpviH9camGOM6WutPd6tZPuBm4wxq621k/3j6gE7gQR8fdoOtNaODHivpfjuth1yKg10grJlyvDSkEH06tuPI2lpVK9ahZHDTrtWjpxdszo97r2bDvf1ICMjg4san8/gvk/mK7N8ubKMe2UEz494hZSUFKKiohg76iWKFCmSr1wn1qtT28pNrnXr1Gbg00/y0BN9SU9PJ7ZiBUa/MDRfmVM//IjtO+JZuGQpC5cszRo/8a1x+SpQumm9NrmwMT27daVLj4eJjIykQvlyjBv9csi5OtV+J/YBNx2vTtO/98MMe/UNrut0L5GRETS76EK6353bLt6Pxk3r1U3nFqdwi6ub9is3uTpFuK9Xubprf3WCcN9Wbtr+TrimvvcGUd2fpOiYqZCRQfraH0mbPTVruqdSVby7Tr37N6euW5zA6X1g5Btv4vV6GfjCv9+HLjy/Ac89nb/agxA54QnGEw5DEWNMPDDMWjvOPxwD7AFaWWu/8RdGh1hrlwYsMxFYiq97hMPW2j7ZMv8PeMda+5Z/eAiAtXZIwDwGGI3v4V7JwHagN77uDjYANa21ewLmbwf8F6gBpFlrc7wFNNPNWjvx1NeGn+T97tnYafnr9zVHPA7cMB55pv6N4zSTkf+Hs+WIE/tACD318aR4896P7AlxYr06hROfeW7aB5wgJdGZ3GIlnMl1Au1XItxx4nM7IvT6kBRnCE5dZzqBjoOwJrlL/h7UezyKT5ob/FA3XbekHnImt1RFF62EgsMbvyHk61Ce2Nohse3O2AKtyAEVaIOfqQKtM6hA6wwq0KqQ5gQq0Gq/EkIFWuEmVKAVLkEFWodQgbZA8e7cGPJ1KE/Fs0Ni27noW7UQQgghhBBCCCGEEEKcWahAK4QQQgghhBBCCCGEEKcJ/T5bhCaFok63gTid6OdczuCmrgicwoluHjwu2l/1M2QhhBOkp518nlNF5xbhFE5cD7mp2wThGopP/tyR3LX1Ggc9s+H6NUHPdIyooqfbIMwIid4DXIG+rQshhBBCCCGEEEIIIcRpQgVaIYQQQgghhBBCCCGEOE2oiwMhhBBCCCGEEEIIIURw8aiLg9yiO2jFUSxdvoJ2d9zJtTffTq++/UhMTAzJTKdy5SpXubrH1an2Z7JoyTIuaNEmKFlu2laZBKP9Xq+XZ4a+yISp04+Z9ugzAxn6yuv5ynfTep39+Txu6ngX7TvdTaeu3Vm7/rcgmLrr2HKLa7i3P9i5Xq+XZ4a8wIQp0wBIT0/nhdFvcF2Hu7j61k5Mm/VpyLg6melUrlzdc862f23gnh6PcPOd93LrPfez7rff850J2lZyzXtuqZtvps68L6jzxeecPWsmxRo2PGp69bf+S+Xnnz9mudIdOlBj/PgCdS3oTPB/fg0awoTJU4KSJ8TJCIsCrTFmoTHm5oDhV4wxicaYqIBx240xE40xXf3DccaYmgHT+xpjfjXGVDTGdDXGTPSPH2KMiTfGrPG/rDFmuH9aTWNMasC0zNcjAe+x3j9uvd/zooD3XGqMaR0wXMgYs8MYM9aJ9ZSQsI/+zw1j7KgRLPh0JtWqVuGVN8aFXKZc5SpXuTrV/kzitmxl5OtjwevNd5abtlUmwWj/hk1x3PvoEyxYvOyYae+8/wE//vxLfhRdtV43xm1m1JixjP/PGGZPn8JD3e/jsaeeCUlXN61Xt2SGs+uGTXHc+/DjLFi8NGvc9E8+I27LVuZOm8TMie8wafpH/PLr+tPu6mSmXN3l6sQ5O+XQIbo98jjdu9zFpx9M4uFuXXlq4JB8ZYK2lVzznht19tnEDuhPXJd7/5+98w6Povjj8Hu5JIQWauggShkFlGIFaVIUpYMoSJXwAwEpUkMLvSiCUoKFjgSQHhARiRQBBVFRQXQUpAdCAggE0nO/P+4SQwwkudsht2be57knudndz352dsru92ZnOfFScyLmzafchx+kLC/apw95n3zyjm2sBQpQauoUSo4PdOq9T2bK15N/naJ7n37sCN3lspZGk1lyRIAW2AU8m+p7E+BboC6AEKIicOtuGwsh3gK6As9JKcPTWeVDKWUNKWUNoBbQTQjxgmNZWPKyVJ/ULcZLjrQqwPvADiFE0btYeQn4DnhFCJEnw6POIvsPHuLRqlUo/0A5ADp1aM/W7V9gc+EGXYWm9qq9aq/aq6rjB4iOjmH42AkEDBnkshaY61yBcccfvGEzHVo1p1mjhnekH/rhCPsOfkfHtq1d0jdTvnp7ezFl3GiK+dm792pVHiEy8gpx8fFu59VM+WoWzZzsNXj9Jjq0bkGzxg1T0kL3fE27Fi/h6elJAd/8NG/amC3bv8x2ryo1tVdzeVXRZh84eIiyZUrToG4dABo3qMf7M6Y4rZdMTj9X2qvzura4WC6MDCAhIgKA20eP4unnh8XLi7zPPE3+BvW5Grzqjm0KtGhOQng4l6ZNu69e77cmQPDa9XRo25pmTY15mi5HY7G4/8dNyCkB2q9wBGiFEKWAWGA9kBxErQeke2UohBgIdAMaSSkvZ7QjKeUt7EHUalk1KaXc5tj2tbus8jqwybFOx6zqZ8SlS+GUKF4s5XuJYsWIirrFrVt3jV1ni6b2qr1qr9qrquMHCJz2Nq+2b4OoVNFlLTDXuQLjjj9w2GBavtD0jrTwiEimvjePdyeOw+rh2iWImfK1TKlSNKxXF7A/Ljd91hwaNaiHt5eX23k1U76aRTMnew0c/hYtm93ZDlwMv0zJO/T9uHQ5Itu9qtTUXs3lVUWbferMOfyKFGb0pGm069qT1/sPIjEx0Wm9ZHL6udJendeNP3+Bm7t3p3wvOXYsN0O/wlqoECXHj+fcoMHYku4so1eDV3F57jyS4uLuq9f7rQkQGDCcli82c0lDo8kqOeUlYT8AFYQQPtiDsl86PpuAkUB9IARok2a7fsAwwF9KGZmZHQkhHgDqAO85kkoJIX5Ks1pXKeXRu0gcAx5OR9cPaAr0BBKAAcCSzHjKLEm2JCzp/HrgYbW6laYqXe1Ve9VezeNV1fEHr92Ap9XKy61bcj7soktayZjpXKk4/mTiExIYGjiJUYPfpFjRIi7rmSlfk7kdHU3A+ElcuhTOoqA5LuuZqW6ZxWtOP36VusnYkpLuGKxiw4aHkz/Y5PR81V7N02YnJCSw98C3rPhoPtWrVSV0z9f0HjSU3Vs34u3tnbHAXcjp50p7dV3Xkjs3Zd99F69SJTnt34tyHyzg4qTJKSNrjcRM+arRZAc5YgStlDIROAg8gSNAK6U8BeQRQhQCamOfBiEtzwEtgJlCiHL32MUbjnlkfwE2A9OklAccy9Kb4uBuwVkAGxCdTnoX4Csp5TXsweTHhBA176GTZUqWKMHliH/i0OGXIyjg60ue3LndSlN71V61V+1V1fFv+mwbR4//RutO3eg9cAgxsbG07tSNcBcuUs10rlQcfzLHfvudcxfCmDEniNZd/VmzaQufh+5izNR3nNIzU74ChF28RMcevbB6eLDi4wX45s/vkh6Yq26ZxWtOP36Vuv/oF+dyxJWU75cjrlCimJ+TWjk7X7VX87TZxfyKUuHB8lSvVhWAJg3rk5iYxLkLYS7p5vRzpb26putVqhQVNm7AlpTIXx07kav8A3iXLUvJcWOp+Pk2CnfuTIEWzSk9Y4ZLHo3wej81NUZjMcHHPcgRAVoHyfPQPoV9/lmAUKA1ECmlvJHONp2llJ8DHwGrhBB3+xkmeQ7ax6SUNaWU813w+RiQ3psSegB1hBCngaNAEtDHhf38i7q1n+bno8c4feYsAGvWb6Rxw/pup6m9aq/aq/aq6vjXr1jCZ2uDCVm9go/nzsYnVy5CVq+guJ9zwQOVXlXoqjj+ZGo+Wo29W9YT8sliQj5ZTMe2rXipSSOmjhnhlJ6Z8jXq1i269u7L842e470ZU/Hx8XHZJ5irbpnFa04/fpW6yTSuX5cNW7eRkJDAjZs32bbzK5o0rOc2Xs2Ur9qredrs+nVqcz4sjGO//Q7A4R+PYLFAmVIlXdLN6edKe3Ve1yNvXh5as5obX3zBuQEDscXGcvvHI8g6z3LipeaceKk5V4ODuf7ZNi4EBLjs0xWv91tTo8kuLEa8UMUMCCGqAZ8CJ6SUrR1pbYFJwHop5UQhxDJgj5RymSMQ2lBKeVoI4YV9BO5nUsrxQogejmU9hBATAKSUE9LZZ3mHXvm7eErZh+N7S2AhUElKeVMIsQeYANwEdgKlpZTRjnUbAluBUlLKm5nKhNvXMzzZe/cdYNa8IOITEihXpjRvT55AwQIFMiV/PzW1V+1Ve9VendJMyvx8b+fDLtLylc4c2Z/B21s9Mn6Eym3OlYrjj7/3PGQBk6ZTqcKD+He+c+r0eQuXcu36dQKHDU5/w1wZj3xwm3zN4FrqoyXLeH/BR1SuWOGO9GUfBlGo4F10M/nCArepW/8hrzn9+J3SjY+9p17AxKlUqvAQ/l06kZCQwNtzF/DNocPEJyTwattW+Hfp9O+NvHKp8ZpNmtqrG3lV0WZnon89/OMR3pkTRHR0NN7e3oweNpgnalS/90bWjGck/E+fK+1Vie7RKjXw69eX4kOHEiPlHctOvdaZxL//BqDY4EF4FipM2Pjxd6xT8OX2FHjxRc7490pJe/R42lkdjfGaXZrJBAROpFLFCvh365LxynkKuM9QTDfCFnHW7YOOFr9ybnHuckyAFkAIcQmYLKUMcnwvAEQCDaSU39wtQOtY9xHgMPAS8BCZD9D+wb9HxH4tpRzo2MdtIPnuNhIYKqX82bH9HuwB2peBWCnl0DT6PwALpZQfZioDMhGg1Wg0mv80WQhQZppMBGjdBhXHn0GA1mkyEaB1G1RcS7nRG2U1mgzJIEDrFJkM0Go0WUZFm62if4VMBWg1mqxytEoNwzUzG6D9T6MDtOkTec7941BFy7rFuctRAdocjw7QajSanI4O0BqvqQO0OkCr0egArcZM6ACtJoejA7SK0AHa9NEB2kyTk+ag1Wg0Go1Go9FoNBqNRqPRaDQat0L/JKdxT2Kjjdc002gsjXlQNWJCxahMM3lVhZm8mgRb3G0luhYztdkJKkYPGvMiMVOjYpSbLcl4TdBti5meyIuLMV7TW9dXU5EYb7ymp7fxmhqNomt3FaNd44emM5e4AXjNWq1EV6NxR3SAVqPRaDQajUaj0Wg0Go1Go9EYi562K9PoKQ40Go1Go9FoNBqNRqPRaDQajSab0AFajUaj0Wg0Go1Go9FoNBqNRqPJJnSAVnMHe/btp+Urr/FCm5cZODyAqKgot9K02WyMnDSNxcFr/rXszZFjmfTu+65YVXL8qnRVeQVHPo+bwOIVK91aU1UeqPAKELp7LzXrNTZMT2UZMINXM9UBM7UtybhaBrZ8uYvWr/ejTc/+dOw7hKO//8HAcVNo07N/yueJF9vTN2CC0/swQxtgs9kYOWEqi1feOYfaxfBw6jVvx9W//3ZJ30x1S4VuyLbttHq1M607dqFjj14cPf6bAU7t6HbQmHpgs9kYOfGfOnAzKoqBAeNo0ak7L73alY9XBLukb/h1ZiqvMTGxjJo8gxadutO8YzdGTZ5BTIzzc06bqQzkdK87d++lZafutH6tB936DuTs+Qtu6VOVrvZqLq8zZs+l4UttaN2pG607dWNwwFiXNY3wau0+BI8m7VK+e9RvjueouXgGfoi1xzDwdMy4WaIs1qEz8Rw9D8/R87A8UivL+zLTvWbOw2KCj3ugA7SaFK5evcao8ZOZN3MGOzavp2yZ0rw7N8htNE+eOk33N99ix669/1q28JNVfP/zL27jVbWuKq8AJ/86Rfc+/dgRussQPVWaqvJAhVeA02fP8fb78wx7kYrKMmAGr2aqA2ZqW5JxtQz8dfY8MxcsYuHMKWxeEkTfbh0ZOHYKcyePZfOSIDYvCWLy8IH45stH4Fv9ndqHGdqAk6dO073f4H/1W5u3fUHn3gO4HBHpkr6Z6pYK3b9On2HmnHksmj+HkDUr6dvrdQYMG+myV9DtoFH14OSp03Tvf2cdmPPRYooX8+Oz1ctZv+xj1mwM4cjRY07pG36dmcbrB8tWkJiYyJbgpWwJXkpsbCwfLXfu5t9MZSCne42JiWV44GTmvzOVkFXLaFTvWaa4OAjETMevvZrLK8CRX44ye/okQlavIGT1Ct6fMSV7vZYoi3XwdCw166YkWWrUwaNhSxLmjCZhcl/wzoVHo7YAWDv1J+mbL0mYNoCEFe9h/d8o8Mh8qMpM95oazb24Z6kXQuwUQrRJ9f1dIUSUEMI7VVqYEGKZEKKH4/seIcTKNDoThBATUi0/IYT4SQhxTAjxjRCiSZr1OwshfhZC/CqEOCqEGCeE8HQs+1MIUSPVuuuFEH+k+p5XCHFTCJFbCPGYEGJXKq1FQoi8GRxzDyHEsjRpDYUQe1IdyyWH/5+EEFIIMcWxrLwQIi7NsqVCiOJp9KoJIWxCiPap0l5ItV1Uqjza5FhuS7U8+TP1XseSVfYfPMSjVatQ/oFyAHTq0J6t27/A5sLNiZGawRs206FVc5o1anhH+qEfjrDv4Hd0bNvaaZ9Ge1Wtq8orQPDa9XRo25pmTY0bNaRCU1UeqPAaHR3D8LETCBgyyDBNVcdvFq9mqgNmalvAmDLg7eXF5JGDKVa0MADVHq5M5NVrxMXb35wdFx9PwLRZjBrQm5LF/ZzahxnagOD1m+jQugXNGjdMSQuPiCR07z4Wz53lsr6Z6pYKXW9vL6aMG00xv6IAVKvyCJGRV1LKmbPodtC4epBeHRgzZCAjB/YDICLyCnFxceTPm88pfUOvM9Px+mSN6vTt2Q0PDw+sViuPiEqEXbqU7V5VamqvkJiUiM1m46ZjtNyt29Hk8vbOYKv771OVrvZqLq9xcXEcl3+waHkwLV/pzIDhowi76Fw7ZZRXjwYtSDqwA9uP+/5Je7oxiV9tgttRYLORuGoeSYccAVUPDyx57P2AxScPxMdlya+Z7jU1mnuR0c8Su4BnU31vAnwL1AUQQlQEbqWzXQchxL2iZb2klDWklNWAgcCnQogqDs0ewEignZSyKlAHqAl8lNaTEMIKVAduCiEeciyvDRyUUkYDnwJjpJTVgUeBeGByBsecGT50+K8B1AK6CSFecCwLS7XsYeASsD7N9j2BdUCf5AQp5Y5U233PP3nUNtU6NdJ8xhhwLClcuhROieLFUr6XKFaMqKhb3LqV3im+/5qBwwbT8oWmd6SFR0Qy9b15vDtxHNYs/Mqm2qtqXVVeAQIDhtPyxWYu66jWVJUHSo5/2tu82r4NolJFwzSVHb9JvJqpDpipbQFjykCZksVpWPspwP7I2Yz5H/Pcs0/j7eUFwIZtOyhWtAhN6z97L5l7YoY2IHD4W7Rsdme/VdyvKPPfmcqDD5R1Wd9MdUuFbplSpWhYzz46x2azMX3WHBo1qJdSzpxFt4PG1YPA4W/969rNYrHg6enJsPGTafFaD56qVdPp+mDodWY6Xus+8xQPlrN7u3DxEsvXrKNZ4+ey3atKTe0V8ubJw8RRw+jo35e6L7YmeN0Ghg3o63Y+Velqr+byGh4RyTNPPs7gfr3Z8ulKqj9alX5DR7gUSHTVa9KnH2A7vOeONEvx0ljyF8D65iQ8xwTh0aILRNt/BElcswCPZq/gOW0F1kFTSVwdBElJmfZrpnvNHInF4v4fNyGjiNZX/BMMLQXEYg82Jgcj6wFfprPdFGCBEKJwRgaklN9jD6T2ciRNAAZKKU86lt8E/IHXhBAPpPYEPA0ccXhIz1MJII9DJwmYCKzNyFNWkFLeAr4DqqWzzAaMB6oJIR4DEEJ4AZ2BsUAtIUQFI/24QpItCUs6hdPDanUrzWTiExIYGjiJUYPfpFjRIi7rqfJqtnw1C2bJg+C1G/C0Wnm5dUtDdVUcv5m8muX8g7naFqPLwO3oGAaPn8bZC2FMGTE4JX3Z2s280a2TS9pmKgOqMFPdUnm+bkdHM2jkaM6eO8eUQNd+u9bt4P3j3YnjOLhjC9dv3CBo8XKnNO5XHhz7TdK5z5t06dCO5+rWcUrDTGUgp3uVJ04StGgZn69dyf7tIbzxejcGjBzjUsDLTMevvZrLa9nSpVg4dzaVK1bAYrHg37UzZ89f4HzYRac1lXi1WrE8XJPERdNJmDEIS558eLTuDp5eePoHkLj8PRJGdyNx9gisrw2AQkWd35cBmL2P1ZiTjAK0PwAVhBA+2AOgX3JnMLQ+sDOd7fZhHyE6N5M+jgEPCyH8gAewBzxTkFJeA34FHgd2Yx9Vi8PHjnt4egvY4pgW4WPgcSnlwUx6yhSOoHEdIF1dKWUc8Cf20bQAzYEzUso/gM1A7yzsK+0UBy9kvFXmKVmixB3z4YVfjqCAry95cud2K81kjv32O+cuhDFjThCtu/qzZtMWPg/dxZip77iVV7Plq1kwSx5s+mwbR4//RutO3eg9cAgxsbG07tSN8IgIl3RVHL+ZvJrl/IO52hYjy0BY+GU69RuC1cOD5XPexje//dG143+cIDExkadqPOq0TzBXGVCFmeqWKt2wi5fo2KMXVg8PVny8AN/8+V3S0+2gevYd/I5wh++8efLQ/PkmHJd/ZLBV+tyPPNj25Vf0HDiEof378EaPrk7rmKkM5HSv+789RK3qj1KuTGkAOndox58nT3Ht+nW38qlKV3s1l9ff/zzB5m3b70iz2cAr+QVcTqDCq+3vq9h++gZioiExgaTvdmN58BEspcqDtw+2Y/YQkO2UxHbxDJbywul9GYFZ+1iNublngFZKmYg98PgEjgCtlPIUkEcIUQj7dAJ3m4l5NPB0BlMdJGMDoh1/AdJrTbwBm5QyArguhCjDP0HjA8ATQohcQHngZ4f/ZdhH0Y7APr3BMiFERjO8pzeW3pIm/Q1HgPQX7EHWaVLKA5k4PoDXgeRXOX8KvJ56Tt97kc4UBzsys11mqVv7aX4+eozTZ84CsGb9Rho3rO92msnUfLQae7esJ+STxYR8spiObVvxUpNGTB0zwq28mi1fzYJZ8mD9iiV8tjaYkNUr+HjubHxy5SJk9QqK+zk372YyKo7fTF7Ncv71LzbHAAAgAElEQVTBXG2LUWUg6vZtug0cSdP6zzJ7wih8cuVKWXb4p6M8U6t6uqMSsoKZyoAqzFS3VOhG3bpF1959eb7Rc7w3Yyo+Pj4u+9TtoHq2h+4iaNFSbDYbcXFxbA/dxTNPZP2N3aA+D3btO8CU2XNYPHfWv6Y/yCpmKgM53WuVhwWHf/yJyCtXAQjdu48ypUpSuGBBt/KpSld7NZdXD4uFqTPf49yFMABWrduIqFThjsfz3cGr7ch+LI/XAy976MOjem1sZ/7AFhEGufNgeegR+4pFS2ApWQ7buZMu7c9VzNrHuiXZPX2BiaY4yMzPKslzvj4FdHGkhQKtgUgp5Q0h/v3rhpTythCiJ7AG2ABcvcc+HgOOSykjhRAnsQd+U4KPQoiiQAXgx1SeXgLySSnPOdb5BegEfCOltAkhKgEdpZSTgU3AJiHEHOxTIvzznOW/uQak7X2LOdKT+VBKOeEeGik4gq8COC6EKAa8CDwuhBiEPfBbCGiHPZ+ylSKFCzN9wjgGDg8gPiGBcmVK8/bkCW6nqQpVXnN6vqoip+eBmY4/p9cBM7UtRhG8cSth4ZcJ3fcNofu+SUlf+t50zpwPo3SJ4vfYOnO48/HfL8xUt1ToBn+6jrCLl9i5ew87d+9JSV/2YRCFChZwzbDBmOlcqSZgUH/Gz5hFy9d6ANCkQT26vfqyU1qq8+DtuQuw2WyMTfV0Vq3HqjF+xBC38Gqm+momr7WffBz/Lp3o+sYAvLw8KeDry4J3p7udT1W62qu5vFauWIGxI4bQ963hJCYmUqJ4MWZPneR2XpP2bsMjb348R80FDw9sZ0+QtGEhxEST+NEUPDr0weLlDUmJJAbPg0jXXnTmKmbtYzXmxpLRXDpCiGrYR3qekFK2dqS1BSYB66WUE4UQy4A9UsplQog9wAQp5R7Huu8DfYHpUsoJ6Sx/CtgGPCmlPC2E6Ih93tbmUsq/hBD5gJXAFSmlv2ObFsDbQKiUcpAj7S3sL996V0q5XAhREDgBvCKl3OVY51VggJSy7j2OtyjwC1BPSnnSMSp3E7BVSvmBEGICQHoBWiFEeUc+lHd89wCmAjWllM2EEEOBJlLKF1NtMwFoKKVsmCrtjjxypNmklK6F9m9fN88rB2OjM14nq+TSjyNoFJCUqEbXQ8H8RmbyqlGDgjJgu/W34ZoAlvyuzy1+34iPMV7Ty/XRoKZHxZuSbZl/6UiWyOntYJyCOuCtqA6YyatGDQlZe0N8pvDM1AORGk3WMNG1e/xQ194tcDe8Zq3OeCV3IU8B9xmK6U5cu+j+cahCJd3i3GU4glZKeUwIUQRYkCp5F/aXbaU3/2xaRmOfdzU1i4QQUdgf/b8NvCqlPO3Y3xohRAKw1jH3rRX7lADTUm2/F6iMfeqCZL4EZmMf3YuU8m8hRHPgHSHEIiAOkNhH2d7reCOFEL0d+7cCubCPAP44E8cKUEoI8ZPjfyv2EbvJ++yBPT9SEwSMEEI8LKX8/V7CqXSTOSGldG7IgUaj0Wg0Go1Go9FoNBqNRqMMt4h9moIMR9Bq/kPoEbTGa2o0Jvpl21ReNWrQI2jVoEfQqkGPoDUPZhqVaiavGjXoEbQas2Cia3c9ghY9gvZuXLvk/nGoQiXc4tw5/2o/E+OYDqF7OovCpJQv3W8/Go1Go9FoNBqNRqPRaDQajSZnokfQ5iTMNIJWBfGxxmt65cp4HWdQMcJHVV1XMWpIlVcVb2hUMRIH1IzGSYw3XhPA6mW8pqpRbkkKdD08jNe0KNBUhO3CH0p0LaUrGy+q4ukMAE8FdcCaI39DvxMVI4dU1S03egNwtqBHpZqH2zfU6ObxVaOrAhV5YKbjNxG2m1cM1zTVEzqqUHFP4KHmuiVx/ybDNa21WxiuCYCvXw6/GLgLegRtptFX/xqNRqPRaDQajUaj0Wg0Go3GWHL6j9hZwDxDdDQajUaj0Wg0Go1Go9FoNBqN5j+GDtBqNBqNRqPRaDQajUaj0Wg0Gk02oac40NzBnn37mTVvAXFxcYhKFZk2fiz58uVzWddmsxEQOJHKlSri362LAU6N9Wqz2QiYOI3KFR/Cv0snEhMTmTEniH3fHiIxMZGenTvSqX0bt/CazMo161i9fiMWi4WyZUozZdwoihQu7JImwIzZc/kidBcFCtjn0nrwgXK8P2OKS5qqylXItu0sXrESi8VCbh8fxowYyqNVHnEbrzabjYBJ06hcwV6uYmJimTjzPY4e/w2bzcZjVaswfvhb+Pg4N5exinzd/Nl2lq5ak/L9ZtQtwsMvs/fzzRQt4nz5MlMdkCdOMuWd2dyMisLDamXS6BFUe+Rht/SqIl+N1gz99ntGzP6QH9ct4u+bUUxcsJTf/jpDHp9ctG3SgK4tn3cLrzabjYDJ0+31tXPHO5a9OXIsxfyKEjhssNNeVZQrMEcZUKmros9S0bckY/T1kBnOVdq+8GZUFGOmvM1fZ86SlJREm+bN6N2ts1t4Vanp7l5DdoSyeNVaLFjI7ZOLMYP7U6VyRWbM+4h9hw7br4c7daBT25Yu+XXnewLVeZATy5Uq3S1f7mLx6vVYLBZ8cuVizKA3WBi8lrMXLqasc/7iJZ6s/igfzJiQrV5Va4Lx9UrV/YCR/Wvoz5L52/djsVgomMeHiR1fpJxfIVbv+5H13/5MbHwCVcqWYEqnF/H2ci7s9cmn61m5dgM+uXJR4cEHCBwxlIIF9HzTWUbPcJBp9AjaTCKEmC+EWJ8m7XkhxF9CiPyO798LIbamWaeHEGJZmrSGQog9qb4/KYTYJYT4QwjxqxBiqRCiaKrlXkKIKUKIP4UQvwghvhNCvGL0MV69eo1R4yczb+YMdmxeT9kypXl3bpDLuif/OkX3Pv3YEbrLAJd2jPR68tRpuvcbzI5de1LS1mzawumz5/hs9XLWL1vI8jXr+OXX49nuNZljx39nySfBrFm6kM/WraJ8ubLMWfCxS5rJHPnlKLOnTyJk9QpCVq9w+UZXVbn66/QZZs6Zx6L5cwhZs5K+vV5nwLCRbuP15KnTdO8/mB279qakfbBsBYmJiWwJXsqW4KXExsby0fKV2e41NW1avEjIquWErFrO+hWL8StSmHEjhrh0MWamOhAdE4N//8H06taZzauW08+/B8PGTnBLryry1WjN0xcu8c6SVSkv/pu+cCV5fHzYtuAd1rw7kX3f/8zu745ku9eTp07T/c237qivySz8ZBXf//yLU7rJqChXYI4yoFrX6D5LRd+SjNHXQ2Y4V+n1hXM+WkzxYn6Oa6yPWbMxhCNHj2W7V5Wa7u71rzPnmBn0MYtmTSdk+Uf07d6ZAWMmsCbkM06fO89nnyxi/aIglq/dyC/Hf3farzvfE6jOg5xYrlTp/nX2PDMXLGLhzClsXhJE324dGTh2CnMnj2XzkiA2Lwli8vCB+ObLR+Bb/bPVq2pNUFOvVNwPGNm/xsTFM/KTz5jj345NI3vSsFpFpm0IZefPkuCvf2Bx/45sGdWL2Ph4lu857NQ+Dn7/IwtXBLN8wRxCVi2j/rO1CZz2jlNaGk1m0QHazBMAPC6EaAUghMgLfAD0lFLeFEI8BsQC1YUQZTMrKoSoAmwBpkkpKwOPAhLYI4RIfn3tQqAiUEtK+RjQEZgkhOhq0LEBsP/gIR6tWoXyD5QDoFOH9mzd/gU2m2sv3Qteu54ObVvTrGljI2wCxnoNXr+JDq1b0Kxxw5S00D1f067FS3h6elLANz/NmzZmy/Yvs91rMtWqPMyOzevJnz8fsbGxhF+OoGCBAk7rJRMXF8dx+QeLlgfT8pXODBg+irCLl1zSVFWuvL29mDJuNMX87L9lVKvyCJGRV4iLd/6tpKrL1ZM1qtO3Zzc8PDywWq08IioRdsm5/FWVr6lZuGwlhQsVoqMLo8fBXHXgwMFDlC1TmgZ16wDQuEE9lwM+qryqyFcjNaNjYhkx6wMCev0zMu74idO0eu5ZrFYPvL08afBkDXYc+C7bvQZv2EyHVs1p1qjhHemHfjjCvoPf0bFta6c8JqOiXIH7lwHVuir6LBV9SzJGXw+Z4Vyl1xeOGTKQkQP7ARAReYW4uDjy53VuBFlOrwNGaXp7ezElYAjFitrfbl/tkcpEXrnGF7u+pl3zF/D0tNqvh5s0ZMuOUKf9uvM9geo8yInlSpWut5cXk0cOplhRe7Cw2sOVibx6LaWdjouPJ2DaLEYN6E3J4n7Z6lW1JqipV6kx6n7AyP410WbDZrMRFR0LwO3YeHJ5WQn57hg9nnuKgnlz4+FhYfwrzWj1ZDWn/P762+/UefIJShQvBsDzzzVg174DhlwPaDR3QwdoM4mUMgr4HzDfEZydBGyRUu5xrPI6sBMIcayXWUYAH0kpQx37SZJSzgBuAx2EEA8C7QF/KeVNxzp/AUOACa4eV2ouXQpPaYAAShQrRlTULW7duuWSbmDAcFq+2MxVe3dgpNfA4W/RslnTO9Iuhl+m5B36fly6HJHtXlPj5eVJ6O691H+xFYd//Il2rZu7pAcQHhHJM08+zuB+vdny6UqqP1qVfkNHuHThoOr4y5QqRcN6dQH7Yz3TZ82hUYN6eHt5uYXXwOFv0fKFO8tV3Wee4sFy9t9vLly8xPI162jW+Lls95oeV//+m6XBaxg9ZKDLWmaqA6fOnMOvSGFGT5pGu649eb3/IBITE13WVeFVRb4aqTk+aAmvNmtE5fLlUtIeExXYsvsA8QkJ3IqO4ctvDhNx9e9s9xo4bPC/6mt4RCRT35vHuxPHYfVw7XJJVbly9zKgWldFn6Wib0nG6OshM5yr9PpCi8WCp6cnw8ZPpsVrPXiqVk0efCDTYxuUeVWp6e5ey5QsQcM6zwCOcj/3QxrVrU3ElauULPZPgKuEnx+XLkc67ded7wlU50FOLFeqdMuULE7D2k8B9nM1Y/7HPPfs0ynt9IZtOyhWtAhN6z+b7V5Va4KaepWMkfcDRvaveXN5M/7VF3jtvU9oMHY+q/b9wJBWz3H68lWuRN2i94JPaTNjMUHb95M/t3NTyVWvVpWD3//ABccPvxu3fk58fDx/X7/ulF7OxmKCj3ugA7RZwBFE3QEsBZ4HRoN9CgKgM7AW+BTwF0KknuiklRDip+QPsCjVsieB9IYPfe1Y9gTwm5Qybcv9NfCQEML1SQwdJNmSsFj+XTg9rFajdmEYqr3akpJILW/DhoeTN+gqvTZ5rgGHdu1gQB9//PsPJikpySW9sqVLsXDubCpXrIDFYsG/a2fOnr/A+bCLGW98F1Sfq9vR0QwaOZqz584xJXCMS1r3qw4c+03Suc+bdOnQjuccI+qyimqvazeG0LhBXcqWKe2ylpnqQEJCAnsPfMur7Vqz8ZMldHnlZXoPGkpcXJzbeVWRr0Zprtq2E6vVg/bPN7gjfaT/a1gs0G7gWN6c8h51alTDy9O5ecFUlqv4hASGBk5i1OA3U0ZTuYKqcuXOZeB+6Kros5Ixsm9RhZnOVXq8O3EcB3ds4fqNGwQtXu6URk6vA0Zr3o6OZtC4yZw9f4EpAUOxpdG3YcPD6l63j2bJg5xcrlTp3o6OYfD4aZy9EMaUEf/MEb9s7Wbe6NbJaZ9grnxViZH3A8kY0b/+EXaZBV8cYOvoXuyd8ia9n6/D4MUbSUhK4tvfTzP79TasHdaD67ejmfPZ107t44ma1en/v568OXw07br52+e6LeCLl6frP9hqNHfDvXpYczAUe3B2gJQy2pHWArgopTwOHACSgNSzx2+RUtZI/gC9Ui2zkf7L2rwdy+62PLllMOx55pIlSnA54p9fhMMvR1DA15c8uXMbtQvDUO21ZIniXI64kvL9csQVShRz7hEZFV7PnD3H90d+SvnevnVLwi5e4vqNm05rAvz+5wk2b9t+R5rNhtMBFFB7rsIuXqJjj15YPTxY8fECfPPnd0nvftSBbV9+Rc+BQxjavw9v9HB+lhLVXj/fuYt2LV0f5QnmqgPF/IpS4cHyVK9WFYAmDeuTmJjEuQthbudVRb4apbkpdB/H/jhFmwGj6TNhJjFxcbQZMJqo29EMe70TWxfMYOnUUdiw8UCp4tnqNT2O/fY75y6EMWNOEK27+rNm0xY+D93FmKnOzT2molyBe5eB+6Gros8C4/sWVZjpXKVm38HvCHfo582Th+bPN+G4/MNtvJopX43UDLsUTsc3BtnL/fxZ+ObPR8nixbgcmep6OPIKJfyK3kPl/mOWPMip5UqVblj4ZTr1G4LVw4Plc97GN799mpTjf5wgMTGRp2o86jZeVWqqxsj7ATCuf93/2ylqPVSGcn6FAHitXi3+vBiJl9VKk+qVyZc7F96eVlo+WZWfTl9wah9Rt27zVK0abFq5hI0rFtOkQT0A/ZIwjVJ0gDaLSClvAH8Dp1Mlvw6UE0KcBk4BvkCfTEoeAmqnk14b+B776NrKQohCaZbXAf6SUl7LrPeMqFv7aX4+eozTZ84CsGb9Rho3rG+UvKGo9tq4fl02bN1GQkICN27eZNvOr2jSsJ7beI2IvMKQUeO4es3+aPDW7TuoVOEhChV0bV5LD4uFqTPfSwkarFq3EVGpwh2P42QVVecq6tYtuvbuy/ONnuO9GVPx8fHJeKMMUF2udu07wJTZc1g8d9a/HvnMKiq9Xr9xg7PnzlOzumsXt8mYqQ7Ur1Ob82FhHPvN/gKQwz8ewWKBMqVKup1XFflqlOa69yaxdcEMNs+bxkcThuPj7c3medNYs/0r5q7cAEDkteus37GHFg3S6wLvn9f0qPloNfZuWU/IJ4sJ+WQxHdu24qUmjZg6ZoRTeirKFbh3Gbgfuir6LBV9iyrMdK5Ssz10F0GLlmKz2YiLi2N76C6eeaKW23g1U74apRl16zZdBwzl+QZ1eW/SWHxy2R8Jbly3Dhu2fUFCQiI3bkaxLXQPTVx4bFwFZsmDnFiuVOlG3b5Nt4EjaVr/WWZPGJVyrgAO/3SUZ2pVT3ekanZ4Va2pEqPvB4zsX6uULc7hE2eJvGF/yPirX/6gdJECvPpsDb448jsxcfHYbDa++uVPHi3n3LXW5YhIur4xgKgo+z4+WLKc5s83cbls5UgsFvf/uAmuDTHQIIQoDjQFKkopLzjSHgKk429GTAcOCCF2SSl3CiEswBggD7BOShkjhFgJLBZCdJNSRgkhKgCzMXgO2iKFCzN9wjgGDg8gPiGBcmVK8/ZkQ3dhGKq9dmrfhrMXwmjd+XXiExJ4tW0rnqpV0228PlGrBm/496Bb735YrVaK+RUlaLbrb5WsXLECY0cMoe9bw0lMTKRE8WLMnjrJJU1V5yr403WEXbzEzt172Ll7T0r6sg+DnA58qS5Xb89dgM1mY2yqUXi1HqvG+BFD3MrrmXMX8CtaxOVRaMmYqQ74FS1C0LszmDjjXaKjo/H29mbezOnkyuXc/FUqvarIV9V1oHeHVoyc/SEt+wVgw8bALu15tHIFt/RqJCrKFZirDKjQVdFnqehbVGGmc5WagEH9GT9jFi1f6wFAkwb16Pbqy27j1Uz5apRm8IbNhF26zM69B9i590BK+uL3ZnD2wkVad+9tvx5u3ZynalZ3ybPRmCUPcmK5UqUbvHErYeGXCd33DaH7vklJX/redM6cD6N0CeeezFHhVbWmSoy+HzCyf32mcnl6NnqaHvNW4WX1oECe3Mz/X3seKl6E67djeHnmMpJsNqqUKc6INs7Nz/tQ+XL07t6FDq/3JikpicdrPEbg8Kzfs2k0WcFi5Bu/cwqOkbINpZSnhRDDgNpSyvZp1tkA/An87li3R6plDYEJUsqGju81gLeBctiD5vuBEVLKCMdyKzAK+zy3iUAMMFNK+WmWjN++nrNPdnys8Zpert1c3xWba3NTpq+p6PR7KJg3SZVXFb+OxcUYrwngrWDUVqKit45aFczFpKIOALg472u6uPgCqXSxmOcBF9sF5x5NzghL6crGi8ZGZ7yOM6iYj8yqf0MnyfUXqf0LVXXLjUZfZAsq+kIV/aAGbt9Qo5vHRI/9qsgDMx2/ibDdvJLxSlnEkt/1ueVNj4p7Ag811y2J+zcZrmmt3cJwTQB8/XL4xcBduBHh/nEoNzl3OkCbk9ABWuM1dYBWB2h1gFYHaHWAVomuDtDqAK0O0JoIHaA1DzpAqwO0JkIHaBWhA7SGawJuE+RzO25Gun8cKn9Rtzh35rkD1Gg0Go1Go9FoNBqNRqPRaDSa/xg6QKvRaDQajUaj0Wg0Go1Go9FoNNmEfn5O4xqqHkNW8Yi7ikdQVaHkEUz3f7JAOSrKq5nKlYrpKBRhu3lVjXDU34ZLWoqUMlyTXHmM1wQ1dSCve7046Z6oeGQeIEnBU1GqpjhQ0b+qerxfhVcPVV4V1C0TTXViqr4wp5M7X3Y7yHZscbcN17T45DVcEzDVtZsKLPkKZbeF7CUhTo2uiqn/FF27qpiOIHHxFMM1AaxvzVGia37cYvYAU2CiKz+NRqPRaDQajUaj0Wg0Go1Go/lvoQO0Go1Go9FoNBqNRqPRaDQajUaTTegArUaj0Wg0Go1Go9FoNBqNRqPRZBN6DlrNHezZt59Z8xYQFxeHqFSRaePHki+fa3NVrVyzjtXrN2KxWChbpjRTxo2iSOHCBjmG0N17GR44iSP7vnJZS5VXFfmqQhMgZNt2Fq9YicViIbePD2NGDOXRKo/kGK8qy6uRZRXU5auqPLDZbAQETqRypYr4d+vilMaWL3exeM0GLBYLPrlyMWZgH6pUqsDk9z/g8M9HAaj/zJOM6OuPJYvzYoZ+e5gRsz7gx/VLSExMYvKHSzl89He75hM1GOH/WpY1bTYbAZNnULnCg/h37gjA0y+0okQxv5R1/Dt3pFWzplnSTcad2+yVmz5jTcjndp1SJZk87E3y5snNpPc/5Ojvf2Cz2XjsEUHg4DfwyZXLab9GlKsUnSlv28/Va68ycPQEzly4kLL8fNglnqz5GB++MzXruhOnUbniQ/h36ZSSfjE8nFd69iUkeAmFCxZ02reKMqCibQXjzlVq5ImTTHlnNjejovCwWpk0egTVHnnYJU0zta2qvKrSBXP0hWbKVzOVV1DTDhiVB/Z+a7uj3yrB5GFv4u3lxZiZ8zh19jxJNhttXmjE/zq1d8mvGeoAuPe5So2+f4Odu/cy9+MleFgsFCjgy5QxIylXprRTWv9cDz2E/2uv8PeNG0yYOYff/jxBHh8f2jVvRtcObV3ya3Qd+OTT9axcuwGfXLmo8OADBI4YSsECvpne3mazMXr3USoVzk/PGg8Sk5DI5H3HOXr5OjbgsWIFGFevCj6eVg5duMI730oSk5Io6ONNQJ2Hebho5veVI1H1roL/IDlyBK0QYr4QYn2atOeFEH8JIfILIToLIX4WQvwqhDgqhBgnhPB0rNdQCLEnzbblhRCnHf8fEkL8JIQ4K4S46vj/JyHEo0KIZUKIHmm2nSCEmOD4/7QQ4rhj/V+FEBuFEIUcy3qk0Uv+PG1Uvly9eo1R4yczb+YMdmxeT9kypXl3bpBLmseO/86ST4JZs3Qhn61bRflyZZmz4GODHMPps+d4+/15hrxIRJVXFfmqQhPgr9NnmDlnHovmzyFkzUr69nqdAcNG5hivKsurkWUV1OWrqjw4+dcpuvfpx47QXU5r/HX2PDM/WMzCmZPZvHg+fbt1ZOC4qYR8uYtT586zZekCNi8J4vBPR9mxZ3+WtE9fuMg7i1elnJ+QXfs4df4iW4LeZvP86Rw+9hs79h/KkubJU2fo/uYQduza+88xnDlLQV9fQj5ZnPJxNjjrzm32MXmCJZ9uYvX8mWxdGsQDZUoyZ8lKPly5lsTEREIWzyNk8Txi4uL4OHid036NKFcAJ0+fofuAoezY/XVK2txpEwhZvpCQ5QuZPHIovvnyMn7ooKzpnjpN936D7ygDAJu3fUHn3gO4HBHpkm8VZUBF2wrGnavURMfE4N9/ML26dWbzquX08+/BsLETXNI0U9uqyqsqXTBHX2imfDVTeQU17YBReWDvtzazev47bF06nwfKlGLOkmDmLAmmhF8Rti6dz7oPZrE6ZDtHfv3dab9mqAPg3ucqNfr+DWJiYhkeOJn570wlZNUyGtV7linvvu+Ulv16aBg7du9LSZs+ZwF5cvvwefASPl04n68PfsfuA9867dfoOnDw+x9ZuCKY5QvmELJqGfWfrU3gtHcyvf3Ja1H03HqYL/8KT0n76MeTJNpsbH7lWTZ3eJbYxEQWHvmLm7HxDNpxhGHPCDa/UpfAelUYsvMn4hIVvThdk+PIkQFaIAB4XAjRCkAIkRf4AOgJtAdGAu2klFWBOkBN4KPMCEspn5ZS1gACgS1SyhqOz9FMenvJsX5V4CwwKtWy1HrJn6xFDO7B/oOHeLRqFco/UA6ATh3as3X7F9hcaDyrVXmYHZvXkz9/PmJjYwm/HEHBAsa88Ts6OobhYycQMCRrN8x3Q5VXFfmqQhPA29uLKeNGU8yvKADVqjxCZOQV4uLjc4RXVWXA6LIK6vJVVR4Er11Ph7atada0sdMa3l5eTB4xiGJF7KMiqolKRF69Rlx8PNExMcTFxxMXF098QgLe3t6Z1o2OiWXEuwsI6PXP6JCkpCSiY2LtmvEJDs2svaU8eMMmOrRqTrNGDVLSjhz9FQ8PD17rM4CWnXsyf/FyEhMTs6SbjDu32dVERXas/Ij8+fISGxdHeORVCvr68sRjVXmj66t4eHhgtVqpUvEhLoRHOO3XiHIFELxh87/OVTJx8fEETHmb0YP7U7J4sazprt9Eh9YtaNa4YUpaeEQkoXv3sXjuLJc8g5oyoKJtBePOVWoOHDxE2TKlaVC3DgCNG9Tj/RmuvZnZTG2rKq+qdM3SF5opX81UXkFNO2BUHtj7rQ9T9VtXKOibnzED/seIvj0BiLh6lfj4ePLnde5t9WapA+De5yo1+v4NEpMSsdls3IyKAuDW7WhyZeE6ODXBGyw5D8MAACAASURBVELo0OolmjWqn5L26+9/0rpZU6xWK95eXjSs8/QdP2hnBRV14NfffqfOk09QwnGN9vxzDdi170Cmr1tWHztL+0fK8MJDxVPSnihZmDdqVcDDYsHqYeGRIr6E3YzmzPXb5PP2pHaZIgA8VCgf+bw9+enSNcOOR5OzyZEBWillFPA/YL4jODsJe/BzDzABGCilPOlY9ybgD7wmhHjgfnkUQngA+YHwjNY1ikuXwlMaNoASxYoRFXWLW7duuaTr5eVJ6O691H+xFYd//Il2rZu7ahWAwGlv82r7NohKFQ3RAzVeVeSrqnNVplQpGtarC9gf9Zg+aw6NGtTD2ytrgSmzegU1ZUBFWVWVr6AoDwKG0/LFZi5plClZnIa1nwLs53xG0EKee/ZpOjR/Ad/8+WnQvhv12nWhXOmSNHo28w8XjJ+/mFdfbEzlB8ulpLVt0gDffHlp0K0/9br2o1zJ4jR6+vEs+Q0cNpiWLzS5Iy0xIZE6Tz7OovffIfjDOew/eJhP1m3Mkm4y7t5me3l6Err/Wxp06MH3vxyj3YtNqPtkLR4sa3/k7sKlyyzfsIVmDZ912qsR5QogcOggWj6f/k3o+q2fU6xoEZo2qJd13eFv0TLNCOnifkWZ/85UHnygrFNeU6OiDKhqW406V6k5deYcfkUKM3rSNNp17cnr/Qc5/YNHMmZqW1V5VaVrlr7QTPlqpvIKatoBI/PA3m8dpEGH1/n+l19p92ITLBYLnlYrw6fOouXrA3iyRrWUfiyrmKUOgPufq9Tk9Pu3vHnyMHHUMDr696Xui60JXreBYQP6OqUVOHTgv66HHqv6MCFf7CQ+IYFbt6PZsXsfEZFXndNXUAeqV6vKwe9/4MLFSwBs3Po58fHx/H39eqa2H1uvCi0qlboj7dmyRSlfMC8AF25Gs+LoGV6oUILyBfMSnZDIgXP2J6COXr7OiWtRRNyONex4/pNYLO7/cRNyZIAWQEoZCuwAlgLPA6OFEH7AA8B3ada9BvwKZO3u3Dk+F0L8BJx3+Er97GerNNMbGDZ6FiDJlpTu/IoeVqvL2k2ea8ChXTsY0Mcf//6DSUpy7TGA4LUb8LRaebl1S5e9pcVoryryVeW5ArgdHc2gkaM5e+4cUwLHuKRlJq/JGFkGVJVV1flqdD0wktvRMQweP52zF8KYMnwQQctWUbiAL/s3B7N3/Qqu34hiyaeZC3qu+mwnVqsH7Z9veEd60KoNds3gD9m7fD7Xb95iycZtLnt/pU0Lxg0bRJ7cufHNn5/XO3UgdG/WpmNIxgxtdpO6tTkYsoo3u79GrxGBKTrH5Am6DBpJ5zbNec4RdHdXln+6gb49jJl7z2hUlgEVbavRJCQksPfAt7zarjUbP1lCl1depvegocTFxTmtaaa2VZVXFbpm6gvNlK9mKq+qMDoPmtR9hoMhwbzZvRO9RoxPOeaZY4bybchKrt+IImjFp1nWNVMdUIUZrluSMVN9lSdOErRoGZ+vXcn+7SG88Xo3Bowc4/LI3GQCBvTFYrHQtnsf+gcE8uxTj+PllfVXGamqA0/UrE7///XkzeGjadfN/g6KggV88fJ07YdlgF8jrtM15BCvVStHwweKkc/bk3kv1OLjH0/Sdt0BQv64wNOliuBlzbFhNY3B5PSSNBR7EHSAlDIaSG7F0mtxvB3L02vtLXdJT0tmtk2e4qAU8D6wQwiR3JKnneLAsPlnAUqWKHHHfHjhlyMo4OtLnty5ndY8c/Yc3x/5KeV7+9YtCbt4ies3brrkddNn2zh6/Ddad+pG74FDiImNpXWnboRHOP+orCqvKvJVhWYyYRcv0bFHL6weHqz4eAG++fO7pGcmryrKgIqyCuryVVU9MIqw8Mt06j8Uq9WD5e/PwDd/Pnbu+4Z2Lz2Pt5cX+fPlpU2zxhw68nOm9DZ9tZdjf/5FmzdH0Wf8O8TExdHmzVFs2b2fdk0b4O3lSf68eWjTuB6Hfjnusv/N27/k9z9Ppny3YcPTyQtzd26zz1wI44ejv/6j82ITwsIjuH4zim27vsZ/+DiG/K87b3R5xWmv94Pj8k8SEhN5qmb17LaSLqraAaPbVlUU8ytKhQfLU71aVQCaNKxPYmIS5y6EOa1pprZVlVcVumbqC82Ur2Yqr6owKg/s/dY//Xxyv/XFngOER14BIG/u3DRvXJ/jqfrxzGKmOqAKd75uuR9eVZ2r/d8eolb1R1NeCta5Qzv+PHmKa5kcQZoRUbduMbx/bz4LXsyyuTOx2WxOvYBMVR2IunWbp2rVYNPKJWxcsZgmjieesvKSsPT4/MRF/D/7niFPV6ZPrQoAJNls5PGysrz102zq8Cxj61bh9PVblPN1btoTjSYtOTpAK6W8AfwNnHZ8jwROArVTryeEKApUAH4ErgFpX7dczJGeEVnddhHwMFAkE9ouU7f20/x89Binz5wFYM36jTRuWD+Dre5NROQVhowax9VrfwOwdfsOKlV4iEIFXZsbaP2KJXy2NpiQ1Sv4eO5sfHLlImT1Cor7+WW88X32qiJfVWiCvQPu2rsvzzd6jvdmTMXHx8dlTTN5VVEGVJRVUJevquqBEUTdvk23QQE0rV+H2eMD8MmVC4AqlSrwheNlBvEJCew+cIgaVTL3Fvd1701h64J32Dx/Oh9NHIGPtzeb50/n8aqCL/Yf/Efz0I/UeNj1x7H+PHmKuQuXkJiYSExMLMHrNvFSk0ZOablzmx1x5RpDJs1MuTnYGrqXSuXLceTX35k672MWzZxEyyYNXfJ6P/jup5955vGa6Y54cQdUlAEVbasq6tepzfmwMI79Zn9hz+Efj2CxQJlSJZ3WNFPbqsqrCl0z9YVmylczlVdVGJUH//RbN4B/+q0D3x8haPkabDYbcXHxfLFnP8/UfCzL+maqA6pw5+uW++FV1bmq8rDg8I8/EXnFPu1A6N59lClVksIF04YdnGPN5q3MXbgMgMirV1m3dTstmmb92lVVHbgcEUnXNwYQFWWfKuKDJctp/nwTl67ddp++zLT9v7Go+RN3TH9gAd74/AeOXbZf324/cZFcVg9EEff8Idt9sJjg4x5kfWz6f5+xwPtCiOZSyr+EEPmwB0rXSCnPCCGsQGEhxNNSykOOuWJ7AaGZ0P4KCBRCLJRS3hJClABaAPPvsn5j4JyUMlII4fqRZUCRwoWZPmEcA4cHEJ+QQLkypXl78gSXNJ+oVYM3/HvQrXc/rFYrxfyKEjQ7829VvJ+o8qoiX1VoAgR/uo6wi5fYuXsPO3fvSUlf9mGQ0xc6ZvJqpvKqKl/dOQ+CN24lLPwyofu+JXTfP2+PXTp7GpPf/4AXu/bG6uHBM7Vq4N/pZZf2FfC/rkz+YBkv9hlq16xeDf/2rj+S9Wav7kx6dw4tO/ckISGBZo0b0sHJudLcuc1+4rGqvNHlFboNHm3XKVqYoClj6DVyAjabjXEz56WsW6vaIwQOdm6uNNWcOXeB0iWKZ7xiNqGiDKhoW1XhV7QIQe/OYOKMd4mOjsbb25t5M6eTy/HjjTOYqW1V5VWVrgrMdI1lJq/ufC2QFqPywN5vdUjTb43GN38+Jsz+gFY9BwDQpN4zdDPgesAocnp91fdvUPvJx/Hv0omubwzAy8uTAr6+LHh3usu6yfTu+hojJk2nRWd/bNgY2Ks7j2VyIMT94KHy5ejdvQsdXu9NUlISj9d4jMDhQ1zSnPnt79iwMW7vsZS0WiUKMa5eFWY2qU7g3mPEJ9nwy5OLec1que0P+RrzYTFqbhKzIoQ4DTSUUp5OlfYyEAD4AFZgNTBNSpngWP4M8B6Q2/HZBQyVUt5OpdHDodsjzf5GAZ2xT2uQBLwtpVydysttIM6xLAEYLKX8xqE3Gzib5hBmSylXZOpgb183/mTbFM1FpaJcqmg4LSYahK6qrqvIV2XtkgJdVV49FMwdpqq+KqgHthuRGa/kDFF/Gy5pKVIq45WySi5Fj0opKAO26649mnY3LAUVBEajo4zXBPBU8Hu3l6KRq2bpXwESE4zXtCoam6CifTXTNUaSay9nSxcV/aDGVNcCqrD9fclwTYuva6MA70pOrwc5vW1NcH5O9XsSr+ClVqquXRX0L4mLpxiuCWB9a46O1KbHrb/dP+iYt6BbnLscH6DNUegArQJNE3XwOkCLDtCa56ZMB2h1gFYHaHWAVgdoTXSNoQO05sFE1wKq0AFaE5HT21YdoNUB2v8CKuJQRpOnQKbPnRDiNexP3nsB70spg4yyYaLWSaPRaDQajUaj0Wg0Go1Go9Fo7i9CiNLAVKAuUAPoLYSoYpS+noNWo9FoNBqNRqPRaDQajUaj0eQ4hBAFgfTerPe3lDL1I5hNgF1SyquO7dYDLwOTjPChA7Q5iSwM29ZoNJrsxJLHvV5GpLk7lryFsttC5tHlSqPRaHI8+hpDo8nZWN+ak90WchbmiENNAMankz7RsSyZUsDFVN8vAk8ZZUIHaDUajUaj0Wg0Go1Go9FoNBpNTuR9YFk66WlfYOLBnS+2sQCGTZatA7QajUaj0Wg0Go1Go9FoNBqNJsfhmMYgM2+TPg/US/W9BBBmlA8doNVoNBqNRqPRaDQajUaj0Wg0mrsTCkwQQvgBt4D2QG+jxD2MEtJoNBqNRqPRaDQajUaj0Wg0mv8aUsoLwBhgN/ATsEpK+Z1R+habzZbxWhqNRqPRaDQajUaj0Wg0Go1GozEcPYJWo9FoNBqNRqPRaDQajUaj0WiyCR2g1Wg0Go1Go9FoNBqNRqPRaDSabEIHaDUajUaj0Wg0Go1Go9FoNBqNJpvQAVqNRqPRaDQajUaj0Wg0Go1Go8kmdIBWo9FoNBqNRqPRaDQajUaj0WiyCc/sNqDJXoQQ3e61XEq5wgnNyVLKcY7/m0opd6ZatlZK+UrWnf5rH75AZSAaOCmljHFVM41+EeCqlNJmpK4m5yCEyCulvJXdPtwZIURlKeUf2e1Dow4hRCEp5bXs9qHRaDQajca9EEJ4SCmTstuHRqPRuAt6BK3muXQ+zwMfA7Oc1Gye6v+30yyr5KQmYA96CSFWAJHANuAr4KoQIkgI4eWkpp8QYr0QoqEQwkMIsQk4C5wQQlRxweuT91jWxVnde2j6Gq3pKkKIFkKIhxz/txFCbBVCTHLhXPW/S3oBIcRqV7wq4GchRD2jRYUQ1RVo1r7HslEG78tTCPGKEGI38KPB2keN1DMKIYRVCOHt+N9XCNFeCFHZRc0mQognHPk5RQixRQgxQghhdUEzvxCiphAijyve0vCVgVpKEUKUym4PmcXRV/1PCDFHCNHRQN3tqf5vYIBe4L0+LujOc9XbXXS/VaF7vxFClHFyu76p/q+aZtn7Tmq2uEu6pxBiupOaSsq/CoQQpYQQq4QQPwshPhRCFDRAs6kR3rK4T6euMYUQ7yT3f2ZGCNHJxe2rZrxWlvSUtFVCiHxpr9GFELlcuRYUQuQWQvQRQnRIk/4i8IsLuoZ7VY0QopDBekII0VYIUc5I3XT2k1cI0duF7Sen+r9pmmVrXfHm0CgohChggM59vXfXaNJDB2hzOFLK11N/gHnAY8B2oJqTspa7/A/g6ojUSY6/ZaWUxaWUJYAHgXzATCc15wHfOz4dgJpASaALMMcFrx8l/5POhdQQZwTT3DynvQDZ44ymQ2u3EGLX3T7/Z++8oyWpqi7+GySJYEAQEAQTbFQkoxLUQRSQoIBkkKRkTCAoSdKIIFnSgCAImMhRPkZAMjoMIoiDW4KgkpPKoOT3/XFuz6tXr7tn+lbVzIPpvdYsuqtfn750V926d5999smM+W3gAGB2SUsCPwMuAeYm/7daQ9KFxZuwpNHAn4BJmTGR9DdJD7T59zdJD2SG3QU4Q9KRkmbLHVsbXCVpX0l1zt8XlBclkhZIv30tCxJJ70ub8YeBs4nz9b11xC4gK56k0wuPty69dlOVAUlankj4jE4b3DuAbwGXSfpiZszDgQOBM4hE1cLAWGBxIJdE2Qh4AhgHPFgHOZdQvgfUBknbSrpd0iRJT0q6vrwB7BF3SNqitgHS2NwCcDLwFeAlYO8qZGcJ8xceH1NDvP2IuXCB9HxU6V8uVq44rk6Yve6ATdxfpwITM9+3feHx2aXXPpUZ8wBJRxeJFEkCfg8slxmzkfO/RSKkzf45kp6W9JikkyXNlRn2DOARYB9gNuq5rsrih1rQ0BrzPcAfJC2TO652mA7X1SlT/pOuKF9PVdHEXLUj8AzwuKTl0rENgb8CVe6NPwW2Aw6RtIukd0m6AvgF8PORNNam1oMKEdAjwFOS/pL2RZWgEK1MIOaWP0r6UtWYbT5jKUknAY8y9P7QKxoRb0naQtJ9xLnwjKR7JW2eG48G9u599NEr+hYHfQChZCDItK8Cu9uuS41Yt0XAWsAStl9tHbD9eMrq3ZYZ88O2N4XJ2dxzbf8HuFXVFFXFzWd5IZW7MS1unjcCiuqTKpvdAwsxfkycB1XxZWBF2/+VdBhwqe3TJI0ic/No+wuS9gQmSNqeOB+2Ana0fUmFsY6u8N62sD0uLcAOAcZL2g14qPD63zNDL0UkFW6V9OWaLAI2I0jaL9q+Q9K6wOnE4nmtKoElrQ/sSGzELyII3x/bPqjimNshd74pbhy/QWwmWnhL/nAAOBLYyPYtkr5GWKesIml+glzNOW/XBj5KJDvuB+a2/VraXN+ROc79gBVs3y1pDeAg6rku5u9Gntg+uNNr3ZA2JjsS19fdxG+/FLCvpLlt52ymVwN+LGkDYk55KmdsJdxJnF+XA7+iMAdUxKeIe9dAml+vZTCBWRfqINcXADZM/z4InAdcYPvpinFnlfQeOoyxwvw6t7pYP+XYPjF4fy1iFWB/4vtoArm/XbcEey5WJkjJWyVtAqxJnKsH285Ngjd1/q9N/C7HE4m1DxBilt2As4D1M2IuaHsNAEnjgD/WMM6mUPsa0/ZmktYCzpN0NjCmuIavgAML46pr3doNVa+HupOVTcxVewErEMKX70j6F/AFYn94WtYoAysQJNzcxLpnT+AqYFHbT2bGbGqsTa0HjwB2AH5LEMiHUXF9TSQ/P2D7CUV13VjggooxkTQ7sCmwEyHaehVYx/b1FcLWLt5KCfn9gK8BNwCzEPeboyS9ZPv8iuOsa+/eRx89oU/Q9kHKav8UuA9Y2vbjFUM26dv6YruFne0XJeUu+Irj/QxDF3lVSn0HOjxu9zwnZm3q5OJNV9KkijfhyeOx/d/0eFXgpPRZAyGeyYPtIyQ9TGzIHgOWtf1IxbHObbstsZWy9FnKiURO7w8sBFwKPEv8bgPA+zNjPg5snEjUCxWlQQ8WXu95UW77+qQcvEhhPbAasEXRP7oCLgDOJcj6+wAkjTS/sSZV/++wfUt6vBpp8Wz7MVUr+5zN9tOSvl3wb5uLWKDmYMD23WlsV0k6ssLYymhiUbsjsGqJ6PuLpBuIDWDP12wip1ciyJibJY1haFLlhoyY6yXl3XrEpnQuIlFxXsV564WWR3o6D+q673a7b/WM9PucApwi6V0E6XOupBeJZOiZmaEXBa6n/bmVPb8S1Tiju8TNml9bjxXVFD8ANgY2qZhY7IY6zodazinbLwG7StoW+AvwJLCK7XsqhG3q/G9hKdtfLjw/WNKfM2O91Hpg+2VJL3X746nEot1UorY/kxm3qTXmryVdR6iJJ6Y1XOu1rLE2tG7thqrn2MKSftLpRdvb9Riv9rkKeN72nYRF148Ji6LFkmilCv5l+xXgiZRY28X2hRVjNjXWptaDs9i+PD0+VdI3KsRq4SXbTwDYvlNSVUEBko4j7k/jiUTVpcBdNV9fdc3XewBr2f5b4dgVkv5CCExyCNom9u599NET+gTtDI5UzvVN4FCiBH02FXxsMlUoSxfI0lHFx1Sf3LoRO7mxH0qqjjnSv+tgstdM7oJ8WqCpG0VdcV9ReK3NSWSkxwFIWgR4JTeopG8B3wV2JlQ0F0vavEX+ZeJXkk6w/aPC58xDbCbeSyZBq/DeO4FQCixs+7kKYyzj78B/iAV6i0TKXZRj+8ZUFvRr4Is1LsaWBLYFbpL0ILFoyr73JHJ3gKEL59bzOhIfdV9XMwEoyns/DYwpPJ8zM+aJxKbkw7ZPS/FWIubwQzNjlufWlzPjlPFoQ2rp19qpMG0/WjEBNCDpfGANQpnRIlEHiAReTszniPLWs9OcuD5wvqSXbedaSZTP07qSHoskEmFU4fFkZJAIxfc+ofCQ/zewK6F0OjMz3ETbtZZMJzxU5f+xG9I1egZR7bOk7WcqxutkOTAKyPWibmRdIWk94PtERcFKwKGStqvQQLCp83/+tB78p6RP2P4dTPYlrKsZbR3f8WNElUOTqO1cUPiaH0z89vtSXyVBC7WMtUu1xyigqo/uJCKpVBeamKuKQpdngS/brmMtUPx9Hq+BnIVpM9Y658PyHPViDTHL48veWxWwEWE/cyFwue3nakqANXFvmb1EzgJg+/6kAu6jj9cl+gRtH1sSDbd2SP+KyFKh2G7S27iTamAUUT6Zg10JAm4+QjX4kqSjgXWpVn5S3NwWH48iPCNzMJei8dRMwJylDVou2dMUDiNK+WYGTkvEycYEgZS1sZB0DVFysqLtBwhVVov829f26d0jdMQngDMlfRbYBliRKJH6BVGamzPW84Blga/Yrq1RkqQ3EyTf5tRkR1Iqk/s5UYq4D0n5k1kqR3rv3cAekr4DrEN8v/Mp/MdOtP3rHuM1Mb+0yqVnYnjpdNVN2fWSTkxxHrY9QWGdsh8padErbJ8s6cpSNcHfiRK03KRSa25p/X/PWXyeox5NaKokrBEVtsKGYj/Cy/eLNZXitmLPC2xAbIDeSmyAclEmT4c8r7BxL3qsXZcZYwgSKb0eMZcuBlwGfKtFfo0w1H6+JtXsocAmhHLs0ppCd7uPjs+MuYQGvZEXLDwexaCPcE+QdAZh6fAl27cq/NMPIJJM29m+OiNsU+f/d4kS2XcQivcvSfomUU6d62/9EQ31m259r6OIyoUctfdzDSlGa19jKuwNTiLIySUrkPLTAt2u/6yGdgU8bfunU/6zqUYT99YiiTapJsIThq6x3lRaY+WKgabFWOtcDw6LVXye+R28s7R+H/I8c+3+HmLvuy1wQtp3vUXSrKkaIhdNiLfmkDRHoVoTiIZm5HNcTezd++ijJ/QJ2hkctt83LT9P0hW2157yX3ZE227AVWD7HwwnYg8BiqXDOShudMsL6esyYz7MoM/awwzdoD08/M+nDmkD1bpB1qKasn2+pFuAeWy3urROAr5q+7rMoV5P+JdN/l1sn5E+5xeEb2rPSEqmL0jaA7iXyGxvYbtKo4nHiM3I8xVitMNEBhVYud5dZaxaen4Fg014slW5RaTytosJxfO8hHfwDwjF7lSji2qs9Tk5ROKcDC2XziUj22F3okphfgabJOxKqPV3rRB3KYXNxa9t32/7n4Tqawfbp2bEK84t5efZ6lHC1mEIJM2czocqWKCD2mkUQ30UpxqSfkf8v46uWH5djLkAQcpuCLyLsLjYvTAn5qLcrKIWssb2TxWWDB+Mp0M3Pr1C4Yu8GOG1PKZGUrZKA89u+PKU/6Rn3EVs7E4lNqlLF190pg+z7fK8PRnKt0/JbtbSBQPAMrYnAaT79wFp4382sEhGzKbO/7MYfr87HfhRhfXgYtVG1RYPNhATmlljnkJ4evd0r58SGlq3Dkt6SHon4R1fVQFYh7VFEVtP+U96RlEEM0wQU8E6o9saK9eSpsmxFsdXHmsuynGLsXO/g2sZun4vPs+15HmVSKJeltbqWxA+v49I+ontvTLG2ZS44lzgtJToewFA0UT6dKKiLAdN7N376KMnjBoY6NtpzOhIi4+dCLP1AYL8GVu1/K7DZ/3H9lvrjlsFkg6xvX96/DkXfDclnWt74+k3uqmHpLlyS+hV6lRaRk7WXwWrjA4xc5u4tAzsFyfOV9t+oWp2V9LywDlEk6Wlgf8DvlMxY1z+jNkIFdVOtlfKjPEl25WbAHSJ/3ZC1fPvmuIdb/trdcRK8X7b5eWBCovyRiHpHXUphxRNcZYH7iFUXd+2fU567Q+2l52e4yvFnZ3YoF9s+6J07FLgX8D2trPK/CQd0O31HFuFlKA5pmJirhzzVeCfBDF7J6UNXhV1ehNQNN04i0imDRAN7rLJLw33m279/7fUg1ml+JJOt/2V9HhR2/fmjrEUN+v6mULM2s/VQuyVge8BHycUX7cTBNuawPW9kmIKe4/TgKtqIKSm5vOy5h1JW9esRmzFfRuhoj2RuAaOIdbGE4h5tufGgU2shSQtaLstYSrpMxWTy7VC0sJV1ntd4jaxbp0XOJmwprqB8LBcg0i4r1NX4q7N527WazVUca7KeX+HmF0tdxpSbWfh9TTWNwIkLQdsk7uel3SB7S/VPKaZifvV+sR6eGYiIXYOsGtd9zBF49naOZE++uiEvoJ2Boek9wI3AzcBvyHKN1YE7pK0iu0Hp9/ohkOD/pMtDBDeQ1cTk3HOBNrq2gtwOPE9tJCtJpH0NzpnWwdsfyAj5rxEdu9p4Fjbr6RywR2Jjrbz5Yy13UK2BtXA9bT3Cl2AOM9yN+b7EeWGLxINkUZJOtx2rvdmawO9M/A12+el8pgTgdsU/raVvIglLU78RlsBz1BN/fWuQtyPFMcm6Vjb38wc4xbEOfT+9PwB4ADbP68wVhhU4taCKajG3lbnZ6WYf7L90QrvH03YRswn6V5g4xrUk2sTirRXJP0IGCfpRdvnkV/2eA1hyVE3jgSeZ+i8ugVwFPBDoktyz+hEaqVrd4ucmETjoWHkbDqvxtreLCPm2cS89w7CL7qIbHV6qWR6GJxXNg1h77CCo2HaGoSCbnRmrCYtj4r+s7+ivnO39rLhKgRsN6S55RzC2/VbwJuJ9dsvgHttfycj7KXAtwn7oLOBM2zfX3GcHRsjJeTYEZS7q9eFicIQrwAAIABJREFUXxJJ2n8TZfl/I+apLxDX8uczYj5IrFNbSc/ymijnWr2MdM63IT6OJPN6UNjvHAEsAdwC7G37XzmxCri4MNY9bB9VMR7Q2Lr1eIKMn0AkP5cl1qwfAX4EfC4zLpK+QKjonybsc+6T9PEU973EddsLiufRnhnvb4f7uhH/VQI3IAZqZKwaahlQxoDts0dKXEkfAX5MnJ+3EEr1SsmQKVSpnVchdO6apCNSJdY2kg4kRAsDwHhHZWwW0npvb+Bx4v93XBzWP4jGnr+vPPA++pgC+gRtH0cAe9kulgIck24kR5Lpv9kU2m32JM0HbE8Qajkb6KY6do5uc2xzokHCsZkxfwY8B8xDNHS7iFiUzUVs0LLQTTUgKUs14JJ9hqQ5CVJmDeL3yhnnzoQy6OOtMaUFyqmSnrE9Nicu8ElgudZiz2FLsE0iLa8lg/hWNIHakFiQLgVcTpS4LVYxq7s98VtBbBiLG7Gu5f+dkFRz+wFfJ37/WQhi9ShJL9nO6YTaQtl3awjqUNZI+hjxPW9M/V7M7634/iMIf+/fEsThYVTztoaCZ5ftexXN6H4j6Uny56ymvGI/BSztobYkz0nalfCorgWSliKSIFsAfyU2wr1iDUmrAdu1SIlEgp1FNPrrGba36fSapNVzYibcRZCUlxMEZV1NdwYcvtHYvkrSkVWClSpU3uL6LF+63beroO5O6y3f8EOAc22PV3jcb08QgZt1IhmmAgcSqr7idXS7pM3InAeSovssSQsSPQoulvQU8BPgvFYZaY94PSnZFrD9eQBFE8aW5cU9isaxOdidUHg9R1yrF+dWOxVQPOfLxEeV6+EM4E9EUnFDQkG8bYV45fG0knOV0cS6Ffiw7U1T/M8T1+x/gFsTeV0FRxD3qEWA/ST9FdiHIIVz/G2L13hdc2BTxP97qV8M1MhYGW75BbEm3pC4hrMI2obijiWu1d8S+9+jqb5v75ZQrGJ3NaSvQRnO7HOgsPNZmUHifzZJ51eofjwd+AdBen+TENScBnw2Pf5EZtw++phq9AnaPlQiZ4FYpCuaBPUesLNydBThu1grbD8OjJFUSeWYUFtJn+3JG+a0kDyF8PX7tO3bM8N+wPYHFB6BtwK7EIu7oyvcjKBB1QBAIj1+TCzMPlphc7I98DkXurfb/rOk9VPsLILW9mc7HP9ZBVXmw8SC9FjgSocNwwM1lNw0QUzsAazlod1Qr5D0FyIBUIWgXZSh3mNF5KqHWoT/FoTy+SOEkmzFzDF2Q9Xfaxbbl6fHp0rKUoyWcB5wXVIjjU/XwEbARcBsmTHnV+cO1tk+mcCr7VSptl+WVMk+RGGfsClBzi9JdHVeJ7e00fYXJO1JKOe3J4j0rQhVyiVVxloY87wE4bED0fBwocyxrpfuA+sR6qm5iN//PNuPVBhi+beq2nilWKFyI/UpXZvqtF13p3WITd0rwIOKhklbEN/DsgS5tH5m3LeVyFkkzUOcB5X8KRNpfDhwuKRliLXGccDcGbE6Kl0VFg05KDfeaqFK4y2AZzVodXWHpGVs3yHpo0CWH7PtY4FjU6JyE+DKlEz7JXCp7f9lhO12/le5Hha0vQaApHHUk0RrgkiEZtatxbF+Bvhq4XnV/ctLrfuIpEcJX88laqpUrGsObIr4b0IM1MhYbQ9JSEhaFjgTuJJYa4ykuG+1fUJ6vF8d++BuVWoVMT9B/nbaC/RM/CZV9nXE3HwDQfx/E9hb0ujiXrEHLG57Q0V16j9sH52OXyhp34x4ffTRM/oEbR+zd3kt94Y/OvN9VZG72W/UZy2pWY4mFCibuFqn0f/AZAXa3KSuyDUMsxHVQCo3PpqkmnXB3zcTM7e74dp+QlJtC/8CibIjQXidlBHmbGLT8DbgXQpvv7pR17k7e4mcBcD2/YkEq4KJtpeZ8p9NHRJZ0FLL3kYQHPuXF78jCGXCK8tztQjbB0m6iSCTWsduVniE7VEhdBMq2qclLW97QvGgwvM5h5hovf844hwYT2zULwXuyiVnW7B9hKSHCeX8Y8CyFQnP1nhHE+ftesR1uxOhfMlGSnSdDZyt8I5eHzhf0su2u/rzdUGri3vrXBiieslQuTSldC02iRvWMK5CQqHuTusAKzrZpEj6InF/vRe4V1Pwp50C3izpTY6mLgDYfkrSsUQCoBJSAmADglBekCj1rxtXAjl9Ce6jeiVCO+wEXCJpElHieqPCmmZe8ol0YHJD2iOBI1uVP4Raq+6qjyqYvI6uI4nWBnWut5tYtz4kaROCjJ2D1BQoqaerkl/Fxpj/BdZ2apyXiSaaZDVF/NcuBqK5sQKT/U0PIEj63V2Dx28DccvNVmu5XtP+cnOi18f/iObE51asgLkv85zshh8C59g+vHhQYYX3Q+ArGTFfhmhmKenx0mtNVZr10ccQ9AnaPiZK2tT2L4sHJW0O3J0Z8x/EpLgEcIvtX1Uc4xQhaQPC1ykHSysauYxKsVo+t5PLiDPHNC+h6FyUWIj9ITdWAcXxPF4TOVuOW4tqoKSaXaLiQrSFmSXN41KjjvRd17EgG01NJIrtPSTtRSjItiWIaiRtCFxU3FT3iCYSCnNImsOlju2JYB9p94nbic6tS7XsESos8EnvL3tblz0Cq6Bs8TDkeQWLh9+RFpKSPksoSG92pgcx8Kib8crcD7hU0mnAH4AXiFK0nYkS6lxsBPweuBC4PCWt6pgDvkU0CdqZsGe4WOFDfV+FeDsQG6dzCTXpuDpJwDT/bUB8J28lvpNc/JPBLu4wtKt7jsqlKaXrWAavqeLjqqibkIJQdrcwmvBQb2HWCnGvAI6WtHvrfiLpTQQJ2FNzsBZSuejaBCn7SaKM+CDbN1cYZzfk/m4vFauU6oJtA4snddsHCVupR4FbM+0dJkNhdbEWcZ2uQKyN9u/6ps7olKAYRSjV6kId12xR7bxg4XFVtXMTatddiWq3+YAtbL+ksCRZl+oJgeJ4/13Dmnidiu+flmhCDNQYkhDgp0QiaOlUqTkS49ZpywdMVvZeRSS/704xNwK+L2l1JwukEYKPOTULLcL2GEnOjDlLWqPPRJv1e2bMPvroCSNt493HtMeewLUKL7zxxDmxEuHn8snMmCcTnps3AvtIUgU1yxCovX3C2wnPwazNvptrYjKRUEZcCOwmqfy5OY0xWuqmmYC3lJRO2R4+NKMa+A1BIK1O+Ey1jldZlJ8K/FLStkmNgqRFCRXKiZnjbIxESZvmSwmC6l3EObo/USq6YGbYbpudBTJjngucJmm71iZUYe1wOrFBrYKODdEkvc32vzu93gFfBLYB/ijpKqJMtNI13OAcADEHlC0eWtdplsVDUuUfD7wo6VSiPPBKwkLheNunZYyzEWWA7d9JWpNoPLQ+oSieAFRd6L+H2DBvC5wg6RpiTpw11+4lxZidUDw+QDRK2ha4SdK+tk/PCHsocAmhwr/R9kBNRPICBCm7IdE48AJCjVOpAV0D5Y3vVJSxjio8Ln5eVpO0YjIhqYcHMuaSdnE/kWLOBnyImFsmOixqFgRetv1Ej2GfVvhkz0nM+1enzxhNEOK52J9owHS/pDuI+WQ5opv1epkxHyP8jc8Etion7RpA7rXwTzXkbZ4SXv8i1m4HETYqy0s6OiexmtZWGxJNbK4i1jGbt7N+6QHdEhSnVIhbto5orTGqrNsWqzCebqh93Wr7H5JOJq75+yWtR6gIzyWavVXBIhr0ty4+bn12T3uCTtUiiiZPO5Fn1dIU8d+EGKiRsUo6hCiTP5RY/84maeHW67lzS0NxWwKjFkYVBEcDtnOaMf8A2Nr2kCSfpHUJoUmud37HppWSFrP914yYs3R5LVcEMyeDa3RKj0dcMqGPNyb6BO0MDkeDmeWJm/k6xOTze2A353fW/BRRejQg6TCiVLQWgpbh9gmvAc9WyUSre8fKAds3Zobek/on86K6qahsgmrm7UXVwOYF1cAXyOtaDOGvBaHoWpMo6fo1w0u+pxq2j02b8XsUJYgzEzfoQ23nNAVqoRESpYi0qT+aUDwtVyFUE5udAwkT/Mcl3UN8r4sRvq6HVgls+8zysURW7Exk5Xsq77R9GXCZwntqyzT2hSSdCJxkO2tjllSIb7H9oKTd07heAY7MJfzSeN/b5TNz/Y2/C4gge8YTjW2eTRuAG4nfslfkNCmZKiTScFgHY0lvTSWpOTFfJdR9l6XfbktiznlE0k9s79U1QHtcD4zx0IZmZ0i6hfBiziFoFyTUiMcC80k6l3yf4CL+mf5dANxJzP9LS1o6jTuL+ARQNJ2baPuBRFB8hVA/j3HvFj3XMtgcpfiYNOYq49yCINDel54/ABxgu5J1hKS9ic3kSwRh/yZJhxOJ58OBXgnabxLNoeYDdrH9vKIE8+uEWjULiTxdXeHj+jFiU3607ZtyYwIr2L6/wvuHoUzKFzCK/H3IEgxPfA0QScpZgRxigvQ7r0xYEz1C2ByMJQjWY4GvZYT9BVFZdgNx7W8JbNlKWuck6xuqdoAG1hdNKJ0Tale7Svo24RO8taQlCSLtG8DShI9qboUKRLO4Fmr1uU7r4q0JW64FyFsDQHPE/7eB39YsBmpqrFsCTxGijbJVTHbfhCbidhMXKDxUc7BQmZxNn3WZpOy9vO1xxecKq4cNiL3ACuRZvTwsaVXbvy3FXg3IItK7rdn76GNaoU/QzuBI2cvz2y32JO2QSXq94NQIyfbTdRJdrYVe2pC0OjbeBtxSIWy7he4Agw2H3pwTtB0xVRXd1E2SshfWSY1aXtAeQiyqPpgZ9n9Ec6mPEOU8AynmrYS3URZsH5g2UR9JMSc6r8lGEbWTKIpS0e2IDd41hAJjJaJEf8cKoZexfXGbz5udUKv2HNv2K8A2kg4kVD4DwPiWSrkOaHhTr7Op0NTL4UV8HHBcKsnaluhk+66Msa1ENN7anVDJ7EyQ058iSJpKnezbfN7HGPTRzVmUzpT+/5+WdLbtZ9PxVtIiB98lztFaIelKD3ZF39t2kQi+jhqaRtl+kug2fkxKfmyTGedgmHwtLU5cB7ZtSVmde1Oi83jgeElLEefpLIpmHifZzlX+n53G9w6GJy6zic8GCIojc5Mm3aBoircfQZjdQCTqVgaOkvSS7SzPb0k7EqTpik5d4CUtQXzff7c9PiPsMsBhDNoobUWUzX+fUOneljPWFOtNwG0OD+q3Ap+roEaCaDTTcc2WWfnTTZWdZYFl+33F5+n+chTJ7z4nZsLawEeJZmj3A3M7vAivBO7IjFm7P7pKfqNlON/rcVdg34xETEdouIVQC1VUfk2pXb9MXPv/TQKTS22fpuhxMDEzZgtnuUOTWEmL5wRM96WdgS8RDd3mBRZ2fiPevwG/sf1o5vs7YWVC3b8z9YmBbnH1vhbDUJ5bRnrcMhT+y19N/xaewp+3Q7c+CXVU/7yPIKi3IypgDyXEGjn4LuEZPpahxP+2xL0gZ3zlZrmvAc8C19j+S+Y4++ijJ/QJ2j7OAr4taQMP7yS6E1GK1SvKE3iVMq4hUHh4XQJ8mCD6ZiXGfw/wxRyirkx6SlqIyD7fTeZGP8X5LV1uZhUW0MXPaGUgdyIUNLU0m0hxP1cx7g+Am4DVWov9RFoeRBBr22SObS4iCTAhlbdtLel2V7AjmAKJcqLtnCZhJwDvJErvvkcoJvYCPktk+3N//xMkvWL78tYBRYfpXxGb/iyk36aY+JhN0vlV1KMpbqemXjkb/bZw+Dv/QWFVkYPDiIZ7v0vPJzkacc1HlKRWJmjbENTnkE9Q3yLpZ8CXbW+f4n+QaIqQq/hvCsVSw40YqtStZKuQVNQ7MTRZd4rtHJVbK+Z+xHX6IkH6jZJ0uO1KSnIA23cC35S0J1GdsDWZ1iy2t6k6ng6om6A4mxpI+DbYA1jLQ5sbXiHpL4RiMbcp4w6E/UbR0/5e4lxYJDPmmYTq9moi4VNWfeaS6csT66FtJf2OIBAfBeaVtJdTx/gecV3OWKaA/Ww/3EBcYJjf/UcrkFMtzJbEBd8uqOnnons5bVdUWZ90wNyEUvI84HIqNFws4T3AHZK+bDuXkB6Cbiq/KmhI7TrgQVuPVUkNYlNVVdUh306aCxVWRMX71M/pcZ6U9EciKXsBQar/U9LfKp7/awJjJP2buJ7GAdfXIIL4RroG6lR+H06MsXZ0WFuMrUAmt3o67EPqzwIcU3V9XYq/BrG+XIvYe+2SGarsuzrktcyYSFqfEJAsB1xEKIp/XKUawPZtCkuaPYk9Rov4X6VCJUj5/3tmYs2+l6Q9bdcuZOijjzL6BG0ffyI2B+MlfdX2pYXXcjfPZW+lIc8rEjNjABMbs1dgMrF0HKFG2b3Le6cISdsRBMKxwA+d38gJovS6EaQM5I4EkdjKQG5cU9xyZjM37kq2P1Q8kErQ9iEy/Tnj25jYiD2naDq0ObE52UXS0rZzCbriGMskyjakRXqPWMn2Eun8fNiDzZvuUnik5WIN4NeJpP0/SV8jrC4OsX10TsC0GL2OsKG4gViEfRPYW9LoElnRK2pt6tVGjdNq5tf6b44aZ94COQvhwYjtx9Pvl40uBHUVVdVOwJYe6mE4F7GZyi3tG9YFuogKCaVOzdfKr/UESe8FbiYI6d8Q5+yKwJ2SVs4pq5W0M7FB/XhBPfkRwtv3GdtjM2KOIjzbnrF9G0zujm6qq/QPIjbP16bnPwUetH1AhbB1ExRNdT2evUTOApCUdN0a0kwJM7eZ72YllLod/bSngGUJIulzhB3FL4GrXc2DFCJxtJHtW9J94Bnbq0ian2gg1jNB241ITJUGObiMQWJqD9tHZcYpj+cthG3QGsD2NanpTiTmkA87eXmn/++fkW/38w2iMVBtsL20ompqE4L0up9I0l5ZhfSxvZmktYDzJJ1N2JpUWQe3IyPrQhNq11cUdgFzEsr3cQCSFiEsj6qgOBeu3OW1qcX9BBn9UcLj9VEqKhxtbw6T76+fIpS5h0t6klDWHl4l/usBhbXFTQxdW9wlaZU2YqapxRnE7zyO2Fu8m5gbqoz1XYQF0fZEz4/zgOUqCoDa9U2oAxcQe4EVnZqupvV8JdieSI1VCp0I43Rf/TUNVJr10UcZfYK2jwGHr+ftwM/TQnSftHHIvdGXSdI6vZZWJ0q8Jy+UEun3dYJsziJoFc0/TicUj59xDSWZ7mDgnz5vs5yYTWQgG4zbtuNx2ujn3pS/R3ikvZX4vRdJJNpsBBGYBUkX2P5SaZwvEwuKCzLDvpzivCSpSiOYIbD9Z0XTpSskPUgQ6atUPGd/CJxTXnwnNeEPiQVgLupu6vUjYuNwK7EhvbFT2WAPGKKMsr114WnVBWStBHVCq2P7mYVjrcX0ieSR1I9Rr7qlHer0dT4C2Mt2sYndMYry8aMIz8hesT3wuSJBl6639YmNWs8ELUFwrgW8OZFolxLE2nZU8189mPBELRLyYwiP6+85vzFn3QTFwqWE7RBUSNjOIWkOl5pYJdKu0tpW0txFpZTt5yT9icwNq+0/EknJvZPqdRPgUEkTgF/avi5zqO+w3bJ3Wo10r7L9WG5iSdKKBOn5DLBdur8uQlxv6xAVIb2i+L1tQVyflVBSzS7hCn0IirB9ssKWpUhK/h1Yp3WPlbRsqtqYrnDYWBwCHJISSRsTjXnvqaKwt/1rSdcRhNJESQ8XXsshfspkZF1oQu16GHGtzgycZvvRJAw4lOr3x1qTlba/lJLrWxDjXohQPy5ve0L+MMHhxf9v4N/Ak8Q9bANCtZqDcvO5Fqo0n2sqqdxtbXEkeWsLiHnqwzB57fa77n8+VfgHsWf7UkvxrrAuzIab82BdkiBSb0p7l19Q/V7deKVqIdZjyvf17aOPntAnaPsAwPaNCu++c4BrJG1aIdZPFWXoH4yntXYBnqlIzhY+82VJWZ5ZJdXs4TWoWqYGpxA3p17RSAayobjdFpy5RM0rth8nmlndmx5j+0VJVUqFmvCGGujwuDJs35PKma4FdqghofAx28NIWNtjktIvG665qVdLiSzpkwTRcYykGwii4/eZw7xd0jYu+UanBXnVjXjdBPWQklFJd9heptIIA5O6JZUqYK70W80EzKmhTRmrWLKotIECojlWBQK8nXoS208kVVYO1iTK495FEB7fJXypl0vKj1ysRzR0muwX52j6uRmx8cslaOsmKCZRc0OchHOB0yRtZ/sFmNx073RC7ZiLU4ALFRVFrXvhYoTdUw5BPwSJOJmQronDiDkx9zqYKY1vFuDTBEHfep4bcyzwE6LU/XsK64QTiUqVD2fG7EZM5eI3RBJ0dULZVoyfS/gAQU6Vnrca8rVwGr2VozdBTk2Gwod4IcJLfx6qW8fMQcwfKwH7AlWbfHUrmc7tXg8NqF1tn69oCjmPo7klxBz21QqJlHaoZU2Y7lc/An6kaBC5HXClpAdtr9BrvJSgWYO4b81DrDHHEV7i2eX9RB+KrMZtXdBUUrmJtQUURCuORpFVFdkQfUK2AS6Q9CtifVkJkna2fXJ6/JHiGl3SsR6sBuwJtu8G9pD0HSLZtw3R8+MKwkpuWGOyqcCB6b+jiITdV3PGNjVI82KWX3YfffSKPkHbx+QFU9qErkFMeLeT3wV3I0IZNAkYkLRRjRv/5yQt5ShDL37m0oSJdw5a3U6/T3gvtY5XamAwBeQuoGvPQDYYt9umZIHMmEXSuLYmFgySSJ02EDdkxFxaUkuFM6r4mGql3UUD++uIUsRjCW/DyY2OekQ3b71K5Y0tuMamXinejcCNKaM9mlAOLpiZ/f8Oce6vSVg8DACrEMqf3LLe1jhrJajboC7yv921WgceZpAsfJihG6oqvpTdythzv5OZJc1j+6niQUnzVoj576TumyTpQ8D3beeWyhfxapGcbcH2pNxkZXp/3QTF067ffxPiOjqNSNbdQ9yvFiOSzNl+wbZPkjQ34Wn9KoO2KYc5v6Fby+riU4QP8+cJEvx4ovw/F9eneaRlozNB0SBmPxJZlYFZbB+XxvsQQfyuYfvWCuMsoq75apo03OmAXtdvtZNTiYRfnTifPk1YvZwH7OIKFgcKe4OTiKTKkh5sQFkFi9K5ZDqre31CI2pX248AjxSe5xBH7fDOlPQdVXhMej531eBJqf91SXuQXzp/M+G7v6vt7Kq0NnjJGbZDU0BTSeUm1haNxLLd6p3xUYKc/w3wdoU/808ySfXtgZPT47KH/KeG/3lvSCKri4GL09pqK0Ik1fN1Vvz9JdVyPhSuyyLeDmxKrC/66KNx9AnaPob4QjlKhQ+QdBNBWuRgP0LZc3cifA9ieJfpXBwAXCTpAMLPcWbCG2g/YpLvGW6ogcEUkHVjnkIG8iTbV4yguIvljGUKaPkZj2Kot/Eo8rqVtjA/cZ522kDklMns5tRcTNIS6TsmPa9C0BTHeB9RjlU+3iselrSq7d8WDyrKSHPVLUOg6DC+GNHMZKLtr0mq6hm9HFFuth5BJmSpBlM535KEt2ur8+ttwNfbKSozP6NWgrqAuhRp6xeSCOX4VTptj640qs6YKGlT20MUI6m87+4O75kSTgV+KWlb2/9I8RYlVJm55Fxxrn+iJnIW4HlJH3CpEYaiWVyl6gfbj0h6n6RvEuOfYPvmzHC1NUEpIm3ytpF0ILA8Mc7xrd8tF2lz9neiUdybifP/v8B/JW1lu2dbCkWn+TWJJl7nEuWzdVQW7U54hc8PrJ2O7UrYEOyaGfNFGGJFtFqrUqUCligkaxcsPM5WkDZA9vSCXtdvTZBTTxLl5xcQfQNayZpPSMpNKkMoyHeskZSEuN/XUeUxBNNQ7VoXriWsGMqPIdYCPSEp+w8i7Ei+k5JzcxH7oa+R19z0CwTxf5akp4hEzzgn//QKyL1/dMMwD/Ka0MTaAoZaMowqPG/Ng9nl+Lb/BHxLQ3tnfI+wg+sVozo8rgWSlgAWZ3AvcBQ12N5QH3m+aun5a4QA7BDbV9b0GX300RV9gnYGh+2byscUfp4LkOc1BnGjuTvFv0pS5Q7oLaR42xMLkJOJCfl3wOa5G8hSyS0MTsb3uILdQUnpWMQoKnTChI4ZyEOJxiAjIm5DG6gioXdd6bXy815wX5XFUQd8lcHmYmcxNAv9ydygrug33AHfBS6RNBYYT9wbViJIxDW6vXFKUHhCnkxkn58lrtm3SjqDjC7Lkj5OkLJfJFSf5wIrVyy/I73/0FQyOWD731XiTeGz/kAo9Co3taO+UskmbBO6zYMQ3/MhmaH3BK6VtDpDz9mVyby+HH7sbwfukTQpxZwFONT2qZnjLP4+dZKVPwDGSRpDEH8vEETlAURZchYkvZloLvVhwud5VuDbkiYC67nHbt62P9HmM95JNLSqdO5Kmgd4NhE17wU2TuduR1/CqcCZwBPA1cTvVdygDpDnG7wj8DRRhr0MMc9MfjG3xD0pJX9YOpb92ycUf5NnaiBnIRSUMzJuTmTapKTOBCY3+DnE9o4ZMf9I/Fatc6qI3KQyxL20bVJW0SBp2J5heqJBtWvtcLXGoO1wJtGxfgFgf4WF0tnAg8BncwLavpywM2k1y1oD+E4i1f5oO8v+zvZuOe+bAqqSxp1Q+9oiYR3ar9dqI0FtvyLpesKXdt4aQtamGE7z3fnAEsC9KbZSkmXzJtfcPeLIGirb+uijEvoEbR+TIWlxYiOxFZGRPTYzVJnUrLMUHdvXANeUj0tayOEV1ivKhNcoQtX2FklfTGVDOeh20/1BZsxhsP0kcJSkD9QVs8m4VeDwNxZBHtzeaSMxQtAtC529IJP0NzovmgZs9/x72b5N0meJhenGKf7vieZj93d985TRUrW+p7XZlzQfQSwcQe8k7a1EY4RLgacI773dWoSHM5sjSdqCmAvel54/ABxg++c58Qpxyw0XWxYXrf/2rEwtnQO1KNJKqLOMr925Pjdxr3mIaHLTMxx+q8sBOzO48fk9oVzPJuttHyjpcMI3doBQePzIY9rsAAAgAElEQVRP0kyZCbuy1UnrfKiqTL4ixd2H8CB8jdiw7mb7qpyYCWMAA2ulhB2KhlPHETZAPaneE4k6FjiBsA+5gFBoPSZpXWf68KbqnLOADSX9lfh/vyo9/4ntH+fEJRJpmwCfA+4kPP2urpKspaFyfA16hZ5re7ykY4jE4B3AZrZzLEQWKCRVio+B7Pn127a/NuU/e8PiKZKXuaT1bF+dlG77A7d0fWcHNFiZcIWk0cXKEYXdxQHEtZ+jyKurauB1D0kHAde3kkiSfgo8aPuAjHDz2P5Wmp//TMxbe5SVn5njfDOwCOFDOzuRrKq1l0IN2Iga91ItpLXF8kRFVW1rC0Il3br3w6AY6GoyKx6SkOZk2txf09ifyAjb1O/8A+AmoirjZZi8tjiImCO26TVgEnq0xlusqgSym5CWbR366GOao0/QzuBQ+FhtSNyIliIypy8Bi1VQt5T9PIc0hqlQejUlTCRj8Wi7XM4AgKRPEyT16JzBNKR07IYtgV1eR3F7hqRdCd+xvwAfkLS97QtqCN1N5VcHytdSlQXQ6DbHNidUc7lJFRJRUrfCA8J7bwkXumI7OoPvQJ4C4mA6f39ZxLfCN7tVFngDoZpcmUhQvGT7/Jy4CT8ifLtuBX4F3FhVOUh9ljGdUKeiY8g8KOkLhLL8ZIJcrBL7Cdp4DUrazHbPTRjTpnQrwjf1/MLxzxPJhCUyxtiYhY7t/wP+r+awqwPLuNCM0/ZLkr4O/IkeCVpi4zgh/duIUPstQBDgxxFEaA4OIhJI90raC/iT7S0VVio3EQ1DekZKyP4R2Dtt0jchFK8TiEaE12XEbKoc/1iiGdKDCu/QzYmN5bLE975+RsyxDF7/xcdVsHINMUYSev1OtiJUxO8GDlZ4hC4EbJSbTFF7n8TJcIYVR8JZRKPgVW0/K2khoifB7MDHcgLaPjOp5ncCViDZpgBjXZOF0OsBkg4m9lmnFA6PIfzzv5eR/HgeJs/PsxPE118rjvEY4np9H7FmuRbYp2AhMUMgiQlq3cO1WwskVekOhIXSZhlhj6fz/fVH5N1fW/1DRgHvLgkAcvuHAKxk+0PFA+nc3Ye45+bgusLjsv9s7jq7dluHPvroFX2Cto+HCW+gY4Erbb8g6YGKBMI/GeoF+TCDnRZFdJptArVOqravVzQLyUITSscpoKmbyki6We0CfMDR0G4pYgNZB0F7rKR/ORpP1YVGstDFzX7Knp8CfBD4tDObOqQs9nZE1v1awjZgJUL1s0PFRf+LRXK2Bdsvqr3naVckhePOwGO2L5I0nijleoVovpODPQjFYNHT7ApJfyE2p9kErVPH25S02gQ4RtINBNnz+8yYTfsv1n7uJuuA44lN/qYNl8qeQvxuveKnhGrobYVyvDOIhnGH5w4mJULnsv1MSga8mThff5F7r52CdUS2khyYqUjOFuK9rLzmYx9ulcUmovtc2/8BblU0tMrF7LbvTY8/Qyjqsf0fRePAyrA9AZiQrt3DiGTlnHXErgkr2v4ogKQvEt/tvcC9Cp/+ntFQYnlWSe+hcxPOEVMJI2kFd/DblLSl7XOAL/UY9jnbjwKPSvoYQYKu0+6+2APaCQuKlRlZBK3tIxTd5a+W9CPCx3QscFC7eWFqkErlbyYamf2GsE1ZEbgz2SY8mBP3dYj1iP4ck5s7pgTTZoRVW69zdvHe8VRVcjbhSeDrwG0Vz89pgaU7rCErVae02bsVla4H2X4hJ247pCTzGEm5JfVN3F+b6B8CYcM0DB70O+8Z7tCANM05O+TEBBYuK3FLn5mjyu2jj57QJ2j7OJvIur0NeJekKkoxYLgiVdLMxIJ2J6ITYlNoghCrstEb3eZYZaVjFzRVljKSyppeSgsabN+p8DetAzsDZ0i6GNjXbbqjZ6CVhYbhZehVstBAqASBo4GfAJu0SoYycQLwTsJ3+gAiE70X4WM2lnw/O+jesKjnc0vSd4HVGCwJm5241tYF9ga+0mtMguwZ1nDC9v1JmVIZify/MZFHownVzIK231tH/Kpo0jZB0rqEavY8YGn36GOagdyk0gqE0m1uwnd7T6Js/oMOy5eeIelDRHfiQwlV5+HE9bUUcb2dljnWphJnz0layvadxYOSliY2qb2ieI1/hijBbyHX5x7CLmIUQXavQiRZWp7Xb64Qt1XS/SlibfR5Qt1zPHBZlbgNoEhOjCbm7Bayfe4lbUvMr5MbuQAn2D4vM+SixDnfqQlnVUuWOnEKqbxV0q22Vyy8tjtwju0H2r6zM4r3wKds71FxjNjeVtI6RK+E+yWtT9z7/kCmbUwh9jGJ+DqdIJKrqvSPIBrj/axw7JikAj6SqOKbEfBqu7Wlo7lXzvrtnek7HAXMXVZVZ6qon7X9OwBJH3HBi1PSsa2E8wjBnW6g+RzD924t27uvEs2schswdkOuP33t91fbD0n6HFExdGvrfKgB3db7lfeZaW29LmGftRopaZuBSQxX4/bRxzRFn6CdwWF7j1QeuDZR3nw0gKQNgYuqZFAlvY/IYG0LvIPwr9uoyng1vKFXC6PI8HJMMRduc/jthKJwfE5MaEzp2PIwKqO1Uc1CU3EbQHmMWYqOMmyPk7QksbEZL2k3wh+z9XqOwqeRLHQ6l8YSm961HQ2nqmIl20skJe3DhUX4XZK2rBi72Lm2iFHE9dArtiZUKJPS81fTgvJkIFdBMoekOVzqrJ7IntrukwrP1A0JJc1D9K6YaRKjmwgq6Wzi/3kMoaBaQUMbJDVheZO72P9XUok9kRR/u9i+sOJYjga+Ybu1Wfh3IlfeD/ycTIJ2apSOkg60fWCPoQ8ALkoKzNuI839FwgKka1l1BzwkaRNiszgHqSQxzStVGnFcRDQzexPRvObPaQ4/mEgEZCHNI2sSPq7nEsTSf7u/a7rh6aTInJOoTLoaQNJoopKpZyhshHYk7oV3E9fSUsC+kua2fUq393fAxIaIlCZQJJHLybncpEhxPqolOZVsEjYFtk7n/TnAN4ClCX/3rAaUhfXwxcQ5dZCke0m9JDLXQiqRs6RYZ6Xy5hkFz0v6gEu+/pI+SPdEdidcy6CSuvgY8lXU2xP2QzDci7PT/usNhQ4VSg8qbG5qbx4laQOiiWQOar+/SjqEuNePJxqEjrF98hTeNjUoilaKqCRakbQgwTVsR5z3cwGLtxNdTCWe7qTM7aOPaYU+QdsHiYS9FLg0lXVuSTQvOI4MO4KUyd8RWI7YRH0Z+HGFkssium1Kc8nU6+ls3L5fZszJqFnpeGDV8UzjuHWjpRho+zxTMdB6738l7U94w11KnAOtcsGeFT4NlqFPJDbkF1JojlX43Jzym9bm6yVJWRv7Llin5nivFshZCOIP269Kei4z5rnAaZK2a5WvSXoboSAatrHsBZI+ThCUXwQeSJ+1sqs1m6gdDZ6v7yHKNz/L8M7S2d3Gu5T4jyJfPVgkUh6vgZwFeH+BnIVoGITtB5LtQ5P4Aj3O7bavkrQ9ce87mfhOfkd0Wb45Ywy7EsnJ+VKMlyQdncaWa0nSsjrZGJifQSJiNULtWmWtsSOxWV4m/Tu0lFAYSWrPbxK+1vMRyYTnJe1HlCivnRlzR2BVD/UG/YvCluUKhvpnvhEx0OFxu+dTi27VNLnVCVsRFhf/lXQYcKnt05L6eyKZBC2D6+HW+CBsCVrIGWu3KpSRVKHVNH4AjJM0hkgAvQAsTyTF9u01mO0megZ0a2470pCdiMuB7dckZSdY2lgnQFSv3kvsu3PQ6f66LtH/IQcbAh9Kc8sixF6jDoK2dtGKpEuIBOIlRMLqFuCBCuQs5KuZ++ijNvQJ2j6GIJWPH02U3y6XGeYCgoRY0fZ9MLmTeR3ja9vQq2LMpjosN6F0/HBNmcxpFbdulFUCxefZvmsAqVzwBKKkeWHbuWRf0/h2AzG7bUorwfYUS4Uk/cH21HZNnUnSXK3fx6lJXCJUc+eZAwkl4+OS7iHujYsRqqRDM2O2cCvwD4L0f4pIAEwm1mtKXI1YuLlu4902j7mdnVtemTMBb1LJNzNTPTZknLaLTTuqJOt6/uyphe1rgGs6vd6LMtf2Pxi+UTyEmMdyFPTF2OcmC5LFJQ0AJ9fgD9jIeqAhLEN4446CyY2jHiWqlT5EXhPG19ymcZPtR8vJwB5wXO4b3yBooppmoKDsXpWwkGn5OWYHtf2+ZEkzsY11wpjMsBMlbWr7l8WDkjYnVNozBGxfkawj9iEaOL1GXKO7Ob9Z3EHA9bavTc9/CjxoO8uDuoSRTp6f06ECEqjf21rSCnTwUZ1KjC49f42wlJjU5m+nFpNst72/2s5dD7/QmltSdVotXFFD1gkLEtUiTxP2MQNpLVBlnJ+oYVx99FEJfYJ2BkeH0uMictRNSxK2BjdJepBo1lJnmXCtC5I2PmN1oQml4+TyI0nX2/501UE2HLdWNKQYQNJ5RCnXVxI5MWLRKr1pERPEItoViYlis4VRxcdMm0V6L0TSz4CzJG3taIaApDkJhfo5OR+eytq3kXQgoWgZAMYncqkqDmbodzjSVSm1oovSFYJkyPJLnJoS/wzMCRQtF4qPc70y75W0RnkDLmlN4L6MeL2gqWu3Z2VuC2mz9znCk75Vnp8FSfsC3wFeBGYh5q7DbWcnVRpUkjeBM4EniGqflxg6t+QmLGtJppcw2SZJI9/XchENNogpPh4FdCSDuqGhc+qVpMCfkyDqxwEkxVu29dMUrBMOJ0+ZuydwraTViUq3mYkmpCsDn8wd6+sRDj/fjp6+vSS/JB1MqAeLqvYxhMDme5nJ35FOyhbRLfmf7W2t9pZv70j/Ns6JCY3NA9cw1IYC2zle8UWU/99raRbXhHWC7eUlfZTgHG6Q9AjR5HV+249ljnOaNYnro49O6BO0fcxNeL+cB1xODf5Ytu8G9pD0HaK8eRtgPklXACfa/nVu7IYWJLU0AWqDJpSOxQ3YW18HcWuHwod4f6KhzwAwATjY0YQpF48BS9p+voYhNo4CMfECUc5diZiwXUvX8wroZVNwGJFMeETSxPTeDwNn2z46dwAK/92VGTyvZpN0vu1K5U5Ts9mSdKrt3I6zIx3tCOm5iVLqh8hsaNOhXLCFAdsf6DWmm2nYth9wVSJ6biDGvAqxoajSfG96ouckgwY96bcjPN4PpcJmV9LOhEXCx23fk459BDhV0jO2x+bGfh1hWWATgvC+E/glcHUF5RTAAh2SKqMIO4kcvJ58LXcvPC4TQNdNw3FMCYcRdh4zA6clhfPGxHVVJXlVu3WC7XslLU8kZdYh5sDfE8rREWX1MwLQS/JrPcKPf3LjsfRdb0bY0uTshxptblsnmqp+ZPj33yLoJlacW5tAEwn/8j1gyPMKVV+1WydIeovtPwG7K/rprEtwDg9IusJ2Tt+b0aXn06JJXB99DEGfoJ3BYXtpSYsRi/yDgPsJT7MrayAmXiEaDVycyv23IkpPswlamlmQDOt+WoQzfU0bUjo2VYreWIl7nZD0GWKDN4bw35uVUGL8UtIWtq/LiWv7a5LeJGnW5OH0VmLT+yfbuY2nGkETxISknVuZ7JGucHJ4Zu+QlPQfS4cnVFG7Snonsfn+L0GizUqcX3tLGt2u5LdmLN9w/OmGstJV0heIctyTiVLPXIxuc2xzwsvv2JyAiqZw+xAleLcAx9RwH7xD0spEwq5lvXAb8KmWBdDrEFN9j9BwT/otCU/6qgro7YHPFa9NR6Ow9QnPzDc8QWv7jwRJt3ciwDYhPHMnAL/MvB+OpfOmP9d/9nXja1lYt83UImMkzWv7yek7sqGwfb6kW4B5bN+VDk8Cvpq7DkpoxDqBuLdeChzhkdt0bySgl+vj1eJeqAXbkyTl2uc00ty2CaQk7bj07xrb/6op9Eq2c22SpjXm71allEmmlu8B3e4JvaAJ64S7JG1j+8bEOVxENDmdj0xf3w5K58aaxPXRRzv0Cdo+SATUIcAhiejZGNhH0j22t6npM54kMk9HVQzVxIJkTmKz3+4GVNXXtFalIzCXpE8SHolzpsdFj8TcjuhNxa0bBxB+vn8sHLtD0u+AY8hU46TN7SXAtinWHYSX37yS9rJ9ScVx14kmiInXk8IJANsPE4uxOvBD4BzbhxcPKhru/JDw4OujAlI57vEEqb6p7ZuqxCsuolMC8BTC0/TTtm/PDHsGMe+NI5RM7ybKeyvB9r0ESTkEkla3Pa5q/C6Y2GDsqUVTnvQzt0uc2H4iqf1mKNieAExI9+7DiM1pz/YRU0OcV1T8j9gEMExO1l1IEJO/SofHpjlmvZGk+LT9CPBI4XkV8UMLtVsnSNqIWEdPAgYkbeSp8KafQdHL9fG8pA/Yvr94UNIHybQqeZ1ZvKxBWGSsCxwm6WliDTyO8DjNtfrYiHwf++mBWu93DdlHQTPWCTsDZyiahe3T4gdsP051vmEIXLFJXB999II+QdvHZEh6E9HAZkFgHkam0qH2BQnwkPP8YLuioRLMfzKoEn6YoYrh7I7oDcatG28tkbMA2L5d0twV4h4JbGT7FklfA56xvYqk+YnO1SOJoG2CmJjeCqfpPdd8zPYwEtb2GEmeHgN6I0HRdOYkwkpnadu1LXJT9cTRhAfxJrarNN5awvaHU9wziaqMWpGInm0JwnY24p6bE+fdxLz1EaIR3XfLCiLbuZ2h60RTnvQzS5rH9lPFg+n7HdEkYJ1Ic/6nCFLh84Si9njgsgY/tlfF/+vp9ziO8AgtdojfEPgeoczvWG31BkET1gn7EZVvd0taI8UZXcdgZ3D8ABgnaQwhKniBuDYPICpJekZKnrW7XkcR6uo3ZY61diRx0V+B0wEkLQisCZwIvJcRbtdWEx6tm1CVdAZd5uwK++XarRNsj1N4ZR8CjJe0G2Gd1Xq9tkZxqt4kro8+php9gnYGh6RZgNWJxf2ngRuJhekuVUs7G0LtCxKaI4dqVzraXrXG8fUUV9IOtk9t4vN7wJySZi5nxlOpTJX57B22b0mPVyNUX9h+TOFNOpLQNDFR+2Za0lyEutEdyhunt1phli6v1dIgYUaFpLMJgmMMcX9ZoVgqm6vOT+f7WGBRQlX/h+qjHVx8235eUnaznTIkjSY8GNcjrrGdgJ9XCHkG8Ceiad6GRAVBI00U22Cqlbnu7kl/ku0rMsdwKmFts23L3kTSosRm/cTMmK8rSDqZICTuIFTKe43Q8vElFF6Wo4B3awT7WgIfLSc2bA8AB0m6ezqNaZqhIeuEgTQPYPsqSUfWMNQZHravUDR03Qf4ESFSuY3w9r2q65s7x5ze/Qh6QrKQ+zShph0NvAm4lqT8zkSxaW4RI46kppn963UNxISGrBMcnrb7E8nuSwm/4FaD454bxamhJnF99NEL+gRtH08C/yYIqR2IbsgAn5A0kkrbgWYWJMCX6xpfCY2UYEpanDArX5xo6jaRUDrU0XG+G3YiNsXTE1cRnYT3aB1Iyu9jCKVrLmZKsWYhFntjCs+zu4w3hCaIicYUTlNT3mj73KY+fyrxsKRVbf+2eFDSakBtGfgumN4K4iaxEKFE/Wz6V0QVdf5E4tq8ENit7I9YU1VE5etC0reIe+tLBIm2PzCu5XVZAQvaXiN9xjhC9VYZTSlz3d6T/mgy523bx6ZS7HskTSLWs7MAh46AROK0wo7A00Qp+jKE/+zkF21ndTFvAIt2eW2kqWu7jWeGSNY1YJ1Qrm6rUuXwRkdPtjS2/49QfA+BpPfafrDXD5d0PnAacFVKTIxYSLoKEHAzIXo5KllfVcWdtpepIc60wGrtDir89LfIvBf+2h08tyWtkhEPaM46QdI6wAnE/nBh289VDHlg6flIbhLXxxsUfYK2jz8SC9LWAh8GF6iLEx58IwqdFiQVsIGkDbp8Xm7HytqVjokw+lX6d0WKsyThPbdxw75eI4FE+g5wmaT7gAnEHLY8Ydze8TecClwv6UTCJ/hh2xMSUbEf1TLxtaMhYqLJzr2vh/LG7wKXSBoLjCe+05UIReIa0+DzfzMNPmO6oCnVP9F0q24sKunaTs9t55DJhxIWKScBNzqa7dSx8Z1c4WL7ZUl1Vbw0rsxNm7+jJB1QMc6Bkg4nyOQBYgP1PxUaPL3B0VQX87pxHe3XPAsQ99yRpEh7SNJaZVJS0pqEoKGP3jGXpKKX/ZzF5yNNCNIkUjJ9L2AF4pqYAPww+ZT3lPxSNHg+iEjSfNfRi2MuIgm4GzBHxhAvJe6tp6TqlzPKlnIjCO8krsl/EGXtT0zf4Ux7lD2xJS1FJO62BEyeqOZalZrjJlHRAcDuZFpHtFGmtojP3xCNQ3teF0k6j+iZ8RXb1+SMq4zWPlrSe4jmpgPAgzPImqKPEYI+QTuDw/bo4vOkGNyAUEu+bXqMqRvUpVslZJOpTRGPTSgdDwLWcKkJTvJLPIowzG8K0z2bbvt54DOpXHh5YkzHumLDIWLR8U1gfmDtdGxXYoG7a8XYtaMTMVEhZJOde0d8eaPt21LyYy+ihOk14PfAyrYf6PrmKUDS6S1/W0lbF5WTkm6yvYrtvap8xkjHlDalmViEWNj/vsaF8zo1xSliQWALwr9yPknnEt6zdaOu+bkRZW7dkPRmQoX7tO3zC8c/DxwBLDG9xjat4OnX0KenNZPtIUSypDmJ9coahBXUSMJeBEFxDfAHwvZkBWAtwuO3j97xMKFKG9Xm+UjqcdAoJC1NJPzPICzZZgVWBG6W9NmCpcTU4kxinbIAsH9SlJ4NPMjwapWpgu2zgLOSn+uWRMXDU4TH+3m2R4wHp+3lFU39Vge2Bn6s6BkwjqhSuScz9HlT/pORg2TzsCmxb1+SUPqvU0GwcxZwTaoqe1bSQoR3/OxEk9dcHFh6Pgp4F/HbLUgQwL3iMWDJtDesBakq8xRgc0L8Myvwfkm/AHbqE7V9TAv0Cdo+AJD0PqIMc1vCZ+X7hC/tSEPtZOrUlF1IOtD2gT3G7aR0/H4FpePbyuRs+qzxqaTlDQ9FDedfi35okt4FHGJ7WKf0qYHDb/mHpWP7po3kFkx/a4chUDRE25yCzYWkc3MXKbYfkvQO4E0txbekTxPEb1XV0OulvHE0cKntbSWNJzY4oyV93qn7fCaKpXLfAIql7W/4a7aBTWkLsxPX7KLJM3Ec8Jsqap+p2dBI+oPtZXuI+QzRtOn4pG7ZFphF0p+BE22flDncouodBpXvLZ+83PL2ppS5deOnBEn/tjT/n0+cY6sQNjh9NIdsxX9KhP04xfhoDeWotcK2Fc1gdiLKh18jEkpLOzqD99EjbI9WNIqcaPt+RR+GrxAE+CHTd3TTFD8ANrd9deHYRZKuJu5la/YYbx7b31L0SfgzsAmwh+1fVh1osgo4HDhc0jLALkQDvSrNeGtHUnn+AvhFEhh9mRBcHE2+Mv+mkuK7/JkjRvEt6ThCVDCeWGdcCtxVpZrS9hEK//2rJf2IsDwaCxzkUv+PHuO2HZOkS4i5oGeC1vbXcsfTBXsTPMi7W/ZOkuYh7lt7E/xIH300ij5BO4MjLZR2JGT8FxE3tx9XKOtvFE2RqVOBLzA8+zdFTEnpKGkd25f3EHKkklvTBJIOJJU2S1rP9tWSvk10WL6l23t7/JxWmdAWRJfYEUPQSlqW8FoaD9xNnFcbAd+XtHpLrdpjzGWAXxPkUcs+ZHXg54mgzCXRIMobP8lgcmXO4vORsNiVtDexGd8lHZqNIGzXJRZkX6kQflSHxzACVOnTAHVvSgGwvQ+ApNmAjxPVAydIWgC41fbO1YbdEdlJQtt3At+UtCdxT9mGsD7IQZOq9yIqnaNtyhpbGAW8uULoFQhv07kJu589iXnxgzUklWZYNKX4Twnko0mqWdsj1tbF4cHatVqrj6mHpD0Ihd/Wio7r5xDJyqWJe8C3puPwpiUWKN0Hgcmd6I/NiPd8ev9LSUW5mu2/Vh1kC8kuYQNiHbwgJRHD9EYSwKwIrJz+fZBYF58MVCl3b7fPHEifBdXuW3VjI0JFfSFwue3n6rBQsn2Mot/L6YQat05bwfJnvSjpxSn/5TTDRsBKRcGL7ackfZn4rvsEbR+No0/Q9nEB0bhkxZZKTNLrXb6fRaZOAVU25f8jFBjtcDDQC0FbJruKaLqZ1b+m/CeNYytiU/5u4OC08F8I2Mj5TeKARsqEmsIPgK3beOStS2yAV8+IeSSwWVGVnBTEN6SYWeVyCQ8T53m75yOlvHErwid3Unr+WlIVn0wQ9FUw0OHxjIK6N6XlOC9K+hfRhO5ZYD6aVfn09BtKusD2l4rHbL9M3HsvyB1Eg+XtdStzD6xtZEPxr6TmeSJ5xe1i+8KGPmtGQu2K/5JqdonCPDvi0CWhAGT7UM/o2IrYY/xX0mFEpcppyddyIjMOQdttjZ6j9iyep0/VQc4mNe7aBCn7SeAyQjl5c9XYDeAhQphxLSHc+INraGzmkm9+KvE/jRBEbFM1fs14D2G/si2RoL4GeIukWVNlYM+QtHB6eDFBzB8k6V6SQMh2rY1zJb2f4ZV20xMztatGdHg8zxCNIvuY/ugTtH0sSUzsN0l6kCgVeb2fF014yjZFrPQ61jLZVX4tC5Jutb1it78ZIRuT52w/Cjwq6WOEV9I6tivdNJsoE2oQC5XJWQDbl0nKVb6/o0jOFmJelRTgVbC+7Wcrxmgar5ZIgzEAtl+VVLUEd9ZEIM1UeNy67metGPv1gLo3pQBI2oxQ460KPABcTTS0mlDHJq1GNNLIKSVSOylTB2znfre1KnMbnEOL/++P98nZ2tCE4v83xOZ+deCucCmaHL+KHUcTOHB6D+ANiAHb/02PVyVVDTgaJk6/UU173CrpW7aPKR6U9B0gp4/COyVtRVxHc6fHk+Hwk+0VjwF3Ef62WxV+t5GIf9pu1Bda/9/encdJUlbpHv91sym2G6Kg6ACO+jguDI2KF0RscMEFUUYRN0REcRlUlEEEQUSUZUTAwY0rCgODIoGv95QAACAASURBVOKIC86wg6goNIILOI8iwnVYxF0aRATq/vFGNlnZVdVdkRkVmZXP9/OpD7lUvXW6qMqMOHHec6Q3UIoijqH0zR+qBF0Vz9cpw5MfSukbvDFwo6QTbO9TY9mp3rM7F9kngFqv15JOYMX3kAdTdsPsUWfNhtwtaSPb13U/WLWCHKZK35jHRj0RF32qtkPvXR0gbE+5OriepDMp/fFWSASNgGE6OV+ZWcXqnqFuA3SfhtYdtO6rrL+1vfeA1m1km1BDZjpAqBvzGppi8rmkhfSfRDyPMmV1mC2UdP9OP0TbXwaQ9ED6v7K/iHLA20l2tN7SYY4N+qS04xTKlvaX2Z5uh8IwmGnXQ+0WH7YX9hXV9Ou2NXhqtrovfKzWc+Fj4FU+Y6SJiv9GLlI05Hbbl7UdxDxzV7UdfRGlQvtsAEkbArV7Wo6gvYELqt1Ol1LOwbegDKOtUwBxPiXh3Xsbyt9unQTt09xHH/c51liyTGVI2meBhwDb2r6qqe81KFVrn6OBoyU9hZrVvu4Z6jhAF/bcv4ey6+mNVS/hYfERynC8twOXce/f6THAgW0GFuMjCdoAoNoqeAblRemhlC1Jh1H6UsaQkNTbF63zBnee7f/pY+kVrr53q3klvgndJ4x/mfazZm/g24Qa1FuFOem5mmteRGnQ39uk/wCmb8+xqpqoaB+0UyiTi3e1/WdYPmn8c5R+ebXZ3qj/8EbaVCelW1L6uPVTlf9kSgXthyVtREl8nw2c23DF9mx/n9en9LSb6uuGpcXHcg1W5g7aTBc+alf5xOAr/kco6Q9levdmAJI+OsCLwOPscOBKymv/8bZvkvQK4FCm7vc5L9m+pUqcvRJ4KuV16lPAaXWOM23vNt1zkuq0ugL4zExVzUOyk66jtx1PR1+V+T1Vs0f0Fi4Mk5nO2yjJxUGvWft8sLuf+Qzf+xu2t6+z/gCdQRnofRLQaffwC+DA7NSJuZIEbaygugr30eojiqvbDqDSe5K/OmUA2Xsk7WP7tJrrLqIMRZouiTAsCdrOAdkC4BFdB2d9HZD1bBNal8FsE2rK/ZicmOhWt+JpP+CbknalnEjdQTlJvQV4Sc01O9af4sLCch6OgYSHU06UbpR0NeXn+ATgZNtHNfVNJf3Y9pObWn8YdJ2U7kzZyjZB2eJa66S0a92rKJOrj6r6Rz8LeC5wkKTbbD+9zrrVYJTHlG8x5fbOw2a55DVDdlI7o6YqcwctFz4as4jJye7exPd81/2+us20nxWrzPbpkr4LrOt7B44uo1TOXdheZHPP9h2U9gEnDnrtqrhmN8p28ftQ5jPM1jrAw4EvUeZjDLIQYtCuoRRWDNrx1X8/DHxoipYsw3KREia/Rr2Ych7TUffcbRvufa2fqs1Nk+eDGzS49qr6IbCb7Y2r88GJIavwjTGQBG3MR7NKpko6xPaB1e3numu6sKTTbL/C9msHHWRlVtVYtqesNpC0PqXauW6C9nrbb6j5tXOp0x/xAZTp77dT/t19XeGW9Fbbn6rurmf7GOCYKrE0bZVCSz5i+5MAkp5UtSmhuv+xOgtWLR22phyYLab8PD9h++JBBMyQV9FWCfo9JB0MbF49vNT2rxr+1hs1vP6w+BvwpU4FhaR/YEDJHkmPoUxw3ory/24ZK26lW9W1dqKcfCwDJiTt1NtDtY+LYDFgkrYFbrL90+r+24GrbfczwXusJfE9yVC/b40S2zcCN3bdH7vdeTPsTgCgbuJP0hLKcNuXVuu/Bfh8nbVsbyrpcZQLqgdTKge/CPzXEO4mu7OJ6vxRuUgJk6uoJV0xU1X1LDwPuI3SQuoc4HzP3WDHYbgI+DbgBElnAO+znb6zMeeSoI2R0lAy9UXc21fmCMobUsdj+4j1n21/YorHHwh82varKH1t+mb75qpfaF2jciLyF+B0StXwNZQ380OAS4BX97HumygVlAAnU21xtH25pM/0sW4T3kg1ZIOSTOru7/rMuotWg5XOrz6QtFa11ekttresuy4lgTIS2xht3wB8ZQ6/5TAcjDaqGqxwNrAvpcczwLuBZ0l6Xu8ghlms+xXK6+dvgQuAM4F9bP+xj3APoPTg+4mk7SgnqEv6WA/KvzsGTNLOlAqnnbsevgX4v5Le0+kjHbOzku2tE7ZPnrNg2tFED96IFRJ/1TH7vsC7gP1nu56kd1GqZe+kFGccCJy9KlvJVxLnzyjH1YdIeiJlgO7+kn5q+/X9rD1g32li0apYoVunldxPh7ndAQN6vbK9gaS/p5xPvBQ4QtJvKOfGZ9v+/iC+z7CyfbakTSh/A5dK2hO4vuv59LePxiVBG6OmiWRqE1OLAbaT9GzKVok/wfIr3SdRrkx2tjv1TdLa9DERHdhlEHHMgcMog4WebftvAJLWpCRSPkbNpvjM/DswbMnrRmOV9HjgzZQ+1L+n/Fz7cf9+Y4qR9jHgoO7eXbbfJGk3So+3l9Zc90vAW23fPIAYOyY6Fem2z5J0ZL8LVgf721MqO6+V9FJgd+AK4JDO61jM2j7As6qLKgDY/qKk71Mu4iVBW89U2/rXAF4O3Eq5gDmfbSqpM6l9Qfdthm97c4yoahfJiZTE31Nq7tY5FPgq5YL9xbYnBjncVtJqlDYJGwDrMmTHwrb3bGjp3oKCBcDDKHMpXmL7yoa+79CoBsX9AjixGvD3Eso8gQOAtdqMbS7Yvl3SgZTf/69R/k4XkP72MUeSoI1R01QydZBrAGB7B0n7AEslvYnSK+l1wJttf7XOmtNUtzyIMnSgn2FGe890YDdE7Q+2tP0P3Q/YvlPS/pTeqYPQ+3MY5iqagcQqqXMC/hbgHym9x+4EHldV1vZjeQ8zSXvbHqve1lNsa+x+3Rrm361BeZTtFbZb2j6hqgCqxfbnJT1W0iHc29t2KaUFyM9qLttbHdN38lTSv1CqPHetqjJOAd4JbEqZFrxXv99jTC3sTs522L6uSixEDb1bZCVtRkkk/Rfl/WFeG6XtzTF6JC0A3kupmn2f7X52aG0AvIZyoXM9SafRZ/KsOhZ8HrATpa/7xZSLoW8bwhYHjbA9Ze9pSc+i/KyXzGlAM+iq9l0ALOqt/rX9rRW/aqVrrk5pGfV8yiDW+wLnAu+n2mHXoKG4CFBdVP84pZjq72zf2nJIMWaSoI1RNqjkRmNJEtsfkXQD5U3tZmCzqhdXXb0HDp2tN4fY/q8+1r1wisceQ6lSGqbtLFNWHFeVA/1sPRqlRFkTsd5A2S52DKXX2B2Srh1Achbgrq7br2HMhg/mhJ81Zniu9sG4pE0prRNOAN5HmTC/BfBtSc/pGkYzG/eX9MyuuBZ1369zskPZnbBFVZFxOPA128dXJ+rDMnxyFC2QtKi3N1415G3NlmKaN6qT9IMoLXXebfsLLYc0J3raaN3P9m1txxTzQ0/V7Ga2/7ef9Wz/HjgWOFbSP1LmJawh6SrKDIFPzrjA1H4D/ImyA2EPoNN/8/9IqvseOC/YvkjSOm3H0eNE4Lrq9g1Mrv6dAOoMKP0D8F3KTpQd67ahqqmv9hyDIOlLlNZxu6effbQlCdoYNU0kp2bc0tbPwlWF2HuBtwJbA2dIerXta+qsN6AG8FOtO+lNUdI7KNvc9wX+rYnvWdNM/z/6+X/1REnXVrc36Lq9gDLRdpg0EevJlIqJBwIPk3R6nzF2m6nqfSxU05XvV1X3vZsyKf0u4MgxqEq5QtLutj/b/WDV4uAXfax7GPBq2+d2PfYVSecC/0qp/pitG4APTnO/7snOhO3bq9vbUPWPri4q1VguKicDX5S0p+1fAkh6JKWXeAa59UHSYsqJ8jXAprZ/3XJIc6m7jdbFTO7xHtGPK6r/XgKc1Pv6b7vO+0vna38I7FXt2tuB0u6rToL2Ssp73eLqo1vd98D5ZNguuN+Hsg1/kAO9jgOeDbwBeKSks4FL+u2/W7Xi+yBwmu1LJR1Nmf/xA+BVtm9wGdDctpuBTXJxLtqUBG2MmoEnU5uqcJN0HuXNcwvb1wLHVUmJb0t6X2/CYhbrbk05geje1vtB2xcPIOZHUyrSoLQTqJVIblB3crJbv4nUx/XxtXNt4LHa3lvSeygnp7sBRwFIejnwFdt3z/T1KzHWQ1ckbUnZIvhuSqXDWyntSLamtJHou8/pkNsHuEjSrpQD8Tsor10bAs/pY92H9yRngeU9X+se5O9o+w99xDSVu6oebosoJ7xnA0jakMnV5TELto+StC7wE0l3Uiq91qZsSzyk1eBGWNUyZC9Kf8tTgLUk/V3n+TEYkDL2FxSjMdsNcjFJX7b9su7Hqp7mX6ZmD27bS2b4fqN0nFxb9+tdlwdREpaXznE4M7L9iGkGep0NnFNnoJftfwGQ9HDK7+yewL9L+jFwlu1P1wz3GMoxz3WSXkgZ7LyYchHs48CONdcdKNtvbzuGiCRoY6TM9XZhSWfaflHNL78I+FD3Vceq7+J3gS8As07QStqWUjn0IcpJ1JrAlsCpkl5j+8KasXaqZt9HmYx97IC2tw9aIweItq9f+WcNhyZilbRB1c/xa8DXqorPXSgXAj5G6XVW10wVvxO253vD/cOBl9n+XnV/me2DJa1HqXqY1wla2zdXFXmvpByM35eyLe9LfQ5JXDTDc3V7kJ7H4CvmDqdUJa0OHG/7JkmvoCTAeoeRxCzY3l/Sh4HHU9r9/HRQgzfH2GuB31K2N+/R89w4DEgZ6wuK0RzbF63scyT9wPaqvgdt3GdIK1W1OvknSv/pzZn5fXe+uIjyt9+5QNNpJXce8I62gprODAO9DqSPnsTVscrngZ8Dz6DMUHk6UDdBu4XtJwNIegmlkvbnwM8lHVQ3zoj5KAnamDf6TKZO55l1v9D2B6d53JL+T81lDwJe1DNF9ApJ3wOOplTlzZqkiygHX0cCfwR26d5+ZfukmvEO1CglUkfM16kSU12DvI4CjpL0lD7XHouqixk8tCs5C/AjANu/ljQWvTKrLf6fm+75WZ6Udlwi6V22j+5Za1/g2zXChAYq5myfXl2UW7erL+4y4I39XFAbd73DUCqbd963xrlXYj9sN570GXIPURnGuqDr9nLDciwU89Zs3oN6e6ZP0s9roKSNKW3OdqNUjx4KvKLueqNklF4DmxjoJWkHSkJ2K8oFue9Va+1s+6o+wu3eibcEeE/X/bE4Fo5YVUnQxnxSO5naBEkXMHMFRp1eTg/oSc4CYPvyPpvX/xK4ltLL6JE9z00AOSmZ37oP8CcN8rJ9eT8LJ6k+eUiW7V277vbV02seqZMY3Ru4QNKLKdsOV6cMCVub+n3y1pf0/umenO6i20y6tkv+sev2TzrPjcGW8aYczL1VTt3vs537494rsTZJD6FUzHXaKF0GfLoaSjTfnU/pFT1BqZhbUj3e+b3KsVA0aTZV2+tTXgenev+s9RooaUdKYvYpwFcoFfWfsT1Wuz2qYZN32P6bpJ0pCcvLe+d1DIEmBnrtSXkd3Ivybx7UcervJG0O3I+yK+9cAElLgL4G5kXMN0nQRjTnAw2suUjS6rYn9S6srqLW/nu2/fp+A4uR1pvgiMG5XNLrbZ/Y/WBVmfWDdkIaOrPeSmz7lqq6+5XAU6s1PkXZNtfP4LVB//73bpekuv9wStVI3XYM4+5EVvy5dmRrek2SNgK+Q6lCP4fyO7oF8CNJW83xRO85Z3s3Sc9icp//y4BDUpUdQ+aafgaLTePLlCGLW3RmUEgaqwvJVQuizwC3Sjqe0iv1G8DbJG1q+12tBjjZwAd62X7eoILr8S7gVGA94G22b5N0AKVtxKB3v0aMtCRoY+xJ+iVTn9AtoFRj1TJTvylJr6q57FnAEZTqsc5aq1HaG5xZc00kfdb27tXtXbuvEkv6tu2t6q4dIyfJjcHalzIY8PnAtyg/360oFRlbthnYqLN9h6T/pFSk/gX4RZ/J2ZsGXSnUu11S0iJKhfp2lAnGUc8JwC2UKpw7WTEBnkrHej4CvMf2KV2PHV1dUDoSeHk7Yc2Nrj7/hwDv5N4+/1/ot89/xAjYhNLW4NuSrqPMyxi3XMH7Ka25HgD8GNiwakm1FtDXjrJBa3CgVxNs+wk9j51KmXnypzYCihhW4/aiGyOuoWTqktoBzaBqgn4c8DvgJbavkfR04N+AjSgHPrO1L/B1SdcASyl/w08FrqI08q+ru//jO4HubTz362PdGA3jPsirMbavk7QJZctwZ4rzZcA7bP+uvchGm6T7USpmd6Zs8wN4gKQTgL2qadaz1Wj1uKRnUypzzgGebPvWJr/fPLcZ5f/9c4EfUk70zh3gdsxxpZ7kLFB6r0rav42A5lgjff4jVtFs3oP2lbQ9cLXtayW9FNgduIJS8T3r90DbPwH2rnq5bw+8HlhP0pnAJ23XLgQZIXfZ/jXwa0k/r25j+6+S+rkA3JgGBno14TeSLgHOBs6x/aNOlXZETJYEbYyaJQ2s+SvKQc2TgO/a/uKA1v1XSi+nDYEDJP0M2B84FjiszoK2bwO2rbbgdbbfHWO77lCcqfQeIKaicv4b90Fejap6Nx5aTdidSLXACuokRjv9YP+ucwIlaT3K6+5HKP3TZqvW6/LKVMnko6iqZm2f08T3GSdVAu1KYD9JT6Ukaw+VtBQ4NZWOtd1nhufG4VigqT7/EdOS9CrbX2B270GbUF73dq0uAp9CKbDYlPrvgQBUbdTOAM6Q9FBKwu9Q+tipN0K6L/LVudA7Zxoc6NWE9Sntcp4JHNPVTudsysXVm1qMLWKoJEEbo6aJZOqngH8ELgb2l6Q6w2CmcKftrwJIugnYGHjSIHq4Ve0TJrVQkPRJ22+rueTENLdjDGSQV7MkvYYyzGPj6v61wEG2P99qYHOkGrjxGMoWt9un+JQ6idEXUl5Pl08GrrYh7kGpUK7jvZT+ewPTUzX7JNvLBrl+gO2lwNJqovnhlME2i9qNamRdLemVtk/tflDSq6mG281zjfT5j1iJ44Av2J7N+88ulF6xt0s6HPia7eMlLQCurhOEpKfZnvT+afs3wEcl3VJnzRG0oaTPUS4cd25T3f+76b+sFU0N9Bq46tjvvOoDSWsCrwL2ofSUTz/+iEoONmLUNJFM3Rp4gu2J6iDnfO6tzupH9wH+7ZRtc02enL8WqJugXVPSo4CFXbc7VW1rDiK4iHEkaSfgAODtlB60a1AqHj4q6U7bp7cZX9Oqf/9JwDJgQtJOvf25Z3lS2vHX7uRs11p/lbTC4y06h1KF8zzKoKXO42kf0qcqEbE1sBPwAkpF7bHA19uMa8TtA5wv6XnApZTzhC0pr1nPbDOwOdJIn/+Ilaizi2Si64LnNsAnAapzmbpxHEfV8kzSJba36HruXZT+zPPdu7m3UOWCrscXABfOeTQzaHCgVyOqqtntqo+nAj8CPkepoo2IShK0MWqaSKbeYXsCwPbvJA2qgrR7nT/NQeVUP/0TF1EqcjtrdE8rTkVtRH17Ay+0/cuux86U9D+UPtTzOkFLSU4/zfZPJG1HqSReMoB1Z6oUqfua9VhJ50/3ZM2J2Ruv/FNitiR9Cng+pd/iaZTBVlNVZ8cs2P65pKcAb6X0oJwAvg/sWbVqme+a6vMfMZM671l3VW2TFgGLqZJckjZkcoHIbHSfR/S2O2m0R/sQOaHn/gTwe8pAyn+e+3DmB0kG/kz5Pf034Ds1ZwVEzHtJ0MaoaSKZ2rvGoLaIdG+N6b4NgO03DOj7dNT+WdjeaIBxRMS97tOTnAXA9i8kzdTvcb6YqAaPYPssSUcOaN3pkqkLKO0U6riZkkAemLQPacybKQM4F1cfh3ZXjaUyuT7btzDD34Gkb9jefg5DmjNz1Oc/xpCk90/z1ALq7VQ7nLJrYHXg+GpQ1CsovWLrvo/N1O5sLIo1bC/sfUzSw4A9gE9QtuXH7P2Y0jf574FrKAPNbmg1ooghlQRtjJomkqm9ydNJ9/tIpL676/ZF037WLEi6gKkPkhYA9+1j3d6tTBExGGtLWru3uq8aHDUO78G9r9GDqphoIkG0rLf9Qr8k3cP0r9kTttN3rZ5UJrdng7YDaNpUff4j+jRTBeqs+7DbPl3Sd4F1bf+oengZ8MYMSRys6qLVhyQN2+CtkWH75ZIWAk+ntDg4vToOPg84y/Z/txpgxBAZh5PDmF+aSKa+u+f+wA7Kbf/7oNaqfABYh/K322nYvwB4WPVcXeNQyRfRhtOA4yW9wfYdAJIeCHyWMnV5vrt/Nbypc3K6qPu+7W9N+5UzWJVEqqQf2N5sFsteWyeWmUxVjRP9S2Vyq8aiki5ikGyvUNUq6SHA7zs7A2useSNwY9f9b9aPEJh+598wDshqw51tBzDKqiFmlwCXSPoMZdjrOynzU9ZqM7aIYZIEbYyagSdTbf/7KkwZr+OdwKATtH8GTgV26yQoJH0YeD1lSEpd60h63XRP2j6pj7UjxtkHgOOBX0v6KeV993HAf1C2Is53NzC5T3j3/QmgTl/XVTXbnnk7TjNgLNWuERFRm6SHUgYdf5wy5+F0SiXhzZK2t/3TNuOr7M29F2B6z68unNtQhoukf6K01YkaJG1CGTb5DMrgyT9R5sjsS3YrREySBG2MlCaSqasyZXyIHAm8qnv7ku33SfoWcBTwnJrrLqIM7pkqoTFB+flExCzZvgt4vaQPUIbNTACX2v5Vq4HNnR1t/6Gl7z2rqqTualdJV9hePPiQIiJiDB1LGTy3FNgJ2Ax4OPBEytCk57YX2nLvmOWuk3lH0i9Z8djhgZSeqa+d+4jmjdMpCdmvAnvZ/m3L8UQMrSRoY6Q0lExtasr4EyVNtWW2U41VZ4jJg6fqLVUN3zmixnod1zcwtCwiAElrUqoGOkNn1pJ0uu1x2C53HuVEdNRkG3fE1MZlmnvEID3B9isBJL0AOM32nynbvR/RbmjRZUnP/XuAP9he1kIs84btxwFIehSwVTXk+wdjVKwQscqSoI1R00Qytakp49dQ+usM0hqSFlZ9fJarGq/XmQLbkROuiAZUPeYuBG6nbGtcE9gL2E/SEtvzfcvcqL62jGrcEU0bdOumiHHQfdFvW+CNXffXnuNYptNEYclISX/zZlTnqccBrwGuohwLP1rSF4C39J7XRoyzJGhj1DSRTG1qyvidDbzRXwQcVH10O4CybaquXfr42oiY3r8C/2F7UoW7pAOq53ZvJaq5s76k90/3pO0PTvfcAPSTZE0FbYwdSWtTekSfZvtSSUcDbwJ+QGmvdIPtY1oNMmI0XS9pZ0oydm2qnq6SXktJWA2DJgpLIgD2pwy5foTtPwJIWhf4DLAf8OEWY4sYKknQxqhpIpk61ZTxrTtP1p0yDnyn78hWtB/wTUm7AlcCd1C2D98C7NDHuntX202mlPYHEbVtbnuFJKztD0lyGwG1oJFq1FXoR37YLNfr7j23QVcl0dhUD8XYOwa4C7hO0guBVwOLKccZHwd2bDG2iFH2z5QKwvWA19i+U9JRwIsZnqRoE4UlEVD6Lm9p+7bOA7Z/K2kX4PskQRuxXBK0MWqaSKb+LytOGf9AdVvABjXWxPaedb5uJWveWv17t6GcNN0DfML2xX0ufeEUjz0G2IfyxhkR9awxw3N3z1kU7bnJ9sGDXnRV+pHbPm2Wyy4ZUHgRo2oL208GkPQSSiXtz4GfS+rduRMRq8j2ryR9Crja9i8kvRR4PHAacF2rwd2ricKSCICF3cnZDtvLJI3DsXDEKkuCNkbNwJOptrfpvi9pdeBlwFuAB9WKsqxzD1Nvk+1UY61WZ13bE5RJmOfXjW2KNSf1lJP0DuDNwL6U6bIRUc8NkraxfUH3g5KeDfy/lmKaS031ch14P/JUDkVMumi0BHhP1/1++txHjDVJ/wLsDOwqaRPgFOCdwKbARyi96VvVRGFJROVuSRvZvq77QUkbA39tJ6SI4ZQEbYyUppKp1VobA3sAuwEPpmy32KnuerYXSloHWM32b6rvsQS4qnN/2Eh6NHBCdXdL29e0GU/EPPBe4KuSPg1cSnnf3ZLyOrNdm4HNkVm1GZiFpoY7Royz30naHFhEueB9Liw/dvnfFuOKGHW7UCrUb5d0OPA128dLWgBc3XJsEU37CHCGpLcDl1GOhbegtNU5sM3AIobNwrYDiKhD0saSDqOcMJwEXABsWHOtHSX9NyV58hDKQdRNtj9o+7d9xLiY0vj/KV0PPxe4srp6PlSqqtlLgC8DS5Kcjeif7cuAZwMbA0cChwIPBZ5h+0dtxjZH3tvQuk0Nd4wYZ3sBJwJfAt5m+7ZqoOFplJZHEVHPRFev9G2A/4blu+Ii5jXbp1CSsScBtwN/Bj4BfND2f7YZW8SwSQVtjBRJO1K23j8F+AolmfqZPieBf5ly8rFFJylZtSfo15GUqccXdh6w/T5J3wKOAp4zgO8xEJIuAjanxPxHYBdJy5+3fVJLoUXMB0so1TK7SbqU8re/RNILciGktqn6kS+/38dwx4hxthg4nOrvSNLrgJsoO4r+gVL5FBGzd5ekB1Gq0xcDZwNI2pAymC9iXrN9InCipHUpFyx+13JIEUMpCdoYNU0kUzehbDf+tqTrgC8wmL+NB3cnZzuq7bhHDGD9Qbq2+nhk9dFtgnLFMyJmSdJ+lArat1UPrUVJ2L4Y2A/YvZ3I5sxjJU3bL9v2tjXXvYEV+5F37k8AddeNGGcnArdQWhvcyeQe0jkWiKjvcOBKyvnF8bZvkvQKyq6agQ/SjBg2kh4PvJEyHO8vkq4GPmt7HOYxRKyyJGhj1Aw8mVr1Mdxb0r7A9sDrgfUknQl8wvY3ay69hqSFticlkCUtZMiGbdjere0YIuap11GGWS2r7t9j+/pqmvPPWoxrrtxMMyefO9r+QwPrRoyzzSiDjJ4L/BA4FTi39zgmImbH9umSvgus29XeaBnwxqmKOSLmk2ow7herjzMpF/w2AS6T9ArbF7UZX8QwSYI2RkqDyVRs3wWcQWli/lBKYuUwrSEjOAAABhRJREFUoO6aFwEHVR/dDgCW1o2zKZK2pjRqfxrljXMppTfQxa0GFjHa7u5KzgJ8CMD23ZJubSmmubSsoQPv8yjJpIgYENtXUqr89pP0VEqy9lBJS4FTk0iKqM/2jcCNXfdrn7NEjJiDge1sX979oKQTgY8Cz2wjqIhhlARtjKQGkqm96/+G8obx0T6W2Q/4pqRdKSc8d1ASCrcAO/Qd5ABJ2hY4mZI82otS4bslcKqk1+SkLKK2hZLub/tWANtfBpD0QFYcdDUfXdvQugtW/ikRUZftpcDSqrfz4cBrKf0zIyIiZuOBvclZANuXSrpfGwFFDKskaGPkDSiZOnC2b62qUrehDAS4h1LlO4wVqQcBL6qqZzqukPQ94Ghg63bCihh5pwAnSdrV9p8BJC0CPgf8R6uRzY0dJd09xeMLKEMiVqu57vqS3j/dk30OjowYW5IWUN7zdwJeQLnAfCzw9TbjioiIkfW3tgOIGBVJ0EY0yPYEcH71Mcwe0JOcBcD25ZLWaSOgiHnicOBTwI3VQIQJ4AnAybaPajWyOWB7Yee2pCtsLx7g8qmijRigqjf284ErKANZ32P79najioiIEXf/ajfGVMdt2ZkR0SUJ2ogAWCRp9ap1xHKSVievExG12b4b2EPSwcDm1cNLbf+qxbDaMjHAtW6yncnXEYP1ZuB3lF0/iyn9Z5c/afvRLcUVERGj6wZgup1NN8xlIBHDLomXiAA4CzgC2LvzgKTVKO0NzmwrqIj5wvYNwFfajqNlg6x4TfVsxOBt3HYAERExv9he0nYMEaMiCdqIANgX+Iaka4CllNeGpwJXAf/UZmARMW8MsoL2sAGuFRGA7evbjiEiIuaXmWYGUOYRHDJnwUQMuSRoIwLbt0l6GfBsYENKIuVK4Djbf201uIgYWZJ+yb2J2Q0kXVvd7gwJq7tl+r2UHpkRERERMbym2vW0DqWtzvVAErQRlSRoIwJJi4FvArvZPrJ67MPAlZJeYPtHrQYYEaNqSdsBREREREQ7emcGSNoB+CRliO7+rQQVMaQWTEwMcsdhRIwiSecBh9i+sOfx7YB9bD+nlcAiIqYg6Vbgsumet73tHIYTERERETOQ9CDgWMrQ3N1tf7vlkCKGTipoIwLgwb3JWQDbZ0k6ooV4IiJmcjNw8Eo/KyIiIiJaJenFlKrZLwGb2v5LyyFFDKUkaCMCYA1JC23f0/2gpIXAmi3FFBExnWW2L2o7iIiIiIiYnqSTgZcDHwIuBp4mafnztr/VUmgRQycJ2ogAuAg4qProdgCwdO7DiYiY0bUr/5SIiIiIaNmjgO8Bz6k+uk0AaUsVUUkP2ohA0v0pQ8IeBVwJ3AFsBtwC7GD79y2GFxExiaR7KAf1vRYAE7ZXm+OQIiIiIiIiakuCNiIAkLQA2AZYDNwDLLV9cbtRRUTMTNIVthe3HUdERERETCbpdTM9b/ukuYolYtilxUFEAGB7Aji/+oiIGBW50hwRERExnLbh3mO1BT3PTQBJ0EZUkqCNiIiIUdZ7sB8RERERw+F5wG3AWcA5wPm2l7UbUsRwWth2ABERERF9SAVtRERExBCyvQHwAuBy4KXAZZK+JelASU9vN7qI4ZIetBERETFSJP2SexOzGwA3VLc7Q8Ie3UpgERERETEtSQ8CXgLsDcj2Wi2HFDE00uIgIiIiRs2StgOIiIiIiJlJWh3YCng+sB1wX+Bc4P1k9knEJKmgjYiIiIiIiIiIgZJ0K/Bd4HTgHNvXtRtRxPBKD9qIiIiIiIiIiBi044CHAW8AdpP0DEnJQ0VMIRW0ERERERERERHRCEkPp7Q42A54GvBj4Czbn241sIghkgRtREREREREREQ0RtKalOTsM4DXAevYfkS7UUUMjyRoIyIiIiIiIiJioCTtQEnIbgU8GvgeZTjY+bavajO2iGGzetsBRERERERERETEvLMnJSG7F3C57XtajidiaKWCNiIiIiIiIiIiIqIlmZ4XERERERERERER0ZIkaCMiIiIiIiIiIiJakgRtREREREREREREREuSoI2IiIiIiIiIiIhoSRK0ERERERERERERES35//LpHhsf0eToAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1800x1080 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(25,15))\n",
    "df_cm=pd.DataFrame(cm,columns=yL_test.columns,index=yL_test.columns)\n",
    "sns.set(font_scale=1.0)\n",
    "sns.heatmap(df_cm, annot=True,fmt='d',cmap=plt.cm.get_cmap('Reds')) # font size\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "weighted F1 Score : 0.7705756343365969\n",
      "Macro F1 Score : 0.7693694115104794\n",
      "F1 Scores : \n"
     ]
    },
    {
     "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>F1-score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>AMAZON</th>\n",
       "      <td>0.813218</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>APPLE</th>\n",
       "      <td>0.605072</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>APPLE_ICLOUD</th>\n",
       "      <td>0.793011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>APPLE_ITUNES</th>\n",
       "      <td>0.699080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CITRIX_ONLINE</th>\n",
       "      <td>0.998124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CLOUDFLARE</th>\n",
       "      <td>0.876582</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CONTENT_FLASH</th>\n",
       "      <td>0.964230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DNS</th>\n",
       "      <td>0.974446</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DROPBOX</th>\n",
       "      <td>0.829181</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EASYTAXI</th>\n",
       "      <td>0.939502</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EBAY</th>\n",
       "      <td>0.619048</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EDONKEY</th>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FACEBOOK</th>\n",
       "      <td>0.705680</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FTP_DATA</th>\n",
       "      <td>0.960000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GMAIL</th>\n",
       "      <td>0.488506</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GOOGLE</th>\n",
       "      <td>0.304348</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GOOGLE_MAPS</th>\n",
       "      <td>0.711027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>H323</th>\n",
       "      <td>0.993631</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HTTP</th>\n",
       "      <td>0.656126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HTTP_CONNECT</th>\n",
       "      <td>0.325773</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HTTP_DOWNLOAD</th>\n",
       "      <td>0.931973</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HTTP_PROXY</th>\n",
       "      <td>0.412234</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>INSTAGRAM</th>\n",
       "      <td>0.729282</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IP_ICMP</th>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IP_OSPF</th>\n",
       "      <td>0.992126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LASTFM</th>\n",
       "      <td>0.962293</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MICROSOFT</th>\n",
       "      <td>0.269006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MQTT</th>\n",
       "      <td>0.917317</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MSN</th>\n",
       "      <td>0.424893</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MSSQL</th>\n",
       "      <td>0.996678</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MS_ONE_DRIVE</th>\n",
       "      <td>0.921311</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NETFLIX</th>\n",
       "      <td>0.766897</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NTP</th>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OFFICE_365</th>\n",
       "      <td>0.817204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RTMP</th>\n",
       "      <td>0.997006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SIMET</th>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SKYPE</th>\n",
       "      <td>0.475248</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SPOTIFY</th>\n",
       "      <td>0.913194</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SSH</th>\n",
       "      <td>0.998336</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SSL</th>\n",
       "      <td>0.347319</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SSL_NO_CERT</th>\n",
       "      <td>0.931507</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>STARCRAFT</th>\n",
       "      <td>0.998331</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TEAMVIEWER</th>\n",
       "      <td>0.993671</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TOR</th>\n",
       "      <td>0.984375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TWITTER</th>\n",
       "      <td>0.258503</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UBUNTUONE</th>\n",
       "      <td>0.975460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UPNP</th>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WHATSAPP</th>\n",
       "      <td>0.712381</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WIKIPEDIA</th>\n",
       "      <td>0.676966</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WINDOWS_UPDATE</th>\n",
       "      <td>0.651163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YAHOO</th>\n",
       "      <td>0.321495</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YOUTUBE</th>\n",
       "      <td>0.374456</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                F1-score\n",
       "AMAZON          0.813218\n",
       "APPLE           0.605072\n",
       "APPLE_ICLOUD    0.793011\n",
       "APPLE_ITUNES    0.699080\n",
       "CITRIX_ONLINE   0.998124\n",
       "CLOUDFLARE      0.876582\n",
       "CONTENT_FLASH   0.964230\n",
       "DNS             0.974446\n",
       "DROPBOX         0.829181\n",
       "EASYTAXI        0.939502\n",
       "EBAY            0.619048\n",
       "EDONKEY         1.000000\n",
       "FACEBOOK        0.705680\n",
       "FTP_DATA        0.960000\n",
       "GMAIL           0.488506\n",
       "GOOGLE          0.304348\n",
       "GOOGLE_MAPS     0.711027\n",
       "H323            0.993631\n",
       "HTTP            0.656126\n",
       "HTTP_CONNECT    0.325773\n",
       "HTTP_DOWNLOAD   0.931973\n",
       "HTTP_PROXY      0.412234\n",
       "INSTAGRAM       0.729282\n",
       "IP_ICMP         1.000000\n",
       "IP_OSPF         0.992126\n",
       "LASTFM          0.962293\n",
       "MICROSOFT       0.269006\n",
       "MQTT            0.917317\n",
       "MSN             0.424893\n",
       "MSSQL           0.996678\n",
       "MS_ONE_DRIVE    0.921311\n",
       "NETFLIX         0.766897\n",
       "NTP             1.000000\n",
       "OFFICE_365      0.817204\n",
       "RTMP            0.997006\n",
       "SIMET           1.000000\n",
       "SKYPE           0.475248\n",
       "SPOTIFY         0.913194\n",
       "SSH             0.998336\n",
       "SSL             0.347319\n",
       "SSL_NO_CERT     0.931507\n",
       "STARCRAFT       0.998331\n",
       "TEAMVIEWER      0.993671\n",
       "TOR             0.984375\n",
       "TWITTER         0.258503\n",
       "UBUNTUONE       0.975460\n",
       "UPNP            1.000000\n",
       "WHATSAPP        0.712381\n",
       "WIKIPEDIA       0.676966\n",
       "WINDOWS_UPDATE  0.651163\n",
       "YAHOO           0.321495\n",
       "YOUTUBE         0.374456"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print('weighted F1 Score : ' + str(f1_score(y_pred1,y_test1,average = 'weighted')))\n",
    "print('Macro F1 Score : ' + str(f1_score(y_pred1,y_test1,average = 'macro')))\n",
    "print('F1 Scores : ' )\n",
    "pd.DataFrame(f1_score(y_pred1,y_test1,labels=yL_test.columns,average = None),columns=['F1-score'],index=yL_test.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "52"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(yL_train.columns)# No. of distinct labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import precision_score\n",
    "from sklearn.metrics import recall_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Macro Presicion : 0.7741715164289672\n",
      "Weighted Presicion : 0.7620908813466739\n",
      "Precision Scores : \n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "    }\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>Precision-score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>AMAZON</th>\n",
       "      <td>0.748677</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>APPLE</th>\n",
       "      <td>0.542208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>APPLE_ICLOUD</th>\n",
       "      <td>0.960912</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>APPLE_ITUNES</th>\n",
       "      <td>0.901695</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CITRIX_ONLINE</th>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CLOUDFLARE</th>\n",
       "      <td>0.865625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CONTENT_FLASH</th>\n",
       "      <td>0.981013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DNS</th>\n",
       "      <td>0.989619</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DROPBOX</th>\n",
       "      <td>0.803448</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EASYTAXI</th>\n",
       "      <td>0.970588</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EBAY</th>\n",
       "      <td>0.688235</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EDONKEY</th>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FACEBOOK</th>\n",
       "      <td>0.732143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FTP_DATA</th>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GMAIL</th>\n",
       "      <td>0.432570</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GOOGLE</th>\n",
       "      <td>0.301266</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GOOGLE_MAPS</th>\n",
       "      <td>0.640411</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>H323</th>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HTTP</th>\n",
       "      <td>0.628788</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HTTP_CONNECT</th>\n",
       "      <td>0.421333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HTTP_DOWNLOAD</th>\n",
       "      <td>0.883871</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HTTP_PROXY</th>\n",
       "      <td>0.387500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>INSTAGRAM</th>\n",
       "      <td>0.891892</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IP_ICMP</th>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IP_OSPF</th>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LASTFM</th>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MICROSOFT</th>\n",
       "      <td>0.194366</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MQTT</th>\n",
       "      <td>0.942308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MSN</th>\n",
       "      <td>0.335593</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MSSQL</th>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MS_ONE_DRIVE</th>\n",
       "      <td>0.894904</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NETFLIX</th>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NTP</th>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OFFICE_365</th>\n",
       "      <td>0.775510</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RTMP</th>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SIMET</th>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SKYPE</th>\n",
       "      <td>0.449438</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SPOTIFY</th>\n",
       "      <td>0.859477</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SSH</th>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SSL</th>\n",
       "      <td>0.348131</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SSL_NO_CERT</th>\n",
       "      <td>0.980769</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>STARCRAFT</th>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TEAMVIEWER</th>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TOR</th>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TWITTER</th>\n",
       "      <td>0.206522</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UBUNTUONE</th>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UPNP</th>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WHATSAPP</th>\n",
       "      <td>0.703008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WIKIPEDIA</th>\n",
       "      <td>0.848592</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WINDOWS_UPDATE</th>\n",
       "      <td>0.593640</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YAHOO</th>\n",
       "      <td>0.273016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YOUTUBE</th>\n",
       "      <td>0.314634</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Precision-score\n",
       "AMAZON                 0.748677\n",
       "APPLE                  0.542208\n",
       "APPLE_ICLOUD           0.960912\n",
       "APPLE_ITUNES           0.901695\n",
       "CITRIX_ONLINE          1.000000\n",
       "CLOUDFLARE             0.865625\n",
       "CONTENT_FLASH          0.981013\n",
       "DNS                    0.989619\n",
       "DROPBOX                0.803448\n",
       "EASYTAXI               0.970588\n",
       "EBAY                   0.688235\n",
       "EDONKEY                1.000000\n",
       "FACEBOOK               0.732143\n",
       "FTP_DATA               1.000000\n",
       "GMAIL                  0.432570\n",
       "GOOGLE                 0.301266\n",
       "GOOGLE_MAPS            0.640411\n",
       "H323                   1.000000\n",
       "HTTP                   0.628788\n",
       "HTTP_CONNECT           0.421333\n",
       "HTTP_DOWNLOAD          0.883871\n",
       "HTTP_PROXY             0.387500\n",
       "INSTAGRAM              0.891892\n",
       "IP_ICMP                1.000000\n",
       "IP_OSPF                1.000000\n",
       "LASTFM                 1.000000\n",
       "MICROSOFT              0.194366\n",
       "MQTT                   0.942308\n",
       "MSN                    0.335593\n",
       "MSSQL                  1.000000\n",
       "MS_ONE_DRIVE           0.894904\n",
       "NETFLIX                1.000000\n",
       "NTP                    1.000000\n",
       "OFFICE_365             0.775510\n",
       "RTMP                   1.000000\n",
       "SIMET                  1.000000\n",
       "SKYPE                  0.449438\n",
       "SPOTIFY                0.859477\n",
       "SSH                    1.000000\n",
       "SSL                    0.348131\n",
       "SSL_NO_CERT            0.980769\n",
       "STARCRAFT              1.000000\n",
       "TEAMVIEWER             1.000000\n",
       "TOR                    1.000000\n",
       "TWITTER                0.206522\n",
       "UBUNTUONE              1.000000\n",
       "UPNP                   1.000000\n",
       "WHATSAPP               0.703008\n",
       "WIKIPEDIA              0.848592\n",
       "WINDOWS_UPDATE         0.593640\n",
       "YAHOO                  0.273016\n",
       "YOUTUBE                0.314634"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print('Macro Presicion : '+str(precision_score(y_test1, y_pred1,average=\"macro\")))\n",
    "print('Weighted Presicion : '+str(precision_score(y_test1, y_pred1,average=\"weighted\")))\n",
    "print('Precision Scores : ' )\n",
    "pd.DataFrame(precision_score(y_pred1,y_test1,labels=yL_test.columns,average = None),columns=['Precision-score'],index=yL_test.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Macro Recall : 0.7786865789893403\n",
      "Weighted Recall : 0.7630746776939917\n",
      "Recall Scores : \n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
       "      <th>recall-score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>AMAZON</th>\n",
       "      <td>0.889937</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>APPLE</th>\n",
       "      <td>0.684426</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>APPLE_ICLOUD</th>\n",
       "      <td>0.675057</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>APPLE_ITUNES</th>\n",
       "      <td>0.570815</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CITRIX_ONLINE</th>\n",
       "      <td>0.996255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CLOUDFLARE</th>\n",
       "      <td>0.887821</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CONTENT_FLASH</th>\n",
       "      <td>0.948012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DNS</th>\n",
       "      <td>0.959732</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DROPBOX</th>\n",
       "      <td>0.856618</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EASYTAXI</th>\n",
       "      <td>0.910345</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EBAY</th>\n",
       "      <td>0.562500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EDONKEY</th>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FACEBOOK</th>\n",
       "      <td>0.681063</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FTP_DATA</th>\n",
       "      <td>0.923077</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GMAIL</th>\n",
       "      <td>0.561056</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GOOGLE</th>\n",
       "      <td>0.307494</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GOOGLE_MAPS</th>\n",
       "      <td>0.799145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>H323</th>\n",
       "      <td>0.987342</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HTTP</th>\n",
       "      <td>0.685950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HTTP_CONNECT</th>\n",
       "      <td>0.265546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HTTP_DOWNLOAD</th>\n",
       "      <td>0.985612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HTTP_PROXY</th>\n",
       "      <td>0.440341</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>INSTAGRAM</th>\n",
       "      <td>0.616822</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IP_ICMP</th>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IP_OSPF</th>\n",
       "      <td>0.984375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LASTFM</th>\n",
       "      <td>0.927326</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MICROSOFT</th>\n",
       "      <td>0.436709</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MQTT</th>\n",
       "      <td>0.893617</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MSN</th>\n",
       "      <td>0.578947</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MSSQL</th>\n",
       "      <td>0.993377</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MS_ONE_DRIVE</th>\n",
       "      <td>0.949324</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NETFLIX</th>\n",
       "      <td>0.621924</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NTP</th>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OFFICE_365</th>\n",
       "      <td>0.863636</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RTMP</th>\n",
       "      <td>0.994030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SIMET</th>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SKYPE</th>\n",
       "      <td>0.504202</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SPOTIFY</th>\n",
       "      <td>0.974074</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SSH</th>\n",
       "      <td>0.996678</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SSL</th>\n",
       "      <td>0.346512</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SSL_NO_CERT</th>\n",
       "      <td>0.886957</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>STARCRAFT</th>\n",
       "      <td>0.996667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TEAMVIEWER</th>\n",
       "      <td>0.987421</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TOR</th>\n",
       "      <td>0.969231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TWITTER</th>\n",
       "      <td>0.345455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UBUNTUONE</th>\n",
       "      <td>0.952096</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UPNP</th>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WHATSAPP</th>\n",
       "      <td>0.722008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WIKIPEDIA</th>\n",
       "      <td>0.563084</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WINDOWS_UPDATE</th>\n",
       "      <td>0.721030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YAHOO</th>\n",
       "      <td>0.390909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YOUTUBE</th>\n",
       "      <td>0.462366</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                recall-score\n",
       "AMAZON              0.889937\n",
       "APPLE               0.684426\n",
       "APPLE_ICLOUD        0.675057\n",
       "APPLE_ITUNES        0.570815\n",
       "CITRIX_ONLINE       0.996255\n",
       "CLOUDFLARE          0.887821\n",
       "CONTENT_FLASH       0.948012\n",
       "DNS                 0.959732\n",
       "DROPBOX             0.856618\n",
       "EASYTAXI            0.910345\n",
       "EBAY                0.562500\n",
       "EDONKEY             1.000000\n",
       "FACEBOOK            0.681063\n",
       "FTP_DATA            0.923077\n",
       "GMAIL               0.561056\n",
       "GOOGLE              0.307494\n",
       "GOOGLE_MAPS         0.799145\n",
       "H323                0.987342\n",
       "HTTP                0.685950\n",
       "HTTP_CONNECT        0.265546\n",
       "HTTP_DOWNLOAD       0.985612\n",
       "HTTP_PROXY          0.440341\n",
       "INSTAGRAM           0.616822\n",
       "IP_ICMP             1.000000\n",
       "IP_OSPF             0.984375\n",
       "LASTFM              0.927326\n",
       "MICROSOFT           0.436709\n",
       "MQTT                0.893617\n",
       "MSN                 0.578947\n",
       "MSSQL               0.993377\n",
       "MS_ONE_DRIVE        0.949324\n",
       "NETFLIX             0.621924\n",
       "NTP                 1.000000\n",
       "OFFICE_365          0.863636\n",
       "RTMP                0.994030\n",
       "SIMET               1.000000\n",
       "SKYPE               0.504202\n",
       "SPOTIFY             0.974074\n",
       "SSH                 0.996678\n",
       "SSL                 0.346512\n",
       "SSL_NO_CERT         0.886957\n",
       "STARCRAFT           0.996667\n",
       "TEAMVIEWER          0.987421\n",
       "TOR                 0.969231\n",
       "TWITTER             0.345455\n",
       "UBUNTUONE           0.952096\n",
       "UPNP                1.000000\n",
       "WHATSAPP            0.722008\n",
       "WIKIPEDIA           0.563084\n",
       "WINDOWS_UPDATE      0.721030\n",
       "YAHOO               0.390909\n",
       "YOUTUBE             0.462366"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print('Macro Recall : '+str(recall_score(y_test1, y_pred1,average=\"macro\")))\n",
    "print('Weighted Recall : '+str(recall_score(y_test1, y_pred1,average=\"weighted\")))\n",
    "print('Recall Scores : ' )\n",
    "pd.DataFrame(recall_score(y_pred1,y_test1,labels=yL_test.columns,average = None),columns=['recall-score'],index=yL_test.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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