exp.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The experiments for the paper, \n",
    "## \"[Class Name Recommendation based on Graph Embedding of Program Elements](https://kuri8ive.github.io/preprints/apsec2019.pdf)\"\n",
    "\n",
    "1. Recommendation before a class is used\n",
    "1. Recommendation after a class is used\n",
    "1. Where does the proposed work well?\n",
    "\n",
    "About the details of these experiments, please refer to the paper."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Settings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/kurimoto/venvs/reco/lib/python3.6/site-packages/chainer/backends/cuda.py:98: UserWarning: cuDNN is not enabled.\n",
      "Please reinstall CuPy after you install cudnn\n",
      "(see https://docs-cupy.chainer.org/en/stable/install.html#install-cudnn).\n",
      "  'cuDNN is not enabled.\\n'\n"
     ]
    }
   ],
   "source": [
    "# -*- coding: utf-8 -*-\n",
    "\n",
    "import sys\n",
    "import os\n",
    "import statistics\n",
    "import random\n",
    "\n",
    "import optuna\n",
    "from IPython.core.debugger import Pdb\n",
    "\n",
    "from core import Model, DataProcessor, Trainer, Recommender\n",
    "\n",
    "\n",
    "sys.path.append(os.path.abspath(os.path.dirname(os.getcwd())))\n",
    "THIS_FILE_DIR_PATH = os.getcwd()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Define functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def prepare_model(index, embedding_dim=250, alpha=0.6, beta=0.2, gamma=0.8,\n",
    "                  delta=0.1, initial_learn_rate=0.25, epochs=30, decay='exp99',\n",
    "                  target_negative_weight=0.1, negative_sample_num=5):\n",
    "    \n",
    "    is_split_by_project_data = 1\n",
    "    #     int(input('How to split data？ if random -> 1， if per project -> 2\\n>'))\n",
    "    data_type = 'random' if is_split_by_project_data == 1 else 'project'\n",
    "    input_dir = 'data/input' if is_split_by_project_data == 1 else 'data/project_input'\n",
    "    output_dir = 'data/output' if is_split_by_project_data == 1 else 'data/project_output'\n",
    "\n",
    "    model_name = 'dim-{}_alpha-{}_beta-{}_gamma-{}_delta-{}_lr-{}_epochs-{}_weight-{}_decay-{}_data_type-{}_negative-sample-num-{}_{}.model' \\\n",
    "        .format(embedding_dim, alpha, beta, gamma, delta, initial_learn_rate, epochs, target_negative_weight, decay, data_type, negative_sample_num, index)\n",
    "    input_path = os.path.join(THIS_FILE_DIR_PATH, input_dir, str(index))\n",
    "    output_path = os.path.join(THIS_FILE_DIR_PATH, output_dir, str(index))\n",
    "\n",
    "    model = Model()\n",
    "    model.model_name = model_name\n",
    "    model.embedding_dim = embedding_dim\n",
    "    model.alpha = alpha\n",
    "    model.beta = beta\n",
    "    model.gamma = gamma\n",
    "    model.delta = delta\n",
    "    model.initial_learn_rate = initial_learn_rate\n",
    "    model.epochs = epochs\n",
    "    model.decay = decay\n",
    "    model.target_negative_weight = target_negative_weight\n",
    "    model.cv_index = index\n",
    "    model.input_path = input_path\n",
    "    model.output_path = output_path\n",
    "    model.negative_sample_num = negative_sample_num\n",
    "\n",
    "    return model\n",
    "\n",
    "def trans_model_for_hsearch(trial, model):\n",
    "    model.embedding_dim = trial.suggest_categorical('embedding_dim', [200, 250])\n",
    "    model.alpha = trial.suggest_categorical('alpha', [0.6, 0.5])\n",
    "    model.beta = trial.suggest_categorical('beta', [0.2, 0.3])\n",
    "    model.gamma = trial.suggest_categorical('gamma', [0.7, 0.8])\n",
    "    model.delta = trial.suggest_categorical('delta', [0.1, 0.2])\n",
    "    model.initial_learn_rate = trial.suggest_categorical('initial_learn_rate', [0.1, 0.25, 0.5])\n",
    "    model.epochs = trial.suggest_categorical('epochs', [15, 30, 50])\n",
    "    model.decay = trial.suggest_categorical('decay', ['exp999', 'exp99'])\n",
    "    model.target_negative_weight = trial.suggest_categorical('target_negative_weight', [0.1, 0.001])\n",
    "    model.negative_sample_num = trial.suggest_categorical('negative_sample_num', [5, 10])\n",
    "    \n",
    "    return model\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "data process started\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 3/3 [00:13<00:00,  4.65s/it]\n",
      "100%|██████████| 12/12 [00:00<00:00, 15.04it/s]\n",
      "24247it [00:05, 4787.25it/s]\n",
      "265133it [00:13, 19113.47it/s]\n",
      "108213it [00:01, 78161.44it/s]\n",
      "33356it [00:13, 2446.60it/s]\n",
      "341185it [00:26, 12954.22it/s]\n",
      "141411it [00:02, 59595.99it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "data process finished\n",
      "elapsed_time for data process : 91.3527102470398\n",
      "model_for_hsearch_1 prepared\n",
      "-----\n",
      "data process started\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 2/2 [00:12<00:00,  6.50s/it]\n",
      "100%|██████████| 12/12 [00:00<00:00, 14.66it/s]\n",
      "24266it [00:04, 4878.45it/s]\n",
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      "140559it [00:02, 63428.09it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "data process finished\n",
      "elapsed_time for data process : 87.51743531227112\n",
      "model_for_hsearch_2 prepared\n",
      "-----\n",
      "data process started\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 2/2 [00:15<00:00,  7.69s/it]\n",
      "100%|██████████| 12/12 [00:00<00:00, 14.65it/s]\n",
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      "141507it [00:02, 63430.76it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "data process finished\n",
      "elapsed_time for data process : 88.18154096603394\n",
      "model_for_hsearch_3 prepared\n",
      "-----\n",
      "data process started\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 2/2 [00:16<00:00,  8.18s/it]\n",
      "100%|██████████| 12/12 [00:00<00:00, 14.67it/s]\n",
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      "141810it [00:02, 63855.07it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "data process finished\n",
      "elapsed_time for data process : 87.65892934799194\n",
      "model_for_hsearch_4 prepared\n",
      "-----\n",
      "data process started\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 2/2 [00:12<00:00,  6.48s/it]\n",
      "100%|██████████| 12/12 [00:00<00:00, 14.75it/s]\n",
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      "111755it [00:01, 80267.59it/s]\n",
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      "141635it [00:02, 62629.61it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "data process finished\n",
      "elapsed_time for data process : 92.79274797439575\n",
      "model_for_hsearch_5 prepared\n",
      "-----\n"
     ]
    }
   ],
   "source": [
    "model_for_hsearch_1 = DataProcessor(prepare_model(1)).pre_process()\n",
    "print('model_for_hsearch_1 prepared\\n-----')\n",
    "model_for_hsearch_2 = DataProcessor(prepare_model(2)).pre_process()\n",
    "print('model_for_hsearch_2 prepared\\n-----')\n",
    "model_for_hsearch_3 = DataProcessor(prepare_model(3)).pre_process()\n",
    "print('model_for_hsearch_3 prepared\\n-----')\n",
    "model_for_hsearch_4 = DataProcessor(prepare_model(4)).pre_process()\n",
    "print('model_for_hsearch_4 prepared\\n-----')\n",
    "model_for_hsearch_5 = DataProcessor(prepare_model(5)).pre_process()\n",
    "print('model_for_hsearch_5 prepared\\n-----')\n",
    "\n",
    "models_for_hsearch = [model_for_hsearch_1, model_for_hsearch_2, \n",
    "                                           model_for_hsearch_3, model_for_hsearch_4, model_for_hsearch_5]\n",
    "\n",
    "def objective(trial):\n",
    "    final_loss_sum = 0.0\n",
    "    tmp_model = trans_model_for_hsearch(trial, random.choice(models_for_hsearch))\n",
    "    final_loss = Trainer(tmp_model).fit(include_be_used=True, is_test=False)\n",
    "    final_loss_sum += final_loss\n",
    "    return final_loss_sum"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### hypar parameter search"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "training started\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 15/15 [02:21<00:00,  9.42s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "elapsed_time for training : 141.47027325630188\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[I 2019-11-23 15:03:48,059] Finished a trial resulted in value: 119754.5078125. Current best value is 119754.5078125 with parameters: {'embedding_dim': 250, 'alpha': 0.5, 'beta': 0.3, 'gamma': 0.8, 'delta': 0.1, 'initial_learn_rate': 0.1, 'epochs': 15, 'decay': 'exp99', 'target_negative_weight': 0.001, 'negative_sample_num': 10}.\n"
     ]
    }
   ],
   "source": [
    "study = optuna.create_study()\n",
    "study.optimize(objective, n_trials=1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'embedding_dim': 250,\n",
       " 'alpha': 0.5,\n",
       " 'beta': 0.3,\n",
       " 'gamma': 0.8,\n",
       " 'delta': 0.1,\n",
       " 'initial_learn_rate': 0.1,\n",
       " 'epochs': 15,\n",
       " 'decay': 'exp99',\n",
       " 'target_negative_weight': 0.001,\n",
       " 'negative_sample_num': 10}"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_params = study.best_params\n",
    "best_params"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Experiment 1 calculation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "index1 start\n",
      "data process started\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 3/3 [00:12<00:00,  4.30s/it]\n",
      "100%|██████████| 12/12 [00:00<00:00, 14.86it/s]\n",
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      "108213it [00:01, 81493.27it/s]\n",
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      "341185it [00:25, 13442.77it/s]\n",
      "141411it [00:02, 63254.92it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "data process finished\n",
      "elapsed_time for data process : 87.58842086791992\n",
      "training started\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 15/15 [02:14<00:00,  8.96s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "elapsed_time for training : 134.39447736740112\n",
      "training started\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 15/15 [01:57<00:00,  7.80s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "elapsed_time for training : 122.27177262306213\n",
      "Trained model object was saved to /home/kurimoto/apsec-test/../data/model/test_fit_model/dim-250_alpha-0.5_beta-0.3_gamma-0.8_delta-0.1_lr-0.1_epochs-15_weight-0.001_decay-exp99_data_type-random_negative-sample-num-10_1.model\n",
      "recommendation started\n",
      "train_classes_shape -> (27526, 250)\n",
      "reco_class_shape -> (9109, 250)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "9109it [00:09, 946.20it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "success_if_related_all : 0.331333803810868\n",
      "success_if_related_not_all : 0.31123505976095617\n",
      "recommendation finished\n",
      "elapsed_time for recommendation : 25.209245681762695\n",
      "accuracy -> 0.3174881984850148 at index 1\n",
      "index2 start\n",
      "data process started\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 2/2 [00:13<00:00,  6.90s/it]\n",
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      "140559it [00:02, 59530.84it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "data process finished\n",
      "elapsed_time for data process : 99.59822082519531\n",
      "training started\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 15/15 [02:33<00:00, 10.25s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "elapsed_time for training : 153.74781203269958\n",
      "training started\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 15/15 [02:10<00:00,  8.67s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "elapsed_time for training : 135.44885873794556\n",
      "Trained model object was saved to /home/kurimoto/apsec-test/../data/model/test_fit_model/dim-250_alpha-0.5_beta-0.3_gamma-0.8_delta-0.1_lr-0.1_epochs-15_weight-0.001_decay-exp99_data_type-random_negative-sample-num-10_2.model\n",
      "recommendation started\n",
      "train_classes_shape -> (27478, 250)\n",
      "reco_class_shape -> (9093, 250)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "9093it [00:09, 942.29it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "success_if_related_all : 0.3303183791606368\n",
      "success_if_related_not_all : 0.3017854321377785\n",
      "recommendation finished\n",
      "elapsed_time for recommendation : 24.756253480911255\n",
      "accuracy -> 0.31045859452325963 at index 2\n",
      "index3 start\n",
      "data process started\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 2/2 [00:13<00:00,  6.84s/it]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "data process finished\n",
      "elapsed_time for data process : 98.82463812828064\n",
      "training started\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 15/15 [02:25<00:00,  9.70s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "elapsed_time for training : 145.58357214927673\n",
      "training started\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 15/15 [01:58<00:00,  7.91s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "elapsed_time for training : 123.92385292053223\n",
      "Trained model object was saved to /home/kurimoto/apsec-test/../data/model/test_fit_model/dim-250_alpha-0.5_beta-0.3_gamma-0.8_delta-0.1_lr-0.1_epochs-15_weight-0.001_decay-exp99_data_type-random_negative-sample-num-10_3.model\n",
      "recommendation started\n",
      "train_classes_shape -> (27603, 250)\n",
      "reco_class_shape -> (8996, 250)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "8996it [00:09, 946.68it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "success_if_related_all : 0.3475836431226766\n",
      "success_if_related_not_all : 0.3011417697431018\n",
      "recommendation finished\n",
      "elapsed_time for recommendation : 24.530369758605957\n",
      "accuracy -> 0.315028901734104 at index 3\n",
      "index4 start\n",
      "data process started\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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      "33338it [00:12, 2685.95it/s]\n",
      "350893it [00:26, 13345.86it/s]\n",
      "141810it [00:02, 61232.01it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "data process finished\n",
      "elapsed_time for data process : 88.32321786880493\n",
      "training started\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 15/15 [02:37<00:00, 10.53s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "elapsed_time for training : 158.0095009803772\n",
      "training started\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 15/15 [02:04<00:00,  8.32s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "elapsed_time for training : 129.9398376941681\n",
      "Trained model object was saved to /home/kurimoto/apsec-test/../data/model/test_fit_model/dim-250_alpha-0.5_beta-0.3_gamma-0.8_delta-0.1_lr-0.1_epochs-15_weight-0.001_decay-exp99_data_type-random_negative-sample-num-10_4.model\n",
      "recommendation started\n",
      "train_classes_shape -> (27569, 250)\n",
      "reco_class_shape -> (9058, 250)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "9058it [00:09, 959.30it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "success_if_related_all : 0.33890469416785207\n",
      "success_if_related_not_all : 0.29554915145693245\n",
      "recommendation finished\n",
      "elapsed_time for recommendation : 24.555268049240112\n",
      "accuracy -> 0.30900861117244427 at index 4\n",
      "index5 start\n",
      "data process started\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 2/2 [00:13<00:00,  6.87s/it]\n",
      "100%|██████████| 12/12 [00:00<00:00, 12.21it/s]\n",
      "24587it [00:05, 4724.74it/s]\n",
      "274409it [00:14, 19479.94it/s]\n",
      "111755it [00:01, 77984.92it/s]\n",
      "33341it [00:12, 2688.68it/s]\n",
      "349330it [00:26, 13279.96it/s]\n",
      "141635it [00:02, 60856.96it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "data process finished\n",
      "elapsed_time for data process : 97.84762454032898\n",
      "training started\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 15/15 [02:34<00:00, 10.30s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "elapsed_time for training : 154.5160849094391\n",
      "training started\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 15/15 [01:59<00:00,  7.96s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "elapsed_time for training : 124.86933875083923\n",
      "Trained model object was saved to /home/kurimoto/apsec-test/../data/model/test_fit_model/dim-250_alpha-0.5_beta-0.3_gamma-0.8_delta-0.1_lr-0.1_epochs-15_weight-0.001_decay-exp99_data_type-random_negative-sample-num-10_5.model\n",
      "recommendation started\n",
      "train_classes_shape -> (27997, 250)\n",
      "reco_class_shape -> (8754, 250)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "8754it [00:09, 949.00it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "success_if_related_all : 0.35844155844155845\n",
      "success_if_related_not_all : 0.3251361610826869\n",
      "recommendation finished\n",
      "elapsed_time for recommendation : 24.09805703163147\n",
      "accuracy -> 0.33538953621201734 at index 5\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.31747476842536804"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy = nodes = edges = 0.0\n",
    "\n",
    "success_have_inside = [[] for _ in range(10)]\n",
    "success_used_outside = success_have_inside[:]\n",
    "success_extended = success_have_inside[:]\n",
    "success_have_methods = success_have_inside[:]\n",
    "success_have_fields = success_have_inside[:]\n",
    "failed_have_inside = []\n",
    "failed_used_outside = []\n",
    "failed_extended = []\n",
    "failed_have_methods = []\n",
    "failed_have_fields = []\n",
    "\n",
    "top_k = 10\n",
    "cross_validation_split_num = 5\n",
    "\n",
    "success_each_ranking_before = [[] for _ in range(cross_validation_split_num)]\n",
    "num_of_recommend_class_before = [0 for _ in range(cross_validation_split_num)]\n",
    "success_if_related_all_before = [0.0 for _ in range(cross_validation_split_num)]\n",
    "success_if_related_not_all_before = [0.0 for _ in range(cross_validation_split_num)]\n",
    "\n",
    "for index in range(cross_validation_split_num):\n",
    "    print(f'index{str(index + 1)} start')\n",
    "    model = prepare_model(index + 1, best_params['embedding_dim'], best_params['alpha'],\n",
    "                          best_params['beta'], best_params['gamma'], best_params['delta'],\n",
    "                          best_params['initial_learn_rate'], best_params['epochs'], best_params['decay'],\n",
    "                          best_params['target_negative_weight'], best_params['negative_sample_num'])\n",
    "    model_name = model.model_name\n",
    "    preprocessed_model_path = os.path.join(THIS_FILE_DIR_PATH, os.pardir, 'data/model/preprocessed_model', model_name)\n",
    "    train_fit_model_path = os.path.join(THIS_FILE_DIR_PATH, os.pardir, 'data/model/train_fit_model', model_name)\n",
    "    test_fit_model_path = os.path.join(THIS_FILE_DIR_PATH, os.pardir, 'data/model/test_fit_model', model_name)\n",
    "\n",
    "    DataProcessor(model).pre_process()\n",
    "    Trainer(model).fit(include_be_used=True, is_test=False)\n",
    "    Trainer(model).fit(include_be_used=False, is_test=True)\n",
    "    model.save(test_fit_model_path)\n",
    "    Recommender(model).recommend(top_k=top_k)\n",
    "\n",
    "    print('accuracy -> {} at index {}'.format(model.accuracy, index+1))\n",
    "    accuracy += model.accuracy\n",
    "    nodes += model.num_of_all_nodes\n",
    "    edges += model.num_of_all_edges\n",
    "    success_each_ranking_before[index].extend(model.recommend_success_count_in_each_ranking)\n",
    "    num_of_recommend_class_before[index] = model.num_of_recommend_class\n",
    "    success_if_related_all_before[index] = model.success_if_related_all\n",
    "    success_if_related_not_all_before[index] = model.success_if_related_not_all\n",
    "\n",
    "    for i in range(top_k):\n",
    "        success_have_inside[i].extend(model.number_data_1st_10th_and_failed[i]['have_inside'])\n",
    "        success_used_outside[i].extend(model.number_data_1st_10th_and_failed[i]['used_outside'])\n",
    "        success_extended[i].extend(model.number_data_1st_10th_and_failed[i]['extended'])\n",
    "        success_have_methods[i].extend(model.number_data_1st_10th_and_failed[i]['have_methods'])\n",
    "        success_have_fields[i].extend(model.number_data_1st_10th_and_failed[i]['have_fields'])\n",
    "\n",
    "    failed_have_inside.extend(model.number_data_1st_10th_and_failed[top_k]['have_inside'])\n",
    "    failed_used_outside.extend(model.number_data_1st_10th_and_failed[top_k]['used_outside'])\n",
    "    failed_extended.extend(model.number_data_1st_10th_and_failed[top_k]['extended'])\n",
    "    failed_have_methods.extend(model.number_data_1st_10th_and_failed[top_k]['have_methods'])\n",
    "    failed_have_fields.extend(model.number_data_1st_10th_and_failed[top_k]['have_fields'])\n",
    "\n",
    "avg_acc_before = accuracy/5\n",
    "avg_acc_before\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Experiment 2 calculation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "index1 start\n",
      "data process started\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 3/3 [00:12<00:00,  4.28s/it]\n",
      "100%|██████████| 12/12 [00:00<00:00, 14.30it/s]\n",
      "24247it [00:04, 4969.47it/s]\n",
      "265133it [00:19, 13602.56it/s]\n",
      "108213it [00:01, 82364.35it/s]\n",
      "33356it [00:12, 2763.19it/s]\n",
      "341185it [00:25, 13472.31it/s]\n",
      "141411it [00:02, 63637.75it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "data process finished\n",
      "elapsed_time for data process : 88.91525602340698\n",
      "training started\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 15/15 [02:17<00:00,  9.15s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "elapsed_time for training : 137.32696509361267\n",
      "training started\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 15/15 [01:57<00:00,  7.84s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "elapsed_time for training : 122.89832425117493\n",
      "Trained model object was saved to /home/kurimoto/apsec-test/../data/model/test_fit_model/dim-250_alpha-0.5_beta-0.3_gamma-0.8_delta-0.1_lr-0.1_epochs-15_weight-0.001_decay-exp99_data_type-random_negative-sample-num-10_1.model\n",
      "recommendation started\n",
      "train_classes_shape -> (27526, 250)\n",
      "reco_class_shape -> (9109, 250)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "9109it [00:09, 998.51it/s] \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "success_if_related_all : 0.39908256880733944\n",
      "success_if_related_not_all : 0.36780876494023906\n",
      "recommendation finished\n",
      "elapsed_time for recommendation : 24.302038431167603\n",
      "accuracy -> 0.37753869799099793 at index 1\n",
      "index2 start\n",
      "data process started\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 2/2 [00:13<00:00,  6.84s/it]\n",
      "100%|██████████| 12/12 [00:01<00:00, 11.95it/s]\n",
      "24266it [00:05, 4739.07it/s]\n",
      "270126it [00:13, 19471.31it/s]\n",
      "109152it [00:01, 78326.14it/s]\n",
      "33359it [00:12, 2666.60it/s]\n",
      "352951it [00:26, 13272.80it/s]\n",
      "140559it [00:02, 59902.21it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "data process finished\n",
      "elapsed_time for data process : 97.64321160316467\n",
      "training started\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 15/15 [02:40<00:00, 10.68s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "elapsed_time for training : 160.15749049186707\n",
      "training started\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 15/15 [02:04<00:00,  8.33s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "elapsed_time for training : 130.2689266204834\n",
      "Trained model object was saved to /home/kurimoto/apsec-test/../data/model/test_fit_model/dim-250_alpha-0.5_beta-0.3_gamma-0.8_delta-0.1_lr-0.1_epochs-15_weight-0.001_decay-exp99_data_type-random_negative-sample-num-10_2.model\n",
      "recommendation started\n",
      "train_classes_shape -> (27478, 250)\n",
      "reco_class_shape -> (9093, 250)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "9093it [00:09, 997.66it/s] \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "success_if_related_all : 0.40123010130246023\n",
      "success_if_related_not_all : 0.36467056407015325\n",
      "recommendation finished\n",
      "elapsed_time for recommendation : 24.234286069869995\n",
      "accuracy -> 0.37578356977895083 at index 2\n",
      "index3 start\n",
      "data process started\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 2/2 [00:13<00:00,  6.85s/it]\n",
      "100%|██████████| 12/12 [00:00<00:00, 12.01it/s]\n",
      "24367it [00:13, 1825.22it/s]\n",
      "267082it [00:13, 19323.08it/s]\n",
      "110214it [00:01, 77944.02it/s]\n",
      "33363it [00:12, 2654.26it/s]\n",
      "351132it [00:26, 13215.30it/s]\n",
      "141507it [00:02, 60530.46it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "data process finished\n",
      "elapsed_time for data process : 97.63396143913269\n",
      "training started\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 15/15 [02:30<00:00, 10.05s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "elapsed_time for training : 150.79953360557556\n",
      "training started\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 15/15 [02:01<00:00,  8.07s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "elapsed_time for training : 126.3682792186737\n",
      "Trained model object was saved to /home/kurimoto/apsec-test/../data/model/test_fit_model/dim-250_alpha-0.5_beta-0.3_gamma-0.8_delta-0.1_lr-0.1_epochs-15_weight-0.001_decay-exp99_data_type-random_negative-sample-num-10_3.model\n",
      "recommendation started\n",
      "train_classes_shape -> (27603, 250)\n",
      "reco_class_shape -> (8996, 250)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "8996it [00:08, 1002.88it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "success_if_related_all : 0.41821561338289964\n",
      "success_if_related_not_all : 0.3655248969235649\n",
      "recommendation finished\n",
      "elapsed_time for recommendation : 24.003393411636353\n",
      "accuracy -> 0.38128056914184083 at index 3\n",
      "index4 start\n",
      "data process started\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 2/2 [00:13<00:00,  6.87s/it]\n",
      "100%|██████████| 12/12 [00:00<00:00, 12.41it/s]\n",
      "24280it [00:04, 4895.35it/s]\n",
      "261265it [00:13, 19605.21it/s]\n",
      "106711it [00:01, 78749.51it/s]\n",
      "33338it [00:12, 2679.05it/s]\n",
      "350893it [00:35, 9969.20it/s] \n",
      "141810it [00:02, 60723.18it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "data process finished\n",
      "elapsed_time for data process : 96.95337319374084\n",
      "training started\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 15/15 [02:27<00:00,  9.82s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "elapsed_time for training : 147.29729199409485\n",
      "training started\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 15/15 [02:01<00:00,  8.09s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "elapsed_time for training : 126.44070935249329\n",
      "Trained model object was saved to /home/kurimoto/apsec-test/../data/model/test_fit_model/dim-250_alpha-0.5_beta-0.3_gamma-0.8_delta-0.1_lr-0.1_epochs-15_weight-0.001_decay-exp99_data_type-random_negative-sample-num-10_4.model\n",
      "recommendation started\n",
      "train_classes_shape -> (27569, 250)\n",
      "reco_class_shape -> (9058, 250)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "9058it [00:09, 1002.63it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "success_if_related_all : 0.3886913229018492\n",
      "success_if_related_not_all : 0.35830931796349663\n",
      "recommendation finished\n",
      "elapsed_time for recommendation : 24.148689031600952\n",
      "accuracy -> 0.36774122322808567 at index 4\n",
      "index5 start\n",
      "data process started\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 2/2 [00:13<00:00,  6.83s/it]\n",
      "100%|██████████| 12/12 [00:00<00:00, 12.48it/s]\n",
      "24587it [00:05, 4747.81it/s]\n",
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      "111755it [00:01, 80043.85it/s]\n",
      "33341it [00:12, 2698.96it/s]\n",
      "349330it [00:25, 13529.25it/s]\n",
      "141635it [00:02, 62319.44it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "data process finished\n",
      "elapsed_time for data process : 91.59060907363892\n",
      "training started\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 15/15 [02:19<00:00,  9.33s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "elapsed_time for training : 140.00790643692017\n",
      "training started\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 15/15 [01:57<00:00,  7.83s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "elapsed_time for training : 122.9627935886383\n",
      "Trained model object was saved to /home/kurimoto/apsec-test/../data/model/test_fit_model/dim-250_alpha-0.5_beta-0.3_gamma-0.8_delta-0.1_lr-0.1_epochs-15_weight-0.001_decay-exp99_data_type-random_negative-sample-num-10_5.model\n",
      "recommendation started\n",
      "train_classes_shape -> (27997, 250)\n",
      "reco_class_shape -> (8754, 250)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "8754it [00:08, 1009.62it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "success_if_related_all : 0.41447124304267163\n",
      "success_if_related_not_all : 0.3850470374649282\n",
      "recommendation finished\n",
      "elapsed_time for recommendation : 23.53250765800476\n",
      "accuracy -> 0.39410555174777245 at index 5\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.37928992237752956"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy = nodes = edges = 0.0\n",
    "\n",
    "success_have_inside = [[] for _ in range(10)]\n",
    "success_used_outside = [[] for _ in range(10)]\n",
    "success_extended = [[] for _ in range(10)]\n",
    "success_have_methods = [[] for _ in range(10)]\n",
    "success_have_fields = [[] for _ in range(10)]\n",
    "failed_have_inside = []\n",
    "failed_used_outside = []\n",
    "failed_extended = []\n",
    "failed_have_methods = []\n",
    "failed_have_fields = []\n",
    "\n",
    "top_k = 10\n",
    "cross_validation_split_num = 5\n",
    "\n",
    "success_each_ranking_after = [[] for _ in range(cross_validation_split_num)]\n",
    "num_of_recommend_class_after = [0 for _ in range(cross_validation_split_num)]\n",
    "success_if_related_all_after = [0.0 for _ in range(cross_validation_split_num)]\n",
    "success_if_related_not_all_after = [0.0 for _ in range(cross_validation_split_num)]\n",
    "\n",
    "for index in range(cross_validation_split_num):\n",
    "    print(f'index{str(index + 1)} start')\n",
    "    model = prepare_model(index + 1, best_params['embedding_dim'], best_params['alpha'],\n",
    "                          best_params['beta'], best_params['gamma'], best_params['delta'],\n",
    "                          best_params['initial_learn_rate'], best_params['epochs'], best_params['decay'],\n",
    "                          best_params['target_negative_weight'], best_params['negative_sample_num'])\n",
    "    model_name = model.model_name\n",
    "    preprocessed_model_path = os.path.join(THIS_FILE_DIR_PATH, os.pardir, 'data/model/preprocessed_model', model_name)\n",
    "    train_fit_model_path = os.path.join(THIS_FILE_DIR_PATH, os.pardir, 'data/model/train_fit_model', model_name)\n",
    "    test_fit_model_path = os.path.join(THIS_FILE_DIR_PATH, os.pardir, 'data/model/test_fit_model', model_name)\n",
    "\n",
    "    DataProcessor(model).pre_process()\n",
    "    Trainer(model).fit(include_be_used=True, is_test=False)\n",
    "    Trainer(model).fit(include_be_used=True, is_test=True)\n",
    "    model.save(test_fit_model_path)\n",
    "    Recommender(model).recommend(top_k=top_k)\n",
    "\n",
    "    print('accuracy -> {} at index {}'.format(model.accuracy, index+1))\n",
    "    accuracy += model.accuracy\n",
    "    nodes += model.num_of_all_nodes\n",
    "    edges += model.num_of_all_edges\n",
    "    success_each_ranking_after[index].extend(model.recommend_success_count_in_each_ranking)\n",
    "    num_of_recommend_class_after[index] = model.num_of_recommend_class\n",
    "    success_if_related_all_after[index] = model.success_if_related_all\n",
    "    success_if_related_not_all_after[index] = model.success_if_related_not_all\n",
    "\n",
    "    for i in range(top_k):\n",
    "        success_have_inside[i].extend(model.number_data_1st_10th_and_failed[i]['have_inside'])\n",
    "        success_used_outside[i].extend(model.number_data_1st_10th_and_failed[i]['used_outside'])\n",
    "        success_extended[i].extend(model.number_data_1st_10th_and_failed[i]['extended'])\n",
    "        success_have_methods[i].extend(model.number_data_1st_10th_and_failed[i]['have_methods'])\n",
    "        success_have_fields[i].extend(model.number_data_1st_10th_and_failed[i]['have_fields'])\n",
    "\n",
    "    failed_have_inside.extend(model.number_data_1st_10th_and_failed[top_k]['have_inside'])\n",
    "    failed_used_outside.extend(model.number_data_1st_10th_and_failed[top_k]['used_outside'])\n",
    "    failed_extended.extend(model.number_data_1st_10th_and_failed[top_k]['extended'])\n",
    "    failed_have_methods.extend(model.number_data_1st_10th_and_failed[top_k]['have_methods'])\n",
    "    failed_have_fields.extend(model.number_data_1st_10th_and_failed[top_k]['have_fields'])\n",
    "\n",
    "avg_acc_after = accuracy/5\n",
    "avg_acc_after\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "success_before = [0.0 for _ in range(top_k)]\n",
    "success_after = [0.0 for _ in range(top_k)]\n",
    "\n",
    "for validate in range(cross_validation_split_num):\n",
    "    for index in range(top_k):\n",
    "        success_before[index] += \\\n",
    "        success_each_ranking_before[validate][index]\\\n",
    "        / num_of_recommend_class_before[validate]\n",
    "        success_after[index] += success_each_ranking_after[validate][index] / num_of_recommend_class_after[validate]\n",
    "result_success_before =list(map(lambda x: x / 5, success_before))\n",
    "result_success_after = list(map(lambda x: x / 5, success_after))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Results for Experiment 1 & 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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     "output_type": "display_data"
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AAQgAAEIQCB1BLw2AP/973+b/fff34pywAEHmAsvvLCeQNOmTTN77723zfxbsmTJUr/Xste77rrLrL/++jUR9s033zSHHnqorXvTTTc1I0aMKNjOww8/bAYMGGB/v88++1jDstzAACyXHNdBAAIQgAAEIAABCEAAAhCAAAQgAIH0EfDaABw2bJi58cYbTaNGjcyoUaNMjx496il00kknmeeff96WyTUAVbhLly5m9OjRplmzZlVX96qrrrLZfAodAnL00UcXbENZgkHWn5YIK1ux3MAALJcc10EAAhCAAAQgAAEIQAACEIAABCAAgfQR8NoAPPLII61RtuKKK5rXX3+9njrffvut2W677ezPW7ZsaS655BKzww47GGUFnnPOOeaDDz6wxqD24Ntjjz2qru5RRx1Vd/iH9v7bfPPNi7ahvqpvijfeeMPeVzkRNgBV50cffWRmzZplD/pYZZVVzAYbbGB23333kv0pp22ugQAEIAABCEAAAhCAAAQgAAEIQAACEHCLgNcG4M4772y0B6D28wsO0Qjj1WEal156qTX5TjzxRHP88cfX/frzzz83vXv3tr/r1auXufbaa6uuzB//+EczZcoUW++YMWNMp06dirZx0EEHmXfeeceWUd91IEg5ETYAi10vQ3LIkCHWFEw65s+fb3744Ye6Zlu0aGGaNGmSdDdoDwIQgAAEIAABCEAAAhCAAAQgkGoCOndg3rx5dfeog1F1jgKRLQJeG4Abbrih+eWXX8xee+2Vd8+88PLfsWPH1jsk5M9//rPNAlxttdXMc889V3Xlte+f9h5UjB8/3rRu3bpoG/379zcvvPCCLXPzzTeb7bffvqw+yQDU6b9bbrmlXRatw1FkrikjUpmFL7/8ct1y6FVXXdU88MADZuWVVy6rrXIvmj59ujVvCQhAAAIQgAAEIAABCEAAAhCAAASSI9C5c+cGSQRK7g5pKR8Brw1Anai7ePFi07dvX3PRRRfVuz/tqTdz5kzTrVs38+STT9b7/amnnmqeeeYZa8yNGzeu6iNE5tuCBQtsvR9++KFp2rRp0TZOP/1089RTT9kyV199tV2mW06899579rThQo7+hAkTjMzRr7/+2la/zTbbmL///e/lNFX2NRiAZaPjQghAAAIQgAAEIAABCEAAAhCAQNkEMADLRuf1hV4bgNrLbu7cuXb/Pu3jF47JkycbLRHWEl+dFHzxxRfXE0qmobLflB0ng67a0VAGYJT7mDRpks2c1FJchQ5RWW+99aJcWpUyGIBVwUglEIAABCAAAQhAAAIQgAAEIACBWAQwAGPhSk1hrw3AHXfc0UydOtVmuz300ENLiRLe/+/KK680e+65Zz3RlHGnE4BrlQHYUEuAo47O888/3xp/ihNOOMFmBSYVWhr96aef1jWnF9AyyyyTVPNlt6M+a/8EmcbKLCUalgB6NCz/3NbRwy091Bs0cUsT9EAPtwi41RueD7f04G8IerhHwK0e+fbO+vnnn5fagktz2bZt27oFld7UnIDXBqAO9tCeeTJjnn76abuXXxB9+vQxWuqqDMCXXnrJdOjQoR5MZQa+//77Zo011rBLgasdDXUISNT70LLoM844wxbfZZddzNChQ6NeWnG5//73v3afwiC6d+9u2rRpU3G9ta7g3//+t13W3axZs0QzJmt9X77Wjx5uKYcebumh3qCJW5qgB3q4RcCt3vB8uKUHf0PQwz0CbvXIt3eWr/Nvt1T3vzdeG4CPP/64Ofvss63JJwf7vPPOMyussIK5//77zciRI+3PtaxVy3xzQybOJptsYk/C0WEbN954Y9XVPOqoo8wrr7xi6x0+fLjRqbvFYrvttjPTpk2zRXRYx4orrlj1PoUrfO2118wRRxxhf9SzZ8+8JynXqgO+voB8e9HXSj9X6kUPV5T4tR/o4ZYeaIIe7hFwq0e8s9DDLQLu9YZnxC1N0AM9KiHg6/y7knvm2voEvDYAFy5caPf/+/zzz+vd2ZIlS6wBeN111xktFc4NnYR7zDHH2DInn3yyOe6446o+Pq666ipz++2323qVaXf00UcXbGPGjBnWhFOstNJK5vXXX696f3Ir1IEjWgatIAMwGm7+8EbjlFQp9EiKdLR20CMapyRLoUmStEu3hR6lGSVZAj2SpF26LfQozSjpEmiSNPHi7aEHelRCAAOwEnrpudZrA1AyyPw7/PDDzTfffFNPlYMPPthmBeaLYP8/GYD33Xef0YEi1Q5l8R122GG2Wu0HOGLEiIJNPPzww2bAgAH29/vss48ZNGhQtbtTr74LLrjAPPjgg/bn/fr1M6ecckrN2wwa8PUFxB/exIZIpIbQIxKmxAqhR2KoIzeEJpFRJVIQPRLBHLkR9IiMKpGC6JEI5liNoEksXDUvjB41RxyrAd/08HX+HUsUCpck4L0BqDucM2eOkYH27rvv2v/Wfn+9e/c2W221VV4As2bNMn379rV7uekAkCeeeMJmAlY7dFjEtttua7777jtb9SOPPGIPLMkNldt3333NxIkT7a9uu+02s/XWW1e7O0vVJ+NUB6NoCbRCRuD6669f0zbDlfv6AvLtRZ+YoA3UEHo0EPgCzaKHW3qoN2jilibogR5uEXCrNzwfbunB3xD0cI+AWz3y7Z3l6/zbLdX9700qDECXZVB24cCBA20X11prLbsXoJb4hmPw4MF1++9tuOGGdv/CfCED8dxzz7W/KpRRePfdd5sePXoY1VMoPvroI9O/f397grJCRmmwVDkplr6+gHx70SelZ0O1gx4NRT5/u+jhlh5M3tDDPQJu9Yh3Fnq4RcC93vCMuKUJeqBHJQR8nX9Xcs9cW58ABmCNR4X2KdRegzpwQ7HyyisbnVCsQ0t++OEHM3r0aDNu3Dj7u+WWW84uR5ZRWK4BePzxx5sxY8aYLl26mC222MKsvfbaZvnllzeNGzc206dPN2+++ab55z//aRYvXmybWHXVVa3h2L59+xqTWLp6X19A/OFNdJiUbAw9SiJKtAB6JIo7UmNoEglTYoXQIzHUkRpCj0iYEiuEHomhjtwQmkRGlUhB9EgEc+RGfNPD1/l3ZEEoGIkABmAkTJUV0sOmQ0BeeumlghVp2fI111xTNHMvSgZgYABG6bEy/6644orEzT/1zdcXkG8v+ijjwOcy6OGWeujhlh7qDZq4pQl6oIdbBNzqDc+HW3rwNwQ93CPgVo98e2f5Ov92S3X/e5M6A1D76Cmjbtq0aWb27NlG++vJ5HIhXnzxRfP444+bCRMmmJkzZ9r9B5Wpt9NOO9k9CZdddtmi3YxiAE6ePNm89dZbdtL38ccf23a056H2O2zTpo3N+NOBJ3/6058S3fMv98Z8fQH59qJ3YdzXsg/oUUu68etGj/jMan0FmtSacLz60SMer1qXRo9aE45XP3rE45VEaTRJgnL0NtAjOqskSvqmh6/z7yS0zFIbqTEAn332WXP99debSZMm1em3ZMkSe7hHcLhG8IsZM2aYvfbay5qDG220kb2OSJaAry8g3170yaqafGvokTzzYi2ih1t6qDdo4pYm6IEebhFwqzc8H27pwd8Q9HCPgFs98u2d5ev82y3V/e9NKgzACy+80IwaNcqqIdMvHPkMQP3+zDPPNE8++aTdG09Lc5PeA8//oVPZHfj6AvLtRV+ZSu5fjR5uaYQebunB5A093CPgVo94Z6GHWwTc6w3PiFuaoAd6VELA1/l3JffMtfUJeG8Aat+8W265pe7OtK/d5ptvbl555RW7FLaQAahDOY488kj7e53Su99++zE+EiTg6wuIP7wJDpIITaFHBEgJFkGPBGFHbApNIoJKqFgW9dBhaD/99JP9n/5bqy9cCfUpWC1SahsWV/qc5n6gh3vqoolbmqBHNvVo0qSJadGihT3Yc5lllrH+RTnh6/y7nHvlmsIEvDYAv/jiC7P77rvbD5Q6Qfe6664zm222mb3bSy+91Nx7770FDUB9AN14443N3Llzza677mquvvpqxkmCBHx9AWVx8pbgsIjdFHrERlbTC9CjpnjLqhxNysJWs4uypMfixYvr9mOuGdAKK54/f35dDc2bN6+wNi6vlAB6VEqw+tejSfWZVlIjelRCr/rXNoQezZo1M506dTItW7aMfUO+zr9j3ygXFCXgtQE4ePBgc+edd1qT79prrzW9evWqu9lSBqAK6uCNf/3rX6Z79+72cA4iOQK+voCyNHlLbjSU3xJ6lM+uFleiRy2oVlYnmlTGr9pXZ0UPmX9Tpkwxc+bMWQqhPq8pk8GVCGcjutQvV/gk3Q/0SJp46fbQpDSjJEugR5K0S7eVlB5qJ7zNWdOmTc3qq69uZAbGCV/n33HukbKlCXhtAO699972gI/VVlvNPPfcc0vdbRQD8PTTTzejR482bdu2tcuFieQI+PoCysrkLbmRUFlL6FEZv2pfjR7VJlp5fWhSOcNq1pAVPaZOnWpmz55t0Wmv5RVWWMGu1NASpnKXLlVTh6Cun3/+uW4JsJZVEQ1LAD0aln++1tHELU3QI5t6yPzT8m8dZDpv3jwLQRmAMgHj/E31df7tlur+98ZrA1DLffUBU8uAhwwZEtsAvOiii8wDDzxg5KJ/8MEH/qvp0R34+gLKyuTNl6GEHm4phR5u6aHeoIlbmmRBD23L8n//93915l/nzp3tnkUuBpNpt1RBD7f0UG/QxC1N0CPbeujvq7ZAW7BggQWxxhprxFoK7Ov82y3V/e+N1wbguuuua/f/0wEeOsgjHFEyAM8++2y79Ld169Zm3Lhx/qvp0R34+gLKwuTNo2GEueGYWDwfjgmCAeicIFl4RmbNmmW++eYby36llVYyq6yyinM6BB1iMu2WNOjhlh4YgOjhHgG3etQQ76zvv//efPvttxaE/r7q72zU8HX+HfX+KBeNgNcG4NZbb21TYXfaaSczbNiw2AbgAQccYN577z2jb6dfeOGFaMQoVRUCvr6AsjB5q4rACVWCHgmBjtgMekQElWAxNEkQdoSmsqDH5MmT6/b+i5udEAFhVYs0xOStqjeQssrQwz1B0cQtTdADPXTwyKRJkywIJTF16dIlMhRf59+Rb5CCkQh4bQAeeOCBNnOvQ4cOZuzYsbEMQH1Dvc0229gMwh133NGeIEwkR8DXF1AWJm/JjYLKW0KPyhlWswb0qCbN6tSFJtXhWK1asqDHZ599Zvco0r5EOmQtzv5E1eIctR4m01FJJVMOPZLhHKcVNIlDq/Zl0aP2jOO00FB6fPLJJ0aHben0+q5du0busq/z78g3SMFIBLw2AG+88Uab+acPlzLwZOQFUWoJcPgE4QsvvNAoG5BIjoCvL6AsTN6SGwWVt4QelTOsZg3oUU2a1akLTarDsVq1ZEEP7f+nL1e1v/Jaa61VLXQ1qaehJm81uZkUVIoe7omIJm5pgh7oIQLl/p31df7tlur+98ZrA1B7zPTq1csoFbZdu3Zm5MiRplOnTlaVYgbgo48+agYMGGDL6VS6l156ydkNqv0fYvnvwNcXUBYmbz6NOfRwSy30cEsP9QZN3NIkC3qUOzFpCKWYTDcE9cJtoodbeqg3aOKWJuiBHhiAbo0BH3vjtQEo4EOHDjU33XSTzQJs27at6d+/v9ltt93M9ddfb+69917784kTJ9rlKOPHj7cmofb703Ha+t35559vtJSYSJYABmCyvNPaWhYm0z5phx7uqYUmbmmSBT0wAN0acz71BnPDPbXQxC1N0AM9MADdGgM+9sZ7A1BG3sknn2yef/75pfaZadasmc0MlMnXpk0bI8MpCF2j2Hvvvc2gQYN81M37PmMAei+hEzeQhcm0E6AjdgI9IoJKsBiaJAg7QlNZ0AMDMMJAoEheApgb7g0MNHFLE/RADwxAt8aAj73x3gAUdG2CqT0Ab731VrNo0SKrQ3jT6cDwCwRq0qSJ6devn80WJBqGAAZgw3BPW6tZmEz7pBl6uKcWmrilSRb0wAB0a8z51BvMDffUQhO3NEEP9MAAdGsM+NibVK4UDxEAACAASURBVBiAAfipU6ea4cOHm5dfftl88cUX9fTo2LGj2W677cwRRxxhOnfu7KNeqekzBmBqpGzQG8nCZLpBAcdsHD1iAkugOJokADlGE1nQAwMwxoCg6FIEMDfcGxBo4pYm6IEeGIBujQEfe5MqAzAswA8//GC+++4789NPP9kDPlZaaSWz8sor+6hRKvuMAZhKWRO/qSxMphOHWkGD6FEBvBpdiiY1AltmtVnQAwOwzMHBZUaH+40YMcK8+uqr5quvvqo7gEJoPvnkEwiVQeCRRx4x5557rr1S2x7ts88+sWrBcIqFq+aF0aPmiGM1EFePt956yxxyyCG2Da1EPPHEE2O1FxQu9++sr/PvsiBxUUECqTUA0dxtAr6+gLIweXN75CzdO/RwSy30cEsP9QZN3NIkC3qUOzFpCKXiTt4aoo/VarN79+5Fq9KX5SuuuKJRuR133NHsvvvupnnz5tVqvmQ933//venTp4+ZMmVK3rIYgCUR5i2AAVgeN1evytI7y1UNwv2KqwcGoA+qpr+PGIDp19jJO8QAdFIW7zqVhcm0T6Kgh3tqoYlbmmRBD18MwJ/nLjBTvpllfpm30LRq0dR06rCCWaZlM7cGTBV7U8oAzG2qS5cuZtiwYeZ3v/tdFXtRuKrLLrvMZv8p1ltvPXtQX7t27Uzjxo3tz2RKEvEJYADGZ+byFXENJ5fvJQ19i6sHBmAaVPf/HjAA/dfQyzvAAPRSNuc6nYXJtHPQi3QIPdxTC03c0iQLerhsAOpQuAmTZpjRr31u3vzgG7N48ZK6AdK4cSOzRY+OZteeq5t1u7Zb6jA5t0ZReb0JG4A33HDDUpWIy+zZs83HH39snnrqKaNsPMXyyy9vRo8ebY24WsdOO+1kJk+ebJZddlnz3HPP2a17iMoJYABWztClGuIaTi71PY19iasHBmAaR4F/94QB6J9mqegxBmAqZGzwm8jCZLrBIcfoAHrEgJVQUTRJCHTEZrKgh6sG4KdTfjDXjBxvJn/zU0m1unRY1px6wIamW6flS5b1pUDYACy2nFZ7aB988MHmP//5j721o446ypx55pk1v80ePXqYBQsWGP2/MgG1JJmonAAGYOUMXaohruHkUt/T2Je4emAApnEU+HdPqTEA3333XfP000/b/Y60f4gMpkWLFkVSpFGjRuajjz6KVJZC1SGAAVgdjlmvJQuTaZ80Rg/31EITtzTJgh4uGoDvfTLdXHHX22bu/GifCzVqWjZvYgYctqnZoPsqbg2iMnsT1QBU9S+++KI54YQTbEtajjtq1KgyW41+WdC/jTbayNx2220YgNHRFS2JAVglkI5UE9dwcqTbqe1GXD0wAFM7FLy6Me8NwFmzZpmzzjrLnhgWhJYyxAkZgBMnToxzCWUrJIABWCFALrcEsjCZ9klq9HBPLTRxS5Ms6OGaAajMv3NveDWW+ReMGpmAg07YKhWZgHEMwM8//9zssssuFsOaa65pnnnmmaIP0k8//WRNwpdfftl8+umnRlmEyuDr1KmT6dmzpznooINM+/bt69VxzjnnmEcffbTkQ3r33XebzTbbbKlyc+fONQ899JAZM2aM0ZhTm61bt7ZtbrXVVuYvf/lL3jaDSvIZYx9++KF54IEHjCbp06dPt6cQFzo5V/OGhx9+2Lz99tv29GKV1ZLp3//+92bnnXc2e+21l2natGnJe4taYP78+ebxxx83//znP23SgpZpL1y40C6VXnvttc3mm29uD27J5RzFANS8afz48XYu9a9//ctMmjTJaH6l+VHbtm3Nb3/7W7P99tubP//5zyUPhlm8eLFdNq4xI0YzZ840ql9sVlhhBbP66qtbLXfddVf779zQfarPMqGVqSpdtQ+kyup/Xbt2NVtuuaXp1auX1btQfPfdd1bL1157zS4t//HHH235NdZYw2yzzTbmwAMPtPdWLHTd/fffb8eD/lsaqw7dy8orr2yzVbU35SabbBJVxnrlpKHqVz/19+GLL76w96yxo0N51IZYaUyJQyHDyYfxHGUsBoCUzPPHP/7R/lP7gV555ZV5GWvbggcffNAoEWjq1KlG74U2bdrYsaJnYf3117djZZ111imq0T/+8Q/z7LPPmvfee8/MmDHDaBzr2dpwww3tydkac/kiV49XXnnFjpn333/fjjnVIQ379u1r30sYgGU/KlxYRQJeG4Dz5s2zf4z0YSOu6RdmiAFYxREVsSoMwIigKFaUQBYm0z4NAfRwTy00cUuTLOjhkgGoz4b9//pSpGW/hUbKah2WNdedsb33ewLGMQBlBh1wwAEWiSatt99+e8EHSUbPxRdfbE2LQtGiRQszcOBAO5EPR7kGoJ6jk046yUybNq1gm61atTLnn3++nSfki1wzQkbVNddcU2/1UK4BKINK96vri8091lprLXPTTTeZzp07V/wSevPNN22yw7ffflu0Lh3Y8thjjy1VJorpcu6559r7KRUyg3VPMvHyhUzD4447zpqIpUL3c+SRRy5V7KuvvrJLzmWClYqhQ4fWmdS5ZWUY/+1vfzO//PJLwWpk/l199dVm6623zltG5rLGrPQuFjK6ZRqVG4cccog1hUrFBhtsYK6//nprrGvcae4aXibvw3iOMhYDDlEMQO1lKiYy64qFnkXtbZov9A459dRTS2ooE3Hw4MFG75VwBAagNJFJWSxbWlrLMNb/K/r3729OPPHEUtLn/X25f2d9nX+XBYmLChLw2gC85ZZb7B9rvQT1rYgc+t12281+E6YXe5MmTZDeUQK+voCyMHlzdMjk7RZ6uKUWerilh3qDJm5pkgU9yp2Y1EKpf3/6nTnvptcrrvqKfj3Nut1qfxBGxR0tUkEcAzBszGn/P5ky+UKZNxdeeKE1JJo1a2YzdpQNpawXTYzHjRtnJ976wl4hw0VZakEo4y4w8YIlx8rw0n/LNAxCWTjKhlIo40fmpOpXdOvWzey55542808mpDICw6uCdLpwnz596nU/bEYoI0zZizJUlLmnZc/KwFKCgZYk6/cKZWsdfvjhNutPscoqq9h5h9i2bNnSGnTPP/+8vW+FMpBkyAV9L0dfZcKdfPLJtm2FzDdlZyqTrXnz5kaZbnqvKDNw1VVXtVmC4YhiusgAeeGFF+y9KmNKJ0Ari0rml7IBgyxL1avfKWtTv88NGRqqR9GxY0ebuab+LrfcctaMk7Enc1B81GauAah5nMaEQmaj7vM3v/mNPRhG8wZlpirTS/er+V+QpRruh35+88032x9JTxk3f/jDH2zWnjKy3njjDauRtomSxsOHDzcbb7zxUreiDEsZxyqjuaRMcGWAaVxrvimzWOPw9ddft3VWYgDut99+NoN10003tVlqGsfKMhQvsVdWmrIPFTIBb731VtunYgagq+M5ylgMhChlAGpMHn/88ba43hU77LCDHb961mQI6rmQjtJIP8tnAOrdo3eDyiqUvat32GqrrWZ11njT8ytjWiGz+O9///tSXwYFBuCQIUPMyJEjbTnp86c//clqqmc0yBbW+0mZnBp/CgzAct6IXFMNAl4bgPojrRewXoL6pqd3797VYEIdCRDAAEwAcgaayMJk2icZ0cM9tdDELU2yoEc5BuCixUvMf38unmlTjpLDHnjPvP1R8aypKPVuuk4Hc9J+f4hSNFaZNss0N00aN4p1TbmFixmAMvD0uUyfqe+99966Jb8y15QJlZv1oj6orEwSHdwhk+fGG2+0yzNzQybGYYcdZpfUytjQUjuZMbkRZQ9ATexl9gUHlGjyrmy83KW2ysK54IILrDGpvmvyL2MlHGEzQj/XPdx5553WcCoUMjBlwChk3CjDMGxUBtcpA+3yyy+3/5QR8Ne//rUs2bSkUdfPmTPHXq9soX79+uVNcJDJKnNru+22K3ifhZYzv/POO3XJE7kdlcEh7nfddZe57rrr7K9lSAbmS1BeppiMMpWVUSVjLR8bldfyZWULhsfLhAkT6rI1ZezJyJMJky/ERdrmaioT95hjjrG/k+k3bNiwvMvAZUAeffTRlqtMUxky4TF0ySWX2OdAoYxHmUv5Qu3IkKxkCbAMKvHK94ypTRm/V1xxRV1/ZGjLWC1mALo6nqtpAB577LFm7NixVjd9EVFoia9MXJnOMgfDIe30RYLMWxl2eo/omc4NmeD6QkTL2hW5Xyjo+VAdMrNVp0xnmYS5prLef4ceeqj57LPP6prAACzrtchFVSDgtQGobwT1DYm+qdM+D4Q/BDAA/dHK5Z5mYTLtMv/cvqGHe2qhiVuaZEGPuAbgq+9PNbc8MsH88N9fs8SyFMu3aWGO3Wdds9X6q9b8tsMGYKnGlNmmzKlTTjklb6aXrg+yvWTyPPnkkzZrplBof7MjjjjC/vr000+3Jk1uRDEAZR7KAFOovDLRCq32ueiii+xeXApNvAcMGLBUk2EzQmaK/q0MoEIhg0v74MloUzaYzMJiofuU8aj+vfTSS0X3IyxUT/getKeh/h034pgu+eoO73GmTFCZZ9I6yGIKrpHJsv/++9t/KitUe+zFCRksp512mr1EJuu2224b53JbNsggVMaXlqbnM5qDSmUaySRWyGyUqRaEzBxlkaoemaoNHTIBlTkm41P7J8qULGYAujqe44zFUhmAMomVobfuuuvaLyniRjiDMJ+hHa5PJqDaE//cPVH1fGjc6hkvNfb1pYnGaHBIKQZgXNUoXy0CXhuASq3VxsP6BvLSSy+tFhPqSYAABmACkDPQRBYm0z7JiB7uqYUmbmmSBT3iGoB9zxtt5sz9dXljFqN1y6bm/st3q/mtxzEAlRWlzDMZPlp+mRuzZ8+2RoSyvbSkV5lxpULZYVpqp+uUIZcbUQzA8F512o9LK4EKhSbrWs6nrBxl9QUT9KB82IxQtk6Q8VWovhEjRtjsH4X2RNT9FAsZSMES11J9zVePTALNc/R5WcsItcS3nKXEcUyXfP0IG4AynoIMSO1LGD7EQwd27LHHHraKfffd12atxYmwuav9HYMl4VHrCLev7LDATCx0ve5LmXsy12TKKDsyCGVayuBUBqKWNOdmGkbtUzXL6X5kkmrptcaC+lZoD0AXx7NYxBmLpQzAwOyVySuNtMw8TgQa69mSyZtvSXu4Pq001NZjCr1LgkxhLevVu0CZ0HpXKqNTdRYKZZ4qU1WBARhHMcpWk4DXBqA2E5abrg8pV111VTW5UFeNCWAA1hhwRqrPwmTaJynRwz210MQtTbKgBwZgvDHXEAagNs/PDa2okWkmI0YnWCpkBGrpp/Z9C4eW3slkUWgiHpzWWezOtUeW9oBr166dPfE0N6IYgMrS0pJihQw2ncRaLMLldTqnMhuDCJsROrhCe9IVC5lSzz33nC0iQ6+UYaD9ALWUVKFJ/xlnnBFrYGj/suDQlEKmaZQKo5ouMsJkeikzSnuWacmilsgWOmBBew3qdOAgZFgqQzI4qER91xJtLcWNsie7jBQtX9Y41LJOZTyqDh1sooy2UiEDN+At41rLakuFDGWZ2dr3UBmBQShzNMi21DOgjFUd3qCxW4vQPT/99NPWWNLydp1CGxiv+dqTASjDq5AB6OJ41n1EHYsqW8oADC/H197/0lzjL6oRqFOztQxdRt55551XUtbgdF8V1BLfYF9Q7Qd68MEH2+uVtRoY5IUqvOOOO+z7Q4EBWBI7BWpEwGsDUG68HjTt26FNUgl/CGAA+qOVyz3NwmTaZf65fUMP99RCE7c0yYIecQ1AlgAnvwRY2VLFIryHnSbXmrjrkI8gwmZL3CdM9XzwwQf1LotiACpjS4aN9hLUScWlQvvUydAKzIfwPmFhM0JmjwynYqGstnz9LtUH/V57i8VdqRTOiNNyWi2rLSeimC7al0wZUTr0JGpoDOTucyZjSqZG+ORcGaUy2LRt0xZbbGH/v5Chp70bdZ9h01EZXjLzdJ0yrQot05apInOlnNA8MjB3db2yuWTa5i7/1fJP9UXjUGZPORmZuf3TOFZ2X7ETrXOvkVmoQ1YKGYAujufgGZTpqii0H2Vwr6UMQK0A1DMb7Aeq65QVqfeITGdlz8qky2fUy1yNYhAXGkva01PJR4rw0nUZgdoXtFjoUJ8guxUDsJynlWuqQcBrA1DfMu200072Ra2NafXtDOEHAQxAP3RyvZdZmEy7rkG4f+jhnlpo4pYmWdAjrgEohWpxCMjPcxea4waPMYsXL6l4EOigjpvO/qNZpmXTiusKV+DKISD5bkqT2eC029zTe7UU9Nprry2bRT4DMooBKPNHmWbK/Auf9FuoI8q60/6ECi3hlSkQRBRjLFyv9mD78ssvy7pnZbJdeeWVsa594oknjE5gVpSTQRj1PmWkKFNSGX8KZUkqE08HdOjUW4UMO/1P5oXMJ4VMYmUm5oYMmeuvv95mkmp+lhtaTqtsSh3mki90qIbGl5ZS5ss+lCEtXXP3CJTxIgOxnFCWn/obDvVdJqf+F5zCG/69shS1L9zZZ5+9VGZpnPZ1uqyWTQcnWmtvRZ00K0NSy6u1v2Zglor3W2+9ZavX3pLqcyEDsJS5pjqSHs9qM84zV8oAVH3ipuX4yt4Mxm+Yv/hpmzBl94a3MpB/EGTwxdErKBvmqzEXmH7KFNXen8VC41oniSswAMuhzzXVIOC1ASgA2vhTD54ebC1pCP9xrwYg6qgNAQzA2nDNWq1ZmEz7pCl6uKcWmrilSRb0KMcArJVKVw5/x7z2768rrr7n+r8x5xyyScX1NGQFxU4BztcvrbAJ9vbL3SNNJ7wGe7zpM3iwBK6S+4tiANYqAzCKYaL9BrU0VqGDMEotAa6Eha5NKgMwbOYqq0m6hvcwC+8BqGSLIMuukAEY3LeuU3abDgcRLxl74cxAZRzKACkUP/74o71O1+taLUvXMuV8Jox+pn5rXCqKndxbji7KkNS96LRXmXAy7oKQYaq5aPv27WNXHTYtZfLKPCqUHRlegl4NAzDp8Sw4cQxAma5K8lGUMtC1z6e+VAg0Uuam9hsNQqaxDgsNDFOZ3kH2qrKC1a9ygwzAcslxXUMS8N4AFDx9u6d057lz59osQH2r0a1bN2sKRtk3QnUEm3k2pBhZahsDMEtq1+5eszCZrh296teMHtVnWmmNaFIpwepenwU9XDIA//3pd+a8m16vWMQr+vU063arzf5fFXcuYgVxDcDwKalaeqlMmyC0XFKGhEIHXZx11lkRe1G4WBQDMLynn/YRLLUnW7E9A+OYEeq19lULDhLRpF/zjFpGUnsAHnTQQeadd96x++4pO6lt27ZL3VbYADznnHPqlsqWMgBz2ehzv64ZOnSo/ZWWgotnqX0cg3q+//57m+hxzz332B9pabCyQIOl6eG91co5hTiOlnqPa9+4YPmpGAYnCsepR3vWff311zbTUnvMFdsrMTj0QvVXwwBMejwH/Q4y5LRfY3BqdD5mMny1dF5RygDMvV6GoMayNAqWVitTMziJXOW1BFhjW+xVttzQsyP9FewBWC5FrkuaQCoMQD3o2pBTewJGNfzCoHWN/tASyRHAAEyOdZpbysJk2if90MM9tdDELU2yoIdLBqA+H/b/60tm8jc/lT0QVuuwrLnujO3L+nxZdqM1uDCuAagTL/W5WqFMHC3rDGLmzJmmZ8+e9oRdZdcEy2wr6XYUAzDOKcAyVnbYYYdIpwBHyQCUARocOJhrJlRy34Wu1VJnLbFVtlItTwHu1atX0cNZAgNQ2Xta8qrDOhRxDcDgPsP7MsrQi7t9k5YO6wBIxWOPPWYPCVFMmDDBLvdUyFi7+eabayFLXZ3KBg1OodZhKDoUJW706NHDLpPOPYQktx4tb9U9BRmQ1TAAkx7PuiedfqtMR4X2wQu+RMjHTYcPBSczxzUAg/rC++3poKIbb7yxrikdYqTDjBQPP/ywkRblhJ4HLdvW88EpwOUQ5JqGIOC9AShnXy+T4FQwQdQHkjghAzBI649zHWXLJ4ABWD47rvwfgSxMpn3SGz3cUwtN3NIkC3q4ZABK/U+n/GDOveFVM3f+otiDoWXzJmbQCVuZbp2Wj32taxfENQC1wb6WYCrynSqq/a506IMid4/Acu49igGojDH1RSHTRVl8hbKmBg4caO677z5b9rDDDjPB4QNB3+JmAH7zzTfWCNVEX8s+ZfhU4xCIYqzC9yA9gpNp4/AtdZ8yV5QEobmQltrmLm0ODEAZMsOGDatrulwDMLxUV9mAMhXjhIwjGTsKLb1dd9117X9r7qf99JSVp3vR3n0bbbRRnKpjlQ0vI11rrbVsVl7c0LZVWuqs/f60bLVQEot018nEQVTDAGyI8aw5u/aXVCgDL3xPYXbz5s2zWurUcEW5BqCM4mCvSbWrLzWCeOaZZ8wpp5xi/ykDT4lE5SQR6fnQIS5BdnCxA1g0NmUay9xXsAdg3CeG8tUi4LUBqJemXgr6li8crVq1sseARzl2Prgud/PXagGmnvwEMAAZGdUgkIXJdDU4JVUHeiRFOno7aBKdVRIls6CHawagdH3vk+nmirvejmUCyvwbcNimZoPuqyQxNGreRhwD8M4776w7tEIna8rsUqZfOPTFeZ8+fWwGkz53X3bZZWb33XcveB/KlHn00UftKZ1bbrllvXJRDEAdCqEJfbD0UksENeHW8tVwyPTS8j+VV9+0ZFeHJuSWiXoiaXCdMgCDpdDaO0wGVufOnQveswwImZBa7lhOaH6jffn0mVmhffP69euXd34jY1ImUu7hGKUMQC2X1f5oinxLWWVwyCxRufAefLkGoJawKhlD87LcZcTBvStzVKcpB8sytZRcB14odOiJjJ/ddtttqcMtwtw+//xze/2cOXPsARlauhk2LJXRpcwuhUw1ZbDmG2tBnToMQkvdZezKUA5CmWfKjNTJw4UinCErjXQybNzQ8vngMBsto9e/cyO8tDn4XTUMQNWV9HhWm3pH6G+EQgcJ9e7de6lb1jgWC425IPIZgNo/UeM1rFsuu/DYzv0SQ4axliBrqbFChuPFF19sTxjPFzLttO2ATgJXFmsQej60N6S0U526Xu+I3FOGZ8yYYQ499NClTtrGAIz7xFC+WgS8NgD1jWPg2Lds2dK+9PWHo9gf42qBo57KCGAAVsaPq38lkIXJtE9ao4d7aqGJW5pkQQ8XDUCNAmUCXjNyfKTlwFr2e8oBG6Yi8y94AsIGoJZe5ob20Z46daoZM2ZM3aRYZYrt8Rc22lRWyzG1VFGnmepzubKktJm/xr2yy2QgyXTIdwJsFANQbchUO+CAA+pOTpUxqcm7DD4lBqj/MqOCkDEpozI3Shlj+d4c6r/mGoFpo/3ntMxYmWbay04GwKxZs6xBqcMilMGkZIRKthlSttvJJ59cZ77JMFPW3Jprrmn3v5OpJlNC5leHDh3qLUUtdZ86PEH7ywXmnowL1a8sR9X9/PPP29OgdYCCMqVk2ilyDcCgHfVJmW1a1qr5mK6T+at2ZMRKI4WMn/BJ0jpgRMvMZdhuscUWNrNP+7PL6NP+f1riq7aDE3OPOuqoulOSw1ppbIczFXXYg/qt8SGjWGNSRqJMGx0wIs2ULRgcCqG6pKmeBV0jA1FjU9meMoFkGippRAdOKHS/ykQsZkQV+iukDFpl0gYh81Z91b50Mkllgum+NbbUh2DcVcsAbIjxrGXbWkKvkB56djfffHP73zKQ9Xuxl6kabC2QzwAM3hd6DnS9sjC1L6QMRBnnzz77rB1zChnSqiv3oBZpKRMwMKRVTmNf5r7+W4a0ll/rnSOzWeNQY1PZsEEEGbJDhgwxI0eOXOq+dGiRlu/ryxKNET0HOqdAz5QCA7DQk8HPa03AawNQfzz0Ete3k/pDFH551xoc9VdGAAOwMn5c/SuBLEymfdIaPdxTC03c0iQLerhqAGokaLL/waSZZvRrn5s3PphmFi/+35YxTRo3Mpuv29HstuUapkfXlcpaDubWaFu6N2EDMEo/ZWwoa0ZLLostjZPZNmDAADtRLhWaDMug2WabbeoVjWoABn/7lQ2nZYyFQkaSsgDzmX+6ppQxVqheZTwqs0zzjnBGXKHyMpEqXWWkzCOZJuGTTfO19/vf/95mWYYjyn3KnFAmZaH7kbGipbsyMoO9IHMNQLWrQ0KihLLrBg8ebM2+IFSvTMBSobGo5dDSttBKL5lI6m9gNharUxlbMm7Cz0dgAJbqi7jI0M7Nuix1Xfj3pe5b40dcRowYUadttQxA9aMhxnPukuZcXsrs07L9YH/IYgZgKdYykWUIB0vFc8vL5Na41f6EUSK3L4EBqL8tyhzVs1QoDjnkEHtP+n8FBmAU4pSpBQGvDcD11lvPvrhk/OnFSPhDAAPQH61c7mkWJtMu88/tG3q4pxaauKVJFvRw2QAMj4af5y4wU76ZZX6Zt9C0atHUdOqwglmmZTO3BkwVe1PKAJQ5p+1zlFGjDC5NdDt16hSpB8q6UYaNMpo+/PBDmymjn8lckYGh7CgdaCFjRW3kizgGoK5XxuKoUaNsxp/GnMweZZupz8qikkmUm/ETbjeKMVbs5pWlpMMDlOn35Zdf2uweJSTIFFKWnjLgdHqysoD080pD96v2tNeYMpuUaSgzTCchKwtSmWrKmsrdlzDqfUo3Lf3WqaYyRaRdx44d7T3ocA1lBGppYyEDUAaIstW0DFnLKpXNJVNYWVTKBpURIybK/tT4yg2Zj8rKe/PNN+2Xu0rwkOGpeZ50VTahluRqCbCMzlKheYaMQGXNKYNLvJTBpyXDqkt1KJtL5l3YiFS90lLXKWtV2VtfffWVmT17tm1S+uoEaJnY6kuh5c6l+hf+ve5Z81hlJGoc6zAJ9VFmUd++fe0zI5MqMHeraQAG/Uh6PCuzVXsAKntVWmncanzouZUuU6ZMMTq4Q5HPAFT2njTSPqV6HlRe9ehZU116n+h6jTeNv1Ih9nqHqT5lBCpTVNmner66du1q/RvMrAAAIABJREFUx56ym5VpuNTfkZ9/tl8s6VnUOJWRKENZz4DGjLI5ZT4q01DvJb0vMABLqcHva03AawNQHyb0cOnbvXL316g1YOrPTwADkJFRDQJZmExXg1NSdaBHUqSjt4Mm0VklUTILevhiAErvIHsjmLwlMQZoozAB9HBvdKCJW5qgB3qIQLl/Z32df7uluv+98doA1DcC+lZn1113tSeQEf4Q8PUFlIXJmz+jiCXArmnF8+GaIjwjrimShWek3IlJQ2jFZLohqGMAukW9eG94RtxSCz3QAwPQrTHgY2+8NgCVhq7/KUU9OH7bRxGy2GcMwCyqXv17zsJkuvrUalcjetSObbk1o0m55GpzXRb0wACszdjJQq2YG+6pjCZuaYIe6IEB6NYY8LE3XhuA2htCx4lrGbA2FNU+CYQfBDAA/dDJ9V5mYTLtugbh/qGHe2qhiVuaZEEPDEC3xpxPvcHccE8tNHFLE/RADwxAt8aAj73x2gAUcG0AqlN0tLHrwIED7VH2hPsEMADd18iHHmZhMu2DDkEf0cM9tdDELU2yoAcGoFtjzqfeYG64pxaauKUJeqAHBqBbY8DH3nhtAOqkKoWOpdcegDopSieN6Xh5ndKjU5S0sXOU0CldRHIEMACTY53mlrIwmfZJP/RwTy00cUuTLOiBAejWmPOpN5gb7qmFJm5pgh7ogQHo1hjwsTdeG4Ay+8IGX3AMd1whVIdMRCI5AhiAybFOc0tZmEz7pB96uKcWmrilSRb0wAB0a8z51BvMDffUQhO3NEEP9MAAdGsM+Ngb7w3AakCXAThx4sRqVEUdEQlgAEYERbGiBLIwmfZpCKCHe2qhiVuaZEEPDEC3xpxPvcHccE8tNHFLE/RADwxAt8aAj73x2gA8+OCDq8Z8xIgRVauLikoTwAAszYgSpQlkYTJdmoI7JdDDHS2CnqCJW5pkQQ8MQLfGnE+9wdxwTy00cUsT9EAPDEC3xoCPvfHaAPQROH3+lQAGICOhGgSyMJmuBqek6kCPpEhHbwdNorNKomQW9MAATGIkpbMNzA33dEUTtzRBD/TAAHRrDPjYGwxAH1VLQZ8xAFMgogO3kIXJtAOYI3cBPSKjSqwgmiSGOlJDWdADAzDSUKBQHgKYG+4NCzRxSxP0QA8MQLfGgI+9wQD0UbUU9BkDMAUiOnALWZhMO4A5chfQIzKqxAqiSWKoIzWUBT0wACMNBQphAHoxBjCc3JIJPdADA9CtMeBjbzAAfVQtBX3GAEyBiA7cQhYm0w5gjtwF9IiMKrGCaJIY6kgNZUEPDMBIQ4FCGIBejAEMJ7dkQg/0wAB0awz42BsMQB9VS0GfMQBTIKIDt5CFybQDmCN3AT0io0qsIJokhjpSQ1nQAwMw0lCgEAagF2MAw8ktmdADPTAA3RoDPvYmlQbgt99+a2bNmmUPmliyZEkkXTbZZJNI5ShUHQIYgNXhmPVasjCZ9klj9HBPLTRxS5Ms6IEB6NaY86k3mBvuqYUmbmmCHuiBAejWGPCxN6kxAMePH2/uuece88Ybb5gffvghlhaNGjUyH330UaxrKFwZAQzAyvhx9a8EsjCZ9klr9HBPLTRxS5Ms6IEB6NaY86k3mBvuqYUmbmmCHuiBAejWGPCxN94bgIsXLzaXXXaZGTlypOUfNeMvLJYMwIkTJ/qon7d9xgD0VjqnOp6FybRTwEt0Bj3cUwtN3NIkC3pgALo15nzqDeaGe2qhiVuaoAd6YAC6NQZ87I33BuCgQYPM8OHD69h37drV/PTTT2b69OlGxt7GG29s5syZY6ZNm2aXBSv081atWpl11lmn7roRI0b4qJ+3fcYA9FY6pzqehcm0U8AxAH2Sw/aVZ8QtybKgBwagW2POp95gbrinFpq4pQl6oAcGoFtjwMfeeG0ATpo0yey+++6W+4orrmhuuukms95665lLL73U3HvvvdboC2f2qfx9991n7r//fqPMwT59+pgLLrjANGvWzEftvO4zBqDX8jnT+SxMpp2BHaEj6BEBUsJF0CRh4JjkBgPQrTHnU28wN9xTC03c0gQ90AMD0K0x4GNvvDYAg+w/GX0y/7bbbjurQSEDMBBo3Lhx5thjj7WZgfvvv7+5+OKLfdTO6z5jAHotnzOdx9xwRgrbEfRwSw80QY+GIIAB2BDU09Em5oZ7OqKJW5qgB3pgALo1BnzsjdcG4L777ms+/PBD06FDBzN27Ng6/qUMQBV8+umnzWmnnWazBLX8V0uFieQIYAAmxzrNLWE4uaUuerilBwYgejQEAQzAhqCejjYxN9zTEU3c0gQ90AMD0K0x4GNvvDYAt9hiC3vi74477miuu+66Ov46FEQnAsvc04S00BJfXTd16lSzzz77mMsvv9xH/bztMwagt9I51XEMJ6fkIAPQLTlsb3hG3BIlC3pgALo15mrdm0WLFpkHH3zQjB492i7/1j7c+plC2/HE+YI9a+bGGWecYZ588knL6p///KdNaHAtsqaJa/xz+4MetVPommuuMTfffHOsd1dD6VHu31lf59+1Uz2bNXttAPbo0cN+yNhvv/3MwIED6xQcPHiwufPOO60B+Oabb5q2bdvmVVd/eJ966imzxhprmGeeeSabI6CB7trXF1AWJm8NNCTKahY9ysJWs4vQo2Zoy64YTcpGV5MLs6BHuROTmgAvUWlDTd4a4l7ztXnbbbeZIUOG1P3q2muvNb17947cPe2nfdxxx1nzKl8EBuAbb7xh3n33XVtEq3d+85vf5C2fNT0wACMPNQr+fwJZe0aSFB4DMEnatNWQBLw2ADfccEPzyy+/mD//+c92378gbrzxRjNs2DBrAD766KPmt7/9bV7G5557rv1969atjfYFJJIjgAGYHOs0t5SFybRP+qGHe2qhiVuaZEEPDEC3xlyx3sjs++yzz+qKbL311kamYNTQl+ennHKKLb7qqquav/zlL/b/g5U3yv5bfvnlTdSJddbMDQzAqCONcgGBrD0jSSof9T0V7lND6VHu31lf599JjoMstOW1AbjLLruYL7/80uy8885m6NChdXopq09/VGUAXnLJJfa033xxwAEHmPfee8+0bNnS/Otf/8qC3s7co68voCxM3pwZJBE6gh4RICVYBD0ShB2xKTSJCCqhYlnQo9yJSUISLNXMnP/+ZJYsWWI/L7Zus2xDdKHB2tQX3zLswtG4cWPz0ksvRV6Kes4559gv0hWjRo0y6623Xt77iTqxbqjJdEOJgAHYUOT9bTdrz0iSSkV9T2EAJqkKbdWCgNcG4IknnmheeOEFm+H32GOP1fH54osvjMxBfaD7wx/+YEaOHFmPnT6E6wRgxeqrr84S4FqMriJ1YgAmDDylzWVhMu2TdOjhnlpo4pYmWdDDFwNw8bxfzJdDjzJLFsw1jZq1NKudfJtp3KKVWwOmhr3RKphHHnnEtqC9sIP/Pvnkk83xxx8fqeVDDz3UbrWjz9sTJkwouOd21Il11swNDMBIw4xCIQJZe0aSFD/qewoDMElVaKsWBLw2AO+66y5z5ZVXmqZNm5q33nrLLuUNQh9mPvroI/uhREuETz31VLPiiivaX2sfEn1rOWXKFPv7Aw880Jx//vm14EudBQhgADI0qkEgC5PpanBKqg70SIp09HbQJDqrJEpmQQ9fDMDZ4583M565pU72drseZ5bbYKckhkGDt6HPYFruKzNBX4I//PDDpmfPnmbu3Lmmc+fO9st1fT4uFVpJM378eNOkSRP7mbtQRJ1YZ83cwAAsNcL4fS6BrD0jSY6AqO8pDMAkVaGtWhDw2gCcNGmS2W233eyHFG1c3KtXrzpGY8eOtRsTBx9gtKxBBuC8efPsCWUKLfto1aqVPQhEe5YQyRHAAEyOdZpbysJk2if90MM9tdDELU2yoIcvBuCU288087/53/53zTt0NZ2OvMqtAVOj3mi5bvDF90knnWROOOEEc/rpp9vPw4rhw4ebzTffPG/r4WuLdU+ZhPrMHZyqWaxsly5drOmYz9zQVj86ZVgHiXz99ddGnx+XW245s9Zaa5kdd9zRbvOjrXwKxTbbbGO+/fZbE7ShPqm+Z5991m4jNHPmTNOpUyfbfrkxY8aMuj5+/vnn5ocffrDJCe3btzfrrLOOUR80R9GcIxxRDED195VXXrH3/8EHH9g+ax7TokUL065dO7vSae+99zZbbLFFye7rugceeMAe2qI51OzZs23W5gorrGDnSN27d7fG8A477GCaN29u6wtrIva6/rXXXjO6T/1b7HW9+vL73//ebLvttvZ+ixnIH374oV06ruQNaaM2tFekWInTHnvsYfkVi7ffftvWoS2cvvnmG7NgwQI7LlSPDpnRPvG77rqrNbjLDd2f5pPqp/qsxJE5c+ZYHaXtRhttZMffuuuuW7SJfDo///zz5vHHH7fG+XfffWf7X+gk6BdffNE899xz9l411nT4zkorrWTbV8JLFO1LMYgyFoM6wu+Aq666yuy55555q//HP/5hT7nWuNU9Lly40Oqj8aIvGrRHqObxYlko9KWEspOlwyeffGK+//57Ozalsd5RSuKJorHa1nOv/mjsz58/36yyyir2i4+DDjrIdO3aNfJepeG+NpQhW+7fWV/n36XGL7+PR8BrA1C3qg8YevHrJaAsv3Bcd9115oYbbqj7kf4YyfQLQn+0rr76avPHP/4xHjVKV0zA1xdQFiZvFYubYAXokSDsCE2hRwRICRdBk4SBl2guC3qUOzFJUql50yaZqXecVa/JVY+4yrTo2DXJrjRIW9oCR2aCPhfLXJAB9uqrr5ojjzzS9udPf/qT+etf/5q3b0kZgMFndK320eS9UMgI0Gd9mU/5ImwA3nHHHaZfv35GYzQcgTlYjhh33nmnTUKQUVEsjjjiCHP22WcvVSSK6bLddtuZadOmleya9kMfPHiwWWaZZfKW1btHiREyPEuFtlX63e9+Z4sFBodMSC0b179LxTvvvGPNuNyQmXnxxRdb4y48H8stt/baa5ubbrrJjsvcWLRokbnwwgvNQw89VKobdn6ngyHLCR0yuemmm1qjqFRoL00Z6sqEzRdhnWU0Syc9d7mRawDK8NY8V9oVCxmdgwYNKmqEl7qHKGMxqKOUASh2OhxIpl2pOOSQQ8x5552Xt5hM77POOstMnz69YDUyitXW0UcfXbCMTMOjjjrKmrj5Qu8a8ZPBGHxhEZxgXqr/GIClCPF7Fwl4bwCWgqpvqW6//XajP0b6dkWx7LLL2m+ntMeJHH8ieQIYgMkzT2OLWZhM+6QberinFpq4pUkW9HDNAJzxzK1mzv+9s9RAWDJ/rlk8r76R0bjFMqZR86WzyVqvtYlp1/sYtwZSBb359NNPbdaNQgbHiBEj7H8rq0hmkzKyNCGWIajPy7kxdepUM3HiRPvjv/3tbzabRqts9KV7OPT5WnUqU0xZN8q4U5x22mn1PnvLtNpyyy2XyjaToTJ69Gh7jbKGZHIoQ6xNmzY2C0rmgkwphX6mLKHVVlutXn8DA1BZRqpHk/wNNtjAHiDYoUMHM2vWLNvHcrYCuvzyy83dd99d16Z4KgNOpqRMS7HStkPKIDv44IPLMgA322wza5Yp0UGmXMeOHW0Gmj5Hf/zxx3YPc2VXKXbffXeb2JAbylrT3uiBkaKMNWVPiom0ViagxoX6KT65BqAMyH333dfI2JFprPsUV2X9KRtLDPXcy7DRPuz5DEDNwQ477DDLQyH20lRmn/qgZA5luelwRoUYSlNli4VDhqu2fwp0133J/FU5taF6lHH2+uuvG7Er1wAUM2UR6h6VYafsSGWMKfNSvNTG008/Xbeq7Nhjj7VjO1+EzTVxe/nll+3Ks7322susueaa1jx+//33rZGlzD6Fxs5+++1nx7pCY1+GprhJA3FWxq7KKfTsyryKsnS/VB8LZSIG15UyAC+77LK694ruRzrrfaDnTyawMin1t1DjTfeYzwBU9qCyk6Wp3i/iJh2kgUxZXa8MSj0HijPPPNOafLmhsvrCI9iiQH3Q1mA6P0C/0x6mes/IvFX9uncFBmDeocwPU0Ig9QZgoJM+hOgPlF6M+iNR7gsyJbo3+G1gADa4BKnoQBYm0z4JhR7uqYUmbmmSBT3KMQCXLF5kFv/y60SumrF4wTwz5ZaTzZKFpbN4CrXbqFkL0+mYa03jZi2q2TVbV+NWbUyjxvmzhqre2P+vUOaJTBSFsl60hDAImUe33nqr/edFF11U75Tg3D7Vag9ALdULTB4ZVfrvfGakDBiZK8oKC5uZ4X4GBmDwM2XhKRuv0lDbwcoj7UGu/cNk/uULGXSTJ0+2SzbDESXrSoaEzFEt1c0XykCS8aTTmxVaoqtlweGQwRGYUzJJZJYUCj2/Mr0C4031K3MyMHhLjQtllsqQC5YQB+0o6031KDRuBgwYUK+Mfhc2+LS0ORgHQT0y/GTYysiRUSlDNF/IaJKZWehk6lL6y3iSoakl0YXmjMosU1alzDtloo0ZMybvCdphndWuljkrwzaXUdAnGb4yxvT3QvUOHDjQmlaKcMaZTCtlyAXmeu7zXOoe447FoHwxA1DcZLzKQNXSXGVq5nt2VZeWpMsMDLJNg/pl4ioLWUbryiuvbE3cfDqqnLKWZV6Lk8Z57nJgZQcPGzbMVq1tA5RRrPEdDi0nP+aYY6zBHQQGYJzRQ1nfCGTGAPRNmLT3FwMw7Qonc39ZmEwnQ7I6raBHdThWsxY0qSbNyuvKgh5xDcD/TnzdzHzuNrNozo+VA/ashiat25qVeh1l2vxuy0R6rsm5DDEZF8oi0yqZ8AF6yuZTto6iR48e9nCQYlELA1DZUMpQVB+VHaYssELml/o2ZMgQc9ttt9luqr/qdzjCBqCMl8AMqAS4kgp69+5ts7AU119/vdlpp/gHyEQxAKP0U0aJjCqx69u3rzWMwiEDZejQofZHMovWWGONKNXaMjKcVJ/MNoVOey5kXBWqVJmHyl5TxtVWW21lV2YVC2XCKbNRpo4yPWUCBaFMOGVXFsp2jHxjVSr42Wef2bGg0D6aMpJyI6yzMhtlHufuBxm+RvsDnnjiiXnrzF1yKqYa11ourOdF2bblRJyxWMwAlCkXGOHFsiKL9THIIJTxet9999lMzELxn//8x+5BqGdSe/ldcMEFdUXFRs+/EoA0ljSGZQLmC+17esUVV9T9CgOwnFHENb4QwAD0RamU9RMDMGWCNtDtZGEy3UBoy2oWPcrCVtOL0KSmeGNXngU94hqAX/z14LzLcWPD9fQCLTte/Yxfl+HWOrTEUsvqFDpoQeZZbuhQg2DPMS2x01K5QlELA1BZVDIjFFpiG2Q+FepDeEmzMvKUkRWOsAF4zz33mE022aRizMpy07JChTKTZIiUE3FMl1L1a4mulqUqmyow64JrlPWk7DCFsjxlnkUNGU466GHkyJH2kvvvv98uoY4T4fb136UOrVDWY2CkKVNO2WBBKItScwgZOdpLsJg5HKePlZTV8myZTMpWDe89H9QZ1lnGXv/+/Ys2p30qtQRWS6O1lDls0ufbc076BKaqlhcXO1SjUMNxxmIxA/DHH3+02biKKGZvbn9k5CmDUKa2DgmREVcq9I6QMa3sP73jghC7ww8/3P5TS6RvueV/p77n1inzXAeCBEuKMQBLUef3PhPAAPRZPY/7jgHosXgOdT0Lk2mHcJfsCnqURJR4ATRJHHnRBrOgBwZgvDGXpAEoUyXY40pLLbW0NDc08b3kkkvsj7VvXbG98WphAMqUVNaPQodO5DsIItxnZfkES3HzHV4SGIAyisaPHx87ey2fmso4DMzTfKZj1BEQ1XTRqcLK7NKehzI8ZTZpuWK+gzS0dFf7moVDWVKBiab9FrUEWiZglExAGU4ylXQYhUKnBWscKetM2WxRQqdMB4deyNArlv2m+rTnoLLAFDJ0w4c8ysAOTB6ZudpXUMZNqTqj9DNfGe2JKUNVTIOTk8NLRcPXFMqaDetc7ITtoC7dlwwwjX09A+HQ0mbpruw47UeokD5a+q0o9FyXuv+oY1H1lNoDMDCjVVaZn8rMk5kXJXNU+1oGpwpvv/32Jb8AUBt6HoO9I2WCB6aw9kTU0vzgXaKxUiy0PD7YVxQDsNSI4fc+E8AA9Fk9j/uOAeixeA51PQuTaYdwl+wKepRElHgBNEkcedEGs6BHXAOQJcDJLAGWkaEJtfbL00EC2jNOm+vnhswmZe5oubD2WdOEuNDEvRYGYHg/u7hPr8y+v//970tdFhiAOjwgmNzHrTe3fPiQA2V8KfOrnIhiumg5qExYZVZFCZkfMkFyQwe25GZAiYmy+WTOiFPu/mmqI8g400ENWpYbjs6dO9v9BmVYadmnxlW+0GEXwcExUe4hXEZjTCcHB/HVV1/ZvSnDJ8PqnrXvoO5F2WMav1HMplJ9kQmk7LpSJzwH9eRmoAU/D+ss8zIf56Cs9sWTHuWGDK9gGX+cOqKMxaC+Ugag/s4p8y7IptN1ymZUtqw0UgaosgTznZoso1iGcbmhrL/gIBV9kRFkEOpU6R122KFotZdeeqlRlrACA7BcBbjOBwKpMQB1qpQ2/9RLRyci6aWjDzlRQt+iBKcDRSlPmcoJYABWzpAajH3eNUnRh79yN3qGY/UIoEf1WFarJjSpFsnq1JMFPeIagCJbq0NAclX7+bN/me+e+HVD+GKx8h4nmWXWXPoghVLXlPP7JA8B0QT42muvtd1UBpf2KysU4SyrYoZCLQzAo48+uu6k2LhMZSxoiWk4AgOwS5cu5oUXXohbZd7yysrS3oSKcjOudG0p00Vzm0MOOaRuPqPl2MralPHWtm1ba3IFB1QEJzLLVCk0p5G5IoNUS5jzhYwnHZIS/jwVGIAqr/0DxVcZhbmhfmiZ5TnnnFPP4JLxEpxWG1cALe/UUvBwaH9I7Wv4xBNP5DVGdejEoYcearQHXblGoE7YDT8jMq5kWikrT6dOh+uVOSrjvNAYK6Vz+N7EqZRRVYyhDMsggy4O6zh9LGUAql0ZtdJIpnG+jEnt6yh9lB0YPmRFy7o1hsqN8AnGqkf1KaIsPVd2avAFAgZguQpwnQ8EvDcA9cLVHyulPweRLyW+mBh68ZT7zZQPIrvYRwxAF1Xxr09ZmEz7pAp6uKcWmrilSRb0KMcATEql6U8MM/+d8M+SzbVZdzuzyh6/bsKfhtDnYh1SoUl53Ci2j1ctDECZj0Gmng5L6Nq1a9wuL1W+FgZgUhmAWoKtE0oVymYK9h3MByTIsitmAAbX6VRimYsyAt955x1rGAZzJ32hqiWV2tdOkW/POZ3cOm7cOHu9+qdlyUHIfNM+gd26dav7mZYfB6ahTsxVNlg1QoeBfPjhh3b5p5Z3a5luOFNSY1f3UugU32J9CExLHR4hI6vQCc+qI1iyWw0DUMu7A/b59pfMp0c1WMYxAKWvToNWlDIctWRZmmusaMxovOgegtCeo8Fyb/0svE9pqROrS903GYD1Cfk6/y6lNb+PR8BrA1Avfh2TLvMurukXxoQBGG/QVKO0ry+gLEzeqqFvUnWgR1Kko7WDHtE4JVkKTZKkXbqtLOjhqgG4ZOEC88W1R5gl8/43+SykWCMdzHHKHaZR02alRfWghIwRZUSVE1omrAMJOnbsWO/yWhiAyvZ66KGHbFs62EAmTiVRCwMwiT0AZZwo60yrmdZff33z4IMPFsSgOZCWvsr8imIA5lakrLMrr7zSaLmxInyQSBTDSQagDCGZiorcwzDCe6vFPYU4jvaaFwZLpufMmWMvLWeJtk531h6HCi2nDfaRy9cXMRd7aVANA1D1SHdlzSlL7tVXX12q2Sh6xGEWlB0wYEDdqd/KFFWWaaEIZxOXMgBz69C41mndes6llyJ82JDMQs3tFZWe9Kwl78qMVShrlz0AjV0h+cknn9TJ0r17d5vRSmSLgNcGoNJztV5fBp5emHpZa+NR7QOhfUv0rQ3hJgFfX0BZmLy5OWLy9wo93FILPdzSQ71BE7c0yYIerhqAc/7zjvl21JVLDQgZfa032tXMGfd0PWOw/X7nmtZrlb8Xl0sjL5zdo8/KOkG1VCir6rXXXrPFlJWXb1+uqAbg0KFDbRaVotjSOpkbMoiCgw9kWsqYqCRqYQCGTYpanQKsgzC0pFaR73CTMBNp1bdvX/ujcgxAXafDVLbeemu7lFWhbC0ZA1ENJy3L1fJkzcc0B3vrrbfqunjrrbfa04cV0rNcMzrqOJBxLGNKoQNPtFIsTsjIPPDAA+0luYeQ5NYTXipcDQNQ9R955JF1xl/uSdxR9Yhzvyo7ePBgc8cdd5R8RlUgfJhQXAMw6Fc4O++CCy6wS4EV2tZHS611n+3atbN7lZa7jPuNN96oM/20/6kOBSkUMiY1fjkFOO7IobyPBLw2APWy0EtaBqA+LGifDMIPAhiAfujkei+zMJl2XYNw/9DDPbXQxC1NsqCHqwZg7vLfZit3Nm13P9k0abuKWfTDt+bHp4aaBTOm1A2YtCwD1mmiyqLTBFdfjGt5rU5yLRXh0zi175mygnKXUkY1ALWvlvbXUui/ZcrlC0369flQhpeMKJ1Yq33eimUjlbqPWhiAixcvNrvttpv57LPPbPPXX3+9XWIdN4otu9T9K7NMUSwDUIabDKPArC3XAFQ74aW6Whq83HLLRTYAdf2GG25olHmnZcBBNqB+rgxDGc8yd3RQiEwtmYS1ivAyUs0NtUdfnNCy4n322cdeUiwDUKapyumdp6iWARg2FWUCy7gKnr1aGYDKygvMdpn9Mv3zhbI999hjj7p9Kcs1AGU2ynRU5GbnXXjhhXWnGp955plGGaTlhPTR869l1Xr36V1SaEuBESNGLLUUmT0AyyHONb4Q8NoA1B9GfbDRMfbaJ4TwhwAGoD9audzTLEymXeaf2zf0cE8tNHFLkyzo4aoB+POn4830J68zi3+ebZYTmfw/AAAgAElEQVTpvplZ5U8nmrmLltiMJU2uWzZpZH//8ydvmcbLLGd/v0y3Dd0aQGX0RhNZZdsoSmXB5FYfPr013yb6UQ1AHQRwyimn2OpPPfVUm1WVLwJzY+TIkWbIkCG2yGqrrWaGDRtmdABGodCSTd3n8ccfb1ZYYYWlitXCAFQDylQ8+eSTbVutW7e2y0QL7RM3c+ZM8+WXX1qDLByl9l3TUtpg38Z8S1m1hHLQoEF1J5eq7nwGoLTTEm7ta6c9/vKFDD/tOajnYdVVV7XLvhXSRNmbWpaq8ZPv5GiVe+yxx+oy7ZTBJUMlHOpncEDLuuuuaw+kkbFcKLQvofaaC8auyn3zzTdm+PDh9hTgQqawTMZ+/frV7SOpdgMzL+rjI7Ncc0wtwxWv++67r95Bc/rdWWedVbdsWnVXywCUwazDT2REKvbee28jU0yGeD4DUMvEtVRYW2IVerZK3Xv4lHAZvzrkJpexslKVURkY36oz1wCcMGGC3Zdfy3i1hDlfyCSWhvqSQXH33XfXmd36t9qRGa0TkTWeZRDmHhYSrlf16SBQZazmnoCs50bvD8Xaa69tx0/uFyAyq3X4UHhvQgzAUiOG3/tMwGsDUGn3etHrA4pe8IQ/BDAA/dHK5Z5mYTLtMv/cvqGHe2qhiVuaZEEPVw1AjYRFP882P//fu6bNettb0y93Mr1kyWLz33+PNcustbFpssxybg2eMnsj8yMwErQUd5dddolckwyb4PN1vv24ohqAMsBkjukzu8wyZaxp76lgaZ+MDZ1AG9Zj4MCB1lRSyHTS8lSZMsogk3bKkJs0aZLNNAuMBJkguaZDrQxA9Uv7mMm8CELGlzK2ZLbJlJGRoaW0ys6TuZa7FLWUARjOShIDmRvipBOAZXoqo+nzzz+3LAPjL58BGLQjY6dnz55mnXXWMe3bt7dZUdJGy3XHjh1r9VFoe6VgHzZpcvjhh9tDHMRW12uPQC3PlA46UESGz+uvv163H3u+vRtVt0wWLctUyFiTIan7UV0yvZSppfeH+qP70/iQoRSETNSdd97Z/rNHjx72WmV0KeNQ/Zw8ebI1ggLTVOaxsg1btWoVecwHBbUnok54Dvqq50jGpepSHzU2ZUgqu1Z7qolDtQxAtamxo0NfZMwplDGpLEqZWLpfmZTaf1BjX+NL7Iod2BMFgMZn8MypPS0r19hSJp2Wvet3+m9pECTe5BqAGgcaLxqvMrxlHK+++urWnJOhp2dWGY7ipVAZGay52cUaUzL0gzGpOpRlK731vpDpp8NoND40XsRDpzZreXI41N/gvAD9XF8QyFzVFwr6na598skn7fOjU8R1irACAzDKiKGMrwS8NgD1zZjSyvVSzj0i3ldBstJvDMCsKF3b+8zCZLq2BKtbO3pUl2c1akOTalCsXh1Z0MNlAzBXyVotp6veiKmsJmUE6UtyhUwjGWRx9tOSOSQDTZlmLVq0sNfLRAoiqgGo8toDTnvB5YvAOAnrIaNF5ZV9Nnfu3JIglNWjrDzdZzhqaQCqHfXxuuuus2ZCsZDpqYyxcJQyAJWNp+ynRx99tGDVMjLESHXpJNx8BqCWUcosLBUy5bT0M2yihA3AUtfLmNFhIMGYyy0vM0eZnTJXggMgitWZa6jJ4Iu61FrGlbK/yl0+Lj2VTRcsrc7XTxnS0l7ZajLqqmkAqj2ZZOecc069g0AKMZOxVcl8WIaiMvw++OCDvE3omZQ5rOfx/PPPt2VyDcDwvnulxov46UuJ3Kzd4Dr9vdTYlRlcKmRmq2/5sj31HpP5HHwRkluXTqXWFx0ycoN9AjEASxHn9z4T8NoAPO200+w3EPp2QcsFCH8IYAD6o5XLPc3CZNpl/rl9Qw/31EITtzTJgh4YgO6MOU2I77nnHtshZfMoqy5uyATRRvwKLUMMDkfQv+MYgCqvz+wys2RMKoMvyO7JZwDKTFJo8q79yZRZpOyh4JAKGX26TplgykrTBv75lrfW2gBUH5UJ9sADD9g+KktNmU4yWpWtqGw7LZ2VcZVrvpYyAAOtZGyOGjXKGjPKfFJ2lrY/UjZnnz59bL3FtFB2lDKd3n77bZsxpT7q0A5lKSozS5lyMmNUl5iGI9iXUdfq/SUTRdcHOigbTVlZ4i8DapVVVik5xJS5JU3VJ9Ul40kZY8F9ac9DZbMpwy93ybFMQO1jqYNP/vOf/9hMOTGR9sokFG9lqPXu3duaoZWE+GgprDLfZA7J+FpppZVsFp4yYmX8qX+lxlhUnQv1VcausuaUTaqxpjmUNFdG5pprrmk22mgjO8a6detWye3aa2V8KiNPmZRa6hvs2yg9tJ+iMvE0FgsZgKpDrPQsSCPtGag+i52+RFDmqZ5Z8QsOuSnWaWmgk53HjBljx5/eB1p+rUxiPV/SQmNX2aTSv1DIcNYzqmw/vUd0n+qLxq2yczWGtYwfA7DiIUQFHhDw2gB888037ek+esHrRaWXEuEHAQxAP3RyvZdZmEy7rkG4f+jhnlpo4pYmWdADA9CtMedTb9KekemTFkFf0cQt1dADPUSg3L+zvs6/3VLd/954bQAKv44O1zcR+rZIe5WUs8+D/zL6dwe+voCyMHnzaTShh1tqoYdbeqg3aOKWJlnQo9yJSUMoxWS6IagXbhM93NJDvUETtzRBD/TAAHRrDPjYG+8NQKUma0mClhQoHV4bgCoNutK0bx/F9KnPGIA+qeVuX7MwmXaXfv2eoYd7aqGJW5pkQQ8MQLfGnE+9wdxwTy00cUsT9EAPDEC3xoCPvfHaANReBEHodCqt59cpQtpjQGag9rXIPVUon0gqo2PBieQIYAAmxzrNLWVhMu2Tfujhnlpo4pYmWdADA9CtMedTbzA33FMLTdzSBD3QAwPQrTHgY2+8NgB18lWuwacTsxRRjD+VU3mV1YbERHIEMACTY53mlrIwmfZJP/RwTy00cUuTLOiBAejWmPOpN5gb7qmFJm5pgh7ogQHo1hjwsTfeG4DVgI4BWA2K8erAAIzHi9L5CWRhMu2T9ujhnlpo4pYmWdADA9CtMedTbzA33FMLTdzSBD3QAwPQrTHgY2+8NgB1JH21YtNNN61WVdQTgQAGYARIFClJIAuT6ZIQHCqAHg6J8f+7giZuaZIFPTAA3RpzPvUGc8M9tdDELU3QAz0wAN0aAz72xmsD0Efg9PlXAhiAjIRqEMjCZLoanJKqAz2SIh29HTSJziqJklnQAwMwiZGUzjYwN9zTFU3c0gQ90AMD0K0x4GNvMAB9VC0FfcYATIGIDtxCFibTDmCO3AX0iIwqsYJokhjqSA1lQQ8MwEhDgUJ5CGBuuDcs0MQtTdADPTAA3RoDPvYGA9BH1VLQZwzAFIjowC1kYTLtAObIXUCPyKgSK4gmiaGO1FAW9MAAjDQUKIQB6MUYwHBySyb0QA8MQLfGgI+9wQD0UbUU9BkDMAUiOnALWZhMO4A5chfQIzKqxAqiSWKoIzWUBT0wACMNBQphAHoxBjCc3JIJPdADA9CtMeBjbzAAfVQtBX3GAEyBiA7cQhYm0w5gjtwF9IiMKrGCaJIY6kgNZUEPDMBIQ4FCGIBejAEMJ7dkQg/0wAB0awz42BsMQB9VS0GfMQBTIKIDt5CFybQDmCN3AT0io0qsIJokhjpSQ1nQAwMw0lCgEAagF2MAw8ktmdADPTAA3RoDPvYGA9BH1VLQZwzAFIjowC1kYTLtAObIXUCPyKgSK4gmiaGO1FAW9Pjss8/MvHnzTKNGjUz37t3t/7saTKbdUgY93NJDvUETtzRBD/RYsmSJ+eSTT4z+v0WLFmbNNdeMDMXX+XfkG6RgJAIYgJEwUajaBHx9AWVh8lZtrWtZH3rUkm78utEjPrNaX4EmtSYcr/4s6DF16lQze/ZsC2bVVVc1yy23XDxICZZmMp0g7AhNoUcESAkXQZOEgZdoDj3QY86cOWby5MkWxLLLLms6deoUGYqv8+/IN0jBSAQwACNholC1Cfj6AsrC5K3aWteyPvSoJd34daNHfGa1vgJNak04Xv1Z0CM8OVF2QpcuXUzTpk3jgUqoNJPphEBHbAY9IoJKsBiaJAg7QlPoEQFSgkWS1kNZf1OmTDGaRyvifsnm6/w7QUkz0RQGYCZkdu8mfX0BZWHy5t5oKdwj9HBLLfRwSw/1Bk3c0iQLemiCMmnSJLNgwQILv1mzZmbFFVc0bdq0Mc2bN3dKkKQnb07dvIOdQQ/3REETtzRBj2zqob+r0v7777+vM/+0vcbaa69tGjduHBmKr/PvyDdIwUgEMAAjYaJQtQn4+gLKwuSt2lrXsj70qCXd+HWjR3xmtb4CTWpNOF79WdFj7ty55quvvjILFy5cCpAmKnEmK/Hoxi+9aNGiuouaNGkSvwKuqCoB9KgqzqpUhiZVwVi1StCjaiirUlFSeqgdmYBByPxT9p+WAMcJX+ffce6RsqUJYACWZkSJGhDw9QWUlclbDSSvSZXoUROsZVeKHmWjq9mFaFIztGVVnCU9lAGopUoyA12N+fPn13XNtexEV5nVsl/oUUu65dWNJuVxq9VV6FErsuXV2xB6lGv+6Q59nX+Xpw5XFSKAAcjYaBACvr6AsjR5a5CBEbNR9IgJrMbF0aPGgMuoHk3KgFbDS7KmhzIWdCKw9gXU/2QKLl68uIaE41X9008/2awKTajiZlLEa4nSUQigRxRKyZZBk2R5l2oNPUoRSvb3SemhDHV9SaVDtbSdRrmZ9L7Ov5NVNf2tYQCmX2Mn79DXF1DWJm9ODp5Qp9DDLYXQwy091Bs0cUsT9EAPtwi41RueD7f04G8IerhHwK0e+fbO8nX+7Zbq/vcmMwbg9OnTzauvvmpmzJhhVlppJbPVVluZ9u3b+6+gp3fg6wvItxe9p8MjcrfRIzKqRAqiRyKYYzWCJrFw1bwwetQccawG0CMWrpoXRo+aI47dAJrERlbTC9CjpnhjV+6bHr7Ov2MLwwVFCXhtAP7yyy/m1ltvtTfYqVMns+++++a92bvuusv87W9/qzuRToWUSnvMMceYk046iSHSAAR8fQH59qJvAGkTbRI9EsVdsjH0KIko8QJokjjyog2iB3q4RcCt3vB8uKWHeoMmbmmCHuhRCQFf59+V3DPX1ifgtQH4zDPPmFNPPdXu3XL++eebAw88sN4dvvjii6Z///55tdd1MgD79evH2EiYgK8vIP7wJjxQSjSHHujhFgH3esMz4pYm6IEebhFwqzc8H27pgQGIHu4RcKtHvr2zfJ1/u6W6/73x2gC8+OKLzf33328NwFdeecW0a9euniK77LKL+eKLL2wZLf3dcMMNzbRp08yECRNsWW2o+dxzz5mOHTv6r6ZHd+DrC8i3F71HQ6KsrqJHWdhqdhF61Axt2RWjSdnoanIhetQEa9mVokfZ6GpyIXrUBGtFlaJJRfiqfjF6VB1pRRX6poev8++KROLiegS8NgD3339/8/7775s111zTPP300/Vubvz48eYvf/mLNf/WXnttc/fdd5u2bdvactdcc4255ZZb7O/IAkz+yfD1BeTbiz55ZZNtET2S5V2qNfQoRSj536NJ8syLtYge6OEWAbd6w/Phlh7qDZq4pQl6oEclBHydf1dyz1xbn4DXBqAO8pg5c6bZaaedzLBhw+rdXdjkGzp0qNl5553ryvz888/2IBDtI7jpppua4cOHMz4SJODrC4g/vAkOkghNoUcESAkWQY8EYUdsCk0igkqoGHokBDpiM+gREVRCxdAjIdAxmkGTGLASKIoeCUCO0YRvevg6/44hCUUjEPDaAFx//fXN/PnzTZ8+fcwll1xS73a1J+C4ceNMq1atzFtvvWWX+4bjyCOPNK+99ppdOqwTgonkCPj6AvLtRZ+cog3TEno0DPdCraKHW3qoN2jilibogR5uEXCrNzwfbunB3xD0cI+AWz3y7Z3l6/zbLdX9743XBuA666xjFi9ebPbbbz8zcODApdRYsGCB2Xjjja1BuNlmmxmdBJwb5557rnn00UdNixYt7FJiIjkCvr6AfHvRJ6dow7SEHg3DHQPQLe7FesMz4pZW6IEebhFwqzc8H27pgQGIHu4RcKtHvr2zfJ1/u6W6/73x2gDU0t2ffvop7xLgd9991xx00EF2jz+d8qt9/nLjggsuMKNGjTLNmjWrOxTEf0n9uANfX0C+vej9GA3l9xI9ymdXiyvRoxZUK6sTTSrjV+2r0aPaRCurDz0q41ftq9Gj2kQrrw9NKmdYzRrQo5o0K6/LNz18nX9XrhQ1hAl4bQDus88+5qOPPjIdOnQwY8eOXUrZIUOGmNtvv90agLfddpvp2bNnPeX79+9vXnzxRbPCCiuYN954g5GRIAFfX0C+vegTlLRBmkKPBsFesFH0cEsP9QZN3NIEPdDDLQJu9Ybnwy09+BuCHu4RcKtHvr2zfJ1/u6W6/73x2gC89NJLzb333mtNviuvvNLsueeeVpHvv//e7LbbbmbWrFl2/z+Zey1btqynVq9evczkyZONlhI/9NBD/qvp0R34+gLy7UXv0ZAoq6voURa2ml2EHjVDW3bFaFI2uppciB41wVp2pehRNrqaXIgeNcFaUaVoUhG+ql+MHlVHWlGFvunh6/y7IpG4uB4Brw3ACRMm2ANAZAA2adLE7LrrrmbFFVc0zz//vPn666/tz/faay8zaNCgejc+Y8YMs/XWW9uf77333uaKK65geCRIwNcXkG8v+gQlbZCm0KNBsBdsFD3c0kO9QRO3NEEP9HCLgFu94flwSw/+hqCHewTc6pFv7yxf599uqe5/b7w2AIX/wgsvNA8++KA1+8KxZMkS07p1a/P444+bTp061VNq5MiR9uAQXacThGUkEskR8PUF5NuLPjlFG6Yl9GgY7oVaRQ+39GDyhh7uEXCrR7yz0MMtAu71hmfELU3QAz0qIeDr/LuSe+ba+gS8NwAXLVpkBg8ebO677z6zcOHCujvs2LGjueqqq8wmm2ySV3dlBn788f9j70ygq6yuvr9vcm/mBAgQwhgiRMYgoOL8ilMtvlJxwDpVK45v1VY/FUVXax0RbIsvtra1ivo6iyNqrVUcqiCKAjJFCBCmQJghkORmuvdb54RcEjLcZ7573/yftVityTn77Of/O2c/nM0ZftQJwLlz51KvXr3QPzxUQGoAwofXw05ioCnwMCCSh0XAw0OxDTYFJgaF8qgYeHgktMFmwMOgUB4VAw+PhDbRDJiYEMuDouDhgcgmmpDGQ+r82wQSFDWggPgEYOM77t27l5YsWUKqY6tLQUaOHEl+v79VCVTZF198Uf9OrRK8+uqrDUiFIk4qIDUASQv0TjLjaAs8eFEBD148lDdgwosJeIAHLwV4eYPxwYsHviHgwU8BXh5Ji1lS59+8qMv3Jm4SgPJRdKw3kBqApAX6eO9V4MGLMHjw4oHJG3jwU4CXR4hZ4MFLAX7eYIzwYgIe4GFHAanzbzvvjLotFUACEL0iJgpIDUD48Maku7TZKHiABy8F+HmDMcKLCXiABy8FeHmD8cGLB/4RCTz4KcDLI2kxS+r8mxd1+d50qARgbW0t7du3jzp37tzm9mD5SGW8gdQAJC3Qy+gN1r0ED+vauVETPNxQ1Z5NMLGnn9O1wcNpRe3ZAw97+jldGzycVtS+PTCxr6GTFsDDSTXt25LGQ+r82z4pWGiqgPgE4KZNm/T7JCUlUY8ePVqlu2HDBpo6dSrNmzdPXxSSkJBAJ5xwAt11111UUFCAHhEDBaQGIGmBPgZoPW0SPDyVO2pj4BFVIs8LgInnkrfbIHiABy8FeHmD8cGLh/IGTHgxAQ/wsKOA1Pm3nXdG3ZYKiE4AqiD485//XL/VpZdeSr/73e9avOHWrVvp/PPP1yv/wuFws9+npqbSc889R0cddRT6hscKSA1A+PB63FGiNAce4MFLAX7eYIzwYgIe4MFLAV7eYHzw4oEEIHjwU4CXR9JiltT5Ny/q8r0RnQCcOXMmPfnkk+Tz+Wj27Nk0fPjwFkR+/etf07///W9d5vAEoCrcr18/+uCDDygQCMinKegNpAYgaYFeUJew5Cp4WJLNtUrg4Zq0lg2DiWXpXKkIHq7IatkoeFiWzpWK4OGKrLaMgokt+RyvDB6OS2rLoDQeUufftiChcgsFRCcAr7nmGr2tNzs7m+bPn9/i5bZt20Zjx47VP09JSaEHHniATj/9dFKrAu+++25avny5TgxOmzaNfvazn6F7eKiA1AAkLdB7iDQmTYFHTGRvs1Hw4MVDeQMmvJiAB3jwUoCXNxgfvHjgGwIe/BTg5ZG0mCV1/s2LunxvRCcAf/KTn5A6A1Cd5zdr1qwWNF566SV68MEHdZLvlltuoV/96leRMiUlJTRu3Dj9u7PPPpsef/xx12nOnTuX3n33XZ143LFjB2VkZFBeXh6deeaZdMkll+j/dvtRic+333470szNN9+stfH6kRqApAV6r7l63R54eK14++2BBy8emLyBBz8FeHmEmAUevBTg5w3GCC8m4AEedhSQOv+2886o21IB0QnA0aNHU1VVFU2YMEFf8nH403T77+eff97ikpCLLrpIJ+NUEu6jjz5yrX9UVFTQHXfcQZ9++mmbbfTs2VMnIUeOHOmaH1988QVdf/31zewjAWhObnx4zenldmnwcFthc/bBw5xeXpQGEy9UNt4GeBjXyouS4OGFysbbAA/jWnlVEky8UtpYO+BhTCevSknjgQSgVz2DdzuiE4DDhg2jUCikV8/dd999LZQ++eSTadeuXTRw4EB67733Wvz+tttuow8//JDS09Pp+++/d4VUfX093XDDDfTll19q+926daOJEydqn9TFJO+//z4tWrRI/65Tp070yiuv0IABAxz3RQ34c889V29/TktLo8rKSt0GEoDmpJYW6M29nbzS4MGLGXjw4qG8ARNeTMADPHgpwMsbjA9ePPANAQ9+CvDySFrMQgKQV/+JlTeiE4CjRo2iYDCoz+9T5/g1fTZu3Ehqi7Da4qtuCv7973/fQmOVNHzttdcoMTGRVqxY4QqDV199NZKcVEm/559/XicBmz7K98YtzMcccwyprctOP+qGZPWuaqXhT3/6U3r22WeRALQgsrRAb+EVRVUBD164wIMXD0zewIOfArw8QswCD14K8PMGY4QXE/AADzsKIAFoR734qSs6AajOzistLSW1EvCNN95oRqXp+X+PPvoonXfeeS2o3X777foGYLdWAKrVf6eeeqo+7089b731lvb18EeVu/DCC6moqEj/6plnniG1etGp5+uvv6arr75a34L8t7/9TW97/vOf/4wEoAWB8eG1IJqLVcDDRXEtmAYPC6K5XAVMXBbYpHnwMCmYy8XBw2WBTZoHD5OCeVAcTDwQ2UQT4GFCLA+KSuOBBKAHnUJAE6ITgOryio8//liv4PvnP/+pz/JrfNQ222XLlukVgJ999hnl5ua2wKFWBv7www+Un5+vtwI7/SxYsICuuuoqbXbMmDH0wgsvtNnEm2++Sffcc4/+/QUXXNDqmYZW/FNnJI4fP15flnLOOefQjBkz6IknnkAC0IqY2E5nUTX3qkn78LqnBA/L4MGDQ1MvwIQXE/AAD14K8PIG44MXD+UNmPBiAh7gYUcBJADtqBc/dUUnANWNunfddZdO8qnttffeey916dKF1LZbdZae+vmIESP01tfDn9raWjr22GOpurqaTjvtNHryyScdpzp9+nS9mk896hKQ6667rs021CrBxlV/aovwvHnzHPHnoYce0onHzp0769WOyjYSgNalxYfXunZu1AQPN1S1bhM8rGvnVk0wcUtZa3bBw5pubtUCD7eUtWYXPKzp5mYtMHFTXfO2wcO8Zm7WkMYDCUA3e4Mc26ITgHV1dfr8v5KSkhaKq+2uKgGokl1qq/Dhz3/+8x99I64q85vf/IZuvPFGx6lde+21kcs/1Nl/xx9/fLttjB07Vl/SoR61bTc7O9uWT+pykcsvv1xflPLII4/obcbqQQLQuqzSAr31N5VREzx4cQIPXjyUN2DCiwl4gAcvBXh5g/HBiwe+IeDBTwFeHkmLWUgA8uo/sfJGdAJQiaaSf+p8u7KyshYa/uIXv9CrAlt7Gs//UwnAl19+mdSFIk4/Z5xxBm3evFmbnTt3LvXp06fdJq644gpauHChLqPOMFQXglh91MpGde6h0ueEE06g5557LmIKCUCrqmIybV05d2pK+/C6owIfq+DBh0WjJ2DCiwl4gAcvBXh5g/HBiwcSgODBTwFeHkmLWUgA8uo/sfJGfAJQCVdRUUHqDL3vvvtO/3913t+4cePavEhjz549dMkll5DaBqwuAJkzZ45eCej0o87927dvnzarVuOpttp7br75Zn2moXrUZR1qa7LVp3H7cUpKCr333nvUr18/1glAdY5jQkKC1df1rJ7qM41PIBDwrF001LoC4MGrZ4AHLx7KGzDhxQQ8wIOXAry8wfjgxQPfEPDgpwAvj6TFLLUrUF0+2vgMGjSIMjIyeIkKb1xXIC4SgK6rZLGB4cOHRyZfK1asIL/f364ltSrx/fff12X++Mc/0rnnnmupZfWvESrBqQb4nXfeSWorctOH4wpASy+KSlAACkABKAAFoAAUgAJQAApAASgABaCAKQWQADQlV9wURgLQRZSxSADW1NTos/5Wr15Nw4YNo9mzZ+tbkrknALEC0MWOGMempf3LWxyj0K8GHvwIgwkvJuABHrwU4OUNxgcvHviugwc/BXh5JC1mYQUgr/4TK2+QAHRR+VhsAf7f//1ffaOxSqip5J9KAh7+cFwBKOVfIKSd9eBi92ZhGjxYYIg4AR68eChvwIQXE/AAD14K8PIG44MXD3xDwIOfArw8khazcAYgr/4TK2+QAHRRea8vAfnxxx/poosu0qtwrrnmGpo8eXKrb4cEoHXo0gK99TeVURM8eHECD148MHkDD5LIy6QAACAASURBVH4K8PIIMQs8eCnAzxuMEV5MwAM87CiABKAd9eKnbtwlANUlICoRpi76UP8/HA4bojVhwgRD5cwUUmfvffnll7rK888/T8cff3y71ceOHUtbt27VZb7++mvKzs420xw98sgjuh11mcb1119PycnJrdZfsGABffPNN/p3xx13XMSv/Px8fXmKF4/UAIQPrxe9w3gb4GFcKy9KgocXKptrA0zM6eV2afBwW2Fz9sHDnF5ulwYPtxU2bx9MzGvmZg3wcFNd87al8ZA6/zZPBjXaUyBuEoDqptsXX3yRli1bZjjp1yiMugF45cqVjveUxpt4leE77riDrrvuujbb2LlzJ5100kn69127dqX58+eb9ufhhx+m//u//zNdr7GCWrGotg978UgNQNICvRcsY9kGeMRS/ZZtgwcvHsobMOHFBDzAg5cCvLzB+ODFA98Q8OCnAC+PpMUsqfNvXtTleyM+ARgMBunWW2+lL774QtNob8WfSvS19nv186KiIsdpqlV8v/zlL7VddR7gCy+80GYbb775Jt1zzz369xdccAFNnTrVtD9IAJqWzHQFaYHe9AsKqwAevICBBy8emLyBBz8FeHmEmAUevBTg5w3GCC8m4AEedhRAAtCOevFTV3wC8Pbbb6cPPvhAE1FbXtWW1s2bN9O6detIJfbU1l61Fbi0tJRWrVpFdXV1+uepqan0k5/8RP9/9VhJuEXrBvX19XTqqafSjh07dNG33nqr1Us5VDl1c29jEvLpp5+mU045JZp5y7/HGYCWpcNqGuvSuVITfxFyRVbLRsHDsnSuVQQT16S1ZBg8LMnmWiXwcE1aS4bBw5JsrlYCE1flNW0cPExL5moFaTyQAHS1O4gxLjoB+MMPP9DPf/5zncTr168fzZo1i3r37k0PPvggvfTSS/rnTVf2qU7/+uuv01/+8heqrKykk08+mWbMmEEZGRmuAXv55Zfp/vvv1/YLCgr0GX1qi2/TZ9q0adp39YwePZpeeeWVVv1RCcQpU6bo30VbUdjeCyEBaB23tEBv/U1l1AQPXpzAgxcP5Q2Y8GICHuDBSwFe3mB88OKBbwh48FOAl0fSYhYSgLz6T6y8EZ0A/P3vf0+vvvqqTvSp8/+OPvporWNbCcBGkdevX09XXnmlXpl31lln0cyZM13TX604VBdyzJs3T7fRvXt3mjhxIg0cOJD27t2rVy9+//33+ndZWVmkEoYqUdjagwSga5gMG5YW6A2/mNCC4MELHHjw4oHJG3jwU4CXR4hZ4MFLAX7eYIzwYgIe4GFHASQA7agXP3VFJwDHjx9PxcXFlJeXRx999FGESrQEoCqozue7+uqrdfLwr3/9K6kbeN161GBTl4B89tlnbTaRm5urVyOqFYBtPUgAukXIuF18eI1r5UVJ8PBCZeNtgIdxrbwqCSZeKW2sHfAwppNXpcDDK6WNtQMexnTyshSYeKl29LbAI7pGXpaQxgMJQC97B9+2RCcA1Xl/5eXldPbZZ9Pjjz8eUfmhhx7SKwJVck9tE05KSmqVgEogrlmzhsaNG0d/+tOfXKf0ySef0LvvvqtvKt61axelp6frrctqFeIll1xCmZmZ7fqABKDriKI2IC3QR30h4QXAgxdA8ODFQ3kDJryYgAd48FKAlzcYH7x44BsCHvwU4OWRtJiFBCCv/hMrb0QnAIcPH07qAg11DqDaDtz4PPbYY/TMM8/oBKDaepudnd2qvnfddZdOyB2+gjBWMDpSu1IDkLRAH+99Cjx4EQYPXjwweQMPfgrw8ggxCzx4KcDPG4wRXkzAAzzsKCB1/m3nnVG3pQKiE4DHHHOMvuFX3aCrVv01Pn//+9/1dlqVAJw9ezapRGFrz+TJk2nOnDn6RuDFixejf3iogNQAhA+vh53EQFPgYUAkD4uAh4diG2wKTAwK5VEx8PBIaIPNgIdBoTwqBh4eCW2iGTAxIZYHRcHDA5FNNCGNh9T5twkkKGpAAdEJwMYtvGeccQb9+c9/jryuOg/wN7/5jU4A3nvvvXTFFVe0KsUFF1xAK1euRALQQEdxuojUACQt0DvNjZs98OBFBDx48VDegAkvJuABHrwU4OUNxgcvHviGgAc/BXh5JC1mSZ1/86Iu3xvRCcDbb79d36I7YMAA/b+Nz9atW+n000/X/6lu23377bfJ7/c3o/XVV1/Rtddeq5OE6tZdtRIQj3cKSA1A0gK9d0Rj0xJ4xEb3tloFD148MHkDD34K8PIIMQs8eCnAzxuMEV5MwAM87Cggdf5t551Rt6UCohOAr7zyCt1///2UkJBA8+fPp86dO0feUK36++6773SC75RTTqFbb71VJ/qCwSDNnTuXHn30Udq7d6/+/fXXX0+33XYb+oeHCkgNQPjwethJDDQFHgZE8rAIeHgotsGmwMSgUB4VAw+PhDbYDHgYFMqjYuDhkdAmmgETE2J5UBQ8PBDZRBPSeEidf5tAgqIGFBCdANyyZQup7b/qmTp1Kk2YMCHyyupMv8suu6xdCcLhsL4g5P3332/zohADGqKIBQWkBiBpgd4CGlFVwIMXLvDgxUN5Aya8mIAHePBSgJc3GB+8eOAbAh78FODlkbSYJXX+zYu6fG9EJwCV/NOmTaNt27bpiz4mTZrUjMgbb7yhbweuq6trlZRK/j355JM0cuRI+SSFvYHUACQt0AvrFqbdBQ/TkrlaATxcldeScTCxJJtrlcDDNWktGQYPS7K5Vgk8XJPWsmEwsSydKxXBwxVZLRuVxkPq/NsyIFRsVQHxCcBoXNeuXUvPPfccLViwgLZv3663C/fp00efEXjVVVdh5V80AV36vdQAJC3Qu4SPjVnwYINCOwIevHiACXjwU4CXR4hZ4MFLAX7eYIzwYgIe4GFHAanzbzvvjLotFYj7BCCg81RAagDCh5dXfwIP8OClAD9vMEZ4MQEP8OClAC9vMD548cA/IoEHPwV4eSQtZkmdf/OiLt8bJADlMxT5BlIDkLRAL7JzmHAaPEyI5UFR8PBAZJNNgIlJwVwuDh4uC2zSPHiYFMzl4uDhssAWzIOJBdFcrAIeLoprwbQ0HlLn3xbQoEo7CiABiO4REwWkBiBpgT4mcD1sFDw8FNtAU+BhQCSPi4CJx4JHaQ48wIOXAry8wfjgxUN5Aya8mIAHMx5LllBtbS0FAgEaIeBOAanzb17U5XuDBKB8hiLfQGoAwoeXV3cDD/DgpQA/bzBGeDEBD/DgpQAvbzA+ePFAAhA8+CnAx6NQdRWVzJhEvvoaCicmUf5tsyghOZWPg614InX+zVpUgc4hASgQWjy4LDUA4S+nvHofeIAHLwX4eYMxwosJeIAHLwV4eYPxwYsHEoDgwU8BPh6VL/o37fzw7xGHup1zI2WNOouPg0gAsmYRS+fYJwDPOOMM1/Xx+Xz0ySefuN4OGjikABKA6A1OKIDJghMqOmcDPJzT0ilLYOKUks7YAQ9ndHTKCng4paQzdsDDGR2dtAImTqpp3xZ42NfQKQubn7mTasrWRcwl5Q6gPtdMd8q8K3akzr9dEaMDG2WfABw8eDCpBJ1bTzgc1vaLiorcagJ2W1FAagDCh5dXdwYP8OClAD9vMEZ4MQEP8OClAC9vMD548VDegAkvJuDBg0f11rVUOmtyC2d6T5pOyT0H8HAyjubfbAUV6piIBKDb2iIB6LbCLe0jAei95vHYIv4ixIsqePDigckbePBTgJdHiFngwUsBft5gjPBiAh7e89j54VNUUbywWcPhmiCFqitbOJOQnEa+pJRmP08vOJa6jbvee8eRAGShOUcn2CcAS0tLPdGtd+/enrSDRhoUQAIQPcEJBfAXISdUdM4GeDinpVOWwMQpJZ2xAx7O6OiUFfBwSkln7ICHMzo6aQVMnFTTvi3wsK+hGQuh2mraMONqCtdWm6nWrKwvkEx5tz1LCYFkyzacqih1/u3U+8NOgwLsE4AAFZ8KSA1A+PDy6o/gAR68FODnDcYILybgAR68FODlDcYHLx7KGzDhxQQ8vOURLC2mLc9NIaKwjYZ91OuXUymld4ENG85UlTr/dubtYaVRASQA0RdiooDUAIQPb0y6S5uNggd48FKAnzcYI7yYgAd48FKAlzcYH7x4IAEIHvwU8MajcKiearatp6qNK6li5Tyq3lJsqWG1JThnwq2UNvBoS/WdriR1/u20Dh3dHhKAHb0HxOj9pQYg/OU0Rh2mjWbBAzx4KcDPG4wRXkzAAzx4KcDLG4wPXjyQAAQPfgq441G4vpaqt66j4MYVOukX3PQjhWuqbDUW6NaHcifeRYHsXrbsOFlZ6vzbSQ1gC1uA0QdipIDUAIS/nMaowyAByEt48BDBA5M3fpjwDeHFBDzAg5cC/LzBGOHFBDyc4aHO9lOr+oIbV+qEX/XmVRSuq3HGOBGlDTqOcsbfQgnJqY7ZdMKQ1Pm3E+8OG4cUEL0CsL6+niZPnkzV1dWUl5dHd955p2G2jz32GG3YsIHS09Np2rRphuuhoDMKSA1A+PA6w98pK+DhlJLO2AEPZ3R00gqYOKmmfVvgYV9DJy2Ah5Nq2rcFHvY1dNoCmDitqD174GFNv1B1FQU3/6gTfsFNRRRUW3rr60wbS0jLolBledR6PS6eQukFx0Qt53UBqfNvr3WK9/ZEJwA//fRT+tWvfkU+n4/uv/9+uvjiiw3zev311+l3v/udrvvUU0/RKaecYrguCtpXQGoAwofXPnsnLYCHk2ratwUe9jV02gKYOK2oPXvgYU8/p2uDh9OK2rMHHvb0c6M2mLihqnWb4GFMu/qqAw2JPpXwUyv8ytYRhUPGKjcpFcjuSSn9hlFKvyGU0m8o7fniVTqw7IuodjIKx1LOz26JWs7rAlLn317rFO/tiU4AqgSeSuQFAgGaP38+ZWZmGua1f/9+OuGEE0itIrz00kt1MhCPdwpIDUD48HrXR4y0BB5GVPKuDHh4p7XRlsDEqFLelAMPb3Q22gp4GFXKm3Lg4Y3OZloBEzNquV8WPFrXuO7A3iYJvxVUs32jpZt7A937UWq/oTrZl9J3KPkzu0QaDNfV0vrHJ1G4ujIqaF9yGvW/dRb5/IGoZb0sIHX+7aVGHaEt0QnACy64gFauXEmFhYU0e/Zs07wmTpxIy5Yto+HDh9Mbb7xhuj4qWFdAagDCh9c6czdqgocbqlq3CR7WtXOrJpi4paw1u+BhTTe3aoGHW8paswse1nRzsxaYuKmuedvg0aBZXfkuqtq4IrLCr3ZXqXkxfQmU1COfUg+u7lMJv8S0thcTVaxeSNtmP9qsnbA/mYL5x1NKyQLy1VU3+x3HbcBS59/m4aJGewqITgAee+yxpDry+PHjafr06aZJqzMD33vvPerUqRN98803puujgnUFpAYgfHitM3ejJni4oap1m+BhXTu3aoKJW8paswse1nRzqxZ4uKWsNbvgYU03N2uBiZvqmrfdEXmEw2Gq27vt4IUdDUm/ur3bzYuXkEjJPQdSal7D6r6UPoMoISXdsJ3tc2Y22/4b6N6X9hZOoJqkTEqqLqfOy96h2p2bI/Y4bgOWOv82DAkFDSkgOgGoVu6pLbzq7D91BqDZ57777qPXXnuN/H4/LV++3Gx1lLehgNQA1BE/vDYwu14VPFyX2FQD4GFKLk8Kg4knMhtuBDwMS+VJQfDwRGbDjYCHYak8KwgmnkltqKGOwEMl/FQiTd/Qu6nhDL/6/bsN6dO0kM+fRMm9C3SyLzVvGCX3PpISAsmm7TRWqFyziLa/94S+BKTxlt/lq4qptrZWH0c2fFCB/n3lqm9IXRaibgFOGzjacntuVJQ6/3ZDi45sU3QC8LjjjqPy8nI6++yz6fHHHzfN8dZbb6V//etf+uzAhQsXmq6PCtYVkBqAOsKH1zpV72uCh/eat9ciePDiobwBE15MwAM8eCnAyxuMD1488A0BDy8UCIfq9Zl9wY0rqOrgLb1Gbto93DdfIIVS+g7Sl3aoc/zUaj+nz+CrryynyuLvKGPEafoi0cNjVjgcogNLP6e0gmMoMS3LC/lMtSF1/m3qJVE4qgKiE4Bq629xcTH16tWL1I3AZp/TTz+dtmzZQv3799eJQDzeKSA1AOEvp971ESMtgYcRlbwrAx7eaW20JTAxqpQ35cDDG52NtgIeRpXyphx4eKOzmVbAxIxa7peNBx7h+jp9K2/jDb3qtt6QgYs1DldXbd9N6dtwO69K+iXn5pMvIdF9CE1akMZD6vzbU6gdoDHRCcDGW4BVBv7pp5+mk046yTCyr776iq699lqdvZ8wYQJNnTrVcF0UtK+A1AAkLdDbJ8XbAnjw4gMevHgob8CEFxPwAA9eCvDyBuODFw98Q8DDCQVCdTVUvWXNoYTf5lUUrg2aNp2Y3qnh7L6Dt/Qm5fQjny/BtB0nK0iLWVLn304ygy0i0QnA//znP3T99dfrJF6fPn30eX7Z2dlRue7atYt+/vOf0+bNm3XdJ554gs4888yo9VDAOQWkBiBpgd45YjwtgQcvLuDBiwcmb+DBTwFeHiFmgQcvBfh5gzHCi4kEHqGaIAVLV1Fww0oKblpJ1aXFFK6vNS1kYmY2pfYbFkn4Bbr21vN2To8EHk31kjr/5sQ8HnwRnQBUAC688EJauXKlZqG2Aj/wwAPtrgScN28eqZWDpaWlOogMHjyY3n777XhgKeodpAYgaYFeVKew4Cx4WBDNxSrg4aK4Fk2DiUXhXKoGHi4Ja9EseFgUzqVq4OGSsDbMgokN8VyoypFHKFhBwU0/Ri7sqN66lihUb/rt/V1yGy7s6DeEUvKGkb9TDruE3+EvxZFHe8JLnX+b7kyo0K4C4hOA6gzASy65hCorK0ndGqSSenl5eTRmzBjq27cvpaWl6d+p1X7ffPMNbdiwQQuiyqanp9PLL79MgwYNQjfxWAGpAUhaoPcYq+fNgYfnkrfbIHjw4qG8ARNeTMADPHgpwMsbjA9ePPANAY/WFFAXYQQ3FlHVxhV6W2/NtvVqZm1arEC3Pnp1n7qwQyX+/FldTduIdQVpMUvq/DvWnOOtffEJQAVEJfbUjb579uzRfNpbHqwSf+rp1KmTvjn4hBNOiDemIt5HagCSFuhFdAYbToKHDfFcqAoeLohq0ySY2BTQ4erg4bCgNs2Bh00BHa4OHg4L6oA5MHFARAdNxIJH3f7dkfP7VNKvdudmC2/ko6Qe/Sml35CGbb19h5A600/6EwsedjSTOv+2886o21KBuEgAqtcqKyujP/zhD/o237q6ujZZ+/1+GjduHN1+++2Um5uLPhEjBaQGIGmBPkZ4PWsWPDyT2lBD4GFIJk8LgYmnckdtDDyiSuRpAfDwVO6ojYFHVIk8LwAmnkveboNLlyyh2tpaCgQCNGLkSMedUwtl6vbtoODB1X1VG1dS3Z4y8+34Eii554DI+X0pfQZTYmqGeTvMa0gbH1Ln38y7gTj34iYB2Kj8tm3b6Ouvv6YlS5aQuuyjoqJCb/Xt2rUrjRw5kk488UTKyckRByreHJYagKQF+njrN4e/D3jwIgwevHgob8CEFxPwAA9eCvDyBuODFw98Q3jxCFVXUcmMSeSrr6FwYhLl3zaLEpJTbTmpEn61u7c0WeG3kurLd5q3meinlF4FlNJ4aUefIykhyZ5v5p3wvoa0mCV1/u092fhuMe4SgPGNK37eTmoAkhbo46fHtP4m4MGLMHjw4oHJG3jwU4CXR4hZ4MFLAX7eYIzwYVK+6N+088O/Rxzqds6NlDXqLFMOhsMhqt2xiao2rNA39Kqz/Oor9pqyoQr7AsmU0meQPrtPneOX3LuAEvxJpu1IryBtfEidf0vvJ9z8RwKQG5EO4o/UACQt0Md7dwIPXoTBgxcPJADBg58CvDxCzAIPXgrw8wZjhA+Tzc/cSTVl6yIOJeUOoD7XTG/XwXConmrKSkht5VUXdgQ3FVEoeMD0S/mS00ht403NG9aQ8MvNJ19iwLSdeKsgbXxInX/HW7+J9fsgARhrAh20fakBSFqgj/fuBR68CIMHLx5IAIIHPwV4eYSYBR68FODnDcYIDybVW9dS6azJLZzpPWm6Pmuv8QnX1ZIq25DwW0HBzT9SuCZo+iUSUjMP3dDbbygl5eSRLyHRtJ14ryBtfEidf8d7P/L6/ZAA9FpxtKcVkBqApAX6eO9u4MGLMHjw4oEEIHjwU4CXR4hZ4MFLAX7eYIx4z2Tnh09RRfHCZg2rJF6ourKFM2plnkrMhevrKFxfS1Tf9kWY7b1JYkaXZgm/QLc+5PMleP/ywlqUNj6kzr+FdQv27iIByB5RfDooNQBJC/Tx2XsOvRV48CIMHrx4IAEIHvwU4OURYhZ48FKAnzcYI94yCdVW04YZV1O4ttrVhv2dciI39Kb2G0r+Lrnk8/lcbTMejUsbH1Ln3/HYd2L5TkgAxlL9Dty21AAkLdDHexcDD16EwYMXDyQAwYOfArw8QswCD14K8PMGY8RbJsHSYtry3BQiCjvacKBrr8iFHTrh16m7o/Y7qjFp40Pq/Luj9i+33hsJQLeUhd12FZAagKQF+njvhuDBizB48OKBBCB48FOAl0eIWeDBSwF+3mCMeMtEXdqx79t/0p7PX2rY0mvxScrp15DwU5d29B1C/owuFi2hWnsKSBsfUuff6IXOKoAEoLN6wppBBaQGIGmB3iAOscXAgxc68ODFAwlA8OCnAC+PELPAg5cC/LzBGHGXSTgcptqdm6lq/TKqWr+UghtWtHrWnxEvElIzqMt/XUoZw06ixNRMI1VQxqYC0saH1Pm3TUyofpgCSACiS8REAakBSFqgjwlcDxsFDw/FNtAUeBgQyeMiYOKx4FGaAw/w4KUAL28wPnjxwD8iucOjbt+Ogwk/lfRbRvUH9thuKG3QcZQz/hZKSE61bQsGjCsgLWZJnX8bJ4KSRhRAAtCISijjuAJSA5C0QO84OGYGwYMXEPDgxQOTN/DgpwAvjxCzwIOXAvy8wRixz6S+spyqNiynqpKGVX51e8rsGz3MQo+Lp1B6wTGO24XB9hWQNj6kzr/RD51VAAlAZ/WENYMKSA1A0gK9QRxii4EHL3TgwYsHEoDgwU8BXh4hZoEHLwX4eYMxYp5JqKaKghuLIqv8aratN32pR6Brb0rtX0g1OzZRcOOKqE5kFI6lnJ/dErUcCjirgLTxIXX+7Sw1WEMCEH0gJgpIDUDSAn1M4HrYKHh4KLaBpsDDgEgeFwETjwWP0hx4gAcvBXh5g/HBiwf+EckYD3VZR/WWNVRZspSC65eRusmXQnXGKh8slZjZlVLzC3XSLzWvkPxZXSlcV0vrH59E4erKqLZ8yWnU/9ZZ5PMHopZFAecUkBazpM6/nSMGS0oBJADRD2KigNQAJC3QxwSuh42Ch4diG2gKPAyI5HERMPFYcCQAeQkOHuAhSgF+zuIb0pJJOByimm0bDl3csbGIwrVBU/ASUjIotf9wnfBL6T+CAtk9yefzNbNRsXohbZv9aLOfhf3JFMw/nlJKFpCvrrrZ77AN2BQCRwpLGx9S59+OwIKRiAJIAKIzxEQBqQFIWqCPCVwPGwUPD8U20BR4GBDJ4yJg4rHgSDjxEhw8wEOUAvycxTeESN3Uq87ta7ypt0rd1FtZbgqWz59EKf2GUGr/ETrpl9SjP/kSEtu1sX3OTDqw7ItImUD3vrS3cALVJGVSUnU5dV72jr5BuPHBNmBTSBwpLG18SJ1/OwILRpAARB+IrQJSA5C0QB9byu63Dh7ua2ymBfAwo5Y3ZcHEG52NtgIeRpXyphx4eKOz0VbAw6hS3pXrqEzq9u+hqg3L9MUdQXVxR/lOc6InJFJyr4ENW3r7j6CU3kea3p5buWYRbX/vCZ1sbLzld/mqYqqtraVAIEDDBxXo31eu+oYS0rL0LcBpA0eb8xOlbSkgbXxInX/bgoTKLRRgvwLwyiuvdB2bWnL9/PPPu94OGjikgNQAJC3Qx3ufAw9ehMGDFw/lDZjwYgIe4MFLAV7eYHzw4tGRviGhYAWplX2Nq/yarqwzSiUpJ+9Qwq/fUEpITjVatc1y6gbhyuLvKGPEaXqL8OFjRG1HPrD0c0orOIYS07JstwcD5hSQFrOkzr/NUUHpaAqwTwAOHjy4xZkI0V7KzO/Vsm4VUIuKisxUQ1mbCkgNQNICvU1M7KuDBy9E4MGLR0eavPFTvnWPMEZ4kQIP8OClAD9v4nWMhOpqqHrTj5Gbequ3riUKh0wB8Hfu0ZDwyx9BqXnDKTG9k6n6VgrHKw8rWnCoI42H1Pk3B9bx5IOIBKBRwRsPT1VJvbae1sogAWhUYefKSQ1A0gK9c8R4WgIPXlzAgxcPJADBg58CvDxCzAIPXgrw8yZexkg4VE8qydewwm+ZTv6p23vNPInpnSnl4MUdaltvoHOOmeqOlI0XHo6IwcCINB5S598MUMeVC+wTgN9++21UwVesWEF/+tOf9JkIqampdPrpp9OoUaOoZ8+elJaWRpWVlVRWVkaLFy+mTz/9VP93UlIS3XbbbTRs2DBtf8yYMVHbQQHnFJAagKQFeueI8bQEHry4gAcvHkgAggc/BXh5hJgFHrwU4OeN1DGiFoOobbw64VeylIIbV1CoutKUwL6kVErNGxbZ1qsu4Tj8pl5TBh0oLJWHA6/O0oQ0HlLn3yzhC3aKfQIwmrafffYZ/eY3v9HJv4suuojuvPNOyspq+wwE1fGnT59Or7/+uk4Czpw5k8aOHRutGfzeYQWkBiBpgd5hbOzMgQcvJODBiwcSgODBTwFeHiFmgQcvBfh5I2mM1O3bEVnhpxJ/9Qf2mBM00U8pfQZHtvUm9xwQ9aZecw3YLy2Jh/235W9BGg+p82/+PUGWh6ITgNu2baPx48fT/v376aqrrqK7777bsPrTpk2jZ599ljp16kTvvvsu5ebmGq6LgvYVkBqApAV6+6R4WwAPXnzAgxcPX4XzAwAAIABJREFUJADBg58CvDxCzAIPXgrw84bzGFEXZFRtWK5v6q1SN/XuKTMnoC+BknOPoNT8QkrpX6iTfwmBZHM2PC7NmYfHUrBoThoPqfNvFrDjyAnRCUC1eu/JJ5+kzp0705dffqmvRDf61NTU0H/913/Rvn376IYbbqBbb73VaFWUc0ABqQFIWqB3ABVrE+DBCw948OKBBCB48FOAl0eIWeDBSwF+3nAaI6GaKgpuLIqs8qvZVmJasEC3Pge39BZSSr9hlJiaYdpGLCtw4hFLHbi0LY2H1Pk3F97x4ofoBOCECRNo1apVdNZZZ+mtvGYftXX4o48+okGDBulVgHi8U0BqAJIW6L0jGpuWwCM2urfVKnjw4oEEIHjwU4CXR4hZ4MFLAX7exHKMqEs6gqXFOuEXVH9KVxOF6k2JlJjZVa/wU5d2qBt7/ZnZpupzKxxLHty04OCPNB5S598cWMeTD6ITgMcddxyVl5fTxIkT6YEHHjDN5Xe/+50+C1BtA/7mm29M10cF6wpIDUDSAr11QjJqggcvTuDBiwcSgODBTwFeHiFmgQcvBfh54+UYCYdDVLNt/cGLO5ZRcFMRhWuDpkRJSM2g1LzhOtmXom7qze4Z84s7TL1AlMJe8nDS73i1JY2H1Pl3vPafWL2X6ASguuk3GAzSiSeeSM8884xpDa+55hqaN2+evjlY3RCMxzsFpAYgaYHeO6KxaQk8YqN7W62CBy8eSACCBz8FeHmEmAUevBTg583SJUv0RYvqmKURI0c66qC6qbduz9aDZ/gt0+f5har2m2rDF0imlL5DIjf1JuX2J58vwZQNSYURs3jRksZD6vybF3X53ohOAI4bN45KSkr0R2nu3LmUk5NjmIi6QOTMM8+kuro6ysvLo3/961+G66KgfQWkBiBpgd4+Kd4WwIMXH/DgxQMJQPDgpwAvjxCzwIOXAry8CVVXUcmMSeSrr6FwYhLl3zaLEpJTbTlZt38PVW1YppN+QXVxR/lOc/YSEim5V8HBm3oLKaX3keRLNH4GvLnG+JVGzOLFRBoPqfNvXtTleyM6Afjwww/TCy+8oJd2jxkzhp566ilKTo5+e5O6AOS6667T235V3SuuuILuvfde+TQFvYHUACQt0AvqEpZcBQ9LsrlWCTxck9ayYTCxLJ0rFcHDFVktGwUPy9K5UhE8XJHVstHyRf+mnR/+PVK/2zk3Utaos0zZqw9WUHDDioMXdyyl2p2bTdVXhZNy+h88x6+QUvoOtZ2ENO0AowoYI4xgEJE0HlLn37yoy/dGdAJww4YN9LOf/YxUQk89BQUFdOedd9Ipp5zSJpmvvvqKHnvsMVq9ejWppecqYThnzhy9ChCPdwpIDUDSAr13RGPTEnjERve2WgUPXjyUN2DCiwl4gAcvBXh5g/HBi8fmZ+6kmrJ1EaeScgdQn2umt+tkqK6Gqjf9GLmpt3rrWqJwyNSL+bvkRm7qVef5JaZ3MlU/ngtjjPCiK42H1Pk3L+ryvRGdAFTyv/TSS/Tggw82O+C1a9euVFhYSL169aKUlBR9TuCWLVto2bJltGvXLk1NJf/U89vf/pYuv/xy+SSFvYHUACQt0AvrFqbdBQ/TkrlaATxcldeScTCxJJtrlcDDNWktGQYPS7K5Vgk8XJPWtGGVuCudNblFvd6TplNyzwGRn4dD9aTKqpt61R+V/FO395p5EtM7H7y0o1Cv9At0Mn6kk5l24qEsxggvitJ4SJ1/86Iu3xvxCUCF4NVXX6WpU6dSdXV1hIja2nv405j0Uz9PSkqiu+++my677DL5FAW+gdQAJC3QC+waplwGD1NyuV4YPFyX2HQDYGJaMlcrgIer8po2Dh6mJXO1Ani4Km+bxnd++BRVFC9s9vtwTZBC1ZUt6iQkp5HPH6BwXa1O9IXr6tSyClOO+5LTKLXfsMi23kC3vnF1U68pMUwWxhgxKZjLxaXxkDr/dhljhzMfFwlARU1tB545cyZ9/PHHkS3BrdFUib+f/OQndNNNN1F+fn6HA87lhaUGIGmBngtvt/wAD7eUtWYXPKzp5mYtMHFTXfO2wcO8Zm7WAA831TVvGzzMa2a3Rqi2mjbMuJrCtYcWUdi1eXh9dUlHcp9BlJo/Qq/0UysIfQmJTjfTIexhjPDCLI2H1Pk3L+ryvYmbBGAjiv3799OiRYuoqKiIdu/eTZWVlZSWlkbZ2dk0ZMgQGj16NGVmZsonJ/wNpAYgaYFeeDeJ6j54RJXI0wLg4anchhoDE0MyeVYIPDyT2lBD4GFIJs8KgYdnUkcaCpYW05bnpphexdeup74ESs49Qq/wS+lfSCl9BlNCIPoljd6/vbwWMUZ4MZPGQ+r8mxd1+d7EXQJQPpKO8QZSA5C0QB/vvQk8eBEGD148lDdgwosJeIAHLwV4eYPxERseFcXf0fZ3HqdwTZVlBwLd+kQu7khRF3ekpFu2hYptK4Axwqt3SOMhdf7Ni7p8b5AAlM9Q5BtIDUDSAr3IzmHCafAwIZYHRcHDA5FNNgEmJgVzuTh4uCywSfPgYVIwl4uDh8sCNzEfqqmiqpJlVLl2MVWtW0x1+3aYblydB9jppIsoc/gp5M/MNl0fFcwrgDFiXjM3a0jjIXX+7SbDjmgbCcCOSJ3BO0sNQNICPQPUrroAHq7Ka9o4eJiWzPUKYOK6xKYaAA9TcrleGDxcl9hUA+BhSi5ThdVFiLU7N+mEn/oT3FhEFFIXeFh70gYdRznjb6GE5FRrBlDLkgIYI5Zkc62SNB5S59+uAeyghuMqAahuAf7yyy/p+++/p61bt1J5eTnV19fT888/3wyv+ggGg0H9M7/fT4FAoIPij91rSw1A0gJ97Ah70zJ4eKOz0VbAw6hS3pUDE++0NtISeBhRybsy4OGd1kZaAg8jKhkvE6quoqr1SyNJv/ryncYrRynZ4+IplF5wjGP2YMiYAhgjxnTyqpQ0HlLn317x7CjtxE0C8JlnnqGnn36a9u7dG2GnEn0+n09fCNL02bNnD5122mmkEoZHHXUUvfrqqx2FN5v3lBqApAV6NsBdcgQ8XBLWolnwsCici9XAxEVxLZgGDwuiuVgFPFwU14Jp8LAgWpMqepXfjo0HE36LKLjpR6JQvWGjiZnZ5PMnUd2esqh1MgrHUs7PbolaDgWcVQBjxFk97VqTxkPq/NsuJ9RvroD4BGBtbS3ddNNNeuWfetTHr+nTWgJQ/f73v/+9Tvyp3//rX/+ivLw89A0PFZAagKQFeg+RxqQp8IiJ7G02Ch68eChvwIQXE/AAD14K8PIG48M8j1B1JVWVNFnlt3+XcSMJiZTSdwilDRil//i79KQN/3sNhasro9rwJadR/1tnkc+PXVRRxXKwAMaIg2I6YEoaD6nzbwdQwUQTBcQnAO+9915688039SslJyfThAkT6Pjjj6f333+f5s6d2+oKQFV20aJFdNlll+nfT5kyha688kp0DA8VkBqApAV6D5HGpCnwiInsSADykr1dbzBGeMECD/DgpQAvbzA+ovNQCx1qtm+gqsaz/DabXOWX1S2S8EvtP6LZGX4VqxfSttmPNnMi7E+mYP7xlFKygHx11c1+h23A0Xk5XQJjxGlF7dmTxkPq/NseJdQ+XAHRCcDly5fTxIkT9Tv16NGD1DbgAQMG6P9+8MEH6aWXXmozAajKHHvssaQGwllnnUUzZ85E7/BQAakBSFqg9xBpTJoCj5jIjgQgL9mRAAQPQQrwchXfEPDgpUDr3oSCFVRZspSq1i6iyrVLqP7AbuNuJ/gppd+hVX6Bbn313Ki1Z/ucmXRg2ReRXwW696W9hROoJimTkqrLqfOyd6h25+bI77EN2DgGp0oiZjmlpDN2pPGQOv92hhasNCogOgH4wAMP0Msvv6w/ZOqijzFjxkTIGkkA/uIXv6CFCxfSwIED9YpBPN4pIDUASQv03hGNTUvgERvd22oVPHjxUN6ACS8m4AEevBTg5Q3GRwMPvcpv23p9lp9K+gU3ryIKhwzD8nfqTqlqW+8Royi1f6Hhm3or1yyi7e89QaHKcmq85Xf5qmJSxy2pCxOHDyrQv69c9Q0lpGXpW4DTBo427BcK2lcAY8S+hk5akMZD6vzbSWawRSQ6Afjf//3ftHbtWjryyCNpzpw5zXgaSQDeeeed9N5771FmZqZOBOLxTgGpAUhaoPeOaGxaAo/Y6I4EIC/d2/MGY4QXK/AAD14K8PKmI4+P+qoDVFXyg17hV7VuMdUf2GMcTqKfUvsNbUj6DRhNga6921zlF81ofWU5VRZ/RxkjTtM2DmcSDofowNLPKa3gGEpMy4pmDr93WIGOPEYcltIRc9J4SJ1/OwILRiIKiE4ANm7hPe+88+jRR5ufWWEkAXjffffRa6+9Rn6/n9R2YjzeKSA1AEkL9N4RjU1L4BEb3ZEA5KU7EoDgIUcBXp7iGwIesVJAJdJqytQqP7WtdzFVl642ucovR5/lp5J+qf2HU0JSqiuvgjHiiqyWjYKHZelcqSiNh9T5tyvwOrBR0QnAESNG6GXp6hxAtR246WMkAdi4AjArK4u+/fbbDtwNvH91qQFIWqD3nqy3LYKHt3pHaw08oink/e/BxHvNkZDlpTl4gAcXBeqr9h+8sXcRVamz/Cr2GnbNlxiglLyheoVf6hEjba3yM9wojpEwI5UnZfFN90Rmw41I4yF1/m0YCAoaUkB0AnDs2LG0bds2Ou200+jJJ580nQC88MILacWKFZSXl0cfffSRIcFQyBkFpAYgaYHeGVp8rYAHLzbgwYuH8gZMeDEBD/DgpQAvb+JtfOhVflvX6RV+levUKr9ic6v8Ovc4eGPvaErJG0YJSSmeA4s3Jp4L6HCD4OGwoDbNSeMhdf5tExOqH6aA6ATgpEmTaP78+ZSdnU1ffvklJSYmRl4v2grArVu30plnnkmhUIjUWYJ/+MMf0Dk8VEBqAJIW6D1EGpOmwCMmsrfZKHjw4oEEIHjwU4CXR4hZ4OG0AvWVapXfkoYLPNapVX77DDfRsMpv2MGtvaMpkN3T8ll+hhuNUhBjxCklnbEDHs7o6JQVaTykzr+d4gU7DQqITgA+++yzNG3aNP1xfOihh0it6Gt8oiUA7777bnrnnXd03alTp9KECRPQJzxUQGoAkhboPUQak6bAIyayIwHIS/Z2vcEY4QULPMCDlwK8vJE4PtQqv+qt6/Rtvfosvy1rzK3y65Krt/Wq8/z0Kr9AMisoEpmwEtBhZ8DDYUFtmpPGQ+r82yYmVD9MAdEJwL1799JZZ51FqjNnZGTQrFmzqLCwUL9iewnAP//5z6T+qORfTk4OffLJJ/p6ezzeKSA1AEkL9N4RjU1L4BEb3dtqFTx48VDegAkvJuABHrwU4OWNlPGhb8pdt4Sq9NbeJRSqLDcspM+fdHCVX0PST63y4/xIYcJZQyd9Aw8n1bRvSxoPqfNv+6RgoakCohOA6kVeeuklnexTyTyVxLv88sv1ll51u+/s2bP1zxcuXEjbt2+nRYsW0SuvvEIrV66kcDisfzdjxgz66U9/il7hsQJSA5C0QO8xVs+bAw/PJW+3QfDgxQMJQPDgpwAvjxCzwMOIAuFQPVVvXduwrbdxlR+FjVTVZVSST93Wq1b6pfQbym6VX3svgjFiGLMnBcHDE5kNNyKNh9T5t2EgKGhIAfEJQPWWDz/8ML3wwgstzslQST71qERf06fx5zfddBPdcssthoRCIWcVkBqApAV6Z6nxswYevJiABy8eSACCBz8FeHmEmAUebSmgzu5rtsqvar9hsdQqv9T+hQeTfqMo0CXXcF1uBTFGeBEBD/Cwo4DU+bedd0bdlgrERQJQvdYbb7xB06dPp/LyhmX4TZN+jQm/xtfPysqiKVOm0Pnnn48+ESMFpAYgfHhj1GHaaBY8wIOXAvy8wRjhxQQ8wIOXAry8ieX40Kv8tqyhyrWLGlb5bV1HZGaVX9delNp4lp9a5edP4iWuRW9iycSiy3FdDTx44ZXGQ+r8mxd1+d7ETQJQoaioqKA333yTvvjiC1qyZIn+78YnKSmJRowYQWPHjqVLLrlEnxmIJ3YKSA1A0gJ97Ah70zJ4eKOz0VbAw6hS3pUDE++0NtISeBhRybsy4OGd1kZa8ppH3YG9+qZenfQr+YFCVQeMuKnL+ALJDav8jhhFaQNHUaBzD8N1JRX0mokkbWLhK3jEQvW225TGQ+r8mxd1+d7EVQLwcByVlZW0f/9+SktLo8zMTPm04ugNpAYgaYE+jrpMq68CHrwIgwcvHsobMOHFBDzAg5cCvLxxe3w0rPIrpso1i/V5fjVla00JEOjWR1/coc7zS+07lHz++L9A0G0mpgCgML7pzPqAtPEhdf7NDLt4d+I6ASieThy/gNQAJC3Qx3EX0q8GHrwIgwcvHhgj4MFPAV4eIWYx47FkCdXW1upL/UaMHOmIc3UH9jTc1qsu8ChZSqGgmVV+KXqVX2PSL9A5xxGfJBnBGOFFCzzAw44CUuffdt4ZdVsqgAQgekVMFJAagPDhjUl3abNR8AAPXgrw8wZjhBcT8AAPXgrw8SZUXUUlMyaRr76GwolJlH/bLEpITjXtoF7lV7qaKtcsaljlt63ElI1A97464Zd2xChK6TukQ6zya08gxCxT3cf1wuDhusSmGpDGQ+r82xQUFI6qABKAUSVCATcUkBqApAV6N9hxsgkenGhgRSYvGg3eYIzwogIe4MFLAT7elC/6N+388O8Rh7qdcyNljTrLkIN1+3c3OctPrfI7dAZ4NAO+JLXKb0RD0m/AKPJ36h6tSof6PWIWL9zgAR52FJA6/7bzzqjbUgEkANErYqKA1ACED29MukubjYIHePBSgJ83GCO8mIAHePBSgI83m5+5k2rK1M27DU9S7gDqc830Vh0M19dRsHRVw9beNYupZvt6Uy8S6N4vkvBL6TuYfInxf5afKYGaFEbMsqqcO/XAwx1drVqVxkPq/NsqH9RrXQH2CcAhQ4a4zs7n89HKlStdbwcNHFJAagCSFujjvc+BBy/C4MGLh/IGTHgxAQ/w4KUAD2+qt66l0lmTWzjTe9J0Su45QP+8rnwXVa5b3JD0K1lK4epKw877klIpNV+t8htNaQNGkj+rm+G6Hb0gYhavHgAe4GFHAanzbzvvjLotFWCfABw8eDCpBF04HHaNn7JfVFTkmn0YbqmA1ACEDy+v3gwe4MFLAX7eYIzwYgIe4MFLAe+92fnhU1RRvLBZw+GaIIVaSej5AilEPqJwXS1RqN6Us0k5efq2XpX0S+kziHyJflP1UbhBAcQsXj0BPMDDjgJS59923hl1hSYAjYBTSTz1GEkUHl4WCUAjCjtbRmoAwofX2X5g1xp42FXQ2frg4ayeTlgDEydUdM4GeDinpROWwMMJFY3bCNVW04YZV1O4ttp4JYMlfclplJY/oiHpd8Qo8md1NVgTxdpTAGOEV/8AD/Cwo4DU+bedd0ZdgQlAI9BefPFFmjZtGtXW1tKoUaPo/PPPp5EjR1KvXr0oNTWVqqqqaOvWrbRkyRJ655136Pvvv6ekpCS666676PLLLzfSBMo4rIDUAIQPr8MdwaY58LApoMPVwcNhQR0wByYOiOigCfBwUEwHTIGHAyKaMBEsLaYtz01R/1xvolbbRZN65Ouz/FTSL6X3kVjl54iqzY1gjLggqg2T4GFDPBeqSuMhdf7tAroObZL9FuBodJ588kl64okndELvoYceovHjx0erQh988AHdc889VFNTQzfffDPddNNNUeuggLMKSA1A0gK9s9T4WQMPXkzAgxcP5Q2Y8GICHuDBSwHvvTnw4wLaMWem5VWAyX0HU9ZRZ1CqWuWX2cX7F+hgLSJm8QIOHuBhRwGp828774y6LRUQnQBcsWIFTZw4UW/7ffTRR+m8884zzHjOnDk0efJkSkxMpFdffZUKCwsN10VB+wpIDUD48Npn76QF8HBSTfu2wMO+hk5bABOnFbVnDzzs6ed0bfBwWtHW7dXt302Vxd/pP1Xrl1G4rsZ0w/5OOZR7yb2U1K2P6bqoYF0BjBHr2rlREzzcUNW6TWk8pM6/rRNCzdYUEJ0A/O1vf0uzZ8+mvLw8+uijj0wTPvvss2njxo100UUX0YMPPmi6PipYV0BqAJIW6K0TklETPHhxAg9ePJQ3YMKLCXiABy8F3PFG/cN8TVmJTvhVFH9HNWVrbTWUNug4yhl/CyUkp9qyg8rmFUDMMq+ZmzXAw011zduWxkPq/Ns8GdRoTwHRCcCf/vSntGHDBr3td/r06aZJqxWAaiWg1QSi6QZRIaKA1AAkLdDHe5cDD16EwYMXDyQAwYOfArw8Qsxyjoe64CO4frlO+FWu+Y7q9+92zHiPi6dQesExjtmDIeMKYIwY18qLkuDhhcrG2qgM1tL8b3+giqpaSk8N0IljjqK0lICxyjEqJXX+HSO54rZZ0QlAdeFHMBikCy64gB5++GHTkO69915688039UUhixcvNl0fFawrIDUA4cNrnbkbNcHDDVWt2wQP69q5VRNM3FLWml3wsKabW7XAw56ydfv3UOWa7xu29pb8YHxrry+BUvoOplBNkGrK1kV1IqNwLOX87Jao5VDAeQUwRpzX1I5F8LCjnv26anXzsrU76YN5JbRgeRmFQocuNEpI8NEJw3vSOSf1p8IB3cjn89lv0GELUuffDsvQ4c2JTgCOGTOG9u/fT8OHD9dbgc0+F198sd4elZWVRd9++63Z6ihvQwGpAQgfXhvQXagKHi6IasMkeNgQz6WqYOKSsBbNgodF4VyqBh7mhNVbe7etj5znV711jWEDCclp+rbetIJj9M29CYEUWv/4JApXV0a14UtOo/63ziKfn/fqmqgvIrAAxggvaOAROx5rNu+lGa8soo1l+6M60S83k267dDQN7NM5alkvC0idf3upUUdoS3QCUJ3dt3z5cp1hf+2112jEiBGGmakAqhKAqu7QoUP1SkA83ikgNQDhw+tdHzHSEngYUcm7MuDhndZGWwITo0p5Uw48vNHZaCvgEV2pUF2N3tqrz/Nb8z3Vl++MXulgCX+XXJ3wU9t3U/oOIV+iP1K3YvVC2jb70Wa2wv5kCuYfTyklC8hXV93sd9gGbFh2RwtijDgqp21j4GFbQksGFq/aTo889y0Fa+oN109JSqR7fjmGRg3KMVzH7YJS599u69LR7ItOAP7973+nGTNmaGa9evWiWbNmUf/+/aMyVOcGXn311bRlyxadALz11lvphhtuiFoPBZxTQGoAwofXuT7ghCXwcEJF52yAh3NaOmUJTJxS0hk74OGMjk5ZAY/Wlaw7sLfJ1t6lFK4NGpNcbe3tM6hhlV/BMRTo2rvNbXDb58ykA8u+iNgNdO9LewsnUE1SJiVVl1PnZe9Q7c7Nkd9jG7AxBE6XwhhxWlF79sDDnn5WaquVf1P+8pWp5F9jOyoJOPWmk9msBJQ6/7bCDXXaVkB0AlB14nHjxtHOnTtJbUtISUmhX/ziF3TeeefRwIEDW7z12rVr6d1336UXXnhBnx2o6nTv3p0+/PBDysjIQD/xUAGpAQgfXg87iYGmwMOASB4WAQ8PxTbYFJgYFMqjYuDhkdAGmwGPBqH01t7tGw5t7d2itvYeOtuqPTl9SamUNmDkwa29R1NiWqYh9SvXLKLt7z1Bocpyarzld/mqYqqtraVAIEDDBxXo31eu+oYS0rL0LcBpA0cbso1CzimAMeKclk5YAg8nVDRuQ8XGm//wmaFtv21ZzcvNpCfuOI3FmYBS59/GiaGkEQVEJwDVC3733Xd0/fXXU1VVlf4LTOOBm+np6dSzZ0+dFFTJvrKyMlKdvvEvOup/1eUf//jHP+iYY3CzmJHO4mQZqQEIH14ne4F9W+BhX0MnLYCHk2o6YwtMnNHRKSvg4ZSSztjpyDzCdbVUtaFha6/6U2dma2/nnMgqv9R+Q8mXaO1svvrKct12xoiGyfHhPMLhEB1Y+rluKzEtyxnosGJKgY48RkwJ5VFh8PBI6IPNLF2zg+7963zbjT7yPydR4cButu3YNSB1/m33vVG/uQLiE4DqddQ5gJMnT6Z165rfJNb09h2VHGz65Ofn0/Tp06mwsBB9IgYKSA1A+PDGoLO00yR4gAcvBfh5gzHCiwl4gEcsFaiv2Ke39lY03tpbY3BrL/kouc+R+iw/vbW3W19XVrNgfMSyd7TeNpjwYgIe3vJ49PmFNG/pFtuNnnRUL7r7ymNt27FrQOr82+57o34cJgDVK6ktA2+//Ta98cYbOiEYCoVasE5ISNA3Bk+cOFFvE05KSkJ/iJECUgMQPrwx6jBtNAse4MFLAX7eYIzwYgIe4OGlAuofv2t3bNIJP7XSrrp0tYmtvSmUdkTj1t7RlJjeyXXXMT5cl9h0A2BiWjJXK4CHq/I2M14ZrKVLf/shhULGjkNoz7OEBB+98uA4SkuxtlraqbeWOv926v1hp0GBuFgBeDjMiooKWrVqFe3evZsqKyspLS2NsrOzadCgQaS2BuOJvQJSAxA+vLHvO009AA/w4KUAP28wRngxAQ/wcFuBcL3a2rvy0NbefdsNN+nv1L3J1t5h5PN7O1nF+DCMyrOCYOKZ1IYaAg9DMjlSaMPWcn3+n1PPk5NPp749jJ2R6lSbh9uROv92S4+OajcuE4AdFaak95YagPDh5dXLwAM8eCnAzxuMEV5MwAM83FBAn6W3ZlFD0m/dEgrXVBlsxkfJvQt00k9t7w107+fK1l6DzrQ4A9BoPZRzTwHELPe0tWIZPKyoZqxObV2I1pXupZUlu6lo/W5atmYnHaiqNVbZQKk//ua/6Mh+XQyUdK+I1Pm3e4p0TMtIAHZM7jF/a6kBCB/emHedZg6AB3jwUoCfNxgjvJiAB3g4oYDe2rtzs074qe29emtvuOXRN6215QukUOoRRzWc5zfwaE+29hp9Z4zoN+nvAAAgAElEQVQPo0p5Vw5MvNPaSEvgYUQlY2VUcu/H9btpZckunfQr3riHauqMxVFjLTQvhRWAVlRDHTcUQALQDVVhM6oCSABGlQgFDCiAvwgZEMnDIuDhodgGmwITg0J5VAw8PBLaYDOSeKitvcGNRZHz/Or2bjP4lkSJWd0iF3ik5A2jBD/PM7Al8TAsvvCCYMILIHhY46H+0WT7niqd7CsqaUj6bdy2nw67I9SacQO1EhN89DLOADSgFIp4oQASgF6ojDZaKIAEIDqFEwrgL0JOqOicDfBwTkunLIGJU0o6Ywc8nNHRKSvcedRX7qfKtU229lZXGn715F4NW3vVn6ScvJhu7TXqNHceRt8jnsqBCS+a4GGMR319iEq2lNPK9Q2r+1TSb3e50VvPjbVhphRuATajFsq6rUDcJAC3bNlC//znP/X5IaWlpaQSTHV1dYb08/l89Mknnxgqi0LOKIAEoDM6dnQr+IsQrx4AHrx4KG/AhBcT8ACP9hTQW3t3lUYu8AhuXmVia28ypeaPaEj6DTya/BmxPWvKCmmMDyuquVsHTNzV16x18GhdMXVj76oNe/TZfWp1n/r/wZp6s/Lq8uqijqH52fpPgs9Hf3x5kSU7TSs98j8nUeHAbrbt2DUgdf5t971Rv7kC4hOANTU1NHXqVHrttddI/cXJ7KPqqARgUVGR2aoob0MBqQEIH14b0F2oCh4uiGrDJHjYEM+lqmDikrAWzYKHReFcqsaBR7i+joKbiiLn+dXtKTP8tomZ2ZELPFLyhlNCINlwXY4FOfDgqEssfQKTWKrfsm3waNBk596qyFbelet30/ot+yhkPg1AAX+CvphjSP+GhN/g/tmUmXboiASVJ1A3AW8s22+5I+TlZtITd5zGYhW21Pm3ZfFRsVUFxCcAr7vuOvrqq68sJf8aFUEC0PvRITUA4cPrfV9pr0XwAA9eCvDzBmOEFxPwAA+lQH3VAapau5gqihdS1bolFApWGBYmueeAQ1t7e+SzmFQadj5KQYwPp5R0zg6YOKelE5Y6Io/6UJg2lpU3rO5bp27o3aXP87PyqORe4+q+ofldaUCfThTwJ7Zras3mvTTlL19ZWlGYkpRIU286mQb26WzFXcfrSJ1/Oy5EBzcoOgE4Z84cmjx5sv7Lj8rQFxYW0gUXXEBDhw6lzp07k9/vN4y3d+/ehsuioH0FpAagjvjhtU/bPQvg4Z62ViyDhxXV3K0DJu7qa9Y6eJhVzN3yXvKo2bXl0NbeTUXGt/b6k5pv7c3MdleUGFr3kkcMX1NU02DCC1dH4BGsqaPijXsbbuddv1vf1FsZNHas1+G0endPpyH9u+qk35D8bOrdPcPSP5osXrWdHnnuW1NJQJX8u+eXY2jUoBw2nUjq/JuNgHHiiOgE4DXXXEPz5s3TA3nSpEl05513xgmW+H8NqQGoI3x4JfU+8OBFCzx48VDegAkvJuDRcXiEQ/UU3PRjJOlXu3uL4ZdPzFBbe4/WK/1S+xeK39pr9MUxPowq5V05MPFOayMtxSOPPeVBnehTF3Wo1X1rN+8jterP7ONP9NGAPp0Pbuftqv+3c6ZzxyKolYAzXllkaDuw2vZ766Wj2az8a9RS6vzbbF9A+fYVEJ0APPHEE2nPnj3Us2dPfYlHQkICeAtRQGoAiscPr5Au06qb4MGLHnjw4oEEIHjwU4CXR0uXLKHa2loKBAI0YuRI287VByv01t7K4u+ocu1iCgUPGLaZlHtE5Dw/9f/VP253tAffEH7EwYQXE+k8QqEwle440LC67+DtvFt3GT8CoSmN9NRA5Ow+lewr6NeFkgPtb+e1S1PtOFy+dhd9MK+Evl6+ldT7ND6JCT46vrAn/feJ+TR8QFeWMVzq/NsuN9RvroDoBKDa8qtu+h0/fjxNnz4dbAUpIDUASf/wCuoihlwFD0MyeVYIPDyT2nBDYGJYKk8KgocnMhtqJFRdRSUzJpGvvobCiUmUf9ssSkhONVS3aaHa3VupQiX8ir/Tl3lQyNjNkz61tbd/4aFbe7O6mm473ipgfPAjCia8mEjjUVNbT8Wb9kZu51XbefdX1loStUd2mt7Gq87uG9o/W9/Wm5AQu38oUTcPz//2B6qoqiWVjDxxzFGUlhKw9G5eVZI6//ZKn47SjugE4GmnnUZlZWV00UUX0YMPPthRmMXFe0oNQNI+vHHRWdp5CfDgRRg8ePFQ3oAJLybgwYdH+aJ/084P/x5xqNs5N1LWqLOiOqi39m5edWhr767SqHUaCySmd45c4JGaP6LDbO01KhDGh1GlvCsHJt5pbaQl7jz2HajWZ/bpCztKduvkX119yMirNSujEntH9MrSyT6V9FMr/Lp2Mv8PNKYbNlmBO4/DX0fq/NskFhSPooDoBOCNN95In3/+OR133HH0/PPPA7YgBaQGIGmBXlCXsOQqeFiSzbVK4OGatJYNg4ll6VypCB6uyGrJ6OZn7qSasnWRukm5A6jPNa3vJlG39FauW3Jwa+8iClWZ2NrbI//geX7HUnJPtbUXx9W0BQzjw1JXdrUSmLgqr2njnHio7bBbd1boRJ/a0quSfpu3G4+NTV8+NdlPg/O60JCDq/uOzOtC6mfcH048jGgldf5t5N1QxrgCohOAH3/8Md1yyy2UlJSkzwDMyeFzy45xBB2zpNQAJC3Qx3vvAg9ehMGDFw/lDZjwYgIePHhUb11LpbMmt3Cm96TplNxzgP557Z6yyCq/qo0rjW/tTQxQSv/hlF5wjF7t58/qxuOlBXiB8cEPEpjwYRLrLae1dSFaV6pu521Y4acu7dh7oNqSQN06pURW96lVfnk9s0idoSftkTY+pM6/pfUL7v6KTgAqcf/nf/6HPvvsMxo7diw9+eSTuAiEe4876J/UACQt0AvpDpbdBA/L0rlSETxckdWWUTCxJZ/jlcHDcUmjGtz54VNUUbywWblwTZBC1ZUt6voCKUQ+onBdreGEnzKSmN6J0gYevLVXbe1N4rdVLapQDApgfDCAcJgLYBJbJmqV3bK1O/WlEwuWlzW7dEJtkz1heE8656T+VDigm+OXThyoqtXbeRtX963esIdq6sxv51X3GfXvmaW38eoVfvnZlNMlLbbCOtS6tPEhdf7tEC6YOaiA+ASg6shqFeCCBQvomGOOoXvvvZcGDx7MEvDcuXPp3XffpeXLl9OOHTsoIyOD8vLy6Mwzz6RLLrlE/7cTjwpGy5Yt03+Ki4v1Tcnqj7rpLisriwYMGKC3TZ9//vnUu3dvJ5o0bUNqAJIW6E2DEVYBPHgBAw9ePJQ3YMKLCXh4yyNUW00bZlxN4Vprq1Ta8zYpJy9ynl9yr4HY2usAWowPB0R02ASYOCyoCXNrNu+lGa8soo1l+6PW6pebSbddOpoG9ukctWxrBVSicfueqoZk38EVfhvKyil86JJbw3aTAokN23n7N1zYMSivi74kIx4faeND6vw7HvtOLN9JdAJwypQpWjt1E/BHH32kE1zq6du3Lx155JGUmZlpSFufz0ePPPKIobJWClVUVNAdd9xBn376aZvVe/bsSY8//jiNHDnSShPN6owaNYoqK1v+y/bhhtXW6ZtvvpluuOEG222aNSA1AEkL9Ga5SCsPHryIgQcvHsobMOHFBDy85REsLaYtz6m/K1qYxR7uaqKfUvOGH0z6HU2BTjh2xmmaGB9OK2rfHpjY19CKhcWrttMjz31LwRpjt4qrNlKSEumeX46hUYOix6b6+hCVbC2PJPzUtt7d5UErrlKXzOTI7bwq6XdE707kT+wYZ51KGx9S59+WOiYqtamA6ASgWumnkndNH/UvGIf/zAj/oqIiI8VMl6mvr9cJti+//FLX7datG02cOJEGDhxI+/bto/fff58WLVqkf9epUyd65ZVX9Ao9O49KAKamptKIESNo0KBB1KdPH50MVYnS0tJSfXFKY5uqHbWCUiUCvXykBiBpgd5LprFoCzxioXrbbYIHLx7KGzDhxQQ8vOdRWfw9bXv3cQq3suXXiDcp/UdQp6PPptT8oyghGVt7jWhmtQzGh1Xl3KsHJu5p25ZltfJvyl++MpX8a7SlkoBTbzq5xUpAdX7gqg17Imf3rdq4m6qqjScXm/rat0em3sar/gzp35Vyu6ZZmnt7r6zzLUobH1Ln386T69gWxScAncCnEoZuJQBfffVVuu+++7SbKumnbitWScCmz7Rp02jWrFn6R2ob80svvWTrtVavXk0FBQXtBuN33nmH7r77blIJU7/fr1cn9ujRw1a7ZipLDUDSAr0ZJhLLggcvauDBiwcSgODBTwHvPFLn+FWuXUwHiuZRxapviepqTDXu75RDuZfcS0nd+piqh8LWFcA3xLp2btUEE7eUbd2umpfd/IfPDG37bcuzvNxMuu/a4+nH9Xto5fpd+tKO9Vv2UcjCQuiAP4EK+naOXNihVvhlpiV5Kwrj1qSND6nzb8ZdQKRrohOAb7/9tmOiq/PwnH7U6r9TTz1Vn/ennrfeeouGDRvWohlV7sILL4wkIZ955hk6+eSTnXanhb0bb7xRX6Cinocffpguuugi19tsbEBqAJIW6D0DGqOGwCNGwrfRLHjw4qG8ARNeTMDDXR7h+jqqKlmqk36Vq75t9aIPIx6kDTqOcsbfghV/RsRysAzGh4NiOmQKTBwS0qCZpWt20L1/nW+wtPPFVHKv6eq+gX07UcCf6HxDcWJR2viQOv+Ok+7C5jVEJwDZqNiGI+pikquuukr/dsyYMfTCCy+06fKbb75J99xzj/79BRdcQFOnTnX99f74xz/SU089pdu57bbbSCUEvXqkBiBpgd4rnrFqBzxipXzr7YIHLx7KGzDhxQQ8nOcRDtVT1YblVLFyPlWsWkChqgO2G+lx8RRKLzjGth0YMKcAxoc5vbwoDSZeqHyojUefX0jzlm7xrNFe3dIjq/tU4q9394wOu53XiujSxofU+bcVNqjTtgJIALrYO6ZPn05qNZ961CUg1113XZutqVWCjav+1BbhefPmuehZg+lf//rX+vIU9WAFoDG5pQV6Y28ltxR48GIHHrx4IAEIHvwUcMYjlfQLbvqRKlaq7b0LqL5iX3TDvgRKSMuiUMXeqGUzCsdSzs9uiVoOBZxVAN8QZ/V0whqYOKGiMRvqnL5Lf/shhazs1TXQhD/RRwN6dz54YUfD+X2dM5MN1ESRthSQNj6QAERfVgogAehiP7j22msjl3+os/+OP/74dlsbO3Ysbd26VZf5+uuvKTs72zXv1Jl/6uIPtf04OTlZnwF4+NmErjVORFIDkLRA7yZDDrbBgwOFQz6ABy8eSACCBz8FrHsUDoeounQ1HVBJv6Kvqf7AHgPGfJTSdzClDz2J0gYeTZufvt3QZSC+5DTqf+ss8vkDBtpAEacUwDfEKSWdswMmzmkZzdLGsnK66bGGo5mceFKTEmnYgG6kzu1Tq/sK+nWh5AC28zqhbaMNaeND6vzbSWawhQSgq33gjDPOoM2bN+s25s6dq2/jbe+54ooraOHChbqIughEXQhi91H21G3D6qmpqaGysjK9uvCrr77SP1MXgDz00EPkxhmI7fkuNQBJC/R2+w/3+uDBixB48OKhvAETXkzAwxwPdSB+zda1+ky/AyvnU335TkMGknsfSRlDT6L0wSeQP6urrlOxeiFtm/1os/phfzIF84+nlJIF5KurbvY7bAM2JLWjhTA+HJXTEWNg4oiM7RpRK/7WbdlHc7/dSO/PK3GswcduOYUG93dvMYljjgo2JG18SJ1/C+4iLF2PqxWA27Ztow8//JAWLVqkV9KVl5frFW6ffPJJM/GrqqqotLRU/6xTp07UvXt3V+Coc/8ak2/Kp/T09HbbUSvyPv74Y13mb3/7G5122mm2/br44ovphx9+aGFH3Xx87LHH6m3A6n+9fqQGIGmB3muuXrcHHl4r3n574MGLh/IGTHgxAY/oPHTSb/sGvb1Xrfar27steiUiSso9oiHpN+RECnTOaVFn+5yZdGDZF5GfB7r3pb2FE6gmKZOSqsup87J3qHZnwz/aqgfbgA3J7mghjA9H5XTEGJg4ImMzIyrGbd5+gJau2Uk/FO+g5Wt30v7KWscbenLy6dS3R6bjdmHwkALSxofU+Tf6nLMKxEUCsLq6mqZNm0azZ8+murq6iEIqwKpEV1FRUTPVKisrSW233b9/PxUUFNCcOXOcVfWgteHDh1NtbUNAX7FihV5t195z++230/vvv6+LqAs6zj33XNt+tZUAzM3NpUsvvVRfOJKT0/IvyrYbjmLg8ACUmJhICQkJbjdr234jT2UoEMDWINuC2jQAHjYFdLg6eDgsqAPmwMQBER00AR5ti+nbv4MCW5aTv3QFJVTsMqR6fWYO1fUaRnW9h1M4vf2VLonbiil5yTuUUFNJdblDKDjqPKoNH/p7R8AXopTF75C/7EcKJaVR9cgJVN+jwJAfKOSMAhgfzujopBUwcUbNvQfqaF1ZFa3dqv4EaX9VvTOG27CS4CO695I8SkniP7dyVQiXjUsbH6FQSC+OanwGDRpEGRkZLqsE89wUEJ8AVImkK6+8Uif5VMLv8Ke1BKAq89hjj+kLOtTv3377bRo8eLDjbDgkAJu+lEp8bty4UW9HfvbZZ3UCtHPnzjRjxgw68cQTHX//9gwengD0tHE0BgWgABSAAlAACsRcgYSK3ZRUtpKSthZR4oEdhvypT+9KNblDqKbnUApldDNUp7GQr6aSAtuLqab3CCKfr2XdcJiSSpdSbU4BhZPSTNlGYSgABaBAowIVwXoq2Vbd8KesmnYfOLRAxQuVhvZLpYtPbjj+AA8UaEsBJAA7Zt8QnwC88cYb6fPPP9f01CUWKhmoLtt48cUX9cq+thKAP/74I02YMEH//rbbbqPrr7/e8R7AYQtwWy+1adMmuuyyy2j79u16Jdubb75JKgh49WAFoFdKx3c70v7lLb5pUGTFs3pPrJDlQRtjhAeHRi/Ag8hXuZf8B1f6JZaXGQIUSusSWekXysxpPXlnyFLzQuBhQTQXq4CHi+JaNA0mxoQL1oRo/bagXuG3rixIZXtqjFVsUqprlp+6ZQVo1eYq03UPr3DN2bl0RG6qbTsw0L4C0sYHVgCiRysFRCcAFyxYQL/85S91Em/gwIF6VVvjTbYPPvigvkijrQSgenm16m3Pnj16O/Bf//pXx3sEh0tA2nupd955h+666y5d5JxzztErAb16pJ5BIO2sB694xqod8IiV8q23Cx68eChvwIQXk47Ko658Fx0omq/P9aveUmwIij+rm769V53rp873U3+fc/rpqDyc1tEpe+DhlJLO2QGT1rWsqa2novW79Rl+6iy/4k17SV3mYebp2imFjiroTiMGdqMRA7tT9y6pejfbzX/4jDaW7TdjqlnZvNxMeuKO01yJmZaditOK0saH1Pl3nHafmL2W6ATgPffcQ2+99ZY+O079b9NtvEYSgJMmTaL58+dTXl4effTRR45DuPbaa+nLL7/Udp9//nm9MrG9RyUi1eUl6vn6668pO9vdm5t2795NJ5xwgm5PJU7V7cBePVIDkLRA7xXPWLUDHrFSvvV2wYMXD+UNmPBi0pF41B3YQxVFX1NF0XwKbmp+FnNbVBIzsil9yAk66adu8nUj6de07Y7Eg9dIwDdEAg98Qw5Rqq8PUfHmvQ0Jv+KdOvlXWxcyhTEzLUCFA7vppJ/606tbeqsxbs3mvTTlL19RsMb8OYEpSYk09aaTaWCfzqZ8Q2FrCkj7hkidf1ujg1ptKSA6AXj22WfrM+0KCwvp9ddfb/aORhKAavXbu+++qw+//O677xzvJdOnT9fnDKrnjjvuoOuuu67NNnbu3EknnXSS/n3Xrl11YtLtp6amRmunHrVdb/ny5W43GbEvNQBJC/SeAY1RQ+ARI+HbaBY8ePHA5A08vFagvrKcKn5coG/vDW5cSRSOPkFOTO9E6YNPoPShJ1JK3yHk83l3aD1iltc9pP32wIMXj478DVGr+TaUldMPxTtp6Rp1U+8uqqo2d46fSsYNH6BW9zX8ye/ViRLU7RwGnsWrttMjz31rKgmo2rvnl2No1CDvL3c08EpxWURazJI6/47LzhPDlxKdABw9ejRVVVXR+eefT4888ojpBOB9991Hr732mr6d143kl1rFp7Yoq0edB/jCCy+0iVqdwadWNKpH3cw7depU17vF6tWrafz48bqdHj160H/+8x/X22xsQGoAkhboPQMao4bAI0bCIwHIS/h2vMEY4YUqHnnUVx2gilXfUEXRPKoqWWYo6ZeQmkHpg47XK/1S8oaRLyExJqDikUdMhHSoUfBwSEgHzXQUJmrr7dZdFQ0Jv+IdtGztTtp3wNw5fv7EBBrcv0tkW++R/bqQ+pnVR60EnPHKIkPbgdW231svHY2Vf1bFtlhP2viQOv+2iAfV2lBAdAJw5MiRVF1dTRdddBGpFX9NHyMrAG+//Xb64IMPqFOnTvTNN9843knUNdunnnoq7djRcLOd2qY8bNiwFu2ochdeeKG+yVg9Tz/9NJ1yyimO+3O4wQceeECfk6iecePG0eOPP+56m40NSA1A0gK9Z0Bj1BB4xEj4NpoFD148lDdgwotJvPAIVVdSxeqF+ky/ynU/EIWir4xJSE6jtEFjKGPoyZTav5B8if6Yw4kXHjEX0iEHwMMhIR00E89Mdu2riqzwU+f47dhj7vINtZhvQJ/OkYTfkPxsSklyNq6pxKRaffjBvBL6evnWZucMJib46PjCnvTfJ+bT8AFdXT8ywcFuFTempI0PqfPvuOkwTF5EdALw9NNP12fmnXzyyfSPf/zDdAJQrX4rLi7WF4i8//77riB5+eWX6f7779e2CwoK9FmAaotv02fatGk0a9Ys/SO1qvGVV15p1ReVQJwyZYr+XVsrClXd/Px8Ou6449r8EKiEo9qa/Kc//UkfNqsetTpR2fTqkRqApAV6r3jGqh3wiJXyrbcLHrx4IAEIHk4qEKoJUuWa7/X23qo1iyhcXxvVvC8phdILjtWXeaQdMZJ8/kDUOl4WQMzyUu3obYFHdI28LhFPTPZX1tCyNTsjF3ds3n7AtJz9cjMjCT+1vTcj1buYVhmspfnf/kAVVbWUnhqgE8ccRWkp3rVvWqwOUEHa+JA6/+4AXcnTVxSdALzxxhvp888/p8zMTPrqq68oOTk5Il60FYBr166lc889V5dXW24ffvhhV4Svq6uj66+/PnLBRvfu3WnixIk66bh37169AvH777/XbWdlZZFKGKpEYWuPkQTg3XffTW+//Tb17NlT33J85JFH6oSjOuNv//79pLb9zp07l0pLSyNN3HDDDfT//t//c+X92zIqNQBJC/SeQo1BY+ARA9HbaRI8ePFAAhA87CoQqq2mqrWLddJPJf/CtdVRTfr8SZRWcHRD0m/AaEoIHPq7WdTKHhdAzPJY8CjNgQcvHtK/IerMvpUluyKr/NaV7qOD6x4MC90jO+3QTb0F3ahLZorhum4UxBhxQ1XrNqXxkDr/tk4INVtTQHQCUK12U6vr1C1x6pKNa665JvKO0RKAKimnzrxTdWfOnElnnXWWaz1EDTbl32f/n73zgI+ruPb/b3e16l2yJMsdW5a7XGXhgm3A9FADf+CDQwuBF+ADJDwIJCGhBHgEHrxHSV7yzKMFCGn0DrZx70W2hYvcbfXetdrV/zNXllyk1b13b9k5u+d+PnzA7JkzM7/vzKzv2Zk5S5b4rSMrKwvPP/+8sgPQ36MnAKilMyJwKgJ/119/vRZzU22oLkDUFnpToUnojHnIBYV5yMWD+subfGoabxGFOSJ29oljveJ4b9Pudehsb1XtuMPlRszIKcqdfiL454yMUS0jgwEFHjLoZFcbmIddSmuvhxITT4cXuw7W9AT8xH97fV0nnbQ+yQlRyBs1AJNyuhJ3ZKXFaS1qix0lHrYIEuRKqPGg+v4dZMwhVz3pAKBIACICd1VVVcoONxFAO+eccxRI/gKAIvOtCBqKpBsi+Dds2DB89tlnttyb8PXXXytZhwsLC5U2x8XFYejQoUofrr32WmUnY3+PlgCgmNgi+cj69euVOwVFluSamhqInYixsbHKbsDc3FzljsELLrhAtU6rRjzVBYjaQm8VP1n8Mg9ZSHS1g3nIxYOZMA+tCnR6O9ByoBCNO1ehefda+Fqb1Is6XYgZkYf48bOVY77OaLlelNU7wGuWFo3stOHvEDvV1laXzExEcG/f0dqexB079lej3ePV1rHjVuIo7cSRaZg0agDyctIxJDPBlndCXY08yVhmHoH2iXI5ajyovn9THiMytp10AFAIKoJ3Jx9fFcG0iy++GF988QU+/fRTZREXR2JFIo5NmzYpgT/x3+LuO5H9V9y9Z+fddzIOgmC0ieoCRG2hDwZbO+tkHnaqrV4X81DXyG4LZmK34v3XJxOPTp8XrYd2Ksd7m75fA19Lg7pYDqeSwCNu3CzE5c6EK6b/Hy7VHQbXQiYewVVCjtqZhxwcTm6FTEzEu9vhsoaeHX6FxVXKfXh6nki3C+NHpGJSTlfA74xByRDJNKg8MvGgopmV7aTGg+r7t5UMw9E3+QCggPbaa6/hmWeegc/n0/SrjfgCcblc+O1vf6vcx8eP/QpQXYCoLfT2k7W3RuZhr95qtTEPNYXs/5yZ2K95fzUGm0dnpw+th7/vOt77/Rp4m2o1CORA9LBxiB87G3FjCuCKS9JQhoZJsHnQUMm+VjIP+7TWWlOwmZRVN3cl7dhTiW17K1DToH4P6cl9E8G93GEpPTv8xH+7I1xauy+dXbB5SCdIkBtEjQfV9+8gYw656kMiACioiGOvIqB38ODBHkhi9594ujPddn8gjv0K2zPPPDPkgFLpENUFiNpCT2U8BNpO5hGoctaUYx7W6GrEKzMxop75ZYPBQ/wdqO3Ynq6dfkWr4G2o1glTpNMAACAASURBVNSxqMFjlDv94saciYiEFE1lqBkFgwc1jexsL/OwU231uoKRdbamofV4sK8rW68IAOp5xKvfGYOSegJ+40akISYqQo8LqW15jsiFhxoPqu/fclGn35qQCQB2B/q+/fZbLFu2DFu2bEF5eTnEQI+JiVHuvsvLy8P8+fNx/vnnw+l00qdHuAdUFyBqCz3hIaKp6cxDk0y2GTEP26TWXBEz0SyVLYZ28RBBv/bS/WgsWqns9uuoq9DUv6jsHOV4b/zYWYhITNdUhrKRXTwoa2Rn25mHnWr3XZdYOwqLK/HJyv1Ys70UvpOSaDidDpw5YSAumj0cE0emazp1pdajxhYPtheL3X1dAb9DpRquIjjN6eCMeCVhR17OAEwYmY7EuEi1asl+znNELnTUeFB9/5aLOv3WhFQAkD6O8OkB1QWI2kIf6iOKechFmHnIxUO0hpnIxcRKHuLF3VNxSNnpJ/7pqCnV1PnIzBGIF3f6jZ0Fd0qWpjKhYmQlj1DRyM5+MA871e5d194jtXj+nU2agnBDsxJw33VTMWpwsq5Gt7Z3oGh/dU/Ar/hILXQm6kV6coxyf58I+InAX1oSjazjuoTyY8xzxAwVzfNBjQfV92/ziLEnoQAHAHkcBEUBqgsQtYU+KHBtrJR52Ci2hqqYhwaRbDZhJjYLrlKdFTzaK4+gaecqZbefp/KIpg67BwzputNv3GxEpmVrKhOKRlbwCEWd7OoT87BL6d71bN5VjidfW4fWdu1ZdKMjXXj4pnxMyc3w2/AOrw97DtVi694KZYff9wdqIP6fnkfs6Ove4TcpJx0D0+JM2X2opw2y2PIckYVEVzuo8aD6/i0Xdfqt4QAgfYYke0B1AaK20JMcHDoazTx0iGWDKfOwQWSdVTATnYJZbL5tyxZ4PB643W5Mmjw54No8NaVdd/rtXIn28hN3H/fn0J2a3XW8VwT9BgwNuO5QKsjzQy6azCM4PMTOv4deXqEr+NfdUhEEfOrOOT07AcWR4QMl9V2JO/ZWYse+SrS0aQ8qCr/izr4JI9N67vEblpUIcfyYH3oBp1BnRm3Novr+HerjyO7+cQDQbsW5PkUBqgsQtYU+1Icb85CLMPOQi4doDTORh4mvrQX7n78FDm87Ol2RGHHfq3BGaT+65qkr79rpt3MV2kuLNXUsIjmjK5HH2NmIzBwetrtm/InF80PTMLLNiHnYJnVPReLqgLueXaLp2K+/1mUPiMOlc89A4d4qJejX0NyuqyPuCCfGDk+F2N0njvXmDE6Gy8V3tfclIs8RXUPLcmNqPKi+f1sOMswqCLkAYHNzM44ePaoEmDo6OjTjnDFjhmZbNjSuANUFiNpCb5yU3B6Yh1x8mIdcPDgAKBeP+k1fovKz/+lpVPpFdyBxysJ+G9nRUK1k7hW7/dqO7tbUIVdCWtedfuPmIGrgSA769aMar1mahpRtRszDNql7Ktq2twK//MMqWysWu/lyhiT3HOsdMzwVUW6XrW2gWhnPEbnIUeNB9f1bLur0WxMSAUAxmN944w18+umn2LdvH8SvWXoeh8OBnTt36inCtgYVoLoAUVvoDWKSvjjzkAsR85CLBwcA5eJxZPG/o710X0+jIrNGYvCtz/RqpLepDo1Fq9FUtBKth4oAqP+dxhWXrCTxELv9ogaPhsPBu2e00Oc1S4tK9tkwD/u07q7p6dfXY+W2Y5ZXPHxgYs8OvwlnpCE22m15naFYAc8RuahS40H1/Vsu6vRbQz4AKCbeT3/6U1RVVSk09Ab/RBkRACwqEn/J5scuBaguQNQWert4Bqse5hEs5fuul3nIxYMDgPLwaCspxtFXH+jVoEG3PKPs0vM2N6Bp1xrlTr+WgzuATvVL8p2xiYgbU6AE/aKHjIXDyTto9BLnNUuvYtbaMw9r9T3de3OrB9f9+jOIe/vMfgamx/Xs8Js4Mh3JCVFmVxGW/niOyIWdGg+q799yUaffGtIBwLKyMlx88cXKcd/uR1ysPXToUCQlJcHl0v6X4TfffJM+TUI9oLoAUVvoCQ2JgJrKPAKSzbJCzMMyaQN2zEwCli7ggpWf/QlNe9afUr6zvRW+tuZePh0RkV0/XHo9mupzRscjLnemkr03ZvgEDvppUs2/Ec8PgwKaXJx5mCyoirtDpfW48/dLTKt0xrhMzJqYrez0y0iJNc0vOzqhAM8RuUYDNR5U37/lok6/NaQDgI899hjefvttZQdfWloaHnzwQZx33nmIiuJfmWQfmlQXIGoLvezjwGj7mIdRBc0tzzzM1dMMb8zEDBW1+/B52nDw+ZvR6WnTXkjF0hEZg7jcfGWnX8yISXC4+OicWeLy/DBLSXP8MA9zdNTipd3jxeerD+DPH2zXYq7J5rl7zsLooSmabNkoMAV4jgSmm1WlqPGg+v5tFb9w9Us6ALhw4UIcPnwYYtff+++/j5EjR4YrR3L9proAUVvoyQ0MnQ1mHjoFs9iceVgscADumUkAohko0np0D4699pCme/v6rcYVibjcGV1Bv5FT4IyINNAqLupPAZ4fco0N5mEtj2MVjdj4fTk2fl+GwuIqiCCgmc8rD5yNIZkJZrpkX6cpwHNEriFBjQfV92+5qNNvDekA4KRJk+DxeDBnzhz8+c9/pk8jjHpAdQGittCH+pBiHnIRZh5y8RCtYSb2M2nesxHlH7zQ55Ff1dY4XUgquBQpc66G082nGVT1MmjA88OggCYXZx7mCtra1oHC4kol6Lfp+3KUVDWZW8FJ3lxOB95+/EJO7mGZwl2OeY5YLLBO99R4UH3/1omFzVUUIB0AFIE/kfzj6quvhjgOzA8dBaguQNQWejojIrCWMo/AdLOqFPOwStnA/TKTwLULpKS3tUlJ5FG36Qt4yg7ocuFOG4Ssa34Bd2q2rnJsHLgCPD8C186KkszDmKriPtEj5WKXX5kS9NuxrwqeDvWEQsZq7So9Oy8bv/jRDDNcsY9+FOA5ItfwoMaD6vu3XNTpt4Z0APD666/H5s2bIY4C//d//zd9GmHUA6oLELWFPtSHFPOQizDzkIuHaA0zsZ5Jp8+Llv3b0LBtCZp3rUOnxoQeJ7csNncmMn5wN5xRMdY3mGvoUYDnh1yDgXno5yEy+W7dI3b5lWHTrnJU1LRoduJ0AIMzEnCorEFzGX+GT/7bbEwclW7YDzvoXwGeI3KNEGo8qL5/y0WdfmtIBwD/8pe/4PHHH0dqaiq+++47RERE0CcSJj2gugBRW+hDfTgxD7kIMw+5eHAA0Foe7ZVHlKBfY+F38DZWG6os85qHEJcz3ZAPLqxfAV6z9GtmZQnmoa6u2OV3oKS+51jvzv1V8Po61Qset0hJiMLUMRmYNiYTk0cPQHyMG3c9uwSHSgMPAg7LSsCL9y9QkjLyY60CPEes1Vevd2o8qL5/6+XC9v0rQDoA2NzcjAsuuAAVFRW4++678dOf/pR5E1GA6gJEbaEnMhwCbibzCFg6SwoyD0tkNeSUmRiSr1dhb0sjmnauQMO2pWg7tkfVuSPCjc4Oj6pd/MT5yLj0blU7NjBXAZ4f5upp1Bvz6FvBxhYPtuzuusdPHO2trm/VLLXT6cDY4amYdjzoNyI7sVegbu+RWjz08gq0tutPChId6cJTd87BqMHJmtvEhoErwHMkcO2sKEmNB9X3byvYhbNP0gFAAU5MvJtvvhktLS248847cfvtt/NOQAIjmuoCRG2hJzAUDDWReRiSz/TCzMN0SQ07ZCaGJYRyxHff1q4jvrvXqx/xdUYgNmca4sfPRcUnL6OzTf1IniMqFsPvfRUiYMiPfQrw/LBPay01MY8ulXy+Tuw7WoeNu8qwsagcuw7VKP9P65OeFI1pYzMxNTcDeTkDEBejvq5s3lWOJ19bpysIKIJ/D9+Ujym5GVqbxnYGFeA5YlBAk4tT40H1/dtkbGHvjnwAUBAsLi7GAw88gJ07dyItLQ1nn302Ro0ahYSEBM3b0S+//PKwHwx2CkB1AaK20NvJNBh1MY9gqO6/TuYhFw/RGmYSOJP2ikPKTr/G7eKIb42qo8iskUiYNF8J/LliE9C0ez3K/vb0KeU6I6LQOqIA0fvXwNHRdspnfAxYVWLTDXh+mC6pIYfhzKOusQ1bdlcod/lt3lWB2sZT14f+hI1wOTD+jDRMzc3EtLEZGJqp/f3nZL9iJ+Dz72zSdBxYHPu997qpvPPP0IjXXzic54h+tawvQY0H1fdv60mGVw0hEQA8fPgwXnnlFfzrX/9S6Om9g0LYi+AhP/YpQHUBorbQ20c0ODUxj+Do7q9W5iEXDw4A6ufhbWlA444VaBRHfEv2qjpwxSUjfsJZSuAvMmPYKfblH/43GguX9fw/94AhqJ14OdojExDZVo/kwvfhqTzS8zkfA1aV23QDXrNMl9SQw3DiIe7t23u4pucuv92Ha9CpfZMfMlJjlWO908dkKsk3YqLMuQdd3DG4vbgKn6zcj9XbS07ZeehyOlAwcSAunjUCE0am6X7fMjQ4uLCiQDjNEQrIqfGg+v5NYSxQaiP5AOCKFSuU+/9aW0/chyG+vPQ8IgBYVFSkpwjbGlSA6gJEbaE3iEn64sxDLkTMQy4e/LKgjYdyxLd4i3LEt2nPesDb0X9BVwTicmYgYdICxIycDIfT1ad9895NKP/oRfia69Gd5Xf7rj3weDxwu92YkJujfN68ay2csYlKFuDYUVO1NZqtTFGA1yxTZDTNSajzqGlohThqK+7xE7v8GprbNWvnjnBi4sh0JegnkngMGhBveQBOZBhetW4rmlo8yjHiWfl5iI1WP06suVNsqFuBUJ8jugUJcgFqPKi+fwcZc8hVTzoAKI7+XnHFFWhvP/EFmp2djZycHCQmJuq6C/Cpp54KObgyd4jqAkRtoZd5DJjRNuZhhorm+WAe5mlplidm4l/J9vJDaCg8nsW3qVZV8qiBoxAvjviOm6Mc8dXyeJvr0bxnA+IndWXIPJ1HZ6dP2W0YmzMdrthELS7ZxkQFeH6YKKYJrkKNh9frw/cHa7BJCfqVofhInS6VstPjeu7yEzvuoiPN2eWnpxGhxkRP32W0ZR5yUaHGg+r7t1zU6beGdADwwQcfxAcffKD8pXrEiBF44oknMHUq/3pOYVhSXYCoLfQUxoKRNjIPI+qZX5Z5mK+pUY/M5FQFvc3iiO9y5W6/9tJiVXmVI74T53Ud8R0wVNVezYB5qClk7+fMw1691WoLBR5VdS092XpF5t6mVpUdxSeJEhXpwqRR6ZiWK3b5ZWJgepyaZJZ/HgpMLBfJxgqYh41ia6iKGg+q798aULCJDgVIBwDnzZuHsrIyxMTE4PPPP0dmZqaOrrNpMBWgugBRW+iDydiOupmHHSprr4N5aNfKLktmAnR6O9BcvBmNhUvRtHsD4NNwxHd0ftcR3zPy/B7xDYQh8whENevKMA/rtA3EM0Ueng4fig5U9QT9DpTU6+r6kMwE5Viv+GfciDREuvu+UkCXUxONKTIxsfvSuWIeciGhxoPq+7dc1Om3hnQAcMKECfB6vViwYIGSBIQfOgpQXYCoLfR0RkRgLWUegelmVSnmYZWygfsNZybt5QeVe/2ULL5N6kfvorJzlJ1+ceNmwxWj7YivXjLhzEOvVnbYMw87VNZeBxUe5dXN2CiO9RaVYdveCrS0eTV3UiTrmDx6AKYqu/wykJESq7lsMAypMAmGNsGok3kEQ3X/dVLjQfX9Wy7q9FtDOgA4Z84cVFVV4eqrr8Zjjz1Gn0YY9YDqAkRtoQ/1IcU85CLMPOTiIVoTbkzEnXsnjvjuUwXiik85fsR3ASLTB6vaGzUINx5G9bK6PPOwWmF9/mXl0e7xYse+qq6MvbvKcLisUVfHRmQnKgG/aWMzMWZYKkRCDyqPrEyo6Gd2O5mH2Yoa80eNB9X3b2OUuPTpCpAOAC5atAgbNmzgHYAExzXVBYjaQk9waOhqMvPQJZflxszDcol1VxAOTLqP+Irdfs17Nqoe8XW43IjNPX7Ed8QkU4/4qgEKBx5qGsj0OfOQiYZcP1iUVDYpiTtE0K+wuBJt7dp3+YmMuWKX3/QxGZiSm4G0pBi5hNbRGp4jOsSywZR52CCyjiqo8aD6/q0DCZtqUIB0APDdd9/Fb3/7WyQkJGDZsmWIjZV7G70GHmFjQnUBorbQh/qAYh5yEWYecvEQrQllJm1lB3qO+Pqa1e/diho0WrnXL27sLLhi4oMCK5R5BEVQg5UyD4MCmlw8mDxa2zuwvVjs8usK+okAoJ5n1OAkTBuTqRzrzR2aApeLzi6//voZTCZ69A8XW+YhF2lqPKi+f8tFnX5rSAcA29vbleO/u3fvxjXXXINHH32UPpEw6QHVBYjaQh/qw4l5yEWYecjFIxQDgOIuv54jvmX7VQV3JaQiYeI8xE+cb8sRX7UG8RxRU8jez5mHvXqr1WYnj87OThwpb8Sm43f5bd9XBZHQQ+uTEBvZc4+fON6bnBCltSgpOzuZkBImSI1lHkES3k+11HhQff+Wizr91pAOAAr5Dx8+jNtuuw0HDx7EZZddhgceeACpqan0yYR4D6guQNQW+hAfRiG9u4kiO54f8lELBSadXg+a925SdvuJf8PX/1E8R0TkiSO+wyfaesRXbQSEAg+1PlL6nHnIQ6u51YNV67aiqcUDcYR2Vn4eYqPdpjZQ1LFtb2VXxt5d5RDJPLQ+DgcwemgKph2/y2/k4GS4nA6txcna8RyRCx3zYB5GFKD6/m2kz1y2twKkA4AvvfSS0iMxmN9++214PB643W5Mnz4dOTk5ytFgrc9dd92l1ZTtTFCA6gLEX7wmwDfRBfMwUUwTXDEPE0Q02QVlJm2l+7uO+O5YDk1HfAfnKkd848fOgjM6zmQlzXFHmYc5CsjlhXkEl4fYhSfu1/tk5X6s2V4Kn6+zp0FOpwNnThiIi2YPx8SR6XCICJzOR/g/WNqATceP9e7cX4UO74k61Nwlx0cpR3qnjcnA5NEZSIyLVCsScp/zHJELKfNgHkYUoPr+baTPXDbEAoBjxozp9RcC8WUfyF8SioqKeHzYqADVBYi/eG0cJBqqYh4aRLLRhHnYKLbGqqgxEUd8G7Z/h8ZtS9BeflC1l66EtK4jvpMWIDItW9U+2AbUeARbL6vrZx5WK+zf/94jtXj+nU04VNqg2oihWQm477qpGDU4WdVW7CDcsqcCG4vKlOO9VXWtqmW6DUTQccywlJ67/M7IToL4f+H88ByRiz7zYB5GFKD6/m2kz1w2BAOAZkAVAUMOAJqhpHYfVBcg/uLVztgOS+Zhh8ra62Ae2rWyy5ICE+WI756NXUd8izdrOuIbN6YA8ZPmI2bYBKmO+KpxpcBDrQ+h9DnzCA7NzbvK8eRr69CqI7NudKQLD9+Ur2TVPfkRuwb3H6tTEneIgF/RgepTdhKq9TAtKVq5y08k8MgbPQDxMeYeO1arX/bPeY7IRYh5MA8jClB9/zbSZy4bYgHA7iPAZoDlI8BmqKjdB9UFiL94tTO2w5J52KGy9jqYh3at7LKUlYnYrd9+8hHfFvVdQFGDxxw/4numtEd81bjKykOt3aH6OfOwn6zY+ffQyyt0Bf+6WymCgE/dOQeZqbEQQcTuoF9tQ5vmjkS4HBg3Iq0r6Dc2E8OyEgI6OaS5QuKGPEfkAsg8mIcRBai+fxvpM5cNsQAgA6WrANUFiL945RpzzIN5yKWAfK2RbY50NNagcftyZbefp+KQqmCuxHQkTJyPhEnz4E6V/4ivWodk46HW3lD/nHnYS1gE/u96dommY7/+WhbpdsLj8UH7TX7AgJQYZYefuMtv0qh005OL2KuivbXxHLFXb7XamIeaQvZ+To0H1fdve6mGfm2kk4CEPp7Q7SHVBYjaQh+6I6irZ8xDLsLMQy4essyRzg4PmvZuQOPW40d8O339CiWy+MaNPVMJ/EUPnwCHwymfsAG2iOdIgMJZVIx5WCSsH7fb9lbgl39YZXmlES4nJoxM6wn6Dc6I511+AarOcyRA4SwqxjwsEjZAt9R4UH3/DhAPF/OjAAcAeWgERQGqCxC1hT4ocG2slHnYKLaGqpiHBpFsNgkWE+WIb0lxVxbfnSvga2lU7Xn0kLFKMo/4sWfCGRWrak/RIFg8KGplR5uZhx0qn6jj6dfXY+W2Y5ZUOjAtTtnhJ7L2iqzB0VERltQTbk55jshFnHkwDyMKUH3/NtJnLttbAQ4A8qgIigJUFyD+4g3KcPFbKfNgHnIpIF9r7J4jHQ01aNzx3fEjvodVBYlIGoB45YjvfLhTslTtqRvYzYO6Xla3n3lYrfAJ/82tHlz36890Jejor3XuCCfycgb0BP2y0+Pt60wY1cRzRC7YzIN5GFGA6vu3kT5zWQ4A8hiQRAGqCxB/8UoygI43g3kwD7kUkK81dswRX0c7mvdsQMPWJWjZtwVQO+LrjkLcmDOVoF/0sPEhdcRXbQTYwUOtDfz5CQWYh32j4VBpPe78/RLTKvyvn83DGYOSTfPHjvpWgOeIXCODeTAPIwpQff820mcuG+IBwMLCQuVOsCNHjqCpqQkej0cTc4fDgSeffFKTLRuZowDVBYi/eM3hb5YX5mGWkub4YR7m6GimF6uYiCO+bcf2olE54rsSvlYNR3yHjleCfiL454yKMbObZHxZxYOMAJI1lHnYA6SksgkfryjGh8v3m1bhc/echdFDU0zzx444AEhhDPCaJRclajyovn/LRZ1+a0LiCPCnn36KF154AYcPqx838oesqKiIPk1CPaC6AFFb6AkNiYCayjwCks2yQszDMmkDdmw2k46GajRuP37Et/KIarsikjIQP2k+EibOC4sjvmqCmM1DrT7+vH8FmIc1I8Tn68TeI7VYs70Ea3eUGsr666+FrzxwNoZkJljTAfbaowDPEbkGA/NgHkYUoPr+baTPXLa3AuQDgM8++ywWL16s9EzsSAjkETsAOQAYiHKBl6G6APEXb+DMrSjJPKxQNXCfzCNw7awqaQYT5Yjv7vXKvX4t+7ZqOOIb3ZXFd9ICRA8dG1ZHfNU4msFDrQ7+XLsCzEO7VmqWng4vtu2txNrtpUrQr7q+Va1IwJ+7nA68/fiFiI12B+yDC2pTgOeINp3ssmIedimtrR5qPKi+f2ujwVZaFSAdAPzuu+/wk5/8pKevSUlJmD9/PnJyciD+OyJCewawK664QqtmbGeCAlQXIGoLvQmopHbBPOTCwzzk4iFas23LFuU6DLfbjUmTJ2tuYNcR3z1K0K9JOeLbpFo2etiE40d8C+CMDM8jvmoi8RxRU8jez5mHMb0bm9uxoagMa3aUYtP35Whp6zDmUGPp2XnZ+MWPZmi0ZjMjCvAcMaKe+WWZh/maGvFIjQfV928jjLhsbwVIBwBvv/12LFu2DGIH3wUXXIAnnngCcXFxzJmAAlQXIGoLPYGhYKiJzMOQfKYXZh6mS2rIoa+tBfufvwUObzs6XZEYcd+rqnfvddRXoXH7MjRsWwpP1VHV+iOSM5SdfiKTrzs5Q9U+3A14jsg1ApiHfh7l1c1Ys6NE2em3Y18VvD5tp2+S4iMxanAyNn5frr/S00o8+W+zMXFUumE/7EBdAZ4j6hrZacE87FRbvS5qPKi+f6uTYAs9CpAOAM6aNQvV1dUYMGAAvvnmG0RGRurpO9sGUQGqCxC1hT6IiG2pmnnYIrPmSpiHZqlsMazf9CUqP/ufnrrSL7oDiVMW9qrb52k7ccR3/zb1I76R0YgbMwsJefMRPYSP+OqByXNEj1rW2zIPdY3FbuDio3XHj/aWYP+xevVCxy0GDYjDzPEDMXNCFnKHpcLpAO56domhOwGHZSXgxfsXKD/+82O9AjxHrNdYTw3MQ49a1ttS40H1/dt6kuFVA+kAYF5eHtrb23HxxRdD3AXIDx0FqC5A1BZ6OiMisJYyj8B0s6oU87BK2cD8Hln872gv3ddTODJrJAbf+ozyZ+WI79HdJ474tjWrVhI9fGLXEd9cccQ3WtWeDXorwHNErlHBPPrm4enwYXtxpXKXn/insrZFEzgRk8sdmoKZEwZi5visPpN0iOQgD728Aq3tXk0+TzaKjnThqTvnKDsJ+bFHAZ4j9uistRbmoVUpe+yo8aD6/m0PzfCphXQA8Pzzz8ehQ4fwwx/+EI8//nj4UAuBnlJdgKgt9CEwVPrtAvOQizDzkIdHW0kxjr76QK8GZf2/X6Kt7AAaty2Bp/qYaoMjUrKOH/E9C+4kPuKrKpiKAc8RowqaW555nNCzudWDjUXlyvHejUVlaGrVdp+fO8KJyaMHKDv98sdlIiVR/ceBzbvK8eRr63QFAUXw7+Gb8jEll9chc2dB/954jtiptnpdzENdIzstqPGg+v5tJ9NwqIt0APCee+7BF198gfz8fLzxxhvhwCtk+kh1AaK20IfMgPHTEeYhF2HmERwelZ/9CU171p9SeWd7K3wadvX11WJHZAzix4ojvgsQNXgMH7UzESvPERPFNMFVuPMQO/uUXX7bS1BYXIkOr7b7/BJi3ZgxLgsFE7IwZXQGoqO0J93rxiZ2Aj7/ziZNx4HFsd97r5vKO/9MGPN6XYT7HNGrl9X2zMNqhfX5p8aD6vu3PipsraYA6QDg2rVrceONNyrZfr/88ktkZ2er9Zc/l0QBqgsQtYVeEtyWNYN5WCZtQI6ZR0CyGSok7u87+PzN6PS0GfIjCseMmIT4SQsQlzsTTneUYX/soLcCPEfkGhXhxkMc/T9QUt8T9Nt7pE4zkKy02J77/MYNT4XL5dRc1p+haM/24ip8snI/Vm8vge+khCIupwMFEwfi4lkjMGFkGv8QYVjtwByE2xwJTCX7SjEP+7TWUhM1HlTfv7WwYBvtCpAOAIpu/vSnP8W3336LGTNmYPHixZwIRDv7oFpSXYCoLfRBhWxD5czDBpF1VME8dIhlkmnr0T049tpD4lY/Qx4z05CJkAAAIABJREFUr34QcaPzDfngwuoK8BxR18hOi3Dg4fX6sGN/1fEkHqUoq1a/77ObQc6QZCWBR8H4gRialWBpEE4cQV61biuaWjyIi3FjVn4eYqPddg4HrqsPBcJhjlACzzzkokWNB9X3b7mo028N+QBga2sr7r77bixfvhw5OTn42c9+hrPOOgsul4s+nRDuAdUFiNpCH8JDSOka85CLMPMIDo/mPRtR/sELAR35dUbFIuPyexE7alpwGh9mtfIckQt4qPJoaevApl3lytHe9TvL0Nji0SR8hMuJSTnpKBifhfzxWUhLitFUziyjUOVhlj7B8MNMgqG6/zqZB/MwogDV928jfeayvRUgHwAUXRJHCF555RW8+OKLyq+T0dHRGD58OOLj4zX9WinKvP766zw+bFSA6gLEX7w2DhINVTEPDSLZaMI8bBT7eFXe1iY0bPoStWs/hK+5XlcD3OmDkXX1g3Cn8vUZuoQzYMxzxIB4FhQNJR7V9a1Ydzxr79Y9FRCZfLU8YrfdjLGZyk6/qbkZQd11F0o8tGhPwYaZyEWJeTAPIwpQff820mcuG6IBwD/96U/K8d/6+nolGCgeEdTT8gh7YVtUVKTFnG1MUoDqAsRfvCYNAJPcMA+ThDTJDfMwSUgNbjrqKlC37mPUb/kaIuGH3ic2dyYyfnA3nFH27vDR285Qs+c5IhdRyjzE318PlzUcv8+vFLsO1WgWNyMlBjMnDMTM8VkYf0YaxM4/GR7KPGTQz4o2MBMrVA3cJ/MIXDsrSlLjQfX92wp24eyT/A7AX/7yl/jnP//Zw7A7AKgHKgcA9ahlji3VBYjaQm8OLXm9MA+52DAP63m0le5D3ZoP0bhzJdCpbYdPX63KvOYhxOVMt77BXMMpCvAckWtAUOPh9XXi+wPVWLO9RAn8lVQ2aRb0jEFJytFeEfgbkZ2o+YdyzRWYYEiNhwldlt4FM5ELEfNgHkYUoPr+baTPXLa3AqQDgB9//DHuv/9+5S8xIvA3YsQIXHjhhRg9ejSSkpJ03QOYn8+Xn9s5QaguQPzFa+coUa+LeahrZKcF87BGbfH91rJvC+rWfICWA4X9VuKMTYKvWT2zZ/zE+ci49G5rGsxe/SrAc0SuwUGBR2t7BzbvqsDaHV33+dU3tWsSUWTRnTgyXTnaK+7zy0iJ1VQumEYUeARTn2DUzUyCobr/OpkH8zCiANX3byN95rIhFgBctGgR1q9frwQAb7zxRjzwwANwOuU4xsCDrX8FqC5A/MUr18hmHsxDLgXMbU2n14PGHStRt/YDtJcf8u/c4UTcmAIkzrgYpX/9HTrb1LN8OqJiMfzeV+GI4Cyb5lLr3xuvWXaqrV6XrDxqG9qwbmepkrl3y+5ytGu8zy8mKgLTxX1+47MwbWwm4mNozW9ZeaiPpNC1YCZysWUezMOIAlTfv430mcuGWABw+vTpaGpqwqBBg/Dll19y8I/QCKe6APEXr1yDjHkwD7kUMKc1vtYm1G/+CnXrP4G3odqvU4c7Cgl55yAp/2K4U7LQtHs9yv729Cn2nRFRaB1RgOj9a+DoaDvlMz4GbA4vPV54zdKjlvW2MvE4WtGoZO1ds70U3x+sxvErrVVFSEuKVgJ+4miv2PHnjqD7Q7hMPFSFDxMDZiIXaObBPIwoQPX920ifuWyIBQAnT56MtrY2XHbZZXj66VNfehi23ApQXYD4i1euccU8mIdcChhrTUd9JerWfaIE/zrbW/w6c8UlIXH6RUicej5csQk9duUf/jcaC5f1/Nk9YAhqJ16O9sgERLbVI7nwfXgqj/R8zseAjfEKpDSvWYGoZl2ZYPLw+Tqx+1BNz31+R8obNXd0+MDE40G/LIwanCzlfX6aO3OSYTB5BNLecCjDTOSizDyYhxEFqL5/G+kzlw2xAOB5552Hw4cP48orr8Tvfvc75ktIAaoLEH/xyjXImAfzkEuBwFrTVnZAud9PSezh8/p14k7LRtLMyxA/8Sw4IyJ72TXv3YTyj16Er7ke3Vl+t+/aA4/HA7fbjQm5OcrnzbvWwhmbqGQBjh01NbBGc6mAFOA1KyDZLCtkN482jxdb91QoR3vFEV9x1FfL43Q6MH5EmnKfn9jtl5UWp6UYORu7eZATKAgNZiZBEL2fKpkH8zCiANX3byN95rIhFgAUd/59+OGHEDsB3333XeZLSAGqCxB/8co1yJgH85BLAe2tURJ77N/Wldhj/9Z+C0YPGYukgssQmzMNDkf/x/u8zfVo3rMB8ZMWKLuCTp8jnZ0+NG5bitic6XDFJmpvMFuaogCvWabIaJoTO3iIpB3rxX1+O0qxaVc52tr9B/lP7lh0pAtTx2Rg5viBmDEuEwmxvYP+pgkhiSM7eEjSVTLNYCZyoWIezMOIAlTfv430mcuGWABww4YNuOGGG5Rsv//85z+Rm5vLjIkoQHUB4i9euQYY82Aecimg3ppOb4ey069uzYdoLz/gv4BI7JE7E0kFlyJ60Gh1x34seI4ELJ0lBZmHJbIG5LS51YNV67aiqcWDuBg3ZuXnITbanKQZpVVNyl1+InPvzn1V8HVqa2JKQpSSsbdgwkBMGpWOSLdLW8EQseL5IR9IZiIXE+bBPIwoQPX920ifuWyIBQBFd5588km88cYbGDVqlPLv1NRU5kxAAaoLEH/xyjW4mAfzkEsB/63xtTWjfvPXqFv3MbwNVX4NHRGRSMg7G0kzf6Ak9jD68BwxqqC55ZmHuXrq9SZ23hYWV+KTlfuVAJ24h6/7Ecdsz5wwEBfNHq4k0xA7aLU+ws/eI7XKLj+RyONgaYPWohiSGa8E/MTR3pwhKRDtCNeH54d85JmJXEyYB/MwogDV928jfeayIRgA9Pl8eOGFF/DnP/9ZCf7dcccdEHcDZmZmMm+JFaC6APEXr1yDinkwD7kU6N2ajvoqJZuvktijrdlvc8WdfEnTL0TitAtMPZrLc0SuEcI8gsdDBOief2cTDmkIzg3NSsB9101VEmz4ezwdXmzbW6nc5ycCf9X1rZo6J+J7Y4anKkd7CyZkIXtAvKZy4WDE80M+ysxELibMg3kYUYDq+7eRPnPZEAsAnnPOOT09KisrQ0dHR88vtgkJCYiPj9f0C674lffrr7/m8WGjAlQXIP7itXGQaKiKeWgQyUYT5nFC7Pbyg6hd8yEadyzvP7FH6kAkzbwU8RPnwemOMp0WMzFdUkMOmYch+QIuvHlXOZ58bR1aNd6/JyoSd/A9fFM+puRm9NTb2NyODUVlWCPu8/u+HC1tHZraJI7yThk9QAn4zRiXhaR48+e6poZIbsTzQz5AzEQuJsyDeRhRgOr7t5E+c9kQCwCOGTOmzwCfOOIhHi3HN4StsCsqKuLxYaMCVBcg/uK1cZBoqIp5aBDJRpNw5yG+T1oPFCqBv5Z9m/tVPmrwGCQXXIrY0TNUE3sYQRjuTIxoZ0VZ5mGFqv37FDv/Hnp5ha7gX7dHEQR84IbpKKluUnb67dhXBa/GC/2S4iORP64ra2/e6AGIjoywv/PEauT5IR8wZiIXE+bBPIwoQPX920ifuWwIBgDNgMoBQDNU1OeD6gLEX7z6OFttzTysVlif/3DlIRJ7NBWtRu2aD9Betr8f0RyIzc1HcsFliB5sT9KqcGWib+TaZ8087NNa1CSC8nc9u0TTsV8zWjZoQJxytHfmhCzkDkuFK4zv8wtET54fgahmbRlmYq2+er0zD72KWWtPjQfV929rKYafd0dn93Y5gn0/evSoaa0eNGiQab7YkboCVBcgagu9OgnaFsxDLn7hxsPX1oL6LV+jft3H6Kiv9AtDJPaInzQfySKxR2q2rdDCjYmt4gZQGfMIQDQDRbbtrcAv/7DKgIf+i4o8IaOHpvQk8RiSmWBZXeHgmOeHfJSZiVxMmAfzMKIA1fdvI33msr0VIB0AZKB0FaC6APEXr1xjjnkwj2Ao0NFQrST2aNj0JUR2X3+PMyYBidMvRJJI7BGXFIymgudIUGT3WynzsJfH06+vx8ptx0yt1B3hRF5O131+4ohvSmK0qf7D2RnPD/noMxO5mDAP5mFEAarv30b6zGU5AMhjQBIFqC5A/MUryQA63gzmwTzsVKC94hBq13yExu3fAT7/l/9HpGQpu/3iJy2wJLGHnj7zHNGjlvW2zMN6jbtraG714Lpffwafxjv71Fp21pRBmD0pW0kKEhPF9/mp6RXI5zw/AlHN2jLMxFp99XpnHnoVs9aeGg+q79/WUgw/77wDMPyYS9FjqgsQtYVeCtgWNoJ5WChuAK5DkYeS2OPg9q7EHsWb+lUlatBo5X4/JbGH0xWAguYXCUUm5qtkn0fmYZ/Wh0rrcefvl5hW4SsPnA0+4muanH064vlhrb6BeGcmgahmXRnmYZ22gXimxoPq+3cgbLiMfwU4AMijIygKUF2AqC30QYFrY6XMw0axNVQVSjw6fd7jiT0+RHtpcT+9dyB29HQkF1yO6CFjNKhkr0koMbFXOWtqYx7W6Hq6V7H774Pv9uHtL743rcLn7jlLue+PH+sU4PlhnbaBemYmgSpnTTnmYY2ugXqlxoPq+3egfLhc3wqEVACwubkZH3/8MdauXYsdO3agpqYGTU1NiIuLQ0pKCsaPH4+CggJccskliImJ4TERRAWoLkDUFvogIralauZhi8yaKwkFHr72FjRs+QZ1IrFHXYXfvjtcbiWxR9LMHyAyTd4kUqHARPMAJGDIPKyDJIJ+63aUYsXWY9i0qxyeDp+plfEOQFPl7NMZzw/rNdZbAzPRq5i19szDWn31eqfGg+r7t14ubN+/AiETAHzttdfw8ssvQwzs7ufkBMcOkart+BMfH4+77roLN954I4+PIClAdQGittAHCa9t1TIP26TWVBFlHh2NNahf/ynqRWKP1hPfI6d33BkTj8RpFyBp+kVBS+yhCcZxI8pM9PSTii3zMJeU1UG/7ta6nA68/fiFiI12m9sB9naKAjw/5BsQzEQuJsyDeRhRgOr7t5E+c9neCpAPAPp8Ptx777346quvcHLATw22CAguXLgQ//Vf/4WTg4Nq5fhzcxSgugDxF685/M3ywjzMUtIcPxR5tFceQd2aD9GwfRng7SexR3KmstsvQST2iKST9ZMiE3NGo5xemIdxLk0tHqzbWYqVFu3066uFs/Oy8YsfzTDeePbQrwI8P+QbIMxELibMg3kYUYDq+7eRPnPZEAwAPvXUU3j99deVIJ4IAIqjvhdffDGmTp2KwYMHK0d9W1pacOTIEWzevBmffvopqqqqFCVEGbEL8Be/+AWPDZsVoLoA8RevzQNFpTrmwTwCUUBJ7HF4J+pWf4DmvRv7dRGVnYOkgssQl5svTWIPPX3mOaJHLettmUdgGoug39odJ4J+HV71471i197IQUnYfbg2sEpPKvXkv83GxFHphv2wg/4V4Pkh3whhJnIxYR7Mw4gCVN+/jfSZy4ZYALC4uBiXXnopxC5A8UK3aNEi/OxnP+v3fr/W1lY8//zzStBQPBEREfjggw8wcuRIHh82KkB1AeIvXhsHiYaqmIcGkWw0kZ2Hkthj11ol8NdWsrdfZWJzpiuBv+ghY0nvEpediY3DU4qqmId2DI1ip9+OEuVOv827KqAl6BfhciAvZwDm5GVj5oSBiI9x465nl+BQaYP2ik+zHJaVgBfvX0B6HQi48zYX5Plhs+AaqmMmGkSy0YR52Ci2hqqo8aD6/q0BBZvoUID0EeBnnnkGr776qvKXsltvvRX333+/5q4/99xz+POf/6yUvfnmm/HAAw9oLsuGxhWgugBRW+iNk5LbA/OQi4+sPHztrWjY+i3q1n2Ejtpy/6K5IpAw8Xhij/TBcokbYGtkZRJgd8gXYx79IxRBv7Xbu4J+W3aXo8PbqcpcBP0mj87A7EnZKJiQhfjYyFPK7D1Si4deXoHWdq+qr9MNoiNdeOrOORg1OFl3WS6gXwGeH/o1s7oEM7FaYX3+mYc+vay2psaD6vu31RzDzT/pAOAVV1yBoqIiJCcnY/ny5XC7tV/O7PF4MHfuXNTV1WHMmDH417/+FW7sg9pfqgsQtYU+qJBtqJx52CCyjipk49HRWIv6DZ+hftPn8LX0k9gjWiT2OB+J0y9ERHyKjh7LbyobE/kVs7aFzKO3vo3N7VizvRQrt+kP+ik7/cb3DvqdXsvmXeV48rV1uoKAIvj38E35mJKbYe2gYO89CvD8kG8wMBO5mDAP5mFEAarv30b6zGV7K0A6AFhQUKAE8M477zwlmYfe55577sEXX3yhBBDXrFmjtzjbG1CA6gLEX7wGoFtQlHlYIKoBl7LwaK86qiT2aCxchk6vx2+PIpIyuhJ75InEHjEGei5vUVmYyKuQvS1jHl16dwX9unb6bd0jjvdq2ennxJTcruO9+eO7jvfqecROwOff2aTpOLA49nvvdVN5558egU2w5flhgogmu2AmJgtq0B3zMCigycWp8aD6/m0ytrB3RzoAOHHiRHR0dODqq6/GY489phvmI488gvfee0/ZOVhYWKi7PBcIXAGqCxC1hT5wQjRKMg+5OAWTh7gHtu3I96gViT32rO9XmKiBI7sSe4wpIJnYQw/1YDLR085wsQ1nHg3N7Scd762A16ct6Dc1NwMiC6/Y6RenM+h3+rgS68T24ip8snI/Vm8vge+kNoikIQUTB+LiWSMwYWQa3/kXhEkZzvMjCHJrqpKZaJLJNiPmYZvUmiqixoPq+7cmGGykWQHSAcA5c+YoGX1nz56N//3f/9Xc6W7D2267TTk6nJ6ejhUrVuguzwUCV4DqAkRtoQ+cEI2SzEMuTsHgoST22L2uK7HHsT39ChI7ahqSCi5F9NDxYfNyHwwmco1KuVoTbjxE0G9NYQlWbDuGrbu1B/2mjekK+uWPMx708zcCmls9WLVuK0SGYRFYnJWfh9hofbsK5Rpd9FsTbvODAjFmIhcl5sE8jChA9f3bSJ+5bG8FSAcARdbf9evXIyoqCkuWLEFqaqpmxtXV1Zg/fz7EXYDTp0/Hm2++qbksGxpXgOoCxF+8xtmb6YF5mKmmcV928vB52tCwdUlXYo+aUv+NF4k9JpylHPWNHDDUeCeJebCTCTFpgtLccOBR39R1vHfl8eO9Wnb6uSOcEDv9uo73ZtkWiAsHHkEZ6AFWyjwCFM7CYszEQnEDcM08AhDNwiLUeFB9/7YQYVi6Jh0A/NOf/oT//M//VHZyzJs3Dy+99BIiIiJUQXq9Xtx1111K0FCUve+++/CTn/xEtRwbmKcA1QWI2kJvHjE5PTEPubjYwcPbVIc6kdhjo0js0eBXAGd0HBKnisQeFyEiIbQSe+ihbgcTPe0Jd9tQ5SGCfqsLRdDvKLbtrdR0vDdYQb+Tx2Co8qA6z5iHfOSYiVxMmAfzMKIA1fdvI33msr0VIB0ArKmpURKAiMEsnqlTp+LXv/61ktXX37N79248/vjj2LBhA8RdMAkJCfjqq6+URCD82KcA1QWIv3jtGyNaamIeWlSyz8ZKHu1Vx1C39iM0Fi5FZ0e7305FJA1AUv4lSMg7B86o0EzsoYeolUz0tINtuxQIJR51jW09iTxE0O/k+/T88RZBv67jvYOQPy7Ttp1+/toTSjxCYY4xD/koMhO5mDAP5mFEAarv30b6zGVDLAAouvP3v/8dv/rVr065z2ns2LGYMmUKsrOzERMTg5aWFpSUlGDTpk0oKipSVBDBP7H774knnsBVV13FY8NmBaguQPzFa/NAUamOeYQ+j9buxB67RWIP/0kDIjNHIPnMyxA3dlbIJ/bQQ53niB61rLelzkME/bp2+h3DtmJtQb9IEfQbm4nZk7IxQ4Kg38mUqfOwfsTaWwPzsFdvLbUxEy0q2WfDPOzTWktN1HhQff/WwoJttCtAegdgdzcXL16sHAUWR3vFIwJ7/h4R+BOPy+XCz3/+c9xyyy3a1WJL0xSgugBRW+hNAyapI+YhFxizeIjEHs27N6B2zQdoO7qr307GjJyC5ILLED1sQtgk9tBD3SwmeupkW/8KUOQhgn6rjh/vLSyu0rTTrzvoJ+70mz42+Dv9/BGhyCOU5xfzkI8uM5GLCfNgHkYUoPr+baTPXLa3AiERABTdErv7RBBQHO1Ve2bMmIGf/exnyi5BfoKjANUFiL94gzNe+OVNLt2t4iESezRuW6ok9vBUl/jvtDMC8RPmInnmpYjMCL/EHnpGA69ZetSy3pYKj9qGNqxWEnkcRaE43ut/822PaJFuF6aPzcCcSYMwfVwmYqLU72S2XvH+a6DCI9g62VU/87BLae31MBPtWtlhyTzsUFl7HdR4UH3/1k6ELbUoEDIBwO7OFhcXY+3atcpRX5Hpt7m5GbGxsUqGYHE0uKCgAGeccYYWbdjGQgWoLkDUFnoLEUrhmnlIgaGnEdu2bFEyq7vdbkyaPFlz47zN9ajf8DnqNn4GX3O933LOqFgkTD0PSSKxR2KaZv/hbMhzRC76MvNQgn6Fx7Bi6zFsF8d7NQb9Zojjvcd3+lEI+p08ImTmIdfItac1zMMenfXUwkz0qGW9LfOwXmM9NVDjQfX9Ww8TtlVXIOQCgOpdZgsZFKC6AFFb6GVgbWUbmIeV6urz7Wtrwf7nb4HD245OVyRG3PeqahIOsctPJPZo2Lak38QersR0JOVfjMTJ50IEAfnRrgDPEe1a2WEpG4+ahtaeO/30Bv3mTM7G9DGZiCaw088fW9l42DEGZa6DechHh5nIxYR5MA8jClB9/zbSZy7bWwEOAPKoCIoCVBcg/uINynDxWynzkIdH/aYvUfnZ//Q0KP2iO5A4ZWGfDWw9uhu1q99H8651/Sf2yBiOpDMvQ7xI7OGS/zihPDROtITniFxUZOBRU996/E6/Y9ixT9tOv6hIcbw3E8qdfsSDfiePCBl4yDVCg9sa5hFc/fuqnZnIxYR5MA8jClB9/zbSZy7LAUAeA5IoQHUB4i9eSQbQ8WYwD3l4HFn872gv3dfToMiskRh86zM9f+7s9CmJPerWfojWw13Z2P09MWdMRlLBpYgZPokTexhEzHPEoIAmFw8WDyXot+0YVmwTQb8qHM+H1m/vRNBPHO+dkzcI08ZmIDoy9ILwweJh8rAKGXfMQz6UzEQuJsyDeRhRgOr7t5E+c9kQDACuWLECbW1tiImJwaxZszQzXrVqFVpaWnSX01wBG/arANUFiL945RrYzEMOHm0lxTj66gO9GjPolmfgHjCkK7HHWpHY45j/BjtdiB8/B0kzL0VU5nA5OhYCreA5IhdEO3lUdwf9th7Dzv3agn7RIug3Lku502/amNAM+p08IuzkIddIlLM1zEM+LsxELibMg3kYUYDq+7eRPnPZEAsA7t27F5dccomyQ2TRokV4+OGHNTP+3e9+h7feeksp+/nnn2PoUM4kqVk8EwypLkD8xWsCfBNdMA8TxdToqvKzP6Fpz/pTrDvbW+Fra+7lweFyo9PXgf62GzkiY5A4dSGSZlzCiT00MtBjxnNEj1rW21rNo6quBau2lWDlNn1Bv/zjQb+pYRD04wCg9eM80Bqsnh+BtiucyzETuegzD+ZhRAGq799G+sxlQywA+NJLL0H8I4J4H330EUaNGqWZ8Z49e/CDH/xAKXvPPffgjjvu0FyWDY0rQHUB4i9e4+zN9MA8zFRT3ZfP04aDz9+MTk+burGKhSshFUn5l3Ql9oiOM+yPHfStAM8ReUZGc6sHq9ZtRVOLB3ExbszKz0NstNtwAwMJ+sVEde30E3f6TR2TiSi3y3A7KDrg+SEXNeYhFw/RGmYiFxPmwTyMKED1/dtIn7lsiAUAb7rpJqxZswaDBw/G119/rZvvOeecg2PHjilHhxcvXqy7PBcIXAGqCxB/8QbO3IqSzMMKVf37bD26B8dee6jfxB1aWpQ892qkzL4KYocgP9YqwHPEWn3VvHd2dqKwuBKfrNyPNdtL4fN19hRxOh04c8JAXDR7OCaOTNd136UI+oldfiu3HkPRgWpNd/qJoF/+uIHK8V6x0y9cg34nM+P5oTaC7f2cedirt5bamIkWleyzYR72aa2lJmo8qL5/a2HBNtoVIJ0FeO7cuaisrMS5556LF198UXuvj1vefffd+Oqrr5CVlYWlS5fqLs8FAleA6gJEbaEPnBCNkszDfk7Nezai/IMX+jzyq9YahzsKGVf8HHE509RM+XOTFOA5YpKQAbjZe6QWz7+zCYdKG1RLD81KwH3XTcWowcl+bStrxfHeY1hxPOin6hRATFQExPHeOZOzMTU3A5FhutPPn1Y8P7SMIvtsmId9WmutiZloVcoeO+Zhj85aa6HGg+r7t1YebKdNAdIBwIkTJ6KjowNXX301HnvsMW09PsnqkUcewXvvvYeoqChs3bpVd3kuELgCVBcgagt94IRolGQeweEkknmUvvc0PFVHNTfAnT4YWVc/CHdqtuYybGhcAZ4jxjUMxMPmXeV48rV1aG33ai4uEnA8fFM+puRm9JQRQb+Td/ppcSaCfjPHdyXy4KBf/4rx/NAyouyzYR72aa21JmaiVSl77JiHPTprrYUaD6rv31p5sJ02BUgHAKdMmYLW1lZcdtllePrpp7X1+CSrX/ziF3j//fc5AKhbOeMFqC5A1BZ646Tk9sA87OfT6fWgfvPXqFn+HnzN9ZoaEJs7Exk/uBvOqBhN9mxkngI8R8zTUqsnsfPvoZdX6Ar+dfsWQcAHF83AkYpGrNx6FN8frNFUbWx0BPLHZ2HOpGwlgMg7/TTJxvebaZPJNiter2yTWnNFzESzVLYYMg9bZNZcCTUeVN+/NQNhQ00KkA4Ann322SgpKUFeXh7effddTR0+2ejaa6/Fli1b+AiwbuWMF6C6AFFb6I2TktsD87CPT2enD01Fq1G99G101JTqqjjzmocQlzNdVxk2NkcBniPm6KjVi7jz765nl2g69qvVpz87EfRgKG8CAAAgAElEQVQTO/3m5A3ClNwBcEeEZyIPIzry/DCinvllmYf5mhr1yEyMKmhueeZhrp5GvVHjQfX92ygnLn+qAqQDgCJzr7i7LyIiAt988w0yMzM18y0rK4MIIPp8Pk4Colk18wypLkDUFnrziMnpiXnYw6XlQCGqv30TbSXFAVUYP3E+Mi69O6CyXMiYAjxHjOmnt/S2vRX45R9W6S2m2T5OBP0mdCXymDKag36ahfNjyPPDqILmlmce5upphjdmYoaK5vlgHuZpaYYnajyovn+bwYp9nFCAdADwrbfewhNPPKFkzrvooovw3HPPaWZ733334bPPPlPKPvjggxAZhfmxTwGqCxC1hd4+osGpiXlYq3tb6X5UL3kLLfu2GKrIERWL4fe+CkcEZ/01JGQAhXmOBCCagSJPv75eubPPzKc76DcnLxuTOehnprR8BNhUNY074/XKuIZme2AmZitqzB/zMKaf2aWp8aD6/m02t3D3RzoAKAbxOeecg/r6rnuorrvuOjz00ENwu/2/ZHo8Hjz55JN45513lDIJCQn4+uuvkZSUFO5jwdb+U12AqC30tkINQmXMwxrRPbXlqFn2Dhq3f+e3gojUbHRUnxro6IyIQuuIAkTvXwNHR9spZfkYsDWs1LzyHFFTyLzPm1s9uO7Xn8Hn6zTF6bypgzB/6hDk5Yidfk5TfLKTUxXg+SHXiGAecvEQrWEmcjFhHszDiAJU37+N9JnL9laAdABQdEcE8h599FFlJ594srOzcc0112DmzJkYMmQIYmNj0dzcjCNHjmDNmjVK1t9jx45B3NMjyjz88MNYtGgRjw2bFaC6APEXr80DRaU65mEuD29zPWpW/gP1Gz8HvB19OnenZSN1/g1o2rXmlAChe8AQ1E68HO2RCYhsq0dy4fvwVB7p8cHHgM1lpdUbzxGtShm3O1Rajzt/v8S4o+MeXnngbAzJTDDNHzvqrQDPD7lGBfOQiwcHAJmHfArI1SJqaxbV92+5qNNvDfkAoEAgjgGL48AioNcd2OsPjbARz/XXX49HHnmEPkWCPaC6AFFb6AkODV1NZh665PJr7GtvRd26j1G75gN0tjX3aeeKT0HK3GuQMPkcOJwuNO/dhPKPXlQyAXdn+d2+aw/ELmuxC3tCbo7yefOutXDGJipZgGNHTTWnwexFswI8RzRLZdhw96Ea/Py//O+a1VvBc/echdFDU/QWY3sdCvD80CGWDabMwwaRdVbBTHQKZrE587BYYJ3uqfGg+v6tEwubqygQEgFA0UexE/DZZ59FU1OTKnSxK/D+++9XAoD8BEcBqgsQtYU+OHTtq5V5GNO60+dFw5ZvULP8PXgba/p0Ju7vSz7zciTNuBjOyOhTbMSOweY9GxA/aYHyA8zpPETm4MZtSxGbMx2u2ERjjeXSASnAcyQg2XQVEj8q7txfjX8t3Yu1O/RlyO6vIt4BqAtDQMY8PwKSzbJCzMMyaQN2zEwCls6SgszDElkDdkqNB9X374ABccG+3+06u7fDhYBA4i7Av/71r1i+fLnyItra2trTq+joaEyaNAlnnXWWckQ4MZFfRoOJnOoCRG2hDyZjO+pmHoGpLJZ9sTOveulf4Knyk7DAGYHE6RcgZfZVmoN3zCMwHlaWYibWqVvb0IZvNxzGl2sP4mhFo6kVuZwOvP34hYiN5sQ5pgp7mjOeH1aqq98389CvmdUlmInVCuvzzzz06WW1NTUeVN+/reYYbv5DZgdgX+DEIBc7AuPi4hAfHx9ubKXuL9UFiNpCL/UgMKFxzEO/iC2HdqL62zfRdnS3n8IOxE+Yi5R518KdnKmrAuahSy5bjJmJuTJ7fZ3YsrtcCfqt3V4K8Wcrntl52fjFj2ZY4Zp9nqQAzw+5hgPzkIuHaA0zkYsJ82AeRhSg+v5tpM9ctrcCIR0AZODyKkB1AeIvXrnGFPPQzqO9/BCql7yF5r0b/RaKOWMyUhfcgKisEdod88t0QFrZVYjniDlKl1c346t1h/D1+kOorG0xx2k/Xp78t9mYOCrd8nrCvQKeH3KNAOYhFw8OADIP+RSQq0XU1iyq799yUaffGg4A0mdIsgdUFyBqCz3JwaGj0cxDXayO+kpUL/srGguXAp2+PgtEZo1E2tk3IGbEJHWH/VgwD0PyWVKYmQQuq6fDq9zp9+Wag9iypwLH84f5dZidHoeF+UPx9frDho4ED8tKwIv3d92ryY+1CvD8sFZfvd6Zh17FrLdnJtZrrKcG5qFHLettqfGg+v5tPcnwqiEkA4BicJeVlaGurg5erxczZvAxGtmGNdUFiNpCLxt3s9vDPPwr6m1pRO2qf6J+/afo9Hr6NIxIyULq/OsRN/ZMOBxOw3iYh2EJTXfATPRLeqi0XtntJ+73q29q79dBpNuFOXnZSuBv/BlpStBu75FaPPTyCrS2e3VXHh3pwlN3zsGowcm6y3IB/Qrw/NCvmZUlmIeV6gbmm5kEpptVpZiHVcoG5pcaD6rv34HR4VL+FAiZAKAY0O+++y4++ugj7NmzB925TcRfxnfu3HlK/6uqqrB48WLl/40ePRqXX345jxCbFaC6AFFb6G3Gant1zKO35D5PG+o3fKYE/3ytfWdFd8YmImXuNUicci4cLvOSDDAP26eAaoXMRFUixaClrQMrthxV7vb7/mDfGbFP9jRycBLOmzkMZ00ZjPiY3nNo865yPPnaOl1BQBH8e/imfEzJzdDWaLYyrADPD8MSmuqAeZgqpynOmIkpMprmhHmYJqUpjqjxoPr+bQosdtKjQEgEANetW4f7778fFRUVSsdOTmwsAoBFRUW9kF955ZXK/xfZgEXW4MjISB4WNipAdQGittDbiDQoVTGPE7J3+rxoLFyG6mXvwttQ1ScPhzsaSQWXInnmpXBGxZjOjHmYLqlhh8zEv4Ti7wp7DtcqQb/vNh9BS1v/O/bioiMwb+pgLJw5TNMOPbET8Pl3NuFQaYMqR3Hs997rpmryq+qMDTQrwPNDs1S2GDIPW2TWVQkz0SWX5cbMw3KJdVVAjQfV929dUNhYVQHyAcANGzbglltugcfjUQJ/IuB3xhlnoL6+XgkI+gsA/vWvf8VvfvMb5fM//OEPmD9/vqpYbGCeAlQXIGoLvXnE5PTEPLp+8BCJPUSCD0/F4b5BOV1InLIQyXOuRkS8dUcLmYd884SZ9GbS0NyOJRsP46u1h3CgpF4V2oSRacpuvzMnDkR0ZISq/ckGYn5uL67CJyv3Y/X2EvhOyhrscjpQMHEgLp41AqIOvvNPl7SmGPP8MEVG05wwD9OkNM0RMzFNSlMcMQ9TZDTNCTUeVN+/TQPGjhQFSAcA29rasHDhQpSXlyudueKKK3DfffchIyMDjz/+OP7yl7/4DQDW1NRg9uzZysvzokWL8PDDD/OQsFEBqgsQtYXeRqRBqSrcebQe2YXqb99E6+Heu5y7gcSNnaXc8+dOHWg5o3DnYbnAAVTATLpEE4G3wuJKZbff6sISeDr6TojTLXFyQhTOmT5E2e03aEB8AMr3LtLc6sGqdVvR1OJBXIwbs/LzEBtt3hF8UxoZZk54fsgFnHnIxUO0hpnIxYR5MA8jClB9/zbSZy7bWwHSAUAR4BOBPvGr+XXXXYdHHnmkp4dqAUBheOGFF+LAgQOYNm0a3nrrLR4fNipAdQHiL14bB4mGqsKVR3vVUVQv+Quad631q1L0sAlIPXsRorNHaVDSHJNw5WGOetZ4CXcmVXUt+Hr9IWW3X1l1c78iOx3A1DGZym6/GeMyEeEynhjn9ArDnYc1ozxwr8wjcO2sKMk8rFDVmE9mYkw/s0szD7MVNeaPGg+q79/GKHHp0xUgHQC89dZbsXLlSsTHx2PZsmWIi4vTFQC8++678dVXX2HAgAHKPYD82KcA1QWI2kJvH9Hg1BRuPDoaqlGz/D00bPkG6Ox7B1NkxnCknn0DYs6YbPuRwnDjEZxRr6/WcGTS4fVhQ1GZsttvY1EZTjp126d4mamxWDhzKM6dMRRpSebfjXlypeHIQ9+Itdeaedirt1ptzENNIfs/Zyb2a95fjcyDeRhRgOr7t5E+c9neCpAOAM6dOxeVlZWYN28e/vjHP57SOy07AH/1q1/h73//O6Kjo7FlyxYeHzYqQHUB4i9eGweJhqrChYfI5lu7+n3UrfsYnR3tfSoTkZSBlPnXIX78HDgc5u9c0oCDjwppEclmm3CZI0LWYxWNStDv2w2HUdPQ1q/SYnffrIkDld1+E0elwym2/9nwhBMPG+Q0XAXzMCyhqQ6Yh6lymuKMmZgio2lOmIdpUpriiBoPqu/fpsBiJz0KkA4ATpw4ER0dHbjmmmvw6KOP6g4A/vrXv8bf/vY3JQOwmMD82KcA1QWI2kJvH9Hg1BTqPDo7PKjf9AVqVvwdvpa+M4k6YxKQMueHSJx6PhwRwb1PLNR5BGeUG6s11Jm0ebxYte2YEvgTyTbUHpFtVwT95k8bgsS4SDVz0z8PdR6mC2axQ+ZhscA63TMPnYLZYM5MbBBZRxXMQ4dYNphS40H1/dsGlGFVBekA4MyZM5Vsv5dddhmefvpp3QHAO+64A0uXLlWShnz33XdhBT7YnaW6AFFb6IPN2er6Q5VHZ6cPjduXo2bZO+ioq+hTRkdEJJJm/gDJBZfBGX3i+gOrNe/Pf6jyCKamRusOVSb7jtYpQb+lGw+jqbWjX5liolw4a8pgLMwfitFDU2w/Gn9y40KVh9FxGqzyzCNYyvddL/OQi4doDTORiwnzYB5GFKD6/m2kz1y2twKkA4CXXHIJ9u7diwkTJihHeU9+1I4Ai+y/s2bNQm1tLaZMmYK3336bx4eNClBdgPiL18ZBoqGqUOMh1qWWfVtQ/e1baC8/0LcCDicSJp+DlLnXICIhVYNK9pmEGg/7lLOuplBiIrLnfrf5iBL423ukTlW0McNSlN1+cyYPQkxUhKq9HQahxMMOvayug3lYrbA+/8xDn152WDMTO1TWXgfz0K6VHZbUeFB9/7aDZTjVQToA+Jvf/AZ//etf4XQ68eWXX2Lw4ME97NQCgJ9//jnuvfdeZSfA7bffrvw3P/YpQHUBorbQ20c0ODWFEo+2Y3tRteQttB4o9CtmbO5MpM6/HpHpJ9a64Cjfd62hxEMmXY20hToTERTfub9aCfqt2HoM7R5vv3IkxEbi7OlDlKQew7ISjUhnSVnqPCwRJYhOmUcQxe+jauYhFw/RGmYiFxPmwTyMKED1/dtIn7lsbwVIBwBXr16Nm2++WQninZ4IpL8AYFlZGa666iolgYgIHn744YcYNWoUjw8bFaC6APEXr42DRENVocDDU12C6qVvo6lold8eRw8Zi9SzFyF6cK4GVYJnEgo8gqeeNTVTZVLT0IolGw4rgb+jFU39iuNwAJNzBuC8gmGYOT4L7giXNWKa4JUqDxO6LqUL5iEXFuYhFw8OADIP+RSQq0XU1iyq799yUaffGtIBQCH/okWLsH79eiUIuHDhQiUZSEpKCvwFAJcsWaLYlJaWKmUuuOACPP/88/RJEusB1QWI2kJPbFjobi5lHh2Ntahd8TfUb/4K8PW9q8k9YAhSF9yA2FHTgnpvmVYwlHlo7SM1O0pMvL5ObN5VrgT91u0ohfhzf096UjTOzR+Gc/OHIjM1lgQaSjxICGqwkczDoIAmF2ceJgtqgjtmYoKIJrpgHiaKaYIrajyovn+bgIpdnKQA+QCgCOT98Ic/RFVVV/Y/kdH3zDPPVAJ833//vfLSLIKEYrff5s2blf8vHnGsaMiQIfjHP/6BxET5jgmF+iilugBRW+hDfRxR5OFra0Ht2g9Rt+ZDdHpa+0TkSkhD6rxrET9xHhxOeXcznd54ijx4jgRfgbLqZny17iC+WXcIlXV9z4nuVrqcDuSPz1Lu9puSmwHxZ0oPzxG5aDEP5iGXAvK1hueIXEyYB/MwogDV928jfeayvRUgHwAUXSouLsbdd9+Nffv2KT0UQT9/jwj8iScnJwevvPKKEgS06/nmm2/wwQcfYPv27aioqEB8fDyGDRuGc889F9dee63yZzMeMblXrlyJtWvXYufOnThw4AAaGhoQFRWlZDyeNGkSRAKVuXPnBm1XEdUFiL94zRih5vmgxKPT60H95q+VXX/epr4TGIhsvsmzrkTi9AvhdEeZJ5RNnijxsEmSoFcjKxNPhxdrtpcqu/227qnA8a9mv3oNGhCP82YOxYLpQ5CSEB10XQNtgKw8Au0P9XLMQy6CzEMuHqI1zEQuJsyDeRhRgOr7t5E+c9kQDQCKbrW0tODVV19Vsvl27wbsC7jY7fejH/0It9xyC2Jj7Tky1NTUhPvvvx/ffvut3zE4cOBAvPDCC5g8ebKhcfp///d/ypHmtrY2VT/Tp0/H73//e2RnZ6vamm1AdQHiL16zR4IxfxR4dHb60FS0Wrnnr6Omawfy6Y/D5UbijIuQPOsKuGISjIkSxNIUeARRnqBULRuTg6X1+GrtIXy74TAamtv71STS7cKcvGxlt9+4EalB+8HKTHCy8TCzbxR9MQ+5qDEPuXhwAJB5yKeAXC2itmZRff+Wizr91oTEDsCTMXR0dCg77LZs2QKR7EMM9JiYGKSnpys736ZOnaocE7br8Xq9Spbh5cuXK1WKdlx99dVK0pG6ujp8/PHH2LRpk/JZUlIS3nnnHYwcOTLg5j3yyCNKZmTxZGZmYtasWRg/fjzS0tKUoKDQRSQ9aW5uVmxE5uT33ntP+dzOh+oCRG2ht5NpMOqSnUfLgUJUf/sm2kqK/cjjQPykBUid9/8QkZgeDAlNrVN2HqZ2logzGZi0tHVgxZajym6/7w/WqCo3anCSEvQ7a8pgxMW4Ve0pGcjAg5JeVreVeVitsD7/zEOfXnZYMxM7VNZeB/PQrpUdltR4UH3/toNlONURcgFA2eC9++67+M1vfqM0SwT9Xn/9dSUIePLzH//xH8ruRfGIXXl/+ctfAu6GqOvIkSPKDkdxF6LIcnz6c/ToUdx6663Yv3+/8tGVV16Jp556KuA6AylIdQGittAHwoZSGVl5tJUdUAJ/Lfu2+JVTJPYQCT4iM4ZSkrzftsrKI2QEDqAjwWIirtvYfagGX649hOVbjqClre9EN91dEoG+BVMHY+HMYThjUFIAPaVRJFg8aKhjfyuZh/2a91cj85CLh2gNM5GLCfNgHkYUoPr+baTPXLa3AqQDgG+88YbSI3Hnn7hDz+2Wa6eA2P03b9485b4/8fzzn/9UduOd/gi7q666CkVFRcpHixcvxpw5cwIar7W1tUhOTlYtKxKkXHbZZYqd2CG5evVq5d92PVQXIP7itWuEaKtHNh6e2nLULHsHjdvFjt++M5hGDRqN1LNvQMzQ3muBtl7LayUbD3mVsq9ldjOpb2rH0o2Hld1+B0sbVDs6cWS6crffmZOyEeWmk/BGtWN+DOzmEWg7w6Uc85CLNPOQi4doDTORiwnzYB5GFKD6/m2kz1y2twKkA4BjxoxRgn/jxo1TsvnK9qxZswY33nij0qz8/Hy8+eabfpso2v/www8rn9u1I++CCy7o2QUokpMIPe16qC5A/MVr1wjRVo8sPLzN9ahZ+Q/Ub/wc8Hb02Xh3WjZS59+A2Nz8kLjLrK9OysJD2+gJDys7mPh8ndi2t0LZ7be6sAQdXl+/4qYkROGcGUOxMH8osgeYk/yKCk07eFDRQoZ2Mg8ZKJxoA/OQiwcHAJmHfArI1SJqaxbV92+5qNNvDekA4JQpU9Da2orLL7/c9iOsWtA/88wzym4+8YgkILfddpvfYmKXYPeuP3FEWGTxtfr54Q9/iMLCQqUacQ9gXl6e1VX2+Ke6AFFb6G0DGqSKgs3D196KuvWfoHb1++hs67pX8/THFZ+ClLnXIGHyOXA4Q3uHU7B5BGkYSl2tlUwqa1vwzfpD+HLdIZRX9z3+u8VxOoDpY7OU3X7TxmYiwtX7egqphTSpcVbyMKmJYeWGeciFm3nIxUO0hpnIxYR5MA8jClB9/zbSZy7bWwHSAcDzzz8fhw4dkjYA+OMf/7gn+Ye4+6+goKDfMTh//nyUlJQoNuJIbmpqqmVjtr29XUkQ0tDQdURrxYoVGDBggGX1ne6Y6gLEX7y2DRFNFQWLR6fPi4Yt36Bm+XvwNvad1MARFYvkMy9H0oyL4YyM1tQf6kbB4kFdNyvbbzYTsbtv/c4y5Yjvpu/L4Ov7pHtPl7LSYrEwfxjOmTEEaUn2XTNhpaZGfJvNw0hbuCwHN2QbAzw/ZCPCc0Q2IjxH5CJCjQfV92+5qNNvDekA4M9//nN88sknSnZfsYNNtuecc85REnKI55tvvlEy7vb33HDDDVi/fr1iIhKBiIQgVj3iPsKHHnpIcS/uJRR/tvOhugBRW+jtZBqMuuzmIRIbNO9ah+qlb8FTdazvLjsjkDj9AqTMvgqu2MRgyBK0Ou3mEbSOEqm4udWDVeu2oqnFo2TTnZWfh9jowO7KPVbRqAT9vtlwGLUNbf0qIHb3zZo0UMnkK+74c4rtf/woCvAckWsgMA/mIZcC8rWG54hcTJgH8zCiANX3byN95rK9FSAdAFy2bBluv/12JdPthx9+qGTZlekR9/7V1dUpTdq0aRPi4uL6bd5dd92Fr776SrH54x//iAULFljSnerqalxyySWoqqpS/L/00ktYuHChJXX5c0p1AeIvXluHiWpldvJoObRTyezbdnS3n3Y5ED9hLlLmXQt3cqZq20PRwE4eoaifGX0SQerC4kp8snI/1mwvhbifr/sRgbgzJwzERbOHK4E5cYduf09rewdWbStRAn879nV9X/T3DB+YiIUzh2L+1CFIjItUMw/Lz3mOyIWdeTAPuRSQrzU8R+RiwjyYhxEFqL5/G+kzl+2tAOkAoOiOCJp9/fXXSgILccw2KSlJGs4TJkyAx+NR2rNjxw5ERET02zaxo/Hjjz9WbJ577jklSGf2I47+3nzzzdiwYYPi+txzz8XLL79sdjWq/k5fgFwulxLIlf3p5inaKVvWadm1s6J9dvBw1pcj8vtvEFHmL/AHdAwYifax58KXlGVFN8n4tIMHGTGC0NCjVW34+4oKlNd2fe/092Qku/HDOQMwKC2ql9mxqjZs2NOArfua0OrpP6FHZIQDeWfEY3pOAgalRaoGFdXaFeqf8xyRizDzYB5yKSBfa3iOyMWEeTAPIwr4fD54vd4eF7m5uYiPD69kbEb0C5Wy5AOAIpAkEmwsXbpUucNOBAQvvPBCJCYG/+idbAFAMekfeOABfPTRR8r4HTp0KP7+978HJWh6egAwVCYU9yN0FHC01CNm73eIPFoIB/q+6KwjMQstuQvQkTYidDrOPSGpQHFJK95dXgVPh8qlfCf1zh3hwLVz0zByYDRa2n0oPNCMzcVNKKlRDyAOSY/E1JFxGDc0BlFu+X+8IQmVG80KsAKsACvACrACrIBFCnAA0CJhJXdLOgD4ox/9SJFXHHnavHkzOjo6enYfiPv2RBKNqKjeuxtOZyKOQYndg2Y/Mh0BFho98sgjPXclZmdn480331S9l9BsTbr98Q5Aq5QNL7+W/BLa3oLIvSvg3r8WDt+JX8lOVtYXm4L2MWejI3s8oHKMMpyIWMIjnAQMsK9i59//fl6Cdh3Bv+6qIlwOnJEVjX2lrejw9h88jI1yYurIeEzLSUBGMh/xDQQXz5FAVLOuDPOwTttAPDOPQFSztgwzsVZfvd6Zh17FrLWnxoN3AFo7Hqh4Jx0AFMd++7rDSAS71O426gbUbVtUVGQ6M1mSgIg+/va3v8W7776r9DErK0sJ/okdgMF6qN5BwHdvBGvE9F2vmTx8njbUb/gMtav+CV9rU58VOmMTkTL3GiROORcOV2DJFORS0NzWmMnD3JaFrjexvt/17BIcKu3K6G72I+LbU0ZnKAk98sdnwR3Bu/2MaMxzxIh65pdlHuZrasQj8zCinjVlmYk1ugbqlXkEqpw15ajxoPr+bQ298PVKPgBoBjoRLLQiAPjjH/8Yy5cvV5oodhgWFBT029z58+ejpKREsVm9erWyg9HoI14OH330UbzzzjuKq8zMTCX4N2zYMKOuDZWnugBRW+gNQSJQ2AwenT4vGguXoXrZu/A29J3owOGORlLBpUieeSmcUTEElAlOE83gEZyW0611294K/PIPq0zvQHpyDBbmD8W5M4YiIzXWdP/h6pDniFzkmQfzkEsB+VrDc0QuJsyDeRhRgOr7t5E+c9neCpAOAK5bt840puK4rtnPM888g8WLFytuxT2Ft912m98qKisrMXv2bOXztLQ0rFpl/IXu9OBfRkaGEvwbPny42V3V7Y/qAsRfvLpRW1rACA8xP5r3bkT1krfgqTjcdzudLiROWYjkOVcjIj7Z0r6EgnMjPEKh/8How9Ovr8fKbcdMqVocB545fqCy2y9v9AC4nP1nCTal0jBzwnNELuDMg3nIpYB8reE5IhcT5sE8jChA9f3bSJ+5bIgFAGUHKnbx3XTTTUozRYBRBN/8Pf/4xz/w8MMPKx9feeWVeOqppwx17/Tgn0iQIuofMUKOZAVUFyD+4jU0LE0vHCiP1iO7UP3tm2g97P/of9zYWUidfz3cqQNNb3eoOgyUR6jqYXW/mls9uO7Xn8Hn0574w1+bxFHf/3nwXAwcEGd1s8PaP88RufAzD+YhlwLytYbniFxMmAfzMKIA1fdvI33mshwA/P/snQeYVcX5/797ty9LW3bpnUVQOiIivYrYAgIGY4nGEhVMTDTFljz/aDQmMSax5meJFSygICBIkSIdpUgXEKQtdenby/+ZQxbBe3fvKTPnztz7Pc/jo7LzvvPO9zPznp2Xc874OgfEMdv9+vXDoUOHrH4/+ugjtGvXLigG0W7kyJFnX0N+9dVX0adPH0+xitd+x48fb/kQxb+33noLLVu29ORTprGpCYg3XpmzwLuvr5OA2G0AACAASURBVNesgfgAb2JiIjp27hzWYdGRvcid9y7ytiyvtG1Ks/bIGHgzUhpmh/XHBucrwPXh74zYtf8Exv5tnrROX/ztQDSpV12aPzoKVoBrRK9ZQR7koZcC+kXDNaIXE/IgDy8KmLr/9jJm2rIA6PscEEU4UYwTV+vWra1vAYpXfM+9nn76abz++uvWH3Xt2vXs9/p+GKwoID700EPWH1f1ROHjjz+Od955x2qnY/FPxGVqAuKN1/clVGmHZYX52PHszxBXWoTy+CS0+NXrlX6fr+RkLo5+8QFOrpkLlJeF9JlUtzkyBt6E1JadbR8ipI8aekTC9eEvh292HcUD/1oordNnftkXFzStLc0fHQUrwDWi16wgD/LQSwH9ouEa0YsJeZCHFwVM3X97GTNtgxUw+huAJgAtKSnBXXfdhcWLF58tyI0ePRrZ2dk4duwYpk+fjq+++sr6WY0aNayn9kShMNRlpwD47LPP4uWXX7bMxeEmv/71r209+XfRRRehYcOGvklqagLijde3KRK2oxOrZuHwjP+cbZd55d3W9/rOvcRpvseWTcHx5VNRXlIU0mdCzbqo3f8GpLfrjbg4nnAaVvgqGnB9eFHPue03u3LxwL/OHDQl4+ITgDJUrNoH14h6jZ30QB5O1FLfljzUa+y0BzJxqpja9uShVl+n3k3jYer+2ykXtq9aARYAfZghYrGJQ0Dmzav8Va369etDFO/EE4CVXXYKgDfffDPcHI4ivjkovj3o12VqAjIt0fvFMxL97HntNyja/+3ZrpPqt0Lj2/9q/X95STFOrPoMRxdNRFn+yZDhBVKro3bvUajRdSjiEhIjMYSo65Prwx+kR08WYNqiHZi26FvkFZRI6VQc+DH+8WFIS+FakCJoJU64RlSq69w3eTjXTKUFeahU151vMnGnmyor8lClrDu/pvEwdf/tjg6tKlOABUAf58acOXMwZcoUrFu3DkeOHEG1atXQtGlTDBkyBGPGjEH16lV/e4kFQB9hcfMWebGriKAwZzv2vv7boBYNb/sLinNzcHT+BJQcPxjSQ1xCEmpeeg1q9fgRAik88EAmaNN+EZI5dj987T9yGh/N34a5K3ahqCT0q+xu4+jVqSF+f8slbs1pZ1MBrhGbQvnUjDx8EtpmN+RhUygfm5GJj2Lb6Io8bIjkYxPTeLAA6OPk0LgrFgA1hhPNoZmagExL9NEwhw7P+D+c3rryvKGUFxWgrDAveHjiKNPySk5EjQugeudBqN3neiRUz4gGabQbA9eHGiTb9xzDpHnbsHjtXkg48DdkkE/e0wsdsjPVDIBezyrANaLXZCAP8tBLAf2i4RrRiwl5kIcXBUzdf3sZM21DbJfLyyvbLVMuKqBOAVMTEG+86uZEKM9lxYX47tnbUF5c6Knj1NbdUGfgzUjKbOzJD42rVoDrQ94MEbfmr7cexsR5W7HmmzMnyVd2pSYnIL/Q/avAzepXx3MPDuDhN/LwVeqJa8QHkR10QR4OxPKhKXn4ILLDLsjEoWCKm5OHYoEdujeNh6n7b4dY2DyMAnwCkFMkIgqYmoBMS/QRgSux04K9W7HvDXHydSVP9dnqKw4Nb30KKY1CH65jywUb2VKA68OWTFU2Ki0rx9J1+zDp863Ytud4pW0T4uMw4OImGNE/G4XFpXjohUUoKCp1HEBKUjyeGtsb2Y1rObalgXMFuEaca6bSgjxUquvcN3k410y1BZmoVtiZf/Jwppfq1qbxMHX/rZpjrPlnATDWiGsyXlMTkGmJXhPcnsLI2/oVDk75Z+hXfsN4DiSnoe7w+5GWfbGnGGhsTwGuD3s6hWpVVFyKuV/uxsfztyHn8OlKHaUmx+OKy1rgR31bok7N1LPtVm85iCffWOGoCCiKfw/f2h1d2tR1HzgtHSnANeJILuWNyUO5xI46IA9HcvnSmEx8kdl2J+RhWypfGprGw9T9ty8wY6gTFgBjCLZOQzU1AZmW6HVi7iWW4tx92P/h0yg+vMe2m8TMxqg/+ndIzGho24YNvSnA9eFcv1P5xZixZAc++eJbHDtZ+avutdKTcW3flhjWswXSU0Of1LttzzE8O2EVdu0PffL1udGJ137vv6Ern/xzjsyTBdeIJ/mkG5OHdEk9OSQPT/IpMSYTJbK6dkoerqVTYmgaD1P330rgxbBTFgBjGH4kh25qAjIt0UeSscy+xffQjn85A7lz3gDKwr/mmNbmUtS95j4Ekr9/QkpmPPQVWgGuD/sz48jxfHyy8FvMWLqzyu/3NahTDSP6t8LAS5oiOTE+bAdirazffgTTF+/A0vU5KDvn1JD4QBx6dGiAq3q2QPtWdfjNv7Bqym/ANSJfUy8eycOLevJtyUO+pl49kolXBeXak4dcPb16M42Hqftvr5xof74CLAByRkREAVMTkGmJPiJwJXdafPwgDk9/Gfk71tr2XO/6h1CtdTfb7dlQjgJcH+F13HPwJD6atw3zvtqNktLKv23ZqnFNjBzQGj07NoQo3Lm58gqKsWTFWpzOL0a11ET07N4JaSmhnx504582zhXgGnGumUoL8lCprnPf5OFcM9UWZKJaYWf+ycOZXqpbm8bD1P23ao6x5t/oAmBpaSni48M/ERFrUE0Yr6kJyLREb8JcqCzG8vIynFw1C0c+fxvlRQWOhpLeoT/qXnufIxs29q4A10flGm75LheT5m3DsvU5KK/iTJvOrbMwcmA2OrXOkvKEHpl4n9cyPZCHTDW9+yIP7xrK9EAeMtWU44tM5Ogoywt5yFJSjh/TeJi6/5ZDi14qFDC6ANi7d2+MGDECo0aNQrNmzUjVIAVMTUCmJXqDpsR5oRYf3Y9D019EwXcbXA0hLjkNze9/HXEJfNrJlYAujbg+zhdOvI771eaDmDRvq/VabmWXeMBPPOknnvjLbiL3NF4ycTmZFZmRhyJhXbolD5fCKTIjD0XCenBLJh7EU2BKHgpE9eDSNB6m7r89IKJpCAWMLgC2bdv27BMS3bp1w+jRo3HFFVcgKSmJsDVXwNQEZFqi13waBIUnnvo7Ib71N+9dlBdXfiBChWF5QjIKWvRAyo5liCs5vz1fA/afPtfHGc1LS8vwxZq91hN/O3NOVAoiMSGAwZc0xfD+rdAwM10JMDJRIqtrp+ThWjolhuShRFbXTsnDtXTKDMlEmbSuHJOHK9mUGZnGw9T9tzKAMerY+AJgBbe4uDPfSKpevTquueYa66nACy+8MEax6j9sUxOQaYle/5nwfYTipN9D015Ewe5NIcNOqF0fJUf3n/1ZYlYTHOswHEVJ1ZFUeAK11k0+75RgvgbsP/1YXx8FRSWYvXwXJi/YhoNH8ysFIL7Hd2XP5rimT0vUrp6iFFSsM1Eqrgvn5OFCNIUm5KFQXBeuycOFaIpNyESxwA7dk4dDwRQ3N42HqftvxRhjzr3RBcDly5fjww8/xOzZs1FY+P3TPxXFQFEAvP7663H11VcjPV3N0xUxN2MkDdjUBGRaopeES6mb8rJSHF8xHUcXTEB5SVFQXwk1MpF51b3W6b8Hpz6HsrwTqDjld/2WrSguLkZiYiLat2lt/Txvy3IE0mpYpwCnZXdVGjudn69ArK6PE6eLMH3Rt5i6aAdO5gXP4QqVMmqkYHi/Vhjao5lvh3HEKhNd1yZ56EWGPMhDLwX0i4ZrRC8m5EEeXhQwdf/tZcy0DVbA6AJgxXBOnjyJTz75BJMmTcLGjRvPjrKiEJiSkmK9Gjxy5EiIV4V5RV4BUxMQb7xy507R4T04NO0FFO79JqTjGl2HImPgzQgkp1o/L807gbytXyK94wDr9f8f8hCvEJ/6ej7SWndDfFoNucHSW1gFYm19HMzNw+SF2zFr+XcoLCqtVJ/GddNxXf9s9L+4MRIT/D24KtaYhJ2kEW5AHhEG8IPuyYM89FJAv2i4RvRiQh7k4UUBU/ffXsZM2ygtAJ47rM2bN1tPBU6bNg3Hjx8PKgaKw0LEtwLF4SEZGRmcExFSwNQExBuvnAljPfW3bAqOLvwA5aXFQU4TatVD1lX3ILV5hyo7JA85PGR5iRUe3+WcsA72WLB6L8rKKj/St02z2tbBHpe2q4+AOOkjAlesMImAtK66JA9XsikzIg9l0rpyTB6uZFNqRCZK5XXsnDwcS6bUwDQepu6/lUKMQedR8QRgKG5FRUWYNWuW9VTgsmXLIE5jFFfFU4Hx8fEYOHCg9VRg3759z/55DM6BiAzZ1ARkWqKPCNwwnRYd3IVD055HYc72EC3jUOOSK5HR/ycIJIX/Nhp56EU4mnmIe8jGHbmY+PlWfLnpQJXCd7uwHkYOyEa7lnUifm+JZiZ6zX570ZCHPZ38akUefiltrx/ysKeTn63IxE+1w/dFHuE18rOFaTxM3X/7yTQW+oraAuC58Pbs2WMVAidPnoycnJyzP6ooBtavXx/XXXed9U+jRo1igXvEx2hqAjIt0Ucc9DkBlJeW4NiSj3F00USgrCQotMSMBsi6eixSmtg/vIc8dCKMoFey9YrOXTTiCb8VG/dj0udbsfm7o5U6EU/49e3SyHrir3kDfV4/5xpxx12VFXmoUtadX/Jwp5sqK/JQpax7v2TiXjsVluShQlX3Pk3jYer+2z0hWoZSICYKgBUDF09wLFq0CM8995y1Ua24KgqBgUAAffr0wU9/+lNcdtllnDEKFTA1AZmW6BUidOS6cP8O61t/RQd2BNvFBVCz+9Wo3W8MAonJjvyShyO5lDeOJh7FJWVYsGo3Ppq/DbsPnKpUu+SkeFx+aTMM79sKdTPSlGvstINoYuJ07Dq2Jw+9qJAHeeilgH7RcI3oxYQ8yMOLAqbuv72MmbYhtt7lFe/GRrk64pXgzz77DBMnTsTKlSvPvhL8w+FXFANFAfDxxx/nE4GK5oWpCYg3XmcTQnzfTzzxJ578E6f4/vBKrNMIWdeMQ0qjC5w5/l9r8nAlmzKjaOCRV1CMz5Z9hykLt+PI8YJKtaqeloRrerfAlb1aoGa6s8K1MgAhHEcDEz/1Ut0XeahW2Jl/8nCml+rW5KFaYef+ycS5ZiotyEOlus59m8bD1P23czK0qEqBqH8CUJwKLIp+4lAQcVqwuCqKfgkJCejfvz+GDBlifSdw5syZyM/PP6tXZmam9epwvXr1OIskK2BqAjIt0UvG5shd4b5tODjtBRQf2hVsFxdArct+hFp9rkcgIcmR33Mbk4dr6ZQYmszj6MkCTP3iW3y6ZCdO5wcfTFMhWN3aqRjeLxtDujdFSnKCEh1lOjWZiUwddPFFHrqQOBMHeZCHXgroFw3XiF5MyIM8vChg6v7by5hpG2IbHo1PAJ44cQJTp061Cn/iVOBzi37ivxs3boxRo0ZZB4BkZWWdVeX06dPWCcIvv/wyjh07Zn28XbQTTwLykquAqQmIN97w86CspAjHvvgAx5ZOAcrLggwSs5qi7tVjkdwwO7yzMC3Iw7OEUh2YyGP/kdPWa75zV+xCUUnwfK0QSHzX77oB2ejTuRES4gNSdVPpzEQmKvWItG/yiDSB8/snD/LQSwH9ouEa0YsJeZCHFwVM3X97GTNto7wAuHTpUqvoN2fOHIhXfs8t/Imn/cSpvz/+8Y/Rq1evKueCODTkmmuuQUFBAerWrYsFCxZw7khWwNQExBtv1ROhYO831rf+ig/vCW4YiEetniNQu9coxCUkSplR5CFFRmlOTOKxfc8xTJq3DYvX7kXZmUPiQ17iJN9RA1vj4rZ1I36irxtQJjFxMz7TbMhDL2LkQR56KaBfNFwjejEhD/LwooCp+28vY6ZtFBYADxw4YL2m+9FHH2Hv3r3nFf3E/zRt2vTs03516tSxPQfuv/9+65VgUThcv369bTs2tKeAqQmIN97QfMuKC3F0wXs4vmJayKf+kuq1sE74Ta7fwt4EsdmKPGwK5VMz3XmIzz98vfUwJs7bijXfHKpSlR7t62PkwNZo2yzDJ/XUdKM7EzWj1tcreejFhjzIQy8F9IuGa0QvJuRBHl4UMHX/7WXMtI2yAuAdd9wB8dRfWdmZ17Yqvu2XmJiIwYMH4/rrr3d9mu+TTz6Jt956y3riY9OmTZw7khUwNQHxxhs8EQp2bzrz1F9uTvAPAwmo3XuU9eRfXLz876WRh+SF6dGdrjxKy8qxdN0+TPp8K7btOV7pKBPi4zDg4iYY0T8bTepV96iGHua6MtFDHf+jIA//Na+qR/IgD70U0C8arhG9mJAHeXhRwNT9t5cx0zbKCoBt27a1CnQVhb/mzZtj9OjRGDFiBDIyvD21wQKg2uViagLijff7eVFWVIDc+e/ixMoZovweNGGSG7SynvpLqttM2WQiD2XSunKsG4+i4lLM/XI3Pp6/DTmHT1c6ptTkeFxxWQv8qG9L1KmZ6mrsuhrpxkRXnfyKizz8UtpeP+RhTye/WpGHX0rb74dM7GvlR0vy8ENl+32YxsPU/bd9ImxpRwGjTwEWBcCkpCTrFF/xbb/u3bvbGTPbaKCAqQnItESvCnX+d+txaNqLKDl2ILiL+ARk9P0xavb4EeIC8apCsPySh1J5HTvXhcep/GLMWLIDn3zxLY6dLKx0HLWqJ+PaPi0xrGcLpKfK+S6lY9EUG+jCRPEwjXFPHnqhIg/y0EsB/aLhGtGLCXmQhxcFTN1/exkzbYMVMLoA+MYbb2D48OGoVasW2RqmgKkJKNZvvGWF+cj9/G2cWPVZyBmX3OiCM0/9ZTb2ZUbGOg9fRHbQSaR5HDmejykLv8XMpTuRX1hSaeQN6lTDiAHZGNStCZIS1RapHcinpGmkmSgZlMFOyUMveORBHnopoF80XCN6MSEP8vCigKn7by9jpm2UFQAJ1FwFTE1AsXzjzft2LQ5/+hJKjgcfnhCXkITa/W5Aze5XKX/q79xZH8s8dFz9keKx5+BJfDRvG+Z9tRslpZUf6duqcU2MHNAaPTs2RHwgTkcJpccUKSbSBxIlDslDL5DkQR56KaBfNFwjejEhD/LwooCp+28vY6YtC4CcA5ooYGoCisUbb1nBaRyZ+xZOrpkTcvakNLkQmVfdi6Q6DX2fXbHIw3eRHXToN48t3+Vi0rxtWLY+B+WV1/3QuXUWRg7MRqfWWdZ3Y2Pp8ptJLGnrZqzk4UY1dTbkoU5bN57Jw41qam3IRK2+Tr2Th1PF1LY3jYep+2+1FGPPu9GvAMcerugZsakJyLRE73XG5G1bhUOfvozSk0eCXMUlJiNjwI2o0W0Y4uICXrtyZR9rPFyJ5KORHzzEoU9fbT6ISfO2Yv324HlZMVzxgJ940k888ZfdJHY/E+EHEx+nmPFdkYdeCMmDPPRSQL9ouEb0YkIe5OFFAVP3317GTNtgBbQvAK5cudIXbpdccokv/bCTMwqYmoBi5cZbmn8KR+b8F6e+nh9yyqY0a4esq+5FYu36EZ3SscIjoiI76Fwlj9LSMnyxZq/1xN/OnBOVRpWYEMDgS5pieP9WaJiZ7iD66Gyqkkl0KqZ2VOShVl+n3snDqWJq25OHWn3deCcTN6qpsyEPddq68WwaD1P3327Y0KZyBbQvAIqTflW/siX8b9y4kfPERwVMTUCmJXo3SE9/sxKHZ/wHpaeOBpnHJaWgzsCbUb3r5RF76u/coGKBhxuGkbJRwaOgqASzl+/C5AXbcPBofqVDq5aaiCt7Nsc1fVqidvWUSEmgXb8qmGg3SIMCIg+9YJEHeeilgH7RcI3oxYQ8yMOLAqbuv72MmbbBChhRAFQNThQAN23apLob+j9HAVMTUDTfeEvzTuLIrNdwasMXIedqaotOyLzqbiTWrKvNXI5mHtqI7CAQmTxOnC7C9EXfYuqiHTiZV1RpFHVqpuBHfVthaI9mSEtJdBBtbDSVySQ2FFM7SvJQq69T7+ThVDG17clDrb5uvJOJG9XU2ZCHOm3deDaNh6n7bzdsaFO5AtoXAG+++WZf+L399tu+9MNOzihgagIyLdHbnW+nNi/FkZmvoPT08SCTuOQ01Bn0U1TvPEj507h2461oF608nOqgS3sZPA7m5mHywu2Ytfw7FBaVVjq0xnXTMXJANvp1bQLx2i+v0ArIYEJt5SlAHvK0lOGJPGSoKM8HecjTUpYnMpGlpBw/5CFHR1leTONh6v5bFi/6OaOA9gVAgopOBUxNQKYl+nCzRxT8Dn/2Ck5vWhqyaWqrLsi68m4k1MgM5yoiP482HhERUVKneQXFWLJiLU7nF0O8jtuzeydHT+R9l3PCOthjweq9KCur/EjfNs1qWwd7XNquPgLipA9eVSrANaLXBCEP8tBLAb2i4frQi4eIhkz0YkIe5OFFAVP3317GTNtgBVgA5KyIiAKmJqBoufGKk1RPb1yMw7NeQ1le8IEKgZRqqDPkNqR36K/dU3/nTtho4RGRRSihUzGP1m0/jOmLd2DZ+v3nFe5Ece6y9g1wZa/m6NAqM+Q8EvYbd+Ri4udb8eWmA1VG1O3CetYTf+1a1tF6TkqQVaoLrhGpcnp2Rh6eJZTqgDykyunZGXl4llC6AzKRLqknh+ThST7pxqbxMHX/LR1cjDtkATDGJ0Ckhm9qAjIt0YfiW3LqKA7P+D/kfbMiJP601pcgc9hdSKieEanpYbvfaOBhe7CaNdy25xienbAKu/afDBtZ0/rV8asbuiK7cS2rrXjCb8XG/Zj0+VZs/i74sJkKh6KI2LdLI+uJv+YNaoTthw2CFeAa0WtWkAd56KWAXtFwfejFQ0RDJnoxIQ/y8KKAqftvL2OmbbACRhcAJ0+ebI2oZcuW6Nixo2O+GzZswNatWy274cOHO7angXsFTE1AJt94xdNWp9YvwJFZ/0VZwakgeIHUdGRefgeqtettzBNWJvNwv3oib7l6y0E8+cYKFFTxjb4fRpmSFI/f3nwJjp8qwEfzt2H3geA5WGGTnBSPyy9thuF9W6FuRlrkB2xwBFwjesEjD/LQSwG9ouH60IsHC4DkoZ8CekVkWs4ydf+tF3XzozG6ANi2bVurUHHjjTfi0UcfdUzj6aefxn//+18EAgFs3LjRsT0N3CtgagIyLdFXECo5cQSHZ/wHedu+CgmtWtseqDP0TiSkn3lCy5TLVB6m6BsqTvHk30MvLHJU/LM73uppSbimdwtc1bslalRLsmvGdlUowDWi1/QgD/LQSwG9ouH60IsHC4DkoZ8CekVkWs4ydf+tF3Xzo2EB8L//tYqImzZtMp+mQSMwNQGZlujFU38n136O3DlvoKwwL2iGBNJqIPOKO5F+YU+DZs/3oZrGw0iRzwlazKdxf59n67VfJ2OtWzsVw/tlY0j3pkhJTnBiyrZhFOAa0WuKkAd56KWAXtFwfejFgwVA8tBPAb0iMi1nmbr/1ou6+dGwAMgCYERmsakJyKREX3L8EA59+jLyv10TknG1i3oh8/LbEV+tZkTmgIxOTeIhY7yR9vH1tkN45KUl0sIQ3/UTB3v07twICfEBaX7piEVyXecAc5ZeZMiDPPRSQL9ouEb0YkIe5OFFAVP3317GTNtgBWK6APiHP/wBH3zwAVJTU7F69WrODx8VMDUBmXDjtZ76Wz0bR+a+hfKi/CCq8dVqWYd8VGtzqY/E1XRlAg81I4+M17+8uRKLv97nuXPxeq84FOTitnWN+d6k50FHyAHXSISEr6Rb8iAPvRTQKxquD714iGjIRC8m5EEeXhQwdf/tZcy0ZQHwPAWuueYa6xCQJk2aYPbs2ZwfPipgagLS/cZbfOwADk1/CQU714Wkmd6hH+oMuQ3xqdV9pK2uK915qBu5/57zCopxw2MzrBN8vV7idN8Jjw9DWkqiV1e0D6MA14heU4Q8yEMvBfSKhutDLx4sAJKHfgroFZFpOcvU/bde1M2PxpgnAFeuXBmk9s0332w9PTJ48GDccssttmiUlJTgwIEDmDlzJubPn2/ZDx06FP/85z9t2bORHAVMTUC6Jvry8jKc+HImcue9i/LigiBI8ekZyLzy56jWupscgJp40ZWHJvJIDWPX/hMY+7d50ny++NuBaFIvOgrR0kRR4IhrRIGoHlyShwfxFJiShwJRPbgkDw/iKTIlE0XCunRLHi6FU2RmGg9T99+K8MWsW2MKgBUn/p5LSrzqKC5RxHNzCXth+5///Ad9+/Z144I2LhUwNQHpmOiLc3NwaPqLKNgV+iTr6p0GImPwrYhPqeaSlr5mOvLQVy1vkX2z6yge+NdCb07OsX7ml31xQdPa0vzRUWgFuEb0mhnkQR56KaBXNFwfevEQ0ZCJXkzIgzy8KGDq/tvLmGkbrIBRBUDZAEXx76677sKvfvUr2a7pL4wCpiYgnW685WWlOL7yUxydPx7lJUVBisfXyETWlXcjrVWXqJ2POvGIWpH/NzA+AWgmYa4RvbiRB3nopYBe0XB96MWDBUDy0E8BvSIyLWeZuv/Wi7r50RhTABSv+/7wEq8FiyJeVlYWmjVrFpaGaJucnIxatWqhdevWuPzyy9G8efOwdmwgXwFTE5Auib7oyF4cmvoCCvduCQmnepfLUWfQzQgkp8mHp5FHXXhoJImSUI4cz8c7MzdjzopdUvzHB+Iwnt8AlKJlOCdcI+EU8vfn5OGv3uF6I49wCvn7c/LwV287vZGJHZX8a0Me/mltpyfTeJi6/7bDgm3sK2BMATDUkCpeC77xxhvx6KOP2h81W0ZcAVMTUKQTvfXU3/KpOLrgPZSXFgdxTKhZF1lX3YPUFh0jztiPACLNw48xRrKPoycKMPHzrZixdCeKS8qkhdKrU0P8/pZLpPmjo8oV4BrRa3aQB3nopYBe0XB96MVDREMmejEhD/LwooCp+28vY6ZtsALGFwDFkG666SYWAA2b3aYmoEjeeIsO7Trz1F/OtpC0a3QbhowBNyKQlGrYbHAfbiR5uI9af8vjpwrx0bxtmLZ4B4qKS6UH/OQ9vdAhO1O6XzoMVoBrRK9ZQR7koZcCekXDW7aIgwAAIABJREFU9aEXDxYAyUM/BfSKyLScZer+Wy/q5kdjdAHQfPljdwSmJqBIJPry0hIcWzoZRxd9CJSWBE2ahNr1kXX1vUht2i7mJlQkeESzyKfyivDxgu2Y+sV25BfKL/wJ7ZrVr47nHhzg+vCmaNZfxdi4RlSo6t4nebjXToUleahQ1b1P8nCvnSpLMlGlrDu/5OFON1VWpvEwdf+til+s+mUBMFbJR3jcpiYgvxN94YGdODT1eRQd2BGCWBxqdr8Ktfv/BIHE5AgTjUz3fvOIzCjV95pXUIwpC7/FlAXbcLoguMgsIkiIj8OQS5uh+0X18fRbK1FQ5LxAmJIUj6fG9kZ241rqB8UeLAW4RvSaCORBHnopoFc0XB968eA9hDz0U0CviEzLWabuv/Wibn40xhcA8/LyUFZ25ttU6enptomIBSCuQCCAtLToPijBtig+NjQ1AfmV6MX3/Y4u/gjHFk8CyoILLYl1GiLr6rFIadzWR2r6deUXD/1GLiei/MISTFv0LT6evw0n84K/KXkmR8ZhULcmGDOkDepmnMmVq7ccxJNvrHBUBBTFv4dv7Y4uberKCZ5ebCnANWJLJt8akYdvUtvqiDxsyeRbI/LwTWrbHZGJbal8aUgevshsuxPTeJi6/7YNhA1tKWB0ATA3Nxf9+vVDSUkJBg0ahOeff97WoEWjcePGYe7cuUhJScGCBQtQo0YN27Zs6F0BUxOQH4m+MOdbHJr2PIoOfhcsdFwANXtci9p9ro/Zp/7OFcUPHt5nu34eCotLMWPJDuuAj+OnikIGGIgD+l/cBD8ecgEaZgb/5cq2Pcfw7IRV2LX/ZNgBitd+77+hK5/8C6uU/AZcI/I19eKRPLyoJ9+WPORr6sUjeXhRT40tmajR1a1X8nCrnBo703iYuv9WQy92vRpdAHznnXfwxBNPWN+SevPNN9G9e3fbJJctW4Zbb73Vsv3jH/+IMWPG2LZlQ+8KmJqAVCb68pJiHP3iA+t7fygPPnE1MbMxsq4eh5RGrb0DiBIPKnlEiUTnDaO4pBSfLfsOH8z5BkdPFoYcYlwc0KdTI4y5vA2a1KtepQzl5eVYv/0Ipi/egaXrc1BWVn62fXwgDj06NMBVPVugfas6/OZfhCYU10iEhK+kW/IgD70U0Csarg+9eIhoyEQvJuRBHl4UMHX/7WXMtA1WwOgC4NixY62n+GrXro2lS5c64is2rpdddhmOHz+OIUOG4N///rcjezb2poCpCUjVjbdg71brqb/iw3tCrNIAavUcgdq9RyMuIdGb8FFmrYpHlMmEktIyzFmxC+/P+QaHj+VXOrzLOjTAT4a2RfMGzp+IFt8RXLJiLU7nF6NaaiJ6du+EtBTO10jPJa6RSBM4v3/yIA+9FNArGq4PvXiwAEge+imgV0Sm5SxT9996UTc/GqMLgIMHD8bevXvRq1cvvPrqq45p3HHHHVi0aBGaNWuGzz77zLE9DdwrYGoCkp3oy4oLcXTh+zi+fGrIp/6S6jaznvpLbtDSvdhRbCmbR7RJVVpahnlf7cF7s7fgQG5epcO75KJ6VuHP6+Ec5KHfDCITvZiQB3nopYBe0XB96MWDBUDy0E8BvSIyLWeZuv/Wi7r50RhdAOzcuTMKCwtx3XXX4c9//rNjGo888ggmTZqE1NRUrF692rE9DdwrYGoCkpnoC/ZsxqFpL6D4yL5gIQPxqN1rFGr1GoG4eD5FVdlMk8nD/WzWz7K0rBxfrN6DCbO2YN/h05UG2OWCLNx4RVu0aZYhZRDkIUVGqU7IRKqcnp2Rh2cJpTogD6lyenZGHp4llO6ATKRL6skheXiST7qxaTxM3X9LBxfjDo0uAHbs2BHFxcUYOXKk9S1Ap9ejjz6KiRMnIiEhAevXr3dqzvYeFDA1AclI9OKpv9z543FixXQA338zrULOpPotrRN+k+s196BwbJjK4BFNSolv8C1Ztw/jP9uC3QcqP5yjQ6tMq/DXrmUdqcMnD6lySnFGJlJklOaEPKRJKcUReUiRUZoT8pAmpTRHZCJNSimOyEOKjNKcmMbD1P23NGB0ZClgdAGwb9++OHToEHr27InXXnvNMdLbb78dixcvRmZmpvUqMC//FDA1AXlN9PnfbcCh6S+i5Oj+YLHjE6zTfWv1+BHi4hP8g2FwT155GDz080IX3zRdvmE/xn+2GTv2nah0WBc2z7AKf51aZykZOnkokdWTUzLxJJ90Y/KQLqknh+ThST7pxuQhXVLPDsnEs4RSHZCHVDk9OzONh6n7b8+g6OA8BYwuAN5000348ssvrVd4xSEgKSkptvEWFBRYh4CIf4snCd9//33btmzoXQFTE5DbRF9WlI/cz9/Bia9mhhQvuWFrZF19L5KymnoXN4Y8uOURLRKJwt9Xmw/i3c82Y9vuY5UOK7tJLdx0RVt0bVNX6Wm8sc5Dx3lFJnpRIQ/y0EsBvaLh+tCLh4iGTPRiQh7k4UUBU/ffXsZM22AFjC4APv/88xD/xMXF4Re/+AXuuece24xfeOEFPPfcc5btXXfdhV/96le2bdnQuwKmJiA3N978HV/j0PSXUHL8YJBw4vt+tfuNQc1Lr0FcIN67sDHmwQ2PaJBIFP6+3noY78zchM3fHa10SC0a1sCNQ9uie7v6Sgt/FQHEKg+d5xSZ6EWHPMhDLwX0iobrQy8eLACSh34K6BWRaTnL1P23XtTNj8boAuDu3bsxbNgwlJaWIj4+Hn/729+s/w93ffrpp/jNb35j2Ynv/02dOhUtWrQIZ8afS1TA1ATkJNGXFebhyNy3cHL17JDKJTduY33rL6lOI4nKxpYrJzyiRZkN3x6xCn/rtx+pdEhN6lW3Cn+XdWiAQCDOt6HHIg/fxHXZEZm4FE6RGXkoEtalW/JwKZwiM/JQJKwHt2TiQTwFpuShQFQPLk3jYer+2wMimoZQwOgCoBjPn/70J4wfP94amnia78orr4R4NbhTp04IBAJnh1xWVoa1a9finXfegSgAiidoRPvRo0dbPnj5q4CpCejrNWusg2cSExPRsXPnSkXL274ahz59GaUnDge1iUtIQsaAG1Gj2zA+9edx2pl24/Uy3C3f5eKdmZux5ptDlbppmFkNNwxtiz6dGyHex8JfRUCxxMMLSz9tycRPtcP3RR7hNfKzBXn4qXb4vsgjvEZ+tyATvxWvuj/yIA8vCpi6//YyZtoGK2B8AbCoqAi33HIL1qxZc94rbuJ7gA0aNEBaWhry8vKQk5Njfe9PXKL4Jy5RJHz77beRlJTEueGzAiYmoLLCfOx49meIKy1CeXwSWvzqdQSSU89TrrTgNI7MfgOnvv48pKIpTS9C1lX3IjGjgc+KR2d3sfCL0LY9x/DuzM34ctOBSiHWy0jDmCFtMODixoiP//4vPvymHgs8/NbUa39k4lVBufbkIVdPr97Iw6uCcu3JQ66eMryRiQwV5fkgD3layvBkGg8T998yONHH+QoYXwAUwxEFvkceeQQzZsw4b3TiCb+Kq6LoV/H/V1xxBZ544gmkp6dzTkRAARMT0IlVs3B4xn/OqpV55d2o0WXI2f8/vfVLHP70Pyg9lRukaFxiyv+e+rsCcXGRK9BEALXSLk278ToRY2fOCetU36Xrcio1y6yVih8PvgCDuzdFQgQLfxUBRjMPJ+x0aksmOtHgB/X1okEe5KGbAvrFw3uIXkzIgzy8KGDi/tvLeGkbWoGoKABWDG3JkiV4/fXXsXLlShQWFgaNODk5GZdeeiluu+026wRgXpFTwMQEtOe136Bo/7dnRUuq3wqNb/8rSvNP4sis13Fq/cKQgqY074Csq+5BYq16kRM8SnuOxl+Edh84iQmztmDR2r3438PKQfQyaiRj9KALMLRHMyQm6HN4TDTyMH3pkIleBMmDPPRSQK9ouD704iGiIRO9mJAHeXhRwMT9t5fx0jYGCoAVQxSvBW/evBmHDx/G6dOnUa1aNWRmZqJt27Z83VeTlWBaAirM2Y69r/82uBAz8BYcX/4JSk8fC/pZXFIq6gy6BdW7DPHlBFZN0PoaRjT9IrTv8Cm8N2sLFqzag7IzXykIumqmJ2HUwNYY1rMFkhP1KfxVBBpNPHydyAo7IxOF4rpwTR4uRFNoQh4KxXXhmjxciKbYhEwUC+zQPXk4FExxc9N4mLb/VowvZt1H1ROAMUvRwIHrnIAOz/g/nN668jxVy4sKIE71tXultuyMrCvvRkLNLLsmbOdCAdNuvKGGeDA3D+/N3oK5X+5GWSWVv+ppibhuQGtc1asFUpMTXCjlj0k08PBHKf96IRP/tLbTE3nYUcm/NuThn9Z2eiIPOyr524ZM/NU7XG/kEU4hf39uGg+d99/+kovt3lgAjG3+ERu9rgmorLgQ3z17G8qLg18hty1WfAKa/fpNxCel2DZhQ3cKmHbjPXeUR47n4/0532D28u9QUhr6kb9qKQkY3j8b1/ZpibSURHci+WhlMg8fZfK1KzLxVe6wnZFHWIl8bUAevsodtjPyCCuR7w3IxHfJq+yQPMjDiwK67r+9jIm2zhVgAdC5ZrSQoICuCahg71bse+MhcVa0h1HGoeGtTyGlUWsPPmhqRwETfxE6eqIAEz/fihlLd6K4pCzkMFOT43FNn1YY0a8V0tPMOaXcRB525pnJbchEL3rkQR56KaBXNFwfevEQ0ZCJXkzIgzy8KKDr/tvLmGjrXIGoKgCKpDh16lR89dVX2L9/P06cOIGysjJs3LjxPGXEn69evdr6s3r16lnfBuTlrwI6J6C8rV/h4JR/Onrlt0K9QHIa6g6/H2nZF/sraIz2ZtIvQsdPFWLSvG2YvngHiopLQxJLSozHNb1bYET/bNRMTzaOqkk8jBPXZcBk4lI4RWbkoUhYl27Jw6VwiszIQ5GwHtySiQfxFJiShwJRPbg0jYfO+28PGGjqUIGoKADm5ubioYcewsKF35/CWv6/4zPj4uKwadOm82QpLi7GwIEDrUNCGjdujNmzZzuUjc29KqB7AirO3Yf9Hz6N4sN7bA81MbMx6o/+HRIzGtq2YUNvCphw4z2ZV4SP52/DtEXfIr8wdOEvMSGAYT2bWwd81K5u7qvjJvDwNuPMsyYTvZiRB3nopYBe0XB96MVDREMmejEhD/LwooDu+28vY6OtfQWMLwAeOHAAN9xwA3JyclBR9Dt3+KEKgOLnL7zwAp577jnrdNYJEyagc+fO9lVjS88KmJCAygrzcXDqc8jbsjzseNPaXIq619yHQHJq2LZsIE8BnX8ROp1fjE8WbsfkhduRV1ASctAJ8XG4/NJmuH7wBahT0/y5ozMPebPOLE9kohcv8iAPvRTQKxquD714sABIHvopoFdEpuUsE/bfehGOzmiMLwCOGTMGa9assehkZ2fj3nvvRY8ePfD8889j/PjxVoHvh08AirY7d+7EFVdcYf187NixGDduXHQS1nRUpiSg09+sxIEP/xJWxXrXP4RqrbuFbccGchXQ8cabX1hiPe330bxtOJVfHHLAgUAcBl/SFD8efAHqZqTJFSWC3nTkEUE5tOiaTLTAcDYI8iAPvRTQKxquD714sABIHvopoFdEpuUsU/bfelGOvmiMLgDOmTPHKtyJIt7FF1+MV155BampZ56iefzxx/Huu+9WWgAUbfr164eDBw+id+/eli0v/xQwJQEd/OTfOLVuQVhh0jv0R91r7wvbjg3kKqDTjbegqAQzluzEpHlbcfxUUejCXxzQ/+ImGDOkDRpkVpMrhgbedOKhgRxahEAmWmBgAVAvDORBHpoqoF9YvIfoxYQ8yMOLAqbsv72MkbbhFTC6APjAAw9g+vTpSEhIwMyZM63v+VVcdgqAd911l/XdQGEniom8/FPAhARUXlKMnf/8GcoL88IKE5echub3v464hMSwbdlAngI6/CJUXFKKmUu/w4dzv8HRk4UhBxcXB/Tp1AhjLm+DJvWqyxNAM0868NBMkoiHQyYRR3BeAORBHnopoFc0XB968RDRkIleTMiDPLwoYML+28v4aGtPAaMLgIMGDcK+ffvQrVs3vP322+eN2E4BUBwc8vHHHyMtLQ2rVq2ypxhbSVHAhAQU6vXf8oRkFLTogZQdyxBXcn6xh68BS5kajpxE8heh4pIyzFm5Cx/M3oLDxwsqjfuyDg1w49C2aNaghqOxmdg4kjxM1MuPmMnED5Xt90Ee9rXyoyV5+KGy/T7Iw75WfrUkE7+UttcPedjTya9WpvEwYf/tF7tY7sfoAmCXLl1QUFCAUaNGWa/8nnvZKQD+8Y9/xPvvv289Qbh+/fpYnge+j92EBPTD138Ts5rgWIfhKEqqjqTCE6i1bvJ5pwTzNWDfp1FE/ma6tLQM877ajQmzv8HB3MqfDr3konpW4a9V41r+CxOhHk37RShCMvnaLZn4KnfYzsgjrES+NiAPX+UO2xl5hJXI9wZk4rvkVXZIHuThRQET9t9exkdbewoYXQDs2rUr8vPzcd111+HPf/6z4wLg/fffb706nJGRgSVLlthTjK2kKGBCAsrbtso6Bbgs7wQqTvldv2UriouLkZiYiPZtWp89JTiQVsM6BTgtu6sUfejEngJ+/iJUWlaOL1bvwYRZW7Dv8OlKA+xyQRZuvKIt2jTLsDeIKGrlJ48okk3pUMhEqbyOnZOHY8mUGpCHUnkdOycPx5IpNyAT5RI76oA8HMmlvLFpPEzYfyuHxg5gdAFw8ODB2Lt3Ly699FK88cYbjguAl19+OXbt2oW2bdti8uTJnA4+KmBKAirNO4G8rV8iveMA60CZHyb68vIynPp6PtJad0N8WvS/4unjFLHVlR833rKycixZtw/jP9uM3QdOVRpXh1aZVuGvXcs6tmKPxkZ+8IhG3VSOiUxUquvcN3k410ylBXmoVNe5b/JwrplqCzJRrbAz/+ThTC/VrU3jYcr+WzW3WPdvdAGw4gk+cfLvF198gfT09LM8w70CLBbs9ddfbxV1brjhBvzhD3+I9bng6/hNTUCmJXpfoUagM5U8ysvLsXzDfrw7czN25pyodHQXNs+wCn+dWmdFQAG9ulTJQ6+RmhMNmejFijzIQy8F9IqG60MvHiIaMtGLCXmQhxcFTN1/exkzbYMVMLoAOGXKFPzud7+zini33347HnzwQVsFQPEK50033YS1a9datq+99hp69uzJ+eGjAqYmIN54fZwkNrpSwUMU/r7afBDvfrYZ23YfqzSK7Ca1cNMVbdG1TV0rj/DiRkHHOaBijeg4TlNiIg+9SJEHeeilgH7RcI3oxYQ8yMOLAqbuv72MmbZRVgAsKSnBsGHDsGfPHmsD/vvf/x633HKLNcrKngDMzc3Fb37zGyxevNiyadeuHSZOnMi54bMCpiYg3nh9nihhupPJQxT+1m49ZD3xt/m7o5X23KJhDetwj+7t6rPw9wOVZPLQa6aZGw2Z6MWOPMhDLwX0iobrQy8eIhoy0YsJeZCHFwVM3X97GTNto6wAKIazfPly6+m/0tJSa3QXXXQRrrrqKixbtgwLFy60NujPP/88Dh06hFWrVmH27NnWycFisy9eHX7vvffQpk0bzg2fFTA1AfHG6/NE8akAuH77YbwzczM2fHuk0h6b1q+Onwxti8vaN0AgwCf+QgnF9aHX+uDmjTz0U0CviJizyEMvBfSLhmtELybkQR5eFDB1/+1lzLSNwgKgGNKMGTPw0EMPWYW9cK/iicKfuNLS0vCPf/wD/fv357yIgAKmJiDeeCMwWaro0iuPzd/l4t0Zm7Fm66FKe2mYWQ03DG2LPp0bIZ6FvyongFcees2u6IiGTPTiSB7koZcCekXD9aEXD/4lEnnop4BeEZmWs0zdf+tF3fxojP4G4Lnyb926FU888YT1ROC5lygIVhT9Kv68e/fueOyxx9C6dWvzCRo6AlMTkGmJ3tDpYSvsvIJiLFmxFqfzi1EtNRE9u3dCWkqiLVvxbT/xjb8vNx2otH29jDTccHkb9O/aGPHxAVt+Y70R14d+M4BM9GJCHuShlwJ6RcP1oRcPFgDJQz8F9IrItJxl6v5bL+rmRxM1BcAKFJs3b7Ze/V29ejUOHjwIMdHFq7516tRBp06drCf+OnbsaD45w0dgagIyLdEbPk2CwhfF/HXbD2P64h1Ytn4/ysrOPNErLvFarng998pezdGhVWbIp4F37DuO8Z9ttmwruzJrpWLMkAsw6JKmSGDhz9EU4vpwJJcvjcnEF5ltd0IetqXypSF5+CKz7U7Iw7ZUvjUkE9+kttURediSybdGpvEwdf/tG9AY6SjqCoAxws34YZqagExL9MZPlHMGsG3PMTw7YRV27T8Zdljie32/uqErshvXstruPnDSKvwtWruvUtuMGsm4ftAFuLxHMyQmxIftgw2CFeD60G9WkIleTMiDPPRSQK9ouD704iGiIRO9mJAHeXhRwNT9t5cx0zZYAaMLgOJpv4pLvM4bH89NuymT3NQExBtvZGbY6i0H8eQbK1BQdOawHztXSlI8fn5dB6zdehgLV+3BOQ8LnmdeKz0ZIwe2xrCezZGcyBxiR9vK2nB9eFFPjS2ZqNHVrVfycKucGjvyUKOrW6/k4VY5dXZkok5bN57Jw41q6mxM42Hq/lsdwdj0bHQBsG3bttZrfg0bNsTcuXNjk6ChozY1AZmW6A2dHueFLZ78e+iFRY6Kf3bGXT0tEdcNaI2re7VASnKCHRO2CaMA14d+U4RM9GJCHuShlwJ6RcP1oRcPEQ2Z6MWEPMjDiwKm7r+9jJm2wQoYXQBs3749SktLceWVV+KZZ54hX4MUMDUB8cbr7yQT3/wb9/d5tl77tRtZtZQEDO+fjWv7tLR9aIhd37HejutDvxlAJnoxIQ/y0EsBvaLh+tCLBwuA5KGfAnpFZFrOMnX/rRd186MxugA4YMAA7N+/H6NGjcLjjz9uPo0YGoGpCci0RG/6lPp62yE88tISKcNITY7HtX1aYXi/VkhPS5Lik07OV4DrQ78ZQSZ6MSEP8tBLAb2i4frQiwcLgOShnwJ6RWRazjJ1/60XdfOjMboAePvtt2Px4sXo1asXXnvtNfNpxNAITE1ApiV606fUX95cicVfV35wh93xNcpKx9PjeqNmerJdE7ZzoQDXhwvRFJuQiWKBHbonD4eCKW5OHooFduiePBwK5kNzMvFBZAddkIcDsXxoahoPU/ffPqCMqS6MLgB++OGHeOyxx5CSkoJ58+ahdu3aMQXP5MGamoBMS/Qmz5G8gmLc8NgMlFV2eoeDwQUCcZjw+DC+8utAMzdNuT7cqKbWhkzU6uvUO3k4VUxte/JQq69T7+ThVDH17clEvcZOeiAPJ2qpb2saD1P33+pJxlYPRhcACwsLMXLkSGzfvh1XXHEFnn322diiZ/BoTU1ApiV6g6cIdu0/gbF/mydtCC/+diCa1KsuzR8dBSvA9aHfrCATvZiQB3nopYBe0XB96MVDREMmejEhD/LwooCp+28vY6ZtsAJGFwDFcETx784770ROTg569+6Nhx9+GC1atCBrzRUwNQHxxuvfxPpm11E88K+F0jp85pd9cUFTPiUsTdAQjrg+VKrrzjeZuNNNlRV5qFLWnV/ycKebKivyUKWse79k4l47FZbkoUJV9z5N42Hq/ts9IVqGUsDoAuDzzz9vjenYsWN47733rBOBxdWmTRu0a9cOGRkZSE62982vcePGcYb4qICpCci0RO8jUuld8QlA6ZIqd8j1oVxixx2QiWPJlBqQh1J5HTsnD8eSKTUgD6XyunJOJq5kU2ZEHsqkdeXYNB6m7r9dwaFRpQoYXQBs27Yt4uLizhtceXl50J/Z4b9p0yY7zdhGkgKmJiDTEr0kXBFxY30D8NEZKCsv99x/fCAO4/kNQM86hnPA9RFOIf9/Tib+a15Vj+RBHnopoFc0XB968RDRkIleTMiDPLwoYOr+28uYaRusgPEFQBlQRRGRBUAZStr3YWoC4o3XPmMvLYtLyvDx/G14Z+YmSKj/oVenhvj9LZd4CYm2NhTg+rAhks9NyMRnwcN0Rx7koZcCekXD9aEXDxYAyUM/BfSKyLScZer+Wy/q5kdjdAGw4hVgGRj4CrAMFe37MDUBmZbo7RPRp+X67Yfx4qS12H3glLSgnrynFzpkZ0rzR0ehFeD60G9mkIleTMiDPPRSQK9ouD704sECIHnop4BeEZmWs0zdf+tF3fxojC4Ami9/7I7A1ARkWqI3aYYdP1WIN6ZtxJyVu6SG3ax+dTz34ABXnwaQGkgMOOP60A8ymejFhDzIQy8F9IqG60MvHiwAkod+CugVkWk5y9T9t17UzY+GBUDzGRo5AlMTkGmJ3oTJIb7bOXflbrw+dQNO5hUFhSy+8hkIxKG0zPm3AFOS4vHU2N7IblzLBCmMj5HrQz+EZKIXE/IgD70U0Csarg+9eLAASB76KaBXRKblLFP333pRNz8aowuAb731lkVAfMNvzJgxSExMNJ9IjIzA1ARkWqLXfTrtPnASL0xciw3fHgkZanbjmrh3VCecyivGk2+sQEHRmZO+7Vyi+Pfwrd3RpU1dO83ZRoICXB8SRJTsgkwkC+rRHXl4FFCyOXlIFtSjO/LwKKACczJRIKoHl+ThQTwFpqbxMHX/rQBdTLs0ugBYcQrwRRddhEmTJsU0SNMGb2oCMi3R6zovCotL8cGcb/DRvK0oKQ1+si81OQE3DWuLq3q1hDjBV1zb9hzDsxNWYdf+k2GHJV77vf+GrnzyL6xSchtwfcjVU4Y3MpGhojwf5CFPSxmeyEOGivJ8kIc8LWV5IhNZSsrxQx5ydJTlxTQepu6/ZfGinzMKGF0A7NKlCwoKCjB8+HA89dRTZGqQAqYmINMSvY5TYtXmg3jpo7XYfyQvZHi9OjbEncPbo07N1KCfi9eF128/gumLd2Dp+hyUnfNasCgU9ujQAFf1bIH2rerwm38RgM/pxhoyAAAgAElEQVT1EQHRw3RJJnoxIQ/y0EsBvaLh+tCLh4iGTPRiQh7k4UUBU/ffXsZM22AFjC4ADh06FLt27WIB0MCZbWoC4o3X/WTLPVGAV6esxxdr9oZ0UjcjDfdc1xHdLqxnq5O8gmIsWbEWp/OLUS01ET27d0JaCj8DYEs8RY24PhQJ68EtmXgQT4EpeSgQ1YNL8vAgngJT8lAgqkeXZOJRQMnm5CFZUI/uTONh6v7bIyaa/0ABowuADzzwAKZPn46OHTvigw8+IFyDFDA1AZmW6HWYEuLwjplLduCtGZuQV1ASFJJ4cm9E/2z8eMgFSElKcBQyeTiSS3lj8lAuseMOyMSxZEoNyEOpvI6dk4djyZQakIdSeV05JxNXsikzIg9l0rpybBoPU/ffruDQqFIFjC4ALliwAD//+c8RCATwySefIDs7m6gNUcDUBGRaoo/0dNi+55h1yMfW3cdChnJh8wyMHdUJzRrUcBUqebiSTZkReSiT1rVjMnEtnRJD8lAiq2un5OFaOiWG5KFEVk9OycSTfNKNyUO6pJ4cmsbD1P23J0g0DlLA6AKgGM24ceMwZ84ciANB3nzzTdSsWZOYDVDA1ARkWqKP1FQQr+e++9lmTPviW5zzmb6z4aSnJuK2a9ph8CVNEfjfIR9uYiUPN6qpsyEPddq69UwmbpVTY0ceanR165U83Cqnxo481OjqxSuZeFFPvi15yNfUi0fTeJi6//bCiLbBChhfABQT+cEHH8T8+fORlZVlFQSHDRuGGjXcPVHESeKPAqYmINMSvT80v+9FHNKxbH0O/u/jdTh8vCBk9wO7NcHPrmmHmunJnsMjD88SSnVAHlLllOKMTKTIKM0JeUiTUooj8pAiozQn5CFNSmmOyESalFIckYcUGaU5MY2HqftvacDoyFLA6ALgLbfcYg1CFB1Wr16NkpKSsyd/Nm7cGBkZGUhODl9kiIuLs54e5OWfAqYmINMSvX9EgYO5efjPx+uwYuP+kN02ykrHvaM6omN2lrSwyEOalFIckYcUGaU6IROpcnp2Rh6eJZTqgDykyunZGXl4llC6AzKRLqknh+ThST7pxqbxMHX/LR1cjDs0ugAoXvsVxbsfXqIgGOrPQ7GuaLtp06YYnwr+Dt/UBGRaoveDaklpGT5ZuB3jZ21BYVFpUJeJCQFcP/gCjByQjcSEeKkhkYdUOT07Iw/PEkp3QCbSJfXkkDw8ySfdmDykS+rJIXl4kk+JMZkokdW1U/JwLZ0SQ9N4mLr/VgIvhp0aXwCUwU4UC1kAlKGkfR+mJiDTEr19Iu5abt6Zax3ysTPnREgHnVtn4Z6RHdEwK91dB2GsyEOJrK6dkodr6ZQZkokyaV05Jg9XsikzIg9l0rpyTB6uZFNqRCZK5XXsnDwcS6bUwDQepu6/lUKMQedGFwBXrFghDVn37t2l+aKj8AqYmoBMS/ThSbhrcSqvCG9M34jPln0X0kGt6sm449r26Nulke2ncd1EQh5uVFNnQx7qtHXrmUzcKqfGjjzU6OrWK3m4VU6NHXmo0dWLVzLxop58W/KQr6kXj6bxMHX/7YURbYMVMLoASKDmKmBqAjIt0cueIeKV+QWr9uC1Tzbg2KnCIPfijfwrLmuOW668COKkX9VXrPNQra9T/+ThVDH17clEvcZOeiAPJ2qpb0se6jV20gN5OFHLn7Zk4o/OdnshD7tK+dPONB6m7r/9oRk7vbAAGDustRqpqQnItEQvE/reQ6fw0qS1WLv1cEi3zRvUwNjRndC2WYbMbqv0Fcs8fBPZQUfk4UAsn5qSiU9C2+yGPGwK5VMz8vBJaJvdkIdNoXxsRiY+im2jK/KwIZKPTUzjYer+20ekMdEVC4AxgVm/QZqagExL9DLIF5eUYuLcrfhg7laIAz9+eCUnxePGoW1xbZ+WiI8PyOjSto9Y5GFbnAg0JI8IiB6mSzLRiwl5kIdeCugVDdeHXjxENGSiFxPyIA8vCpi6//YyZtoGK8ACIGdFRBQwNQHF2o137dZD1lN/ew+dDjlPLm1XH3eN6IC6tdMiMo9ijUdERHbQKXk4EMunpmTik9A2uyEPm0L51Iw8fBLaZjfkYVMoH5uRiY9i2+iKPGyI5GMT03iYuv/2EWlMdBV1BcDFixdj+fLl2LBhA44ePYrTp0+jWrVqqF27Ntq1a4cePXqgZ8+eMQFX50GamoBMS/Ru58Cxk4V4bep6zP9qT0gXmbVS8fMRHdCjfQO3XUixixUeUsTywQl5+CCywy7IxKFgipuTh2KBHbonD4eCKW5OHooFduGeTFyIptCEPBSK68K1aTxM3X+7QEOTKhSImgLgnDlz8Le//Q27du0KC7xp06b4zW9+g8GDB4dtywZqFDA1AZmW6J3SKysrx+wV3+GNaRtxKr84yDwQiLNe9f3J0LZITU5w6l56+2jnIV0wxQ7JQ7HALtyTiQvRFJqQh0JxXbgmDxeiKTQhD4XiunRNJi6FU2RGHoqEdenWNB6m7r9d4qFZJQpERQHwL3/5C958801riOKUUjtXXFwcbr31Vvzud7+z05xtJCtgagIyLdE7wbYz5wRenLgWm3bmhjRr07Q27h3VCS0b1XTiVmnbaOahVDhFzslDkbAe3JKJB/EUmJKHAlE9uCQPD+IpMCUPBaJ6dEkmHgWUbE4ekgX16M40Hqbuvz1iovkPFDC+APjKK6/gmWeegSjoieJfQkIC+vTpg65du6JRo0ZIS0tDXl4e9u7di9WrV2PhwoUoKSmxZBA2v/71r3HnnXdyYvisgKkJyLREbwdrQWEJ3pu9BZMXbEdpWXABvVpKAm656iIM7dEc8YE4Oy59axONPHwTT0FH5KFAVI8uycSjgJLNyUOyoB7dkYdHASWbk4dkQSW4IxMJIkp0QR4SxZTgyjQepu6/JaCii3MUMLoAuH//fgwdOhRFRUVW8W/QoEH4wx/+gHr16lUK+eDBg3j88ccxe/Zsq01KSgpmzpyJ+vXrc2L4qICpCci0RB8O6YqN+/Gfj77GwaP5IZv27dIId1zbHrVrpIRzFZGfRxuPiIgosVPykCimJFdkIklISW7IQ5KQktyQhyQhJbkhD0lCSnRDJhLFlOCKPCSIKNGFaTxM3X9LREZX4iG4crvvzGoo13PPPYcXXnjBepLv2muvxdNPP207yt///veYPHmyZTt27FiMGzfOti0belfA1ARkWqKvjNThY/n4v8nrsHRdTsgmDepUw90jO6Jrm7reYSv0EC08FErkq2vy8FVuW52RiS2ZfGtEHr5Jbasj8rAlk2+NyMM3qW13RCa2pfKlIXn4IrPtTkzjYer+2zYQNrSlgNEFwB//+MdYu3atdcqveLVX/NvuJU4HFq8K5+fno2PHjnj//fftmrKdBAVMTUCmJfofoiotLcO0xTvw7sxNyC8sDSKZEB+HkQNaY/TgC5CcGC+BtFoXpvNQq47/3snDf83D9Ugm4RTy9+fk4a/e4Xojj3AK+ftz8vBXbzu9kYkdlfxrQx7+aW2nJ9N4mLr/tsOCbewrYHQBsFevXsjNzcWAAQPw4osv2h/1/1qKJ//mzp2LjIwMLFmyxLE9DdwrYGoCMi3Rn0vom11H8eKktdi+53hIcO1b1cG9IzuhSb3q7sH6bGkyD5+l8qU78vBFZkedkIkjuZQ3Jg/lEjvqgDwcyaW8MXkol9hxB2TiWDKlBuShVF7Hzk3jYer+2zEYGlSpgNEFwPbt26O0tBSjR4/Gn/70J8eoxfcCP/jgA+vgkPXr1zu2p4F7BUxNQKYlekHodH4x3pmxCdOX7ECoQ7JrVEvCz65ph4HdmlivxJt0mcjDJH2dxkoeThVT355M1GvspAfycKKW+rbkoV5jJz2QhxO1/GlLJv7obLcX8rCrlD/tTONh6v7bH5qx04vRBUCvTwDee++9+Pzzz317AlA8bThlyhSr2Hjo0CGkp6ejWbNmGDx4MMaMGWP9v4xLFEW3b99u9bNhwwbr35s3b0ZBQYHlfsSIEfjLX/4ioyvXPkxNQCYlevF5z0Vr9+HVKeuQe6IwJKsh3Zvi1qvbQRQBTbxM4mGivk5jJg+niqlvTybqNXbSA3k4UUt9W/JQr7GTHsjDiVr+tCUTf3S22wt52FXKn3am8TB1/+0PzdjpxegCoJdvAObl5VnfABT/7tChg/UkoKpLfG/wwQcftIqNlV0NGjTAP//5T3Tu3NlzGPfddx9mzZpVqR8WAN1LbEqi33/kNF766Gus2nww5GDFa75jR3VCu5Z13IuhgaUpPDSQypcQyMMXmR11QiaO5FLemDyUS+yoA/JwJJfyxuShXGLHHZCJY8mUGpCHUnkdOzeNBwuAjhFHpYHRBcB///vf1rf/xGuL1113Hf785z/bhvTYY4/hww8/tGzvuece/OIXv7Bt66SheBrv5z//Ob744gvLLDMz03plOTs7G8ePH8e0adOwatUq62c1a9bEhAkT0KpVKyddBLUVTzaKpw0rrlq1akH8s3PnTuuPWAB0L6/uib64pAwfz9+G92dvQVFJWdBAkxICGHN5Gwzvl43EhIB7ITSx1J2HJjL5FgZ5+Ca17Y7IxLZUvjQkD19ktt0JediWypeG5OGLzI46IRNHcilvTB7KJXbUgWk8WAB0hDdqGxtdANy3bx+GDRuGoqIiC5D470ceeQR16lT+VJM4NOTJJ5/E9OnTIV6RTE5OxsyZMyGewFNxvffee/jjH/9ouRZFvzfffNMqAp57Pf3003j99detP+rWrRveffddT6G8/PLLEE8dtmvXzvqnSZMm+Oijj/DQQw9ZflkAdC+vzol+w7dH8MLEtdh94GTIAV7cti7uvq4j6texf1q2e6X8sdSZhz8K6NULeejFQ0RDJnoxIQ/y0EsBvaLh+tCLB+8h5KGfAnpFZFrOYgFQr/kTqWiMLgAK0V566SX861//Ont4QWJiIvr164cuXbqgYcOGSE1NRX5+PnJycrB69WosWLDAKhiK4p94+u+Xv/wl7r77biX6i6f/RCzie3/iEkU4UZD74SXajRw5Eps2bbJ+9Nprr6F3795SY2IBUI6cOib646cK8ca0jZizclfIQWbUSMadwzugV8eGxh3yEY6ajjzCxRzNPycP/eiSiV5MyIM89FJAr2i4PvTiwQIgeeingF4RmZazWADUa/5EKhrjC4BCOHEC8Pjx463iRkVhrzJBxc8rrptuugmPPvqoMu2XLVuGn/70p5b/7t274+233660r0mTJuHhhx+2fi5eZ37qqaekxsUCoBw5dUr0Yi7PXbkbr0/dgJN5Z56CPfcSB/pe1asFbh52IdJSEuUIoJkXnXhoJk1EwiGPiMheZadkohcT8iAPvRTQKxquD714iGjIRC8m5EEeXhRgAdCLetFjGxUFQIHj008/xT/+8Q/s2bMnLJ3GjRvjgQcesF4ZVnn99a9/tZ7mE5c4BOTOO++stDvxlGDFU3/iFeHFixdLDY0FQDly6nLjFa/5itd9xWu/oa5WjWtah3y0blJbzsA19aILD03l8T0s8vBd8rAdkklYiXxtQB6+yh22M/IIK5GvDcjDV7ltdUYmtmTyrRF5+Ca1rY5M48ECoC2sUd8oagqAgpR4ImrRokUQT96J12nF9/7EKb9paWnIyMjAhRdeiB49eliFNvG0oOrrjjvuOHv4h/j2n+i7qqt///7Wq8riWrp0qRWzrIsFQDlKRjrRFxaX4oM53+CjeVtRUvr906wVo0tNTsBNw9riql4tER9QP8flqOreS6R5uI88Oi3JQz+uZKIXE/IgD70U0Csarg+9eIhoyEQvJuRBHl4UYAHQi3rRYxtVBUDdsAwaNOjsE4niVF7x5GFVl3gleeXKlVYTcRCIOBBE1sUCoBwlI3njXbXlIF6e9DVyjpwOOZieHRvgruEdUKdmqpzBGuAlkjwMkMf3EMnDd8nDdkgmYSXytQF5+Cp32M7II6xEvjYgD1/lttUZmdiSybdG5OGb1LY6Mo0HC4C2sEZ9IxYAFSIW3/07fvy41cOqVatQrVrVp6+OGzcOs2fPttqLk3wHDBggLToWAOVIGYlEn3uiAK9OWY8v1uwNOYi6GWm4e0QHXHJRfTmDNMhLJHgYJI/voZKH75KH7ZBMwkrkawPy8FXusJ2RR1iJfG1AHr7KbaszMrElk2+NyMM3qW11ZBoPFgBtYY36RiwAKkTcvn17FBcXWz1s2LABCQkJVfYmvks4bdo0q80zzzyDq6++Wlp0uhcA4+PjEQgEpI1XlaMKnsK/OHFa5VVWVo4V35zErFW5KCwOft1XvOHbu11NDOhUC0kJ+munQis/eaiIP9p8kod+RMlELybkQR56KaBXNFwfevEQ0ZCJXkzIgzy8KFBWVobS0tKzLtq0aYP09HQvLmlroALGFwC3b99u3ZxEMaZVq1a2EVTYJScno0WLFrbtnDRkAbBytX74NxBOdI2Ftjm5RZi28ij2HjlTQP7h1SQzCVd3r416tdQWIWNBa46RClABKkAFqAAVoAJUgApQASoQSwqwABhLtL8fq9EFwL1792LIkCHW4R/Dhw/HU089ZZviQw89hMmTJ0M8efb555+jbt26tm3tNuQrwPYLgHwC8IxWhcVlmLvmKJZsOoHy4If+kJoUwNCLM3Bx63QEfDjIxu5cj1Q7/k1opJQP3S956MVDREMmejEhD/LQSwG9ouH60IsH7yHkoZ8CekVkWs7iE4B6zZ9IRWN0AfDVV1/F3//+d+tE3w8++AAdOnSwraN4Z//666+3bH/3u9/h1ltvtW1rtyEPAbFfADTlbyBUfetBFLGXrc/B/328DoePF4QUbsDFjfGza9qjVvVku1Mw6tup4hH1wikaIHkoEtaDWzLxIJ4CU/JQIKoHl+ThQTwFpuShQFSPLsnEo4CSzclDsqAe3ZnGg98A9Ag8SsyNLgDeeeed+OKLL6yn9xYuXOgYSZ8+fXD48GH069fPOnRD9nXHHXdY8YnrzTffRI8eParson///sjJybHaLF26FBkZGdJC0v0bgLFcADyYm4f/fLwOKzbuD8m7UVY13DuqEzpmZ0mbD9HiyLQbb7ToXtk4yEM/wmSiFxPyIA+9FNArGq4PvXiIaMhELybkQR5eFGAB0It60WNrdAFQnJK7f/9+iMLZSy+95JjK3Xffjfnz56NRo0aYO3euY/twBn/961/x2muvWc0efPBBiIJlZZcoRPbq1cv6cZ06dbBkyZJw7h39nAVAR3JV2ljmjbektAyfLPwW42dtRmHR9x9kreg8MSGA0YMuwKiB2UhMiJczgCjzIpNHlEkTkeGQR0Rkr7JTMtGLCXmQh14K6BUN14dePFgAJA/9FNArItNyFguAes2fSEVjdAGwU6dOKCoqwujRo/GnP/3JsYaPPfYYPvzwQ6SmpmL16tWO7cMZiKf4Kl4tFt8DfPvttys1mTRpEh5++GHr59ddd52j7xmGi0P8nAVAOyqFbyMr0W/emYsXJq7FzpwTITvt3DoL94zsiIZZPJmpKiqyeIQnzxZ2FCAPOyr524ZM/NU7XG/kEU4hf39OHv7qHa438ginkP8/JxP/NefvvXppHk08WAA0Z26pjDQqCoBODwCpEFQcBPLxxx9bJwivW7dOus7imG3xevGhQ4cs36II165du6B+RLuRI0di06ZN1s/Etw3F68kyLxYA5ajp9RehU3lFePPTTfhs2c6Qh3zUSk/G7T9qj35dGlnfp+RVtQJeeVBfuQqQh1w9ZXgjExkqyvNBHvK0lOGJPGSoKM8HecjTUpYnMpGlpBw/5CFHR1leTOPBAqAs8mb7MboAWPEK8CWXXIK33nrLMYmbb74ZK1eudP0NQTsdjh8/Hv/v//0/q2nr1q2tbwGKV3zPvZ5++mm8/vrr1h917doVEyZMCOn63CJeuCcKf+iABUA7tMK3cZvoxSEfC1btwWufbMCxU4UhO7risub46ZUXIj0tKXwgbGEp4JYH5VOjAHmo0dWLVzLxop58W/KQr6kXj+ThRT35tuQhX1OvHsnEq4Jy7clDrp5evZnGgwVAr8Sjw97oAuDPfvYz61t5SUlJ1mEbNWvWtE3l+PHj6N27N0pKStCtW7cqX8+17TREQ+H/rrvuwuLFi62fZmVlWa8sZ2dn49ixY5g+fTq++uor62c1atSAKBiKQmGoy24BcPfu3Zg4ceJ5LrZs2YJ58+ZZfyYO3BDF03MvcUDJZZdd5mWojmxNTUBuEv2+Q6fw0qSvsWbrmSdBf3g1b1ADY0d1Qtvm8g59cQTD4MZueBg8XO1DJw/9EJGJXkzIgzz0UkCvaLg+9OIhoiETvZiQB3l4UcDU/beXMdM2WAGjC4DiVdm///3v1quSN910Ex555BHbjJ944gm88847lu0vfvEL3HPPPbZtnTYUi00cAlJRgAtlX79+fTz77LPWE4CVXXYLgMuXL8ctt9ziKMxx48bhvvvuc2TjpbGJCSivoBhLVqzF6fxiVEtNRM/unZCWklipDMUlpZg4dys+/HwrikvKgtolJ8XjJ5e3xbV9WyIhPuBFzpi15S9CeqEnD714cPNGHvopoFdEzFnkoZcC+kXDNaIXE/IgDy8KmLj/9jJe2oZWwOgC4JEjRzB48GAUFBRYo3vggQdwxx13hGX9yiuv4JlnnrHaJScnY9asWahXr15YO68N5syZgylTpljfGxSxV6tWDU2bNsWQIUMwZswYVK9evcouWAD0SsC5vXh1d932w5i+eAeWrd+PsrLys04CgThc1r4BruzVHB1aZZ73zb61Ww/hpUlrsffQ6ZCdXtquPu4a0QF1a6c5D4oWZxXgL0J6TQby0IsHC4DkoZ8CekXEnEUeeimgXzRcI3oxIQ/y8KIAC4Be1IseW6MLgALD888/b/1TcWCCOBlYPA146aWXWq/bVlyHDx/GsmXL8O6772LNmjUQhR1h8/Of/xz3339/9BA1ZCQmJKBte47h2QmrsGv/ybCqNq1fHb+6oSsya6bitanrMf+rPSFtMmum4K4RHXFZhwZhfbJBeAX4i1B4jfxsQR5+qm2vLzKxp5NfrcjDL6Xt9UMe9nTyqxV5+KW0/X7IxL5WfrQkDz9Utt+HaTxM2H/bV58t3SpgfAFQFPLEq6vi6bofnpoqvg2YlpaGvLw8FBUVndVI2Ihr4MCBePHFF91qRzsPCuiegFZvOYgn31iBgqJS26NMjA8gPj4upI14WvDaPi3xk6FtkZqcYNsnG1atgGk33mjnSR76ESYTvZiQB3nopYBe0XB96MVDREMmejEhD/LwooDu+28vY6OtfQWMLwCKoZaVleEf//iHdZKu+O9wVyAQwG233YZf//rXiI+PD9ecP1eggM4JSDz599ALixwV/6qS6IKmtTB2VGe0bGT/kBoFkkelS/4ipBdW8tCLBzdv5KGfAnpFxJxFHnopoF80XCN6MSEP8vCigM77by/joq0zBaKiAFgx5J07d+K///0vFi1ahL179wYp0ahRI/Tt2xc//elP0bx5c2dKsbVUBXRNQOLp0HF/n2frtd9wgqSlJOCWKy/CFZc1R3wgLlxz/tyFAvxFyIVoCk3IQ6G4Ll2TiUvhFJmRhyJhXbolD5fCKTIjD0XCenBLJh7EU2BKHgpE9eDSNB667r89IKCpCwWiqgB47vjFIRviu3+nT5+2DtvIzMxEnTp1XEhEExUK6JqAvt52CI+8tMTzkDu2ysQDN12MjBopnn3RQeUKmHbjjXaW5KEfYTLRiwl5kIdeCugVDdeHXjxENGSiFxPyIA8vCui6//YyJto6VyBqC4DOpaCFnwromoD+8uZKLP56n2cpenVqiN/fcolnP3RQtQL8RUivGUIeevHg5o089FNAr4iYs8hDLwX0i4ZrRC8m5EEeXhTQdf/tZUy0da4AC4DONaOFBAV0TEB5BcW44bEZKCs7c0iMl0sc+jHh8WFIS0n04oa2YRTgL0J6TRHy0IsHC4DkoZ8CekXEnEUeeimgXzRcI3oxIQ/y8KKAjvtvL+OhrTsFWAB0pxutPCqgYwLatf8Exv5tnseRfW/+4m8Hokm96tL80VGwAvxFSK9ZQR568WABkDz0U0CviJizyEMvBfSLhmtELybkQR5eFNBx/+1lPLR1pwALgO50o5VHBXRMQN/sOooH/rXQ48i+N3/ml31xQdPa0vzREQuAus8B/mKqHyEy0YsJeZCHXgroFQ3Xh148+JdI5KGfAnpFZFrO0nH/rRfR2IiGBcDY4KzdKHVMQHwCULtpEjYg0268YQdkeAPy0A8gmejFhDzIQy8F9IqG60MvHiwAkod+CugVkWk5S8f9t15EYyMaFgBjg7N2o9QxAcn8BmB8IA7j+Q1A5fPOtBuvckEi3AF5RBhAiO7JRC8m5EEeeimgVzRcH3rxYAGQPPRTQK+ITMtZOu6/9SIaG9GwABgbnLUbpa4JiKcAazdVqgzItBuvWeo6j5Y8nGum2oJMVCvszD95ONNLdWvyUK2wM//k4UwvP1qTiR8q2++DPOxr5UdL03jouv/2gxX7+F4BFgA5GyKigK4J6Otth/DIS0s8a/LkPb3QITvTsx86qFoB02680c6TPPQjTCZ6MSEP8tBLAb2i4frQi4eIhkz0YkIe5OFFAV33317GRFvnCrAA6FwzWkhQQNcEVF5ejnF/n4dd+0+6HmWz+tXx3IMDEBcX59oHDe0pwF+E7OnkVyvy8Etp+/2QiX2t/GhJHn6obL8P8rCvlR8tycMPlZ31QSbO9FLdmjxUK+zMv2k8dN1/O1Odrb0qwAKgVwVp70oBnRPQtj3H8NALi1BQVOp4bClJ8XhqbG9kN67l2JYGzhUw7cbrfIRmWZCHfrzIRC8m5EEeeimgVzRcH3rxENGQiV5MyIM8vCig8/7by7ho60wBFgCd6cXWkhTQPQGt3nIQT76xwlERUBT/Hr61O7q0qboPXHcAACAASURBVCtJJboJpwB/EQqnkL8/Jw9/9bbTG5nYUcm/NuThn9Z2eiIPOyr514Y8/NPabk9kYlcpf9qRhz862+3FNB6677/t6s523hTQvgA4aNAga4QjRozAuHHjzhvtvn37rP9PT09HjRo1vClBa18VMCEBiScBn52wytbrwOK13/tv6Mon/3ydRfybaZ/lDtudab8IhR1QFDQgE70gkgd56KWAXtFwfejFQ0RDJnoxIQ/y8KKACftvL+OjrT0FtC8Atm3b1vqW2o033ohHH330vFFV9TN7w2erSClgSgIS3wRcv/0Ipi/egaXrc1BWVn5WsvhAHHp0aICrerZA+1Z1+M2/CEwm/iIUAdGr6JI89OLBzRt56KeAXhExZ5GHXgroFw3XiF5MyIM8vChgyv7byxhpG14BFgDDa8QWChQwMQHlFRRjyYq1OJ1fjGqpiejZvRPSUhIVqEOXdhXgL0J2lfKnHXn4o7OTXsjEiVrq25KHeo2d9EAeTtRS35Y81GvstAcycaqY2vbkoVZfp95N42Hi/tspE7YPr4D2BcBOnTqhqKgI1157LZ5++unzRsQnAMMD1rWFqQnItESvK39ZcZGHLCXl+CEPOTrK9EImMtX07os8vGso0wN5yFTTuy/y8K6hbA9kIltRb/7Iw5t+sq1N42Hq/ls2t1j3p30BcODAgcjJyUF2djamTp3KAmCUzFhTE5BpiT5KpkulwyAPvQiTh148RDRkohcT8iAPvRTQKxquD7148B5CHvopoFdEpuUsU/ffelE3PxrtC4D3338/Zs6caX1fbfTo0dZhIFlZWQgEAhDFQfHnw4cPx3333eeJRsOGDT3Z09iZAqYmINMSvTMq5rUmD72YkYdePLh5Iw/9FNArIuYs8tBLAf2i4RrRiwl5kIcXBUzdf3sZM22DFdC+ALh48WLcfvvtIQ9YEAc0iEsUAb1cwn7jxo1eXNDWoQKmJiDeeB2CVtycPBQL7NA9eTgUzIfmZOKDyA66IA8HYvnQlDx8ENlBF+ThQCyfmpKJT0Lb7IY8bArlUzPTeJi6//YJZ8x0o30BUJB45pln8MorryiDIgqAmzZtUuafjoMVMDUBmZboo33ukYdehMlDLx4iGjLRiwl5kIdeCugVDdeHXjx4DyEP/RTQKyLTcpap+2+9qJsfjREFQCHzqlWrMGXKFOtJvRMnTqC4uBj79u2znv5LS0tDzZo1PdH4/PPPPdnT2JkCpiYg0xK9MyrmtSYPvZiRh148uHkjD/0U0Csi5izy0EsB/aLhGtGLCXmQhxcFTN1/exkzbYMVMKYAGAoeTwE2d0qbmoB449VrzpEHeeilgH7RcI3oxYQ8/n975wFuVXG27VdFEREV7GCLXVGxi72gJnaxKwpqsISgn4qxYUuMIPYYTTAWxIrGhooNa+zYFcGoiQURQREUEQvKfz3z/et8m3P2PnuvumfOuee6vNRz1sy8635mzT7r2e/MoIdfBPyKhufDLz34Egk9/CPgV0ShzVmhvn/7pXr40WAAhq9hkHcQ6gQU2kQf5OCIETR6xIBVwKXoUQDkmF2gSUxgOV+OHjkDjtk8esQElvPl6JEz4ATNo0kCaDlWQY8c4SZoOjQ9Qn3/TiANVZohELQBeNhhh7lb23nnnS36b9QOg0CoE1BoE30YoyF5lOiRnF0eNdEjD6rp2kSTdPyyro0eWRNN1x56pOOXdW30yJpo+vbQJD3DLFtAjyxppm8rND1Cff9OrxQtlBII2gBEynAJhDoBhTbRhztCaoscPWrjVNRV6FEU6dr7QZPaWRVxJXoUQbn2PtCjdlZFXIkeRVCO1weaxOOV99XokTfheO2Hpkeo79/xVOHqagQwAKsR4ve5EAh1Agptos9FPI8aRQ+PxODEWb/E+P/R8Iz4JQt6oIdfBPyKhufDLz0UDZr4pQl6oEcaAqG+f6e5Z+o2JdDiDEAN7DfeeMOdFjxt2jSbOXOmtW/f3jp27Ghrr722rb/++rbwwgszFupMINQJiA/eOg+cRt2jB3r4RcC/aHhG/NIEPdDDLwJ+RcPz4ZceGIDo4R8BvyIKbc4K9f3bL9XDj6bFGICfffaZXXXVVfbAAw/Yjz/+WFGZtm3b2u677279+vWzzp07h69goHcQ6gQU2kQf6PCoOWz0qBlVIReiRyGYY3WCJrFw5X4xeuSOOFYH6BELV+4Xo0fuiGN3gCaxkeVaAT1yxRu78dD0CPX9O7YwVGiWQIswAB999FEbOHCgaVDPmTOnquTzzDOPywIcNGiQ7bTTTlWv54LsCYQ6AYU20WevnF8togd6+EXAv2h4RvzSBD3Qwy8CfkXD8+GXHooGTfzSBD3QIw2BUN+/09wzdZsSCN4A/Ne//uWy+X7++ecG82/RRRe19dZbz7p06WLt2rWzWbNm2cSJE+3tt9+26dOnmwxAGYVt2rSxoUOH2lZbbcXYKJhAqBMQH7wFD5Qq3aEHevhFwL9oeEb80gQ90MMvAn5Fw/Phlx4YgOjhHwG/Igptzgr1/dsv1cOPJmgDUMbezjvvbF988YVTQobfySef7LL6ZO41LjIJR48ebRdffLF9+umn7tdLL720PfLII7bggguGr2ZAdxDqBBTaRB/QkEgUKnokwpZbJfTIDW3ihtEkMbpcKqJHLlgTN4oeidHlUhE9csGaqlE0SYUv88rokTnSVA2Gpkeo79+pRKJyEwJBG4C33HKLnXfeeS6jr2vXrnb99dfbIossUlXmGTNm2BFHHGFjx451dc8880zr1atX1XpckB2BUCeg0Cb67BTzsyX08EsX9PBLD0WDJn5pgh7o4RcBv6Lh+fBLDz5D0MM/An5FFNqcFer7t1+qhx9N0AZg37597dlnn3XZfg8++KCtsMIKNSvy8ccf26677mq//PKLbbnllnbttdfWXJcL0xMIdQIKbaJPr5TfLaCHX/qgh1968PKGHv4R8Csi5iz08IuAf9HwjPilCXqgRxoCob5/p7ln6jYlELQBuO2229qUKVNsk002sRtvvDG2vn369LGXXnrJLQN++umnY9enQnICoU5AfPAm1zyPmuiRB9XkbaJHcnZ51USTvMgmaxc9knHLqxZ65EU2WbvokYxbnrXQJE+68dtGj/jM8qwRmh6hvn/nqWFrbDtoA1AHffz000+23377uaXAcctZZ51l//znP22BBRZwy6QoxREIdQIKbaIvTtH69IQe9eFeqVf08EsPRYMmfmmCHujhFwG/ouH58EsPPkPQwz8CfkUU2pwV6vu3X6qHH03QBuCmm25q2s9PS3kvueSS2GoMGDDARo0a5fYNHDNmTOz6VEhOINQJKLSJPrlCYdRED790Qg+/9ODlDT38I+BXRMxZ6OEXAf+i4RnxSxP0QI80BEJ9/05zz9RtSiBoA3D33Xe3Dz74INES3jlz5tj2229vkydPtlVWWcUeeOABxkeBBEKdgPjgLXCQ1NAVetQAqcBL0KNA2DV2hSY1giroMvQoCHSN3aBHjaAKugw9CgIdoxs0iQGrgEvRowDIMboITY9Q379jSMKlNRAI2gDUsl+dBKyTfAcOHGiHHnpoDbf8v5eUniB88MEH29lnn11zXS5MTyDUCSi0iT69Un63gB5+6YMefumhaNDEL03QAz38IuBXNDwffunBZwh6+EfAr4hCm7NCff/2S/XwownaAHzzzTftwAMPdAbgfPPN50xAmXnVyh133GF/+tOfbPbs2a7uiBEjrFu3btWq8fsMCYQ6AYU20WcomZdNoYdfsqCHX3rw8oYe/hHwKyLmLPTwi4B/0fCM+KUJeqBHGgKhvn+nuWfqNiUQtAGo2znxxBPtoYcecncmM2+ttdaynj172gYbbGCdO3e2hRZayL777jubNGmSvf7663bPPffYuHHjTEuAdf0uu+xil156KWOjYAKhTkB88BY8UKp0hx7o4RcB/6LhGfFLE/RAD78I+BUNz4dfevAlEnr4R8CviEKbs0J9//ZL9fCjCd4AnDVrlvXu3dvefvttZ+jVUmT+qegU4eHDh1u7du1qqcY1GRIIdQIKbaLPUDIvm0IPv2RBD7/04OUNPfwj4FdEzFno4RcB/6LhGfFLE/RAjzQEQn3/TnPP1G1KIHgDULf0/fff2wUXXGBa2vvLL79U1Xneeed1S4dPPfVUW3DBBatezwXZEwh1AuKDN/uxkKZF9EhDL/u66JE907QtoklagtnWR49seaZtDT3SEsy2PnpkyzOL1tAkC4rZtYEe2bHMoqXQ9Aj1/TsLrWjj/wi0CAMwup0JEyY4E/DFF1+08ePHuz3+otKmTRu3PLh79+52wAEH2PLLL884qCOBUCeg0Cb6OkpcSNfoUQjmmjtBj5pRFXYhmhSGuqaO0KMmTIVdhB6Foa6pI/SoCVOhF6FJobirdoYeVREVekFoeoT6/l2oqK2gsxZlADbWa8aMGW7/P+0D2KFDh1YgZzi3GOoEFNpEH86ISBYpeiTjllct9MiLbPJ20SQ5uzxqokceVJO3iR7J2eVREz3yoJquTTRJxy/r2uiRNdF07YWmR6jv3+lUonZjAi3aAERufwmEOgGFNtH7OwKyiQw9suGYVSvokRXJ7NpBk+xYZtESemRBMbs20CM7llm0hB5ZUMy2DTTJlmfa1tAjLcFs64emR6jv39mqRmsYgIyBuhAIdQIKbaKvi7gFdooeBcKuoSv0qAFSwZegScHAq3SHHujhFwG/ouH58EsPRYMmfmmCHuiRhkCo799p7pm6TQlgADIq6kIg1AmID966DJeKnaIHevhFwL9oeEb80gQ90MMvAn5Fw/Phlx4YgOjhHwG/Igptzgr1/dsv1cOPBgMwfA2DvINQJ6DQJvogB0eMoNEjBqwCLkWPAiDH7AJNYgLL+XL0yBlwzObRIyawnC9Hj5wBJ2geTRJAy7EKeuQIN0HToekR6vt3Ammo0gwBDECGR10IhDoBhTbR10XcAjtFjwJh19AVetQAqeBL0KRg4FW6Qw/08IuAX9HwfPilh6JBE780QQ/0SEMg1PfvNPdM3aYEMAAZFXUhEOoExAdvXYZLxU7RAz38IuBfNDwjfmmCHujhFwG/ouH58EsPDED08I+AXxGFNmeF+v7tl+rhR4MBGL6GQd5BqBNQaBN9kIMjRtDoEQNWAZeiRwGQY3aBJjGB5Xw5euQMOGbz6BETWM6Xo0fOgBM0jyYJoOVYBT1yhJug6dD0CPX9O4E0VGmGAAYgw6MuBEKdgEKb6OsiboGdokeBsGvoCj1qgFTwJWhSMPAq3aEHevhFwK9oeD780kPRoIlfmqAHeqQhEOr7d5p7pm5TAhiAjIq6EAh1AuKDty7DpWKn6IEefhHwLxqeEb80QQ/08IuAX9HwfPilBwYgevhHwK+IQpuzQn3/9kv18KPBAAxfwyDvINQJKLSJPsjBESNo9IgBq4BL0aMAyDG7QJOYwHK+HD1yBhyzefSICSzny9EjZ8AJmkeTBNByrIIeOcJN0HRoeoT6/p1AGqo0QwADkOFRFwKhTkChTfR1EbfATtGjQNg1dIUeNUAq+BI0KRh4le7QAz38IuBXNDwffumhaNDEL03QAz3SEAj1/TvNPVO3KQEMQEZFXQiEOgHxwVuX4VKxU/RAD78I+BcNz4hfmqAHevhFwK9oeD780gMDED38I+BXRKHNWaG+f/ulevjRYACGr2GQdxDqBBTaRB/k4IgRNHrEgFXApehRAOSYXaBJTGA5X44eOQOO2Tx6xASW8+XokTPgBM2jSQJoOVZBjxzhJmg6ND1Cff9OIA1VmiHQIg3ADz/80MaNG2fTpk2zmTNnWvv27a1jx4629tpr269+9SsGhAcEQp2AQpvoPZA61xDQI1e8sRtHj9jIcq+AJrkjjtUBesTClfvF6JE74lgdoEcsXIVcjCaFYK65E/SoGVUhF4amR6jv34WI2Yo6aTEGoAb0jTfeaCNGjLAvvviiooRLLbWUHXTQQXbYYYfZwgsv3Iqk9utWQ52AQpvo/VI9+2jQI3umaVpEjzT08qmLJvlwTdoqeiQll0899MiHa9JW0SMpufzqoUl+bJO0jB5JqOVXJzQ9Qn3/zk/B1tlyizAAX3/9dRswYIBNmjTJ5syZU1XJeeaZx5Zddlm79NJLbf311696PRdkTyDUCSi0iT575fxqET3Qwy8C/kXDM+KXJuiBHn4R8Csang+/9FA0aOKXJuiBHmkIhPr+neaeqduUQPAG4NixY6137942a9Ysd3cyAOedd15baaWVrEuXLtauXTv3u4kTJ9pHH31kv/zyi8kA1HULLbSQ3XTTTda1a1fGRsEEQp2A+OAteKBU6Q490MMvAv5FwzPilybogR5+EfArGp4Pv/TAAEQP/wj4FVFoc1ao799+qR5+NEEbgLNnz7bddtvNPv74Y6dEhw4d7JhjjrF99tnHOnXq1EQd7Ql4991329VXX20zZsxwJqCMwlGjRtl8880XvpoB3UGoE1BoE31AQyJRqOiRCFtuldAjN7SJG0aTxOhyqYgeuWBN3Ch6JEaXS0X0yAVrqkbRJBW+zCujR+ZIUzUYmh6hvn+nEonKTQgEbQDec889dvrpp7uMvuWXX96GDRvmsv6qFWUDHnnkkc44VN3Bgwfb3nvvXa0av8+QQKgTUGgTfYaSedkUevglC3r4pYeiQRO/NEEP9PCLgF/R8Hz4pQefIejhHwG/Igptzgr1/dsv1cOPJmgDsH///vbYY485E+/OO++MtZRXS4f3339/p+AOO+xgV111VfhqBnQHoU5AoU30AQ2JRKGiRyJsuVVCj9zQJm4YTRKjy6UieuSCNXGj6JEYXS4V0SMXrKkaRZNU+DKvjB6ZI03VYGh6hPr+nUokKjchELQBKONOB39069bNnf4bt+g04DfeeMM6d+5sTzzxRNzqXJ+CQKgTUGgTfQqJgqiKHn7JhB5+6aFo0MQvTdADPfwi4Fc0PB9+6cFnCHr4R8CviEKbs0J9//ZL9fCjCdoAlPH3448/uj3/zj///NhqDBw40O666y5r27atvfnmm7HrUyE5gVAnoNAm+uQKhVETPfzSCT380oOXN/Twj4BfETFnoYdfBPyLhmfEL03QAz3SEAj1/TvNPVO3KYGgDcCNNtrIvvvuO9t9993toosuiq3vKaecYvfdd5+1b9/eXn311dj1qZCcQKgTEB+8yTXPoyZ65EE1eZvokZxdXjXRJC+yydpFj2Tc8qqFHnmRTdYueiTjlmctNMmTbvy20SM+szxrhKZHqO/feWrYGtsO2gD89a9/7Q7yWHHFFe2RRx6JrV/a+rE7pEIDgVAnoNAm+pY+5NDDL4XRwy89FA2a+KUJeqCHXwT8iobnwy89+AxBD/8I+BVRaHNWqO/ffqkefjRBG4BnnHGG3X333e4QkEsvvdR22WWXmhV5+OGH7YQTTnB1e/bsaYMGDaq5LhemJxDqBBTaRJ9eKb9bQA+/9EEPv/Tg5Q09/CPgV0TMWejhFwH/ouEZ8UsT9ECPNARCff9Oc8/UbUogaAPw2Weftb59+zoTr127dnb55ZfbNttsU1Xn5557zo477ji3fFh1r7nmGttqq62q1uOC7AiEOgHxwZvdGMiiJfTIgmJ2baBHdiyzaglNsiKZTTvokQ3HrFpBj6xIZtMOemTDMctW0CRLmunbQo/0DLNsITQ9Qn3/zlIz2jIL2gCUgIcffri9+OKLTkuZeT169HCHgmywwQbWsWPHBo2nT59ur7/+ut1zzz02evRomzNnjru+e/fuNmzYMMZCwQRCnYBCm+gLlrXw7tCjcOTNdogefumhaNDEL03QAz38IuBXNDwffunBZwh6+EfAr4hCm7NCff/2S/XwowneAPzqq6/swAMPtAkTJjSYgJEsCy64oMsMnDVrln3//fcNasn8U9Hegbfddpt16tQpfCUDu4NQJ6DQJvrAhkXscNEjNrJcK6BHrngTNY4mibDlVgk9ckObqGH0SIQtt0rokRvaxA2jSWJ0uVREj1ywJm40ND1Cff9OLBAVyxII3gDUXU2dOtVOO+00e+aZZ2qWWUuFBw8ebIsvvnjNdbgwOwKhTkChTfTZKeZnS+jhly7o4ZceigZN/NIEPdDDLwJ+RcPz4ZcefIagh38E/IootDkr1Pdvv1QPP5oWYQBGMmgp8B133GEvvfSSMwUbF5l9m222mcsY1L8p9SMQ6gQU2kRfP4WL6Rk9iuFcay/oUSup4q5Dk+JY19ITetRCqbhr0KM41rX0hB61UCr2GjQplne13tCjGqFifx+aHqG+fxerasvvrUUZgKVyTZ482aZNm2YzZ8609u3bu/0Al1566ZavaCB3GOoEFNpEH8hwSBwmeiRGl0tF9MgFa6pG0SQVvswro0fmSFM1iB6p8GVeGT0yR5q6QTRJjTDTBtAjU5ypGwtNj1Dfv1MLRQNzEWixBiA6+00g1AkotIne71GQPjr0SM8wyxbQI0ua2bSFJtlwzKoV9MiKZDbtoEc2HLNqBT2yIpldO2iSHcssWkKPLChm10ZoeoT6/p2dYrQkAhiAjIO6EAh1Agptoq+LuAV2ih4Fwq6hK/SoAVLBl6BJwcCrdIce6OEXAb+i4fnwSw9FgyZ+aYIe6JGGQKjv32numbpNCWAAMirqQiDUCYgP3roMl4qdogd6+EXAv2h4RvzSBD3Qwy8CfkXD8+GXHhiA6OEfAb8iCm3OCvX92y/Vw48GAzB8DYO8g1AnoNAm+iAHR4yg0SMGrAIuRY8CIMfsAk1iAsv5cvTIGXDM5tEjJrCcL0ePnAEnaB5NEkDLsQp65Ag3QdOh6RHq+3cCaajSDAHvDcDevXs3hD/PPPPY8OHDG/6/9HdpVG7cbpq2qFsbgVAnoNAm+trUCPcq9PBLO/TwSw9FgyZ+aYIe6OEXAb+i4fnwSw8+Q9DDPwJ+RRTanBXq+7dfqocfjfcG4Jprrmky6ObMmeP+PX78+Abq0e/SyFCu3TTtUbc2AqFOQKFN9LWpEe5V6OGXdujhlx68vKGHfwT8iog5Cz38IuBfNDwjfmmCHuiRhkCo799p7pm6TQkEYQBGYZczALMQtXG7WbRJG80TCHUC4oPXr5GNHujhFwH/ouEZ8UsT9EAPvwj4FQ3Ph1968CUSevhHwK+IQpuzQn3/9kv18KPx3gAcM2bMXJQ33XTThv9v/Ls0cpS2m6Yd6tZGINQJKLSJvjY1wr0KPfzSDj380oOXN/Twj4BfETFnoYdfBPyLhmfEL03QAz3SEAj1/TvNPVO3KQHvDUBEa5kEQp2A+OD1azyiB3r4RcC/aHhG/NIEPdDDLwJ+RcPz4ZcefImEHv4R8Cui0OasUN+//VI9/GgwAMPXMMg7CHUCCm2iD3JwxAgaPWLAKuBS9CgAcswu0CQmsJwvR4+cAcdsHj1iAsv5cvTIGXCC5tEkAbQcq6BHjnATNB2aHqG+fyeQhirNEMAAZHjUhUCoE1BoE31dxC2wU/QoEHYNXaFHDZAKvgRNCgZepTv0QA+/CPgVDc+HX3ooGjTxSxP0QI80BEJ9/05zz9RtSiBoA7BHjx7ujnr27Gn9+/ePre/VV19td9xxhztd+LHHHotdnwrJCYQ6AfHBm1zzPGqiRx5Uk7eJHsnZ5VUTTfIim6xd9EjGLa9a6JEX2WTtokcybnnWQpM86cZvGz3iM8uzRmh6hPr+naeGrbHtoA3ANddc05l3vXr1sjPPPDO2fkOGDLFhw4a5NsaPHx+7PhWSEwh1Agptok+uUBg10cMvndDDLz0UDZr4pQl6oIdfBPyKhufDLz34DEEP/wj4FVFoc1ao799+qR5+NBiAGIB1GcWhTkChTfR1EbfATtGjQNg1dIUeNUAq+BI0KRh4le7QAz38IuBXNDwffumBAYge/hHwK6LQ5qxQ37/9Uj38aFq1ATh48GAbPny4tWnTxsaOHRu+mgHdQagTUGgTfUBDIlGo6JEIW26V0CM3tIkbRpPE6HKpiB65YE3cKHokRpdLRfTIBWuqRtEkFb7MK6NH5khTNRiaHqG+f6cSicpNCLRqA/D444+3Rx991BZddFF76aWXGB4FEgh1Agptoi9Q0rp0hR51wV6xU/TwSw9FgyZ+aYIe6OEXAb+i4fnwSw8+Q9DDPwJ+RRTanBXq+7dfqocfTas1AJXxd9hhh9n3339v3bp1sxEjRoSvZkB3EOoEFNpEH9CQSBQqeiTCllsl9MgNbeKG0SQxulwqokcuWBM3ih6J0eVSET1ywZqqUTRJhS/zyuiROdJUDYamR6jv36lEonITAsEYgL17924S/JgxY9wBHksttZStuOKKNck7e/Zsmzx5sn322Wc2Z84cV79fv3523HHH1VSfi7IhEOoEFNpEn41a/raCHn5pgx5+6aFo0MQvTdADPfwi4Fc0PB9+6cFnCHr4R8CviEKbs0J9//ZL9fCjCcYAjE78LUUuA09FJl7cEtVdcsklbeTIkdapU6e4TXB9CgKhTkChTfQpJAqiKnr4JRN6+KUHL2/o4R8BvyJizkIPvwj4Fw3PiF+aoAd6pCEQ6vt3mnumblMCQRmAWQrYrl0769Gjh5100knWuXPnLJumrRoIhDoB8cFbg7gFXoIeBcKuoSv0qAFSwZegScHAq3SHHujhFwG/ouH58EsPvkRCD/8I+BVRaHNWqO/ffqkefjTBGIBa7ltalMHXp08fl/234447uv38qhVd27ZtW1tsscVsueWWs3nnnbdaFX6fE4FQJ6DQJvqc5POmWfTwRgoXCHr4pQeaoId/BPyKiDkLPfwi4F80PCN+aYIe6JGGQKjv32numbpNCQRjAJYTL1oW3KtXLzvzzDPRNyACoU5AfPD6NcjQAz38IuBfNDwjfmmCHujhFwG/ouH58EsPvkRCD/8I+BVRaHNWqO/ffqkefjRBG4BXXnmlU2Dddde1bbfdNnw1WtEdeXRAoAAAIABJREFUhDoBhTbRt/QhhR5+KYwefunByxt6+EfAr4iYs9DDLwL+RcMz4pcm6IEeaQiE+v6d5p6p25RA0AYggoZLINQJiA9ev8YceqCHXwT8i4ZnxC9N0AM9/CLgVzQ8H37pwZdI6OEfAb8iCm3OCvX92y/Vw48GAzB8DYO8g1AnoNAm+iAHR4yg0SMGrAIuRY8CIMfsAk1iAsv5cvTIGXDM5tEjJrCcL0ePnAEnaB5NEkDLsQp65Ag3QdOh6RHq+3cCaajSDAEMQIZHXQiEOgGFNtHXRdwCO0WPAmHX0BV61ACp4EvQpGDgVbpDD/Twi4Bf0fB8+KWHokETvzRBD/RIQyDU9+8090zdpgRalAE4Y8YMe/XVV238+PE2bdo0mzlzpv3yyy9VddfpwIMGDap6HRdkRyDUCYgP3uzGQBYtoUcWFLNrAz2yY5lVS2iSFcls2kGPbDhm1Qp6ZEUym3bQIxuOWbaCJlnSTN8WeqRnmGULoekR6vt3lprRllmLMAC//vpru/jii+3++++3H374IZGuMg0pxREIdQIKbaIvTtH69IQe9eFeqVf08EsPRYMmfmmCHujhFwG/ouH58EsPPkPQwz8CfkUU2pwV6vu3X6qHH03wBuCnn35qhx12mH3++ec2Z86cqooo26/xdfoZBmBVdJleEOoEFNpEn6loHjaGHn6Jgh5+6cHLG3r4R8CviJiz0MMvAv5FwzPilybogR5pCIT6/p3mnqnblEDQBqCMvJ49e9q7777r7myNNdawPfbYw5577jl74YUXLFraq6XAEydOtFdeecXefvttd+1CCy1k/fv3t44dO7r/VzuU4giEOgHxwVvcGKmlJ/SohVJx16BHcaxr7QlNaiVVzHXoUQznWntBj1pJFXMdehTDOU4vaBKHVv7Xokf+jOP0EJoeob5/x9GEa6sTCNoAfOihh+zEE090Rt9WW21lf//7361NmzZ23nnn2S233OJ+3jizb+zYsXb22WfbuHHjbJlllrHrrrvOVlllleqkuCJTAqFOQKFN9JmK5mFj6OGXKOjhlx6KBk380gQ90MMvAn5Fw/Phlx58hqCHfwT8iii0OSvU92+/VA8/mqANwAEDBtioUaNs3nnntdGjR1uXLl2cIs0ZgPr9999/b0cccYS9/vrrttpqq9mdd95pbdu2DV/NgO4g1AkotIk+oCGRKFT0SIQtt0rokRvaxA2jSWJ0uVREj1ywJm4UPRKjy6UieuSCNVWjaJIKX+aV0SNzpKkaDE2PUN+/U4lE5SYEgjYAd9ppJ9MegF27dnUmXlSqGYC67uOPP7Zdd93VnRKs6/fbbz+GR4EEQp2AQpvoC5S0Ll2hR12wV+wUPfzSQ9GgiV+aoAd6+EXAr2h4PvzSg88Q9PCPgF8RhTZnhfr+7Zfq4UcTtAG40UYb2XfffWd77rmnDRkypEGNP//5z3bzzTe7JcBvvPFGxey+Qw891O0LuMUWW9j1118fvpoB3UGoE1BoE31AQyJRqOiRCFtuldAjN7SJG0aTxOhyqYgeuWBN3Ch6JEaXS0X0yAVrqkbRJBW+zCujR+ZIUzUYmh6hvn+nEonKTQgEbQCus8469vPPP9tBBx1k55xzTsPNXXbZZXb11Vc7A/Cpp56ypZdeuqz0AwcOtLvuusuWXXZZe/LJJxkeBRIIdQIKbaIvUNK6dIUedcFesVP08EsPRYMmfmmCHujhFwG/ouH58EsPPkPQwz8CfkUU2pwV6vu3X6qHH03QBmD37t3t66+/tr333tsGDx7coMawYcNcRqAMwBtvvNE22WSTskodf/zx9uijj9oCCyzgXpIoxREIdQIKbaIvTtH69IQe9eFeqVf08EsPXt7Qwz8CfkXEnIUefhHwLxqeEb80QQ/0SEMg1PfvNPdM3aYEgjYADzjgAHv77bdtyy23tGuvvbbh7p555hk76qijnAHYr18/O+6445rcufb+23nnnd0egosuuqi99NJLjI8CCYQ6AfHBW+AgqaEr9KgBUoGXoEeBsGvsCk1qBFXQZehREOgau0GPGkEVdBl6FAQ6RjdoEgNWAZeiRwGQY3QRmh6hvn/HkIRLayAQtAF49tln2x133NFkCe+MGTOcKfjTTz9Zp06d7L777rPFF198LhylWYLaS1B7BlKKIxDqBBTaRF+covXpCT3qw71Sr+jhlx6KBk380gQ90MMvAn5Fw/Phlx58hqCHfwT8iii0OSvU92+/VA8/mqANwFGjRtmAAQNcpp+W8i6//PINipx88sn2wAMPuN9pD8A+ffrY6quvbrNmzbInnnjC7r33XncCsH5/1lln2SGHHBK+mgHdQagTUGgTfUBDIlGo6JEIW26V0CM3tIkbRpPE6HKpiB65YE3cKHokRpdLRfTIBWuqRtEkFb7MK6NH5khTNRiaHqG+f6cSicpNCARtAGoQb7755jZ79mw76aST3LLfqEyePNmdDvzNN99UlH3OnDnWtWtXGzFihM0///wMjwIJhDoBhTbRFyhpXbpCj7pgr9gpevilh6JBE780QQ/08IuAX9HwfPilB58h6OEfAb8iCm3OCvX92y/Vw48maANQ+EePHm1Tp051WX7bb7/9XIqMGzfO7f83ceLEskrpcJDLL7+8yfLg8GX1/w5CnYBCm+j9HwnpIkSPdPyyro0eWRNN3x6apGeYZQvokSXN9G2hR3qGWbaAHlnSzKYtNMmGY1atoEdWJLNpJzQ9Qn3/zkYtWokIBG8AVpPyxx9/dMuDX3jhBZsyZYrNO++8bqmwzELtE0ipD4FQJ6DQJvr6qFtcr+hRHOtaekKPWigVew2aFMu7Wm/oUY1Qsb9Hj2J5V+sNPaoRKv73aFI88+Z6RA/0SEMg1PfvNPdM3aYEWrwBiOh+Egh1AuKD16/xhB7o4RcB/6LhGfFLE/RAD78I+BUNz4dfeigaNPFLE/RAjzQEQn3/TnPP1MUAZAx4QiDUCYgPXk8G0P8PAz3Qwy8C/kXDM+KXJuiBHn4R8Csang+/9MAARA//CPgVUWhzVqjv336pHn40ZACamR6GhRdeOHw1A7qDUCeg0Cb6gIZEolDRIxG23CqhR25oEzeMJonR5VIRPXLBmrhR9EiMLpeK6JEL1lSNokkqfJlXRo/MkaZqMDQ9Qn3/TiUSlZsQaNUGoE4IHjZsmN1yyy02ZswYhkeBBEKdgEKb6AuUtC5doUddsFfsFD380kPRoIlfmqAHevhFwK9oeD780oPPEPTwj4BfEYU2Z4X6/u2X6uFH0yoNwGnTpjUYf999951Tcfz48eGrGdAdhDoBhTbRBzQkEoWKHomw5VYJPXJDm7hhNEmMLpeK6JEL1sSNokdidLlURI9csKZqFE1S4cu8MnpkjjRVg6HpEer7dyqRqNyEQKsyAKdOnWrXXnutjRgxwr7//nubM2eOAzLPPPMUYgA+/vjjNnLkSBs7dqx98cUXbtnxiiuuaDvuuKMddNBBuSxDrkeftTxnoU5AoU30tWgR8jXo4Zd66OGXHooGTfzSBD3Qwy8CfkXD8+GXHnyGoId/BPyKKLQ5K9T3b79UDz+a4AzAd955x26//XZ75ZVXbMqUKfbDDz/YEkssYd26dbMDDzzQNt988yaqzJgxw4YOHeqW+up6GX8y/fTvdu3a2cEHH2ynnHJKbmrOnDnTTj75ZHviiScq9rHsssva5Zdfbuuvv34mcdSjzziBhzoBhTbRx9EkxGvRwy/V0MMvPXh5Qw//CPgVEXMWevhFwL9oeEb80gQ90CMNgVDfv9PcM3WbEgjKALzgggts+PDh7i6i7L3olmToqRx++OF26qmnNtzpww8/bOeee659/fXXcxl/Cy20kPXq1cuOOOII69SpU25j4+eff7ZjjjnGnnnmGdeHzMr999/fVl11VRfTAw88YK+99pr73aKLLmq33XabrbLKKqniqUefcQMOdQLigzeu0vlejx758o3bOnrEJZb/9WiSP+M4PaBHHFr5X4se+TOO0wN6xKFVzLVoUgznWntBj1pJFXNdaHqE+v5djJqtp5dgDMArr7zS9I9KlL1XKlP0M/1b2Xa//e1v7bLLLrN//OMfcxl/7du3t0MPPdQZf4sttljuSmu58TnnnOP6keknA1MmYGkZMmSIXX/99e5HG2+8sctUTFPq0WfceEOdgEKb6OPqEtr16OGXYujhlx6KBk380gQ90MMvAn5Fw/Phlx58hqCHfwT8iii0OSvU92+/VA8/miAMwMmTJ1uPHj1MmW1R2XTTTW2ttdaytm3buqXAL774ok2aNMn9umPHjs4EHDhw4FxLffv06WNHHnmkLbLIIoUop3i33XZbt9+fyt13321du3Zt0reu23fffRv2Ibzuuutsq622ShRjPfpMEmioE1BoE30SbUKqgx5+qYUefunByxt6+EfAr4iYs9DDLwL+RcMz4pcm6IEeaQiE+v6d5p6p25RAEAbg1Vdf7bL5lN2nvfL+9re/2ZprrjnX3cj40nVXXHGFu26++eaz2bNnu2tkpp1//vm29NJLFzoGZErKdFSRYXnTTTdV7P+uu+6yM844w/1+n332scGDByeKtR59Jgk01AmID94kaudXBz3yY5ukZfRIQi3fOmiSL9+4raNHXGL5Xo8e+fKN2zp6xCWW//Vokj/jOD2gRxxa+V8bmh6hvn/nr2Tr6iEIA7Bv37727LPPOmNPe+Q1d1DGgAEDbNSoUU5FXb/bbrvZRRdd5P676HLhhReasvlUlJF41FFHVQxBWYJR1p+WCD/33HOJwq1Hn0kCDXUCCm2iT6JNSHXQwy+10MMvPRQNmvilCXqgh18E/IqG58MvPfgMQQ//CPgVUWhzVqjv336pHn40QRiA22+/vX3++ee2+uqr28iRI5ulrtOBtcefipYH6+TdxRdfvC5KybiMDv/Q3n/du3dvNo7tttuuYRnzCy+8kOhwknr0mQRuqBNQaBN9Em1CqoMefqmFHn7pwcsbevhHwK+ImLPQwy8C/kXDM+KXJuiBHmkIhPr+neaeqduUQBAG4IYbbmizZs2yPffc03RgRnNFA1sHaSjjb7PNNrMbbrihbrpr38JPP/3U9f/444/bcsst12wsMi5ffvlld40OAtF9xC316DNujLpeJyB/8MEHDVWXX35508nMvhfFrOXmWmKuQ10o9SWAHvXl37h39PBLD0WDJn5pgh7o4RcBv6Lh+fBLDz5D0MM/An5FFNqc9d1339mECRMaIOpddtFFF/ULKtHkTiAIA1D7/cnQ69Wrl5155plVoUTX77333on30qvaSQ0XaN8/GV0qr732mukE4uZK//79bfTo0e6SoUOHmjIf45Z69Bk3Rl2vg1tKJ6AkbVAHAhCAAAQgAAEIQAACEIAABCAAgXgElICz1FJLxavE1cETaNEGYK2GYV4qrrPOOvbTTz+55t955x1r06ZNs11p/8IHHnjAXXPJJZfY7rvvHju0evQZO0gMwCTIqAMBCEAAAhCAAAQgAAEIQAACEEhNAAMwNcIgG8AAzFG2ephx9egzCUIyAJNQow4EIAABCEAAAhCAAAQgAAEIQCAdAQzAdPxCrY0BmKNy9ViOW48+kyD88ccfbfr06Q1VdWCL9tWjQAACEIAABCAAAQhAAAIQgAAEIJAdAe1j/8MPPzQ0uNhii9kCCyyQXQe0FASBoAzAdddd17bZZpuqYK+88kq3Z2Ct16tB7b+XdanHgRz16DNrbrQHAQhAAAIQgAAEIAABCEAAAhCAAAQgkB2BoAzA7G67aUvjx4/PvPm+ffvaM88849odPny4de/evdk+tttuO5s0aZK75oUXXrBOnTrFjqkefcYOkgoQgAAEIAABCEAAAhCAAAQgAAEIQAAChREIygCcM2dOLmCULZiHAXjhhRfadddd52I++eST7aijjqoY/5dffmlbbrml+/3iiy9uzz//fKJ7rUefiQKlEgQgAAEIQAACEIAABCAAAQhAAAIQgEAhBIIwAA877LDcYdx0002Z96EsvsMPP9y1q735muvjrrvusjPOOMNdu88++9jgwYMTxVOPPhMFSiUIQAACEIAABCAAAQhAAAIQgAAEIACBQggEYQAWQiKHTrTR5rbbbmtffPGFa/3uu++2rl27NulJ1+27774NWYjXXnutbb311okiqkefiQKlEgQgAAEIQAACEIAABCAAAQhAAAIQgEAhBDAAc8Z866232h//+EfXy2qrreb2AtQS39IyZMgQu/76692PNtxwQ7vtttvKRiUD8fTTT3e/ay6jMMs+c8ZD8xCAAAQgAAEIQAACEIAABCAAAQhAAAI5E8AAzBnw7Nmz7eijj7bnnnvO9bTkkkva/vvvb6uuuqpNnz7dRo0aZa+++qr73SKLLGIy72QUliu1GoBZ9pkzHpqHAAQgAAEIQAACEIAABCAAAQhAAAIQyJkABmDOgNX8t99+6w4BefLJJyv2tswyy9hll13mMgArlVoNwCz7LAAPXUAAAhCAAAQgAAEIQAACEIAABCAAAQjkSAADMEe4jZt+7LHHbOTIkfb222/b1KlTrX379rbCCivYTjvtZAcddJB16NCh2WjiGIBRQ2n7LBAPXUEAAhCAAAQgAAEIQAACEIAABCAAAQjkQAADMAeoNAkBCEAAAhCAAAQgAAEIQAACEIAABCAAAV8IYAD6ogRxQAACEIAABCAAAQhAAAIQgAAEIAABCEAgBwIYgDlApUkIQAACEIAABCAAAQhAAAIQgAAEIAABCPhCAAPQFyWIAwIQgAAEIAABCEAAAhCAAAQgAAEIQAACORDAAMwBKk1CAAIQgAAEIAABCEAAAhCAAAQgAAEIQMAXAhiAvihBHBCAAAQgAAEIQAACEIAABCAAAQhAAAIQyIEABmAOUGkSAhCAAAQgAAEIQAACEIAABCAAAQhAAAK+EMAA9EUJ4oAABCAAAQhAAAIQgAAEIAABCEAAAhCAQA4EMABzgEqTEIAABCAAAQhAAAIQgAAEIAABCEAAAhDwhQAGoC9KEAcEIAABCEAAAhCAAAQgAAEIQAACEIAABHIggAGYA1SahAAEIAABCEAAAhCAAAQgAAEIQAACEICALwQwAH1RgjggAAEIQAACEIAABCAAAQhAAAIQgAAEIJADAQzAHKDSJAQgAAEIQAACEIAABCAAAQhAAAIQgAAEfCGAAeiLEsQBAQhAAAIQgAAEIAABCEAAAhCAAAQgAIEcCGAA5gCVJiEAAQhAAAIQgAAEIAABCEAAAhCAAAQg4AsBDEBflCAOCEAAAhCAAAQgAAEIQAACEIAABCAAAQjkQAADMAeoNNkyCPz888/2n//8x8aOHWvvvPOO+/e7775r33//vbvBnj172gUXXNAybjaAu/j222/tueees5deesnGjRtnH330kc2YMcPatm1rSy21lK233nq2++6729Zbb23zzDNPAHcUdohvvfWWvf322+6f999/36ZNm+b++emnn2yRRRaxVVZZxTbbbDP3nHTp0iXsmw08+tNOO83uueeehrvo37+/HXfccYHflf/hH3bYYTZmzJiaA3388cdtueWWq/l6LkxHQJ8j999/v73wwgv2+eefmz5jOnbsaEsuuaStv/76tummm9pOO+1k8803X7qOqN2EwF//+le78sorY5ORJjfddFPselSoncCnn35qd955p/tb67///a97LhZYYAHr1KmTrbXWWu6Z2HXXXW3++eevvVGuTEVA7yK33Xab+zyZNGmSew9ZYoklbM0117TddtvN6THvvPOm6qM1V87jfe/jjz+2ESNG2DPPPOM0++WXX9y7yhZbbGEHHHCAe5YoEKgXAQzAepGnX+8J6AX50UcfrRgnBmBxEg4bNswuu+wy++GHH6p2uvHGG9tFF11knTt3rnotFyQnsMEGG9h3331XtQG9OMhwOuaYY6peywXZE3j66aft6KOPnqthDMDsOZdrEQOwGM5xe5Ghcf755ztTfM6cOc1Wf/nll90XGpRsCSQ1APfZZx8bPHhwtsHQWgMB/a116aWX2o8//tgslV/96ld2xRVX2Oqrrw69HAnImNLfvtdee22zc9WGG25ol19+uS299NI5RtNym876fe/222+3QYMGNSSMNCanL5X69evn/jamQKAeBDAA60GdPoMgoMlZGRlRWWyxxUz/KPNMBQOwOBnPPvts0weqiv7A0TdoXbt2tcUXX9yZgm+88Ybdd999DYaUsmjuuOMO93tKPgRkALZr185lXq6xxhouc6lDhw42e/Zsmzhxoj311FP22muvNXSuP7D4YycfLSq1KqNDWbH69nmhhRZqeD4wAIvRodQAvOqqq6p2uuWWW7pnipIfgenTp9tvf/tbl9EffZ7svPPObg7T/DVz5kxT5oayzZX5r4wbDMDs9VBG04cffli1YWWU/+EPf3CZ5Sq33HKL6Us+SvYEbr75ZjvvvPMaGtZn/A477GDLLrusywL84IMP7O677274HFG2rDJolTFLyYfAOeec47LIVGQa7bLLLta9e3dr3769KVNT/N977z33+9VWW81uvfVW5qsEUmT5vjdy5Eg75ZRTXBTKylR25uabb25t2rRxfxPri6fIYB8wYECTL2gThE8VCMQmgAEYGxkVWguBoUOHupcBGU36Z/nll3d//Jx++ukOAQZgcSNBfwTpj50jjzzSfZCWW+og00kvdtFLBZkC+eqjPzr1B2dzy63vvfde0/JTZdnoj58nnniCb6jzlWWu1iPjXC9wv/nNb0zZHSoYgMWIUGoA/vvf/y6mU3pploA+I5599ll3jT5PTjjhBLeNRLkyefJk9yWS5i5KfQiMHj264YsjZZ09/PDD9QmkhfeqJaX6YlV/86r8+c9/tv3337/JXX/11VfWp0+fBtPp8MMPb/ibuIUjKvz29CWE5igVfYF3zTXXNDG/lSF47rnnui+8VQ4++GD3/5R4BLJ639PzoSXyMsz1nqJtDnr06DFXMEpY0HMza9Ys99kiE3fllVeOFzBXQyAlAQzAlACp3roIYADWR29lbSj7slrRHo177bWXu0yZNNrbiYyaatTy/f2xxx5rTz75pOtEy+7222+/fDukdUdAY/+II45w5qv+uFXGU7TnFgZgMYMEA7AYzrX2Uvr5zYtyrdTqe13p58fJJ59sRx11VH0DaqG9P//88+7zQmXdddd1ewBWKsruj7b00Jfjeq4o2RPQWP/Xv/7lGlbigUyjckWrLpTpry+/lSWorYvYSza9Hkne97T9kJZrq+jz/8wzzywbiL6MjfaQl3aXXHJJ+oBpAQIxCGAAxoDFpRBI8oEAtWIJKNMpygJUKr42SabUj4D+sPnHP/7hAjjxxBNNL3SUfAnom+U99tjDJkyY4JafaA+h0j23MADz5R+1jgFYDOdae9HyOR1qoGwabcy+8MIL11qV6+pAYMqUKbbddtuZspyUKSPjieWm+QihLCQZrCrVDAn9faW/s1TIysxHDx0YoSXYyszUKosXX3yx2S/B//a3v9lf/vIXFwzLSrPRJMn7npbMazWSymOPPeZWjpUryhDUgYXaR1ufR/rCdsEFF8wmcFqBQA0EMABrgMQlEIgIJPlAgF6xBJRhppNpVbQsolu3bsUGQG9zETj++OPtkUcecT8jA7CYwaHlWzopU1mzo0aNcqcFYgAWw760FwzA4plX6vHVV1+1Qw45xP1a5vjFF1/sT3BEUpaAvjiKMmO0jE4mByUfAqUZgOuss47dddddFTtSRn/0RZ72z9RnCyVbAlpKqu1uVLQNgfRprjz00ENuOwMVGYfRvoHZRtW6Wov7vqc9MnUis8oqq6xiDz74YLPA+vbt676IUtHy7m222aZ1AeZu60oAA7Cu+Ok8NAJxPxBCu7/Q49XGutrHZsaMGe5WtNcTGQP1U1V7/inbTBkc2mdL/y8zipIfAW0y3atXL1MGgU6h23fffV1nGID5Ma/UcqkBqEymcePG2bRp09y2BEsttZR7UVO2jTZ1p+RLQMvglQmromVZ0kZL5f75z386Xb7++mtnmK+99tr261//2m0lwd5/+WpSrXXpEB269ve//90dSEHJh4AOU9t2223d/KRSyx6A2uNMB4dstNFG+QTViludOnWq+1tWJa4BqANCSg9ga8UYU9163Pc9GX5a5aKy995725AhQ5rtXxmb0ZcaOuhIhiAFAkURwAAsijT9tAgCcT8QWsRNB3QTpfqwN01xwr388svuBVpFJuznn3/uTtGMNtvXi7ReKHRwDiU/AnqJk3GhJVrKHrjhhhsaOsMAzI97pZZLDcDmepcBqL2DZApS8iGgLyJ0oISKjEBlzMgArFRWX31193JWaQlXPlHSakTglVdecV9kqOhLvKefftrtb0bJj4Ay9U866STTnnIqG2644VynAL///vvuBFMdFKJli8ro1xYTlOwJSIP111/fnX6tJcAvvfSSLbroohU7kkF++eWXN/xeewcuvfTS2QfWilqM+76nPZajbNhatllRlu0ZZ5zhiGrlkp4nCgSKIoABWBRp+mkRBOJ+ILSImw7kJrRkQtk0+uZURR/GOo2Lkj+BAw44wN58880mHekP10022cS0DFj/puRL4MILL7TrrrvO7SWjPZ1WWGGFhg4xAPNlX651GYA6/VeZHFpWpxcymRg6XVZ7/uglTYe0qHTp0sVuv/12MpZzkunAAw80nb6oon3LZJLPP//8LlNDGUz6kkKHSOnwAx06FWmiz/xaDqDKKexW26wOPYgOlzj66KPdvmaU/AmMGTPG/vSnP5nMvnJFz4xO0j7ooINMp8tT8iNw6KGHmr5cVRk4cKD17t27bGdaYaG/fbW/aVQeeOABW2211fILrhW0HPd9b/DgwQ1fup511lkm/Zorjz/+uPXr189domznK664ohVQ5RZ9IYAB6IsSxBEEgbgfCEHcVAsIUllnOsFOWQOhZJMQAAAgAElEQVQqO+64o1111VUt4M7CuIVKBuAyyyxjOm1zn332IbspZynfeust91Kml4Fyy0kwAHMWoEzzr7/+uikTeYEFFijbufYqlTn+2Wefud9rDyDtBUTJnkDp4VBqXdk0ypDVkt/S8sUXX7jTNrWfk4qMQxkilOIIlG6Qr16VmbbSSisVF0Ar7klfSMgE1AmlWhpfriyyyCIum1/ZghxckN9gue+++9xnuYoyLq+//nq3bURp0VYfmp9uu+22uX6uPQAbX5tfpC2z5bjvezL9tO+4SqUl9KWktErmyCOPdD/aaqut3Je3FAgURQADsCjS9NMiCMT9QGgRN+35TegPoFNOOcVlPKko60lZHM0tl/D8loIOT6eaffLJJ6ZvN4cNG+b2Y1QGjZbdRXvaBH2DHgYvA1x7/b333nvOcNK+Zo2Xy2EAeiicmf3nP/9xWWjSUEXarbfeen4GG3BUpfvJ6TaaO5BImYBaSq8i81bZmpwYXJz4ega0T6PKxhtvbLfccktxnbfinrSKQgdJRMtNlZ2kw1f0RZ5Oox07dqz7TNdybBXNUzqopWPHjq2YWn63rr9tjzrqqLm2UtFJ5toyQvv8ffrpp+7vXmWZd+rUybQFiJZnq3AAXnpd4r7vYQCmZ04LxRHAACyONT21AAJxPxBawC17fQv6tvrss89u+Natc+fO7vTT5ZZbzuu4W0twEyZMcCdvTpkyxS23054na6yxRmu5/cLuM9pMWqafXp5lAjYuGICFyRG7I5kd0k3l97//vcsKpGRLQFnI77zzjmtU2TQyOSplZuqa0iXDMjl0QAKlGALKZFb2rIoy0dg7Nn/us2bNcpn6WkaqL09lIFXKulTGWWTKaulpdFJz/lG2vh6UDXvaaac17F9ajoD2yNR+pVqa/c0337hLHn74YbfVASU5gbjveywBTs6amsUTwAAsnjk9Bkwg7gdCwLfqfegy/84991zTUgcVfUst86903zPvb6IVBHjvvffaqaee6u5UG4ZHJ3G2glsv5BaVraQNpLVZuF4AlA1brmAAFiJHok6UxXHyySe7ulqqKkOXki0BLetVJp+KNtfXfovNlfPOO8+dcKpSy35O2UbbeltTRmx0sISyLnWQlE7NpuRLQMsPtYesik4yPfbYYyt2qCx/GeIym3QSsPYylQlFyY/A888/7/bElDH+5ZdfOu76olv7XGtvQGUEduvWzW0Bor2XtXdghw4d8guoFbQc932PQ0BawaBoQbeIAdiCxORW8icQ9wMh/4haZw8y//74xz827HuizfVl/q244oqtE4jHd61lRTqRVmWJJZZwpwNTsiMwaNAgGz58uHsh0Gb5bdu2Ldv4iy++6LKeVDbbbDO3jEhFWQJaVkSpH4HSvYC23HJLt9cTJVsCpVlLtey3pC8qhg4d6oKoZohkG2nrbm3IkCEN45/9F4sbC6UnlmsLlXXXXbfZzvVZEy0F1gm0O+ywQ3HB0lMTAloGvOeeezZ8pisDkJKOQNz3vQcffNB9VqhoWw/NZc2VaOWGrim3b3O66KkNgeYJYAAyQiAQg0DcD4QYTXNpjQQam39LLbWUM//YJLxGgAVfpr3NopcJLQPWPkKU7AhoL7Mbb7wxcYPa40nLhyj1I6ATG6NTTskAzEcHLWlUJp9K3AxAbTPRq1evfAKj1QYCs2fPdpllynBSYT/M4gZH6R6ZtRy6ovlK85aKlgBrKTClfgT0N7AOnlDRoWzKYKakIxD3fU8HR+22226u01VWWcVkCDZX+vbta88884y7hG0m0mlF7fgEMADjM6NGKyYQ9wOhFaPK5dYbm39adqI/fNjrJBfcmTSqgyn22GMP15YyNbVciJIdAQzA7FjWq6XSzcN/97vfuY34KdkS+Pzzz2277bYzfYbE3QNQBx9wgFG2epRr7bHHHnN7YKqsvvrqDQd75d8zPZTukanTsaOs/UpkDj74YHvttdfcr7V8WFm1lPoQ0Jwm/aJTmzHOs9Ehyfve9ttvb5999pkLQAfhVdqPXIe16JnRcnptcaDtKdjqIBvdaKU2AhiAtXHiKgg4Akk+EECXHQEt+7311ltdgzL/lPm08sorZ9cBLWVOoHTpnZaaXn755Zn3QYPVCbAHYHVG9bjiww8/dCfO6gRHFU5vzE+F0mWOtZ4CrL21tP/WggsumF9gtOwIaN+5J5980v336aefbtq3kVIMAR00cc8997jOdKK8tpaoVD7++GO3T6MyNrX1hMyLxRZbrJhA6aUJgdtuu83th62y6aabui/FKekJJHnf0z6aMsRV9HkTnWbeOBp9qaQDjlSUNXjppZemD5gWIBCDAAZgDFhcCoEkHwhQy4ZA6absmH/ZME3aiv7gVNal9pLThtPlijaj1h9C+sNG31Cr6A9T/YFKKZ4ABmCxzPXlxDrrrGMbbrhhxY6VsdG/f3+bOHGiu6aWvemKvYuW1Zs20NcJsyo66VSZTmuvvfZcN6nlpzKe3n//ffdzmVLRvk4ti4Zfd/PFF1+4DE2ZStoqQpninTp18ivIFhyNDlvRIVIq+kzX31v7779/kzuWTtr/L8o2095/2gOQkg+BN954w81R5U4s199V+sJIX7LquVFm88iRIzkILyMpkrzvTZ061R3Mogw/meM6GETbrJSWN9980/r06WM6ebtNmzZ23333uSXDFAgUSQADsEja9BUUgQkTJpg2Qy4t2mg3+oZ6jTXWMKV7lxZtrF9t6URQEDwJtnRDdv1xetJJJ9WU+ac/nDp37uzJXbScMKJsgWWXXdYtjdNyrcUXX9y9uM2YMcO07FfLHyJjQ3d+zDHHON0o9SGAAVgs9379+rlnQKeS6zNBz4iyZPRSMGXKFNOhLNpE/5dffnGBdenSxR1qpGXylPwIXHzxxXbNNde4DjRf9ezZ0zbaaCP3IjZ+/Hj3mT99+nT3exm4yjivdLBOflG2vpalibRRYR/M+uh//PHHm/b/i4q+rJN5oTlJGcrav1cGk07/VdF8JgOKw9fy00t/N2mp9TbbbOP2Utae1z/99JPp/eTRRx81vZOoKENZn/G6jhKfQJbve8qk1d/IKvq8V7asDvfSf0vLe++9tyHjnwOm4mtFjWwIYABmw5FWWiABnZjZu3fvWHembI7jjjsuVh0urk6gdOlW9av/74rBgwe7vVEo2RIoXS5UreUOHTo44++QQw6pdim/z5EABmCOcMs0HRmAtfSqzD8tucP8q4VW+mv0hZIMJ2UpVyrSRNnLyhSk5E9App+Ww6tce+21tvXWW+ffKT3MRUAHdmkp6V133VWVjFYA6Dlaa621ql7LBckJyAB86qmnmm1g1VVXdRmbzWWbJ4+gddTM+n1PXxxpiW+0tUdjivPNN5/LLpfpToFAPQhgANaDOn0GQSDrD4QgbtrTIDEA/RLm22+/dfv+vPzyyy5r5pNPPrFp06Y1LENRNqAyZPUSpxc7mYCU+hLAACyWv54JfYa89dZb9u6775qWBukZUfbGwgsv7DL+NthgA3dATrdu3YoNjt6cJsr2e+6552zy5Mlu7tK8JU20J6NOo6UUQ+DVV19t+IJIWeVPPPGEy5ah1IeAPtO1/FHZSp9++qnp817ZslqSraxYZQVqP99yy1LrE3HL7VVLrfU86G8taaHPEa2C0VzVtWtXt9xUf2Mpg5mSnEAe73sfffSRjRgxwp30O2nSJLcVjjI4tVLswAMPbLL9RPLoqQmB+AQwAOMzowYEIAABCEAAAhCAAAQgAAEIQAACEIAABIIhgAEYjFQECgEIQAACEIAABCAAAQhAAAIQgAAEIACB+AQwAOMzowYEIAABCEAAAhCAAAQgAAEIQAACEIAABIIhgAEYjFQECgEIQAACEIAABCAAAQhAAAIQgAAEIACB+AQwAOMzowYEIAABCEAAAhCAAAQgAAEIQAACEIAABIIhgAEYjFQECgEIQAACEIAABCAAAQhAAAIQgAAEIACB+AQwAOMzowYEIAABCEAAAhCAAAQgAAEIQAACEIAABIIhgAEYjFQECgEIQAACEIAABCAAAQhAAAIQgAAEIACB+AQwAOMzowYEIAABCEAAAhCAAAQgAAEIQAACEIAABIIhgAEYjFQECgEIQAACEIAABCAAAQhAAAIQgAAEIACB+AQwAOMzowYEIAABCEAAAhCAAAQgAAEIQAACEIAABIIhgAEYjFQECgEIQAACEIAABCAAAQhAAAIQgAAEIACB+AQwAOMzowYEIAABCEAAAhCAAAQgAAEIQAACEIAABIIhgAEYjFQECgEIQAACEIAABCAAAQhAAAIQgAAEIACB+AQwAOMzowYEIAABCEAAAhCAAAQgAAEIQAACEIAABIIhgAEYjFQECgEIQAACEIAABCAAAQhAAAIQgAAEIACB+AQwAOMzowYEIAABCEAAAhCAAAQgAAEIQAACEIAABIIhgAEYjFQECgEIQAACEIAABCDQmMDzzz9vRxxxhPvx5ptvbjfccIOXkC677DIbOnSoi+1//ud/rF+/fl7G6UNQJ598st1///0ulAsvvND22msvH8IiBghAAAIQgEDQBDAAg5aP4CEAAQhAoKUTOOyww2zMmDEVb3OhhRayRRdd1FZZZRXbZJNNrGfPnrb00ku3dCzcHwQaCGAAtrzBgAHY8jTljiAAAQhAoP4EMADrrwERQAACEIAABCoSqGYANq44//zz27HHHmu///3vbZ555oEsBFo8AQzAlicxBmDL05Q7ggAEIACB+hPAAKy/BkQAAQhAAAIQqMkAXHfddW299dab69oZM2bYu+++a++9995cP+/Tp4+dccYZkIVAiyeAAdjyJMYAbHmackcQgAAEIFB/AhiA9deACCAAAQhAAAI1GYD9+/e34447ruy1r732mg0YMMA+++yzht/feeedJtOQAoGWTAADsOWpiwHY8jTljiAAAQhAoP4EMADrrwERQAACEIAABFIbgGpg/Pjxbg/AOXPmuPYOOOAAO++886ALgRZNAAOw5cmLAdjyNOWOIAABCECg/gQwAOuvARFAAAIQgAAEMjEA1Ujv3r3tpZdecu396le/socffhi6EGjRBDAAW568GIAtT1PuCAIQgAAE6k8AA7D+GhABBCAAAQhAIDMDcPDgwXbDDTe49tq3b29aGlxL+emnn+yBBx6wJ5980t555x376quvXCZhp06dbIMNNrBddtnFdtxxx1qaarjm448/tpEjR9qLL75oEyZMsGnTplmbNm1siSWWsDXWWMM233xz23XXXV0fzZV///vfdu+999oLL7xgkyZNspkzZ9piiy3mDM5tttnG9t9/f/f/zZV//vOfduaZZ7pL9ttvPzv//PPt559/dvd833332fvvv+/uWScqb7TRRvbb3/7WunXrNleTP/74o40aNcruuece++ijj9z9LL744u4+jjrqKFt55ZWbjeHggw9u0OOWW26xjTfe2CZPnmx33HGHPf7442759qxZs6xLly620047Wd++fV08pUX3r7rPPPOMTZw40d3Diiuu6PQ5/PDDrW3btjVrpP7uvvtue+6555w+06dPd2Nm2WWXdfd00EEHubabK+WMmu+++861K7affPKJaZ9KcRLXQw891I2nWsuHH37YcL9itcACC7j4evToYQceeKA78TqJAfjBBx+4sam6YvrNN9/YwgsvbCussIJttdVW7t6XWmqpWsN0Y1M6vv766zZ16lSnm9jtvvvuLit3wQUXtMsuu8yGDh3q2vyf//kf69evX83tl7tQY19MVJ5++mlbZpll3Li866677F//+pf7nTRde+21nR6lRfevMfTKK6+Y/nvKlCmm8d2hQwfr3LmzO1FcGcQ6XbxaKTeu9WzomdMXEBqnGtfiudlmm9kRRxxhq666auxxVamC7uP44483jTsVxXP22WfbvPPOWy10fg8BCEAAAhBoVQQwAFuV3NwsBCAAAQiERqD0FODm9gCM7qvUZNCJwGPHjq16yzIvzjrrLGcCNVdk3FxxxRVVjZEffvjBZETKAJg9e3azbSrGl19+2dq1a9fkOpmSgwYNshEjRtgvv/xSsR2ZLQMHDrS99tqr4jWNDcCTTjrJTjjhBBszZkzZOjIPdA977723+72MqN/97nfu3+WKjKm//OUvtsMOO1SMobFRIsPiD3/4gzNpypXlllvObr75Zmd4qchg0pJuGTXlyuqrr27Dhw+vaqiKpWK9/vrrK7al9mXWHn300c5cqXSidGMDcK211nLXV+KkdrWPpcZytXLTTTfZhRdeWDFG6X7RRReZxpBMJRUZl5EBXq59jc0///nPpv0xmxtTGo+nnHKKHXLIIc2GqTGqZ0emcKUiXf7617+6a/I0AGUiDxkyxHSPpaVr165zGYBiP3r06Gr4nYEmU1ljtDkzrfG41hcHJ554on3xxRdl+9C4+tOf/mT77rtvxRhqzQCUyXzaaaeZdFDR6ecafxQIQAACEIAABJoSwABkVEAAAhCAAAQ8JhDXADz99NMbXvaVEaTMoOZK4xdoGR/KfFMWkEwfZRS98cYbLtNMRdlpMk8qZe19++23duSRR9qbb77Z0O1CCy1kG264ocvWkjmgbCMZk5HxpSxFZZ6VFpkzxx577FzxK8tPGUSLLLKIy9rSUufoxV91ZcQow6xcKTUAZeopO1HZWsrMUiae7vfrr792GWHKWFOZb7757NZbb3X3LLNCGVXKElN21JJLLukMDmU3KrtJReyUIajry5VSo0QnNF988cXO3JLBJz7iJOPs1VdfbdjHUeaRMtWUpXjqqae6ZldaaSV3uIuy/ZQd+fbbbzd0t91229nVV19dUXLpKANOZlFUNE50unTHjh1ddqW0KzWDFfe5555bts1So0aH0Nx4442OizQSV2V7KrNSnDQ2oiIj+de//nXFOJXlKJMoKjL5pL1YSSdpr3/LeJWRK6NQpTkDUP0rq1K6R0VZesqQU7xqT+xLjSvdn7I7KxVl8pUus5cpqTj1b2VYymDWGFU/ytiTqamSdQagTDqZoSp6zmTWK5tPY1b3fdtttzXcwj777OOyfGXEKcNP40nXarwre1HjKcosVCXNQVH2bLVxrcw7jWuZ29JeWZ9iofY0BiJzUobi7bff3uRU86j9WgxAsVQmr+YUzVWKsdLzX1FAfgEBCEAAAhBoRQQwAFuR2NwqBCAAAQiERyCOAahsOy3TlTmmIoNFRkul8u6777plfnop1wu0zBFlfMkMKS1axqlsqMg42X777RsymRq3reybRx55xP1YhoKyjZSd1TjDT0aUzBFlrF166aXO/CotypRSNmNUZAYqu0eGT1RkKsgUUwajikwiGR3lTj4uNQDVhow3LbOVyVRqZmrpopZmRkunt9xyS9enlkb36tXLnbRcalaKtbKkZJSqNHfwSqkBqDZlXMi00FLW0gw73Y+yDSNjUX3+/e9/dzwvuOCCJkux77//fqdPlNEmBjIUyxWxjgxCLck855xz3HLa0v4V10MPPeQM1ci0Uwbbzjvv3KTJUqMm4nrMMcc4hjJXoyKuGhtRxqVMp2icNG70P//5j8u8jDIdu3fv7gw+GVtR0ZiV4SUTSLpHRnBzBqA4yvBW0XLtP/7xj7bpppvO1b2eIWWcirPaFHP9vwzSxkVLbWXkRkX7b4pH6TJsjVH9TPddGmfWBqDMPJlq0lNmdame4lj63Iib7kdjW4Z24yL9ZRBrbEo3FZl166+/ftkx1XhcaxxqPMqME7+oyBCVmaolxyrqX1mo5Uo1A/Dyyy93z0T03EsvLbemQAACEIAABCBQmQAGIKMDAhCAAAQg4DGBOAbg3/72N7e0Myp6udZLdqWiF3Qtv1XRy776qlSUGSZjIVraKfNjnXXWmety7TtWmi2lWH7zm9/Epqv92LbddtuGPb1kSsq8KVdkBMmAUEaTSiVTodQA1HUyisSn3NJGZb/J7CpdIhrtG1guBpk7ETtlUun/y7VbapSoHZkW2h+uXLnyyivdstGoqD1l1yn7sFwpzfyslLElI1emsO5L2X5isvzyy1fUR3sDKptTRZmIMhobl1KjRr+T8Sdzq1xR5qdM1++//979utwY0s+V0ScDUkV7RWrpc6mZWNp26X1HupZbAqzssz59+riqMh9laDW3b2TpeClneMso1M91Tyrah1JLi8sVZcPJGNY+k1HJ2gBUuzLMtadmVkXZkNESaJlrl1xySdmmG49rZeXpeSlXdFJ5tKxeY1pjrFw2cSUDUGNX2ajST0VfHOhLjq233jqr26YdCEAAAhCAQIslgAHYYqXlxiAAAQhAoCUQqGYAKkNLmXzK+oqym3TfykqTOVKpaAlutAeXMua0rLda0VJUZfZUal+ZflpCq7LHHnu4pYBJiva90153KspSUzZSaQZT4zaVrScTIiqPPvpok8MrGhuAWlIrc6lS0SEQUcajMrq0lFqmWaUiAyIygx588MGyhyeUGiWN92Vr3K6M1lLztFo257PPPusOLlHREm6ZZo2LmIqtipZqKqOxWtE4ijIspf+aa645V5VSo0ZLPpUp2ZxWWn4sfVSUrdZ4jz1lnIlllNF33XXXuUM5KhVdr2XPkalYKQNQGaSKTUUZkKrTXFEWnPgrs1PZdDJ1SzNjn3jiCZelqaLsVo2Pxge2lLav38vIjkrWBqCW/CpTMeuicScG0lZmXblSOq61nLq5/RBVP1qCrP/+xz/+4cz+xqWcAahMRv08yhyVgSstK2UmZs2C9iAAAQhAAAKhE8AADF1B4ocABCAAgRZNoNQArOVG9VIsI6jUbChX76qrrmpYHiyjUEZPtaLljNrHTKWxaSgDRnu+RcaNDKjGp+hWaz/6fekhBcpAi/a+a66+Mp+0dFRFmVjKyCotpQagTg8u3betXLsyyKIsIxlS1157bbPhK87IIKlkapQaJcpobE4jGVDiF+2ZVi27q1QbnbgbGbGlQWt5eLS3n/bQq3ZysuqWLsUuZxqWGjVayly6b185YKVZqlpyrr3rSovM3uh0XO2zqKzSaqe5lu7DV84AlHGksSmWMumUDVitTcWkJdCRkdpY09LTtjX2Sperl7tvZa7p2Yn2F8zaAKyWwVtp8Mpo1pcByg7VlwlipbEXFZm1Ucw6bbfcycil41qH62gJeHNFB/ZEXziUM4FVt7EBqGXqGhcatypaDq4M3mqnCdc653AdBCAAAQhAoDUQwABsDSpzjxCAAAQgECyBOAag9tvSvmaNza9yN6+lujJXVJQNVengitK62rcvyjKSOaOss6iULhfUsjz9fy0mS7nYdJLuxIkT3a+0FFbLRqsVGSAy+VTKHVpRagBWy6ZTG8pevOaaa1x7tZiQOvVUmX8qlcy6UqNExpqWkDZXtJz5yy+/dJdo+a2W4VYqMreifeqUsfjWW2/NdakOd9hiiy3cz5ShV8sY0bVathrt21duKXapUdPcISxRMDpURWNURVmW0X9Hvy9d+izDUkZ1taIlvzLkVMoZgGIR3a+Wm+6yyy7VmnS/1+E30dJyxal4o1L6XGofwGh5cXMNK2NQmYMqWRuAze37WC4mGa1aYq4lubWWSlmzcce19iCMDPVKRnjpuNIpvxr/kRYy8GX+6eAeCgQgAAEIQAACtRPAAKydFVdCAAIQgAAECidQajQo6670MALtLaaN9bVUNTowQQFquaMMqebKXnvt5ZYOJy0ykUpPn1WmkJZ3qqy22mpzLUeO24eWM+reVO6++27TctlqRQcC6GAAFS3dLN0LUT8rNQC19HnQoEHNNikTTyadig6v0AEkzZVqhxaobqlRolNulZXWXFHGWHQa61NPPeVOwK1UtCddxElG8Lhx4+a6tHTvtWosK/2+XIZf6X0PGTKkYX+3Sm2U6lBuX0VlEIqNSrXTZ6M+tCRUGqmUMwAfe+yxqvpVY9LYqNKeeNGefrWa1KVLsLM2AMVAexvWUkrHdi3XR9dUMhnjjuvS/itxKB1XOuBE41tlrbXWcuZfpVPI49wP10IAAhCAAARaGwEMwNamOPcLAQhAAAJBEai2B6BuRkv0dKBE6R6A1ZaMlmbZJQHS2GTSvl/K1FGptAddLf1o+WHpPnM6DEIntlYrpVlg5ZbsVjOeGrdfi0lRWidvA1B7yC2zzDIVMVQzAEsPKqnGstLvyxl2tdx3aXvVdNAYivaQa+7wl9I2Sw+fKWcAlo7NpPfe2KgqfX6UzVbLIRSlWaVZG4DVxkd03433ItRp0dqTTwf6aHzphOvSPRxrMfdquaaUey3PVum40gEw0R6PyjzWYTi1zAlJtaYeBCAAAQhAoKUSwABsqcpyXxCAAAQg0CII1GIA6ka1x5iW9UbLcrW/m7LyKh1MUJrBpOw5GRppSkgZgM2d6BsxqMWkCMkAVLZmdDKrxka0l1oazVU3awMwjwxA7fcYnUxc7fCVWnn4lgFYqwHYu3fvBu2V0amlzTrkpFLZc8897d///rf7daWs1bwNQC0tVyZwtAQYE7DWUcp1EIAABCAAgbkJYAAyIiAAAQhAAAIeE6jVANQtfP75525/s2j5bHN712nPMh2GoKKliQcccEAqCqV7ACqL6JVXXslkD0DtAae94KqVOHsAtkYDcNKkSQ0n32pvxjfffLPZ03qr8Y5+n7UBmMcegBqL0YnHOsRCh1mkLUn2ANQhFtp7T6UeGYDaJkDL65UtqgxeHVrT3MnWysbdbLPN7Ouvv66rAXjhhRe6/TJ1UBEmYNqRS30IQAACEGjNBDAAW7P63DsEIAABCHhPII4BqJspzVzTYRAyHJQx07hceumldvXVV7sf9+zZ0y0hTlPyOgVYJxqfcsopVUPbbbfd7IMPPnDXVTsFuDUagOKiZapTpkxxjLSMUuZO2pK1AVh6CrDMOi3vbS5DTfGfcMIJpqXiKuWWAMsQ33TTTRtOqFYfyy23XKpbLz0FWGNPz1NzJZddvt8AAAh3SURBVO9TgGvJACw9KVrLfVWnuaI9QrVXaFTqlQEoA1BxyIg84ogjMAFTjVwqQwACEIBAayaAAdia1efeIQABCEDAewJxDUC9JCtbZubMme7elDVz+umnN7nP1157zR1KodKuXTt3OmnajfWVcaisIhUtHdRpn0nKzTff7LISVWRUjB49utlsNZ3WquWMUdH1K6ywwlxdV9t7rnGcLW0JsO5PSynvuOMOd6vlDkpJolXWBuC0adOcUfnTTz+5cIYNG9ZwenG5+DTet912W5s1a5b7dTkDUD8vHZu1msrN8dDzolN9VXTqtYzKDh06VKyirMO+ffs2/L4eGYAyf6O9CmtZBn7OOec0nPqtwOttACqGcibgTTfdZDoZmAIBCEAAAhCAQPMEMAAZIRCAAAQgAAGPCcQ1AHUrpeZVc+beIYccYlq6qyJjQHsBzj///FVpaCmhMv4WWWSRua4tPYxBv9BJvDKa4pZvvvnGmTrRUmYZLcryKlcUi+4jOpF4yy23dKeENi4YgGYffvihKVvt559/dniizKpa9NFBM+UySbM2ABVLaUafTn29/fbbTdms5crAgQPtzjvvbPhVJQPwhRdecGa4isa4Du7o3r17LbfuDtlpfO9aRiujPcqoLHdCctS4npX999/f3nvvvYb+6mEASnedPB09V7feeqtttNFGZRm8/PLLpm0CorGii3wwABUHJmBNw5aLIAABCEAAAk0IYAAyKCAAAQhAAAIeE0hiACqLSod6RC/6OhxERk3joiV+Bx10UEP2lPYHO+OMM2y99dYrS+S///2vPfjggy4raNCgQbbNNts0ua5///4uY0+lTZs2pv+X8SIjsrTIWNDJtFqKeskll7gsqtIydOhQZ2RGRe0ce+yxcxmUMl+U3RgdfCJjR7HpRNPGBQPwf4mUmsNaWqvMOI2PcnvByeSScXbvvfe6PR3LLRnNwwDUUu699967IQtwiy22sCFDhpiWBEflhx9+cONm+PDhbkxEGYOVDEDVK41VJ90ef/zxbm/AxmNP18q0k6GtwyemT58+VyZcFEPpmNLPtDz1pJNOmitbVctutYRd+22WxlkPA1Axql8diqKy/PLLu/Gw7rrrzvW4jBo1ymWLKotYbKJ5xBcDUMFiAnr8oUVoEIAABCDgLQEMQG+lITAIQAACEICAWRIDUNwuvvhiu+aaaxxCvcQ/+eSTpmV/jctjjz1mAwYMcIZHVFZccUVbe+21XYafjJapU6e6k0CjbCddp7bLGYAzZsxwht/YsWMb2lP/G264oVvOq4MFZIro9zJWVLQcWQeHlBYZhMr8KzWdtER5k002cScbf/bZZ85AVAZgVGRaHHrooWWHDQbg/2LRXnSnnnqq3XfffQ2cZEzJBJIhtOCCC9q3335rEydOdJpHS2uXWGKJhuXdpYDzMADVvozh888/v6ErGXbax69z587O/NEpxho/+rlMrWi5eXMGoMbyMccc40zNqMiY7tatm2tXHJR9+sknn7hsvchU1O+jpdONB9dxxx3nTtuOip4x7a0YjVHFqXb0TOl50XJVlXoZgNJUe2BGz40OhFl//fVtpZVWcj97/fXXnfYq2iJA1+v5VPHJAFQ8Ggeaa8aNG+fik0GsccNy4LJTID+EAAQgAAEIGAYggwACEIAABCDgMYGkBuBXX31lPXr0qGkZrV6gtZQyepGuhkNGkU7nXWONNcpeKtNIe/jdc889znBqrmhpp4w8GU+Ni4wTmUBaAtpcOzIqFb+yxioVDMC5yShzTifuyvCqVmQS6STmv/71r00uzcsAVEc33HCDM7IjI65x59Jdxp9MQGXfqTRnAOr3MpavuOIK13ap6V2JgUxBGWEaX+WKTDP9rtRQbXzdqquu6p4XPQ/KbFWplwGovmVY/uEPf2j2/nXPygbWMmBfDUDdi0xgaY8JWO0p5vcQgAAEIAABwwBkEEAAAhCAAAR8JpDUANQ9adlktB+eDijQwQWN9+0rvXctedQJqXrhV7afsvlk0Gl56Morr+wypbbaaiuXMVTtZFa1q6WcI0eOdBlXythTxo7MGu2nJvNQSzt32WWXspmJpXEpC0lLMdXO559/7kxNZVgp00dZVQcccEDVNjAAm45yZfpJHx3couXgWjouQ0vZmMrWXG211VzWnfZj1P+XK3kagOpPy851KIxilPYaP8suu6zbf0/L1/Xfzz//fM0GYHQPX375pVvarKW5GqcykrTkWffepUsXNz6VyafxVcvhOIpBRrUOpFHGrManDqLRnov77LOPy8KNe7BMtXlJsSmbVqWWU4BL25swYYI7YEVcJ02a5JbrK4NOmbqKV3sFqsgI9NkAxASsNkr4PQQgAAEIQOD/CJAByGiAAAQgAAEIQAACEIAABCAAAQhAAAIQgEALJoAB2ILF5dYgAAEIQAACEIAABCAAAQhAAAIQgAAEIIAByBiAAAQgAAEIQAACEIAABCAAAQhAAAIQgEALJoAB2ILF5dYgAAEIQAACEIAABCAAAQhAAAIQgAAEIIAByBiAAAQgAAEIQAACEIAABCAAAQhAAAIQgEALJoAB2ILF5dYgAAEIQAACEIAABCAAAQhAAAIQgAAEIIAByBiAAAQgAAEIQAACEIAABCAAAQhAAAIQgEALJoAB2ILF5dYgAAEIQAACEIAABCAAAQhAAAIQgAAEIIAByBiAAAQgAAEIQAACEIAABCAAAQhAAAIQgEALJoAB2ILF5dYgAAEIQAACEIAABCAAAQhAAAIQgAAEIIAByBiAAAQgAAEIQAACEIAABCAAAQhAAAIQgEALJoAB2ILF5dYgAAEIQAACEIAABCAAAQhAAAIQgAAEIIAByBiAAAQgAAEIQAACEIAABCAAAQhAAAIQgEALJvD/AKtNxnB+lleWAAAAAElFTkSuQmCC\" width=\"640\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%matplotlib notebook\n",
    "import matplotlib\n",
    "# matplotlib.use('Agg')\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    " \n",
    "plt.style.use('default')\n",
    "sns.set()\n",
    "sns.set_style('whitegrid')\n",
    "    \n",
    "x = [i+1 for i in range(10)]\n",
    "before = np.cumsum(result_success_before)\n",
    "after = np.cumsum(result_success_after)\n",
    "\n",
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(1, 1, 1)\n",
    "\n",
    "ax.plot(x, before, label='Before classes are used', marker='o')\n",
    "ax.plot(x, after, label='After classes are used', marker='*')\n",
    "\n",
    "ax.legend()\n",
    "ax.set_xlabel(\"Recommended rank\")\n",
    "ax.set_ylabel(\"Ratio of correctly recommended class names\")\n",
    "ax.set_ylim(0, 0.5)\n",
    "\n",
    "plt.xticks(np.arange(1, 10+1, 1))\n",
    "\n",
    "plt.show()\n",
    "# fig.savefig('result.pdf')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Experiment 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<img 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\" width=\"640\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%matplotlib notebook\n",
    "import matplotlib\n",
    "# matplotlib.use('Agg')\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "sns.set()\n",
    "sns.set_style('whitegrid')\n",
    "sns.set_palette('Paired')\n",
    "\n",
    "x = np.array(['before classes are used', 'after classes are used'])\n",
    "x_position = np.arange(len(x))\n",
    "\n",
    "# before\n",
    "ratio_if_related_not_all_before = np.array(success_if_related_not_all_before)\n",
    "ratio_if_related_all_before = np.array(success_if_related_all_before)\n",
    "\n",
    "ratio_if_related_not_all_before_mu = ratio_if_related_not_all_before.mean()\n",
    "ratio_if_related_all_before_mu = ratio_if_related_all_before.mean()\n",
    "ratio_if_related_not_all_before_sd = ratio_if_related_not_all_before.std()\n",
    "ratio_if_related_all_before_sd = ratio_if_related_all_before.std()\n",
    "\n",
    "# after\n",
    "ratio_if_related_not_all_after = np.array(success_if_related_not_all_after)\n",
    "ratio_if_related_all_after = np.array(success_if_related_all_after)\n",
    "\n",
    "ratio_if_related_not_all_after_mu = ratio_if_related_not_all_after.mean()\n",
    "ratio_if_related_all_after_mu = ratio_if_related_all_after.mean()\n",
    "ratio_if_related_not_all_after_sd = ratio_if_related_not_all_after.std()\n",
    "ratio_if_related_all_after_sd = ratio_if_related_all_after.std()\n",
    "\n",
    "# ---\n",
    "\n",
    "y_not_related_all = np.array([ratio_if_related_not_all_before_mu, ratio_if_related_not_all_after_mu])\n",
    "e_not_related_all = np.array([ratio_if_related_not_all_before_sd, ratio_if_related_not_all_before_sd])\n",
    "\n",
    "y_related_all = np.array([ratio_if_related_all_before_mu, ratio_if_related_all_after_mu])\n",
    "e_related_all = np.array([ratio_if_related_all_before_sd, ratio_if_related_all_after_sd])\n",
    "\n",
    "error_bar_set = dict(lw = 1, capthick = 1, capsize = 20)\n",
    "\n",
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(1, 1, 1)\n",
    "\n",
    "ax.bar(x_position, y_not_related_all, yerr = e_not_related_all, error_kw=error_bar_set, width=0.4, label='Classes not related to \\nall types of \\nprogram elements')\n",
    "ax.bar(x_position + 0.4, y_related_all, yerr = e_related_all, error_kw=error_bar_set, width=0.4, label='Classes related to \\nall types of \\nprogram elements', hatch=\"//\")\n",
    "ax.legend(loc=\"upper left\")\n",
    "ax.set_xticks(x_position + 0.2)\n",
    "ax.set_xticklabels(x)\n",
    "ax.set_ylim([0.0,0.6])\n",
    "\n",
    "plt.show()\n",
    "# fig.savefig('change-if-own-all-types.png')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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