{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "96ec678e-b20c-4213-8616-542010f46342",
   "metadata": {},
   "source": [
    "\n",
    "# Dirty Entity Resolution Tutorial\n",
    "\n",
    "In this notebook we present the pyJedAI approach in the well-known ABT-BUY dataset. Dirty ER, is the process of dedeplication of one set."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5274855c-ba95-49b1-ba68-4f50ca2bbd89",
   "metadata": {},
   "source": [
    "# How to install?\n",
    "\n",
    "pyJedAI is an open-source library that can be installed from PyPI.\n",
    "\n",
    "For more: [pypi.org/project/pyjedai/](https://pypi.org/project/pyjedai/)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "4697d149-c1a4-4767-9ed1-14444485e409",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Python 3.8.17\n"
     ]
    }
   ],
   "source": [
    "!python --version"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "776843a2-570d-4d87-bb1d-b5b61f07b1da",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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      "Requirement already satisfied: wordcloud>=1.9.1 in /home/jm/anaconda3/envs/pyjedai-new/lib/python3.8/site-packages (from ydata-profiling->pandas-profiling>=3.2->pyjedai) (1.9.2)\n",
      "Requirement already satisfied: dacite>=1.8 in /home/jm/anaconda3/envs/pyjedai-new/lib/python3.8/site-packages (from ydata-profiling->pandas-profiling>=3.2->pyjedai) (1.8.1)\n",
      "Requirement already satisfied: PyWavelets in /home/jm/anaconda3/envs/pyjedai-new/lib/python3.8/site-packages (from imagehash==4.3.1->ydata-profiling->pandas-profiling>=3.2->pyjedai) (1.4.1)\n",
      "Requirement already satisfied: attrs>=19.3.0 in /home/jm/anaconda3/envs/pyjedai-new/lib/python3.8/site-packages (from visions[type_image_path]==0.7.5->ydata-profiling->pandas-profiling>=3.2->pyjedai) (22.1.0)\n",
      "Requirement already satisfied: tangled-up-in-unicode>=0.0.4 in /home/jm/anaconda3/envs/pyjedai-new/lib/python3.8/site-packages (from visions[type_image_path]==0.7.5->ydata-profiling->pandas-profiling>=3.2->pyjedai) (0.2.0)\n",
      "Requirement already satisfied: MarkupSafe>=2.0 in /home/jm/anaconda3/envs/pyjedai-new/lib/python3.8/site-packages (from jinja2->torch>=1.6.0->sentence-transformers>=2.2->pyjedai) (2.1.1)\n",
      "Requirement already satisfied: patsy>=0.5.2 in /home/jm/anaconda3/envs/pyjedai-new/lib/python3.8/site-packages (from statsmodels<1,>=0.13.2->ydata-profiling->pandas-profiling>=3.2->pyjedai) (0.5.3)\n",
      "Requirement already satisfied: mpmath>=0.19 in /home/jm/anaconda3/envs/pyjedai-new/lib/python3.8/site-packages (from sympy->torch>=1.6.0->sentence-transformers>=2.2->pyjedai) (1.3.0)\n"
     ]
    }
   ],
   "source": [
    "!pip install pyjedai -U"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "6d2e5cf7-ff2e-4271-9242-fe3d638263e9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Name: pyjedai\n",
      "Version: 0.1.0\n",
      "Summary: An open-source library that builds powerful end-to-end Entity Resolution workflows.\n",
      "Home-page: \n",
      "Author: \n",
      "Author-email: Konstantinos Nikoletos <nikoletos.kon@gmail.com>, George Papadakis <gpapadis84@gmail.com>, Jakub Maciejewski <jacobb.maciejewski@gmail.com>, Manolis Koubarakis <koubarak@di.uoa.gr>\n",
      "License: Apache Software License 2.0\n",
      "Location: /home/jm/anaconda3/envs/pyjedai-new/lib/python3.8/site-packages\n",
      "Requires: faiss-cpu, gensim, matplotlib, matplotlib-inline, networkx, nltk, numpy, optuna, ordered-set, pandas, pandas-profiling, pandocfilters, plotly, py-stringmatching, PyYAML, rdflib, rdfpandas, regex, scipy, seaborn, sentence-transformers, strsim, strsimpy, tomli, tqdm, transformers, valentine\n",
      "Required-by: \n"
     ]
    }
   ],
   "source": [
    "!pip show pyjedai"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "15d28272-269a-4e87-bb03-0a45a5492a06",
   "metadata": {},
   "source": [
    "Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "a0890ce6-3a10-4e66-913f-78095bd786a1",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package stopwords to /home/jm/nltk_data...\n",
      "[nltk_data]   Package stopwords is already up-to-date!\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import sys\n",
    "import pandas as pd\n",
    "import networkx\n",
    "from networkx import draw, Graph\n",
    "\n",
    "from pyjedai.utils import print_clusters, print_blocks, print_candidate_pairs\n",
    "from pyjedai.evaluation import Evaluation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "af77914f-5e76-4da8-a0ad-1c53e0111a0f",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Reading the dataset\n",
    "\n",
    "pyJedAI in order to perfrom needs only the tranformation of the initial data into a pandas DataFrame. Hence, pyJedAI can function in every structured or semi-structured data. In this case Abt-Buy dataset is provided as .csv files. \n",
    "\n",
    "<div align=\"center\">\n",
    "    <img align=\"center\" src=\"https://github.com/AI-team-UoA/pyJedAI/blob/main/docs/img/reading-process.png?raw=true\" width=\"800\" />\n",
    "</div>\n",
    "\n",
    "\n",
    "### pyjedai <Data\\> module\n",
    "\n",
    "Data module offers a numpber of options\n",
    "- Selecting the parameters (columns) of the dataframe, in D1 (and in D2)\n",
    "- Prints a detailed text analysis\n",
    "- Stores a hidden mapping of the ids, and creates it if not exists."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "3d3feb89-1406-4c90-a1aa-dc2cf4707739",
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyjedai.datamodel import Data\n",
    "\n",
    "d1 = pd.read_csv(\"./../data/der/cora/cora.csv\", sep='|')\n",
    "gt = pd.read_csv(\"./../data/der/cora/cora_gt.csv\", sep='|', header=None)\n",
    "attr = ['Entity Id','author', 'title']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fda32323-c74d-4374-b322-5c11a175c3ea",
   "metadata": {},
   "source": [
    "Data is the connecting module of all steps of the workflow"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "e257597d-ea77-4090-ba34-e1038d8f9a0d",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = Data(\n",
    "    dataset_1=d1,\n",
    "    id_column_name_1='Entity Id',\n",
    "    ground_truth=gt,\n",
    "    attributes_1=attr\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "93464edd-b88a-40d9-aa4d-7fe1523db662",
   "metadata": {},
   "source": [
    "## Workflow with Block Cleaning Methods\n",
    "\n",
    "In this notebook we created the bellow architecture:\n",
    "\n",
    "![workflow1-cora.png](https://github.com/AI-team-UoA/pyJedAI/blob/main/docs/img/workflow1-cora.png?raw=true)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9c068252-4a69-405a-a320-c2875ec08ea5",
   "metadata": {},
   "source": [
    "## Block Building\n",
    "\n",
    "It clusters entities into overlapping blocks in a lazy manner that relies on unsupervised blocking keys: every token in an attribute value forms a key. Blocks are then extracted, possibly using a transformation, based on its equality or on its similarity with other keys.\n",
    "\n",
    "The following methods are currently supported:\n",
    "\n",
    "- Standard/Token Blocking\n",
    "- Sorted Neighborhood\n",
    "- Extended Sorted Neighborhood\n",
    "- Q-Grams Blocking\n",
    "- Extended Q-Grams Blocking\n",
    "- Suffix Arrays Blocking\n",
    "- Extended Suffix Arrays Blocking"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "9c1b6213-a218-40cf-bc72-801b77d28da9",
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyjedai.block_building import (\n",
    "    StandardBlocking,\n",
    "    QGramsBlocking,\n",
    "    SuffixArraysBlocking,\n",
    "    ExtendedSuffixArraysBlocking,\n",
    "    ExtendedQGramsBlocking\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "7ee34038-1352-440e-8c34-98c5cf036523",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "8605af0eba7d43c3972e821952576ebd",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Suffix Arrays Blocking:   0%|          | 0/1295 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bb = SuffixArraysBlocking(suffix_length=2)\n",
    "blocks = bb.build_blocks(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "b0ac846d-0f13-4b90-b4c8-688054ed7ffe",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "***************************************************************************************************************************\n",
      "                                         Μethod:  Suffix Arrays Blocking\n",
      "***************************************************************************************************************************\n",
      "Method name: Suffix Arrays Blocking\n",
      "Parameters: \n",
      "\tSuffix length: 2\n",
      "\tMaximum Block Size: 53\n",
      "Runtime: 1.3212 seconds\n",
      "───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n",
      "Performance:\n",
      "\tPrecision:      4.28% \n",
      "\tRecall:        75.77%\n",
      "\tF1-score:       8.10%\n",
      "───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n"
     ]
    }
   ],
   "source": [
    "_ = bb.evaluate(blocks)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9fd79ed0-4073-4789-9f9c-09e61da1f4ef",
   "metadata": {},
   "source": [
    "## Block Purging\n",
    "\n",
    "__Optional step__\n",
    "\n",
    "Discards the blocks exceeding a certain number of comparisons. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "ca78a044-589d-48c1-b508-72d5d3205a1c",
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyjedai.block_cleaning import BlockPurging"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "56f77f40-1c76-4f03-b0dd-5bb592f586e0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "09fe8fc8d2354083ae352d33cc3280e0",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Block Purging:   0%|          | 0/3420 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bp = BlockPurging()\n",
    "cleaned_blocks = bp.process(blocks, data, tqdm_disable=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "9ad1950f-9c89-484e-926e-54892dcd93d6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Method name: Block Purging\n",
      "Method info: Discards the blocks exceeding a certain number of comparisons.\n",
      "Parameters: \n",
      "\tSmoothing factor: 1.025\n",
      "\tMax Comparisons per Block: 1378.0\n",
      "Runtime: 0.0678 seconds\n"
     ]
    }
   ],
   "source": [
    "bp.report()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "e3a7cbdf-7ed5-499d-b8da-aa7da3167660",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "***************************************************************************************************************************\n",
      "                                         Μethod:  Block Purging\n",
      "***************************************************************************************************************************\n",
      "Method name: Block Purging\n",
      "Parameters: \n",
      "\tSmoothing factor: 1.025\n",
      "\tMax Comparisons per Block: 1378.0\n",
      "Runtime: 0.0678 seconds\n",
      "───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n",
      "Performance:\n",
      "\tPrecision:      4.28% \n",
      "\tRecall:        75.77%\n",
      "\tF1-score:       8.10%\n",
      "───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n"
     ]
    }
   ],
   "source": [
    "_ = bp.evaluate(cleaned_blocks)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "62b067d8-fbc4-4689-ba72-7ab5baf88a70",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Block Cleaning\n",
    "\n",
    "___Optional step___\n",
    "\n",
    "Its goal is to clean a set of overlapping blocks from unnecessary comparisons, which can be either redundant (i.e., repeated comparisons that have already been executed in a previously examined block) or superfluous (i.e., comparisons that involve non-matching entities). Its methods operate on the coarse level of individual blocks or entities."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "9c2c0e42-485a-444e-9161-975f30d21a02",
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyjedai.block_cleaning import BlockFiltering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "bf5c20ac-b16a-484d-82b0-61ecb9e7f3ea",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "879b37c5522a413ab0507969adc0d2ab",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Block Filtering:   0%|          | 0/3 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bc = BlockFiltering(ratio=0.9)\n",
    "blocks = bc.process(blocks, data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "25fd0be0-91c3-4d0b-b596-c66dccba3c79",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "***************************************************************************************************************************\n",
      "                                         Μethod:  Block Filtering\n",
      "***************************************************************************************************************************\n",
      "Method name: Block Filtering\n",
      "Parameters: \n",
      "\tRatio: 0.9\n",
      "Runtime: 0.3659 seconds\n",
      "───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n",
      "Performance:\n",
      "\tPrecision:      5.08% \n",
      "\tRecall:        73.73%\n",
      "\tF1-score:       9.51%\n",
      "───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n"
     ]
    }
   ],
   "source": [
    "_ = bc.evaluate(blocks)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9cd12048-bd0c-4571-ba70-488d46afcdd6",
   "metadata": {},
   "source": [
    "## Comparison Cleaning -  Meta Blocking\n",
    "\n",
    "___Optional step___\n",
    "\n",
    "Similar to Block Cleaning, this step aims to clean a set of blocks from both redundant and superfluous comparisons. Unlike Block Cleaning, its methods operate on the finer granularity of individual comparisons.\n",
    "\n",
    "The following methods are currently supported:\n",
    "\n",
    "- Comparison Propagation\n",
    "- Cardinality Edge Pruning (CEP)\n",
    "- Cardinality Node Pruning (CNP)\n",
    "- Weighed Edge Pruning (WEP)\n",
    "- Weighed Node Pruning (WNP)\n",
    "- Reciprocal Cardinality Node Pruning (ReCNP)\n",
    "- Reciprocal Weighed Node Pruning (ReWNP)\n",
    "- BLAST\n",
    "\n",
    "Most of these methods are Meta-blocking techniques. All methods are optional, but competive, in the sense that only one of them can part of an ER workflow. For more details on the functionality of these methods, see here. They can be combined with one of the following weighting schemes:\n",
    "\n",
    "- Aggregate Reciprocal Comparisons Scheme (ARCS)\n",
    "- Common Blocks Scheme (CBS)\n",
    "- Enhanced Common Blocks Scheme (ECBS)\n",
    "- Jaccard Scheme (JS)\n",
    "- Enhanced Jaccard Scheme (EJS)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "1f7d75f3-6bed-482d-a572-c3b4927236a5",
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyjedai.comparison_cleaning import (\n",
    "    WeightedEdgePruning,\n",
    "    WeightedNodePruning,\n",
    "    CardinalityEdgePruning,\n",
    "    CardinalityNodePruning,\n",
    "    BLAST,\n",
    "    ReciprocalCardinalityNodePruning,\n",
    "    ComparisonPropagation\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "c92e0ca3-5591-4620-b3f4-012a23637416",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "bca23c01ae524c64a17da2f21ce719a2",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Weighted Edge Pruning:   0%|          | 0/1295 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mb = WeightedEdgePruning(weighting_scheme='CBS')\n",
    "blocks = mb.process(blocks, data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "f469e387-e135-4945-b97f-da14d391c6b1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "***************************************************************************************************************************\n",
      "                                         Μethod:  Weighted Edge Pruning\n",
      "***************************************************************************************************************************\n",
      "Method name: Weighted Edge Pruning\n",
      "Parameters: \n",
      "\tNode centric: False\n",
      "\tWeighting scheme: CBS\n",
      "Runtime: 1.2461 seconds\n",
      "───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n",
      "Performance:\n",
      "\tPrecision:     74.46% \n",
      "\tRecall:        48.51%\n",
      "\tF1-score:      58.75%\n",
      "───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n"
     ]
    }
   ],
   "source": [
    "_ = mb.evaluate(blocks)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6aeff39a-b51b-4166-a55b-f8452ec258a7",
   "metadata": {},
   "source": [
    "## Entity Matching\n",
    "\n",
    "It compares pairs of entity profiles, associating every pair with a similarity in [0,1]. Its output comprises the similarity graph, i.e., an undirected, weighted graph where the nodes correspond to entities and the edges connect pairs of compared entities."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "f479d967-8bac-4870-99bd-68c01e75747b",
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyjedai.matching import EntityMatching"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "bcb37bf3-1da1-4830-9659-41cf85a536a9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "63113eb6e9c3473b8b784753bea9a7a9",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Entity Matching (jaccard, white_space_tokenizer):   0%|          | 0/795 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "em = EntityMatching(\n",
    "    metric='jaccard',\n",
    "    similarity_threshold=0.0\n",
    ")\n",
    "\n",
    "pairs_graph = em.predict(blocks, data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "4d606bfc-3265-4042-93f3-22a1117c4886",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "draw(pairs_graph)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "00bc2e82-9bc1-4119-b8cb-4a1c18afee19",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "***************************************************************************************************************************\n",
      "                                         Μethod:  Entity Matching\n",
      "***************************************************************************************************************************\n",
      "Method name: Entity Matching\n",
      "Parameters: \n",
      "\tMetric: jaccard\n",
      "\tAttributes: None\n",
      "\tSimilarity threshold: 0.0\n",
      "\tTokenizer: white_space_tokenizer\n",
      "\tVectorizer: None\n",
      "\tQgrams: 1\n",
      "Runtime: 7.9103 seconds\n",
      "───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n",
      "Performance:\n",
      "\tPrecision:     74.46% \n",
      "\tRecall:        48.51%\n",
      "\tF1-score:      58.75%\n",
      "───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n"
     ]
    }
   ],
   "source": [
    "_ = em.evaluate(pairs_graph)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "607ee1fb-bed4-4751-9fa8-429874457f72",
   "metadata": {},
   "source": [
    "### How to set a valid similarity threshold?\n",
    "\n",
    "Configure similariy threshold with a Grid-Search or with an Optuna search. Also pyJedAI provides some visualizations on the distributions of the scores.\n",
    "\n",
    "For example with a classic histogram:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "c04c0482-a0d2-4ebf-b9c8-f6cff1863e5d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "em.plot_distribution_of_all_weights()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "75b6218c-3983-413e-a904-53bba33ea381",
   "metadata": {},
   "source": [
    "Or with a range 0.1 from 0.0 to 1.0 grouping:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "3e624fb5-cb48-4081-b90f-0e59adf88d26",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Distribution-% of predicted scores:  [3.0010718113612005, 7.725973561986424, 13.844230082172205, 23.97284744551626, 18.854948195784207, 13.71025366202215, 8.145766345123258, 4.948195784208646, 3.858520900321544, 1.9381922115041088]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "em.plot_distribution_of_scores()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "93b72120-4578-4d5c-a408-a24ee78bf6cb",
   "metadata": {},
   "source": [
    "## Entity Clustering\n",
    "\n",
    "It takes as input the similarity graph produced by Entity Matching and partitions it into a set of equivalence clusters, with every cluster corresponding to a distinct real-world object."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "500d2ef7-7017-4dba-bbea-acdba8abf5b7",
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyjedai.clustering import ConnectedComponentsClustering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "aebd9329-3a4b-48c9-bd05-c7bd4aed3ca9",
   "metadata": {},
   "outputs": [],
   "source": [
    "ec = ConnectedComponentsClustering()\n",
    "clusters = ec.process(pairs_graph, data, similarity_threshold=0.3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "3d2aa574",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "***************************************************************************************************************************\n",
      "                                         Μethod:  Connected Components Clustering\n",
      "***************************************************************************************************************************\n",
      "Method name: Connected Components Clustering\n",
      "Parameters: \n",
      "Runtime: 0.0894 seconds\n",
      "───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n",
      "Performance:\n",
      "\tPrecision:     76.51% \n",
      "\tRecall:        51.54%\n",
      "\tF1-score:      61.59%\n",
      "───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n"
     ]
    }
   ],
   "source": [
    "_ = ec.evaluate(clusters)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6e44642b-b0d9-4f8d-9fe4-4fca2ad716aa",
   "metadata": {},
   "source": [
    "# Workflow with Similarity Joins\n",
    "\n",
    "In this notebook we created the bellow archtecture:\n",
    "\n",
    "![workflow2-cora.png](https://github.com/AI-team-UoA/pyJedAI/blob/main/documentation/workflow2-cora.png?raw=true)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2b07ea13-58cb-498a-949a-4702ea9ee4ce",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Data Reading"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a0b7acf5-32b3-45b1-8347-197dfa869fb9",
   "metadata": {},
   "source": [
    "Data is the connecting module of all steps of the workflow"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "3006b051-8348-4922-a627-56441a1db7b7",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from pyjedai.datamodel import Data\n",
    "d1 = pd.read_csv(\"./../data/der/cora/cora.csv\", sep='|')\n",
    "gt = pd.read_csv(\"./../data/der/cora/cora_gt.csv\", sep='|', header=None)\n",
    "attr = ['Entity Id','author', 'title']\n",
    "data = Data(\n",
    "    dataset_1=d1,\n",
    "    id_column_name_1='Entity Id',\n",
    "    ground_truth=gt,\n",
    "    attributes_1=attr\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b3eedb4c-b86f-4f98-abf2-9d6f7d0271c5",
   "metadata": {},
   "source": [
    "## Similarity Joins"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "afd97b7e-4bf8-4256-b9fc-a301e413e834",
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyjedai.joins import EJoin, TopKJoin"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "7bc8ba43-b059-4839-8958-0b31fab95e46",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c518d093ae0047529a0ff47fa6e0696e",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "EJoin (jaccard):   0%|          | 0/2590 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "join = EJoin(similarity_threshold = 0.5,\n",
    "             metric = 'jaccard',\n",
    "             tokenization = 'qgrams_multiset',\n",
    "             qgrams = 2)\n",
    "\n",
    "g = join.fit(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "8fe34c67-4679-4279-8e7e-35437783ecf3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "***************************************************************************************************************************\n",
      "                                         Μethod:  EJoin\n",
      "***************************************************************************************************************************\n",
      "Method name: EJoin\n",
      "Parameters: \n",
      "\tsimilarity_threshold: 0.5\n",
      "\tmetric: jaccard\n",
      "\ttokenization: qgrams_multiset\n",
      "\tqgrams: 2\n",
      "Runtime: 49.7947 seconds\n",
      "───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n",
      "Performance:\n",
      "\tPrecision:     65.80% \n",
      "\tRecall:        93.03%\n",
      "\tF1-score:      77.08%\n",
      "───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n"
     ]
    }
   ],
   "source": [
    "_ = join.evaluate(g)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "27c7bc36-c39b-4bab-a728-971195763728",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "46361f79a17e4b98be1fccbe2ddd8549",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Top-K Join (jaccard):   0%|          | 0/2590 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "topk_join = TopKJoin(K=20,\n",
    "             metric = 'jaccard',\n",
    "             tokenization = 'qgrams',\n",
    "             qgrams = 3)\n",
    "\n",
    "g = topk_join.fit(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "e1061a7a-9ccb-41ec-8bdf-42412d4d249c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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XrlwRJzUJCQkJiWcnOzub8ePHM23aNGQyGc2aNePOnTtcvHgRMzMzsrOzqV69OmfPnsXS0pJWrVrh7e3N7du3CQkJITIykri4ODIyMp7rfS0tLU0MGF5eXvj5+VG/fn1sbGzIzs7m/v37hISEsGbNGrZt2yZWKzHi4uJCsWLFuHnzpnjPNNKsWTMmTpxI5cqVX/oaSbw8kqAsADIyMrC0tDSJb6xWrRoGg4Fz585RsWJFLl68iIuLi+hezo3RkvcoNjY29OrVS3RDazSa134uRjIzM/n999+ZOXMm4eHhADRo0IBbt25x//59GjZsiKurK2vWrDHZL7coNqLRaDh06BDVq1enffv2bNq0iWrVqnH69Ok3dj4SEhIS7wKXL1+mW7duYvOMe/fusWrVKmxsbEhJSUGlUj02XvJZkclkWFpaUqxYMXx9fXFxccHMzEz0dt29e5fw8HDu3LlDSkoKSqWSGjVqEBgYSNOmTQkICECpVGIwGNi5cydffvkld+7cwc3NjfDwcAwGA+bm5rRv3x69Xs+GDRtMxlu/fn1Gjx5N8+bNJXd4QSJIvHG2b98uAOKfXC4Xxo0bJzg7OwuA4OLiIlhZWZlsAwgymUzYsGFDnuUODg7CjBkzhJSUlII+NcFgMAhz584VzMzMBKVSKQCCpaWlAAhVq1YVZDJZnvHn93f+/HkBEHr37i0oFApBo9EU9KlJSEhIvDXo9Xph2rRpglqtFtzc3IQmTZoIKpXKZJ41ztGv80+j0QgBAQHClClThNDQUCEkJESYN2+e8NFHHwm2trYCINja2go9e/YUgoODBUEQhPT0dOGLL74QAKF27dpClSpVBEAwMzMTFAqF0L59e6FatWp53qt69erCzZs3C/jKv79IgrIAGDZsmCCXy00E5dy5cwV3d3fxR56f8Jo6darQsWNHk/2mT58uaLXagj6lPISGhgq1a9cWZDKZUKRIERNRnN+kY7wexgnO1dVVFJQlSpQQAOHhw4cFfVoSEhIShZ7s7GyhUaNGeeZctVotKBSK1y4in2Q4UCgUgqurq9CyZUthyJAhwvDhw4UuXbqIBpVmzZoJe/bsEQwGgxAUFCS4ubkJzs7OwjfffCOo1WqhePHigpeXlwAIdevWFc8LEFQqlaDRaIT58+cLBoOhoD+G9w5JUBYAvr6+QsmSJcUfnVKpFObOnSuULFkyj2XSaN0DhNWrV5sIsOvXrxf0qTwRnU4nDB06VJxEcp+XmZmZyb/Gv4CAAJPXvXv3Fr777jsBEEaPHl3QpyQhISFRaElNTRXmzJkjWv5KlCgh+Pv7P5cYdHFxER/wK1SoIEyfPl0YPHiwEBgYKPj4+IjHft1i1M7OThg5cqRw4cIFoWzZsoK7u7uwa9cuoUiRIkKVKlWEmTNnCpaWloKjo6MACFZWVoKZmZm4f/PmzYXo6OiC/kjeKyRB+YZ58OCBAAj9+vUzeWKbO3euUKJECRMB+ajYyv13+fLlgj6VZ0Kv1wu9evXKM35zc/PHTibGp01A+Oqrr4R///1XAISPP/64oE9HQkJCotCh1WqFSZMmCQ4ODqKgMnq8nmQ19Pb2FipWrCgAQuPGjYWvv/5aNFgULVo0j4v8VVkpNRpNnnuAubm5iUEl9/6enp6CjY2NULRoUWHbtm2Cra2tUL9+fSE4OFj0hAGCtbW14OLiIlotfXx8hNjY2IL+eN4bpOjVN0x2djYAtWvXFpcplUoiIyPF4uJGHB0dAXB1dc1znHLlyr3mkb4a5HI5CxcuxN7eHvjvXDIyMrC1tRW3MSIIgkmNM0tLyzc4WgkJCYm3i9DQUOrXr8+3334r1u+FnJrDABYWFigUCnG5UqmkQoUKhIWFUblyZa5du8bHH3/MrVu3mDJlCpBTHighIQELCwvMzc1fqPe28IR838zMTDE51c3NDblcTkZGBqmpqQBUqFCBtm3b4uLiAkB4eDhpaWlERUXRrl072rdvz+nTpxk7diyHDh3i559/RiaTkZ2dTWpqKh06dECpVHLnzh0CAwN5+PDhc49f4gUoWD37/hEVFSUAwrZt24RixYqJFkpvb+/HPs3lF/PyoqRmaoXLEUnCufAE4XJEkpCaqc132askKytLtDpqNJqnPtkqFArxidPDw0OyUEpISEg8gsFgEH7//XfB0tJS8PT0FMqXL28yj1paWprcO1QqldCzZ0/B3NxcuHz5slCjRo3X6rp+mT+jd87Hx0f46quvBE9PTxOLpUKhEOPtf//9d0EQBGHKlCkCIBQvXlxQKpXC0qVLBZVKJahUKqF27dpCampqAX9i7z7Kl9CiEi/JL7/8Qs+ePdHr9YSEhODk5JSnBhfkPC0ai84qlUp0Oh1ZWVmYmZk90/uExKSw6uRd/r0ew73Ep9cRkwEeDhY08nHmsxoeeLtYP3WfJ/Hrr7+SnZ2NjY0NycnJeVpCPkru9lz37t3j6tWrAKJFU0JCQuJ9xmAw0KdPH5YtW8YHH3zA0aNHxXJtGo2GgIAADh8+DCDW8v3555/5/vvvqV69OlWqVBG9ZYURY1m8GzducOPGDSwsLKhVqxanTp3CwcGB2NhYVCoVCoWCAQMGcPHiRaZOncq+ffvYuXMnNjY2rF+/ng0bNtC+fXtOnTrFd999x8yZMwv2xN5xpDqUb5iMjAzs7OyYMmUKQ4YMQaFQiOs0Gg2ZmZmiaMwPV1dXoqOjGT9+PD/88MMT3+teQjrD1p7n3N2kFxqrQgZ6Abydrfi0ujutKhTD2eb5altGRUXh4eGBTqejTp06T+14o1AoREGpVqvJzs7G29ubkJAQbt++LXVGkJCQeK8RBIFhw4YxZ84cAgICTOrz2tjYUKFCBXGeLVasGLVr1+bSpUsYDAZCQ0Mfe1xjc4z8Ot+8CZ5maID/WhQLgkDt2rU5fvw4Wq0WhUJB6dKlWbRoEU2bNiUzMxOAo0ePcu7cOYYOHYpCoSA4OBg/P7/Xfi7vK5KgLACaNGmCubk527Zto1KlSly8ePGZ9zX+6EqXLk1ISMhjt5t3IJSpe25geMWfrlIuw9vZipGBZfigbN7YztwkJydTpkwZYmJiALC3tycxMRFAtFZaW1uTkpIC5IhJMzMz0tPTAahatSpnz54V21Pm1y1IQkJC4n3i+++/55dffqF8+fJcvnwZe3t75HI5Dx48oGjRokRFRQFQpEgRTp48SZUqVUhNTX3hwuXGB/tHlzk7O1OuXDnq1atH5cqVcXNzIzo6mvPnz3Py5EkuXbrEvXv3xH3lcjmlS5cmICAAS0tLwsLCOHbsmBg3+SSqVq3KxYsXTc7B0dGRPXv28MEHH5CYmEjx4sWJjo7Gx8eH27dvo9PpGDVqFOPHj6dy5crcvHmTBg0asGvXrheKCZV4OpKgLAAmT57MTz/9REJCAnv27KFt27Ym652dnYmNjX3iMeRyOVqtNt+uAP3+PMOeqzGvdMz5YWeuYkHXqtQo6ZhnXUZGBn5+foSHh9O6dWu2bdtmst7Z2Zns7GySkpLEZQ0aNODgwYNAzoRVunRp0d3t7e3NzZs3X9/JSEhISBRyli1bRq9evahUqRIXLlzA1dUVDw8Pzp8/j0wmQ6vVYmNjw8OHD/n7778ZPXo0N27ceO73yW0tNP7fxsaGYcOGMWzYMDFh9GkI/9+i988//2Tz5s3cvHlTPF7RokX54osv6Nu3L4MHD2bt2rVPPNbXX39NmTJlGDx4sChSzc3Nad26NevXr0cmk1GqVClu3boljr1EiRLcvn2bAwcO0KhRIwD27dsn/l/i1SIJygLg4sWLVKpUiQ0bNpCVlcVnn31msj6/J0IjxlaEAJ9++imrVq0yWf+mxKTJmCoXZ3rnSuLrpKQkqlSpwp07d+jfvz9hYWHs3r1bXG98ira0tDTJandxcRGtmSVLliQ5OVmMKd2wYQMdOnR4MyckISEhUchISkqiTJky2NnZERISgqOjI6NHj2bUqFHiNkaPl5WVFampqSaZ3bmtex9//DHW1tYsXrxYXPaoiHR0dCQ+Pp4KFSrQoUMHSpUqRWRkJOnp6Wi1Wh4+fMjSpUsZOHAgGo0GlUqFtbU1Hh4eYt9uJycnE2vgtWvXmDJlCitXrkTIKVuIRqOhV69eODs7m4RxqVSqPF6pcuXKsXPnTlq0aMGVK1dM1gUGBnLw4EF8fHwIDg7Gzs6OpKQkYmNjKVKkCK1atWLv3r0MHDhQiqV8TUiCsgAQBIEWLVoQGhpKgwYNOHz48BPd17mxt7fHy8uL8+fPA5CQkCCW5Jl3IJTJu5//afRVULe0Iyt712Tt2rX06NGDrKws+vfvT4MGDfj000/F7SwsLESXtoODAwkJCfkez9/fn5s3b5KZmYmlpeUzuUUkJCQk3lVGjhzJvHnzyMzMxMrKioMHD9KyZUsAYmJisLS0pGLFihw7dsxkv0djE7ds2cKBAweYPn16nvdQqVQUK1aM+/fvizGVuUWdjY0N1tbWqFQqEhMTefjwIe7u7qLHLDk52WSutrCwwMvLC09PTypWrEhgYCB16tQhKSmJuXPnMmvWLAwGA3K5nNTUVPz9/blw4YIYS587pt6IXC5n69attG3bNs+6GjVqcObMGZPlq1evpkuXLixevJh+/frh7e39QlZbiacjCcoC4tq1a1SoUIEKFSpgZ2fH3bt3uX379jPtq1KpMDc3Jzk5GVtbW5KSkriXkE79KfspyA/T9sFVLi0ahUajYfny5aSmptKnTx8gZxIwThzm5uakpaWJy4wYYyUBKleuLAabT5s2jS+//PLNn5CEhIREISAkJAQ/Pz9sbW1JSEhgw4YNnDp1ilmzZokZ0T4+PiZCyVgFxLjezs4OmUxGsWLFuHbtWp7km9zizd3dnTZt2uDj4yNaGz09PZ9aaUMQBBITEwkLCyM8PJywsDDCwsK4c+cOJ0+eJDY2FnNzc+rXr09gYCD+/v5MmTKFoKAg6tWrx6VLl0hNTUWv1/Phhx+yZcsWk+omuTEaJFQqFTqdDkEQsLS0RC6Xk5KSgpeXF2FhYXTs2JH169cTHh6Ol5cXAHfv3sXd3f3FPxCJfJEEZQEyfPhw5s6dS9WqVSlatCh79uwRrXdP4tEnTnNzcxr9so0rsU8vCfS6MI7H9vRSOjWszIIFC4iIiAD+E5PGJ157e3sePHhgsr9xMrOyskImk5GZmSk+GWdlZaFWq9/sCUlISEgUEsaNG8evv/5KZmYmVatW5cSJEzg7OwM582N6erpYJQT+m3ON4VO1atXi3LlzZGdn4+LiQnR0tMnxH7UEGoWZUqnEzs4Ob29v6tSpQ/PmzalatapJdZJnxWAwEBwcTFBQEEFBQRw6dIjMzEwqVaqEv78/69ato1ixYtja2nL+/HmUSiUbNmygXbt2Tzyusfi68Z7i7+/PpUuXxPukhYUFycnJKBQKSpUqxe3bt9mxYwctWrR47nOQeDKSoCxAkpKS8PDwIDs7m+7du7N161ZiY2OfWrZBpVJhMBjECUDl6E6xvvPfxJCfjCAgGPREzuiIIAh53BEAfn5+XLt27YmHyW25tLKyErPAJfIiCAJxcXHcuXOHS5cucenSJW7dukVKSgparZaUlBSuXr2Kq6srnp6eqFQqbGxsKF26NOXKlaNEiRJ4eXnh4eHxzHVNJSQk3iy1atXi7Nmz6HQ6Ll68SHp6OjVr1jQxLpQoUYI7d+4AOdbJBg0asGfPntc2JqVSiZubGz169OCrr77CysrqufbPzMxk3759zJkzh127duHi4kJWVhaOjo4kJycTFxeHn58fY8aMoVu3bsB/ZfOMtTXhPwOLn58f169fz7f00MKFC+nbty916tTh2LFjbN++XQwXkHh1SIKygPn8889ZtWoVrq6u3L9/n8DAQIKCgvJYIR99nVt02X/QD+uqbQpNKQRt8gMi5/UwWWZpaYlGozGxTOZOPvL19eX69et5XBtGd4VEDhERESxatIitW7dy+/ZtkpOTX1ndOIVCgYWFBcWKFcPX15cGDRpQo0YNKlSoYNIOU0JC4s2RlJSEg4MDgiBQvXp1Tp48yf/+9z/GjRv30uXUihUrRqVKlThw4AB79uyhTp06JusfPnzIrVu3CAoK4uDBg9y4cYO4uDjS09PzGAzUajX+/v589dVXdO7c+bnuR5cvX2b69OmsXLkSnU6Hvb09CQkJyOVyAgMDCQgI4OeffxYTdWrVqsXx48dNjuHo6Mj//vc/Bg0aBOSUTYqLi0OlUmFnZ0dERAQNGzaUBOVrRBKUBcymTZvo0KEDTk5OPHjwgC+//JJp06YBeUXk4wq/Fuu/CJV90Tc25mch895VYlaNQiaTYWZmJrpiIEcMW1lZkZycDOTE/oSFhYmxPrlJSUl57iffdwm9Xs8ff/zBqlWrOH36tHjN5HI5jo6OuLi44OXlha+vLxUrVqRcuXLI5XKWL1/Ojh07xKzMlxWdcrkcS0tLnJ2dqV69Or1796ZRo0b5lq2SkJB4dezcuVMUP1OnTmXkyJHUrFmTkydPolar8fPze2wt49x1KfPDGH/YuXNnKlSogKenJ56ennh5eVGsWLEnurYfPHjApk2bmDlzJjdu3DARmEqlkj59+jBz5szn8nxER0czdOhQNmzYII5PLpfTunVr/vnnH3Q6HTY2NqSkpOR7L7x27Ro1a9bk4cOHFCtWjKioKHG7w4cPM2rUKI4fPy4JyteEJCgLmKSkJJycnPjuu++YPHky2dnZYtLK41ox5kamNsd9xLpCY52E/+Ip4/+ZTPr1w+JyjUbDiBEjWLFiBYmJiWRkZCCXyylTpgzXr18HcgLHjVa3vn37snDhwgI5h4ImNjaWIUOG8Pfff4sWCGdnZ+rWrUvPnj1p3rw5SmVO59Tr168zceJE9uzZ80whE68KuVxOqVKlaNOmDf3796dMmTJv5H0lJN4nNm/eTPv27YGcVoTG0kEPHz7ME/sol8uxsbExqe/7NGxtbXF0dOThw4cmHiSlUom7uzteXl5UrFiRDz74gAYNGuT7gC8IAqGhocycOZNFixaJc5ZMJqNJkyasWrVKjPl8FiZPnszo0aMBGDhwIAsXLsTV1ZWIiAjxnNVqNe3atWPdunXifqVLlyYiIkJ0hxsTd8zNzRk5ciTLly/n/v373LlzR0zQkXh1SOaFAsbOzo4aNWpw4sQJ/vnnHwwGg1ibMT4+nsqVKz9xf6Wda6ESk4A4Hqc2I4GcoOlJkyaRlJREeno6kZGR4g9eLpeLYrJ8+fIkJSWJCTwLFiwomBMoQC5evEidOnVwdXVl/fr1uLq6MnXqVJKSkoiJiWHjxo3UrVuXrVu38tlnn6HRaPDz8+OPP/4gOjpaDMT38vLC398fd3d3NBrTdplyuRwzMzMsLCxMXNmOjo44ODhgbW2NlZXVUwPvDQYDISEhTJ8+HR8fH1xdXVm+fPlT26dJSEg8PxqNBm9vby5dusTDhw8B8ridzczMKFKkiMmyUaNGmdwjihQpQp06dbCwsAByrJ63bt0iPj6e1NRUrly5wvbt25k1axadOnXCycmJjRs30rp1axwcHGjQoAE///wzJ0+eFB9eZTIZ3t7ezJ07l9TUVObMX4hDSX9Urt4cvHQLVzdPBg0a9Mxzw6hRo8QH5vnz59OsWTPR0mo8Z2tra9q0aSPuY2lpSWhoKDKZjLJlywKI95msrCw2bdrE/fv3KVq0qCQmXxOShbIQsHbtWrp06cKePXtYunQpW7duNSn4bWZmZuIOzu36tvCtR5F2o9/4mJ+VgQH29G3oy++//87s2bPFwuX5kbs1459//knXrl3f1DALnJSUFNq0aSN2CqpcuTKzZ8+mbt26QI4FYPfu3UyfPp29e/fmCYUwMzNDEIR8wwZeBKOYzC+xyoi7uztubm6cP3/eJKRBqVTSqVMnli5dmkfMSkhIPB9GC6WlpSWrVq16bNazpaUlMpmM4sWLc+PGDWQyGYGBgaxZswYHBweT7Tp27MiKFStQKpV89tlnLF++/IljEASBmzdvihna+/fvJzVTS+nKtfjks240bd6c2DQ9e6/FcO5uEtEPM01K2AmCgC4lDlnEFf4a14/6lZ7uzbC1tSU5ORkvLy/Cw8NRq9Um85tSqaRHjx4sWbIEgAoVKhAcHIyNjQ06nU6smGKcH7OyshAEgd69e5sUdJd4dUiCshAgCAL169cnMTGRLVu2UL58ebKzs3F3dycsLEzcTqlU5ik069R2FJZl6xfAqJ8NtSGL+3O6kp2djUajMRHKRmH82WefmXT8adu2Lf/8809BDLdA2LJlC126dCEjI4NatWqxevVqPD09gZwn61WrVjF9+nSuXLmSb6Hfl0EmkyGXyxEE4amucktLSywtLUlMTDT5DjZo0IDmzZuzdu1aLly4YHLszz//nHnz5knCUkLiBTl8+DD169fPE0NvZWWFVqslKysLlUrFoEGDmDVrFvDf3Dpx4kSOHTvG1q1bTY5ZpUoVrly5QlZWFhYWFty6dQtXV9enjiUkJoVVJ++y70YMdxOev0ydIAjIZDKsZNnM+7wu9csUyXe7tLQ0bGxsRI9Lly5d+OOPP/JsZ2FhgUKhICUlBYVCgcFgQBAEkyxwlUpFyZIlxRqdu3fvpmnTps89domnIwnKQsK5c+eoVq0aU6dOxdramn79+qFSqRg8eHCeNlG5WxYWxoSc3AiCwN1f2+RZbhQx06ZNY/To0aJAsbe35969e1haWr7pob5xtFotn3zyCRs3bkSlUrFgwQJ69uwJ5Fy3hQsXMm7cOKKjo/O0qXwcKpUKFxcXSpcuTdGiRXF0dMTS0pLLly9Tvnx5srKyyMrKIikpibCwMCIiIkhISMiTuKNQKFCr1eKkbGFhgV6vFy0ECoVC7I5hxNfXl65du3LlyhVWr15tMqZvvvmG7777TipNJCHxHGi1Wn744Qd+/fVXk+VlypQRe1YbDAZcXV2ZNWsWH3/8MfDf79PFxUX8feeuqqFUKqlXrx43b94kLi6Orl27ipa+/LiXkM6YzcEcDo1HIQP9S6oGo7B0szNnSY9q+LjamKzfsWMHrVq1El8HBQXRqVOnfGNDhw4dytKlS0lLSxPnSU9PTxISEsQGGmXLluXSpUt4eXlx+/btQhcm9q4gCcpCxIgRI5g9ezZr167l6tWr/Pjjj1SpUoVmzZoxceLEPNsXxoScRxEEgQcrhpIWHWayXKlUsnbtWj799FNRpFhYWHDhwgW8vb0LYKRvlqysLAICAggODqZcuXLs27dPDFqPioqid+/e7Ny5E0dHxzxF4HMjk8kICAhg8uTJ1KlTR4w7ehGSkpI4efIkmzdvJigoiLCwMFFkqtVqFAoFGRkZeHl5ERcX91iBW7x4cTp27MiOHTtMWop6eXnxzz//4O/v/8JjlJB4X7h+/Tpdu3bl7Nmz4jJfX18sLCzw8PDg77//Fpd7eHjg7u7OiRMnRA+Gn58fkZGRYrzloxQvXpyffvpJ7GZ2+vRpqlWrlme7Nafv8uOWK+gMAnrD65ELZko5Lcq5MrhRabxdrBk+fDjz588nOzsbe3t7evbsycyZM/P1ovTo0YMVK1aIry0tLcnOzkar1WJvb09SUhIajYaMjAyGDh3K7NmzX8s5SEhJOYWKqVOn0qVLFz799FMCAgJo2bIl586dIyoqimPHjuVxGzp6+hRqMQn/71L1rGKyzNfXl59//pkOHTqIYtLa2prjx4+/F2JSp9PRsGFDgoODadGiBcHBwaKY3LBhA76+vgQFBQGYiElHR0eT43Ts2JGMjAxOnjxJgwYNXkpMQk6CWLNmzfj999+5desWycnJLF26lKZNm5q4kGJiYkhLS8PDwwM3N7c8xxEEgfnz55OUlGQS7xUWFkbVqlWZPHnyK3XbS0i8SwiCwG+//UblypXFhEUj9+7dIzAwkE6dOpksT0lJ4ejRo7Rt21ZcFh4ezgcffJDn+MauY/Hx8SxdupQaNWpgZmbG559/LsawG/ltfwjfbAomS2d4bWISIEtn4O+LkQTOPESHeUfYvOcQOp0OKysrWrRowfLly0X3d25cXFxYsWKF6K5Xq9V07NgRrVaLs7MzKpUKQRDIyMhAoVDkOT+JV4skKAsRCoWC5cuX07x5c9q3b8+AAQPw8vJi+fLlDBgwgIsXL9KkSRNx++TUp7dpLAzI1TnZhHK5nPr16xMaGso333wjrreysiIoKOi9sFwZDAZatmzJiRMnxGxt40PBDz/8QKdOnUhOTjYp7l66dGnkcjkPHjxAJpNRtGhRDh48yPr161/ahRwWFsaCBQv45ptv6Nq1K3Xr1sXNzU3M9O7Vqxd79uwxsXIYheXdu3e5f/8+ZmZmJmI2MjISlUpF+fLl+fvvv2nRogXW1tbIZDJ0Oh2jR4+madOmz+TCl5B43/juu+8YOnQoVapUEX8jRqGYlpZG8eLF88QAJiYm0rp1a77++mtxWXp6Ojt37jTZTi6Xi1bIrKwsTp48SVxcHAaDgZs3b9KyZUtSU1MBWHH8DlP33Hxt5/k4zt5LQmg1FovyH1CnTh1u375NQkICCoVCjC03EhMTQ7t27cRkzxIlSohtJY1JPZBj2HiWGFGJl0NyeRdCMjMz6dixI9u3b6dfv34cOHCA0NBQNBoNM2bMwM3Njc6dO5Nt6UyxXnMKerhPJWH/UtShB0lISMhTNqJatWqsXbuWkiVLFtDo3iw//PADP//8M/7+/pw+fVp84h49ejSTJ0/Os70x890o2IxZmzY2Nnm2fRxpaWkcOXKE69evExoaysmTJwkJCXmuWnXPQn6F9729vQkPD8fW1pakpCR0Op2YdVmnTh22bt0qJexISPw/kyZN4ttvv+XHH39k0qRJZGVl4eLiQlRUFC4uLsTFxdGnTx8WLVpE8eLFiYyMFPcdO3Ys/fv3p3jx4uKy3HGTkBPP7OvrS3BwMJBTisgYB20wGFAX8cSnZW8sfWoS+fDVVIx4GeTB27iz/ffHrq9WrRpnzpwRO+gYr4kgCLi4uJhUFalYsSIVKlTgzz//fBNDfy+RBGUhxWAwMHfuXEaNGoWbmxtarVZseVW+fHm+/PJL1BbWjDmvKdRub0EQiPi9D/qHOT/s3KJjzJgxjB8//qn1Dt8V7t27R8mSJZHJZISHh1O0aE4yVffu3cVJTqFQYGlpSXJyspi16OnpSVxcHDVq1GD79u1PFWB6vZ7z58+zZ88eNm/ezPnz5wvUxWz8zHN/9hqNBkEQaN68OevXr0elUhXY+CQkCgPz589n0KBBjB07lrCwMDGr+eTJk1SvXp0DBw7QqFEj1Go1Dx8+pFevXibJb76+vly7dg1vb29CQ0MBxALogiAgl8vFphmQE0MZERFB2bJluRnxAIfmgzEvUQXBoEcmLzxzcuKu30i+sAswbTlstDp+8803DBs2TJxfvvvuO3755RdxfxsbG5RKJT4+PpQqVUoSlK8RyeVdSJHL5QwdOpTz589ja2sruhaNZWN69erFV8OHYiPPfvrBChghJU5s0ScIAmZmZmzfvp1ffvnlvRGTAIMGDUKn0zFmzBhRTPbt21ec4Jydnfnqq69EN41CoaB8+fIkJydToUIF/vnnnyeKyWvXrtG1a1dsbW0JCAjgu+++48yZM08Uk8abjKurK35+fjRo0ABra2u6d+/OkCFD6N+/v+gKL1as2Au1WszvmTUzMxN/f3+2b98u9t6VkHhfOXv2LIMHD2bYsGF06NBBFJO1atWievXqADRs2BCFQkF2djaNGzfOEyJ0/fp1duzYIYZFubi4kJSUhEwmo3379mLTDONvOCIigoCAAO4q3XDrvwBzz4oAhUpMCoKAbZO+KG1d8qyztbXl8uXLXL58Wdy2atWqeUokpaSkMHbsWC5evEiFChXeyLjfV14uil/itePr68vx48eZNWsWM2bMIDExUXwKrVq1KnvP7cWiYvNCNQmICAKGjGSTzDzjZGns0vC+cPLkSbZt24aTk5MYP7pgwQKxwG7ZsmXZsWMHJUqUAHJcVT4+Pnh5eZGQkMCOHTse2/LswIEDjB07liNHjojLH1ev0sLCgho1atCnTx/at2//3O5mrVZLREQEYWFhhIWFcebMGbZv325SL/VxKJVKkzJDp0+fxs3NjcWLF9O9e3fq1av3XGORkHgXEASBYcOGUbZsWaZOncr//vc/cd2jtRdbtGjBtm3bOH78uMlvyUi7du2YOnUqkFPo2xgfWbZsWTZt2gTkNMrIyMhAJpNxQ1kCxybdMfy/B6GwIZPJQKnCoflgYteONbmXODk5cf78eZMi5cHBwSYufgAfHx/KlStHenq6VH/yNSO5vN8isrOzWbt2LT/99BO3bt1CLpdToW5TkmoPKeihPZbEPfPJCN6DnZ0df/75J82aNSvoIRUIQ4cOZe7cufz000/88MMPbNu2TWwb5ubmxuXLl2nVqhVHjx5FqVTi5ubGtGnT6NChAytXruSzzz7Lc8x///2X/v37c+vWLQAxjuhRihUrxvDhw/n888/ztGV7Vdy9e5f58+czadKk59pPqVSi0+lwcnLi1q1bzxUb+rrJyMggJiaGrKwstFotGRkZpKen4+DggEqlQqVS4eTkhK2tbUEPVeItZs2aNXzyySfs3buXJk2aEBAQwJkzZ8RSN7lZtGgR/fr1y/c4Go2GzMxMrKysUKvVpKSkMHLkSPE3aWNjQ3JyMl9++SULFixAVqoOji2/eO3n96pIWvUlqowE4uPjKVGiBAkJCWKyoLEKhUqlwtHRkejoaNEFHhQUxL///suyZcuIjIx8IS+LxLMhCcq3EEEQ2LJlC9988w03btygSOef0HhWLHRWSkGvI2F+V/r378+YMWNM2n+9bxjbh506dQpfX1/s7OzEp+1Lly7h4uKCi8t/bp2tW7fy3XffYWlpydGjR02sBxkZGQwaNEhsl2ZhYUF6enqepJiqVasybdo0scvGm2DXrl20aNHiseuNAjI3xvE7OzuzZcsWatSo8drGJwgCERERHD58mAsXLnD79m1u3bpFWloaiYmJJtnsj1o6HoednR1eXl54eXnh6emJl5cXJUqUoFatWmI5KAmJ/NDr9ZQsWZIqVaqwefNmEhMTxXmydu3aHD161GT7sLAwSpQowaeffspff/2V53jGOcCYjDNmzBgmTJgA/Bd/qFQqGT1+Mn888ESmVBdKy2QeBIHaRbSs/qo9kL8HxsbGhvDwcOzt7cVlXbt2Zd68eZQpU4bWrVuzaNGiNzrs9w3J5f0WIpPJ+PDDD/nwww9JTk5m9PjJ7DDoEWTyQjM5CIJAbasHLIiIKFRWp4Lg7t27hIeHY2VlRZUqVahRo4YoJrt3706FChXEDjkymYwWLVpgaWnJpUuXOHz4sMlnevbsWdq0aUNUVJRokbS1tSU9PV0Uk/Xq1WPx4sWUKfP0frmvmubNm7NixQp69Oghno9KpRLFmU6no1y5cty5c0fstWu0GDx48IDatWszb948+vfv/1LjePDgAQcPHmTdunWcOnWKmJgYMjMzn9pe8llQq9XY29vj5eVFmTJlMDc3Jy4ujoiICM6dOydaNQFKlSpFQEAADRs25KOPPpIEpoQJ586d4+7du6xcuRKA8+fPi+s+/PDDPNt7eXnRtGlTTp06ZSIWjRgFpfH3NnHiRHx9fbl+/TpyuRy1Wk1mZiYrb+iRuyihkNwvnopMxsGb8eLL3GKyRYsW7Ny5k6lTp5rU3SxVqhR//PEH3377LQ8fPuSHH354o0N+H5EslO8If50MY8zfVwp6GCaETWz19I3eA4wurfr16zN9+nSqVasm9tCOjo5GEASKFy+OVqtFJpNx7do1li9fztKlS4mKihIF1+LFi+nXrx+CIKDRaDAzM8PR0ZHbt28DOU/tf/zxB59++mlBni4A5cqV4+rVqwAMGTKE3377zWS9nZ2dSdkio8XB2dmZ2NhY/vjjD7p16/bM75eZmcmhQ4dYtGgRe/fuzVMSSS6XI5fL0ev1CIKAUqkU45AtLS0xGAzcu3ePiIgIHjx4QGpqqtim8lWiVCrx8PDA399ftGpWrVqVGjVqSJnu7yG//PILv/76Kw8ePEClUhEUFCTG+c2ePZuhQ4fm2efKlStUrFiRiRMnkpqayvjx4/M9ttEbYBSZ1tbW6HQ6dJZFKNZn3ms9r9eBIAjcm94JQZspLps3bx7Lli1Dq9WalASSy+WEhIQgCAJly5ZlzJgx/PjjjwU19PcGyUL5jvBpDS+uR6fwx4m7BT0UIKeVlkQORmuBmZmZGDcpk8nw9/fHycmJ8ePHi7GPvr6++Pj4sHv3bpo2bSqKyTVr1tC3b1+xB3qdOnUIDQ0VxWTJkiU5fPgwxYoVK4AzzMumTZvw9fUF4NatW2I9zapVq3L27Fkx+zS3ew5yLIvFixenZ8+eWFlZ8dFHHz32PfR6PVu3bmXmzJkcPnzYxPpoYWFB5cqVqVq1KufOnePIkSN4eHjQoEEDkpKSuHTpErdv3+batWt5jqtWq03Kk7wocrkcOzs7sWyJTqcjKSmJ27dvEx0djbW1NYmJiWRnZ2NtbU3Dhg0JDAwkMDAQH5/C3wVL4uXZs2cPjRs3fuzDRHp6OuHh4YSFhYn/3r17F3d3d7799tsnZi3rdDqTuOqUlBR8fHyIdW9Q6EoDPQsymQx3vyrcvXQMgNatW6NWqzl9+jQeHh4m5YAWL16Mq6srTZs2xcXFxaTgu8TrQ7rrv0OMbu5X0EMQ6Vfv/ShU/jxkZmYSFRWFhYUFBoNB7H6xa9cuUTxUrVqVhIQEzp8/T2BgIAA7duzgk08+QS7PCWmYMWMGsbGxhIeHA1CzZk1CQ0MLjZiEnMxKY0moo0eP8sEHH2BpaUmFChWoWrWqSbynm5ubmKyl1+uJiYnB1dWVLl26cOLEiTzHTk9PZ968eXh6evLRRx9x8OBBDAYDNjY2jBw5kqioKJYuXUp8fDyzZ8/m3r17eHl5ce/ePVasWME///xDeHg4jo6OlC1bFi8vL5OwjOzsbDIzM/NNcAKeWegZDAYSEhIICwsjNDSUsLAw0XKanp5OTEwMer0eX19fmjZtSnx8PCNHjsTPzw9PT09++ukn4uLinvmaS7x9XLhwgVq1agE53/2bN//rTDNmzBgsLS0pW7YsLVu2ZMiQIaxdu5bIyEh8fHxQqVRiQh6Qpy0hkOc7fOPGDSxKBbx1YtLIg6T/Ypxbt25N3759kclk3L37nyFlzpw5fPrpp3z00UdcuHCBdevWvXdVRQoKyUL5DmFppsTTwYLwhIJvyTiyqU9BD6HQYZz0vLy8uHr1KvXq1SMxMZGTJ0+K21SsWFGsQ1m8eHGuXLlCmzZtRAG2fPlyFi5cKHa68Pb2zpO0U1hQqVTo9XqSk5Px8fFh/fr1pKSk0LVrVy5duiTe7O7evcvNmzcpUqQIiYmJ6PV6oqKicHV1ZfDgwZw+fRq5XE5mZiaTJk1ixowZ4jWCnNaUEyZMoEOHDiQmJjJw4EDWr1+Pk5MTgCi8nZ2dKVmyJAaDgcuXLxMZGWnSaSQ3arVaLHP06E3ZWKTd+Jk8T9RQ0aJF8fPzIzY2lhs3bqDVarl+/brYs9nf35/SpUuL5zpp0iR69OjBiBEj8PGRflPvGgaDgbNnz9K5c2f+/fdfEhISxHWurq6MHTtWTPYqVqyYSYvTq1evUr9+fapVq8bVq1fJysrigw8+ICIiQrS8P5qoJ1Obo7B7e1sQ6rIzKVu2LPfv32fAgAF51n/xxRcMGDCAjz/+mIMHD7Jjxw5q1qxZACN9P5EE5TtGIx9n/jwZjt5QcKGxP7YqPJbSwoCxiLlRULq4uHD16lUsLS3Zv38/BoNBFITu7u4m+7Zr1050vS5YsIB79+6J9SbVajUXLlwolGUwjEkwkHNTMwrAhw8f0qxZM0aMGEHdunU5cuQIOp2ORYsWUadOHQ4ePEhKSgpyuZy4uDgiIyNZvnw5lStXpmvXrly/fl28QZYoUYJVq1aJFp5t27bRrVs3UlJSgBz3uVKppGHDhqKVMDY29pnGn52dLbrhZTIZ1tbWODs7Y2tri16vp3z58jg5OVGkSBESEhK4efMmoaGh3L9/X3z//IiKiiIqKgo7OzvGjh1L1apV2bBhAytWrECv14suzaSkJBwdHfH392fTpk0sWLCADz/8kJkzZ+Ll5fVCn4lE4SEkJIQZM2aQmprKhg0bqF69OoMGDSIwMJBPP/2UiIgIoqOjnxhHXLZsWXbv3k3jxo1xcXHh7t277N+/ny5dujBo0CC++OKLPA87SjvXQvnw+UwIAg2rlmP39i35rh4+fDhff/01rVu35t9//2XTpk00btz4DQ/y/UYSlO8Yn9XwYPnxsAJ7/6K2ZvSsK7m7Icf6EB0djVKpNClzYYx7bNOmDVqtFoVCIVq6pk+fLtac3Lhxo9hC7eeff6ZNmzZi4XOAlStXFqgrR6fTERsbK9ZpzM7OJjk5GXt7e3bs2AH8VxIot0XRGFuZ+2Y3fPhwGjVqRJ06dfj333/F+C9XV1e++OILsrKyUKvVovgePXo048ePF918AwcO5Pff/+v5a2FhId5k9+7dKy7Pr9/40xAEgeTkZJNzuHDhApCTTOTg4EDx4sWpUaMGffv2xdXVFYVCwcaNG1m/fn2ecwVISkrihx9+wMbGhuXLlzN9+nQ+/vhjdu/eDeSEDPj6+hIUFIRer6d+/fqcOnUKf39/5syZQ/fu3d9eYfAec+TIEaZNm8Y///xDkSJFUKvVeRJGWrZsyeLFi0lNTSUqKkp8IM2PqlWrsn37dlq0aIFMJsPT01MsJxQSEsKHH37IlStXUCqVOZZ1xdub+KVNjDIRk7lLkP3+++84ODhQoUIF1Go127Zte29rHhckUpb3O0i3JSc5dvvBG7dSKuUyQn9p+Ubfs7Cg1+s5c+YMQUFBHD58mDt37hAeHp5vLUNbW1uTeocymQwzMzMyMzPzTQZRq9U0bNiQ6OhoLl26BICnpyd37tx57aIiOzubM2fOcPPmTbE7jjE54N69e8/cI9zBwYGEhAS8vb0ZMmQIw4YNE8WmpaUlaWlpODs706RJE0qXLm3SLQRybh4GgwEXFxc2bNhA7dq1gRxR26ZNG3bt2vXY7kAFibm5OZATP2tjY2PyueemWbNmbNq0iX379tG+fXtsbW2Jj4+nV69eeHh4sGDBAqKioihVqhS3bt2iffv2LFiwQHTrSxRuEhMTGTx4MKtXr8bPz48vv/ySrl27UqdOHfz8/MSyQQCbN2+mffuceoudOnVi3bp1Tz1+WFgYrVq14urVq1SoUIHbt2/j7OzMihUrOHjwIOPGjUOv1+NQ0h/rzhOeerzChiAYSD23nYSgBcB/NTXlcjlz587lyJEjrFq1io4dOzJ//nzpd1FASILyHeReQjofzDhIlu7la+49D4e/boS7w/sT/BweHs7OnTsJCgpi3759JCUlYWNjQ/369cW2icb4py1btvD9998D0LRpUw4cOECTJk1wdnZm9erV1K9fX7SkXb58mTp16ojio27dukRGRoqWTcipsdanTx+6dOnySl2ggiBw9epVgoKCCAoK4uDBg6SlpQE5rntj4W7jv25ubmg0GlQqFQqFgocPHyKXy/noo4/IysrCx8eHGzdu4OnpSXh4OE5OTqSkpJiU4wkICOD06dMAzJ8/n759+4rxlEbkcjmlSpXiyJEjYi3HO3fu0KhRIzFGEjDJGH+UZ83clslkWFhYYG5ujkKhQCaT8fDhQzIyMrC2tiYlJeWZLZ3u7u4UKVKEK1eukJWVJe6X31hsbGzYsWMH8fHxdOjQgWrVqnHx4kXc3d1ZsmQJV65cYdSoUWg0GrKysrCysuLQoUOUKlXqqeOQKDiCgoLo2bMnaWlp/Pbbb2KCHcC3337LkiVLiI6OFpfp9Xr8/f3Fslu3bt2iZMmne310Oh1169bl5MmTuLi4YGNjQ2hoKKNGjeLLL79k9OjRrNmwGefBK99K63bEogHoEyJMQl5q167N+vXrMTc357fffuOzzz57K8/tXUESlO8oa07f5ZtNwW/s/X5tX4GPAzze2PsVFIIgcPjwYaZNm8bWrVuRy+XUrFlTLPdSuXJlzp07R3BwMGFhYdy5c4erV68SHh7+xNi6l8HS0hJfX1/8/f0pUaIEfn5+NGrUCI2VLdeikglPSEcGeDhY4FfUBkuzvJEuN2/eZNasWfz9999ERkaiVqupW7cuTZs25YMPPqBcuXLP3Pd70KBBzJ8/n549e3L//n2CgoIoV64cV65cITw8HDs7O5N2hU5OTsTH5xQtLleuHD/88APjxo0TE1UgRwzeuXMHD4+c79i5c+eoXbu2KExzCzy5XI6fnx/Xrl17JYXMH4dcLhcF57N01TG2hzOKUsjbOUgul7Ns2TIUCgXdunVjwIABnDlzhnPnzrFkyRIaNGhA9+7dOXLkiFiS6MiRI7i5ub2285R4MbRaLSNHjmTOnDkEBgaydOnSPJ/TgQMHaNSoEefOnaNy5cri8oMHD9KwYUMA/Pz8RHH5NPR6Pa1bt2b37t0IgoCNjQ1paWm4uroyfPhwevbsSZ2ph8gkb0Z4YSYz4gYxf44UXxsfVIsVK8YXX3xBv379TDrkSBQMkqB8h/ltfwhT99x8+oYvyddNfRjcqPRrf5+CRKfTsXHjRiZPnsy5c+coUqQIHh4eZGZmEhkZSXJy8ku5W18kts8YF2V8X5lMhpmzJ5YBHTAvXR2FmUW+nTCszZQ08XVmUMNS3Ag+z5QVf3M++ApW5mra1ylP26aNqFev3gvFZ968eRM/Pz/MzMyIiIigTp06XLt2DRsbGzEWcdOmTXTo0AHIEcNJSUlUqVKF4OBg0TWeHxcvXsTf358VK1bQs2fPfK9X9erVuXnzZp7C5s+LsfC8XC7HzMxM7PYjCAI6ne6Z2zK+KCtXriQkJIQJEyZw/vx5ZsyYwbJly1i3bh3t2rVj+vTpfP/99ygUCjw8PDh06JDUhacQodfr6d69O+vXr2fatGkMHjw43+S57OxsXFxc6NKlC/PnzzdZ17FjRzZu3AjkdLz55ptvnum9tVot7du3Z+/evVStWpUTJ04gk8kwGAyYmZnhP2gW0arCU2LsWYhZN47M22fEBzB/f39GjhxJly5d8i2XJFEwSILyHWfN6bv8uOUKOoPwymMq5TKY+NG7bZmMiIhgzJgxrF69+rF1CZ/Gi4jF3DwpNtDonlXauqBp1B+Ne7nnOraxBE5ulHIZ3s5WjAwswwdln73EiFarpWrVqgQHBzNjxgyGDRsmlg4C+OSTT/jrr7/w9/cnODhYLPQ+btw4KlasiF6vx8rKitTUVNzc3Lh//77J8Xfv3s2KFSvy7WEMT75OjyN3OzpATKAyGAxP/byNbSUFQRC3fVTkPw1jHKnRepmbzp07c+jQIapWrco///xD165d2bhxI1u2bKF58+bs2LGDDz/8EKVSib+/P8eOHRNrf0oUHIIgMGDAABYvXszatWvp2LHjY7fNzMxk/PjxTJw4kcGDB5OQkEBkZCRJSUkkJiaa1FfMHc5hY2Mjfvfs7OwoVqwYVlZWqNVqrKyscHFx4dChQ5w9exZHR0f8/PwIDg5G71kDh+ZDkBXCyhCPQxAEIn7rTnV/X5o3b07Tpk2pUaOG5NouhEiC8j3gXkI6YzYHczg0HpkMXsUnXsxOw9q+td65mMmsrCxWrVrFpEmTuHXrVh6XqUwmQ6FQmLgpjSgUCrHjxeMSbF6E2rVrc+zYsTzvJQgCBoMBK/+mODQblFOs+BVPsnbmKhZ0rUqNko5P3M5gMNCkSRPRhbd3714uXLhgUsT8xIkTODg4mPQYX7VqFcuXLxfDA3Q6HWXLluXu3bukp6ebXL9HrZePuoufFaVSKbZgfJUYawQax2TMSn+WMTo4OPDVV1/x3Xff5Tuutm3bMnnyZL7++mv27t3LqVOnKF++POvWreOTTz7BYDCwaNEi+vTp80rPSeL5GTVqFFOmTGHZsmV8/vnn4vLU1FQOHjxIUFAQx48fJyQkxCROODfGJgavKslM5eiOY8thmBX3fSXHe9Os712FgNKPz3aXKBxIgvI9IiQmhVUn77L1UiQP0l7MZScHhn/gzRdNyjx127eFxMREVqxYwcqVKzl37lyeG7qtrS3Ozs6i4MkPY2IK5HRB0ev1Yva2Uqk0qWv4OJ6UUGLEKM5q1aqFm5sbt829iS9W+znO9sX4wM+Zxd0D8l0nCAIffvghW7dupVq1ahw7dgyVSkXTpk0JCgrK2f+DD1i9ejWurq7o9Xrs7OwoVaoU3377LR07dmTr1q20b98erVZLy5YtOXDgAOnpL1eg/2mWYaVSiY2NDW5ubvj4+GBtbY1SqSQ4OJhz587l6eOdW8Q/DaO1VKVSUbx4ccLCwl7oHMzMzLC3tychIQGdTkfbtm05d+4cpUuXZu/evchkMpYuXUrv3r2xtrbm3r17JvGpEm+WvXv3EhgYyMCBA2nVqhXBwcEcPnyYkJAQQkNDEQQBlUqFTqdDEASsrKywsrIiMTHxtfSNV9sXxapxP8xLVH76DoWYzQNrU9lDipEs7EiC8j3lRnQy32wK5vy9pKf2dTWur1PSgUkdKr4zVsk7d+4wY8YMFi5cmGcyN1oiDQZDHgGhVCqpXbs2AwcOpGbNmnh4eCCTyVi4cCEjR47ExcWF8ePH07lzZzIzMxk6dCirV69+JXF3MpkMR0dHdu7cybEYGdOPRL/0MZ8VO3MVW4fUNfn8MzIyaNGiBQcPHsTPz49z586h0WiIjo4W6+cpFAqOHTtGgwYNyMzMFJNSfvzxR6ZMmULt2rVZsWIFbm5u4k3WxcWFBw8evHQs5LNiTLDJz81tbm6OlZWVWGfTOGXa29ujUChIT08nIyPjieK1dOnSfPXVV/z444/Ex8eLlqdH3fTGDjyP9iUXBIGff/6Z33//nZCQEABmzJjB8OHDgRwL5tatWxk2bBgzZ8582csh8RSuXbvGypUruXbtGmFhYURHR5OUlJQnbOFFkMvlODrmeASSkpJeONTGyr8p9oH9kSmUb22rRSPbh9alXDHpQamwIwnK95yQmBTafDkZipUjQ2FlEpciCAKGh9Gk3zpD8rntJIZdw8rKqgBH+2o4e/YskyZNYtOmTSZuJYVCgZ2dHQ8ePAD+/wlfrRYtZebm5ixevJhPP/3U5HjR0dH07t2bHTt20K9fP6ZNm4aVlRUbNmygW7duZGZm4uzsTEBAAEePHhVFkqOjo/hez4vS1oVifeeDQvVGY4nkMpjwUQW6BHiwd+9e2rdvT0pKCnXr1mXt2rVcvnyZO3fu8OWXX4rXzRgn+Fzv8//JMK/iBv2qkclkFC9e3KR8kpeXFxqNhnPnzon9lvPDwcFBTOoxxm3mF/uZX7iEhYUFw4YNw9vbmy+++ILU1FQmTJjAqFGjiIiIoFSpUqjVarHTkMSrIykpiYULF7Jx40aCg4NNvpdqtRpra2sUCgWxsbFoNBoyMzPx8vJCJpNx584dzMzMniku91VhU6sz9g265xsj/bYhAy6Pa5ZvdQqJwoUkKCX47LPPCA8P5+btcDKUVmTrDNjZWJEadQdDdgbZ2dkYDAYePHiAg4NDQQ/3hcnKyuLHH39k8uTJODs7ExcXJ960bWxsxE4oKpWK77//nkmTJpGZmYkgCNSrV4+dO3diaWlpcszr16+L5T2WLFlCq1atyM7O5qOPPmLHjh2o1WoaNGjAkSNH0Ov1lChRgtu3b7/0jcX54/FovCoX2M3CI/EChxfkZBkHBgby8OFDTp06Jbr6jdOKsWi5ETMzM5ydnXn48CFOTk707t2bU6dOcfLkSaKj35y19UUpWrQorVu3xtnZWSzwHhYWRkRETn08hUJB9erVqV+/PmfPnjXp0mMkv9CGR6/To/GYRhQKBQqFgmbNmrF161YA6tWrx4oVKxg3bhx//PEHu3fvpmnTpq/61N87dDod48ePZ/78+WJZK7lcjqenJx988AG9e/cmICAAmUzGn3/+yeeff45MJuOTTz4hLi6OPXv2oFKp8v2t29jYYGtrS3R09CsTmTKZDI1Gg1nZRtgGDnolxywMeDpacPCrRgU9DIlnQBKUEowfP545c+ZQokQJrl+/TkpKCq6urqSkpKDVatHpdBgMBqKionB1ffas38JEcHAwXbt25dq1a5QoUYKbN3PKKRktQQqFAicnJ7RaLUuXLuWzzz4jLS0NpVLJ77//Tu/evfMcMywsjLp162JnZ8e+fftwdnYmODiYevXq8fDhQ4oWLUpUVJQo+l7VT03l6J5jnSxgHuycTerFPdjb29OkSRNq1qzJ8uXLuXz5MoDJzVSpVKJUKjE3Nyc5ORmlUolWq803HlEul4utKAsSGxsbnJ2dRdd7fuMJCAjAwsJCFHqCIBAfH09oaCipqalYWlqi0WjyWKKN9QGflnSR2wXu7u5Oeno6mZmZqNVqEhMT8fHxISMjg4cPH7Ju3TqaNWtGhw4d2LBhwyu9Fu8TiYmJfPHFF6xbt47s7GwsLS1p0qQJPXr0oF27dibW3wcPHjBw4ECxxWaVKlW4ePFivp+rUqnEwsLCpIXnq0Zp50qxPvPeuOfidaGQy+hWw5NxbZ+veoVEwSD5RSTw8fEhPj6eYsWKiZOlsWdy7onR6KJ72/j999+pVq0aer2eypUri2LSeKPu2LEjmzZtIiYmhjFjxvD555+LYvLChQv5isnIyEiaNGmCRqMhKCgIZ2dnQkNDCQgIIDk5GbVaTVRUFEC+4ujRyb5u3brIZDKsra3zbKdWq022t6rcAkF4s12QHkUQBJxbDGHr/uPExcXRo0cPfv75Z1FMQk4ZIWdnZ9Hdl5mZSWJiInq9nqysLPGalCpVSkxocnJyQq1WF7iYBEhOTiY0NJTExETUajWenp64uLiYCIrTp09z8eJFtFot8fHxXLp0ieDgYFJTUwFIS0vLN6whd93S3J+to6OjSemf3PGU9+7dY/fu3TRp0oTExEScnJy4ceMG1tbWuLu789lnn2Ftbc3Ro0dfy/V410lPT6dFixY4OjqycuVKHB0dmTdvHikpKfzzzz+0b9/e5LMPCgqifPnybN26Vfz+njt37rEPCTqd7rWKSQCHZoNAJn8nxCSA3iDQtea7W5buXUMSlBL4+PgAOW43483LeMM3GAzizf1ls24LggULFjBw4ED69OlDqVKlOHXqlLjOwsKCXbt2sXr1asaMGUONGjWYMmUKSUlJyOVyjhw5QrlyeZ+MdTodrVu3Jjs7m71791K0aFEiIiIoX768eN3yS8AxNzenX79+xMTEYDAY2LRpE5BjyTty5AhNmjTBysoKa2trsViv8Vi5BZZ5qWrIZAX705XJZOiRM/bAA5q070qbNm1MEmiUSiUNGjQgNjaWrKwsk9ACmUyGvb09Y8aMISIigoULF4qWzPj4+Jd6cDGKMWMPbYAmTZoQEBBAjRo1MDMzM9n+cbGGGo0Ge3t78caclZVFeHg4MTExeHt7U7NmTfEzSkpK4vDhw5w/f57Y2FiKFy9OpUqVqFixImXLln3qmHN/tg8ePECv14vn8Wix8okTJ/L333+zePFisTWnTqfj5s2bKJVK0tPTiYuLK3Q9zQs7O3fupEiRIuzatQs/Pz+CgoKIjIxk4MCBecRZSkoKw4cPp2nTpkRHR5OZmfnGYiM1Gg01atTAzs4OyJnDAgICcHd3x9ylBOYlqiBTvBuxhgq5jHqlnSjtbP30jSUKBZKglMDb2xvIEQHGOK7MzExRTBpveLljvN4GVq1axcCBA/niiy/Iyspiy5Yt4rpGjRoRExNDs2bNWLlyJVevXqVUqVLExsYik8nYs2cPNWrUyPe4Cxcu5MKFC2zevBkvLy/27t2Lm5vbY8t+GK0YqampLFiwQBQJH330EcuXL0er1SKTyTh8+DAymYxq1ao9NiNcpjZHaVd46rElZEFY2a44fzwepa0LKpWKb7/9FgsLCw4ePChuZ2NjA0CRIkX47bffuHfvHj///DMALVq0eKH3ViqVomXIGNuq1+txd3cXkyZ8fHzEuo1t2rTJ8xkZhW7Tpk0ZOXKkeKM2GAwkJiaKfbeNMY0AN27c4MSJE2K2/6NxtXXq1OHw4cNcuHCBK1euiL+hrKwsJkyY8MTi40bBaxSEj8aVbty4kUuXLtG7d2/Gjx8P5Fh4Bw0aRHR0tOhVMFrhJZ6MwWCgc+fOtGzZUgx3uXLlCh988EGebc+ePUunTp2wsbFh1qxZBTDanHn55MmT4sNbeno6p0+f5v79+1hWao5geHceJJRyGRM+qlDQw5B4DiRBKYGlpSXu7u6iiIQcd+WjT91vk4Vyx44d9OjRgx49etChQweWLFkiruvRowf79u0ThcDff/9NlSpVWLNmDYIgMHToUJo0aZLvcRMSEhg7diw9e/bEzs4Od3d3AgMDTbaRyWRYWlqiVquZPXs2Fy9epHXr1vlaw3r06MGJEyews7MjKyuLhw8fsn//fho1amTS+lCtVqNWq1HauRZKd5bGsyLuAxbi1fhTJk6cKLr2ypQpQ7FixRAEgSVLlnDjxg0GDRqEpaUlGzZsoESJEi9cTkmn06HVapHL5aSlpVGuXDnq16/PvXv3xGoElpaW6PV6evXqxffffw/kWIRnz55NnTp1UCqV4gPE4cOHOXPmDHPmzEEul+Ps7Iyrq6tJcfLcn2F6ejrHjh3DysqKli1bistXr15NyZIl87ie1Wo13377LSdOnHhsX/TPPvtM3DY/lEol27dvB6BatWoA7Nq1i7S0NMaPHy+Oc9myZc93Md9D0tPT8fDwYP369ZQvX56IiAh69uxpsk1MTAzffvstHh4eVKtWrdDGpgqCgNqr8ltfHig349uWe2dK1L0vSIJSAsix5OTu2pBfh4+3xUKZmppKnz59aNasGQsWLDCJgWzdurXJzVar1bJv3z6Sk5MxGAxYW1szY8aMxx57/PjxZGdnU6RIEby9vfO0BzQmLfn6+nLx4kWGDh361BIuNWrUICYmhnbt2pGWloZcLuf48eOkp6eLFitjYXSVmfkTj1VQyOQK9MjJrtwJm1qdcXJy4vPPPyckJARvb28uXbpEr169UCgUZGRk0LlzZzp16vRMYrJEiRL5Lre3zyl0bKzZl5aWxuHDh5k1a5YotsLCwqhWrRrLli3DyckJyLFc79+/nzNnzrBnzx5OnjyJm5sbp0+fply5cpw5c4aiRYsSGxtLdHQ0HTt2JCQkhO7du+exLqpUKmJiYtixYwft2rUThWBcXBx169ZlzJgxeR7MqlWrxi+//JLvOS1duhRvb+/Hfmd0Oh0TJ05k1apV4u9z8uTJLFq0CF9fX/FazJo1663Imi8odDod5cqVIyIigtGjRxMcHEyRIkXIzMxk7969jB49mjJlyuDq6sqkSZO4d+9eQQ/5iQ+Shc1z8bJ83dTnnW7p+64iZXlLADBkyBD27dvHtWvXHrvNmjVr+Pjjj9/gqF6M7777junTp3P9+nWCgoLo27cvkGMtu379usnEfOTIEerVqye+XrlypWglehS9Xo+TkxNKpVIsI5K7RE67du34559/6NevH3PmzBHdsc/Dli1b6N27N/Hx8chkMszNzUXLsFwuR+HkSbFec577uG8a74RTHFg6gUmTJjFixAhRIGVnZ9OiRQv2799vEjuYX3kVmUzG9evXxUSn/JDL5QQHB4uxrvPnz2fAgAHo9XosLCxyRLhKhZOTE1ZWVri7u9OhQwcGDx7Mli1baNOmDZBjrerUqRM7duwActzIy5YtY//+/YwbN45BgwYxZ84c0tLSmDp1Kr/++qsY62lmZibGurq5uRETEyN2QpHL5XTo0IHVq1ebiNHs7GyxdmHumoYqVU527qNC2/idtbe3F13xrq6uxMTE8ODBA3r06MGFCxe4f/++eF07d+7M2rVrn/OTe/cxGAxUqFCBq1evMmLECKZNm8ahQ4eYOXMmu3btIjMzM08Zp8LGo7VLVc4l3op54Uko5DKUchnj25aTxORbimShlAByxNbt27cpUqTIY5+EU1JS3vConp/bt28zbdo0vv76azw8PPjuu++AnBvy2bNn85zbpUuXxGVWVlaPFZMAmzZtIikpSRSTLi4u4s174sSJbN26lW7dujFv3rwXEpOQ0/EkLi6OTZs2Ubp0aZMwA4PBgC4xqlBkQD8RQeCGdUVmL1nFyJEjRTGp1+vp3Lkz+/btMzkHmUyWb1KDXC5n/vz5T8yM9fX1JTY2VnzdoUMHEhMT+fDDD0VRptVqqVWrFiEhIXz11VeMHTuWzz//XBSTGRkZfPfdd+zYsUMUprdu3WLHjh2MHTuWBQsWMG/hEgZ9N4GQBC2d+o3g8vUQGjduDOQk7MhkMipUqMD9+/dRKBTI5XKxJNWGDRvo27evSYmksLAwsrOz8xRu12q1/PDDD3lc3oIgYG5uLvYg37Bhg5gANmfOHH799VciIyMRBAEzMzOsrKzYuHHjK+nO9K7RrFkzrl69So8ePahevTrVq1enYcOGhISEMGTIEEqUKPFEMVkYisYbxaS1tTVVq1alVZsPC3hEL45CnjP/1i7pyN4RDSQx+RZT8L8MiUKBj48PWVlZFC1a9LETpjGrtDAzY8YM7O3tGT16NCEhIaLY6Ny5c75dfnJ3kqhZs+Zjj3vv3j0Tsenu7k5MTAwAnTp1Yty4cXz44YcsWbLkldxwPvroI27evMn58+epU6eOeExBm4kuKeqlj/9akclQqNQc03qKiwRBoEePHvzzzz95Nn+cQNZoNMyaNeuJrj61Ws2wYcPEeNjt27fTsmVLjh07xuDBg4GcXuybNm3C0tKSXbt2ickxACfOnKdyozYs3nqIPhMW8+3ibQyZ9gfqYr5MX7aB1j+uYGVSaTxGrmenUImP5h+j1ZwjfPD7RbTNv6f9L6tRF/EkKyuLq1ev0rdvXzIzM1GpVBgMBtEFvWzZMkaMGCGe6+jRoylevHi+35VJkyZRsmTJPMu1Wq34G/T29kahUFC3bl3Gjx/P559/jqdnzvWuW7cuo0aNQq/XM2/evMdeu/eRw4cPs3fvXkqWLMn+/fv55JNPsLOzY+fOncybN49Zs2Zx584dk30e/YyepZf7y5L7O1+9enX69+9PuXLl8s04P3v2LNu35v1dvQ14OlrQrYYne0fU58/eNaSYybccyeUtAeRYTEqUKEG9evU4efJkvpaNsWPH8tNPPxXA6J4db29vmjZtyty5c/n111/55ptvALh586aYzZ6buXPnMmTIEACWL19Ojx498mwTFxdH6dKlRUuZq6urGJ8mk8moUqUKer2eEydO5ClL86oQBIHRo0czZcoUHJoNwqpSi0KZnPMoe0fUp7SzNdOmTeOrr74CTMMEcvOoG+9pLRuNnUEyMjKoUKEC9+7dQ6VSkZmZyb59+7CxscHHxwdra2vRCpqdnc2XP03BokJTtl28S1ya/onX8Wmt6xRyGXqDQFb4BeJ3zEGhy6DbwC/5Y9VfKBDIjL+HZ3FXsQXfqlWrcHZ2JjAwkI8++oi///77ma7FoyxdupRevXpx4MABNBoNHTp0ICIiAoBevXqxYMECzM3NKVq0KHfv3n3scd43ihUrJtaH/fjjj/n222+pWLEiZ86coVatWmJcqkwmw8XF5ZnjUB/3nX4UpVKJwWAwEaXGWrMGgwG9Xv9EwWpmZoa/vz9RUVEm8dsylQb3L9cX6jlBIQMPBws+re5B7dJOeDlaSu0U3zGkT1MCAA8PDzQazRNdta+7KO/Lcvv2bUJDQ5k6dSqQc9OFHLdQ6dKln7r/J598ku/yypUrk5ycLN7kc7v+ixQpwtmzZzl06NArFZPZ2dmcP3+e27dvi+39wsPD8fDwIO7Oeawrt3z6QQoYhQzm7LpI/O75/PXXX0DODfXRZC/IaR94/Phxk2XPUlUgIyODkiVLYmlpiUwmIy4ujrlz51KtWjUxs9/4eSntXHFsO4h1qWXgeBjw5ESHZ1mvN+SICDPPShQbsBiAIJmMoj1ykoIEQUCXHENayCkqedzj66+/xtbWlsqVK7Njxw7MzMzEWMzc/bufZgWbPn06lStXFgviG8WPXq8nISEBpVJJpUqVOHPmDImJiWIC0/tM3759iYqKQqPRsGbNGj78MMdNfODAAZo0aSJec3d3d8qWLcvu3buf+dhPEpMymUwsO6XVarG3t0ehUBAfH0/58uWpXLkyWq2WhIQE8Xf+uBJkWVlZnD59Wnxt/D0ZPRcq+2LPPObXjfFhLDvkOPunDcbH891JGpLIH0lQSgA5NzNvb2+TckGPJkoU9hjKPXv2oFQqadQop++r0TJTo0aNxwqD3IWn8yvVMmHCBCIiIqhXrx6HDx/G1taW5ORkypcvz+XLl0lOTqZz584miT0vgiAIXLt2jaCgIIKCgjhw4IAYx+Xg4ICnpydeXl60b98emVMJNqW+1Nu9EfQCbDh6jcj/F5OQtzc15IQpfPvtt9SoUYMTJ07kscw9zvpjXHbnzh0iIiLIysqiVKlSLF++nAEDBrB7924qVKhAcHAwVv5NcWg6AF5jWZX8vmMymQyFrSs2VVqRKFegcz3PraB5WJET/+js7CxmEOcWlE8SKEqlksuXL3Po0CEUCgWLFi3i3LlzYjvHHTt2EB8fj7e3N2fOnOHff/+lY8eOr+ek3xLGjx/P4sWLkcvl3Lp1i2LFcoTX6dOnxfkCcpKxwsPDX1lWt/E7kXsezV1N49atW2LJqwYNGtCvXz88PDywtLTk4cOHhIeHc/v2bdatW8fFixdNjquxskVnbo+5WoMuO5OMO+dR2roUitJBChlotdkkBP2OPuQIv7unPbF6hsS7gSQoJUR8fHy4efOmeDN79AZpbCdXWImMjKRo0aLY2NiIhaSBx9b8A6hdu/Zj16Wnp/PTTz9ha2vLuXPnkMvlPHz4EKVSSdmyZbl8+TKZmZlMmjTphcccHBzMnDlz2L59O5GRkajVaurWrcv3339PkyZN8PHxEYuCG7kS+ZBNc4688Hu+SZT2RZGpNAja/Lvf1KtXjwsXLmBnZ0edOnU4deoUer1eFJFPc/1CjmX5r7/+wsPDg0WLFtG4cWMWL17MjRs3qFOnDubVPiLGpfpT3devlf+/yWs8KuDS8zeS9y/GIfI8mzdvFksc5Se2c2NmZiYm4hgfYrZu3cqgQYOoUKECt27dYsSIEUydOpUhQ4ZgY2ODRqNh9+7d77WgnDZtGj/++COQ83BpFJOhoaHUrVtX3E6j0XDr1q1X9r4qlQo7Ozvi4uKAnAfWR0OJ9Ho9d+/e5c6dO2zbts1knY2NDfXq1SMwMJC//vqLEiVKMObXOfxx9DaaUlVR2hU1+T4X6Pf7/zGGgNQu5cTFJd+SFX0Fg6Ulq1evZvr06QU+PonXi5SUIyHi4+NjEjNk7OdtpDCX0TBiHG9mZuYzxTSZmZmJ5VwevaH36NGD7Oxsvv/+e9LS0kRh2qtXLxISEoCcTO/H1Ul8HIIgsGfPHpo1a4a/vz87d+7kk08+YdeuXSQmJvLvv//yzTffEBAQkEdMAng5WvK2TMsymQyl/eNdXVOmTOGff/6hf//+Jp+FMZlFr9eb9DfP74ZkLPR9//59xowZQ9u2bRk4cCC3b99m/bkIYlyqP3bfN41MoUSmVGPXdDB9Z27ExcXlidvnTgipWrUqkPP9mTJlCvv27aNTp07Url2b69evM2bMGHx9fQFYu3Yt0dHRWFpaEhIS8vpOqJCzcOFCvvrqK9G93blzZyDnu1K9enUTgfcyLT/z49dffyU1NVUUrSdPniQjIwN7e3s0Gg0LFizgs88+o2jRnN+HUqnEwcFBDDtycHDg3r2cMImKtRvjM2AOm7PKYVOtNSr7Ynm+z8bXbyQt4tH3EAQUGQkmCTbWsiwCAgKIj48nJiaGy5cvv/5xSRQokqCUEPHx8TEpwWK0FBl5mzrl5OZx8UhGPDxyylQ86pLZtm0b7u7uhIeHA/8JzqZNm4rXxc3N7bnGsmHDBipWrEizZs2Ii4tj1apV3L59m6lTp9KsWTOT7jiPw9JMicdblA0pU6jw9fXN06LQ0dERQRBISkqiWbNmAOLNNHdf8NyhFsabZW6r88OHD3FwcGDgwIGcO3eOLVu2YDAYyFRYYlW/J1C48g6N350/LiTSZUzeFn65RaRRWANcuHBBXH/t2jXatm1L5cqVOX/+PPXq1ROTniCnePqBAwdeW5LY28CePXsYMGAAQ4cOFa3cffr0ITs7m/r165u4nl8GBwcHk9dFihQB4JtvvqFmzZomHoy0tDQSExOpX78+/fr1Y+nSpdy9e5dr164xffp06tSpg5mZGXK5nJSUFC5duoRHoy64D1iIoqgfAMJTHidf14OTsYVo5r2r2AaNJ3LpUFI3/Ujd1MOM9Iwk/LeejGjgZtJ7u0iRIri6uqJUKp8rJlXi7UQSlBIiPj4+wH8dSB590n3VT/CvGpVKRVpaWp5xnzlz5olP7cZalRMmTBC3Cw4OJjMzk1atWrFz505UKpVozTDWAgRES+XTSEhIoEuXLnTq1InixYuzY8+//Ll1P351W3AzLp20rCe7Ox+lkY/zc21fkAh6LW3bts3z/QkMDGT37t3Y2dkREBCAn5+fKB5ziyrjDTo3jx4rISGB33//nTZt2tCwYUMAHJoPQZDJoRDbc++61kVpm2OlNNavzJ2QExcXh7W1NWZmZqSnp2NpaYlOp6N58+ZUqlSJkJAQypUrxz///IOZmZn4HZ04cSIpKSlPDPd4l8nOzuaLL76gQYMGzJw5k9TUVORyOVZWVsyePTtPaSB4fLtLI+bmOV2qcnsNPvroozxlhYydjrKzs2nQoIH4GQiCwLp16wBMGkTIZDJ8fX0ZOnQoW7ZsITo6mjlz5mBnZ4dNrc7oqnyMHvn/f5cLEIOe+L9/5cHaMQSfP4M29g52+iRO7NxAnRrV0Ov1JtZwowu+WbNmmJmZceLEiQIcvMSbQBKUEiKPCkojxifeR4swFzZq1qzJgwcPuHLlislTemJiIjdv3nzsfu3btwdyrGLGOCZj/b7BgwcTHR2NtbU1SqUSjUbDli1bREF57969p2bk7tmzhwoVKhB0KphPpm4ivfEoBu3LoNVvR8S6huXH7abBlP2M23KFkJinJz99VuPtKP4rCAIKpYrQ0FAqVaokLjc3N8fOzo7z589TvXp1lEolgYGBYgkVo+tbLpeLMWiPtj18FL1eT0ZGBvXr18ehRFnMS1RGpijcYeJyhZIiHccC8OGHH+b7XUpPT88Te/fBBx8QGhqa83CyY4dYY/X48eNUrFhR3D4zM7NQuPrfNHPnziUkJIRZs2aZCL7Y2Fixp3tuOnbs+NQi8Mban7ljejdt2iR6bmQymVhpQCaTMXLkSMaNG8fs2bMBuHr1KocPHwYQa4bmh6WlJYMGDWL8qr3YN+guHrsgEAQBstOJ3/Ubd6e0I/3GEbELFEDJkiW5desWkZGRJvvp9XquXbuGh4cHzZo1Iy0trdDH4Eu8PJKglBCxtbXFxcUFjUaTbzxOYe+6UbduXTQaDXv27EGj0Zi4WLdu3frY/ezt7cU4yM8//5z79++L7sLy5csDOe5uKysrfHx8WLVqFXZ2duLyQ4cOPfbY48aNo1Xn7ti2+w7rjydzMsGM8IT0PE5YAQhPSOfPk+EEzjxEtyUnuZeQc6NKy9JxJfIh5+8mciXyIWlZOrxdrKlX2ilvLFMhxKX7dE6YB+Baqpy4zHj9DAaD6JZ1cnKiSpUquLi45Pvwotfrn+rC3bFjB+PHj8ezSVdkhczV/TjURTxxrdSITZs25bs+t0Xc0tISuVzO3r178ff3JygoyMSjYIzNXbNmDTY2NkRHR5sI+feB+Ph4fvrpJ/r374+/v7/Juh49euQJgTGGBzwOo4UxODgYhUIhxpIb50ijoPTx8aFo0aKEhobi5ubGlClT+P3339mwYQNqtZqVK1c+c3OIewnp/LTt8W1wXyuCgKDXkbB/GXd/bcP9WZ+QfjF/d7UxwelRi+/p06fFUBZ3d3eAfLthSbxbSIJSwgQfHx8TK0luQfm0WMSCxtzcnAYNGrBz504AMXsWcrqPPGlCM8b3JCQkEBgYaJKEkxsvLy/8/f1NSngMHDgw32NOnDiRaZuP4zFwEVl2XsB/dQsfh3H90VvxNJy6nyo/B1F+3G5azclrzXSyUiPodRS2GMHcGG+6ymJ+XPFqj5V/UyDns8ovDKFNmzYmcbyPWuyeNeEgRuH01FizwoIgCChqdXuqBbZHjx6kpKRgMBgoUaIEu3btwtn5v9CHS5cuERMTg6enJytXrhQtnvkV9H+X2bx5M6mpqYwbN05cZm9vj8FgYNeuXXm2r1q1qthONTdGF7idnR0qlQpBEEweUo2lvnKTmJiIjY0Njo6OyGQy+vfvz4ULF3BycmLXrl1cv35d3PdJjNkcjO4pc8Xz8rT3FAw5llebjEgiF/Yn5eRGFAoF1tbWj9334MGDwH+tII2/9z179mBra0tAQMCrGr7EW4AkKCVM8PHxITU11WQCMf6/sFsoIaeEzN69ezl+/DiffvqpuPzBgwdiu7388Pb2FuvRhYWFERMTY3INjMJGJpMxc+ZMQkNDAShevDjXr19n9erV4raxyZkM+HU5My8ZcGz5BXrkTxWSj2IQcuo4JqRlP9aauflCBDKlisIcI2hEplAiyBQ4tvwCm1qdsba2FrM+c4vG4cOHY2NjY5LslNvdl52dnW9Mpcl7qc1R2r09RZRlMhlKW2dkTxizh4cHK1asIDMzE39/f4oVK5Yndm/GjBk4Ojry559/Ur58eTHj+31LzNm9ezc1atQwEdu5Hy5z4+TkxLJly/K4lDUajfi9VKlUYib2o4k8Y8eOFf8fGhpKRkYGrVq14sqVK6Ll0tvbmz/++AP4z5L3yy+/EBYWlu+YQmJSOBwa/9xzxtN4XBa4IAhoEyJJObedGvF7uPLbQHQPc9rK6vV6kwS53McBxHO4ceMGKpUKHx8ftFotf/31F02bNhULukNOhzGJdxtJUEqY4OPjI8asPcrT6uQVBrp27UqVKlUYNmwYH3/8MXK5XCw78/PPPz/23CCn/IxCoSAzM5Ps7GxSU1O5cuUKxYsXJy0tDblcTnh4OI0aNaJ3795ATkyW2sKaYXM3U2vSXry+3U71iXvZlVQEc69Kr/ls32CZkFfB/9+I7Bt0x1CiJidOnMDDw4MLFy6I52Bra8uECRO4f/++aLEz3pSMN7K4uLg8HZ2M4QmtWrVCaef61sUNCoKATdW24utHb75OTk58/fXXCIKQb9enU6dOsWLFClq0aMGJEyeYPHkyixfndO55nwSlTqfj33//pWnTpuKyS5cumQg/yLmekHPds7Ozxe+f8bpnZmaaeCiM4vxR/v77b5P3Bhg0aBBZWVkmoTCNGzemXr16WFlZoVKpOHr0KKVLl6ZLly4cOXLEZG5ddfIuCvlrytQ26Ek9v5PIpUOJWvElkUuHYrH9eyIX9iNx70J2rMnpLvZoRQYjKpUKW1vbPMvPnTtHnTp1sLKyYt68eYSEhIjJjsYHx8aNG7+Wc5IoPEiCUsIEHx+fx7q234YYGIVCwaxZszh9+jSbN28W3YRmZmbodDoaNGjw2PMwNzcXaxpCjuWsatWqFC1aFEEQSExM5Pz588TGxjJy/FTcPhyBc79FFB36Fxb1ehD10Hjd3qyYyd1J5m0Ql4Ig8LBUU2RWTty6dYuIiAjOnj0rru/ZsyeVK1cWy7EYP6/c5/boZ5iVlcW1a9fQ6XQsWLzkDZzFq0Umk2FeKseKZmFhIdaDtbGxYefOnZw+fZoLFy7g6+uLWq02sU4KgsDw4cPx9PRk9erV9OnTh4sXL4rlrt4nLl++TFJSEg0aNOD48eOMHTuWatWq5SmOb3RxP3jwwGR57jq8xiSS+/fvc/ToUXF5brFlFIJKpVL8TLKzs3FzczMpkyOTyZg1axapqalotVpUKhXTpk3jzJkz1KtXDycnJzp06MDPsxez/szdV26dFMchV2BRtj4WZmq00SFoY+9w/XJO+I5KpaJUqVJAjlU190Ob8QHNzMxMtFgqFArx4eby5ctiKbQff/yRfv36UbFiRQDWrFkD8F4X139fkAlvwx1I4o0REhJCmTJl8l3n4OCQZwIurHz22Wds2bKFnTt30rRp0xy3jlaLXq+nevXqHD9+PI/L0Mjq1atFd3n9+vVNLA1KWxcafP07oakqcpzPb5clrLAg6HVk3r1E7Nr/LEfm5uZ4eHjg5+eHra0t69aty1OgvmjRokRHRz9ROJev15yUOkNe6/hfB4IgcG96J7GrkLm5Offu3cPR0ZGtW7fStm1b/vnnH4YMGUKHDh3Euqnjx4/nxx9/RKVS0bZtW2bMmEHZsmXp3r078+bN488//6Rr164FeWovjbENobGvfWRkJFlZWWi1Wq5cucKRI0fo0KED58+f5+bNm49t1wk5lQM0Gk2+dXWtra3JyMhAp9NRpkwZIiMjnyk7uWjRokRFRQFQpkwZmjRpwrp16zh27jLXE/VkZOsxVytYMfl7Nv21HIB169bRvn17Tp48yabdB9kaY0WWfcnX3vHGePyMO+dI2DUXV2uV6BEwCu/q1asTEhIiuvjt7OxISkoSrd1ZWVlYWlpSqlQpLl++jMFg4NKlS0yYMIGdO3cSEhJCkSJFEAQBCwsL7O3t82SCS7x7SIJSwgSdToeFhQVKpTJPpq2Njc0zZykWNKmpqTRt2pTr168zcOBAJkyYQMWKFbl06RKCINCkSROCgoIeO3FPmDBBdNm0bduWLVu25PSDDuyPTKkEWcH3y30cxhuGQa9DJlcUavdv7MafyQy7YNKaUS6XY25uTnp6er6iQCaTYWVl9dje8jKVBvcv1xfq834ckUuHoo3NibNbtGgRffr0ISsri/Lly1OyZElmzpxJ2bJl2bFjBy1atGDmzJmMGDECtVpNo0aNWLt2Le3bt+fixYssWbKEdu3aceHCBSpWrEhGRgbh4eEmYiw+Pp74+Hh8fHxQqVSoVCqKFCmCp6cndnZ2BXINb926xZ49e9i/fz8hISGEhYWZxPGZmZlRvHhxNBoNOp0u35Jgj9bzfLTcT8mSJbl9+3ae7Rs1asTJkydJT09n+PDhVKxYkZ49e4rb2dnZkZyc/MRSYealq+PRahAZarucvtqPXENBr0P74B6KKzsIObCJv4Nj+XHLFXQG4bVZJvNDMOgR9DoSgxaQemkPkJO8lJiYiEajMan16ujoyIMHD1CpVKJ3oHjx4qSkpJCcnEz37t2xsbFh7ty5rF69WqyzOWPGDL788kvGjh3LTz/99MbOTaJgkASlRB78/PxISEgwybaFnED1M2fOoFarUalUmJmZ4ezs/NTs1IIiKSmJRo0aER0dTcmSJTl27Bg1a9bk5MmTCIKAl5cXx44dEwPuH8Xc3FycVIs07IZFzY8LRb/c5yH18n5Szm1Hae+KeanqWJWtX9BDyoMgCOiSosi4dYbU8zvRPriXZxu52hylkydKe1cQwMXOnI4ftmHZ0qU8THyALjEKQZuJ0tYFhxZD30D86ushbvMEskJPYmtrK7plhwwZwoIFC7h06RJBQUGMGjWKhIQEli9fzpAhOZbYVq1asWrVKrp3787u3bsZMmQIhw8f5uLFi/j7+xMeHp7n9/w0bGxs8PT0xMvLS/yrXbs21apVM0m2eFmSkpLYu3cvQUFBBAUFcefOHRQKBTVq1KBChQom7+/p6UmRIkXYuXMn33//PZcuXQLAysqKVq1aUaNGDb788kuuXLnCwIEDOXToEL/++iuLFy9+YgtKY5/0OnXqcPr0abKzs7GwsODIkSNUq1ZN7BJjYWHB+vXradWqVd5juJWjSPvvUFjYPHWeMK5XoEdPwc2fxnEkHvyD5OPrcHBwQKPRPNaa6Obmxv379wFwd3fn3r2c3+oXX3zB7NmzWbBgAf369QNyyik5OTkhCALx8fGPjcuUeHeQBOV7TmxsLCdPniQsLEx0Ke3du5fk5ORnisdTKpW4u7ub3Hg8PT0pU6YM1apVe2r3iddNbGwsDRo0ICoqCiennJi9Fi1acOzYMR4+fIhcLmf27NkMHjw4z76jRo1iypQpONdqh3mDPgUw+pdDEHIK5yz5zJ/G5XMm/z4rTnIzvXC2bRQMemRyheiKkynV2NTqjHnp6sjNLJ56gxZ02ciUOd+3t0n058aRZM7/+hmCILBjxw6OHDnChAkTWLBgAd27d6dcuXKUK1cOOzs7/vzzT5RKJRMmTKB48eKMHTuWW7duATkWOYVCgb29PS1btjT5bRqte0aLpCAI6PV6tFot2dnZxMXFia7l3G7mO3fukJGRga2tLY0bNyYwMJDAwEBKlSr1Qtf71q1bzJgxg2XLlpGeno6Pj494zIYNG+bpYy8IAitWrOCHH34QRY2Xlxfjx4+nS5cuqFQqbty4ga+vL5s3b6ZDhw6ULl2aGzduUL58ea5cuQL8Jx7zo1ixYqKYsrKyokOHDtjY2DBv3jzRwrlz507mz5/Pli1b/vvcWo7AskJO0snb+t1L3L+M5JMbTZYplUoxTtRonTRePwsLC9LT08Vtpk6dysiRI8V9O3XqxIYNG/Isl3h3kQTle0ZGRgaHDx8WrQHGeopqtRpPT088PT2JjY3lxo0beSZdpVLJvn37xJtPZmYm9+/fN7nphIeHi4HtVlZWNGjQQLxJ+Pn5Fchkm5iYyJAhQ/jrr7+wsbEhOTmZWrVqUaRIEfGm4Ovry9atW00yaA0GA05evlh3noRMpeZtjJcUBAFDRgpRc7vl/N9goMhHY7DwqV1ora059fBkyB4T4/qu0yDtEOGXTnLt2jViY2PFG/KkSZP4/vvvMTc3JzU1FUdHRzw8PDh//ry4b4sWLRg0aBBqtZpmzZqxbt06OnXq9ErGpdPpOHXqFEFBQezdu5cTJ06g0+koVaoUgwYNok+fPnlEYH4cO3aMadOmsXnzZhwdHRk8eDC9evXCw+Px3Z8iIiLo0aMH//77L5BTO/LXX3+lcePGJt9hQRDw8PCgYsWKbN++nfHjx/PDDz+YCEowFUqAiSsXclzg1apV49SpU0ybNo2ffvqJ5ORkzM3N8fT05NixYzg7O6PT6XD++H+Yl6hcaH9Pz4JRBiQdWEHyyQ0A+cahenh4cPfuXfG18TpOnz6dESNGiMt/+eUXvv/+e7y8vLh9+/Zbe10kng9JUL4H6PV6/v77bxYsWMChQ4fIysqiaNGiotBr0KABxYsXF5NUli5dSp8+ffJMJrn7WT+JzMxMLl++LLqxjhw5QnZ2NsWLF6ddu3Z88cUXj038eZ2sW7eOAQMGkJaWRnZ2NhqNhtGjRzN58mQxXtTDw4MlS5bwwQcfABA4YQs3k4RC38LvacSsH0fmrTPia5uaHbGr373Qira3+eb8MgiCgFNKKMXu/su2bdsYPHgws2bNYsGCBQwZMiSnCPr/F5tOSkrCz8+Pu3fvYmlpyR9//EGzZs0wGAwEBAQgl8s5efLkY5PPXpbk5GQOHjzI+vXrWbNmDebm5vTt25dhw4aJ3VFyEx0dTd++fdm2bRtlypThyy+/pHv37mKP7Mexdu1a+vbtK1rDZs+eTd++fR/7/ejduzfr1q0jNTWVsLAwPD09TQTlo/GVgNgdLCMjQ1wvl8tp0aIFO3bsoHv37qxYsQL4rxZtYmIic049xMq/yYtcvkLHo+7vRylevDhOTk4mTR2USiWdO3dm1apV4rLffvuNoUOHit+/x9UAlXj3kATlO0xaWhrLli1jxowZ3L59m3r16tG+fXsCAwMpW7bsYyfko0ePUrdu3TzLlUrlC5UOSk9P5/Dhw+zevZtVq1YRFxdH27Zt+eqrr6hTp84bFQ6RkZEMGjSIf/75R1zWvHlzGjduzC+//CImHdnY2NCl/wh2K97+Tg+CIJAde4foZV+YLFfauuDYZiQat7LvrYArjBiyM4mY2RmAgIAArl27RnJyMpDjyjYYDLRp04bs7Gx27dpFp06dmD9/Po6OjkDOA2Hv3r05duwYtWrVeiNjjoiI4LfffuP3338nNTWVrl27Mm3aNLH008aNG+nfvz9KpZI5c+bQoUOHpwpdvV7PgAEDxHqaVatWZc2aNfnW4czNunXr+Pjjj00egKtWrcq5c+dQKBSipT4/cscFGvH29iY0NBRLS0tSU1NxcHBAp9Mxf+Nevt2bUwD8XfvtPNgxi+zrB8XrZ2try7hx40yskF26dGHNmjVigpjBYGDatGmMGjUKmUzG+vXr6dChQ0GdgkQBIAnKd5DMzEwmTpzInDlzSE5OplOnTowcOfKZnxTj4+Pz7UYil8vz1HN7kbGtWrWKadOmce3aNapXr84vv/wiWgTfFFeuXGHixIn89ddfoiW2atWqDBo0iPHjxxMeHo79B/2wrtIqJ1PzLUfQ67g7pV2+61SO7lhVboF5qYC3sij4u0jksmFoY3LiIXO7Hrt06UJmZibbtm3D2tqaOXPm8Omnn4qf2bVr16hbty7Nmzc3sRrlR1ZWFnfv3hVDVnL/m5ycjFarRavVEhISglKpFGsTWlhY4O7uLsZk5o7PBFiyZAk//fQT5ubm/Pbbb/z999/88ccffPTRRyxYsOCpnY4g5yFowIABLFq0CEEQGDhwILNnz36mZKDMzEzMzc1RKBSiW/ubb77h119/Ncle9vLyytOtJneZMAsLC5PEPGMPb+M6p35LkWkseRtDYZ6EIAig1xK5eBC6pGgsLCxo3Lgx27ZtE7dZvHgx9+7dY9KkSTx48IAHDx7Qo0cPsSf68uXL6dGjRwGdgURBIQnKd4zz58/TtWtXbt26xaBBgxg2bJg40T8Pjo6OJCQkmCx7FYLSiLGv7sSJEzly5AiDBw9m8uTJWFi82YSR6Ohohg8fzoYNG8Rzs7a2pkOHDhx3ak6m0uqNjud1IQgC9+d0w5Ce9MTtZCoNSvuiyBQqkMlw7TZVEpgFQOqVAzzYOlV0v9rZ2VGqVCnOnj2Lp6cnI0aMoFevXmIXKMhp61e3bl0cHBw4ePCgaB00kpaWxsGDB8UYyCtXrohCVSaTUbx4cTGO2ti/WqVS8ddff/Hhhx9iZmaGVqslLS1NFKJ379418Vq4u7vzwQcfULVqVdauXcvhw4dRqVT89ttvT3RT50YQBEaNGsXUqTnfvR49erBkyZLnct2rVCp0Oh3bt2+nZcuWXL58mQoVKgA58eLZ2dlYW1uLpafMzc3FsJfcolOhUDB8+HCOHj3KmTNnRIFqXioA504/PvN43jYEg57M8IvErh2bJ0Sge/fuTJgwgTJlyjBgwAAqVarEoEGDxKSu3377jUGDBhXg6CUKCklQviPodDomT57Mjz/+SPny5Vm5ciXlypV74ePVrl2bU6dOmQhImUz2xPprL4LBYGDevHmMGjUKd3d3Vq5cSUDAm3cz63Q6NmzYwLfffktYWBgytTnuI9a9U2Iq9u9JZFw/8lz7FOu/EJV9sdc0IonHYUE21ya1B/6zUAYEBPDVV1/Rvn37PJa6yMhI6tWrh1wu5/Dhw2ILwbt377Jy5Ur27NnDsWPH0Gq1uLm5ERgYSO3atSlRogReXl64u7u/UEUGvV5PVFSUmJR39uxZgoKCxHhFmUyGXC6nVKlSnDhxAnt7+6cec9q0aXz11VcoFAo++ugjVq9e/dxlioydsUqXLs2ZM2ewsrLCysqK9PR0qlWrxpkzZ0wsv48m6Tx6rD179vC///2P/fv3I7NyomivOcjNCme1hFdJ9JLBZMX913HJzMyM+Ph4+vfvz7Zt2/Dy8uLSpUuoVCpcXV1ZuXIl9esXvtJkEm8GSVC+A6SlpdGmTRsOHjzIN998w48//vjS5Xp69uzJ6tWrTTK9X4egNHLjxg26devGuXPnWLBggdgruyBITEzkm19/Y7esSoGN4XXwYNc8Ui/seK593iW3/9tE7q45vXr1YsSIEZQrVy7fB5yjR4/SrVs3dDodhw8fxtPTkzNnzjBt2jTWr1+Pubk5jRo1EpPwfHx8XuuDkk6n48MPPyQoKIiGDRty/vx54uPjsbGxYdGiRXTs2PGx1sawsDD8/Pywt7fHyclJrHv7vFhZWZGWloaVlRUBAQFs376dfv36sXLlShPLZG7Mzc3R6/X5Jh4qFAqGDh3K3jvpPCzdDJlS/U49bOaHIAiknN1K0r+LROEdFBTEhg0bWLBgAfBfwfOePXsyc+bMZ8ryl3h3KZwpnhLPTFZWFu3bt+fUqVPs27ePX3755ZXUfixTpkwe9/brfPbw8fHh6NGj9O3bl759+4r9XwsCe3t7Bgx6+1r3PQ2Zmea590k9v1MSkwWATCajTrN2qFQqwsLCKF++fB4Bk52dzZgxY6hfvz5FixblwIEDXL58mYYNGxIQEMDJkyeZOXMmUVFRbNmyhaFDh+Lr6/vahdCMGTPYvXs3mzZtYs+ePURHRzNp0iRSU1P5+OOP8fPzY+HChfkKt1GjRqHRaIiKimLWrFkvPJdVr14dmUxG7969OXHiBJ06dWL27Nmo1WpRTD4aEmB0eefuYW1Er9ez9GQkKX5t3wsxCf/fkcqnljjvV69endatW7NgwQLxGjk7O7N582aWLl0qiUkJSVC+zeh0Oj755BMOHjzI1q1badCgwSs7to+Pz2NdQK8LlUrF3Llz6datG926dWPr1q1v9P1zo1a+ez8NXUKU+H8HBwdsbW2fuo/2wT0y7pxD0L/Z74IEWFjb4O7uzpEjR0wSQgDOnj1LjRo1mDp1Kj///DMbN25k5MiRtG7dmuzsbDZs2EBISAhDhgzByurNxQFHR0fzv//9j8GDB9O6dWsgx7o3evRo9u3bh1qtJisriwEDBlC9enUuX74s7nvo0KH/Y++842u63zj+Pnfk3uw9iQQhNrFnjBJUqVmt1RpVra1GlbZqdVBFVbVatdWsXRIzqB2EGrESCdl73eSO8/vj/u5prgSxqbxfr74qZ37PuWc85xmfh/XrjS0zu3btSsuWLR95HO3bt0cQBK5cucLmzZsJCQlh0KBBfPbZZ9Iy3bt3x8HBwWy9/Px86tSpg1qtNvOi2tQIwrF5P+C/V9F9PwQbFwSl8UM0JiZG+gho2LAh27dv58KFC3Tu3Pk5jrCEF4n/3lvzFWLkyJFs27aNDRs2PNbDtyj8/f2f6PaKi0wm47fffqNTp0706NGDU6dOPXilp4Cvs/V/rHYT+H+LzBMnTpCcnEybNm2KtVrKrh+NfX9LsmOeKSePHZUMxIMHD2IwGNi5cyevvfYadevWRavVcvz4capVq0bNmjU5fPgwmzZt4u+//6Zbt27PpSXqpEmTsLCwYMqUKYXmNW/enB9++IGoqCiWLl2KTqejTp06fPfddxgMBubOnYuvry+pqal89dVXjzWOoKAgDAYDwcHBlC5dmnXr1rF7926WLFmCi4sLAMuXL+fChQuoVCqzdY8dO8bYsWNRKBR4eHigsHfHsc0Hr+T1LwgCVu7Gos7Y2FgcHBw4cOAAoaGhdOjQ4alpnJbwclJyNbyknDx5koULFzJnzhzJE/Ak8fPze24PC4VCwerVq6lUqRLDhg17anmb98NapaCM038r6d6QnYZMJpM6k2zYYOyIoVaruXbtGt27dy+0jkqlQpceT2rIz6+UZ+ZFIC3mKr169aJ06dLMnTuX6tWr06FDBzIzM1m7di2nTp1i0aJFdOrUifr163P+/Hm6dOny3MZ76dIlfv/9d6ZNm3bP4puBAwdSq1YtfvzxR06cOMGwYcMYN24cLVu2JCQkBGdnZ/z9/R+78UGNGjWoWLEiFhYWjBo1ik6dOnHu3Dk8PT1JSkpCLpej0WgICgoiKiqqUJh7+vTpjBo1isTERMp0m4AgV7yy13/VgHrI5XLq1KnD5cuXn2gkrIT/FiUG5UuIKIqMHDmSGjVqPDV5BpVKRalSpZ7Ktou7/3nz5nH8+HFWr179XMbQ0t8Nuew/9BLR5jJt2jQqV64sGZNg1O2rUaMGGzZsQKFQ4OTkJL08TUVZWeHBpB5cDjzdXNoS/o9Bj5WFnLFjxxITE0NISAgVKlQgNDSU48eP0717d95//32WLFnCzz//zNatW6XK7ufFli1bsLKyum9BnVwuZ968eZw4cYKNGzfy3XffsW/fPs6fP09WVhbR0dEEBQU99lgEQeD7779Ho9EQEhLCjh07KF++PKGhocycOVO6hi9evEifPn347bffCm3j22+/pWLdQPRuFV/pPOKL16P57LPPpHaTJZRwL0oMypeQNWvWcPToUebOnftUw1qVK1d+atsuDs2bN6dHjx5MmDCBrKysZ77/3g3KoDf8N4wnURTx93Li008/ZcyYMVJhQpcuXbCwsCAnJwcbGxtkMhkpKSlFGo0ZR9eRvHM+oi6/JKfyKSMKMgwyJc7OzpQrV45u3bqxefNmmjVrBsCwYcNYtWoVK1euZPDgwS+E9yw4OJiWLVs+sJAmMDCQ1q1bs2LFCgBatGjB22+/DUBCQkKRXboehddff5127dphaWnJBx98QFRUFHK5nIkTJxIWFkaDBg0A2LNnD8OHD6dv376AeY5knG2F//eWf3WZ/+10vvjiiyKLlUoooSAlBuVLyKJFi2jbtu0Tz5u8mypVqjzV7ReHr7/+mjt37rB169Znvu8K7rY083P5T3gp9bmZvNmhHYBZWHTbtm3o9Xp+++03MjMzyc7O5vz58/f0dmWFBxP760doboUDvPIv26eFIAgcCY9g8+bNeHh4mAmYT548mZ9++onFixfTs2fP5zjKf8nKyuLw4cPF9i6+/vrrhIaGkpOTAxjFxkuXLg0YdSifVEHg999/j06nIyMjg1atWhEbayxMq1mzJseOHZPuhfT0dFasWCG1rxQEwdgpp3zdV9o7CVA/oPrzHkIJLwklBuVLRkZGBkePHn0mlXXPqzCnIOXKlaN69eqEhIQ8l/3P7FIdxUtuUIqiiMLKjkWHImnatCk3b96U5hkMBvbv38+AAQMAY/7q7NmziYuLu+f2dOnxJKz9nDuLPyQzbAfalDslYfCngcwo5p2cnCx1kDp+/DgzZ87kq6++eq5arXdz5coVtFotDRs2LNbyQUFB5OXlcejQIWmaWm2sJj558iSLFi16IuOqVKkSq1atIicnh/j4eNq0aUNycrI0f/Xq1bRs2VISTk9OTkYURURRJN8gQ+Hg+UTG8TLj62z9vIdQwktCiUH5krF//350Ot0TyTN6EEUZlM/DcAgKCiI4OPi57NvbyYovOz16x6EXAVMIz6phT8KzzCVkxo4dS61ataS/f/31V5YtWyb97e7uXmQ4tW7dumiTo7G/FsydXwYTPacHCZtmPJ0DeEX5btY3XL9+nStXrtC8eXMMBoOUOz1u3LhCy+v1ejQaDZmZmSQnJ5Oenk5OTg5arfap3zum7Rc3LFqlShXc3d3NDEoTb7zxBp9//rmZ4fc49OjRg8WLF5Odnc2NGzdo1KgRJ0+eBIxG7NatW6lTpw52dnZmWoqCnesLkUrwPPFyUGOterguRSW8upRcKS8Z+/fvp1y5cpQrV+6p76sog1Kr1T6y2LAoiqSlpREZGUlUVJTUri0yMpK0tDS0Wi1arZaTJ0+iUCioW7cuSqWSrKws7ty5w4gRI6hbty6+vr74+vpSqlSph27J9ii8Xa8MSVl5zA6OeOr7epqIoohD68FoboWjTY1FpVLx7bffMm/ePIKCgihTpgw//vijtLyFhYXUz9lgMJiFIc+ePQsYO5u4ubmRmpqK5uoxcm+GoS5TA0Fe8mh5HERRZPXP8zi6cz0ymYzWrVuzdOlSjh8/zhdffMH3338v3Tume+l+ecZyuZzSpUvj6+uLj4+PdA/5+PhQqVIlvLyebXtNQRCwt7eXir6srKykvN5OnTpx8OBBpkyZwg8//PBE9jdgwAAyMzOlyu2GDRvy+eef8+mnn2JjY8POnTtp2bIlkZGRDBs2jHXr1pEmL8kZDKr8fAu9Sni5KGm9+JIxcOBALl26xN9///3U9yWKIpaWlmbtF9PS0ooliA3GcOrZs2cJCQkhJCSEkydPkpGRIc23sLDAzs4OS0tLY5uvzExEUSQjIwNLS0sMBgN5eXmo1Wo0Gg1yudyse49MJqN06dI0b96cN998k9deew0HBwfJW/KkvQt/nLzFF1v/IV9v4GW9a0S9Ds2tcBLWfo6Xlxf79u1jx44drFq1irCwMLNlBUGgadOmLFiwgNatW5Obm1uk0TJu3DgpV01h747noIXIFBbwint3Hgdtyh3u/DIYMP4OAQEBnDlzRrq2bWxsJKPQZBg6OTlJHwBXr16lfPnyiKKIVqslJyeH6Ohosw85Uz4hGEPDptaMLVq0MMvZLA6nTp2iXr16nDp1ijp16hRrHX9/fzp16sSsWbPYtWsX7du3x8fHh7Zt2+Ln58fEiRO5c+fOE60s3rBhAx988AF5eXnk5uZSp04dfv75ZwICAkhLS2P48OGsXLmSbt260aX/cCYdfvbFgC8Se0YH4uf2cNdCCa8uJQblS8azNCjB2ILx6tWr0t8xMTH3lRPKzMxkw4YN7N69m71795KUlIRCocDOzk56iD+qrqRMJpM8kjqd7oHbkclk0gvW0dHR7AVc0FNTtmzZYmtuRqfk8Omf5zl0LQlRFF/akNjtxUPQJccA4O3tjaOjI5cuXUKr1Zot17RpU7p3786tW7f48ccfzT4uwGjYiKJIQEAAR48eRa/XY1urLU7thj+zY/nPIRrIOL2d1D2/FJo1e/Zs+vfvj6Oj42NfexqNhujoaLOPvsjISBQKBQ0bNqRbt24MHDiwWMZlWloazs7OLFq0iPfff79Y+y9oUObk5ODk5ET9+vW5desWR48excvLi1WrVtGrV6/HOs67iY2NZeDAgfz111/Y2dlJBTtjx46lXbt2bNy4kcGDB6MT5Di+//tLe48/DjIBmpR3YcXABs97KCW8RJTkUJZwX+4Oe6empha53O3btxk/fjxeXl4MGDCALVu2kJSUBBiNv5SUFLKzsyUjUCaTYWFhgZWVFSqVCgsLC7PwtVwuR6FQmBl6BoOB/Px88vPzzYxJS0tLfHx8CoXA1Wo1AQEBjBw5kr59++Lt7c2NGzdYsmQJgwYNonXr1vj5+eHh4cE777zDkiVLiI6Ovu/58HayYsXABjgfW0j2+b0YtHn3Xb4onvc3nGjQYxvwuvR3dHQ04eHhZsbkwoUL+e2337Czs2PcuHHMmTOnkDEJ4OzsTHZ2NhUqVJC8x8KNo+jPbDbuq+R79eERZMhvFP3BuGfPHpRK5RMxctRqNRUqVKBHjx788ssv3Lhxg6tXrzJ//nycnZ0ZN24c3t7ejB8/npgY48eHKIrodLpCv6uDgwMNGjQgODi4WPvOy8sjJiZG8j5aWVnRrFkz0tPTiYqKIjY2lpo1a7J79+7HPs678fT0ZMeOHSxatAhBEJDJZISFhfH666/j7OzMyJEjSU1NJTMlCV1a7IM3+B9EKZcxs0tJdXcJD0dJotNLhp2d3X0rcJ80tWrVYvv27dLfiYmJZvPDw8OZNm0aGzduNHvJaDQa6d8KhQK93rx1n52dHR4eHri7u+Pp6YmrqysuLi6IosjGjRvJz8/n+vXrGAwGLCwsyM/Px8vLi6ysLLOwOUBubi5RUVGA0bjs2LEj1tbWbNu2jWPHjnHs2DHq16/P3LlzadSoEWB8oUVHR3P9+nVCQ0MJCQlh7dq1Rr1Gf3/atm3L+++/T7Vq1Yo8LzfO/k1a2k6Sd87FwqMC9k3fwcqnBihVcHfTRlFEn5dNNUeRU2cvYO3fqPg/wFNAkMmxqdUezc0z5F4/WeQyH330EeXLl+ezzz5jxYoVjBo1StINLIhcLsfT01Pq8Sso1ShcfUkID8UyOweHRj1AkMErLr1SbAx6rLJi8HC1JulG4dm7du3Cy8uLMWPG0KhRI3x9fSlTpoxUBf4wZGZmkpubi1arJT09nUuXLpGSkkJCQgIeHh60aNGC8PBwZs+ezaxZsxAEweweVigUKJVKbG1t8fHxITs7m7CwMBYsWEDZsmXx9fWlQoUKReZcHzlyhJycHLP2nwMGDJA6A40bN442bdqwcuXKJx4JEEWRa9euoVQq6dChA7v3HiBDVGHh6UaWXkta4r9GZO71UyjqvIEgvFq+l6mdquL9H+sUVsLTpyTk/ZKxdetW3nzzTa5du0b58uWf+v7Wrl0riQ4DrFu3jh49epCXl8eHH37I77///tT2ffcLzNbWFgsLC2xsbHB1dZXC6FFRUZIHpeC6LVq04KOPPmLq1KmcP38egDp16jBlyhQ6dOhQ6CWVkpLCvn37CAkJYdu2bcTGxtK2bVs+/vhjWrduLS2fkpIi6dUVHKe1tTU1atfjVoaOVK3CaFdmJlHJw5aYyOvY29tz9epV3HpOw7JswHMNmZv2rc/NInHjNPJi/nngOm5ubiQkJBSabl+mEhZVWqH0qYXCwfOuYxJBpwWFBXIB9Pd52ogGPYJMjigaAOGVCzWKooig12J54Dsiwo7i5uYmfTzenT98N25ubpQrV45mzZrRpk0bmjZtilqt5sqVK1y6dKlQAU9kZCTp6ekPHJOVlZUUXk9KSkKr1eLv70///v2xsrKSjNGoqCjCw8M5deqU2VitrKxo3ry5lJ9ZtWpVBEHgk08+4ffffyc2NlaKQoiiSNOmTbl9+zZRUVEMHz6cH374gfz8/McS1dbr9Vy4cIHQ0FAOHTrEoUOHSNYqsQloj5VfA+T2bmbXmiiK6NJiyb1+Cs3NM7j1+OKR9/0yMi7In6Et/Z73MEp4CSkxKF8yMjMzcXJyYv78+Xz44YdPfX9nz54lICBA+vuXX37B29ubzp07FxkCvRu1Wo21tTVyuZy8vDyysrLu+2K8HzKZDAcHB8qVK0epUqVISEgoVFxgYWGBXq8320e5cuUYNGgQc+bMkcLw9evX55NPPiEgIAAfH59Cxkt+fj7r1q3ju+++4+zZs9SoUYPx48fTq1cvEhMTcXd3LzQ2URSxs7PDx8eH8PBwybPq6+tL5cqVCQkJQafT0aVLF0LzfLGu3sroy3zOhpMoimhuniPxz+mIWs2DV/g/Cnt3yvacRL5TOckYvM9O4P+G993nWhRFBG0uWRHHST+6FnRanN8Yg9r75ZZrehTKJx/j5B/zqF+/Ptu3b+eHH35g/PjxuLm5Ubt2bQ4cOGDm/W/fvj09evQgJiaGy5cvExISQmJiopQ/XLCK2sfHB5VKRXp6OrGxsWg0GiwtLalZsyZ169bF0dGRatWqYWVlhU6nIzU1VTJAo6KiuHHjBtHR0dJHXpUqVWjfvj1t2rShWbNmWFlZ0bVrV06ePMm+ffuIi4vj6NGjhISEcOjQIfLy8vD09KRVq1YcOHCAli1bFvJ6m4p7KleuTFJSEomJifc1KLPzdEQmZ5OvM2ChkOHrbI1SMHD69GkzA9IU1VA7l8K+/SjUpSs/8IPu3w+clzdXurjIZQIKmcDUTlXpWa/M8x5OCS8pJQblS0jz5s1RqVTFzld6HLKzs7Gx+Ve70NPT08yAK4harcbf3x9BEPjnn3/QarXI5XJkMplZ3pXp4fy4l55MJsPDwwMvLy/0ej137twhJSWlUGFJcTEV7/j7+1O/fn3eeust6tWrx/79+/nuu+/YuXMn7du355tvvqFGjRpm61pZWZGfn49erycgIIBz587RoEED/v77bwRBwNvbm1mzZtGzZ08CAgLIyMggJk+Na9dJyK3sJIPreVLQM5N15i90KTH3/I1sagTh2OYDBLniiXQSMb28c2+GkbLrR3Tp8SidvbFr9BaWfvWQqazv/1J/Ac7fo2IyWFIPLiPj6Hrq1KnDgQMHsLGx4cqVK1SqVAmA3r17s2jRIjp06EBoaKi0vq+vLz179uSvv/4iPNzYwcjT0xO1Ws2dO3fIy8ujfPnyZGRkkJiYSM2aNWnQoAHVA+qSr7LHxc0DS5USbwc1TnbWeHt7Y2lpWeRYdTody5cvZ8yYMWg0GmxsbEhOTkalUtG0aVPeeOMNJkyYwMSJE5kyZYq0Xm5uLocPHyYkJITly5cTHx9PxYoV+eyzz+jZs6eZwThkyBCWLFmCIAjk5+eTnp5upg95NT6TVcdvsf9KArdScjC7QkURXXocOddOknV2F/qUGCnf2q5Bdxya90MoZgHeq4BcJqA3iDTzc2Fml+olYe4SHosSg/IlxBSGDgkJoXXr1k99fwqFAoNMicLRE0GuRNRr0aXGSt4sKysrBEEgOzu7UJi6KIqzTFFYWlqiVqvJzs6WcvaeBS4uLgwcOJAqVaowYcIE8vLyChUnKZVKtFotgiDg4uJCamoq/v7+/PPPv6HkK1eu0LdvX06cOEGzZs0kUWerig2xb9obtZsvevHJyx09NAY93GXcFcSu0Vs4Nu/3VDw3ol6HaNDjnXCMlV9+SGRkJKGhoXw7Zx4yJ29k9m7otDr0WcmIOi2IBqzVKjIz0vHsPgmZY6mXyqMkF0CnzSMleBHZ5/dIFfMmCacbN25Qvnx5ZDIZlStX5sKFCwAsXbqUgQMHmhWntWrVinfffRc3NzcuXbrEqVOnOHjwIHfu/NvJSOnsjU1AeyzL1y2UnnD3B4U22VigZmNjg6WlJVZWVtjZ2WFhYYEgCFy9epXMzEwMBgPVqlUjISGBhIQElEoler2eTz75hE6dOuHj4yMJ5CclJVGhQgUaN26MwWBg165dlCpVipEjRzJixAhUKhVarZauXbuya9cudDod7dq1Y8uWLcRn6SSFBRkihrtzlQtw9weKQ6uBWPs3fqmujaeNj7MVLSu60adhmRJpoBKeCCUG5UuIKIoEBgaSmprK2bNnn5q49+lrdxi1YAM3NJbFfvncD3t7e9zc3IiKikIul9OrVy9atGghGWiXL1+WQmuxsbHk5uY+leMqChsbG3x8fMjNzSUjI4OsrCyz0KIJQRDw8PAgNja2UF5b5cqVuXbtmpmH1N/fnytXrgDGHtpvvPEGgwYNAoznsOA2unTpQnx6LueTdDi2H/n8X3yiAVGvRzyzkei9KwGjZ9L59RFPd7f/f+mXST1LU6dsfHx8kMvljBgxQmoV2a5du0Kapvn5+ah9a1Gqy3h0KrsX2ngweYbqlrbmn6WTuXzqiFS9nZ+fz+7duwkKCpIMytKlSxMfH09eXh6XL19m0qRJbN68GZlMZnYN3utjTWHvjlO7oViWrf3A9ISivMUPQqVS4ejoiFqtJiUlpVDhnFqtpkyZMuh0OmJjYzl69Cg1a9bkwoULzJkzh5UrV1KpUiVWrFhBzZo1yc3NxdfXl6SkJGQyGZU6DCS70usYRB6qwEvU60AQXvl+3AUZ0cqPDwLLl3TAKeGJU2JQvqScPn2aevXq8f333zNy5Mgnuu3olBw++O0AF1PEYr98ZAlXiN40C12asYhAoVDQoEEDOnfuTMuWLfHz8yMiIoJWrVpRqVIlOnbsyLFjxwgNDSU7O9tsm3K5HCcnJ9LT04ulN/kgVCoVtra2UgFBcVEqlTg5OaHT6aSx3I8hQ4awaNEinJ2dSU5OxsLCggYNGpi1l5s/fz7Tpk0jICBASllo3LixpCtaunRp4/F2nILS8dl2L7kfek02ORFHsanaAmTyZ2eoHV9JTOh69Ho9MpkMg8Eg/R9g6NChbNiwgfh4o9FjMqjsvf2RVw3CpmbQczEqXW0ssFIpuJVsHpIVRREPazkt/N3QX9nP4tnTsLe3p3LlygQHB0sC5hYWFiQnJxMbG0vFihXp0aMH69evZ+jQoSxevBhra2MKQEpKygPHYlurLQ6vDUaQyR+qg5HJW5wa8jNZ4Q+XXuPt7U1SUhK5ubk4OTnRu3dvjh07JrU8BOMHWOvWrQkKCsLT05P+/ftz+fJlRo4cSYUKFRgxYgRarRabBt2fmkf8VaOk4KaEp0mJQfkSM2LECBYuXMjGjRt58803n8g2lx+5zpRt/6A3iI/08vG8fYhp/dsTGBiIWq0GjAUu8+bNY/LkyVLnDlOXHJPwuYeHBwaDgfT09EIG5r141NB5cShotDzMOra2tqSnp+Pi4iIVAFWuXJnbt29LXpsPP/yQn376SeoABFC+fHmuX78ubcux9WBsa7/xQuV7PY8XulyAX/vVxlWXTJ8+fTh37pw0T6FQUKZMGW7c+Fdfx8vLizt37vz79we/PDfDvJ9LFM1qVyMyOZthI0Yx7uPR7N64kvDTJ6UiroEDBzJjxgwp//jWrVtUqVKFixcv4u/vz6RJk+jXrx8rV66kT58+ZtsXBAFHR0dSU1PveR88fnqCCAj45VwiOXSV1HLTwcGBjIyMh75H3n//fT766CMuXbokKSpERUXh4OCAq6srN2/eNPtwexYe8f86JQU3JTwrSgzKlxi9Xs8777zDli1b2LFjx2PnU87+6wILQqMe23AYG1SRYS0rkJqayqJFi5g7dy4JCQmo1WpatGhBREQEN27cwNbWlry8PPLz8x9oHNrZ2aHVap9pGNzCwgIvLy9cXV2Jj48nISGhyDD4oyCXy5HL5eTn51OxYkXkcjmXLl2SDFmHlgOxb9Dliezr5cZo0CiTrxO5buY9w69qtRpPT09u3rxpZqgbDfMOzz7kadCTfW4XSbt/AozGr7u7O7dv30Yul6NUKtmzZw9NmjSRVomIiKBKlSrGnGWDAa1Wi4eHBzk5OWYhZCsrKxwcHMwMZ1tbW3Jzc5+qMZa8cx65/+wrVGT3KJi6Xt0vF9rUxlNQWJR4Jh+BkoKbEp41JQblS05+fj6dO3fm4MGDbNu2jVatWj3SdpYfucbn2688sXHVzLvA3l+modPp8PLyklqzXbt2TVrGlD/o5+fHG2+8wd9//82JEyfuuU2FQvHAsPODeJpezcdBEASsrKzIzs6WvEol/Ito0CMY9CQHL8Jw7TA5OTnSPBcXF9LS0tDpdKhUKkkqR61Wo7d2xev9n57LmK0NOSQtHSZpd1pbW6PT6aTxyWQyWrZsSZMmTQgICKB27dps376doUOHUqZMGW7duvXAfVhaWhb5kfWkjTFRFBF1WuKWjkKbXPS4lEolBoPhkWXB7sat51TUZWo8VKSkBKP8bZmSgpsSngMlBuV/gNzcXN5880327NnD2LFjmTZtGiqVqtjrX49Pp/WcAxiEJ5MbJ4oi6LV0s7xIk5qV6N69uzTP0tKS/Px8VCoV77//PiNHjsTGxgY/P79CifyCIGBnZydVkt4LU/s0gNatW1O/fn2WLFnC7du3i1y+TJkyxMXFPdNK8eJSEuK7N//K6ywn4+g6s4+D1q1bc+LEiULXkKenJ6V6f0WC4PDMDRNRFMn4bRAVy/mwbds23NzcMBgMXL9+nX379vHJJ5+Ql5eHpaWllAvp5OSEVqslMzPzgdt3cnKSpIFMWFpaotFocH3ry6djjIkihowEsq4eL3YxXnEQBAG5XC59MCqdvZ/bh8CLjkImUMHNhrFB/rxW2b1ILc6SgpsSngclBuV/BL1ez5w5c5g8eTL+/v6sXLmykFbivag7dimJcqcnGhaUC1DZWc5f4ztgMBiwsrIiJycHT09PRo0axeDBg3FwcCA1NZVSpUqZeVnkcjlWVlbSS7WgZ1Iul0sGZLly5YiIiDB2qKlRg9zcXM6ePYvBYMDPzw9fX1/27t2LKIpSlxdBEFCr1Rw+fJhFixaxePHiIscvCAKenp5mL+vHwbT/gh60u6vEFfbuxpeo/Mn0av4vk7xzHlnhITg6OhaSkbKwsDAr5urR/0NOu7dHJz777jt3lgxnxfyv6NmzZ6F5kZGRNG3aFHt7e/744w+uX7/OggUL2Ldv33296IIgYGlpaealLcizMMYEUUQUBIi7zO0/Z2HITHzk4jmZTEa9evWoXbs2x48f58yZM5TqNAqZf4uS6myMHsfWld3oWNOLhmWdcbNTP+8hlVBCkbw4Gf8lPBZyuZxx48ZJVZR169ZlxIgRZgULRbH1wAmSlK5P/MGtF+FCkh6ZoxfOzs7k5+czffp0IiMjGT9+PHZ2dqxbtw43NzczY9LOzg69Xk92drbUn1in0yEIglSE0KdPHzw9PYmMjMTHx4fMzEwuXbpE+fLlWbRoETdv3uTq1auEhIRw7tw5ZDIZCQkJVKhQAUdHR3Jzc2ncuDGrV682G/PdskgPMiYfxjhJSEhAJpOh1WpxcXGR5F4UCoV0nE7thpYYk8VAFEUc2wzBxdef1NRUM2NSJpMxePBgDAaD1M1o0/JfMJxc+1zOq0xhwWuvvVbkPF9fX/bs2UNCQgIDBgxg48aN7N+/nw8//PC+UmCiKN7TmASwCWiPaHgyYed7jsHUnMDVD89BC7GqVvz8bSsrK9q3by/9bTAYOH78OL/88gthYWGIoojBo3KJMQk083MhdFxLFverR6eapUqMyRJeaEoMyv8YNWrU4OTJk3z22WesWbOGChUq0L17d44ePVpoWVEUmbR0l1HI+ikgGvTYBryOq6srx48fZ9KkSeh0OhYtWkSlSpXo2bNnoZzIjIwMKlSoYPbSVCgUODs7M3/+fPr06cOyZcuIj49Ho9Hg5eXFpk2bSEhIYN26dbz//vv4+vpK26tevTpbt24F4Nq1a6SkpEhtIHNzc/n000+lavT7IZPJEARB6uGtUCgQRZFSpUoV+3wYDAYMBgO5ubkIgoCXlxc6nY6cnByUzt5Ylq1dYkwWA+H/uoKyBr0LzTMYDCxYsACAUqVK0bBhQ/R6PZF7V5F6cDnw+B2aHoZKFfxwcXG59/xKldi9ezfh4eGsXLmSWbNmsX79+sfKFbYsX/eZGWOCXIGgsMD59RHYNXpLmm5KubG0tKROnTpm6+Tk5BAeHo6fn5/Z9W7y1gsWRt3bV5mqnnbsGR3IioENSoppSnhpKDEo/4OoVCo+++wzbt26xcKFCzl//jyNGzemUaNGzJo1SwoLb9q0iRS150MJBT8MgkyOfaXGUreOESNGUKZMGYYOHVpk+0ZnZ2e8vLy4evWq5LUD6NixI+fOnWP16tXMnTsXgDfeeINjx45x+PBhunTpglx+72Po0KED/fr1kwwJvV6Pra0tBoOBypUr8/bbbwNFGxqWlpbY2dnh5+dH27Ztyc7OZtiwYcjlciwtLbl9+zZ2dnZ4ehb/BZidnS15QL28vFAoFEavUkn2SbER5Aosy9ZG4VwaMMrYdO7cWZpvaWnJmTNnOHbsmDQt4+g6knfOR9TlP3UPHgCiiI/zg42BzZs3k5+fj0Kh4NNPPyUxMfGRd/k8jDGTUejYvB82NdoAkJeXh62tLfn5+Zw+fZpu3bqZGY+3b9/m2rVrRYuwO3i8kh9WAmAhl/FN1+rsGNGspJimhJeOEoPyP4ylpSUffPABly5dYsuWLTg5OfHFF18QEBCAh4cHIz8ej/Ipv3xEa2dK+5anY8eObN26lT59+tC8eXOysrLMlmvWrBmZmZncuXOH2rVrk52djUqlYtmyZaxcuZLXXnuNo0ePUrlyZS5dusT69etp0KBBscYQGhrKvn37pL9lMhmTJ09GrVYzZMgQdu3aZba8re2/D3K9Xs/8+fNJTU0lPj6eunXr8ueff3L48GEqV66MTCYjIyMDrVZLjx49in1eTPlmsbGx6HQ6rPybvJIv0cfB5AEHSEtLY/PmzdK83NxcqRORhYWFND0rPJjYXz9CE3Xu7s09cZT56cjF+3sat27dyrRp0xg2bBgODg5Sfu2j8lyNMVHEpd1QY1ceJydUKhVyuRy1Ws2mTZuK9cEkCAKCXPnA5V4I/n888sc83XKZcQNN/VzYO6Z5iVZkCS8tJQblK4BMJqNTp07s2LGD1NRU9u3bR//+/UnKE+Bpv3wEgYkz5xAREcHNmzdJT09n//79ZosEBgZy6tQptFotH374IWFhYdjY2HDhwgUaNmyIt7c3ly9fpnfv3oSHh+Pv71/s3a9du5aWLVuSnJyMSqWSXrb79u2ja9euZGdnExcXZ7ZOfn6+FAavU6cOw4YNY86cOdy8eRONRkNSUhJbt27l6NGjfPLJJwAkJSVx9OhRvL29i3la/p+DJooIFpbIbZyKfUwlGBFkcizL1b3vMnq9vlA1vy49noS1n5N1YR+i+HhdmO6FXCZgm3F/2Z+8vDzGjBlD+fLlWbBggSSE/yiYrqfnaowJAgbRmAucnZ1NUlIS+fn5aDSaBxqT06dPl1JVatWo9gwG+/h4O1kRMiqQvg198XG2KrKzuKe9msoetnjZqwvNFzD20+7bwKckvP2MEEWR/Px8srOzSUtLIy4ujqSkJNLT08nJyTFLNRFFEZ1O98RksF4FSrQFXjFUKhUtW7ZELpczd8XmZ7LPzl27U6GMI19//TVLly41m1e9enXOnDlDbm4u33//PePGjUOlUnHixAkUCgW1atUiPT2dadOmMXny5Ifa744dO+jTpw/29vYolUpGjhzJpEmTEEWRPXv23PNBMXfuXEJDQ1mzZg1ly5YFYPLkyfz555+0bduWBg0aMGvWLAYMGMCMGTNwdHRk3LhxxMTEYGNjQ8WKFYmIiLjv2Aq+YNW+tUq8k4+IwtETQalG1GoeqDHq6upKUlISKpUKjUZDxtH12FR7NN3WB6E3iHhmX7tva8TJkyebdUd6VAoet6jXPmDpp4spFSHFxhXyYu67rJOTk3R+Nm/ezNy5c+ncuTMn9+3Eu1b/F/6eeK2SOxXcbZnSqSpTqPpA+Z4SeZ9nQ15eHuHh4URGRhIVFUVkZKTZfw/qxFbwY7/gNIVCgUqlws3NDV9fX7P/fHx8KF++/EPl1P8XKbmaX1GOHDmCjeWzqRi0UMjIysri888/N3v5KRQKUlJSyMzMZP78+cydOxedTseOHTtwcHCgcePGZGZm0q9fv4c2Jvfv30+3bt1wdXUlNzeXkJAQbG1tmTRpklRhDZh1VTGRmJiIt7c3MpmMTZs2cfz4cerWrcuRI0cYM2YM33//PQ4ODkyfPp1ff/2VsWPHkp6ezvTp08nKysLNzY0aNWqwcePGYoX5rKu0fKhjK+FfBEFA4eiJNuGmdK4LGioFSUxMlCrtd+zYQefOnTHkZiCobZ+s8SKKGDSZVPJyYMGmo2RnZ2NtbQ0YX3ZHjx7lhx9+YNOmTY+1G5PsVMFrTJca+9x7XptSEVL3/FJonp2dHdnZ2ej1egICAggNDUWr1XLq1CkpB1bUatClxb5QveyLok9D89C0tUpBVS/7ey7/oPklPBqiKHLu3Dk2btxIcHAwZ86cQas1fljJ5XIUCoXUgayolBKTsahUKs1k6bRaLXl5eVJXKK1Wi1arRRAEsrKyOH/+PNnZ2WaKC76+vrRt25Y2bdrQsmVLnJxerchTiUH5ipKXl4eVIduYB/SUXz6+ztb0equbdJObqFmzJqdPn6Znz56oVCpu3LjBO++8Q926dQkMDCQhIQEbGxu+++67h9rf7du3efPNN6latSphYWGsWbOGGjVqSEaGKX/Rzc2N/v37880330jrKhQKQkJC0Ov1eHl5ERMTw5EjRxg5ciQzZ84kLCyMpUuX4uDgwPbt26WX99SpU4mLi+PXX3/l+PHjREVFsXPnzvvKu5QqVYqsrCxUpSs/1PGVYI5cqaLglVXQmKxTpw4pKSncvHkT+Pe379ChA0qXMsgs7Z78gAQBmaUdS9bsJD8/n71795KRkcHq1as5cOCAmUzW43RuKsrD/iIYY4JMjm3FhkUalBkZGVhYWKDX69m7d+89t6G5EYaytgcIL2ZWVp0yDiVFM88RURRZtWoV8+bNIzw8vFBai6OjIx4eHuTn5xMXFyd5JYu63woai8UhMzPTTCPZZKyCUVv2119/5eeffwagbNmydO3albFjx+Lh4fFYx/wyUCJs/ooyZcoUfvvtNyy6zkRv9fS+omzEXNa/W5UqVaoUupmVSiUymYyoqCj8/f3Jzc0lNTWVUaNG8ccff5Cdnc3333/PiBEP1zmmX79+7Nq1C2dnZ1xcXAgNDUUQBFJSUiTZH4Bhw4YRHx/P+vXrpWmurq6SQdKkSRPCwsJo2bIlK1asoGLFinTt2pX69eszYMAAAM6ePUvNmjUBuHXrFj4+PgCMHz8eJycnKceyIBYWFtIDULCwxHv0uhc+vPcik7xqLFnRl+8538LCgsqVK3Pu3DnefPNNtm/fjl6vf6p9vgVAcfMI19d9jVqtJjc3lwoVKpCQkEB6evoT35/pXmrdujV3PJuQ7FT1ueo4iqJI9JweiFoNZcqUITo6uliGs1qtxs3Njdhs8YXtlCMAoeNaluQ7PmN0Oh2bN29m/vz5HD16VMp39PLyolmzZjRu3BhRFNm3bx/79u0rVPh5PywsLHB0dMTNzQ1HR0epz3x0dDR37tyRjM2CxuPdODs7Szn/8fHxnDp1iujofztJValShY8++oj+/fubqZj8lygxKF9RTAalvN7biBWaPZ2Xj2hAdes4NXURrF+/vsjw8tdff41SqeTjjz+mR48eTJw4kTp16tCuXTuOHj1KQkICSmXxCw2OHz9Ow4YN6dOnD6tWreLkyZOSDt6CBQsYPny4tGxQUBBHjhwxy6lp2bKlVDTUu3dv9u/fT0ZGBsnJyUycOJG1a9dy9epVnJyc0Ov1TJ8+nfHjxwPGAiCTBJGdnR0JCQl4enqSmpoqbT8wMJCzZ8+SmZmJm5sbLhUCyGo67CFPbAkmChoupg8WPz8/s57xd1OzZk3Onz+Px/uLnqonT5+dRswPfZ7a9u/Fi9K2cMfwphiSbzFy5EgOHjz40J103HpORe1TA0H2YgXSJrT158MWfs97GC8FBoOB2NhYbt26RVZWluQJPHPmDLVq1cLCwgKlUom1tTVlypTB09MTuVxOdHQ0wcHBnDt3jkuXLnH+/HkSEhKeq7Ta3bmVarUahUJBXl5ekd5N0zG5ubkRExPDzZs3MRgMKJVKevXqxddff/2f81q+WHdqCc8MNzc34uPj8bp+GMG/xdPZiSDjRvAykkRj2Ldp06bs2bNHmq1Wq/n444+pXr06AD179mT46LH4N3yNBL0Vjdr3IN8g8DB1q6NHj6ZWrVrExMTQrl07yZg0GAyMGjUKMBqSwcHBXLt2rVBOTUBAAPv376du3boYDAZcXFy4c+cOR48epW3btsyZM4cbN27QokUL/v77b65cuSKtu2PHDqpWrcqdO3dITU0lJyeHjh07snz5cmmZzp07ExoaKskf/bzxG0ZufzL9kF9FDJosRK2GChUqcOnSJan9qK+vL9nZ2eTm5uLo6GjmKTh37twz0WuUWdmjdPFBmxQFPF54+2HQZSSiibmEyqvic/VSNmjUhOxb/zzy+im7fsRz0EIQ5C+MB79tFfcSY7IIMjIyOHjwIOfOnTMrhLl161ahcPT9KKogpiAWFhZYWVmh0WgKOSeeNqYxme7ju/dvihKY3inZ2dnodDqio6O5ceMGMpmM0qVLk5yczLJly1ixYgV9+/Zl3LhxVK1a9Zkey9PixUxQKeGp07p1a7RaLbLMeHJvhiF7ws9ruUygoa89toZsKYRcpUoVs2UaN25MYmIi1xOzcWr7EVPPqYhuOJrc5qNIqt2fiz5vUnXKbhp9vZdJf57nanzmffcZGRnJ0aNH+fjjjzly5IhZe7epU6dKOWcNGzYEICoqykwQ3cLCQpJumTVrFvv376d169bSss2aNUOtVhMcHExQUBCZmZlS2OXixYusXr2afv36SV7K+fPnExQUZDZGCwsLateuLYXGf5g75yHOagl3I1NZo7B35+rVq7Rr105qP2pnZ0daWhqlS5cmOjqalStXMnnyZOn3fhZ6jYIg4NT+X+/zwxiTBbUzH4SlpSVKZ2+cg4bgM2wpZcasR136+bcuzNeY5w8rFArp/Bfn+HTp8aSG/PxCGZM/972/TNWrgk6n4++//+bLL7+kadOmODk50alTJ+bOncvZs2elRgPfffcd27ZtIzw8nJs3bxITE0N8fDwJCQncvHmTESNG4OrqKm1XFEVUKpVUxHY3+fn5pKWlFVlcI5fLpQ5NTxK5XI5M9q+pVPA+9vLyws7OmIddVNHP1atXuXHjBq1atWLWrFk0bdpUOjaDwcCKFSuoVq0aAwcOJCMj44mP/VlT4qF8RalQoQI+Pj7k5eWRsutHbD5cjDE76Mk8vBUygVk9aiMPbc/q1avx8vIqlDtWr1M/2i48idf7PyGKIulaocj6oNh0DatO3GLViVt42av5umt1Aiu6FVouODgYhdqaJJ0awaUs5es0JztPh7VKweLFiwGoXbu21D1Fr9ebFTY0adKEjRs3olarcXR0JC4ujjZt2jBnjtHos7S0pFq1avzzzz/07t1bag8piiKjR4/G19eXkSNHEhsby08//cSaNWvYsWOH2RgvXbpEUFAQv/32GwDnDu/Bxq8HJXknj45Tu6EkrP2cPXv20LZtW3bt2sWJEyeYNm0a8+bNQyaT0adPHwRBoF69esydO5cW3d57JmNTl6qMwrk0uuT7S+jcTY8ePdi0aRP29vaSTqqpmMVgMODg4EBqaioKe3ds2w3FsmxtY/efF6T/tSiK6FJjEQSBxo0bo1KpOHnypFTMUFyvVVZ4MDJrBxyb93vk6nXTeqJeh1yu4GGVR2UCjA3yp2slG44cOUJkZCS3b98mJiaGK1eu4OjoiCiK3L59m4sXL/LOO+9gY2ODSqXC0dFRkpXx9fXF0dHxhTGQH4UrV67w/fffs2bNGjIyMnBwcKBVq1YsWLCANm3aUL58+QduIzY2lrFjx7J+/Xq0Wi0WFhZUq1YNFxcXYmJipHQVpVKJVqvFwcGBtLQ0s/zFoj7O3N3dyc7OvqexqdfrsbOzo1KlSmRnZ3Px4kVEUTTbbv369fn666/x9fXl1q1bZtJDV65cISIiwkwv9s6dO4CxU1dWVhZeXl60a9eOU6dOER4eLi23b98+Dh8+zPz581m1ahUHDhzgyy+/JDQ0FIBly5YREhLCqlWraNasWXF/jheOEoPyFUUQBNq1a8fq1avRZWbC6XVQ5+0ntv2pnari7WRFtWpGkWKdTiclSSvs3XHt/jl/xLn8v8qcYj9k76Rr6Pf7SSq42rDkvXp4O1lxNT6TVcdvseaKLaVG/sHcKwKe787ho23RCNuiKeNkRW6VN1Dm/UWPHj2YOnWq2TYVCgU6nY7c3FxycnLo0qULf/31F5aWljRp0qTQsoDZF+v27dsJDg5m8+bNqFQqfH19EQSBxMRELC0tzdZPSUmhc+fOfP311wAkxd3Gz1ZJbObz1Q98WRFkMqkFoy45huDgYMqVK8eRI0d48803uXjxotRBRxRFDAYD58+ff2Z6jfeT0Lkfq1atAjCrCJfJZFJP+dTUVGxqBOHY5gPJE/m8PZIF0aXGImqNIUFHR0e2bdtG9+7dpfSRhyHj6DoMOek4Bw1BJldgeIiPXlGvQzToydj/G++8Vock79aciM5EJoDhvl9xIiBA4nXit33H0K9uMbSY+1y4cOE951lYWODq6oq3tzdNmjSha9euNGjQ4L6tY583oigSGhoqeRvd3d0ZOXIkHTp0oE6dOtIz8UFER0fTt29fQkNDEUURFxcXWrZsyblz57hw4QKenp5kZmaiVqupXr06p06dks6LWq0mLy+vyKIYGxsbsrOzJePOFJL29PREFEXi4uJo27YtH3/8MS1btpTeNSkpKezbt4/g4GC2bdtGXFwcJ06coFWrVvj5+bF06VLefffdQseRlJTE3r17WbNmDcHBweTm5pKWlibNW7JkCVOmTGHt2rXMnj2bJUuWSILqQ4YM4euvv2b58uUcPHiQs2fP0qtXLy5dusTt27cJDAxk/PjxTJ8+/aFqB14USopyXmEuX75M1apVpRt0yA+b+eu24rE9AeOC/Bna0phnNHXqVL744gvAGPK+pSqLY+v3kckf/1tGLhMo52zN1cQs5DIB/X3eEKJBjyCTU0qWwYkfR6NLj5fmCYIg/afX6/nll1+YNGkSr7/+OvPnz8fe3p6VK1fSu3dvGjVqRNWqVenevTvt27fn/fffZ/369TRs2JCdO3dK583U63vdunV06NBB2tfQoUN57733qFevnrRcxynLCc91uO/4S7g3okFPZtiOIo22cuXKMXr0aLp160ZQUBBXrlwxaskp1XiPWf9MvEXalDvc+WVwsZY1vQyVSiVePmW5k6FFkCuxsVSTGh2BqNUgk8lwaPI2tk16PXfNyaKQCZB2cqvZ73Hs2DE6depEZmammZH8IDw8PIiLi8PCwoI3er7LEa0Pat9a0v18L0zzddHnqS+7TuzVC5w9exZBEOg/ciL7orVkWHsbhfELnD9RFNGlxZF7/SSZZ3Y+tGf5UVAqlVSpUoUuXbrQt29fypUr98B1RFEkKSlJ8p4lJiZKBS+HDh3C1dWVypUro1QqsbCwwNPTU/KS2tsXXwvz2LFjjBgxgpMnT1K1alXGjBlD7969Hzq0vGjRIoYPH45Op6N8+fLUq1ePvXv3kpSUROfOnenSpQsjR46kfPnylClThk2bNhlTRpycyMjIoFy5cmb56gCenp7ExsYW2le9evXw8/Nj/fr11KhRgyVLlkhKHPdCFEWOHTvG1KlTzVrx9urVi+XLl9/T4BdFkYiICD7++GN27txp5jmdPHky06ZNIzY2llatWnH5srkSxaBBg5g/fz5qtZp+/fqxcuVKwPgM6Ny5M+vXr3+hPzSKosSgfMUZPXo0c+fORalU4uPjQ9VOgwkTyiNXKhEfIsVWNOgRRAPJuxeyZPL7Ul/rqVOnMn3+r9ToOpR4qzLIn4bu30MgGvSIeh2pIT+TFR6MWq1Gq9Wi1+vp06cPq1ev5oMPPmDZsmVERERw5swZOnbsSEREBBUqVKBevXrUrFkTX19fPvvsMxwcHChXrhz79u0ze1CbDMrWrVuzZcsWafrw4cPp16+fZFB6e3vzWtc+7LdsUmisJRSfexltLVu2ZNu2bVhbWxMdHY2/vz9169bl8OHDeH2wGIXD06+yLFiJ/iCavN6Ni/nOWJavi8KhsLGjT4vDQWkgy+bF7siR8PtwcuNvSn+bwpf3oyjvU0FlCJVKRV5eHkpnb2wC2mNZrm7RBmFqLLk3TpF3IYSyzlZotVpycnK4fft2oVCpoFQbtyFXIuq1Zp7VB2HyFj+oNZ/JKLjXcqZwrIlSpUoxbNgwxo8fj0wmIyEhgT179nDkyBFu3rwphWELatyaetYrlUopF8/W1hatVkt+fr7ZebW3t5c6vJQvX57mzZvTokULKRcQQKvVMm3aNGbMmEGdOnWYOnUqbdu2feiPl5SUFDp16sSRI0dQqVQMHjyYVatWodFo6N+/P6NGjUIQBJo1a4arqyuVKlVi3bp1WFpakp+fT7Vq1XB2dmbfvn0AUjGj6ZgtLS1p2rQpu3btkq4fT09P4uPj+fTTT/nss88eKh8Z4MaNG3z55ZesWLECURRxcHDgyJEjhWoA7kav19O5c2e2b98uTevRowfr1q0jLy+PDh06sH//fgwGg/ThWLFiRVavXk2tWrXo27cv69atkyrHO3TowLZt2164D8b7UWJQvuKkpqbi7u5uLNCRyfjiiy/4+ofFOLUbhqJ0NQRExPuFmP6ft6WJPEvOwV95PbABmzdvZv/+/ZT2r0nfH/4iUqN+JgLqxcXk1Uk9uJzcU3+i1Wrp3LkzBw4cICgoiE2bNjFlyhQmTZrEiBEj2LZtGzdu3JAqhr/99lt+//13zp07R+XKlQkNDcXFxUXafn5+PiqVyqwriIm7DcrKlSvTpk0bEqv15Mi1pBcqbPkycbfRplAo8Pf3559//qFSpUqcPXsWlUrFlClTmDp1KqIo0m3mH4Rl2jwg9PlkuLNkONqEm0XOs7e3Jxs1TgVyIe9/Hfw/HPsiIhpo6OtI6p/TCA4OfuDiRVW+m4xPkwF59/KCIEgGUlEGob21mrfeeksSlzaxePFiBg8unqf4RUQmk2FhYYGzszOVKlWiYcOGvPbaa9SsWfO+uZkGg4GEhASp8rpgFfbFixe5desWCoWCBg0a0KZNGypWrMjs2bM5d+4cX3zxBRMnTix2WLsg58+fp1GjRmRnZ1OrVi3Kly/Pxo0b6dGjBz/++COurq7ExcXRqFEjLCwsGDp0KCNHjsTS0hKNRsPYsWM5cOAAJ0+eBKBBgwYcP35cuj7atm1LamoqJ06cwMfHhz///JNWrVqRlpZG+fLl2bRpEzVq1Hjk852YmEjTpk2lNro//PADw4Y9WOLt8uXLNGrUSAqD+/v7S+OuX78+6enpxMQYPd8WFhYYDAa+/PJLxo4dS9u2bbl58yaWlpZcvnyZ7t27s27dy6NTXFLl/Yrj6OjIjz/+CBgf1tOmTeO1hrW4vfITultdolsNN7QpdwonQYsi2pQ7ZIbtJGPNOOL/mExWXBSNGzemXr16dB33Ha/NOUB03v/bO75AN4Tp5nRs3g+393/BrnIT7O3t0ev1nD59Gh8fH8aMGYMoiuzevZugoCAEQeDgwYPk5+eTkpLCuXPnUCqVhISEmBmTAKtXr5b2YzImTV/Jfn5+UteWatWqSQ/q9s5piHrdMzn+/yKCIGDjUVb6W6fTUbduXapWrcrly5dxdXXF39+fX3/9VbqWt3437pkYkwCC/N75UHqfBngOWoi6jPHl9+CPihfnXiqIKBo/Pg9t+I3Tp08Xex1ACqEKgiB5Mk3GZEEPkykPVvpbq0GbcJP82AhIjeHAnt3k5+dLxSEpKSmsWbMGd3f3hzYmBUF4oUKOBoMBjUbD7du32bt3LzNmzKBVq1Y4OzujUChwc3Nj6NChJCQkmK0nk8nw8PCgYcOGvP3220yYMIGffvqJv/76i8jISK5evcr8+fNxc3Nj1qxZ9OrVi3PnzjFw4EA+/PDDRzImr127RsOGDcnOzuaDDz4gKSmJPXv2sHLlStauXStVdo8bN46srCxmzpzJmDFjJK/v1q1b2bBhg2RMenh4cPz4cQRBQKfT8f3335OWlsaJEycICAjg0KFD9OzZE0dHR3bv3o2VlRX16tUzC18/LK6urly5coVp06YBRmfA3fn3RVGpUiUSExMlp8GVK1fw9vbmxIkTzJs3j5iYGD766CMEQSA/Px8vLy8mTZrEl19+yYIFC4iJiaFfv35UqlSJDRs2SPn2LwMlHsoSAHjttdfYt28fzs7OZGZmolKpyMrKYtKkSUyfPh1BqcbJx5/M7FzJE+Bb2lMyjsAYYpHJZIxavIulYckvlFfyXpi8lYb8XPT7fsAQF8GhQ4fw8/Nj06ZNdOvWjT179vDaa68xZMgQli9fLuWAzZw5k4kTJxbaZuvWrQu1lfP19ZUe3jNnzmT58uWMGzeOTZs28frrrwOw6VwcQoNnL4T9OLxIOXyxy8YYDYsCuLi4oNVqSU9Pp1SpUgiCQExMDBYWFjRp0oSEmv3IUrveY4tPjqI8lIIgYNuwx2NVML9oFPT+ZxxdZzZPoVBga2trJvQPxjzXihUrFnr529jYSIV8BbtL3Y1MJmP06NEsXryYpk2bsnPnTn788Uf+/PNPM93bu7nbA+rs7IyFhYWUl+fs7Ex6erpkwD6sMPvzRqFQ4OfnR4cOHbCwsEClUuHp6SmFu8uUKYNarZaWNxWkVKlShYCAAFasWAHAu+++y+jRo6lYsWKx9hsTE0O1atVIT09n+PDhLFmyhNq1a7Nq1Sq8vb2l5Y4dO0ajRo2YMmUKs2bNIjs7G4VCwZYtW5g2bRrHjx9HpVKh1WopVaoUMTExGAwGfvrpJ7Zs2cKePXtQq9WEhYXRvXt3EhMTOXz4MOXKlSMvL48ePXqwZ88edu3aRWBg4GOdyz179hAUFIQoinz33XeMGTPmgesYDAZq1arF+fPnpWlLlixh69atnDp1ikGDBjFlyhQAunfvzoYNG/jqq6+4ffs2y5Yt4+zZs9StW5f09HSOHz9O3bovvmRViUFZAmDM/7CxsUGj0eDp6UlaWhq5ublSfk+lSpW4cuUKSqWy0IP9vffeY/369WRnZ2NTIwjn1x+uVeKLgiiKVHVTsXNMGzQaDZUrV6Zq1aps2LCBRYsWMWbMGOn4Td0cPD3NxbH1ej3W1tZmLyobGxtycnJwdHQkMTERJycn0tLSOHjwIM2bN2fx4sVMmTKFDh06UP3tscwOjrh7aC8kmRcPYFO5OVD8Kv2nSVFGW7169Th69CiBgYH8/fffgFG8/syZM5w8eZLlW0LYoq3+VMd/rxzKl/leKQ7JO+eRFR7yyOvfz4g00bJlS7Zs2YKtrS1z5szh448/lp5ZdxuMtra2ZGZm4urqSmJiImAMr4uiiE6nw8nJidq1a+Pi4kJmZiYnT54s5O0rzpjh3rJIJnWIZ22cmvINc3JyzPbt4eGBr68vLi4u7Nu3j8qVK3Pw4EGsra1JSkpi0aJFLFiwgISEBHr37s38+fNxdHS8536ys7MpV64cCQkJDB48mHXr1lG9enV27dpl1m7QYDBIHky5XM4///yDwWCgYsWKREdHFyrcMoW57e3tEUWRzMxMRFHknXfeIS8vT5LlKSgQrtFo6NChAydPnmTfvn2PbZCFhobSvLnxebd27VreeuutB66j1Wrx8vKSpIYEQWDevHmMGDGC33//nZkzZ3L9+nUEQWDo0KHMnz+fWbNmMXXqVMaNG0f79u2pV68e7u7uxMTEPJK3+FlSEvIuATA+cA4dOoRMJiM2NrZQwvkbb7xBzZo1yc/Pl0IAJpYuXUqlSpVQOXvhFPTBc22P9TgIgsDFxHwqf/4Xn85ayO34JMqVK4ePjw+jR49GJpNJXq5x48YVMibBGBa5O++rS5cuGAwGgoKC+Ouvv0hLS6NDhw5SHs3ly5dJTExk/PjxDGtZga+7VkfxpJXmnxCiKGLQ6UjeOY+UrbPJPr/vhTAmTcUYd2N6mQwcOFCatnXrVmxsbFizZg3bVv6CIefJ99YuSFGFHgp7dxzbvLz3SnFwajccVekHdwC5V1j5QcZk48aN2bVrFzY2NixdupRvv/0W+NdYM92HgiBQu3ZtNBoNcrlcMiZNBXlOTk7IZDJSUlLYs2cPf/zxBzt27ChkTBZUcDCN2crKitKlS+Pu7o5MJiM/P/++4zYYDE/FmJTL5VSqVIm2bdtSpUqVQoUoer2erKwsqSCkZs2adO3alY4dO1K2bFl2795NTk4Op0+fJiAggKFDh3LkyBFGjBhBZGQkixYtYvv27VSvXp2QkHt/JHzyySckJCTQpUsXNm/ejJ+fH9u3b8fKyoqUlBR+/vlnWrRoga2tLSdPnuTixYucP39eOicRERFFqgCYcvw1Gg0ZGRnSfbNmzRo2bdpEWloatWrVwsPDg7Zt2/L7778TFxfHxo0bqVq1Ku3atXvoj4O7CQwMZP369YCx+vvixYsPXEepVEp6lDKZTNIsNhVyzps3D4PBgF6v5/DhwwwbNoxPP/2Uhg0bEhwcTN26denVqxfx8fHMnTv3scb/LCjxUJZghqk7jMFgMKu6FASB119/nR07dmBpaUn9+vU5fvy4VCEN/++9W6YGwhOQBHoREEURfXo8pWVphK2bj6cVxMXFYW9vz40bN7CxsTFbPuJGFNUatTQrEKheuSK3bt0iIyOD69ev06BBA5KTk7lz5w7jxo3jxIkTREVFMXLkSL766itpW9EpOfRfdpJrCVnP+rDviyb6AsnbvzeTXXLrOQ3LsgHPNWyrTblD4u9D79lT1yRAD0YD/8yZM9y6dQtRFPF6YyTyyq0QZE/++1qGSNqpbYUkjTzemY5F6Wr/mXulKERRBFEkZc/PZIXtKHKZ8ePHU6VKFd57771ib7dHjx4MHDiQTp060alTJ7RaLVu2bKFcuXLcuHHDbNkyZcpQuXJlQkJC7mvI2dvbk56eXqjS3NnZmQkTJjBkyBCWLFnCt99+K+kddu/enY8++oh33nmH+Pj4e236oSiq0v1hsbW1pWvXrrRs2ZKdO3eybdu2QkaanZ0der2e7OxsVCoVer2erVu3kpOTQ0hICCEhIdy4cQNra2sGDhzIqFGjUCqV9O/fnz179jBs2DC++eYbM69jZGQk5cuXx9LSkho1apCUlMS3337LqlWrCA0NNTPoLC0t0ev1kucxPz+f2bNnM3bsWKk1LkCHDh04dOjQY3WRsbOzIycnh7Jly9K7d2+aNWtGkyZNHrmrTtOmTTly5Ah+fn5EREQU65nXrFkzDh8+jI+PD0lJSWg0GhwcHEhMTKRJkyZcunSJtLQ09u7dy4ABA7Czs+PixYukpKSQlpaGj48PNjY2REVF4eTk9EjjfhaUGJQlFGLr1q107tzZrJqyIG3btmX37t24urqi0+moWbMmR85fx+v9n57DaJ8B/69kz4s6S9LOH/h9wWz69DHmOoZHp7H48A0OX4klRWNuUImiiKU+m4Sz++jgb4+QEcfKP9bz7rCxvPV2L3p064KXrZLM1CQiIiKwtbUttOvQiEQ+2RTOnXQNj1Ld+7hGniiKGPKyyb12gvSj6yRdPlN1rUxlg8zaHptqbbAsX9s47xkblSYdyvR9v973Zbx48WJmzJhBZGQkYHyp5ebmYuVRDtf35j+18d1ePMRMz1Dp7P3fvVfugT43g+x/DpB15i+0yca+6k2aNCE2NlYyAovT63zJkiX0798fgDFjxvD9999jZ2cneXQK0qNHDzIzM4sszFCr1dSsWZPu3bvz3nvvMWzYMNauXSvNL1u2LNu2baNq1apcvHiRvn37cu7cOXr16sXIkSPZuXMnX3zxxRPzMJuaKzwspnSae133np6ejBgxgsqVKzN58mQuXLhgNt/kSTTdC126dGHs2LE0atSIGzdusGzZMn788UdSU1Pp2rUro0eP5vTp04wfP14qejEZlS1atODgwYMMHDiQ3377DScnJ6ntrrW1NXXq1OHtt9/mrbfeokWLFjg5OUmdYqysrLC1tb1nW8WCWFtbk52dbfa3STj8QefQtK6lpSWBgYG0adOGNm3aUL168dNeYmJiKFOmDKIoml2P98OUL6pQKDAYDFhZWZGVlcX58+fZsmULM2bMIDc3l5o1azJx4kSpfe+WLVvo1KkTFSpUICoqShI9f1EpMShLKJK//vqLrl27ShpwBZHJZDRs2JCzZ8+i1WqxtbXF840RZHoE/Kdlb0S9DrlMYGTrStxKzWFHeCwa3YO9CSYZGIM2D5nCwqxQSRRFXC0F3gjwpXeDMlRwL2xUAlyNz6TDmFlkO1ZAYfPg9m0F9fg0N89gVbkZln71kamsC60riiIyfR4dAnxpXdmdJb8vZf/+/UwYOoD2jWryTo+uXL16FaWzN7Z1OmJZsSFy66LHILW5e8beyruNtrspW7Ys33zzDf369ZOu6YL6f0/Duy7qdeTdOk/82s/Mpju2Hoxt7Q7/6XulKESDAUEmI/dmGCm7fkSfkYBarSY3N7eQkVAUgiDQp08f5s2bx9KlSxkzZgz29vbk5uYWCjM7OTnx3nvvSW1TC06fPXs27733nlQxXKpUKTPv2cSJE5k5cyYGg4H58+fzySefUK5cOVasWEFAQABffvkl06dPL2TEFccgfhA+Pj689dZbdOjQgXbt2pGfn0/FihUlUWxLS0uzdpymadWqVZMqou9GJpOhVqsZNGgQlpaWzJo1y2zsgiBw+fJl9u/fz5w5c4iIiKBx48bMnz+fOnXqkJOTw/Lly5kzZw5Xr16lffv2fPDBB/Tq1YvAwEC2bNlCcnIyXl5eUtcaURSRy+V06NCBr7/+mkqVKknPg9jYWLy8vAgICCA8PLxIfU5BEGjRogWJiYnEx8eTkpKCKIr4+vqSnJxcqI2vjY0NFhYWZikNtra25Ofno9Vqi9Q3tbCwkGTdPD09GTVqFIMHD8bBweGBv9OQIUP4+eefcXR05NatW4WiVXdTMLe+devWHD58GI1Gw6xZs2jevDn169eXlg0ODmb69OmEhoayePFiBg0axMiRI/ntt9/w8/Pj7NmzDxzf86LEoCzhnkyfPp3PPjO+DNVqNZaWloUqNK2srMjJyaHUB4tROBbOKfzPYapcf8IV7Ca9z9KKTFrZxOOkEqVcLdN/Q4YMwcXFhYSU9P97B62RWTsg5qRj0OUDAoiGewo016tXj7MXLiE4lqJLv8G89lprtq9ZwrYVPzNl8kRGjBiBXC5HJpMxbNgwli5dSu/even18TSm7LxKQpa22IbiszIoRb0Oza1wEtZ+ft/l5HI5BoMBuVxepBdD4eBBmSG/oBeFJ/K7iqKIqMsn9tePzNIDALw++AWlo9dj7+NlRdTrQDSQEryIrPD7a1Wq1WoMBgNly5Zl9OjRfPLJJwCkpaXx0Ucf8eeffxbqlvLNN98wYcIEs2kymYy1a9fSvXt3adrFixepWbOmdD3MnDmTM2fOEB0dTUhICF27diUkJERKRzG18Lt69eoDj9HBwYG+ffsSGBiIvb0977zzDsnJyfj7+zN8+HBq1apFbGwsCxYs4ODBg0Vuw8PDg/j4eARBoHXr1pIHdvDgwXz44YfUr19fSvG4uwCpatWqXLlyxexaN2keDh06lL1795p5KwVBYPHixfTv358dO3bw+eefc+HCBaZMmcKECRNQKBTo9Xr+/PNPhg8fTn5+Ph999BHffvstnTp1wt3dXZKfA+jbty/z5s0rVMBjMBj4/PPPmTFjhjTNVBhVq1YtrK2tpfPRtWtX/vzzTzMjvWPHjpw5cwY3Nzfu3LlDixYt+OWXX4qM7hQkMzOTwYMH88cff5hNL1j4JQgCCoWC9u3b89VXX91XyDwsLIw6deogCALfffcdo0ePvu/+wSgT988//9C6dWv279+PXq/Hx8eHiIgI3NzcsLCwICcnhw8//JAaNWrQr18/qaL8jz/+4J133gGMBrmHx9NvyPAolBiUJdyTzp07s2XLFipWrMjNmzelh1fB7hUAgoUl3qNfHvHVFxnRoAeDnuzQ38kOD0Gv16PX65+LZImp57qFq88LKWtzP6PNRPv27fnrr7+kv4sSywajwWFVrfUTrbouWOVsumdK7hUj95MXgn+9fe3atSM7Oxt7e3u2bdvGjz/+KIlLOzg4kJGRYXZvLFy4kPLly9O2bVtpmlwuJyMjwyzf7+LFi2YVwabQ4q+//soHH3xA06ZNCQsLY9OmTdjZ2TF69Ogie5Df7ZV0cnLi77//xt/fHzCKezdr1oz09HRGjRrFN998w4ULF9izZw/ff/898fHxODg4kJaW9tjezZo1ayKXywkLCwOMeog1a9Y0C+WbaNSoERkZGfzzzz9m01u2bMmePXvQ6XRMnTqVr776ivr167NixQr8/IztdJOSkhgyZAgbN26U8glNYy9dujQVK1YsJJuWm5vLokWLmDJlSqF8yJ49e7Ju3To+/fRTvvrqK+n3NOW1durUSSoIOnv2LM2bN8fR0ZEtW7ZIv1txSE1NxcXFhRkzZrB8+XIuXbokGZTW1tb4+Phw69YtSaqqUqVKrFq1itq1axfalsFgwMbGhvz8fFq0aHFfeaqC5/zEiRNYWVnh7+8vabWeOHGCbt26IYoiarUaKysrdu/ejaenJwMHDuTXX39l48aN0sfQsmXL6NevX7GO+VlTUuVdwj25ePGiFHrIyMigc+fOgFGOQSaToVAokMlkOJau8Mq/IJ8UgkyOoLDAptUHzNoZTn5+Pnq9nlOnTgEUko2QyWSSSPCDMH3hAvzxxx+SALBSqSQgIID169fzxx9/sGrVKkbM/QPvD3/FwtXHOK4X8PcVBIGMYxvQ36dKu6AxCdwzP8tgMJAVHkzqweUAj/xyN62XenCZmWSO6QNM4eDxQp7LZ03B5gI2NdqYzZPJZFhaWvLTTz+xceNGSR/xzJkzjBo1irfffht3d3fS0tLMjElfX19Kly5tZkyCsRK4oDF569YtqlevLv29atUqyShp2bIlBoOBo0eP8sknnzBt2jQaNmxYpDEJ5p54W1tbqb0nGK+92rVrk56eTtu2bQkNDZVyCSdMmEBcXByiKJKamvpEcjHPnTsnGZNgVI9Yu3YtZcqUkeSKTJw6dYrLly9jYWFhpkW5f/9+GjRogEKhYPr06Rw6dIjExERq1arFkSNHAKO26/r165k6daqZMalUKklOTqZ9+/Zm+9qwYQNeXl6MGTOG3NxcevXqJc0rVaoU8fHxiKIopRmY6NSpE6VLl6Z9+/YYDAZatmwpzTOFvOvUqVPs8+Po6Ej9+vUJCwvj4sWLfPnll+h0OgRBwN/fn6ioKPLy8hg0aBBBQUFcuXKFOnXq0KxZs0K/v0wmo0GDBuj1ekJDQ83aYN4LQRCk3ElTv3YrKyuz3N9SpUoRHh4uddm5u/936dKlC017kSgxKEu4JyZZGz8/P9RqtSTmOnnyZNzc3NDpdBgMBrI190+iLuHRmB0cwdqTtwCkB9rd+UZ2dnZSztCD2L17t/RvvV5PUFAQcrkcOzs7Ll++zBtvvEHPnj1J8azHljgbDC9oR5aCODTrjfeY9Xh98AuOrQejdPa+57KCIJi9PMHovbpw4QIRERGEh4eTdXwDyTvngy7fWIz1EIh6HaIun+Sd88g4ur7oMdynY86riCiKOLYZgsLeHTC+YKdMmUJUVBRDhgzh8OHD5OXlERQUxMiRI6lYsSLp6elFVlXXqFGDN99802xalSpV6NGjh/R3SkoKfn5+kuHyxRdfmBk4poIHS0tLJk+e/MDwtpWVFaIo8tlnn6HRaJg9ezZgNFI7dOgghZx3795NWFiYWQhaqVTi4uKCj48PFStWLNRxC4zXrLOz82N9hNy6datQhMPCwgK9Xk9+fj6enp5mOYCnTp0iKCgIMEozmQS2X3/9dclgvXjxIjNmzEAURZRKpTTW3Nxcad309HR69epFjx49SEtLo3Hjxty4cYPx48dL+8rIyODAgQMA1K9f3yxEnpKSQvny5SX9yIJSSKbjedhORq1ateLQoUMAfP7551y6dAlPT0/CwsLo2bMnEyZM4Pfffyc5OZkdO3ZQsWJFDh8+TOPGjWnVqhVRUVHStkzdmLRa7T3TFgqSlZUlGfamD9u6deuaGZSm0P2OHUZVBFN1vum5/yJ1biqKEoOyhCLJyckhNzcXURSlG8f0Mu7evTuxsbEcO3aMfv364ep8b6HbEh6Pz7f+Q6/3hzN8+HCAQp4G05dscTBVXAqCwP79+6XppmTxX375hT9O3npphNVNCIKA0tEL29od8Hr/J9x6TpUMlIJUrVoVHx8fs2n16tWjatWqVKhQQdIIzP1nL3d+/QjNLaN+nPgAw9I0X3MrnNhfP7qvmLeoLyxr9CojCAKCTI5Tu6GA8bnzzTff0KpVKzp16sTHH3+MnZ0dq1ev5tChQygUCsnrbDJkTGzduhVRFM3kYApKcYExj82UuuPs7Mznn/+be7tx40aWLl0KGD1F/fr1Iy4urpCmIxhD2+7u7uTk5DB9+nTGjx9PYGAgX375JUqlkj59+jzQ66jVaklKSiIqKoqIiAiSkpIkw1GhUGBhYYEoitjZ2bFq1SrAWJVdEKVSibOz8333UxTZ2dnSvm7evIler6dMmTKA8Rmzd+9eKcRqY2PD1q1b8ff3p23btgQHB9OgQQPy8vL4+eefpWeJKRexevXq7N+/n6pVq7Ju3TqUSiVz587l0KFDlC5dmsqVK0u/nUajoXXr1oBRks3UrhCMeY9gDD0DUs5npUqVuH79+kMfMxjTJAqma1WsWJEbN25QtmxZlixZgl6v5+jRo2RlZdG1a1c+/PBDZs+ejUKh4MiRI1SvXp1ly5aZ/bYKheKB40lKSuLChQvSO/TKlSuAMU3hyJEjxMfHk56eLv2WR48exd7eXrqWT58+jYeHR6Hn/4vGiz26Ep4bpvAG/PslZrq4TV9XDRo0YNmyZVw+eegl8GW9nGjytezNdJNeagqFAi+vxy/oKGhQKhQK3n//fabMXsDkzefvs9aLjalqWu0bgNcHv2Df+G2z+RcuXODKlStm3h5T0UdaWpqU0qHX69GlxxP/x2fcWfwhmWE7iuxnL0r97Hdwe/EQ3vFI5td537B48eJ75jjpUmP/02Lmj4IgV2BZtjZLN+2ievXqGAwG6tWrR0ZGBhcuXCA3N5eZM2cCSCLR1apVw9bWFmtra7NtyeVy6fkkk8nMwt9Lly41K+BZvXq19ILeuXMnb7/9NnK5HBsbGypWrMjy5cuxs7MrVEHu4OBAq1atiI+Px9PTkxkzZmBra8vevXsxGAyFir5MoU4TarUad3d36tevT6tWrQgMDMTf3x9HR0fJA6XT6aT93rx5k169eiGTydizZw9+fn7S88DLy8vMEDQVa3zwwQfMn39/KayC4XqNRkN0dLQkFefi4sLGjRvZtm0bYIyE/PXXX7i4uEh5rd9++y2DBw+mSZMmZtvdvHkzrVu3luR5Dh8+zMiRI5HJZCQnJ9OnTx/JqPfz88Pf3x8bGxtSUlIoW7YsNjY2ODs7S7+VaYxhYWEYDAbatm3L7du3AYrUnX1YVCoV58+fx9PTk6+++or9+/cTFhbGoEGDGD16NDExMRw6dAilUomNjQ3vvfce3bp1kwzT4hh5e/bsMeZ8///ej4gwfrRbW1tLnuLMzEyp2vvIkSOUKlVKWn/37t0EBQWh1Wpf6JSZEoOyhCIpmGR8t4fybikha5WCMk5WlPDkEWRyLMvWxqdGAzOZk4el4ENPFEWuX79OZGQkrq6u3Llzh2nTpmHdYQLFUEF64REEAQQZDoF98PpwSSFvZUGD7sCBAzg4OODo6CjpU4Ix36pevXqI6bGk7vkFw9bPiZ7TgztLhhO7bAx3lgw3/v3LYHIOLUNMiyU5OZl3332XQYMG8fvvv1O1atXCEk1aDbq0OEq4C4Oek2lqgoODJaNOEATs7OwKGeCCIHDhwgVSUlIKCXYX9CaWLl2aiIgIqbPKkCFDpHn+/v5SaPbrr7+WwtNbt27FYDCwa9cuKlWqVKiAxORZ27BhA2CsuC2qs4sJV1dXRFGUQspr164lNzeXuLg4jh8/zt69ezl48CCXL18mJSUFrVbLl19+ia2tLfXq1cPV1VV67hoMBjIzM7l27ZrU/jUqKorJkyfj6OiIwWAgMTERmUzGL7/8woABA2jUqBFg1Pwsqm2f6dya/m/qtZ2UlIRcLqd///7SPGdnZ0nzsWHDhowbN07ajlqtlq71t956Cw8PDzQaDTt27JCMpKSkJAIDA9mzZw/vvvsuAHfuGD/UnJycyMzMJC4ujlatWqFSqYiNjSUjI0PablxcHKtXr6ZVq1bStOPHj9/z3D8M1tbWXLx4EUdHRz755BNiY2P54Ycf+PHHH5k7dy67du1i+/btpKamUr9+fUJDQ9m8eXOxt//XX39RoUIFKT3J0tLSbL6DgwOWlpb4+vpKx2rKfY2Li+PcuXPUqVOHmJgYatas+USO+WlQYlCWUCQnTpwAwN3dXXoY3sugBGjp71bipXxaiAayPAJwd3dHr9ffU2/ufhRVJe7v749KpUKj0TAn+BJyhxdTiuJRML1wlPZueH2wGLsG3e+5rCm0ZnrIy2QyJk6cyNmzZ6Xcpbi4OEStBm3CTfJjI9Am3JRkmXQ6HSqVitWrV9OvXz+6detG+fLl+eeff4r0RuZeP/nAMPorh0zOn8ciaNWqFXZ2dsyfP58DBw6QkZFRyONXpUoV3NzcgMLXdcFn061bt6hRowb29vao1Wry8vIkg9CUV/nDDz8wceJE7O3t6du3L56enuTk5CCXy6WwZEG0Wq2UZuLn5ycZaYIgSJ5CMHr0wBg6X7VqFUqlki5duhSr//Prr79OZmYmbdu2JTExkZMnT6JUKqV9mYxm07U5duxYypYtK03z8TGqMrRt25YPP/wQMKYSxMfHS6kzBTEV5oiiyK1bt6SPz2bNmpGcnMyUKVMAoz7i6dOncXFxISwszCzMW6tWLbPinMTERDZt2kRgYCBgzJVs164diYmJ/P3338yYMQNBEEhPT0cQBDNvZuvWrYmPjycrK4uzZ8+i0WioWLEi5cqVkySMAgMDUavVRYrW348bN25I1052no5/7qRz5lYq/9xJR2lpw+bNmxFFkbfeeovsPB3NO/dm1LS5zPxpOcdOn2HDhg2EhYXRtWtXqRBHp9Pd10t59epV1qxZg7+/v7Rc3759gX+jfgqFgpYtW7J3714EQaBv375cvnyZgIAAfv75Z5RKpdQ287XXXnuoY36WlBiUJRRJREQESqVS8k7CvwZlUZWyvRuUoSSQ95QQZAhe1czEjAtydy5ZcdFqtVy6dAmFvTurw9OM2pr/RQQBx5bv4dLl0yJnKxQKbG1tyc3NlUJ+X3/9NVqt1swgvFeoSa/XS91KVqxYwaZNm8y8nXeTdeavV07UvDgoHD25fO2m1NqwYAGCQqHA29sbd3d37Ozs7tmK724Jn+bNm9O+fXtpuilEOmPGDMqVK8eIESNo164d6enpvP322/z+++8AuLm5FfkxYMrnc3Fx4dq1a+j1egRBoEuXLmbGmKWlJba2toSFhREVFUViYqJUsPMgAgICcHZ2loTez5w5w4QJEyTD2lTZbHoe37x506y6OzY2FkEQOHHihGQ4h4eHY29vz/z589m6davZ/kzeTtPYTYZrdnY2jo6OzJo1C51OJ7XH3LJlC+7u7mYeyoLSPbm5uQwcOFCq9s7JyeGNN97g2rVrBAcHU6lSJUqVKkWrVq0Ao5xPcnIyfn5+JCcnk5eXh7OzM7a2toiiyJ49e2jbti35+fkkJSXx1VdfMWPGDDQaTSGdyvuRnadj1z9xOHUcS6Ov9lJ1ym46/HCYLj/9TYcfDlN1ym6G78+l7MhVxDceJc3/M9sPrwE/8FNiBSafhCYTlrDm8EVQqCR92/tpVo4dOxZPT0/pvWlnZ4e1tTWenp5SgVBSUhJjx45l5cqVyOVyPvjgAyIjI6lduzbffPMNo0eP5tixY9SrV++RcmafFSUG5f/RarXcvHmTAwcOsHTpUr788kv69+9PmzZtaNq0KQ0aNMDa2thlpG7dujRq1IjAwEA6duzIsGHDmD17NuvXr+fkyZMkJia+1HlSoiiSkJCAQqGQtMfg36+pojyUFdxtaeZXuEqxhCeDwtETQflvhXKDBg2Af8V4765eLg6ma9Sp3VAQZE9UqP1FwmQIWvs3xrUIo7KgB8x0TkwFTAWRyWRFFmiA0RNVrVo1ZDIZ9vb2bNq0ifDwcLZu3Yqnp7ngvzY5mtybYUaB7xIkBEHgncEjpA+kgooGOp2OmJgYrKysqFChguSRu3v9gnh4eKBSqbh27VqhXLtSpUpx8+ZNlEolu3btQhAErl27xvr161GpVEV+EOzbt08ykpKSkiQjd/bs2VKuoclj2qtXL+zs7BAEgU2bNtG1a1dJKuZByOVymjdvzrlz5wgICCA4OJjPP/9c6uBiukYrVKhQ5Ppjxowx5vdqtVKhh16vlzQnO3bsSJMmTczyT7Ozs6Wxm3I3T58+Tc+ePcnNzWXGjBnExsbSpUsXGjduzKeffsqWLVukRhemnEATBbc9ZswYTp8+zc6dO6lVq5Y0/aefjO1HN23ahI2NDd7e3sjlciZOnEjv3r2lyMGPP/5IUFAQMTExDB48mK+++oqYmBhatmxJSkoKq1evvue5vBqfyZSt/9D4G6MBKTYZRKzSi9iMwu8wgHSNDoOlPQqbwv2yBUEgNQ8i9U44vzkR7zHrKTN8GY6tB7Nw5aYitxccHMzWrVsZMGCA1E9+6NCh7Nmzh2bNmnHgwAHkcjndunXj7NmzJCQkSP9WKpX89ddf2NnZ0bt3bzZv3lxIxeBF45U1KNPT09m8eTNDhw7F398ftVpNuXLlaNmyJf3792fRokVcunQJBwcH/Pz8qFGjBkFBQSiVSkniQRRF7ty5w9atW5k8eTJvvfUW9evXx83NDSsrKxo1asTUqVM5duzYI/VpfV7ExcVJkkBFeSiLMigBZnapXhL2fkoIgsAXsxdIreTi4+MpXbq01MO2a9euj7RdpbM3lmVrv9CJ3k8SK//G2DXoVmi66eVVFAXPTcECDUEQUCqVCIJAfHw8CxcuxNLSEgsLCwYOHIhGo6FVq1Y0bty40DZTdv2IaNC/1B+eT4MNmzYXWWjx888/I4oi3333HcuWLTMTJTd51O6WVElMTGTBggVMmzYNgKZNmwJGYyctLQ25XI5Wq0Uul6NUKhk5cqSkDWnC9NtbWVnRvHlzFi9eDBijAr6+vixYsIBJkyZJxphpXyZvX1JSEqdPny6kjfkgTG0D/f39uX37NkqlUjImTBqMBT1VBXMYZ86cKUkQffjhh5JxVzDX8/XXXy90z98d6TAYDNK1+/PPP5sd3+uvv47BYGDv3r1cunSJFStWSGF+CwsL9u3bB8DZs2f55Zdf+OqrrwrdBxUqVKBhw4ZoNBpsbW2JjIxEr9dTu3ZtfvrpJ+m9HBoaiqWlJQ4ODuTl5dG3b1/69OlDnz59kMlkDBkypJAOZHRKDn1/O06buaEsPxbJnbSi31mPgyAIGNQO2NV5gxOuben0fTDRKf+OIyIign79+hEYGCgZzw4ODrz11luEh4dz8+ZN6bpp0aIFY8aMwcLCgjlz5jBv3jyqVavG+vXrmTlzJtOmTcPZ2ZkRI55c44WnwZNrXPsSEB4ezsaNGwkJCeHEiRPo9XrKly9PmzZt+Pjjj/Hx8ZH6pN65c4fw8HDOnTtHTEwMycnJ0g25Zs2aB+5Lo9Fw7Ngxjh07xhdffCFNl8vlWFhYYGlpiaurK56ennh7e1OlShUqV65M2bJl8fX1lW7O54Gpy0FeXp6ZQWnyztzLoPR2sqJvwzIsP3br6Q/yFWTm19+iT7iOXC4nMjJS8ljo9XpWr15NhQoVitUWriA2Ae2lXuOvAqIo4tD8XXIuH75nd527u5+Y/q3X6wkMDCQ9PZ1z584hiiL169fnyJEjGAwGZsyYwbRp0xgzZgxly5alVatWUou4u7epS48nNeTnJ9qZ57+AqNciKNVYuftgbWuPu6szF47ul1QJTEZQQXkdmUwmFawV7ISUmppKrVq1pCIGk64lGD1yJ06cID8/n549e6LRaEhKSgL+/WgwtQTU6XS4uLjw0UcfSV1U3N3d+fnnn3nzzTcxGAxYW1tTuXJlM8F0U7hWFEXatDEXby8uBY2+Hj16sGzZMqka+7XXXpM0HE3PZFNfctOx7Ny5E3f3whJaZcuWJSsri4CAAM6cOQMYo3QymcwsL3Xnzp1YWVkRFxeHra2tFNotU6YMlSpVIjg4mBUrVuDr60u5cuUICQkhPz+fM2fOEB8fz6hRo6hUqZKUywnG0HNkcjb5OgM/rPyTRtX8iI2NJTY2FicnJ/z9/bGysuLkyZPSPdOlSxcmTZrExIkTOXHiBJmZmQwdOYZuH4xjx8HjNO41il/nfkVlT3u2hd/hi63/oDMY1zU87W82wfjxEB6nofX3B/myU1Uau0Pr1q1xdHTEyspK0k1dsGABn332Gba2tlIufPfu3Rk1ahSCIPD111+zZs0arl27hlwup1evXpQtW5YNGzawYsWKB/YMf9785w1KURTZvXs33333HXv27MHR0ZHXXnuNhQsX0qZNG7y8vDhy5Ah79uxh0aJFnD17tpDXwMLCQrrQ7e3tycnJISkpibi4uPtW+BWFXq8nNzeX3NxcUlJSikz8BuPXZ+XKlQkICKBTp07SF+vt27cpU6YMSqUSpVKJnZ0d3t7ej5xHVxSmhxRgZlAKgnDP1nUmpr5ZnXWnY9Bo/wPlwi8YNatXpZRVFTQaDbt37yYtLU1q2wY8tDEJYFm+7itjTIKpAlzApeNY4laOK3KZu+9/a2trcnJyEEWR06dP4+zsTM2aNTl37hxHjhyhTZs2hISEsHv3blq1asXIkSMlyZYbN25QunRpqUlAwRd2VngwMmsHHJu/mG3UnjWiKOLaZRJKB3cp/SID8G70EaFpsTi2tiPu/w6gqlWrsn79etzd3SUJGTDmTZrkZnQ6HfXq1ZMKDE0evezsbAYPHiwJZpuen6GhoWbjKegp1Wg0kpdOrVazZ88e3n//fTQaDb6+vqSmphIUFERYWBiOjo5UrlyZ2NhY/v77b3x8fB5amaEoz/WNGzcAJKO2efPm0jxTa8+3336bL7/8ktq1axMREUFeXp60fFH89NNPNGzYUPr77iKntWvX4uXlRU5ODk2bNjUzcFu1akVISAiRkZHMnj2bqVOnmq27cOFCDh48yK5du4hM0bDqeAT7ryRwKyXHLN++1Kg/0KbGknv9FNrL+1mxYgWHDh1i/PjxHD9+HEEQyMzMZOnSpVSoG0i/RfuQ1RiMe8UBnBAEXDs1Iw3o/vOxB53Wp4tMTp5OzyebziO/YBQmt7S0ZNeuXajVagICAnBxcWH79u3SKi4uLqxduxYXFxecnZ156623qFixIjKZjNdff52vvvqKVq1a0bBhQzMB/heV/6xBmZ+fz+rVq/nuu++4cOECderUYc2aNXTv3h2ZTMbOnTsZNmwY+/fvJzc3F6VSiU6nQxRFSpcuTbt27WjQoAE6nY7Q0FB27tzJ2bNni71/CwsLVCoVSqVSMsBML6XikJyczOHDhzl8+DA//PDDfZeVyWR4eXnh6+tr9l9AQAC1atV6aDHUM2fOSB6VgjmUULiPd1Ese68+PRc/55v7P4Yoipza/xcntebn3hS6u7uDTnEQLCxROHg+eMH/IKrSlbErXZGMmAeLuGdnZ9OvXz+WL19O7dq1OXToEKIo0qxZMw4dOkRISAilS5fmzp07TJgwgerVq0saf15eXsTExKBQKFAoFIXunZyLB7Fv8g6CXPHKpB3cD6VjYaUBk3C9onYHvj4v58hvx2kfYJTDuTvXtaARKAgCXbt25dKlS6SlpZmlNXz55ZfSv3NyciR9SxOmPtImTB4/MErwXLp0iUOHDuHq6sq+ffuoWrUqDg4OrF+/ntatW9O2bVtEUSQyMvKeebf34+rVq5QuXZqsrCzJWbBu3b89zx0cHMwMRRsbGzQaDb169ZIkcEypAKZzVFQxx93P97sdBjY2NtKx3+1ldXR0JD09Ha1WS/ny5UlOTsbW1haNRoNWq+W3336jWsMWrIxx4NCBUOQyAX2R7sJ/f1+hbieso84ybsrXHNi/n6lTpzJz5kwU9u6k1nkPtXc14yp5+hc059s4Jn21DmTHRxNzagd2dnbk5eXx9ddf062bebpNeno6gYGBhIaGsmTJEtq2bUtOTg6BgYEsXLiQdu3aodFoWLNmzQsvag7/UYPywoUL9OnTh3PnztGxY0cWLFhAYGAgeXl5/P7778yZM4fLly9TpkwZ1Go1ubm5NGnShJ49e+Lm5sbff/9NSEgIv/766wP3ZWNjQ9myZalYsSKlSpVCo9GgVquJjo4mLi6OZs2a4ejoiIWFBfb29pQqVYobN26wadMmjh49WmQPUJlMZiaCejdKpZLAwEAGDBiAq6srUVFRREZGSv8dOHCA27dvI4oiLi4uvPbaawQFBdGmTRtJZ+x+3Lx5EysrK+RyeaGH0P0MSq1WK2mrOWc4kGznV+RyJTw8utRYSabmbh7FmIRXu6+0KIp4t+yFw40QMxH/e7F8+XIsLCwICwvD29ub5ORksrOzpTSDmJgYnJycSE1N5fz58zg7O1OhQgWOHTN+WOl0OmPfYKUalUtpREGOQZePY6uBxm4xr+jvUJAHnQOTJ/1QRDyHrhiwqRFk9PLKZKhUKnJzcyXjx93dnfj4eL7//nsCAgLYv38/w4YNQ6FQoNPpSExMxMPDg2vXrnH+/HlEUUQul2NpaUlWVhZ16tTh2rVr3LplTN8xGAzSut7e3gwZNhKlW1lmLvqFDLkdWNoRnSlyNjqNDn0+wNHVgypVqty32v9epKWlcfz4cfr168fEiRMZNWoUgBSaBhg3bhx79uzB0tKS3NxcMjIycHBwkIpeTPmkBVMtChYFpaWlIZPJCmlT3h190mq10rSiomC5ubn4+vpKXuJ27drx3nvv0aFDB9Kdq6IM/Ii/byQD3MOY/BfT72tRuhq3vSrz3rRfWDNjBlbVWvPLmQx4ySIpNi0GISREkH77BgsXLqRjx45m6gTu7u5069aNhQsXMm3aNIYMGcKdO3do3bo1y5Yto3PnzsTHxxMaGirpU77o/KcMSoPBwNy5c5k4cSJ+fn6cPn2a2rVro9Vq+eabb/j+++9JTEykYsWKWFtbc/v2bd5++20GDBjA7t27+eqrr7h165ak+VQcsrKyOH/+POfPF91h5O6m8kVR8KY3yRCIooharaZevXokJSVx6dIllEolWq0WrVbL3r172bt3Lw0aNGDKlCkMHDjQ7IFsyuEMCQkhODiYgQMHAsbOCg0aNKBChQr8888/eHp6olKpJEkaFxcXUlNTsbKywtramgkTJlCrVi3s7OyknKLY2Fjy8/NRKpVcuXKFkJAQQkJC2LdvH9nZ2dL5c+4+BcuyAWYdGUp4eGQC1HJXUrN9e86fP098fPwT6RBRVHvCVwVBEEizLs0/xTAmwejdSU5OJj8/n5ycHOrWrUuZMmXYuHGjFNI2eYKUSiWZmZmSMal09sYmoD2W5euicPAsuRceF5kcUZTh/PoIZNYOZBxdVyj1qEKFCsTHxxMbGyul7aSmpkofX3/88QcODg40b95cetaPHTtWSlVwd3fH0dFRMigBBHtPHAPaE2rfDFWfbngJAtNP5MGJwzj2X8SfGgH3vrP5NQZ+nbIbdccvSTwfilXMw+nGmjruuLu7k5aWRtu2bfnpp58kGSGAjz/+mJo1a6LRaCSv4qxZsyQvVkiIsf2nl5eXZOwtXbqUAQMGAMaK9QYNGpi1qbwba2trLCwsyMvLu6dzIz8/nyZNmkjn6Y033qBatWrYNXoLx+b9MIgi4kMmMQpyBcjkHNWXo8bIxWRYeRmnvWQIcgUWrUcyUBHOkCFDzGwKT09Phg0bxqRJk+jduzczZ84kNzeXCRMm0Lt3b9q3b09UVBT79++XpKpeBgTxP1JmmJCQQM+ePTlw4ABjxoxhxowZqNVqLl68SN++fQkPD6dLly6cO3eOmzdv0qVLF5ydndm5c6fU8N3UP7Uor+HzRKVS0a5dO2JiYggLC5O08gpSpUoVpk6dikql4uTJk9y4cYPz588TFRVFenr6U6kmLRhqNf1boVDQrFkzgoKCaNiwIatvKAi+mv6ALZXwIPaMDsTPzdZsmsFg4OLFiyxbtoyFCxc+9HXr8d48VB7lH7zgfxRRFIme0+Oent+7cXd3JyEhQbqXFi5cyN69e9m0aVOR95fC3h2ndkOxLFv7lSp8etZk7f2ZwNIKtm7datSFVKqxL+1HjiYfUa+VvPs2NjZSmNje3h4rKysSExMlBY7x48cze/ZsDAYDPXv2pHbt2kYh7YK/o2hAEIofejR9THvYWvBt91oEVnR94DrdunXj4sWLvPXWW8yfP5/bt2/j6upKbm4uoihibW3N5s2bpRC0IAiUKlWK6OhoqeBl0AdDOH3iONaGHNKSjAUharWa5ORkLCwscHV1ZdSoUbz33nv4+vpKXvKC75UyZcowduxYqbJ40qRJfPTRR5JTY+TIkezfv59OnTpx+vRprl27Rl5eHp8v28UfN0qudRNxf0wiL/IcCoUCS0tLPD09qVKlCps3b8bFxUXyqi9YsIDc3FwmTZpExYoVWbVqFTVq1HjOo384/hMGZWpqKi1atCA+Pl5qzWQwGPjhhx+YMGECZcuWpXXr1ixatEjKNdFoNFKCvIuLC2q1WkqcfxhUKhUuLi6UKlUKhUKBg4MDFStWJDo6mv3795OSkoJcLkcUxWJ7PR+Xgh5PlUqFk5MTarWa/Px84uPj0el0Zp7QcuXK0aVLF/z8/Ni7dy8bNmxArVZTunRprKysiI01tpW71/jvNsQrV65MmzZtaNOmDc2bN+diYj4frDxNWu7je9VeNeQygcblnFkxsMF9l8vKymLMmDGSrMmDUDp74/X+T09iiC81d5YMR5tw84lsq6DBYlMjCMc2HyDI5C+ld+VlQRRFRF0+AZknOBadhaJMzUJeYFEU0aUZiz6yzvyFLiXGmPLg7U10dDRWVlYoFAr69OnDwoULAfCvWoNvFi7hvcnzsK3fFUHxuDmuIiDgZqtiyhtVaO7vhrWq8HURGhpK8+bNWbhwIZMmTaJHjx7o9Xp+++03aZlGjRpx7tw56XmrdPbGrk4HVGXrFEpjufvYtcnR0jwLCwvkcrnk3b27wlsQBGrWrPlQtQOl/Gsg7zgF5Mon4ol/2aNboiiiz0jEN/w3bt++jUajITs7m6ysLERRlPqDf/XVV6xZs4bQ0FA+/vhjpk2b9kjaws+bl96gzMrKok2bNkRERBAaGkrVqlXJy8ujR48ebNu2jQ8//JD9+/dz+fLl5z3U54pMJqNevXoEBQXh7++PUqnk2rVr7N69m5MnT6LX66lVq5ZUEQnQuHFjatWqRenSpTlz5gybNxfWiLO2tkaj0aDX6ylbtiyBgYGIosjBgweJiopCoVAQFBTEmDFjMHhUYc6eq0TEZ6J/qa+6ofZ7egAA79hJREFUZ4QoolLK2TO6Od7F6JWek5ODn5+fVOV6t1SNCUEQcOowBptqLZ/4kF82YpeNIT/WvDCnbNmy3Lx5k+nTp/Ptt9/esyvL3QhKNQpHT2xrtce29usv/cvwZcF0ngVExPso4Zq8xLk3w0jZ9aMkGzV//nyOHTvGjsNhqKu1Ru5d45mkJtiqFLxWyY2hLf2o4G6LXq+nbt26KCyt8anbmkM30who0Izjh/ahuXaCvOgLZt70h/GA3+vYHwaZTMb69euxsbHh2rVrDB06lFKlSpGcnIyXlxc3btyg7tilJMgcS7zxdxH760foU29LnZW8vb25desWzZs3x8LCgpCQEHx8fFi6dCktWrR43sN9ZF5qgzI/P5/27dtz8uRJ9u3bR926ddHpdLz11lts376dMmXKmPUbfRhMCdhgzIkyJTRbWVlx6dKlB1Y6vwzY2Njg4+ODlZUVkZGRUuN6E1WrVkWn00ktxkyYqlc9PDxITU3lxo0blCpVCi8vL06fPo2dnR2DBw/mjTfeIDw8nMWLF3Pu3Dlq1arFmDFj6NmzJ2kaA3+evc3BiEQi4jNJzsovad1YBAHaiyz9bDD29vYPXLZv376sXLkSgGHDhnH16lV2795dyPMAUHrUWuRq66I280qRsPlrNNdPFRn2HjFiBFu2bJFSYoqiJD/y5UPU6xANelJDfiYrPPiFSE0o72JNemoiiVoVgkwGRRjGoiiCKKKJvkBeVDh2jXo8tAfcdOyaIytIObntoQv6VCoVjRs3Jjs7m3PnziGTycjLy6Nhw4acvBJdEvUoAlEUyTrzF5n7F+Pp6Sl5xV1dXYmMjKR69ep8/PHHvPPOO4+kCPAi8VIblN9++y2ffvop+/btIzAwkJSUFAIDA6UWUyacnJzIycl5oBFoehm8jKfkXt4oMIY2TK24CtKqVSsWLFhA5cqVEUWR1atXS90HDAYD3333HVOmTEEul5ORkYGfnx+CIGBpaUmLFi04efIkx44dQ6/XY2VlRW5uLtbW1tSpU4ewsDA0Gg0DBw7km2++4eTJk8yePZtdu3bh5eXF9OnTee+996RzXlDs1kIhQ6cXWX4skh3nY19ZTcvUg8vIOfkntra2jBs3juHDh99T2NZksIOxIOHKlSsIgsClS5eYM2cOGzdulNqkCRaWeI9eV2L8/J/7hQUbNmzIjz/+iIODA23atJG0AF8EI6SER8fk1cy58jeW5esiUyjv6918EXlUD7hpvdSDy8k4uu7BKxQTx9aDsa3doeReKAIxO4VbP/RDrVaj1WrR6/UEBQXx8ccf06ZNm//Ms/ilNSjj4uKoUKECAwYMYMyYMYwbN44NGzZIRpXJKLpbo89UKf0qYDoHpUqVkir9ypUrR2xsrFlVpFKpRKVSkZ+fb9ZariByuRw7Ozu0Wi1WVlYMGTKEFi1a4OPjw/nz59mzZw87duzg5k1jPlqZMmXo3LkzS5YswdXVleXLl9O0aVP++ecfZsyYwZo1a3jzzTf55ZdfcHNzu+9xhEensfjwDU5GphCXcW9R9aeF6QFs0OsQ4KnmxMllAgqZwNROVfluaHdsbGyoWrUqP//8Mw4ODkycOJEhQ4ZgaWlpNr6AgADOnTsHQFhYGAEBAYWO4fz58wQHB7N0+0GyGg15asfwsnKvsKBKpaJRo0bExcVx+fLl/+dHDjFqR74E2nAl3J9XOTUh+a/5ZJ0LfuztyOVy3Af9hNLR6wmM6r+HKIok/dSP15o3JSgoiLZt296zF/vLzEtrUA4YMIDNmzfz7rvvMn/+/EIl+aY8suJwP+/e88LUJ/heBt79eGSh63ucB5Om2f226eTkRKtWrXjvvff4/fffpcrXevXqodVqOXfuHOPHj+fLL79EpVLx559/MnjwYGQyGYsXL6ZTp07FGmN2no4LUfG8238gmvx8fl28GJ3Cip9Db3AmOu0+4rmPxr/hsUVoosKfnlfKoAeZnKZ+LnzVpTreTlasX7+et956i927d+Pv78/06dP5/fffcXd3Z9KkSQwaNAgLCwu2b99Ox44dAaPX2dQ6816sOXadiVte7Zzi+yHqdSAaSAleRFa4+cvWoeUA7Bs8Wt/0Ekp4kTAVNMX++tEj5VQWpCTq8WC2fNiQmmUKi8v/l3gpDcrIyEjKli2Lq6sriYmJODk5YWtrS1RUlFmlZUFeRKPxSSKXy7G1tZXa8N3d6eFuTALldnZ2yGQyaT0TJo9ljRo1CAsLk/TNevToweXLl+natSunT5/m+vXr3L5926wLhUwmw8PDA4PBIOlb1qtXj7Nnz9K0aVN27NiBpaUl8fHxDBo0iO3btzNz5kwmTpxY7OONjIykYcOGlCtXjq1bt3L06FE27fmb0FgRjWN5FI6FKz31WSkIShVytQ2iwXBf75LJYMyPOofm0O/kp8ai0+nIz89H4VTamDdXrm6h/Zj2VdwHqyiK6FJjyb1xiswzO9Elx1C7dm0WLFhAw4YNad68OcnJyZw9e1YqpJo6dSorV66kTJkyfP755+zevVvqorFy5Up69+5daB9Xrlzh+PHjzJ49m5uiKy4dPy7uqX4lKSos6Pz6aGxqvPacR1ZCCU8O0WBAE3WWhLWfP9Z2lG5l8Rpw/45urzp/ftiYgDKOz3sYT5WX0qDs06cPq1atAuDtt9+mQoUKTJs2rdBy/3Uj8m7uZUzb2tqSmZlJly5d2LVrlxTuFgQBFxcXEhMT8ff3N+srLggC8+bNY8SIESxZsoT+/fsD8P777xMeHs7x48fN9pGfn8/SpUtZtGgR//zzj+RZNf0GCoUCV1dXUlJSaN26NX/++acklD5lyhSmTp3KDz/8wLBhw4p9vFu3bpVaWel0Onx9fWnRooWxtWbwXpLzZQhypZkWHRQopCjCIDTl02munybjzA50yfeXkjJV9pr2Y+XfDLv6byIoLKTjL7Bx4//1OrL+2UfG6e0o89LQ5WYX6YkWBIEKFSoQERHB119/zYQJE6R5Fy9eZMqUKaxfv96s6CYuLg53d3fu3LkjCc7v3LlTyp8EsPKuimvvb4p9nl91knfOQ+HsjX2Drq90eLSE/y63Fw954LPuflh4VsTz3TlPcET/PXYMb0pVrwcXV77MvHQG5Zw5c/j444+RyWTs2LEDGxsbmjVrVmi5exlXxaFghfe9MLW8srCweKSwdEEcHR3NXviPS1FVvabpU6dOxcHBgenTp0veQ0EQkMvlZsesUCiIjo5m9OjR7N+/n4iICOzs7Bg+fDihoaFSvt69SEhI4P3332f79u3SWEznSiaT0b17d1avXi1pdI4dO5Y5c+awdOlS3n333ftu+9SpU3z33XesX79eapX29ttvc+fOHY4cOVIoNG9tbY2NjQ3JycnSMarVary8vNDoRBI1oLKyIS8n654tDk292QVBID8/36zA6+4PF7lcbmyfaeeJTa0gZBZWGPJzyDobjC75FrVq1aJ58+a4uroSHx/P/v37uXDhwn1/OwB/f3+2bNmCv7+/NM0UFgfjb/bGG28QERHBxYsXAcyEc2vXrs2kSZOYM/9HohuOLjGMioEoimDQl2hJlvCfJjfyLAl/TH7k9Us8lPdHAC5MaVuk9uh/iZfKoPztt98YNGgQAJ9++in16tWja9euhbyQxfFMPozBaWNjw6effkrHjh3x9fXF0tKSKlWqkJWVRWJiolmRz/32fb95nTt3JiQkRGqvZXrZy2Syh86H/P7774mJieG7776TDN9mzZpx6NAh/sfeeYdHUXVx+J1t2U3vISGB0HtvobfQEUFpCoggiFhonw1QRAVEKSKIgBTpRZAmNYD03kMLhBKSkEp6z5b5/lh3YEkCAQOEZN/n4SE7c2fmzuzszLnnnvM7YDSGBw4ciI+PD19//XWeBoyTkxMHDhzAz8+PESNGMHXqVD799FO2bduWb11PvV7Pl19+yc8//5xDemj8+PGSZ1kURd5//32WLFnCoUOHaNq0aY59xcTEMGzYMDZv3kyZMmUYPXo0jRo1olOnTlLZOxNqtRq9Xv/cErAeNvwe/V5NAxKlUom1tXWO0INH1QRMBmh++yqTyfD392fRokXs27dP8h6bcHFx4Y033mDHjh3cu3cPHx8fJk+ezNy5czl58qRUFs1C/rB4JS0UdUTRQMT8oc8cSyko1fiMWW/5neRBaRdrDn5a9HV/XxmD8q+//qJXr17Y2NiQmppK69at2b9/v1kbk/FUUPTr14/ly5dL8YMA58+f58svvyQgIECqoWrC1taWtLQ0qYSVRqPB2tqauLg4s6lfmUyWp1fTJKpsokWLFgQGBuaIcXwSv/32Gx999JFktEydOpVffvmFcuXKce3aNeLi4pDL5QwYMIDjx4+bTXcD0rn5+vrSo0cPVq5cSVRUFBMmTGDlypWEhIQ8VX8MBgMVK1Y00wWVy+XcuHFD0vjU6/U0btwYnU7H6dOnpWQggC1btjB06FBEUeSXX37B2dmZESNGEBwcnONYCoUCg8GAwWCQDD+VSkWdOnVITEzMca5PKrkpl8tRKo2VH7RabZ7ea5VKhU6n+88VkR7npXwcvr6+zJs3j08++YSbN28CRq+kwWCQql2U9e+Pvn7f/9S/l4nFuLNgoeARRZHMuxf/k5fSa9jvlizvXJDLBAY0Ks3EbtVedleeO6+E5kVCQgJvvfUWVlZW7Ny5E8DMmBQEgQ4dOjzWmHxYZuVJmLyeK1eulIzJ2NhY3nzzTerWrcupU6dyGJOOjo5kZWUhiiJOTk4sX76ctLQ0YmNjCQgIoHVr4+jEZIwIgmBmqJp42JhUKBQcPnzYzJjUaDT5Ej/98MMPKVOmjPR5/fr1dOzYkcTERO7fv8+GDRtwc3Nj6dKluVYCEQQBhUJBSEgIer2e2NhYLl68KCXzPC0ymYy1a9cCSDJBer3ezBMpl8v55ZdfOH/+PEuXLgWMlZCGDBlC9+7d8fPz49ChQ0ybNo1OnTqZGZMPG59+fn589dVXqNVqyZjMzs4mMDBQkk8y4evri06nIz09HUEQ8PX1ZdiwYaxfv57Lly+TkpKCTqcjIyOD9PR0yaDcv38//fv3x9PTU9pXdnb2Yw3B3L43X19fypUrh7X1g0o4ee3Dzc3tsfdxSEgII0aM4ObNm6hUKhQKBefOnePmzZvIZDK69hmIoc6br3hc8avcdwsWCieCIKDxrY3CxfuZ95Fx6wyi4enVRYo6eoNIf79SL7sbL4RXwqB866230Gq1LFu2jHfffddsnZ2dHRqNht27d0vLTIkaJm+g6e8noVKpOHPmDH/99ZfZ8r///pvq1atz6NAhVq5cSaVKlcz2Z29vT2JiIlqtlj59+hAdHc2AAQOM5cAEgXbt2rFv3z42bTKKVGdnZ6NWq6Vaqnmh0+kQRRFHR0dpmVarzXfMpkmEGYzC1y1btuTy5cvEx8fz5ptvcu/ePYYMGZKrxFJmZqZkvMyfPx9ra+tcvbJPQ/369Xn33XfR6/VMnDgRMCaRvP3221Kbxo0b069fP8aNG0d0dDSdOnVi7dq1LFy4kJ49e1KzZs0ctWUFQaBXr15s3bqVypUrc+nSJb7//nsyMzPx9fXls88+o2/fvmRkZJCamoqLywPphpCQEORyOSNHjkSr1XLnzh3mz59Pz549qVatWq5C4nK5HA8PD9zc3PI0zkw1Wh/m4e/NdF+GhIRw9+5dKlasSM+ePalevbrkgbOxseGTTz6RYiRjY2PJyMjIdSBiwmRkZ2dn4+bmxrBhw0hLS8NgMHBGVgEDwivp4RNFESt0OB7+mczwq9IyCxYsFAyiQY9dnc7PvH3q+Z0WUfNHkMsEmpd3pby73cvuyguh0BuU586dY/fu3dSoUYPJkyfnKKVoZ2cnTVVaWVnh7u4uecBM3kCZTPbEeMly5cqRkpJCvXr1pGWpqakMHTqUbt260bBhQy5fvswbb7zByZMnpVhHGxsbkpOT0Wg07Nq1i7Vr1+ZpJHbv3p3g4GDatWtHRkYGWq2W8uXL59r2888/l/5OSkqS9vnwdKuprmp+0Ol0khfM5Mk1aUA+3IeH95eSkoKtrS3Z2dk4Ojpy4MCBZ/ZQmpg4cSJxcXFUqVKFgwcPArBmzRo2btwotfnuu++IiYmhQ4cOnD9/nh07drBixQoGDhxodv5yuZzhw4dz584dpk+fzj///ENoaChJSUmIosjw4cPZuXMnf//9N2vXrkUmkyEIAnFxcdI+vLy8CA8PZ9asWY817sFowOzduxc/Pz+qVq3Kzz//LCU2PWrkabVaUlNTsbGxoWTJkmbrKleuzJQpUxg8eLCUDHXhwgW2bNlCcHCwZCilpaUxZ84cjh8/zvTp06VjyOXyHPvMjcjISBYsWGA0xtx9sSpd+5VMLjFdj7srxxN4bD/RKz8nYuFwUs7+jTYh8kH2vAULFp4ZQSZHU7Z+vto+PKNiQhsXRsadc0YdVwsAKGQCU3rUeNndeGEUeoPS5Onz8PAgMDDQ7MXt7OxMREQEAM2bN2fEiBFoNBp27NghtTHF0j3Om1GhQgUuX75sNiWZnp5O586dWbNmDQsXLmTr1q14eHiYeec0Gg1paWl4eHgQFxdHhw4dnng+Hh4e7N69W0pSedhAfthz9NNPP0l/i6JoltBiyvJdvXq1mRfySeTWVqvVSvF2YDSi7e3tUSgUyOVyyRCPiooiLS1NMiif1TtUunRpqlatSkBAAC1atKBZs2aA0atsMjBLlSqFnZ0dly5dYuPGjQwYMIBDhw6ZXZ86deoQGxvL3LlzOXr0KNWqVWP58uWSoSWXy7l48SK1atWSMqjVajWiKNKiRQvgQfhAiRIlHttnrVbLypUrqVatGu3atTOTTJLL5TRv3pwffviBgIAAbty4QXp6OuvWrcPe3p7s7Gzu3bvH0KFDcXV1BSAoKIhjx44xY8YMrl+/LnmCGzdujF6vp3Tp0ixevJjFixfj5uZGWFgYn376qTQVrtVq6dChA1euXJGunwlra2uqVKkCmN9P1jXbv7LTUYIgkHZpH1nhxpKqMpkMbVwYCXt/J2LBUGoFLydm89SX3EsLFl59FE6eCEp1ruvkcrk06DY5cR4dhMfvmoto0FtmD/7lu27V8HHOaXwXVQq1QXn//n2uXr2Kl5cXe/fuRSaTYWVlJa03ZfYqFAp2796Nh4cHsbGx3L1712w/j/M8eXl5cf78edTqBz+irKws3njjDc6dO8eePXsYMmSI9HLeunWr1M5Uuzo4OPipYjQFQWDUqFHMmjVL8rh98MEHuf4IH56eBWP8ockA3Lx5M4cOHcr3cSdNmpRj2cPHdHd3RxAEkpOTEQRBMmJNSS4ZGRnS9f8vUknt27dn9+7diKJo5ont1KkTADNmzCA1NRUnJyf69+9PaGgo1tbWUl/9/f05deoUoijSt29f+vXrR5cuXSQPX6VKlfjqq684duwYWq0Wg8GAtbU11tbWfPXVVxw/fpzy5cvj4uJCbGzsY/t6+fJl6tWrx4ABA7h27Zq0vHr16mzevJmUlBQOHTrE559/Trt27ahQoQIajYbevXsTFBRE27ZGIezFixfj4eFB5cqVAeN9VL16dUJCQjh58iQqlYojR46we/duvL29GTJkCDdu3CAsLIz+/fsDxvumRg3jaHfJkiX06tWLSZMm4eHhIfUrPT2dyMhImjVrZvbdasrVf+Wmo0z9z7hzjrR/5kvLH40x3bpxPfLMvEX8LViwkD8EQUDlYj77YW9vL70PTO8EpVLJ559/Tvfu3aV2CoUCR6WehD0LXsmwmoLms/aV6NOgeMROmijUBuX27dsBpGnFcuXK5Ui8qVSpEjqdTnp555apa4pBy429e/diY2MjfdbpdPTr14/9+/ezZcsWGjduLK3T6/WMHj1a+iyTybhx4wZ2ds8WHzFy5EiqVTNmfu3fvz9XT5mT0wNlfUEQiImJkablnZycGDt2bK7TD7lhavfwlO/DuLu7I4qiVLMbjOdoMnojIyMlw/tZ4ygBWrduTXh4OGFhYbRq1UpanpGRwdChQ5k0aRKdO3cmLi6O2NhYnJycSE9PRyaT0aZNG7Zv387Zs2epUaMGe/bsYe3ataxatUqSlBo7diyTJ08GjEaJTCajefPmBAQEMHPmTDp37kyrVq0e+9AzGAx8//331K5d20wj0sHBgc2bN3Pp0iVef/31xw4kPD092bFjB/PmzUMulxMeHk5ISIiUyJOeno6/vz+rVq0iICAAg8HAiBEjOHjwIFOmTGHmzJlUq1aNtWvX0qJFC2QyGZcvX6ZcuXKAUdy8VatWREebS30kJiZy9OhR6bOg0qBw9ORVwmRM6qOD2Tu+O0FBQWaDPhOmpK7sjLQX2j8LFooqovBg4KnRaEhOTkYURcm54enpSb169fjpp584d+6c9I7S6XRs2bKF1MAAEg4u/3dnxctTKZcJWClk/PhGDT5qnXs4W1GmUBuUpmo4er0eFxeXHN46pVLJ4cOHsbGxYffu3dSsWTPXl8769evzPIYp3tLE9OnT2bx5M+vXr5e8Sya2bdtmpnf466+/4uX132QSKlasiJubG9evX6du3bo51j88HV23bl1kMhmnTp3Cw8ODzMxMgoODqVGjRr5GhNWrV0etVjNo0KBctS1NDwZT9RWT6LiJhIQE6fr+lzhKe3t7wOjlNBnjGo0GQRBYtGgRKpVKSi5xcHCQRN8VCgVLlizh6tWrdOzYEV9fXy5dukSfPn3YtWsX169fp02bNgwbNswsm95gMPDJJ58wbdo07OzsckhBPUpkZCR16tRhwoQJ6PV6afq/ffv23Lhxg9dffz3f5yoIAh988AHr1q0jNTUVBwcHUlNT0Wg0JCQkMHjwYH788Ue2b99Ot27duHLlCps3b+bLL7/k2LFjhIWFIZfL+eOPP/j7778RBEGSbMrLmDVVIDLh5FPhlfMYmPor96jAustJlC5dmuPHj+dod/ToUXx8fBCRWabZLFgoAET9Az3chx04JkdEZGQkp0+fpkSJEsTFxZGSkiKFi/n7+wOQfPxP4nbMxqDLLhYxlXKZ8XnVpKwLe0e3LHaeSROF2qA8d+6c9Pfs2bM5ffq02XqtVktkZCStW7dm8+bNCIKAUqmU1pumZ3PTDTSt27dvn7QsIiKCSZMm8cknn9CtWzez9mlZOtbsPIzKsyJK9zIISjUffPDBfz9JoFatWqhUKrO+5GbwZGVlUbNmTcD44zZlmj+ccf44w+HMmTP069ePc+fOSbI8SqVSuhYmI7Nx48aS0fnw/gRBKBCDMjeqV6+Os7MzYPRO3bhxAzAmJNna2iKTyfjiiy/IzMykffv2lC9fnp07d0oxk7/99hsA4eHhZGVlYWdnh0KhoEmTJrRv357evXuzZs0apkyZgr29PZcvX8bOzs4shAIgNDSUatWqERgYiFqtxtHREYVCQbt27diyZUuOAUh+6dGjB3/88QfR0dFoNBrpOq5fv55Zs2Yxbdo0ateujVKp5P3330cURU6cOEF2djbOzs507tyZevXqMX/+fPR6PTKZzCy56ssvv5SO9ahAemp6wX5XLwrTvbcyMIn+3/xGWFiYtM7R0REXFxcUDh5kN3kfr0GzXlIvLbwwLAOG544oiugScqp+PIperycqKork5GR0Op0UApWdnS2FmKUGBsD278kMDTTu+xWN4X4cAkbR8gGNSrN3dAtWvNeoWMVMPkqhNihTUlIAKFGihFT5xISpDvWoUaP44IMPOHnyJOvWrZO2gcdPy5YsWZJq1aqxYcMGadm4ceNQq9VMmDABgODoFCZuvULLafupPnE3J5xa4zlwJl6D5+AzZj2Nf9jHR6vOsfl8OGdC4jkfmsCViCTSsp5uRKZQKJgwYUKO/j6awZ2YmEhCQgLW1tbodDrJGHpYu/JxWd/Jycm89957vP3224wbN46kpCRjzMy/o8sbN27g4eGBnZ0d8+bNw8rKyixe7eEY1v8y5f2wJ8n0t6+vr1RR5uEylL179yY1NRVXV1cGDBiAv78/7u7u7Nq1S/J0gtFTpVKpuHHjBlWqVCElJQVfX1+2b98ueahVKpWkxXny5El0Op1ZUsuZM2eoWLEiCQkJeHt7o1Qq0ev1NGjQgL/++itf+p+PY8CAAfzyyy/ExMRgZ2eHUqkkJSUFQRD4/vvv+e6772jcuDHx8fH8+eefTJgwgaFDh3Lo0CGSkpIkw7hRo0Y5Bg6jRo0yUygwIQiCmcfhVUQURQ6nleCNAUOlZYmJiQz5YQmeQ37DqpQxrvRV88JaeEos3+9zJ6/Ss2B8/s+dO5eoqChSUlKoW7culStXlp5bYDQ0TU4de3t7/BvXoZ9XglGV4dx2dIlRvMpasiXsrXitpidz+tZm+yfNuDyxAwc/bc3EbtWKjTTQ4yi0GiKiKEqjnpEjR/LPP/9gY2MjyfWYpof3799P69at6dSpEyNGjDDbx+Mq59y+fZsff/yRL774guPHj2Nra8uyZcuYN28eqaIVIxaf5PDN+8hlAnpDzh+AIAhEpWSx/XIk2y/nHNGVdramdSV3+jUqhZejhpC4NLJ1BmlfcpmASiHDzdOb/Xt2sX37dr777jvpnF1dXYmJiTHbZ3h4OGCcap84cSKxsbHI5XIp010ul+Pg4GBmVD+Mn58fjRo1wsfHh0qVKjF9+nRJrxGMSVAmo0SpVDJq1Ch+/PFHaXu9Xl8gHkqT0Labm5s0pe/o6Gg2YDBVi/H09EQQBAYOHMiECRPQarUEBASYhT9ERUVJoQgeHh6SUPsff/yBg4MD4eHhpKen4+joSPfu3Rk9ejR6vZ7bt29L90xQUBBNmzZFp9Ph7+/P8ePH8fb2xsrKir///jvfcapP4pNPPmH9+vVERUVJnsSxY8eSmppKZGQkixcvBmDy5Mmkp6czadIk3N3d2bNnD35+fnz33Xf8+uuvNGjQQNqnKIps27aNZcuWUaNGjRwGuy4h8pWuMCMIAqJcjnPHj4hZZxzsOTbty9rbcmQKmcXQsGChABANejJun8lzvcFgYMKECQQHB1OxYkXOnTvHoUOHaN68OZ9//rn0bjA9f7Kzs/nrr7+IiIggIiKC9esXkbD3d0p/vgUKeYKgLDMJu+x4Pu7TiSqedgwd2I8KJRzZujHv8DkLhdhD+XDs4BtvvEFcXJyZV6xjx45cvXqVb775hgkTJlC7du0cGbt5lcgzsX79eurUqcPIkSPZtGkT9vb22NXugP/PBzl22xgvkpsxmR/uxqez9HgI7WYdotrE3XSZc4Qe847Rc8Fxei44To95x+gy5wi77duT1uYz/rf6JDWbP5AdylH/WalG6V6GCn7taNy5F26exooGD3vp5HI5bm5uefZp4sSJyGQyvL296du3L5s2bSI6OtpsivTh6fORI0ea96GAprx3795No0aNcHBwICAgAKVSaYyDe8gQMhgM1KhRQ8oG9/LyYu3atUydOjVH3OqxY8ekv3/88Ufu3btH48aNJe9jQEAAgiCwceNGrly5wpgxY/D29kan09GhQwciIyNp0qQJWq2WGTNmsG/fPrp168b169dZsGCB2TX+rwiCwKxZs7h165Y0qk9PT+fChQv88MMP0hR7UFAQzZs3l6bYq1evzvjx45k9e3aO+NcyZcrwyy+/0KJFi1zjCKtVKv+vZ+DVRZAp0JSpy4xFq3Co2wmH5v3/XWExJi08PZZ425wIMjkp53c8tk1cXByzZs3iww8/RKFQEBMTk+Namgb77u7uJCcnU6pUKTp06CBNhWvjwnLstzAwo1cttn/SjCsTOzDI+RZ3V33FoCalaVbBnX6dW3Jg7+4c4UQWzCm0BuXDlW8uXbpEamqqmYHo6OhIRkYG1atX5/PPP+eHH36QPDAODg6oVKrHfvl2dnacOXOGZs2acfr0aVasWEHVXmMYv+UqWQ95Ep8/AkonLzZfiSO2wTDc+3xnjAvLzkbt4YuT//t4DfsdnzHr8Ro8h+xWI3nj99Okdf4e7+GLEOr3QeniAxhHhCEhIXl6oh7OZm/fvj1Xrlxhz549AJJn8tq1a5JX99Gs8xIlSkhT3s9qUOp0Ovbt20frdh25EpHEpkPnqd+uO/ei7wPmU/bdunUjKCgIpVLJ0qVLqV+/Pu+8806OfZpqc2s0Gqk0pymmEowGZYMGDWjdujVffPEFUVFRJCUl4efnh5ubGy1btiQhIYHhw4fz119/UalSJfbu3Uv//v3x8/N7pvN8HPXq1WPQoEFmpSPnzZuHvb09P/zwAzqdDq1Wa1aWEmD06NH4+PgwfPhws+V37tzh0qVLeX7vly9fJvN2ESiLJhq4Z1cVhzZDLAaBhWdGNBgQddmkXT/25MbFBFGvI+POOXRx4fneRqfT0bNnTxwcHPj555+l5abKa2vWrEGtVlOiRAkGDx4syZslHFhWsJ0vAERRxFeeSDUvB2ysjDHzSUlJUh5Hhw4dSElJMdMgtpCTQmtQmqRaAA4cOCBJB4FxOvTq1au0a9eOsWPH8u233/LWW29JLxlT5ZrHkZKSgkajYc6cObRs2ZIo67JEuuWvSsDzQMRoDKhL1cRr6DxKDp2Hx6BfsavbBaWTVw5jQRAE5A4lsK3TCa+h8/Do8z1ql5IkJibm62Xr7++PIAisWbMGQJr6TUlJYcaMGQBS7W0TDRo0+E+yQcHRKbz3226s+/zEqux6dJlzhJtlexBRcyC7bNtR8sM/sGo6UDKQ27dvDxizzy9evMjMmTNzTVYyxcHWrl2bw4cPY2dnR+3ataX1poGDTqdj3bp1uLq6kpKSQsuWLZk8eTK3b9/Gzc2N5s2bc+TIEZo0aUJqaipTpz4/sewhQ4Zw//59KbM+ICAAgIEDB0pGtUkeyIRarWbatGmcP3/e7P42leaMi4vL06hMObfjldOhzIEgY8Pp2yDIX9npewuFg5h1E7i/aQpxO2YXiyzkxyGKIqJBT/yuuU+9rY2NDSkpKYwdO9ZsfzKZjCZNmlC1alWaNWvG4sWLpXe4eO8SCsOzx+A/D3QJkRz6Z4/02eRMMYVP1a1bF2dnZ+k5bSF3Cq1B+bCe5M6dO6UEEjBOh5o0Be/cucOvv/5qlpCg0WjMyhU+islTp9PpUKlUHL0QhFO7YYXC6yHIFSBXInc2Tmk/yQgwrbcqVQO3d2djU6Ndvl62alsHKjZsw8V7Sai8KpOmcUflWRGFmy+Tf5zO2rVrGfz+cFQeZaXMdntnt2ea8g6LT2fA4pO0m3WIg/f0KJ1ykVoSBBT2btjU7ojX0Hn4fLQU0aOSdCxvb+8cVWFM60yDj3LlyhEVFSVVpDFhiv2cMmUK165dQ6fTUblyZaZNm8bMmTPR6/VMnz6dnTt3UrNmTYKDg+nQoUO+yhs+Kw0aNMDR0VH6rkxxsDKZTJoyMmW9P3ouYJ7JnZiYKP396PS8aV9FpSyaoLB6JctHWihECODQtA9gzESO+H0Y2vuhQPGcChcEgYQ989ElRT+58SOkpaXh4OCQI8bcNBg2lbsdPHiwVNQhMzOT5O3T/3vHCwhT7OjDRUseRS6XU6FCBek5bSF3Xokn861bt6hatarZj/3evXtcvnyZDz/8kO+++07yZoGxvN+1a9dy1VoE44+gWbNmHDlyhBIlSiC2/AhBVni8Hs/SD0GuAJkcl84jkNk4knz8zxxt7qUamLj1Cvuvx3A3Ph1aj0YBeAI/ntPjOXAmYHyofnFOj8dHK81i1HYjcmXZFZz83yckIX8jzLWnQ/lm6xV0phCCJxnI/x5PZuvCwKVn8HxvLgmbp9ChQ4dcr8uRI0ekRKbz589jMBjMxOBNnD17lt27d1O6dGkSExPZvXs3DRo0ICYmBmtra/r06cMXX3xBnz59mDt3LrNnz87X+T0rCoWCtm3bSlP0D2fTOzo6cvfuXa5cuUKpUqXYs2cPhw8f5sCBA5Lx+HD4x8O/i0djbx8WsY/fNRfPIb9BIbrXLVh40QiCDE2ZuihcvNHFhaNLiiZi0YeofWvj1mM8glX+q569ypgS9RIOLiM1cM+TN/gXQak2lmiUKxH1WpJzyQwPDg5m48aN0ud//vnHbNYx9spxeryj5VyCkpeNKXb0rv3j+2J5Zj6ZQmtQmryLpkztR8spqtVqPvvsM06cOMH27dvNXNGjRo3izJkzLFy4MM/9t23blpMnTxKnVeJVps7zOYkXjOmGd2r5Doa0BOkhoXDwwLnjx3xxMAW5kIL+CYNwQRAgVy+QQHhiJnZ1uzD9qpyTi08ypUeNPHW3ft0fzPSAG896MgCo3ErjNnguDr65Dw4CAgJwc3MjNjaWW7duIQhCjnCH1NRUdu/eja+vL9HR0ezZs4eUlBRiYmIkaaQjR44QFRWFs7OzlKzzvKlWrZokR2QyECMiIqRs/vHjx/PFF1/8p2OY7glRFNElRZOwZwEunUc8YSsLFoo2okGPXZ3OJP2zSBrMZYZcIOznXnj0n4bau8orrYzwJESDHgx64vYtIvUJiTgAShcfbOt0QlOuPgpHT7PrIooiusRIMm6dIfX8Tinp5s0338TJyYm6desyatQoateuzYULF5DL5ej1egK+60/zCX9yLUF8adda1OvIDA3EKjOBRMMDWTjT9HxBJmQWBwrtlHedOkYjr27dusjlctLS0qQbTqPRkJmZSWRkJIsWLWLv3r1m3si9e/fy+++/8+GHH+a5/2+//ZZBgwZhV7fzq5+s8AiiKOLU7gMUDh7Y1myP15Df0JQ2CqI/yZjMD6Zp9mO34/D/+SBrT4fmaLP2dOizG5OPIJMr+CvMil/3B+dYFxoaSoUKFYAH0/C3bt1CFEUMBgNz5szh/v37aDQa7t27x5YtW2jatCm7du2SjEkrKyvee+89wHhvWVtbU7Zs2QLp++MIDTVeN1Nlm8aNG1OyZEnJq5ibSoGLi8tja9M/iiiKZh7Mh8uiFcfpPQsWwPgM05Stn6MuPED0ys9IDTQWmShqvxHT+QgyOYJChXP74XgN+x0n//el2PWHUTh44N7nO7yGzntsPL/SyQu7ul3wGjpPSiwFo6bw6tWruXz5Mu3bt0epVEphOGXLlmXfV91JDdz3ksQajLGjiXvmYTAYzCQG9+zZg729vVn1utTUVLPCKRZyUmgNSlM2b0ZGhmQsmn4MGRkZUszGlClTuHPnDp06dZK23bp1K1FRUfzyyy9Ur149z2P8/vvvWFds8uonKzyCIAgIMjnufb43eqMUquei+6U3iGTpDHy58ZKZsRcWn843W68U+PGmB9xgXS7G68NSR6IoEhUVxd69e+nYsSMjRozAxsaGjIwMVq1aRbt27QCjZ7N+/fpotVpmzZolZSaaYn5eBNevX0cQBNLS0qTKOI/yaBJSXFxcnqEc+cVUFk0sJmXRLFjIDYWTJ4JSjdzKGqV7GbMqaJkHfyd61ZcYMoyavkXFsHwaY9C2Zns8h/yGupTRGZHfeH516Vp4DZ2Pa+eRWFdrRba9N2pbBwIDA2natKn0TKtQoQJarRa7q5tYO8QPR43JWHtxCisJe+ZTytkmh151QEAAbdu2ld4tsbGxXL58WZJ6s5A7gliIfylubm7o9XpKlSpFcHCwWaJO586d2bFjB2q1GrlczmeffcbEiRONIsiiSMuWLTlw4AChoaH4+vrm+kBwee1TbKq2LLLTGi+aH9+oQZ8GpRiw+CTHbsc9F+klK4WMvaNbStPsffv2JS4ujsjISIKDg8nOzkapVCKXy1GpVGRkZFCyZElCQkLYv38/rVq1IjMzEycnJ0aMGMFPP/3EsWPHWLt2LbNnz6Zly5acOXOG1NTUJ/YlJjmTE3fiyMjWo1HJ8Svjgrt9zlryj6LX6/niiy+kbHoT9vb2Ulbhi8AYCvERmjJ1EQ36IjewsmDhSeiSY5HbueaYwtUnRpF+6zSp53eicCyBQ4sBqFxL5Yg/FkURDPoiE5cs6nX/JqmcxaZSkwKbihZFEUNWOj6yBG5u/Y3Ym8ZyjGq1Gpm9B1/9/hcKtQ1hcan89c8ZFM6ez+2aPhw7mnx8PceOHaNJkybY2dmRnJxMVFQU3t7e/Prrr1J55dWrV9OvXz8iIiLw9PQs8D4VFQptDCUYZWNWr17NV199xf/+9z+pegrArl27eO+991i8eDEajUbSwbK3tyc9PZ2DBw/Svn171q5dy8SJE/nmm2/M9u3e53s0RSR2Mi9edFzKhK1X8HTQcPjm/ed2DK3ewLhNl1jxXiPAGGubkZGBv78/ISEhZGdno9VqEUWRzMxMRo4cyeTJk6lQoQJLliyhVatWJCUlkZmZSalSpRCUakKSdLhUqofSvQyHjp1EoVBwKTwBnQFUChm+LjbYWBl/KnuvRjFjzw2CY1IfJBo9hEImUMHdlv+1q4h/VXMdT1EUWbBgAV988UWuhuOLNCYBdEnRxKybIMVHWVdsjMI+b2F8CxaKGrnd74IgoHDyxK5uF+zrdyPjzjnub5yMLikambUjVqWqI1OqMWgzyQq9jCE9Ec8h81C55pwyftUwJXfaVGpi/FxA7w9BEJCrbYjABuueU/CMj0CusUOusTM+F088kAVUuBoVTgwZKWSEXUafEkeNdr0ITcj87wNfgx5RryN+z3xSA/dga2vLvXv3ACQFk/Hjx+Pg4EDv3r2lzUwKIBZj8vEUag/lvXv3KFWqFB4eHjRp0oRt27ah1+ul2LIqVarQuHFjlixZIsVVgjEmTa1Wk5aWhpOTEx9++CGzZs2SXtgunUdjW7PtSzuvoopcJuBqrSQ6JROE5xtNsXd0C8q72/HDDz8wYcIEHBwczDKaAT7//HOpdOSCBQukmu96W3e6jPyBUn6dSNAqzB+aoohIzgepnRLStQb0yEAUH1uhRTLks1Lh4hZIjCArI437d66hy0wrsGtQ0Cjdy+A1eM7L7oYFC4UKk9cuYc8CUgNz1yHUlGuAe69vcl1nIS9EIB/P0Yc+G7LSUcjliEr1Y9s+iqmEshAVRPimaZJE0vvvv48gCCxYsIAmTZowe/ZsGjRowJw5c/joo48AY2hS9erVmTRp0n9OkizqFGqDEmDo0KEsWrSIr776ij/++IN79+6hUCgko7JGjRr4+vry999/Sx5MV1dXkpOTUSqVODg4kJCQgMFgICsrCyvvanj0MwpWF4UpiuKITIDqVvFkHVvJgQMHyM7OxsrKiqysLJRKJTqdDlEUcXR0JCwsDFtbW/R6PbWbtiWr1htkO5d7ppHu03p8c3sg5pYN+bIxXTtX77LY9PvFUs6wEGK6l3zsZIQmaS3hCS+YB9Oky3OVZAPwHrkGmdrW8l55zggYTdHMe0Gkn9mMTq9H41sHTZnayB/JQEcU0ejT6NO8Ov39SlGrTAmz0LkLFy7QqFEjsrKy+PDDD7lw4QJJSUlcuHBBip/s2rUrV65c4dq1a5IX00LuFHqDMisrCxcXF7Kysti4cSN9+/YlMzPTLDvP1taWbt26sXbtWmm5TCbD19eXiIgImjVrRtOmTZk+fTpO7y1EprGz/OifEy9qml0bH0Hkwgdi9AqFAisrK9LScvcA2tZsj1O7YcbsxpcsjG0yZjPunCN+19xnEhQuKB4nB1JcKWzxpFJmruW7KRQkHl1H+vUj6B7RXzQ5Kyzf04vB+LsQ0YVfJXbXXORpsegFBWeu30UvCty9c4s32jVnzYql9OnTh8OHD9OiRQtp+5EjRxITE8OWLVtIT0+nbdu2HDx4kL1799KyZUvAGFrXqVMnNmzYwJtvvvmSzvTVodAblAB//vknffr0QaPRsHDhQgYMGIBKpcpR/s/NzY309HRJYkgURTQaDVlZWbRp04Z3x89g/O7C4RWy8N8QRZGwmb1yCOrmhn3j3ji1fKfQ6crlZyotv9jY2JCWloZarc5XFSNLQk7uuJxfxh29E5qy9Y1ZwIXofrFQuMhtxsESTvXyyLhzjqqpF9m/bQOiKNK2bVsiIiIIDAzkxIkTkpEIRnm47du306ZNG5ycnEhKSsJgMLB27Vr69DFWUQoKCqJFixbUqFGDvXv3Wp4F+eCVMCjBKA80fvx47O3tGTFiBJMmTZKMxvwgCAKeg+egdCvN4+I2LLw6RCz5BG3MHemzIAhmAw0HBwf0vn64dPrkZXXxiZiM3JQLu8kIOY8hLQlDVmoO7wcYyzWePn1amqIG6NKlC9u3b0cmk9GjRw/WrFlDixYtCAwMNJva8fb2lgTTC5O3tjAh6nXE/dZP8nKbKoI4d/gYK68KCM85LtisL4Vs8GMhbx6dcXDu+DGaMnWe+Tu0fPfPhmgwoFLI+L57DazCz/LGG2+wfft2lixZwl9//WXWdtq0afz444+4uLhw/fp1ABYuXMiQIUMACAkJoXnz5jg4OHDw4EFJO9PC43llDEqAL774gp9++glra2v69+/P77//Lq0zvWQfZ2SW+myz5QVahIhcNoZ2dSuwcuVKVq1axSeffMLp06dJSUnB398fuYM73u8vQCcKr9wDWhRF9MmxpAefYHi7GkRcO8vSpUsRBAG1Wk1GRgaVK1fm3Xff5csvvwRg3bp19O7dm5s3b9KsWTPUajWhoaFmvweHJn1wbDHA8tJ6BFEUMWQkE73qS7PY1saNG3M/QySz7WcICpXlmlnIk4dnHKy8q2FTow3wdKEKktZy8EnSrh/DpmoL1P9mlVt4CgL/xif5CoGBgTnUM2rXri2V3L179y5arZZ3332XP/74AzBWKzNNjR8+fNiS2f0UvFIGpSiKjBw5kjlzjJmoderU4fz581Ipp4fLzJmWmZDZOOH98XLLC6EIEb1sFJmRN5HJZLi7uxMfH4/BYKBEiRIkJyej7vQp6lI1X+lBhMnwy7xznrhdv6JLikYul2MwGLC2tpa8aeXKlSMsLIz58+fz7rvvcvnyZVq2bEn58uW5cOECWq0W25rtLWUXH4PJ01TZEfb98B765BjpBW+5dhbyw8PJO1lhV3B7Yzxya/snDuBM6/XpycRunExW+IPCEN6j1iJX276I7hcp4nb8QmrgHrPZGblcjpeXF5mZmcTHx6PX6/H29ubOnTsoFAr+/vtvhgwZgkql4vDhw/j6+r7ck3jFeKUMShNr1qxhyJAhZGRkYGNjQ1ZWFmq1mpQUY1UDUxb4w95Kt3odsW738cvstoUC5EkxlEoXH7yGznvBvXp+iAYDol6bI96yefPmHDlyBCcnJ0qUKMHVq1epUKECr732GikpKSxbtgy5XI5WZY/nkN8sXrZ88Ghsq0qlIjs7u9DG4loonJgMGk25Bk8URs++H0rioeVk3jpjtg9BpcFn9J+W++0pEUUR9FpKnFvIqX92SsuVSiUymcws/2LPnj00atSIMWPGsGjRIrp27cqiRYvw8PB4GV1/pXklXTdvvfUWzZs35+233+bw4cPScplMhq2tba4C0Vkqe6xfZCeLMS/ihWuKMRQEAWtra9LT082mdm3rdCpSySaCTAaCCpfOI5DZOErSJab7Pz4+npSUFBQKBcHBwcycOROFQoHBYCA7Oxv3bmONMZOWF9MTMYk7P3yt5XI5ycf/xJCWiHOH4SB/dWv65vb7tBjJBYsoiji1+4DMu4Fk3DpNxq3TAHkKo+eF0rW05Xt5BgRBAJmcELfGwAODUqvV4uHhQXR0NI6OjrRo0YLExERq1apFTEwMCxcu5L333rNc82ek0NbyfhLe3t4cOHCAX3/9FRcXF7RaLQaDgeTkZKysrFAoFGYGhrpcg5fY2+LF8/4xGkuDGUfyoihKtbAfRlOufpExJk2YrqtTy3ewrdkux3qtVivpswLodDoMBgNKFx80Zeq+0lP/L5pHr7Xp/koNDCBiychXtrazaDCAXkfSyY3Ebp1O7N/Tif17huUFWsAIgoAgk+Pc8SOz5Yb0RDKCjpB2aS8ZQUcea0wCKBxLPHa9hccgk6MpUxeFi7HyTtWqVenWrRvR0dG4uroiiiLnz5+nV69elCtXjosXLzJkyBDLb+E/8MoalGD0SH700UeEh4ezePFiKlasCEB2drb0YjWJk1q5+76sbhYrRIMeXXKssb7tc0KQyUk5vyPv9SoNCseiG0ht8n4oHPI3JWPy1lp4ekzXWmb3oESfLi4UXWLkS+zVsyPIZAgKJQ6N3sC53ftYeVYEve7JG1p4agS5wsygebadFFx/iiOiQY99nS5MnDiR7Oxsdu3ahb29PXFxcaSmptK8eXPOnj3Lnj17KFeu3Mvu7ivPK21QmlCr1QwePJigoCB27tyJv78/Mpnx1HQ6HYJKg2DJknshCDI5cTt+waDXPRcvjqjXkXHnHLq48DzbKBxLFOlRZl7ej7woit7aF0Ve1zrj1plX3kiXa+yxq9sVt+6WcnLPC9Ggx65O5xzLBaUalVclrKu2wrpaK1RelXN9R+kSonIss5B/BJkcp2pNmThxInfu3CE7O5vk5GT69+/P7du3WbVqFXXr1n3Z3SwyFKk5MEEQ6NixIx07diQ5OZkDBw6wY8cOthy7VKQNjMKELikKxf2bpB9eim2bYQW6b1EUEQ164nfNfWw74RWOb8svD3s/HmdcF3Vv7Ysgt2uden4n9vW7veSe/XeEfwfelhjK54Mgk6MpW58EfkflUR775m+j8aludHLkEsdqyEon4/ZZ0oMOo0+JQ2HrbPlu/iNatRPO7p6kJsbh4uLCxo0badq06cvuVpHklczyflo2ngtnzPqLL7sbxYJhzcsytnMVAH7dH8z0gBsFun9T5uTjULqXwWvwnAI9bmFENOhJObedhL2/59mmuFyL545BT/Ij19q9z3evvCyVheePKIqIumxkSqtn3t5iUP43IpZ8Qp/2TZkzZw6Ojo4vuztFliIx5f0kirzFXIh4q6GP9PfHrSsw9Y0aKGT/7WFoGvMkHFxG+uV9NGrUiDlz5mBjYyPFyMrlD6Z09akJr2zSxNNg8n48tk0x8Na+EHK51vG75iIa9MXiXrPw7AiC8MzGpGl7C/+NH36axooVKyzG5HOmWBiUpZ0tgkEvCl9XcwHevg1Ksf9/rSjv/mzCvKJeh6jLJvPQYr7p6UdmZiaHDh1i3rx5pKWlodPpUCgUZiL2clunYvMQVjh5PjY+WNRrX2BvijaPXmtdUjQJexYUm3vNgoVXlY7t/F92F4oFxcKgrOJp/7K78NIY418BK4UM+Qt45zlZ5+4N83G2Zu/olix7tz4qbSrAE706poSHzNBAXpNfJPLIX4wZMwZRFKlVqxZXr16V2j4slQPFyysnCAIKp7xjJHUJkRYPWgGR27VODQwg4eBy4Mn3tAULFl48AuDrYvOyu1EsKBYGpY2VAjur4hfnZKOSM6JtRfaObkmTcq7P/Xhj/Cs+dv3+NfMIntGXxFX/Iz1wN9qkmBwvYVEU0SVEkHJuOxGLhjO/T3Xm/vgtgiCQlZVFmTJlCAoKeuxxiptX7nEGtKjNfGUlbgoj7r2/Q+1b22xZ8vE/idtddKoyWbBQlCjlYo1NMXz/vwyKzVVuW8WdzRciXnY3XihfdzUmx/g4W7PivUYcvxpC3+WXEISCH0coZAIDGvvmuf7WrVt8//33KBQKUiNuog+7jlqtZs+BQ1y4eY9PP/8SUa/FXSNwL+QWANOnT6dHjx4AXL9+nbp165Keni7t08HBAVEUc1RGMnnlistUpKjXMmHCBBwcHNBqtWRnZ5OVlcXmzZu5cuUKGbfOoKjbVcrotfDsyG0c8eg7iezYu8Ru+A5dUjQA2vjwYnO/WbDwqiCIBsqpM4vV++BlUmzeMB+1Kv+yu/BC0Sjl9G1Q2mxZ6r1g7m/+CZ7D1NzPvWrluU4URVq3bo3BYABAr9cjl8u5fv06WanJjHznTbIjb9C2biXCQ27h6+tLxYoV+eSTTxBFkV9++YXKlStLxqQgCKjVaq5fv55rmc3O7duizEoq8HMsjBg9upFMnTqViZN/YPritfy2bgeLN+7mTrhxAJVx85TFmCwgTC8llVtpvN5fgG3N9gDY1e3yMrtlwYKFXBAFGSsmvk+tWrVYunSpWQ1vCwVPsfFQVvCwo0FpJ07fTXjZXXkhLH03Z6nJCxcukH79CCVUI4nSagrsWHVLOfJa7ZJ5rp89ezZhYWEoFAop3vGnn37C0dGRbt26YTAY+OCDD5g/fz49evRg06ZNbN++HZVKxaeffsqMGTOkfbm6upKenk6DBg1YuHBhjmM5OTlx5MgR7JWVkFVuU2DnWFhxVEFax4/xbeDP/XTzWFJXUUSXEoeAaBmhPwcEuQKXziOwrtIcTZk6L7s7FixYeAjRoMdTlsyCVYv49ddfGTRoEOPGjePjjz/m448/xt6++OZWPC+KhQ6libD4dFrPOIDOULRP2TPzLg7XtpKeno5Wq+XmzZvcv38fR0dHEhMTqVWrFmltPkNr5Wj0Vj6DoWEyUEo7a1jU3Yc7d+4QEhLC3bt3zf6PiYlBq809plEQBERRxMfHh5iYGMqVK4erqyu3bt3i6NGjLF++nAkTJkjtu3fvzpYtW2jbti0A+/fvN8vuNtGvXz9GT/yJNxeff+rzepWwGIkWLFiwkBOT9mfkog/RJUXTsGFDPv/8c3bv3s3y5cvx8PBg+fLltGzZ8mV3tUhRrAxKgLWnQ/ly46WX3Y0Cx2Rc6MIvUeLaBnx8fLCxsUGpVJKamsqGDRuwtbUlNTWVN998k+joaIJ9X8eqZGXgKbXORBER0EXdIHH912ZT0S4uLpQuXZqKFStSrlw5Ll++zObNm6VNHR0dKVeuHNWrV2fZsmX4+vpiZWVFcHAwrq6uxMTE5HrI8uXLc//+fXr37k18fDxnzpwhJCTErI0gCCgUCpKTk1Gr1QxYfJLDN+8/zWW0YOGpsRj2FiwUPmyvbuHq34vMEj9dXV2ZMGECGzZs4PDhw4wZM4ZJkyahVltKMxcExc6ghOdTwaUw0K2GB7Pfzl3oWqfToVarsba2Jjk5mb/++ouePXuy4tBVvtl5G70IRgn4vF+MphenqNdxf+t00q8fAcDGxga9Xk92drYUJwng5uZGfHw8er0epVKJVqtFJpMxZ84cxo0bR2ZmJkePHsXPz4/vv/+enj17UqFCBcaPH8/kyZNzHF8ulzNq1CjS0tKYP39+jvW9evVi/fr17NmzB39/f8Li02n780GydYYcbS1YsGDBQtHC9I5KOLiM5OPrqVevHi1btmTWrFlm7yZHR0c6d+7Mhg0bqFixIps3b6ZcuXIvsedFg2IZqW+q4KJSFI3Td9QoWTfUL09jEiA4OBi9Xk/FikZpn1u3bmFrawt3z9Ls3p9oT6xCl55sHM3lIuUjiiL6jGSUFzbwtvwE6dePcOTIEURRJDU1lYyMDLKysrh79y4HDx5k+fLlvPXWW9KUtOl/g8HAjBkzSEpKokGDBowaNQqNRsPZs2fp0sWY2PDTTz+ZHd/k/VEoFPz888+5GpMAzZs3x8vLi507dwLG7PbvulV72stpoZBhuv8eXWbBggULJkS9DnTZxO34hZQTGwA4e/YsM2fOpFatWtK7TyaTkZiYyOrVq/Hw8CAhIYG2bdsSHh7+MrtfJCiWHkoTYfHpjNt0icM37yNQeEo0yhAxIGDISjeW7JLJeNRzqJAJVHC35dP2lWhbxeOJ+1y3bh19+/Zl2LBh9OnTh3fffZfQ0FAAatasSbt27WjXrh3NmzcnJt3AzxsOsvfgYcLuBOOeGcEH/d8kMTGR33//nfv370vZ1yNGjMjzmL1792b9+vUA2NnZYWdnR0RE7tJNcrk813jIp6F06dKULVuWkydPcuvWLUqUKAEUXY90Ucfkbci4cw77OweZsWAZGVo9SkFk6U/j2bX/MPZ+vbAu3xDBytoy7WzBQjHE9L7Uh18m6u+fJSkvQRDw9fXlzp07UltfX19CQkJwcnIiJSVFShJVq9X4+Phw5MgR3N3dX8p5FAWKtUFpIjg6hVUnQ9l/I4bQuPQCNSwVMqjmaY+dWkGjsi54O1lzJiSBI7fu53oseXo8CVePUEOdwLiP36N9+/bEpmRx4k4cGdl6NCo5fmVccLd/upiPMWPG8PPPP1OqVClCQ0OxsbGhcuXKbN++HQ+PBwZpUlISI0eOZNmyZTRr1ozPPvuMrl27IvtXdiYjI4Nff/2Vzz//HIAhQ4Ywc+ZM7OzszI538eJF6tevb1bF5uEs70eRyWQ4OTkRFxcHgL29PR07duTPP/+kWrVqBAcHM3HiRBISEpg2bdoTz1epVNK2bVu+/fZbGjZsyNrToXy1+XKRT8gqKoiiSOr5nViHnyL86hn+/vtvunbtyrFjx+jSpQuJiYmUL18ef39/5s+fj0ylwc6vF45Ner/srluwYOGFIELqfZKDjlNejOTLD9+lZs2a9OvXj1OnTkmtunfvzoEDB0hMTDTb2svLC3t7e65fvy4NXsuWLcv58+dzvM8s5A+LQfkIaVk6QuLSyNYZUClkuNlaERqfzt34dASglLM1UUkZzD1wi+CY1FwNlPx6D03HytIZ2LZ5Ez9O+BRPV2eWLVtG06ZNC+ycdu3aRffu3cnKyqJRo0Z89913DB06lLfeeoupU6dK7Q4ePMg777xDQkICs2fPZuDAgbl6feLj43FxcWH48OEsX74cd3d3li9fTtOmTQkICGDGjBns2bMnz/7IZDIqVarEwIED+frrrzlx4gQLFixgyZIlksG5fv167t27x6hRo2jUqBGOjo7s2rWLxYsXM2TIkFz3a2VlRcOGDTl9+jSZmZlmywcOHMj/Jkxh/N/Xi4101KuMNj6CyIXDEEWRcuXKERQUxMcff8yCBQuQyWR89dVXXLp0iU2bNknbyOVybBq+iVPLdyyJMhYsFGFGtinP+y3KoVHK2LZtG9OnT+fw4cM0b96cZcuW4ebmxsCBA9m4cSMAJUuWpEKFChw4cMBsNszLywsHBwdu374taVQ2aNCA48ePI5fLX9r5vapYDMr/SExy5n/yHiYkJNC/f3927NjBsGHDmD59ujG2sQBIS0vjs88+Y968eVJSTGpqKgqFAmtraxYsWCAZZ99//z3ffPON9IP09fXNc7/p6enY2NiwatUqGjVqxDvvvMOxY8dwc3MjNjaWEiVKEBcXl0MuaOrUqXz55ZeUKFGCKlWq0Lx5c/744w9p6n3gwIEsX26si9y4cWPatm3LpEmTqFu3LmXLlmX9+vW0adOG/fv3Aw9kh0wIgoBcLmfq1KksXLiQqKgo7O3tCQsLk9Y3bdqU4V9+x4j527Gt6IeoVFsMj0JIWvBJ4jZOQhRF1q1bx9SpUzl//jze3t4MHDiQGTNmSIMGa2trPv74Yw4cOMCpU6ewr9MRhzZDEWRyBHmxkdq1YKFIYxokftq+Ih+3rpBj/YEDBxg0aBD3799n9uzZvPvuu9y6dYuqVaui1WoRBIEKFSpw48YNvLy8pPArHx8fRFEkLi6OzExjVZ2ePXuybt06aWbOQv6wXK3/iLu9mm61StKnQSm61Sr5VMZkamoqnTt35sSJE2zbto358+cXmDF58uRJ6tSpw9KlS5k+fTparRZ3d3dsbGwICQnBYDBIWW0//PADEyZMYOLEifzzzz+PNSbB6PEDyMzMRKfTkZ2dDUBsbCwymYyoqCgMBgNKpbHGtKursY64ab95jWHOnj0LGEeN0dHR/Pjjjzna37x5U/rb2to8bs6UvPHpp59y/fp1kpKSJGPStP7IkSP069qGuG0zCJvVh7CZvYhc/ikNddfoVhpal3eklLP1Y3LdLbwIbCo0wq33t6icPRk5ciTnz5+nQoUKJCYmMnnyZDIzMxEEAXd3d/r06cO0adPIyMigdOnSJJ/fReSiD8kMDQSMAscWLFh4hRENiP8m3DiEn8i1SatWrbh48SK9evVi8ODB9OjRQ5Kis7a2RhRFbty4gZubGxEREZQpUwaAiIgIoqOjMRgMeHl5AbBhwwbGjBljSf57SiwG5UsiMzOT119/nStXrrB7924pw7kg2L17N82bN8fJyYkLFy7g5+cHQNWqVQFjhjdAuXLlmDt3LuPGjWPixIlMmDAhX25+uVyOXC4nICCAOnXqEBsbi5ubG2DM4u7cuTN169bFyckJAH9/f5RKpRTXkpycnKvYeXBwMGD0JF64cIFWrVoBcOfOHdLS0li8eDH37z/QlUxLS5OMWxMVK1akTZs2CILARx99hJOTE2XKlGHLli3s2LGDHj16SEaowWBA1GaSHRHEXzO/YM7wriwd0oyYAytA1L9wo9Ly8DJHXaomHoN+JdW9JmC8P1JTUwHjvWxnZ0dcXByrV6/Gzc2NK1eucPfuXQB0SdHErJtAxMLhpF7Ybbm2RQTL91i4eN7fh1xmfAo3q+BG6rovSL+8jy+++EJ6DjyKvb09S5YsYdOmTRw8eJAuXbqgVCqZN2+e1CY2NhaNRkNoaCienp7o9Xpq1qxJVlYWERERODs7A/DLL78wceLE53p+RQ2LQfkS0Ov19O7dm+PHj7N9+3bq189b7udpOXz4MD169KB9+/YcPnyYihUrEhho9NSYDMtbt25hZWXF8ePH+fjjjxkzZoxZRZonkZiYKE1F+vj4EBoaSmxsLK1atcLf358dO3Zw48aDrGrTdPtff/0FGBN7zp49i1wuJyUlRdIHM/1/7949AgMDWbRokXS8nTt3MnTo0BxJPWlpadSrV0/6fO3aNZYsWUKvXr1YuHAhM2bMIDExkVGjRmFnZ8fGjRuJioqSRqcmPD09CQkJ4b2Z68ms+SYGZC8s69/kQbNMvZsjyBUIChUunUdg39g82ebq1askJyej1+vJysoiJibGTGfOhDYujPiA39AlRr6oblt4jjwa5mLh5aJPiUPU63L9TuQCVHK35ftu1ehe2wsrmSHf350AlHaxZkCj0uwd3YKV7/kxb/okDAYD0dHRZrH/udG9e3d2795NYGAg3bt3p2fPnjRs2FB67mdkZEgyeoIgEBgYyPjx4xFFEY3GWJa4Xr16fPfddxw/fvzpLkoxxhJD+RKYP38+w4cPZ8eOHXTq1KnA9nv27FnatGlDvXr12L59u/TDGDRoEEuXLmXt2rX06dOHkSNHsnPnThITE2nVqhXr1q3L05jR6XScPn2affv2cfXqVW7dusX58+dzeBhlMhkymSzPLO7HYWdnh4+PD1evXgXAxcUFnU5Henq6tD9RFBk/fjx//PGHmfTQoUOHaN68OXK5XDIovL29Cbx6nQ49+5OUksbUH39iyg9TuHTxIu/0f5up4/+Ho61GEl4XlGoUTp6M/nkF6y4lPnX/nxVRFMGgRxsXjtzGEZm1g8WofAxxO34hNTDvZK/H4eT/PnZ1uyDILIH2rzKiQY8+JQ6Fg0XapTCQcmwt8z/uRoPmbQmMznhsLkG/fv0IunmHBeu2mSW5VvE01tR+OBnW18UGG6uc8c9VqlQhKCgIlUpFeHi4NDOWFwcOHKBTp0506NCBDz/8kA4dOvDtt9/yzTffSG3Kly/PzZs3mTt3Ljt37mTbtm3Su7NixYqoVCpOnDhhiafMBxaD8gWTkJBAhQoVeO211/jjjz8KZJ9arZagoCDatGmDu7s7M2fO5P79+3h4eKDRaBg4cCC3bt3iyJEjNGnShNdee42rV68SHR3NjRs3KFmypLQvURS5dOkSGzZsYN++fZw/f56MjAysrKxwc3MjOjo6z9rcJqysrMjKypKy6WxsbChTpgyXL19GEATUajUZGRlP1J4UBAEHBwczuYc6depw/vyDGt2vv/46mzdv5sSJE7To2hvbOp3QlKuP0tEz7xrlooiTWsDOSs6tsEgU9m4v1ZCzZCQ/mUdr86pUKil2Nz8oXXzwGjrvyQ0tFHq08RFkRVzHplpLBMHykn+ZDKvrwNhezZ7YLiEhgbJly/Lhhx/mWgUtv6xcuZIBAwZIf/fr1++J22zfvp2uXbvy66+/MnbsWL744guuXLnCmjVrAKhUqRLXr1/Hzs6OoKAgfHx8AOOM2eTJkxk/fjzLli3jnXfeeeZ+FxcsBuULZtSoUSxevJgbN27g6en5xPZ6vZ6IiAju3LnD0aNHOXnyJHfv3iUqKoqUlBSysrKeyitobW2NVqtFq9XSoEEDWrduTVJSEjExMQQGBnL37l2z/QmCgJOTEyqVKtdpxdyMQg8PD7Kzs0lISMDV1ZX79+9z+vRp3n77bYKDgylRogQKhYKIiAiaN29ObGwsDg4OZlMLNjY2ZGRkAJgd02RI2NnZkZKSAsCxi9f57XQCh2/eRzTo8++FEsW8jU4LhQ5BNNC4nCvLBzXgxx9/5Kuvvsr/tko17n2+x8qrosVL+YojiiK6xGiUTiVedleKPbP71qZbrZJPbGd675me/8+KTqdDpVKh0Wjo2bMny5Yty9d2gwYN4u+//8bPz4/k5GT27NmDg4MDWVlZkgNEJpMxdepUduzYwZEjR9DpdAwZMoSkpCROnjxJSEiIZeD/BCwG5QskJiaGkiVL8t133zF27Nhc2+j1es6cOcOePXvYtm0bZ86cyWGwKZVK7O3tcXZ2xsnJCSsrKw4fPkzp0qVp06YNer2exMREoqKiuHfvHvfu3ct3H62tralWrRodOnSgW7dupKamMmbMGC5cuAA88D4qlUqGDRvGnDlz0Ol03Lt3j5CQEO7evUtISAjHjh1j9+7dZn1+7bXX2LRpE6IosmjRIoYOHYqNjQ2pqakIgkDlypW5du0arVq1IioqSpraqFatmplXEoxT7AaDAdua7XHpMBy5QvlvPXILRZ3YpZ+QHnXnie2ULj6Sx1rh6FmkXwbFzctd3M63UCKKnBrn/0Rlk6tXr1KzZk0mT57MF1988Z8P6+vry71793B1dSUiIiJf90FkZCQVK1akQYMGHD58mNTUVObNm8fo0aOBB46TWrVq0blzZ8aOHYtOp8PHx4fFixfTvn17Ll26RPXq1f9z/4syFoPyBbJq1Sr69+9PVFSUWXWatLQ01qxZw86dO9m3bx9JSUmSwQRQtmxZWrZsSffu3fH398fa2howJq8cPHiQKVOmcOfOHRo2bEhYWBihoaFPnJZ+FmxsbEhLS5OmsJs2bZpnXe2srCxpBOju7k5MTAwlSpQgKioKMBqYDRs25OjRo4CxbOL8+fPp1KkTJUuWJCwsjFWrVjF06FBJIkYURcmgBbBv3NsiYl3MEA16Us5tJ2Hv73m2UTh44NzxIzRl6j6dx/oVw3TfZ8feRenkCYLMortp4YUhF+DWlMerk2RnZ9O+fXvCwsK4evVqDlWOZ6Fr165s374dMGZsm2TpnsTUqVMZP348BoOB5ORkbG1tUavVUuhMuXLlCA0NZdGiRQwcOFDa7vbt21StWpXJkyczZsyY/9z/oowlAOUFEhAQQO3atSVjMjIyknHjxuHj48P777/PqVOnyM7ORiaT0b17d1atWkVUVBS3bt1i3rx5lC5dmpkzZ+Lv74+bmxve3t7069ePK1eukJmZyYEDBwgNDX2mxJj8kJaWJv1/+fJlli5dSteuXVm3bl0OL6qVlRW9evUC4O233wbg/v37rF69Gk9PT7RaLUePHkUQBARB4O7du1Lg871790hJSaF///5cvnwZV1dXSdPSZEza1myPU0tjTIvFmHzxiKL4UvQdBZkcTdm8VRFsa7bHc8hvqEvVlNoXNUx6q5l3L3Jv4QdELv6IiIXD8627afEhWPjPiCIVPR5fnlCv19O/f3+OHz/OokWLCsSYBJ65gs17771nFj4lCALVqlWTPjs5OaHVaqXkUBMpKSm0bNnSbMbNQu5YDMoXhCiKBAQE0L59e65du8agQYMoXbo0s2fPxtvbG6VSSUJCAu+//z7Xrl1j1KhRBAUF0atXL1xcXFCr1dSuXZuvv/6affv2kZ6eLtUihQdxhlqtNt8vDLX6wVRFlSpVzJJzwOiRVCjMPR5KpZK+ffvi4OCAwWBg+/bt9O3bF6VSSc2aNdm3b5/Udtq0achkMnbs2EHPnj2RyWS89957jB8/3uy6mPr77bffSlqZPXv2BIyjxqNHj5r98BUOHjh3+DBf52jh+fGyjDWFkyeCMuc0m33j3rh0HoGgUBVpT51pAKXxrY11xSaAue5myrntaOMjcjwHRFHEkJVuGYA9JRYDPBcEgU/bV8pztcFg4P3332fjxo2sXbuW1q1bv8DO5Y6bm1uOoh2dO3eW/nZ0dEQulxMXFwcY1UYATp8+Te3atSWdZAt5U3SfuoWMjIwMoqKiuHPnDjVr1sTDw4N3332X7du3ExYWxsiRI3F2dubo0aPUq1eP1NRUrK2tJa3GsmXL0rVrVxo0aMCWLVvYsmWLmXyOCQcHB7RaLenp6Y/tjyAIZvWur127lmO9ySP5MFqtlrVr15otMyX6XLp0CX9/fzQaDe3bt+ftt9+mYsWKBAUFsXDhQgIDAwkJCeHjjz+mVKlSUsnFhzFlgu/Zs4egoCAqV65MxYoVOXHiBN9//z2TJk3CreeEIm0wvAqYQhBehnEiCAIKJ0/E+DDJG1/cPNamc3Rq+Q6GtARJTkkbF0bC3t9J4HdJDkuQKxH1WnQJkYjaTNz7fI+mTB1LqEg+MBmTlmtljkrQ07aKR67r0tPTGTFiBEuWLGHFihX06NGjQI9tmqV6FsqUKUNISAgqlQqAHj16SFnn5cqV48CBA9J7r1GjRlKCTn4SaC1YPJQvjNu3bwPGkk6jRo3i9ddfZ+HChZQoUYL69eszbdo0vvnmG1JTU2nRogXOzs5kZGTQtm1b9u7dS9++ffnrr78YMGAAGzZskGIk5XI51tbWkm5WUlKSZEw+rJvl7e1t1h8/Pz+qV6+Op6dnrvpaTzMqT09PN5vyzsjIYMuWLfTp04egoCDAWBbL1tZW2m9YWJiZoPvDfTC1qVevnpRQpFKp+P7775m/6QAqt9L57puF58fLfMEKcqVkTCocPHBqN6xYepJEUcSp3QcoHHK+3EVtJtqYO2RH3kAbcwdRaxxAxqz7mtTAfdL2FvJGEATSLu2zGJMSxhmlbo45nRlg9ObVrVuXVatWsWTJEvr371/gPbhw4QJWVlZ4eHhIXsT8YvI+mpwppqpxgJR9fvHiReBBZTlT3L+FJ2MxKAuI6OhoNm3axMSJE+nduzcNGjTAx8cHW1tb5HI5NWrUAIwP8OnTp/Pbb78BcO7cOfbt24eDgwPlypXj0qVL7Nixg1q1arFu3TpKlixJp06dmDJliplH0s7OGL+i1+vJyMjA1dUVGxsb7Ozs+OGHHwgJCZFkeDw9PQkPD2f8+PF4eXlhZWXFrl27eP3119Hr9TniSsBoqDo6OuY4TysrK/r27SslBj2MIAj07NmTTZs20bVrV7PpclEUOXfunFmy0JkzZ7Czs0Mmk+Hk5JRjej09PR1vb28GDhwoVfv542q2Ue7HQrFG1D+4j5w7foQgkxfLl74gCAgyOc4dP3qq7eJ2/Ez0qi8xZBiltyyGpTmm65FwcBlxO2aRcHC52fLii0D2rZO4iUlmS7VaLd9++y2NGzfG1taWc+fOMWjQoAI/empqKtHRRh3a9u3bP9VvPiMjI8dM3JIlS6S/9Xo9Op2O69evY21tTWxsLDY2NqjVaklWyMLjsWR5PyPJycksXryYDRs2cOHChRxTzAqFAjs7O6ysrKSs7ujoaBQKheRZcXJywsvLC71eT1hYWK5TzCYe1ns0TTdWrVqV7Oxs6tWrx5UrV0hISODw4cNSeamlS5dKP+pPPvmEWbNmoVar0Wq1Zlnkcrmc999/nw8++IAaNWpw4cIFZs6cydq1a9HpdJQrV04q1/jwdIPps5ubG/fv35cetk5OTqxcuZI2bdpQpkwZoqKiUCqNHiW5XE6JEiUIDw83Oz9BEKhRowaBgYFm1wgeSARVbNCSrLafPdP3ZeFFIMILqIAuiiL3ZvVB0GcjOHhaBMv/5d7CD9DFhT+54SNoyjXAocUAVK6loJga5o8i6rKJD5hnVpnJtmZ7nNoNMw5eimnIjSwlhlKXl+Hg4MCWLVtIS0tj6dKl/Pzzz4SEhDB+/Hi++uorKYkyv8QkZ3LiTpxZpR0bK0WO6jmL5s9l1KhRAKxYseKpPKAbN27kzTffBIzlfB0cHFCpVGi1WhQKBW+//TbLlxsHDp999hnr1q1DJpNRu3ZtIiMjKV26NOvWrXuq8ypuFM9fxTNy7949Pv30U/bu3cv9+/cBoyHk7e1Njx49aNq0KfXr16dWrVqoVCq2bdtGjx49GDBgAL/++iuurq5otVrKly/PmjVrqF+/PqIoMnz4cBYsWMA777xD2bJlpZgO042u0+nMppQ9PT2ZPXs2b775Jo0aNWLv3r0IgmBmTAKSdqS3tzfdunWjevXqkofQ09OT2NhYFAoFYWFhODs7S9vVqVOHFStWMGXKFEqXLi1NC2zbto1vvvmGM2fOYDAYJOMyNjaWbt26sW3bNlxcXIiNjaVLly706NFDGkn+888/iKIojfLWrVvH6tWr2bJlC2A0EgIDA1Gr1TRo0ICjR4/mqPEd61QVO0ssUyFGQNTrnvvLVpcQiT7LOIBzqtOpSEsD5RfRoMeuTufHyinlRcat02TcOg2AzNoRq1LVkSnViAY9rl3HFLvfm5iZytxupXhtunmZz9TAADLvXiwWklRm/FsAIisqmKilo+n6yScsXryYsWPH8vvvv5OYmEjPnj1Zv349derUeeLu0rJ0hMSlcTT4PmtOhxIan54vDWEB0Cfb4+w/jIxLxgTX/JKdnc3YsWOpWrUqV69eRSaTERAQYBY6dvv2bQRBQKVS0bNnT6ZNm0bNmjWl8sNDhw7N9/GKKxYPZT44ffo0n3zyCSdPngSMHrhGjRrRv39/evXqJQX4PsyhQ4do164drVu3ZvDgwQwdOpTk5GQUCgWtWrVCrVaTlpZGcHCw5K0zefwUCoXk7XvYkPTw8ECn00lxIJUqVSIxMZH79+9z6tQp6tata9aHChUqcPPmTcqUKcOdO3ckz6adnR2lSpXiypUrLF68mMGDB+d57iqVCl9fX4KDg/Hy8mLZsmV89dVXXLhwATs7O0RRlPozatQoFixYQJMmTQgPD+f69euAUX/z7Nmz/Pzzz4iiSIUKFQgODmbkyJF0796d/v375xBfd3d3x9vbm3PnzknLvD5ZgdLGKX9fmoUXjyhiyM5AZmX93JIYHtWh9Br2O0onrwI/zquINj6CiN/fz3f7kiVLEhsbi06nw9XVlbS0tByzJF7DFho1LosBxvKeWqKWjqJEyZJEx8Yh6rXoUxOQ2zohU6gQAblcgWDjjHWlJliVrIzczrXIG92GzFQi5vRDr9ejUqnQKayxr+RHozadaNLYj9Y1y1C3lFOu9bcBgqNTWHUylP3XY7gb//iE0SdhMuSbl3dlSo8a+DjnDL96lJkzZ/LZZ5/Rq1cvzp49y40bN9BoNGYzbhqNhoyMDH766Sf27NnDrVu3iI6O5vXXX2f16tWEhoZKZRkt5I7FoHwMW7duZfTo0VJCTd26dfnll19o1sy8dmlcXBz//PMPV65c4e7du1y/fp2TJ0/mKFNYEGg0GmxtbUlLSzObZn/rrbf4+uuvqVSpEjKZjKysLDQajWSYtmjRgj59+vDRRx/RuXNnduzYQdmyZc2CkvM6Xp06dTh+/Dg1atTg1q1bbNiwgf79+5Oenk5qaipTpkxhwoQJAHTv3p3t27fz5ptvEhUVxYEDB7C2tqZUqVJUr16df/75h/j4eN566y02btxI2bJlmTBhApmZmUyZMiVPaQZBpcFn9J9F/sH9qiPqdcRunIx7r2+e2zFMU7uWe8IcURQJm9lLSr55HHZ2dmRmZqLVarG1taV8+fLEx8fnUF5w8n8fu7pdiocnDtCnJSKzdjC7p/I7ODI9awUociVdRVHk/qYfcGg5AKVTSfhXP/hRlHKB5hVcGduxChU87AiLT2fcpkscvnkfuUxAbyg4c0MmgFIu49tu1ejboFSe7cLDw6lWrRr9+vUjICCADh06cOLECclZYW1tTVZWFnq9Hl9fX2bPnk23bt2YNGkSX331FdWrV0etVnP69OkC63tRxTLlnQvp6en07NmTnTt3IpfL6dKlC3PmzJGmk7Oysjh27BgBAQHs2bOHc+fOIYoiSqUSg8GQQ+S7IMnIyJBqXD/MmjVrpGL3KpUKg8EgPeC6dOnCr7/+yrJly3B3dycxMREgXy78hg0bSlluX375Jb///jt9+/aladOm7Ny5k02bNvH1119TvXp13njjDTZv3kzVqlX5888/sbOzo1atWgQGBkqamnPnzqV8+fKsXbuWtWvXMn/+fN566y3KlCnDyJEj8fX1ZezYsTmCp9W+tS2Gw6uATE5WZDAZd86hLlWzQKe/Rb2OzNBAKU5Q4VjCck88hElOSRuTe1lK08yHjY0NycnJgFGLtkOHDri5uSGTyVixYgUpKSnSNqkXdmFfv9sL6f/LwmQwigYDchvHHOvze4+Z2omiiED+DdFXAUEQcHtj3BPbafUi/wTF8k9QLCUd1MSmZUtGZEEakwAGEbJ0Br7ceIn7qVl83LpCjjaxsbG0a9cOR0dHBg0axLx58wgMDDSb+RJFEb1ejyAIbN68mZ49e9KuXTtSUlKws7Pj8uXLHDhwoED7XlSxeCgfYf/+/XTv3p3k5GRq1apFQEAA7u7ugDEje+bMmWzatIn09HTJA/iwnmNeWFtbo9fr/5OGVn4xTW0DZlp0ckQUWQkYsjLIzs7G09OToKAgSXIoN6ZMmcLkyZNJT0+ne/fuLFu2DH9/f65evUpWVhYlS5bk2rVraDQaBgwYICXy2NrakpqayrZt27h58yajP/sSa4/SrFi1BhuNFZ2a1ad0yRLcvn2bs2fPMnPmTNatW4ednR3vvfce7u7uTJ8+ndjYWABcu4/DpnKT537tLPx3YjZPRRsZjOeQ34wi4wXwUjVOR2YTuehDdEnRAKg8K+I5cOZ/3ndRInLZGLIjbxToPt37fFfgg4PCgvScfE6GX1EyKgs7P75Rgz4PeSoTExNp06YNERERHD58mJkzZ7J48WIpbtI0xW3i999/Z8OGDRw9epSAgAC6du1KVlYWnTt3Zv369S/8fF5FLAblQ4wZM4aff/4ZmUzGlClT+PzzzxFFkZ07dzJjxgz279+PjY0NGRkZOaR2lEqlVBO0IFGr1RgMhjz3bWVlhbOzM5GRkSiVSrRaLUoXH2zrdEJTrj4KR88c0ze6xEgybp0h9fxOOjery+bNm/M8/pkzZ2jQoAEqlQobGxvi4+OJi4ujWq26JOgUyJVWjP3iM8YMHcC9u7epVKmSlNGudC1F6bb9kJWsQabSzvzBKopoEyNpXNqeHwZ3poKHHaGhocyePZulS5cSFxdnlole8uMVKGwt8ZOvAve3zyLt0l5sa7bHpfOIAttv3I5fpKxbtVqN3t4Tr8FzCmz/RYGIJZ/k6aF8VhQOHgU6OCgMiAYDgkxmMfiKEDIBDn7aGh9na2JiYujRowfXrl3jwIED3L17l27dHnjaTcolJrp06YJKpWLHjh3s3LmTjRs3Mn/+fORyOUFBQTkq7FjIHYtB+S9ffPEFP/30Ex4eHhw+fJgKFSqwe/duRo8eLXngcptqVqlUz8WQNBmHJkw1rx8Xl6lw8Mh3BqJpfcadc8TvmosuKRqlUom1tTUODg74+vpSsWJFqlWrxvTp00lJSSFD6cDQn5ZzMUZHeGKmWZzQw4Zqxs1T2Dd646n6YZMcSgNuUq+yL61bt+bWrVt0794dAJmVNd6j1lke/K8IsVt+ZMqwNxk5ciTvrzhDwNXoZ35xm7ZLOLiM5OMPvAS1atUi8Op1fMast9wX/5KfGEp7e3sUCgXx8fEA+Pv7M3fuXAwGAwMGDOD8+fOUKlWKO3fMjdKCHhy8LERRRJ+ehCKXqW0Lrz7udlZ8VT2dIUOGIIoi27ZtIyAgQIrxB/Dx8SEsLEz6XL58eerXr8+GDRvYtGkTZcqUkXSj//rrrwKv9FOUsRiUwA8//MC4ceNwdXXl+vXrqNVqRo0axcKFC828ZCbq16+Pvb09//zzT4H249FjyWQy1Go12dnZZrqMufGsGmmiXodo0JOwZwGpgQG5tnk6Q9U08jcgCPkXgn24H2nXDmHl6o2oUOPiXYZRn37J3NMJ+d6XhZeHKIro1n9KSNAlFhwJYXrAjf9sTKZfP0bspim5trFkeT9A1Ou4P/dtHBwcSE5Ofqyu7aMIgoCVlRUymSzPsq32jXvj1PKdV8qrJxr0pF07jO7KHrp06sCfa9fi+e7PRcrbasGclMA9NJHdonXr1sycOTOHgsjDODg4ULJkSW7evMny5cvx9/encuXK3L9//4kKKBZyUuwNyj/++IPBgwfj4ODAtWvXOHPmDAMHDiQh4YEBY21tLRl6jo6OuZZiksvlZsHuj9aqbtu2LTNmzGDQoEFcvnyZrl27EhwczOXLl1Gr1Tg4OBAdHf1M5/BfH/QPvEDLST7+p9m6FynmKwXHv0IvLAvmiHododO6U6/XJ9wv16HA9vvwdLcJmUyGQ5shxSoL+XGIokjCwkGkxN+XlgmCwGeffcann36Kra0t9+/fJzQ0lJs3b3Lt2jXWrl3L3bt3AeNzztramuTk5DxnXV5Fce+H5ZSKcjyohQek7ltA3Om/zZZZW1ubDZZM4utVqlRh5cqV2NnZUa9ePeLj4/n666/57rvvXmifiwLFupZQUlISH374IUqlks2bN9O3b1+6detmZkxWr16d9PR0MjMzyczMNDMmBUHgtdde4/bt2+h0Oj777DOpfKDJOFQqlVy8eJG9e/dSsmRJLly4gMFgQCaTcfnyZcBYV/RZjUnbmu1xavmO1J9nwbSdU8t3qNDhHWm5fePeuHQeYRzNv4AHsKkfFmMyJ6IoIv/3ssgL6eURRRFdXBgKBw9iSrUqsDJ1edWrNhgMpJ7faTEm/0UQBGxLlDFbtnXrVn788Ufc3NzQaDT4+PjQtGlTBg4cyNSpU7lz5w5LliyRZkcEQeD06dP07Nkz12OkBgYQuehDMkONpVBFw+MVLQqDv0Lh5ImgVKN08UFTpq7FmCwG2LQcJD0vBEHAxcXFzJg0FQwZM2YMp06d4vZtY/x/fHw8P/zwg8WYfEaKtUH58ccfk5mZSefOnenYsSOHDh2SjBlbW1sUCoVk9D2MlZUV33zzDfHx8WzdupUyZcpw48YNFixYgE6nQxAEsrKy8PLywsPDgxEjRlC6dGk8PDwkiYK//vrrP/df4eCBU7thBfrizqz+OgoHjwIxVC0UDCaP7eohfuwZ1YKWXjK08RGF4mX9MIIgMHdYR6q++32B1tZ+XL1qbVwYGXfOIeofHxJSXEhJexDn3b9/f7p27frY9oIgMGjQIBYuXCipVbRr147x48fj6OiY6za6pGhi1k0gYuFwnOMuo0uIMFZTeQhRFDFkZ2Isx/lyMckp2f5bVclCMUCuwLnjR5JUlqn4hokGDRpw6NAhRo8ezTvvvEP37t3R6XRs3LiRL7/88iV1+tWn2A7VgoKCWLlyJSqVii1btiCTyczkdrKysqTa0wqFQpL76dChA8uXL5ekhAD27NkjJZCA8WFqZWVFREQECoWCRo0akZKSkkM02ESjRo2kKjyQMyEnL5w7flTgL25kcly6jkFVorxl6rmQIAgCb9YtSaOyLoSHh7P60x7o1Y54DvkNClEsmKNGSbXSHiRpvAq8orcgV6ApUxeFi3eOetXxu+Yar4WlDjVZGca4yVq1arFs2bJ8bzd48GBSUlIYNWoUbm5uvP7663zzzTeMHj0ayD35UBsXxsUlXyGKItVr10Ph5EnYvUiSE+NBkOM1aFaBndd/RZAr0ZSrb/FmFxMEQWb0Rjt6wv0HCThlypRh2bJluLi4MGPGDJYtW4Zer6dEiRJs3749R7U5C09HsfVQmkS91Wo1arUaQKo1rVar0ev1uLq6AkbjUhAE5s2bx86dO82MycOHD/Paa6+hUqkkl3rVqlX5/vvv6dOnD2q1mo0bN5oJqZqoU6cOcrnczJgE8mVMPq/pG0GuQO1TrUANVQvPhmlw07y8KzN61QbgzTffJCsrq8AHEwVBD9dYenw+87l5gUz1qh9FlxRNwp4FhepavAxEUUSXEImbmxtXrlxh165dT7X9yJEjGT16NMnJyRgMBubNm8ewYcMAYy1ka+ucJe5M9+jlC2e5cmQ3pW1FtDF3sK3RpnB5AwUZCsfiUULSghHRoMe2difA+J5fuHAh8+bNY+rUqVSrVo2VK1ei1+sZPnw4d+7csRiTBUCxNCjPnTvHkSNHEASBJk2akJmZicFgkCrMVKpUCYD79++j1+uRyWRcvHiRDz74QHppZWZm8vXXX9OqVSuysrJITU2V9j9u3DiCg4P5888/SU1NzTE1adrH+fPnn7qqjjQl/5ynbyxxRi8X0z3TrpwdK95rBEBAQACnTp2iUbvXC10sWGrgXiZ+1J8Ejddz8wIJMjmasvXzOH4ACQeXGz8UslCAF4UuIZIWTRpx584dunbtyptvvsnBgwefah/ffvstTk5OVKpUiVu3bnHmzBlpXV7Z3ya0Wi3nz59HLpcXKm+gKIrIbZyL/YCjuGF6XtSrV48mTZrwySefSKFtAF5eXuzbt4/ffvtNcipZ+G8UO4MyKCgIf39/wJjdZRrFm7K0582bx7Vr18zke9avXy/pUgHs3buXcuXKMWnSJOzt7XnrrbfQ6XS89tprgDF2aeHChXnGuP2X2DfTtoXpgW3h8TzNj8z0/RoyUohdOw7n4O3cuHEDvV7PO++8g0wmo9mgcYXC+2Pqa8adc8TtmIWg0jx3L5ApwSI3ko//SdyO2Rh02cUuplI06KlfUsOBAwewsbFh7dq1NG/enNdffz1fCX+ZmZns27ePyZMnY2Vlxb59+9Dr9Zw7dw5HR8d8G2OiKGKQqwqVN1AQBNx6WOLiiiMKJ0+uXL9JVFQUDg4OAFSuXJk1a9YQHBxMmzZtXnIPixbFyqCMjo7G398flUoFYCbe6+npycWLF9myZQvZ2dnIZMZL061bN9544w3AWEd75MiRtGvXDlEUcXBw4OzZs2zYsAF7e3tOnDjxQs7jRby4LRQMi96pzzuNfSntknO60AxRRNTryI65Q/T6iYTPfhu7jGh++eUXKlWqhJubG9HR0bRs2ZK9VyNf+mDCVK+4ijqJO0v+x+3bt/l2+tzn7gUyJViYfsOP8mgWcnFBkMmZ8cHr0mcrKyvWrFmDTCbjq6++ynO76Ohovv76a7y9vfH392fp0qU0bdoUb29v2rdvj7u7O97e3iiVStzc3KTvN7fpbxOFssZ6YevPC+ZJTozCMEB9HgiCgN7amatXr9KwYUMOHjzIqVOn6Nu3r6TIYqHgKFZXdPz48WRkZNCpUydWrVolVb6xsbHh0KFDaDQadu/ejY2NDWlpaSiVSubNmwcYp6fffvttQkJCGDNmDDNnzmTu3Ll88sknaLVaY8nDf3WtnjeF8oFtIQfNy7viX8UD/yoeTKQaaVk6QuLSyNYZSM3SEZWUiUEU0ajkzPxiOAd3G3XTSpUqRZggUL9+fY4fP05WVpYkZXXg6Al8Go556e9Ha5WcsZ0q07GaJ3cSs8iWO/JPsusLObYgV2IwGLC3t5d0X0uXLi3pKZqykE36rEUdAWha3hU3KwNRUVFotVqys7NJTU1lxIgRfPvtt/Tp04fGjRtjY2MDwNWrV5k5cyYrVqxAqVQyZMgQBg0aRM2aNREEgQkTJvDLL7/Qq1cvFi9eDEBsbKwkt/K46W9B/mKeg09DcXxemozI9Bsn0KfEoilb3+jhf6TCmSEpCn34JVKvHsKp1/dF7lp9+/1kBnVrTYkSJV52V4o8xUbY/OzZszRo0IA5c+awYcMGDhw4IK07fvw4fn5+vP3226xZswYwjsDbtGnD33//zdmzZ2ndujXly5dnxYoVDBs2jKSkJE6fPo2NjQ0GgwEnJyeysrKeGGf0NDg7O9O4cWOaN2+Or68vbm5uKJVKQlLg68PJBXYcCwWPlULG3tEt8XF+gmcSiIuLw93dXQqzUKlU+Pr6smLFCn788Ue2bt0qVUpSupcp9vWrTfWqy5QpQ2hoKHq9nlmzZjF69GgzT4x1tTa4vTbmJfb0xWDQZhO5aDi6pCdPbTs7OwMQHx+Pra0trVq1omXLltjZ2REdHc2VK1cIDAzkxo0bjy3z+jgs92jhwEYp4H17G8e3rmTbtm2o1Wrik9O4G59OakYm+uwsNLpUdJlppKenk5aWxmq9HxSxUKrtnzSjmpfDy+5GsaBYeChFUWTkyJFUrVqVt99+m88//1xa16hRI/z8/Lh06RJr166VlmdlZdG+fXuuXr1Khw4dqFq1Knv27CE8PJyjR4+yceNGevTogcFgQKPRkJSUlOsD+FF1/sfxcOnFcePGMXny5FzbOUckweEjT3MJLLxgvutWLV/GJBizaw0GA1ZWVmRlZZGdnc2tW7do1KhRjrYK1YsJHk84vAbbGq1ROhauUb0pk7lcuXLcunVLUmQwGZPVqlUjNDSU1NRU9Cn3n7zDIkDCnnn5MiYBqYa3TCYjNTWVbdu2sW3btmc6rlwuzzWpUJcQaZEce87kdn1FUQRRREiNZdFHnfGvWoLY2AZU2LaaNWvWSLNtj+PcL4cIikwuMiECAuDrYvOyu1FsKBYG5dmzZzl69Cjbtm3jp59+MtNTM8kHfffdd9jZ2ZGcnIyTkxMJCQlUrVoVf39/vLy82LFjB3Z2duzevRsrKyt27twpJfSYps4f5XF1cU1UqlSJ69evA5gZpL/88gtly5aldOnS+Pr64uPjg5WVFWHx6Uzefu0/XQ8Lz5fP2leiT4NS+Wp76dIlVq1aBWCmd5rbi/p///sfg8Z8TZc5z38w4dikV6H0VIjaLNBlER8fj7e3N+HhRk1Kd3d3XFxciIqKIiUl5d+2mS+zq88Vqc75ua14pN2mYt26yOVy7O3tUSqVqFQqUlJSuHfvHuHh4TmeQ7kNfq2srBBF0ez5aNLmNU11lyxZkmXLltG1a1eysrLQaDRSbJrUN20musRIS43154g+JY7722ag8qqMIJejS4whK+Q8QlYKd+7cwdvbOBB0c3Nj4sSJ/O9//+ODDz6gVq1aj93vp+0qMmTF2RdxCi+EUi7W2FgVCzOnUFAsrnRAQAC2trZUq1aNN954gypVqnDp0iUA2rdvj1arZffu3ZQsWZLk5GQqV67M8ePHGTduHNbW1gQEBEhTRQEBAZQtW5aFCxeaeRQfRqVSodPpnjhlpFAocoidC4KAo6MjCQkJDBkyxGy5V/NeKBq9ZUzIEIpVPlWhRy4TUMgEvutWLV/GZEpKCpMnT+ann36SlqWlpZm18fX1ZfXq1QA0adIEFxcXfF1sEHj+9UcKkyTRw8hUauTOJUmIC5fiSpVKJa+99hqLFi2S2nl6evLbmlWM2vVsJU0LO4IgkBq4l7iA34nNR1swnwGRyWRSuI7pvjMNZkzbiKIoFWkQBAGdTse9e/fw9/fHw8OD6OhoNm3aRIcOHYiMjMTb21vaf8atMygsNdafG1nhV8gKvURWqPE9plAo0Ot0zJkzB29vb7O2H330Eb/++iszZ858oti9f9USOGqUJGY8WQu5sCMToHVF9yc3tFBgFAurZPfu3bRp04aAgAD0ej2JiYnSOh8fH06ePElKSookKF6vXj0EQeDUqVMsXrxYCuYVRZGDBw8SEhKCg4OD9PB8dOohOzs7X8akTqcjIyNDyigXBAFnZ2eio6Ml8fTJkyezd+9e3vlxNYom74BMgWgxJgsNcpnxu29S1oW9o1s+1pjMzs5m4cKF1KxZE3t7e3788ccc2ZemxK4qVapw584dGjduLN2vAQEBdPRvjTYh8vmczCuAKIrY1emMIAiSlJdWqzUzJn/++Wfu3r1LO7/aL6mXz5+kk38Rt2MWYNTTa9GiBQMHDmTChAlMmTKFTz/9lB49emBjY/NAiuqhZ5K3tze9e/fm0KFDZGVl8fHHH5tlvZq2cXd3x8PDQyrLqNFoAKO3vEGDBnz55ZdotVo8PT1JSEjA09OoPmGpsf58STq5UfpbqVSi0+kYN24cH3/8cY62SqWSHj16EBAQkC/JugX96xVoX18WBhH6++VvpshCwVDkk3IyMjKwt7dn1qxZ7N+/nzt37phVrbl79y6LFi3it99+QyaTERsby8iRI/nll1/o2bMn69evl9qaKukIgoBarc5zqtvR0VEyAkzGoulh7u3tTc2aNdmxY4fU3sHBgaSkJOnzkiVLeO211yhXrhzJycmMmbuBv8IswquFjdIu1rSu6E5/v1KUd7fLtY3BYGDz5s1Mnz6dEydO5PpAl8lkjB8/nm+++YY1a9YwYMAABEHg7bff5uDBg9K0rgkn//exe07en1ci9i0lhrtzB5stMpUrnTJlCmPHjpWWt5y2n7vxBZco91IRDQiigfrCbV6r6kLVqlWpXLkytra2OZrGxMRQuXJlyYvr4+NDdnY2fn5+HDt2jLi4uGdKujHdG6IoUqdOHS5evEjHjh3ZunUrcrnxfhw7dixTp07F453pqL0q/4cTtpAbhuxMwn/uZRRs/zeO9f3332f+/Pl5/nb37t1Lu3btCAwMNNNUzosxf15g4/l7Bd31F4YANCvvKhWFsPBiKPIGZWxsLO7u7mzcuJFBgwbRtGlTAgICpKzZrl27YmdnR2RkJDdu3CAiIoKmTZty9OhRrl69SpUqVaR9BQUFUaVKFeRyuTQF9DCCIODv78+ePXsAY9C6wWBAJpNJMXG1atVi6NChOUaSD9fKtbKy4vTp0zg6OlK1YXOc+s1EplRBgVdItvBf+PGNGrl6JEVR5NChQ0yZMoX9+/c/tpTm8OHD6dChA8ePH+fYsWOcOHHise3Lly9Ps8492W/drEDO4VVEFEXCZvaSYiRr1arFtWvXyM7OJjY2ViqZCjBx6xWWHg95ST0tGGSC0dvSvLwrU3rUeGKy14oVKxg0aBB6vR4vLy/c3d25dOnSU1flehrkcjmNGjWiT58+9O7dm6ioKJr0GoZ7zwnP7ZjFEVEUSb20j/h/vdNgdGDY29tL69PT04mLi8PJyYnMzEy0Wi2iKJp9/xqNBpVKhZWVFd7e3lKsvul/X19ffjiRyomQxBd8hgWDUi7wz5hW+U6MtFAwFBuDcv78+XzwwQe0atWK7OxsAgMDpXKJ7du3R6fTERERQVBQkBQftHPnTjp27Cjtq3PnzuzcuTPPYzVu3Jjjx48DYGdnh0qlMptil1k74lixPtmijPKlvIm4eIiY0JvS9gqFgs8//5wpU6ag0Wg4ceIEEw/e52x4qmX6qBAiAIc+ay09tC5dusT333/Pzp07zUpx5sWTFABkMhkNGjSgf//+zJo1i/DwcGnqccDikxy7HYfeUKR/vnlikg4yXcPSpUvj6upqVioQIDg6hXazDr2kXv538uMFN2FSpjCVlnuemGIs88LFxYUu3d/ggGu3wu/xfsW4t/ADdHHhT26YDxQKBVZWVpKXOz4+XhrQymQyKgz4nowSNY2ujFfoe8xrsG/h+VJsDMrffvuNDz/8kObNm2NnZ0dmZib//PMPALa2tjRs2JDk5GTpheTk5MS7777LzJkzAVi9ejX9+vUDMBNUzgulUsnx48fxHzgadcNeqFxLgUyeQ1QWg57s+6EkHVpBxq3TCIJAgwYNOHXqFGoPXzwG/fo8LouFAqKsk4ryN9ezceNG4uLicm2jVqvRarWP9RDJZDJcXV1p3Lgxw4cPp0ePHmRkZHD+/Hlq167NyJEjmT17NidOnKBRo0aExafj//NBsnTPphX4qhO5bAy66Jv4+PhQs2ZN/v77b3x9fQkICKBChQpmbd/6/SjHbye8Ui9EgLlv1aFLzQeZ0g8L46sUMnxdbKQM1suXL1O/fn2zxBow3ldyuVwyEhwdHSlZsiRubm4cOnRIkj1TKBSkp6cXuBfTa9hClE6Wql4FgWjQk3n3IjHrHnh9TXGver3+P5X0fRS5XI6joyNKpZIkKzdceoxDrn78gKawMLRZGcZ3qfqyu1EsKfLZHaai7w8n4oBR+w+MxmRqaiqBgYG4uLhIP9BWrVpJnqahQ4dKxmT58uWfaEwCvD1yAu9sjsLhtS+wci+DIFfkGKkLgoAgV6ByL4N7r2/wHrGaEjWbc+rUKZRKJdY1OhTZklhFhdsJ2fyxcWeexiQY6yQ/+qJWqVSUL1+eESNGcPv2bfR6PdHR0WzevJkOHTqwcuVKAJo1a0ZqaipjxhgFut977z0AfJyt+bZbted0VoUfUa9l0KBBBAYGkpKSQunSpVEoFNSqVUu6pgAJCQmEbZyGqH+1slbtrBR0qelFcHQKE7deoeW0/VSfuJsuc47QY94xusw5QvWJu2nw3XY8u46gbqvOObK0wZjtbiozN2jQIN544w1SUlI4cOCAFEMpiiLW1tZUrlyZEiVyr8JlKrX4tN7GjFunEcXiOegpSMR/S7PG75prtlyn06HT6cyMSUEQEAQBlUqFWq2WprfzQi6Xo1AopHh/MBqocXFxREdHk3H3EuGz3iJhyw/oEyMRDYY8jVelXKByiZwxvS+KN+uWtBiTL5Ei76EEY9Z2yZIl+fvvv2nWrBn29vZs2bJF+pGZZHrq1avH+fPnMRgMvPbaa/z999/UqFGDmzdvkp2djV6vl+IrH4d7t0/RVG0FovhUXhHTVyEPP0f8tpnY9J1m0XJ7BUgPuUDs2rzrJZtwdXXlm2++YdCgQVIJvMfh5+fHyZMnqVmzJidPnqRv375s2bKF9evX07NnTwB+3R/M9IAb//kcXiVEUeS7GqkM7NeXDRs20KtXL3bu3EmzZs2YPn06c+fOJT4+niZNmnD9+nWys7P5328bWXwp9yS6QoUoIgJjm7tzKMLAsdtxUgxlnpsY9AgyOZkh54nb+SuGlFh+/PFHunTpQokSJRgyZAgbNz7ICjbJB8lkMtRq9X+u7mVKiMp1nYsPXkOfLKht4cnE7fiF1MA9z/04eQnWA1LxBZm1I9bl6qFy8qRenTp80KcDHepWkjzmL+O5NLR5GcZ3thiTL5NiYVCOHTuWxYsXk5SURL169UhMTOTq1av07t2b9evXU7t2bS5cuGC2jSAIksD5xIkT+eabbwCkOt8mHs7oBnDv8z2aMnX+c58NumwEudISf/QKIBr0hP70eq7rBEGgQ4cOrFmzBkdHx6fab2JiIu7u7mi1Wvz8/Pjzzz8pX7481tbWxMXFSR6FtadD+WbrFXQGsVjEVJZ0UHH0y3acO3eO1q1b06pVK7Zs2SKtj4+Pp2fPnuzfvx+AmjVr0qNHD9J9m/Pn9VdA7DwzBaxsn36KXjSAQY939DEWjR3MkCFDOHbsmJk3ycrKCq1WKxmUD2d6y2QyrKysyMjI4L333mPLli3cv38fe3t7UlJS8PT0pFy5chw+fDiHMoVJZL5Dhw4kJCRw+vTpB9JDfb5D7VvH8iz7DyQcXEbycaPiSF7xqzKZDGtra9RqNSqVCplMRnx8PDqdzkys/mlQq9VS3PbDKJVKlEol6enpKJVKFAoF8+bN45133pG+5xfxXBIApVzG96/nT//XwvOlWBiU+/fvp02bNtSvXx+9Xs/58+cJDQ3FxcUFBwcHdDodzs7OpKenSz8e049WLpdTv359wsLCiIiIMFsHUKNGDUkkXfJMWihWPJp1DMYX9/vvv8/MmTPN9P2eltOnT+Pn54fBYMDd3Z2OHTuyfPlymjVrxqFDh6SHd1h8OuM2XeLwzfvIZUKRNiy71/ZiWE01LVq0oGzZsuzdu1eKi161ahVTp04lPDycSZMmoVQqWb16NRcvXiQzMxO72h1wajc8RzxzUcEk+5RwcDnJx/+UnlU9evTAwcGBFStWSN4nBwcHmjRpQv/+/bl48SIzZ85EFEWGDx9OUFAQe/fuZfDgwYwbNw4/Pz/i4+Px8/NDp9ORmZlJxYoV+eeff7CyskKv15OcnExmZiZqtZqmTZtib2/Ppk2bsHLxMsaCWwbIT4XpHZN4YCnJJ//KsV6j0WBjY4NWqyUlJeWZa68/Lfb29igUCqmM58ODi3bt2rFmzRpcXFyA5/dcMu0rv8oHFl4MxcKg1Gq1VK9eHb1eT2RkJOnp6SxevJjBgwczbdo0Pv/8c2QyGba2thgMBilDVy6X4+npSXh4OH5+fpw4cUKKTTHFK1lbW5OdnY28RCU8+k0Fnj7OyMKrT/Sf36CMvYGfnx+fffYZ7dq1K7B9//PPP/j7+yMIAgaDAR8fH8LCwujSpUuOOszB0SmsOhnK/hsxhMalP/eKOi+DFqU07Js0AGdnZw4ePEhGRgY//PADK1euJCkpCS8vLzQaDXfv3pWkvVxdXfHx8UGpVHJf6YG++bCXfBYPMBmBosGAICu4sPb2DrEcXTGN8PDwB+Uo/33c//HHHwwcONBYcSc1FVdXV3Q6HZ07d8ZgMLB9+3ZKlSrF9evXUavVXLlyhSZNmpCWlkabNm3Ys2cPU6ZMYdKkSXTp0oX169czd+5cxowZQ9OmTaUEx7Jly5KWlkZmyXo4tv+wwM6tuJB69RBxWx9U03pcdr21tTXu7u74+vri7e2Nt7c3Tk5OlCxZksmTJ6NUKhkxYgQxMTGEhYURFhbGvXv3CAsLIy4u7qmTerp3787169e5du2aWfiEnZ0du3btokmTJlLbZ3kuKWQCukcMUAFjOcX8Kh9YeLEUC4MSYMeOHXTp0gWAsmXLUr58eXbv3o0oilSoUIFbt24B0LBhQ06dOiVt97CnUq/X4+TkhFarNZOFadu2LdervYdMY2cxJl8gplu3MFzzt+uXZMqbtZ/b/rds2UKPHj2ke1Emk6HVannzzTfZsGFDrttcDEvk9d8eH+/7KqKNjyBy4TAcHR3JyMgwm5Jzc3OjZs2aVK1aVfpXpUoV3NzcpDbnQxPoMe/Yy+h6njwXQXm9lnu/f4AhJRaZTEb//v3ZtGmT5E00HW/q1KmMHTuWmjVrkpCQQHh4OO7u7pw/f16qfANGI3TwYKOgfNu2bbl16xaDBg1i0qRJNG/enJs3b9KzZ08WL15MbGwsx48fZ8aMGWzduhVra2usmr2Lfd3OBXuORRh9VjrhP/eWPpuqq5nw8vKiU6dOvP7667Rs2VLSonyUf/75h7Zt27J69WreeuutXNtkZ2dz+fJl1q9fz8qVK3MUU8gLGxsb+vfvz6pVq0hLS5M0l+VyOTt27KB9+/Y5tslNrQDIVcHgccoGFgofxcagBOjUqROHDx9GqVSSmJgo6Uympqbi6+ubI1P3YbFxk2Fpa2uLXq83q5Izbu5qVoXl/mO2UPCIogFBkJEdexeVW+mX3R0Aetfz5qeetZ7rMYKCgmjbti0RERGSOgGAs7Mzu3fvpn79+mbtJ269woqTd4vc9HduIQY2Njb4+fnRsmVLGjduTMOGDfN8wV6JSKLLnCMvqrsvDVGvIzM0kOqxB1i6dClKpRJPT0/atm3L3r170Wq1TJ48mW+//VYaMGs0Guzt7Tlx4gS+vr5m+zMYDDRs2JCgoCBcXV25e/cugYGBtG7dmh49erBo0SJ++uknPv/8c44fP46fnx8A169fZ8aMGSzbug/PQbNfwpV49TAYDEQuGIouKVpKhAGoXr06I0aMoGPHjvj4+DxxPzqdjrp162JnZ8eRI0ekWY7o6Ghu3LjBtWvXSEpKIisri9TUVA4dOkSLFi2Qy+UsWrSI+/fvS/sy5RTkRtmyZSlTpgz79u0DHgzyDxw4QIsWLf7r5bDwilCsDMqgoCDq1KlDdnY2JUuWxMbGhsDAQJRKJfHx8ZQqVUpKuLGzs0Ov12Nvb09UVNRj91v6w0WIdh6FwlNWlBFFEV1CJBm3z5ByfgdiYiTjVx9m2fn4l901RrWtwCj/is/9OKIo8v7777No0SIEQcDW1laazqxUqRKDBw+mXbt21KpVi9YzDhadsoOPMLxMIgO6tsbW1pZTp05x/Phx6V9iYiKCIFC9enWaNGlC48aNady4MRUqVEAQBNKydFSfuLtIhgPkRsDI5lQsYTQSGzduzMSJE+nbty9vvfUWFy5ckAbLVlZWODs7s2/fPipXzr1k4rFjx2jatClgTAKZPXs2R48e5caNG8THx9OxY0fWrFnDqFGjpERGE3sPHOa9XUmW52Q+eDijWxAE2rVrx7Rp06hZs2a+ts/KyuLu3bt89dVXrF+/Hnd3d2PoQS4SZgWJqbiHKXlVJpNx8eJFqlev/tyOaaHwUKwMSjDWNO3QoYNR10sUzWr//vDDD4wbN05qa2Njg0wmIy0tDTs7O1JTU3P8GG1rtse50yeWh+RzwFoOIX+MRi8KiHotuoRIySvl4OBAWFgYdnZ2UjZhllYHwsuRVj3wv5b4ur44/bUjR47w9ttvExYWBjzwoJv+d/PywXrAb6+cmHd+2TS8CXVKOeVYbjAYuH79umRcHjt2jKtXrwLG6i0m43KzrjYxGUX/0SeXCQxoVJqJ3apx6NAhWrZsSdOmTTl58qSUxGH6/6233mLu3Lk4OeW8rg/j5+dHeHg4ycnJtGnThh49ejBo0CDeeecdjh07Ro0aNbh//z4HDx40227Xrl28tznMIoWWBw8Sqh5kdPfs2ZPZs2dj7+z22KlfnU7H6dOn+euvv/jzzz+l58LDmOIcHRwccHZ2xsPDAzDq5FaqVInU1FQp2SYqKop79+6ZeSifhEk1wNnZmfj4eMqWLcvt27exs7MjNDT0qVUuLLx6FLtgBH9/f9asWUOfPn2QyWSMGzcOLy8vBg4cyKlTp2jcuDFpaWkEBgZK3kqZTEZGRgYGg8FsGty+cW+cWr5ToBUKLPyLKHJ72ZdkRQTnWFW+fHmCgoKQy43lKHvVLcmlPRtYFJyGpkxdSZfvRfIijUkwCp6HhoZy+PBhRo4cyfnz54EHcaV6tVORNSYBVIrcBw4ymYwqVapQpUoVKd4vMTGRkydPcuzYMY4fP86PP/6IvGFf7Op2KfIlTfUGkf3XY6jxVxDjx48H4OjRo6hUKvz8/Lh+/TqxsbEMHjyYxYsX52uf7du3Z+bMmaSlpREWFoa/v79xEOPmRnBwME2bNpVi0k3odDr69euHWLcnynpdX9rArzAjCAKiXochzZgxbVuyAhXfGk/flUGExp8z86gLgLeTBk9DHElnt3Hw73W5Ctt7eXmxYcMGGjRoID0v88u4ceP4+eef2bFjBz179kQQhMcWcDANTOLj45HL5YSEhODl5UVERASdO3fm2LHCFbdsoeAplr/q3r178/vvv2MwGBAEgXfffZd169axb98+OnfuzMWLF+nQoYPU3mAwkJ2djZWVlWRM2tZsj1PLd4DCkRRS1MiKvkVW+BUAswoOAHv27CE7O5utW7fSpEkTFAoFU8b/j5h1E4hYOJyUc9vRxke8MEPfXv3yxmXNmzfn3Llz3Llzh86dO6NUKgFIzXgF9BafEQGkQP784OjoSIcOHfj2228JCAggPj6elROHFXlj0kRIXBq93urP9evXAfDx8UGlUnHy5Ek6d+6MXC6nwf/bu++4Kuv2geOf+wz2BkFQEByQ4kJz5t7aMJuWmZZmlmXpo5VZT7brqSwr22aZWlq5TcOVs9x7gQNEkQ2yD2fcvz/4cSfhjHEAr/fr1Ss95x7XfcBzrvO9v9/ratfumo/Xv39/7ct2dna2NtJVkrBcqsj55MmTycjIwClhpySTV6LT4ztoPEFjvsJ3+If8uPMs8RllV0WrQEJmAdszHTne+D4875yKS51ghgwZwsyZM/H09KRLly7ExBRXnrjeZPLgwYN88MEHPPfcc/Ts2ZMZM2aQnp5O9+7dMRgM6PV6IiIitPebEiWfhVarFZvNhsFg0NoQ//TTT+V4YURNcMPd8r7YmjVruO222ygqKtJuFf7+++/069cPq9VKeHg4p0+fLpOYGLzqEjhqJorBQZLJSpT+2wzMxzdp37w7dOjAyZMncXNzIy4u7qr7K0YnDN6BuDbtjmeneyotztfviGR4p9BKO/712rt3L298+h27/cuusKwNGvi6sHFSz3IfZ/is7Ww7lV7rFi1dSsPjC3AqTGPVqlXo9XomT57M008/TVBQEA4ODrz33ntaO9qrsVgsGI1GnJ2dcXd359y5cxiNRp5//nneffddhg0bxqFDh7RmEWfPniU4OBhFUYiLi2PqmvM1/nWvThUmABRUHAx6ujqfZ+5rT9KiRQvWrl2Lp6fndR8rNjaWrl27EhgYyJYtW3B1dcVms2lzZz08PNi+fTsXLlzg448/5s033yQ5OVnbv169epw7d077e8mCLxcXF7Kzs687uRU1xw39VbFv374cOXKEoKAg7Q3iueeeIzMzE71ez8qVK/Hx8dGKtJbw6f8kSi0tjFyd+PQfh9XJCygu4rt9+3bS0tKuKZkEUM2FmFNO43piDfpK+lHpFapVMgkQFRXFnM8+oDb+dup1Cj3D/SvkWG8NaYFBVxtfpbL+3LGT9PR0PDw8cHR05O233yYoqHguY9u2ba/aTvZiJX2fdTqdtsjJ6B9GOu4Y/cNIzczG3f3v+oAl1Qfef/99QkJCasXr7uFkqFbv/yoKJrOVtTkB9HrqXTZt2vSvksmSKQze3t5ER0drLWJ1Oh1PPPEE27dv591336WoqIjIyEhee+019uzZU6p5w8Wd5ABt3UF+fj5PP/10Oa5SVHc39AhlCZvNxoMPPsiCBQuA4jfMSZMm8eqrr3Lo0CF69uxJQEAAsbGx0pu2ihWlxnN+1rh/vb+XlxexsbH8edbE0wv2VVxg/++T+1tze+t6FX7citD9vQ21cpX32gndKqyg8U87z/DCooMVcqzq7KcRreh4U32tZuDevXtp3bo1ANOmTePjjz8mNTX1mkePHP1DcWs9AM+bOqO6+pa6JauqKh6Kibs73UT8+nl8N+NtwsPDtVvuUPNf95GdQtm8diUnXSMrp4ZoOb17V4tLtiK02WwkJSURHx9PXFwcCQkJ5OfnYzabiYmJYdGiRbi7uzNixAh8fX1xd3cnNDSUBg0a4OTkRGRkJPPnzyc2NpbXX38dBwcHxo4dS9OmTXnssceA4uRz4sSJTJ8+/ZLde+bOncuwYcMq/TUQVU8Syv938uRJGjdujL+/PykpKUBxv9KhQ4fSsWNHJk2ahLu7O7aou3FpNeCGmX9VHST/+BKF8fuuut0/+866uroSExOjjcQM+WwrexOyKiyuNiFeLHrilgo7XkWbtuwwc/6KowbfWSxFr1Po3NCXH0Z1qNDjfrohlvejYyr0mNWJAhya1h9XR4NW9qd9+/Zs374d+LsU0MW1Iy8nISOfKYsPsOVE+lUXv+kUsKlQeHova98eRfOwwFLPl7zu1TEhu5o1z3ajb4cWtLrrCQ45NsNazW72ORp0LH6sLSf2/cW6des4evQocXFxxMfHl+rr7enpiaOjI3l5edrIorOzM1arFVVVtbmQFzMajQQEBJCamoq3tzepqan89ddf9OnThwsXLqDT6Rg4cCBvvfUW7dq1u2Qf8XfffZfnnnuucl8EUeUkofx/JpMJX19fpkyZQv369XnhhRdK1Z90cHBAp9PhM+JjKXtRhVRVxZqbwbmZIy67jaIo+Pv7l5rH4+npyZ49e2jYsGGpbbu/t574jIJ/HuK6NfBxYePk8s/jq0yxyTn0/WiTvcOoMI4GHWsndK+Uvr0lpacsNrVGz+27FH1+OivGtqNp06aoqkqLFi04fPgwK1as4NZbb8VisRAWFkb79u359deyPaNLlLxGZqvtur6kKKgY9DruaBlE94g6dAzzxd/DCYA3f9zAl7sz0Rsda0ZdUFWloUsRXz7QgvDwcL7//nv2O7fk1z3nrr5vVbJZKTxzgOSfXiY0NJQ2bdrg6elJbm4uiYmJnD17lpSUlFINOirali1b8PDwuGztzM8//5yxY8dW2vlF1ZOE8iK33XYb+fn5rF+/HlVVWbNmDVOmTGHPnj0A6BxdqP/sghr3bbo2OPf1WCzppduB1atXj/Pnz+Pm5kZ2drb2eN++fVm+fDmOjo6XPFZ5Ryqr+8jkxYbP2s7mE9deS646u9xtvIqSkJHPi4sPsvlEGnqdcsXE8mrPVxeqzUrOvtVkRn+OwWCgY8eOjBs3jgceeICAgADi4uJwcnJi/vz5DBs2jHXr1tGrV68yx6noUVy9AiE+LnSvb+R/UyfQ88k3OJxmufqOdqWCzcq5r8ZiySoebOjXrx/pnZ4mzVQ9PxMe9o1j78bVbNq0qdR7ZFXR6/XYbLZSC1sNBoM2r3LOnDk89NBDVR6XqBySUF7kk08+4T//+Q+ZmZnaZGSAw4cP89lnn7Fk4y6Mt//XjhHeuHKPbCR92XtAcR1KnU5HTEzpDziDwcDcuXO5//77r3q85fvOMeHn/ViuIykw6BQ+vLdVtZ0zeSkJGfl0f39Djb/tPblfBON6Nq6Sc8Um5zBv+xk2xKRwJj2/TP2/EF8Xeob706GhD0/M21MlMZWHqqpYspIoOLmT3L2rMKf/XfT65ptvZuvWrRiNRrp06UJOTk6ZRRZVNd/RBVPxKmB3X5RqWlro3bta0KehK6tXr+ahhx6iTlAwzsM/q5aDDKrNSs6elWSu/eqa9ylJAEtaNFaWiIgITpw4AcCuXbu0+byiZpOE8iInT56kSZMmfPjhh5csobH3TCZDPpfirPZgNeVTNO8pGjZsyI4dO8q82fXt25eff/75ulc2ztl2mg/XxZKZX7Z2XglvFyMT+4RXu9Xc12rGuhg+XFu2QHx1p9cpGHQKr90RWakjk1eSZ7JctkNJTWvhWDLnsfD0XtJXf4rlwt9TRNq1a8fDDz/M+PHjeeqpp5gxYwaKovDj9nheXHKoyq6xJEZPZyMXCsx2aVJwORd/qTGZTDg5OfHWZ3P48oyPnSO7PHNGIolfjbniNiUl8+xBURSaNm3Kli1brtqhSVR/klD+w+OPP87ChQuJjY3Fz8+v1HOHEy9w6ydb7BTZjU1VVRKm36u1XiwxePBgvv/++39VIuOf4tJy+e3QefJMVlwd9QxqHljlHXAqyy3vruNcVs0odl5yO7lrYz/eGtKiUuZMVpSauJJetVlRrRYy135F7v7fSz1Xklz0H/4USY1vJ6vg8l+0KjtGbFYKzx7B4OGPwTvQbqOABp3Cm3c2L/OlpkmTJrQbcC/b3Krv9JeL3zdLWiO6uLigqmqp+ZP16tUjMLB40dT58+e1OpLBwcF4eHiQnp5OZmZmqW48/1TexLRBgwY8+OCD9O/fn06dOuHg4PCvjyXsQxLKf0hJSaFJkyY89NBDzJw5s9RzNW1EorY5P2cSRYnFLRfvv/9+5syZI0Vyr1FNuPV98e3khzqGVFhpoMo0bdlhftgeXyPmU17s777Rc8j+c2Gp53wHTcC1RfE8Snveyr04xpxdy4qTSr0RHy8PAgY8QY5z3Up/3ds18Gb6fa0v+aXmqaee4rc/D2Dr93ylxlBe2QumkHn6IJ6enly4UNzW0WAwEBISQkBAgLZIp2ReY1W5OJ5/cnV1pXv37tx///0MHTpUkssaQhLKS5g+fTqTJ09mxYoVDBw4sNRzNXFEorZw2reAr198jK5du9o7lBqputX+e+2OZrRt4HPJ28k1RW1YSW/a9C1J2xYB4H//6ziHRVW7Uj7pv80g98AaunbtSl5eHje1vYVdAQMxWSp+np+rg56+zQJ4qmfjK36pWbZsGXfecz8h//mlwmOoSOk/vkBu/KFSjymKgl6vx2KpnguhjEYjgYGBnDlzhsDAQMaPH8/jjz8ut8Wrueo589nOxo8fz+23385dd93Fpk2lPyx6Rvijr+FdHmqqt958S5LJchjaLoRJ/cLtHQZQPB/t4U5hRAZ5EhXiTWSQZ41LJgGaBLjTtbFfjX1PUFUVh84P4RTamqDHvsA5LAqoPi0FoThGn35PENikBa1atWL//v1EhYfQRj1ZoecZenN9Dk/rz+FXB/DR/VFXHSHv2bMnbk5GXGzVe4DBVJCHwWAgIiKCOnXqAP+/UKuaJpNQ3A/+7NmzGAwGgoKCeOWVVwgODmb8+PGlysOJ6kUSykswGAz89NNP3HLLLdx2223s2rVLe25Yh5Aad3urtgipxnPpaoqnejbhnbta4GCo+n/6ep2Co0HHu3e1qLIV21WhJrcSVBQF9EYChr6B0ad6Vi9QFAWdwYi1zb3YbDasVisvvvgiP7/zLJkb51TIOSb3i+Cdu1td15cad3d3Xn75ZZL3rqe6/vhVVcXZkovFYiHmVDxZihsOgeEY/cNQjE7XfbySdptVwWazYbPZ2LNnD82bN2fkyJHMmzeP5s2bs2TJkiqJQVwfueV9Bbm5ufTr14+jR4/y1Vdfce+99wLFtf22nkyr1vPRaqPD/9/tQ5TfxTUXK1tNWmTzb1W36QS11cX1aMPDw3n++edxjuzNK8sOUWS1wXWUG6qIKgImk4lmHXthHfDiv9q/sqmqCqqKarOg6I2lksGLy0mpsZsZ2Lk1Xbp0oU2bNiQlJXHs2DEOHjzI+vXrSzX5sIeSW/Qvvvgi+/fvZ+nSpYwcOZIZM2bg4eFh19jE3yShvIqsrCzGjBnDzz//zLBhw/j000/JsTnQ58ONlTJ/R1yam6OBQ9P62zuMWqek5uJvhxJJySnbIu1KvJyNvHxrUw6ey75qzcaassimPMpb/PvveYsqxa+euFhJXUWP2N/57LPP6Nq1K+vWrWPGjBls2XuUekMmQd2m6BWwXuFTraK/4CxbtozH5u/HJSwKtQb/3DzN6ej+msOpgzuvuQi6Xq/XFvNUVfmhtm3bctddd/H222/j6+vLqlWraNq0aaWfV1ydJJTXQFVV5s+fz7hx43B3d+frr78m06cZUxbLiERVubNVEB8NjbJ3GLVansnCgl1nmPNnPAmZ+f8/taP0B6RBp9DE341J/SLo3TSgzP6Xq9l4o/i3LRxVqwUFlTqpe0j2boHi4Fyt5jFWF9asJB6pE8+mTZvYtm0bZrOZDh068J///IchQ4ZwOr3gmorSV+QXHFVVGffcf1mhRqEzOEAN/rmpNhsXNv/AhT9/BopXW+fl5TFhwgTy8/OJi4sjPT2dY8eOkZub+6/OUVGJZ8+ePTl79ix5eXls2bKFsLCwch9TlI8klNchISGBkSNHsn79etq2bUubh6YQnXz981DE9Vs7oVutH+GqTnJzc/EODMFYP5L/TZ9BSFBAqR7M4vKup4UjNivo9BSc3kPG6pkoBgeCHvu86oKtYVRVJfPrR+jZpTN9+/alb9++hIeHXzL5rsovOKqqctszb3HYtXWlHL+qlIySu2efpkH8alauXHnF8j4lSpLEgIAAbdFMSd3LfzIYDP96QVBwcDAJCcWdnoxGI1BcfsjDw4PNmzcTFBT0r44rKoYklNdJVVWio6P54IMPWLNmDSE9h6Lr8CCqrG+qNM2D3FnxdDd7h3FDadWqFQcOHOCJJ57gs88+s3c4NdKVWjgWL5bI4fabG/FQxwasXzyfr776ipQGvXFrPUBGJ69g6RMdaRXia+8wyrBarTS7dyKmiH7VruzSv5F/fBupi9+ydxhluLm5kZeXh6qqODo6YjKZcHR0JCQkhD179uDmVjuaUdREklCWw/79+5k+fToLV67D586pGOuEIPOfKpYCbJrcs1Yu5Kiu5s6dy/Dhw/H29iY9Pb3GfzBWByWjZQUmM8sWL+Kr6W+SfO4MUNylpEePHowZM4bntppJyq4ZHY3sZfETnYkKqX71CM+ePUtwcDBuLfvh3fdxFJ0eRV+zp3xk/vEd2X/9gpubG23atOHgwYNkZmaW2e7f3sa+eL9rPUb9+vVJT0+noKBAGwV1dnamoKCA4cOHM2dOxaz8F9dPEsoKcP78eVasWMEvmw9x1KMNOne/WvENtTp4vn8ET/SoPSVmqruCggI8PDywWCwcOnSIyMhIe4dUK6mqytq1a3n33XfZvn07ubm5KA7OBE9YKO8bV7Hy6S5EBpW/1WpFUlUVBwcH7VauwTMA34FP4RQaddV+5CXP24oK0TlUsyklqo2+pq1sXLmI2NhYe0ej2b59O1OmTGH9+vVA8eIgo9FIYWEhS5YsYfDgwXaO8MYkCWUFs1qtfL78T97ffuU5J+Lq+jcL4MvhN9s7jBtKhw4d2LFjBw899BA//PCDvcO5YcTHx/P2Fz+wGll4diUKcOg6y4fl5+dTVFSE2WzW/nN2dsZoNGI0GnF1dS13Eh8VFcW+ffu0v7u4uBATE0O7PndQWP9mnBvejNE7sNSCHVVVsWSep+DULgpP78X/3lfKFUNlMWckkvjVmFKPeXp6kp2dfdkRRQcHB4qKiqtGBAcHExoaSk5ODqmpqVqf8Cu52milp6cn06dP56WXXuL8+fPaanNFUTAYDOzdu1e+DNuBJJSVYO+ZTIZ8vs3eYdRokkxWvUWLFnH33Xfj7u7OhQsXZKSsisn7xtU18HVh46SepR6zWq0cOnSIEydOEB8fT1xcHHFxcdqfr1YCx8nJiQYNGhAaGlrq/2FhYURFReHkdOVRw08++YTx48eXemz+/Pk88MAD5OXlMWLECH799VcUoxMG70AcXdww5ediyTwPFhPu7u7o2w/Fvc2tVxzJtKekn6ZiituPk5MTjo6OV1ykU6dOHdLS0lBVlREjRvDdd9+Ven79+vX07t2bkSNH8v3331/1NvfFyenlNGvWjOzsbM6eLa5R6ufnx5EjR7TOQKJqSEJZCQ4nXuDWT7bYO4waS25zVz2TqfiDzWw2s2vXLtq2bWvvkG448r5xZXqdwvAODZh2RyRxcXGsWbOGNWvWsG7dOjIyMoDikcHQ0NBSyWFQUBBOTk4YjUbMZjM5OTn4+vpiNpspKioiJSWlVAIaFxenzRN0dnama9eu2oryli1blvqilZKSQkBA6fJZwcHBxMfHl9pu2bJljBw5kgsXLlxy5XPQ418Xj2BWQ6qqYs3N5NzMhy/5fMnooJOTk1aT0mw2M2TIEBYtWlRqW6vVys0334yjoyOvvvoqAwYMuK5YLl4h/s9RzKCgIFxcXDhx4gR6vZ7Ro0fzxRdfXNfxRflIQlkJ8kwWmk/7nZr2wjbwceFsVoHdWks2D/Lg82FtZQGOHfTo0YONGzde8kNAVI2a+r5RlfoU/cnGZQs4ceIEOp2Opk2bEhUVRVhYGLm5uRQVFRWP+On1pKenU79+fZo2bYqLiwsODg4EBgbSoEEDXFyu/B6TnZ1NbGwsf/zxB2vWrGHTpk0UFBTg7+/PgAEDePLJJ2nfvj0uLi4UFpZeRLV161Y6d+5c5pipqamMGTOmTNvAmjJ31nQ+lrQl72C5UFwWyNXVFZPJhMViQafTodPpsFqtqKrKmDFj+PLLL8sc4+uvv2bMmDFs3LiRESNGEBcXxwsvvMA777wDQPPmzTl06FCZ/S4uoH4xPz8/srKysFgshISEcO7cuVLb7du3j1atWlXUSyCuQhLKStL9vQ3EZ+TbO4yrurj9WOdGflXeAcjFQUe/ZnV5qmdjqTNpJ6tWrWLQoEE4OzuTm5uLTiclsOylprxvVDmblYL4/aT9PK34r5cY5bseBoOBOnXq0LRpUyIjI4mMjKR79+5ERERcMrErLCxk27ZtrFmzhl9++YUTJ07g7u5OTk5Oqe26du3Kpk2brnjunTt38uabb7J06VIAjP5hBD36Sbmupyqoqgo2Kzkbvqbg0DotkXZ2dsbLy4vz58/j5ubGr7/+Sr9+/crsv23bNvr27cvdd9/N6dOn2bJlCx06dGDz5s34+/uTlZVFeHg4MTGX7zZ1qZqYXl5eZGdnY7PZmDRpEr/99htHjhwBoFGjRsTGxlb7ZL22kISykkxbdpgftsfbbbTvanQK2FTKtB+r7J7ETgYdzYLcGdExlD7N6t5wnVSqG7PZjLu7OyaTiT/++IPu3bvbO6Qb2rRlh/n+zzgZpbyIqqqoliLOf/OkNjqm1+vR6/UoioLVai1VKFun0+Hu7q59MSq5fX25Ua6LOTs706JFC+655x5GjBiBv79/mW2sVisTJkzgk0/KJoHHjx8nPDz8mq4rLi6O999/n9nLNlBn2P+uaZ/qInPjHKwHVuLt7a3NW+zduzfLli275Ojvvn376NGjBy1btsTZ2Zno6GiaNWvGjh07yM/P117ni+dL1qtX76oLeFxcXMjPzy/1s33ggQf48ccftW2mTp3K66+/LkllFZCEspLEJufQ96Mrf1O1l3pezvRtGnDZ9mPl7Ulc4oF2wdwc6lPc8szHhaaBHpJAVjMDBw5k9erV9OvXj99//93e4dzwqvP7hj3lrvsS8/GN5Ofnl5pDV+LiWoYl/zcYDJjNZnx8fPDy8sLPz4+6desSEBBASEgIhw4dYufOnZw5cwaz2XzJ8/r5+XHfffcxZcoU6tevDxTfur5UohkUFMSOHTuoV6/edV3bjthE7vt273XtUx2k/zaD3ANraNq0KR9//DF9+vS55HZHjx6le/fuBAcH4+fnR3R0NKGhoezduxcvLy9+/PFHHnzwQfz8/EhLS9P2u/hW+JU4OTlpo6X33XcfCxcuLLPNtGnTeOWV6rmKvjaRhLISDZ+1nW2n0u0+SlnHzZH2oT7c3iqQrk3qXFNS9297El98C/3+diHlCVtUsk2bNtG9e3ccHBzIzc3VWpkJ+xo+azubT6RdfcNarqSWb+bG78n+/97SUDwCWbIgo7y3vi9mMBS/L5YkrCX/HkqSzaioKD788ENuvfVW8vLySu2r1+vx8/OjqKiIzz77jKFDh17zeWvi3FlVVVFUK1/cFsSAru0uu813333HM888Q506dcjOziYtLY3w8HDtNreqqvTp04fMzEz0ej27du0Cin/GAwcO5MCBAyQmJl52ZFlRFG3uJsCKFSuYPn0669evx8fHp7hV5/+PUE+fPp0JEyZUwqshSshkqUr01pAWGHRVP8zuoFfoGV6HL4a14fC0/uyc2oeZw9owoHngNY8QDm0XwtoJ3encsLjFmf4q11HyfOeGvqyd0F2SyWrOarUycOBAABYvXizJZDXy1pAWOBhu7Ldm1WpBtRSRsfoTzPtWoNf/XU7HZrNhtVr/VTJ5pdueFoul1OhnSc1KKL4VvnfvXnr06FEmmQR4+eWXOXLkCH379uWBBx7gscceu+b4XB0NhNSwhYiKooDOwLyYS19jSkoKQ4YM4dFHH6VBgwacPn2a9PR0xo8fz5EjR7QR3qVLl7J+/Xpee+017dY5FCf3q1ev5umnny5zC11RFO3nqKoqN910k/bcwoULufXWW7Wfn8FgQFEUQkNDmThxIt98801FvxTiIjJCWckqek6im6OBXJOlzOOBHk7cHOrNY10b0rK+V4WdD67ck1gBQnxd6Bnuf9lb6KL6ufvuu1m0aBFdunRh8+bN9g5H/ENlz2Wurkq6xhSc3kPG6pnanMl/jkRVJ15eXpw7dw4XFxdUVWX27NmMHj2asWPHMnPmzGuau1fd59xfydoJ3bT3fZPJxNy5c5kyZQp5eXnodDpyc3Px8/Nj5cqVtG/fXtvPZDLRrFkzwsPDef/992nevLn2nMFgIDQ0lMOHDxMREUFcXNxlz280GhkwYADLly/H2dmZ3r17k5KSoo122mw2jEYjI0aMYNasWWzZsuWSq/BF+cmEtko2tF0IabmmCpmTOLlfBON6Ntb6AhdZbDgYdIT6ulbq3MQmAe5MuyOSaURW+blFxduxYweLFi3CYDCwdu1ae4cjLqEi3zeqD5XCuP0YvOqi9wwoMweypGtMzt7fsKT/PVoVFBRETk5OmRXVTk5OREZG0rhxYyIjI2nVqhWNGjWiQYMGuLm5acdNT08nPj6e06dPs3v3brZv387Ro0dJSkrSjuXh4YG3tzdJSUmYTKZS57la15Z27dppxc8VReHRRx9FVVVGjx6Nh4fHNc0DHNYhhO/+jLvqdtWNAszeGsfEbkF88cUXzJgxg9TU1FK1KZ999lneeecdHB0dtf1UVWXKlCmcOXOGFStWlKkXabFYmD59OocOHbpkMlmnTh1SU1OB4ikJJQllQUEB69ev57///S/Ozs5s3LhR22bw4MHs2bOH8ePHs2PHDqlmUQlkhLKKyJxEUR3YbDa8vLzIyclhwYIF3HffffYOSVzBTzvP8NKSQ1hq4MjVP13cwq+ka4yiN6JazVgyz6OaixdWGI1G+vbtS926dVm4cCG5ubnF+ygK4eHhDBkyhKFDh9KiRYtyJQVFRUXMnTuX+fPns3XrVm1hR+PGjTEYDBw7duyaj+Xl5cXPP/9camHKRx99xIQJE/joo4945plnrnqM6jLn/rqpKpacNApi/iJn72+Y0xPw9fVl8uTJTJ48+ZI/ozfffJOXXnqJGTNmMGrUKOrUqUNRUZE2Ah0YGMjZs2fp3r07sbGxJCcna/u6ubmhqmqpqQdjx45lwYIF2nzJPXv2sHbtWp577jmguNj8XXfdxT333EPXrl359ttveeSRRyrzVbkhSUJZhRIy8nlx8UE2n0hDr1Ou+MZR8vw/y/oIUR7Dhw9n7ty5tGnTht27d9s7HHENEjLymbhwHzvjM69rv2aB7jzf/ybGzN1dpbVlL0W1WcnZs5LMtV9pj3Xs2JG//vpL+3tISAhfffUV586dY+LEiVq9waCgIKZPn87gwYOv2gbx37JarSxcuJBXX32V48ePA+Dv74+iKKWSmX8aMWIE33//vTaK+fLLLzNt2jQtiRo/fjyzZs0iJibmqqu/EzLyq7wOcEUqWUQV7GJj/rjel/3MKmlV+dprr/HCCy9w8803c+DAgVLbfPrppyQmJvLWW2+VWf3dtm3bUu9dOp0OLy8v7rzzTr799lsAsrKymD17NhMnTkSn0zFo0CBsNhsrVqzggQceYMOGDZw6deqqBe7F9ZGE0g5kTqKwhwMHDtCqVSv0ej0XLlzA1dXV3iGJ6xCbnMPMP06w7lgKOYVl51EDuDsZ6B3hz1O9/m4UUF3mY577eizWjHPo9XoiIyPZv38/UDxfbsqUKej1eqZPn16q93bJYpxrmTtZ0g3HxcUFX19fwsLCuOmmm2jcuDGNGjUiLCyM+vXrX3UB2vbt25k0aRJbtlxbG8wBAwawevVqrYZily5dmDdvHiEhIVy4cIEmTZrQv39/fvjhh6seq7r8rCrCTXXdeLJ7Yxr5uxHq64qDTuWtt95i2rRp/Oc//+Hdd99l6NCh/PLLL2X2femll3jjjTe49957+fnnv1f4e3l5aV2QoHjU+qabbuLo0aOMGzeOmTNnAsWLfebOncuyZcswm80MGjQIVVVZsWIFR44cITIykt9++01bmCgqhiSUdiZzEkVVUFUVHx8fsrKymDVrFo8++qi9QxLlkGeycPR8NvEZ+ddU57Wiasv+G6rVQuGZA7DhUzIyMnBwcCA/337dgNzd3WnSpAk333wz3bp1o1GjRkRGRuLuXvrLe0mbwGsRHBxMQkKC1o7Q1dWVNWvW0K5dO7755hsee+wx/vzzTzp27HjVY2k/K1WFWlSMW5+fQdbRbQzv2IC3pzzDk08+yXfffac9X9Jr3dfXl/T0dMaMGcOsWbNKfZmYMmUKb7/9dqnjlsynLHl/MxgMjB49mh9//JGsrCxUVaVLly54enqyYsUKVFWlQYMG3H333Xz44YdVdfk3BEkohbgBjB07li+//JJmzZpx+PBhe4cj7OCzP07wv9+PV+k5S7rc3HTyZ9Ys+ema93NyciI0NJSwsDAA0tPTOXPmTKmFNAaDAaPRqNWltNlsV0xUdTrdZUv56HQ62rdvz6BBg+jTpw8333wzgYGB2kjYP7Vv3x5PT0/WrFmjPebg4ICzs7O2Ih1g48aNNG3alBYtWhAVFcW8efOu6fp/2nmGKb/uw4auVnV4KenQpks5TsKi97BkFf88GzVqRGpqqjY6fffdd2ujiyV8fHxo3LgxO3bsKHPcks5JUDzH0sXFpVQZorZt2xIUFMSyZcsAeOyxx9i6davWolFUDEkohajlYmJiiIiIQKfTkZ6ejpeXl71DEnZij6Qyd/2XpO9YXuoxnU6Ho6MjBQUF2mMGg4EWLVpQp04djh8/ztmzZyu1TJDBYMBisWgJm06nQ6/XU1RUpI2WXcnPP//MI488Qm5uLs7Oztq19OnTh7Vr19KoUSPy8vLYvHkzs2bNYtasWSQlJV3zQqK/Dp3gro+icajToHwXWg2pNiuq1ULmmi/p09CFOnXq8PXXXwPQq1cv1q9fX2Z1/ZgxY/jqq7/n4Jb0Ug8MDCQ5OZmmTZty9OhRre6lTqejQ4cO/Pnnn7i4uPD888/z3//+F4C5c+cyfPhwsrKy8PT0rNqLr8UkoRSiFlNVlYCAAFJTU/nwww959tln7R2SsLOquP19uS43V3NxYnYlTk5O+Pn54evri6+vL7m5uTg5OVFQUEB8fDxGo5Hc3Fzy8/OvmhheTK/XY7PZrlgmyNvbm8zMTNzc3Pjggw94/PHHAahfv742Kta8eXOtAHtRUREzZ85k0KBB7Nmzh6ioqGuO55ZbbuGocyQe7QZrr2ltUXI9jfOPsu7jyQB4enpy4cKFMqPJJcl/iYsTfoPBwG233cbu3btJSEiga9euWm3dtWvXaivvL55ysGTJEoYMGUJqaip+fn5Vcr03ApmsJ0QtNmnSJFJTU2nYsKEkkwKAp3o2wc/N8V+VMbsWJaNPGWu+oODweqA4UbtUq0SdToeqqjg7O5Ofn68lkxcnEM2bN2fAgAF06dKFhg0b0qBBAzw8PK45nrNnz7J27VpWrVrF2rVrycjIuOy2VxsR1el0XLhwgYCAAJKTk9m0aRNhYWGcPXuWpKQkbUVyXFwcubm5/O9//+Pll19m06ZNuLi4sG7duutKKBcsWECDBg1wLMzArfsjFFlsKLWkfmJJcnzCpSkeHe4me/uvpZLJklqW8Hc7TH9/f1JSUkollz/++CNbt25l3bp1AMTGxgLF03xKRh/d3Ny4+eabq+zablS14zdTCFHG6dOnmT59Ooqi8Oeff9o7HFGNXG9r1Wvy/6N6hfH7Of/Nk7gmH9ASgotbJZZ8yLu6umqjgfn5+Vrh64CAAIYPH878+fNJTk7m4MGDvPfeewwePJgWLVpcVzIJxSOHI0eOZMGCBaSmprJv3z7eeOMNAgMDr/sSdTodDg4OODk5YTAYmDdvHt26dcPFxQWr1ap1gsnNzSUgIICtW7cyefJkPvzwQ3x8fLQ6idcT+xNPPEHS1l+ps/NLWgcWl02qTTcWVVXFq8dIPDrcAxR/+XBwcCiT3A8cOJCUlBTtSwjAs88+yz333KPVpXR0dNTm2b766qssX74cRVEYMmSI1qu95Jyi4klCKUQtpKoqt9xyC1D8xlrSO1eIEsE+LvwwqgNrnu3G8A4NaODrwqXSSncnA64O+ks8c5G8dLJ3L+fc12NJWfBf1Nw08vLyytT5Gzx4sDYKWVKYuqSMT7NmzZg7dy4JCQl8++23PPDAAxX+e6vT6WjVqhVTp04lLi6OOXPmEBERcU37uru7s3fvXho3bkx8fLzWQ3rTpk1cuHABVVX57bfftMdTUlJYu3YtEydOxNfX94ojo1fy/vvvExYWxp9rlmNb9zHTe3uTt3cl5qyUWpEYlYxUevccSZ0ewzGbzRQVFZV6/tFHH2XVqlUApUa5R40aBcAff/yBzWbDZDJhMBiIiIjA39+f2bNnoygKb775ZqlzHjt2DDc3N5k/WcFkDqUQtdDLL7/MG2+8Qf369UlISLB3OKKGuFIZs5LnsgvMpOaa8HNzRG81cWfvW0g6G39Nxy+ZI3exgQMHMnnyZHr06GGXOYKqqvL+++9rXVWupFevXqxevZqOHTuyZ8+eUs8ZDAY8PT1p1aoV69ev1x7fvHkzBw4cYNy4cUyYMIHp06dfd4znz5+nTZs2JCUlcc899zB+/Hi6d+8OBkccfOuh9wrCo/N9OPqHoao2FKXmjhWl/zaD3APFq+d1Oh0333xzmZXd7u7u+Pr6curUKfLz87VWmwaDAS8vLx555BHuuusuOnXqxMCBA/ntt99K7d+zZ0/c3d21Vd+iYkhCKUQtc+7cOYKDg1FVlTNnzhAcHGzvkEQtlJeXR2RkJPHx8RgMBkJCQjh16lSpbXQ6HTqdrtSct4v9/PPP3HPPPVUR7mWlp6fj7+9/2ZJCJUpWHfv7+xMdHU1UVJRWrkhRFLy8vAgPD+fMmTOcP39eq6c4depURo0aRcOGDXnwwQevuXTQP8XHx9OmTRsyMzNp1KgRAwcO5JNPPim1jdE3GLeogbg07oDe07/GLeIpKTN1/psnsVz4u0NRSVmgi0cu582bx5AhQwgJCSEtLQ2dTsfs2bMZMWIES5YsYfTo0WRkZBAfH0/9+vW1/XJzc/Hx8eHDDz9k3LhxVXp9tV3N/RojhLikW265BVVVmTx5siSTolJYLBbat29PfHw8Op2On3/+mdOnT5fapk6dOtpK50sJCgqyezJps9mIiorSksLLcXd3R1VVjEYjKSkptG/fnsjISMLDwwG0BUcNGzbk/PnzANrt/s2bN2vtG3Nycv51rA0aNGDbtm0EBARw6tQpPv30U23OZsn8QHN6Aplrv+LcF6PIWP/Nvz6XvSiKgqLT4zPg70Rv2LBhdO/enaKiIjw9PXF1daVTp06EhITg4+OjtWU8fvw4f/zxB76+vkybNo20tDQmTZpUKpkEWLhwIWazmX79+lXptd0IJKEUohZ59913iY+Pp06dOrz77rv2DkfUUm+//TZHjhxBURStNd4/b3ZNnTr1ko+PHz8eR0fHSuvLfT3GjRt31SkhHTt25MSJE0RFRWE2m/Hw8KCoqIijR4+WKp6dm5tLcHCwltydO3cOgCNHjvD777/j6OjI0aNHyxVvREQER48eZejQoaiqyo4dO3B1dcVisWgtJ7V4di7FWphXrvPZg6I34BzWhl53PsjJkyc5d+4c69evJzg4mOzsbPLy8jAajXTt2pXCwkKguFB5ZmYms2fPJiAggAMHDhAWFlZm7mROTg5Tp07lvvvuo0mTJva4vFpNbnkLUUskJycTFBSEzWbj+PHj2uiJEBUpIyODunXrYjabeeaZZ/joo48YMmQIS5YsKbVdq1atSvXrtlgs6HQ6CgsL8fT0xGKxkJaWdt2rtivKhg0b6NWrF/B3kewSF9c5dHZ2ZteuXdSvX59WrVoRFxd32cLnDzzwAKtWrSIrK0sb8VRVlTZt2lBQUMCFCxe0RLO8fvnlF0aNGkVBQUGpWEoKfAP4DX4O16bdKuR8Va1bXRu/vf4IGRkZ+Pv7k5qaWmZaQkl3otjYWAYMGMDRo0cpLCxEp9Oxbt264nmmF5kyZQofffQRx44do0GD2lcw3t5khFKIWqJbt27YbDYef/xxSSZFpRk9ejRmsxl3d3deffVVTpw4ccnFDQcOHND+XDKHMiAgAKPRqJUIeuONN6om6H9IS0ujb9++QHH5okslkyWrzwsLC+nXrx86nY4dO3bg7OyM1Wq95C3y9evXU1RUpI1SlozX7Nmzh7Zt21boNdxzzz0cO3aMESNGaLECHD16VOtLborZVqHnrEp/JJgp0Lng7u5OcnKylky6uroCxbUlzWYzr776KqNGjWLPnj3Ft8wVhfnz55dJJvfu3cv06dN57rnnJJmsJJJQClELfPrpp8TExODl5cXnn39u73BELZWZmcnixYsBmD59Op6enkyaNKlUQlOiJGkEtHaDJbcodTodPXr04KOPPtIKUVe2PJOFw4kX2BOfQfv+d2HTFcecn59fKjm0WCw4OTmh0+lo1KgRqqqSlJTE22+/TZ06dViwYAE2mw0HBwdtH0VRMBgMZGRkUFhYqHVfKTlu9+7dady4cYVfU2BgIF9//TUJCQlMnTpVS2RLEuTcE7tqbGkhxeCA78MfYQst7m7j5+eHk5MTeXl5dOzYETc3N2666Sa+/PJLli9fTkBAAPn5+Xz99dfcd999pY517Ngx+vXrR8uWLXn++eftcTk3BLnlLUQNl5GRoS2A2Lt3L61bt7Z3SKKW+u6773jkkUdwc3MjKyuLDRs20LdvX4KCgjh//vxlkxeDwaAlYQUFBTRu3Jg+ffqwatUqWrRooRWgrmixyTnM236GDcdTOJORz8XRqaqKLTuFvNjt5O5dhTk9AQ8PD7Kzs3FwcGDixIm0bduWe++9Fy8vLwoKCjhy5AgNGzYkPDy8TCIcEBBAv379+OGHHwgLC9OKcOfk5LBq1Sp++eUXtm/fzsGDByv8OktkZWXRp08fdu/erT1W76k5GNx8Ku2clamkPaNlz2LORc/CwcGBt99+m/fee4+cnBytlqmPjw9ZWVnMmDGDp556qtQx4uLi6NKlC15eXmzcuLHUPFNRsWSEUogarkePHthsNoYNGybJpKhUP/zwAwD9+/dHURQmTJjALbfcQm5ubqnt/pkcWiwWFEWhsLCQw4cP07t3bzZs2MDHH3/MypUrmTx5crlG0kpGH/eeydT+P+SzrfT9aBNz/ooj/h/JZEmMes8A3NvcStBjnxNw/+vkK854e3vj7u7OCy+8wG233QbAhQsX8PHx0Ua3RowYUSaGoqIiLVnMzMwkPz9fGykMDQ0lOjpa6ytdWby8vNi1axfz58/H2dm5OK6UuEo9Z2Uq+T0ytBlC5B2P8eqrrzJ16lSSkpK0EWKDwYCPjw9btmwpk0zu3buX3r174+TkxJo1aySZrGQyQilEDfbtt98yatQo3NzctD64QlQWV1dX8vPzmTt3Ls2aNaNNmzZ8++23PProo8DftRov5ubmRm5urvZcnz59GDt2LPfccw+nT59m+fLljB8/ntdee42XX375mmO50ujjv1HSgzxz7Ve8+eggxo8fD0BwcDBnz55l9OjRfP/992RkZHD8+PFr6g3t5uZGXl4eu3fvpk2bNvz2228MHDiwnJFem7NnzzJs2DD2murg0/ORKjlnpVFVbP9fn5K8dLy8vEhPT0dVVR5//HE++OADbW4lFLf6/N///scrr7xCs2bNWLJkCaGhofaL/wYhnz5C1FBZWVk8/vjjAERHR0syKSpdfn4+AF27diU6OhpXV9dSo5GXGp/Izc0lNDRU227t2rWYTCb0ej2//vorTz/9NG+++Sb//e9/ee+99646UpmQkc/wWduvOPr4byg6ffG8vYFPozb9u0ZhixYtip9XFMxmM3/88QctW7a85DEMBoM2n7R169bk5uYSERHBypUrcXBwKLNQpDLVr1+fDRs28MKj9q31WSH+vz5l4B0TsVqtpKWlERkZyerVq/niiy9KJZMnT56kW7duTJ06lYkTJ7J9+3ZJJquIfAIJUUP17dsXi8XCnXfeSadOnewdjriBODs7Ex0dTY8ePdDrS/f5vtRcSAcHB22VrouLCy+88AL33Xcfb775Junp6UyZMoUXX3yR5557jsGDB2uFwP/pp51n6PPhRradLC5mbavg+2slsX+4/iQLdp4B0K7Pw8OD0NBQVq9ezR133KHtYzQatf0sFgtmsxlFUZg8eTIAffr04X//+x9jxowp09u8sul0Op4ZUQsSSorrU+rqRdKwdWfWrVvHgQMH6N+/v/b84cOHGTVqFM2aNeP8+fNs2rSJd955p9TiMFG5JKEUogb66aef2LVrF87OzlphaSGq0tatW+nZs2eZx729vQG0/soAMTEx1KtXD0VRyM/PJysri927d2M2m5k2bRqKovDmm2+yZMkS/vrrL1q0aMHSpUtLHffTDbG8sOggJosNaxVM1PrvssMkZOSXeqxHjx4sXLiQ1atXA8UjkmazucyoalhYGC+99BIAiYmJGI1GXn311coP+hJcHQ24/38/9ppOp8BD076kV69e2hSKdevWMWjQIJo3b87q1at5/fXX2b9/P126dLF3uDccSSiFqGFyc3N5+OGHAVi2bJlWKkSIqqKqKkVFRVq9wxI+Pj7aytuShTr33nsvUNw5piTxysnJ4dy5c3h4ePD555+zZ88eAAYPHsyhQ4fo1KkTd955J/feey+7du3ip51neD86pqouDwCLTeXFxQe12/w2m40DBw6QmpqqbXOptpIdOnSgsLCQ06dP0759exYvXsxrr72Gj4/9Vlr3bupvt3NXJJsK644lsXTpUp566ikiIiLo06cPiYmJzJkzh9OnT/Pcc8+V+b0UVUMSSiFqmEGDBmE2m+nTp0+lrxoV4mL+/sWJyaFDh7THGjZsCBTfLvbz88NkMpW6zejg4KB9AbpYXl4eycnJGI1G+vfvT0xMjHaOJUuWMHv2bHbv3k2nPrcx5ec9UCEzJa+d1aay+UQae04mArBo0SIt8S1xqfmeQUFBJCUlodfriYmJoWPHjtpcZ3sZ16Pia2Day5mMAobcO5TVq1fTu3dv1qxZw969exk+fHip2qCi6klCKUQNsnTpUjZv3oyDgwPLly+3dzjiBjNgwAAA5s2bBxQnVO3bt8fDwwMnJycuXLhA/fr1MZlMBAUFads++OCDPPPMM6WO5eDgQJ06dTCZTKSlpdGhQwfi4+OB4uR05MiRxMbG0vO5r1B1BqDi61RejV4BpUlx68Kr9fwusWLFCmw2G05OToSEhLBy5Uq730VoEuBOuwbedo2hoiiKwuqtuzlx4gSff/45ffr0qZQapuL6SUIpRA2Rn5/P/fffD8DChQtxcnKyc0TiRjNmzBgAli9fTmBgICdPnsRoNNK7d28URSE1NZVBgwah1+tJTEzUOsYMGDAAk8nElClTtGMVFRWRmppKv37FK6qzsrIIDw9nw4YN2jan0vKJydGDnRIGqwrODYvLA13cR/qfi2vq1q1LQEAAAGazGYPBQGBgINHR0dqcUnubfl9rDLrakXjVCQi0dwjiEiShFKKGuPPOOzGZTHTu3JnBgwfbOxxxA+rUqROOjo6kpqbSunVrfv/9d6B4GkZ+fj42m43s7GysVitGo5HMzEygeFTpiy++4IcffmD06NFA8Qpkq9XK77//TkBAAEFBQRQVFdGrVy9CQ0PZtm0bzy7cZ69L1Ri8A1GMf3958/T01OZVQvHqbwcHB23uKBQXMl+/fr2WZFYHwT4uvHFnc3uHUSEcDJK6VEdS2FyIGiA6Opr+/ftjMBjIysoqVXdNiKo0c+ZMnnrqKa1NYWJiIj4+PjRp0oSEhAScnJwoKirCZrPRp08f1q5di16v19ou6nQ6evfuze7du8nOzsZisWjHdnV1xWw2U1RUhMEzgKCx31SL25mJ3z5NgIOZs2fPlnrc29ubfv36sWDBAu2xiIgI9uzZU+Ulgq7Vpxtiq3yBU0VSgEPT+uNaS1au1yaS5gtRzRUWFnLnnXcCxb2UJZkU9vTkk0/i7+9PdnY2UFzCytHRkRkzZgDFt4br1q2Loih07NiRqVOnYrVatcSxpA1eVlZWmZXPeXl5FBUVYfQNJmDkjGqRTAIoemOZZPKll14iMzOzVDLp4ODAH3/8UW2TSYCnejbhnbta1NhRvhBfF0kmqykZoRSimrvzzjtZunQpUVFRZVaZCmEP27dvp2PHjgC4u7tz+vRpfHx8aNSoEadPn9a2c3Z25uzZs4waNYrly5djtVrR6/X4+Pjg5OSkLXQxGAxYLBYMngH4DX4ex6Bwu1zX5SR++zTmlNMoioKiKERGRnLs2DHMZjNQnCQXFhby1ltvlZonWp0lZOTz4uKDbD6RZu9QrplepzC8QwOm3RFp71DEJUhCKUQ1tmnTJrp3745eryctLQ0vLy97hyQEAJMmTeKDDz4AYMSIEXz33XccOnSIli1bYjQaKSoqAmDUqFG89tprREZG4uPjQ2FhIYmJxaV4wsPDMRgMHD16FNcWffEZMA4UXbUZmYTilexJnzyIragAi8VSql+5Xq/H29ubtLQ06tevz4kTJ2pcZ5aSnuhL9p0jq8Bs73Cuau2EbjT2lzqT1VHNHPMW4gZgMpm49dZbAfjkk08kmRTVyvvvv8/IkSMB+P7774mOjqZ58+a8/fbbFBUV4eHhAcCsWbNITk5m9erVpKSk0LRpU3744QcaN25MTEwMR44cIaDXw/gOGl/cT7saJZNQPGfPbDZrt+xVVUWv11OnTh2sVitZWVk4OjqydevWGpdMQnFJoWl3RLLvv/149fZIqutCcL1OoWtjP0kmqzFJKIWopkaOHElubi433XQTTzzxhL3DEaKM2bNnc/vttwPQv39/PvvsM5577jnatGlDdna2Nt+3Q4cO+Pv7s3z5crZu3cqvv/7KgQMH2Lx5M+2HPotju3vteRlXpigYvIvL1LRu3Zpu3bppJZKcnJywWCwsXLiQkJAQOwdafiM6h7JxUk98XKtfgXCDTuGtIS3sHYa4AkkohaiGtm/fzk8//YSiKPzxxx/2DkeIy1q2bBkjRowAYNy4cXTr1o3o6GgaNWpEQUEBer0es9lM48aNMRqN/PLLL6xatYp27dqRqzqS0aivna/g6vz867JlyxZatGjB1q1bi+d7GgwUFhby/vvvc8cdd9g7xAoT7OPCnpf60rmRr71DKeW1OyIJ9qm+i52EJJRCVDtms5n+/fsD8M4771SrWnZCXMp3333HmjVrUBSFLVu2UL9+fV5++WVCQ0NxdnYGild/d+nShfXr17NhwwYMBgMjP/sds9V2laPb36qVy5kzZw4//PCD1r/bYrHwwQcf8J///MfO0VWO+aM78lz/iGpxC3xyvwjub1fzR4BrO1mUI0Q18+ijjzJ79mzCwsI4deqUvcMR4polJCTQrFkzcnNzgeJV3k5OTphMJiwWi7ZQx8HBgWFPTmK9U2d7hnvNvNa8xv7dOwC0RTkffvghzz77rH0DqwIJGfk8s2Ave85kVel59ToFg07htTsiJZmsISShFKIa2bdvH1FRUSiKQlxcXK2YlyVuLEVFRYwbN45vvvlGe6wkCQsJCeHMmTMAePcZg3vb26vdIpx/MmcmkvhlcctJg8GAp6cn33//vbZg7kYRm5zDK8sPs+1keqWeR69TsNpUujb2460hLeQ2dw0iCaUQ1YTFYqFu3bqkp6czdepU3njjDXuHJMS/tnXrVh588EESEhL458eMwWDAf9RnGL2D7BTdtVFtVnL2rMS64yeys7O56667+PLLL7Ue5TeihIx8nv/1ANtOpaNTwHaNGUQ9L2faNfAGBfYmZHEmPZ+Ld1UoLlreM9yfhzqGyGruGkgSSiGqiaeeeoqZM2cSFBTE2bNnq/3IjRBXk5uby8cff8zHH39McnKyVgBccXAmeMLCGvE7fu7rsdRxsPLOO+8wfPjwGhFzVSipX7khJqVMcggQ6OlEm2Bvbm8VSNcmdcp0t8kzWYhLz6PIYsPBoCPU11U64NRwklAKUQ0cOXKEyMji7g8xMTE0adLEzhEJUXFMJhM//vgjH3zwAYcOHSK4ZSd0g6baO6wrUlUVQ9oJXunmw9ChQ3FwqH6ldKoLSQ4FSEIphN1ZrVbq1atHcnIyzzzzDB999JG9QxKiUqiqSnR0NF/9Gs1u3172DueK9MCGST0I8XW1dyhC1AiSUAphZ8899xzvvfcefn5+JCcno9NJNS9Rux1OvMCtn2yxdxhX9O5dLWR1sRDXQT65hLCj2NhY3n//fQDWrVsnyaS4IYRW81G/+9vVl2RSiOskn15C2InNZqNXr16oqsqoUaNo2bKlvUMSokq4OhpoUE3LwbQJ8eLdu1rZOwwhahxJKIWwk1dffZWzZ8/i5eXFl19+ae9whKhSPSP87R1CGTc38GbRE7fYOwwhaiSZQymEHZw+fZpGjRqhqip//vknHTt2tHdIQlSp2OQc+n60yd5haML8XNnwnx72DkOIGktGKIWoYqqq0qdPH1RV5b777pNkUtyQmgS407Vx9SkQ/vXwtvYOQYgaTRJKIarYe++9x6lTp3Bzc2Pu3Ln2DkcIu3lrSAuMevsXCg/ydJLOLEKUkySUQlShhIQEpkyZAsDSpUsxGo12jkgI+wn2ceH1wc3tHQbv3t3C3iEIUeNJQilEFVFVlX79+mGz2bj11lvp1at6F3YWoioMbRfCpH7hdjt/E383ujapfguEhKhppDeSuCEUFRVx9uxZ4uLiOHLkCAcOHODEiROkpaVhsVgwmUycOnUKgIiICAwGA46OjtSrV49mzZoRERFBgwYNCA0NJTg4+F+NLH766accO3YMZ2dnfvnll4q+RCFqrKd6NsHPzZEpiw6W6QldmfQ6hW9HtKvCMwpRe8kqb1ErnTt3jrlz5zJv3jxiY2MpLCys0OP7+vrSvXt3Bg8eTN++fQkMDLzi9ufPnyc4OBir1cqyZcu4/fbbKzQeIWqDhIx87vliK8k5RVVyPumGI0TFkYRS1Ao2m40lS5bw8ccfs2PHDgoKCko9rygKzs7O+Pn5ERYWRnh4OKGhobi5ueHk5MTRo0dp2rQpACaTiZycHE6dOkVsbCxnzpwhOTm5zDEvFhISwu23386dd95Jr169SnW8UVWVqKgo9u/fT48ePdiwYUPlvAhC1BIz1sXw0drYSh2tnNwvgnE9G1fiGYS4sUhCKWq0/Px8Ro0axS+//ILFYtEe9/b2plevXjz66KO0atWKwMDAcrc1zM7OJj4+nn379rF8+XLWrFlDVlZWme1CQ0N56aWXGDZsGE5OTnzzzTc89thjODg4kJaWhru7rCYV4moSMvJ5cfFBNp9Iq7BjKoCDQcdrd0TKyKQQFUwSSlEjnT17lnvuuYft27cDxSOQbdq0Ydy4cdx33324ulZ+r2BVVTl8+DDR0dEsXryYrVu3oigKNpsNAE9PTx5//HE++OADrFYrP/74I0OHDq30uISoTWKTc5i3/QwbYlKIT8//V8dQABXo2tiPt4a0ILiatn0UoiaThFLUKBaLhf79+7N+/XoAHB0dGTNmDB9++CF6vd6usZ06dYqPPvqIr7/+GpPJxMX/tBo3bkxsbKwdoxOi5sszWYhLz+NkSi5rjiaz/+wFzmRcOcls4OtCz3B/HuoYIrUmhahEklCKGmPjxo30798fk8mEk5MT06dP54knnrB3WGVkZmby5Zdf8uabb5Kbm6s9/sADD/D555/j6elpx+iEqF1Kkswiiw2rrfjjTK9TcDDoCPV1xdVRipkIURUkoRTVnqqqDB06lIULFwJwxx13sHjx4nLPiaxM6enpBAYGYjabgeJb8gD+/v4sWrSIzp072zM8IYQQokJV309kIYDCwkKaNm3KwoULMRqNrFmzhqVLl1brZBLg7rvvxmw207JlSxYtWoSXlxc6nY7k5GR69eqlzf0UQgghagMZoRTVltlsJioqisOHD+Pl5cXZs2erZLFNef3666/cc8896HQ6EhMTCQgIICkpibvvvpsdO3ZgtVpxdXVl69attGzZ0t7hCiGEEOVWvYd5xA3LZrPRtWtXDh8+jJubG6dPn64RyWRWVhYPPfQQAO+99x4BAQEA1K1bl99++42WLVtiMBjIy8ujZ8+enDhxwp7hCiGEEBVCRihFtfToo48ye/ZsHB0dOX36tNaJRlVV0tLSiI+PJy4ujri4OO3PZ86cIS8vD7PZTHJyMiaTCTc3N7y9vTEajbi6ulKvXj3q1atHSEgIYWFhhIWF0aRJE/z9/bV5juXRv39/oqOjadKkCcePHy9zzLS0NLp27crJkyexWq20b9+ebdu2Vci5hRBCCHuRhFJUOzt27KBDhw4ArFy5ktatW7NmzRpWrlzJ+vXrSU9P17bV6XQYDAYURcFkMqHT6bQ6kIqioCgKqqpytV9zRVHw8PCgYcOGdOrUiRYtWhAaGkqTJk1o2LDhNSV8K1as4Pbbb0dRFOLi4ggJuXTh5MTERJo3b052djZWq5WFCxdy7733XuvLI4QQQlQ7klCKakVVVZo1a8axY8fw8PBAp9NdshtNVfLw8KBz58488MADDBw4kDp16pTZJicnh7p165Kfn8+0adN45ZVXrnjMzz//nHHjxqGqKoGBgZw8eRJnZ+fKugQhhBCiUklCKaoFVVXZuHEjEydOZO/evZfdTqfToSgKVqu1UuLQ6/V4e3vj5OREampqmVFPgKCgIO644w5eeeUV6tatC8CQIUNYsmQJwcHBxMfHX3VE02KxEBUVRUxMDEVFRXz66aeMGzeuUq5JCCGEqGySUAq7Kiws5JVXXuGzzz4rVQQcoH79+kRERODo6MiuXbtISUm57HEURUGn0+Hs7Ixer8dqtVJUVKTVgfy3v+Z6vR6j0UhhYSHu7u44OzuTlpamJZg333wzw4cP55lnnkFRFI4dO0Z4ePg1Hfv3339nwIABAPTu3Zu1a9f+qxiFEEIIe5OEUthFUlISTz/9NIsXL77kaOPAgQPZuXMnaWlpdojuyhRFISIigoKCAuLj47XH77zzThYvXnzNx7FYLPj5+XHhwgWMRiM5OTk4OjpWRshCCCFEpZKEUlSpzMxMxo4dy88//1xq1LBHjx54eHiwbNmyy+6rKAqenp44OTmRkpJS6jb0P+l0OhwdHbUEzWazYTabKSgo0BbqXHxcnU5XIbfRW7RoQXR0tHYr/Gruvfdeli9fjslkYu3atfTu3bvcMQghhBBVTepQiiozZ84c6taty8KFC7VON3Xr1uW2225j06ZNZZJJRVFwdXXFwcGBRo0aoaoqWVlZJCUlXTKZvHjeos1mw2Kx4OzsjL+/P40aNSIqKgqAli1b0qlTJxo1aoSbmxuqql4ymdTr9Tg5OV3TtZVcz8GDB6lfvz5ffvnlNe3Xr18/ioqKAKQmpRBCiBpLEkpR6bKzs+nfvz8jRozAZrNpcxzr1atHUlISq1atKpMghoeHo6oqFouFoqIiTp06dV3n1Ol0mM1mzp8/T0xMDHv37mXbtm3UrVsXJycnQkJCuO+++1i4cCE5OTkcPXqUhx9+GDc3N+0YqqpSWFgIcMXEUq/Xlzqv1Wpl7NixNGvWjOzs7CvGGRoa+q/ndwohhBDVhSSUolL9+eefNGnShOjoaBo2bIjVatVGAy9cuACg/d1gMGj7xcTEAGAymYCyi2oCAgKYOHEiO3fupKCgAKvVis1m02pOlpynoKCA06dPs2HDBr777jvGjh1L06ZNSUlJ4bvvvmPQoEH4+vryxBNPEBERQXR0NPPnz6dJkyalklyDwYC/v/8lr9FqteLk5MR9992nXYOiKBw9ehR/f3/2799fES+lEEIIUX2pQlSSP//8U3VyclIVRVE7d+6s6nQ6FVB9fHxUo9GoAtp/rVq1KvX3y/3XtWtXNS0trULis9ls6tGjR9WPP/5Yvf3221U3NzcVUAMCAtRp06apS5cuVUNDQ1VFUVRAdXBwUD/55BPV0dHxkrHpdDp13bp1aq9evUo9riiK+umnn6o2m61MDNHR0dp2X331VYVclxBCCFHVZFGOqBT79++nS5cuFBQU0Lp1a/bv34/FYqFOnTqkpqYCaPUdmzRpQmxs7CWPU7KAxtHRkVmzZjFs2LBKi9lsNrN9+3bmz5/Pd999h6qqDBs2DLPZzJw5c7SYHR0dKSgowMXFhYKCglKjp3q9nn379vHXX38xbtw4bX4kwB133MGCBQtK3T5fuHAh999/PwC7d++mTZs2lXZ9QgghRGWRhFJUuJiYGLp06cKFCxe46aabOH78OCaTCaPRiJubG1lZWSiKgs1mK7Pi+mIODg5YrVaaNm3KqlWrqF+/fpVdQ1paGl988QWffvopycnJ9OjRg0OHDmlljIxGIwaDgYcffpjvv/9em2sJxUnnX3/9ha+vL7fccgtJSUlAcXJ8xx138PPPP2M0GgF47LHHmDVrFs7OzuTk5GiLe4QQQoiaRD69RIWy2Ww8/PDD2jzGpKQkbR5k/fr1yc/P17ZzdnamQYMGlzxOSaIZERHBxo0bqzSZBPDz8+Oll14iLi6Or7/+moMHD2KxWLTnzWYzt912G99++y0ffvhhqX1tNhudO3fG3d2dLVu24OXlBRTPA12+fDmPPPKINt9zxYoVqKpKly5dJJkUQghRY8knmKhQ8+fPZ/v27eTk5ODt7a2N6Hl5eVGnTh1MJhOqqhIQEMDkyZOJi4vDwcFB278kqVJVFT8/P9atW4ePj49drgWKV3ePHj2aPXv2UFBQAPxdnuiXX37By8uLVatW0bdvX1xdXbXnLBYLN910E/7+/mzZskVbrKPT6Zg3bx4TJ07kyJEj2ujl6NGj7XB1QgghRMWQW96iwuTm5hIREYGzszMJCQkYDAZtRPLNN99k6tSpQPGK6e3bt9OxY0etNBCAu7s7Xl5eJCQkAMWlg44cOVKqLE9lyDNZOHo+m/iMfBQgxMeFpoEeuDr+vep80qRJfPDBBzg4OKDX6ykoKECv16MoChaLhfnz5zN8+HCtXFGJFi1asGvXLn766SdGjBgBFJcKiouLo02bNuzZs+ea+38LIYQQ1ZUklKLCvPfee7z00kt4eHiQlpZGUFAQiYmJ+Pr6kpeXR2FhIXq9nhdffJH09HQ+++yzUvs//fTTfPLJJ8DfC3a+/vrrShm9i03OYeYfJ1h3NIUck+WS27g7Guh9kz996tm4o3s77ZZ1cHAwHTt2pLCwEC8vL/Ly8pg0aRJ//fUXSUlJHD16tNRxpk+fzoQJE/D09CQnJwdVVQkMDOT8+fMAbN26lc6dO1f4NQohhBBVRRJKUWG6d++OXq9nw4YNhIWFcfr0aZycnHB3dyc1NZW6deuSlpbGli1b6NSpk7YYx9HRkZYtW5KSkkJOTg5OTk4kJiYC4OrqypYtW2jdunWFxJiQkc/EhfvYGZ95XfsVJhyiadZONqz4BYBt27Zxyy23aEXaGzduzOjRo3n99ddRFIWCggJcXV3Jzs7G0dGRhIQEXnzxRb755hvg7zmiTZs25ciRIxVybUIIIYS9yBxKUSGys7PZtm2bdou7ZBGNzWbT5lH6+fnRuXNn3njjDZydnbV9TSYT7du35+zZs2RkZPDZZ5/RrVs3jEYj+fn59OzZk+PHj5c7xp92nqHH+xuuO5kEcKwfSUKrR/lp5xkAOnfuTL9+/bBarbi6unLixAmioqLIy8vTCriXFEY3mUy88MIL3HPPPdrxVFVFr9fLQhwhhBC1gnyaiQrxxx9/YLFYSExMxMXFhT179gBQVFSEo6MjAKdOnaJbt26sWrWq1DxDd3d3jh49qvXWHjRoEIsXL6ZJkyYYDAays7Pp3r07cXFx/zq+TzfE8sKig1j/5Xi8oihYbCovLDrIpxuKa2a+/PLLAOTl5QFw9OhRdDqd1r4xNzdXSxhnz55dpn1jVFQUhw8f1m59CyGEEDWVJJSiQhw/fhw3NzeSk5NxdnbWkiydTkdhYaE22uji4oLVasVsNmu1GLt3787WrVuxWq306tULo9GIj48Pa9euJTg4GL1eT2pqKm3atOG333677th+2nmG96NjKuxa34+OYcHOM1oR8pLFNDt27EBRFLy9vbXHGjRooN3efuihh7RjhISEaP3J16xZU2GxCSGEEPYgCaWoUCWdYUqSRRcXF+DvPt27d+/G29sbAE9PTwDq1auHyWQiPj6efv36accKDAxk/fr1BAQEoCgKmZmZ3HrrrTz22GPk5uZeUzwJGflMXXKoYi7uIi8vPURidvG1lswF/eOPP4Di16Dk+h0cHPDz8wPg7NmzQHGSraoqGRkZ3HTTTZJQCiGEqPEkoRQVxmq1oigKhYWF2m3fqKioUttcvAK6ZI5hTk4OLi4uWCwWwsLCSm3foEED9u/fz1133aU99s0339C0aVNWr1592S47JSYu3IfVVvHrzoqsKq8sL72aOzExEavVSmxsrHb9iYmJWqvJklFLnU6n3Qp3c3PTnhdCCCFqKkkoRYVQFEVLKPPy8rTFOSEhIQBarUmbzUZeXh5eXl5kZGQAxQt6rlRr0sfHhwULFjBv3jzc3d3R6/WcPXuWgQMHEh4ezvfff1+qZ3aJ2OScf7UA51ptPZWBwbd+mfqR8fHxWnegnJwc7XFVVfH09MRisWjzSoUQQojaQBJKUSFatWpFUVGRllyZTCZtviSgLcIpLCzEarVSr149bZSupETQlSiKwoMPPsiRI0e4/fbbURQFRVE4ceIEI0eOpG7durz11lvEx8dr+8zccKKiL7MM96hBAFp7RShOHEvmkBoMBm677TbtufDw8DLbCyGEEDWdJJSiQnTp0gWDwYDNZtPmS/bp04e4uDgt+atXrx7Z2dmoqorBYKBjx44AJCQkaIlnVlbWFc9Tv359Fi9ezPHjx3n88cdxcHDQ5ldOnTqV0NBQgoKCGD16NKsPnavUawZwbdweVVUvGberqysWi4WWLVtqj5WsAvf19QUgPz+/zOpvIYQQoqaRhFJUCGdnZ1q3bo2qqnh4eADQv39//vrrL5o1a4aqqphMJnJzc1FVFbPZzJgxYwBITU0lPz+fhg0bsn79+ms6X5MmTfj8889JTEzk1VdfpW7dutpzSWmZ/LB6GwWXboBToXSeASjG0gnhm2++CRTP/3R3dyc/P1+7pX/kyBEKCgpo2LAhACdPnqRr166VH6gQQghRiSShFBWmpFd1yejbzp07adWqFTk5OSiKQlpaGkVFRaiqSkJCAg899BBBQUHa/vXr1yc6OvqqC20u5uvry8svv8zq7UcY8dUfNHtxCSETfyZw+HtV0htbURQcfOtpi3A6dOjA5s2bAbhw4QJ9+vRhxYoVpW79W61WioqK8PLywmQylVrZLoQQQtRE0npRVJiioiJ8fHyw2WwUFBSgKIrWi7skudPr9doCnePHj7Nt2zYeeeQRACIiIjh+/DgHDhygRYsWVz1fbHIOn244wW+HzmP+txXLK4Bh/Yec3LEOgGeffZYZM2bg6elJVlYWv/32G4MGDdK2bdeuHefPn6dOnTrk5+eTnZ3NuXPnqiT5FUIIISqLjFCKCuPg4MCIESMoKCgAimtQfvzxx9xxxx04OTmh0+m0ZBJg0aJFjBgxgoCAAKA4wfT399c60FzOppgUOr+zjr4fbWLp/kS7JpMACfGntT8vXboUVVWxWq2MGjWKadOmac85OzuzZ88ebrvtNvbu3cupU6d4+OGHJZkUQghR48kIpahQhYWFeHt7U1hYCICHhwcRERHk5OQQExODu7s7Fy5cAIoXraSkpHDw4EFtgY6zszMFBQWsWbOGPn36lDp2QkY+j363k9jUaytqXhVUVSVh+r3UC/AjMTERm81GnTp1KCwsZMiQIcyZMweDwYDFYiE8PByr1Yq7uztnzpzBYDAQGxurzTkVQgghaioZoRQVysnJiZkzZ2p/r1u3LseOHcPPzw9fX18uXLiAq6srUNwD+8knn6RDhw6MHDkSnU5HQUEBzs7OPPPMM6X6ff+08ww9PvijWiWTAJbMJBRrEXXr1tVWuKemptK3b1/mzJmjjcoajUZiYmIYNGgQ+/btIyMjg7fffluSSSGEELWCjFCKCqeqKmFhYVpNyCeeeILZs2cTGRnJ0aNHyc/Px2g0agnjyy+/zLhx44iKiiIpKUlblHPXXXexcOFCPt90qkJ7cVcUVVXJ3/cbtzglsnTpUq1nd7NmzThy5EipbY1GI927d2fXrl0UFhbSqlUrtm3bptXiFEIIIWoySShFpThw4IBWRgiKk8rff/+dpKQk9Hp9qQ4yUFzH8j//+Q+jRo0iKytLa8vY9r7xpDWsvqugi5a8TNLxfdp1uri4YDQatdv6UFx7sn379pw6dYrMzEwaNGjA1q1b8ff3t1fYQgghRIWS4RFRKVq2bKktSNHpdHz++ee0bduWoUOHkpOTg7u7e6ntt2zZwpAhQ2jcuDGOjo7o9XoMngGkBHe/rjJCVano3DHOH9tbKr66dety4cIFjEaj9lhYWBjnzp0jMzMTf39/1q9fL8mkEEKIWkVGKEWlsVqtREZGEhsbi8FgoKioiEaNGvHEE0/wv//9j9TU1DLJopOTE4WFheh0OvzunYZTSEsUvcFOV3BpqqqCqpL45WNYLiQD4OnpiaIoZGVl4erqqrVe9PX1RafTkZ6ejq+vL9u2baNx48b2DF8IIYSocJJQikq1Z88eunbtiqqqFBYWaglkp06daNmyJStWrODcubItEn3CmuF+//+qOtxrlrlhNtnbfwXA0dERk8kEFK9qz87OBiAgIIDk5OKEs0+fPsybN09GJoUQQtRKcstbVKo2bdqwcuVKVFUlJCQEKO4us2fPHr788ktcXV0ZPXq0tvK7hNqoC6rNao+Qr8qSl0nuzsVAcTLp5OSEwWDAaDRqyaSTkxPJyck4ODjwzTffEB0dLcmkEEKIWksSSlHpevTowS+//MK5c+fo1KkT3t7eFBUVAZCVlcU333xDQUFBqYTLudHNKDq9vUK+IoOrNzrvIOrVq4fJZKKwsBCLxVKqzFFhYSFdu3bl2LFjjBo1SoqXCyGEqNUkoRRV4tZbb+WXX37hyJEjODk5MXr0aLy9vUlJSQEgMDCQ8PDw4rmIDs4YvALtHPHlqaoN96hBJCUlAWi3u0v07NmT/fv3s2nTJsLCwuwRohBCCFGlJKEUVWbw4MEcPHiQm266ia+//pphw4bx7bffaqugt2zZgtVqpdUtvav1iJ6i6HBueDNW69+35PV6PQ888ACJiYmsX7+eli1b2jFCIYQQomrJohxR5Ww2G5988gkvvPACXl5ePPXUU7Rt25affvqJlStXkm30IXDEdHuHeUUlLRdDggJ4/vnnGTt2bLVOgoUQQojKJAmlsJsTJ07w3nvv8f3336PX63nkkUeYMGECKUUOPDDngL3Du6rvhkbQo5WUABJCCCEkoRR2l5KSwmeffcbMmTNJT0+nfeeuJHWdDFTvEb/FT3QmKsTb3mEIIYQQdidzKIXd+fv7M23aNM6cOcOsWbMIDvTHmpNh77CuysEg/3yEEEIIkBFKUU1NWbSfH3eetXcYV3R4Wn9cHatXFx8hhBDCHmSIRVRLj97S0N4hXFEDXxdJJoUQQoj/JwmlqJaaBLgT5Olk7zAuSQF6hkvXGyGEEKKEJJSi2nrnrupZy1EFHuoYYu8whBBCiGpDEkpRbXULr0Njfzd7h1FG18Z+NPZ3t3cYQgghRLUhCaWo1maPaFetigc56BXeGtLC3mEIIYQQ1YoklKJaC/Zx4dk+Tewdhub1wc0J9nGxdxhCCCFEtSIJpaj2nukdTgNf+ydxk/tFcH87mTsphBBC/JMklKJGmPtoBww6+9z8NugU3r2rBeN6SptFIYQQ4lIkoRQ1QrCPC2/c2bzKz9uugTcb/tNDRiaFEEKIK5DKzKLGGNouhLRcE+9Hx1TqeRwNOgZG1uWpXo1lNbcQQghxDaT1oqhxftp5hleWHcZiU7HaKu7Xt2U9T967pyURdT0q7JhCCCHEjUASSlEjJWTk8+Lig2w+kYZep1wxsVQoLkZ+KQ18XegZ7s9DHUNkNFIIIYT4lyShFDVabHIO87afYUNMCmfS80sljgoQclHCGOjpTFx6HkUWGw4GHaG+rtKPWwghhKgAklCKWiPPZJGEUQghhLADSSiFEEIIIUS5SNkgIYQQQghRLpJQCiGEEEKIcpGEUgghhBBClIsklEIIIYQQolwkoRRCCCGEEOUiCaUQQgghhCgXSSiFEEIIIUS5SEIphBBCCCHKRRJKIYQQQghRLpJQCiGEEEKIcpGEUgghhBBClIsklEIIIYQQolwkoRRCCCGEEOUiCaUQQgghhCgXSSiFEEIIIUS5SEIphBBCCCHKRRJKIYQQQghRLpJQCiGEEEKIcpGEUgghhBBClIsklEIIIYQQolwkoRRCCCGEEOUiCaUQQgghhCgXSSiFEEIIIUS5SEIphBBCCCHKRRJKIYQQQghRLpJQCiGEEEKIcpGEUgghhBBClIsklEIIIYQQolwkoRRCCCGEEOUiCaUQQgghhCgXSSiFEEIIIUS5SEIphBBCCCHKRRJKIYQQQghRLpJQCiGEEEKIcpGEUgghhBBClIsklEIIIYQQolz+D9rYBfhCJ85JAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "draw(g)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "a4c57244-0c98-4598-8ae2-06cdd0c48385",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "***************************************************************************************************************************\n",
      "                                         Μethod:  Top-K Join\n",
      "***************************************************************************************************************************\n",
      "Method name: Top-K Join\n",
      "Parameters: \n",
      "\tsimilarity_threshold: 0.25547445255474455\n",
      "\tK: 20\n",
      "\tmetric: jaccard\n",
      "\ttokenization: qgrams\n",
      "\tqgrams: 3\n",
      "Runtime: 33.9919 seconds\n",
      "───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n",
      "Performance:\n",
      "\tPrecision:     58.34% \n",
      "\tRecall:        63.75%\n",
      "\tF1-score:      60.92%\n",
      "───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'Precision %': 58.340434597358325,\n",
       " 'Recall %': 63.74534450651769,\n",
       " 'F1 %': 60.923248053392655,\n",
       " 'True Positives': 10954,\n",
       " 'False Positives': 7822,\n",
       " 'True Negatives': 814451.0,\n",
       " 'False Negatives': 6230}"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "topk_join.evaluate(g)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e686ba55-cbbe-4133-8256-fa5339e70720",
   "metadata": {},
   "source": [
    "## Entity Clustering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "e48627ef-0be6-45dd-b3ba-863818eadbfb",
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyjedai.clustering import ConnectedComponentsClustering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "a9d5a28d-79f0-479f-b154-f5f7e3661897",
   "metadata": {},
   "outputs": [],
   "source": [
    "ccc = ConnectedComponentsClustering()\n",
    "\n",
    "clusters = ccc.process(g, data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "f95539fe-2569-4569-8929-2f4220f14157",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "***************************************************************************************************************************\n",
      "                                         Μethod:  Connected Components Clustering\n",
      "***************************************************************************************************************************\n",
      "Method name: Connected Components Clustering\n",
      "Parameters: \n",
      "Runtime: 0.1218 seconds\n",
      "───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n",
      "Performance:\n",
      "\tPrecision:      2.05% \n",
      "\tRecall:       100.00%\n",
      "\tF1-score:       4.02%\n",
      "───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n"
     ]
    }
   ],
   "source": [
    "_ = ccc.evaluate(clusters)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "778b9b19-0964-4095-a293-a8336fe0607a",
   "metadata": {},
   "source": [
    "<hr>\n",
    "<div align=\"right\">\n",
    "K. Nikoletos, J. Maciejewski, G. Papadakis & M. Koubarakis\n",
    "</div>\n",
    "<div align=\"right\">\n",
    "<a href=\"https://github.com/Nikoletos-K/pyJedAI/blob/main/LICENSE\">Apache License 2.0</a>\n",
    "</div>"
   ]
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