{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# MolDrug without receptor\n",
    "\n",
    "One of the strengths of MolDrug is the ability to optimize on the chemical space based on docking results. However it could also be used to optimize some QSAR function when the information of the receptor is not available. Some previous of this application where show [here](https://moldrug.readthedocs.io/en/latest/notebooks/how_to.html#Create-your-own-cost-function)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Imports and creating working directory"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyexpat import model\n",
    "from moldrug import utils\n",
    "from rdkit import Chem\n",
    "from rdkit.Chem import AllChem, Lipinski, Draw\n",
    "import multiprocessing as mp\n",
    "import shutil, os, gzip, requests, tempfile\n",
    "\n",
    "tmp_path = tempfile.TemporaryDirectory()\n",
    "wd = tmp_path.name\n",
    "# or\n",
    "# os.makedirs('tutorial_mwr', exist_ok=True)\n",
    "# wd = os.path.abspath('tutorial_mwr')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Getting the CReM data base"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "url = \"http://www.qsar4u.com/files/cremdb/replacements02_sc2.db.gz\"\n",
    "r = requests.get(url, allow_redirects=True)\n",
    "crem_dbgz_path = os.path.join(wd,'crem.db.gz')\n",
    "crem_db_path = os.path.join(wd,'crem.db')\n",
    "open(crem_dbgz_path, 'wb').write(r.content)\n",
    "with gzip.open(crem_dbgz_path, 'rb') as f_in:\n",
    "    with open(crem_db_path, 'wb') as f_out:\n",
    "        shutil.copyfileobj(f_in, f_out)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Define the function\n",
    "Let's assume that our QSAR model give us that the best molecules are those ones that have more hydrogen bonds acceptor and donors. Because MolDrug minimize we must use the negative of this number."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def QSAR_model_cost(Individual:utils.Individual) -> utils.Individual:\n",
    "    \"\"\"Simple cost function\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    Individual : utils.Individual\n",
    "        An initialize Individual\n",
    "\n",
    "    Returns\n",
    "    -------\n",
    "    utils.Individual\n",
    "        The individual with cost sum of HBA and HBD with negative sign\n",
    "    \"\"\"\n",
    "    NumHAcceptors  = Lipinski.NumHAcceptors(Individual.mol)\n",
    "    NumHDonors = Lipinski.NumHDonors(Individual.mol)\n",
    "    model = NumHAcceptors + NumHDonors\n",
    "    Individual.cost = -model\n",
    "    return Individual"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Set MolDrug run"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "Creating the first population with 50 members:\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 50/50 [00:00<00:00, 1272.61it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Initial Population: Best individual: Individual(idx = 35, smiles = CC(N)C(=N)NO, cost = -7)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Evaluating generation 1 / 10:\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 50/50 [00:00<00:00, 997.56it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Generation 1: Best Individual: Individual(idx = 35, smiles = CC(N)C(=N)NO, cost = -7).\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Evaluating generation 2 / 10:\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 48/48 [00:00<00:00, 417.55it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Generation 2: Best Individual: Individual(idx = 35, smiles = CC(N)C(=N)NO, cost = -7).\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Evaluating generation 3 / 10:\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 49/49 [00:00<00:00, 265.61it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Generation 3: Best Individual: Individual(idx = 148, smiles = CC1(C)N=C(N)N=C(N)N1O, cost = -9).\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Evaluating generation 4 / 10:\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 46/46 [00:00<00:00, 212.73it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Generation 4: Best Individual: Individual(idx = 202, smiles = O=C(O)C1(O)CC(O)C(O)C(O)C1, cost = -10).\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Evaluating generation 5 / 10:\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 49/49 [00:00<00:00, 173.32it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Generation 5: Best Individual: Individual(idx = 202, smiles = O=C(O)C1(O)CC(O)C(O)C(O)C1, cost = -10).\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Note: The mutation on Individual(idx = 153, smiles = CC1(O)C(=NO)CC2CC1C2(C)C, cost = -5) did not work, it will be returned the same individual\n",
      "Note: The mutation on Individual(idx = 153, smiles = CC1(O)C(=NO)CC2CC1C2(C)C, cost = -5) did not work, it will be returned the same individual\n",
      "Note: The mutation on Individual(idx = 153, smiles = CC1(O)C(=NO)CC2CC1C2(C)C, cost = -5) did not work, it will be returned the same individual\n",
      "Evaluating generation 6 / 10:\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 47/47 [00:00<00:00, 122.85it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Generation 6: Best Individual: Individual(idx = 202, smiles = O=C(O)C1(O)CC(O)C(O)C(O)C1, cost = -10).\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Evaluating generation 7 / 10:\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 50/50 [00:00<00:00, 129.32it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Generation 7: Best Individual: Individual(idx = 352, smiles = OCC(O)C(O)C(O)C(O)C1SCCCS1, cost = -12).\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Evaluating generation 8 / 10:\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 50/50 [00:00<00:00, 118.14it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Generation 8: Best Individual: Individual(idx = 426, smiles = NNC(=O)C(O)C(O)C(O)C(O)CO, cost = -14).\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Evaluating generation 9 / 10:\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 49/49 [00:00<00:00, 88.03it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Generation 9: Best Individual: Individual(idx = 426, smiles = NNC(=O)C(O)C(O)C(O)C(O)CO, cost = -14).\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Evaluating generation 10 / 10:\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 49/49 [00:00<00:00, 86.79it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Generation 10: Best Individual: Individual(idx = 499, smiles = NNC(=O)C(O)C(O)C(O)C(O)C(O)C=O, cost = -15).\n",
      "\n",
      "\n",
      "=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+\n",
      "\n",
      "The simulation finished successfully after 10 generations with a population of 50 individuals. A total number of 534 Individuals were seen during the simulation.\n",
      "Initial Individual: Individual(idx = 0, smiles = CCCC, cost = 0)\n",
      "Final Individual: Individual(idx = 499, smiles = NNC(=O)C(O)C(O)C(O)C(O)C(O)C=O, cost = -15)\n",
      "The cost function droped in 15 units.\n",
      "\n",
      "=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+\n",
      "\n",
      "Total time (10 generations): 335.81 (s).\n",
      "Finished at Mon Aug 29 01:17:50 2022.\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "ga = utils.GA(\n",
    "    Chem.MolFromSmiles('CCCC'),\n",
    "    crem_db_path = os.path.join(tmp_path.name, 'crem.db'),\n",
    "    maxiter = 10,\n",
    "    popsize = 50,\n",
    "    costfunc=QSAR_model_cost,\n",
    "    mutate_crem_kwargs={\n",
    "        'ncores': mp.cpu_count()\n",
    "    },\n",
    "    costfunc_kwargs={}\n",
    "    )\n",
    "ga(12)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Draw.MolsToGridImage([ga.InitIndividual.mol, ga.pop[0].mol])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you can see, this simulation is really good: On every single carbon atom it is bound, at least, one HBD and/or HBA! (Your results could differ)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0.5, 1.0, 'best_cost')]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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9YjIAr8yn35GBO56ym+pCCm1LpvpxcZf3yQuP4n+PVds62nP499fZc2NqU8r3vURnpcoSCmWma3zrLfZ6Eeelcx+JdhDirge1/zsdi/H1y45zfH7JCYwneX5rQ7xPvULQ0Z7DmxbP5H/7zX/SBf3u848FOtpzODE3if89rSmFCxZO5X/Hd2C9iA3YCYyO9hyuXDIbAPAv58xHR3sOFx03AwBw3MzmUSeDFzcHOj1fnGsBAJx5ZOuovv81c6cAAM47ZuqovoffgS3rMCvch3HjnKOYcJnWlMKjV12I73QsxuXtOUzOJvDmk2ZhXmt21MdotHDqvPr3f7EGxePVhskCtdANHzuzakPi8nYmLz509jzPb4moPanIYzYvO9pzSFkejo+cd+SEWgvk9ZElW3l5y8mzIFsW7J2fPi9yfc9fOA0AcEV7LrBv3ygoUPf8R/SyRwIxHK6/UOYKeWNK5f++cNF0/sw7Tp1bU71OPYLJi46T2Ry789PnesoZKwMWYPPuuFnNAIDbPnUO3noKWxc3fvws5CZnPfURfze1MYUFUxvGXXZ3tOcwezILK7ziNbNHXJcl1hi99TWzx7RtOSE08r/eeXIghU5DUkFzJoGCFalDawoA3nvGER4KHaC6eXTGkaz9V57ibP/rjmXz/4rXsLU7oyWNpCJjZks63qdeQZhpzcNPXXiU7/znBuw4GopDJR0zJ6UBAF+77Di0Nqa5U0SMYIvBEBuwExx06kLeAjo1LozBZO53hRAD9r2x0b4nMMCzH4+uokNZiHXDrJoo2p0Uhf27jMa0iqQqH9YnZkNFJ1dkPVCr4vFqwoY9A/zftYRZEoeiGJJHoDt/Jd3gz40F6JBIbNtEQM9gEUlVxqRMgq/VfFkXEo1F73/6fTFEhoh97jc+o4GyywNL625WS9pXfm3cW9sY0f7QkmUHMH6yT8ywORaGAbWrZNH2AOzKiG7J/OGANaAZRtV7wWiBrrjUOi4iqM/H+o7femHdB8n9nsEiWhtTSKuyL4WNWEYtHlgAGCqy8XaPLfFrGlZf9w6W0NqYRFM6gQHr3niMwx8kc4PWti0jxmftlzQDL+8fxCWLZ0KRJWzcM4DewSJmtaQd9YthI+aBHSdEJfcmpZ/ua5ESGkRqHIWs203sfsGiqVi5fj+6+/JQJAm6aSLXksGFx7IT0Oa0KhiwrB6jb8Da2Y9Hgkr9LCp4w0UdKTX47lRQHfNlHWXdQEKRMVDQ0JRKIKnKke8sUB3d/U/jElR3d9vEcZQAUMsmZxP4+mXH+86voP4hZbManr2g+UVlOu/Aesvt7OrGN25dx3kbw+o9Woi6LsN+J7Y/qC+Cyrhj7W7+951rd+P4WZM8z4TVr0AHXkNFuCGGTN6wagd+8eDm0LlVzVgE1cs0Ta6E39zVjae2HIjcp1HbXAs6u7rxh8e3oaQZ0HQDmywDwXkYpYW+X/y8rSkFIJxsXqRE2D9YxDPbDnrmzMHhskMG+LU1Sn3o8ymCR3+goGGQG7AZbNw7iEJZ5wclEoD71+/D2dfez++G+cklvzqRMdiSTbB+8JF9Q0UNTWnVYUhXiyj7GLV/oFDidaH6aLrB5+OOA0M4+9r7Pf1Y1k3IwliNRC6NdO7S4c9jL/dg/lW3h8qTSu+iPtcMw9OPJU3HsNVmv/aNRC7+3yNb+N8PbNiHpcfP8DyzYt0eFDUDqizhpX3OtSgBuOeFvXxeLprZ5GlT2Pup3hTd4l6jdLCkm+z521bvQlEz8OcntkEzTBTKBjLJ6HrBSOGnD7RU2Eei7L/ud4hzWpYAw0Tg+g6qY5T5EKTfBM3dKPun3/sBhPYB7YllzfRtAx3al/T6HVhE7afOrm58744XoRkmrn9yG0zTxE/uf8nxzPPdh+r+3sMdsQE7DqiGVLzEDVi2+Mig8/OgRCXrdhO7/+mJ7fxvSivf3ZfHX59in09vTnPDmTzBo32hXOSfrRVR+lkTDPHhso7JVZQvnv4OFjRMbkgyA9a6cxXlxMxdR7H/xXFx192vbeLzogg+OFzGsr+vdrTb793iO2wDtvIJt7sc9/yiMo9ozfLP3ck3Oru6sezG1Y77J0H1Hi1Usy7Dfie2P6gv/BSKq25a40jh/8uHtuCoaU2OTb5S/Ugu9Az4eWDttfTd21/ka9hvblUzFmH10l2n2VH7NErZtc4Jd5mGCTy99SA6u7pxQq6ZPzdQ0CLL1P0DTC7uODgc+F5RZt++Zjf+8uR23zkjygB3W6PWhz5/7xlzhfaU+XqmjJcDBQ13Ps8OTWi0u/vyWHbjakCyD/jC6gTYkTKTwzywJR2TMgmUdaMmD2wlOeOWl4SiZqBkHZ5pApXX1p5hR5upXZpu8HvQI5FL9Zi7lAWXXh8kTwBUfBf1+bbe4cB+9GvfSOWiqCv8/ZmdOH1+q2c+FwUP+Zqdh9DZ1c0jNtxj9Inzj+TlhRmw7noTbdb2A0OO5+hgaUfvkKMuFIF246rteP9Z8wPfU08E6QNh+0jU/Vfsc/ecpn9GGdtq5kOYfhM0dyvtn36yzi2v/H7X1mTJJl33bQNBq5NzJmo/uZ8bDjgEXblhPzq7uiMdLtR7z5yoiEOIxwHVkIqT16rXEr70Oz8Dthqy7iggYTBjUnocQohH7oGN0s+iwVBtuLKft6a/UEZT2vLARugjvzoGQax7Nb8D2Fi651dY/1C/R/GUVKoLlUmKgSpLHgV2+YoNvskT/Oo9WqhmXVb6XRCCylu+YoPDQwewUCfx2Sj14wasjwdWXEvuAyj33KpmLMLq9Z93b/Q8H6VPo5RdK/zK1E3Wtn7Hmi5XLVO37B/yfEYQr33845mdkeaMu63V1Cdf1vGPZ7uF9tiezxw3YMu4/qkdnveWDdMRnRJWJ0AMISYPrLdtwyUd2aTCQzOrRbUyj1DUdD7fdcOOCHC3jtpVNkzupRuJXKrH3B2qsP9ReVHeRX3+0r7Biv0otq+ectHdb37PGCb7/G9Pb4cb+bKOPzy+DYD/PlLp/QDwsmuN0rpcv3fA9/mfrnw58B31RtQ5Xo0u4CdDwhIVVRrbauZDWN3C5m5Y3fxkXZi8ot/t6iuwZzUz9J310m2j9lPU9utGNF1oNPbMiYrYAzsOqIZUPNAD63PCXStZdyVMaUhi+wHmWSBDerTvCNFmOzSCO7BR+lk0YKs1lsU7ivTvgYJmZb1UUNIMmKYJKYRrrloieXq+FgJ692/C+oeM+eGSDk03oCrBZ11R6rKrL8/ncmtj0qPAhpVRS1trQTXrsprvozwf5d1RniHF2++OZb4cvpaizK1a617NdyMteyRlug+lqpWpYXJRPKBwe73CEGUOBNWHvE4AM1b7uQc2bX2mOZ6ppU5AtDuww2UdmaQKzTBrMmBrHXPxDmxZN7gHNugdkgTolsweyRyux9yNsnVHrSNFvUSNnqokD+rR/rBngppO15j89pEo73e3nw793AeIBIquGAtUMzeq0QWi9HnUelQzH+q9dwK167O07su6EfreeoUQR+2nWsa8Hu99JWDUPLCSJC2XJGm9JElrJEm6WZKkloDntkqStFaSpOckSVo1WvWZSKiGVJyEbd9wGWXdwLC1CemG6TkpqoWsuxIkCcgmFe8dWG10ExtQ6NRIQoij9LMjhLjKd/l5YAcKZR5CDFQ29KslkqfnayGgd/8mrH/EvhiswLUXpS6zWjJ8Lrc2pDwn52Fl1NLWWlDNuqzm+yjPR3l3lGfIm3AoX/YYEZTEpFK9qh2LsHrNsDIqRv1NNWXXirAyxXk5UChXLVPdPH4ixKiZhlT0O3VR5kBQfSZbHtGGpILBou2BndFshxBPyiQi1yWoHsMlDbIENKWtjJk+ci9f0pBNKGhKqZH5O8PeGRXiHVjRA+uHmS1pGKb1nG6MSC6NxtwNKi/Ku2huJ0MOI/1+W2+5GFWmTcr4+1darbusrQ0pR8RE1Pe7+UDJcHXzxBLEe+SjjWrmRjW6QJQ+j1qPauZDlDlS7XoYiT4LMNkU9s56hRBH7adaxrwe730lYDRDiO8BcIJpmicC2Ajg6pBnLzBN82TTNJeMYn0mDKohKReV0INDJYdh4Q4jXrZ0oWdzIrLuZIBwDoMiSWjJJJBJqNyQHOsQYs0wa86+FkaGThDDTqo1lkUDdrDIshUOFjU0pRN8M6x02u03F4Ig1r2a3wFs03bPrzBy72ooLyrVhcqkcWxrSnnKXLZ0Ifx0f796jxaCyO4rvX/Z0oVIJ6Ktr6Dy/MZClZ1tX7Z0IdIV6ifKBLdnTRzTMFL3oPYGjYUfATuV94nXHul5PkqfimVXIqCvFn7zVZbY5+K87C9ooTLVT+ENU3ZFWXD0tMZI69fd1qC9412nz/H9nBLmTG5Iot8KIW5MqdxoHSyWcfaCVs97E7Lk6fegOgEUHmwf3Pkls3KGEFd/BzZIRlSC0wNreu5lEzIJBf9x0TH273RjRHKpVnkiQpbCD0WovCg6BRl7MyelPPuiG2L7qtFXRLC146y7IsEr01x1kazPz5jvnZeZhII3n8zoqNg+EjyPli1dyDl9RbS51ijJzPltDb5r8rKTZno+Gy1E3dfd8jpsXfjJkICl7ft8lDqG7WtB7Qmbu2F185N1YfIKcB5OlDUj9J310m2j7l1R2y9LwXtzpfJGumdOVIyaAWua5t2maZI28ASA2aP1rsMNRFJOQmfWpHQgqbjowds/WHSk/neHvHS05/CeM+bwv0Wy7n89205CkFYlxzPvPWOuZ6PNtWSwaGYjZkzKIJtUMFTSYBgmV4pHO6W3qEjW6oXtaM/hq29ykpS7+1lMmV5tuLLbWzNc0mGYzAtBBwaV+onmglv00rjQBtzW6CSA72jP4XuXn+B5npQBsbyUKmP5207yzK+O9hxet2iaowx6x3DJ612u1AaCeHLeIhLQ62wc2xqSPDRZLOONJzizU7ZkEr71Hi10tOfwrtPs9dPWmAxcl+7fff0ye541Ct41cRz85p9YBn0ugW1Wp86b7Hi2oz2HZRfbm5BfeYWywTcvOmwiiCHE5xzVxv89ozntmVtTG5MQRcKkjBo4Fh3tOZwwy05+JNbrdRZvNdUpap+KZS893uYpDevDqHCvuaQqY15rAzraczxLL8DmPZOpdiKkmZas/k7HYnxcMM7p7mc6RBEhRTmTUJBJqLjmisVoTnu9TLQvNKdVT1up7iSvs0mF1+eaKxZzeUH1XDybZbGeYiWYGyiU0ZhSuae0v6BhzpQsFMm+F9uQUrD8ypOw/G0neYynoP7Pl3Rkkkpo5Mmw9QxlIq4WHe053s+Ac23NaE7jvWfM5X1H3ImA04DVXXflqL5NVl+LXL0lzUBHew6XLnYaMJOz0eRSR3sOn77oaP53tXPXNE0YJvC6RdN4u0SDUCyP5gXJ/xk+OgX1eVMmgW+9xd473HuPLMHRPiqb5oJbXoS1X5RpCUXCUdObPPP5y5cey//OJBRMbkigoz2H3OQskorkmJfXXLEYJ89hqRbbGpJWgi7/PbajPYfZLXYESIOVSTiTdK45WpdTm1IOuTC9mWUWP3ZmM8YK1Nc0f6ku4p7qnkcd7Tlkk9H3nI72HL5wsb8xE2WOUh2pThKA711+QuDeIMorv/e4n2lO23KkKaXwQ1uShyTraE8hefXNNx/v2wfTmlL4xPkL+N8l3bDXi1U2jTWA0Lu01aCjPYfzjpnq22b3c6LsbskkeOQMeZsTioRFM5oiyQ63fTG9OVWV3DmcMFZJnP4FwJ0B35kA7pYk6RlJkj4aVIAkSR+VJGmVJEmr9u/fPyqVHEtcdtIs/u87PxNMal8sGzxkpnew5Eg05Jco4zVzGVn3R86d7yBrXjKPCf2T5rTwjHpvX8IIvb/TsRgnzW7B6fOn4IRcMy5YOBWPXnUhGlIJNKVVZJIKTBPYO1CAbpgsw+4Y8cACwHCFu3thOP1Idoo7ozntS16t6bXfgR0oaGhrTPJ/cwWhCg8sALzh+OkwAXzh4oWYMyWDjpNn8XEhAfuDK0/01P3SE+05dM9/nIfvdCzGCbMm4cwjW7Hl2kux9dpLMa81izccPyNwfk1tYhv8SbMnOfrH6YGt7C15y8msLlMakrjj0+fxz9/2mtm8TPLKtFp95g5Nnt6cQUNS4cbg/Z8/f8yF7qSMfTr/3cujC/3XHWsbWd9/60n83zS7Fs1o8p1/IhZMbQQAPP/NpTiitQGtjSnPM+cvZAcObY0p3/KKmo7Zk5nC5zZgxTEV7zT/9kOnuoxgHb1DJfy/C47GT9/dDgC44WNnhdadQkKPmd7oqBcdUlzxGvb3f7795KrHdLoV7vrGE2ZU7MOo6GjPQVUkfOL8BTjjyFY0Wx7JgUIZksSMFJr3i3M2ldFP3tUuyFQma9/6mtn4/BuYQhhGNk+K8uzJGfQMFtHRnsP7zjzC8cw1VyzG5msutWi0pgUqhcdbBwZHCwZBR3sOWUup++enzkFHew5lS/4wA7bMs6Q3p6m9GvoLGiY3sPl0ZFsDzl84jSuWsydncOmJzID7j9cfE9j/wyUdDUmF05AVfZKSDJc0ywOr1uSBHSpqODBUxkmWUW4C/N//+faT8K03nwDZUvjOFLzKLISY1adsOO/ATrXoj95qySlxPyDZPbc1C0WW8FnLO/v0ly+KPAdPt7yIb7FkejVzl6p5fG4SjpnehNcfNx2XWMb0Jy84ylNeR3sOZy9gB1M3fuxMz7uozzXdxMXCYaEJdli29dpL8akLjwIAx/dU9ry2BgDAbz64JHI75kxmmedf/PbFOH1+q8PQIpx/DJNp173tRLzl5FlQZZnXd4o1L+dOyeKiY6ejoz1nX0UJ2Ed4u0wTvUOszece3cYPJzx3YDnFkomO9hwySQUfPmc+7v7sa616jC1neUd7DlcumY3GlIr3n3kEJmUSWPEZVpe5U7Kece8dLGKwqDvWBQCcPKclcM6dfRQzrH75vlOw9dpLceq8yTjjyCmR52hHew7/es6R/H1n+ERxiM+eNKcFZxw5BW86cSbmtzX4zl2Sszd+/Gy881R2kPzL95+Kd5/OZOSVS+Y4ZB3ptDQvTps/hZcnmqA/esfJOM46hEipMvewdrTnuH74mw+cyp+vZ3Qh6e6nzpsc2rcd7TmkVBn/es58PPf1N6Dra2/A1msvxcvXXIKt116KxblJvvpAEDrac2iwDmp+/t5TXpHGKzDCJE6SJN0LYIbPV182TfMW65kvA9AA/DmgmLNN09wlSdI0APdIkrTeNM2H3A+ZpvlLAL8EgCVLlkxoZukoHEx9wyW+QYV56Uq67VF5//89xU/OAeZtcb/r/EVsA3MLXZ4MaqDoSQgFMCNxamMKpsk+7+zqxrPbDkIzTGzY0w8AuOy/HwFgUxHc/OxO/ODujaPCNTVQ0CBJ4PUJQiX+M4r7DxJK4mnbE5t78V/3borcnoFiGTMmpdEzWHJQVDSmVR6mFjS2Yr2nWad/bY0pZBM2526+pPMslH6btNim3sESslNU9A6VcIKgcLc1ptA7WAyckzQXelxJf/Ilnfe/OJeCyqF+FPsBAH79yBbc+fweFkJs1XfPIZYNsP1b9zjK6B0qoq0pxb1YojFQb16zIB65XzxkZ5y898W9vpyFfvUQQyY3WJyiIhdvr8uY9MNggd0jJCX/pX2DHp7KhTOaAAAHhoowDBOyy0NWKBuYkmWfffC3Tzv49iiSQZKALT12Jk4x4VNnVze+e8eLMEzgT49vxZWWIkF30v368bq71mOXNaZ7+wuO72lekOIqroeoY0pzlGhtquUv9OMhnNWSRlk3kVYVJBWJr6X+gobGpIrmjJ0pV5Q/V/78cf4u4oYsaDpvV5gspzmy/cAwipqBs6+9HwumNjie+eHdG5FJKJiUUXH7mt249bldvn1DSviaHX2Yf9XtmNWSwWcuOpofJGg8YQn7/5ObDyBf1rHzIEvk8drl9wMAvv3PF5BJyNzj09qYdMzVksb2n4Qi+Wa+JzDvqhB5YvWnOMYAsHpHH3b25TFU1LHg6jtCuWVFdHZ147u3vwgA2Li3n39O/37Pr5/EzElp3m5xfpeE8dF15x3Y3da83U9XY4SIHPoNGf00h4fLOpqtA6BKc5jWXKVIHL9y3riYyR5Vkfi45C2lNGhPpH73+57mc0n3ei1f2NWPzq5u9OfLMExg0VfvAuDkhCXD3y9BXFA/9A6V0JRWkU4wmbbHJR/EumaTCtIJhc8z6neA5qUz+ov2kVO+fY+HB3hSJgETJt83t/YO8UMbtwFLhy2UHEg3TCiKhPte2AsA+M7tL+K3j26tas8Z6X5V1AykEzKa0gkMFjWefM29J4qy8IVd9rqQ4KULcpbPyqG9NpNUcWi4VBXnsTjHzrzm/lCu2pJmoDmTQFtjynOwSqCxKmkGn8csgzh7z3PbDzr2Q9N0zkfSYVKKhKKg1/3Pyk1Yt4vtyZphYnefPQdpron6lZ+uWA33tvNwocT/H6arfvp1R2G4pPNDGTeSqhzKMe6GaZo8WtO9v7+S+GFHZMCapnlR2PeSJH0AwJsAvM40/VOHmaa5y/r/PkmSbgZwGgCPAXu4ICoHk2gwhJ34HBwquqgd7H/f+fxu/M/Klx3vuuHpnZ7nAFto9w4V+bvF0Fy6w2SYwOb9g7j6prV8oz+U1xx1po3sC/9YwxWkenNN9Rc0tDYwYTcckHwmCv/ZO05lketBHmPNYBtFoWzg1ud28TZHac+AVcekKnNPBsDC0cIUF3e99/Yzgb5p7wAySYV/3itQofidAovzpmewiDlTsugZKPJTP4Bt/Kt39AXOSVv4Fx0Zk4dLOu//Act4CZvbrz9uulUnE7d07XLUk557g/XMXev2AGDGnVhGzyCrO4XBUYh8vXnN/Mpz88gBwM3PduPsBW38HWH1EPlDH1i/l7eP0GNtYKEHIlZ4pyRJyJc0vLR/iGchdXMgGiZwcLjkOZUdLJSxUdiMxTpSEqfWhiS299p8pTTPPLyJw2X87tGtAOCbLMX9PMBkhdhOCtHPWop3Wa9+TGmObusdqpq/MIiHkCgVXt43gKTA2UxKc1Na5UrN01sOOOpD73rrKRRZYNO0hEVcPLml1/FMd1/ekxly/2ARy25cDd0KHw1q2/4BVn+RI/Mrnc/zckiOrd55EAA8tAp9eeGKRtlAQWPzs60xhZf2DfLvipqBlCojpSqBWVoB27sq3oH1mx8b9tplV+KWJbjLyZeFvAVlrzEKwDG/xSROmmE47sDaRhlbA04PrG1INaZUfmCRL+loTicizWG6ihE2L4LKIcNKlSW0Nabw3I4++3AzICqpzA1Y7/d0sFjWDdy2ximjh0o6lt242pPgSuSEpbLdBkhYP/QMFtFmyaggzzvVNZtUkErI3CM6UCxzA7atMYUdFhMC9aV7HxF5gN1ZvnceyKPRkkHuKAkyYgzDNmBf3jeIPzy2jT9TzZ5Tj/2qUNaRUhXrMNzEPktHEPdEtywU/20CODBUDtxz3ImrsgkFGw4VquI8Xtfd5/g7jH+2qBlIKjJarasMhbLuuW5BOk5R07mxVigbvK5dO/ocMpFA85H2CTdF0KMv2/JbN0xs6R3i/VIQ5D5Bc4UQV8u9LfYV1a27L5h7ubsvj6/esg4A+FpxI6UqVWWuLwnZ1qkOr0R+2NHMQnwxgC8CeLNpmr7s7pIkNUiS1ET/BvAGAM/7PXu4ICoHk/uUOwj9BQ2u9cjx+8e2+vKssd85Jzu9o1A2sNPaCMQTtLyVYKMhpWD3oUIkXir3XYF6ck0NFMr8XoLfZgxE4z+72TKm3EKJUNZN7n1wb96V2mOH46lWghTW581plSd+8QspDKp3Z9cuNKS8GZ/Zu7zCS5w3vYMlFMo6Bop2WDMAtDamsG+gGDgniS+0qBkOzsHhso4Zk1K8nUH1pnLEulz/lD9/38oN+wAEz5vewRLaGlNIW2GIpFjUm9fMl5/Qh0dOMypzFlI9ROX+hd0Dnnea1u/DwOYT8xLsOJj3UGiIHIiATWnF32Ga0E14aEI4t29ZQ1KV0ZJNOu/WWzQRfu0r+mzwhKB5fN2K9fzfmssDS0pwNWNKG/DuQ4Wq+QsryYgHN/UgodhhZQMWj3NTyg4hvt+at+53/XP1bgBMpkbxwN5jeXRE+LWmbJgeme9uW++gVx6IRpJu9fuDG3sC6+Ooh8W92dqYdBgoJc1AUpXZIV9IeDQlaBI9sPXgRwZq438V57d4B1YzTGi66Ul6w5Vf4Xf23GdzgjJHk3yOMofp+7B5EVTOj+/bBABQZRmtDSn0Dpb4mg/ywNJ688sbwRMj6ib+94GXPd+XDdN/PlrcrUEe2LB+oENJAIH8v3nugVWRtujnDItqieRhW2OS74diQq6oMGHzv7rHgmQ398CaJo9Y8GtTJdRjvyqWyQPLdBM66BL3xDBZSPj+Xet9P6dyyIjMphT0DJaq4jx+Zntf6LvFNpc0HamEjDYrZN+Puot01pJm8CzmRU3ncieouT3CIXzYcwSSdYDtfe8XjEO3s6Na7m2xr6huRc0MlWEka9pCPbDRZaC4/klmvBL5YUfzDuxPATSBhQU/J0nSzwFAkqRZkiTdYT0zHcAjkiStBvAUgNtN07xrFOs06ojKwbRfUBLCPLBhi9Ed9inC64G1J+7WXibIxYRQpIBkEmoozUAl1INrqmSdmM9oTnvqWe27DuXtU2c/aLqBRI0cp1zZtbJqOu7AJoKTOAWV2TNYREYIIRYPOfw2/pLLA0uCSjzFa2tMBc6hXX159AwU0WQZ8OL78iUN05psrsiweu/qyzv6N+ikMIzyYFdfnik7jXYIMW2y9eY1q5V3LaweYnhl0Pqp9N5+IWQuyNslKo9uT0iYl4dx++o8PBlgm2JCkfi8qTTX/cr0gxiiRfOCDFiqYzVjSnIuilyqlmPvUL6MhOLvgbWpsfznLc3zQlnnCcroeoUfwuZ/FIhtqcSFSKGw1dzf29WXR2tDCgeHy/zuctEyYJkHNuSwsKQjk1D4wV1JC+daDHp/NZ9XQlKRIUsuD6zOxkdM8tSUUvlaEucY/YbWZSZB4buV5SGBZHnY3eigcihElkKIB4ua7/UfEeWAEGJNN/hnZd3gHr2oYDLe8ugMFT3fBf2GDiUB5oF1J+8DwA9OKYQYYH3vCCFuSPFrE2F9GYaSMKfFgEAqzzBMGIbJrs0E3KsdKx5O8lCSAU8eR6p71LL2HPKGbLPyWV9wAzaphMoUv/dFyRmyS6h3yvLAAt5DEMOww72LmiF4YPWKhtuBoSJ0w4x0TcddLztc3d7f3M6Oarm36Xl2B9vWr6KgtSHIA1td3hlxbOiA+pXIDzuaWYiPMk1zjkWPc7Jpmh+3Pt9lmuYl1r83m6Z5kvXf8aZpfne06jNWiMrBJC7gWhMitYZQNrgVTlG5pT16WBDSwyUNmSS751MLVQGhHlxTJMimWQZsUBbiKO8ixVkzTPhFsWuGCVWRAtsc9o6BgoZmQdkdEEKIbQ+sd2yDypw5KW1x7rJyeh0eWB8DVvTADpW4AG91GLDBc2TmpDT6CxqOse5VigbRUFFHSyaBpCLzk9GwuS3OYb9EHYAzM6inLi1pHBgqoa0xycOaaJOtN69ZrbxrYfUIC6+M+t6BQpnf0wriCRVpWtwHWGEGBnH7ZgWlaGpjyvLsFCvWz2/+BT0/TcjoSAaBO4Q46piyzOfRFZNqOfYmZxMshJjucFthi6K3qCFgPnPOU0HhAoK9bWHzPwrEtlQS0WREV/POWS0Z20MyXIJpmijpBlKqgnQi/A7WcFlDQ0qFqshQZAlFTa8LP3LY50Eg2UvZ4IuazuUTHTDQGgCAhTOauNGu6d5xJDmfFUKIo9Z3OOQqSaX2Tbf2P0WWMNWS6bSFBUUlkZHpzqjvvONn8uRVUTGrJcNDr3sGSp7vgn7TO1Ti9/qoz935HMQQYvv6iM4PiAG2jxkmOzSqlQFBpNQRvbckuzXD5EaJmGvE3aZKqMc8Lmg6UqrXAwsweRO1rKCQVNsDS9nMw+WE3/uCeHP9flfSDKQSMtdN3IevQyWNz+2icAdWDCH2Q1KR2bwYLqFnqBRZd6V6Udni/uZ2dlTLvU3PD5V0FMoG16+ioC1gXVZ7B1aUD3RA/Urkhx2rLMSvGkThagSc9xuDBDIZXEE8cG86caaH74meDboDK0I8kS3rJrJJBdmkAgnwtMEP9eZnJJDxPa2JQoj9FfMo/KNHCUlS/EKOyrqBhCw76BnE3we1h7zEtrfGTl7UlE6E0ugE1fsLFy+yDFjWXvLStzYkPSHh7vb0DBa5wSsmAqATPfcMyiQUfNSiAVnIDVhbMcmXvZQXYbxmYjtzAnWB+Fz73BakVMmXo+z/nb8Ahsk23BR5YK2T8XrzmvnxMyZkybPOiI9Q/J2bs5Dq4TYe/Zbs599wjPdDAaLH4ZwFbZ7vMwnFk31SBG3GQWNEVCf2vbKkFTJaCmmfDAn+Htigefz+M+fxf7s9sDRPoo7pQSvZHR3EVMtfWElGdLTPQtIRQqxZURUqX3PHz2r2XT+nWVmImQdWDD31l1enzG3x/dxdth+noV/b3PNVdSjoRug73VAszmHiyOwZKPE2pVTZkVzHDzS3AKZUliyuxZHyIwPV8SwDwGusNjelVaRUxWEw6YYBzTB51mnAln8HhksOmeoOIeZJnEq2XAqSB4R8hDuwQWvhY5Z8TsiyJ7lLJQ+s+9CXZHhLNoGybuC9AjUUISH7H+QSJyx5pnpdB0pB+8LnXn80Dg47PbBiXQhU10xStaNvNJ0fHABwGD7sPqUUyYASIT4trlGa17oQPfHaY9pq3nOizItKKJQNpBMKb/+uQ3nhOx3Lli6seIgFAG8+eZbv5wV3Eifr/35dGsR5PLslE5l/lu7ATg0wYMU5we7AsvoVynqo3DllXgsAZqT1DBQxrSlVkVNV3NepH0T9yu1QCuPeDuN5pv2Z5EvYeJHsDnJKpVSlZg8s1eOVyA8bG7B1Rkd7Dh88ex7/O4j7STzFDLrLQRN26fHT+eaZVGS0WJvvibNbHPxRrQ1JzpkYlIWY0JRSeTy8mAUwk1Sgm3Dw101znQoRR9UXli7ii7JafscwUN3pBDrotNnNd5VNyJygfbLFP5oSFqxfGLGmMw8shcsSwrh5WR1tY7UpxTIFDhZZFlkHnYSPMkv1TgrCr9XivsskFb6h9w6W0JhSMbUpFSGJU4kbvG1CGAopPuIMozm55AimgC+yBKzo8R0uMY9KU1rl3Jgd7TkHf6jILybOYc1gwpoM2YQi4ZorFmPOlCya0gmLw0121OXUea28vrT50ybW0Z7Dly5d5PveWtDRnsMHBPqSXEsay688CZ+0KCQANobphOxJ9f+lS2zOwpnCHHHfDzzjyCnItWQgAVwJEWmP/CAmLTlVoAQAbA68U46YzD9zh2HRRv/2JbO5Qunm9s0mVYdSSFmqqX0fPsfmi2a/PdGRkVcEn8eWx8umDLDrTkpvxnUHln5LsmxSxst5Ctinx/Na2UGUSD8mohLHnjuMi5Tq846ZhoQieUKIm60kToZhYmpTGlObkpg5ic1nGguiKyoISUeA4APJ3JQsGlMKl6eTswnMaE7hhFwznyu5lgyWX3kSrrl8cWDbNN2ACUYfIY7zOUfZVBakiB/R1oBsQuZcmuQ1aMkkHHzFF1mUPeQB6B0qcqOLG7AhoZtDRZ1T+KQSMooWh+rXBB5ugBkGVBeS21Mbw9dzR3sO/yLwmBNPomT9u0UwRhtTCqfToIPE/oLT+6gbpoN/lx/gDZQcvOA0joNFjdPJAfZ+1NGew79dYMsMvzkYxQPb0c44vak/aC28bhHbyxVZckTVNKXUwKikoBBiWr9TskmUdQNnWQdkrQ1Jx7z74dtPdvRnNqlwTliKpnDLnY72nGNdUj+cc8xUmMLhE/W5+zCW6x8J2wM7UND4ATFg72M9g0WUNAMNKdXBH0s86CSLJmVUm1fT0lfKhn33WRwPSuRjmLYBuzjHdCuSadXoNx3tOcde4sfnXAnuEOJdjsy5bG1Nb05BFayibELm64Lyhxw9rTGgfCuEWLVDiAHgc29wGjMJRQrkPG5Iq1g4vYkbRS2ZBO9fNw8x88AqfBzd+RvE/aXk9sCGyJ03nsCokXoG2BWqI6c24porFnN56jbYKM8L1UvMeE1whxDT+iQQX/B3Ohbjra+x+2VWi7PNdDBM+pVYqmj35loyOGtBK5pSqiexFSElJBqMAlpT4vUI2gvJWK4Hp/p4Y2QxTTF8QV6vXEsGj151oe8z4ilm0P1MUiDa507G7ClZ/PaRrTh6eiNkSUJf9yEUNB0dJ9vp7a9964lYt+sQsHYP8mWdeReFUFainACAOVOy2NzDMkKKSRQojIOoWH77wVNx9PRGnPP9lQCAH1x5ElKqjE9d34XzF07F9+9aD80w8akLj67bQqANzk7iFCzAzjm6jbfpqkuORXdfHr94cDM+b6UH/y8rEQbgn8hJMwyosszvrCYUCWXdxC/fv8RBR+OGGC4shhBTFlkyToNO3jvac/jD41vxrJUIYV4b22gakiqGSxq/P9HamAzM3iiW3St4YNua7FM8MYQooUgwTOC+z70W6YSCB6zkNEdPc4YQG4aJQpnRZ9D9XoJu2n107RUn4oJFjMNPFK67+vJoySbw6FWvw9dueR43PduNt5w8Cw9v6kFKZZvH9gPD+OE9G3H3Z89DQ0rFYy+xZDOtDeIdWLvMI9vsjfh3HzptxOTykwUj/97/OB+ZpIKnthzAj+/dhD9/+HSs2noQP7p3o2MNAYzv9WtWxsAfv+NkziNHdZ2cTeDgcBmnzmvFXz7CPK6/emgzvnvHiyjphuPQwg0xaYkYwpZQJLzW4ubs7Ormn7tPsWmjP/uoqXh5/xAME7jhY2fy74csLxmnTGlIQjdNR9ZZCtt/4urXYYZlsP3g7g2Bdyk72nP40b0bcdLsFvzLOfPR8bNHnfeJLIOgIUUhxKbjtz2DRXzn9hfxobPnh1LozGtrwKptB3n/rPnGG9CcTuDCHzyAY2c142fvfo1v/eg9a3Yewt+e3o7zF07D+j39+PZbTsC7f/0ko9ERuAEHuQc2AdNkoW1DJQ0zJmVw6yfPwRnfuw9nLWhFR3sOj1pztlA2XB5Y/zVfKOuYlEnipv93Fk7/3n343BsW4hcPvYyjpjXhR+842fP8lzufxwfOmuc4NAFshbt9bguKmo79g0X881Pn4jN/7eLPUD+XdQMN6YTvPtTdl8fZ194PwD4wIYWdDAUAdhKngBA2w2AJSrIuDywAXLhoOsS8jB89bwHOtg7BtvYM4fwfPIDPLz2m4t5BBtzTX77IN/z13Ovux44DeeRasvzZprSKg8OyY+7qhgnNMPg8kiTgKEvJ7x0qOkIpi5oO0zT5oUaDD4XNrEl2+N1DX7jAEaYqPluJD/xsYR9712lHoKM9h5f3s3WpKpLjKsicKdlA7lMad3eyFlqTUxqS2NwzxMfnZ+95Dc440snh2dGew8GhEtq/fQ8+/wabZoPWsh8NCh1KnLWgFX/5yBkAgBd3M1oXGo/GlBVC7PbAlskDq3CDqse6t0eyijx3vYMlFDWWoffy9hy+dss6fOXSY/Hhc5m3em13P1bv6MMdnz4P7//Nk1g4owlvPimHj//pGQDApAyTz+J4iB5YMtIVWeLcomddez8+/brq9BuaFwlFwoUBfM5hYAasfwhxocxooXoGS/jIaxfgixcv8v39oq/e5TEUxe8BcL0na/Uz6Tz/857X4JbnurFp72Bg3YdLOo6Z3oiT57bg3hf34ekvX4QLfvAAtvQM4fcfOo0fDAFsLSUVGQ0pFZmEwseXIO4ZjjuwGgvDpX0VsPUPADhmuqW7WFeoTpzdgo72HK/zd29/Ab96eAsA4IHPn4/fPbYVNz3LmDpM0+QyTTxU8dPH37h4Jj57A8vG/MWLF/HyG4WrCLd+8hyHvkXrhOoo1j2bZIk/T5o9Cbd88hx86vquQAodgBmw1dz9Jp1+zpSsg9quoz3HKeUeWHZ+aP6XwwGHd+0nKNbvIc6p4E2rZ7DEhVPQyYqoQLQ1pFDSDeztL2KypWQUygY27rUznpZ1w2GkDbpOtUTvy9wpWRTKLNW2eAeFTpmJ2oXx3wmn1dObuBI+XNK5wKc21wOkcJBXNMyA3Si8V7yHSgu4Z7DIvRR+IRhl3URCkbjy9dpjGMF3pfaICZvovly/cGcnFRJCTBDrQ4pjJqnAMJkQpwyOQdkbSdA2pVUrhLiITEJxjJeo+Lz2mKmMIsBSjOiEcOakNJrTKvfEkUKRdYUQA8CGPf0472hvH7kVeOqHhTOaMFjU0N2XdxhwtLnR/O2xNtqpTUlPEif3u6Ikj6gE4jYGwGmCqD9VWeKbiTtbYlkYzw3C2qO6zp6cBeDsd36YERIKJSrKgH1fTJUlnLmgjde35BhztweWEnOwpDvu9+VLOhqS9ql+W1OK8/LRdYX1ewYwKZPgh0dUF78DFELvYIkftADe+3aAfRLuVuRpLIMMZGrj/Dbmgd3SM4SkKnOPqhixEIZ8md3xz1gh+lSPdEJGQpGhGSbyJRYK3JRW0SiEOw5bCYoA4JgZTXzch4UwN1G5CDJW3JlFB4sahot26K0bQf0uZhBtbUzxaB5RWaVDzbJucq+UG7MmpXk/0t3rVoehIHhgQ5I40cFJxpI75IEV60oQ2zp3ShaZhBJp79iwpx+tDcnAu5sLpzdb9U86QlaTqoxBl3IqemAnZ5M8WWDPYNFzBzZf1vmdWXcIMeCUAX6yPgqNDmufXQ7JYRpDykJMmDslGyGJk3M90foi3YF+H6TAivOfIHpg3fkkaJ36JZmrFEI8XNKgyBL39AP29Zkm17ykgxVKLAY451iRe9RYUsWmVMLhbW/JsvaXfAxYw7SpdOggYuakNJrSatX6zYY9A0gqMs44srUm3ahQNpBWbVntMLg1HZt7BqEZJvfuuZFOKGhKqTyBjxvFMuN5F2l0AKfet3BGM7b2DgWue5a4TXUkeKP/i9dbNN2AYdrvam1MVuGBZSHEtK8Cto4mC4dPPQOMHtJtBNK8SSdkzJ2StQ4r2RiLfTpQ8O5Zzv4S9n1Bd1jvWLfONtHfR7RmeWQB1Z2iQux1U/RQ4okgqrcANlIPaH3PnZLFgeGSI7EgGethodmHC2IPbJ3R2dWNW1czL8n+gaKDh4tIhLtdWb8e3rSfe7JEcANWsZWensEiplghMc9uO4D/FjyMj7/cw0/SAKYg0YZV0nWL3JiV+YjlPciXdT7ZmfHEJjqd2jSlE7jvRZv+4WN/XIU3ncjCNsRFf/1T2/HQxv01ESO7CZ5JeH34D08jIUtYu/MgTv7m3R5ybQD4+q326f4z2w7wU97hko6/r9rhqOPta3fhg2fZoWgAO2Q4lC9zBYK8Kp+/cTV+dM9Gfj/AUT9N5/34yT8/A0mSUNAM3PQsG/ezr70fHzuPvSdMcRE30btf2Iuzr70fp89nIaJ/f2Ynntx8AJphIpNQIEumg8R72dKFPKFPvqRj495BbNo7CNN6P43DyhdtChDycl76k0fQkklwpfstP3sEg0UNv398G+59cR+/e7Vp7wCe3X4QhbKB+VfdzkNgDgzugwSWov9PT2zDsqULubeOQPN1nzWPzvn+SqQTMiZb4WnE03j5/zwGCXZ4zdv+93F8finzXD61pRf/ff9L6O7LO+6P3PvCXh5KWwsxd2dXN/65Zjf/++aubnzsvAV83iVUGS/vZ/Ph9O/dB8Cec8fNsj2/X7tlHX7x4GbHHdhN+9jvfnTPRjSlWVg4GbC3rdmFXz20xVFXwJ5bJoDtFsXVmp19AJjS+OTLPSjqJuZfdTsmWf2XL+m498W9OPmbd3NCdNq801bSHbfHbLik4eAQ8JtHNgMA/vLkNixoa0BRMzD/6jscz57z/ZW8L5vSKjbvH/LMv472HAplHYNFDW2NKT7m/T7hWAmFZTxet+uQoxxSwNxeGRonWt+/eZjVeUvPENoakpyvWEx6FgbKsk53zEUjkMbn+qe28fHIWgrH2dfeD1WRcNRUpigpErBuVz/mX3W7cDBhONZy0KEVhQVmEgoUWcJAoYxh61DBD0TP5VcOq7vMQsCHirj52Z14/OVe/swDG/bhtPlTUNYNqAEXh295bheXT9fe+SKSqswVpO/c/iJ+ZfV5MuAOrLj2AHvuix5Yd/jfx//4DL50ybHoaM/h1tW7oBkGfvvoVty9bq9n7frtl6JsEyFZEuSxl3v52lmxbi9UWcKQcK+MvGy0vx0YKuHy/3kUAPDZv612HDyVrEy4ABwhxHRg0tnVjd8/tpU/f3PXTrz79CMc/UKKa0mzf+OWV4C9jykS8Hz3IQDCgZoiYcW6PVxOPvzS/sA5Rkbmmp32OhP3VNrf6PAl6B5pQpGRSSj8AEW3svOmVAlFzcCRV9+BSZkElz1UTu8Q03m+ces6vl9/+PdP41tvOQH7LO7iD/9hFXKCDKHwc0mSuEfwa7ew/vjuHS9CkSWYVru+edsLSCdkTEonkLCSL4pyjuboYEHjod9iwi6Sn44DV57EyeBJnCjM8pbndqFYNvDnJ7fjr0/tgG6ajrr7obOrG797bCtKuoGnNvdy2e3en8L2rqKmI5VQ0JBUIElwUKqtWLeH83N/9/YXYZpeHs/Orm4Ml3X87rGt+OPj26CbJlqE8WpIqVAkOOQo4NT7+vMs/8CxX73LMdZU16GShoYUyxwtZg0GnCwb1NdJVUZnVzf29hdwc1c3ntpygLfZfQdVpH0slA2+vgFbjzFM4KL/fAAA8K1/vgAAHlmyzWLcKJQNnHvdShw3s8nORi3MG5Kziixh58Fhz1535gI7SuFXD2/Brx/ewmtE48OM9ibe/9fc+SIA4I0/fojLWao7wNb63v48bn52J57awnS9IPmWUlmyqrO/fz929RUcOpOoD3M9NUsHHyw51lFfugOzWjL4/OuP4QfMhbKBJm+6ksMKsQFbRzCi4DX8BMcwwYmCAXhI3Ql/fGIbD30QwU/AE84TWDpFXLFuryPt/1+f3oGzhIUmCoWNewYcPJ80iW96dgcWzWBhI9mkwhUYMjwee7kH1wlcYrsOFfA7i+D7vhf3OOpbCzGym1xZpGCh08CVG5xchgeHy/iPG56DIkuO07IHN/bgGOtEbvWOPvxspfN319yxHi2ZpKNuu/sK2H5gmIdv5QWh1t2Xx7IbVwOSfSrnpogp6ibcbI7dfXl8707WZ2EeWHcSnu6+PG5bw/r92/98gY8t9c1QKc+fu/qmtTj9SGbE0XOmUI5ItE0YFtomtuNQ3laSu/vy+O7tTPDesnoXb7fYQtEuond94KwjIKIpraKzqxu/eGgz/6xQNrBHK+IrnWvxj2d28s/FsvvyZXzzNrYZ3bp6t6dtADPAyINbLTF3Z1c3rrppjWPd/PDujZjelOZhro9u6nGQ2AM2ofvrj53u+JzeuXB6A28jABwYLvO6kHJ3zR3r+Zr2m1sAU06+0rkWN66y+6cojAGNG9VfHEc6zX1yywHfO4u9g0Vs7RnmStqhvIZndxzy7SexL4eKZWzpGfLMLwBYMo/NwbbGJPfi+YUQq4oECcADG/bz0+Duvjz2WHKGvOAEt1w4YIWPdfflsVgI7c8mVfQNB9OJEWwDlt0fFJOYkIfy+3fZfHi0Vkyw8dm4dwBf6VzLD/5M2PK5pBsOuR4U6lXQmAErSRJLEpXXrGRp/tuweP/cUY5AgdHWmERZN3H1zWsdc/rXD2/BMdObPCHwBOpfUuYODJf5fCSQ/F29ow8p14GIe3wA4LbVu3De0VOZ99/qgxXrnHvEvoGiQzbR3HevXb/y/Z6jujywcT9/ZrBo/0YzTOwVvFCaYSJf1PDElgP8M1H+iVENjMqFzbvGlB2JNFzSef3EA8pv3fYCXtjdj388083rTfvJUFHztMlPBugmsH7vADq7unGklYDw6a0H8OcntvP1N2S17+Znd+Ly18x29A9FiDz2ci9fZ6KMoMPqRzax/goLIWyy7oGzfrPmuuaVRYAd1t4zWMKyG59z7BH9BY3v1wRxHMUEYE9Z40JjcmCo5JmXhbKBolbELc/t8hys0BztG2aHQ5SQjUD5O0TjhfOMGrbXW5YlzxohuRm2z7jnhSi7xd8B4XtXwYrWkCQJjSlnFNTPVr7M9QpaT2JdqA7UFqq3OF40ruRgof4nve/JLb24/qkdvO7ib6muJY2NWzrBKF50ww7JFb2R1Ncb9vTjx/du8l3zYtROsWxw+VEs6xjIl7BOkPFOPcYrH4P0n+6+PPb2F3iyLnF/HLDap8osKoxEKZVFuWUIoj5Chwt3Pr8bZx3V5qPTCmwfQt1JdfzCP2x9JGhuUaQa3YUW3++nD/dZ++VDG3v58919eVzduZbX95XggY1DiOsIRhTsNFiIKDiMjD2IKNr2wCqO0AiRGsZdzqqtB/nfotBb290PP/xs5cuuEGIm7Emx/OVDmz1tIoF+c9cuT3nVEiPXQlIPsMMBd6gHC49lJ25PbO71eD+LmuGp246Dw6Fcu2XDrIownUCCPMwDS3c6HO/zCW/xQ76s46GNPaHf+xFtRwG9O2q782XdYXAB7AR3+YoNHi+gacKqV3D76L1BvJ8la73UQsztVyeaF+Tx+OMT2+DX9LJu4i6XQk7vXL3Tu76oLkH3of3mVlk3cf2TOyqOfxj+8tR235DPQwWtIn+oCKr/5v3DcP+KvuOZrxtSSFm8sn7hWAlZRtmHI5X+docVhskFtyyMElKeFzywJd3gRg6FEAPha0635m3QmnCHwPmBlFKAGQcU3hdIOxVw953fX1MVHp7pntMlnea06Wuk+PVvkKy7dfVu5mURFD7f31vrklHXWGvpcedBEBAsm8S1Gzb+7jVO7YwCTTdR0oO5ekWUNIN7ZprTCSgyy20wXNZ861fQjECZq5vA8hXrI/W5aTrbdPOz3b5l+uoMZGxVaB8dLLizB4sQr49ogiFWCX6i3W+/pnEcFu5P/+XJ7T7lBfeR+6CO/r3bytpLOSoIk7N2RBr/jU8WYkWSqpqDhEr6TJg+KJZJ0RqAHd5Pyajc8sVvPUTd9+l3WZfe99tHtobKw3xZh24C2YSdeKio2QeDPT4e2PvX7w9ss2jAlnTdEcFRjLhe3eVe/+QOz7Ux0inKuuHYH2mdl3XTow/myzpuXe3Vdd2gZ6rVaYPWhYgHhQM6P/itL8DLVSvuE7XyKU8kxAZsHRFGFFyJLNjve5pgKdVOPw4EKzwAHF5WUakKWlD7+ot2GvuEKoSSFCFJwUTYgNcbSaiGGLneJMoksIYClFr3+2oxTquqT8gmUKVMrvr31RgrI4X7TktTWq2aALwahK2psDkV9hsyYIPuDQHBfR7Uol19eX5XKypG2j89A0XfEOJait3Vlw+UHbv68jwZXVtTyvIsOu9takIYZNj73aGyYWMoJsrIRDRgh6wMzCTf+qz5SkmcoiBsXA7lyzwUOPAOrKbzJDVNqQRXFoPkeVPK/+57UbON77DEHzSn/YyUauTugaGSdSBitytsHYkZM933tAlBfUnlVrNfVtMWzTAiGWEA62cxhBhgY5Uv6TXJNjGTbCXs6stzpT0oEc9un7056NDPDfJwhs39xnSCR3FFLbda7OrLI29x0APhstfvt2lV9g0h7u6jUFhnCDFFrzk8sGXbw8oNWFmqSWeLMhcr7V2azqieuKxIOxNZVapLLfqXqPex/0ebq9mkwikXBwo2l6ufBzZMXxwolKHILBcJ88BS5ELtRlbYWizpTn5ZmudB0zzK9Kc1VQ+d1l3GIR9P80gRhbt+oiM2YOuIMKLgSmTBft+LSZwmC/d4Mkk1kH9LVIZERTLorktrY4orgA0phf9+/0ARjSk1tN4jIfuu5dkoIF2NaFoqvS+IY7deCDvlGumbA3i0OYKItkcDFJpFd4ya04mqCcCrQdiaCptTYb+hw4xpzcGKQtB0CWrRrJZMZAOJMNL+mdac8oTWaXptm9WslgzPBOr3HSUQoiRk7qRfZUpEo0ih7XJ7GsPG0OuBrby5U5gihcodsELS0gklcibGsPr35+0EbmEe2JTggaVwvSDeQndfiuVQ3cWrJW7MaslAC/DAViN32yxqK3E+ha0j0QM7JYDXMKgvqdxq9stq2lKNIVYSQohpbBuSKoaKek2yzZ0nIAwzJqX5mm0LOKRwyynDJ8IhCLSmgxJ8AewOtu2BHR1ld1ZLxnEPPEz2+v1WlHNiVlkyAIhKifQf8mIWda/RK/afIks16WxR5mKlvYvCscVoDQCBCczc761F/xL1PlWWMMuHy90PLITYOhQUIspElg3ydpOO4FcHYnBIqbLjDuyhAKM3CsLWYllzemDpfUERCVHUROrDeui07jKC+m4kiEOIYzjgTxQsY9nShb7fEYKIosUskAlF5sI3m1QwJZvwLCpVlnDczCa+kETlZ+6UjK/Bc+Ups3lCh0zSzmC7b6CA5nTCt960GZw8p8VTXrXEyGH9EgZZ8gobCbZBOq+1wfN9SpU9dWttSIYKp4QshYZZBSGTUJAU+CX9IEleA5r61k3H4Ff+afMmh37/rtPn1NS3Sau9UYniMwkFF58wA4BtXDSlVd+xlSVUrFdCkZBSpMA+UGUpcE1Vmn/Lli70KG20/sgD+4nzFyDh8+6EIuG1x7R5Ps8kFMxvy/p+vmzpQt6PbkM2IXvbmFLlmseN8MkLj0IqoaAoZC2kNV7NgQ3V/4KFUwO/6yEPrJBp1MmpZykGsozJDQmPUkHVcRtqYXJB5DnOJtVIHlgxiRNgZ5emsGcgvG9UWQodl34hg3QYjY7oVaFEJ9nAO7CVsxCLlFkiaE6XdMO3XX79GyTr3nfmEUgnFGiGycdz2dKFnvlM8jWlKlzuXbp4hqe8INkkrl1x3fj9Xlzj1ewhdEc0yjooaga/g0xjm0kqyJc1LFu6MHDtBtX73y5YELnP/+Xs+dzYfs/pc33b98Gz5jnbJrAeVDoDO3kOu0de6Q6smMQJqLxGguC3X9M4Dgl3YD8l8KcS/PpIlqw5klB8r+uQAUsZiOkAwvcOrLWeNBeNTti8Ctpnli1dGKov0O/C9i5xfYt1JwM2qB/FOkRZD5LVhwAceh/buxeFlsGzFzsMWNvrul/wwNL4XN6eC2wzZeBPqYqD3YIM2Gp1sCAZIzJS+MnpOZOznsPoTELB+T57oAgJ4DpArTqt+D733HpzAP85wW99Ad7PRN0n9sDGcICIgnPC6cl3O07gvFQiibCoyF18/AzfpDOiBxawlcRsUkFrYxqzXCe6bzhuOma2ZPk9D1H5ackmsaCtgRM8kzA8eW4Lhot0B9YOsSvrJprSqqNNEsAz8AE2zU2bYLRUS4xM5ZMy0JhSuFKba8ng+FneNPHZpIIfvv1kfO71xzg+a0orKFmCrzmTwKWLZzp+95FzvVyT2ZSKk2a38Pa1ZBJ8k8smFSy/8iR8VnjPpIxT2STycMAeUyKIbkipvtQ9ADvtNUzgomOnOfr2MxcdDQA41kpSJMH2bhGIVP2SxUyozbTmgfv93+lY7Bi7lkzCPokWSMdbMgl+ONKUVvGOU+cAAP7jDcfwuSyKwWxC5v0wOZvANVcsxuJcCwCnJ47GlpQIWQJOnt3iqJe77MnZBJa/7SRMbU6jfc4kLnDF9fLahVMda4pO71syiYrzr6M9h/edMdfx3gstjlVSzi9dPAvLrzwJLRn71LMprWL5207CO05lyaponcxoZuTlx+da0NaQcIwl1YXW7wfPOoK/M9eSwfIrT8J7Tp/rqN9Vb1zkO26EhpQCRbI9W00phW9S9P/L22fz03vapOmawOWvyTnq+N4z5jrKF9ce1f8MITEcYM+/jvYcegdLaBA8myzsVQwhtj2wrQ0pHD+ric+dSZkEnwNuQ43GltqUa8mgKcXeIRpt2SQz1Ct5npgBayfiOTBUQlKVIVv0HQDwmrktAMD7XDQC3nTiTD4uolJAso9RrVi0aLq/Qc08sLZSSh7/wBDitIqhku5pm0gBNCWb5PVUZInPr7cvmc3ntF8EgJ9cX37lSVj+tpN4faY0sHnx+mNneOZTR3sO7z/DmbjtP17P+FxFzkLilZzenHKsC7cMSCiSY+12tOdw5RI7QZFbtolr3N0WkuESvN5tuqP3huOmO56nfblF8HQUXVmIAfvOdUd7DnMnZxwK4UfOnY/vdCzmMhxg64nW1OuOnY5rrljMqYtasgnP/kLys31uC0+cdOGi6Y720TNu/lbxOsz0phTfU0Udlvaq6c2s38OiQ8QQdoqkeP1xdjIbd/ZscbzcMv2Hbz8Zy99my9TpzSk+jvmSxufc5e3OpFTivBT3kRNnT0JHew7phD3X/AxYogOi307id2DFJE7s32xPtg1YP32O6hS0z3S057je4Zbd4u+obAq/zSQU/l0xyANr6X+XCcZMpfVA9QCc8yCpyDiytYH/LuPQ+xIefbUlk+DltDYk8anXsYOGrJXECbBDhCXJmaCS+vq8Y6bimisWc+9uQ8pu84BFQchor+zDTDKKr2jP+a5vUV+rpP/kWjK48hQ2v8qa6euBnNfWgLamJJ+P6YSMa65YjHecyvZp0pvd8/uY6Y1QFcXR/6J+5a6vBDj0d5LdQXOLuLNpDMX3ZxIyfvj2k3kmYgBIqzKmNDBdStzT33HaHP7MK8EDG2chrjNIOP3PAy/hurs24JITZzm++8HdG3DavCn44TtOhqYbOOrLd+Lo6V4jDRDvwLKF0dqQxEtghmY6IWPfAPv+V+9fgo/8YRWOmt6ETXsHkEkqSKlOAveiZmD2lCx+96HTAACb9w/iwv98EPmSQKOTsGl0AFtwisTQANscvnP7i1zp/M0HTsWHfvc03niCvyEepc+uvmkt8oaOa644Ed+8bR1ef9wMXHPFYnzq+i6s2zWAk+e04K8fPQOLvnoX/u2Co9DRnsNjL7MkRn/5yOm454W9+P1jW/kdjOGSjrmtLIvjXz96Bt75yydw5gKv96ysG5jf1oCb/+1sx+dv//njMEwTHe05PL2VZUY8+6hWfP+tJ+Kc76/EdW89EW8/dY6nPBHX3rnecdIrgoT6iXNa8PP3LeGf7zgwjO/ftYEnEXr2q6/HS/sHceXPH7fLveJEXHTcdPzaorm4+7PnOe75uPtWHJPlK9bj5w9uRtfXXo9zr1uJU+dNwY/ecTIA4MIfPIBjpjfhuFlM6XzLSTl87LwFvuUOFjWc8PUV+PhrF6CjPcfpJCikkepDVCtXWZn1FlhZot31cuOnK1/C1KY0jpyqY86ULH71ftZHJ35jBeYInHAd7Tk8+lIPbnxmJz7sc0DhhyXzpuA3j27FHZ8+F1f+/HHkrPJKVqcnFZnXb92uQ7j0J49g+dtOxMUnzMRtVqKGd582F79+ZAtu+n9nYVZLBrev3Y22pjTu+sx5nveRgXTKEVPwf/JWmAAe+eIFkCTJQ5r+VmuDdffPa759Dy5ZPAOyJOG21bvwP+95Dd75yyfwi/ctwf8++DIe3tSDxpSKg8NlpC3eTsBOBkJr/KwFrVj+tpMc7/xOx+LQ/nLPrW+/5QS80VLSelz8dU1plVMBAbZXKKHISKgSpjal8cbFs/D9u9bj469dgH9YpPKFsuHJmNvRnsNP7t+EY2c042fveQ0u/M8HMLB/yBE2S4pGvqwHhjoD4EoyPX9wuMSVR3onkdK/9L1LoMgSLvvvR6AZJl7c3Y9T50/hdbrluW7s6S/ixd39mDsly+95cs7GgDVfLOsepVRsgxucL7agcVoE1leWh0ZVoCoyJmeTODBUwgULp+Ibbz4e53x/JU6aw6IzgpI4UVv81suanYdww6od+Pplx+PTf33Ow7lJ2bqPzzU7fkeKNYUBsufZ/+/493M9PIf0/m/cug43rNrh8TI0phhVygvfurhimHdQW7749zX426od/G8am8WzW/A/7z2Ff/7B3z6FA0MlvOu0uTwLKGUhliQWOgywPXK4xCjpeoZKePups9Fxcg5v+/njON0yKM9a0AZgA+a3NWD3oTya0gnsHyiiWDbQ0Z7D+j0D+PmDL+Nj5y3g8gtg+1RjSsWb/vsR9AwWIVsKORlT1L7HX+7Fu371hIf/WOSoVhUZ2YSCgaLGqFJME//4xJnoHSzho398hofdV/bAsud0SzaeOm8K7nx+D772puPQ1pTCv1/fxZ+/cskc3LhqJzSD7Zu0r4iYMyWLt/7vY/hOx2JuDNPhEmB7HQHg+2+1DQeA7Zlf+PsaAMCRFq2VmKxO5L3eI9DBUFsA0QNrhw2To0C8A0uGm9jv//bnZ7F+Tz/u+9z5gX0GADMmZZBQJGz8zhshSRLO+N59OO+YNlznkrsd7Tn87ekdeHxzL+ZOyfL3eD2wzhDieZZe84WLF+L/ne/1WLvr/c81u/DJv3TxeXDhomnoHSxyYx5wyiBR7/vtY1sxKZPAH/7lNCz+xgoMFDR8+NwjcaoV/ZVNqnZWeyuEeEZzmnOLS5LE11xStffVt/z0ETSkVF5HimDRdMORkZ48sGcuaMP3Xf0XBW65cOvqXfjLUzusO7A6rxfNAQqJPvPINty6ehdmNKe5zAeA6z9yOo6a5tXXP/u357ieSO/94j/W4ANnzcOXLjk2sH4nfmMF+gsaXrdoGn75/iWBz9HBpwngnKPa8KcPnw4AOOHrK3CldVi5SeCknj+1EYa1Dn/50GbMnJTGbz54Kv785Db+jJul4HDEqHlgJUn6hiRJ3ZIkPWf9d0nAcxdLkrRBkqSXJEm6arTqM9ZIyKxr3XduBoRQM1WRIUvwKLGEYogHNpVQeBbbtsYkEooETTdQ1k2oMkum0u/KjCmeFouUAPmyjpQqs0v0CVGQ+RtFtOnRxXd2FyvpIXKOiuGSxhPF7Bso4sBQiXs2qD6LZjQhnVDQmFJ5hjt6X1tjCk0p1XHRfrikYaBQ5vcqAPh6QzXd9OVJXDijCRv2DsA0TW6oD5d0O+FVSCItQlJQ5Nyww8Od5dBGsvPAMBRZwqSMkwYAsClHipptHERFNqlCN0xG/SGEbgHAMdNZm4csj3xYGxusQxJKMEIbwBTBG08gxdUww0/8RdCdO3G9UP3ddx4pPDYoYYwb5E1IKJIjTI6Hu6r2fKBTbyqb2kl8y2WuqOt8k3GD5t9QUYNm3bOi+V4QlE5JAhoDwkkpWUlJY0YeGWv9BY2v84PDZaiyBFWRufJDBgT1WSZR/Zmle/71CEllel3k8Szs1csDq8oSEorsUBwGCmWHEu5331OcoySPxPdluBwLvgfL+K+dIcQHh8q8j2hO9g4xbzJ5rlobk9hpGeOigtfamOKfi3c8bQ9sQAixpnuUUla2/5hQ9tF+l3davAML2B65Y6Y3cVlA/V4OCCEOA90rFq+wkOEtzlf3eFEfJVXZwwuZDgmpWzSjCcMlHTsPOhOXbNjTjwVTG6uSb2645U0hIISYuGtJBlAiqn7rbp5sPU9JnHYfKmCgoGHhjGYHHzBgt3lffwGFssG9cDQvSGaQl4r2s7bGJDdSegZLtjHl2p+on92h83RYRHydA5wCh5Ujzg9KchiehTiBfFlHWTe4N5jkTkHTPR6cqY0pvh6I5syNY6Yzw3OjoGyLa1wRwoUXznAekJBOYJi2TBWT1YkhkaQL0DqzQ4idHljRaytmIZZ9YrBTIfu5iN7BIlobUi6uan9jgfbyl/cPCtynTscF1Z30P0p0FaSfuUFc2zQPBgpllhFdWBvE083KteVR0tIrAXvebtw7wNvDkjhZIcR5ti/kWjIolA3+TElYU4SFM5qwYY89BwYKGprTKpIuxwuNYzogp0m1oOtRJc3gskD0kmcTCsqayeu+7cAw8iWdr+2ghIxu/beksRDlppBDVcAe06D1Ytebtd8wnXfi2xqTXDcR9Z+dB4b5mmprSnEZI/btKyGEeLQ9sD8yTfMHQV9KkqQA+BmA1wPYCeBpSZJuNU3zhVGu16iDlKBbn+vGz1a+bJEipx3JPgAmOEhwuYmtz7Xu26UsAugHNuwDAHzmr89xQwFgi0CVZb7RDBY09A2XcP1T2/HQxv1YtnQhiprhULCzKdoENQyXNH6qrioyP5EK8mjQhk2LIZ2Q0daYclzc92tPEIm3mLjhpX2D1iJNobOrG/9cyzxedz6/G2cc2YpWYcH2Dtr378Q+JSWDjB/atMUTaoJmGFB9FKS8ZTwdefUdPKxM9FaHZYIW+2nz/gEPKTYLE7JP/0TQOAwUNUxrSkGWJQf5NQA8sqkHl7fP5htKWCIONyikjtoiHliYpoktPUP4jsUDe/7ylfj6Zcf7ejYkSUJbYwo91mZKm9Rtz7Hx+vot66Dp7ARQFLhRM/KmVRYa2l8oc0UeYP3uzjBNRtBzOw7yvvYjXqd28My4suzg2CsLnxMoeRptAPRMgxByBTDlNR1gnFObRdqkgQLLiit6DUwTOPe6lb5E5mnrTquhMCoPkXNVDL8lQ4E2/NtW78LPVr7Ew7s+f+NqFMp6VZESXdsPOv6+4ant+PkDL6NbyJR49rX344JFU3HXut0YKuqckJ36WpElbiSQ0jhQYLKHFLyBQtmT9IfurnZ2dWP9bkZV9K+/W4Wr3rgIHe05Pn+HizpxyANwypeZk9IwTXYgQwb8gaESD0Um+XBgqOiQI22NKW4EiEam+PnkrNN4B/w9sLpFBZJ2KaVA8EERKZIDBc3RnuYMeavYvrDNMqavf2o7ZlrhebphK52JKpOIZZIKDNOW78yAtT2wBLcBe8l/PYwvXLzIcXDnNrb9QN6y85avREsmgZKmc77EhCJxrspaICrNqizx9ea+v0p7Hq3nxpSKl/cPYMOeQeTL9nzOplTs3XUIl/33IwCA/7p3I/71nPkAbBlIijHJqcmuzLe0Jnq4AWsfxN77wl4AwFc6n+feQlEeASz0EgAe3LQfX791Hd9bPnwuq0dTSuWHWrMmpbHLylZ88Y8f5l7u4aKGpCJzI8sPYgQAGT+kNxTK3juEl/33w3xO/Oqhzdx7JeK+F/dxipof3r0RumlCArC7j83hzq5ufvjyiT89gy9evIiXIWYfJ5kqJnHy8ygRH/mqbcw79q+/fxoAsGrLAfyPpZcBbJ4ZogfWx7AXD2aort+4dR2XrZOzCXz9suPRM1h0XHPIWPqIHwYKGhSJGZfHfOVOXmfWNra+//wE85p9744XIcE2YJvT0dT3NTv6HH/vPDCMVELxrMlsUsUhl35KeiVgz9+bu7o5rUsmqUB2JVyiKL4Tvr4Cs1oyuNjiUBX3/pJmoHeohPlX3Y5JGZbt+sXd/UgoEk8EmRKSwQUdDlcL0rdEGp2WbAL7BopIKjISqgzNMJAvs3lsmsBxX7sLzVad/ORYZ1c3bljF6LPOvOY+fPHiRTj3aKa7ByU6pd/Rod2fntiGBVMbA+VcSjDgxUiW1saU5yAMYPojyYm2hiRe3jfIPhd0hSe39OJH92z01c8PF4x3CPFpAF4yTXMzAEiS9FcAbwFw2BuwdJr1rX++wDdxSu2+vXeIP5e0vBJ+JOf/eIaF161cvxfX3GlzS+0fLDqMxVbLA1vWTew6mEf3oTwPpSVi5JQqOz2wLkNGvCuUTbIkHEGLj8qxDVjGU7tul82F6deeIBJvSt0OsFN3gIU437BqJ3/mUF7D1TetxfRmccGWIEvsBE2s64zmNA4Ml6x7FbYB65eBkjzWIjq7ujmnlwnb8Ng3UBAM2MpLJ1/SsHl/wUOKDQBLrBAcd8KPlCrDivRBq2XEX+fiBLvluV049+ip3LsiV+FhIcN7yPK8kyexs6sb97641/HsweEylv19NQAvYTtgnf5Z3ri1O/sAgCv2vUMl3tZTjpjMfxPdA6tgqKRhsOj0wPopA+R9W7PzEO9rP+J1aod4L7MprfJTcM5ZKigvlDyNTldpM/d4YDXdYcyIoDaLSS4GCmVMb057jMMgIvOkSllgFSQV2WHciIYEGa600V5754sOTtvBohY6pm50dnXjFw9udny2VljnYr3/9MR2x99X37QWZx3VioQiQZKY4T1Y1Bwe2OGSjlxLBpt7hgI9sN0H87hx1U6uzOzpL/A+8vNGuWUPKfAv7R3AJSew0Oe8kFCJ5EPvYMlx382PextwnoCL2eGbQzywtieymhBipjTdsXYXfvPIVocsBIDv37UenV27+AHoweEyvmsdPpW5B9b0TUgWBjqcofmaUhVHCDHhmW0HHL/bdYiNyxlHTra9kZqOREhCts6ubvz8wZf5326qjbJuVjVf3RDlTTap2B5YT4I/dkBEnkYTpkOe0HyeOSmF/QMlTsXTM1jCj+/dBMC+8uP2TFL4N92NpqsKFL3SO8gyv658cR++ess6/jvad1au34ejrKsXgB118NentvNx7u7L49o71/P3kQG7T6Cl6e7L4zePbgHA5H+lxDiijNG4bGQZfYtlHc9sdY5/n0D1Icp/8dD66pvW8pBT+r8J4OFNvfhK51r845lu3re7DxUcZYiKe1K15VzB1e+KLEE3TCRVGXeu3YOrb1rLdTDSNW5dvcshF93c45U8sJ1d3Vh242oezQPY++WM5jQPcQbCPbA9A0UP5zjJwT8/sQ2PvNTL1z3NF9KRwowjQmdXN77cuc7x2Z7+IprSqsermU0qlgFrl5tQZQznddz87E4H/RQlwXvspV5+P5MM2DU7DwFg49rdl8cfLAOcxqyzqxt3rN3DnxHXfFk3+YFOcybBjfV0lVR0QSBZX9INfpjVkqGDTPuQdeeBYUhW/Uyhbfe9uAfvPM2+++/ea2jO/od1rz3IS06/o/l0cLjsu+8TRN1d3JdaG5LY1ssOf8R7x4AdbdXamETvUNGKJtT4+ujs6nbIj7D3T1SMdhKnT0qStEaSpP+TJGmyz/c5ADuEv3danx32UKxTUz83/UObbI9aUmUnXEHE8ADwvw9u9nxHcjNrZQ6mcrb2Dnv4FvNlHf2FsmMzVxVm0A6VdAwXdYcSleVhbkEhxEy402lOSmUeWPEEKIyoO4zoeeNedlJ0+9rdvr/fc6jAjYneoSKmNDAvpVjXac0py6OjoSmd4MqKX6i2phueE+7lKzb4Zg/uGy7zk7koHth9A0VfUmyxfLcBK0l2GHdbYxLLV2zwzCHNMHkZ1YbXkbenl2dAZX8vX7HBs5ECbA76Ebaz+tmHCY+81Ov5ntra6vDARg8hPjhUgml6lX1PCLGlHITl8RHJwcV7mWLIKx0IuL0SYnQBGVGk5GuiBzYgzIk2n4OCAUsK5kMbezzP+xGZk6ehpOkshJi8I0XNRZfl9MBWO6ZuLF+xIZTQPgz5so4nNx/gxktCYTKKFP2+fBlFzeARGG4DtqwbKOkGntp6IFCW0HymdUl19pMv96/f71i3ZOTTnBwu6bxfAXe2YzGEWDBghUMLip4o+rw7KLOou2wRNO//8tQO3/bc8LT3c85paS0GTa9eRtDhHM3XpBhCLMiiJzYf8Pw2X9bx9NY+wQOrhyqffvLNjWrmqxui16chZUc8uGU+Uf/Qntuf13xl95aeYQ+PLK0Pkunu9cLvXWpuDyyF/hUxpSGJH9yz0Xecf/2I8wCJ9gc3hzmVP0kIiXQf2lIdh0taxcNEmqMDxTI37FVZ4tEg96/fF/p7txwL2/c1w8T1T3rns1iGmMwwLISYnmtOq4HvDKJ/F2l03BA9sMtXbHAYr4SybmJXX8HFVa3yay5uBPHVA8B96/f51n37QZsiqBL82m+CHTS7vYkkS8WIp4TMQoiD1t9vHtliJ3Ea9ucMpnlKY7Z8xYZIodiih7leIcRiNB7NFzpgSloZ6TXDRHdfwZcv+if3v+T4O0jP/aWVnyTokCFMP/aD2H5xbonhwb1DJUfiQdpX2hpTKJQNDFk6Ma0Pt/wIe/9ExYhmhSRJ90qS9LzPf28B8L8AFgA4GcBuAP/pV4TPZ76iRZKkj0qStEqSpFX79+8fSbXHBH4hKATxxIlCiMPIj/f4EJYTaDKrsgxNN4Oz3ppe44F5szR+P0z8HAhefHR3V/TAtjUmMVDQuHIaRtQd1tZB8uAF3GcsaIYj9KrN587l9OY0SpqBg8PsNJEMCPeCBdh9SPdJdFD9DNMm1o5iwPq9j8ovBhiwgH3C3taYCu3Hsm5WzTFKyin1L7UjbEyCvmPh3GwsaNz8fitmt45a31RC8b3rk02qPh7YaMkIqB3ivUwxUYk7iRChtSHJ+U7tzLFOb5t4v9HbFucGD9hrZyCk30SkE0x5ojFPKDIyCQUHhkoOA4B7YCucWEclWx8pKftgUeP5AJKKbGV/ZPXdZ3lDpjezsFd3JmIaVz/PLNWNjEZxDgTVuS9f5p5zwO4rccwdIcRiGKBwd1hUIChTL8DWclKVHRyThCBuRyA4hJiM6QND/rIwSAkH7EOakm76XpEIQ4Z7YNl4JIUQYtE4D1p3g0UNukW547664sZoz8NAD6wPBU5R07lsCOKLDct1TTLd7YHlIcSa8y6hmMshTNbv63d5VirsPc0RDJuhol7xYKNZ9MDy0FqZ5yfoD1iXIsQ2VRpD3X3y7vpdOqHwO4W0p6eEJE70f7pH3JROVD1vaGyCDFiS92Hl6qbpiNLIJpie5UalLLBBB7JkZIclrSME1dM0veGwtEeL5dKh4+4AHXRvf4GXU4mzldZi1DFpFg5iwq4gVAOqg5iLge7AJhQZqkJX8fw7f3efsx+C2tJT4Z5ymF7nW2/FPwKorSGJA8PsvnyPdfea1giNJ0Uu9A4WMVAoe5LpRXn/RMWIDFjTNC8yTfMEn/9uMU1zr2maummaBoBfgYULu7ETgJjKdTaAXQHv+qVpmktM01wydWo4J9NEQFjiDPEkMaGy0N8w8uOZIaTS5A1g5RihYUFuY4lCW/IlzbEpknIedscioch8kadUWVgkTNkKI+oO+k48ZQoifW9KqY4FK/JPEkgh3j9QQFM6YZ+6BXlgXX0WNhY8GU4EAzbI2zirJeOhSBLBBU9DMrQfizV4YMlzSMoThY6HtTnoO3b/ogTTND2UCu7f0jyN7IFV7buuXg+s/x3YSqC68Luu3ANrhxD7rZ+2xhTnO6Vxo/sl5QieJlK2xA2eqAIq9RuBQuXENd6UVrH7ENtwxJA6oPKdoahk6yMlZW9IKnx9JVRnEqd9A0wZsA1Yp5JH4yom2XDXjeavOCcC52tD0nFVgvpKHHNH8rEgD6zwueiBTaoyUkJOAxEeDyzlHLDuBvuB6jIpoP1uTl0RlDW2rBs8cUlUUFsP5VlSMMXyugHOe4ZBa1kMpS4ImZf9MNrzkNYFJRELugNLSZs0w4AkBbctrCe5BzYohJgbsGxsDgyVYBgsm3FrY7Csn+7aCyvJ0KD5IiKKB7bRJ4RYlSVuNDamKu+BYpsqjWHQfJ7lE9ZPdU8lvAnDbANWrXre0Bj51SWlKvxgplK5jusHKYUffIsIOpgjVIr8jxJCHFZPd86GbIISXjlDiDXdDNTHZrakBR7YcAOWoiGijsmk0TBgKYRYM1DQdCiyxA9BWSIrGWXdDOx7d92D2jK5wetYifK7oM/FtSruP62NKZgmi5Yhh06bNf8z3IC183iIfOXVvH+iYjSzEIsknJcDeN7nsacBHC1J0nxJkpIA3gng1tGq01hC5SeE3i5+r8VDCdh3YP2I4VWLG+oLPqTSZCDTZE7IrJxpTSnP4ssk/OuSSSoYLuuONPb0ORAeosI3EFXmCX0A24BdtnShx4gPI/EGgNPnTQHAlIsvvMGf6Pv1x0/nC1bMgOoIIbYWcO9QCU1pNTCEmLhY3eFkYUTUdC8pyh3Yo6c1+pJiU1ItwD+pkXhy5lcXInEv60Zkg5C/n0KIh8gDy9qxbOlC37tyCUXyJWwHmGGnGSb68xpOnD3J+y6BkJvmaTUhxAR3whu3ATtc0kIVS3dd6OAloUhojuCBbWtMOu7AypI9bqTYFUJCiFWFZfg+6LoDCwDHz2r2PO9HZE50EUXN5vVsSqvYZZ0Iz2tlVEDuJE5+tkvYmLoxElL2TELBibMncVlI94tI0aQ5SOvV64Fl43LpSTN8ZcGypQuFO7C2MhhU53eeNgeKwPtKYyjKXfHQLsod2FZXgrJUQvYNuQ7KLJpJKoGJdMiTds5Rrb7tedfpc3z6xTqsG0EIMcmIg8MlR6gma4fdtlmT0j57jYKLjp0GwMrEWTZClc8o86ua+epGStinEoo9Nt47sHYIsSpLWDC1wVd2E1ewCDdHrjskOsgDqxsmDuXL6BlgB7FBffHvFzqpUiRJQspnYVNbJ3GPkuQpTwyXr5T8r0lIFEchxOwwg4XtnuQj80W45VjYWCcUKWA+O8ugg3I+L1UFJd2AYZjcuz5VONQOeqe792gel3T/+QE4vXdB85F0HjFKI5tUfEOBSd4FGUsd7bM8dRdFRZQQ4rA+dx9y+ul9CVlCSTfwifMX+JbxhaWLBB5YJs/dhjH1CfVf1D1FjCSoWxZinsTJ5JmYxaskpANJ8PfCu8fdry2ZhIKlVuKqoGiIoN8FzStRbyIDFbDnWc9gEb2WQ4ccZLRniUwKlO05k5A97Qt7/0TFaN6BvU6SpLWSJK0BcAGAzwKAJEmzJEm6AwBM09QAfBLACgAvArjBNM11QQUeTqBF++mLjuaTj0IzLhW4YSmEuKM9h3cLJMO5lgxTXJKKL+n86xYxL/RUIZOmpptoTCVw/Kxm7tnJtWTw7becAMAn460VjpkvOUOIG7ggCzbSaPPjVA50ymN5qjraczj7qFZHe9wk3uLyaWtM4vQF7PnWhiQuP2W2p83XXLEYr1vEBEPvYIkvWMBWPFVZ4plM6f6k7YF13c3gdyGdC9lNMi4KZPfd0TAc0dbgeG5SRuV9EMUD29aY9Ix9SpVxhMUZV8nj7gc7hNhqh3WK3tGew/IrT3J4uyZnGRF20KV+UuT3DxaRm5xFS0b1jJc7g2Q1SZwIUe7Azp7iPDlsTCl8g2hrTDrqImYhbkqrKGqGRaHhz5nZ2pjCoXzZylJq8LsygMsDG7IpJxUZB4e8IcTTJ2XQ1pAI7De7P2QPV6oYIkfcgO4kTu5yKo2pG+7555YJInn8e8+Yy5MgpVRGAJ+bnOVKQdKKEiEFnyIGpwV4YOmg4ryjp/nKgo72nIMOzF3npOsQ8Q3HzQBgry/qK1GRF5W3qaISKoTViZmSW1we2GSgB9Y/hDhMjqSseTZnSgOuuWIxn3OqLKG1IYHvdCz26ZcTocqSkIW4+hBi4jw9OGTnTUj7JHFKqgqOn9XsGZdTrIPIolbZA0tjRWPk9rZXO1/doPonVaawhWUhpvapsowjWp2ym9r21lPsPZq3+fLFvL1UBmDPq8keD6w9P3qshIyirKe+oD3tzSd72z7JlTAu15LBR6wsxGTAzpyU8cyPLwgKaqWDDf8kThKPBpnZksUkQea3ZBKYnE0EyjFRlgBOw+1DZ88LmM/OMtq4B5bWsBXaLhyMcQ9sKuGrOzWnFc+6W2xxGtMYBSVxomc62nNoSimOQ9/J2QT+3wXM0Gt1GLBe6jfqVwD4l7PnO+Y9Ffm1Nx3vqfsRU2wO9CghxO72T84GezUpqsjhgbX0youOZXqXWM9MgvG6chod63D/628+jtdt1qQ03nAcZSGWHXWiv7OWfJDglIeUbR2ofxKnMo8OURxXSShju24CFy2ayuuYSShoySQ8cojaMtPyUDenmY53zHSWEr8xQIf2m5d++z7B6YH1HqD2DpbQa0Vy2HSbqucZltg0gUmZJJYc0cLtlErvn6gYtSzEpmm+L+DzXQAuEf6+A8Ado1WP8QJNjPOOnooH1u/HU1sP4LXHTMXta3c7BERKtdOUL7Ay1118/Az8/H2n4Kudz2O1ldHNTcj8s5UvYcUL+2wPrKUclg0DR7U24/T5rbj+qe149KoLMVAo4/N/X+Px9mUsY2DYZcDSxA874UtwA5b9nxKe9AhZD+nk+UNnz8PXLzve8fuLT5iBz/zN/nteawMvg4S/Hzn9E5tZsqAdB4YxVNI9HtimtOrwjjanEx5jg6AL93rc6GjP4cktB3DPC3twZFsjnrIyLvYOlkJDzESkVNmRpOHi42fy9nAaHZ93Z4U7sO5++NgfV2FrD8s6V0sSJxpnSiAi0uj49XcYaO71DhZR0gy0ZJN4YNkFvs+28RDiaBuRqPQ2OwxY1WGs6IaJomZg7pQsdhyw72/873tPwdruQ7jurg340TtOxrlH29cOxGzDopeh7BNODtgbwIGhEg/bFjdC0zQr3vVLJWRPFmKAKbttTWnc9ZnzKvQHJXEykMzaRhB5Mee3WQasS7EjZXH5207ElUvmuIuNBHFe/OXJ7fjSzWsBACs/fz5/r4gv/H017l+/Dx3tOTywYR9fX5wH1kV30ZJJIKXKnvvAImXV0uNn+M5NnsTJ5ZXvaM/hpytfwkv7BnmWY5r72aSKg8NeHlgADt4+McOweFquKjImZxM4OMx4pimKJqXKSFkJbtwoukOIuQEbvAVLksRD3ImQ/oXd/UgoMtoa07yd7n754j/WQNNNmKaJslF7CHHfcImvDzevMMDm8FlHteEHV57k+P3frez5FKZXSfnsaM/h5q5u9OXLuOXfzsZZ19znW24tEL3tqizZHlhPCDGr42BRg2pRVZHs/um72/Em69C5s6sbADOA131zKe+XL9601ubxtEITpzWnsPNgnh9ykMwX79dtPzCMQtlw7Hl3Pr8bW3uG0dGew/fvWu97HUncr992ymz84MqT8PCm/fjpypf53cFWyygW58euvjy+bWWqrpzEiQzYspDcSObRIIWyjikNKaz8/Pmh5YgQ67PjwDDOvW4lAPCD6Up7kMcDm7APHoo+IcR+ZZ573f2OvQIAjpnejNU7+/kYuqOyALu/ipqBg0MlDBR1fPmSYzFU0vDjezdh1Vdej3ssGqRWl+wolJmXWGQMIAP29cdNx1fedBzKuoGjv3wnUirz2KYTiqfu7/n1E9jaO4zGlBqY2dsNsYwHNuzDB3/7tKPv7Hp6Q4hVhemVdIDx1Tcdh+6+PH54z0au38nWNQhqz1tfMweZhIrP/O05/PZDp+Gfa3ZBkpxrrqM9h4c39eDxl3vw8fMX4Gu3rMNTX74IP71/E37/OMtaPBohxKQLlqwkTmmBTiihSg59qv2IKUgmVKzd2Yfjc5PwokXj5kZHew5vOXkWFnzpDrz/zHnoaM/hJ/exzORhTqBq9C3RCSb2RZsQHtwzwO7AkpwhGTFFoAIkasl0Qsb05gxmthRwytzJ+PE72yPVY6JhvGl0XrEgRVgzTO7p29LD6HOCeGD3W0YFKXglIVxQRGdXN375EKMe+OMTW3HUtEbG12WYzIskS0glZB5S4+ft6+zqxuodfXxDv2HVTjz6Ui8uWDQVKy2+2U/+5Vl86ZJj/dN6u+7cPbGZZVNd9vc1+PG9m7Bs6UJuJBGPociZNsk6XVMkdtq1attBLiBe3N3Peffc7169g9GOfPgPqwAAP73/JcyalIFpuXMODpfxZUvBZn0t0Oi4DNiycK/HD1MbkzgwVEJrg+052z9YRENSDeXPI4hGriIBN3XtxA2rdmBWSwZvOI6F2aVcm0hnVzdWWcbyF/6xBl929b/zzqb//AhDhhuw5EmuXQSs6e4DALzjl08gnZAxKSRl/D/X7AYAfOs2FmBRSXCLhq4jhNgyEHTDhCJL/GTbcCVdeHDjfn4KrLk875phQLGyDW/cy+bcku/ci3RC9j3VJg61M6+5j4V8Ag6vPq2hME9TUpEdd4QoAUqhrEfiuCMDVhxzMTyJjOP71u/D2dfej0+cfyQANl+B6KT3lfDSvgH+7/f8+gl8Yekiz1iWNAM9g4zjL52wMyazJE6GJ8Ry1bYDKOsGfvnQZty+Zjdf9zS2YV5KkUbHzTt9wIoGIa69rCu5hX8SJ3v8b1+zm1NanSfw83Z2dXNlreNnj0KS2PwiD+zWnkGc/M27uazLJmSuuH7qL1348qXHwrTm65aeoUBZ19nVjUP5Mv785HY8sGE/p2HJV/BqUn4C3TBhmv4HdGEgGcEOCFOOvnLzwPopaLair4eG1jvrLHH5zBLr1Sc4LCV4YFVFEqhWnOVTnYdLGqeKIYjhoGssujDdMPG6/3yQj1tKkQUaHQOKxBLcAMC/X98FwN6HS5qBbELGcNnAv/6e7WM/uW8T501NWmGxdqSIc68R+SMBm1aF5BzpGV3b+zxzS+zXStE7d1pUJz+4eyNXglVLtxgoaCiUq7/CImLVlgOcquST13d59jo/9Fr35j9342r88J6NPMqrYM01ANhqtf/GZ3bisZd7PWvL79D4jufZ/kSH3D72K9+T/rlmF/73AaZ//fzBl3GORSNz46rtuPZOlsX1w78XuKp5pnSd37cE7ENMks1E2caTp/nU0+aRrm3fFvcB8WCps6sbd1l98Pkb1+Arl7KxoENHMbSarsLsOlTg8yuVYM/JEptXxBG+9McPoTGlQgZ8svszGr6egSIkiRla4l4o7m8jmWciHEmcNB2phMwj7JKK7FgT2aTCkzwWy3ro4bskSRanPBu7gUIZ6YRcNzlG+tNgUXOs6ae2MF3x0399DgBw/VPbcJTlCPvmbS/g1w9vwQWLpkIC8MN7NgIA/vD4Nqgy1Verm24wHogN2FECbZC6YXChuLV3CLLkTNxCWd4AO6yTNtmi5l00bt4p4kedOSmNTEKxkhKxU1LdMC3aCmfGWyrD7SlwcznuGygGckPRQk+rCjq7uvH1W19wlHP1TWvRYhmpG/b045aubkfaeeIyFO0O0Vvpx0tFCqqI4ZKO/7jhOcdpZK+QtbMprXIFoOQ2ZHSbTsUPrY0pGCaw4+Aw97j0DJYiJXACnEaYCdtg7u7L409Psn4WNykaFzp42O/T/2LW3FqSOLk9sFHb4kZnVzd+YvEfAkRsX0RnV7djrrjn64EKfGcEUekVjUoKc8qXdTSm7IzET2918qn+4bGtOPcY5nV1e941655bZ1c3blzFPComgHzZQKFccrShs6sbf7bGyoTtFXxwIzvk0Qw7dC3M0yQeVEzO2tQ9ReseTiXQgVRZCHMWlZibn7Nz33X35fEdy8tSLel9GMS+AIBdfQXfNSpy/Il9mlBZggx3kptfPLjZlyvZDt8KTyaXVGSs3tGH/33gZQfvNIGUL4o24AasiwcWsBU8mrduPu1V2w7gH890cy+amJ0zpcoYLmnYsNfp3RkWDPb9g0Usu3G14xJekKy7+qa13PPV3Zd3XLkIU6YUi/rCjjSoVkY4I4QAwQNrGWmGYWKw5K/8pARPVaGsBybhEiHug7VcjQgC9VNKlaHKcogHltV5sKizxFoOA5YZb51d3b5cxwBbn2Sgvrj7kGOvIS7WZ7cdxIfOno89h/KOOQEwuUJ8t8SzqnGvp11XmheiF3fdrn50dnVzWX7Lc92+dWQGiV1W2OEnvYdAmbAf2rQfaStDfDEk83oldHZ140udz/Oszn57nd9v7t9gM1B09+Vxs+URL5RtOXz90zscz7jL9Vs7lGTpaevwOCgLMQBcd5dNLdY7VOLG7zdufYHv3UFc1U4Dlu0BzsRxSfQNs9B9P353dwRHtXBS07Cy3Ht0z6A9FuxgyeRr87ntB/GXp7z9S1MpnVBwy3O78N/327oBMRS4dYPWxiRKmoFtB4YxJZuE4kpoRx7YoL6oBWISp6KVeNFO5uc0ODMJ24CNchAnUvLV0zB0r0VxL/rbU9sdz+bLhoOn3a3TEzTDxAu7+z00hYcbRvMO7KsadDeirJv8ZHS4xJRu8SSKpWa3iM3JA1smCgSvhy2IP6q7Lw/NMDgtjB3ypXs8sGF8bG4EcUPZd1DkwDrttTbujXsHfTnTgHBaAj8eOT9KSsMMpqxpSiUgSZLjhJ9gUwP4C0cKHR0u6TxTau9gMdL9V8CpILibz7nRBAUgCjdYU0rFYEmDYR1OVErE4UZaVSBJ1d3l9cPyFRv4Zk0wTXjmSrV8Z7yeVr8osuSieKI7j5r1f29YHsAOKyjc3I+XLqHIvnx0JuCZc34hob95ZAt7j+BRrHQHljC1KWWHEEdUAtOqwjddPwPWfe+S6uRHRVQr/PrCb40G9Smd5ufLuuNEPahMMYQ4DJmkgic291aUaaTgZ7gHlhQXbxbioHnrx1VJSKoy9rooT/xQNsyKHHxB/I0Ed+SGCOIyDLrjXwlif4vJ+gDwjK+DJY0pPz4RC0mXARtlfqvWXTsAgXfRa4F4B1a1+gUIvgM7XNSsQxG7znRVwm9u07glhQRRXdsP+dblgY3M+NoVQElCfLdE16IZXl5qv3lhWHLXPgAInltOD2xwHwfpCL97dCvnga10vzkMtewLrI3+HKOFsu6IXAsrN8xwv/dFFgLsF5UlHsz41iHgvXQg5L7m0M89sF6KrqBDTVr3tcrzRocBG6wPUt3pYInW5i2rd/vK62FhDwzidnaPLbV1w54BX5YCCoWPcsAbFXYSJ+bYSSfsJE5JVXaMezapojmdQEk30F8oV7wK0ZRWeWRVUHRKLQjbiypQaIfCMNmecjh7YGMDdpRAG6RuOLlZ3ZNFDCHucXtgfUJ0gniaipqBks5SvKuyk3i+6DJgq+V68nue7lWlEkoobyrVrVZUwyPnBxLY4gk/gf5O+MULwRk6RolmDgyVImdljRL2Iho2UbjBmtIJmCZTIGvhgZVlifOHArUbsFF5zKrlOyPQZuE+8CEv2rB1Yh7GAUun6h4PrMHuukapWyVuRpbNkO43Bo8FnfpnEgqahZPaqEogbbIDBZv+ojFVeeOxDdiRb6Yj6a9dfXnby1XQ+D21sHcNl6MZsNmk4oje8IMs3FsnhZL6XZLsk3+Sz0HtCOKqBNgYB/FwR0E1si7MKGQeWBNlLTzCJAgsszz7N801SWLZm8lIGPTxHom/B+zDnTBjm5BQJN53pYC76LVAzEIsKqdBd2CHSjpUReJ1VmWJe4JC53ZC4ft40AGHne08eA7t6sszY7jMuCjdhnZYHdxXJfx+J/Zr2OFn0Hv2DxSRVmXrDmx4hukw1LIvhH1H9Yny27B9mSLD/JI4VbvX0nuzPCTfecefPJNihBE3YIMyB9czhNh6R9hY0HUEur4QyEltKXtpVY48tnQ94eX9g75JHmnd1ev+K+BN4pRSFfuagSJ7eKOpn/cPFCvu083C9a5+K1lSPVDLXlQNYg9sDA/o3pFmmA4F2j1ZxCROFPpKBp+fBzaQQ1Vl98s03bQ2YK8HtloerrB32kmclMr8biMI/6iGR84P1N+qLHkUB85tF6AsiXQZ0y2FWzPM6B5Yq4/Cmi8qd1G4wcTMkCyJU/V9m00q3BNR6x3YqDxm1fKdEeyTZmf9xHAsAMiXg7n06Fm3YseoMuRIdavEzagZdlKiUA8sN56ssKQiJXGKpgTS5jlY0vjhUaWNR4J4B3bkm9RI+mtWS4bP1cFSZQN2VksGeboDWyHbZiapcPoYN+idWeHeutsDKz5H/RTUjjDu1ZQqRzq0CkI1si5szqgyUzptuqjq6iRJEs9ELLYnnVC4B9YOfwwOIS5pRuQw06QrhLjayJLAckUPrHBQGeSBHSpqDm7eKQ1JHr4YNreTwh3YII8R8aaGccTPaskwKibL6+V+NqwOYYcn9DvxsDbMIAt6z7TmFL+PH8Z9XQm17Ath37GDej0Sf2dYu219wftMqoY5Oaslw+WN+7B1oKAhm1Qcd9TJExm0ZtIj9MA2JBXeR1RWFJmdL7G5JepEIviVshB90P05lVXWTU9yLsC+AxvlACwq+HUynsTJ9sB6QogFA7ZnsFhRjonXuwaLWl2u7QC17UXVIDZgY3hAC0UTwi8A72RJWFneAB8PrObdyIP4o46Z0WiFELN7kXZ6ed3OeFslDxeV7ccNZdMryL7liVQAaVUO3FjCDDA/Hjk/rlJKHOAHEoJJ1euBJW67oCQnImE0hRAD0Y0+6qNpTalAHl9xfKNwg7mz5tZyKkwbqvuuVzUI46et9FwUvjH7ro9zo+ZZZy3Dlbys7nYkFAnHzmCp7GmcCZp1x27Z0oUeg8PdBr/6SxLw769j3IzOEOLgvqT6sU3R5YGNoARSf5imXRbJkiaBMoiQSShoSCn80KYep8FRxjJsXtBcN01ndIM3GyYrk8a2kqzKJhXMa23wfa59TgsrQ8yyLvD+Edx9GtRWP65KEkkpVcbC6Y2hdQXY9RK3vKqGMxMID6tjobKGnVG1hkMu6q+kMDdTlucNEBPQ+IQQK7T3WEpihPmtWnfteOKpgKiYamHfgVWgCP3g7hNPEidrTop0KGHzP6nakVRzp2Thp1vSXJyUUX15q7lMsqKyKJ+FiKB5sWzpQq5nuOeGOLdkWeJ7T9jBRtB7PnH+Ak7pxa4/1G//qLQvhOkZlMSJcVyGl0tzwu9g/fT5UwAEJHHyod0ChEMSxb/fg0KIGaWJc/20uZKmuUH7QBQKHT9QsiHA7oewsaA5Qld23nXaXN9nZ09mRlYyQB+U4NUNxD2AsjaL8ob6pl4UOoAVbWNd26PrDXxcPSHECpqsKKeybkYzYIuUxKl+IcRhe5GfPlwtYgM2hgeOLMQRQogLZd2h1ALwpeYI4o+a19rI79uqssQ3sULZEDywsqcMwJ/LsRI3leiBpfI491dLGv9qcdLNb2tAWTfx5pNmecqY0sA4/qrhkfPjKv3h2092lDOrxTY2xRNVbwix5aEIEAKTMgku0KY3O4nJo4D6e8G0RgevLOPxZVmIxU0vCjeY1wNb/RLOWklxak3gJNaVNm9VlnD8rOZAnrSofGeEoGQVlASDTrPp///+uqP4OwDgDcdN52HfHs+7waIUOtpz+MqbjuWfJxQJR09v8uUuFI2Uea1ZXH7ybF4WrdewxDp2+KriOKmNHkLsvbtGsmRqU9q3jydl7HtFtR5UiIgylvQMGWgJRcLR0xpZAhmhDuSBTaoyrr3iRM65OqXB5uylu7KVIjiySRXNmQSuuWKxZ22+3uJ+FRPn0RwSZau7T4Pa6sdVefS0Rt6WeW1eA1a0lXItGSy/8iSHvArrR0paRjyDhHAPrOTYd2rxZlI/uj2wtgEbEkKcEEOIo81vuh/Nr3WodUraInhgRTnvzkJsJ3GyaHSsPhM9TmHzP6Xad2CbMwksaGtwPNeUUpCbzDg80wkVp86bHMi5TfMyX9Y9Hlh3HcjLc/EJM3jffeVNx4bOLdJNwmSCm6uTwjnfdOIszgM7khDiWvYFv9/8++uOBsBoqojWp1K51KbXHzvNw49KtGC+NDrWwcz7zjyCr2lWB3aY+Z4z5vryatpRQ85oIb9EPxU9sNYeMxLvnk2N5dUH3X1GBygUFn/hIn9O7lwLzW1bH6T5rcoSZk/OeMZW5NMW9wP6P/VBPUOIATvag/JPODywjhBi1SHfqkviVK75kMGNsL3ITx926/AiPzvp+uI+eTjfgT18Te8JDhKALINbsAeWPIN0t6AppdpJnHw8sIA/f9RDm/azU1vDMmCFEOIiKTKqHFpGNbBpdGwheGCohG/98wXc8e/n4uFNjFZnflsDVu88hMkNSSQVGeu/fTEWf2MFhko65k5pqLoeYc+Lnx/zlTtR0gwuRBKq5KVT4SHE/oJJliVMaUhi30CRG0NAdAOWThNbG1LoaM9h074B/OLBzbj/86/Fj+/dhOSG/Z7sepX6g+bPYEFjIeY1KKdkuNZ6/5XQ0Z7DHWt3Y/uBYZQ0A0e0ejlB6blq5xoZ++6Nmk4i3SHEly6ehU9ecDQMw8SRX7oDR01rwvPdLJmKlz7J4KF0l7fPxlc71+FLlyxCZ9cux+GHWP/lKzbgjCNbsa13iCeEAWDRwkS5A2vfv2xKJ/gdQpZIIloSJwIZW9Q3TWnVt48pE2Q9T1ijjGVHew43rNphhZAa3AgQD1umcrJ1pvC84fjpOP7rK/ABi0cPYApflDmaTbI73cQ722llZE4qMuZZCmlGiJrwCyF2e2DD2ur+/MO/X4UNewcZjQ4dWk1twH2fOx/v/tUTeHn/IPb2F/HtjhPwvjOOcJQTho72HF7eP4ifrnwJt3zybJz23fvQlFIxUNRC55oqyyzyp8YQYsBeZ0mHASvzvanfRQEigmTSUEmDZlT2XFAdxWynoxFCLBqtbsOQniuUWQ4JMiJFLxEQPCdED2xB0zFnSha//dBp/Ptzr7vfptHRDSyY1oQbPn6Wf515/+m+3nOxDn94fCu+dss6DBY13ndvPjmH954xz7dsgPV1oVz58FPkpH3naXPwzdte4LqFaRJVyMgOQavdF9y/2bx/kCcNYvetvdypbtBYn330VPz8fUsAALet3oVPXd/FdbUwD2z73Bb8/ZkEOk6ehW++5QRs6RnCD1ZsxImzJ6G1MYnzj5mG77/tRP47O2rIG0Lsls0U9RXkdUwHXK2pBvRb8cA1cF5b84/224Qi+z77zzW7HPXraM+hIaXiI39YhZQqY+Yk/2toRBtEHlh+Z12x87jU6uUPAq1VHkKs2rJODLHPJhWUHFQ+0UKITdOsOz1N1L0oKr52y/P4g8W3G3tgY3hge2DZqTLtl94QYnZyS+HDuckZFDQdpmkyGp2Ii1e8g6MKIcSFssHvLdWLS4vex8oUsjUKpMqU5ZYUyK09Q2htZPeJjrFCO4PuU9QD2aTiuF9CJ/wiykblEDsKIWtrTHJPUCZiCDH1N/XLwhnN0AwTm/cPoViuLfyXhGL/CEKIyaszEg5YsT4DIzCmgxAUQuw+zXZnqiVS9aJmJy9zZyGme+IAHJEKmhGs1GWSCvJljfc5KcAsGYTzjrkf6DvywJasJBJ+URa+vxfkgG1sJRz/dyOoD8cC2aSC4RLjrVW5rHDe9ZIkkdpGxdwpWWyweHkBNrZR5mg2qWCo6JwPAFuzJGOyPiHEYqhl0lKYajH2SMESE4IstGTcMdObeGbihdObqi47k2TGQr/FKZuzQvUqJXHSDXNEIcSBHljN6YH18wTRXKU6R+WBLQvUP2H3RKuBmMQpERJCLLYzoUj8vmNrQ7Q9KqXa+4ufZ1LMUlyJJoj6b7ioVQylFiNyqO8qyWGa48kI8yKlKihqOj/sVWTJDtuNmKBrNCHm+ihGDGmm+rcJY0t7u02z5OeBtSML8iWd6wFiXgY/o7Qh6YwaIgz4JPqZ2mQZchVCiEci05u5BzbawRIALl+D5GPKx1tKsneopAfqKbS+Wl1JnFKJ0fPAkqyhJE7phL0e/HhgCZXq0ZhWoRsmBosahkv6hDYMxbbEHtgYHth3YNmpcmOKpdj+0xPbsXL9fpsAXZWRL2n40G+fBsAMPdME/vHMTmzrHcbm/UN4brs/0b3jfYrEBaSqSHxRFjWdb6z1NGDtS/ter0pnVzd+/xg73fn9Y1tZu3qH7fAYqx73vrjPQ7ReD3R2dWMgX4ZugpefkGVs6x3C2dfej+6+PBRJ4lncntp8ABcsnOZbzub9gwCAT/zpWagSoCOa57KzqxvfuZ1x4/7jmZ04aXYLdh9i2eTe+F8PO0Iaq8FjLzEqhk//9TnIErDjwHDVZRCv5kg9sAClji8jk1DqEqZKeOxl5sG/uasbT205wOfIQ5tY+z/7t9X4wYqNOHXeZABe6o+SZofOP7ejD2dfez929eUxqyWDlqzKFRRVYcZooayz5E4BGzQZZCWL4oOomdbt7scfn2Bz/V9+9zS+dMmxgR4aADiUL+GXD20GALx2+UoA0RR88USeFCmiCXrkpR7fdTRSzsCRIJNUkS/rUIT7nqLy88LufsBklCJU96aUirvX7cX8q27HrJYMWhsTFedoZ1c3Vq7fj3xZx9nX3s8T5QCs7A/835MAgGe2HcTZ196PCxZNxW2rmbfg6pvWcpm5/cAwNMOsWh51dnXj/vWME/iCH6zkhxkPb+pBZ1c3T0QFAJ/+axe+ePGiqmQdKb8HhpgxSHbdf9//Em56ttu3rkSjQwc3tRws8TBra952dnVj/e4BlHQDJ3/zbn5Y2vGzR/EFV5vus6hIiIt4w56Biu+jxFO0ZhN1kiX3vMA4iW96ttsxl4J4YAHg4FAJ19zJ6n7Dqh04ITep4pglVZkfFPtRByVVxT5Qq0ATVMkDK4Lu6FFOBL+2uZGIEEJs11vmkV3st7KjbfW8n1gL7APIaEmlOru6+fr/6i3Po6gZ6GjPcQOW5p9fghwyKodKTKei+UQe1sECGS7+eRvICKR6rNvV75E5z27rA8Dkh1sWdXZ140f3bAQA/PjejZiUSVStN3V2dWPNTvaON//0kYryiIcQcw+s/9yifhf7X4xe8JtrnV3d2HGA6URfumkthooaWqxQ7qQi4861jF/Xry9GgoQi46V9AxgoaPjdY1tx07M7AAC/f3wb5zAH2LhJwm31KCHEAPBnSx/48b2bcOOqnXXXb+sB8fB2IhvalXD41nyCw85CzE6VNcMWXiK59ub9g9AMOwMxcYldfbOTxL4SyXdCkXmISkJ2e2Ar39GrFnYIseiBZQLrFw9t5qfBB4eZ4rW1ZwjnHTMVnV3dWLXtIP9NlLZVAyJ9pmhhKj+tSjhU0Di1j5iC/DePbsHCGc67j1QOKR1ERg9UNvzcxOD9BQ3LblwNMXMH0X64yb0rlfu9O9fzvw0TeGLLgarKEOtfLwN2sKhBlqS6cTd2dnXjx/faROgicfffn9np+HzvGsapKHrq2H00O3nZXc/v4QpYd18euw+B3wkB6G6fEeoZySYVDBd1lDTdYRA8sGE/X6f7BoqBc5kU5PW7B/ncI8/cpggKvvO+poTOrm785z02r57fOqpHuFmtyCYUDJc0ZBIKPywQ58c/ntnJeU27+/JYduNq6KbJ1ycbp7xjnNxwr7NuH7qB/oLt9XCTuvcOlfi61KqQtUHvJwoOgHnElt242sHduvtQoWpZR8ovXTHZsHfQ0R6/8jiNjl67B5ZCiFOqwttJB6F9lmcVYIcEYh3Ywd2LjrJuXb0L5x49NVJYp7iHjRSdXd345m0v8L9FD5j3Dqy9vjb3DPF52F/QIo2ZSKFEoYnO7+0IqUo0QdwDW9Iq3v8WPbCabkCSKmf9Tyje9RhYF+tur25FK4keWKD+3rFqwfUcKyS0rTFY1rnXa89giY8tXTWiMfSbfiT3Dw07KegoooPkeVDm/Lyw519901qPzPHb36h+ABx1PzhcrlqW0HtJx4wij2g/JEqzoDnjF+7bKkTYuZ0nbpmyf5DtnR86Zx4ANg5fuvl5/nw99cSipuPZ7X38b3GPoKz9Eth4q+noc52iUX54j1d3qUe96wnSJyQJaKxDJN54IQ4hHiWorpAUN2UTEUU/seWA7+8rEd27kVRk/g5VkfhJWKFse2Dr6SETkzgRWoW06G6YYCdyfmTkldpWDYJInw/mbePVjZJmeN4fROQOVE5+5PfbsmH69ks17fYjCNcNs+q+s0+O6xFCrLIQx0K5bvNr+YoNvmTp1z+5w9N+UgJEpY28BkEhxIZpKxuAdbdP0x13Y93IJlUMl53cu5phcuNVrKffeNBv/LjbVm7Y7/tOEWlHCLE/Wbz73SQDmiLwxdYbGR5CbPomjXGvhbJhetanYQJ7hYMjN8LWaFT4rcuo8qjS+8uCF7Tasgm0Vg9aSnOU+aYqMr+6AtR2B5bem1Tliu0U6+A3L8t6ZRnFFWXLY12PJE5+dSEE3YEF4JmHUcaMeWCJv133HBaLd2S1CtctKFnQcFGvaMiLWelLuomELDt4s/3AQ4ir8MCKod0OD+w4hxCLuT78PN8ignSD5Ss28P2jHBJCTEp/n3Uoz7P5KzKSioy9A+ww1e+aWEKRuBEYVA+//Y3qF1b3qKiljITHAxse2iz2fzapOuRIlLrc8DTzhvYNl0fc3iAcHCoH6oIiJEmCIks8Wi4s8ztgj7v7qlo99dt6gWezTqqePCyHE2IDdpRAG6Q7dbqIXX15fpcoCsKIvFXH/R77AnxB00fnDqxwt4gwORt+X6itMVkTgXk1qLUc9+/CyslWOImrpg71eLbaNmdcJ8cjASlQplmbouyHkRJ3k9egpPkrr4Bzk0mpCg8hDlKcySATuXeDquNX/7C1J3q0guDmLI0yF8YzhJhCrjXDPhSohbM4bAzrJTNqLbtesiYMWZcHNkp5KvfAUthnLTQ6dghxNX1Rq4wig6GSolwNwt7p9lJW2hsr1d9xB9aH+9b2ZLKDmrD2UV2GqvDA9lse2Chj7RfSH1wXhddbkSVIktuAHV8PLF1RsGl9gusTNjdJiY/igaWoMse9+pSCff1kwHoPDDMJhc/tave3XX35uuz9tZThptEJihywPbDO/ienhnt9Bb2zd5DJOffBX5S6RkVQ2SLEJxqJzqcijU7wQfFo7lW1YDyjs+qJUTNgJUn6myRJz1n/bZUk6bmA57ZKkrTWem7VaNVnrEEbsvvivohZLRlMykSfQGFE3uJmlJAlfgJcKBuj4oFN+nhgFVkK5HsFmDCrhcC8GtRajvt3YeVkK6RHr6YO9Xi22jbXO4SYECUpSBRUS9ztfq3bA+sHZxgcu7/GQogDPLCWAiImzgqa6371D1t7U7KVPaTujLlR5kKKb1Jj74FtSLGEFsPCPb5aDtAyIR6eesmMWsuul6wJA91XDzNg3eUxD6zJvUkj8sAqwXPNrw61yii688rzONQhhDjsnWEe2GrLot8Xy8zQY5yR3hBi5smsHNbNOWmLekWD1JnEycsb6wfq2yh3o5OqTW9EfSa2bbw9sACLNGEeWG/otoiwuUl7SynEA0tj0cdDiO29L5tQAkOI6VkyAqvd32a1ZOqy99dShsojIyp4YIlv2dX/lFXZvb6C3snpdALeUw+ZH2XeiwdHTRGTXoUZg6O5V9UCHp11GCdwAkbRgDVN8x2maZ5smubJAP4B4KaQxy+wnl0yWvUZa9DCJ4qNIOL61x873f/3sv/zQRAFiyoQsRfKtge2nlli/UKIAWB6U8qXpB1gIcS1EJhXg6DyZzanfMnlAaZcuN/vVw4NSSXDz++3CVnyzAFJ8pJ7V1uuKktV9x1tvNlU/TywQP0OSMKIu92fK5Lzrg1g30ejgxu3F0OSbL4/gM3hIoUQV0ziZD/Tkk14lI6guUwHSn4JVt56ymzfd4pwZ8yNso7GNYmT9e7BouZ7585tzPqtDwA4cfakwHf49QEQfLDgB7/3RpVHQe+vR9kEHkJsGbDufvMrj/HAsqzaQG0GLIXNpRL+cy2oDn7PJn3kqxvE0UqKcrIOIcRh9XbLBHFvjLqmRaRUBUVdpNRye2BtGeN+n7es6B5YurvJQ4gjjDUdFkSR1ynhYME2YCdOEieAhfYWNZ1nlQ1CmMx0J3Hy63ZJYvd/KWJG1AMySQV7+/1DiAG219LcXrZ0oe869tvfqH710JtqKYPmKXmPg+YtDyF29T8lcnKPS1BdPnrekQCAI1ozo6YnzplS2Zic3mQnoGriHtho4fxu1FO/rRfGUzeoJ0b9+ExiFzLeDuD60X7XRAIJRIrjv7w950sUvWTeFAC2wKS04lcusZXaKCTfoqKUUJiglSR2H6eks2ygUU5no8LNA0s4clojD7+QAAevZmtjqiYC82oQVP7xuRbMbE77KgSfe8Mxnvf7lXP8rGYAlQ1Yv98uv/IkLH/bSZwQXpElLGhrqJoD95orFjsE+2Unzqy672wPbH3uwBLqFUIcRtx9zRWL+VzPtWRwzPQmzHBxzCUpiZO19s48cgonqp/SkMTM5jRmT/YmcWLZQQOSOKXYCbrIzdySTWLx7GZOSxQ2l2m9XHbiTOSsNUHv8suA7YYzhFiOtI7sU9bxCSEGWJg1Kb7i/PjMRUf7rg+a27mWDNKqhGNnBhuw1AdTLJlJp/evWzSNl92SSWByNuEhdXe/txZ55B4D97tGUjaB5tYBy+uzbOnCiuVRCHGpDiHESWGuzbC4sBOCDHXXgZ4VI4s+ecGCim32hCrWwQPrHh9xHbjLVxWZ7w2vmdtS9ZhR1Aft9+77crYnszJNEMkKwwzmKBfrnU0qPIlTlCiYZFUhxIIBS4fWgjEy3iHErA4ypwsMq0+YzOQGrG7wUGk/JFWZ30d3hBAnVR7x42fIZJN2CHFHew6fvPAo/p17f/OrXz30plrK4DQ6NYYQE5WO+7AkqC6XnjgTADB7cnbU9MTc5CwApoO55TbJONFjSuNZie5OlC8pVR4V/bZeoPFqPMwN2LGo/bkA9pqmuSngexPA3ZIkmQB+YZrmL8egTqMO9+X3U+dNwXVvOynwOcME3rFkDt62ZDau/PnjmDOFLbJfvX8JXn+cv5fWrxyAbc50WljQDBiGWVfvq/g+94kbhYwosoQXvrUUKVXBcV+7C8MlnRvntZIvR4Vf+SvW7UFDSkVjScdbTp6F57sP8Ux0bzxhZqRyPvu357C2u5+H9VVbB4AJjo//6VkokoTZ1hhXg472HJ7b0YffWfREp1gHINWAJ3Gqg/LRPAoGLBBO3H3Dqh0oagb+8Ymz8PZfPO7x+CcVi0bH8nbMmdKAN504C1fdtBbfePPx+On9mxwKbDrBMnhrhhmoOGcTiqV82kmckoqM6c1pHDdrEu5etwePXnVhYHto/Z1+ZCt+9M52nHXNfSjpJnoGi5F4YN0hxGF9JLYLGC8aHbu+pPiKSsyli2fhE+cf5fnd7kMFfP+u9bjj38/Fku/eUzFhWkd7DtOaUnj3r5/E8rediA/+9mm0HzEZv/rAqVXVt1Z5FEWWjUTWkSFJHtjLTpqFD597ZOhvVKLRqUsSJ/Z/aucp376HJecydDy47Hwc0drg+W1Hew7TmlN4968YhdHS4/3lqwh3CHE9D8Oo///ngZdw3V0smYrio4gnFRl5Q8eCaY248RNnVfUeMvQGrZwWfndgS5phj0mI91NcJ1H4cJvSKqfRqSqEOGISJ4AdLPiFEI83DyzA+nq4xLjIK3nJgtYrdVtZNwJDeQHmSTxESZwEPUCUU74e2ITKjUAAOOWIyQCAv3zkdJy1oK1i/Sp9FxXVlkEGa56H9vv3jc0D6wohDrgDG1QXuiqRVOVR0xOTwsHxrz/gDPo8NFzGSd+623c8K0UbNAgOgQ+dPR9XvXFRvapcd4wnR3w9MSLpI0nSvZIkPe/z31uEx96FcO/r2aZpvgbAGwH8myRJ5wW866OSJK2SJGnV/v2Vs3aON2idU8ryoM1C/LytKckXyf6B4PsUfhA3LhI6zLPEwh7rvdH40egAdsjI/LYGHjZCbZgqhGWMNRKKjLJuYLDIiMZbBY6yqDQTmTrcHaX+KVXIRBkGsR9rKcNNwD4SjEYIceV3MoUNYBurux2pBEuYUhSyfpIxWyzr0ITMuADbmEjxDGqDuKGRcq0qEsq6WTF0jeokltPWlEKPlbI/yj2yID7VMIznJiV69znvpFDvoDYvmtEEAFi36xDKuhkp0Ri1b591B60eyckmCqgt5IGNErKpyrJ1F5PuW9YiI6wQYtd6WDijCfmyjkxCwZzJwQdwC6c38X9Hmd/k1c3XMYTY+45ww5DWaC20Q9RP/ZZc8vLAWonlyIAN8TCLsqRSCDHA5v9AQUPZCI4gEUHGc5Rn7XBm3aFXECaKB/ZQ3r/fo0IW7sCG9XlQCHFDBQM2I3hgAfDknc0T3IBI8sgIdh87yDPtl4UYsPWdqLqBnRx09OYVvYP2GhE0x8WxbY4YQqzIEg/p9yt7IiFO4gTANM2LTNM8wee/WwBAkiQVwBUA/hZSxi7r//sA3AzgtIDnfmma5hLTNJdMnTp1JNUeE0gSUzjzFUKiRGW0tSHFJ1a1BmzSFUIMMGVnw55+/P2ZnegbLuPsa+9HZ1d39Y1xobOrGz+5jznUP/O3Ll5mZ1c3/v4MS4PefXAYnV3d6Ozq5pnlLv/Zo3V5fy1IKDIGChp0w0RTOuEg2Y4SrtbZ1Y1bn2N1/8gfVtXcDtFwjuJ58y2jwb7zWQvdxJodfQCAa+5cP+I50SgktKq3lz8IpLB1dnXjxd39WLlhv6MdSUXGUFHjWYI1w+R3m0q6gbLhvOuaTijcIA7mgRXayRVAdihSrJA8pLOrm6fR/+ZtL6Czq9sxhlGVLjJeoij3nV3d+PXDmwEAX+18fszXnagA+PHABs39rb1DAIB3/5p57/7zno0V5yjJSLqDVo/Q+ImCDL8Dy+ZnlINIVZbQN1zC9+5gnNFv/Z/Hqh7/dd2HAACfu3G1o//FqzHnXrcysNyHN/XwQ9x3/OKJiu9PCIoya0P9ZYlomPgZKSS/ank3yYT+PHlg7TI6u7px07NsD37r/z4GIFxui4cGlQ6rOru6sa13CHc+vwf3vbAXw8XKrAZ0WBA1iRMADBc13i/iHBzvO7CdXd3YuGcQT2xmdITX1rinuUOIg5BSZU5l5Q4hBlh/uY2vzq5uPLmlF6t3HsKCq+/AvKtuxxf/sQbAxDcgxCROYXNx9faDABhXrdj/m3uYPL/urg2RxuWutbsBMO7oeumrIjq7urFyPXOA/fnJbZ7y77Tev2LdXpx97f34Suda3LaaffaxPz4TWp/Orm5+BeJ7d7w4brpuFDz2ci8A4C9Pbh+Vfh4rjPbquQjAetM0d/p9KUlSAwDZNM0B699vAPCtUa7TmEGRJZuYPUAxTjk8sCmuzJIBG/WETnWcLrN/a7qOVVv7eHr2epAqB5GBr9p2AP94ppt/ni8bWHbjakCy05a7Se/HEglF4ndXmtIqv5tB34XB3eZ9A8Wa2yEmHKrV4BONYOIMjIrOrm784fFt/O+RzolsUoEiS9ANc0w9sAcs4nM3GTzAjCORnqrs8MCyu66iByaVkDFQDD9oyvp4YBMyM2DD+Afdc+fAEFsvi3PN9vsj9lsqoWCgqFUcc/c7e613AmO37hwGrA8PrJ/B39nVjevuWu/5vNIcJSVwj2XAVgo7PpyQUmXIEkuGJUnR5kp33zCn+gBYv1Qz/p1d3bj+qR1CeXku45/Y3Ov53F0uzT9iq4jyfq4oly0e2FE4DBPlvF+YKBlmtdwZpnEhTyAd0LjXImWqfW57Hy5v90/eJo5xmDFFZdO92oJmYM9AEZ1d3ZHuNUZL4sTaEeSBHc8QYmq/m3ezlj1NTOIU5vQW+0xkIyCZ0+wySKmOxO9KuhhxyT68qcc3DH+igF+FKwcbsJ1d3fjjE9v536K8uOHpnZ7PAf9x6ezqxldveT7y89XCvRYPDpcd5fu9/09Cu8L0PrfMG4mOONro7OrGT+9/if9d734eS4y29HknXOHDkiTNkiTpDuvP6QAekSRpNYCnANxumuZdo1ynMYMqyxV57cTP2xqSfEPYP1idB1a8U0MbTV9e83CLjZRUOYyE2/152bC5COv1/lqRUGQuXJrSCYcHrFKIXT1IxAlNKdW+Q1mjwVeN8e3G8hUb6kq0LUl22MxoKJ1+aEonUNTNwDFJWt52gqabKGts8CmRijjmKVXBoGXABt1NEw0yngRFtUKIQ/gHg+bOC7sH+N+RPbCkYFfwwNZzvtYKvxBi+r8k+R/esHr7Ux+F1Z9CiMk4qEdo/ESBJEn8bhVLzFd5va/t7vd8Vs34B8mI65/cEUme1zL/eKhiMfzAdyRQrMMpWQLn/fSrQy0h10l3CLFl+Pn1BQDctmZ3xbKA8H7wK9s0UXGc1SquItghpHZG5ImSxCmob4Hq5R21rdI9YvFwQcwhQTLHfV0jrI4Au5c9kZGQ7fEPmoth8qIaXcNP/tdz36okl8L2n0r1mQh7blQsX7HBQzE4UetaCaPqgTVN84M+n+0CcIn1780AvJmNXiFQFYkviGAD1hYKrY1eD2xjBc5RgjOE2PLAjgIZdLUk3PV+f60Q7zU1pVTHKWulRBn1IBEnSJKEtoYkdh0q1MSLCcAR/lytEVzPthCa0ioO5cujonT6wX3KLWJXXx4pKykTQTNs/sVimdGLiHVNJ2QebpwImAtBIcSDBQ0FBN+PCerXQSHUL7oBa4UQV1A8R2OMq4VfCLF9v8nfEKtUv6DvWdiejP0DrzwPLMDaM1DUIs+TIO7xqONfrYx3P1/L/FNdIcSjcRhGcj4oyoK8jUEyIAz0W/suJntHUJvDeH2dd2CD+6HWdU7yI1IWYqsdQ0UdUxoS1u8Y37therMtjyVqlRd+II98UTNC71+KMkz0jme4AevcmyrVYXdfIXIdxwN0WFooG5icrW6Piyovav28WlQqfyTycSLsuVFxONW1EsZP+rwKoMpSIA8swZHEqdFO4jRQ0JBNKpFPg1WfBBWjQQZdLQl3vd9fK8S+aEqrPFsyUHkjrweJuIi2JuJGqzWEuPYw5Hq3BbBPnccyhDgIs1oynj4p6yY/CS7pVhInRxZib3iwG75JnGQZJSuJU9BdsKB+JVofILoSmFKjKZ6jMcbVIuMTQkyn+UGGWKX6hX3flE4IHtiJfa+sWtBhQNT7hg0B/M5Rx79aGe9+vpb5R/tjvhweCTES0DwMStJE8qsmD6xCd2CtEGJrrILaLEYABdUDCDema13n1YQQix5YkpmSJAUm7RlLjEReuCGLHtgIFEfuKI+slZHYvTfVs47jAQe7RcC6Gam8qPXzalGp/JHIx4mw50bF4VTXSogN2FGEGEIctCmSgJAlxivJMr2x76q54J9whB2xfx85tQFuGTJSUuUgAmo/Eu6EkDm1Xu+vFQ4PbDqBqU1MgZCkypke60EiLoKUl1oN2GxS5RtotYpevdsC2PN0LJM4Ad5DIWqH+16WZhg8iVOxzLyxDg+sYBhECiHmIeASNOsObNBdsKD+fsepcwCww6aoCjP3wFYY89EY42rh54GVZQmqLAUaYn71JlSqf3Na5dcuGl5xHlgrhDjifcPT5k32llHF+Fcj4/3KrWX+uZM41eIFrQSS80HynuRxlMy/nt8mXCHE1t9Bc/qdp80Jradaoa5BZcsSKo4zv5MeyQPLyh8W7sACdl+NpwE7EnnhBhlbZd2skMSJmAicuhkdGjWlnCHEYXWUUHmsxhtituygg9ORyotK5dSrjyqVHzZWleozEfbcqDic6loJr6yj6gkGRZZ4qGDQZkEbwZSGpOOOSb6sV0V/IW74tNEc0ZrFwaES9lrhyLmWDJYtXTiii9r02+UrNmBXXx6zhDKXHDHF83nQs2ONhMsDSws4jM6AENbmWtBaZWp5/zKSGD6Qr9porHdbADukd6w9sK9bNA13rdsLCXC0Y/Pdg47ny7pNKVLSDcb36gohJgSHEPt7YMvWndogoyyov4+Z3oSfP7i5KgWQ6llpzEdjjKuF2B9iXydVOTDcWqx3d18eiiRBN81IcqsprfLsoK+0EOJqPbCLZk7Cgxt7LH5MvWq5X62Md5dby/zzGLCjksSJ1m24B7aWqxC0Jg/lnTyw1OZv3bYOB4bLaE6r6C9ouHDRtPDyVBlaSQ893HKXDQDHzWyuOM61eGABZ7+lEwoSilaTsV8vjEReuEHt0I1wA5b6wy1j6O9Gl9PBXUdCSpUxrSk14ZPmiPkWgvadkcqLKOXUA5XK9/v+gkVTsXL9/or1mQh7blQcTnWthNiAHUUkFDsLcVD4BW0m4r3GtHWHr1YPrCqE6g1b7//GZcfhg2fPr64BAQgimA77fLzhNmAbkio76Y6orNSTVJvGeiRcZ22NKew4kK9J0as3QbjtER1bDywpiff8x2tx1LRG/r2bokXTbQ9soaxDN0YWQizeISvrJoqaHkqJ5Nff+6z7mlE4Mnk96Y5eBMVztEjgo0KWJWSTCucPJCQUOdRor7Xe4mHfKzaEOOJcUWUJJoALFk7D+j39uO9z51f9zmplfK3PEWiOEA1FLVyslWB7YMPvwNZCo0Prv9+Hj7SjPYfJDUl84P+ewvvOPAI/W/lyxXekVJl5PSsYiB3tOeQmZ3Dlzx8HgEgZbd1J1ULrkfDqFQBr33hT6AD1k3OVKJYI1B/uKI9swB1Ydx0/89cuPLu9D62NSZ6cbSLDcTUtZL6MVF7U+ny1qFT+SN4/3ntuNTic6hqGOIR4FKHIElecA5M4WcqoeK+RNr+oCZwA5+kobUwp1c7GKlKvvBohZkBtSKqQZQmtDcmKCsJogLIIj8gD2zByL269MNZZiOl9W3qHATizMgPOk+J0QoZm2B7YoZL3TrpoGARt0g0+SZySqmTR6ITzwPphSpbCyKvxwEZL4jRRQEqdqAQlVblm/uMwiLLylZSFGLDbE7XfVJklJRssaoeNMT82HlhK4hQeQjwyD6wzhJhAY0jfV2qffR+3cl2cGfUrP58QDuAqweGBdYUQj8Y6Hi9ENWADPbD8Dmx41FxrYwo9g0UMFLQJzwELwHP4GCPGREI8I0cR4oIPUjrve3EvAODRl3o5oTApqlE5YN3vorAj8RS47VVvwLI+aUwx47WzqxsHhkroL2hjTuS8pZeFuH791nU1vbuzqxuPvdwDAHjPr58YVxLqzq5u3Gy9/4O/fWpM6kIhy1v2D0KVJc86EY36xpRqhRCz8NJh4nt1GLle76obGR8vrRqBBzYI/1yzG7LEONiizIHOrm7cv34fAOCCH6w8LIjHSckjJaizqxsHBktYvaOv7muOlMGoXKmHE8gIjTrHyNDoL5QPm3Bqzjc5igYseV6DDJSRJHF6eNN+AMBzO/oAwDO/SX5QiHElI5nXJcIBq3g4XanfGGfnNgDAlT9/vOIa9OOk7ezqxsv7B9EzWBzzvXO0IAvJQsISUpIH1n0w9NyOgwCAn9y3KbRP2hpTGC7p2NdfOCwMWEmy85hEuW4VI8ZYIp6RowjF516qiM6ubnzvjhf530QoXLDCqKoRcH6ZC50GbHDWw1cDSClpTic46TTRDFG/j8VG3NnVjRuftt9T7bup7uSp2NtfHLO6B9WF7nkTefdo14VOufsLGlobkx5OR1HpyiZVaLrBec+GLJ5JUTFMRUjiJMuSzcMq8CgWyuxObTXhdG7S80pzwE3A3t1XGLcxrwaUmVOVZd4Golao95qjOZFNKJG4Ug8nZPgd2OghxICdyf5wgMpDiHXIERLr1QLaF4O8lKkqjEYRnV3duO6u9Y7PDg6Xsezvq/n8rtYDy8OZIxjTzWlVuNYQXHdagxSVtae/shxxyEbFXsd0IDiWe+doQpxvfhzBhKTC+kM8GOrs6sZvH93K/w7rE4q06y9oVeU4GU9QBE0l/vEYMcYasQE7ihA3H78Na/mKDSj4EDf3WBxx1RiwqsNYtjywDoqeV7cHlnhym9LquJJOB5F+R333RCLMHq+6pBMyn+8iHRIh6TBgFVcIMXlAnGHGhDDllU7dxWQv1P5qQoir7beJNObVgJQ8VZFGvQ0kK7NVXLs4XJCtkrKElPH+fPmwMWBFD+xohSpWykIs8jtXg+UrNkD3obws6yaf3yQ7uAFb4TAiqUQ3piVJ4oZRWN1rWYNJlwf2cJVFleBwNkSh0RHW4vIVG/gBKSGoT0RHwuHggQXE0PvYXIgxsRDPyFGE371UEUHEwXS6WVUWYp+7KnRHRZElTMocHqd9owXqn6a0Oq5EziN990QioR6vukiSxDf/Vp/IAtFrwEKIDduALXqTxERJ4gTYYYBiEie/MiqhXkTuE514nFM9KfKot4EbsIeJwVYNqk3iRPNyoKDxu3kTHbSmSroxagasWsEoTFVx71RE2Bym7+gwZ4AM2AqGKYWqRvVERzFga1mDjsguRTpsZVElRPXA0hwR5Uw1fSI6Eg4XD2w1d6ZjxBhLxDNyFOE0YL1dHUQcTEKyqizEordXyEIMMIqeMKH8aoDKDdjEuBI5j/TdE4mEejzrQpv/VJ/IAocHNqVC002eTI0ncaoyCzFgc/35eWqq8cDWi8h9ohOPk9dJlaVRbwPdg67E43c4grzK1Xpg82Wdz9mJDlUJP+ytyzsqZCG2751WpxaFzWH6ruokTlUaDWQYhRnftaxBJyetfNjKokoQ771G8sAKkR7V9Emrw4A9PA6XbNqlV7cOGWPiITZgRxFqhQxuQYTCC6czSpDqPLDe+7akVL/aw4cBZwjxeBI5j/TdE4mEejzrEu6BFZM4KdCMSh5Yp5chCJmkM9tykBe3Eqrtt4k05tWAZyFW5FFvw6vDAxutbeIcPlySOPldgak3yLAP9sA6k45FxbKlC309qglF4vM7ochIKjI3YCt5ecXoqSjgWelD+q7WNcizM8vSYSuLKkE8swhN4uQTQlxNn4gZo5sPEwNWjUOIY0xQHB4r6DCFuOD9NsUgQuEHNuzDmu7+EfDAWgastSG/2hM4AfZYNKXVcSVyHum7JxIJ9XjWxTZgK92BZVmIS5SFuES8zIL3VI3mgSWlhZ9IC89WQ4dTbb9NpDGvBlkhC/Fot4EncTpMaGOqASnHUZM4iR7G7GESQkzZTsu6OWo0UbRu652FmObwN25dhz7LQJ2cTeDrlx3vmN+ZpFK1BzZqQina48OMjFrXYFKVMVTSocijv47HC6LRGsoDq3ppdKrpk3RCQVNKxUDx8EniVG00QIwYY4XDY3c7TKEKJ75BmTH9CIWf2NwLoMoQYtne8OhddGIfe2BtA582jfEkch7puycSCfV41YXG0W9uOz2wLAtxSXMmHhE9JlFDiMkg8+NorJYHdqIRvI8GMi4e2NFsA8nKw8XjWA3IKI/OA2vPy8PJI51QZJR1fdRCiCt7YG1PY7WIMrezVRiwdAc2ugHL5GClTLG1rEF2OFfm8u5wlEWVEJUHlg4q3Qdl1fRJa2PSMmAPD/XbvgMbhxDHmFgY0ZGKJElXSpK0TpIkQ5KkJa7vrpYk6SVJkjZIkrQ04PdTJEm6R5KkTdb/J4+kPhMNPPSiioXf2dWN29bsAgB8+q/PRU5Pn/B5V5fFTXZzV/crhq+tVjy5hR0K/O8DL7/q++JwR2dXNx7ZxHhwr7nj/7f35+Fxnvd5933+ZgMG+04AA+6kQFIbQdGyJTmOZdmmd5Hs49ZpkqZ5kqjNkzROm9A146R12qR2rDRLnTSJ4izOGzteSWqLREuyJTmSLJkkSJEUSYmriMFCACSIfb/eP2YBhgRILAPMgu/nOHAAuOeemWtm7lnO+V3Liesey1jA9ETXBB0Zn1gHNsY3zfjV6d6kJ6+9++E/eVH7GsNznsRpKdjXGNa3DzRJWpz1gWPP72feaMu65/eR6Ov4w/tPzei2+TKwC7E0uWv+QlVgp5+FeF9jWH/67FuSpF/7RuOCHD+xx2ImywTlzPK+OB9dW/xLT8/sGJmNuY4NziRmpthDMt18Ifsaw/rCU5FlDx/ef3JO9/G+xrCauwYlSb/0Dwcy4nUq9npCBRbpZr5H5DFJOyW9OHmjmW2S9ClJt0r6kKT/a2ZTvZN+VtJzzrn1kp6L/p814utnzfCJH1tjLbZWZfss1tWMvSHGKrH7GsP6h5cvxE/PlvXa5mJfY1hf+eG5+P9L+b7IdNeuidrZN3zdYxn7ljzH55XPa9EKbOIyB9ONX53qA+PEdUYuo/lqZP3EY+GrU17GUrfY6wPvawzrj773Zvz/bHp+72sM6+9fmd3r+OSqXaZM4iQlrq+8EOLvkddcfux4jXX/7ei9/jUlGfKvGUN/I7OpwEa+LJr72uI3bYtvdjMiZ6obVehjx8iV/sgxcqV/ZNb3cewyYsvopXIN99lgFmKkq3kdkc65E865qRYAe1DSN5xzQ865c5JOS7p7mv2+Gv37q5K2z6c96Wa231zNZ401M1PA64lf53zXG80mD+8/dV2AWar3RaabyXMk9oEr4PPI5/Fo3ElD13Uh9ly3vzT1JCjTXef+463x/2fbhTibLfZakQ/vP6XBLH1+z+W1a3KlLFOW0ZEWvqvidGNgF+t4DQYSx9DfSGAWFdiFfq+fvO51NvNEh155phjulYxjJFPX0J34Yim7H39knoX61BWSdHHS/03Rbdda5pxrkaTo76rpLtDMHjKzA2Z2oL29PamNXSgT3/jO7Ik/3zXW/F6Lv+Fl63ptc8F9kT1m8ljGPnDl+Dzx515s8qaYyRVYM5tyXOvNrjP2bbyUOBHUUrfYz7dsfn7P5bZ5Jx3DmTQGdqG7Kk5XYVus42fypGY3M9Ft9+b7LnT7A/EKbHZ/SXejCmwy7uNMfZ2iAot0ddMj0syeNbNjU/w8eKOzTbHNTbFtxpxzjzjntjrntlZWVs7nohaNb5ouS9OZ7xprPq8nPgFFtq7XNhfcF9ljJo9lQgU2+twbuqaKde2HyNwbfGCc7jrL8idmkcyhAhu32M+3bH5+z+W2Te5dkEkBdsErsJ6pK7CLdfxMXlbqZmLDIGYyf8ZCtz9niVRgY8fFVF2lk3EfZ+rr1FTLxgHp4KavpM659zvnbpvi59EbnK1J0vJJ/9dJap5ivzYzq5Gk6O9Ls2l8uvPN8pur+a6x5vd64t++Z+t6bXPBfZE9ZvJYTq7ATg6kgYSlphKfk7l+rwJez5SzhU93nTu31E2cnwps3GI/37L5+T2X2zb5A3hGTeI0yzkjZss7zaSKi3X8xLpzz2SZoNlUYBe6/QHf7NakzVQ3CrDJuI8z9XVqocemA3O1UANkHpP0dTP7I0m1ktZLem2a/X5O0hejv28UijOOb5ZdiOe7xprfa/EPAdm6XttccF9kj5k8lrEPiAGfN+FNtzDHp87RYUnXf4jN9Xvl847O6jpL8wPxycGYxGnCYj/fsvn5PZfb5k/oQpxBY2B9C/tB2RcPKImXv1jHz2y6EOfMYubfhW7/RAU2uwNMbC3YqQJsMu7jTH2dmmrdcyAdzOvdzcx2SPqypEpJT5rZYefcNufccTP7lqQ3JI1K+hXn3Fj0PF+R9JfOuQOKBNdvmdkvSHpb0ifn05504/PM/E0oZj5rrPknTeI038vKNtwX2eNmj6XP65HXY9EuxBPPh4Jcnzr7IgH22g9juX7PDT+gTXWdsWV1pMSJoLD4z7dsfn7P9rZN/gCen0kVWO/Mq45z4bvBGMfFOH7ycmbehTg+7nQWX34vVPsDS2QWYs8NKrBScu7jTHyd8tGFGGlqXgHWObdX0t5pTvt9Sb8/xfZfnPR3p6QH5tOGdBaflGKRPtz6vJbVa7UBM5Xj80QmcZr0fCjImXi5u/ZDbK7fO+sxXhOVXs+0awcCi23yFzEZ2YV4gd4vb9RFdDHk+WexjE5sDGwavK7kzKI7cyaLV2CnGEaylNGFGOmKI3IBxV7wA4vwzdW+xrDe7uzXGy3dSV/IHMgk+xrDGhgZ02vnLscXnpek/EkBdvKb8b7GsE629Kijd3hWz53YN9O5VF+RRiYHtEzsQrxQXRUfPRyZhuM7B5tS8h450y7E+xrD+tL+k5Kk//zNwyl/L18yATZ6+/gyMtHE2HTuF6SXzHl3y0ATXZYW9gNubIHs0fHIRM+xhcwlZVx3FWA+Ys8FF53zfPJSN4WTK7DRN+NrF5efzXMn9obO+Fekk/gXpz5PRnX7nBhyk/w2x57nMal4j5zJOrCxdsbWC+3oHU75e3msGuzN8grcjZbRWcoWemw6MFcckQsoPgvxAldoMnWBbCDZpnouxBTkTu5C7Jl2/5k+d2Jv6ARYpJPY+04mLaEjTVpGZwHeL9PhPXImFdh0aOe1YmNg/Vke7FLdxTxdsQ4s0hVH5AKKvRAu9At/pi6QDSTbjY75goQuxHbD/Wfy3JkIsLyMIn3EKkh5GfbFSnys3QK8X6bDe2TeDCqw6dDOa+UslUmcojcv22/nbC30+szAXPHJawEt1uD3TF0gG0i2Gx3zCRXY6HNyPs8duhAjHcW6x+flZNYIoYWs9KTDe2RsPPKNbl86tPNagSVSgaMCOzUmcUK64ohcQN4FnlUxJlMXyAaSbarnQkzhFLMQz+e5E6/A+giwSB+xD+CZ2oV4JsvMzFY6vEfGHo8bjbFMh3Zea8kso3ODdWCXsvjYdAIs0kxmfUWbYRayS9RkmbpANpBs1z4XygsC6uiNrP2aP0WAnc9zJ/aBO4cuxEgjsVlDp/siJ13F3i8XYtb+dHiPjE/idIMvtNOhndeKdSHO9i6ksZ4LLKOTKLBEHn9kHgLsAoqPgV2Eb64ycYFsYCFMfi4cvHBZ/+ovXpGUOAZ28rfsc33u+OhCjDTk9WZ2BXah3i9T/R4Z70J8ky+0U93OawVisxBn+RrzXiqwU/It4udYYDY4IheQP971ghdEIBUmL2FVmBsbg2ayJHzLHmAWYqSh2PtOJq0BK028T2ZrV8WZTOKUjuLrwGb55xgPY2CnxCzESFcckQtoMSuwAK43+UNXQY4/si1JlYR/fr1FkvT4kWbd98Xva19jOCmXC8zHk683R34fbcmo4zKQ5bOd/uBkmyTp2webMuZx2dcY1u89+YYk6Te/dSQj2jxXVGCvt68xrD997i1J0i/9w4GsfvyReTLrK9oM44vP3sYLIpAKk788is1CnIxKwr7GsD6371j8/3DXgHbvOSpJadX9D0vLvsawfufRzDwus7nSs68xrN99/I34/5nwuOxrDGv3nqPxdWk7+4bTvs3zQQU20bWPf3vPUFY//sg82fdOkUZilZ5sfEMGMsHkGT8LcpLXhe/h/afib+wxAyNjenj/qXlfNjBXkeNyPGFbphyXvixeriMTH5el9hoXe69gEqeIpfb4I/Nk3ztFGsnmN2QgE0x+7uUFfDK78TIWM9XcNTCr7cBiyOTj0p/FXYgz8XHJxDbPB+vAJlpqjz8yz7ySlZl90syOm9m4mW2dtP0DZnbQzI5Gf79vmvN/3szCZnY4+vOR+bQn3UzM3sYLIpAKk7sLB3weBbyepHyhVFsSnNV2YDFk8nHpz+IvfDPxccnENs8H68AmWmqPPzLPfN8pjknaKenFa7Z3SPq4c+52ST8n6f93g8v4Y+fc5ujPP8+zPWmFSZyA1Jo8YZPf61GOz5OUMbC7ttVft85m0O/Vrm31875sYK4y+bjM5jGwmfi4ZGKb54MKbKKl9vgj88xrEifn3AlJ1y1J4ZxrnPTvcUm5ZpbjnBuaz/VlmtgbcbYuCwCku8m9H3J8HgV83qR0IY5NYvHw/lNq7hpQbUlQu7bVM7kFUiqTj0ufN3uXa8nExyUT2zwfVGATLbXHH5lnMWYh/leSGm8QXn/VzP6dpAOSfsM5d2WqnczsIUkPSdKKFSsWpKHJFnshDGThGzKQCSZ/eRSrwCarwrO9IcSbOdJOph6XsffJQJZ+4ZuJj0smtnmufFRgr7OUHn9knpu+U5jZs2Z2bIqfB2dw3lsl/YGk/zDNLn8haa2kzZJaJP3v6S7LOfeIc26rc25rZWXlza46LbxypkOS9DuPHs+Ydd+AbBL7UOKxyHqtbd2DOtnaw/MRSCP7GsP64lMnJUm/+e3sXm8U6cnLLMRARrlpBdY59/65XLCZ1UnaK+nfOefOTHPZbZP2/2tJT8zlutLRvsaw/vKFs/H/M2HdNyDbxKqtHpN27zmq0XEniecjkC6W2nqjSE+sAwtklgXpq2NmJZKelLTbOffSDfarmfTvDkUmhcoKD+8/paHRzFr3Dcg2Xo/JTBobF2vaAWmI9SaRDmIjvQiwQGaY7zI6O8ysSdI9kp40s/3Rk35V0jpJvzNpiZyq6Hm+MmnJnS9Fl9p5XdL9kv7zfNqTTlhDC0gPfo9HbprTeD4CqcV7JdIBFVggs8x3FuK9inQTvnb770n6vWnO84uT/v7Z+Vx/OqstCSo8xRswa2gBi8vnNY2OS+NTpFiej0Bq8V6JdMAkTkBmyc7p/tIAa2gB6cHnMZXk+Xk+AmmI90qkAyZxAjILAXaBbG8I6Qs7b1eoJCiTFCoJ6gs7b2dSCmCR+b0eleXn8HwE0hDvlUgH8XVgWfYQyAiLsQ7sksUaWkDq+bwmv9fD8xFIUzw3kWpUYIHMQgUWQFbzeTwK+HipAwBMLV6BZQwskBH4VAcga+1rDKu1e1BHLnbpvi9+X/saw6luEgAgzTCJE5BZCLAAstK+xrB27zmqsej0w+GuAe3ec5QQCwBI4CXAAhmFAAsgKz28/5QGRsYStg2MjOnh/adS1CIAQDpiHVggsxBgAWSl5inWlrzRdgDA0hSbvIlJnIDMQIAFkJVqS4Kz2g4AWJqowAKZhQALICvt2lavoN+bsC3o92rXtvoUtQgAkI6YxAnILKwDCyArxdaVfHj/KTV3Dai2JKhd2+pZbxIAkCAWXH0EWCAjEGABZK3tDSECKwDghmLrwHoIsEBGoAsxAAAAlixv9NMwkzgBmWFeAdbMPmlmx81s3My2Ttq+yswGzOxw9Ocvpzl/mZk9Y2ZvRX+Xzqc9AAAAwEztawzrz39wWpL0bx55hbXCgQww3wrsMUk7Jb04xWlnnHOboz//cZrzf1bSc8659ZKei/4PAAAALKh9jWHt3nNUVwdGJUlt3UPavecoIRZIc/MKsM65E865U/O4iAclfTX691clbZ9PewAAAICZeHj/KQ2MjCVsGxgZ08P75/PRFsBCW8gxsKvNrNHMXjCzn5hmn2XOuRZJiv6umu7CzOwhMztgZgfa29sXor0AAABYIpq7Bma1HUB6uOksxGb2rKTqKU76nHPu0WnO1iJphXOu08zukrTPzG51znXPtaHOuUckPSJJW7dudXO9HAAAAKC2JKjwFGG1tiSYgtYAmKmbBljn3Ptne6HOuSFJQ9G/D5rZGUm3SDpwza5tZlbjnGsxsxpJl2Z7XQAAAMBs7dpWr917jiZ0Iw76vdq1rT6FrQJwMwvShdjMKs3MG/17jaT1ks5Osetjkn4u+vfPSZquogsAAAAkzfaGkL6w83aFSoIySaGSoL6w83bWDwfS3E0rsDdiZjskfVlSpaQnzeywc26bpPdI+h9mNippTNJ/dM5djp7nK5L+0jl3QNIXJX3LzH5B0tuSPjmf9gAAAAAztb0hRGAFMow5l3nDSbdu3eoOHLi2NzIAAAAAIBuY2UHn3NbrtmdigDWzdkkXUt2Om6iQ1JHqRiBrcXxhIXF8YSFxfGGhcYxhIXF8LZ6VzrnKazdmZIDNBGZ2YKpvDIBk4PjCQuL4wkLi+MJC4xjDQuL4Sr2FXAcWAAAAAICkIcACAAAAADICAXbhPJLqBiCrcXxhIXF8YSFxfGGhcYxhIXF8pRhjYAEAAAAAGYEKLAAAAAAgIxBgAQAAAAAZgQCbZGb2ITM7ZWanzeyzqW4PMo+Z/a2ZXTKzY5O2lZnZM2b2VvR36aTTdkePt1Nmti01rUamMLPlZvYDMzthZsfN7NPR7RxjSAozyzWz18zsSPQY+93odo4xJI2Zec2s0cyeiP7P8YWkMLPzZnbUzA6b2YHoNo6vNEKATSIz80r6c0kflrRJ0k+Z2abUtgoZ6O8lfeiabZ+V9Jxzbr2k56L/K3p8fUrSrdHz/N/ocQhMZ1TSbzjnNkp6l6RfiR5HHGNIliFJ73PO3Slps6QPmdm7xDGG5Pq0pBOT/uf4QjLd75zbPGm9V46vNEKATa67JZ12zp11zg1L+oakB1PcJmQY59yLki5fs/lBSV+N/v1VSdsnbf+Gc27IOXdO0mlFjkNgSs65FufcoejfPYp8AAyJYwxJ4iJ6o//6oz9OHGNIEjOrk/RRSV+ZtJnjCwuJ4yuNEGCTKyTp4qT/m6LbgPla5pxrkSIBRFJVdDvHHObMzFZJapD0qjjGkETR7p2HJV2S9IxzjmMMyfQnkj4jaXzSNo4vJIuT9D0zO2hmD0W3cXylEV+qG5BlbIptrFOEhcQxhzkxswJJ35X06865brOpDqXIrlNs4xjDDTnnxiRtNrMSSXvN7LYb7M4xhhkzs49JuuScO2hm753JWabYxvGFG7nPOddsZlWSnjGzkzfYl+MrBajAJleTpOWT/q+T1JyitiC7tJlZjSRFf1+KbueYw6yZmV+R8Po159ye6GaOMSSdc65L0vOKjA3jGEMy3CfpE2Z2XpGhWu8zs38UxxeSxDnXHP19SdJeRboEc3ylEQJscv1Y0nozW21mAUUGdT+W4jYhOzwm6eeif/+cpEcnbf+UmeWY2WpJ6yW9loL2IUNYpNT6N5JOOOf+aNJJHGNICjOrjFZeZWZBSe+XdFIcY0gC59xu51ydc26VIp+zvu+c+xlxfCEJzCzfzApjf0v6oKRj4vhKK3QhTiLn3KiZ/aqk/ZK8kv7WOXc8xc1ChjGzf5L0XkkVZtYk6b9L+qKkb5nZL0h6W9InJck5d9zMviXpDUVml/2VaNc9YDr3SfpZSUejYxQl6bfEMbZkmdnnJa2LhoBkqJH01ehMnB5J33LOPWFmr4hjDAuH1zAkwzJFhj1IkZz0defc02b2Y3F8pQ1zjm7aAAAsVQsQYAEAWDB0IQYAYJ7M7LNmdsbMeszsDTPbEe1S1jV5AqNo99qB6OQgMrPPmFmLmTWb2S+amTOzdTe5rqCZ/W8zu2BmV83sX6JddWVmnzCz49Hrfd7MNk463381s3C0jafM7AEz+5AiFfh/Y2a9ZnZkYe4hAACSgwALAMD8nZH0E5KKJf2upH+UVCZpj6SfmrTfv5b0gnPuUjQ8/hdFxoiuk/STM7yuP5R0l6R7o9fxGUnjZnaLpH+S9OuSKiX9s6THzSxgZvWSflXSO5xzhZK2STrvnHta0v+S9E3nXIFz7s453n4AABYFARYAgHlyzn3bOdfsnBt3zn1T0luKzFz5dSUG2H8b3SZFwuzfOeeOO+f6FQm+N2RmHkn/r6RPO+fCzrkx59zLzrkhSf9G0pPOuWeccyOKBN2gIkF3TFKOpE1m5nfOnXfOnUnKjQcAYBERYAEAmCcz+3dmdjjadbdL0m2SKiR9X1LQzN5pZislbVZkWQZJqpV0cdLFTP57OhWSchWp+F6rVtKF2D/OufHoZYacc6cVqcx+XtIlM/uGmdXO+AYCAJAmCLAAAMxDNJj+tSJddMudcyWKLLtg0RD5LUWqsP9W0hPOuZ7oWVsUWTMwZvJagtPpkDQoae0UpzVLWjmpXRa9zLAkOee+7px7d3QfJ+kPorsymyMAIGMQYAEAmJ98RUJguySZ2c8rUoGN+boi3Xt/WhPdh6VIsP15M9toZnmS/tvNrigaiP9W0h+ZWa2Zec3sHjPLiV7eR6OTM/kl/YakIUkvm1m9mb0vut+gpAFFuhVLUpukVdHuyQAApDXerAAAmAfn3BuS/rekVxQJg7dLemnS6a9K6lOki+9Tk7Y/Jen/SPqBpNPR80uR0HkjvynpqKQfS7qsSCXV45w7JelnJH1ZkUrtxyV93Dk3rMj41y9Gt7dKqlJk9mFJ+nb0d6eZHZrdrQcAYHGxDiwAAGkguuTNMUk5zrnRVLcHAIB0RAUWAIAUia4XGzCzUkUqqY8TXgEAmB4BFgCA1PkPioydPaPImNRfliQzO25mvVP8/HQqGwsAQKrRhRgAAAAAkBGowAIAAAAAMoIv1Q2Yi4qKCrdq1apUNwMAAAAAsAAOHjzY4ZyrvHZ7RgbYVatW6cCBA6luBgAAAABgAZjZham204UYAAAAAJARCLAAAAAAgIyQkV2I09m+xrAe3n9KzV0Dqi0Jate2em1vCKW6WQAAAACQ8QiwSbSvMazde17XwMi4JCncNaDde45KEiEWAAAAAOaJLsRJ9PD+U/HwGjMwMqb/+cQbCncNiDV3AQAAAGDuqMAmUXPXwJTbO/uGdd8Xv6/CXJ/qlxWqvrpQG6oLVV9dpPrqQhUH/YvcUgAAAADIPATYJKotCSo8RYitKAjo0++/Radau/Vma68eP9Ksr706Gj+9pjhX9dWTgu2yIq2tyleOz7uYzQcAAACAtEaATaJd2+q1e89RDYyMxbcF/V799kc3JYyBdc6ptXtQJ1t7dCr6c7K1Ry+f7tTwWKQLstdjWlORn1Ct3VBdqFBJUB6PLfptAwAAAIBUI8AmUSyk3mwWYjNTTXFQNcVB3V9fFd8+Mjau8x198WB7srVHR5q69MTrLfF98gNe3RKv1E4E29L8wOLcSAAAAABIEcvEiYW2bt3qDhw4kOpmLJreoVG92Ta5WtutU609utI/Et+nqjAnXq29ZVmhNlQXaf2yAuX66YYMAAAAILOY2UHn3NZrt6dFBdbMflPSw5IqnXMdqW5PuinI8WnLilJtWVEa3+acU3vPUEK19lRbt/7hlQsaGo10Q/aYtKo8P3F8bXWRVpTlyUs3ZAAAAAAZJuUB1syWS/qApLdT3ZZMYmaqKspVVVGu3nNLZXz72LjT+c6+iVDb2q0TLd16+nirYsX2XL9HtywrnDQjcmQ25MrCnBTdGgAAAAC4uZQHWEl/LOkzkh5NdUOygddjWltZoLWVBfrI7TXx7f3Do3qrrTehWvuDU5f07YNN8X3K8wPXVWtvWVagvEA6HCYAAAAAlrqUJhMz+4SksHPuiBldWhdSXsCnO5eX6M7lJQnbO3qHEqq1p1p79I3XLsZnUjaTVpTlqX5Z4tq1q8rz5PN6UnBLAAAAACxVCx5gzexZSdVTnPQ5Sb8l6YMzvJyHJD0kSStWrEha+5a6ioIcVazL0X3rKuLbxsed3r7cP7HMT1u3Trb26NkTbRqPdkMO+DxaX1Vw3TI/VYU54ssIAAAAAAshZbMQm9ntkp6T1B/dVCepWdLdzrnWG513qc1CnC4GR8Z0+lKvTrb26M22iaptW/dQfJ+SPP911dr66kIV5NANGQAAAMDMpN0sxM65o5Lii6Ca2XlJW5mFOH3l+r26LVSs20LFCduv9A3rVFtPQlfk7x4Kq3foQnyfutJgNNRGg+2yQq2pzJefbsgAAAAAZoiyGOatND+gd60p17vWlMe3OefUdGUg2gV5Itg+f6pdo9F+yH5vZMKpayeOqi3OpRsyAAAAgOukTYB1zq1KdRuQPGam5WV5Wl6Wp/dvWhbfPjQ6prPticv8/PjcZT16uDm+T2Gub9ISPxNdkYuD/vg++xrDenj/KTV3Dai2JKhd2+q1vSG0qLcRAAAAwOJKmwCLpSHH59XGmiJtrClK2H51YCRhXO2p1h49dqRZX3t1NL5PTXGu6qsL5THph291aGQsUskNdw1o956jkkSIBQAAALIYARZpoTjo1ztWlekdq8ri25xzark6mFCtPRn9+1oDI2P63N6jutI/rNUV+VpTUaBQaVBeD12RAQAAgGyRslmI54NZiJe21Z99UjM5agNej1aU50UDbX7kd2WBVlfkq6IgwDhbAAAAIE2l3SzEwFzVlgQV7hq4bnuoJFeP/uq7da6jT+fa+3S2o09n23t1rqNPL5xq1/DYeHzfwhyfVldGQm3sZ01FgVZV5Kkw13/dZQMAAABIPQIsMs6ubfXaveeoBkbG4tuCfq92bdugioIcVRTkJHRFlqSxcafmrgGd7ejTuWioPdvRp4MXruixI82a3BGhsjAnoWobqdzma3lZnnJ83sW6mQAAAACuQYBFxolN1DSbWYi9nolZkX/ylsqE0wZHxvT25X6dbe+LVG87IgH32RNt6ugdju/nMamuNC8h1K6pKNDqynzVFOXKw3hbAAAAYEExBha4gasDIzrfEQm2Z9t7IxXc6E//8EQFOMfnSeiOHAu4qysKVJrnZ7wtAAAAMAuMgQXmoDjo153LS3Tn8pKE7c45XeoZuq5qe6qtR8+80abRcZdwGQldkieNvc0L8BQEAAAAZopPz8AcmJmWFeVqWVGu7llbnnDa6Ni4mq4MxMfZxsLtj852ak9jOGHf6qLceKidPOZ2eVme/F7PYt4kAAAAIO0RYIEk83k9WlWRr1UV+br/mtMGhsfiXZDPdUx0SX7y9RZdHRiZuAyPaUXZxHjbWNV2TUWBlhXl0CUZAAAASxIBFlhEwYBXm2qLtKm26LrTrvQNTxpjG50pub1PL53p0ODIxBJAeQGvVpVfX7VdU1Gg4jyWAAIAAED2IsACaaI0P6C78gO6a2VpwvbxcafW7sGJLsntkYB7PHxVTx9r1dik8bZl+YFr1rbN15rKAq0sz1OunyWAAAAAkNkIsECa83hMtSVB1ZYEdd+6ioTThkfH9fbl/uuqti++2a7vHGyK72cm1RYHozMjJ1ZtQ6VBeadZAmhfY3hWyxUBAAAAC4kAC2SwgM+jdVUFWldVIGlZwmm9Q6M6f03V9lxHn/YeCqtnaHTiMrwerSjPS5wpuSJfp9q69YV/PqmBaPflcNeAdu85KkmEWAAAAKQEARbIUgU5Pt0WKtZtoeKE7c45dfYNR6q27ZGAe7Y9Em5fONWu4bHxaS5RGhgZ039/7JicnMryc1SWF1BZQUDl+QG6KAMAAGDBmXPu5nulma1bt7oDBw6kuhlA1hkbd2ruGtDZjj793N++Nqvz5gW8KssPJPyU5wdUGv1dlp+TcFpRro/ZlAEAADAlMzvonNt67XYqsADivB7T8rI8LS/LU6gkqHDXwHX71BTn6mu/+E5d7htWZ9+wLl/z09k3rM7eYb3V1qvOvqGEGZQn83tNpXkzD7yleX75WBsXAABgSSPAApjSrm312r3nqAZGxuLbgn6v/uuHNmhNZYHWVM7scvqHRxPC7eXeYV3pn/i7sy/y//HmbnX2Dql7cHTKyzGTioP+SKCNBt/ygli4jf2dkxCC6dYMAACQXQiwAKYUm6hpvrMQ5wV8ygv4VFeaN6P9R8bGdaU/WtGdFHA7eydXeYd0obNfh97u0pX+4YSlhBKvm27NAAAA2YQxsAAy2vi4U/fgSGKVd4puzVcmhd/ZdmuOBF1/PPCWF0Sqvsnq1sxyRQAAAIkYAwsgK3k8ppK8gEryAhnZrXlfYzihqzbLFQEAAEyPAAtgyZlTt+a+YV3unwi411d559at+dWzlxPGGUuR5Yoe3n+SAAsAAHANAiwA3ITf61FVUa6qinJntP9MuzV39g5fF15jwl2DevDP/kWrKvK1qjxfqyvytaoiX6vL81Wc50/mzQMAAMgYBFgASLLZdGu+74vfn3K5ovyAV4W5fh04f0WPHWnW5OkKSvP88TC7sjxfqyry4gG3KJdwCwAAshcBFgBSaLrlin5/x+3xLsSDI2O6eLlf5zr6dL6zT+c6+nW+o0+vnO3UnsZwwuWV5wcmVW3z4n+vqshXQQ4v+QAAILPxaQYAUmgmyxXl+r1av6xQ65cVXnf+wZExXeicCLfnO/p0rqNP/3K6Xd89NJSwb0VBTiTURgPt6ni4zVNegLcDAACQ/lhGBwCyVP/wqM539EeCbTTcnu/o17nOPrX3JIbbZUU58bG2K6+p3l47czIAAMBCYxkdAFhi8gI+baot0qbaoutO6x0ajQTaeNW2Xxc6+/TsiTZ19A4n7FtTnDupapsXD7rLy/IItwAAYFERYAFgCSrI8em2ULFuCxVfd1r34IguRCu1kaptn8519unpYy260j8S389Mqi0OatWkUBsLuivK8hTweRbzJgEAgCWAAAsASFCU69ftdcW6ve76cHu1fyTeJflcPNz264nXW3R1YCLcekwKlQavCbaRoLu8LE9+L+EWAADMHgEWADBjxXl+3ZlXojuXl1x32pW+4YSq7fnOyPjbvY1h9QyOxvfzekx1CeE2Lz6pVKgkKB/hFgAATIMACwBIitL8gErzA9qyojRhu3NOl/uGE5YAigXdA+cvq294Ygkhn8e0oixPKyeF2ljQrS0Jyuuxxb5ZAAAgjaQ8wJrZf5L0q5JGJT3pnPtMipsEAEgiM1N5QY7KC3J018qyhNOcc+roHU7okhwLuq+eu6z+SeE24PVoeVkwYaxtZNbkPNUWB+W5Sbjd1xi+4XJFAAAg/aU0wJrZ/ZIelHSHc27IzKpS2R4AwOIyM1UW5qiyMEfvWHV9uL3UMzQp2PbHA+6/nO7Q4Mh4fN+Az6OVZXnXrW+7uiJfywpz9diRZu3ec1QDI5FAHO4a0O49RyWJEAsAQAZJdQX2lyV90Tk3JEnOuUspbg8AIE2YmZYV5WpZUa7etaY84bTxcae2nsFouO2PV3AvdPbphTfbNTw6EW5z/R6NjjmNjieuez4wMqYvPX2SAAsAQAYx59zN91qoKzc7LOlRSR+SNCjpN51zP55m34ckPSRJK1asuOvChQuL1UwAQAYZH3dq6R6Mrm8bqd5+5V/OTbt/RUGOQqVB1ZUEI79LgwpF/w6VBFWY61/E1gMAAEkys4POua3Xbl/wCqyZPSupeoqTPhe9/lJJ75L0DknfMrM1bopU7Zx7RNIjkrR169bUpW4AQFrzeCwSQEuCum9dhSTpqWOtCncNXLdvYa5P799YpXDXgE60dOuZE20J1VtJKsr1KVSap1BJJNxeG3DL8gMyY3IpAAAWw4IHWOfc+6c7zcx+WdKeaGB9zczGJVVIal/odgEAlo5d2+oTxsBKUtDv1f988LaELsTj404dfUMKXxlQuGsg4ffFy/360dlO9Q6NJlx20O9VbUmu6krz4qF2csitKsxl9mQAAJIk1WNg90l6n6TnzewWSQFJHSltEQAg68RC6s1mIfZ4TFWFuaoqzFXDNcsBSZGJpboHRtXU1Z8Qbpuifx8NX9XlvuGE8/i9puriXNWVTATcyV2Wa4qDCvhY+xYAgJlI9RjYgKS/lbRZ0rAiY2C/f7Pzbd261R04cGCBWwcAwOz1D4+quWsi1E4OuOErA2rrGdTkt14zaVlhbkK4vTbk5gVS/X0zAACLK2VjYG/EOTcs6WdS2QYAAJIpL+DTuqpCrasqnPL04dFxtV4djFdxJ4fbwxe79NSxFo2MJX65XJYfiI/rTQi4pUHVleSpKOhjHC4AYEngK10AABZRwOfRivI8rSjPm/L0sXGn9p4hhbv61XRNwD3d3qsX3mxPGMsrSQU5vuvCbXwsbmlQlQU5BFwAQFYgwAIAkEa8nsiY2eriXN218vrTnXO60j8Srd72K3xNd+WDF67o6sBIwnkCPs9EBXeK5YKqi3Ll8zIOFwCQ/giwAABkEDNTWX5AZfkB3V5XPOU+PYMj182i3BT9/f1Tl9TeM5Swv9djqi7KnbJ6GyoJqrYkqFy/d0bt29cYvulkWQAAzBUBFgCALFOY69eGar82VBdNefrgyJiau65fKqipa0Cvnbus1u5BjY0njsOtKMiJTyw1OdzGfhfm+rWvMZywXFG4a0C79xyVJEIsACApCLAAACwxuX6v1lQWaE1lwZSnj46Nq7V78Pr1cLsG9EZLt5450abh0fGE8xTl+tQ/PKbRa4LvwMiYvrT/JAEWAJAUBFgAAJDA5/WorjRPdaVTTzQ1Pu7U0Td0XcD9h1cuTLl/c9eg7v/D57W6Ij/+s6YiX6sr81VdlMsEUwCAGSPAAgCAWfF4TFWFuaoqzFXDitL49udOXFK4a+C6/QtzfNpUU6SzHX16+UyHBkcmqrdBvzcSaiujoTYecAtUnOdflNsDAMgcBFgAAJAUu7bVJ4yBlSIB9X9uvy3ehXh83KmtZ1Dn2vt0tqNP56I/x8NX9fSx1oSxt2X5gSmrtqvK82c8qRQAILsQYAEAQFLEQuqNZiH2eEw1xUHVFAd177qKhPMPj47r4pV+nWuPhNpIwO3VD99q13cONiXsGyoJJoTbWAU3VBJkSSAAyGLmnLv5Xmlm69at7sCBA6luBgAAWCS9Q6M6P6liGwu4Z9t71TM4Gt/P7zWtLJ9UsZ0UcCsLchhvCwAZwswOOue2XrudCiwAAEh7BTk+3RYq1m2hxLVvnXO63Dc8KdBGqrbnOvr0wpvtCbMlF+T4ErskV0Z+r6rIV1Eu420BIBMQYAEAQMYyM5UX5Ki8IEdbV5UlnDY27tTcNXBd1bbx4hU9/nqzJndCqyjImajYVk5UcFeU5ynHx3hbAEgXBFgAAJCVvB7T8rI8LS/L03tuqUw4bXBkTBcv909MJNXep7MdvXruZJs6DgzH9/OYVFead13VdnVFvmqLg/J46JIMAIuJAAsAAJacXL9X65cVav2ywutOuzowEh9vOzFTcq8OnL+svuGJGZZzfB6tKr++aru6Il9l+QHG2wLAAiDAAgAATFIc9OvO5SW6c3lJwnbnnNp7hhKW/znb3qe3LvXouZNtGhmb6JNclOvT6sqCxImkoj/5OXz8AoC54hUUAABgBsxMVUW5qirK1bvWlCecNjo2rqYrAwnL/5zr6NOrZzu1tzGcsG91UW7C0j+xYLu8LE9+lgACgBsiwAIAAMyTz+vRquiMxvdfc9rA8JjOdyZWbc919Oqpoy260j8S38/rMa0oy0ucKTkadKuLcq/rkryvMXzDNXcBIBsRYAEAABZQMODVxpoibawpuu60K33DOtc5MYlULOC+fKZDgyMTSwAF/V6tmjTG9nLfkL57KKyh6DJB4a4B7d5zVJIIsQCyGgEWAAAgRUrzAyrND2jLitKE7ePjTq3dgxNdkqNV2+PNV/X08VaNjbvrLmtgZEz//bFjKsnza21lgUIlzJIMIPuYc9e/AKa7rVu3ugMHDqS6GQAAAItueHRc9b/9lG72CS7X79GaigKtrSrQusoCra3K19rKAq2uyFeun7VtAaQ3MzvonNt67XYqsAAAABkk4POotiSocNfAdadVF+fq/3yqQacv9epMe+Sn8e0reuL1ZsVqFmbS8tI8ra3M17qqAq2tnAi5pfmBRb41ADA7BFgAAIAMs2tbvXbvOaqBkYl1aYN+rz77oQ26e3WZ7l5dlrD/wPCYznX06XR7r85c6o3/fulMp4ZHJ8baluUHEqq1sWBLd2QA6YIACwAAkGFiEzXNdBbiYMCrTbVF2lSbOJHU2LhT+MpAvFobq9zuP96my30X4/vl+DxaU1mgtZWRYBur3K6ppDsygMXFGFgAAABc53LfcCTYXpoItqfbe9V0ZSChO3JdaTASaqMV21jALaM7MoB5YAwsAAAAZqwsP6Cy/DK9Y1Vid+TBkWh35Pg428jfr5zpjC/rI0ml0dmQ100KtWsrCxQqDcpLd2QAc0SABQAAwIzl+qde13Z83CncNRAfXxup3vbpmTfa9I1ruiOvrshPqNaurczXmooCBQN0RwZwYwRYAAAAzJvHY1pelqflZXm6v74q4bQrse7I8XG2fToWvqqnjrZofFJ35FBJMKFaG5spuSw/IDOqtgAIsAAAAFhgpfkBbc0v09YpuiOf7+zTmUsTXZJPX+rVq+c6NTgy0R25JNYdedIMyeuqClRXmkd3ZGCJIcACAAAgJXL9Xm2oLtKG6uu7IzdfHYhXa2PB9rmTbfrmgeH4fgGfR2sq8uPV2rWTZkfOC/AxF8hGPLMBAACQVjweU11pnupK8/Te+sTTuvqH4+NrY+Ntjzdf1VPHJrojS9HuyFUT3ZBjVdtyuiMDGY0ACwAAgIxRkhfQXSvLdNfK67sjX+jsT1jP9vSlXv343GUNjIzF9ysO+hNC7UR35KB8Xs9117evMTzj9XYBLDwCLAAAADJert+r+upC1VcXJmwfH3dq6R6MhNpJwfb7J9v1rQNN8f0CXo9WVeQlBNuLV/r15z84HR+PG+4a0O49RyWJEAukiDnnbr7XQl252WZJfykpV9KopP/POffazc63detWd+DAgQVuHQAAALLZ1f6RSDfk2E90zO2Fzr6E7sjXqinO1Su7H1i8hgJLkJkddM5tvW57igPs9yT9sXPuKTP7iKTPOOfee7PzEWABAACwUIZGI92RP/jHL067z5rKfG2uK9GdyyM/G2sKleNjHVsgWaYLsKnuQuwkxaadK5bUnMK2AAAAAMrxeXXLskKFSoIKdw1cd3pRrk9rKgr04lsd2tMYlhTpgryxtkib64rjoXZ1eb48LPMDJFWqK7AbJe2XZJI8ku51zl2YZt+HJD0kSStWrLjrwoUpdwMAAACSYl9jWLv3HE2YBCro9+oLO2/X9oaQnHNquTqoIxe7dLipS4ff7tLR8FX1D0f2L8r1RcJsvFJbrKrC3FTdHCCjpKwLsZk9K6l6ipM+J+kBSS84575rZv9a0kPOufff7DLpQgwAAIDFMNtZiMfGnU5f6k0ItafaejQWHVRbW5yrzSsmQu3toWLl56S6UySQftJ1DOxVSSXOOWeRBbmuOueKbnY+AiwAAAAyxcDwmI43X9Xhi106fLFLR5q6dPFypGuyx6T1VYXavHyiSlu/rHDKJX2ApSRdx8A2S/pJSc9Lep+kt1LaGgAAACDJggGvtq4q09ZVE2vXdvYO6fWmq/FA+703WvXNAxclSbl+j26rLY6H2s3LS1RXGlSk3gMsbamuwL5b0p8qEqQHFVlG5+DNzkcFFgAAANnEOae3L/dHAu3FqzrS1KVj4asaGo2sQVuWH9CddcXavLxUdy4v1p11JSrND6S41cDCScsuxHNFgAUAAEC2Gxkb16nWnmiojVRq37rUq9jH95XlebqzriReqb21tki5fpbyQXYgwAIAAAAZrmdwREfDVyNV2miobbk6KEnyeUwbagrjoXbz8hKtrSxgKR9kJAIsAAAAkIXaugcTqrSvX7yqnqFRSVJBjk+3h4rjMx9vXl6i6mKW8kH6I8ACAAAAS8D4uNPZjl4dnlSlPdHSrZGxyOf+ZUU5kTC7okSb60p0e12xCnP9KW41kChdZyEGAAAAkEQej2ldVaHWVRXq/7mrTpI0ODKmN1q6I4H2YpeONF3V995okySZSWsrC6IV2shEUfXVhQr4WMoH6YcACwAAAGS5XL9XW1aUasuK0vi2rv5hHWm6Gg+1z5+6pO8eapIkBXwe3VpblDCedmV5Hkv5IOXoQgwAAABAzjk1XRnQkaZolfbiVR0NX9XAyJgkqTjoj6xLW1esO6MzH1cU5KS41chWdCEGAAAAMC0z0/KyPC0vy9PH7qiVJI2OjevNtt54qD18sUt/9oN2jUdrYHWlwWiojQTa20PFCgZYygcLhwosAAAAgBnrHx7VsXC3Dl+8oiMXr+rwxS6FuwYkSV6P6ZZlhdq8vDg+UdT6qkJ5WcoHs8QsxAAAAAAWRHvPkF5vilRoY0v6dA9GlvLJC3h1W6hYm5eXxENtbXFuwnjafY1hPbz/lJq7BlRbEtSubfXa3hBK1c1BGiDAAgAAAFgUzjmd7+xPqNK+0dyt4bFxSVJFQU68Sts3PKq/f/m8BkfG4+cP+r36ws7bCbFLGAEWAAAAQMoMj47rZGt3QpX2THvftPuHSoJ66bPvW8QWIp0QYAEAAACkle7BEd3x+e9Ne/rP3bNS96yt0D1rylWc51/EliHVmIUYAAAAQFopyvUrVBKMTwI1WY7Po28daNJXX7kgj0m3hYp179oK3beuXFtXljHb8RJFgAUAAACQMru21Wv3nqPx9WaliTGwH7m9Rocvduml0x16+UyHvvLDs/rLF84o4PVoy8oS3be2Qveuq9AddcXyez0pvBVYLHQhBgAAAJBSM52FuG9oVK+dv6yXT3fopdOdeqOlW5JUkOPT3avLdO/act23rkL1ywrlYemejMYYWAAAAABZ5XLfsF4506mXz3To5TOdOtcRmRSqPD+ge6Jh9r61FVpRnpfilmK2CLAAAAAAslq4a0Avn46E2ZdOd+hSz5Akqa40GK/O3rO2XFWFuSluKW6GAAsAAABgyXDO6Ux7r146HanQvnKmU92Do5KkW5YVRCeEqtA715SpKJcZjtMNARYAAADAkjU27nS8+Wo80P74/GUNjozLY9IddSW6b1257l1bobtWlirXzwzHqUaABQAAAICoodExHbrQFR8/e/hil8bGnQI+j7auLNV96yp079py3R4qlo8ZjhcdARYAAAAAptE7NKrXznXqpdOR8bMnW3skSYU5Pr1zTbnuWxcZQ7u+qkBmzHC80KYLsKwDCwAAAGDJK8jx6X0blul9G5ZJkjp6h+IzHL90ulPPnmiTJFUU5EQnhIp0OV5exgzHi4kKLAAAAADcxMXL/XrlTKdeigbajt7IDMcryvLiYfbeteUqL8hJcUuzA12IAQAAACAJnHN661KvXjodCbOvnu1Uz1BkhuMN1YXRGY7L9c415SrIodPrXBBgAQAAAGABjI6N62j4ql4+E5vh+IqGR8fl9ZjurCuOTghVoS0rS5TjY4bjmSDAAgAAAMAiGBwZ06ELV+LdjV9v6tK4k3L9Hr1jVVm8QntrbbG8HiaEmgoBFgAAAABSoHtwRK+evRxZsud0p061RWY4Lsr16V1rIrMb37euXGsrmeE4hlmIAQAAACAFinL9+sCmZfrApsgMx+09Q/Ew+9KZDn3vjcgMx8uKcuKTQd23rkK1JcFUNjstUYEFAAAAgBR6u7M/2t24Q6+c6VRn37AkaXVFvu5dG5nh+J615SrLD6S4pYuHLsQAAAAAkObGx53evNSjl0536uXTHXr13GX1Do3KTNpYXRRZsmddhe5eVab8LJ7hmAALAAAAABlmZGxcrzdd1cunO/TSmQ4dutCl4bFx+TymhhUl8S7HDStKFfB5Ut3cpElpgDWzT0r6vKSNku52zh2YdNpuSb8gaUzSrznn9t/s8giwAAAAAJaigeExHYzOcPzy6Q4dDV/VuJOCfq/esbpM90XHz26qKZIng2c4TvUkTsck7ZT0V9c0apOkT0m6VVKtpGfN7Bbn3NgitQsAAAAAMkYw4NW711fo3esrJElXB0b0o7Od0Qptp77w1ElJUkmeX/esiXQ3vm9tuVZX5OvRw816eP8pNXcNqLYkqF3b6rW9IZTKmzNrixJgnXMnJE01JfSDkr7hnBuSdM7MTku6W9Iri9EuAAAAAMhkxUG/tt1arW23VkuSLnUP6uUznXrpdIdePtOpp461RvfzqXdwTGPRHrjhrgHt3nNUkjIqxKZ61G9I0o8m/d8U3QYAAAAAmKWqolxtbwhpe0NIzjldiM5w/HtPvBEPrzEDI2N6eP+pjAqwSRvla2bPmtmxKX4evNHZptg25aBcM3vIzA6Y2YH29vbkNBoAAAAAspSZaVVFvn76nSs1ODI+5T7NXQOL3Kr5SVoF1jn3/jmcrUnS8kn/10lqnubyH5H0iBSZxGkO1wUAAAAAS1JtSVDhKcJqbUkwBa2Zu1TPs/yYpE+ZWY6ZrZa0XtJrKW4TAAAAAGSVXdvqFfR7E7YF/V7t2lafohbNzaKMgTWzHZK+LKlS0pNmdtg5t805d9zMviXpDUmjkn6FGYgBAAAAILli41wzfRbiRVkHNtlYBxYAAAAAstd068CmugsxAAAAAAAzkpEVWDNrl3Qh1e24iQpJHaluBJAkHM/IJhzPyDYc08gmHM+IWemcq7x2Y0YG2ExgZgemKnkDmYjjGdmE4xnZhmMa2YTjGTdDF2IAAAAAQEYgwAIAAAAAMgIBduE8kuoGAEnE8YxswvGMbMMxjWzC8YwbYgwsAAAAACAjUIEFAAAAAGQEAiwAAAAAICMQYJPMzD5kZqfM7LSZfTbV7QHmysyWm9kPzOyEmR03s0+nuk1AMpiZ18wazeyJVLcFmA8zKzGz75jZyehr9T2pbhMwV2b2n6OfN46Z2T+ZWW6q24T0RIBNIjPzSvpzSR+WtEnST5nZptS2CpizUUm/4ZzbKOldkn6F4xlZ4tOSTqS6EUAS/Kmkp51zGyTdKY5rZCgzC0n6NUlbnXO3SfJK+lRqW4V0RYBNrrslnXbOnXXODUv6hqQHU9wmYE6ccy3OuUPRv3sU+WAUSm2rgPkxszpJH5X0lVS3BZgPMyuS9B5JfyNJzrlh51xXShsFzI9PUtDMfJLyJDWnuD1IUwTY5ApJujjp/ybxgR9ZwMxWSWqQ9GqKmwLM159I+oyk8RS3A5ivNZLaJf1dtEv8V8wsP9WNAubCOReW9IeS3pbUIumqc+57qW0V0hUBNrlsim2sU4SMZmYFkr4r6dedc92pbg8wV2b2MUmXnHMHU90WIAl8krZI+gvnXIOkPknMvYGMZGalivRaXC2pVlK+mf1MaluFdEWATa4mScsn/V8nuj8gg5mZX5Hw+jXn3J5UtweYp/skfcLMzisyxON9ZvaPqW0SMGdNkpqcc7GeMd9RJNACmej9ks4559qdcyOS9ki6N8VtQpoiwCbXjyWtN7PVZhZQZPD5YyluEzAnZmaKjK064Zz7o1S3B5gv59xu51ydc26VIq/P33fO8Q0/MpJzrlXSRTOrj256QNIbKWwSMB9vS3qXmeVFP388ICYlwzQIsEnknBuV9KuS9ivypPuWc+54alsFzNl9kn5WkSrV4ejPR1LdKCBdmNl5M3t/qtuBJe0/Sfqamb0uabOk/5Xa5gBzE+1J8B1JhyQdVSSjPJLSRiFtmXMM0QQAYLaiXZF/0Tn3bJIu7/OS1qWiKmxmf69Id9TfXuzrBgBgNqjAAgAAAAAyAgEWAIC5e4eZvWFmV8zs78wsV4rMeBztdt9lZi+b2R2xM5jZfzWzsJn1mNkpM3vAzD4k6bck/Rsz6zWzIze6UjMri15fc/S690067ZfM7LSZXTazx8ysNrrdzOyPzeySmV01s9fN7DYze0jST0v6TPS6H1+A+wkAgKSgCzEAAHMQ7ULcK+nDiixh8rikHygye+bTkj4u6YCkn5H0u5LqJa2S9KykdzrnmqNrLHudc2dm04XYzJ6MXvd/jP6+1zn3gpm9T9K3JH1Q0nFF1lW80zn3HjPbpsgYyQckXZW0QVKXc66FLsQAgEzhS3UDAADIYH/mnLsoSWb2+5K+LKlc0l9NWt7kq2b2W5LeJSksKUfSJjNrd86dn+0VmlmNIqG53Dl3Jbr5hejvn5b0t865Q9F9d0u6Eg3KI5IKFQmurznnmOETAJBx6EIMAMDcXZz09wVJtZJWSvqNaPfhLjPrUmSN8Frn3GlJvy7p85Iumdk3Yl18Z2G5pMuTwutktdF2SJKcc72SOiWFnHPfl/Rnkv5cUpuZPWJmRbO8bgAAUooACwDA3C2f9PcKSc2KhNrfd86VTPrJc879kyQ5577unHu3IkHXSfqD6PlnOqbnoqQyMyuZ4rTm6OVKkswsX5GKcDh63f/HOXeXpFsl3SJp1yyvGwCAlCLAAgAwd79iZnVmVqbIJEzflPTXkv6jmb0zOnFSvpl91MwKzazezN5nZjmSBiUNSBqLXlabpFVmdsP3Zudci6SnJP1fMys1M7+ZvSd68tcl/byZbY5ex/+S9Kpz7ryZvSPaJr8iY3YHr7nuNcm6UwAAWCgEWAAA5u7rkr4n6Wz05/eccwck/ZIi3XWvSDot6d9H98+R9EVJHZJaJVUpEnwl6dvR351mdugm1/uzioxpPSnpkiLdkuWce07S70j6rqQWSWslfSp6niJFwvUVRboZdyoyyZMk/Y0i43K7Js9oDABAumEWYgAAAABARqACCwAAAADICCyjAwBAGjKz3mlO+rBz7oeL2hgAANIEXYgBAAAAABkhIyuwFRUVbtWqValuBgAAAABgARw8eLDDOVd57faMDLCrVq3SgQMHUt0MAAAAAMACMLMLU21nEicAAAAAQEbIyApsOtvXGNbD+0+puWtAtSVB7dpWr+0NoVQ3K+twPwMAAABLDwE2ifY1hrV7z1ENjIxJksJdA9q956gkEa6SiPsZAAAAWJoIsEn08P5T8VAVMzAypv/xxBvKz+GuTpb/8cQbU97PD+8/RYAFAAAAshipKomauwam3H65b1i/9A9MOrXQprv/AQAAAGQHAmwS1ZYEFZ4iRFUW5ujv/v07UtCi7PTzf/9jtfcMTXnarm8f0Y4tIb1rdbk8HlvklgEAAABYSATYJNq1rT5hbKYkBf1efe4jG3VbqDiFLcsun/vIxuvu54DPo811xXrqWKu+fbBJtcW5erAhpB0NId2yrDCFrQUAAACQLATYJIqNv2R23IV1o/t5YHhMz5xo077GsB558az+4vkzurW2SDsaQvrE5lpVFeamuPUAAAAA5sqcc6luw6xt3brVHTjAmFLcWEfvkB4/0qy9jWG93nRVHpN+Yn2ldm4J6QOblikvwPc3AAAAQDoys4POua3XbSfAYik4falXexubtK+xWeGuAeUHvNp2W7V2NtTpnrXl8jJeFgAAAEgbBFhA0vi404/PX9bexrCePNqinsFRLSvK0fbNIW1vCGljTVGqmwgAAAAseQRY4BqDI2N67sQl7W1s0vOn2jU67rShulA7t4T04OaQlhUxXhYAAABIBQIscAOX+4b1xOvN2nMorMMXu+Qx6b51FdrRENK2W6uVn8N4WQAAAGCxEGCBGTrb3qt9jWHtPRzWxcsDCvq92nbrMu3YUqf71pbL5/WkuokAAABAViPAArPknNPBC1e0pzGsJ440q3twVJWFOXrwzlrt2BLSppoimTH5EwAAAJBsBFhgHoZGx/SDk5e051BYPzh1SSNjTrcsK9COhjptb6hVTXEw1U0EAAAAsgYBFkiSK33DeuJoi/YeatKht7tkJt2zplw7GkL68O01KmC8LAAAADAvaRlgzWyzpL+UlCtpVNL/55x77WbnI8AiXVzo7NPexrD2NoZ1obNfuX6PPrCpWjsbQvqJ9RWMlwUAAADmIF0D7Pck/bFz7ikz+4ikzzjn3nuz8xFgkW6cczr0dpf2Njbpiddb1NU/ooqCgD5+Z612NtTpthDjZQEAAICZmi7Aprqvo5NUFP27WFJzCtsCzJmZ6a6VpbprZan+28du1fOnLmlvY1hf+9Hb+ruXzmtdVYF2NIT04OZa1ZXmpbq5AAAAQEZKdQV2o6T9kkySR9K9zrkLNzsfFVhkiqv9I3ryaIv2Njbpx+evSJLeubpMO7dExssW5fpT3EIAAAAg/aSsC7GZPSupeoqTPifpAUkvOOe+a2b/WtJDzrn3T3M5D0l6SJJWrFhx14ULN825Ez9H5wAAHLdJREFUQFq5eLk/sr5sY1hnO/oU8Hn0gY3LtKMhpJ+sr5Sf8bIAAACApPQdA3tVUolzzllkgOBV51zRzc5HBRaZzDmnI01XtfdQkx5/vUWX+4ZVlh/Qx++o0Y4tdbqzrpjxsgAAAFjS0jXAnpD0y865583sAUlfcs7ddbPzEWCRLUbGxvXim+3a0xjWM2+0aXh0XGsq8rW9IaQdDSEtL2O8LAAAAJaedA2w75b0p4pMJjWoyDI6B292PgIsslH34IieOtqiPYfCevXcZUnSO1aVakdDnT56e42K8xgvCwAAgKUhLQPsXBFgke2arvTr0cPN2tsY1ulLvQp4PXpgY5W2N4R0f32VAj7GywIAACB7EWCBDOSc07Fwt/Y0NunxI83q6B1WSZ5fH7ujRjsa6rRlRQnjZQEAAJB1CLBAhhsdG9cP3+rQnsawvne8VUOj41pVnhcfL7uyPD/VTQQAAACSggALZJGewRE9faxVexvDeuVsp5yTtqwo0Y4tdfrY7TUqzQ+kuokAAADAnBFggSzV3DUQHS/bpDfbeuX3mu6vr9LOLSHdv6FKOT5vqpsIAAAAzAoBFshyzjkdb+7WvsawHj3SrPaeIRXl+vTRO2q1c0tIW1eWMl4WAAAAGYEACywho2PjeulMp/YeatL+420aGBnT8rKgdmwOaXtDSGsqC1LdRAAAAGBaBFhgieobGtX+45Hxsi+d7tC4k+5cXqKdDSF9/M5alTFeFgAAAGmGAAtAbd2DevRwWHsOhXWytUc+j+m99ZXa0VCnBzZWKdfPeFkAAACkHgEWQIITLZHxsvsOh9XWPaTCHJ8+cnuNdmwJ6e5VZfJ4TPsaw3p4/yk1dw2otiSoXdvqtb0hlOqmAwAAIMsRYAFMaWzc6ZUzndrT2KSnj7Wqf3hMoZKgNtUU6sW3OjQ0Oh7fN+j36gs7byfEAgAAYEERYAHcVP/wqL53vE17G8N64c32KfepLcnVy599YJFbBgAAgKWEAAtgVlZ/9klN9+qweXmJNtYUamNNkTZUF2lDTaGKcv2L2j4AAABkr+kCrC8VjQGQ/mpLggp3DVy3PT/Hq1y/R/98tFX/9NrF+PZQSVAba4q0saZQG6ojv1eW58vrYe1ZAAAAJAcBFsCUdm2r1+49RzUwMhbfFvR79fvbI2NgnXNq7R7UyZYenWjt1omWHp1s6dYPTl3S2LiL739LdaE2VhdqQ/VExbY4j2otAAAAZo8AC2BKsYmappuF2MxUUxxUTXFQ92+oip9vcGRMpy/16o2Wbp1s6dHJ1m7tP96qb/w4sVobD7TRiu3qCqq1AAAAuDHGwAJYcM45XeoZSgi1J1q6daa9L16tzfF5VF9dqI3RMbWRam2hSvICKW49AAAAFhtjYAGkjJlpWVGulhXl6v76iWrt0OiY3mrr1cnWSPfjE63deuZEm755YKJaW1OcGw+zsTG2q8rz5fN6UnFTAAAAkEIEWAApk+Pz6rZQsW4LFce3OefU3jOkE609OtHSrZMt3TrZ2qMX32zX6KRq7S3LChO6IW+sLlJpPtVaAACAbEaABZBWzExVRbmqKsrVT95SGd8+NDqmM5f6IqG2NRJqf3Dqkr59sCm+T3VRbkL34001kbG1VGsBAACyAwEWQEbI8Xm1qbZIm2qLEra39wzFQ+2JlkjV9qXTHRoZi1RrAz6P1lcVJITaDTVFKqNaCwAAkHEIsAAyWmVhjioLK/WeSdXa4dFxnWnvTQi1L7zZru9MqtZWFebEux9vii7vs6YyX36qtQAAAGmLAAsg6wR8nuiET0Xa0TCxvaN3KLJubXTCqJMtPXrlTKeGx8Yj5/N6tK6qICHUbqwpVHlBTopuCQAAACYjwAJYMioKcvTu9Tl69/qK+LaRsXGdbe+Lh9oTLT36l7c6tOdQOL5PZWHOpO7HkTG2ayoKFPBRrQUAAFhMBFgAS5rfG1l/tr66UNsVim/v7B3SyehMyCeia9f+3Uvn49Vav9e0rqpQG6sLJ00cVaTKQqq1AAAAC2VRAqyZfVLS5yVtlHS3c+7ApNN2S/oFSWOSfs05t38x2gQAN1JekKP71uXovnWJ1dpzHX0JofalMx3a0zhRra0oyNHGmklL/FQXaV0V1VoAAIBkWKwK7DFJOyX91eSNZrZJ0qck3SqpVtKzZnaLc25skdoFADPm90bWn71lWaEe3Dyx/XLfsE62dOtEa0/0d7e++soFDY9GqrU+j0XG1sbXrY2Mra0syJGZaV9jWA/vP6XmrgHVlgS1a1u9tjeEpm4E5oz7eXFwPwMAFtKiBFjn3Akpsr7jNR6U9A3n3JCkc2Z2WtLdkl5ZjHYBQDKU5Qd077oK3TupWjsaq9bGQm1Lt149d1n7DjfH9ynPD6g8P6AzHX0aG48s+xPuGtBnvvu63rrUo3evq7zuujA3/3K6XX/9w3PxLxW4nxfGVPfz7j1HJYkQCwBICnPOLd6VmT0v6TdjXYjN7M8k/cg594/R//9G0lPOue/c6HK2bt3qDhw4cKNdACAtXekb1snWnugSP93a2xiOr1kLZKvKwhy99lsPTPVFNgAAUzKzg865rdduT1oF1syelVQ9xUmfc849Ot3Zptg25Sc5M3tI0kOStGLFijm1EQBSrTQ/oHvWluueteWSpG8faJpyP5P09V961yK2LLv91F//aMrt3M/JNd393N4zpG1/8qJ2NNRpe0OtaoqDi9wyAEC2SFqAdc69fw5na5K0fNL/dZKap9rROfeIpEekSAV2DtcFAGmntiSocNfAlNtjIRfzF+J+XhTT3c/FQb8Kc/36g6dP6kv7T+qeNeXa0RDSh2+vUUEOCyIAAGYu1dNiPibpU2aWY2arJa2X9FqK2wQAi2bXtnoF/d6EbUG/V7u21aeoRdmJ+3lxTHc//+4nbtV3f/levbDrvfr0A+sV7hrQru+8rq2/94z+0z816gcnL2k0ukQVAAA3sihjYM1sh6QvS6qU1CXpsHNuW/S0z0n6fyWNSvp159xTN7s8xsACyCbM2ro4uJ8Xx0zuZ+ecGi92ae+hsB5/vVld/SOqKAjo43fWamdDnW4LFTFeFgCWuOnGwC7qJE7JQoAFACA7DI+O6/lTl7S3MaznTlzS8Ni41lUVaEdDSA9urlVdaV6qmwgASAECLAAASGtX+0f05NEW7W1s0o/PX5EkvXN1mXZuiYyXLcr1p7iFAIDFQoAFAAAZ4+Llfu1rDGtvY1hnO/oU8Hn0gY3LtKMhpJ+sr5Tfm+ppPAAAC4kACwAAMo5zTkearmrvoSY9/nqLLvcNqyw/oI/fUaMdW+p0Z10x42UBIAsRYAEAQEYbGRvXi2+2a09jWM+80abh0XGtqcjXjoaQtjeEtLyM8bIAkC0IsAAAIGt0D47oqaMt2nMorFfPXZYkvWNVqXY01Omjt9eoOI/xsgCQyQiwAAAgKzVd6dejh5u1tzGs05d6FfB69MDGKu1oCOm99VUK+BgvCwCZhgALAACymnNOx8Ld2tPYpMePNKujd1gleX597I4a7Wio05YVJYyXBYAMQYAFAABLxujYuH74Vof2Nob1vTdaNTgyrlXledreENKOhpBWluenuokAgBsgwAIAgCWpZ3BETx9r1d7GsF452ynnpLtWlmp7Q0gfv6NGJXmBVDcRAHANAiwAAFjyWq4ORMbLHgrrVFuP/F7T/fVV2rklpPs3VCnH5011EwEAIsACAADEOef0Rku39h4K69EjzWrvGVJx0K+P3lGjHQ0hbV1ZynhZAEghAiwAAMAURsfG9dKZTu091KT9x9s0MDKm5WVB7dgc0o4tdVpdwXhZAFhsBFgAAICb6Bsa1f7jkfGyL53u0LiTNi8v0Y6GkD5+Z63K8hkvCwCLgQALAAAwC23dg3r0cFh7DoV1srVHPo/pvfWV2tFQpwc2VinXz3hZAFgoBFgAAIA5OtHSrX2NYe07HFZb95AKc3366O012t4Q0t2ryuTxMF4WAJKJAAsAADBPY+NOr5zp1J7GJj19rFX9w2MKlQS1vaFWOxrqtK6qINVNBICsQIAFAABIov7hUX3veJv2Nob1w7faNe6kO+qK4+NlKwpyUt1EAMhYBFgAAIAFcqlnUI8dbtbexrCON3fL6zG9Z32Fdmyp0wc3LWO8LADMEgEWAABgEbzZ1qM9h8J69HBYLVcHVZDj04dvq9aOLSG9a3U542UBYAYIsAAAAItofNzpR+c6tfdQWE8da1Xv0KhqinP14OaQdm4J6ZZlhaluIgCkLQIsAABAigwMj+mZE23a1xjWC2+2a2zc6dbaIu1oCOkTm2tVVZib6iYCQFohwAIAAKSBjt4hPX4kMl729aar8pj07vWV2tkQ0gdvXaa8gC/VTQSAlCPAAgAApJnTl3q0tzGsfY3NCncNKD/g1bbbqrWzoU73rC2Xl/GyAJYoAiwAAECaGh93eu38Ze1rDOvJoy3qGRzVsqIcPbg5pB0NIW2sKUp1EwFgUaU0wJrZJyV9XtJGSXc75w5Et39A0hclBSQNS9rlnPv+zS6PAAsAALLV4MiYnjtxSXsbm/T8qXaNjjttqC7Uzi0hPbg5pGVFkfGy+xrDenj/KTV3Dai2JKhd2+q1vSGU4tYDQHKkOsBulDQu6a8k/eakANsgqc0512xmt0na75y76SsvARYAACwFnb1DeuL1Fu1tDOvwxS6ZSfetrdCKsqD2NIY1ODIe3zfo9+oLO28nxALICmnRhdjMntekAHvNaSapQ1Ktc27oRpdDgAUAAEvN2fZe7WsMa+/hsC5eHphyn1BJUC999n2L3DIASL7pAqwnFY2Zxr+S1DhdeDWzh8zsgJkdaG9vX+SmAQAApNaaygL9lw/W68Vd92u6qZ2auwaUifObAMBMJS3AmtmzZnZsip8HZ3DeWyX9gaT/MN0+zrlHnHNbnXNbKysrk9VsAACAjGJmqi0JTnmak7TtT17UXzx/Ri1Xp67SAkAmS9pCY86598/lfGZWJ2mvpH/nnDuTrPYAAABkq13b6rV7z1ENjIzFt+X6PfrYHbU6296rP3j6pL60/6TuWVOuHQ0hffj2GhXksL4sgMyX0lcyMyuR9KSk3c65l1LZFgAAgEwRm6hpulmIL3T2aW9jWHsbw9r1ndf1O48e0wc2VWtnQ0g/sb5CPm86jSIDgJlbrFmId0j6sqRKSV2SDjvntpnZb0vaLemtSbt/0Dl36UaXxyROAAAAN+ec06G3u7S3sUlPvN6irv4RVRQE9PE7a7WzoU63hYoUmUcTANJLWsxCnCwEWAAAgNkZHh3X86cuaW9jWM+duKThsXGtqyrQjoaQHtxcq7rSvFQ3EQDiCLAAAACQJF3tH9GTR1u0t7FJPz5/RZL0ztVl2rklMl62KNef4hYCWOoIsAAAALjOxcv9kfVlG8M629GngM+jD2xcph0NIf1kfaX8jJcFkAIEWAAAAEzLOacjTVe191CTHn+9RZf7hlWWH9DH76jRji11urOumPGyABYNARYAAAAzMjI2rhffbNeexrCeeaNNw6PjWlORr+0NIe1oCGl5GeNlASwsAiwAAABmrXtwRE8dbdGeQ2G9eu6yJOkdq0q1o6FOH729RsV5jJcFkHwEWAAAAMxL05V+PXq4WXsONelMe58CXo8e2Fil7Q0h3V9fpYCP8bIAkoMACwAAgKRwzulYuFt7Gpv0+JFmdfQOqyTPr4/dUaMdDXXasqKE8bIA5oUACwAAgKQbGRvXv7zVoT2NYX3veKuGRse1qjwvPl52ZXl+qpsIIAMRYAEAALCgegZH9PSxVu1tDOuVs51yTtqyokQ7ttTpY7fXqDQ/kOomAsgQBFgAAAAsmuauAT16uFl7G5v0Zluv/F7T/fVV2rklpPs3VCnH5011EwGkMQIsAAAAFp1zTsebu7WvMaxHjzSrvWdIRbk+ffSOWu3cEtLWlaWMlwVwHQIsAAAAUmp0bFwvnenU3kNN2n+8TQMjY1peFtSOzSFtbwhpTWVBqpsIIE0QYAEAAJA2+oZGtf94ZLzsS6c7NO6kO5eXaGdDSB+/s1ZljJcFljQCLAAAANJSW/egHj0c1p5DYZ1s7ZHPY3pvfaV2NNTpgY1VyvUzXhZYagiwAAAASHsnWiLjZfcdDqute0iFOT595PYa7dgS0t2ryuTxMF4WWAoIsAAAAMgYY+NOr5zp1J7GJj19rFX9w2MKlQS1vaFWOxrqtK6K8bJANiPAAgAAICP1D4/qe8fbtLcxrB++1a5xJ90eKtaOhpA+sblWFQU5qW4igCQjwAIAACDjXeoZ1GOHm7W3Mazjzd3yekzvWV+hHVvq9IGNyxQMMF4WyAYEWAAAAGSVN9t6tOdQWI8eDqvl6qAKcnz60G3V2tkQ0rvWlDNeFshgBFgAAABkpfFxpx+d69TeQ2E9daxVvUOjqinO1YObQ9q5JaRblhWmuokAZokACwAAgKw3MDymZ060aV9jWC+82a6xcadNNUXauSUyXraqMDfVTQQwAwRYAAAALCkdvUN6/EhkvOzrTVflMend6yu1syGkD966THkBX6qbCGAaBFgAAAAsWacv9WpvY5P2NTYr3DWgvIBXH7q1Wju2hHTv2gp5GS8LpBUCLAAAAJa88XGnH5+/rL2NYT15tEU9g6NaVpSjBzeHtKMhpI01RaluIgClOMCa2SclfV7SRkl3O+cOXHP6CklvSPq8c+4Pb3Z5BFgAAADM1+DImJ47cUl7G5v0/Kl2jY47bagu1I6GkB7cHFJ1ca72NYb18P5Tau4aUG1JULu21Wt7QyjVTc863M+4VqoD7EZJ45L+StJvThFgvxs9/VUCLAAAABbb5b5hPfF6s/YcCuvwxS6ZSesrC3Sus08jYxOfl4N+r76w83bCVRLtawxr956jGhgZi2/jfkZadCE2s+d1TYA1s+2S7pPUJ6mXAAsAAIBUOtveq32NYf3582c0Nn79Z2Wfx7SiPC8FLctOb3f2a3SK+7miIEcv7Hqv8nOYbGspmi7ApvRoMLN8Sf9V0gck/eZN9n1I0kOStGLFioVvHAAAAJakNZUF+i8frNeXv396ytNHo0vzIDnOtvdNub2jd0i3/vf9Wlmep43VRdpQU6gN1UXaVFOkutKgPEy8tSQlLcCa2bOSqqc46XPOuUenOdvvSvpj51yv2Y0PQOfcI5IekSIV2Pm0FQAAALiZ2pKgwl0D120PlQT1Z/92SwpalJ0a3/7+lPdzWX5A//7eVTrZ2q2TLT3a/0arYp1H8wNebagp0obqQm2oKdKmmkLVVxepgGpt1kvaI+yce/8czvZOSf+PmX1JUomkcTMbdM79WbLaBQAAAMzFrm31U47N3LWtPoWtyj7T3c//7WObEsbA9g+P6s22Xp1o6dbJlm6daOnRY0ea9bVX347vs6IsTxuqC7WxpkgboxXbFWV5VGuzSEq/onDO/UTsbzP7vCJjYAmvAAAASLlYeGJ23IU10/s5L+DT5uUl2ry8JL7NOafmq4M60dytk62RUHuitVvPnmhTbFhtXsCr+liojf6ury5UYa5/sW4ikmixZiHeIenLkioldUk67Jzbds0+nxeTOAEAAACYp4HhMb3Z1jMRalu6daKlW92Do/F9lpcFtaF6ItRuqCnSSqq1aSMtZiFOFgIsAAAAgNlwzqnl6uB1ofZcR1+8Whv0x6q10VAbnTyqiGrtoiPAAgAAAMA1Bkei1dpo9+MT0fG1VwdG4vuESoIJ42o31hRqZXm+vFRrF0xaLqMDAAAAAKmU6/fqjroS3VFXEt/mnFNr96BOtvTojZZunWzt0cmWbv3g1KX42sBBv1e3VBdOdEGOzohcHKRau5CowAIAAADADAyOjOn0pd5IqI11Q27tVld/YrV2Q3xcbeT3Kqq1s0YFFgAAAADmIdfv1W2hYt0WKo5vc87pUs9QQqg92dqt599sj1drc/0e1S8rjI+pjcyIXKTiPKq1s0WABQAAAIA5MjMtK8rVsqJc3V9fFd8+NDqmt9p6dbJ1ItQ+c6JN3zxwMb5PbXGuNkS7H8fG2K4qz5fP60nFTckIBFgAAAAASLIc39TV2vZYtTY6rvZES49efLNdo9FqbY7Po1uWFcYnjNpQU6hNNUUqyQuk6qakFQIsAAAAACwCM1NVUa6qinL13muqtacv9U7qgtyj505c0rcONMX3qS7KjYTamqJoF+RCra5YetVaAiwAAAAApFCOz6tba4t1a21xwvZLPYMJofZES7d++FZHvFob8Hl0y7KC6NI+kVC7oaZIZflTV2v3NYb18P5Tau4aUG1JULu21Wt7Q2jBb18yEWABAAAAIA1VFeaqqjBX77mlMr5teHRcZ9p7E0Lt86fa9Z2DE9XaZUU5E6E2OmnU0aYu/fa+4xoYGZMkhbsGtHvPUUnKqBBLgAUAAACADBHweaLBtChhe3vPkE62Tl7ep0cvnzmrkbHpl00dGBnTw/tPEWABAAAAAIunsjBHlYWV+on1idXasx2RsbW//s3DU56vuWtgkVqYHEtrxC8AAAAALBEBn0cbqou0vSGkUElwyn1qp9mergiwAAAAAJDldm2rV9DvTdgW9Hu1a1t9ilo0N3QhBgAAAIAsFxvnyizEAAAAAIC0t70hlHGB9Vp0IQYAAAAAZAQCLAAAAAAgI5hz068LlK7MrF3ShVS34yYqJHWkuhFAknA8I5twPCPbcEwjm3A8I2alc67y2o0ZGWAzgZkdcM5tTXU7gGTgeEY24XhGtuGYRjbheMbN0IUYAAAAAJARCLAAAAAAgIxAgF04j6S6AUAScTwjm3A8I9twTCObcDzjhhgDCwAAAADICFRgAQAAAAAZgQCbZGb2ITM7ZWanzeyzqW4PMFdmttzMfmBmJ8zsuJl9OtVtApLBzLxm1mhmT6S6LcB8mFmJmX3HzE5GX6vvSXWbgLkys/8c/bxxzMz+ycxyU90mpCcCbBKZmVfSn0v6sKRNkn7KzDaltlXAnI1K+g3n3EZJ75L0KxzPyBKflnQi1Y0AkuBPJT3tnNsg6U5xXCNDmVlI0q9J2uqcu02SV9KnUtsqpCsCbHLdLem0c+6sc25Y0jckPZjiNgFz4pxrcc4div7do8gHo1BqWwXMj5nVSfqopK+kui3AfJhZkaT3SPobSXLODTvnulLaKGB+fJKCZuaTlCepOcXtQZoiwCZXSNLFSf83iQ/8yAJmtkpSg6RXU9wUYL7+RNJnJI2nuB3AfK2R1C7p76Jd4r9iZvmpbhQwF865sKQ/lPS2pBZJV51z30ttq5CuCLDJZVNsY5pnZDQzK5D0XUm/7pzrTnV7gLkys49JuuScO5jqtgBJ4JO0RdJfOOcaJPVJYu4NZCQzK1Wk1+JqSbWS8s3sZ1LbKqQrAmxyNUlaPun/OtH9ARnMzPyKhNevOef2pLo9wDzdJ+kTZnZekSEe7zOzf0xtk4A5a5LU5JyL9Yz5jiKBFshE75d0zjnX7pwbkbRH0r0pbhPSFAE2uX4sab2ZrTazgCKDzx9LcZuAOTEzU2Rs1Qnn3B+luj3AfDnndjvn6pxzqxR5ff6+c45v+JGRnHOtki6aWX100wOS3khhk4D5eFvSu8wsL/r54wExKRmm4Ut1A7KJc27UzH5V0n5FZk/7W+fc8RQ3C5ir+yT9rKSjZnY4uu23nHP/nLomAQAm+U+Svhb90vyspJ9PcXuAOXHOvWpm35F0SJFVEBolPZLaViFdmXMM0QQAAAAApD+6EAMAAAAAMgIBFgAAAACQEQiwAAAAAICMQIAFAAAAAGQEAiwAAAAAICMQYAEAAAAAGYEACwAAAADICARYAAAAAEBG+P8D9b6665erhioAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1152x648 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "fig, ax = plt.subplots(nrows=3,  figsize = (16,9))\n",
    "ax[0].plot([individual.cost for individual in ga.SawIndividuals], '-o')\n",
    "ax[0].set(title = 'cost')\n",
    "ax[1].plot([cost for cost in ga.avg_cost], '-o')\n",
    "ax[1].set(title = 'avg_cost')\n",
    "ax[2].plot([cost for cost in ga.best_cost], '-o')\n",
    "ax[2].set(title = 'best_cost')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.9.13 ('moldrug')",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.13"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "580997a69bc0f3991857025e1d93e87ed090e2c1fa4aff0ca8e9824f56baf8cb"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}