Dynex_Portfolio_Optimisation.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "1ac5ba64-ca2a-47fd-9e6a-10deb078480b",
   "metadata": {},
   "source": [
    "# Challenge: Portfolio Optimisation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "4e46625b-214e-4e2c-9621-93433fd0a9fb",
   "metadata": {},
   "outputs": [],
   "source": [
    "import csv\n",
    "import numpy as np\n",
    "import dynex\n",
    "from autoqubo import Binarization, SamplingCompiler, SearchSpace, Utils\n",
    "import yfinance as yf #!pip install yfinance\n",
    "import pandas as pd\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6a74f476-2f0e-437c-b504-362123c39816",
   "metadata": {},
   "source": [
    "# Real World Example (Markowitz Mean-Variance Model)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "591cc28b-3685-454d-bd13-1fae3de6a5a8",
   "metadata": {},
   "source": [
    "https://arxiv.org/pdf/2303.12601.pdf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 369,
   "id": "31bcc0dc-1ba5-4ead-8395-012945d517ce",
   "metadata": {},
   "outputs": [],
   "source": [
    "# settings:\n",
    "symbols = ['AAPL', 'MSFT', 'GOOG', 'AMZN', 'META']\n",
    "start_date = '2018-01-01'\n",
    "end_date = '2024-01-01' \n",
    "budget = 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 370,
   "id": "cfbc2f18-cb7d-4e81-9955-dff105df4bc9",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[*********************100%%**********************]  5 of 5 completed\n"
     ]
    }
   ],
   "source": [
    "data = yf.download(symbols, start=start_date, end=end_date)['Adj Close']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 381,
   "id": "cbcf7b3a-4dae-46f2-84c6-1be3d4d6df2e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ticker            AAPL        AMZN        GOOG        META        MSFT\n",
      "Date                                                                  \n",
      "2018-01-02   40.670971   59.450500   53.250000  181.227707   80.080925\n",
      "2018-01-03   40.663902   60.209999   54.124001  184.474274   80.453598\n",
      "2018-01-04   40.852764   60.479500   54.320000  184.134628   81.161713\n",
      "2018-01-05   41.317890   61.457001   55.111500  186.651962   82.167969\n",
      "2018-01-08   41.164433   62.343498   55.347000  188.080444   82.251816\n",
      "...                ...         ...         ...         ...         ...\n",
      "2023-12-22  193.353287  153.419998  142.720001  353.015472  373.888580\n",
      "2023-12-26  192.803986  153.410004  142.820007  354.453918  373.968445\n",
      "2023-12-27  192.903839  153.339996  141.440002  357.450714  373.379547\n",
      "2023-12-28  193.333298  153.380005  141.279999  357.940216  374.587280\n",
      "2023-12-29  192.284637  151.940002  140.929993  353.584839  375.345886\n",
      "\n",
      "[1509 rows x 5 columns]\n"
     ]
    }
   ],
   "source": [
    "print(data)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "086926a9-63a7-4d29-83ef-98adbefed474",
   "metadata": {},
   "source": [
    "## Calculate mean returns and covariance matrix:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 371,
   "id": "21b60a72-8d08-4beb-9380-85164791cf84",
   "metadata": {},
   "outputs": [],
   "source": [
    "trading_days = 252; # number of trading days per year\n",
    "returns = data.pct_change().dropna();\n",
    "mean_daily_returns = returns.mean();\n",
    "mean_annual_returns = mean_daily_returns * trading_days # annualized\n",
    "cov_matrix = returns.cov();\n",
    "n = len(symbols)\n",
    "cov_matrix_np = np.zeros((n, n))\n",
    "cov_matrix_np = np.array(cov_matrix)\n",
    "cov_matrix_np = cov_matrix_np * trading_days # annualized\n",
    "mean_vector_np = np.zeros(n)\n",
    "mean_vector_np = np.array(mean_annual_returns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 391,
   "id": "324fbe71-b600-4576-b0f9-bb32da57a30e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean vector:\n",
      "Ticker\n",
      "AAPL    0.309906\n",
      "AMZN    0.219349\n",
      "GOOG    0.211479\n",
      "META    0.204932\n",
      "MSFT    0.303639\n",
      "dtype: float64\n",
      "\n",
      "Covariance matrix:\n",
      "Ticker      AAPL      AMZN      GOOG      META      MSFT\n",
      "Ticker                                                  \n",
      "AAPL    0.100343  0.069905  0.066677  0.077528  0.071554\n",
      "AMZN    0.069905  0.125078  0.074557  0.091815  0.074206\n",
      "GOOG    0.066677  0.074557  0.097505  0.088823  0.071861\n",
      "META    0.077528  0.091815  0.088823  0.182890  0.078538\n",
      "MSFT    0.071554  0.074206  0.071861  0.078538  0.090745\n"
     ]
    }
   ],
   "source": [
    "print('Mean vector:')\n",
    "print(mean_daily_returns*trading_days)\n",
    "print()\n",
    "print('Covariance matrix:')\n",
    "print(cov_matrix*trading_days)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0e76f8a9-1eff-4b33-8159-740b08c35766",
   "metadata": {},
   "source": [
    "## Portfolio Optimisation Model:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 374,
   "id": "53d95724-9efe-4013-ae85-787dc7ff62ee",
   "metadata": {},
   "outputs": [],
   "source": [
    "def variance(x):\n",
    "    \"\"\"\n",
    "    Variance\n",
    "    \"\"\"\n",
    "    return x@cov_matrix_np@x\n",
    "\n",
    "def mean(x):\n",
    "    \"\"\"\n",
    "    Mean return\n",
    "    \"\"\"\n",
    "    return x@mean_vector_np\n",
    "\n",
    "def constraint(x):\n",
    "    \"\"\"\n",
    "    Budget constraint\n",
    "    \"\"\"\n",
    "    return (x.sum() - budget)**2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 375,
   "id": "ea440149-9fa0-47c6-bd08-451bbdd887b9",
   "metadata": {},
   "outputs": [],
   "source": [
    "A, B, C = 1, -1, 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 376,
   "id": "07527a9c-6f9a-4dba-a16a-18421d76297a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Mean-variance portfolio optimization model\n",
    "def f(x):\n",
    "    return A*variance(x) + B*mean(x) + C*constraint(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e5c8f352-4803-41f3-b484-3ec0fb86cdd2",
   "metadata": {},
   "source": [
    "## Create QUBO formulation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 377,
   "id": "51a0c176-4b83-4c23-874e-d6e4e066717b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "QUBO generation successful\n"
     ]
    }
   ],
   "source": [
    "s = SearchSpace()\n",
    "weights_vector = Binarization.get_uint_vector_type(n, n)\n",
    "s.add('x', weights_vector, n * n)\n",
    "\n",
    "qubo, offset = SamplingCompiler.generate_qubo_matrix(fitness_function=f, input_size=s.size, searchspace=s, use_multiprocessing=False)\n",
    "\n",
    "if SamplingCompiler.test_qubo_matrix(f, qubo, offset, search_space=s):\n",
    "    print(\"QUBO generation successful\")\n",
    "else:\n",
    "    print(\"QUBO generation failed - the objective function is not quadratic\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 397,
   "id": "1db9bf02-bff7-4317-9b29-cee304b76af6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[-19900.20956279    400.40137333    800.80274666   1601.60549333\n",
      "    3203.21098665    200.13980919    400.27961838    800.55923676\n",
      "    1601.11847353   3202.23694706    200.13335453    400.26670907\n",
      "     800.53341813   1601.06683626   3202.13367252    200.15505622\n",
      "     400.31011243    800.62022486   1601.24044972   3202.48089945\n",
      "     200.14310807    400.28621613    800.57243227   1601.14486454\n",
      "    3202.28972907]\n",
      " [     0.         -39600.21843891   1601.60549333   3203.21098665\n",
      "    6406.42197331    400.27961838    800.55923676   1601.11847353\n",
      "    3202.23694706   6404.47389411    400.26670907    800.53341813\n",
      "    1601.06683626   3202.13367252   6404.26734505    400.31011243\n",
      "     800.62022486   1601.24044972   3202.48089945   6404.96179889\n",
      "     400.28621613    800.57243227   1601.14486454   3202.28972907\n",
      "    6404.57945815]\n",
      " [     0.              0.         -78399.63413115   6406.42197331\n",
      "   12812.84394661    800.55923676   1601.11847353   3202.23694706\n",
      "    6404.47389411  12808.94778822    800.53341813   1601.06683626\n",
      "    3202.13367252   6404.26734505  12808.53469009    800.62022486\n",
      "    1601.24044972   3202.48089945   6404.96179889  12809.92359778\n",
      "     800.57243227   1601.14486454   3202.28972907   6404.57945815\n",
      "   12809.1589163 ]] offset: 1000000.0\n"
     ]
    }
   ],
   "source": [
    "print(qubo[:3], 'offset:',offset)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e3099000-da3f-409b-a4a9-3cf6d537b2e1",
   "metadata": {},
   "source": [
    "## Compute on Dynex"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 398,
   "id": "5f2d9fc8-d2af-4fb8-87c0-94245b56a3fd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best solutions (minimize):\n",
      "[ÐYNEX] PRECISION CUT FROM 10.0 TO 1\n",
      "[DYNEX] PRECISION SET TO 1\n",
      "[DYNEX] SAMPLER INITIALISED\n",
      "[DYNEX|TESTNET] *** WAITING FOR READS ***\n",
      "╭────────────┬─────────────┬───────────┬───────────────────────────┬─────────┬─────────┬────────────────╮\n",
      "│   DYNEXJOB │   BLOCK FEE │ ELAPSED   │ WORKERS READ              │ CHIPS   │ STEPS   │ GROUND STATE   │\n",
      "├────────────┼─────────────┼───────────┼───────────────────────────┼─────────┼─────────┼────────────────┤\n",
      "│         -1 │           0 │           │ *** WAITING FOR READS *** │         │         │                │\n",
      "╰────────────┴─────────────┴───────────┴───────────────────────────┴─────────┴─────────┴────────────────╯\n",
      "\n",
      "[DYNEX] FINISHED READ AFTER 0.00 SECONDS\n",
      "[DYNEX] SAMPLESET READY\n",
      "   0  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 ... 24     energy num_oc.\n",
      "0  0  1  1  1  1  1  1  0  1  0  1  1  0  1  1  1  0 ...  1 755.648561       1\n",
      "['BINARY', 1 rows, 1 samples, 25 variables]\n"
     ]
    }
   ],
   "source": [
    "print(\"Best solutions (minimize):\")\n",
    "sampleset = dynex.sample_qubo(qubo, offset, mainnet=False, num_reads=10000, annealing_time=1000)\n",
    "print(sampleset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 400,
   "id": "e72bc613-3958-4460-bee9-265a7020d9ae",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x=[30 11 27  1 31], energy=755.6485607431969, obj=755.6485607438183, constraint=0 obj(baseline)=826.0096115957166 (-70.36105085189831)\n"
     ]
    }
   ],
   "source": [
    "sol = sampleset.record[0][0]\n",
    "x = s.decode_dict(sol)['x']\n",
    "e = sampleset.first.energy\n",
    "print(\n",
    "    f\"x={x}, \"\n",
    "    f\"energy={e}, \"\n",
    "    f\"obj={f(x)}, \"\n",
    "    f\"constraint={constraint(x)} \"\n",
    "    f\"obj(baseline)={f(np.repeat(100/n, n))} ({f(x)-f(np.repeat(100/n, n))})\"\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "55085626-caa7-46ee-bc37-3a8d37528030",
   "metadata": {},
   "source": [
    "## Plot Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 401,
   "id": "2d7a64bc-b957-4755-b609-d6dcf8cb6458",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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jRwZ8vYkTJ9KvXz/uuOMO9u/f3+IAtylcLheKovDFF1/4/d7ov0tCiK5JggohxAl1/vnnc9NNN7F69eoGk3bff/99QkJCWLJkielJ/cKFC5v8npMmTeJXv/oVZ511FhdddBEffvghNpv2z19GRgYul4vdu3czZMgQ45zjx49TVFRkGjakBwtff/0169at4/e//z2gJWW/+OKLpKamEh4ezrhx4xpsT0MBzNtvv80zzzzDM888w8yZM5v8WUG7iT7vvPN44403mDt3Lj169PB7nJ7MGxUVVW/hNN17773H/Pnzeeqpp4xtVVVVFBUVNauN99xzj6nehOcwonPOOYfIyEgWLVqE3W6nsLDQNPRp2bJl5Ofn88EHH5gS4vfv39+stujee+89+vbtywcffGD6GT300EPNul7fvn0B2LZtW73HJCQkEBkZidPpbPRnEKjLLruMRx99lCFDhtQbLOq/17t27fLZt3PnTnr06EF4eDghISGEhoYavVqevM/t168fqqrSp08fBg4c2PIPIoToVCSnQghxQkVERPDiiy+yYMECzj777HqPs1qtKIpimiL0wIEDfPTRR81639mzZ/P222/z5ZdfctVVVxlPiM844wwAnnnmGdPxf/vb3wBMs0z16dOHtLQ0nn76aWpra5k6dSqgBRt79+7lvffeY9KkSUbAUp/w8HAAnxvybdu2cf3113PllVdy++23N+tz6u666y4eeugh/vCHP9R7zLhx4+jXrx9PPvkkZWVlPvs9p4C1Wq0+T+CfffZZnylcAzV06FBmz55tfHkGYqGhoZx//vl8/vnnvPjii4SHh3Puueea2gLmHoGampom5Z744++6a9asYdWqVc26XkJCAjNmzOA///kPhw4dMu3T38NqtXLBBRfw/vvv+w0+GpuG15/rr7+ehx56yBQAektJSWH06NG89tprpt/Dbdu28dVXXxl/F1arlTlz5vDRRx+ZPkNmZiZLliwxXXPevHlYrVYefvhhn98VVVXJz89v8mcRQnQe0lMhhDjhGhrDrzvzzDP529/+xumnn87ll19OTk4Ozz//PP3792fLli3Net/zzjuPhQsXcvXVVxMVFcU///lPRo0axfz583n55ZeNYTVr167ltdde47zzzuPkk082XWP69Om8/fbbjBgxwni6PnbsWMLDw/n5558DGm6i30DfdtttzJkzB6vVyqWXXmokE8+YMYM33njDdM6UKVOMJ9+BGDVqFKNGjWrwGIvFwr///W/mzp3LsGHD+OUvf0laWhpHjx7lu+++Iyoqik8//RSAs846i//+979ER0czdOhQY3hWfHx8wG1qiiuvvJLXX3+dJUuWcMUVVxiBGGjfi9jYWObPn89tt92Goij897//bdKwI3/OOussPvjgA84//3zOPPNM9u/fz0svvcTQoUP9Bl2B+Mc//sG0adMYO3YsN954I3369OHAgQN89tlnbNq0CYD/+7//47vvvmPixInccMMNDB06lIKCAjZu3MjSpUspKCho0ntmZGSwYMGCRo974oknmDt3LpMnT+a6664zppSNjo42nf/www/z5ZdfMn36dH7zm9/gcDh49tlnGTZsmOlvsV+/fjz66KPcd999HDhwgPPOO4/IyEj279/Phx9+yI033shdd93VpM8ihOg8JKgQQnRIp5xyCq+88gr/93//xx133EGfPn14/PHHOXDgQLODCtBuVktLS/nNb35DVFQUTzzxBP/+97/p27cvr776Kh9++CHJycncd999foe96EHFtGnTjG02m43JkyezdOnSgPIp5s2bx6233srbb7/NG2+8gaqqXHrppeTm5lJeXs6NN97oc87ChQubFFQEatasWaxatYpHHnmE5557jrKyMpKTk5k4cSI33XSTcdzf//53rFYrb775JlVVVUydOpWlS5fWW1StpU455RRSUlLIysoyDX0CiI+PZ/Hixfzud7/jwQcfJDY2liuvvJJTTz21Re255ppryM7O5p///CdLlixh6NChvPHGG7z77rtGvkdTjRo1itWrV/OHP/yBF198kaqqKjIyMkx5LklJSaxdu5Y//elPfPDBB7zwwgvEx8czbNgwHn/88WZ/nsbMnj2bL7/8koceeog//vGP2O12Zs6cyeOPP25KTB85ciRLlizhzjvv5I9//CM9e/bk4YcfJisry+dv8fe//z0DBw7k6aef5uGHHwYgPT2d0047jXPOOafNPosQov0paksf7QghhBBCCCG6NcmpEEIIIYQQQrSIBBVCCCGEEEKIFpGgQgghhBBCCNEiElQIIYQQQgghWkSCCiGEEEIIIUSLSFAhhBBCCCGEaJFOWafC5XJx7NgxIiMjURSlvZsjhBBCCCFEl6SqKqWlpaSmpmKx1N8f0SmDimPHjpGent7ezRBCCCGEEKJbOHz4MD179qx3f6cMKiIjIwHtw0VFRbVza4QQQgghhOiaSkpKSE9PN+6/69Mpgwp9yFNUVJQEFUIIIYQQQrSxxlIOJFFbCCGEEEII0SISVAghhBBCCCFaRIIKIYQQQgghRIt0ypyKQDmdTmpra9u7GUI0m91ux2q1tnczhBBCCCEa1CWDClVVyc7OpqioqL2bIkSLxcTEkJycLDVZhBBCCNFhdcmgQg8oEhMTCQsLk5sx0SmpqkpFRQU5OTkApKSktHOLhBBCCCH863JBhdPpNAKK+Pj49m6OEC0SGhoKQE5ODomJiTIUSgghhBAdUpdL1NZzKMLCwtq5JUK0Dv13WfKDhBBCCNFRdbmgQidDnkRXIb/LQgghhOjoumxQIYQQQgghhDgxJKgQfs2aNYs77rjDWO/duzfPPPNMu7XH24IFCxg9enSTzvH+TG1NURQ++uijE/Z+QgghhBDtRYKKDuSaa65BURSfr9NPP729m9bh3HXXXXzzzTetfl0JBIQQQgghmq7Lzf7U2Z1++uksXLjQtC04OLidWtNxRUREEBER0d7NEEIIIYQQSE9FhxMcHExycrLpKzY21ti/e/duZsyYQUhICEOHDuXrr782PV1ftmwZiqKYCv9t2rQJRVE4cOAAAPn5+Vx22WWkpaURFhbGiBEjeOuttwJu47XXXstZZ51l2lZbW0tiYiKvvPKKz/GqqpKQkMB7771nbBs9erSp7sIPP/xAcHAwFRUVABQVFXH99deTkJBAVFQUp5xyCps3bzaO9x7+5HA4uO2224iJiSE+Pp57772X+fPnc95555na4nK5uOeee4iLiyM5OZkFCxYY+3r37g3A+eefj6IoxjrAxx9/zNixYwkJCaFv3748/PDDOBwOY7+/n4sQQgghRHfRLYIKVVVxOp3t8qWqaqt9DpfLxbx58wgKCmLNmjW89NJL3HvvvU2+TlVVFePGjeOzzz5j27Zt3HjjjVx11VWsXbs2oPOvv/56vvzyS7KysoxtixcvpqKigksuucTneEVRmDFjBsuWLQOgsLCQzMxMKisr2blzJwDLly9nwoQJxvSpF110ETk5OXzxxRds2LCBsWPHcuqpp1JQUOC3TY8//jhvvvkmCxcuZOXKlZSUlPgdxvTaa68RHh7OmjVr+Otf/8qf/vQnIwBYt24dAAsXLiQrK8tYX7FiBVdffTW33347O3bs4J///Cevvvoqf/7zn4HW+7kIIYQQQnRW3WL4k8vlYsWKFe3y3tOnT29SwbLFixf7DOu5//77uf/++1m6dCk7d+5kyZIlpKamAvCXv/yFuXPnNqlNaWlp3HXXXcb6rbfeypIlS3jnnXc46aSTGj1/ypQpDBo0iP/+97/cc889gHYjftFFF9U7JGnWrFn885//BOD7779nzJgxJCcns2zZMgYPHsyyZcuYOXMmoPVarF27lpycHGPo15NPPslHH33Ee++9x4033uhz/WeffZb77ruP888/H4DnnnuOzz//3Oe4kSNH8tBDDwEwYMAAnnvuOb755ht+8YtfkJCQAEBMTAzJycnGOQ8//DC///3vmT9/PgB9+/blkUce4Z577uGhhx5qtZ+LEEIIIQQVx+DoJ2CLgNQzIDiuvVsUkG4RVHQmJ598Mi+++KJpW1yc9suUmZlJenq6ceMKMHny5Ca/h9Pp5C9/+QvvvPMOR48epaamhurq6iYVDLz++ut5+eWXueeeezh+/DhffPEF3377bb3Hz5w5k9tvv53c3FyWL1/OrFmzjKDiuuuu48cffzQClM2bN1NWVuZTEb2yspK9e/f6XLu4uJjjx4+bAiKr1cq4ceNwuVymY0eOHGlaT0lJIScnp8HPunnzZlauXGn0TID2PayqqqKioqLVfi5CCCGE6OYqs+GjNPO2mZ9B2hnt054m6BZBhcViYfr06e323k0RHh5O//79W/x+nsOuvCsxP/HEE/z973/nmWeeYcSIEYSHh3PHHXdQU1MT8PtcffXV/P73v2fVqlX8+OOP9OnTp8Hv8YgRI4iLi2P58uUsX76cP//5zyQnJ/P444+zbt06amtrmTJlCgBlZWWkpKQYw6U8xcTEBNxGf+x2u2ldURSfwMNbWVkZDz/8MPPmzfPZFxIS0qL2CCGEEKIbqSmGoi0Q2R8qj0HcOFBd4KgAewSs9B1GzsY7IGUOWAIf+dIeukVQoShKk4YgdVRDhgzh8OHDZGVlGUnOq1evNh2jD+HJysoyErw3bdpkOmblypWce+65XHnllYA2POznn39m6NChAbclPj6e8847j4ULF7Jq1Sp++ctfNni8oihMnz6djz/+mO3btzNt2jTCwsKorq7mn//8J+PHjyc8PByAsWPHkp2djc1mMyVL1yc6OpqkpCTWrVvHjBkzAK0nYePGjU2uZWG323E6naZtY8eOZdeuXfUGe4H8XIQQQgjRzbkc8F6MedsZ22DjbyHba4KXuAkw6zPYcDuMfqzDBxTQTRK1O5Pq6mqys7NNX3l5eQDMnj2bgQMHMn/+fDZv3syKFSt44IEHTOf379+f9PR0FixYwO7du/nss8946qmnTMcMGDCAr7/+mh9//JHMzExuuukmjh8/3uS2Xn/99bz22mtkZmYa+QYNmTVrFm+99RajR48mIiICi8XCjBkzePPNN418Cv1zTp48mfPOO4+vvvqKAwcO8OOPP/LAAw+wfv16v9e+9dZbeeyxx/j444/ZtWsXt99+O4WFhSiK0qTP1Lt3b7755huys7MpLCwE4I9//COvv/46Dz/8MNu3byczM5O3336bBx980GhvYz8XIYQQQnRjZfvhbbvv9t0v+gYUA2+D09dCSAJMXQThGSemjS0kQUUH8+WXX5KSkmL6mjZtGqANbfrwww+prKzkpJNO4vrrrzeN8wftSftbb73Fzp07GTlyJI8//jiPPvqo6ZgHH3yQsWPHMmfOHCO3wXvq1UDMnj2blJQU5syZY8onqM/MmTNxOp3MmjXL2DZr1iyfbYqi8PnnnzNjxgx++ctfMnDgQC699FIOHjxIUlKS32vfe++9XHbZZVx99dVMnjyZiIgI5syZ0+ThSU899RRff/016enpjBkzBoA5c+awePFivvrqKyZMmMCkSZN4+umnycjQ/sgD+bkIIYQQohtbdbX/7buf993W/4a2bUsbUdTWnPP0BCkpKSE6Opri4mKioqJM+6qqqti/fz99+vTpNuPdFUXhww8/bFZg0BJlZWWkpaWxcOFCv/kG7cnlcjFkyBAuvvhiHnnkkfZuTot0x99pIYQQoktZ5DFy4owtEBQLi4eAo8y9PawnjH4cel9+4tvXgIbuuz11i5wK0bpcLhd5eXk89dRTxMTEcM4557R3kzh48CBfffUVM2fOpLq6mueee479+/dz+eUd6w9TCCGEEN2Iywk7/s+9PuUtiBmhLfe/CXZ6DFE/7/CJbVsrk+FPoskOHTpEUlISixYt4j//+Q82W/vHphaLhVdffZUJEyYwdepUtm7dytKlSxkyZEh7N00IIYQQ3VXWl7DlQfd670vdy/2ucy9P//DEtamNtP/doGixEz2CrXfv3if8PRuTnp7OypUr27sZQgghhBBupbvdy2lnm/dFD4GT/gWqE9LPO6HNagsSVAghhBBCCNHaVFWbLlY34iHfY/pff+La08Zk+JMQQgghhBCtrWirezlhulborguToEIIIYQQQojWVviTe3nI3e3XjhNEggohhBBCCNE1Ocphyx9hw51aAbrGHFsCP14JhZtb/t4FG7XXlNOh59kNH9sFSE6FEEIIIYToOsr2wdpfwcBboKYAttXVqyrbAzM/qf+8n+6GzCe15eyl4KqB9Hkw4UWw+KmG7SiHwx9C6hkQHGfeV5UH+/6jLQ/4Vcs/UycgPRVCCCGEEKLr2Pk0ZH8N358L+evd23O+r/+cvNXugAKg6jjUFMLeV2DzA6C6fM/5+TlYdRW8Hw/fzQVHhXvf3pe1wnaxYyGt/et5nQgSVHRjO3fuZNKkSYSEhDB69OiAzrnmmmtMlbtnzZrFHXfc0Sbta6kDBw6gKAqbNm1q0XVOxGdUFIWPPvqoTd9DCCGE6PKOL9Nu9nW7n3cv1xaDs8b/ednfaK/BPXz3ZT4BKy8zb1NV2PR793rWl3Bgkcf619pr/xtBUegOZPhTB3LNNdfw2muvAWC32+nVqxdXX301999/f4sKzF1zzTUUFRX53LQ+9NBDhIeHs2vXLiIiIpp17Q8++AC73U+X4Anm7zOmp6eTlZVFjx5+/oFogo7yGYUQQgjRiA23N7y/Og/CUn23F+/QXvtdBzse992f9aV53bMStm79b8Big57nQ1FdTkaPyY23uYuQnooO5vTTTycrK4vdu3fzu9/9jgULFvDEE08061pOpxOXy093XZ29e/cybdo0MjIyiI+Pb9Z7xMXFERkZ2axz25rVaiU5ObnFFb878mcUQgghug1HBRx6H2pL3dsqs6A4U1t21ULRloav8VEabP2Te71oOyyZCAfrehmiR0DyL7TlsHT3cbUl7l4OZzVs+YPvtV21sPqXsPoabegUCkQNbMonBKC0tJT9+/eza9cudu3ahcPhaPI12oMEFR1McHAwycnJZGRk8Otf/5rZs2fzySdaUlFhYSFXX301sbGxhIWFMXfuXHbvdldqfPXVV4mJieGTTz5h6NChBAcHc+211/Laa6/x8ccfoygKiqKwbNkyFEVhw4YN/OlPf0JRFBYsWADA1q1bOeWUUwgNDSU+Pp4bb7yRsrKyetvrPTSosTb6c+jQIc4991wiIiKIiori4osv5vjx48b+BQsWMHr0aP75z3+Snp5OWFgYF198McXFxcZ+f5/Re/iT/rmXLFnCmDFjCA0N5ZRTTiEnJ4cvvviCIUOGEBUVxeWXX05FhXtcpOdn1K/h/XXNNdcYx3/88ceMHTuWkJAQ+vbty8MPP2z6B2H37t3MmDGDkJAQhg4dytdff93g90cIIYQQwOb74YcLYc11ULwTnFXwzSnw2VBYpGj7ARSLVhdCN/t7yLjUvb71Ifi4N6y9CX68AvLXuveFJsGMj+HCAjjvEFzmdCdpV9Xdm1Rla+9tCYbLXNqXpyMfaa8hCWANadJHrK2tZcOGDRw8eJCsrCyysrI4cuRIk67RXrrH8CdVBWdF48e1BWtYi8bShYaGkp+fD2hDfHbv3s0nn3xCVFQU9957L2eccQY7duwwhudUVFTw+OOP8+9//5v4+HhSUlKorKykpKSEhQsXAtqT96ysLGbPns3pp5/OXXfdRUREBOXl5cyZM4fJkyezbt06cnJyuP7667nlllt49dVXA2pvIG305HK5jIBi+fLlOBwObr75Zi655BKWLVtmHLdnzx7eeecdPv30U0pKSrjuuuv4zW9+w5tvvsldd91FZmamz2c8duyY3zYuWLCA5557zghOLr74YoKDg1m0aBFlZWWcf/75PPvss9x7770+506ZMoWsrCxjPTMzkzPOOIMZM2YAsGLFCq6++mr+8Y9/MH36dPbu3cuNN94IaMPNXC4X8+bNIykpiTVr1lBcXNxhc1KEEEKIDmXX37XXQ+9qX8m/gJKd7v16onVwAkx4QZv1qf+NkDhd23bkY3BWaseUH4Q9L2uBgafQVLCFAqHaumKBkCSoOKIFE+HpsP+/dccmu+/xrGG+95rBiU3+iHv27DGWbTYbDoeD3Nxcevfu3eRrnWjdI6hwVsA7zcsZaLGLy8AW3uTTVFXlm2++YcmSJdx6663GjfrKlSuZMmUKAG+++Sbp6el89NFHXHTRRYAW4b7wwguMGjXKuFZoaCjV1dUkJycb2/RhQREREcb2f/3rX1RVVfH6668THq61+bnnnuPss8/m8ccfJykpqcE2B9pGT9988w1bt25l//79pKdr3Yyvv/46w4YNY926dUyYMAHAaFdaWhoAzz77LGeeeSZPPfUUycnJfj9jfR599FGmTp0KwHXXXcd9993H3r176du3LwAXXngh3333nd+gIigoyHiP/Px8rr/+eq699lquvfZaAB5++GF+//vfM3/+fAD69u3LI488wj333MNDDz3E0qVL2blzJ0uWLCE1VRvT+Ze//IW5c+c22m4hhBCiW7NFaDMq6bLr6ekPSYKY4TDtf+5t0YNhXjZ8OwfyV7u3u6rdy8m/gKghfq6XrAUVW/4AKXPcQ59CPO6LRiyATfd4nde0oEJVVXJycoz1iRMnkpOTQ0JCQpOu015k+FMHs3jxYiIiIggJCWHu3LlccsklLFiwgMzMTGw2GxMnTjSOjY+PZ9CgQWRmZhrbgoKCGDlyZLPeOzMzk1GjRhkBBcDUqVNxuVzs2rUroPMDaaP3Oenp6UZAATB06FBiYmJM5/Tq1csIKAAmT54ccLu8eX5/kpKSCAsLMwIKfZvnH7U/tbW1XHDBBWRkZPD3v//d2L5582b+9Kc/ERERYXzdcMMNZGVlUVFRYXxePaDQP4sQQgghvKgq7H8Dlp4Mmx8EGhj5MfyP7uWIPv6PsUfByIfrv8aUN/2PLgmpe2CZtQQ23uneHjvavTz0bpjllcwd2d/nUvn5+WzevNkY1qSqqrEvJycHVVWxWCzMmDEDu91OWloaQUFB9be5A+kePRXWMK3HoL3euwlOPvlkXnzxRYKCgkhNTW1yknFoaChKN5m6rLk8h2EpiuIzLEtRlAYT3AF+/etfc/jwYdauXWv6GZWVlfHwww8zb948n3NCQpo2rlIIIYTo1vYt1PInAHKWaa9Rg81DngBO+ifETYBtdQnYISn1XzPlNLhche1/0epPGNtP13Ig/IkeCscW+25PPcO87l0Ab8QC02pFRQVbt24FtBxUgPDwcIqLizl48KARYCQmJmKxdL7n/p2vxc2hKNoQpPb4auINfnh4OP3796dXr16mm9UhQ4bgcDhYs2aNsS0/P59du3YxdOjQBq8ZFBSE0+ls9L2HDBnC5s2bKS8vN7atXLkSi8XCoEGDAjq/qW0cMmQIhw8f5vDhw8a2HTt2UFRUZDrn0KFDphyJ1atXm9oV6GdsDX/729945513+Pjjj31mzRo7diy7du2if//+Pl8Wi8X4vJ55GatXr/Z+CyGEEEIc/tB328hHtQDAU69LtF6DqEGAYk7Krk9Pj4d/E16Ak7+o/9hh90HiTN/toV5T0wbFupdjR0OoO7hxOp2sXbsWb6WlpRw4cMDUY9HcGTnbW/cIKrqAAQMGcO6553LDDTfwww8/sHnzZq688krS0tI499xzGzy3d+/ebNmyhV27dpGXl0dtba3f46644gpCQkKYP38+27Zt47vvvuPWW2/lqquuajSforltnD17NiNGjOCKK65g48aNrF27lquvvpqZM2cyfvx44zi9XZs3b2bFihXcdtttXHzxxUZ+Q6CfsaWWLl3KPffcwxNPPEGPHj3Izs4mOzvbmInqj3/8I6+//joPP/ww27dvJzMzk7fffpsHH3zQ+LwDBw40fZYHHnigobcUQgghupeCjdqsTt69A/YoSDsTpr0Lsz6HsJ6QMheCorWHuCd/DXPWQpKfAMBb9GAY8GutpkTf6xo+NigGTnrZva5YteAmfoLXcR5BReIs066ffvrJtB4aqiWCe852qYuNjfXZ1hlIUNGJLFy4kHHjxnHWWWcxefJkVFXl888/b7Qw2w033MCgQYMYP348CQkJrFy50u9xYWFhLFmyhIKCAiZMmMCFF17IqaeeynPPPef3+NZoo6IofPzxx8TGxjJjxgxmz55N3759+d///mc6rn///sybN48zzjiD0047jZEjR/LCCy80+TO21A8//IDT6eRXv/oVKSkpxtftt2vFdubMmcPixYv56quvmDBhApMmTeLpp58mIyMDAIvFwocffkhlZSUnnXQS119/PX/+85/bpK1CCCFEp7TzGTj+ne/2tHO0KVrtEZA6F849CCd/7t4fng7x433Pq8+EF2DGB2ANIGchLB2C48EWCecegOF+HgjaY9zLEe5czerqatP0/NOmTWPw4MEAptEhAGPGjGlxfa32oqie/S2dRElJCdHR0RQXFxMVFWXaV1VVxf79++nTp4+MYe8iFixYwEcffWTUm+hu5HdaCCFEt/LNqXD8W205ejgUb9OWT16i5US0l6pcrZfCO3fC06K6Ye+nfAPJp+B0Otm0aROlpVrBvokTJxq9FJs3bzZyK0AbQu19X9sRNHTf7alzhkJCCCGEaH0530N1HqR7jDc/+jkc/QTGPKk9IRairaiqVjhODyimf6hN4brnJSg74K503V7qS+T2NGURlO6GpJMBLbdUDyhCQ0ONgAIgJSXFFFR0xICiKSSoEEIIIYR2Q7e0biz6tHch/QJwlMPyM7Vtxdu1SsMNPaUVorlcDni/B9QWu7dFDdIK0Q3+bfu1q6l6X2Zara5218Ho0aOHaV+PHj2wWCy4XC5GjBhxQprXliSnQnR4CxYs6LZDn4QQ4oQpdVfy5YeLYNO92hNiXe4P8H48HGtglhwhmqtsnzmgANPsSZ3V3r17AYiOjqZPH3P9DIvFwtSpU5kxY0annfHJk/RUCCGEEAJ+uMC8nvmEuWKw7sCbWpKsEK2p9GffbfboE9+OVuSZnG21Wv3WnrBarSeySW1KeiqEEEKI7qZwMzgqzduKtvoeV3UcQtNg4K3ubTXFvse1pl3PwgfJsOxs+GqqNgRLdH0lXkFF1KAm1/rqaDyDivT09HZsyYnRZYOKxioiC9FZyO+yEKJVHfkEvhgNP1xs3h5cTxJqj4kw6lFtPn+A6pw2bR4bbtOCmWOLIe9HOOqnkrHoevSeimH3w6SFMPmN9m1PnZZMkqoHFfHx8Z229kRTNGn402OPPcYHH3zAzp07CQ0NZcqUKTz++OOmastVVVX87ne/4+2336a6upo5c+bwwgsvmIqnHTp0iF//+td89913REREMH/+fB577LFWmZc3KCgIi8XCsWPHSEhIICgoCKWTR7qie1JVlZqaGnJzc7FYLAQFBTCPthBCNGZ3XZ7EscWw4Q4Y94y27j2eXReaphUdG3QbHPkQaktORCvdnJWNHyM6t32vw95/a8uRg6Dv1a126draWg4cOEBkZCRJSUkB3xM6HA42bdpEVVUVY8eOJSwszOeYsrIydu/eTe/evX2ChqqqKo4dOwZgFOrt6pp0F798+XJuvvlmJkyYgMPh4P777+e0005jx44dhIeHA/Db3/6Wzz77jHfffZfo6GhuueUW5s2bZxQjczqdnHnmmSQnJ/Pjjz+SlZXF1Vdfjd1u5y9/+UuLP5DFYqFPnz5kZWUZP0whOrOwsDB69erldyymEEI0mWfwsOvv2vCiAb8BV422bdKrsPoa9zFhadqrPbLu/FKoLQNHqW8ibU0x7FsIvS6GsNSmty1/ne+2Svm/vEsr3AKr57vXowa26uXz8vI4evQoAKWlpQwYMKDB410uF6WlpezatYuKigoAjhw5wsCBvu06dOgQxcXFbN68mejoaIYPH24U+83KysLlchEdHe0z61NX1aSg4ssvvzStv/rqqyQmJrJhwwZmzJhBcXExr7zyCosWLeKUU04BtArLQ4YMYfXq1UyaNImvvvqKHTt2sHTpUpKSkhg9ejSPPPII9957LwsWLGiVp7FBQUH06tULh8OB0+ls8fWEaC9WqxWbzSa9bUKI1lOZZV7f+2/3U2IU6H2lNvNT1XFtU2hdUGGrm0PfUQpLJkDZXq2isWdgse7XcPAt2PsKDL0HEmdAeEbgbct80ndbVV7g54vOp2izezkkSSt210JOp5Pt27cTFhZmuq88fvw4qamphISE+E2QLi0tJTMz0wgmdHl5efTp08cIGHSexxUXF3P8+HF69uypfayiIkDrpegu/4e3aLxRcbH2tCMuTpuzesOGDdTW1jJ79mzjmMGDB9OrVy9WrVrFpEmTWLVqFSNGjDANh5ozZw6//vWv2b59O2PGjPF5n+rqatM8vyUljXe9KoqC3W73+QUQQgghuiVV1XolKrWntqScDlnmh4XYo8Bi1RKztzyobfPpqShxD4HKXw89z3aff+gd7bV4G6y6GkKSYZ5XEFOf4p3u8z3VFvpuE12HPpVx+oUw+VWwhbf4knv37qWgoICCggJTgrTD4WDdunUkJiYydOhQ0zlOp5MtW7ZQW1vrc72amhq2bdtmuketra013ZsCVFZqQ/X03g7QppLtLpo9nsLlcnHHHXcwdepUhg/Xosrs7GyCgoKIiYkxHZuUlER2drZxjGdAoe/X9/nz2GOPER0dbXx1hwx6IYQQolUceBvejYa3LPBupDbMKaK/UfHXJLruRmvI77RlWwRE1xXlsvup9ltTYF5XvUYHVPn/f90vPaCIGQFn7YSh99a9R1Hg1xCdT3VdT1T00FYJKHbt2mUa/l5TU+NzTE5ODmVlZaZ9eXl5fgMKnedMTgCZmZk+xx89epRt27ZRUFCAy+XCYrGYKmh3dc0OKm6++Wa2bdvG22+/3Zrt8eu+++6juLjY+Dp8+HCbv6cQQgjR6akqbLzDN7l6wE3+awD0u057tYbAnLVw7gEIqRsPbg2FUK88Cc/ci5ba+pD2WrRVm040tu6pcN6PrfceomNxOd1D74JbnnegqipZWeaeMe+hTLr169ezdat7GuWCAi1ATktLM3IYe/fuTd++fQGtJ0OfjdHhcBjHDxkyxJSnkZeXx/bt2wGw2+3dZugTNDOouOWWW1i8eDHfffedMXYMtHFjNTU1xjgy3fHjx43M9+TkZI4fP+6zX9/nT3BwMFFRUaYvIYQQQjSgKgf2vOzOjdClngmD7oCe52q9D8mnga1uaFPGpe7jbOEQ7FHlV1Gg53m+71NxtHXbHRRnfq3Kgarc1n0P0f5UFZad7p4gwNLyGUA9ew70GUX1YUj+lJaWUlJSwrp164x70cjISMaOHcvw4cPp3bs36enpRmCgX19/tVqtJCUlkZaW5vXRVFMbuosmBRWqqnLLLbfw4Ycf8u233/qUGx83bhx2u51vvvnG2LZr1y4OHTrE5MmTAZg8eTJbt24lJ8c9z/XXX39NVFSUz/g2IYQQQjSDqsK3v4B1v9LWPYeV9L1Gu4ELTYZ5uTDrM5i7UUu6bmz4ydinYNo72vh33ZGPtNfyQ/7P+WY27Hii4eu6HO7lWV9or4kz3NtKdjV8fnuoLYFtf4ZjXzZ+rHDLXwcfpsH350H2Uvf2HpNbfGl9OFNQUJDphj4oKIiTTjqJjIwMQkJCTOds3LiR8vJy07ERERHGjE16jq5+/draWtasWQPQaN5ud8vrbVJQcfPNN/PGG2+waNEiIiMjyc7OJjs720hMiY6O5rrrruPOO+/ku+++Y8OGDfzyl79k8uTJTJo0CYDTTjuNoUOHctVVV7F582aWLFnCgw8+yM0330xwcHDrf0IhhBCiuyndDUVb3OsZl2vDS0KSIOkU93ZrkBZgRPaH8F6NX9caAr0ugilvunstDn+gvXreIHo6/g1suqfh667+pXs5bmzdewVD4ixtuaKegKW9OKtgySQtmX3ZXMj5ob1b1Hkc+1KbJvjoJ+5tv/gRYkc3+5K1tbXs3r2b/Px8QLuZ96wrERsbS1hYGH369GHUqFENXstfIKDPILVhwwbWrl1rbPcMXPRhUp6kp6IBL774IsXFxcyaNYuUlBTj63//+59xzNNPP81ZZ53FBRdcwIwZM0hOTuaDDz4w9lutVhYvXozVamXy5MlceeWVXH311fzpT39qvU8lhBBCdGdFW83rPSbD2Xu0HInguJZf3xqk9VoA5CzX6lZ4BhX9bwp8jHxVHhyoq54cO9o8DCait/aqzxDUUeSvh5JM9/pOP1PhCv8cXsORLHZIaHovhT7EaMeOHaxcuZKjR4+yf/9+QAsCEhK0CvGKopCYmGicFxoayqxZs0zbdN7BiOd2necQK89q27169TImLtJlZDRhOuUuoEkhVCClykNCQnj++ed5/vnn6z0mIyODzz//vClvLYQQQnQvh96DQ+/C6MfdN9eBqvTIc4geruVPBLXy1JYRfSGsJ1QcgcKNWo8EwOzl2tCl49+6Z/YBcFZrU9ounQFh6TDzE+2GssojsfYXXk/8o4dpr0WbtSFdHSXp9dC75vWyA+3SjE6p1iuocNU/45K34uJidu7cSWpqKocOHSItLc00nF4XFBREcnIycXFxRr0nb/6Ch8GDB/utX1HfSBrvNADvIVcRERGNfqauREr0CiGEEB3JT/fAIgV+uEibZnXz/U07v2g7lOzUlvv/Cs7c2jq9E/7EjNZeCzZqCdUAMSO1V4tXMdusr+CDRCjertXHyPpK264nkkcP883p0AuhHf4AvjtNy62oNU/t2S5+/od5vSrAWhzC3VORepYWVPb/VcCn7tixg8rKSvbu3UttbS0HDhzwe1xQUBCKohAcHFzvECTvqV5tNptRd81bz549CQ/3zTfyDjY83ysmJqZbzfwEElQIIYQQHUfhFsj0Smo+8iH8cClsfaTx87+eBp8Ph90vauttFUzoIuum0vSsimyrezrb5yrzEKh9/zHXsSjbq73qCd4hfmaAjBnhXs5eCosHw7entrzdra0qV+uJEW45K+DnF7Sha3tfAYeWf2v0VPQ8B847BuOfC/iSDoej3n3jxo0zlv31QnjzLEo3YMAAJk6cWG8QEBERwYgR7t/F4OBg+vfvT2RkpOk4z14O733dQffKIBFCCCE6Ms/k1b7XwP43tKTgQ3W5i+nzIGaY/3OdNZC70rwtqI2DCn1YVnFdfoElyJ0TMfReGHIPfDZMyz/I/sZ8rj4VbX5d4queoO3Juy6GfryzSksaP9FKfoZ9r7rXZ36qVQ6vKYSDb2lP30NaXm+hS1gxzzz8bc312pA5vUfLFhHw98rlcqEoCqGhoT5F6AB69OhBZGQkSUlJlJSUGDM3NSQkJISMjAxqampITU1ttFchODiYpKQkVFVl8ODBRi0LT549Fd0tSRskqBBCCCE6huylsOUP2vJJ/4L+10PWEqj0GFpTvr/+oOLgIt9tIUmt305Pek9ERV1RWqvXE2JFgajBWlDhnaCrfy49uIgc6Hv9+m70yg9D1AD/+9rSsrlQts+9Hj8JYsdq+SSrf6l9Py6Qmhpkf2MOKHSe3zt/xRe9OJ1Ojhw5wqFDh+oNKACjJMGQIUOa1EzvnIiGKIrS6PU9eyr8BR1dXff7xEIIIURHk79OqyuhSztTe1W8prf8+QV4P1EraudJdcG2R83bFAskzmz9tnrSg4rKY9qrvzoXPc81r/eZr70e+C983BtKdmjr9SWST/ETLFVlN7mprcLzphi0Nnv2sPi7ke5utv8Fvp3d+HGehRXroc/o5HQ6jYAiNjaW4cOHG/UmQkNDO8wNvGc7OkqbTqTu94mFEEKIE+3Qu7D6Om32I2+le2DzA+ZtoSna68DfQHACUPfEPusLqM6FtTeBo8J9fHGmlqNgDdOmjZ34Hzh5CYSnt8WncfO+MfQXVPS5Gnqe716P8Zh2s/yg+0a9vifXvS+DiH7mbe0RVHjnTCTO0BKN48b5P767OvaF/+1nbIFTv3WvBzA0r6CgwGdbz5496dGjB5MmTWLixImMGTOmuS1tEz169CAkJKTepO+uTIY/CSGEEG3p0Lvww8XacvIvoPel7n17/gVrb3Svh6bA8D+61/W8hINvwY9XmK+7/3UYUDdzTuFP2mv8eAjPgH6/5ISI6G9e9xdUKIoWWBz5UFuvr8hZQ8Nhpr8H2d/CvoVQvA0KftJmnIoZoU1dO+YJCIpt1kcISPFO+OEC9/rFFe6cjviT2u59O6PKegI+e7Q2LbAugEkEqqqqTOsWi4WYmBhj3XsGp45g2DBteGJ3m/kJJKgQQggh2tbe/7iXyw+4l0v3mAOKiP5wzm7f8xUFkk723V68XbtJy1+j3WCDVv+hlZWWllJUVERaWprvkI6gaDh9I3w5tuH39+zRiBygBR/evTb2qPobETta+yrergUVOx4z77cEwYQXAvk4zbP/VSje4V63edzMRvSBwb+DnXXFAF1OsPjWOujydj6jBXhldYUKz9gGjjL4apK2HhQDdo8Zkewx9V5KnzbWO6iIioryW0eiI+mOwYROggohhBCiLen5BgA/Pwfp58PB//kOE7E3UChLHw7lqbZEe3K/5jr3tmDfKsFN5XQ6yc3NJS4uDpvNxtatW6mpqcHhcPhPbI0a5F5OOsX/RT0DBnuUFkB5TkMLASXu1ntMW1fcdtY0vH/kI+6gwlkBlm42nWjeGtj4W/e6YtNmerKGaD1tFrv7d2DOWrAENxh4HTp0iLw83/yU5GQ/0w6LDkOCCiGEEKK1OSph19+1fADP2Zsqj2q1FvxqJM1x3LOw/RFInKUVxastgePLzMeENB5UVFZWsnXrVtLS0khLSzO2q6rKmjVrjKfD4eHh9OrVi5oa7Yb64MGD5Ofnm5JkAbB5zPhU32xTnj0V1nCI9BdUNNBT0dgxFrv/7a3F0UjBPc/pbR3l7ify+9/UAr8pb0BoF74h1nsjdLGj3b05Yx4374uf0Ojl9N85u91O3759ycvLo6KigoSEhFZorGgrElQIIYQQrW37o9osOFsfAlcjT7l1/a9veP+gW7SvA29pQcWRj3yPieznu83LoUOHqKioYPfu3SQnJxvDSfbv328ablJeXs7hw4dN55aVlXHw4EEGDRpk2k7fa6Fgg1bQzJ+wnjD6cS2R3Brkf5iUv3wMb7Ul/re3dVChv29Ef5jyX9/9iqJ9NmeFe1iXqsKqK7Xlfa/CsN+3bRvbS02h77ZAep0aoBe5GzhwIAkJCUYPRXceWtQZSFAhhBBCtDa9B0EPKIJ7QK9LYPfz2nrUIBi+QKvdkHKaNnyk10WBXdvfDVvMKK3oXOqZjZ5eXe2exWjNmjVMnjwZRVE4dOiQz7FlZWVYLBbsdrtxXm1tre9FJ73SeLuH3uNeDs/w3R/IDWN9T/uVNr6dqS3WXoc/AD0m+T8mPB1KdmnD2gbd4r/KeEe382ktKHJUwKBb/Q+78+Sshi/8FC30zB1qBj2o0AvISTDROUhQIYQQQrSWoq2w5CSt4rOnmFHaTVp1jjZEaPhD5mrC/m6y6xOW5rtt7katLkUAPIOKmpoatmzZQt++fQkODjbtM5oeE0Nqairbtm0zzmmx9HnmMfiBGvBrOPy+uwq3rq17KvSn8Q0kFzPodlj3G8h8XJuV68jH7n0B/mzaVflB2Hinez33B/jF9w2fk/uDOYCI6K8lave7rt5TGpKTk0N+fj7l5VpvT3esSt2ZdYLfciGEEKKT+PYXvgEFQOoZWu/EtHdg/LPmgKKpYkf5bgvwprWystK4YdMVFhayYcMGnE6n33MiIyPp0aMHo0Zp71vfcU0S3gvmbob+v2raefZIGP+c7/a27gko36+9NhT89b1Wy/moOKLNUlW4yb3P3+9ER+NdhyN3RePn1HjUkeh3HZy+DqZ/CIPvrP+cejgcDnbs2MHx48eNbcHBwU2+jmg/ElQIIYQQrcHlgCr3DREDb3MvN6UnIhCj/hLwoUVFRfz000+sXLmSNWvWAFoCbGpqquk4fcjJ8OHDTdv1Gzs998LpdFJcXEx2dgsL0MWO1AKs4X+EU5cFfp6/ZHTVUf/x2Uu1/BbPGgmBOvQ+/HQvVOVo6xF96z/WGgyxdYXYVv/SXF3b1QmCioa+h/WprUtgTz0DJv5bmzY2/Tzte9FE/nrJ7PY27oESrUr6lYQQQojWsNujTsIZW7R6DD//Q1sPTfV/TgOOHDnC4cOHGTZsGFFRXrMeDf29doO78lJtuQGHDx+muLjYWA8NDWXo0KGmbZ7i4uJITEwkJ0e7kdZv7PSgorq6mp9+0ortRUZGEh4eQIJ1fSw2GPlw084Jz9DyTw69697WUE/At7/QXqOH159I7o+qwg8XutcVq1aXoyH6LFB6MUKjfb43zB2Ov+9h3lotUIga6P8cR6n2amv5FLreQUVISIjkUnQyElQIIYQQLeWsgi11lbAnvKRVegZtKEjJzvqTe/04cOAABw4cMNYPHjzIiBEjzAcpCmRcotWFCG54KFVpaalpPTk5mcjISCoqKnyOtVgsWCwWUlNTfYIKfXy76vHEv7q62ieocDgcbT8W/qSXtdmmKrO1GZdK9/o/zrN34vtzYerb2vetPoWboGyflvNR5dUTYwng6fvwP0DWEt/tnWL4k582fjVRe728nl4efardhmqsBEgPKiIjI0lKSpLpYzshGf4khBBCtFT5YW2GIFs49POYGjb9PG0q0QCfuFZXV5sCCtByHg4dOkR2drbphh6AkIQGr11bW+uTWK0HCQkJCfToYQ5IgoKCAIiOjiYmJga73U5EhHbD6K+S8Z495qJzx48f54cffvCZirbVBcXAOXvh7F3aesF6rTaIN++q3SsvrX8YVHU+fDEGVlwA+eu0YNCTZy2K+iRM9T9drquT9lToHL4BKAC1ek9Fy4MKPciNioqiZ8+ekk/RCUlQIYQQQrRUda72GpLUYKXgxhQUuBNfIyIisFgsuFwu9u3bx86dO/32LtTH6XT6rUqsBw4Wi4Xhw4cTFuYuXhcXFwdoU3iOHDmSyZMnG70OVqvVCDB0FRUVpkAnMzMTgL176+k5aG2haRAUC6hQutt3/6H/+W6rzvd/reId7uWsr/wEFQHe5PaZ76cd7/pu62gaCio8c4U81bbe8Cf9d9vz91F0LhJUCCGEEC2lV80O9l/ROisri61btxrJ0PXRg4qMjAzGjRvnc4PlWZyuMdu3b2fXLu1Jfnx8PDExMURERBAdbc4LGDBggLEcH++ufK0PhdLpgYZnFW6AkpJ6CtKdCIoCUXUVyvcthMrjcPQzUF2w+0VY46egYK1HLsmef2t1JcCcWH38W22KVU+WAHoqQKtl4a3yWPMSxU8kPZk8aojvPs+q8KbtR7XXFlQLV1WVzMxM8vO1YE+Cis5LggohhBCiJVxOd0JvmG9CdmFhIbt27SI/P99vz4FOVVVjf3x8PIqikJKSQkiI+2a2sRoRhYWFLFu2jL1795p6PSIjIxk9ejTjx4/3mVHHs/chJiamwesHBQUxYMAAxowZY2wrKipq8Jymqq2tJS8vj/379/PTTz81PoWtHlTsegY+TIblZ8GBReY6EaY3KNJeyw/C2htg2RnaNLB6bxNA7krfp/P6tLKNsYZoQ7OG3AP9b3Rv1/MPGpOzAr6dAyU/B3Z8a9F7KkKSfPeV7XMvqy44+D8oOwDldQUTw3o1+22LiopM08i2KPFftCsJKoQQQojm2vUcfOIxzWi/G3wOOXr0qLHsb9pM0AKKtWvXGkOJ9Bv9tLQ0Jk2aREqKVtm4oqKCrKws/1Wtgc2btSrOnjkNFouFxET/PSig5ViMGzeOCRMm+M2b8Cc6OpqBA7UZgQoLC332t2TWnu3bt7Nt2zYOHjxIcXFxg4EYoM2y5e3Y5+6b+GnvwnlHtNmfwF3IzrNnYscTWi+HzlUN+15t9mcgoi+MeRzGPu3eVrQtsHOXzoDsr2DNtc1//+bQZ6iyhUNEP/M+z6Fg2x7VclPW3uDuqQj3k0cSIM/f5cGDBxvD80TnI0GFEEII0Rwlu2HDrVBR97S23w2QerrPYZ69C/v3m592u1wusrOzOX78OJWV7kRjz2FH4A4yDh8+zK5du/jpp598krb9BSyRkZFMmjSp0SElzZkaVu/VKC4uxul04nK5jH0tGcLi3fPheV2/7FG+22xh7hoKtkitCnmQ1l4jqKj16Dk49jmUtkHPgC3M/RT/6ylw4O3Az63KbfyY1qQPC7NFwOzvYcKLMK5uSuSc5dqrswa2PqQtZy919+Y0MgNZQ/Tf29jYWJKTmz+MSrQ/CSqEEEKIpqothd3Pm7eNedzvod55FJ5PZjMzM9m5cyc7d7qfBPfv39/nGt55EBUVFT7F53JzfW9Cw8LC2uzJb2hoKMHBwaiqSklJialSd2vO3FNfr4whzU/tCWu4x3SndUnEevChJxd7DkeqLYaiLS1raH1Uj+Fb2x8J/LxAbtQrj8PeV6DGf82RgK2/HTb+VlsOTdWG8Q34FaSdpW3LW6W9R/5q/+cHxTX7rfWgW4Y9dX4SVAghhBBNteE22PV3bdkWDnPW1c1C5FZRUYHL5fK5KT58+DDbtm0jMzPTJxAYP348PXv29Hk7fzdcZWXmMfp6XQlPnvkYrU1RFCIjtRv2iooKUz2MRnsX6uH9mSCAoCI83V0XRGcLc08nq093aqv7HurTo3oGFdW57qAiZmQTW92Ifh7DmJweOTH567QhVi6PoKPimHvZ39S03rY/qiWjfzm2+e1z1bqLNILWq6OL6AORA7XAaPfz7srinizBYA0N+O0qKyvZvHmzMWxOz/2RKWQ7PwkqhBBCiKY4/IF5vP0ZWyB+vOmQnJwc1q5dy759+4yb4tBQ7cbr0KFD5OXlmZJTQUuC9p6yVacoijHdq84zgbmmpsaYhckzEbutn/7qQUtVVZVpFih9WJfnkK5AHDt2zGdbYzNmAb434IrNtzCbHlQ464INf4nTMSNgmMfsTX2v0XIyADIua7wd/gy5B4bcpS1X1c2i5HLANyfD6l9qs1bpDr/vXvYKUv3SE6jL9kFhM3tavGe5CjXP7kXKHO118wP+p8YN7hFwHRbQgurCwkI2b97Mrl27jB4uyaXo/KSithBCCBEoVdWKo+kG/EZLyq1TVVXF2rVrjSf1R44cAbQciYiIiAZvshtLbu7Xrx9RUVEoisL+/ftNN9v6DX1ERATDhg2juLgYm81mmiK2LehPl6urq03Dn0pKSigpKcFqtTJ9+vSAruV0On0CLQgwqFC8bme2P+pe1nsqrHpPRV07a/0EFUmzoddF2lCpuLEQUpfgfu4BCPXtQQqIPQKGPQiZT2rvffgDsMe425G1BPrXTX175EP3ed4F81TVffOevx6yvwY8fmcOLoLYZvSy6DM46cK8gor08+HnZ7VlPeixRYKjrmcq+dQmvZ3nZABZWe6par2H+InOR4IKIYQQ3VNVHhT+pN0UKQF23B//1r2cMgfGP2vavWnTJr9Df0JCQkxPYnv06EFkZCRlZWXGEKjGehXCw8MJDw83brxLS0spKytj3759xtCjsLAwQkNDjV6RtqYXxnM4HH4L8zU6HayH0tJSnE4nwcHBpKWlkZuba2xrlJ6E7Xdf3RN/m1dQUeOnCF7ybO3G3TvhPjyj8TY0xDOZ3DMoBXOPSalHhXLPYnQFG7VZoRwVWgL1xt+C0ytAPfg29P0lHHgThv5eGwLWmNoy+NYrKAj1mhY56WSIG69VLdfzQ2JHatPuQvN7cDzMmDHDZ3IC0fnIT1AIIUT3obq0J74b7oAPEuC702DPy4Gfv6MuGXvAr+HkL03BSFVVlak4nWc+Q3R0NMnJycbNflpaGhkZGQwbNowhQ4YQFxfHoEGDAmqCfiNfXV3N+vXrKSgoMIZYneghJPpT55qaGp/ZqJpKnw5XURR69epFr17arEkBFfzTa1X4Y6kbDuYdVFRm+x6bOC3Q5jaNokD/X/nfp7en4ghUuKcCNgUVO/9Wd5wK637lG1CANozp29mw7RFtWFUgPHtGQCt85y+A6nmeeT3pFAiO14ZKNbGnwl/QLQFF1yA9FUIIIbqHHU/A5vvMs/EArPs1xI6FHic1fL6zBnJXaMsDb/HZfeDAAWN54sSJZGVlceiQNrQkMjKSyMhITjpJew/PoU5JSUkkJfkpOFYPPajw50QHFXpbGrrxV1U1oLoVelCiX0v/LBUVFdTU1DT82QbdAYWbtZvyvB/raaxHUOFywN5/+R7jb3ra1uKdTK7Tg4qvZ5i3H/lQG+YUPz7wKVsrtOF2HHoH+F/jxx/5yLx+xhaw+Pn98k5eD4qDMzNBsbqDtgDpP+fg4GCqq6vbfIieOHEkNBRCCNH1ZX8Lm+7xCig8bnS/ngoFPzV8jSMfaU+PQ1O0J7oeHA6HMcXr2LFjCQ0NJSEhwdiv3xAritKiwnCAMeOSP/UlercVvaeiobyHpgyBAvfni4qKMp5g+xtaZWILhWlvm5OsvenDoArWw8/PubePfw5QIOPyJrWzydLPhwQ/PSHOcm0GKH8Vu5fWBRq1Jb77GuKdY+KPswaOfeFe732V/4ACIO1MOGUpJM4Caxj0uhhCEiC46VPJ6j0VaWlpjBo1KuAeOtHxSVAhhBCia9v+F99x4wB958NJdU+rVYc2LafnEJ789bB4COz5lza05Kffadv73eAz243+dN1utxMVpT3tjoiIIDg4GIvF0mAg0FQWi8UYGqTLyMggOTmZ2NgAZgxqRQ31muh27dplmm7Wm6qq7Nixw1gfOnQooAVgevJuQEOgAKIGmtd7THYvh9d9z0p2umsy2CK0ZPt5OTDlv4G9R3OFpsAvVkD6PPN2R7mWB6HzrPngrNSG7FXX5X9EDYIeUxp/r0Bu9msK3MOoTvoXjP9H/ccqFm2Y0ylLYV62VseimfSgwmKxEBsbK7M+dSESVAghhOjaNtfz9NoeDZEDzNtKd2uvLicsmeC+Ad3+f9rQkqghMOh2n0vpBbw8b5AURWH8+PFMmDCh1efg79Onj8/64MGDW9wL0lShoaGNjofPzc1lw4YN9e53OBymGhueuSh6DkqjPRW6iH6QUpdknXYOTP/AvS/MT67A0Hu1ADGkR+DJ+i3lMVsYoP1erZ6vLUcOhCmLzPvz17tnWhr+EMz6TMvpGXZ//e9hD2AmJT1B3BapzT7VULK7zmJ1FxMMkP7z1fN+PIMK0bVIToUQQoiuzWLXCnzpTnoZ9v5bmyGnOs98bN4q7Wn30U/d2xzlsOclbXnovT5PgZ1OJ1u2aDUCvJ+62u12U92I1qI/xS8ubmEl5RayWCxMmTKFH374AdA+b21tLeHh4bhcroDqVHgWt0tLSzMFRvqMWGVlZaiqSnl5OSEhIfX3kCgKzPxEewLvnR8R7qeYXCBP/VtbQ/UnIvpCkFdAUHHQ/ftrsWs3/xNegPLDWi+cP45y/9tNx3jV8WhlxcXFlJeXU1RUZASNvXv3prpamypXgoquR4IKIYQQXVdNofuGbOSj0OcqbRhM/xu0bd5Jptse0eoDrL3J//VCkn02edZWCAsLYBrPVjJo0CB27NjhMxTqRPOsOxAaGsrEiROxWq2sX7/edFx9Cdt6PkZISAgDBph7jvSgoqCggJUrV+JwOAgLCzMS3v2y2P0nD1tDtMrPnjMnxTeSnN8WGgwq+vj2MtSWmoMKnWc9id5XmIdQNRZU5K+DA3U9ItaGpzJuDlVV+ekn3xwlz8kMJKjoeiSoEEII0XWV1SW/hiTBcD/DoILjYcSfYOsf647faw4ohv9Be5q9bK627jH0w+VysX37dvLz3fUOTuQNflhYGOPHj2/8wDamKAo2mw2Hw0GfPn2MXoTg4GBTQTyXy0VZWRlFRUUkJCQYAZjeU+Gv98GzdocefAQ8FMqfoBjw7D1p5lN6VVXJysri6NGjhIWFMXTo0MCHnlk9aohMf99ctyKir/+gQq1LhFc8ggrFAuOfh5JMGPuMNkOT6oTN92tBhWexPE+le2DJRKAuf0it9T2mBVRVZdeuXY0eJ0FF1yNBhRBCiK6rbJ/26j2O3dOIP0C/6+CjNN99Pc+FuHEw5G4o3m56sl1WVmYKKE466aRWz53oLMaMGUNtbS0xMTHGtj59+hAREWFMq5udnc3u3VrOyv79+5k6daoxXArwO0ys1YeOtdJT+YqKCn7++WcAysvLqaioaLR4ocGzFyH1DPO+0DTf4U+OenoqAAb+xr089B6oKdaCCtWhzVRm81MEMW8NRkAB2iQErWjv3r3GTGiehgwZQk5OjvE30xbDAkX7kjBRCCFE15WzXHttKKiA+meziRunvY75q5YgWzfl5tGjR9m4caNxWFRU1Akd+tTRhIeHmwIK0KaG7du3r9ED4RmAAUY+iD6z0wkJyGJHu5cb+51ogHeuiL+b6Hr1ulgbRtf/V9qQLM/CfcHx2pSt0cPd2+ob/uSPPUqb0Qq0SQb8FSQ8tti8nnZ24G0PQEmJ/+lvIyMjychwJ8tLUNH1SFAhhBCi4ynaBvvf1Hoayg83frw/ququRxDep+FjvVnD4Bc/+N3ldDqNJ+4AMTExjBhRT2EzYSSveyeV67MA6UGFPtOTN3/f26bWvjCM+asWTIT3gZmfNe8awLZt20zrZWVlgZ8ckgDnH4WTXtTWT/5Se43oB4kztCFLc3+Cofdp2xvqqfCmKNoMUqBNkbzyUt9jCjdprxNeglF/hjFPBN72AOgzoXmzWq2mIW4SVHQ9MvxJCCFEx+KsgW9ONs/MNOh2GP04WJvwNLvUfePvM8ykMRcWgtX//Pl79+41rQ8aNEhukBoQFhZGRUWFTyCgzwKkD3+qr15BfHw8ISEhploVDofDlCAesIg+cM7exo9rIs8ZrALiOX1teAZc5tLqUVjqPpPFpvVaQF1ORd31AylqF38SFNb1ovmrrK0X0oufAHFjm9buRqiqagQV48ePx2q1smbNGkDLmdH/TrwDDNE1yE9UCCFEx5KzzHeq111/h8osmPY/v6f4lfej9powDRIm+z3E5XL5JoxaQ+sNKJxOJ8eOHTPWhw8fXu8TdqGJjY0lLy/PZ7seVOjBRkNBwqBBgygoKODwYa3XyuFwEBwcjNPppLKykvDw8BNeowMwgp0mBxXeFAUUr8+vTwrQlJ4K0IrU6VMgg2/Cdm1dzQvvKXdb6OjRo+Tk5Bg9UKGhoVitVgYNGoSqqsbPd9o0rap4e/y8RNuS4U9CCCE6luxvtNeMy7WaEqPq5uI/9C5UHq//PG+5dUFFPbUIjh49yvfff+8z1p8wP/UM6ug3tQCDBw+mR48egbenm0pJSTEVtEtMTASaFlTExsbSr18/4zq5ubmsWrWKFStWsH79enJzc9uq+SZOp5OioiKjrfoUuPrMVK3KVhdUeM7+FEhQkXSyeb3YY6iW6nIX0mvFoCIrK4vdu3cbQ9zCwsKM71FKSgqpqe6cJZvNJr0UXZQEFUIIITqWyrqegLgxWj2JYfdB9DBAhfy1gV/H6KnwH1ToeRFbt27VNkx6FUJTYeoiv8eDdvME0LdvX5KTfWtWCF8Wi4X0dHegpie0l5WVUVxcHFBQodNvRg8cOGAEJeD+ubS1n3/+mU2bNhlt1oMcp9NJdXU1qr/E6OayewQVVXUVx5UAgorgeEic5V7/fCR8MxuKd7oL3kGrBhXeU8h6BpGi+5CgQgghRMeQ8wMc+9J9AxWc4N4XV1ePoWBdYNeqKdKmgAXo4X/ok6fi4mLoO19LoNVnfPJSUlJi3MimpKQE1g4BmG8y9eFilZWV/PTTT0bdiaYEFd5O1FAaz0KHoM1YpecJrFq1yqis3ir0ngrP3/lAeioAZnxgXj/+DXw2BI594b6OxTc/qaioiNLS0oCb6HQ6fZLWgdYNrkSnIUGFEEKI9uesgqXTtSJzJTu1bSGJ7v3xE7TX/HXgrIb89dpQDp3qMk+fWVaXjBuSbL6OfrjXTc+RI0f8HlNUVERtbS2qqrJvn1bzIj4+XhKzm8gzGKhv6t3OEFR4s1qtpvoUhYWFrXdxj0KLhkCDiqBYOG2V73Z9NqjIAT6F8Wpqati0aRMbNmwIOCjIycnxmy/jmVQvug8JKoQQQrS/gp/cyxVasTRCPWpHxNUFFQXrYf0tsGQC7HlZ2+ZyahWCPxuq9VAAVNflSYRovR0ul4uff/6ZLVu2UFJSYpoSFrQx+p4J2KAFGps2bWL37t1kZWVRVFSExWKhX79+rfGJuxXPgCEkJMRvENDRgwrvG+3o6GgURQm86F1T2VoQVAD0mASTX69nn++QQM+8kMzMTJ9aHIHQf4Z63ozoXiRTRgghRPs7/J55XbGai4LFjtJeq/Ng77+15S1/0HItfrgIquqGpRx4U6uCrQcVQfFkZ2ezc+dO41IFBQXGcnBwsDGk6eeffyYlJQVFUXC5XMbUsTk5OeTkaEOyMjIyunWRu+bynC7WZrMRHBzs8zS7JUGFzwxebcBzStxp06YZ7Y2IiGibN7T7uW4gU8p6Cq2nqOPwP/he2iMwy8nJoaKigvHjxwf8VsHBwYwbN46CggISEhIaP0F0OdJTIYQQon25auHAG+ZtUYPNNSmswRDWy3yMNQy+O90dUIDWi/HpQCjeoa0Hx7N//36/bxsfH8+oUaNM2/Qb3fpmE2qzp9JdXFBQEIMHD2bo0KEoiuK3enYgQUV9x5yIngo9qFAUBavVaryn9++EZ49GRUUFhw8fbl6Ogc1fUNHE2hz2aN9tEX0hvJfPZu82BlLQT/+exMbGMn78eIKCgkhOTm5eDRHR6UlQIYQQon3lrHAnZ+tiRvoeN/5Z87o9EpwVvsc5K2H7owCowQmmWYI82Ww2n4JrJSUlFBcXk5mZCUBcXByDBg0y9svNUvMlJycbw2L8FboLJDCor5J2W/9cKisr2bx5M6D1ini2NSIiwtRT4nlzvnbtWvbu3Wv0dDWJ1U+PmK2JQa2f4IF6ApzmBD6es2BJnpGQoEIIIUT7yqtLKA31mFHJX1CRMse8rs/u1AAn/ovY6Ww2m6nWRGZmJj/95M7viI6OJiUlhYSEBMLCwoiKat2CYd1Vc29A66u63dANcU1NTUBP3RuiDwcC38DGYrEwevRoY13fX15ebmpDk1k8AqV+N8C5B5tWUR60SQpO/RZOW+2x0eX30JYEFSdi+Jno+CSnQgghRPvSZ2pKmKYVuAN3DoWnhm6oQlO0itteatHOCQ0NNfIh8vLyOHr0KBkZGQAMHTqUffv2+Z0BKjY2FoBhw4ahqqpUAW4lzQ0qUlNTOX78uOmGHRq+IV6zZg1Op5Px48c3O/+hseJ2UVFRKIqCqqpGRWnP3okW96RE9vff6xAI72J4Vv81JFoSVEgPngDpqRBCCNHeKuqqVEcPc2/z11PRkL7XupdT5kDiDEicRVHS1YAWVCQnJxMVFUXfvn2ZOnWqkXBtsViIj4/3e1nPngkJKFpPamqq37yKxthsNgYOHOizXb+R90e/8W3OdK8FBQUcPXrU1DvhORzOk/60Xg9APAOfFtdt6DGpZecDTHxFK4w3caHf3d7fQ6vVSnFxsU8A56m2thZofpAouhbpqRBCCNH2aoqhpgAi+vju03sYekyG2LEQFFP/rDX+hCTCoNugphB6XQRJs4xde3/4AfAdNuM9XCMmJsZnv+eQFtG6goODmThxIoqicOjQIZ/vf0P8PRXXb24bsnfvXmpra+nbt29A76OqqlHMTk/G7tevX72FDyMjIykqKiInJ4c+ffqYejcaCnoadOZ2KNmlBckt1e9a6PtLn/oUOu/Ax+l0GkMBZ82a5fccPV+pOQGi6Hqkp0IIIUTb+/48WDwYijN99znqxrvbY+D09XDqN/Xe+DDpNehztXlbTaEWWEx43hRQOJ1O48ausSep3r0QkydPlvyJNqYnPGdkZBAd7WeWonp43sDqU5fq+Q7evG/mDx06FPANvmeCv77c0DCftLQ0AA4ePEheXp4pqGh2T0X0UEg/v3nn+tNAb5veRn/5EfV9z/RckfpyXUT3IkGFEEKItlWZDTnLwFUD+/0U43LU3RDawhq86QGg79Uw+TU4Z697m8v/U+rS0lJjuWfPnk1qsgzn6LjsdrtRryI9PR3Qbvr9zQzlL0G6sfwInWfxN/2choIKz4T/bdu2mepwNLun4gTSgwp/v/v19QQFGrSL7kGCCiGEEG0re6l7uXQ3uBxw9HNw1N20OT2CikBF9IWIusrW/ubiR5seFrSn2U0ZniFJpx3fxIkTmTBhAlFRUUaAUVlZSV5eHtu2baO2thaXy2VMA+sp0Bt8fzfSDdUpURTFVJyvVYY/nUCBBhVlZWVs2LCBY8eOBRRsie5DggohhBBtK+sr9/Lh92HT72H5mbDuV9qc+XpPhb95+Rsy6zNIPQNO/tLvbj2oCHQYkz4ERx/GIjouu91u3OCHhoYCWuHCbdu2kZeXx8GDBzl48KCpt0FXX60Lb/56NBqbPcpfEjm0QqL2CdDQ8Ce9x8fhcLB+/XpKS0v5+eefjXMkqBDQjKDi+++/5+yzzyY1NRVFUfjoo49M+8vKyrjlllvo2bMnoaGhDB06lJdeesl0TFVVFTfffDPx8fFERERwwQUXcPz4cYQQQrQSZw3krqx3aNAJc/QzOPBf87adT2mv+18HVzVQd8PlUdjL4XA0nnwbNUgLLPzMjKOqapODimHDhjFkyBB69+4d0PGiYwgJ0aZILSoqMrbV1tYaP//+/fszadIko7eqvqBCVVUyMzPZv38/qqr6/P4FcuOcmJjo9/etM/VUKIpiBGo6vc6H55BCTxJUCGhGUFFeXs6oUaN4/vnn/e6/8847+fLLL3njjTfIzMzkjjvu4JZbbuGTTz4xjvntb3/Lp59+yrvvvsvy5cs5duwY8+bNa/6nEEIIYbbhVvh6Gmxd0D7vr6rarDXLz3JvG/5H3+MO/s+9bA2tO1Vl06ZN/Pjjjyxbtoxly5axfv16srOzA3776upqampqUBQl4NoEQUFBJCUlSSGvTka/AfasM2KxWEzVnkNCQowb3/pu8MvKyjh+/DgHDx6ktLTUp6ci0N8Lf/k7namnQlEURo0aRd++fenTR5utTQ/Y/PXeWCwW+ZsRQDOCirlz5/Loo49y/vn+ZyP48ccfmT9/PrNmzaJ3797ceOONjBo1irVr1wJQXFzMK6+8wt/+9jdOOeUUxo0bx8KFC/nxxx9ZvXq132sKIYRooj0va6/bH2uf99+3UJvtSZcwHTIu8z1u9TXaqyUIVbGiqirl5eWUlZWZbsTKysrYu3dvwDdn+mw9njeTomvSeyo8Wa1Wn8Js+o2vZ09FdXW10SPhmdRdWlrq06MRaG9DQkKCEejo733s2LGAzm1P+udTFIWQkBB69eplJJ8XFhbicDj89vLI35fQtXpoOWXKFD755BOOHj2Kqqp89913/Pzzz5x22mkAbNiwgdraWmbPnm2cM3jwYHr16sWqVav8XrO6upqSkhLTlxBCiACEpZ+Y9yncAj8/r+VLrLoGDrzp3mcJglO+gujB9Z6uhvdh06ZNrF+/3vg3XlEU0tLSjBl+amtr/Y6R90e/QZRZabo+z+RondVq9Uki1l/1G2OHw8HatWtZvXo1LpfLNNypvLzc5wY60FwMRVEYN24cgwcPJi4uztje0XsrPHsqdGFhYYSGhqKqKlu3bvU7JFGmkxW6Vi9+9+yzz3LjjTfSs2dPbDYbFouFf/3rX8yYoRVuyc7OJigoyKfQTVJSUr1d24899hgPP/xwazdVCCG6vpCE1ruWqvqf8lV1aXUoyve7t1k8bubHPAnWuqfJw/8A2x7xuYSz1xUUFxYDkJ+fD2gJ0/379ze2VVRUUF1dbVTC9lRRUUFBQQHx8fE4HA6ZP78bqa8auh4E6EGH9/Cnqqoq45i8vDyfGY5a8rtjs9mMCu65ubmAFsR05CDXX6K2oij06NGDw4cPU1xcbHyPQkJCjClz9VohQrR6T8Wzzz7L6tWr+eSTT9iwYQNPPfUUN998M0uXLm385Hrcd999FBcXG1+HDx9uxRYLIUQX4/lE1N4KBdxUFb45FT5IgKLtvvsrjpgDCnAniKeeBQN+7d6ecrrftyiJdw+pLS7WggvP6Tv1IS6ec/97Wrt2LXv27GHNmjVs2LDBSCyVoKLrs1qtPnkz5eXlPj0V3sOfPIczZWVlmQrolZSUkJeX1+K2hYWFGUGNv5oZHYn+ffHOj0hKSjKW9e+RZ2DvuV90b60aVFRWVnL//ffzt7/9jbPPPpuRI0dyyy23cMkll/Dkk08CkJycTE1NjWmWBoDjx4+TnJzs97rBwcFERUWZvoQQQtSjtti9bIts+fUcpXD8W6jOh/2vaQnYmU+Co1zbX93Azdek/4DFo1M8zCOJ9dRvtdeMyzhS4L7B028GPYMKfbmkpARVVY2cCYfDwdGjR33eNisrC5DhT92VZ0DgPfxJDyY8g4rKykojEG1temC7bt06ysvL2+Q9WiovL499+/YBvoF4REQEqamppm2RkZGkpaXRu3dvn5miRPfVqsOfamtrqa2t9YlyrVar8cc7btw47HY733zzDRdccAEAu3bt4tChQ0yePLk1myOEEN1T7o/uZaUVnh3VFLmXdz0DmU9oy5ZgGHSr+/1iRkJIEmR/ra1bQyDYa2hKeC+Y9i4ExUDSyXD2biqVeArW+xYp8wwqoqOjOXz4MFlZWdhsNg4fPsywYcPIzc0lJyen3qZ39HHsou3Vl1PhmSPhOWSutQUFBRlP+A8cOMCwYcMaPF5VVSoqKggLCzPlN7Slbdu2Gcv+eve8hxxaLBYGDBjQ5u0SnUuTg4qysjL27NljrO/fv59NmzYRFxdHr169mDlzJnfffTehoaFkZGSwfPlyXn/9df72t78B2n8M1113HXfeeSdxcXFERUVx6623MnnyZCZN8p1rXAghRBOU7NYKy+mc1S2/pmdQ4Vn3Iu9HiB6mTV8LENwDel/pDiomvuI/qOl1oXs5sj/5HlOB6kJDQ02zyuiF6QBjCOyePXuMHov6yPCn7qGhHin9xryh4U+eQ6UiIyON0RRBQUHYbDbT0KiWaOz3FbQHrdnZ2fTv39/v9LStzTvw9vc3ExMTg91uN3Iq6htZIrq3JgcV69ev5+STTzbW77zzTgDmz5/Pq6++yttvv819993HFVdcQUFBARkZGfz5z3/mV7/6lXHO008/jcVi4YILLqC6upo5c+bwwgsvtMLHEUKIbm7TPeZ1V4BBharCsc8hfgKEJJr31Rb5P+fg21Do0cOQdjb0vVr7agL9hs0z+dOzlwL83zQ2NpVlWloaKSkpTWqL6JwGDhzIxo0bcblc9c7S1NDwJ114eDgRERFGUDFu3DiOHTvGwYMHmz11akZGRoN1Hrzpk9YcPHjwhAQV3jM6+ftbi4iIYMqUKUDHTzgX7afJQcWsWbMa7E5OTk5m4cKFDV4jJCSE559/vt4CekIIIZpJz28ISYSqHHAFOKTj6GL4/hwITYXzvXIUPHsqvJVkaq/n7IWIvvUeplfI9jf+Wn96GxERUW9QATBy5Ei2bNnS8OdAG6qRlJRERkZGo8eKriE0NJQpU6ZQVlbGhg0b/B7j3VPhL/iIiIggPT2dI0eOEBQUhN1uJyMjg+DgYGJjY5vVttjYWMaPH8/69etNN/ClpaWoqlpvnuiJqv/gHejU17un9/hIQCHq0+pTygohhGhHNYUAVCbNI/TgSzhqygP7hz77K+218hhU5UFID/c+RyPJpVFDGgwoQBuzXVxczJgxY3xuojyDCj3B1l9Q4T0VueeQlIEDB5Kfn0/Pnj2bffMnOje9enpqaqrfYnPeORX+eioiIiIIDg42cjz1QMQ7Ubmp9Bvx2tpa48Hs5s2bcTgcjB492ud327O9bS2QngohAiF11YUQoiup1mo8HCvUblyqKgIsFqp43MAc+8y8z+lnGtexf3Mveydje3E4HBQVFaGqKkf85E/oN3melZG9pwgF36kuPSUkJDBixAgJKLo5RVEYOHCg3336E3g9IdvfUCT99y44OJjg4OBWa5dngb6jR49y9OhR4/31mcq8NfT73po8a4QlJSW16ucW3YsEFUII0VW4nFBTAEC1RStIZXUFVoGaCo/6P0c+9rquV1DR/0YYeJt7vaHhUWBUyPZe1ulBhefQqKZMUxkeHi5PV4WJHhx4FsbTg4ri4mJ+/vlnv9Wh/fWQtQbPXoc9e/aYJrzxDG48K8afiEkGysrKjKAmOjqaIUOGnLAZp0TXI8OfhBCiqyhYr+VQ2CIpDRoEQJArr/5K2J7KPYKK7K+0wMJRAb0vM3oqXOkXYxlyJ8SOAYsVpr0Hq6+BkX9q8NJ6MTvQiteVl5dTXl5OXFwcNpvNCCrsdjsTJ05EUZQm3djUl5gruq/hw4eTnZ1NWlqasc3zJt3f8Cho2yFHPXv2bLCnDjDtPxGBsl69Hmi1Ga5E9yVBhRBCdAUuB6z7jbacegbVtdoMTla1RsuzCI5r+HzPngpHOXx/nrb84+WoA36DAuQWlBEVPpJQa93NWa8LIH0eKApZWVnk5eXRr18/QBvucezYMRITE30qE69btw7QJvYYPHiwMbbdarU2OvSiT58+5Ofnm3o8ApmmU3QvISEh9O7d27Stvt+t3r17k5WV1eK8icbU1/PgmdtRUFBgLJ+IGiue7ydES0lQIYQQXUH+GijcCPYoXGOexLVuD7WWKOyuEqg40nBQ4ayBquPacuRAKP3ZtFvZrU357VBt5Obm0qtXL4+dCk6nk127dmnNyM/HYrGQnJzMsWPHOHDggHGo95Pa7OxsBg4caNw8BfKUOCMjg4yMDMrLy43gRArciUBYrVasVqtPz1ZKSopPANIWvAvI6fT2HDlyxDT8yV8ieWtyOp2m4Hzw4MFt+n6i65OcCiGE6ArK9gFQFT6C79dp47WrrEnavvKDDZ9beQxQtQrZKXPqPcylBJluenTelYhdLpdPlWur1WoORup4jidvSmJqeHi4URCvvps1Ibx5JkwDjBgx4oQlJsfHx5uKOOr04MEzzwICD5adTmezhgA6HA5UVUVRFGbOnGnKPxGiOSSoEEKIrqDsAACFNe7pWqusdVVv9y2E2gZmgaqo6z0IS9PqVNTDpQRRXu47vay/hFfPYCE+Pp4xY8b4Hf5RWloKaLP2NHW2m2HDhpGens7w4cObdJ7ovrx7w+LiGhkW2IoURWHYsGE+251Op9+/oUB6KsrKyli9ejVr1qxpcmChH2+1WiU5W7QKCSqEEKIrqNZ6Bmos7psko6fiyIew7MwGzs3VXkOSIONSUGyQOMPnMD2oOHToEGvXrqW8vBxVVY3ZY+x2O5GRkaZzJk6cyIgRI4zZeLwDgK1btwLNm+kmKCiIfv36SU+FCJh3T8WJvpkOCgoyJY+DdnPvmReUkKDN3BZIT8XRo0epra2lpqaGwsLCgNvhWXn8RE1dK7o+yakQQoiuoK4+hRqkDWEICQmhqizJvT/3B99zirbB4Q/Arg3JcFhjUELTsZ57EOyR8PVUKNpqHO4iCKfTyb592lCr48ePExYWZgQVYWFhBAcHG70PFovFVHsC6g8eTuQTY9F9hYaG+p3W+ETyHm7ldDpZv349oE2Fm5iYSG5ubkA9FZ49gvn5+fTo0aOBozW1tbWsWbPGOPdEFdkTXZ8EFUII0dk5KuHgWwA4rVqAkJSURHlhsvm4jXfB4DshrG6I0+cjTLvzSpxkbdnCmDFjtA1e9SccFnMl7Pz8fNOUlNHR0SQkJOBwOLBarSQkJPg8CfYOMnT601kh2lKvXr04flyblKC9fuc8e0sSEhLIzc011oOCgoy/Ge+goqqqim3bthETE0P//v0Bc29Gfn4+VVVVFBQUkJycXG8PRElJiSkYkaFPorVIUCGEEJ1d8XZj0eVy13yosnkFFTufgpJMmOVVMbtOrSWK4uJiamtrtTnyreYCdNVWc2+Cnl+hKAonnXQSISEhKIrCyJEj621qUFAQ48aNA2DDhg3G9piYmIY/oxCtIDw8nClTppCbm0tSUlLjJ7Sxvn37moKK4OBgIxjwDBgKCwvZvHkzoOVR9OvXD0VRTIFHTU0Nu3fvJj8/n8LCQr/5G4DPZAtVVVV+jxOiqWQgnRBCdEZZX8H628BZDVXumZbyQqcD2s2JkVPhKWe59lqZ7bOr0pYOuIMFXOZZnTzzNTyFhYURGhoa8BPPyMhIIiMjGTBgAKBNNSvjusWJouc1eOdXnCiJiYlERkbSu3dvn+GA9fVUeM+mduzYMbZt2+Yz85rec+gZqHgrKyszrcvfnmgt0lMhhBCd0Xd1U786q2D/awCoKXOpUUMBlZCQEBxKhO95dfkTfD3VZ1dxkPZks6ysjLKyMhKrytBvecptvaiw9/bblOYkWQOkpaWRnJwsY7pFt2Kz2YzeOm/19VR4B0C7d+9u9vvrQUV8fDxlZWUMGTKk2dcSwpOEp0II0Znt/ZfRo+CMn2bciISFhYG/noPwDHA5jboWnqqt2hjzkpIS9uzZw/GQUwAoDRrCuoRXcSnuBNMJEyYYy57js5tKAgrR3XkO/bPb7UZQ4dlT0ZK/MW/6tTIyMpg8ebIMPRStRoIKIYToCpJOpSLjN4D2tNNqtWK32ykO8qrhULrHby8FgEPRpoPVh1rsj7qO7bF/YHPc/4GiEBUVRWxsLGPGjCE8PJzExERAkqyFaAnPHCTPmhGePRV6HYuePXu2+P0861MI0Zpk+JMQQnQ2NcW+26a+RfYhLRgIDw8HtJmWNsf9lXHhKwgvXQU5y7SaFHpditgx2lS0FYcoChrl07PhUoLIDT3FWI+IiGDgwIHG+pAhQ0hNTSUqyjwrlBAicBaLBUVRUFWVyMhII4Dw7KnQAwHvOjD1qS9gUFXV6Klor5wS0XVJT4UQQnQ2K+b5bKp2BRv1Inr16gVoQYXLEkp+yq9g6iLf66SdDecdpOa8AjbFPwVo8/jXxzuhU1EUYmJiJNFTiBaaMmUKkyZNwm63Gz0VDofDCCz0XgtFUYwewobUN2mCqqrGtaSnQrQ2+Z9ACCE6m+Pf+mwqKC43nnTqY6T1AGHfvn0czqn0OYcg7bhqpw0UK0FBQURHR9f7thI8CNE27Ha7UcPF8+/s4MGDgDmoGDhwIGlpaUatCn/0ng1vnrkZElSI1ib/QwghRGdScdS9nDLXvbmiAsAUFHgWmtu7/5B75iddUCyAMS1lUFCQqdqv902HFMkSou15/p0dO3YMMAcVNpuNAQMGkJKSYhynBxhpaWnG8ZWVlT4F9PShVTabTf6eRauToEIIITqT/DXaa8xIiHUneOo3C3a73djmXb3aFdTDtF7hCGLPnj1GMSy73U6PHtoxVqvVZ8y19FQI0fY8/870v2vPoEJntVrp0aMHISEhpKSkMGvWLPr37288GFizZg0bN240Xbu6uhrA9PBAiNYiWTpCCNHR7X1FK3A37D7IX6tt6zEJBt0Be/+DmnEp2dlaMbuGgoqayhI8t+zan0Nx8BFjPSgoiMjISMaMGUNwcDA//fST6XwJKoRoe95/Z555EN69C8OHD0dVVWO7oijEx8cbPRzehe70Hk0JKkRbkKBCCCE6MlctrLleW44dC+WHtOWowRCaDPOyqaiohHXrfE71DipqrTGEuNyVdqtsqab9ekCiD6HynNISml/kTggROO/AoaKiot6gwt82ffY3Ty6Xi3379nHkiPYQoaHcKSGaSx47CSFER7R3Iez5F1S4exJYdjrUlgJQUWtj3759uFT3EAkw3yx4P/HcG/Vrym0Zxnq1xTwcyjsI8X6aKUGFEG1PT8bWVVRUGLkRgeRBREREmNYPHz7M999/bwQUYWFhpKent2KLhdBIT4UQQnQ0FUdhzbXa8uT/mvc5SgDYfySP3NBD2Gw2Ixiw2Wx+n1LqioLH8FPqG0xL2Eg5PSDLHHR434wMHTqUNWvWGOveQYcQom2kpqaSn59Pfn4+DoejwZ4Kb97/Buzdu9e0brPZZCijaBMSVAghREeTs8K9vPkB93LkAKOnwqloNw779u0zdsfGxvpcauTIkWzZssVYDw0NhUG38fNPPwHmInreQUNoaCgzZ84kNzcXh8PRYA0LIUTr0idKaGpQ0VhRu/qmmxWipSRUFUKIjqYk071ccci9bAkyggqH4nuD37NnT59tcXFx9OnTx1h3OBw4nU5KS0t9jvU3vEkvtpWamuqzTwjRdvTgoLa2tklBBcDYsWPrDS68p5kVorVIUCGEEB1N6R7/22uKoFKrU+G0hJl2DR8+vN7ky4yMDCZMmABAZWUlK1asMG4sPAMJGRIhRMehT5zg2VMR6N9oVFQU06ZNY8CAAT77vCdgEKK1yPAnIYToaA4u8r+90l34rsqaYtrlb+iTJ8+pZnUpKSkMGjSIkpISScIWooPRi086nc4m91ToPIc0xsbGUlhYSEZGRgNnCNF8ElQIIURHcvRz9/LQe2HH434P8+6paOwJpr+hEPrsTlFRUU1spBCirel/0y0JKjz/7ocOHUpNTQ1hYWENnCFE80lQIYQQHcmxz9zLqWeCNRSOLoaC9Q2e1tjNhsViwWq1mpI0G0voFEK0n9boqfCcFtpms/ntsRSitcj/KEII0ZGU7XcvRw2BxOkw+LfwrjtfQm1mOpzdbpegQohOQg8qXC5Xi4Y/DRo0CKvV2uRzhWgqycoTQoiOQnVB/mpteer/IEQrTqfaIlEzLjcO2xz/FCkpKUyePBmbzRbwzEzeQYQ8tRSi49KDiuLi4mYHFaDlTiUmJrZq24TwRx5TCSFER1HyM9QUakOe0s83Nm/cuJEa5TecNO95du07RlFODj3CwwkODmbq1KkB32h4BxESVAjRcfnLk5LeBtGRSU+FEEJ0FHmrtNe48RSVlFNYWIjD4aC0tJTqmlrKqhVj+JL+FLMpNxmRkZGm9Yaqbwsh2pf+N+5JggrRkUlPhRBCdBRFmwFwxoxl06ZNAIwePdrYXVBQYNSXaE5Nid69e5OUlERmZiYRERF+b1qEEB2DBBWis5GgQggh2pPLAWtv0hKyi7VK2hV29zzyubm5xnJeXp5xo9GcgMBisRAeHs748eNb2GghRFvzfnAwdOhQeRAgOjQJKoQQoj0dfAv2/Uf7CksHoNLe29jtGVRYrdYW9VQIIToPzwAiKSlJkq1Fhyf/KwkhRHsqO+BerjisvXj0VNTU1BjLLpfLyKmQoEKIrs0zqJCK96IzkP+VhBCiPTkrzOu2CCqdoX4PdblcRk+FDIMQomvzzJ+QhwiiM5DhT0II0Z7K9ppW1eBEjh8/7vfQigp3ACJBhRBdmwQVorOR31IhhDjR8tdDxRH3socaS4yxPGTIkHovERrqvzdDCNH1yKxPojOQoEIIIU6k4h2wZAJ8lK7lU5TvN+2utcUby2FhYQwePJiEhASfy8hNhhDdR1RUVHs3QYhGyfAnIYQ4kXKWu5c/6eOzuyR4BKANd4iIiCAyMpK4uDjTLFBCiO5hwoQJVFRUEBMT095NEaJRElQIIcSJ5KhocHeOogUVo0ePNnoj7Ha76RjvythCiK4pPDyc8PDw9m6GEAGR4U9CCHEi+Qsqhj1gLBbRC8B0I6EoCuPGjSM1NZX09HSGDRvW5s0UQgghmkJ6KoQQ4kRylpvX+10HA2+Fo4upSDwPyiyEhYX5zO4UGRkpPRRCCCE6LOmpEEKIE8nhFVQExUJoEpyxiaPRVwAQERHRDg0TQgghmk96KoQQ4kTyCipcthi2b91KTEwMxcXFAMTHx/s7UwghhOiwJKgQQogTqabAtFrkTCC/KJ/8/HxCQkIAjFchhBCis5DhT0IIcSJVHPNYUSiJPsVYq66uBnxnexJCCCE6OgkqhBDiRDn6ORSsc6/3vxGX6v5nWFVVAGw26UQWQgjRuUhQIYQQJ8qel8zrA2+mosJ3ilkJKoQQQnQ2ElQIIcSJ4HLA0U+15UmvwVm7UKOHU1RUZDrMarViscg/zUIIIToXeRwmhBAnwqbfG4uZRyqxOiCZUhwOh+kw6aUQQgjRGcn/XkII0RZqyyBnGST/AqzBsPMpY1dZtZ3yY8coKSnxOU2CCiGEEJ1Rk/vYv//+e84++2xSU1NRFIWPPvrI55jMzEzOOeccoqOjCQ8PZ8KECRw6dMjYX1VVxc0330x8fDwRERFccMEFHD9+vEUfRAghOpT1N8Pys2HDbbDnZdMupxIGQFlZGVarlZSUFGOfzPwkhBCiM2pyUFFeXs6oUaN4/vnn/e7fu3cv06ZNY/DgwSxbtowtW7bwhz/8wTTv+m9/+1s+/fRT3n33XZYvX86xY8eYN29e8z+FEEJ0FNlLofI47H9dW9/zMqy9ydhdG5xOtbWHsR4bG0tkZKSxHhwcfMKaKoQQQrSWJvezz507l7lz59a7/4EHHuCMM87gr3/9q7GtX79+xnJxcTGvvPIKixYt4pRTtPnZFy5cyJAhQ1i9ejWTJk1qapOEEKJj2P0irPsNpJzuf/8F+Rw5fBz1iLtn1m63mxKzY2Nj27qVQgghRKtr1SlGXC4Xn332GQMHDmTOnDkkJiYyceJE0xCpDRs2UFtby+zZs41tgwcPplevXqxatao1myOEECfWxju116wvfff1uw6C43Co5mc5NpvNlKwdFxfXli0UQggh2kSrBhU5OTmUlZXxf//3f5x++ul89dVXnH/++cybN4/ly5cDkJ2dTVBQEDExMaZzk5KSyM7O9nvd6upqSkpKTF9CCNGhVBeAs8p3++zv4XIVJv4bAKfTadptt9sJCwsz1oOCgtq0mUIIIURbaNVpRlwuFwDnnnsuv/3tbwEYPXo0P/74Iy+99BIzZ85s1nUfe+wxHn744VZrpxBCtLq81b7brKGQMNW0yXsK2cjISGJiYhg6dKgpt0IIIYToTFq1p6JHjx7YbDaGDh1q2j5kyBBj9qfk5GRqamp8Cj4dP36c5ORkv9e97777KC4uNr4OHz7cms0WQoiWKz/gu+38LFDM/8xWV1cby4qiEBMTg6IoJCYmEhoa2saNFEIIIdpGqwYVQUFBTJgwgV27dpm2//zzz2RkZAAwbtw47HY733zzjbF/165dHDp0iMmTJ/u9bnBwMFFRUaYvIYToUMoPmtctQRAUbdpUUFBAaWmpsZ6YmIiiKCeidUIIIUSbavLwp7KyMvbs2WOs79+/n02bNhEXF0evXr24++67ueSSS5gxYwYnn3wyX375JZ9++inLli0DIDo6muuuu44777yTuLg4oqKiuPXWW5k8ebLM/CSE6Ly8g4qIvj6HHDt2zLQuMz0JIYToKpocVKxfv56TTz7ZWL/zTm22k/nz5/Pqq69y/vnn89JLL/HYY49x2223MWjQIN5//32mTZtmnPP0009jsVi44IILqK6uZs6cObzwwgut8HGEEKKdeA9/Gv6QaTUrK4u8vDxA66EIDg4mKSnpBDVOCCGEaFuKqqpqezeiqUpKSoiOjqa4uFiGQgkh2p/LCR8kQE2hNtuTYqMmaiw5ubkkJydjs9lYsWKFMfPTmDFjiI6ObuSiQgghRPsL9L67VWd/EkKIbifzKfjpLm3ZEgTxE8EaxM4tWygoKGDPnj2MGjXKNJWszSb/9AohhOhaWjVRWwghuh09oACI6ANWrc5EQUGBsXnz5s2mUySoEEII0dVIUCGEEK0ltKex6FnQznNbSkqKFLgTQgjR5UhQIYToHkp2w0cZsPOZtnuPMHdQoQcOcXFxxrZx48YxaNAgmUZWCCFElyN98EKIrm3FBXD4A/f6xt/C4Dta59re81wkTjcW9crZKSkpWCwWwsLCsFqtrfO+QgghRAcjQYUQousq3GQOKFqbq9q0WtPjVHZt3UpSUpIRVNjtdoYPH952bRBCCCE6AAkqhBBdV95q/9sdFWDzzXloMqc5qNixv4iioiLy8/ONbZI/IYQQojuQnAohRNeV/Y17+cztQF0uQ21Jy67rKNdePXoqXNM/oqioyHRYbGys34RtIYQQoquRoEII0TUV74TD72nLUxZB9FCw1xXtaUlQse5meCcSjn4Gh97VtlmCKImc6XNoampq899HCCGE6ERk+JMQomvKW+leTjtbe7VHQW2x9tUcqgt2v6AtLz/Lvd1VQ0VFhc/h8fHxzXsfIYQQopORngohRNdUlaO9Jp0M9ght2R6tvTa3p6JkZ727KisrTetWqxWLRf6JFUII0T3I/3hCiK6p8pj2GjfBvc0Y/tTMnooVF9b/dn6CCiGEEKK7kKBCCNE1HftCe43o494W3EN7rcpt3jVLMuvd5R1UhIeHN+89hBBCiE5IggohRNfjckDZXm059Uz39tBk7bUqu3nXTTm93l3l5dqMUGlpaQQHBzNw4MDmvYcQQgjRCUmithCi66nO014VC4R6zMAUUhdUVDYzqLBHNnpIv379GDBgQPOuL4QQQnRS0lMhhOhaVBdkL9WWg3uAxSO3ITRFe63Kat61vYrdeQsJCZHkbCGEEN2S9FQIIbqW3S/C+lu05bB0876W9lS4fIOKo2HnkRcyFYDQ0NDmXVcIIYTo5CSoEEJ0DaoKNYXugAIgYZr5mJbmVDirfDbtjrndWJbq2UIIIborCSqEEJ1bVQ5s/B0cfAtUp3lf0snmdc+eClUFRWnaezUy/CkuLq5p1xNCCCG6CAkqhBCd277X4MAb/vclzjCv6z0VrmqtVkXlMbAEQ2S/wN7Lz/AnTzExMYFdRwghhOhiJKNQCNG5le/3vz31TAiKNVZra2vZvnMvLltdVe2df4PPhsGn/bVei0Dow58GaUOe1CH3mnZLwTshhBDdlQQVQojOy1kFBxZpy/1vMu+b/Lpp9eeffyY3N5cq6qpqb3vEvbORHgif43pdAhfk4xzuvsZJJ53UlJYLIYQQXYoEFUKIzuvHK7VhTACpZ5j3BcUAkJeXx7Jly8jN1apoO5QI3+s4Khp/r6o8qDisLYcmQXAcnv0bMvOTEEKI7kxyKoQQnZOzGg6/716PGmzer1ioqqpi27Ztps0OS7ifa1UAjSRZH3wLXLUQNw4i+lJaWmpcW1EUlKYmfQshhBBdiPRUCCE6p13/MK+H99KK3XkoLCz0OS26ZpvPNhzljb/f/te01z5XA3D48GGqq7XhUGqgORlCCCFEFyVBhRCic9rzT/fytHfAGgLxk0yHFBcXG8t9+/bFarViVX1rTTQ6/ClvDRRsAMWG2utSamtrJZAQQgghPMjwJyFE56O6oOKQtjxnHcSP15ZPehF+LINBt6KqKkVFRQBYLBbS0tI4cuSI/+s5Gwkqcldqr6lz2Z9VxqFDO1r+GYQQQoguRHoqhBCdT1Wult+AArGj3NvDesLs7yB9HkVFRVRVVWGxWJg6dSpWqxWbzcaOmAd8r9fY8Ke6BG01rBeHDh1qvc8hhBBCdBESVAghOp/Kuh6HkCSw2P0eUlGh9T7ExcUZ9SOsVislQUN8D64tqf+9VBfsekZbtMgMT0IIIYQ/ElQIITqXqhzY9HttuW7aWH/Ky7Xeh+DgYGOb3W5H9Tfqs6ag/vdzlLkPy9vcpKYKIYQQ3YXkVAghOg/VBR+mgurU1kt21ntoQYEWKHjWjwgKCqJM8dOzUd1AUFFbaizuZE7T2iuEEEJ0E9JTIYToPCqz3AEFwKg/+z1MVVWqqrRZnnr0cE8za7fbcSlW3xNq8ut/T4+gQk08xe8h0dHRDTRaCCGE6Pqkp0II0XmUH3QvW4JgyD1+D3M4HMZyUFCQaVnFT09F5pNajYuh9/q5WF1QEdbTNJRKN2nSJNN7CCGEEN2R9FQIITqH2lIo3eNen7sZLL7PRVRVpaxMy4OwWq1YLO5/5oKCgnAp9TxL0fM0vOk5FbZIXC6Xz+6QkBDTewghhBDdkfRUCCE6vppi+KSvO6G691UQPRjQeiUOHjxIeXk5iqLgcrmMStp2u7lXQuup8PhnT7GB6qBB+vAnu/+gQgghhBASVAghOoPCjeYZmsIzjMWsrCwOHz7s97SIiAjTelBQECgevQqhqe4iegAFGyFurPkielBRT0+FEEIIIWT4kxCiM/Ce5SnlNABqamrYu3dvvafFx8eb1n1yHyL7mde/HOderjwOX4yDVVdq69JTIYQQQtRLggohRMeX+ZT2mnQKzPwMEqcDsHHjRuOQtLQ0n9O8gwqbzYaiKO4NkQP9v1/O9/BhstZDYpzsDir0HpDIyMimfhIhhBCiS5KgQgjRsR3+AMrqeiN6Xw5pZwDgdDqNaWPBPHUsQM+ePX16JhRFMW9LO9v/e275o+82j56Kvn37MmzYMEaOHNnEDyOEEEJ0TZJTIYTomFy18MNFcORj97ae5xmLnr0U4Fsron///n4va7fbWZP4Gv1TI6is7kVPfwcpfp632CNxVWlBhc1mIy4uLpBPIYQQQnQLElQIITqmQ++7A4rQNDh3P1jcszmVl5cbywMHDsRisWCxWBrNe7BarZTZerE1B1Bz/AcVfjpxHUqY0TPir16FEEII0Z1JUCGE6Jj2v+penvQfU0DhGThMmjSJkJAQQKsZUVFR0eBlbTbPKWX9VNcGvz0VFTXatoiICAkqhBBCCC+SUyGE6HiqciH7a2357D3GbE+6mpoaQMuR8LzBHzx4MIqikJGRQX3y8/Mbf38/wUZpXfqGd/K3EEIIIaSnQgjREeWtBtUFUUN8p30FioqKAK1nwnM2p6ioKKZNm4bVWk8PRKD89FSUVKgQ7JsQLoQQQgjpqRBCdDTHvoD9r2vLPSb77K6urmbnTq1uhb/8icYCCn9Tz5q4nPj7p7FGDSYkJMSnoJ4QQgghpKdCCNER5KwARzmEJsOyM9zbk081HaaqKqtWrTLWU1JSmvxWaWlpHD16tP4DXNVg8Q1MXEoQ0dHR5joXQgghhAAkqBBCtDdHJSydoS0P/p17u2LVit150HMpdL17927y2zUaFDirzNPY6ufhxG63+zlBCCGEEBJUCCFOPEcFHPtcW7Z5DCfa+ZR7efIbWs+Fh+rqamM5Nja2WW9tsTQy6tNZ6XdzcdAIYmzyT6YQQgjhj/wPKYQ48TbdCz8/V//+wXdC70tNm/Ly8sjLy3MfMnhws97au6diW+yfGF7oUUHb4TElbWgqDP8DO8qGoxY7zNPRCiGEEMIgidpCiBOvaEvD++MmmFarqqrYtm0b2dnZACQmJja7VoR3T0Ve6HTzAdW57uXQVNT+N5Fb4gSQoEIIIYSohwQVQogTr/yweT04wZxP0eMk027vgnYtKT7XaE7F11PdyxF9cDqdqKoKQFxcXLPfVwghhOjK5LGbEOLEqS2Dg29B+X5tXbGB6oD0edBjkvu4MHPxuspKc55DeHh4s5vQaE6FpzFP4XRqvRSKokiithBCCFEPCSqEECfOqivdMyv1uhjGPAFHPoI+V2kJ233mQ/QwnyldCwsLjeXU1FQSExOb3YQmTQkbmoqjUiulbbVaZTpZIYQQoh4SVAghTgxXLWR9pS0rVhj7NISlwqDb3MdMftV0iqqqZGdnGwnaffv2pVevXq3WJJvNhsPhqP8AxWL0VEg+hRBCCFE/yakQQrQ91QWfj3RP13ppjRZQNKKkpIRdu3YZ682dRrY+YWFh9e9UbKAoRlDRWKVuIYQQojuTR29CiLZ37HMo2aktp8wBJbDnGcXFxQDY7XbGjRtHSEhIqzRH76FISEigpKTE7zGqxca+vXspLS0FJKgQQgghGiJBhRCi7e38m3t59F8DOsXpdHLs2DEA0tPTWy2gAJg4cSIOh4OqqqoGjrJx+LB7lioJKoQQQoj6NXn40/fff8/ZZ59NamoqiqLw0Ucf1Xvsr371KxRF4ZlnnjFtLygo4IorriAqKoqYmBiuu+46ysrKmtoUIURHp6qw7VE4/p22fmYmxI4M6NTc3FyqqqoICgoiNbXxoVJNYbfbCQ0NbTBQcLjM65JTIYQQQtSvyUFFeXk5o0aN4vnnn2/wuA8//JDVq1f7vRm44oor2L59O19//TWLFy/m+++/58Ybb2xqU4QQHd3xb2DLH9zrEb39HuZyuXwSpnNztSJ0KSkpbXZD3+B1vYZoSU+FEEIIUb8m/089d+5c5s6d2+AxR48e5dZbb2XJkiWceeaZpn2ZmZl8+eWXrFu3jvHjxwPw7LPPcsYZZ/Dkk0+2+hNJIUQ7yl3pXraGgdV3CJOqqmzYsIHa2lrGjx9PTk4OBQUFFBQUALRo+tjGNBRUqJiDCAkqhBBCiPq1+uxPLpeLq666irvvvpthw4b57F+1ahUxMTFGQAEwe/ZsLBYLa9as8XvN6upqSkpKTF9CiE6geLt72ea/YF1NTQ3l5eXU1NSwfft29uzZYwQUQUFBDc/Q1EJNCSpk+JMQQghRv1YPKh5//HFsNhu33Xab3/3Z2dk+Tx5tNhtxcXFkZ2f7Peexxx4jOjra+EpPT2/tZgsh2kLeavdy3Di/h3jmU+mzPenGjx/fpgXnGqqurSrSUyGEEEIEqlUfvW3YsIG///3vbNy4sVVvBO677z7uvPNOY72kpEQCCyE6uvLDUOGePYkhd5l3l5dz+PBhU7VsXVRUFIMGDSIoKKitW1kvGf4khBBCBK5VeypWrFhBTk4OvXr1wmazYbPZOHjwIL/73e/o3bs3AMnJyeTk5JjOczgcFBQUkJyc7Pe6wcHBREVFmb6EEB1cft1wxpBkmLMWkk81dhUWFrJu3Tqys7Oprq4GIDzcPTwqISHBtN4epKdCCCGECFyrBhVXXXUVW7ZsYdOmTcZXamoqd999N0uWLAFg8uTJFBUVsWHDBuO8b7/9FpfLxcSJE1uzOUKI9qQXu0s9HeInmHYdOXLEtB4cHGzqfUxKSmrz5nn6Ofq3PtsU1Wlal5wKIYQQon5N/l+yrKyMPXv2GOv79+9n06ZNxMXF0atXL+Lj403H2+12kpOTGTRoEABDhgzh9NNP54YbbuCll16itraWW265hUsvvVRmfhKiK8hfpwUU+lSykQNMuysrK8nPzzdtS0xMJD4+nqCgICIiIk74sKdj4eeQFXY6sdWbGFlwLwChzmOmY6SnQgghhKhfk4OK9evXc/LJJxvreq7D/PnzefXVVwO6xptvvsktt9zCqaeeisVi4YILLuAf//hHU5sihOhoqvJgyUnmbf/f3p3HyVXV+f9/3Vt7V3f1lu4kne6ELEASCNkTwiI7CA4iwg8IODC/QRg1jCJfQVQUcZzh+1NGGQR1HL+DjoKjOHxRkUUEJQJJTEKCScxK9qWXpPfuqupa7u+P213V1dWd7k5VV2/v5+PRj7733HNPnZtDwv3U2fJnAfYqT/X19ezYsSNxac6cOQSDQaqqqnA4HKxYsSKXtU1hGW7qvcuwyi/GqP3jsNVDRERkNBp0UHHxxRdjWdaA8+/fvz8traSkhGeffXawHy0iI13r++lpeVVEo1HWr19PJBJJJuflpQ1zGsqVngYqfu5PaX3lg9T4Lk1J1/AnERGRvun/kiKSHfEIbEpd4QlXERQv4MiRIykBhWmaLFiwIKfV60t5eXnK4hExdxmbJnwHsOd6zJo1i2AwSEFBwXBVUUREZMRTUCEi2XHkt1D3VvL84peg/GJw+jh6NDk/YebMmVRUVIyYOQpnnnlmalARS07QdrlclJWVDUe1RERERhUFFSIyOPt/Bhhw2i2p6d2HPl27GwrsuRThcDixbOz555+Py+XKUUUHpmdw0z2omDx5cq6rIyIiMiopqBCRgWnaDrEQvHOrfV44F/IqwVNin7cfsX/P+VxKQLFmzRrAni8xGuYldA8qtCKdiIjIwIz8/8OLyPBr3Qu/nZua9vJ8e2O76w7AiT9D83Y73VeZyNJ9WJHb7R4RE7H7E41GATsIGg31FRERGQkUVIhI/6pf7z09VA0/96SmFc5JHLa1tSWOR9qwp75s2bIFGBkrUYmIiIwWWd1RW0RGuEgrNO8c/H3th/vP0yVgb3RpWRYNDQ2J5NG2epKCChERkYFTT4XIePLWjXDsVZi2Es57Bvp7cW47ZG9mF6pOphlOsKJ93+O1955ob29PTNDOz89nxowZmdY+pxRUiIiIDJx6KkTGk/oN9u8DP4MT6/rPv+/HqQHFom/BjfUnvSUcNdi2bRvr168H7M0ulyxZMmqGP3VRUCEiIjJwCipExhOjW+fkiQ2957Gs5LGrKPVa/ixwFcBFvwVHHhQvSrt9zZo11NXVJc7z8vIyqPDwUVAhIiIycAoqRMaReCQ5cZqGjT0uxuD5ifB/J0HouJ0WbUlen/UPMPlK+3jKNXBTKyx5Mnm9aD6hFS+kfabP58tO5XNMQYWIiMjAaU6FyHhhWRix9uR5V09FPAp7n4aaP0KocwnYXU/AxMsg1NnjcOa9sPjbABw8eBDDMKiqqgJ3YbK8854hbFTCgU0pH+v1eofmeYaYggoREZGBU1AhMk5YsTAG8eR5818xokHY8wN4997UzPv+C7b+UzKvK8DRI0cwDIO9e/cCMGnSJFyObkOb/NOINIXSPldBhYiIyNinoEJkDIvFYhw8eJD8/Hy8W/4XXYu6xnFhWhHY/hhs+Ur6jW0HUk6DMR+7d+9OSQuFQrjyp8H028FdQjDqYOvWrYA95KlrN2q/35/158oFBRUiIiIDp6BCZIyyLIut61/jtEOfo9l7DmUtP0tcCzsm4Isd6z2g6MW+6nboMTXi8OHDzJ49G2PFjwFoPHYsca28vNweHiUiIiLjgiZqi4xR7+/ZzcyDqyiMbKOqW0AB0GEWD6qsiFmSllZTU8PatWuTZXZ0AFBYWMhpp502+AqPMO3t7f1nEhEREUBBhcjwO/oqrL8Hwiff/2Gw8rZ9jvzo3rT09WU/JOIYXFARdExKltttidhwOIxlWQSDwZSgYiwMHSorKxvuKoiIiIwaGv4kMpwO/hLe+n/s45KFMPPO7JRrxSnseC8tub7wQ8xZcTPNv3+h9/scXoglJ1vXz3iEw41uwk47qJgzZw7l5eW8+eabiTxbt27lxIkTiXOPx5OdZxhGy5YtGxPPISIikivqqRAZTnv+PXFoZaunYt9P4Bd+/NGDAHSUXpS4ZJR/AL/fT8SRPpwJIGROSJ44/TSW3Ua991z8fj9VVVWUl5en9UJ0DygACgoKGG0WLlyYcp6Xl4fD4Rim2oiIiIw+CipEhkssDNW/T5wamx+AQ89nXGx8/adTehuMZd9PHLsqr8AwDGKu0l7v7SA/cdy4/Hd0RCIATJgwgZkzZyYCCpfL1efnj8YdtAsLCznrrLMAqKysHObaiIiIjD4KKkSGy18eSk/70w2Zza2INGNGG1OSnAXTaTj395xY8EvyJ9vfyMfdyfkC8W6jIDu69WBs3huiurraLsOZOlLynHPO6bMKo/Ub/rKyMs477zxmzpw53FUREREZdTSnQmS4bH+s12Rrzw8wznpw4OW0HYDt/wqxELHJV9P9lb7BvZBip4fiGZelfoanPHHc7J7LX0r+P9zxesCkKPweNb7LwEh+59CzZ6KgoIDFixfz7rvvYllWyrXRPEnb7XYPdxVERERGJQUVIsNl8lVw7NW05ODxnQxqANHaO6Hmdfu4ZjUADe4FHJ/4cQoqz+v9Hu/ExGGb8zTippeQaW9W9/akF7CM1H8aenvZLigo4JxzzuG995ITwsfCUrIiIiIyeAoqRIaJFW3HANqc0/BHkztYx0KDGP5kWVjH19DVN+Bo3QlANO90Tr9wVZ+3mXmTE8dxw5tapJH+z0LP4U9duvdgnHvuuXi93l7ziYiIyNimORUiw+H4Wji+BoD3A59IuWREmgdezrHfYcTSN2mLFi096W1OX3KitkH0pHk9Hg9+v7/Xa6bZ9xApERERGT/UUyGSa/EYvHMbhhWl1nsJ9Z7l1PguZWLwDQCM6CCCioZ3e012VlzUa3oXd7c9GAwrGVS4XC4inSs+ASxYsOCkm9n5fD6KiopwuVyjdoK2iIiIZE5BhUiuNb4HrXuJGn52Ft0HhsH2oi9x3HsBZzV8DVfHITvwMPt/Se9orcYNxAwvDiu5jGxRxYKT3td9jkS7c1ri2Ov1pgQV/e2ObRgGCxac/LNERERk7NPwJ5Fca7AnNgfzziZm5uP1epm/YCHBwguJGj7c0To4+lvY8wMIHT9pUR0tx+wDK55Iqyv8CK5+VjFyu928O+E77M//W4JTbk+k5+cn96lYsGDBqF7JSURERHJHPRUiudZ+CICQ054sPWvWLIqLizl70QU0HTuH0vA6WH2dnffAf8Nlb/RdVoe9m3WTZz4l4fUAWIse77cKbrebZvfZNLvPprKgCKcnD8uy8Pl8iTyjcWdsERERGR4KKkRyKRaGuj8B0BovApI7UHu9Xk4ElkPdumT+mj/0UU4H/NyT2P+6pvTvqGm/lGb3XJZNmtpvNXpOqp47dy4ATU1NieuaIyEiIiIDpaBCJFcizfBcYeK02TWX0tLSRFABQMXVUPdE/2WtSQ5ZanOeRumcGzhWXU1VWdmAhix1X7Wp++Z1gUCAhQsXpvRYiIiIiPRHQYXIUIhHwez216v9MLxQlTjdU7iKBu9SFk5N7VUoKJvVS1k9Jm3Ho3Dw54nTLaX/m6UTJlA+cWL6vQPQPQgxDIPCwsKT5BYRERFJp4naItm27yfwXACOvmyfWxa887GULIf9NxIIBNJe4F35k7B6/rWs7TEEqj65jOym0sfxlJxxSkOVpk+fjs/no6qqqv/MIiIiIiehoEIk29bcDrEgvHmtfX7of6D2zcTlvXN+BdhDjXpye3x0mEWpiW9cAe1Hkue1fwTguPcCmjzzKS4uPqVqTps2jeXLl+PptmeFiIiIyKlQUCEy1Pb+yP494+/YM38NB5vsYKK3YUYOh4OIoyQtPXbwheRJZ4DS6J4PQFlZWVarKyIiIjJYCipkZNj3E6j543DXIss65yo0bAagvvijHK6zN6ibMmUKEyZM6PWuuCN9Kde2+gPJk/bDdprTno+RMtFbREREZBhoorYMvz0/hD/fZR/fap0872himBCqg6A9dGnbEUcijJ81a1afqzRZjl6ChHBd8nq0DQOIGfYKTdqgTkRERIabeipk6MSjcPg30LKn7zzRNvjz3clzawwFFRhwfC0AQWclMdMOFubMmXPSQMBBR+K40T3PLqlzkzsAK9IKQNz0sWLFiqzXWkRERGSwFFTI0HnnNlj9YXh5Yd9Dm353PtAtkIiHc1Gz3LAicMxeAarevRiXy8WiRYuY2M/SrzFXaeK4rugmAApO/Ibg83OJBevtQAwIlFRokrWIiIiMCAoqZOgcX2P/jrbCnz4KsVDq9Wg7NL6XmhZpyU3dhko8mjy24rD7ewCc8C6noqKi1xWfejpa/hnqPUv4S8mj5J95C0FHBQC+0Hbad/83RswOKvIKNUFbRERERgYFFTJ0Ohq7HTdA8Bjsewaa/mqnVb+evN41jyA6yoOKUE1aUhwXje6FfU7M7qni9BX8pfSbFJx5MwXFk/hz+Y8T1wq2rsLo7NnJK1BQISIiIiODJmrL0IhHkwGC6bGHNR38JWx+wE5bGbeHRnVxBSDWDpHm3Nc1m+o3ABA1/Dgtu0chYhYQN70DXqWpsLCQCy64AIfDgWEYLFqynMiL+bis1pR8pjt9lSgRERGR4aCeChka3Xsp8mfYv/f9KJn2Qo9dnN2dezZ0NAxlrYbe+/8HgKN5f0ODZxEAdb6LcDqdg9r12ul0JiZzFxQU0HTO/0m5HjECOJzuLFVaREREJDPqqZCh0TlXImLkEzeK8EBy2BMkllkF4PLVsOWr0Lwzdefo0ab9CBz9LQDVeddw1FXAnNJ69tZVkOf1ZlR0YPoV0G36SdgxYVBBioiIiMhQUk+FDI1D/wPAcd8HaAqfZIWi4kVQfiHkVdrnf30UWvfloIJD4MivwYrT5D6bdtdUYq5yajwXEjd9FBUVZVS0w11AvNt3AGFHOaapv74iIiIyMuitRAYlFotRV1dHLBbrO5NlwVF7KdXj3gsIO8r7zNo29xs0NjZC4Vw7oemv8PsPZLHGOdS82/7lmgNAJBKhpsaeuF1cXJxR0abDQcRMrhwVdpSpp0JERERGDAUVMii7du1i27Zt7NixI/1i+2F7GdWW3dC2nzhOGt0LCDl6X6XIMl2s3+dg8+bN1BV8KLWc0ahzCd2Qc3IiqSv4KiwszKhowzCIOJL7V4Qc5QoqREREZMRQUCGD0vXNe11dXTJxz3/Ab+fZk6833Q91fwKg2X0WMdNHyD2917KiV2xIHG97/zghx8k3hRvR2o/ACXv37HrP4pRLhmHgdGY+fSnqSi5JG3GUnnRXbhEREZFcUlAhmYk0w5/vhqat9vmOb8GWrwHQ6pqFaZqE8+cl83vL4bxn4bxnifhmpRS1q/A++6DgdHhlGfz5k7l4guw4sR6AeGAeQedUHA5HYqO7ge5P0Z+oKzmMzPQEFFSIiIjIiKHVnyQzNX9IT2s/CECrayZ+vx/DyGfjhKc4y/M23ikXw2krATj2/vspt8WMziVSW+y5CdSvh7mfh/zThqjyWdRmTy7v8Nq9Mm63m9mzZ3PixAkmTsxOD0zcnQwqnN7MhlOJiIiIZJOCCjklXeP5Y4dfoa+R/bW+S5k3YwaHDh2i3j2XLXlLaTvcxuz8ahwOB0eO2MvHmqZJPB7HMlzphQSPjIqgwmo7gAHUtvmg0J5DkZeXN+AN7wYi7kkGJy4FFSIiIjKCaPiTDJoz3mQHFW2HMA79AoDtRQ8SdCQnKLc4Z2GZXgKBAC6XHSy0tdk7TO/YsYNt27YRj8cBmDNnDoZhYPUW4256AOo3DfETZS7ecgCwV2UyTZPp03ufR5IJy9stqPAVZb18ERERkVOlngoZlPL215jb+C8cKL0XVr+JGamn1TmDWt8l1PiuBCzmTW5h55EogUAAh8PR7yTl4uJiHA4HcaOXfMffgVcWwQc3Qskie0L0tn+G01dB0VlD8oynIt56EAcQdkxk2bJleDwn2ZvjFBm+isSxw1OQ9fJFRERETpV6KmTA4vE4cxv/BYBpJx6HBrsHYUvJP2MZbjAMMEy2Hy+lw1FKfn4+wEmDiry8PJxOJw6Ho/eeii6vdK6otPkLsPt78NLZWXmmjHU0wPbHcDW/C0Be2Zl4M9w9uy9GXrInyHTlD8lniIiIiJwKBRUyIKFQiObmZqKGPyU9YgaIeSu58MILE2nRaBQAv9/O2zX8qTeTJk1K5Om1p6KnzkngI0I8Aq99wF5Gt5NZMG3IPs70T0keO4cmcBERERE5FRr+JAOyYcMGotEoKwwfTqstkR418ikqKsLhcHDaaaexf//+xLWugMHn8/VZblVVFQAej4fIQP5z9Pa9O3fO/fUbyaV0gTguPAWVQ/ZxLm+AvQV34rTaKcqfOmSfIyIiIjJYCiqkX9FoNNH7YBmpnVtR009RUREAp512GoWFhbz33ntMmDAB07TzFhUV4fV6CYVCOByOxC7TixcvTuy14PF4aOlt9afu/vwJqH0zeR7rAIc7C094impeTzkNO8rwniSAypTT6eRgwccAWGKqk1FERERGjkG/maxevZprr72WiooKDMPghRdeSFyLRCJ8/vOfZ968efj9fioqKrj99ts5evRoShn19fXcdtttBAIBioqKuPPOO2ltbc34YST74vE4GzZs6JaSuuFa1PBTVlaWOC8uLmb58uXMnj07keZwOFi2bBnLli1jxYoVuN1u3G53YnhU131pw5/Ofjj1fM+/Q6g2eR5pOuXnylg8lrZHR8zwnbRXJlPdh5F1LekrIiIiMhIMOqhoa2tj/vz5PPXUU2nX2tvbeffdd/nyl7/Mu+++y/PPP8/OnTv58Ic/nJLvtttuY9u2bbz22mu8+OKLrF69mrvvvvvUn0KGTGNjI6FQKHFuWNGU63FHftpKRz6fL21ytmmaiUnZS5cuZenSpYmeDLB3nU6ZqD3vETjnqyev3HAGFdu/kZYUNzxDsupTl+5BhameChERERlBBj386eqrr+bqq6/u9VphYSGvvfZaStqTTz7JsmXLOHjwIFOnTmX79u288sorrF+/niVLlgDwne98h2uuuYbHHnuMioqK3oqWYdK1twTAzBkzcB9rTrkedwQGXWZvE7cNw6By2kyots+PHT1MdXQTC09WUEdjelrjFvjLw3DO16BoiFaIirbDzscTp0HHJHyxaprylhEwjL7vy5Bpmpx++unEYrEhDV5EREREBmvI51Q0NTVhGEZi3P2aNWsoKipKBBQAl19+OaZpsm7dOq6//vq0MsLhMOFwOHHe3NyclkeGRiQSAaCyspKqSQGwIinX487BBxV9KSpJTsI22vfT1NRPT0T4eHraq+dCrB1adsGHtqZfH6z9P4NYCGb+v8m0TZ9LDMPaVPo4ETNAfnQfLcVXUZX5J57UlClT+s8kIiIikmNDOoYiFArx+c9/npUrVxII2C+f1dXVlJenruDjdDopKSmhurq613IeffRRCgsLEz9dKwbJ0OuaVO0wYlD3Ttp1y5W9oMIwDJrcdu9Cre+S/m8I9fLfS6zd/t1+OPMKxcLwzq2w7u+x2rt91tGXAdhX9S2aPPNpd02n1ncpbk9e5p8pIiIiMgoNWVARiUS46aabsCyL733vexmV9YUvfIGmpqbEz6FDh7JUS+lPV1BR8deV8OaH7DSj22TkLPZUmKbJeyXfYEPZf1DvObf/G4K9B6EA+DNfcjUWrEsch1uO2AeWBaEaAGrCEwE7KDYMg2nThm6PChEREZGRbEiGP3UFFAcOHOCNN95I9FKAvXdBbW1tSv5oNEp9fX1iX4OePJ6hnQArfYvFYpS3/x5Py6ZE2pFJ9zP12NcAsDxlfd06aIZhEDd9tJqzBnZDz56KWHJCOXmZv+BH22roWmMp1l5j91zs/i7EggB0mMUAzJ8/H7/fr8nTIiIiMm5lPajoCih2797NH/7wB0pLS1Our1ixgsbGRjZu3MjixYsBeOONN4jH4yxfvjzb1ZEMhEIh2o5uZFnjP6ekd1Su5HjD73HHGgiVfShrn2cMdpJzz56KlveTx66CjOsTa08Gv3lrroU18cR5xMgnbto9Nl6vVwGFiIiIjGuDDipaW1vZs2dP4nzfvn1s3ryZkpISJk+ezI033si7777Liy++SCwWS8yTKCkpwe12M2fOHD74wQ9y11138f3vf59IJMI999zDLbfcopWfhtHx48fx+Xwpe0ccPXqU/Miu1IzTVpJfUMTWEjvQmOHJ/OW9y6BfzHv2VLzebR5GPHVC+amIBWsSxwbJgCJm+Nhe/EUKCgqYNm1ar6tZiYiIiIwng/56dcOGDSxcuJCFC+3FPu+77z4WLlzIV77yFY4cOcKvf/1rDh8+zIIFC5g8eXLi5513kpN8n3nmGWbPns1ll13GNddcwwUXXMAPfvCD7D2VDEpLSwtbt25l/fr1Ken19fX4YvbGhZZvClz0Iiz7d/Lz8xN5svlC3VtPxd6CO/u+ofZN2PhZe9hT6DiEk3MgiIf7vm8AIpEITUc2paV3XPAyf5r8EvXeFZxxxhlMmDAho88RERERGQsG3VNx8cUXY1lWn9dPdq1LSUkJzz777GA/WoZANBpl586difNYLIbD4SAcDtPa2kp53F5NyThtJUyxhzrlOeI4nU6i0SjFxcVZq0tvQcXBgo8RM7yc3mxvthh2TSFolFDUscXOsPNxe1L21JtSb4x3ZFSXHTt2MLFlS1r6uj1WIhR3u90ZfYaIiIjIWDHk+1TIyGVZFmvXriUaTe6S3d7eTkFBQWIvEK+zcxiRM9k7YZomixYtIh6P4/V6s1afvoY/Rc3kZ78/42nqm0JcUN1tl/bw8fSeiVhmPRWuQz+hPPSHtPSYkXxeBRUiIiIiNs0uHYeam5s5cOAAwWAwJaAAaGxsBJKb3rmMzpdzZ+rciby8vJRhUNnQs6eia+J+xCxMpDV35BM1enyuFYNYj56JSGYbJM5u/Gav6YHCQpxOJxMmTBj8xHIRERGRMUo9FePQ7t27aWlpYd++fWnXgsEglmXZwYVlUdDaORfGld0AojfdeyqmT5+Oz2evrlTvWc6B/I/R5J5LKBwGwyBsluCJ19uZQ3XpPRUN70LNH2DiADbR68em0ieY3vJDmtznsGjRoozLExERERlr1FMxzliWRbjxANNafow3eiyRfvq0CRhWlGAwyL59+6itrWVCaDXOaIOdwRj6FY66f/PftemefcFkX+BO6r0rEklhR7dd2cN1vc+h2P3vWanXpHkfZfOEf6Ou4jNZKU9ERERkrFFPxTjT1NTEguP3khc7hCdWhzPeSnnoTTgKhc4ZbPU9Q0ODHUiUhd5K3ujw9VFi9nTvqXA4HCfJCXGj23yGUF3vcygcHlh7J8y+D4rOOuV6Ta6YQqCwSEvHioiIiPRBPRXjTE11NXmxQwBUtP/WDig65Uf3Egraqz1hxSkOb7SPS8+FqTfmpH7z589n0qRJ/e5ZkhJUdOupaHVOJ+SYaKfv+y/Y+5+w7iTL0nZp3gVv3QQNmyEeI+K0l4qtn/c0AH6/XxOzRURERPqgoGIcsSyL5pptJ83jjp8AwB/dizvegOXww+VvgpmbTq3i4mJmz57db6/ACc+5yZNwck6FZTip8V2Wmrl5R/8fvOYOOPgcvLwQ/qcEV/Q4AHH/rEHVX0RERGQ8UlAxjsTjcQra1p00T37kfQD8kf12QslicIy8b+iP+j9C08wv2yeRZoi2AhDHndqLAVBwev8FNnTb6K7bylGGw5NpVUVERETGPAUV40g0Gk0MabKKF8Ksu+G02+DS38P02wEIdOzAEW+nsu15AAzfpGGr78lYhoOG8r8Do3PuRXvnzt+GE4sevRyeAex67S7sNdl0Zm8fDhEREZGxShO1x5FYNEpx+F0AjMX/BuUXJi/W28GGL3aIOY3/QiCy3U4fyAv5MOmIRO36hWpg348AiBuu9J6KnsvN9sKKReh11wlz5PXSiIiIiIw06qkYR6Id7bjjjfZJ0TmpFzuHCBUatUwIvZ1MH8lBRUdHsn4N79m/LYt4z+Vvo8GTFxSqw4g09HrJdGr4k4iIiEh/FFSMI7FwY/LE2WMzu86gwtu+NTU9r2poK5WBjo4OcAU6zywADuXflB5UxNpPXtBJJnJrToWIiIhI/xRUjCORdvvb+LjhBbPHPhD5M3u/qWR4d5BeunRpyvmcOXM4++yzAXv377gjL+V6yDkJb7Q6tZBYPz0VzTv7vKQ5FSIiIiL9U1AxBjU3N7Njxw527dpFMBgkHA5z/Phxwu32crFxhz/9Jmcfm9sVnvqmcdng9/uZOHFi4tzr9eL32/WPRCLUN6fupB02y2nw9AiEOleGShNpgdcuxNp0PwAtrvRVogxTG96JiIiI9EcTtceg/fv3U19fD4BhGImgItCxlyoAZy9BRW9KFtu7Ug+z7pvOmaaZcp7XsTdxHDECxE0vTe75bJzwFPlFFZy553qsYA1GPAI9A4Rjr0DdW4kJ2i2uM9lS8i9MCP2JM5qesD+vn529RUREREQ9FWNSQd0vueDY3zCn4Z+JtR6ivvYQeZGDOOKdcwsS8xB6WPjN5PHVm+HyPw15XQei+0Z4pmni6Pai74sdSxxHzAL7wDBocc/lWFshMdwYxHufN9HRlHIaNzx0OCYQMZPLy5qm/oqIiIiI9Ec9FWPQaTVfx8BiYvD3RA4GKIq3Min4u8R101fe+41n/CNs+ZrdO1E0D4yR8ULdPYjweOyeE5/PRzCYOlfCYYVSbzQMQs5J+KMH4aVz4KY2cHabgxFKnXsRM+yyG9328KmgYxJOBRUiIiIi/dIb0xgUNZM9ERVtv0kJKADMsvN6v9HhgesPw3X7R0xAAfZO4F2cTjsO7hoCtSfwycQ1h5W+ylPYLEueNP4l9WIwNagwOgOOiKOItyb9ivXlP8Ywet29QkRERES6GTlvjkJHRwe1tbVYlnXKZcRiMeIkv9k3iaRnKrug7wJcgYHPuciR8vJyDMOguLg4kTZhgr0/xRH/DYT88wA4lL8Sr9dLeXmyJyZmdl8dKvU/93jnLtxdfPmlTJ8+nUAgQNQMEDfcGv4kIiIiMgAa/jRCWJbFO2+/jUkEc96ixEvzYEU6OnDHm3q/OPPjEKqFiRefekWHgcfj4bzzzkv0UgBUVlZy5MgRQqEQB2b9iCnuAxw8nI/XMBKrQwHEjG6rWkVbksftR4i1HEgJMwyHi2nTpuHxeGhubrbT1FMhIiIi0i99DTtCHDt2jJnN3+WCY9fQUbvhlMuJBE9gEku/MP0OWP4fcNGvRsSKToPlcrlSXvANw2Dq1KkAdMRdRCZcjGW4ME0zpXch6KxMFhKxAwX2/ze8UImr+d2Uz3B3HAFSJ2crqBARERHpn4KKEaK9ejNVbb/EJIav/vVTLqep9n0AYkYeLP1u8sK5/5lpFUecrlWhotFoYt5Fz6DikP/G5A1dQcU7KxNJ9Z6lVPuuwMKkvfwjKeWKiIiIyMBo+NMIUVT3bOI43NFLT8MAtR3fA0DMUYBj5l3Q8j5MumxETbzOlq7hUN2DCsMwUnoX4qaP43mXMqH9DTuoOPg/KWUc9t9AvWcJ7wc+xWlFiwEoKipiypQp5OWl7tYtIiIiIr1TUDFCODtqEsfh1lpaWlooKCgYVBnhcJhJ1Y8D4MgrA9MJix7LZjVHlK6gIhKJ0NDQkDgOhVKXlu2gc17FziegdU/KNctwguEg4igiErEntRuGwemnp++uLSIiIiK9G3tfX48ikUiEjo4OAIx4cjlUZ7yN6urqvm7r0/Hao+RF9gPgKJ6XlTqOZN2HPx09aq/kFAwGKSy0N69zOBwYhmEPBYO0gAJIWSkrEOhjU0AREREROSkFFcPEsizWrFnD2rVricViGLFkUDGl/QU6gs2DL7T697isVqKuCbDiJ1ms7cjU1VMRj8cTQ54mTpxISUkJ55xzDsuXL8fj8RA1+14i1zJczJ49m3nz5qUsWSsiIiIiA6egYpgEg0FmNDzO7OMPEwoGMeOpu0PnHf/toMv0Nq21yy69EkxHP7lHv66eCCCxt0dlZSWGYVBSUoLb7cbtdhMzegQVE1YkDi2c+Hw+SktLtdKTiIiIyCnSnIph0lp/kMq2/wtAS/MuHD2DiraN/RfS8B4EzgSHFwCjcx8GyzMpu5UdoQzDwOl0JuZCQHKn7S4Oh4Oo2WPCtTM/cWg6PeTn5yMiIiIip049FcMkUpsMGoJNR3F0zqmIzlwFgDt86OQ7a+98El5eAJvuT6Z1DaFyjZ+X5O4b4kH6crAOh4O44e1xU/LPp2xiBQ7H2O/VERERERlKCiqGidWwOXEcajyIw7J7KszShQD4YkdSvoFPs7kzmNj1ZLLMDrunwhxHQUUsllx+1+l0puxR0ZUWJzXQsJzJ4VD+gqIhrZ+IiIjIeKCgYpi4WrcnjiONe3BabQCY5RcC4InVnXSytuWd2O3EIhwOJ1aQMsZRUNG1ehakD30C8Hg8xI3UoKIlGE8cG6Nwd3ERERGRkUZBxTDJ60gubxoIbgAgbnqh4HRiph8Di2jj7j7vj0a7bZAXC9LY2IjDsvdn8PhLhqbSI1xvw8Wqqqqw0oKKZD7DkR6IiIiIiMjgKKgYBvF4HG/sWOK8NGSv2oSvAgyDWOcSqJ4t9520jMRxRzPBYDAx2dvpKcp+pUeB7kOhujidTvLyU5eKjcSTgYRhKqgQERERyZRWfxoCkUiEAwcO4Ha7MQwjscxpl1gslrLak4k9d8IsOw8Ad7QWAF/z2j4/w4gnh/3EOpqJdkTwxDp35XYXZetRRryqqioOHToE9B5UANBjiFPc6BZIOFyIiIiISGbUUzEEDh8+zOHDh9m7dy/vv/8+J06cSLkeiwQTgUSKOZ8DoG3mg4mkvlaAMjuHOgEcP7afln2v4bJaibkmQMniLDzF6DB9+vTEkrAzZszoNY/hSF39KW4kY2n1VIiIiIhkTkFFlkUiEQ7s348/sg8s+5vz1tbWlDyxcFPafeHSS6B4PgC++Z9Llle3CeLp38Cb8WRQUXNwGwWRXQB0FC4Fc/x0QJmmyeLFi1m6dCkVFRW95jGcqUFF9zkWplMTtUVEREQyNX7ePnNk/97dzKt/kNLwnwE4lnc1ofh3AHulou3bt+OJVjO7543Tbk0cmp4S4jgwieH+/WKY+XFY/h/JvPEoJtHE6ZyGr1PruxSAmLt8SJ5rJDMMA7/f3+d10+VLOe8wkxPZ1VMhIiIikjn1VGRZ/tEfJwIKgMntL+OpfwOAQ4cO0dDQgFn9Utp97ooPJE8Mg7jR7UX4/R+mZj6xLuXUE6+nILLTPvZPyPAJxh6HK3VHbWfR6ewoup9txV/FMPVXQERERCRT6qnIskDLm2lpZugoAI7QIS4+eknKNctwYFgxjPyZKelx0wOx1GFTAARr4LULAKj2XUGg46/kxY5Q1LEFAFfe+FxO9mR6BhWG08uxvGsAmNltAr2IiIiInBoFFdlmONKTIo0AFNe/kHph8RMYU2+y50CYqffFjT7G+u/+XuKwzvsB8iPvp153BgZb4zHPcBeknJvOZC9Qzx24RURERGTwFFRkmTN0MD0xZk+qjjoKk2kf3gv+06CPb8ots0dQYVnQ9FfY+kgiqd67nGmtz6Tm0zfvaRyuPDrMYtzxBjuh2zwKQ39eIiIiIhnT17RZZDXvwRM5TBwHzVfuT17oDCqsaDsAzRNugPzpJw0ALDN1xSIO/De8dHZKks9fmP5SHDjzlOs/VpmmSciRnMDefTUoBRUiIiIimVNQkS3xKLxhr8DU7D4bf3ElwRmfBSASauHYsWMQbbPzOvteqShRXM+gYvODaXkmT56MQbflZs/9MUy87NTqP4YZhkGHWZo8dyioEBEREckmBRXZUv8uRru9s3NL4AM4HA7MzgnCptXBzp07MbomXrvy+y3OMlOXQSV4NOW02ncVkyZNwrCSS8sy43YNf+qFYRh0OIqTCd2GlmlOhYiIiEjm9EaVDc07YcvDidPWyrsAUoIKACti91QYzoEEFT16KroHD8DuwntwuVypPRXSK9M0U/am8PkLT5JbRERERAZLE7WzYeNn4dgrANS7F1NYZA+16VplyCBi/44OpqfC2/c1TE6fs9gus0ewIel69lQ43V7OP/98DMPQ8CcRERGRLFBPRTYcezlx6LEamDhxIpDeU2HEOnsqXAX0y9F3UBEzfBSX2N+8VwduBOCEZ/ng6z1O2D0VE1LOXS4XTqdiahEREZFs0FtVFrQ7ppAXOwJAk/98/A57zwnDYfdUJIMKe/WngQQVhtF3vBdyTMLb+RnVgZtoMGYSdFZx/qk/wphmGAYnvMtpcZ1Ou3Mqbs2jEBEREckqBRUZCofDOC17WNMh/02Epn0qebGzt6E4vJH8jl04rCCQvhlbr6xIn5eq865kRueL8dRp09i9u4OysrJTfIKxzzAMLMPNxrIfALBAQ55EREREskpBRQZisRhr3nmHD8TtoCJ6+j9SOX1+MkPn0rEmMZYc/wfanFPtc9cAdr2O9z1XojbvKmZ1BhVTpkyhpKQEj6ePHbglbYUnrfgkIiIikl0KKjJwcP9uzmj6NmbnCkzTz1gMzm5LwbpLUvK74i32b19R/4X30VPxl5L/TcyVWq7P5+s1r9h6TsbW5GwRERGR7FJQkYH8bf+LsvaXkgnOHi/3ntKUU3e8AQCHZwBLmvbRU1HvWYZHE4wHpWcQoZ4KERERkezS21UGAsE/J0/mfTU9Q4+gIsFd1H/h8T7mVBhGYnUpGZieQYR6KkRERESyS0HFKbIsC2esGYCOq3fCvIfTM7mK0u8z3ODtPyhwV1yQOD7ovznlWmVl5eAqO86pp0JERERkaGkczSmKhltwYS8V68wr7z2T6UhLinkqcJ5kudgu3gVfJubO492aKYQc5fhiR6n1XcKCBQtwu90Z1X28cThS20E9FSIiIiLZpa9sT1G0vRYACwemewBzJDpZeVUDy+j04TjnIdpc04mZfraVfI063yUUFg78s8RmmmZKIKaeChEREZHsGvTb1erVq7n22mupqKjAMAxeeOGFlOuWZfGVr3yFyZMn4/P5uPzyy9m9e3dKnvr6em677TYCgQBFRUXceeedtLa2ZvQguRaq3wtAxFEEg/jm28yfltHn6lv2U+P1Jnco15+hiIiISHYNOqhoa2tj/vz5PPXUU71e/8Y3vsETTzzB97//fdatW4ff7+eqq64iFAol8tx2221s27aN1157jRdffJHVq1dz9913n/pTDIOO4+8BEM2b2U/OHuP5A9MH9TkrVqzA5XIN6h5J130fD/VUiIiIiGTXoOdUXH311Vx99dW9XrMsi8cff5yHHnqI6667DoD/+q//YuLEibzwwgvccsstbN++nVdeeYX169ezZMkSAL7zne9wzTXX8Nhjj1FRUZHB4+ROmbsOAFfpOSfP6MyHaEvi1PAPrqfC4/Ewf/58NmzYwKRJkwZdT7F1DyrUUyEiIiKSXVn9ynbfvn1UV1dz+eWXJ9IKCwtZvnw5a9asAWDNmjUUFRUlAgqAyy+/HNM0WbduXTarM6TMuQ/AZW/gmvOPJ8/oKkg9z5s66M/Kz8/n/PPP58wzzxz0vWJTUCEiIiIydLK6+lN1dTVA2j4KEydOTFyrrq6mvDx1tSSn00lJSUkiT0/hcJhwOJw4b25uzma1T413Angv6T+fMz/13D/4oALQEKgMdZ9TISIiIiLZNSoGlz/66KMUFhYmfqqqBriC0khQ+ZHU81PoqZDMlZaWkp+fnxbQioiIiEjmshpUdI35r6mpSUmvqalJXJs0aRK1tbUp16PRKPX19X3OGfjCF75AU1NT4ufQoUPZrPbQOueR1HOnb3jqMc6ZpsmSJUuYO3fucFdFREREZMzJalAxffp0Jk2axOuvv55Ia25uZt26daxYsQKwVzNqbGxk48aNiTxvvPEG8Xic5cuX91qux+MhEAik/IwaDi9B55ThroWIiIiIyJAZ9JyK1tZW9uzZkzjft28fmzdvpqSkhKlTp3Lvvffy9a9/ndNPP53p06fz5S9/mYqKCj7ykY8AMGfOHD74wQ9y11138f3vf59IJMI999zDLbfcMmpWfhosw4oNdxVERERERIbMoIOKDRs2cMklyQnK9913HwB33HEHP/rRj3jggQdoa2vj7rvvprGxkQsuuIBXXnklZaLsM888wz333MNll12GaZrccMMNPPHEE1l4nJHJ5TRAcYWIiIiIjFGGZVnWcFdisJqbmyksLKSpqWl0DIV6fjKEOle2unXU/XGLiIiIyDg10PfuUbH606hnRYe7BiIiIiIiQ0ZBRS5oToWIiIiIjGEKKnIhrp4KERERERm7FFSIiIiIiEhGFFTkwkW/AlcAVvxkuGsiIiIiIpJ1g15SVk7BxEvgxgYwFMOJiIiIyNijt9xcUUAhIiIiImOU3nRFRERERCQjCipERERERCQjCipERERERCQjCipERERERCQjCipERERERCQjCipERERERCQjCipERERERCQjCipERERERCQjCipERERERCQjCipERERERCQjCipERERERCQjCipERERERCQjCipERERERCQjCipERERERCQjCipERERERCQjzuGuwKmwLAuA5ubmYa6JiIiIiMjY1fW+3fX+3ZdRGVS0tLQAUFVVNcw1EREREREZ+1paWigsLOzzumH1F3aMQPF4nKNHj1JQUIBhGMNSh+bmZqqqqjh06BCBQGBY6iCnRm03uqn9Ri+13eim9hu91Haj23C3n2VZtLS0UFFRgWn2PXNiVPZUmKZJZWXlcFcDgEAgoL+go5TabnRT+41earvRTe03eqntRrfhbL+T9VB00URtERERERHJiIIKERERERHJiIKKU+TxeHj44YfxeDzDXRUZJLXd6Kb2G73UdqOb2m/0UtuNbqOl/UblRG0RERERERk51FMhIiIiIiIZUVAhIiIiIiIZUVAhIiIiIiIZUVAhIiIiIiIZGbdBxaOPPsrSpUspKCigvLycj3zkI+zcuTMlTygUYtWqVZSWlpKfn88NN9xATU1NSp5Pf/rTLF68GI/Hw4IFC3r9rFdffZVzzz2XgoICysrKuOGGG9i/f/8QPdn4kMv2+8UvfsGCBQvIy8tj2rRpfPOb3xyqxxoXstF27733HitXrqSqqgqfz8ecOXP4t3/7t7TP+uMf/8iiRYvweDzMmjWLH/3oR0P9eGNertrv2LFj3HrrrZxxxhmYpsm9996bi8cb03LVds8//zxXXHEFZWVlBAIBVqxYwauvvpqTZxzLctV+b731Fueffz6lpaX4fD5mz57Nt7/97Zw841iVy//vdXn77bdxOp19vtsMhXEbVLz55pusWrWKtWvX8tprrxGJRLjyyitpa2tL5PnsZz/Lb37zG5577jnefPNNjh49ykc/+tG0sv7+7/+em2++udfP2bdvH9dddx2XXnopmzdv5tVXX+X48eO9liMDl6v2e/nll7ntttv4xCc+wdatW/nud7/Lt7/9bZ588skhe7axLhttt3HjRsrLy/npT3/Ktm3b+NKXvsQXvvCFlHbZt28fH/rQh7jkkkvYvHkz9957Lx//+Mf1cpOhXLVfOBymrKyMhx56iPnz5+f0GceqXLXd6tWrueKKK3jppZfYuHEjl1xyCddeey2bNm3K6fOONblqP7/fzz333MPq1avZvn07Dz30EA899BA/+MEPcvq8Y0mu2q5LY2Mjt99+O5dddllOni/BEsuyLKu2ttYCrDfffNOyLMtqbGy0XC6X9dxzzyXybN++3QKsNWvWpN3/8MMPW/Pnz09Lf+655yyn02nFYrFE2q9//WvLMAyro6Mj+w8yTg1V+61cudK68cYbU9KeeOIJq7Ky0orH49l9iHEq07br8qlPfcq65JJLEucPPPCAddZZZ6Xkufnmm62rrroqy08wvg1V+3V30UUXWZ/5zGeyWm/JTdt1mTt3rvXII49kp+JiWVZu2+/666+3Pvaxj2Wn4jLkbXfzzTdbDz30UJ/vNkNl3PZU9NTU1ARASUkJYEeEkUiEyy+/PJFn9uzZTJ06lTVr1gy43MWLF2OaJk8//TSxWIympiZ+8pOfcPnll+NyubL7EOPYULVfOBzG6/WmpPl8Pg4fPsyBAweyUHPJVts1NTUlygBYs2ZNShkAV1111aDaX/o3VO0nQy9XbRePx2lpaVH7Zlmu2m/Tpk288847XHTRRVmquQxl2z399NPs3buXhx9+eAhqfnIKKrD/wbv33ns5//zzOfvsswGorq7G7XZTVFSUknfixIlUV1cPuOzp06fzu9/9ji9+8Yt4PB6Kioo4fPgwv/jFL7L5COPaULbfVVddxfPPP8/rr79OPB5n165d/Ou//itgj/mWzGSr7d555x1+/vOfc/fddyfSqqurmThxYloZzc3NBIPB7D7IODWU7SdDK5dt99hjj9Ha2spNN92UtfqPd7lov8rKSjweD0uWLGHVqlV8/OMfz/pzjEdD2Xa7d+/mwQcf5Kc//SlOp3PInqEvuf/EEWjVqlVs3bqVt956K+tlV1dXc9ddd3HHHXewcuVKWlpa+MpXvsKNN97Ia6+9hmEYWf/M8WYo2++uu+7i/fff52/+5m+IRCIEAgE+85nP8NWvfhXTVEyeqWy03datW7nuuut4+OGHufLKK7NYO+mP2m/0ylXbPfvsszzyyCP86le/ory8/JQ/S1Llov3+9Kc/0draytq1a3nwwQeZNWsWK1euzKTawtC1XSwW49Zbb+WRRx7hjDPOyFZ1BydnA61GqFWrVlmVlZXW3r17U9Jff/11C7AaGhpS0qdOnWp961vfSiunr3FrDz30kLVkyZKUtEOHDvU7Tk4GZqjbr0s0GrUOHz5shcNh66WXXrIAq7a2NhuPMG5lo+22bdtmlZeXW1/84hfTyr/wwgvTxuH/53/+pxUIBLJS//FuqNuvO82pyK5ctd3PfvYzy+fzWS+++GLW6i65/bvX5Z/+6Z+sM844I6N6y9C2XUNDgwVYDocj8WMYRiLt9ddfH5Jn6m7cBhXxeNxatWqVVVFRYe3atSvtetekmV/+8peJtB07dgx6ou99991nLVu2LCXt6NGjFmC9/fbbmT/IOJWr9uvN3/7t31orVqw45bqPd9lqu61bt1rl5eXW/fff3+vnPPDAA9bZZ5+dkrZy5UpN1M5QrtqvOwUV2ZHLtnv22Wctr9drvfDCC9l9iHFsOP7udXnkkUesadOmZVT/8SwXbReLxawtW7ak/Hzyk5+0zjzzTGvLli1Wa2vr0DxcN+M2qPjkJz9pFRYWWn/84x+tY8eOJX7a29sTeT7xiU9YU6dOtd544w1rw4YN1ooVK9JeJnfv3m1t2rTJ+od/+AfrjDPOsDZt2mRt2rTJCofDlmXZ0adhGNYjjzxi7dq1y9q4caN11VVXWdOmTUv5LBmcXLVfXV2d9b3vfc/avn27tWnTJuvTn/605fV6rXXr1uX0eceSbLTdli1brLKyMutjH/tYShnde4/27t1r5eXlWffff7+1fft266mnnrIcDof1yiuv5PR5x5pctZ9lWYm/j4sXL7ZuvfVWa9OmTda2bdty9qxjTa7a7plnnrGcTqf11FNPpeRpbGzM6fOONblqvyeffNL69a9/be3atcvatWuX9cMf/tAqKCiwvvSlL+X0eceSXP672V2uV38at0EF0OvP008/ncgTDAatT33qU1ZxcbGVl5dnXX/99daxY8dSyrnooot6LWffvn2JPD/72c+shQsXWn6/3yorK7M+/OEPW9u3b8/Rk45NuWq/uro669xzz7X8fr+Vl5dnXXbZZdbatWtz+KRjTzba7uGHH+61jJ7fpP3hD3+wFixYYLndbmvGjBkpnyGnJpftN5A8MnC5aru+/l294447cvewY1Cu2u+JJ56wzjrrLCsvL88KBALWwoULre9+97spS+PL4OTy383uch1UGJZlWX3NtxAREREREemPlq8REREREZGMKKgQEREREZGMKKgQEREREZGMKKgQEREREZGMKKgQEREREZGMKKgQEREREZGMKKgQEREREZGMKKgQEREREZGMKKgQEREREZGMKKgQEREREZGMKKgQEREREZGMKKgQEREREZGM/P8wfAhdkplpegAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Return (equally weigthed) : 205.3663989425734 %\n",
      "Return (optimized)        : 210.65131760114411 %\n",
      "Improved objective by     : -70.36105085189831\n"
     ]
    }
   ],
   "source": [
    "portfolio_equally_weighted = [];\n",
    "portfolio_opt_weighted = [];\n",
    "pul = budget\n",
    "pul_opt = budget\n",
    "returns_np = np.array(returns)\n",
    "\n",
    "for day in returns_np:\n",
    "    # equally weighted portfolio:\n",
    "    budget_day = pul;\n",
    "    for i in range(0, len(day)):\n",
    "        asset_weight = (100/n) / budget_day * 100;\n",
    "        pul += asset_weight * day[i]\n",
    "    portfolio_equally_weighted.append(pul)\n",
    "    \n",
    "    # optimised weighted portfolio:\n",
    "    budget_day = pul_opt;\n",
    "    for i in range(0, len(day)):\n",
    "        asset_weight = x[i] / budget_day * 100;\n",
    "        pul_opt += asset_weight * day[i]\n",
    "    portfolio_opt_weighted.append(pul_opt)\n",
    "\n",
    "plt.figure(figsize=(8, 5))\n",
    "plt.title('Markowitz Mean-Variance Model')\n",
    "plt.plot(data.index[1:], portfolio_equally_weighted, '-', color='gray', label='Equally weighted', alpha=0.5);\n",
    "plt.plot(data.index[1:], portfolio_opt_weighted, '-', color='orange', label='Portfolio optimized');\n",
    "plt.legend() \n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "print('Return (equally weigthed) :',portfolio_equally_weighted[-1]/budget*100,'%')\n",
    "print('Return (optimized)        :',portfolio_opt_weighted[-1]/budget*100,'%')\n",
    "print('Improved objective by     :',f(x)-f(np.repeat(100/n, n)))"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
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