{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# 3. Unconfined Aquifer Test - Anisotropic unconfined aquifer\n", "The description and data for this example are taken from the [aqtesolve](http://www.aqtesolv.com/examples/ione.htm) website. \n", "\n", "Lohman (1972) presented data from a constant-rate pumping test performed in an unconfined aquifer with delayed gravity response near Ione, Colorado. The thickness of the unconfined alluvium was 39.4 ft. The fully penetrating test well pumped at a rate of 1170 gallons-per-minute (gpm) for 4270 minutes. The drawdown data were recorded in an observation well located 63 ft from the test well at a depth of 19.7 ft below the static water surface." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false, "jupyter": { "outputs_hidden": false } }, "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import pandas as pd\n", "\n", "import ttim" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false, "jupyter": { "outputs_hidden": false } }, "outputs": [], "source": [ "# problem definition\n", "H = 39.4 * 0.3048 # thickness [meters]\n", "xw, yw = 0, 0 # location well\n", "xp, yp = 63 * 0.3048, 0 # Location piezometer [meter]\n", "Qw = 1170 * 5.45 # discharge well in [m3/d]\n", "z_obswell = -19.7 * 0.3048 # elevation of observation well" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false, "jupyter": { "outputs_hidden": false } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "minimum and maximum time: 0.0006944444444444445 2.965277777777778\n" ] } ], "source": [ "# loading data\n", "data = np.loadtxt(\"./data/pumptest_neuman.txt\") # time and drawdown\n", "time, dd = data[:, 0], data[:, 1]\n", "td = time / 60 / 24 # t in [days]\n", "ho = -dd * 0.3048 # observed head [meter]\n", "print(\"minimum and maximum time:\", td.min(), td.max())" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "# layer definition\n", "nlay = 12 # number of layers\n", "zlayers = np.linspace(0, -H, nlay + 1)\n", "zcenter = 0.5 * (zlayers[:-1] + zlayers[1:])\n", "layer_obswell = np.argmin(np.abs(z_obswell - zcenter))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Flow is simulated with a quasi three-dimensional model consisting of one aquifer which is divided into `nlay` model layers. The top and bottom of the aquifer are impermeable. The horizontal hydraulic conductivity $k$, phreatic storage $S_y$, elastic storage $S_s$, and vertical anisotropy $k_v/k_h$ are unkonwn. The variable `p` contains all unknown parameters. The well is modeled with the `Well` element. TTim divides the discharge along the layers such that the head is the same at the well in all screened layers." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "self.neq 12\n", "solution complete\n" ] } ], "source": [ "Saq = 1e-4 * np.ones(nlay)\n", "Saq[0] = 0.2\n", "ml = ttim.Model3D(\n", " kaq=10, z=zlayers, Saq=Saq, kzoverkh=0.2, phreatictop=True, tmin=1e-4, tmax=10\n", ")\n", "w = ttim.Well(ml, xw=xw, yw=yw, rw=0.3, tsandQ=[(0, Qw)], layers=range(nlay))\n", "ml.solve()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ ".................................................................\n", "Fit succeeded.\n" ] } ], "source": [ "cal = ttim.Calibrate(ml)\n", "cal.set_parameter(name=\"kaq0_11\", layers=np.arange(12), initial=100, pmin=10, pmax=400)\n", "cal.set_parameter(name=\"Saq0\", layers=0, initial=0.1, pmin=0.01, pmax=1)\n", "cal.set_parameter(\n", " name=\"Saq1_11\", layers=np.arange(1, 12), initial=1e-4, pmin=1e-5, pmax=1e-3\n", ")\n", "cal.set_parameter_by_reference(\n", " name=\"kzoverkh\", parameter=ml.aq.kzoverkh[:], initial=0.2, pmin=0.01, pmax=1\n", ")\n", "cal.series(name=\"obs1\", x=xp, y=yp, layer=layer_obswell, t=td, h=ho)\n", "cal.fit()" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "# cal.parameters\n", "k, Sy, Ss, kzoverkh = cal.parameters[\"optimal\"].values" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0.5, 0.98, 'TTim Aquifer Test Analysis in Unconfined Aquifer')" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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L6dOnqVGjRonPt3//foBCk4f/9ddfGIaR7+9vaGgoQJ6pgKV9nYUp6pjF/Zv866+/yM7OPqfPjkWLFnHkyBFuuukm999BVlYWN910E2lpae7fRdfPxZefU8X5vHDxxmeUiIj4B63CJiIild4777yDYRgkJCSQkJCQZ/97773HU089hdlsplGjRuzZsydPnfzKatasyYkTJ/KUF5YDpzCuFdFmzZpV4Op2uUdGAXkSNJf2vCaTieTkZPc/0LnlLuvYsSMffPABhmGwdetW5s2bxxNPPEFgYCAPPfRQic+9c+dOvv76awBatGiRb50VK1Zw7bXXlui4CxYsoFWrVixatMjjZ5Tf+5Wf3r1706FDB1599VXq1q3L5s2bPXIsAdSpU4cZM2YwY8YM9u7d6x6NNGjQIH7++ecCjx0REUFERAQOh4NNmzYxa9YspkyZQtOmTd0j5MqD63cpIyMj3/0ZGRl5ft+Ko0mTJgDuxOr5adCgAdWqVSMzMzPPPldibH9YIbC4f5OnTp3CZDIV+7MjP66cX1OmTMmTbNy1v1+/fu6gV3HO5RoNdfbv/dlBnpIoyecFeOczSkRE/IMCSCIiUqk5HA7ee+89Wrduzdy5c/Ps/+STT3jxxRf5/PPPGThwIFFRUSxbtoy9e/e6/3l2OBz5JvUNDw9n69atHmWrV6/myJEjpWrrlVdeSf369dm2bZtXl80uysCBA3nmmWfIyMjgpptuKtZzTCYTl1xyCS+//DLz5s1j8+bN7n1njwYqjOuf5rfeeosLLrjAY9/x48cZMmQI77zzTokDSCaTiYCAAI9/Xvfs2VPkKmy5TZ48mTvvvJOsrCyaNm3KjTfeWGDdpk2bMmbMGH744QdiYmKKtVS52WymW7dutG3bloULF7J58+ZyDSB1796dunXrsmjRIqxWq8e+bdu28dNPP/HYY4+V+LgXXXQRrVu35p133mHq1Kn5Bhnq1KlDt27dsNvtvPDCCwQGBgKQnZ3NggULsFgsXHTRRaV7YV5U3L/JgIAAunbtit1u5/nnn3cHbv7++2/++9//Fnme7du3s27dOoYOHZrveZ566ik+/vhjDhw4QJs2bQgJCSE+Pp6pU6e6f8d///131q5d6x7BBbhXZNu6dSv9+vVzly9btqxYrz8/pfm8EBGRykEBJBERqdQ+//xzdu/ezbPPPpvvcvKuUSZvv/02AwcO5NFHH2XZsmX07t2bxx57jNq1azN79uw8uYcARo8ezf/93//x2GOP0atXL7Zt28arr75KUFBQqdpat25dZs2axa233srBgwex2WwEBwezf/9+fvjhB/bv389rr71WqmMX5sorr2TChAmMHTuWTZs28a9//Ys6deqQmZnJ119/TceOHbnrrrv45JNPmDNnDtdffz3nn38+hmFgt9s5dOgQffr0cR+vY8eOJCUl8d///peQkBDOO+882rRpk+e8p0+f5v3336ddu3aMGzcu37YNGjSIZcuWsX//fvfIluIYOHAgdrudiRMnYrPZSEtL48knnyQkJKTQKWa53XzzzUybNo01a9bw6KOPuqdMunTr1o2BAwfSqVMnGjRowPbt25k/fz49evQoMHj0+uuvs3r1aq677jpatGjBP//8455Cec011xT79XnDeeedx4wZM7j33nvJzs5m2LBhNGjQgP/973/85z//oWXLlkyePLlUx549ezaDBg2ie/fu3HPPPbRo0YI//viDFStWsHDhQgBmzpxJnz59iIqK4r777iMgIIA5c+bw448/Eh8f7xcjV0ryN/nkk0/Sv39/+vTpw7333ovD4eDZZ5+lTp06eaaens0VSH3ggQfy5PcCZyBq1apVLFiwgOjoaJ588knGjRvHDTfcwPjx4zl06BDTp0/PM4WtWbNmXHPNNcycOZMGDRrQsmVLVq1ahd1uL/XPpLifFyIiUvkogCQiIpXa22+/TUBAAGPHjs13f+PGjbnhhhtISEhg7969dOjQgS+//JJ7772XW2+9lQYNGjB69GiGDh3KhAkTPJ57//33c/jwYebNm8cLL7xA165dWbx4MUOGDCl1e2+++WZatGjBc889xx133MHff/9NcHAwnTt3ZsyYMaU+blHeeOMNunfvzhtvvMGcOXPIzs4mNDSUK6+80v0P7YUXXkj9+vV57rnn2L17NwEBAbRp04Z58+Zx6623uo8VGxvLpEmTGD58OMeOHaNXr155EusCfPrpp+zZs6fQqW8TJkzAbrczf/58pk6dWuzXM3bsWPbt28frr7/OO++8w/nnn89DDz1Eeno6M2bMKNYxAgMDGTRoEAsWLODOO+/Ms793794sW7aMl19+mWPHjtG8eXNuueUWHnnkkQKP2blzZ7744gsef/xx9uzZQ926denQoQPLli2jb9++xX593jJ16lTCwsJ45ZVXuO222zh+/DihoaHYbDYef/zxPHnHiqtfv36sWbOGJ554gsmTJ/PPP/9gsVg8klb36tWL1atX8/jjjzNmzBiys7O55JJLWLZsGQMHDvTWSzxnxf2b7NOnD0uXLuXRRx9l2LBhNGvWjIkTJ3L8+PFCf+dOnTrF/Pnz6dy5c77BI4Brr70Wi8XC22+/TXR0NLfffjsAzz77LFarlfDwcB5++GG++uqrPH9r8+fP59///jcPPvggDoeDQYMGER8fzxVXXFHqn0lxPi9ERKTyMRmu5UVERESkQPPmzWPs2LGkpKS4p4VI5Xby5EnCw8O56qqrWLx4sa+bI1KkMWPGkJSURGpqqq+bIiIilZBGIImIiIjksn//fnbs2MG7777L3r17S5UcXERERKSyUQBJREREJJdPP/2UsWPHEhISwpw5c7jssst83SQRERERn9MUNhERERERERERKVQ1XzdARERERERERET8mwJIIiIiIiIiIiJSKAWQRERERERERESkUAogiYiIiIiIiIhIoRRAEhERERERERGRQimAJCIiIiIiIiIihVIASURERERERERECqUAkoiIiIiIiIiIFEoBJBERERERERERKZQCSCIiIiIiIiIiUigFkEREREREREREpFAKIImIiIiIiIiISKEUQBIRERERERERkUIpgCQiIiIiIiIiIoVSAElERERERERERAqlAJKIiIiIiIiIiBRKASQRERERERERESmUAkgiIiIiIiIiIlIoBZBERERERERERKRQCiCJiIiIiIiIiEihFEASEREREREREZFCKYAkIiIiIiIiIiKFUgBJREREREREREQKpQCSiIiIiIiIiIgUSgEkEREREREREREplAJIIiIiIiIiIiJSKAWQRERERERERESkUAogiYiIiIiIiIhIoRRAEhERERERERGRQimAJCIiIiIiIiIihVIASURERERERERECqUAkoiIiIiIiIiIFEoBJBERERERERERKZQCSCIiIiIiIiIiUigFkEREREREREREpFAKIImIiIiIiIiISKEUQBIRERERERERkUIpgCQiIiIiIiIiIoVSAElEREREPKSlpREZGUn79u3p1KkTH374oa+bJCIiIj5mMgzD8HUjRERERMR/ZGZmsnfvXjp37sy+ffu47LLL2LFjB3Xq1PF100RERMRHqvu6ASIiIiLiX0JCQggJCQEgODiYhg0bcvDgQQWQREREqjBNYRMRERGpYNasWcOgQYMIDQ3FZDKxdOnSPHXmzJlDq1atqFWrFpdffjnJycmlOtemTZvIzs4mLCzsHFstIiIiFZlGIBUhOzub3bt3c95552EymXzdHBERESmAYRj8/fffhIaGUq1a5b5HdvToUS655BLGjh3L0KFD8+xftGgRU6ZMYc6cOVx55ZW88cYbDBgwgG3bttGiRQsALr/8ck6cOJHnuV988QWhoaEAHDhwgFtuuYW5c+cW2p4TJ054HCs7O5uDBw/SqFEj9Z9ERET8WEn6T8qBVIT09HTdcRMREalA0tLSsFgsvm5GuTGZTHz00Udcf/317rJu3bpx2WWX8dprr7nL2rVrx/XXX8/MmTOLddwTJ07Qp08fxo8fz+jRowutO336dGbMmFGq9ouIiIjvFaf/pBFIRTjvvPMA5w+zXr16Pm6NiIiIFOTw4cOEhYW5r91V1cmTJ/nuu+946KGHPMr79u3L2rVri3UMwzAYM2YMvXv3LjJ4BDBt2jSmTp3q3s7KyqJFixbqP4mIiPi5kvSfFEAqgmvYdb169dQBEhERqQCq+pSpP//8E4fDQdOmTT3KmzZtyp49e4p1jG+++YZFixbRqVMnd36l+fPn07Fjx3zr16xZk5o1a+YpV/9JRESkYihO/0kBJBEREZFK6OyOoGEYxQ6uXXXVVWRnZ5dFs0RERKSCqtwZJkVERESqmMaNG2M2m/OMNtq3b1+eUUkiIiIixaUAkoiIiEglEhAQwOWXX87KlSs9yleuXEnPnj191CoRERGp6DSFTUREqoTs7GxOnjzp62bIOahRowZms9nXzfALR44c4ZdffnFvp6SksGXLFho2bEiLFi2YOnUqo0eP5oorrqBHjx68+eab/PHHH9x5550+bLWIiFQ0DoeDU6dO+boZcg682X9SAElERCq9kydPkpKSopwulUD9+vVp1qxZlU+UvWnTJqKiotzbrhXQbr31VubNm8ewYcM4cOAATzzxBJmZmXTo0IHPPvuMli1b+qrJIiJSgRiGwZ49ezh06JCvmyJe4K3+k8kwDMNLbaqUDh8+TFBQEFlZWVpFRESkAjIMgz/++INTp04RGhpKtWqavV0RGYbBsWPH2LdvH/Xr1yckJCRPHV2z/YfeCxGRii0zM5NDhw4RHBxM7dq1q/yNm4rK2/0njUASEZFK7fTp0xw7dozQ0FBq167t6+bIOQgMDAScyaCDg4M1nU1ERKQMOBwOd/CoUaNGvm6OnCNv9p90G1ZERCo1h8MBOBMLS8XnCgIqH4OIiEjZcF1jdeOt8vBW/0kBJBERqRI09Lpy0PsoIiJSPnTNrTy89V4qgCQiIiIiIiIiIoVSAElERERERERERAqlAJKIiEgxOBwOkpKSiI+PJykpyZ1byR+Eh4cTExPj62Z4TWV7PSIiIlWV+k/lpzxejwJIIiIiRbDb7YSHhxMVFcXIkSOJiooiPDwcu91e5udOS0vj9ttvJzQ0lICAAFq2bEl0dDQHDhwo83OLiIiIlJb6T5WPAkgiIiKFsNvt2Gw20tPTPcozMjKw2Wxl2gn67bffuOKKK9i5cyfx8fH88ssvvP7666xatYoePXpw8ODBMjt3YRwOB9nZ2T45t4iIiPg/9Z/yqgz9JwWQfMCfh/GJiMgZDoeD6OhoDMPIs89VNmXKlDL7HJ80aRIBAQF88cUX9OrVixYtWjBgwAC+/PJLMjIyeOSRR9x1//77b0aOHEndunUJDQ1l1qxZHseaPn06LVq0oGbNmoSGhjJ58mT3vpMnT/LAAw/QvHlz6tSpQ7du3UhKSnLvnzdvHvXr1+eTTz6hffv21KxZk7feeotatWpx6NAhj/NMnjyZXr16ubfXrl3Lv/71LwIDAwkLC2Py5MkcPXrUvX/fvn0MGjSIwMBAWrVqxcKFC7300xMRERFfUP/JqTL2nxRAKme+HMYnIiIlk5ycnOfOWW6GYZCWlkZycrLXz33w4EFWrFjBxIkTCQwM9NjXrFkzRo0axaJFi9wdseeff55OnTqxefNmpk2bxj333MPKlSsBSEhI4OWXX+aNN95g165dLF26lI4dO7qPN3bsWL755hs++OADtm7dyo033kj//v3ZtWuXu86xY8eYOXMmc+fO5aeffuLmm2+mfv36LFmyxF3H4XCwePFiRo0aBcD//vc/+vXrh9VqZevWrSxatIivv/6au+++2/2cMWPGkJqayurVq0lISGDOnDns27fP6z9PERERKR/qP1Xi/pMhhcrKyjIAIysr65yPtWTJEsNkMhmAx8NkMhkmk8lYsmSJF1osIiK5HT9+3Ni2bZtx/PjxEj83Li4uz2d2fo+4uDivt3v9+vUGYHz00Uf57n/ppZcMwNi7d6/RsmVLo3///h77hw0bZgwYMMAwDMN48cUXjYsuusg4efJknuP88ssvhslkMjIyMjzKr776amPatGmGYRjGu+++awDGli1bPOpMnjzZ6N27t3t7xYoVRkBAgHHw4EHDMAxj9OjRxoQJEzyek5ycbFSrVs04fvy4sWPHDgMw1q9f796/fft2AzBefvnlfF93Ye+nN6/Zcm70XoiIVFzn0ncyDPWfKnP/SSOQykneYXwvAPuBqeUyjE9EREouJCTEq/W8yXXtMJlMAPTo0cNjf48ePdi+fTsAN954I8ePH+f8889n/PjxfPTRR5w+fRqAzZs3YxgGF110EXXr1nU/vvrqK3799Vf38QICAujUqZPHOUaNGkVSUhK7d+8GYOHChVx77bU0aNAAgO+++4558+Z5HLdfv35kZ2eTkpLC9u3bqV69OldccYX7mG3btqV+/fpe/EmJiIhIeVL/qfL2nxRAKid5h/HVARoDtYGyHcYnIiKlExERgcVicXcyzmYymQgLCyMiIsLr577gggswmUxs27Yt3/0///wzDRo0oHHjxgUew9XusLAwduzYwezZswkMDGTixIn861//4tSpU2RnZ2M2m/nuu+/YsmWL+7F9+3ZiY2PdxwoMDMzzc+jatSutW7fmgw8+4Pjx43z00UfcfPPN7v3Z2dnccccdHsf94Ycf2LVrF61bt87TiRMREZGKT/2nytt/ql7uZ6yiMjMzzypxZV+vVkQ9ERHxFbPZTGxsLDabDZPJ5JEM0nXRjomJwWw2e/3cjRo1ok+fPsyZM4d77rnHYx7/nj17WLhwIbfccou7HevXr/d4/vr162nbtq17OzAwkMGDBzN48GAmTZpE27Zt+d///sell16Kw+Fg3759perIjRw5koULF2KxWKhWrRrXXXede99ll13GTz/9xAUXXJDvc9u1a8fp06fZtGkTXbt2BWDHjh15EkuKiIhIxaH+U9Eqav9JI5DKSd7hea4AkrmIeiIi4ktWq5WEhASaN2/uUW6xWEhISMBqtZbZuV999VVOnDhBv379WLNmDWlpaSxfvpw+ffrQvHlznn76aXfdb775hueee46dO3cye/ZsPvzwQ6KjowHnKiBvv/02P/74I7/99hvz588nMDCQli1bctFFFzFq1ChuueUW7HY7KSkpbNy4kWeffZbPPvusyDaOGjWKzZs38/TTT2Oz2ahVq5Z734MPPsi6deuYNGkSW7ZsYdeuXSxbtox///vfALRp04b+/fszfvx4NmzYwHfffce4cePyJL0UERGRikX9p8JV2P5TkVmSqjhvJYE8ffq0YbFYciXRjjHAMOBJdyLtsLAw4/Tp015quYiIGMa5J4J0OX36tJGYmGjExcUZiYmJ5fZ5nZqaaowZM8Zo1qyZUaNGDSMsLMz497//bfz555/uOi1btjRmzJhh3HTTTUbt2rWNpk2bGjExMe79H330kdGtWzejXr16Rp06dYzu3bsbX375pXv/yZMnjccee8wIDw83atSoYTRr1sy44YYbjK1btxqG4UwCGRQUVGAbu3TpYgDG6tWr8+z79ttvjT59+hh169Y16tSpY3Tq1Ml4+umn3fszMzON6667zqhZs6bRokUL4/333zdatmypJNoVnN4LEZGKy1t9J8NQ/6my9Z9MhpFrPJnkcfjwYYKCgsjKyqJevXrndCy73Y7NZgPAMF4E7gFmYjI9AlDmkVgRkaron3/+ISUlhVatWnnc3ZGKqbD305vXbDk3ei9ERCou9Z0qH2/1nzSFrRx5DuNzrbZWrVyG8YmIiIiIiIiIlJYCSOXMarWSmprKjTcOBeCCC9oQHR3NwIEDfdwyEREREREREZH8KYDkA9OmTSMh4UMAfvnlN+677z5q167NAw884OOWiYiIiIiIiIjkpQBSOXvggQd4/vnnMYxTOSXOt8DhcPD8888riCQiIiIiIiIifkcBpHJ08uRJXnrppZyt7JyvZo86L730EidPnizXdomIiIiIiIiIFEYBpHI0Z84cHA5X8mxXAMnzLXA4HMyZM6dc2yUiIiIiIiIiUhgFkMrRr7/+mmvrdM7X6kXUExERERERERHxLQWQylHr1q1zbblyINUoop6IiIiIiIiIiG8pgFSOJk6ciNnsynnkynMU4FHHbDYzceLEcm2XiIhUPqmpqZhMJrZs2eLrpoiIiIhUCOo/FU4BpHIUEBDA1KlTc7ZcASTPEUhTp04lIMAzqCQiIlWLyWQq9DFmzJgi64SFhZGZmUmHDh18/XJEREREypz6T2UvbwIeKVPdu3fP+c41ha1mAftFRKSqyszMdH+/aNEiHnvsMXbs2OEuCwwM5JlnnnFvh4SE8O6779K/f393mdlsplmzZuXTYBEREREfU/+p7GkEUjlyOBxER0fnbJ3I+XomgGQymZgyZUquldpERKQqatasmfsRFBSEyWTKU5Z7G6B+/foeZWcPwU5KSsJkMrFixQouvfRSAgMD6d27N/v27ePzzz+nXbt21KtXjxEjRnDs2DEfvnoRERGRklP/qexpBFI5Sk5OJj09PWfL9csV6N5vGAZpaWkkJycTGRlZ3s0TEakSDAN8dX2vXRtMJt+c22X69Om8+uqr1K5dm5tuuombbrqJmjVrEhcXx5EjR7jhhhuYNWsWDz74oG8bKiIiIn5D/Sf1n0ABpHKVe0gdHM/5GlhEPRER8aZjx6BuXd+c+8gRqFPHN+d2eeqpp7jyyisBuP3225k2bRq//vor559/PgA2m43ExMRK3wESERGR4lP/Sf0n0BS2chUSEpJryxW+zfuX4FlPRETEezp16uT+vmnTptSuXdvd+XGV7du3zxdNExEREfFL6j85aQRSOerZsydmszknx9GRnFLPAJLZbKZnz57l3jYRkaqidm3nnSxfndvXatQ4s/qnyWTy2HaVZWdnl3ezRERExI+p/6T+EyiAVK7Wrl2bK0G266/vPI86DoeDtWvXKgeSiEgZMZl8PwxaREREpCJR/0lAU9jKVUZGRq6tv3O+nldEPRERERERERER31IAqRzt378/15YrgFQTCCiknoiIiIiIiIiIb5kMwzB83Qh/dvjwYYKCgsjKyqJevXrndKz58+dzyy235GyZgdM53zcGDrjrvf/++4wePfqcziUiIk7//PMPKSkptGrVilq1avm6OXKOCns/vXnNlnOj90JEpOJS36ny8Vb/SSOQypHnyCIHcDTn+6BC6omIiIhUDLNnz6Z9+/Z06dLF100RERERL1MAqRwdOHDgrJKsnK/nFVFPRERExP9NmjSJbdu2sXHjRl83RURERLxMAaRyVK3a2T9uVwApqIh6IiIiIiIiIiK+o0hFOerZs+dZJa4AUv0i6omIiIiIiIiI+I4CSOVo27ZtZ5XkPwIpbz0RETlXWjOictD7KCIiUj50za08vPVeKoBUjlJTU88qOZTztX4R9UREpLTMZjMAJ0+e9HFLxBuOHTsGQI0aNXzcEhERkcrJdY11XXOl4vNW/6m6NxojxdO6deuzSg7lfA0qop6IiJRW9erVqV27Nvv376dGjRrKM1dBGYbBsWPH2LdvH/Xr13cHBkVERMS7zGYz9evXZ9++fQDUrl0bk8nk41ZJaXi7/6QAUjmaOHEi9913Hw6HI6fkUM7X+u461apVY+LEieXcMhGRystkMhESEkJKSgq///67r5sj56h+/fo0a9bM180QERGp1FzXWlcQSSo2b/WfFEAqRwEBAUydOpXnn38+p+RQztcG7jrZ2dl88sknWK3W8m6eiEilFRAQwIUXXqhpbBVcjRo1NPJIRESkHLhuwAUHB3Pq1ClfN0fOgTf7TyZDmbEKdfjwYYKCgsjKyqJevXrnfDyHw0H9+vU5cuQIcCfwGmAHhgLOP1SLxUJKSoo6ySIiIiXg7Wu2lJ7eCxERkYqhJNdsJYIoZ8nJyTnBI8hvCpthGKSlpZGcnFzOLRMRERERERERyZ8CSOUsMzMz19ahnK8NiqgnIiIiIiIiIuI7FSaA9NdffzF69GiCgoIICgpi9OjRHDp0qMD6p06d4sEHH6Rjx47UqVOH0NBQbrnlFnbv3l1+jc5HSEhIrq2/cr7mDSB51hMRERERERER8Z0KE0AaOXIkW7ZsYfny5SxfvpwtW7YwevToAusfO3aMzZs383//939s3rwZu93Ozp07GTx4cDm2Oq+ePXvmym3kCiDV96hjNpvp2bNneTZLRERERERERKRAFWIVtu3bt7N8+XLWr19Pt27dAHjrrbfo0aMHO3bsoE2bNnmeExQUxMqVKz3KZs2aRdeuXfnjjz9o0aJFvuc6ceIEJ06ccG8fPnzYi68E1q5di8PhyNnKHUCqBmQDzkTba9euJTIy0qvnFhEREREREREpjQoxAmndunUEBQW5g0cA3bt3JygoiLVr1xb7OFlZWZhMJurXr19gnZkzZ7qnyQUFBREWFnYuTc8jIyMj19Zfub4PKqSeiIiIiIiIiIjvVIgA0p49ewgODs5THhwczJ49e4p1jH/++YeHHnqIkSNHFro03bRp08jKynI/0tLSSt3u/Ozfvz/X1mng75zvGxRST0RERERERETEd3waQJo+fTomk6nQx6ZNmwAwmUx5nm8YRr7lZzt16hTDhw8nOzubOXPmFFq3Zs2a1KtXz+PhTU2aNDmrxDUKqWER9UREREREREREfMOnOZDuvvtuhg8fXmid8PBwtm7dyt69e/Ps279/P02bNi30+adOneKmm24iJSWF1atXez0gVFLNmjU7q+Qg0AJoVEQ9ERERERERERHf8GkAqXHjxjRu3LjIej169CArK4tvv/2Wrl27ArBhwwaysrIKXa3MFTzatWsXiYmJNGrUqMC6vnMw52uDQmuJiIiIiIiIiPhKhciB1K5dO/r378/48eNZv34969evZ/z48QwcONBjBba2bdvy0UcfAXD69GlsNhubNm1i4cKFOBwO9uzZw549ezh58qSvXko+OZtcAaSGRdQTEREREREREfGNChFAAli4cCEdO3akb9++9O3bl06dOjF//nyPOjt27CArKwuA9PR0li1bRnp6Op07dyYkJMT9KMnKbd6WNzm2K4DUqIh6IiIiIiIiIiK+4dMpbCXRsGFDFixYUGgdwzDc34eHh3ts+4u8ybHzH4GkJNoiIiIiIiIi4i8qzAikyqJ58+ZnlRzI+dqwiHoiIiIiIiIiIr6hAFI5i4iIwGKx5CpxBZDOTGELCwsjIiKiXNslIiIiIiIiIlIQBZDKmdlsJjY2FpPJlFOSNwfS8OHDMZvN5d42EREREREREZH8KIDkA1arlfvuuy9nK+8UthdeeAG73V7u7RIRERERERERyY8CSD7gcDiIj4/P2XIFkBp71JkyZQoOh6Nc2yUiIiIiIiIikh8FkHwgOTmZ9PT0nK0/c77WB5zT1gzDIC0tjeTkZB+0TkRERKR0Zs+eTfv27enSpYuvmyIiIiJepgCSD2RmZubacuVAqgY0KKSeiIiIiH+bNGkS27ZtY+PGjb5uioiIiHiZAkg+EBISkmvLAfyV833jQuqJiIiIiIiIiPiGAkg+0LNnz7NWWXNNYzuzEpvZbKZnz57l2i4RERERERERkfwogOQDa9euPStBtiuA1MRd4nA4WLt2bbm2S0REREREREQkPwog+UDe3EZ5A0j51xMRERERERERKX8KIPlA3txG+3O+NiminoiIiIiIiIhI+VMAyQciIiKwWCyYTKacEs8AkslkIiwsjIiICJ+0T0REREREREQkNwWQfMBsNhMbGwuQE0Tal7OniTuoFBMTc1aibRERERERERER31AAyUesVisJCQk0b96c3COQLBYLCQkJWK1WXzZPRERERERERMRNASQfslqtpKam8p//TAWgadOOvPvuuwwZMsTHLRMREREREREROUMBJB/7+OOPiYl5BIC9ex1cc801hIeHY7fbfdwyEREREREREREnBZB8yG63Y7PZ2Lfvx5ySYAAyMjKw2WwKIomIiIiIiIiIX1AAyUccDgfR0dEYhsGZHEgBQL2cMpgyZQoOh8NXTRQRERERERERARRA8pnk5GTS09Nzto4DR3O+bwKAYRikpaWRnJzsi+aJiIiIiIiIiLgpgOQjmZmZZ5Xsy/kaXEQ9EREREREREZHypQCSj4SEhJxVsjfna1OP0l27dpVLe0RERERERERECqIAko9ERETQvHnzXCWuAJLnCKS33npLeZBERERERERExKcUQPIRs9nMhAkTcpXkPwIpPT1deZBERERERERExKcUQPKhCy+8MNeWKwdS0zz1lAdJREREKoLZs2fTvn17unTp4uumiIiIiJcpgORDnnmQXEGiZnnqKQ+SiIiIVASTJk1i27ZtbNy40ddNERERES9TAMmHPPMg7cn5mjeApDxIIiIiIiIiIuJLCiD5kGceJNcIpLNXZ1MeJBERERERERHxLQWQfOxMHqSCRyCB8iCJiIiIiIiIiO8ogORjZ/IguQJItYF6hdQTERERERERESlfCiD5WEREBBaLBZPpHyArp/TMKCSTyURYWBgRERE+aZ+IiIiIiIiIiAJIPmY2m4mNjc3Zco1Cco42MplMAMTExGA2m8u/cSIiIiIiIiIiKIDkF6xWKwkJCQQEHMwpcY5AslgsJCQkYLVafdc4EREREREREanyFEDyE1arleuv7wbAzTc/QGJiIikpKQoeiYiIiIiIiIjPVfd1A+SM0NBqOV8vIzLSt20REREREREREXHRCCQ/0iwnd/bu3b5th4iIiIiIiIhIbgog+ZHmzZ1fFUASEREREREREX+iAJIfcQWQMjJ82w4RERERERERkdwUQPIjrgBSejoYhm/bIiIiIiIiIiLiogCSH3EFkI4ehXfeSSApKQmHw+HbRomIiIgU0+zZs2nfvj1dunTxdVNERETEyxRA8iMrVtgxmQ4BMG7cY0RFRREeHo7dbvdtw0RERESKYdKkSWzbto2NGzf6uikiIiLiZQog+Qm73Y7NZsMw0nNKnMORMjIysNlsCiKJiIiIiIiIiM8ogOQHHA4H0dHRGIYBuDJoOwNIRk4ypClTpmg6m4iIiIiIiIj4hAJIfiA5OZn0dNfII88AEjiDSGlpaSQnJ5d720REREREREREFEDyA5mZmbm28gaQ8q8nIiIiIiIiIlI+FEDyAyEhIbm2XCORwoqoJyIiIiIiIiJSPhRA8gMRERFYLBZMJhPwR07pmQCSyWQiLCyMiIgIn7RPRERERERERKo2BZD8gNlsJjY2NmcrLedrC4CcoBLExMRgNpvLv3EiIiIiIiIiUuUpgOQnrFYrCQkJhIa6VlprCNTFYrGQkJCA1Wr1ZfNEREREREREpApTAMmPWK1W/vjjR+rUOQXAHXc8zbvvvsuQIUN83DIRERERERERqcoUQPIzH3/8MSdP/gLAG298xjXXXEN4eDh2u93HLRMRERERERGRqkoBJD9it9ux2WycOvVrTokzD1JGRgY2m01BJBEREfFrs2fPpn379nTp0sXXTREREREvUwDJTzgcDqKjozEMgzMrsTkDSM4ymDJlCg6HI/8DiIiIiPjYpEmT2LZtGxs3bvR1U0RERMTLFEDyE8nJyaSnp+dseQaQwBlESktLIzk5udzbJiIiIiIiIiJVmwJIfiIzMzPXliuA1LKIeiIiIiIiIiIiZU8BJD8REhKSaysl52urIuqJiIiIiIiIiJQ9BZD8REREBBaLBZPJBKTmlDYHarjrNGrUiIiICB+0TkRERERERESqMgWQ/ITZbCY2NjYnYfYe4DhgBsLcdQ4cOMDHH3/soxaKiIiIiIiISFVVYQJIf/31F6NHjyYoKIigoCBGjx7NoUOHiv38O+64A5PJRExMTJm18VwNGTKERo0a5Wyl5nw9M43NZDJpJTYRERERERERKXcVJoA0cuRItmzZwvLly1m+fDlbtmxh9OjRxXru0qVL2bBhA6GhoWXcynOTnJzMgQMHcrby5kHSSmwiIiIiIiIi4gvVfd2A4ti+fTvLly9n/fr1dOvWDYC33nqLHj16sGPHDtq0aVPgczMyMrj77rtZsWIF1113XZHnOnHiBCdOnHBvHz58+NxfQDF5rrDmCiCFF1FPRERERERERKRsVYgRSOvWrSMoKMgdPALo3r07QUFBrF27tsDnZWdnM3r0aO6//34uvvjiYp1r5syZ7mlyQUFBhIWFFf0kL9FKbCIiIlKRzZ49m/bt29OlSxdfN0VERES8rEIEkPbs2UNwcHCe8uDgYPbs2VPg85599lmqV6/O5MmTi32uadOmkZWV5X6kpaWVqs2l4bkSmyuAdL57v8lkIiwsTCuxiYiIiF+aNGkS27ZtY+PGjb5uioiIiHiZTwNI06dPx2QyFfrYtGkTQE5QxZNhGPmWA3z33XfExsYyb968Auvkp2bNmtSrV8/jUV5cK7E5/ZbztbV7v2EYxMTEYDaby61NIiIiIiIiIiI+zYF09913M3z48ELrhIeHs3XrVvbu3Ztn3/79+2natGm+z0tOTmbfvn20aNHCXeZwOLj33nuJiYkhNTX1nNpeVqxWKwkJCYwfP5WDBwGaAPWAw7lWaBMRERERERERKT8+DSA1btyYxo0bF1mvR48eZGVl8e2339K1a1cANmzYQFZWFj179sz3OaNHj+aaa67xKOvXrx+jR49m7Nix5974MvbXX38Ae4BmOEchfc/Bgwex2WwkJCRgtVp920ARERERERERqTIqRA6kdu3a0b9/f8aPH8/69etZv34948ePZ+DAgR4rsLVt25aPPvoIgEaNGtGhQwePR40aNWjWrFmhq7b5msPhIDo6GsMwgF9ySi8AyCmDKVOm4HA4fNNAEREREREREalyKkQACWDhwoV07NiRvn370rdvXzp16sT8+fM96uzYsYOsrCwftdA7kpOTSU9Pz9n6NefrBe79hmGQlpZGcnJyubdNRERERERERKomn05hK4mGDRuyYMGCQuu4RugUxF/zHuWWmZmZa8tzBFLB9UREREREREREyk6FGYFUVYSEhOTaKjiA5FlPRERERERERKTsVJgRSFVFREQEFoslZxqbK4B0YZ56f/75Z7m2S0RERKQos2fPZvbs2WWSq9HhcJCcnExmZiYhISFERERgNpu9fh4RERHJn0Yg+Rmz2cxLL72Us7Uz52sIcJ5HvalTpyqRtoiIiPiVSZMmsW3bNjZu3OjV49rtdsLDw4mKep6RI38kKqo/4eHh2O12r55HRERECqYAkh9q0qRJzneHgT0533uuHKdE2iIiIlIV2O12bDYb6en7gVnA08BPpKd3xmazKYgkIiJSThRA8kOeCbJ35Hy9qIh6IiIiIpWLw+EgOjo6Z6GUE8AjQAbQGvgvhrGMSZNe1qhsERGRcqAAkh/yTJDtCiC1KaKeiIiISOWSnJyckxfS5QOcfaJngJPAQPbs+ZJbb03n6FGfNFFERKTKUADJD7kSaZtMJs7kQToTQDKZTISFhREREeGT9omIiIiUh/xHWx8FpgEdgeVATRYubEnbtrB4MRhGuTZRRESkylAAyQ+ZzWZiY2NztjwDSM6gEsTExGjlEREREanUCh9tvRMYAAyhWbPjpKfDsGFw9dXw00/l1EAREZEqRAEkP2W1WklISKBp079yStoA1bBYLCQkJGC1Wn3ZPBEREZEy5zkqOy/nqOzv2bUrgBkzoFYtSEyESy6Be+6BrKxybrCIiEglpgCSH7NarfzxRxIBAQ4gkAUL1pKSkqLgkYiIiPil2bNn0759e7p06eKV4+UelX12ECn3qOy6dc089hhs3w5WKzgcEBMDF10E770H2dleaY6IiEiVpgCSnwsIMNO+vXOqWt263TRtTURERPzWpEmT2LZtGxs3bvTaMV2jsps3b+5Rnt+o7PBwWLIEVqyANm1g3z4YMwauvBK++85rTRIREamSFECqAC6+2PlV8/lFRESkvNxwww00aNAAm83m66ZgtVpJTU0lMTGRuLg4EhMTCx2V3bcvbN0Kzz8PdevC+vXQpQvccQf8+Wc5N15ERKSSUACpAujQwfl11aq9xMfHk5SUhMPh8G2jREREpFKbPHky77//vq+b4WY2m4mMjGTEiBFERkYWOSo7IADuuw927IBRo5yrs735pnNa25w5zmluIiIiUnwKIFUAWVlrAVi9eg8jR44kKiqK8PBw7Ha7j1smIiIilVVUVBTnnXeer5txzkJDYcECWLMGOnWCv/6CSZPgiivgm2983ToREZGKQwEkP2e323nmmdE5W20B5922jIwMbDabgkgiIiJV0Jo1axg0aBChoaGYTCaWLl2ap86cOXNo1aoVtWrV4vLLLyc5Obn8G+pHIiKceZBefRXq14ctW+Cqq+CWWyAz09etExER8X8KIPkxh8NBdHQ0kAIcBWoCrQEwDAOAKVOmaDqbiIhIFXP06FEuueQSXn311Xz3L1q0iClTpvDII4/w/fffExERwYABA/jjjz/cdS6//HI6dOiQ57F79+4St+fEiRMcPnzY4+GPqld3jj7auRPGjweTCebPdybcfuEFOHnS1y0UERHxXwog+bHk5GTS09MBA9iWU3qxe79hGKSlpVX5O4oiIiJVzYABA3jqqacKTCL90ksvcfvttzNu3DjatWtHTEwMYWFhvPbaa+463333HT/++GOeR2hoaInbM3PmTIKCgtyPsLCwUr+28tCkiTMf0rffQrdu8PffcP/9cMklsHKlr1snIiLinxRA8mOZHuOpXUuwdSiinoiIiFRlJ0+e5LvvvqNv374e5X379mXt2rVlcs5p06aRlZXlfqSlpZXJebztiitg7Vp45x1nUOnnn50ruNls8Pvvvm6diIiIf1EAyY+FhITk2nIFkC4uop6IiIhUZX/++ScOh4OmTZt6lDdt2pQ9e/YU+zj9+vXjxhtv5LPPPsNisbBx48YC69asWZN69ep5PCqKatVg7FjntLboaDCbYckSaNcOnnwS/vnH1y0UERHxDwog+bGIiAgsFgsmk4n8Akgmk4mwsDAiIiJ80j4RERHxX87+wxmGYeQpK8yKFSvYv38/x44dIz09nS5duni7iX6lfn2IiYHvv4deveD4cXjsMbj4Yli2DHLST4qIiFRZCiD5MbPZTGxsbM6WK4DUBghwdwBjYmIwm82+aJ6IiIj4ocaNG2M2m/OMNtq3b1+eUUmSV8eOkJgIH3wAzZvDb7/BkCFw3XXOUUoiIiJVlQJIfs5qtZKQkEDz5tnAQaAGcDEWi4WEhIQCk2eKiIhI1RQQEMDll1/OyrOyQa9cuZKePXv6qFUVi8kEw4Y5cyI99BDUqAGffw4dOsC0aXDkiLOew+EgKSmJ+Ph4kpKSvLYyblkdV0RE5FwogFQBWK1Wfv89lUsvdY46uu++OFJSUhQ8EhERqaKOHDnCli1b2LJlCwApKSls2bKFP/74A4CpU6cyd+5c3nnnHbZv384999zDH3/8wZ133unDVlc8devCzJnw448wYACcOgXPPANt28LUqd/SsmU4UVFRjBw5kqioKMLDw7Hb7ed0TrvdTni4948rIiJyrhRAqiDMZjO9ezcA4Pjxtpq2JiIiUoVt2rSJSy+9lEsvvRRwBowuvfRSHnvsMQCGDRtGTEwMTzzxBJ07d2bNmjV89tlntGzZ0pfNrrAuugg+/dSZC6lVK8jIgJdf7kpGxnxyr5CbkZGBzWYrdbDHbrdjs9lIT0/3KD/X44qIiHiDyTCUErAwhw8fJigoiKysLJ+vKBIXB6NGQc+e8M03Pm2KiIiI3/Gna3ZVV5nfi6NHHYSGvszhwxOB2sBpYDYQA6RiMpmwWCykpKSU6Iafw+EgPDw8T/DIpbTHFRERKUxJrtkagVSB5NxkZPNmBwsXfqA58SIiIiLlbOPGZA4fvh9oByQA1YFoIAVYh2H8m7S0UyQnJ5fouMnJyQUGj8C5il5aWlqJjysiIuItCiBVID/+aMdkOsY//5i5+ebpmhMvIiIiUs4yMzNzvvsDuBG4BvgScADdgVggnbvvbsvbb8Nff5X0uN6pJyIi4m0KIFUQdrudYcNsGMaWnBLncCTNiRcREREpPyEhIWeVrAL6AM2BycA6wMxPPzVj3Dho2hSGDIEPPoCjR0ty3HOrJyIi4m0KIFUADoeD6OhonOmqvs8pvQwAVwqrKVOmaDqbiIiISBmLiIjAYrFgMpnO2rMXmIXJdCUhIVfy1FPZdOzoXLlt2TIYMQKCg51f//tfOHmyuMd1MplMhIWFERERUSavS0REpCgKIFUAnnPiXQGkS937NSdeREREpHyYzWZiY2MB8gR7XNuvvnovjzxSja1b4ccf4ZFH4Pzz4dgx50ikwYOhWTMYPx5WrwaHo3jHjYmJUQJtERHxGQWQKgDPue6bc75eWkQ9ERERESkLVquVhIQEmjdv7lFusVhISEjAarW6yy6+GJ56Cn75BTZsgClTICTEmRtp7ly4+moIC3OWN29u5cMPi3dcERGR8mYyXHOgJF/+sAxtUlISUVFROVsBwN85X1sBqe56iYmJREZGlnfzRERE/II/XLPFqaq8Fw6Hg+TkZDIzMwkJCSEiIqJYI4QcDlizBuLjISHBM9F2q1YwbFg2F130HbVq/VKi455ru0REpOopyTVbAaQi+EMHyOFwEB4eTkZGRk7Oow1AV2A4sAhw3plKTU1V50BERKosf7hmi5Pei+I7eRK++MIZTPr4Y89E2+3awbXXQr9+EBEBtWoVfTy73U50dHSu9AfOfmJsbKxGMImISB4luWZrClsFkHdO/PqcPd3cdY4fP87HH39c/o0TERERkVILCICBA2HhQti715kjacgQZ/n27fDii9C3LzRs6AwmxcbCzz9DfreA7XY7NpvNI3gEWrVXRES8QyOQiuBPd9DsdjsTJkzgwIF+wEKcy8T2BM4kV9T8eBERqar86Zpd1em9OHeHDjlHJi1fDitWwO7dnvtbtID+/Z2jk66+GurWdY5YPzt45GIymbBYLKSkpGjEuoiIuGkKmxf5UwfINZUtPT0A+BU4AdQDnOvAqmMgIiJVmT9ds6s6vRfeZRjw009ngklr1jinvrmYzdCuXRY//vg8sAL4Dsi/i6+cmSIikpumsFVSycnJOXeVfgP2AzWBS9z7DcMgLS2N5ORkH7VQRERERLzNZIIOHeC++2DlSjh4ED79FCZPhjZtnEm5f/wxCHgK2AjsA+KAMYDF41hatVdEREqruq8bIMXnecHfAAwEuuPsKBRUT0REREQqkzp1nPmQrr3WuZ2aCq+8soOXX/4JuAZoDIzIeQD8DHwJrOS88yx5jqdV20REpDgUQKpAQkJCcm25AkjdgFmF1BMRERGRyiw8HJ5//gI+/PAa0tP34uwf9sMZTOoCtM153M2QIQZdu0KfPnDNNZCZ+RH33TdZq7aJiEiRlAOpCP40h9+VAykjIwPDuBpYiTMX0gWAciCJiEjV5k/X7KpO74VvuFZhA2dqA6cgoDdwDaGho9m9+7yznnUE+ArnCKUvgG35Ls6iUUoiIpVTueRAcuXaWbFiBZs3b+bEiROlPZQUk9lsJjY2NmdrU87X1kAT94U+JiZGF3MRERE/pf6TlCWr1UpCQgLNmzfPVZpFWNgmlixpRkbGefz+O7z9Ngwfnk21an8CdYHrgJeBn4A0DOMtDONG/v3vx3E4HNjtdsLDw4mKimLkyJFERUURHh6O3W73xcsUEREfKdEIpN9//53XX3+d+Ph40tLSyP3UgIAAIiIimDBhAkOHDqVatcqRn9sf76DZ7Xaio6NJT18OXAxcT1jYZmJiYjTUWEREqix/vGaD+k/+9F5UFcUZLZSUlERUVG+gI86pbn2BfwGBuY+ExZJJevo7wHLgW8ABkO8oJRERqXhKcs0udgApOjqad999l759+zJ48GC6du1K8+bNCQwM5ODBg/z4448kJycTHx9P9erVeffdd+nSpYtXXpAv+WsHyOFwYLXuYdmy5thsaXzwQahGHomISJXmj9ds9Z/8570QT/Hx8YwcOfKs0lpABM78Sf2ADmftP4RzqtunwOeYTPs80idompuISMVTkmt2sZNoBwQE8Ouvv9KkSZM8+4KDg+nduze9e/fm8ccf57PPPuP333+vFB0gf2U2mxk+vDnLlsHWrfVZvHixLtQiIiJ+Rv0n8Vf5L7ryD84cmyuB+4DmOEcm9QP6AA0BW84DDGMjaWmfMHfuFho1+p177okuMBm3gksiIhWfkmgXwZ/voL311udMmDAA51DiBsDfWjVDRESqLH++Zlc1ei/8n+fiLHn/HTCZTDRo0ICDBw/mlFQDrgAG4MyZdHagMxP4DPgEZwDqqHua23333Ud8fLxWehMR8UPlkkRbfMtut3PHHdfhXIXNDPQEICMjA5vNpqSGIiIiIlKg3IuzuAI9Lq7t6OjoXKXZOHMgzQC6AiHAbcASTKYjOdu3Ax8BfwIfYxhjMIxGPP/88x7BI1CfVUSkIipVAOnAgQNMmjSJ9u3b07hxYxo2bOjxkLLlcDiIjo7OuVu0Jqc0AjizZOuUKVNwOBy+aaCIiIjkof6T+Jv8V21zjg5KSEjgkUcewWKx5AkwOe3BZJpHkyZ3YRiNcCbifhnnzc1awGDgHWAPkAREAy3dz1afVUSk4il2DqTcbr75Zn799Vduv/12mjZtWsBFRcpKcnJyrrs4ycBYnKtmOBmG4V4mODIy0gctFBERkbOp/yT+yGq1MmTIkALzE8XGxmKz2TCZTB5T3Vy/v6NGjSImJgZYlfOYijP59vXADcBlQK+cRwywCfgQ+BDDSCEtLY1Zs2bRtGlTj3MrZ5KIiP8pVQDp66+/5uuvv+aSSy7xdnukGDIzM3NtuUYgdcV5t+efAuqJiIiIL6n/JP7KbDYXeNPRNUopOjpvguyYmBgaNmyYE0DK7cecx1NAC84EkyJw5lG6AngW2Ags5p57YoDf3ccdMWKEciaJiPihUk1ha9u2LcePH/d2W6SYPFfN+BXYDdQEuhVST0RERHxJ/SepqKxWK6mpqSQmJhIXF0diYiIpKSlYrVYiIiIKmeYG8AfwChAFNAMmAF/iXASmC/A8kApsAKJJTz+lnEkiIn6qVKuwbdy4kYceeojHHnuMDh06UKNGDY/9lWm1DX9cRSTvqhkLgFHAk8BjmEwmLBYLKSkpGuorIiJVhj9es3NT/0kqK7vdjs1mA8h3Rbf8NcE5KukmIBLnojDgDCytAuIAO/C3+xnq44qIeF+Zr8JWv359srKy6N27N8HBwTRo0IAGDRpQv359GjRoUKpGS/HlXTXjy5w917jv/sTExOjCKiIi4kfUf5LKqqBk3GFhYdx///2YTKZ8RijtB97EmXw7FLgbWIczkNQXmAfsBeKBPkA1jzyfIiJS/kqVA2nUqFEEBAQQFxenJJA+4jkf3RVA6kpQUAvuuec2hgwZ4tP2iYiIiCf1n6QyKywZd/fu3fPkUPK0D5id8zgfGIFzdH07YHjOIw14D5inPJ8iIj5SqilstWvX5vvvv6dNmzZl0Sa/4u9DsB0OB08//TQzZowgO/tCnEkKP1aiQRERqXL8/Zqt/pNUZblXVdu7dy/33HNPMZ51Gc7VhkcCDd2lnTod4p576mOzQWCgVmsTETkXZT6F7YorriAtLa1UjRPv+vjjj5k+fTrZ2StySq4BlGhQRETE36j/JFWZa6W3ESNG8O9//7uIxNsum4F/45zidhOwHMhm69b6jB0LTZqcol69D4mKepSRI0cSFRVFeHi4+r8iImWkVCOQPvzwQ6ZPn879999Px44d8ySB7NSpk9ca6Gv+fAfNlUzbORx4CLAU+BnncF8lGhQRkarFn6/ZoP6TSG4lTbztCja98can7N8/gFmzjrBnT91cNX4CXgMWYDIdJiEhQSPxRUSKoSTX7FIFkKpVyztwyWQyYRgGJpMJh8NR0kP6LX/uACUlJREVFZWzFQQcwJl4MAw4M8c8MTGRyMjIcm+fiIhIefLnazao/yRyNrvdnic3UlhYGMOHDyc+Pj5PeUxMDFarFYfDQcuW4WRkhOOc4nYT4AomHQHiadp0CRkZn+omqohIEUpyzS5VEu2UlJRSNexc/PXXX0yePJlly5YBMHjwYGbNmkX9+vULfd727dt58MEH+eqrr8jOzubiiy9m8eLFtGjRohxaXbY8EwhmARuB7jhXrningHoiIiLiC77oP4n4s8ISb8+cObPA3EbJyclkZKTjvGH6NXAPMBq4C7gYGM/evePp1CmLBx44j5CQrzlwIEM5kkREzlGpAkgtW7b0djuKNHLkSNLT01m+fDkAEyZMYPTo0fz3v/8t8Dm//vorV111FbfffjszZswgKCiI7du3U6tWrfJqdpkKCQk5q+QznAGkgeQOIOWtJyIiIuXNF/0nEX/nyo1U3HLI7+boYc6s4hYBTASGsm1bEGPGALQH1gIPYLGghWZEREqp2FPY1q1bR48ePYp10KNHj5KamsrFF198To1z2b59O+3bt2f9+vV069YNgPXr19OjRw9+/vnnAlczGT58ODVq1GD+/PmlPrc/D8F25UDKyMjImTveGfgeOAo0xmQ6oRxIIiJSZfjjNduX/Sdf8sf3QioPzzQOBWkK3A7cAbhmHpwGEoAYlix5oMDRTyIiVUmZrMJ2yy230KdPHxYvXsyRI0fyrbNt2zYefvhhLrjgAjZv3lyyVhdi3bp1BAUFuYNHAN27dycoKIi1a9fm+5zs7Gw+/fRTLrroIvr160dwcDDdunVj6dKlhZ7rxIkTHD582OPhr8xmM7GxsYArseAWIA2oA1wNQExMjC6EIiIiPuLL/pNIZRUREVHkKm5m85/Af4BWOBebScQ5+WI4sJ5hw8IIDr6bqKhrtIKbiEgxFTuAtG3bNoYMGcJjjz1GgwYNuPjii+nTpw+DBg3iqquuonHjxlx++eX8/vvvrFy5ktGjR3utkXv27CE4ODhPeXBwMHv27Mn3Ofv27ePIkSM888wz9O/fny+++IIbbrgBq9XKV199VeC5Zs6cSVBQkPsRFhbmtddRFqxWKwkJCTRv3jynxDmlr06d4Vp9QkRExMd82X8Sqazy3kQ9w7V9Jil9NrAM6I1ztP67wAlOn+7CwYOvAb8B9wH1yMjIwGazKYgkIlKAUq3CtnnzZpKTk0lNTeX48eM0btyYSy+9lKioKBo2bFjs40yfPp0ZM2YUWmfjxo188cUXvPfee+zYscNj34UXXsjtt9/OQw89lOd5u3fvpnnz5owYMYK4uDh3+eDBg6lTpw7x8fH5nu/EiROcOHHCvX348GHCwsL8fgi2w+EgOTmZ5cvh2WcjCQ01SE83UciNGRERkUrF36dNeav/5M9mz57N7NmzcTgc7Ny502/fC6kcClrFbejQocTExBTyzKbAnThzJbluUmcBrwOxWCxm5s2bx759+zS1TUQqvZL0n0oVQPKWP//8kz///LPQOuHh4cTFxTF16lQOHTrksa9+/fq8/PLLjB07Ns/zTp48SZ06dXj88cd59NFH3eUPPvggX3/9Nd98802x2ujvndGznTgBjRvDkSOwaRNcfrmvWyQiIlI+Kto1uzLTeyHlxXUTNXceo+Tk5GLkSAKoCYzEOQKpfU7ZCWA+8ALgvHltsViUeFtEKq2SXLNLtQqbtzRu3JjGjRsXWa9Hjx5kZWXx7bff0rVrVwA2bNhAVlYWPXv2zPc5AQEBdOnSJc+opZ07d1bqVVBq1oS+fcFuh2XLFEASERERkcorv9XaXDmSziw0U5ATOKe0zQOuAx4ErgLG5TyWAv8hI2MTNpuNRYsW0aRJEyXdFpEqq9g5kHypXbt29O/fn/Hjx7N+/XrWr1/P+PHjGThwoMcKbG3btuWjjz5yb99///0sWrSIt956i19++YVXX32V//73v0ycONEXL6PcDB7s/LpsmUFSUhLx8fEkJSXlmgsuIiIiIlI5FZYjKX8G8AkQAVyJM3AEcD3wLYaxHMOIYPjwEURFRSnptohUWRUigASwcOFCOnbsSN++fenbty+dOnVi/vz5HnV27NhBVlaWe/uGG27g9ddf57nnnqNjx47MnTuXJUuWcNVVV5V388vVtdeCyWSwZYuJqKjRusiJiIiISJWSd6EZJ4vFQqNGjQoJLK0FbgDa4RyZdBroC3xFdnYS0N9dU0m3RaSq8WkOpIqgIs7ht9vtDB0ajHMI7kTgNeDMHRitziYiIpVRRbxmV1Z6L8Rf5Jcj6eOPP8ZmswEUMcUNIBx4ALgNZ84kgI3AdOAzTCYTFouFlJQUTWcTkQqpJNfsCjMCSYrH4XAQHR2Nc7lSgMHufa4L5JQpUzSdTUREREQqPVeOpBEjRhAZGYnZbC5wdFL+UnHekG0FvAQcBboAnwLrMYy+pKWlkZycXGavQUTEX5Q6ifaqVatYtWoV+/btIzs722PfO++8c84Nk9JJTk7OWcr0v8BzQBRQFzgCOINIrovc2QkHRUREpGyp/yTiH6xWK0OGDHGPTgoODmbMmDGFJN7OBO4FZgL3A3cD3YDlwFq++OIEvXpBsVIuiYhUUKUKIM2YMYMnnniCK664gpCQkGImp5PykJmZmfPdz8Au4EKc87btBdQTERGR8qD+k4h/OXsFt9jYWGw2GyaTqZCpbX/iXK3tRZxT2yYCPZk5E5KT4Ykn4F//yjttTtPbRKQyKFUA6fXXX2fevHmMHj3a2+2RcxQSEpJraxnOOyVWzg4gedYTERGRsqb+k4h/c01ti46OzhnR72Q2m/NJ/7APuA94kbp1n+TUqdv4+msTvXtDQMC3nDz5OLAGcCbujo2NVQ5SEanwSpUD6eTJk/Ts2dPbbREviIiIwGKx5NzV/DCndAhQG3Am0g4LCyMiIsJXTRQREamS1H8S8X9Wq5XU1FQSExOJi4sjMTGRDz74AJPJlGfUoLNsD++914BffzUxYMCvwAlOnuwBfAV8AVyeZ7U2h8NBUlIS8fHxJCUlKTepiFQYpQogjRs3jri4OG+3RbzAbDYTGxubs/Ut8CvOHEiD3Re9mJgYDaMVEREpZ+o/iVQMZyfettls+Sbdtlgs7tWNmzVz8L//RQIXAHOAk0AfYBOGsQjDaMOUKVNISEggPDycqKgoRo4cSVRUFOHh4e7gkoiIPzMZRa9dmUd0dDTvv/8+nTp1olOnTtSoUcNj/0svveS1BvpaRV2G1m635wy/nQD8H/AJYWETiYmJ0fBZERGplPz9mq3+k0jF5nAUnNsoKSmJqKioXLVbAjOA0Tjv2TuAd3PK0j2O67rJ6wpGiYiUp5Jcs0sVQPL8cDzrgCYTq1evLukh/VZF7gA5HA4WLvyOW2/titmcTUaGQdOmGnkkIiKVk79fs9V/Eqm84uPjGTlyZD57LgaeAq7P2f4HeBXnam4H3bVMJhMWi4WUlBTNFBCRclXmAaSqpDJ0gC6/HDZvhjlz4K67fN0aERGRslEZrtmVhd4LqWryjkA6W3fgGaBXzvZh4DngZeCYu1ZiYqLHynAiImWtJNfsUuVAWrlyJcePHy9V46T8uW6GLFhgKGGfiIiIj6j/JFJ5eS5kk5/1QCTQH/geqIdzZNJOYAyuf8syMzPLuqkiIqVWqgDS0KFDqV+/Pj179mTatGmsWLGCI0eOeLtt4iXDh4PJZLB2rYmoqDFK2CciIuID6j+JVF65F7LJb7W2M1YAlwMjgBSgOc7cSJuBawgJCSmP5oqIlEqpAkh//fUXSUlJDB48mO+//54bb7yRhg0b0r17dx566CFvt1HO0YYNdgwjMWdrhLv87CVFRUREpOyo/yRSuVmt1gJXa1u8eHGuEUoG8AHQFrgX+Au4BFjJM8/04scfy7vlIiLF45UcSD/++CMvvPACCxcuJDs7u1JNjaroc/gdDgfh4eGkp/cF3gb+B3Ry71fCPhERqSwq2jVb/SeRyqmg1drsdjs2mw0Az3/BGgGPUr36ZE6frka1anDbbfDEE+AakFTYCnAiIueizHMgbd++nddff53hw4cTEhJC7969OXz4MC+++CKbN28uVaOlbCQnJ5Oeng4sAU4AHXMeToZhkJaWRnJyso9aKCIiUjWo/yRSNZjNZiIjIxkxYgSRkZHuQE9BI5TCwmqzZEkLtm+vhs0G2dkwdy5ccAHMmAFxcR8THh5OVFSUUlGIiE+VagRStWrVaNKkCVOmTGHw4MFcfPHFZdE2v1DR76B5Lim6BLDiXAFimke9uLg4RowYgYiISEXl79ds9Z9EBIoeTbR2Ldx7L6xf7yrZDfwfMA/IBs7kVUpISMBqtZZj60WksinJNbtUAaQpU6awZs0afvrpJzp37kxkZCSRkZFERERQt27dUjfcH1X0DpDnkqJWnEGkDKAlcGaovJYMFRGRis7fr9nqP4lIcRkGLF7sYNSodByOljml/wPux5mI2xlEat68OfPmzWPfvn2a2iYipVLmASSXQ4cOkZyczFdffcVXX33F//73Pzp37sz6M+HyCq+id4BcOZAyMjIwjOpAOhAMXA98rBxIIiJSaVSUa7b6TyJSHM4bwf2AiThHIDXM2bMcmApsz/Mci8VCbGysRiWJSLGVeQ4kl+zsbE6fPs3Jkyc5ceIEp06dIjU19VwOKV7muaToaeCdnD13uoe+xsTEKHgkIiJSTtR/EpHiyMzMBE4CMUBr4MWc7f7AVmAWzgTcZ2iVZREpS6UKIEVHR3PJJZcQHBzMHXfcwe7du5kwYQI//PADe/bs8XYb5Rx5Jux7K6e0L82a9dC8aRERkXKi/pOIlESIawk2AA4B9wHtgY+A6sDdwC4gGqgBnFndbcqUKfmu7OhwOEhKSiI+Pp6kpKRKtfqjiJS9Uk1hs9ls7nn7HTp0KIt2+Y3KNATblbBv8uQ2/O9/ITz4YDbPPHNOg9BERET8hr9fs9V/EpGS8ExFcfa/bJHAy0DnnO0dOKe1feaucXaOU7vdTnR0dM4KzU6a8iYi5ZYDqSqojB0gux2GDoXgYIMFC9bw55+7lXRPREQqvMp4za6o9F6IeIfdbsdmswHkE0SqBowFngaa5pR9jjOQ9LPHKsuu45x9DK3mJiJlHkB6//33C91/yy23lPSQfqsydoBOnYKmTY/z11+BwI1AAqA7ECIiUrH5+zVb/ScRKY38Rg55Og94FJgCBACngVdZtuwKBg26yj2SqaDna1EdkaqtzANIDRo08Ng+deoUx44dIyAggNq1a3Pw4MGSHtJvVcYOkN1uZ+jQbTgvNKuAawDdgRARkYrN36/ZVaH/NHv2bGbPno3D4WDnzp1++16IVDSuVBSZmZkEBwczZsyYfKa2XQC8AAwBoFEjgyefNHHRRV9xzTWRRZ7j7ClvIlI1+GQK265du7jrrru4//776devnzcO6Rf8vTNaUmfuQFQDUnAOfb0Q+AXQHQgREam4KuI1W/0nESmNgqa2mUwmDOMaLJYE0tOdf3thYYdIS7MCiYUeM/eUNxGpOkpyzfZaBuULL7yQZ555hujoaG8dUspAcnJyzvDVPziTZG+Ce79hGKSlpZGcnOyL5omIiFQp6j+JSGl4rrJ8hsViYcmSO0lJqcesWdCgAaSl1QdWA0uAVgUe03PVNxGRvLy6BJfZbGb37t3ePKR4WWZmZq6tN3K+jgUCC6knIiIiZUX9JxEpDavVSmpqKomJicTFxZGYmEhKSgpWq5Xq1eHuu2HXLpg0KRtnXiQrsB1n0u267uOYTCbCwsKIiIjwzQsRkQqjemmetGzZMo9twzDIzMzk1Vdf5corr/RKw6RseN5Z+Az4DTgfuIUzASXdgRAREfE29Z9ExNvMZnOheYsaNYJXX63GBRes5p57wJn79GFgDPAQsBAwiImJUfoKESlSqXIgVavmOXDJZDLRpEkTevfuzYsvvlipgg+VbQ6/KwfSmaR7dwOzgF1AW0wmQzmQRESkQvL3a7b6TyLiS0uW2Lnjjk85cGAazoTbEBDwPTNmHOKhh6I86uZO2h0SEkJERIT+NxCppHySRLuyqowdIM+ke7Vx5kNqCFgxmZZqFTYREamQKuM1u6LSeyHinxwOB6tWfc28eUEsXdqB48edE1JGj4ZnnoHQUOf/CtHR0Tl5U50sFguxsbH6H0GkEirXJNqGYaAYVMXimXTvKDAHgICAh5k+fTonTpwgKSkJh8Ph03aKiIhUVuo/iYgvmM1m+vbtRVxcZ379tTpjxzrL58+Hiy6Cm2/+kaFDR3kEjwAyMjKw2WzY7XYftFpE/EWpA0jvv/8+HTt2JDAwkMDAQDp16sT8+fO92TYpQ7mT7s2ZczFm82lOnryCxx9fzsiRI4mKiiI8PFwXCRERES9S/0lE/EVICLzzDnz7LXTvDkePwsKFHYCfgOs96roC3lOmTNFNZpEqrFQBpJdeeom77rqLa6+9lsWLF7No0SL69+/PnXfeycsvv+ztNkoZcSXda9rUwOF4L6f0fvd+3WkQERHxHvWfRMQfdekCa9fCww9vAzJwLrDzEbASuNhdzzAM0tLSSE5O9k1DRcTnSpUDqVWrVsyYMYNbbrnFo/y9995j+vTppKSkeK2BvlbZ5/C7kmqnp9fFuaxnNtAWZ1JtZ4JPJdUWEZGKwN+v2eo/iYg/i4+PZ+TI8cCDOG8q1wIcOFdqfgw4AEBcXBw33XSTkmyLVBJlngMpMzOTnj175inv2bMnmZmZpTmk+EhycnLOHOefgf/i/JW4x71fdxpERES8Q/0nEfFnzpUgj+IMFrUDEgAzMBHnzeXJQHV27dpFeHg4UVFRSn0hUsWUKoB0wQUXsHjx4jzlixYt4sILLzznRkn58eywvpDzdQwQXEg9ERERKSn1n0TEn0VERGCxWDCZTEAqcCMQCWwBGgCxmM0/8fjj65VkW6SKql6aJ82YMYNhw4axZs0arrzySkwmE19//TWrVq3Kt2Mk/st5p8FlDbAe6I5z2Or9BdQTERGRklL/SUT8mdlsJjY2FpvNhslkykmc/RVwOXA78BQOx0XAZzmPqcAOwDlrwWQyMWXKFAYOHMjatWs1vU2kEipVDiSA7777jpdffpnt27djGAbt27fn3nvv5dJLL/V2G32qss/hd+VAysjIyLlI9Ac+B44B52My7VMOJBERqRAqwjVb/ScR8Xd2u53o6GiPUUZhYWHcfPMkZs4045zKFgCcAmYDTwB/ues2adKE/fv3u7ctFguxsbFYrdZyegUiUhIluWaXOoBUVVSFDpDdbsdmswGuJTrX4RyFFIPJNJWEhAR94IuIiN+rCtfsikLvhUjF5nA48iTJXrx4MSNHjgQuAF4EBufUPgDMAF4DTuc5lnNKHPqfQsRPlUkA6fDhw8VuQGXqKFSVDpDnnYY+wBeYTP/w5purGTfuWl83T0REpEj+eM1W/8l/3gsROTdJSUlERUXlKrkGeAnomLP9M3AvzultnrSys4j/Ksk1u9g5kOrXr++OHhfF4XAU97DiJ6xWK0OGDCE5OZnduzP5z3+y+OmnILZuVfBIRESktNR/EpHKwpVk+0zqiy+BS4FxOKextQU+Bb7AmR/pJ/dzXSs7JyUlYTablR9JpIIqdgApMTHR/X1qaioPPfQQY8aMoUePHgCsW7eO9957j5kzZ3q/lVIuzGYzkZGRAAQHQ58+8Oab8OCD0Ly5b9smIiJSEan/JCKVRf5Jth3AG0A88AgQDfQFfgDeAh4H9rmPcdNNN3Hw4EH3tvIjiVQspcqBdPXVVzNu3DhGjBjhUR4XF8ebb75JUlKSt9rnc1V1CLZhQK9ekJwMkybBq6/6ukUiIiKF8/drdlXoP82ePZvZs2fjcDjYuXOn374XIlJ6+SXZPpM4uxXwHGDL2XMYeAZ4Gfgnz7FcgagZM2Zw4YUXalSSiA+UeRLt2rVr88MPP3DhhRd6lO/cuZPOnTtz7Nixkh7Sb/l7Z7Qsffmlgz59zFSv7mDBgm+x2brqw1xERPyWv1+z1X8Skcri7CTbPXv2pHXr1rmmt0XgTLTdJecZfwAPA3FA4f9+alSSSPkqyTW7WmlOEBYWxuuvv56n/I033iAsLKw0hxQ/Y7fbGTs2HEjk9Gkzw4fvJDw8HLvd7uumiYiIVEjqP4lIZeFKfTFixAgiIyMJCAggNjYWcK26lgx0A0bhDB61ABYA3wL/KvTYGRkZ2Gw2/d8h4odKNQLps88+Y+jQobRu3Zru3bsDsH79en799VeWLFnCtddWnsTLVfEOmt1ux2az5dw9uALYmLOnKybTJi3BKSIifsnfr9nqP4lIZZff9LYGDUL5669bgGmA6/PgY+BBYEe+x9GqbSLlp8ynsAGkpaXx2muv8fPPP2MYBu3bt+fOO++sdHfQqloHyOFwEB4e7vGhD+8BtwBfA/8iLEwf5iIi4n8qwjVb/ScRqezOnt7mcDi45pprgCbAdGACzrWcTgNzc8r25nusxMRE9yI/IlI2yiWAVFVUtQ5QUlISUVFRZ5U2B3YCtYGbgA/1YS4iIn6nql2z/ZneCxFxcd2gPpMfqS0wE7g+p8YR4IWcx1GP58bFxeVZeEBEvKvMcyBdeeWVPPzww6xcuZKjR48W/QSpMDIzM/MpzQCezfn+OaBmAfVERESkIOo/iUhVZDabz8qP9DNwA85cSBuAujhHIf0CTARquJ8bEhLi/t7hcJCUlER8fDxJSUk4HI5yegUi4lKqANLAgQPZvHkzQ4cOpUGDBvTo0YOHHnqI5cuXc+TIEW+3UcpR7g9pT88DaUA4cE8h9URERCQ/6j+JSFVltVpJSEigefPmuUqTge7AjTiDR82A2cB2YAQWSwsiIiIAZ26l8PBwoqKiGDlyJFFRUVrgR8QHzmkKm8PhYOPGjSQlJZGUlMTq1asxmUycOHHCm230qao2BDvvENPcRgELMJmOkJoawG+/rXXPbY6IiFBOJBER8amKcs1W/0lEqqrc+ZF27drF9OnTATCM6sB44DGgKQCtWh1izpz6HD1q58YbbXn+N3GOZkIL/Iico5Jcs6ufy4l27drFDz/8wA8//MDWrVupV6+eO0osFZNriKnNZsNkMp31QR0P/BvD6Ea7dvEcOzbSvcdisRAbG6sPbxERkSKo/yQiVZXZbPbIo9qhQ4dcq7bNAd6jXr3HOX16Cikp9RkwAAICQjCMrjinu51hGAYmk4kpU6YwZMgQ3cwWKQelmsI2bNgwQkJC6NWrF19++SU9e/Zk+fLl/Pnnn3z00UfebqOUs/yHmEJYWHNGjtwIwLFjw4DO7n0ZGRnYbDYNIxURESmA+k8iIp6sViupqakkJiYSFxdHYuInHDw4ld9/r8HUqVCjRjYnT/YA1gN2nAm4zzAMg7S0NJKTk33RfJEqp1RT2KpVq0bjxo0ZM2YMUVFRREREULdu3bJon89V5SHYZy/B2bNnT1q3bk16+nPACCAJOLNim8lkwmKxkJKSojsAIiJS7vz9mq3+k4hIycya9TGTJx8AbgXMgAN4D3gC+N1dLy4ujptuusnjfxel2BApnjJfhe3gwYPMnTuX06dP8+ijj9K4cWO6devGgw8+yOeff16qRov/cQ0xHTFiBJGRkaxduzZneOlDwHEgkjPLb+oOgIiISGHUfxIRKZmOHYOA24GOwEc4g0i3ATuBV4FQwDk1WEm2RcreOSXRdvn111956qmnWLBgAdnZ2ZVqSUXdQTsjPj6ekSNdeY+eBB4F/gAuBs6sHhMXF8eIESPKv4EiIlKlVbRrtvpPIiKFy7vATzec/4f0yanxD7Vqvcs//0wH9nk8V0m2RYqnzJNoHzx4kK+++sq9eshPP/1Ew4YNGTJkCFFRUUUfQCqkkJCQXFszgZHA+cB/gMkF1BMRERFQ/0lEpKTyLvCzAegL9MIZSIrgn3/uAm4BXgGeB/4CPJNsDxw4kLVrtYK0yLkq1Qgks9lM48aNiYiIIDIyksjISDp06FAW7fM53UE7I+8dgKuBL4FsIAKTaZ1yIImIiM/4+zW7KvSfZs+ezezZs3E4HOzcudNv3wsRqVjsdnuu1dqcLJYwoqL+w/z5FwFdc0qzgJeBmJzvnZo0acL+/ftzPVcrSIu4lKT/VKoA0o8//ljpOjwF8ffOaHmz2+3YbDaAnCDS2zjnIW8HLmPJkoX6IBYREZ/w92u2+k8iIqV39gI/ERERLF68OCfFxiCcibU759Q+hDOIFEPuQJKLpreJnFHmSbR90fn566+/GD16NEFBQQQFBTF69GgOHTpU6HOOHDnC3XffjcViITAwkHbt2vHaa6+VT4MrKavVSkJCAs2bN88puRfYA7TDZvteH8AiIiIFqCrBIxGRsnD2Aj9mszlX6oz/ApcBNwE/AfWB6UBqztf6HsdyjaGYMmUKJ0+eJCkpifj4eJKSkipVPjoRbyt1Eu2EhAQWL17MH3/8wcmTJz32bd682SuNy23AgAGkp6fz5ptvAjBhwgTCw8P573//W+Bzxo8fT2JiInPnziU8PJwvvviCiRMnsmTJEoYMGVKs8+oOWv5y3wHYubMT06dfTPXq8N130KmTr1snIiJVUUW4Zpd3/8lXKsJ7ISIVX94UGwAmYCjwOOAK3GcBsThHJP3lcQxNb5OqrsxHIL3yyiuMHTuW4OBgvv/+e7p27UqjRo347bffGDBgQKkaXZjt27ezfPly5s6dS48ePejRowdvvfUWn3zyCTt27CjweevWrePWW28lMjKS8PBwJkyYwCWXXMKmTZu83saqJvcdgMceu5gbboDTp2HcOFDQXkREJK/y7j+JiFR2riTbcGZaGhhAAtAJsAH/A4KAx3COSHoaaOw+Ru7gEUBGRgY2mw273V62jRepgEoVQJozZw5vvvkmr776KgEBATzwwAOsXLmSyZMnk5WVd47puVq3bh1BQUF069bNXda9e3eCgoJYu3Ztgc+76qqrWLZsmTsinZiYyM6dO+nXr1+Bzzlx4gSHDx/2eEjhTCZ49VUICoKNG+Hf//5FQ0BFRETOUt79JxGRqiBvig2nJk0aA0uAS3COSPoBqAc8jDOQ9BIQmud4uae36X8ZEU+lCiD98ccf9OzZE4DAwED+/vtvAEaPHk18fLz3Wpdjz549BAcH5ykPDg5mz549BT7vlVdeoX379lgsFgICAujfvz9z5szhqquuKvA5M2fOdOdZCgoKIiwszCuvobILDYWRI51D7197LZSRIx8hKiqK8PBwRe9FREQo//6TiEhVYbVaSU1NJTExkbi4OBITE0lPT8diseAcmGQHLgWuBzYCdYB7gN+AOUBLj+MZhkFaWhrTp0/XTXGRXEoVQGrWrBkHDhwAoGXLlqxfvx6AlJQUSpJSafr06ZhMpkIfrulmZ4YknmEYRr7lLq+88grr169n2bJlfPfdd7z44otMnDiRL7/8ssDnTJs2jaysLPcjLS2t2K+nKrPb7bz22hXAaqA28CZg0hBQERGRHN7qP4mISF5nJ9kOCAg4a3qbAXwMdAX6AWuAmsBdwC/APKC9xzGfeuop3RQXyaVUAaTevXu7k1fffvvt3HPPPfTp04dhw4Zxww03FPs4d999N9u3by/00aFDB5o1a8bevXvzPH///v00bdo032MfP36chx9+mJdeeolBgwbRqVMn7r77boYNG8YLL7xQYJtq1qxJvXr1PB5SOIfDQXR0NM4P5QnAMeAaYIqGgIqIiOTwVv9JRESKp+Dpbd8DvYB/ASuA6sCtOFdwWwp096ivm+IiTqVahS07O5vs7GyqV68OwOLFi/n666+54IILuPPOOwkICPBqI7dv30779u3ZsGEDXbt2BWDDhg10796dn3/+mTZt2uR5jiuT+GeffeaRmPKOO+4gJSWFL774oljn1ioiRUtKSiIqKipXyQTgDeAkzg/f7wFITEwkMjKy3NsnIiJVg79fs8u7/+RL/v5eiEjVknsF6ZCQEHr27Enr1q1zrd52OfAQYOXMGIuvgGeBzwHnKCaLxUJKSgpms9kXL0OkTJTkml3iANLp06d5+umnue2228o1P9CAAQPYvXs3b7zxBgATJkygZcuW7jt5AG3btmXmzJnuu3iRkZH8+eefvPrqq7Rs2ZKvvvqKu+66i5deeom77rqrWOdVB6ho8fHxjBw58qzSJTg/gHfg/EA+SlxcHCNGjCj39omISNXgz9dsX/WffMWf3wsREXCm4LDZbAC5phFfBNwP3AK4gvr/w5lwOw446XFT/OzAVEREhIJLUuGU5Jpd4ils1atX5/nnny/36UgLFy6kY8eO9O3bl759+9KpUyfmz5/vUWfHjh0eq5h88MEHdOnShVGjRtG+fXueeeYZnn76ae68885ybXtlFxISkk/peCAdaAPEFFJPRESk8vNV/0lERPKX//S2nTj/j2kFvAD8DXQE3sW5cts0du1y5rKz2+2Eh4cTFRXFyJEjlStJqoRSTWG7/vrruf766xkzZkwZNMm/6A5a0RwOB+Hh4bmGgLr0wplUuxqNGt3F3r2vKiIvIiJlxt+v2VWh/zR79mxmz56Nw+Fg586dfvteiIi4uEYRrVq1iqeeeuqsvUE4A0rRgAWAWrUcREWl8vnn/YBfPWq7FnhKSEjAarWWddNFvKJMp7ABvPHGG0yfPp1Ro0Zx+eWXU6dOHY/9gwcPLukh/Za/d0b9Rf5DQAGeAh6hdu2TvP32dxhGqoZ3iohImfD3a7b6TyIi/qvgm+IANYBh1KjxEKdOXex6BvARzpFKG9w1c+dKAjTFTfxemQeQqlUreOabyWSqVMOz1QEqPrvdTnR0NOnp6e4yi6UVDkcimZktga+BSMCBxWIhNjZWkXkREfEaf79mq/8kIuLfCrop7hpZ9OGHCaSmns9992UCA3I98xsgFmdA6TQAM2bM4K233jrrfyP9DyT+p8wDSFWJOkAlc3YiuT///JMbb3wQ50ps9YDHgSc0vFNERLxO12z/ofdCRCqq/G6Kh4WFERMTg9VqzbWA0MXAVGAUUDOnZhowB3gLOJDn2PofSPyRAkhepA5Q6bmGgTo/fEcCC3EO9ewLrNZSmCIi4lW6ZvsPvRciUpEVtrpaUlISUVFRuWo3A+7MeTTNKTuO83+fV3Cu4naGpriJvynTAFJ2djbz5s3DbreTmpqKyWSiVatW2Gw2Ro8e7Y6qVhbqAJVe3g/Xd4CxwEGgO7ALwGMpTBERkdLy52u2+k8iIpVDwbmSAoBhOBNuX56rPBHn9Lb/AtnuUk1xE39Rkmt2wZPx82EYBoMHD2bcuHFkZGTQsWNHLr74Yn7//XfGjBnDDTfccE4Nl8olMzPzrJK7gHVAQ5wfoPULqCciIlJ5qP8kIlJ5mM1mYmNjAc4K/p/EZFoAXAFcCSzCmQ8pClgK/AZMA4IBePzxxz2CRwAZGRnYbDbsdnvZvgiRUqpeksrz5s1jzZo1rFq16qyRJbB69Wquv/563n//fW655RavNlIqppCQkLNKTgDXAxuBNsBi4Np86omIiFQe6j+JiFQuVquVhISEfBYQsjBu3Dgef/xxYC1gwXkTfQLQEvgPMB34EGeupLUexzUMA5PJRHR0NEFBQezbt09T28SvlGgKW9++fenduzcPPfRQvvv/85//8NVXX7FixQqvNdDXNAS79Aoe3tkJ50oFdalbdx6HDo3WB6KIiJwzf71mq//kP++FiIg35ZcrCcjnf6CawI3AJJypPFy24AwkxQFHCzyPprZJWSqzKWxbt26lf//+Be4fMGAAP/zwQ0kOKZVYwcM7twKjgWyOHBnD668reCQiIpWX+k8iIpWT2WwmMjKSESNGEBkZidlsLuB/oBPAAkymnjjzI80FjgGdgTeB3TgDSZ3zPY+mtom/KFEA6eDBgzRt2rTA/U2bNuWvv/4650ZJ5eEa3tm8eXOP8rCw7xg1ahsA0dGwcqUvWiciIlL2qlL/afbs2bRv354uXbr4uikiIj5T0P9AFouFGTOGAOOB5sAUYAdQD+dUt++BDcDtQB3381wjmaKjo1m1ahXx8fEkJSXhcDjK/sWI5FKiKWxms5k9e/bQpEmTfPfv3buX0NDQSvWLrCHY3pHf8M5q1czceivMnw/168P69dCmja9bKiIiFZW/XrPVf/Kf90JEpDwVf4pbL+AOYCjO1dwADgMLcY5W2pzv8TW1TbyhJNfsEgWQqlWrxoABA6hZs2a++0+cOMHy5cvVAZJiO3ECeveGtWvhwgudQaSGDX3dKhERqYj89Zqt/pP/vBciIv7Abrdjs9kAzsoV2xgYgzPp9oW5yr/HGUiKAw65S11T5BISEhgyZEieYJXyzEpxlFkAaezYscWq9+677xb3kH5PHaCyt28fdOkCf/zhDCYtXw41avi6VSIiUtH46zVb/Sf/eS9ERPyF3W7Ps4rbGSYgCudUtqE4k3ADHAeWAO8ASYBz1baGDRsSGBiYZ0U4jU6S4iizAFJVpA5Q+di6FXr2hKNH4c47Yc4c8Mi7LSIiUgRds/2H3gsRkaLlnuIWHBzMmDFj8lnBuiEwChiHczVrl1TgvZxHSp5j5x6dpCCSFEYBJC9SB6j8LFsG118PhgGzZsHdd/u6RSIiUpHomu0/9F6IiJRcwVPbXK7AOSppOFA/V3kS8C7O0UlH3aUmkwmLxcIvv/zC2rVrNb1N8lWSa3aJVmETKUuDB8Ozzzq/j46GL77wbXtERERERETKS0Grt52xCedqbSHACOALIBuIxDkSaQ/O6W2RgAnDMEhLS8NisRAVFcXIkSOJiooiPDwcu91e1i9HKiGNQCqC7qCVL8OAsWPhvfcgKMiZVLttW1+3SkREKgJds/2H3gsRkdIr3tQ2FwtwC87k27kTb/+BcxW3+cB2j2e4prctWrSIJk2aaGRSFacpbF6kDlD5O3ECrr4avvkGWreGDRugUSNft0pERPydrtn+Q++FiIj3FD21zeVKnMGkm/Cc4rYJZyApHtjvLjWbzR4rgCrxdtWkKWxSodWsCR99BOHhBr/+ClddtY/PP19TqZY3FhERqYxmz55N+/bt6dKli6+bIiJSaRQ0tc1isdCoUSP3iCL4BrgDaAbYgGXAKZy5k2KB3cDnwM1A3Tz/X2VkZGCz2TS9TQqkEUhF0B0037Db7UycOIe9e5cAQcBqQkPvYNasZxURFxGRfOma7T/0XoiIeF/uqW2uKWcff/xxEaOTGuNMuj0a6Jqr/BjOAFMcsAI4CZxJvJ2SkqLpbFWEprB5kTpA5c81RNP5q9kdZ3K484CVwBCWLFmgIJKIiOSha7b/0HshIlJ+7HY70dHRpKenu8uaNGnC/v37z6p5Ac7k26OANrnKDwF2YBGwGjhNYmIikZGRQP6BKwWXKg9NYZMKy+FwEB0dnSt6vh7oDxwB+gAfER39oKaziYiIiIiI4JzilpqaSmJiInFxcSQmJpKeno7FYsk1vQ3gF+BJoC1wOfASzmlt9YHbcI5EygRe58svHTgczuBUeHi4VnETQCOQiqQ7aOUrKSmJqKiofPZchXO+bl3gc1asqEPfvv8q38aJiIhf0zXbf+i9EBHxveIl366G83+tYTjzJgW799Sv/w+HDs3FmXx7HeA8hisolZCQoJkhlYBGIEmFlZmZWcCer4HrgKPAAB58sDUnTpRfu0RERERERCqSgpJve04/ywbWAJOAUKAvderE06CBwaFDtYC7cSbn/h3niKWeuGJRU6ZMweFw4HA4SEpKIj4+nqSkJM0WqcQUQBK/EhISUsjeNcBA4BhbtjTnxhvh5MlyapiIiIiIiEgFk9/0tg8++ACTyXTW9DYwmbIxmb7k/fdrsmjRGuBa4H3gMBAG3IMzmJSOYbxCWlprnnjiP5riVoVoClsRNAS7fDkcDsLDw8nIyMh3mKXJZKJx42H8/Xcc//xjYsgQWLwYAgJ80FgREfErumb7D70XIiL+Lb/E22FhYcTExGC1WomPj2fkyJE5e2rizEs7FBiMc5Vsl33AUiABSAROa4pbBaNV2LxIHaDyV9Bc3dwfRHXrWhk8GE6cgBtugEWLoEYNnzRXRET8hK7Zvjd79mxmz56Nw+Fg586dei9ERPxYYaurFZybNgC4Gmcw6XqgUa59B4FPcAaUviAsrCG//PILa9eu1QpufkwBJC9SZ9Q3ioqIAyxfDkOGOKex2WwQF6cgkohIVaZrtv/QeyEiUrEVNTPEqTrQC2fy7RuAprn2HQdWcN55q/j77zicwSWwWCzExsZitVoLDWBJ+VEAyYvUAfKd4nygfPaZcwTSyZNw002wcCFUr+6jBouIiE/pmu1dn3zyCffeey/Z2dk8+OCDjBs3rtjP1XshIlLxFTYzJG8YoRrQE2cg6QagVa59p3Hms10GLMNkSuW+++4jPj7eY8BA7uCSlB8FkLxIHSD/98knYLXCqVPQu/dexo5NxGJppgi2iEgVo2u295w+fZr27duTmJhIvXr1uOyyy9iwYQMNGzYs1vP1XoiIVA4FzQwZN24cjz/+eCHP7MSZYNIlZ+37H/AxzoDSJsAZklDuJN8oyTVbq7BJhTdwIEydug44xerVTRk9+jRRUVcr+7+IiEgpffvtt1x88cU0b96c8847j2uvvZYVK1b4ulkiIlLO8lvFLSUlhUceeQSLxZJnJbcztgIzgM7A+cBUXEm2oSPwKPAtkAG8CQzGMAIBmDJlCg6Ho0xfl5SOAkhS4dntdp577krgJuAUcDOwmPT0A9hsNgWRRESkylmzZg2DBg0iNDQUk8nE0qVL89SZM2cOrVq1olatWlx++eUkJye79+3evZvmzZu7ty0WCxkZGeXRdBER8TNms5nIyEhGjBhBZGQkZrMZs9lMbGwsQCFBJJcU4GWgN848Sc7/1/j/9u48PqrybPj4bwibKIQdAgmGKmJ5WERECgom4kZV0BhRsFZstbXaCq9aq7XvU/R9FMUNVLBVW1AfQSoEtZaqtBBEsW4YK9qKVBCIQRYhLCLg5Lx/DAmEhCwwycwkv+/ncz44Z+455xovwn24uBe2ASnA1URGJG0iCOaxZs1w/vSnd0pdIRwOk5ubyzPPPMOkSZN45plnyM3NtdBUy1wtRgktHA4zduzYvXNwnwcuAWYS2RUghSAYwbhx4xgxYoTT2SRJ9caOHTvo06cPV155JRdddFGZ92fNmsW4ceOYOnUqp5xyCr///e8ZNmwYH3/8MV26dCl3wdSK/oKwa9cudu3aVfJ669at0fkikqS4lZWVxezZs8tMcWvXrh0bNmw4yKe+Ap7ZezQGMoBzgfOIjFQ6BziH0aPhf/4Hzj0XmjdfxO9/fwX5+Z+XuVrbtm0ZPXo0Xbt2pV27dnTu3NmlTGqQayBVwjn88a387SUHE6lgtwI+BYaxcOETZGRk1HZ4kqRaZJ9dvlAoxNy5c7ngggtKzg0YMIATTzyRRx99tOTcd7/7XS644AImTJjAkiVLuPfee5k7dy4AY8eOZcCAAYwePbrce4wfP57bb7+9zHlzIUl134GbHw0aNIhjjjmmkh3cynM8kULSuTRocBpFRfv/w8Vm4GVg3t5fNx70Kq1bt2bs2LHcdtttFpKqwDWQVG8UFBSUc3YxkR0AVgLdgDdZvHh3rcYlSVK82r17N++99x5nnXVWqfNnnXUWS5YsAeDkk09m2bJl5Ofns23bNubNm8fZZ5990GveeuutFBYWlhxr1qyp0e8gSYofB05xa9y4cTWmt+3v34RC95OW9kO+/LKIGTOKaNZsDpFiUStgFPA08CXwFpE1lr7HgWWNr776it/+9rckJydz5ZVX8vTTTzvtLUqcwqaElpKScpB3/g0MBF4CTuLOO4fSqxfs94+vkiTVSxs3biQcDtOhQ4dS5zt06MC6desAaNiwIffffz+ZmZkUFRVx880306ZNm4Nes0mTJjRp0qRG45YkJY6DTW9LS0vj0ksv5b777gMoNUKpuNg0adIk2rZNIiUll6+/ziZSIBpAZKrb94G+wMl7j/8GNgGvEBmZ9CqRAlNkOvf06dOZPn16qdjatm3LD37wA0aMGOF0t2pyBJIS2uDBgytY/f9LIJOmTf/Orl1JZGXBww/XdoSSJMWnA/vOIAhKnRs+fDjLly9nxYoV/OQnP6nt8CRJCe5gO7hNnDiR2bNnl9qsASIbNsyePZusrCxg/9kmRcCbRHZuOxHoBFwJzCIyta0NMBp4ClgHvA/cQ2TR7rL/uLFx40YmTZpEZmYm7dq148orr3R0UhU5AkkJrXj1/+zsbEKhUDkV7B089dRWFiyA3/0Orr8eVq2Ce++FBpZPJUn1UNu2bUlKSioZbVRs/fr1ZUYlSZJ0OIqntx0oKyuLESNGlFo76cDRQAefbVIATN97JBGZxjYMOBs4CThh73Ez8DXwGpGRSfOBZaWutHnz5lKjlNq2bcvUqVO5+OKLD+Xr1nn+FVoJr3h45MEq2BdffCFTp8Ldd0fOP/AAXHIJ7NwZg2AlSYqxxo0b069fP+bPn1/q/Pz58xk0aFCMopIk1TcHrp104FSy4tkmFQsDbxAZndQfaE9kNNKTwBdAMyI7uz0AfLj33FPA5URGMpW2ceNGRo4cyc0333w4X63Oche2SrijS+I4cPX/8uazzpwJY8bA7t0waBC88AK0bRubeCVJ0WWfvc/27dtZsWIFAH379uWBBx4gMzOT1q1b06VLF2bNmsXll1/O7373OwYOHMhjjz3G448/zkcffcTRRx992Pc3F5KkaMjJySE7O7uau7ntrydw5t7jNCIFpf19DPwN+DuQC2wteee5554jOzsbqNrfNRNVdfpsC0iV8AGo7lm0KLKY9pYt0K0b/PWvcMwxsY5KknS47LP3yc3NJTMzs8z5K664omSY/tSpU5k4cSIFBQX07NmTBx98kCFDhkTl/uZCkhQtOTk5ZRbjPjSNiezWfSZwBpHpbvtPyvoWeBdYACygbdtPWbfuM1544YUy909NTWXy5Mkl6zUlcoHJAlIU+QBUN/3rXzBsGHz+ObRrB3/+MwwYEOuoJEmHwz47fpgLSVI0FRdo8vPz2bBhA6tWrWLGjBls2LDhMK7aCsggUlAaChx3wPu7SE8vYNWqaURGKL0N7AH2bUQxe/ZsgDIFpqZNm3LSSSdx9NFHc/TRR3P66aeXO00vHlhAiiIfgOquggI47zxYuhSaNoWpU+HKK2MdlSTpUNlnxw9zIUmqafsXlf7+97/z3HPPsX379sO4YiqQSWT3tqFA2gHv7wAWAwuJTHd7nzZtWvDVV19VaYpdo0aNGDBgAGlpaTRo0CBuCksWkKLIB6C6bft2GD06MgIJ4Kqr4OGHIwUlSVJisc+OH+ZCklTbwuEwd955J5MnT+arr76KwhWPJVJMKj7aHfD+NuB1IsWkXOA9Iot6V0+LFi144oknYrbzmwWkKPIBqO4rKoIJE+D//l8IAujbF2bPhu98J9aRSZKqwz47fpgLSVKsHDjdrU2bNmzatIlVq1Yxbdo0tm7dWvlFgNatW+9XiAoB/0WkkJRBZEHu1gd8YhuRHeEWsa+gtKfKcf/yl79kwoQJtb6WkgWkKPIBqG7bf7GzL77owd1392bjxhAtW8LTT0emuEmSEoN9dvwwF5KkeFTVUUqhUIjx48fz29/+9mAtgF5EikkZlF9Q+hp4k0hBaTHwD+CbCuMrLnYV69y5M1dddRV79uxh9erVdOnSJerT3iwgRZEPQHVXeav5d+zYnxYt/sry5W0A+PWv4Y47IA7XOpMkHcA+O36YC0lSPCseSPD8888zffp0CgsLS95LS0tj0qRJjBgxgvT0dPLz86uwxlEI6E2kkHQaMARoe0Cb3cA7RIpJi4mMVirkULRp04bHHnusZBe4w2EBKYp8AKqbcnJyyM7OLvMHQSgUIgga8f3vf8S8eccCMHQozJgB7dvHIlJJUlXZZ8cPcyFJShT7z0o5cNpY8d8bgVJ/d4z8vTGgRYsWB5kSFwK+y75i0mCg8wFtioBlRNZRKj7WVCv2OXPmHHYRyQJSFPkAVPeEw2HS09NLjTzaXygUIjU1lbvvXsVPftKAHTugc2f4059g0KBaDlaSVGX22fHDXEiS6oryZq4Uj1IKh8OMHDmyilfqyr5i0mDguHLarCZSSHpj7/EhkUJT+VJTU1m1atVhTWezgBRFPgDVPbm5uWRmZlbabuHChbRvn8FFF8G//w0NG8L998MvfgGhUC0EKkmqFvvs+GEuJEl1SUWjlG6++WbuvffeQ7hqe+BU4JS9v54INDygzVYiaycVF5TeAraXarFw4UIyMjIO4f5771CNPvvA6KQ6r6CgoMrtMjLg7bfhqqsiI5DGjoXXXy9izJg3KCxcW2sr40uSJEmSYiMpKemgRZqJEydy8skn8+Mf/7jKO7xFrAdy9h4ARwInExmddArwPaAFcNbeAyK7u5UeDFHVv99GgwUk1TspKSnVate8OTz7LJxyCtxwQxHPPdeA555rC1wLLCM1NZXJkydHZQEzSZIkSVJiyc7O5sILLyQ3N5cFCxawatUqVq9ezdtvv83u3bureJUdwMK9B0ADIju9nbL3GAgsKfOpqv79NhqcwlYJh2DXPcVrIB1sNf3iNZBWrlxZamRRTk4OF110P/AnIgug7QbuACYSCn3L7NmzLSJJUgzZZ8cPcyFJUuTvnvsXlYIgYM2aNSxdupSvv/66pF2bNm3YtGlTyeLcB5cEhEteuQZSnPEBqG6qaDV9oEwxqPTC2+2APwDn7333feDHpKVtLFN0kiTVHvvs+GEuJEk6uPLWVHrhhRfKLNZdGXdhizM+ANVdFa2mf+APYfkLb48CHgbaAN8Cd/PKK4M566zTajhySVJ57LPjh7mQJKn69i8sffrppzz00ENs2rSpTLs2bdrw2GOPRWUGjAWkKPIBqG6raDX9/c2cOZPRo0eXc4X2RIpIka0bO3cuZM6cZAYMqNGwJUnlsM+OH+ZCkqTDt/8UuNWrV9OlSxdOP/10MjIyojbzxV3YpCqqaDX9/R18YbL1wCXALGAq+fkdGDQIbrgB7rgDjjgierFKkiRJkuqPpKQkhg4dytChQ2MdChBZ1ltSJQYPHkxqamrJGkkHCoXm0qnTmVx2WRFFRXDffdCnDyxeXMuBSpIkSZJUAxKmgHTnnXcyaNAgmjVrRsuWLav0mSAIGD9+PJ06deKII44gIyODjz76qGYDVZ2UlJTE5MmTAcoUkYpfP/zweP73fxvw5z9Dp07w6adw2mlw/fWwfXuthyxJkiRJUtQkTAFp9+7dXHzxxfzsZz+r8mcmTpzIAw88wCOPPMI777xDx44dOfPMM9m2bVsNRqq6Kisri9mzZ9O5c+dS51NTU0vt2nbeefDRR3DVVRAE8PDD0KsX/P3vsYhakiRJkqTDl3CLaE+fPp1x48axZcuWCtsFQUCnTp0YN24cv/rVrwDYtWsXHTp04J577uGnP/1ple7nIpA6UFUX3gaYPx+uvho+/zzy+uqr4d57ITm5FgOWpHrCPjt+mAtJkhJDdfrshBmBVF0rV65k3bp1nHXWWSXnmjRpwmmnncaSJUsO+rldu3axdevWUoe0v+KFt0eNGlXp6vdnngkffgjXXRd5/fjj0LMnzJtXS8FKkiRJkhQFdbaAtG7dOgA6dOhQ6nyHDh1K3ivPhAkTSE5OLjnS0tJqNE7Vfc2bwyOPwKJFcOyxsHYtnHsuXHEFfPVVrKOTJEmSJKlyMS0gjR8/nlAoVOHx7rvvHtY9DlzwOAiCg+6kBXDrrbdSWFhYcqxZs+aw7i8VGzIEPvgAbrwRGjSAp56CHj1g7txYRyZJkiRJUsUaxvLmP//5z7n00ksrbJOenn5I1+7YsSMQGYmUkpJScn79+vVlRiXtr0mTJjRp0uSQ7ilVplkzuO8+yM6GH/0I/vUvyMqCkSMji223bx/rCCVJkiRJKiumBaS2bdvStm3bGrl2165d6dixI/Pnz6dv375AZCe3RYsWcc8999TIPaWq+t734P334Y474J574E9/ggULIkWkSy6BCgbJSZIkSZJU6xJmDaTVq1eTl5fH6tWrCYfD5OXlkZeXx/bt20vaHH/88czdOx8oFAoxbtw47rrrLubOncuyZcsYM2YMzZo1Y/To0bH6GlKJJk3gzjvh7behd2/YuBFGjYILL4SCglhHJ0mSJEnSPjEdgVQd//3f/82TTz5Z8rp4VNHChQvJyMgA4JNPPqGwsLCkzc0338zOnTu59tpr2bx5MwMGDODVV1+lefPmtRq7VJETT4R33omMRPp//w9eeCGy4PaDD0YW2nY0kiRJkiQp1kJBEASxDiKebd26leTkZAoLC2nRokWsw1Edt2xZZG2kd96JvD7jDHjoIfjud2MblyQlAvvs+GEuJElKDNXpsxNmCptUH/TsCUuWwMSJkSluf/sb9OoF48bBli2xjk6SJEmSVF9ZQJLiTMOGcMMNYf74x39w0klrCIdh8mTo1g0eewzC4VhHKEmSJEmqbywgSXEmJyeH9PR0LrtsIO++2wU4k4YNl7NxI/z0p3DSSbB4cayjlCRJkiTVJxaQpDiSk5NDdnY2a9eu3e/s3/j2257AOJo1201eHgwZAhddBCtWxChQSZIkSVK9YgFJihPhcJixY8dS/rr2ewiFHiI5uT/nnptPKFRETg706BHwf/4PfPVVrYcrSZIkSapHLCBJcWLx4sUHjDwqLQgCCgr+yV/+kkoQ9Ab+yp49ISZNgmOOgQcegF27ai1cSZIkSVI9YgFJihMFBQXVaP0R8H3gbOCfbNkCN94IPXrArFlQ7iAmSZIkSZIOkQUkKU6kpKQcwqdeBU6kVaubSEkJ+OwzuPRSGDAAcnOjHKAkSZIkqd6ygCTFicGDB5OamkooFKrmJ8Ns3nw/f/jD69xxBxx1FLzzDmRmwrnnwrJlNRKuJEmSJKkesYAkxYmkpCQmT54McAhFJFiy5FWOPXYmTz75BtdeW0TDhjBvHvTpEzBsWAGPPPICubm5hMPhaIcuSZIkSarjQkH5Wz5pr61bt5KcnExhYSEtWrSIdTiqB3Jychg7dmyFC2pXJjU1lV/96glmzPgv3nwzde/ZXcDvSUl5kkceuY2srKyoxCtJ8cI+O/amTJnClClTCIfDLF++3FxIkhTnqvP8ZAGpEj6MKhbC4TCLFy+moKCA9u3bM2bMGPLz86nqj2soFNqv7QDgHuC0va93Ao/yxz9258orz41+8JIUI/bZ8cNcSJKUGCwgRZEPQIoHOTk5ZGdnA1S5iFTW6cD/AwYBEAp9zciR6zn99Pc47rg2DB48mKSkpKjEK0mxYJ8dP8yFJEmJoTp9tmsgSQkgKyuL2bNn07lz58O4ygLgFOAc4B2CoBmzZqXz05+eRWbmm6SlnUhOTk50ApYkSZIk1SkWkKQEkZWVxapVq1i4cCEzZszgN7/5zSFe6RXgZOB84H2gOXArBQVLuOiilfzhD3+JVsiSJEmSpDrCApKUQJKSksjIyGDUqFEMHTr0MK/2EnAiMBx4FzgSuJGrrx7K9dcXkZ9/uNFKkiRJkuoKC0hSgho8eDCpqamEQqHDvNKfgf7AMOAfBEFTHn64Ad/5Dvz0p/Cf/xx+rJIkSZKkxGYBSUpQSUlJTJ48GaBMEWn/11UvML0MDATOJC1tFbt3w2OPwXHHBYweDf/8Z3TiliRJkiQlHgtIUgI72OLaqampzJkzhzlz5hzCwtt/Y82arsAQ4K8UFYWYORP69IFzzw14+OGlzJw5k9zcXMLhcLS+iiRJkiQpjoWCQ98TvF5wG1olgnA4zOLFiykoKCAlJYXBgweTlJRU5r327dszZswY8vPzqfqPfl/gV8DF7Ks5LwbupXPnPB56aBJZWVlR/06SVF322fHDXEiSlBiq02dbQKqED0Cqa3JycsjOzgaoRhEJ4FjgZuAKoPHec/8CHuB///ccOnduU24BS5Jqi312/DAXkiQlhur02U5hk+qZg017q9wK4CdAOnA3UAh8F3icH/zgFDIzX2X06GvJzMwkPT2dnJyc6AYuSZIkSYoZC0hSPZSVlcWqVatYuHAhM2bM4De/+U01Pl0A3AqkATcCa4COwF3AWuAR1q49kuzsbItIkiRJklRHWECS6qmkpCQyMjIYNWoUQ4cOPYQrbAMeAL4DXA58ABwJXAf8myB4kWuumc2337rQtiRJkiQlOgtIkhg8eDCpqamEQqFD+PS3wP8CJwCnAy8CRcB5bNgwg27dvuGJJ2DnzujFK0mSJEmqXRaQJJGUlMTkyZMBDrGIVGwhMALoDjwEbGfVqiO5+mro0iXghz9cxdSpc8nNzSUcdmSSJEmSJCUKC0iSgIMvrp2WlsYvf/lLQqFQNYpLK4CxQCo/+9kK2rXbwcaNIZ5+Op3rrjuXzMwCUlIuZs4c10iSJEmSpERgAUlSiQMX1164cCErV65k4sSJ5RaXkpKSDnqtUChEWloLTj89jw0bWgIXAYuBxsAoNmzIITv7GK65Zik7dtTgl5IkSZIkHbZQEARBrIOIZ1u3biU5OZnCwkJatGgR63CkmAqHwyxevJiCggJSUlLYuHEjI0eOBGD/P0qKRyrNmjWLG264gbVr1+53lb7AtcBooBkAyckBY8aEuPZaOO642vkukuoe++z4YS4kSUoM1emzLSBVwgcgqWI5OTmMHTu2VJEoLS2NSZMm0bp1azIzMw/yyZbAlcDPgG4lZ884I+C005aRnv4RqakdGTx4cIUjnSSpmH12/DAXkiQlBgtIUeQDkFS5A0cmFRd9Zs6cyejRoyv5dIhf/ervfPxxJi+9FBAExessrQaeICXlrzzyyK1kZWXV8LeQlOjss+OHuZAkKTFUp892DSRJhy0pKYmMjAxGjRpFRkZGyYihlJSUKnw64JxzQowZk0MQfAe4G9gIdAHuoKDgH1x0URK33fYGbtwmSZIkSbFhAUlSjRk8eDCpqakH3b0tstB2GoMGDWLs2LHAKuBWIJXIGkm5QBIwgrvuOoXOnXcycuQ/ee65NwlbTZIkSZKkWmMBSVKNSUpKYvLkyQBlikjFrydNmsSSJUsOWGh7FzATyASOBx4ANvHll0fw3HO9GTnyZI46aiG/+tWb7NlTC19EkiRJkuo5C0iSalRWVhazZ8+mc+fOpc6npqYye/ZssrKyKCgoqOAKnwA3Ap2BH1A8Kumbb85g4sSBtG//DbfcAsuX19Q3kCRJkiS5iHYlXARSio6DLbQNkJubW8FubeXpBvwIGAN0LDnbo0chP/1pcy69tAHt20cxeEkJwT47fpgLSZISg7uwRZEPQFLNC4fDpKenk5+fT/X+SGoInAtcBQwjsl4SNGhQxFlnNeCyy2DECGjePPoxS4o/9tnxw1xIkpQY3IVNUkKpaK2kin0LvACcT2Th7XHA2xQVNeDll+Hyy6FDBxg1Cv78Z9i9O9qRS5IkSVL9YAFJUlw42FpJVbcOmAwMALrTosUDdOsWsHMnPPssDB8OKSnws5/B4sVQVBS92CVJEVOmTKFHjx70798/1qFIkqQocwpbJRyCLdWu/ddKat++PWPGjDmEqW0RCxYs5KijMpgxI1JEWrdu33tdusDo0ZGjV68ofgFJMWOfHT/MhSRJicEpbJISVlJSEhkZGYwaNYqhQ4ce4tS2iHXrCujfHx58ENauhVdfhTFjImsirV4Nd98NvXtHjnvuiZyTJEmSJJVlAUlSXDucqW0pKSkl/52UBGeeCdOmwZdfwnPPwQUXQOPG8OGHcMstcPTRMGQI/O53sGlTFL+EJEmSJCU4p7BVwiHYUnyoztS2UChEamoqK1euJCkpqcLrbt4Mc+bAM8/AokVQfLlGjeCccyJT3IYPh2bNauJbSYom++z4YS4kSUoM1emzLSBVwgcgKT7l5OSQnZ0NUKqIVDzVbfbs2WRlZVXrmmvXRtZKeuYZyMvbd/6oo+DCCyPFpDPOgIYNDzt8STXAPjt+mAtJkhKDayBJqvMONrUtNTX1kIpHkc/CTTfB++/DRx/BbbdB166wfTs8/TQMGwadO8P118M//rFvtJIkSZIk1XWOQKqE/4Imxbf9p7alpKQwePDgSqetVUcQRIpFzzwDs2bBxo373vvOdyKjki67DI4/Pmq3lHSI7LPjh7mQJCkxOIUtinwAklRszx74298ixaTnn4cdO/a9d+KJkWLSpZdGRinVdGFLUln22fHDXEiSlBgsIEWRD0CSyrNjB7z4YqSY9Mor8O23kfOhEPzXf60nP38imzc/ARQCkal1kydPPqSpdZKqxj47fpgLSZISg2sgSVINO/JIGDUKXnoJCgpg6lQ45ZTIlLdly9qzefN9wJdADnA5a9d+Q3Z2Njk5OTGOXJIkSZKqzwKSJB2mtm3hZz+DRYvCdOw4ELgVWAY0AS4EngLWEQS5/OhHH/Ovf4VjGa4kSZIkVZsFJEmKksWLF7Nu3T+Au4FeQB/gDiAPSAKGUFj4G3r0SOL44+Hmm2HxYghbT5IkSZIU5ywgSVKUFBQUHHDmn8Bvgb7A0cDPgVdJSgrzySdw770wZAh06ABXXAFz5sC2bbUdtSRJkiRVzgKSJEVJSkpKBe+uBqYAZ/PCC0v405/gBz+AVq1g0yZ46inIzo5Mhxs2DB59FNauraXAJUmSJKkS7sJWCXcRkVRV4XCY9PR08vPzKe+P1lAoRGpqKitXriQpKQmI7N72xhuRHd1eeAH+85/SnznxRBg+HM4/H/r2jezyJql89tnxw1xIkpQY3IVNkmIgKSmJyZMnA5Fi0f6KX0+aNKmkeATQsCGcdhrcfz98+il8/DHcfXdkR7dQCJYuhfHjoV8/6NIFrr0WXn4Zdu2qta8lSZIkSRaQJCmasrKymD17Np07dy51PjU1ldmzZ5OVlXXQz4ZC8N3vwq9+Ba+/DuvWwbRpcOGF0KxZZErbo49Gpri1bRuZ8vbUU7BxY01/K0mSJEn1nVPYKuEQbEmHIhwOs3jxYgoKCkhJSWHw4MGlRh5V1zffwIIFkaluL74I+6/X3aBBZMTS+edHprt17x6FLyAlIPvs+GEuJElKDNXpsy0gVcIHIEnxpqgoMrWtuJj0wQel3z/uuEghafhwGDgwMk2uWLQLW1I8sc+OvSlTpjBlyhTC4TDLly83F5Ikxbk6WUC68847+ctf/kJeXh6NGzdmy5YtFbbfs2cPv/nNb5g3bx6fffYZycnJnHHGGdx999106tSpyvf1YVRSvPv8c3jppUgxaeFC2LNn33tt2sC550aKSd988yK33HIda/fb3i01NZXJkydXOLVOShT22fHDXEiSlBjqZAHpt7/9LS1btmTt2rX84Q9/qLSAVFhYSHZ2NldffTV9+vRh8+bNjBs3jm+//ZZ33323yvf1AUhSItm6FV55JVJM+stfYPPm/d/dBSwA/rz3WFuyuPesWbNo166dI5OU0Oyz44e5kCQpMdTJAlKx6dOnM27cuEoLSOV55513OPnkk/n888/p0qVLlT7jA5CkRPXtt/DGG/D880U8/PBqwuH0A1osA14GXqZBgzcpKvq65J39RyY57U2Jwj47fpgLSZISQ3X67IYVvlvHFBYWEgqFaNmy5UHb7Nq1i1377Y+9devWWohMkqKvYUM47TQIgteYNCkTOB44HxgBfA/oufe4aW/xaCGRgtIrrF27guzsbG666SZmzpxZ7rS3ESNGWFiSJEmS6ol6U0D65ptvuOWWWxg9enSFVbUJEyZw++2312JkklSzCkq2bPv33uNeoBVwBnDO3qMTcO7eA2AVQfAq9977CrC91PXy8/O56KKLaNOmDZs2bSo5b2FJkiRJqrsaxPLm48ePJxQKVXhUZ72ig9mzZw+XXnopRUVFTJ06tcK2t956K4WFhSXHmjVrDvv+khRLKSkp5ZzdDDwH/BjoDPQGfgn8nchaSenAT4A5wEbgDWA8cApBECkG7V88gn2FpQ4dOpCZmcno0aPJzMwkPT2dnJwcILILXG5uLjNnziQ3N5dwOBztrytJkiSpBsR0DaSNGzeycePGCtukp6fTtGnTktfVXQNpz549jBw5ks8++4wFCxbQpk2basXoHH5JiS4cDpOenk5+fj5V+yO/GTAEOBs4C+hxwPtbgVxgPvA3IqOaDq54oW6nw6mm2WfHD3MhSVJiSJg1kNq2bUvbtm1r7PrFxaNPP/2UhQsXVrt4JEl1QVJSEpMnTyY7O5tQKFSFItLXFC+uHZFKpJB0JjAUaAcM33sArCVSSPr73qNg/4uV3O/ee+8tcyenw0mSJEmJIaZT2Kpj9erV5OXlsXr1asLhMHl5eeTl5bF9+761OY4//njmzp0LwLfffkt2djbvvvsuzzzzDOFwmHXr1rFu3Tp2794dq68hSTGRlZXF7Nmz6dy5c6nzVSvGrAX+CIwCOgAnAr8iUjT6hkiBaQzwNPAF8BEwmUiBKbnCKxcXlw5lOtzBOE1OkiRJir6YTmGrjjFjxvDkk0+WOb9w4UIyMjKAyDSJadOmMWbMGFatWkXXrl3Lvdb+n6mMQ7Al1SXhcLjUiJ6NGzcycuRIgCpObztQU+AUIgtyDwX6UfrfJsLAe8CCvccbREY4HZri6XCzZ88ud3TSCy+8wNixY8udJpeVlXXI91VisM+OH+ZCkqTEUJ0+O2EKSLHiA5Ckui4nJ6dM0SUtLY1LL72U++67D6hOcakVkMG+glL3A97fDfwDWLj3+AeRRburLhQK0bp1a4444ohSMR84DW7/9gCzZs2iXbt2ZabDHVhUc5pc4rLPjh/mQpKkxGABKYp8AJJUHxysiFJecam4UFO19ZQ6A5nA6UQKSl0OeP8bYAn7CkpvA3ui9bVKKS4WFUtNTWXUqFEHXdjbEUuJxz47fpgLSZISgwWkKPIBSFJ9V15xqbypYlUrLH2HSDEpc++RcsD7XxMpKC0istPb20RGLdWe/afJWURKLPbZ8cNcSJKUGCwgRZEPQJJUvqoWliqeDtedfcWkDKD9AXfZCbxJpKC0CHiLyKilmhUKhUhNTWXFihUsWbLE6W0Jwj47fpgLSZISgwWkKPIBSJKq5/Cmw32XSCHptL2/djjg6ruIjEp6jUhB6U1gOzWlXbt2bNiwoeS109vim3127E2ZMoUpU6YQDodZvny5uZAkKc5ZQIoiH0YlKXqqOx0uUlA6DRiy99dOB1zxW+B9IgWlxcDrQNmFtKPF6W3xzT47fpgLSZISgwWkKPIBSJJqXlULSx07nsq2bSeyY8eJRApK6eVc7SMixaTigtLqqMZaPL1t5cqVTmeLM/bZ8cNcSJKUGCwgRZEPQJIUOwcrLGVnZwMQBKnA4L3HEKBHOVdZTaSQVHx8BBQddmwLFy4kIyPjsK+j6LHPjh/mQpKkxFCdPrthLcUkSVK1JSUllSnSZGVlMXv27L2jk9YAM4AZpKWlcccdU2nRYhjPPvsFS5cewapVrQmHuwCj9x4AW4isnfTG3uNtIru/VU9BQcGhfi1JkiQp4VhAkiQlnKysLEaMGFHuYt2R99MA2LED3noLHnvsY55/fiO7dp0ItASG7T1g3zpKkYJS69af8NVXH1YaQ0pKStS/lyRJkhSvnMJWCYdgS1LdEA6Hyc19nbfe2sm6dcewbt0xLFnSgPz8sm2TktYSDi8GlhApLH1IpNAUH2sgHWynu/rOPjt+mAtJkhKDU9gkSTpAUlISQ4eextCh+84FAaxeDUuWwBtvRH794AMIh1OBUXsPiExxWwq8TRC8xa9/PYYGDWJTsMnJySmzuHhqaiqTJ092ZzhJkiTVGEcgVcJ/QZOk+mX7dnj7bXjiiY+YO/dLvvmmL9CqTLsOHeDkk2HAgMjRvz8kJ9dsbDk5OWRnZ3Ng1x0KhQCYPXt2vS4i2WfHD3MhSVJicBe2KPIBSJLqr3A4zKJFi1m6dDsbNx7D1q3defvtBnzwAXz7bdn2xx+/r6A0YAD06gWNGkUvlvT09FIjj/YXD1PrYs0+O36YC0mSEoNT2CRJioKkpCROPz2D008vfX7nTnj//chIpbfeihwrV8K//x05nnwy0q5pU+jXr/RIpaOPhr0Dhqpl8eLFBy0eAQRBwJo1a1i8eHGZneskSZKkw2UBSZKkajriCBg0KHIUW78+UlAqLiq9/TZs2RJZW+mNN/a1a98+UkgqLir17w8tW1Z+z4KCgirFVtV2kiRJUnVYQJIkKQrat4fzzoscAEVF8OmnpUcpffBBpND05z9HjmLFU9+Ki0q9e5ed+paSklKlOKraLtqqujOcO8jVbVOmTGHKlCmEw+FYhyJJkqLMNZAq4Rx+SVK0fPNN2alvn31Wtl3TpnDiiaWLSmlpYbp2TSc/P7/MItoQ2zWQKtsZrrho9MILL/DMM8+wYcOGctsdLvvs+GEuJElKDC6iHUU+AEmSatKGDWWnvm3eXLZdu3bQpUsB7703FXgbeAsoBGK7C1tlO8PddNNNzJw5s8LFvyE6sdtnxw9zIUlSYrCAFEU+AEmSalMQlJ36lpcHe/aU13oFsJTk5M+4/vpT+cUvTqVdu9qLtbKd4aoqWqOn7LPjh7mQJCkxWECKIh+AJEmx9s03kSLSvqJSwH/+U/5WbmlpkelvfftGfj3xROjU6dB2fqtMbm4umZmZUbvewoULD2sHOfvs+GEuJElKDNXps11EW5KkONe0KXzve5EjIsRXX0XWU1q6dN+xfDmsWRM5Xnhh3+fbt99XTCouLHXtevhFpWjv+OYOcpIkSfHLApIkSQmodWsYOjRyFNu6NbLT2/6FpY8/juz89vLLkaNYy5alRymdeCJ06wbVmUEW7R3fYrWDnCRJkirnFLZKOARbkpTIdu6EDz8sPVLpww9h9+6ybZs1gxNOKF1U6tEDGjUq/9rFayAdbGe4qnINpLrHXEiSlBicwiZJkgA44gg4+eTIUWz3bvjXv0oXlfLy4OuvYcmSyFGscWPo1at0UalXr8h1k5KSmDx5MtnZ2YRCoUMqIhXvwjZp0qTDKh5JkiSpZjkCqRL+C5okqT4IhyNrKC1dWnoKXGFh2bZJSZGRScVrKm3btohHH/0pX3zxSUmbtLQ0Lr30Uu677z6AgxaX0tLSmDRpEllZWYf9Heyz44e5kCQpMbgLWxT5ACRJqq+CAFauLD1SaelS2LChbNtQKCA1dSedO2+gV68ww4cfTb9+SSxZksO4cWNZu3ZtSdt27dpx2WWXMWLECAYPHhy1kUf22bE3ZcoUpkyZQjgcZvny5eZCkqQ4ZwEpinwYlSRpnyCAL74oW1Tarz5USrt20Lt3QNu2a2jTJp9+/RoyevSJNG0a/elq9tnxw1xIkpQYXANJkiTViFAIOneOHOefv+/8hg37pr598EHk+OSTyPm//z0EdNl7wDXXQP/+8PrrketJkiQp/llAkiRJh61dOzjrrMhR7Ouv4aOPIgt0FxeV/vlP2Lo1suaSxSNJkqTEYQFJkiTViGbNIiON+vffdy4IYNWq8hfnliRJUvyygCRJkmpNKARdu8Y6CkmSJFVXg1gHIEmSJEmSpPhmAUmSJEmSJEkVsoAkSZIkSZKkCllAkiRJUlRMmTKFHj160H//ldMlSVKdYAFJkiRJUXHdddfx8ccf884778Q6FEmSFGUWkCRJkiRJklQhC0iSJEmSJEmqkAUkSZIkSZIkVcgCkiRJkiRJkipkAUmSJEmSJEkVsoAkSZIkSZKkCllAkiRJUlRMmTKFHj160L9//1iHIkmSoswCkiRJkqLiuuuu4+OPP+add96JdSiSJCnKLCBJkiSpjAsvvJBWrVqRnZ0d61AkSVIcsIAkSZKkMq6//nqeeuqpWIchSZLihAUkSZIklZGZmUnz5s1jHYYkSYoTFpAkSZLqmNdee43zzz+fTp06EQqFeP7558u0mTp1Kl27dqVp06b069ePxYsX136gkiQpYTSMdQDxLggCALZu3RrjSCRJUkWK++rivrs+27FjB3369OHKK6/koosuKvP+rFmzGDduHFOnTuWUU07h97//PcOGDePjjz+mS5cu1b7frl272LVrV8nrwsJCwOcnSZLiXXWenywgVWLbtm0ApKWlxTgSSZJUFdu2bSM5OTnWYcTUsGHDGDZs2EHff+CBB/jxj3/MVVddBcCkSZN45ZVXePTRR5kwYUK17zdhwgRuv/32Mud9fpIkKTFU5fnJAlIlOnXqxJo1a2jevDmhUChq1926dStpaWmsWbOGFi1aRO26ih5zFP/MUfwzR/GvLuUoCAK2bdtGp06dYh1KXNu9ezfvvfcet9xyS6nzZ511FkuWLDmka956663ccMMNJa+Lior46quvaNOmDaFQiP79+/POO++U+9nqvBevv18r+g6xvG51P1+V9ofb5mDvlXfefNfcZ6vavrJ2/mzH5rr+bNeMeMx3bfxsV+f5yQJSJRo0aEBqamqNXb9FixZx9UOjssxR/DNH8c8cxb+6kqP6PvKoKjZu3Eg4HKZDhw6lznfo0IF169aVvD777LNZunQpO3bsIDU1lblz59K/f/9yr9mkSROaNGlS6lzLli1L/jspKemgv78O5b14+/1a0XeI5XWr+/mqtD/cNgd7r6LPmO/of7aq7Str5892bK7rz3bNiMd819bPdlWfnywgSZIk1UMHjqwOgqDUuVdeeSVq97ruuuui/l48qak4D/e61f18VdofbpuDvZcouYb4zHdN5Loq7fzZjs11/dmuGfGY71j9bB9MKHClyZjYunUrycnJFBYWxlXVVfuYo/hnjuKfOYp/5qjuC4VCzJ07lwsuuACITGFr1qwZzz33HBdeeGFJu7Fjx5KXl8eiRYtiFGnl/P1av5jv+sNc1y/mO3E1iHUA9VWTJk347W9/W2a4t+KHOYp/5ij+maP4Z47qn8aNG9OvXz/mz59f6vz8+fMZNGhQjKKqGn+/1i/mu/4w1/WL+U5cjkCSJEmqY7Zv386KFSsA6Nu3Lw888ACZmZm0bt2aLl26MGvWLC6//HJ+97vfMXDgQB577DEef/xxPvroI44++ugYRy9JkuKRBSRJkqQ6Jjc3l8zMzDLnr7jiCqZPnw7A1KlTmThxIgUFBfTs2ZMHH3yQIUOG1HKkkiQpUVhAkiRJkiRJUoVcA0mSJEmSJEkVsoAkSZIkSZKkCllAkiRJkiRJUoUsIMXI1KlT6dq1K02bNqVfv34sXrw41iHVSa+99hrnn38+nTp1IhQK8fzzz5d6PwgCxo8fT6dOnTjiiCPIyMjgo48+KtVm165d/OIXv6Bt27YceeSRDB8+nLVr15Zqs3nzZi6//HKSk5NJTk7m8ssvZ8uWLTX87eqGCRMm0L9/f5o3b0779u254IIL+OSTT0q1MU+x9eijj9K7d29atGhBixYtGDhwIH/9619L3jc/8WXChAmEQiHGjRtXcs4cqb546aWX6N69O926deOJJ56IdTiqQRdeeCGtWrUiOzs71qGohq1Zs4aMjAx69OhB7969ee6552IdkmrItm3b6N+/PyeccAK9evXi8ccfj3VIOlCgWvfss88GjRo1Ch5//PHg448/DsaOHRsceeSRweeffx7r0OqcefPmBbfddlswZ86cAAjmzp1b6v277747aN68eTBnzpzgww8/DC655JIgJSUl2Lp1a0mba665JujcuXMwf/78YOnSpUFmZmbQp0+f4Ntvvy1pc8455wQ9e/YMlixZEixZsiTo2bNncN5559XW10xoZ599djBt2rRg2bJlQV5eXnDuuecGXbp0CbZv317SxjzF1osvvhj85S9/CT755JPgk08+CX79618HjRo1CpYtWxYEgfmJJ2+//XaQnp4e9O7dOxg7dmzJeXOk+mDPnj1Bt27dgrVr1wZbt24Njj322GDTpk2xDks1ZMGCBcGLL74YXHTRRbEORTXsiy++CN5///0gCILgyy+/DDp37lzqOVF1x7fffhvs2LEjCIIg2LFjR9C1a9dg48aNMY5K+7OAFAMnn3xycM0115Q6d/zxxwe33HJLjCKqHw4sIBUVFQUdO3YM7r777pJz33zzTZCcnBz87ne/C4IgCLZs2RI0atQoePbZZ0va5OfnBw0aNAhefvnlIAiC4OOPPw6A4B//+EdJmzfffDMAgn//+981/K3qnvXr1wdAsGjRoiAIzFO8atWqVfDEE0+Ynziybdu2oFu3bsH8+fOD0047raSAZI5UX7zxxhvBBRdcUPL6+uuvD2bMmBHDiFTTFi5caAGpHurVq1ewevXqWIehGrZp06agS5cuwYYNG2IdivbjFLZatnv3bt577z3OOuusUufPOusslixZEqOo6qeVK1eybt26Urlo0qQJp512Wkku3nvvPfbs2VOqTadOnejZs2dJmzfffJPk5GQGDBhQ0uZ73/seycnJ5vQQFBYWAtC6dWvAPMWbcDjMs88+y44dOxg4cKD5iSPXXXcd5557LmeccUap8+ZIiaKyaedQ8RIAX3zxBZ07dy55nZqaSn5+fm2Ermo63FwrsUQz3++++y5FRUWkpaXVcNQ6FNHI9ZYtW+jTpw+pqancfPPNtG3btpaiV1VYQKplGzduJBwO06FDh1LnO3TowLp162IUVf1U/P+7olysW7eOxo0b06pVqwrbtG/fvsz127dvb06rKQgCbrjhBk499VR69uwJmKd48eGHH3LUUUfRpEkTrrnmGubOnUuPHj3MT5x49tlnWbp0KRMmTCjznjlSotixYwd9+vThkUceKff9WbNmMW7cOG677Tbef/99Bg8ezLBhw1i9ejUQ6UMOFAqFajRmHZrDzbUSS7TyvWnTJn74wx/y2GOP1UbYOgTRyHXLli354IMPWLlyJTNmzODLL7+srfBVBQ1jHUB9deADTRAEPuTEyKHk4sA25bU3p9X385//nH/+85+8/vrrZd4zT7HVvXt38vLy2LJlC3PmzOGKK65g0aJFJe+bn9hZs2YNY8eO5dVXX6Vp06YHbWeOFO+GDRvGsGHDDvr+Aw88wI9//GOuuuoqACZNmsQrr7zCo48+yoQJE+jcuXOpEUdr164tNWJO8eNwc63EEo1879q1iwsvvJBbb72VQYMG1Urcqr5o/mx36NCB3r1789prr3HxxRfXaNyqOkcg1bK2bduSlJRU5l9r169fX+Zfh1WzOnbsCFBhLjp27Mju3bvZvHlzhW3Kq4xv2LDBnFbDL37xC1588UUWLlxIampqyXnzFB8aN27Msccey0knncSECRPo06cPkydPNj9x4L333mP9+vX069ePhg0b0rBhQxYtWsRDDz1Ew4YNS/7/mSMlsqosAXDyySezbNky8vPz2bZtG/PmzePss8+ORbg6DC73UL9UJd9BEDBmzBhOP/10Lr/88liEqSioSq6//PJLtm7dCsDWrVt57bXX6N69e63HqoOzgFTLGjduTL9+/Zg/f36p8/Pnz7eaXsu6du1Kx44dS+Vi9+7dLFq0qCQX/fr1o1GjRqXaFBQUsGzZspI2AwcOpLCwkLfffrukzVtvvUVhYaE5rYIgCPj5z39OTk4OCxYsoGvXrqXeN0/xKQgCdu3aZX7iwNChQ/nwww/Jy8srOU466SQuu+wy8vLy+M53vmOOlPCqsgRAw4YNuf/++8nMzKRv37788pe/pE2bNrEIV4ehqss9nH322Vx88cXMmzeP1NRU3nnnndoOVVFQlXy/8cYbzJo1i+eff54TTjiBE044gQ8//DAW4eowVCXXa9euZciQIfTp04dTTz2Vn//85/Tu3TsW4eognMIWAzfccAOXX345J510EgMHDuSxxx5j9erVXHPNNbEOrc7Zvn07K1asKHm9cuVK8vLyaN26NV26dGHcuHHcdddddOvWjW7dunHXXXfRrFkzRo8eDUBycjI//vGPufHGG2nTpg2tW7fmpptuolevXiUL1X73u9/lnHPO4eqrr+b3v/89AD/5yU8477zzrJhXwXXXXceMGTN44YUXaN68eUkHkpyczBFHHEEoFDJPMfbrX/+aYcOGkZaWxrZt23j22WfJzc3l5ZdfNj9xoHnz5iVrhhU78sgjadOmTcl5c6S6orKpmMOHD2f48OG1HZZqQGW5fuWVV2o7JNWgivJ96qmnUlRUFIuwVAMqynW/fv3Iy8uLQVSqKgtIMXDJJZewadMm7rjjDgoKCujZsyfz5s3j6KOPjnVodc67775LZmZmyesbbrgBgCuuuILp06dz8803s3PnTq699lo2b97MgAEDePXVV2nevHnJZx588EEaNmzIyJEj2blzJ0OHDmX69OkkJSWVtHnmmWe4/vrrS4ZkDh8+/KCLx6m0Rx99FICMjIxS56dNm8aYMWMAzFOMffnll1x++eUUFBSQnJxM7969efnllznzzDMB85MIzJESnUsA1B/mun4x3/WHua4bQkF5W1ZIkiRJMRIKhZg7dy4XXHBBybkBAwbQr18/pk6dWnKuR48ejBgxwoWVE5i5rl/Md/1hrusmRyBJkiQp5iqbdu4SAHWHua5fzHf9Ya7rPkcgSZIkKeZyc3NLTTsvVjztHGDq1KlMnDixZAmABx98kCFDhtRypDpc5rp+Md/1h7mu+ywgSZIkSZIkqUINYh2AJEmSJEmS4psFJEmSJEmSJFXIApIkSZIkSZIqZAFJkiRJkiRJFbKAJCmmcnNzCYVCbNmypdbvHQqFCIVCtGzZMirXev7556vcvvh7h0IhLrjggsO+vyRJkiTVJAtIkmpNRkYG48aNK3Vu0KBBFBQUkJycHJOYpk2bxvLlyw/7OgUFBQwbNqzK7Yu/98iRIw/73pIkSZJU0xrGOgBJ9Vvjxo3p2LFjzO7fsmVL2rdvf9jXqe53KP7eRxxxBLt27Trs+0uSJElSTXIEkqRaMWbMGBYtWsTkyZNLpm6tWrWqzBS26dOn07JlS1566SW6d+9Os2bNyM7OZseOHTz55JOkp6fTqlUrfvGLXxAOh0uuv3v3bm6++WY6d+7MkUceyYABA8jNza12nOPHj+eEE07gj3/8I126dOGoo47iZz/7GeFwmIkTJ9KxY0fat2/PnXfeWepz+09hW7VqFaFQiJycHDIzM2nWrBl9+vThzTffPNT/fZIkSZIUU45AklQrJk+ezPLly+nZsyd33HEHAO3atWPVqlVl2n799dc89NBDPPvss2zbto2srCyysrJo2bIl8+bN47PPPuOiiy7i1FNP5ZJLLgHgyiuvZNWqVTz77LN06tSJuXPncs455/Dhhx/SrVu3asX6n//8h7/+9a+8/PLL/Oc//yE7O5uVK1dy3HHHsWjRIpYsWcKPfvQjhg4dyve+972DXue2227jvvvuo1u3btx2222MGjWKFStW0LChf/RKkiRJSiyOQJJUK5KTk2ncuDHNmjWjY8eOdOzYkaSkpHLb7tmzh0cffZS+ffsyZMgQsrOzef311/nDH/5Ajx49OO+888jMzGThwoVApOAzc+ZMnnvuOQYPHswxxxzDTTfdxKmnnsq0adOqHWtRURF//OMf6dGjB+effz6ZmZl88sknTJo0ie7du3PllVfSvXv3Skc43XTTTZx77rkcd9xx3H777Xz++eesWLGi2vFIkiTtz01I3IREigX/GVxS3GnWrBnHHHNMyesOHTqQnp7OUUcdVerc+vXrAVi6dClBEHDccceVus6uXbto06ZNte+fnp5O8+bNS90rKSmJBg0alDpXfP+D6d27d8l/p6SkALB+/XqOP/74asckSZLqp4yMDE444QQmTZpUci4eNiH5/ve/f9jXKSgooFWrVlVuX/y9x44d6xqSUgxYQJIUdxo1alTqdSgUKvdcUVEREBkxlJSUxHvvvVdmVNP+Raeaun9VrhMKhUpilSRJOhxuQuImJFIsOIVNUq1p3LhxqYWvo6Vv376Ew2HWr1/PscceW+qI5cOVJEnS4XATEjchkeKJI5Ak1Zr09HTeeustVq1axVFHHUXr1q2jct3jjjuOyy67jB/+8Ifcf//99O3bl40bN7JgwQJ69eoVlSHWkiRJtc1NSNyERIon/hRKqjU33XQTV1xxBT169GDnzp2sXLkyateeNm0a//M//8ONN95Ifn4+bdq0YeDAgRaPJElSwjpwE5KKFG9CUryOZHZ2Nk8//TRffvklRx11FD169CjZhOSSSy4p2YRk7dq1dOrUCYg8q7388stMmzaNu+66q1qxFm9C0rx585J7ffLJJ8ybN48GDRrQvXt37rnnHnJzcyssIBVvQgJw++2381//9V+sWLHCNSSlOGABSVKtOe6448oMQ05PTycIgpLXY8aMYcyYMaXajB8/nvHjx5c6N3369FKvGzVqxO23387tt99+WDFW5V5AmeHd+3+HA78TRNYKOPCcJElStLgJiaSaZgFJUr02atQo2rRpw9q1a2v1vosXL2bYsGHs2rWr5F/ZJEmSDpWbkEiqaRaQJNVbn376KUCZh6bacNJJJ5GXlwcc2kOaJEmqH2pjE5LBgwdH/fqS6h4LSJLqrWOPPTZm9z7iiCNien9JkpQY3IREUrxoUHkTSZIkSVIs3HTTTSQlJdGjRw/atWvH6tWro3btadOm8cMf/pAbb7yR7t27M3z4cN566y3S0tKidg9JdUcocFVXSZIkSVIVhEIh5s6dywUXXBCzGMaMGcOWLVt4/vnnYxaDVB85AkmSJEmSVGWjRo0iNTW11u+7ePFijjrqKJ555plav7ckRyBJkiRJkqpoxYoVQGQTkq5du9bqvXfu3El+fj4Q2YSkY8eOtXp/qb6zgCRJkiRJkqQKOYVNkiRJkiRJFbKAJEmSJEmSpApZQJIkSZIkSVKFLCBJkiRJkiSpQhaQJEmSJEmSVCELSJIkSZIkSaqQBSRJkiRJkiRVyAKSJEmSJEmSKvT/ATiZk5ridSx9AAAAAElFTkSuQmCC", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "hm1 = ml.head(xp, yp, td, layers=layer_obswell)\n", "plt.figure(figsize=(14, 6))\n", "plt.subplot(121)\n", "plt.plot(time, ho, \"ko\", label=\"Observed\")\n", "plt.plot(time, hm1[0], \"b\", label=\"TTim\")\n", "plt.xlabel(\"time [min]\")\n", "plt.ylabel(\"Drawdouwn (m)\")\n", "plt.legend(loc=\"best\")\n", "plt.subplot(122)\n", "plt.loglog(time, -ho, \"ko\", label=\"Observed\")\n", "plt.loglog(time, -hm1[0], \"b\", label=\"TTim\")\n", "plt.ylim(10, 0.01)\n", "plt.xlabel(\"time [min]\")\n", "plt.ylabel(\"Drawdouwn (m)\")\n", "plt.legend(loc=\"best\")\n", "plt.suptitle(\"TTim Aquifer Test Analysis in Unconfined Aquifer\")" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false, "jupyter": { "outputs_hidden": false } }, "outputs": [ { "data": { "text/html": [ "
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$T$ [ft$^2$/day]$S_y$$S$$k_h/k_r$
Lohman (1972)220000.200.3
AQTESOLV229800.150.0081660.25
TTim22920.1507040.157260.0059390.186627
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" ], "text/plain": [ " $T$ [ft$^2$/day] $S_y$ $S$ $k_h/k_r$\n", "Lohman (1972) 22000 0.2 0 0.3\n", "AQTESOLV 22980 0.15 0.008166 0.25\n", "TTim 22920.150704 0.15726 0.005939 0.186627" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "r = pd.DataFrame(\n", " columns=[\"$T$ [ft$^2$/day]\", \"$S_y$\", \"$S$\", \"$k_h/k_r$\"],\n", " index=[\"Lohman (1972)\", \"AQTESOLV\", \"TTim\"],\n", ")\n", "r.loc[\"Lohman (1972)\"] = [22000, 0.2, 0, 0.3]\n", "r.loc[\"AQTESOLV\"] = [22980, 0.15, 0.008166, 0.25]\n", "r.loc[\"TTim\"] = [k * H / 0.0929, Sy, Ss * H, kzoverkh]\n", "r" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This model is similar to the first model except for the `Well` function. Here, a `DischargeWell` is used and the discharge is evenly divided over all the layers. " ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "self.neq 0\n", "No unknowns. Solution complete\n", "..............................................................\n", "Fit succeeded.\n" ] } ], "source": [ "ml = ttim.Model3D(\n", " kaq=10, z=zlayers, Saq=Saq, kzoverkh=0.2, phreatictop=True, tmin=1e-4, tmax=10\n", ")\n", "Qp = Qw / nlay # deviding Qw over the layers equal\n", "w = ttim.DischargeWell(ml, xw=xw, yw=yw, rw=0.3, tsandQ=[(0, Qp)], layers=range(nlay))\n", "ml.solve()\n", "cal = ttim.Calibrate(ml)\n", "cal.set_parameter(name=\"kaq0_11\", layers=np.arange(12), initial=100, pmin=10, pmax=400)\n", "cal.set_parameter(name=\"Saq0\", layers=0, initial=0.1, pmin=0.01, pmax=1)\n", "cal.set_parameter(\n", " name=\"Saq1_11\", layers=np.arange(1, 12), initial=1e-4, pmin=1e-5, pmax=1e-3\n", ")\n", "cal.set_parameter_by_reference(\n", " name=\"kzoverkh\", parameter=ml.aq.kzoverkh[:], initial=0.2, pmin=0.01, pmax=1\n", ")\n", "cal.series(name=\"obs1\", x=xp, y=yp, layer=layer_obswell, t=td, h=ho)\n", "cal.fit()" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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$T$ [ft$^2$/day]$S_y$$S$$k_h/k_r$
Lohman (1972)220000.200.3
AQTESOLV229800.150.0081660.25
TTim22920.1507040.157260.0059390.186627
TTim uniform discharge well23009.9979540.1531230.0078750.197499
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" ], "text/plain": [ " $T$ [ft$^2$/day] $S_y$ $S$ $k_h/k_r$\n", "Lohman (1972) 22000 0.2 0 0.3\n", "AQTESOLV 22980 0.15 0.008166 0.25\n", "TTim 22920.150704 0.15726 0.005939 0.186627\n", "TTim uniform discharge well 23009.997954 0.153123 0.007875 0.197499" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# cal.parameters\n", "k, Sy, Ss, kzoverkh = cal.parameters[\"optimal\"].values\n", "r.loc[\"TTim uniform discharge well\"] = [k * H / 0.0929, Sy, Ss * H, kzoverkh]\n", "r" ] } ], "metadata": { "kernelspec": { "display_name": "artesia", "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": "3.11.10" } }, "nbformat": 4, "nbformat_minor": 4 }