diff --git a/examples/01_Binary_Precipitation.ipynb b/examples/01_Binary_Precipitation.ipynb index 8fe487f..b4ce509 100644 --- a/examples/01_Binary_Precipitation.ipynb +++ b/examples/01_Binary_Precipitation.ipynb @@ -77,7 +77,7 @@ "matrix = MatrixParameters(solutes=['ZR'])\n", "matrix.initComposition = 4e-3 # Mole fraction\n", "matrix.volume.setVolume(Va, 'VA', atomsPerCell)\n", - "matrix.nucleationSites.setNucleationDensity(dislocationDensity = 1e15)\n", + "matrix.nucleationSites.setDislocationDensity(1e15)\n", "\n", "precipitate = PrecipitateParameters('AL3ZR')\n", "precipitate.gamma = 0.1 # J/m2\n", @@ -122,7 +122,7 @@ "\tAL3ZR\t0.000e+00\t\t0.0000\t\t0.0000e+00\t5.7737e+03\n", "\n", "N\tTime (s)\tSim Time (s)\tTemperature (K)\tMatrix Comp\n", - "4069\t1.8e+06\t\t24.5\t\t723\t\t0.0127\n", + "4069\t1.8e+06\t\t29.1\t\t723\t\t0.0127\n", "\n", "\tPhase\tPrec Density (#/m3)\tVolume Frac\tAvg Radius (m)\tDriving Force (J/mol)\n", "\tAL3ZR\t1.596e+22\t\t1.5498\t\t5.8031e-09\t3.4655e+02\n", diff --git a/examples/02_Multicomponent_Precipitation.ipynb b/examples/02_Multicomponent_Precipitation.ipynb index 6609bbe..a399177 100644 --- a/examples/02_Multicomponent_Precipitation.ipynb +++ b/examples/02_Multicomponent_Precipitation.ipynb @@ -56,7 +56,7 @@ "matrix = MatrixParameters(['AL', 'CR'])\n", "matrix.initComposition = [0.098, 0.083]\n", "matrix.volume.setVolume(Va, 'VA', 4)\n", - "matrix.nucleationSites.setNucleationDensity(bulkN0=1e30)\n", + "matrix.nucleationSites.setBulkDensity(1e30)\n", "\n", "precipitate = PrecipitateParameters('FCC_L12')\n", "precipitate.gamma = 0.023\n", @@ -137,13 +137,13 @@ "\tFCC_L12\t0.000e+00\t\t0.0000\t\t0.0000e+00\t2.4244e+02\n", "\n", "N\tTime (s)\tSim Time (s)\tTemperature (K)\tAL\tCR\t\n", - "5000\t1.3e+04\t\t13.2\t\t1073\t\t8.8453\t8.5602\t\n", + "5000\t1.3e+04\t\t32.9\t\t1073\t\t8.8453\t8.5602\t\n", "\n", "\tPhase\tPrec Density (#/m3)\tVolume Frac\tAvg Radius (m)\tDriving Force (J/mol)\n", "\tFCC_L12\t6.195e+20\t\t11.2806\t\t3.2974e-08\t9.0518e+00\n", "\n", "N\tTime (s)\tSim Time (s)\tTemperature (K)\tAL\tCR\t\n", - "7691\t1.0e+06\t\t20.3\t\t1073\t\t8.8171\t8.5673\t\n", + "7691\t1.0e+06\t\t50.6\t\t1073\t\t8.8171\t8.5673\t\n", "\n", "\tPhase\tPrec Density (#/m3)\tVolume Frac\tAvg Radius (m)\tDriving Force (J/mol)\n", "\tFCC_L12\t8.583e+18\t\t11.6262\t\t1.3883e-07\t2.1529e+00\n", @@ -232,13 +232,13 @@ "\tFCC_L12\t0.000e+00\t\t0.0000\t\t0.0000e+00\t2.4244e+02\n", "\n", "N\tTime (s)\tSim Time (s)\tTemperature (K)\tAL\tCR\t\n", - "5000\t1.3e+04\t\t13.3\t\t1073\t\t8.8606\t8.5200\t\n", + "5000\t1.3e+04\t\t32.9\t\t1073\t\t8.8606\t8.5200\t\n", "\n", "\tPhase\tPrec Density (#/m3)\tVolume Frac\tAvg Radius (m)\tDriving Force (J/mol)\n", "\tFCC_L12\t6.242e+20\t\t11.4616\t\t3.3070e-08\t9.0275e+00\n", "\n", "N\tTime (s)\tSim Time (s)\tTemperature (K)\tAL\tCR\t\n", - "7688\t1.0e+06\t\t20.5\t\t1073\t\t8.8335\t8.5244\t\n", + "7688\t1.0e+06\t\t50.4\t\t1073\t\t8.8335\t8.5244\t\n", "\n", "\tPhase\tPrec Density (#/m3)\tVolume Frac\tAvg Radius (m)\tDriving Force (J/mol)\n", "\tFCC_L12\t8.686e+18\t\t11.8159\t\t1.3905e-07\t2.1499e+00\n", @@ -273,7 +273,7 @@ "outputs": [ { "data": { - "image/png": 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", + "image/png": 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", "text/plain": [ "
" ] diff --git a/examples/03_Multiphase_Precipitation.ipynb b/examples/03_Multiphase_Precipitation.ipynb index 0930637..7bb3262 100644 --- a/examples/03_Multiphase_Precipitation.ipynb +++ b/examples/03_Multiphase_Precipitation.ipynb @@ -130,14 +130,6 @@ "execution_count": 4, "metadata": {}, "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Nucleation density not set.\n", - "Setting nucleation density assuming grain size of 100 um and dislocation density of 5e+12 #/m2\n" - ] - }, { "name": "stderr", "output_type": "stream", @@ -161,7 +153,7 @@ "\tU2_PHASE\t0.000e+00\t\t0.0000\t\t0.0000e+00\t7.1719e+03\n", "\n", "N\tTime (s)\tSim Time (s)\tTemperature (K)\tMG\tSI\t\n", - "10000\t6.1e+04\t\t154.0\t\t523\t\t0.0631\t0.2068\t\n", + "10000\t6.1e+04\t\t166.9\t\t523\t\t0.0631\t0.2068\t\n", "\n", "\tPhase\tPrec Density (#/m3)\tVolume Frac\tAvg Radius (m)\tDriving Force (J/mol)\n", "\tMGSI_B_P\t2.552e+22\t\t1.0228\t\t4.5242e-09\t7.9550e+02\n", @@ -171,7 +163,7 @@ "\tU2_PHASE\t0.000e+00\t\t0.0000\t\t0.0000e+00\t-3.5897e+02\n", "\n", "N\tTime (s)\tSim Time (s)\tTemperature (K)\tMG\tSI\t\n", - "17084\t9.0e+04\t\t241.1\t\t523\t\t0.0566\t0.2032\t\n", + "17084\t9.0e+04\t\t268.5\t\t523\t\t0.0566\t0.2032\t\n", "\n", "\tPhase\tPrec Density (#/m3)\tVolume Frac\tAvg Radius (m)\tDriving Force (J/mol)\n", "\tMGSI_B_P\t4.536e+21\t\t1.0329\t\t7.6846e-09\t4.6375e+02\n", diff --git a/examples/04_Precipitation_with_Elastic_Energy.ipynb b/examples/04_Precipitation_with_Elastic_Energy.ipynb index 502379b..fcdfd6c 100644 --- a/examples/04_Precipitation_with_Elastic_Energy.ipynb +++ b/examples/04_Precipitation_with_Elastic_Energy.ipynb @@ -53,7 +53,7 @@ "matrix = MatrixParameters(['TI'])\n", "matrix.initComposition = 0.019\n", "matrix.volume.setVolume(7.11e-6, 'VM', 4)\n", - "matrix.nucleationSites.setNucleationDensity(bulkN0=1e30)\n", + "matrix.nucleationSites.setBulkDensity(1e30)\n", "\n", "precipitate = PrecipitateParameters('CU4TI')\n", "precipitate.gamma = 0.035\n", @@ -119,7 +119,7 @@ "\tCU4TI\t0.000e+00\t\t0.0000\t\t0.0000e+00\t1.9660e+03\n", "\n", "N\tTime (s)\tSim Time (s)\tTemperature (K)\tMatrix Comp\n", - "4127\t1.0e+05\t\t57.4\t\t623\t\t0.1758\n", + "4127\t1.0e+05\t\t53.9\t\t623\t\t0.1758\n", "\n", "\tPhase\tPrec Density (#/m3)\tVolume Frac\tAvg Radius (m)\tDriving Force (J/mol)\n", "\tCU4TI\t1.476e+23\t\t9.3686\t\t5.0967e-09\t1.2302e+02\n", diff --git a/examples/13_Nucleation_Rate.ipynb b/examples/13_Nucleation_Rate.ipynb new file mode 100644 index 0000000..6a5e390 --- /dev/null +++ b/examples/13_Nucleation_Rate.ipynb @@ -0,0 +1,169 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 13 Nucleation Rate\n", + "\n", + "This example will compute the nucleation rate with various precipitate parameters.\n", + "\n", + "Note: the `computeSteadyStateNucleation` function does not account for the number of nucleation sites\n", + "\n", + "### Comparing multiple phases in Al-Mg-Si system" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "from kawin.thermo import MulticomponentThermodynamics\n", + "from kawin.precipitation.NucleationRate import computeSteadyStateNucleation\n", + "from kawin.precipitation.PrecipitationParameters import MatrixParameters, PrecipitateParameters\n", + "\n", + "phases = ['FCC_A1', 'MGSI_B_P', 'MG5SI6_B_DP', 'B_PRIME_L', 'U1_PHASE', 'U2_PHASE']\n", + "therm = MulticomponentThermodynamics('AlMgSi.tdb', ['AL', 'MG', 'SI'], phases)\n", + "\n", + "matrix = MatrixParameters(['MG', 'SI'])\n", + "matrix.volume.setVolume(1e-5, 'VM', 4)\n", + "\n", + "gamma = {\n", + " 'MGSI_B_P': 0.18,\n", + " 'MG5SI6_B_DP': 0.084,\n", + " 'B_PRIME_L': 0.18,\n", + " 'U1_PHASE': 0.18,\n", + " 'U2_PHASE': 0.18\n", + " }\n", + "\n", + "precipitates = []\n", + "for p in phases[1:]:\n", + " params = PrecipitateParameters(p)\n", + " params.gamma = gamma[p]\n", + " params.volume.setVolume(1e-5, 'VM', 4)\n", + " precipitates.append(params)\n", + "\n", + "fig, ax = plt.subplots(figsize=(5,5))\n", + "T = np.linspace(50, 400, 100) + 273.15\n", + "for prec in precipitates:\n", + " nucRate = computeSteadyStateNucleation(therm, [0.0072, 0.0057], T, prec, matrix)\n", + " ax.plot(nucRate.nucleation_rate, T-273.15, label=prec.name)\n", + "ax.set_ylim([100, 400])\n", + "ax.set_xlim([1e-24, 1e-3])\n", + "ax.set_xscale('log')\n", + "ax.set_xlabel(r'Nucleation Rate ($s^{-1}$)')\n", + "ax.set_ylabel(r'Temperature ($\\degree C $)')\n", + "ax.legend()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Comparing nucleation on different nucleation sites for Al-Zr" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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9Ntu3b2fy5MlkZWXx/fff89NPP9G6dWvA8IdGxYoVC2zjvffeMz6uXLky8+fPp3HjxmRlZeHk5GRc9tVXXz30M48bN45Zs2bx6quvAoaf77lz5/juu+/o3bs3MTExVKtWjRYtWiCTyahUqVKxP6cllImWs0ajoVu3bkiSxKJFi0p8f6NHjyY9Pd14u379eonvUxCEsufQoUOcOHGC2rVrk5eXV2DZvUf07vj2229p2LAh3t7eODk5sWTJEmJiYh66j/tbif7+/iQmJj70PXXr1sXBwcH4vGnTpmRlZXH9+nWuXLmCRqOhefPmxuUqlYpnnnmGyMjIh273fsHBwcZgvr+2K1eukJ+fT5MmTYzLPTw8Cv2Bc/ToUTp37kxQUBDOzs60bNkSoNDPpaif5x3Z2dlcuXKFvn374uTkZLxNmjSJK1euAIZe7CdOnCA0NJShQ4eyZcsWkz5rabP6lvOdYI6OjmbHjh0F5s/08/Mr9Euq1WpJSUnBz8/PuE5CQkKBde48v7PO/WxtbbG1tTXnxxAE4UFUDoYWrKX2XQxVq1ZFJpNx4cKFAq9XrlwZAHt7+0Lvuf/w9y+//MKIESOYNWsWTZs2xdnZmZkzZxpPwT2wRJWqwHOZTIZery9W3Y9LLpcj3XfIv6hObU9aW3Z2Nu3bt6d9+/asWrUKb29vYmJiaN++Pfn5+QXWfdDpBMB4hc7SpUsL/DEAoFAoAGjQoAFRUVFs3LiRbdu20a1bN9q0acPvv/9e7HpLk1W3nO8E86VLl9i2bVuhnnlNmzYlLS2No0ePGl/bsWMHer3e+A/UtGlT9uzZU+AXa+vWrYSGhhZ5SFsQhFImkxkOLVvidt954gfx9PSkbdu2LFiwgOzs7Mf6mPv376dZs2YMHDiQ+vXrU7VqVWOrztxOnjyJWq02Pj948CBOTk4EBgZSpUoVbGxs2L9/v3G5RqPh8OHD1KpVCwBvb28yMzMLfNYTJ06YVEOVKlVQqVQF/vhITU3l4sWLxufnz58nOTmZadOm8dxzz1GjRo1HHhUoiq+vLwEBAVy9epWqVasWuN17WtPFxYXu3buzdOlSfv31V9auXWvyefbSYtGWc1ZWFpcvXzY+j4qK4sSJE3h4eODv78/rr7/OsWPH2LBhAzqdznge2cPDAxsbG2rWrEmHDh344IMPWLx4MRqNhsGDB9OjRw8CAgznsN58800mTJhA3759GTVqFGfOnGHevHnMmTPHIp9ZEISyaeHChTRv3pxGjRoxfvx4wsPDkcvlHD58mPPnz9OwYcOHvr9atWqsWLGCzZs3ExISwsqVKzl8+HChPjHmkJ+fT9++ffnyyy+5du0a48aNY/DgwcjlchwdHfnwww8ZOXIkHh4eBAUFMWPGDHJycujbty8ATZo0wcHBgc8//5yhQ4cSERFh8vXXTk5O9O3bl5EjR+Lp6YmPjw9ffPEFcvndNmFQUBA2NjZ88803DBgwgDNnzjBx4sTH+swTJkxg6NChuLq60qFDB/Ly8jhy5AipqakMHz6c2bNn4+/vT/369ZHL5fz222/4+flZ76VrJdoX/BHudJG//9a7d28pKiqqyGWAtHPnTuM2kpOTpZ49e0pOTk6Si4uL9O6770qZmZkF9nPy5EmpRYsWkq2trVShQgVp2rRpJtUpLqUSBPMoy5dSSZIk3bx5Uxo8eLAUEhIiqVQqycnJSXrmmWekmTNnStnZ2cb1KOLS0NzcXKlPnz6Sq6ur5ObmJn344YfSZ599VuByoKIupbr3siNJkqSuXbtKvXv3fmCNd7YxduxYydPTU3JycpI++OADKTc317iOWq2WhgwZInl5eRV5KZUkGS6dqlq1qmRvby916tRJWrJkSZGXUt1rzpw5UqVKlYzPMzMzpbfeektycHCQfH19pRkzZhT6TKtXr5aCg4MlW1tbqWnTptJff/0lAdLx48clSbqbE6mpqQX2VdT+V61aJdWrV0+ysbGR3N3dpeeff15at26dJEmStGTJEqlevXqSo6Oj5OLiIrVu3Vo6duxYkT9Da7iUSiZJxbyWoBzLyMjA1dWV9PT0Aue8BUEwTW5uLlFRUYSEhGBnZ2fpcgShSA/7PS2tPLDqc86CIAiCUB6JcBYEQRAEKyPCWRAEQRCsjAhnQRAEQbAyIpwFQRAEwcqIcBYEQRAEKyPCWRAEQRCsjAhnQRAEQbAyIpwFQRAEwcqIcBYEQbASffr04eWXXy71/V67dg2ZTGby5BZCyRHhLAiCUEzx8fEMGzaMqlWrYmdnh6+vL82bN2fRokXk5OQ88fbnzZtn8gQTwtPJ6udzFgRBsAZXr16lefPmuLm5MWXKFMLCwrC1teX06dMsWbKEChUq0KVLlyLfq9FoCs19XBRXV1dzly2UUaLlLAiCUAwDBw5EqVRy5MgRunXrRs2aNalcuTJdu3bln3/+oXPnzsZ1ZTIZixYtokuXLjg6OjJ58mR0Oh19+/YlJCQEe3t7QkNDmTdvXoF93H9Y+4UXXmDo0KF8+umneHh44Ofnx/jx4x9Z6//+9z9q1qyJnZ0dNWrUYOHChQWWHzp0iPr162NnZ0ejRo04fvx4oW389ddfVKtWDTs7O1q1asWPP/6ITCYjLS3NuM6+fft47rnnsLe3JzAwkKFDhxaYA3rhwoXGbfj6+vL6668/snbBQLScBUGwKEmSUGvVFtm3vdIemUz2yPWSk5PZsmULU6ZMwdHRsch17t/O+PHjmTZtGnPnzkWpVKLX66lYsSK//fYbnp6e/Pfff/Tr1w9/f3+6dev2wH3/+OOPDB8+nIiICA4cOECfPn1o3rw5bdu2LXL9VatWMXbsWBYsWED9+vU5fvw4H3zwAY6OjvTu3ZusrCw6depE27Zt+emnn4iKimLYsGEFthEVFcXrr7/OsGHDeP/99zl+/DgjRowosM6VK1fo0KEDkyZN4ocffiApKYnBgwczePBgli1bxpEjRxg6dCgrV66kWbNmpKSksHfv3kf+rAUDEc6CIFiUWqumyeomFtl3xJsROKgcHrne5cuXkSSJ0NDQAq97eXmRm5sLwKBBg5g+fbpx2Ztvvsm7775bYP0JEyYYH4eEhHDgwAHWrFnz0HAODw9n3LhxAFSrVo0FCxawffv2B4bzuHHjmDVrFq+++qpxP+fOneO7776jd+/erF69Gr1ez/fff4+dnR21a9cmNjaWDz/80LiN7777jtDQUGbOnAlAaGgoZ86cYfLkycZ1pk6dSq9evfjoo4+Mtc2fP5+WLVuyaNEiYmJicHR0pFOnTjg7O1OpUiXq16//wM8pFCTCWRAE4TEdOnQIvV5Pr169yMvLK7CsUaNGhdb/9ttv+eGHH4iJiUGtVpOfn0+9evUeuo/w8PACz/39/UlMTCxy3ezsbK5cuULfvn354IMPjK9rtVrj+ezIyEjCw8MLzFPctGnTAtu5cOECjRs3LvDaM888U+D5yZMnOXXqFKtWrTK+JkkSer2eqKgo2rZtS6VKlahcuTIdOnSgQ4cOvPLKKzg4PPqPIUGEsyAIFmavtCfizQiL7bs4qlatikwm48KFCwVer1y5smE79oW3c//h719++YURI0Ywa9YsmjZtirOzMzNnziQi4uGf/f6OZDKZDL1eX+S6WVlZACxdupQmTQoejVAoFA/dj6mysrLo378/Q4cOLbQsKCgIGxsbjh07xq5du9iyZQtjx45l/PjxHD58GDc3N7PW8jQS4SwIgkXJZLJiHVq2JE9PT9q2bcuCBQsYMmTIA887P8z+/ftp1qwZAwcONL525coVc5aJr68vAQEBXL16lV69ehW5Ts2aNVm5ciW5ubnG1vPBgwcLrBMaGsq///5b4LXDhw8XeN6gQQPOnTtH1apVH1iPUqmkTZs2tGnThnHjxuHm5saOHTuMh9yFBxO9tQVBEIph4cKFaLVaGjVqxK+//kpkZCQXLlzgp59+4vz5849smVarVo0jR46wefNmLl68yJgxYwoFnjlMmDCBqVOnMn/+fC5evMjp06dZtmwZs2fPBgznwmUyGR988AHnzp3j33//5euvvy6wjf79+3P+/HlGjRrFxYsXWbNmjfH66zsd30aNGsV///3H4MGDOXHiBJcuXeLPP/9k8ODBAGzYsIH58+dz4sQJoqOjWbFiBXq9vtB5e6FoIpwFQRCKoUqVKhw/fpw2bdowevRo6tatS6NGjfjmm28YMWIEEydOfOj7+/fvz6uvvkr37t1p0qQJycnJBVrR5vL+++/zv//9j2XLlhEWFkbLli1Zvnw5ISEhADg5OfH3339z+vRp6tevzxdffFGgIxsYOpH9/vvvrFu3jvDwcBYtWsQXX3wBgK2tLWA4F757924uXrzIc889R/369Rk7diwBAQEAuLm5sW7dOl588UVq1qzJ4sWL+fnnn6ldu7bZP/PTSCZJkmTpIqxdRkYGrq6upKen4+LiYulyBKHMys3NJSoqipCQkAIdkgTrN3nyZBYvXsz169ctXUqJe9jvaWnlgTjnLAiCIBSycOFCGjdujKenJ/v372fmzJnGQ9ZCyRPhLAiCIBRy6dIlJk2aREpKCkFBQXzyySeMHj3a0mWVGyKcBUEQhELmzJnDnDlzLF1GuSU6hAmCIAiClRHhLAiCIAhWRoSzIAiCIFgZEc6CIAiCYGVEhzATaBIS0EkScnt7ZDY2li5HEARBeEqJcDbB1Y7/h9OdIfqUSuT29sabzMEBuZ3d7cf2yO0d7ll273M7ZPa3nzvcXm5vj9zhnvXt7JDJxUENQRCE8kqEsymUSrgzoJpWiz4zE31mZonsSmZnZwhsZycUTs7IXZwN987OKJzv3Dshd3YxrGO8v7uOTCn+eQWhLOnTpw9paWmsX7/e0qUIFia+vU0QGnEQZzs79Gq14ZajRq/OQbrzXK2++/jeZTlFLFffXn7vstuTtgNIubnocnPRpaSgecx6Zfb2d8PayQm5i4sh0J2cUbg4I3dyNgS6iwtypzvBfnsdZ2fkjo6iBS8I94iPj2fq1Kn8888/xMbG4urqStWqVXnrrbfo3bv3E89VPG/ePMSIygKIcDaZzMYGhY0NitsTl5uTpNcbwjs31xDY2TnoszLRZWSgz8pCl5mJPiPT8FpmFvrMjNv3mYZlmZnosrKQcnIM21Or0arV8ICJ2R9JJkPu5FSwZX5/K/52yN9pxRuD/U7r3c7OOIuNIJRlV69epXnz5ri5uTFlyhTCwsKwtbXl9OnTLFmyhAoVKtClS5ci36vRaArNy1wU1xL4XnlS+fn52JRQH5uS3HZZJ8LZBGP2j8HN1Q07hR12SsPNXmGPndIOR5UjjipHnFROONo44qh0xMnGCUeVI3aK4gWUTC5H5uiI/DHmir2XpNGgy8oyBHpGBvrMrNshfyfYDSGvy8pEn5mFLvP2OveEvKTRgCQZD91riXu8YpTKR7beFS7OKNzcUHh4oHBzR+nhjsLDA3kRE9gLgqUMHDgQpVLJkSNHCsznXLlyZbp27VqgxSuTyVi4cCEbN25k+/btjBw5kjFjxtCvXz927NhBfHw8QUFBDBw4kGHDhhnfd/9h7RdeeIHw8HDs7Oz43//+h42NDQMGDGD8+PEPrfWHH35g1qxZXL58GQ8PD1577TUWLFgAQExMDEOGDGH79u3I5XI6dOjAN998g6+vLwDjx49n/fr1DB48mMmTJxMdHY1er0cmk7F06VL++ecfNm/eTIUKFZg1a1aBP0jOnDnDyJEj2bt3L46OjrRr1445c+bg5eVl/Dx16tRBqVTy008/ERYWxo4dO5gwYQI//PADCQkJeHp68vrrrzN//vwn+vcq60Q4m2Br9FYU9g+fs7Uocpn8bnDfE+IOKocHvuZk44SD0sEY8HeW2yge/VemTKVC6e4O7u6P8zEB0Ofloc+43TJ/SLA/qPWuz8wEvR60WnSpqehSU00+PC+zt0fpbghqhYc7SnePu489PFC4exiC/PY6cicn0UovgyRJQlKrLbJvmb19sX5nkpOT2bJlC1OmTCkQzAW2dd92xo8fz7Rp05g7dy5KpRK9Xk/FihX57bff8PT05L///qNfv374+/vTrVu3B+77xx9/ZPjw4URERHDgwAH69OlD8+bNadu2bZHrL1q0iOHDhzNt2jReeukl0tPT2b9/PwB6vZ6uXbvi5OTE7t270Wq1DBo0iO7du7Nr1y7jNi5fvszatWtZt25dgXmqJ0yYwIwZM5g5cybffPMNvXr1Ijo6Gg8PD9LS0njxxRd5//33mTNnDmq1mlGjRtGtWzd27NhR4PN8+OGHxprWrl3LnDlz+OWXX6hduzbx8fGcPHny4f8g5YAIZxN81OAjZHYy1Fo1ubpccrWGm1qrJluTTbYmmyxNlvFxtiYbCQm9pCczP5PM/CfvPKaSqwq20h8W9jb3hH0Ry5XyB//zy21tkXt7o/T2fqw6JUkqdFhen3lvyN8J9tthn5aKNjUNXUoKupQUJI0GSa1Go1ajuXmzWPuUqVTGoDaEtsd9j2+HuoeHYT1XV3FO3QpIajUXGjS0yL5Djx1FVozzxJcvX0aSJEJDQwu87uXlRe7tviKDBg0qMC/ym2++ybvvvltg/QkTJhgfh4SEcODAAdasWfPQcA4PD2fcuHEAVKtWjQULFrB9+/YHhvOkSZP45JNPCrTIGzduDMD27ds5ffo0UVFRBAYGArBixQpq167N4cOHjevl5+ezYsUKvO/7/9+nTx969uwJwJQpU5g/fz6HDh2iQ4cOLFiwgPr16zNlyhTj+j/88AOBgYFcvHiR6tWrGz/DjBkzjOv8888/+Pn50aZNG1QqFUFBQTzzzDMP/HmUFyKcTdCjRg+T5u/US3pytblkabLI0mSRo8kpEN5Z+VnkaHPIyi96+b2Br9YaWhYavYa0vDTS8tKe+PPYKe4ejr8T5o5KR+NheUebB/8BcO9jB5UDclnBkJPJZCicHFE4OaLy8zOpLkOwZxuDWpuSii41BW1KCrqUVMNrqfc8TktDyslB0mjQJiaiTUwkrzg7kstvH06/r1V+b6jf20J3c0NWjPOGQvlx6NAh9Ho9vXr1Ii+v4G9do0aNCq3/7bff8sMPPxATE4NarSY/P5969eo9dB/h4eEFnvv7+5P4gH4kiYmJ3Lx5k9atWxe5PDIyksDAQGMwA9SqVQs3NzciIyON4VypUqVCwXx/LY6Ojri4uBhrOXnyJDt37sTJyanQ+65cuWIM54YNC/4h9sYbbzB37lwqV65Mhw4d6NixI507d0ZZzq82Kd+fvoTJZXIcVA44qBzwweeJtqXT68jR5hhDPVubTXZ+wZZ6kX8AFPFans7wJZKryyVXl0tybvITf1YHpQPONs642roabjaGexdbF9xs3YzPXW1dcbFxMT4u6ny8IdidUDg5QVBQsfavV6vRpaYag9wY6vcH+e3Hdw673/kDIJ8rxdqP3NUVlY83Sl8/lH6+qHz9UPr6oPLzQ+nrh8rPF7mLizi8bgKZvT2hx45abN/FUbVqVWQyGRcuXCjweuXKlQGwL2I79x/+/uWXXxgxYgSzZs2iadOmODs7M3PmTCIiIh667/s7kslkMvR6fZHrFlXH43jQofuH1ZKVlUXnzp0LHD24w9/f/4HbDgwM5MKFC2zbto2tW7cycOBAZs6cye7du4vVie5pJcK5jFDIFTjbOONs4wxP1l8MjU5jCGrt7aAvoqX+oOcFwj4/G62kBSBHm0OONoeEnASTarGR2xQZ2gXC3NbF+NzN1g1XW1cclA4FAvDOAC6qgIBi7VfKz0eblmY4H55yT6u8UAvdcK9LSzN0kEtPJy89nbxLlx+4bZmdHSpfX5R+hrBW+vgagvxOgPv6oPD0FIfUb5PJZMU6tGxJnp6etG3blgULFjBkyJAHhtfD7N+/n2bNmjFw4EDja1euFO+PwuJydnYmODiY7du306pVq0LLa9asyfXr17l+/bqx9Xzu3DnS0tKoVavWE+27QYMGrF27luDgYJNbvfb29nTu3JnOnTszaNAgatSowenTp2nQoMET1VSWiXAuh1QKFW4KN9xwe6LtSJJEvj7fGPCZ+Zmk56WTnp9uuL/ncUZeRsHX89LRSlry9fkkqZNIUieZtG+lTGkI7fuD/AEB72pjCHlnG2fkMjkyGxtUPj6ofIp3REPS6dClp6NLTkaTmIg2PgFNQjzahES08fFoEhLQxsejS0tDys0lPzqa/OjoB29QpULl7Y3S73bL+05L3M8PpY+vIdS9vcVhdCuycOFCmjdvTqNGjRg/fjzh4eHI5XIOHz7M+fPnCx2uvV+1atVYsWIFmzdvJiQkhJUrV3L48GFCQkLMWuf48eMZMGAAPj4+vPTSS2RmZrJ//36GDBlCmzZtCAsLo1evXsydOxetVsvAgQNp2bJlkYfhTTFo0CCWLl1Kz549+fTTT/Hw8ODy5cv88ssv/O9//yvQsexey5cvR6fT0aRJExwcHPjpp5+wt7enUqVKT1RPWSfCWXhsMpkMW4Uttva2eNp7mvReSZJQa9Wk5aUVCvSM/IwCIX5vwKflpZGvz0craUnJTSElN8Wk/cplcsPhdxtDK7yogPew88DTzhMvey887T1xsXFBplCg9PBA6eGBbbVqD9y+PjcXbWIimvh4tAkJaBMS0MQnoE2Iv32fgDYpCTQaNDdvPryzm0yG0ssLpa/v3UPofr7YVKiAKqgSNpWCUDg7m/T5hcdXpUoVjh8/zpQpUxg9ejSxsbHY2tpSq1YtRowYUaBFXJT+/ftz/Phxunfvjkwmo2fPngwcOJCNGzeatc7evXuTm5vLnDlzGDFiBF5eXrz++uuA4f/sn3/+yZAhQ3j++ecLXEr1pAICAti/fz+jRo2iXbt25OXlUalSJTp06ID8IUeJ3NzcmDZtGsOHD0en0xEWFsbff/+Np6dp3ylPG5kkhqN5pIyMDFxdXUlPTzepQ5hQMnK1uYVb6A8L+Nuv3+lUZyqlXImnnSee9rcDu4jHnvaeeNrdDvJHnG+WNBq0t27dDvDEe4I7Hs2dlnhiImgeffGZwt0dm6AgVJWCsLkd2DZBQaiCggwd2Kzs3Hdubi5RUVGEhIRgZ2dn6XIEoUgP+z0trTwQLWehzLkzAIyvo69J78vX5RdqlaflpRV4LS0vjeTcZJLVySTnJpOZn4lWryUhJ6FY59NVcpUxqO+0vO8NcC+72695euLsV++B4Snp9YZrw2+3wI1BHh9HfuwN8mOi0SXdQpeaijo1FXUR14XKXVywCQoqMrwVnp5WF9yCINwlwlkoN2wUNnjZe+Fl71Xs9+Tp8khRp5Ccm8wt9S2S1bfvbwf4LfUtUnJTSFYnk6nJRKPXEJ8dT3x2/KPrkdsUCnIPOw/jYy97LzwreuJZrTHuqsIDrOiystHEXic/Oob8GMM5bk10DPkxMWgTEtBnZJB75gy5Z84U2rfcwQFVpUrG8LapZGht21SqZDjXLTqrCYJFiXAWhIewVdji7+SPv5P/I9fN1eaSkptiDPF7A/3e1vgt9S2yNdnk6/OJy44jLvvRQ6PaKmzxtvfG38kfPwc//Bz9DI+d/PB/pjL+LzbHS3W3B7FerSb/+nU0MTG3w9sQ4JroGDRxcehzcsiLjCQvMrLQvmR2dthWroxtaCi2odWxCw3FNjQUpYeHaT88QRAemwhnQTATO6UdAU4BBDg9+nKuXG1uofA2Pr4nzG+pb5GjzSFPl0dsViyxWbEP3KazytkQ1o6GAPd38sevsh9+dcLxd2pPgIMPKrkKfX4+mthYQ0u7QHjHoLlxAyk3l9xz58g9d67A9hXeXthVNwS1XWh1bENDsalcGbmYuEAQzE6EsyBYgJ3SjgpOFajgVOGR66q1am6pb5GYk0hcdpzxsPmdx3HZcYbhYTWZZKZmcin1UpHbkSHD2977boA7+eH/jD9+rZrh7/gafo5+uMmd0Ny4Qd6lS+RduEjexQvkXriIJiYGXdItspNukX17TGQAlEpsQ0IKt7J9fB56Tlv0QxWsmTX8fore2sUgemsL1i5bk10osO8PcY3+0b2/bRW2+Dv64+voi7+jv/FWSemLf6IGu2vx5F24SO7FC+RduGgYaa0IClfX24Edil2NGtjXr4dNcDB6SeLixYv4+PiU+0tlBOuVnJxMYmIi1atXL3R9dmnlgQjnYhDhLJR1eklPSm7KQwP8lvrWI7fjqHIkyDmISi6VCHIOpEqeKxUTdXjEZiK7EkPuxQvkR10Dna7QexWurtjXq0d+x5fICw7GJyAARzGTmGBFJEkiJyeHxMRE3NzcCgw7eocIZysiwlkoD/J1+STkJBQK8NjMWGIyYojLjkPiwV8XLjYuVHKpRLB9BWqmOxGUJOF1Ixu7S7FozkYi3Z4YQpLJ0HbpgvTCC8gd7JHZ2CKztUGmUiEr55MdCNbBzc0NPz+/Iv9wFOFsRUQ4C4LhsrLYzFiiM6KJyYghOvP2fUb0I68B91K40SjLi7B4GypF5+JxORFFZi6Smxvc8wWo8PTErkYNbGvVwr5mDWyqVBEdzoRSpVKpHjjUKIhwtioinAXh4dRaNTEZMcRkxtwN74xoYjJjHni43DNDovoNibrxdtS6qcDnRhZyXcGvI5mNDfYNGuD0XAscWzyHbfVq4jC4YFEinK2ICGdBeHzZmmxjSzs6PbpAgKfmpRrXs9FIVI6H0FiJ0BtQ86YMx+yC566VPj44PtcCp+eew7FpUxSurqX9cYRyToSzFTH+Y5zbhYt3BbB3AztXUIgZgwThSWTkZ3A17SqX0i5xMeWi4T71Ipn5mSBJ+KdA3SiJelclakdL2GrvvleSy5BqVcO95Yu4v/AidrVri5HNhBInwtmKGP8xPnPGxfaeQ2oqR0NI27neDWw7t4Kv2XuAgwc4eN69t3UpcJ5NEIS7JEkiISeBi6kXjbdLqZeITb5KtRgt9a4awjrwvqPlakcVGXVDsG3RlKqd38TbO8gyH0B4qolwtiLGf4wpNXEhE/IynmyDcuXtsPa8G9r2Hve9dk+YO3iCjaMIdKFcy9flczX9KpdSDa3rm1dPYXf0PFXPZxJ+TcIh7551lRAZ6kByi5q4t2pDWIWG1PCogUoc7RKekAhnK1LoH0Ovg9z027e0u4/VaQVfU6eBOgVy7tySQZP9eEUobAu3wO+/2bsXfG7jYL4fgiBYqZTcFC4lRXLz0G60+w/hczgKn6R843K1DRyuJiOijg3aRrWp41eXcO9w6nrVxc+x6MtlBOFBRDhbEbP+Y2jUd4P6zk2dWvC58XZ7PW3u4+1LaV9EmD/osaeh9a4Sc+wKZZskSaScPkbMulWwfR92SXdHMcu0g4M1ZPxXS8a5QBlejj6Ee4cbbl7h1PKshYNK/FErPJgIZyti0d7akgSanPsCPaVwkKtTCr6uy3/0toti42QIaidfcPK5fe8LTt73PPYBRx8R5ILVkyQJ9YkTZPzzD2n//ouUcrd3eIoTbKsnZ2t9GelOhtazQqagpkdNng14lmf9n6WeTz1sFbaWKl+wQiKcrUiZu5RKkiA/674wLyLQ731NnQJ67aO3fS9b13sC3KfoEHfyBQcvUIiRnwTLkrRacg4fJv2ff8jcshV9hqHviF4p52p9X/6sqyHCK7VA3w5bhS31ferzrP+zPBvwLDXca6CQP3iACuHpJ8LZipS5cH4ckmTo6JZ9y3DLSjDcspNuP0685z4RdHmP3qaRrIjWeBEtcSdfw3lzcTmMUML0+flkbtlK6qpVqI8fN76uqFGNGx3qsb16Hv/dOkySOqnA+1xtXXnG7xlDWPs/S6BzoDhnXc6IcLYi5SKcTSFJhg5vxsC+HdrZiUW8lgSSvvjblitvB/W9t9sh7uwPrhXApYJhHRHighmoz54lddVqMjZsQMo3nA5SuLnh+sbrZHZ9ngjNRQ7GHeRw/GGy7+vQGeAYQBP/JjSr0IznKjyHo8rREh9BKEUinK2ICOcnoNcZDp8/KsSzEg2H1otLrgTnAHC55+Za8fbjCoZ7J18QhyCFYtKmppL2+++k/vwz2ptxAMjs7HB/8008P3gfXJ05c+sMEXERHIw7yImkE2jvORWkkqtoGtCU1kGteSHwBTzsPCz1UYQSJMLZiohwLiXa/IKH0bPvOZSeGQ8ZNw23rPjitcZlCkNru1B4B4BLxbsBLs6HC/eQtFqydu0ieen/UJ88CYDcwQH33u/g+e67KG5/B+RocjiWeIyDNw+yO3Y31zKuGbchl8lp4NOANpXa8GLgi/g7FZ56UCibRDhbERHOVkanNYR2xo3bt9uhnR5793FmHEiF5xQuRCYHJ7/bh8vvaXW7VLj72NlPDNVaDkmSRPaePSTOm0feuUgA5C4ueL73Lu5vvY3CybHAulfTr7ItehvbY7YTmRJZYFu1PWvTOqg1rYNaU9mtcql+DsG8RDhbERHOZZBedzvAb94T4Dcg/Z4wz7xZvB7qdwLcLQjcg8G9ErhVMty7Bxta5+Lw+VNLkiQyt23j1vz55F26DIDC3R2fESNwffWVIjuE3ci6wY6YHWyL3sbxxOMF5sGu4lqFl6u+TKcqnfCy9yq1zyGYR7kI5z179jBz5kyOHj1KXFwcf/zxBy+//LJxuSRJjBs3jqVLl5KWlkbz5s1ZtGgR1apVM66TkpLCkCFD+Pvvv5HL5bz22mvMmzcPJycn4zqnTp1i0KBBHD58GG9vb4YMGcKnn35a7DpFOD+l9HrDofN7W98FAvz2vV7z8O3IVeAWeDuwg+8L7xBDD3TRo7fMk3Q6MjZu4tY335AfHQ2AQ+PG+E0Yj23lB7eGb6lvsev6LrbFbCMiLsJ4nlohU/Bcxed4uerLPF/xeVRycXSmLCgX4bxx40b2799Pw4YNefXVVwuF8/Tp05k6dSo//vgjISEhjBkzhtOnT3Pu3Dns7AwDYLz00kvExcXx3XffodFoePfdd2ncuDGrV68GDD/I6tWr06ZNG0aPHs3p06d57733mDt3Lv369StWnSKcyzG9HnJuQfp1SI2G1GuQFm14nBYNaTGPbn3bOBdubd957FZJDLNaxkgaDSkrVpK0YAGSWo1MpcKzXz88+32A3PbhA5Zk5Gew+dpm1l9ez6mkU8bXPew86FS5Ey9XfZlq7tUesgXB0spFON9LJpMVCGdJkggICOCTTz5hxIgRAKSnp+Pr68vy5cvp0aMHkZGR1KpVi8OHD9OoUSMANm3aRMeOHYmNjSUgIIBFixbxxRdfEB8fj42NDQCfffYZ69ev5/z588WqTYSz8EB6naF1nXY7uO+E9p0gz4p/9DYcfQq3tr2qgWc1cPQs6U8gPKb82BvET/yK7N17ALAJDsbvqwk4PvNMsd5/Je0Kf17+k7+u/EVybrLx9TqedXil2it0qtxJDCVqhcp9OF+9epUqVapw/Phx6tWrZ1yvZcuW1KtXj3nz5vHDDz/wySefkJp6d0g+rVaLnZ0dv/32G6+88grvvPMOGRkZrF+/3rjOzp07efHFF0lJScHd3b1QLXl5eeTl3R1kIyMjg8DAQBHOguk0aki7fk94X7snvKMhL/3h77d3B8+qhqD2unNfDTwqg1IMK2lpkiSRuXkLCZMno01KApkMr8GD8BowAJmieP0QNHoN+2/s549Lf7Andg9ayXAkxtXWle6h3elZo6c4N21FSiucrfYakvh4Q4vD19e3wOu+vr7GZfHx8fj4+BRYrlQq8fDwKLBOSEhIoW3cWVZUOE+dOpUJEyaY54MI5ZvKHryrG25FUafe09q+ZnicchWSLxsOpatTIfaw4XYvmdzQQe1OWHtWvdvadvYT57hLiUwmw6VDexybNyNh+nTSf1/LrW8WoD56jICZM1B6PvrIh0qu4oXAF3gh8AWS1clsuLqBXy/8yvXM6yw5tYRlZ5bRpUoX3qn1jujpXY5YbThb0ujRoxk+fLjx+Z2WsyCYnb274RZQr/Cy/BxIuQK3LhnCOvny3cd5GXdb4pe3FnyfjRN4VikiuKsa5gUXzE7h7EzApEk4Nm5M3PgJZP/3H1GvvEqF2bNwuH3KrTg87T3pXbs3b9V8i53Xd7Ls7DJOJZ1i7aW1rL20lpYVW9K7dm8a+TYSw4Y+5aw2nP38/ABISEjA3//uBfwJCQnGw9x+fn4kJiYWeJ9WqyUlJcX4fj8/PxISEgqsc+f5nXXuZ2tri+0jOnYIQomzcQC/MMPtXpJkGJgl+dLdsL51yfA8Ndow6UncScPtfi4VwbcW+NQC39qGm2c1UNqUzmd6yrl27Ypd7drEDvuI/CtXiO7dB9/PR+PRq5dJ21HIFbSp1IY2ldpwIvEEy88uZ0fMDnbH7mZ37G7CvcIZ1mAYz/gX7/y2UPZYbTiHhITg5+fH9u3bjWGckZFBREQEH374IQBNmzYlLS2No0eP0rBhQwB27NiBXq+nSZMmxnW++OILNBoNKpXhUoWtW7cSGhpa5CFtQbB6Mhk4+xpuwS0KLtPmQ2pUwcC+ddlwn5MMGbGG26Utd98jV4JX9YKB7VPLMKKaaJ2ZzLZqVULW/ErchAlk/PU3CRMnoUtJxWvwoMdq7dbzqcdcn7lEZ0Sz8txKQ0/vW6fou6UvzSs05+MGHxPqEVoCn0SwJIt2CMvKyuLyZcNF/fXr12f27Nm0atUKDw8PgoKCmD59OtOmTStwKdWpU6cKXUqVkJDA4sWLjZdSNWrUyHgpVXp6OqGhobRr145Ro0Zx5swZ3nvvPebMmSMupRLKl5wUSLoAiWch4RwknIXEc4ZD5EWxdb2nlV0LfGob7u1cS7fuMkqSJG59u5BbCxYA4P5mT3y//BLZE07Yckt9iyWnlvDbhd/QSlpkyOhYuSOD6w2monNFc5QuPES56K29a9cuWrVqVej13r17s3z5cuMgJEuWLCEtLY0WLVqwcOFCqle/27kmJSWFwYMHFxiEZP78+Q8chMTLy4shQ4YwatSoYtcpwll4akmSYdjTxHOQcMYQ2onn4NbFB1+/7Rp4N7D9wiCgvuHyL9HKLlLK6tUkTJwEkoRLx5cImDYNmc2Tn0aIyYhhwfEFbLy2EQClXEmP0B4MqDsAV1vxB1RJKRfhXFaIcBbKHW2+IaAT72lhJ5w1jJpWFDs3Q0gH1IcKDQz3LhVEYN+W8e+/3Bj1GWg0uHTuTMD0aU/cgr7jbPJZ5h2dx4G4A4BhQJPPnvmMDsEdRKexEiDC2YqIcBaE29SpkBhpCOqEs4ZOZwlnQJdfeF1Hn7uBfSe0nXwKr1dOZO3ezfVBg0GrxaPve/iOHGnW7f938z+mH5rO1fSrADxf8Xm+bPKlmBHLzEQ4WxERzoLwENp8Q8v65jG4edxwSzhX9KxgLhUKBnZAfXAoP/Mep61fT9xnowEMvbjfeces28/X5fP96e9ZenopGr0GB6UDQxsMpUdoDxRichazEOFsRUQ4C4KJNGqIP1MwsJMuAEV83XhUhqCmEPSs4d6z6lN9OPzWkqUkzZ4NMhkV5s/DpW1bs+/jatpVxh8Yz/HE4wDU9a7LzOdnila0GYhwtiIinAXBDPIyIe7U7bC+HdopVwuv5+B1N6iDmoJ/+FM1n7YkSSRMnETq6tXIXVyo/Mc6VBUqmH0/eknP7xd/Z87ROWRpsnC1dWVKiyk8X/F5s++rPBHhbEVEOAtCCVGnQuwRiDkAMQcNj3V5BddROUDFRndb1xWfAVunordXRkgaDdfeeovck6ewr1+fSitXIFOWzLATsZmxjNw9kjPJZwB4t867DKk/RExR+ZhEOFsREc6CUEq0eXDzxN2wjjkAuWkF15EpDJdwBTWF4OYQ/BzYu1mg2CeTHxtL1MuvoM/KwnNAf3w++qjk9qXLZ9aRWaw+bxj/ob5PfWY+PxNfR99HvFO4nwhnKyLCWRAsRK+HWxcKhnVaTMF1ZHKo0BAqt4IqraBi4zJzGDzj33+5MfwTkMkIWfs7drVqlej+tlzbwrj/xpGlycLXwZfv2n5HFbcqJbrPp40IZysiwlkQrEj6jdthfQCi9hiux76XjZNhWNM7Ye1V3ao7mN0Y/gkZ//5rOLy9elWJX5sckxHD4B2DiUqPwsXGhQWtF1Dfp36J7vNpIsLZiohwFgQrlh4LV3bC1Z1wdZdhDPF7uVS4G9SVXwBH65obWRMfz5WXOiKp1QRMn4Zr164lvs+03DQG7xjMyaST2Cpsmf78dFoHtS7x/T4NRDhbERHOglBG6PWQcPpuWEcfKNzBzL8e1Pg/CO1omOTDClrVt75bQtKcOSi8vai6aRNyx5Kf2lOtVfPpnk/ZdX0Xcpmcic0n0qVKlxLfb1knwtmKiHAWhDJKo4bo/wxBfWWXIbjv5RYEof8HNTpCUDNQWGaiPn1+Plf/rxOa69fxHf0ZHr17l8p+tXotkw5OYu2ltchlcma1nEWbSm1KZd9llQhnKyLCWRCeElmJcHEznP/HENja3LvL7NygegdDUFdpXeqXa6WuWUP82HEo/fyoumWzWSbHKA5Jkhh/YDzrLq1DJVexoPUCmgU0K5V9l0UinK2ICGdBeArlZxsOf1/4Fy5sBHXK3WUKW6jc0nD4u2aXUhliVJ+fz5XWbdAmJeE/eTJur71a4vu8Q6fX8emeT9kSvQV7pT1L2i6hnk+9Utt/WSLC2YqIcBaEp5xeB9cjDC3q8/9AatTdZXIVVG0DYa9D6EtgU3Lng5O//57EmV9jGxpK5T/Xl9h+iqLRaRiyYwj7b+7H3dadNZ3X4OfoV6o1lAUinK2ICGdBKEckCZLOG0L63HqIv+c8tcrR0JoO72bo+W3m66l16elcavEckkZDyPo/sKtRw6zbfxS1Vk3vjb2JTIkkzCuM5R2WY6MoncPrZUVp5YF5JhQVBEF4Wshk4FMTnh8BA/bBwAh4bgS4VQJNNpxeA6teh1mh8M8nEBNhCHQzULi64tSqFQDp6/80yzZNYa+0Z06rObjaunL61mmmH5pe6jUIBo/VctZoNMTHx5OTk4O3tzceHk/3lG+i5SwIApJkGPv79G9wdh1kJ91d5lYJGrwN9d8G5yc7FJy5YwexAweh8Pai2q5dyBSlP9Xjvhv7GLhtIBISU1pMoXOVzqVeg7WyupZzZmYmixYtomXLlri4uBAcHEzNmjXx9vamUqVKfPDBBxw+fLjEChUEQbAomQwCG0PHGTD8PLy1Fur2NIxIlhYNOybBnNrw61twebvhmuvH4NSiBXIXF3RJt8g9c8bMH6J4WlRowYf1PgRg6qGp3FLfskgd5Vmxwnn27NkEBwezbNky2rRpw/r16zlx4gQXL17kwIEDjBs3Dq1WS7t27ejQoQOXLl0q6boFQRAsR6E0dBJ7ZTGMuAQvL4bAJqDXQuTf8NOr8E192DvbcPmWCWQ2Njg++ywAWfv3l0T1xfJB2AfU8qxFZn4mUyKmWKyO8qpYh7V79uzJl19+Se3atR+6Xm5uLsuXL8fGxob33nvPbEVamjisLQhCsSScg6PL4eQvkJdueE2uNHQia/y+YQatYoxIlvrrGuLHjcO+YUOCV/1UsjU/xIWUC/TY0AOtpGX2C7NpW6mtxWqxFmWyt3ZWVhZOTmV7ntWiiHAWBMEk+Tlw9g9DUMceuvu6f11oNhRqvfzQ0cjyY29wpU0bUCgIPXwIuYNDiZf8IPOPzWfp6aX4OPjwzyv/YKe0s1gt1sDqzjnPmTPnocszMzNp3779ExckCIJQ5tk4QP1e8P5WGLAfGvUFpT3EnYS1fQ2HvA8uNgyEUtTbK1ZA4e0FOh25Fy6UcvEF9a/bHz9HPxJzEvn1wq8WraU8KXY4f/7556xYsaLIZdnZ2XTo0IHk5OQilwuCIJRbfnWg02z4+Cy88Dk4eBrmpN40CmbXgu0TizwvfWdu59zIyNKuuABbhS0D6w4EYOnppWTmZ1q0nvKi2OG8cuVK+vfvz19//VXg9ezsbNq3b09SUhI7d+40e4GCIAhPBUdPeGGUIaT/bzZ4VIbcNNj7NcypA/9+CpkJxtXtatYEIPfcOQsVfFfnKp0JcQ0hPS+dFeeKbqQJ5lXscH799df55ptv6NmzJ7t27QLutpgTEhLYtWsX/v7+JVWnIAjC00FlD437wuAj0G0lVGxsmNby0Hcwvx5sHQc5KdhVrw5A/tWoh2+vFCjlSgbVGwTAL+d/IffeCUOEEmHSCGHvv/8+48aNo2vXruzatYuXXnqJmzdvsnPnTgICAkqqRkEQhKePXAG1ukDfrfDOn1ChEWhyYP9cmFcXVcIOADQ3b1q2zttaB7XG39GftLw0NkZttHQ5Tz2Th+/89NNP+fDDD2ndujU3btxg165dVKxYsSRqEwRBePrJZIZxut/fBj1/Ad86kJeB6vz3AGgTEpDUWZatEUPruXtodwB+Pv8zYlqGklXsmcVffbXg9GUqlQovLy+GDRtW4PV169aZpzJBEITyRCYzzHpVrT2c+wPFjsnI5DlIetDOao6q2wyobtkrYl6r9hqLTi4iMiWSc8nnqO318LEvhMdX7Jazq6trgVvPnj2pVatWodcFQRCEJyCXQ53XkA06hNzFMG6ELikWVneDVd0g+YrFSnOzc6NVoGFijs3XNlusjvKg2C3nZcuWlWQdgiAIwr0UShTuPujSotBVfwOSf4dLm+HqTmg62DBrVgnOLf0g7YLbsenaJrZEb+Hjhh8jK8aIZ4LpxJSRgiAIVkp+e8RFfc3uMPAAVGkNunzYNxsWNIZLW0u9phYVWmCvtOdG1g3OJp8t9f2XF8UK5wEDBhAbG1usDf7666+sWrXqiYoSBEEQQG5nGCpTyssFr2qGmbB6rDZMUZlxwzCv9J+DITej1GqyV9rTokILAP67+V+p7be8KVY4e3t7U7t2bTp27MiiRYs4fPgwN27cIDk5mcuXL/PXX3/x6aefEhQUxJw5cwgLCyvpugVBEMofmcwwicbAg/DsQEAGx1fCwqZwpfQGgXrG7xkADseLaYJLSrHCeeLEiVy8eJHmzZuzcOFCnn32WYKCgvDx8SE0NJR33nmHq1evsmTJEg4ePEh4eHhJ1y0IglB+2ThAh6nQ5x9wD4aMWFj5Mmz4+IHjdZtTY7/GAJxIPIFGpynx/ZVHxe4Q5uvryxdffMEXX3xBamoqMTExqNVqvLy8qFKliugUIAiCYGaSpH/4CsHN4cP/DKOKHV4KR36A6P8MI495Vy+xuiq7VsbDzoOU3BTOJp+lnk+9EttXefVYHcLc3d2pW7cuzz77LFWrVhXBLAiCUAKkHDXAw6eMtHGE//vaMMqYkx8knYclL8CZtSVWl0wmo6anYezvi6kXS2w/5ZnorS0IgmCl9NmGQ9Ryx2JcMlX5Bei/B4KfA002/P6eYTINbX6J1FbdzdAyv5R6qUS2X96JcBYEQbBSujvhfPuSqkdy9oW310OL4Ybnh76D5R0hK8nstVVzrwaIlnNJEeEsCIJghSRJQpeeDoDCxaX4b1Qooc046Pkr2LlC7GH4vg3cumzW+iq7VgYgNrN4l9kKphHhLAiCYIV0qamgMfSEVnp5mb6B0A7w/nbDNdGp1+D7thATYbb6fB19AUhSJ6HRix7b5vZY4azVatm2bRvfffcdmZmZANy8eZOsLMvPnCIIgvA00CYmAqDw8EBmY/N4G/GqZpjtKqA+qFNgRRc495dZ6vOw80ApUyIhkaxONss2hbtMDufo6GjCwsLo2rUrgwYNIinJcC5j+vTpjBgxwuwFCoIglEfahAQAlD4+T7YhJx/D9dDVO4A2F37rDad/f+L65DI53g7eACTmJD7x9oSCTA7nYcOG0ahRI1JTU7G3tze+/sorr7B9+3azFicIglBe5UfHAGATWPHJN2bjCN1XQb23QNLDug/McqmVs40zAFkacdTU3Io9CMkde/fu5b///sPmvsMswcHB3Lhxw2yFCYIglGf50dEA2FSqZJ4NKpTQ5RvD4xM/wdoPQCaH2q889iYdlIbrr9UatTkqFO5hcstZr9ej0+kKvR4bG4uzs7NZihIEQSjvjOEcHGy+jcrlhoCu1wskHfzeF87/+9ibc1AZwjlHm2OuCoXbTA7ndu3aMXfuXONzmUxGVlYW48aNo2PHjuasTRAEodzKu2y49MkmJMS8G74T0HV7GgJ6bV+4efyxNqWSqwBEb+0SYHI4f/311+zfv59atWqRm5vLm2++aTykPX369JKoURAEoVzRpqaijY8HwDY01Pw7kCugywLD/NCaHFjdHdKum7wZ/e2xv+UycVWuuZl8zjkwMJCTJ0/y66+/cvLkSbKysujbty+9evUq0EFMEARBeDx5Fy4AoAoMRFHc0cFMpVDCG8vhhw6QeNYQ0O9tArviD3giwrnkmBTOGo2GGjVqsGHDBnr16kWvXr1Kqi5BEIRyK/fsOQDsapRAq/ledi7w5q/wv9aGgP5rMLzxo2He6GLQSlpAhHNJMOknqlKpyM3NLalaBEEQBEB98iQAduHhJb8zt0DosRrkSjj3JxxdVuy35mgMHcGcVCXUui/HTP5zZ9CgQUyfPh2tVlsS9QiCIJRrkiShPnECAId69UpnpxUbQZvxhsebRkPC2WK9LTPfMEKkCGfzM/mc8+HDh9m+fTtbtmwhLCwMx/umMlu3bp3ZihMEQShvtHFxhqE7lUrs6tQpvR0/Owiu7obLW+G3PtB/L6jsHvqWO+F8ZzASwXxMDmc3Nzdee+21kqhFEASh3Ms5fBgAu5o1kZdmJ1u5HF5ZDIuaw62LsPdrePHLB66u0+tIy0sDwM3WrXRqLEdMDudly4p/PkIQBEEwTfaBgwA4Pvts6e/c0Qs6zoA178C+uVDndfCpUeSqybnJ6CQdCpkCL/vHmDVLeCjRxU4QBMFKSJJE9kFDODs828QyRdTsAtVfAr0G/h4Gen2RqyVkGybm8HbwRiFXlGaF5YLJLeeQkBBkD+lmf/Xq1ScqSBAEobzKj7qGNj4emUqFQ4MGlilCJoOOMyFqD1w/CKd/g7rdC60Wlx0HgK+Db2lXWC6YHM4fffRRgecajYbjx4+zadMmRo4caa66BEEQyp2s3bsBcGjcqHTPN9/PLRCeGw47JsKuKYbJMZQFJzuKzjCM/V3JxUwTcwgFmBzOw4YNK/L1b7/9liNHjjxxQYIgCOXVnXB2atnSwpUAz34IEd9B6jU4vgIav19gcVR6FADBLsGlX1s5YLZzzi+99BJr1z75/KCCIAjlkS4ri5yjRwErCWcbR3j+9tHQ3TPgvmkhjeHsGlzKhZUPZgvn33//HQ8PD3NtThAEoVzJ2r0bNBpsgoPNO03kk2jYB1wDISvBcO75Np1ex5X0KwBUca1ioeKebiYf1q5fv36BDmGSJBEfH09SUhILFy40a3GCIAjlRebmLQA4t2tn4UruobSBZ/rB1jFwcBHUfxtkMq5lXEOtVWOvtBfnnEuIyeHctWvXAuEsl8vx9vbmhRdeoEaNoq+HEwRBEB5Mn5ND1t69ADi3t6JwBmjwNuyaBonnDD24K7fkXLJhYo4aHjXEZVQlxORwHj9+fAmUIQiCUH5l7dmDpFajqlABu1q1LF1OQfbuUO9NOLwUDv8PKrfkbLJh7O1anlZW61PE5HPOCoWCxMTEQq8nJyejUIi/oARBEEyV/vcGAFw6dnzoOBIW0+Adw/3FzZCbwbGEYwDU9a5rwaKebiaHsyRJRb6el5eHjY1NkcsEQRCEounS0sjaswcAl86dLFzNA/iFgVd10OWRdXYdF1IvANDAx0IDpZQDxT6sPX/+fABkMhn/+9//cHK6O0WYTqdjz5494pyzIAiCiTI2bwGNBtvQUOyqV7d0OUWTyQzjbO+awsmzP6OX9FRwqoCvoxgdrKQUu+U8Z84c5syZgyRJLF682Ph8zpw5LF68mJycHBYvXmzW4nQ6HWPGjCEkJAR7e3uqVKnCxIkTC7TeJUli7Nix+Pv7Y29vT5s2bbh06VKB7aSkpNCrVy9cXFxwc3Ojb9++ZGVlmbVWQRCEx5F+e5pd1y6dLVzJI9R+BYCI9IsANPRtaMlqnnrFbjlHRRkuOG/VqhXr1q3D3d29xIq6Y/r06SxatIgff/yR2rVrc+TIEd59911cXV0ZOnQoADNmzGD+/Pn8+OOPhISEMGbMGNq3b8+5c+ewszPMRdqrVy/i4uLYunUrGo2Gd999l379+rF69WqT6hm46ihtwoNpGepDsKeDdZ4bEgShzMi7fBn1yZOgUODapYuly3k4r2rgGsh/thoAmgU0s3BBTzeZ9KCTyFagU6dO+Pr68v333xtfe+2117C3t+enn35CkiQCAgL45JNPGDFiBADp6en4+vqyfPlyevToQWRkJLVq1eLw4cM0atQIgE2bNtGxY0diY2MJCAh4ZB0ZGRm4uroS+NEa5LYOAAR62NOyujcvVPehRTUv7FSiM5wgCKZJmD6DlGXLcGrdmsBvF1i6nEe69Uc/WmUcAGB399142JW/gafu5EF6ejouLi4lth+TL6UCiI2N5a+//iImJob8/PwCy2bPnm2WwgCaNWvGkiVLuHjxItWrV+fkyZPs27fPuI+oqCji4+Np06aN8T2urq40adKEAwcO0KNHDw4cOICbm5sxmAHatGmDXC4nIiKCV155pdB+8/LyyMvLMz7PyMgA4OO21TgUm8uR6BSup6j56WAMPx2MwU4l57lq3rSt6cuLNX3wcrI1289AEISnkz4/n/Q//wTA7bXXLFxN8ex394EMqKlXlMtgLk0mh/P27dvp0qULlStX5vz589SpU4dr164hSRINzDzF2WeffUZGRgY1atRAoVCg0+mYPHkyvXr1AiA+Ph4AX9+CnRJ8fX2Ny+Lj4/Hx8SmwXKlU4uHhYVznflOnTmXChAmFXu/bojIfu7iQnaflwJVkdl9MYsf5RG6kqdl6LoGt5xKQyaBBkDttavrStpYPVbydxOFvQRAKydy8GV1KCko/P5yef87S5RTLzvwkAFpmpIE2D5SiIVJSTA7n0aNHM2LECCZMmICzszNr167Fx8eHXr160aFDB7MWt2bNGlatWsXq1aupXbs2J06c4KOPPiIgIIDevXubdV/3Gj16NMOHDzc+z8jIIDAw0Pjc0VZJm1q+tKnly1eSxLm4DLadS2RbZAKnb6RzNDqVo9GpTN90nhAvR9rU9KFNTV8aVnJHqTDbcOaCIJRhqT//AoBbtzeQKR/rIGapytXm8l+S4frmF7OzDCOGBdS3cFVPL5N/IyIjI/n5558Nb1YqUavVODk58dVXX9G1a1c+/PBDsxU3cuRIPvvsM3r06AFAWFgY0dHRTJ06ld69e+Pn5wdAQkIC/v7+xvclJCRQr149APz8/AoNmqLVaklJSTG+/362trbY2hbvL0KZTEbtAFdqB7gyrE014tLVbItMZNu5BA5cSSbqVjZL90axdG8U7g4q2tXy4//C/WlaxROVCGpBKJdyz59HfewYKJW4vf66pcsploNxB1Frc/FHSY18Ddw8IcK5BJkczo6OjsbzzP7+/ly5coXatWsDcOvWLbMWl5OTg1xeMMAUCgV6vR6AkJAQ/Pz82L59uzGMMzIyiIiIMP6R0LRpU9LS0jh69CgNGxq6/u/YsQO9Xk+TJk3MWi+Av6s9bz9bibefrURWnpY9F5PYdi6BHRcSSc3R8OuR6/x65DruDira174d1JU9RYtaEMqRlBUrAXBu2wbVfafdrNXma5sBeNEhEBlXIfmyhSt6upkczs8++yz79u2jZs2adOzYkU8++YTTp0+zbt06nn32WbMW17lzZyZPnkxQUBC1a9fm+PHjzJ49m/feew8wtFo/+ugjJk2aRLVq1YyXUgUEBPDyyy8DULNmTTp06MAHH3zA4sWL0Wg0DB48mB49ehSrp/aTcLJV0jHMn45h/mh1eg5FpfDP6Tg2nYknOTufXw5f55fD1/FwtKF9bV/+LyyAZyt7iKAWhKeY9tYtMv7+GwCPd96xcDXFo9aq2RGzA4AO3g2B3ZAWbdminnImh/Ps2bONA3hMmDCBrKwsfv31V6pVq2bWntoA33zzDWPGjGHgwIEkJiYSEBBA//79GTt2rHGdTz/9lOzsbPr160daWhotWrRg06ZNxmucAVatWsXgwYNp3bo1crmc1157zTjiWWlRKuQ0q+pFs6peTOhSm4h7gjolO5+fD13n50N3gtqPLnUDaBLigVwuOpMJwtMkdfXPSBoNdnXDcahfNg4L74ndQ442hwpOFajr94zhxVQRziXJpOucdTod+/fvJzw8HDc3txIsy7qU5HVtWp2eiKgUNpyKY9OZOFJzNMZlFdzsebl+AK/Ur0hVH6eHbEUQhLJAr1Zz+cXW6FJTqTB7Fi4dO1q6pGIZumMoO6/vpG+dvnxUsS0samaYrWrUNUuXVuqs8jpnhUJBu3btiIyMLFfhXJKUCjnNq3rRvKoXE7vW5uDVFDacusk/p+O4kabm251X+HbnFepWdOXVBhXpXDcAD0cxwYgglEVpa9ehS01FVaECzu2sbN7mB0hWJ7M31jDXdOcqnUHhaFiQmw6SZBh3WzA7kw9r16lTh6tXrxISElIS9ZRrSoWcFtW8aFHNi/FdarM9MpF1x2LZdTGJk7HpnIxNZ+KGc7wQ6sNrDSrwYk0fbJViZDJBKAskjYaUH34AwKPve2Xi8imADVc3oJW0hHmFUcWtCmjUhgWSHvKzwNbZsgU+pUz+7Zg0aRIjRoxg4sSJNGzYEEdHxwLLS7KZX57YqRT8X7g//xfuz62sPP4+eZN1x25w+kY62yIT2BaZgIudklcbVOTNJkFU9xX/QQTBmmX8+y+amzdReHjg9uqrli6nWCRJYv3l9QC8XPVlw4tKO5CrQK8xtJ5FOJcIk8fWvvfSpntHvpIkCZlMhk6nM191VqK0zjEUx6WETNYdv8H64zeIS881vt6okjs9nwni/8L9xTjfgmBlJJ2Oq506kx8VhfdHH+E1oL+lSyqWE4kneHvj29gqbNnRbQcuNre//6YGQV46DD4KXlUtW2Qps8pzzgA7d+4siTqEYqrm68yoDjUY0S6UfZdvsToimm2RiRyJTuVIdCoT/j7Lqw0q0qtJENVEa1oQrELGxk3kR0Uhd3XF/a1eli6n2H65YBjFrGNIx7vBfC9xvrnEmBzOLVu2LIk6BBMp5DJaVvemZXVvEjNyWXPEcCnWjTQ1y/+7xvL/rtE42J03mwTxf2EB2CjFtdOCYAmSTsetRYsA8OzTG4VT2bjyIiU3hS3XtgDQPbS7haspfx7rG3vv3r289dZbNGvWjBs3bgCwcuVK9u3bZ9bihOLxcbFj8IvV2PNpK5a/25h2tXxRyGUcvpbKx7+epMX0HSzYcYmU7PxHb0wQBLPK2LiJ/CtXkLu44P7WW5Yup9jWXlyLRq+hjmcdanvVLrhQ0lumqHLE5HBeu3Yt7du3x97enmPHjhmnVkxPT2fKlClmL1AoPoVcxguhPix5pxH7R73I8LbV8XG2JTEzj6+3XKTp1O2MXneKSwmZli5VEMoFSaMh6RvDgEee7/ZB4Vw2TjVpdBp+Pm+YQ+HNmm8WXKi/3UsbwKZsHAUoi0wO50mTJrF48WKWLl2KSqUyvt68eXOOHTtm1uKEx+fnasfQ1tXYN+pF5nSvS50KLuRp9fx86Dpt5+zhnR8OsftiEib2BxQEwQRp69ejiY5B4eFRZobqBNh0bRNJ6iS87b3pEHzfbIP5WcDt7w0711Kvrbww+ZzzhQsXeP755wu97urqSlpamjlqEszIRinnlfoVebleBQ5fS+X7fVfZci6BPReT2HMxieq+Tgx8oSqdwv3FmN6CYEb6vDxuLTSca/bq3w/5fZedWitJklh5zjAxR88aPVEpVAVXyE033CtsQGWHUDJM/jb28/Pj8uXCs5Hs27ePypUrm6UowfxkMhnPhHjw3duN2D2iFe81D8HJVsnFhCw++vUEbWbvZs3h6+RrxbkkQTCH1J9WoY2LQ+nnh9vtaW/LggM3DxCZEom90p43qr9ReIXsJMO9g2fpFlbOmBzOH3zwAcOGDSMiIgKZTMbNmzdZtWoVI0aMMOtczkLJCfJ0YGznWvw3+kVGtg/F3UHFteQcPl17ilZf72LlgWvkap6+69UFobTo0tK49d13AHgPG4a8mPPDW4Olp5cC8Fq113Czcyu8QoahEzAuFUqvqHLI5MPan332GXq9ntatW5OTk8Pzzz+Pra0tI0aMYMiQISVRo1BCXOxUDGpVlXebB7M6Iobv9lzlRpqaMX+e5Zsdl+n3fGXeeraSGNREEEx0a/F36DMysA0NxbVLZ0uXU2wnEk9wJOEISrmS3rV7F71Seqzh3rVi6RVWDpnccpbJZHzxxRekpKRw5swZDh48SFJSEhMnTiyJ+oRS4GCj5P3nKrP301ZM7FqbAFc7EjPzmPRPJC/M3MUvh2LQ6sThbkEojvyYGFJXrQLAZ8QIZIqy88ftklNLAOhSpQt+jn5Fr5R23XAvwrlEPXYPIBsbG5ydnfH398epjFxULzycnUrB202D2TWyFdNfC6OCmz3xGbl8tu407efuYdOZONG7WxAeIWHGDCSNBsfmzXFs0dzS5RTbyaST7L2xF4VMQd86fR+8YtJ5w71XtdIprJwyOZy1Wi1jxozB1dWV4OBggoODcXV15csvv0Sj0Tx6A4LVs1HK6d44iB0jWjKmUy3cHVRcScpmwE/HeHnhf/x35ZalSxQEq5R98CBZ27aDQoHv6M8KzD9g7RadMPQs71ylM0EuQQ9eMTHScO9dsxSqKr9MPuc8ZMgQ1q1bx4wZM2jatCkABw4cYPz48SQnJ7Po9jB1Qtlnq1TQt0UI3RpVZOmeq/xvXxQnr6fx5tIIXqzhw5hOtQjxKhuXhwhCSZO0WhKmTgPAvUcPbKuWnQkhTiSeYP/N/ShlSvqF93vwiuo0yLxpeOxTo1RqK69MDufVq1fzyy+/8NJLLxlfCw8PJzAwkJ49e4pwfgo526kY3i6Ut5sG882OS6yOiGHH+UT2XbrF+8+FMKhVVRxty8bctIJQUlJ//oW8CxeQu7riNXiQpcspNkmSmHN0DgBdq3Yl0DnwwSvHnTTcuwaJAUhKmMmHtW1tbQkODi70ekhICDY2NuaoSbBS3s62fNW1Dls+fp6W1b3J1+lZuOsKrWft5q+TN8X5aKHc0t66RdJ8wzCdPh9/hNLd3cIVFd/eG3s5lngMW4UtA+oOePjKsYcM9xUblXxh5ZzJ4Tx48GAmTpxoHFMbIC8vj8mTJzN48GCzFidYp8reTix/tzFL32lEoIeh09jQn4/TY8lBLsSLcbuF8idx1mz0mZnY1a6N2xtFDNxhpXR6HXOPzQXgzRpvPriH9h2xRwz3FRuXbGGC6Ye1jx8/zvbt26lYsSJ169YF4OTJk+Tn59O6dWteffVV47rr1q0zX6WCVZHJZLSt5ctz1bxYsucq3+68TERUCp2+2cuQF6vx4QtVUInhQIVyIOfIEdL/+AMAv7FjytSlU39f/ZtLqZdwtnGmb9hDemiDYcKL6xGGxyKcS5zJ4ezm5sZrr71W4LXAwIecoxCeanYqBUNbV+PVBhUY/9dZtkUmMnvrRTaeiWfm6+HUqSDOSwlPLyk/n7hx4wFwe+MN7G83WMqCHE0O848ZDsV/EPYBrraP+L8afxLUqYaZqALqlXyB5ZzJ4bxs2bKSqEMo4yq6O7D0nUb8dfIm4/46S2RcBl2/3c+HLaswpHVVbJVlpzUhCMWV/MMP5F+5gsLTE58Rn1i6HJMsO7uMJHUSFZ0q0qtmr0e/4cpOw33wc3D/ZBiC2YnjjoLZyGQyutarwNaPW9IxzA+dXmLBzst0/mafOBctPHXyr10zzjrl+9lnKFzLzlGi+Ox4lp9ZDsDwRsOxURSjM+/V2+Fc5cWSK0wwMjmck5OTGTRoELVq1cLLywsPD48CN0HwdrZlYa+GLOzVAC8nGy4mZNFlwT5WR8SIHt3CU0HS64n7cgxSfj6OzZrh0un/LF2SSb4+8jW5ulwa+DSgTVCbR79BnQrR/xkeV21dssUJwGMc1n777be5fPkyffv2xdfXt0yNgCOUro5h/jwT4sEna06y+2ISn/9xmv2XbzHl1TBc7cVhMaHsSluzhpwjR5DZ2+P31YQy9T0YERfB5mubkcvkfN7k8+LVfnEL6LWGUcE8q5R8kYLp4bx371727dtn7KktCA/j5WTLsj6N+d++q8zYdIF/TsdxMjaNBW82oF6gm6XLEwSTaeLiSJz5NQA+H3+MTcWyMwGERq9hasRUALqHdifUI7R4b4z8y3Bfs1MJVSbcz+TD2jVq1ECtVpdELcJTSi6X0e/5Kvz+YTMCPeyJTVXTbfEB1h2LtXRpgmASSZKIGzMWfXY29vXr497rTUuXZJKfzv3ElfQreNh5MKheMUcxy8uEy9sNj2uIcC4tJofzwoUL+eKLL9i9ezfJyclkZGQUuAnCg9QLdOOfoc/RtpYv+To9w9ecZNrG8+j04jy0UDakrfmN7H37kNna4j95Upm6pvlG1g0WnlgIwMcNP370pVN3RP4NWjV4VgV/ccS0tJgczm5ubmRkZPDiiy/i4+ODu7s77u7uuLm54V6GhqwTLMPFTsV3bzVkcCvDpACLd1+h/8ojZOVpLVyZIDxcfmwsCdOnA+D98UfYVq5s4YqKT5IkJh+cTK4ul0a+jehapWvx33zyZ8N9eA8oQ+fWyzqTzzn36tULlUrF6tWrRYcw4bHI5TJGtA+lmq8TI38/xbbIRF5b+B8r+j6Dr4udpcsThEIknY640Z8j5eRg36ghHu+8Y+mSTLI5ejN7b+xFJVcxpumY4n9vp8dC1F7D4/BuJVegUIjJ4XzmzBmOHz9OaGgxOxIIwgN0rVeBSp6O9FtxhAsJmby++D9W9X2WIE8HS5cmCAWkLF9OzuHDyBwcCJgyBZm87AwRkZabZuwE9n7Y+1R2NaHFf2wlIEGlFuBeqWQKFIpk8m9Yo0aNuH79eknUIpRD9QLdWPthMyp5OnA9Rc3ri//jYoIYsESwHrmRkSTOnQeA3+ejsQkKsnBFppl+eDopuSlUdavK+2HvF/+NOg0cXW543Pi9EqlNeDCTw3nIkCEMGzaM5cuXc/ToUU6dOlXgJgimCvRw4Lf+TQn1dSYxM49u3x3g5PU0S5clCOhzc7kxciRoNDi1aY3rffMKWLs9sXvYcHUDcpmcr5p9VbyRwO648C9kxYOjD9ToXHJFCkWSSSYO2SQv4nCOTCZDkiRkMhk6nc5sxVmLjIwMXF1dSU9Px8XFxdLlPLXScvLpvewwJ6+n4WKn5Nf+TanpL37eguXEf/UVqat/RuHtReW//ipT8zSn56Xz6p+vkqhOpE/tPnzSyMSxv5f9H0Tvg+dGQOsxJVNkGVRaeWDyOeeoqKiSqEMQcHOwYdX7Tej9wyGORqfyzg+H+H1AUyp5Olq6NKEcyti6ldTVhp7KAVOmlqlgBpgcMZlEdSLBLsEMrDfQtDffOGoIZrkSGolD2pZgcjhXqiQ6BQglx8lWyQ+9G9N9yQHOx2fy1vcR/D6gmejFLZQqzc2bxH1paC16vPceTs+1sHBFptl0bRMbozaikCmY0mIK9kp70zaw3zCVJGFvgGsF8xcoPNJjdTlcuXIlzZs3JyAggOjoaADmzp3Ln3/+adbihPLJ1UHFiveeIcjD0Ems9w+HxHXQQqmRtFpujPwUfXo6dmFh+Hw0zNIlmSQpJ4lJBycB0DesL2HeYaZtIOXq3eE6mw0xc3VCcZkczosWLWL48OF07NiRtLQ04zlmNzc35s6da+76hHLKx8WOn/o2wdvZlvPxmYxYc1LMaCWUiqR581AfPYrc0ZEKs75GZmNCJyoL00t6vtj3Bel56dT0qMmA8AGmb2TvLJD0ULUt+NY2f5FCsZgczt988w1Lly7liy++QHHP0HWNGjXi9OnTZi1OKN+CPB347u2GqBQyNp2NZ+GuK5YuSXjKZe7cSfLS/wHgP3lSmbtsauW5lRyIO4Cdwo5pz09DpTBx9reUKDhxe0SwlqPMX6BQbCaHc1RUFPXr1y/0uq2tLdnZ2WYpShDuaBDkzldd6wDw9ZYL7LqQaOGKhKeV5sYNbn42GgD3t97CpUMHC1dkmvMp55l3zHA99sjGI00bbOSOvV+DpIOqbSCwsZkrFExhcjiHhIRw4sSJQq9v2rSJmjVrmqMmQSig5zNB9HwmCEmCj389QVJmnqVLEp4y+rw8YocOM5xnDg/H99ORli7JJDmaHEbuHolGr6FVYCveqP6G6Ru5demeVvNn5i1QMFmxw/mrr74iJyeH4cOHM2jQIH799VckSeLQoUNMnjyZ0aNH8+mnn5ZkrUI5Nr5LLWr6u5Cao+HL9afF+WfBrOInTiT37FkUbm5UnDO7TJ1nliSJiQcnci3jGj4OPkxoNuHx5jzYPsHQaq7eQbSarUCxByFRKBTExcXh4+PDqlWrGD9+PFeuGM4BBgQEMGHCBPr27VuixVqKGITEOpy7mUGXBfvQ6iXm9ahH13riEg/hyaWuWUP82HEglxO4dAlOzZtbuiST/HHpD8b+NxaFTMH37b+noW9D0zcSEwE/tAOZHD78D3zEUdAHKa08KHbL+d4M79WrF5cuXSIrK4v4+HhiY2Of2mAWrEetABeGtq4GwNg/z4rD28ITU586RcJEw2VH3sOGlblgvpR6iSkRUwAYVG/Q4wWzJMHWsYbH9d8SwWwlTDrnfP+hEgcHB3x8fMxakCA8zIcvVKF2gAvpag3ztl+0dDlCGaZJSCR20GCk2+Nme/b7wNIlmSQrP4vhu4aTq8ulqX9T+oY9ZgPp9O9w/SCoHOCF0eYtUnhsJoVz9erV8fDweOhNEEqSSiFnTKdaAPx86DqXE7MsXJFQFhk6gA1Bm5SEbbWqBEybXqbmppckiTH7x3At4xp+jn5Me34actljjCmVlwVbb4+b/dxwcAkwb6HCYzNp+M4JEybg6upaUrUIQrE8W9mTNjV92RaZwLSN5/lf70aWLkkoQyRJIn78BHJPnkLu6krFb79F4VS2xm9ffnY522K2oZQrmdVyFh52j9kw2jMTMuPAPRiaitHArIlJ4dyjRw9xGFuwCp+9VIOdFxLZFpnAqdg0wiu6WbokoYxI+WEZ6X/8AXI5FefMLnMDjRy4eYC5x+YC8Fnjzwj3Dn+8DSWehwPfGh53mAYqMX69NSn2cZCydMhHePpV9XGiS13DIbhl+69ZthihzMjcto3Er78GwPezz3Bs1szCFZnmeuZ1Ru4ZiV7S06VKF7qFdnu8Den18Pcw0GsMl05VL1sDrpQHj9VbWxCswXvNQwDYcOomCRm5Fq5GsHa5585xY+SnIEm49eyB+9tvWbokk+Rochi6YyjpeemEeYUxtunYx280HVtu6ARm4wT/NwtE48vqFDuc9Xq9OKQtWJWwiq40DnZHo5NYdTDa0uUIVkyTkMD1DwciqdU4NmuG3+efl6mjgXcmtLicdhkvey/mvDAHW4Xt420sIw62jjc8fnEMuFY0W52C+TzWlJGCYC3eaRoMwPoTN8XRHaFIuqwsrvcfgDYhAZvKlakwdw4ylYkTQljYguMLjB3A5rwwB19H38fbkCTB30MhLx0CGsAzZevysfJEhLNQprWu6YOdSk5MSg7n4jIsXY5gZSSNhhvDPiLv/HkUXl4ELvkORRkb5e/vK3+z9PRSACY0m0A9n3qPv7HjP8GlLaCwhZcXgVzx6PcIFiHCWSjTHGyUtKzuDcCmM/EWrkawJpIkETd+PNn79yOztydw0SJsKpatQ7jHE48z7r9xAPSt05cuVbo8/sbSYmDT7UFGXvwCfGqYoUKhpIhwFsq8l+r4A7D1XIKFKxGsya1vFpC+dh3I5VSYPQv7sDqWLskk0RnRDN0xFI1eQ+ug1gxtMPTxN6bXwR8fQn4mBDaBpoPNV6hQIkQ4C2VesyqeAFxIyCQrT2vhagRrkLJ6NbcWLgTAb+xYnFu1snBFpknNTWXgtoGk5aVRy7MWU1pMebwRwO7YNwei94HKURzOLiNEOAtlno+LHQGudkgSnIpNs3Q5goVlbNpsnMzCa/Bg3Ht0t3BFpsnV5jJkxxBiMmOo4FSBb1t/i4PK4fE3GHsEdhomx+D/vgbPKuYpVChRIpyFp0L9IHcAjsekWbYQwaKyDx7k5siRhmuZe3THa9BAS5dkEp1ex+i9ozmZdBIXGxcWtl6Il73X428wNx3W9jXM01znNajb03zFCiVKhLPwVKgVYOiBeyVJTIRRXqlPnuT6wEFIGg3ObdviN2ZMmbqWWZIkJkdMZlvMNlRyFfNazaOyW+Un2SCsHwip18AtCDrNEYONlCEinIWnQoCbYVxgMVJY+ZR78SIx/foj5eTg2KwpAbO+RqYoW+dVF51cxG8Xf0OGjGnPTaOR3xNO6HLgWzi/ARQ28MZysBOTFpUlIpyFp4Kfiz0AcekinMub/JgYYvr2RZ+ejn3dulT85hvkNjaWLsskv57/lUUnFwHw5bNf0i643ZNtMOYgbB1reNx+ClRo+IQVCqVNhLPwVPBzvd1yFuFcrmhu3CC6Tx90SbewDQ0lcMl3yB3L1vSP/179l8kRkwEYWHfg409mcUdGHKzpffs88+vQ+H0zVCmUNhHOwlPBRmn4VdboxRCe5YUmIYHoPu+ivRmHTXAwQf9biqKMzTe/M2Ynn+/7HAmJ7qHdGVB3wJNtUJMLv74FWfHgXRM6zxPnmcsoEc7CU0F++/tHjK9dPmiTkojp3QfN9euoAgMJ+nE5Sm9vS5dlkoNxBxmxewQ6SUfnyp35vMkTTsYhSfDPJ3DjCNi5Qc/VYOtktnqF0iXCWXgqyDB8qYmG89NPm5xMzHvvkX/tGsoAfyotX4bK9zEngrCQE4knGLpjKPn6fFoHtear5l892SAjABHfwYmfQCaHN5aBxxP09BYszurD+caNG7z11lt4enpib29PWFgYR44cMS6XJImxY8fi7++Pvb09bdq04dKlSwW2kZKSQq9evXBxccHNzY2+ffuSlSUuuXma5Gp0ANgorP5XWngC2uRkYvr0Ie/SZZQ+PlRatgxVhQqWLsskp5NOM2DbANRaNU39mzLj+Rko5con2+jFzbD59rjZbb+CKi8+eaGCRVn1N1lqairNmzdHpVKxceNGzp07x6xZs3B3dzeuM2PGDObPn8/ixYuJiIjA0dGR9u3bk5t7t2NQr169OHv2LFu3bmXDhg3s2bOHfv36WeIjCSUkKSsPAB+Xx5zjVrB62lu3iO7d+24wr/gRm0qVLF2WSc4ln6P/1v5ka7Jp7NeYeS/Ow0bxhD3L407Bb++CpIcG74hxs58ST/jnWsmaPn06gYGBLFu2zPhaSEiI8bEkScydO5cvv/ySrl27ArBixQp8fX1Zv349PXr0IDIykk2bNnH48GEaNTJcN/jNN9/QsWNHvv76awICAkr3QwklIinTEM7eTiKcn0baW7eI7tOH/MtXUPr6UunH5dgEB1u6LJNcSLlAv639yNRk0sCnAQteXIC90v7JNppxE1Z3B002hLSE/5stOoA9Jay65fzXX3/RqFEj3njjDXx8fKhfvz5Lly41Lo+KiiI+Pp42bdoYX3N1daVJkyYcOHAAgAMHDuDm5mYMZoA2bdogl8uJiIgocr95eXlkZGQUuAnW7c71zaLl/PTRxMcT/dbbhmD28zO0mMtYMEcmR9J3S1/S89IJ9w5nYZuFTzZeNhiG5lz1BmTeBK9Q6LYCFCrzFCxYnFWH89WrV1m0aBHVqlVj8+bNfPjhhwwdOpQff/wRgPh4w/y9vvd1BvH19TUui4+Px8fHp8BypVKJh4eHcZ37TZ06FVdXV+MtMDDQ3B9NMLPIOMMfUFV9nC1ciWBO+bGxhmC+0/mrDB7KPnvrrDGYw7zCWNxmMY6qJ7wWW5MLv/SChDPg6AO91oC9m1nqFayDVYezXq+nQYMGTJkyhfr169OvXz8++OADFi9eXKL7HT16NOnp6cbb9evXS3R/wpM7cyMdgDq3x9gWyr68qCii33obTWwsqqAggleuxCYoyNJlmeRU0ik+2PIBmfmZ1PWuy3dtv8PZ5gn/gNTr4I9+cG0v2DjDW7+De7BZ6hWsh1WHs7+/P7Vq1SrwWs2aNYmJiQHAz88PgISEhALrJCQkGJf5+fmRmJhYYLlWqyUlJcW4zv1sbW1xcXEpcBOsV65Gx6VEQ+/7OhXK1iAUQtFyL1wg+u130MbHY1OlCpVWrixzvbKPJx4vcI7ZLMEsSbBxFJz7E+Qq6LEK/Ouap2DBqlh1ODdv3pwLFy4UeO3ixYtUun1YKyQkBD8/P7Zv325cnpGRQUREBE2bNgWgadOmpKWlcfToUeM6O3bsQK/X06RJk1L4FEJJO3wtBZ1ewsfZFv/bw3gKZVfOseNEv/0Oulu3sK1Rg0orV6Dy9Xn0G63Ifzf/K9Are1GbRU9+KBtg+wQ4vBSQwavfQeWWT75NwSpZdW/tjz/+mGbNmjFlyhS6devGoUOHWLJkCUuWLAFAJpPx0UcfMWnSJKpVq0ZISAhjxowhICCAl19+GTC0tDt06GA8HK7RaBg8eDA9evQQPbWfEjvOG46MtAr1KVNTBAqFZe3dS+yQoUi5udg3aEDg4kUoytiRqx0xOxixewQavYbmFZoz54U5T94rG2DvLNg3x/C40xzD/MzC00uycn///bdUp04dydbWVqpRo4a0ZMmSAsv1er00ZswYydfXV7K1tZVat24tXbhwocA6ycnJUs+ePSUnJyfJxcVFevfdd6XMzMxi15Ceni4BUnp6ulk+k2A+er1een7GDqnSqA3SxtNxli5HeALp//4rnasTJp0LrSFFv/+BpMvJsXRJJttwZYNU98e6Up3ldaSPd34s5WnzzLPhg4slaZyL4bZ/vnm2KTyW0soDmSSJwYgfJSMjA1dXV9LT08X5ZytzPj6DDnP3olLIOD62HU62Vn0wSHiAlJU/kTBlCkgSLh1fImDaNGRlbNrHVZGrmH5oOhISXap0YUKzCU8+8hfAkR9gw8eGxy1HQavPn3ybwmMrrTwQ32RCmfbLIUNP+tY1fEUwl0GSJJE0ew7Jt8cvcOvZA78vv0SmUFi4suKTJIkFJxaw5JThdFvPGj357JnPnnysbIAjy+4Gc7Mh8MLoJ9+mUCaIbzOhzMrV6Pjj+A0AejwjrkUvaySNhrgvx5D+558AeH80DM/+/ctUvwGdXsekiEn8fvF3AAbXG0y/8H7m+QxHlsGGjwyPmw6GthPF6F/liAhnoczadCaedLWGCm72PFetbE0XWN7psrK48dHHZO/bBwoF/l99hdtrr1q6LJPkanP5bO9nbI/ZjgwZXz77Jd1Cu5ln4/ceyn52ELSbJIK5nBHhLJRJer3E4t1XAOjeOBCFXHxxlRWa+Hiu9x9A3oULyOztqTh3Dk4ty9YlQam5qQzZMYSTSSdRyVVMe24a7YLbmWfjB76FzbfPKz87ENpPFsFcDolwFsqkbZEJnI/PxMlWSe+mwZYuRyim3MhIrvcfgDYxEYWXF4GLFmEfVsfSZZnkesZ1Ptz+IdEZ0TjbODO/1Xwa+TV69BsfRZJgz9ewc5LhefOPoM14EczllAhnocyRJIn5Owxzdr/TtBKuDmKw/7Iga88ebnz0MfqcHGyrVSVw8eIyN+rX6aTTDN4xmJTcFAIcA1jUZhGV3So/+YYlyTDAyJ3rmFt9Cc+PEMFcjolwFsqcjWfiOXMjA3uVgr4tQh79BsGiJEkideVKEqZNB70eh6bPUnHevDI3uMiWa1v4Yt8X5OpyqelRk29bf4u3gxn6Ouh18M9wOLrc8LzdZGgm5mQu70Q4C2WKOl/H5H8iAXj/uRA8xfzNVk3SaIifOIm0NWsAcH3tVfzHjStT1zBLksT3Z75n3rF5ADxX4TlmtpxpnuE4Nbmw7n2I/BtkcsN8zI3effLtCmWeCGehTFm8+wo30tQEuNox8IWqli5HeAhtaio3PvqYnIgIkMnw+fRTPPr0LlOXSuXr8plwYAJ/XfkLgLdqvsWIRiNQyM1wHXZuumHax2t7QWEDr30Ptbo8+XaFp4IIZ6HMiEnOMfbQ/uL/amFvU3YGqihv8i5f5vqgQWiiY5A7OBAw62ucW7WydFkmSVYnM3zXcI4lHkMhUzD6mdF0r9HdPBvPiIPVb0D8acO0jz1XQ8jz5tm28FQQ4SyUCTq9xIjfTpKn1dO0sicdw4qe7lOwvMxt27j56Sj0OTmoAgKouHgRdtWrW7osk5xPOc/QHUOJy47DSeXE1y2/pnmF5ubZeMJZWPUGZNwAR294a62Y9lEoRISzUCb8sC+KQ9dScLRRMOP18DJ1aLS8kPR6bn27kFvffguAQ5MmVJg7B6W7u4UrM83ma5sZs38Maq2aSi6VmN9qvnl6ZANc2Qlr3oG8DPCsBr1+Aw/RqVEoTISzYPUuJmQyc4thXu8vO9Ui0MPBwhUJ99NlZXFz1Gdk3Z5b3f2dt/EdORKZquxc5qaX9Hx74lvjGNnNA5oz/fnpuNq6mmcHx1fB30NBr4VKzaH7T+DgYZ5tC08dEc6CVcvJ1zJk9XHytXpahXrTo7EYQ9va5F26ROyQoeRfu4bMxga/8eNxe/UVS5dlkvS8dEbvHc3eG3sB6F2rNx83/Ng8Hb/0Otj+Feyfa3ge9gZ0/RaU4koD4cFEOAtWS5IkPlt7mgsJmXg72zJdHM62Oukb/iFuzBgktRqlnx8V58/DPjzc0mWZ5ELKBT7a+RGxWbHYKmwZ13Qcnat0Ns/G8zJh7QdwcaPh+XMjoNUXIDfDjFXCU02Es2C1lu2/xl8nb6KUy1jYqwE+znaWLkm4TcrPJ2HGTFJ/+gkAx2ZNCfj6a5QeZesw7YarG5jw3wRydblUcKrAnBfmUNOzpnk2nhoNP/eExLOgsDW0lsPfMM+2haeeCGfBKh24ksyUfw2DjXzxfzVpHFy2vvSfZpq4OG4M/wT18eMAeA7oj/eQIWVqDuZ8XT5fH/man8//DEDzCs2Z/pwZzy9f2wdrekPOLXDyhR6roaIZxt8Wyg0RzoLVuZiQSb+VR9DqJbrWC6BPs2BLlyTclrlrF3GjPkOXno7c2ZmA6dNxfrFsXb8cmxnLiN0jOJt8FoD+4f35sO6H5jm/LEkQsRg2fwGSDvzCoecv4Fq2xhAXLE+Es2BV4tNz6f3DITJztTQOdmf6a+I8szWQNBqS5s0j+X/fA2BXpw4V5szGJrBsddDbEbODL/d/SWZ+Jq62rkxpMYXnK5pp8I/8HPh7GJw2DFVKWDfoPA9sxNUFgulEOAtWIyNXQ59lh4hLz6WKtyNL32mEnarsHCp9Wt1/GNv97bfxGTkCeRkaH1uj1zDv6Dx+PPcjAOHe4Xz9/Nf4O/mbZwep1+DXtwwjfskUhjmYmwwQs0oJj02Es2AVsvO0vLfsMOfjDT2zl7/7DG4OZefL/2mVsXkLcWPGoM/IQO7sjP/kSbi0a2fpskxyPfM6o/aM4vSt0wC8U+sdPmrwESqFma7BPv8vrB9gGCvbwQu6/QjBLcyzbaHcEuEsWFxOvpZ3lx/mSHQqLnZKlvVpLAYasTC9Wk3ClKmk/fYbAHZhYVSYPavMHcb+9+q/fHXwK7I12TjbODOx2URaV2ptno3rNIbrl/+bb3heoZEhmF0rmmf7QrkmwlmwKHW+jr7Lj3AoKgVnWyUr+zahTgUz9ZgVHkvu+fPcGP4J+VevgkyG5/vv4z10SJka7StHk8OUiCn8eeVPABr4NGDac9PMdxg7/Qb8/h5cP2h4/uxAaDMBlOJoj2AeIpwFi8nO09Jv5REOXE3GyVbJj32foW6gm6XLKrckvZ6UFStImjUbSaNB6e1NwIzpODZtaunSTHLm1hlG7x3NtYxryGVy+of3p194P5RyM33dXdxiOIydkwy2Lobrl8VUj4KZiXAWLCI1O58+yw9z8noaDjYKlr/bmAZBZWuChKeJ5uZNbo7+3DD3MuD0wgv4T51Spiat0Oq1fH/6exafXIxW0uLj4MO056bR2K+xmXaQB9vGw8GFhud+4YbD2B5mmhRDEO4hwlkodXHpat7+/hCXE7Nwc1Cx/N1nqCdazBYhSRIZGzYQ/9VE9JmZyOzt8R01Crfu3crUJWzXM6/z+d7POZF0AoD2we0Z8+wY8w0qcusS/P6uoTc2QJMPoc14UIlR64SSIcJZKFVXkrJ45/tD3EhT4+9qx8q+z1DVx9nSZZVLurQ04r/6iox/DeM+29UNp8L06dgEB1u2MBNIksT6y+uZdmgaOdocnFROfN7kczpV7mSePy4kCY7/BBs/BU0O2HvAy4sgtMOTb1sQHkKEs1Bq/rt8iw9XHSNdraGytyMr+zahgpu9pcsqlzJ37CBu3Dh0SbdAocBr0EC8+vVDpiw7XwlJOUlMODCB3bG7AWjo25ApLaYQ4BRgnh1k3zIMKnJ+g+F5yPPwyhJwMVOnMkF4iLLzP1Eo034+FMOY9WfQ6iXqBbrxfe9GeDqJKfNKmy49nYQpU0n/09CL2aZyZQKmT8M+LMzClRWfJEn8G/UvUyKmkJGfgUquYlC9QfSp3cc8Q3ACXNwMfw6G7ESQq6DV59B8GJhr+4LwCCKchRKl00tM/TeS/+2LAqBL3QBmvB4uRv6ygKzdu4kbMxZtYiLI5Xi+9y5eQ4Ygty07fyQlq5OZdHAS22K2AVDToyaTW0ymmns18+wgLwu2fAFHlxuee9eAV5eAf13zbF8QikmEs1Bi0nLy+fjXE+y8kATA8LbVGfJi1TLV0ehpoEtLI2H6DNL/+AMAm+Bg/KdOwaF+fQtXVnySJLHp2iamRkwlNS8VpUxJv7r9eD/sfVRyM11/Hf0frB8IqYY/JGk6GF4cIzp9CRYhwlkoEadj0/lw1VFiU9XYKuXM6laXTuFmOhcoFIskSWRu3kz8xEnokpNBJsOjd2+8PxqG3K7sBE58djyTD05mV+wuAKq5V2Ny88nmm3c5P9sw0lfEd4AELhXh5YVQuaV5ti8Ij0GEs2BWkiTxy+HrjPvrLPlaPUEeDix6qwG1A8SoX6VJk5BI/FdfkbV9OwA2VargP2limWot6yU9ay+tZfaR2WRpslDKlfQL78f7dd4337jY1/bBn4MME1cANHgH2k0CO/H7KliWCGfBbNT5Osb8eYbfj8YC0KamL7O61cXVvuwM+1jWSXo9ab//TuLMr9FnZoJSiVe/fngO6F+mZpGKyYhh/IHxHI4/DEC4VzgTmk2gqntV8+wgLxO2TYDDSw3PXSpCl/lQ1UzjbgvCExLhLJjFmRvpDP3lOFeTspHLYGT7GvR/vjJyuTi/XFryLl0ibtx41MeOAWAXHo7/xInYhVa3cGXFl6/LZ9mZZSw5tYR8fT72SnuG1B/CmzXeNG9P7A3DIcPwRyQN34W2X4Gdi3m2LwhmIMJZeCJ6vcTSvVf5essFNDoJXxdb5nSvR7MqXpYurdzQq9XcWriI5GXLQKtF5uCA99AheLz9NjJF2ekVfyT+CF8d/IqodEOHrKb+TRnTdAyBzmaaCSszATaNgrOGjnG4VYLO86BKK/NsXxDMSISz8Nji03MZvuYE/11JBqB9bV+mvRqOu2PZOXxa1mXt2UP8VxPRxBpagU6tW+P3xeeoAspO57u03DRmH53NH5cNoelh58GoxqN4KeQl8/Ts1+vh+ArYOtYw57JMAU0HwQujwUZMTSpYJxHOgskkSeKvkzcZ++dZ0tUa7FUKxnepRbdGgeIyqVKiiYsjYdp0MjdvBkDp74/fl1/g3LrsnDPVS3r+vPwnc47OITUvFYA3qr/BsAbDzDcmdsI5+OcTiPnP8Ny/nuHcsrhuWbByIpwFkyRk5PLFH6fZFpkIQHhFV+Z2r0dlbycLV1Y+6PPzSVm2nFuLFyOp1aBQ4PH223gPGYzc0dHS5RXb2eSzTDk4hVO3TgFQ1a0q45qOo55PPfPsIC8Tdk2Dg4tA0oHKAV78Ep7pDwrxtSdYP/FbKhSLJEn8djSWiRvOkZmrRaWQMax1Nfq3rIJKIbd0eeVC1t69JEyaTH50NAD2DRviN3YMdqGhFq6s+NLz0pl/bD6/XfwNCQkHpQMD6w3kzZpvmmcwEUkynFPe/AVk3jS8VqMTdJgGbmY6dy0IpUCEs/BIsak5fP7HGfZcNIz0VbeiKzPfqEt1XzGbVGnIj71BwrSpZG0zXLOs8PbCd+RIXDp3LjOnEXR6Hesvr2fusbmk5aUB0DGkI580+gQfBx/z7OTWJfh3JFzdaXjuHgwvzYTq7cyzfUEoRSKchQfS6PR8vy+KedsuodbosFHKGd62Ou+3CEEpWsslTp+dza0lS0lZtgwpP994CNtr8KD/b+++w6I608aPfwcYepMiiFIFrKhRQcGuKLEnmpiY7Gpi1rzJxsTdbLKp75vyS3N3E1M3Jmv6mjVrYk+wa8SGir0roICAitI7M8/vj6ODo2JAQGb0/lwXl/CcmXPu5xycm/Ocp2Draj2PEVLOpDBr+ywOXzgMaE3YL/Z5kWj/6KY5QEUh/Po3SJ4DxhqwdYD+f4b+fwK9rHomrJMkZ3FNyWnneXnxAY6fLQEgJtSLtydE0V6eLTc7ZTRSuGQp5957j5pzWmuFc9+++L34Ao6R1jNmObskm/dS3mPlSa3Tmpvejce6P8bkTpObpgnbaIA987SpN0u180TECK0J27t94/cvRAuS5CzM5JVU8tYvh1m46zQA3i72vDiqExN6trWaJlRrVrZrN2fefpuK/fsB0AcF4ffXZ3EdNsxqzn9ZdRlfHPiCbw5+Q6WhEhudDRMjJjLjjhl4OXo1zUFObdXGLOfs1X72joA734aI4U2zfyFamCRnAWhN2PO2nWL2muMUllej08HkmCD+mtABT2cZt9zcqrKyOPfebIp++QUAGxcXfB5/jFZTpljNtJsGo4GlqUv5ePfHnC3XevNH+0fzXPRzdPBqok5r+Se1aTcPLtR+dnCHwc9D9HSws47zJER9SHK+zSml2HD0HG/8fIjUc6UAdG7jzpt3d+WOoFYtHN2tz1BQQN6cz8ifNw9VXQ06HR4TJ9D6T3/Czsd6ZlnbcnoL76a8y7H8YwC0dW3LM72fYVhQE93xl+fDxn/A9s/BUAXotEUqhv4vuPo2fv9CWBhJzrexY2eKeePnw6Ze2N4u9jw9IpL7egdKh69mZqyqIv/f88ibMwdjUREALnGxtH72WRw7NdFSiDfB0QtHeS/lPbZka5N8uNm78WjUo0zuNBkHW4fGH6CmCnbMhV9nQUWBVhY2GIb/P2jTrfH7F8JCSXK+DZ0vqeSDtceZl5yBwajQ2+qY1i+UJ4aG4+4oK0g1J2U0UvRLIudmz6b6tPZc3yEyktbPPoNL//5W81w5tzSXj3d/zNLUpSgUdjZ2TO44mUejHsXT0bPxB1AKDi3WmrDztbm28e2kLecYPgys5DwJcaMkOd9GSiprmJuUxr82plFaZQC0+bBfGNmJEB/rmV3KGimlKE1K4uzs96k8rA0psmvdGt+ZT+Fx111Ws0BFfkU+c/fPZf6R+VQZqwBICElg5h0zCXRvgkk+lNLGKa99HbJ3a2WufjDkJejxoMzuJW4b8pt+G6isMTBvWwYfrz/BhVLtA7VrW3deHNVJVo+6Ccp27eLse+9RvjMFABtXV7wfmYbX1KnYOFvHwgul1aV8e+hbvjn4DaXVWt+EXn69eLrX03TzbaLm5aydsOZVOJmk/WzvCrEzIO5JcJAhfOL2Isn5FmYwKhbtPs3s1cc4XVAOQKiPC8+M6MDIrv6y1nIzqzh6lHOz36dkwwYAdA4OtHrwQbyn/wG7VtbR2a7KUMV/j/6Xf+3/FxcqLgDQyasTT/V8in4B/ZqmGf7sYVj3BhxZrv1saw+9H4EBf5HOXuK2Jcn5FmQwKn7Zn8OHa4+bJhHxc3fgT/GR3NOrncyF3cwq09LI++Sf2rAopcDWFs8JE/B54o/o/f1bOrx6qTZWs+TEEj7f9zk5pTkABLsHM6PHDEaEjMBG1wS/Q+dTtZm99v8XlBF0NtB9sjY0yjOo8fsXwopJcr6FGIyKny8m5RMXk7KHk54/Dm7P1LgQHPXW8VzTWlWdPEnep59SuGy5toYw4DbyTnyfegqH0NAWjq5+aow1LEtdxmf7PuN0idZhrbVTax7v8Tjjw8c3zcxeF9Jh499h73xtxSjQFqcY+r/QumPj9y/ELUCS8y3AYFQs35fNh2uPm8Yquzva8Uj/MB7qF4KHk/TAbk5VmZnk/fNTCpcuBYOWbFyHDcN3xhNWMyyqxlhDYnoin+79lMziTAC8Hb15JOoR7o28F0c7x8YfJP+UlpT3fF+blCNGaHfKbXs1fv9C3EIkOVuxGoOR5fty+HDdcdIuJmUPJz1/6B/K1H4hMiyqmVVlZXH+s88oWLQYamoAcB00CJ8ZM3CK6tqywdWTwWhgxckVzNk7h5NFJwHwcvRiWtdpTOowCSe7Jlg4oiADkt6F3f/WFqYAaD8MhrwI7Xo3fv9C3IIkOVuh8ioD/92Zyb+S0sjK1zp6eTjpmT4glKlxIbhJUm5WlenpnP/scwqXLTPdKbv074/vkzNw6t69haOrn2pjNctTl/PFgS84VaStD+3h4MHDXR5mcsfJOOuboBd53gnYNBv2za9NymFDtKQcGNP4/QtxC5PkbEXyS6v4duspvtl60jQkytvFnmn9Q5kSGyxJuZlVHDvG+TmfUbRihemZsktcHD4znsC5Z88Wjq5+Kg2VLDmxhC/2f0F2aTagJeUpnafwQMcHcLVvgiFLZw5qd8oHF2kdvUCb1WvQ8xAc2/j9C3EbkORsBbILypmblM78HRmUXZw8JNDLiUcHhHFv70Dp6NXMyg8e5PycORSvXmMqcx0yBJ/H/sdq7pTLa8r58diPfH3ga9OiFN6O3jzU5SEmdZjUNHfKp1Ng47tw9OfassiRMPAZab4WooEkOVuwfVkFfLkpneX7cqgxKkBblOKxwe0Z1dVf5r9uRkopynbs4PzcuZRuvDgphk6H24gR+Dz2P1bT0auwspAfjv7AvMPzTOOU/Zz9mNZ1GhMiJjS+o5dSkLYBNn+gzewFgA663KWNU/aPatz+hbhNSXK2MDUGI6sOneHLTensPJVvKo9r781jg9ozIMLHauZftkbKaKR47VrOz51Lxd59WqGNDe6jR+PzP4/iEB7esgHWU25pLt8d+o4fj/1IWU0ZoK0U9YeoPzCu/TjsbRu5vKKhRpv7evMHkHvxPOlsodt90P/P4BvZuP0LcZuT5GwhCsuqmb8jg2+3njLN5qW31TG2WwAP9wslqp1HC0d4a1NVVRQuW8b5L76kKi0NAJ29PR4T7sb74YexDw5u4QjrJ60gjS8PfMnP6T9Tc7ETVmSrSB7u+jAJIQmNH6dcVar1ut76sdYLG0DvrC3f2PeP0Mo6zpMQlk6Scws7cbaYb7ac4seULMqrtefJXi72/K5PEL/rG0xr9yYYXyrqZCgpoeC/C7jwzTfUnDkDgI2bG60mT8Zryu+tYk1lpRR7zu3hywNfsiFzg6k82j+aaV2nNc00myXnYMe/tPWUyy+26Dh7Q5/HIPoP4OzVuP0LIcxIcm4BVTVGVh7M5d/bTpGcfsFU3tHfjWn9QxnXPUA6eTWzqqzT5H/3HQU//oixVBsjbte6NV5Tp+J53yRsXS1/oYUaYw1rTq3h20Pfsj9vPwA6dAwNGsq0rtOaZkGK3P2wbY42xaZBGyFAqxBtMYruD4C9dSzcIYS1keR8E2VeKOM/2zP4785M8kq0DzobHcR38uOhfiHEhnnL8+RmVr53L+e/+priVatMw6Hs27fH++GHcB83Dhv7Rj6LvQmKqopYeGwh847MI7c0FwB7G3vGth/L1C5TCfVo5FShRiMcXwlbP6ldIQq0WbzinoRO48BG/ngUojlJcm5mBqPi12Nn+fe2DNYfPYvSOl3T2s2B+2OCmBwTSBuPJpiFSdRJGQwUr1nLha+/pnz3blO5S1wcXg8/hEu/fuhsLL/ne2ZxJvMOz2PR8UWmTl5ejl7c3/F+JkVOwtvJu3EHqCzRptZM/hQuaM/d0dlC53Ha82SZOESIm0aSczPJyi/jx5QsFuzMMnXwAugX7s3v+gQT39lPVodqZoaCAgp+Wkj+999TfVpbxEGn1+M+dixeU6fi2MHyexQrpdieu53vD3/PhqwNGC9O6hHuGc6UzlMYFTYKB1uHxh3kfCrsmAu750FloVbm6AE9p0LMo+AZ2MhaCCEayqqS8zvvvMMLL7zAzJkzef/99wGoqKjgL3/5C/Pnz6eyspKEhAT++c9/4ufnZ3pfRkYGjz/+OOvXr8fV1ZWpU6fy9ttvY2fXtNWvqDaw6tAZFuzMZNOJPNNdsoeTnnt6tePBPkGE+Vr+s0xrV3H0KPn/nkfhsmWoigoAbD09afXAZFpNnoydr+WvEVxWXcay1GX858h/SC1MNZX3a9uPKZ2nENsmtnGPQIwGOLZS6+SVuq623Dtc6+TVfTI4yO+qEC3FapLzjh07+Oyzz+jWzbyTy5///Gd+/vlnFixYgIeHBzNmzGDChAls3rwZAIPBwOjRo/H392fLli3k5OQwZcoU9Ho9b731VpPEdjC7kP/uyGTxnmwKy6tN5XHtvZnUO5A7u/pLB69mpmpqKF6zlvx//5uynTtN5Q4dO+L1uwdxHzMGG0fL7/l+qugU84/MZ/GJxZRUa8t+Otk5Ma79OB7o+ABhnmGNO0BpHuz6FnZ+BYUXh0Kh01aHipmuLUhhBU38QtzqdEpdur+zXCUlJfTs2ZN//vOfvPHGG/To0YP333+fwsJCfH19+f7777nnnnsAOHLkCJ06dWLr1q307duXxMRExowZQ3Z2tulues6cOTz33HOcO3cO+3p0ACoqKsLDw4PCwkLc3d0BbZ7rZfuy+WFHJgezi0yvbePhyL292nFPr0CCvKUna3Orycuj4MefyJ8/n5pcrXMUtra4DR+O1+8exKlXL4vvZGcwGticvZnvj3zP5tObTeXB7sFM7jiZce3H4WbvduMHUAqydmpN1wcX1va6dmoFd/week8DL+tYb1qIlnatfNAcrOLO+YknnmD06NHEx8fzxhtvmMpTUlKorq4mPj7eVNaxY0eCgoJMyXnr1q1ERUWZNXMnJCTw+OOPc/DgQe64446rjldZWUllZaXp56IiLflWVBvYtD+HhbtOs+HoWdOUmnpbHSM6+zMpOpD+4T7Y2lh2MrB2SinKtu+g4If5FK1eA9Vaa4Wtlxeek+6l1f33o/f3b+Eof9u5snMsOrGIH4/9SE5pDqANhRrQbgCTO04mLiAOG10j7mLLC2DffyHlazh7sLY84A6Ing5dJ4BeOiMKYYksPjnPnz+fXbt2sWPHjqu25ebmYm9vj6enp1m5n58fuRfvonJzc80S86Xtl7Zdy9tvv81rr712Vfngf6ynTNV2vukS4M7Enu246462eLlY/hAca2coKKBwyRLy5/9AVXq6qdyxWzdaTZ6M+6iR2Dg0snNUMzMqI8k5ySw4toD1GeupUdosXu727owPH8/9He4nyD3oxg+gFGRu1xLywUVQc7Ezop0jdLlbS8rtejW+IkKIZmXRyTkzM5OZM2eyevVqHG/i88IXXniBp59+2vRzUVERgYGBlFQYaNfakfF3tOXuO9oS6deIpkZRL0opKvbuJX/+DxQlJqIutmjonJ3xGDuWVvdNwrFz5xaO8rddqLjAkhNLWHBsAZnFmabyHr49mNRhEsODhzduEYryfNj7g5aUzx2uLW/dGXo9DN3u1ZqxhRBWwaKTc0pKCmfPnqXnZWvlGgwGNm7cyMcff8zKlSupqqqioKDA7O75zJkz+F9s1vT392f79u1m+z1zcZpG/zqaPh0cHHC4xh3Yl1OjGdotGBtptm52hoICCpctp+Cnn6g8csRU7tChA60m34/7mDEWP4uXURnZlrONRccXsTZjLdVGrfndVe/KmLAx3NvhXiJbNWI4l9EI6b/CnnlweBnUaD3TsXOCrhOh10PaUo0W/sxdCHE1i07Ow4YNY//+/WZlDz/8MB07duS5554jMDAQvV7P2rVrmThxIgBHjx4lIyOD2FhtUffY2FjefPNNzp49S+vWrQFYvXo17u7udG7gHVdMmJck5makjEbKkpMp+PEnilevRlVpHZd0Dg64jxxJq/vvw7F7d4vv4JVTksPiE4tZfGIx2aXZpvIu3l2Y1GESd4bc2bj1ky+ka5OF7P0PFNbehePXVUvIUfeCk+eN718I0eIsOjm7ubnRtWtXszIXFxe8vb1N5Y888ghPP/00Xl5euLu78+STTxIbG0vfvn0BGDFiBJ07d+b3v/89f/vb38jNzeXll1/miSeeuObdsbj5qnNzKVi4kMKFi6jOyjKVO3TsiOfEiXiMHYPtFf0KLE2VoYp1GetYdGIRW7O3otA6C7rp3RgVNooJERPo7N2I5veqUji0RJso5NSm2nJHD+h6D/R4ENr2lLtkIW4RFp2c62P27NnY2NgwceJEs0lILrG1tWX58uU8/vjjxMbG4uLiwtSpU3n99ddbMGphrKqiZP0GCn76kdJNm03zXNu4uuI+dgyeE+/BsUtni79LPnLhCItPLGZ52nIKL82uBfTx78PdEXczLGjYjT9LNhohc5vWbH1wMVSVXNygg/ZDtITccQzoLX/8thCiYaxinHNLu1nj2m51Sikq9u2jYPFiin5JxFhYm8yco6PxvGcibiNGYONk2cN7zpWd45f0X1iaupRj+cdM5a2dW3NX+F3cFX4XgW6NmPLy3FHY9wPsW3DZRCGAVxj0eECbvcujXSNqIIS4UTLOWdwyqnNyKFyylMIlS8yGQNm1bo3H+PF4TpyAfUhIywVYD+U15azPWM/StKVszd5qmuNab6NncOBg7g6/m7iAOGxvdLWm4jNw4CctKefsqS13cNcWnujxIATFSrO1ELcJSc6iWRhLSylavZrCxUsoS07m0kTjOkdH3IYPx+Ou8bj07YvO1nKnNTUqIylnUliWuoxVp1ZRWl1q2tbDtwdj248lISQBDwePGztAVSkc+VlLyKnr4GLCx8YOwodDt0nQYaRMFCLEbUiSs2gyqrqa0i1bKFz+M8Vr16LKykzbnKOj8bjrLtwSErB1dWnBKH/bsfxj/JL2C4npiWa9rdu6tmVs+7GMCRtDsHvwje28phJOrNXuko8mwmUJn3bR0O0+6DIBXBq5/KMQwqpJchaNooxGyvfsoXDZMooTV2AoKDBt0wcH4TF+PB7jxmPfrm3LBVkPWcVZJKYn8kv6L5woOGEqd9G7kBCSwNiwsfT063lj02kaqiHtV21e68PLa5dlBGgVCt3v14Y/ebdvgpoIIW4FkpzFDak4eoyi5csp+vlnqrNr7y5tvb1xHzkS99GjcOrRw6J7W+eV57Hy5EoS0xPZe26vqVxvo2dA2wGMChvFoHaDbqy3tdEApzZrd8iHlkL5hdptbm20u+OuE6BtL3mOLIS4iiRnUW9VmZkU/ZJI0fLlVB4/biq3cXHBbfhw3MeMwaVvH3RNvE52UyqsLGR95noS0xPZlrPN1LFLh46YNjGMDh3NsOBhuNvfQC9MowEyk7VhT4cWQ8mZ2m0uvtB5vDZzV2BfWZZRCHFdlvspKixCVWYmRStWULxiJRUHa1c20un1uAwaiMeYMbgOHmzRayVfSsirTq5ia85Waow1pm1RPlGMCh1FQkgCvs6+Dd+5oUabFOTQEq1z1+UJ2dFT62ndZQKEDABb+e8mhKgf+bQQV6nKyqJ4xQqKEleYJWRsbHDuE4PH6NG4DR+OrccN9lK+CQorC1mXsY5Vp1axLWebWUIO9wwnISSBUaGjbmwFqJpK7Rny4SVw5BfzJmsHD62HddcJEDYE7GS1MiFEw0lyFgBUZZ2meOXFhHzgQO2GiwnZPeFO3IbHY+dtub2IzRJy9jbTcoygJeQRISNICE4gzDOs4TuvLocTa7Tnx8dWQGVR7TZnb+g4GjqNh9CBkpCFEI0myfk2YygppSo9jcrUVKpS06hMS6MqNZWqkydrX2Rjg3NMDO53JuA2fLjFJeQqQxWnik6RVphGWmEa6YXppBemcyL/hFlCjmgVwYjgEYwIGUGYRz0TsqFaW1gi75j519nDUF07NAxXf+g0BjqNg+B+0mQthGhS8olyC1JKYbhwQUvAaWlUpmoJuDItjZrc3Gu/ycYG5+ho3EfeiVt8PHY+Pjc36GsoriomvTD9qiScVZyFQRmu+Z56J+SKQsg7cTH5HoW849r3F9LgsiZwMx6BWjLuPA7axUinLiFEs5HkbMWU0Uh1dg5VaalaAr70b2oqhsvmrb6Srbc3DmFh2LcPwyGsPfbtw3Ds1Ak7L6+bGL1GKUVeeZ4pAacVpJFelE56QTpny8/W+T4XvQthHmGEeoQS5hFGmEcYEa0iaOfW7vKdQ1H21XfB545BSR1/pADoncEnAnw6gE+k9r1vR/DtIMOehBA3hSRnK6EMBsp2plCWsrO2OTo9HVVRce036HTo27atTcBhoTi0b49DWFiLLb+olOLwhcMk5ySTWpBqSsLF1cV1vsfXyZcwjzBCPEK0JOypJWJfJ1/zMdSGGsjYCrv/o90FnzsK509ctpLTNbj6X0zCkdqX78V/3QLkrlgI0aIkOVswVVND2Y4dFK1cSfHqNRjOn7/6RXo9DiHB2Ie1x6F9WO2/ISEWsbqTUooDeQdYfWo1q06t4nTJ6ateY6OzoZ1rO+1O2DPUdEcc6hF6/fHG1RWQtgGOLLu61/QlOlttNSfTHfDFu2HvcHDybLJ6CiFEU5LkbGFUdTWl27dTvGIlxWvWYMjPN22z9fDAZcAAHDpE4tC+PfahodgHBlrcpB9GZWTfuX2sOrWK1adWk1ta24TsZOdEbJtYOnp3NDVHB7kH4WDrUL+dVxbD8VVweBkcX21+Z+zUCtoPA7/OtXfDrUKl97QQwupY1qf6bUpVV1O6LZmilSsoWbPWbH5qW09P3IbH45ZwJy59YtDp9S0X6HUYjAZ2n93N6lOrWXNqjdnzYmc7Zwa1G8TwkOH0C+iHs965YTsvzYOjv2jzUqetB0NV7Ta3gIvDmMZKr2khxC1DPslaiLGsjJJNmyhZu5biDb9ivKwDl22rVtp0mHcm4BwTY3F3xpdUGipJzklmXcY6NmRu4HxFbbO7q96VwYGDGR48nLiAuIbNT62U1nHr2Ao4ugIyt9Uupwjg1V5Lxp3GQkBPeT4shLjlWOan/i2q5sIFStavp3jNWkq3bEFVVpq22Xp74zZiOO4JCTj37m2xCbmwspCNWRtZl7GOzdmbKa8pN21zs3djaOBQRoSMoG+bvtjbNqA52VANp7ZcTMiJkJ9uvt2/mzaMqdMYree09JoWQtzCLDMD3EKqMjIoXrOW4rVrKd+9G4y1d4D6du1wi4/HbdhQnHr2RGdr24KR1u10yWnWZ6xnXeY6dp3ZZTbG2M/ZjyGBQxgSNIRov2j0tg1odi+7oD03PpaorXF8+axbtvYQ0h8iR0JkArS6wfWThRDCCklybmLKaKTiwAGK16+nZM1as9WbABw7d8Y1fhhuw+JxiIywyCUVjcrIofOH+DXrV9ZnrOdo/lGz7RGtIhgaOJQhQUPo7NW5/nVQSptp6/gqOLby6uZqZx8tEUfeCe2HgINbE9ZKCCGshyTnJmAoLqZ082ZKNvxKSVKS+ZAnW1uco6NxGzYMt2FD0QcEtFyg11FcVcyW7C0kZSWx6fQms+fHNjoberbuydCgoQwOHEygW2D9d1xeoA13OrFGuzsuzjbf7tf1YkIeqa1tLM+PhRBCkvONUEpRlZ6uJeNff6UsJQVqaqd8tHFxwaVfP9zih+E6cGCLTfpxPUop0grT2Ji1kaTTSew+s9tsXmoXvQuxbWIZEjSEgW0H4unoWb8dG42Qu7c2GWduh8un2rRzvNhcfaeWlD1vYFUoIYS4xUlyboCSrdso27mTkg0bqM7MNNtmHxqK66BBuA4ehHPPnujsLW9sbUVNBdtzt7MxayObTm+6akKQEPcQBrYbyMB2A+nZumf9nx+X5kHqutqEXJZnvt0nEsLjta/gONC3/OQoQghhySQ5N8DpGTNwvdhpS6fX4xwdjevgQbgOGoR9sOV1WFJKkVqQypbsLWzJ2UJKbgoVhtrpPu1t7IluE82AtgMY2HYgge71bK6uLoeMbVpzddoGyNkLqNrt9q4QNhjCh2mTgkhnLiGEaBBJzg1g5+ODZ/wwXAcNwiU2FhsXl5YO6Sr5Fflszd7KluwtbM3eetXiEf4u/gxsO5AB7QYQ4x9TvwlBjAbI3gPpG7RknJEMhkrz1/hFack4PB4C+8isXEII0QiSnBsgbEUiHh4eLR2GmWpDNXvO7dHujrO3cPj8YdRld7EOtg709utNbEAscQFxhHuG/3bvaqW0RSMu3RmfTNKWWLycWwCEDdLukEMHgXubpq6aEELctiQ5N4AlDHu61FSdnJvMluwt7MjdYTYRCECHVh2IC4gjNiCWnn496zdvdWEWnNwM6b9qCbnoigUqHDwgdICWiMMGa4tIWMD5EEKIW5EkZwunlCKzOJPk3GS252xne+52LlSYr77k5ehFXEAccQFx9G3TF19n39/ecUGGloxPboJTmyD/pPl2W3uteTpssPbVpofMWy2EEDeJfNpaoJySHLbnaok4OSeZM2VnzLY72jrSo3UPU0KOaBWBje4644OVgoJTWiK+lJALM8xfo7PREnBIfy0ZB8WCfQMXqBBCCNEkJDlbgLzyPHbk7iA5J5ntudvJLDYfpqW30dPNtxt9/PsQ0yaGKJ+o689brZQ2N/Xlybgoy/w1OlsIuENLxiH9tbtkx+usnSyEEOKmkeRcD0ppHayKiop+45UNk1WUxbMbnyW9yHyRBxudDZ29OtPLrxe9/HoR5RuFk13t2OCK0goqqLhyd5qjK2DVy1CSa16us4OAHloSDoqFdr3BwbV2exVQ1bT1E0KIW82lPHApLzQXnWruI9wC0tLSaN++fUuHIYQQwkKkpqYSFhbWbPuXO+d68PLyAiAjI8PihlLVV1FREYGBgWRmZuLubn3N19YeP1h/Haw9frD+Okj8La+wsJCgoCBTXmgukpzrwebiYgweHh5W+wt1ibu7u1XXwdrjB+uvg7XHD9ZfB4m/5dk08yI9sgSQEEIIYWEkOQshhBAWRpJzPTg4OPDKK6/g4FCPmbYslLXXwdrjB+uvg7XHD9ZfB4m/5d2sOkhvbSGEEMLCyJ2zEEIIYWEkOQshhBAWRpKzEEIIYWEkOQshhBAWRpKzEEIIYWEkOTeBzMxMBg8eTOfOnenWrRsLFiww2758+XI6dOhAREQEc+fObaEor+/uu++mVatW3HPPPVdtCwkJoVu3bvTo0YMhQ4a0QHS/7XrxW8P5v5I1nPMrWeN5vpw1nvO6fu+t6VrUVQdruB7X++xv9DVQotGys7PV7t27lVJK5eTkqICAAFVSUqKUUqq6ulpFRESorKwsVVxcrCIjI1VeXl4LRntt69evV0uXLlUTJ068altwcLAqLi5ugajqr674reX8X8kazvnlrPU8X87azrlS1/69t7ZrUdf/XWu4HnV99jfFNZA75ybQpk0bevToAYC/vz8+Pj5cuHABgO3bt9OlSxfatm2Lq6srI0eOZNWqVS0Y7bUNHjwYNze3lg7jhtUVv7Wcf2sn57llXOv33tquhTV/9tT12d8U1+C2SM4bN25k7NixBAQEoNPpWLx48VWv+eSTTwgJCcHR0ZE+ffqwffv2GzpWSkoKBoOBwMBAALKzs2nbtq1pe9u2bTl9+rTFxn8tOp2OQYMGER0dzbx58xr8/paMvynO/5VuRn0ae84bqrF1ao7z3BBNcU2s7ZzX5WZei+b8v3AzrkdTxn/5Z39TXIPbIjmXlpbSvXt3Pvnkk2tu/+GHH3j66ad55ZVX2LVrF927dychIYGzZ8+aXtOjRw+6du161Vd2drbpNRcuXGDKlCl8/vnnVhl/XTZt2kRKSgpLly7lrbfeYt++fVYVf1O7GfVp7DlviTq1pKaIX855wzVnHW7G9Wiq+Jvls78ZmuEtGqAWLVpkVhYTE6OeeOIJ088Gg0EFBASot99+u977raioUAMGDFDffvutWfnmzZvVXXfdZfp55syZat68eTcWvGq++JXSnv1c65nz5Z555hn11VdfNWi/l7vZ8Tf1+b9Sc9bnksae84a6kTo193luiKa4JtZwzi+58ve+pa5FU9bhSjfjetxo/Nf67G+Ka3Bb3DlfT1VVFSkpKcTHx5vKbGxsiI+PZ+vWrfXah1KKhx56iKFDh/L73//ebFtMTAwHDhzg9OnTlJSUkJiYSEJCgkXFfz2lpaUUFxcDUFJSwrp16+jSpUuj93tJc8ff3Of/Sk1Rn+Y+5w1Vnzrd7PPcEPWJ3xrPeV0s5Vo0pg6WcD3qE39dn/1NcQ3smqYa1isvLw+DwYCfn59ZuZ+fH0eOHKnXPjZv3swPP/xAt27dTM8svvvuO6KiorCzs+Pdd99lyJAhGI1G/vrXv+Lt7W1R8QPEx8ezd+9eSktLadeuHQsWLCA2NpYzZ85w9913A2AwGJg+fTrR0dFWE39zn/8rNUV9mvucN1R96nSzz3ND1Cd+azznUPfvvSVci8bUwc/Pr8WvR33iv95nf2OvwW2fnJtC//79MRqNdW4fN24c48aNu4kRNdyaNWuuWR4WFsbevXtvcjQNV1f8YB3n/3LWcs6vZG3n+XLWes7r+r23pmtRVx2s4Xpc77O/sdfgtm/W9vHxwdbWljNnzpiVnzlzBn9//xaKqv4kfstyq9UHrL9O1hi/NcZ8JWuvQ0vHf9snZ3t7e3r16sXatWtNZUajkbVr1xIbG9uCkdWPxG9ZbrX6gPXXyRrjt8aYr2TtdWjp+G+LZu2SkhJOnDhh+jk9PZ09e/bg5eVFUFAQTz/9NFOnTqV3797ExMTw/vvvU1paysMPP9yCUdeS+C3LrVYfsP46WWP81hjzlay9DhYdf4P6dlup9evXK+Cqr6lTp5pe89FHH6mgoCBlb2+vYmJi1LZt21ou4CtI/JblVquPUtZfJ2uM3xpjvpK118GS49cppVSzZH0hhBBC3JDb/pmzEEIIYWkkOQshhBAWRpKzEEIIYWEkOQshhBAWRpKzEEIIYWEkOQshhBAWRpKzEEIIYWEkOQshhBAWRpKzEEIIYWEkOQtxk+l0OtPar83p1VdfpUePHs1+HEtUVVVFeHg4W7ZsuSnHe/7553nyySdvyrHE7UGSsxDAQw89hE6n45133jErX7x4MTqdroWiqr9rJfxnnnnGbEWd5hISEoJOp0On0+Hs7ExUVBRz585t8H6a8o+WOXPmEBoaSlxc3DW3//3vf+eBBx4A4Pvvv2fo0KHXfN2vv/5KYGDgbx7vmWee4ZtvviEtLe3GgxbiMpKchbjI0dGRWbNmkZ+f39KhNAlXV1e8vb1vyrFef/11cnJyOHDgAL/73e+YPn06iYmJN+XYV1JK8fHHH/PII4/U+ZqtW7fSr18/AJKSkkzfX2nJkiWMHTv2N4/p4+NDQkICn3766Y0FLcQVJDkLcVF8fDz+/v68/fbbdb7mWk3F77//PiEhIWZlX375JV26dMHBwYE2bdowY8aMOveZmZnJpEmT8PT0xMvLi/Hjx3Py5EnT9h07djB8+HB8fHzw8PBg0KBB7Nq1y7T90rHvvvtudDqd6ecrYzUajbz++uu0a9cOBwcHevTowYoVK0zbT548iU6nY+HChQwZMgRnZ2e6d+/O1q1b64z9Ejc3N/z9/QkLC+O5557Dy8uL1atXN7oOoCXInj174ujoSFhYGK+99ho1NTV1xpKSkkJqaiqjR4+u8zWXJ+dNmzbVmZyXLl3KuHHjAPjxxx+JiorCyckJb29v4uPjKS0tNb127NixzJ8/v+6TJEQDSHIW4iJbW1veeustPvroI7Kysm54P59++ilPPPEEjz76KPv372fp0qWEh4df87XV1dUkJCTg5uZGUlISmzdvxtXVlTvvvJOqqioAiouLmTp1Kps2bWLbtm1EREQwatQoiouLAS3xAXz11Vfk5OSYfr7SBx98wLvvvss//vEP9u3bR0JCAuPGjeP48eNmr3vppZd45pln2LNnD5GRkUyePPm6yfByRqORn376ifz8fOzt7U3lN1qHpKQkpkyZwsyZMzl06BCfffYZX3/9NW+++WadMSQlJREZGYmbm5tZ+TvvvIOnpyeenp7k5uYyaNAgPD09OXDggOmPo02bNplef/DgQc6ePcvQoUPJyclh8uTJTJs2jcOHD7NhwwYmTJjA5Yv6xcTEkJWVZfaHlRA37KYsTCmEhZs6daoaP368Ukqpvn37qmnTpimllFq0aJG6/L/JK6+8orp372723tmzZ6vg4GDTzwEBAeqll16q81iAWrRokVJKqe+++0516NBBGY1G0/bKykrl5OSkVq5cec33GwwG5ebmppYtW3bNfdYVa0BAgHrzzTfNXhMdHa3++Mc/KqWUSk9PV4CaO3euafvBgwcVoA4fPlxnfYKDg5W9vb1ycXFRdnZ2ClBeXl7q+PHjdb6nvnUYNmyYeuutt8zKvvvuO9WmTZs69z1z5kw1dOjQq8rz8/NVenq6euWVV1RCQoJKT09Xn3zyiYqOjlbp6ekqPT1dlZeXm17/5ptvqnvuuUcppVRKSooC1MmTJ+s8bmFhoQLUhg0b6nyNEPUld85CXGHWrFl88803HD58uMHvPXv2LNnZ2QwbNqxer9+7dy8nTpzAzc0NV1dXXF1d8fLyoqKigtTUVADOnDnD9OnTiYiIwMPDA3d3d0pKSsjIyKh3XEVFRWRnZ1/VfNuvX7+r6tmtWzfT923atDHV63qeffZZ9uzZw7p16+jTpw+zZ882ay240Trs3buX119/3XRuXF1dmT59Ojk5OZSVlV3zPeXl5Tg6Ol5V7unpSUhICNu3b2fixImEhISwe/duxo0bR0hICCEhIWbvW7JkialJu3v37gwbNoyoqCjuvfde/vWvf13VN8HJyQmgzriEaAi7lg5ACEszcOBAEhISeOGFF3jooYfMttnY2Jg1ZYLWNH3JpQ/o+iopKaFXr17Mmzfvqm2+vr4ATJ06lfPnz/PBBx8QHByMg4MDsbGxpmbvpqbX603fX+qpbjQar/seHx8fwsPDCQ8PZ8GCBURFRdG7d286d+7cqDqUlJTw2muvMWHChKu2XSsBX4pl//79ZmVJSUmMHDkS0JLnhg0b+POf/0x5eTl6vZ533nmHF198kRdffBGAnJwcdu/ebXpubWtry+rVq9myZQurVq3io48+4qWXXiI5OZnQ0FAALly4ANReNyEaQ+6chbiGd955h2XLll3VGcrX15fc3FyzBL1nzx7T925uboSEhNR7CFPPnj05fvw4rVu3NiW3S18eHh4AbN68maeeeopRo0aZOpnl5eWZ7Uev12MwGOo8jru7OwEBAWzevNmsfPPmzaYE2lQCAwO57777eOGFF8yOcyN16NmzJ0ePHr3q3ISHh2Njc+2PrzvuuIMjR46YXaPevXuzZ88evvjiCwIDA9m3bx9Lly7FycmJffv2sWfPHh577DHT65ctW0ZcXBxeXl6mMp1OR79+/XjttdfYvXs39vb2LFq0yLT9wIED6PV6unTpcmMnTojLSHIW4hqioqJ48MEH+fDDD83KBw8ezLlz5/jb3/5Gamoqn3zyyVVDhl599VXeffddPvzwQ44fP86uXbv46KOPrnmcBx98EB8fH8aPH09SUhLp6els2LCBp556ytQpLSIigu+++47Dhw+TnJzMgw8+eNUd+qU/CHJzc+scCvbss88ya9YsfvjhB44ePcrzzz/Pnj17mDlz5o2epjrNnDmTZcuWsXPnzkbV4f/+7//49ttvee211zh48CCHDx9m/vz5vPzyy3Uee8iQIZSUlHDw4EFTmZOTE+Hh4aSnpzN48GDCw8PJysqiX79+REZGEh4ebpaIL++lDZCcnMxbb73Fzp07ycjIYOHChZw7d45OnTqZXpOUlMSAAQMa3HoixDW18DNvISzC5R3CLklPT1f29vbqyv8mn376qQoMDFQuLi5qypQp6s033zTrEKaUUnPmzFEdOnRQer1etWnTRj355JOmbVzR8SknJ0dNmTJF+fj4KAcHBxUWFqamT5+uCgsLlVJK7dq1S/Xu3Vs5OjqqiIgItWDBAhUcHKxmz55t2sfSpUtVeHi4srOzM8VyZYcwg8GgXn31VdW2bVul1+tV9+7dVWJioll9AbV7925TWX5+vgLU+vXr6zx3V8ZySUJCgho5cmSj6qCUUitWrFBxcXHKyclJubu7q5iYGPX555/XGY9SSk2aNEk9//zz14zpUoe3adOmqTfeeOOq15SUlChHR0ezDm2HDh1SCQkJytfXVzk4OKjIyEj10Ucfmb2vQ4cO6j//+c914xKivnRKXfEATQghrNy+ffsYPnw4qampuLq6Nui9Cxcu5OWXX+bQoUP1fk9iYiJ/+ctf2LdvH3Z20pVHNJ40awshbjndunVj1qxZpKenN/i9rq6uzJo1q0HvKS0t5auvvpLELJqM3DkLIYQQFkbunIUQQggLI8lZCCGEsDCSnIUQQggLI8lZCCGEsDCSnIUQQggLI8lZCCGEsDCSnIUQQggLI8lZCCGEsDCSnIUQQggL8/8Biv2Gibf0+f8AAAAASUVORK5CYII=", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from kawin.thermo import BinaryThermodynamics\n", + "\n", + "therm = BinaryThermodynamics('AlScZr.tdb', elements=['AL', 'ZR'], phases=['FCC_A1', 'AL3ZR'])\n", + "therm.setGuessComposition(0.24)\n", + "\n", + "D0 = 0.0768 #Diffusivity pre-factor (m2/s)\n", + "Q = 242000 #Activation energy (J/mol)\n", + "diff = lambda T: D0 * np.exp(-Q / (8.314 * T))\n", + "therm.setDiffusivity(diff, 'FCC_A1')\n", + "\n", + "a = 0.405e-9 # nm\n", + "Va = a**3 # nm^3\n", + "atomsPerCell = 4\n", + "\n", + "matrix = MatrixParameters(solutes=['ZR'])\n", + "matrix.volume.setVolume(Va, 'VA', atomsPerCell)\n", + "matrix.GBenergy = 0.3\n", + "matrix.nucleationSites.setDislocationDensity(1e15)\n", + "matrix.nucleationSites.setGrainSize(100)\n", + "\n", + "precipitate = PrecipitateParameters('AL3ZR')\n", + "precipitate.gamma = 0.25 # J/m2\n", + "precipitate.volume.setVolume(Va, 'VA', atomsPerCell)\n", + "precipitate.nucleation.gbEnergy = matrix.GBenergy\n", + "\n", + "nucleationSites = ['dislocations', 'grain boundaries', 'grain edges', 'grain corners']\n", + "N0 = {\n", + " 'dislocations': matrix.nucleationSites.dislocationN0,\n", + " 'grain boundaries': matrix.nucleationSites.GBareaN0,\n", + " 'grain edges': matrix.nucleationSites.GBedgeN0,\n", + " 'grain corners': matrix.nucleationSites.GBcornerN0\n", + "}\n", + "\n", + "fig, ax = plt.subplots(figsize=(5,5))\n", + "T = np.linspace(300, 1200, 100)\n", + "for site in nucleationSites:\n", + " precipitate.nucleation.setNucleationType(site)\n", + " nucRate = computeSteadyStateNucleation(therm, 4e-3, T, precipitate, matrix)\n", + " ax.plot(N0[site]*nucRate.nucleation_rate, T, label=site.capitalize())\n", + "ax.legend()\n", + "ax.set_xlim([1e-20, 1e20])\n", + "ax.set_xscale('log')\n", + "ax.set_ylim([300, 1300])\n", + "ax.set_xlabel('Nucleation Rate (#/s)')\n", + "ax.set_ylabel('Temperature (K)')\n", + "plt.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "calphad_312", + "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": "3.12.7" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/kawin/diffusion/DiffusionParameters.py b/kawin/diffusion/DiffusionParameters.py index 11bb3d0..4d1fbac 100644 --- a/kawin/diffusion/DiffusionParameters.py +++ b/kawin/diffusion/DiffusionParameters.py @@ -682,22 +682,4 @@ def reset(self): # Max composition change a node can see per iteration in the homogenization model # There is not an analagous method for von Neumann stability as we have for the single # phase diffusion model, so this is a naive approach to numerical stability - self.maxCompositionChange = 0.002 - -class DiffusionParameters: - def __init__(self, elements, - temperatureParameters = None, - boundaryCondition = None, - compositionProfile = None, - hashTable = None, - homogenizationParameters = None, - minComposition = 1e-8, - maxCompositionChange = 0.002): - self.temperature = TemperatureParameters() if temperatureParameters is None else temperatureParameters - self.boundaryConditions = BoundaryConditions(elements) if boundaryCondition is None else boundaryCondition - self.compositionProfile = CompositionProfile(elements) if compositionProfile is None else compositionProfile - self.hashTable = HashTable() if hashTable is None else hashTable - self.homogenizationParameters = HomogenizationParameters() if homogenizationParameters is None else homogenizationParameters - - self.minComposition = minComposition - self.maxCompositionChange = maxCompositionChange \ No newline at end of file + self.maxCompositionChange = 0.002 \ No newline at end of file diff --git a/kawin/precipitation/KWNBase.py b/kawin/precipitation/KWNBase.py index 1b67137..c08d126 100644 --- a/kawin/precipitation/KWNBase.py +++ b/kawin/precipitation/KWNBase.py @@ -484,12 +484,6 @@ def setup(self): if self._isSetup: return - if not self.matrixParameters.nucleationSites._parametersSet: - #Set nucleation density assuming grain size of 100 um and dislocation density of 5e12 m/m3 (Thermocalc default) - print('Nucleation density not set.\nSetting nucleation density assuming grain size of {:.0f} um and dislocation density of {:.0e} #/m2'.format(100, 5e12)) - self.matrixParameters.nucleationSites.setNucleationDensity(100, 1, 5e12) - self.matrixParameters.nucleationSites._parametersSet = True - self.matrixParameters.nucleationSites.setupNucleationDensity(self.matrixParameters.initComposition, self.matrixParameters.volume.Vm) for p in range(len(self.phases)): self.precipitateParameters[p].nucleation.gbEnergy = self.matrixParameters.GBenergy self.precipitateParameters[p].validate() diff --git a/kawin/precipitation/PrecipitationParameters.py b/kawin/precipitation/PrecipitationParameters.py index 6dcf7e9..f457888 100644 --- a/kawin/precipitation/PrecipitationParameters.py +++ b/kawin/precipitation/PrecipitationParameters.py @@ -109,15 +109,34 @@ def __call__(self, t): class MatrixParameters: def __init__(self, solutes): self.solutes = solutes - self.initComposition = None + self._initComposition = None self.volume = VolumeParameter() + self.volume._updateCallbacks.append(self.update) self.nucleationSites = NucleationSiteParameters() self.GBenergy = 0.3 self.effectiveDiffusion = EffectiveDiffusionFunctions() self.theta = 2 + @property + def initComposition(self): + return self._initComposition + + @initComposition.setter + def initComposition(self, value): + self._initComposition = value + self.update() + + def update(self): + # update nucleation site volume and bulkN0 + # only do this once both volume and composition is defined + # if bulkN0 is set before composition or volume, then we leave to the user defined bulkN0 + self.nucleationSites.VmAlpha = self.volume.Vm + if self._initComposition is not None and self.nucleationSites.VmAlpha is not None: + if self.nucleationSites._compositionDependentBulkN0: + self.nucleationSites.setBulkDensityFromComposition(self._initComposition) + class PrecipitateParameters: ''' Parameters for a single precipitate @@ -162,7 +181,7 @@ def validate(self): self.nucleation.gamma = self.gamma if self.nucleation.description.isGrainBoundaryNucleation and not isinstance(self.shapeFactor.description, SphereDescription): - raise ValueError('Nucleation is set to grain boundary nucleaiton and shape factor not set to spherical. \ + raise ValueError('Nucleation is set to grain boundary nucleation and shape factor not set to spherical. \ If using GB nucleation, shape factor should be spherical. If shape factor is spherical, nucleation should be bulk or dislocations') # If strain energy is not constant, then switch to description that matches shapeFactor diff --git a/kawin/precipitation/parameters/Nucleation.py b/kawin/precipitation/parameters/Nucleation.py index 679e16a..45417c5 100644 --- a/kawin/precipitation/parameters/Nucleation.py +++ b/kawin/precipitation/parameters/Nucleation.py @@ -265,7 +265,7 @@ def setNucleationType(self, site): def _validateInputs(self): if self.gamma is None or self.gamma == 0: - raise ValueError(f"Interfacial energy (gamma) is not set. NucleationBarrierParametesr.gamma = {self.gamma}") + raise ValueError(f"Interfacial energy (gamma) is not set. NucleationBarrierParameters.gamma = {self.gamma}") if self.gbEnergy is None: raise ValueError(f"Grain boundary energy (gbEnergy) is not set. NucleationBarrierParameters.gbEnergy = {self.gbEnergy}") @@ -350,14 +350,50 @@ def Gcrit(self, dG, Rcrit): return Rcrit**2 * ((self.areaFactor * self.gamma - self.gbRemoval * self.gbEnergy) - self.volumeFactor * dG * Rcrit) class NucleationSiteParameters: - def __init__(self, grainSize = 100, aspectRatio = 1, dislocationDensity = 5e12, bulkN0 = None): - self.setNucleationDensity(grainSize, aspectRatio, dislocationDensity, bulkN0) + def __init__(self, grainSize = 100, aspectRatio = 1, dislocationDensity = 5e12): + self._grainSize = grainSize + self._grainAspectRatio = aspectRatio + self._dislocationDensity = dislocationDensity - self._parametersSet = False - self.GBareaN0 = None - self.GBedgeN0 = None - self.GBcornerN0 = None - self.dislocationN0 = None + self.VmAlpha = None + + self._bulkN0 = None + self._compositionDependentBulkN0 = True + self._GBareaN0 = None + self._GBedgeN0 = None + self._GBcornerN0 = None + self._dislocationN0 = None + + @property + def grainSize(self): + return self._grainSize + + @grainSize.setter + def grainSize(self, value): + self._grainSize = value + self._GBareaN0 = None + self._GBedgeN0 = None + self._GBcornerN0 = None + + @property + def grainAspectRatio(self): + return self._grainAspectRatio + + @grainAspectRatio.setter + def grainAspectRatio(self, value): + self._grainAspectRatio = value + self._GBareaN0 = None + self._GBedgeN0 = None + self._GBcornerN0 = None + + @property + def dislocationDensity(self): + return self._dislocationDensity + + @dislocationDensity.setter + def dislocationDensity(self, value): + self._dislocationDensity = value + self._dislocationN0 = None def setNucleationDensity(self, grainSize = 100, aspectRatio = 1, dislocationDensity = 5e12, bulkN0 = None): ''' @@ -379,16 +415,64 @@ def setNucleationDensity(self, grainSize = 100, aspectRatio = 1, dislocationDens self.grainSize = grainSize * 1e-6 self.grainAspectRatio = aspectRatio self.dislocationDensity = dislocationDensity + if bulkN0 is not None: + self.bulkN0 = bulkN0 + + def setGrainSize(self, grainSize = 100, aspectRatio = 1): + self.grainSize = grainSize * 1e-6 + self.grainAspectRatio = aspectRatio + + def setDislocationDensity(self, dislocationDensity): + self.dislocationDensity = dislocationDensity + + def setBulkDensity(self, bulkN0): self.bulkN0 = bulkN0 - self._parametersSet = True - def bulkSites(self, x0, VmAlpha): + def setBulkDensityFromComposition(self, x0): #Set bulk nucleation site to the number of solutes per unit volume # This is the represent that any solute atom can be a nucleation site #NOTE: some texts will state the bulk nucleation sites to just be the number # of lattice sites per unit volume. The justification for this would be # the solutes can diffuse around to any lattice site and nucleate there - return np.amin(x0) * (AVOGADROS_NUMBER / VmAlpha) + self._validateVolume('bulkN0 from composition') + self.bulkN0 = np.amin(x0) * (AVOGADROS_NUMBER / self.VmAlpha) + self._compositionDependentBulkN0 = True + + @property + def bulkN0(self): + return self._bulkN0 + + @bulkN0.setter + def bulkN0(self, value): + self._bulkN0 = value + self._compositionDependentBulkN0 = False + + @property + def dislocationN0(self): + if self._dislocationN0 is None: + self._validateVolume('dislocationN0') + self._dislocationN0 = self.dislocationSites(self.VmAlpha) + return self._dislocationN0 + + @property + def GBareaN0(self): + if self._GBareaN0 is None: + self._validateVolume('GBareaN0') + self._GBareaN0 = self.grainBoundarySites(self.grainSize, self.grainAspectRatio, self.VmAlpha) + return self._GBareaN0 + + @property + def GBedgeN0(self): + if self._GBedgeN0 is None: + self._validateVolume('GBedgeN0') + self._GBedgeN0 = self.grainEdgeSites(self.grainSize, self.grainAspectRatio, self.VmAlpha) + return self._GBedgeN0 + + @property + def GBcornerN0(self): + if self._GBcornerN0 is None: + self._GBcornerN0 = self.grainCornerSites(self.grainSize, self.grainAspectRatio, self.VmAlpha) + return self._GBcornerN0 def dislocationSites(self, VmAlpha): return self.dislocationDensity * (AVOGADROS_NUMBER / VmAlpha)**(1/3) @@ -420,18 +504,8 @@ def grainCornerDensity(self, grainSize, grainAspectRatio): def grainCornerSites(self, grainSize, grainAspectRatio, VmAlpha): rho = self.grainCornerDensity(grainSize, grainAspectRatio) return rho - - def setupNucleationDensity(self, x0, VmAlpha): - if self.bulkN0 is None: - self.bulkN0 = self.bulkSites(x0, VmAlpha) - self.dislocationN0 = self.dislocationSites(VmAlpha) - - if self.grainSize != np.inf: - self.GBareaN0 = self.grainBoundarySites(self.grainSize, self.grainAspectRatio, VmAlpha) - self.GBedgeN0 = self.grainEdgeSites(self.grainSize, self.grainAspectRatio, VmAlpha) - self.GBcornerN0 = self.grainCornerSites(self.grainSize, self.grainAspectRatio, VmAlpha) - else: - self.GBareaN0 = 0 - self.GBedgeN0 = 0 - self.GBcornerN0 = 0 + + def _validateVolume(self, term): + if self.VmAlpha is None: + raise ValueError(f'NucleationSiteParameters.VMalpha must be set to compute {term}.') diff --git a/kawin/precipitation/parameters/ShapeFactors.py b/kawin/precipitation/parameters/ShapeFactors.py index 868147a..44aa504 100644 --- a/kawin/precipitation/parameters/ShapeFactors.py +++ b/kawin/precipitation/parameters/ShapeFactors.py @@ -305,6 +305,13 @@ def setPrecipitateShape(self, precipitateShape, ar = 1): General shape setting function Defaults to spherical + + Parameters + ---------- + precipitateShape: str + 'sphere', 'needle', 'plate' or 'cubic' + ar: float or callable + Aspect ratio. If callable, then it must be a function of equivalent spherical radius ''' descriptionDict = { SphereDescription.name.upper(): SphereDescription(), diff --git a/kawin/precipitation/parameters/Volume.py b/kawin/precipitation/parameters/Volume.py index 299608b..d73a2f7 100644 --- a/kawin/precipitation/parameters/Volume.py +++ b/kawin/precipitation/parameters/Volume.py @@ -15,6 +15,7 @@ def __init__(self, value=None, volumeType=None, atomsPerCell=None): self.atomsPerCell = None else: self.setVolume(value, volumeType, atomsPerCell) + self._updateCallbacks = [] def setVolume(self, value, volumeType, atomsPerCell): ''' @@ -44,4 +45,7 @@ def setVolume(self, value, volumeType, atomsPerCell): self.Vm = self.Va * AVOGADROS_NUMBER / atomsPerCell else: valid_values = "['VM', 'VA', 'a', VolumeParameter.MOLAR_VOLUME, VolumeParameter.ATOMIC_VOLUME, VolumeParameter.LATTICE_PARAMETER]" - raise ValueError(f'Unknown volume type {volumeType}. Values must be: {valid_values}') \ No newline at end of file + raise ValueError(f'Unknown volume type {volumeType}. Values must be: {valid_values}') + + for callback in self._updateCallbacks: + callback() \ No newline at end of file diff --git a/kawin/tests/test_nucleation.py b/kawin/tests/test_nucleation.py new file mode 100644 index 0000000..ceeb29c --- /dev/null +++ b/kawin/tests/test_nucleation.py @@ -0,0 +1,121 @@ +import numpy as np +from numpy.testing import assert_allclose +import pytest + +from kawin.precipitation import PrecipitateParameters, MatrixParameters +from kawin.precipitation.parameters.Nucleation import NucleationBarrierParameters, NucleationSiteParameters + +def test_nucleation_barrier_updating(): + ''' + tests that the nucleation barrier factors in precipitate parameters + will automatically update with updated values + ''' + prec = PrecipitateParameters('phase') + + with pytest.raises(ValueError): + value = prec.nucleation.GBk + + prec.gamma = 0.3 + assert_allclose(prec.nucleation.GBk, 0.5, rtol=1e-3) + + types = ['bulk', 'dislocations', 'grain boundaries', 'grain edges', 'grain corners'] + # Order is [area factor, volume factor, gb removal, area removal] + test_values = { + 'bulk': [12.566370614359172, 4.1887902047863905, 0.0, 1.0], + 'dislocations': [12.566370614359172, 4.1887902047863905, 0.0, 1.0], + 'grain boundaries': [6.283185307179586, 1.308996938995747, 2.356194490192345, 0.8660254037844386], + 'grain edges': [4.078042913449462, 0.6718303352064217, 2.0625519078301955, 0.8102657977661342], + 'grain corners': [2.975471716584403, 0.42215773311582705, 1.7089985172369215, 0.7375575391181026], + } + for t in types: + prec.nucleation.setNucleationType(t) + assert_allclose([prec.nucleation.areaFactor, prec.nucleation.volumeFactor, prec.nucleation.gbRemoval, prec.nucleation.areaRemoval], + test_values[t], rtol=1e-3) + + # test if gamma is too small, then ValueError is raised for grain boundaries, edges, corners + prec.gamma = 0.1 + for t in ['grain boundaries', 'grain edges', 'grain corners']: + prec.nucleation.setNucleationType(t) + with pytest.raises(ValueError): + value = prec.nucleation.areaFactor + + with pytest.raises(ValueError): + value = prec.nucleation.volumeFactor + + with pytest.raises(ValueError): + value = prec.nucleation.gbRemoval + + with pytest.raises(ValueError): + value = prec.nucleation.areaRemoval + + # assert that if gb nucleation, then setting shape to non-spherical will raise ValueError + with pytest.raises(ValueError): + prec.nucleation.setNucleationType('grain boundaries') + prec.shapeFactor.setPrecipitateShape('plate') + + # assert that if non-spherical shape, then setting gb nucleation will raise ValueError + prec.nucleation.setNucleationType('dislocations') + prec.shapeFactor.setPrecipitateShape('sphere') + with pytest.raises(ValueError): + prec.shapeFactor.setPrecipitateShape('plate') + prec.nucleation.setNucleationType('grain boundaries') + + +def test_nucleation_barrier_shape(): + ''' + test that shape of nucleation barrier factors are same length as GBk + ''' + prec = PrecipitateParameters('phase') + + gbk = prec.nucleation.description.gbRatio(0.3, np.linspace(0.1, 0.2, 10)) + values = prec.nucleation.description.areaFactor(gbk) + assert gbk.shape == values.shape + + values = prec.nucleation.description.volumeFactor(gbk) + assert gbk.shape == values.shape + + values = prec.nucleation.description.gbRemoval(gbk) + assert gbk.shape == values.shape + + values = prec.nucleation.description.areaRemoval(gbk) + assert gbk.shape == values.shape + +def test_nucleation_barrier_rcrit(): + ''' + test value of Rcrit and Gcrit + ''' + prec = PrecipitateParameters('phase') + prec.gamma = 0.2 + prec.nucleation.setNucleationType('grain boundaries') + + rcrit = prec.nucleation.Rcrit(10000) + gcrit = prec.nucleation.Gcrit(10000, 3e-9) + + assert_allclose(rcrit, 3.9999e-5, rtol=1e-3) + assert_allclose(gcrit, 1.9437632e-18, rtol=1e-3) + +def test_nucleation_site_updating(): + ''' + test that updating volume and composition will update nucleation site parameters + ''' + matrix = MatrixParameters('A') + + with pytest.raises(ValueError): + value = matrix.nucleationSites.GBareaN0 + + with pytest.raises(ValueError): + value = matrix.nucleationSites.GBedgeN0 + + # GB corner N0 does not depend on molar volume + value = matrix.nucleationSites.GBcornerN0 + + matrix.volume.setVolume(1e-5, 'VM', 4) + assert_allclose(matrix.nucleationSites.VmAlpha, 1e-5, rtol=1e-3) + + matrix.initComposition = 1e-3 + assert_allclose(matrix.nucleationSites.bulkN0, 6.022e25, rtol=1e-3) + + # If bulkN0 is set, then don't update + matrix.nucleationSites.bulkN0 =1e30 + matrix.initComposition = 2e-3 + assert_allclose(matrix.nucleationSites.bulkN0, 1e30, rtol=1e-3) \ No newline at end of file