From 4389f49501d9e23ad01d3fc70f683fbe6b4b80cf Mon Sep 17 00:00:00 2001 From: vtulus <47393746+vtulus@users.noreply.github.com> Date: Wed, 23 Aug 2023 21:28:21 +0200 Subject: [PATCH] Squashed commit of the dev branch Latest commit: 3cddca5199a771a71a3a --- .gitignore | 3 +- data/processed/lci-GAC_project_db.xlsx | Bin 0 -> 12546 bytes data/processed/png_files/fig_LCA_MS.png | Bin 0 -> 614196 bytes .../processed/png_files/fig_LCA_SI_sankey.png | Bin 0 -> 1981391 bytes .../png_files/fig_LCA_SI_sensitivity.png | Bin 0 -> 100513 bytes data/processed/scenario_table.xlsx | Bin 0 -> 9233 bytes data/processed/svg_files/fig_LCA_MS.svg | 5990 ++++++++++++ .../processed/svg_files/fig_LCA_SI_sankey.svg | 8323 +++++++++++++++++ .../svg_files/fig_LCA_SI_sensitivity.svg | 1414 +++ notebooks/LCAscores_breakdown_sankey.ipynb | 1305 +++ .../LCAscores_sensitivity_scatterplot.ipynb | 368 + notebooks/LCAscores_total_barplot.ipynb | 538 ++ notebooks/LCAscores_total_doughnutchart.ipynb | 1322 +++ notebooks/howto_sensitivity_AB.md | 9 + notebooks/import_LCI_data.ipynb | 306 + notebooks/project_path.py | 16 + 16 files changed, 19593 insertions(+), 1 deletion(-) create mode 100644 data/processed/lci-GAC_project_db.xlsx create mode 100644 data/processed/png_files/fig_LCA_MS.png create mode 100644 data/processed/png_files/fig_LCA_SI_sankey.png create mode 100644 data/processed/png_files/fig_LCA_SI_sensitivity.png create mode 100644 data/processed/scenario_table.xlsx create mode 100644 data/processed/svg_files/fig_LCA_MS.svg create mode 100644 data/processed/svg_files/fig_LCA_SI_sankey.svg create mode 100644 data/processed/svg_files/fig_LCA_SI_sensitivity.svg create mode 100644 notebooks/LCAscores_breakdown_sankey.ipynb create mode 100644 notebooks/LCAscores_sensitivity_scatterplot.ipynb create mode 100644 notebooks/LCAscores_total_barplot.ipynb create mode 100644 notebooks/LCAscores_total_doughnutchart.ipynb create mode 100644 notebooks/howto_sensitivity_AB.md create mode 100644 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targetsourcevalue
01-(naphthalen-1-yl)-1H-imidazole production wi...1-iodonaphthalene production2.163055
11-(naphthalen-1-yl)-1H-imidazole production wi...Cu/PCN catalyst production, at 10.8 wt% Cu0.402700
21-(naphthalen-1-yl)-1H-imidazole production wi...market for dimethyl sulfoxide0.314026
31-(naphthalen-1-yl)-1H-imidazole production wi...market for imidazole2.729986
41-(naphthalen-1-yl)-1H-imidazole production wi...1-(naphthalen-1-yl)-1H-imidazole production wi...5.609766
51-iodonaphthalene productionmarket for naphthalene sulfonic acid2.163055
6Cu/PCN catalyst production, at 10.8 wt% Cupolymeric carbon nitride prodution0.330835
7air separation, cryogenicmarket group for electricity, medium voltage0.251784
8calcium carbide production, technical grademarket group for electricity, medium voltage0.074602
9cokingmarket for hard coal0.318650
10cokingmarket group for electricity, medium voltage0.022442
11dicyandiamide productionmarket for calcium carbide, technical grade0.310739
12ethylene oxide productionmarket for ethylene0.280362
13ethylene oxide productionmarket for oxygen, liquid0.120335
14ethylene oxide productionmarket group for electricity, medium voltage0.047217
15glyoxal productionmarket for ethylene oxide0.565643
16glyoxal productionmarket for oxygen, liquid0.188715
17glyoxal productionmarket group for electricity, medium voltage0.076679
18imidazole productionmarket for ammonia, anhydrous, liquid0.338462
19imidazole productionmarket for glyoxal1.286795
20imidazole productionmarket group for electricity, medium voltage0.085404
21market for calcium carbide, technical gradecalcium carbide production, technical grade0.308768
22market for coal tarcoking0.821164
23market for ethyleneethylene production, average0.282943
24market for ethylene oxideethylene oxide production0.566556
25market for glyoxalglyoxal production1.266251
26market for hard coalhard coal mine operation and hard coal prepara...0.478516
27market for hard coalmarket group for electricity, medium voltage0.015134
28market for imidazoleimidazole production2.702893
29market for naphthalene sulfonic acidnaphthalene sulfonic acid production2.078794
30market for oxygen, liquidair separation, cryogenic0.319764
31market for wood preservative, creosotewood preservative production, creosote1.019797
32market group for electricity, medium voltagemarket group for electricity, medium voltage1.436761
33naphthalene sulfonic acid productionmarket for wood preservative, creosote1.081765
34naphthalene sulfonic acid productionmarket group for electricity, medium voltage0.331818
35polymeric carbon nitride produtiondicyandiamide production0.316818
36wood preservative production, creosotemarket for coal tar0.828883
37wood preservative production, creosotemarket group for electricity, medium voltage0.033588
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" + ], + "text/plain": [ + " target \\\n", + "0 1-(naphthalen-1-yl)-1H-imidazole production wi... \n", + "1 1-(naphthalen-1-yl)-1H-imidazole production wi... \n", + "2 1-(naphthalen-1-yl)-1H-imidazole production wi... \n", + "3 1-(naphthalen-1-yl)-1H-imidazole production wi... \n", + "4 1-(naphthalen-1-yl)-1H-imidazole production wi... \n", + "5 1-iodonaphthalene production \n", + "6 Cu/PCN catalyst production, at 10.8 wt% Cu \n", + "7 air separation, cryogenic \n", + "8 calcium carbide production, technical grade \n", + "9 coking \n", + "10 coking \n", + "11 dicyandiamide production \n", + "12 ethylene oxide production \n", + "13 ethylene oxide production \n", + "14 ethylene oxide production \n", + "15 glyoxal production \n", + "16 glyoxal production \n", + "17 glyoxal production \n", + "18 imidazole production \n", + "19 imidazole production \n", + "20 imidazole production \n", + "21 market for calcium carbide, technical grade \n", + "22 market for coal tar \n", + "23 market for ethylene \n", + "24 market for ethylene oxide \n", + "25 market for glyoxal \n", + "26 market for hard coal \n", + "27 market for hard coal \n", + "28 market for imidazole \n", + "29 market for naphthalene sulfonic acid \n", + "30 market for oxygen, liquid \n", + "31 market for wood preservative, creosote \n", + "32 market group for electricity, medium voltage \n", + "33 naphthalene sulfonic acid production \n", + "34 naphthalene sulfonic acid production \n", + "35 polymeric carbon nitride prodution \n", + "36 wood preservative production, creosote \n", + "37 wood preservative production, creosote \n", + "\n", + " source value \n", + "0 1-iodonaphthalene production 2.163055 \n", + "1 Cu/PCN catalyst production, at 10.8 wt% Cu 0.402700 \n", + "2 market for dimethyl sulfoxide 0.314026 \n", + "3 market for imidazole 2.729986 \n", + "4 1-(naphthalen-1-yl)-1H-imidazole production wi... 5.609766 \n", + "5 market for naphthalene sulfonic acid 2.163055 \n", + "6 polymeric carbon nitride prodution 0.330835 \n", + "7 market group for electricity, medium voltage 0.251784 \n", + "8 market group for electricity, medium voltage 0.074602 \n", + "9 market for hard coal 0.318650 \n", + "10 market group for electricity, medium voltage 0.022442 \n", + "11 market for calcium carbide, technical grade 0.310739 \n", + "12 market for ethylene 0.280362 \n", + "13 market for oxygen, liquid 0.120335 \n", + "14 market group for electricity, medium voltage 0.047217 \n", + "15 market for ethylene oxide 0.565643 \n", + "16 market for oxygen, liquid 0.188715 \n", + "17 market group for electricity, medium voltage 0.076679 \n", + "18 market for ammonia, anhydrous, liquid 0.338462 \n", + "19 market for glyoxal 1.286795 \n", + "20 market group for electricity, medium voltage 0.085404 \n", + "21 calcium carbide production, technical grade 0.308768 \n", + "22 coking 0.821164 \n", + "23 ethylene production, average 0.282943 \n", + "24 ethylene oxide production 0.566556 \n", + "25 glyoxal production 1.266251 \n", + "26 hard coal mine operation and hard coal prepara... 0.478516 \n", + "27 market group for electricity, medium voltage 0.015134 \n", + "28 imidazole production 2.702893 \n", + "29 naphthalene sulfonic acid production 2.078794 \n", + "30 air separation, cryogenic 0.319764 \n", + "31 wood preservative production, creosote 1.019797 \n", + "32 market group for electricity, medium voltage 1.436761 \n", + "33 market for wood preservative, creosote 1.081765 \n", + "34 market group for electricity, medium voltage 0.331818 \n", + "35 dicyandiamide production 0.316818 \n", + "36 market for coal tar 0.828883 \n", + "37 market group for electricity, medium voltage 0.033588 " + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_hetero" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "6f8f12bf", + "metadata": { + "notebookRunGroups": { + "groupValue": "2" + } + }, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "d2c51b50000e465dac1ef029d5e311cd", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "SankeyWidget(align_link_types=True, layout=Layout(height='453.54330709199996', width='1776.377952777'), linkLa…" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot_sankey(\n", + " df_links=df_homogen[df_homogen.target != df_homogen.source],\n", + " size=(w_px, h_px),\n", + " save=True,\n", + " file_name=\"homogen\",\n", + ")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "f00a4dda", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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targetsourcevalue
53market group for electricity, medium voltagemarket group for electricity, medium voltage14.787129
18air separation, cryogenicmarket group for electricity, medium voltage2.297423
28chlor-alkali electrolysis, membrane cellmarket group for electricity, medium voltage2.133812
59trichloromethane productionmarket group for electricity, medium voltage1.562060
14N-methyl-2-pyrrolidone productionmarket group for electricity, medium voltage0.357757
26butane-1,4-diol productionmarket group for electricity, medium voltage0.333475
33cumene productionmarket group for electricity, medium voltage0.300437
36dehydrogenation of butan-1,4-diolmarket group for electricity, medium voltage0.270366
56phenol production, from cumenemarket group for electricity, medium voltage0.243044
30chlorine production, liquidmarket group for electricity, medium voltage0.237712
37heat production, at hard coal industrial furna...market group for electricity, medium voltage0.046197
21benzene chlorinationmarket group for electricity, medium voltage0.029376
17acetylene productionmarket group for electricity, medium voltage0.000061
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targetsourcevalue
61trichloromethane productionmarket group for natural gas, high pressure1.163064
38heat production, natural gas, at industrial fu...market group for natural gas, high pressure1.131604
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targetsourcevalue
60trichloromethane productionmarket group for heat, district or industrial,...5.784491
58trichloromethane productionmarket for chlorine, liquid5.032062
59trichloromethane productionmarket group for electricity, medium voltage1.562060
61trichloromethane productionmarket group for natural gas, high pressure1.163064
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" + ], + "text/plain": [ + " target \\\n", + "60 trichloromethane production \n", + "58 trichloromethane production \n", + "59 trichloromethane production \n", + "61 trichloromethane production \n", + "\n", + " source value \n", + "60 market group for heat, district or industrial,... 5.784491 \n", + "58 market for chlorine, liquid 5.032062 \n", + "59 market group for electricity, medium voltage 1.562060 \n", + "61 market group for natural gas, high pressure 1.163064 " + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_homogen[\n", + " df_homogen.target.isin([\"trichloromethane production\"])\n", + "].sort_values(\"value\", ascending=False)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "901f36a7", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3.9.7", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.7" + }, + "vscode": { + "interpreter": { + "hash": "bd958e837233136b65ef886a306db27124b4a48f9a20f2930a4727b08bf88f0b" + } + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks/LCAscores_sensitivity_scatterplot.ipynb b/notebooks/LCAscores_sensitivity_scatterplot.ipynb new file mode 100644 index 0000000..2d1dda4 --- /dev/null +++ b/notebooks/LCAscores_sensitivity_scatterplot.ipynb @@ -0,0 +1,368 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "dc075a87-3c79-48c8-a6fb-1b9e21df49f5", + "metadata": {}, + "outputs": [], + "source": [ + "import textwrap\n", + "from os import mkdir\n", + "from pathlib import Path\n", + "\n", + "import matplotlib as mpl\n", + "import matplotlib.pyplot as plt\n", + "import openpyxl as xl\n", + "import pandas as pd\n", + "from matplotlib.ticker import ( # ScalarFormatter,; FixedFormatter,; NullFormatter,\n", + " FixedLocator,\n", + " LogLocator,\n", + " MultipleLocator,\n", + ")\n", + "from project_path import ROOT_DIR\n", + "\n", + "plt.style.use(\"default\")" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "8535d1ce", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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cycle_numberHomogeneousHeterogeneous
170.3326735.609766
237.4307905.251403
420.9798495.072222
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" + ], + "text/plain": [ + "cycle_number Homogeneous Heterogeneous\n", + "1 70.332673 5.609766\n", + "2 37.430790 5.251403\n", + "4 20.979849 5.072222\n", + "6 15.496202 5.012495\n", + "8 12.754378 4.982631\n", + "10 11.109284 4.964713\n", + "20 7.819096 4.928877\n", + "30 6.722366 4.916932\n", + "40 6.174002 4.910959\n", + "50 5.844983 4.907375\n", + "60 5.625637 4.904986\n", + "70 5.468961 4.903280\n", + "80 5.351455 4.902000\n", + "90 5.260061 4.901004\n", + "100 5.186945 4.900208" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_scenarios = pd.read_excel(r\"../data/interim/results_scenario_table.xlsx\", header=2, index_col=0).T\n", + "df_scenarios" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "a33f6c18", + "metadata": {}, + "outputs": [], + "source": [ + "pngFilesDir = Path(\n", + " ROOT_DIR, r\"data/processed/png_files\"\n", + ") # full path to folder w/png files\n", + "svgFilesDir = Path(\n", + " ROOT_DIR, r\"data/processed/svg_files\"\n", + ") # full path to folder w/svg files\n", + "\n", + "# Generate required folders if needed\n", + "if not Path.exists(pngFilesDir):\n", + " mkdir(pngFilesDir)\n", + "if not Path.exists(svgFilesDir):\n", + " mkdir(svgFilesDir)" + ] + }, + { + "cell_type": "markdown", + "id": "58a7d8f3", + "metadata": {}, + "source": [ + "Preliminary colors:
\n", + "\n", + "heterogeneous
\n", + "homogeneous
\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "05c1ea13", + "metadata": {}, + "outputs": [], + "source": [ + "## BAR CHART colors\n", + "\n", + "allcolors = {\n", + " \"heterogeneous\": \"#ef8a62\",\n", + " \"homogeneous\": \"#67a9cf\",\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "7165ff82", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plot\n", + "\n", + "# Figure specifications (fonts, sizes, figsize, etc.)\n", + "size_legend_font = 8\n", + "size_tick_font = 6\n", + "size_label_font = 7\n", + "mpl.rc(\"xtick\", labelsize=size_tick_font)\n", + "mpl.rc(\"ytick\", labelsize=size_tick_font)\n", + "mpl.rc(\"axes\", labelsize=size_label_font, linewidth=0.6)\n", + "mpl.rc(\"font\", family=\"Arial\")\n", + "# mpl.rc('text', usetex=True) # no LaTex on this machine\n", + "mpl.rc(\"mathtext\", default=\"regular\")\n", + "# mpl.rc('lines', )\n", + "# mpl.rc('markers', )\n", + "marker_style = dict(lw=0, marker=\"D\", ms=1.5, mec=\"r\", mfc=\"none\", mew=0.7)\n", + "\n", + "# Size of the figure\n", + "size_in_mm = (85, 85) # input the desired size in mm (width, height)\n", + "\n", + "# Variables to define figure's empty space from its borders to the spines of the axes\n", + "from_left = 13 # in mm\n", + "from_right = 2 # in mm\n", + "from_bottom = 10 # in mm\n", + "from_top = 2 # in mm\n", + "\n", + "fig = plt.figure(\n", + " figsize=[x / 25.4 for x in size_in_mm],\n", + " dpi=300,\n", + " # tight_layout = {'pad': 0}\n", + ")\n", + "\n", + "gs = fig.add_gridspec(\n", + " nrows=1,\n", + " ncols=1,\n", + " # width_ratios=[1, 1, 1],\n", + " # height_ratios=[0.86, 0.16],\n", + " # wspace=0.15,\n", + " # hspace=0.005,\n", + " top=1 - from_top / size_in_mm[1],\n", + " bottom=from_bottom / size_in_mm[1],\n", + " left=from_left / size_in_mm[0],\n", + " right=1 - from_right / size_in_mm[0],\n", + ")\n", + "\n", + "ax = fig.add_subplot(gs[0])\n", + "\n", + "## Scatter plot ################################################################\n", + "cycle_num = df_scenarios.index.values.tolist()\n", + "\n", + "ax.set_xlabel(\"Cycle number (-)\")\n", + "ax.set_ylabel(\"GWP100 ($kg_{CO_{2}eq} kg^{-1}$)\")\n", + "\n", + "ax.scatter(\n", + " cycle_num,\n", + " df_scenarios.Homogeneous,\n", + " s=12,\n", + " edgecolors=\"k\",\n", + " linewidth=0.4,\n", + " color=[v for v in allcolors.values()][1],\n", + " label=df_scenarios.columns[0],\n", + ")\n", + "ax.scatter(\n", + " cycle_num,\n", + " df_scenarios.Heterogeneous,\n", + " s=12,\n", + " edgecolors=\"k\",\n", + " linewidth=0.4,\n", + " color=[v for v in allcolors.values()][0],\n", + " label=df_scenarios.columns[1],\n", + ")\n", + "ax.set_xscale(\"log\")\n", + "ax.set_yscale(\"log\")\n", + "# ax.grid(True)\n", + "ax.xaxis.grid(True, which=\"both\", ls=\":\", lw=0.3)\n", + "ax.yaxis.grid(True, which=\"both\", ls=\":\", lw=0.3)\n", + "ax.set_axisbelow(True)\n", + "ax.set_xlim([0.9, 110])\n", + "ax.set_ylim([0.9, 110])\n", + "# ax.yaxis.set_major_locator(LogLocator(base=10, numticks=10))\n", + "\n", + "# # LEGEND\n", + "handles, labels = ax.get_legend_handles_labels()\n", + "lg = fig.legend(\n", + " handles,\n", + " labels,\n", + " frameon=True,\n", + " loc=\"upper right\",\n", + " fancybox=False,\n", + " framealpha=1,\n", + " edgecolor=\"k\",\n", + " borderpad=0.4,\n", + " # borderaxespad=0.5,\n", + " bbox_to_anchor=(0.97, 0.97),\n", + " ncol=1,\n", + " markerscale=1.5,\n", + " handlelength=1,\n", + " fontsize=size_legend_font,\n", + ")\n", + "lg.get_frame().set_linewidth(0.5)\n", + "\n", + "figNamePNG = \"scatter_sensitivity.png\"\n", + "figNameSVG = \"scatter_sensitivity.svg\"\n", + "\n", + "plt.savefig(str(pngFilesDir / figNamePNG)) # export fig as png\n", + "plt.savefig(str(svgFilesDir / figNameSVG)) # export fig as svg" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.7" + }, + "vscode": { + "interpreter": { + "hash": "bd958e837233136b65ef886a306db27124b4a48f9a20f2930a4727b08bf88f0b" + } + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks/LCAscores_total_barplot.ipynb b/notebooks/LCAscores_total_barplot.ipynb new file mode 100644 index 0000000..5d206b8 --- /dev/null +++ b/notebooks/LCAscores_total_barplot.ipynb @@ -0,0 +1,538 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "dc075a87-3c79-48c8-a6fb-1b9e21df49f5", + "metadata": {}, + "outputs": [], + "source": [ + "import textwrap\n", + "from os import mkdir\n", + "from pathlib import Path\n", + "from pprint import pprint\n", + "\n", + "import brightway2 as bw\n", + "import matplotlib as mpl\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import pandas as pd\n", + "import productivity_boosters as prbs\n", + "from matplotlib.ticker import ( # ScalarFormatter,; FixedFormatter,; NullFormatter,\n", + " FixedLocator,\n", + " LogLocator,\n", + " MultipleLocator,\n", + ")\n", + "from project_path import ROOT_DIR\n", + "\n", + "plt.style.use(\"default\")" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "c847b277-050c-4c01-9ac8-f715c3dcd999", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Databases dictionary with 3 object(s):\n", + "\tGAC_project_db\n", + "\tbiosphere3\n", + "\tei39cutoff" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "bw.projects\n", + "bw.projects.set_current(\"GAC_project\")\n", + "bw.databases" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "ebf5e2bf-86f4-48d9-9a35-a91a943f9028", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "('IPCC 2021',\n", + " 'climate change: including SLCFs',\n", + " 'global warming potential (GWP100)')" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ipcc_methods = [\n", + " met\n", + " for met in bw.methods\n", + " if \"ipcc 2021\" in str(met).lower()\n", + " and \"climate change: including SLCFs\" in str(met)\n", + " and \"no LT\" not in str(met)\n", + " and \"GWP100\" in str(met)\n", + " or (\"ipcc 2013\" in str(met).lower() and \"no LT\" not in str(met) and \"GWP100\" in str(met))\n", + " # or (\"ipcc 2021\" in str(met).lower() and \"no LT\" not in str(met) and \"GWP100\" in str(met))\n", + "][1]# [:2]\n", + "ipcc_methods\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "345341b7-e690-4136-aa58-c0c357b91366", + "metadata": {}, + "outputs": [], + "source": [ + "DB_NAME = \"GAC_project_db\"" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "7be1da1e", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'1-(naphthalen-1-yl)-1H-imidazole production with heterogeneous Cu/PCN catalyst (10.8 wt% Cu)' (kilogram, GLO, None)" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "hetero_system = [\n", + " ds\n", + " for ds in bw.Database(DB_NAME)\n", + " if \"1-(naphthalen-1-yl)-1H-imidazole\" in ds[\"name\"] and \"Cu/PCN\" in ds[\"name\"]\n", + "][0]\n", + "hetero_system" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "8af8420f", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'1-(naphthalen-1-yl)-1H-imidazole production with homogeneous Cu2O/Altman-Buchwald-ligand catalytic complex' (kilogram, GLO, None)" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "homoge_system = [\n", + " ds\n", + " for ds in bw.Database(DB_NAME)\n", + " if \"1-(naphthalen-1-yl)-1H-imidazole\" in ds[\"name\"]\n", + " and \"Cu2O/Altman-Buchwald-ligand\" in ds[\"name\"]\n", + "][0]\n", + "homoge_system" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "fc030a43", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "CalculationSetups dictionary with 5 object(s):\n", + "\tcatalysts_only\n", + "\tcontrib\n", + "\tsolveLCA\n", + "\tsolveLCA_scenarios\n", + "\ttrial" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "bw.calculation_setups" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "e809aeb7", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Duration: 0:00:00.950457\n" + ] + } + ], + "source": [ + "result = prbs.pretty_lca(FUs=[{hetero_system: 1}, {homoge_system: 1}], methods=[ipcc_methods], setup_name=\"solveLCA\")" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "7a719be0", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " activity name \\\n", + "0 heterogeneous \n", + "1 homogeneous \n", + "\n", + " IPCC 2021 - climate change: including SLCFs - global warming potential (GWP100) (kg CO2-Eq) \n", + "0 5.609766 \n", + "1 70.332673 " + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "result_plot = result.iloc[:, [1, 5]]\n", + "result_plot[\"activity name\"] = result_plot[\"activity name\"].replace(\n", + " {\n", + " result_plot[\"activity name\"][0]: \"heterogeneous\",\n", + " result_plot[\"activity name\"][1]: \"homogeneous\",\n", + " }\n", + ")\n", + "result_plot\n" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "a33f6c18", + "metadata": {}, + "outputs": [], + "source": [ + "pngFilesDir = Path(ROOT_DIR, r\"data/interim/png_files\") # full path to folder w/png files\n", + "svgFilesDir = Path(ROOT_DIR, r\"data/interim/svg_files\") # full path to folder w/svg files\n", + "\n", + "# Generate required folders if needed\n", + "if not Path.exists(pngFilesDir):\n", + " mkdir(pngFilesDir)\n", + "if not Path.exists(svgFilesDir):\n", + " mkdir(svgFilesDir)" + ] + }, + { + "cell_type": "markdown", + "id": "58a7d8f3", + "metadata": {}, + "source": [ + "Preliminary colors:
\n", + "\n", + "heterogeneous
\n", + "homogeneous
\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "05c1ea13", + "metadata": {}, + "outputs": [], + "source": [ + "## BAR CHART colors\n", + "\n", + "allcolors = {\n", + " \"heterogeneous\": \"#ef8a62\",\n", + " \"homogeneous\": \"#67a9cf\",\n", + "}\n" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "0ace9627", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\vtulus\\AppData\\Local\\Temp\\ipykernel_608\\526853604.py:59: UserWarning: FixedFormatter should only be used together with FixedLocator\n", + " ax.yaxis.set_ticklabels(result_plot[\"activity name\"], rotation=-90, va=\"center\")\n" + ] + }, + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plot\n", + "\n", + "# Figure specifications (fonts, sizes, figsize, etc.)\n", + "size_legend_font = 8\n", + "size_tick_font = 6\n", + "size_label_font = 7\n", + "mpl.rc(\"xtick\", labelsize=size_tick_font)\n", + "mpl.rc(\"ytick\", labelsize=size_tick_font)\n", + "mpl.rc(\"axes\", labelsize=size_label_font, linewidth=0.6)\n", + "mpl.rc(\"font\", family=\"Arial\")\n", + "mpl.rc(\"mathtext\", default=\"regular\")\n", + "# mpl.rc('lines', )\n", + "# mpl.rc('markers', )\n", + "marker_style = dict(lw=0, marker=\"D\", ms=1.5, mec=\"r\", mfc=\"none\", mew=0.7)\n", + "\n", + "# Size of the figure\n", + "size_in_mm = (36, 110) # input the desired size in mm (width, height)\n", + "\n", + "# Variables to define figure's empty space from its borders to the spines of the axes\n", + "from_left = 4 # in mm\n", + "from_right = 2 # in mm\n", + "from_bottom = 11 # in mm\n", + "from_top = 2 # in mm\n", + "\n", + "fig = plt.figure(\n", + " figsize=[x / 25.4 for x in size_in_mm],\n", + " dpi=300,\n", + " # tight_layout = {'pad': 0}\n", + ")\n", + "\n", + "gs = fig.add_gridspec(\n", + " nrows=1,\n", + " ncols=1,\n", + " # width_ratios=[1, 1, 1],\n", + " # height_ratios=[0.86, 0.16],\n", + " # wspace=0.15,\n", + " # hspace=0.005,\n", + " top=1 - from_top / size_in_mm[1],\n", + " bottom=from_bottom / size_in_mm[1],\n", + " left=from_left / size_in_mm[0],\n", + " right=1 - from_right / size_in_mm[0],\n", + ")\n", + "\n", + "ax = fig.add_subplot(gs[0])\n", + "\n", + "## BAR CHART ################################################################\n", + "height = 0.8 # the height of the bars (thickness)\n", + "bars = ax.barh(\n", + " y=result_plot.iloc[:, 0],\n", + " width=result_plot.iloc[:, 1],\n", + " height=height,\n", + " color=[v for v in allcolors.values()],\n", + " edgecolor=\"k\",\n", + " linewidth=0.3,\n", + ")\n", + "\n", + "ax.bar_label(bars, fmt=\"%.1f\", padding=-7, fontsize=5)\n", + "\n", + "ax.yaxis.set_ticklabels(result_plot[\"activity name\"], rotation=-90, va=\"center\")\n", + "ax.tick_params(\n", + " axis=\"y\", which=\"major\", length=0.001, # pad=-100\n", + ") # almost invisible, pad = separation of text from axis\n", + "\n", + "ax.set_xlabel(\"GWP100 ($kg_{CO_{2}eq} kg^{-1}$)\")\n", + "ax.grid(True)\n", + "ax.xaxis.grid(True, ls=\":\", lw=0.3)\n", + "ax.yaxis.grid(True, ls=\":\", lw=0.3)\n", + "ax.invert_yaxis()\n", + "ax.xaxis.set_minor_locator(MultipleLocator(5))\n", + "ax.xaxis.set_major_locator(MultipleLocator(20))\n", + "ax.set_axisbelow(True)\n", + "\n", + "figNamePNG = \"LCAscore_total_barplot.png\"\n", + "figNameSVG = \"LCAscore_total_barplot.svg\"\n", + "\n", + "plt.savefig(str(pngFilesDir / figNamePNG)) # export fig as png\n", + "plt.savefig(str(svgFilesDir / figNameSVG)) # export fig as svg" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3.9.7", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.7" + }, + "vscode": { + "interpreter": { + "hash": "bd958e837233136b65ef886a306db27124b4a48f9a20f2930a4727b08bf88f0b" + } + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks/LCAscores_total_doughnutchart.ipynb b/notebooks/LCAscores_total_doughnutchart.ipynb new file mode 100644 index 0000000..4a904af --- /dev/null +++ b/notebooks/LCAscores_total_doughnutchart.ipynb @@ -0,0 +1,1322 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "dc075a87-3c79-48c8-a6fb-1b9e21df49f5", + "metadata": {}, + "outputs": [], + "source": [ + "from os import mkdir\n", + "from pathlib import Path\n", + "\n", + "import brightway2 as bw\n", + "import bw2calc as bc\n", + "import matplotlib as mpl\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import pandas as pd\n", + "from textwrap import wrap\n", + "from project_path import ROOT_DIR\n", + "\n", + "plt.style.use(\"default\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "c847b277-050c-4c01-9ac8-f715c3dcd999", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Databases dictionary with 3 object(s):\n", + "\tGAC_project_db\n", + "\tbiosphere3\n", + "\tei39cutoff" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "bw.projects\n", + "bw.projects.set_current(\"GAC_project\")\n", + "bw.databases" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "ebf5e2bf-86f4-48d9-9a35-a91a943f9028", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "('IPCC 2021',\n", + " 'climate change: including SLCFs',\n", + " 'global warming potential (GWP100)')" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ipcc_methods = [\n", + " met\n", + " for met in bw.methods\n", + " if \"ipcc 2021\" in str(met).lower()\n", + " and \"climate change: including SLCFs\" in str(met)\n", + " and \"no LT\" not in str(met)\n", + " and \"GWP100\" in str(met)\n", + " or (\"ipcc 2013\" in str(met).lower() and \"no LT\" not in str(met) and \"GWP100\" in str(met))\n", + " # or (\"ipcc 2021\" in str(met).lower() and \"no LT\" not in str(met) and \"GWP100\" in str(met))\n", + "][1]# [:2]\n", + "ipcc_methods\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "085f88cb", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[('ReCiPe 2016 v1.03, endpoint (H)',\n", + " 'total: ecosystem quality',\n", + " 'ecosystem quality'),\n", + " ('ReCiPe 2016 v1.03, endpoint (H)', 'total: human health', 'human health'),\n", + " ('ReCiPe 2016 v1.03, endpoint (H)',\n", + " 'total: natural resources',\n", + " 'natural resources')]" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "recipe_methods = [\n", + " met\n", + " for met in bw.methods\n", + " if \"recipe\" in str(met).lower()\n", + " and \"2016\" in str(met).lower()\n", + " and \"endpoint (H)\" in str(met)\n", + " and \"total\" in str(met)\n", + " and \"no LT\" not in str(met)\n", + " \n", + " # midpoints\n", + " # and \"(H)\" in str(met)\n", + " # and \"endpoint\" not in str(met)\n", + "]\n", + "recipe_methods\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "345341b7-e690-4136-aa58-c0c357b91366", + "metadata": {}, + "outputs": [], + "source": [ + "DB_NAME = \"GAC_project_db\"" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "7be1da1e", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'1-(naphthalen-1-yl)-1H-imidazole production with heterogeneous Cu/PCN catalyst (10.8 wt% Cu)' (kilogram, GLO, None)" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "hetero_system = [\n", + " ds\n", + " for ds in bw.Database(DB_NAME)\n", + " if \"1-(naphthalen-1-yl)-1H-imidazole\" in ds[\"name\"] and \"Cu/PCN\" in ds[\"name\"]\n", + "][0]\n", + "hetero_system" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "8af8420f", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'1-(naphthalen-1-yl)-1H-imidazole production with homogeneous Cu2O/Altman-Buchwald-ligand catalytic complex' (kilogram, GLO, None)" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "homoge_system = [\n", + " ds\n", + " for ds in bw.Database(DB_NAME)\n", + " if \"1-(naphthalen-1-yl)-1H-imidazole\" in ds[\"name\"]\n", + " and \"Cu2O/Altman-Buchwald-ligand\" in ds[\"name\"]\n", + "][0]\n", + "homoge_system" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "fc030a43", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "CalculationSetups dictionary with 5 object(s):\n", + "\tcatalysts_only\n", + "\tcontrib\n", + "\tsolveLCA\n", + "\tsolveLCA_scenarios\n", + "\ttrial" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "bw.calculation_setups" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "ed4364c5", + "metadata": {}, + "outputs": [], + "source": [ + "def turn_to_scientific(val: float, n_decimals: int, excl_power_range: int = (-1, 1)) -> str:\n", + " \"\"\"Converts values to scientific notation of the type N x 10^m.\n", + "\n", + " Parameters\n", + " ----------\n", + " val : float\n", + " Value to (possibly) transform to scientific notation\n", + " n_decimals : int\n", + " Number of decimals of the leading digit\n", + " excl_power_range : tuple of int, default (-1,1)\n", + " (optional) Power range not to be transformed to scientific notation\n", + " By default [-1,1) powers will not be transformed\n", + "\n", + " Returns\n", + " -------\n", + " sci_val: str\n", + " Value transformed into scientific notation of the type n.nn x 10^m\n", + "\n", + " Notes\n", + " -----\n", + " Adapted from https://stackoverflow.com/a/31453961/14485040\n", + " \"\"\"\n", + "\n", + " left, right = excl_power_range\n", + "\n", + " s = \"{x:.{ndp:d}e}\".format(\n", + " x=val, ndp=n_decimals) # pylint: disable=invalid-name\n", + " digits, power = s.split(\"e\")\n", + " if (\n", + " int(power) not in range(left, right)\n", + " ): # power is not in provided range -> transform\n", + " sci_val = r\"${dig:s}\\times 10^{{{pw:d}}}$\".format(\n", + " dig=digits, pw=int(power))\n", + " else: # -> leave unchanged\n", + " sci_val = \"{x:.{ndp:d}f}\".format(x=val, ndp=n_decimals)\n", + "\n", + " return sci_val" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "96359b04", + "metadata": {}, + "outputs": [], + "source": [ + "def do_recursive_calculation(\n", + " activity,\n", + " lcia_method,\n", + " lca_obj=None,\n", + " total_score=None,\n", + " amount=1,\n", + " level=0,\n", + " max_level=3,\n", + " cutoff=1e-2,\n", + "):\n", + " \"\"\" \"Adapted from https://github.com/brightway-lca/brightway2/blob/master/notebooks/Contribution%20analysis%20and%20comparison.ipynb\"\"\"\n", + "\n", + " results = list()\n", + "\n", + " def get_lca_attributes(activity, lcia_method):\n", + " result_item = dict()\n", + " result_item[\"database\"] = activity[\"database\"]\n", + " result_item[\"activity name\"] = activity[\"name\"]\n", + " result_item[\"reference product\"] = activity[\"reference product\"]\n", + " result_item[\"unit\"] = activity[\"unit\"]\n", + " result_item[\"location\"] = activity[\"location\"]\n", + " result_item[\" - \".join(lcia_method) + f' ({bw.Method(lcia_method).metadata[\"unit\"]})'] = lca_obj.score\n", + " return result_item\n", + "\n", + " if lca_obj is None:\n", + " lca_obj = bc.LCA({activity: amount}, lcia_method)\n", + " lca_obj.lci()\n", + " lca_obj.lcia()\n", + " total_score = lca_obj.score\n", + " elif total_score is None:\n", + " raise ValueError\n", + " else:\n", + " lca_obj.redo_lcia({activity: amount})\n", + " if abs(lca_obj.score) <= abs(total_score * cutoff):\n", + " return\n", + "\n", + " results.append(get_lca_attributes(activity, lcia_method))\n", + "\n", + " if level < max_level:\n", + " for exc in activity.technosphere():\n", + " res = (\n", + " do_recursive_calculation(\n", + " activity=exc.input,\n", + " lcia_method=lcia_method,\n", + " lca_obj=lca_obj,\n", + " total_score=total_score,\n", + " amount=amount * exc[\"amount\"],\n", + " level=level + 1,\n", + " max_level=max_level,\n", + " cutoff=cutoff,\n", + " )\n", + " )\n", + " results.append(res[0])\n", + " \n", + " return results" + ] + }, + { + "cell_type": "raw", + "id": "2e24d83d", + "metadata": { + "notebookRunGroups": { + "groupValue": "1" + } + }, + "source": [ + "\n", + "r_dict = {}\n", + "for lcia_method in ipcc_methods:\n", + " r = do_recursive_calculation(\n", + " activity=hetero_system, lcia_method=lcia_method, level=1, max_level=2\n", + " )\n", + " r_dict[lcia_method] = r\n", + "r_dict\n", + "for met, res in r_dict.items():\n", + " pd.DataFrame.from_dict(res)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "1e5346d5", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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reference productIPCC 2021 - climate change: including SLCFs - global warming potential (GWP100) (kg CO2-Eq)
11-iodonaphthalene2.002083
2imidazole2.526824
3Cu2O/Altman-Buchwald-ligand catalytic complex54.210020
4N-methyl-2-pyrrolidone11.593745
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" + ], + "text/plain": [ + " reference product \\\n", + "1 1-iodonaphthalene \n", + "2 imidazole \n", + "3 Cu2O/Altman-Buchwald-ligand catalytic complex \n", + "4 N-methyl-2-pyrrolidone \n", + "\n", + " IPCC 2021 - climate change: including SLCFs - global warming potential (GWP100) (kg CO2-Eq) \n", + "1 2.002083 \n", + "2 2.526824 \n", + "3 54.210020 \n", + "4 11.593745 " + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "res_hetero_gwp = pd.DataFrame(\n", + " do_recursive_calculation(\n", + " activity=hetero_system, lcia_method=ipcc_methods, level=1, max_level=2\n", + " )\n", + ")\n", + "\n", + "res_hetero_gwp_plot = res_hetero_gwp.iloc[1:, [2, 5]]\n", + "# res_hetero_gwp_plot\n", + "\n", + "res_homoge_gwp = pd.DataFrame(\n", + " do_recursive_calculation(\n", + " activity=homoge_system, lcia_method=ipcc_methods, level=1, max_level=2\n", + " )\n", + ")\n", + "res_homoge_gwp_plot = res_homoge_gwp.iloc[1:, [2, 5]]\n", + "res_homoge_gwp_plot" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "d2fa6930", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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reference productReCiPe 2016 v1.03, endpoint (H) - total: ecosystem quality - ecosystem quality (species.yr)
11-iodonaphthalene13.811482
2imidazole11.161289
3Cu/PCN catalyst (10.8 wt% Cu)2.135037
4dimethyl sulfoxide1.744744
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" + ], + "text/plain": [ + " reference product \\\n", + "1 1-iodonaphthalene \n", + "2 imidazole \n", + "3 Cu/PCN catalyst (10.8 wt% Cu) \n", + "4 dimethyl sulfoxide \n", + "\n", + " ReCiPe 2016 v1.03, endpoint (H) - total: ecosystem quality - ecosystem quality (species.yr) \n", + "1 13.811482 \n", + "2 11.161289 \n", + "3 2.135037 \n", + "4 1.744744 " + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "res_homoge_eq = pd.DataFrame(\n", + " do_recursive_calculation(\n", + " activity=homoge_system, lcia_method=recipe_methods[0], level=1, max_level=2\n", + " )\n", + ")\n", + "res_homoge_eq_plot = res_homoge_eq.iloc[1:, [2, 5]]\n", + "res_homoge_eq_plot.iloc[:,1] *= 1e9 # multiply by 1e9\n", + "res_homoge_eq_plot\n", + "\n", + "res_hetero_eq = pd.DataFrame(\n", + " do_recursive_calculation(\n", + " activity=hetero_system, lcia_method=recipe_methods[0], level=1, max_level=2\n", + " )\n", + ")\n", + "res_hetero_eq_plot = res_hetero_eq.iloc[1:, [2, 5]]\n", + "res_hetero_eq_plot.iloc[:,1] *= 1e9 # multiply by 1e9\n", + "res_hetero_eq_plot" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "d2fa6930", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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reference productReCiPe 2016 v1.03, endpoint (H) - total: human health - human health (DALYs)
11-iodonaphthalene9.280134
2imidazole5.577956
3Cu/PCN catalyst (10.8 wt% Cu)1.624831
4dimethyl sulfoxide1.038294
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" + ], + "text/plain": [ + " reference product \\\n", + "1 1-iodonaphthalene \n", + "2 imidazole \n", + "3 Cu/PCN catalyst (10.8 wt% Cu) \n", + "4 dimethyl sulfoxide \n", + "\n", + " ReCiPe 2016 v1.03, endpoint (H) - total: human health - human health (DALYs) \n", + "1 9.280134 \n", + "2 5.577956 \n", + "3 1.624831 \n", + "4 1.038294 " + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "res_homoge_hh = pd.DataFrame(\n", + " do_recursive_calculation(\n", + " activity=homoge_system, lcia_method=recipe_methods[1], level=1, max_level=2\n", + " )\n", + ")\n", + "res_homoge_hh_plot = res_homoge_hh.iloc[1:, [2, 5]]\n", + "res_homoge_hh_plot.iloc[:,1] *= 1e6 # multiply by 1e6\n", + "res_homoge_hh_plot\n", + "\n", + "res_hetero_hh = pd.DataFrame(\n", + " do_recursive_calculation(\n", + " activity=hetero_system, lcia_method=recipe_methods[1], level=1, max_level=2\n", + " )\n", + ")\n", + "# res_hetero\n", + "res_hetero_hh_plot = res_hetero_hh.iloc[1:, [2, 5]]\n", + "res_hetero_hh_plot.iloc[:,1] *= 1e6 # multiply by 1e6\n", + "res_hetero_hh_plot" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "d2fa6930", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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reference productReCiPe 2016 v1.03, endpoint (H) - total: natural resources - natural resources (USD 2013)
11-iodonaphthalene1.762329
2imidazole3.773720
3Cu/PCN catalyst (10.8 wt% Cu)0.202768
4dimethyl sulfoxide0.669991
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" + ], + "text/plain": [ + " reference product \\\n", + "1 1-iodonaphthalene \n", + "2 imidazole \n", + "3 Cu/PCN catalyst (10.8 wt% Cu) \n", + "4 dimethyl sulfoxide \n", + "\n", + " ReCiPe 2016 v1.03, endpoint (H) - total: natural resources - natural resources (USD 2013) \n", + "1 1.762329 \n", + "2 3.773720 \n", + "3 0.202768 \n", + "4 0.669991 " + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "res_homoge_nr = pd.DataFrame(\n", + " do_recursive_calculation(\n", + " activity=homoge_system, lcia_method=recipe_methods[2], level=1, max_level=2\n", + " )\n", + ")\n", + "res_homoge_nr_plot = res_homoge_nr.iloc[1:, [2, 5]]\n", + "res_homoge_nr_plot.iloc[:,1] *= 1e1 # multiply by 1e1\n", + "res_homoge_nr_plot\n", + "\n", + "res_hetero_nr = pd.DataFrame(\n", + " do_recursive_calculation(\n", + " activity=hetero_system, lcia_method=recipe_methods[2], level=1, max_level=2\n", + " )\n", + ")\n", + "# res_hetero\n", + "res_hetero_nr_plot = res_hetero_nr.iloc[1:, [2, 5]]\n", + "res_hetero_nr_plot.iloc[:,1] *= 1e1 # multiply by 1e1\n", + "res_hetero_nr_plot" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "a33f6c18", + "metadata": {}, + "outputs": [], + "source": [ + "pngFilesDir = Path(ROOT_DIR, r\"data/interim/png_files\") # full path to folder w/png files\n", + "svgFilesDir = Path(ROOT_DIR, r\"data/interim/svg_files\") # full path to folder w/svg files\n", + "\n", + "# Generate required folders if needed\n", + "if not Path.exists(pngFilesDir):\n", + " mkdir(pngFilesDir)\n", + "if not Path.exists(svgFilesDir):\n", + " mkdir(svgFilesDir)" + ] + }, + { + "cell_type": "markdown", + "id": "58a7d8f3", + "metadata": {}, + "source": [ + "Preliminary colors:
\n", + "\n", + "heterogeneous
\n", + "homogeneous
\n", + "\n", + "Sequence hetero:
\n", + "...1
\n", + "...2
\n", + "...3
\n", + "...4
\n", + "\n", + "Sequence homo:
\n", + "...1
\n", + "...2
\n", + "...3
\n", + "...4
" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "05c1ea13", + "metadata": {}, + "outputs": [], + "source": [ + "## Doughnut CHART colors\n", + "\n", + "allcolors = {\n", + " \"heterogeneous\": \"#ef8a62\",\n", + " \"homogeneous\": \"#67a9cf\",\n", + "}\n", + "\n", + "hetero_colors = [\"#ea7a52\", \"#ea987a\", \"#e4b6a6\", \"#dec8c0\"]\n", + "homoge_colors = [\"#459fcc\", \"#7eb1cc\", \"#adc4cc\", \"#c2cecb\"]" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "141e2794", + "metadata": {}, + "outputs": [], + "source": [ + "def plot_doughnut(ax, data, explode, colors, startangle, tot_loc = (0.45, 0.47)):\n", + " \n", + " radius = 0.65\n", + " \n", + " def label_w_value(label, val, allvals):\n", + " pct = np.round(val / np.sum(allvals) * 100, 2)\n", + " absolute = np.round(val, 2)\n", + " return \"{:s}\\n{:.1f}% ({:.2f})\".format(label, pct, absolute)\n", + " \n", + " def value_as_label(val, allvals):\n", + " pct = np.round(val / np.sum(allvals) * 100, 2)\n", + " # absolute = np.round(val, 2)\n", + " absolute = turn_to_scientific(val, 1, (-1,3))\n", + " # return \"{:.1f}% ({:.2f})\".format(pct, absolute)\n", + " # return \"{:.1f}% ({:s})\".format(pct, absolute)\n", + " return \"{:.1f}%\".format(pct)\n", + "\n", + " # wedges, texts, autotexts = ax.pie(\n", + " wedges, texts = ax.pie(\n", + " data,\n", + " explode=explode,\n", + " colors=colors,\n", + " # autopct=lambda pct: func(pct, data),\n", + " # pctdistance=1.2,\n", + " wedgeprops=dict(width=0.3),\n", + " startangle=startangle,\n", + " radius=radius,\n", + " )\n", + "\n", + " # bbox_props = dict(boxstyle=\"round4,pad=0.3\", fc=\"w\", ec=\"k\", lw=0.72)\n", + " kw = dict(\n", + " arrowprops=dict(arrowstyle=\"-\"),\n", + " # bbox=bbox_props,\n", + " zorder=0,\n", + " va=\"center\",\n", + " size=6,\n", + " )\n", + "\n", + " for i, p in enumerate(wedges):\n", + " ang = (p.theta2 - p.theta1) / 2.0 + p.theta1\n", + " y = np.sin(np.deg2rad(ang))\n", + " x = np.cos(np.deg2rad(ang))\n", + " horizontalalignment = {-1: \"right\", 1: \"left\"}[int(np.sign(x))]\n", + " connectionstyle = \"angle,angleA=0,angleB={}\".format(ang)\n", + " kw[\"arrowprops\"].update({\"connectionstyle\": connectionstyle})\n", + " ax.annotate(\n", + " # label_w_value(labels[i], data[i], data),\n", + " value_as_label(data[i], data),\n", + " xy=(x*radius, y*radius),\n", + " xytext=(x * np.sign(x), y),\n", + " horizontalalignment=horizontalalignment,\n", + " **kw\n", + " )\n", + "\n", + " # total value in the center\n", + " ax.text(\n", + " x=tot_loc[0],\n", + " y=tot_loc[1],\n", + " s=turn_to_scientific(float(np.sum(data)),1,(-1,3)),\n", + " transform=ax.transAxes,\n", + " fontdict=dict(fontsize=7, fontweight=\"bold\"),\n", + " )\n", + " return wedges, texts" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "0e7267a7", + "metadata": {}, + "outputs": [], + "source": [ + "labels_hetero_gwp = res_hetero_gwp_plot[\"reference product\"].values.tolist()\n", + "data_hetero_gwp = res_hetero_gwp_plot.iloc[:, 1].values.tolist()\n", + "explode_hetero_gwp = (0, 0, 0.1, 0)\n", + "\n", + "labels_homoge_gwp = res_homoge_gwp_plot[\"reference product\"].values.tolist()\n", + "data_homoge_gwp = res_homoge_gwp_plot.iloc[:, 1].values.tolist()\n", + "explode_homoge_gwp = (0, 0, 0.1, 0)\n", + "\n", + "# category_label_gwp = \"\\n\".join(wrap(res_hetero_gwp_plot.columns[1], 36))\n", + "category_label_gwp = \"Global warming potential\\n ($kg_{CO_{2}eq}\\ kg^{-1}_{23}$)\"" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "0e7267a7", + "metadata": {}, + "outputs": [], + "source": [ + "labels_hetero_eq = res_hetero_eq_plot[\"reference product\"].values.tolist()\n", + "data_hetero_eq = res_hetero_eq_plot.iloc[:, 1].values.tolist()\n", + "explode_hetero_eq = (0, 0, 0.1, 0)\n", + "\n", + "labels_homoge_eq = res_homoge_eq_plot[\"reference product\"].values.tolist()\n", + "data_homoge_eq = res_homoge_eq_plot.iloc[:, 1].values.tolist()\n", + "explode_homoge_eq = (0, 0, 0.1, 0)\n", + "\n", + "# category_label_eq = \"\\n\".join(wrap(res_hetero_eq_plot.columns[1], 36))\n", + "category_label_eq = \"Ecosystems quality\\n ($10^{-9}\\ species·yr\\ kg^{-1}_{23}$)\"" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "0e7267a7", + "metadata": {}, + "outputs": [], + "source": [ + "labels_hetero_hh = res_hetero_hh_plot[\"reference product\"].values.tolist()\n", + "data_hetero_hh = res_hetero_hh_plot.iloc[:, 1].values.tolist()\n", + "explode_hetero_hh = (0, 0, 0.1, 0)\n", + "\n", + "labels_homoge_hh = res_homoge_hh_plot[\"reference product\"].values.tolist()\n", + "data_homoge_hh = res_homoge_hh_plot.iloc[:, 1].values.tolist()\n", + "explode_homoge_hh = (0, 0, 0.1, 0)\n", + "\n", + "# category_label_hh = \"\\n\".join(wrap(res_hetero_hh_plot.columns[1], 36))\n", + "category_label_hh = \"Human health\\n ($10^{-6}\\ DALYs\\ kg^{-1}_{23}$)\"" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "0e7267a7", + "metadata": {}, + "outputs": [], + "source": [ + "labels_hetero_nr = res_hetero_nr_plot[\"reference product\"].values.tolist()\n", + "data_hetero_nr = res_hetero_nr_plot.iloc[:, 1].values.tolist()\n", + "explode_hetero_nr = (0, 0, 0.1, 0)\n", + "\n", + "labels_homoge_nr = res_homoge_nr_plot[\"reference product\"].values.tolist()\n", + "data_homoge_nr = res_homoge_nr_plot.iloc[:, 1].values.tolist()\n", + "explode_homoge_nr = (0, 0, 0.1, 0)\n", + "\n", + "# category_label_nr = \"\\n\".join(wrap(res_hetero_nr_plot.columns[1], 36))\n", + "category_label_nr = \"Natural resources\\n ($10^{-1}\\ USD_{2013}\\ kg_{23}^{-1}$)\"" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "3dd427a4", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(figsize=(6, 3), subplot_kw=dict(aspect=\"equal\"))\n", + "\n", + "w, t = plot_doughnut(\n", + " ax=ax,\n", + " data=data_hetero_hh,\n", + " explode=explode_hetero_hh,\n", + " colors=hetero_colors,\n", + " startangle=35,\n", + ")\n", + " \n", + "# legend\n", + "leg = ax.legend(w, labels_hetero_hh,\n", + " # title=\"Inputs:\",\n", + " loc=\"lower left\",\n", + " frameon=True,\n", + " fancybox=False,\n", + " edgecolor=\"k\",\n", + " # columnspacing=0.7,\n", + " fontsize=7,\n", + " bbox_to_anchor=(0.7, 0))\n", + "\n", + "leg.get_frame().set_linewidth(0.4)\n", + "ax.set_title(\n", + " category_label_hh, fontdict=dict(fontsize=7, fontweight=\"bold\"))\n", + "\n", + "# plt.setp(at, size=7, weight=\"normal\")\n", + "plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "3dd427a4", + "metadata": {}, + "outputs": [ + { + "ename": "NameError", + "evalue": "name 'data_homoge' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m~\\AppData\\Local\\Temp\\ipykernel_9828\\1287425807.py\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[0;32m 3\u001b[0m w, t = plot_doughnut(\n\u001b[0;32m 4\u001b[0m \u001b[0max\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0max\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 5\u001b[1;33m \u001b[0mdata\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdata_homoge\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 6\u001b[0m \u001b[0mexplode\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mexplode_homoge\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 7\u001b[0m \u001b[0mcolors\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mhomoge_colors\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;31mNameError\u001b[0m: name 'data_homoge' is not defined" + ] + }, + { + "data": { + "image/png": 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daWlpAAC32x1wz9LHH3+M0dFRvPbaa3jttdf84+Xl5Thy5MjDvwMimlcU36c0G3ifEpGcZv0+JSKiaGMpEZFUWEpEJBWWEhFJhaVERFJhKRGRVFhKRCQVlhIRSYWlRERSYSkRkVRYSkQkFZYSEUmFpUREUmEpEZFUWEpEJBWWEhFJhaVERFJhKRGRVFhKRCQVlhIRSYWlRERSYSkRkVRYSkQkFZYSEUmFpUREUmEpEZFUWEpEJBWWEhFJhaVERFJhKRGRVFhKRCQVlhIRSYWlRERSYSkRkVRYSkQklbBKqaGhAenp6dBqtTAajWhtbX3g/AsXLsBoNEKr1SIjIwMfffRRWGGJaP5TXEo2mw2VlZWora2F0+lEUVERiouL4XK5Qs7v6enBhg0bUFRUBKfTiTfffBMVFRU4efLkQ4cnovlHJYQQShbk5eUhOzsbjY2N/rHMzEyUlJTAarUGzX/jjTdw9uxZdHV1+cfMZjO++uorXLlyZVqv6fV6odfr4fF4oNPplMQloiiKxrmpVjJ5ZGQE7e3tqK6uDhg3mUxoa2sLuebKlSswmUwBYy+++CKamppw//59xMXFBa3x+Xzw+Xz+xx6PB8DED4CI5DF5Tirc2zyQolIaHBzE2NgYDAZDwLjBYEB/f3/INf39/SHnj46OYnBwEElJSUFrrFYr6urqgsZTU1OVxCWiGTI0NAS9Xh+R51JUSpNUKlXAYyFE0NjPzQ81PqmmpgYWi8X/+Pbt20hLS4PL5YrYG58JXq8Xqamp6O3tnVOXncw98+Zqdo/Hg+XLl2Px4sURe05FpZSYmIjY2NigXdHAwEDQbmjS0qVLQ85Xq9VISEgIuUaj0UCj0QSN6/X6OfU/bJJOp2PuGTRXcwNzN3tMTOTuLlL0TPHx8TAajXA4HAHjDocDBQUFIdfk5+cHzT9//jxycnJCfp5ERI82xfVmsVhw6NAhNDc3o6urC1VVVXC5XDCbzQAmLr3Kysr8881mM27cuAGLxYKuri40NzejqakJu3btity7IKJ5Q/FnSqWlpRgaGkJ9fT3cbjeysrJgt9uRlpYGAHC73QH3LKWnp8Nut6OqqgoHDx5EcnIyDhw4gN/+9rfTfk2NRoO9e/eGvKSTGXPPrLmaG5i72aORW/F9SkRE0cS/fSMiqbCUiEgqLCUikgpLiYikIk0pzdWvQ1GS+9SpU1i/fj0ef/xx6HQ65Ofn4/PPP5/BtP9P6c970uXLl6FWq/Hcc89FN+AUlOb2+Xyora1FWloaNBoNnnzySTQ3N89Q2v+nNHdLSwtWr16NhQsXIikpCdu3b8fQ0NAMpZ1w8eJFbNq0CcnJyVCpVDhz5szPronIeSkk8Le//U3ExcWJTz/9VHR2doqdO3eKRYsWiRs3boSc393dLRYuXCh27twpOjs7xaeffiri4uLEiRMnpM69c+dO8fbbb4t//vOf4tq1a6KmpkbExcWJf//731LnnnT79m2RkZEhTCaTWL169cyE/ZFwcr/00ksiLy9POBwO0dPTI/7xj3+Iy5cvz2Bq5blbW1tFTEyMeP/990V3d7dobW0VzzzzjCgpKZnR3Ha7XdTW1oqTJ08KAOL06dMPnB+p81KKUsrNzRVmszlgbNWqVaK6ujrk/L/85S9i1apVAWN//OMfxQsvvBC1jKEozR3K008/Lerq6iId7YHCzV1aWir++te/ir17985KKSnN/fe//13o9XoxNDQ0E/GmpDT3u+++KzIyMgLGDhw4IFJSUqKW8edMp5QidV7O+uXb5Neh/PTrTcL5OpSrV6/i/v37Ucv6Y+Hk/qnx8XEMDw9H9I8Zf064uQ8fPozr169j79690Y4YUji5z549i5ycHLzzzjtYtmwZVq5ciV27duGHH36YicgAwstdUFCAvr4+2O12CCFw8+ZNnDhxAhs3bpyJyGGL1HkZ1rcERNJMfR1KpIWT+6fee+893L17F5s3b45GxJDCyf3tt9+iuroara2tUKtn559MOLm7u7tx6dIlaLVanD59GoODg3j11Vdx69atGftcKZzcBQUFaGlpQWlpKf773/9idHQUL730Ej744IOZiBy2SJ2Xs75TmhTtr0OJFqW5Jx07dgxvvfUWbDYblixZEq14U5pu7rGxMWzZsgV1dXVYuXLlTMWbkpKf9/j4OFQqFVpaWpCbm4sNGzZg//79OHLkyIzulgBluTs7O1FRUYE9e/agvb0d586dQ09Pj//vS2UWifNy1ndKM/V1KJEWTu5JNpsNO3bswPHjx7Fu3bpoxgyiNPfw8DCuXr0Kp9OJ119/HcDEyS6EgFqtxvnz57F27VrpcgNAUlISli1bFvAdXJmZmRBCoK+vDytWrIhqZiC83FarFYWFhdi9ezcA4Nlnn8WiRYtQVFSEffv2zciVQDgidV7O+k5prn4dSji5gYkd0rZt23D06NFZ+YxAaW6dToevv/4aHR0d/sNsNuOpp55CR0cH8vLypMwNAIWFhfj+++9x584d/9i1a9cQExODlJSUqOadFE7ue/fuBX0/UWxsLIDIfu1spEXsvFT0sXiUTP7KtKmpSXR2dorKykqxaNEi8Z///EcIIUR1dbXYunWrf/7krx6rqqpEZ2enaGpqmtVbAqab++jRo0KtVouDBw8Kt9vtP27fvi117p+ard++Kc09PDwsUlJSxO9+9zvxzTffiAsXLogVK1aIV155Rerchw8fFmq1WjQ0NIjr16+LS5cuiZycHJGbmzujuYeHh4XT6RROp1MAEPv37xdOp9N/K0O0zkspSkkIIQ4ePCjS0tJEfHy8yM7OFhcuXPD/t/LycrFmzZqA+V9++aV4/vnnRXx8vHjiiSdEY2PjDCeeoCT3mjVrBICgo7y8XOrcPzVbpSSE8txdXV1i3bp1YsGCBSIlJUVYLBZx7969GU6tPPeBAwfE008/LRYsWCCSkpLE73//e9HX1zejmb/44osH/nuN1nnJry4hIqnM+mdKREQ/xlIiIqmwlIhIKiwlIpIKS4mIpMJSIiKpsJSISCosJSKSCkuJiKTCUiIiqbCUiEgqLCUiksr/AZuuQ0PznT01AAAAAElFTkSuQmCC", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(figsize=(6, 3), subplot_kw=dict(aspect=\"equal\"))\n", + "\n", + "w, t = plot_doughnut(\n", + " ax=ax,\n", + " data=data_homoge,\n", + " explode=explode_homoge,\n", + " colors=homoge_colors,\n", + " startangle=75\n", + ")\n", + " \n", + "# legend\n", + "leg = ax.legend(w, labels_homoge,\n", + " # title=\"Inputs:\",\n", + " loc=\"lower left\",\n", + " frameon=True,\n", + " fancybox=False,\n", + " edgecolor=\"k\",\n", + " # columnspacing=0.7,\n", + " fontsize=7,\n", + " bbox_to_anchor=(0.7, 0))\n", + "\n", + "leg.get_frame().set_linewidth(0.4)\n", + "ax.set_title(\n", + " category_label, fontdict=dict(fontsize=7, fontweight=\"bold\"))\n", + "\n", + "# plt.setp(at, size=7, weight=\"normal\")\n", + "plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "0ace9627", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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9lbYl97iCEEk33nP16lWt+6lfv76IjIzU+bz+fg6bNGki6tSpo3H/WbNmFQ8fPlS7j8R8t0/KcySPO3Qdl1Aczxs7dqzGOqampmL37t3xPrdERKRM8XNXCCF27twpfdZ6eHiIsLAwtfXkY9vqYoGIiAitY73yHwcHB3H9+nWpXkL6Ck2ePHki1R0/frzSNl2vdQghxKJFi6TrY5p+DAwMxJo1a1TaoHgO4hsPyOuNHDlSZbw7e/bsIjo6Wiq7detWYWFhobF9hoaGYubMmWqPc/jwYWFubq71+f39999q3weRkZGiR48eWuva2dmJI0eOqNSNa5xfTv59HlCNVV1dXQUA0bt3b5ElSxal45qbm4uvX79KZbdv3671WoWFhYXYv3+/yvGjo6PFkCFDpHEydT/29vYax1kUxzvU/chkMo1jekTaMHFCj06fPi39E3fo0CFJ963tQuWJEyeEsbGxACBMTEzEoEGDhI+Pj/Dz8xNLly4VOXPmlOp2795dZd/yzlP+gebg4CBmzZol/Pz8hLe3t2jYsKFUP3fu3Cof/O/evRPW1tYCgLCyshIjR44Uhw4dEpcuXRJ79+4Vffr0kdoHQBw6dEipfkISJ4QQonDhwgJQTXoQInaw2sjISNpv3rx5VcqEh4dLHcCMGTOkx2/duiUN/Ds5OYnJkyeLo0ePiosXL4qdO3eKdu3aSedKJpOpJBbIXyv5BYZy5cqJQ4cOiRMnToh+/fqJb9++KZ13R0dHAUDkzJlTLF++XFy4cEFs3LhRusACQPryX7hwYbF582Zx6dIlsX79eqUyv587XRInnJ2dBRB7IUi+3127domyZctKZUqVKqVy7oKCgkTGjBmlc9C1a1dx/PhxceHCBTF9+nQpiUceKCUmcSJTpkwCiL1ws2/fPuHn5yfmzJkjHBwcpDJbt25V2ceGDRuk7dbW1mLcuHHC19dXnD9/XsyaNUvaLwAxdepUqV5ISIi4efOmmDhxotJ79ubNm0rnMSIiQpQuXVoq06pVK7F3715x5coVcejQIdG9e3fpPZgzZ06Viw/y18fKykpYWloKCwsLMWLECHHq1Clx7tw5MX78eKWLf4cPH5bqvnz5Uty8eVO62Fa8eHFx8+ZNcfPmTREUFCSE0B5QJdV7XN17i4jSn6RMnPjx44dwc3OT9te4cWOxZ88ecfHiRbF+/Xrh4eEhbZs4caJS3YR+7gsR+wXW3d1dqjtt2jRx+vRpcenSJbF9+3ZRoUIFjbFU1apVBQBhY2OjcZBEMUY8ePCg9HiHDh2kx3v37i2OHj0qrl69Kry9vZUGDfLmzSsligQFBYmbN2+Knj17StvlfcDLly+lfX/9+lXkyJFDABBGRkaiR48ewtvbW1y5ckXs3btXtGrVSqpfpkwZlcQG+fEdHByETCYTmTJlEnPmzBHnzp0TO3fuFAUKFJDq16xZUxgYGIgcOXKIlStXikuXLqmUWbVqldL+16xZI22rUaOG2LVrl7hy5Yrw8fER48aNkwY7TExM4nVxSgghvnz5Il28kMlkwtPTUxw/flycO3dOjB49WpiZmQkjIyMpDkqOxInmzZtL+6hXr57Yvn27uHDhgjh+/LiYN2+ecHFxkba3aNFCpb58my6JE7q8JxT/P9TFTXLy90yzZs00liEiSq9Sc+JEco0rJOV4j7OzszA0NBS9e/cWx44dExcuXBCzZ88W9vb2UpmlS5fqfF5/P4fyn8KFC4s1a9aICxcuiK1btyol5FaoUEGlfmK/2yfHmJiu4xKKcbh8/OTff/8VZ8+eFQcOHFCK9+zs7NQmyxIRke7UJSEoXjsZO3as2nraEieGDRsm1S9XrpzYsGGDOHv2rDh16pRYvny5yJcvn7S9ZMmSUr349hWJTZyI61rHwYMHpX24ubmJuXPnCh8fH6k/rlu3rrTdwsJCfP78Wek4SZE4YWBgIExMTMT06dPF+fPnxerVq5XGIv777z9pzDt79uxi1qxZ4ty5c9J1tLx580r7WrhwodIxgoKCpGsezs7OYsGCBVLddevWiYIFC2occxJCiC5dukjbc+bMKZYuXSr8/PyEj4+PGDRokHQdxcjISJw5c0apblImThgYGAiZTCZGjBghzp8/L7Zu3ap0bczb21tpbG3MmDHC19dX+Pr6irFjx0pJJ+bm5ioJqYMHD5bqli5dWqxbt05cunRJ+Pr6igkTJkgxn6mpqbh69apS3ZMnT0p1S5QoIcWqp0+fFjNnzlRKUL1w4YLGc0CkDhMn9GjFihUaO5jE0nShMjIyUhrgNDc3V/uhERISIg3sAhDHjh1T2q74RStjxozi6dOnKvsYNGiQVGb27NlK2xTvalC8uKto9+7dUpmuXbsqbUto4oT8TgljY2OVO+fkH/CKX04/fvyoVObEiRPStnv37kmPN23aVACxA/W3bt1Se+zZs2dLdSdPnqy0TfG1cnFx0XhXn+J5z5Mnj3TBW+7atWtKX4DLli0rQkNDlcr4+/tLnf3AgQM1tkNT4gQA0aZNG5W7diMiIpTugP29E+zbt6+0bcOGDSrP7fHjx1JihaagUJvfM2a7du2qMoPL48ePpTuDs2bNKn7+/Clt+/Lli7CxsZHe0+ruKnn//r3IkyePAGIzSf39/ZW2K74v1Q2KTZ8+XQCxF2S2bNmi9nkcOXJEp9fH3NxcXLt2TaX+nj17pDKtW7dW2S5/D6k7v9oCqqR8jzNxgogU+zP5xVpdft68eaOyrxEjRkj7UvziJvf9+3cpecLKykrKiE/s575iv7Nt2zaVupGRkaJcuXLSF3zFJIO1a9dKdffu3av2HHXv3l0AsRc15AMAYWFh0hfjnj17qq03fPhwad/nz59X2qb4WayO/CK6qampOHXqlNoyirHrvHnzlLYpDlo4OTmpzIrw7t07pTjL3d1dJZb5/PmzdCGhUaNGStvk75sCBQqoHRQ5dOiQtO/43lHQr18/qa66manOnz+vlJyY1IkTt2/fluorzjCi6Nu3b9LgmZmZmUriiqa2aeuDtb0nQkJCpIThhg0bqm3ThQsXpPr79u3T+LyJiNIrxb5RfmFClx9t34s1fd4r0iVxIrnGFZJyvMfAwEApgVTu6tWr0sWYMmXKaDwPmig+v7///luEh4crbQ8PD1dK5vw9pknsd/vkHBOLa1xC8fXPkSOH2vha8e7WhM7kRUREsdQlIbx+/VoajzA2Nla6ziCnKXEiMDBQ+l5dvnx5le+FQsSORxQtWlT6LH/9+rXSdl37isQmTgDar3XIExUzZMig0kY5xe/qmzZtUtqWFIkTAMSiRYvUlgkJCZGuJZQsWVJphgW5X79+STNQmZmZiffv30vbFM/zxYsXVep++/ZNuoE5V65cStsUr0OVL19ehISEqNS/cOGCNJuFi4uL0nshKRMnAIihQ4eqrR8aGird5OHg4KByvUQIIY4ePSrFbe3bt5ce9/Pzk2KlXr16Kc3yIRcQECDdwFKkSBGlbfLX38nJSXz//l2l7u3bt6UbQbt166bxHBCpYwDSG8X1eTJmzKi17K1bt7T+yNcsjsu+ffukNYXGjBmDcuXKqZSxsrLC1q1bYWxsDACYPXu2xv3NmTMHOXPmVHl8xowZyJ49OwBg5cqVStvevXuHTJkyoUSJEqhbt67a/TZp0gSmpqYAYte5Sgr16tUDAERGRqqsIXbq1CkAQPPmzWFiYgIA0nrWckePHgUQu75T/vz5AQBCCAQGBiJDhgz4+++/UbhwYbXHbt26tfS7tufToUMHWFpaxvlcJkyYAAcHB6XHihcvDhcXF+nvadOmwczMTKmMh4eH9Ho9e/YszuP8zsjICPPmzYOhoaHS48bGxujUqZP0971796Tff/36hTVr1gAAGjVqhI4dO6rs96+//sL8+fPj3R51XFxcsHjxYmlNVMVjTJkyBUDsumRHjhyRtq1evVpaM3zevHnIkyePyn6dnZ2xbt06AEB0dDTmzZunc5uio6OxcOFCALHnoG3btmrL1alTR9q2evVqREREqC3XtWtXFC9eXOXxpk2bIkOGDACUX4PESOr3OBGRoqJFi+r88/s6mDExMdLncqlSpTBixAiV/VtbW2PcuHEAYvsjef+f2M99xbVC3d3dVeoaGRlh/PjxGDhwIGbMmIHw8HBpW/PmzWFhYQEA2LZtm0rdiIgI7N69G0DsZ6uRkREA4OvXr1K/oO6YANC/f3/07NkTM2fORKZMmdSWUefLly/YsGEDAKB3796oWrWq2nI9evRA+fLlAQBLlizRuL8hQ4bA1dVV6bHMmTOjbNmy0t9jx45ViWUcHR1RunRpAKpxivycZ8+eXToniurVq4f+/ftj4sSJqFixosa2/S4qKgqbN28GAJQpUwYDBgxQKVO+fHmMHDlS533Gl7+/P3LlygUTExOMHTtWbRkbGxspbg4LC4tz3fXEsrKyQpMmTQDExsBfv35VKbN161YAgIODgxRnExGRevXr19c55kkpyTGukJTjPfXq1UODBg1UHi9RogSKFCkCIHHfew0MDLB8+XJpDEjOxMQEXbp0kf729/eXfk+K7/b6GhP73fTp05E1a1aVx3v16iX9rvjciYgoaWTLlg3Tp08HEHudonv37oiJidGp7r1795AzZ06YmZnhn3/+ka7fKDIyMkKzZs2kv/U5TqvpWse3b98QExMDW1tbeHp6Ilu2bGrrJ/d4s7GxMbp27ap228aNGxEUFAQAWLVqFWxtbVXKmJubY8WKFQBiv6fLr4EAcY8b2djYYNKkSRg2bBiGDh2q9B6YO3cugNjXcsuWLbCyslKpX65cOWn84NWrV9i1a1eczzehFGMDRT4+PtLrMmXKFHh4eKiUqV27NmrWrAkAOHnyJIQQAGLH4oQQcHZ2xvz582FgoHqp2tXVFZMnTwYQe330woUL0jb5+XV0dIS1tbVK3UKFCmHMmDEYO3YsGjVqFJ+nSwQmTuiR4oeh/ANDnaioqDi/WHt6eup0zBMnTki/a+oUgNiLz3Xq1AEQm0AQFhamUsbKygotW7ZUW9/ExATNmzcHADx+/Bhv3ryRtm3btg0fPnzA5cuXNR7fwMAATk5OAKD22AlRtmxZ2NvbA4j9kFbk6+sLAKhVq5Z0YfjMmTNKZY4dOwYgdsBDTiaT4cyZMwgMDMTOnTs1HtvZ2Vn6XdvzKVWqlC5PBdWrV1f7eJYsWQDEdvryCxu/c3R0BAD8+PFDp2MpKliwoMYLMTly5JB+V9z3yZMnpees7X3aokUL6fVJjE6dOqkM7Mi1a9dOSvo4fPiw9Lj8/8LW1hYtWrTQuO+yZcuiYMGCAIDjx4/r3Kbbt2/j/fv3ADS/dnK1a9cGAPz8+RNXr15VW0YebKjj5uYGIGGvrzpJ/R4nIkoqV65ckS4et2/fXmO5Jk2a4Pbt2/j586fUhyf2c18x0aJr1644ffq0ykBHrVq1MH/+fPTr10/pS661tTUaN24MAPD29lb5vD5y5AiCg4NVnpeTk5PUT06bNg2bNm1CaGioUt3s2bNj+fLlGD58OHLlyqXxef3u9OnT0me3rv3U06dPleI7RXHFKQA0JmdoilPk5/zo0aMYN26c2sSBhQsXYty4cahUqZLW56DIz89PSgrQdPEDAHr27KnzPuOrdevWePLkCUJDQ1GgQAGN5VK6r5Unu0ZERGDv3r1K26KioqS4oGXLlioXnYiIKPVLjnGFpBzv0fa9Vz7+kJjvvYULF1aKTRT99ddf0u/yuAxImu/2+hoT+518zO93mp47ERElnd69e0t97MWLF7F06VKd6lWuXBkPHjzAr1+/lK5R/C61jNNqutZha2uLmzdv4uvXr5g5c6bG+sn9PAoUKABzc3O12+Q30WbIkEHjjYQAkCtXLikx4vTp09LjiuNGzZs3x/Xr11Xqtm3bFrNmzUKvXr2kxIHIyEjpulTNmjVVbkpR1L17d+nm0fhcq4gPe3t7jeNLhw4dAhB7/UDbeMqqVasQEBCAt2/fQiaTISYmRmpv+fLlpWRRdeTxFKD+/D548AA9e/bE69evVepOmDABkyZNUpuIS6QNEyf0yMbGRvr906dPKXLM+/fvA4hNjIjrTkR5xxYeHq52doKCBQtqHSSV3wEAqL8LQN4ZhIWF4cGDBzh48CBmzZqFtm3bIlu2bNJgvK4Zl3ExNDRErVq1ACgnTnz58gW3b98GAFSpUkW6G1Jxxol3797h7t27AKDxg1b+fH7+/Ik7d+5g7969mDp1Kpo1a6bUyWt7Puqy/X9nYWEhfYH+nbyTcXBwUJtxqlhGW7KOJto6asXs0aioKOn3O3fuSL+rmyVBzsjIKEnurlG8m/V31tbWUiDz+PFj6XH5/0XhwoU1njc5+f/Fy5cv8fPnT53adOvWLen3/v37QyaTafxRnJHj+fPnaveny+ug+BoklaR4jxMRKRKxy8bp9PP7zESKn+OKMcfvTE1NUahQIaWkusR+7hcpUgQ1atQAEBvjVK1aFc7OzujQoQM2b96MwMBArfuUf9aHhoZi//79Stu2bNkCIHbQWj77AhD7GTx06FAAsXdndOzYERkyZED9+vWxcOFCnWcfU0exn/r777+19lPyGTwASLOY/U6exPc7xS/DmuJQTXHKkCFDYGhoCCEEJk+eDGdnZ5QtWxYTJkzA5cuXExTXAMp3ymp7H2XOnFlr/5sU5P2sEAJv3rzB6dOnsWrVKgwYMAAlS5bEtGnTpLIp0ddWr15dik1/nx3l+PHjUvJKhw4dkr0tRER/uhcvXugc86SE5B5XSIrxHl3HH6KjozWW00bT3a0AlOLGyMhI6fek/G6f0mNiiqytrdXeOQtofu5ERJR0ZDIZVq1aJV1bGT16tNoLv9rqyy+Yf/jwARcuXMD69esxdOhQVKxYEb1795bK6nOcVpdrHfL+8Nu3b7hx4wZ27tyJCRMmoEGDBihWrJhULjmeh7b2yfv8oKAgrf29TCaT+nnF/r5+/frIly8fAODcuXMoUaIEXFxc0KNHD+zZswchISFqj6s49lSyZEmt7Xd0dJSudcjHuZKatnMkH5fLkSOHxrgCiL3Jx9XVVXrPBgQE4Nu3bwCAPXv2aD23ijOhKZ7fPn36SPHgypUr4eLigiJFimDkyJE4ffp0slwbofRDdZ5bSjGKWdzv3r3TWM7IyEjjl1E3Nze8fPlS52PKpxeKa2mQ38soLisip+lLtpz8DgR19b9//44FCxZgx44dePDggdqOTyaTJfmgQb169bBjxw7cunULX758gYODg3SXqIuLC9zc3FC5cmUsXLgQd+/eRXBwMOzt7aUMQwsLC1SpUkVlvx8/fsScOXOwd+9etUkm6qYaUkcxmUYTdVMP/U7dFNZJQdsyIopLYyi+borTUim+J9TR5X0Zl8yZM2vd7ujoiCdPnii1KzH/F7osrSLff3ypmxYb0O11SOr/naR6jxMRJRXFpFP5MkW6SorP/Z07d6Jbt27SnfifP3/G5s2bsXnzZhgYGKBy5cro2bMnWrZsqbJ8VI0aNZA5c2a8f/8e27dvR7t27QAAISEh8Pb2BqB+Fo3Ro0cjLCwMM2bMQGRkJEJDQ3H48GEcPnwYAwcORP78+dGxY0f069dPWg4kPucjvjT1U8kRq1SqVAnbt29H7969ERgYiJiYGFy6dAmXLl3CxIkT4ezsjFatWmHYsGFaL4T8Lj7vo8yZM8cr7o6v/fv3Y+nSpbhw4YLa5MyU7msNDAykO2B8fX3x8eNHKeFFnuDj7u6uduk/IiJK3ZJzXCGpxnsSMv4QH7qcg9/3n1Qxk77GxOQS8tyJiChpeXh4YPTo0ZgwYQJCQkLQp08fHDx4UKe6Z86cwYIFC+Dr66v2e3lqGaeN61rH8+fPMWvWLBw8eBBv375V2Z7cz0Nb+xLS5yu+FsbGxjhy5Ag6duwozSDx+vVrrFq1SkqaqVWrFvr166c0q4LicXUds3r27Jna63dJQds5ko+nJHRMLr4Uz2/u3Llx6NAheHp6IiAgAEDszGC3b9/GzJkzYWdnh2bNmmHo0KFqlxAh0oaJE3pUsmRJGBgYICYmRmkJjeQk/9Lz+wC+OopZ++rKx7UPxS9+incYPn36FDVq1FAaeDY3N0fevHlRqFAhlC5dGrVr10a1atWSfHC6bt260jk/ffo0mjZtilOnTgGAlBBRuXJl6QvquXPn0LBhQ2mZjho1aqgsA3H58mXUq1dPqXOytrZGvnz5UKhQIZQtWxa1a9fWKcNSl9cluZIikoviuu5xZYYmxZfyuM6P/H2teOdMUv5fqKOY4bhp0yat03Ar0uU9kxKS8j1ORJRUEpM9nhSf+/b29tizZw/u3buHnTt3wtvbG7du3YIQAjExMfD19YWvry+2bduG3bt3K/VPhoaGaNu2LebMmYNjx45JyZx79+6Vlt9Qlzghk8kwefJk9O3bFzt37sSBAwdw/vx5qa+9f/8+Ro4ciVWrVuHs2bNxJhPKKZ7LkydPqqx3roniMl2KkitWad68OerVq4f9+/fjv//+g4+PjzSF9IcPH7BgwQKsXr0a3t7eahNd1YnPQExil6PQFAfFxMSgU6dO2Lx5s/SYTCZDjhw5kD9/fhQvXhxVqlTBiRMnMGXKlES1Ib46duyIWbNmITo6Grt27UK/fv3w69cvaaYUbcvkEBGRfuhyR2Zy9dX6HO+JL12/zytKiu/2qeEcJeS5ExFR0hs1ahR27twJf39/eHt7Y8eOHWjVqpXWOl5eXkqzEQKxd/Tny5cPxYoVQ6VKlfDy5Uv06tUr2dqt6+wP2vqbAwcOoFWrVkpLcDg4OCBfvnwoXLgwypcvjzx58midwTqxtLVP3ueXL18eixcv1ml/8iXC5VxcXHD69GlcunQJu3fvxqFDh/Dw4UMAsUtient7w9vbG3379pWOoXh9JD5jVgnp23V5HXU5R/GlWG/gwIFal3dX9PusFpUrV8bjx49x5MgR7N27F0ePHpVulv369SvWrFmDjRs3YsOGDWjTpk2C2krp0591BTaNcXBwQMWKFXHmzBkEBATgxo0bStMPJdcxAd2WBlEso24APa4sNsW1pxWzzlq3bi19+Rs8eDA8PT2RP39+lY4lMWtVauLk5ITixYvj6tWrOHnyJJo2bSqtjSQfYM+QIQMKFCiAu3fv4syZM2jQoIGU2PL72mGhoaFo3rw5vnz5AhMTE4wZMwatWrXCX3/9pdSpJMdz+VMozjLx+fNnrXeNxDW1uS50fV8qLi3h4OCA9+/fx/v/Qr7WfFwU/39MTU21TgWe2vA9TkSpleJncHwz65Pyc79AgQIoUKAAJk2ahMDAQJw4cQIHDx7E3r17ERYWhv3792PdunXo3r27Ur2OHTtizpw5iIyMxIEDB+Dp6Yldu3YBAMqVKydNt6iOs7MzBgwYgAEDBiA0NBTnzp3D4cOHsX37dnz8+BFPnz7FsGHDpFkBdDkfctbW1qm6n7KwsECbNm3Qpk0bxMTE4Pr16zh27Bi2b9+O+/fv4+fPn/D09MTTp091uij0e5yijaZ1vnW96/X79+9qH1+yZImUNFG6dGmMHz8eFStWhJWVlVI5Xe8+SkoFChRA0aJFcfPmTezevRv9+vXD4cOHpRkxmDhBRKQfCelvUoI+x3tSQlJ8t0/r54iIiHRnYmKCVatWoWLFioiJicHAgQOlpcbVOXjwoJQ0kSdPHkyePBk1atRQGavQ9UK/OrrMZpzYWOPdu3do3749wsLCYGNjg0mTJqFx48Yqy3SpW/49pTg4OODDhw8IDg5O9BhJmTJlUKZMGcyePRtv3rzBiRMn8N9//8Hb2xvR0dFYsmQJGjVqhJo1ayrFGvEZs1Ksl9gxCl3J33cJGZOTi4yMTNT5NTY2RsOGDdGwYUMAse+ZY8eOYdeuXbh8+TIiIyPRo0cP1KlTR+drOUSpY86edKxv377S79OnT0/24xUsWBBA7FpJcX3wXrlyBUDsh4+6OwofPHig9YP32rVrAGI/qOUfflevXsX169cBAF27dsXcuXNRqFAhtV8QNU3/nFj16tUDAJw4cQJBQUHw9/cHEJuhJidPojh9+jSuXLkiffj/njjh7e0trTs5duxYjB07Frlz51bJxIvPGmVpjWLHd/PmTY3lYmJicOfOnUQf7/bt2xq3BQcHS1M3FSpUSHpc/n9x69atONfwlP9fZMuWTadlOgBI65kBkBJ1NLl8+TLmzJmDPXv2JEkiSWLxPU5EqZXiZ6u2z/7IyEhkz54dJUqUwLJlywAk/nM/OjoaT58+hZ+fn1JZR0dHtG7dGlu2bFH6vJcv+aWoUKFCUl+0f/9+hIaGSrNgaboY/f79e5w6dUopO9/c3By1atXC/Pnzcf/+fWmZCnXH1CQ+/dSxY8ewYMEC7N+/P0UH9L9+/YpLly7h/fv30mMGBgYoWbIkxowZgzt37khx2suXL/HgwQOd9lu0aFHp96tXr2osFx4ejkePHqndppigIZ8x5HdCCI1L8y1fvhxA7KCDj48P6tatq5I0Aeivr+3QoQMA4Pz58wgKCpKWkylVqpTS0oNERJT85H2Opv4GgPT9LaWlhvGe5JbY7/bp4RwREVH8lCtXDr179wYQu1Ty0KFDNZaVf3c0NDTEsWPH0KJFC7UXgxPz3TElYo0tW7YgJCQEQOyNBAMHDlRJmgD0O94s7/MfPHgQ53W02bNnY82aNUpjRJGRkXjw4IHU78tly5YNnp6e+O+//5RudpGP4bi7u0tjT9rGKIDYpAn5tY48efJIj+syRgEk/nWUn6MXL15Ir6c6O3bsgIuLC6pWrYrHjx8jR44cMDc3BxB3PPX582dMnToVW7ZswePHj6XHf/78ievXr+P58+dK5QsUKIChQ4fi0qVL0v/Vjx8/cP78+YQ8RUqnmDihZ82bN0fp0qUBALt378aSJUt0rnv37t14Z3PVqFFD+n3t2rUay718+RLHjx8HAJQtW1b6IFMUGBgIX19ftfV//PiBnTt3AogdVLWzswMAPHv2TCqjOFD9u127dknTDCVmKm515IkTjx8/xvbt2yGEQPbs2ZXu7KxatSqA2Asq27dvBxCbAPD7UgS6Ph/5PoCkfz6pXfXq1aWlWhSnoP7d0aNH47zTUxeK5/p3GzdulJJ9GjVqJD0u/7/49u0b9uzZo7G+n5+flGhTrVo1pW3apvouVaqUNJXU1q1btf7fjhgxAsOGDUPz5s3x7ds3jeUSIiHrwvE9TkSpVYkSJaT1mbV99p89exZv3rzB9evXpS+Pif3cb9u2Lf766y/UqFFD45fQ0qVLS/GPpjIdO3YEABw/fhyHDh1CaGgoTExM1E7NuWzZMmTJkgXVq1fX+IUvQ4YMKFGiBAAoTXcJaO8DqlWrJg3Yr1y5EhEREWrLRUdHo0+fPhg0aBBat26tcX9J7cGDB7C3t0fZsmWxYsUKtWUMDAxQs2ZN6W9tgwOKChUqJCWbbNiwQeNUlbt27dJ4XuSvMwC8evVKbZlr165pnLFC3tfmzJlT45rjX758gY+Pj/R3UvS1usYFbdu2hZGREaKjo+Ht7Y0jR44A+F9CBRERpRx5n6OpvxFCpNhSsL9LDeM9yS2x3+2T+xylljXtiYgofqZPny59L123bp3GC/XyfsTe3l5togEQuwTEvn37pL9/70fi6ivkscbXr181zkig+N00If6E8Wb5+IIQQusMHr6+vhg+fDi6desm3awDABUqVEC+fPnQrFkzjXXr1Kkj/S4fwzAyMpJu8PXx8dEY8wHA6tWrpd8Vx6x0GaMIDQ1NdDKB/JgxMTHStUB1vL298fr1a/j5+SFLliwwMTFBpUqVAAD+/v7STUTqLF68GGPGjEH79u1x7tw5AEBISAhsbW1RokQJTJo0SWPd2rVrS7/rOkZEBDBxQu9kMhm2bNkiTU/Tr18/DB8+XOsdfD9//sSUKVNQpkwZKZNL1zWMmjRpAhcXFwDA5MmTcfnyZZUyP378QLt27aTOaNCgQRr316tXL5UvikII9O7dW8qoHzhwoLRNcckOTXdCXr58WSmzUtMgdUKVLFkSGTNmBAD8+++/AKCyDnblypUhk8kQExMjDdA3aNBAZV+6PB9vb2/pOEDSP5/UzsHBQbpzdu/evdixY4dKmQ8fPmDAgAFJcjw/Pz+1wcz9+/cxYcIEALFJMBUrVpS2de3aVcrkHDx4MJ4+fapS/+PHj+jSpQuA2Kze/v37K22XJ4cAqlNqmpqaomfPngBig84OHTpI69ErWrJkCc6ePQsgtmPPmTNnnM83PuRtjM8dwnyPE1FqZW5ujs6dOwOIvRNe8QuqXFhYGAYPHgwAsLKyQosWLQAk/nNfnoQZGhqKcePGqW3fsWPHpDsFNa3J2a5dOxgaGuLXr18YNWoUAKBu3bpql0irU6eONMDh5eWldqaMd+/eSV8kf1/+TVs/lTVrVunL/NOnT9GvXz+1s4r9888/UjZ/p06d1M6KkBzy5s0rzX62ePFitet9R0dHY/fu3QBil/Pw8PDQad8GBgZSrHvv3j2MHTtWpczLly8xYsQIjfuQz2ACxA5c/N7H//jxQyke/p28r71//77aOz6+f/+Otm3bKiVeJEVfq+09oShTpkzSdLFTpkzBp0+fYGRkFOfau0RElPQKFCgAALh+/TouXbqksn3u3Lm4f/9+SjcLQOoY70luif1un9znSNe+nYiIUhdra2ssXbpU+lvTzJjyfiQwMFBlJgMgts/o0aMHnjx5ovSYorj6CnmsIYRQapOcn5+f0gX7hNClP1y5ciU2btwo/Z3SMUO3bt1gYWEBAJgxY4bai/vBwcHo1auX9Lfi7PLycaOXL19qvFlaMTFEcdxIfp0kMjISHTp0kJbKVOTn54cpU6YAALJkyYKWLVtK2ywtLeHm5gYAOHToEF68eKFSf+TIkQgKClLbLl01b95cus7m5eWl9jhnz57F1q1bAQAtWrSQxpEUx0i6du2qdnaRq1evYvbs2QBi3zPyMQhra2vp2s6OHTvU/i/ItwGx105/HyMj0ibuhX8p2eXMmROnTp1C/fr18fbtW8yePRvLly9H8+bNUbVqVbi6usLAwACvX7/GyZMnsXfvXqUp+ypWrIhVq1bpdCwjIyOsW7cONWvWxK9fv1C5cmX06dMH9evXh6WlJW7duoU5c+ZIFxA6deqEJk2aqN2XhYUFnjx5ghIlSmDUqFEoWLAg3r17h4ULF+LMmTMAYpMN2rRpI9WpUKECMmbMiE+fPsHb2xvNmjVDp06dkClTJrx79w4HDhzAli1blIKDpF6fUyaToXbt2ti0aZM0OK24TAcQe7G/YMGCuHPnjnTH5u/LdMgfMzMzQ1hYGJYsWYLw8HA0adIE9vb2ePnyJXbu3Il9+/YpXXzQ53qj+jJ9+nQcPHgQnz59Qtu2beHr64vmzZvDysoKV65cwb///qs09bauiUCaDBgwANeuXUO7du1gaWmJ06dPY8aMGfj+/TuMjIywbNkypakwHRwcsGTJEnh6euLDhw8oUaIEBg0ahGrVqsHIyAgXL17E7Nmz8eHDBwDAmDFjpDt65TJlyiT9Pm3aNAwePBhGRkZS1uy4ceNw4MABPHz4EIcPH5aOUaBAAXz69Am7du2SZuSwtLTEggULEnUO1JG38fbt21i/fj3y58+PLFmyqMykoojvcSJKTrdu3YpXeXNzc6XpBydOnIj9+/fj5cuX6Nu3L/z8/NCmTRvY2dnB398fM2fOlAYMpk2bJmXdJ/Zzv3Xr1pg2bRoeP36M2bNn48GDB+jYsSNcXFwQHByMs2fPYtGiRQBiM/379Omj9vk4OzujRo0aOHbsmBR7abqLP0eOHOjQoQM2bNgAPz8/FCtWDAMGDEDevHkRFRWFu3fvYs6cOQgKCoJMJpMSMeQU+6lx48ahbdu2sLCwkKZWnD9/Pk6fPo1Pnz5h1apV8Pf3R58+fZAzZ068ffsW69atk5ZoyJw5MyZPnqzbi5YEZDIZxo0bh86dOyMoKAglS5bE4MGDUaJECVhaWuL58+dYunQpLl68CADo37+/xpkb1Bk4cCD27NmDixcvYtq0abh//z66desGR0dHXL58GdOmTdM6NaeLiwvKlSsHPz8/3L59GzVq1MCQIUPg7OyM27dvY968eXj8+DHc3d1VppEEgGbNmmHRokUIDQ1FlSpVMHLkSOTPnx8/f/7ElStXsGLFCpVBhKToa+N6Tyjq0KEDDh8+LL1P69SpAycnp0S3gYiI4qdt27bSlMYNGjTA2LFjUbJkSQQGBmLTpk3YvXu3xv4muaWG8Z6UkJjv9sl9juIalyAiotTr77//RsuWLbXeud+sWTNpOYj69evjn3/+QfHixREVFYXbt29jxYoVePjwoVKd3/uRuPqKVq1aYdKkSYiJicGYMWMQGBiIBg0aICoqCkePHsWSJUtgaWkJS0tLjbMqxqVp06aYNm0aAGD06NH4+PEjatasCUtLSzx9+hSbNm1SmUErpWMGR0dHLFy4EN26dUNERATq1KmDHj16oFGjRrCwsMCtW7cwa9Ys6caO7t27o0yZMlL9vn37YvHixQgMDMSAAQNw6dIlNGnSBFmzZsWnT59w9OhR6Zqeq6sr2rVrJ9WtXbs2PD09sX79epw9exZFihTB0KFDUaRIEfz69Qve3t5YunQpwsPDYWBggI0bN6osKd62bVtMmzYNYWFhqFq1KsaMGYMCBQrg9evXWLFiBU6ePJnomNHU1BTLly9H06ZN8fHjR5QsWRLDhg1DxYoV8evXL5w6dQrz5s1DTEwM7OzspNcciL1pqH379ti8eTMCAgJQtGhRDB06FBUrVkR4eDhOnz6N+fPn49evXwCABQsWKN28M27cOJw5c0Z6fv3790eFChVgb2+P169fY926ddJsmS1btkSuXLkS/DwpHRKUagQFBYkePXoIY2NjASDOn8KFC4tNmzaJmJgYlX2NHz9eKvfkyROV7f/995+wtrbWuv+BAweKiIgIlbqVK1cWAESjRo2Ep6enxvp169YVP3/+VKl/6NAhYWJiovXYjRs3Fu3atRMAhLGxsdJ+1q1bJ5Xz8fFJ0Lnetm2b0vGePn2qUmbAgAHSdicnJxEdHa12XytXrhQGBgZan0+PHj1EjRo1BACRJUsWpfpxvVZy8vOeNWvWJClTuXJlndshf7xdu3Ya9+vr6yuVW7Vqlcr269evi4wZM2o9Rzlz5hQARO3atTUeJ65j9+jRQ9ja2qo9hrW1tTh27JjG/Sxbtkzre9PQ0FD8+++/av/nfvz4IZydnZXK//46vH37VpQsWVLre8XJyUmcOXNGZf9J8T7ZvXu3yvG8vLyEEEK8ePFC5TG5lHqPE1H6IP+cSshP4cKFVfb39OlTkSdPHo11ZDKZGDNmjNq2JOZz/8GDByJbtmxxfqafPXtW6/nYsmWLVN7Ozk6EhYVpLBsSEiKqVKmi9ZjGxsZi4cKFKnVfvXolzM3NlcqWL19eqYy/v7/IlSuX1v3nyJFD3L9/X2X/nTp1kspERkaqbX98yri6uqpsGzZsWJzvkXbt2omoqCiN51CTL1++iPLly2vc79ixY7XGQ/fv3xdOTk4a6w8ePFhMnjxZ7XMLDg4WBQoU0Pq8smXLJubNmyf9vXLlSqV9aGqbtj5Yl/eEXGhoqFJ8tX379nifYyKi9Eax33vx4oXO9eR1fv/OLoQQkZGRolGjRhr7i3z58okrV65If/v6+irVT+5xhZQa79ElptBEW38u5+PjI5Vbt26dyvbEfLdPznMU17iELq9tZGSkVLdTp05aziQREcVFl89dRR8+fBD29vYaY4Hw8HBRrVo1rX2InZ2dWLVqlfT36NGjlfahyxj2zJkzte7/9OnT0jj++PHjlerqOg48ZswYrc/DwMBAjBs3TuTLl08AEOXKlVOqn9yxgNzixYvj7LfbtWun9jra2bNnhZ2dnda6bm5u4sGDByp1IyIiRNeuXbXWzZAhgzhy5IjadoeEhIgyZcporFuxYkVx5MgRjbGqq6ur1jECRWvWrNF6jjJlyiT8/PxU6oWHh4vOnTtrfY7GxsZiwYIFao87f/58YWhoqLV+9erVxY8fP+J8DkSKuFRHKuLg4IAVK1bgxYsXmDNnDurWrYvs2bPD1NQUpqamyJYtGypUqAAvLy+cP38et27dQvv27RN0d36jRo3w9OlTeHl5oWjRorCxsYGFhQXy5s2L7t274/r165g/fz6MjY217mfdunVYs2aNdMefra0tKleujE2bNuHQoUPSdEaK6tWrh2vXrqF9+/bIli0bjI2NYW5ujhw5cqBZs2Y4dOgQ9u3bJ00ZHRkZqbQuV1KoXbu2NONAtmzZ1C6JULVqVen3unXralz/q3v37jhz5gyaNGmCTJkywdDQEJaWlsidOzfat2+P8+fPY8WKFdL0TO/evcOFCxeS9Pn8CYoVKwZ/f3+MGjUK+fLlg4WFBaytrVG5cmXs3r0bK1askJaHSczU3yVLlsSdO3fQpUsXODs7w9zcHPnz58eIESPw6NEjaappdXr16oWHDx9i0KBByJcvHywtLWFtbY0CBQpg8ODBuH//PkaOHKn2f87S0hLHjx9HrVq1YGtrCzMzM5iamipNpZUlSxZcunQJW7Zswd9//43MmTPD2NgYVlZWKFasGMaPHw9/f39pja+k1qxZMyxbtgz58uWDmZkZ7O3tdcrW5XuciFKznDlz4vbt21i0aBEqVaoEBwcHGBsbI0uWLGjdujUuXLigcXaExHzu582bF/fv38f06dNRvnx5ODg4wMjICA4ODihVqhQmT56MR48eKS0NpU7jxo1hZBQ7CVyLFi2Ups38nZWVFU6ePIlNmzahbt26yJw5M0xMTGBlZQUPDw8MGDAAd+7cUVlOCgCyZ8+OI0eOoHz58rCysoKlpaXK1NIeHh64f/8+li1bhho1aiBjxowwMjKCra0typUrh9mzZ+Pu3btqZyRICbNmzcKZM2fQrl07uLu7w8zMDGZmZnB1dUXbtm1x/PhxbN68WWlWKV3Z29vj7NmzWLlyJcqVKwdra2tYW1ujatWqOHTokNZ1MwEgX7588Pf3x4gRI5AnTx6YmprC0dERdevWxaFDhzB37lyNde3s7HDp0iVMnDgRhQsXhrm5OYyNjeHk5ISKFSti1qxZuHfvHvr27StNaartLiRd6fKekDMzM5NmX7OxsUHDhg0TfXwiIoo/IyMj7Nu3Dxs2bEDlypVha2sLS0tLFCpUCFOnTsW1a9eQOXNmvbUvNYz3pITEfLdPznOky7gEERGlXpkyZZKWJlDHxMQEx44dw/z581GqVClYWVkpjUOMGzcODx48QLdu3VCoUCEAwO7du5VmCtalrxg+fDjOnTuHZs2aIWPGjDAxMUGOHDnQt29f3LlzR2X27oSYPHkyDhw4gNq1ayNDhgwwNDSEtbU18ufPj549e+LmzZuYOHEiatSoASB2KSt1y3Ymt759++LRo0cYMmQIChYsCBsbGxgbG0tLnh49ehSbN29Wex2tYsWKePDgAby8vFCiRAnY2trCyMgITk5OqFSpEubNmwd/f3/kzZtXpa6xsTFWr16Ns2fPon379nB1dZXGGUqXLo1Zs2bh0aNHqFOnjtp2W1lZ4ezZs1i4cKH0XrG2tkapUqWwePFi+Pr6Jtnyq126dMHDhw8xYMAA5MmTBxYWFjA3N0fBggXh5eWFe/fuoWzZsir1TExMsHbtWpw/fx4dO3aEu7s7zM3NYWZmhr/++gu9evXCrVu3NC7xPnDgQFy7dg3du3dH3rx5YWFhARMTE2TLlg2NGzfGrl27cOLECZXZOIjiIhOKn5pERHpkY2ODkJAQ9OvXT5riXBenT5+WEl1WrVqFbt26JVcTiYiIktTVq1dRqlQpALFrP8aVaEH6I0+gadeunTQFd3pSoEAB3L9/H126dMGaNWv03RwiIiIiIiIiIqIkxRkniChZff78GbVr10afPn1w6dIljeWuX7+OkJAQAEDBggVTqnlERER6tWnTJgCxM2dUqFBBz60hUu/GjRu4f/8+AMDT01O/jSEiIiIiIiIiIkoGRvpuABGlbXZ2dvDz88Px48fx6NEj+Pj4qCx7Eh4ejmHDhgGInXpUvuQDERFRWnbr1i2sX78eANCzZ88ELb9GlNzCw8MxcuRIALFLknBWFCIiIiIiIiIiSouYOEFEycrY2BidOnXCkiVLcOrUKVSpUgXdu3eHu7s7IiIi8PDhQyxbtgx3794FAIwdOxbZsmXTc6uJiIiSx9KlS3Hx4kVERUXh0KFDCAkJgZOTE3r06KHvphFJnj17hiFDhiBjxoy4cOECHjx4AAAYM2aMnltGRERERERERESUPJg4QUTJ7t9//8Xjx4/h4+ODc+fO4dy5c2rLDR06FF5eXincOiIiopQTFhaGzZs3S38bGBhg+fLlsLW11WOriJTZ29vjwIEDSo81btwYbdq00VOLiIiIiIiIiIiIkpdB3EWIiBLHysoKx44dw65du9CoUSNkzZoVxsbGsLW1hYeHB3r27IkrV65g9uzZMDQ01HdziYiIkk3RokWRNWtWmJmZoXjx4ti/fz+aNm2q72YRKXFwcECpUqVgamqKrFmzYuTIkdixY4e+m0VERERERERERJRsZEIIoe9GEBEREREREREREREREREREekDZ5wgIiIiIiIiIiIiIiIiIiKidIuJE0RERERERERERERERERERJRuMXGCiIiIiIiIiIiIiIiIiIiI0i0mThAREREREREREREREREREVG6xcQJIiIiIiIiIiIiIiIiIiIiSreYOEFERERERERERERERERERETpFhMniIiIiIiIiIiIiIiIiIiIKN1i4gQRERERERERERERERERERGlW0ycICLSwc+fP5EjRw706tVL300hIqIU8OXLFwwaNAiurq4wMzNDnjx5MGnSJPz8+VPfTSNK9Rg3ERHpz549e1ChQgVYW1vDyckJtWvXxqVLl/TdLErHGBcQEf2Z+PlNaQ3f06QLJk4QEcUhKioKbdu2RUBAgL6bQkREKSAoKAhly5bFggULYGtri969eyN//vyYMGECqlatiq9fv+q7iUSpFuMmIiL9mTZtGpo3b47Xr1+je/fu+Pvvv3Hu3DlUrFgRZ8+e1XfzKB1iXEBE9Gfi5zelNXxPk66M9N0AIqLU7PPnz2jVqhV8fX313RQiIkohI0eOxOPHj9G+fXusW7cORkaxIfPevXvRrFkzjB49GkuXLtVzK4lSH8ZNRET6c/PmTYwdOxYlSpTAyZMnYWNjAwDo27cvSpcujSFDhuDatWt6biWlJ4wLiIj+TPz8prSG72mKD844QUSkwZo1a+Dh4QFfX1/UqlVL380hIqIUEB0djZ07d8LS0hKLFy+WkiYAoGnTpqhWrRpWr16N4OBgPbaSKPVh3EREpF+LFi1CTEwMVq5cKSVNAEDx4sUxevRolC1bFpGRkXpsIaUnjAuIiP5M/PymtIbvaYovzjhBRKTBokWLYGtri82bN8PMzAzHjx/Xd5OIiCiZffr0CSEhIShVqhRsbW1VthcpUgSnTp3CxYsXUa9ePT20kCh1YtxERKRfhw8fRq5cuVC0aFGVbZMmTdJDiyg9Y1xARPRn4uc3pTV8T1N8ccYJIiINZs2ahQcPHqBOnTr6bgoREaUQMzMzAEB4eLja7d++fQMAPH/+PMXaRPQnYNxERKQ/nz9/xsePH1GwYEG8ePECbdq0gaOjIywtLVGrVi1cv35d302kdIZxARHRn4mf35TW8D1N8cXECSIiDWrWrAkTExN9N4OIiFKQvb09cubMifv378Pf319p269fv6TMdHkCBRHFYtxERKQ/7969AxCbQFG8eHHcuXMH7du3R7169XDq1ClUqFAB586d03MrKT1hXEBE9Gfi5zelNXxPU3wxcYKI0gU3NzfIZDKtP1WqVNF3M4mIKBnEtw8YOXIkoqKi0KhRI/j4+ODHjx+4f/8+mjZtiuDgYACAEEJPz4Yo+TFuIiLSv/h8Fv/48QMAcP78eVSuXBk3b97E/PnzsWvXLhw4cABhYWHo2rUrYmJi9PiM6E/FuICI6M/Ez29Ka/ieppRgpO8GEBGlhJw5c0rTr2vi4uKSQq0hIqKUFN8+oHv37njx4gVmzJiBWrVqSY+XKlUK06dPR//+/WFhYZFs7SXSN8ZNRET6F5/PYkNDQ+mxhQsXKt1VV69ePdSqVQvHjx/H9evXUbJkyeRpMKVZjAuIiP5M/PymtIbvaUoJTJwgonTh5MmT+m4CERHpSUL6gGnTpsHT0xPHjh1DaGgoihYtiurVq2Px4sUAAGdn56RuJlGqwbiJiEj/4vNZbGtrCwDImDEjsmfPrrK9WLFiOH78OJ49e8bECYo3xgVERH8mfn5TWsP3NKUEJk4QEREREamRO3du5M6dW+mxK1euAADy58+vjyYRERERqXB3d4eRkREiIyPVbpc/zhmziIiIiIiINDPQdwOIiIiIiFKTNm3aIFOmTAgLC1N6/MePHzh8+DCyZs2KQoUK6al1RERERMpMTU1RqlQpBAcH4/r16yrbr169CgCMX4iIiIiIiLRg4gQRERERkQIPDw98+vQJq1evlh6Ljo5G9+7dERwcjFGjRkEmk+mxhURERETKevXqBQAYOHAgfv36JT2+Z88enD17FtWqVYObm5ueWkdERERERJT6MXGC/mj//PMPZDIZtmzZAgAICAiATCaDTCbDmDFj9Nw6+l1af32+f/+OjBkzwt3dHT9+/NB3c4iI9OL3vlmbR48ewczMDNmyZdN5/6Ghofj3339RrFgxWFlZwdraGoUKFcKkSZPw9evXRLT8fwYPHgx3d3cMHDgQLVu2xMiRI1GmTBls374dTZs2Rc+ePZPkOOlBWu/7k1NaP3eMm4goNUkL8UuHDh3QunVrXLhwAQULFsTQoUPRsmVLtGzZEhkyZMCyZcsStN8qVapI/ZG6HxMTEzg6OqJIkSIYMGAAbty4kaDjXL58WWm/jx8/1rmum5sbZDJZvBNDunfvLh2vWrVq8aq7e/duqa6HhwfCw8PjVT++GBcQEaVuaSGWSC6KsYSuFPs9T09PrWXv3buH0aNHo0SJEsiUKRNMTEyQKVMmFC1aFIMHD8aFCxfi1cbkjHfSAsYklNYxcYL+WH5+fpg1axaKFy+Otm3b6rs5RLCxscH48ePx4sULDBkyRN/NISJKcfHpm8PDw9GlS5d4DTK/e/cOJUqUwKhRo3Dz5k38/PkTP378wN27dzF+/HgULVoU/v7+iX0asLa2xoULF+Dp6YnLly9jyZIliIqKwuLFi7Fjxw4YGRkl+hhE6R3jJiJKLdJK/AIAW7ZswdKlS2FtbY2lS5fizJkzaN26Na5cuYLcuXMnyTF+FxkZiaCgINy+fRuLFi1C8eLF0bVr13gPNG/atEnp77Vr1yZlM9WaNWsWMmXKBADw9fVVaYMm379/x8CBAwEABgYGWLNmDUxNTZOtnekB4wIi+pOlpVjiTxIZGYn+/fujSJEimD59Oq5fv45Pnz4hMjISnz59wq1btzB//nxUqFABjRo1wsePHxN1rKSIdyj1Y0xCHPWlP1J0dDR69eqFmJgYzJgxg9NlU7KrUqUKhBBxluvRowfmzZuH1atXw9PTE+XKlUuB1hER6V98+uaoqCi0atUKfn5+Ou8/KioKjRo1gr+/P2QyGXr16oUWLVrAwMAAe/fuxeLFixEQEICGDRvixo0bsLGxSdTzcXZ2xpo1axK1D6L0inETEf0p0lr8YmBggN69e6N3796J2o8mN2/eVPpbCIGwsDB8+PAB165dw+rVq/Hp0yesXbsWr169wpEjR3RKOI2MjMSOHTsAAK6urnj58iU2bNiAKVOmJGvCqp2dHRYuXIhWrVoBAIYOHYr69evDwcFBaz0vLy+8e/cOADBgwAD2X3FgXEBEaVlaiyUU6fr5rS9du3aVkh5LlSqFDh06IH/+/LCxsUFISAhu376NtWvX4s6dOzhw4ABevXqFM2fOxHmOkiveIf1jTEI6EUR/oEWLFgkAonLlykqPv3jxQgAQAISXl5d+GkcapZfXZ926dQKAKFasmIiJidF3c4iIUoSmvvl3nz9/FtWrV5f6AwAia9asce5/6dKlUvmFCxeqbN+2bZu0fdy4cQl9GkSpBuMmIqLkx/glbpUrV5baGJcvX76IChUqSOUHDBig0zH+++8/qY68XwAg/vvvP53qu7q6CgDC1dVVp/K/a9CggXTMbt26aS175coVYWBgIAAId3d38fPnzwQdM74YFxARpU6MJeIWn1hCTrHf69Spk8r2EydOSNv79++vsc+IiooS3bt3l8qOGjUq0W1MaLyTVjAmobSOS3XQHycsLAxTp04FAAwbNkzPrSFS1a5dOzg7O+PGjRvYt2+fvptDRJTsdO2bDx06hOLFi+PkyZMAYu+K1NXChQsBAB4eHujbt6/K9tatW6NBgwYAgEWLFiEqKkrnfROR/jBuIiJ9YfyS9Ozt7XHgwAFkzZoVALB06VK8ePEiznryu0WzZs2KDh06wNHREQCwevXq5GusgiVLlsDKygoAsGbNGo1roUdHR6Nnz56IiYmBTCbDmjVrYGFhkSJtTC8YFxDRn4SxhP7IZwh1cHDA7NmzNc70YWhoiEWLFsHV1RUAsGzZMsTExCTq2AmNd+jPwpgk/WLiBP1x1q5diw8fPsDZ2Rl169bVd3OIVBgbG6N9+/YAIAXPRERpmS59c6tWrdCgQQO8evUKMpkMY8aMQcWKFXXa/8OHD/Hw4UMAQNu2bTUOMnTq1AkAEBwcDF9f3wQ8EyJKaYybiEhfGL8kD3t7e0yfPh1A7PTis2bN0lr+69ev8Pb2BgA0aNAAhoaGaNq0KQDgyJEj0pIYycnFxQVTpkwBEDsdd69evRAZGalSbsGCBdL03T179kSVKlWSvW3pDeMCIvqTMJbQnwcPHgCIXeLLxMREa1lTU1O0bNkSQGzc8ebNm0QfP77xDv15GJOkX0ycoD/O8uXLAcQGHYaGhvGqGxwcjEKFCkEmk0Emk0n7+t2lS5fQunVrZMmSBSYmJsiaNSs6duwIf39/AICZmRlkMhnWr1+fqOdCynR5ff6U16ZDhw4AgBs3buDKlSt6bQsRUXLTpW++fPkyAMDNzQ0+Pj6YPHmyzvtXXP+zUqVKGstVqFBB+v3MmTM67z8l3b17F7169UKePHlgbm4OKysr5MqVC507d8bZs2fV1gkICJD6xqNHj+LHjx8YPXo0/vrrL5iZmcHR0RENGjTA0aNH4zx+cHAwJk2ahJIlS8Le3h5mZmbIkSMHOnXqJL1G2rx58wbjx49HsWLFYG9vD3Nzc+TNmxf9+/dXe4eFYtvHjBmT5G1KyPnUxdatW6V2r1ixQmvZpUuXSmWfPHmi9JxPnz6N48ePo2jRojAzM0OmTJlQt25d/Pr1K8Ftk0tLcS3jJiLSB8Yvyad169aws7MDABw8eFBr2Z07dyI8PBwA0LhxYwCxrwkQO8NDSvVP/fv3R8mSJQEA9+7dw7x585S2y2MgAMiePTtmzpypcV/JFZ9ow7iAiCjlMZbQH/kME48ePcKXL1/iLN+5c2ds2bIFPj4+yJAhQ5K0IT7xTnrCmIT+ePpeK4QoPq5fvy6tn3TkyBGV7drWV/r586coW7astH3BggVqjzF//nwhk8mU1huT/5iamoodO3YIU1NTae1N0l1iX58/7bXJnDmzACB69+6t76YQESWbuPpmubJly4qZM2eKsLAw6TH5GpJxres5bNgw6Rhv377VWtbc3FwAEE2bNo3fE0kBq1evFoaGhmr7MflPz549VdZOVOw/t27dKvLnz6+x/tChQzUe/9SpU8LBwUHr8QcNGiSioqLU1t++fbuwtLTUWNfCwkLs379fY9vVrX2ZmDYl9Hzq4tevX8La2lqntWrLly8vAIhSpUqpPOcJEyaotLFcuXI6tSG9xbWMm4goJTF+0V1C1iUXQohGjRpJ9Z48eaKxnLwfdXR0FJGRkUIIIaKjo0WWLFkEAJEzZ844+3JXV1cBQLi6usarjb+7deuWMDIyEgCEtbW1eP/+vbStWbNmOr1nkis+YVxARJS6MJbQXUJiCcV+r1OnTirbO3fuLG2vWrWqePHiRYq3UQjd4520hDEJpXWccYL+KAcOHAAAGBkZoXLlyjrXi4iIQJMmTXDx4kUAwMyZMzFgwACVcps3b8agQYMghECmTJkwf/58+Pn54fDhw2jTpg3Cw8PRvn17REREJM0TIgC6vT5/4mtTrVo1AJCmHSUiSot07ZvPnz+P4cOHw9TUNN7HkE/RbGRkhMyZM2stmyVLFqU6qcWjR4/Qu3dvREdHI1euXFi1ahX8/Pxw7tw5LF26FG5ubgCAFStWYNu2bRr3M3jwYNy/fx/FihXDtm3bcPHiRaxYsQLZs2cHAMyZM0eaLlLRtWvXUKdOHXz58gUODg6YMGECTp06hUuXLmH9+vUoVaoUAGD+/PkYMWKESv1Dhw6hdevW+PnzJ6ytrTFmzBj4+vrC19cXY8eOhYWFBX79+oXWrVvj0aNHOp2TxLQpqc6nJubm5mjevDkA4Ny5c3j79q3acgEBAdJdQPI7ERRNmjQJ1tbWWLJkCc6fP48FCxZoXftWF2k1rmXcREQpifFL8subN6/0+507d9SWef78OS5cuAAg9m5dIyMjALFrv7du3RoA8OzZM5w+fTp5G/v/ChcujKFDhwIAQkJCpNmyTpw4gT179gAAPD09UadOHbX1kzs+UYdxARGRfjCW0K9+/fpJs3z4+voiV65cqFmzJhYvXizNWJASdIl30gvGJJRm6DVtgyie5Jl/+fLlU7tdXbZbdHS0aNGihfT4lClT1Nb9/v27cHZ2FgBE9uzZxevXr1XKLFiwQCn7Td/Zbn+ahL4+f+prM2vWLKk9T58+1XdziIiSRVx9sy5147rLom7dugKAsLW1jXOfhQoVEgCEh4dHvNuTnMaPHy8ACENDQ7X92Js3b4S9vb0AIGrUqKG0TbH/BCDq1KkjwsPDlcp8/PhR5MqVSwAQlpaW4t27d9K2mJgY4eHhIQAId3d38ebNG5XjR0dHi/bt20vHuHnzprQtNDRUuLi4CADCwcFB+Pv7q9Q/evSoMDAwEABE+/bt1bZd8U6ExLYpMedTV6dOnZKOPXfuXLVlpk6dKgAIIyMj8enTJ5XnDEAcPHgwQcdPb3Et4yYiSkmMX3SX0DswFfuZlStXqi0zceJEqYyfn5/Sths3bkjb2rVrp/VYSTXjhBCxs07lzJlTABAGBgbi6tWrokCBAgKAyJw5swgODtZYNznjE8YFRESpC2MJ3SXHjBNCCLFw4UKNsxZkzJhRtGzZUqxdu1Z8/vw5WdoohG7xTlrDmITSOs44QX+UGzduAADy5cunc52ePXti165dAIBx48bBy8tLbbldu3bhw4cPAICFCxciW7ZsKmUGDBiAWrVqxbfZpIUur8+f+trkz59f+v3atWt6bAkRUfJJSN8cX/J1r83MzOIsa25urlQntfj48SMAwMrKCpkyZVLZnjVrVkycOBH//PMPPD09Ne7HysoKGzZsgImJidLjGTNmxKJFiwAAP3/+xNatW6VtR44cwYMHDwAAc+fORdasWVX2a2BggEWLFsHKygoAsGTJEmmbj48PXr16BQCYMmUKPDw8VOrXrl0bNWvWBACcPHkSQgiNzyEp2pQU59PT01Nac1PdnaxVqlSBi4sLAGi8K1R+nuvUqQMnJyeV7dmyZUODBg3U1k2ItBzXMm4iopTE+CX5WVpaSr8HBQWpLbN582YAQI4cOVC2bFmlbUWLFpVenz179uDr16/J09DfmJubS2txx8TEoHbt2rh37x4AYNmyZdJa5uokVbynK8YFRET6w1hC//r3749jx44hV65cKts+ffqEnTt3okuXLsiSJQu6du2KwMDAJG+DLvFOesCYhNISJk7QH+Pjx48ICQkBAOTMmVOnOiNHjsTq1asBACNGjMDEiRM1lj106BAAwMHBAX///bfGcj169NC1yRQHXV+fP/W1UQzanj9/rseWEBElj4T0zQlhYBAbsspksjjLyi/Yy+ukFnny5AEAfPv2Da1atcLDhw9VyvTv3x/Tp09Hu3btNO6nSZMmyJgxo9pttWvXlrYdPnxYevzo0aPS79WrV9e4bzs7O2l5DMVEAnk/LJPJ0LZtW431V61ahYCAALx9+zbO1yqxbUqq86mNTCZD+/btAQBXr17Fs2fPlLbfvn0b9+/fBwCp3O9KliyZoGOrk9bjWsZNRJRSGL+kDMULN+qe18WLF/HkyRMA0NhXyx8PCwvDli1bkqGV6tWoUQMdO3YEAHz58gUA0Lp1azRq1EhrvZSIT+QYFxAR6Q9jidSjZs2aePToEU6ePIm+ffuqfT0iIyOxdu1a5M2bF7du3UrS48cV76QHjEkorUmf/8n0R3r//r30u62tbZzlN2/ejJkzZ0p/Ozg4aC0vX4OqSJEi0vpY6sgH7ylx4vP6/KmvjeL7NL2sL0dE6Ut8++aEks84EBYWFmdZeZmErB+anDp06ABnZ2cAwL59++Dh4YE8efJg4MCBOHz4sE7PDYDK3ZiKZDIZChcuDAB4/Pix9LjiwIC1tbU0y4K6n1OnTgEAXrx4IQ28yPeVI0cOra9z9uzZ4erqqtOgTmLblFTnMy7yiyYAsH37dqVt8gs4NjY2aNiwodr66mbSSIj0ENcybiKilML4JWV8+/ZN+l3dLA2bNm2SfteUmNmuXTsprlizZk3SNjAOc+fOhbW1NYDY11I+s5c2KRWfMC4gItIvxhLxo8sYgTZxJSQYGBigWrVqWLx4MZ4+fYqXL19i7dq1aN26tdLrExQUhDp16khJkUkhrngnrWNMQmkREyfoj/Hz50/pdxsbmzjLv3z5EsD/goWJEydqzQiTT6no6Oiodb+a7vKk+InP6/OnvjaKnari+5eIKK2Ib9+cUPJB61+/funcJnt7+2RrT0JkyJABx48fR6FChaTHHj9+jIULF6J+/frIkCEDWrdujcuXL2vdT+bMmbVul/eVnz59kh5LyHSR0dHR+PHjh9K+MmTIEO/9aJLYNiXF+Vy/fj2EEBBCoEqVKmrL5MmTR5o1QjFxQgghLd/RrFkzaVrU3yXV/0V6iGsZNxFRSmH8kjLk0zADqvFLREQEduzYIf2dL18+tcmTbm5uUtLkzZs3pWnRU0KGDBmkwf8MGTLE2afKyyVFvBcXxgVERPrFWCJ+FJcajYqK0qlOaGio2vq6cHFxQefOnbFt2zZ8+PAB8+fPh4WFBYDYPnHp0qXx2p822uKd9IAxCaVFTJygP4ZiZqK2bDRFHTp0wP79+wHEdra9evXSWFY+rVJMTIzWfca1ZjfpTtfX5099bRTfp4nNrCUiSo0S0jcnhKurK4DYQfbPnz9rLSvP/s6SJUuytSehChYsiFu3buHEiRPo3bu39LyA2IGQHTt2oGzZskrZ+r8zMjLSeozo6GiVcvKBiRw5cuDmzZs6/8gHFnQd2IiPxLYJSJrzqQv5rBP37t2T1jg/e/Ys3rx5AyA2ntEkKfv/tB7XMm4iopTC+CVlXL9+Xfq9ePHiStsOHTqUoLs9U3rWiYRIqfiEcQERkf4wlogfxZkY5DdDxEWx3O8zOYSGhuLFixe4dOlSnPsxMzPDwIEDsWvXLumxEydO6NQGXWiLd9ILxiSU1mgfeSVKReRTUwG6ZVnWrVsX69atg6GhIVq2bImdO3fCx8cHmzZtUjvA7ejoiHfv3sUZhAQGBsa/8aQiPq/Pn/raKGYgKr5/iYjSivj2zQnl4eEh/f7s2TM4OTmpLff+/XvprgTFOqmJTCZD9erVUb16dQDA06dPcfz4cezbtw8nTpyAEAKjRo1Co0aNpHWyFcV1kUHeV8qniQb+N1Xi58+fUahQoXivuym/YyUpp7NMbJvkEns+ddGmTRsMGTIEkZGR2L17NwoUKCANumTLlg2VK1dO0H7jIz3EtYybiCilMH5Jft+/f5dmh3B3d1e5iKO4TMfatWulO2rViYmJgaenJ0JDQ7F161bMnj1b40xPqUVyxyeMC4iI9IuxRPxkz55d+v3t27c6LWmhOGuBYn0AGDJkCJYvXw4ACAgIUEpS1KRevXr466+/8OTJkyRbbiGueCc9YExCaRFnnKA/hmLHozgFkibFihWTssHmzZsnfREfMmSI2g/eIkWKAIhdZ0l+t6Y6N2/e1Km99+7dw4ABA5A/f35YW1vD0tISRYsWxZQpUxASEqKx3vPnzzFw4EDky5cPVlZWsLOzQ5EiRTB69Gi8fv1ap2P/CeLz+iT1a5NSFN+nSbXGORFRahLfvjmhFNc2vHDhgsZy58+fl34vV65csrUnIcLCwnDnzh3cv39f6fFcuXKhT58+8PHxwb///gsg9gLB8ePH1e7n9u3bGo8RHR0tbVecIjpfvnwAYu/YULwbQp2VK1dixYoVSndgyOu/ePFCawyzY8cOuLi4oGrVqnj8+LHW4yS2TUl1PnWRIUMG1K1bFwCkuygOHToEIHZN9oQmfcSHvuPalMC4iYhSCuOX5LdlyxbpAk6bNm2UtgUHB0v9aOnSpdG5c2c0b95c40/Lli3RqFEjAMDXr1+xZ8+elH0y8ZBS8QnjAiIi/WIsET+K4xNxff+Xu3r1qvR74cKFlbblypVL+v3o0aM6t8PS0hJA0s3KoS3eAWKXpejbty9cXV1hYmICR0dHNG3aVOO4TkxMDJYtW4ZChQrBwsIC2bJlw+DBg/H9+3et7fj58ydy5MihcaaHL1++YOjQocidOzfMzc2RK1cujBkzJkmWnWBMQmkREyfoj+Ho6Cjd8ShfO0lXWbJkwaRJkwDEZqsNGTJEpUz9+vUBxH6JP3z4sMZ9Kd4ZoY4QAtOmTUORIkWwbNkyuLu7o0ePHujQoQNCQ0MxduxYFC1aFG/fvlWpu3TpUuTNmxeLFi1CpkyZ0KtXL3Tq1Ak2NjaYPn06cufOHefx/0RxvT5J9dqkNMX36V9//aXHlhARJY/E9M3xkStXLhQsWBAAsHHjRo1T9m3YsAFA7AwJKTELQHy4urqicOHC6Nu3r8YyderUkX5XXM9T0Z49exAZGal2m7e3tzQrhPwCAwDUrFlT+n3RokUaj//s2TP07t0bvXr1wsSJE6XHq1WrBiD2S/zOnTs11vf29sbr16/h5+cX50BEYtuUVOdTV/LlOm7duoXDhw8jICAAgPZlOpJLSsa1KYlxExGlFMYvySsoKAiTJ08GAJibm6Nnz55K23fs2IGIiAgAQOvWrXXap2J/u3r16iRqadJL6fgEYFxARKQPjCXip2bNmtINB4sWLYpzGYbw8HBp7MHBwQFlypRR2v73339Lv8+cORPfvn2Lsw0vX76UEhsrVaoUr/arE1e88+7dO5QoUQJLly5Fzpw5MXDgQFSrVg0HDhxAmTJlcO7cOZV9Dh48GH369IGpqSkGDhyIwoULY/78+ahatarGmCEqKgpt27aVxih+FxISgooVK2Lu3Llwd3dH//794ebmhqlTp6J8+fI6L52iC8YklGYIoj9IjRo1BACRN29etdtfvHghAAgAwsvLS2lbVFSUKFy4sLTdx8dHafu3b9+Eg4ODACBy5Mgh3r9/r7L/nTt3SvUBiHXr1qmUmTFjhgAgihYtKl68eKG0LTo6Wnh5eQkAwsPDQ0RGRkrbVq1aJQCInDlziuvXr6vs18/PT2TOnFkAEJs2bdJ0ilK1hL4+SfXapLRZs2ZJ7Xn9+rW+m0NElCzi6pu1qVy5sgAgsmbNGmfZpUuXSp+pM2bMUNm+fft2afvIkSPj3Zbk5unpKQAImUwmDh48qLbMP//8Iz2HEydOSI8r9p8AxNChQ1Xqvn37Vri6ugoAIkuWLOL79+/StoiICJEtWzap/saNG1Xqh4WFiQoVKkhltm3bprQtY8aMAoDIlCmTeP78uUr9M2fOCAMDAwFAtGvXTm3bFfv+xLYpMeczIcLDw4W9vb0AIHLlyiUAiMKFC6stqy3eiY/UENemJMZNRJSSGL/oTv58dRlCDAwMFJUqVZLKT5w4UaVM2bJlBQBhYGAg3r59q1MbIiMjpVgEgHjy5InSdnkM5OrqqtP+dBXf/SZnfMK4gIgodWEsET8tWrSQ2jlw4EARHR2ttlxUVJTo2rWrVHb8+PFqy7Vt21YqU7ZsWfHs2TONx/706ZMoVaqUACDMzc3Fhw8fVMokdbzTuXNnAUDMnDlT6fGTJ08KAwMDlffN5cuXBQBRt25dpXMzYcIEAUBMnz5d7fOqWrWq1I6ePXuqlBk9erQAIKZNm6b0+PDhw9W2TxeMSSitY+IE/VGmTJkifcEODg5W2R7XQLWfn5+QyWRSgsKvX7+Utq9fv16qnzlzZrFgwQJx8eJF4ePjI3r06CFdEJD/rF+/Xqn+06dPhYmJiXBwcBCBgYEan4e8Q9u8ebMQQogPHz4Ic3NzYWdnJwICAjTWu3//vjA2NhZ2dnbi48eP2k5VqpSY1yexr40+yAPCXLly6bspRETJJq6+WZv4DBZERUWJokWLSp/zHTt2FD4+PuLMmTNi0KBBwtDQUAAQbm5u4uvXrwl8NsnnwYMHwtTUVAAQJiYmom/fvmL//v3i0qVLYt++faJt27ZSH1i6dGmlur8nTgAQDRs2FAcPHhQXL14UCxcuFM7OztK2Xbt2qRz/+PHjUl8pk8lE+/bthbe3t/Dz8xNr164VBQoUkOrXqlVLxMTEKNXfu3evtD1Dhgxi+vTp4vz58+L48ePin3/+kZ6bnZ2dePnypdq2/973J6ZNiTmfCdWzZ0+l12D27Nlqy6VE4oQQyR/XpjTGTUSUkhi/6E7xQsLNmzeVfq5fvy5Onz4ttm3bJvr27StsbW2lsg0aNFC5KPL06VNpe+XKlePVjoEDB0p1//nnH6Vt8gQHGxsbMXToUJ1+dEnaiG/iRHLGJ4wLiIhSF8YS8fPu3TvpplAAolChQmL+/Pni+PHj4sqVK+LYsWNi2rRpIk+ePEoJEWFhYWr39+PHDykZQp4Q0aZNG7Fp0yZx5swZcfHiRbF3714xYMAAKT6RyWRi9+7daveXlPFOTEyMsLW1FU5OTmoTRKpVqyYAiKdPn0qPtW/fXgAQly9fViobHh4uHBwchLu7u9Ljq1evFhkyZJDGSzQlTrRp00Y4OzurxAW3b98WAETTpk3Vng9tGJNQWsfECfqj3L9/X/rA3Lt3r8p2XQaqu3XrJpVRl4U5adIk6YP99x8LCwspyw9QvvNRCCHGjh0rAM2ZkHIXL14US5YskTrHiRMnCgBi9OjRcZ6DPn36CABi7ty5cZZV58ePH2Ls2LEid+7cwsTERDg5OYk2bdqIR48eqZR9/fq16NKli3B2dhYWFhaiSpUq4vr166JRo0bC1NQ03sdO7OuTmNcmpcXExAhHR0cBQIwYMUKvbSEiSk5x9c3axGewQIjYWRXy5s2rth+Q7+fevXsJeRopYteuXcLMzExj+4HYWQx+v/tBsf9s06aN0kwNij/GxsZav1Du3btXWFtbaz1+jRo1lGarULRmzRphYmKisW6mTJmEn5+fxrar6/sT06aEns+EunDhgrRfbXfJplTihBDJG9emJMZNRJTSGL/oTvFCgi4/hoaGYuDAgSIiIkJlX+PHj5fKLVu2LF7tuHbtmtIge1RUlLRNnuAQn5+bN2/GecyEzGSRXPEJ4wIiotSFsUT8PXnyRBQvXlynfrpFixbi27dvWvcXHBwsevbsqXKhXd1P5syZxf79+zXuKynjnfDwcDFv3jyxZMkStceqV6+eACBu3bolPZY9e3ZhbW2tNtGiYcOGAoDSTSqFCxcW7u7u4siRI8LX11cA6hMnNJHPVNK3b1+d68gxJqG0jokT9McpVqyYACA6d+6ssk2XD+2goCDpw87IyEipg5K7cOGCaNWqlciSJYswMTERmTNnFp6enuLZs2dSRwRAZepFecd/8eLFeD2nihUrCgDi/PnzcZY9deqUACAqVqwYr2MIIURISIh0/ipWrCiGDRsmOnToIExNTYWNjY24evWqVPbNmzciS5YsAoCoWbOmGD58uChevLiwtrYW+fPnT7bEibhen4S+Nint/PnzUltu376t17YQESU3bX2zNvEdLBBCiNDQUDFjxgxRrFgxYW1tLUxNTYWHh4cYNWqUCAoKim/TU9zTp0/FoEGDRMGCBYW1tbUwNjYWzs7Oonbt2mL16tVKy3jJ/d5/BgYGikGDBgkXFxdhZmYmcufOLXr37q0ybbU6nz59EmPHjhUlSpQQ9vb2wsjISGTMmFHUq1dPbN++XWWmid89f/5cDBgwQOTJk0dYWFgIc3NzUbBgQeHl5SU+f/4cZ9uTuk0JOZ+JoRgbaZKSiRPJGdemJMZNRKQPjF90E9eFBDMzM5ElSxZRrVo1MWHCBK3xSM6cOaU+S13cEBcPDw/puIoXP1JT4oQQyROfMC4gIkp9GEvEX3R0tNi3b59o166dyJMnj7C0tBQGBgbC2tpaFChQQPTo0UOnaySK/P39xdSpU0XlypVFjhw5hLW1tTAzMxNubm6ibt26YtWqVXEmYSRlvKPN58+fhY2NjTA1NRU/fvwQQsQmWgCxs3CoI5916/jx49Jjx48fF+Hh4UIIEa/EicDAQLFx40bh4OAgbG1t1d5MGxfGJJTWMXGC/jhbtmwRQOxU0JqmakpOe/bskT4sr127prRNvubmly9f4rVPeSeiS5Dz4cOHeAdWcgMGDBCA6tTS169fl4It+cWJdu3aqZSNjo6Wpo1KSOJEctP22ggRe+769OkjXFxchLGxsciQIYNo0qSJ2o47KChIDBkyRPz111/CzMxM5MyZU3h5eUkBTVz69esngNipsoiI0jp9981pXVJdiKfEef/+vTQN6saNG/XdnCShLXaKT9wkROJiJ8ZNRKQPjF+IlDEuICKKH8YSFF8dO3YUAETXrl2lx969eycAzcuYjRs3TgDql2UVQvfEidmzZ0v9vKWlpTh79myCn0dyY0xC+mQAoj9Mq1at4O7ujq9fv2L37t1Jtt8NGzagRYsW+OeffxAWFqaxnK+vLwDA0NAQHh4eStu+fv0KALCysorXsb99+wYAsLGxibOsg4MDACAwMDBex4iKisK6deuQI0cODBkyRGlbsWLF0KZNGzx48AAXL15EWFgY9u3bB1dXV6WyBgYGWLBgAUxNTeN17MRKitfm3bt3KFGiBJYuXYqcOXNi4MCBqFatGg4cOIAyZcrg3LlzUtmQkBBUrFgRc+fOhbu7O/r37w83NzdMnToV5cuXx48fP7S2NzQ0FFu2bAEAjBo1KqFPm4joj5FcfTNRarJ161ZER0fD2toaTZs21XdztEps7BSfuAlIXOzEuImI9IXxC6UXjAuIiJIHYwmKDy8vL2zcuBEuLi6YOXOm9HhkZCQAaLzmIn9cWx+ui8yZM2Po0KFo1aoVIiIiUKdOHRw+fDhR+4wvxiT0R9B35gZRQixfvlwAEKVKlUqyfW7btk3KYlu3bp3aMjdu3BCWlpYCgKhTp47Kdvn0zZ8+fYrXseUzVWhaU1zRly9fEjTjhHzdNXd3dzF+/HiVn5o1awoAYvHixeLevXsCiF1LTJ1SpUql6IwTSfHadO7cWQAQM2fOVHr85MmTwsDAQOTNm1d6bPTo0QKAmDZtmlLZ4cOHq93H71auXCkAiDJlyuj4DImI/nzJ0TdTLM44oX8BAQEia9asAoDo06ePvpsTp8TGTvGJm4RIXOzEuImI9InxC6UHjAuIiJIPYwmKS3R0tLTchpOTk7h3757S9k+fPgkAokqVKmrry2ec2Lt3r9rt8VmqQ+7ixYvC2NhYODo66jzDdlJgTEJ/AiZO0B8pIiJCFCpUSGVtp8QICQkRzs7O0jIUffv2FQcPHhSXL18W3t7eYvjw4cLGxkYAEObm5uLBgwcq+6hQoYIAIC5cuBBn+58/fy79XbZsWQFAXL16Nc52njt3TgAQFSpUiNfzU1yPSdvPpEmTpLJdunRRu6969eqlaOJEYl+bmJgYYWtrK5ycnER0dLTK/qtVqyYAiKdPnwohhGjTpo1wdnYWv379Uip3+/ZtAUA0bdpUY1sjIyOFu7u7MDAwiPN9QESUliRH30yxmDihH7t37xatW7cWHTt2FE5OTtK6ps+ePdN30+KUmNgpvnGTEAmPnRg3EZG+MX6h9IBxARFR8mEsQdr8+vVLNGnSRAAQWbJkEffv31cpEx4eLgwMDETRokXV7kOedHHy5Em12xOSOCGEkJZk9/HxiVe9xGBMQn8CI11npiBKTYyNjbFu3TqULl0aY8eORY0aNSCTyRK1TysrK+zZswcNGzZEUFAQlixZgiVLlqiUc3JywrZt25A3b16VbXXr1sX58+dx9OhRlCtXTuOxjh49ioYNG6JevXo4dOgQGjdujIsXL2Lv3r0oUaKE1nbu27cPANCkSROlx9+8eYPBgwfD19cXRkZGqF+/PubMmQM7Ozvp+QFAixYtsHPnTq3H8Pf3BxA79ZE6nz590lo/qSX2tYmMjMSECRNgYmICAwPVFYrMzMwAQJqaaevWrWrb8eDBAwCx01ppsm7dOjx//hwDBw7U+h4gIkprkqNvJtInmUyG7du3Kz02ffp0uLu766lFuktM7BTfuAlIeOzEuImI9I3xC6UHjAuIiJIPYwnS5OvXr6hfvz78/Pzg4eGBo0ePwsXFRaWciYkJcuTIgRcvXkAIofL+ef78OQCoLE0el8jISJw7dw6RkZGoXbu2ynZXV1cA8V8SPjEYk9AfQd+ZG0SJIZ9qZ8uWLUm2z6CgIDF58mRRpkwZYWtrK4yMjETmzJlF2bJlxcyZM7UuwxEQECBMTU2Fvb29CAwMVFsmJiZGWhZj/vz5QgghPnz4IOzs7IS1tbVSRtzvHj16JCwtLYWNjY34+PGj9Hh0dLQoUaKEqFWrlrh9+7a4fPmyKFasmGjcuLFUJiwsTJiZmYmcOXOqzcjbtGmTGDt2rHj48KGIiooStra2ImPGjCrZeN+/fxfW1tYpOuOEXGJeG00+f/4sbGxshKmpqcZpqQIDA8XGjRuFg4ODsLW1FY8ePVJb7tu3byJTpkzC3d1d/Pz5M95tISJKC5Kjb07vOOOEfvj7+wt3d3dhamoq8uXLp3EaydQsqWMnXeImIXSLnRg3EVFqwviF0gPGBUREyYexBCkKDQ0V5cuXFwBE2bJlRVBQkNbynp6eAoC4efOm0uPh4eHCwcFBuLm5aayracaJ8PBwYWpqKuzs7ERERIRKvfr16+s8C3pSY0xCqRkTJ4iSmHzNqWLFiomXL18qbQsLCxODBg0SAETevHmVkhI2bdokAAg3Nzdx/fp1lf1evXpVuLi4CABi48aNSttu3rwpAIj3799Lj50/f17IZDLx/ft36bEuXboIAGLy5MlK9f39/aWO5cOHD0IIIYYMGSIAiOHDhyuVlU8NpY/EieTQsWNHAUB07dpV7fbZs2dLF6ssLS3F2bNnU7iFRERERKlDXHGTEIydiIiI0gvGBUREROoNGDBAABClSpXSeiFf7vTp0wKAqFu3roiMjJQenzBhggAgZsyYobGutqU62rZtKwCI8ePHKz2+c+dOAUAULlxYxMTE6P7EUinGJJSUZEIIkaxTWhClMzExMejfvz+WLl0KY2Nj1KtXD7lz58bnz59x6tQpvHr1Cu7u7jh27Bhy5cqlVHfTpk3o0aMHIiIiULVqVRQrVgwxMTG4ceMGzpw5A1NTUyxcuBDdunVTqvflyxdcuXIFderUkR7z8/ND+fLl8fnzZzg6Okrlypcvj4cPH6JMmTIoV64cvnz5gl27duHnz59YsWIFevToAQD4+fMnKlWqhBs3bqB8+fIoVaoUrly5gmvXrsHQ0BDR0dEICwtL5rOZvLy8vDBt2jS4uLjg5s2bcHBwUCmzdetW3LhxA2/evMHevXthbGyMXbt2oV69enpoMREREZF+6BI3AYydiIiI0gPGBUREROq9fPkSuXLlQlRUFDw9PaUlMX7XrVs3ZMuWTfrb09MTGzZsQOHChVGnTh3cvXsXhw8fRqlSpeDr6wsLCwu1+zl9+jSqVq2Knj17Yvny5Urb3r9/j3LlyiEgIADVqlVDsWLF4O/vj8OHD8PJyQlnzpyJ9xIgqQ1jEkpqTJwgSianT5/G8uXLcePGDbx9+xYAkDdvXjRv3hwDBgyApaWl2nqvX7/G0qVLcfToUWldKzc3NzRo0ADdu3eHm5ubTsdv0aIF7t+/D39/f6XHv337hhkzZmD37t14+fIl7OzsUKRIEQwfPhw1atRQKhsSEoJJkyZh+/bt+Pz5M4oVK4YZM2agQ4cO+PDhwx+bOBETE4MhQ4ZgwYIFcHJygq+vL/Lnzx9nvUuXLqFSpUqwtbVFQECAxteQiIiIKK1IaNwEMHYiIiJKaxgXEBERabdu3Tp06dIlznIXL15EmTJlpL+joqIwd+5crFmzBgEBAciSJQuaNGmCcePGwc7OTuN+tCVOAMDnz58xYcIE7N+/Hx8/fkTGjBlRv359jBs3Tilx40/DmISSCxMniNKgGTNmYNSoUThy5Ahq166d5Pt3c3P7YxMnQkND0a5dO+zbtw9ZsmSBj48P8uXLp3P9Dh06YPPmzfDx8VFJNCEiIiJKSxIbNwGMnYiIiNIKxgVERESUGjAmoeRkpO8GEFHSmjx5MsaNG4fFixcnS9LEn+zr16+oX78+/Pz84OHhgaNHj8LFxUWpTGRkJM6dO4fIyEi1508+tVZgYGCKtJmIiIhIH3SJmwDGTkREROkB4wIiIiJKDRiTUHJj4gRRGjJo0CAsXLgQy5YtQ69evfTdnFQlLCwMDRo0gJ+fH8qWLQtvb2+1610JIVCvXj2Ym5vj06dPMDY2Vtp+69YtAECuXLlSotlEREREKU7XuAlg7ERERJTWMS4gIiKi1IAxCaUEA303gIiShnyWiQ0bNjBpQo2RI0fiwoULKFWqFHx8fDR2qCYmJmjWrBm+fv2KqVOnKm3btWsXDh06hMKFC6N48eIp0WwiIiKiFKdr3AQwdiIiIkrrGBcQERFRasCYhFKCTAgh9N0IIkqc27dvo1ixYhgxYgQGDhyotM3JyQmGhoZ6alnq8PLlS+TKlQtRUVHw9PSUpmH6Xbdu3ZAtWza8f/8e5cqVQ0BAAKpVq4ZixYrB398fhw8fhpOTE86cOQMPD48UfhZEREREyS++cRMAxk5ERERpFOMCIiIiSg0Yk1BKYeIEURowbtw4TJ48We22Bw8eIG/evCncotRl3bp16NKlS5zlLl68iDJlygAAPn/+jAkTJmD//v34+PEjMmbMiPr162PcuHFSx0tERESU1iQkbgIYOxEREaVFjAuIiIgoNWBMQimFiRNERERERERERERERERERESUbhnouwFERERERERERERERERERERE+sLECSIiIiIiIiIiIiIiIiIiIkq3mDhBRERERERERERERERERERE6RYTJ4iIiIiIiIiIiIiIiIiIiCjdYuIEERERERERERERERERERERpVtMnCAiIiIiIiIiIiIiIiIiIqJ0i4kTRERERERERERERERERERElG4xcYKIiIiIiIiIiIiIiIiIiIjSLSZOEBERERERERERERERERERUbrFxAkiIiIiIiIiIiIiIiIiIiJKt5g4QUREREREREREREREREREROkWEyeIiIiIiIiIiIiIiIiIiIgo3WLiBBEREREREREREREREREREaVbTJwgIiIiIiIiIiIiIiIiIiKidIuJE0RERERERERERERERERERJRuMXGCiIiIiIiIiIiIiIiIiIiI0i0mThAREREREREREREREREREVG6xcQJIiIiIiIiIiIiIiIiIiIiSreYOEFERERERERERERERERERETpFhMniIiIiIiIiIiIiIiIiIiIKN1i4gQRERERERERERERERERERGlW0ycICIiIiIiIiIiIiIiIiIionSLiRNERERERERERERERERERESUbjFxgoiIiIiIiIiIiIiIiIiIiNItJk4QERERERERERERERERERFRusXECSIiIiIiIiIiIiIiIiIiIkq3mDhBRERERERERERERERERERE6RYTJ4iIiIiIiIiIiIiIiIiIiCjdYuIEERERERERERERERERERERpVtMnCAiIiIiIiIiIiIiIiIiIqJ0i4kTRERERERERERERERERERElG4xcYKIiIiIiIiIiIiIiIiIiIjSLSZOEBERERERERERERERERERUbrFxAkiIiIiIiIiIiIiIiIiIiJKt5g4QUREREREREREREREREREROkWEyeIiIiIiIiIiIiIiIiIiIgo3WLiBBEREREREREREREREREREaVbTJwgIiIiIiIiIiIiIiIiIiKidIuJE0RERERERERERERERERERJRuMXGCiIiIiIiIiIiIiIiIiIiI0i0mThAREREREREREREREREREVG6xcQJIiIiIiIiIiIiIiIiIiIiSreYOEFERERERERERERERERERETpFhMniIiIiIiIiIiIiIiIiIiIKN1i4gQRERERERERERERERERERGlW0ycICIiIiIiIiIiIiIiIiIionSLiRNERERERERERERERERERESUbjFxgoiIiIiIiIiIiIiIiIjSpDdv3mDw4MHw8PCAhYUFLCwskD9/fowcORKfPn1SKiuTyXT+8fT01LkNp0+fjnN/jRs3Vql36tQplCpVCmZmZnB2dka/fv0QEhKi8TgdO3aEoaEh7t27p3PbiCiWkb4bQERERERERERERERERESU1M6dO4eGDRvi69evMDQ0RK5cuRAdHY1Hjx7B398fGzduxLFjx1CoUCEAQPny5bXuLzg4GP7+/gCAv/76S+d23L59GwCQOXNmuLu7qy2TL18+pb/9/f1Rt25dREREwMPDA4GBgViyZAlu376NM2fOwMBA+f7427dvY8uWLejYsSMKFCigc9uIKJZMCCH03QgiIiIiIiIiIiIiIiIioqTy9etX5M6dG58/f0adOnWwdu1aZM6cGQDw/PlzdOzYERcuXECOHDng7+8PMzMzrfuLiYlBrVq1cPLkSdSqVQtHjhxRSV7QpEuXLli3bh3+/fdfjBw5Uqc67dq1w9atW7FixQr06NEDoaGhqFWrFs6fP4+DBw+iQYMGSuVr1aqFc+fO4fHjx8iePbtOxyCi/+FSHURERERERERERERERESUpqxfvx6fP39GlixZsHPnTilpAgDc3d2xb98+2Nvb48WLF9i9e3ec+5s+fTpOnjwJJycnbNy4UeekCeB/M04ULFhQ5zrnz5+HhYUFunXrBgAwNzdHz549AcTOpKHIx8cHPj4+GDBgAJMmiBKIiRNE6diTJ0/QtWtXuLi4wMTEBM7OzmjcuDF8fHw01nn16hV69+6NHDlywMTEBPb29qhevbpOQYUm4eHhmDNnDkqVKgVbW1uYm5ujQIECmDJlCkJDQ9XW4bpeRERERERERERERESkia+vLwCgQYMGsLa2Vtnu5OSEcuXKAQCuXr2qdV8PHz7ExIkTAQDz589HpkyZdG5HVFSUtLxHfJbQeP/+Pezt7ZUSNJycnAAAX758kR4TQmDEiBGwt7fHqFGjdN4/ESlj4gRROnXs2DEULlwYa9euRVBQEPLlywdDQ0Ps378ftWrVwvDhw1Xq3LhxA0WKFMHy5cvx/v175M2bF2ZmZjh16hRatGiBXr16xbsdHz9+RKlSpTBs2DDcuHED2bNnh7OzM+7fv4+xY8eiQoUK+Pbtm1Id+bpeV69ehbu7O2JiYrBkyRLUq1cPMTExKsfgul5EREREREREREREROnLmDFjsHHjRnTp0kVjGSEEACA6OlrrvoYMGYLIyEhUqlQJbdu2jVc7Hj16hLCwMNja2sLFxUXnevb29ggMDFS67vHx40cAgIODg/TY5s2bcevWLXh5ecHOzi5ebSOi/2HiBFE6FBgYiDZt2iA0NBStW7fGu3fvcOvWLbx9+xZbtmyBoaEhZs+ejT179kh1oqOj0aZNGwQHB6NSpUoICAjAnTt38P79e6xcuRIymQwrVqzAli1bdG6HEAItW7bEnTt3kC9fPvj7++PevXt48eIFzpw5g4wZM+LGjRsYPXq0Ur2pU6ciIiICK1asgL+/P16+fIkKFSrg/PnzOHz4sMpxhg8fDhMTE0yaNCnhJ42IiIiIiIiIiIiIiP4YJUuWRIcOHVC6dGm12wMDA3H69GkAQP78+TXu5+jRozhy5AgAYPbs2fFuh3yZjvz58+PGjRsYMmQIatWqhbp162LYsGG4e/eu2noVKlRAeHg4Vq5cCQAICwvDmjVrAACVKlUCEDuj99ixY+Hi4oJ+/frFu21E9D9MnCBKh1avXo3g4GC4ublh/fr1sLW1lba1bdsW3bt3BwAsX75cevzixYt4/PgxAGDr1q1wdnaWtnXv3h0dOnQAAKnT1sXu3btx9uxZ2NjY4OTJk8idO7e0rVKlSvj3338BABs2bEBkZKS0jet6ERERERERERERERFRYgwcOBC/fv2ChYUFmjVrprHc1KlTAQA1a9ZEyZIl430ceeLEnTt3ULx4ccybNw8+Pj44evQo5syZgyJFimDMmDEq9UaPHg0TExP07t0b+fPnh6urK86ePYtKlSqhbt26AICFCxfi5cuXmDJlCkxNTePdNiL6HyZOEKVDbm5uaNOmDfr06aO2Iy1UqBAA4OXLl9Jjr1+/BgA4Ojoia9asKnXkwcKrV690bsf69esBAMOGDVNKxJBr1qwZJk6ciJkzZyI8PFx6nOt6ERERERERERERERFRQk2ZMgVbt24FAIwbNw4ZM2ZUW+7atWs4f/48AOCff/5J0LHkiRNhYWHw8vLC8+fPER4ejidPnmDQoEEQQmDq1KmYOXOmUr3ixYvj5MmTKF++PJ49ewYA6NevHw4dOgQDAwMEBwdj+vTpKFy4MNq1ayfV+/nzJ16/fq10QyoRxc1I3w0gopTXunVrtG7dWuP2a9euAQD++usv6TH5uluBgYF48+YNsmXLplTnzp07AGKTMnQRHR2NU6dOAQCaNm2qtoyNjQ3GjRun8rjiul7y5Alt63rNnj2b63oREREREREREREREREmTpyICRMmAAAaNmyIESNGaCw7f/58AECxYsVQrVq1BB2vQYMGyJIlC+rXr680s0WuXLkwb948ODo6YsyYMZg4cSK6dOkCR0dHqYx8mXJ1pk6diuDgYGzduhUGBgb49esXevTogR07diAqKgo2NjYYOXIkRo0aBZlMlqC2E6UnMiGE0HcjiCh1+Pr1KxYsWICJEyfC0NAQJ0+elNbJEkKgWLFiuHXrFqpUqYLt27cjU6ZMAIDt27ejffv2iImJwcGDB1G/fv04j/Xw4UN4eHjA0NAQ4eHh+PLlCzZs2IALFy7gx48f+Ouvv9CxY0eUKVNGpW6zZs2wd+9eLFu2DL169UJYWBhq166Ns2fPwtvbG/Xr10d4eDjy5MkDIQQeP37MKaqIiIiIiIiIiIiIiNKxqKgo9OvXDytWrAAA1K5dG//99x/MzMzUlg8PD4ejoyN+/PiB+fPnY+DAgcnSLsXjbNmyBW3bto2zzsuXL5EnTx6UL18eJ0+eBAD07t0by5cvR6tWrVCrVi1s3boVJ0+exLRp0zgrN5EOmDhBRNizZw/Gjx+Pp0+fIjw8HNmzZ8eyZctUEiA+fPiADh064MSJEzA1NUWePHnw5csXvHnzBpkzZ8bcuXO1zmSh6NixY6hTpw4cHR2xdetWtGnTBkFBQSrlBg8ejDlz5ihlQ16/fh3lypVDREQE8uXLh8DAQHz69AmVKlWCr68vDAwMMGvWLIwYMQIbN25Ehw4dEneCiIiIiIiIiIiIiIjoj/X9+3c0b94cPj4+AIBWrVph48aNMDEx0VjH29sbf//9NwwMDPD69WtkyZIl2dpXunRpXLlyBVOmTIGXl1ec5du3b4+tW7fiypUrKFGiBD59+oTs2bOjSJEiuHz5MoDYpUHc3NwQFhaGwMBAGBlxIQIibQz03QAi0r8rV67g/v37CA8PBwAEBwfj4MGDCAkJUSpnZGSEEiVKwMLCAuHh4bhz5w7evHkDAHB0dISxsbHOx5Tv++fPn2jSpAmyZs2KkydPIjQ0FO/evcPEiRNhYGCAefPmcV0vIiIiIiIiIiIiIiJKkDdv3qB8+fJS0sTw4cOxbds2rUkTALB//34AsctlJDZpIiwsTOv2mJgYANDpOsvNmzexdetWtGrVCiVKlAAA3L17FxEREfg/9u47PIqqjQL4me2bnpDQAiSUAKH3GpEuAjZQUSmCIjZEinRFPxUpIkixgQhKUbAhiGKhg9J7CUgLLYX0zfYy3x8xKwvp2WSy2fN7njwkU+6cgJLLzDvvjYmJcR6n0WjQtm1bZGRkIDY2tgTpibwDCyeICK+88gqysrJw8+ZNrFy5ElqtFp999hm6d+8Om80GIHti0bFjR8yePRudOnXCgQMHYDabcfPmTcydOxfnz5/Ho48+eleRQ16MRqPz19DQUOzcuRPdu3eHRqNBtWrVMGPGDLz++usAgHfeeQdpaWku5+es62UymZCYmIjFixfDz88PwH/res2ePdu5rteQIUMQFBSEWrVqITQ0FO+99x7YcIeIiIiIiIiIiIiIqOKKj49H165dcerUKcjlcnzyySeYO3euS5frvOzZswcA0LNnz2Jf/8SJEwgMDIRWq8WRI0dyPcZkMuHMmTMAgEaNGhU45qRJk6BQKDBz5kzntsTERAC4a9mRwMBAAEBGRkax8hN5ExZOEBFq1KgBX19fVKtWDU8//TR2794NjUaDQ4cOYfXq1QCAqVOn4sKFC2jevDl++eUXtG3bFiqVCtWqVcPEiROxdu1aAMC0adNw4cKFAq/p4+Pj/Hzs2LEICgq665gpU6ZArVZDr9c71+gqSFxcHJYsWYLu3bujT58+AIAJEyZgzZo1GDhwIJYvX462bdti+vTpmD17dqHGJCIiIiIiIiIiIiIiz2KxWPDAAw/g4sWLUKlU+Pbbb/HCCy8U6tzMzEycO3cOANCxY8diZ2jQoAFksuzHsStXrsz1mCVLlsBgMCA0NBQ9evTId7zffvsNf/75J1588UXUqVPHub1SpUoA4OwSnuPmzZsAsruGE1H+WDhBRHdp0KABBgwYAADYsWMHRFHE999/DyC7MCK3VlEDBgxA8+bNYbfbsX79+gKvcXuhRIsWLXI9RqvVom7dugCAS5cuFSr79OnTYbFYMGfOHABAUlISvvjiC7Rr1w7ffPMNnnnmGfz888+oUqUK5syZ4+yoQUREREREREREREREFcecOXNw+PBhAMBHH32ERx55pNDnHjt2zNm1ulWrVoU6JzY2FrGxsUhOTnZuU6vVGD9+PIDsAolFixY5l+VwOBz4+OOPMXXqVADA7NmzodVq8xzf4XBg8uTJCAgIcHbsztG+fXuo1Wps2LABV65cAQAcPnwYe/bsQXh4OOrXr1+4b5zIi7FwgsgLpaam4vDhwy4/vO8UEREBAEhISEBSUpJzaY2GDRvmeU5OC6nLly8XmCE6Otr5udlszvM4hUIBIHtyURCu60VERERERERERERERBaLBQsWLACQ/Zxh5cqViImJyfPjvffeczn/xo0bAAA/Pz+EhIQU6prR0dGIjo7GkiVLXLZPmzYNTzzxBERRxKuvvoqwsDC0bdsWVapUwcsvvwy73Y633noLzz77bL7jr1q1CsePH8ekSZMQFhbmsi8oKAjjx49HZmYmGjdujObNm6Njx46wWq2YPXt2oZYmIfJ2CqkDEFHZa9u2LS5duoS5c+di4sSJuR4TFxcHAAgPD4e/vz8EQYAoioiPj0ezZs1yPScpKQkAEBAQUGCG6tWro2bNmrh27Rr279+P++67765j7Ha7c9mPnM4T+eG6XkREREREREREREREdPLkSaSlpQEAbDYb9u7dm+/x9erVc/n61q1bAICaNWuWOItcLsfXX3+NAQMG4PPPP8ehQ4dw/PhxhIaG4vHHH8err76KTp065TuGyWTCjBkzUK1aNYwbNy7XY2bOnIng4GB89NFHiI2NRcOGDfHGG2/gscceK/H3QOQNBDGnzwwReY0XX3wRn376KaKionD69Om7lt64cuUKGjVqBKPRiB9//BEPP/wwOnbsiH379uGRRx7BDz/8cNeYFy5cQKNGjWC1WrF582b07du3wBxTp07F7NmzER4ejrNnz8Lf399l/yeffIKXXnoJgYGBSEhIuKv44Xa//fYb+vTpgzFjxmDhwoV3bR82bBi+/PJL5/bu3btj+/btiI2NRYMGDQrMSkREREREREREREREREQVE5fqIPJCEydOhFarxT///IOnnnrKZcmOo0eP4r777oPRaESXLl3w0EMPAQDeeustCIKAH3/8ERMmTIBOp3Oec/z4cfTr1w9WqxUdO3bE/fff73K93Nb1yslRpUoV3LhxA/3798e1a9ec+37//Xfnul5Tp07Nt2iC63oRERERERERERERERERUXGx4wSRl/r5558xaNAgGAwGqNVqNGjQACaTCefPnwcAdOjQAZs2bUJoaKjznMWLF2PcuHGw2+3w8fFBw4YNkZWV5TynefPm2LJlC6pWrepyrZy1s95880289dZbLvsOHz6M+++/H7du3YJCoUCjRo1gMBicS3Q89dRTWLVqFWSyvOu8vvzySwwfPhzvvvsupk+fftf+adOmYdasWfDx8UG9evVw9uxZWK1WrFq1CkOGDCn6bx4RERERERERERERERERVRjsOEHkpfr374/jx49j1KhRqFatGs6ePYvExETExMTg008/xa5du1yKJgDglVdewYEDBzB06FBUqlQJJ0+eRHx8PDp06IAPP/wQ+/btu6tooiCtW7fG2bNnMWXKFNSrVw/nz59HamoqunbtirVr12LNmjX5Fk0Udl2vuXPnIiwszLmu1/r161k0QURE5MFsNhvatm0LQRCwcuVKl32CIBT6Y/jw4UW67qZNm3DfffchJCQEfn5+aNasGd555x3nuqm52bZtG9q1aweNRoOqVati9OjRLt277jRs2DDI5XKcOnWqSNmIiIiIiIiIiIioeNhxgoiIiIiIPM7bb7+NN998EwCwYsUKlwKImJiYfM9NS0vDmTNnACDPjlW5efnll/Hxxx8DAEJCQlCnTh1cvHgRaWlpqFGjBn799Vc0adLE5ZwzZ86gZcuWsFgsiI6ORnJyMm7duoWYmBjs3LnzrgLR48ePo1WrVhg2bBhWrFhRqFxERERERERERERUMgqpAxARERERERXFsWPH8O677+a5f8+ePXnuczgc6N27N86cOYPevXtj6tSphbrm4sWLnUUTM2bMwOuvvw6lUgmz2YwpU6bgww8/RM+ePREbG4ugoCDneTNnzoTFYsFnn32GUaNGwWg0onfv3tizZw9++eUX9O/f3+U6EydOhEqlwttvv12oXERERERERERERFRyXKqDiIiIiIg8hsViwbBhw2C326FWq4t8/qxZs7B161aEhYXhq6++yndJsBw2mw3vvPMOAGDUqFH43//+B6VSCQBQq9VYsGABOnbsiMTERPzvf/9zOXfPnj3w8fHByJEjAQBarRbPP/88AGD37t0ux/7xxx/4448/MGbMGNSsWbPI3xsREREREREREREVDwsniIiIiIjIY8yYMQMnT57Eq6++iqpVqxbp3NjYWGdhw4cffogqVaoU6rxDhw7h1q1bAIBJkyblesyrr74KAFi1ahVuXw0xPj4ewcHBLgUaYWFhAIDU1FTnNlEUMWnSJAQHBxe6CwYRERERERERERG5BwsniIiIiIjII+zbtw/z5s1D/fr1MXPmzCKfP378eFitVnTp0gVPPfVUoc+Li4sDAAQGBqJu3bq5HtOgQQMAQEpKCv755x/n9uDgYCQnJ8PhcDi3JSYmAgBCQkKc21avXo1jx45h+vTpLkt9EBERERERERERUelj4QQREREREZV7RqMRTz/9NERRxIoVK6DVaot0/pYtW/Drr78CAObNm1ekcwVBAADn8hy5sVqtzs+vXLni/DwmJgZmsxlLly4FAJhMJixfvhwA0KVLFwCA2WzGG2+8gVq1amH06NFFykZEREREREREREQlx8IJIiIiIiIq9yZPnozz589j3Lhx6NSpU5HPz+lQ0atXL7Rt27ZI59apUwcAkJycjOvXr+d6zOnTp52fp6WlOT+fNm0aVCoVXnzxRTRu3BgRERHYtWsXunTpgvvvvx8AsGjRIsTFxeHdd9+FWq0uUjYiIiIiIiIiIiIqORZOEBERERFRubZjxw4sWbIEDRs2xLvvvlvk8w8dOoQ9e/YAAKZMmVLk81u2bIkaNWoAAN5555279ttsNsydO9f5tcVicX7eunVrbN26FZ07d8bFixcBAKNHj8bmzZshk8mQlpaGWbNmoXnz5hg8eLDzPL1ej2vXrrl0siAiIiIiIiIiIqLSoZA6ABERERGRp9u/fz+ef/55xMbGSh2l3PD398ewYcMwb94851IXxaHT6TB8+HDIZDKsWLECGo2myGN8+OGHAIBWrVqhe/fuRT5fLpfjvffew7Bhw7B06VKIoojJkyejVq1aOHPmDCZPnozLly/D19cXer3+riU9YmJinIUbd5o5cybS0tKwdu1ayGQyGAwGjBo1CuvWrYPNZkNAQAAmT56MqVOnluj3kYiIiIiIiIiIiPLGwgkiIiIiohIaPnw4iybuYDabMX/+fHTu3BkDBgwo9jjjxo1DXFwcJk2ahA4dOhQrx08//QQAGDZsWLFzDB06FNeuXcMbb7yBZcuWYdmyZc59wcHB+P777zFixAjo9XoEBAQUasy4uDgsWbIE3bt3R58+fQAAEyZMwJo1azBo0CD07t0ba9euxfTp0yEIAqZOnVrs/ERERERERERERJQ3QRRFUeoQRERERESeShRFhIeHIz4+Xuoo5dIXX3yBESNGFOvcX3/9FX379kV0dDSOHj0KtVrtsj8yMhJxcXFYsWIFhg8fnusYP//8Mx544AHIZDJcu3YN1atXL1aWHCdOnMDy5ctx9uxZaLVadOjQAc888wxCQkLg6+sLq9WKY8eOoXnz5gWONWTIEKxduxYHDhxAmzZtkJSUhJo1a6JFixbYv38/AMBkMiEyMhImkwnJyclQKFj7TkRERERERERE5G4yqQMQEREREXkyQRDw7LPPSh2jXGrfvj2eeOKJYp+/bt06AMDZs2eh0WggCILLR1xcHABgxIgREAQBXbt2vWuMnG4TMTExJS6aAIBmzZph4cKF+P333/HTTz9h6tSpqFKlCk6ePAmr1QqFQoGGDRsWOM7Ro0exdu1aDBo0CG3atAEAnDx5EhaLBTExMc7jNBoN2rZti4yMDHY1ISIiIiIiIiIiKiV8XYmIiIiIqISmTp2Kr776ClevXnVu02q12Lx5c6GXbahoAgICUK9ePQiCUOwx6tevj86dO+e5/9ChQzCbzYiKikLlypXRtGnTu47Zs2cPAKBnz57FziGKIr744gskJibi+eefR6VKle465vYCjTs7Y+Rm0qRJUCgUmDlzpnNbYmIigOxiidsFBgYCADIyMor9PRAREREREREREVHeWDhBRERERFRCPj4+WLhwIR555BHnNqPRiI0bN2LBggUSJvNs06ZNw7Rp0/Lcn7NUx7Rp03JdqiMzMxPnzp0DAHTs2LHYOQRBwHvvvYdLly4hNDQUo0aNctmfkpKCjz/+GADw6quvFjjeb7/9hj///BNjxoxBnTp1nNtzCjKuX7/ucvzNmzcBAKGhocX+HoiIiChvOXOK/KSlpSEoKMj59eHDh/H+++9j586dSElJQeXKldG1a1e8+uqraNu2bbFynDp1Cu+++y62b9+O9PR0VKtWDX379sX06dMRHh6e6znbtm3DlClTcOLECQQFBeHRRx/FrFmz4O/vn+vxw4YNw5o1a3D8+HE0adKkWDmJiIiIiCoiQRRFUeoQRERERESeThRF9O/fH7/88otzm1wux5EjR9CsWTMJk1VcOQ85VqxYkWvhxK5du3DvvfcCyC5uCAkJKXDMnOUwQkNDXQoV3n33XbzxxhsIDQ3FH3/8gRYtWgAALl26hCeffBIHDhxATEwMdu/ene/4DocDrVq1wuXLl3HhwgWEhYU596Wnp6Nq1apQq9U4fvw4IiMjcfjwYXTs2BGVK1fGtWvXStTBg4iIiO6WkZGBoKAgyOVydOjQIc/jtmzZAj8/PwDAihUr8Nxzz8FutyMgIAB16tTB9evXkZycDLlcjvnz52PMmDFFyrF792707t0bJpMJoaGhiIiIwLlz55CVlYXg4GBs27bNOf/IcebMGbRs2RIWiwXR0dFITk7GrVu3EBMTg507d0Imc12l+fjx42jVqhWGDRuGFStWFCkfEREREVFFJyv4ECIiIiIiKoggCFi0aJHLMg12ux0vvfQSHA6HhMm8140bNwAAfn5+hSqaAIDo6GhER0djyZIlLttfe+01xMTEIDk5Ga1bt0Z0dDSaNm2KqKgoHDhwAC1btsTGjRsLHH/VqlU4fvw4Jk2a5FI0AQBBQUEYP348MjMz0bhxYzRv3hwdO3aE1WrF7NmzWTRBRERUCo4fPw4AiIqKwp49e/L8yCmaOHv2LF544QXY7XaMHTsWiYmJOHr0KBITE/H+++/Dbrdj3LhxOHjwYKEzpKam4qGHHoLJZMLkyZMRHx+PQ4cO4ebNmxg4cCDS0tIwcOBAWCwWl/NmzpwJi8WCzz77DGfOnEFcXBxiYmKwZ88el2LeHBMnToRKpcLbb79dgt8xIiKi8iUlJQWvvfYaRo0ahfPnz0sdh4g8GAsniIiIiIjcpG7dupg6darLtr179+Krr76SKJF3u3XrFgCgZs2aJR5Lo9Fg69atmD17Npo0aYIrV67gwoULaNasGebMmYO9e/ciODg43zFMJhNmzJiBatWqYdy4cbkeM3PmTMydOxdhYWGIjY1Fw4YNsX79egwZMqTE3wMRERHdLadwomnTpoU6fuHChbBYLOjUqRPmz58PjUYDAJDJZHjttdfQp08fOBwOLF26tNAZFi1ahLS0NHTo0AGzZ8+GQpG9urK/vz/Wrl2L2rVr49KlS3fNKffs2QMfHx+MHDkSAKDVavH8888DwF1dsP744w/88ccfGDNmjFvmRkREROXFc889hw8++ADLli1Dly5dYLfbpY5ERB6KS3UQEREREbmRyWRCkyZNcPHiRee2sLAwnDt3rsAH60RERERUtkaOHInly5fjf//7H2bMmFHg8R9//DG2bduGhx56CEOHDr1r/+TJkzF37lz06tULv//+e6Ey5Cw/tmzZMmcRxO1mzpyJ119/Hd27d8fWrVud21UqFSpXrozr1687t/3222/o06cPRo4ciWXLlgHIXlKuVatWiIuLw6VLlxAUFFSoXEREROWd3W6HRqOBzWZzbjt//jyioqIkTEVEnoodJ4iIiIiI3Eij0WDx4sUu227duoXXX39dokRERERElJeidpx46aWX8N133+VaNAEAhw4dAoBCP7CJj49HXFwcACAmJibXYzp37gwgu5OZ1Wp1bg8ODkZycrLLsnCJiYkA4LJM2erVq3Hs2DFMnz6dRRNERFShJCcnuxRNAHAur0VEVFQsnCDyQiaTCcOGDUO9evUwffp0sPEMERGRe91///14+OGHXbZ98sknOHz4sDSBiIiIiOgudrsdp0+fBgBUr14dCxYswIABA9CjRw8MGzYM69atcylKyE98fDxefvllbNu2DX5+fhgzZkyhzrtw4QIAQBAE1K5dO9djIiMjAQBmsxlXr151bo+JiYHZbHYuC2IymbB8+XIAQJcuXZznvPHGG6hVqxZGjx5dqExERESeIj4+3uVrmUyGypUrS5SGiDwdl+og8kLvvvsu3njjDefX3333HQYOHChhIiIiooonLi4O0dHRMBqNzm3t2rXD33//DZmM9ctEREREUjt79iwaNWoEAPD394dOp7vrmHbt2mHDhg2oVq1armMsWbIES5YswcWLF2Gz2dCoUSMsX74cHTp0KFSG77//Ho8++igCAwORnp6e6zE6nQ4BAQEAgH379qF9+/YAgMOHD6NTp06wWCxo1KgRkpOTkZSUhC5dumD79u2QyWR4//33MWnSJHz11Vd5dskgIiLyVL/++iv69u3r/Lpq1ap3FVMQERUW79gSeaGEhASXr7/77juJkhAREVVcERERLoWKAHDgwAF8/vnnEiUiIiIiotvlLNMBAG3btsWuXbug1+uRkpKC1atXo2rVqjhw4AD69u0Li8WS6xi7d+/GuXPnnG3CExMT8dNPP7ksqZEfg8EAIHu5t7xotdq7jgeA1q1bY+vWrejcuTMuXrwIABg9ejQ2b94MmUyGtLQ0zJo1C82bN8fgwYOd5+n1ely7dq3QGYmIiMqrO591VK1aVaIkRFQRsHCCyAu1aNHC5ett27ZxuQ4iIqJSMGHCBDRo0MBl29SpU5GcnCxRIiIiIrqTKIqYPn06QkJCIAgCPzzgo127djh16lSJ/+xr1aqFV155BWPHjsUff/yBe+65Bz4+PggJCcHgwYOxc+dO+Pj44NixY84lMO40d+5cGI1GxMXFYcGCBTCbzZg9ezYef/zxQmWQy+UAspfqKIw7O5fFxMRgz549MJlMSExMxOLFi51ru8+cORNpaWmYPXs2ZDIZDAYDhgwZgqCgINSqVQuhoaF47733eE+IiIg81p3dJfLqEEVEVBgsnCDyQt26dXP5OikpCWfOnJEoDRERUcWlUqmwZMkSl22pqamYOnWqRImIiIjoTnv27MF7772HtLQ0qaNQIR08eBAvv/xyicfp1KkTFi1ahAULFuS6lFr9+vWdnRp+/PHHXMeIiIiARqNBrVq1MHbsWPz8888AgA0bNmDHjh0FZsgpcjCZTHkec/vSb7d3n8hPXFwclixZgu7du6NPnz4Asot616xZg4EDB2L58uVo27Ytpk+fjtmzZxdqTCIiovKGhRNE5E4snCDyQnXq1EGtWrVctm3fvl2iNERERBVbz54973rj8PPPP8e+ffskSkRERES30+v1UkegYsjKyiqT67Rs2RIAcPny5UIdf++996JTp04AUKjCidDQUABAZmZmnktn3N6trHLlyoXKMX36dFgsFsyZMwdA9kszX3zxBdq1a4dvvvkGzzzzDH7++WdUqVIFc+bMcS41QkRE5Em4VAcRuRMLJ4i8kCAI6N69u8u2bdu2SZSGiIio4ps/f77zbcIcL730Eux2u0SJiIiIKEevXr3QqlUrqWNQEQQFBbmtS4LD4YDFYsl3PwAolUoA2QUI+/fvz7fgJiIiAsDdD3NyEx0d7bzO1atXcz3mypUrAACNRoOaNWsWOObRo0exdu1aDBo0CG3atAEAnDx5EhaLBTExMc7jNBoN2rZti4yMDMTGxhY4LhERUXnDjhNE5E4KqQMQkTS6d++OlStXOr/esWMH7Ha7c21NIiIicp/w8HC89dZbeO2115zbjh49ik8++QSjR4+WMBkRERHJ5XJs3LgR7dq1w82bN132jRgxAhMmTJAoGeVGEATUq1cPKpWqxGN16dIFf/31F1566SUsWrQo12OOHDkCAGjUqBFsNhtq1aoFs9mM9evX47HHHsv1nLi4OADZc8CCBAcHIyoqCv/88w/+/vtv1K1b965j/vrrLwBA+/btC3XfZtKkSVAoFJg5c6ZzW2JiIoDsYonbBQYGAgAyMjIKHJeIiKi8YeEEEbkTCyeIvFS3bt1cvk5LS8Px48f5lg0REVEpGTNmDFasWIHTp087t73++ut47LHHUKVKFQmTERERUXh4ODZt2oR77rkHBoPBuX3FihXo27cvHn30UQnTUWlp0qQJdu/ejR9++AEzZ86Ev7+/y/64uDisW7cOAPDEE09AoVCgW7du2LJlCz7++ONcCyf27duHv//+GwDQv3//QuV4/PHHMXPmTCxduhRDhgxx2WexWPDFF18AAIYPH17gWL/99hv+/PNPjBkzBnXq1HFur1SpEgDg+vXrLsfnFAvlLBlCRETkKURR5FIdRORWXKqDyEvVqFEDUVFRLtu2b98uURoiIqKKT6lU4uOPP3bZlpGRgUmTJkmUiIiIiG7XqlUrrFmzBoIguGwfOnQoDh48KFEqKk3jxo2DWq3GjRs3MGjQIJeHL8ePH8d9990HvV6PLl26YMCAAQCyC18FQcCOHTvwyiuvuCzZsXXrVjz88MMQRRFDhw5Fy5YtXa4XGxuL2NhYJCcnu2wfM2YMgoODsXv3bowZM8a5dIhOp8PgwYNx6dIl1KlTB4MHD873+3E4HJg8eTICAgLw+uuvu+xr37491Go1NmzY4Fz64/Dhw9izZw/Cw8NRv379ov3mERERSUyn07kUvALsOEFEJcPCCSIv1r17d5evt23bJlESIiIi79ClS5e73iL86quvsHv3bokSERER0e0efvhhzJ0712WbyWTCgw8+iKtXr0qUikpLVFQU1qxZA41Gg19//RURERFo2rQpoqOj0aJFC5w7dw5t2rTBjz/+CJks+zZq586d8emnn0KhUGDJkiWoUqUKWrVqhcjISPTs2ROJiYl44IEHsHTp0ruuFx0djejoaCxZssRle+XKlfHVV19BpVJh8eLFqFatGtq2bYvw8HB89913CAoKwsaNG6FUKvP9flatWoXjx49j0qRJCAsLc9kXFBSE8ePHIzMzE40bN0bz5s3RsWNHWK1WzJ49+66CISIiovLuzmU6AHacIKKSYeEEkRe7c7mOXbt2wWq1SpSGiIjIO7z//vsICAhw2fbSSy/xZzAREVE5MWHCBDz33HMu2xISEvDAAw9Ap9NJlIpKy8CBA3Hs2DE899xzqF69Os6fP4/4+Hh07NgRixcvxt9//42QkBCXc0aNGoUDBw7gqaeeQmBgIE6ePAmdTodevXrh66+/xk8//QSNRlOkHP3798ehQ4cwaNAgKJVKHDt2DH5+fnj66adx+PBhNG7cON/zTSYTZsyYgWrVqmHcuHG5HjNz5kzMnTsXYWFhiI2NRcOGDbF+/fq7CnuJiIg8wZ3LdAQEBMDHx0eiNERUEQiiKIpShyAiaSQmJt5VgfnXX3+hY8eOEiUiIiLyDosXL8aYMWNctn3wwQcYP368RImIiIjodlarFffffz+2bt3qsr1fv3746aefIJfLJUpGRERERADwzTff4Mknn3R+3aBBA8TGxkqYiIg8HTtOEHmxKlWqoEmTJi7btm/fLlEaIiIi7/Hiiy+iRYsWLtvefPNN3LhxQ5pARERE5EKpVOLbb79FgwYNXLZv3rwZEyZMkCgVEREREeVITEx0+ZrLdBBRSbFwgsjLde/e3eXrbdu2SZSEiIjIeygUCnz88ccu27KysvDaa69JlIiIiIjuFBwcjM2bN6NSpUou2xcuXHjXz3EiIiIiKls1atRw+bp169YSJSGiioKFE0Rerlu3bi5f7927FyaTSaI0RERE3qNjx4549tlnXbZ98803d7UEJyIiIunUrVsXGzZsgEqlctk+ZswY/PbbbxKlIiIiIqK+ffti4MCBEAQBbdu2xdixY6WOREQeThBFUZQ6BBFJJy0tDZUqVcLtfxUcP34czZo1kzAVERGRd7h16xYaNGiAtLQ057aGDRvi+PHjdz2gISIiIumsXr0aQ4cOddkWEBCAvXv33rUEJhERERGVHZvNBplMBpmM74oTUcnwbxEiLxccHIxevXo5v1YoFAgNDZUwERERkfcICwvDrFmzXLbFxsZi/vz5EiUiIiKi3AwZMgRvvPGGy7bMzEz079//rvW1iYiIiKjsKBQKFk0QkVuw4wQR4eLFi3jllVdw48YNTJ48GU899ZTUkYiIiLyG3W5Hx44dcfDgQec2Hx8fnD17FrVq1ZIwGREREd1OFEU8+eSTWLduncv2Dh06YNu2bdBqtRIlIyIiIiIiopJi4QQRERERkcQOHTqEdu3auSydNWDAAHz//fcSpiIiIqI7GY1GdO/eHfv27XPZPmjQIKxdu5ZvOxIREREREXko/muOiIiIiEhibdq0wQsvvOCy7YcffsCWLVskSkRERES50Wq12LBhAyIiIly2r1u3Dm+99ZY0oYiIiIiIiKjE2HGCiEqdaLcBdtt/v9qsgN0OKJQQ1FoIKg0EvpVDREReLjU1FQ0aNEBycrJzW926dXHq1CloNBoJk7mXaDXDYcgCRBGCXA7I5IBcDkGmyP5VrpA6IhERUYFOnTqFTp06QafTuWxftWoVhgwZIlEqzyNazXDodYDoAORKCApF9lxAroCgUEodj4iIiMqY6HBkzwv+/VV0OACHPXsbAEHtwzkCEZUaFk4QUbGIoghHWhLsKfFw6FJhz0yDIzMVjqw0ODLT4NClwaHPAGxWoKC/ZgQBUKohU2kgaHwgqLQQfAMgDw6DPLgK5MGVIQuunP2rb0DZfINEREQSWLFiBZ555hmXbf/73/8wY8YMiRIVnsOggy3xKuxJ1/6dB+ggGnVwGHQQDVlwGHRwGDIBq6Xgwf4tppD5BEAWWAnywFDIAitBFhjq/FweFArBNxCCIJT+N0dERJSL3377Df369YPdbnduU6lU2Lp1K2JiYiRMJj17RgpsCVey7xPoMyEaMuHQZ2bPD/SZcBgysgsmrOb8B8oposj58A2APLASZAH/zguCKkEWXAXyStUhDwgum2+OiIiIisRhNsKRfgv2tFvZv6bf/msyHEZddnGEw1Go8QS1FoKPf/Y9A98AyAJC/psXBIZl3zMIrpL9sgYRURGwcIKICuQw6mFLuAJ74lXYEuKyH4okXoVoNpZ5FkGlgbxSNSiq14GiRj0owutCUTWCb6cSEVGF4HA4cM899+Cvv/5ybtNoNDh9+jTq1KkjYbL/OEx62BOvOYskbInXYEu6BjErvezDKJT/zQuq14aieh0oq9WGoNaWfRYiIvJKH3/8MV5++WWXbZUqVcL+/ftRt25diVKVHdFug/3Wddjir/z3kXAFokFX8MlulnO/QB5aHYrwOlBGREMRXpf3C4iIiMqIaLfDdvMSrFdjYbt6DrbkeDjSb0E06cs+jEIFRbVIKHOeIYTXgzwsnC9fEFG+WDhBRHexpybCeuU0LJfPwHrlLBypCVJHyp9CCUXVSCjC60JZMwrKus35pgkREXms48ePo1WrVnDc9qZF//79sWnTJknyOAw6WC+ehOXiCVguniz/8wJBBnmVmlDWaghlRAMoazWAPKSq1KmIiKgCGzt2LBYuXOiyrWHDhvjrr78QHFyx/m1qT0mA5cIxWK9fgD3+CmxJ1wC7TepYeVOqoKwRBWVENJSRDaGo1RAyFlgSERG5hcOkh+3qeVjjzsIaFwvr9QsFd5OSkKDWOl/IVIbXg6JGPciDK0sdi4jKERZOEBEcRj2sF0/AcuE4LBeOw5GWJHWkkhEEKKpGQlm/JVT1W0JZswHbchERkUfJ7QHMTz/9hAcffLDUry3arLDGnYXlwnFYL5yELf6ycy1RTyULqQJ1dFuoottBGRENQSaTOhIREVUgdrsdDz30EDZv3uyyvXv37tiyZQuUSs9dh1u0mGC5eBKWf47B8s+x8l9AWRCZDIoqEVBGRkMZ2QiqqBbsVEVERFRIDpMelvNHYb18Bta4s7AnXff8+wWBlaCKbgd14w5QRkZDkPE5ApE3Y+EEkZdymAywnD0A88m/YLlwvHy/IVJCgsYHynrNoa7fCqpG7SDT+kkdiYiIKF8ZGRlo2LAhEhL+ezgRERGBM2fOwMfHx+3Xcxh0MJ/cC/OZA7DGnQWsFrdfo7wQfAOgbtAGqkbtoKrXHIJSJXUkIiKqAHQ6HWJiYnDixAmX7c899xw+++wzj2oL7dBnwnz2ICxnD8By4QRgq7jzAijVUDdsA3WzGKjqt4Sg8NwiFyIiotLgMGZlP0c4ta/iP0fwDYA6p4iiblMu90XkhVg4QeRFRIsJ5thDMJ/YC8s/RwGbVepIZU+ugKp+K2hadIGqYRveFCEionJrzZo1GDJkiMu2N998E2+99ZZbxhftdljOH4Hp6A5YYg9V6JsfeVJpoKrXHOpG7aBu3BGCSi11IiIi8mBXr15F+/btXQofAWDevHmYMGGCRKkKR7SaYTq+B+ZjO7OLKB2e/fZocQhaP6gbtYe6eQyUtZuwQxUREXkt0WqG+cwBmI/thOXiSa+8XyBofKFq2Brqxh2yO1Qpeb+AyBuwcILIC1ivnoNx368wnzlQrtcYK2uC1g/qZjHQtOkBZfU6UschIiJyIYoiunfvjh07dji3NWvWDMePHy/RuLabl2E6ugOm47sh6jNKmLLiEDS+0LTuBm37PpBXqiZ1HCIi8lAHDx7EvffeC6PR6NwmCAJ++OEHPPzww9IFy4Mt+SZM+3+D6egOiMYsqeOUGzL/YKibdoK6WQyUNetLHYeIiKjUiaII65UzMB/dCfOpvyGaDVJHKj9UGqgbtYe2c38+RyCq4Fg4QVRBiTYrzCf3wvj3L7DduCh1nHJPUb0OtJ37Q900BoKc65gREVH5cObMGbRs2RIWS3aL7IceeggbNmwo8jii2Qjj4a0wHd4Ge0Kcm1NWMIIAZb3m0Ha4H6r6rfi2KRERFdn333+PRx991GWbj48Pdu/ejVatWkmU6j+iww7L2UMw7t8C66WTAG8N5kteuSZ8ujwMdbN7eL+AiIgqHNFmhenoDhh3/wR7SrzUcco9Ze3G0HZ+ILubtQctxUZEhcPCCaIKxp6RAtOB32A8+AdEfabUcTyOLCgU2k79oW3TE4JaK3UcIiIi7Ny5E++99x5CQkIwd+5c1KxZs9DnOoxZMP79C4x/beZbpMUgC64Mbbv7oGnTAzIff6njEBGRB5k9ezamTp3qsq169eo4cOAAwsPDJclkz0yD6dAfMB36E46MFEkyeDJZcGX4dH4AmjY92K6biIg8nmgxwXjgdxj3boIjM1XqOB5HHlod2k79oGnVjfMCogqEhRNEFYQ9LQmGbd/CdGwn4LBLHcfjCVo/aNr1hk+nfpD5BUkdh4iIqEgcWRkw7N0I0/7fIJqNBZ9A+VOooGndHb7dHoXMP1jqNERE5AFEUcSzzz6LFStWuGxv2bIldu3aBT8/vzLLYk9Lgv7Pb2A+udcr1yh3N8E3ENqOfaHtcD9kWl+p4xARERWJ8wWLv3+BaNBJHcfjCT7+0LbrnT0v4P0CIo/HwgkiD2fPTINhx3cwHfqTN0BKg0IJTese8O3+OGR+gVKnISIiypc9IwXG3RtgPPQnYLVIHafiUWng06kftPc8BJmGD0qIiCh/FosFvXv3xs6dO122P/jgg/jhhx8gL+VlHxwGHQzbv4PxwG+AzVqq1/JGgton+4WLzv35oISIiMo9hy4Nhr2bYDrwO1+wKA1yBdTN74Fvt8cgD6kidRoiKiYWThB5KIdBB8POH2Dcv4UPRsqAoNZCe8/D8Il5gK23iIio3LFnpsKwbT1MR7azkLIMCD7+8Ll3ALTt+0BQqqSOQ0RE5Vhqaio6dOiAf/75x2X7hAkTMG/evFK5pmg1w/jXZhh2/QjRZCiVa9BtFEpo2vSEb49BXNqLiIjKHYcuDfpt32bfL7DxOUKpUyih7XA/fLo+ys5URB6IhRNEHka0WmDYtQHGvRtZGSoBWWAl+PZ8EuoW90KQyaSOQ0REXk60WWHcuwmGHd9DtJikjuN1ZIGh8On+ODStukKQle5bw0RE5Ln++ecfdOjQAampruuHL126FM8995zbriM67DAd3g7D1nVw6LhWeVkTtH7w7TEImvb3cV5ARESSEx0OmPZvgf7Pr1lIKQHBxx++3R+Hpt19EEq5yxgRuQ8LJ4g8iOWfY9BtXAZHaoLUUbyeolpt+N4/DKq6zaSOQkREXsp89iCyflnJeUE5IA+rAb9+w6GKail1FCIiKqd27tyJXr16wWr9b8kMhUKBX3/9FT179izx+OYzB6D/Yw3sSddLPBaVjLxKLfj1ewaquk2ljkJERF7KeuMisn76DLYbF6WO4vXkYeHwe+A5zguIPAQLJ4g8gEOXhqzNK2A+uVfqKHQHdcuu8Os3AjKtn9RRiIjIS9jTk5G16XNYYg9KHYXuoGnVHb59h7MdJxER5WrlypUYMWKEy7bAwED8/fffiI6OLtaY9sxU6H74GNZ/jrojIrmRuvk98Os7AjK/QKmjEBGRl3CYDND/sRam/b8BokPqOHQbdbMY+PUdDpl/sNRRiCgfLJwgKsdEhwOmA79B/8dattMqx2R+QfB7aBTUjdpLHYWIiCow0WGH8e9fYfjzay7LUY7J/EPg9/DzUDdsI3UUIiIqh6ZNm4ZZs2a5bGvSpAlOnDgBQRCKNJbp+G5kbfocojHLnRHJjQStH3z7DIWmdY8i//kSEREVhenEHuh/WQmHLk3qKJQHQe0Dn56DoO3Ql8uAE5VTLJwgKqdsyTeh+3YRbNf/kToKFZK6aSf4PTASMl++TUJERO5lS74J3foP2WbTg6hb3Au//s+wKxUREblwOBwYNGgQvvvuO5ftp0+fRqNGjQo3hj4Tuo1LYTn1d2lEpFKgjGwE/4EvQx5SVeooRERUwdhT4qHbuAzWC8eljkKFpKzTBP6PjYU8gN0niMobFk4QlUOmI9ug27Qc4NukHkfwCYDfAyOhadZZ6ihERFRBmI7tQtZPn7HLhAfK7kr1PNSN2kkdhYiIyhGDwYCuXbvi4MHsZbeCg4MRHx8PtVpd4Lnmsweh2/ApxKz0Uk5J7iZofOA/YDTUjdmtkoiI3MN44HdkbV4B2CxSR6EiEnwDEPDYGKiiWkodhYhuw8IJonLEYTIga+NSmI/vljoKlZCmdXf4PfAcBKVK6ihEROShRIsZWT9/DtPhbVJHoRJSN78Hfg89D5laK3UUIiIqJ7KysvD+++/j1q1bGDNmDBo2bJjv8Q6TAfrNK2A6wnmBp9N26g/fPkMhyBVSRyEiIg8lWs3QbVwG85HtUkehkhAEaGMegm+vpyDI5VKnISKwcIKo3LBe+weZ6xfAkZoodRRyE0W12gh48jXIK7EVJxERFY0t8Soyv5kPe9I1qaOQm8gr10DA4MlQhFaXOgoREXkYy+XT0H23GI70W1JHITdR1KyPgCcmQB4UKnUUIiLyMPbUBGSunQdb/GWpo5CbKGrWR8CgcZAHV5Y6CpHXY+EEUTlg2L0B+j++Buw2qaOQmwkaX/g/+grU0W2ljkJERB7CeOhPZP38BWA1Sx2F3EzQ+ML/8VehbtBa6ihEROQhDH/9DP2vXwIOh9RRyM0EH//s+wWcFxARUSGZYw9B9+0iiCa91FHIzQSNL/wHvAR14w5SRyHyaiycIJKQaLNC9+MnMB/bKXUUKk2CAO09D8O315MQZGy5RUREuRMtJug2fAbz8V1SR6HSJMjg0/MJ+HYdKHUSIiIqx0SbFVkbl8F0eKvUUag08X4BEREVguhwwPDn1zDs+hHgI70KTdO+D/z6DoegUEodhcgrsXCCSCIOgw6Za+bAeuWs1FGojCjrNkXAUxMh0/hKHYWIiMoZhz4DGV/OhO3GRamjUBlRN+kI/4GjIag0UkchIqJyxpGVgYy1c2GLi5U6CpURZWQjBDz5GmR+gVJHISKicsahz0TmugWwXjwhdRQqI4qa9RE4bBpkPv5SRyHyOjKpAxB5I9utG0j7ZAqLJryM9eJJpC99HfbMVKmjeIzIyEgIgpDvR3p6eqHHczgcWL58Obp06YKQkBCo1WrUr18fEydORFpaWq7nHDt2DN27d4ePjw9CQkIwdOhQJCQk5HmNGTNmQBAE/PLLL0X9donIS9lTE5D+2TQWTXgZ86m/kfbpVNhT8/6ZQkRE3seWdB1pn05h0YSXsV45g/Sl02FPvyV1FCIiKkesNy8h7aOJLJrwMrZr55G+7HXYM1KkjkLkddhxgqiMWS6dRObaeRCNWVJHIYnIgkIROPwNKMJqSB2lXMvIyEBQUBDkcjk6dMh7bbctW7bAz8+vwPH0ej0efPBBbNu2DQBQv359AMCFCxfgcDgQGRmJ3bt3o0aN//5ckpOTERUVhfT0dNStWxdmsxnXr19HVFQUjhw5ctd14+PjERUVhbZt22L79u3F+baJyMtYb15CxpczIWalSx2FJCJo/RA4/A0oa9STOgoREUnMcvk0MtfM5f0CLyYLrITAETN4v6CQIiMjERcXl+8xaWlpCAoKKtR4e/bswQcffIC9e/dCp9MhMjIS/fr1w7hx4xAeHp7rOceOHcP48eOxb98+aDQa9OvXD++//z6qVq2a6/EzZszAO++8g82bN6Nv376FykVE3sly+TQyV82GaDZIHYUkIgsKy54XhFaXOgqR12DHCaIyZDq2Exkr3+VNEC/nSE9G+tLXYb16Xuoo5drx48cBAFFRUdizZ0+eH4UpmgCAF198Edu2bUP16tWxf/9+nDt3DufOncOxY8cQFRWFK1eu4Pnnn3c5Z8GCBUhPT8e0adNw4cIFXLlyBYMGDcI///yDlStX3nWNN998E3q9HnPnzi3x909EFZ/l4glkfD6DRRNeTjRmIeOL/8HKN4sLbdu2bRgwYACqVasGlUqF6tWrY8iQITh7tnjd3K5evYqRI0eiRo0aUKlUCA8Px7Bhw/Idjx2piMjdTCf2ImPlO7xf4OUcGSlIX/YGrDcvSR2l3MvIyEBcXBzkcjk6d+6c54dCoSjUeHPmzEGXLl2wYcMGGI1GNGnSBGlpafjggw/QpEkT7Nix465zkpOT0a1bN2zfvh3Vq1eHr68vVq9ejS5duiAr6+7/l+Pj4zF//nx07dqVRRNElC9z7KHs5wgsmvBqjvRb2c8RbnBeQFRWWDhBVEZMR7ZB990SwG6TOgqVA6JBh/Qv3oI59pDUUcqtnMKJpk2blnisAwcOYNWqVZDL5diyZQvatWvn3Ne0aVN89tlnAIBff/0VN27ccO7bs2cPAOCVV14BAMjlcowePRoAsHv3bpdrnD17Fl988QUef/xxtG3btsSZiahiMx3fnd1pwmyUOgqVA6LZgIyV78By+bTUUcq9KVOmoEePHvjxxx8hCAKio6ORmpqKNWvWoFWrVvjjjz+KNN65c+fQqlUrLF++HFlZWWjevDlMJhNWrVqFVq1a4bfffrvrHD4kISJ3M+7fAt36BYDNKnUUKgdEfSYylr8Jy5UzUkcp19z5ssWmTZswZcoUiKKI5557DgkJCTh48CBu3ryJ999/H+np6ejbty8uX77sch5ftiCi0mA6tguZa+YCNovUUagcEPUZ2fOCS6ekjkLkFVg4QVQGjIe2QvfDJ4DokDoKlSdWMzLXzIHpxB6pk5RLOTdBmjRpUuKxvvzySwDA008/nWshRteuXfHuu+9i0aJFkMvlzu3x8fEAgNDQUOe2sLAwAEBqaqrLGFOmTIFMJsPMmTNLnJeIKjbD3p+h+3YhiynJhWgxIePLmbBcOC51lHLriy++wJw5c6BUKrFixQrcuHEDx48fx82bN9GvXz+YTCYMGTIEer2+UOPZbDb0798fKSkpGDJkCOLj43Hw4EHEx8dj9OjRMJlMeOKJJ5CS4rquLB+SEJE7mY5sQ9amzwGupEu3EU3ZRZXmc0ekjlJuufNlizfffBMA0Lt3byxduhS+vr4AAJlMhtdeew1PPPEEjEYjxo8f73IeX7YgInczxh6C8cRewGGXOgqVI6LZgIwv34X59H6poxBVeCycICplxoN/ImsDiyYoDw4HdN8ugvnMAamTlDvuvAmS8/bpgAEDct0vCAKmT5+O0aNHu6xDGhwcDABISkpybktMTAQAhISEOLft3r0bGzduxPPPP4969bhGPRHlzfDXz9D/soIPRyh3VjMyVs2G+dxhqZOUOyaTCRMnTgQALFy4EMOHD4cgCACyfyavWbMG/v7+SEpKwsaNGws15urVq3HhwgXUqlULy5cvh1arBQCoVCosWrQIMTExSE9Px4IFC1zO40MSInIX08m90P34CecFlDur5d+XLfZKnaRcctfLFgkJCTh69CgAYNKkSbkeM3bsWADZnSluf4mCL1sQkTsZYw8ha+cG2EwGKOq1lDoOlTc2KzK/mQfjoa1SJyGq0Fg4QVSKjAd+R9ZPn/ImCOXPYUfmuvmw/HNM6iTlht1ux+nT2e3Kq1evjgULFmDAgAHo0aMHhg0bhnXr1sHhKFwxksFgwMWLFwEAjRs3hk6nwyeffILHHnsMPXv2xLPPPptrG24AiImJAQAsWrQIAOBwOPDJJ58AALp06eI8btKkSfD398cbb7xRvG+YiLyC6dhO6H9ZKXUMKu9sFmSumQvzGb5JcrucBxVRUVEYNWrUXfsDAwOxePFifPDBB2jQoEGhxszpDjF06FCoVCqXfYIg4IUXXgAAfP311y77+JCEiNzBfPYgdN8uAgr57xryUnYbdOs/5EOSXLjrZYu4uDjn561bt871mJy5hd1ux6FD/y25ypctiMhdcoomABEQHbDZLZDXLfnLZFTBOBzI+vFjGA/9KXUSogpLIXUAoorKeOB3ZG1cyqIJKhybFRlr5iDw6dehqt1Y6jSSO3/+PIxGIwCgV69e0Ol0LvtXrVqF+fPnY8OGDahWrVq+Y127ds1ZZHH9+nXce++9uHr1qssxX3zxBQYNGoQvv/wSarXauX3s2LFYsWIF5syZg++//x5msxnXrl1DgwYNMHz4cADAt99+i3379uHtt99G5cqVS/qtE1EFZT53GLrvP+K8gArHbkPm1x8g4InxUDfuIHWaciGne9RDDz3ksqzW7Z5++ulCj+dwOHDgQHbHr5xCyTt17twZAHDp0iVcu3YNNWvWBOD6kKR69eoA8n9IMnr0aD4kISIXlgvHkfnNB1y2iwpHdCDrp08h8w+CukHuD/a9TW4vW+zevRsZGRkIDw9Hv3798Nhjj0EmK/idwZwOVgCgVCpzPcZqtTo/v3LlivPzmJgYHDhwAIsWLcLs2bP5sgURFYsp9vB/RRM57HbYIUAe2Qj2K2ekikblVNZPSyEPrARVFDuTELkbO04QlQLz6X3I2riMD0eoaKwWZK6aBeu1f6ROIrmcN0cAoG3btti1axf0ej1SUlKwevVqVK1aFQcOHEDfvn1hsVjyHev2oosBAwZAEARs2LABer0eycnJWLJkCbRaLdatW+dsv5mjZs2a2L17N+677z7cuHEDOp0OQ4cOxc6dO+Hr6wur1Ypp06ahatWqLmudmkwmXLt2DWaz2T2/IUTk0axXziLz63lco5SKxmFH5vqFsF49L3WScuHEiRMAsrtHiaKIH374AcOHD0fPnj3x+OOPY/ny5S4PNQpy48YNZ5Fm3bp1cz2mZs2aziKN8+f/+3NgRyoiKgnrlbPIWD0HsBX+7ywiOBzQfTMf1puXpE5SLtz5ssX48ePx448/Ytu2bVi1ahWeeOIJdOzY0dklKj+1a9d2fn7s2LFcj8kp0gCAtLQ05+djx45FcHAw5syZg6ioKERGRuKbb77J9WWLiRMn8mULIrqL+coZ6Hb9CJeiiRx2G+wyOeS1GpZ5LirnHHZkfv0BbDcvS52EqMJh4QSRm1mvnEXm+oWAyHabVHSi2YiML9+FLfFqwQdXYLVq1cIrr7yCsWPH4o8//sA999wDHx8fhISEYPDgwdi5cyd8fHxw7NgxLF++PN+xcm6mAIDZbMbOnTvx0EMPwcfHB5UqVcLLL7+Mjz76CACwdOlSnDt3zuX8xo0bY8uWLTAYDEhLS8NXX32FKlWqAAA+++wzXLhwAW+++SZ8fX1ht9sxbtw4BAcHo1atWggKCsLYsWOL9CCHiCoWW8IVZKyaBVjzL/IiypXNgozVs2FPSyr42Aoup422UqnEvffei4EDB+LLL7/E1q1b8e2332LkyJFo3bo1rl27Vqjxbm+pnbPMxp3kcjkCAwMBAMnJyc7tfEhCRMVlvX4BGV+9B1hZYE1FJ1pMyPxqFuzpyQUfXMG582WLsLAwdOzYEQDw7rvvQszlJajbl9y6fTy+bEFEJWFLTYRu27f5v3xps8KhVEMezg525Eo0G5Hx1XucFxC5GQsniNzIlnwTGatnAzY+HKHiE41ZyFg1Cw59ptRRJNOpUycsWrQICxYsyLW1Zv369TF48GAAwI8//pjvWD4+Ps7PR4wYgYiIiLuOGT58OCIiIuBwOLBp06ZCZdTpdHj77bdRv359jBw5EgAwb948fPjhh7jnnnuwfPlyPPDAA1i4cCFGjx5dqDGJqGKxpyYgY8U7EE16qaOQBxP1Gcj4aiYcXv7fUU4HqXHjxuHgwYP48MMPkZSUBL1ej02bNiEyMhInT55E//79C3xAAgAGg8H5uUajyfM4rVZ71/F8SEJExWFLuo6Mle9ANBsKPpgoDw5d6r/zAu/+78idL1sAwHvvvQeZTIYtW7bgkUcewalTp2C1WvHPP/9g2LBh2LZtm3OprjuX8+DLFkRUHA6TARlbVkEsxEsWotUMh9YPsmq1CzyWvMt/8wLvvl9A5E4snCByE4dBh8yvZkI0ZkkdhSoAR1oSMr+eB5Fr3uapZcvsNdwuX86/JVlQUJDz8xYtWuR6jCAIaNy4MYDsdcwLY86cObh16xZmzZoFhUIBm82GefPmoUaNGti8eTOeeeYZrF+/Hs2bN8fy5cuRkJBQqHGJqGJw6DOQvuJtOLLSpY5CFYA96Toyv/4Aot17l3vJ6SB169YtfP3113j11VcRFhYGHx8f9O/fH1u2bIFSqcSJEyfw5ZdfFjhezhIcgOva5nfKeev0zkJOPiQhoqJwmI3IXDOH9wvILeyJV5G59n2vvl/gzpctAKBr16744osvoFKp8NNPP6Fp06ZQqVSoX78+vv32WyxbtgxRUVEAgICAgEJl5MsWRJQX0WFH5h9r4dClFXxwzjkWE0S/YMiq1CzFZOSJ7IlXkbnGu+cFRO7EwgkiNxDtNmSufR/2FD4YJfexXj6NrJ8LfjOionI4HPm+MepwZC+Hc+fbHneKjIx0vi2a3xueCoUCAKBWqwvMdvPmTSxYsAAdOnTAgAEDAGS3EE9OTka7du1cMt1zzz2w2+04dOhQgeMSUcUgOhzIXL8QjtREqaNQBWK9cBxZP38udQzJ5HSQat68OR5++OG79jdo0ABPPvkkAOCnn34qcDw/Pz/n5yaTKc/jcvblzCUKwockRJQb3fdLYE++KXUMqkCsF08g66fPpI5RrhX2ZYscTz/9NM6ePYspU6bgvvvuQ9++fTF9+nScOXMGzzzzDBITs+f24eHhhRqPL1sQUV6y9v4M683C/d10O9FsgBhUGbLQ6qWQijyZ9dJJ6H74WOoYRBUCCyeI3CBr8wpYL5+WOgZVQKYDv8O4f4vUMcpcly5doFKp8Nprr+V5zJEjRwAAjRo1yncsuVyONm3aAAD279+f53Hnzp0DANStW7fAfG+++SYMBgPmzp3r3JZzE+XOdt85a6NnZGQUOC4RVQyG7d/BeuF4wQcSFZHpwO8w7NkodQxJ5HSQyqt7FAA0adIEQOG6R4WGhjo/T0lJyfUYm83m/PlduXLlQuXkQxIiupNhz0ZYTu+TOgZVQKbD26Df/p3UMSTjrpctblenTh3MmjULW7ZswebNm/Huu++idu3aSElJQVxcHACgadOmBY7Dly2IKC/GM/thOnOg2OeLRj0QGg4hpKobU1FFYD62E/o/vpY6BpHHY+EEUQmZT/4Fkxc+2Kayk7V5BSyXTkkdo0w1adIEdrsdP/zwg3NN89vFxcVh3bp1AIAnnniiwPFyWnSuX78e165du2v/r7/+inPnzkEmk+GRRx7Jd6wzZ85gxYoVePDBB3HPPfc4t1eqVAkAcP36dZfjb97MfrPs9gc0RFRxWS4ch2H7t1LHoApMv2UVzLHed2M9OjoagPu6R1WvXt1Z3JjXm6jXrl2D/d/lUerXr1/gmHxIQkR3slw5A/1vq6WOQRWY4c+vYT6T9wsCFZU7X7YAgHXr1mHOnDm4cOFCrvs3bswuXI2MjESdOnUKHI8vWxBRbiw3LyNr7+YSj+Mw6CBUqQUhKMwNqagiMez4DuZTf0sdg8ijsXCCqATsqYnQbfhE6hhU0dltyPz6A9gzcn8bsiIaN24c1Go1bty4gUGDBrm8jXn8+HHcd9990Ov16NKli/PBBABcvHgRsbGxiI+PdxlvxIgRaNy4MfR6Pe6//36cOXPGue/w4cMYNWoUAGDUqFEFtt2cPHkyAGDWrFku26OiolC1alXs3bsXBw8eBABcuXIFGzduhFarRfv27YvxO0FEnsSekYLM9R8CokPqKFSRiY7slu9eNC8AgA4dOgAADhzI++2s2NhYAIXrHgUA7dq1AwD8/XfuN5b++usvAEBERASqVy+4HS4fkhDR7Ry6NOi+mQ847FJHoQpO98PHXjcvcPfLFkuXLsWUKVPwxRdf3LXPbDbj/fffBwC8+uqrBY7Fly2IKDd2XRoy/1jrtnmBQ58JoXpdCP4hbhmPKg7dhk9hT78ldQwij8XCCaJiEu02ZH4zH6LJIHUU8gKiIRO67xZDdJTfh3FXr17F7NmzMXnyZFy9erVEY0VFRWHNmjXQaDT49ddfERERgaZNmyI6OhotWrTAuXPn0KZNG/z444+Qyf77UdajRw9ER0dj6tSpLuOpVCps2rQJ9erVw+nTp9GkSRM0atQIjRs3Rps2bXD9+nX06NED8+bNyzfXrl278PPPP2PEiBF3vbUik8nwv//9D3a7HTExMWjevDkaN26MlJQUTJs2zdlinIgqJtFuR+a6+RD1mVJHIS8gGnTQfbuwXM8L3O2pp54CkL0Mx48//njX/qSkJHz9dXZb0kcffbRQYz7++OMAgBUrVuTa6vvTTz8FAAwfPrzAsfiQhIhuJ9rtyPxmPhy6NKmjkBcQjVnQrf/Qq+YFpfGyBQAsWLAAf/75p3N7YmIiBg4ciLNnz6Ju3bp48cUXC8zGly2I6E6i1YKMLavc/hzBkZUOoVYDwDfIreOSZxONWchcvxAii3eJioWFE0TFpP9tFWw3cm/hR1QarJdOwrh7g9Qx7nLq1CkMGzYMdevWxdSpUzF37lzExMTAarWWaNyBAwfi2LFjeO6551C9enWcP38e8fHx6NixIxYvXoy///4bISGFr6quXbs2jh8/jpkzZ6J58+a4evUqbty4gfbt2+Pjjz/Gli1b4Ovrm+8YEydOhFarxVtvvZXr/lGjRmHFihWoXbs2zp49i/DwcHz00Ud4/fXXi/KtE5EH0v++Gra4WKljkBexXj4Nw84fpI5RZho2bIiRI0cCyH64sWnTJue+hIQEDBo0CDqdDs2aNXN5QALk/ZBkyJAhqFu3Li5duoSnnnrK+caqxWLBmDFjsGfPHgQGBuKVV14pMB8fkhDR7fS/r4b1ypmCDyRyE+uVMzDs+E7qGPlKSkrChg0b8u0eVVjuftli8ODBeOyxx2AymdCrVy/Uq1cPLVq0QK1atbB582ZERERg69atBS4HxpctiCg3+gO/w56aWCpjO3RpkNduDGj9SmV88ky2uLMwbOMyskTFIYiiKEodgsjTmGMPIXPVrIIPJHK31r0RcN9TUPv6SxpDFEXs2bMHc+bMwebNua/Nd+7cuUKtB05E5OnMZw8ic/VsqWOQN2rVE/69B0PjHyB1krskJCTgo48+wvfffw9BELB48WJ07969RGMajUY89thjzrlHzZo1ERYWhlOnTsFisSAiIgJbtmxBw4YNXc6LjIxEXFwcnn76aaxcudJl38GDB9GrVy9kZGTAz88PDRs2xKVLl5CamgqVSoUtW7agW7du+ebatWsX7r33XowcORLLli27a//SpUvx/PPPQ6VSoWHDhrhw4QIMBgPeeecdFlcSVUDmMweQuWaO1DHIG7XsDv/7hkDjHyh1EhexsbGYP38+vvrqK5jNZgDAJ598ghdeeKHEY587dw4ffPAB/vjjD9y8eRNarRaNGjXCU089hRdeeAEKhcLl+PzmBA6HA59//jmWL1+OU6dOwWazoW7dunjkkUfw2muvITg4uMA87du3x8mTJ/HPP//kuQzoypUrMXv2bFy6dAmRkZEYO3YsXnrppWL/HhBR+WZNvIr0n5YCpfwYTh4UCvv5IwC7Y1OO5l3h12cotAFBUich8igsnCAqIoc+E6kfvgrRwFbcVHZEtS8c9w2HEXIotT4Ib9IagqzsmwY5HA5s2rQJc+bMyXM9cABo2rQpjh49CrlcXobpiIjKnsOYlT0vyEqXOgp5EVGthdh7BAyCAkqND6o3be3yNqWUTp48iQULFmDNmjUuy1/UqlULly5dKvHcQBRFrF27FsuXL8fRo0dhNpsRERGBgQMHYty4cc6lMW6X30MSIHsd9HfeeQe//fYbEhMTERgYiHvvvRevv/46WrRoUWAmPiQhohwOkwFpH47hEh1UprLnBcNhEJRQarT/zguk/be4KIrYtWsX5s2bh59//vmu/e3atcP+/fslSEZEVHZEuw1p338Ee1pSmVxPHhwG+5mDgNVUJtej8klUaSH2fhoGmQoKtQbhTdtAxnv0RIXGwgmiIsr8bjHMR3dIHYO8Sf02MDW9F7bblr4IrFYDIbXqllkEi8WC1atX4/3330dsbN6t6LVaLUaOHIn//e9/hXobg4jI0+k2fArTwT+kjkHeJKoVTM26ucwLAqrWQKWIspsX3EkURfz++++YP38+fv/991yPCQ4ORkpKCgRBKON0RERlR7fpc5j2/Sp1DPIm9VrA1LzHHfOCcFSKqCdJHJvNhu+//x7z5s3DoUOH8jzu+eefx6efflqGyYiIyp7+0FYYDm8r02vKQ6rAfmofYDOX6XWpnKjTHKaWPV3mBf6VqyO0dpSEoYg8S/l4LYnIQ1gunmDRRHEo1ZCFVIUiIhqq+i2hjIyGolptyEKqQvANBBQqqROWS6JMDvG+EciK7uwy2QGAjPjrMOkySj1DZmYm5s2bh9q1a+PZZ5/Ns2giJCQEb775Jq5evYpFixaxaIKIvII1LhamQ39KHcOzyGQQfAMgDwuHIqIhlHWbQhnZCIpaDaAIrwt51UjIAisB5aR7QnkiyuQQew9HVqN77poXZCaUzbzgTmazGStWrECzZs3Qp0+fPIsmBEHAe++9x6IJIqrQrNcvwLT/N6ljeBZBgODjD3lo9ex5QZ2mUEREQ1EjCopqtSGvXAOy4MqAQil10nJHFGRw9H4aWY3vzWVecAPGzPQyzaPT6fDhhx+iXr16eOKJJ/IsmlCpVHjhhRfw4Ycflmk+IqKyZktLguHozjK/rj01EYomHQC5ouCDyxuZDLKgMCiq1Yaiet3s+UCtBlBEREMZ2Sj7fkFACOcFuRAFGcSeQ5HVtOtd8wJd0k0YM9gNLTefffYZBEHA559/nucxKSkpGDduHOrUqQOVSoUqVapgwIAB2Ldvn9tyHD58GEqlMt97Jtu2bUO7du2g0WhQtWpVjB49GjqdLs/jhw0bBrlcjlOnTrktp7dgxwmiQhKtFqQtHgd7SoLUUcolmX8IlBHZDz1kASGQ+YdA5h8MWUAwZBrfAs8X7XaIFiMc6cmw3rgA2/WLsN24AFviVcBuK4PvoJwJrwdLh4dguWOiczuVjy+qN2ldKg8hEhMTsXDhQnz88cfIyMj7QUytWrUwYcIEPPvss/D1LfjPmYioohDtNqR9NBH2xKtSRymXZEGhUFSJgLxyTSiq1IS8ci3IQ6pA0PgU6ueWaLfDoUuDIyMZ9oxk2BOvwRZ/GdYbF71zWZRqdWDp9Ei+8wKlxgfhzdqUSXFCcnIyPv30UyxZsgSJiYl5HqdWqzFkyBCMGzcOjRs3LvVcRERSER12pH8yBbabl6SOUi4JvgFQVI2Eotq/H1UjIPMPhqD1g1DIJSUc+gzY027BnhIPe3I8bDcuwHr1HERjVimnL4eq1oal84AC5gVahDdtU+pLfN64cQOLFy/Gp59+mu+9g+DgYLz44osYPXo0qlWrVqqZiIikJooOpP+0LPu+tkQUlarCdnw34LBLliEvgtYPyloNoKhRD/KQKpAFhUEeFAZZQEjR5gXpKXCkJsB649/nCDcuQTQbSjl9OVQlApaYgbBY836GotT4ILypNEt/l1cHDx5Ejx49oNPpsGzZMowcOfKuYxITE9G5c2dcvHgRPj4+iI6OxvXr15GYmAi5XI6lS5fimWeeKVEOs9mMVq1a4cyZMwCyO3re6cyZM2jZsiUsFguio6ORnJyMW7duISYmBjt37rxr6dbjx4+jVatWGDZsGFasWFGifN7IA8vOiKRh2P4tiyZuI/gGQhXVAqqo5lBGREMeXLlk48nlELR+kGn9oKgWCbTpCQAQbVbY4q9k3xS5fgGWf45V/AcmXR6FPqQGxHxuggCAxaCHLikeAVWqu+3SFy5cwLx587By5UqYzXm3dGvatCkmT56Mxx9/HEolq3yJyPsY92xk0cRtBJ8AqOo2hbJuM6jqNXPLvEAeFAp5UCju/CljT02ANS4WlnOHYTl/FKLZWKJrlXv3DIC+Uq0C5wVWkwGZiTcQWLVGqUU5f/48FixYgC+//BJGY96/76GhoXjppZfw0ksvoUqVKqWWh4iovDDu+5VFE7cR/IL+vV/QAsrajSEPCCnxmDLfQMh8A6Gs8d8SFKIowp54Fda4s9lzgwsnIOrLvgNTWRJjHoEhNKIQ8wIjMhNvIrBa6cwLTpw4gQ8++ABff/01rPlkqV27NsaNG4cRI0bAz8+vVLIQEZU3ptP7JS2aAABbSgIUzbvAdmwnIDokzSLzD4ayXnMoazWAMqIh5JVrlrjgP2degPA6UDftBODfeUHyDdj+fYZgiT1c8QspOj0MfeXaEPMpmgBuu19QrWYZBSvfduzYgQEDBuTbsQEABg0ahIsXL6JXr15Yt24dgoOD4XA48P7772PKlCl44YUX0LFjR0RHRxc7y/Tp051FE3mZOXMmLBYLPvvsM4waNQpGoxG9e/fGnj178Msvv6B///4ux0+cOBEqlQpvv/12sXN5M3acICoEW+JVpH000Ts7H9xG5h8MTZseUEW3g6J6HUnaLYsOO6yXz8B8ci/Mp/dBNOT/w82TiEFhsHcfApOt8JXAMoUSNZq3g1xRsjq4w4cPY86cOfj+++/hcOQ9mb733nsxefJk9OnTh+22ichr2VMTkbpoLGC1SB1FUoJPADTNY6Bu3gWKGvWkmRfYrLBcPAnL2QMwnz1YoYorxcAw2LsPhsle+JtcMoXi33mB+4oaRVHErl278MEHH+Dnn3/O9e2HHA0aNMD48eMxdOhQaLVat2UgIirP7BkpSFv4asUv5CuALCAE6mYxUDdqD0XN+pK80Sg67LBePAnTiT2wnN5fsR6WBIbC1n0wTPbC38aVyf+dF7jpZQdRFPHnn39i3rx5eS7PlaNdu3aYOHEiHnnkEcjlhXt7mIioIrDr0pH27UKI5eR+gaJSNdiO7QDK+DGg4BcETbPOUDfuCEVEQwnvF5yA5fQ+mM8egmjILPMMpcY/BLaeQ4s4L5D/Oy/w3mXTTSYTZs+ejXfffRd2+3/PYHLrOLFjxw5069YNfn5+uHz5MkJDQ132DxkyBGvWrMHgwYOxevXqYuXZu3cvunTpAo1GA4Mhe96a2z2XiIgIJCcnQ6fTObtLrF69GkOHDsWkSZMwZ84c57F//PEHevfufdd2Kjx2nCAqhKyNS726aEJRsz60HftC3aQjBInXRxNkcqjqNoWqblP49X8W5rMHYDr4B6yXTpX5BNCtWvWEMbIZHLai/XfmsFmRfuMKKkXUK/jgO+Tc9JgzZw62bt2a53GCIODhhx/G5MmT0b59+yJfh4iootFtXOa9RRMyOVQNWkPTqitUDVpLPy9QKKFu0ArqBq3g98BImM/sh2nfr7BeOStprhJr2R2m2i1gL/K8wIb0G3HFmhfcyWq14ttvv8X8+fNx+PDhfI/t1q0bJkyYgPvvv/+uFpFERBVd1uYvvLpoQlmnKbTt74Mquh0EiR+QCzK5s9OF+OAoWM4fgenwNljOH/Hs+wUtusFYp2XR5wV2G9KuX0Fo7agSXd5iseCbb77BBx98gBMnTuR5nCAIeOihhzBhwgR07tyZL1sQkVfK2v1TuSmaAABbSjwULbrCdnR76V9MkEEV3Tb7xcuoFoVedqPU4iiUUDdoDXWD1vBz2GE5fwzGvZtgvXRS0lwl1uxeGOu1Lsa8wI7Ua5cRVqdBKQUr3y5cuIDu3bvj2rVrkMvlePfdd7Fs2TLExcXlevzKlSsBAA899NBdRRMA8OKLL2LNmjXYsGEDjEZjkV8e0ev1ePrppyGTyfD222/jtddey/PY+Ph4VK5c2eV+S1hYGAAgNTXVuU0URUyaNAnBwcGYOnVqkfLQf1g4QVQA89mDnn/zvTgUSqibdoa2w/0urTDLE0GhhKZpZ2iadoY9JQHGvzfDeOB3jypyEdW+cNw3HEbIgSJOdnJkJt6Ef+XqUGl9CnW8zWbD999/jzlz5uDo0aN5HqdUKjFs2DBMnDgRDRp454SKiOhO5lN/w/pP3n93VlgKJTStusOny8MlXoajtAhyhXNeYEuIg3HfFpiO7gBs5eemVUFEtRZi7xEwCIoSzQsCKleHspDzgjulp6dj2bJlWLRoEa5fv57ncQqFAk888QTGjRuHVq1aFetaRESezhx7GJbT+6SOUeYElQaa1t2haX8fFGGlt0RUSQhKFdSNO0DduANsCXEw7NoA88m95XKt97xkzwuGwyAoiz0v0CXdRECV6lD5+Bb53PT0dCxduhQLFy7EzZs38zxOo9Fg+PDhGDduHOrXr1+snEREFYH5yhlYrp2XOsZdbCnxULTsVnrFE3IF1M3vgU+XR6AICy+da5SQIJND3bA11A1bw3bzMgx7N2XPCzzpOYJKC7H30zDIVMWeF2TdSkBA5WpQ+wW4OV35d/36dVy7dg0dOnTAkiVL0Lp1ayxbtizP4//++28AQExMTK7727VrB4VCAb1ej0OHDuGee+4pUp6JEyfi4sWLeP3119G6det8jw0ODkZycjIcDoezeCIxMREAEBLy35J4q1evxrFjxzBv3jwEBQUVKQ/9h0t1EOVDdDiQtmSCd61hrtLAJ+YhaNvfB5lfoNRpisyeEo+s31Z7xs2rqNYwNesKWwFrkxaGNjAEVRs2zfcYURTx+eefY/bs2bh0Ke/1d/39/fHCCy9g7NixqF69eomzERFVFKLDgbRF42C/lffD5ApHpYG2bS9oYx6CPCBY6jRFZk9PhmHbuuwCinyWoioX6rWEqXl398wLgkJQtUH+84I7Xb58GQsXLsTy5cuRlZWV53FBQUF4/vnnMXr0aNSoUT4flhERlQXRbkfawjGwpyRIHaXsyBXQtOkJ3+6PQeYXJHWaIrOnJsKwZyNMh7eV/8LKus1hatHTTfcLglG1YbNCHx8XF4cPP/wQn3/+eb5zgrCwMIwePRovvvii861HIiJvlvb9EtiS46WOkSdlSFVYj+1w44AqaFr3gM89D0Ee5Hk/B+yZqTDu3QTjvl8BW8l/3paqOs1haumeeYHazx/VGrX0us5QFy5cwPnz59G3b1/ntsjISMTFxd21VIfD4YBarYbNZsPvv/+OXr165Tpmzvmff/45nn322UJn+fPPP9G7d280bdoUBw8exF9//YVu3boByH2pjoEDB+KHH37AJ598ghdeeAEmkwn33Xcfdu3ahZ9//hn9+vWD2WxGgwYNIIoizp8/D7VaXeg85IodJ4jyYT6x26uKJlTRbeHXfyTkQXe3HvIU8krVEPjURFjjYpH165ewlcMqX1EmB3oNg14TALhhsgMAxoxUGNJT4BNUKc9jXn/9dbz33nt57q9SpQrGjh2LF154gRWJRES5MJ/c6z1FE4IAdcuu8LtviEc+GMkhDwqF/4CXoY15EPrf18Jy9oDUke4iyuRAzyHQa4PcNy9IT4UxIw3awIKLXfbt24f58+fj+++/hyOf4pLatWtj3LhxGDFiBPz8/NySk4jIk5mO7vCqogl1k47w7T0Y8krVpI5SbPKQKvB/8Dn43PMQ9Fu+gvnU31JHuosoyIBeQ907L8hIK/B+AQD8888/mDFjBr799luXdb/vVL9+fUyYMAFDhw4tcltqIqKKynzlbLkumgAAa2oClM27wHp8V4nHUjftBN++IyAPCCn44HJKHhACv/ufhrbD/dD/tjq7A0U5IwoyoMdg6H1D3DYvMGfpoE9Jgl9oFbeM5ynq1auHevUK11k9LS0Ntn+7euRXHFqpUiXExcUhOTm50DkyMzPxzDPPQKFQYOXKlVCpVAWeM23aNPz888948cUXsXjxYiQnJyMpKQldunTB/fffDwBYtGgR4uLi8NVXX7FoooRYOEGUB9Fug37rOqljlAlZUCj8+o+EOrqt1FHcRhnREMEvzILp5F7of18DR2qi1JGyhdeDtcNDsFitbl9jNfXqJWgDQ/KsFl2/fn2u2+vVq4eJEydi2LBh0Gg0bs1ERFRRiA4HDNu/kzpGmZBXjYT/w89DWbPitFpWVK6JwCGTYbl0ErofPyk/84JqdWDt9EipzAtS4i4ivGnrPOcFDocDY8aMwUcffZTvOJ06dcL48ePx8MMPQy7x2vVEROWFaLfBsMNL5gVVasH/kZegrBkldRS3kQdXRsCTr8Fy6SSyfloKe3Ley1CUqaq1Ye08oHTuF8Tlf78gLi4OHTp0cFkn+05dunTBa6+9hn79+rmssU1ERIDhSCktg+Fm1rQkKJrdA9uJ3cU6XxZSFf4PjoQqqqWbk0lHHlwZAU+Mh6Vdb2RtXFZ+XpipEgFrzEBYrDa3zwvSb1yFb6XKXtd1orAMBoPz8/yel+QUkN5+fEHGjBmDa9eu4c0330TLloX7/6h169bYunUrpkyZgkOHDiEwMBCjR4/GrFmzIJPJkJaWhlmzZqF58+YYPHiw8zy9Xo/U1FRUrVoVSqWy0Bm9HQsniPJgOvhn+bmpXlpkcmg79YNvj0EQVBXzgbmmaWeoo9vBsOM7GHZ87/ZJRpF0eRT6kBoQ3VQdeier0QBDWjJ8Q3KvguzUqRMuXLjg/LpNmzaYPHkyHnnkET4IISIqgFd0m5Ar4NvrSWg7PQChgv5cUNVpipBX5kP/+5rsdpxSzgtiBkAfWqsU5wV6ZCUnwj+saq77z507l2fRhEwmw8CBAzF+/Hh06NChVPIREXky05HtcKQlSR2jdMlk0MY8lH2/QFExb7Sq6jRF8CvzYdj5Q/b9AkfeXRZKm9j5ERjCIkpvXmAy5Dsv2LFjR65FEzKZDI8++igmTJiAdu3alUo2IiJPZ7l6HrZbN6SOUWi29FtQNu0Ma1E6LMgV8OnyMHzuHQhBWfAb8p5IVacJgl/5AIYdP8Cw41tpl/vs9BD0letAtNpKZXirKf/nCN7u9mcl+RWX5CyrUdiC0k2bNuHLL79EixYtMH369CJliomJwZ49e3LdN3PmTKSlpWHt2rWQyWQwGAwYNWoU1q1bB5vNhoCAAEyePBlTp05lsUwhsHCCKBeixQzD9m+ljlGqFOF14T/gJSiqRkodpdQJCiV8ez4JRc0G0H27EKIx7zU6S4MYFAZ798Ew2RylPuFKv3E1zwnPRx99hKioKGRkZOD+++9Ht27d+IOSiKgQvKHbhLxSVfgPmgBleB2po5Q6QaWBX/9noWrSEbrvP4IjtWzbrIuB/84L7KU/L8iIvwa/0Cq5/rzP7R/2fn5+GDlyJMaMGYPatWuXajYiIk+V3W3ie6ljlCp5parwf3QMlLUaSB2l1AkKJXx7DIKqXnNkfvMBHJl5d1woFYGhsHUfDJNdLP15wc285wWdOnWCIAjOBwC+vr4YOXIkXn31Vc4JiIgKoPeQbhO3s2akQtG4A2yn9xV4rCykKgKffA2K6hX/54EgV8C3x+NQ1mkM3foPy35e4B8CW8+h/84LSregM7/nCN7u9uVJTSZTnsfl7CvM0mUpKSkYNWoUlEolVq5c6bYOEHFxcViyZAm6d++OPn36AAAmTJiANWvWYNCgQejduzfWrl2L6dOnQxAETJ061S3XrchYOEGUC9ORbXBkpUsdo9Som8XAf+DoCvvWSF7UDVpB8fL7yPx6Hmw3LpbNRVv1hDGyGRy20qkOvZPFkJXn2qV+fn54/fXXyyQHEVFFUtG7TaibdYbfwy9CpvauNapVkY0Q/NJc6NYvgOX80bK5aMvuMNVuAXsZzQusRgOM6SnwCQ69a1+DBg3w3nvvYe7cuahatSqeeeYZPPfccwgKCiqTbEREnsp8fBcc6bekjlFqNK27w6//SAgq71obWRnREMGjP0Dmtwth/edY2Vy0RTcY67Qsu3mByQBDWgp8Q+6eF0RFRWHPnj34+uuvERUVhaFDhyI4OLhMchEReTLL9YuwJV6VOkYxiLBlZUAR3Q62swfyPErVsA38Hx0Dmda3DLNJT1W7MYJHfwDd90tgOXe4bC7a7F4Y67Uus3lB9nOEVPgEhZTJ9TyJn58f1Go1zGYzUlJS8jwuOTkZAFC5cuUCx3zppZeQkJCAt99+G82bN3db1unTp8NisWDOnDkAgKSkJHzxxRdo164dvvnmGwDAU089hcjISMyZMwcTJ06EQsHSgPxwQTqiO4iiCOPfv0gdo9T4dHsMAYPGeV3RRA55cGUEjZoJTdvepXodUe0L+4MvIatGozIrmsiRcfNamV6PiKgiq9DdJmRy+D30PAIGjfe6ookcMq0vAoZOg7bLI6V6HVGtheOBF5FVs0mZ3QTJkZ7PvGDq1KlIS0vD2bNnMXHiRBZNEBEVQBRFGHb/JHWM0iGTw6//s/Af8LLXFU3kkPkGIHDYdPj0fBIoZMvl4nDOC2o1LfN5QcbNvB/uderUCYsXL8aYMWNYNEFEVEiGI9ukjlB8ogibIQuKBq3v3ieTwafXUwgYMsXriiZyyHwDEDB0Knzvf7p05wUqLRz9X0BWZHMJ7hfElen1PIVMJkODBtmd1y5fvpzrMVarFTdv3gQA1K9fv8Ax169fDwCYMWMGBEFw+ejWrZvzuJxtK1euLHDMo0ePYu3atRg0aBDatGkDADh58iQsFgtiYmKcx2k0GrRt2xYZGRmIjY0tcFxvx7ISojtYzh+BPfmm1DHcT6GE/yMvQtPiXqmTSE5QKOH/8PNQRjSA7qelgNXs3gtEtYa5WVfYSmlt0oKYdBkwZ+mg9vOX5PpERBWJ+dTfFbPbhEqDwCdfg6p+S6mTSE6QyeB33xAoq9dB5vdL3D8vqNcS5ubdJZsXmLMyYdJlQOMfKMn1iYgqEkvsIdiTKt68QFBrEfDka1BFtZA6iuQEmQy+3R6FokpNZK5bANjc/PO7bnOYW/SUbl6g18GUmQ5NQJAk1yciqkgsNy/DGn9F6hglIzpgMxmhqNcStgvZnRgFtQ8CnnoNqnrueyveUwmCAJ+YByEProzM9R+6f15QuxnMrXpJNy/QZXJekIf27dvjxIkT+Pvvv/Hss8/etf/AgQOw2WzQaDRo2bLge2udO3fOc19GRgZOnTrlclyVKlUKHHPSpElQKBSYOXOmc1tiYiKA7GKJ2wUGBjqvRflj4QTRHYx/bZY6gtsJPgEIHDwJyshoqaOUK5qWXSEPrY6MFW9DNBtLPJ4okwO9hkGvCQAkmuzkyEi4hsr1GkmagYioIjDt/1XqCG4n/Ps2pbJGPamjlCvqpp0Q6B+EzK/ec9+8oOcQ6LVBks8LMhNusHCCiMgNKmK3CZl/CAKfng5FtUipo5Qr6kbtEThsGjJXz4FoyXtt68ISBRnQa2i5mBdkJNzgAxIiIjcwHNkudQT3EB2w2S2Q120KR0IcAp9+A8rwOlKnKlfUjTsgcNh0ZK6Z4577BYIM6DEYet8QyecF6Tevomo5mRfcvHkTv/zyC3755RecOHEC9erVw+eff44aNWqUeZbHH38cy5Ytw3fffYe5c+ciJMR1SZNPPvkEADBo0CBotQV3cd2zZ0+e+3bs2OHsOpHfcbf77bff8Oeff2LMmDGoU+e//18rVcpewv36dddi75zuGKGhdy/ZRq64VAfRbWxJ12G9cFzqGG4lD62O4BdmsWgiD8qa9REwdCqgVJVsoOp1YX1kLPRqf0AU3ROuBPSpybCZS35zh4jIm9mSb8J65azUMdxK9u+SVSyayJ0qshECh8+AoPEp2UDV6mTPCzSB5WNekJYMm8XNnTSIiLyM9foF2OIq2LzAPxhBz73Dook8qOo2Q+Azb0LQ+pVsoKq1YR0wrtzMCwxpybDyfgERUYlYb92A9cZFqWO4j90OUeOHoOdmsmgiD6q6TRH4zFsQfAJKNlCVCFgHjIXeJ7hczAuMGWmSzQvsdjv++usvTJ8+HS1btkR4eDiee+45/Pjjj7h48SJ+++03TJs2TZJsPXr0QExMDDIyMvDwww87Ozk4HA7MnTsXa9asgVKpxOTJk+86NzY2FrGxsUhOTi6VbA6HA5MnT0ZAQABef/11l33t27eHWq3Ghg0bcOXKFQDA4cOHsWfPHoSHhxdqWRFvx8IJottUtG4TsuDKCHz2f5BXqip1lHJNVbsxAgdPAuTFbMLT5VHo2z0Ai8TVoS5EEbpbCVKnICLyaKbDW6WO4FYy/2AEPfMWFKHVpY5Srilr1c++GVLchyQxA6Bv/2C5mxdkJlbApeiIiMqQ+dhOqSO4leATgMBn3uT9ggIoa9ZH0HPvQPALKtb5YudHoO/wcPmaFwDQJd6QOgIRkUczxR6WOoJbyXz8Edh3OBRhvF+QH2WNegga9W6x5wXo9BD0HQfAYrW5NVdJZZXhc4Tk5GSsWbMGTz31FCpXrozOnTvjvffew7Fjx3I9/tatW2WW7XaCIODLL79EeHg4du/ejYiICLRp0wbh4eGYPHkyBEHAihUrEB199wvL0dHRiI6OxpIlS0ol26pVq3D8+HFMmjQJYWFhLvuCgoIwfvx4ZGZmonHjxmjevDk6duwIq9WK2bNnQxCEUslUkbBwguhfDqMepgp0I0TwDUDg8DcgDwgp+GCCKqolAp6YAMjkhT5HDAqDbcBYZAVVh+hwlGK64slKTpQ6AhGRxxLtdpiP7JA6htsIGt/seUFIwWskEqAMr5v9hqlKU/DB/xIDw2B7ZCyyQmqUy3mBLim+XOYiIvIEot0O04m9UsdwG0Hji8ARb0BRuabUUTyCokotBA6dChRhXoDAUNgeeRX6SjUhOuylF66YdLcSOC8gIiom0W6D+eIJqWO4jaDxReADI6EIYgv/wlCEhSNw2LSizQv8Q2B75FVkhUaUy3lB1q0EiKXU/cLhcODIkSN455130LFjR1SuXBlDhgzB119/jdTU1HzPDQkJwZQpU0olV2HUqVMHx44dw9ixYxEeHo6TJ0/CZDKhT58+2Lp1KwYPHlzmmUwmE2bMmIFq1aph3LhxuR4zc+ZMzJ07F2FhYYiNjUXDhg2xfv16DBkypIzTeiZBLK3/G4g8jPHQn8j68ROpY7iFoNIg8Nn/sQ13MZhO7IFu/UJALOAGQqueMEQ2g8NWvqpD71StUQuuaU5EVAzmMweQuWaO1DHcQ6lG0IgZUEY0lDqJxzHHHkLm6jkFzwtadIexTgvYy/m8oEqDJvAJqiR1DCIij2M+dxiZX70ndQz3UGmy5wW1GkidxOOYzx5E5pq5Bc8LmneFsW4rzguIiCoo08WT0P35jdQx3EMmR2C/EVBVry11Eo9jjj2Ufd+ooELEZvfCWK91uZ8XVG3YDNrAYLeMlZGRgT///BObN2/Gr7/+ioSEwne0CAwMRO/evdGvXz/0798flSpxrkJlq5h96YkqHvOJPVJHcA9BgP9jr7Joopg0zWIgWszI+vHjXPeLal847nsaRiiAcj7ZAbK7TrBwgoio6CrMMh2CgIBB41g0UUzqhm3ge//T0P+yItf9oloLsfdwGASlR8wL9Cm3+ICEiKgYKtIyHQEDR7NoopjU0W3h128Esn5enut+zguIiLyD+dwRqSO4jV/n/iyaKCZ1wzbw6/8ssjYuy3W/qNJC7P00DDKVR8wLdLcSil04IYoizp49i19++QWbN2/Gnj17YCvC99ykSRP069cPffv2RceOHaFUKouVg8gdWDhBBMChS4P10mmpY7iFT48noG7UTuoYHk3bpgfsCVdg/PsX1x1RrWFu1hW2crY2aX70qbdQKaIeBBlXZiIiKix7Zhos5yvGjRCfewdAHd1W6hgezadzf9hT4mHav8V1R72WMDfv7lHzAkNaMkSHg/MCIqIicJiNMJ89KHUMt9B27g91k45Sx/Bo2o59YU9LhHHvz6476jaHuUVPj5oX6NOSUcnhgIzzAiKiQnMYdLBcvyB1DLfQNGoPLZ8jlIi2fR/Y05Jg3P2T647azWBu1cuj5gWG1GTYbTbIFYV7bGwwGLB9+3Zs3rwZv/zyC+Li4gp9LR8fH/To0QP9+vXD/fffj1q1ahU3NpHbsXCCCIDp5N6CWy16AHXTzvDt9qjUMSoE3z7DYI2Lhe3mJYgyOdBrKPSaQMCDJjsA4LDZYEhPhW8I16gjIios89HtBbda9ADKOk3g02OQ1DEqBL9+z8AWfxm2q+ey5wU9h0CvDfK8eYHdDkNGKnyDOS8gIiosy+m/AatF6hglpoiIhu99w6SOUSH49hkG6/ULsMXFQhRk2fMCn2CPmxeIdjuM6SnwDQmTOgoRkccwXzpVIZ4jKKvXgV/nflLHqBB8ew+G7do/sF45kz0v6DEYet8Qz5sXiA7oU5IQUKV6nseYzWZ88cUX2LhxI7Zv3w6z2Vzo8evVq+fsKtGlSxdoNBp3xCZyOxZOEKFiLNMh8w+G38PPSx2jwhAUSgQ8MR5pP3wCS+s+sFitgChKHatYspITWThBRFQEpgrQjlvmH4yAQeMgyORSR6kQBLkcAY+PRdq3i2Fpc79Hzwv0KbdYOEFEVASmY7ukjlBigl8QAp6YAEHOeYE7CLJ/5wXrF1WMeQELJ4iICs188ZTUEUpM0PoioOcTvF/gJoJMDv/HXkX6+oUwtekDi9XmsfOCrJTEPAsnRFHEvffei/379xdqLJVKha5du6Jv377o27cvoqKi3BmVqNSwcIK8nj01AbZr/0gdo8T8+j8LmcZX6hgVirxSNagffgm6S+ekjlIixvSUIrXZIiLyZvaUBNiTrksdo8T8H30FMr8gqWNUKPLgytAMGA3dxVipo5SIIS0FDrblJiIqFHtGSoVY1jNg4GjIA4q3ZjXlTh4UBs2AV6C7eFbqKCViSE+Bw26HjEU1REQFsuszYU0s/HIE5ZVfp/6QafkcwZ3kQaFQD3wFmRc8e15g1mXCbrVCrlTete/s2bMFFk3UqFHD2VWie/fu8PPzK62oRKWGT9HI65lP/iV1hBJTNWzDdUpLiX9YVRhSk2FIT5E6SrGJoghDWgr8w6pIHYWIqNwznzskdYQSU7e4F6p6zaWOUSH5hVaBPvUWDGkePC9w2GHkMl5ERIViPrHH49txq5t2hqp+S6ljVEh+oZX/nRckSx2l2ESHA4a0ZPiF8n4BEVFBLJdPe2wngRyqiIbQ1GsmdYwKya9SZehTkjz6fgGQXVTpH1b1ru116tRBQEAAMjMzndvkcjk6d+7s7CrRpEkTCIJQlnGJ3I6FE+T1LP8ckzpCiQgqDfweeE7qGBVapch6MJ5Ig+jB690bM1JZOEFEVAiWWM8unBC0fvDr+7TUMSq0SpFRMGZ49rxAn5LEwgkiokKwnD8qdYQSETQ+8Os3QuoYFdp/8wK71FGKTZ9yi4UTRESFYL7k2ct0CCo1/GIelDpGhVYpsj5MuoNw2GxSRym27Bcw7y6c0Gg02LNnD+bPnw+lUomePXuiV69eCA5mVzOqWFg4QV5NtJhhverZyzD49HwS8iDe+C5NCrUGgdVqIv2G57ZiM2akQRRFVnwSEeXDYTbCesWz2yr69hkKmW+g1DEqNIVKjcDqtZB+/YrUUYrNkJ4C0eGAwOU6iIjyJNqssF7z7PsFvr0GQ+bPm9mlSaFSIah6TaR58LzAmJnGeQERUQFEqwXWhKtSxygR33a9Iffj/YLSpFCpEFKrLpI9eOnvnBdFcpsXNG3aFCtWrJAgFVHZ4YyYvJr1yhnA7rnVf4rqdaHteL/UMbxCYLWakKvUUscoNofNCos+q1jn2gxZsBn1bk5ERFT+WC+d8ux5Qc360LTuIXUMrxBYrYZHzwtEhwNmvU7qGERE5Zrt+gXAapE6RrEpwutB06631DG8QkC1mlCoNVLHKDbOC4iICmZNuubRy3cpQqtD06i91DG8gl9oFSg1WqljFJvosMOUlVnwgUQVFAsnyKtZLp6QOkKJ+D38PASZXOoYXkEmlyOkZh2pYxSZTKGEb0gYQmvXL/SNHLvFDGP8VaSdOoCEHZuQuHMT9HHnSzkpEZH0rB4+L/Dt+QQ7C5URmUyOkJq1pY5RIsbMdKkjEBGVa5bLp6WOUCK+fYawg0AZkclkCPb0eUFGmtQRiIjKqwIxRQABAABJREFUNWuC53YiBgCf1t14v6CMCIKAwGo1pY5RIpwXkDfjUh3k1SwXjksdodiUdZtBGV5X6hhexbdSGNJvXoW1HHdfEGQyaPwDoQ0MhiYgGCof3wInxaLdBnPqLZhTEmBOSYQ18+6JkTn1VmlFJiIqNywXPLdwQhHREKp6zaWO4VV8K1VGRsL1Ynd0kpopMx0Ij5A6BhFRuWX14MIJZe3GUNVpKnUMr+JXqTIy4697bOcGEwsqiYjyZU303GU6FJWqQRURLXUMr+IXWgVpN+Jgt5iljlIsxoxUwMOLQomKi4UT5LUcWRmwe/CER9upv9QRvI4gCAgKr4VbF85KHcWF2s8f2oBgaAKDofELKPCtIlEUYc1IhTklAabkBFjSkwFH/q3mrJmpcNiskCmU7oxORFRu2DNTYb91XeoYxebb/XGpI3gdQRAQVK0Wki6ckTpKsViNBoiiWKy3jhw2KwS5gm8sEVGFJdptsF713LWpfbo/JnUErxRYvRaS/vGcghuZQgmNfwA0/kHQBAQVexzRbocgZzdUIqq4RFGELfGa1DGKzadVV/7brYwJMhkCq9VEatwFqaMUi0WfBYfDDhm7nZMXYuEEeS3LpVOAKEodo1jklapB1aCV1DG8km9IGNLUl2EzmyTLoNT4ZHeUCAyC1j8IMkXBf5VbszKdHSXMKYkQbdaiXVQUYUlLhiasWjFTExGVb7a4WKkjFJuiVgN2m5CIT0goFBotbCaj1FEKJFepsx+O+AVC7R9YqK5UOewmIyxpt2BOuwVL2i1YdemoHHM/lH6BpZyaiEgatusXAKtnviGoCK/HbhMS8QmuBKXGB1aTQeoouZIrVdAEBGYXSvgHQqn1KV4BpdUCc+otWNKSYE5Ngk2vQ7UeA7g0DBFVWPa0JIgW6e4Fl4Q8uDJUtRtLHcMr+VeuivQbcXAU9T58OWHRZ0Hjz3/zk/dh4QR5LdsNz6z2AwBtx76sEpWIIAgIqBqO1LiLZXZNuVL179IbQdAGBkOhUhd4jt1shDk50VksYXfDjRtzWhILJ4iowrIlXJE6QrFpO/aTOoLXEgQBAVXCy+VbJEqtDzT+gdD4ZxdKKNWaQp9rzcrMfhiSdguW1GTYjXcvR2JJT2HhBBFVWNYrntlNCAB8ujwsdQSvlXO/IOXKP1JHAQAo1BrnXEATEASlRluscexmEyypSTCnJcGcegs2XQYA1xeRrJlpUAVVckNqIqLyx5oQJ3WEYmO3CenIZHIEVquBtGuXpY5SLGa9joUT5JVYOEFeyxZ/ReoIxSJofKBp1U3qGF7NP6wq0q5dhljA8hbFJcjl0PoHQRMYDG1AEFQ+vgWe47BZYU5Ngjk5u1DClpXh9lzWjDS3j0lEVF7YPPRGiODjD3WjdlLH8Gr+YVWQdv0yRLtduhCCALWv/78PRwKg9g+EvJDLa4kOR/YSXv92k7CkJcNRiLesrekpQI06JU1ORFQuWS6dkjpCsQg+AVBFt5U6hlfzC62CtGuX4bDbyvzaSo0WmoAgZ7GEoghFk7ezGw3Z9xfSkmD5t6NEQSxpt1g4QUQVltVDl/sWND5Q124idQyvFlC5OjJuXpNkXlBSFv3dL1AQeQMWTpDX8tQ3SzWte0BQF+8tAXIPmVwBn6BK0Kfecs+AggCNX4CzUELtF1BgJbDocMCSnvJvR4kEWNJTAbF0CjlyFOZmCRGRp/LYeUGLLhAK+YCcSodMroBvSBiybiWU4TXlUPsF/rsueSBUfgGQFbI9tsNmhSUt2bn0hjUjpVhFH5aMlCKfQ0TkCUSHA7ar56SOUSzqpp0gyHmrT0oyuRw+IaFlMi9Q+fg6l93QBARCrlQVaxybXndbocQt2I36Io9hSee8gIgqLk/tOKGp1wKCXC51DK8mUyjgX7kqMuKvSx2lyMxZfBZA3on/miKvZM9IgajPlDpG0QkyaDv2lToFIfstkpIUTqh8fKEJCIY2MAga/yDICjGJterSYU5OgCklEZbUJIhlXKlqN+khOhxct5SIKhyHMQuO9GSpYxSLpnUPqSMQUOqFE3KV2lkkkb0muW+h263aTcbbukncglWXDohigecVxKpLh2i38QEdEVU4joxkj13HXNPiXqkjEAC/SqUwLxAEqHz8oA34bxmuwnaXup0oirBlZcKcmpS9LFfqLTjMxhLHs2all3gMIqLyyGHMgiMzVeoYxaJp0FLqCITs+wWeWDhhNRngsNsL9dyCqCLhXS7ySp66TIeiem3IgytLHYMAaINCIFMo4bBZC3W8QqXO7ijxb1eJwrwJYjPqYU5JzF5+IzURDrPEN+9EETZDFpR+AdLmICJyM09dpkNRvQ4UVSOkjkEAtIHBRZoXFESp9XEWSaj9A6EsQqtta1aGs5tEcd8aLRRRhFWXDlVQaOmMT0QkEXtq2XUQcid5papQ1qovdQwCoAko+bxAEASo/Pz/6yjhH1isBxeiKMKamQZLapJzblCYJbmKyqbP4osWRFQhWRM8c5kOeWAoFKHVpY5BAFS+/pCr1LBb3P/zt7SZ9TpoA4KkjkFUplg4QV7JU9txK+s2kzoC/UsQBPiGhEGXdDPX/TKFApqAIGgDsosllJqCl1dxWC3ZhRIpCTCnJJbLpTFs+sxyXTgRGRmJuLjCPQC99957sWPHjkIde+DAASxcuBC7d+9GQkICtFotGjVqhCeffBIvvPACVKq7C2GOHTuG8ePHY9++fdBoNOjXrx/ef/99VK1aNddrzJgxA++88w42b96Mvn3ZWYaoLNk9tHCCa5iXH4IgwDc4FLpb8cU5GWpf/9sKJQIK/Qap6HDAmpF6W0eJ5FJ5GJIXm1HPwgkiqnDsKZ5ZOKFu3kXqCPQvQRDgWykMusTc7xfkeo5MBrVfADQB2YUS6iIsw3U70eGAJSP130KJJFjSkiG6qbCzgAvDptdB6R9Y+tciIipD9nQ3LdVcxtR1mkgdgf6Vc78gM/GG1FGKzKLPYuEEeR0WTpBXsnto4YSqHgsnyhOfoBBn4YQgyKD2D8juKBEYDJWPX4EttEW7HZb0WzAlZxdLWDPT3NI6uzSVx2KO27Vt2xY1atTIc7/RaMSRI0cAAFFRUYUac+HChRg/fjwcDge0Wi2io6ORnJyMffv2Yd++fVi7di1+//13BAT8V1CSnJyMbt26IT09HXXr1oXZbMbq1auxf/9+HDlyBH5+fi7XiI+Px/z589G1a1cWTRBJwFM7UakatJE6At3Gt1JYoQonZHIF1P4B0PgFQuMfAFURHow4bFZY0pKdHSWs6SkQHfaSRi82u9Eg2bWJiEqLPaUYRXDlgKpBa6kj0G18Q/IvnJDJ5VD/WzSp8Q+C2tevWN0asu8rJP+79MYtWNKTIdqlmRvYsjJYOEFEFY49K0PqCMWiqt1I6gh0G5/gSh5ZOGFzw3JeRJ6GhRPklezJHngjRKmCMiJa6hR0G01AEAKr14Q2IBhq/8Aivw2SsHOTW9YSLUs2Q/kunPj222/z3T9ixAgcOXIETZs2xcKFCwscb+/evRg3bhxEUcSkSZPw9ttvQ61WAwC2b9+OIUOGYP/+/Xj++efx9ddfO89bsGAB0tPTMW3aNMycORN2ux2DBw/GunXrsHLlSowePdrlOm+++Sb0ej3mzp1bjO+aiErKEztRCb6BUFSvLXUMuo3GPxCCIIMoOly2y1Xqfx+KBEDjHwil1rfA4socdpMB5rRkWNKSYEm9BWtWRrkqsrSbWDhBRBWPJ3acENRazgvKGY1fAASZDKIje14gUyiz5wMB2cUShXnZIjc5RZTZhRJJsKSnAnfMPaRizcpAwb02iYg8i0PveYUTglINRaVqUseg22j8A13mBZ7CZva85UWISoqFE+SVPLHFlrJWQwiFbNtMZUMmlyOkZp1in68KDIEpybMqTct7x4n8rF27FitXroRGo8E333wDHx+fAs95//33IYoiHnjgAcyZM8dlX7du3fDll1+iV69e+OabbzB37lzUrFkTALBnzx4AwCuvvAIAkMvlGD16NNatW4fdu3e7FE6cPXsWX3zxBR5//HG0bcu2+0RS8MQ3S1V1mxTrZjuVHkGW3X3KbrU6iyTU/oFQqjWFHsOalQFL6i3n0ht2o74UE5ccCyeIqCKyp3pe4YSiVkMIMrnUMeg2gkyGoOq1nAUTSq1P8QolrJbsIonUpOxuU+W4U6XNQ9/KJiLKj0OfKXWEIlNUrlGsLkZUerKX5AqEKTNN6ihFYrOYpI5AVOZYOEFex2E2QjRmSR2jyLhMR8WjCglj4UQZSU5OxssvvwwAeOONN9CoUeHa1W3fvh0A8OSTT+a6v0ePHvD394dOp8OhQ4echRPx8dkPYUND/1v3PSwsDACQmprqMsaUKVMgk8kwc+bMInxHROQuos0K0QMf/ioj2XazPKrasFmxC1oyzh1D1qWzbk5UurhUBxFVNKIowp6aKHWMIlPVaSx1BMpFUHhEsc/NijsP/bULsOk8pxjB6qH3C4iI8mP3wI4Tyiq1pI5AudAGBnle4QQ7TpAXYtkZeR2HLh2KBm0gr9UQQnAVQO4Z9UPKuuWrcGLv3r0QBKFQHx9++GGxrqHX6zF//nx06tQJoaGhUKvVqFOnDl544QVcvHjRvd+QBFQBIVJHKDKH2QiHzSZ1jCJ74403kJ6ejnr16uG1114r1DkOhwPr1q3D0qVLcc899+R6jHjbmz7229aRDQ4OBgAkJSU5tyUmZt+ADQn578999+7d2LhxI55//nnUq1ev8N8QEbmNw0PfjFNUk6Yd95kzZzB69Gg0btwY/v7+0Gg0iIyMxODBg7F79+58z42NjcVLL72E6Oho+Pn5QaPRICIiAk8++SR27dpV4mxGoxEffPABOnbsiODgYPj4+KBBgwZ49dVXy2zeUJIuIEr/YDcmKRt2U/nuiBEZGVno+WrXrl2LfZ2rV68iMDAQgiDgypUruR5z7NgxdO/eHT4+PggJCcHQoUORkJD3W+0zZsyAIAj45Zdfip2LiIrOoUsDrJ53g1gZWfaFE0ajEZUqVXL+PfrWW2/ddcxbb71V6L+Hb//I6+/S/Dz33HOFHj89Pb3E339ps5uMHlU0AcDjliIlIiqIaLdB9MBicWXV4hfuudPp06cxevRo1K9fH76+vvD390f79u3x4YcfwuzmB/ILFy50+Vm/Y8cOt47vDtqAIKkjFJnDboPD7nnPAohKwjOeGBO5kcOkhy2n44R/MOAfAkGjhUythSBXQhAEiA47YDFBNGZBzMqAIyMFsErXlkhQayV7QJKXEydOlOr4Bw8exGOPPYa4uDiX7ZcvX8Znn32G1atX45tvvkH//v1LNUdpUvj6Sx2hWGwGHVQBnvNw59SpU1i2bBkAYNasWVCpVIU6TyaToU+fPvkes2XLlv+zd9/hUZVpG8DvM72kFwglEEoIvUnoIEVEUFERKSrIuiiouKKiqKtgYxVBAf0UsAC7IoJYdhEURRQpShekQ4AEQoD0On3mfH8ERiYJJJNM8s4k9++6uJI5c+acOwGSd97znOdFQUHxXTXt2v01Wdm3b1/s2rUL7777Lt588024XC4sWrQIANC/f3/3fs8++yyCg4Px0ksvefU1EZHvBOJ6pZAkqATcQfL666/j5Zdf9igUA4CUlBSkpKRg5cqVePTRR/Hee+9BUaIt6LJlyzBlyhTYbDaP7WfPnsXZs2exatUqPPnkk3jnnXcqle3o0aO47bbbcPr0aY/tJ06cwIkTJ/DRRx/hgw8+wMSJEyt1/JqgCYsUHcFrLpsVstMJSemf7eETExPRuHHjaz5vNpuxb98+AEB8fHylziHLMh588EHk51+7hW9mZiYGDhyI3NxctGjRAlarFStWrMDOnTuxb98+BAUFeex/4cIFvPPOOxgwYACGDx9eqVxEVDmBuHwXFAqoGtb8fMHcuXNLddMTqbrnKGpaYI4LbJBlFySJ9+kRUe1QvEyHfy6PdE2SBFX9WNEp8MYbb2DWrFmw2+0e23ft2oVdu3bh008/xY8//ojIyKr/vktJScGLL75Y5eNUN5VOLzpCpTisVmgMvJRMdQf/tVOd4zKVXKZDhmwxXXuNZkMwYAiGpNFB0uohqTWQJAUk2QXZZoVsLSoursjPBqppCRBlRIzfrUtWnZMSBw8exM0333zdu0CKioowevRoHDx4EC1atKi2LNWpuFhHBTnAqjZdAba22Zw5c+B0OpGQkICRI0f67LiFhYV46qmnAADdunVDmzZt3M9NmzYNy5Ytw5w5c/DVV1/BarXi3LlzSEhIcF+0W7NmDXbs2IFXX30V9erV81kuIvKOqzBXdASvKcLrQ9LW7BvuhQsXVqjI64MPPkBISAjeeOMN97YtW7bgoYceKlVwUdL8+fPRtGlTPPHEE15lu3TpEgYPHuxeJqksZrMZDz74IAwGA0aPHu3V8WuKyhAUkOMCp8Xkt8Wga9asue7zf/vb37Bv3z506NABCxcurNQ53n//fWzatOm6+8yfPx+5ubl44YUXMHv2bDidTtx3331YvXo1li9fjqlTp3rsP2vWLBQVFeGtt96qVCYiqjxXTuAt06EIjYKkUtfoOX/66Se89tpr1XJslUoFg8Hg1WtkWcbhw4erJY8ogVg4AchwWa1QBuiFISKikpwB2KFSGRoJhUYnNMNrr72GmTNnXnefffv2Ydy4cfjxxx+rfL4pU6agsND/l2ZXqtSQFMrim3YDiMNmgcZgFB2DqMawcILqHJe5cmsuyjYL5GtdMNYagGgDoFJDoTMWF1colIDsAhx2wGKCbCqAMz8bKMoFZO8qVRWRMZXKXJ0OHjzo/jwmJsbjAklJ3bt3r/BxXS4X/v73v3sUTcTFxeGRRx6BLMt4//33ce7cOQDFF0Fee+01LF++3Ov8/kCSJKgMQbAX5IqO4hU5gJbqSEtLw+rVqwEUd3coeQd0ZdlsNowePRrHjx+HUqkstRxNbGwstm7diqeffhpbtmyBVqvF+PHjMXfuXBiNRtjtdrzwwguIiYlxF18AgMViQUZGBurVqwetVuuTrER0fXIAdpxQNajZtps5OTmliiY6dOiAe++9F0qlEmvWrMHu3bvdz82dOxcPPvig+w7+WbNmeRRNNGjQAFOmTEFwcDBWr16NnTt3up97+eWXMWXKFK9+Bj7zzDMeRRNxcXF46KGHoFQqsWjRInf3KlmW8dhjj2HIkCHuJZX8jVJngKPo2p0L/JHTUuS3hRPXs3LlSixfvhw6nQ6rVq3y+iIdACQlJWHGjBkwGAwwma7dwnfbtm0AgMcffxwAoFQqMXXqVKxevRpbt271KJw4evQoli5ditGjRyMxMdHrTERUNaVvtPB/yoianS/44osvMHHiRDgq8L7wzjvvRFxc3HX3WbJkCXbs2OF+/N5773ldWH7q1CkUFf21fNSoUaNw6623XnP/yvzMr2lKrR4KjRYuW2AtHeO0mlk4QUS1hivA3psBgCIoTOj59+/fj1deecVj2+23345hw4bh4MGD+PDDD93zAxs3bsTmzZurtGzip59+ig0bNlQlco1SabWwB9jyL44AG4sQVRULJ6jOqdaJEIf92neuqtRARH0gqmFxcYVGC0mpAmQZktNeXJhhKoCrIAdyfhbgcrlfqgyvX32ZK+nQoUPuzzt27Oiz1tdr1671uPjSuHFj7N69G1FRUQCAkSNHom3btu5Jmq+//hqffPIJlH7aIro8KmNwwBVOuBz2MrfLsgybzQaz2Qyz2QyLxeLx8XrboqOjcfvttyM6OtqnWT/44APY7XY0aNAA999/v0+OaTabMWrUKHz//fcAgLfeegt9+vQptV+7du2uOXBfsmQJkpKSsGjRIhiNRjidTkyfPh2LFy+GxWKBTqfD5MmTMXfuXKjVNXv3GFFd4yoMvIkQZWSDGj3f119/7V6WCABuuOEGbN++3V3c8NRTT+HGG2/E9u3bAQBOpxMrV67ErFmzYLFYsHXrVvdrg4KCsHPnTsTGFrcO/cc//oGePXtiz549AIDc3Fzs2LEDN954Y4WyZWVl4fPPP3c/btCgAfbs2eNu9/nggw+iU6dO7sKKzMxMfPzxx3jmmWcq++2oVoFYOOEwmxBopX6ZmZl47LHHAAAvvfQS2rZt6/UxXC4XJk6cCJPJhAULFmDatGnX3PfKv78r41kA7jFPyTb3zz33HBQKBWbPnu11JiKqOtkaWN31gJornMjNzcXTTz+NpUuXVvg1nTt3RufOna/5/N69e91jAAC4++67MWXKFK+zleyIOXLkSIwbN87r4/gbhVYfcIUTgdahkojoegJxaU+lMVTo+Usu7zl58mQsXrzY/bhevXoehRVr1qypdOFERkaGxw1pgUCl0QVc4YRcTvdQotqGhRNU58hWs9gALidcpnygrN+PkgIIiQRCoyHpDFBodJBUKqhiW9V4zOtJTk5GXt5fA8fKrgldllWrVnk8nj59usckc3x8vHuSOjY2FrGxsbBarQFxx0hZVMYQ0RG8NvfNN/Dl5t9LFT9YLBbIXnZTuVqbNm2we/duGI2+a/11pdvEmDFjoNFoqny89PR03HHHHe47ombOnOn1AL2goACvvvoqWrVqhUmTJgEA5s2bhwULFmDIkCEYO3YsNmzYgIULF8JsNmPJkiVVzk1E1xaIEyGKGp4IubrwAQAmTZrk0RFCqVTi4YcfdhdOAH9dwMjJyfGYNBkwYIC7aOLKa8eOHetx0eR6S26UtGXLFo87XseNG+exRmp0dDT++c9/etzRv3r1av8tnNAH3njGJXpsXQkvvfQScnNz0bJlS0yfPr1Sx5g3bx62b9+O+++/H3fcccd1CyeudDhJT09Hw4YNARQvMQMAERER7v22bt2KtWvXYurUqWjZsmWlchFR1ci2wPuZpqyBDpXvv/8+nn/+eY9Cyqq60m3yyu/xsLAwvP/++5U6VsnCCV/OUYik1Org8N23vEY4A7D4iIjoWgKy44TAuea8vDyPm8hUKhVef/11j33uu+8+980UsbGxuOGGGyp9vieeeAKZmZmVfr0IqgDsMOy66gZforqAhRNU58hyAPygl12QzYVwmou7Y0gG/2p/fPUyHYBvJyVKrhF9yy23lNrno48+8tn5RFNoAm+wlJ2ZUS3rxx49ehTHjh2r0oD5aocOHUJSUhKA4sKJqjp69CiGDx+O5ORkSJKEt99+G08++aTXx5kzZw4yMjKwePFiqFQqOBwOzJs3D40bN8b69euhVqvx4IMPonPnzvjkk0/wyiuvICbG/5brIaotXAG4ZqkiqGYLJ7p16waLxYLU1FSkpqaiVavSBZ1XFysAcLfLjo6Odv+sA1BmgV3JN+HedB9KTk72eFxWtpJdgf744w+YTCa/LLpU6vwvU3kC7e6TQ4cOuceSb7zxRqUKKw8fPoyZM2eiQYMGePfddz0KisvSt29f7Nq1C++++y7efPNNuFwuLFq0CADQv39/937PPvssgoODSy2NQ0Q1R7YE1h2AAKCMqP4OlRs3bvQomqhXrx569OiBb7/9ttLHXLp0KQ4cOOB+/Morr6B+/cp9LSXnKGpL8ZlCK3aN+soItA4ZRETXE4hddGp6vuBqW7duhdX61++Bbt26edwQCRRfR7jSxbcqvvvuO4/ukzExMbh48WKVj1vdVJrA+90us3CC6hjfLPZOFEgC8Ae9whAkOoKHkndz7N+/H/3790d4eDiCg4PRvXt3LFy4EDabzavjXrx40aNKVKfTIT4+Hi6XC9u2bcOqVauwefNmjwFYoJNUgbcMg0FXPQO8qKiocte/9cb//vc/AECTJk3Qo0ePKh1r8+bN6N27N5KTk6HT6fDFF19UqmgiLS0N8+fPR8+ePTFy5EgAQEpKCjIzM9G9e3ePZTn69esHp9PpcRc2EfleIF4gqemOE1OnTsWqVauwbds2JCcnY9CgQaX2KXnB4krxg0qlwrBhw9zbt2zZ4tFRwul04osvvnA/NhqN6NatW4Wzmc2edwaXtXRXyeIzl8uFY8eOVfgcNSkgCycCoSj5KnPmzIHT6URCQoL7d7E3HA4HHnjgAVitVnz44YfubhLXM23aNISHh2POnDmIj49HXFwcVq1ahYSEBPdyd2vWrMGOHTvwzDPPoF69el7nIiLfkAPwAomk813Hvoro168fdu3aha5du1b6GBaLBTNnznQ/btasWaWW6Lji6jkKg8GAN998E61bt4Zer0dMTAzuvvtubNmypdLHF0Wp0YuO4L0AnHMjIro2SXQAr4nsOHH10t4A0KFDBwDFyxN+++23+PLLL31yM15BQYHHuGHYsGEYOnRolY9bEwKx44TsCqybJYiqioUTVPdUYSkBUSS1f/1CLVk4sXz5cmzduhW5ubkoLCzE7t27MW3aNPTo0cOrdtsnT570eNyoUSPs27cPCQkJ6NevH8aNG4eBAwciJiYG7777rk++FtEUAVg4oVJ696tDo9EgJCQE9evXR9OmTdG6dWt06dIFvXr1wqBBgzB8+HBMmTIFGzduLHXHclVs27YNAHDTTTdBkir/RufXX3/F8OHDkZubi8jISPz8888YNWpUpY41a9YsmEwmvPXWW+5tV1p160oUpISGFl8YLe8uViKqosCbB6nxwonyOByOUssK9ezZ0/35m2++6f6ZVlBQgB49emD27NmYP38++vTp41EgNn36dPe+FXH1MgcAcPbs2VL7nD9/vtS2kp0q/IUiAO8+CaQLJGlpae5lvJ599lkoFN6/HX799dexd+9ePPDAA7jtttsq9JrY2Fhs3boVQ4cOxfnz51FQUIDx48fj119/hdFohN1uxwsvvICYmBiPJcAsFgvOnTtXq4qGifyd7LSLjuA1SVszF9ebN2+OZcuWYfPmzWjatGmVjrVs2TKPuYLp06dXemlFk8mE06dPezyeM2cOjh8/DovFgkuXLuHrr7/GjTfeiOeee65KuWuaMgA7TgRCQWVKSgqmTJmCuLg46HQ6xMXF4d5778W+ffsqfcxNmzbh9ttvR1RUFLRaLZo1a4bJkyfj+PHj13zN/v37MWjQIBgMBkRERGD8+PHXvVt65syZkCQJ3333XaVzEpF3qjCdKIxCL+4GzJJz+02aNMFbb72Fhg0bYsSIEbjnnnvQvn17dOvWDXv37q30eZ5//nmcO3cOQPHNF1e6+QUCVSD+bg+g9/xEvsClOqjuCYA3caX42Sit5F2l17J//34MGzYMO3bsKHVRuCy5ubkej81mM4YMGVJqe25uLp544gkcO3YMH3zwQUVj+ycp8OrXhg0bhvZDRkCv10On03l8LLlNp9OVefdvTbhyIa5Xr16VPsaZM2dwxx13wGw2o3Hjxvjpp5+QkJBQqWMdOXIEy5Ytw4gRI9CvXz/39ivFIqmpqR77p6WlAUCplnZE5GMB+HMYfrbM07PPPutRiKDRaHDXXXe5H7dt2xa//PILJk+ejN27d+PcuXN48cUXPY4hSRIef/xxzJo1y6tzd+7c2ePx119/jVmzZnn87lm1alWp1/lyjXZfkhT+NeariH//+994/5tx7iLFq4sVK7stMjISU6ZMwejRo32a9YMPPoDdbkeDBg1w//33e/36ffv2Yfbs2WjUqBEWLFjg1WvbtWvnsd7v1ZYsWYKkpCQsWrQIRqMRTqcT06dPx+LFi2GxWKDT6TB58mTMnTvXozsVEVUDZ+DNF0g1UHT31ltvoWXLlpUqOCvJ5XLhnXfecT8OCwvDAw88UOnjHTp0qMJrb8+ZMwehoaF4/vnnK32+mhSIS3X4+8WVTZs2YeTIkcjPz4dSqUSHDh1QWFiIzz//HKtWrcK8efM8ihgr4tVXX3WPYSMjI9GuXTucPn0aH374If7zn/9gxYoVuPvuuz1ek5mZiYEDByI3NxctWrSA1WrFihUrsHPnTuzbtw9BQZ4XPi9cuIB33nkHAwYMwPDhw6v2TSCiivOzOXl/V3IO//PPP8eRI0dK7bd37170798f69atw8CBA706x2+//eZRKPHKK69UuaCzJimUgXdJ1t9/txP5WuD9LyWqorLW1vZ7fnRRx2q1lqoe7dq1K0aPHg2VSoX169fjl19+cT934MABzJ07t0JrNRcWFno8vnLh+FoWLVqEnj17YsKECV58Bf4lEC+QtGvbFn3a3iA6xnWdPXvWvexLRVrInjp1Cna7HaGhoWjQoIF7+6RJk5CXlwe9Xo/169dXumgCAGbMmAGgeD31q8XHxyMmJgbbt2/H7t27kZiYiOTkZKxduxZ6vb7Ky4wQUTkCcSLEj4pAFyxYgPnz53tse+CBB9CwYUOPbQqF4rqFdE2bNsVdd93ldYegxMRENG3aFCkpKQCAw4cP45577sGcOXMQGRmJFStWYOHChaVeV3LM4Tf8aMxXUYUFBThx4oTPj7tlyxa0adPG3d7VF650mxgzZozXdzZbrVZMmDABDocDH330EcLCwnySqaCgAK+++ipatWqFSZMmAQDmzZuHBQsWYMiQIRg7diw2bNiAhQsXwmw2l+ruQkQ+5nKITuA9HxQzlKdVq1Y+O9bGjRuRlJTkfjxp0iQYjZVfbqRkR0ylUolx48ahV69eOH/+fKnuFjNnzsTYsWPRrFmzSp+zpgRixwl/GqeWlJqairvvvhv5+fno2bMnVq1a5b7YtnHjRtxzzz14+umn0bBhQ4wdO7ZCx9y0aZO7aOKtt97CU089BaVSCavViueeew4LFizA/fffjx49eqBx48bu182fPx+5ubl44YUXMHv2bDidTtx3331YvXo1li9fjqlTp3qcZ9asWSgqKvLoXklENSEA5wsg7tpHyffZZRVNXGEymTB27FgcPny4wjeN2Ww2TJo0yV0w2bVrV0ybNq3SecUIvGtTLJyguibwZuaIqsqP38RdS1WWGfC1oqIiTJkyBTfffDPi4uLw3HPPYffu3ZgxYwaefvppbNq0CQ8//LDHa/7v//6vQgUrZd0lolKpMHfuXGRkZODEiRO48847PZ6fNWsWnM4AXmfLj/5uKywA/g9d3Za9IlXHgwcPRps2bTzuPNqzZw9+/vlnAMXr5D766KPo27fvNf98//331zz+li1bsG7dOvztb39D27ZtPZ5TKBR45ZVX4HQ60bdvX3Tq1Ant2rVDVlYWXnjhBZ9dmCGisvnT79gK85Mi0CVLlpS6Iy86OrpUgdjevXvRq1cv7Nix45rHSk5OxsCBAzF79myvMiiVSrzyyise27755hu0atUKkZGReOKJJ2C3l267rlKxftxXquv/kNPpdBfE+MKhQ4fcF+rGjBnj9etnzpyJw4cP48EHH8SwYcN8lmvOnDnIyMjAG2+8AZVKBYfDgXnz5qFx48ZYv349HnzwQXzxxRfo1KkTPvnkk+u28CaiqpMD8L2lFGB3Li5btszj8SOPPFKl48XFxWHChAno2bMnmjVrhi1btuDTTz/Fo48+itmzZ2P//v0eF6wdDkcAtfQOvGlbf7648s477yAvLw8xMTFYv369x1zBkCFDMGfOHADAU089BYvFUqFjzp07FwAwbtw4PPPMM+5CYa1Wi3feeQdt2rSBxWLBv//9b4/XXVla9PHHHwdQPKa9UiyxdetWj32PHj2KpUuXYvTo0UhMTPT2yyaiquB8gVfKmttv0aIFfvnlF+Tl5eH777/3uMkiPT29zBsdruX111/H0aNHART/3Pzwww+FdTqurEC8qdeff7cTVYfAG4ETVVUg/qCvgTtIKioiIgLvvfcefvjhB5w5cwZvvPGGR7tOSZIwe/ZsjzbC6enpOHz4cLnHLusuk0ceeQTTp09HVFQU4uPjsWbNGrRp08b9fHJycpXWRBNNdgTeHU2BMMDLyMgAAOj1evdSGN769ddf3Z9nZWVh+/bt1/1z6dKlax7rmWeegV6vx8svv1zm8w8//DCWLVuGZs2a4ejRo2jUqBHef//9Uq3siagaBOAd/v5QOPHRRx/hkUce8fidoFAosGzZMo+fuy6XC6NHj4bZbHZvu/fee3H8+HHk5ubi888/R0REhPu5F198EevWrfMqywMPPHDdiy5Go7FUEV1wcLBX56gxAVCcWJKjmsYybdu2xaBBg3x2vP/9738AitfZrUw3pyvdKpYuXQpJkjz+XH3XcrNmzSBJ0jV/518tLS0N8+fPR8+ePTFy5EgAxeuuZ2Zmonv37h7j6X79+sHpdLqXIiOiahKAP4dlh010hAorKipy/zwGgI4dO6J58+ZVOuZNN92Ef//73/j9999x+vRp9O7d2+P5evXq4ZlnnvHYdqVA3t/JrsAr5PGHceq1fPfddwCKu5xcPf684kr3kwsXLuDHH3+s0DH79OmDESNG4MEHHyz1nCRJ7s5ZJYtBr3RBufou6+joaABAdna2x77PPfccFAqF1wXGROQDAVk4Ie7UJef2JUnC6tWrMWDAAISEhOCWW27BmjVrPPb56quvKnTsQ4cO4c0333Q//sc//oEbbvDvjshlkV3++3vymgKwYzZRVQRWWTqRL/jxm7hrke2BMxECFL/xa9KkCU6dOuXelpycjPbt21/3dSXXcARQqj2iSqXC3Xffjddff9297Y8//kD37t2rmFoMl61idzH4E4XK/9fWHjFihFcFHsnJyaW2Pf3003j66ad9kmfnzp3l7jNx4kRMnDjRJ+cjIi8E5ESI2Is6y5Ytw+TJk0v9nH333Xdx6623emz7/vvvcfr0affjjh07YsWKFe4uBWPHjoVarcaoUaPc+7zxxhu47bbbvMr0wQcfICEhAS+//LLHuqrNmjXDp59+iscee8xjwjo0NNSr49eUQLyTY9itt6LdzXcA8CyuvPJ5ZbZFRERgwIAB0Gq1Pst55c7Om266qVJdMhITEz3uWL6a1Wp1FzR069YNWq0WTZo0KfeYs2bNgslk8mi7faUQU6fzbM9+5d9sXl6e19mJqOIkte9+7tQU2eSny0+VYePGjR538t9+++01ct6SyzeW9f7PHwVi4YQ/32hxZSx4rQttSqUSLVu2xIEDB7Bjxw6MGDGi3GNeb1lap9OJP/74A0DxEp1XCw8PB1B8o9GVu6+vjAGuLurYunUr1q5di6lTp6Jly5bl5iEiEnnto+TcfsuWLUv9zO3duzcaN26M1NRUAMVddSwWS6n3P1dzuVyYNGmSu5tkkyZN8Nprr/k4fc1QabUIa9QULqcTsstZxkeXx2N/mCOQAvGGI6IqYOEEUQBwWYpER/Ba/fr1PQonTCZTua+Ji4srta3kGukAEBsb6/E4KyvL+4B+wmmzio7gNUUATiYSEV1TAL4BdJnFjQvWrFmDSZMmlZoUnzNnDh577LFS+5csHBszZkypi9Z33nknNBoNbLbiQtFdu3bB4XB4vZzGE088gSlTpri7ADVq1Ah9+vSBUqnE2bNnPfYtOXntLwKxE1V8q1boltBZdIxyXSls6NWrV6VeX/LOqKslJye7u06sWbOmzDFtSUeOHMGyZcswYsQI9OvXz739SseWKxOJV6SlpQFAhdf/JaLKkQx+2pHoOlzmwCmcWL9+vcfjmiqcqF+/vsfjisxP+AN/uFjiLZPZDGV+PoKCgjy6k/qDK2PQqzs6lXTlolxVi2tOnTqF5557DidPnkRMTEypjhR9+/bFrl278O677+LNN9+Ey+VyLyHTv39/937PPvssgoODr1ugQUTVKABvtBBZdFfyfVBZ8/pA8dz+1e93srOzr7kvAJw7d85jbuHs2bNl3oB5tYEDBwIoXsLZnwomVRotwhvHVXh/WZZLF1g4nXC5XJc/Okt9dBddOF1/Pb6yz+XH3hTYSH72+5yourFwguocSasXHcFrIi+QlOR0OnHhwgWkpqbi3Llz6NSpE1q1alVqv5ycHI/HFZnkbd68OfR6vUc77/T09FKDrpLrpRkMBi++Av/iCsTCCQ0LJ4ioFgnAiRBXQU75O1WDbdu2Yfz48aV+D7/55pt49tlny3xNyWWMyloiQ6lUIjg42F0I6XA4kJGRgQYNGnidUavVllre4dy5cx7jEp1OhxYtWnh97JrgDMBi2UC4++Ts2bPIzMwEUPqu47KcOnUKdrsdoaGhlfp3WBEzZswAUNxh5Wrx8fGIiYnB9u3bsXv3biQmJiI5ORlr166FXq+v1DIjRFRxCv31J+H9kRxAhRNbt251fx4UFITExMQqH7OoqAipqalITU1FWloa7r///lJFmpWZn/AHsj3w5gve/b/38dZ/vgBQ/HccEhKC4ODg634sbx+j0eiTIozmzZvj8OHD2L9/f6kuaQBgsVjcndJK/pupqBdeeAGrV69GcnIyXC4XevXqhaVLl5ZaQnTatGlYtmwZ5syZg6+++gpWqxXnzp1DQkKCuxPlmjVrsGPHDrz66quoV69epfIQUVUF4HyBqUDYudu1a+fxOD09vcz9vJ3b9+duRtVNkiRIShUUShXshXlwFeZDUiqhVKohqVVQKHWQVCpIyst/KjjHJbtcl4stXGUWX1z9UWMovbw6UW3GwgmqcxS6wPtBL/tR4cSTTz6J9957z/142rRpmD9/vsc+GRkZOHHihPuxQqFAly5dyj22QqFA7969sWnTJve27du3l1qG4+pjA2V3qggUrgCcCGHhBBHVJlIALD9Ukis/u/ydfCwtLQ2jRo2C1er5e+v11193XwAuS8n1ow8fPlxqn+zsbI/uUZIkudsXl8dkMmHFihU4d+4czp07B71e775b74or61lfMWjQICiVygodv6Y5zIFxB6yHALj75Pz58+7PmzZtWu7+gwcPRkpKCh544AEsX77c53m2bNmCdevWYdKkSWjbtq3HcwqFAq+88gomT56Mvn37onXr1khKSoLJZMJrr72GsLAwn+chor9I+sCbLwiUjhO5ubke7+U7dOhQ5YvhsiyjUaNGHssYdezYEZ06dfLYb/v27R6Pu3XrVqXz1pRA7FBpsvyVubCwEIWFVf/32aBBA8yePRt/+9vfqnScESNG4PDhw/jggw8wZcqUUsUMb7/9tnspmSud0Ly1adMmj2Xqzp07h3Xr1qF169Ye+8XGxmLr1q14+umnsWXLFmi1WowfPx5z586F0WiE3W7HCy+8gJiYGDz11FPu11ksFmRkZKBevXo+XdKMiMpWmSX+RBNZONGnTx9IkuQudDh16hQuXryImJgY9z4ulwtJSUnux2FhYX67lKa/MV84i4KkQ9fdR1IqLxdRqCGplFAo1cWPLxdXKK76XFJdfqxUQVIV76dUqiBpNO5CDMXlfYnqEv6LpzqHEyFVM2DAAI/CiY8++ghTpkxBQkKCe9sLL7wAp/OvtmBDhgyp8AWQ0aNHexROzJkzB/fdd5+7uj4rKwuff/65+3mVSuXR3jjQuKyBNxHCwgkiqk0UwWGiI3hNRMeJyZMnl+oeMWXKFPzzn/+87utK3kn6n//8B0899ZRHt6qSa5N26NDhuuubXk2r1eLpp592T8qrVCo8/fTT7jWgc3JyMGfOHI/X3HvvvRU6tgiB2HEiEJbwysjIAADo9fpSF0lEeOaZZ6DX6/Hyyy+X+fzDDz8MjUaDN998E0ePHkVcXBymTZuGRx99tGaDEtVBgdhxwlWYV/5OfuDPP//0uGO0c+fOVT6mJEno378/vv32W/e2GTNmYP369e4iyfT0dLz99tserxs9enSVz10TXPbKXbwXqeiqDqK+cuHCBUyZMgW33XYboqOjK32cJ598EkuXLkVaWhpuvPFGLFy4EH379kVeXh6WLl2Kl19+GVFRUcjMzLzuch7Xs2rVKjRs2BDnzp3Df/7zH7z55pt45plncP78+VI3HbVr1w4bNmwo8zhLlixBUlISFi1aBKPRCKfTienTp2Px4sWwWCzQ6XSYPHky5s6dW+msRFQBAXjB2FUkblzQpEkT9OzZE7///juA4iK0F154AUuXLnXvs2rVKo8bJwYMGFBugUpUVBSWLVt23X0+/vhjj0LJGTNmoHXr1uUu6RFIXA57ufvIl5flAHx5zUEqLsi4XFyhuLrDhUfxxdXFGWpISiU04dFQBeD1OKrbAu8nP1EVKQLwB7VTwJ2l13LbbbchNjYW586dA1DcFrN79+6YMmUK4uLisHbtWo83fpIkYebMme7HEydOxL///W/342XLlrnbEALAfffdh9dff919/EuXLqFbt27uieIlS5bg4sWL7v3Hjh3rFxPgleUoyhcdwWssnCCi2kQRHFH+Tn6mpjtO7Nq1C+vWrfPYptFo0KFDh2vejR8TE4NbbrkFw4YNQ8OGDZGWlgYAMJvN6N69OyZPnoxGjRrhp59+8rjYAaDUGtDLly/3uMPw6i4ASqUSI0aMwMqVKwEUL/MxcOBAPPbYY9BoNFi0aBHOnDnjfm379u0xZsyYSn0faoIzADtOBMIkyIgRI7xq7+rtGrhxcXFeHf/q9XmvZeLEiR5jZCKqGZKh9JJS/s6Zfk50hAo5deqUx+OSXSGu5XrjAAB49NFHPcYSP/zwA3r06IFx48bBbrdj0aJFHp2H2rVrh1GjRlXyq6hZLqvvixCqW15h9RSB2my2Uq3dvRUdHY3vvvsOt99+Ow4fPoybbrrJ4/kpU6YgKCgI8+bNQ0hISKXO0axZMwBAy5Yt8eqrr6Jly5Z44IEH8N577+Gxxx5zF/deT0FBAV599VW0atUKkyZNAgDMmzcPCxYswJAhQzB27Fhs2LABCxcuhNlsxpIlSyqVlYjKpwjEcYHg6wjPPfcc7rjjDvfjZcuW4cKFC+6uPyV/Zk2dOtX9+bWuGwQFBZX73mjz5s0ehRO33HILBgwYULUvxs+IW/Jbhux0QHY6AADOcva+WkSXvgExZ0B0NRZOUJ0TiIUTjuyL5e9UQzQaDZYsWYLbb7/d3VUiPz8fb731Vpn7v/jii+jdu3eFj280GrF48WKMGDHCffxz587h+eefL7Vv/fr1MXfu3Ep8Ff7BZbfBaQm8CyTKALizlIioohQhgVc44ajhCyQl744DiievH3vssWu+5sYbb8Qtt9ziXjpj5MiR7t/reXl51xw3dOvWDVOmTPEq3/PPP48vvvgCDkfxm/jU1NQyxw1BQUFYunQpVCr/fQvk9KPl2SpKqb/+erRERIEkEDtOOC6dFR2hQlJSUjweN2rUyCfHveWWWzBu3DiPzpR79+7F3r17S+0bHByMFStW+PVY4Gr2/JrvMlZVSz7+BO8odSgoKEB+fj7y8/Pdn5f8eL3nrl4eTqVS4YUXXkD9+vWrnK9r1644evQoPv74Y/z6668wmUyIj4/H+PHj0atXL/eFOV/9+5wwYQKef/55pKWlYdu2bRUqnJgzZw4yMjKwePFiqFQqOBwOzJs3D40bN8b69euhVqvx4IMPonPnzvjkk0/wyiuveLTBJyLfURgrV0QlkjMvq/ydqtGIESNw7733um9uAIANGzaU2WFnwoQJGDx4cE3GC2hOk/90Ja8oBbsiUQAKjHcKRD6k0AVe4YQzNxOy0+E360kNGzYMK1aswEMPPXTN9SoVCgWeffZZvPrqq14ff/jw4Vi5ciX+/ve/X/P4TZs2xfr16wP6zaHsdEBXvzHs+TmBc6FEkiCpNaJTEBH5jCK4YktJ+RNneipku61Gfh7LsozvvvuuSscYMWIEVq1ahUmTJnmsQV5S79698fXXX3u9XnP79u3xySefYNKkSbDby25d2bhxY6xatarU0iH+xGk1u+/gCCTKACxKJiK6loBc2jMnHbLNAklTsWWuRCk5BoiI8F3x6rJlyyBJksdFmpIaNGiAVatW+WSJkJrgstvhKBK3Tn1lBYdHIio8qsrHsdls7iKKqKgoBAf77q7vkJAQPPXUU3jqqadKPbdv3z4AxUvHlUeWZVy4cAFnzpxBr169oFAoytyvadOmSEtL8+ieei1paWmYP38+evbsiZEjRwIoLjrKzMzEyJEjPZbl6NevHw4cOIA9e/bgtttuK/fYROQ9ZSAWTuRnQ3Y6IV1eskqE5cuXQ5IkfPbZZ9fc595778WHH35Yg6kCnyNQrh9cRVLxOgIFnrJHdES1WCDeQQKXE86cdNEpPIwdOxZHjx7FjBkz0KlTJwQFBUGn06F58+Z48MEHsWPHDrzxxhuVPv7o0aNx+PBhPPnkk2jVqhX0ej1CQkKQmJiIN998E4cPH0a7du18+BVVji01Cdlr3kX+z2tg+nM7bGlnKtw2S6kzILJrP8QMGIEGg0ciMnEgQhI6Q9+gCVRG/2wFp1Bryl13jogokARixwm4nHBcTCl/Px84duwY8vOrvqzUqFGjcOzYMcyaNQuJiYkICQmBSqVC/fr1MXz4cKxYsQJbtmyp9J2EEyZMwO7duzFhwgTExsZCrVYjIiICPXr0wJw5c3DkyBH06dOnyl9HdbLlir0zqDIklRoKFe8gIaLaQwrE+QJZhuOS/y/XUVTkOdnvy8IJrVaLzz77DD/99BPGjBmD2NhYaDQahIeH44YbbsDrr7+OQ4cOoX///j47Z3Wz+9GSrd5Q+agTlUajQWRkJJo1a+azoomtW7finXfewU8//VTm86dPn8bBgwcBoEJ3QKempqJRo0bo27dvmR1OrrjSbaUiXSxmzZoFk8nk0Z3t0qVLAACdzrM4KjQ0FEDpoiQi8p1AXKoDTgccGefL368aqdVqrFixAt999x3uvPNOxMTEQK1WIyYmBnfeeSfWr1+Pzz77zOubJuoyl8MekEt4Kfz4BsyTJ0/i73//O5o0aQKNRuP+97lx48ZKHW/Xrl2477773McLDQ1Fr1698O6778Jms5X5mv3792PQoEEwGAyIiIjA+PHjr1toOXPmTEiSVOUbnOj6JNmbxViJagGX1YKs5a+JjuG14IGjoGvVRXQMKqFo788w7dlUYqsEZUgEVFENoYpuWPwxqiEUOu8mEFwOO+z5ubDnZ8OenwNbfjYchfmAwB/bqqAQ1O93q7DzExH5muxyInPWWKCKaybXtKARD0PfY6joGORDecf3o/D0UdExvKIKDkP9vsNExyAi8hmXqQBZsyeKjuG1oDsfgT7xJtExyIcKTh9F/vH9omN4R6FAw5tH++3NFq+99hpmzpyJXr164bfffiv1/IMPPohly5bhjjvuwH//+98KHbNNmzY4duwYJk6ciGXLlpV6/osvvsCYMWOg0Whw9uzZ6xYJHzlyBB07dsStt96K//3vf+7tx48fR+vWrdG/f3/8+uuvpfJu2LABQ4fyfQFRdZCdDmR+PEt0DK8Zu98MQ5cbRccgH7LlZiLj98pd0BepwU13+2XxxA8//IC77roLZrMZBoMB8fHxyMjIQFpaGgBg+vTpXi0Rv3DhQjz11FNwuVzQ6/WIj49HZmam+3g9evTAjz/+iJCQv7rYZGZmIj4+Hrm5uWjRogWsVitSU1MRHx+Pffv2ISjIs6D7woULiI+PR2JiIn755RcffBfoWthxguochVYH+MmSF96wp50WHYHKYC9zPVkZzvwsWE8fRNHOH5C3fhmy/j0bWZ+9hbwfVqBo78+wJh+Fs+j6dwUoVGpoI6IRFJeA8I49Ub/vcDQccg+ie92MsHaJMMS2gDo0AlDUXOs1hZ+3fyUi8pakUEJhDBUdw2uO80miI1AZCn//HoU7f4A15RhcVotXr9XHNEFwy/bQRtYX2lbVGyovi0KJiPydwhAMKQCX97SfOSQ6AvlYIHacUOqMfls0AQD33Xcf1Go1fv/9d7zxxhtwXS6ctlgseOmll7Bs2TJotVq8/vrrpV576tQpHDt2DBcuXPDYPnPmTADFbelnz57tsWzcF198gb///e8AgBkzZpTbWW3GjBkAUKp7a3x8PGJiYrB9+3bs3r0bAJCcnIy1a9dCr9ejR48e3nwbiMgLklIVkF0n7BeSRUegMjgLciDLlbtpx14QeN2FJJXaL4smMjMzMW7cOJjNZowdOxZpaWnYv38/zp8/j88++wxKpRLz5s3DV199VaHjbd++HU8++SRcLheeffZZ5OTk4MCBAzh//jx+/vlnNGzYEDt37sTkyZM9Xjd//nzk5ubihRdeQFJSEpKTkzFmzBicPHkSy5cvL3WeWbNmoaioyKMrFVWPwLt6TOQDytBIOLMviY7hFdu5k6IjUAmyw+7VQNRVmAdbYR5syX/dTSrpjVBFFnekUF/uTqG8Ttt4SamEJiwSmrBIXJnOk10uOArzYMvPgf2qP9WxTroqANf2IyIqjyI4Aq6CHNExvGI7uV90BCpBdjpgPrITcNhhBgBJgjKiPtQxcVA3iIM6pul118jVhEZAE1o8BpBdLtjzsmHNyYAtJwPWnAzI9rJbO4qk1AfexUUiovKoYprAnhxYHYBsJw9AdrkgKXh/VG1hywu8wgmVn48LmjdvjnfeeQePP/44XnjhBSxcuBCNGzdGUlIS8vLyoNPp8N///hft27cv9drBgwcjJSUFDzzwgMfFjHHjxuHo0aN47bXX8OKLL2Lu3Llo2bIl0tLS3EUWDz/8MF5++eXrZtuyZQvWrVuHSZMmoW3bth7PKRQKvPLKK5g8eTL69u2L1q1bIykpCSaTCa+99hrCwsKq+q0houtQhkbCZSoQHcMr9kspkGUXJInjAn8hyy7kfPU+IMvF8wMNm0HdoBlUUQ0q9Pdky8mogZS+5a/jgo8//hg5OTmIi4vD8uXLPZaMuffee7F161YsXrwYixcvxt13313u8ebOnQtZlnH77bdjzpw5Hs8NHDgQ//73vzFkyBCsWrUKb731FmJjYwEA27ZtAwA8/vjjAAClUompU6di9erV2Lp1K6ZOneo+ztGjR7F06VKMHj0aiYmJVf4e0PWxcILqJFVETMAVTrhMBXBkXYQqMkZ0FLrMdv4U4LCXv+N1yOYi2FNPwp56EldWKZM0OqiiGriX+FBFNYQyLOqagyhJoYA6JBzqkPC/jivLcBQVXC6iuLLUR06VL7qogwPvrmwiovIowqOAtFOiY3jFlZ8NR9oZqBo2Ex2FLrNfOuc5LpBlOLMuwpl1EZbDOwAAipCIy4UUTaFuEAdVaFSZx5IUCmjCo6AJjwLQpvj3emEerNnFhRS2nAw4LaYa+KquT+mjdcyJiPyJsn7TgCuckE35cJw/BXVsvOgo5AMuuw1OU6HoGF5TGvzzAsnVpk6dijZt2uDtt9/Gjh07cODAAcTExGDkyJGYMWMGEhISvD7mq6++ikGDBmHBggX47bffcODAAUREROCOO+7AI488UqFlNJ555hno9fprFlg8/PDD0Gg0ePPNN3H06FHExcVh2rRpePTRR73OS0TeUYZEBlwHB9lmhSPzAtTRjURHocsc6echW4tn/20px2BLOQag+DpARQopbHlZNZrXF7bs3I1vFq1AfHw8WrVqhfj4eMTHxwsv+IuLi8O4cePQpUsXj6KJKzp27AgASElJqdDxriybMW7cuDKfHzx4MIKDg1FQUIA9e/a4CyeuFFhGRf01LxQdHQ0AyM72LKB97rnnoFAoMHv27Aploqph4QTVSarIGFiTDoiO4TXbuRMsnPAjVwY4vibbLLCnnYE97cxfG1UaqCJjPAsqwq/dyluSJKiDQqAOCgEaNnVvd5iLYM/7q5DCnp8Dl9Vc5jHKog4Kq+yXRUTkt1QxzWA7vFN0DK9Zj+9l4YQfsVdg+RRXfjas+dmwntgHAFAYgqCKKe5GoW4QB1VkTJmTJJIkQR0cBnVwGNC0+KKYw1To7kZhy86Aoyjfp19PRSgDsJ09EVF5VPVjRUeoFNuJP1g4UUtYMi+KjlAp/npnaUmDBw/G4MGDvXpNcnLydZ8fMGAABgwYUOlMO3eW/15k4sSJmDhxYqXPQUSVowyNFB2hUuznT7Fwwo/YUsvuJi7bLKULKWKaQt2wOdQN/yqkiO49tPj9f3Y6bFnpxZ2pKrnsR03589gJrFy5stT26Ohoj0KKK5+3bNkSRmP1jyXGjh2LsWPHXvP5PXv2ACheKqs8LpcLq1evxrlz59CvX78y95Fl2f250+l0fx4eXnwTbHp6Oho2bAgAuHSp+GbviIi/OpJv3boVa9euxdSpU9GyZctyM1HVsXCC6iRVRGAWH9jOnoChc3/RMegy29kTNXcyhw2OS2fhuHT2r20KJVQR9T2LKSJiIF1n7TCV3giV3gh9zF+TgU6rGfa8HNgud6aw5+fAaS4q8/XsOEFEtZGqUXPRESrFdnwPjANHiY5Bl1lPH/b6NS5TIWynD8F2unhtekmjhap+k7+W96jXGJKy7LdsKkMQVIYgGBoVF884rZa/CilyMmDPzwGueoNeHdhxgohqI1X9puXv5IdsJ/fBOHi06BjkA+aLZ8vfyQ8pDUGiIxAR+VygFk5YTvzB6wh+xJr0Z4X2k20W2M4eh+3scQClCylC4jtAaqWAy+nw+0KKlAtld3zPyMhARkYGtm/fXuq5Ro0auQsprv7YvHlzaDTXvubhC7m5uVi4cCGWLVsGlUqFGTNmlPsahUKBW2655br7bNiwAQUFxcv9tGvXzr29b9++2LVrF9599128+eabcLlcWLRoEQCgf/+//u8+++yzCA4OxksvvVSZL4sqgYUTVCcpA7Rrg/1CMpwFOVAGh5e/M1Ure3oqXEV5YkO4nHBkpsGRmQZgb/E2SQFlWNRVy3w0gCqyIRRa3TUPo9Tqoaynh65ew78ObbfBlvdXIYU9Pxuy0wmFpnT7KiKiQKdu1EJ0hEpxpJ6CMzcTyrCyl3ugmmNLOwNnbtXXHJVtVtjPnYT93OW7UZQqqKIb/bW8R0xTKDRl/05XanXQx8S6iyNdDjtsuZmwZRcXU9hzsyC7nGW+trLUxhCfHo+IyB8o6zcRHaFSHKlJcOakQxleT3SUOs9lMUFSKiGpvX//7HI6YM1Iq4ZU1e/q5UOJiGoLZVi06AiV4sxJhz3jPLtO+AH7pbOVni8I5EKKpHPej2fOnz+P8+fPu5e/uEKhUCAuLg5DhgzByy+/jJgY313f++qrrzBr1iwkJSXBarUiNjYWixYt8iheqKzCwkI89dRTAIBu3bqhTZs27uemTZuGZcuWYc6cOfjqq69gtVpx7tw5JCQkuDtMrVmzBjt27MCrr76KevU4xq8pLJygOklpDIGkM0K2lH1Xvf+SYTm2F8bEm0QHqfMsR3eJjlA22QVnTjqcOemwntx/eaMERUg4VFENoWncEvo2ieUeRqHWQBcVA13UX4MQX19sISLyF4qgMCiCw+EqyBEdxTuyC5Y9P8F407VbDFLNsByppnGB0wHHxRQ4LqbAvB+AJEEVEVPcjeLy8h4KQ3CZL1Wo1NBFNYAuqgGA4t/jtrwc2HLSi4spcjMh222VjqYyBrOgkohqJYXeCEVoFFx5maKjeEeWYdn3C4yDx4hOUueZ9v4C87Hd0Ma1gbZlJ2hi4yEpyl5msySXxQyVMaS4c1QAkVRqqK4xJiEiCmTK8GhIWj1kL5Y69hfWE3+wcMIPWI7v89mxvCukyIQtOx3W7Euw5dZ8IcXEKY+i27ETOHHiBE6ePIkzZ87A5apcBpfLhdOnT2PJkiXIyMjAV1995bOcu3btwuHDf3UQzcnJwbfffov+/fsjOLjyYxubzYbRo0fj+PHjUCqVWLBggcfzsbGx2Lp1K55++mls2bIFWq0W48ePx9y5c2E0GmG32/HCCy8gJibGXXwBABaLBRkZGahXrx60Ws7JVAcWTlCdpYqMgf38KdExvGY58QcM3QaVuf411QyXzQJL0kHRMbwgw5WfDVt+NiRIFSqcKEtFJ3qIiAKRqmFz2I7vFR3Da5a9m2AYdA9/RgvkMhfCesb7ZToqRZbhyLoAR9YFmA/9DgBQhkRe7kZRvLzHtVrJSgoltOFR0IZHAc2L19l0FObBmp0BW046rNkZcHkxGagO0Ja1REQVoaofC1ugFU4AsOz7BYaB90BScL5AFNnphCVpP+Cww5r0J6xJf0LSGaFt0QG6+E5Ql9PRRGUMRr0+t8BekAfThWSY01KuuZSmP1GHhEOSJNExiIh8TpIUUNdv4r5QHUgsSX/C2GsY5wsEkh12WE9V33WEaxdSNIO6YTMEt2yPEEXHGi+kUKg1eOiRxzy22Ww2nDlzxl1IcfXH1NTUCh/7+HHf/l98/PHHMXPmTOTn5+PHH3/EM888gyVLlmDv3r34/fffoVJ5fxndbDZj1KhR+P777wEAb731Fvr06VNqv3bt2mHDhg1lHmPJkiVISkrCokWLYDQa4XQ6MX36dCxevBgWiwU6nQ6TJ0/G3LlzoVarvc5I18bCCaqzVBGBWTjhKsyFPfUUNLHxoqPUWdaT+wFH5e/QFElVr7HoCEREfilQCydc+dmwHdsLbdvuoqPUWZZjewGBXZmc+Vlw5me572JRGILd3SjUMU2hjIwps+BWkiSog8OgDg4DmhaPKx2mwsvtPYuLKRxFBdc8r4ZLxBBRLaas3xQ48YfoGF5z5WbAdnwvtJUslqeqs6UchWwxeWyTLUWwHN4By+EdUIZEQhvfCbr4ztcsdgQAdXAoQoM7ISS+I2w5GTClJcN88VyVukVVJ01ohOgIRETVRt2gWUAWTsiWItjOnoA2rk35O1O1sJ45AtlmqbHz+UshhSootNQ2jUaDhIQEJCQklHrOZDIhKSmpVEHFyZMnkZ6e7rHvQw895LOcANC4cfH1EqPRiAceeAA9e/ZE586dsWfPHqxYscK9bEZFpaen44477sCOHTsAADNnzvToGFERBQUFePXVV9GqVStMmjQJADBv3jwsWLAAQ4YMwdixY7FhwwYsXLgQZrMZS5Ys8er4dH0snKA6SxXpu3WQapr58A4WTghkPrJbdIRKY+EEEVHZVA2bi45QaZbdP7JwQhBZlmE+6l/jApepANbTh2A9fQjA5YmS+k2KCykaxEEV3QiSsuy3gSpDEFSGIBgaNQMAOK2W4kKKnAzYsjNgL8gBZBkAoAljxwkiqr1UMU1FR6g0884NLJwQyLR/y3Wfd+ZnwbT3Z5j2/gxVvVjo4jtB26IjFHpjmftLkgRtRD1oI+ohrO0NsGRcgOl8MiwZ54FKtruuDuoQFk4QUe2lbhAnOkKlmQ/+xsIJgUQv9y2qkEIdEu7V/gaDAR07dkTHjh1LPZeXl4eTJ0/i7NmzaN68OTp37lzpXBWRkJCAkSNHYuXKldi8ebNXhRNHjx7F8OHDkZycDEmS8Pbbb+PJJ5/0OsOcOXOQkZGBxYsXQ6VSweFwYN68eWjcuDHWr18PtVqNBx98EJ07d8Ynn3yCV155BTExgXu909+wcILqrEAe8NhSjsGemQZ1VEPRUeoc69njcGZfFB2jciQF/80QEV2DqlEL0REqzXZyPxwXUwL6Ik+gsp87CVeBf69BLtsssJ07Adu5E8UblCqo6zWGumFzGLsNvu5rlVod9DGx0MfEAgBcDnvxZEpORnGnCiKiWkrdrJ3oCJVmTzoA+/nTUDcK3KLQQGVNPgpHxvkK7+9IP4fC9HMo/P07aBq3hDa+M7RN20BSa8rcX1Iooa/fGPr6jeGy22C+eA6mtGTYstPL3L8mseMEEdVmquiGgEoTkB2I7WmnYUs7DU0A3ywSqGypSbBfSBYdw0NNFVJowqN9ljk0NBTdunVDt27dfHK87OxsnDlzBk2bNkVUVNmdNJs2LZ5fu3ix4teBNm/ejLvuugu5ubnQ6XT49NNPMWrUKK/zpaWlYf78+ejZsydGjhwJAEhJSUFmZiZGjhzpsSxHv379cODAAezZswe33Xab1+eisrFwguosZUgElKGRcOZliY5SKaY9mxB6y3jRMeoUWZZh2v2T6BiVpopueM0JGCKiuk4ZGglFaCRcgTgukGUU/bIGoeOmi05S55gO/iY6gvecDtgvJEN2ucotnChJoVJDF90AuugG1RSOiMg/KEMjoazfBM5LZ0VH8Z4so2jjSoRNfFF0kjpFlmWY9myq3ItdLtjOnoDt7AkUqjXQxLWFLr4z1I1aQFKUXm4LKF473BjbAsbYFnCYi2BOS4EpLRmOwrwqfBWVI6k1UBqCavy8REQ1RVIooa4fG5DLfgPF1xE0I1g4UdOKAuA6wrUKKbQtO0EX3wm6qOIuBrLTAau7kCIdttys6xZSaMP9d2nPxMREnD59Gm+99RaeeeaZMvdJSUkBADRq1KhCx/z1118xfPhwmM1mREZG4ttvv0WvXr0qlW/WrFkwmUx466233NsuXboEANDpdB77hoYWL4mSl1fz47/arOzRN1EdoYltJTpCpdlSjsHuxZ0MVHW2M4fhyEwTHaPSNE1Krx9GRER/0bTqKjpCpSjqx8Kl1nFcUMOsyUdhTz0pOkalaRo2Ex2BiMivBeq4AADsJ/+A7cxh0THqFNuZI3BkXajycWS7DdaT+5H33XJkf/YWCn/7rtwxnkpvRHCLtqjfbzjq9bkFQc1aQ6HVVzlLRekiYyBJUo2dj4hIhEDuXm2/kAxbamAWfQQqa/JRONLPiY7htSuFFCXHNJJSBV1UDEJadUR0z5vQcMjdiEwciOAW7Yq7S0h/XWpW6o1Q6gw1Hb3Cbr75ZgDARx99BLvdXur55ORkfPPNNwCA22+/vdzjnTlzBnfccQfMZjMaN26M7du3V7po4siRI1i2bBlGjBiBfv36ubdHRhYvlZqamuqxf1pa8bWqa3XOoMph4QTVaerYeNERqqTSdzOQ12TZhaIA/36zcIKI6Po0CTeIjuAVKbIhVF0GwqULgiPrAop2bBAdqc6QHXYU/v6d6BhVomarViKi69K06iI6QpUU/fiZ6Ah1hizLKNrr+/kCl6kA5oPbkfv1B8hevQBF+36Bs5wlwtQh4Qht3QUxA+9AVPeBMDRuDkmlvu5rqkobxTW1iaj2UzcI7MLz6vg9RWWrUhcqP6Fp3PK6z1+vkMIY699L4T7zzDPQ6/U4efIk7r33XmRmZrqf++OPPzB06FCYzWb0798fd9xxh/u5U6dO4dixY7hwwbOoZNKkScjLy4Ner8f69euRkFD5azAzZswAALzxxhse2+Pj4xETE4Pt27dj9+7dAIoLPNauXQu9Xo8ePXpU+pxUGpfqoDpN06AZoFQBTofoKJViO3sc1pRj0DZtLTpKrWc5vg/OHPHrhlaWwhAMdXTFWksREdVVmpYdAbUGsPv3uqVSWD0om7WDI/uSx10A9rTTsJ49Di0L5aqd6cBWuPKzRceoPJUa6pimolMQEfk1ddPWkLR6yFaz6CiV4jh7HNaju6Ftkyg6Sq1nPfUnnNmXqvUcztwMmHb/BNPuTVDFNIEuvhO0zTtAcY07OiVJgjYyBtrIGIS17QZL+nmY0pJhybjg1RrlFcElvIioLlDXaxzQ1xEcF1NgTT4KbVwb0VFqPevpQz7pQiWMUuX1fMGVQgpdABRTNm/eHF988QXGjBmDL7/8Et9++y0SEhJgsVhw4sQJAEDPnj3x1VdfeXTUGjx4MFJSUvDAAw9g+fLlAIA9e/bg559/BgAYDAY8+uij1z33P//5TwwbNqzM57Zs2YJ169Zh0qRJaNu2rcdzCoUCr7zyCiZPnoy+ffuidevWSEpKgslkwmuvvYawsLBKfjeoLCycoDpNUmugbhAHe2qS6CiVVrjtW2gaNoek1oiOUmu5TAUBfxcvu00QEZVPUmuhadYethP7REcpkxQcAVXLTrDnpl/zTXjRjg3QNGoBSclhfnVxFuTAtH+L6BhVooltVe13nxIRBTpJqYK6RUfYjuwUHaXSCtcvhaZ5e0g1uGxDXeMyF6Hwt/U1eEYZjospKLyYgsLt66Fp0gq6lp2gadr6mr/bJaUS+gZNoG/QBC6bFeaLZ2FKS4YtJ7PM/b2hCgr163bcRES+IqnUUEc3hv1isugolVa4fV3xfAGvI1Qbl8WEohodF/ieukFcrZ8vuO2223DgwAHMnTsXP/74I44ePQqDwYC+ffvi/vvvx4MPPgi1uvzvwa+//ur+PCsrC9u3b7/u/pcuXbvQ9konjJdffrnM5x9++GFoNBq8+eabOHr0KOLi4jBt2rRyizXIe5Isy7LoEEQimf7chqLfvxcdo0r0HfsiqFfZlWpUdXk/rgz49WFDht7PimIiogow79yAwrUfiY7hyRgKdXwX2POzAEfp9RdL4rigeuX9sAK25KOiY1RJ8KB7oIvvLDoGEZHfM+/eiML/LhYdo0p03Yci+I6HRceotfI3fg7r6UOiY0DS6KBt1g7a+M5QN4yDJJW/OrPDVAhzWgpMaclwFOVX6rxBzVojtHVgL2tDRFRRRXt/DvglGPQdeiOo962iY9Ra+Zu+gDXpgOgYVWLscQsMnfuJjkEkTPmjaKJaThPbSnSEKjMf/A32zDTRMWol65nDAV80AaUKmkb+vbYYEZG/0CTcIDrCX3RGqDrdCCkmDvbsixUqmgAA85/bYTt7oprD1U22sycCvmgCCiU0TbjMGxFRRWjiA/+CsGX3j7CdPig6Rq1kSfrTL4omAEC2WWA5vhd56z5B9mfzULhjAxxZF6/7GpUhCMEt26F+/1sR3XsojHEJUGh1Xp1XGwAtuYmIfEXXspPoCFVmPvQ77BeSRceolaxnDgd80QQAaJu1LX8nolqMhRNU56nC60ERFCY6RtXILhT8vAZyBS+oUMW4zEUo3Pat6BhVxqVciIgqThkWDWX9JmJDaPVQdewHqXE8HDmXINutXh5ARv7mL+EyFVZLvLrKZTGhYNta0TGqTNOohdcXRYiI6iplWBSU9WJFx6gaWUbB1x9AtppFJ6lVXKZCFG73z/kCV1EezAe2IufL95C95l2Y9m+BszDvuq/RhEYgrE1XxAy8A5HdBkDfMK7cpd8klRra8Hq+jE5E5NeUoZFQxTQVHaNqZBn5m7+qxDwDXY/LXISCrf8THaPKVDFNoQyNFB2DSCgWThAB0LbsKDpClTlz0v32TXsgkl0u5G9aDZepQHSUKtM0TRAdgYgooGhadxNzYpUa6g59IDVtA0duBmSbpdKHks1FyP9lDbgqn2/ITifyN66EqyBHdJQq0/DuESIirwgbF/iQKycdBes+ER2jVinY8g1ki0l0jHI5sy+haOcPyF45F7nffgzzsT1wWa89xpQkBXTRDRDRqRdiBt+F8E69oI1uCEhSqX119RpBUiqrMz4Rkd/RtQr8blSu/GwU1IKbBf1J4ba1kM1FomNUmS6hq+gIRMKxcIIIgK61H7XlrgLLsb2wHNsrOkatULTrR9jPnxIdo+oUCmibtROdgogooOg69q3ZEypVULXrBUWLTrDnZflsEt6emgTzga0+OVZdV/jbOtjTzoiOUXWSAto4Fk4QEXlD12WA6Ag+Yd33C0zb14mOUSuY9m+FLeWY6BjekWXY086g8NdvkPXpG8j7cSWs5Sw/plCqYGgYh6huNyJm4J0IbXMD1FfdhWpoEOB3XRMRVYK2eQegnI48gcB64g+Y9nO+wBdMB7b5zdJdVaJSF//7JqrjWDhBBEAVGgV1gzjRMXyiYNta2NNTRccIaJZTB2vNhSZN0zZQGIJFxyAiCiiqmKZQNamBbj0KBVRtekDRqiscBTlwmX2/tEbR7p9gTz/n8+PWJebDO2A5skt0DJ9QN2oOhd4oOgYRUUBR1WsMVWy86Bg+UbTh37Cd+EN0jIBmSfoTRTt/EB2japwO2M4chvnwjgq/RKnVISiuFer1vhn1+9+GkPiO0EbFVGNIIiL/pNDqoG3aRnQMnyja9QOsyUdExwhotrMnAn9ccJm2WTsoNFrRMYiEY+EE0WW6WtB+EwDgdCD/h0/hzMsSnSQg2TPOo2Dz16Jj+Iy+bXfREYiIApI+cUj1HVySoEq4AYo23eEoyoOrKL/6zuVyIv+Hz+DIy6y+c9RitvOnUPjbetExfEbfhuMCIqLK0HUdJDqCb7hcyF/1Dhy82aJSbGlnULD5KwC1Yyk0XavKteNWGYMR3LIdJAWnlYmobtImBP5yHQAAWUb+pjVwZKaJThKQHFkXkb9pNSC7REfxCV1C7ejKTlRVHOESXaZt1g5SLamoc5kKkbtuKZyFeaKjBBRH9iXkfbcccNhER/EJRUgE1I1aiI5BRBSQtB16Q9L5/s58VcvOULbrBYe5CK6CXJ8fvywuUwHyvv2ERZVecuZlIX/j54CrdkyCKAzB0MS1Fh2DiCggaTv2BdQa0TF8QraakPfpG3BxvsArjpwM5P/4GeB0iI7iE5JGC20zLt9FRFQZmsbxkPRBomP4hsOGvA0r4KzOGzpqIWdhLvK+/zdkm0V0FJ9QBIdD3bCZ6BhEfoGFE0SXSWoNtC06io7hM67CXOStXwaXuUh0lIDgyMtE3vqlPltX3h/o2yRCkiTRMYiIApKk1kLX5UafHU/ZrD2UHfvCYbPAmZ/ts+NWlKsoH7ksnqgwZ1E+8jZ8CtlqFh3FZ3Stu0FSKEXHICIKSAqdAdr2vUXH8BlX9kXkfjILriIWT1SEy1RYfHGkNo0LWnWFpFKLjkFEFJAkhQK6+E6iY/iMqygPed8th8tUIDpKQHAW5SNv/fLq7R5aw3StuvA6AtFlLJwgukqtWa7jMmduRvGgpxYVA1QHZ0EO8tYthcvk+7XlhVEo2V6LiKiKdD5YrkPZpDWUnfrD6XTAmSt2uQxXUR5y130ipHAjkDgLcpC79iM4czNER/EdSQFdm0TRKYiIApq+5zDREXzKmX4O+f9bApe5Fr0PrgbOwjzkfvsxXAU5oqP4jiRB376X6BRERAFN16qWLNdxmTP7EnL+9yGcten3XTVw5mUh938f1q75Aki17t8zUVWwcILoKup6jaGMqC86hk85MtOKf5lz0FMmR/Yl5K79uNa1KdU2aweF3vct5omI6hJV/SZQNa3c0gbKRi2h6nwjnACcOem+DVYFrsK84s4TLJ4okyMnHbn/+xCuWvb90TRpBWVQqOgYREQBTd24JVSN40XH8BlF/Vg4bHbk/u8jOGto+bBA48jNrIUXRwBNbCsoQyNFxyAiCmiqyAZQRsaIjuFTrvxs5P7vIzhyatfvPV9xZF1E7toPa1cxJQB1wzgoQyJExyDyGyycICpB37a76Ag+58zNQO5/F8OecV50FL9iO3cSuf9bAldhrugoPqdry7tKiYh8Qe9l1wlFTFOougyAU6mCI/tS9YSqIldhLnK//QSOPLEdMPyN7UIyctd+VKvabV6hq4XjWyIiEfQ9bxEdwScU0Y0hh9aDbDXBmZeJ3LUfwuFHhZ7+wH7xbHExZS2cL2C3CSIi3zB07Cs6gs+5ivKQu/Yj2DPTREfxK/aLKcXzBbWpY/Vlta0LO1FVsXCCqARd625QGIJFx/A5l6kQed9+DOvZ46Kj+AXz0d3I2/AfyDar6Cg+pwyLhqZhc9ExiIhqBW373pAMIeXup4huDFWXAXBpDXBkXaz+YFXkKsxF7lcfwJJ0QHQUv2A58Qfy1i2FXAuXN1OG14MmtvbcIU1EJJK2Qx9IxvLHBf5MimwIOSIGsqXIvc1VmIfcbxbDkvSnwGT+w5L0J3LXfeLxPaotlGHRHBcQEfmItmUnKMOiRMfwOdlSVHwd4fRh0VH8gvX0IeSuXwbZZhEdxeeUoVHQtugoOgaRX2HhBFEJklIFQ5cbRceoFrLdhvwNK1C0+yfILpfoOELITgcKt69H4Zb/ArX0e2DoOlB0BCKiWkNSa2Doe/u1n4+IgarLQLgMIcUFE7Jcg+mqRrZbUbDpCxT8+jVkh110HCFklxNFu35EwS9fAi6n6DjVwtB1ICSJb/uIiHxBUqlh6HeH6BiVJkXEQIpuBNlc+m7J4nHBahRs/R9kp0NAOvFkpwOFO39AwaYvgFr6PdB37CM6AhFRrSEpFDB0HSQ6RrWQbVbkb1yJgm1r6+64wGFHwdb/IX/j50AtnTMxdBsEScH5AqKr8X8EURl0bRKhCPC7SK5JdsG07xfkfvtxnVvH1JGXidz/LoH50G+io1QbZUR9aFt2EB2DiKhW0fccBqlENyopJAqqLgMhh0TCkXUBkAO3GM9ybC9yvllU51p0OzLTkPvNYpj++FV0lGqjDIuGtkV70TGIiGoVfc9hUAQH3jrQUlg9SPWbwGUquO5+liO7kPvfJXDmZ9dQMv/gyLqInK8Xwbx/C4DAKYT1hiIkArqErqJjEBHVKtqWHaAMixYdo9pYDu8sHhfkZYmOUqMcOenI+WYRLEd2iY5SbZQR9dltgqgMLJwgKoOkVMHQub/oGNXKcTEFOV++B+upg6KjVDtZlmE+tAM5X/4fHLV8fTZjt5t4VykRkY9JWj30fS53nQgKg6rLAMiRMcUFE7WkS4Ez+xJyvv4A5mN7REepdrLTgaJdPxYXi9TycYHhBnabICLyNUmthWHQPaJjeEUKiYLUsBlcRfkV2t+RmYacr96HpS7MF7hcMP3xK3K+/gDObP9fbq0qjF0GQFIoRccgIqpVJEkBww21s+vEFe5xwcm6sdSn+ejuy+OCS6KjVCtjt8GQJEl0DCK/I8lyAPUTJqpBstOB7M/frvDEQiDTNElAUO9boQyNFB3F5xxZF1H42zrY086IjlLtVNGNED7yUdExiIhqJZfVjPzvlsORnQ7ZYRMdp1ppW3SAsecwKINCRUfxOfvFFBT8+g2cuRmio1Q7ZVg0wkf/g4UTRETVQHY6kbPwH3BmBcCF9uAIKGJbwVWYW6mXqxu1QFDvW6GKqO/bXH7AmZeF/F++hOPSWdFRqp0yJBLhY55g4QQRUTWQZRdyvvy/Wn+hHQA0sa0Q1Oe22nsd4ffvYD9/SnSUaqeKaojwux8THYPIL7Fwgug6zId2oHD7t6Jj1AylCvoOfWDsOgCSWiM6TZW5zIUo2rURluN7A2q9+aoIHT4Rmth40TGIiGot04GtKNqxQXSMmqFSw9CxDwyd+0NSa0WnqTLZbkPRrh9hPryjzowLggeNhi6+k+gYRES1luXAVhR8sUB0jOsLCoOiaRu4CnKqdhxJAV3bRBi73QSFzuCbbAK5zIUw/fErzEd2AXVk3fbggaOga9VFdAwiolrLevoQ8jd+LjpGzVCqYOjYF/rO/aHQBP58gctchKLdG2E5tjegl2H1RsiwCdA2SRAdg8gvsXCC6DqKu068A1dRnugoNUZhDIGh60DoErpCUqpEx/GabLfBfHgnTH/8AtlmFR2nxqgbxCFsxEOiYxAR1Wqyw47sVfPr1rjAEARDlwHQtUkMyHGBy2KC+fAOmA/vhGwuFB2nxrDbBBFR9ZNlGbnvPwPHBT/tbmgIhrJ5Bzjzs312SEmrh7HbYOjadg/IzgUuqxmm/VtgPrQDqOUdxK6mDItC+D1PQFJwXEBEVF1kWb7cdSIAulH5iKQ3wnjDIOhaJ0JSBt64QHY6YD74G0x/bK5T1xFUMU0RfsfDomMQ+S0WThCVw3x4Jwq3rRUdo8YpDEHQt+8NXdseUGh1ouOUy2UqgPnQ7zAf2QXZahYdp8aFjXgI6gZxomMQEdV65mN7UPjrN6Jj1DiFIRj6zv2hb5MISaUWHadczrwsmP7cDsuJfYDDLjpOjQu99W/QNG4pOgYRUa1nPb4X+f/5l+gYpemDoGzZCc68rGo5vCIoFLo2idC36Q6F3lgt5/All80K88HtMP+5HbLNIjpOjeNdpURENcN65jDyf1wpOkaNUxiCoWvbHfq23aHQB4mOUy6X1QzL0d0wH/q9TizTXlLo7X+HpmFz0TGI/BYLJ4jKIbtcyP1mERyZaaKjCCFptNC1ToQuoatfrmnqyEyD+dDvsJw8ALicouMIoY6NR9jwiaJjEBHVCbLsQu7XdXhcoDdC27wDdC07QlW/CSRJEh3Jg/3SWZgObIMt+UidWZKjJE2zdgi9+V7RMYiI6oycD1+EI+Wo6Bh/0RmhjO8CZ15m9Z9LqYK2eXvo2/WAun6T6j+fF2RZhv3CGVhP/AHr6cOQ7XXnTtKraZokIHTYBNExiIjqjJyv3q+z8wVQqqBt0QH6Dr2hjmooOk0pjsw0mI/uhuXE/jrVeepq6obNEXb730XHIPJrLJwgqgB7xnnkfrO4zqxxdS3KiBjo4jtB27ITlEGhwnI4ctJhTfoT1tOH4MzNEJbDLyhVCB81FaqwaNFJiIjqjOJxwaI6e2H+CkVQGLQtLhdRCJoUkWUZjqwLsJ87CWvyUTjSzwnJ4TdUakSMngZlcJjoJEREdYY9+ShyP3pRdIxiWj2UCd2EvE9WRTeCLuEGaJq2FjtfkJsJ64k/YDm5H67CXGE5/IJShYh7/gFlaKToJEREdYb9QjJy134MoG7PF6jqN4E2ri00TVtDFS5u3tqRkwHb2eOwnj4IR3qqsBx+QZIQdsdkqOvHik5C5NdYOEFUQYXb18N86DfRMfyEBFW9xlA3bA5No+ZQ128CSa2ptrO5rGbYL6bAfjEFtrPH4cy+VG3nCjSGxCEwdh0gOgYRUZ1TsO1bWA7vEB3DbyjDoqFt2RGa2HiowutX77jAYoIt9SRs507CnnoSLlNhtZ0r0BgSb4Kx60DRMYiI6pyCr9+HZe/PYkOodVC27Q5nTrrYHABUUQ2gadIa6ivzBUpVtZ1LdtiL5wvSzsCWehKOjPPVdq5Ao+98I4J63Cw6BhFRnVOw5b+wHN0tOobfUIZEQtO0NTRxraGOaQpJoay2c8lOR/GY4OxxWM8ehys/u9rOFWj0HXojqPetomMQ+T0WThBVkGy3Inv1QriK8kRH8T8KJVTRjaCu3wSq8HpQhteDMjQSCp3Bq8PIsguuwjw487LhzM+CI+si7BeTiyd++KOqFGVkDMJHPlqtg00iIiqby2ZBzuoFcJkKREfxP5IEZWgkVBENoIyMgSqqAVSRMVAavb/71GUqgCM3E868TDjzsmC/cKb4ggjHBaUoQyIRPvof1XpxioiIyuayFCHn3SfhyssSE0CthbJtD78omihFpYa6XmMow6KL/4RGQhUWDUVwGCRJUeHDyE4nXKYCuEz5cOZmwp6eCkdGKhxZF+vssp3XozCGIGLMk9VazEpERGVzWS3I+YLzBWVSqaEKiy6eI4iIgSoiBqrIGCj0Rq8P5SzKhzP7Ehw5l4o/Zl+CIycdcNirIXhgU4ZGIXzUVEgqtegoRH6PhRNEXrAmH0H+D5+JjhEwJI0OCkMwJI0WkloLSaMr/qhQQHbYITtsxR/tNsgWE5wFOZzwqChJgbC7pkAd3Uh0EiKiOsty6iAKflolOkbAkHQGKHRGSCoVoFRDUqqKL/IrVZBUxZ+7bNbiiyLmwuJJJk54VFjILeOhbdpadAwiojrLduIP5P379Zo/sUoNVfvecARaZ0alCsqQCCh0RkCpLL4hQKGEpCz+CEmCbC6C05QPV1EBZIsJdb3tuTc4LiAiEst6+hDyN34uOkbAkLT64vkCvREKrR6SSgNJrQYUSsg2K2S79fJHC1w2K2SLCbLNIjp2YJAkhI14GOqYJqKTEAUE3o5E5AVtXFto4trClnxEdJSAINsscHIAUy30HfuwaIKISDBdiw6wHN8L+7mToqMEBNligtNiEh2jVtK27MSLI0REgmladYHuhsGw7N1UcydVqqBq3weO7Is1d05fcTrgzEkHb53wPW2rrhwXEBEJpm3eHpq4NrAlHxUdJSDIVjOcVjOQlyk6Sq2j79CHRRNEXqh4TzwiAgAE9b0NklorOgbVYcrQSBi7DRYdg4iIAAT3uwOShuMCEkcRFIagviNExyAiIgDG4ROhCI2qmZMplFB17BuYRRNUbRTGUK5fTkTkJ4L73QnJy6WsiXxJGRYNY+JNomMQBRQWThB5SWkMhbH7zaJjUJ0lIaj/XVyPjIjITyiDwxHU707RMaiukiQEDxwFhVYnOgkREQFQ6AwIvuuRGjiRAqpO/eHIYtEEeQq+8S6OC4iI/ITCEITgfneIjkF1laRA8MC7eR2ByEssnCCqBH37ntCw7SEJoGubCE3DZqJjEBHRVXQtO0KX0FV0DKqD9J36cVxARORnNPGdoetWjXf2SQqoOt0IR9aF6jsHBSRdm0RoYuNFxyAioqtom7eHNr6z6BhUB+k79YW6XqzoGEQBh4UTRJUUPHAUFEFhomNQHaKKaoigXsNFxyAiojIE9bkdyrAaas1NhOJxgbE6L8wREVGlGYdPhKI6xgWSBFVnFk1QacqI+pwvICLyU0F9bociKFR0DKpDlBH1udQ3USWxcIKokhRaPUIGjwEU/G9E1U/SGRBy831srUVE5KcktaZ4XKBUiY5CdYFKjeBBoyEplaKTEBFRGRRaPYLvecK34wJJgqrzABZNUCmSWouQIfdCUmtERyEiojIotDoEDxoNKPj+jWqAQoHgAXdD4vwUUaXwii9RFahjmsDYY6joGFTbSQqEDB4LZXCY6CRERHQdqqiGHBdQjQjufydU4dGiYxAR0XVo4toi6PaHfHY8Fk3QtQQPGAkVO58REfk1TYM4BPUbIToG1QFBvW+FOrqR6BhEAYuFE0RVZOjYF9oWHUXHoFrM2H0INI1biI5BREQVYOjQG9rm7UXHoFpM37k/dFwjl4goIOgTb4K+961VPo6qC4smqGx6jj2JiAKGvnU36Dv2FR2DajFd2+7Qt+spOgZRQGPhBJEPBN94F5QRMaJjUC2kad4ehs79RccgIiIvBA8cBVW9WNExqBbSNG0NY/chomMQEZEXjMMegDq+S6Vfr+4yAI6si74LRLWGKqYpjD1vER2DiIi8YOw5FJq4NqJjUC2kbtgMQb1vEx2DKOCxcILIByS1BqFD74Ok1YuOQrWIMrweQgaMFB2DiIi8JKnUCB16PxRBYaKjUC2ijKiP4EGjIUl8C0dEFEgkhRIhY5+EshItk1Wdb4SdRRNUBmVIJEJvvg+SQik6ChEReUGSFAgZNBqqyAaio1AtoggOR8hN4yApOS4gqirOuhH5iDIkAiFD7wdUatFRqBaQNDqE3HwfJLVWdBQiIqoEhSEIocMmQNLw5zhVnaQzFBfj8N8TEVFAUuiMCB3/PCR9UIVfo+7UH47sS9WYigKVpDMgZPgEKPRG0VGIiKgSJLUGIbeMh8IQLDoK1QKSRofQW8ZzXEDkIyycIPIhTYM4hAwZB7Din6pCpUbI0PuhCosSnYSIiKpAFVEfITeNA9ghgKpCoUTIkHFQhkSITkJERFWgjGyAkHHTKzRfoOrUH/ac9BpIRQFHqULo0PFQhXK+gIgokCmDQnkTJlWdUlV8HSGivugkRLUGZ3GJfEzbJAHBA0cBkiQ6CgUipQqhN98HTcNmopMQEZEPaGLjEdSHa0xSJSkUCLlpDDQNm4tOQkREPqBp0QFBtz143X1UHfvCwaIJKoskIWTQaKhjmohOQkREPqCu1xghA+4GwOsIVBkSggeO4nUEIh9j4QRRNdC17IigfneIjkGBRqFAyE1joYmNF52EiIh8SN+uB4w9hoqOQYFGKp4E0TZrJzoJERH5kL7HLTAMGl3mc+oOfeDIzazhRBQogvrcBm1zjguIiGoTbYsOMCQOFh2DApCx1zDoWnQQHYOo1mHhBFE10bdJhLHHLaJjUKCQFAgeNBrauDaikxARUTUwdO4PQ+IQ0TEoYEgI6n8ndC07iQ5CRETVwDh4DAwDR3lsU7XvBXtelqBE5O+MvW+Fvl1P0TGIiKgaGLsOhKHrANExKIAYutwIQ8c+omMQ1UosnCCqRobO/aDvfKPoGOT3JATfeBcrRImIajlj1wEwJN4kOgYFgKA+t0LfupvoGEREVI2MN42D4caRAABV255w5OeKDUR+y9j7Vhg69BYdg4iIqpExcQiMPXkTJpXP2GMojN1vFh2DqNaSZFmWRYcgqu0Ktq2F5fBO0THITwX1HQF9ux6iYxARUQ0p2rMJpr0/i45BfsrY4xYYOvcTHYOIiGqIaccGFP25DeD0HJXB2Gs47yglIqpDzEd2oXDbWo4LqDRJQlC/O6Bvkyg6CVGtxsIJohpStGsjTH9sFh2D/IoEY69hnAQhIqqDinb/BNO+X0THIL/CcQERUV1VtPdnmPZsEh2D/EzxuKCv6BhERFTDLEl/ouCXNYDLJToK+QuFEiGDR0PbvL3oJES1HgsniGqQ+ehuFG5dC8gc9NR5ShWCB9wNXcuOopMQEZEgpgPbULRjAwAOx+s8hQLBN94NXavOopMQEZEgpj+3o+j378FxAUFSILj/ndC1vkF0EiIiEsSacgz5Gz8HnA7RUUg0lQahQ++DpnFL0UmI6gQWThDVMGvKMeT/tBpw2ERHIUEknQGhQ++HOqap6ChERCSY9fQh5P/yJeCwi45Coqg0CBkyFtomCaKTEBGRYJZTB1Hwy5e8SFKXqdQIGTwG2rg2opMQEZFgtrTTyN+wArLdKjoKCSJp9QgdNgHq+k1ERyGqM1g4QSSAPSMVed9/CtlcKDoK1TBlaBRCh02AMjRSdBQiIvIT9ktnkbdhBWRLkegoVMMUhiCE3DIB6uhGoqMQEZGfsF9MQd4PKyBbTKKjUA2T9EaE3jIe6nqxoqMQEZGfsKefQ953/4ZsNYuOQjVMYQhG6K1/gyqivugoRHUKCyeIBHHmZyPv+3/DmZspOgrVEHWDOITcfB8UOoPoKERE5Gec+dnI++7fcOZxXFBXKMOiETr8ASiDw0VHISIiP+PMy0Lehv9wvqAOUYZFF99kERIhOgoREfkZR/Yl5H3/H7gKc0VHoRqiCIlA2K1/47iASAAWThAJ5LKYkPfDCjgupoiOQtVMG98ZwTfeBUmpEh2FiIj8lMtiQv6Pn8F+IVl0FKpmmmbtEDxgJBQanegoRETkp1xWM/J/XAl72mnRUaiaFY8L7oZCoxUdhYiI/JTLakbB5q9gSz4qOgpVM02ThOJxgd4oOgpRncTCCSLBZKcDRTt/hPngbwD437H2kWC4YSCM3QaLDkJERAFAdjlRtGsjzAe2geOCWkihhLHHUBg69hGdhIiIAoDscqFo908w798CjgtqIUkBY/ebYejcT3QSIiIKEKY/t6No5w+Ayyk6Cvna5fkCfYfekCRJdBqiOouFE0R+wnr2OAo2fwXZzPXNawtJH4SQQfdA07il6ChERBRgrGePo+CXL7m+eS2iMIYi5KaxUMc0ER2FiIgCDMcFtY+kNyJk8FhoGjUXHYWIiAKMPSMV+T+this/W3QU8hFlSCSCbxoDdXQj0VGI6jwWThD5EZepAPm/fAl7apLoKFRF6sbxCBk0Cgp9kOgoREQUoJxFeSjY9AWX7qgF1I1bImTQaLbaJCKiSnMW5iL/p9VwXDorOgpVkbpBHIIHj4bSGCo6ChERBSiX1YKCLd/AdvqQ6ChURdqWnRDU7w4u2UXkJ1g4QeRnZFmG+cA2FO3eyJZbgUipgjFxCPQd+7ClFhERVZnscsG092eY/tgMcNgeeBRKGG4YCEOXGyFJCtFpiIgowMlOJ4r2bIL5wFZAdomOQ95SqmDsPgT6DpwvICIi3zAf3oHC378HnA7RUchbKg2C+94OXUJX0UmI6CosnCDyU/aMVBT89AWc+Vmio1AFqaIaInjgKKgi6ouOQkREtYwt7QwKt3wDZx7HBYFCVS8WwTfexXEBERH5nD09FQWbv4IzJ110FKogVVRDBA+6B6rweqKjEBFRLePITEP+T6vhzMsUHYUqSBXZAME3jYUqLEp0FCIqgYUTRH5MtltRuGMDLEd38y5Tf6ZQwNBlAAxdB0BSKEWnISKiWkp22GHatxmmA1vZlcqfqdQwJt4EfYfe7DJBRETVRnY6YNr7C0wHtgAudp/wWwoFDJ1vhOGGgZwvICKiaiPbrSjc+QMsR3bxOoI/kxTQt+sJY8+hkJQq0WmIqAwsnCAKAI7MNBRsXwfHxRTRUagEdcPmCOp9K1SRMaKjEBFRHeHIvoSCLf/lGud+SN2wOYJvvAvKkAjRUYiIqI6wZ5xH4a/fwJF1QXQUKkHdoBmC+t7O7lNERFRj7BnnUbjtWzjSz4mOQiWoG7VAUK/hvI5A5OdYOEEUQCwnD6Box/dwmQpER6nzlCGRMPa6Bdq4tqKjEBFRHSTLMixHd6Fo54+QbRbRceo8SW+EMXEI9G0SRUchIqI6SJZdsBzdg6LdGyFbTKLj1HkKQzCMvYZB17KT6ChERFQHybIMy7E9KNr1I8cFfkAZGgVjz1ugjWsjOgoRVQALJ4gCjGy3omjfZpj/3M423QJIGh0MXQdC374n22kREZFwLlMBinb/BMvxfYDMNt01TqWBoWMfGDr3g6TWik5DRER1nMtqhmnvzzAf3sn5AhEUCujb94bhhkFQaDguICIisVwWU/G44MgujgsEkLT64usI7XpCUnK5LqJAwcIJogDlyMtE0W/rYTt7QnSUukFSQNemG4zdboJCbxSdhoiIyIMjJx1FO3+ALeWY6Ch1g6SALqErDN0GQ2kMEZ2GiIjIgyM3A0W/fw/b2eOio9QNkgRti44wdBsEVWiU6DREREQenHlZKNz5A2xnDouOUjcoFNC16Q5jt8FQ6Ayi0xCRl1g4QRTgbOdPw7T/V9hTk0RHqbXUsfEI6jmM65ISEZHfs188i6I9P8F+/pToKLWWpmlrGHsMhSq8nugoRERE12VPPwfT3p95w0W1kaBp1hbGboM5X0BERH7PfjEFhTs2wHHprOgotZYmthWMvYZxvoAogLFwgqiWsGemwbx/C6ynD7NVty8olNC27AhDxz5QRTYQnYaIiMgrtrQzMO3dBHvaGdFRagdJAW2zdtB36gt1vcai0xAREXnFnp56uYCCHSh8RdMkAYZug6GObiQ6ChERkVdsaWdgPvQ7bMlHeR3BR9SN42Ho1A+axi1ERyGiKmLhBFEt48zLgunPbcVrnTsdouMEHEmjg65td+jb92LrbSIiCniOrAswH/wNlqQ/OS6oBEmtgS7hBug79oEyOFx0HCIioiqxZ5yH+c9txTdccK1z76nU0MV3hr5Db95JSkREAc9ZkAPz4Z2wHNsD2WoWHSfwKFXQtewEfcc+7DxFVIuwcIKolnKZCmE6uB2WI7sg2yyi4/g9RXA49B16Q9/6Bkhqreg4REREPuUyF8F8ZCcsR3bBZSoQHcfvKQzB0LfvBV3b7lBo9aLjEBER+ZTLVADz0d0cF1SQwhAMXbue0LftzrXKiYjIL+3btw+rVq1CQkICJk6cCKVSWeHXynYbLCf/gPng73DmZlRjytpBYQiGrnU36Nv1hMIQJDoOEfkYCyeIajnZboM15SisJ/fDlpoEuNh+y02hgLphC+jbdIMmri0khUJ0IiIiomolO52wnj4Iy5HdsF9MAcC3Am4KJTRNEqBr1RmaJgmQlCrRiYiIiKqV7HLCeuYILId3wn4hGRwXXEWhgKZxPHStuhTPF3hxAYqIiKgmHTx4EL169UJRUREAYN68eXj66acrdSxbahLMB3+D7ewJcFxwFUmCunFL6Nt0h6ZpAiQFxwVEtRULJ4jqEJe5CNZTB2FJOgDHpbOi44ghKaBuEAdtiw7QNmsHhd4oOhEREZEQzsJcWE8dhDXpTzgy00THEUZVvwl08Z2hbdGBd5ESEVGd5SzIhfXUn8XjgqwLouMIo4pqAG18F+jiO0Gh512kRETk32RZxqBBg7B582b3tttvvx1r166t0nGdeVmwHN8Ha8oxOLMvVjFl4FKGRUPbrB10bbpx+U6iOoKFE0R1lDM/G5akA7CePFD7W3BJEtQxTS8XS7RnCy0iIqISHHmZsCYVXyyp9eMCSFBFxUDTtA108Z2hDI0UHYiIiMivOHLSi8cFpw/CmZspOk41k6CKbghNkwRom7fnGuVERBRQvvrqK4waNcpjW1U6TpTFWZADW8pxWFOOwp52BnA5fXZsv6NUQd0grnhc0CSB8wVEdRALJ4gIjuxLsKedhi3tDOwXzkC2mERHqjJJZ4C6fhOoG7WAtnl7KI0hoiMREREFBEduBuznT8GWegr2tNOQbRbRkapM0hmgadwSmthW0DSOZxElERFRBTnzsmA9exy2cyeKl/Nw2EVHqjqVBprGLaBp2hraJglQGIJFJyIiIvKa2WxG27ZtkZyc7N7WpEkTHDt2DHq9vlrO6bJZYU89CWvKMdjOHq8V1xEUxpDiuYKmCdA0aglJrREdiYgEYuEEEXmQZRnO7EuwpZ2G/UohhdUsOla5FEFhUDeIgzqmKdQN4qAMi4YkSaJjERERBTRZdsGRkVZcSHH+FOyXzgbEBRNJZ4AqqhHUMU2giY2HKroRJEkhOhYREVFAk50O2C+ehT3tNOzpqXBkpAbEfIGk0UFVr3HxnEGDZlDXawxJqRIdi4iIqEpmz56NF1980WPbF198gXvuuadGzi/LLjgunYM15Rgcl87BkX0xMMYFag2UkQ2giY2HtkkCVFENRUciIj/Cwgkiuq7iQoqLsF9MgSP7Epw56XDkpIurJpUkKIyhUIZEQBkeXVwoERMHZVComDxERER1iOxywZmXCUfWRTiyLsCRdQHOrItwmQqEZXIXSUQ3hCq6EVRRjaAMDhOWh4iIqC5x5GXCcSkV9vRzxeOCvEzI5iJheRTGECjD60EVXg+qiBio6sfyxgoiIqp1zp8/j1atWsFk+muOvn///ti8ebPQ33nOwjw4si/CmXURjuyLcGRdhDMvE3C5aj6MUgVlWBRU4fWhiqhfPD6IqA9FcDjHBUR0TSycIKJKcZkK4cjLhCs/G84rfwpyIFvNkO1WyDYrZLsNgJc/YpQqSGoNFDpDcXFESCQUIRHuz5Uh4bwzhIiIyM+4zEVwZF+CqzAXzqJ8uIry4CrKh6soH86i/MsXUCrxtkNSFI8LDMFQBIVCaQyBIijs8rig+A/baxMREfkXl9UMZ14mnLlZcOZlwFmQC5e5ELK5CC5zIVwWU+XWR5ckSDojlMZgKAwhxeMDYwiUQWFQhkdDGV4PCo3O918QERGRnxk/fjxWrFjhfixJEvbt24fOnTuLC3UNstMBZ27G5RswLsJVmAuXxQSXpQiypejyuMDLwgqVGpJaC4VGC0mthaTRFV9PuFwcoQyvD2VoJCQFO08SkXdYOEFE1UaWZch2G2S75XIhRXFBBSQJkkoNSaWBpNZc/qguHvCwjTYREVGtIzudkB02yA474HRAvvznyueQAUldPPEhqa76qFKLjk5ERETVwGU1F9944XICTqfnR8iQlOq/xgJXzx/wAggREdVxO3bsQK9evTy2PfTQQ/jwww8FJaoaWZYh2yzF1xEcdsgOG3Dlc9kFxeXCCMldJKGFpFCKjk1EtRQLJ4iIiIiIiIiIiIiIiIj8mMvlQq9evbBr1y73tpCQEJw8eRL16tUTmIyIqHZgmTYRERERERERERERERGRH1uxYoVH0QQAzJo1i0UTREQ+wo4TREREREREREREVKtZrVbk5OQgJiZGdBQiIiKvFRQUICEhARcuXHBva9WqFQ4ePAiNRiMwGRFR7cGOE0RERERERERERFRrbdy4EeHh4WjYsCGeeOIJ0XGIiIi89sYbb3gUTQDA/PnzWTRBRORD7DhBRF7ZsWMHli9fjo4dO+KRRx6BJEmiIxEREVEdkpGRgYsXL6J9+/YchxAREVGF9OjRw6O1+R9//IHOnTuLC0REROSF06dPo02bNrDZbO5tw4YNw3fffScwFRFR7aMSHYCIAse5c+fQv39/2O12AIDJZML06dMFpyIiIqK6YsOGDbjrrrtgsVgwdOhQfP/99yyeICIionJdfaEJAH7++WcWThARUcCYPn26x+8ylUqFd955R2AiIqLaiUt1EFGFrVmzxl00AQBbt24VmIaIiIjqmgULFsBisQAAfvjhB2zevFlsICIiIgoIiYmJHo+3b98uKAkREZF3Nm3ahG+++cZj2+OPP47WrVsLSkREVHuxcIKIKuz48eMejyMjIwUlISIiorooPz/f4/GhQ4cEJSEiIqJA0qdPH4/H27dvB1cvJiIif+dwODBt2jSPbVFRUZg5c6aYQEREtRwLJ4iowkoWTrRq1UpQEiIiIqqLmjZt6vE4OTlZTBAiIiIKKCULJy5duoRTp04JSkNERFQxH374YakbBmbPno2wsDAxgYiIajkWThBRhZUsnEhISBCUhIiIiOqikoUTKSkpgpIQERFRIGnRogXq1avnsY3LdRARkT/Lzs7GSy+95LGtU6dO+Pvf/y4oERFR7cfCCSKqkPz8fFy8eNFjGwsniIiIqCbFxcV5PGbhBBEREVWEJEllLtdBRETkr15++WVkZ2d7bFuwYAGUSqWgREREtR8LJ4ioQk6cOOHxWKFQoEWLFoLSEBERUV3EjhNERERUWSycICKiQHHkyBF88MEHHttGjRqFAQMGiAlERFRHsHCCiCqk5DIdcXFx0Gq1gtIQERFRXVSycCIjIwNFRUWC0hAREVEgKVk4ceTIkVJ38hIREYkmyzKmTZsGp9Pp3qbVajF37lyBqYiI6gYWThBRhZTsOMFlOoiIiKimlSycAICzZ88KSEJERESBpmvXrtDpdB7bfv/9d0FpiIiIyrZu3Tps3LjRY9szzzxTaulKIiLyPRZOEFGFlOw40apVK0FJiIiIqK4yGo2Iiory2MblOoiIiKgiNBoNunfv7rGNy3UQEZE/sVqteOqppzy2NWrUCM8995ygREREdQsLJ4ioQkoWTrDjBBEREYlQsutEcnKymCBEREQUcEou18HCCSIi8ifvvvsukpKSPLbNmTMHRqNRUCIiorqFhRNEVC5ZlrlUBxEREfmFkoUT7DhBREREFVWycGLXrl2w2WyC0hAREf3l4sWLeO211zy29erVC/fee6+gREREdQ8LJ4ioXOfPn4fJZPLYxqU6iIiISISS67qycIKIiIgqqlevXh6PLRYL/vjjD0FpiIiI/vLPf/4TBQUFHtsWLlwISZIEJSIiqntYOEFE5Sq5TIfRaESjRo0EpSEiIqK6jB0niIiIqLIiIiLQtm1bj21croOIiETbs2cPli1b5rHtgQceQGJioqBERER1EwsniKhcJZfpaNWqFStdiYiISIiShRPJyclighAREVFAKrlcx5EjRwQlISIiKl4m+4knnoAsy+5tQUFBeOONNwSmIiKqm1g4QUTlKtlxgst0EBERkSglCycuXLjAtcmJiIiowu6++26PxyXHFkRERDVp1apV+O233zy2/fOf/0SDBg0EJQoccXFxkCTpun9yc3M9XnP27Fk88sgjaNasGTQaDcLDwzF48GB8+eWXlc7x7bffYujQoYiIiEBQUBA6duyI1157DTk5Odd8zc8//4zu3btDp9MhJiYGU6dOLbVUy9UmTJgApVKJQ4cOVTonEZVPkq8uYyMiKsPdd9+Nr7/+2v145syZeOWVVwQmIiIioroqNzcX4eHhHtuSkpLQokULQYmIiIgo0Lz11lv47LPP0LNnT7z77rvQarWiIxERUR1UVFSE1q1bIzU11b2tefPmOHz4MHQ6ncBk/i8vLw9hYWFQKpXo2bPnNffbsGEDgoKCAAD79u3DTTfdhJycHGi1WrRq1QoZGRm4ePEiAGDy5MlYvHixVzkee+wxfPDBBwCKlwRr3rw5Tp06hZycHDRu3Bjff/892rdv7/GaI0eOoEuXLrDZbGjTpg0yMzORkZGBvn374tdff4VC4XnP+4EDB9C1a1dMmDCh1JIuRORb7DhBROUqeSFi6NChgpIQERFRXRcWFoaoqCiPbawFJyIiIm88++yzOHDgAJYsWcKiCSIiEmbu3LkeRRMA8Pbbb7NoogIOHDgAAIiPj8e2bduu+edK0YTT6cS4ceOQk5OD/v37Izk5GX/++ScuXLiADz/8EJIkYcmSJfjss88qnOG9995zF03MnDkTFy9exO7du3HhwgVMmzYNqampuOmmm0p1vZg9ezZsNhuWLFmCI0eOICUlBX379sW2bdvw3XfflTrPM888A41Gg1dffbWS3y0iqigWThBRuWbOnIl7770Xbdq0wdy5c9G7d2/RkYiIiKgOmzp1qvvz7t27o3nz5gLTEBEREREREXnn7NmzmDNnjse2wYMH44477hCUKLBcKZzo0KFDhfb//fffceLECQDAypUrERMT437uoYcewvjx4wEAn3zySYWO53A48NprrwEAHn74YbzyyitQq9UAAK1Wi/nz56NXr164dOlSqe7d27Ztg8FgwKRJkwAAer0ekydPBgBs3brVY9+NGzdi48aN+Mc//oHY2NgKZSOiymPhBBGVKygoCJ999hmOHDmC6dOni45DREREddzMmTPxww8/YPXq1di0aVOpNpZERERERERE/uzZZ5+FxWJxP1YoFFiwYAEkSRKYKnBcKZwouQzGtZw7dw4AEBUVhUaNGpV6PjExEUBxQUtF7NmzBxkZGQCK/y7L8sQTTwAAPv30U49OmRcuXEB4eLjHXEZ0dDQAIDs7271NlmU8++yzCA8Px/PPP1+hXERUNSrRAYiIiIiIiLwhSRJuvvlm0TGIiIiIiIiIvLZ161asXr3aY9sjjzxS4SIA8r7jRJMmTQAAmZmZSE1NRePGjT2e//PPPwEAcXFxFTpeSkoKACA0NLTUUudXJCQkAACysrJw8uRJtGrVCgAQHh6OzMxMuFwud/HEpUuXAAARERHu169YsQL79+/HvHnzEBYWVqFcRFQ1vDWLiIiIiIiIiIiIiIiIqJo5nU53J4IrwsPDSy3nQNfmdDpx+PBhAEDDhg0xf/58jBw5EoMHD8aECROwevVquFwuj9f07t0bnTt3BgCMHz/eXagAAKtWrcLSpUshSRKefPLJCmW40hnkyvIcZbHb7e7Pk5OT3Z/37dsXVqsVH374IQDAYrG4lwjp378/AMBqteKll15CkyZNPJYrJaLqxY4TRERERERERERERERERNVs2bJl+OOPPzy2vfrqq4iMjBSUKPCcOHECZrMZADBkyBAUFBR4PP/pp5/inXfewX//+180aNAAQHGhw/fff4/x48fjp59+QtOmTZGQkIDs7GykpqaiQYMGeOedd3DrrbdWKEPz5s0BXLuDBQB3cQcA5OTkuD9/4YUXsG7dOjzyyCN47733kJmZifT0dPTv3x/Dhg0DALz77rtISUnBf/7zH2i1Wi++O0RUFew4QURERERERERERERERFSN8vLy8MILL3hsa9u2LaZMmSIoUWC6skwHACQmJmLLli0oKipCVlYWVqxYgZiYGOzatQvDhw+HzWZz76tSqdCtWzcYDAZYrVb8+eefSE1NBQBERUVdt3tESV26dHEXS7z22mulnnc4HHjrrbfcj6/OccMNN2DTpk3o06cPTp06BQCYOnUq1q9fD4VCgZycHLzxxhvo1KkT7rvvPvfrioqKcO7cOY9OFkTkW+w4QURERERERERERERERFSNXnvtNWRkZHhsq1+/Pp577jlBiWpWt27dMHLkSGg0miodp0mTJnj88cehVCrx9ttvQ6EovkfcYDDgvvvuQ2JiIrp06YL9+/fjk08+wSOPPILU1FQMHDgQSUlJuOmmm/Cvf/0LnTp1chdbvPTSSxg1ahTmzJmDZ599ttwMSqUS//rXvzBhwgR8+OGHkGUZM2bMQJMmTXDkyBHMmDEDZ86cgdFoRFFRUamijL59+2Lbtm1lHnv27NnIycnBypUroVAoYDKZ8PDDD2P16tVwOBwICQnBjBkz8Pzzz7uXDCEi35BkWZZFhyAiIiIiIiIiIiIiIiKqjVJTU9GsWTM4HA7RUYSaNm0a5s+fX+3nefjhh/HRRx9hyJAh+PHHHzF+/HisWLECnTp1wu7du0sVMnz99de4++67oVQqcezYMbRs2bJC5/nXv/6Fl156CS6Xy2N7eHg4VqxYgb/97W9IT0/H+vXrMXz48HKPl5KSgoSEBPTp0webNm0CADzyyCNYvHgxxowZg5tvvhkrV67Epk2b8K9//QvPP/98Bb8jRFQRLJwgIiIiIiIiIiIiIiIiqiZr1qzB6NGjRccQrlWrVjh+/Hi1n2fRokV49NFH0bJlS5w4cQJGoxFmsxmrV6++5t9D586dceDAAcyePbvUkirX8+eff+KTTz7B0aNHodfr0bNnTzz44IOIiIiA0WiE3W7H/v370alTp3KPdf/992PlypXYtWsXunXrhvT0dMTGxqJz587YuXMnAMBisSAuLg4WiwWZmZlQqbi4AJGv8H8TERERERERERERERERUTXp3r07DAYDTCaT6ChC9e/f3yfHcblccDgc11z240oHCLVajfT0dJjNZgBA69atr3nMtm3b4sCBAzhz5oxXWTp27IiFCxeW2r5v3z7Y7XaoVKrrnveKP/74AytXrsSYMWPQrVs3AMDBgwdhs9nQt29f9346nQ6JiYlYt24djh07hvbt23uVl4iujYUTRERERERERERERERERNWkadOm2Lp1Kz799FPk5uaKjiNEYmIiJk6cWOXj9O/fH7/99hseffRRvPvuu2Xus2/fPgDFxRDBwcGQJAmyLOPChQvo2LFjma9JT08HAISEhJSbQZZlLF26FJcuXcLkyZMRGRlZap///e9/AIC+fftCq9WWe8xnn30WKpUKs2fPdm+7dOkSgOJiiauFhoYCAPLy8so9LhFVHAsniIiIiIiIiIiIiIiIiKpR165d0bVrV9ExAl779u2xdetWfP3115g9ezaCg4M9nk9JScHq1asBAGPHjoXBYECPHj2wY8cOLFmyBEOHDi11zKSkJGzZsgUAMHjw4HIzSJKEf/3rXzh9+jSioqLw8MMPezyflZWFDz74AADwxBNPlHu8H374AT/99BP+8Y9/oHnz5u7tVwoyUlNTPfZPS0sDAERFRZV7bCKqOIXoAERERERERERERERERERE5XnyySeh1Wpx/vx5jBkzBhcvXnQ/d+DAAQwdOhRFRUXo378/Ro4cCQB4+eWXIUkSvvnmGzz99NMoKCjweM2tt94Ku92OXr16YdiwYR7nO3bsGI4dO4bMzEyP7X/7298AAP/85z+xf/9+9/bTp09j+PDhyMzMRN++fXHnnXde9+txuVyYMWMGQkJC8OKLL3o816NHD2i1Wvz3v/9FcnIyAGDv3r3Ytm0bGjVqhFatWlXoe0ZEFcPCCSIiIiIiIiIiIqIynDx5En//+9/RpEkTaDQaxMTE4M4778TGjRsrfIznn38ekiThp59+qnSO3377DSNHjkS9evWg0WjQoEEDjBkzBnv37r3ma37++Wd0794dOp0OMTExmDp1qseFopImTJgApVKJQ4cOVTonERFRdYuPj8dnn30GnU6H77//Hk2bNkWHDh3Qpk0bdO7cGcePH0e3bt3wzTffQKEovgw6dOhQLFy4EEqlEu+88w5iYmJwww03ICEhAZ07d8aJEyfQqVMnfP3115AkyeN8bdq0QZs2bfB///d/HtunT5+Ovn37IjMzEzfccAPatGmDDh06ID4+Hrt27UKXLl2wdu3acr+eTz/9FAcOHMCzzz6L6Ohoj+fCwsLw1FNPIT8/H+3atUOnTp3Qq1cv2O12vPnmm6WyElHVSLIsy6JDEBEREREREREREfmTH374AXfddRfMZjMMBgPi4+ORkZHhbo89ffp0zJ0797rHWLduHe666y44HA5s3LgRN910k9c5PvnkE0yePBlOpxOhoaFo1qwZTp8+jfz8fKhUKixduhTjx4/3eM2RI0fQpUsX2Gw2tGnTBpmZmcjIyEDfvn3x66+/ui8kXXHgwAF07doVEyZMwLJly7zOSEREVNOOHz+Ot99+Gxs3bkRaWhr0ej3atm2Le++9F1OmTIFKpSr1mn379mHBggXYvHkzLl68CJ1Oh3bt2mHs2LGYPHkydDpdqddcKU6YNWsWXn75ZY/nbDYb5s+fj5UrV+LEiRMAgNatW2PcuHF4/PHHodfrr/s1WCwWJCQkwG63IykpCQaDodQ+sixj3rx5eP/993HhwgUkJCTgpZdewj333FPRbxURVRALJ4hqGYfDgV69emHPnj1YtmwZJk6cWGqfs2fP4o033sCGDRtw/vx5GI1GdO3aFY888ghGjRpV4XNt3rwZAwcOrPD+JfP8/PPPeO655/Dnn38iLCwMo0aNwhtvvFFqTbIrJkyYgM8++wwHDhxA+/btK3xeIiIiIiIiIiJvZGZmolWrVsjJycHYsWOxePFihIaGAgBWrlyJCRMmwOl04ssvv8Tdd99d5jG++OILTJgwAVarFQAqVThx6tQptGnTBna7HY8//jjmzp0LrVYLs9mMf/zjH/j444+h0Whw9OhRjzXR77vvPqxcuRJLlizBww8/DLPZjJtvvhnbtm3Dt99+i9tuu83jPDfffDO2bt2KEydOIDY21quMRERERES1AZfqIKpl/vWvf2HPnj3XfH7fvn3o3LkzFi9ejAsXLqB169bQ6XT4+eefcc8992DKlCkVPldoaCj69Olz3T+NGjUCUFyVefUb+CNHjmDYsGHYvXs3mjdvDpfLhffffx/Dhw+Hy+Uqda4DBw7gs88+w4QJE1g0QUREROWqTFvts2fP4pFHHkGzZs2g0WgQHh6OwYMH48svv/Tq3Js3b4YkSRX+s3z5co/Xs602ERGReB9//DFycnIQFxeH5cuXu4smAODee+/FQw89BABYvHhxqdfm5ubisccew5gxY9xFE5X1+eefw263o3Xr1liwYAG0Wi0AQK/XY9GiRfh/9u47PMoq7R/4d2aSyaT3SgsBEpoGpQiCrKssCMvqihUURcQOFhQsLIoKiuW3unZUikpTUWy4KiiIqAhIh4SQ3vukTG/n90c28zKpM8lMJjPz/VwX17sz8zznOQm+PPeccz/3nZKSAoPBgI0bN9qct2/fPgQFBWHBggXW4++66y4AwC+//GJz7M6dO7Fz507cf//9TJogIiIiIp/Vuk4NEXmso0ePYuXKle1+bjabMXv2bCiVSkyePBkff/wxEhISAADvvfce7rrrLqxZswaXXHIJbrrppk6vd8EFF2Dfvn3tfl5ZWYn09HQAwDPPPIPJkydbP1u1ahUMBkObTz58++23rZ58WLJkCeRyOZ555plO50VERES+rWVZ7eHDh6OqqgpffvklvvzyyzbLah8+fBhTpkyBUqlEQEAAhg4diqqqKvz000/46aefcNddd7W5MdKW5uTSjuTn56OkpKTd5NJzy2q/+eabOHbsWLtltZlcSkRE5HzJycmYPXs2LrjgAmuywrnOP/98AEBBQYHN+7///jv+8Y9/oKamBoGBgXjzzTcxf/78Ls+jqKgIADBy5MhWcYCfnx8uuOAC5ObmorCw0OazsrIyxMXF2ZzT3De9trbW+p4QAkuXLkVkZCQef/zxLs+TiIiIiMjTseIEkZcwGAzWMpFtfaEHmr68N/fZ2rx5szVpAgDuuOMOaz/MtWvXdns+QgjMnTsX5eXl+Mtf/oInnnjC5nM++UBERESuUF1djdmzZ0Or1eLGG29EaWkpjh49ipKSEmzatAkymQwvv/wyPvvsM+s5LZNL8/Pzcfz4cZSVleHdd9+FRCLBmjVrsGnTJrvm0Jxc2t6fzz//HGazGUDHyaWnT59GQUEBJk2aZE0ubYnJpURERK5x4403YvPmzViyZEmbnzdX+xwyZIjN+2fOnEFNTQ2mT5+O48eP47bbbuvWPPr37w8AOH78eKsKnRaLBadOnQLQlOhxrsjISFRXV9ucU1FRAQCIioqyvrdx40YcPXoUy5YtQ0RERLfmSkRERETkyZg4QeQlnnzySZw4cQIPPPCATULEuZqfUoiJibG20DjX2LFjAaDVUwpdsW7dOvzwww8IDAzE+++/3+qpiLKyMkRGRvLJByIiInKqrpTVZnIpERER2auurg5PP/001q9fDz8/Pzz66KM2n48cOdKa8Dh48OBuX+/WW29FcHAwsrKysHjxYhgMBgBND9A8/PDDyMzMRHh4eKuqFpMmTYJer8e7774LANDpdNZYpjlpU6/XY/ny5ejfvz8WLlzY7bkSEREREXkyJk4QeYH9+/fj5ZdfRmpqKlatWtXucc1PKVRXV6O4uLjV58ePHwfQ+ikFRzU0NGDZsmUAmp6CbGuhgE8+EBERkSs0l9W+99577S6rzeRSIiIi6sxnn32GkSNHIiEhAStWrEDfvn3xxRdf2FSOAoAxY8Z02rLLEX379sUPP/yA1NRU/Oc//0FcXBwuvPBCxMfH49VXX8WFF16I3bt3Iykpyea8J554AnK5HPfccw9GjBiBAQMGYO/evZg8eTKmT58OAHjttddQUFCAlStXtlu9lIiIiIjIVzBxgsjDabVa3HrrrRBCYP369QgMDGz32IsvvhijRo0CAMydO9eaqAAAW7duxbp16yCRSPDQQw91a07PPfccKioqEBcXh6VLl7Z5DJ98ICIiIlfoSlltJpcSERFRZw4cOIBTp05Br9cDAJRKJb7++ms0Nja6/NphYWEYM2YMAKC+vh5HjhxBXV0dACAxMbHNc0aPHo0ff/wREydORE5ODgBg4cKF2LFjB6RSKZRKJZ5//nmkp6fjpptusp6nVqtRVFQEo9Ho2h+KiIiIiKiXYeIEkYd79NFHkZWVhYceeggXX3xxh8dKJBL897//xZQpU7Bnzx4MGDAA6enp6NevH2bPno24uDhs3rwZf//737s8n/r6erz55psAgAceeADBwcFtHscnH4iIiKgndVRWm8mlRERE1JlFixZBpVKhtLQUGzZsQGBgINasWYPLLrsMJpPJZdf98ccfMX78eGzevBmLFi1CTk4O9Ho9MjMzMX/+fOzYsQOTJk3Cnj17Wp07adIk7Nu3DzqdDhUVFXj99dcREhICAFi1ahWUSiVWr14NqVQKjUaDm2++GREREejfvz9iYmLw3HPPQQjhsp+NiIiIiKg3YeIEkQfbs2cP3njjDQwdOhQrV6606xw/Pz+MGTMGQUFB0Ov1OH78uPXJypiYGPj7+3drTu+++y5UKhVCQ0Nx7733tnscn3wgIiKinmBPWW0mlxIREVFn+vbti+DgYCQmJuLWW2/FL7/8AoVCgUOHDmHjxo0uuabRaMQdd9wBtVqNe++9F6+99hpSUlIgl8uRlpaGtWvXYtGiRdBoNFiwYIHdCRwFBQV44403cNlll+GKK64AADz88MPYtGkTrrnmGqxduxZjx47FsmXLsHr1apf8bEREREREvQ0TJ4g8VGNjI+bNmwepVIr169dDoVB0ek5xcTEmTJiA1atX4+KLL8aBAweg1+tRWlqKF198EVlZWbj22mvx4osvdmlOFosFb7zxBgDgzjvv7LRsNJ98ICIiIlezt6w2k0uJiIjIEWlpaZg1axYAtFntwRn+/PNP5OXlAQCefPLJNo956qmnIJPJkJOTgwMHDtg17rJly2AwGPDCCy8AACorK7Fu3TqMGzcOW7duxfz58/HNN98gPj4eL7zwgksrahARERER9RZMnCDyUA899BAKCgrw8MMPY/z48Xad8/jjjyM7Oxvp6en49ttvMXbsWMjlciQmJmLJkiXYvHkzgKYnHbOzsx2e0++//47CwkIATWWuu4pPPhAREZGz2FNWm8mlRERE1FJtbS3+/PNPVFdXt3vMgAEDAADl5eUumUNBQQEAIDIyEvHx8W0eEx0djbi4OACwJll05MiRI9i8eTNuuOEGjBkzBgBw4sQJGAwGTJo0yXqcQqHA2LFjUV9fj8zMzO7+KEREREREvR4TJ4g80H//+1+sXbsWw4YNwzPPPGPXOUIIfPbZZwCaEiPaempy1qxZSE9Ph9lsxieffOLwvLZv3w6g6amL9PR0h89vxicfiIiIyFnsKavN5FIiIiJqaezYsRgzZgzWr1/f7jHNiQ19+vRxyRzCwsIANFUdVavVbR5jNBpRV1dnc3xHli5dCj8/P6xatcr6XkVFBQC0qmYaHh4OoKnlGBERERGRt2PiBJEH+vjjjwEAGRkZUCgUkEgkNn+av7jfdtttkEgkuPTSS1FZWQmtVgsAGDp0aLtjDx8+HIB9Tym09OWXXwIAbrjhBofPbcYnH4iIiMiVWpbVZnIpERERtWXq1KkAgPfee6/NVlj5+fnWe/w//vEPl8zh4osvRkBAAEwmE9atW9fmMVu2bIFWq4VcLrdZN2nL999/j127duGee+5BSkqK9f3o6GgAsLYpa1ZaWgqgqW0ZEREREZG3Y+IEkQdKTU3FxIkT2/0TEBAAABgyZAgmTpyI8847D6GhoZBIJACAsrKydseurKwEYN9TCueqqKiwPoE5ZcqUrvxYAPjkAxEREXWPo2W1mVxKREREbVmyZAkCAwNx9uxZzJkzxya2OHLkCKZNmwatVovJkyfjqquu6vb1MjMzkZmZaXOd8PBwLF68GADw6KOPYv369TCbzdbPt23bhvvuuw8AsHjxYkRGRrY7vsViwaOPPoqwsDD861//svnsoosuQkBAAL744gvk5+cDAP7880/s27cPffr0QWpqard/PiIiIiKi3o6JE0Qe6IknnsC+ffva/ZOQkGBz3Ouvv46goCBcdNFFAIA1a9a0OW52djb27t0LALj88ssdmtPBgwcBAH5+ftbFfEfxyQciIiLqLkfLajO5lIiIiNqSkpKCTz75BEFBQdi2bRv69u2L9PR0pKWl4cILL0RWVhbGjx+Pzz77zBpLdMewYcMwbNgwvPHGGzbvP/vss5gzZw60Wi3mz5+PhIQEjBs3Dn369MF1110HlUqFOXPmYOXKlR2O/9FHH+HYsWNYunQpYmNjbT6LiIjA4sWL0dDQgBEjRiA9PR0TJkyA0WjE6tWrnfLzERERERH1dkycIPIhK1asgEQiwfbt2/Hwww+jsbHR+tmxY8fw97//HUajERMmTMD06dNtzm3ryYdzHT58GEDTF/3AwECH58YnH4iIiMgZHC2rzeRSIiIias/MmTNx7Ngx3HnnnUhMTERGRgYqKiowadIkvPPOO9i7d6/L77cymQybNm3C559/bl2rOXLkCAwGA6ZPn47PP/8cmzZtgkwma3cMnU6HJ598EomJiXjooYfaPGbVqlV48cUXERsbi8zMTAwdOhSffPIJbr75Zpf8XEREREREvY1ECCHcPQkicq7k5GQUFBRg/fr1mDdvns1nr7/+Oh566CGYzWYEBQVh6NChUKlUyMrKAgCkp6fju+++s1ataNb8dMFTTz2FFStWtLrmXXfdhXfffRczZ87E119/7fCcP/jgA8ybNw8rV67EsmXLWn3+xBNP4Pnnn0dQUBAGDx6MjIwMGI1GfPTRR/wST0RERFa5ubkYOXIktFotrr32Wrz99tvWDY0jR47gxhtvRFZWFiZPnow9e/ZAIpHg+++/x/Tp0yGEwOLFi7FixQqEhoYCaEouvf7665GVlYUJEybg119/tXnqsrkdRkxMTJsbJ8888wyeeuopnHfeeTh+/LjDP4/FYsGFF16IvLw8ZGdn2zwhWldXh4SEBAQEBODYsWNITk7Gn3/+iQkTJiAuLg5FRUV8QpSIiIiIiIiIiMgOrDhB5GMWLVqEAwcOYO7cuYiOjsaJEydQVlaG8ePH49VXX8X+/ftbJU3Yo6qqCgDQr18/h8/lkw9ERETkLF0pqz1t2jT85z//gUwmw7///W8kJCRg9OjRSEtLw6hRo5CVlYX09HR8/vnnrRIR2iur3aykpAQAMGDAgC79PCyrTURERERERERE5HqsOEFERERERF4nOzsbL730En744QeUlJQgKCgI5513Hm6++WbMnz8f/v7+rc45fPgwXn31VezZswfl5eVQKBQYMWIEbrzxRtx1111QKBStzumsKtesWbOwfft23HPPPXjrrbcc+hl0Oh3S0tJgNBqRnZ2NoKCgVscIIfDyyy/jzTffRFlZGdLS0rB8+XJcd911Dl2LiIiIiIiIiIjIlzFxgoiIiIiIiIiIiIiIiIiIiHwWW3UQERERERERERERERERERGRz2LiBBEREREREREREREREREREfksJk4QERERERERERERERERERGRz2LiBBEREREREREREREREREREfksJk4QERERERERERERERERERGRz2LiBBEREREREREREREREREREfksJk4QERERERERERERERERERGRz2LiBBEREREREREREREREREREfksJk4QERERERERERERERERERGRz2LiBBEREREREREREREREREREfksJk4QERERERERERERERERERGRz2LiBBEREREREREREREREREREfksJk4QERERERERERERERERERGRz2LiBBEREREREREREREREREREfksJk4QERERERERERERERERERGRz2LiBBEREREREREREREREREREfksJk4QERERERERERERERERERGRz2LiBBEREREREREREREREREREfksJk4QERERERERERERERERERGRz2LiBBEREREREREREREREREREfksJk4QERERERERERERERERERGRz2LiBBEREREREREREREREREREfksJk4QERERERERERERERERERGRz2LiBBEREREREREREREREREREfksJk4QERERERERERERERERERGRz2LiBBEREREREREREREREREREfksJk4QERERERERERERERERERGRz2LiBBEREREREREREREREREREfksJk4QERERERERERERERERERGRz2LiBBEREREREREREREREREREfksJk4QERERERERERERERERERGRz2LiBBEREREREREREREREREREfksJk4QERERERERERERERERERGRz2LiBBEREREREREREREREREREfksJk4QERERERERERERERERERGRz2LiBBEREREREREREREREREREfksJk4QERERERERERERERERERGRz2LiBBEREREREREREREREREREfksJk4QERERERERERERERERERGRz2LiBBEREREREREREREREREREfksJk4QERERERERERERERERERGRz2LiBBEREREREREREREREREREfksJk4QERERERERERERERERERGRz2LiBBEREREREREREREREREREfksJk4QERERERERERERERERERGRz2LiBBEREREREREREREREREREfksJk4QERERERERERERERERERGRz2LiBBEREREREREREREREREREfksJk4QERERERERERERERERERGRz2LiBBEREREREREREREREREREfksJk4QERERERERERERERERERGRz2LiBBEREREREREREREREREREfksJk4QERERERERERERERERERGRz2LiBBEREREREREREREREREREfksJk4QERERERERERERERERERGRz2LiBBEREREREREREREREREREfksJk4QERERERERERERERERERGRz2LiBBEREREREREREREREREREfksJk4QERERERERERERERERERGRz2LiBBEREREREREREREREREREfksJk4QERERERERERERERERERGRz2LiBJGHOXv2LG6//Xb0798fcrkcCQkJ+Oc//4mdO3d2aTyLxYK1a9di8uTJiIqKQkBAAFJTU7FkyRIolco2zzl69Cguu+wyBAUFISoqCnPnzkV5eXm713jyySchkUjw7bffdmmORERERERERET2SE5OhkQi6fBPXV2dXWPl5+d3OtaoUaNancd1EyIi6i2ceV8EgMLCQixYsAB9+/aFXC5Hnz59cMsttyAjI6Pdc3hfJCJPIRFCCHdPgojs8/333+Pqq6+GVqtFUFAQhgwZgqqqKpSWlgIAHnnkEbz00kt2j6dWq3HllVfip59+AgCkpqYCALKzs2GxWJCcnIxffvkFffv2tZ5TXV2NIUOGoK6uDoMGDYJer0dxcTGGDBmCw4cPIyQkxOYaZWVlGDJkCMaOHYvdu3d391dARERELnD27FmsXr0aO3fuRHl5OaKiojB+/Hjcd999+Nvf/ubweKdOncLq1avx008/oaqqCuHh4bjooovw0EMP4fLLL2/znKNHj2Lx4sXYv38/FAoF/v73v+Oll15CQkJCm8c/+eSTePbZZ7Fjxw7MmDGjw/mYTCZMmDABhw4dwvr16zFv3rxOf4b8/HwMHDiw0+OaPfXUU1ixYkWP/DxERETUtvr6ekREREAmk2H8+PHtHvfdd9+1Wr9oy5dffol//vOfiIqKwrBhw9o8ZsiQIVi/fr31NddNiIiot3D2ffHMmTOYOHEiampqEB4ejiFDhiA3Nxe1tbVQKBT44osvMG3aNJtzeF8kIo8iiMgjVFVVicjISAFA3HjjjaKurs762aZNm4RMJhMAxLZt2+wec+7cuQKASEpKEn/88Yf1/ePHj4shQ4YIAGLGjBk25zzxxBMCgHjiiSeEEEKYTCZxww03CADi9ddfb3WNO+64QwAQBw4ccPRHdrqioiLx4IMPiqFDh4rAwEARGBgohg8fLpYuXSoqKiocHu+XX34R//znP0VsbKxQKBRi6NCh4uGHHxbFxcXtnnPkyBHx17/+VQQGBorIyEhx8803i7KysnaPX758uQAgduzY4fD8iIiI7PHdd9+JwMBAAUAEBQWJ9PR0kZSUJAAIAOKRRx5xaLxvv/1WKBQKm/Gio6Ot4z333HOtzqmqqhIRERECgBg0aJDo27evACCGDBkiGhsbWx1fWloqgoODxaWXXmrXnJ5++mnr9devX2/XOWVlZWLixIkd/hk0aJB13I0bN/bYz0NERNTTsrKyxPz580W/fv2Ev7+/iI+PF1dddZX44YcfujTerl27xMyZM0V0dLSQy+UiOTlZ3HnnnSIzM7Pdc+z5Pv3zzz8LAGLo0KFO+T7dHEPcfffddp/jyesmRETkXc69L3aX0WgUgwcPFgDEzTffLDQajRBCCL1eLxYuXCgAiIiICFFdXW1zniffF529n+Cq+Odc3E8g6h4mThB5iOeff14AEMnJyUKn07X6/O677xYAxJQpU+wa748//hAAhEwmE8ePH2/1+U8//SQACIlEYpMIMHnyZAHA5ub8yy+/CADi+uuvtxnj9OnTQiaTtXrfHfbu3WvdwJDJZCItLU0MHjzYmnCSkJAgjh07Zvd4q1evFhKJRAAQISEhYsyYMSI+Pt4aIO7evbvVOdxEISKi3sbZiZlKpVJERUUJAOLaa6+1jmcymcSTTz5pTTL45ZdfbM5z5ULKkSNHhL+/v8OJE53RaDRixIgRAoC4/fbbe+znISIi6mnOTrI8N6ExOjpaXHDBBSI8PFwAEAqFos24w97v06+99poAIGbOnOmU79OzZs0SAMSbb75p9zmeum5CRETep/m+eN1113V7rPXr1wsAon///kKv19t8ZrFYxKRJkwQAsWzZMpvPPPW+6Oz9BFfGP824n0DUfUycIPIQW7ZsEbNnzxYvvvhim5+/9dZb1pumPe69914BQMyfP7/Nzy0Wi1i5cqV4/fXXbYKa5koURqPR+l5mZmabSRtXXnml8Pf3F2fPnrVrTq6iVCpFbGysACCuuOIKUVpaav0sJydHTJw4UQAQAwcOFFqtttPxvvrqK2uQc8cddwiVSiWEEMJsNouXXnpJABCBgYEiNzfX5jxuohARUW/j7MTMjz76yJpEqFarW33evGBy2223tfm+sxdS9Hq9OO+884RUKhUBAQFOTZxYsGCBACDS0tJa/ayeujBERETUkrOTLHft2mX9Pv3iiy8Kk8kkhBBCp9OJBx980Lp5UFRUZHOevd+nb7/9dgFAjB492infp5urS/388892n+OJ6yZEROSdmu+LTz/9dLfH+stf/tJmYkSzjRs3CgAiJSXF5n1PvC86ez/B1fFPM+4nEHWfFETkEW688UZs3rwZS5YsafPzQ4cOAWjqrWmPnTt3AgBmzZrV5ucSiQTLli3DwoULbXpxR0ZGAgAqKyut71VUVAAAoqKirO/98ssv+Oqrr3DXXXdh8ODBds3JVTZs2ICqqiokJSXhk08+QWJiovWzlJQUbN++HZGRkcjLy8O2bds6He+pp54CAEydOhXvvvsugoODAQBSqRSPPPIIbrzxRmi1WixevNjmvH379gEAFi1aBACQyWRYuHAhgKbf17kyMjKwbt06XH/99Rg7dmwXf3IiIqKOJScnY/bs2bj33nsREBDQ6vPzzz8fAFBQUGDXeEVFRQCAwYMHIygoqNXnzfe0wsJCm/fLysoAADExMdb3YmNjAQC1tbU2xz722GOQSqVYtWpVp/N58sknceLECTzwwAM28Ux3/fjjj3j//fchkUjw/vvvt/pZXfXzEBER9bT3338fSqUSycnJ2LBhA8LDw62fzZkzB3fccQcA4J133rFrvJdeegkAMHv2bCxZsgQymQwAEBAQgH//+98YNmwYdDodPvjgA5vz7P0+fezYMQDAkSNHuv19WqVSITc3FwAwcuRIu8/zxHUTIiLyTs33xfPOO69b41gsFhw4cAAAMGnSpDaPmThxIgAgNzfXujYAeOZ90dn7Ca6OfwDuJxA5CxMniDxcXV0dnn76aaxfvx5+fn549NFHOz1Ho9EgJycHADBixAg0Njbi7bffxnXXXYcpU6bg9ttvx/fff9/muc2B0WuvvQagKWh6++23AQCTJ0+2Hrd06VKEhoZi+fLl3fr5nGH37t0AgJkzZyI0NLTV57Gxsbj44osBAAcPHuxwrPLychw5cgRA08/YlgcffBAA8PXXX9tsjnAThYiIehtnJ2b2798fAHD27Fmo1epWnx8/fhxAU8LGuVyxkLJ//368/PLLSE1Nder91Gw2W+/1c+fObXPRyBMXhoiIiNri7CTLiRMn4sorr8T8+fNbfSaRSKwbOy3Hs+f7tNlsxqlTpwA0PdgwaNAgzJo1C5dffjluueUWfPzxx7BYLHbNE2iKW4QQSEpKQnV1NZYtW4YZM2Zg6tSpWLhwIX799dc2z/PEdRMiIvI+594Xk5KS8Morr3T5vlhSUgKtVgsAGDRoUJvH9OvXz5oQkJWVZX3fE++LztxPAFwb/zTjfgKRk7i75AURdc22bdvEiBEjrGWn+/XrJ7755hu7zm0uhYX/9Rjv37+/9fW5f2644YZWZbsLCwutZToHDx4s+vXrZy1T3dyy4pNPPhEAxDPPPOP0n7srDhw4ID788EOxf//+do+ZMWOGACDuu+++Dsfav3+/9fejVCrbPEapVFqP+f77763vjxs3TgAQJSUl1vd+/vnnVmW79+7dKwCIhQsX2vkTEhEROZdSqRQrVqwQEolE+Pn52V2eWqVSWXueX3/99aK+vl4I0dQC7OWXXxYAhFwuF0eOHLE5b/HixQKAePTRR4UQTe2vbrzxRgFAvPHGG9bjxo8fL0JDQ0VFRUWH89BoNCI1NVVIpVLx66+/CiGEGDBggFNadbz99tsCgAgICGhVRtNVPw8REVFvNX/+fAFAzJgxo9tjmUwmaznvlm1K7fk+ffr0aet3cX9//zbXOcaNG2dTbrsjzS1RAwMDrS1JWv6ZP3++MBgMNud54roJERF5n3Pvi6Ghod26Lx46dKjTNXEhhIiKihIAxNatW63veeJ90Zn7CZ3pbvwjBPcTiJyJiRNEHmrp0qU2QU5ISIi46667RENDQ6fnHjx40HpebGysGDBggPjiiy+EWq0W1dXV4o033hCBgYECgLj77rtbnX/y5Ekxbdo0ERgYKCIiIsTcuXNFeXm5EEIIg8EgBg8eLBISEqyBjxBCaLVaUVhY2Gb/dHerqqoSQUFBAoB46623Ojz2jz/+sP7uzv35zlVZWWk9Zs2aNdb3uYlCRES9XXcSM5tlZGRY+4oHBweLUaNGibi4OAFADBkyROzatavVOc5eSFm0aJEAIB5++GHre85InDCZTCI5OVkAEHfccUe7x3niwhAREZEjuppk2Z7s7Gxx7bXXCgAiISFBVFdX23xuz/fpLVu2WL+LT5o0Sezdu1eo1WpRU1MjNm7cKBISEgQAMWrUKKHX6zud01133SUACIlEIu6++25x6tQpodfrRUFBgVi5cqU1OeOee+5pda63rZsQEZHnOfe+eNlll3Xrvti8MQ9AaLXado/r06ePACDWrVtn87633Rcd2U/oiDPiHyG4n0DkTEycIPJQRUVFQqVSidLSUrFhwwYRGxsrAIgxY8YIo9HY4bnnBjphYWEiPz+/1THr1q0TAIRUKhWZmZl2z+v1118XAMTbb78thGjaYHjwwQeFQqEQAIRCoRAPPPBAqycy3GnOnDkCgAgKCuo0uDg3KWLfvn1tHrN7927rMatXr7a+z00UIiLq7bqTmNksPz9f3HfffW0+mTlx4kTx008/tXmesxZSdu/eLSQSiRg6dKjNgo4zEiea79NSqVRkZWV1eKy3LQwREREJ4Zwky3M9/vjjIiUlRUilUgFATJgwQWRkZLQ6zp7v088++6x1DLPZ3GqMM2fOOLTJsXHjRnHHHXe0e+zGjRutiRUnT560+2f2xHUTIiLyPL/++qtYtGiRePDBB7t9X/z111+t3+s7+r7aXIFyw4YNds/TE++LjuwntMWZ8Q/3E4ici4kTRF4iMzPTGkx0tiFwbmmtBx54oM1jLBaLdYPhpZdesmsODQ0NIjY2VqSmplqTN1avXi0AiL/97W9i7dq14rrrrhMAxJ133unIj+cyzQsrLZMcOjJhwgQBQFxxxRXCYrG0+nzKlCnWMVsGLNxEISKi3qw7iZlCCHHs2DHrOTfccIM4efKk0Ov1Ii8vTzz66KNCKpUKmUwmNm/e7NC87F1IqampEQMGDBAymUz8/vvvNmM4I3Fi4sSJAoC45pprujyGEJ65MERERCSEc5Isz9Vcgrr5T9++fdtdg3DG9+k77rjDukbhDM2ltVetWmXX8Z64bkJERN7L3vvisWPHrPfqurq6do9rbtXx8ccf23V9T7wvdmU/oaWejn+IyH5MnCDyIs2ZjrfeemuHx2VnZ1tvyh1tHjT36Wqr7GRbli1bJgCIzz77TAghhNFoFDExMaJv3742C//p6elCJpOJsrIyu8Z1lRUrVlh/D1deeWWbSRBt2b17tzUb9KqrrhInTpwQBoNBZGVliblz5wqpVGrNBH3++eftng83UYiIqLdxJDFTCCEmTZokgPb7nP+///f/BAARHh7eYV/UczmykJKWliYAiKVLl7Yap7uJE0VFRUIikQgA4osvvujSGI7+PL1lYYiIiKhZd5MsW8rNzRU6nU6cPXtWLF++3Nr+4sEHH3RoHHu/TzcfN3jwYIfn2pbrr79eABALFiyw63hPWzchIiLv9tZbb9l1XywpKbGuo+fk5LR5jNFotFae3L17t13X97T7Ylf3E1rq6fiH+wlE9mPiBJGHqKmpEYcOHRJVVVXtHvP4448LAGLatGkdjmUymURgYKAAIN555512j7vyyivtvmGXlJSIoKAgMX78eOt7zQkas2bNsjl24cKFAoD4+uuvOx3XFYxGo7VXafPvq6PebG3ZsGGDkMvlrUqQKxQKsXbtWmvW6JtvvmnXeNxEISKi3srexMyysjLr/fCPP/5o8xiTySSio6MFALFp0ya7rm/vQkpKSooAIIYMGdLmUxXdTZxoXpAICwvr1lMbnrYwRERE1BFHkyw788EHHwgAQiaTibNnz9p1zrnfp/V6vdDr9e1+n26uHjVs2DC7xjYYDMJkMrX7eXNfcnseOPGkdRMiIvIOZrNZ6PX6dj9/44037L4vhoeHCwBi165dbX6em5trXRMoKSnpdDxPui86Yz+hI92Nf7ifQOQ8UhCRRxg7dizGjBmD9evXt3tMQUEBAKBPnz4djiWTyTBmzBgAwB9//NHucWfOnAEADBo0qNP5PfXUU9BoNHjxxRet71VUVAAAFAqFzbHh4eEAgPr6+k7HdbaGhgbMmDEDa9asAQDccMMN+Oqrr1rNsTO33norMjIy8Nhjj2HatGmYMWMGli1bhtOnT2P+/PnWn72zv4tmL7zwAqqqqvD888/Dz88PJpMJL7/8Mvr27YsdO3Zg/vz5+OSTT5Ceno61a9eivLzcsR+ciIioDbW1tfjzzz9RXV3d7jEDBgwAgE7vPc1xCAAMHTq0zWNkMhlSU1MBAHl5eZ3Or7S0FK+88grGjx+PWbNmWa9TXV2NcePGwd/f33qsXC4HAJw9exYKhQISicTmT/P8brvtNkgkElx66aWdXv9cX375JQDgn//8JwICAhw6tys/zyWXXAKz2YxDhw516VpEREQ9IS0tzXpP27NnT7fHu+WWW5CUlASz2Yx9+/bZdU7z92mZTIagoCAsXry43e/Tv/32GwBg+PDhHY6pVCoRFRUFuVxujQHacuTIEbvGAzxn3YSIiLzD5MmTIZfL8cgjj7R7zOHDhwHYdx8bN24cAOD3339v8/Pme+yAAQOQlJTU6Xiecl901n5CR7oT/3A/gci5mDhB5CGmTp0KAHjvvfdgNBpbfZ6fn4/t27cDAP7xj390Ot5NN90EAPjkk09QVFTU6vP//ve/OHPmDKRSKa6++uoOxzp9+jTWr1+PK6+8Epdccon1/ejoaABAcXGxzfGlpaUAgJiYmE7n6UzFxcWYOHEidu7cCQBYsmQJtmzZYt1ocVRKSgqef/55fPfdd9ixYwdWrlyJgQMHoqamxro5c95553U6DjdRiIjIXZyZmBkWFmb932VlZe0eV1lZ2er49jiykJKQkAAAGDJkCCZOnNjqT3OyQ/Pn9tyjm1ksFuvi0JQpU+w+rzs/DzdMiIioN3BmkqUQAqWlpfj1119hsVi6PR5g+3360ksvhdlsxrZt29r8Pj1q1CgIIQAAN954Y4fjRkZGWmOLDRs2tHnMtm3bkJOTA7lcbv0u3x5PWTchIiLvMXLkSJjNZnz++edobGxs9XlBQQE+/vhjAJ3fFwHg+uuvBwCsX78eBoOh1efvvPMOAGDevHmdjuUp90Vn7Ce4Ov7hfgKRk7m54gUR2SknJ8faXuPaa6+1adlx+PBhkZqaKgCIyZMn2/TWys7OFhkZGaK0tNRmPL1eL0aMGCEAiBEjRohTp05ZPzt06JDo27evACDuvvvuTuc2c+ZMIZPJbMYQoqkUWEJCgpDJZOLAgQNCCCHy8vJEdHS0CAwMtLu3uTOUlpaKQYMGWUteNff96oqtW7eK1atXt1s2a926dQKASE5Otmu8BQsWCABi79691vd+/fVXAUDMmTPH5tjm8t4bN27s8vyJiIia3X333db2Fm31vMzLy7PGH9u3b+9wLIvFIpKSkgQAsXjx4jaP2bNnj7W05cmTJzsc79SpU0Imk4krr7zS5v3MzExrzHOu2267TQAQ3333XZvjdadVx4kTJ6zztrdsZkvO/nmIiIh6QnMrrBdffLHdY5rbes2fP7/DsQoLC6330+Y1grY0xxMffvhhp/M79/t0VlaWCAgIsF7j6quvth539OhRERUVJQCItLQ0YTabrZ+1t27y4YcfWsd67LHHbFp1ffrppyIkJEQAEP/61786nacnrJsQEZF3Ofe+OH36dJs2kEePHhVpaWnW76L23Be1Wq11ff2aa64RDQ0NQoimfYZFixYJACI8PFxUV1d3OjdPuC86az/B1fFPM+4nEDkHEyeIPMjXX38tgoKCBAAREBAgzj//fGvCBAAxfvx4m4QKIf5vk6CtvuS5ubli8ODBAoCQSCRi2LBhYvjw4dbxLr/8cqFSqTqc088//ywAiAULFrT5+Zo1awQAIZfLxfnnn2+d/7PPPtvl34Oj9Hq9GD16tHUen3/+ebfGu+yyywQA8fjjj7f6TKfTiWHDhgkA4pVXXul0LG6iEBGROzk7MbP5vi+VSsULL7xgs8Gwe/duER8fLwCIG2+8sdO5OXshpaPEifZ+nmbN/UbDwsI6nXdP/TxEREQ9wZlJlkIIMXToUAFAzJs3r83PP/74Y+t39/Ly8g7Hauv79LZt24RcLreuc4wcOdJ6zeY/n376qc04Ha2bPPLII9bzQkNDxYUXXij69OljfW/BggU2m01t8YR1EyIi8k7btm0TCoXCep9peV8cM2aMqKmpsTmno/vigQMHRHh4uAAgQkJCxJgxY6yJiXK5XPz000+dzskT7ovO3k9wdfwjBPcTiJyFiRNEHubs2bPizjvvFMnJycLf31+Eh4eLSZMmiXfeeafNRYyOAh0hhFCr1WLVqlVi1KhRIjg4WISHh4uLLrpIvPXWW8JoNHY6n3HjxonAwEBRXFzc7jHr168XaWlpwt/fXwwZMkS8+eabdv+8zvDMM89Yg8H33nvP7vPa20T56KOPBAChUCjEzp07re+Xl5eLv//97wKAGDRokM1mUXu4iUJERO7m7MTMpUuXWs8NDw8XY8aMEcnJydb3LrvsMrckZnaUONFZvPTcc88JAGLkyJEdzrs9nrAwRERE1BZnJ1lu3rzZGhOsXLnSZh3j448/tlZxWL58eadza+/79OnTp61z9vPzE+Hh4eKCCy4QwcHBbX6f7iwO2Llzp7jqqqtEXFyc8PPzE3FxcWLmzJnim2++6XSOQnjGugkREXmvzMxMcccdd4jk5GQhl8tFeHi4mDBhgnj99dfbXP/v7L6Yn58vbr/9dtG3b1/h7+8vYmJixDXXXCOOHDli13w84b7o7P2Enoh/uJ9A5BxMnCAir6bX60VkZKR1wWTixIkd/lm1apX13PaCRIvFIq677jprsDNo0CCRnp5ufaplwIABIj8/v9O5cROFiIh6C2cnZu7evVtcc801IiEhQfj5+YnIyEhx6aWXinXr1gmTydTpfFyxkNKdxImHHnrIWt60KzxhYYiIiKg9zk6yXL58uU2S5ejRo0ViYqL1vTvvvJNVHIiIiMgtXLGfIATjHyJPIRFCCBAReak///wTY8aMsfv4W2+9FRs2bAAAJCcno6CgwOa9ZhaLBe+//z7Wrl2LkydPwmQyYdCgQbj66qvxyCOPIDIystNrXXTRRThx4gTOnj2LPn36tHnMhg0bsHr1auTm5iI5ORkPPvgg7r33Xrt/HiIiIiIiIqLuys7OxksvvYQffvgBJSUlCAoKwnnnnYebb74Z8+fPh7+/v83xHX2fBoA9e/bg1VdfxW+//QalUomoqChMmDAB99xzD6ZNm9bpfPh9moiIiFzBVfsJAOMfIk/AxAkiIiIiIiIiIiIiIiIiIiLyWVJ3T4CIiIiIiIiIiIiIiIiIiIjIXZg4QURERERERERERERERERERD6LiRNERERERERERERERERERETks5g4QURERERERERERERERERERD6LiRNERERERERERERERERERETks5g4QURERERERERERERERERERD6LiRNERERERERERERERERERETks5g4QURERERERERERERERERERD6LiRNERERERERERERERERERETks5g4QURERERERERERERERERERD6LiRNERERERERERERERERERETks5g4QURERERERERERERERERERD6LiRNERERERERERERERERERETks5g4QURERERERERERERERERERD6LiRNERERERERERERERERERETks5g4QURERERERERERERERERERD6LiRNE5FIHDx7E7Nmz8dBDD6GhocHd0yEiIiIiIiIiD1dTU4MzZ87AaDS6eypERETUTZ9//jkWLFiADz74AEIId0+HiHyYRPBfISJyEZVKhbi4OGi1WgDArbfeig0bNrh3UkRERNRtGo0G2dnZSEtLQ0BAQI9eW6vV4l//+hf27t2Lq666CsuWLYNEIunRORAREZH7/PLLL7jyyitRV1eHv/71r9ixYwcCAwPdPS0iIiLqgp9//hmXXnqp9fXHH3+M66+/3n0TIiKfxooTROQyf/75pzVpAgB27drl9GvU19fjqaeewoIFC3Dw4EGnj09ERES2ioqKMHLkSKSnpyMtLQ1lZWU9ev21a9fi3//+Nw4dOoTly5fjq6++6tHrExER+TKLxYI//vgD+/btc9sToS+++CLq6uoAALt378bvv//ulnkQERFR9+3evdvm9Y4dO1x+zfz8fBw6dIiVq4ioFSZOEJHLSKW2/8RoNBqnX+Oxxx7DM888g7Vr12LKlCmora11+jWIiIjo/2zduhV5eXkAgIKCArz55ps9ev0ffvjB5vXp06d79PpERES+bMmSJRg/fjwuueQS3HvvvW6ZQ2Zmps3rmpoat8yDiIiIuq/lHkJjY6NLr7d582YMHjwYY8eOxT//+U+YTCaXXo+IPAsTJ4jIZRQKhc1rnU7n1PGFENiyZYv1dUNDA86ePevUaxAREZEtlUpl8/rw4cM9ev3CwkKb1/Hx8T16fSIiIl8lhMC7775rfb1mzZoerzwFABUVFTavGQsQERF5ruDgYJvXarXapdd78sknYTabAQDffvstsrOzXXo9IvIsTJwgIpdp2fNcr9c7tZRncXEx6uvrbd5LSEhw2vhERETUWmpqqs3rrKysHr1+UVGRzev+/fv36PWJiIh8mUQisf5vIUSPJ1BqtdpWT6IycYKIiMhzBQUF2bx2RdXqZjU1NcjJybF5rzmJgogIYOIEEblQy4oTFovFqaWvTp48afM6LCyMmyc9ZM2aNZBIJHj//fc7PO6zzz7DFVdcgdjYWMjlciQnJ+Puu+9GcXGxw9fcs2cPJBJJh3/++c9/tjrvp59+wrhx46BQKJCQkICFCxd2WPLtlltugUwma/XfFxERNUlLS7N5nZeXB4PB0CPXVqlUrdpy9evXr0euTURE5OskEgnS09Nt3jt69GiPzqFltQmAiRNERESerCcTJ44cOWLzWqFQtFrjICLf5ufuCRCR92pZcQJoatfh7+/vlPFPnDhh83rkyJE2T7+Qaxw8eBBLlizp8BiTyYR58+Zh06ZNAIABAwYgPj4eZ86cwZo1a/Dpp59i9+7dOP/88+2+7rFjxwAAiYmJSElJafOY4cOH27w+ffo0pk+fDoPBgGHDhqG6uhpvvvkmjh07hp9//rlVD71jx45h06ZNuOWWWzBy5Ei750ZE5EuGDBli89pisSAnJwfDhg1z+bVbVpsAmDhBRETUky644ALs27fP+trdiRNyuRzh4eE9OgciIiJynp5s1dEyceK8886Dnx+3SYno//BfBCJymZYVJ4Cmdh2hoaFOGb+txAlyrT179mDWrFkdVmwAgGeeeQabNm1CWFgYtmzZghkzZgBo6kt/3XXX4cCBA7jppptw/Phxu5NdmhMnHnjgATz66KN2nbNq1SoYDAasWbMGd955J7RaLaZOnYp9+/bh22+/xcyZM22OX7JkCeRyOZ555hm7xici8kXh4eGIj4+32bjIysrqkcSJwsJCm9cxMTGtnk4hIiIi1xk1apTN65YbEK7WMnEiPj6eD1AQERF5MHdWnLjgggtcdi0i8kxs1UFELtNW4oROp3Pa+C1bKZx33nlOG5ts6XQ6rFixAlOmTIFSqezw2JKSErzwwgsAgK1bt1qTJoCmPvRbtmyBRCLByZMnsX//frvn0Jw44cjf8759+xAUFIQFCxYAAAIDA3HXXXcBAH755RebY3fu3ImdO3fi/vvv59PLRESdaFnKMisrq0eu27LiBP+9JiIi6lktEydycnLQ0NDQY9dvK3GCeq+O2nx21orz3D/z5s3r1jz+/PNP+Pv7d5hkwzafRETu4c6KE0yc6FmMC8gTsOIEEblMe606nMFkMiEjI8PmPVaccI3s7GxcdtllKCoqgkwmw8qVK/Hee++hoKCgzeM3b94Mg8GAyy67DNOnT2/1eUpKCl599VUIIRATE2PXHEwmE06fPg3Asb/nsrIyxMXF2bTkiI2NBQDU1tZa3xNCYOnSpYiMjMTjjz9u9/hERL4qNTUVe/futb4+c+ZMj1y3ZcWJ/v3798h1iYiIqMmIESPg5+cHk8lkfe/48eOYNGlSj1y/srLS5jUTJ3qvztp8Tpw4scPzlUqldR2gZas4R+j1etxyyy02/822xDafRETu01MVJ9Rqdau1CyZO9BzGBeQpmDhBRC7TVuKEXq93ytjZ2dmtxuJNyTWKi4tRVFSE8ePH44033sDo0aPx3nvvtXv8zp07AQCzZs1q95j777/foTmcOXMGOp0O4eHhDm2SRUZGorq6GhaLxRrQND+hFBUVZT1u48aNOHr0KF5++WVEREQ4NDciIl/krooTTJwgIiJyr4CAAAwfPhzHjx+3vnf06NEeS5xgxQnPYE+bz3379rX7mcViwdSpU3H69GlMnTq1Ww84LFu2zLrR0h62+SQicp+WFSd0Op3NWq6znDhxAkII62upVMoK1j2EcQF5ErbqICKXkUgkrZInnFVx4sSJEzavExIS7K5eQI7p27cvduzYgd9//x2jR4/u9PjmBbQRI0bAYDDgww8/xJw5c3D55Zfj5ptvxqeffmoTpNqjuU3HiBEjcPjwYSxevBhTp07F9OnT8cgjj7T676HZpEmToNfr8e677wJo+u9v7dq1AIDJkycDaErmWb58Ofr374+FCxc6NC8iIl+Vmppq89pdiRNs1UFERNTzWrbraFn22pWYONG7OdLmsyPPP/88fvzxR8TGxuLDDz/s8ubZr7/+ildeeaXV08wtsc0nEZH7tPVvtCuqTrSMV4YOHdrp/YG6h3EBeSImThCRS7kqcaJlryhmh7rO4MGDMWPGDLuO1ev11oUsjUaD0aNH49Zbb8WWLVvw008/YdOmTbj++utx+eWXo66uzu45NCdOHD9+HKNHj8Yrr7yCnTt34rvvvsP/+3//D6NGjcK//vWvVuc98cQTkMvluOeeezBixAgMGDAAe/fuxeTJk61tRF577TUUFBRg5cqVbVZJISKi1lomTlRUVKC+vt7l1y0qKrJ5zYoTREREPa9l4sTRo0d77NpMnOi9srOzkZqaiqeffhoAsHLlSgwYMMDhcTIzM61jvPrqq13+O1ar1bj11lshlUo7fQK0rKwMkZGRbPNJROQGLStOAD2TOME2Ha7FuIA8FRMniMilFAqFzWtntepoWWGAiRO9w7nltubNm4fS0lJ88MEHqKurQ319PTZu3Ijo6Gjs3r0bs2fPtnvc5sQJnU6HZcuWITc3F3q9HmfPnsWDDz4IIQRWrVqFF1980ea80aNH48cff8TEiRORk5MDAFi4cCF27NgBqVQKpVKJ559/Hunp6bjpppus56nVahQVFcFoNHbn10FE5LVSUlIgk8ls3nN11QmLxcLECQ+1Zs0aSCQSvP/+++0eU1hYiHvuuQcDBw6EXC5HZGQkLr/8cmzbtq3L1/3tt98wa9YsxMXFQS6XIzExETfccAP+/PPPds/56aefMG7cOCgUCiQkJGDhwoUdlhO95ZZbIJPJWiX1EhF5s5YbDSdPnuyx705MnOi9zm3z+ccff2DZsmVdGmfx4sUwGo2YPHky5syZ0+X5LFmyBDk5OXjsscc6rZ55bpvPZh21+Vy2bBnbfBIROYm7Kk60TAQl52JcQJ6KiRNE5FItEydc1apj5MiRThmXuker1Vr/d21tLb7//nvccsstCA8PR1hYGG666SZ8+umnAIDvvvsOO3futGvcmTNn4rbbbsPWrVuxcuVK66bK4MGD8corr+DZZ58FADz99NOorq62OXfSpEnYt28fdDodKioq8PrrryMkJARAU78ypVKJ1atXQyqVQqPR4Oabb0ZERAT69++PmJgYPPfccw63FiEi8nZyuRwDBw60ec/ViRNVVVWtEjBZBrH3O3jwIJYsWdLhMYcPH8aoUaPwzjvvoKysDEOHDoVCocBPP/2E6667DnfffbfD1127di0mT56M7du3w2AwYMSIEdBoNPjkk08wfvx4fPTRR63OOX36NKZPn46DBw8iJSUFFosFb775JmbMmGGzYNLs2LFj2LRpE2655RbGokTkU9LT021eGwwGZGRk9Mi1mTjRezna5rMt3333Hf773/8CAF5++eUuz2XXrl145513cP7552P58uWdHs82n0RE7hMYGNjqPbVa7dRrGI3GVvsJrDjhWowLyFMxcYKIXMoVrTo0Go21ekAzVpzoHc7NEJ45cybGjBnT6pi//vWv1gDjyy+/tGvchQsXYt26dbjmmmva/PyRRx5BSEgINBoNfvjhB7vGLCgowBtvvIHLLrsMV1xxBQDg4YcfxqZNm3DNNddg7dq1GDt2LJYtW4bVq1fbNSYRkS9JS0uzeX3mzBmXXq+wsNDmtUwmQ2JiokuvSd2zZ88eTJs2rcOKDWazGbNnz4ZSqcTkyZORn5+P48ePo6ysDO+++y4kEgnWrFmDTZs22X3dnJwc3HPPPTCbzVi0aBEqKipw5MgRlJeXY8GCBTCZTFiwYAFyc3Ntzlu1ahUMBgPWrFmD06dPo6CgwJqA+e2337a6zpIlSyCXyzst80lE5G0iIyNblVruiXYdBoOhVX9sJk70Ho60+WzPqlWrAAB/+9vfMHbs2C6N0dDQgPnz58PPzw8bNmyAXC7v9By2+SQich+pVNoqecLZFScyMzNbPYjBxAnXYlxAnoqJE0TkUq5o1XH69GmbCgASiQTDhw/v9rjUfWFhYdbeXx2VO2t+KrPlhkVXBQQEWP8byMvLs+ucZcuWwWAw4IUXXgAAVFZWYt26dRg3bhy2bt2K+fPn45tvvkF8fDxeeOEFmEwmp8yViMhbpKam2rx2dcWJlm06+vbt26pdCPUOOp0OK1aswJQpU1ptcLX0+++/W//b2bx5MxISEqyf3XHHHZg7dy4AWJ/usMeWLVtgNBoxdOhQvPrqq9ZFjMDAQLz99ttISUmBwWDAxo0bbc7bt28fgoKCsGDBAuvxd911FwDgl19+sTl2586d2LlzJ+6//35WPiEin9Rys6EnEicqKytbvcfECe9x6NAh7Nu3DwDw2GOPdXmc+++/H0VFRXjiiSfs3hRjm08iIvdq2a7D2RUnWrbp6N+/v03LBep9GBeQuzBxgohcKiUlxeZ1UlJSt8ds2UN60KBBbfZCo57n7++PQYMGAeg4ScbPzw9A64okHemsWklzCW1/f/9Oxzpy5Ag2b96MG264wVoV48SJEzAYDJg0aZL1OIVCgbFjx6K+vh6ZmZl2z5WIyBe0TJxwdcWJlpsl3KzunbKzs5Gamoqnn34aALBy5cpWTyWfqzkhJiYmBn369Gn1efNTJS0rjnSkecyRI0daEzqb+fn5WRdLWo5ZVlaGyMhIm3NiY2MBNLUgayaEwNKlSxEZGYnHH3/c7nkREXmTlonyLTckXKFlMrtCoUBkZKTLr0s949VXXwUAXHjhhbjsssu6NMbXX3+NDz74AKNGjXK4lzrbfBIRuU/LPQRnJ0a2TPBktYnej3EBuQsTJ4jIpZYuXYqYmBgATa0bLr744m6PWVZWZvOaPaV7l/HjxwMA/vjjj3aPaU5CaE6y6Mjx48cRHh6OwMBAHD58uM1jdDodTp8+DQB2VR9ZunQp/Pz8rOW+gP/rlduySkp4eDgAoL6+vtNxiYh8Scv+5q4uTThixAib11OnTnXp9ahriouLUVRUhPHjx+OPP/7odHGif//+AIDq6moUFxe3+vz48eMAgOTkZLvn0Dzm8ePHrYmVzSwWC06dOtXmmJGRkaiurrY5pzk+OPdppI0bN+Lo0aNYtmwZIiIi7J4XEZE3aZk40ROJ5gMGDLD5vjd79uxWCXLkmfR6vbWV5y233NKlMWpqanDnnXfC398fGzZssOuhCnuwzScRkes9/vjj1nYd119/PYYOHerU8ZvXjZsxcaJ3Y1xA7sRvF0TkUhdffDFycnJQUFCAL7/80ik3qOYKAc1mz57d7THJeZrLU/388884dOhQq89PnjyJH3/8EQBw7bXXdjpeWlqadTFsw4YNbR7zxhtvQKPRICYmBpdffnmH433//ffYtWsX7rnnHpts5ujoaABotWlTWloKANYEICIiajJ+/Hhr8oJEIsG9997r0utdcskleO211zBhwgTcd999ePDBB116Peqavn37YseOHfj9998xevToTo+/+OKLrZtvc+fOtSYqAMDWrVuxbt06SCQSPPTQQ3bP4dZbb0VwcDCysrKwePFiGAwGAIDBYMDDDz+MzMxMhIeHY/78+TbnTZo0CXq9Hu+++y6ApsTM5hYhkydPBtC0gLN8+XL0798fCxcutHtORETeZuLEiTaVHxMTE11+TYlEgr179+KFF17AW2+9hXfeecfl16SesXPnTqhUKkilUlx33XVdGuPee+9FeXk5li9f3irBtzvY5pOIyPWuvvpq5ObmIisrC1u3boVEInHq+M2VBJtNmTLFqeOTczEuILcSREQeaNOmTWL27NnivffeE2az2d3T8TkDBgwQAMR7773X5ud/+9vfBADRp08f8euvv1rfP3v2rBg5cqQAIKZPn97qvIyMDJGRkSGqqqps3n/mmWcEACGRSMR//vMf69+52WwWb775pvDz8xMAxPvvv9/hvM1ms0hPTxdhYWGisrLS5jOlUikCAgJEWFiYyMvLE0IIcejQIeHv7y/69OkjLBZLp78XIiJfYzabxS+//CLOnj3r7qlQL9ZZ3FBWViamTJkiAIiAgABx/vnni759+woAIjExUWzZssXha/76668iNTVVABDh4eHiggsuEBEREQKAuPDCC8Xhw4dbnXPo0CEhl8sFADF8+HARFxcnAIjJkydbY48XX3xRABAffvihw3MiIvI2GzduFP369RPnn39+m/+uEnUWAzRbsGCB9Z7bVQAc+rN+/fpOxzx8+LCQSCTixhtvtL63a9cuAUAsXrzY5tiZM2cKAOLEiRNd/hmIiMg1CgsLxejRo0VwcLB47LHH3D0dn8W4gDyBn0uyMYiIXGzOnDmYM2eOu6dB7diyZQtmzJiBAwcOYOLEiRg8eDCCg4Nx8uRJmM1mpKenY926da3OGzZsGADgqaeewooVK6zvP/HEEzh9+jS2bt2KBx54AE8//TRSUlKQn5+P6upqSCQSrFixArfffnuH8/roo49w7NgxrFy5slWmcUREBBYvXoznn38eI0aMwODBg5GRkQGj0YjVq1c7PdOZiMgbSKVSTJo0yd3TIA/n5+eHMWPG4LfffoNGo7G25wCaKj51pWJZWFgYxowZg6ysLNTX1+PIkSPWz9p7Knr06NH48ccf8dhjj+HQoUMIDw/HwoUL8fzzz0MqlUKpVOL5559Henq6tcIWAKjVatTW1iIhIcFp5T+JiDzBTTfdZPPvIVFX7du3D0D3ngCeOHFiu5/V19fj5MmTNsfFx8d3OibbfBIReYd+/frh0KFDEEJwjdcDMC4gd2LiBBEROV10dDT27duHd999Fxs3bsSpU6dgsVgwcuRIzJ49G4sWLbIp69oZmUyGLVu2YNasWXj//fdx6NAhHDt2DDExMbj++uvxwAMP4OKLL+5wDJ1OhyeffBKJiYntlvtetWoVIiMj8eabbyIzMxNDhw7F8uXLu1wSjIiIiDpWXFyMv/71r8jOzsaUKVPw3HPPIT09HTU1Ndi4cSOWL1+Oa6+9Fi+88AKWLl1q15g//vgjrrrqKqjVaixatAgPPvgg+vbti7y8PLz44otYt24ddu/ejR07duDSSy+1OXfSpEnWRZqWVq1aBaVSic2bN0MqlUKj0eDOO+/Exx9/DJPJhLCwMDz66KN4/PHHuRhHRERkp4aGBpw5cwYAMGHChC6P0979GwD27NmDv/71r50ed67mNp/3338/23wSEXkJfk/r/RgXkLtJhBDC3ZMgIiIiIiIi75ScnIyCggK89957WLBggc1nc+fOxcaNG5Geno6DBw+2qtjw+eef45prroFMJkNmZiYGDx7c4bWMRiPS0tKQl5eHe++9F2+++WarY+6//368/vrrGDRoEDIzM+Hn1/nzBAUFBUhLS8PEiRPx448/AgDuuecevPPOO7jhhhswdepUbN68GT/++COee+45PP74452OSURE5O06igGa7d27F3/5y18AADU1NYiKiup03MzMTABNmxH2bEicu0Fiz1K4xWLBhRdeiLy8PGRnZ9tUrKyrq0NCQgICAgJw7NgxJCcn488//8SECRMQFxeHoqIibswRERG1gXEBeQKpuydAREREREREvkcIgc8++wxAU1uuttpczJo1C+np6TCbzfjkk086HfPPP/9EXl4eAODJJ59s85innnoKMpkMOTk5OHDggF1zXbZsGQwGA1544QUAQGVlJdatW4dx48Zh69atmD9/Pr755hvEx8fjhRdegMlksmtcIiIiX1dSUgIACAkJsWtzBGhq8zls2DC88cYbLplTc5vPpUuXttvms6GhASNGjEB6ejomTJjANp9EREROwLiA3I2tOojIaTQGE+o0eqj1JuhNZuhNFhjMZhhMFhjNFpgtAkIIWAT+938F/GRSKPxkUPjLEOjvB4W/DP7+flDI/RAV6I9Af5m7fywiIiLqArXeiDqNARqjCQaTBXqT2fp/tRYJChqNMFsAk0VAJgEUflIE+Mug8JNCIZNC4S+Fwk+KELkfooP8ERPEuMDbVFZWQqvVAgCGDh3a7nHDhw/HsWPHrAkRHSkoKAAAREZGttujNDo6GnFxcSgrK0NeXl6n7b6OHDmCzZs344YbbsCYMWMAACdOnIDBYMCkSZOsxykUCowdOxbffPMNMjMzMXLkyE7nS0TkK/QmMxp1RtRqjajUmNCoN0NlMEFlMEMI4Nw1ZQmaSmk3v6XwkyI0wA9hAX4IU8gQqfBHNOMCr1FVVQWgqf98b8A2n0RErmcyW1CvNaBea4DGYILeumZghskiYPrfXkLz/oH/OX/8pFLIZVL4+0kR6C9DqEKOsEB/BPgxLvAGjAvI3Zg4QURdVqxU4+esMtRp9KjXGqA3WZwybmhoCH4oUAEAgvyliAr0R2JoAPpHKNAvXIH+4YFIjlAgJID/hBEREbmTxmBCZaMWlQ061Kh1UGr0qFMboNToYTC3HxdEhYdgR57K4esF+UsRHxKAhBA5EkIC0DdcgUFRgUiJCoLCj8X0PE1oaCgkEgmEECgrK8P555/f5nGVlZUAgLCwsE7HbD6msbERarUawcHBrY4xGo2oq6uze8ylS5fCz88Pq1atsr5XUVEBoClZ4lzh4eEAgPr6+k7HJSLyNlqDCRWNWlQ2aFHZqEO1SocGrQGNOqM1LogIC8F/8x2PAdoS5C9FdJAc8SFyJEcEYmBkIFIiA9EvQgG5jHFBb5Cfn9/pMffffz/uv/9+h8Z1tPP0pZdeavc5CoXCmojZHolEgiVLlmDJkiUOzYOIyJeo9SZUNGhQ2ahDvdZg3UOo1xqg1pvg2L/knUuMDMHhGhMSQgOQEBKAPmEBGBjZFB+Ech+hV2BcQJ6A/1oQUZdZLAJZFc5fFNaZ/u+mpTFaoDHqUdygx8GSBpvj4kPkGBQVhMFRgUiNCcaIuBAEy5lZSkRE5AoagwkFNY0oqFGhokGLqkYdGvXGLo3V1UKFGqMFeUot8pRam/elEqBPmAKDowIxKCoIg6KDkBYdxCTLXi4oKAgXXXQR9u/fjzVr1mDatGmtjsnOzsbevXsBAJdffnmnY1588cUICAiAXq/HunXrsGjRolbHbNmyBVqtFnK53KZiRFu+//577Nq1C/fffz9SUlKs70dHRwMAiouLbY4vLS0FALv6qhIReTKNwYSSOjVK6zQoqVOjrE6DBl3ncYGfE6sVa4wWaOp1KKrX4dA56wUyCdA3XGFNphgUFYQRccGMC4iIiHrAsaIa7MoosSsucCaDBciv0yG/Ttfqs5ggfwyJDsKQmGCkRgdhaGwwwhgXEFEb+C8DEXVZVEiAS8ZV21m5okJlQIXKgN8K6wA0bZoMjgrC+Qmh+MuAMAyOCYZM6ruJFJmZmTh8+DAuvfRSJCUluXs6RETkYUwWC7IrG5BX3Yj86kZUNGid9kSIszs8WgRQ9L+Nk915SgBNcUFKVBBGJYTikv6hSI0JgUzmu3FBb7VixQpMnz4d27dvx8MPP4wVK1YgNDQUAHDs2DFcf/31MBqNmDBhAqZPn25zbmZmJoCmJIXmRIXw8HAsXrwYzz//PB599FGEhITglltusf7db9u2Dffddx8AYPHixYiMjGx3bhaLBY8++ijCwsLwr3/9y+aziy66CAEBAfjiiy+Qn5+P5ORk/Pnnn9i3bx/69OmD1NRU5/yCiIh6Cb3JjLyqRmRXNSCnqgG1an2XxpE4/fnS1swCKKjToaBOh5/zbeOC9IQQTO4fxriAiIjIRQL8ZD2eNAEAenP7n1VrjKjW1OP3oqaHQCUAUqICkZ4Qisn9w5AWy7iAiJowcYKIuiwkwB8BflKnteho1mjo2ngWAWTVaJBVo8FghQpVpWqEB4cgMiwcMRGRUAS4JtGjN/rPf/6DxYsXw2KxICkpCQcOHECfPn3cPS0iIvIgQgCfHsqFyeL8DY6e2DSxCCC7RoPsGg1SAlSoKfu/uCA6PAKBLVoskHtMmzYN//nPf/DQQw/h3//+N9555x0MHToUKpUKWVlZAID09HR8/vnnkEhsU26GDRsGAHjqqaewYsUK6/vPPvssCgoKsHnzZsyfPx9Lly7FwIEDUVJSYq0IMWfOHKxcubLDuX300Uc4duwYVq5cidjYWJvPIiIirAkaI0aMwODBg5GRkQGj0YjVq1e3misRkaeqVunw9bECFNWqYXawDHKbnDFGF9jGBWrUlKkRFhyCqLBwREdEIDCAcQEREZEzJEYEueW6KqP9ewoCQE6tFjm1WqQo1Kgtb4oLIsPCEBMeyfUCIh/Ghn9E1C1Rwc4PIpS6DtJD7RQIPYQQqFM1Iq+0GAdPn8DRMxkoriyH3mBwwix7r9dffx0PPvggLJamYLG0tBR79uxx76SIiMjj+Muk6B8V4u5pOEWwxGATFxzKOIkjZ077RFzgCRYtWoQDBw5g7ty5iI6OxokTJ1BWVobx48fj1Vdfxf79+5GQkGD3eDKZDJs2bcLnn39urVJx5MgRGAwGTJ8+HZ9//jk2bdrU4RNFOp0OTz75JBITE/HQQw+1ecyqVavw4osvIjY2FpmZmRg6dCg++eQT3HzzzY79AoiIerGQAH/nJU0AMJu7/32/u0KkTXFBfXNccPokjmSeRnEF4wIiIqLuCg+UI1je889s13ZUcqIDYVKjNS7ILy1pWi/4X1ygM3StwhYReS6JEG5K9SYir/DpoVycLFU6dcxDdYADCaKtDI30x3X9O17sCAsORmxkNOIio+Dn5z3Fd9566y1r+elmEokEJ0+exPDhw900KyIi8lT7zpZjZ0aJ08eNCw/Gl3lqp4/bliER/rhxQMdxQXhIKGIjoxATEQl/L4oLiIiInGHDb1nIq250ylghAf7YWdbz5bubJYf7YW5yx9cPCw5BTGQk4iKj4O/n30Mzc49Tp05h0aJFOHv2LO677z489thj7p4SERF5gY37z+JsZUOPXjNDLUWd3rFNhX6hfpiX0nFcEBocjNiIKMRGRkHu791xwcmTJ7Fo0SKcOXMG9957b6uWlUS+gIkTRNQtP2aWYG9WudPGC/CTYU9l955AmTc8GP38VXYdK5VKERsZhaSYWIQEBXfruu62Zs0a3H333a3ef/HFF7FkyRI3zIiIiDxdWb0G7/yc4fRxY8OC8FW+xunjtmXusGAky+2LCyQSCWIiItEnNg6hwd5RbYOIiKi7fsupwPenip0ylkwqwb5q9y1F3jQ0GCkB9scFsRFRSIqNQ2iwZ68XtOXgwYOYNm0alMr/exjm6NGjSE9Pd+OsiIjIG/yUWYqfs8p67Hr+Min2Vjn+JOaNacEYonAkLohEYmwcwrxwvaCtuODw4cO44IIL3Dgrop7Hx6mIqFuindyqQ+EvA9C9xIl+QQKw8wEWi8WCippqVNRUIzQoGIkxsYiNjIJU6lmdjN577702kyaee+45Jk0QEVGXJYQFIiTADyq9yd1T6bIBwfbHBUIIVClrUaWsRWhQMPrExSMmIhISicS1kyQiIurF0uLDnZY4YbYIhPhLoDK6J3kiOcSxuKBSWYNKZQ1CgoKQFBPnkesFbdm3bx9mzJiBxkbbSiItXxMREXVFUnhQj14vSO4HwPF2WymhjsYFtahU1vpMXKBS2ZdUQuRNPP//o4nIraKCA5w6nn8HvabtIZMIyMy6Lp3bqFEjqzAfB04dR1FFWa/ovWqPtWvX4s4772z1/rPPPovHH3/cDTMiIiJvIZFIkBIT5vRxe6ronVQi4GfpelyQmZ/bFBeUl8Fo8tzkESIiou6IDlEgJsR53/2jFO4pcy2BgL+la73KVRqNdb2goKwEJg+OC3bu3ImpU6e22hyZNm0aJk6c6KZZERGRN0mM6NnEia623AzoZlzwx6njyC8r8ej1gvbigr/97W+YNGmSm2ZF5D5MnCCibol2cuKERNq9JzovSgiE2dK9hAejyYT80hIcOHUCheWlMPXiBIoNGzbgjjvuaPX+ihUr2IOMiIicYlCc8xMnLD2UODEmXtHtREiD0Yj8shIcPHUc+aUlMJk9d0GkLfX19bjjjjuQkJCAG2+8ETpd1xJNiIjIu6XGRzhtrLAA9yxHXhCngLmb93GjyYTC8jIcPH0CBWW9e72gLV999RVmzpwJrVZr8/7UqVPx+eefs8oWERE5RXigHMHynit4L+lC1YdRsfJuf783mUwossYFnrde8PXXX7cZF/ztb3/D9u3bGReQT2LiBBF1S3CAPxR+3asSYUPSvX+WRsU6by4mswkFZaU42PxESS9bEPnwww8xf/78Vk/tLl++HE899ZSbZkVERN5mUKwLKk5YeiZx4sJY5y3UmC0WFFWU4eCpEyiuKIfZ4nj/1N6msLAQEydOxPvvv4+Kigp8/PHHeO2119w9LSIi6oXS4sOdNlaov3uWI8fEOa/ShclsRmF503pBb3/gotnWrVsxa9YsGAy2pcyvuuoqfPXVVwgK6tmng4mIyLsl9WDVCVMXlhjGxDsvLjCbzU2Jlac8J7Hy448/bjMuuPLKK/HVV18hODjYTTMjci8mThBRt0U5sWSnuZv7KHEBzg9KTP8LfA6dPoGy6qoeKy/ekU2bNmHevHmt5vLEE0/g6aefdtOsiIjIG4Uq/BEfGujUMXuq4kSCwvnJDSazGXmlxf+LCyphEZ6ZQHHkyBGMHz8ep06dsnmfvc2JiKgt/aJCEOjvnAcVAv3c8/RiYqBr1guaH7gorizvFesFbVm3bh3mzJnTqhLX7Nmz8emnnyIgwLnVRImIiHqyXYe2C5sKfQJds17QnFhZXFHea9cL1q1bh9mzZ7dqPXbDDTdg27ZtUCgUbpoZkfsxcYKI2mXvF35ntuswdmONIdRfAovRdeWljSYTsosKcOTMadQ1NrjsOp3ZsmULbrnlllZ/P4899hhWrlzJElpEROR0zm7XYe6BihPBfoDFqO38wC4yGI3ILirEn6dPoba+zmXXcYX//ve/uOSSS1BWVmbzfnx8PBYtWuSmWRERUW8mk0ow2EnxgFzW899ZFTIALowLTGYz8kqKcTjzFJRuXC9oyxtvvIHbb7+91RrC7bffjo8++gj+/s574paIiKhZUnjPVSyoNziWoBAgA2By3T5C8wMXhzNOQ9lQ77LrdEV7ccFtt92GTZs2MS4gn8fECSKCRQgoVWrklFXicHYB9p44gx0HjuFUYald50cFOy8DUdeVulr/M6mPokee7lBrtTiRnYXTudnQ6fUuv965PvnkE9x8882wtCgPvmTJEjz33HNMmiAiIpdwdruOnqg4MbFPYI/EBTqDHqdys5GRlwOD0dD5CW727rvv4h//+AfUarXN+8OGDcP+/fsRFxfnppkREVFvlxof4ZRx/N3wtXViUmCPxB8anQ4ns7NwOi8HOkPPrhe0ZfXq1W0mRd5///149913IZM5sfUqERF5PbPZYncbip5s1VGtMXV+0DkmJCpara+7glavw8mcs037CL0gLnjhhRfajAsWLVqE999/n3EBEQDnNf0lIo+hMxhR26hGbaMKtY1qKFWaNvt0q7X2ZV06s+KE2tT1gGV4pBTowepXNfV1UDY2IDkxCUmx8S5PWti2bRvmzJnTKqhbvHgxXnjhBSZNEBGRywyIDoGfVAKTkypFtBV3ONuIqJ6NC6rrlFA2NCA5qQ8SY2J73X3ZYrFg2bJlWL16davP/vKXv2D79u2IjIx0w8yIiMhTDIkLg1QCdDcckKLn21mMjJaiJy9bU6eEsqEe/eIT0Dc+AVJJzz67JoTA8uXLsWrVqlafPfHEE6xWSUREdtEZjKisa0BNgwq1KjUaNFqkp/RHSkJsp+eGB8oRLPeD2uBYUkNXVGiMDh1/fnTPbo3W1NdB2dCAfgkJ6BuXAKm05+OCJ598EitXrmz12WOPPcYHMonOwcQJIh8ghEB1gwpltXUor62HSmdfdqO9x0U5MXGi0cGyWucKkxnRjbyLLrFYLMgtKUZ1nRJD+g9EkIv6f23fvh2zZ89u1Y/0gQcewMsvv8zAhoiI7KbW6VGurIdMKkVyfIxd5/jLpBgQHYKcqkanzMFZCRgdiZCZejwuMFvMyCkuRGVtDQb3G4CQoJ57wqYjer0e8+bNw9atW1t9NmfOHKxbt469zYmIqFOBcj/0iwpBQY2qW+P0REWolqL8TDA5tqfSbRaLBQVlpaiuUyJtQAqCAwN75LpCCCxevBivvvpqq89WrVqFJ554okfmQUREnsdiEahpVKFCWY+KugbUq1u3uapTaeweLykiCGcrXdvCKkjuB73ZseSMaH9jz8cF4ty4YCCCA3tmvUAIgYcffhivvPJKq89WrlyJZcuW9cg8iDwFEyeIvJTRZEaFsh5lynpUKOthMNlXQutcKq19iRPRIc5LFlDqHJ8nAPQNkcFkdF+5qwa1GkcyT2NAYhL6xDm3+sSXX36J66+/HiaTbQC4aNEivPLKK0yaICKiTjVotCiqqkVpTR0a/1dRKjRQYXfiBNDUrsNZiRNmFydOJATLYDK6rl9pZxo1ahw9k4H+iYnoF5/o1nt1bW0trr76auzdu7fVZ8uWLcOzzz7LWIKIyEeptDqU1tahX2w0AuX29bNOjQ/vduKEvSW+nSUmUOrWuECt1eLImdNITuzj9PWClsxmM+655x689957rT579dVX8cADD7js2kRE5JmMJjNKapQoralDdX0jTJ1UiHQkcSKxBxInFP5+AOxPnIhW9Ia4IAMDEpPQNy7B5XHBvffei3fffbfVZ6+88goefPBBl12byFMxcYLIi1gsFpTW1qGgogaV9Y3dforDbLFAqzcgMEDe4XFBcj8E+sugNXZ/8aNO37XSXROTFAC6t3jTXRZhQV5pMarrlRianAKFvPtPbn799de47rrrWiVN3HvvvfjPf/7DjQ4iImqXRm9AcVUtiqpr23xKpFGrg0ZvQFAn9/lmg2LDAJQ4bX5yKdCNQlMduiQpAI4snLiCgEBBWSnqGhuRljwQAf72/Z6dKTc3FzNmzMCZM2ds3pfJZHj77bdxxx139PiciIjIvdQ6PQoqa1BSrbQmU/rJZHaV3AaAtPgI7DzdvXjA2MMloSb3UQBQ9+g1WxJCIK+0GLUNdUjtPxAKF1R6MplMmDdvHjZt2mTzvkQiwXvvvYfbb7/d6dckIiLPZLFYUKasR1FVLcpr62FxYB+hQaOFxWKxq91EUnhwd6ZpFz8/x9peTOolcUF+aQlq6+uROmAgAns4LlizZg3XA4jawcQJIi9Qr9Yiv6IaRVU1Xaos0RGVTt9p4gTQ1K6jpM7+bNO2BPjJYLR0bf4poXD3/ohV4/+qT6QOGIjo8Iguj7Njxw5cc801MBpt64bdddddeP3115k0QURErZjNFhRV16KwsgbVDZ0nFFbWNdhddSIhPAghAX5QdTHJsaUAmRSGTp5k6apBYZJeExfUqxpxOPM00vonI6obcYGjDh48iJkzZ6KystLm/ZCQEHz66ae44ooremwuRETkXiazBSU1ShRUVLcZH5TX1tudOBEbqkBUcABq1V2v+Kg19uxNenB4b4oLVDiceQqD+w1AXFS008bV6/WYPXs2tm/fbvO+TCbDRx99hNmzZzvtWkRE5LmqG1QorKxBSY0Sxi7uI1iEQL1Gi8iQzpMikiJc347CAscSJ1LDJUDPFr9qV4NahSOZpzCob3/ER9tfEbQzHcUFH3zwAW666SanXYvI2zBxgshDGU1mFFfXIr+iGkoHymM5Sq3TIzY8tNPjooIV3U+c8Jehq1GLArresg4CoKn06OncbPSNS0ByUh+Hkxz++9//YtasWa2SJhYsWIC33nrLroxeIiLyHVq9ATnlVcgvr3IoibJCaX/iBNBUdeJYcW1XpthKgEyCRhf1FA2EvnfFBSYTTuVmIyk2DgOT+rr8Pv7ll19i9uzZ0GptK40kJSVhx44dGDVqlEuvT0REvUNNgwoFlTUorq6Fydx+smJVfQPMZgtkMvvuT6nx4difW9n5ge0wWwRC/CVQGV3buqtZMAy9Ki4wWyw4U5CHBrUag/r26/ZDERqNBtdccw2+++47m/flcjk++eQTXHXVVd0an4iIPJvZYkFRVS2ySyvQoHFOi4o6lcauxInwQDmC5X5QG1x3J3a0kmWo1AAnFM52GrPFgqzCfDRq1Ejp2w9SSffWC7RaLWbNmtUqLvD398fHH3+Mq6++ulvjE3k7Jk4QeRitwYic0grklVfD2AN9QVVa+4Kp6ODul5OSy2RdOu+8GDlMpq4/7eJKxZXlaNCoMDQ5xe4S3eXl5bj22mthMBhs3p8/fz7WrFnDpAkiIrKqaVAhp6wSJTVKdKVDV1V9A4QQdi/YOzdxQgpXPOYxLMofJpOh8wPdoLSqEg1qFYYPHIwAuWtad7z++ut44IEHWrVsGzlyJL799lv069fPJdclIqLeQQiBkpo6ZBWXo05t38MNZotAZX0DEqMi7Do+rZuJEwAQpfCHyuj6+3VqZO+NC8qqK6HWaTAseRDk/v5dGqOxsRH/+Mc/8PPPP9u8HxgYiO3bt2PatGnOmCoREXkgvdGE3LJK5JZXQe/kak9KlQYD7Tw2MSII2ZUNTr3+uVRG+zMnBoX7w9gD8UdXlFVXQa3VYtjA7sUFV155Jfbs2WPzvkKhwPbt21l5ksgOTJwg8hBqnR5nistRWFnjUM+x7lLp7EtIiA7pfuJEVxMCxsXLAfTOxAkAaFCpcCTzNEakDEFocOeZuN9//z00GtsFrnnz5uG9995j0gQREQFoKqmdUVQGpap7fTkNJjOUKg2iQu3rOzooNgwSAM6IRPy7li/ZqYsS5AB650IIAKg0GhzNysCIlCEICXJe2VKLxYIlS5bg3//+d6vPpkyZgm3btiE8PNxp1yMiot7FbLGgoLIGZ0sqoLbze/y5ymvr7U6cGBAdigA/KfSmrrfcCguQAo1dPt1u43t5XNCgUuHImdMYNnAQwoJDHDpXqVRi+vTp+OOPP2zeDw0NxTfffIPJkyc7c6pEROQhGjU6nC2tQGFVDSwW1+wj1DlQATsp3LWJE0q9/Q9kTEjs5XGBuntxwYwZM7B//36b90NCQvDNN9/gL3/5izOnSuS1mDhB1MuptDpkFpejqKqmS0+Sdv/69i24RAUrusJQBh4AAQAASURBVH0t0cXqlH0CzRAuKvXtLEaTCcezzyBtwEDERER2eOyYMWMQEBAAvb7pdz937ly8//77TJogIiJU1zfiVGEpatroT95VlXUNdidOhCj8ER8WiPIGbecHd0JuZzlwR/ULEkAvjwsMRiOOnc3E0OQURIdHdHs8rVaLuXPn4rPPPmv12bx587BmzRrIXVThgoiI3MtoMiO3vBLZpZXdepq0TFmPC+w8ViaVYHBcOE6VKrt8vRD/nvl+2z/YM+KC42fPYHC/AUiws7+5EAJXX311q6SJyMhIfPfddxg3bpwrpkpERL2YWqdHRmEpCqucUyWyIw0aLSwWi13r1UkR9q03dFWVxv74Z0CIB8UFffsjISbWrnOEEJg1a1arpImIiAh89913uOiii1wxVSKvxMQJol5KbzThdGEJ8surnfJUZ1epdXq7SnhHOaFVh7kLmRN+EgGJWefW35G9LBYLMvJykNKnH/rExbd73IgRI7Bz5058/vnnGDt2LG644QbIutjGhIiIvINSpcapglJU1jn/KY3KugYM7Zdo9/GDYsOckjjh54L9EplEQGbWuaABiPNZLBaczs3GwD590TcuocvjVFVV4aqrrsLvv//e6rOnn34ay5cv73bvdCIi6n0sFoHc8ipkFpXCYOr+nU9nMKJOpUFEiH3VkNLiu5c4Eejn+nuTVCLgZ/GMuEAIgbOF+TAYDeifkNTp8cePH2/VniMuLg47d+7E+eef76ppEhFRL6QzGJFRVIr8ippWLRtdxSIE6jVaRIZ0nhSRFOG8SostyaQSVGvtu9NLIOBv0XtOXFBUAL3RiAGJnccFp06datWeIzY2Fjt37kR6erqLZknknZg4QdTLWCwCOWWVyCwqg9Hs/tu42WKBzmBEYEDHTykGyf0Q6C+D1tj1ORu7ENdNTAqExdL9zZuelFtSBJ1Bj5Q+/drdyLjkkktwySWX9PDMiIiot1Hr9DiRX4zSmjqXXaOmUQ2T2Qw/O5P0BsWF4decim5f198FlZTGJwbC7GFxQV5JMbQ6PQb36+9wgsPZs2cxffp05OTk2Lzv5+eH999/H7feeqszp0pERL1ESY0Sp/JL7G6taa+y2jq7EyeGxId3q31XgMz1iRPj4hUwm3Uuv44zFZSVwmgydbheAABJSUk2lSr79OmDXbt2YejQoT01VSIicjOT2YKzJeU4W1IBk6Xr7bO6qk6lsStxIjxQjmC5H9SGrlfGak+Q3A/CzhISYzwwLigsL4XRZMSgvh2vFyQmJkKhUECna/r5kpKSsGvXLgwbNqynpkrkNVj3nagXKautx66jp3Aiv7hXJE00s3cxJjqke+06dGbHl1zOj/HMSgylVZXIyMuBxQ1BLRER9X4WiwWZRWXYdeSUS5MmgKYnGarq7W8yPiAqBP5O2Ozwlzp/w2SUh8YF5TVVTXGBsD8uOHToECZMmNAqaSIsLAzfffcdkyaIiLxQbaMae0+cwR+ZuU5PmgCAcmW93ccGyf3QN7Lrpbf9e6AY0gWx/q6/iAuUVlXiTEFeh3FBbGwsvvjiC0ybNg3z5s3Db7/9xqQJIiIfUlKtxA+HTyKjqMwtSRMAoFRp7D420UVVJwL87X82/MI4z3yOvKy6Cpn5uR3uI0RHR+PLL7/E9OnTceutt+K3335j0gRRF3nmvxREXqZRo8OxvCKXlN92BpVWh9jw0E6PiwoOQLFS3eXrqI2OB3kxcjNMhi5f0q1q6utwOi8HwwcOsqsfHBER+Yaq+kYczSlEo7bnnoSoqGtAYlSEXcf6yaQYEBWK7KruxS2uaG0eG2CG2YPjgsy8XAxNTuk0LrBYLLjllltQU1Nj836/fv3w7bffYuTIka6cKhER9TC90YQTeUUu71muVGmgMxihkNuXcJCWEIGiLq4BSHug4Wa8wuSxcUGVshYmkwnDBg5qt3XnFVdcgSuuuKKHZ0ZERO6k0RtwLLcQZbX2Jzu6Sp0DiRNJ4UHIrnT+3ofEgTX1BIUFFg+NC6rrlDCZTRg+cHC7ccHUqVMxderUHp4ZkffhTh2RGwkhcLakAj8eO91rkyYABypOBAd06zqNBscSJyICJDAbPKscd0vKhnqcyj0LMytPEBH5PL3RiENZefjlZFaPJk0AQKXSsThkUFxYt68pc3LFiVB/CSxGzyq72VJNfZ1dFakkEkmrShOjRo3C/v37mTRBRORliquV2HXklMuTJpo5UnUiNT68y9dxdQ/2YD94fFygbGzAyZyzMPeiiqREROQeQjS199515FSvSJoAgAaN1u5qykkuqjhhFvatKwT6AcLo2fsIdY2NOJGdBRPjAiKXYuIEkZuodXr8cjILJ/KLYbG4/kmL7lBp7UuciAruXquOOr1jN/1L+gT2wDMqrlfX2IjTudls20FE5MNKqpXYebjnNkVaUun00DhQ8ntQrBMSJ5xcovuSvgqXb8L0hNqGepzuJHlCIpHgvvvus76eOXMm9u7di6SkpJ6YIhER9QCdwYj9mTk4cCYXeqPze4K3p6y2zu5j48MCEREk79J1zGbXfv+9pE+gV8QFDWrV/x624CYJEZGvatBo8fOJMziWWwSTi++fjrAIgXqNfckIieFdb+/VEZ2dv46JSd4RFzRq1DjFpEoil2LiBJEb5JZV4ccjp1HdoHL3VOyi1tn3lEZ3K04odY4tBg2N6IGmqD2krrGByRNERD7IZLbgcHYB/jiTC4PJvV98KxyofhUfFohQRff6hju74sSwCO/5aqNsqO80Lnj55Zdx9OhRHDp0CF9//TVCQztvq0ZERJ6hoLIGu46cQmlNXY9fu7Ku0aGKiF2tOuHquGd4lPfEBfUqFdcLiIh81NmSCvx0NAO1jV1vj+1K9rbriAiSI0ju5/TrNxjsiydGRrXd3sITNSVVMi4gchXv+RZB5AG0egP2nTqLo7mFMHnQjU2t09uVkRkV0vXEiQA/GYwO/kpCpB7alKwdysYGnCnI84rsVyIi6lydSoPdxzKQX1Ht7qkAgMNtw7pbdUImce79LkxmdOp47qZsbEBGfm67cYFUKkV6ejpGjx7dwzMjIiJXMZrM2J+Rgz/P5rstodJssaCqvtHu49PiI7p0Ha2Lq2iEe1lcUNfYiMwO4gIiIvIuBqMJv53ObqpW3Yv/7VfamTgBAEnhzm/XUauzL16K9POuuKBe1YiMvBzGBUQuwMQJoh5SVd+In45lOLwp0RuYLQJaQ+fBRaC/X5czRxX+jmV9Dgjzg8noXYkTAFBdp0ReSbG7p0FERC4khMDZkgrsOZ6JRm3v6b1dWd/o0JfubidOwHkVJ/qG+MFktL/ViKeora9DdlGBu6dBREQ9oE6lwU/HTqPUgVYZrlLuQO/05JgQyGWOLy+aLQIh/q6pIpkYLPPKuKCmvg5ZhfncJCEi8nI1DSr8eCwD5Ur778fuYm/FCQBIinB+4kSFuvM9i7hAKUzG3rP24iy1DfV8CJPIBZg4QdQDsksrsO9UVo/2JXU2lZ0bO11t1+Hn4ELLxKTutQXpzUqqKlBSWe7uaRARkQsYTWb8npHTK58aMZrMDj0tMig2tFupD87s1DHJi+OC8ppqFJSVunsaRETkQnnlVdhzIhNqXe94OKDMgY0aP6kUg+K6lkwZqXB+yW7Au+OCytoaFJQzLiAi8kZCCJwpLsfek2eg1feOmKAzDRqt3S0jnJ04EeAng8rY+brKJX0VTr1ub1KlrEVBWYm7p0HkVZg4QeRCZrMFB7PycDyvGL1sb8RhKp19T2tEdTFxQip17J+jgSFduozHyC0pRpWy1t3TICIiJ1Lr9Pj5RGavfmqkwoHKWMEB/kgID+zytZyZOJES6ryxeqPC8lJU1ta4expERORkJrMFh7LycCSnEBZL71k00OoNqFfbn0yZFh/epeuEyV3Tb3xwmGsqWfQWReVlqOR6ARGRV2l+yOJUQYlH7SNYhEC9xr4HLhPDg516bXsrX3t9XFBRzvUCIidi4gSRi2h0euw5kYmiKu/4MqvW2ps40cUMTon9AYwEAv4W7yuv1dKZgjzUq+zvLUtERL1XdX0jdh/LRIOdCwru4mhLse6063Dm0kUAevfv1RmyCvMZFxAReRG1To89xzNQ2EvXDMocaNcxJD68S/f1ULlrliUV8L42HS2dLchDo1rt7mkQEZETaPUG7D15plc/ZNGROpV996OIIHmX23y3xc/PvgTMIF+ICwrz0aBWuXsaRF6BiRNELlBd34ifjmWgXq1191ScRqWzs1VHSNcqTlgcWGa5IE4Bs9lz257YSwiBzLxcGIyd92ojIqLeK7+iGvtOnYXB1PvvXbWNKhhNZruP7w2JE+fHymHygN9tdwkhcDovBzq99y/6EBF5O6VKgz3He3dCpSObNyEB/ugT6fhTpIF+zn8CdHi0P0wm7/8ObRECp/OyoTd6Ril3IiJqW51ag93HMz16H6HOgZafSeFObNdhx4OYaZE+FBfk5kBvYFxA1F1MnCBystKaOuw7fRYGBzYdPIHKzooT0V1s1WFw4Nc1Js6/S9fwRAaTERn5ORCeVKONiIgANG10n8gvxuHsAlg85N9xIYCqevurGvSPDoG/rKtfKZzzOxkXJ3fKOJ7AZDIhMz8XFmFfD1kiIup9KpT1+OXEGeiNvTvpr7ZRDb0DSfypXWjXEeDMvl3/c1G878QFBqMRp3OzYbaztzwREfUu5cp67D1xBjqDZ2/sKx1InEiMcF7ihEF0HkeMT/SduMBoMuJUbjbMFu/alyLqaUycIHKivPIq/JGZ06t6kzqLWqe3a/O+q606dA78zhIVvnXzb1CpkFda7O5pEBGRA4QQOJxdgLMlFe6eisMcadfhJ5UiOTrEhbPpXFKgb8UFjRo18ktK3D0NIiLqgoKKavyWkQ2Th2x0lzvQriOtC4kT/i5Ylewb5H3rMR1RaTTIKSpw9zSIiMhBeeVV+D0jGyazZ8QEHWnQaGGxM7ZxZsUJjanze35/H4sL1FoNsgsL3T0NIo/GxAkiJ8kqLseRnEInPTvZ+1iEgFbfeaknhb8MwV3oVaY22hdcKWQATL23nKmrlFRWoErZO3vfEhGRLSEEDp3NR0Fljbun0iUVDiROAN1o1+GEKhxyKSAx+2BcUFWBmvo6d0+DiIgckFFYij+zC5xx++sxZQ6060gID0J4oGPVIaVOXkHxkwhIzZ5b6ryrKmpruF5ARORBMovKmvYRPCgm6IhFCNTb2X4syYkVJ+r1He8nSCUCMh9cL6hU1qCy1jPXo4h6AyZOEDnBqYISnCzw/if/VDr72nVEdaFdh8rOxImLkxQ+W576bGE+tHrfC/aIiDyJxSLwx5lcFFV57uK1WqeH2s57PgAMiuti4oQTTEhU2P1ki7fJKsiDzmD/3xMREbnP8bwiZBSVuXsaDqusa3DoPpsaH+HQ+M5uSTk+MdBn44KzRQWMC4iIPMDpwlKcLix19zScrk6ltuu4iKAABHXhocu2VGk6bnt2UYLCZ9tWZBcVQqtnXEDUFUycIOqmE/nFOFNc7u5p9AiV1r6bbXSI4+066nT2BTHnRcscHttbmC0WZBXkO31xiYiInMNssWB/Zg5Ka+rcPZVuc6TqRFxoIMIUjj1hCjhns+T8GN+NC0xmMzLzcn02oZSIyFOczC9Gdmmlu6fRJSazBVUNKruPT3WwXYfZ7NzNjFGxztmI8URmsxln8nO5XkBE1IudKihBpgcmUtqjTqWx+1hntOuQAKjQGDs85oJYx9cpvIXZwriAqKuYOEHUDZlFZR7Zu7yrVDr7qh10peKEUtdxhqh1bH/7jvNWDWoVSip95785IiJPYbZY8HtGDsodKGndm1X2RLsOJ3x/j5H75tMjzRo1ahSWed/TSkRE3uJUQQmyPHzNoLzW/thmYEwo/GX2LzUaTM5N/ouV+/p6gRqF5YwLiIh6o4zCUq9++FKptj9xItEJ7TqCAvxg7mRNIS7At+OCRo0aBWXeXyWdyNmYOEHURbllVV5ZVqsjdleccDBxIsBPCoMd6yUxCilMBraqyC8rgVrre31biYh6KyEEDmblOZxs0JtV1TU69GRCV9p1WLr55ENEgAQmA++HxRUVUGvtX6QiIqKekVFU5hUbJOXKOruP9ZdJkRIbavfxOqPzNjTCAyQwMy5AYXkZGlT2VwkhIiLXO1Nc7pEtuxzRoNbCYrHvO74zKk4o/DuuMhXsL4HFyH2Eoopy1Ksa3T0NIo/CxAmiLiiuqsXR3EJ3T6PH2dvvPCrYsVYdAZ0EOs0u6et4CxBvJIRAVmEeS20REfUSR3IKvaI9x7mMZjNqG+3rUQo0VZyQOHiN7iZOTO4T2K3zvYWAQFYhW3kREfUmZ4rLkeElD1qodQY0aOxPSHCkXYfJIhDs52gE0bZJSYHOKGblFc4WFbCVFxFRL5FXXoVTBd7/1L9FCNTbGS8kOaHihFTWcdvOS/oo+B35f84WFcBiYVxAZC/fbf5H1EUVynocPJvv7mm4hVqnhxACEknHCxvRIQFIjg5BRFAAIgLlCArwQ4BMBrmfFH4yKSQAJBLAaBbQGU3QmQQiogyo0RhQqTKgsF6HujZadwwJkwC+XZHbSqXRoKiiDP0Tktw9FSIin3aqoAT5FdXunoZLVNY1IDosxK5jg+R+SAwPQml968oHYQp/RAUHNMUFQXIEyZviAn8/KcanSSCFBM2hRaPeBKXOBKXWiFqNEWWNepQ06lHfRlyQFsG4oJlKo0FpVQX6xCW4eypERD7PGzdIymrrERZkX8JiWnwEvkFhu0kMYQp/xIQoEBYoR6jCHxdJ/CH3lyHQTwaJBGje4xAQ0JksqNeZUKczWdcLCup0qNe3jguGRTIuaKbRaVFSUYF+CYnungoRkU+rUDb41MOXdSo1IkM6T4qICApAkFwGjcGMkAA/xIUGIjpEgUC5DIH+fgj0lyHATwapVAKpRALp/9YLNAYTVHoTVHojNCbgPI0EZY16VGuMra4xPFIKMFcAAKDV6VBcWc59BCI7MXGCyAE1DSrsz8z12WxFixDQ6A0IVnTciiPAT4bbJqY5NPaEFq8bdCbk1GqQU6vFiYpGnKhQIVhigG93JrNVVF6GuMhoKAIca41CRETOkVde7RUluNtTUdeAYf3t/2I9KC4M9VoDBkSHIDkmFIlhQYgNUyDQzspSHdEYzShQanGiUoUT5SqcqlQhRGqAiRskVvllpYgOj2RcQETkRuXKehzN8b4NknJlPdL62pecF6rwtyZThgT4ITkmFEnhwegTGYT4sECnxAU1GiPylFrkKTU4XanG8YpGhEmNMDIusCosL0NsZBTjAiIiN2nQaPHHmRz40jZCncr+FpK3TkhFWGDTgxVd9c/zm/5vo96E3FotsmrUyKhS43h5I8JlRpiYOGFV9L+4IDCAFb2JOiMRvroDTOQgjd6A3ccyoHdiD05PNHHEEMRHON7HvLssQkCtUaO2oR61DfVQadjLGwCiwyMwPGWwu6dBRORzKpT1+C0j26sXQSQSYOa4UfD367gEZjOj2QI/qaTTylTOYBMX1NdDpWVcAACRoWEYOTjV3dMgIvJJ9WoNfj5xBiaz963SSwDMGJdud5vNghoV5H5SJIQF9lxcoNWgtr4etQ11XC/4n8iwMIwcxLiAiKin6QxG7DmeCY3e4O6p9KiIkCBclj7M3dOAEAKq/8UFyoZ6NGrsb0PqzSJCw3Ae1wuIOsXECSI7mC0W7D1xBkoHsia9VXpKPwxKjHP3NKDT61FWU4WKmmoYTb6dzDJyUCoiw3o+mYWIyFepdXr8dCwDRh8od3DR0BT0iY509zQ6pTcYUF5TjbLqSp+PC4YmpyA2Msrd0yAi8ik6gxG7j2dC68UbJGOGJKN/XLS7p2EXvcGAspoqlFdXMS5gXEBE1KPMZgv2nvTNfQSpRIIrx18AqdT1SZOO0BsNKK+uQll1NYym1m09fEnagIGIi/KMeI7IXaTungCRJziaU+iTwU5b1Fq9u6cAAFAEBGBgUl+MG3E+0gYMRFhwsLun5DY5JYWwCO97qomIqDcyWyzYn5njE0kTAFBZ1+DuKdglQC7HgMQkjBtxPlL7JyMksPO+qt4qv6yEcQERUQ+yWCz4IzPHq5MmAKCstt7dU7BbgFyO5MQ+1vWCUB9eL8gtKYLFwriAiKinHDqb57P7CBYh0KDRunsarQT4yzEgsQ/GjTwPackpCAsOcfeU3CavtBhmxgVEHep+Y0EiL5dbVoWCyhp3T6PXUOl6R+JEM6lUirioaMRFRUOt1aCkshIVtdXunlaP0up0KK2qRN84+3rOEhFR1x3NKUS9uvctBLiKpyRONJNKpYiPjkF8dAzqVY0oqapETZ3S3dPqUTq9HuXV1UiKdX+FMCIiX3A0twg1jd5fArqyrgEWi+h1T5F25Nz1ApVGjdKqSlQqa+FLxXcNRmPTekE81wuIiFwtu7QCJTV17p6GWylVGkSE9M4HGaQSKeIioxAXGQWVRoPS6kpU1tb4YFxQgX7xie6eClGvxYoTRB2oaVDheF6Ru6fRq6h0OndPoV3BgUFIHZCMC4cOR3hIqLun06OKystgMvt2CVIiIlfLK/e9ZEq1zgBVL6k25ajwkFAMHzgIY4efh5iI3t9uxJkKy0thNvtGVRQiIncqqqpFfoVvJO4bzWZUNzS6expdFhIUjNQBAzF62AhEhoW7ezo9qqiC6wVERK6mVGlwMr/E3dNwuzq1ZySThgQFIbV/MkYPG4EoH4sLiivKYfLxVmZEHWHiBFE7dAYj/jiTC4sPZRzaQ60z9PoszODAIJw/JA3DBg6CQh7g7un0CJPZjJLKCndPg4jIaykb1TiW65vJlJ5WdaIlRUAAhg0chBEpgxEgl7t7Oj3CaDKhuLLc3dMgIvJqap0eR3MK3T2NHlWu9Jx2He0JDFBg5KAhGJ4y2KfWC4oruF5AROQqRpMZB7iPAAAe16YkMECBEYOGYETKYCgCfCcuKOJ6AVG7mDhB1I6jOYXQGYzunkavI4SAxkN6t8ZERGL0sBFITuoDmVTm7um4XElVJbNFiYhcwGQ240CW7y6CVHh44kSzqPAIjB42An3j4iGB55QZ76qSygoYjIxliYhcwSIEDmblwehj1X3Kaz0/caJZ9P/iggGJSZBKvH95tKSKcQERkasczSmEupe1t3aXBrUWFovnrZ1EhUdg9NARGJDYB1Kp98cFpVWVMBg9Y4+HqKd5/78ARF1QWFmD0to6d0+j11Jpe2+7jpakUin6xSfiwqHDERLUO/urOYvZbEYxq04QETndyfwSqHW++4Wyur7Ra5JGZFIZBvbphwuGDkNoULC7p+NSZosFRRVl7p4GEZFXyigsRW2jZ5SidiaVTo9GjeesB3RGKpWif0ISRg8fgYjQMHdPx6UsFgsKy0vdPQ0iIq+TX1GNoupad0+j17AIgQaN1t3T6JKmuCARY4aNRKQPxAUF5VwvIGoLEyeIWtDqDT5bitteKg/MoFUEBCB9yFAkxsS6eyouVVpVASOrThAROU1VXSNyy6vcPQ23MprNUHrZ5lBwYBDSU4diYFJfd0/FpcqrqxkXEBE5WVVdI84U+255Y29o19GSQh6AkYOGoG9cgrun4lLlNdWsOkFE5EQqrY77CG3wtHYdLQXI5RgxaAj6xXt3XFBRU82qE0RtYOIEUQuHswt8rtymo1Raz0ucAJqyRgf3G4ChySmQeWnJLbPFghJWnSAicgqT2Yw/s/PdPY1ewVvadZxLIpGgb3wChqcM9tq4wCIsKKuudPc0iIi8htFkxqGzee6ehluVeWl1TolEgoF9+nr1eoEQAqWMC4iInOZITiHMFou7p9Hr1Kk9/8ELiUSC5KS+GDZwkHfHBVWMC4ha8s7/jyfqorzyKq/cGHA2lc6zS3PGRkZhVNpwBCsC3T0VlyirroKFQTsRUbedyC+GRs/sewCo9OL4KDo8AucPGQq5v7+7p+ISpVWVjAuIiJzkdGEptAbffmK/plEFgxdXM4qNjEJ66jAoAgLcPRWXKKuu4iYfEZETFFbWoKq+0d3T6JXqPLzixLliIiIxKm0YAgMU7p6KSzTFBXyImOhcTJwg+h+N3oAT+cXunkavFeDvh9jwUAxKjMOAuBh3T6fbghQKpKcN9co+piazCZVK9tYjIuqOyroG5JVXu3savYayUe3VmyQhQUEYlTYMIYFB7p6K0xlNjAuIiJxBqVIjp4xP5QkBVCi9N6ESAIIDA3FB2jBEhYW7eypOZzKZUFlb4+5pEBF5NL3RhON53Edoj1ZvhBDC3dNwmiBFIEalDUNUeIS7p+J0JrMZFTWMC4jO5efuCRD1FsfzimAyM+u+mUQCxISFIjEqHIlREQhWeN/TFjKpDCNSBiMzPxc19XXuno5TlVZVICHa8xNciIjcwWIROJpT6O5p9CphQYHQGYyQ+3nv14cAfznOH5KGMwV5XhcXlFSWMy4gIuoGIQSOZDM2aFZeW49+sVHunoZL+cn8MDxlMM4U5KHKyxIQSyqb1gskEom7p0JE5JFO5hd79YMFjpL7+SExKhzRYSGIDg1BSGCA191j/GQyDB84CFmF+V6XgFhSVYHEmFiv+zsj6irvXfkkckBlXQNKa+rcPQ2385fJEB8ZhsSoCCREhsPfT+buKbmcVCrFsIGDkJmfi+o6pbun4zRqrRb1qkaEh4S6eypERB4np6wSKp3e3dNwu5iwEPSJiURiVASCAuTunk6PkMlkGDZwELKLClBe4z0VRzQ6HZQN9Yj0widniYh6Qk5ZFerU3lN2ujvCgwMRGRrs7mn0CIlEgrQBAwHAq5IntHodahvqEe2FT84SEblaVX0jCiq9a+O8K2RSCRKjItAvNhrxkWGQ+sCmu0QiQWr/ZADwquQJnV6Pmvo6xEREunsqRL0CEyfI51mEwLHcIndPw62SoiKQkhiLmPBQnwhyWpJIJBianIKMvByvesK0tKqSiRNERA7SG03ILCpz9zTcRiqVoF9MFAYlxSEi2PvaVthDIpFgcL8BMJnNXpVUWVpVycQJIqIu0OoNOF1Y4u5puFVQgBwpCbHoExPpldUoO2JNnhBAVZ33JE+UV1cxcYKIyEEWi8CRnAJ3T8Ot4sJD0S8uGklRET7x0GVL1uQJIbyqJWZ5dRUTJ4j+h4kT5PPyyqrQqNW5exo9TiaVIjk+GoOT4n1u4aMtzckTp/NyoGyod/d0nKKmrg4GoxFyf393T4WIyGNkFJbCaDa7exo9TuHvj5TEWAxMiEEA7xvWTRKTyYQ6VaO7p+MUyoYGGE1G+Pvx75eIyBEn80t8tq1neFAgUvsmoE9MpE8+ZNFMIpEgNTkZxhwT6hob3D0dp1A2NHC9gIjIQXkVVVBpfa86pZ9UipSkOAxKiEWgj1Sj7IhEIkHqgIEwms1es4+gbGyAwWiA3J9/v0RMnCCfZjSZkeFjT5XKpFKkJMYitU8CAvz5T8C5pFIphiWn4EhWBrQ6z0+mERCoUtaiT1y8u6dCROQRGjRa5FVUuXsaPSo0UIG0vgnoGxOF/8/efcfJVZb9H/9OL9t7yWbTeyEJoRhaBEFQKcrzE0SUFgxdBQUREVSQB+RRqgUkFAEVfUBUeESq0gklJCSkJ5tskt3N9r7Tzu+PJWMmu9lsmdkzZ+bzfr32lcyZc85cySY799z3dV+X3Z6+CyL9sdvtmjlxsj5cv1Yd3V1mhzNiveOCJpUXFZsdCgBYRktHp7bXp85uwsEqzM7U1IpSleZRqWgPu82umRMmadXG9Wrr7DA7nBFjvgAAhiYYCqdddUqH3a5JZUWawjpCHzabTTMmTOwdF3RYf1wgSXWNjaooKTU7DMB0drMDAMy0tnqXAqGQ2WGMCptNmlRWrM8ePFtzxlcw2NkPh8OhmRMmyWFPjR+PqdSHFQASbdWWahmG2VGMDofdppmV5Tpu3kxVFheQNLEfDodDMydNlsuZGuOmVOrDCgCjYXXVTrNDGFWleTlaPHe6jp4zjaSJfjgcDs2cODllqjfVNtabHQIAWMaGHbXqCabHOoIkjSsu0GcPnq3ZrCPsl8Pu0KyJk1OmelMt8wWAJBInkMa6A0Ft2lVndhijIsvn1eI503XQxLHyulPjjTyR/F6fplSONzuMuGjr7FBXj/WrZwBAotW3tKm2OTVKLx9ISV62PjN/lqaPLSNhYhC8bo9mTJgkWwqUKG/r7FBnClTVAoDR0NDarpqm1Ci/fCAel1OHTZ+oRTMnKz8rw+xwkprb5dK0cePNDiMuOrq61NHVaXYYAJD0eoIhbdxZa3YYo6IwO1PHHjRDB08ZzzrCILicLk0bN8HsMOKis7tL7Z2MCwASJ5C2Nu6sVSSS+ttKp5SX6Nh5M5TH5MeQFOXla0xRapSspOoEABzYuuoas0NIOK/bpcOmTdQRM6cow+sxOxxLycnM0oTyCrPDiIu6JnaRAMBgrK7aYXYIo6KyKF/HL5ilMQV5ZodiGXnZOSnT4oLdpQBwYOuraxSKRMwOI6FsNpvmTqjQ0XOmKTfTb3Y4lpKbla2K4tRocUGVSoDECaSpQCikzTWp3cM8w+vR0XOmac6EipRpOzHaJoypUHZGptlhjFhdI4kTADCQpvaOlK82MaGkUMfPn6UxhSyKDFd5UbGy/NZPRN3d2CAjXXrSAMAw1Ta1qr613ewwEsrndmnRzMlaOHWC3CnSkmo0jS8bo0yf9ReW2GgBAAPrDgS1uSa1q1b7PW4dM2eaJpenRlKgGcaVlyvTnwLjgmbGBQCrqUhLm3btViiculmiE0uLdNy8GSrMtv6iv5lsNpumj59o+cSTrp5uym8CwABSudqEzSbNm1Sp+ZPHyeV0mB2OpdlsNk2pHGf5lh3dgYA6urrMDgMAktqabaldbWJ8SaE+M3+WSvNyzA7Fsux2u6aNnyi7xecLAsEg8wUAMICNu+oUTuGq1eUFuTpu3kxadY2Q3WZPiXWEQDBIuw6kPWv/LwaGIRQOa1OK9iRz2G06fPokzZtUKaeDxZF48LjdGltaZnYYI9bYkh69eQFgqFo7u7SzodnsMBLC7XToiJlTNLG0yOxQUkaGz58SJTib2hgXAMD+NLS2q6k9NSeMbTZpweRxWkBCZVz4vV5Nqqg0O4wRY74AAPoXDke0NUWrVtttNh00cawOnz6JMUGc+DwpMi5obTY7BMBUJE4g7WytrVcgFDY7jLjzuJw6avY0lRfkmh1KyhlTVCKfx9q94BtbmQgBgP6karWJLJ9Xi+dOV3FuttmhpJzK0jL5PF6zwxiRptbUbk0DACOxMWU3Wtj1qRmTNb6k0OxQUkppQaHlW3wyXwAA/du2uyEl1xF8bpeOmTtdk8qKzQ4l5ZQUFCon0+LjAhIqkeZInEBaiUQMbdiRepMgGV6PjpkzjZJaCWK32zVxjHWzRTN8PuVkZpkdBgAkna6egKrrU69/Y3FuthbPna5Mn7UX95OV3W7XlMpxZocxIq0d7QqHU28CEABGqrMnkJKVqDwup46eM5XWHAkyvnyM2SGMSGtHu4KhkNlhAEDS2bizzuwQ4i7D69bRc6YpL9Nvdigpa3x5hdkhjEhbZ4eCoaDZYQCmcZodADCadjU1qyuQWj/0s3xeHTlrinwet9mhpLT8nBzlZ+dYYieG0+FUXla28rJ7v9wu/m0AQH+q6hpkpFir0sqifB08ZbxsNpvZoaS0nMwslRYUqqah3uxQhsUwDDW3taogN8/sUAAgqWzaVacUGxoo0+vREbOmKMNr7SqKySwnM8sy8wX709TWquK8fLPDAICkUdvUqraubrPDiKtMn0dHzZrKOkKCZWdkKj8nV40tzWaHMmxNra0qzi8wOwzAFCROIK1srbXm5Pb+ZPu9OnLWVHndLrNDSQsTK8aq6eNWGUm4ypaVkaG8rBzlZWcry5/BghkAHIBhGCk3LigvyCVpYhSNLS1TbUODDIsusTWROAEAMULhcMqNDfKzMvSpGZPlcTH9l2jjy8dYO3GipYXECQDYS6q17sr0eXT07GmsI4yS8WVjLJ040djaQuIE0hafnJA2unoCqmtKnX7OWT6vjpo9VR4Xg53R4vN4VZJfkBS7S90uVzRRIjcrWy4nP84BYCjqWtrU2RMwO4y4Kc7N1qFTJ5A0MYq8bo8KcnNV39xkdijD0mThxR0ASIRtdY0KplAf85wMn46YOUUup8PsUNJChs+vorx87W6yZhu4lo42s0MAgKTR3tWt2ubUWUfwe9xsvhxlGT6fivMLVNfYYHYow9LSzrgA6YuVNqSNqroGi+4H7MvpsOvwGZNImjBBeVGJKYkTNptNORmZysvuTZbI8NGHDgBGYmuN+Ulw8ZKb4dfh0yfKbrebHUraqSgutWziRHcgoEAwQEsvAPhEKlWb8HncWjRjMkkTo2x82RjVNzclZZXKA+kJBNQTCMjjZlwAANt2WzMJrj9el0tHzpoqP+05Rt24snLtbmq05LggEAyqO9Ajr5tWb0g/JE4gLRiGoao6a2b39eeQqROU5fOaHUZayvD5lJOZNSpZl16PR3lZ2crLzlFuZpYcDia9ACAeeoJB7WpsNjuMuPC5XfrUjEly8h5hiqyMDGVnZKq1o93sUIalrbNTBTlMoAFAW1e3mjs6zQ4jLlwOh46YOZn+5SbwejwqzstXrUV3l7Z3dZI4AQCSqlMkccJms+nwGZOU6WPx2wxetydpqlcPR0dXF4kTSEskTiAt1Le2q6O7x+ww4mL62DKV5eeaHUZaKy8qTkjihN1uV25mVrSqhM9DcgwAJMK2ukZFLJjxvy+n3a5PsTBiujHFJWrdYs3EifbODhXk5JodBgCYbnuKLJBI0iHTJijb7zM7jLRVnF9gicQJp8OhLH+GsjIylJWRqSx/Bi1AAUBSU1uH2lNkHWHWuHLlZ2WYHUZaK7ZI4oTT4egdE/gzlZ3ROz5wOhgXID3xLx9poSpFSm6W5uVoxtgys8NIewU5ufK43OoJBkZ8L7/Xp/zs3qoS2RmZlFkHgFGwo8GarRX2NXfiWOVm0LrJbAU5ufJ6POrusd7kWntnauyuBoCRSpWdpbPGjVFpXo7ZYaS1nMysuM0XxFOGzxezGOLzeGWz2cwOCwCSTqokU5bkZWtKeYnZYaS97IxMed0edQeSZ77AJlvvuGCv5Em/lw2cwB4kTiDlRQxDu5pazA5jxDK9Hh0ydQIfbJOAzWZTWWGRtu7aMeRrnQ6Hcj9pv5GXnS0PfcUBYFR1BYJqbOswO4wRK8nN1viSQrPDgHrHBWOKSrSpepvZoQxZG4kTAJAyO0vHFORqWkWp2WGkPZvNpuL8fG2vrTEtBpfTFU2QyPJnKsvvp/UnAAyCYRiqrrf+Rguvy6WFU8azjpAE9owLttXsMi0Gt8ulLH9vm9GsjAxl+jPkYPMmsF8kTiDlNbS2KxgKmx3GiDjsdh0+Y5JcTj7oJouSgsJBJ05k+TOU90lViSx/BoNWADDRroZms0MYMZfDofmTx5kdBvZSmJtnycSJYCionkCAfuYA0loq7Cx1Ox2aN4mxQbIozi8ctcQJm82mTJ9fWRn/WRChHzkADM/uljZ1B4NmhzFiC6eOl8flMjsMfKI4v2DUEifsNpsy/f7exMlPxgZ83geGhsQJpLyaRutXm5g+towepUnG7erdwdHa0XfXstvpiiZK5GZl0ycUAJLIzsZms0MYsTkTKuT38ME3mbhdLmX4fOro6jI7lCFr7+xgIgVA2jIMQ9Up0MJr9vgKeVx87kwWfq9XWf4MtXXGv8qZx+1W9ieLIVkZGcr0+Wn5CQBxsiMFNlpMqyhVcW622WFgLz6Pd7/rCCPldXuiY4Jsf6Yy/D7ZbYwLgJHgUxVS3i6LL5Bk+bz0I0tS+dm5au3okM1mU3ZGpvKys5WfnaMMH/3mASAZBUNh7W5pMzuMESnJo0VHssrLyrFk4kRnd7cKzA4CAEzS0tml7oC1d5YWZGdqXDE/yZNNcX7BiBMnHHa7Mv17FkN6+5C72UEMAAlT12ztDZg5GT7NrCw3Owz0ozi/cMSJE3vGBb3tuDKV5c9gXAAkAIkTSGltnd2W71U6b1Kl7HZaOySj4vwC+X0+5WZmmd4v1DAMWoAAwAHUNLXIMAyzwxg2l8OhBZThTlp52dmqrjOvn/lwdfVYe6wMACNR19RqdggjYrPZNH9SJZ8Fk1BBTu6Q23j5vN5ogkR2Rob8Xh/fWwAYJR3dPeroDpgdxojMnTCW940klZ+TI20f2jW9Faz2tNxgXACMFhInkNJ2NTWbHcKIjCnIVVFOltlhYD88breppa3bu7pV29Sq2uZWNbS166SFc+Q0OYEDAJJZTZO1d49MG1sqHy06klZ2ZqYcdrvCkYjZoQxJd6Db7BAAwDR1zdZOnJg6poS2nklqz3xBT6D/RTinw9FbWtvfmySR5c+QkzafAGCaWosnU5bl57COkMQ8Lre8bo+6A/1vXHA6nNEEiaxPqk05HYwLADPwPw8prabRugskNps0a9wYs8NAEgmFe0vM70mW6Ninmkp9S7tK83NMig4Akl99a7vZIQyb2+nQxNIis8PAAOw2u3KzstXQ0mx2KAfkdDrldfdO3GTSYgxAmgqHI6pvs+7YIMPr1vSKMrPDwAByMrJUF2iQTTb5fb69FkMy5fd6zQ4PALCXuhbrJk7YbNKc8RVmh4EDyMnMVHdjj2yyKcPn602gzMhUtj9DPsYFQNIgcQIpKxyJqLFtZH2jzDS+pFCZPt4w011zR6fq9lSVaG1XZIAS83UtrSROAMB+dPYE1NVj3bKbk8qKqSpkAXnZyZE44XT0JkZ4PJ5ogoTX7Zbnk1/NbjEGAMmgvrVNkYh1W3jNrBwjh8NudhgYwJjiEpUWFirT75fDznsvACQrwzC0u7nN7DCGrbKogHUECxhTXKqSgkJl+jPksDOGA5IViRNIWc3tnQMuMiczu92mGWPLzQ4DJggEQ6prblVNc6vqmlvVHQgO+trdLdYd4ANAojVYuNqE02HXpPJis8PAIGRnjE5pVKfDIa/bI88nSREez57kCBIjAGCwai3cpsPndmlMYZ7ZYeAAMv1UdQIAK2hq71AwHDY7jGGx2aRpVKCyhAwf7dUAKyBxAinLyuW4xxTkyet2mR0GRoFhGGpq71BNU6tqm1rV1D78KimtnV0KhyPsOgKAftS3Wje5bGJpkdz0vLYEn8cTl/s4HY7/JEXsqRjh+U/VCKqPAMDI1bdYd85gYlmR7Dab2WEAAJASdlt4TDC2qECZvvh8DgUAkDiBFNZo4V6l44sLzQ4BCdQVCKq2qUW1Ta3a3dKqQCg+Gc2GIbV0dio/KzMu9wOAVGLVihN2u02Ty0vMDgODZLfb5XV71B3oGfA8h93R20LD4+knQcItp4OPaQCQSOFIRC2dXWaHMSwOu00TSorMDgMWYxiGOrp7KOUOAP0YyUY2s02mOiWGIBQOq7GtQ41tHSrNy1FuJtWxgH0xI4eU1dTWaXYIw5Lhdaswh4XvVBKJRFTf2q7a5lbVNbUmdIKuqZ3ECQDYVyAYUmtnt9lhDMv44kKqUFmMz+tVMBSU55O2GXsnR+xJkHBRQQQATNXa0SXDoq09xxYVyO3ifQQD6w4EP1kYaVdjW4ea2jslGTr58PlUKwGAffT+jLSebL9XuRksfKN/hmGoras7mijR2Nahts4u7T0CJnEC6ItPWkhJXT0BdQeDZocxLOOKC2XjQ6zldXT3qOaTqhL1LW0KRSKj8rrNFh3oA0AiNXVY92djZXGB2SFgiGZOmCS7nbZZAJDMmi08NmBnKfYVjkTU3N75n4WR9g519QT6Pbe1o4tFEgDYS08wuN+fmcmuojDf7BCQRALB0F5jgXY1tXUqGN5/pWsrV1oBEonECaQkq2aJSiyQWFUoHNHuljbVNvcmS3R0D1yiO1FInACAvlo7rFmK2+d2KY+JbcshaQIAkp9VPzcV52Qp2+8zOwyYrKO7Z6/do+1q6ehSZJAVVJraO0mcAIC9NLdbc75AksYWkTiRriKGoZaOLjW2tavpkzFB+xDXI6y8hgYkEokTSEmtFu1VWpKXLb/HbXYYGKSWjq5ookRDa/ugJyoSqbWrS+FwRA4HizYAsIdVe5iX5edShQoAgASwasUJNlqkn1A4rKa2TjW2t0eTJXqCoWHfr8Wi//YBIFFaOq35czE/K0MZXo/ZYWCUdAWC0fZbjW0dam7vVHiEFa67A0EFQiG5aSUKxOB/BFKSWbv9R2p8caHZIWAAgVBIdc1tqm1qUW1zq7oDydcOxjB6FwjzszLMDgUAkkabRRMnygtyzQ4BAICUs2eHnhUV52abHQISaN9e5E1tHWrdpxf5SA11NyoApDqrVqik2kTq6tOCq61dXQlah+jo6pE7i2ViYG/8j0BKsuIHQbvNptK8HLPDwF4Mw1BTe2c0UaKprSOuExaJ0t7VTeIEAOylravb7BCGzOVwqDAny+wwAABIOe1d3UlRLXCocvw+ed0us8NAHEV7kbf/J1FioF7k8dBuwXExACRSqwV/Ltps0piCPLPDQJz8pwVXb0WJobTgGqn27h7lsY4AxCBxAinJih8E87IyaK+QZJrbO/XKyrVmhzFknT0Bs0MAgKTRFQgqFB5Z+UIzlObnyE6bDgAA4s6qFSqpNmFt8ehFHg9dPQFFIhHZ7cw/AYDU+3PRagqzs0imtKi9W3A1tHaoqX1kLbhGyoobkIFEI3ECKScUDpv6ZjNchdmZZoeAfeRm+uV02C234EbiBAD8R4cFkyklqTw/1+wQkCJYHAGAWJZNnMgjccJKEtGLPB4MSR3dAWX5vWaHAgCmC0cillxHKMhiHcEKRqMF10hZdc4MSCQSJ5By2rusOQlSRDnupGOz2VSYnaWaphazQxkSEicA4D+s+jORNh0YrEAopM7ugDp6etTZHVBnT0CdPT3q6O79tSw/V4dMnWB2mACQNDq6rTc2cNhtbLZIYqPZizwe2ru7SZwAAFl3voAWzckpEPqkBVdrhxrb29XU1pnwFlwjRcUJoC8SJ5ByrLp7JC+TAU8yKsqxYuKENf8PAEAiWHH3iNftksfFMB29gqHwXkkR/0mI6OwJqLM7cMCJGCuWngWARLLi56WC7Cw5qB6UlF79aL0aWttHrRd5PFh1wxEAxBuJE4inLTX1Wl21w+wwhsSqa2lAIjEji5TTFbDegCfD65HL6TA7DPQj2+8zO4Qh6+wJyDAM2Ww2s0MBANN1B5N3t9/+5Gb4zQ4BoygYCquzJ6CO7p5otYi9K0iMdIeKVScDASBRunqsNzYoyqHaRLIKRyKWSpqQSKoEgD2s+PPQ73HLzUaLpGTFuZyeYEiRiCG7nXUEYA9+wiLlBELJXf6oP1Z8U00Xfo/b7BCGLBIx1BMMyet2mR0KAJiuJ4nLJO9PTob1kvawf6FwuE+ViL0rSCR67Nptwf8DAJBIVtxskemjrUKyys3wq7Gtw+wwhiQQsl5FNgBIBCsmmVtxk1+6yM205vcmEGIdAdgbdf6QcoIW/AA42m+qixcvls1mG9LX+PHj+9zHMAw9+uij+sxnPqOioiJ5vV6NHz9eF1xwgdasWTPiOCORiB555BF99rOfVVlZmTwej8rLy/WlL31Jzz333IjvPxhWTJyQrFmaHgASoduCPw/jvTiyZs0aXXbZZZo1a5aysrKi79df/epX9eqrr/Y5/8YbbxzyOMFms2nr1q2Diuevf/1rzHUPPfTQkP9M48ePH3J8ixcvHvLrDEYoHFFrZ5dqGlu0aVedVm2p1ttrN+mlDz/W399eob++tUIvrlijNz/epA83b9eGnbXa2dCs5o7OUUn4jRiGwpFIwl8HAKzAMAxLflbK9CYmcaKrq0sFBQXR98obb7xxv+eGw2H5fL5Bveeedtpp+71PKBTS/fffr2OPPVYlJSVyu90aO3asvva1r2nFihUj+vM89thjgx4X/OUvfxnRa+2Rm2m9jTBW3HAEAIlgxSTzRCdOWGnd4M9//rNOPfVUjRkzRh6PRzk5OVqwYIGuu+461dbWjvg1hsrjclkyASHIuACIQcUJpBwrfgD0uq23ON/T06PTTz9dzzzzTMzxqqoqLVu2TI8++qjuv/9+ff3rXx/W/Xfv3q2TTz5Zb7/9dszxXbt26amnntJTTz2lCy64QL/+9a/ldCbuR5nDYZfH5bTc5Fo4Yr3/BwCQCD0WbNWR6fXE7V433XSTbrzxRoX3afdQVVWlqqoqPf7447rkkkt09913yz4KvdNbW1t1ySWXJPx1Eunj7bvU2tEVrSBhhTFCKByWYxS+vwCQ7EJhayaSZfriNzbY289+9jM1NjYO6tz169eru7t7RK+3efNmnXbaaVq1alXM8erqaj366KP6wx/+oHvvvVff+MY3hnX/lStXjii+4YjnuG20UHECAHqFRtgW0QzZ/uSvQjUa6wZf+tKX9Nprr8UcDwQC+uCDD/TBBx/o3nvv1R//+Ed99rOfHfafYzh8bpflEnIYFwCxmD1DyrFihpzb6TA7hAPKycmJeXz55Zf3GfzsLRAI6Pzzz9frr78+5NcyDEOnnnpqn6SJfT3wwAO6+uqrh3z/obJi1QmrTggCQLxZYVF7XxlxmoC/8847df311/dJmtjXL3/5S1133XUjei2n0ym//8A7Lq+++mrt2LFjRK81XPuOZYarenejdjQ0qam90zL/voIhxgUAIEkhC1bg8bpdCUl+e+GFF/STn/xk0OePNClhx44dWrx4cZ+kib2FQiFddNFFfRZCBsuMxAlXAjdyJAoLJADQy4rzp74k3IA5musG4XBYn/vc5w44VmhpadEXv/hFffDBB0N+jZGw4rjAiutpQCJZ738xcABW/AA42m+o3/ve93Tuuefu93nDMPSd73wnuvPE6/XGlNH+4IMP9MADD8Rcs2TJEs2dO1fLli2LltcMh8O64oor9N577w0pvqeeekpvvvlm9LHL5dLSpUs1bdo0Pfvss/q///u/6HN33nln9LlE8Xs8amrvTNj9E8GKA38ASISwxX4e2my2uJR2bGpq0vXXXx9zbM6cOTrrrLPkcDj0pz/9ScuXL48+97Of/Uznn3++pkyZotNOO63fUpt7+81vfqO33nor+vjuu+9WcXHxgNe8+uqruu+++4b+h+nH7bffrvb29v0+39XVpW9961sKfNLHvqioSHfffXdcXtvndqmta2Q7bkebFXdSAUAiHCiZMBl5XfEv+fzEE0/o3HPPVWgI8yf7Jjzccccd+01KrKys7HPsiiuu0Pbt26OPi4qKdNlllykrK0sPPPCAVq9eLal3PuIHP/iBXnnllUHH1l+Ms2fP1lVXXbXfcxcsWDDk+/fHChth9sUCCQD0smJLQ4cjsXuhk33d4MEHH9S7774bc+wrX/mKFi1apOrqav36179WS0uLpN55gW9+85v697//PaTXGAkrjgusuJ4GJBKJE0g5VvwAONpvqCeeeOKAz995550x5Tp/9rOfaf78+dHH999/vyJ7DSwvuugi/epXv5IkffWrX9XUqVPV0NAgSXr//ff17rvvauHChYOO79FHH415/Mgjj+jMM8+UJF122WU68cQT9dxzz0nq7Wf25JNP6tprrx30/YfK702+TN4DYYEEAHpFDMPsEIbEbrPF5T5PPvmk2traoo8PPvhgvf766/J4eqtZXHnllTrmmGOiOzzC4bAef/xx3XDDDZo3b57mzZu333u/9957MRMVp59+ui666KIB4+nu7taFF14oI07fj//6r/8a8Plvf/vb0aQJqbdKVX+LOMNhxZ6ljAsAoFfQYgmVUnzfd5qbm3XVVVdp2bJlQ75272oOXq9Xl19++aDbfK1YsUJPPvlk9HFmZqZef/11TZkyRZJ0zjnnaMqUKdF5iH/961+qq6s7YFLm3hobG2OqWi1atGjAhZ94cVtwZykLJADQy4obz5wJTpxI9nWDhx9+OObx7bffHpMoefbZZ2vBggUKftK29dVXX1VVVZXGjRs36NcYCSpOANZHqw6kHCtODLscyZOJuG3bNn3/+9+PPl60aJEuvfTSmHOeeuqpmMcXXnhh9Pf5+fl9FjP2niAZDJfLpZKSEklSRUVFNGlij89//vMxj6uqqoZ0/6GiVQcAWFe6Jk68+uqrMY+XLFkSTZqQJIfD0ad/+GDKW0ciEV1wwQXRHaq5ubm69957D3jdj3/8Y61bt24woY/Y8uXLddddd0Uff+UrX9HJJ58ct/tbcYEkaMHxMQAkgiUrTsQpceLee+9VZWXlsJImpNhxwqRJkwadNCFJf/jDH2IeL1myJJo0IfXOI1x88cU644wzdNVVV+mOO+4YUXySYu6fSA6HXXZ7fMZvo8UwrLnLGgDiLRyx3rggEe27BsvsdYNgMBjT2tvhcGjp0qUx58yePVtHHnlkzLHRbOVlxYoTVmxlBySS9Wb9gBSUTJmI3//+99XZ2duWwmaz6d5775Vtr0Wc6upq1dTURB+73W4ddNBBMfdYtGiRfvOb30Qf710KfDD++Mc/SpJ2796t2traPs/v3r075vFQdqEMR7wWsUZTyIIDfwBIhHhVOBgt8Zp4X7hwobq7u1VdXa3q6mpNnTq1zzkFBQUxjzs6Og5432XLlunDDz+MPv7Rj34UTXbcnw8//FA/+9nPoo9LS0tjxhLxduWVV0Z3uPj9fv3P//xPXO8fkbX+TUlSOGK9mAEgEayYYO6I09jg+eefj6lGVVxcrMMOO0x/+9vfDnhta2trzIaFoSYlvPDCCzGP+9vNetNNNw3pnvvat5XIaCVOSL1Jld2B4Ki9HgAgPqw4Lkh0xYmBmL1u0N7ergsuuEDV1dXasWOHwuGwMjMz+5w3nLmOeLHiRgvrrXwAiUXFCaQcm8UWue02m6kDnr2tWLFCjz/+ePTxWWed1adU98cffxzzuKysTI59KmbsWwp77dq1w4qnqKhIs2fPjjlWU1PTpz/6SSedNKz7p7IICyQAYLmkCUmyxekj62WXXaY//OEPeu2117R161Yde+yxfc7Zd4GhqKhowHt2d3frhz/8YfTxhAkTDtiiIxwOa8mSJdEKFXPnztUFF1ww2D/GkD399NN67bXXoo+//e1vq6ysLK6vYcl/V9YaHgNAwoQN6y2QJOKz3VFHHaV33nlHCxYsGNT5+44ZWltbdfrpp6usrEw+n0/Tpk3Td7/73T6bHKTealX7ziPMmTNHkvTuu+/qj3/8o55//nm1t7cP80/Ta9/dpC+++KIOPfRQZWVlKS8vT8ccc4weeuihhLyPW3F3KQDAmvOnDrs57znJsG6Ql5enX/3qV/rb3/6m999/P2ZTx94++uijmMcHmuuIJ5cVxwTMFwAxkmO1FkhjyfRmesstt8RMIlx77bV9ztm2bVvM47y8vD7n5Obmxjyurq4ecUnUv/71r7rqqqs0c+bMmCoUZ5xxhj71qU+N6N4HYr0htPUSiAAgEazWpkOSbKNU6jkUCsXs8pCkww8/fMBrHnzwQe3atSv6+Dvf+Y7c7oHbWf3iF7/Qu+++K0my2+2677775EzgDoxbbrkl+nufz6dvf/vbcX8NK06uWbF6FgAkghV/HsZzPDNx4kQ9+OCDeuWVV4bU63vfpISXXnpJTz75pGpqatTd3a3169fr9ttv1/Tp0/X666/HnLtjx47o7lRJcjqd6ujo0CGHHKJDDjlEZ555pk444QQVFxfr+uuvjyZbDtW+Md59991avny52tvb1dzcrH//+98677zzdMIJJ8RU3oiHZKoiOljW+58AAPFnwWFB3CpRDVUyrxvs7fnnn49JxnA6nVq4cGHc7n8gziRqyT5Y8drAA6QKEieQcqy2YJwsfSW3bNmi//3f/40+Pv744zVr1qw+5+27C8Tv9/c5JyMjo8+xkZbEuuGGG/Tzn/9cTU1N0WNnnHGGHnrooRHdd1Cstz5iyYE/AMSbFSsDjNaCztVXX62tW7dGH7vdbn3xi1/c7/mRSEQ///nPo49zc3N1zjnnDPgamzdv1g033BB9fMkll+iwww4bftAH8O9//zum3+nZZ5/dp0RnPFjx35XVxscAkCjpnDhx2223acOGDTr33HNlH2J/9H0rTuxPY2OjTjzxRK1fvz56rLm5OeYcr9erz3zmM9HEyj26urp000036Utf+tKQF1AMw9Dq1asHde4LL7ygL3/5y3F9Pw/FccEHADB6HEN8PzSbw2435bNdsq8b7NHY2KiLL7445thnP/tZ5eTkxOX+gxHPJBAA5rDWOwMwCFabBgmFI0kxAX/ffffFvLF/85vf7Pe8fQcy/e0a3bcEl9R34DRU+2as5ubmavHixQoGE99HNBSx3oDHihOCABBvVpsEkUYn8e2OO+7QL37xi5hj55xzjsrLy/d7zfPPP6+NGzdGHy9ZsqTfCY+9feMb34juMK2oqNBPf/rTEUR9YL/61a9iHl9xxRUJeZ0kGLYNGeMCAOhlt1lvbBCJ02aLqVOnDjlhYo99qzmMGTNG3//+93X33Xfr61//eswiTnt7e8yixb5zAe3t7X0+3+/tb3/7m26++eYhxbdp06Y+cxWLFy/Wz3/+c/30pz/VwQcfHPPcP/7xDz366KNDeo2BBILDq5JhLsYGADDc90WzhCPmrCMk+7rBntc++eSTtWnTppjj/VXGSKRgODk2yQ4F0wVALGu9MwCDYMUddUGTMxEjkYh+97vfRR+PGzdOn/vc5/o9d9/B2WD/vkfyfeno6FBjY2PMsebmZl188cU6/PDDtX379mHfezBCIesNeKw4IQgA8Waz2UwrYzlciZ54/81vfqMrr7wy5lhRUVFMi4v+PPjggzGP993Fsa9ly5bpxRdfjD6+5557lJWVNcRoB6+5uVl/+ctfoo+POuoozZ49OyGvZckWMBYcHwNAItgtNi6QkuN958QTT9Rpp52mmTNn6qSTTtKaNWt0880367LLLtPDDz/cJwnhpZde0po1ayTtP/Hj6quv1s6dO1VVVaWLLroo5rnbb7+9T6WKgdjtdl100UX69Kc/rYqKCt199916+eWX9e1vf1vXXnut3nzzzT5zHHffffeg738ggZD1NlsAAMxrezESgWG2tBquZF83kHqrVp166ql64403Yo6ff/75OuKII0Z076Eye51neKz3/wBIJFbWkHKsOC8cNPlD9iuvvKIdO3ZEH59yyin7HbDsu7u0v/6j/R070K7UgTgcDq1du1ZdXV1atWqVjj322Ohza9as0ec///lh90EdDCsOeJwOfrwDgGS9/pKBUDhh5Z7vv/9+XXzxxTGTGXa7XQ8++OCALS06Ojr09NNPRx/PnTtXEydO3O/5tbW1+s53vhN9/MUvflGnnnrqCKMf2JNPPqnu7u7o49NOOy1hr5UMC1hDRcUJAOhlxUSySMT8950f/OAHeuqpp7R69Wo9++yzys7Ojnn+rLPO0vz582OOvfTSS5L6nws4+eSTdeutt6qsrEyVlZX61a9+peOPPz76fFtbm/75z38OOr6JEyfqV7/6lV566SVt375dl112WczzLperT5Loe++9p5aWlkG/xv6EI5GkacE6FBb8rwAAcWe1ihOS1DPKVY6Sfd2gu7tbp5xySszGDam30tbe7UZHS3CUE1vigfkCIJb13hmAA7DaAokkdQUS325iIH/9619jHp9yyin7PXffgUxXV1efc/aU5R7ouqHwer2aNm2avF6vZs+eraefflpjxoyJPr9q1So988wzw77/gVgxccKK5ekBIBFcFhwXdHQH4n7PBx98UEuXLu2zA+Suu+7S5z//+QGvff7552OSEk4++eQBz7/sssvU1NQkScrOzo7rjs79GcpYZqQ6e3oSdu9EYVwAAL2sWHHCKovyCxYsiHm8detWSVJmZmafc88888w+x84444yYxx988EH8gpM0e/Zsud3u6ONIJBKX6pVWbNPhdjosmUQEAPHmsODPwtFOnEjmdYOenh6ddtppeuGFF2KOl5aW6tlnn1VOTs6w7jsS3Sav8wyHy2m9eTMgkZhBQ8px99M7K9l19cR/gWQo9k46yM7O1tFHH73fcysqKmIe91c+c89iyd7X9Ne/bLgyMzN14oknxhzbtxRXPLV1dh/4pCRDxQkA6GXFhMp4L8z/6U9/0pIlS/okTdx666269NJLD3j9vsmJB0qc+POf/xz9fWtrqyoqKmSz2aJfP/rRj2LOP++886LPDUcgEIiZKJk+fbomT548rHsdiGEYau+y3rjA47Le+BgAEsFpwUSyNou875SUlMQ83rMwMnbs2D7zAeXl5X2uHzt2bMzjhoaGuMZnt9tVWFjYb4wjMdol0+PBivNmAJAIVqw40TnK6wjJum4QDod15pln6rnnnos5XlRUpBdeeEGTJk0a8j3jwewNssPBuACIxf8IpBy3BTPkugLmJU7U1NRo48aN0cdHHHFEzC6MfU2dOjXm8c6dOxWJRGIGmvvu2pgxY8aQYopEItq1a5eqqqrkdru1cOHCPufsmzHa0dExpNcYLMMw1NrZNzs22XlcLrNDAICkYMVEsnhOhLz22mv62te+1qe/+H//93/r6quvHtQ9Xn311ejvMzMzdcghh8Qtvnh4//33Y8YBn/70pxP2Wp09AYWToGT6UDEuAIBeVvx52BMMqScYMjUJbvfu3aqurtb27duVm5vb76LJvgshe5IUPB6PJk2apPXr10efq6ur63P9vmMVv98/6PiCwaB27Nih6upqVVdX65hjjlFZWVnMOYZh9GnNsW8ixXAEgtarUOkmoRIAJFkzwbyje/QqICbjusEeV1xxhf7yl7/EHCsqKtKLL76oWbNmDeue8WD2BtnhoOIEEMt6M8nAAVhxIqQzASW5B+vtt9+OeTxv3rwBz58wYYLy8/Ojj7u7u7VixYqYc955552Yx/0lPuzPihUr5PV6VVFRoSOOOEIXXnhhv+etXLky5vHerTviqb27xzKlUffm81jv/wEAJIIVPwDGa1ywc+dO/dd//Zd69qlgcdNNN+maa64Z1D2am5tjFjrmzJmTdLtyhjqWGQkrJlO6nQ5LlqYHgERwOR2WbF/UZuL7z5NPPqni4mItWLBAp556qr75zW/2e97rr78e83jveYCjjjpqwHMlxYw3JGn8+PGDjvH000/XhAkTdNRRR+krX/mKnnjiiT7nrFixIibRMj8/XxMmTBj0a+xPhwVbeLGzFAB6WXMdYfTed5Jt3WCPBx54QL/85S9jjuXn5+uFF17QnDlzhny/eAmHI5asOGHFBCIgkaz3aRE4AI/bej/oG9sSUy1hMD788MOYxwcaANntdp100kkxx37zm99Ef9/a2qo//elPMc9/6UtfGnQ806dPl9frjT5esWJFn/v985//7NO77DOf+cygX2MoWjust0Bit9mYCAGAT/gG2A2RrOI1Ab906VLV1tbGHLvooot03XXXDfoeK1eujGnxMZikhAcffHDAr1NPPTXm/AsuuCD63HAMdSwzElZs32XFyUAASCSv23o/F1tMTJw48sgj5dzr8+WKFSv06KOPxpzz2GOPxWxuyM3N1fHHHx99/OUvfznm/Pvuu0/r1q2LPu7u7tYDDzwQc85QKkgtXrw45vGtt94aU9UiHA7r+9//fsw5/+///b9htwnbm5nzOcNFxQkA6OW14DpCW9foJU4k27qBJFVVVfVJ4nQ4HPrrX/+quXPnDule8dbY3tGnRaoVWHFsDCSS9d4ZgAPwWnByuKWjU8FQ2JRdsZs2bYp5fNBBBx3wmosuukiPPfZY9PH9998vm82m2bNn6+GHH46ZoDj44IP7ZI4+9NBDOu+886KPzznnHD300EOSJK/Xq/POO0933XVX9PmvfOUr+sc//qEFCxZo9erV+u1vfxtzv8WLFyesbHhzx8h7no42r9sVlwkgAEgFfo/1EifisTj/zjvv6O9//3vMMbfbrTlz5kTfc/dVWlqqE088MebYcMYJ55577oDPb926VU8//XT08ZFHHtnnmq1bt8bsAh03bpy2bt3a7/32jtFutyd0h0mrRfrM741JEACI5XW7RrXMdTy0mpi4V1xcrNNPP11//OMfo8fOOeccvfDCCzrssMO0cuXKPp/Rr7nmGvl8vujjz3zmM1q4cKHeffddSb2JEosWLdLll1+urKwsPfLIIzGJF0cccUTMmONA44Kvfe1ruuGGG9Te3i5J2rVrlw466CAtXbpUhYWFevzxx/Xmm29Gz/d6vYOuvnUgjW3tcbnPaPKw0QIAJEleC260aO7oVCgcltOR+HWEZFs3kHrbju7bsvuQQw7Rhg0btGHDhn5jOvLIIzV58uQDxj5SDa3WGxO4HNasxgYkEiNlpBwr7iw1JDW0tas0L2fUX7uqqirm8WBaXhx55JE6++yzo7tMDMOIyR7dw263xyRADNaPf/xjPfPMM9HBWTgc1rJly7Rs2bI+5xYVFfV7PF5aLFhxwscCCQBE+b0es0MYsraubnX2BEaU9PGLX/yiz7FAIKBLL710v9ccc8wxfRInhjNOGG17x5iXlxezUBNv1qw4wUc+ANibFTdbmNmqQ5Juv/12vfTSS9q9e7ckKRKJ6OGHH9bDDz/c59xjjz1WV111Vcwxu92u++67T0cddVR0saOxsVE/+tGP+lzv9/v7nV8YSFFRkf7nf/5HS5cujR6rqanp9/6SdM8998SlTUcwFDY1qWW4rFipFQASwW/BVseGYai+dXTWEZJt3aCxsVGPPPJIn+NvvfWW3nrrrf1e9+CDD45O4oQFkynZaAH0RSoRUo7fa73ECUmqNykjsaWlJfp7l8ulzMzMQV33wAMP6LTTTtvv8y6XS8uWLdOiRYuGHFNOTo5eeuklLViwYMDzpk2bpn/9619xmfDYHzNLog6XFbOlASBRrFhxQpLqmluHfa1hGHr22WfjEsfe4wRJMf1Kk8XeMSYyvlA4opZO61Wi8ln0/wAAJIoVJ4hbTf5cWlFRoRdeeEETJ04c8LzPfe5zevLJJ+XqJzll/vz5euaZZ1RUVLTf6/Pz8/Xss89q1qxZQ47xG9/4hn7+85/3+9p7eDwe3X333brggguGfP/+WLFNhyRl7tUeFQDSmc9jvY0WkrS7uW1UXifZ1g1efvlldSbpZ3LDMNTYar1xQabPmv8HgEQicQIpx+9xW7K8UEPL6Ax49rV3aauhLDa43W499dRTeuKJJ3TCCSeosLBQbrdblZWVOu+887RixQqdc845w46rsrJSb731lh588EGdcMIJKioqksvlUkFBgT796U/rV7/6lVauXKkZM2YM+zUOpDsQVFdPIGH3TxSfBbOlASBRMiyaUFnbNPzEibVr16q1dfjX723fEpjJmDgx3LHMUNU2tygSsV6/0iwfiyMAsDcrfl4KhMKmV0OcO3euVq5cqTvuuENHH3208vPz5XK5NGbMGJ188sn685//rGeeeUY5OfvfAXvMMcfo448/1vXXX6/Zs2crIyNDGRkZmjt3rq677jqtX79exxxzzLBj/Pa3v62VK1fq0ksv1fTp0+X3++X3+zVt2jRdfvnlWrFihS677LJh339fVmzTIbFIAgB7eF1O2S3Y7nj3KK0jJNu6wTvvvDPka0ZLa2e3guGw2WEMGfMFQF82wzCsN/sHHMCLK9aYPqkwVHabTScfPs+SSR+pamttvd7fWHXgE5PMvImVmli2/108AJBunn7zA4UjEbPDGBK306HPH3qQbBacxElV767fom27G80OY8gWz52m/KzB7QwCgHRQ09iiNz7eaHYYQzZjbJlmVJabHQb28trqDSOqEmYGm6RTPzVfduaeAECS9MIHqy3ZdukLhx0kt5PWS8li867dWrF5m9lhDNmCyeM0vqTQ7DCApMIoGSkpO4G9rRMlYhiWLfOYqnY2NJsdwrDkZFjv3z8AJFKG13q76gKhsJrbk7MEZTqKGIZ2NbUc+MQklGXBcTEAJFK2RT8v7bDo59NUFTEMNVlwDsfvdZM0AQB7ycnwmx3CsIxW1QkMToNJbdhHiooTQF+MlJGSsvzW/IE/krLciK9gKGy5nSN7kDgBALFyLfpzsdai70OpqL6lTcGQ9cpuet0uuZwOs8MAgKTi97jldFhvOqy1s0vtXdbbEZuq6ppaLVmSO9NrzfkyAEiUHL815wtInEge4XBEu5qazQ5jWEicAPqy3idFYBCsmjixbXeD6J6THGqaWhSx4Pciw+uR08ECCQDsLTfTmjtIaixa4SAV7WxsNjuEYclmEgQA+pVt0UUSqk4kj+311mvfJUmZjA0AIIZVK1HtbGhmHSFJ7GxsVihsrfawkuRxOeV20e4F2BeJE0hJVmzVIUndgSC7S5NEVV292SEMC9UmAKCvXIuW3mxs61BrZ5fZYUDSLosuVFk1mRgAEs2qiRM7G5rMDgGSQuGwZVt7ZjM2AIAYVq040R0Ialcjmy2SwbbdDWaHMCxUmwD6R+IEUlKGzyOHRXs2VtVa8402lXT2BFTXbM1yZ1Yd7ANAIlm14oQkba7ZbXYIaa++pU1dgaDZYQxLbmaG2SEAQFKy6uJxU3unOnsCZoeR9nY0NCscsd7OUknKz8o0OwQASCo+j1tupzV33W+ttebGv1TS1RNQnUXbr+dnMV8A9MeaK8vAAdhtNsv+4N/Z2GzZyflUsd2iWaKSlGPRXdUAkEhOh0OZPo/ZYQzLtroGhSzYPzuVbNhZa3YIw1aYzeIIAPTHqtWopN6xAcy1xaKJrU6H3bJJQwCQSFat4Fvb1EJCpcm21NTLqg1TSKYE+kfiBFJWgUUnig3D0OZddWaHkbbC4Yg27bTmJIgk5Vl4VzUAJFJehjUTKkPhiKpYIDFNe1e3Zcufet0uZXitmTAEAImWl5Uhu81mdhjDsmlXnWWrHaSC5vZONbZ1mB3GsORnZchm0X/3AJBIVt2AaUjatJN1BLNEIhFtqbXuOoJV18+ARCNxAinLyjvsttTUKxxmIsQMm3bVqTtozYofmT6PfB632WEAQFKy6kSIJG3YUSvDsOoeBmuzcrWJAnaPAMB+Oex2y44NeoIhWnyayMpt1NhZCgD9K8rJMjuEYdtSu1vBEFUqzVBd36SeYMjsMIYly+eVx2XNFjVAopE4gZSVn5Vp2Uz6QCjE7lITBENhrd9RY3YYw1ack212CACQtIpyrTsR0tkTUHV9k9lhpJ3OnoClF6asnEQMAKOhMNu6Y4MNO2sVIaly1LV1dVt6rsaqyUIAkGgF2Zmy2625jhAKR7S1tt7sMNJOJGJo7fZdZocxbFSbAPaPxAmkLKfDbum2BR9v36lAyJoZi1a1fkeNAhbO0LXyoiAAJFq23yef22V2GMO2rnoXCySjbO12a/+dMxECAAMrzLHuz8mO7h5LJ/dZ1Udbqy1dBYzECQDon8Nut3TFvt45bdYRRtOmXXVq7+4xO4xhK2BMAOwXiRNIaVbeadcTDGlN1U6zw0gbPcGg5XvCWbmsHACMhuJc61bmae3s1sYd1m0bYTUd3T2W3lHqdNiVk+EzOwwASGpWrlIpSWu371Q4QovP0bK7pU27GlvMDmPYcjP8cjspyQ0A+2PleVXWEUZXdyBo6WoTklRg4cprQKKROIGUVmjhAY8kbanZreb2TrPDSAvrttcoZOFJJyZBAODASiycOCFJH2/fpc6egNlhpIUPN2+39I7SktwcSy8GAsBosHqVyq5AUFtqdpsdRlowDEOrtlSbHcaIlObnmB0CACQ1KydOSNLmmt1qauswO4y0sGbbTgXD1q1aneXzKtPnMTsMIGmROIGUVpSTJafDuv/MDUkrNm+z9MS9FXT2BLTZ4hNOxbTpAIADKrJ44kQ4EtGHm7eZHUbK21bXoJom6+4olaTyglyzQwAAS0iFpMoukioTbtvuRjV3WHtTS1keiRMAMJC8rAxLryNI0gesIyRcc3unttbWmx3GiJTl55odApDUrP1OAByAw25XqcU/HDa2dWjb7kazw0hpH2yqsnQPc8na5ecBYLR4XE7lZlh3Z6kk7Wps0c6GZrPDSFldgaA+3LLd7DBGxG6zWX78CwCjZUxhntkhjEgwFNZ7G6vMDiOlhcMRranaYXYYI+J1uZRr4eoqADAa7DabSnKt/Tmqub3T8psDk53V5wskNloAB0LiBFLemAJrT4RI0kdbqxUMWbf8UzLbvGu3aptazQ5jRNxOp+XLyQHAaEmFD4gfbt6mkIXLQiazDzZWWX7MVZiTJZfTYXYYAGAJ2X6f5UsV1zW3atOuOrPDSFkfb9+prkDQ7DBGpDSfFl4AMBgVRdZfR1hTtVPdFn/fSlbbdzeqobXd7DBGxOtyWbpVHTAaSJxAyivJy5HDbu0PiD3BkFamQDZjsmnv6taqrdbuUyr1LgIyCQIAgzO2KN/sEEasKxDUmqqdZoeRcqpSoEWHJJVTdhMAhqQ8JTZb7FB7V7fZYaScXY3NWr+j1uwwRqws39o7qAFgtJTm5Vi+XUcwHGYdIQE6unu0YpP1W6eWkUwJHJC13wWAQXA67JYvsyX1TuZbvX9WMglHInpn3RaFIxGzQxkxq5eXBYDRlOH1KD8rw+wwRmzjrjptp5VX3HT1BLRyc2pMLrE4AgBDMyYFqlGFIxEtX7+VvuZx1NHdo3c3bDU7jBFz2G0qzqG1JwAMhsNuV1lertlhjFh1fRMtO+IoHIno7bWbFUyByp9lKTDuBRKNxAmkhVQoyy1JKzZvU1N7h9lhpIQPN29Xc0en2WGMmMflVDFtOgBgSMYWWr/qhCS9t3GrGtsYF4xU72LTlpSYBCnIypDP4zY7DACwlLzMDPlT4GdnU3uH1lXXmB1GSogukFi8fZckleXnymHx3dMAMJpSZYPays3bLd9WIlms2lKdEusILoeDdQRgEBg5Iy2U5efKbvF2HZIUiRh6e+1m+pSN0Nba+pSp3lFRmE95LQAYojGFeUqFn5yRiKG3Pt6kzp6A2aFYlmEYenf9VtWnyITSuJJCs0MAAEtKlc0WH2/fpbrmVrPDsLyVKbLRQpIqiwrMDgEALKU0L1suh8PsMEYsYhh6e91mdbGOMCKba3anTPWOiqJ82e0sCQMHwv8SpAWX06ExKdC3VJI6ewJ68+NNCoet32LCDPUtbVqx2fr9yPaoLEqNXdMAMJq8bpeKclOjZHF3MKg3P96oEOOCYflwy3btaGgyO4y4cDrsqkiRaioAMNrGFadG4plh9CZVUpFq+LbVNWhLimy08LicKslLjTEvAIwWu92eMu0MugNBvbFmg0IpUF3RDLub2/RhCq0jjC8mmRIYDBInkDbGp9AOvKb2Dr2zfgv9S4dod0ubXl+zUZFIavy9Zfu9ysvKMDsMALCkVEo8a+no0rsbGBcM1brqXdq8KzV2jki9LWiclOIGgGHJyfCpIEU+W4UiEb2xZoNaO7vMDsVy6lvb9cGm1FkgqSwqoEIlAAzDhBRaR2jp6NLbazcrwnzBkLR1duvtdZuUKn9t2X4f6wjAIDGzhrRRlJOlTK/H7DDiZldjs1Zs3s4iySDtbmnTG2s2KhxJnR25k8qKzQ4BACxrTGGe3E7rl9/cY2dDs1ZuqWZcMEhVdQ1aXbXT7DDianxp6kzuAYAZJpQWmR1C3ARCYb2+eoM6u3vMDsUydje36fXVG1JqzoCxAQAMT0F2prL9PrPDiJva5la9v7GK+YJBam7v1L8/WqdAKHUqdUxgTAAMGokTSCsTy1JnIkSSttTs1vL1W1Lqg30ipGLShNvpoFcpAIyAw25PqWpUkrRpV53e3bA1ZSorJcquxha9v3Gr2WHEVU6GT3mZ7B4BgJFItaTKrkBQr63eoG56mx9QbVOr3vg4tZImCrMzleXzmh0GAFhWqi00b6tr0NvrNqfUe10i1Le269WP1qsnGDI7lLhx2O2sIwBDQOIE0sq44kI57Kn1z766vkmvr96gYAplQMZTKiZNSL2tZxyU4waAEZmYQjtL99i+u1FvfLyRHqb7sXnXbr21dmPKlNvcY0JJ6v1bBoDR5rDbNa44tRZJ2rt79PqaDQqEUmfyP952NbbozY83KpxiiaepVEEFAMwwrrhALkfqJFRKvZUqX/tovQIplBQQTzVNLXp99XoFU2w+ZWxRvlwplBwMJBqrbkgrLqdDlcWp09N8j/rWdv1r1Tp19QTMDiWppGrShE3SRNp0AMCI+b0elRfkmh1G3NU1t+rfq9azw3QvhmFoxaZtWrF5W8olTbhTdHwLAGZItd2lUm9v81c+XKvWzi6zQ0k6Oxua9fbaTSnX993vcauiMM/sMADA0pwOh8aVpN4u/Ya2Dv1r1Traee2jur5Rb368KeUSKSXafQNDReIE0s7kshKzQ0iI1s4uvbKSyZA9Nu2qS7n+pHuUF+TK73GbHQYApIQp5ak5Lmju6NS/Vq1TexeTIYFQSK+v2aDNNbvNDiUhJpYWy5liO6EAwCyZPq9KcrPNDiPu2rt79MrKtdrZ0Gx2KEmjt2R56iVNSNKUMSWy2WxmhwEAljeprFip+NO0ratbr6xcp+aOTrNDSQpbanbrnXVbZKTgmKAsP0c5GT6zwwAshcQJpJ0svzdlM++7AkH9a9U67W5pMzsU04TCES1fv0Ufbt6ekhMgElmiABBPBdmZys/KMDuMhOjo7tG/Vq3V7ub0HRe0d3XrXyvXqS5F/w4cdpsmlVOKGwDiaVpFqdkhJEQoHNFbazdpzbadKbkwMFiRSEQrNm3Tuxu2plwVKknyuJwan2ItZwDALBlej8oLUnMdoTsY1L9XrVNtU6vZoZgmHI7og41V+mDTNrNDSZhpFWVmhwBYDokTSEvTx6buG0YwFNZrH63XR1t3KJKC1RYG0tLRpVdWfqztuxvNDiVhCrIzVZiTZXYYAJBSpqfwB8meYEivrl6vVVuq025cUNPYoldWrlVbV7fZoSTM+JIieVwus8MAgJRSmJOlguxMs8NImLXbd+mttZsVDKVW/+7B6Oju0Sur1qVsFSqpd6OFw8F0LwDEy4zK1J0vCIUjen3NBq3cvD0lqzYPpKWjSy+v/FhbauvNDiVhinOzU3ajEJBIjKSRlrL9vpStOiFJhqT1O2r08sq1aulI/dYdhmH0/nk//Fitnam7OCJJsyrLzQ4BAFJOaX5OSi+QSNKGnbV6eeXatCjF2RMMafn6LXrj440KpPCikN1m09QxqdlqBgDMNj1Fq07ssauxWa+kyXzBHltr6/XiijVqbk/dsZDTYdfEMipRAUA8Zft9GluUb3YYCbVxV51eWvGxmlL4PXIPwzC0YUetXl6Z+usIqT6eBRLFZqRzfT6ktdbOLr3wwRqzw0g4u82mKWNKNL2iLCV3HbR1duuDTVWqb203O5SEK8nL1hEzp5gdBgCkpPqWNv37o/Vmh5FwNps0pbxEM8aWp+S4YPvuRq3csl09wZDZoSTcxNIizZtUaXYYAJCyXlm5Vo1tHWaHkVA2mzT5k3GBMwXHBZLUHQjqg01V2tXYYnYoCTelvERzJlSYHQYApJz2rm49/8HqlGzxtDebTZpcVqIZlWVyOhxmhxN3rZ1dem/D1rRIECnIztQxc6aZHQZgSSROIK29s26zquubzA5jVPg9bs2bWKnS/ByzQ4mLju4efbx9l7bXNShdfogde9AM5Wb6zQ4DAFLWa6s3qK45Pfp7Zng9mjO+QmX5ObLZbGaHM2LNHZ1auXl7WiRSSpLDbtcJC2bJ53GbHQoApKy65la9tnqD2WGMCr/HrbkTxqq8INfsUOImFI5o065ard9RmxZtSVwOh044eLY8LqfZoQBASnpvw1ZV1TWYHcao8LldOmhiZcqMC3qCQW3YUauNO+sUSZPl0CNnTVFxbrbZYQCWROIE0lq6VJ3YW1FOlqaUl6gkL9uSCyXdgaDWbt+lrbX1aTPQkaQxBXk6bPpEs8MAgJTW1N6hlz9ca3YYoyrb79O0ilJVFOZZclzQ3tWt9TtqVVVbnzaJlJI0Y2yZZtC+CwAS7t+r1qVNUp4kFWZnas6ECuVlWrcfdiQS0eaaeq2r3pUWFaj2mD1ujKZSkhsAEqazu0f/fH91Ws1H52dlaMqYEpXn51pyvqA7ENT6HTXaUlOvcCRidjijhqrVwMiQOIG098HGKm2prTc7jFGX7fdqcnmJKovyZbcnf0nOQCik9dU12rRrd1oNdCTJJukz82cpy+81OxQASHlvrd2knQ3NZocx6jK8Hk0dU6JxxQWWGBfUNrdq08461TSlftntffncLp2wYHZKtloBgGTT3N6plz782OwwRt3YwnxNKi9WfpZ1EigMw1BVXYPWbt+lzp6A2eGMKr/HreMXzJLDAmM4ALCyFZu2aXPNbrPDGHWZXo+mjClRZXGBJd5rOnsCWr+jpnfjZSS9lj9tNps+M3+msnysIwDDReIE0l4gGNI/3/9IgTQo3dgfr9ulSWXFmlBaKLcz+Uo6Nnd0altdg6pqGxQMp+f3aHxJoRZMHmd2GACQFtq7uvXCB2vSahfJ3nxuV3RCJNnGBeFwRNt2N2jTrjq1dnabHY5pDpk6QWOL8s0OAwDSxvsbq7Q1DTdbSFJuhl8TSgs1tig/aXudB0Nh7Who0vodNWrv6jE7HFMcOnWCKhgbAEDC9a4jrFYglD4VjfbmcTk1qaxYE8uKkm6+QJLaurq1cUetquoa0nZOZ3J5seZOGGt2GIClkTgBSNpSs1sfbNpmdhimctrtGluUr7KCXBXnZJm627SzJ6Ad9U3atrtBLR1dpsWRDDwup46fP0tu+pQCwKhZs22n1m7fZXYYprLZpMLsLJUX5KosP1d+j9uUOMKRiBpa21XT1KJtdQ1pm+i6R35WhhbPnW52GACQVno+2WwRTOP3IJfDobFF+ZpQWqScDJ/Z4SgciaimqUXbdzeqpqkl7XaT7o2xAQCMrq219Xp/Y5XZYZjKabf3zhUU5Ko0N8fUaoidPQFV727U9vpG1hFcTp2wYLZczuRMdgWsgsQJQL0lHV9ZuVZN7Z1mh5IUnA67SnJzVJqXraKcLPm9noS+XsQw1NbZpV2NLdrZ0KzmDr4Pexw8ZbzGFReYHQYApJVwJKIXP1ij9u703LXYn9xMv8rzc1VekKtsf2IXTFo7u1Tb1Kra5lY1tLYpnMaLIftaPHe6pcqmA0Cq2LSrTh9u3m52GEkhPytDJbnZKszJUn5WxqiV7DYMQ3Utbdq+u1G7GprTtiLlvo6ZM00F2ZlmhwEAacMwDP171To1tHWYHUpScNjtKsnNVnFutopzs5SZ4BYRhmGosa1DNU0tqmlqSftkib3Nn1SpCaVFZocBWB6JE8AnGts69MrKtWaHkZT8HreKcrKUk+GT3+OR3+NWhtczrOzFnmBILR2daunoUktnl1o6OtXW2Z225bMGUpSTpaNmTzU7DABIS3XNrXpt9Qazw0hKHpdTWT6vsvxeZfl8yvZ7leXzyjeEqhSGYag7GFJnd486ewLq7OlRW2e36lra1B0IJjB666J1FwCYxzAMvbjiY7V2Mjm/N7vdpvzMDBXmZKkojokUhmGovatHzR2dam7v7P21ozOtq370Z1xxgQ6eMt7sMAAg7bR0dOmlD9eI6ey+/B63CrMzlenzKsPriX55hlhN2TAMdQWCau3sUltnt9q6er9aO7pInuxHToZPxx40QzabzexQAMsjcQLYSzr3Lh0Ol8Mhv8ctv9ctv8cjh733jXnPD5VwOKJQOKxgOKxQOKK2rm4WQwbJbrfpM/NmJjxLFwCwf8vXbdH2+kazw7AMl8OhDJ9HLodDDrv9ky9bdFwgQ+oJhdTZ05sskc5ltYfK53bpM/NnUXITAEy0u6VNr3603uwwkprdZlOG1yOv2yWv2yXfJ796Xb2/elwuGTIUiRiKGIYikYgCobACoZACoZC6eoJq7uhUS0enQuGI2X+cpOZ1u3Q8YwMAMM3KLdu1cWed2WFYhsvhiEmisNkkm2yy2WyKGIbCkUj0q7M7oLauboUjjAUGw2az6dNzpys30292KEBKGFqaF5DiZo8bo5qmFhb3BykYDvdWjWDXTdxNqygjaQIATDZnQoVqmlrYzTBIwXBYzbQ9S4gFk8ezMAIAJivKydLE0iJtrtltdihJK2IY0R2hSKz5k8YxNgAAE82sLNeO+iZ1sY4wKMFwOFpBCvE1vaKUpAkgjkanESFgEW6XUwspcwiTZfm8mjamxOwwACDted0uHTRxrNlhIM2NLylUSV622WEAACTNHl+hTK/H7DCQ5sYW5assP8fsMAAgrTkdDtolwXS5mX5NG1tmdhhASiFxAthHcW62JpcXmx0G0pTdZtPCqRNkj0NfWADAyFUWF2hsUb7ZYSBN+TxuzRlfYXYYAIBPOB12LZw6QXSPhlk8LqcOmkBiLwAkg+LcbE1h8xtMYrfbtHDKeNltjEyBeGJlDujHrHFjlOP3mR0G0tDMynLlUVoLAJLKvImV8nvcZoeBNHTwZMpwA0Cyyc/K0NSKUrPDQJqaN6lSbhedlwEgWcyqHKPcDOZyMfpmVY5RNmtYQNyROAH0w2G3f7Lrn2w9jJ6inCyylAEgCbmcDh06bYJI4sdomjqmRMW5tOgAgGQ0Y2w5iyQYdRNKCzWmIM/sMAAAe7HbbTpk2gQ5qB6MUVSYnUnVdCBB+GkO7EdOhk+zx40xOwykCY/LqUOmTpCNVTkASEr5WZmaMbbc7DCQJopysjSLcSgAJC273aaFU8ezSIJRk5vp11xadABAUsryeXXQRH5GY3SwjgAkFp/wgAFMKitWWX6O2WEgxdkkHTptorxul9mhAAAGMK2iVEU5WWaHgRTndbuYBAEAC8j2+3Tw5HFmh4E04HY6dNi0iSTqAEASG19CVSAknk3SoVMnykc7WSBhGHEDA7DZbFo4ZYKyfF6zQ0EKm1FZzkIcAFiAzWbTodMmKsPLB1Qkhs1m02EkUwKAZVQU5Wsq7RaRYAdPmaAMr8fsMAAAB3DwlHHK9vvMDgMpbOa4MSrKZR0BSCQSJ4ADcDkdOnzGJLkcDrNDQQoaW5Sv6WPLzA4DADBIHpdTn5oxWU4Hw2jE39wJFSrIzjQ7DADAEMwaN0bFudlmh4EUNbWilEqoAGARTodDn5oxSW4n6wiIv4rCPE2rKDU7DCDlMeMLDEKWz6tDp00QBZMRTwXZmZR2BQALyvb7dOi0iYwLEFeVxQWaVFZsdhgAgCHqrUhFRQDEX3FutmZVlpsdBgBgCDK8Hh02bRKtFxFXuRl+HTx5vNlhAGmBxAlgkErycjR34lizw0CKyPR69Knpk2SnRykAWFJpXo7mTKgwOwykiJLcbC0gmRIALMvtdOrw6ZPk4PMd4iQ3w6/Dp09k4Q0ALKgoN0vzJ1WaHQZShNft0uEzJslB5VNgVPA/DRiCSWXF7ATEiLmdTi2aOUVul9PsUAAAIzC5vETjSwrNDgMWl5fp12HTJ8rOwggAWFpOhk+HsdCNOPB73Fo0c7KctIwFAMsaX1KoqWNoq4CRcTsdOnLWFPk9brNDAdIGiRPAEM2dUKGxRflmhwGLstts+tSMScr0UcYVAFLBvEmVKi/INTsMWFSm18PCCACkkNK8HC2cMt7sMGBhbqdDR8ycIq/bZXYoAIARmjWuXJXFBWaHAYty2u1aNHOKsv0+s0MB0gqJE8AQ2Ww2LZwyXmNYJMEQ2WzSIdMmqCA70+xQAABxYrfZdOjUCSrJyzY7FFiMx+XUEbOmyONiYQQAUsnYonzNm0h5bgydw27Tp2ZMVpbfa3YoAIA4sNlsOnjyOFUU5pkdCizGbrPp8BmTlJ+VYXYoQNohcQIYBpvNpkOmTlRpXo7ZocAibDbp0KkTNaaAgTIApBq73a7Dp09SUU6W2aHAIlyf7CbN8FKBCgBS0cSyIs2sLDc7DFiIzSYdMnUiGy0AIMXYbDYtnDqBSpUYkkOmTlBxLht0ADOQOAEMk91u02HTJ6o4l0USDCyaNEF2MQCkLIfdrk/NmMRkNw7I7XTqqFlTlZvpNzsUAEACTR9bpsnlxWaHAQuw22w6bNokFtUAIEX1VqpkEyYGZ/6kStYRABOROAGMgMNu1+HTJ6uQRRLsB0kTAJA+nA6HFs2YrDwWxLEfHpdTR88haQIA0sWc8RWaVFZkdhhIYnabTYdNJ2kCAFIdmzBxIDabdPCU8ZpQytgRMJPNMAzD7CAAqwuFw3pr7SbVNbeZHQqSCEkTAJCegqHeccHuFsYF+A+f26UjZ09Vlo++5QCQbtZs26m123eZHQaSjMNu0+HTJ6skj1LcAJAuwuGI3lq3SbVNrWaHgiRit9l06LSJJFICSYDECSBOIhFD723cqu27G80OBUnAbrfpkKkTNKaApAkASEeRSETL12/VjoYms0NBEvB73Dpq9lRleD1mhwIAMMnGnbVauaXa7DCQJBx2uxbNmKwidh4DQNqJGIY+2FilqroGs0NBEnDa7Tp8xiQV55JICSQDEieAODIMQ6urdmj9jlqzQ4GJPC6nDp9On3sASHeGYWjllmpt2lVndigwUbbfp0UzJ8vvcZsdCgDAZFW19Xp/Y5WYiEtvLodDn5oxSYU5JE0AQDpbU7VTa6upSJXO3E6HFs2covysDLNDAfAJEieABNi4s04rt2w3OwyYIMvn1aKZk9lRCgCIWlddo9VVO8wOAyYozcvRodMmyOlwmB0KACBJ7Gxo1jvrNivCdFxayvB6tGjGZGX5ad0FAJC21OzWik3bSKpMQz63S0fMmqJsv8/sUADshcQJIEGq65v07votTIakkaKcLB02faLcTqfZoQAAkkxVXYM+2FjFuCCNTCkv0ezxY2Sz2cwOBQCQZBpa2/X22s3qDgbNDgWjqDA7U4dPnyS3izkDAMB/7Gps1jvrtigciZgdCkZJYXamDps+UR6Xy+xQAOyDxAkggRrbOvT2us3q6gmYHQoSbFxxgeZPGie7ncURAED/mj4ZF3QyLkhpdptN8yZVanxJodmhAACSWFcgqLfXblJjW4fZoWAUjC8p1LyJlcwZAAD61dTeqbfXbmK+IA1MLC3S3IljZWeTBZCUSJwAEqwnGNLy9VtU19xqdihIAJtNmj2uQlPGlJgdCgDAAhgXpDaPy6nDpk2kZzkAYFAikYg+2LRNVXUNZoeCBJoznjkDAMCBBUIhvbdhq3Y1tpgdChKATRaANZA4AYwCwzC0trpGH2/baXYoiCOf26VDp01UQXam2aEAACzEMAx9vG2X1lbvMjsUxFFxTpYOnjpBPjelNgEAQ7NpV51WbqkWU3SpxeNyauGUCSrJyzY7FACARRiGoQ07arW6aocYFaQOr9ulw6dPUn5WhtmhADgAEieAUVTX3Krl67eoJxgyOxSMUGlejg6eMl4eepMCAIapprFFyzdsUTAUNjsUjIDNZtOsynJNGVMiG6U2AQDDVN/aruXrt9DqM0UU5WRpIQmVAIBhqm9p0zvrtqg7GDQ7FIxQaV6OFkweJy9jAsASSJwARllXT0DL129RfWu72aFgGBx2m2aPr9CksmKzQwEApICunoCeeO5FZRXwvmJFGV6PDp06QXnsGgEAxEEwFNaHW7ZrG607LMtmk2aMLde0ilISKgEAI9IdCOrdDVtp9WlRrCMA1mQ3OwAg3fg8bh01e6o2rViurs5Os8PBEORm+PXpg2Yw2AEAxM26j9doyemn6L7bb2FcYDGVxQU6bt4MkiYAAHHjcjq0cMp4HTZ9otxOqhtaTabXo2PmTNf0sWUkTQAARszrdmnRjEl66S9/UmcHmzCtJC8zQ8ceNJN1BMCCqDgBmOCtt97SokWLVFxWrku+d71mL1hodkgYgNNu14zKck0uL2byAwAQN4FAQIceeqg+/PBDSVJxWbkuvuYHmrvwUJMjw0D8HrfmTaxUaX6O2aEAAFJYdyCo9zdWqaapxexQcAA2SZPKijVzXLmcDofZ4QAAUsivf/1rXXzxxSosKdFFV1+n+YctMjskDMBus2lGZbmm0soTsCwSJ4BR1tPTowULFmjNmjXRY5897XRd/N1rFeZ/Y9Ipy8/RQRMr5fe4zQ4FAJBibrzxRv3oRz+KOXbddddpyeXf0qqt1QqGwiZFhv6wKAIAMMPW2nqt2lKtYJhxQTLKy/Rr/qRxys30mx0KACDFbN++XbNmzVJbW1v02Be/crbOu+IqxgVJqDg3SwdNqFSW32t2KABGgLp/wCi7+eabY5ImJGnxYQv12YVz9dHWam3b3WhSZNibz+3SQRMrVV6Qa3YoAIAUtGLFCt18880xx2bPnq3rr79eHo9H5fm5WrN9p7bU7BZpzuYryM7UQRPHKjeDRREAwOgaX1KoPzz8oKoamnXcF06T3U7X3WTgcjg0c1y5JpYWsaMUABB3hmFo6dKlMUkTknTRuV/TMQfP1uqqHaqqrRfTBebze9yaO2Es6whAiqDiBDCKPvzwQy1cuFChUCh6bMGCBXr77bfl/KR/aVN7pz7aWq3dLW37uw0SyG63aVJpsWZUlrGbFACQEIFAQIcccohWrlwZPeZwOPT222/r4IMPjjm3tbNLq7ZWq7apdbTDhHoTKWeOG6PKonwWRQAApti+fbumT5+uzs5OTZ4xS+d/8ypNmz3X7LDSWkVhnuZOGCuv22V2KACAFPXII4/onHPOiTl2/vnn64EHHog+Zr7AXA67XdMqSjVlTIkcJLYCKYPECWCUhEIhHX744Xrvvfeix5xOp5YvX6558+b1Ob+mqUUfbd2h1s6uUYwyfdls0rjiQs0YWyYfbTkAAAn0wx/+UD/5yU9ijl1//fX68Y9/vN9rappatGpLtdq6uhMdHiR5XE5NqyjVhNIiJkAAAKY688wz9cc//jH62Gaz6ZW3lqvD5lJXIGhiZOmnMDtTs8aNUUF2ptmhAABSWE1NjWbOnKmmpqbosbKyMq1Zs0a5ubl9zq9rbtWqrdVq6WAdYbRUFOZp9vgK2nsDKYjECWCU3Hbbbbrmmmtijl133XW66aab9nuNYRiqqmvQmm071c2ESMKMLczXjMoyZfroPwYASKz3339fhx56qMJ79SOdO3euli9fLrd74A/cEcPQtroGra+uUXt3T6JDTUsuh0NTxpRocnkxlacAAKZ75ZVX9OlPfzrm2NKlS/XrX/9a4UhEW2rqtX5HDfMFCZaX6descWNUnJttdigAgDRw+umn68knn4w59vTTT+uUU07Z7zWGYWj77kat3rZTXT2BRIeYtioK8zStokw5GT6zQwGQICROAKNg/fr1Ouigg9Td/Z9dojNmzNAHH3wgj8dzwOvD4Yiq6hq0YWetOlgoiZuy/BzNrBzDQAcAMCoCgYAWLlyoVatWRY85nU698847mj9//qDvYxiGdjQ0aV11DTtK4sTpsGtiabGmVpTI/Un7NAAAzBQKhbRgwYKYcUNeXp42bNiggoKC6LFwJKKttfVaV00CRbxl+72aWTmGnuUAgFHz5z//Wf/v//2/mGNnnnmmfv/73w/q+kjE0Pb6Rm3YUaPWTipWxoNN0tiifE2rKFOWn42XQKpjVhBIsEgkogsuuCAmacJms+mBBx4YVNKEJDkcdk0sK9KE0kLtbGzWhh21amzrSFTIKc1ht6uyKF+TyouV7SdhAgAwen7yk5/ELH5I0ve///0hJU1IveOIisJ8VRTma1dji9ZV72JcMEwZXo8mlRVpXHGhXE4qTAAAksevfvWrPuOGm266KSZpQur9jDuprFjjSwq1tbZe66traOExQgVZGZpUXqIxBbmy2WxmhwMASBMNDQ269NJLY44VFhbqrrvuGvQ97HabxhUXqLIoXzVNrdqwo0b1re3xDjUt2Gw2VX6SMJHpG9w6DgDro+IEkGD33nuvLrvssphj3/zmN3XHHXeM6L4Nre3asKNWOxubR3SfdOH3uDWxrEjjSwrZSQoAGHXvvfeeDjvssJgWHQcddJDeeeedA7boGIyG1nZtqdmtHQ1NCkcY3h9IUU6WJpcXqzQvhwURAEDS2b17t6ZOnarm5ubosblz5+q9996T8wCfZyORiHY0NGtzzW41sFAyaHabTRWFeZpUXqy8zAyzwwEApKGvfe1revTRR2OO/f73v9eZZ545ovs2tnVo/Y4a7WpoFrMFB+b3uDW+pFDjSwrldbvMDgfAKCNxAkigqqoqzZ49W+3t/5msmDBhglatWqWMjPh8EO/o7tG2ugZt291IG49+FOVkaVJZscryWRgBAJijp6dHCxcu1EcffRQ95nQ6tXz5cs2bNy+urxUIhbR9d6O21tbTxmMfbqdDYwrzNbG0iDZdAICkduGFF+q3v/1tzLF//etfOvroo4d0n5aOLm2p2a1tuxsUCkfiGWLK8LicmlBapImlRSyOAABM88wzz+gLX/hCzLFTTjlFf/nLX+I2p90VCGp7XYOq6hrU1kUbj73ZbFJZXq4mlBaqODebdQQgjZE4ASSIYRg66aST9Nxzz8Ucf+GFF3Tccccl5DUbWtu1bXeDquubFAyFD3xBisryeTW2KF+VRfnyeymjBQAw13XXXaef/vSnMcduvPFG3XDDDQl93aa2Dm2prdfOhiYF0nRc4LDbVZ6fq4qifJXkZstuZ/IDAJDc3n33XR166KHae7ruK1/5ih5//PFh3zMUDkcTK5vaO+MRpqU57HaV5eeoojBfpXnZstvtZocEAEhjLS0tmjVrlnbs2BE9lpOTozVr1qi8vDwhr9nY1qFtdQ2qrm9M2/kC6T/rCONKCuUjgRKASJwAEubhhx/WueeeG3NsyZIluv/++xP+2uFIRDVNLdq+u1F1Ta0KRVJ/Z0mWz6vyglxVFOYpJ8NvdjgAAEjqXfw4/PDDY1p0zJs3T++8845crtH5UG4Yhupb27WzoVm7GpvV2RMYldc1i91uU3FOtsYW5assP0dOh8PskAAAGJRIJKIjjjhCb731VvRYRkaG1q1bpzFjxsTlNTq6e7SzoVk7GprU2NYRl3tagc1mU0lutioK81RekMv4AACQNJYuXar77rsv5tgDDzyg888/P+GvHYlEtKuxRTsbmlXT3JIWmzGz/V6VF+SpojBP2X6qUQKIReIEkAA1NTWaOXOmmpqaosfKy8u1evVq5ebmjmoskUhEu1vaVdPUrNqmVrWnSDsPp8OuwuwsFedmqTg3m0EOACDp9PT06OCDD9bq1aujx5xOp959910ddNBBpsXV3N6pnY3NqmlsUXNHauw6zfZ7VZybreLcbBVlZ8nhYOcoAMB6+tuAccstt+h73/teQl6vqycQTaKob20/8AUW43Y6VJSTrZK8bJXl58rjcpodEgAAMV566aU+1amPP/54Pffcc6PeLiJiGGpoaVdNU4tqm1vU2pka7TzsdpsKsjJVlp+jsvxcZVChGsAASJwAEuC//uu/9L//+78xx55++mmdcsopJkX0H509AdU1t2p3c5ua2jssk0hht9lUkJ2popzeZInczAzZ6TUGAEhi3//+93XLLbfEHPvRj36kH/7whyZF1FcwFFZDW7saWnu/Gts7FIkk/8eDTK9HeZkZKsrNUklutnwet9khAQAwIi0tLZo2bZpqa2ujx6ZMmaJVq1bJ40n8BH8wFFZ9a5vqW9pV39qm5vZOJf+IIJbdZlN+VoZK8rJVnJOt3Ew/PcoBAEmro6NDc+bM0ZYtW6LHMjIy9NFHH2n8+PHmBfaJrk/WERraOtTU3qHWji5LjA1cDocKsjNVkJ2pwuxM5WX6acsFYNBInADi7H//93/1X//1XzHHzjzzTP3+9783KaKBBUNhNbd3qqm9Q03tnWru6FSHyckULqdDOX6fcjJ8ysnwK/uT3zsY4AAALGL58uU6/PDDFdmrXdb8+fP19ttvj1qLjuGIRCJq+mRc0NrZrbaubrV1dpna8zTT51FeRoZyM/29Xxl+uZyU1wYApJarrrpKP//5z2OOPfPMM/rc5z5nSjx7kivrW9rU1N6p1s4u9QRDpsSyP5k+j3Iz/jM+yM/KoAUHAMAyvvWtb+nOO++MOXbPPffo0ksvNSmigYXCETV/sobQ+EkyhdmtQN1Op7L9XmX5vMrO8KkwO1PZfh+JkwCGjcQJII4aGxs1c+bMmB0ihYWFWrNmjYqKikyMbGgCoZA6unvU2R1QZ09AnT096tjr96Fw5MA3GYDDbpfP7ZLX7ZLP7e791eNShtejnAy//OwaBQBYWHd3tw4++GCtWbMmeszlcum9997TnDlzTIxs+LoDQbV1dau1s0tdPQF1B0PqDgTVEwyqJxBSTyio4XyqcDkccjrs8rp7xwF+j1t+j0d+r1sZn/xK4iQAINV9/PHHmjt3rkKh/yQmfOELX9Df/vY3E6PqqycYVGtH73igpbNLrZ1d6uwJqCcQTNgOVKfDLr/HLd8nY4Qsn/eTRAkfSRIAAMt64403dOSRR2rv5bmjjjpKr7zyiqWqI4QjEXV2B9TR3aOO7h61f/JrR3ePeoIhBcNhjWQJ0uV0yONyyuNyyeNyyud2K8vvVbbPqyy/jzZcAOKOnypAHH3729+OSZqQpLvuustSSRNSb6amO9OpvMyMfp8PRyIKhSMKh8MKhiMKR8IKhT85FonIZpNsNptssslht8nhcMhht/cujLhc7BIFAKS0G2+8MSZpQpJ++MMfWjZpQpK8nyQ8FuVk9fu8YRgKhMIKhcMKRyIKRwwZRiSaTGEYhux2e2+ihNMhl8Muh93OLhAAQNozDENXXHFFTNKE2+3WL37xCxOj6p/H5VJRrktFubHjAcMw1PNJUmV3IKjuYO+vwXBYkYihiBH55FdDkUhEEcOQ9swX2O1yOHrHCC6H45MFEpf8Xrf8HrfcTqYuAQCppbu7WxdccEFMQoHX69Vvf/tbSyVNSL0bJLP8XmX5vfs9JxQOKxgKK7jn11BYYeM/GzNtsslus8lu7/3V6fhPsoTdzpwBgNHFpw8gTv7xj3/okUceiTn2hS98QWeeeaZJESWOw9672CEyOgEAiPH222/rZz/7WcyxBQsW6JprrjEpotFhs9k+mdhgbAAAwFA89dRTeuGFF2KOfec739HkyZNNimjobDZbNMkSAAAM7Mc//rHWrl3b59jUqVNNiiixnA6HnA6HfGYHAgCDQKsOIA7a2to0e/Zsbdu2LXosOztba9as0ZgxY0yMDAAAjJbu7m7Nnz8/ZgLE6i06AABA4nR2dmrmzJmqqqqKHquoqNDatWuVkdF/BUgAAGBd77//vg499FCFw+HosYULF+rNN9+UkypLAGA6a9X9AZLU9773vZikCUm6/fbbSZoAACCN3HDDDX12jdxwww0kTSTI+PHje1uDDeJr8eLFMdeuXbtWF154oSZNmiSv16usrCwtWLBAN910k9ra2gYdwyuvvDLoGGw2mx566KGY61966SUdeuih8nq9Ki0t1WWXXTbg63/961+Xw+HQRx99NJS/KgBAkrrttttikiak3rkEkiYAAEg9wWBQ559/fkzShMvl0rJly0iaAIAkQeIEMEKvvvqqfvnLX8YcO/bYY7VkyRKTIko9w1kYGcoixrnnnjvs2AzD0BFHHCGbzRbTk3ZfLIwAQGp76623dPvtt8ccO/jgg1O+RYeZDjnkEB1xxBH7/VqwYEH03ClTpkR//6c//Unz5s3Tb3/7W1VXV2vy5MkqKCjQihUrdP3112vhwoWqrq4eVAw5OTkDxnDEEUdEE2ltNpsmTpwYvXbNmjU66aSTtHz5ck2cOFGRSET33nuvPve5zykSifR5rQ8//FCPPfaYvv71r2v27NnD/WsDACSJrVu36tZbb405tnjxYn35y182KSIAAJBIt956qz788MOYY9dddx2bLeJsuJssgsGg3G73gOfn5uYOOZ4///nPOu6445Sbmyufz6epU6fqyiuv1M6dO/d7DWsJgHlIYwNGoKurSxdccEHMMZ/Pp/vvv182m82kqFLPIYccooqKiv0+39XVpffff1/SfxZGjjjiiAHv2dTUpDVr1sRcMxzXXnut3njjjQHP2bMwEggENGPGDNXX1+vee+/Vhx9+qH/961+y22Nz2FgYAQBr6erq0nnnnRez2O12u/Xwww+zaySB/vSnPw34/Hnnnaf3339fc+bM0Z133ilJ2rx5s772ta+pp6dHX//613XXXXcpJydHkrRixQqdddZZ+vjjj/XlL3/5gO/vkjR//ny99tpr+32+rq5OBx10kKTenrVHH3109Lmbb75ZgUBAv/nNb/SNb3xDXV1dOuGEE/Taa6/p2Wef1Re+8IWYe333u9+V2+3Wj3/84wPGBQBIfldeeaW6u7ujj+12u+666y7mEgAASEGrV6/WT37yk5hjc+bM0bXXXmtSRKlrOGsJUu8cfjAYlN/v1/z58/u9Nisra0ixLFmyRA888IAkqbS0VBMmTNCGDRv0i1/8Qg8++KD+/ve/91nHYC0BMJkBYNiuvvpqQ1LM189//nOzw0o75557riHJmDNnjtHR0XHA88PhsHHccccZkowTTjjBCIfDQ37NUChkfOc734n53geDwX7PPeusswxJxm9+8xvDMAyjs7PTOPLIIw1Jxt/+9rc+5x9//PGG1+s1tm3bNuS4AACjb9/3A0nGT3/6U7PDSmuPPfaYIcnwer3G6tWro8e/+c1vGpKMefPmGaFQqM91H3/8seFwOAxJxquvvjqiGCKRiHHCCScYkoxjjjmmz3ijsrLS8Pv9Mcd/97vfGZKMq6++Oubcf/7zn/0eBwBY056f63t/XXbZZWaHBQAAEiAUChmHHXZYzPu+3W43li9fbnZoaWl/awkPP/ywIck48cQT4/I6v/3tbw1JhtPpNH73u99Fj7e0tBhnnHGGIckoLi7us57BWgJgLlp1AMP07rvv9inJffjhh+uKK64wKaL09Pjjj+uhhx6S1+vVH/7wB/n9/gNec8stt+jFF19UUVGRHnnkkT5ZmgeyYcMGHXfccX2+//vz2muvye/3R9u3+Hw+LV26VFJvq5e9Pf/883r++ed1xRVXaOzYsUOKCwAw+t588039z//8T8yxQw45RN/97ndNigj19fW69NJLJUnXX3+9Zs6cGX3u5ZdfliR9+ctflsPh6HPt9OnTNX36dEnS8uXLRxTHsmXL9M9//lM+n0+//e1v+4w3du3apby8vJjjRUVFkqTGxsboMcMwdPXVVysvL4/dSACQAgKBQJ95g8LCQioKAQCQou688069/fbbMce++93vauHChSZFlL4GWkvY00YlXq1Tfvazn0nq/V6fffbZ0ePZ2dl66KGHlJeXp7q6Oj311FMx17GWAJiLxAlgGAKBgM4///w+JbkfeOCBfifhkRgDLYzsz9q1a/WjH/1IknTHHXeopKRkSK957733atasWfrXv/6lsWPH9ulJ2x8WRgAgNXV1dencc8+VYRjRY263Ww899BAtOkx0/fXXq7m5WZMnT9Z3vvOdmOfuvPNOLVu2TKeeeup+r9/z/QyHw8OOobW1Vdddd52k3kmSyZMn9zknLy9P9fX1MePJ2tpaSVJ+fn702KOPPqoVK1bouuuuG1Y/VQBAcrn77ru1du3amGM//elPlZeXZ1JE1jHcnuWS9N577+nMM89UWVmZ3G63KioqdPbZZ48oUfKNN97Ql770JRUXF8vtdqusrExnnHGG3nvvvf1eQ89yAEgvGzdujH423GPq1Km64YYbTIoofR1oLWFP4kQ82l0EAgGdcsopOumkk/TVr361z/NerzfaJqSqqirmOdYSAHMxowsMw6233qpVq1bFHBvswj3iZ6CFkf258sorFQwGdfTRR+uss84a8mvumVS5/PLLddNNN0X7oQ1k74WRPQOegRZGbr/9dhZGAMACrr/+eq1fvz7m2I9//GPGAyb66KOPdP/990vqrTDldrtjnl+8eHGfhZS9rV69WmvWrJEkzZo1a9hx/PSnP1Vtba2Ki4t19dVX93vOkUceqSeffFL33XefLrroInV3d0d7nx599NGSpJ6eHl1//fWqrKzUZZddNux4AADJYdeuXdFE/j0OPvhgnX/++SZFZC3D7Vn+4IMP6sILL1Q4HFZ2drZmzZql6upqPfbYY/rDH/6gn//850OuHvrAAw9o6dKlCofDysnJ0axZs7R582Y98cQTevLJJ7Vs2TJ97Wtfi7mGnuUAkF4ikYiWLFmi7u7u6DGbzaZly5bJ5/OZGFl6OtBaQjwrTrjdbt122237fb6trU3r1q2TFDtmkVhLAExnaqMQwII++ugjw+VyxfQkmzt3rhEIBMwOLa2sWrUq2oP8T3/606Cu+b//+7/o9+ydd94Z1uv+9a9/NTZv3hx9/PLLL0fvGQwG+73mS1/6kiHJ+NWvfmUYhmF0dXUZRx99tCHJ+Pvf/24YhmF0d3cb48aNMyorK43u7u5hxQYAGD2vv/66YbPZYsYDhx566H7fCzA6zj77bEOSMW3aNCMcDg/p2lAoFH1/HjNmzLDfj5ubm43MzExDknHzzTfv97x3333XcLvdhiRj5syZRnFxsSHJOProo6Ox33bbbYYk45FHHhlWLACA5HLOOefEjB0kGW+++abZYaWM/nqWr1mzJvp++61vfcvo6uoyDMMwwuGw8bOf/SzaZ34ocwQbN26Mzgtdfvnl0TFDZ2ensWTJEkOS4Xa7jU2bNsVcR89yAEgvv/zlL/u8719++eVmh5WWDrSWsGPHDkOS4XA4jA0bNhg33XSTcfLJJxvHHXecsWTJEuPZZ5+NWywrVqwwFi9ebEgyZs2a1WddibUEwFwkTgBDEAqFjMMOOyxmsONwOIx3333X7NDSznAWRvZMSBx//PFxi2MwiRMsjABAauno6DCmTJkSMx7weDzGmjVrzA4tre3YsSO6iPHAAw8M6dpIJBJd6JBk/P73vx92HHve07OysoympqYBz3311VeNI444wvB4PEZxcbFx2WWXGW1tbYZhGEZjY6ORl5dnHHTQQTFjnfb2dmPbtm0k7QKAxbzxxht9Fk/OOeccs8NKGY899pghyfB6vcbq1aujx5cuXWpIMhYtWmREIpE+15144omGJGPJkiWDfq2f/OQnhiRj+vTpfeYjgsGgMXHiREOS8aMf/SjmucrKSsPv98dc87vf/c6QZFx99dUx5/7zn//s9zgAwBqqqqqiCfV7vsaPHx/9vIfRdaC1hGeffdaQZDidTsPr9fYZs0kyPv/5zxutra3DjuHcc881Kisro/f73Oc+Z+zcubPPeawlAOaiVQcwBHfddZfefvvtmGPf+c53dPDBB5sUUXrauXOn/vjHP0qSrr766j7lLPvz7rvv6rXXXpMkfe9730tofPs6+OCD9eKLL+p73/ue3n33XeXk5Oiyyy7TLbfcIrvdrqamJt1yyy066KCDYnqedXR0qLGxUaWlpXK5XKMaMwBg/37wgx9ow4YNMcfOOeccRSIRrV692qSorKOsrCymvGS8/PKXv1QwGFRZWZnOPvvsQV8XDoe1dOnSaJuMyy+/XGeeeeawYohEIrrnnnskSd/4xjcOWC7zyCOPjI5P9nXzzTerqalJjz/+uOx2uzo7O/WNb3xDf/zjHxUKhZSdna1rrrlG1157rWw227DiBQCMjnA4rMsvvzzmWFZWlv77v//bpIhSy0A9y+fOnavTTz9dp556ar/vl3PnztU//vGPPv3FB7J9+3ZJvT3Q952PcDqdmj9/vjZv3qxt27bFPLdr1y4VFxfTsxwAUpxhGFq6dKna29tjjt9///3KzMw0Kar0NZi1hD1tOkKhkL74xS/qmmuu0axZs9TS0qL//d//1fe+9z0988wzOuOMM/Tss88OK45nn31WdXV10cebNm3SP//5T51zzjkx57GWAJiLxAlgkDZt2qTrrrsu5lhhYaG++MUvauXKlSZFldxcLpemTZs2qMSGoRjOwsgdd9whSVqwYIGOPfbYuMYzGCyMAEBqePPNN6PvKXu77777dN99941+QBbkcDh01VVX6dZbb43rffdMhJxxxhlyu92Duqajo0NnnXWW/vrXv0qSzj333H6/v4P15ptvRhdJ9u1rPhRVVVW65557dOyxx+rEE0+UJF111VV67LHHdMYZZ+iEE07Q448/ruuuu042m41FFQBIcsuWLdN7770Xc+zGG29UaWmpSRGlloF6ll9yySW65JJL9nvtu+++K6lvf/GBVFZWSpJWrlwZ039cUkwi7fjx42Ouo2c5AKSHRx55RP/4xz9ijs2bN0/Nzc3685//bFJUyW/evHmaPHly3O87mLWEuXPn6uKLL1Zpaal++MMfRo97vV5dcsklmjt3ro4++mj93//9n/7v//5PJ5100pDjeOONN1RRUaFNmzbpnnvu0a9//Wude+65ampq0re+9a2Yc1lLAExkdskLwCqOO+64fks08TXwV0VFhbF58+a4fi8mT55sSL39SQeju7s7WhrtjjvuiGssg2nVMZCtW7caHo/HOPbYY6PHLrroIkOSccYZZxgPPPBA9N/eT3/603iGDgAYhq9//eumv7emyldVVVXcvi+rVq2K3newveJ37NhhzJ8/P3rdt7/97X5LeA/FVVddZUi95T9H4qtf/aphs9mM5cuXG4ZhGLW1tYbb7TYOPfTQ6DldXV1GSUmJkZOTM6wxCABgdDQ2NhqFhYUx74EzZsyg5VKcHKhn+f7s3LnTuOSSSwxJRmZmprF27dpBX7t9+3YjIyPDkGR885vfNHp6egzDMIyenh7jW9/6liHJyMnJMXbs2BFzHT3LASD1NTQ0GHl5eaZ/3rbq1//8z//E/Xsy1LWE/Tn++OMNScaFF14Yl7h+9KMfGZKM7Oxso7m5eVDXsJYAJB6JE8AgrF+/3vRBg5W/vvGNb8TtezGchZG//e1vhiTDbrf3mbgYqZEmTrAwAgDWsmeCna+RfTmdTmPXrl1x+77cdNNNhiSjsrJyUMkPq1atMioqKqLjgzvvvDMuceyZkPnhD3847Hu8//77hs1mM84888zosRdeeMGQZFx55ZUx537hC18wJBmrVq0a9usBABLrsssu6/M++Pzzz5sdVso4UM/yfd19993GtGnTDKfTaUi9vcMHO7ewt9dff92YOnWqIfUmScyfP9/Izc01JBkLFiww3n///T7X0LMcAFLfo48+avrnbSt/jRkzJq7fj+GsJezP1VdfbUgyPvOZz8Qltvb2dsPlchmSjJdffnlQ17CWACRefOvnAykqIyPD7BAsLZ69255++mlJvaUxDzvssCFdc+SRR6q8vDxusYzUBx98oMcff1xnnHGGFi5cKElatWqVAoGAjjzyyOh5Xq9XhxxyiFpaWrR27VqzwgUASLrhhht03HHHyemk491wjR07Vr/5zW/iWp58TwnLz3zmMwcsRblmzRp9+tOfVnV1tXw+n5588kldccUVI46htrZWGzdujMYxXFdffbWcTqduvvnmmHtLvWOCveXk5EiSWlpahv16AIDEWblypX75y1/GHPvSl740ovcJ/Mdgepbv69VXX9W6desUCoUk9b7HPv300woGg0N67ezs7Ojn+JaWFn3wwQdqbm6WJJWVlfV7zZ6e5UcccYQ2bdokSbrsssv0zDPPHLBn+fbt24ccIwBg9E2YMMHsECxt3zZXIzWUtYRwOBwdH/QnEolI6m1PfiCRSETbtm3TW2+9td9zMjIyVFhYKEmqqak54D1ZSwBGBzO+wCCUl5frl7/8pW666aZBvYmhl8vl0vHHHx/TF2ykhrIw0t81yYSFEQCwnuLiYr3wwgtmh4F97OlP/qlPfWrA85qamnTSSSepvr5eOTk5evbZZ7Vo0aK4xLB8+XJJktPpjE5iDNVzzz2nF154QVdccYUmTpwYPV5QUCBJqq6ujjl/586dkhSdbAEAJA/DMHT55ZdHJ9n3qK+v15lnnmlSVOaYMWOGli5dGtekSWlwPcv3ddttt+nhhx9WXV2dnnzySV1//fX67//+b61du1ZPPfXUoO7x4osv6tRTT1VHR4cuv/xyfetb31JFRYW2bNmi2267TcuWLdPLL7+sZ555RosXL465lp7lAJDaFi1apF//+tdatmyZWltbzQ7HUubPn6+f/OQncb3nYNcSxo8fr23btun222/XlVde2e8577//viRp5syZB3zd119/XUcffbRsNpt27dqlkpKSPud0dHSovr5ekjRmzJgD3pO1BGB0kDgBDNLFF1+siy++2Oww0t5gF0b2aG1t1bp164Z0zWhgYQQAgPjYtm1bdLJhwYIFA577ne98R9u2bZPdbtcf//jHISVN7NmpUVhY2O/78Z5JlBkzZsjn8w36vntEIhFdc801ys7O1g9+8IOY5w477DB5PB795S9/0datWzV+/Hi99957eu211zRmzBhNnTp1yK8HAEis5557Tv/+97/7HO/vWDp48cUX4/5n31Nt4owzzpDb7R7UNePGjZPUu/P0W9/6lubPn6/FixfrL3/5i1555ZU+iQ77CgaDuvDCC9XR0aFLLrlEd911V/S5adOm6YEHHlBGRobuvvtuLVmyRGvXrh1UpbKqqirdc889OvbYY3XiiSdKkq666io99thjOuOMM3TCCSfo8ccf13XXXSebzaZrr712UH9eAMDoW7p0qZYuXWp2GNDg1xJmzpypqqoqPfroo7riiiv6vHe/8847evnllyVpUAmwhxxyiHJzc9Xc3Kxf//rXuuGGG/qcc++99yoYDKqwsFCHH374gPdjLQEYPbTqAGAZQ1kY2WPFihUyDGNI16xdu1Zr166Nvla8DWVhRBILIwAADGDHjh3R3+9ZDOnPrl279Mgjj0iS/H6/fvKTn+jII4/c79eyZctirp8xY4ZmzJihe+65Z8A4BophIL/73e/04Ycf6uqrr1ZRUVHMc7m5ubryyivV2tqqWbNm6aCDDtKnPvUpBYNB/fd//ze7TgEgCe2ZqEevN998M673++ijj6Itss4444xh3+eYY46JJlK+8sorBzz/vffe05YtWyRpv9U1b7jhBjkcDm3atEnvvPPOoOK47rrrFAgEdOutt0qS6urqtGzZMh166KH6wx/+oPPPP19///vfVVJSoltvvXXAUuIAAGBoawnXXHONbDabPvjgAy1ZsiSmWsgrr7yiU089VYZh6Oyzz+5TYbK/tQS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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plot\n", + "\n", + "# Figure specifications (fonts, sizes, figsize, etc.)\n", + "size_legend_font = 8\n", + "size_tick_font = 6\n", + "size_label_font = 7\n", + "mpl.rc(\"xtick\", labelsize=size_tick_font)\n", + "mpl.rc(\"ytick\", labelsize=size_tick_font)\n", + "mpl.rc(\"axes\", labelsize=size_label_font, linewidth=0.6)\n", + "mpl.rc(\"font\", family=\"Arial\")\n", + "mpl.rc(\"mathtext\", default=\"regular\")\n", + "# mpl.rc('lines', )\n", + "# mpl.rc('markers', )\n", + "marker_style = dict(lw=0, marker=\"D\", ms=1.5, mec=\"r\", mfc=\"none\", mew=0.7)\n", + "\n", + "# Size of the figure\n", + "size_in_mm = (180, 100) # input the desired size in mm (width, height)\n", + "\n", + "# Variables to define figure's empty space from its borders to the spines of the axes\n", + "from_left = 2.5 # in mm\n", + "from_right = 2.5 # in mm\n", + "from_bottom = 2.5 # in mm\n", + "from_top = 2.5 # in mm\n", + "\n", + "fig = plt.figure(\n", + " figsize=[x / 25.4 for x in size_in_mm],\n", + " dpi=300,\n", + " # tight_layout = {'pad': 0}\n", + ")\n", + "\n", + "gs = fig.add_gridspec(\n", + " nrows=3,\n", + " ncols=1,\n", + " height_ratios=[0.1, 1, 0.3],\n", + " hspace=0.1,\n", + " top=1 - from_top / size_in_mm[1],\n", + " bottom=from_bottom / size_in_mm[1],\n", + " left=from_left / size_in_mm[0],\n", + " right=1 - from_right / size_in_mm[0],\n", + ")\n", + "\n", + "doughnut = gs[1].subgridspec(\n", + " nrows=2,\n", + " ncols=4,\n", + " width_ratios=[1, 1, 1, 1],\n", + " height_ratios=[1, 1],\n", + " wspace=0.01,\n", + " hspace=0.01,\n", + ")\n", + "\n", + "category = gs[0].subgridspec(\n", + " nrows=1,\n", + " ncols=4,\n", + " width_ratios=[1, 1, 1, 1],\n", + " wspace=0.2,\n", + ")\n", + "\n", + "legend_gs = gs[2].subgridspec(\n", + " nrows=1,\n", + " ncols=2,\n", + " wspace=0.001,\n", + ")\n", + "\n", + "ax0 = fig.add_subplot(doughnut[0])\n", + "ax1 = fig.add_subplot(doughnut[1])\n", + "ax2 = fig.add_subplot(doughnut[2])\n", + "ax3 = fig.add_subplot(doughnut[3])\n", + "ax4 = fig.add_subplot(doughnut[4])\n", + "ax5 = fig.add_subplot(doughnut[5])\n", + "ax6 = fig.add_subplot(doughnut[6])\n", + "ax7 = fig.add_subplot(doughnut[7])\n", + "\n", + "ax_label0 = fig.add_subplot(category[0])\n", + "ax_label0.axis(\"off\")\n", + "ax_label1 = fig.add_subplot(category[1])\n", + "ax_label1.axis(\"off\")\n", + "ax_label2 = fig.add_subplot(category[2])\n", + "ax_label2.axis(\"off\")\n", + "ax_label3 = fig.add_subplot(category[3])\n", + "ax_label3.axis(\"off\")\n", + "ax_legend0 = fig.add_subplot(legend_gs[0])\n", + "ax_legend0.axis(\"off\")\n", + "ax_legend1 = fig.add_subplot(legend_gs[1])\n", + "ax_legend1.axis(\"off\")\n", + "\n", + "## DOUGHNUT CHARTS ################################################################\n", + "## GWP\n", + "w_hetero, t_hetero = plot_doughnut(\n", + " ax=ax0,\n", + " data=data_hetero_gwp,\n", + " explode=explode_hetero_gwp,\n", + " colors=hetero_colors,\n", + " startangle=35,\n", + " tot_loc = (0.45, 0.47)\n", + ")\n", + "w_homoge, t_homoge = plot_doughnut(\n", + " ax=ax4,\n", + " data=data_homoge_gwp,\n", + " explode=explode_homoge_gwp,\n", + " colors=homoge_colors,\n", + " startangle=75,\n", + " tot_loc = (0.41, 0.43)\n", + ")\n", + "\n", + "ax_label0.text(\n", + " x=0.5,\n", + " y=0.0,\n", + " s=category_label_gwp,\n", + " fontdict=dict(fontsize=size_label_font, fontweight=\"normal\"),\n", + " horizontalalignment=\"center\",\n", + ")\n", + "\n", + "## EQ\n", + "w_hetero, t_hetero = plot_doughnut(\n", + " ax=ax1,\n", + " data=data_hetero_eq,\n", + " explode=explode_hetero_eq,\n", + " colors=hetero_colors,\n", + " startangle=35,\n", + " tot_loc = (0.43, 0.47)\n", + ")\n", + "w_homoge, t_homoge = plot_doughnut(\n", + " ax=ax5,\n", + " data=data_homoge_eq,\n", + " explode=explode_homoge_eq,\n", + " colors=homoge_colors,\n", + " startangle=75,\n", + " tot_loc = (0.38, 0.43)\n", + ")\n", + "\n", + "ax_label1.text(\n", + " x=0.5,\n", + " y=0.0,\n", + " s=category_label_eq,\n", + " fontdict=dict(fontsize=size_label_font, fontweight=\"normal\"),\n", + " horizontalalignment=\"center\",\n", + ")\n", + "\n", + "## HH\n", + "w_hetero, t_hetero = plot_doughnut(\n", + " ax=ax2,\n", + " data=data_hetero_hh,\n", + " explode=explode_hetero_hh,\n", + " colors=hetero_colors,\n", + " startangle=35,\n", + " tot_loc = (0.42, 0.47)\n", + ")\n", + "w_homoge, t_homoge = plot_doughnut(\n", + " ax=ax6,\n", + " data=data_homoge_hh,\n", + " explode=explode_homoge_hh,\n", + " colors=homoge_colors,\n", + " startangle=75,\n", + " tot_loc = (0.38, 0.43)\n", + ")\n", + "\n", + "ax_label2.text(\n", + " x=0.5,\n", + " y=0.0,\n", + " s=category_label_hh,\n", + " fontdict=dict(fontsize=size_label_font, fontweight=\"normal\"),\n", + " horizontalalignment=\"center\",\n", + ")\n", + "\n", + "## NR\n", + "w_hetero, t_hetero = plot_doughnut(\n", + " ax=ax3,\n", + " data=data_hetero_nr,\n", + " explode=explode_hetero_nr,\n", + " colors=hetero_colors,\n", + " startangle=35,\n", + " tot_loc = (0.44, 0.47)\n", + ")\n", + "w_homoge, t_homoge = plot_doughnut(\n", + " ax=ax7,\n", + " data=data_homoge_nr,\n", + " explode=explode_homoge_nr,\n", + " colors=homoge_colors,\n", + " startangle=75,\n", + " tot_loc = (0.40, 0.43)\n", + ")\n", + "\n", + "ax_label3.text(\n", + " x=0.5,\n", + " y=0.0,\n", + " s=category_label_nr,\n", + " fontdict=dict(fontsize=size_label_font, fontweight=\"normal\"),\n", + " horizontalalignment=\"center\",\n", + ")\n", + "\n", + "## LEGEND ################################################################\n", + "leg0 = ax_legend0.legend(w_hetero, labels_hetero_gwp, \n", + " loc=\"lower right\",\n", + " frameon=True,\n", + " fancybox=False,\n", + " edgecolor=\"k\",\n", + " # columnspacing=0.7,\n", + " fontsize=7,\n", + " title_fontsize=8,\n", + " bbox_to_anchor=(1, 0))\n", + "leg0.get_frame().set_linewidth(0.4)\n", + "leg0.set_title(title=\"Heterogeneous\", prop={\"size\":8, \"weight\":\"bold\"})\n", + "\n", + "leg1 = ax_legend1.legend(w_homoge, labels_homoge_gwp,\n", + " loc=\"lower left\",\n", + " frameon=True,\n", + " fancybox=False,\n", + " edgecolor=\"k\",\n", + " # columnspacing=0.7,\n", + " fontsize=7,\n", + " title_fontsize=8,\n", + " bbox_to_anchor=(0, 0))\n", + "leg1.get_frame().set_linewidth(0.4)\n", + "leg1.set_title(title=\"Homogeneous\", prop={\"size\":8, \"weight\":\"bold\"})\n", + "\n", + "## EXPORT ################################################################\n", + "# figNamePNG = \"LCAscore_total_barplot.png\"\n", + "figNameSVG = \"LCAscore_total_doughnut.svg\"\n", + "\n", + "# plt.savefig(str(pngFilesDir / figNamePNG)) # export fig as png\n", + "plt.savefig(str(svgFilesDir / figNameSVG)) # export fig as svg\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0ea92f9e", + "metadata": {}, + "outputs": [], + "source": [ + "bw.__version__" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e686a71f", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3.9.7", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.7" + }, + "vscode": { + "interpreter": { + "hash": "bd958e837233136b65ef886a306db27124b4a48f9a20f2930a4727b08bf88f0b" + } + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks/howto_sensitivity_AB.md b/notebooks/howto_sensitivity_AB.md new file mode 100644 index 0000000..3f23b52 --- /dev/null +++ b/notebooks/howto_sensitivity_AB.md @@ -0,0 +1,9 @@ +## Instructions to perform the sensitivity analysis using Activity Browser (AB) + +- start AB, activate `GAC_project` +- go to `LCA Setup` -> select `solveLCA_scenarios` calculation setup, from dropdown menu select **Scenario LCA** +- define "**Reference flows**": homogeneous/heterogeneous reactions systems +- define "**Impact categories**": `IPCC 2021, climate change: including SLCFs, global warming potential (GWP100)` +- define **Scenarios**, add -> load `data\processed\scenario_table.xlsx` +- press `Calculate` +- (optional) go to `LCA results` -> "Export all data `Excel`" to export the results \ No newline at end of file diff --git a/notebooks/import_LCI_data.ipynb b/notebooks/import_LCI_data.ipynb new file mode 100644 index 0000000..82823bd --- /dev/null +++ b/notebooks/import_LCI_data.ipynb @@ -0,0 +1,306 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "dc075a87-3c79-48c8-a6fb-1b9e21df49f5", + "metadata": {}, + "outputs": [], + "source": [ + "import brightway2 as bw\n", + "import bw2io\n", + "import productivity_boosters as prbs\n", + "from project_path import ROOT_DIR" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "c847b277-050c-4c01-9ac8-f715c3dcd999", + "metadata": {}, + "outputs": [], + "source": [ + "bw.projects\n", + "bw.projects.set_current(\"GAC_project\")" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "0e3979c0-10c9-490a-859a-4cd2a0e76007", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Biosphere database already present!!! No setup is needed\n" + ] + } + ], + "source": [ + "bw.bw2setup()" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "f898ed57-189d-4163-a908-29c5a02e775f", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The database is already present! No import needed.\n" + ] + } + ], + "source": [ + "prbs.import_ei_database(\n", + " dirpath=Path(\n", + " str(Path(ROOT_DIR).parent)\n", + " + r\"\\bw2Hub\\data\\external\\ecoinvent\\v39\\ecoinvent 3.9_cutoff_ecoSpold02\\datasets\"\n", + " ),\n", + " db_name=\"ei39cutoff\",\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "8af12383-936b-43bb-82ce-3a6bdb1a0d52", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Databases dictionary with 2 object(s):\n", + "\tbiosphere3\n", + "\tei39cutoff" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "bw.databases" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "8c1c495a-1b50-4794-981f-3e1a85ca445d", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Extracted 3 worksheets in 0.06 seconds\n", + "\n", + "Importing database: GAC_project_db\n" + ] + }, + { + "data": { + "text/plain": [ + "'This database contains the activities related to the GAC heterogeneous catalyst (Cu/PCN) vs homogeneous (Cu2O/Ligand) catalyst performance comparison.'" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "FOREGROUND_DBS_PATH = Path(ROOT_DIR + r\"\\data\\interim\\foreground_dbs.xlsx\")\n", + "fdbs = bw2io.importers.ExcelImporter(FOREGROUND_DBS_PATH)\n", + "print(f\"\\nImporting database: {fdbs.db_name}\")\n", + "fdbs.metadata[\"description\"]" + ] + }, + { + "cell_type": "raw", + "id": "bfcee97d-5f5a-458c-a58c-b8364ee74f86", + "metadata": {}, + "source": [ + "for u in fdbs.unlinked:\n", + " print(u)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "5a6d983a-96f9-4925-aea7-bb7613c2aadc", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Applying strategy: csv_restore_tuples\n", + "Applying strategy: csv_restore_booleans\n", + "Applying strategy: csv_numerize\n", + "Applying strategy: csv_drop_unknown\n", + "Applying strategy: csv_add_missing_exchanges_section\n", + "Applying strategy: normalize_units\n", + "Applying strategy: normalize_biosphere_categories\n", + "Applying strategy: normalize_biosphere_names\n", + "Applying strategy: strip_biosphere_exc_locations\n", + "Applying strategy: set_code_by_activity_hash\n", + "Applying strategy: link_iterable_by_fields\n", + "Applying strategy: assign_only_product_as_production\n", + "Applying strategy: link_technosphere_by_activity_hash\n", + "Applying strategy: drop_falsey_uncertainty_fields_but_keep_zeros\n", + "Applying strategy: convert_uncertainty_types_to_integers\n", + "Applying strategy: convert_activity_parameters_to_list\n", + "Applied 16 strategies in 20.96 seconds\n", + "Applying strategy: link_iterable_by_fields\n", + "Applying strategy: link_iterable_by_fields\n", + "11 datasets\n", + "44 exchanges\n", + "0 unlinked exchanges\n", + " \n" + ] + }, + { + "data": { + "text/plain": [ + "(11, 44, 0)" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "fdbs.apply_strategies()\n", + "fdbs.match_database(db_name=\"ei39cutoff\")\n", + "fdbs.match_database(db_name=\"biosphere3\")\n", + "fdbs.statistics()" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "81501b96-ff93-4f92-a6ab-d9f87072cffd", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Writing activities to SQLite3 database:\n", + "0% [###########] 100% | ETA: 00:00:00\n", + "Total time elapsed: 00:00:00\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Title: Writing activities to SQLite3 database:\n", + " Started: 11/06/2022 18:29:08\n", + " Finished: 11/06/2022 18:29:08\n", + " Total time elapsed: 00:00:00\n", + " CPU %: 50.40\n", + " Memory %: 1.30\n", + "Created database: GAC_project_db\n" + ] + } + ], + "source": [ + "# if no unlinked activities, write database to disk\n", + "if list(fdbs.unlinked):\n", + " list(fdbs.unlinked)\n", + "else:\n", + " fdbs.write_database()" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "f1fc47da-3281-4a99-a341-6c9604614372", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Databases dictionary with 3 object(s):\n", + "\tGAC_project_db\n", + "\tbiosphere3\n", + "\tei39cutoff" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "bw.databases" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "bacd93bc", + "metadata": {}, + "outputs": [], + "source": [ + "# del bw.databases[\"GAC_project_db\"]" + ] + }, + { + "cell_type": "markdown", + "id": "966c1b74", + "metadata": {}, + "source": [ + "### Export valid lci database to excel (this file will be shared)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9f58785d", + "metadata": {}, + "outputs": [], + "source": [ + "# bw2io.export.write_lci_excel(database_name=\"GAC_project_db\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c06d946c", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python (bw2)", + "language": "python", + "name": "bw2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.7" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks/project_path.py b/notebooks/project_path.py new file mode 100644 index 0000000..3729077 --- /dev/null +++ b/notebooks/project_path.py @@ -0,0 +1,16 @@ +""" +Adapted from https://stackoverflow.com/a/51028921/14485040 + +This script has to be included in the `notebooks` directory and +has to be imported (first line) in every notebook that uses +scripts/data from directories relative to the root directory of +the project. +""" + +import sys +import os + +ROOT_DIR = os.path.abspath(os.path.join("..")) +# `..` indicate a relative path to root directory +if ROOT_DIR not in sys.path: + sys.path.append(ROOT_DIR)

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sulfonicacid production + + + + market fornaphthalenesulfonic acid + + + + market forhard coal + + + + coking + + + + market forcoal tar + + + + woodpreservativeproduction,creosote + + + + market forwoodpreservative,creosote + + + polymericcarbon nitrideproduction + + + + + dicyandiamideproduction + + + + market for calcium carbide + + + + calcium carbideproduction + + + + market group forelectricity,medium voltage + + + + hard coal mineoperation andhard coal preparation + + + + Cug/PCN catalystproduction, at 10.8 wt% Cu + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + b + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + 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butan-1,4-diol + market forbutyrolactone + N-methyl-2-pyrrolidoneproduction + market fornaphthalenesulfonic acid + market foracetylene + + + market foracetyl chloride + + + market formonochlorobenzene + + + market groupfor natural gas,high pressure + + phenol production, from cumene + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + market forbenzene + benzeneproduction + benzenechlorination + acetyleneproduction + market foroxygen,liquid + air separation, cryogenic + chlorine production, liquid + cumeneproduction + market forchlorine,gaseous + chlor-alkalielectrolysis,membrane cell + market for heat,district or industrial,natural gas + market for imidazole(reactant) + 1-iodonaphthalene production(proxy, reactant) + + L1 ligandproduction + + + 2.0 + 2.0 + 2.0 + 2.1 + 2.1 + 2.1 + 2.1 + 2.3 + 2.3 + 2.5 + 2.7 + 2.9 + 3.4 + 3.7 + 4.0 + 4.1 + 4.9 + 5.0 + 5.2 + 5.4 + 5.6 + 5.6 + 5.8 + 6.5 + 6.5 + 6.5 + 7.1 + 7.2 + 7.3 + 7.8 + 9.6 + 11.6 + 38.2 + 38.3 + 53.9 + 54.2 + 70.3 + 14.8 + 2.3 + Cu2O/L1 catalystproduction + + diff --git a/data/processed/svg_files/fig_LCA_SI_sensitivity.svg b/data/processed/svg_files/fig_LCA_SI_sensitivity.svg new file mode 100644 index 0000000..e0e6777 --- /dev/null +++ b/data/processed/svg_files/fig_LCA_SI_sensitivity.svg @@ -0,0 +1,1414 @@ + + + + + + + + 2022-11-14T19:06:32.503483 + image/svg+xml + + + Matplotlib v3.5.3, https://matplotlib.org/ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + 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a/notebooks/LCAscores_breakdown_sankey.ipynb b/notebooks/LCAscores_breakdown_sankey.ipynb new file mode 100644 index 0000000..9aebf15 --- /dev/null +++ b/notebooks/LCAscores_breakdown_sankey.ipynb @@ -0,0 +1,1305 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "dc075a87-3c79-48c8-a6fb-1b9e21df49f5", + "metadata": {}, + "outputs": [], + "source": [ + "from pprint import pprint\n", + "from typing import Dict, List, Tuple\n", + "\n", + "import brightway2 as bw\n", + "import bw2analyzer as ba\n", + "import bw2calc as bc\n", + "import bw2data as bd\n", + "import pandas as pd\n", + "import productivity_boosters as prbs\n", + "from ipysankeywidget import SankeyWidget\n", + "from ipywidgets import Layout" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "c847b277-050c-4c01-9ac8-f715c3dcd999", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Databases dictionary with 3 object(s):\n", + "\tGAC_project_db\n", + "\tbiosphere3\n", + "\tei39cutoff" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "bw.projects\n", + "bw.projects.set_current(\"GAC_project\")\n", + "bw.databases" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "ebf5e2bf-86f4-48d9-9a35-a91a943f9028", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "('IPCC 2021',\n", + " 'climate change: including SLCFs',\n", + " 'global warming potential (GWP100)')" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ipcc_methods = [\n", + " met\n", + " for met in bw.methods\n", + " if \"ipcc 2021\" in str(met).lower()\n", + " and \"climate change: including SLCFs\" in str(met)\n", + " and \"no LT\" not in str(met)\n", + " and \"GWP100\" in str(met)\n", + " or (\"ipcc 2013\" in str(met).lower() and \"no LT\" not in str(met) and \"GWP100\" in str(met))\n", + " # or (\"ipcc 2021\" in str(met).lower() and \"no LT\" not in str(met) and \"GWP100\" in str(met))\n", + "][1]# [:2]\n", + "ipcc_methods\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "345341b7-e690-4136-aa58-c0c357b91366", + "metadata": {}, + "outputs": [], + "source": [ + "DB_NAME = \"GAC_project_db\"" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "933ae682-a9fa-4d30-9007-f03a97f29990", + "metadata": { + "notebookRunGroups": { + "groupValue": "1" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "'1-(naphthalen-1-yl)-1H-imidazole production with heterogeneous Cu/PCN catalyst (10.8 wt% Cu)' (kilogram, GLO, None)" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "act_hetero = [\n", + " ds\n", + " for ds in bw.Database(DB_NAME)\n", + " if \"1-(naphthalen-1-yl)-1H-imidazole\" in ds[\"name\"] and \"Cu/PCN\" in ds[\"name\"]\n", + "][0]\n", + "act_hetero\n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "c2f02f15", + "metadata": { + "notebookRunGroups": { + "groupValue": "2" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "'1-(naphthalen-1-yl)-1H-imidazole production with homogeneous Cu2O/Altman-Buchwald-ligand catalytic complex' (kilogram, GLO, None)" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "act_homogen = [\n", + " ds\n", + " for ds in bw.Database(DB_NAME)\n", + " if \"1-(naphthalen-1-yl)-1H-imidazole\" in ds[\"name\"]\n", + " and \"Cu2O/Altman-Buchwald-ligand\" in ds[\"name\"]\n", + "][0]\n", + "act_homogen\n" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "17f92333-acf9-4ca4-995f-9f11e248b655", + "metadata": {}, + "outputs": [], + "source": [ + "# bw.Database(DB_NAME).random()\n", + "#TODO should be accepting `methods=str`. Correct\n", + "\n", + "# prbs.pretty_lca(\n", + "# FUs=[{act: 1}],\n", + "# methods=ipcc_methods,\n", + "# )" + ] + }, + { + "cell_type": "markdown", + "id": "ad610209", + "metadata": {}, + "source": [ + "### Functions" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "96359b04", + "metadata": {}, + "outputs": [], + "source": [ + "def print_recursive_calculation(activity, lcia_method, lca_obj=None, total_score=None, amount=1, level=0, max_level=3, cutoff=1e-2):\n", + " \"\"\"\"Taken from https://github.com/brightway-lca/brightway2/blob/master/notebooks/Contribution%20analysis%20and%20comparison.ipynb\"\"\"\n", + " if lca_obj is None:\n", + " lca_obj = bc.LCA({activity: amount}, lcia_method)\n", + " lca_obj.lci()\n", + " lca_obj.lcia()\n", + " total_score = lca_obj.score\n", + " elif total_score is None:\n", + " raise ValueError\n", + " else:\n", + " lca_obj.redo_lcia({activity: amount})\n", + " if abs(lca_obj.score) <= abs(total_score * cutoff):\n", + " return\n", + " print(\"{}{:4.3f} ({:06.4f}): {:.70}\".format(\" \" * level, lca_obj.score / total_score, lca_obj.score, str(activity)))\n", + " if level < max_level:\n", + " for exc in activity.technosphere():\n", + " print_recursive_calculation(\n", + " activity=exc.input, \n", + " lcia_method=lcia_method, \n", + " lca_obj=lca_obj, \n", + " total_score=total_score, \n", + " amount=amount * exc['amount'], \n", + " level=level + 1, \n", + " max_level=max_level, \n", + " cutoff=cutoff\n", + " ) " + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "916c1c18", + "metadata": {}, + "outputs": [], + "source": [ + "def get_named_edges(traversed_dict: Dict, act: bd.backends.peewee.Activity) -> List:\n", + " rev_activity_dict, _, _ = traversed_dict[\"lca\"].reverse_dict()\n", + " named_edges = []\n", + " for edge in traversed_dict[\"edges\"]:\n", + " named_edge = {\"impact\": edge[\"impact\"]}\n", + "\n", + " act_from = bw.get_activity(rev_activity_dict[edge[\"from\"]])\n", + "\n", + " named_edge[\"from\"] = act_from[\"name\"] # + \" (\" + act_from[\"location\"] + \")\"\n", + " if edge[\"to\"] == -1:\n", + " named_edge[\"to\"] = act[\"name\"] + \"(start)\"\n", + " else:\n", + " act_to = bw.get_activity(rev_activity_dict[edge[\"to\"]])\n", + " named_edge[\"to\"] = act_to[\"name\"] # + \" (\" + act_to[\"location\"] + \")\"\n", + " named_edges.append(named_edge)\n", + " return named_edges\n" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "86da16f5", + "metadata": {}, + "outputs": [], + "source": [ + "def prepare_dataframe(lst: List[dict], group: bool = True) -> pd.DataFrame:\n", + " to_return = pd.DataFrame(lst)\n", + " to_return = to_return[[\"to\", \"from\", \"impact\"]].rename(\n", + " columns={\"to\": \"target\", \"from\": \"source\", \"impact\": \"value\"}\n", + " )\n", + " if group:\n", + " to_return = to_return.groupby([\"target\", \"source\"])[\"value\"].sum().reset_index()\n", + " return to_return\n" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "19374137", + "metadata": {}, + "outputs": [], + "source": [ + "def plot_sankey(\n", + " df_links: pd.DataFrame, size: Tuple, save: bool = True, file_name: str = \"test\"\n", + "):\n", + " links = df_links.to_dict(\"records\")\n", + " layout = Layout(width=size[0], height=size[1])\n", + " sankey = SankeyWidget(\n", + " links=links,\n", + " linkLabelFormat=\".1f\",\n", + " layout=layout,\n", + " align_link_types=True,\n", + " # margins=dict(top=10, bottom=0, left=250, right=300),\n", + " )\n", + " if save:\n", + " sankey.auto_save_svg(f\"../data/interim/svg_files/{file_name}.svg\")\n", + " return sankey\n" + ] + }, + { + "cell_type": "markdown", + "id": "4d011371", + "metadata": {}, + "source": [ + "## Print before Graph Traversal" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "2e24d83d", + "metadata": { + "notebookRunGroups": { + "groupValue": "1" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1.000 (5.6098): '1-(naphthalen-1-yl)-1H-imidazole production with heterogeneous Cu/PCN\n", + " 0.386 (2.1631): '1-iodonaphthalene production' (kilogram, GLO, None)\n", + " 0.386 (2.1631): 'market for naphthalene sulfonic acid' (kilogram, GLO, None)\n", + " 0.110 (0.6177): 'naphthalene sulfonic acid production' (kilogram, RER, None)\n", + " 0.260 (1.4611): 'naphthalene sulfonic acid production' (kilogram, RoW, None)\n", + " 0.487 (2.7300): 'market for imidazole' (kilogram, GLO, None)\n", + " 0.352 (1.9760): 'imidazole production' (kilogram, RoW, None)\n", + " 0.034 (0.1892): 'market for ammonia, anhydrous, liquid' (kilogram, CN, None)\n", + " 0.060 (0.3385): 'market for ammonia, anhydrous, liquid' (kilogram, RoW, None)\n", + " 0.012 (0.0663): 'market group for electricity, medium voltage' (kilowatt hour, RAS, No\n", + " 0.031 (0.1711): 'market for formaldehyde' (kilogram, RoW, None)\n", + " 0.171 (0.9603): 'market for glyoxal' (kilogram, RoW, None)\n", + " 0.015 (0.0840): 'market for heat, from steam, in chemical industry' (megajoule, RoW, N\n", + " 0.130 (0.7269): 'imidazole production' (kilogram, RER, None)\n", + " 0.043 (0.2431): 'market for ammonia, anhydrous, liquid' (kilogram, RER, None)\n", + " 0.014 (0.0775): 'market for formaldehyde' (kilogram, RER, None)\n", + " 0.058 (0.3265): 'market for glyoxal' (kilogram, RER, None)\n", + " 0.072 (0.4027): 'Cu/PCN catalyst production, at 10.8 wt% Cu' (kilogram, GLO, None)\n", + " 0.059 (0.3308): 'polymeric carbon nitride prodution' (kilogram, GLO, None)\n", + " 0.056 (0.3168): 'dicyandiamide production' (kilogram, GLO, None)\n", + " 0.056 (0.3140): 'market for dimethyl sulfoxide' (kilogram, GLO, None)\n", + " 0.016 (0.0909): 'dimethyl sulfoxide production' (kilogram, RER, None)\n", + " 0.038 (0.2114): 'dimethyl sulfoxide production' (kilogram, RoW, None)\n", + " 0.017 (0.0968): 'market for methanol' (kilogram, GLO, None)\n" + ] + } + ], + "source": [ + "print_recursive_calculation(activity=act_hetero, lcia_method=ipcc_methods)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "26cb5309", + "metadata": { + "notebookRunGroups": { + "groupValue": "2" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1.000 (70.3327): '1-(naphthalen-1-yl)-1H-imidazole production with homogeneous Cu2O/Alt\n", + " 0.028 (2.0021): '1-iodonaphthalene production' (kilogram, GLO, None)\n", + " 0.028 (2.0021): 'market for naphthalene sulfonic acid' (kilogram, GLO, None)\n", + " 0.019 (1.3523): 'naphthalene sulfonic acid production' (kilogram, RoW, None)\n", + " 0.036 (2.5268): 'market for imidazole' (kilogram, GLO, None)\n", + " 0.026 (1.8289): 'imidazole production' (kilogram, RoW, None)\n", + " 0.013 (0.8888): 'market for glyoxal' (kilogram, RoW, None)\n", + " 0.771 (54.2100): 'Cu2O/Altman-Buchwald-ligand catalytic complex production' (kilogram, \n", + " 0.767 (53.9492): '4,7-Dimethoxy-1,10-phenanthroline (Altman-Buchwald ligand) production\n", + " 0.011 (0.8021): 'market for acetic acid, without water, in 98% solution state' (kilogr\n", + " 0.012 (0.8247): 'market for acetic acid, without water, in 98% solution state' (kilogr\n", + " 0.544 (38.2841): 'market for trichloromethane' (kilogram, RER, None)\n", + " 0.016 (1.1572): 'market for aniline' (kilogram, RER, None)\n", + " 0.032 (2.2780): 'market for acetyl chloride' (kilogram, GLO, None)\n", + " 0.058 (4.0567): 'market for monochlorobenzene' (kilogram, RER, None)\n", + " 0.093 (6.5463): 'market for phenol' (kilogram, RER, None)\n", + " 0.165 (11.5937): 'market for N-methyl-2-pyrrolidone' (kilogram, GLO, None)\n", + " 0.027 (1.8813): 'N-methyl-2-pyrrolidone production' (kilogram, RER, None)\n", + " 0.021 (1.4707): 'market for butyrolactone' (kilogram, RER, None)\n", + " 0.137 (9.6176): 'N-methyl-2-pyrrolidone production' (kilogram, RoW, None)\n", + " 0.102 (7.1649): 'market for butyrolactone' (kilogram, RoW, None)\n", + " 0.024 (1.6702): 'market for methylamine' (kilogram, RoW, None)\n" + ] + } + ], + "source": [ + "print_recursive_calculation(activity=act_homogen, lcia_method=ipcc_methods)" + ] + }, + { + "cell_type": "markdown", + "id": "78d47983", + "metadata": {}, + "source": [ + "## Graph Traversal" + ] + }, + { + "cell_type": "markdown", + "id": "9eeb4d35", + "metadata": {}, + "source": [ + "### Documentation regarding Graph Traversal\n", + "\n", + "```python\n", + "t = bw.GraphTraversal().calculate(demand={act:1}, method=ipcc_methods[1])\n", + "t.keys()\n", + ">>> dict_keys(['nodes', 'edges', 'lca', 'counter'])\n", + "# ---\n", + "t[\"nodes\"][-1]\n", + ">>> {'amount': 1, 'cum': 9.327382788679612, 'ind': 9.327382788679611e-06}`\n", + "```\n", + "\n", + "from [bw documentation](https://2.docs.brightway.dev/lca.html#graph-traversal): \n", + "`t[\"nodes\"]` - dictionary (each node = row index in `A` matrix)\n", + "- `amount`: The total amount of this node needed to produce the functional unit\n", + "- `cum`: The cumulative LCA impact score attributable to the needed amount of this node, including its specific supply chain.\n", + "- `ind`: The individual LCA impact score directly attributable to one unit of this node, i.e. the score from the direct emissions and resource consumption of this node.\n", + "\n", + "```python\n", + "t[\"edges\"][0]\n", + ">>> {'to': -1,\n", + " 'from': 21263,\n", + " 'amount': 1,\n", + " 'exc_amount': 1,\n", + " 'impact': 9.327382788679609}\n", + "```\n", + "\n", + "from [bw documentation](https://2.docs.brightway.dev/lca.html#graph-traversal): \n", + "``t[\"edges\"]`` - list \n", + "- ``to``: The row index of the node consuming the product.\n", + "- ``from``: The row index of the node producing the product.\n", + "- ``amount``: The total amount of product from needed for the amount of to needed.\n", + "- ``exc_amount``: The amount of from needed for one unit of to, i.e. the value given in the technosphere matrix.\n", + "- ``impact``: The total LCA impact score embodied in this edge, i.e. the individual score of from times amount." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "3fbab094", + "metadata": { + "notebookRunGroups": { + "groupValue": "1" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Calculated 404 LCAs.\n" + ] + } + ], + "source": [ + "t_hetero = bw.GraphTraversal().calculate(demand={act_hetero: 1}, method=ipcc_methods, cutoff=0.05)\n", + "print(f\"Calculated {t_hetero['counter']} LCAs.\")\n", + "\n", + "# Named edges in a list\n", + "ne_hetero = get_named_edges(traversed_dict=t_hetero, act=act_hetero)\n", + "# Clean dataframe with renamed columns\n", + "df_hetero = prepare_dataframe(lst=ne_hetero, group=True)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "a9b4181b", + "metadata": { + "notebookRunGroups": { + "groupValue": "2" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Calculated 716 LCAs.\n" + ] + } + ], + "source": [ + "t_homogen = bw.GraphTraversal().calculate(demand={act_homogen: 1}, method=ipcc_methods, cutoff=0.028)\n", + "print(f\"Calculated {t_homogen['counter']} LCAs.\")\n", + "\n", + "# Named edges in a list\n", + "ne_homogen = get_named_edges(traversed_dict=t_homogen, act=act_homogen)\n", + "# Clean dataframe with renamed columns\n", + "df_homogen = prepare_dataframe(lst=ne_homogen, group=True)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "be16a4e0", + "metadata": {}, + "outputs": [], + "source": [ + "# un = ba.GTManipulator().unroll_graph(\n", + "# nodes=t_hetero[\"nodes\"], edges=t_hetero[\"edges\"], score=t_hetero[\"nodes\"][-1][\"cum\"]\n", + "# )\n", + "\n", + "# un[0].keys()\n", + "# un[2]" + ] + }, + { + "cell_type": "markdown", + "id": "7621aa6c", + "metadata": {}, + "source": [ + "## Plot results" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "c7e56c3c", + "metadata": { + "notebookRunGroups": { + "groupValue": "12" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "('1776.377952777', '453.54330709199996')" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Layout size in mm\n", + "mm_to_px = 3.7795275591\n", + "\n", + "w_h_in_mm = (470,120)\n", + "# w_h_in_mm = (200,70)\n", + "(w_px, h_px) = str(w_h_in_mm[0]*mm_to_px), str(w_h_in_mm[1]*mm_to_px)\n", + "(w_px, h_px)" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "d5c957d8", + "metadata": { + "notebookRunGroups": { + "groupValue": "1" + } + }, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "598b5b96cbe54a65af0d231aeadeebb4", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "SankeyWidget(align_link_types=True, layout=Layout(height='453.54330709199996', width='1776.377952777'), linkLa…" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot_sankey(\n", + " df_links=df_hetero[df_hetero.target != df_hetero.source],\n", + " size=(w_px, h_px),\n", + " save=True,\n", + " file_name=\"hetero\",\n", + ")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "2ba3498e", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "

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