From a8602a168acd77b0e65a5d2dc15d4e80148f14c1 Mon Sep 17 00:00:00 2001
From: pinbal-wizard <thomwilliamspencer2@gmail.com>
Date: Wed, 17 Apr 2024 14:18:34 +1000
Subject: [PATCH 1/2] Fixed Bug with dh parameters in 02-direct-kinematics
 "Model Function for Direct Kinematics"

---
 3-kinematics/02-direct-kinematics.ipynb | 124 +++++++++++++++++++++---
 1 file changed, 112 insertions(+), 12 deletions(-)

diff --git a/3-kinematics/02-direct-kinematics.ipynb b/3-kinematics/02-direct-kinematics.ipynb
index 0afc6f2..7fe9e7a 100644
--- a/3-kinematics/02-direct-kinematics.ipynb
+++ b/3-kinematics/02-direct-kinematics.ipynb
@@ -71,7 +71,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": null,
+            "execution_count": 2,
             "metadata": {},
             "outputs": [],
             "source": [
@@ -141,9 +141,52 @@
         },
         {
             "cell_type": "code",
-            "execution_count": null,
+            "execution_count": 3,
             "metadata": {},
-            "outputs": [],
+            "outputs": [
+                {
+                    "name": "stdout",
+                    "output_type": "stream",
+                    "text": [
+                        "Requirement already satisfied: roboticstoolbox-python in /home/conner/.local/lib/python3.8/site-packages (1.1.0)\n",
+                        "Requirement already satisfied: ansitable in /home/conner/.local/lib/python3.8/site-packages (from roboticstoolbox-python) (0.9.10)\n",
+                        "Requirement already satisfied: typing-extensions in /home/conner/.local/lib/python3.8/site-packages (from roboticstoolbox-python) (4.10.0)\n",
+                        "Requirement already satisfied: scipy in /home/conner/.local/lib/python3.8/site-packages (from roboticstoolbox-python) (1.10.1)\n",
+                        "Requirement already satisfied: progress in /home/conner/.local/lib/python3.8/site-packages (from roboticstoolbox-python) (1.6)\n",
+                        "Requirement already satisfied: matplotlib in /usr/local/lib/python3.8/dist-packages (from roboticstoolbox-python) (3.7.5)\n",
+                        "Requirement already satisfied: pgraph-python in /home/conner/.local/lib/python3.8/site-packages (from roboticstoolbox-python) (0.6.2)\n",
+                        "Requirement already satisfied: spatialgeometry>=1.0.0 in /home/conner/.local/lib/python3.8/site-packages (from roboticstoolbox-python) (1.1.0)\n",
+                        "Requirement already satisfied: spatialmath-python>=1.1.5 in /home/conner/.local/lib/python3.8/site-packages (from roboticstoolbox-python) (1.1.9)\n",
+                        "Requirement already satisfied: numpy>=1.17.4 in /usr/local/lib/python3.8/dist-packages (from roboticstoolbox-python) (1.24.4)\n",
+                        "Requirement already satisfied: swift-sim>=1.0.0 in /home/conner/.local/lib/python3.8/site-packages (from roboticstoolbox-python) (1.1.0)\n",
+                        "Requirement already satisfied: rtb-data in /home/conner/.local/lib/python3.8/site-packages (from roboticstoolbox-python) (1.0.1)\n",
+                        "Requirement already satisfied: colored<1.5.0 in /home/conner/.local/lib/python3.8/site-packages (from ansitable->roboticstoolbox-python) (1.4.4)\n",
+                        "Requirement already satisfied: importlib-resources>=3.2.0; python_version < \"3.10\" in /usr/local/lib/python3.8/dist-packages (from matplotlib->roboticstoolbox-python) (6.3.0)\n",
+                        "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.8/dist-packages (from matplotlib->roboticstoolbox-python) (1.4.5)\n",
+                        "Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.8/dist-packages (from matplotlib->roboticstoolbox-python) (3.1.2)\n",
+                        "Requirement already satisfied: pillow>=6.2.0 in /usr/lib/python3/dist-packages (from matplotlib->roboticstoolbox-python) (7.0.0)\n",
+                        "Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.8/dist-packages (from matplotlib->roboticstoolbox-python) (1.1.1)\n",
+                        "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.8/dist-packages (from matplotlib->roboticstoolbox-python) (24.0)\n",
+                        "Requirement already satisfied: python-dateutil>=2.7 in /home/conner/.local/lib/python3.8/site-packages (from matplotlib->roboticstoolbox-python) (2.9.0.post0)\n",
+                        "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.8/dist-packages (from matplotlib->roboticstoolbox-python) (0.12.1)\n",
+                        "Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.8/dist-packages (from matplotlib->roboticstoolbox-python) (4.49.0)\n",
+                        "Requirement already satisfied: pre-commit in /home/conner/.local/lib/python3.8/site-packages (from spatialmath-python>=1.1.5->roboticstoolbox-python) (3.5.0)\n",
+                        "Requirement already satisfied: websockets in /usr/lib/python3/dist-packages (from swift-sim>=1.0.0->roboticstoolbox-python) (8.1)\n",
+                        "Requirement already satisfied: zipp>=3.1.0; python_version < \"3.10\" in /usr/local/lib/python3.8/dist-packages (from importlib-resources>=3.2.0; python_version < \"3.10\"->matplotlib->roboticstoolbox-python) (3.18.0)\n",
+                        "Requirement already satisfied: six>=1.5 in /usr/lib/python3/dist-packages (from python-dateutil>=2.7->matplotlib->roboticstoolbox-python) (1.14.0)\n",
+                        "Requirement already satisfied: nodeenv>=0.11.1 in /home/conner/.local/lib/python3.8/site-packages (from pre-commit->spatialmath-python>=1.1.5->roboticstoolbox-python) (1.8.0)\n",
+                        "Requirement already satisfied: identify>=1.0.0 in /home/conner/.local/lib/python3.8/site-packages (from pre-commit->spatialmath-python>=1.1.5->roboticstoolbox-python) (2.5.35)\n",
+                        "Requirement already satisfied: cfgv>=2.0.0 in /home/conner/.local/lib/python3.8/site-packages (from pre-commit->spatialmath-python>=1.1.5->roboticstoolbox-python) (3.4.0)\n",
+                        "Requirement already satisfied: virtualenv>=20.10.0 in /home/conner/.local/lib/python3.8/site-packages (from pre-commit->spatialmath-python>=1.1.5->roboticstoolbox-python) (20.25.2)\n",
+                        "Requirement already satisfied: pyyaml>=5.1 in /usr/lib/python3/dist-packages (from pre-commit->spatialmath-python>=1.1.5->roboticstoolbox-python) (5.3.1)\n",
+                        "Requirement already satisfied: setuptools in /usr/lib/python3/dist-packages (from nodeenv>=0.11.1->pre-commit->spatialmath-python>=1.1.5->roboticstoolbox-python) (45.2.0)\n",
+                        "Requirement already satisfied: distlib<1,>=0.3.7 in /home/conner/.local/lib/python3.8/site-packages (from virtualenv>=20.10.0->pre-commit->spatialmath-python>=1.1.5->roboticstoolbox-python) (0.3.8)\n",
+                        "Requirement already satisfied: filelock<4,>=3.12.2 in /home/conner/.local/lib/python3.8/site-packages (from virtualenv>=20.10.0->pre-commit->spatialmath-python>=1.1.5->roboticstoolbox-python) (3.13.4)\n",
+                        "Requirement already satisfied: platformdirs<5,>=3.9.1 in /home/conner/.local/lib/python3.8/site-packages (from virtualenv>=20.10.0->pre-commit->spatialmath-python>=1.1.5->roboticstoolbox-python) (4.2.0)\n",
+                        "Note: you may need to restart the kernel to use updated packages.\n"
+                    ]
+                }
+            ],
             "source": [
                 "# install the robotics toolbox for python\n",
                 "# this library is helpful for kinematics and dynamics of robot manipulators\n",
@@ -154,9 +197,57 @@
         },
         {
             "cell_type": "code",
-            "execution_count": null,
+            "execution_count": 4,
             "metadata": {},
-            "outputs": [],
+            "outputs": [
+                {
+                    "name": "stdout",
+                    "output_type": "stream",
+                    "text": [
+                        "DHRobot: Two-Link Manipulator, 2 joints (RR), dynamics, standard DH parameters\n",
+                        "┌────┬────┬────┬──────┐\n",
+                        "│θⱼ  │ dⱼ │ aⱼ │  ⍺ⱼ  │\n",
+                        "├────┼────┼────┼──────┤\n",
+                        "│ q1\u001b[0m │  0\u001b[0m │  1\u001b[0m │ 0.0°\u001b[0m │\n",
+                        "│ q2\u001b[0m │  0\u001b[0m │  1\u001b[0m │ 0.0°\u001b[0m │\n",
+                        "└────┴────┴────┴──────┘\n",
+                        "\n",
+                        "┌─┬──┐\n",
+                        "└─┴──┘\n",
+                        "\n"
+                    ]
+                },
+                {
+                    "data": {
+                        "image/png": 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bg9Tb+Gv52hP9Sz0fC9YXpDcloDUwIntjc0MBgI3RG/NLJpMoFAoYGxtTOszNTKlUQiKRgMPhwMjICG0sbaDd+PV87dlnQitHS/Qnej4WMzMzkGW5ro8F2RubHwoAbEo/GfoMDAygUCjQ4d9BavnaA0A8Hid7YxvCzIZYT0kzPhbV7I0pO2Ac5t/liY6jPfwFQUAsFrOMUp4sy8hms0ilUohGo+A4TtE7J7oDyw54PB4Ui0UMDQ2B53myN7YxjfhYqEtItSSKeZ6v0B0gA6PeQAGAzWBpOLYAmaGPz+dDOBw2/catZ+hj9tHEfoTpCgCo6Wuv3fiJ/qURHwv2mSADI3NAAYBNUBv6sAVXKBQQj8cRCoUQDAZNv7isWKawA4362ms3fqJ/0fOxYEGi2t64UQOjUqmklB/IwKhz0A5qA9hdM0vTAkA2m8XMzIxllPKsauhjN7S+9tU2fpYhoN+jPXA6nXOyA434WOiNGaZSKQBAMBgkEaI2oQCgz2ERdDqdxtDQkNK9m8vlLKOUx8oU/WDoYzf0Nn51WtjtdivBAGUH7EEjPha17I0Z1QyMKDvQOBQA9DFqQx/293g8jnK5jLGxMUso5fWzoY/dqJcWJntje9KsjwWDDIzahwKAPkXb6c/u/FkK3QpjW1YrUxDNUS0tTAZG9qURHwv2nyzLDRsYqXUH6HP0GhQA9CFaQx+e5yEIAjweD0ZHR01/Z0WGPvajGV97EiGyD3o+FtlsFjzPY2pqqmEDI1EUIQgCCoUCZQdUUADQR9Qy9OE4DsFg0BKHPxn6ELXSwgCQTqcpO2AzWHaAqZcGAoGGfSy0kwVkYDQLBQB9Qj1DH9ZcY2ZEUUQ8Hocsy5YpUxDdR5sWPn78ONxutxIQkIGR/dBTqmQlpHo+Fo0YGNklO0ABQB+gPfz1DH1yuZzRl1kTQRAwPT0Nt9uNSCRi+kwFYRwOhwN+vx8ul6spX3uif2ElIq3LpdrHgj1OBkavQavD4qgNfdjhb0VDn1gshkAggOHh4b5caER3aMfXnuhPavlYqA2M9Hws7GZgRAGAhalm6COKomWU8srlMpLJJIaGhhAMBo2+HMLCNOtrTyUme1BNqbIRHws9AyOWaWJCRFbODpj/hCB0qWbo43Q6MTY2ZvoIVb0QR0ZG4Pf7jb4kos+o52tPBkb2Q8/AqJqPRTV7Y7b3qn1VWAnCak3LFABYkH4w9EkmkxBFEaFQiA5/ouvo+drrpYX1Nn6if2nUx6KevTEAyx3+AAUAlkKv09/Khj7UpEUYRbWNvxFfe6I/0fpYqBsC1T4WsizP0aLo1udj//792Lt3L2RZxubNm7Fp06aKxz/+8Y9jZmZGCT5uvfXWpvq+aPe1COzwF0VRSe8zpbxIJIJAIGDwFdaHlSmYGmEsFjP6kgiiIV97tYERYQ+q2RsXCgVIkoREIqEYrZ188skd//6iKGLv3r3YuXMnAoEAbrrpJqxevXpOr9S2bduwaNGilr4HBQAWQN3pzyQwmaHP6OioJTYlK5Yp0uk0eJ4nX3ubUc/XHpj1qGBe9Wb/LBPto1aqZGVXn8+H559/Hv/8z/8Mj8eD5cuX47zzzsPy5cs7ckN2+PBhLFiwAJFIBAAwMTGBgwcPYv369W2/NoMCAJPDapXAa53+iUQCPM9bxtCHlSmsYugjyzJisZhSplDXA9nsuVaHnOhP9AyM2KRNMpkkAyObwrQozj77bNx111147rnn8Nxzz+Gb3/wm7rrrLpx66qmYmJjAhg0blAO8WZLJZMW/jUQiSCQSc5539913g+M4bNiwAZdddllT34MCABNTLBZRLBbh8/mU7lOWNh8bG7NEo5LVDH1kWUY2m4XL5cLIyIjSb8Gc61j6b3p6mnztbYjT6QTHcRgYGIDH42nY157oX9xuN5YvX461a9cCAGKxGCYnJzE5OYlly5a1HAA0wvXXX49IJIJ8Po/bb78d8+fPx4oVKxr+9xQAmBRBEJDP55HNZuH3+8HzPKanp+HxeCyhlKcuU1jF0Id1hbtcLoyOjkKSJEiSBOA15zqn04lUKoWhoaGKbmHytbcfjfjaq0WIzL5mic4wMjKCiy66CBdddFFbrxMOhyvu+BOJBBYvXlzxHBZcBAIBrF27FocPH6YAwMqoDX0YTClvYGAAQ0NDpj9cZFlGPB5HuVy2TJmiWCwqOgqBQKDme1xr4ydfe/tSz9deK0Jk9nVMGMvixYtx9OhRJBIJBAIBTE5OYsuWLcrjoigil8shFApBEAQcOHAAGzdubOp7UABgIrRjfsDsL3l6ehrDw8MYHBw0+Arroy5TWMXQR12mKBQKTW/M2o2ffO2JRnzt2eMkUUzo4XQ6ce211+KWW26BJEnYvHkzgsEgdu3aha1btyIQCODWW2+FKIqQJAkrV67EmjVrmvoeFACYBD1Dn0KhAFEULaOUx/M8YrGYZQx9WKd/JpNRyhSFQqGt12zG156yA/ZBz9ee+RWw7AAZGBFaVq1ahVWrVlV8bceOHcqfb7/99rZenz5pJqCaoU+xWITL5bLE4W/FMkUikUCpVML4+HjXyhR6aWF1doDSwvaDDIwIs0ABgMHoGfrEYjFIkoShoSFFgcrM5PN5JBIJyxj6sFEuWZZ7WqZQb/wA5qSFydfeftQzMKrla08Q7UIBgIHUM/RpNx3dbWRZRiaTQTqdRjQatUSmQhAETE9Pm6JMUS0tTBu/fdFKFOv52jPpYtKiINqFAgCD0DP0mZ6eht/vt4xSXjKZRKFQwNjYWFP600bByhSBQADDw8Omeo8bTQtTfdg+VMsOpNNpFAoFFAoFMjAi2oJ2kx7TD4Y+TClPFEWMj49b4lCyUpmiVlo4k8kAAGZmZsjX3maw7EA+n4fP54Pb7SYDI6ItzL9z9xF6hj6ZTAapVMoyhj6iKEIQBKVMYfYudiuWKbSo08J+vx+JRAJut5t87W1MIwZG6hKS2dcpYQwUAPSIfjH0SaVScDgcGB0dNf1BY8UyRT3Yez4wMEC+9oSC1sComq89KVUSaigA6AH9ZOjj9/tRLpdNv4FIkoR4PA5BECxTpmgF8rUntGh97ZmPhdbXnpQqif7cFU2EttPf6oY+HMdVyBSbEaaeyHEcxsfHbbPBNetrb5f3xe4wHwu1vbHWwIh8LOwJBQBdRBAECIIAYHZzZoY+Xq8XkUjE9AtNr0xh9tHEcrmMWCxmmfe4m9TztaeN337o+VioswPkY2EvKADoAmpDH9bpXywWEY/HLaOUZ8UyBTP0CQaDCIVCpn+Pe4merz1t/ES17AD5WNgDCgA6DNOXd7vdSno/l8shmUxa0tDHKmUKpqAYDocxMDBg9OWYnkbSwuRrby8a9bEQRdESewJRHwoAOgjbSBOJBKLRKDiOU8xmrDKCZkVDn2w2C1mWMTo6Cp/PZ/QlWY5mfO37tZmSmEs1e2MmYsbzPPlYWBxazR1CPebH/s4MfawygmZVQ59isah0wxPtU8/XHoAiRkMbvz1QK1VKkgSO4+B0OsnHwuJQANABtJ3+siwrXddjY2OWuGuycpkiEokgmUwafEX9idbXnmW4eJ5HLpcjX3ubwnGcrssl+VhYC/OfTCZHz9BHkiS4XC6Mjo5aIoVuNaU8bZnC7GOJ/QTbzFmGiHztCbI3ti60QtugmqGPw+HA0NCQJQ5/q5YpzGjoYzdo4ye01LM3ZkqV5GNhDigAaIF6hj6ZTMb0mx3rmpckyTJlCisZ+tgN8rUn9NBTqiyXy+RjYRLMv+ubjEYMfbLZrMFXWRtBEBCLxcjQh+ga9TZ+lh2gjd8+qJUqycfCHFAA0AR6hj4zMzPI5/OWMvSZnp6G3+9HOBw2/cbbj4Y+doM2fkIP8rEwHgoAGqSWoY9VzGaYn3woFEIwGDT9gmKGPqIoWuY9JupDGz+hpRkfC1mW6TPRIWhHbYBqhj4Oh8MySnmiKCop9EAgYPTl1IWVKTiOs0SZgmgN8rUn9KjlYyEIAjiOU5pQSamydSgAqEO/GPpIkoShoSFLHP7M0Mfn81miTEF0jkZ87dmBQCJE9kDrY5FOpyGKIgRBIB+LNqEAoAps82HKZ8zQx0pmM+oyhcvlsoShj3qawgplCqJ7VPO1Z/LamUyGfO1tCJs4CQaD5GPRJhQA6KAd8wOsrZQ3NjaG6elpg6+oPtlsFjMzM2ToQ+jCDIwymQzC4TBkWaaN3+Y042NBQeJcKADQoO70Z7K+7I5jZGTEEnrzrEzh8XgQjUZNvxGyMkUul7PMNAVhLCw7UGvjp7Sw/ajnY6EVITL73thtKABQoXf4JxIJlEoly4ygFYtFxONxSxn6xONxlMtljI2NWaJMQZgP7cZPvvaE1seCaVFo7Y3t7GNBAcCrMAlT9kFQK+WNj49botPfamUKJvDDcZxl3mPC/DTqa2/njd+OMKVKrYGRnX0s7PFT1kEURWQyGRSLRYyOjkIQBExPT8PlclliBE1dprCKUh7zE7eKaRJhXfTSwtqNn9LC9oJ8LGaxdQDAfvFqNzmrmc1Y2dCH4zgMDg7S4U/0DPXGD2BOWph87e1Hsz4W/YRtAwC9Tn9RFDE9PY2hoSEMDg6afvFb2dBneHhYmeElCKOolhYmAyP7olWqZNkB5mPBRIjK5XLXlSr379+PvXv3QpZlbN68GZs2bZrzHEmS8NGPfhTRaBQ33nhjU69v/hOjC+gZ+pRKJfA8bymlPKuVKbSGPvl83ujLqokoihBF0RKBFdE+1dLCWgMjWZYhy7LRl0v0AL3sQCaTQblcRiqVwvHjx/HjH/8Yy5cvx7p16zA6Otqx7y2KIvbu3YudO3ciEAjgpptuwurVq+c4of70pz/F6OgoJElq+nvYbmerZuhTLBbh8Xgscfhb1dDHimUKSZIq6oGEPaiWFi6VSpAkCalUStkzKDtgHziOU9Qo2ZTVokWL8Mgjj+DrX/86Fi1ahImJCUxMTGDJkiVtfS4OHz6MBQsWIBKJAAAmJiZw8OBBrF+/XnlONpvFvn37cMUVV+BHP/pR09/DVgGAnqFPPB6HIAgYHBys6AUwK1ZTylMb+lilTMGi+1AoBLfbreiQs7QwMFvKoI3fPqjTwuVyGQMDA5BlmXztbYzD4cDIyAje+ta34sorr4QoinjiiScwOTmJO+64A5IkYfv27TjzzDNbev1kMqkc/gAQiUSQSCQqnvP1r38dW7ZsaflnMP9u3CH0DH2mp6cVs5l8Pm/6ACCTySCVSiESiVgiU8EMfZxOp2XKFDzPQxAEjIyMwOPxQBCEinog2/DJ196+OBwO5feuzQ5oDYzI3tg+hEIhrF+/HuvXr4ckSXjhhRdwwgkndO37/eEPf0Aul8PSpUvx9NNPt/QatggA+sHQZ2ZmBvl83jJKeVYz9GFlCpYN8vv9ig8Eg238HMchEomQrz0BoLq9sdrAiOyN7QXHcViyZElbrxEOhyvu+BOJBBYvXqz8/Xe/+x2eeeYZ/PVf/zXK5TKKxSL++Z//GVu3bm34e/R1ANAPhj5MKY/neYyPj1sihW7VMoUgCIq9aCOQrz2hRWtgVMvXnuyNiVosXrwYR48eRSKRQCAQwOTkZEW6/81vfjPe/OY3AwCefvpp/PCHP2zq8Af6OACoZehjFbMZSZIgCAIcDgfGxsYscUfJDH2sVqZgaoTaGlujkK89oUctX/t0Oq34GZCvPaHF6XTi2muvxS233AJJkrB582YEg0Hs2rULW7duregPaJW+DAD6xdAnnU4rh7/ZNwYrGvp0s0xRb+Nn2QHa+O2D1tee2RuzcgEZGBFaVq1ahVWrVlV8bceOHXOet3TpUixdurTp1++7AKCWoc/4+LglzGZYmcLr9UIURdMfDpIkIZFIgOd5yxj69LJMUW/jB0C+9jaE2Rurg0SyNyZ6SV8FANpOf6sb+jgcDmSzWaMvqSaiKCIWiwGA5coURpWCaOMntNTytVcbGHk8HhIhIjpG3wQA2sPfikp52jJFLpcz+rJqwqYpPB4PotGo6Q8qdZnCLKWgWhu/nq89YQ/q+drncjlIkkQGRkRbWD4AUBv6OBwOOBwOSxr6sDKFVZTyisUi4vE4BgYGFEUsM8OmKcrlsqnLFPU2fjahIAgCbfw2QetrH4/H4XK5lM8FGRgRrWLpAECWZeRyOWUjdDgcitnM0NDQHM1kM2LFMgW75uHhYQwODhp9OXVRlyms8h4Dczd+URRRKBQgCALi8Tj52tsUlhViJaRqBkZ28rUnWsOynw5WOz1+/LgyH29FpTyrlSny+TxEUcTIyAj8fr/Rl1QXnucRi8XgdrsRiURM/x7Xwul0KqWhsbExXV972vjtBfnaE+1gyV1C3enP/m5Fpbzp6WlLlSmYoY/L5bLE4c9KQVYpUzRDIxs/pYXtRT1fe6ZUSQZGBMOSAYAgCBVjfjMzMxBF0XJKeUNDQxgcHDT95qwuUwwNDZl+MgGAUgqySpmiHfQ2fjZZQL729kVPqZJJxpKBEQFYNABg9X4m8SvLMsbHxy2R3rVimUJt6FMqlYy+pJrIsoxMJoN0Oo1oNGqJTEWnUfcG1PK1p43fPqiVKrUGRuRjYV8sGQAAr6m4AbOmCWY//K1cpvD7/ZYy9CkUCpaZpug2tXzt1Rs/pYXtBflYEIBFA4BCoYCpqSkEg0FFLtfMqJXyrFamsIqhjyzLiMVilioFGQGlhQktzfpYEP2DJXfJcrmsqLhlMhmjL6cmoihienoaHMdZRinPamUKURQhSRIAWGKawizUSguTr719qedjwVRWSanS+lgyABgaGprj1W5GmFKe1+tFJBIx/UKxapkimUzC4XBgZGTE9O+xmWnE156VVUiO1h7o+VikUilIkoRkMgmAfCysjCUDACtQLpeRSqUQDAYRCoVMfzBZ2dAnEAigUCiY/j22EtV87VkTaDweJ197G+J0OuF0OuFyuRAIBMjHwuJQANAFJElSzGasMIJmZUOfSCQCp9OJYrFo9CX1NSwt7PV6MT09jaGhIfA8T772NqZZHwsKEs0HBQAdhBn6yLJsmflzK5YpmKEPK1OYfTSxH2F3euRrTzC0PhYsO8B8LNhnhgyMzAMFAB1CbejDcZwlRtCsZuhjxTKFHahmb6zd+CktbB/U2QHmY8FKSGp7Y1KqNBYKADqA1tDn+PHjRl9SXXK5HJLJpGUyFVYsU9iRRjd+9hzKDtgDPZdL8rEwHnqn28SKhj7pdBqZTAYjIyPw+XxGX1JdWJnC4/EgGo3S3YKFqGdvrBUhot9t/6P2sQCgaFGQj0XvoQCgDZjZjJUMfViZwipKef1s6GM3tPbG6o2ffO3tC1Oq1GYHyMei+1AA0CLMbGZoaAjBYNDoy6kLu/MHgPHxcUssJKuVKYjmaHTjp7Swfajmcqn2sXA4HHA6nYohHNE6tKqaRJZlZLNZSynlCYIAQRDgdrstU6awu6GP3WjG155EiOxBNR+LbDar+JSQj0V7UADQBFZVymNSxKFQyBKHfzKZRLFYtEyZgugs9XztASCdTsPn89HGbyOYUiXP85BlGX6/n3ws2oQCgAaRJAnxeByCIFjGbEZt6MOEOcyMeppibGzMEu8x0X20EsVTU1Nwu9208dsY8rHoDLTDNoDa0Gd8fNz0d9HAXEOffD5v9CXVRBAExGIxOJ1OS5QpCGNwOBxwOBzw+/0IBoPka08AaMzHgpWYSIviNSgAqEO5XEYsFrOUUp5VyxR+vx/hcNj07zFhHsjXntBSy8eC3QiRj8UsFADUoFgsIhaLWdLQxyplCubmFwqFEAwGTf8eE+alEV972vjtRz17Yzv7WJj/hDAIZjYTDocxMDBg9OXUhSnlORwOyyjlsU7vaDRqiWkKwlrQxk9o0bM3trOPBQUAGqyslGelMkUqlYIgCAgGg3T4E12HNn5CD7v7WFAAoEKtlDc+Pm4JsxkrlylYXZYgek21jZ987e1LNR8Lpj3AfCxkWe6ZFsX+/fuxd+9eyLKMzZs3Y9OmTRWP79y5E/l8HoIg4Pzzz8eVV17Z1OtTAPAqrJvY5XKRUl6X0Br6xONxg6+IIBrztVcbGBH2oJqPRbFYVPRKgNl9eMGCBR3//qIoYu/evdi5cycCgQBuuukmrF69ukJ59qabbkIgEIAoivjHf/xHrFy5Eq9//esb/h4UAGB2BC2bzSr1c7On/6hM0X2YGiHP85QWthn1DIyA2cyb0+kkAyOboFaq5DgO5XIZXq8Xzz//PL74xS8iFArhnHPOwerVq7F06dKOTF8dPnwYCxYsQCQSAQBMTEzg4MGDWL9+vfIcVj4VRRGCIDT9WbR9AMDMZlgq2uybvBUNfYrFIuLxuGUMfWRZRjweR7lchsfjqagHulwuJQVo9p+DaB89A6NEIgFBEBCPx8nX3qY4HA4EAgGcc845uOuuu3Do0CE899xzuP/++zEzM4Nly5bhnHPOwdq1azE8PNzS90gmk8rhDwCRSASJRGLO8z760Y/ij3/8I9785jfjpJNOaup72DoAUBv6ALMHlZlRK+VRmaI7MK8Hp9OJkZER5aBXm5Kw3wP52tsPl8sFjuMwODgIj8dDvvYEfD4fVq1ahY0bN0KWZbz88suYnJzEY489hpNPPrnlAKBRPvWpT6FQKOCzn/0s/vjHP2LRokUN/1tbfkL1zGYymYzRl1UTQRAwPT0Nl8tluTKFVQx9WCOYy+XC6OioIiwDvJYWdrlcSKVSCIVC5GtvcxoxMCJ7Y3vhcDjwute9Dq973etw2WWXtfVa4XC44o4/kUhg8eLFus/1+/1YtmwZDhw4QAFALVjzRqFQsEwKnZUpAoEAhoeHTb+RWLFMwd5jdtBzHKfUe7WQrz2hpZ6BEfnaE82yePFiHD16FIlEAoFAAJOTk9iyZYvyOOv+D4VC4HkeBw8exJ//+Z839T1sFQBY0dBHkiRMT09jaGgIg4ODpj9MrF6mKBaLzTfSkK89oUErUazna8+CAZIoJvRwOp249tprccstt0CSJGzevBnBYBC7du3C1q1bIYoi7rzzTgiCAFmWsXbtWqxcubKp72Gb3ciKhj7FYhGCIFhGKc+KZQptKajdPpBmfO0pO2APqmUHtAZG5GtPaFm1ahVWrVpV8bUdO3Yof77tttvaen1bBABWNfQpFArKnaXZsZqhDysFFYvFrpUp6qWFaeO3J9UMjMjemOg1fR8AWFEpj5UpQqEQCoWC0ZdUl0KhgHg8bhlDH3WZYmxsrGdpeb2NnwmL0MZvT/QMjKr52pOBEdFp+joAsKKhj7pMYYXDP5PJIJVKIRKJWCJTIQiC0uxnZJlCvfHXSguTr729aMTXXpIkpe5LQSLRDn0ZADCzmVwuR0p5XYKVKfL5PEZHRzuifNVtzFymaNTXngIB+6D1tWcGRplMBvl8HoVCgeyNibbouwBAO4JmBbMZq5UpmFIez/OWmaawUpmilq99Pp8HAKRSKdr4bQYzMCoUCvD5fHC5XGRvTLSF+XfuJlCbzVhtBM0qZQpJkiCKopJCt8J7bLUyhRa1rz3P80gkEnA6nbTx2xiyNyY6Qd8EADzPK5r+kUjE9B94qxr6sMak0dFR0x806lKQVcoU9WDv+eDgIG38hEKjvvakVEmo6YsAoFwuY2ZmxpJKeePj45YqU/h8PvA8b/r3WJIkJBIJ8DxvmVJQK5CvPaGlmq89CwiYgRH5WBCWDwDYPPfQ0FCFT7JZEUUR8Xjcskp5TqcTqVTK6EuqiboUZJUyRSdo1teeNn57UM/emHws7ItlAwCm4gbAMoc/U8pzu90YGRkx/QasV6Yw+2gim6bweDyIRqO23sxo4ye0kI8FocaSAYDa0AeAJernZOjTfYrFIuLxOAYGBjA0NGT697iX1Nv4ydfenpCPhb2x5G9UXT8/duyY0ZdTl3w+j0QiYZlMhRUNfdg1Dw8PY3Bw0OjLMT3VNn7ytbcvjfpYsCkgwvpYcmUPDQ0hFApZIoWezWaRSqUUsxmzY0VDn1wuB1mWMTIyYon32Gw0Y2BEwYA9qOVjwfM8eJ6HIAjkY2FxLLmaXS5XVa92s2BFpTwrlilYKYjNyhPt0YivPQBFjIY2fnugVqqUJAkcx8HpdJKPhcWxZABgdtj8OUuhW+GuiSnlDQ0NYXBw0PQLWF2mCIfDij4B0Vm0EsU8zyOZTFY0jZGvvb1wOBxwOp0YGBggHwuLY/6TyWKIoghJkiDLMsbHx02fQgesp5SnLVPwPG/0JdkClh0AgOHhYQCgjZ9o2MfC4/FQkGgyKADoIOVyGbFYDA6HA8PDw6Y//K1YpjCzoY/daGTjp7SwvajlY8GydGRgZB4oAOgQakOfbDZr+s1OkiTE43EIgmC5MoUVDH3sRiMbP/na2w+1jwUrIZGBkXkw/65vAbLZLGZmZhRDn1wuZ/Ql1UQURUxPT4PjOCpTEF1Bu/Hr+dqzYIA2fntABkbmgwKANmDNftls1lKGPtPT0/B6vYhEIqbfeK1YpiAqqeZrzzZ+gNLCdoR8LIyHAoAWsaKhT7lcRjweRzAYRCgUMv2CUhv6WKVMQdSn2sZPaWH70oyPhSzL9JnoELSjtgAz9GGd/lbodpYkCel0GpFIBAMDA0ZfTl2YoY/D4bCVoY/d0Nv4KS1M1POx4DhOka8mH4vWoQCgSdSGPpFIxPQbEjP0EUURoVDIEoe/1coUROdoxNee9RTQxm8PtD4WqVRKCRTJx6I9KABoAisq5bEyBRvJMjvqaQorlCmI7lHN157neaRSKfK1tyns7j8YDJKPRZvQu9MgVjf0mZ6eNvqS6pLL5ZBMJsnQh9CFpYWz2SzC4bCiQEf2xvalGR8Lyg7MhQKAOsiyjEwmg3Q6TYY+XYKVKTKZjGWmKQhjIV97QksjPhZqLQrqK6IAoCZqs5mxsTFLpNCtXKawyntMmI9Gfe1p47cPWqVKlh1gBkbkY0EBQFWsqJRntTIFy644HA7LTFMQ5ofSwoSWatkBu/tYmP9UMwBBEBCLxSyjlCfLMrLZLFKplKXKFDzPW6ZMQViTemlhtvGTr729IAOjWSgA0MAMfXw+nyXMZqyolMfKFA6HA4ODg3T4Ez1Db+Mvl8vka29jmvWx6CcoAFAhiiKmpqYsYzZjxTIFM/QZGhpShF4IwgjUGz+lhQlGPR8LjuPgcDiUDGY397D9+/dj7969kGUZmzdvxqZNm5THSqUS7rzzTkxNTYHjOFx00UW45JJLmnp9858YPYLNF1tJKc/qhj6FQsHoS6qJJEkQRdESgRXRPo2mhWVZhizLRl8u0QP0fCwymQx4nkcymUQsFsPPf/5zLF++HGvXrsXw8HDHvrcoiti7dy927tyJQCCAm266CatXr67o77r88stx5plnolgs4iMf+QgmJiYwb968hr+H7Xc2ZuhTLBbh8XgscfizMoVVlPKsWKYol8uYnp6GJEkV9UDa+O1BrbQwk9UulUpkYGQznE6n8pkYGhqCJEmIRCL47//+b/zbv/0blixZgomJCUxMTOCkk05qa28+fPgwFixYgEgkAgCYmJjAwYMHsX79egCzBlpnnnkmAMDn82H+/PlIJpMUADSKLMuIx+Mol8sYHBwEz/NGX1JdrKaUZ0VDH6Y0FwwG4fF4lKYxVg8EZksZtPHbB3VauFwuK+OGZGBkXxwOB+bNm4e3v/3t4DgOxWIRBw8exOOPP47vfOc78Hq9+Lu/+zucccYZLb1+MplUDn8AiEQiSCQSus+NxWI4cuQIXv/61zf1Pcy/G3cJZjYDAOPj48jn86YPALLZLGZmZhAOhy2RqVCXKaxi6MOmE6LRKLxeLwRBqKgHFgoFZLNZ8rW3MWyywOv1kq89oRCNRnHhhRfiwgsvhCAIePbZZ/G6172u69+X53ns3r0b73rXu5oWUbNlAMDzPGKxmOUMfayklGc1Qx9WpuB5HoODgwgEAhBFseI5bOPnOA7RaLSqrz1t/PaCfO0JLS6XC8uWLWvrNcLhcMUdfyKRwOLFiyueI8sy7rnnHkxMTGDNmjXNX2dbV2hB2AjawMAAhoaGTL8Y1Up54+PjSv3JzFi5TMHu5JmAjCAIyvVrD3Ta+AktzfjaU5BI1GLx4sU4evQoEokEAoEAJicnsWXLlornPPjgg/B6vXO+3ii2CgCsppQnSZJyAFlFKY8Z+lipTMFKQWNjY0gmk8rXOY6r2MglSQLP85BlGZIkVWzejWz8lBa2H/V87cnAiKiG0+nEtddei1tuuQWSJGHz5s0IBoPYtWsXtm7dClmW8Z3vfAcLFizA9u3bAQDveMc7cM455zT8PWwRAFjV0CedTgMARkdHTX9g9EuZQpZlpbtbfdfOmnxyuRwGBgaUO3714+rfkXbj1/O1V2vT08bf/9QzMCJfe0LLqlWrsGrVqoqv7dixQ/nzQw891Nbr930AYGVDH7fbPedO04xYtUwRj8crSkGSJMHn8yGXy2Fqako5pH0+H8rlMjKZDIaHh5UAUpIkJRvANAMcDsecYKCarz1t/PammoER+doTvaKvP1VMKU8URcuMoKnLFBzHIZvNGn1JNZEkCbFYDJIkWa5MMTw8jMHBQQCvpfj9fr/SAMicw1iJgOkAiKIIp9OpHPLsZ1YHAiwYYP/Vyg7obfyUFrYXzRgYkRYF0SnMfyK2CDP0cTqdljCb0StT5HI5oy+rJoIgYHp62jKGPnplCqbqJkmSclgDs4e0z+dDsVgEx3EYGhoCz/PI5XKYmZmB2+2Gz+er0Ixnd/4ul2tOdkCSJABzSwXqjR9AXV97ov9pxNc+n89DlmUyMCLaoi8DACsa+li1TBEIBDA8PGyJ95iVKdh7zA5oWZbnBC+iKCKRSMDhcCjBDUv9q7MD2WxWOcRZQKA+5NXZAVEUK3oH9LID9XztWXmFZSGI/kcrUZxIJOB0OsnXnmibvgsAmNkMGfp0D0mSMD09balpCm2ZQn34az8jPM8jkUjA4/HoBje1UvjJZFK5Y/f5fEojoToo0MsOyLJcUVYA9NPChUJB0bEgX3v7wYJGr9cLv99PBkZEW5j/tGkCppTHzGbMDitTWMXQhynhiaKIaDRqmfdYW6ZghymAOYdmqVRCIpHAwMBAQwGk+pAOhUIQRRHFYlFJ4TscDiUz0E52gKWF/X6/4qmgzg6Qr709IV97oh36IgBgKm65XM5SZjNWNfRhd8Bmhxn6qMsU6nq89j3P5/NIpVIYGhpq+edzOp0YGBhQRgXVXvMsO8ACglrZAdaUWC07QL72hJZmfe3NfsNBdB/LBwDMzU8QBIyNjVliBI2VKayklMfKFMPDw6afTABee4+HhoYwODgIh8Oh3GUDlYe/LMvIZrPIZrOIRCIdCyD1GvxY70Amk1HG/3w+nyIOVC87wDIX6vHQWr722o2f0sL2oZ6vPflYEJYOANhmKIqiZcxmrGzoMz4+jlKpZPQl1SWTySCVSimlIHW9Xd3pD7yW2SiXyxgZGelqAKnu7GaCQ6x2K4qishmz3gGgcmpAEARkMhlFH6LaZEG1tLB246e0sH3Q87UnHwvCsgEAa4QCZm0SzX74s0xFLpezjFKelcsUrBSkbfZT/wzMA0CWZYyMjPT0M8R6A3w+35zegXQ6rYwhsk1ZFEXE43G43W5lsoVlB7TBgPr/2o1fLy3MNn1KC9sH8rEgAIsGAMViEVNTUxgYGEAmkzH9h1OWZcTjcZTLZcuUKaxm6MPeY57nlWmKWp3+giAo41RGO0KyBr/BwUHlkGb1fJYdAKBIyOrJD+v1Dmifw/6u3fjL5TL52tuYZn0siP7BsgEAq+2avR6tNpuxilKe1coU6nE6Vgqq1elfLpeRSCTg8/lM6QipTuHn83nMzMzA5/Mp45cshc96B9TTAtregUYlisnXnmDU87HgOA6SJJFSZR9gyQBgeHh4jle7GWFlCrfbbfhdZiNY1dCHCfaMjo7W7fQvFAqYmZlBMBjEwMCAaTcvbWMi+12wFH6pVEIymVTU4FhAwAIAbXaA/VfPwKheWpjdAZIcrT3Q87GYmZlRSlLkY2FtLBkAWAGe55FKpSrMZsyMVQ19YrEY/H4/isViRV1cW+8HZjMbWkMfM8L6RYrF4pzGRL3O7mKxiEKhgFQqBZfLpfQOqLMD1USIGskOAK+lhVkTaCKRIF97G+J0OivUKsnAyNrQb6gLSJKkmM1YQSmPRfOyLFumTKE29HG73SiVSlUPf5bZKBQKiEajpq5jss+OKIoYHR2t+btQN/gFg0ElO1AsFiuyAywg6ISBkc/nw/T0NILBIHieJ197G1PLx4JJFKsDAvpMmA8KADoIM/SRZdkyMrlMKc/KZYpisQhJklAsFueMtUmShJmZGQiCgJGREVPflbBAzOl0YmRkpOnfhV6DX7FYrDAwYgFBOwZGABRBIz1fe9r47Uk9Hwu1CJEVbjLsgHl3Q4uhNvRhdTGz0y+GPuzAYb0AaqW9mZkZOByOlg7UXsLzPOLxeMcaE2ul8OPxeMXdm8/na1iiWI9GN35KC9uHavbGWgMj8rEwFlqNHYAp5YmiiPHxcUxNTRl9SXXJ5/NIJBKWyVTUMvRxOByIRqMV0rvpdFrpWB4YGKjaF2AGWMqejQF24xqrGRixiY9a9sbAa6UCZlHNSgaNGBhV87Wnjd8eVLM3Jh8L46EAoE3Uhj7MbMbMsDJFOp1GNBo1dTMco1FDH3b4ALMBTiAQgMvlmnP4qKV3jSaXyyGdTve0MbERAyOtvbHD4UA2m1UaE10uV8MGRtV87WnjtyfkY2EeKABoA6aU5/P5FHU2M6MuU7AUutnRK1M0a+gzODjYsPRur1A3JnbSf6AV6hkYMelhAIhGo8pUQrP2xvU2fvK1tx/kY2EsFAC0CDObCYVCDdnGGg07cFinvxVqseoyRSOGPplMBrlcTvdArSa9y8oFWundbv4+WSDG87zpGhO1nd1MNIm956xPoRP2xtU2fvK1ty+N2BsDUEqAZt93zY55dh4LYTWlPFEUIQgC3G43xsfHTZH6rkc3DX30pHdZdmBmZqZCXIc1E3YKURQrhIvM/LvgeR7JZBJerxfDw8MAoLxPrMeiFXtjoHEDIz1fexIhsgfV7I1zuRxKpRKmp6fJx6JNKABoArWhDzObMTusTOFwOBAKhUy/SIww9GlEXEcrvdsKgiAgHo/D4/GYfuqiWmMiO6SHhoaU90ltYNSovbG6VMB6DPSyA+zfaA2MMplMRY8C0f+wdSoIAmRZhs/nIx+LNqEAoEHYQcPzvCUNfZi2u5lh0xSCIDRl6ONyuTA8PNyRg6AVcZ1GKJVKSCQSGBgY6FjJSJZlSDLAOQBJBpxcZ36/rDFR3UehRz0DI/X7pGdv3KqB0dTUFJxOJ/na2xjysegMFAA0gNrQh5nNmB1tmaJQKBh9STURRRHT09PgOE4pUzRi6OP3+7vqVqgnrsPSkGpxnVqiN6wxMRQKdbRkFMsL+L/PpAEAHg54z4poW69Xr4+iFrVS+NWyKPWyA+x11f9nGSA24UG+9gRA9satQgFAHaxo6MPKFFYy9GH1vEgk0pShz+DgYM+uU2uMwtQH1cY82gY5ZugTDoc7/rtQl8J5qd3Xaq6PohbqLIq2x0JrYFQrO6D9jz1HDW38hJZa9sbZbFYRaiMfCwoAasJG0Kxo6GOVMgVrNAsGg8qdfC1N/1wuZxpDH47j5timqqV3WRZjaGioK/0ikioCaKctrhN9FLXohIER8JrWPMsMNWpgVM3X3s4bv53QE8FiwYDdfSwoAKgCG0EbHh7u6V1mq6jLFFYx9BFFEZlMBpFIREmNW9XQR334qJUhPR4PUqkU0un0HHGddhE60AzPGhPdbnfH+ihq0Y6BEfsMDAwMwO12z5Eo1rM3rrXxq8s3dtv47Yp6zJV8LCgAmIMVlfKsWKZIp9MQBAHBYFARoGF3dnqd/swhz2xz81pYY6LT6UQ0GlWyAGqXtGQyqWwyTISolY1GENu71l71UdSi0R4LYLavZXh4uKIxsRl7Y/XGr5YoVqeF7bLxE7PY3cei/36iNmACLcVi0dJKeWZGXaZgd4LqTn9t8KKemze7oQ87ULWGPo1I72p7BxqBl1pPAbA+ik43JraDtseCHdLZbBaCIMDhcKBUKgFAx+yNtRs/+drbl0Z9LNhe1Qv279+PvXv3QpZlbN68GZs2bap4/L777sNvfvMbRKNR3HbbbU2/Pn2yX4XdfUiShLGxMUsseqsb+sTj8ZpjfjzPI5FIWGpunmU0al1rPeldPXEdPYQWAgBZlrvamNhJOI5DqVRSehMAzLE3rmZg1Iy9cbO+9kT/U8vHgn0mmdNoqVTCvHnzOn4Noihi79692LlzJwKBAG666SasXr26Yq9fv349LrzwQtx7770tfQ/zn3I9QBAEZDIZACBDny7BDH3cbrdyJ8+CLr27XjY3302HvE7RjqGP3uGjtk1lqWk9AyNerAwAJFkGV+N9YhMixWLRlH0Uaqo1Jmob/IrFYlUDo1YkioH6aWFg9vPpcrks0WtDtI96zJXjOJTLZbjdbjz33HP44he/iNHRUUxMTOC8887Daaed1pEbyMOHD2PBggWIRCIAgImJCRw8eBDr169XnnP66ae35T5r+wCgXC4rTnPajcCM9JOhj8/nQzabRS6XqzDmYXrw9YRojKYbjYnqu456BkbaEoAgAZ4q55FV+yiq9bTUSuFX67HQEyGqZ2CklxZmgmCxWIwMjGwI+ywNDAxgxYoV+NznPocnn3wSzz33HHbv3o1yuYzly5djYmIC5557bsvZ2WQyqRz+ABCJRJBIJDr1YwCweQCgNvQBoNQXzYq6u9yKhj5sIbC7MJYG146FAbOSs2Y2/JAkCTMzM1019FEbGOlJ76YybgCvHVaCKMPjnPteiaKIeDwOjuMs00fRTGNiKz0WrRoYuVwucByHwcFBuN1uXQMjsje2F4ODgzj//PPx5je/GZIk4cUXX8Tk5CR++tOf4oQTTsAZZ5xh9CVWxfwnSJfQms2wEoBZEQQBsVgMHMdZpkyRzWaRSqWUMgXr9Nca+rjdbrhcLgiCoETWgiC0Jb3bTYwy9NFK7/6umAEgKI9PJ5IYCVZK7/I8rzj4mV3LolMCT630WNSTKG7F3ph87e0Hx3E4+eSTcfLJJ2PLli1tvVY4HK64408kEli8eHG7l1iB7QIAKxv6+Hw+hMNh028k1Qx9mKyv9sBU13tHR0eVQ77WWJjP5zNkUzVLYyLHcZAdTqgDAM7lrhDXcblcKBaLGBwcNL1ldTab7YrAUys9FrWyA0yIiAUGjdgbk6890QqLFy/G0aNHkUgkEAgEMDk52XZQocVWAYAVDX0kScLU1BRCoZDpN3GgtqEPMFfWt5ahT7WxsFKphHg8XrG59yI70A1Dn3bQ9gC4fX6MDIYgSRLS6bRiipLL5SAIgnLQmenw6bXAUzM9FlqJYva+qqcMmrU3JgMjolGcTieuvfZa3HLLLZAkCZs3b0YwGMSuXbuwdetWRCIR7NmzBwcPHkQmk8EHP/hBvPvd78batWsb/h62CQCsaOhTLBYhCEKFUp6Z6bahj7bxSyu9qzcW1imYoY+ZGhMFzRSAIMpK6UV9oDYivWsERjcmqnsstL0DzN5YXXpKJpNwOp0VfRSNGhhV80ZQGxiRrz2hZdWqVVi1alXF13bs2KH8+a//+q/ben1bBADMbMbj8SAajZo+2mZlikKhoNytmB1WpmjW0KdVIZpquu+1xsJawcxz89oMQFmcnRDheR6jo6PKgdoNe+N2MZvAE2vw07M3npmZUdL9gUCgIvXfCXtjnufJ154whL4PAIrFIuLxuKUMfeLxOMrlMkKhkOltfIHZ9zgWizVt6NPJA7WVsbB6dNIhrxtodQBm0hkEB2ob+jQqvdvNHguz9FHUgqXwgdnsTyAQgNPpRLFYrEjhN2pvXEuimHztCaPo6wAgl8shmUxa1tCnWCwafEX1yWazmJmZQTgcbsjQpxdCNJ2Q3u22Q14n0CoBSnBgZKTxDFczPRZMAKVdzNZHUQsm8BQOh5XGRHUWRc/eWN1jUc3euJ6BkV6ApjYwYjLHZh2RJaxDXwYArLEok8lYRinPqoY+2WwWIyMjSrOT3pgfYGy9t9mxMJaeriVEYwb0mgDbORC63WNhxj4KPeo1JlazN2Y/XyP2xo0aGFXzRsjlcigUCuRrT7RF3wUAVjb0sVKZghn6sGkKvdEohpnqvbXGwliHtyRJ8Hq9CIfDpt5UtSUAQerca3eyx4JJV+dyOUQiEVOP3jYr8NSOvXGrBkbFYlHJNJCvPdEOfRUAqM1mrGboY5UyBduo2JgfU+urNuPPhGi8Xq8p673qsbBCoYBkMgm32w1BEHDs2DHdsTCjYQfqbAbgtfezFXOgRmm1x8LsfRRqOiHwpNdjoc2iaHss2jUwIl97olXMsaN1AGY243K5LKOUZ0VDHybVyzbIWmN+7C7ICoY+TIiG1XvZz1UsFpXsgHoszOv1GvLzKEJWhSKAysNUmxHoFtoeC5ZF0fZYeDwe5HI5ADBtHwWjG42JtbIo1XosGpEoZqU2NXb3tSdaoy8+CTzPI5lMwu/3W0Ypz6plCjZXzlLl1cb8WAOVVeu99cbCmO67uneg26j7KIYjUeBP6YrHO1kCaAY9cR025gnMKuCxerUZx9p61ZhYLYvCGmlZj0UtAyMm6sRxHARBaNjASM/XngUnZvt9EL3D8gEAq0dbTSlPFEXLlSmGhobgcrkU1TR2F6Lt9LdavVcQhLr1Xj1lN624jnYsrJMwQx+1EM3/WRnBAwcSKIiAywHMCxr/WWKHVqlUwsDAAAKBgBI4sSxKNXtjI2CNe63qUbRKI5Mq6t4Bh8OhNP9Fo1F4vd6mDIz0fO3T6TQZGNkc43eMNmAGPmqnOTPDDH2cTqdlyxT5fB6SJEEQhDmNRlat9zbbmNho41enpHerGfpwbKMXJXhdHE4fMV6kSM/QR6uPXywWK6R3e5lFYZhN4KnepAr7nQ8PDytZqlbsjcnAiFBjyQCApdCZjKbRi7cRrGjok0wmUSgUlDKFLMvgOA4OhwNTU1MVY2FMKtXMc/OMTtd7WxkLaxSr9FE0IvBUTR+fZVF61WPBAtVSqWTKQFWdHVBnDF0uF2ZmZpo2MOJ5HgCUtduIgZHa3pgMjPoXSwYAiUQCxWIR4+PjOHbsmNGXU5dCoYB4PG65MoWeoQ9rstSmLNndhtl/vm7Xe/WyA+x9YgGS+pCrtamyPopOO+R1mlYEnmrp47MeC7XRU6eyA0zgSZKkCudJMyIIAuLxONxuN0ZGRpQGQK2BEetFYdmmRuyNgcYNjAqFQoX6IXs9wvpYMgBgB6mZFy+DNfhEIhFTN8MxWJminqEPS1m63W6USiVl82E/byvSu93GCCEaph9fS1xHOxbGGhPz+bwl+ig6IfBULYvSyR4L5jypNfQxI9WMstQGRkNDQ8r7pGdgxLIotSSK2WFeKzvA/k2pVFL6B8rlshJ8kIGRdbFkAMDkMM0MSzPmcjmMjo6aehNn6JUpWjH0UYvrNCq9203M0pjY6FiYIAgQRbHC0MeMdEvgqRVxnXqwA1XbR2FGmjHKqjapUqvHolp2gAUF2uewv6sDNPY4GRhZG/PuLhaG3cGxTn+z1Rj10CtTVDv86zVQVfNcZ++JXsqyW5i5MVE7FsY2bvaez8zMmC6LwuiloU+7BkYscAgGgxgYGDDV+6im3cbEej0WzRoY1bI3ZhlAMjCyNhQAdBj14hkbG7NUmaIbhj6tpCw7hRUMfRiiKCKdTsPj8SAcDlf0Dpghi6LGyMbEWgZG7Hetfp/Y+GE/9lHUolaPRSsGRuqAQFv/r2ZglMvlkEqllACNiRCZNQCzIxQAdBCe5zE9PQ2Hw4Hh4WFTHziASlUul6sw9GFpwVqGPq02ULWSsmwFdb3XzIY+gH5jYqMGRjJ624xlNoGnegZGABQrX7O653ViXdWjkR6LegZGwOxnled5OJ1ORZComoER8FqZq1wuI5fLKWUuMjAyBxQAdAhWxx0YGFA+6GZGlmXE43GUy+WGDX20QjTt0krKshGqNVCZkUaEaGoZGEnirB8Ayxh0a1M1Sx9FLdjh43K5lD6KgYEBRUehGQOjXtGNdVWPVg2MyuUyUqkU/H4/BgcHlf2imggRoK9+qDYwYtkBMjAyBgoAOkAul0MymVQMfZg+gVkRRRGxWAwAmjL06WYDVaspSy16QjRmpJ16r7rHwvlSApBkwIGKLIr6fWr392XmPgot6sZEtdhWMwZGvaIX66oRGumxcDqdKBaLCAaDc0TXmrE3VhsYqcs32WxW0TcgA6PeQQFAG1jR0IfnecRiMbjdbiU1bkZDn2ZTlgAUIRor1XvbP1AdAGRwjtmxzU73WFipj6JWY2Ij0ru97LEwq8CTXo8F88pgcsSCIFTctbdrb6znMKkeMSQDo+5B72qLWNHQh22QAwMDyh2HFQx9GklZMnMiM8i61qLb9d5aPRbMFa7RHgu1EM3w8LApUubVaFbgqdEei26MtZllXdWDZanUiol6OhbsfeqUvXE1AyMWEJi1l8OKUADQApIkIRaLKZ3+VohOJUlCKpXC8PCwksKzqqGPOjvAUr6iKMLpdCKRSNQdCzOKTgvRFCcnce4vHoUnl0I6+jrwofVwv/5k5fF2eiys2EfR6oFaq8cik8noSu+2gtnXlZpqAk/VdCz0DIwatTduxMCIlSWYvTHHcYqxlJmzUmbH/CeXybCqoY8oisqMP/MTlyRJd8wvmUyC53lLCdGw34X6ziEejwNAw9K73aTTQjSFfb9C7nv/D5FX/z6YSyH1L4fgPmUxApveBPdJJ1U8X6/HYu+BJERZAIcMLp7PVxyC6XS65w55zaLuo+jkgVpNx0Kvx6LR9WHVdVUrUK2Vwq/WY9GqgZE6O8D0RFiAxgIBMjBqHvN+Ck1IuVzG9PQ0/H6/5Qx9mMtXrWY/tvABWLbeW28sTC9l2W06LUQjiyLyP/uZ7mP8C4eReuEw3IsXI7DpIrhPPFH3eRzHoTybjYWI2Y2e3cmxbAoLpszYkNWrxsRO6Fj0w7qqRys9Fq1mB9i/ZeVAPQMjsjduDAoAGsSqhj6iKGJ8fByxWEyJtoG59X51vdfswQ2r99ZroKolvVstZdlpumHoI2UykHPZms/hDx9G6vBhuJcsmQ0EFi2qfA2NmIvL5VI230gkoigTtiq9202MbExstseiH9dVI7TSY1HPwIhlB9SQvXF7UADQAJlMBqlUynKGPtoyhSAIundz3XbI6yTt1HtbSVm2CktVFgqFjii7qeFCIcDlAgSh7nP53/0Oqd/9Du5TT50NBBYuBAAIUmUAEIsnIEuV9d5WpXe7iZkEnhqxNxZFET6fr+tyye3STaOsWj0WLIvSqL2xKIqKEJF63JB9n2r2xkwUiq1xs+hAGA0FADVgacZ8Pm95Qx+3241UKoVsNjtHKrVR4xEj6XQDVTfHwiRJwszMDHieb8shr+q1cxw8Z5yJ8pNPNPxv+OefR+r55+E+7TQENr0JwtjrKh8XZcwbnVvvrSW9qxbX6UWPhZkNfbQ9Fvl8XgmWyuUyjh8/3pCORa8xojGxlR4L9rnMZDKQJAmhUAgAak4WVAvQmIERa4K1s4ERBQBVYGlGnuf7wtBnaGgIoVBISY/NzMwoCycQCJg6uOlFvbdTY2HqBqrR0dGu3WX416xtKgBg8M89h9Rzz6G4fCWw+ELl66HhcEPX2u0eix+96ERJcsDDAX920msZDrXAk1UMfSKRSMXhU09614hrNVrgSd1joQ3E1T0WbrdbEQvSrqtGDYyqCY0x4Tb2u7BTdoACAB3USnlWM/RhZQpW79cKcbBNh9V4A4EAeJ7H1NRURzzXO40R9d5WUpZAbx3yXCedBOe8eRCPHWvp3xePHAUWv/Z3oQVLgW70WDz4nAfyq+JGMoCLTxKQzWYtLfDUivRutzGjwBMb/9P2WOTzeeRyOQCzh3ShUKjQsajXO6B9Dvu7Vv2QfS872RtTAKCBGfp4vV5EIhHT/+LVhj6sTNGooY86uGlVerebsHqvy+UyVIimkZSl0+lEPp/H4OBgT/ooHA4H/GvWIvuf/7elfy+4Ku/2Zv7f/8Pg+avhmj+/5WvqbI+FAz980YW14XhX+ig6TTMCT+3aG7eLWdZVPTiOg8PhQKlUwsDAAPx+f1v2xrUkilkg24i9cT9BAYAKSZIwNTVVoZRnZvTKFK0a+rTiFtZNzCpEo5eyTKfTShqxUChUBE/dvG7vORNIfv8HcPOlpv+t6KwMAIp/PIqZyV/Dc+ZSBDa9Ca4TTmjr2rQ9FiyLUqvHQpLx6t3/q9coSooKndnn5ls19Gmmx8Ln87V9WJt1XemhZ5TF3qd27Y15ngcA3YCgmr0xG5FlgYkgCF03MNq/fz/27t0LWZaxefNmbNq0qeLxw4cP4wtf+AJ4nsfGjRtx5ZVXNvX65l1VPYa5VIXDYVObyDD0yhSdMvQxOmVpFUMfYPZaS6USIpEIPB5PW9K7zeLwePDySctw4u/213iSA5Dn5vcFl0fz99mAoHzoaZQPPQ3P0mWzgcC8eR25Vm0WRa/HAi4/gMoO9F455LVKpw19utljYZV11YhRVrUbFhY0NGJv3KiBkTZAy2Qyyu89mUzikUcewdlnn401a9Z0dIJCFEXs3bsXO3fuRCAQwE033YTVq1dXmDHdf//9uOGGG7Bw4UJ87GMfw+rVq7FIM/JbC9sHAGxcK5/PKw0iZkfP0KeWpn+7xiN6KUvtptSJlKUsy4qhj9k1/VkDlVonHcCcrmNtFqXTPRZ/XLKiZgAQ2PQmiLkcSv/7G+DV4BCYWwIQnZUBQfnpp1B++il4lp01GwiMj7d9rYxqPRZTmbL2iR37nt2g24Y+neqx6Id1VQu9GxbWSFjL3hhozcCIXdPQ0JBiWvStb30L9913H8444wxMTExgYmIC8+fPb+szcfjwYSxYsACRyKze58TEBA4ePIj169cDgCKBfuKrYl/r1q3D/v37KQBoFFmWkUgkUCqVEAwGlbSQmSmVSojFYk0b+nSqgaraptRuylLdQGWFei9roKpW762WRel0j0U+GMHxsZMwPvWi7uOlp55C+eq3wXX22fAePIDib38LCMKcAED7d0b5qSdfCwQu3NTRQIDBsgOucuXvXJJlHDt2rCXp3W7TDYGneuj1WGizKNoei35bV43AcVzdLIr2hqVVA6P58+fj2muvBcdxSKfTOHDgACYnJ/H1r38d4XAYf/M3f4PTTz+9pZ8jmUwqhz8ARCIRRVGy2uOHDh1q6nuYYzUZgNrQZ3x8HPl83vQBQC6XQzKZxPDwsJKpqGXow4RoujnfW63xi00lNJKyrGY8YkZaFaJpJWXZKL8/eaJqACAeewWeeBzDZ54Jx6IT4d/wBhR+8XOISany56oSAAAAZBnlJ59A+akn4T1rOfwXboJrbKzh62uEfD4PMZfGRQvc+Pmf/OBlB5wch7GxMRSLxYoJjHbsjdulV+uqHtWyKOoeC6/XC57nlU7/flxX9aiVRal2w1JPopj9WdtndcIJJ+CEE07AJZdcglKphKeffhontNlL023MW1zrIoIg4Pjx44qJjBlGYGrBNp1kMoloNIrBwcGKTn9g7uHPrIpHRkZ6tkmxxRQKhTA2Nobx8XFlzDAej+P48eOKNwGLqlkDlRU2KSay5PF42tqkWHYgGAxidHQU8+bNU+qLyWQSx44dQyKRmD0UVWn7arxywskoBEJVH3c9+cRrXglDQxh8y2a4L9hY8Zx7n3gaq/7lfpx3/5dxyQNfxxUPfXPuC8kySk8cxMxdn0PmoX+HGJuu+j0ffvhhvOMd75jz9SuuuAKFQkH1krOf7VQqhRNGw3jnUiCXnB1t5ByvSe+OjIxg3rx5CIVCkGUZ0WgUH/jABxCPx5HL5fDSSy8hGAzi1ltvrfleVeP73/8+Pv/5z9d8TrV1deTIESUtW42DBw/ixz/+cUvX1gisvyISiSjvU6lUgiAIEEURMzMzyGazSkBgJjq1rhqB3bCw9ykcDsPpdCKbzeLYsWOYnp5WavzsfeI4ThkLFEVRGUOUJAmCIIDneWU/Y3i9XqxYsQJDQ0MtX2s4HK64408kEhV3/HqPh8Phpr6HeXfbLmFVQ59isYixsbE5hj7a69c6eRkZ3NQT13G73Yo8cTjcmBCNUXTa0EdN2z0WDg5HF0/g1Cd+ofv6paefwkAmA07VPCS4vQCKyt/fvfo8zHvqEYT9Przn7LNrX7Aso3TgAEoHD8J7zjkIvHETnCMjDf2s//f/vja22KwQjfp9ikQieOqpp+B0OlEoFPC1r30NS5YsUWq/zWZR/vzP/7zm4+2uqyeeeAKHDh3Cm970pob/DTNlahZBEJBOp+H1ejE8PKwrrqOnY2EE3VxX9WhGDdTj8SCfz1cIPAGvZQe6UVpZvHgxjh49ikQigUAggMnJSWzZskV5nAVLR44cwcKFC7Fv3z5s3bq1qe9h3h23CxQKBUxNTSEYDFri8GcRZrlcrjj8axn6MA+AaDRqqswGW2xDQ0MYGxvD0NAQeJ6fdaUrlzE1NYWZmZmK7IBZyGazFaWXbn5uWMoyFAphdHQU4+PjGBgYUExlWBYln89XvE8vnbx81h9AD1FE8bf/W/ElrReA67y1cM1/HVKlMq7/4X8DAB588ilc8JV/AwA8Mx3D+7/7PQDAw0f+iEse+Dou+urXcPNddyPxuTuR+dZDEOOxuj/fGWecgWw2iz/84Q8499xzcf311+Piiy/G+9///jl3prIs4+KLL8YPfvAD3ffp/PPPx4EDBzAyMoJf/OIXeMtb3qIEzF/96lexbt06nHfeebjiiisULfgPfOAD+PCHP4w3vOENWL58OX75y18CAL761a9ix44dynP+9m//FuvWrcOKFSvw85//HLFYDN/+9rdx5513Kutq/fr1OHLkSMV1/f73v8dFF12EdevWYcOGDTh48CBEUcSnPvUpPPjgg1izZg1++MMfIhaL4corr8Tq1atx8cUXK6/zgQ98ADfccAM2bNiAO++8s+77qaVYLCo9QkyMimVRotEo5s2bp9yVplIpHDt2TMmiCA14S3SSXq6rRmA3LOrsgMPhQCaTwfHjx5HJZBAIBJS+K+C1foNuBABOpxPXXnstbrnlFmzfvh1/8Rd/gWAwiF27dil3/u973/tw11134YYbbsDZZ5/dVAMgYKMMgBUNfTKZDAAohj617vytZOijbqBivQOsfsm8vhuV3u0m3TT0aZR6PRai5AHgQNnjB049FajSBFT839/Av/ENcLx6ePFi5WErerzwnHEGhs45B9/90pcAjsNvX34ZIa8Xx3M5/Pbll3Hu/BNQFAR85Cc/xTeufCteFwzifd/9f/jh736HS17NCnjPmUDgwgv1LkFBEAQkk0kcPnwYe/fuxRlnnIFLLrkE+/btw/nnn688Lx6P44YbbsAll1yi+zpbtmzB17/+dZx22mnweDyYN28e4vE4xsfHcfHFF+Pyyy9HqVTCfffdh89+9rP48Ic/rPSb/PznP8fPf/5z7Nq1CxdccMGc13755Zfxq1/9Ck888QSuueYa7Nu3ryGvjHnz5uF73/sevF4vnnzySezYsQPf+9738NGPfhSHDh3Crl27AADbtm3DunXr8K1vfQvf+ta38OEPfxjf/OZs2SWRSOAXv/hF0595tq5qGfo0YmDU7R4LM6yrerAbFpallGUZgUAA5XIZDz/8ML73ve9h6dKlOPfcc7FixYqulS5XrVqFVatWVXyNBaoAcOqpp+Kzn/1sy6/f9wGAVQ19pqen4Xa7lUaTWp3+eoIZZqSa8YhaXKea53qvU5bM0EcQBNP0JuilLB1TMwBmr5dfugzuKgGAlE6j/MwheJedBQDgNRkAFhC4Bgcx75RTkP/Ld+Cl7/0AW844A4+9/DJ++/IruPbss/BCMomTw8NY+KoZy+WnnYbf/ukVXLJ4MSBJKD2+H6UDk8j7/JDLmrE+zH4G4vE4PB4PlixZgjPPPBMAsHz5chw5cqQiABgcHMQlF+sf/gCwZs0a3HjjjfjmN7+JK664AsViUXmfjh07hh07dmBqagqFQgErVqxQGsAuvPBCJJNJnH766XPu4BlbtmxBoVDA2NgYgsEgstna9suMUqmEbdu2KeUJptWhZd++fUo5ZMuWLdi+fbvy2OWXX97UwduqoU8tffyZmZmKSZVO6Vh02yirk6gFntT+Axs3bkQ0GsUTTzyBf//3f8eePXuU8b8VK1bghBNOMPUNmBrzvvsdQK2UNz4+buoPG0Nt6MNxHHK5XNXDvxHBDLPQTL23luc6k97tlrgOMLfea9behNdUyGYln8NnnIncCSdAfuUV3ecXf/2oEgAImt5CQVV1WbNmDf7zJz/B65Ytw4Xvfz++dvddeHJqCsvHxnE4mYAek8eO4R9+8jMAwC1v2ABRllF+/nlk/uPbCLzxjXCGI0p5Z3BwUNFEUP8s2obHYqkEoPphxsoAd955Jx5//HHlDhoAtm/fju3bt+NNb3oT/uu//gtf+9rXEA6H4ff7lfJYsVhUpL+LxWJFUy2TemZNYiyVri67lEpzFRj37NmDE088EV/+8peRy+WUAKce6nXdTIayk4Y+3VYD7ZVRVieoJfDk9Xpx/vnnK8HqsWPHMDk5qYz/feITn8App5xi1KU3hXl/A20iiiKmp6chSRLGxsYscfhnMhnE43FEIhFFppMtRGDu4c88AEZGRkx9+EuShHg8rtxNN7NJsZTl8PAwxsbGlCwO6+eYmppCKpVCqVTqSHczE1lyuVyIRqOm3qSKxSIkkdUiZ7MoA+dX70bnf/975I/+cTZboMkAqHsC1q5di3vuuQdr1qzBxIYN+OUfj2J88WKEzj0Xp0Qi+ENyBi+l05BkGd99/nmsft18TMybh/96x1/iv97xl1j9OmY1LKP02G+R/OydSD70Dcwc/SM4jms4SyXLMj7xiU/UfM7WrVvxyU9+EtFotOLr6XQa8+fPhyzLePDBBysec7vdCIVCSjPfwMCA0t19/PhxFItF/Md//AfC4TBefPFFZLNZnHDCCVi0aBGefPJJAMAzzzyD3/3ud3OuJ51OK3eAX/va15Sva7MI69atwze+8Q0As42R2jRvI7SzruqhnlRhExjtTKpYbV1ptVYYbD9S+6yccMIJuPTSS3HzzTfj/vvvx+tf/3oDr745zH8qtkC/GPpwHAdBEDA1NTWnLpdIJCBJUluCGb2ANa+53e62jUdqpSy14jqtZAes2EfBcV5AkoFXNfS9Zy1H7gffh/yqN4GW7K9+hZkNG1EsV95ZqwOCNWvW4JVXXsHatWvhdDoxf/58nH322QhedTUCb3wjPuPz4bqv7IUgS7hg0SL82Skn636vn714BOfd/2Xl79+6+mqgUICYSjX0M4aHh3Ho0CHcc889+Ju/+Rvd5yxevBiLFy+e8/V/+Id/wFVXXYVwOIz169fj6NGjVb9PIBDAwMAAAoGA0mszPj6ODRs2oFgs4o477oAgCFi7di1GRkawYsUKnHPOOTjttNPmvNYHPvABXHPNNfjXf/1XvOUtb1G+zpr61q5di507d+If/uEfsHXrVjz44IOIRCL453/+54beE0Yn11UjtGNgZMV1pSfw5HK54PF4dH1WGGa+EdPDIZttKLQB2GwrALz00ksYHx9Xol8WvQWDwabMLjKZDIrFIkZHR9u+vpdffhnRaLShWpy6TMGieLUKlcPhUMbCSqWSIlbkdDoxPDxsGttePXppPKJOWZZKJZTL5aZSlqyPolYDlRlQS1dHIhF869k8cryEATeHd5w9OwOc++F/ofCw/kigw+NB6MN/j4deKCGn0r0a9gBvOXWg4cYvYXoahZ/9BKWDB3W9BmridMK36lz43/BGOHXmpP/25z4kSxzCXgm731DUeYHOoxai+chHPoK3vvWtePOb36x8nkqlkq6BkRGYzdBHrQbKMnHsfZIkCZlMxnLrSrt3sxsPduffL/RVBkBPKc/MNGrow5SsyuWyEvVzHKfU05r1XO8FzHikV42JrRoYqfsojFR2awQ24sbzPEZHR1/NcMy90/eddx4Kv3xY92CWy2UITz6JNyxdhR+/kEHp1eY/jxNN9Vi4RkcRvPrt8L/hQhR++hOUnnyi8UBAFFH8za9RfOy38J27Gv6Nb9ANBHoFO1C19d56OhZGTKr0el01QjUDo3Q6DUmS4HK5FIvdbtgbt4v+unoNj8ej7M1mu/Z26YsAgKXQM5mM6evhDL0yRSOGPmrBjPY91zuPWRoTG01ZlsvlrtRQOw1roAJQV4jGGY7Ac9rpKD/7jO7jxV8/ivlr1sDFOVASZQy4OVy+NKw7FlbPwMg1Nobg2/8S/jdeiPxPf4LyU082Fwj8+tHKQCBUXdGwG+itq3vvvXfO86pJ77KAgOO4rk6qmGVd1YMF4rlcDg6HA9FoVBHYacbAqFfUWldMk4NdY78d/kAfBADs8Od5XhHLMTtMh7pdQ59mlKx6sdjMajyiZ+nJNm5WZslms8r7ZLaeCnW9t1EBK9+atVUDAHF6Cvzvfw8gUvH1ZnostAZGrvFxhP7yGuSP/hHZ//kfcIfnNsjV+AFRfHQfir/9X/hWnwcMNOdp3irtGPpo7Y3Z+6TOoqjfp3YOD7OuKz3Uhj7qA1WrY2GGG5Za64rtrexr/Xj4AxYPANiBKQgCxsfHTbdx69GqoU8jC9/IlKWVDH2YPSproGIBQSc81ztNqw1U7sWLwUWjkOJx3ceLv94HLLus5ms0OxZWKBSQcrkx9Pa/hCedRv4nP0b56aca/2EFAcV9v4K05s8A7zAgS/j9zNcwPvAGDLgXNP46dei0EE0jOhatiutYaV3VM/Qx0w1LrXXFsjn9mPLXYt5PUx0EQcD09KwZSSQSMf3hzzadTCaDaDSqbKqs4Y+NlDDaFczoZcqSCWZwHGfquXlAv4HK6XS27bneDdoReHJwHPznrUHuB9/Xfbz8zDOQl17a+OvV6bFgwfjAwGwjoXPePITe8U4Ir7yM/E9+gvKhp5u6fgAoSym8kvsxwEs4efTapv+9Hr0QoqmlYyFJ0pxAvBpqIRorrqt6GHXDUmtdsb2ATWH1O5YMAEqlEo4fP45AIIBsNmv6X1Q1Qx8mPKK9/m4IZjSTsmxmU6wlmGE2WANVLeORatK7vUxZdqre6125Ern/+RGgZ3MtSbNKfVxrd78sO+Dz+ZBMJlEul+Hz+VAul3H8+PHXxsKiIwi+450QX3llNiPwTG2/ctkhQ/RJwKttBP6XZcjPHYB89dvhaPNO3QghmnrSu9V6LPptXdWjFzcs9dYVG/Ozy+EPWDQAyOfzCIVCGBwcRC6XM/pyaiJJEmKxWIUgkfrw1y4WnueRSCTg8XgUM49O06mUJbv7Y3c7Zt6kstksMplMU/VeI1KW6npv28pu/gC850ygpDECUiiXAV/rh6q63qvWo1CPhcVfLUH4/H54t1wJbzyG0s9+VrU/wSE7wPEy4AI8KeDcPRKOvJVv+/DvxbqqR6M9FhzHIZ/PIxgM9uW6aoRO37DUW1f93OlfC0sGAOFwuCH1KaNhZQqXy2VqQ59WUpaNGI+YgU7We7udslTXezsl8ORfs6ZqANCOBAir97pcrjlCNNXGwnK5HHinC+5LLoVnzRrI+34F4fnntReFSw58D2X44C8XAADzD3iBd7Z8qaYVotHrsWCz6MDsnTULCsym99FLQx/1DYs2EG/khqXeulIH72Z6j3uBJQMAK8CkL/1+v9JhagVDn0ZSlkycSO2LbUa6aejT6ZSluoGqk/Ve1wnz4TrxRAhVTG9aoZl6r3oCA1D1WDidKP3ZJXBMrIDrt/8L/P73yr/Z+KeHMTCdglOUIXnccHzohpav1SzrqhEKhQLK5TKi0SjcbndDOhZGYGRjIvNkaPSGBUDNdeXz+fq+078WFAB0AWbQEQqFlDuOWp3+Zp3v1aYsWQ1VEARwHIdkMll1LMxoem3o007KspoQTafwr1mHTI0AQG4im9auEM2cHotIBKXXn4ziH/4APLoP3B+PoDA8gGLQh+E/JeHJl4Cf/hLye69p6vuYeV1pqSZE06r0bjcxm1FWvRsWAMq6VMOyCuzPdoUCgA7DZstDoRBCodAcWV/t4d8pJ69uoxbMYOWMTruFdQqj673NpCzZZ6CbfRSepUvhGByEXMXSVs+2d85zXh2dzGQyHTtQK3oszj4b4rJlyB0+jPIvfgb5xRcRf8tFGHjdieDOPA3eGvrretdqpXXF7vCrCTzp6VioeyzU72M3swNGr6t6qG9YXC6XElQ7HA7MzMzgnnvuQTgcxvLly7F+/XpTCBEZDQUAHYJtOvl8vsIxSt3spx3z0xPMMCPVBDNakd7tNmar99ZKWbL3iUk7i6LYlXSqw+WC79zVKPzsp7qPy3wZUiEPzq/fy9ErIRqn04nQaacBp52G8ot/gBgMoexyzTbJHTvWUI9FP6yretTqseiWjoXZ1lUtWH8Ss38GZj/D733ve3HgwAE8+uij+PrXv46FCxdiYmICExMTOPXUU039WekWFAB0ALWhz/j4OKampiru/LVRZj3BDDPBOpTr1XvbcQvrFFYw9GG9AcxLfnh4GJIkNSy92yq+1eeh8IufAyo/ezWl/fvhX3/BnK8bVe/1nDRrqcr6ytU9FiyLou2xsOK6CgQCbR2oVXssOqhjYYV1BdRuTHQ4HDj99NNx1llnQZIk5PN5HDx4EJOTk/jMZz6DZcuW4e/+7u8MvHpjoACgTZihj8PhUAx92NddLtecBdftem8nabXe20zK0ufztb1Ry7KMTCaDXC5nekMftRCNut7brPRusziHhuA5c+msVr8OhV//Gr5158Oh+l2YSeCpXo+F2+2GIAjwer1N3U0bQTcNffR0LLSTKo3qWFh1XekFquoxP47jMDg4iPPPPx/nn38+JElCtkp5rN+hAKANqhn6sPqTNmXJNvZ2BDN6QacbqKqJ63QiZWm1em8tIZpmpXeb/fz41qypGgBIiTj4w7+D59RZj3smROP1ek1X79X2WLA7VKfTiWKxiKmpqZald7tJrxsTq02qNKJj0S/rqhFDH47jEOqBCVUsFsM999yjfFa3bNmCtWvXVjzn2LFj2L17N3K5HM466yxcd911Xf38UgDQIsViEbFYDMFgUEmNs5R/JBKpSMWl02k4HLPufYODgwgEAqbZlLR0UohGD+2m1E7K0kr13mYbqOpJ77LsQDOz/O7Xnwzn2DjEqeO6jxcefRSeU0+zlMCTujHR7/e3Jb3bTbq9rhqhWhYlnU5DFEXlffJ4PEoHvZXXldkMfZxOJ97znvfgpJNOwszMDG666SZMTExUBIIPPPAArrrqKqxcuRJ33nknHn/8caxcubJr10QBQAtUM/RRd/qzxRYIBJBKpVAoFODz+VAoFCqc55qV3u0m3RCiqUerKctaQjRmoxMNVHo9FsViEZJcBuCAKIlIp9M1eywcDgd8a9Yg993v6H4P/vnnkPnTS8hyTsvWe/XGwrRZlG70WNTCiHVVj2pqoOobFr/fD57n4XA4TLm+rGboEw6HEQ6HAcz2/oRCIWSzWSUAkGUZzz//PLZt2wYAuOCCC7B//34KAMyC2tBnZGRE2WTqGfoIgqDUe5kSYKfcwjqFGYxHGk1ZOp1OZLNZBAKBho1HjKIbQjTqHgsnlwRECZzDoXSV1+qx8E6sQP6/fwj51SbECmQZ+UcfReQv3mKJem89gadqWZRu9FhUwwzrqhGYDj5bV6xRtV2vkG5hdUOf3//+95AkCSMjI8rXMplMRcYtEokoo9fdwvjfpEWQZRmJRAKlUqlpQx/1wm9WyaoXi82sxiN6KctcLqdIpfI8j1wu1xV743bpfb2XQyQSruixyGazc3ssPB54J1ag+OtHdV/H/fKfTO03344QTbUeC3aYdFrHwqzrSg+1oQ/LaqrfJ+0EhlE3LPXWFQv4zHz4Z7NZ3HPPPdi6davRl0IBQCOoDX3Gx8eVbtJOGPoYnbK0Ur1XEASUy2WEw2G43e45WZRO2Ru3C2ugKpVKXa/3njXuQ1mU4XG+VuesZ2DkOfNMQBMAyIsWYfD8C+A780zTfgY6KUSjlx1g71MndCyssq7UAk96hj5aNVB1FmVmZqYii9LtG5Z668oKhj48z+OOO+7A5ZdfjtNOO63isWAwiGw2q1x/IpFQSgbdggKAOvTS0KfRlGWnxHWYYEannbw6TTUhmmpZFJayNKLxizUmSpLUk3rv8nm1f2+6BkZ+P8SFC197UiCAoff9n55KyjZLt4VoOI6rK67TqI6F1ddVLRqZVOnGDUs150mGFQx9ZFnGnj17sHTpUmzYsGHO4w6HA0uWLFEa/x555BHd53USh9yOJZhBCIKgHMAvvfQSxsfH277LymQyKBaLGB0dVb5WKpUQi8UQCASUO45GDH260UClXmzFYhE8z7ecsmS9DPl83hLzvayBKhKJ1D3MtSnLcrncs5SlWogmHA6bNgUJAMVjx/DQy04UJQ5+J/CmeeWaY2FGYrQQjXpSpVQqVZ1UUTcmhsPhvlpXjb6m+n3q1A1LvXVlFUOfZ599Fjt37sSiRYuUr33oQx/CD37wA1x00UU45ZRT8Morr2D37t3I5/NYtmwZrrvuuq6uQwoAXkUbABQKBcTjcQwNDSkpvFqGPr0WzFCnLNliU2/e1Rab2ngkGo2aoqGnGup6b6vKbuosSrFY7FrK0ooCTz8+7kFBAAbcHK5ZPlzxPqnHwliDXK9/JjMK0ah7LIrFIgRBULID5XK5K86TnaYT66oeajXQdm5Yaq0rFrCyPxPNY95PqYFkMhmkUilEIhElJWg2Q59WUpZqQx8rz/c2Q7fFdYDX6r1WE3jipgoAXstm6Y2FGTWpYlYhGr0ei0KhgEwmozSeqcd8zZJFYfTK0KeWGigrZ9a7Yam1rtRjfmZ7j60EBQAq2N1xPp/H6Oio8gEzu6FPLT1wNhbm8XhQKpXg8XgU1UKz0q0GqkbFdZpJWVq73luo+vxakyrd7rEwy7pqBNZExxQTWSCu1rGoZ2DUK4xsTGzWwKhWYyIbWdTzWSGagwKAV2EfSlEUMT4+rszsV+v0N7PxiHaxsUOK4zhFj79RPfBe08t6bzsGRrWMR8xGu0I09TzXO9n4ZeZ1pUWvMVFPx4IFBOyu1YhJFbYHmEHgqdYNC+uCB2a74rVlHxbA0+HfGSgAwOwHkH3wmKFPrU5/Vpeq55BnBlh6cmhoCAMDA7pjYWZo/DK63lsrZcnuRtWlgnQ6XdV4xEx0Woim1lhYu+I6VlpXjQg81TMw0r5P3fh5jV5XjcBuWHw+n9KfxFRTH3nkEfzv//4vli5divPOOw9LliyhtH8HMe/O1SOYoQ/HcXC5XHA6nTU7/fUEM8xINcEM3bEwg1OWZqz3VktZZrNZCIIAh8OBgYGBqhkiM9ALIZpO9VhYfV3Vw4geCzOuq2qoGxPZuDUAnHfeeXC5XHjyySfx4x//GH6/HxMTE5iYmMBZZ51leDbD6th6CkBt6ONwOFAqlRCNRiGK4px6PzCr4FStLmUmtPXeRt8b7fhcL1KW6npvJBIxdb2XNVCx0gC7o2vGwKhXNFLvfeBgEjlewoCbwzvO7rzgSCNjYfWEaMxEq+uqHuoei1Kp1BE1UCuuq3qGPoIg4LnnnsPjjz+OyclJHDt2DJ/73Ocwb948A6/e2tg2AGCGPuFwGAMDA8hkMsr8rl6nP6v3RiIRy9R7o9FoywtfnbJkY2GdTlky7Xq3221ZQx+9sbBmPNe7QaP13m4HAGqqjYWxjd2s6WlGp9ZVPfR0LJrtseiHdQXU7/SfmprC6OioKbNvVsF2JYBqhj6sQS4Wi1WkLNlkgCiKpq/3qhuo2q33djtlacV6r96B2oj0bq96LMxc79X2WLDPqiAIAKDMetfTsTCCTq6rerTbY9Ev66oRQ5+xsbGuX+Mdd9yBQ4cOYdmyZbjxxhvnPP7xj39caRgGgFtvvdXUN4hazHuadQG1oQ/LGrBOf6/Xi3nz5s0ZCwNmI9FOqWV1i24L0XTSwIjVezvpkNcN1PXeRg9Uo3os1AJPzHnSrIiiiGQyCY7jMDIyAofDUXcszKiDzGiBp2Z6LERR7LjzZDeot67MZOhz6aWX4o1vfCN+8YtfVH3Otm3bKtT9rIR5d4kO04ihj8PhUBYbW/gcx8HhcGB6eropPfBe0mshmlbHwtT13l445LVDJxqoejUW1g8CT7XGwozqsTCbwFMtHQtW72frTRRFU34O6q0rsxn6LF26FE8//bTRl9E1bBEAdMLQRz0WFo/HAcAUKUujhWiaSVmWSiWUy2VLzM13Q4immbGwRu/g1fVe1r9iVti6qidEo53AYD0W6ixKt3ssjF5XjcAC8VKpBAAYGhqCJEktGRj1gnrrygqGPnrcfffd4DgOGzZswGWXXWb05TRF3wcAnTL0aVbJqhdSqWYUotFLWRYKBaTTaciyDJfLpWjyd8PeuF16JUSj7rHQ9g402mPRbYe8TtKqwFOveyzMuq700Ao8saBRq2PB1EDVHhi9vmGpt66sYuij5frrr0ckEkE+n8ftt9+O+fPnY8WKFUZfVsP0dQCQz+eRSCQwNDSEYDAIAB0x9KmnZNXtlKUkSZiZmTG9EI3D4QDHcSgWi0pHMgucumFv3C5G1XsdDkfTPRZW6qPoZGNiN3ssrLKugNcEnlgfhXaPMdMNS63GRKsb+kQiEQBAIBDA2rVrcfjwYQoAjIY1mWgNfVjNv9OGPr1MWaoFM0ZHRw1vkqmFnhCNy+VqSXq326iFaIyu99brseA4DpIkIRgMmloIpdtCNLV6LFgWpdEeCyuuK+Y/UO+zauQNS6111Uinv5kRRRG5XA6hUAiCIODAgQPYuHGj0ZfVFH2nA8A2nXw+j5GREWWOlD0fqIw0uy2YoU5ZMtGYVlOWvXLy6gTNGo+oU5bFYrGnKUsrCTzNzMygWCwq9rPtSO92UwfAaCGaejoW6rv7fl5X9eimjkWtdWUVQ59PfvKTePHFF1EqlTA4OIht27bh29/+NrZu3YpAIICdO3dCFEVIkoSVK1fimmuuMfXnR0tfBQCSJCEej0MQBKUm1oihj8vl6olghjplWSqVlMXWSMrSSvXedo1H1CnLUqkEnue7krK0usCTeiyMNVg2Y2/crQCg1+uqHuwGQB2Isx4LjuOQyWQwODjY9+uqETpxw1JvXZGhj3nomxKAKIqKpv/4+HjdTn8jBDNaTVk2YjxiBjpV7+1FypLVewVBsEy9VytEU2sszKgeCzMK0VTrschmsyiXywBmswD5fL4r9sbt0kuBp3Z7LOqtK/WYHx3+xmOuT3qL8DyPmZkZeL1exeveCoY+jYyFAbN3/5FIxPRz890Soul0j4W63tttZbd2acbQp1l7405jlnVVDyY8xPM8wuEwXC5X1+yN28VIgadmb1iY0Fq1dWXVTv9+xvIlgKNHj8LhcCAYDCp3HJIk6Rr6WEWIhqV2WUcygK64hXUKpuxmRL232ZSlXeu9ej0W//OKGwURCLgdeOfZkZZf2yrrCqhsTIxEInMCIbWOBRtXbbXHol2MXFf10OuxAKBM+6jfV7WhD/s7YQ4sHQBks1kkk0kMDQ0hFAoBQM3Dnzl5WaHeq26gcjgcHXcL6xRmEqKp12PBNlQr9VF0ozGRvU8PPZNFQQB8nIxLFsot9VhYeV3VO1DVPRZqA6NGeyzawUzrqh5MiZD97kulEr785S9jwYIFOOecc7Bq1Sq43W5TZ9rsiiUDAJ7nkUwmkclkIMsy5s2bpzT86R3+6gYqs2v6qwUzwuHwnEXTCbewTsHU/sxU71Wjfp+YWprH48Hg4GDX7I3bRd1AFQ6Hu1rvZU2AAbcDVyxu3t64n9ZVIzRib9wJzL6u1OgJPAmCgMceewxPPPEEDh06hEKhgGXLlmFiYgIrVqzAyMiIwVdNMCwZAExNTSGXy2F0dBTHjx/H2NgYnE6nbleput7bTWW3TtCKEI1RKUurCdFks1mlUa7eWJiR18rqvdFotOvXpDcFUC2Lou2x6Pd1VY9qkyrt6lhYbV1Va0xUd/q/9NJLmJycxOTkJJ577jlcfPHFePe7323QlRNqLBkA5PN5ALN18ZdeegnRaFR3wVmx3tuOEE0vUpZqJy+r1nvVY2Esi2J0j4X6QA2Hwz2p9zYyBqjNNrEJjXK5DI/HY4n0dC8MfdSTKuosCgsI6gVI/bCuGKzTH5hb78/lcsjn8xgdHe3Z9RLVsWQAwHoAZFnGK6+8MicVx+RnOymY0U26JUTT6ZSlut7Lgi6z0ky9Vy+L0sseC6Pqvc3qALBDKpPJwOFwVLxPLNtkpnVmlMCTnrgO07HQm1Tpp3VlFkOfO+64A4cOHcKyZctw4403znn88OHD+MIXvgCe57Fx40ZceeWVBlyl8Rif92wRNuY3OjqqpOLYWJjT6YQoiso4kpk2JTXdNh7RGwvT6oE3mrLUGo+YqSNZS7NCNI14rnerx8JKAk/5fB7ZbBbDw8MIBAIVIkRaAyMjeyyMNvRpxsDI7XYrY79WX1dmGvO79NJL8cY3vhG/+MUvdB+///77ccMNN2DhwoX42Mc+htWrV2PRokU9vkrjsWQAoG7yY57qXq8XkiQhFospEXc2m0Uul+uoW1in6LXxSC1xHa1bmDZlWU2Ixoy0K0TTS3Edqws81TIwYj0WvZ5UMaPAUzVxnXQ6rTQtDwwMKD1MRh+eeljN0Gfp0qV4+umndR9LJBIQRREnnngiAGDdunXYv38/BQBWgX3gmMyvKIooFAr44he/CFmW8Vd/9Vdwu90Vs6pssbXjFtYpzGA8Uk1cJ5vNVriFOZ1OpFIp+P3+njrktUI3hGjqieu00mNh1XpvLUOfegZGvZhUsYLAEwu0OY5DoVBQ3g/2mWI3NI0YGPWKWuvKioY+yWRScfEDZh39Dh06ZOAVGYclAwD1B83hcKBYLOKOO+5AsVjE3//93yvZABYosO5fvZRlrxebGRsTq6Us8/k8eJ5X6r3MhMZsC10tRNPNeq86i6KeKGBjW7IsV2Sb9LID6npvNxzyOom63jsyMtJwtkOdRWHZARaIq7MonZxUMeO6qoZef9Lg4GBVNVD1+9TLn6veurKKoQ9RHUsGAAyHw4Hjx49j165dWLBgAW6++WblgFJnB1jDoNEpS6vUe9mGLAgChoeH4XQ65+iBd8reuF2MrPdyHFfXc13dY8GkUlnvitnrvawxsV1Dn0Z6LNqZVLHKugJqCzw1csPSq0kVbWOidl15PB64XC5LHv7hcBiJREL5eyKRQDjcWTdMq2DpAODYsWO4+eabsXHjRrzzne+c80FkdyIsSmXBAPvQNpKy7NRi0xPMMCPV6r16euDZbLaiB6PX2QF1Y6LR9d56PRYMp9NpOllXLd009Ol0j4WV1lU6nUY+n2/Y0MeoG5Z660p9k2W1wx+Aoltx5MgRLFy4EPv27cPWrVuNvixDsOQYIEMURUxOTmLVqlU1n8d+RPaBZXPgLBjQvgW1xHWaXWzqem+3nbzapVkhGj098F6NhVlJiIbdoaozK2bIouiNARopRKPusWA6FtUmVfp5XTXyet1SA623rszU6V+LT37yk3jxxRdRKpUwODiIbdu24dvf/ja2bt2KSCSC559/Hl/60pfA8zwuuOACXH311UZfsiFYOgBoFVmWIcuyMk2gzQ5on9uq53o9wQwzwRY+gJbvUNXvk9pzvdMpSyvWe9VCNJ3wXO8E6gDgmuXDpjP00RoYAahomuN53hbrqh6dUgOtta7I0Kc/sWUAoEYbDDSaHagnrtOs8YiRdEOIRp2yLJVKHUtZsrtpKwg8NWLoU8/AqJvZgSeOFVAWZbidwIm+sqkNfVh2oFAoIJ/PQ5ZlZVKlWQOjXmGEwFOrNyy11hUr81k15U9Ux/YBgJZq2QFBEOYEA9VSlm63W7kDtkp6upsNVJ1KWVqt3lsoFJo+ULXvU7fHwqxq6BMKhSq0+Bs1MOoVZmlMbOSGpda6suKYH9E4FADUoNnsgCiKyOVyyGazADAntWu2LIBRQjTNpizrGY+YCbXAU7v1Xr0ei06OhTGBJ47jTB+o1mpM1JPeNbLHwqwCT3o3LBzHQZIk5VrV7xO7mWF7INF/UADQBKw/gAUELBhgY4a//vWv8fjjj+Paa6+dI5XKsgNmSFmaSYimXsqSSaVaqY+iW42Jneyx4Hke8Xi8ow553aJZgSejeizMtK7qwRoTS6WSYu504MAB/O53v8Py5cuxevVqRKNRAFTv72coAGgRbXbgu9/9Lh566CG8733vw5o1a+Y8v5pbWK9TlmY3HtGmLJmgUzAYhN/vN10WhdHrxkRtj4UkSQ0bGNnJKKtXPRZmX1dq9PqTZFnGkSNH8Oijj+Kpp57Cyy+/jMWLF2NiYgIrVqzASSedZOoMEdEaFAC0iSRJ+Ld/+zfs27cP27Ztw+mnn66bHVBjVMpSXe+NRqOmPUyB1xqoWA2yk57rncboem8zPRasMbGf+yhq0Y0eCyuuq2oCT2zML5lM4uDBg5icnMTBgwfh8Xhw5513IhQKGXTlRDegAKANisUi7r77brzyyivYsWMHxsbGAOj3DjALY+2YIdCblKXa0CccDps6mq9W7602FmZkj4UZ673V7I2B2WAlGo2avo+iF42J9XosGvm+6sZEq64r4LVOf2Buyl8QBLzwwgs47bTTenq9RPehAKANisUiHnjgAbztbW+rWZtkAYFWophZGmuf2+mUJVv4Vqr31jtQ1dK7RvRYWKXey94n5pAHoG3p3W5ilMATa/BtpseiX9aVmTr99+/fj71790KWZWzevBmbNm2qePzjH/+4IrMNALfeeqspx1atgnlnfiyAz+fD+9///rrPU9sXs7+zA0orQqQ25gEqU5aZTKbplKVV6r1q45FGDtRa0rvMc71bPRaNOuSZAUmSkE6n4XA4MD4+DofD0TV743YxUuDJ4XDUlN7V9lgIgtAX68pMhj6iKGLv3r3YuXMnAoEAbrrpJqxevRrBYLDiedu2bbOldW83oACgx6iDAVYmcDqdyriNnoGRy+VSvMRruYVpU5aNCNGYgXrGI41Qzd640wZGrTrkGUE1IZpa9sZG9VgY3UehpZ5XCAAlg2JW6q0rNuZnhsMfAA4fPowFCxYoVr0TExM4ePAg1q9fb/CV9S8UABiInqxmLQMjtVuY2rZXzy2sWCy2daD2im4Y+lSzNy6VSkp2oJUeC3W91+xz88x6t5ahj9beWN1jEY/HK95Hn8/XtZ/X7AJPbE26XC6Iogie5zE4OAhRFLtmb9wuVjT0SSaTyuEPzEonq137GHfffTc4jsOGDRtw2WWX9fIS+w4KAExEveyA1q+gWsqSLRqPx6OIfZhR4U1d7x0ZGenaRuR0OjEwMKBkUdj7pM4O1Oux6Jd6by20WRQ9e+NO9lhYVeBpdHRUWU/dsDduF7XAk966soqhjx7XX389IpEI8vk8br/9dsyfPx8rVqww+rIsi/lOBQKAfnaA4zhF/U3P3rhQKOCRRx7BmjVrFKlUrb1xu25hnYIJ0Xi93p7We2v1WLAsirbHQs/Qx4x0sjGx2z0WVuqjUAeqo6OjFT9rp+2N26XWumKZL/XfzUQ4HK64408kEli8eHHFc1iGIBAIYO3atTh8+DAFAG1AAYBF0GYHgNkMAMsOHDlyBLfddhtOPPFEXHrppcpBxrIDLLVrhpSlmRoT6/VYOJ1OiKKIUCjUkAqdUajrvd04UDvZY2GlPopmGxM5jpvTY6HNonSrx6LWurKCoc/ixYtx9OhRJBIJBAIBTE5OYsuWLcrjTGo9FApBEAQcOHAAGzduNPCKrQ+NAfYBTz/9ND7zmc9g48aNeNe73lUhRNRJe+NOYBUhGkmSlAPV5XKB5/mu2Ru3i9FCNCyL0oiOBeujcLlcukI0ZqLTjYnV1EA70WNRa12ZqdO/Ho899hi++tWvQpIkbN68GW9605uwa9cubN26FYFAADt37lT2tZUrV+Kaa64xzTq0IhQAWJxf/vKXuPfee/Ge97wHmzZt6pq9cbtYsd4rCIIiRNOO9G43MZvAk564DnufOI5DKpVCIBCo2phoFrot8KSnBsp6LJqZVKm3rsjQh6gFBQAWplQq4eabb8Y73/lOnHPOObrP0QYE9SSK9cR12k1Zquu97TrkdZtGhGiqZVF63WNhBUMflh3I5XIQBAEOhwN+v79r9sbtYpTAk54aaL0eC2boU8150uPxKAG8GT8bhPFQAGBxmknrtWJvrL6bayVlqWc8YlZaFaJp1t64E5ipj6IWaiEa9p6yg64V6d1uX+vMzIwil2xUY2I1NVD1+8TKPnrrijVwsvVp1s8GYTymCwBKpRLuvPNOTE1NgeM4XHTRRbjkkkvmPC+dTuNzn/sc4vE4Fi1ahOuvv97U8+5mpJ69sZpaKctqY2HVhGjMSKfqversAPNc73SPhRUFnrSGPnoGRkb2WLBAVZIk0xn6aN8nFry7XC7dw1/93pl5zRHGY8oA4IUXXsCZZ56JYrGIj3zkI/jIRz6CefPmVTxv7969GBsbw8UXX1zxZ6I1qhkYsXFDLfVSlqzZq5YQjVnophCNJEkV71M7PRbMIS+fz1uij6IZQ59aWZRu91hYydCHBarMwpfneTz00ENYvHgxVq5ciVNOOWWO9DhBVMN0xViv14szzzwTwKxgxfz585FMJucEAPv378euXbsAABs2bMADDzxAAUAbaDcNWZbhdDqVZiR1qUCSpLriOsDs78/Mnf69aEzkOK6uuE4jPRbqeq9aiMaMtCLwpB2f0xPX6UaPhdUFngqFAk499VQ89dRT+O53v4tIJIKJiQlMTExg6dKlpg4SCeMx7y4CIBaL4ciRI3j9618/57F8Pq8cLtUkI4nWUW/aLDPADiitvTG7+3e73XjllVcQCoUQCAQgiiJisVhX7I3bxQghmlriOrWkd9mBCqDv5ub16JW4Tj8IPPn9flx++eW4+uqrkc/n8eyzz+Lxxx/H/fffj5mZGbz97W/HX/zFXxh49YSZMSwA2L59+5wZdQC4+eabEYlEwPM8du/ejXe9612mtVq1C3oiRFoDo1KphH/5l3/BM888g0996lOKaI56LCydTleMhbEGuV5vvGYRoqkmrpPNZpXsgMfjQaFQgNvtRiQSMe0hBXSvMVFPXEdtYNRKj4UV+yj0AlXmUSDLMgKBAFasWIEVK1ZAlmX86U9/MvXnhTAewwKAO+64o+pjsizjnnvuwcTEBNasWaP7nEAgoGQBEokEwuFwty6VUKHXXFQsFrF7925MTU3hlltuQTgc1jUwGhoaqhif0xoY9aLxy6yGPnoGRtlsFrlcDsDsnXUqlTJVFkVNrwx9ahkYseyA+vOkDe5YH0WhULCUUdbo6Oicn0X9OdCuG4fDgQULFvTkOvfv34+9e/dClmVs3rwZmzZtqnj88OHD+MIXvgCe57Fx40ZceeWVPbkuoj6mLAE8+OCD8Hq9FTKQWlasWIGHH34YF198MR5++GGsXLmyh1dIMBKJBG677TYMDQ3h05/+tHI3W8veuJrnOhsL65a4Dqv3WqExsVQqIZ/PKweqnoFRJ+yN28VogadmDIycTidSqRR4nu+Y82S3UAs8mdnQRxRF7N27Fzt37kQgEMBNN92E1atXIxgMKs+5//77ccMNN2DhwoX42Mc+htWrV2PRokWGXTPxGqZbAfF4HN/5znewYMECbN++HQDwjne8A+eccw6+8Y1v4JRTTsGqVatwxRVX4M4778T3v/99LFy4EG9/+9sNvnL7IUkSPv3pT2PJkiX4P//n/ygbaqP2xvU81zvZ+MUaqILBoOk1/fXqvdUMjLLZrKLz3mtxHbMZ+tQzMGI6+MFg0HQZFDW1BJ7MZuhz+PBhLFiwQDHpmZiYwMGDB7F+/XoAszcIoijixBNPBACsW7cO+/fvpwDAJJguAIhGo3jooYd0H3vb296m/DkUCuGWW27p1WUROnAch5tuugmjo6NV/eabsTdmjV+dNjDKZrOKEI2V672MegZG2vepG4eEWfooasGyA263G+VyWfl85XI5pFIp02RR1DRi6AMYf/AzksmkcvgDcxuy9R4/dOhQT6+RqI7pAgAzcN999+E3v/kNotEobrvtNt3n7NmzB88884xyoNx4441zRhXtwNjYWEPPa8XeuN5YWL3Gr36q91bDiB4LtcCTVQ191DoWzN7Y6EmVWo2JVjL0IawDBQA6rF+/HhdeeCHuvffems9773vfS70HLVLP3rhadqDRsTD1gWr2eq+6MbHRuflqdLvHwkp9FLUaE+vpWGiNnrr5czZj6GO2wz8cDlfc8ScSCSxevLjm49SwbR7MuysayOmnn46pqSmjL8M26CmX6WUHBEFQNsF6Y2HsTslssq5auilE0+keCz0hGjOi7qNopDFRPYEBVPZYZDKZrvZY1BN4YoY+ZnXzW7x4MY4ePYpEIoFAIIDJycmK5m02aXPkyBEsXLgQ+/btw9atWw28YkINBQBt8NWvfhX//u//jomJCbz97W83XXRuVZrNDqjHwli9n+M4iKKIqampmmNhRtJLIRq1uE6zPRZqQ59eOuS1QicaE5vpsWgns1RL4Mkqhj5OpxPXXnstbrnlFkiShM2bNyMYDGLXrl3YunUrIpEI3ve+9+Guu+4Cz/O44IILqAHQRJjOC6BX1BMimpqawmc/+9mqPQDJZBLDw8PgeR579uzBGWecQVLEPaCWgdHk5CT+9V//FX//93+P+fPnA4CuvXEtA6NeYSYhmmr2xuyOt1AooFQqzTH0MRu9cJ5Uv0+lUqnlHotaRllk6EP0CttmAGoJETUCq2N5PB5s3LgRjz76aCcui6hDNYniX/3qV7j//vtxzTXXYNGiRYqBUa2xsEY81zuNGRsTq0nvFgoFZLNZAFAMnpxOp6myKIxeCTzV6rGQJGlO74AetZwnWbnBrCl/or+wbQDQLslkEuFwGJIk4bHHHsPChQuNviTbwTbIhx56CD/84Q+xfft2nHXWWVXtjatJ7/ZKXEeSJMzMzJheiIbjOHg8HmQyGbjdboRCIZTL5aYNjHqFUYY+rfRYsMZEvT4Kp9MJj8djymY/oj+xbQmgFnv27MHBgweRyWQwNDSEd7/73Vi7di2+9KUv4aKLLsIpp5yCW265BZlMBrIsY8mSJXj/+99vuBCKHfniF7+Ip556Cjt27KiQPq1mb6zuHVCjZ2/cybEwtUOemSSI9aglRKOW3i0WiwBgaI+FWuDJTIY+evbGTqcTgiBgeHh4zlQCG/Oz853/HXfcgfHxcVx77bXK177//e/j17/+NT7xiU/Y9n3pJhQAEJbmsccew+LFizE8PFz1Oewjzu6s1BLFzN5Y+3yW2i2VShAEoa2xsE445PWKZgx91NK7RvRYWEXgiWV+WM+AIAh46qmnMDU1hXPOOQfLly83ncCPESQSCXz4wx/GP/7jP+Kkk05CPB7H9u3bccstt1CGtUtQAEDYDm12QCtCpEU9FsY28UbHwmrVe80Ga0xs1dBH3WPBsijd6LFQ91FYoTGR6VGwkVRJkvDkk0/il7/8JZ555hnIsoyzzz4bExMTOOecc2oGs/3Oj370I/zsZz/Drbfeis985jNYtGgRybx3EQoATEojaoTHjh3D7t27kcvlcNZZZ+G6664z9QFjRmplB1jvgPb56tRurbGwWvVeM9ENQx91j0WxWFSyKO32WLC7aUEQEIlETNtHAVQa+oTDYV1DH0mS8Ic//AEHDhzA5OQkXnjhBZx88sm45ZZbTB3YdAtZlvGP//iPCAaD+NOf/oTPfOYzVFrtIhQAmJRnn30WHo8H9957b9UA4M4778Qb3vAGrFy5suLPROs0kx1gmgTasTDWxc3uUM0+N8+EaKLRaNcO1E70WFipj6JWY2ItQ590Oo3nnnsO5557bk+v10y89NJL2LZtGz760Y9i+fLlRl9OX2Pe8Nnm1FMjlGUZzz//PLZt2wYAuOCCC7B//34KANqkWQMj7VgY0+BnY4i5XE7JEpjtblUURUUEqNuGPu1K7/ZLH4V6zE8vgAmFQj0//A8fPowvfOEL4HkeGzduxJVXXjnnOb30PmHNvK973eu68vrEa5hrRyIaJpPJVGwuWhcuon2aNTDK5/P48pe/jEsvvRQnnXSSUi7ohr1xu9QSouk2taR3mYGR+n1ih7+V+iisZOhz//3344YbbsDChQvxsY99DKtXr9ZV6yPvk/6DAgADqadGSJiLWhLFU1NT+NSnPoVwOIz58+crd/vdsDduF3YNZjH0qSe9CwB+vx+BQMDwa62GtjGxmqGP2Q7/RCIBURRx4oknAgDWrVuH/fv3k1yvTaAAwEDaUSNkuvcsTU0uW71FHQy88MILuP3227Fy5Uq8733vUzb6RuyNWaNgI/bGncDshj7q3gBg9o46EAhAEARMTU11xd64XdR9FHoCT2pDHzMd/sCsoJn6ZiMSieDQoUO6zyXvk/6DAgCL4nA4sGTJEjz++ONYuXIlHnnkEWzYsMHoy7Idjz32GO6++25cddVVuOyyyxTRIaCz9sbtonbIs5Khz+joqNIF3qr0bjdRNyaOjo5WHIpmMvSplm18//vf39C/v+aaayq8T370ox911fvkoYce6tprE69BAYBJUasRfvCDH9RVI3zHO96B3bt34ytf+QqWLVuGFStWGH3ZtuPo0aP4q7/6K6xZs0b5mtbemN35sd4BrUSxnr1xsVjsmPSuLMtIpVIoFostO+T1CrWhj7YxUU96t1gsGtZjwfoo9BoTzWboUy3bmEgkKnqHqmUSyfukP6ExQILoIXoSxersgHY5VhPXYQddvTSsnhCNWWGGPi6XC8PDw02lmPWkd7vZY9GooY8V0uQf+chH8P/9f/+f0gS4devWOT0Aau+T++67D/PmzcNb3vIWg66Y6BQUABC6mG00qF+pZW+sJ0KkFddh0rt64jr1hGjMBJub70Rjojo7UCwWwfN8R3ss+s3Q5/nnn8eXvvQl8DyPCy64AFdffTUAkPeJDaAAgNBlx44d+OAHP1jzrmDPnj1Ys2YNjQZ1iGoGRqyhUEstcR2O45BMJnvukNcKakOfwcHBjr++JEkV75Msyy0ZGNXro2B9HXY29CGsBfUAEHOg0SBj0PYOALN3lOzOXl0qkCSpqrgOEyJyOp2K+Uw37I3bRZZl5HK5rhv6cBxXYQPdSo8Fa0wslUq6fRTqTn+zvc8EUQ0KAIg50GiQOdBTJWQHlNbemPUG/PrXv8aJJ56IkZEROBwOFItFZLNZpS7diIFRL1DPzUej0Z7p3rPOfPb91D0W8Xhc18CINSZKkoTR0dE5GQO1lDEd/oSVoADAxlhtNMjO6IkQqSWKBUHAv/7rv+JXv/oVbrzxRiWVXk1cR9sg18uDS92YqDc330ucTmdFdoD1WDCJYrfbrWRT9JoofT6faTr9CaJZKACwMTQaZE20B065XMaePXvw+9//Hp/4xCewcOHCinIB6w1g/QCsQY75FvRSXEc9Nz8yMmJ4JkKNesIiFAopvQkcx0EQBBw7dgw/+MEPsHTpUpxzzjmIRqPKvyMIK0IBADEH5rR25MgRLFy4EPv27cPWrVvnPE89GvTYY49h4cKFBlytvUmn0/inf/onOBwOfPrTn0YwGARQmR3Q2htrDYzU4josO9ANcR2rGfqwxkTW6Z9KpeDxePCf//mfuO+++3D66adjxYoVmJiYwIIFC0z98xCEHjQFQOhCo0HW4IEHHsDx48fxN3/zN7p19GbtjdXGPOVyuWPiOmxuXs8hz2w0Yuhz7NgxPPHEE3j88cfx1FNPIRQK4ZprrsH69esNumqCaB4KAAjCwgiCAI7jGkqls6XOZtTriRB1SlyHzc0PDQ0hEAg0/0P2CK2hjzagqmboUy6XcejQIUQiEZqUISwFBQCEadi/fz/27t0LWZaxefNmbNq0qeLxRsSJiMZpJTvAegdYdqCWuI4sy8hkMsjlcroOeWZCkiTMzMyA53lEo9Gqhj6AeWr+9913H37zm98gGo3itttu033OsWPHsHv3buRyOZx11lm47rrrTHP9hPGYpwOHsDWiKGLv3r3YuXMn/umf/gnf/e53kclkKp7DfMvvuusuTE5O4o9//KNBV9sfOBwOcBw3x8DI5/PB7/crhx4LEJh50cjICObNm4dgMAhRFJFMJnHs2DEkEgnk83ml12BmZgaFQgEjIyOmPvyZYiIb81Mf/qwx0GyHPwCsX78eO3bsqPmcBx54AFdddRU+//nPI5PJ4PHHH+/R1RFWgAIAwhQcPnwYCxYsQCQSgc/nw8TEBA4ePKg8rhYn4jhOESciOoM6GGBwHAePxwO/3z9HJIcZGIXDYYyPjyMajcLtdiOXy+H48eM4duwYSqUSQqGQoWN+9eB5HrFYDC6XC9FodI6bn5ln/E8//fSayomyLOP5559XTMIuuOACWjNEBeZdmYSt0BMfUo8iNiNORLSPnu5ALXtjJq5TKBSU5zqdTszMzABAS9K73aZRQx+zHfyNkslkKhoutWuKICgAIAiiJo3aGz/11FP4/Oc/j6uuugobN25USgt60rssIGjF3rgT1GpMNJOhTzWxrptvvrkiICaIVqAAgDAF4XB4jvjQ4sWLaz6uJ05EdB/1ocjukB9//HHs2bMHV1xxBd785jcrQUEt6d1sNqsrvdtN1IY+eo2JakMfow9/oLpYVyMEg0Fks1nld0RrhtBi/CecIAAsXrwYR48eRSKRQLFYxOTkJM4++2zlcbU4kSRJ2LdvH1atWmXgFROM73//+/jCF76AG264AZdffrnSOxAIBBQxIXaXz6R3I5EI5s2bh3A4DKfTiUwmg2PHjiEWiyGTyYDn+Tljie3CGhPz+bxuY6LH44HL5bJ02l+Nw+HAkiVLlMa/Rx55hJw7iQpoDJAwDY899hi++tWvQpIkbN68GW9605uwa9cubN26FZFIpKo4EWEcP/zhD/Ef//Ef+MhHPoKTTz654rFq9sZ6Y4ZAbXvjdrMDzNBHlmVEIpG+MPTZs2cPDh48iEwmg6GhIbz73e/G2rVrK8S6XnnlFezevRv5fB7Lli3DddddZ4rMBmEOKAAgCKJlcrkc8vk8RkdHaz5PT4SI6Q4we2Pt85lEcalUgiAI8Hg8FRLFjR7UgiAgkUjA6XQiHA7POQDJ0IewKxQAEMSr1BMi+vjHP640sQHArbfe2jMb236jGREiABUSxaVSCU6nsyF743K5jEQioRghqQ94lmFgfyYIu0FNgASBSiGiQCCAm266CatXr1bMdRjbtm0judcOoB0zdDgcdQ2MXC5XQ/bGTHeAufkxQx/1IW+mTn+CMAoKAAgClUJEABQhIjJ36T566XemPOjxeOZkB9T2xqFQqKJ3gNkbO51OlMtlDA8PzxnzY4Y+Wk1/grAbFAAQBOoLETHuvvtucByHDRs24LLLLuvlJdqGetkBbalAbW/M8zxSqRTK5TI4jkMqlcJvfvMbFItFrFixAgsWLNA19CEIO0IBAEE0yPXXX49IJIJ8Po/bb78d8+fPV2RWie6glx1QixBJkgRBECBJEnK5HPbs2YMlS5bgz//8z+F0OiEIAhwOB37729/im9/8Jk444QSsWLECExMTOP30000tU0wQ3YZCYKLn5PN5bN26FQcOHFC+9qc//Qnvfe97cezYMUOuSU9oSKu0xv4eCASwdu1aHD58uKfXSOgbGLndbhSLRdx2220oFAp405vepCgMut1uvOENb8CnP/1p3Hvvvbj66quRyWRw99134/3vfz8++9nP6jYdEoQdoPCX6DmBQADvfe978S//8i/47Gc/C4/Hg3vvvRdvectbMG/ePEOuSS1EFAgEMDk5iS1btiiPi6KIXC6HUCgEQRBw4MABbNy40ZBrJWZhpYKXXnoJu3btwmmnnYYPfOAD8Hq9SnZAkiRl5j8YDGLt2rVYu3YtJEnCkSNH8Ic//IFKAYRtoTFAwjBYGn3+/Pn4r//6L9x2222GpmRrCREFAgHs3LlTqT2vXLkS11xzDY2PGcyzzz6L2267DZdeeimuuuqqit4BtaKfWX5P9913H37zm98gGo3itttu033Onj178Mwzz8Dv9wMAbrzxRsMCY6K/oQCAMIxYLIbt27cDAHbs2IFTTz3V4CsirMbU1BSeffZZbNiwwehLaYhnn31WyXjVCgDWrFlDsr1E16ESAGEYkUgEkUgEgiBUGP8QRKOMjY1hbGzM6MtomNNPPx1TU1NGXwZBAKAmQMJAfvCDHyAQCMDv9+N//ud/jL4cw7njjjvw3ve+F3feeafu44cPH8a2bdvwoQ99CN/61rd6fHVEL/nqV7+K7du348EHH6QmRaJrUAaAMITp6Wl8+9vfxic+8QnwPI9PfepTOO+88zA8PGz0pRnGpZdeije+8Y34xS9+ofv4/fffjxtuuAELFy7Exz72MaxevZpUCU3I9u3bdQ/tm2++ec5kiR7XXHMNhoeHwfM89uzZgx/96Ee4+OKLu3GphM2hAIAwhPvuuw9/9md/hoULFwIANmzYgK985Sv427/9W2MvzECWLl2Kp59+WvexRCIBURRx4oknAgDWrVuH/fv3UwBgQu644462/n04HAYwa0+8ceNGPProo524LIKYA5UAiJ6zb98+vPLKK3jrW9+qfO3tb387nnvuuQptAOI1GlUqJKxPMpkEMGth/NhjjylBMkF0GsoAED1n3bp1WLduXcXXfD4fvvjFLxp0RQTRG/bs2YODBw8ik8nggx/8IN797ndj7dq1+NKXvoSLLroIp5xyCu6++25kMhnIsowlS5bgkksuMfqyiT6FAgCCsAB6SoUsVUxYh7/+67/W/foHP/hB5c87d+7s1eUQNodKAARhASKRCDiOw5EjRyBJEvbt24dVq1YZfVkEQVgYEgIiCJPwyU9+Ei+++CJKpRIGBwexbds2fPvb38bWrVsRiUTw/PPP40tf+hJ4nscFF1yAq6++2uhLJgjCwlAAQBAEQRA2hHoACIIAMDu+dujQISxbtgw33njjnMc//vGPY2ZmBm63GwBw6623wuPx9PoyCYLoEBQAEAQBoL4QEQBs27aNtAcIok+gJkCCIADMChExBzqCIPofygAQBNEwd999NziOw4YNG3DZZZcZfTkEQbQBBQAEQTTE9ddfj0gkgnw+j9tvvx3z58/HihUrjL6srlIqlXDnnXdiamoKHMfhoosu0hXmSafT+NznPod4PI5Fixbh+uuvp/4IwvRQCYAgiIZgUsSBQABr167F4cOHDb6i3nD55Zdj9+7d+PSnP43//u//xrFjx+Y85z//8z9x3nnn4e6778bY2Bh++tOfGnClBNEcFAAQBFEXURSRTqcBAIIg4MCBA7bQqPd6vTjzzDMBzMpVz58/X9HqV7N//35s2LABwKyx1f79+3t6nQTRClQCIAgCQKUQ0Qc/+MEKIaJAIIBbb70VoihCkiSsXLkSa9asMfqSe0osFsORI0fw+te/fs5j+XwegUAAABk1EdaBAgCCIAAAH/vYx+Z8bceOHcqfb7/99l5eTk/Zvn07JEma8/Wbb74ZkUgEPM9j9+7deNe73gWfz2fAFRJE56EAgCAIUxCLxXDPPfcglUrB6XRiy5YtWLt2bcVzjh07ht27dyOXy+Gss87CddddB4fD0fb3vuOOO6o+Jssy7rnnHkxMTFTNegQCASULQEZNhFWgHgCCIEyB0+nEe97zHnzuc5/DRz/6UXzlK19BsViseM4DDzyAq666Cp///OeRyWTw+OOPd/26HnzwQXi9XmzZsqXqc1asWIGHH34YAPDwww9j5cqVXb8ugmgXCgAIgjAF4XAYJ510EgBgeHgYoVAI2WxWeVyWZTz//PPK6OEFF1zQ9Wa7eDyO73znOzh8+DC2b9+O7du348CBAwCAb3zjG3jssccAAFdccQUeffRRfOhDH8KxY8ewadOmrl4XQXQCKgEQBGE6fv/730OSJIyMjChfy2QyGBwcVFL+vWi2i0ajeOihh3Qfe9vb3qb8ORQK4ZZbbunqtRBEp6EMAEEQpiKbzeKee+7BBz7wAaMvhSD6GgoACIIwDTzP44477sDll1+O0047reKxYDCIbDYL5mBOzXYE0R4UABAEYQpkWcaePXuwdOlSRVRHjcPhwJIlS5TGv0ceeYSa7QiiDRwyC6cJgiAM5Nlnn8XOnTsr7IY/9KEP4Qc/+AEuuuginHLKKXjllVewe/du5PN5LFu2DNdddx04ju5jCKIVKAAgCIIgCBtCoTNBEARB2BAKAAiCIAjChlAAQBAEQRA2hAIAgiAIgrAhFAAQBEEQhA2hAIAgCIIgbAgFAARBEARhQygAIAiCIAgbQgEAQRAEQdgQCgAIgiAIwoZQAEAQBEEQNoQCAIIgCIKwIRQAEARBEIQNoQCAIAiCIGwIBQAEQRAEYUMoACAIgiAIG0IBAEEQBEHYEAoACIIgCMKGUABAEARBEDaEAgCCIAiCsCEUABAEQRCEDfn/AfodsjjnZQRxAAAAAElFTkSuQmCC",
+                        "text/plain": [
+                            "<Figure size 640x480 with 1 Axes>"
+                        ]
+                    },
+                    "metadata": {},
+                    "output_type": "display_data"
+                },
+                {
+                    "data": {
+                        "text/plain": [
+                            "PyPlot3D backend, t = 0.05, scene:\n",
+                            "  robot: Text(0.0, 0.0, 'Two-Link Manipulator')"
+                        ]
+                    },
+                    "execution_count": 4,
+                    "metadata": {},
+                    "output_type": "execute_result"
+                },
+                {
+                    "data": {
+                        "text/plain": [
+                            "<Figure size 640x480 with 0 Axes>"
+                        ]
+                    },
+                    "metadata": {},
+                    "output_type": "display_data"
+                }
+            ],
             "source": [
                 "import math\n",
                 "import roboticstoolbox as rtb # load the robotics toolbox\n",
@@ -204,7 +295,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": null,
+            "execution_count": 8,
             "metadata": {},
             "outputs": [],
             "source": [
@@ -227,8 +318,8 @@
                 "\n",
                 "    # Denavit-Hartenberg parameters for the 3 DOF robot\n",
                 "    # end effector: H10 * H21\n",
-                "    H10 = dh(t1 + 40, 1000, 0, 0)\n",
-                "    H21 = dh(t2 + 10, 1000, 0, 0)\n",
+                "    H10 = dh(t1,0, 1, 0)\n",
+                "    H21 = dh(t2,0, 1, 0)\n",
                 "    # the tool has a length of 100 mm\n",
                 "    Htool3 = dh(0, 100, 0, 0)\n",
                 "\n",
@@ -267,12 +358,21 @@
         },
         {
             "cell_type": "code",
-            "execution_count": null,
+            "execution_count": 10,
             "metadata": {},
-            "outputs": [],
+            "outputs": [
+                {
+                    "name": "stdout",
+                    "output_type": "stream",
+                    "text": [
+                        "[  1.40883205   1.40883205 100.           0.          -0.\n",
+                        "  50.        ]\n"
+                    ]
+                }
+            ],
             "source": [
                 "# Test of the function dk(t1, t2)\n",
-                "pose = dk(0, 0)\n",
+                "pose = dk(40, 10)\n",
                 "\n",
                 "# print the end effector pose\n",
                 "print(pose)\n",
@@ -421,7 +521,7 @@
             "name": "python",
             "nbconvert_exporter": "python",
             "pygments_lexer": "ipython3",
-            "version": "3.10.6"
+            "version": "3.8.10"
         }
     },
     "nbformat": 4,

From 485f6e49c3ab8935fa907eec526388f10bd46b5a Mon Sep 17 00:00:00 2001
From: pinbal-wizard <thomwilliamspencer2@gmail.com>
Date: Wed, 17 Apr 2024 14:21:01 +1000
Subject: [PATCH 2/2] Left outputs in

---
 3-kinematics/02-direct-kinematics.ipynb | 116 ++----------------------
 1 file changed, 8 insertions(+), 108 deletions(-)

diff --git a/3-kinematics/02-direct-kinematics.ipynb b/3-kinematics/02-direct-kinematics.ipynb
index 7fe9e7a..2f43b69 100644
--- a/3-kinematics/02-direct-kinematics.ipynb
+++ b/3-kinematics/02-direct-kinematics.ipynb
@@ -71,7 +71,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 2,
+            "execution_count": null,
             "metadata": {},
             "outputs": [],
             "source": [
@@ -141,52 +141,9 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 3,
+            "execution_count": null,
             "metadata": {},
-            "outputs": [
-                {
-                    "name": "stdout",
-                    "output_type": "stream",
-                    "text": [
-                        "Requirement already satisfied: roboticstoolbox-python in /home/conner/.local/lib/python3.8/site-packages (1.1.0)\n",
-                        "Requirement already satisfied: ansitable in /home/conner/.local/lib/python3.8/site-packages (from roboticstoolbox-python) (0.9.10)\n",
-                        "Requirement already satisfied: typing-extensions in /home/conner/.local/lib/python3.8/site-packages (from roboticstoolbox-python) (4.10.0)\n",
-                        "Requirement already satisfied: scipy in /home/conner/.local/lib/python3.8/site-packages (from roboticstoolbox-python) (1.10.1)\n",
-                        "Requirement already satisfied: progress in /home/conner/.local/lib/python3.8/site-packages (from roboticstoolbox-python) (1.6)\n",
-                        "Requirement already satisfied: matplotlib in /usr/local/lib/python3.8/dist-packages (from roboticstoolbox-python) (3.7.5)\n",
-                        "Requirement already satisfied: pgraph-python in /home/conner/.local/lib/python3.8/site-packages (from roboticstoolbox-python) (0.6.2)\n",
-                        "Requirement already satisfied: spatialgeometry>=1.0.0 in /home/conner/.local/lib/python3.8/site-packages (from roboticstoolbox-python) (1.1.0)\n",
-                        "Requirement already satisfied: spatialmath-python>=1.1.5 in /home/conner/.local/lib/python3.8/site-packages (from roboticstoolbox-python) (1.1.9)\n",
-                        "Requirement already satisfied: numpy>=1.17.4 in /usr/local/lib/python3.8/dist-packages (from roboticstoolbox-python) (1.24.4)\n",
-                        "Requirement already satisfied: swift-sim>=1.0.0 in /home/conner/.local/lib/python3.8/site-packages (from roboticstoolbox-python) (1.1.0)\n",
-                        "Requirement already satisfied: rtb-data in /home/conner/.local/lib/python3.8/site-packages (from roboticstoolbox-python) (1.0.1)\n",
-                        "Requirement already satisfied: colored<1.5.0 in /home/conner/.local/lib/python3.8/site-packages (from ansitable->roboticstoolbox-python) (1.4.4)\n",
-                        "Requirement already satisfied: importlib-resources>=3.2.0; python_version < \"3.10\" in /usr/local/lib/python3.8/dist-packages (from matplotlib->roboticstoolbox-python) (6.3.0)\n",
-                        "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.8/dist-packages (from matplotlib->roboticstoolbox-python) (1.4.5)\n",
-                        "Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.8/dist-packages (from matplotlib->roboticstoolbox-python) (3.1.2)\n",
-                        "Requirement already satisfied: pillow>=6.2.0 in /usr/lib/python3/dist-packages (from matplotlib->roboticstoolbox-python) (7.0.0)\n",
-                        "Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.8/dist-packages (from matplotlib->roboticstoolbox-python) (1.1.1)\n",
-                        "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.8/dist-packages (from matplotlib->roboticstoolbox-python) (24.0)\n",
-                        "Requirement already satisfied: python-dateutil>=2.7 in /home/conner/.local/lib/python3.8/site-packages (from matplotlib->roboticstoolbox-python) (2.9.0.post0)\n",
-                        "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.8/dist-packages (from matplotlib->roboticstoolbox-python) (0.12.1)\n",
-                        "Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.8/dist-packages (from matplotlib->roboticstoolbox-python) (4.49.0)\n",
-                        "Requirement already satisfied: pre-commit in /home/conner/.local/lib/python3.8/site-packages (from spatialmath-python>=1.1.5->roboticstoolbox-python) (3.5.0)\n",
-                        "Requirement already satisfied: websockets in /usr/lib/python3/dist-packages (from swift-sim>=1.0.0->roboticstoolbox-python) (8.1)\n",
-                        "Requirement already satisfied: zipp>=3.1.0; python_version < \"3.10\" in /usr/local/lib/python3.8/dist-packages (from importlib-resources>=3.2.0; python_version < \"3.10\"->matplotlib->roboticstoolbox-python) (3.18.0)\n",
-                        "Requirement already satisfied: six>=1.5 in /usr/lib/python3/dist-packages (from python-dateutil>=2.7->matplotlib->roboticstoolbox-python) (1.14.0)\n",
-                        "Requirement already satisfied: nodeenv>=0.11.1 in /home/conner/.local/lib/python3.8/site-packages (from pre-commit->spatialmath-python>=1.1.5->roboticstoolbox-python) (1.8.0)\n",
-                        "Requirement already satisfied: identify>=1.0.0 in /home/conner/.local/lib/python3.8/site-packages (from pre-commit->spatialmath-python>=1.1.5->roboticstoolbox-python) (2.5.35)\n",
-                        "Requirement already satisfied: cfgv>=2.0.0 in /home/conner/.local/lib/python3.8/site-packages (from pre-commit->spatialmath-python>=1.1.5->roboticstoolbox-python) (3.4.0)\n",
-                        "Requirement already satisfied: virtualenv>=20.10.0 in /home/conner/.local/lib/python3.8/site-packages (from pre-commit->spatialmath-python>=1.1.5->roboticstoolbox-python) (20.25.2)\n",
-                        "Requirement already satisfied: pyyaml>=5.1 in /usr/lib/python3/dist-packages (from pre-commit->spatialmath-python>=1.1.5->roboticstoolbox-python) (5.3.1)\n",
-                        "Requirement already satisfied: setuptools in /usr/lib/python3/dist-packages (from nodeenv>=0.11.1->pre-commit->spatialmath-python>=1.1.5->roboticstoolbox-python) (45.2.0)\n",
-                        "Requirement already satisfied: distlib<1,>=0.3.7 in /home/conner/.local/lib/python3.8/site-packages (from virtualenv>=20.10.0->pre-commit->spatialmath-python>=1.1.5->roboticstoolbox-python) (0.3.8)\n",
-                        "Requirement already satisfied: filelock<4,>=3.12.2 in /home/conner/.local/lib/python3.8/site-packages (from virtualenv>=20.10.0->pre-commit->spatialmath-python>=1.1.5->roboticstoolbox-python) (3.13.4)\n",
-                        "Requirement already satisfied: platformdirs<5,>=3.9.1 in /home/conner/.local/lib/python3.8/site-packages (from virtualenv>=20.10.0->pre-commit->spatialmath-python>=1.1.5->roboticstoolbox-python) (4.2.0)\n",
-                        "Note: you may need to restart the kernel to use updated packages.\n"
-                    ]
-                }
-            ],
+            "outputs": [],
             "source": [
                 "# install the robotics toolbox for python\n",
                 "# this library is helpful for kinematics and dynamics of robot manipulators\n",
@@ -197,57 +154,9 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 4,
+            "execution_count": null,
             "metadata": {},
-            "outputs": [
-                {
-                    "name": "stdout",
-                    "output_type": "stream",
-                    "text": [
-                        "DHRobot: Two-Link Manipulator, 2 joints (RR), dynamics, standard DH parameters\n",
-                        "┌────┬────┬────┬──────┐\n",
-                        "│θⱼ  │ dⱼ │ aⱼ │  ⍺ⱼ  │\n",
-                        "├────┼────┼────┼──────┤\n",
-                        "│ q1\u001b[0m │  0\u001b[0m │  1\u001b[0m │ 0.0°\u001b[0m │\n",
-                        "│ q2\u001b[0m │  0\u001b[0m │  1\u001b[0m │ 0.0°\u001b[0m │\n",
-                        "└────┴────┴────┴──────┘\n",
-                        "\n",
-                        "┌─┬──┐\n",
-                        "└─┴──┘\n",
-                        "\n"
-                    ]
-                },
-                {
-                    "data": {
-                        "image/png": 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",
-                        "text/plain": [
-                            "<Figure size 640x480 with 1 Axes>"
-                        ]
-                    },
-                    "metadata": {},
-                    "output_type": "display_data"
-                },
-                {
-                    "data": {
-                        "text/plain": [
-                            "PyPlot3D backend, t = 0.05, scene:\n",
-                            "  robot: Text(0.0, 0.0, 'Two-Link Manipulator')"
-                        ]
-                    },
-                    "execution_count": 4,
-                    "metadata": {},
-                    "output_type": "execute_result"
-                },
-                {
-                    "data": {
-                        "text/plain": [
-                            "<Figure size 640x480 with 0 Axes>"
-                        ]
-                    },
-                    "metadata": {},
-                    "output_type": "display_data"
-                }
-            ],
+            "outputs": [],
             "source": [
                 "import math\n",
                 "import roboticstoolbox as rtb # load the robotics toolbox\n",
@@ -295,7 +204,7 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 8,
+            "execution_count": null,
             "metadata": {},
             "outputs": [],
             "source": [
@@ -358,18 +267,9 @@
         },
         {
             "cell_type": "code",
-            "execution_count": 10,
+            "execution_count": null,
             "metadata": {},
-            "outputs": [
-                {
-                    "name": "stdout",
-                    "output_type": "stream",
-                    "text": [
-                        "[  1.40883205   1.40883205 100.           0.          -0.\n",
-                        "  50.        ]\n"
-                    ]
-                }
-            ],
+            "outputs": [],
             "source": [
                 "# Test of the function dk(t1, t2)\n",
                 "pose = dk(40, 10)\n",