From 8c915bfa85c603ad3d827d12196fcf381ab2ad8d Mon Sep 17 00:00:00 2001
From: rch <rostislav.chudoba@rwth-aachen.de>
Date: Tue, 5 Apr 2022 18:29:04 +0200
Subject: [PATCH] Summer semester 2022

---
 bending/bending_3pt.ipynb                     |    2 +-
 environment.yml                               |   24 +-
 extras/pull_out_animation.html                | 3640 +++++++--------
 extras/pullout1d.ipynb                        | 3837 ++++++++--------
 index.ipynb                                   |    4 +-
 .../2_1_1_PO_observation.ipynb                |    4 +-
 .../2_2_1_PO_configuration_explorer.ipynb     |    4 +-
 tour2_constant_bond/fragmentation.ipynb       |    4 +-
 tour3_nonlinear_bond/3_1_nonlinear_bond.ipynb | 3991 +++++++++--------
 .../3_2_anchorage_length.ipynb                |    2 +-
 ...t_Damage_Evolution_Damage_initiation.ipynb |  116 +-
 tour7_cracking/7_1_bending3pt_2d.ipynb        |  243 +-
 tour8_rcbeam/8_1_rc_beam_mkapa.ipynb          |   22 +-
 13 files changed, 6042 insertions(+), 5851 deletions(-)

diff --git a/bending/bending_3pt.ipynb b/bending/bending_3pt.ipynb
index ca2150b..7d89e34 100644
--- a/bending/bending_3pt.ipynb
+++ b/bending/bending_3pt.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "id": "adolescent-tunnel",
    "metadata": {},
    "outputs": [],
diff --git a/environment.yml b/environment.yml
index ef56fa2..ba99595 100644
--- a/environment.yml
+++ b/environment.yml
@@ -2,22 +2,22 @@ name: base
 channels:
 - conda-forge
 dependencies:
-- traits==6.2.0
+- traits==6.3.2
 - matplotlib==3.3.3
-- numpy==1.19.4
-- sympy==1.7.1
-- scipy==1.5.3
-- k3d==2.9.7
-- ipywidgets==7.6.3
-- ipympl==0.7.0
+- numpy==1.22.2
+- sympy==1.10.1
+- scipy==1.8.0
+- k3d==2.12.0
+- ipywidgets==7.6.5
+- ipympl==0.8.8
 - ipyregulartable==0.2.0
 - ipytree==0.2.1
 - pyface==7.3.0
 - traitsui==7.1.0
-- importlib_resources==5.1.2
+- importlib_resources==5.4.0
 - pip
 - pip:
-  - bmcs-utils==0.0.30a0
-  - bmcs-ibvpy==0.0.17a0
-  - bmcs-cross-section==0.0.48a0
-  - bmcs-beam==0.0.12a0
\ No newline at end of file
+  - bmcs-utils==0.0.31a0
+  - bmcs-ibvpy==0.0.19a0
+  - bmcs-cross-section==0.0.51a0
+  - bmcs-beam==0.0.17a0
\ No newline at end of file
diff --git a/extras/pull_out_animation.html b/extras/pull_out_animation.html
index 027cb62..a378708 100644
--- a/extras/pull_out_animation.html
+++ b/extras/pull_out_animation.html
@@ -1,1824 +1,1824 @@
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diff --git a/extras/pullout1d.ipynb b/extras/pullout1d.ipynb
index 143aa50..4b96a57 100644
--- a/extras/pullout1d.ipynb
+++ b/extras/pullout1d.ipynb
@@ -81,16 +81,7 @@
    "cell_type": "code",
    "execution_count": 1,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/rch/miniconda3/lib/python3.9/site-packages/traits/observation/_has_traits_helpers.py:70: RuntimeWarning: Trait '_wrappers' (trait type: List) on class ActionItem is defined with comparison_mode=<ComparisonMode.equality: 2>. Mutations and extended traits cannot be observed if a new container compared equally to the old one is set. Redefine the trait with List(..., comparison_mode=<ComparisonMode.identity: 1>) to avoid this.\n",
-      "  warnings.warn(\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "%matplotlib widget\n",
     "import matplotlib.pyplot as plt\n",
@@ -536,8 +527,12 @@
     "  - `lambda` functions are used to define a callable function on the fly inline\n",
     "  - `def` functions are used for more complex expressions\n",
     "\n",
-    "Names of functions representing derived expressions start all with `get_`. \n",
-    "The parameterization of expressions we will related only to variables that change with a discretization or with a material behavior (e.g. number of finite elements $n_E$, material stiffness, maximum load, etc.). As we want to show on examples this parameterization allows us to show several application examples at the end of the notebook. Configurational parameters, e.g. the dimensionality of the problem $n_a$, number of composite layers $n_C$, number of element nodes $n_i$ are assumed constant within a notebook. They are there to prepare an adoption of the whole notebook for 2D or 3D configurations. "
+    "By convention, names of functions representing derived expressions start all with `get_` so that\n",
+    "it is possible to associate an executable function to the underlying symbolic expression.\n",
+    "\n",
+    "The parameterization of expressions will be introduced only to variables that change with a discretization or with a material behavior (e.g. number of finite elements $n_E$, material stiffness, maximum load, etc.). As we want to show on examples, this parameterization allows us to show several application examples at the end of the notebook. \n",
+    "\n",
+    "Configurational parameters, e.g. the dimensionality of the problem $n_a$, number of composite layers $n_C$, number of element nodes $n_i$ are assumed constant within this notebook. They are there to prepare an adoption of the whole notebook to 2D or 3D configurations. "
    ]
   },
   {
@@ -629,7 +624,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -666,7 +661,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
@@ -681,7 +676,7 @@
        "⎣2   2  2   2⎦"
       ]
      },
-     "execution_count": 14,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -705,7 +700,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
@@ -718,7 +713,7 @@
        "1"
       ]
      },
-     "execution_count": 15,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -736,7 +731,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -753,7 +748,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -771,7 +766,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
@@ -824,7 +819,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [
     {
@@ -837,7 +832,7 @@
        "[-1/2  1/2]"
       ]
      },
-     "execution_count": 19,
+     "execution_count": 15,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -849,7 +844,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -865,7 +860,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [
     {
@@ -908,7 +903,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -926,7 +921,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [
     {
@@ -959,7 +954,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -978,7 +973,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1003,7 +998,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [
     {
@@ -1013,7 +1008,7 @@
        "         [ 0.5]]]])"
       ]
      },
-     "execution_count": 26,
+     "execution_count": 22,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1036,7 +1031,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1061,7 +1056,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [
     {
@@ -1070,7 +1065,7 @@
        "array([[[0.5]]])"
       ]
      },
-     "execution_count": 28,
+     "execution_count": 24,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1091,7 +1086,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 25,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1145,7 +1140,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 26,
    "metadata": {},
    "outputs": [
     {
@@ -1154,7 +1149,7 @@
        "array([1., 5., 4.])"
       ]
      },
-     "execution_count": 30,
+     "execution_count": 26,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1378,7 +1373,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 27,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1387,7 +1382,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 28,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1415,7 +1410,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 29,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1438,7 +1433,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 30,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1460,7 +1455,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 78,
+   "execution_count": 31,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1484,7 +1479,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 79,
+   "execution_count": 32,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1522,7 +1517,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 80,
+   "execution_count": 33,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1564,7 +1559,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 81,
+   "execution_count": 34,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1590,7 +1585,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 82,
+   "execution_count": 35,
    "metadata": {},
    "outputs": [
     {
@@ -1599,7 +1594,7 @@
        "(array([-1.,  1.]), array([1., 1.]))"
       ]
      },
-     "execution_count": 82,
+     "execution_count": 35,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1620,7 +1615,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 83,
+   "execution_count": 36,
    "metadata": {
     "scrolled": true
    },
@@ -1632,7 +1627,7 @@
        "        [ 0.5,  0.5]]])"
       ]
      },
-     "execution_count": 83,
+     "execution_count": 36,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1674,7 +1669,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 84,
+   "execution_count": 37,
    "metadata": {
     "scrolled": true
    },
@@ -1693,7 +1688,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 85,
+   "execution_count": 38,
    "metadata": {
     "hide_input": false
    },
@@ -1715,7 +1710,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 86,
+   "execution_count": 39,
    "metadata": {
     "hide_input": false,
     "scrolled": true
@@ -1724,7 +1719,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "840b7cbabd0f4cb5870aae6d0887f380",
+       "model_id": "0588338298164f12aa406b201ccb4582",
        "version_major": 2,
        "version_minor": 0
       },
@@ -1752,7 +1747,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 87,
+   "execution_count": 40,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1776,7 +1771,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 88,
+   "execution_count": 41,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1814,7 +1809,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 89,
+   "execution_count": 42,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1832,7 +1827,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 90,
+   "execution_count": 43,
    "metadata": {
     "hide_input": true
    },
@@ -1851,13 +1846,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 91,
+   "execution_count": 44,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "50842a526ade4901b343d971ca0467d9",
+       "model_id": "4fcdc9e571204cd39844f66209f48dbe",
        "version_major": 2,
        "version_minor": 0
       },
@@ -1886,23 +1881,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 92,
+   "execution_count": 45,
    "metadata": {
     "scrolled": true
    },
    "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "<ipython-input-92-ded473f3bb57>:1: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
-      "  f, (ax1, ax2) = plt.subplots(1,2, figsize=(9, 4))\n"
-     ]
-    },
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "cd36b482fc2b417194aef70adfb02f34",
+       "model_id": "52f41f2219c54e438631362aa7118391",
        "version_major": 2,
        "version_minor": 0
       },
@@ -1954,7 +1941,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 93,
+   "execution_count": 46,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1975,7 +1962,7 @@
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   {
    "cell_type": "code",
-   "execution_count": 94,
+   "execution_count": 47,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1985,7 +1972,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 95,
+   "execution_count": 48,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2003,7 +1990,7 @@
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   {
    "cell_type": "code",
-   "execution_count": 96,
+   "execution_count": 49,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2027,7 +2014,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 97,
+   "execution_count": 50,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2043,7 +2030,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 98,
+   "execution_count": 51,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2053,7 +2040,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 99,
+   "execution_count": 52,
    "metadata": {
     "hide_input": false
    },
@@ -2061,7 +2048,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "40ba66544acd45429ad3066d78507167",
+       "model_id": "a2142913bf5c44e7bd6d2a6e3ab348b3",
        "version_major": 2,
        "version_minor": 0
       },
@@ -2099,7 +2086,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 100,
+   "execution_count": 53,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2150,7 +2137,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 101,
+   "execution_count": 54,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2245,7 +2232,7 @@
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   {
    "cell_type": "code",
-   "execution_count": 102,
+   "execution_count": 55,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2256,13 +2243,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 103,
+   "execution_count": 56,
    "metadata": {},
    "outputs": [
     {
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-       "model_id": "724e23c51ce74707ab159cdb2171fbf7",
+       "model_id": "6320ad201cbf4c26bca5bd79f87cd487",
        "version_major": 2,
        "version_minor": 0
       },
@@ -2282,7 +2269,7 @@
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   {
    "cell_type": "code",
-   "execution_count": 104,
+   "execution_count": 57,
    "metadata": {},
    "outputs": [
     {
@@ -2300,7 +2287,7 @@
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   {
    "cell_type": "code",
-   "execution_count": 105,
+   "execution_count": 58,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2319,13 +2306,13 @@
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   {
    "cell_type": "code",
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+   "execution_count": 59,
    "metadata": {},
    "outputs": [
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-       "model_id": "c07c54804b4641c6b2cbd72ff7dedd16",
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        "version_major": 2,
        "version_minor": 0
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@@ -2348,13 +2335,13 @@
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   {
    "cell_type": "code",
-   "execution_count": 108,
+   "execution_count": 60,
    "metadata": {},
    "outputs": [
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+       "model_id": "090972d5019348a99c6bb36f0dee766a",
        "version_major": 2,
        "version_minor": 0
       },
@@ -2383,13 +2370,13 @@
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   {
    "cell_type": "code",
-   "execution_count": 110,
+   "execution_count": 61,
    "metadata": {},
    "outputs": [
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+       "model_id": "e61be4f1cc66467e8cfea3585e937c46",
        "version_major": 2,
        "version_minor": 0
       },
@@ -2422,13 +2409,13 @@
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   {
    "cell_type": "code",
-   "execution_count": 111,
+   "execution_count": 62,
    "metadata": {},
    "outputs": [
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+       "model_id": "f394c570b7074282919da66c6d70557a",
        "version_major": 2,
        "version_minor": 0
       },
@@ -2473,7 +2460,7 @@
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+   "execution_count": 63,
    "metadata": {
     "hide_input": false
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@@ -2573,7 +2560,7 @@
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    "cell_type": "code",
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+   "execution_count": 64,
    "metadata": {},
    "outputs": [
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@@ -2595,7 +2582,7 @@
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    "metadata": {},
    "outputs": [],
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@@ -2606,1833 +2593,1833 @@
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        "</video>"
@@ -4441,7 +4428,7 @@
        "<IPython.core.display.HTML object>"
       ]
      },
-     "execution_count": 167,
+     "execution_count": 66,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4474,7 +4461,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 125,
+   "execution_count": 67,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -4490,7 +4477,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 126,
+   "execution_count": 68,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -4501,7 +4488,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 68,
+   "execution_count": 69,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -4549,7 +4536,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 69,
+   "execution_count": 70,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -4562,7 +4549,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 70,
+   "execution_count": 71,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -4582,7 +4569,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 71,
+   "execution_count": 72,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -4600,7 +4587,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 72,
+   "execution_count": 73,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -4609,7 +4596,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 73,
+   "execution_count": 74,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -4633,7 +4620,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.10"
+   "version": "3.8.8"
   },
   "toc": {
    "base_numbering": 1,
diff --git a/index.ipynb b/index.ipynb
index 5bc1a19..1a0a68e 100644
--- a/index.ipynb
+++ b/index.ipynb
@@ -366,7 +366,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -380,7 +380,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.8"
+   "version": "3.9.0"
   },
   "toc": {
    "base_numbering": 1,
diff --git a/tour2_constant_bond/2_1_1_PO_observation.ipynb b/tour2_constant_bond/2_1_1_PO_observation.ipynb
index c354ba9..dfa0611 100644
--- a/tour2_constant_bond/2_1_1_PO_observation.ipynb
+++ b/tour2_constant_bond/2_1_1_PO_observation.ipynb
@@ -246,7 +246,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -260,7 +260,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.10"
+   "version": "3.9.0"
   },
   "toc": {
    "base_numbering": 1,
diff --git a/tour2_constant_bond/2_2_1_PO_configuration_explorer.ipynb b/tour2_constant_bond/2_2_1_PO_configuration_explorer.ipynb
index 832ab77..7413bc2 100644
--- a/tour2_constant_bond/2_2_1_PO_configuration_explorer.ipynb
+++ b/tour2_constant_bond/2_2_1_PO_configuration_explorer.ipynb
@@ -286,7 +286,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -300,7 +300,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.10"
+   "version": "3.9.0"
   },
   "toc": {
    "base_numbering": 1,
diff --git a/tour2_constant_bond/fragmentation.ipynb b/tour2_constant_bond/fragmentation.ipynb
index b909290..9240d24 100644
--- a/tour2_constant_bond/fragmentation.ipynb
+++ b/tour2_constant_bond/fragmentation.ipynb
@@ -847,7 +847,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -861,7 +861,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.8"
+   "version": "3.9.0"
   }
  },
  "nbformat": 4,
diff --git a/tour3_nonlinear_bond/3_1_nonlinear_bond.ipynb b/tour3_nonlinear_bond/3_1_nonlinear_bond.ipynb
index 001221e..dc2cd39 100644
--- a/tour3_nonlinear_bond/3_1_nonlinear_bond.ipynb
+++ b/tour3_nonlinear_bond/3_1_nonlinear_bond.ipynb
@@ -182,1833 +182,1833 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [
     {
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+       "dHNjAAAAAAAAAAEAAAABAAAAMgAAAAEAAADcc3RzegAAAAAAAAAAAAAAMgAASKkAAAx+AAAKFQAA\n",
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+       "AAfNAAAEZwAABWEAAAQrAAAAFHN0Y28AAAAAAAAAAQAAADAAAABidWR0YQAAAFptZXRhAAAAAAAA\n",
+       "ACFoZGxyAAAAAAAAAABtZGlyYXBwbAAAAAAAAAAAAAAAAC1pbHN0AAAAJal0b28AAAAdZGF0YQAA\n",
+       "AAEAAAAATGF2ZjU4LjQ1LjEwMA==\n",
        "\">\n",
        "  Your browser does not support the video tag.\n",
        "</video>"
@@ -2017,7 +2017,7 @@
        "<IPython.core.display.HTML object>"
       ]
      },
-     "execution_count": 6,
+     "execution_count": 1,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2117,7 +2117,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -2145,13 +2145,36 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
     }
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAASIAAABYCAYAAABRcPqlAAAACXBIWXMAAA7EAAAOxAGVKw4bAAAWRUlEQVR4Ae2dS5IdNRaGryu8AONeANFmB2CvwMW8B2CvwGYHdFT0AEZdgXcAXgGGQc96AF6BDTuADhbgwjug/0/WkZWZUr6z7r11jyLy6n109N/Un0dSPm799ddfuzHu66+/vqNy5/J/HFPeyzgCjoAjMBaBszEFRT5PVe5PHV+MKe9lHAFHwBGYgsAgEYmEvpVAjh8V/nSKcC/rCDgCjsAYBHqJSMTzpYRgDf2s8OdjBHoZR8ARcASmInC7VkHEc668b2L+JBJSXer9FuteKR7WleTfU9oPOj6Oebn3qfJ/zhM87Ag4AqeBQJWI1H2mY7hnIoi374LDvyobpnLyf9UB4VzoYFrHYvc/dTxU+K2Ob3X4mpMAcXfcCOg8ZtbA+c057xfTGX9nkYgEJtYQ1gvu8p03+pd6zyUDQnohP1lTCufEc7ckUWWoT93PFR5NgCVZnuYIbI2AztFf1AZj5LWOXxR3y34G6GeVOkYYMPxUMsDqMTL5n+oT3uVyFIbofic9d0rHguIIdfI8DzsCh4aAztdgCcln6YFxgg8hHb1Tnz7TAbFey1gsWkRCETLATQI1Kv27/A9i+CfJ+EzHM4RlDiuJvIZTnV+VAPnZ2lQj3yOOwIEhwC4y5+xO5ywX1mT9k3aMTv2AXDEmmGKGZZTr6EeNiIwFbcF5rC6QC9YUu2wQEleI0pwZi8jJRiC4O3oEro6+B+qAxio75Ixdxuwnik+dCanafFcjorkSIZc76gSsinulcLhivIu+/1V6Z2r2PtdDjoAjsDUCGoN31AabScxa2Dz6aOs2a/KHiGgSK6oj39UaytP32eFcDw87AnMQ0PnL0gUDGMt+p/jd6IepmeIM8PwWFgb4D0oPswP5NiNg5sFCNxflBzpIZ5Nm8CKtMsh/owOH/H8qbdR4VTnTL+ih+GoENFev26EbB/KjTvAHAw5/7IXiWFSYiu4cgYNBQOckVj6EwT1xO/lpbUhhzmHS2T1LhKLwT+Tp4HYYCInpD49NQQKUY6eY6RHnf+8FXfVYAoF4bH0qEIrSbJNJwa5TeQjouY5AgIr3lu9K6E+ZqxdSD42IAJajvbiNru4cgWNAABLivrlEQlFpFoDZhWL9NBCI4lc6zhUPhCCfTZ5eq0b5kAh1TIaiYXE5kCKRmkO2jlr2ovSxeqkcZPhIxxcKf2KNrkZEEjruMX5reaIv+bcmVvHijsC1IqBzFGsIonjVblh57AaT/FiHkQikkwhL+b0kRGVzKotVBPlAbKOfAVVZLDl0xKJieveN/F4LTGVGO8mq6qU8m/EgD0JKbk0icqJIsHrgRBFggOP6CIXBmDusotFOg5ndaGYMNo3bEdeBxTXKqSzkh0UCGbAEwu44i9WzZyLIjPWreikfAoaQWRxvuLNGzCOOgCOwBAGzbhpX+5ZAK2PJfaRlZRq+BjLWDBd+LCHWm75UvK/NRn2LqA5TNWSFxWr5v+nAQposC5mqN1svJyL7V9x3BBYioIHIFR9i6UyVlHcexQ+u5dTUkAxIIt3bp/CkaVlNLumShVUFISGftSwspFGEpHKL9Roiog9R0p0j4AgUEWCgtgfrQ6U90uBsT8FYj7EdMxNWqm95JR8yY3ctObXDNOc7+W9T4oKA5CALQmKt56XC7X6UpC/W61bpVbFq3Bae/63wv0ote5ojcKoIxMFp9xFBJtxiwq0mYY1FPmtFrNkYORDHwsjvI2KnDBKhDOnfK7/3VhXl22IvdWgXx/RqtcXmdyKn/U7VS+XpN1ZUmBLS2hARsaC1uJOSkd/cdaV4L+DTYPDSjoAjcEwIaPx3iGhoara4f2oUUxK2h9Be62D70p0j4Ag4AgmB1bbvk8RuALO0+H4iioqggtmqoL9/CEDcOQIbIaCxxnSO9ydNcYxLu+9pSr1O2TjWmZKypnRP8fCYivxn10FEzJVf6sAyYl7Ire1hC1M+c16IiMOdI+AIbIiAxhtrS2ldZsOmiqLjuC/e73RWrPE+cdGumRqGYML7ieQDADdvMT8MTvnc3OTrRQaI+47AiSIwRER/LMSFLcD7yBDhYAVBOmHngDR3joAj4AiAwNZTM+aAo95P5H+HI+AInC4CmxKRrKDFW/+n+9d4zx2B00FgaGq2KRIiKt7PwkNyd3Xw8F1aP9q0YRfuCDgCB4XAphbRUE9FPOFpXJWb/dTvUBtj8yMJsnjefihxrIhNy0kvHmzsxenQ+7AFQGNw2aJdl7kuAnu1iNbtynxpOpl5xza3yh8kCdEz6cZzSo3njPIeK+/g+5Dru1Z4CJe12nE52yJw8kSkE5mbvI7lo3i895gNgIY79D5IvyqBNjoyP1LEZb44r3ndCJw8EQlwBvbWA2WV/1UDmlsfziPx5DIPvQ/3cmXXDvfgsnZTLm8jBPa6RrRRn6aKZWAXXyKudAa4vf9llYd1V5AZyEh65TeCVvswFQzpB2nwzpzS6x82sRxXarOEy9Tue/k9IdAhIp0U9noBVOJO6Bvr4gDgtveOUx5WEq9uYAGbQXmhIx/8nTpDCSvJ5H3IvHgr6CKZEEexD0P6tPMli/+eW/DDFz7RV0eRpNt158ZXbLOBy1x9vN5+EOgQkdTIzeiDXbxdCS76WiNb8qoP685sv1emBiX5EGDfA8CQDuXMEa71wcqM9lvEw20VHTdSz069WsJQm8o/V11Ikr7yqBBrQu079Nu4qNj1OOnCRgH6cdFq63U9Shx5KyUiCo9k0C+Byvb6TXacPJzAJTf0sG5n0bgghPc55WQ+JJOBlpNMQWSHdKp9UNtYcqP1VPmEhcIM/lz3oEuUWdVT+RBpuw/3lc7jPrmDTBpvFlS82KYqUfYDKsunv3wPrP2xhtXImHbGOunD0+yXOl7r4BWrm0xfx+pzrOVKRGTv2100DTkSQBhonNgNp5OJgRQe1o1hBhE3W9ob+CBow0nBYTdSJlfUIeIIumUtFvtAvmRN1jOTy0cD2+RhMqt6qs3OVE5pfFxwDF7FNqVH+v6VwvQ/EabC5tq4WPpmvvoULCH5fMeM9hkzENLRO/WH853lCKzzzgVp7Q6e5QLVIIPS7m7mynaQTnqyOMtJsMhJBgOVE6jttnhYdy2Z6GsL6LuePrT7NDWOdQI+1+mKbaqP+UCAqNOXVTPlGrhk6VsGIdeAETrq6JtSb6nHarLVh6c6OL/oG2uFOfartdMW1LaIYEAcDH/Ic10Is2PJBM2n/3AC8ZKmHHBO9rUf1l1LJidI2+oo9WE6Eq0aLUxaudtE+9pUHhdHPllTIsgSLtso2ZR61YweZ0yY8qgV5xVWHe8MK1mdm3UuEZEa5oqCMijwZLMWD08wBMEfkF7YJCxWf1h3DZmSEchXfk6aINrpA4lLnNq49hdo9bWpPPoICXEHfONxF8VruCyB4MbXjbhhfDALYof02v9zAzkQUVSIqQMkNJkNVZ+TxHYOTHbur/IS/lzgWmHpzrehMKnbVtFaTYySo/ZZWGZqwk4VDwC/0tFepzPCachUuWvrw0g9G/op0ibOdn5vXG2yIAw+EBBlkZc/d1fEhYJbuIgBA5j/a6c4/xl+mDLKhxjRyabQjZ0+5VOPfC7+lzrozwMdpI9ak5EM6r/RgUN+IOkQG/jJ9At6KL4aAc3V69ZXX30FaPzRgDF5jquGMZchMIgMMxWAknWh8E5lSqY0WbOc5MHgEEd+Ms6SZZUkC70v5dOXg3PSC6JnCladMh96H7YAdQwuW7SLTLUdPpYoP61ZKQxhks7uWSJghRkfLNqnc1Zhdv9ekK6DMQJxDV60o6w0RVXcCKw9ZZe4907lGOvPdQQCVLx9oXtfeEZorl40dVvHIx0oNoeEAB1wU4cU3unoEI/SAIG2ADrfBVHS/p104o9Fx0N1L6RfL0keQR+2wHYQly0a7ZEJCbHGmkgoluXizPY+1quNjyulnSseCET+BzqG/mPGKnVMBuKRHUiRSM0hW0cte1G65A7qpTKMLyxJHOVxT9ArfNdMAayBsFil8Oz1EdWFmJhWpCsELcV0WBsHEY0yBVUOa8vqhcrxJ5jCCvNHth1by4322wWIq4x9RLKU7WlHjoD+3/Z9Rqv3SG00LCLFOf+ZXXBRTxdna1hpnHO8RSHMGOSHmYj8wfM1k8EAxnLCMqZ9iK1NekquO5VHBjowtvigxewxb61Emb16qUzjTn3i6CD/o9sIUsCsAQoSn6vYY4nDzGw4yYO9IQi7NaCRX4uofNHUjHIWTc0kY/MTtdYvT7+xCNhVvs+qgaxyV7qY5vmNsM5bpudM7zAcwkWauI5Abo3ClQgylIVBECwU+RAIYz9NGytVq8nIjPX79HqqMvld8RhApH18ZpIVYdCjIAqFDlreBB+ieT2hvBd1BG4SAmaZMMBrzspYfh9pWZmGr/GJ4cCFlFsWsIxYxO9rs1HfIqrDVA1ZYYYi/zcdWEiTZSFT9Yb0gmOK/JCIKCpnFsjgfDOWT56U4GqAlZLPXVO+BxyBm45APPchFgii4ZRnF/fJY8sESQYkgfUSnMJMyzptWf4UX3KwqiAk5LOWhUEyipBUbpReKtd4pEftBONH6b82iEgJsCtA5jfzKTrKYQ212X5URS+0PwT0nzNPdzcPAQZqe7A+VNoj4dqegjENYbDnu56l+n2aQGaN/0vyGHftAd4nozdP8pAFIbHE8lLhdj9K9SfrFeWie9i4ul2QynYiW8Ww1ZS1ogcq3wBJcXeHjwCWrLsJCMRBdKEq93Vw0cbK4b4viIa1UAYX0xQu6jgwJh5ISD4Dl/FFOqR1V/738jsL3ErPHTcas7ib37PH9MpmMnnZReGoy5A+1sYkvSSbfkPM6Z7FsGtm0vBVCJBswXlwOzGvOxSWbBgQMy7MSYfK1/KjnEWL1TXZx5Cu/vMnmol+pfjYE6bTPdUd9UDqmm12lPCEk0FA55GRciDPGN+dFRDIV/Fh/EN0XGnsanOI+m2mk/44rE6unlirLPyxU7mp20ebm3bIhe8FgUg6nL+sP/EpMaZ97PZdlaZm+ToP7LXYRQVgQKwtLBmu6G/kz9ouVL18nr1YvyMTwH/yXBjwh76Q37gHRXHyyZt8g6rq1Fy1TbXHf8paB2WwdPPtWUXdHQoC+q/4n7h3aYrjPFprA4q2OzpI/hedqRkaKsNu9uNegynrRFM6eNJlhStXAwh5yPEfpItDrPdSlfhD3+pgnh3yYx6E0Jh/Kx6c8iEo8nOH1YtllTvIJP3vUW6tzT+V33hpmeJ+j1aOpocHEShZRIOVvMByBDRYucpM2npVHUiEG8dYuyPMWh7rbsGyjDJZLC0SnNI7i5pK610jUn5vm2o7f1yHspCjO0dgEgJnk0p74X0jAPGEdTsRBFYQi9RbT1N724x6GC4QYGOqaBnuOwJ9CLhF1IfO4eUx0PN7vNgyXmv+XuvtqDalB9M+tqi31qemp6cfMQJOREf052mQp3Wb61J7TJsqA1mF+2YU5nGDWZsQ19Unb+fwEPCp2eH9J7M1EgGwJcpDh9wgd6Ew60dDLi2EDxUs5asNdkJok0VrNjk661Clep7mCOQI+K5ZjoaHHQFHYC8IuEW0F9i9UUfAEcgRcCLK0fCwI+AI7AWB2mL1f6I27Rvd9qKkN+oIOAI3G4EaEf0jdvu/8n079mafA947R2DvCPjUbO9/gSvgCDgCTkR+DjgCjsDeEahNzfaumCvgCNx0BHTfFQ8+P9dxTwdvUjj4e7CkI7py7xg3sK52g61bRELUnSOwNQIatNx93nBKs7c5XimDm1CPwUFEd3TkDzsv1nuIiD5c3IILcAROHIFoRTB4a27R3e01oVukqy88ZM3bH1a13oaI6I8tOuMyHYETQ2DMozZHA4lI6O3ayg4R0drtuTxH4KQQ0KAd+wK8k8Kl3dlVFqsFNvPf/MsC7Xb8TY9tRDx+9AjovGe6xblvHzJovCpX+VhC9k5xvr5h3zQrfrFD+ay/mPXEV3HCl0HkN1wsx7ueafdvOng1TJgqyT9XHJ2QdamDaR+ySOddUfjUJZ88wqxPUR/SJI3xyjfT0Bd9sIAox7SMNOqSn949pTB1kU1ZcME9VnpaS1KYekW9Fz/0KuG8h4bGeYEWi26AQGPJqYzfFJnQ8MBNQEDnNAOPQfmpwmmNR2HGAW+9TK9CURjCYOAW11ViHRUJ7/sOO1FKQz67Ux8pnMsnndf25q8I5u0H7UH/p9Je6EAfxh86BINA8iAK8tn5srd7Whr9SS/bU5j67dcVoxdvCg1EJJ+6vF648cZRxflybPhij/xevW9LwGwXhQM6bwoMTuGdjgbxKI6iF7EIrIh7ovS374L+6wgcHQKQ0I86hxNJxB5wEeZLqRBPYxwM9PC+yqeBTF0dVMHKyLfJaZet/tSuws908Jmuz3TYWLyiruJmKaVPgymNb6FR7rEOIyLSGI+QSyIihdEjtaU4Dtm5u68I+mOZ5WMaI8Vcr95LiQigE9hSAtbLFTElACldDRRGwcD2VsB9R+BYEIjnORfUV22dlWcEwiBPY6NdrhCvPdd5x8pm7TJ22o62HugwImIcJgJR3fa4/F75WDF8VQfrhikYhId1ZeQFCWIR9TrVhXSvVIh3UkFi1IGkjeTgBfCq6n2mzDUd4JcUfyql6JS5MIdVGgq6cwSODQEGFa49uN+lvvvd4ty2dpmWMabSoSa5uEMuuYMcik51ISz0NwPhgdKw5rCMbJ2qMU0rCnqfyFoQxIOOjG+mZWYRDeq9yCJSY21HB9ICVpZJZ2uMnxXzoCNwFAiYpXGnR1sr0ymiAcoMgUE/1ZnMxnJIj5A+oqTaCx1M5y7lvyFBjjTGq1lWpPU61YdormKfQr8UZvOKDylCRoN6n/W2MCEzKoOZ1zFHlfadjhwUOoo52Ck7oUkv6gjsBYF43nI+pzUdU0R5ZvmzJmIuP/ctbbKftcvMo+Oytjt5lQRIAhJ5rsOIhzTWlpiimUWjYK/D+oN4klN9pnnIZO2IcQ4GVb2HiOiOKo91WEPGfNU6UgqlKZu29aqFPcMROFwEHkq1R/F8zrVkWsLicb7gS/h+XqgVvtuK90VpF6IwwgtlFafdfPwxdnvHr+pAENQJ60Tyd1kau3C5PLJz15bNO9LbacQNh169a9v3bPXBlFgyWC+DTuW4AhTve7DKKoNMWHbNzyGbePcdgWtFIJ7PTEXexobD+a10G3xJnzg+iDO4LxVnLYaL8oUOLszIoB7TJOQw7iCbkK6yaclDYfLDFEg+Y5V7iZgGMcugDnVzmcX7llRmp/JYPtQzi4i0YN3Iz3frSDd9jQTR94kOSBbr0KZ3CgadmEImLBSu6l0jIkiFjrADsIrlYkrID8QW4zv5faxLh9w5Ao7ADUfgrNI/mxvyeRrMq0Uukg4yYW1kwqww+tUiwV7ZEXAEbgQCRSISUWBOmaXSWISa2WvuH8Ccw7fjqdoxk3amWK/mCDgCNwGB4tSMjokkIA7uCYIs/u6kIRTcOQKOwCYIFC0iWhLxYBVxgxJTM7b33DkCjoAjsAkCVYvIWhMh2cL16B00q+u+I+AIOAJjEKhaRFZZRMS2IQvLrOnkN2lZEfcdAUfAEViEwCARIV0ExBSNx/nbz7KQ7c4RcAQcgUUI/B+QkULL48wNuwAAAABJRU5ErkJggg==\n",
+      "text/latex": [
+       "$\\displaystyle \\begin{cases} \\frac{s \\tau_{1}}{s_{1}} & \\text{for}\\: s \\leq s_{1} \\\\\\tau_{1} + \\frac{\\left(s - s_{1}\\right) \\left(- \\tau_{1} + \\tau_{2}\\right)}{- s_{1} + s_{2}} & \\text{for}\\: s \\leq s_{2} \\\\\\tau_{2} & \\text{otherwise} \\end{cases}$"
+      ],
+      "text/plain": [
+       "⎧          s⋅τ₁                      \n",
+       "⎪          ────            for s ≤ s₁\n",
+       "⎪           s₁                       \n",
+       "⎪                                    \n",
+       "⎨     (s - s₁)⋅(-τ₁ + τ₂)            \n",
+       "⎪τ₁ + ───────────────────  for s ≤ s₂\n",
+       "⎪           -s₁ + s₂                 \n",
+       "⎪                                    \n",
+       "⎩           τ₂             otherwise "
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "s = sp.symbols('s')\n",
     "tau_1, s_1, tau_2, s_2 = sp.symbols(r'tau_1, s_1, tau_2, s_2')\n",
@@ -2176,13 +2199,36 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
     }
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/latex": [
+       "$\\displaystyle \\begin{cases} \\frac{\\tau_{1}}{s_{1}} & \\text{for}\\: s \\leq s_{1} \\\\\\frac{- \\tau_{1} + \\tau_{2}}{- s_{1} + s_{2}} & \\text{for}\\: s \\leq s_{2} \\\\0 & \\text{otherwise} \\end{cases}$"
+      ],
+      "text/plain": [
+       "⎧   τ₁               \n",
+       "⎪   ──     for s ≤ s₁\n",
+       "⎪   s₁               \n",
+       "⎪                    \n",
+       "⎨-τ₁ + τ₂            \n",
+       "⎪────────  for s ≤ s₂\n",
+       "⎪-s₁ + s₂            \n",
+       "⎪                    \n",
+       "⎩   0      otherwise "
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "d_tau_s = sp.diff(tau_s, s)\n",
     "d_tau_s"
@@ -2213,7 +2259,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -2234,9 +2280,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([0.  , 0.5 , 1.  , 0.55, 0.1 ])"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "get_tau_s(np.array([0, 0.5, 1, 1.5, 2]), 1, 0.1, 1, 2)"
    ]
@@ -2262,13 +2319,39 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 7,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
     }
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0f28e0125e194326b39b6eb8251f829e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "image/png": 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",
+      "text/html": [
+       "\n",
+       "            <div style=\"display: inline-block;\">\n",
+       "                <div class=\"jupyter-widgets widget-label\" style=\"text-align: center;\">\n",
+       "                    Figure\n",
+       "                </div>\n",
+       "                <img 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' width=800.0/>\n",
+       "            </div>\n",
+       "        "
+      ],
+      "text/plain": [
+       "Canvas(header_visible=False, toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Bac…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "fig, (ax1, ax2) = plt.subplots(1,2, figsize=(8,3), tight_layout=True)\n",
     "fig.canvas.header_visible = False\n",
@@ -2295,7 +2378,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2311,7 +2394,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2377,13 +2460,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Warning: Update progress bar from model - not implemented yet\n"
+     ]
+    },
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "8bbc617c76f14998b8d1aa1d540d40ee",
+       "model_id": "47522a9f4d23434f84a850d9fa4e1a91",
        "version_major": 2,
        "version_minor": 0
       },
@@ -2393,18 +2483,6 @@
      },
      "metadata": {},
      "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 4 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
     }
    ],
    "source": [
@@ -2464,7 +2542,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 11,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -2489,7 +2567,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2512,7 +2590,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2531,13 +2609,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Warning: Update progress bar from model - not implemented yet\n"
+     ]
+    },
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "5ab6003c61d64aff8cf261a20d25f45b",
+       "model_id": "a028464d64fc462690a3eb76500261b8",
        "version_major": 2,
        "version_minor": 0
       },
@@ -2589,49 +2674,49 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "calculate d=16 mm, L=5d\n",
+      "calculate d=16 mm, L=10d\n",
+      "calculate d=28 mm, L=3d\n"
+     ]
+    },
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "7b9ae791bfc64a6980c3aed83360420b",
+       "model_id": "b52ee7cc2dea4158bf4a3a57e7ebed69",
        "version_major": 2,
        "version_minor": 0
       },
+      "image/png": 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+       "\n",
+       "            <div style=\"display: inline-block;\">\n",
+       "                <div class=\"jupyter-widgets widget-label\" style=\"text-align: center;\">\n",
+       "                    Figure\n",
+       "                </div>\n",
+       "                <img 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' width=800.0/>\n",
+       "            </div>\n",
+       "        "
+      ],
       "text/plain": [
-       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
+       "Canvas(header_visible=False, toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Bac…"
       ]
      },
      "metadata": {},
      "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "calculate d=16 mm, L=5d\n",
-      "calculate d=16 mm, L=10d\n",
-      "calculate d=28 mm, L=3d\n"
-     ]
-    },
-    {
-     "ename": "TypeError",
-     "evalue": "cannot unpack non-iterable AxesSubplot object",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-27-efa9a6e3b430>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     28\u001b[0m \u001b[0mpo_rilem\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     29\u001b[0m \u001b[0mpo_rilem\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhist\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot_Pw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolor\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'green'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 30\u001b[0;31m \u001b[0mpo_rilem\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmaterial_model_\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0max_bond_slip\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     31\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     32\u001b[0m \u001b[0;31m# The code sequence can be certainly shortened by using the loop.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/PycharmProjects/bmcs_utils/bmcs_utils/model.py\u001b[0m in \u001b[0;36mplot\u001b[0;34m(self, axes)\u001b[0m\n\u001b[1;32m     37\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxes\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     38\u001b[0m         \u001b[0;34m\"\"\"Alias to update plot - to be overloaded by subclasses\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 39\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate_plot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maxes\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     40\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     41\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mupdate_plot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxes\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/PycharmProjects/bmcs_ibvpy/ibvpy/tmodel/mats1D5/vmats1D5_bondslip1D.py\u001b[0m in \u001b[0;36mupdate_plot\u001b[0;34m(self, axes)\u001b[0m\n\u001b[1;32m     30\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     31\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mupdate_plot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxes\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 32\u001b[0;31m         \u001b[0max_tau\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0max_d_tau\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0maxes\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     33\u001b[0m         \u001b[0ms_max\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0ms_max\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     34\u001b[0m         \u001b[0mn_s\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m100\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;31mTypeError\u001b[0m: cannot unpack non-iterable AxesSubplot object"
-     ]
     }
    ],
    "source": [
+    "%matplotlib widget\n",
     "fig, (ax, ax_bond_slip) = plt.subplots(1,2, figsize=(8,3), tight_layout=True)\n",
     "fig.canvas.header_visible = False\n",
+    "ax_d = ax_bond_slip.twinx()\n",
     "\n",
     "print('calculate d=16 mm, L=5d')\n",
     "d = 16.0 # [mm]\n",
@@ -2659,7 +2744,7 @@
     "po_rilem.reset()\n",
     "po_rilem.run()\n",
     "po_rilem.hist.plot_Pw(ax, color='green')\n",
-    "po_rilem.material_model_.plot(ax_bond_slip)\n",
+    "po_rilem.material_model_.update_plot((ax_bond_slip, ax_d))\n",
     "\n",
     "# The code sequence can be certainly shortened by using the loop. \n",
     "# It is deliberately omitted here as the focus is not on programming."
@@ -2685,12 +2770,39 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 16,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "912484f83ead4fe4baf3c99fea78beb8",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "image/png": 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",
+      "text/html": [
+       "\n",
+       "            <div style=\"display: inline-block;\">\n",
+       "                <div class=\"jupyter-widgets widget-label\" style=\"text-align: center;\">\n",
+       "                    Figure\n",
+       "                </div>\n",
+       "                <img 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' width=800.0/>\n",
+       "            </div>\n",
+       "        "
+      ],
+      "text/plain": [
+       "Canvas(header_visible=False, toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Bac…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "fig, (ax, ax_bond_slip) = plt.subplots(1,2, figsize=(8,3), tight_layout=True)\n",
     "fig.canvas.header_visible = False\n",
+    "ax_d = ax_bond_slip.twinx()\n",
     "d = 32.0 # [mm]\n",
     "po_rilem.w_max = 0.12\n",
     "po_rilem.time_line.step = 0.05\n",
@@ -2700,7 +2812,7 @@
     "po_rilem.reset()\n",
     "po_rilem.run()\n",
     "po_rilem.hist.plot_Pw(ax, color='magenta')\n",
-    "po_rilem.material_model_.plot(ax_bond_slip)"
+    "po_rilem.material_model_.plot((ax_bond_slip, ax_d))"
    ]
   },
   {
@@ -2828,9 +2940,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 17,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/latex": [
+       "$\\displaystyle 25.3$"
+      ],
+      "text/plain": [
+       "25.3"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "p_b = 100 # [mm]\n",
     "t_f = 0.11 # [mm]\n",
@@ -2877,9 +3004,31 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 18,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Warning: Update progress bar from model - not implemented yet\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "cf738f42e54c44f1ba32ab81b991b1b9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "VBox(children=(HBox(children=(VBox(children=(Tree(layout=Layout(align_items='stretch', border='solid 1px black…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "po_cfrp = PullOutModel1D(n_e_x=300, w_max=1.5) # mm \n",
     "po_cfrp.geometry.L_x=100 # mm\n",
@@ -2903,9 +3052,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 19,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAC4AAAAPCAYAAACbSf2kAAAACXBIWXMAAA7EAAAOxAGVKw4bAAAC90lEQVRIDc2W0VEUQRCGV8oAToyAMwPQDCADMQMgAyifjjcLMgAjUMyAIwLlMsAMpC6D8/v6pse9Ye+kfLKrZrvnn+6e3u6e2e0Wi0X3P47JZLLbxgU2YozFX3aFzs/Px4gnZTqCO78AnxasMrCrOum6beQjsHkP2yiie1EUfsHfMNznZ2N0B2Ycs4IrS3s+Xhh9UdA4Axd7z/oN4xD5m8pwje8ZV8iXBduF3zH2wNrNVVkhdLT/BG99HvTtkR/QMynuqV/1tZvDa8aPkY8BbxnhkHlm+iNyYp+Rt9GJoJE75BnjB6JVOBBbR+i5zwie/jrkeZm39vo9XOdrqyxYDt8k3kZMh/KGrMJQVrXfx2bU6LdTA8nS99e+M3mOfbWJHmdDs/uqoghgBilFP/eCelzCK097VXrLyEoF0Dz2mV83mNNMhuu1GgN6FaqHsyIIBKkDD9AJcmwEt6RA0XfyPr0uEw/0IGH7t2poZ09XwiZaC0D/+rbHo2IrgQN60Az6HUMFe7dPZsP1lrSTNgWXQQ214NJ61V5fX4kp9OEG/gD3EE+30kIO4IG4ZNiLXxj3yNkyqhz5AKvBIxt0BpMlV+1fKCvnHgaYfp3r2zaM1l0JvL8TimZXwxvkkWtw5zsMr8hThu1kJjxc0qbAh87G0upPi+RZSbzl+h+z7zhaBSFKDY/+6WnbKmbX4Yt06Bh8ve8Llh+UtYFrx1A9kqDQo8TCHr1b1rx242PT00txlBn3o2BbpINUeC73xafY+1KbyFJboZay//NG8nYaiiX02GcWGUfJDYc21oEUDjGw3/0I7WSQcDewIuuyw1Ilv8RZnQoiaOv5yhe/Rj7rKxTZfSKWzLhKlqcShgZpUF6J6dBstb1qMOrUNkP267hgWMlKzL1aH+H6DkJ2jw+MOPhLNH4p4hCWeYfeaZHjaxr/KgIs+DYBFgWDfPKThV5mzA0lD2+WeInwBPNfwyq250E7fZgMD6NXb72fkYOwc//Mui1iws7Atet+Ay3orKZAztJRAAAAAElFTkSuQmCC\n",
+      "text/latex": [
+       "$\\displaystyle 39.05$"
+      ],
+      "text/plain": [
+       "39.05"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "f_t = 3550 # CFRP sheet strength in [MPa] - see the table above\n",
     "f_t * t_f * p_b / 1000 # breaking force of the sheet 100 x 100 mm in [kN]"
@@ -2940,11 +3104,50 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 23,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "evaluating pullout curve for L 5\n",
+      "evaluating pullout curve for L 10\n",
+      "evaluating pullout curve for L 50\n",
+      "evaluating pullout curve for L 100\n",
+      "evaluating pullout curve for L 200\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "abf659e575944510988c3231dc20a0d8",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "image/png": 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M+vXrc/78eTp06MDevXu1DksIIUqtqCjlq5SNjT7f4nD58fSENWvAzg7WrZOCcUIIIe6QBF1lhS0EN3HiRCpUqMChQ4fYsGGDiaPSRtSpCOz2DKSaG5y96Yzf8xHorB98A0OYlq+vL7t37yYwMJAbN24QFBTE77//rnVYQghRKh07pnyVcnbOwdq68McFBsLixcrz6dNh0yYTBCeEEKLUkQRdZYUZQQeoXLkyY8aMAeCtt94iKyvLlGGZXdyNWC6u7ICfdzaxSTZ4PvYPNo7uWoclbqtSpQqhoaH06tWL1NRUHn/8cRITE7UOSwghSp2TJ5Up7hUqFH023HPPwYsvgl4PTz4JZ8+qHZ0QQojSRhJ0lRU2QQd47bXXcHNzIyIigu+//96EUZlXRno6f3/WnI51k0nJ0EHn9VT09NM6LHGXChUqsG7dOpycnEhOTiY2NlbrkIQQotQ5eVL5KuXoWLyF5PPnQ7t2EBenFI1LTlYzOiGEEKWNjdYBlDVF6XVepUoV5s2bx61btxg2bJgJozIfvV7PLx88xBN+V8jJgdh6n1G7US+twxIFsLW1xdHRkZSUFOmPLoQQRZSQABcuKCPo9vbFqydjbw8//wytWsHhw/DCC/D996DTqRmpEEKI0kISdDU41+WybWeSnRJ46KGHinToyJEjTROTRlZ/8jhDG/wLwMmKL9Ko43iNIxIPYripJAm6EEIUTXg4XLvmyZYtQ/n22+IXQK1eHX76CYKC4McfoU0bmDhRtTCFEEKUIpKgq6HaI3gPeYThQ0p2mvT0dC5fvoyvr686cZnZph/ep2+l1VhZwbHMLjQJDtE6JFEIhtaAGRkZGkcihBCly7//QkqKE5UrN6KkH92dOsGnn8L48fDaa9C8OYXqqy6EEKJskTXoFuLw4cMEBATQr18/MjMztQ6nyC6dP0X92HdwsocTiXVo8tQWmZ9XShgSdBlBF0KIojl4UHls1Uqd840dC8OHQ3Y2PPYYXLyoznmFEEKUHpKgq0GfQ1ZGGlkZ6cXuaV6jRg3i4uI4evQon3/+ucoBmpZer2fvlz2p65lDbJId9Z7eB1YyOaO0kARdiLJpxowZ6HS6PFu1atW0DqtMOXgQHBxSqVXrMJGRkSU+n04H//d/yuj5tWswaBDIr2YhhChfJEFXw+kvsfnZkfVTHHjllVeKdQoPDw/mzJkDwDvvvMOlS5fUjNCkNn77Dv0bnQMgvel8rB0raRyRKArDGnSZ4i5E2dOkSROuXLli3I4cOaJ1SGVGcjL89x+4uiZw7txaNm7cqMp5nZxgzRrw8IB9+5RRdSGEEOWHJOgqs7Ep/sjxyJEjad++PUlJSbz66qsqRmU6l6LPUvfaB1hbwdHklvi0e0nrkEQRyQi6EGWXjY0N1apVM25VqlTROqQyIzxc6V9eubKynKu4M+jy4+urFIvT6WDpUvjyS9VOLYQQwsJJgq4ya2vrYh9rZWXF4sWLsbKyYtWqVWzdulXFyNSn1+v5O6QnjbxyuJFsS6Mn/9A6JFEMkqALUXadOnUKb29vfH19efzxxzl79qzWIZUZhvXnfn7KV6mcnOL1QS9Ijx7wwQfK87Fj4Z9/VD29EEIICyUJuspKMoIO0Lx5c8aMGQPAmDFjLHra8cbv3mew32kAUpp8hE0FT40jEsUhbdaEKJvatm3LN998w+bNm/nyyy+JiYnhoYce4saNGwUek56eTkJCgnFLTEw0Y8Sly79KR1GaNFF/BN3gjTdgwADIyFDWo1+9qvolhBDC4ly6dInhw4dTqVIlnJycaN68OQcNd0VRft/OmDEDb29vHB0d6dKlC8eOHdMwYnVJgq6ykiboAO+++y5Vq1bF19eX+Ph4FaJS35VL56l1ZQY21hCZHIDPQxO0DkkUk7RZE6Js6tWrF4MGDSIgIIBHHnmEDRs2ALBixYoCj5k9ezaurq7GrXHjxuYKt9QxfFf09zfNCDooU9yXL4dGjeDSJaWyeyls9CKEEIUWFxdHhw4dsLW15Y8//iAyMpJPPvkENzc34z5z587l008/ZeHChezfv59q1arRvXv3MnNTWRJ0lZVkiruBm5sbBw8eZOPGjRa5XlCv17NtYU/8a+RwK9WaBk9s0jokUQIyxV2I8qFChQoEBARw6tSpAveZOnUq8fHxxk2NyuRlUUoKGP7T+PsrI+imSNABXFxg7VpwdoadO+H1101yGSGEsAhz5szBx8eHZcuW0aZNG2rXrk23bt2oW7cuoOQhn332GdOmTWPgwIH4+/uzYsUKUlJS+OGHHzSOXh2SoKtMjRF0gOrVq6Oz0D7iG36YwxC/EwAkNpiFTUVvjSMSJSEJuhDlQ3p6OsePH8fLy6vAfezt7XFxcTFuzs7OZoyw9Dh8GHJyoGpV8PJSvkqZYoq7QaNG8M03yvP58+H77012KSGE0NT69etp3bo1Q4YMwdPTkxYtWvBlrkqZ586dIyYmhh49ehhfs7e35+GHH2bPnj1ahKw6SdDVUKEWZzP8SbJrRP369VU99fXr13nhhRf44w/LKMAWc/ki3hfews4GTiT74dNRbuWXdtJmTYiy6bXXXmPHjh2cO3eOf/75h8GDB5OQkMCIESO0Dq3UM0xvb9UKnJwcGTBgAAMGDDDpNfv3hzffVJ6/8IJSRV4IIUqLxMTEPDVOChoYOnv2LCEhIdSvX5/Nmzfz0ksvMX78eL65fZcyJiYGgKpVq+Y5rmrVqsb3Sjt1hnvLO+9HqTPyUeqY4NTz5s1j6dKlhIaGcvToURwcHExwlcL78/NePB2QTUKaNXWG/qEskBOlmoygC1E2Xbx4kSeeeILr169TpUoV2rVrx969e6lVq5bWoZV6uRN0W1tbmjZtapbrvvuucu3Nm2HgQDhwQOmXLoQQlu7umibvvPMOM2bMuGe/nJwcWrduzaxZswBo0aIFx44dIyQkhKefftq4390zjfV6vcXOPi4qGUG3cG+88Qbe3t6cOXOGOXPmaBrL3u1rGdzgKADxdd/B1kW+5JUFkqALUTatXLmSy5cvk5GRwaVLl/jll1+k6JtKDBXcW7Y073WtreGHH5Q+6efOwRNPQHa2eWMQQojiiIyMzFPjZOrUqfnu5+Xldc9nlZ+fHxcuXACgWrVqAPeMlsfGxt4zql5ayQi6hXN2dubTTz/l8ccfZ/bs2Tz11FPUqWOKsfr70+v1XP5zDO384Ux8Veo+8ZbZYxCmIW3WtJednU2mlGa2WLa2tqoUABVlQ1oaGLr5tGqljPacOHECvV5Po0aNsLIy7diHh4dSNK59e/jzT3j77Tv90oUQwlI5Ozvj4uLywP06dOjAiRMn8rx28uRJ4+wvX19fqlWrxpYtW2jRogWgLNPcsWOH5oOZapEEXQ2nl5AZ9jK/HoSbfot58cUXH3hIejqEhEBwsHIn/H4ee+wxvvzyS7Zu3cr48eP57bffzD6FY8eG5fRpdAUAl86f5zu1PToavv0WbtyAhIS8W2IipKbC1Knw7LNmDV08gLRZ045erycmJoZbt25pHYp4ADc3N6pVq1Zmps+J4jt8GLKyoHJlqFEDMjOzWL16NaBUwTfc9DSlZs1g6VIYNgxmzYLWrZV+6UIIUdq98sorPPTQQ8yaNYvHHnuMffv2sWTJEpYsWQIoU9snTpzIrFmzqF+/PvXr12fWrFk4OTnx5JNPahy9OiRBV4M+B1vrHKwofBXXhQvhtdegenUlQU9JgU2blA/d210EjHQ6HQsXLqRp06Zs2LCB9evXExwcrP6fowA5OTkk7HkNO384mVCLBk2G3LPPvn3Qpw9cu3b/cy1YIAm6pZEp7toxJOeenp44OTlJ8meB9Ho9KSkpxMbGAty3ArooHwzT21u1Uu5V5x4xN2Ul97s9+STs3w+ffQYjRoCfn1LtXQghSrPAwEDWrl3L1KlTeffdd/H19eWzzz5j2LBhxn0mT55Mamoqo0ePJi4ujrZt2/Lnn3+Wmc4jkqCrrDDTIPV6+Oor5XlEBAwZoiStq1bBtGnw/vv3HtOoUSNeffVVPvzwQ9577z369etnti/zf/48n96NbwLg2X3JPe+vXw+PP66MkAcEwKOPKn1bc29JScoXiNOnlT+/5CGWQxJ0bWRnZxuT80qVKmkdjrgPR0dHQFnf5unpKdPdyzlDgTjD+vPcn8Wm6oVekLlz4dAh2LFDGUH/5x/lM1cIIUqzPn360KdPnwLf1+l0zJgxI98ic2WBJOgqK8zas3374PhxcHSEyZOV14KDlQT9p5/gvffyT2Dfeust9Ho9kydPNltynpWVBYffxrox/JfUkEZ1e+R5f9EiGD9e6QfbqxesXg0VK957nsxM5SZEcjJcuQLe0jrdYkibNW0Y1pw7OTlpHIkoDMPfU2ZmpiTo5VzuCu6g3Qg6gK2t8t2hVSv47z8YORJ+/hlMvAxeCCGECcmvcJUV5ovbsmXK46BBd+509+kD9vZw8iQcOZL/cRUqVODDDz/Ew4w9VTZ8+x6PNk4iJwd8ei83vp6To9xcGDtWef7888pIen7JOShfIgxr7U+eNH3covBkBF1bMq29dJC/JwFK/ZijSjMTixhBB6haFX75BezslOJxZaRGkhBClFuSoKvsQSPoqamwcqXy/Jln7rzu7KxMDQdlFP1B9Ho9oaGhJr1bn56ejvO5uQCcSG9BhertAKWC7ZNPwkcfKft98AEsWQI2D5iPUb++8igJumWRBF0IIQrn6FFlRpi7O9Sufed1w2e/Fgk6QNu2Sm0bUJbKbd6sSRhCCCFUIAm6yh40gr52LcTHKx/sXbrkfW/I7dprP/2krNMuiF6vp3///gQFBfHLL7+UKN77Wf/VZIIapZGVDXWCvwXg5k3o0UOZUmdrq1Rtf/PNwq0pb9BAeTx1ymQhi2KQNmtCCFE4dxeIMzCMopt7intuL7ygzGbT65X+6OfOaRaKEEKIErDYBH327NkEBgbi7OyMp6cn/fv3v6cnnl6vZ8aMGXh7e+Po6EiXLl04ZmhOak6O1TmVWJMkm9oPrPD7/ffK44gR964R69NHmaJ24sSdKXT50el0xr5/EydOJCkpqSTR5yspMZHq10MAOJXTCfvKTUhLg6Ag+PtvZWr+pk0wfHjhzykj6JZJ2qwJIUTh3F0gzqB379707dsXBwcH8weVy8KF0KYNxMUpReNSUjQNRwghRDFYbIK+Y8cOxowZw969e9myZQtZWVn06NGD5ORk4z5z587l008/ZeHChezfv59q1arRvXt3EhMTzRtsjb7Uf/E8Iz4+R1BQ0H13XbMGwsOVtdt3c3WFnj2V59u33/+SU6ZMwdfXl0uXLvHuu+8WK+z7Wf/lOB6ql0l6po56/ZcDMGOGUnW+ShXYvVtJ1otCRtAtk0xxF0XVrl075s2bZ/x56NCh6HQ64+/ny5cvY2dnx/Hjx7UKUQiTuLtAnEGLFi1o2bKl8fepVuztlfXoVaoon9ejRt1/Rp4QQgjLY7EJ+qZNmxg5ciRNmjShWbNmLFu2jAsXLnDw9qejXq/ns88+Y9q0aQwcOBB/f39WrFhBSkoKP/zwg8bRF8zeXul1Xrly/u9/8IGSwI4bd//zODo68vnnnwMwb948VWcO3Lxxg0bp3wEQZfsotq51+OefO2vOlywBf/+in9cwgn7mDGRnqxSsKDFJ0EVRubm5GW+ERkdHs3nzZpydnYmLiwNgyZIlBAUF4efnp2WYQqgqMxMOH1ae352gW5IaNZSlctbWyqy9218VhBBClBIWm6DfLT4+HsBYwfzcuXPExMTQo8edtl/29vY8/PDD7NmzR5MY1RAQAPXqFW7f3r17ExwcTFZWFmPHjlVt7duGJS/QslY2KRlW1O//NWlpSuuWnBwYNgz69y/eeX18lBsUGRlw4YIqoQoVSJs1C6HXK30ItdiK+LvD3d3duLRm4cKFDBs2jCpVqhAXF0dmZiZLlixhwoQJREVF0bp16xL/pxk5ciS///67yY8R4n6OHVM+v1xdoU6dvO9FRUVx6tQpi7nR+fDD8PHHyvNJk2DnTm3jEUIIUXilIkHX6/VMmjSJjh074n976DYmJgaAqlWr5tm3atWqxvfulp6eTkJCQp5NFWeWkbDMlh/H2/HHH38UuNuKFUoBlz//LNxpC1MM9rPPPsPR0ZHt27fz448/FjLggsVcuUQrm18BuFRxMFZO1Xj7baW/arVqsGBB8c9tbQ116yrPZR265ZARdAuRkqL0KdRiK+JCVcMIenJyMkuXLmXChAm4uLgQFxfH2rVrcXZ25lFDWwohyojc68/vLoz6888/88MPP3Dr1i2zx1WQCROUjivZ2UoR2osXtY5ICCFEYZSKBH3s2LEcPnw43wT07t60er2+wH61s2fPxtXV1bj5+PioE2BOGi72WdhZZZKVlVXgbhs3wldfKevC7ufcOaW4S9u2D7507dq1mTZtGg0bNsTb27uIgd9rx/eTaFxdT0KaNfX6fkFY2J278EuWQElbsMs6dMsjCbooKsMI+ooVK2jfvj0NGjQwJuiLFi1i/Pjx+f4enjNnDv7+/gQEBPC9oWIm0LdvX1q1aoW/vz9r1qwBYPr06fj5+dG7d29iY2PznGfZsmW0adOGpk2b8vbbbxtfv98xQpRU7grud9O6zVp+dDr48kto2hRiY2HwYKWPuxBCCMv2gM7V2hs3bhzr169n586d1KhRw/h6tWrVAGUkPXfl9NjY2HtG1Q2mTp3KpEmTjD8nJCSol6Tfdr82a4a1a02b3v8c7u6wYYOy3u34cXjQMs7XX3+d1157rcTFaTIyMqie/CsAFx374JvjzsiRyuzXp5+Gvn1LdHpAKrlbImmzZiGcnMAEHRkKfe0icHNz49ixY8yfP59FixYB4OLiwq5du4iIiGDDhg33HHPgwAFWr17NgQMHSElJITAwkK5du+Lt7c2KFSvw8PAgPj6etm3bUr16dTZt2kRERAQ3btzAz8+P0aNHAxAZGcnGjRsJCwtDp9MRHBxMWFgY1tbWBR4jhBoKquAOdwYLLClBB+V/7bVrlZsK//yjjKp/8YXWUQkhhLgfix1B1+v1jB07ljVr1rBt2zZ8fX3zvO/r60u1atXYsmWL8bWMjAx27NjBQw89lO857e3tcXFxybOpraDR+9TUO0npgxJ0Nzfo3l15/tNPD76mnZ1dnuS8uGvRt675nI71M8jOgXq9Puatt5SYvb3hs8+Kdcp7GEbQJUG3HNJmzULodFChgjZbAb+3CuLu7s62bduws7PjkUceAZQEPSQkhOeee46KFSvec8yuXbsYNGgQDg4OeHh40K1bN/bv3w8ohS6bNWtG586duXDhAtu2bWPAgAHY2dnh5eWVpzvG1q1bCQsLo1WrVrRs2ZLjx49z5swZ9uzZU+AxQpRUVtad2W/3G0HXsg96QerUgR9/VP43/7//U2byCSGEsFwWm6CPGTOG7777jh9++AFnZ2diYmKIiYkhNTUVUBLhiRMnMmvWLNauXcvRo0cZOXIkTk5OPPnkk5rFbXV3c/PbIiOVNeWVKytruR9kyBDl8eefC3/tzMxMPvnkE3r16lWsu/iphz8B4GRKI/Yfq4ehi9KSJcqovhpkirvlkSnuoqgMU9wnTJhgfM3FxYXU1FTG5tdDMh+G5UihoaHs3r2bvXv3EhERQc2aNXFwcCjwZqder2fUqFGEh4cTHh7O6dOnGT58+H2XNwlRUsePQ1oaODvnX8jVEqe45/boo/Dee8rz0aNh3z5t4xFCCFEwi03QQ0JCiI+Pp0uXLnh5eRm3VatWGfeZPHkyEydOZPTo0bRu3ZpLly7x559/4uzsrFncBSXouae3F+Y7ZHAw2NrCkSNw4kThrh0bG8s777zD5s2b+eabbwoZseLIv7vpXvcKAK6tZxintj/zDPTuXaRT3ZdhintUlFINV2jPkKBnZmZa7JdLYVkGDx6MXq/n+eefN74WEhJCdnb2PbOdDDp27MiaNWtIT08nLi6O0NBQAgMDSUhIoFKlSjg6OrJv3z5OnjxJhw4dWLt2LRkZGcTExBAaGmo8T1BQEKtWrTK2dLt48SI3bty47zFClJRhenuLFpDfx7zh5pAljqAbTJ2qfLfIyIBBg5R16UIIISyPxSboer0+323kyJHGfXQ6HTNmzODKlSukpaWxY8cOY5V3rRQ0glPY9ecG7u5we+Zooaa5A1SvXp133nkHUG5eGL7AFsaR9ZNxdoRLic589O1jnD4N1avDp58W+hSFUq2aUjQ6JwfOnlX33KJ4DGvQQUnShTCF1q1bM2TIEFq1akXnzp2ZOXMmXl5e9OzZk/j4eJo3b86iRYsICAjA09OTnj170rRpU1588UU6d+5sPI+/vz9TpkyhS5cuBAQE8Nhjj5GcnEybNm0KPEaIkrpfgTiw/BF0UG4sfPMNNGyoVHQfOlSZui+EEMKy6PSWfLvXxBISEnB1dSU+Pr5k69Gj13Bq/YuEnbHD74lfCQwMvGeXgQOVQi1ff62MShfG11/Dc88pSf2DKr8bZGZm0rx5cyIjI3n55ZdZvHjxA4+Ju3mTmK8r4+etJyxjAh2e/Qy9Hv74Q5kWp7aWLeHQIVi3Dvr1U//8omjS0tJwdHQEKPn/C6LQ0tLSOHfuHL6+vjg4OGgdjniAgv6+VPscsVAXL17Ex8eH6OjoPIVay5sOHWDPHvj2Wxg+/N73IyIiSEtLo1GjRri6upo/wCI4fhzatFFqUk6aBJ98onVEQoiyTD5His5iR9BLFZ+B1B9zjac/vZRvcg6wZo1SKK4oy+P791e+FIwYUbie6AC2trbGqspffPEFBw3z8u7jrx/ews9bT0qGFZ9vmIFer8RpqjbGsg7dsuQeQZd16EIIkVd2NoSHK88LGkFv1qwZbdu2tfjkHJTOMCtWKM8//VQpICeEEMJySIJuRg4OUJROaB4esGuXcoe7gKXt+erSpQtPPvkker2e0aNH33fKXU5ODs5XlPXqkSmd+PFnN3Q6eOutwl+vqKTVmmWxsrLCxkbpuCgJuhBC5HXiBKSkKA0PDDeYS7uBA5U16aDM1DMswxNCCKE9SdDLqI8//hgXFxf+/fdfYyuj/Oz443seaZQMwMpDcwHlg/tBvddLQkbQLY+0WhNCiPwZJqI1bw7W1vnvc/XqVc6fP09KSorZ4iqp996DHj2U2X0DBkARytYIIYQwIUnQ1XD2G2KX2PP9hAr5JsPHjysF3yZOLN7p4+Jg2TI4f77wx3h5ebF8+XIiIiJo27ZtgfvF7HoXG2s4EVedeV+3AeDNN4sXZ2HJCLrlkVZrQgiRP0OCXtD0doANGzawfPlyzhflg1pj1tbwww9Qu7ZStHXYMGU6vxBCCG1Jgq6G7GQ8K2Zgr0shK5+SqGfOwNat8PffxTv9k0/Cs88qH6RFMWDAABo3blzg+2dOHaerz2kA/jgzmZwcZd15y5bFi7OwDCPoly5BcrJpryUKx7AOXRJ0IYTI60EV3KF0VHHPT6VKSgFbBwelMOzMmVpHJIQQQhJ0leXXB/3SJeWxevXinXPgQOWxsO3W8hMeHk64ocrNbWGrX6OaG1xPtmfaFy8DMG1a8a9RWB4eygZw+rTpryceTKa4CyHEvXJylK4jcP+b16WhD3pBmjeHL79Unr/3ntJhRQghhHYkQVdZfn3QL15UHovbWWDAAGUq2qFDymh8Uf3444+0atWKZ599luzb89dSUlKol7MZgC1Rg0hJtaVTJ+jYsXgxFpVhFF2muVsGmeIuhBD3OnlSaUfm6AiNGhW8X2kdQTcYPhzGj1eeP/WUUhhPCCGENiRBV9n9RtCLm6BXrgxduyrPf/656Md369YNFxcXDh06REhICACbfvyQdnWzycyGaV/PAcwzem5gWIcuheIsgyToQghxL8P09ubN4Xazi3yV5hF0g48/hk6dIDFRGRhITNQ6IiGEKJ8kQVdZfgm6YQS9uFPcAQYPVh6LM83d09OTWbNmAfDWW28RExMDJ5Ve6bsvNONcTA1atVKquZqLjKBbFlmDLoQQ9zIUiHtQbZbSPoIOYGsLq1eDt7dS3PaZZ6AU328QQohSSxJ0lZliBB2Uu9lWVsqXhbNni378qFGjaNWqFfHx8Yx7cTiPNroJwJw17wNK5fZ8ZuebjIygWxZZgy6EEPcqTAV3KBsJOkC1avDLL0qy/ssvMHeu1hEJIUT5Iwm6Guw8OHuzIilUwtHR8Z63DTlPSUbQPT2hSxclid6zp+jHW1tbs3jxYnQ6HdUztuJkD6euubPp3974+UH//sWPrThkBN2yyBR3IYTIq7AF4gCaNm1KUFAQ1UvyQW8h2rWDzz9Xnr/5JmzZom08QghR3txnRZUotFpDqTN2KHUKePvUKUhPv//6tcJYsECpfu7lVbzj27Rpw7PPPsuwWl8B8H9bnwJ0TJ2qjM6bU716yuP160qfd3d3815f5CVT3IUQIq8zZyAhAezt4T4dSwHu29K0NBo1Cvbvh6++gscfV2YS1K6tdVRCCFE+yAi6mdjbK5XYS6JJk+In5wZ9uzYhsC5k58A3OyZTu7by4Wtuzs53/iwyzV17MsVdFEW7du2YN2+e8eehQ4ei0+lITk4G4PLly9jZ2XH8+HGtQhSixAzT25s1U6Z8lyc6HSxcCIGBcPOm0u41NVXrqIQQonyQBL2Uysoq3nGpJ78FYOcJH64lVGfKFO2+eBimuUuCrj2Z4m5BkpML3tLSCr/v3d+mC9qvGNzc3Ei8XeI5OjqazZs34+zsTFxcHABLliwhKCgIPz+/Yp1fCEtgqOD+oOntALdu3eLy5cskJSWZNigzcnBQ1qFXqaJM9X/xRSkaJ4QQ5iAJuhqifuD8Ike+meDG6dOn87y1bh088ojSvkQNx45Bt27KevSiSktLo6FDBAA/7n4CLy8YNixTncCKwVAoTtaha08SdAtSsWLB26BBeff19Cx431698u5bu3b++xWDu7u7MRFZuHAhw4YNo0qVKsTFxZGZmcmSJUuYMGFCkc8bFRVF69atixXT3UaOHMnvv/9usv1F2VfYAnEAoaGhfPnll0RERJg2KDPz8YFVq5QZgN9+C4sWaR2REEKUfZKgqyHjFrXc03DSxZN119D2iROwdSscOaLOpSpVgtBQ2L0bLlwo2rF/b1xGi1o5ZGXD2gOT6NlzI82bN2Lr1q3qBFdEUijOcsgadFEUhhH05ORkli5dyoQJE3BxcSEuLo61a9fi7OzMo48+qnWYQhSbXn9nBL0wCbqhintp7oNekK5d71Rzf+UV2LVL23iEEKKskwRdZbq7epXdVLqZUamSOuevVg06d1ae//xz0Y69cegLALZF1uJmclV0uj84e/YsY8eO1WTtsbRasxyyBt2CJCUVvP3yS959Y2ML3vePP/LuGxWV/37FYBhBX7FiBe3bt6dBgwbGBH3RokWMHz8enU53z4j4a6+9xvLly4mKiqJZs2aMGDECPz8/hg4dmm9iM2fOHPz9/QkICOD77783vt63b19atWqFv78/a9asMb4+ffp0/Pz86N27N7GxscbXly1bRps2bWjatClvv/32A/cX4tw5uHUL7OyU+i8PYvjsL+1t1gryyitKvZqsLBg8GC5f1joiIYQouyRBV1lBCbqHh3rXGDJEefzpp8Ifk5KSgl+FowCs3juM7t3h00/fw9PTk//++y9PwSdzyT2CXgYHHUoVmeJuQSpUKHhzcCj8vne3fCxov2Jwc3MjISGB+fPnM3HiRABcXFzYtWsXERERjBgx4oHnOH78OFOnTiUyMpKrV6+y665huQMHDrB69WoOHDjAjh07ePvtt7l8OytYsWIFBw8eZPfu3bz55pvo9Xr27dvHpk2biIiIYOnSpey53Y8yMjKSjRs3EhYWRnh4OIcOHSIsLKzA/YWAO9PbAwKUJP1ByvIIOihF45YuBX9/uHpVSdLlfq4QQpiGJOgqM0eCPnCg8mG5dy9ERxfumL83fEWzmobp7a/w1FPKl+yPPvoIgHfffZcLRZ0zX0J16ih/jsREZSBQaEemuIuicHd3Z9u2bdjZ2fHII48ASoIeEhLCc889R8VCrG1v2LAhjRo1QqfT0aJFC6KiovK8v2vXLgYNGoSDgwMeHh5069aN/fv3AzBv3jyaNWtG586duXDhAjExMezZs4cBAwZgZ2eHl5cXQUFBAGzdupWwsDBatWpFy5YtOX78OGfOnClwfyHgToJemAJxUPZH0EG5n7d2Lbi5QVgY3L43J4QQQmWSoKvMHAm6lxd07Kg8L+w097iI/wPgr6O+pOsr07+/8vpTTz1Fp06dSElJ4ZVXXlEvyEJwcIBatZTnsg5dWzLFXRSFYYp77kJwLi4upKamMnbsWONrNjY2eRKW3DeADP/mAKytrcnOzr7vNfV6PTqdjtDQUHbv3s3evXuJiIigZs2apKenG9/P77hRo0YRHh5OeHg4p0+fZvjw4QXuLwQUbf053BlBL8sJOkC9evD998rN9ZAQWLZM64iEEKLskQRdZeZI0KFo09yTk5Np4hIJwKq9TzFw4J2ZrTqdjkWLFmFtbc2aNWvYtGmTuoE+gKxDtwwyxV0UxeDBg9Hr9Tz//PPG10JCQsjOzsbX19f4mqenJ5cvXyYxMZGkpCS2bNlS6Gt07NiRNWvWkJ6eTlxcHKGhoQQGBpKQkEClSpVwdHRk3759nLx9d69Dhw6sXbuWjIwMYmJiCA0NBSAoKIhVq1YZW8BdvHiRGzduFLi/EHp90Sq4Q9mf4p7b//4HM2Yoz19+GQ4c0DQcIYQocyRBV4OtCxfi7EjJcTFOFTbIzlbuNKudoA8aBD16wPPPP3j99o7f/o+AGnoysuDXAxN46qm87wcEBBhHwjZu3KhuoA8gldwtgyTowhTs7OyYPHkyLVu25LHHHiMgIKDQx7Zu3ZohQ4bQqlUrOnfuzMyZM/Hy8qJnz57Ex8fTvHlzFi1aZDxnmzZt6NmzJ02bNuXFF1+k8+1qmv7+/kyZMoUuXboQEBDAY489RnJycoH7C3H+vHJz3dZWWYNeGHXq1KFTp055blCVZW+9Bf36QXq6suzu2jWtIxJCiLJDpy8Pt3sLkJCQgKurK/Hx8bi4uJjsOoYk3Uqj2yErpzXm8SbH2XCoLqN+OM2FC0pP09wSExPZuXMnvXv3Nmts8+cr69gGDry3QLUwnwULFjBhwgQee+wxVq1apXU45UJaWhrnzp3D19cXh7uLvwmLU9Dfl7k+R7Ry8eJFfHx8iI6OpkaNGlqHYxZr1ig3wVu0uDPVXdwrPh4CA5UZcEFBsHkz2NhoHZUQwtKUx8+RkpIRdDOwttYuOU9MTCTA7T8AVv/zNE8+eW9yDuDs7Gz25BxkBN1SyBp0Icq+2bNno9PpjJX3Rf6KWiCuvHJ1VYrGVagA27bB1KlaRySEEGWDJOil3KVLyij0lSv5v7/ztxCaVFemt687OO6e6e35iY2NZf78+eoGWgDDGvTTp6GM19axaDLFXYiybf/+/SxZsoSmTZtqHYrFK2qBOFBamV6/fp3ExETTBGWhmjSB5cuV5x9/DDIBSwghSk4SdDWcX81/8yqwfGJVrl69anz57Fno3h1eeMF0lx46VJkiXlA198RjXwOw+XB9atZ150HfzZKTkwkICGDixImsX79e3WDzUbu2MiUuLQ0uXjT55UQBpM2aEGVXUlISw4YN48svv8Td3V3rcCxacQrEgXIDZNGiRezYscM0gVmwwYNh8mTl+bPPwpEj2sYjhBClnSToaki/TqOqKVTQx+ZpFXT5Mvz1F5jy83rwYOUxv2ru8bdu0cztBACr/xlZqNHzChUq8MwzzwAwfvx4UlJS1Ao1XzY2Sj90kGnuWpIp7kKUXWPGjKF3797GnvWiYBcvKgXPrK0LXyAOykcf9Pv54AN45BFISYEBA+DWLa0jEkKI0ksSdJXlbrNmqhZruQ0apDzu2nXvNPedv4XgVx3SM3WsPziaJ58s3DmnT5+Oj48P58+fZ/bs2eoGnA/DOnRptaYdmeIuRNm0cuVK/v3330L/Lk9PTychIcG4lbcp24bp7U2agKNj4Y8rT23W8mNjAz/+CLVqwZkzMGyYLFsTQojikgRdZeZO0H18oF07ZVre3VXQk48r09v/iGhAmw5uVK9euHNWqFCBefPmATB37lxOmThzNqxDlxF07UiCLkTZEx0dzYQJE/juu+8K3Slg9uzZuLq6GrfGjRubOErLUtwCceV9BB2gcmWlAr6DA2zcCO++q3VEQghROkmCrjJzJ+gAQ4Yoj7nXocfdvEnLSqcBWP3Ps4Wa3p7bwIED6dmzJxkZGYwdO9akowIygq49WYMuRNlz8OBBYmNjadWqFTY2NtjY2LBjxw4WLFiAjY1NniVZBlOnTiU+Pt64RUZGahC5doqz/hxkBN2gZUv44gvl+cyZ8Ntv2sYjhBClkSToKtBz5wM5vwTd1DV5DOvQd+6EmBjl+Y71n9PAC9IydGw9/jIDBxbtnDqdjs8//xw7Ozv+/PNP1q5dq27QuUirNe3JGnQhyp5u3bpx5MgRwsPDjVvr1q0ZNmwY4eHhWOfTc9Pe3h4XFxfj5uzsrEHk2ilOBXe4k6CX5xF0gxEjYMwY5fnw4fLZLoQQRWWjdQBlgV4PhrRcixH0mjWhbVvli8W+fdCvH6Sd+AYCYGNEIx7p5UzFikU/b/369XnzzTe5fv06QUFB6gduvI7yeO4cZGaCra3JLiUKIFPchSh7nJ2d8ff3z/NahQoVqFSp0j2vC6Wwa0wMWFlBs2ZFO9bw2V/eR9ANPv0UwsNh926laNw//1Cs7yFCCFEeSYKuBmtHriZYkaG3zzMikZYGOp3pE3SAFSvAywtcXJR+rC0rnwVg9d7nGPlO8c/7zjslOLiQqldXKuZmZUFsLIVeKy/UI1PchRDlnWF6u58fODkV7dhq1arRrl07qlatqn5gpZCdnbLsrmVLiIxU2q+tWqV8JxJCCHF/kqCrwKres1St9yzD7nr966/hyy/NU8m0YcM7z/dv/4mHvSArGw5cfo7vVOqso9frSUhIwNXVVZ0T3mZlBZUqKcn5tWuSoGtBprgLUT5s375d6xAsVnGntwPUrFmTmjVrqhtQKVetmpKkd+mitIINDITXX9c6KiGEsHyyBt3ErK3NP2U7JvxHAP4540m/QW7YqHAb5syZM3Tr1o0BAwaYZApflSrK47Vrqp9aFIJMcRdF0a5dO2OnB4ChQ4ei0+lITk4G4PLly9jZ2XH8+HGtQhSiyIpbwV0U7KGHYP585fkbb8Bff2kbjxBClAaSoJch27Ypd6htrv8DwF9HOzLs7mH9YrKysiIsLIzQ0FBWrlypzklzMSTo16+rfmpRCLlH0GUNpXgQNzc3Y3/s6OhoNm/ejLOzM3FxcQAsWbKEoKAg/Pz8tAxTiCIpbgV3gMzMTOLj40lKSlI3qDLgpZdg5EhlNuHjj8P581pHJIQQlk0SdBVknVtNxMcuLH/FJ8+H8xNPKC3QoqPNE4eDAxw8eJVO9W8BcPDS06qNBPj6+jJt2jQAXn31VRISEtQ58W2VKyuPMoKuDcMadJBp7lrR6/UkZyRrshX1poy7u7vxd93ChQsZNmwYVapUIS4ujszMTJYsWcKECROIioqidevWJf5vM3LkSH7//XeTHyPKr5gYpUicTgfNmxf9+OPHj/PZZ5+ZtONJaaXTweLFyo2PGzdg4EBITdU6KiGEsFyyBl0F+tQrNPNO5MSFxDx9ZX/7DZKTYc4c88TRti20a7gCT1dISrOiSqNeqhZkef311/nmm284deoU77zzTp4priUlU9y1ZRhBByVBz/2zMI+UzBQqztamzHHS1CQq2FUo9P6GEfTk5GSWLl1KWFgYe/bsIS4ujrVr1+Ls7Myjjz7KeRkqE6WEYf15w4bFqzYubdbuz9ERfvkFWrdW/luPHq3U6ZGicUIIcS8ZQVeD/t4+6Dk5SnIO5mstYm0N/dr+AsCO47V4pIfdA44oGnt7ez7//HMAPv/8cw4fPqzauWWKu7Zyj6DLOnTxIIYR9BUrVtC+fXsaNGiAi4sLcXFxLFq0iPHjx+dpOWkwZ84c/P39CQgI4Pvvvze+3rdvX1q1aoW/vz9r1qwBYPr06fj5+dG7d29iY2PznGfZsmW0adOGpk2b8vbbbxtfv98xQtxPSQrEwZ0EXZYIFaxWLVi5UikMu3w5fPGF1hEJIYRlkhF0Fei5N0E3JOdgvgRdr9fTuoaSNP91tDtvTlb/Gj179mTQoEH88ssvjBkzhp07d+b7RbyoZARdW9bW1lhbW5OdnS0JukacbJ1ImqrN+lUn26L1lHJzc+PYsWPMnz+fRYsWAeDi4sKuXbuIiIhgw4YN9xxz4MABVq9ezYEDB0hJSSEwMJCuXbvi7e3NihUr8PDwID4+nrZt21K9enU2bdpEREQEN27cwM/Pj9GjRwMQGRnJxo0bCQsLQ6fTERwcTFhYGNbW1gUeI8SDlLRA3J2b8zKCfj/dusGHH8LkyTBhAjRtCh06aB2VEEJYFknQ1ZDrhrnhQ9qwFF2nU6Z2mcOZU5G0r5MGwF/HnuWZK3cSXzXNmzePTZs2cfPmTa5evUq1atVKfE5Zg649e3t7UlJSZA26RnQ6XZGmmWvJ3d2dbdu2Ubt2bR55ROnj6OLiQkhICC+++CIV87kruWvXLgYNGoSDgwMODg5069aN/fv3ExwczLx581i/fj0AFy5cYNu2bQwYMAA7Ozu8vLwICgoynmfr1q2EhYXR6vZQZ1JSEmfOnOH69esFHiPEg5SkQBzIFPeieO01OHAAVq+GwYOV2QteXlpHJYQQeRm+lxRF9+7dcVQh8ZMEXQX6fKa4GxL0ihXNt8bq2I6vqFcBrsbbcjS6DevXK3en1ebj48O2bdto0aIFtir1kJMRdO0ZEnQZQRcPYpjiPmHCBONrLi4upKamMnbs2EKdQ6/Xo9PpCA0NZffu3ezduxdHR0caNWqEg4MDaWlpBR43atSoPFPbQblxqMZsHlH+XLt2p5hrixbFO4fh355McX8wnQ6++gqOHVO2IUOULjR26q7KE0KIEunfv3+R9tfpdJw6dYo6deqU+NqyBl1ldyfozs7mu3bGhT8A+OuoH1276mjTxnTXatOmjWrJOcgadEtgWIcuCbp4kMGDB6PX63n++eeNr4WEhJCdnY2vr2++x3Ts2JE1a9aQnp5OXFwcoaGhBAYGkpCQQKVKlXB0dGTfvn2cPHmSDh06sHbtWjIyMoiJiSE0NNR4nqCgIFatWmVs6Xbx4kVu3Lhx32OEuB/D+vP69cHFpXjnkBH0oqlYEdauBVdX2L0bJk3SOiIhhCWaPXs2Op2OiRMnGl/T6/XMmDEDb29vHB0d6dKlC8eOHTPJ9WNiYsjJySnU5uRUtOWC9yMj6CrQ62xISIUsvY0xQU9JUd4z1/rz7OxsatmfAuDvk33ZvBlUzJ8LlJGRwbx583jkkUeMU06Lw5Cg37ihFNizkltHZmeo3C4JujCF1q1bM2TIEFq1aoVOp2PmzJl4eXnRs2dPFi1aRPPmzWnWrBkBAQF4enrSs2dPmjZtSsOGDencubPxPP7+/kyZMoUuXbqQk5ODs7MzK1eupE2bNgUeI8T9lHR6O4CrqystW7bE1dVVnaDKgfr14bvvoG9fWLQIAgNhxAitoxJCWIr9+/ezZMkSmt41HXju3Ll8+umnLF++nAYNGvD+++/TvXt3Tpw4gbOKI6MjRowo0nT14cOH41Lcu7x30enL8XyshIQEXF1diY+PV+0/aG7Z2ZCeDireUCnQv3tDaXY6CGsreHb9Gb5eWfLpFYXx2muv8cknn9CmTRvCwsKMowhFlZEBhs5e167dWZMuzKdBgwacOnWKv//+m44dO2odTpmXlpbGuXPn8PX1xcHBQetwxAMU9Pdl6s8RrV28eBEfHx+io6OpUaOG1uGYxODBSguwjz5S1kcL85oxA2bOVL4D7N5dshslQgjLU5zPkaSkJFq2bMnixYt5//33ad68OZ999hl6vR5vb28mTpzIlClTAGVgqWrVqsyZM4cXX3zRlH8Us5FxShOytjZPcg5wdu/XWFvBicsVCXy4Dno9REYqLU1MadKkSTg7O7Nv3z6++uqrYp/Hzk6Z6gayDl0rMoIuhCiPSlrBXZTM229Dnz7KgMbAgbLUTYiyKjExkYSEBON2v++bY8aMoXfv3sZCtAbnzp0jJiaGHj16GF+zt7fn4YcfZs+ePSaL3dwkQS8jrGK3A/DXsRb07AknT0KTJsp0scRE013X29ubmTNnAvDGG29w48aNYp9L1qFrS9agCyHKmxs3ICpKeV6SBD0nJ4fU1FRSDOvbRKFZWcG330K9enDhAjz+OGRlaR2VEEJtjRs3xtXV1bjNnj073/1WrlzJv//+m+/7MTExAFStWjXP61WrVjW+ZyppaWns27eP33//nfXr1+fZ1CZr0FWQenoVx9aO4ehVd4Z/eBwbGxt++035wAkKgpdeMu31U1JS8PO4CEBEzGOMqQN6vfJhd/o0/PknDBpkuuuPGzeOZcuWceTIEaZOncqSJUuKdZ4qVZR4ZQRdG4YRdGmzJoQoLw4dUh7r1gU3t+Kf5/Lly3z11Ve4ubnl6W4gCsfNTSka164dbN0K06bBnDlaRyWEUFNkZCTVq1c3/mz43plbdHQ0EyZM4M8//7zv8r+7u7YYOsOYyqZNm3j66ae5ns8ook6nIzs7W9XryQi6CvRJF2hd/QZOGaeNLVaOHoWfflJ6fZragZ2/4ucN2TngUv9JQGlj0q+f8v66daa9vo2NDYsXLwZg6dKl/PPPP8U6j/RC15ZMcRdClDdqTW83fDGUKu7F5+8PX3+tPJ87V/kOJYQoO5ydnXFxcTFu+SXoBw8eJDY2llatWmFjY4ONjQ07duxgwYIF2NjYGEfO7x4tj42NvWdUXU1jx45lyJAhXLly5Z7q7Won5yAJuioe1Afd1K4c+g6AA2cr83B3D+PrwcHK44YNpp8u1rFjR0aMGIFer2fixInF6gUrU9y1JVPchRDljaHFWkkLkxkKpJbjuruqeOyxO4X6nnlG6ZMuhCg/unXrxpEjRwgPDzdurVu3ZtiwYYSHh1OnTh2qVavGli1bjMdkZGSwY8cOHnroIZPFFRsby6RJk0x6EyA3meKugtwfx1ok6I4JewHYFtmBcbl6iT70EHh4wM2bSmXUhx82bRxz5swhMTGRWbNmFWuaiSFBlxF0bcgIuhCivFFrBF36oKtn9mzlxsm2bTBgAOzff6eIrBCibHN2dsbf3z/PaxUqVKBSpUrG1ydOnMisWbOoX78+9evXZ9asWTg5OfHkk0+aLK7Bgwezfft26tata7Jr5GaxCfrOnTv56KOPOHjwIFeuXGHt2rX079/f+P7IkSNZsWJFnmPatm3L3r17zRwpyoLv28ydoF+/do3AGnEAXM55Ks/1bGyUyqjffKNMczd1gl61alV++eWXYh8vCbq2ZA26EKI8uXULzpxRnqs1xV1G0EvOxkbpQNO6NZw6BU89Bb/+qhSTE0KIyZMnk5qayujRo4mLi6Nt27b8+eefqvZAv9vChQsZMmQIf//9NwEBAdja2uZ5f/z48apez2IT9OTkZJo1a8YzzzzDoAIqnD366KMsW7bM+LNhiq656dEuQT+w7RsedYeUdB01W/a+5/3gYCVB37rVtHHkJyoqitq1axd6f1mDri0ZQRdClCeGAnG1a0OlSiU7l4ygq6tKFaU3fceO8Ntv8P77Sjs2IUT5s3379jw/63Q6ZsyYwYwZM8wWww8//MDmzZtxdHRk+/bteWYK63Q61RN0i70f2atXL95//30GDhxY4D729vZUq1bNuHl4eBS4r0nlumFu7gQ97rgyYv33iZo80vPeaoc9esDGjVDMum3FotfrGTNmDHXr1mXXrl2FPk7WoGtL1qALIcoTNfufywi6+lq3hpAQ5fmMGUo9HSGE0MJbb73Fu+++S3x8PFFRUZw7d864nT17VvXrWWyCXhjbt2/H09OTBg0a8MILLxAbG3vf/dPT00lISMizqUNHVjbouXM3xRwJul6vp1JmOAC7TnejWbN796lYEXr1gvt0KlCdTqcjIyODnJwcRo8eTVYhK9TJFHdtyRR3IUR5YkjQS1ogDsDBwYGAgACaNGlS8pMJo2eegZdfVlYSDhumtGIVQghzy8jIYOjQocbZUqZWahP0Xr168f3337Nt2zY++eQT9u/fT1BQ0H1H/2bPno2rq6tx8/HxUSUW51ZTsHlKz9AFd6a2bdsG8fHQt68ql8jX2dMnaF8nFYBMj2ctan3W7Nmz8fDw4MiRI3z++eeFOiZ3gi6DEOYnU9yFEOWJWhXcQSliNHDgQPqa8kO/nPrsM2jfXvlONWDAnQEQIYQwlxEjRrBq1SqzXc+CUrqiGTp0KL1798bf35++ffvyxx9/cPLkSTbcZw7U1KlTiY+PN27R0dEmi8/aGlxcIJ8Wf6o5suMrnB3heqIN/p3bF7hfTg688QY0bgx3tQ00mcqVK/Phhx8C8M4773D58uVCHKM8pqfLB7AWZIq7KKx27doxb948489Dhw5Fp9ORnJwMwOXLl7Gzs+P48eNahSjEfSUkwMmTynM1EnRhOnZ28PPPUK0aHD0Kzz8vN/GFEOaVnZ3N3Llzefjhhxk3bhyTJk3Ks6mt1Cbod/Py8qJWrVqcOnWqwH3s7e1xcXHJs5VmyWc2ArD1WCN69Cj4r9LKShnRP34cfv/dXNHBc889R5s2bUhMTOQ1Q2PT+6hQ4c5UfFmHbn4ygi4Ky83NjcTERACio6PZvHkzzs7OxMUpHSWWLFlCUFAQfn5+WoYpRIEMBeJq1rxzc7gk9Ho9WVlZZGZmlvxk4h7e3vDTT0qF91Wr4NNPtY5ICFGeHDlyhBYtWmBlZcXRo0c5dOiQcQsPD1f9emUmQb9x4wbR0dF4eXmZ/dqJJ1axd64330xtanxt5Eh44QXTJZrZ2dn4Oii3/49c64On5/3379dPeVy3zjTx5MfKyorFixej0+n48ccf2bZt23331+lkHbqWZA26ZUjOSC5wS8tKK/S+qZmphdq3ONzd3Um6Pc1l4cKFDBs2jCpVqhAXF0dmZiZLlixhwoQJRT5vVFQUrVu3LlZMdxs5ciS/F+GOZFH3F6WbYXq7GgXiAFJTU/nggw+YNWuWFIozkY4dwTBxZ/JkZeBBCCHMITQ0tMDtQflNcVhsm7WkpCRO56oGcu7cOcLDw/Hw8MDDw4MZM2YwaNAgvLy8iIqK4s0336Ry5coMGDDA7LFmx5+iXY0rRF9W5o/n5CitzfR6eO8901zz6KEwAn2V4msV6z37wP2Dg2H6dPjrL0hOVkarzaFVq1a8/PLL/Pjjj9y8efOB+1epAtHRkqBrQUbQLUPF2QVXlvxf/f+x4ck7y3g8P/YkJTMl330frvUw20duN/5ce35trqfce8dQ/07RkwnDCHpycjJLly4lLCyMPXv2EBcXx9q1a3F2dubRRx8t8nmFMBc1C8QBeVru5OTkYG1trc6JRR5jxsD+/cp3rKFDlb/HmjW1jkoIUR6kpaVx+PBhYmNj87TU1Ol0qtcfsdgR9AMHDtCiRQtatGgBwKRJk2jRogVvv/021tbWHDlyhODgYBo0aMCIESNo0KABYWFhJm1S/yCGj+fU1Dvro0wVzoVDv2BrA1HXHGjXrf4D9/f3V3q9pqXBli2miakgs2bN4uTJkwwePPiB+xqmGsoUd/OTNeiisAwj6CtWrKB9+/Y0aNAAFxcX4uLiWLRoEePHj0en090zIv7aa6+xfPlyoqKiaNasGSNGjMDPz4+hQ4fmO+o4Z84c/P39CQgI4Pvvvze+3rdvX1q1aoW/vz9r1qwxvj59+nT8/Pzo3bt3nq4ey5Yto02bNjRt2pS3czVTLmh/Ufap2WINyFPZV0bQTUengy++gBYtlO8JgwYp32uEEMKUNm3ahI+PD+3ataNfv370798/z6Y2ix1B79Kly30/5DZv3mzGaO7PEKbhMXeBM0dH01wz/twe8If9Z2sT/PKD99fplFH0+fNh/Xowwb+lArm6uhZ6X5nirh2Z4m4ZkqYWXCHR2irvqFzsawUnlVa6vPdfoyZElSiu3Nzc3Dh27Bjz589n0aJFALi4uLBr1y4iIiLuW6zT4Pjx46xatYqGDRvStWtXdu3alaezxoEDB1i9ejUHDhwgJSWFwMBAunbtire3NytWrMDDw4P4+Hjatm3LgAED2L9/P5s2bSIiIoIbN27g5+fH6NGjiYyMZOPGjYSFhaHT6QgODiYsLAxra+t89xdlX1ISnDihPFdrBD13gp57ZEWoz9ER1qxR+qQfOKCMqi9dqnzPEUIIUxg7diyPPfYYb7/9NlWrVjX59Sx2BL10uZ2Z3/50MNzNdXTEZK3P3PXK+vPo5NbcHvh8IMM69N9/h+xs08R1P3q9nl9//fW+a1MlQdeOTHG3DBXsKhS4Odg4FHpfR1vHQu1bHO7u7mzbtg07OzseeeQRQEnQQ0JCeO6556hYseBp+gYNGzakUaNG6HQ6WrRoQVRUVJ73d+3axaBBg3BwcMDDw4Nu3bqxf/9+AObNm0ezZs3o3LkzFy5cICYmhj179jBgwADs7Ozw8vIiKCgIgK1btxIWFkarVq1o2bIlx48f58yZMwXuL8q+8HDlhnr16qDW96y7p7gL06pdG378UfmO9fXXsGSJ1hEJIcqy2NhYJk2aZJbkHCRBNwlDgu7gcP/9iis9PR2/qrcAsKpS+HWenTpBw4ZKon67ALNZnTt3jsGDB7NgwYICZ0AYprhLgm5+MsVdFJZhinvum20uLi6kpqYyduxY42s2NjZ5kpXc/7bsc/WgtLa2JvsBdw31ej06nY7Q0FB2797N3r17iYiIoGbNmqSnpxvfz++4UaNGER4eTnh4OKdPn2b48OEF7i/KPrWnt4NMcddC9+4wa5byfNw4CAvTNh4hRNk1ePBgtm/fbrbrSYKugrs/jE2doB+P2E2d21XbfVv2KvRxtrZKq7WlS8HNzTSx3U+dOnUYN24cAOPGjcs3ETSMoMsadPOTEXRRWIMHD0av1/P8888bXwsJCVG6S/j6Gl/z9PTk8uXLJCYmkpSUxJYiFMDo2LEja9asIT09nbi4OEJDQwkMDCQhIYFKlSrh6OjIvn37OHm7mXWHDh1Yu3YtGRkZxMTEEBoaCkBQUBCrVq0ytoC7ePEiN27cKHB/UfYZKrir2f9cRtC1MXmysg49MxMGD4aYGK0jEkKURQsXLmTNmjWMHDmSTz75hAULFuTZ1Gaxa9BLI8PHs6kT9PP/rqO5C5yOcaT5II8iHav1gNHMmTNZtWoVp06d4qOPPuKtt97K875McdeOrEEXarOzs2Py5Mm0bNmS+vXrExAQUOhjW7duzZAhQ2jVqhU6nY6ZM2fi5eVFz549WbRoEc2bN6dZs2bGc7Zp04aePXvStGlTGjZsSOfOnQHw9/dnypQpdOnShZycHJydnVm5cmWB+4uyT+0K7qAk6IYlG1LB3Xx0Oli2TBl8iIyExx6DrVuVAQkhhFDLDz/8wObNm3F0dGT79u15bsrqdDrGjx+v6vV0+nI8FyshIQFXV1fi4+NxcXEp9nn02VlkpKegR4eDkzPZ2RAfD1lZPLA/eXF880Ybnm66n5/3+zHo08giJ905OUqbkpo1QYO28fz44488+eSTODg4EBkZmWfEbdcuZSp+3bqQq8ueMIM///yTnj170qxZM8LDw7UOp8xLS0vj3Llz+Pr64mCqu3lCNQX9fan1OWKpLl68iI+PD9HR0dSoUUPrcEosORlcXJTPwUuXwNtb64iEGk6ehMBASEiA8eOVgrhCCMtQFj5HqlWrxvjx43njjTfyLGkyFZnirgKdtQ32Ti44OCk91aytwcPDNMk5QCVOAXAxpXWxRsSffBLatYPvvlM5sEJ6/PHH6dq1K2lpaUycODHPe7IGXTuyBl0IUdYdPqwk59WqSXJeljRoAN9+qzxfsEC77zdCiLIpIyODoUOHmiU5B0nQS5309HQae90CwMaz8OvPc+vUSXlcv16loIpIp9OxcOFCbGxsWL9+Pfv27TO+Z5jinpAAMtPavGSKuxCirDPF9Pbc9Hq9FInTSL9+8PbbyvMXXoBDh7SNRwhRdowYMYJVq1aZ7XqyBl0Ft/5bzbHfphCVVI1hM8P45x+l7UdAAOQqaKyKyPC/aXE7ia3TuvAV3HPr21eJa88eZaTakBSbU+PGjfnoo49o1KgRbdq0Mb7u7q7MQMjOVgrFyQiH+UiROCFEWWeKCu4Gc+fONXYyqFSpkvoXEA/0zjtKb/SNG2HgQOW5/FUIIUoqOzubuXPnsnnzZpo2bYrtXYUuPv30U1WvJwm6CrJuRtKhehQx+6MBpVjJkiXQq5f6CXrUwd9o4aYUiGvxhHuxzlGzJrRoodxd/v13eOYZdWMsrLunt4PS07RSJYiNVW4eSIJuPjLFXQhtrC/GdKbu3bvj6Oj44B1FHqao4G5gGDmXEXTtWFkp09sDA+HMGWVJ38aNyo1/IYQoriNHjtCiRQsAjh49muc9U7RslQRdTbf/gkxZxT3h/B5wg/Do2gwuQYG34GAlQV+/XrsEPbcrV66QmZlJzZo1qVxZSdCl1Zp5yRR3IbTRv3//Iu2v0+k4deoUderUMU1AZVRqKhw7pjw3xQi6YW2itFnTlrs7rF2r1Nr580946y2YPVvrqIQQpZm5W7HKGnQVmLMPemWdUtr8SlrrEp2nXz/l8c8/lS8tWlq/fj2NGjVi1KhR6PV6abWmEZniLoR2YmJiyMnJKdTm5OSkdbil0uHDyvKpKlXAFIWEDaMoMoKuvYAA+Oor5fmHH8Ivv2gbjxCidHrzzTfz1MoyF0nQTcBUCXpaWhqNq90CwLZa8QrEGTRvDj4+kJKi9AzVkp+fH2lpaWzevJm1a9dKgq6R3Am6fMEUwnxGjBhRpOnqw4cPL5Mt3Uwt9/R2E8xIlBF0C/P44zBpkvJ85EilT7oQQhTFlStX6NOnD15eXowaNYoNGzaYZSBLEnQVGT7vTZWgR4b/je/t1m312xSvQJyBTqf0Cd25Ex4t2alKrH79+kyePBlQ1qW7uSUDkqCbm2ENOkBmZqaGkQhRvixbtgxnZ+dC7x8SEkJlQ09KUWimLBAHkqBbojlzoGtXSEqCAQMgPl7riIQQpcmyZcu4evUqq1evxs3NjVdffZXKlSszcOBAli9fznUTrceVBF0F5prifna/UkjoVIwTzdsUr0BcbgMGKC3XbCygEsHUqVOpVasW0dHRREa+B8gadHMzjKCDrEMXQpQ9piwQBzLF3RLZ2MDKlcqShpMnYcQIkPsnQoii0Ol0dOrUiblz5/Lff/+xb98+2rVrx5dffom3tzedO3fm448/5tKlS6pds0SpmVSevYuJi8QlXtgLleDIxdoMLGNtQ5ycnFiwYAHBwcHs3fsJMIJr1/y0DqtcyZ2gp6enU7FiRQ2jEaJ8i4yM5MKFC/fcLOtnKCAiiiQ9HQyFd02VoNeuXZukpKQ8v0uF9jw9Yc0a6NgR1q2DWbOUwnFCCFEcfn5++Pn5MXnyZK5du8Zvv/3GunXrAHjttddUuUaJEnSpPKuo1GYKt+qPpGt3pSfeu+/Cq69ChQrqXqeK9SkALqeXrEBcbuHh8OWXULfunbVaWunXrx99+vTh999/B8YQG7uVOwsHhKlZW1tjZWVFTk6OFIoTQiNnz55lwIABHDlyBJ1OZxyNNYzOZmdnF/pcISEhhISEEBUVBUCTJk14++236dWrZDVMSqMjRyAzEzw8lFajphAcHGyaE4sSCwyExYvh+efh7beVmzTl8H8DIUQJ5XfzvHLlysYEXS0lntwcExODp6dnofYtyhq70sTargJuVe/cdHBxUTY1paWl4e+lLJ5yrP4/1c4bGal8aDVpon2CDrBgwQK2bt1Oampzrl3LBOweeIxQj729PampqTLFXQiNTJgwAV9fX/766y/q1KnDvn37uHHjBq+++ioff/xxkc5Vo0YNPvzwQ+rVqwfAihUrCA4O5tChQzRp0sQU4VssUxeIE5bvuedg/374v/9T+qMfOKAMTgghxIOoefO8MEq0Bl0qz5pP5KG/qX27unmDdj1VO2+vXsoarWPH4MwZ1U5bbL6+vmzadB74lBs3JDk3N2m1JgqjXbt2zJs3z/jz0KFD0el0JCcrBR4vX76MnZ0dx48f1yrEUissLIx3332XKlWqYGVlhZWVFR07dmT27NmMHz++SOfq27cv//vf/2jQoAENGjTggw8+oGLFiuzdu9dE0VsuQ4E4U01vF6XD/PlKf/Rbt5Q6PLd/ZQkhxH0Zbp5fvXoVJycnjh07xs6dO2ndujXbt29X/XolStCl8qziZuTP7PzYj9WzugPK3dlXX1Xu1Krl5D/Kev+TV5xoHuim2nnd3aFzZ+V5MUoKmES9eh4A3LghxVzMTRJ07ej1ypdFLbai1rRyc3MjMTERgOjoaDZv3oyzszNxcXEALFmyhKCgIPz8pI5EUWVnZxvrP1SuXJnLly8DUKtWLU6cOFGi865cuZLk5GTat29f4H7p6ekkJCQYN8Pfc2ln6gruoHwnmjVrFmcs4W63yJe9Pfz8s7Iu/cgReOGFov/+E0KUP2rePC8MqeKugozrh+js/R82N3YCsHYtfPop/PefetdIiQ4D4Ojl2qi9UsBQc0jl5RPFZriHk519hIcfDuLIkSPaBlSOGFqtSYJufikpULGiNltKStFidXd3JykpCYCFCxcybNgwqlSpQlxcHJmZmSxZsoQJEyYQFRVF69Ylr5kxcuTI27UpTHuMJfD39+fw4cMAtG3blrlz57J7927efffdYtVvOXLkCBUrVsTe3p6XXnqJtWvX0rhx4wL3nz17Nq6ursbtfvuWFhkZSjIGph1Bz8rKIjMzU/WpjkJd1avDTz8pswd//FEZVRdCiPsx1c3zgpQ4QbeyssLa2vq+m40l9PEyA8OyttRU5VHNKu6etqcBuJoZqN5JbzMk6Lt2KaPWWrOzA1dXgPfYtSuUMWPGSNsaMzGMoMsadHE/hhH05ORkli5dyoQJE3BxcSEuLo61a9fi7OzMo48+qnWYpdJbb71l7KP9/vvvc/78eTp16sTGjRtZsGBBkc/XsGFDwsPD2bt3Ly+//DIjRowgMjKywP2nTp1KfHy8cbvfvqXFsWNKku7mBr6+pruO9EEvPTp3hk8+UZ6/9hqYYIaqEKIMUfvm+YOUOHNeu3Ztge/t2bOHzz//vMwnV3f/8dRus5aammosEFfBR70CcQa+vhAQoIwwbNwITz2l+iWKrHJliI//BAeHDfz99998++23PP3001qHVebJFHftODnB7UFpTa5dFO7u7kRHR7NixQrat29PgwYNjAn6okWLGD9+vLFwSm5z5szh22+/RafT8cYbbzBs2DBAWSt9+fJl0tPTeffddxk4cCDTp0/n559/pk6dOvd8hixbtoyQkBDS0tLo378/7777LsB9jykteva8U2OkTp06REZGcvPmTdzd3fP9b/ogdnZ2xiJxrVu3Zv/+/cyfP5//+7//y3d/e3v7PG3CEhISinxNS2MoENeypWkLxEkf9NJl3DhlKeJ338Fjjyn/TmrU0DoqIYQleuutt4x1dt5//3369OlDp06dqFSpEqtWrVL9eiVO0PNrK/Lff/8xdepUfvvtN4YNG8Z7771X0suUDrc/+NVO0I/9u5PWtwvENWzfQ52T3iU4WEkOLOXGf5UqcOaMD4MHv813373B66+/Tr9+/XBzc9M6tDJNprhrR6dTvzWjqbi5uXHs2DHmz5/PokWLAHBxcWHXrl1ERESwYcOGe445cOAAq1ev5sCBA6SkpBAYGEjXrl3x9vZmxYoVeHh4EB8fT9u2balevTqbNm0iIiKCGzdu4Ofnx+jRowGlxcnGjRsJCwtDp9MRHBxMWFgY1tbWBR5TGqSkpPD666/z66+/kpmZySOPPMKCBQuoXLkyHh4eql1Hr9eXu/+/zVUgTkbQSxedTqkZdPSo0nJ20CDYuVNZpy6EELmpffP8QVRdg3758mVeeOEFmjZtSlZWFuHh4axYsYKapmo6ainuuluudoJ+cu+dAnFNW7mpc9K7vPWWUsV9xAiTnL7Iqty+IdG+/Ss0atSI2NhYpk+frm1Q5YBMcReF4e7uzrZt27Czs+ORRx4BlAQ9JCSE5557zrhOK7ddu3YxaNAgHBwc8PDwoFu3buy/XUlz3rx5NGvWjM6dO3PhwgW2bdvGgAEDsLOzw8vLi6CgION5tm7dSlhYGK1ataJly5YcP36cM2fOsGfPngKPKQ3eeecdli9fTu/evXn88cfZsmULL7/8conO+eabb/L3338TFRXFkSNHmDZtGtu3bzfOXCgvzFEgDu6MoEuCXno4OcGaNeDhAfv2wdixWkckhCgtPDw8TJKcg0oJenx8PFOmTKFevXocO3aMrVu38ttvv+Hv76/G6S2eHkOCrvwlqT7F/ZLSEufYFV+K0NWuSOztLas3rCFBv3XLzjhCt3jxYv41zFUUJiFT3EVhGIrETZgwwfiai4sLqampjC3kN1y9Xo9OpyM0NJTdu3ezd+9eIiIiqFmzJg4ODgV+6On1ekaNGkV4eDjh4eGcPn2a4cOHG89XWq1Zs4avvvqKJUuWsGDBAjZs2MCvv/5aooJjV69e5amnnqJhw4Z069aNf/75h02bNtG9e3cVI7dsmZkQEaE8N9cIukxxL118fZVicTodLF0KX36pdURCCEuRkpLCmDFjqF69Op6enjz55JNcv37d5NctcYI+d+5c6tSpw++//86PP/7Inj176NSpkxqxlTqmKhJX9XaBuNhs9QvE3S0zE06eNPllHshQyf3aNQgKCuLxxx8nJyeHzz//XNvAyjiZ4i4KY/Dgwej1ep5//nnjayEhIWRnZ+NbQBWujh07smbNGtLT04mLiyM0NJTAwEASEhKoVKkSjo6O7Nu3j5MnT9KhQwfWrl1LRkYGMTExhIaGGs8TFBTEqlWrjC3dLl68yI0bN+57TGkQHR2d57OzTZs22NjYGCvFFsdXX31FVFQU6enpxMbG8tdff5Wr5Bzg+HFITwcXF6hb17TX8vT0pGbNmjgVtaiD0FyPHvDBB8rzsWNh715t4xFCWIbcs9ueeOIJVWa3FUaJ16C/8cYbODo6Uq9ePVasWMGKFSvy3W/NmjUlvZTFqtJ2MlcuD6RtB2V4e98+JUlXo1psamoq/t5KkR7nmuoXiMstIgK6dFGmfEVHg5WGTfgMI+jXrimPn3zyCe3atWPMmDHaBVUOyAi6MJXWrVszZMgQWrVqhU6nY+bMmXh5edGzZ08WLVpE8+bNadasGQEBAXh6etKzZ0+aNm1Kw4YN6dy5s/E8/v7+TJkyhS5dupCTk4OzszMrV66kTZs2BR5TGmRnZxtvkBnY2NiQlZWlUURlg2F6e4sWpv9My71GUZQ+b7wBBw4oU94HD1b+7VStqnVUQggtGWa3Pf744wAMGzaMDh06kJ2djbW1tcmuW+IE/emnny7V0wrVYOvojlfdO6PbtWurd+6jB3cQeDtZ9etg2pGPRo0gKwsuX1aqmarQvrjYDAm6YRaJt7d3num0wjRkDbpQU+3atTlw4IDx5ylTpjBlypQ8+zg4OPDnn3/ec+x7771XYIHRp59+Ot+uDvc7xtLp9XpGjhyZp4J6WloaL730EhVyVQ8syze7TcGwKsrU09tF6afTwbJlEBkJ//2nVHb/6y+wtdU6MiGEVu43u83Hx8dk1y1xgr58+XIVwhAFOb77NwJ94MSVCjQZ7GbSa9nbQ8+e8MsvsG6dZSTohhH03NLS0ti6dSu9e/c2b1DlgIygC6GNEflU6Bw+fLgGkZQt5qrgLsoGFxf49VcIDFQqur/+Onz2mdZRCSG0otXsthIn6AbfffddgV8mXn/9dT766CO1LmVxrkeu5eim97ipr0mfcb8yfbqy/nzaNLjr77TI0q/sBR+IjPGlYQnPVRjBwUqCvn49aDkQlXsNem5JSUm0bNmS06dPs3fvXtq0aWP+4MowWYMuhDaWLVumdQhlTlaW0j4LTF/BHeC3337jv//+o1u3brQ0xwWFSTRsCN98AwMGwPz5SrJezhofCCFu02p2m2orssaOHcvvv/9+z+uvvPIK3333nVqXsUhpMXvpUu0Q1jGbSU6GuXPh3XfVqYruZa8UiLueY/oCcQD/+x9YW8PhwxAVZZZL5iv3CHrugrgVK1akffv26PV6Ro8eXaIKx+JeMsVdCFFWnDih1IOpWBEaNDD99TIyMkhJSSEzM9P0FxMm1b+/MsgC8MILd270CCHKlxEjRuDp6Ymrq6txGz58ON7e3nleU5tqI+grV67k8ccfZ/369cbiPOPGjWPNmjWlrppusenutFizti75uqWUlBQCqisF4lx9zTOdu1Il6NgRduxQRtHHjzfLZe9hSNDT0iAlBXLdpGLu3LmsW7eOgwcPsmTJErNUUywvZIq7ENp49tlnC7Xf119/beJIyg7D9Pbmzc1T9FT6oJctM2cq/4Y2bYKBA5UCch4eWkclhDAnrWa3qfaR9eijj/LFF1/Qv39/Dhw4wOjRo43JeaNGjdS6jEXKPcKrZou1w/u3U+v2VG//TuZrjdOvn/K4fr3ZLnmPChWUNfFw7zT3qlWr8v777wPw5ptvEhsba+boyi6Z4i6ENpYvX05oaCi3bt0iLi6uwE0UnrnXnxv6oEuCXjZYW8P330OdOnDuHDz5JMikPSGEOag2gg7w+OOPExcXR8eOHalSpQo7duygXr16al7CwumMI+hqJOj/7fmNdrWUAnENh7qU/ISFNGAAJCYq69G1otMpo+gXLyoJ+t2V8V9++WW+/vprDh06xBtvvCGjSiqREXQhtPHSSy+xcuVKzp49y7PPPsvw4cPxkOG6EjF3BXfDCLo+9117Uap5eCht19q3h82b4Z134Pb4gBCiHNBqdluJEvRJkybl+7qnpyctWrRg8eLFxtc+/fTTklzKsuX6MFYzQc+KPQi14PjV2jQ0Xau9e/j6Kh9CWsudoN/N2tqaxYsX0759e5YtW8Zzzz1Hhw4dzB9kGSNr0IXQxuLFi5k3bx5r1qzh66+/ZurUqfTu3ZvnnnuOHj16lPt2pkWVnQ2HDinPzVWvTUbQy6ZmzWDpUqVQ3AcfKDd8BgzQOiohhDksX76cWrVq0aJFC7PefC1Rgn7I8Ol3l7p165KQkGB8v7x8sdDlWoOeq9hfsblbXwDgZnbjkp+sFLq7F/rd2rVrx3PPPcfly5fx8vIyX2BlmIygC6Ede3t7nnjiCZ544gnOnz/P8uXLGT16NJmZmURGRlKxYkWtQyw1Tp6E5GRwcgJzrbKTNehl15NPwv79Ssu1ESPAz898/66EENrRanZbiRL0clP8rQgMA48lba8GUNPtJgC2lczfwDUrS+mF/scfsHixOn+eoiqo1VpuixcvxtbWttzcBDI1WYMuhGXQ6XTodDr0er0kfMVgmN7evLmyltgc3Nzc8PLywtnZ2TwXFGY1d64yK2PHDmUE/Z9/lL7pQoiyS6vZbWaoa1r2VWnzCuf8/6DpyL9o3RoiIuDnn0t2zoT4eBpUU1q11PR/WIUoi8bKCsaMga++Uj6MtJC71VpB7Ozs8vzPIV9kS0amuAuhnfT0dH788Ue6d+9Ow4YNOXLkCAsXLuTChQsyel5EhgJx5mxH3rFjR0aNGkUrcy16F2ZlawurVkH16vDffzByJMhXDiHKPsPsti1bthAZGUmTJk0YPXo0tWrVIikpySTXLFGCfvjw4SIlRMeOHSMrK6skl7RIDi5e+DZ9lNp+D1GxIjRtCk2alOycJyLCcHWC7Bxo2LqFOoEWgZUV9O2rPNeqmvuDprjndv36dV544QVGjBhh2qDKOJniLoQ2Ro8ejZeXF3PmzKFPnz5cvHiRn376if/973/Gtc2i8MxdIE6UD1Wrwi+/KLMK166FOXO0jkgIYU7mmt1Wok/9Fi1acOPGjULv3759ey5cuFCSS5Ybp/7dBUDUNQeqeqmwoL0YDO3W1q3L20rOXAozgm4QFRXFV199xXfffSdLL0pApriLwmjXrh3z5s0z/jx06FB0Oh3JyckAXL58GTs7O44fP65ViKXOF198gYuLC76+vuzYsYMXXniBgQMH3rOJB8vJkQRdmE7btrBwofJ82jSlursQouzSYnZbidag6/V6pk+fjpOTU6H2L6vTZmMj1xG55SMSrWvj2+U71q6FevXgiSeKf87ES+HgCeduVKGuRsurH3kEHB0hOhrCw6GFmQfyC7MG3aB169a89NJLhISEMGbMGCIiIrC1tTVtgGWQjKCLwnBzcyMxMRGA6OhoNm/ejLOzM3FxcVSoUIElS5YQFBSEn5+fxpGWHk8//bTU0lDJ6dNKq1AHB6WYl7ns3r2bAwcO0Lx5cx5+2PxL04T5vPAC7NunVHd/4gk4cEDply6EKFtGjx7NypUrqVmzJs888wwrV66kUqVKJr9uiRL0zp07c+LEiULv3759exwdHUtySYuUeulvulTZzfqIQ4SHw9tvQ48eJUvQHTJPARCbWluVGIvD0VH5c6xbp0xzN3eCXpQRdIAPPviAn376iePHj/PZZ5/x+uuvmy64MkrWoGvv9iB0vqyt87ZwvN++VlbK/8MP2rdChaLFB+Du7m5cd7Vw4UKGDRvGpk2biIuLo2rVqixZsoSvvvqqyOeNiopi8ODBHDhwoOhB3WXkyJEMHjyYPn36mGR/tS1fvlyT65ZFhtHzZs3ApkTfcoomPT2dW7dukZKSYr6LCs0sXAiHDyuJ+sCBsGeP0jVACFF2fPHFF9SsWdM4u21HAYW51qxZo+p1S/TRtX37dpXCKDvUquJezSkGgHT7gBJGVDLBwUqCvm6d+XujF2UNOihJw9y5c3n22WeZOXMmTzzxBDVq1DBdgGWQjKBr736zpf73P9iw4c7Pnp5QUC7w8MOQ+1d07dr5/79UnOUrhhH05ORkli5dSlhYGHv27CEuLo61a9fi7OzMo48+WvQTC6ECQ4E4c09vlzZr5Yu9vbIevWVLpTjwqFHw7bdKy10hRNmg1ew2M95bLsNyfcM1JOglmV2dk5NDncrK9FFXn7YliazE+vRRRuKys5URuOKMthWXIUG/dQsyMwv333TEiBEsXbqUPXv28Morr/DTTz+ZNMayRtagi8Jwd3cnOjqaFStW0L59exo0aICLiwtxcXEsWrSI8ePHo9Pp7hkRf+211/D396dLly4EBwfTvHlz9u3bR9OmTVm5cuU915kzZw7ffvstOp2ON954g2HDhgHQt29fLl++THp6Ou+++65xbfb06dP5+eefqVOnDvpcv5eXLVtGSEgIaWlp9O/fn3ffffe++5vb4cOH8ff3L3QxuGPHjtGwYUNszDk8XIpoUcEdMP79aflvSZhXjRrw00/QrRt8/z0EBsKECVpHJYRQi1az2+TTXVU6VUbQL144h28V5QO+fktt17FVqQIXL4KXl/mv7e6u3BzIyVFG/goTg5WVFYsXL6Zly5Zs376dq1evUrVqVdMHW0bIFHft3a9jx939nGNjC9737lwvKqrYId3Dzc2NY8eOMX/+fBYtWgSAi4sLu3btIiIigg25h/kLcPz4cVatWkXDhg3p2rUru3btwsfHx/j+gQMHWL16NQcOHCAlJYXAwEC6du2Kt7c3K1aswMPDg/j4eNq2bcuAAQPYv38/mzZtIiIighs3buDn58fo0aOJjIxk48aNhIWFodPpCA4OJiwsDGtr63z310KLFi2IiYmhiuGu5AO0b9+e8PBw6sii13vo9doViJMR9PLp4Yfh44/hlVfg1VeV5YCdO2sdlRCipLS8eS4Jugpy3y3PVFqXlyhBj9y3nZrWkJiqo37TmiWMruS0SM5BSTAqVVLWoF+7Vvg4mjVrxsqVK+nWrRseHh6mDbKMkSnu2ivKLBVT7fsg7u7ubNu2jdq1a/PII48ASoIeEhLCiy++WKiqpg0bNqRRo0aAkqBGRUXlSdB37drFoEGDcHBwwMHBgW7durF//36Cg4OZN28e62/3f7xw4QIxMTHs2bOHAQMGYGdnh5eXF0FBQQBs3bqVsLAwY2/qpKQkzpw5w/Xr1/PdXwtScFU9Z89CfLzyGdy4sXmvLSPo5deECbB/P/zwAwwZotwkql5d66iEECWh5c1zVRL08+fPc/jwYapWrUqbNm3UOGWppNOpM8X90vF/oD6cuupCS0fLWcyUkqKM4NmbsetblSpKcl7YdegGQ4YMMU1AZZxMcReFYSgSNyHXXE4XFxdSU1MZO3as8TUbG5s8o4m5/13Z5/pFYm1tTXZ29n2vqdfr0el0hIaGsnv3bvbu3YujoyONGjUiPT3d+H5+x40aNYq33347z+vz5s2zmKrpUnBVPYbp7U2blrwWTFEZEnQZQS9/dDr48ks4dkxZjz5oEOzYYd7vS0IIdWl587zECfqPP/7IyJEjyczMRKfT0aJFC/74449C320oa9SY4p5z6xgA0fHemHkJXYHGj1c+fFasgMceM991i1rJ/W56vZ6ff/6ZwMBAateurVpcZZWMoIvCGDx48D2jhCEhIYSEhOR5zdPTk8uXL5OYmIhOp2PLli3GkewH6dixIy+99BKvvvoqKSkphIaG8u6777Jv3z4qVaqEo6Mj+/bt4+TJkwB06NCBsWPH8uqrr3Lz5k1CQ0N59tlnCQoKYujQoYwbNw53d3cuXryIo6NjgftrQQquqkfL/udOTk5UrlzZZH1xhWVzcoI1a5R/e//8o4yqf/GF1lEJIYpLy5vnJU7QZ86cyVNPPcWbb75JdHQ0r7/+Om+88UaxWuyUVlUCx/Hff61p2KgSLasqrclKcn/CVRcFQFx2A3UCVIGDA6SlKe3WzJmgF6UXen6mTZvG7NmzCQ4O5tdff1UtrrJK1qALNdnZ2TF58mRatmxJ/fr1CQgofFeK1q1bM2TIEFq1aoVOp2PmzJl4eXnRs2dPFi1aRPPmzWnWrJnxnG3atKFnz540bdqUhg0b0vn2IlB/f3+mTJlCly5dyMnJwdnZmZUrVxa4vyjdtCoQB9C8eXOaN29u/gsLi1GnDvz4o9Jx4//+Tyka99xzWkclhCgOLW+e6/QlXCxlZ2fHqVOnqFWrFgAnTpygZcuWJN+vQa+FSEhIwNXVlfj4eFxcXLQOxyjsXQfa10vn23MzeGqamXubFWDXLujUCdzclMJUJZnCXxQvv6zcgX7nHZgxo+jHR0ZG0qxZM7Kysvj999/p3bu36jGWJTExMXh5eaHT6cjOzraYKcBlUVpaGufOncPX1xeH3M3NhUUq6O/LUj9H1HLx4kV8fHyIjo62+LaVer1yU/fmTThwQJtRdCEAPvgA3npLmU35999Qjld/ClHkz5HZs2ezZs0a/vvvPxwdHXnooYeYM2cODRs2NO6j1+uZOXMmS5YsIS4ujrZt27Jo0SKaNGliyj+K2RSuLN19ZGVl5RnOb9iwITk5OcTExJT01OVSamoq9asq04u9G3bSOJo72rdXvvjcuqUk6+ZS0inujRs35pVXXgFg3LhxpKamqhRZ2WRYg67X68nKytI4GiGEKLzz55Xk3NYW/P21jkaUZ1OnQnCwsuxx0KD7d9wQQuS1Y8cOxowZw969e9myZQtZWVn06NEjz+Dv3Llz+fTTT1m4cCH79++nWrVqdO/encTERA0jV0+JE3SAFStWsGfPHpJu9weysbEhJSWlROfcuXMnffv2xdvbG51Od8/0ZL1ez4wZM/D29sbR0ZEuXbpw7NixEl2zuK4e30Doop788eVL/PknzJsH+/YV71wnjx6gsrPy3M+CbrlaWys90QHWrTPfdUuaoAO8/fbb1KhRg3PnzjFnzhx1AiujchfukmnuQpjf77//LkXGiskwvd3fX5viXMePH2fx4sWFajMoyjYrK/jmG2jYUGlVO3QoyD1vIQpn06ZNjBw5kiZNmtCsWTOWLVvGhQsXOHj7l7xer+ezzz5j2rRpDBw4EH9/f1asWEFKSgo//PCDxtGro8QJeseOHXn//ffp2LEjbm5u1K9fn7S0NL766itCQ0NJSEgo1nmTk5Np1qwZCxcuzPd9S7pzknz+L7q6/0n22e9ZvRomTYKtW4t3rv/+2QnAhet2ePlYVqGZ4GDlcf16ZSqhOZR0DTpAxYoVmTdvHgAffvghZ86cUSGysil3gi6F4oQwv+DgYK4XtW2FALQtEAfKMohr164RHx+vTQDCori4wNq1ULEibN8OU6ZoHZEQ2kpMTCQhIcG4FfZ7puF3qqF18rlz54iJiaFHjx7Gfezt7Xn44YfZs2eP+oFroMQJ+s6dO4mPj+fEiRN89913DBgwgM6dOxMSEmLsQ+3n51fk8/bq1Yv333+fgQMH3vOexd05yZWtlrTN2q1o5e7Q2euVsLTlv927K8Xizp1TWomYg2EEvaTfVwcNGkT37t1JT09n3LhxJQ+sjLK2tjauO5cEXQjzkx7axWcYQdcqQZc2a+Jufn5K9xuATz9VCsgJUV41btwYV1dX4zZ79uwHHqPX65k0aRIdO3bE//baJcMy6qpVq+bZt2rVqmVmibUqfdAB6tevT/369Xn88ceNr507d44DBw5w6NAhtS5jPO/97py8+OKLql6vKEraZs0uTWkZFJNSU6WI1FOhgjI7wMMDPD3Nc001prgD6HQ6Fi5cyJAhQ5g0aVLJAyujdDod9vb2pKWlSYIuhEbCw8Pp0KEDFSpUML526dIl/Pz8ij0rrazT67Wt4A4Yb25Kgi5yGzhQWZM+e7ZS0b1JE2jaVOuohDC/yMhIqlevbvzZvhBrkcaOHcvhw4fZlU8BrLsLGev1epMVNx4xYgRdunThmWeeAeD8+fNERkby0EMP4erqqvr1VEvQ8+Pr64uvry9DhgxR9bz3u3Ny/vz5Ao9LT0/Pk3So9UUn93hHZqbyWNwE3dPxMgCptpZZhfCDD8x7vdwj6Dk5yrqu4mrQoAHh4eFSmfwBDAm6rEEXQhu9evVCp9NRu3ZtYxu48+fP4+bmpnVoFuviReVzwsZGu+THMIIusyDE3d57T7mB9OefMGCA0mXA3V3rqIQwL2dn5yJ1Oxk3bhzr169n586deaq/V6tWDbjTecggNjb2ntxQLZs3b+all14CIC4ujpYtW5KSkoKHhwfbtm3LU2FeDaoUidNKUe+czJ49O8/UCh8fH9VjKskUd71ej28lZZ1FRe+2KkZVelWqpDxmZysV5Esq97+PkhYyLKsMdzRlBF0IbZw8eZK///6byZMn4+3tzZEjR7h16xZLlizROjSLZRg9b9JEWYqlBZniLgpibQ0//AC1a8PZszBsmPK9RghxL71ez9ixY1mzZg3btm3D19c3z/u+vr5Uq1aNLVu2GF/LyMhgx44dPPTQQyaJKT4+3niTYPXq1Xh7exMfH8+TTz7J1KlTVb+eSUfQTaW4d06mTp2aZ3pzQkKCOkm68W65rkRT3GNjrlCvqvLB3qDVwyWPy0SuX4cNG8DbW1mXbkr29kqhlYQE5bq360OUSE5ODvPmzWPWrFns3r2bRo0alfykZYih1Zok6EJow9nZmbp169K+fXutQyk1tJ7eDnduAMsIushPpUpK0bj27eGPP2DGDGVkXQiR15gxY/jhhx9Yt24dzs7OxpnTrq6uODo6otPpmDhxIrNmzTIusZ41axZOTk48+eSTJonJx8eHc+fO4ePjw5o1axgxYgR2dna88MILdOqkflvsUjmCXtw7J/b29ri4uOTZ1FaSKe5H9+3AzgZS0nU0aF5f3cBUtGQJjBwJn31mnuuptQ7dQKfTERoays2bNxk7dqx8mbqLYQRdprgLYX59+/bFtrhVRssxrSu4g3Jz08XFJU/tACFya94cvvxSef7+++ZtWytEaRESEkJ8fDxdunTBy8vLuK1atcq4z+TJk5k4cSKjR4+mdevWXLp0iT///BNnZ2eTxDRy5EjGjh3L1KlT2bZtG/379wcgOzvb2GZcTRY7gp6UlMTp06eNP587d47w8HA8PDyoWbOm2e+c3E+VVi9z+EhDavf15JPHlJHegICin+fCkTCoA6djK9DUyXLvnQQHw7RpSiu5pCSlhYgpVa4MZ86om6AvWLCAv/76i61bt7J69WqGDh2qzsnLAJniLoR21sk39iLLXSBOywS9bt26vPLKK9oFIEqF4cNh/35YsACeekp5rvLyVSFKtcIMnOl0OmbMmMGMGTNMHxDKLGy9Xs+ff/7Jhx9+SL169QDYv38/NWuqX9jbYhP0AwcO0LVrV+PPhqnpI0aMYPny5UyePJnU1FRGjx5NXFwcbdu2Nemdk/txqdqIplVLPk0688YRqAMX4qphyQU+GzeGunWVpHnzZhg0yLTXU3sEHaBOnTpMnTqVGTNmMGnSJP73v/9p8m/HEskUdyFEaXLlCly9qhQRlerYojT4+GM4dAj+/lspGvfPPyBfQYSwXDqdjmnTpjFt2rQ8r1+9etUkg8MWO0zbpUsX9Hr9Pdvy5cuBO3dOrly5QlpaGjt27DD2xyutnDkLwM2sehpHcn86nTKKDrB+vemvp1Yv9LtNmTKFunXrcvnyZbPdgSsNZARdPEi7du2YN2+e8eehQ4ei0+lITk4G4PLly9jZ2XH8+HGtQhTliGH0vHFjcHLSNhYhCsPWFlavVmr5HD8OzzyTq5yREKLUeP3115k+fbrq57XYEfTS5Op/mzm+8//IdqxLjNVHJCdDv35wu5ZdodVwuT1E7NJc9RjV1q8ffPqpUiwuK0tpbWMqphhBB3BwcODzzz/nf//7H/Pnz+eZZ54p9Td51CBr0MWDuLm5kZiYCEB0dDSbN2/G2dmZuLg4KlSowJIlSwgKCsLPz0/jSEV5YAnT2wGuXLnChg0bcHNzY/DgwdoGIyxetWrw88/w8MPwyy8wZw688YbWUQkhDHIXFn+QTz/9VNVrS4KugsQzv9Ol4lp+P+rCO6s/4swZ8PcvWoKemZlJPc9UAKrV72CiSNXToYNSUf3GDdizBzp3Nt21KldWHtVO0EHpNzxgwAB+++03wsLCJEFHprhrRa/Xa9b6z8nJ6b4tKu/m7u5uLIqycOFChg0bxqZNm4iLi6Nq1aosWbKEr776iqioKAYPHsyBAwdKFN/IkSMZPHgwffr0MekxonQyFIjTsoI7KDc1L126RFpamraBiFKjfXv4/HN46SWltk/LltCjh9ZRCSEADh06lOfngwcPkp2dbex5fvLkSaytrWllgrvDkqCrIHcxg+L2QT99/Ah+7srzxu3UL9evNhsb6N0bvvtOWUdlygTdVFPcDebPn8/7779P48aNTXOBUkamuGsjJSWFiqauuFiApKSkIlWeNoygJycns3TpUsLCwtizZw9xcXGsXbsWZ2dnHn30Uc6fP2/CqIVQWMoIurRZE8UxahTs2wdffw1PPKH8e65dW+uohBChoaHG559++inOzs6sWLECd3clYYuLi+OZZ56RNmulQXHbrB3bux2AK7dsqF7bVd2gTGTmTKU4z4QJpr2Oqaa4G/j4+EhynotMcRcPYhhBX7FiBe3bt6dBgwa4uLgQFxfHokWLGD9+fL4j8nPmzMHf35+AgAC+//574+t9+/alVatW+Pv7s2bNGgCmT5+On58fvXv3JjY2Ns95li1bRps2bWjatClvv/228fX7HSPKppgYuHxZqY3SrJm2sVhZKV+pcnJytA1ElCo6HSxaBIGBcPMmDBwIqalaRyWEyO2TTz5h9uzZxuQclO9C77//Pp988onq15MRdBXpuDOCXtQE/ea5/RAAZ66541X4maaa8vU1z3VMnaDnFh4ezt9//824ceNMfzELJSPo2nBycjJJL83CXrso3NzcOHbsGPPnz2fRokUAuLi4sGvXLiIiItiwYcM9xxw4cIDVq1dz4MABUlJSCAwMpGvXrnh7e7NixQo8PDyIj4+nbdu2VK9enU2bNhEREcGNGzfw8/Nj9OjRAERGRrJx40bCwsLQ6XQEBwcTFhaGtbV1gceIssswvb1RI9O3/HwQSdBFcTk4KOvQW7VSZiW++CKsWKEk70II7SUkJHD16lWaNGmS5/XY2FhjTR41SYKusuIm6NYp/wFwJclH5YjMIzsbrK1Nc25TrkHP7fTp08Z1JB07dqRFixamvaCFkjXo2tDpdEWaZq4ld3d3tm3bRu3atXnkkUcAJUEPCQnhxRdfzHeq/q5duxg0aBAODg44ODjQrVs39u/fT3BwMPPmzWP97ZYQFy5cYNu2bQwYMAA7Ozu8vLwICgoynmfr1q2EhYUZ/19NSkrizJkzXL9+vcBjRNllKdPbQaa4i5Lx8YFVq6B7d/j2W2jTBsaO1ToqIQTAgAEDeOaZZ/jkk09o164dAHv37uX1119n4MCBql9PprirwPhhrNMVe4p7FfuLACRbl66qx0eOQFAQ5GpZrzrDCHpqKpiyhla9evUYMmQIOTk5jB49utyOgsgIungQwxT3CbnWt7i4uJCamsrYQn6j1Ov16HQ6QkND2b17N3v37iUiIoKaNWvi4OBQYNE6vV7PqFGjCA8PJzw8nNOnTzN8+HDj+UT5YkjQtS4QBzKCLkqua1eYO1d5/sorsGuXtvEIIRRffPEFvXv3Zvjw4dSqVYtatWoxbNgwevXqxeLFi1W/niToKivuCHptj1sAOFULVDcgE3N3h9BQ5UPk6lXTXKNiRbidM5p8FP2TTz6hYsWK7N27l+XLl5v2YhZK1qCLBxk8eDB6vZ7nn3/e+FpISAjZ2dn4FrD2pWPHjqxZs4b09HTi4uIIDQ0lMDCQhIQEKlWqhKOjI/v27ePkyZN06NCBtWvXkpGRQUxMTJ5CLUFBQaxatYq4uDgALl68yI0bN+57THk1e/ZsAgMDcXZ2xtPTk/79+3PixAmtw1KVYYq7JYygW1tb4+joiIODg9ahiFLslVfg8ceVFrZDhig1FoQQ2njzzTfZt28fTk5OLF68mBs3bnDo0CH+/fdfbt68yeLFi00y+1ESdBVUafECB10+wLvbZ/z2G6xdC25uhT/+VtxN6lXNAqBu84dNE6SJ1KihfDHS6+H3301zDZ3OfNPcq1evzsyZMwGYMmUKN2/eNO0FLZBMcRem0Lp1a4YMGUKrVq3o3LkzM2fOxMvLi549exIfH0/z5s1ZtGgRAQEBeHp60rNnT5o2bcqLL75I51xtIvz9/ZkyZQpdunQhICCAxx57jOTkZNq0aVPgMeXVjh07GDNmDHv37mXLli1kZWXRo0cPkpOTtQ5NFdeuQXS08twSViRVrlyZyZMnF3oWiRD50elg6VKlXW9MDAwefGfwRwhhXleuXKFPnz54eXkxatQoduzYQcOGDWnWrJlJlyXq9OV4sVRCQgKurq7Ex8fj4uKiWRw7//iVznEDyMiCzAGZVHAuXaUB3nsP3n4b+vWDdetMc40WLSA8HP74Ax591DTXMMjMzKRFixYcO3aMF198kS+++MK0F7Qw06ZNY9asWYwbN44FCxZoHU6ZlZaWxrlz5/D19ZURt1KgoL8vS/kcKYxr167h6enJjh07Cn0D4+LFi/j4+BAdHU2NGjVMHGHRbN6sfB40aABlbGKAEJw+rVR2v3ULXn4ZTDCLVgizsOTPkcLQ6/Xs2rWL3377jfXr13Pp0iW6d+9Ov3796NOnD5UNo4gqkhF0C3AuYg8AZ2KdSl1yDkpiDrBli+nWiJuzkrutra1xPcmSJUsIDw83/UUtiExxF6Jsio+PB8DDw6PAfdLT00lISDBupqhOqxZLKhAnhNrq1YPvv1dG1ENCYNkyrSMSonzS6XR06tSJuXPn8t9//7Fv3z7atWvHl19+SfXq1encuTMff/wxly5dUu2akqCr4OrJrfy9bARbf5jJ8uXwww9FOz41NgKA8zc91Q/ODJo2hVq1lCJuf/1lmmuYM0EH6Ny5M+PGjePzzz8nICDAPBe1EFIkToiyR6/XM2nSJDp27Ii/v3+B+82ePRtXV1fj1rhxYzNGWTSWlqAnJyezYsUKvvnmG61DEWXE//4HM2Yoz19+GQ4c0DQcIQTg5+fH5MmT2b17NxcvXmTEiBH8/fff/Pjjj6pdQxJ0Fdz67xc62X9D6tHPeOYZGDGiaMc7688AcD2jjgmiMz2d7s4ouqmmuJtrDXpuCxYsYMyYMVibqn+chZI16EKUPWPHjuXw4cMP/AIxdepU4uPjjVtkZKSZIiw6Q4E4S6jgDkr19qioKKKiorQORZQhb72lfMdKT4eBA837PUgIofj999/z7dBRpUoVnnvuOdatW8drr72m2vUkQVfB3av4i1rBvbpzDAA5FZuqFJH5DRqk3Ont1s005zeMoF+/bprzP0hqaiq3bt3S5uJmJiPoQpQt48aNY/369YSGhj5w/Z+9vT0uLi7GzdnZ2UxRFs2NG2DIgy0lQZc+6MIUrKzgm2+UWgvR0XcqvAshzCc4OJjrZkxCJEFX0+3P5KIk6Dk5OdStolTUrVznIRMEZR4PPwwbNsCTT5rm/Oae4p7bjh07aNKkCa+++qr5L64BWYMuRNmg1+sZO3Ysa9asYdu2bQW2wCuNDKPn9eqBq6u2sRgY+qCDJOlCXa6uSoegihVh2zZ44w2tIxKifDH373RJ0FWR9y+tKAn62ZP/4VNJed6kXelqsWZOWibodnZ2nDt3jq+//po9e/aYPwAzkynuQpQNY8aM4bvvvuOHH37A2dmZmJgYYmJiSE1N1Tq0ErO06e1wZwQdyHcqpBAl0bgxLF+uPP/kE1i1StNwhCh3wsPD72lTeunSJZN0cJEEXUWGNL0oCfrRPaEAXE+0xqde6SwSl9uFC7Bihfrn1WINukH79u159tlnARg9ejRZZXxumUxxF6JsCAkJIT4+ni5duuDl5WXcVpWBb/aWViAO8o6gS4IuTGHQIJgyRXn+7LNw9Ki28QhRnvTq1QtXV1fq1avHwIEDmTp1Kq+//jpubm6qX0sSdBWUZA36tbP7ATgd64pVKf/bSEpSphuOHAmnTql7bq3XoH/44Ye4u7sTERFhbMFWVskUdyHKBr1en+82cuRIrUMrMcMIuiUl6LlH0GWKuzCVDz6ARx5R2toOGKD0SRdCmN7Jkyf5+++/mTx5Mt7e3hw5coRbt26xZMkS1a9V+ppuW7Lbn8e2toU/RJekVMi9lFjdBAGZV8WK0KWL0g993TpQsZihMUGPi4PMzKL9N1bn+lWYPXs2L730EtOnT+exxx6jWrVq5g3CTGQEXQhhyW7dgjNK8xNatNA0lDysrKywtrbGyspKRtCFyVhbw8qVys2p06dh+HBYv55SP8gjhKVzdnambt26tG/f3uTXkv+dVVCl2Qj+sZtMhVYfsGoVfPxx4Y+tZHsBgCRdQxNFZ16Gdmvr16t7Xnd3pZ0bKNV7tfD8888TGBhIQkICr7/+ujZBmIGsQRdCWDLD6LmvL3h4aBtLbjY2Nrz11lu8+eabODg4aB2OKMMqVYI1a8DBQSnQ++67WkckRNnWt29fbM04OigJugoq+7aj7eA5BA0ZzWOPQZ8+hT+2pnscAPZVLGieXgkYEvTdu9Wdjm5trXwggXY9QK2trVm8eDE6nY7ExEQyMzP/v737Do+i6h44/p30QjoJhN4hEGroSEdAEQEBURABwfKiAioivhbAAj9ERV4EFFFERUWUpqCIUkPvAqFIT0hoIaSTOr8/ht0kJJu6PefzPHnYcnfm7IbNzJl77r2WCcTEpAddCGHNrLG8XQhza9UKPv9cuz1jBvz6q2XjEcKerV27Fj8/P7PtT0rcLSg1JYX6lbRxvjWb2scM7jVqQIsWcOSIdlV31CjjbTswUEv6LTUOHaB169YcPXqUpk2bWi4IE5Mx6EIIa6abIM6aZnAXwhKefBL27YMFC7RS9wMHoH59S0clhH14+eWXi932448/Nuq+JUE3gmtnt3N2308kZNYhyf1lgoPhvvuKft2x/Xto6w5Z2RDa3n66AgYM0BL0tWuNn6CfPGm5HnQde07OQUrcRdHat2/PsGHDeOmllwAYNmwYP/30E0lJSXh6ehIdHU2tWrU4evQoISEhFo5W2BtrnMFdZ8WKFWRmZjJw4EA8PT0tHY4oBz7+WDvn2rkTBg6EvXu1OYGEEGVz+PDhPPcPHjxIVlYWDRtqw5LPnDmDo6MjYSY4GEmJuxHcOracTiwg/cT7PPqoVmpUHP8eCgfgwg1XvHxKMPW7ldOVuW/dCsZckcySS60V5Pr16zzzzDNERUVZOhSjkhJ3URRfX18SExMBiIyMZOPGjXh5eREXpw3ZWbx4MT169JDkXBhdQkLOKiHW2IN+9uxZzp49a7dDoIT1cXGBlSshOBgiImDMmPyrCwkhSm7Lli36n/79+9OtWzeioqI4dOgQhw4dIjIyku7du9OvXz+j71sSdCPQLaei+4NY3GXWkqKPAHDpVkUTRGU5LVvCL7/AhQvgZMQaDUsvtXav0aNH88UXX5SoBMYWSIm7ZSUnJxv8uXPnTrHbpqamFqttafj5+ZGUlATAp59+yogRIwgMDCQuLo6MjAwWL17MxIkTS7zdixcv0rp161LFdK/Ro0fz22+/may9sAxdh0aNGjkXba2Jbqk1WWZNmFNwMPz8s7bCzc8/l2yyYiFE0T766CNmzZqVZxy6n58f7733Hh999JHR9ycJugkUd5I/1/QzAFxPrWHCaMxPUeCRR8DHx7jb1SXo1tKDPnPmTBwcHFi5ciWbNm2ydDhGIz3ollWhQgWDP4MHD87TNigoyGDbBx54IE/bWrVqFdiuNHQ96MnJySxZsoSJEyfi7e1NXFwcq1evxsvLi759+5b6MxDCEGufIM7h7lpXssyaMLeOHeGTT7TbU6fCX39ZNBwh7EpCQgLXrl3L9/j169f1FYXGJAm6Edx7nby4PeiVPaIBSHdrYtyA7JS1lbi3aNGCF154AYAXXnjBbhJa3Rj07OxsMo05RkHYDV0P+rJly+jQoQMNGjTQJ+gLFixgwoQJKIqSr0d88uTJfP3111y8eJHmzZszatQoQkJCGDZsWIE9jrNnzyY0NJSmTZuyfPly/eP9+/cnLCyM0NBQVq1apX/8rbfeIiQkhH79+nH9+nX940uXLqVt27Y0a9aMt99+u8j2wnpZ+wRxkqALS/rPf2D0aMjOhsceg0uXLB2REPZh0KBBjBkzhp9//pmoqCiioqL4+eefGTt2LI888ojR9yeTxBlRSUvcawUkAOBdtY2JIrKsTz6B5cu12UXbti379qytBx3gnXfeYcWKFZw5c4aPPvqI//73v5YOqcx0Peig9aI7GXOcgiiSrnS8II6OjnnuF5ZU6hIFnYsXL5Yprtx8fX05ceIE8+bNY8GCBQB4e3sTHh7O0aNHWb9+fZHbOHnyJCtWrKBhw4Z0796d8PBwqlevrn/+wIED/PTTTxw4cICUlBTatGlD9+7dqVKlCsuWLcPf35/4+HjatWvHoEGD2L9/P3/88QdHjx4lNjaWkJAQxo8fT0REBBs2bGD37t0oisKAAQPYvXs3jo6OBbYX1s2aJ4gDKXEXlqUosGgRHDumfVceeQTCw8Hd3dKRCWHbPvvsMyZPnswTTzyhn2PEycmJsWPHMmfOHKPvT3rQjeGeA3FxStxvXr9O3aAsABq0to8l1u61a5e25MfatcbZnrWNQQfw8fHhw7uDvd577z2jJkGWkjtBl3Ho5ufp6Wnwx83Nrdht3e85IzPUrjT8/PzYvHkzLi4u9OrVC9AS9EWLFjF27Nhilc43bNiQRo0aoSgKLVu2zPfdCQ8PZ/Dgwbi5ueHv70/Pnj3Zv38/AHPnzqV58+Z06dKFy5cvc/XqVXbt2sWgQYNwcXEhODiYHj16APD333+ze/duwsLCaNWqFSdPnuTcuXMG2wvrlZQEp09rt6UHXYiCubnBqlVa1eGhQ1qvulwvEqJsPDw8WLhwIbGxsRw+fJhDhw5x69YtFi5caJIVOyRBN4Hi9KAf3b0dZydISVNo0Nw+F60cMED7d90642zPGnvQAUaMGEHXrl1JTU1lRnGn8LdiuXvM7aVsXxiXrsQ990Rw3t7epKam6od9gPZ/KXeikvv/U+4LQY6OjmRlZRW6T1VVURSFLVu2sHPnTvbs2cPRo0epUaMGaWlp+ucLet0zzzzDkSNHOHLkCGfPnuWJJ54w2F5YryNHtESjalWoVMnS0RRMetCFNahRA1asAAcHWLZM61UXQpSdp6cnzZo1o3nz5iZdSlMSdCMIaDqcndnPodZ9na++gpEji37N5eO7ADh7zRNnF/v8NTzwADg6wvHjcP582benG4N+86Z1XQ1WFIUFCxbw4osvMnfuXEuHU2aKoshEcaJQQ4YMQVVVxo0bp39s0aJFZGVlUbt2bf1jQUFBREdHk5iYSFJSUokmU7zvvvtYtWoVaWlpxMXFsWXLFtq0aUNCQgIBAQG4u7uzb98+zpzRJtvs1KkTq1evJj09natXr7JlyxYAevTowYoVK/RLwEVFRREbG2uwvbBe1l7eDjBp0iTefvttgoODLR2KKOd69IDZs7XbEydqVY1CCNsgg0uNoHKD7lRu0L1Er0mPPQa14XJ8EM1MFJel+ftDly6wZYvWiz5pUtm2p+tBz8yE27ch10oHFtekSRP+97//WToMo3F1dSUtLU1K3EWZuLi4MGXKFFq1akX9+vVp2rRpsV/bunVrhg4dSlhYGIqiMGPGDIKDg+nTpw8LFiygRYsWNG/eXL/Ntm3b0qdPH5o1a0bDhg3p0qULAKGhobz22mt069aN7OxsvLy8+PHHHw22F9bL2mdwh/xzPwhhSa+8Avv3w08/weDB2ndIrh0JYf0UtRzXYSUkJODj40N8fDze3t5m3ff3L9VleJvzfHu4FyPn2M8SXff65BN46SXo3h02by779ry9ITERzpyB+lY6MkBVVSIiImjSxHZn5w8KCuLGjRscO3aM0NBQS4djl+7cucOFCxeoXbt2vrHlwvoY+n1Z8jhiDlFRUVSvXp3IyEiqVatm0VhCQ+HECfj1V3joIYuGIoTNSEqCDh20asZOnbRzseJOZiyEMVjTccRWyKVeI7hxfg97f3mDdV9/we+/w7lzRb+mitfd2ZcrFL9HyRbpxqFv3w63bpV9e9Y6Dl0nISGBnj17EhYWxrni/EewUrql1qTEXQhhDZKT4eRJ7bY196Bv3LiRlStXyrJ9wmpUqKBNGufjAzt3wssvWzoiIURRJEE3gmuHltAubSYZx17nwQfh++8Lb5+dnU29oGQAKtbtaIYILad2be2K7eDBkJBQ9u1Z21ro9/Ly8sLR0ZG0tDQmTJhgsxMFyRh0IYQ1+ecfbW3nypWtu0T33LlzREREFLpcohDmVr8+fPeddnvBAm3iOCGE9ZIE3QhUVN0NoOhl1i6d+5dq/lrjph06mzAy67BjhzabaK1aZd+WNS61lpuiKHz66ac4OzuzYcMG1hprjTkz0yXoMgZdCGENbGGCOMgZg26rF2eF/XroIZg+Xbv97LM5czoIIayPJOhGpDscFzW259iubQDcSHCkah0rXSvGiIy5kpG1l7iDtr7zq6++CsDEiRNJSUmxcEQlJyXuQghrYisJum6ZNVkHXVijt97SEvW0NBg0yHo7O4Qo7yRBNwb9hXLtwFxUgn7t7H4Azt3wNmryas1UVRs/WNZh2dZe4q7zxhtvUKNGDS5fvsz7779v6XBKTErchRDWRNfb16qVZeMoivSgC2vm4ADffquVvF++DI89pq2MI4SwLpKgG5HueFxUgq4kngDgSkIVE0dkPd54Axo3ho8+Ktt2bKEHHcDDw4N58+YBMGfOHE6fPm3hiEpGStyFENYiNVWbvR2svwddl6BLD7qwVr6+2qRxnp7w99/a+ZkQwrpIgm4EKnmvlBc1Bt3f6TIAiYqVrhNmAvfdp/27bl3OhYzSsPYx6LkNGDCABx98kPr169vchEHSgy6EsBb//ANZWRAUBFWrWjqawulK3KUHXViz0FBYulS7/cEHsHKlZeMRQuQlCboRFbcHvbqvll06+7cwbUBWpEcP7WrtlStlm5jEVnrQQTtRW7ZsGUeOHCHM2rt97iFj0IUQ1iJ3ebu1DwuTHnRhK4YOhbvT5TBmTE6VihDC8pwsHYA98A8Zwo596SQGNeB//4PWrQ23zUhPp14lLempEXqfmSK0PDc36NNHK6tat670ZYq2MgZdp6IuYBsjPehCCGthKxPEAQwfPhwAJyc5vRLWb+ZM7QLY339rk8bt36+tly6EsCw5ghhBtdAHqRb6IMVZMC3i8EGae2rruTbraN9roN/r4Ye1BH3tWpgxo3TbsKUe9NzS09P5+OOPSUlJ4Z133rF0OEWSMehCCGuhS9CtfYI4yKk+EsIWODnBDz9oHUv//gsjR8KaNdpkckIIy5GvoJmd3LcdgEuxLvj4u1s4GvPq10/7o3/0KFy6VLpt6BL0lBTtx1Zs27aN119/nZkzZ3L8+HFLh1MkKXEXQliDtDTQ/cm0hR50IWxNYKDWeeLqCr/+Cu+9Z+mIhBCSoBvBzUsHObThA1Z//SPbt0N8vOG2CVHaYLqLsX5mis56VKwInTppt3/9tXTb8PLKGeNvCxPF6dx///0MGDCArKwsnn/+eaufQEhK3EVh2rdvz9y5c/X3hw0bhqIoJCcnAxAdHY2LiwsnT560VIjCThw7pi0DFRAANWpYOpqi7d27lzVr1nDx4kVLhyJEsYWFwWefabenT4f16y0ajhDlniToRnBlz3xa3X6NO4cm0LUrHDliuK1L2hkArqVUN09wVuatt7Q//OPGle71imJ749B15s2bh7u7O9u3b2f58uWWDqdQUuIuCuPr60tiYiIAkZGRbNy4ES8vL+Li4gBYvHgxPXr0ICQkxJJhCjuQu7zd2ieIA7hw4QJHjx4lNjbW0qEIUSKjR8N//qNNeDxiBJw9a+mIhCi/JEE3pmLM4h7kFgVAqnP5PHG9/3548EFt0rjSstVx6DVr1uTNN98EYPLkydy+fduyARVCStwtQFUhM9kyPyWs6PDz89MvHfjpp58yYsQIAgMDiYuLIyMjg8WLFzNx4kQuXrxI68JmzSym0aNH89tvv5n8NcL66GZwt5XydpnFXdiyTz6BDh20StBBg8DGVogVwm7IJHEmUNg66LUCbgNQoUrZT1rLK1taC/1er7zyCsuWLePMmTNMmzaNefPmWTqkAkmJuwVkpcBPFSyz70eTwMmz2M11PejJycksWbKE3bt3s2vXLuLi4li9ejVeXl707duXS6WdbEKIu2xpBneQddCFbXNxgZ9/1r5vx4/D2LHw44+2Ub0ihD2RHnQj0B2IVVX7C2aoBz0xIZ66QZkA1GvZxSyxWaPz5+G112Dq1NK93lZL3EFLfD/99FMAvvjiC25Y6ZuQBF0URteDvmzZMjp06ECDBg3w9vYmLi6OBQsWMGHCBH2iktvs2bMJDQ2ladOmeYZ59O/fn7CwMEJDQ1m1ahUAb731FiEhIfTr14/r16/n2c7SpUtp27YtzZo14+2339Y/XthrhO1JT9fGoINtzOAO0oMubF+VKlqS7uQEP/0EH39s6YiEKH+kB90EDCXoR3aG09kZ7qRDkzZNDb4+4kYEPq4+VKpQib1Re2lTtQ0ujvazdMvVq/DBB9pam+++W3jFQUFstcRd5/7772fWrFkMGjSIQN2bsTIyBt0CHD20nmxL7bsEfH19OXHiBPPmzWPBggUAeHt7Ex4eztGjR1lfwAxDBw4c4KeffuLAgQOkpKTQpk0bunfvTpUqVVi2bBn+/v7Ex8fTrl07qlatyh9//KEfyxsSEsL48eMBiIiIYMOGDezevRtFURgwYAC7d+/G0dHR4GuEbTpxQkvSfX2hdm1LR1M8ugtTkqALW9apk1bu/sILMGUKtGwJPXpYOiohyg9J0I1IV9FmKOG8+M9OOleHs9c9CHV1LLDN5fjLtPy8Ja6OrgwOGczXR7+mY/WOrHtsHQEeASaK3LzatYOgILh+HbZvh549S/Z6Wy5x15la2vIBM5Ex6BagKCUqM7ckPz8/Nm/eTK1atejVqxegJeiLFi3i2WefpUKF/KX64eHhDB48GDc3N9zc3OjZsyf79+9nwIABzJ07l3Xr1gFw+fJlNm/ezKBBg3BxcSE4OJgeuc4M//77b3bv3k3Y3ZrnpKQkzp07x82bNw2+RtgmW5sgDnJ60KXEXdi68eNh3z745hsYNkz7PtrCSgpC2AMpcTci3eHYUA/6nRtHAbgcZ7jXdNXJVaRnpZOYnsjXR78GYFfkLjp+1ZHzceeNGK3lODrCQw9pt9euLfnrbb0H/V4HDhzg1q1blg4jDylxF4XRlbhPnDhR/5i3tzepqam88MILxdqGqqooisKWLVvYuXMne/bs4ejRo9SoUQM3N7cCS+R1r3vmmWc4cuQIR44c4ezZszzxxBP67Qn7YWvjz0F60IX9UBRt6bWWLbUOkcGD4c4dS0clRPlg0wn69OnTURQlz0/lypXNHodfwwFsS3yYq65PMWuWVo5XEI+scwDEZtQyuK3Vp1YDUMNHu0zpoDhQ3bs6Z2LP0OHLDuy/st+YoVvMgAHav2vXlngCaZseg36v2bNn07ZtW9544w1Lh5KHlLiLwgwZMgRVVRmXa73ERYsWkZWVRW0Dtcj33Xcfq1atIi0tjbi4OLZs2UKbNm1ISEggICAAd3d39u3bx5kzZ+jUqROrV68mPT2dq1evsmXLFv12evTowYoVK/RLukVFRREbG1voa4RtsrUZ3AH69OnD5MmTad++vaVDEaLM3N1h1SoICIADB7RedSkOEcL0bL7EvUmTJvz111/6+46OBZeOm1LNlo9Qs+UjdC2iXXCFGACyPZoU+PyN5BuEXw4HoEetHnx99GseafQI8x6YR7/v+3Hk6hG6LevGiiEreKjBQ0Z8B+bXq5f2h//yZfjnH2jevPivtace9Pbt26OqKp9//jlPPfUUbdq0sXRIgJS4C+Nr3bo1Q4cOJSwsDEVRmDFjBsHBwfTp04cFCxbQokULmjdvTtOmTQkKCqJPnz40a9aMhg0b0qVLzqSaoaGhvPbaa3Tr1o3s7Gy8vLz48ccfadu2rcHXCNuTkQFHtaIzm5kgDsCtLGuICmGFatWCH36Avn1h6VJtmOKzz1o6KiHsm6La8ECp6dOns2bNGo4cOVKq1yckJODj40N8fDze3t7GDe4eqqpyeb4jNSuq/Jq2jP5jnszXZuflnTzy0yNU8apCTGIM15Kv8dvjv9GvQT8S7iQQ9kUYZ2+dxUFxYOGDC3m2tW3/hRwwANatgxkzINdEzEWKiIAmTcDfH2JjTRefuYwcOZLvvvuO1q1bs2fPHotcZLrXihUreOyxx+jatStbt261dDh26c6dO1y4cIHatWvLSb0NMPT7MudxxBKioqKoXr06kZGRVKtWzWz71V249faGuDhwsOl6PyFs3+zZ2uo7zs6wbZu2XroQxWGp44gts/lD3r///kuVKlWoXbs2jz32GOfPGx6nnZaWRkJCQp4fY7gVdZzDmxbz6/cbOHiw4PKfmKgoalbUnmjSvnuB2+lUoxPRL0fzasdXuZZ8jQD3AHrX7Q3A0iNLOXvrLO5O7mSr2Ty3/jn++/d/bXoimgEDtMninEpYx6Ercb91CzIzjR+Xuc2ZMwdvb28OHDjAkiVLLB0OIGPQhRCWlXuCOFtKziMiIli/fj2nTp2ydChCGNWUKTBkiFbdMmSItiKPEKa0cOFC/UXxsLAwduzYYemQzMaGDnv5tWvXjm+++YaNGzfyxRdfcPXqVTp27EisgW7VWbNm4ePjo/+pXr26UeK4uOMDWt54lvjwp2jduuA2R3ZsBeBWkgO1Gxq+euTo4MjGcxsBeLTJozg7alPCDwoZRNOgpqRmpuLkoGW0s8Jn8eSaJ0nPss1xwk88AdHR8N//lux1AQE5M/raQw965cqVeffddwF4/fXXrWJtdBmDLoSwpNwJui2JjIzkwIEDREVFWToUIYxKUeCrr6BxY+3c7dFHtWRdCFNYsWIFkyZN4o033uDw4cN07tyZBx54gMuXL1s6NLOw6QT9gQceYPDgwTRt2pRevXrp195dtmxZge1ff/114uPj9T+RkZHGCSRXL7aTU8HLwVz9dy8A5657oTjkbxB/J55sNZvUjFRWnVwFwIimI/TP1/CpQfhT4TxQ7wEyszNRUHBQHPjun+/o/0N/ktIttH5yGbi4aDO6l5Sjo1beDvYxDh1g/PjxtGjRgri4OKtYgk3GoAshLMkWJ4iDnGXWZBZ3YY+8vGD1am3oyY4d8Morlo5I2KuPP/6YsWPHMm7cOEJCQvjkk0+oXr06ixYtsnRoZmHzk8Tl5unpSdOmTfn3338LfN7V1VXfM2gqhpZYU+OPA3AlsTIFTQP24u8v8tf5v3gs9DGS0pOo5VuLjtU75mnj7erNusfX8eKGF/ns4GeoqoqTgxN/nvuT7su6s374eoI8g4z8jkwvOxv+/RcaNiz+awIDtd5zW14LPTcnJycWLFjAww8/bBWz/0qJuxDCUjIzQTe1jK0l6LLMmrB3DRrAt99qwxTnz4c2bWDkSEtHVXqqqnLhwgVSU1PzPefr60vVqlUByMrKKnToire3t74yV1VVIiIiDLatUKECNWvW1N+PiIgwOGTVw8Mjz+oop06dIisrq8C27u7u1KlTR3//zJkzZBgoc3B1daVevXr6+2fPns13znfv9swlPT2dgwcP5uuw6t27N7t27TJ7PJZgVwl6WloaJ0+epHPnzmbdb85XSsHZueA2Pg4XAbidXS/fcxlZGfx25jfi7sSx78o+AB4PfbzANX2dHJxY2G8htf1q89pfr6GqKj6uPhyIPkCnrzqx8YmN1PEz/5eptG7c0CYC0iXbXl7Fe509LbWm07FjRy5duoSnp6elQ5ESdyGExZw6BampUKEC1K9v6WhKRteDbsvzwwhRlIcfhrfegnffhWeegdBQbb10W3Ty5ElWrlxZ4HMtWrTIk6D//PPPBrfTuHHjPENnC2tbv379PAn66tWryTQwqVKtWrXyJOjr1q0r8GICQJUqVfIk1Bs2bCA+Pr7AtoGBgXkS9I0bN3Lznl6vqlWrGjVBT0xMzDP/l6GO05s3b5KVlUWlSpXyPF6pUiWulpPJD2w6QZ88eTL9+/enRo0aXL9+nffee4+EhARGjRplsZgM9aBX89EySUff/OuJ7bi8g7g7cQS4B7A3SiuFz13efi9FUZjSaQo1fWqSmJ5Il5pd6PNdH87eOkvHLzvy+4jfaRlsG38pK1bUTsJiYmDjRm3ikeKwp6XWcsudnKuqWuBFGnOQEnch7MP27duZM2cOBw8eJCYmhtWrVzNw4EBLh1UoXXl7y5a2NUEcSIm7KD+mT9fmitiwAR55RFsnPSDA0lGVnG7eqgoVKlBR1/tzV0CuN6QoCrVq1TK4nUDdieldhbUNCspb7VqzZk2DveL3Jqk1atQweG4WcM8voFq1avj5+RXY1tfXN8/9qlWrUqFChTyP3ft5lFXjxo3z3J82bRrTp0832P7ec2BLnhebm00n6FFRUTz++OPcvHmTwMBA2rdvz549e/JclTILVYW7/18KStCzs7OpF5QCQNWQjvmeX3NqDQCNKjZiZ+ROmldqTpOggtdKz21Y6DD97V1P7aLHsh6cij1F16+7snrYanrW6Vny92JmiqKVSX34IaxdKwk6aH+A1qxZw/Tp0/nzzz/z/XE2BylxF8I+JCcn07x5c8aMGcPgwYMtHU6x2OoEcSAl7qL8cHCA777TStzPnYPHH4fffy/d3EKWVL9+fdzd3fH39y+0t9jZ2bnYHYCKopSos/CJJ54odtvHHnus2G2HFPekGsxy4TYiIkJfkQAYHHZcsWJFHB0d8/WWX79+3SLnxJZgY9em8/rxxx+Jjo4mPT2dK1eu8Msvv+S7OmNuBSXoZ44do+Ld0u0W9+Utv1dVVZ+g375zG4DhTYeXeL/Ojs6kZ6fj5OBEYnoiDyx/gB+P/1ji7VjCww9r/65fX/xl03QJur2MQc9NVVVmzpzJP//8w6uvvmqRGCRBF8I+PPDAA7z33ns88sgjlg6l2HQJuq2NPwcpcRfli5+fNmmchwds2gRvvmnpiEqucuXKtG7d2iJjrcsbLy8vvL299T+GEnQXFxfCwsLYtGlTnsc3bdpEx475OzrtkU0n6NbCp94D/BHdkyuOjzFhQv7nT+zZBsDlWCf8g7zzPHco5hCRCZF4OHtw4sYJFBQeD328xDGoqkqQZxCZ2Zk4KA5kZGfw+C+PM2/PvFK9J3Pq0EEri4qLg/Dw4r3GHseg6zg4OLBw4UIUReHbb79l+/btZo9BxqALISwhK8t2J4gDaN26NRMmTKBXr16WDkUIs2jaVFt+DeD//g9++cWy8Qj78PLLL7NkyRK++uorTp48yUsvvcTly5d57rnnLB2aWUiCbgT12o2g7+S/eG3+XCZNyv983KUDAFy46ZvvOV3vuW5ity41u1Ddp+Trswd6BrL5yc08EvII2WpOad2kjZOY+tdUq76a7+QEDz2k3V63rnivsecSd4A2bdrwzDPPANoSbIZm4TQV3Rj0rKwsg+OihBD2Jy0tjYSEBP1PYmKiWfd/5gwkJ2s9ciVZ2cNauLu74+fnh4eHh6VDEcJshg3LWXJt9GgoZAJzq3P58mUuXLhASkqKpUMRuQwbNoxPPvmEd955hxYtWrB9+3Y2bNhg/mHMFiIJuhk4pWrLMlxNrpbvuYGNBvJKh1f065iXprxdx93ZnZ+G/MRL7V/K8/jsnbMZs3YMGVnmTfJKQlfmvnZtnmXlDbLnEnedmTNnEhAQwIkTJ5g/f75Z95277EjK3IUoP2bNmoWPj4/+x9zDxnQTxLVoYXtjWYUoz/7v/6B7d0hKgkGDwMDk4VZn48aNfPPNN0RFRVk6FHGP8ePHc/HiRdLS0jh48CBdunSxdEhmIwm6Edy++i8H/17JlvXhXLqU//kAl0gAUhzzdweEVQljdIvRXLx9EWcHZ4Y0Lv6EDgVxdHDk4z4fM6/vPJS7M9cpKCw7uoyBKwaSnJ5cpu2bSu/eMHkyfP118drbc4m7jr+/P7Nnzwa0mS6vXLlitn3nTtClzF3cq3379sydO1d/f9iwYSiKQnKy9vclOjoaFxcXTp48aakQRSm9/vrrxMfH638KW8vXFGx5/DlAZGQkf/75J0ePHrV0KEKYlZMTrFgB1atrlTCjRoEtzJV4584dANzc3CwciRA5JEE3grN/TSPs2qNcWf8oI0fmf76W/y0APCoXPCXt98e+B+CB+g/g7+5vlJgmtJvAqmGraBrUlO8GfYe7kzsb/t1Az296cjPF+rqdK1SAOXOgc2dtZvei5O5Bt+Lq/TIbM2YMHTp0ICkpyeA6nabg7Oysvy096OJevr6++tLnyMhINm7ciJeXF3FxcQAsXryYHj16EBISYskwRSm4urrmmcTHy8vLrPu35RncAWJiYti9ezenT5+2dChCmF1gIKxaBa6uWkXkzJmWjqhokqALayQJuhHkThDvncU97c4d6lXSeiDrtMhbmjFn5xz+OPsHy/9ZDhS+9nlpDGw0kMPPHmZ4s+H8/eTf+Lv7s/fKXu776j4u3r5o1H2Zmy5Bz8iwnTKq0nBwcOCzzz7j999/Z1JBExyYiKIosha6pWQmG/7JulP8tpmpxWtbCn5+fiQlacNyPv30U0aMGEFgYCBxcXFkZGSwePFiJk6cWOLtXrx4kdatW5cqpnuNHj2a3377zWTtbUVSUhJHjhzhyN2Z1y5cuMCRI0e4fPmyZQMrQHY2HD6s3bbVHnSZxV2Ud61bw8KF2u2339aWXrNWqqpKgi6skk2vg249cg7EuToeATiyaxftXCA9E5q1zznjuJZ0jdf+eg317msruFSgf4P+Ro/M0UEbxNehegdeav8Sb295m9Oxp+n4ZUf+eOIPmlVqZvR9lsWmTbBmDbz1FlSubLidm5vW656UpPWi+/qaK0Lza9asGc2amf/35OrqSnp6uiTo5vZTBcPPVXkQuq3Puf9LEGQZmNgmqCv02ppzf20tSCugemZ4yRMJXQ96cnIyS5YsYffu3ezatYu4uDhWr16Nl5cXffv2LfF2hfEdOHCA7t276++//PLLAIwaNYqvizumyEzOnoXERO3vu60WX+gSdFkHXZRnTz0F+/bB55/D8OFw4ADUrWvpqPLLzMzUf1clQRfWRHrQjUhFydeDfvawtm7Y+etuuLrnZO+/nvkVFZVAD60r+JGQR3B3djdZbNlqNlsubtFfEIhJiqHL0i5su7jNZPssjTfe0K68/vpr0W3Lwzj0e8XExPBrcT4cI5Cl1oQhuh70ZcuW0aFDBxo0aIC3tzdxcXEsWLCACRMmoChKvh7xyZMn8/XXX3Px4kWaN2/OqFGjCAkJYdiwYQX2OM6ePZvQ0FCaNm3K8uXL9Y/379+fsLAwQkNDWbVqlf7xt956i5CQEPr168f169f1jy9dupS2bdvSrFkz3n777SLb25Nu3bqhqmq+H2tLziGnvL15c208qy1S7o7RkgRdlHfz5kH79nD7tjZpXLIVToGk6z1XFCXP0D4hLM1GD4HWJfd55b3f75SrhyEYLt4KoFGux387o5VSJmdof7GGh5Z+9vbicFAc2DB8A2PWjuGH4z8AEJ8Wz/3f3s/3g78v8+R0xjJgAOzfr41devrpwtsGBsLFi+UnQT99+jRt2rQhMzOTkydPmnypCSlxt5BHkww/p9wzrfXgwpLKe66/DrhY2ojy8fX15cSJE8ybN48FCxYA4O3tTXh4OEePHmX9+vVFbAFOnjzJihUraNiwId27dyc8PJzq1XOWmDxw4AA//fQTBw4cICUlhTZt2tC9e3eqVKnCsmXL8Pf3Jz4+nnbt2jFo0CD279/PH3/8wdGjR4mNjSUkJITx48cTERHBhg0b2L17N4qiMGDAAHbv3o2jo2OB7YXl6GZwt9XydpASdyF0XF3h55+1+SSOHdPO6ZYvL948Q+aSu7xdsabARLknPehGdm8PukfWvwDcTMtJpjKyMth8YTMAKRkp+Lj60KN2D5PH5urkynePfMfr972eE0t2BkNXDmXh/oUm339xDBig/fvXX0VfbbX3tdDv1aBBA1q0aEFqaiovvfRS0S8oI10PuiToZubkafjH0a34bZ3ci9e2FPz8/Ni8eTMuLi706tUL0BL0RYsWMXbsWCpUKKRM/66GDRvSqFEjFEWhZcuWXLx4Mc/z4eHhDB48GDc3N/z9/enZsyf79+8HYO7cuTRv3pwuXbpw+fJlrl69yq5duxg0aBAuLi4EBwfTo4f2N/Xvv/9m9+7dhIWF0apVK06ePMm5c+cMtheWY+sTxIGUuAuRW9WqsHKlVhHzww/wySeWjigvT09P+vXrl2cYkBDWQBJ0I7u3B72SRzQAGe45a8nuu7KPxPRE3O+eQPdr0A9nR/OU1jgoDszsOZPPH/ocx1y9cc9veJ43N79p8av+TZpA7dqQlgZ//ll4W12Juz2vhZ6boigsWLAAR0dHVq9eze8mnnlFStyFIboS99wTwXl7e5OamsoLL7ygf8zJySlPopL7Yk/upfwcHR3JysoqdJ+qqqIoClu2bGHnzp3s2bOHo0ePUqNGDdLS0vTPF/S6Z555Rj9R2tmzZ3niiScMtheWoar20YOu+z9l6WOpENaiSxf46CPt9quvwtatFg0nDw8PD1q3bk2bNm0sHYoQeUiCbgQ+tXuy7lwHorIH8OCDeZ+rHZAAgF+NnC//n+e0zFM3gduAhgPME2guz4Q9w6+P/4qnsyc9amk9R+/veJ+nf32azOxMs8ejoyg5vejr1hXetrz1oAM0bdpUnxS9+OKL+vIsU5ASd2HIkCFDUFWVcePG6R9btGgRWVlZ1K5dW/9YUFAQ0dHRJCYmkpSUxKZNm4q9j/vuu49Vq1aRlpZGXFwcW7ZsoU2bNiQkJBAQEIC7uzv79u3jzJkzAHTq1InVq1eTnp7O1atX2bJlCwA9evRgxYoV+iXgoqKiiI2NNdheWMb589qKHC4u2oVaW1WvXj3+85//MGCA+Y/rQlirF1+EESMgKwsefRQiIy0dkRDWTcagG0HDzuNo2HkcD9/zeOyNG9QK1HqFQtrllM8cuXYEgKT0JJwdnOlbzzKzHT9Q/wGOjz9OTZ+aLDm0hOfWP8eXh7/kevJ1fhzyIx7OHhaJ6+GHtTKo337T/pg7Ohbcrjwm6ADTp0/nxx9/5Ny5c8yePZtp06aZZD9S4i7KysXFhSlTptCqVSvq169P06ZNi/3a1q1bM3ToUMLCwlAUhRkzZhAcHEyfPn1YsGABLVq0oHnz5vpttm3blj59+tCsWTMaNmxIly7aspahoaG89tprdOvWjezsbLy8vPjxxx8NtheWoStvb9YsfyWaLXFzc5PZoIW4h6LA4sVw4gQcOQKDB8P27dqKDZYUFxfH7du38fX1xc/Pz7LBCJGLopbjOqyEhAR8fHyIj4/H29vb6Nvf9PNK7k9/lPgUBe+nslAcckrfXvnzFebumUufun3444k/jL7v0lhxfAXDVw0nW82mQ7UO/Pr4rwR4BJg9jowMqFQJfHxg82at5L0gX30FY8fCgw9CMeaksisrVqzgsccew9XVlYiICOrUqWP0fXTu3Jnw8HBWrlzJkCHWMYmgPblz5w4XLlygdu3ackJvAwz9vkx9HLG0qKgoqlevTmRkJNWqVTPZfqZOhdmz4dln4bPPTLYbIYQFXbigDWGJi4Nx4+CLLywbz44dO9i8eTMtWrSQqhcTMtdxxJ5IibsRJNy8xJHwvzkY/g+3b+c8fuXUbgDOXvfUJ+egjVHbHaU9Z4nydkNO3TxFtqqNF90dtZvOSztzOf6y2eNwdtZm/Dx/3nByDuVzmTWdRx99lD59+jB+/Hgq6j4II5Mx6EIIc7GHCeIAbt26xdatWzlw4IClQxHC6tSurU0WpyiwZInWq25JumGC7u6mW+ZYiNKQBN0ITq6fQovLvYj4/kE+/DDn8cxbxwCIiq+Up/3VpKvsjdoLwMMN7y2Mt5zXO7/OqOaj9PdP3jxJhyUdOH79uNljqVq16KU4ymuJO2gXedavX8/HH39ssl47GYMuhDAHVc1J0G15gjjQEvRt27ZxUPeGhBB59OkD77+v3X7hBdizx3Kx5F5mTQhrIgm6keUeO+fjcB6A21k53cAdvuxA/x/6o6LSukprqnpXNXeIBrk4urB0wFKmd52ufyw6KZr7vrqPHZd2WCSmjAzDy62V5wQdtJmvdVRVJSMjw6jblzHoQghzuHRJK3l1dobQUEtHUzayzJoQRZs6FQYN0s7xhgyBa9csE4ck6MJaSYJuZLnXQa/qdV274d0MgPNx59kTtYeD0dqV9YENB5o5uqIpisK0btP4esDXODlocwjGp8XT65terDm1xqyxzJkDQUGwYEHBz+sS9ORkSE01X1zW5vjx43Tp0oUZM2YYdbtS4i6EMAddZ3NoKORafc8mSYIuRNEUBb7+Gho1gitXYOhQLVk3N0nQhbWSBN0Ics+zp+tBV1WVukFa12/l+h0A2HQu7xJDAxpZz/jze41qMYo/RvyBt6s3Lo4upGenM/inwSw+aL4BQ56ecPs2rF1b8PPe3jmfd3lZC70g//77L+Hh4cyZM0e/5JQxSIm7EMIc7KW8HWQddCGKy9sb1qwBLy/YsUNbI93cJEEX1koSdCPT9aBfPPMvlXy0A3TLLt0A2HReS9BVVOr41aFJoHUv9tqzTk92PrWTbaO3MbblWLLVbJ797VlmbJ1hlpOPh+8Oz9+9G65fz/+8opTvieJ0Bg4cyAMPPEB6ejovvPCC0X43UuJuHnIibxvk92Q6hw5p/9pDgi496EIUX8OG8M032u158+C778y7f0nQhbWSBN0Ycp236RL0f3ZuBSA6zpGgqhXJys7i7wt/69sNaDhAf6XdmoUGhdK+Wnu+6P8Fb3Z+E4Dp26bzn/X/ISs7y6T7rlZNm9FXVQ0vo1bex6GD1mMzf/58XF1d2bRpE7/88otRtisJumk53y3/SElJsXAkojh0vydnW16k2wrlniDO1mdwh5wEXS7oCFE8AwfCG29ot59+Wlsn3Vw6d+5Mz549ZQ10YXWcLB2AfVH0JdexF/ZBCJy74U0V4ED0AW7fuY2Cgopa9PJq8fHaX62TJ+Gpp+Cll3KyUQtQFIXHmz7O7J2zycjO4PODn3Mt+RrfP/I97s6mW57i4Ye13pW1a2HMmPzP63rQy3OJO0DdunV57bXXeOedd5g0aRJ9+/alQoUKZdqmrsRdxqCbhqOjI76+vly/Wx7i4eFhExftyhtVVUlJSeH69ev4+vrmmZxRlF1kpPb328kJmjWzdDRlp/sOSw+6EMU3Y4Z2oe6PP+CRR+DAAfD3N/1+W7RoYfqdCFEKkqAbQYUanVm1+QyRGa3o3lh7TEmKACAmqQoAf577E9DK2/3d/elUo5PhDWZkQN++OWtPzJoFH38MI0fCpEnQxDKl8Q0CGvBUy6f4/ODnAKw5tYbe3/Zm3ePr8HM3zdXHAQNg+nT4809tIrh7l6qUHvQcU6dO5dtvv+XChQu88847fPDBB2XanvSgm17lypUB9Em6sF6+vr7635cwHl15e5MmYA9VphUrVmTs2LFSaSFECTg6wvLl0KYNnD8Pw4drlZNyPVSUV5KgG0FozxcJ7flinscCXC4DkOxQH4Cq3lWp5FmJa8nXeKjBQ/oZ0vNRVXjyyZzkvHp1qFRJu5y4ZIn206ABzJypXWY0Y4+bk4MTi/otoo5fHV776zUAwiPDuW/pfWx8YiPVvKsZfZ/Nm0ONGnD5Mvz1F/Tvn/d5SdBzuLu7M3/+fB566CE2b95MZmYmTk6l/4pLgm56iqIQHBxMUFCQ0ZfJE8bj7OwsPecmYk/l7aBVHlWrZvxjoRD2zt8fVq2CDh1g40Z4++2c9dJNISMjg6ioKNzd3eXiq7A6kqCbSHXfWABcA1sCMKbFGN7b/h5QxPJq06bBjz9qt6tVg6NHwdcXdu6EiRO17oYzZ7SFI/38YPJkrfz93q5lE1EUhSmdplDTpyYjV48kIzuDiBsRtPuiHX89+RchgSFG3h9MmACJiQUXDkiCnle/fv1Ys2YN/fr1K1NyDrLMmjk5OjpKAijKJXuawV0IUTbNm8OXX2o96DNnQuvW2nrppnD79m2++eYb3N3dmTJliml2IkQpySRxRpAcf42Ig4f598QF0tMhMyOT+pW0mSFrNb0PgOPXj3Ph9gXcnNzoXbd3wRv69lt4913ttrc3hIdrSbiiwH33aUn6//0fBARobeLitJk1fH21XverV038TnMMCx3G30/+jY+rDwDRSdF0+qoTuyJ3GX1fr7yilbnXqZP/uaAg7d8rV4y+W5s1YMCAMifnIMusCSFMK/cEcfaSoKemprJz5052795t6VCEsEmPP66N5gQYNQpOnTLNfmQGd2HNJEE3gn9+eZ7Gp1ux+9OubN0Kx/bvp4IbZGZBi/s6cvz6cVacWAFArzq98HTxzL+RmJicWdCcnWHbNqhZM28bNzd47TWtu3jNGgi521udnq4l9zVqaBPKHTtmsveaW+eandk7bi9jWoyhbZW2xN2Jo9c3vfj19K9m2T/klEXu3g0yJ09ed+7cYc6cOcTHx5fq9VLiLoQwpehobQlNBwf7mCAOtAT9r7/+YuvWrZYORQib9cEH0LWrVj05aBAkJBh/H6mpqYAk6MI6SYJuBLkXU3F2hjMHdgBw4YYLHhXcGPLTEGbumAlgePb2efMgK0vrLV+3DgqbWVJRtNnTIiLgxAno3VsbvJORAUuXamc6TZtqC0qaOGttWLEhXw34is2jNvNg/QdJzUxl4I8D+fLQl0bdT3Y2/PorDB2qvU2dli3B01MrJjhxwqi7tHmDBw9mypQpTJs2rVSvlwRdCGFKut7zxo3Bw8OysRiLzOIuRNk5O8OKFVC1qtaDPnq0VnFjTNKDLqyZJOhGpeDiAonR2rS0F2P9uBx/mdOxp1HvpvH9G/TP/7IFC2D2bO32119rM7gXV+PG2mwaN2/Crl1aBqsocPy4Nuu7nx+8+SaYeK1lTxdPVj+6mnr+9cgmm3G/juO9be8ZbS3Y9HRtfcyff84Zog/aH/FOdyfE377dKLuyGxMnTgRg/vz5HD16tMSvl2XWhBCmpJvB3V7K20HWQRfCWCpVgl9+ARcXWL1aG+FpTJKgC2smCbox5DoQu7iAW7o2YOZaag02ndukf65DtQ5UqlAp53VpaXD//fDCC9r9997TxpKXhqJoU1/+9BP89hsEB2uPJyRo02D6+MCjj0JUVOm2XwxOjk48WP9B/f23tr7F8xueJys7q8zbdnPLGZM0e3bewoAuXbR/t20r827sSu/evRkyZAjZ2dk8//zzJe7RkR50IYQp2dv4c8hJ0KUHXYiya9cOPv1Uu/3GG9qSu8YiCbqwZpKgG5mzMwS5azOWpbs2YtP5nAQ9X3n7e+9pa4cBjBsH//2vcYJ48EFtcN9ff2k14ACZmbBypbZsW79+cPiwcfaVi4PiwLy+8/ikzycoaGV+iw4sYsjKIdzJvFPm7f/nP9rceSdOaOtj6ugS9O3bjV8CZevmzp2Lp6cnO3fu5JtvvinRayVBF0KYkr0tsQY5Je6qqkovuhBG8PTT2o+qwmOPwYULxtmuJOjCmkmCbgQqeXvQawfcBsCrWqs8CfrARgNzXnTtWk5Ze7VqsGiR8dc079lTqyE8f14bs65bxmnDBu2MqHt3rW7IyFf6J7afyKphq3Bx1Eqk15xaQ49lPYi/U7rJynR8fLQkHWDWrJxkvE0bcHXVPtJ//y3TLuxOtWrV9GPQp0yZQlxcXLFfK8usCSFMJSZG+3FwKHzKFVuj60EHKXMXwljmz4e2bbX5hgYNMs6ozQYNGtCjRw/q1atX9o0JYWSSoBtZRnoitQMzAXCqU5VbqbcAaODfgIYVG+Y0fPHFnNnOvv4ajLAslkG1a2uzvt++DZ99pl2CdHSErVvhkUe0zHfyZEhKMtouBzYayPbR2/F18wVgd9RuOi/tTHRidJm2O3Gilozv3q2tQgda+Xu7dtptGYee38SJEwkJCeHGjRu8/fbbxX6dLLMmhDAV3fjzRo20iT7thSToQhifq6s2Hj0wEI4ehWeeKXvFZK1atejcuTN1ClrDVwgLkwTdCCpUbc/PhxsQmdaZyIhtODlC0h044XZG3yZP7/nx41q5OWjrSPTsaaZAK8Czz8IPP2g1QqNHa48nJcFHH2nrqQ8YABcvGmV37aq148DTB6jrVxc/Nz+OXT9Gxy87cvrm6VJvMzg4J2xdAQJoHyPIOPSCuLi4sHDhQh588EEm6QbyF4OUuAshTEWXoNtTeTtof2+ffPJJRo8enSdZF0KUTbVq2qmzoyMsX671qgthr+ToYQTN+05hyJzTvPHVd8Rd2gXA2WsePNZsGG5O2tiWAY1yjT/XTQTn4KD1nltC9erakmx79miTyymKtszbunVaj3uLFvD332XeTV3/upx64RQHnzlIff/6XIq/RIcvO7A3am+ptzl5MrRunXc+vdzj0EV+3bp1Y/369dStW7fYr5EEXQhhKvY4QRxoPei1a9emZs2a+vHoQgjj6NoVPvxQu/3yy2U754uOjiYmJkaG8QmrJAm6kaXHastZXb4dyMX4i9zJvEOQZxDtqt6twV6/PmeCtueeg1q1LBOoTrt22vJsV67A449rs9yBVkPUq5f213DNGi15LyUnBydq+9Vm51M7aRrUlLg7cXRe2pkNZzaUanv16sH+/dqk9DodOmijBC5fhkuXSh1quVGcseiyzJoQwlTsNUEXQpjWxIkwfLh2Wjp0aOkXJ/rll19YvHgxV69eNW6AQhiBJOhGcCc5gcvnIrlxNZYK6jkA4jJrsfbUWkBb+9zR4e4Ebfv2af96ecEHH1gi3IIFB8P330NiIsyYodUSOTpqlycHDYKqVWH8eO35Ugr0DOSx0McAyMjO4KEfHuLLQ18aJXxPz5wTPelFNywpKYlx48ZRv359bt68WWhb6UEXQpjC9evaSbWi2NcEcaCNO9+/fz979+4lQzfPjBDCaBQFvvgCmjfX/pYMGaKtWlxSMou7sGaSoBvBnm9GUWNvDX6fEUZwBe1KXDT+LD+2HMi1vFpUVE5tzmefWefMOK6u8PbbEBmpdUW//jr4+WlTpC9apI1T79MHzpwpclMF+W/n//LpA5+ioKCiMu7XcczYOqNUk+kkJmpD52fM0O5LmXvR3Nzc2L9/P7Gxsbz++uuFtpUEXQhhCrrx5w0aaNeq7c2GDRv4448/pPpICBPx8IBVq7TT0717YcKEkr1eVVVJ0IVVkwTdCHKvUlYvSOth3nInkrg7cbg4utCrTi/t8t5rr2lrQ3TqpJWTW7uqVWHmTG1Su+7dtcuW2dnw55/QsCE0aZJ3QfJier7t86x9bC3ODlo5/fRt0xm3bhzZasmWezt4UBuP/n//p10/kIniiubk5MTChQsBWLJkCXv27DHYVpZZE0KYgj2XtyuKoh97nm3kJUyFEDnq1NEKPxUFFi+GJUuK/9qMjAz991MSdGGNJEE3gqy7x2Anx0yq+Gl39nloPcy96vTC3dkdnn5a+0sCMG+e8dc8N6UqVWDzZoiNhbFjtXXNACIi4KGHtEHhv/xSonHq/Rv2Z/fY3Xi5aN0nXx35iv7f9ycts/i9tV27akPo79yB//1Pu+6hKNpa6DExJXqH5UqnTp0YfXcq/PHjx5Nl4PemG4OemZkpJ5pCCKOx1xncdXQJuiyzJoRp9e0L772n3X7++ZxRpEXR9Z47ODjgrJt7SQgrIgm6EWRnawdhb3ctubwW70B8hQQAhjYeqi1p9t13WuNevWy328DPT7tEqastDwrSHj93ThsEVK+e1uNejAnIAMKqhHHsP8eo6lUVBYUNZzfQ7/t+JKQlFOv1igJTp2q3FyzQJsVv3ly7v2NHSd9c+TJ79mx8fX05fPgwixYtKrCNrgcdpMxdCGE89tyDDjlrocuFTSFMb+pUGDgQ0tNh8GBtXHpRcpe3y2oLwhpJgm4EumOwLkH/95qH/rmHGjyklbOrqjbp2rffWiJE43Jy0ta3uHZNqyd/800ICNDWT3/jDe12t27wzz9Fbqqmb02Ojz/OyqErqeBSgb8v/E3XpV25mlS8WTUffhgaNYL4ePj8cxmHXlxBQUG8//77ALz55ptcu3YtXxtJ0IUQxhYbm7PSRsuWlo3FVKTEXQjzcXCAZcu0kZdRUdoKP5mZhb9Gxp8LaycJuhHojsE+7tpY3dOxWtl2s0rNqLj3mDaDBcCLL0LlypYI0XS6dIF339Umlfv0Uy15V1UtcW/eHOrXh59+0h4zwNfNl8GNB7N11FYCPQI5cu0IjT5txJmbRU9E5+CgDe0HmDtXW24NJEEvjmeffZZWrVrh7OzM6dOn8z2fu+xLxqELIYxBV95erx74+Fg2FlPR9aBLibsQ5uHtDatXQ4UK2unnlClFtfemR48etG3b1jwBClFCkqAbgW5uMy937ZLd6SR3AEaEDocnntCe9PaG2bMtEZ55uLtrA4Di4rQLEboZ6s+ehWHDtF71d96BQpadCasSxhf9vwAgPi2eZp81Y1fkriJ3PXy4tipcTEzO2PNjx7SeGmGYo6MjP/zwA6dPn6aLrvQgl9xjs6QHXQhhDPZe3g5S4i6EJYSEwDffaLfnzoUffjDc1tfXl86dO9OuXTvzBCdECUmCbgRuQS1Zd7QGDg7a1fIzmdqa54P3J0N0tNZo/ny4O+mWXatQQZuxLTFRW0qualXt8bg4mDYN6tbVlpq7fbvAlw9oNIC5feYCkJaVRuelnfk54udCd+niAq++qpU19eyp/ZEGCA831puyXw0aNMDf39/g87LUmhDCmHQJur1OEAcwePBghg8fjre3t6VDEaJcGTRIWx0YtDmNizHSUgirJAm6EfQaNY2HZl7Az0O7Wn7G/Q71/etTd87dNR/q1YMnn7RghBagKPDss9qAoB07oHVrrRc9MlLLpitXhvbtC5xyc1L7Sax9bC2OiiPZajZDVw5l7u65he5uwgRYsQKaNZNx6KWhqiorV65k6dKleR6XpdaEEMakK3G35x70unXrUr9+/TzzeAghzOPdd6F3b0hN1RL2guYtvn37NjExMSQnJ5s/QCGKQRJ0Izl5+B98PLQl184FxjDUtZVWb+3goGWO5dl998H+/Vqy/uWX2vrpaWna2Px27aBWLfj66zzj1B9u+DD7nt6Hh7M24d7Lf77MpD8mFWt3kqCX3Pr163n00UeZOHEi0bqqD3KWWpMedCFEWcXFwfnz2m177kEXQliOo6O2qnGtWtrfmxEj8q8CvG/fPhYvXszOnTstEqMQRZEE3Qgy0jM5vusvAC7ecCLdJZMBX+/WnnzpJTkT0XFzg6ee0mqOpk7NmSHo0iUYM0a7P3mylrwDrYJbcfqF0wR6BAIwb+88Jv85mWzV8Li+s2dhwwbt9qFDkFC8FdvKvQcffJD27duTmJjIK6+8on9cStyFEMZy+LD2b+3a2qqd9ioiIoLDhw9L75wQFhIQAKtWaaedv/8OM2bkfV5mcRfWThJ0I1g96xGGVXwVgDPXPfHLcqH1nsvaOuFvvWXh6KyQgwPMmqV1p3z3nXa2Bjnrq/v5wf/9H9y6RTXvapx98Swjmo4A4KPdH/Hk6idJzyq45Hr+fFi+XJuzLjsbdhU9x5xAm9Ro4cKFODg48OOPP7J582ZAEnQhhPGUhwniAP766y/WrVvHrVu3LB2KEOVWy5bwhTbvMO++C2vX5jynO6eRBF1YK0nQjSD3Uipn4irQ6lI6Dip5e4lFfoqi1R6dP6+duXXqpD2WmqrN8lG9Ojz9NN57D/PdI9/xzcBvcHJwYvmx5TRe0JiEtPzd4y+/rK30lpqq3Zcy9+Jr2bIl48ePB+D5558nPT1dX+IuY9CFEGVVXhJ03TrossyaEJb1xBPawkIAI0eCbkVZ6UEX1k4SdCPIvZLKmVR3xh5ES8wnTLBYTDanVStt2vXr12HJEm0N9ZQU7Xa3blC1KiN3JrLm0dUoKJyLO0etT2px6falPJupWVNbdk1HEvSSeffddwkKCuLUqVPMnTtXetCFEEajmyDO3kd9yTJrQliPjz6Czp21Is1Bg7R/JUEX1s4uEvSFCxdSu3Zt3NzcCAsLY8eOHWbdf+5j8GkVHjkFLFqkzVQhSqZiRW1tjMOH4e+/c5Zpi46G55+nX6thzEhqA0DcnTgaLWjE/iv782xiypSc23v35vSmi6L5+voyZ84cAN555x39iaYk6MKuXAUqA253/71q2XBMzdLHSID4ePj3X+12eUnQpQddCMtzdoaffoIqVeDkSW3KI0nQhbWz+QR9xYoVTJo0iTfeeIPDhw/TuXNnHnjgAS5fvmy2GLJyTQ+ZpCbgWrcBPP642fZvlxQFevTQZn5fvRoaNtQeT0nhrQ/38d0qcFDhTuYdOnzZgVc2vsKVhCuANkn8ww9rzTMzYc8eC70HGzVy5EjGjBnDunXrqFChAiAl7sKOeALBwDUg7e6/wXcft0PWcIyEnAniatTQrsPaM12Ju/SgC2EdKleGX37RkvVffoFbtyRBF9bN5hP0jz/+mLFjxzJu3DhCQkL45JNPqF69OosWLTJbDEqGNhFMZhZ0i7oOK1eabd/lwsCBcOoUnDihJe0ODoz4B7YsBZcsyFKz+HjPx1SbW41uS+7jkz2f8MT4KP3Lf/vNcqHbIkVR+Oqrr+jZs6cssybsiyeQYuC5FOwySbeGYySUj/XPdaTEXQjr0769NpEwwF9/dSQoqCMVKnhZNighDHCydABlkZ6ezsGDB5k6dWqex3v37s0uM07f7ZR1DYCEVJhYoRs0a2a2fZcrjRtrZe8JCTB5Ml22b+fEp6cZPgSOVIYMR9h2ZSfbruwEXsIh6DDZ11vw9TenOH5lBVejm6OqCmRm4pqWib+ac+X0lts1/Cue0+54uHP1ajPUbEfIysTlTiYBudu63sQ/8Iy+7fVroWRlOUNWJs53Mqiouue0dbmFf9Ap7Y67GzduhpKZ4QJZWTjdSScwV9s459v4VYrQt70Z25iMdDfIzsYxNY2g3Nt1TMQ/+Jh2x82V2LgQ0tM8QM1GSblDZdUjp61DMv5Vjt5t68Kt+EakpVa42zaVympOVnJLScW/6t2uLlcX9h28DsDzE+bwyqvf44mzvm0aWXgH3MHdwwucnEhK8Sf+1t1a4YwMPHBGydXWyz8VD09vcHIk6U4A8TcNt/X0u6MdOB0dSUmvSNyNGH1bd5xwuNs6nSzcfVLx8vYGRwdSMyty69rd7WZm4q466ttmkIWrdyrePt7g4MAdNYhbMdGoBtq6VEjFx09rm6YGEnv1GqqaDZmZuKkOON69vplBNs4VUvD18wYF0h0CuRl9E1XNgsws3FRF3zaTbBw9kvEL8LnbtiKxMXFkZ2dAVhau2QpOudo6uCfjX1GbaDLDKYCb0fFkZ6fna5tFNrglExCotc109uNmdDJZWXcgKwuXbAXnXG1V1yQqBvnebevDzeg0srJSCmyb7ZJIYCVtTawsZ29uxGSSlZl0ty04ow3lyUYlyyWRikE+KIpClosXN6OzycxMhKwsnLPBJVfbTOckAit5oSgOZLt4ciPGgcyMeO27kQ2ud9uqqKQ7JRFYqQIODo6oru7ciHEhIz0OsrNxylJztYV0p2QqBrnj6OiE6urGjRh3MtJjcchUeTSlDgqg3P09N6MZAxlIINpSjqSQU/5uB6zlGAnlZ4I4yD9J3Pnz5w0uuebg4ECTJk309y9evEhiYqLBbTdt2lR/+9KlSyQUspZokyZN9BcLIiMjuX37tsG2ISEhODlpp4RXrlwpdAb6hg0b6i/exsTEcPPmTYNtGzRooJ/L5Nq1a1y/ft1g23r16uHurh3nbty4wdWrhsee1KlTB09P7dgVGxtLdHS0wba1atXCy0tLxG7dusWVK1cMtq1RowY+dyf3vX37NpGRkQbbVqtWDb+76wUmJCRw6dIlg22rVKlCQEAAAElJSVy4cMFg2+DgYCreLTNJSUnh3LlzBttWqlSJoKAgQCvb/lc3jqQAgYGBVK6s/XFLT0/ntG7GtAIEBARQpUoVADIyMjh16pTBtn5+flSrVg3QKkojIiIMtvXx8aFGjRqA9v04fvy4wbZeXl7UqlVLf//48eMGh414enpSp04d/f2IiIg81a06HTvC+PEeLFzYifBwmDYNunbVfu7+esQ9fHzgwQctHUX5Y9MJ+s2bN8nKyqJSpUp5Hq9UqVKBf9jT0tLy9AQWdmAriawMbZuXY11o8e1qo2xTFMLbGxYvBlWl3vbt7Fu6FHX5dg6nXGBbLfi1IWyrCdlDhsPCCG7dbMSfK6ZZOmqz+ufeBw4NL6hZwW0PP5brzp8ApCRGkJIYwb2nbNcNn5eVqS2FtM3HlG0vFr9tZHHbAlElqC6+Yvj8MJ/oqKLb6MQYPkfN56rhc98ytb0WU/y210swTtxQ24UFPNaYxjkJOkAL7GZMekmPkZD/OFlYslgSugTd3sefA/Ts2ZM7d+4QHBwMwLZt2wwOKXBxccmToIeHhxeakOVO0Pfu3cvJkycNtg0JCdEn6AcOHOCff/L9tdd79dVX9Qn64cOHOaj7hRVg0qRJ+gT9n3/+YU8h48ief/55fYIeERHB9kJmbn366af1Cfrp06f5+++/DbYdPXq0PkE/e/Ysf/zxh8G2I0aM0Cfoly5dYt26dQbbDh06VJ+gR0VFsWrVKoNtBw4cqE/QY2JiCm3br18/fYJ+48aNQtvef//9+gT91q1bhbbt1q2bPkGPj48vtG2nTp30CXpycnKhbdu0aaNP0NPS0gpt26JFC32CnpmZWWjbJk2a6BN0oNC2DRo0yJOgr1mzpsCkG7SLMLkT9N9++41UAxMQNW9elSeeqMuqVXDjBvz8s/YjCtakiSTolmDTCbqO7mq1jqqq+R4DmDVrFjNmzDD6/qOymvN3RBKHYurTwtfX6NsXBiiK/tKnArTKzqZVRAQvbd9OzKGtzMzczrdtZ5F8rQ0oClnXmoOqnawoqoqDmvN/JNstAQe/s/rtZl1rBurdSf5UFcfcbV2TcfA/ndP2eihkOxfc1jkFh4p3rzwrkHWjMWS5FdzWKQ2HwBM5bW+GQKa7gbYZOAQey2kb2wAyKtx9VsUxO1dbxywcgo7mtL1VD9K9C26rqDhUPqxvm3n1QbhzDij4oIiTN4qTdtKhZjhAluETS5y8UJwq3m3rCFlnDbd19ERxDipmWw8UZy0BUTOdIfOM4bYO7igu2gmKmuUCGYZ7EHBwQ3EJztX2X8BAyaqDK4pLlbttnSHjPJBpoK0ziot2MqNmO0P6RcDAGH/FCcW1+t22TpAeCdwx0NYBxbVmrrZXMFjPrSgorrXutnWE9KtAkoG2KoqrduKjqo6Qdh0wcHFTyQaXOiiKgqo6QHosqLcLaVsLRXHUKlvSb4NqoNdOUcGlBoridLdtPKixhbSthqK4aNURaUmg3sADBdd7mjaiET7csxSmgXBtWXGPkWCa42R2Ntx3H7i6lo8EPXeiAFqPqKOBSWOdnZ3z3K9cubLB0vh7f2dBQUH6ya6Kah8YGEjt2rUNts0dX8WKFYvdNiAgoNC2ud+fn59foW11iTxoPa2Ftc09dtjb27vQtrqkH7Re2cLa6pJ+3e3C2urmaAHw8PAotK3uAoEu9sLa+uRantfV1bXQtr65zjldXFwKbevv76+/7eTkVGjbirkminB0dCx2WwcHh0Lb6i4m6BTW9t4Li7Vr1zaYoOsuPOjUrFnT4NC8ihUr8u238OWXsH8/bNmizVVUyFepXMt1jUSYkaLa8DSj6enpeHh4sHLlSgYNGqR/fOLEiRw5coRt27blaV9QD3r16tWJj4/H29ubMsnM1GZtN3DSI4QQwoIqo00IV5RKlKgHPSEhAR8fH+McR4yspMdIyH+cvHLlCo0bNyYyMlLfSyaEEEIUV1RUFNWrV5fjSAnY9CRxLi4uhIWFsWnTpjyPb9q0iY4dO+Zr7+rqire3d54fo3FykuRcCCGs1REjt7MBJT1GQv7jZO6ePyGEEEKYns2XuL/88suMHDmS1q1b06FDBxYvXszly5d57rnnLB2aEEIIa1EZ8MDwLO7cfd5OJojTkWOkEEIIYVtsPkEfNmwYsbGxvPPOO8TExBAaGsqGDRuoWbOmpUMTQghhTZIxvNSax93n7YwcI4UQQgjbYtMl7jrjx4/n4sWLpKWlcfDgQbp06WLpkIQQQlijZCAGbay5691/Y7DL5FxHjpFCCCHszcWLFxk7diy1a9fG3d2dunXrMm3aNNLT8068e/nyZfr374+npycVK1ZkwoQJ+dpYG5vvQRdCCCFKpDJ2s5SaEEIIUR6dOnWK7OxsPv/8c+rVq8fx48d5+umnSU5O5sMPPwQgKyuLfv36ERgYSHh4OLGxsYwaNQpVVZk/f76F34FhkqALIYQQQgghhLAZffv2pW/fvvr7derU4fTp0yxatEifoP/5559EREQQGRlJlSracrgfffQRo0eP5v3337e61Vd07KLEXQghhBBCCCGEdUpMTCQhIUH/Y2it+rKIj4/H399ff3/37t2Ehobqk3OAPn366Id8WStJ0IUQQgghhBBCmEzjxo3x8fHR/8yaNcuo2z937hzz58/Ps0rJ1atXqVSpUp52fn5+uLi4cPWq9Y51kwRdCCGEEEIIIYTJREREEB8fr/95/fXXC2w3ffp0FEUp9OfAgQN5XhMdHU3fvn0ZOnQo48aNy/Ocoij59qGqaoGPWwsZgy6EEEIIIYQQwmS8vLyKNeb7hRde4LHHHiu0Ta1atfS3o6Oj6d69Ox06dGDx4sV52lWuXJm9e/fmeSwuLo6MjIx8PevWRBJ0IYQQQgghhBAWV7FiRSpWrFistleuXKF79+6EhYWxdOlSHBzyFod36NCB999/n5iYGIKDgwFt4jhXV1fCwsKMHruxlOsEXVVVABISEiwciRBCCFukO37ojif2Jjs7G4CYmBgLRyKEEMIW6Y4fuuOJsURHR9OtWzdq1KjBhx9+yI0bN/TPVa5cGYDevXvTuHFjRo4cyZw5c7h16xaTJ0/m6aefttoZ3KGcJ+iJiYkAVK9e3cKRCCGEsGWJiYn4+PhYOgyju3btGgBt27a1cCRCCCFs2bVr16hRo4bRtvfnn39y9uxZzp49S7Vq1fI8p7to7ujoyPr16xk/fjydOnXC3d2d4cOH65dhs1aKaq+X/YshOzub6OhovLy8yjxRQEJCAtWrVycyMtKqr8gYYuvxg7wHa2Hr78HW4wd5D+akqiqJiYlUqVIlX2mdPcjMzOTw4cNUqlSpTO8vMTGRxo0bExERgZeXlxEjtB/yGRVNPqPikc+paPIZFc1Yn1F2djbXrl2jZcuWODmV677hYivXn5KDg0O+Ky5l5e3tbdUnk0Wx9fhB3oO1sPX3YOvxg7wHc7HHnnMdJycn2rRpU+bt6IYCVK1a1ep/n5Yin1HR5DMqHvmciiafUdGM+RkZs+e8PLC/y/1CCCGEEEIIIYQNkgRdCCGEEEIIIYSwApKgG4mrqyvTpk3D1dXV0qGUiq3HD/IerIWtvwdbjx/kPQjrI7/PoslnVDT5jIpHPqeiyWdUNPmMLKdcTxInhBBCCCGEEEJYC+lBF0IIIYQQQgghrIAk6EIIIYQQQgghhBWQBF0IIYQQQgghhLACkqALIYQQQgghhBBWQBL0Ylq4cCG1a9fGzc2NsLAwduzYUWj7bdu2ERYWhpubG3Xq1OGzzz4zU6SGleQ9rFq1ivvvv5/AwEC8vb3p0KEDGzduNGO0BSvp70Fn586dODk50aJFC9MGWAwlfQ9paWm88cYb1KxZE1dXV+rWrctXX31lpmjzK2n8y5cvp3nz5nh4eBAcHMyYMWOIjY01U7T5bd++nf79+1OlShUURWHNmjVFvsbavs8lfQ/W9n0uze9Ax5q+yyIvezhOmlpJPqOYmBiGDx9Ow4YNcXBwYNKkSeYL1ILs4VzF1EryGYWHh9OpUycCAgJwd3enUaNGzJ0714zRWoY9nC+aQ0k+p61bt6IoSr6fU6dOmTHickIVRfrxxx9VZ2dn9YsvvlAjIiLUiRMnqp6enuqlS5cKbH/+/HnVw8NDnThxohoREaF+8cUXqrOzs/rzzz+bOfIcJX0PEydOVGfPnq3u27dPPXPmjPr666+rzs7O6qFDh8wceY6Svged27dvq3Xq1FF79+6tNm/e3DzBGlCa9/Dwww+r7dq1Uzdt2qReuHBB3bt3r7pz504zRp2jpPHv2LFDdXBwUOfNm6eeP39e3bFjh9qkSRN14MCBZo48x4YNG9Q33nhD/eWXX1RAXb16daHtrfH7XNL3YG3f55LGr2NN32WRlz0cJ02tpJ/RhQsX1AkTJqjLli1TW7RooU6cONG8AVuAPZyrmFpJP6NDhw6p33//vXr8+HH1woUL6rfffqt6eHion3/+uZkjNx97OF80h5J+Tlu2bFEB9fTp02pMTIz+JzMz08yR2z9J0Iuhbdu26nPPPZfnsUaNGqlTp04tsP2UKVPURo0a5Xns2WefVdu3b2+yGItS0vdQkMaNG6szZswwdmjFVtr3MGzYMPXNN99Up02bZvE/uCV9D7///rvq4+OjxsbGmiO8IpU0/jlz5qh16tTJ89j//vc/tVq1aiaLsSSKkxxa4/c5t5IkuLlZ+vusU5L4rem7LPKyh+OkqZXlONy1a9dykaDbw7mKqRnjMxo0aJD6xBNPGDs0q2EP54vmUNLPSZegx8XFmSG68k1K3IuQnp7OwYMH6d27d57He/fuza5duwp8ze7du/O179OnDwcOHCAjI8NksRpSmvdwr+zsbBITE/H39zdFiEUq7XtYunQp586dY9q0aaYOsUileQ/r1q2jdevWfPDBB1StWpUGDRowefJkUlNTzRFyHqWJv2PHjkRFRbFhwwZUVeXatWv8/PPP9OvXzxwhG4W1fZ+NwdLf59Kwpu+yyMsejpOmZozjsL2zh3MVUzPGZ3T48GF27dpF165dTRGixdnD+aI5lOX/UsuWLQkODqZnz55s2bLFlGGWW06WDsDa3bx5k6ysLCpVqpTn8UqVKnH16tUCX3P16tUC22dmZnLz5k2Cg4NNFm9BSvMe7vXRRx+RnJzMo48+aooQi1Sa9/Dvv/8ydepUduzYgZOT5f+rl+Y9nD9/nvDwcNzc3Fi9ejU3b95k/Pjx3Lp1y+zj0EsTf8eOHVm+fDnDhg3jzp07ZGZm8vDDDzN//nxzhGwU1vZ9NgZLf59Lytq+yyIvezhOmpoxjsP2zh7OVUytLJ9RtWrVuHHjBpmZmUyfPp1x48aZMlSLsYfzRXMozecUHBzM4sWLCQsLIy0tjW+//ZaePXuydetWunTpYo6wy43y8b/QCBRFyXNfVdV8jxXVvqDHzamk70Hnhx9+YPr06axdu5agoCBThVcsxX0PWVlZDB8+nBkzZtCgQQNzhVcsJfk9ZGdnoygKy5cvx8fHB4CPP/6YIUOGsGDBAtzd3U0e771KEn9ERAQTJkzg7bffpk+fPsTExPDqq6/y3HPP8eWXX5ojXKOwxu9zaVnT97k4rPm7LPKyh+OkqZX2OFye2MO5iqmV5jPasWMHSUlJ7Nmzh6lTp1KvXj0ef/xxU4ZpUfZwvmgOJfm/1LBhQxo2bKi/36FDByIjI/nwww8lQTcySdCLULFiRRwdHfNdTbp+/Xq+q046lStXLrC9k5MTAQEBJovVkNK8B50VK1YwduxYVq5cSa9evUwZZqFK+h4SExM5cOAAhw8f5oUXXgC0ZFdVVZycnPjzzz/p0aOHWWLXKc3vITg4mKpVq+qTc4CQkBBUVSUqKor69eubNObcShP/rFmz6NSpE6+++ioAzZo1w9PTk86dO/Pee+/ZRC+ZtX2fy8Javs8lYY3fZZGXPRwnTa0sx+Hywh7OVUytLJ9R7dq1AWjatCnXrl1j+vTpdpmg28P5ojkY629S+/bt+e6774wdXrknY9CL4OLiQlhYGJs2bcrz+KZNm+jYsWOBr+nQoUO+9n/++SetW7fG2dnZZLEaUpr3ANrV6NGjR/P9999bfMxwSd+Dt7c3x44d48iRI/qf5557joYNG3LkyBHatWtnrtD1SvN76NSpE9HR0SQlJekfO3PmDA4ODlSrVs2k8d6rNPGnpKTg4JD3z4yjoyOQ01tm7azt+1xa1vR9Lglr/C6LvOzhOGlqpT0Olyf2cK5iasb6f6SqKmlpacYOzyrYw/miORjr/9Lhw4dtorPF5ph1SjobpVuG4Msvv1QjIiLUSZMmqZ6enurFixdVVVXVqVOnqiNHjtS31y0f89JLL6kRERHql19+afHlY0r6Hr7//nvVyclJXbBgQZ6lFG7fvm2pt1Di93Ava5iVs6TvITExUa1WrZo6ZMgQ9cSJE+q2bdvU+vXrq+PGjbOJ+JcuXao6OTmpCxcuVM+dO6eGh4errVu3Vtu2bWuR+FVV+0wPHz6sHj58WAXUjz/+WD18+LB+WRFb+D6X9D1Y2/e5pPHfyxq+yyIvezhOmlppjmG670lYWJg6fPhw9fDhw+qJEycsEb5Z2MO5iqmV9DP69NNP1XXr1qlnzpxRz5w5o3711Veqt7e3+sYbb1jqLZicPZwvmkNJP6e5c+eqq1evVs+cOaMeP35cnTp1qgqov/zyi6Xegt2SBL2YFixYoNasWVN1cXFRW7VqpW7btk3/3KhRo9SuXbvmab9161a1ZcuWqouLi1qrVi110aJFZo44v5K8h65du6pAvp9Ro0aZP/BcSvp7yM1a/uCW9D2cPHlS7dWrl+ru7q5Wq1ZNffnll9WUlBQzR52jpPH/73//Uxs3bqy6u7urwcHB6ogRI9SoqCgzR51Dt0yIof/btvB9Lul7sLbvc2l+B7lZy3dZ5GUPx0lTK+lnVND3pGbNmuYN2szs4VzF1EryGf3vf/9TmzRponp4eKje3t5qy5Yt1YULF6pZWVkWiNx87OF80RxK8jnNnj1brVu3rurm5qb6+fmp9913n7p+/XoLRG3/FFW1kTpTIYQQQgghhBDCjskYdCGEEEIIIYQQwgpIgi6EEEIIIYQQQlgBSdCFEEIIIYQQQggrIAm6EEIIIYQQQghhBSRBF0IIIYQQQgghrIAk6EIIIYQQQgghhBWQBF0IIYQQQgghhLACkqALIYQQQgghhBBWQBJ0IYQQQgghhBDCCkiCLkQ51a1bNxRFQVEUjhw5YpEYRo8erY9hzZo1FolBCCGEyE2Oj0IIS5IEXYhy7OmnnyYmJobQ0FCL7H/evHnExMRYZN9CCCGEIXJ8FEJYipOlAxBCWI6HhweVK1e22P59fHzw8fGx2P6FEEKIgsjxUQhhKdKDLoQNa9++PXPnztXfHzZsGIqikJycDEB0dDQuLi6cPHmy2Nvs1q0bL774IpMmTcLPz49KlSqxePFikpOTGTNmDF5eXtStW5fff/+9TK8RQgghzCU7O5uZM2dSv3593NzcqFSpEiNHjizRNuT4KIQwB0nQhbBhvr6+JCYmAhAZGcnGjRvx8vIiLi4OgMWLF9OjRw9CQkJKtN1ly5ZRsWJF9u3bx4svvsh//vMfhg4dSseOHTl06BB9+vRh5MiRpKSklOk1QgghhDnMmjWL77//nsWLF3P69GlWrVpFt27dSrwdOT4KIUxNEnQhbJifnx9JSUkAfPrpp4wYMYLAwEDi4uLIyMhg8eLFTJw4scTbbd68OW+++Sb169fn9ddfx93dnYoVK/L0009Tv3593n77bWJjY/nnn3/K9BohhBDCHDZu3Ei/fv3o3r07NWvWpFOnTowdO7bE25HjoxDC1CRBF8KG6XrQk5OTWbJkCRMnTsTb25u4uDhWr16Nl5cXffv2LfF2mzVrpr/t6OhIQEAATZs21T9WqVIlAK5fv16m1wghhBDm8PDDD/Phhx/Su3dvPvvsM27dulWq7cjxUQhhapKgC2HDdD3oy5Yto0OHDjRo0ECfoC9YsIAJEyagKEqJt+vs7JznvqIoeR7TbTM7O7tMrxFCCCHMYfLkyZw8eZJevXoxf/586tWrx4ULF0q8HTk+CiFMTRJ0IWyYr68vCQkJzJs3j0mTJgHg7e1NeHg4R48eZdSoUZYNUAghhLASDRo0YMqUKRw6dIiUlBQiIiIsHZIQQuQjy6wJYcP8/PzYvHkztWrVolevXoCWoC9atIhnn32WChUqWDhCIYQQwrI++OADKlWqRJs2bXB0dGTJkiX4+fnRsWNHS4cmhBD5SIIuhA3TlbjnngjO29ub1NRUXnjhBQtGJoQQQliHO3fuMHPmTC5fvkyFChXo1KkTmzdvxs/Pz9KhCSFEPoqqqqqlgxBCmF+3bt1o0aIFn3zyiaVDQVEUVq9ezcCBAy0dihBCiHJOjo9CCEuSMehClGMLFy6kQoUKHDt2zCL7f+6556QMXwghhNWR46MQwlKkB12IcurKlSukpqYCUKNGDVxcXMwew/Xr10lISAAgODgYT09Ps8cghBBC5CbHRyGEJUmCLoQQQgghhBBCWAEpcRdCCCGEEEIIIayAJOhCCCGEEEIIIYQVkARdCCGEEEIIIYSwApKgCyGEEEIIIYQQVkASdCGEEEIIIYQQwgpIgi6EEEIIIYQQQlgBSdCFEEIIIYQQQggrIAm6EEIIIYQQQghhBSRBF0IIIYQQQgghrIAk6EIIIYQQQgghhBWQBF0IIYQQQgghhLACkqALIYQQQgghhBBWQBJ0IYQQQgghhBDCCkiCLoQQQgghhBBCWAFJ0IUQQgghhBBCCCvw/1TCXgCCc9eRAAAAAElFTkSuQmCC",
+      "text/html": [
+       "\n",
+       "            <div style=\"display: inline-block;\">\n",
+       "                <div class=\"jupyter-widgets widget-label\" style=\"text-align: center;\">\n",
+       "                    Figure\n",
+       "                </div>\n",
+       "                <img 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' width=1000.0/>\n",
+       "            </div>\n",
+       "        "
+      ],
+      "text/plain": [
+       "Canvas(header_visible=False, toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Bac…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
+    "%matplotlib widget\n",
     "fig, (ax, ax_bond_slip) = plt.subplots(1,2, figsize=(10,4), tight_layout=True)\n",
+    "ax_d = ax_bond_slip.twinx()\n",
     "fig.canvas.header_visible = False\n",
     "for L, color in zip([5, 10, 50, 100, 200], ['red','green','blue','black','orange']):\n",
     "    print('evaluating pullout curve for L', L)\n",
@@ -2952,7 +3155,7 @@
     "    po_cfrp.reset()\n",
     "    po_cfrp.run()\n",
     "    po_cfrp.history.plot_Pw(ax, color=color)\n",
-    "po_cfrp.material_model_.plot(ax_bond_slip)"
+    "po_cfrp.material_model_.plot((ax_bond_slip, ax_d))"
    ]
   },
   {
@@ -2997,9 +3200,9 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "bmcs_env",
    "language": "python",
-   "name": "python3"
+   "name": "bmcs_env"
   },
   "language_info": {
    "codemirror_mode": {
@@ -3011,7 +3214,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.10"
+   "version": "3.9.0"
   },
   "toc": {
    "base_numbering": 1,
diff --git a/tour3_nonlinear_bond/3_2_anchorage_length.ipynb b/tour3_nonlinear_bond/3_2_anchorage_length.ipynb
index 62ed81a..e561aa3 100644
--- a/tour3_nonlinear_bond/3_2_anchorage_length.ipynb
+++ b/tour3_nonlinear_bond/3_2_anchorage_length.ipynb
@@ -844,7 +844,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.10"
+   "version": "3.8.8"
   },
   "toc": {
    "base_numbering": 1,
diff --git a/tour5_damage_bond/5_1_Introspect_Damage_Evolution_Damage_initiation.ipynb b/tour5_damage_bond/5_1_Introspect_Damage_Evolution_Damage_initiation.ipynb
index cb32b7e..a6757d4 100644
--- a/tour5_damage_bond/5_1_Introspect_Damage_Evolution_Damage_initiation.ipynb
+++ b/tour5_damage_bond/5_1_Introspect_Damage_Evolution_Damage_initiation.ipynb
@@ -205,7 +205,6 @@
     "\n",
     "<a id=\"damage_general\"></a>\n",
     "\\begin{equation}\n",
-    "\\label{EQ:bond_damage_model}\n",
     "\\tau = \\left(1 - \\omega(\\kappa)\\right)E_\\mathrm{b} \\; s\n",
     "\\end{equation}\n",
     "\n",
@@ -258,7 +257,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 2,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -342,7 +341,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [
     {
@@ -351,10 +350,10 @@
        "$\\displaystyle \\begin{cases} 0 & \\text{for}\\: \\kappa < \\kappa_{0} \\\\\\frac{\\kappa - \\kappa_{0}}{- \\kappa_{0} + \\kappa_{u}} & \\text{for}\\: \\kappa < \\kappa_{u} \\\\1 & \\text{otherwise} \\end{cases}$"
       ],
       "text/plain": [
-       "<ibvpy.tmodel.mats_damage_fn.LinearDamageFn at 0x7f067eff9f90>"
+       "<ibvpy.tmodel.mats_damage_fn.LinearDamageFn at 0x7f7a32753b30>"
       ]
      },
-     "execution_count": 2,
+     "execution_count": 3,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -374,13 +373,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "34f156bb4c5f47519e1ffc35c0880b10",
+       "model_id": "0cb5fdb9c48a4585a4a16aecc5baabdb",
        "version_major": 2,
        "version_minor": 0
       },
@@ -407,13 +406,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "9cc17a70350040f3b4184a3a4bbabffe",
+       "model_id": "2808693547334520ae3f6afc0ef77d9f",
        "version_major": 2,
        "version_minor": 0
       },
@@ -518,7 +517,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
@@ -527,10 +526,10 @@
        "$\\displaystyle \\begin{cases} 0 & \\text{for}\\: \\kappa < 0 \\\\1 - e^{- \\left(\\frac{\\kappa}{\\lambda}\\right)^{m}} & \\text{otherwise} \\end{cases}$"
       ],
       "text/plain": [
-       "<ibvpy.tmodel.mats_damage_fn.WeibullDamageFn at 0x7f0676d3d360>"
+       "<ibvpy.tmodel.mats_damage_fn.WeibullDamageFn at 0x7f7a20ad1040>"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -551,13 +550,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "2c72e24999204400871912bae54ecf3e",
+       "model_id": "3b8f365709c0405aa192a29b97593e08",
        "version_major": 2,
        "version_minor": 0
       },
@@ -582,13 +581,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "19a30992fd334c5fb78376e636514f56",
+       "model_id": "9b0bcd5dcc6647f6845d21f1718fb1df",
        "version_major": 2,
        "version_minor": 0
       },
@@ -669,12 +668,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": "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\n",
+      "image/png": "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",
       "text/latex": [
        "$\\displaystyle 1 - e^{- \\kappa}$"
       ],
@@ -683,7 +682,7 @@
        "1 - ℯ  "
       ]
      },
-     "execution_count": 8,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -704,29 +703,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 10,
    "metadata": {
     "hide_input": true,
     "slideshow": {
      "slide_type": "fragment"
     }
    },
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "726f5bc44e1f48cebd5db0f09d524de0",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "get_omega_kappa = sp.lambdify(kappa, omega_kappa_) # executable expression\n",
     "fig, ax = plt.subplots(1,1,figsize=(7,4), tight_layout=True) # plottable area\n",
@@ -769,19 +753,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/latex": [
-       "$\\displaystyle \\begin{cases} 0 & \\text{for}\\: \\kappa \\leq \\kappa_{0} \\\\1 - \\frac{\\kappa_{0} e^{\\frac{- \\kappa + \\kappa_{0}}{- \\kappa_{0} + kappa_\\mathrm{f}}}}{\\kappa} & \\text{otherwise} \\end{cases}$"
+       "$\\displaystyle \\begin{cases} 0 & \\text{for}\\: \\kappa \\leq \\kappa_{0} \\\\1 - \\frac{\\kappa_{0} e^{\\frac{- \\kappa + \\kappa_{0}}{- \\kappa_{0} + \\kappa_\\mathrm{f}}}}{\\kappa} & \\text{otherwise} \\end{cases}$"
       ],
       "text/plain": [
-       "<ibvpy.tmodel.mats_damage_fn.ExpSlopeDamageFn at 0x7f067f320540>"
+       "<ibvpy.tmodel.mats_damage_fn.ExpSlopeDamageFn at 0x7f7a326a8130>"
       ]
      },
-     "execution_count": 10,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -794,13 +778,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "81933ae6d5e84c5e9baed16a3b8be4b7",
+       "model_id": "60a98a2ec5bd421f9ce20dfa1253ee38",
        "version_major": 2,
        "version_minor": 0
       },
@@ -861,7 +845,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
@@ -870,10 +854,10 @@
        "$\\displaystyle \\begin{cases} 0 & \\text{for}\\: \\kappa \\leq \\kappa_{0} \\\\1 - \\begin{cases} 1 & \\text{for}\\: \\kappa < \\kappa_{0} \\\\\\frac{\\kappa_{0} \\left(1 - \\frac{1 - e^{- \\frac{\\alpha \\left(\\kappa - \\kappa_{0}\\right)}{- \\kappa_{0} + \\kappa_{u}}}}{1 - e^{- \\alpha}}\\right)}{\\kappa} & \\text{for}\\: \\kappa < \\kappa_{u} \\\\0 & \\text{otherwise} \\end{cases} & \\text{otherwise} \\end{cases}$"
       ],
       "text/plain": [
-       "<ibvpy.tmodel.mats_damage_fn.AbaqusDamageFn at 0x7f08a4a99db0>"
+       "<ibvpy.tmodel.mats_damage_fn.AbaqusDamageFn at 0x7f7a206ec360>"
       ]
      },
-     "execution_count": 12,
+     "execution_count": 13,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -892,7 +876,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "8e3bda61362d4e2a82553ce46e075e34",
+       "model_id": "3615c46c950e4e5491b99d17afc60a42",
        "version_major": 2,
        "version_minor": 0
       },
@@ -945,7 +929,7 @@
        "$\\displaystyle 1 - \\begin{cases} 1 & \\text{for}\\: \\kappa < \\kappa_{0} \\\\e^{\\frac{\\left(\\kappa - \\kappa_{0}\\right) \\left(\\sqrt{E_{b}} \\sqrt{- E_{b} \\kappa_{0}^{2} + 4 G_{f}} + E_{b} \\kappa_{0}\\right)}{E_{b} \\kappa_{0}^{2} - 2 G_{f}}} & \\text{otherwise} \\end{cases}$"
       ],
       "text/plain": [
-       "<ibvpy.tmodel.mats_damage_fn.GfDamageFn at 0x7f0676e81130>"
+       "<ibvpy.tmodel.mats_damage_fn.GfDamageFn at 0x7f7a2084a090>"
       ]
      },
      "execution_count": 15,
@@ -974,7 +958,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "ce548ed7fbb84aab8b619ed48e666984",
+       "model_id": "af5c5ba340aa4cdd85a1c9aa0d356e5f",
        "version_major": 2,
        "version_minor": 0
       },
@@ -1100,7 +1084,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": "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\n",
+      "image/png": "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",
       "text/latex": [
        "$\\displaystyle \\sqrt{s_{x}^{2} + s_{y}^{2}}$"
       ],
@@ -1137,22 +1121,7 @@
      "slide_type": "fragment"
     }
    },
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "1001f8b3b8f14a9cb8acc70d46feae13",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "get_kappa = sp.lambdify( (s_x, s_y), kappa_, 'numpy' )\n",
     "phi = np.linspace(0, 2*np.pi, 500)\n",
@@ -1183,13 +1152,13 @@
     "Given a strain and stress tensors $\\boldsymbol{\\varepsilon}$ and $\\boldsymbol{\\sigma}$, \n",
     "the elastic limit can be introduced using the maximum \n",
     "principal tensile stress $\\sigma_i$ and divide it by the $E$ modulus as follows.\n",
-    "\\begin{equation}\\label{eq:rankine_eq}\n",
+    "\\begin{equation}\n",
     "\\kappa = \\frac{1}{E} \\mathrm{max}(\\sigma_i), \\qquad i=1,2,3  \n",
     "\\end{equation}\n",
     "The principal stresses $\\sigma_i$ are obtained as the eigenvalues of the stress tensor $\\boldsymbol{\\sigma}$.\n",
     "\n",
     "Another option is to define a norm of the strain tensor expressed as follows\n",
-    "\\begin{equation}\\label{eq:mazars_eq}\n",
+    "\\begin{equation}\n",
     "\\kappa = ||\\langle\n",
     "{\\boldsymbol{\\varepsilon}}\n",
     "\\rangle || = \\sqrt{\\langle \\boldsymbol{\\varepsilon} \\rangle  : \\langle \\boldsymbol{\\varepsilon} \\rangle} = \\sqrt{ {\\langle \\varepsilon_1\\rangle}^2   + {\\langle\\varepsilon_2 \\rangle}^2 + {\\langle\\varepsilon_3 \\rangle}^2}\n",
@@ -1281,7 +1250,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 19,
    "metadata": {
     "hide_input": false,
     "slideshow": {
@@ -1292,7 +1261,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "dec8067eaf8848e1a02a6481a92d96ad",
+       "model_id": "a06f38fda3784ad4a5ab7912d79b906a",
        "version_major": 2,
        "version_minor": 0
       },
@@ -1430,13 +1399,6 @@
     "</div><div style=\"background-color:lightgray;text-align:center;width:10%;display:inline-table;\"> <a href=\"#top\"><img src=\"../icons/compass.png\" alt=\"Compass\" width=\"50\" height=\"50\"></a></div><div style=\"background-color:lightgray;text-align:right;width:45%;display:inline-table;\"> \n",
     "    <a href=\"../tour5_damage_bond/5_2_PO_cfrp_damage.ipynb#top\">5.2 Pullout behavior governed by damage</a>&nbsp; <img src=\"../icons/next.png\" alt=\"Previous trip\" width=\"50\" height=\"50\"> </div> "
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
@@ -1456,7 +1418,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.8"
+   "version": "3.9.0"
   },
   "toc": {
    "base_numbering": 1,
diff --git a/tour7_cracking/7_1_bending3pt_2d.ipynb b/tour7_cracking/7_1_bending3pt_2d.ipynb
index a725552..5215b10 100644
--- a/tour7_cracking/7_1_bending3pt_2d.ipynb
+++ b/tour7_cracking/7_1_bending3pt_2d.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "satisfactory-appreciation",
+   "id": "faad10ef",
    "metadata": {},
    "source": [
     "<a id=\"top\"></a>\n",
@@ -11,7 +11,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "alert-longer",
+   "id": "d27fe4ad",
    "metadata": {},
    "source": [
     "[![title](../fig/bmcs_video.png)](https://moodle.rwth-aachen.de/mod/page/view.php?id=643780)&nbsp;part 1"
@@ -19,7 +19,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "pursuant-stations",
+   "id": "57e7d886",
    "metadata": {},
    "source": [
     "<div style=\"background-color:lightgray;text-align:left\"> <img src=\"../icons/start_flag.png\" alt=\"Previous trip\" width=\"50\" height=\"50\">\n",
@@ -28,7 +28,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "reduced-comparison",
+   "id": "a170843e",
    "metadata": {},
    "source": [
     "In the notebook [6.3](../tour6_energy/6_3_localized_energy_dissipation.ipynb#top) we have analyzed the correspondence between the damage function inducing softening and the propagation of discontinuity macroscopically observable as debonding in a pullout test. We mentioned the correspondence between the locally and globally energy evaluated energy dissipation indicating the energy dissipated at a material point can be used to estimate the total amount of dissipated energy by relating it to a localized crack of a known known surface area. In case of an interface area between two material components, like CFRP sheet and concrete, the area is well defined.\n",
@@ -37,7 +37,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "innocent-beijing",
+   "id": "227ade22",
    "metadata": {},
    "source": [
     "<div style=\"background-color:lightgray;text-align:left\"> <img src=\"../icons/destination.png\" alt=\"Previous trip\" width=\"50\" height=\"50\">\n",
@@ -51,7 +51,7 @@
     }
    },
    "cell_type": "markdown",
-   "id": "descending-watch",
+   "id": "c752b196",
    "metadata": {},
    "source": [
     "![image.png](attachment:9fb77393-4c12-4784-8c03-00258f81aa5e.png)"
@@ -59,7 +59,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "liked-status",
+   "id": "325abb80",
    "metadata": {},
    "source": [
     "In the present notebook we apply the damage model inducing material softening to a two-dimensional case of stable crack propagation. In particular, we are going to use the three-point bending test as a means of  rendering an elementary crack-propagation scenario, namely a straight crack propagating through a cross section. After discussing the test setup, we provide the test results in terms of load-deflection curve. Then, we will construct a numerical model that can reproduce such a behavior. Similarly to the previous lecture, we will consider the link between fracture and damage concepts that are both included in any finite element computation involving strain-softening material behavior"
@@ -67,7 +67,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "square-action",
+   "id": "064081fa",
    "metadata": {},
    "source": [
     "# **Why notched three-point bending test?**"
@@ -75,14 +75,14 @@
   },
   {
    "cell_type": "markdown",
-   "id": "contrary-mortality",
+   "id": "9e0b4bc5",
    "metadata": {},
    "source": [
     "An isolated tensile crack propagation can be initiated using a notched specimen. The most common configurations used to study the cracking behavior for a tensile crack denoted as mode I are the wedge splitting test and the notched, three-point bending test. Both these tests aim to characterize the material behavior in terms of the softening law describing the relation between the tensile stress transmitted across the localization zone and the corresponding crack opening. \n",
     "\n",
     "Due to its simplicity, three-point-bending test on a notched concrete has become a standard (RILEM) to determine the fracture energy $G_\\mathrm{f}$ characterizing the cracking behavior of concrete. The test induces a single crack in the notched section propagating straight upwards from the notch in a stable manner. The energy is dissipated in a local region in the crack vicinity so that it can be directly ascribed to the area of emerging crack surface. \n",
     "\n",
-    "The numerical simulation of this model can be readily performed using the material model with the damage function presented in notebook [5.1](tour5_damage_bond/5_1_Introspect_Damage_Evolution_Damage_initiation.ipynb). An example of the geometry and boundary conditions of the three-point bending test is provided by the  [Petersson (1982)](https://portal.research.lu.se/portal/files/4705811/1785208.pdf) test series using the following setup."
+    "The numerical simulation of this model can be readily performed using the material model with the damage function presented in notebook [5.1](../tour5_damage_bond/5_1_Introspect_Damage_Evolution_Damage_initiation.ipynb). An example of the geometry and boundary conditions of the three-point bending test is provided by the  [Petersson (1982)](https://portal.research.lu.se/portal/files/4705811/1785208.pdf) test series using the following setup."
    ]
   },
   {
@@ -92,7 +92,7 @@
     }
    },
    "cell_type": "markdown",
-   "id": "individual-cherry",
+   "id": "f4afa6b3",
    "metadata": {},
    "source": [
     "![image.png](attachment:aa0b3375-f959-416c-ad75-641d96347767.png)"
@@ -100,7 +100,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "arctic-olympus",
+   "id": "f7883cb7",
    "metadata": {},
    "source": [
     "# **Boundary value problem and its FE discretization**"
@@ -108,7 +108,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "quantitative-earth",
+   "id": "d859ff7c",
    "metadata": {},
    "source": [
     "The numerical model simulating this test is assuming a [plain stress](https://en.wikipedia.org/wiki/Plane_stress) described by the stress tensor with the enumeration of spatial dimensions using the index variables $a, b$ = [1,2]\n",
@@ -133,7 +133,7 @@
     }
    },
    "cell_type": "markdown",
-   "id": "joined-empire",
+   "id": "d870f6a4",
    "metadata": {},
    "source": [
     "![image.png](attachment:b68d13da-8f97-4a34-bac4-2a9bb271b6c0.png)"
@@ -141,7 +141,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "sharp-english",
+   "id": "3c94f304",
    "metadata": {},
    "source": [
     "The discretization is using quadrilateral finite elements with bilinear shape functions. A fineness of the regular mesh is defined by the number of elements in $x$ and $y$ directions denoted as `n_e_x` and `n_e_y`, respectively. A maximum deflection is given by the parameter `w_max`. The maximum number of iterations to find an equilibrium within a single load increment is controlled by the parameter `k_max`."
@@ -150,7 +150,7 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "id": "every-cursor",
+   "id": "b23c9819",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -167,7 +167,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "promotional-thriller",
+   "id": "f4d93b41",
    "metadata": {},
    "source": [
     "# **Material model**\n",
@@ -181,7 +181,7 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "id": "vital-emergency",
+   "id": "c1eddcc0",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -191,7 +191,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "fiscal-nashville",
+   "id": "ca8b11f3",
    "metadata": {},
    "source": [
     "## Damage function"
@@ -199,7 +199,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "prompt-guest",
+   "id": "fe75d037",
    "metadata": {},
    "source": [
     "The exponential damage function with the two parameters $\\kappa_0$ defining the onset of damage and $\\kappa_\\mathrm{f}$ controlling the slope of the exponential softening branch at the onset of damage presented in notebook [5.1](../tour5_damage_bond/5_1_Introspect_Damage_Evolution_Damage_initiation.ipynb#exp_slope). Assuming the concrete tensile strength $f_\\mathrm{ct} = 3.3 \\mathrm{[MPa]}$, we can set the parameters $\\kappa_0$ and the slope of the softening branch $\\kappa_\\mathrm{f}$ as follows"
@@ -208,7 +208,7 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "id": "preliminary-northeast",
+   "id": "c78374b0",
    "metadata": {},
    "outputs": [
     {
@@ -217,7 +217,7 @@
        "$\\displaystyle \\begin{cases} 0 & \\text{for}\\: \\kappa \\leq \\kappa_{0} \\\\1 - \\frac{\\kappa_{0} e^{\\frac{- \\kappa + \\kappa_{0}}{- \\kappa_{0} + \\kappa_\\mathrm{f}}}}{\\kappa} & \\text{otherwise} \\end{cases}$"
       ],
       "text/plain": [
-       "<ibvpy.tmodel.mats_damage_fn.ExpSlopeDamageFn at 0x7f5068f4c2c0>"
+       "<ibvpy.tmodel.mats_damage_fn.ExpSlopeDamageFn at 0x7f422e67c860>"
       ]
      },
      "execution_count": 3,
@@ -234,7 +234,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "awful-cylinder",
+   "id": "0f85c0e5",
    "metadata": {},
    "source": [
     "To achieve a fast convergence of the nonlinear simulation, the tangential algorithmic stiffness will be activated by setting the parameter `D_alg = 1`. Note that `D_alg = 0` corresponds to the secant stiffness which requires large number of iterations but can cope with large stress redistribution within the simulated domain. "
@@ -243,7 +243,7 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "id": "hollywood-failure",
+   "id": "f6861e1e",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -252,7 +252,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "likely-bottle",
+   "id": "b1bb2cf3",
    "metadata": {},
    "source": [
     "The important feature of the provided damage model is the possibility to evaluate the fracture energy, i.e. the amount of energy to achieve a zero stress state. This value can be accessed as a `G_f` property of the material model."
@@ -261,7 +261,7 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "id": "framed-wages",
+   "id": "f1b0b705",
    "metadata": {},
    "outputs": [
     {
@@ -281,7 +281,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "harmful-confidentiality",
+   "id": "9cd4c0f8",
    "metadata": {},
    "source": [
     "## Strain norm"
@@ -289,7 +289,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "neural-services",
+   "id": "d5e27b2f",
    "metadata": {},
    "source": [
     "[![title](../fig/bmcs_video.png)](https://moodle.rwth-aachen.de/mod/page/view.php?id=643780)&nbsp;part 2"
@@ -297,7 +297,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "certified-polyester",
+   "id": "2a73936e",
    "metadata": {},
    "source": [
     "The scalar damage material model can combine different measures of equivalent strain with a different type of damage function. \n",
@@ -319,13 +319,13 @@
   {
    "cell_type": "code",
    "execution_count": 6,
-   "id": "cross-definition",
+   "id": "7d053725",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "d7fd39efabe74251a5a9c5595770bb61",
+       "model_id": "fc51658e45264e52b57851522cdf2572",
        "version_major": 2,
        "version_minor": 0
       },
@@ -344,7 +344,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "requested-framework",
+   "id": "ebde4d4a",
    "metadata": {},
    "source": [
     "Further strain norms can be chosen from the options\n",
@@ -358,7 +358,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "modern-helping",
+   "id": "ff35af5f",
    "metadata": {},
    "source": [
     "The mathematical expression delivering the three provided strain norms look as follows:\n",
@@ -372,7 +372,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "complex-texture",
+   "id": "69596be2",
    "metadata": {},
    "source": [
     "**Rankine strain norm:**\n",
@@ -393,7 +393,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "searching-chocolate",
+   "id": "b39c4189",
    "metadata": {},
    "source": [
     "## The importance of algorithmic stiffness"
@@ -401,7 +401,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "sealed-threat",
+   "id": "d9078a6a",
    "metadata": {},
    "source": [
     "The rate of convergence strongly depends on the quality of prediction of the stress state for the next step. The parameter `D_alg` can be used ton control the calculation by choosing between the secant and algorithmic, tangential stiffness. Derivation of the material stiffness might appear tedious at the first sight but in fact, it only requires the evaluation of the chain derivatives involved in the constitutive law. "
@@ -409,7 +409,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "rational-duncan",
+   "id": "f0c7b866",
    "metadata": {},
    "source": [
     "Let us exemplify the derivation of the algorithmic material stiffness on the example of the isotropic damage model. In contrast to the version presented in notebook [5.1]() for the bond-slip law, we are applying the damage model to a 2D continuum described by the strain and stress tensor tensors $\\boldsymbol{\\varepsilon} = \\varepsilon_{ab}$ \n",
@@ -418,7 +418,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "pretty-trust",
+   "id": "0e288f89",
    "metadata": {},
    "source": [
     "\\begin{align}\n",
@@ -428,7 +428,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "social-formation",
+   "id": "c9ef932e",
    "metadata": {},
    "source": [
     "where $\\kappa(\\varepsilon_{cd})$ represents the equivalent strain described above, and $\\omega(\\kappa)$ represents the particular damage function. The algorithmic stiffness is derived as the derivative of the above stress tensor with respect to a strain tensor. This means that every stress component must be differentiated with respect to every strain component. The result of this expression is a rank 4 tensor $D_{abcd}$ describing the instantaneous stiffness of a material point undergoing damage. The resulting expression delivers the material stiffness in the following form"
@@ -436,7 +436,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "lesbian-oklahoma",
+   "id": "9baa14d2",
    "metadata": {},
    "source": [
     "\\begin{align}\n",
@@ -450,7 +450,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "actual-visibility",
+   "id": "6c965fb4",
    "metadata": {},
    "source": [
     "The parameter `D_alg` is denoted here as $\\theta_{\\mathrm{alg,stiff}}$ and is provided as an input parameter in the material model. By setting this parameter to zero, the second term in the above expression is canceled. Then, only the secant stiffness is available. The importance of the algorithmic stiffness can be studied in the simulation of the bending test. The default value  $\\theta_\\mathrm{alg,stiff} = 1$ is activating the algorithmic stiffness. By setting $\\theta_\\mathrm{alg,stiff} = 0$ one can see the strong reduction of the performance."
@@ -458,7 +458,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "accepting-thanksgiving",
+   "id": "49520dc3",
    "metadata": {},
    "source": [
     "However, in some situation, for example when simulating simultaneous crack propagation in reinforced specimens the tangential stiffness might induce divergence. The reason for such behavior is the fact that the bond stress can rapidly change the sign. In these situations, advanced control strategies are required."
@@ -466,7 +466,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "behind-official",
+   "id": "fcffb664",
    "metadata": {},
    "source": [
     "# **Simulation example**"
@@ -474,7 +474,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "widespread-ambassador",
+   "id": "acaf4d78",
    "metadata": {},
    "source": [
     "[![title](../fig/bmcs_video.png)](https://moodle.rwth-aachen.de/mod/page/view.php?id=643780)&nbsp;part 3"
@@ -482,7 +482,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "comparable-fiber",
+   "id": "3c986379",
    "metadata": {},
    "source": [
     "Fist, let us ensure that the `Rankine` strain norm is used in the model. "
@@ -491,7 +491,7 @@
   {
    "cell_type": "code",
    "execution_count": 7,
-   "id": "considerable-oracle",
+   "id": "9ef06290",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -503,13 +503,20 @@
   {
    "cell_type": "code",
    "execution_count": 8,
-   "id": "dated-meeting",
+   "id": "249946d3",
    "metadata": {},
    "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Warning: Update progress bar from model - not implemented yet\n"
+     ]
+    },
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "9a39766278494a51822921e8dbb1ec26",
+       "model_id": "a51614aaec564df48ac39cbffc00116e",
        "version_major": 2,
        "version_minor": 0
       },
@@ -527,7 +534,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "corporate-spray",
+   "id": "d09cb5b3",
    "metadata": {},
    "source": [
     "## Evaluation of energy dissipation"
@@ -535,7 +542,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "brief-dominican",
+   "id": "ed566bdb",
    "metadata": {},
    "source": [
     "If a standard finite element discretization is combined with a softening material model, \n",
@@ -554,8 +561,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
-   "id": "greek-conditions",
+   "execution_count": 9,
+   "id": "755fd2d7",
    "metadata": {
     "tags": []
    },
@@ -566,7 +573,7 @@
        "5000.0"
       ]
      },
-     "execution_count": 10,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -578,7 +585,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "painful-vietnam",
+   "id": "85435f7b",
    "metadata": {},
    "source": [
     "The specific energy $G_\\mathrm{f}$ is available as an attribute of the material model. It has the value"
@@ -586,8 +593,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
-   "id": "rising-cemetery",
+   "execution_count": 10,
+   "id": "87caf60f",
    "metadata": {},
    "outputs": [
     {
@@ -596,7 +603,7 @@
        "0.06435413610343986"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -607,7 +614,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "extreme-cooperation",
+   "id": "fbca4ec6",
    "metadata": {},
    "source": [
     "Thus, the total dissipated energy should read"
@@ -615,8 +622,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
-   "id": "prospective-differential",
+   "execution_count": 11,
+   "id": "354c32ce",
    "metadata": {},
    "outputs": [
     {
@@ -625,7 +632,7 @@
        "321.7706805171993"
       ]
      },
-     "execution_count": 12,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -637,7 +644,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "polar-brand",
+   "id": "37bcbc0f",
    "metadata": {},
    "source": [
     "The total dissipated energy is available in the `hist` model component as a last element of the recorded array, so that we can pick it using the index `-1`"
@@ -645,8 +652,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
-   "id": "circular-blank",
+   "execution_count": 12,
+   "id": "e7bb40e7",
    "metadata": {},
    "outputs": [
     {
@@ -655,7 +662,7 @@
        "282.5506041549446"
       ]
      },
-     "execution_count": 14,
+     "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -667,7 +674,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "descending-circulation",
+   "id": "b28f50aa",
    "metadata": {},
    "source": [
     "# **Parametric study**"
@@ -675,7 +682,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "hispanic-repeat",
+   "id": "5c75a714",
    "metadata": {},
    "source": [
     "[![title](../fig/bmcs_video.png)](https://moodle.rwth-aachen.de/mod/page/view.php?id=643780)&nbsp;part 4"
@@ -683,7 +690,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "starting-antique",
+   "id": "6bd734ee",
    "metadata": {},
    "source": [
     "What happens when we change something? In particular, what happens if we change the volume of the dissipative zone $V_\\mathrm{diss}$?"
@@ -696,7 +703,7 @@
     }
    },
    "cell_type": "markdown",
-   "id": "formed-wales",
+   "id": "0e4ef66f",
    "metadata": {},
    "source": [
     "![image.png](attachment:5e6eb343-c973-4ada-8019-9bd37e5f9fec.png)"
@@ -704,7 +711,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "biblical-conference",
+   "id": "a44dc7fd",
    "metadata": {},
    "source": [
     "<div style=\"background-color:lightgray;text-align:left\"> <img src=\"../icons/run.png\" alt=\"Run\" width=\"40\" height=\"40\">\n",
@@ -713,8 +720,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
-   "id": "partial-blues",
+   "execution_count": 13,
+   "id": "63321a16",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -724,8 +731,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
-   "id": "lasting-absence",
+   "execution_count": 14,
+   "id": "7d93f849",
    "metadata": {},
    "outputs": [
     {
@@ -733,8 +740,22 @@
      "output_type": "stream",
      "text": [
       "calculating F-w and G for crack band L_cb = 1 [mm]\n",
-      "calculating F-w and G for crack band L_cb = 2 [mm]\n",
-      "calculating F-w and G for crack band L_cb = 4 [mm]\n"
+      "\n"
+     ]
+    },
+    {
+     "ename": "StopIteration",
+     "evalue": "Warning: convergence not reached in 500 iterations",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mStopIteration\u001b[0m                             Traceback (most recent call last)",
+      "Input \u001b[0;32mIn [14]\u001b[0m, in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      4\u001b[0m bt\u001b[38;5;241m.\u001b[39mreset()\n\u001b[1;32m      5\u001b[0m bt\u001b[38;5;241m.\u001b[39mgeometry\u001b[38;5;241m.\u001b[39mL_cb \u001b[38;5;241m=\u001b[39m L_cb\n\u001b[0;32m----> 6\u001b[0m \u001b[43mbt\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m      7\u001b[0m Fw_dict[L_cb] \u001b[38;5;241m=\u001b[39m bt\u001b[38;5;241m.\u001b[39mhist[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mFw\u001b[39m\u001b[38;5;124m'\u001b[39m]\u001b[38;5;241m.\u001b[39mFw\n\u001b[1;32m      8\u001b[0m G_dict[L_cb] \u001b[38;5;241m=\u001b[39m bt\u001b[38;5;241m.\u001b[39mhist[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124menergy\u001b[39m\u001b[38;5;124m'\u001b[39m]\u001b[38;5;241m.\u001b[39mG_t[\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m]\n",
+      "File \u001b[0;32m~/PycharmProjects/bmcs_apps/tour7_cracking/bmcs_bending/bending3pt_2d.py:504\u001b[0m, in \u001b[0;36mBendingTestModel.run\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    503\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mrun\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[0;32m--> 504\u001b[0m     \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msim\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m~/PycharmProjects/bmcs_ibvpy/ibvpy/sim/sim_base.py:59\u001b[0m, in \u001b[0;36mSimulator.run\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m     56\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mrun\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[1;32m     57\u001b[0m     \u001b[38;5;124mr\u001b[39m\u001b[38;5;124;03m'''Run a thread if it does not exist - do nothing otherwise\u001b[39;00m\n\u001b[1;32m     58\u001b[0m \u001b[38;5;124;03m    '''\u001b[39;00m\n\u001b[0;32m---> 59\u001b[0m     \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtloop\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     60\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m\n",
+      "File \u001b[0;32m~/PycharmProjects/bmcs_ibvpy/ibvpy/sim/tloop.py:65\u001b[0m, in \u001b[0;36mTLoop.__call__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m     63\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__call__\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[1;32m     64\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39minit()\n\u001b[0;32m---> 65\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43meval\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m~/PycharmProjects/bmcs_ibvpy/ibvpy/sim/tloop_implicit.py:52\u001b[0m, in \u001b[0;36mTLoopImplicit.eval\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m     50\u001b[0m         \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mrestart \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[1;32m     51\u001b[0m         \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m---> 52\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mStopIteration\u001b[39;00m(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mWarning: \u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m     53\u001b[0m                             \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mconvergence not reached in \u001b[39m\u001b[38;5;132;01m%g\u001b[39;00m\u001b[38;5;124m iterations\u001b[39m\u001b[38;5;124m'\u001b[39m \u001b[38;5;241m%\u001b[39m k)\n\u001b[1;32m     54\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m\n\u001b[1;32m     55\u001b[0m \u001b[38;5;66;03m# accept the time step and record the state in history\u001b[39;00m\n",
+      "\u001b[0;31mStopIteration\u001b[0m: Warning: convergence not reached in 500 iterations"
      ]
     }
    ],
@@ -751,7 +772,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "reverse-olive",
+   "id": "9ed57a60",
    "metadata": {},
    "source": [
     "<div style=\"background-color:lightgray;text-align:left\"> <img src=\"../icons/view.png\" alt=\"Run\" width=\"40\" height=\"40\">\n",
@@ -760,14 +781,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
-   "id": "deluxe-religion",
+   "execution_count": 15,
+   "id": "dbc926a6",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "73592155a8d04608a7007572e70562f7",
+       "model_id": "caf92e4a39a5440c9083748cd2365dae",
        "version_major": 2,
        "version_minor": 0
       },
@@ -784,7 +805,7 @@
        "(0.0, 575.4982493017241)"
       ]
      },
-     "execution_count": 16,
+     "execution_count": 15,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -807,7 +828,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "after-product",
+   "id": "f668ef2b",
    "metadata": {},
    "source": [
     "<div style=\"background-color:lightgray;text-align:left\"> <img src=\"../icons/caveat.png\" alt=\"Run\" width=\"40\" height=\"40\">\n",
@@ -816,7 +837,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "incorporated-choice",
+   "id": "9e269f0a",
    "metadata": {},
    "source": [
     "By changing the element size within the crack band we change the volume of material which dissipates energy. As observed, a larger element size leads to a larger amount of energy dissipation which makes the structural response more ductile. Why does it happen? The displacement approximated within a finite element is continuous function. Using low-order elements, with, e.g. bilinear shape functions, means that the strain within the element is of even lower order, i.e. constant or linear. Therefore, strain cannot localize within a smaller size than the size of the element through which the crack propagates. As a consequence, upon softening, an element exhibits a nearly uniform increasing strain which results in energy dissipation over its whole volume. The obtained results are therefore biased by the mesh size which documents an ill-posed model behavior. \n",
@@ -826,7 +847,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "included-greece",
+   "id": "164afbd3",
    "metadata": {},
    "source": [
     "# **How to make the results mesh independent?**"
@@ -834,7 +855,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "amended-greensboro",
+   "id": "b1d99e39",
    "metadata": {},
    "source": [
     "The finite element codes solve this problem by adjusting the slope of the softening branch to keep the energy dissipation within a crack band invariant for a changed size of an element. This is the most common technique used in commercial finite element codes."
@@ -842,7 +863,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "starting-sacrifice",
+   "id": "3a1151da",
    "metadata": {},
    "source": [
     "The above examples explain that the correspondence between the local value of $G_\\mathrm{f}$ and the total energy $G_\\mathrm{total}$ is established by realizing that energy dissipation is linked with the crack propagation. Indeed, if $G_\\mathrm{f}$ is related to a unit area of the crack surface, the total energy dissipation in the whole test with a localized crack of the area $V_\\mathrm{diss}$ is \n",
@@ -854,7 +875,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "bottom-framework",
+   "id": "3b9a5f95",
    "metadata": {},
    "source": [
     "In the above example, the crack was represented by a band of elements with the width specified by the $L_\\mathrm{cb}$ model parameter. Since the crack developed over the whole notched cross section, we can express the dissipative volume as"
@@ -862,7 +883,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "removed-bennett",
+   "id": "0f3d130b",
    "metadata": {},
    "source": [
     "$$\n",
@@ -872,7 +893,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "smoking-anthropology",
+   "id": "0d8e5c32",
    "metadata": {},
    "source": [
     "To ensure that the amount of dissipated energy for a changed $V_\\mathrm{diss}$ due to different mesh size does not change, we need to correspondingly adjust effective value of dissipated energy in depending on $L_\\mathrm{cb}$. In other words, we need to introduce\n",
@@ -888,7 +909,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "boxed-toolbox",
+   "id": "0029b8a6",
    "metadata": {},
    "source": [
     "This scaling must be achieved by adjusting the material parameters of the model depending on the size of the discretization element $L_\\mathrm{cb}$. In present model we use the exponential damage function with the two parameters $\\kappa_0$ and $\\kappa_\\mathrm{f}$ controlling the onset of damage and the slope of the descending branch, respectively. The above specified scaling of energy dissipation can be achieved by scaling the parameter \n",
@@ -901,7 +922,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "asian-letter",
+   "id": "eec20ea5",
    "metadata": {},
    "source": [
     "To verify, that this scaling correspondingly affects the effective amount of energy dissipation by evaluating the value of $G_{\\mathrm{f}}^{L_\\mathrm{cb}}$ for the values of $L_\\mathrm{cb} = [1,2,3,4]$. These values should lead to the corresponding fractions [$1, \\frac{1}{2}, \\frac{1}{3}, \\frac{1}{4}$] of $G_\\mathrm{f}$. Assuming the reference value $\\kappa_f$ corresponding to the length $L_\\mathrm{cb} = 1$ we obtain"
@@ -909,8 +930,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
-   "id": "strategic-prior",
+   "execution_count": 16,
+   "id": "de3e4eb3",
    "metadata": {},
    "outputs": [
     {
@@ -919,7 +940,7 @@
        "array([1.        , 0.49416785, 0.32557841, 0.24129989])"
       ]
      },
-     "execution_count": 17,
+     "execution_count": 16,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -936,7 +957,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "facial-mission",
+   "id": "7d79a704",
    "metadata": {},
    "source": [
     "Which roughly correspond to the desired fractions"
@@ -944,7 +965,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "spare-brighton",
+   "id": "7547180a",
    "metadata": {},
    "source": [
     "Let us now apply this scaling by changing a single line in the parametric study. This line is denoted as `REGULARIZATION`."
@@ -952,8 +973,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
-   "id": "taken-chest",
+   "execution_count": 17,
+   "id": "d8725cb0",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -963,8 +984,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
-   "id": "amateur-saver",
+   "execution_count": 18,
+   "id": "b1819e94",
    "metadata": {},
    "outputs": [
     {
@@ -999,14 +1020,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
-   "id": "enhanced-timing",
+   "execution_count": 19,
+   "id": "7aebaf1a",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "98a95ffaca22478b853daf450f039f60",
+       "model_id": "d568ddeebbbc4cd0b9c29c042f51ea28",
        "version_major": 2,
        "version_minor": 0
       },
@@ -1036,7 +1057,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ranging-familiar",
+   "id": "1f20fbbd",
    "metadata": {},
    "source": [
     "Even though the difference in the predicted peak of the load deflection curve shown left is still apparent, the amount of dissipated energy up to the control displacement of $w=2$ mm does not change with the element size any more. Let us remark that the applied scaling of $\\kappa_\\mathrm{f}$ was only approximate. A more sophisticated scaling and a smaller size of load increment would be required to achieve even a better match of the load deflection curves."
@@ -1044,7 +1065,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "subject-costs",
+   "id": "6c704aa2",
    "metadata": {},
    "source": [
     "The described kind of scaling can be found in the most common finite-element codes. In the present example, the position and profile of the crack was known a-priori. Finite element codes implementing material models with softening behavior must implement a general procedure for the described crack band regularization. This regularization is performed in each material point by including the size of the containing element and the orientation of principle strains to reflect the width of the softening zone correctly.\n",
@@ -1060,7 +1081,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "parental-tablet",
+   "id": "612c43c6",
    "metadata": {},
    "source": [
     "**Note the dimensionality of $G_\\mathrm{f}$:** To be precise, let us note that in contrast to the example shown in notebook [6.3](tour6_energy/6_3_localized_energy_dissipation.ipynb) for the CFRP pullout simulation, the damage function was applied to the bond-slip law, describing the behavior of a unit area of the interface. On the other hand, in the present notebook, we applied the damage function in a two-dimensional constitutive law. "
@@ -1068,7 +1089,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "front-protection",
+   "id": "ec731531",
    "metadata": {},
    "source": [
     "<div style=\"background-color:lightgray;text-align:left;width:45%;display:inline-table;\"> <img src=\"../icons/previous.png\" alt=\"Previous trip\" width=\"50\" height=\"50\">\n",
@@ -1080,7 +1101,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "ambient-typing",
+   "id": "5c74602c",
    "metadata": {},
    "outputs": [],
    "source": []
@@ -1088,9 +1109,9 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "bmcs_env",
    "language": "python",
-   "name": "python3"
+   "name": "bmcs_env"
   },
   "language_info": {
    "codemirror_mode": {
@@ -1102,7 +1123,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.8"
+   "version": "3.9.0"
   }
  },
  "nbformat": 4,
diff --git a/tour8_rcbeam/8_1_rc_beam_mkapa.ipynb b/tour8_rcbeam/8_1_rc_beam_mkapa.ipynb
index 0f22c54..b07864e 100644
--- a/tour8_rcbeam/8_1_rc_beam_mkapa.ipynb
+++ b/tour8_rcbeam/8_1_rc_beam_mkapa.ipynb
@@ -277,7 +277,25 @@
    "execution_count": 1,
    "id": "47ced80d-59d8-474d-be9b-869804021ac5",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "ename": "ModuleNotFoundError",
+     "evalue": "No module named 'shapely'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
+      "Input \u001b[0;32mIn [1]\u001b[0m, in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m get_ipython()\u001b[38;5;241m.\u001b[39mrun_line_magic(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mmatplotlib\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mwidget\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m----> 2\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mbmcs_cross_section\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mapi\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m MKappa\n\u001b[1;32m      3\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mbmcs_cross_section\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mapi\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m ReinfLayer, BarLayer, FabricLayer\n\u001b[1;32m      4\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mbmcs_cross_section\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcs_design\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcs_shape\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m TShape\n",
+      "File \u001b[0;32m~/miniconda3/envs/bmcs_env/lib/python3.9/site-packages/bmcs_cross_section/api.py:1\u001b[0m, in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mbmcs_cross_section\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mmkappa\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mmkappa\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m MKappa, MKappaParamsStudy\n\u001b[1;32m      2\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mbmcs_cross_section\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mmatmod\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m \\\n\u001b[1;32m      3\u001b[0m     ConcreteMatMod, EC2PlateauConcreteMatMod, EC2ConcreteMatMod, PWLConcreteMatMod, SteelReinfMatMod, CarbonReinfMatMod\n\u001b[1;32m      4\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mbmcs_cross_section\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mmatmod\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mec2\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m EC2\n",
+      "File \u001b[0;32m~/miniconda3/envs/bmcs_env/lib/python3.9/site-packages/bmcs_cross_section/mkappa/__init__.py:2\u001b[0m, in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 2\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mmkappa\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m MKappa\n",
+      "File \u001b[0;32m~/miniconda3/envs/bmcs_env/lib/python3.9/site-packages/bmcs_cross_section/mkappa/mkappa.py:7\u001b[0m, in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m pyplot \u001b[38;5;28;01mas\u001b[39;00m plt\n\u001b[1;32m      5\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mticker\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m PercentFormatter\n\u001b[0;32m----> 7\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mbmcs_cross_section\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcs_design\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m CrossSectionDesign\n\u001b[1;32m      8\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mscipy\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01moptimize\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m root\n\u001b[1;32m      9\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mbmcs_utils\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mapi\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m \\\n\u001b[1;32m     10\u001b[0m     InteractiveModel, Instance, Item, View, mpl_align_xaxis, ParametricStudy, \\\n\u001b[1;32m     11\u001b[0m     SymbExpr, InjectSymbExpr, Float, Int, Bool, FloatRangeEditor, FloatEditor, HistoryEditor\n",
+      "File \u001b[0;32m~/miniconda3/envs/bmcs_env/lib/python3.9/site-packages/bmcs_cross_section/cs_design/__init__.py:2\u001b[0m, in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 2\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcs_design\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m CrossSectionDesign\n\u001b[1;32m      3\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcs_layout\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m CrossSectionLayout\n\u001b[1;32m      4\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcs_shape\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m CustomShape, TShape, Rectangle, Circle, ICrossSectionShape, IShape\n",
+      "File \u001b[0;32m~/miniconda3/envs/bmcs_env/lib/python3.9/site-packages/bmcs_cross_section/cs_design/cs_design.py:2\u001b[0m, in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcs_layout\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m CrossSectionLayout\n\u001b[0;32m----> 2\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcs_shape\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m Rectangle, Circle, TShape, CustomShape, ICrossSectionShape, IShape\n\u001b[1;32m      3\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mbmcs_utils\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mapi\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m Model, Item, View, EitherTypeEditor\n\u001b[1;32m      4\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mtraits\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mapi\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mtr\u001b[39;00m\n",
+      "File \u001b[0;32m~/miniconda3/envs/bmcs_env/lib/python3.9/site-packages/bmcs_cross_section/cs_design/cs_shape.py:9\u001b[0m, in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpatches\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m Polygon\n\u001b[1;32m      8\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpatches\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m Circle \u001b[38;5;28;01mas\u001b[39;00m MPL_Circle\n\u001b[0;32m----> 9\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mshapely\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mgeometry\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01msg\u001b[39;00m\n\u001b[1;32m     11\u001b[0m \u001b[38;5;28;01mclass\u001b[39;00m \u001b[38;5;21;01mICrossSectionShape\u001b[39;00m(tr\u001b[38;5;241m.\u001b[39mInterface):\n\u001b[1;32m     12\u001b[0m     \u001b[38;5;124;03m\"\"\"This interface lists the functions need to be implemented by cross section classes.\"\"\"\u001b[39;00m\n",
+      "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'shapely'"
+     ]
+    }
+   ],
    "source": [
     "%matplotlib widget\n",
     "from bmcs_cross_section.api import MKappa\n",
@@ -576,7 +594,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.8"
+   "version": "3.9.0"
   }
  },
  "nbformat": 4,
-- 
GitLab