diff --git a/doc/rtd/_static/custom.css b/doc/rtd/_static/custom.css
index 8b0e9e3af..d771bd2d9 100644
--- a/doc/rtd/_static/custom.css
+++ b/doc/rtd/_static/custom.css
@@ -24,4 +24,10 @@
 
 .wy-nav-content {
   max-width: none;
-}
\ No newline at end of file
+}
+
+@import url('https://fonts.googleapis.com/css2?family=Roboto:wght@400;700&display=swap');
+
+body, h1, h2, h3, h4, h5, h6 {
+    font-family: "Roboto", sans-serif;
+}
diff --git a/doc/rtd/conf.py b/doc/rtd/conf.py
index e4fd9c7ef..9d65eeb0f 100644
--- a/doc/rtd/conf.py
+++ b/doc/rtd/conf.py
@@ -19,7 +19,7 @@
 # -- Project information -----------------------------------------------------
 
 project = "MLPro Documentations"
-copyright = "2024 South Westphalia University of Applied Sciences, Germany"
+copyright = "2025 South Westphalia University of Applied Sciences, Germany"
 author = "Detlef Arend, Steve Yuwono, Mochammad Rizky Diprasetya, Laxmikant Shrikant Baheti et al"
 
 # The full version, including alpha/beta/rc tags
diff --git a/doc/rtd/content/00_start_page/images/api_reference.png b/doc/rtd/content/00_start_page/images/api_reference.png
index aec8af9a5..dbdc1318d 100644
Binary files a/doc/rtd/content/00_start_page/images/api_reference.png and b/doc/rtd/content/00_start_page/images/api_reference.png differ
diff --git a/doc/rtd/content/00_start_page/images/chatgpt.png b/doc/rtd/content/00_start_page/images/chatgpt.png
new file mode 100644
index 000000000..0ce8abc40
Binary files /dev/null and b/doc/rtd/content/00_start_page/images/chatgpt.png differ
diff --git a/doc/rtd/content/00_start_page/images/example_pool.png b/doc/rtd/content/00_start_page/images/example_pool.png
index af8ed9ac4..a63a6a337 100644
Binary files a/doc/rtd/content/00_start_page/images/example_pool.png and b/doc/rtd/content/00_start_page/images/example_pool.png differ
diff --git a/doc/rtd/content/00_start_page/images/extension_hub.png b/doc/rtd/content/00_start_page/images/extension_hub.png
index b94e650e7..fa8a45367 100644
Binary files a/doc/rtd/content/00_start_page/images/extension_hub.png and b/doc/rtd/content/00_start_page/images/extension_hub.png differ
diff --git a/doc/rtd/content/00_start_page/images/icons intro.drawio b/doc/rtd/content/00_start_page/images/icons intro.drawio
new file mode 100644
index 000000000..dee3aa6ee
--- /dev/null
+++ b/doc/rtd/content/00_start_page/images/icons intro.drawio	
@@ -0,0 +1,118 @@
+<mxfile host="Electron" agent="Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) draw.io/26.0.16 Chrome/132.0.6834.196 Electron/34.2.0 Safari/537.36" version="26.0.16">
+  <diagram id="EHj-KjT-_PD9RqYpWqSy" name="Page-1">
+    <mxGraphModel dx="2884" dy="387" grid="1" gridSize="10" guides="1" tooltips="1" connect="1" arrows="1" fold="1" page="1" pageScale="1" pageWidth="1100" pageHeight="850" math="0" shadow="0">
+      <root>
+        <mxCell id="0" />
+        <mxCell id="1" parent="0" />
+        <mxCell id="s8sjKYFR_AVLs2IwWPgZ-1" value="" style="whiteSpace=wrap;html=1;aspect=fixed;rounded=0;shadow=0;glass=0;sketch=0;strokeColor=none;strokeWidth=1;fontSize=22;fontColor=#6600CC;fillColor=none;gradientColor=#FFFFFF;gradientDirection=west;opacity=80;" parent="1" vertex="1">
+          <mxGeometry x="244.16000000000017" y="1560" width="170" height="170" as="geometry" />
+        </mxCell>
+        <mxCell id="oZE7QmULsiBy4WYUOifm-9" value="" style="whiteSpace=wrap;html=1;aspect=fixed;rounded=0;shadow=0;glass=0;sketch=0;strokeColor=none;strokeWidth=1;fontSize=22;fontColor=#6600CC;fillColor=none;gradientColor=#FFFFFF;gradientDirection=west;opacity=80;" parent="1" vertex="1">
+          <mxGeometry x="437.1600000000002" y="1560" width="170" height="170" as="geometry" />
+        </mxCell>
+        <mxCell id="oZE7QmULsiBy4WYUOifm-8" value="" style="whiteSpace=wrap;html=1;aspect=fixed;rounded=0;shadow=0;glass=0;sketch=0;strokeColor=none;strokeWidth=1;fontSize=22;fontColor=#6600CC;fillColor=none;gradientColor=#FFFFFF;gradientDirection=west;opacity=80;" parent="1" vertex="1">
+          <mxGeometry x="51.16000000000011" y="1131.5" width="170" height="170" as="geometry" />
+        </mxCell>
+        <mxCell id="oZE7QmULsiBy4WYUOifm-6" value="" style="whiteSpace=wrap;html=1;aspect=fixed;rounded=0;shadow=0;glass=0;sketch=0;strokeColor=none;strokeWidth=1;fontSize=22;fontColor=#6600CC;fillColor=none;gradientColor=#FFFFFF;gradientDirection=west;opacity=80;" parent="1" vertex="1">
+          <mxGeometry x="51.16000000000011" y="1560.5" width="170" height="170" as="geometry" />
+        </mxCell>
+        <mxCell id="oZE7QmULsiBy4WYUOifm-3" value="" style="whiteSpace=wrap;html=1;aspect=fixed;rounded=0;shadow=0;glass=0;sketch=0;strokeColor=none;strokeWidth=1;fontSize=22;fontColor=#6600CC;fillColor=none;gradientColor=#FFFFFF;gradientDirection=west;opacity=80;" parent="1" vertex="1">
+          <mxGeometry x="245.16000000000003" y="1340" width="170" height="170" as="geometry" />
+        </mxCell>
+        <mxCell id="oZE7QmULsiBy4WYUOifm-2" value="" style="whiteSpace=wrap;html=1;aspect=fixed;rounded=0;shadow=0;glass=0;sketch=0;strokeColor=none;strokeWidth=1;fontSize=22;fontColor=#6600CC;fillColor=none;gradientColor=#FFFFFF;gradientDirection=west;opacity=80;" parent="1" vertex="1">
+          <mxGeometry x="51.16000000000005" y="920" width="170" height="170" as="geometry" />
+        </mxCell>
+        <mxCell id="oZE7QmULsiBy4WYUOifm-5" value="" style="whiteSpace=wrap;html=1;aspect=fixed;rounded=0;shadow=0;glass=0;sketch=0;strokeColor=none;strokeWidth=1;fontSize=22;fontColor=#6600CC;fillColor=none;gradientColor=#FFFFFF;gradientDirection=west;opacity=80;" parent="1" vertex="1">
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+        </mxCell>
+        <mxCell id="oZE7QmULsiBy4WYUOifm-4" value="" style="whiteSpace=wrap;html=1;aspect=fixed;rounded=0;shadow=0;glass=0;sketch=0;strokeColor=none;strokeWidth=1;fontSize=22;fontColor=#6600CC;fillColor=none;gradientColor=#FFFFFF;gradientDirection=west;opacity=80;" parent="1" vertex="1">
+          <mxGeometry x="245.15999999999997" y="1131" width="170" height="170" as="geometry" />
+        </mxCell>
+        <mxCell id="oBciCZLcTB8yx7cMriY3-1" value="&lt;font color=&quot;#000099&quot; style=&quot;font-size: 23px;&quot;&gt;Welcome&amp;nbsp;to&lt;br style=&quot;&quot;&gt;MLPro&lt;/font&gt;" style="whiteSpace=wrap;html=1;aspect=fixed;strokeWidth=3;rounded=0;glass=0;shadow=1;gradientDirection=west;fontStyle=1;fontSize=22;align=left;verticalAlign=top;perimeterSpacing=1;spacingLeft=11;spacing=0;spacingTop=3;strokeColor=#000099;" parent="1" vertex="1">
+          <mxGeometry x="61" y="930" width="148" height="148" as="geometry" />
+        </mxCell>
+        <mxCell id="sD8p_Dm-KN44XBGqJHIv-26" value="&lt;font style=&quot;font-size: 23px; color: rgb(0, 0, 153);&quot;&gt;API Reference&lt;/font&gt;" style="whiteSpace=wrap;html=1;aspect=fixed;strokeWidth=3;rounded=0;glass=0;shadow=1;gradientDirection=west;fontStyle=1;fontSize=22;align=left;verticalAlign=top;perimeterSpacing=1;spacingLeft=11;spacing=0;spacingTop=3;strokeColor=#000099;" parent="1" vertex="1">
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+        <mxCell id="i9t-HrYGtDAK3b97exIO-2" value="&lt;font style=&quot;font-size: 23px;&quot;&gt;&lt;font color=&quot;#000099&quot;&gt;Project&lt;br&gt;MLPro&lt;/font&gt;&lt;br&gt;&lt;/font&gt;" style="whiteSpace=wrap;html=1;aspect=fixed;strokeWidth=3;rounded=0;glass=0;shadow=1;gradientDirection=west;fontStyle=1;fontSize=23;align=left;verticalAlign=top;perimeterSpacing=1;spacingLeft=7;spacing=0;spacingTop=6;strokeColor=#000099;" parent="1" vertex="1">
+          <mxGeometry x="447.1600000000002" y="1571" width="148" height="148" as="geometry" />
+        </mxCell>
+        <mxCell id="oZE7QmULsiBy4WYUOifm-1" value="" style="whiteSpace=wrap;html=1;aspect=fixed;rounded=0;shadow=0;glass=0;sketch=0;strokeColor=none;strokeWidth=1;fontSize=22;fontColor=#6600CC;fillColor=none;gradientColor=#FFFFFF;gradientDirection=west;opacity=80;" parent="1" vertex="1">
+          <mxGeometry x="50.00000000000003" y="1340" width="170" height="170" as="geometry" />
+        </mxCell>
+        <mxCell id="HKH38EIKG9Yy371c5PlH-7" value="&lt;font style=&quot;color: rgb(0, 0, 153);&quot;&gt;&lt;font style=&quot;font-size: 23px;&quot;&gt;MLPro-RL&lt;br&gt;&lt;/font&gt;&lt;span style=&quot;font-size: 18px;&quot;&gt;Reinforcement&lt;br&gt;Learning&lt;/span&gt;&lt;/font&gt;" style="whiteSpace=wrap;html=1;aspect=fixed;strokeWidth=3;rounded=0;glass=0;shadow=1;gradientDirection=west;fontStyle=1;fontSize=22;align=left;verticalAlign=top;perimeterSpacing=1;spacingLeft=11;spacing=0;spacingTop=3;strokeColor=#000099;" parent="1" vertex="1">
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cOBES+U+t3bi81zMaLVpFv29wxbeAh4qL/l12z4cEtlxc3XuWQjJl0h7Ip4LM7hJZZMMzDLgOeFP2AGzjOyhVLJvbEofOY053grxktE0dpc1JNrsSybt7Kgvp6CRIKCAouKcRRYUBD3Ptkq5/e1tsQtLrc3VxH3l8z5eom1jP64wUcFOz2sbI6Fv81FXSOVES2Ugukvl4ShfcJQIPjFPTkcSw+kWE6bfk1RUTJW7kVpELiKZBOTRyUYJxjlndIpKiJf6AiaLAp2FcRlYDsXT121y+QiQ68GPtxiMW25Z/Pyfrm+V5beMMY327uEXYYp5fHfbYmlfnwkW7iyMWKFUsm9sdbUM6c7p4WCWg8590JtF4uSjQXACkSvxdKe3EjOn4h3MwmYF8AMM44HJqg9AkvGGrB1Wg9OB0PxkdCTPtBLeL20cMqqV0z2ShSGL/RVsPPnr467rcllePdZgwSyX6biwaqHN16eF0knRqxQElNeL0jJ/g6IeVh37yBNOms2fLj1tOnXrCqK4kWgj/UslnQi0MBrDlH0FsEp8m5/KrdDjJERgL2ZdGwzk9M+4K/xROKPi8pvuD9Ihc/3xpOgijXBztrak82Cz8rsGLBfGfr6kW4G7o4RKZRqqt19sd3zFLkzgAYc/zOI1dvDmz6167Tp11xdFMXpTizp5Ebhqd7bOyR/MmgBougg4mgPW5Sx38YKQImsZ6f7mGLB2zE7DTjfB8HDS8quv12hf7I7wXQyA78Z6U8B9o27a5fnLVzdMCKFcuf0koKiSH+LbLKMO5uV6rd1q49kFUssFm89s/yG7wVec5p8uMDkLxBaAMoWsM+AHYItHquTqU5iO0brgThdYF6j5FRk5kuEJhmUAhV09W8LgDcijvFokYJg3aKyVbf5UHd3NAJ0NANLetTga3dvufyxgX5IhxMjUCjVrsDHZyFVYez18kO1Z6SLWCRWxLyfgHiTZReIB9sEetywHc542aSXZLaZWGxzU7cOhKYzpl4/qcDZdB9phnM63rAZQrNNNgfTfveatjwvFYgPYZzl4v57lVOuu61m2xVPdzYDm3hVPvqvdVtX/CaXD+pwYMQJpbJiVkKtje8GKkB/GOIwN/bwpk/tOmfK1deGrvA00FlIy7Oc1wy8YugvSA8pjO5sSYRbei9w2QNb2QHsAJ4Afl3FmmBn+fY3AW8COxuxoItgxFxMVznnTlxYtvLnCr7zJ3l93OCjQD1wfVPeDNwrRphQTC78zkTgA0Arzm75w8bLhnTyec6Uq0dFsaJTzDM6y56FJoy/IvujpEdaA3//g69fvmUg6l3LRRFb+DPYE5Wl377DJVhqZm910pvM7K0ZpyaE3ofpVIvCWwX/gBEiftju2zYQ7TncGVFCmc9NMcLwdKQ3Ak+1OhvSvH+VpTeMiWIs9d4+J1iQ8ZPJbCvSb8z5XzS78IHcvZCymh1sBX44n9U/HVcWLgZ9DOws2uYxbb3LHKErzUgBv2yN2Y0Pv54XSW8ZUUKZND0oDkP+waAV6X8ffH35gHydD4XK0hvGBImsImlC+pNJd7UG/kcPvr6iSxuNj8dh+zSwBaDjQeXAHFAp2CTQvhBt9I7aCLcjKfdcKvIPlLL2hZ7a9DiXpKitvmvJzMkvWtL+zmQXgjqmyBCRw14aqJ7tSGHECCVjgbHS4DWFuXFX6VVbSm8YQ4wLvLdPdhLJPuDXMn2nKWh5LPOLfUAcnAZ170jH8tVk0pNvcWDLg0IgkuZGhkVy5g2zIIhq9f5nDfezVvl1s1M/+0v21lX732/klfms/urYstRTwN+Dnd9uYhYkPKpaOHXVQ/nsx71nxAilYPoTJako8REgBv72oQqadtxx1QnqucBhX6BjDsl9glu98dV1Wy/tsB5hvGsK1F0M+idgFlhA2pyrjucZESKSiFAQSrSJJn0cvTmEEyKL1b0Q/4e7vLnvvjG8OatpvK13ufXM6eOfjoXBHmTvByXasx+3JXTdt652+f0D+oAOU0bExq0q1gTJMHYC2FmInZINSdC0KtYEU3aVLuhJJPduPbBoZ7xrinHhJyG4D+zrwFFAAekPVAeReMgQiSMtEh0QiRwhcpGUiERZCH+XcvbzJwo+svqJ+Me6SR9X7e/ftGK9D/wXMf0sI3BgIDge8W9H0s7N/jAihNJSvqtAuPdhKsZ4aGiC0pl2lO4oxbiSDiJRA+gnXUVywQngVoK+DhwNJOgmmIcnnbkru0hcu0jajjsiOUVSLEJTQ9xHWgNb/XDBJe/M3m5ZzebLN2cRSwzj7UfKzs3+MuyFUk2126fWo4ALkDWYuR8OhUmz6ri1cWLRBenxfhpBEuxXrTH/H+0iMT4eN96zENy1oPfSg0DggEjaRZApkrQotF8k0f7/pl1AiiAWmluQlK26t+gT/5y9lgNiMVOmu0+BoQ9i/NMAPKLDmmEvlDunlxTI73dXebgl1rJusNtQxZpg564dCxzucg7kj4wMHjPj6+0WJOOc0bD93WDXks7N3mOUm0yRRBmi6FkkHXudtvNdhJsR4j7zu6J/+trviy/LEr4pLRaJL3WK8FiA+NCi8lVvGYBHddgyzIWS4a4yhCFO907fNLbTkMvANsv8tZk9CRQvAvf/gBPoo0iy9SRRVpG0H2s/37VfpxAmpxRc0hQGn85eq2xPbXyLEX3FpDZPZ5NgPKYrK0tvyGH6uZHNsBbKsnkT4jKdh1FhpheHwl1l/vzV8dAnzskccmE0gH7U0f2j7jSwfwE7noM812wiiXoQSZjRsxzoSVymSNp+d4rQ2KTj724ZfUXWYdjjXJLyieK7BasP3I7iYEtd4Jf183EdtgxjoZjC3ZooeC+QlOPWwd8rYRq9KTndG//M/iGXhcAfWmN2Y3vvZrzzDWD/CHYKvRBJWgSuW5GE3Yhkfy/SaZh2oDwRSorQNC/3wR+P+fRF2dpQs+HDrbHI3Qjc336fwHiTW7F42qqBirp5WDFshTKfm2I+5k4H3oTxEkOwwHjccVfFA9kZHRcVtdfkv39gXvKuKRB8AHg/vRRJdOClzjoniTJE1EEMWUQSdhTJfqtYCnd8iH30ptGfyWY6tuS2N9Sb+W9wIH1DIOzNPtL5Wc4/4hm2Qpk0PSgG/yHSNv9fDMUC4/Q908c4lPFVtlCmhzsOAYMTQRfS0+JtLMCfMAv7xLnoqotxn34XnPFGolGFWXqSriLqSSRRF5G0H8elnE71Tn+brUk1LIzi1vooxp8yDhdKfLTquOpuNooduQxLoaTdVVpONw3efvjOpBc5m04wadGBo9rrFf2gfQhovGc66fA+x3ZbUCzAzjkJvvwB3GffTbBiGfFPvZPEl6qIvfdU/Kiig4jEpQXQZcKfXSRRxu8eFUfi9OuKP3d6lpbZ5kmNuxGZuWYCsDfV7xn3hgF5iIcRw1IoBdOfKHGOjwABZndkLuQNFpumb0o43PlkmINBT7X3JmkrF28FLqCH5+iPm4l9aBHuvLeg2ZPRuNFo6jjiZx1LwcVvx500q4NIOpuIMyfxUY/Dra6/hziFuJN94LL2Ks88U51SYXhXp4y/cUsF5w3MUzx8GHZCqWJNEFns6HZ3FXP+p0PRjgI/erRJizMOJU322wMGhW1TwU4HZndXhkfYCbPQKfMg6PSoA0f8zRXEFsztViSZpuCDiSTM6Ik6lCcVptBJX5vw/7L2KpGN3SOzzPlfHGlJfvjVkWEnlE3TNyW81wVD6a5SxZogUOoosDdmHE5Z5DMiJ8ZnAG+hW7eU9IvqxxRByais9WhsEYwfk3ViH3YSSWcRdJ2TdLR+dVh3QTNDI/uC4obXQh/w4AHXFgWI4/PDr44MK6FUU+1GUXgUUtVQuqvsm7c1ZhEncWDY5YHNYUEsYz9INBV4Y9erD4gkRET1+7C6vVnrieobSdU1dDERd3CG7GAqdh3+u6NIXBcRhUrPa3zaJyxrW2uojiyuZ0CvpI+YgJL88Ksjw0ood04vKbAouhizWUPlrgIQayoIwDIm6BYaPHBgEn/+eNIiKe18re/UOyT//AqpP3Tdb2VhRMvjr9L42KsZK+yuy4uf6QPWs0i6LlJG0F5OQaTgmH8prc5qKi5OxvZiZC7mxnE6o39P8fAiZ/tRTpt+TdHolMpwFIQuURAzxnj5hKKowTv2xpJBgy9Q64GQoKbRqRvLfOCrMGvF9JOh2s8dtrTGVBgsMGuPwqVQ4tEDZySmAm8gY9hlZHoAZ/Qor+0k+u46wmREwZLjCcrH4/c20/TAC9Tf/ACNz245YL3q8uLTsztLlpX8DhP7DsM3JnqvWUCXDV9btpAqKfO/F+6fSN+Usx7mXkciAygU0zlTvlOaJDUnCFyFRUz1zo4Fipz3o7zceEyFnqDeGTujuOrx7Fs09fonFWhTFH2rNnKxpTLNBj3VkrAh21DUMCYWJFL+mIxDoY98hmUoGA9+Znusx64iaesZgNBD9PirtOxowN35ZHpO0pKi+cWtNL6wlTDpO4ikw4vfYe6Rzbq1f2jVo0ja/n+Cx83Mdr+PUxstofQ5v/9+JMzGVh1XnVj7THUy2zVHGgMgFNPiyddNxn372JSzZYG5E9vj6XaIa9X2dZa0P5SowJv0uvf2koi/gHEqQ74f3lQYriz2BOMzDvowsIz2+AKw0dBVJPvnCbC/p4i8EW6oI3ytvtPQzPVbJN37iHWsI5ISERqd/Z6rzQfX7CVqN3SZkI3aunPCWNIhko54+iWUdNjQlaea4ouBM2Wc0Zd4um3bUmcJzUIsaTvcjPdhZekNY3KV5qAnqljrdnpN7WTLMmLxjLzoNhqY1K1IejDpZi4WhvTuRe+407HvIkmvw7jxZszo5ratyBU2NUdRcn/4VkkJs1LyQgEOeTJvOnvG9eWNJP/R5K4C+xTYgEz+BAWgT7i4X1455brjB6LMvrCDZxU5NzbjkIE1dHLITIBGdRbJwUy6Ay8Sl8WRMtuqvQgNhVK3G8gaXauBDojCCCJZ1qHakcghCKXaLZly4+wodJdjuiodTYRssXUPiY4RDoMvVJZ/e8nBrxpIziKIVJhxwBva2OmkRAijO4vkYCbdzJc/Y/W864JhpzIztwGHWers4hPWuU6El0uEUvYFHaBob+CBrRmHFFgwZSCf7EimT0KpYk2weGrp/Mj5K8CuAMYf9KJDJwG8z3n/75VTVr71oGfnDpNIZR6IcI2RtHO/CbfzfCKrSbejSKIuvUzGAmGW36JOIsn+e+f99R1E1ejNHSzpkM/425mYccaka8sG/pGOPPowRzHtKr9pmpl9VpDpA5VLAhwLAvSvlVOu+3w60HSuuY9I4/cFbRsUBc6MqZlnhJD0uMYuHr77v+pdh1I9iSTbLsbuTcDpcnyW37tcmykq51ojo6m7u05ZgSA5NsPiHTc4NxaPj1849fqXndwec77We/ZGqXDz4ZO/snf0UijpaOqGPgoaLJG0VU3c0NnOBfWLylZ9cV3t8tdyWV0N+CWKbe3JVOpxu1Lo9Qi9oYsnbxdrVeff+i+SznOWbNuEO/c8kbHLuy5DyP20FEcukVLmAqqTmINxEtJow9dhWu+MOsXjL6XFw94IVwe+LkolNgfbwwZivrsqRjS9EsqyedcnWpuC95nZcgZTJAdIgKow2wJ8PrdVfcmSiVUtsWT7l9WEGLVzz5RSYDNACmehw3znOUEvRNK1B+rmRe+lSDp7FWeK5EDP44ggaQSN2e85q0m81bx9R1ICbJJQBcYkk04QtjAtHuqEbZbpdcWT6xFbwWbKhEHhGZNWl02dUrvzcFiLOahQqql2D7TEjgH/SXI7JzkYBYgPV05Z+auabSv+ePDT+8Zp068pKlKsnPCGaUrphE5mbmeRTaVNKI2ktjsSLxo6J3Pi3HFNpLN1q+8iObCY2BeRZIsLJkK5bd7xYrZ7z2ISN0FdMhGs+sPGS3dVsSbYVb71RIvcNJyfDprWLh7RLh4WYow2tBsksKmxePIz9TsnbF44ZdUuc9oCvi6VYksuMxznioMKJZ3dKroYNHcwGtQ9JsR4J/eFytIbPtDfB11ZcXOhTzaVuiia5gJm491MMztKaB7ejqKjIdWhaBrwOEAZ/7dxK++/P0IXR2jioYqky2Q/cy6jrteme4jOv2cILYsPWJurfdJjr4zy/rlsz2IHz0rBhLknoewAABzcSURBVFIdGDVFJnup3STelmLiCdK5WYC0YaezeASTDZ0ONh4YK/RBk5sgWSPYepleL4jzMtjORWXfrvPYBvB1WFhHlKgbzuLpUSjVVLsHKDzK5KsONTPngGLEEYsUDxcDfUr5kCmMIHAV1to41Rlz5NzReE4wUSa0A/xmLz2qdO95ZtvlMdB84NcAAntNsVrwz4fS2zr2CJ3nG9nMuIcmks69VwdXme5EkhbwNkNP/XjbP2cfelXMipFsPCXjSATqMXJ+NvGcNv2aosIo8a+C44E9gmsMP8NQhaAYR4WhBTIrS2c35imZNqHYJmK2eTiLp0eh1FTMSijZ+F4ZQ9ybZGKFIvgoBxFKT8Joc7EZj9gHbDHxjGG3OeNlHC8a7jlF/ihzGULxvC2z/CgMX4/isSc8vK33Ium6phK1v+idh3BZFimz/X7wuY2I4BWTHuzuWRXtbUgkC4Nz7MDXMAI929d/mXGFMZ/cxxYTJmPXPVuXf6WKNcHuaTtOiHATiMKJiBlgR4GbJbNCHLMMN78n8QjbYGKvkdrpFKs74Ejbd+aXV48qVunRGGMc+CAV7uhNFuWeh17J5BiZ3nHIiZ5zggvwtuDs0uuOyrzBdmEEkZXjNE2tjTM7CWMCAjM2m+w5oe0y+2s6h2K4qTnmn87wVtbZM65viELtA8aAAmHHZdY5m59tfEEfuj8yLo7ExE5f8CzWLZfVzyvXIglRo8k9nbRk1he/mmpXUxTMcd46OYFGA5L8dC0XRWzu6LFcxZqgbtrW45Wy0eCmIWYg5oEmydukzuLx8JQ8O52LbfKezYvKvv16X8VTxZpg15Rtx0ams2X+bUjj8fgocBsXT111n4maniyq3QqlijVBvbYfZZZ9c9LQYUKM9YHedc6UG3/sA6ZF5me3C8OcHS0xD6McgYkteDYh3Y/ZRu/sJeftqdY4m3pIpmNNzaMaCuJNT6Q9D9J1hrHgncA1kB5+PeF41Bm3eHOXHkwkPb74HeYv3bi7dLZ+dZ7EdzPEi6TnPdx+17bPZB12/WnehHjQ6M8y1G6o8YJXOm5SG1jaxPPXjker3ZLy8dO9Z3q7eCTN9WZjZZQhxnl0rOhZPF6+IYjY2xqFde1rPdVUuwembj3GcJ8R/A2oCKNtyciZYe8G++/F01b9R3eC61YoOyoa467VFnWT+nmoiXu5C73zk2UcLdkJbcIQaAeeLcCjiK3AMyZ7PRmzp/qSZapkXGOydR9rUPsGJotLWkibUABOavnha48n/vFuE++M0IzeiqSry0k2k27/RZKSGj36Y2sUdLtloXlPEHcxOyvjUBLjl4MfbLDa/34LG4GMtZ70+l3Cu3kmTcLZUZLmmvkShyaZKM0innofuJ0xF399Udm3X8Ki1+4T3qFzgL+hy/KGCRgH+rj39hpwbbbWdT/0amyKWUxv7taLbmiJCd6O2QKDHcoQhmSvGLyoIPZ0c7fpqA/OHesvTy6Z8q3bvVw1MDHbkE9gf4jZIy4KbvHw6ajTi957kXSbD6XNRJzxeyeR9OTu4tGTEfp5dxPiaqpdTYJ5eCozDu8zl7rtUJ7ZwNMhG3IbB1KJWxRM7yoelRr+jYKJ4AOkpwWtGNNRj2uAhRKX0VehpGyfEowZO7zmJxkYKcQdkt07EMLoiszxo21me+828bft5mkfC5YDV7Sf9bamH9TeX3jJb0Cnh3B6dpf7bEEgujPpdrZ+9cI9JYtIQtzzHved8VtrHujuDmsqxpa4Vv9BDgy7IrBnmp1lNSMPD7qkEqereDTPsOnCJgtKDTfNZOUHKxhTt+uE3QqlxBepVVY6XHWCSMq49Z7ay//74CcfGsltr7QG5ZN+gPEO0pP6hGHvW1S+6r/XbVn+53QzsDUt9Q9OGDXxq2bumxEckyuRdF5/6cEVZps5W9NcvO+Xv9+6Nsp2b5VUx2LJYIGZ3mdqf6S0RvDLkZdSu3vxxJ2bYWYLMoOSHwrdeg83l0QOYzi7WctkOZ0/1VAdRik9LLgnfSR7ioSLWBvVNxXd0+rcf4Zyrx+wOGWPExy2RX/sHPkxHBiRNHqnX8RG8V8PvvC5hu7urWB6SYm34BIT7V9ab8Z6F9kwGXb1F9kDW1fsWLdl+RNhIvWbA2kuusWEurWcHczNPuvXaJhgOhBgOnfs2NGCuIG2utpSJJzbtui5n4u4trkpMfo3IWmxZBNJ5mp9l5X1Tr1Q2EkkYSeRhJ1E0ubev8ujHzQnm758x/oV2WMk0R6yNnF6h1QWsNekHw5VEtkcYmFkuzH/w4OclzTst9392K1QsmzkGW6YM9XnupIaqsMgan4I9Ku2agWMh9jyzrkPL9r19b2xcS0/8Qo+E0kvdLZ+7RdNFpF0fumjTiKJOh3L0stsM2kVoxq/+GDd57r9elaxJnDlfzkxkH2CzFQWxkPJmL8lV89xKHl405Utgfx3hP2+m1OawO4z43vdldH90CsIvA3v/dIWaRB6FGDstlktyK4F2hekYjLeLuOyTvlE7J1bbmpi31t/Hsl9NEL3ZLV+dWsJ65hyrrNIDmwKay+zXSTueS++GBtl3/z9K5/fQ7cWGNOu8tpp+PgnM4KPm8QOw327L+bzkYVs/JayzWb2/xA3A4+DNgObgD8b/ETYf/YU47pboSRckyHbloNWDxTN3oJu91cMJGu5KPKJumdN+jYHhnsFiH8065om4SIuil5pKH4ocvFPRLirI7Sz83yjc8aszsOt7K4onYZpzu2KpJ9GgT4S1tbf3NNwC2DZvOuLvQUfRJnrCZbC9OuhCjY4WKzlomjd1hWPFozyn8TrKsHVyL7hxL8nY3bVPbUrurUOQg9Wr2RRY+SSo54fpubhCOPl1ljzoAm5ZkN16+Jpq74PHGPGR8BkaDxmn66rn7ieTqkpqqn2ttdevoqr/nXs2OT/hrLlHr0rQhO7WsI6i6QbHzHaQiBJyRA94vHfShbYPZUb6vdWU93jjqnK0hvGtDbaBTL+yQ6sJ4RAjTeuH3mWrkPC2j4mv+nrhd32KKUbjk15879nMCbMfSdEemSQ/3Htns11uzzuGwfGuun5iqR/W1S26t2dexYhq6Y6uWdP4i8Fu/f9UzJybwud+0wo91Dm0CrL/vZsk/xk5NyGSPx3iujdLhF/V0lt+a9qNly5uzciIcYFwOcyrFwRxqMHG3LkSdNtj7KWi6JlrStfbC3UM22B6YYTKSMagkiS1X7SljXr66dt/ze8TQS9GQhkHA/6+s5dE8ctnrbq1539hdpe5FZrsBcv4ZLryivKVyX3BbPM/KIUwQkRzPFQGqJZESqOpD0hbk8kdkRyr4Xw15DoXteYWh9OGbOveP20cC0X9cIiaTp7xqqyMLK/kfFxOmc1xq452JAjT5oevYebS4qTtO77jUPDSShesD45BBmCIf0Bmb959WPjpoZfMfElsOPTgfxsHug/zTRzUdmqH2bzRBUyIMUGUoa9eBVXvQwpt6ECNz4sUcrvVWs0WkVBo+1zJTY6Nt62xRpsxvoCX05tVE21p9uVkc5UuyVTbqyIQvcxYZfQcXdqC9K3m4JUt+bQPB3peT/KhteSi6dO+KVJHwNm9XTuINJq0i2D77R3gMe5JFU1sfrX9fUTCw33ubaU2cIoB/sCZpMqp6z8aU9blttEEwKwYWDbl47gGZ4ayV8o+Bhd4xxEoGePkHnJgHCQBcdqHxUWv2Km7w9Ocw6KH6oMwZ1Z+0x1csKEnWsEX5OUKYgCnC6RC760aOoNHzq79LqjBq9V1a5y6o0V6Qie/qpOIvGkPXPvB4ow+1zndaA83RMc7IQNu38VVYx58DU5dz5oqHOQ7zHpunu3XX77ELcDgGd31EQT9l3wfOEYvxEYDTYXFACB0FywhTg3cW7xsvjcoqXhK02/y9ECabVbVPY3M+cUF54vx8WYLgcdA8TbTmhCPGjoR/hojRQsAB0vM79g1qn3P7ujZkA8MI6asCwWJfUWxHmC3a/uu/26gSh3ONCLcEWyojEra5sb7b8c+jIwNGIRKUw1w231+HEuSVFbfVfl9NL1LrJtyBZjOqp9sxfwIY8WKQjWLZ56wx1eetU5v6E/21nTZKTZcMz14m3y7gIg00vWwHaBfidz32kOWh6bPmFvVLdzwtWSu8akj9TVT3yEIci6PNLo9XaT06ZfU1QUJr6E7Mr9Ec8Hl3rMfWLd1kuHfNjVDVoyZ3VJ1JJ6L9h7ZDoL2O84KfAGr2M8iexJ4Z7D2SvJ1viG3sa+yowDoEBzQCc540SMEzLMvu00AxslW+O9bsk0AZ82/Zqioij+ZdAK4K/AewYisOCyeSsLkvvcR01cL2PDPVuXz+lvmcOFvuzLUmX5Nyc6K1gJujhnLcpOK9BsZjeZ+R8PTmjVQ6OKNcGOKbVvDFzwfmQLzXhLlg+LATsMexJ4yjm32Ue+2Ym9XmqQt6QFSjpzxURRgZcVSEGByY+X13SJo7sRB6QF8jpwv8z9oSnWsqbrpN20qOyGmZL+14yTZH5lFLn/6G/Ek8NZKH3Jj2I1W/bVL55WWG2eQuBsMr6YOaIR7CFDryC9S/BJp2DaorJVN62rXT5kGbl6Yi0XRWzj6fms/tLY8rAGbBnGqYgTOPC8BEwWWgIsMW9IMoOdMuqRmmW0GDYBp1EOigwrkqkItflKdP3E7RcIZg+1xrnzwdcv7cZ3S7andvWWseXhV8BuNrmPEPNPAT8Z8AdymHDQyXxHauzVhlN2zRo3+jEZMZNmCYpz0C6T2VaT1jqLvukLozWKgpTQdKDSYO6c4vP2zio6f9OGptuHZbjOWm7zrzbc/vKEfResKyiONgqSoAU9XCJgFOk54FRgetvfJW1xC+JZrmkxqFV6+FSD2f+0xvne/Vsuf+D1vXf0uOJSy21+6tglr8fNTQIqBXNmjV764IbG320/pBvm8J7M91EoADW2Ye/tu2ZNOvuPilxSxhjEeNJpGgaCJuAxnPtxMmYr79tyxQsbdt/ZcvLMUx5taimqdzBeqBJjgQKi6YVnv7ax6c7h6GYDpF/Iifsu3Fg4JipAei/oRbCfG6qTscOcYr3/2KgFqAd7EXgYdI+k28z8Lck4v+iNQDLZtPd30dySPz4HOhF0slMwds7Y8x54teH2Q1pfOZyFcqip6axmw5W757P6W2PLW/8qc5VIC82sP3lMmhHr8XYfzn7T7MIHHn79wNh67TPVySrW3FI3ZfvLcnyOdCq7L8VisbLKKdcN63lLcWlYgHQxkJTZzyZsnfLl3dN2nGCmSZH5E03McUhmvsRQsVACswSyBpNrdeZbDVpl7Aarw/GihfZUcyK1pX+LhrLxW9ZsrivfVi3jp4ZVmbe/0E2AhSOZAQmyMp/V8ZKy5CLBhUKz06mXraIX1rEWYDvYC8DTkvtTa0x39bzqXu0qp5fOcZFdZtL7ZDbRYK15vyoXwbv7SzXVrmZa6YnO+3tAzSj1rnVbrvxz1zNNleX/NTFQ4QSPLyL0hZFXfawg0dRMczPQnKuV9MqK6kIlS1fIuApsm/f+bw/lWR7Ok/kBjUZUxZpgZ/n2NyF/onl3grDJ4CZiVoAYY5CU0YqsAVMLYqvEenz4SFPHSI0HbXdlxbVjlUx8XMbfIzvGjHsEN02cUPfb4ZRmoLLi5kIlG78gzxcQtzYHyQ8OQ9cRnTF15aQ47vs4luL1u1SYvKSvyYIOZ6EMYJ75/YGb/wz8uT0KRiyIyl3KjfKO8Y6o2Zs1RdH+L+Whhheymg1X7q46rvpb9fWlm0HLhZ1jxrz6+tLJi6et+nn/F/QGApNvXlUWc3ovotXw/zMMRQJgU7dOra8r3/Z1mU5EdnY8FrsEqB7qhg0Xhml8u94zn9XxcWXhYsM+DpwHNCGt9FH486Getxx3XHVi8s4Jfyu5mwVPtsbdoqF05jwYB4Zg9q9Ai5d7f82WS7vbZ96Fw7lHOcT02cOHx7kkdU/tjrt8oM8a/BDwmH1BLvj8ECdJZfqekjGSu5AhC1PaN2o2VLcazT8A3QpMcOar846TaUa8UNJU+5pNl72cirsvYHY96EWJi+Tcl7LtPBwMqlgTRBY7GjhTUOtHhj+VlW6ZuastkMYm0HyZXZoZw+xI5TARCgD2h42X7po4cedXBV8TetzBORjfqK8v/XCnaCk5Z9P0TQnvdYGg2Iw/jJTtth0DaViqbdX+wr6WY8K9bdL15YeLyEb8HCUbQz9vMS2ZcuNsH/i7MMYb/mP31q74v9zXO2C0+fUVXQt2MT04TnZI2OSCuYZfCu4DYHtID4V3mqkesxacbY6F/pXeJO4ZbhyWQklT7Sqnl85RZJ9O58SgeLDWW5bNW1nQ0qR/lGmlmR5NJXTecJ+fdKaKNcG28p0nxsz/CJjX2XGyPTGPd1qMMUfiaKMtYVOXHZUysEbgxTbv6T+Z2XMWBY8Pl9RzB+MQXFhGCmlXm+kT3vFgEHmHNE/irZKbUTFm6e6TZ5362kBtWOrM9MLF453FvwJMltNN9226rNeWo+HCs6y1U2aeXN/UVLhX0iLk3mzOv7Zh3+1PVZZOnto8rnGR4FKkSxFvA+YBJWT3SRNpF6cyxEmgpRJH4ZhQMXppuKHxjk2DeW+HwmEslDQb9/y25Q1Tlz0chSQxpgGny+lNzS1FjXNKzn/9UP2auqOS6lhQXHIa8ElgK5H/wquNd4yo3qSdZ3fURGXjzn5xv+OkNK9izCM75NyFwi4lnVgpmzAOhgPNkjgTaeac4vOahrODKxwBQgFYX39HNKHhwicKisNtQiXA25BOAQWzx5y34dV9t+8ZqLqOnnJ+kSf+OcSJMlu7btuKEe26vmnv76LZxY8+L+l0jDdLOlnYOZB1L0xfCRzMGQkOroeT1atHHueS1KTaqbcqcl/AuAWjALMvYPzzovJVbxmIOqqpdqlYwTzgHFCDOf/TgSh3aJH5gtHbMPsNEJDOED1g6TYMHNJc4N9isfgnBts62VuOiB6lnWdZa6823rZ9XuEjD5kjQpoHLASmVow5r2HDvtv7Z42p+IcCReHHBUuBh5uD8NpNe+8MB6LtQ0U11e61wvjRwIcQxxz0gkNDoEInTjRoXjDzlEdzNX88VI6YHuUAsrvrLt9SMMa+iuy/kD0NvEuOry4sW/meQ/+imVy4a2I63TitOP1smPp19QHTA5PHlUp2BeKdua4rHcuZT9fVT8xxXX3niOpRMuk8bxGcjukUpIJDmbfMpzxeNMotQfZRYH1rYP/el01Uw5H5lMcLi3UO4ksc2qS9rwgoEDp6dvF5dw7k3LG/DKj38EjjcS5JVdWuubV+ct16C6wF2Vlt85aJi8pX/bA9T2NHqt2Z5aXTYmaz5VVmgRXLU4DCGObPBgUy/m/k5xoxjZr0rUkQ+9Agp1APgGMl+0fg3wax3h45YnuUdtLzlpN3zB5f+Jg8BppN1nmLqbJ08tTZ44oWBt4+iNzfIrsYeA/iAtC5iBkg4fTXeYXnbshdwLvcM5/y+OgSnW3iUwxOb5JJAJo1r+jc24fLMzzihZKmxl7de8fuWZPOeVzeNQLTQGfKcWLFmKXbZ4xZVnf06D9PsUCfEFyG9A6ggo4LbEr/rQCz43FuYsWY88J+GwiGiGNKl41SYP8GnDAE1Qso8M5t3rDv9oeHoP4u5IWSwYbdd7Zkm7cEosSLM4RdxsHXD4QoRJyIOKGi5B3rNzT89pXBaP/AYZoz8YkZeP8VBtAU3Nc2SIpebbj9f4am/o7khdKJWm7zJ+/78Isto5qfNGdliBPBLRS8ib69NE5QKuyoeUXnPjBchhC9Yf788nhib7hY8PdD1wonmUqmF579s+GwCHkEmocPzlouiu7Zvv1pL/sXxDNtmbUO5csaAPN9LFg+wE3MKYltjTGHnTy0rTCZrDiIuzcNbTvS5IXSLdUeWusw+rnwpYQZ76ucct3xA9Ou3FPQWhCYUTHU7QAUWDBlqBsBeaF0SxVrgkCFRwFv7l9J6aj2CoKqAWnYIJCyfQKNHep2CIRjyNsBR/g6Sk/sm7c1ZvvcSWhAJrMxed6+aPo10wagrJxjvnCM8KVDnRDawJm3vFCGM7tbQjdKiRkD864oQPYWovhnBqS4HCP5BMbMoW4HgHDDYtSTF0o3JP1oFZIaPTBbQE3AONAVA1JcrhniniQDL8K6oW4E5IXSLcWJWu9aJm42DYhUDKPWHL8ciMJyjUwB2LHAmUPcFC+5rUPcBiAvlG7ZMRo/tZUB8dcSpAzuuXfL8hFhJq7k3lgw5Zml5oZcKM2R6fkhbgOQt3plodotKls1a2p9aRXSkoEo0SBl+BGzb76GhZFL2J+BoVzoi0AvDZcwT/keZT/VblHZpBnCvz2dONQuMCgj/bL0JzaVAdvkeXBg2jkoWKrF7Q1ido/BBUPUhpTM7hyiuruQF0oXgbgLBGUmthvcJ+wJ0HuAQwstajQgfrZu+4qXB7bdOWbHsS1+6lPfldxicp+CsDMe2DCcomsewULJKpCpXmyWVIP5exzB/VFB61NBa+I1w75IH1OHC5Imbm2N2Y05uomcUcPC6By7+tGQoj8hFg9y9Ulkt9xbe/mwGHbBESmUarekvGy6+fAUL1vUUSDch7hNXo82BanHHt50eTNAZcXNq13LvvFIH6L3PUujmd1r6OsjdBOX7dxWsqtkauomwUn08SPRDzzGi0T240Gqr1ccxpEiO5MhEPxCOZ2LMdtgs8R6E7c57x5tCloey7bXfdm8lQWtTcHH8Pb+Thl+OyDUZNhzSPcG5m+5u/byx3J+azkkIyf9lYNQnQHbQV9cV3vZdwehvl5zBAilfwLJZD6r42PLWxfK3Fne7HhJEw0KlM7mW4exRWKLl93X4lJ3j/zgEgCmt5evnJGw4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kJUZC1ERdZCXLsWuGuYEzv3/IHFr6fgvY++MNoI1DldO8SJ3Xjn3C8YewZAih+NHP/Vlp+MHE6kXPdObfDxGwtMG35yjgSS7KddX9dzOPkuLFEAuGNIf3y+4mnccG2v8578S9K8SQM8Pf0+vDb/YYRVNfTcPXQdA/7+74YaAE2IUqcMleW7bbuwc88fRiKIlIuMqIG3Fk3Fg3cMVroBynm01oHM2HjngLI/SkSVkd1mw4Ipd2PqmOEIDfF8p+ArunfCtb09HrwLABACV6Fnz7+u/BtqAPY31LYBMHQJ89WVa40cTmQKTRO479br8e7iRxFdx9R5FdWlRGqMwzkLSUmmdhtE5H0P3jEYg/opmReFgkLDbxJH1C+o3ercvxgbw5Sc7BaAoasAb636GLv27jdUBpFZunRohU/eWIDe3TqauYwAMDlmX/EH9bsmcToWkZ/o3LYF7hqWoCyvS4cSh/qVm1vX/3rlyfAcRl1KQ7ufFbvcmDjnOb8eVkGVW62a1bF8wWRMuWeYybcExFVut/ub2M4DO5i4CBF5yZ1DBijdf6RvT4fxoVGaaPHX/zRakAwS7wEwdPbO3LoDDz7xgpkT2YgMEULgrmFOpDw/0+PNWspHXiA1fVO0w3mziYsQkcmEEOjVVW0vHxVZy/B8AgH9r6uMBkcBAwe/Sj0U43B+BuBKIznvrPkUJ07l4amp9yK8WlWjZRGZonPbFlj/+nyMnfksPt6YadYyoQJ4LcbhrNhcf/bPRD4jKjKiQk/7l9cFDWKxe+8Bj4+XEn+NPjXcAACAEHhFSmMNAACs3ZCOr7/7GbcMvBqdL26OyFqG99nwmqohIX4/dYvOiqgRjlfnPoQX316DOYuXo9jFkdZE9E9GnvgvPddYUyEg/torWkkDkNXQnhyzzzUbQBOjWcdOnMJTy5IVVOVdndpchDVLH7e6DPISIQRGD+6PLh1a447J87DvwEGrSyIiqhAlDQCSk92IT3wGUi5UkkdUSbRr2RQfvTYX42ctxtoN6VaXY9jIm/rh6svirS6j0hh073Q+wEyVlpoGAMAZFCyrgpDHAISoyiSqDGqEh+Hlxx/Asnc/xMxnX6/Ue4jXCA8z+SFH/6J+E18i7zH8FgAAxHRLbBQiQ1LBkz8FKCEERtx4Lda8/Bga14+xuhwiojIZbgBi4523wSV/kEBvFQURVWYXN2+Cj16bi36Xd7O6FCKiUhlpAESsI3G6lFgKoLqqgogqu+phVfHCnIlYNG2MaU8CExEZ5dEzAK1bJwXnVnO9KiFvUl0Qkb9IuqYnWjZthNGT5+G3P7KtLoeI6B8qfgUgKcl2tKorGQBP/kRlaNP8Aqx7fR4SrrzE6lKIiP6hwg1A9L7ihUKgvxnFEPmjsKqheG7mON4SICKfUqEGIDreOVFA3GtWMUT+LOmannh/6eOcGElEPqHcDUBs54EdNIk5ZhZD5O9aNmuE9a/Px5CEPlaXQkQBrlwNQLNmfatA6K9JIKjsTxNRaUKqBGPupDuxaNoYVA3l6Awiska5GoD82qFTpMDFZhdDFEiSrumJD5Y9geZNGlhdChEFoDJfA6wTlxQN6RrvjWKIAk3zJg3w4StPYtXHX8LlNrar4I879mB56npFlRGRvyuzAbCj+EFAVPVGMUSBKDSkCgb1u9xwzgf/28wGgIjKrdQGILJTYgyEHK1ywerh1dAoNgo1a4SpjLXchY3rW10CERFRuZXaAATb5M0SCFWxUFy7Frjn5kT06toBdptNRSQRVQIn807j0ade8fj4+tF1MO72GxRWRERAGQ2ABAYZXUDTBKaOGY7bb7wOmsbNM4kCTVFxETZ986PHx1/EhySJTFFiAxDTxdkSOtobCRdCYMGUe3DDtb2MxBAREZFiJb4GKHTj435vu+EanvyJiIh8UIkNgBRwGAmOjYrElHtuNhJBREREJil5EJBEnJHg0YP7o0owBwcSERH5ovM2AHXj+0cB8PjJG00TuL7vZR4XRUREROY6bwMghGhsJPTi5k0QUSPcSAQRERGZ6LwNgKbbaxoJbdmssZHDiYiIyGQlXAHQI4yE1qllqH8gIiIik523AZBS1DASKjjvh4iIyKed/y0AoUsjocdOnDJyOBEREZmspCsAhUZCsw8dNXI4ERERmez8DwFq4riR0C0//QIpDV1EICIiIhOdtwHQ3cg2EnrsxCns+PV3IxFERERkovM2AG7Y/jAavOL9z4xGEBERkUnOuxvg4czknBiH8yiA2p4GJ3+4AfePvgnVQkM8Lo6IKr+a4eF4Zd4kj48PDamisBoiOqfE7YAh8SMEenoafOzEKcx/6R1MHTPc0wgi8gN2uw0tmzWyugwi+peSNwPSxJdGw5eu+ADf/LDDaAwREREpVmIDIHTxudHwYpcbw+9/HHt+zzIaRURERAqV2ABk6bU/B3DM6AK5x0/imlsfwAf/22w0ioiIiBQp+RmALS8Ww+FcA+Bmo4uczDuNkQ/NRY/OF2NIQh+0vvAC1KltaNqwz7HZbAirGmp1GUREROVScgMAANBfBjTDDcA5X37zI7785kdVcT6lU5uLsGbp41aXQUREVC4lPwQIIDtj1UYh4Z9nbCIiogBWagNwlphtfhlERETkTWU2AFmZbZMl8IM3iiEiIiLvKOMZAACYrguZeC+E3ABAmF0QERGpEd+uJe6+2enx8W2aN1FYzX8NvPpSxLdv6fHxNcKrKazmn4LtdkP/7WrVrK6wGnOUowEA9DN5W7SQar8BMPdPAxERKdMjri16xLW1uowS3TLwaqtLKFFwcBAm3z3M6jJMVeYtgAbd+8dqIVU3gCd/IiIiv1HqFYC6nRKauorFBgD1vVMOEREReUOJDUA9h7O2DnwAnvyJiIj8znlvATRr1reKG1gDoLmX6yEiIiIvOG8DkFcr5FEBdPV2MUREROQd/2kA6jmcXQUw0YpiiIiIyDv+2QAkJdl0YCkAmzXlEBERkTf8owGI3ls8DIDnUxmIiIioUvj/BqDTqCAhxCMW1kJERERe8tdrgDHaoRsBoXTYj03TUDcyAsFB5Ro4WKlF1alldQlERETl9rczsxihKvSy+Pa4Y0h/dOvYBkEBcPInIiKqbOwAUL9r/2ZuNy4zGlYlOAizJtyOIQl9jFdGRGQCl8uNXXv34/cDB7E/5zAOHT2GvPwCFJ4pgq7rqBoagrBqoageVg0NY+uifkxdNGkYa+rGM0RWsAOA26UNgjC2059N0/DinInoc0mcmsqIiBT5/UAOPtv8PTK+/xk/79qLwjNFFTpeCIELGkSjY5vmiO/QCl07tobdxpelqHI7e31eiCuMBk26eyhP/kTkMwoKz2DthnSkfPQFdu/dbyhLSok9v2djz+/ZWPnhBkTWqoHreneFLqWiaom8zx7V9spqMDj1r/VFF2DUTf0UlURE5LnTBYV4a9UneGfNZ8jLP23KGkdyT+DVlR+Zkk3kLXZbaLVuUiLYSMikO4fwchgRWUpKiVUff4kX3lyNYydOWV0Okc+zS4k2RgIaxkahZ5cOquohIqqwrINHMGfxcnzzw06rSyGqNOyQogWE5/ex+l3RDZpm6PlBIiKPfbXlJ0xdsMy0y/1E/soOIVsYCXC04+RgIrLGm2kfY/HrKdB1PoxHVFF2ANFGAtq3aqaoFCKi8nvhrdV45d0PrS6DqNLSBBDu8cGaQK2a1VXWQ0RUppffeZ8nfyKDNGmgAageVg02TSv7g0REiqzdkI6lKz6wugyiSk8DEOrpwRWdpkVEZMQve/7AnMXLITmAh8gwDYDHj84WnilCQeEZheUQEZ1fcbELMxa9iuJil9WlEPkFDcBJIwG//ZGtqBQiopItS/4Qu/cdsLoMIr+hAcgzEpCxdYeiUoiIzu9w7nG8veoTq8sg8iuaBAztkvF5+veqaiEiOq9lKz7gM0dEitkBsR2Ql3sa8OmmLcg5nIvoOrVU1kVEBAA4lZ+PtRvSTcmOjKiBuHYt0Oni5qhbOwIRNcLhcrlw7MQp/JF9CBnfb8e323bxWSfyS3ZIbIeBSb4utxsvv/M+ptx7s7qqiIj+9P6nXyv/6b9Pj864eeDV6NWlQ5mjzPMLCpG2biOWvfshtv+6T2kdRFbSNImtRkNeWvE+dhncb5uI6Hw2bP5OWVaThrF499npeG3+w7i8W8dy7WNSLTQEQxL6YP0b8zFrwu0Ir1ZVWT1EVtKyZGQGAEN7ZxYXu3D31IXIO12gqCwiIuDEqXz8tHOPkqxLHG3x4StPokdcW4+Ot2kabrvhGqx6aQ5ioyKV1ERkJQ1bXiwG8JnRoJ92/oZRk+Yiv6BQQVlERMBPO/fAreuGc+LatcAbC6agepjxn95bNG2IlOdnomb1MMNZRFbSAEBKuU5F2Iavv0ff4ffj222/qIgjogD3q4L3/quHVcXzsyYgKMiuoKKzGsZGYf7ku5XlEVnBDgB2e9AKt9s1HwbGAp+ze+8BXHfbQ4hr1wKXOtqhcb1oBAcHGS7U10XUCEePzhdbXQaRX9l34KDhjMEDrkBM3doKqvmnvj3j0fqiC7Dtl9+UZxN5gx0A9m9Ozo11ON+VwC2qgjO37kBmAA0J6tTmIvRY+rjVZRD5lZOn8g1nDLz6MgWVlJzNBoAqq7+28nNr+hIrCyEi+jejzxQJIdCkYayiav6rWeN6pmUTme2vBuDg16vSBcA9NonIZxS73IaOD6kSjNCQKoqq+a+IGh7vpk5kOe0f/+bWJgLgVltERER+7h8NQNaW93YIiJesKoaIiIi8Q/v3L7gL8+4HsN2CWoiIiMhL/tMAHPxhfb6u4wYAHOtHRETkp/7TAADAwW9Sf4LESADGR3ARERGRzzlvAwAA2Zmpb0opR4BNABERkd8psQEAgJzMtFch5J0CKPZWQURERGS+UhsAAMhOT3sRAr2lRJY3CiIiIiLzldkAAEBWeuqXNk3EA+BWf0RERH6gXA0AALilvAlAiIm1EBERkZeUqwGoG98/SgBTzS6GiIiIvKNcDYBN1x4CEGZyLUREROQl9rI+UM/hrK0Do1Ut2Lh+NFo0bYTIiBqqIn1Cw9i6VpdARERUbmU2AG4phgshQ40u1LZFU0y+exh6xF0MIYTROCIiIjKgzAZACDnC6CJDE67ErAkjEBwcZDSKiIiIFCi1AYiOH9AKEi2NLNC/T3c88dBo/tRPRETkQ0p9CFCT4joj4c0a18PTU8fw5E9ERORjSm0AJHCZkfA594/iZX8iIiIfVFoDIADRzdNgR7uW6NH5Yk8PJyIiIhOV2ADUi0+sB6Cmp8FDnVd6eigRERGZrMQGQEJv5XGoJnDlJXGeHk5EREQmK+UWgKjvaWiLpo1QPayqp4cTERGRyUq+AiBFtKehLZs18vRQIiIi8oJSbgHA41m9tWtW9/RQIiIi8oISGwAhZRVPQ8Or8fI/ERGRLyv5CoCQ5dop8HxO5uV7eigRERF5QSknea3A09Cjx096eigRERF5QckNgJAe/xi/fdc+Tw8lIiIiLyhxMyABmQN4NsN/x57fcfxkHmpWD/O4MKJAsP3XfUgc/QhOnMqzuhQiCjAlXwGQNo9/jJdSYv3GTE8PJwoIe/fnYPCYGTz5E5ElSrwCoEvs0gxs4rc8dT1uuLaX5wFEfuzgkWO4acyjOHjkmNWlkAF2mw01wj2/0lk11OOXrYgMK7EBOJj53m8xDudRALU9Cd7y4058+tW3uLxbR4+LI/JHJ/NOY+i4Wdh34KDVpZBB7Vo2xfZPXre6DCKPlPaqnwRg6Dr+1AVLUXimyEgEkV8pKDyDoeNmYdsvv1ldChEFuLLe9f/YSPhvf2RjzPRF0HVpJIbILxS73Bg1aS6++WGH1aUQEZXeAAjd/YHRBd7/bDPGz3qWVwIooOm6xH2PPo1Pv/rW6lKIiACU8gwAAGR9s3pnTJzzWwgYupH/7gf/w48792DSXUPRu2tHaJrA1u2/4uQpTgz0d106tkaQ3WZ1GZZ7ZP7LSFu/0eoyypS5dTsKCgutLuMvh4/yIUkis5TaAACAEOJVCWn4Sb7tu/fh5vGzERsViRZNG+LHHXtwOPe40Vjycds/ed3QU9L+YP5LK/DKyrVWl1Eun6d/j8/Tv7e6DCLygjIbgOAiffmZYDELgJIt/rIOHkHWwSMqooh83rJ3P8T8l1dYXQYR0X+UueHP3u/TjktgiTeKIfInqes2YurCpVaXQUR0XuXb8a/YPh8Ar9cTmvrSngAAIABJREFUldOnX32LsTOe5hswROSzytUA5HyXfBjADJNrIfILW376BaMfnodil9vqUoiISlS+KwAAst11noUE32EiKsX23fswZOxMnC7wnSfpiYjOp9wNALa8WCw1fRgAfmcjOo+9+3Mw+L4ZfL2ViCqF8jcAAHLSV/0MIUaZVQxRZXXk2AkMHTeLm/sQUaVR5muA/5adnrI8Nj6hnZRighkFEVU2J07l4cZ7pmPP71nqw4WYJaX8obwf14DrJXCD+kKIyN9UuAEAgKz0tPtjHM4wAKMV10NUqRSeKcLwiY9j++59yrMFxKNZ6SnTK3JMdLyzpeCLB0RUDhW6BfA3Mjuj3V0CgvMBKGAVu9wY+dCTSP/+Z+XZAmJJVkbFTv5ERBXhaQMAYLqelZFylwQeAMD3nSig6LrE2BnmbO4jgHezMtreozyYiOhvDDQAZ+VkpM6FFL0B7FdQD1Gl8MiCpUhdZ8LmPkJ+EpFvHwZM19WHExH9P8MNAABkZ6Z8oRfmtxAQjwLgvr/k1+a/tAKvJH9oRnSmKzTIuW1bMv8OEZHplDQAAHDwh/X5WRkp04XubgvI5QBcqrKJfMUrySZt7iOwQ7hd1xzekJynPpyI6L88egugNFnfrN4J4Ob6XZMm6W7XUAkMAtAOgFC9FpWsdkR1NL+gIWpHKNnE0WNBduV/xCyTum4jHllgyuY++4XLflXWllRuk0lEXmPad+f9m5MPAHgCwBN14/tH2aRtJYAenmQNqAF0qqa0PJ+SXQwsOawmq0nDWDww+iZc06sL7DabmlAyc3Ofo1LoV2VvSf5dTZy+UkLbribLegLyAUB0troOIn/klR/PDqWvPhjjcHp8iutcFUiqqbIi3/LMITU51/XuigWP3IOwqqFqAgkAkLl1B0ZNmmvC5j4iXxe47mD6KmXvEeaczVL/XqJFYhzOIQDYABCZwH+uz1ZSbgArTxjPubxbRzw3azx/6lds++59uHnCHBQUnlEdXQSIgQfT3/tadTAR+a8zZ4qVZSl7CJA8s/U0cNDg17NedCSWzJ7Ak79i+w4cxOD7ZuDEKeXP5ekCcnh2xnvrVAcTkf/a/O02fPLlN8ry2ABYLOO08YwZ40bwsr9iR46dwJCxM83Z3EeICVkZaW+rDyYif3X8ZB7GPLoIbl3diBA2ABbLMLhzbMtmjdC3Z7yaYggAcDLvNAbfN8OUzX2ExIzs9JSnlAcTkd+SUmLsjGdwIEfti0JsACy2rdDY8TcnXqWmEAIAFBSewbDxs/DTzt/MiH8+KzN1mhnBROS/XnhrDdZvzFSeywbAQsdcwAmDD5b37dlFTTGEYpcbox+eh8ytO0xIF2nZjeyc709EFbJ1+694bMkbpmSzAbDQPoMP/13QIAZ1a/vx+5FepOsS42Y+g082bVEfLuQn1XILBiE5mZtmeVlRUbEpuzUSecPJvNO4Y/I8FBerG6yrS/Hjuf/NBsBC2QYnvre+sLGSOgiYunApUj76wozozGK7nrh791rl7xFS2dy6jpvGzDDl8imR2R54bAn2HTioLE9ApOfokY+f+3c2ABbKM/gwZ+2IGmoKCXDzX1qBZe+q39xHQux2C3e/I5tWn1IeTuVWeKYIIx54Am+t+sTqUojKbXnqeqz+ZJPKyDxNiGHY8uJf157ZAFjotMGpsuFhVdUUEsBeWbnWnM19gP3CjisOpa9W176Tx9y6jvsfW4Ilb66yuhSiMm3/dR+mLVymNlTKO/env7fr77/EBsBCLoMNQF6+giECASxt/UY8Mv9lM6JzpdCvyv4qZZ8Z4eQZKSVmPv0aZj3zOqRUvqcDkRIFhWdw5+QFKDyjcFdwgTeyM9P+8yQhGwAL1TY4iDn3OK8se+qzr77FfY+asbmPyNeFuDZH4Xx/Uuu5N9IwftZiuNx8JpN8z+R5L+OX3/5QlieAX8+g8O7z/R4bAAtFGpzcu+NXRRvIBZhvt/2CUQ/PU765jwCKpa4nHUxP4Xx/H7fi/c8w4sEn1f6URWTQ6k824Z01n6qMdAlgWG762pPn+002ABaqF2Ts+F179+PYCV4FqIjtv+7DkPtm4XSBwQlM/6UDYnjON2lrVQcHMgmYMZQBAPDxxkwMvm8GTubxVhpZb+/+bEyc/ZzaUCGnHshI3VzSb7MBsFDDKkCI8Px4KSVfb6qAfQcOYvAYUzb3gYAYm5WR8pby4ACXk5E6CcACs/K//u5nJN4xBYeOHjdrCaIyFRUVY/TD85F3ukBZpgD+l53e/onSPsMGwEI2AE2rGMt4I+1jJbX4u0NHj+OmMY+atbnPrKyMlGfUBxMAmZ2ROkFITAJgypN7P+/ai4RRD2Pv/hwz4onKNPPZ1/Hjzj0qI48KIW4Gppf6sjkbAIt1MPgm35Yfd+LLzB/UFOOnTp7Kx+D7Zpj1Df6F7PSUR8wIpv+XlZn6uIAcCcCUJ/f27s9BwqiH8fOuvWbEE5Vo/cZM5XNIBOTtB9JT9pf1OTYAFutSzXjGw/NeQlGRwbnCfqrwTBFunjDHrG/sK7Mb2c/7dC2pl5WRtlRAXg9A+QMcwNmrRIl3TMHX3/EFDvKOrINHMHbGM6pfS30+KyMtrTwfZANgMUc1IMjAcwAAsHvvAUyY8xzfbf6XYpcboybNRcbW7cqzJfBptdzCoZzv711ZGWlpUpN9BXDCjPxzW0Hz2Roym8vtxl2PLMTxk0qfSfrZZrOPL++H2QBYrLoGdFNwFeC9tZ9j0pMvKt00ojLTdYnxs541Z3MfyG9cQW4n5/tbI+frtA3QtV4ATJmyWHimCLc/+CRWvP+ZGfFEAM6OIFf8w0mhTWg37d+cXO4nCQ2+iV5+4fVa3gigpSfH9g4HWocqLsiH6AA+UfA239btv+KL9K24oEEMGsTUNR5YiU1duAxvmvOA5E5ZHHTFoYxUE54mpPI6lbU9p2bDi9KktPUDEKE6X//zDZvQkCqIa9tCdTwFuI0ZP+CBx5dA5UVbITHhQEbK6godo2750sU4nCkAnJ4cOzMGSFL+V9x3FOjAZb8AJw1uDvR3Fzauj1YXNka9qEgIzWtfZp+QdfAIUtdtNCP6AOyiO0f8+o7ITokxdpv8SABtzVrjziEDMOXemyFEYP09InMczj2OPkPHK331VAAfZGWk9kMF35QxOIyWVAjVgBsigJePqsvctXc/du0t8yFQKicBnBDAtQd48vcpR7akZDdun3DZmWCxGsAlZqyx5M1VOJx7HPOn3IMgu9cumpIf0nWJMdOfVj134lCxtN8OD16T5TMAPmJILeMPA5JZRL4A+h7ISN1qdSX0X3u/Tztus9mvEsD7Zq2xcu3nGPkQRweTMc8tT8Xn6d+rjNSFkMMOZyZ79I4zGwAfERME3OTHtzkqq3Pz/Usbp0nW2785uSCrkT0BEEvNWmP9xkwk3TWV47fJI99t24W5L76tNFNILMhKT1vv6fFsAHzIHZFAOK8w+hJdStzK+f6VRHKyOzsjZaQQcr5ZS2z56Rck3T3NnImS5LdOnMrDaPUbkG2JOG2fbCSADYAPqWUHJgb2w/u+ZmJ2ZuqbVhdBFSKz0tMmQsixMHF0cP/bJ+G3P7LNiCc/NH7WYuzPOawwUeQLtzZ027ZkQ/ek2AD4mBsigHiD44FJASFmZWekLrS6DPJMdnraIiFwO0waHfxH9iE4R0/Gtl9+MyOe/MgrK9di7YZ0pZlCyDFZW94zvFMmGwAfIwDMrQ/U5fsZVuJ8fz+QlZ66TEjdCUDdFmt/c+jocSSMmoyNGdyLg85v+6/7MPPp19SGSvFeVnrqMhVRbAB8UF07MK8eYOdbAVbgfH8/kpW5ao2UMG10cH5BIW6eMBvrvsgwI54qsfyCQox8aK7qN0f2VSnWb1cVxgbARzmqAXNigACb4WMpAfyP8/39T05m6uduHT2kRJYZ+WeKinH7g0+aNXmSKqlJT7yAPb8r/SOnS00O3/t9mrIhAmwAfFj/msC0aDYBXrKlKMg9gPP9/dPBb1J/stvdlwFQuun6OW5dxwOPP4/nlqeaEU+VzLsf/A8r136uNFNAzMz5Om2Dykw2AD7uxgjgqXpACJsA00iI3W7hvvbIptV8wduP7d+8erdL2rsDUDqJ5RwpJWY9uxyPLFgKXefOnIHqtz+yMWXey2pDBTZlNbLNVBvKBqBSuLI68GpjIDbI6kr80gFhxxWH0lebsrMc+ZbDmck5VYpkLwCmbBYBAEtXfIDxs56Fy807SYHmTFExRk6ai7zTSp87PQ6bGGLGrUk2AJVE+1BgVROgXw2rK/EruVJqV3Fzn8BybnSwhFhj1hrvfvA/jHiQo4MDzaOLXsHPu/YqzZRSjjbrexQbgEok3AbMrQcsbwy0DrG6mkrvtAZcl5P53jarCyHv2785uSCnam6iABS/o/X/Pt6YiUH3PoqTp/LNWoJ8yAf/24xXV36kNFNKvJKTmfau0tC/8fvtgHeeAX447dmxANC2KtC8iufHm0UC+CofeCMX+OKUSdNO/JQAinVdDuCIXwIgYuOcT0qBiWYt0LJZI7y1aCqiIrnZh7/6I/sQ+gyboLrZ+8VV1d7p8IbkPJWhf+f342a+zAPmGri7e3+UbzYAAkD3amf/OeYCPs8HNuWdbXh+OwMU8xmkknC+P/2dzMpMvT/WkXBYQjwOE34o2r57HxJGPYy3n56KxvVjVMeTxYpdbtw1ZYHqk38RhBxs5skfCIAGIBBE2IGEGmf/Ac6e/E+4gTwdyNetre3fcl3AyN+Npog0XYoJnhxp12TRgYyU/UYrIP+SlZH2ZGy887CUeBEmfF/cd+AgBoycjDefegRtml+gOp4s9MTzb2HLT78ozZQCk3PS07YoDT0PNgB+KEgAkXYg0upCzuOQS0WKfupgZpop73NT4MpKT30lNm7AESm0FQBCVecfzj0O5+jJWPrkg7jU0U51PFlgw9ff4/k309SGSnyck9FuAWD+TAk+BEhE9KeszFVrIMXV5o4OnoP3P9tsRjx50eHc4xg742nVMx8OF+viFmC6V67dsgEgIvqb7MyUL9w6egA4YEZ+UVEx7pw8n6ODKzFdl7hn2lM4dFTZVF4AkEITtx/ZkuK1fabZABAR/cvBb1J/0uG6BMAuM/LPjQ6e/9IKM+LJZIteWal+F0iBZ7K+TlmtNrR0bACIiM7jYMaa31zSfilMHB08/+UVHB1cyaR//zMWLlX+av42m2Z/SHVoWdgAEBGVwFujg++b8TSKXZzm4etOnMrDvdMWqR7zXGgT2uD9m5OVzg8uDzYARESl2Pt92vFquYV9IMV7Zq3x3trPcfuDT6CgkJtR+iopJcbOeBb7cw6rjh63P/09xfcTyocNABFRGXbvXnsmu7HtRgCKt3n7fx9/+Q2S7pqGYye4KaUveumd97HuiwylmQL4IDsj9QWloRXABoCIqDySk93ZGamjJPCEWUt8u+0XOEdPQfaho2YtQR74YcevmL14uerYAwK4BWcnu1uCDQARUfnJnIzUhyDkWJj0jfuX3/5AwqjJ2PN7lhnxVEH5BYW465GFKC5WMsXsHF2T8pYDGamWdnpsAIiIKig7PW0RhLhFAMVm5P+RfQjO0VPw404OvLTag48/b0Yz9viBzLRPVYdWFBsAIiIPZKenLIcmrgdgytPbh3OPY+qCZWZEUzm9teoTpHz0hdpQgYxsd53pakM9wwaAiMhDWV+nrDZzdLCUnA9gpQ83fK06Mk+TGIYtL5py5aii/H4zoA6hwB0GdsXpoHxLECLyJ9mZKV9Exw3sLoS+DkA9q+shHyblnQcy09RuHWiA3zcAHaue/YeIyCw5me9ti3L0u0SDfR2AC62uh3yPAN7Nykx7w+o6/o63AIiIFPjb6OCtVtdCPmdPoSgcaXUR/8YGgIhIkcOZyTmuqvYeEPITq2shn/JZbvrak1YX8W9sAIiIFDq8ITmv2tEz15k5OphIBTYARESK7d699kx2tdxBAA5ZXQtRSdgAEBGZYcMGF4Aiq8sgKgkbACIiogDEBoCIiCgAsQEgIiIKQGwAiIiIAhAbACIiogDEBoCIiCgAsQEgIiIKQGwAiIiIApDf7wZIRERkJQE0iXY4k8r7ealj+8FvUn8ysyaADQARkc/auz8bDzz2vNVlBKwdv/6uJEcCvQXQu7yfFxpmA5iiZPFSsAEgIvJQjCPhA0BElfKRukbyDx09jjfS1huJICoRGwAiIo+JiwE0sLoKIk/wIUAiIqIAxAaAiIgoALEBICIiCkCV4hmAR7LP/kNERERq8AoAERFRAGIDQEREFIDYABAREQUgNgBEREQBiA0AERFRAGIDQEREFIDYABAREQUgNgBEREQBiA0AERFRAGIDQEREFIAqxShgIiJ/1LaeDUuGhlhdBiky4d1CfP2b2+oyyo0NABGRRaoEAY1q8UKsvwgJFlaXUCH8k0dERBSA2AAQEREFIN4CIF9UBCC/5N8Wp71WCRGRn/JaAyAFvtIkXOX+PNAbQG1P1low5W4M6ne5J4eSQW5dR4Ou1xuNSc7OSB2qoh4iIjo/rzUAOemp8yry+RhH4iZAdjOrHiIiokDGZwCIiIgCEBsAIiKiAMQGgIiIKACxASAiIgpAbACIiIgCEBsAIiKiAMQGgIiIKACxASAiIgpAbACIiIgCEBsA8jkCCLK6BiIif8cGgHyOBAbGOBJHW10HEZE/88kGoH7XpFBAxlpdB1nGBsglMY7Eh60uhIjIX/lkA+ByuRYDaGx1HWQpAcjZ0Y7EZ4DpPvnnlIioMvO5b6wx8QmjhMCtVtdBvkFA3hPj+H45Oo3icwFERAp5bTvg8qjncLbTJZ6yug7yNWJwjP1QXVfPJOfhDcl5VldDRL7ri10u5OZ7fvxVrW0IDRLqCvJhPtMA1O+aVMvtdqcCMtRoVlRkLRUlkS+R4gr7afe6+l2T+u3fnJxrdTlE5JvmrivCd3+4PT7+60lhqF9TYUE+zEduAUzXdLfrdUBeYDTpxut6o1fXDiqKIg9oQqBrx9Ympctubrfr8/pdk+qZtAARUcDwiQYgOu6HyRK41mhOk4axmDlhhIqSyENCCLy9aCqu693VrCXauN2uTbGd+zc3awEiokBgeQMQ5XD2FkJOM5pTJTgIL86ZiLCqhu8gkEHBwUFYMnsChiT0MWuJRlKzfRUVn9jFrAWIiPydpQ1AbNyABhrwDgCb0azHHhiFVhc2Nl4UKWHTNDz50B2YcPuNZi1RS5Py49j4hCvNWoCIyJ9Z1wB0GhUkNe1tAHWMRvXv0x2D+l2uoChSSQiBCSNvxMzxI6BppjxVGyalWBMdl3CDGeFERP7MsgYgVju8EBLdjeZc0CAGcyfdqaIkMsmIG6/FoqljEGQ3fKHnfIKFEG9xdDARUcVY0gDExjsHSYG7jeZUCQ7CC7MnIrxaVRVlkYkG9r0MLz/xIEJDqpgRbwPk89EO5+NmhBMR+SOvNwCxnfs3lxIvqsiaMX4E2jQ3/OYgeUmfHp2R/NyjiKgRbkq+AB7k6GAiovLx6jfKOj2TwqRmSwFg+AzQ7/JuGObk81+VTcfWFyHl+VmIrmPOsCaODiYiKh+vNgBBp13LALQymtO4fjTmPnyXgorICs2bNMCql+agSUOzNnwUg6Nth9dGdu9vzqUGIiI/4LUGIMbhHC+BJKM5wcFBeGHORFQP433/yqxBTF2kvjDLtFs4Arg8yGX7JLZTv0hTFiAiquS80gD8ObDlMRVZ0+4bjoubN1ERRRarU6smUl+YjUsd7cxZQMIhbfYvYuMGNDBnASKiysv0BiCqm7OuJuVKAMFGs67t1RW3Xt9XQVXkK6qFhuD1+Q+bOTq4pRTaRo4OJiL6J3N3A0xKstn2ud6RgOHNWxrVi8L8ybzv74/OjQ4Of+x5vL36EzOWaCQ128YYx8BrsjPe+8aMBahyiolLnAMhJ1m1fuZeN+o/eMqq5UsUEgSEBQs0rKWhRayGdvU19G4ehJgagbFNbqAwtQGI+d01SwK9jOYEBdmxZNYEVA+vpqIs8kE2TcO8h+9ErZrhWPx6qhlL1AH0z+rFJTgPZKZ9asYCVLnEOJzjAOtO/r6ssBgoLJY4ku/Gt3+48VY6AJxB23o2DOliR2KHIIQG+WYzkNDBjs6NPR86Fm7KqBLfZNpXMDbeOUBKpKpY49Fxt2HkoOsUVEWVwQtvrcaMp1+DlNKM+DNSiKE56SkrzQinyiEmPnEYpHwNJn4P9Gc1QwXu7hWMEd2DEGznf8Jzhi4rwIadLhVRs7MzUqeoCCqNKc8A1O2U0FRKvAoFf7muutSB2280vFMwVSKjB/fHU1Pvhd1myujgKkLKdzg6OHBFOxKvFVIuBU/+HjteIDH7wzPoNf80Nv3qtroc8pDy77D1uyaFAnIdgMaGs6Lr4K1FjyCkSgBdkyEAQOsLG6NN8yb46PN0uNzKv8FoAK4Lj23hzsva8YXqcPJdsZ0Tu0PI1QBCrK7FH5wokEj5thini4CuTeywBfgMzpTvXNh7VFcRtTHvwI7PVASVRvmXy+VyLQbQ3miO3WbDczPHoUZ4mIKqqDLq06MzVi6ZYdroYAgxMzbO+SxHBweGqM7ONlKTqwFwiIhCugSWfF6EocsKcLLQlNt2ZBKl3/hi4pwjhcCtKrIm3T0Undu2UBFFlVjH1hdh5ZIZiIqMMCVfCtzN0cH+L7pLQmMhsA6AOTOoCZt2u3D986eRm88moLJQ1gDUczjbQWCRiqzLu3XEHYP7q4giP9CyaSOsfvkxjg4mj0R3SKojdPGREDDrDxD96edsHYNeOo0TBWwCKgMlDUD9rkm1dIhUAKFGs2KjIvH09PsgBJ/Pof/njdHB9mLbuvpdk/gToh+pFd+3urC7PgLAQVBe8nO2jpHLC+BSciuczKSiARBut2sZIA1/Zz5339+0e75UqZk9OlgAXd1u15ccHewfWrdOCq6ih6yEQEerawk0X/3qxmNrC60ug8pguAGIiUuYDGCAglrwwOib4GjXUkUU+alqoSF4bf7D6Nsz3qwlODrYHyQl2XLDXG9DoI/VpQSqFzcW45PtSt6JJ5MYagCiHM7eEGK6ikJ6de2Au4Y5VUSRn6sSHIQXH7sfQxOuNGuJRlKzfVXP4TRtgwIylYj53bUEEolWFxLIpAQeTCnk8wA+zOMGIDZuQAMNeAcKZglERdbC09Pug6bxvj+Vj03T8MRDo3HPLaZ9j6+lA+vqxSVcbtYCZI4Yh3MWJEZaXQcBB09KPLGuyOoyqASeNQCdRgVJTXsbQB3DBWgCz0wfg9oR1Y1GUYARQuDhu4Zi1oTbzWoew3UhPoyOS7jBjHBSLzreeTeAh62ug/7fm18XYdchPhHoizxqAKJtRxZAoruKAiaOHIQecW1VRFGAuu2Ga/DU1DEIspsyOjhYCPFWjMN5hxnhpE5MXMJQIfGM1XXQP7klMG/9GavLoPOocAMQG+8cJCDvUbF4905tcO/wgSqiKMBd3/cyLH3yIYSGmDI22gZgSbTD+bgZ4WRcbLyzD4TgfH8ftfYnF/Yc4VUAX1OhBiC2c//mUuJFFQvXqVUTz84YB5vGKaykxhXdOyH5uUdNe41UAA9ydLDvieoyIF5KkQog2Opa6Px0Cbz2VbHVZdC/lPsbWWT3/uHSZksDYPi7q6YJLJ45zrTxrhS4/n90sDnzfDg62LdEdXa20XRtLSCrWV0LlS55SzGKXHwjwJfYy/vB4GLbUgkoGc4/9tYk9Oh8sYooov9o2bQRVr00GzeNmYHf/sg2YQUxOMZ2uJbe9srrD/6wPt+EBagcorskNBY6PgKg/CeJqqEhmPfwnahds4bqaJ/mcrvxR/Yh/LDjV6St/xKnC9QN8zlZKLHhFzeubFXu0w6ZrFz3y2LiE8dCyoUqFuzWsQ1WLJ7OS/9kuiPHTmDIfTPx4849puQL4GvNZr92/+bkXFMWoBJFdXPW1VzYCOAi1dlBdhtenfcwenXtoDq6UjlxKg+PLnoN76z5VFnmgPZBWHyT/+7EPHRZATbsVDL8aHZ2RuoUFUGlKfMsHNs5sbuQ8kkVi0VG1MDimbzvT94RGVEDK5fMQPdObUzJl0AXt9u1sV58Yn1TFqDziuzeP1xz4UOYcPLXNIGnpo4J+JM/ANQID8OCKXdj9v3qRips3OWCzmcBfUapZ+K68f2jdCHflYDh+52aJvDMo/fxvj95VXi1qnjzqUfMHB3cSpf6Jo4O9o5mzfpWCS62pQHoZEb+jHEj4LzqEjOiK61br++LcSOSlGTl5ktsz3ErySLjSm4AkpJsdml7W9UWmvfcnIjL4turiCKqkOA/RwcPHnCFSSuIhlKzfRkdlxhn0gIEAElJtvyI0Dcl0NuM+LG3JeG2G64xI7rSm3D7ILRv1UxJVsZvvATgK0psAGJ+d82SQC8Vi8S3b4WJowapiCLyiE3TMHfSnbj3FtPmTkQKIT+LjRtoVpcR6ETMXvdzENKUL+Aw55V4YPRNZkT7BU0TmD1Rza2A7Qd5BcBXnLcBiI13DoDEgyoWqFWzOp6bOQ52mylT2ojKTQiBSXcNwaPjboMQpsyLCZNCfz/K4TTlJ9RAFuNwzoSQo8zIvq53V8x5wJRov9Kh9YWIb9/KcM7ObF4B8BX/aQDqdkpoKiVehYKJWkIILJo2BjF1axuNIlJm5KDrsGiaaaODq2gA73UpFBOfcB+AyWZk94hri2cfHcsHk8tpSILx3ZV/O8oGwFf84099457DQ2yaeBdATRXhdw5NwOXdOqqIIlLq+r6X4Y2Fj6BaqP++kuQPYh2JgyHFAjOy27VsimVPPojgYM50Kq/L4tsZzjh2WqKYdwF8wj8agKKCk0sgoOSM3bltCzx4x2AVUUSmuMTRFm8tmooa4WFWl0LnEd05oS8gX4WBbctL0rRRPbyx8BGEVQ1VHe3X6tSqicb1YwxlSAkcyeNEQF/w11+smPiEUVJDkGPoAAAgAElEQVTK4SpCa4SHYfGMsWZdYiVSJq5dC6x6aTZvU/mYqC4D4oWmJat4BfnfouvUwluLHuEW5B5qXD/KcIaUbAB8gQYA9RzOdpDiKRWBQggsfORuNIipqyKOyHQXXdAAq16agyYNlbzxSgZFxw1srenah2bM968eVhVvLJzC708G1KppvHEKC+Gmjb5Aq981qZYOkQpAybWwUTf1w9WXmTZ0hcgU9aPrIO3F2bi4eROrSwloMd0SGwmhrwOgfDen0JAqePOpR9DqwsaqowNKcJCxizJCANWqsAHwBZrudr0OyAtUhHVofSEevmuoiigir4uMqMHXwSwU3SGpDlxyPYB6qrOD7Da89Nj96HQxBzYalXv8pKHj64QJ2Hj+9wmaBK5VEVQ9vBqenz0BQUHc6YkqL82c+QBUhsju/cNFULFp8/0XPnIvevONJCUO5x43dPwFkXzl0lco+0osmMz7/kRUca1bJwUHFdtWAqKzGflTxwxH4tWXmhEdcIqKirFt115DGY1rswHwFUq+ErfdcA2u6dVFRRQRBZKkJFtuNdebAK40I/6+W6/HqJv6mREdkL77eReKiooNZbSOZQPgKwx/Jdq2aIqp996iohYiCjDR+4oXArjejOxhzis5i0SxlWs/N5zRtSlvE/sKQw2AEALPz57ASVpEVGEx8YkzBcS9ZmRf24vz/VU7eSofaes2GsqIqCrQvC6vAPgKgw0A0Lh+tKpaiChAxMY5x0DKKWZk9+h8MRbP4Hx/1ea9tAL5BYWGMq5oaQe/LL6DXwoi8qpYR+JgKbDQjOy2LZpi2dyHeFVSsW+3/YJXV641nHOTg18XX8KbMUTq1Y+JT+hkdRGqFNv1X45sWn1KRVZMnPNqs+b7N2kYizef4nx/1fYdOIhbJz4Ol9vYDj51wwVCgoAfDvjvTkCnCpWNOL4vxpHQQUixrhj2dw9nJueoCv47NgBE6o2DFOOsLkIVe5GtJwDDT39FxSd2gcRKCWnKfP+3n57K+f6KpX//M+6YvMDwu/8AcOiUxDVPn1ZQVUAIA8Q1UuAaO1zzo+OcH2rQn8jKXPWVykXYABCR6aLjBrYWUv8AgPL5/jWrh+GtRVM5h0SRoqJi/PjLb3ht5Vqkrf/S8E/+ZJhdCPSX0PrHxDk/Fro2JmvLezuUBKsIISIqSUy3xEZw6R/BpPn+r89/GC2aNlQdbYmt23/FHZPnWbZ+scuNg4dz4dZ1y2qgUgj0kTZ9a6wj8bGsqrmzsGGDy0gcGwAiMs3Z+f6udQDqq84Ostvw4mP3o3PbFqqjLXOmqAj7Dhy0ugzybcESclrM6YjL7d373/jHptVZngbxLQAiMkVsp35VEeRaBUD5DjxCCDw56U5czvn+FLh6FBfZMuvFJbT3NIANABEp16xZ3yrSblslgK5m5D867lbceF1vM6KJKg0hEKsL8Vls54EdPDmeDQARqZWUZDtdK2Q5pLjCjPgxwwfi9huvMyOaqDKKkJq+PqaLs2VFD+QzAERUKgFcFBOfkFfuA/a6R0mBJDNqGZLQh/P9if4rUuhYU79rkmP/5uTc8h7EBoCISifwIqSowOeVDUP5h2t6dcHjD46GEBWohShASKCp2+16G8DVZ/+1bLwFQEQ+r3unNlg8Yxzn+xOV7spYR+I95f0w/zYRkU87N9+/Cuf7E5VJQj4e2ympXIMx2AAQkc9qXD8GyxdORni1qlaXQlRZVNU11xPl+SAbACLySVGRtbDi2WmoU6um1aUQVSpC4MbybEjGBoCIfE71sKp4Y+Fkzvcn8oyArk0q60NsAIjIp4SGVMEbC6eg9UUXWF0KUeUlpDMqbmCT0j7C1wCJyGcE2W14Yc5Ev5rvXxG1alRHv8u7WV0GecHRYyexY8/vyD1+0qwlNJvmHg5gakkfYANApFhYo1qoGuU7+9If256D4lOFVpdRJiEEFky5B1d0L/PWpd9q1rgeXpgz0eoyyIt27vkDL769Bive/xS6rnaGhpRiGNgAEHlPrYtj8H/t3XlcVdXeBvBn7XOYZ0QZHFO7qeWUok1mvdktvSKieW+j9V6zTN/qlg1aWVY2mpWlWZaVNjiggJqpaaY5IKCkOQ8lKnAAEZCZwzl7vX9UXjNA5Kwz8nw/n/7w7L2f/cs0fmevtdeKGtDJ2WWcVT233C0agKn/+V+MHDzQ2WUQOdRlHdtixrPjMWzQNZjw/Luqnwh0iOybcEX+juS9dR3kHAAicrqH7x2JsbdzfX9qvgb274WvZ05BgJ+v0lyhYXB9x9gAEJFT3Rk/CJMe4vr+RD26dMLbUxq9kF+jCODa+o5xCIAc7vCxk1i3eUeTrtUMGh66K15xReQsQ268Cm9MGsf1/Yl+F3fTNVh8zZXYsC1TVWTf+g6wASCH23c4C6/M/qJJ13p7e7EB8BBc35+obs+Mv1tlA9C63XX/CDuxZVXx+Qf4N4+IHK5r5/aY98bTXN+fqA7dLu2APlf8TVme1exd594AbACIyKEuaRuNxbOmIjgowNmlELms+L9fpyxLSp0NABE5V2REOBa+9zwiwkKcXQqRS+vXs6u6MGEIqutjNgBE5BAhQYH4+r0paBcT6exSiFxet0s7wGgwKMmSUtb5uI2TAImoQfNnPIN+PW1fmtfLaIS/4neciTyV0WBAWEgQThWV2B4mUGcnwQaAiBoU6O+HkKBAZ5dB1OyEBgcqaQCklHUuBcohACIiIhdUVV2jJkiIiro+ZgNARETkgorPlCnJEdDz6vqcDQAREZGLyS8sRkWVmk28DAZpqutzNgBEREQuJn33AVVR5myf0qy6DrABICIicjE/pP6kJEdIHMLGjZa6jrEBICIiciGFxWeQvPZHJVlSiPT6jrEBICIiciHvf74MNeZaJVkScnN9x9gAEBERuYjvt2Vi3pJVquJ0Ly/ruvoOsgEgIiJyAYePncQjU2dC16WaQIHUk1tX5NZ3mA0AERGRk6Vm7sOIcVOUvfsPAJD4sqHDXAqYiIjISSqqqvHBF8mYNT8JtRaryuiyWi/rVw2dwAaAiIhcWmVVNZIUzYp3FbW1FhzNysE3G1LVbPhzHiHk3MKtKxp8nMAGgIiIXNqZsgo89dqHzi7DjYgKqwFvXugszgEgIiLyKPK1/G3JBRc6iw0AERGR58gKKKp+qzEnsgEgIiLyHO0rw/0mN+ZENgBERESeQ0jIF2L6D58NTG3wZzwbACIiIg8jpRgf3W/39IbOYQNARETkmR6P7j/iP/UdZANARETkqaScEd0vfkBdh9gAEBEReS4NMMyPuHZYUB0HiIiIyHPJS7wt2uvnf8oGgIiIyMNJKR6Mviqh67mfsQEgIiLyfAYp5QvnfsAGgNyKbtVh1XW75ddaLHbLJiJyJiHFqMi+CVf88Ws2AORWLFYrcvJO2S0/KzvP5gyh8a8VEbkkTWjygbO/cGYlRE2RmrnPbtnbdu61OcPgy002icg1CYg7O3ce7ANwO2ByQx8v+gajhtwITRNKc/MLi5GybovNOUY/LwXVENEfggL98eyEe5xdhkMUnC7G0eM52LJjD2pr7TIk2aI83HcYgEQ2AOR29h/JwszPluKxMaOUZeq6xGMvvw+zudbmLJ+IAAUVEdEfAv39MGF0grPLcKj8wmK8P38ZPktcDSml0mxNiCEAEjkEQG7p7U8WY9nqTUqyrLqO52Z8jI3bd9mcJYwafCMCFVRFRM1ZZEQYpk28H5+8/hR8vNU+VZRSDgI4B4DclFXX8ciL7+GtuYtQa7E2OaeopBRjJ03H50vXKKkroE0ohOKhCSJqvgbf0B8zX3hE9ZBnm+irErqyASC3JaXE2/OWYNBdj2Hdlh3Q9cY/JqusqsZnS1fjulH/hzWb0pTVFNYlUlkWEREADBt0Le67bbDaUCn6cg4Aub0jWdm4d+KraBvdCiMHD8RVvbuhT/fLEODn+6fzCk6XIOPnA9ic/jNSvtuM0vJK5bWEXh6lPJOI6KkH78TK9dtwqqhETaCUXdgAkMc4aSrAu58mnv11gJ8vwkODYbFaUVRSihoFE/waEtyxBfxa/WW/DSIimwUH+uPe227FW3MXqQkU6MIhAPJYFVXVOGkqgKngtN1/+ANAq2s62v0eRNR83RU/CEaDQU2YRCc2AEQK+EcHI+zyaGeXQUQeLDIiHN27qPqiIYLZABAp0C7uCgj+bSIiO+vXs+uFT2oUyQaAyFYRfdoiuFOEs8sgombgb5e0URUVxAaAyAY+Yf5oH3/FhU8kIlIgPDRYUZKoZQNA1ESatxGXjo6FwYdr/xORYwT4+ynJkVKeYQNA1ARCCHS+80r4x4Q4uxQiakbOlJUryRECJVwHgOgiCU2g47+uRGhXLvpDRI51urhUVdQpNgBEF0EYNXS640qEXxHj7FKIqBnae+iYkhwJsYcNAFEjeYf64dLRsQhoHersUoiomUrffUBJjgbsZgNA1AjhV8SgfUIPeAV6O7sUImqmDv5yAoePnVSSJSF2sQEgaoBXsC/axV2OFj1aO7sUImrmvkj+TlXUaZP/aTYARHUx+HkhZmBnRF7XEZqXorW3iYia6ERuPhauWK8qbhU2brSwASC349cqCEITqMxTNhv2LN+WgYi89hJE9GkHgzd/8BOR80kpMWXGPFTXmBUFYjkAsAEgtxPQJgQd/3Ulyk8W4/RP2Sg9WoiqgnJAyosPEwL+kYEI6RqF8O7RnOBHRC5n+txFWLdlh6q4IqFb1gBsAMiNBbYNQ2DbMABAbbkZFSeKUFVYgZrCctSUVMFSbobVbAGsOiAEDL5eMPga4dPCH77hAfCNCkZwhzAY/Dixj4hcj1XX8dbcRZj52VJlmRL42LRzZSXABoA8hFegN0K7RYHf34nI3Ukp8WP6brwzL1HZa3+/s2hW4wd//IINABERubTiM2WY9MZHzi7DIU4VleBIVrbKFf/OEsBXuTsTT/zxazYARETk0qprzFj5/TZnl+Huyg1e1mfO/YCbAREREXk88drJrStyz/2EDQAREZFn2x1QVDXj/A/ZABAREXmuSmi44+jR1TXnH2ADQERE5KGEwMOm7cl1vkrASYBEREQeSEi8lJue/Gl9x/kEgIiIyPN8lJuR/EJDJ7ABICIi8iQC75nSe46/0GkcAiAiIvIMVinwaF5a8mwg+YInu3QDUFVdgyNZ2U26tkvHdvD29lJc0X/tPXQMutSbfH3Xzh3gZbTPbnMWqxX7j2Q1+XpfH2/87ZK26goiIiJHkNKKTY092aUbgF+O5+LWe59s0rWpSXPQvnWk4or+K+7+Sagx1zb5+sxvPkFUy3CFFf1XaVlFk3/fAOCyjm3xw8KZCisiIiIHMGoaZgG4oTEncw4AERGR5xgY1X/kkMacyAaAiIjIgwipT23MeWwAiIiIPEtsTGz8NRc6iQ0AERGRh5FCjLvQOS49CZCIiCgiLARr5k93dhlKmc21OJadh9TMfUhe+6NNk8rrJoZ3uOE+36yNn1fXdwYbACIicmleXkb06NLJ2WUo17dHF4wacgMmj78LE6fNxvqtO1XGB5mrS/8OYEV9J3AIgIiIyIlahodi/oxnMPb2oUpzdV0f1NBxNgBEREROJoTAC4/+L+JuuuDcvcZnQlzf0HE2AERERC5A0wTenDwOEWEhqiIv73DDfb713k/VXYiaPSmh11qb/I+0Nn1paSLyDCFBgXh63J2q4oy1FSVd6j2o6i5EzV11USV+fvP7Jl8f2D4c3cZfp7AiInJHtw25Aa9+8CWKz5TZnGXVtM4AdtV1jE8AiIiIXIiPtxdG3jpQSZYmEV3vMSV3ICIiImUGxHZXkiMFWtV3jA0AERGRi+nbo96h+4sioPvVd4wNABERkYsJCwlCUIC/7UG64FsARERE7iQ8NMj2EIHK+g6xASAiInJBRqPtL+rpUtT7KgEbACIiIhd0uviMzRlC00vrO8YGgIiIyMVU15hRWl5hc47UUW8XwQaAiIjIxWTuOwxdlzbnCCF/qe8YGwAiIiIXk/bTASU5BoP3vvqOsQEgIiJyIVJKLFuzyeYcAZiyUxOL6jvOBoCIiMiFbErbjV9P5NqcIyF/bug4GwAiIiIXYbFaMW3WAkVpYm1DR9kAEBERuYjZC5Kx/0iWkiyD0L5p6Di3AyZSRDNo8Alr+tKd3sE+CqshInezYt1WTJ+7UFXcoey0ZUcaOoENAJEi3qF+6DlpkLPLICI3I6XE7AXJePOjhUpe/QMACXnBToINABERkYPpukRO/imkZu7DJ4u/wd5Dx1TGV0ujmHOhk9gAEBGRSzMVnEafuLHOLsONiK/ytyUVXOgsTgIkIiLyEAKohSZnNOZcPgEgogbN+HgxWoQF25Th6+ONiWP/hXYxkYqqIqI6Scw0bU9u1DKCbACIqEHbMvcqyVmzKR3TJz+EYTdfqySPiP7iZG2A8cXGnswhACJyiLKKSox7bgaem/EJzOZaZ5dD5Gl0KbRxpzYmljf2AjYARORQny75FnH3T0ZWtsnZpRB5EDElL23ZtxdzBRsAInK4PYd+xd/veQIp3212dilE7k8gyZSe9NrFXsYGgIicoryyCuOnvINJb3yEGg4JEDWJhFhp0Ix3A7joFYTYABCRUy1IWotbRj+BQ7+edHYpRO7mqzxrxMjs1MSqplzMBoCInO7wsZOIGzMJyWs5JEDUCFYh8ZIpPfke7Jzb5MdnbACIyCWUV1ZhwvPv4JGpM1FZVe3scohcVRak+J/cjOQX0ITH/udiA0BELmXp6k2IGzMZR7KynV0Kkaup8vI2X2nKSPpRRRgbACJyOQd+OY5bRj+Br1LWObsUIlfiV1vrM1RVmM0rAUopIYRQUctfs217ukEeqjAzG4WZ/Hbo6aprzHjytTnYsecQXnnifvj7+Tq7JCLnk/JxAF+oiLLpCYCuS5wqOqOijjrlFZy2WzYRuYfF32zArfc9iQNHjzu7FCJX0CsmduQgFUFGADpsaAQOHM1Cqxa9VNTyF/v5F56IABzNysHQMZPwyhP34/a4m5xdDjmYpgmEBAU6u4xGKy2vgJR2fIIt9LEA1tsaYxRAmQRCmhrwRfJ3GNhffQNg1XUsXPG98lwick9V1TV4fNpsbN25F68//SACOCTQbERGhOPA+gXOLqPRasy1OPTrCSxc8T2WrPoBVdU1SvMlMLTlDaMCL2bd/7oYJVAKGxqAtZvSkZq5D1dfebktdfzFp0u+xYncfKWZRNQ0AtiQm558UV+9Y2Lj20pNWwgJpdv/LVu9Cbv2H8VHr0xEt0s7qIwmUsLH2ws9unRCjy6d8O9/DsEDk6erXujK36uyNg7AQltCNABZtgRYdR3jp7yNnLxCW2L+JDVzH6a9P19ZHhE5Xm7G8pMmv+IbIPA6bHxf+Xy/HM/BP/79NL5I/k5lLJFyl3Zog+Ufv4aundorzZUQo2zN0ISQe2wNyS8sxtAxk7Dv8DFbo7Dqh1Tc9Z+XUWux2pxFRE62caPFlJY8GRJDAJxSGV1jrsXTr3+Icc/NQFlFpcpoIqWCA/3x5bvPISwkSGXsAAA2vYKnQSJTRSX5hUUYOmYSps9diOoa80Vff6qoBI9MnYkHJr/VpOuJyHWZMpLXGAzG3gA2qc5esW4rbhn9JPYc+lV1NJEy0a1a4NkJ96iMjGjVZ3hHWwI0zeC1Booez9WYa/HOvET0Hz4Or37wJY5m5TR4vlXXsXPvYUx85QNclfAQlq7eZN+Zk0TkNNmpiTmm9sabIMQ0/Pb2kTJZ2SbE3T8Zny1drTKWSKnb427CZR3bKsszGtDPpuuzUxNzomMTfoLAlaqKOlVUglnzkzBrfhJahoei9+WXIiayBcJDgmGxWnG6p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g/QCNgECCAxnrrdFgxq9b1XLo/HSbpQUj50JiCpbLcv0goY6fXEbbQCTqqPnOR7WFy+Tm77hc4CJBFLAAAS6cm5tviJXTNDTBotaXnoPEDSUAAASK6Cxf99W2Z2HEd7SjQQAroia0yAAki4txsI7fbFUosZDYSAzmAGAEDDeOK27PxMFO0u0UAI2BQKAAANZdFce+W/b82MjN2HS3oxdB6gXlEAAGhIT96WvS8TRbvJaCAErI/t8SVuA0R6Lb6V2wDTYI8TfYg8niNpt9BZgHpBIyAADW/x91c3EOpYGY9zGggBklgCAJASj8224p+/n7kksmiQyR8JnQcIjacBAkiVRXNtsQo+ZM9n41PlulxS39CZgBCYAQCQPgWL/zw3MzuTjfZ0p4EQ0okCAEBqLbzJliy+NTpS5iNcejV0HqCW2AQIIPX+PDc7f6+T/DdSPM2lk0LnAWqBPQAAoNUNhCSN3POk0u0mmyVp+9CZgGpiCQAA1vLnudn7pGg3o4EQGhwFAACsY9FcW/GnWzKTYo8+5dITofMA1cDTAAFgA95uIFRqi8eJBkJoMMwAAMBGPDbbin+6JXOJezRINBBCA6EAAIBOWDTXFv9p58wQM42WtDx0HqCnKAAAoLMKFi+8OTPbomhPiQZCSDZuAwSALlp4ky2RdOTeJ5VaZDZT0laBIwFdRiMgAOimhWsaCJnF08xoIIRkYQkAAHpg0Vx75U+3ZEbKfLikF0PnATqLJQAAqICFN2Xv2+sk3y2Tic+Xa4L4BQt1jv9BAaBCFs21FY/flJnkUfQp0UAIdY4CAAAqbOGNtiBqivaWa5KkjtB5gPWxfU4ue+gQQCiP35RhFQxVtddXfY+oXL7OZPuFzgKsjT0AAFBFi663xSr4kH2fj0+VdLlMfUNnAiSWAACg+goW//HmzOzYaCCE+kEBAAA1svAmW/LHm6IjTT5CrldD50G60QgIAGrssZtWNxDK5uJpchoIIQz2AABAAIvm2iuSRn7s5NLtbjZL0vahMyFdWAIAgID+cFP2vmIp2k2mSyTFofMgPSgAACCwRXNtxWM3ZCZFooEQaoclAACoE4/eaAuGnuyDllt8maQxofOgsbEJEADqyIM3WZukMz52Svk5ma4InQeNiyUAAKhDf7gxc6Wk74XOgcZFAQAAderNPtEkSc+EzoHGxB4AAKhTf73K2j92SqlgZreFzoLGwwwAANQxz2XulPRa6BxoPGwCBIA69thsK378q+WfS/pi6CxoLCwBAECdM9dCGQUAKoslAACoc2b+j9AZ0HgoAACgzrmzWIvKYw8AANS7yLYNHQGNhz0AAFDnTNondAY0HpYAAKCODWr1nKQjQudA46EAAIA6lvPyCElbhM6BxsMSAADUqaEne3Obx1P5OY1qYBMgANSptmx8iUz/GjoHGlM2dAAAwHt9clR5nKSxoXOgcVEAAEAdGXaGN73RFl8m6YzQWdDY2AMAAHViyGgf/GZbPMek3UNnQeNjDwAABDao1XvnPJ7qHo+XKRM6D9KBJQAACOiTo0rDTPEsmXYInQXpwhIAAARwwGk+IC7HF0tqDZ0F6cQMAADU2ODWUktcjmdI2jp0FqQXBQAA1Mj+X/GBnvUZkh8bOgvAJkAAqDq3IaPjUa54mqR+odMAEnsAAKCqDhztu5asPMfcDgqdBVgbSwAAUAWDWj3Xy+JxZcUXmFtT6DzAuigAAKDChoz2waZ4jtPQB3WMAgAAKmRQq/dusniqKR4v0dAH9S1rbAIAgB7b/7TSMPN4lkRDHyRDVvLQGQAgsQ44zQcoLl8sVys/T5EkLAEAQDcdcFqpRV6eIaOhD5KH2wABoIv2H+MDo2J5hlw09EFi0QgIADrN7YDT4lFWLk9TREMfJBtLAADQCQeO9l09Ks+RREMfNAQKAADYiEGtnuuVice5yhdIoqEPGgZ7AABgAw4aXRysKJ7jchr6oOEwAwAA6xjU6r37ZEpTXTZechr6oCGxCRAA1nLQaaVhsZVnSUZDHzQ0lgAAQKsb+mSi1Q19otBhgBpgCQBA6h10WqlFVp4hp6EP0oMCAEBq7T/GB2bj8gyJhj5IH2a6AKSQ20GnlVuzcfkpMfgjpXgaIIBUOXCs7xrF5TlyGvog3VgCAJAKg1o91y8Xj1OZhj6ARAEAIAUOOr042BTPEQ19gHdwGyCAhnV0q/dekaOhD7A+NAIC0JAOGVMatlw09AE2hCUAAA3lgNN8QC5Tvtil1tBZgHpGAQCgYRwyptTiKs+QaOgDbAp7AAAk3mFjfGB59cB/LD/TgM6hERCABHM7eEy5tew09AG6ik2AABLpsLG+a8nLs00ays8xoOtYAgCQKINaPbdZUzwu9vIFEQ19gG5jEyCAxDj09OJgRTT0ASqBAgBA3Tu61XuvaqKhD1BJFAAA6tohY0rDVhoNfYBK42mAAOrS8NN8QHuufLFo6ANUBTMAAOrOYWeWWtqdhj5ANVEAAKgbh43xgW7lGe7c0w9UG42AANQBt0+PLbd6VH5KxuAP1AJ7AAAEddhY31VWni1paOgsQJqwBAAgiEGtntuiVzxOKl8gGvoANUcBAKDmDj2rODhyGvoAIdEKGEDNHN3qvdt6laaa09AHCI0ZAAA1cfhZpWFtKs8yp6EPUA94GiCAqhp+mg8oNq1u6GOS+JkD1AdmAABUzWFnllqKRkMfoB6xBwBAxR02xgdG2fIMiXv6gXpFIyAAFeR2+Nnl1ihbfkoM/kBdYwkAQEUcNtZ3jaLybDkNfYAkoBMggB4Z1Oq5bfrG49xp6AMkCTMAALrtyLOKg2XxHHca+gBJQwEAoMuObvXeHX1KUyUa+gBJRQEAoEuGnVUaVrTyLBMNfYAkYw8AgE4ZPskHxO3li2VrGvoASDRmAABs0pHjSi1xR3mGjIY+QKOgAACwQYdN9IHZEg19gEZEJ0AA6+E2/Jx4lJfK0yT1C50GQOUxAwDgXY6a4LuWS+XZTkMfoKHxNEAAklY39NmufzwujssXWERDH6DRMQMAQEeNLw52p6EPkCbsAQBS7OiC946Xlqa6r27ow88DID2YAQBS6jNnF4+Ml5WvkdHQB0gjCgAgZYZP8gEqli+W1Bo6C4Bw6AQIpMjwCaUWK9HQBwAzAEAqHDPRB8ZeniGnoQ+A1aLQAQBUk9vwc8qtcVx+isEfwNqYAQAa1FETfFd5ebZEQx8A78UeAKDBtLZ67u+bx+Pk5QtkNPQBsH7MAAAN5KjxxcF/t3iOaOgDYBMoAIAGcHTBe2t5aapsdUOf0HkA1D8KACDhjplQPNJX0NAHQNfQChhIqOGTfECmvLqhD/+OAXQVTwMEEuiYCaUWxTT0AdB9LAEACXLMRB9oKs9wcU8/gJ5hCQBIBLdjJ8ajXOVpkvrx7xZATzEDANS5oyb4rhmVZzsNfQBUEAUAUKdaWz338ubxOKl8gURDHwCVxSZAoA4dN7k4+OU4niPR0AdAdbAHAKgjRxe8d3ZlaarHNt5o6AOgilgCAOrEcZOKR3pb+RqnoQ+AGqAAAAIbPskH5OLyxe5qDZ0FQHrwNEAgoOMmlVqk8gxFNPQBUFvMAAABHDPRB2aNhj4AwolCBwBCOrngzbW9otuxk8qtmaj8lBuDP4BwmAFAqi0taltJL9TiWp87t3332MpzJA2uxfUAYGO4DRCpZnF5H1W5AGgpeL7cVprisskm5at5LQDoLBoBIdVcOkrSD6t1/uMmFweX2+M5MqOhD4C6wh4ApJpLLS0F36LS520peN/PTi5NN9lDdPMDUI9YAkCqmdQ/bi+dJ+mcSp3z+CmlYXF7+RqTtq/UOQGg0uxzk0seOgQQWNncP33nxbkHe3KSlsm+dVnlK036UoVyAUDVsAQASBk3u/u4czs+2t0THD+p9KWylZ9g8AeQFPa5KcwAAGssddPJd387e09n3/DZ83wH8/IsScOqmAsAKs6OpwAA1nWPxfG351+cf2xDB7RM8O08XzpDbmdJ6l3DbABQERQAwAa4/Em5PWCRPWny19zVz+U7muyTkg4SjbQAJFhWYvwH1sekj8j8I3J/51/J6rtm+DcDIPl4GiAAACnEXQAAAKQQBQAAAClEAQAAQArRChgAgBTiaYAAAKQQSwAAAKQQBQAAACnEHgAAAFKIGQAAAFKITYAAAKQQSwAAAKQQSwAAAKQQBQAAACnEHgAAAFKIPQAAAKQQSwAAAKQQBQAAACnEEgAAACnEJkAAAFKIJQAAAFKIAgAAgBRiDwAAACnEHgAAAFKIJQAAAFKIJQAAAFKIGQAAAFKIAgAAgBRiEyAAACnEHgAAAFKIJQAAAFKIAgAAgBRiCQAAgBRiEyAAACnEEgAAAClEAQAAQAqxBwAAgBRiDwAAACnEEgAAAClEAQAAQAqxBwAAgBRiBgAAgBRiEyAAACnEEgAAACnEEgAAAClEAQAAQApRAAAAkEJZYxMAAACpwwwAAAApRAEAAEAKcRsgAAApRCMgAABSiCUAAABSiAIAAIAUYg8AAAApxAwAAAApxCZAAABSiCUAAABSiCUAAABSiAIAAIAUogAAACCFeBogAAAplA0dAKnRJtnfJX/LpDaXlpu0TJJc6mdSX5eaJdtM8vdJag6cFwAaGgUAKm2ZSY+69HuZPRObP5ftyD53zXl6SWbeqTO429e+rYGlfGlnK/suZrarSZ906ROS+lY3PgCkg42+uNi5H8rA+q2S9HOX/8KkBa/vnFs8f4SVq3Ghlnme2XxJcc/INURuh0s6XFKvalwLABqdjb6EAgBd9pbkPzGL7llhmZ/NnWArQoQ46TLv0zsuDzOLj/PYjpKpf4gcAJBEFADoNJeeNPNrV1ruulCD/oacXPDmpt7lEXI/W9LeofMAQL2jAMCmxC6f525XzZmUWxA6TGe0Xlw8wMzPkOx4casrAKyXfY0CABvi+mUU+YSZE/MLQ0fpjtHT2nezclSQdLzEUy8AYG0UAFifB6LYJ8+cnH80dJBK+NolHZ+Q2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cOBES+U+t3bi81zMaLVpFv29wxbeAh4qL/l12z4cEtlxc3XuWQjJl0h7Ip4LM7hJZZMMzDLgOeFP2AGzjOyhVLJvbEofOY053grxktE0dpc1JNrsSybt7Kgvp6CRIKCAouKcRRYUBD3Ptkq5/e1tsQtLrc3VxH3l8z5eom1jP64wUcFOz2sbI6Fv81FXSOVES2Ugukvl4ShfcJQIPjFPTkcSw+kWE6bfk1RUTJW7kVpELiKZBOTRyUYJxjlndIpKiJf6AiaLAp2FcRlYDsXT121y+QiQ68GPtxiMW25Z/Pyfrm+V5beMMY327uEXYYp5fHfbYmlfnwkW7iyMWKFUsm9sdbUM6c7p4WCWg8590JtF4uSjQXACkSvxdKe3EjOn4h3MwmYF8AMM44HJqg9AkvGGrB1Wg9OB0PxkdCTPtBLeL20cMqqV0z2ShSGL/RVsPPnr467rcllePdZgwSyX6biwaqHN16eF0knRqxQElNeL0jJ/g6IeVh37yBNOms2fLj1tOnXrCqK4kWgj/UslnQi0MBrDlH0FsEp8m5/KrdDjJERgL2ZdGwzk9M+4K/xROKPi8pvuD9Ihc/3xpOgijXBztrak82Cz8rsGLBfGfr6kW4G7o4RKZRqqt19sd3zFLkzgAYc/zOI1dvDmz6167Tp11xdFMXpTizp5Ebhqd7bOyR/MmgBougg4mgPW5Sx38YKQImsZ6f7mGLB2zE7DTjfB8HDS8quv12hf7I7wXQyA78Z6U8B9o27a5fnLVzdMCKFcuf0koKiSH+LbLKMO5uV6rd1q49kFUssFm89s/yG7wVec5p8uMDkLxBaAMoWsM+AHYItHquTqU5iO0brgThdYF6j5FRk5kuEJhmUAhV09W8LgDcijvFokYJg3aKyVbf5UHd3NAJ0NANLetTga3dvufyxgX5IhxMjUCjVrsDHZyFVYez18kO1Z6SLWCRWxLyfgHiTZReIB9sEetywHc542aSXZLaZWGxzU7cOhKYzpl4/qcDZdB9phnM63rAZQrNNNgfTfveatjwvFYgPYZzl4v57lVOuu61m2xVPdzYDm3hVPvqvdVtX/CaXD+pwYMQJpbJiVkKtje8GKkB/GOIwN/bwpk/tOmfK1deGrvA00FlIy7Oc1wy8YugvSA8pjO5sSYRbei9w2QNb2QHsAJ4Afl3FmmBn+fY3AW8COxuxoItgxFxMVznnTlxYtvLnCr7zJ3l93OCjQD1wfVPeDNwrRphQTC78zkTgA0Arzm75w8bLhnTyec6Uq0dFsaJTzDM6y56FJoy/IvujpEdaA3//g69fvmUg6l3LRRFb+DPYE5Wl377DJVhqZm910pvM7K0ZpyaE3ofpVIvCWwX/gBEiftju2zYQ7TncGVFCmc9NMcLwdKQ3Ak+1OhvSvH+VpTeMiWIs9d4+J1iQ8ZPJbCvSb8z5XzS78IHcvZCymh1sBX44n9U/HVcWLgZ9DOws2uYxbb3LHKErzUgBv2yN2Y0Pv54XSW8ZUUKZND0oDkP+waAV6X8ffH35gHydD4XK0hvGBImsImlC+pNJd7UG/kcPvr6iSxuNj8dh+zSwBaDjQeXAHFAp2CTQvhBt9I7aCLcjKfdcKvIPlLL2hZ7a9DiXpKitvmvJzMkvWtL+zmQXgjqmyBCRw14aqJ7tSGHECCVjgbHS4DWFuXFX6VVbSm8YQ4wLvLdPdhLJPuDXMn2nKWh5LPOLfUAcnAZ170jH8tVk0pNvcWDLg0IgkuZGhkVy5g2zIIhq9f5nDfezVvl1s1M/+0v21lX732/klfms/urYstRTwN+Dnd9uYhYkPKpaOHXVQ/nsx71nxAilYPoTJako8REgBv72oQqadtxx1QnqucBhX6BjDsl9glu98dV1Wy/tsB5hvGsK1F0M+idgFlhA2pyrjucZESKSiFAQSrSJJn0cvTmEEyKL1b0Q/4e7vLnvvjG8OatpvK13ufXM6eOfjoXBHmTvByXasx+3JXTdt652+f0D+oAOU0bExq0q1gTJMHYC2FmInZINSdC0KtYEU3aVLuhJJPduPbBoZ7xrinHhJyG4D+zrwFFAAekPVAeReMgQiSMtEh0QiRwhcpGUiERZCH+XcvbzJwo+svqJ+Me6SR9X7e/ftGK9D/wXMf0sI3BgIDge8W9H0s7N/jAihNJSvqtAuPdhKsZ4aGiC0pl2lO4oxbiSDiJRA+gnXUVywQngVoK+DhwNJOgmmIcnnbkru0hcu0jajjsiOUVSLEJTQ9xHWgNb/XDBJe/M3m5ZzebLN2cRSwzj7UfKzs3+MuyFUk2126fWo4ALkDWYuR8OhUmz6ri1cWLRBenxfhpBEuxXrTH/H+0iMT4eN96zENy1oPfSg0DggEjaRZApkrQotF8k0f7/pl1AiiAWmluQlK26t+gT/5y9lgNiMVOmu0+BoQ9i/NMAPKLDmmEvlDunlxTI73dXebgl1rJusNtQxZpg564dCxzucg7kj4wMHjPj6+0WJOOc0bD93WDXks7N3mOUm0yRRBmi6FkkHXudtvNdhJsR4j7zu6J/+trviy/LEr4pLRaJL3WK8FiA+NCi8lVvGYBHddgyzIWS4a4yhCFO907fNLbTkMvANsv8tZk9CRQvAvf/gBPoo0iy9SRRVpG0H2s/37VfpxAmpxRc0hQGn85eq2xPbXyLEX3FpDZPZ5NgPKYrK0tvyGH6uZHNsBbKsnkT4jKdh1FhpheHwl1l/vzV8dAnzskccmE0gH7U0f2j7jSwfwE7noM812wiiXoQSZjRsxzoSVymSNp+d4rQ2KTj724ZfUXWYdjjXJLyieK7BasP3I7iYEtd4Jf183EdtgxjoZjC3ZooeC+QlOPWwd8rYRq9KTndG//M/iGXhcAfWmN2Y3vvZrzzDWD/CHYKvRBJWgSuW5GE3Yhkfy/SaZh2oDwRSorQNC/3wR+P+fRF2dpQs+HDrbHI3Qjc336fwHiTW7F42qqBirp5WDFshTKfm2I+5k4H3oTxEkOwwHjccVfFA9kZHRcVtdfkv39gXvKuKRB8AHg/vRRJdOClzjoniTJE1EEMWUQSdhTJfqtYCnd8iH30ptGfyWY6tuS2N9Sb+W9wIH1DIOzNPtL5Wc4/4hm2Qpk0PSgG/yHSNv9fDMUC4/Q908c4lPFVtlCmhzsOAYMTQRfS0+JtLMCfMAv7xLnoqotxn34XnPFGolGFWXqSriLqSSRRF5G0H8elnE71Tn+brUk1LIzi1vooxp8yDhdKfLTquOpuNooduQxLoaTdVVpONw3efvjOpBc5m04wadGBo9rrFf2gfQhovGc66fA+x3ZbUCzAzjkJvvwB3GffTbBiGfFPvZPEl6qIvfdU/Kiig4jEpQXQZcKfXSRRxu8eFUfi9OuKP3d6lpbZ5kmNuxGZuWYCsDfV7xn3hgF5iIcRw1IoBdOfKHGOjwABZndkLuQNFpumb0o43PlkmINBT7X3JmkrF28FLqCH5+iPm4l9aBHuvLeg2ZPRuNFo6jjiZx1LwcVvx500q4NIOpuIMyfxUY/Dra6/hziFuJN94LL2Ks88U51SYXhXp4y/cUsF5w3MUzx8GHZCqWJNEFns6HZ3FXP+p0PRjgI/erRJizMOJU322wMGhW1TwU4HZndXhkfYCbPQKfMg6PSoA0f8zRXEFsztViSZpuCDiSTM6Ik6lCcVptBJX5vw/7L2KpGN3SOzzPlfHGlJfvjVkWEnlE3TNyW81wVD6a5SxZogUOoosDdmHE5Z5DMiJ8ZnAG+hW7eU9IvqxxRByais9WhsEYwfk3ViH3YSSWcRdJ2TdLR+dVh3QTNDI/uC4obXQh/w4AHXFgWI4/PDr44MK6FUU+1GUXgUUtVQuqvsm7c1ZhEncWDY5YHNYUEsYz9INBV4Y9erD4gkRET1+7C6vVnrieobSdU1dDERd3CG7GAqdh3+u6NIXBcRhUrPa3zaJyxrW2uojiyuZ0CvpI+YgJL88Ksjw0ood04vKbAouhizWUPlrgIQayoIwDIm6BYaPHBgEn/+eNIiKe18re/UOyT//AqpP3Tdb2VhRMvjr9L42KsZK+yuy4uf6QPWs0i6LlJG0F5OQaTgmH8prc5qKi5OxvZiZC7mxnE6o39P8fAiZ/tRTpt+TdHolMpwFIQuURAzxnj5hKKowTv2xpJBgy9Q64GQoKbRqRvLfOCrMGvF9JOh2s8dtrTGVBgsMGuPwqVQ4tEDZySmAm8gY9hlZHoAZ/Qor+0k+u46wmREwZLjCcrH4/c20/TAC9Tf/ACNz245YL3q8uLTsztLlpX8DhP7DsM3JnqvWUCXDV9btpAqKfO/F+6fSN+Usx7mXkciAygU0zlTvlOaJDUnCFyFRUz1zo4Fipz3o7zceEyFnqDeGTujuOrx7Fs09fonFWhTFH2rNnKxpTLNBj3VkrAh21DUMCYWJFL+mIxDoY98hmUoGA9+Znusx64iaesZgNBD9PirtOxowN35ZHpO0pKi+cWtNL6wlTDpO4ikw4vfYe6Rzbq1f2jVo0ja/n+Cx83Mdr+PUxstofQ5v/9+JMzGVh1XnVj7THUy2zVHGgMgFNPiyddNxn372JSzZYG5E9vj6XaIa9X2dZa0P5SowJv0uvf2koi/gHEqQ74f3lQYriz2BOMzDvowsIz2+AKw0dBVJPvnCbC/p4i8EW6oI3ytvtPQzPVbJN37iHWsI5ISERqd/Z6rzQfX7CVqN3SZkI3aunPCWNIhko54+iWUdNjQlaea4ouBM2Wc0Zd4um3bUmcJzUIsaTvcjPdhZekNY3KV5qAnqljrdnpN7WTLMmLxjLzoNhqY1K1IejDpZi4WhvTuRe+407HvIkmvw7jxZszo5ratyBU2NUdRcn/4VkkJs1LyQgEOeTJvOnvG9eWNJP/R5K4C+xTYgEz+BAWgT7i4X1455brjB6LMvrCDZxU5NzbjkIE1dHLITIBGdRbJwUy6Ay8Sl8WRMtuqvQgNhVK3G8gaXauBDojCCCJZ1qHakcghCKXaLZly4+wodJdjuiodTYRssXUPiY4RDoMvVJZ/e8nBrxpIziKIVJhxwBva2OmkRAijO4vkYCbdzJc/Y/W864JhpzIztwGHWers4hPWuU6El0uEUvYFHaBob+CBrRmHFFgwZSCf7EimT0KpYk2weGrp/Mj5K8CuAMYf9KJDJwG8z3n/75VTVr71oGfnDpNIZR6IcI2RtHO/CbfzfCKrSbejSKIuvUzGAmGW36JOIsn+e+f99R1E1ejNHSzpkM/425mYccaka8sG/pGOPPowRzHtKr9pmpl9VpDpA5VLAhwLAvSvlVOu+3w60HSuuY9I4/cFbRsUBc6MqZlnhJD0uMYuHr77v+pdh1I9iSTbLsbuTcDpcnyW37tcmykq51ojo6m7u05ZgSA5NsPiHTc4NxaPj1849fqXndwec77We/ZGqXDz4ZO/snf0UijpaOqGPgoaLJG0VU3c0NnOBfWLylZ9cV3t8tdyWV0N+CWKbe3JVOpxu1Lo9Qi9oYsnbxdrVeff+i+SznOWbNuEO/c8kbHLuy5DyP20FEcukVLmAqqTmINxEtJow9dhWu+MOsXjL6XFw94IVwe+LkolNgfbwwZivrsqRjS9EsqyedcnWpuC95nZcgZTJAdIgKow2wJ8PrdVfcmSiVUtsWT7l9WEGLVzz5RSYDNACmehw3znOUEvRNK1B+rmRe+lSDp7FWeK5EDP44ggaQSN2e85q0m81bx9R1ICbJJQBcYkk04QtjAtHuqEbZbpdcWT6xFbwWbKhEHhGZNWl02dUrvzcFiLOahQqql2D7TEjgH/SXI7JzkYBYgPV05Z+auabSv+ePDT+8Zp068pKlKsnPCGaUrphE5mbmeRTaVNKI2ktjsSLxo6J3Pi3HFNpLN1q+8iObCY2BeRZIsLJkK5bd7xYrZ7z2ISN0FdMhGs+sPGS3dVsSbYVb71RIvcNJyfDprWLh7RLh4WYow2tBsksKmxePIz9TsnbF44ZdUuc9oCvi6VYksuMxznioMKJZ3dKroYNHcwGtQ9JsR4J/eFytIbPtDfB11ZcXOhTzaVuiia5gJm491MMztKaB7ejqKjIdWhaBrwOEAZ/7dxK++/P0IXR2jioYqky2Q/cy6jrteme4jOv2cILYsPWJurfdJjr4zy/rlsz2IHz0rBhLknoewAABzcSURBVFIdGDVFJnup3STelmLiCdK5WYC0YaezeASTDZ0ONh4YK/RBk5sgWSPYepleL4jzMtjORWXfrvPYBvB1WFhHlKgbzuLpUSjVVLsHKDzK5KsONTPngGLEEYsUDxcDfUr5kCmMIHAV1to41Rlz5NzReE4wUSa0A/xmLz2qdO95ZtvlMdB84NcAAntNsVrwz4fS2zr2CJ3nG9nMuIcmks69VwdXme5EkhbwNkNP/XjbP2cfelXMipFsPCXjSATqMXJ+NvGcNv2aosIo8a+C44E9gmsMP8NQhaAYR4WhBTIrS2c35imZNqHYJmK2eTiLp0eh1FTMSijZ+F4ZQ9ybZGKFIvgoBxFKT8Joc7EZj9gHbDHxjGG3OeNlHC8a7jlF/ihzGULxvC2z/CgMX4/isSc8vK33Ium6phK1v+idh3BZFimz/X7wuY2I4BWTHuzuWRXtbUgkC4Nz7MDXMAI929d/mXGFMZ/cxxYTJmPXPVuXf6WKNcHuaTtOiHATiMKJiBlgR4GbJbNCHLMMN78n8QjbYGKvkdrpFKs74Ejbd+aXV48qVunRGGMc+CAV7uhNFuWeh17J5BiZ3nHIiZ5zggvwtuDs0uuOyrzBdmEEkZXjNE2tjTM7CWMCAjM2m+w5oe0y+2s6h2K4qTnmn87wVtbZM65viELtA8aAAmHHZdY5m59tfEEfuj8yLo7ExE5f8CzWLZfVzyvXIglRo8k9nbRk1he/mmpXUxTMcd46OYFGA5L8dC0XRWzu6LFcxZqgbtrW45Wy0eCmIWYg5oEmydukzuLx8JQ8O52LbfKezYvKvv16X8VTxZpg15Rtx0ams2X+bUjj8fgocBsXT111n4maniyq3QqlijVBvbYfZZZ9c9LQYUKM9YHedc6UG3/sA6ZF5me3C8OcHS0xD6McgYkteDYh3Y/ZRu/sJeftqdY4m3pIpmNNzaMaCuJNT6Q9D9J1hrHgncA1kB5+PeF41Bm3eHOXHkwkPb74HeYv3bi7dLZ+dZ7EdzPEi6TnPdx+17bPZB12/WnehHjQ6M8y1G6o8YJXOm5SG1jaxPPXjker3ZLy8dO9Z3q7eCTN9WZjZZQhxnl0rOhZPF6+IYjY2xqFde1rPdVUuwembj3GcJ8R/A2oCKNtyciZYe8G++/F01b9R3eC61YoOyoa467VFnWT+nmoiXu5C73zk2UcLdkJbcIQaAeeLcCjiK3AMyZ7PRmzp/qSZapkXGOydR9rUPsGJotLWkibUABOavnha48n/vFuE++M0IzeiqSry0k2k27/RZKSGj36Y2sUdLtloXlPEHcxOyvjUBLjl4MfbLDa/34LG4GMtZ70+l3Cu3kmTcLZUZLmmvkShyaZKM0innofuJ0xF399Udm3X8Ki1+4T3qFzgL+hy/KGCRgH+rj39hpwbbbWdT/0amyKWUxv7taLbmiJCd6O2QKDHcoQhmSvGLyoIPZ0c7fpqA/OHesvTy6Z8q3bvVw1MDHbkE9gf4jZIy4KbvHw6ajTi957kXSbD6XNRJzxeyeR9OTu4tGTEfp5dxPiaqpdTYJ5eCozDu8zl7rtUJ7ZwNMhG3IbB1KJWxRM7yoelRr+jYKJ4AOkpwWtGNNRj2uAhRKX0VehpGyfEowZO7zmJxkYKcQdkt07EMLoiszxo21me+828bft5mkfC5YDV7Sf9bamH9TeX3jJb0Cnh3B6dpf7bEEgujPpdrZ+9cI9JYtIQtzzHved8VtrHujuDmsqxpa4Vv9BDgy7IrBnmp1lNSMPD7qkEqereDTPsOnCJgtKDTfNZOUHKxhTt+uE3QqlxBepVVY6XHWCSMq49Z7ay//74CcfGsltr7QG5ZN+gPEO0pP6hGHvW1S+6r/XbVn+53QzsDUt9Q9OGDXxq2bumxEckyuRdF5/6cEVZps5W9NcvO+Xv9+6Nsp2b5VUx2LJYIGZ3mdqf6S0RvDLkZdSu3vxxJ2bYWYLMoOSHwrdeg83l0QOYzi7WctkOZ0/1VAdRik9LLgnfSR7ioSLWBvVNxXd0+rcf4Zyrx+wOGWPExy2RX/sHPkxHBiRNHqnX8RG8V8PvvC5hu7urWB6SYm34BIT7V9ab8Z6F9kwGXb1F9kDW1fsWLdl+RNhIvWbA2kuusWEurWcHczNPuvXaJhgOhBgOnfs2NGCuIG2utpSJJzbtui5n4u4trkpMfo3IWmxZBNJ5mp9l5X1Tr1Q2EkkYSeRhJ1E0ubev8ujHzQnm758x/oV2WMk0R6yNnF6h1QWsNekHw5VEtkcYmFkuzH/w4OclzTst9392K1QsmzkGW6YM9XnupIaqsMgan4I9Ku2agWMh9jyzrkPL9r19b2xcS0/8Qo+E0kvdLZ+7RdNFpF0fumjTiKJOh3L0stsM2kVoxq/+GDd57r9elaxJnDlfzkxkH2CzFQWxkPJmL8lV89xKHl405Utgfx3hP2+m1OawO4z43vdldH90CsIvA3v/dIWaRB6FGDstlktyK4F2hekYjLeLuOyTvlE7J1bbmpi31t/Hsl9NEL3ZLV+dWsJ65hyrrNIDmwKay+zXSTueS++GBtl3/z9K5/fQ7cWGNOu8tpp+PgnM4KPm8QOw327L+bzkYVs/JayzWb2/xA3A4+DNgObgD8b/ETYf/YU47pboSRckyHbloNWDxTN3oJu91cMJGu5KPKJumdN+jYHhnsFiH8065om4SIuil5pKH4ocvFPRLirI7Sz83yjc8aszsOt7K4onYZpzu2KpJ9GgT4S1tbf3NNwC2DZvOuLvQUfRJnrCZbC9OuhCjY4WKzlomjd1hWPFozyn8TrKsHVyL7hxL8nY3bVPbUrurUOQg9Wr2RRY+SSo54fpubhCOPl1ljzoAm5ZkN16+Jpq74PHGPGR8BkaDxmn66rn7ieTqkpqqn2ttdevoqr/nXs2OT/hrLlHr0rQhO7WsI6i6QbHzHaQiBJyRA94vHfShbYPZUb6vdWU93jjqnK0hvGtDbaBTL+yQ6sJ4RAjTeuH3mWrkPC2j4mv+nrhd32KKUbjk15879nMCbMfSdEemSQ/3Htns11uzzuGwfGuun5iqR/W1S26t2dexYhq6Y6uWdP4i8Fu/f9UzJybwud+0wo91Dm0CrL/vZsk/xk5NyGSPx3iujdLhF/V0lt+a9qNly5uzciIcYFwOcyrFwRxqMHG3LkSdNtj7KWi6JlrStfbC3UM22B6YYTKSMagkiS1X7SljXr66dt/ze8TQS9GQhkHA/6+s5dE8ctnrbq1539hdpe5FZrsBcv4ZLryivKVyX3BbPM/KIUwQkRzPFQGqJZESqOpD0hbk8kdkRyr4Xw15DoXteYWh9OGbOveP20cC0X9cIiaTp7xqqyMLK/kfFxOmc1xq452JAjT5oevYebS4qTtO77jUPDSShesD45BBmCIf0Bmb959WPjpoZfMfElsOPTgfxsHug/zTRzUdmqH2bzRBUyIMUGUoa9eBVXvQwpt6ECNz4sUcrvVWs0WkVBo+1zJTY6Nt62xRpsxvoCX05tVE21p9uVkc5UuyVTbqyIQvcxYZfQcXdqC9K3m4JUt+bQPB3peT/KhteSi6dO+KVJHwNm9XTuINJq0i2D77R3gMe5JFU1sfrX9fUTCw33ubaU2cIoB/sCZpMqp6z8aU9blttEEwKwYWDbl47gGZ4ayV8o+Bhd4xxEoGePkHnJgHCQBcdqHxUWv2Km7w9Ocw6KH6oMwZ1Z+0x1csKEnWsEX5OUKYgCnC6RC760aOoNHzq79LqjBq9V1a5y6o0V6Qie/qpOIvGkPXPvB4ow+1zndaA83RMc7IQNu38VVYx58DU5dz5oqHOQ7zHpunu3XX77ELcDgGd31EQT9l3wfOEYvxEYDTYXFACB0FywhTg3cW7xsvjcoqXhK02/y9ECabVbVPY3M+cUF54vx8WYLgcdA8TbTmhCPGjoR/hojRQsAB0vM79g1qn3P7ujZkA8MI6asCwWJfUWxHmC3a/uu/26gSh3ONCLcEWyojEra5sb7b8c+jIwNGIRKUw1w231+HEuSVFbfVfl9NL1LrJtyBZjOqp9sxfwIY8WKQjWLZ56wx1eetU5v6E/21nTZKTZcMz14m3y7gIg00vWwHaBfidz32kOWh6bPmFvVLdzwtWSu8akj9TVT3yEIci6PNLo9XaT06ZfU1QUJr6E7Mr9Ec8Hl3rMfWLd1kuHfNjVDVoyZ3VJ1JJ6L9h7ZDoL2O84KfAGr2M8iexJ4Z7D2SvJ1viG3sa+yowDoEBzQCc540SMEzLMvu00AxslW+O9bsk0AZ82/Zqioij+ZdAK4K/AewYisOCyeSsLkvvcR01cL2PDPVuXz+lvmcOFvuzLUmX5Nyc6K1gJujhnLcpOK9BsZjeZ+R8PTmjVQ6OKNcGOKbVvDFzwfmQLzXhLlg+LATsMexJ4yjm32Ue+2Ym9XmqQt6QFSjpzxURRgZcVSEGByY+X13SJo7sRB6QF8jpwv8z9oSnWsqbrpN20qOyGmZL+14yTZH5lFLn/6G/Ek8NZKH3Jj2I1W/bVL55WWG2eQuBsMr6YOaIR7CFDryC9S/BJp2DaorJVN62rXT5kGbl6Yi0XRWzj6fms/tLY8rAGbBnGqYgTOPC8BEwWWgIsMW9IMoOdMuqRmmW0GDYBp1EOigwrkqkItflKdP3E7RcIZg+1xrnzwdcv7cZ3S7andvWWseXhV8BuNrmPEPNPAT8Z8AdymHDQyXxHauzVhlN2zRo3+jEZMZNmCYpz0C6T2VaT1jqLvukLozWKgpTQdKDSYO6c4vP2zio6f9OGptuHZbjOWm7zrzbc/vKEfResKyiONgqSoAU9XCJgFOk54FRgetvfJW1xC+JZrmkxqFV6+FSD2f+0xvne/Vsuf+D1vXf0uOJSy21+6tglr8fNTQIqBXNmjV764IbG320/pBvm8J7M91EoADW2Ye/tu2ZNOvuPilxSxhjEeNJpGgaCJuAxnPtxMmYr79tyxQsbdt/ZcvLMUx5taimqdzBeqBJjgQKi6YVnv7ax6c7h6GYDpF/Iifsu3Fg4JipAei/oRbCfG6qTscOcYr3/2KgFqAd7EXgYdI+k28z8Lck4v+iNQDLZtPd30dySPz4HOhF0slMwds7Y8x54teH2Q1pfOZyFcqip6axmw5W757P6W2PLW/8qc5VIC82sP3lMmhHr8XYfzn7T7MIHHn79wNh67TPVySrW3FI3ZfvLcnyOdCq7L8VisbLKKdcN63lLcWlYgHQxkJTZzyZsnfLl3dN2nGCmSZH5E03McUhmvsRQsVACswSyBpNrdeZbDVpl7Aarw/GihfZUcyK1pX+LhrLxW9ZsrivfVi3jp4ZVmbe/0E2AhSOZAQmyMp/V8ZKy5CLBhUKz06mXraIX1rEWYDvYC8DTkvtTa0x39bzqXu0qp5fOcZFdZtL7ZDbRYK15vyoXwbv7SzXVrmZa6YnO+3tAzSj1rnVbrvxz1zNNleX/NTFQ4QSPLyL0hZFXfawg0dRMczPQnKuV9MqK6kIlS1fIuApsm/f+bw/lWR7Ok/kBjUZUxZpgZ/n2NyF/onl3grDJ4CZiVoAYY5CU0YqsAVMLYqvEenz4SFPHSI0HbXdlxbVjlUx8XMbfIzvGjHsEN02cUPfb4ZRmoLLi5kIlG78gzxcQtzYHyQ8OQ9cRnTF15aQ47vs4luL1u1SYvKSvyYIOZ6EMYJ75/YGb/wz8uT0KRiyIyl3KjfKO8Y6o2Zs1RdH+L+Whhheymg1X7q46rvpb9fWlm0HLhZ1jxrz6+tLJi6et+nn/F/QGApNvXlUWc3ovotXw/zMMRQJgU7dOra8r3/Z1mU5EdnY8FrsEqB7qhg0Xhml8u94zn9XxcWXhYsM+DpwHNCGt9FH486Getxx3XHVi8s4Jfyu5mwVPtsbdoqF05jwYB4Zg9q9Ai5d7f82WS7vbZ96Fw7lHOcT02cOHx7kkdU/tjrt8oM8a/BDwmH1BLvj8ECdJZfqekjGSu5AhC1PaN2o2VLcazT8A3QpMcOar846TaUa8UNJU+5pNl72cirsvYHY96EWJi+Tcl7LtPBwMqlgTRBY7GjhTUOtHhj+VlW6ZuastkMYm0HyZXZoZw+xI5TARCgD2h42X7po4cedXBV8TetzBORjfqK8v/XCnaCk5Z9P0TQnvdYGg2Iw/jJTtth0DaViqbdX+wr6WY8K9bdL15YeLyEb8HCUbQz9vMS2ZcuNsH/i7MMYb/mP31q74v9zXO2C0+fUVXQt2MT04TnZI2OSCuYZfCu4DYHtID4V3mqkesxacbY6F/pXeJO4ZbhyWQklT7Sqnl85RZJ9O58SgeLDWW5bNW1nQ0qR/lGmlmR5NJXTecJ+fdKaKNcG28p0nxsz/CJjX2XGyPTGPd1qMMUfiaKMtYVOXHZUysEbgxTbv6T+Z2XMWBY8Pl9RzB+MQXFhGCmlXm+kT3vFgEHmHNE/irZKbUTFm6e6TZ5362kBtWOrM9MLF453FvwJMltNN9226rNeWo+HCs6y1U2aeXN/UVLhX0iLk3mzOv7Zh3+1PVZZOnto8rnGR4FKkSxFvA+YBJWT3SRNpF6cyxEmgpRJH4ZhQMXppuKHxjk2DeW+HwmEslDQb9/y25Q1Tlz0chSQxpgGny+lNzS1FjXNKzn/9UP2auqOS6lhQXHIa8ElgK5H/wquNd4yo3qSdZ3fURGXjzn5xv+OkNK9izCM75NyFwi4lnVgpmzAOhgPNkjgTaeac4vOahrODKxwBQgFYX39HNKHhwicKisNtQiXA25BOAQWzx5y34dV9t+8ZqLqOnnJ+kSf+OcSJMlu7btuKEe26vmnv76LZxY8+L+l0jDdLOlnYOZB1L0xfCRzMGQkOroeT1atHHueS1KTaqbcqcl/AuAWjALMvYPzzovJVbxmIOqqpdqlYwTzgHFCDOf/TgSh3aJH5gtHbMPsNEJDOED1g6TYMHNJc4N9isfgnBts62VuOiB6lnWdZa6823rZ9XuEjD5kjQpoHLASmVow5r2HDvtv7Z42p+IcCReHHBUuBh5uD8NpNe+8MB6LtQ0U11e61wvjRwIcQxxz0gkNDoEInTjRoXjDzlEdzNX88VI6YHuUAsrvrLt9SMMa+iuy/kD0NvEuOry4sW/meQ/+imVy4a2I63TitOP1smPp19QHTA5PHlUp2BeKdua4rHcuZT9fVT8xxXX3niOpRMuk8bxGcjukUpIJDmbfMpzxeNMotQfZRYH1rYP/el01Uw5H5lMcLi3UO4ksc2qS9rwgoEDp6dvF5dw7k3LG/DKj38EjjcS5JVdWuubV+ct16C6wF2Vlt85aJi8pX/bA9T2NHqt2Z5aXTYmaz5VVmgRXLU4DCGObPBgUy/m/k5xoxjZr0rUkQ+9Agp1APgGMl+0fg3wax3h45YnuUdtLzlpN3zB5f+Jg8BppN1nmLqbJ08tTZ44oWBt4+iNzfIrsYeA/iAtC5iBkg4fTXeYXnbshdwLvcM5/y+OgSnW3iUwxOb5JJAJo1r+jc24fLMzzihZKmxl7de8fuWZPOeVzeNQLTQGfKcWLFmKXbZ4xZVnf06D9PsUCfEFyG9A6ggo4LbEr/rQCz43FuYsWY88J+GwiGiGNKl41SYP8GnDAE1Qso8M5t3rDv9oeHoP4u5IWSwYbdd7Zkm7cEosSLM4RdxsHXD4QoRJyIOKGi5B3rNzT89pXBaP/AYZoz8YkZeP8VBtAU3Nc2SIpebbj9f4am/o7khdKJWm7zJ+/78Isto5qfNGdliBPBLRS8ib69NE5QKuyoeUXnPjBchhC9Yf788nhib7hY8PdD1wonmUqmF579s+GwCHkEmocPzlouiu7Zvv1pL/sXxDNtmbUO5csaAPN9LFg+wE3MKYltjTGHnTy0rTCZrDiIuzcNbTvS5IXSLdUeWusw+rnwpYQZ76ucct3xA9Ou3FPQWhCYUTHU7QAUWDBlqBsBeaF0SxVrgkCFRwFv7l9J6aj2CoKqAWnYIJCyfQKNHep2CIRjyNsBR/g6Sk/sm7c1ZvvcSWhAJrMxed6+aPo10wagrJxjvnCM8KVDnRDawJm3vFCGM7tbQjdKiRkD864oQPYWovhnBqS4HCP5BMbMoW4HgHDDYtSTF0o3JP1oFZIaPTBbQE3AONAVA1JcrhniniQDL8K6oW4E5IXSLcWJWu9aJm42DYhUDKPWHL8ciMJyjUwB2LHAmUPcFC+5rUPcBiAvlG7ZMRo/tZUB8dcSpAzuuXfL8hFhJq7k3lgw5Zml5oZcKM2R6fkhbgOQt3plodotKls1a2p9aRXSkoEo0SBl+BGzb76GhZFL2J+BoVzoi0AvDZcwT/keZT/VblHZpBnCvz2dONQuMCgj/bL0JzaVAdvkeXBg2jkoWKrF7Q1ido/BBUPUhpTM7hyiuruQF0oXgbgLBGUmthvcJ+wJ0HuAQwstajQgfrZu+4qXB7bdOWbHsS1+6lPfldxicp+CsDMe2DCcomsewULJKpCpXmyWVIP5exzB/VFB61NBa+I1w75IH1OHC5Imbm2N2Y05uomcUcPC6By7+tGQoj8hFg9y9Ulkt9xbe/mwGHbBESmUarekvGy6+fAUL1vUUSDch7hNXo82BanHHt50eTNAZcXNq13LvvFIH6L3PUujmd1r6OsjdBOX7dxWsqtkauomwUn08SPRDzzGi0T240Gqr1ccxpEiO5MhEPxCOZ2LMdtgs8R6E7c57x5tCloey7bXfdm8lQWtTcHH8Pb+Thl+OyDUZNhzSPcG5m+5u/byx3J+azkkIyf9lYNQnQHbQV9cV3vZdwehvl5zBAilfwLJZD6r42PLWxfK3Fne7HhJEw0KlM7mW4exRWKLl93X4lJ3j/zgEgCmt5evnJGw4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AjyAx9IWLkhFg8iwgiBEBFWSqwIBTSnDFI7ROyRaRkiXJODLfHOovIlsAU+H+CKdcqij7dDnCtQ3tB3PaQXCC+QXqB8oFvhr/zMV4uUyiJ0t2ilULFG+lFO0RQl7e4Ma2trrK+vM91t0+l0ybIhJ0+e5dy5i6xcWuQTn/gEjz76KJ1WRBzrMZncb+c8/C5hs3+yavyTBd4LisKMFVc3Ps/7axciJ4W0vx9qchqfCItylG4w3tJb73PqzGleeeUIp0+f5oVXXqYsS/I8EKu1G0nbDIdIKUmSBF/JQZ1z2Ep9EAnQSpGmcfO+tVzM2rKKTv/hqJVQzoH0o3TF+ARyvXDT0wt1pOt8tKE/QggRrL3waCFASaQP5a1QezJoDxIRSl5FgayKXVpohNTIpIuqomnpFsAZvMmhGOCLAWU5oChzvDPY/nkkDmEtihLnQiQsfInAcq29Kbz3dDptvAnFr7IswxJLVCSsIpwzoVjgNOAwxhOpiFjHSCn52S8e5/LKGv1+n49/7DA7b9kSLi5/7fd+r0AIUErgXJ0brHXZoLXcYEC0sREn3God9fjyeILfHEVRXFFnMMZQlkGG9ealNc6dO8fRo0c5duIUi4uLrKyssNbrMxwO6U53MKWjKIpqKR+MZCKtkVJTFv1QSwlbxWDr/C9VRFsV4LIsC7p6Rivl67ESHJ8AxnFlE9c7x01PLzSILHULb7MDMK4ZSqVoIbyvimUFCon0Lvyv86gormYqj3FBvmZFSA0IIek7hZQRWreRyVzIv3oPNuxiPN0/D67Amz6uWMXnK5hyBV8OwRXEtK/6HeoTlQ/7wZFIKiIdARJcaCssvaHIB7RaLVQUkec5zli0jgFPNhgyt7CFI0eOMOit4Z3lC5/9DPNzM9f9hP+uYjDIWFtb49KlS1y6dImVlRXW19fp98Py1MlRzi9JEqamppidnWV2dpZ2u83dd9zWkMbm/Pgk0n171OoPYwyrq6ucPXuWY8eOceLECS5dusTrpxcZDAb0egNKG1YdOgoBRZK2GA7yaoWi0brW2AvyvMTajDgiBDdViKmFwDbnxIdWfCHQSiK0QgjfFOZCjeadEe+owAfCh5zuaHL/PSDd+mA1BjbNX6qwxMsm6LXVT1nPbkFXgvPgUOAVCDC2fj7QuDe66mZIVEjyg8fbkUEjMgIJy3N7gyzMg/KAyXFmGG42h7VTeJtDOYSyjyoHKJ8R+xKJBRJwEmvBSQkiwgqFBazwpFGOLX3Y700poigJqRMHOtIMy5y4M8eFlYz/9z/8HWtDy7/6l/8RwgqUFE20FkXRBqvI4XBIa8w16WaiPp81qV2dzIKrU2hoEdUp8SBV8LaQYTk3yCxn3zzPkaNvcPrsGd44dpLl5WUuXbpEv99vOoucc02hcWM1XY51H0r2bN/Btm3b2L9/H/tv38fOnduZm5ul022hpUTWhRPnKj/n8HpehGuuTjNZPyq6OEE4N9z8nUuC3zObPKHH0CRZq+9UfaFmnJnRebPeIavaymBYcOHCBY6dWeTSpUucOHGC48ePc+HChQ3nodbPIyVaxs1yvX5cuKhy5qvGsAeJQwmBV4pCeLSQKEJ6z1aEV3qHwyMjhXQe5QhyUA8IF/xzpUCUBT5NSHIPOqI3HLC+NeGuUxnn5wRaXLu5QW7OSYmabH9PcrrvRjgXLrTaf1PoCK08PpI4l6CTO0N6ogxE7IseZdGnNAXeFYhyKSxRTIHwBdr3UEKQaFfld7t4WZkqOzDOAlXzReXZubbao9vtsj7o84tf/ILbdu/kc5863Oy0XC/txk046gjkZqMoCqKq+DGOpplFeAaDAUop0lYLwWigKhWes9bvc+7ceV546WWeePIpXnrlCHlpmJqexpiQtinLshnoQON3ujnnPt696L3n5OkzvPb6G/zgR4+hteSWW7byvnsO8MADB9m7dy8HbttDu90OZDvW9eidozRlmBy1RutQhd9McDcb43GeryYiIUTTSeUaUhRIIUc62gpKS4ZZwfLaKisraywuLnLs+AleeeUVTp8+zdJaNhZZblSSbF6WXy0y9G2NqUjY+nAOvRRIHVaiOEtpDNp6IiuIhCSRClkR+HqRoYRAS4WIIxwhmHHeUeICUWuFUQox3cXpKfwtLfJ8iWFaMrVur/hMNwsT0q2gqm4ZLwUeiRfgSUBFSBw9t4AUQTOsfGjKsCYDU+C8QeVv4k0JRQ9Z9PGmB+UAbzOcLbEi9OaEZVFQSng8QgTdcEvp4BkhJXmec/z4cb7xjW9w1/593LZvF7DRQKd2Q6r7xG+2wmGzM1Odb6snClPmdLpd6u2Zgo2mwlg4depNnv31czz99LM8//zzDPOStN3BCUVRZpx98yJJpDZUq+v3gaubLI0ThHMO4SUqimlHGucti0srLP70l/zil0+glOL2vXs4fPgwH37kQ2zfvp2kWi1JKYmjOKSxwrtW309tfM+bnJ5wVdG1llTVk3G9Eqh/rzZYwNowya2trjMYDDh5+hSvvfYaR44c5eKlSwyHQ4Z56MKM45gsKzY1owTUx/jtNOVyOEQoSaIUSifVZ7AUJqT3ZnQKCoz2WGAoPLlw5L6g9I6FJKJ0Hl+U+Dx4rGitieIOsVYUu+cY3jZPfuBWuGcPOpKIS8uU/+b7lEeOwvSVTQ83CxPSraCq/K/zHuvrpLrAV5ZxuVVVzpZAjpFHuCqf5D2qvRusQdkcaQaIfA2XrVFm65g8R9kzCOcQokCKinIFzVLbGE+328WUIXfVand4+dXX+Juvf4P/6r/8L9DKbyDacdQqh5uJ+jMVRRio473q1lp0FOGMx/rg4F9aeP3YSX7805/z9LPPcO7cebIswzqH1hH9PMcaT9RqM7PQJu/3NpDtuLv/VSOrWrrUFGTqPCB4HxhVSoVUGqkUrxw9wfHT5/n6N7/DPXffxaOfOMxDB++n006rV3RBq+08Alm1rVbF4GrnkJsJ7wxCRUCQa1g3Mp+K4xgP9Ad9LiwucebMOY4dP8XxYyc5c+YcKysrZDZnbW2NsrR0Oh2iOAahgzqgtMzMzDRqAWNMM7nVk+3Vmk+az+Y9MhJ4a8nzAm9BetBS0dEapSLO2j5KKCIhUS7Uj1MEUyqs8JLLQ0w7YTDfZXDrAvn+rdg7duB3b0XOdZkVmjyCTMAwlbRLR5J3EJ0201u2QPHOdLzXExPSbTDm94vFVvnlesC2lQWqXFK1j5sbu9AK0UZoh4vaSGaR3R14k2PLHGMMrfUEk/cww+WQE6ZEE8Ti0juKoiRJ0tFyPNIUOfz4pz/jox/9KB94+P5mt4yaeOv7N5twa9T53M3kqJQiz8PeVALFK0eO8YPHfsTPn3iKixcv4hHEcYyIIoTxlNZVaQNFaQyXLl1mqhU3RDtupFST7ubq82YBf17mVXogtFUHOVJJNhyGNuEopSgNFxYvcfHiRZ5//nn279vLRx75EO9//8Ns2TpLpKOmCaPIytHy/V1QhNNRFNIK1odzoEJu/NTZ85w+fZqf/OzH9NYHLC1d5vLlFdb7Q8rSVo0kkiTVtNtdbNVZUlqqTWXDMV5aWroiT17jN0k35Cro9iQKjUJ4Ac5TOA/Wc4tJEbHGSMVAWfLEU7RadOZmiGemePEPbkMlKXGng57qolotiCKskFg8i87gtGZaRnS9oIvHFkP84josrsFMwrsFE9KtEETQ1RQrQMvQz+9FOESxL6oBL0a2b3406K1awTpPSei+EUojI4lvB3N12bkf2V/GrZzGrp/HlWt4V4TigZAgHFmWEcUpxllWV9aZne6yut7jB4/9iIP33kWSJE2xqi5eXM3u7mbgaimO8ZyiTiSnzl7ihz96jB889mPefPMCcZqQttr0+32WV9dJ07T5Xs450jSlE7dJY0uZD5vXHG/vraOvzRHv5vtJpEKHYF42j2mtSdsdlFL0h3lIGSiBwHN5pcfi08/xxrFT/PSXT/ChD70/tHDfeTvtJCZOk2Y1FFIPN3ficy4U/7SEYWE5deoUL7z4Ek898xxHjx4lz3PK0lIUBaZq3tEqQkVB97q+thr2DIujEMlai688LcrS0G63N6RTNt/Gz/3VVh7pao6UGrTCKw9a4YTEKI9Tgn7kibbMondtwe+axdwyjdk5S7ljnsH8LNL0kTICrfEqCo1LxiJLiywdXRkzBIT0rJAz1ALVgbSraW+ZoygHN+hMvD0mpFvBO4LPghAI4ULwUvk+gCSvo1ocUoYllUA1YlGlPF6Ell/nDcJavNcIEdzF+slukFNIKpLtEdzUnEHIUKSxRRkiWO9AhrxtlKQ8++sXOHLkCAcPHmxMlse9e98NGB9o4y2jeZ6zvLzMz55+mccff5znn38+FN10TL8XBkK73SZKWk3+NYoVWkiEtxRZQVEUJEnSvM/VBn896DeT7SjnKFFjHRJ1FB6E+mHT0sKGAo/WmjhJsDmcu3iJxeVVfvnkM9xzz9185JEP8eDB+9i7ZzezU23AVaqSmxtJlcahY8nipWV+8tOf8Z3v/YAjR1/HWE/SbiNtOEZx0qalVPAhcY7CGvI8J20lKCVxJjTzODxRlCB18A0RfrRjy/juDXXUW5ajyWw87VOfj9b0DJmz9DD0E4Gb7RBtnSPdOkc01WXl4V3E3TbRTBfRbWMV5N4yQGCx3OFmKa2jzB05BitC5KyUQmjNydQRGc9CAcmwJE1jhhIuqgLfNdyyfNNOzRWYkO4GSIL4xlUK+0Ci3gvKuBpUPsjwg6MZIYVmIXIhUa9xRFRKCGehalVelAlTcprp6R1EMse7PtZkwbzdeqwPxFsUBdbD7OwsS4uXaKcRS8uXefnll7nzzjs3yMPqDfTeDYU059zIYWqMBF977TV+9rOf8c3HnmQwGFQie9HoaY0tyYYDiKOQikCgVYhKbUXe3U6bojTNa48bpwgRUhObN0Ot/7e578wGRYOUGq0kcRSGgCktcRxTWoMxDmMcUsfE7UprKuG1o8d47bXXuWXrPB/6wMN87PAj3HXH/rG8782DjjVPP/sCX//613ny6WcZ5Ibu7DzOC9bX1+noMKkVxoQIUUp85R2ttUY6jzMWL0b5eIfDVmTq/cZNYWvUKw3YSLj17/XP03s6RHMzJLu2Mr9vB/7WrWRbpljvagqtiNFY7yl9eA3tJC0vib1AOjiuyyAnFAKkRuDQXqCsR+LYkktKIfCxJhOaOImYGUq2XCqZPZ9zfvrmp4BqTEi3gpB1dVqCj0eyxorLYnuNLTskUHtHEOortelKaKmBOfkmzsbkcpZ+cjt+a4KONfLcUWZdwaoK1WUlfPCC6PfoVoPZeXj5laN89R8nVQqiuoC8D0viqhFjQ26xJpzmMbtB/bA5DxmWpxuJewNJvU1UrZTDOoOQMTKKuLTa55dPPM23vv09nn72GWZm5hqJm7WWvKyj4ZAnNXlOpDU6jqoUDqO2b6+xNttQRBzlcEMxU1AP9o2ypoakRQsp6642h7MViUuIlMYpx9AUKARRpPDe4Cpy0kJjjGsmluW1AV//9mN8/8eP8/73v5/Dhw9z34Hb2LIwh5IgvacsMmIlm/7jzGoiKaoJSVD7OtUTprebrCs3L9EbhYTFe0uWFbTaXfDh3H37J0/wta99jddff51Op4OUnqxfNeR4Rx57ktLTNWEVUQrPQDgKHAbLFhFjncWUBudKhKp3ww6fZ7X63I2vrA2WjUHuK0gKj00Vw25CbyZluG0KuecWurftorttK9m+WXIh6LFpfzMgtaLR66tm5DkcoTCGIrQmuSrKadRfo0nVOhvMsrwj8QoKQ24svamE9WFE5Ed+1jcbE9K9Qagj48ZxSbUQugUqpuQahF5haekyvd6A2dkudaOr867yPq8aDcZRy7eq5aCOo6vWe8YNY+o+hebpQiCVwjnIStfIj4qiaMxJTCX58TaoDZZWVnn1yGs89cxzvH7sJEVpmJubY3V1faNByZh43ntPlFRknOeN9lbrGFsaymFOnIy6ydI0ZWZmhh07drB79262bNmCEiEXnGWhc21paYnFxUWWlpbo9/usrQ9wFbGFZbFspHfDvCDpdCmKgsIYpHJEUiGkwjqHLcoNeug6r3758mV+/OMf8/TTT7PzlgUOHTrExw9/hNv330ac1CuSYNKS6JFXhLMWU6dFhA+R29uZtn+gTMIAACAASURBVAiBNcFc21potdthYjKeb3zjm/ztD37MsWPHmki0rPYMrEl9rciwcYRJFLYsMc6ijSD1oC1cUkOkVug07LRi8aHN1xisseyQU5jCUnhL6R0+iog6KUm3S5wknPzwDlqzs7S3L7B1YYayk9KPJX0lWJWSmI3RbzMmrlJ4+33HhHRvILy3OCwIiYjbqHQWE3cxhYG3Id4jR9/gyNHX+cAHHkQSxPlCysoIPXiKjuc3A6mA0ApV7VvhfNg3rjZIH7+dOnWKfr/P6uoq6+tBu1nf8jxneXW9yenVTQq1fMg5h6kIuLQ+GBVVW2EnUYTF02olG7wPwmu5EKUKQGoiqbG2pCxyyiwjTWPmp6fpdFt89KMfZvv27ezbt4/t27fT6XSqNEWYIDYnV/Lc0ev16Pf7ZFnG8vIyR48e5Znnfs0bx48zGGSVmb3H4RkOQ6Gu2YyQMKlJFRo+yiJr9uVzzpEkCZ1Oh6IoWF5expqcE6dO893v/4B733c3H/3wh3jg4P1s2TKP1jHDYU6rlTTEGpboY+f8ipXJlVA6HB+tNR6JkPCDxx7jW9/+Pq++8QYQ8uP1+ak/bxzH7HaSwnjWXc7AlchIkyYtUhSitEAW3tt4RF6SlNB1ikQlJFKzlhXIqTZ6+wxmzzz5vgV6uxcw22dwMy3SKKYUgmUV2u+dDNuhy0jRkhrfH24aC+9NwoUJ6d4wiKa12eGkQugOKpnFRC0KsxbWiNfAzOw8l1dWyctAsrpimcGwII5jdCxZX89YWlpieXmZXq/HYDAgyzKKouD85RWKomiItCak4XDYmJDUBiZ1dLRR/D9KL2zWZIYoPhhKI0H60FxirSUvCowtmWlPbcj/1c+ribjf75NEijjStKe63LJ1gXvuPsD7H36I/fv3hWhWKbSWeA/WBs8NXR2IshjfwRXaiaSdTMPCNABFsYNDD97Ll774OU6cPsOLL73M8y+/wqlTZ1hZW8VZQeks3osmojbGkJcF2aCkFcmmESXPgwwwTVOiKGJ6eprecEAURQyGOU889SxPPfMsO3bs4AOHHuaBBx7g/QfvAcBYHyJWpQCJs2HFkLY6Vz3vTfehUCHz5QV5aYh0zLETZ/i//+IvWVpaDsXIKGqKg/XkURc1yyLHKUEriZnSobDmBiWDok9pPXfIlEIKhpFkPZb05mLsfJdoYY54eooTH99FnLbpTHVptdtEUYzyntJZjLO4NK1sTQVYT2REqGn0HbgC2776VkvN+HgXyO5uFCake4MghMILixMG7xRSpAjZRsgIh31bwdHyep9vfe+HHDtxCutyIqkqZcAqcZRy5s1zDAaDJrIb9zx1zmH8qMhRV53H1Q/DSjxem3z4ume+7izj2m2UoTCl8FJQljllViIVTHW7JK2E9curTadaFEVNtAshuowVtFspe3bdyqGHHuDwRx/h7jv3h8jQg/em0siGyFhqQhOAKaporm7GcM13Hh17QRyH9t6Z6TaHHryfhx68n9X1AY8/+STP/foFnn32BfI8J8tzyjInywfgJTqOaLfbZL31htTTNG0msLArSESappRlST8LuWdROt44doLTZ87x7e/9kDv2bOfQoUMcOnSI3bt3j7KRMiJtRRRFcYUD2gYHrcrzQWmNLQvW+kP+v7/8K46fOk0St3HSNSsPKWXzGfM86JPzVGGcRVmDtALXzxnmBXKuS+eWrTyTDNDTU0S3LCBuXcDtXIDt89ht8xRTXRaWV5rtzQsZ1A9WiNDgIwV5mSOkQipBJBUg8DYUJG1piN9CvzuJdCf4rcGjKymar/wXYqQPJpWeAri2IYd1kudfeJk33niDPB8SRYp8WFS7TEQk7ZGGd1w94EIT1YbdlEMH3Ciq9b4Srl/Fx6CORG1xbdI1ZXg9T6hyp3GMEJ5imLG2vEK328H7kcSoKIqGaJRKueeu2zh8+DCf+uSjbNs6jwTyLAMg1hGyimhrDwEpZZDqVZX08dbjcR0v1BGVR0hJJETosvKOTjvl85/6BJ//1Cd49tcv8eSTT/Pzn/+c02ffJNIRSSvFGMPayipJpCiKAq11k1qoj5/WmmGRN7ppIQQohfOerCwx/T7Pv/QqL7x8hL/42r9n//79fOSRD3Lw4MGwA20nCR1gV1wzo/1KqhohpSnRUczPH/shP/zRT+h0ZxgMc5TwTaeYEGKDIXgcx2wZCs6XfdZagmLrFO279yK3TlHu3kKxax7etw3iCBcFLaxBUNrQJOSzHlNpGvqCav+EompUIUwUU0lQfpRlQe4cVLllGWniOMGXV98E4L0U4daYkO6NghcI6RHS4r3CmeB2Jp1F+oy3I90obZFlA4ZZTlkapNRYD1PTMxjj6GXDjYRT5fO8lJXIPWCcmMbTB05sNIrZfF8LueH59f36p9K1+34SCmxFXgnwFVOdLtbZpnBWpzI6nQ579+5l3759/Of/4qt0u13aaavJe6bpuBTrShWFH59klHrLASyEwNuRKkDr0YVf56nf/8C9HLznbj7z6Md58uln+MXjT/Dqa69TGMt0tw2IRtObZVnjcFZ/pyTSeDWyl7TQpCOMMeTW0UlTcuN56dWjvHLkNbrdLnfeeSf33H2Ajz3yEJ1Oh+npaZJKnigQmMp6NKlsEk1esry2yje//S2GecFMOoOXhiRRzbF1zjWEW68qesbB3h3ED+5B37+faN+t2Nkuy9pxgYzdLsU5jy2DfM5IcErgJKAkK4Nh5Y4XIaVCE4XJzFXHvJ8TK0lLJchI4qoVhynCROD1lWu5t5L3/b5jQro3CF7UJBckTqHiHzrghL/2VkBAE2VFsW6iPGMdUmqGw1XiVrqhGcCUIwmSlArh/cjtxIfPEO5WxTctcVVRLZi8RBsKR8JfOUCageI9lhKbmeq1PVpFxDrCWk+e5cTtqCEEpRTT09Pcc889fP7zn+ejH36QxJnK/T/YX3ovqoJRVWPyXBG9emQT6TZfjStdsIBG3eE9eBOMh4QI0i2pFeCIleTOO/axf/8+Dh8+zK+eepof//TnvHLkVUrjmzbi+piNrwpMmYXjj2yibqoUjveeVjuoI8rhMJzHKGLx0mUuLj7Or59/kW/8h79g7969PPDAA9x3333s3buX6elptNKNnVmWFbRaCX/1N3/LyROnieKEQZ6hoxhj8g25+CiKmvRRr9ej98jdpB+9B/fRA5yfTiiKkq5xtGXMraQMvEH4YJsYCUkLUBYog8dIniTBv8J6vHFN+knKYMe40hJIGRqLZH19KxteUwj0Jk59L0a4NSake4PQMkOGSpCrGJ1KomEPv/4m2lkMLWS1TK1lTHWKoCZSJUrwYKqCkffQ6XTIiiFxGoXOuYqhgjsW1C4s3luK0jR53PGIs8nt1sUpKq9fqcnLEi8EkW5jy/6VX2ps3EQqYTAYVBNDTFYWlCY0HAgRWkkhCBtu27uHL37+szz68Y+xMNfFOxBydCmOd45VyjU27DzJxoh7/KM0m4q+BYKiQ2x4RoPqLaTw7Ll1Gzu2fYZHDt3Pm2++yV9/83scfeMNzp+/iIxidJKQF8HrNU3bKFME8b4XTZeirjoXnXOUwwECSHSIPIXzxHGCtZbBYMhQCC48/wbPvnKKbvcx5ubm2L9/Pw8//DD33nsv26ZSklbMSq/g+RePMMhKtI4wzoE3JGjORgVThWEHCX1TknhYiiXLnzpI/q++yFAFJUt3IBAiCR7NdRdefcxEUMgW9aFR1a1uFmpOUvhhcZRA6kMBbfRnQUMvVwli30uR7WZMSPcGwQqBI3RNSOERJsOVA4TJkHharVbVH182y/+afCEstccLY+NLSe89Ook3Nj+wsVARp8mIpLwPqq6q88P6UPBotcI+daEQlzfV8NXeOlOdaxulDwZDFha2kGUZeZ4x052iNxywurrKli1bKAZD2u2Uhx54kD/+R3/IA/cfQHjIKinVzUbTpFAd43opvWvXLubm5rj34CFefPFFfvH4E7zw0iucv7iI8QV4gcn6RFIgZViFBNvC0B5svAMvaSVRk2qoI+F6wquN6etzNRgMWFtb4/jx4zz++ONMT0/zwN230+lMoeOIN954I/hNyKChjqIIbyyt0pImKcPcMnAlFxNH8qF72fKxB7hQTehX25J+vItsgt8+JqR7g1AKjfWh6yZyBbJcx2eXoRyihGgaDoQQpGmKUqoZoEophnk5WipLhYoiorHOntBUcGWutUa9W3H9+Lj/rfceFUXN+0eRwjmBt2FH4tmZrayt9675/aanZ1lcXKLVahHHCZcvL9PtdpjaspWzZ8/yvjv28tWvfpXPfeazdNtRiC8FtNIEuPmGMZsnqtodDcKKYjDM+cChg3zg0EGOHD3B4796kl+/8BKnT59meXUNpcLxsy7s/xUUI45IJ6Rpii3zZtkPG3fUrUm4LsTVsNayurpKv9/n4tmTSCmZnZlnaWmZOEqQSlEUOSKOcVLQHnqE9vSFo4wki7dOMf+pe5h/+E6iTZLE93KkebMxId0bBFNpLJUD7YbIYhmTLyPsEKkFrip61AWamoSBaskeikr1QK13UIC6kFWVtwE7NqDrv3daI0OZqxXM2u2kirRN2IdKCKzJKfIBw/4aOrm6jrRGWVg6aZeyzPEW2mmLPMtoxQmPfuww/+Kf/An79+9nqh2iZ2eDuY+s2VfcXNIdj/bqAlizLY9ztNMkpGCc5+4793Hgzn0Mh3/MLx5/gieeeIJfv3KUXq8XJj8VJFOyLINxLNDv9xv977g5z/itXr2M9MYjRUmZhbz+aq+P1KGd2lGdQ2vJtaQlFL0sQ8YRKkrp3H8H+ftuZUk7pBlFuJtz3pMo98ZiQro3CFaCQhFZi8x6iOEllF1FeItANxd/vQQFmogXIM+zJscrIRSC5EitYMqiuV9vYc1YB1hvfSW4Z8UxUaSbwRxFUZNGmNp5C0kUE0URW7ZsYWqqw4kTJ/j1s8++7fdzzqFk+MyRkuRFRjdJOPyRD/Knf/qn7LplDnCURRY8beMY74JfQ71dz83EuGlQTXo1hAitu1KpsCeaB+8daRLxmU8e5jOfPMzpiyv88pe/5Cc/+RmnT58mLw3ooKVeGwyYm5u/IjU0/vr1BFuTbd35V0e/U90OsVQY48CLZvPHOiWSuYJ2K8YOerRUi14k2XbfXZyf7lCsZ+g0pIeuppUd10xP8NvHzb/a3yPwvkTJFO1K/GAVny2h3AChBE5EG6LSOI43FLyMMSjpUYomhVC30FLlDDtJINAkSSpiDfeTJCGKIh566AHSNKXdblcpgJhWq0W73SZNU1pVzlcqTZomWA9vvHGcb37zW8g4GRn4vAWSWLO6uozSklgrti7M8Udf+gO+9Id/wEy3xerqMjMzM0Rx3Pi1RlGEknG1lP9tn4FrY/PebuMIqw+LqdIvSimEB+cM3kuElGxbmOUrX/4iX/r85zh58iRPPPkUTz31FOfOnaO0lvVqE0fY2PQwvgvGeMqnTkUkSWifHgwzkiTBetOQct2hJ4THl4ZiqkViE1pOsphqpnZvRXhJWyaNNek4JlHuzcGEdG8QPCVKJEhTYgcr+OEqSuQIlVBYTRr5DT65w+GQ4XDI7Owse/fuZff2LbRaLaanp5menqbb7dLpdOh0OkGsn0TEcbyBaOv7WoOpevHf2oPX4Vzl5wCcOXmWb33nu/ziiV+R5SVxfO1i12DQY35hjkFvnR23bOWf/7P/mM99+pNIAcYUzMzM4Fwwaq/TJR4w1mCtu+mkO47xJXija9YKSShcIUF4gZJ1C3JBEodCZhpJ7jmwnwN37ufzn/4kR48e5cTpU3z7hz8ny7LG3nKc8GrCrVM+dZPDuHol7OIQQ2GIomSDnaIQgq6VrFIwH8cwNORbZhjMtcnzkpmkQ2bz5v+v9j0nuHGYkO6NgrNI5ZHOUmYZlH2kNkAb4wTej7YSr4lxfn6eT3/603z5y19m55apkArQo0FTR59ShNTheNDiPTjnkdUGi1FUsZqvZD9jSgbvPZgCGYWuqPPnLvKd73yH73//+yytrrFjx06GvatIxsYQJxFLS4t86APv58/+5T/nvnsOYG2BcYY4ihsSSdudZqAXNmzwGMU3/zIc72gbX3LXJJUXhjjWSBlsMqwtiZQGIUI3WaUzVgrwAiVg546t7Ni+lQ+XH+TRz/0Rx48f57nnnuOll17i/PnzDAaD0bEYI+Jx/W/tpaDjsFNxaS1pRcR126+WimkVczYfsFC5gbFtjkFHM+wPGUYhH381Vcu4SdIENwY3/2r/PcHmC7l+bHTbDoOzyJUXSfPXg4upmAahiVSON9DtTrPS61MAcdqiGPb51Icf4sCt0yCv7Fjb4OZ4FWdHNfYPHrAmaIHxUBaeSAMyNGj4qI0B+v2Sf/+N7/OXf/UNvIC5uXkGgz4IRZzohii63S5ZPxjqzM3NkS0t8gef+hRf+cofc+/77gIC6QvqLq3wU9YfDkjUu2egX410xh9LxiaG0PC36XyM56XHzoUQEMeaPVs1e7bex0cfvofFxUVOnjzJq6++yosvvsiJEye4PBzl5y2hGFq/bjCXcWSDNWItybIBU1NTLC8vE8cxhbGsK8FsLsi70BECs9ZH55bbXZs3fUn7Lbq/JimGG48J6d4AeO+JhMGXOc5koQttbGkZcqmS9fV14mTk1nTHHfvZsWPHNcX+vykEox17raUxQneVoL1wlkgrvva1r/Hd736XbrdLfxgcydI0wWPp93Pa1edbvRycrebn51lZWeGffuUrfPKTn+Dee+8Ke8WVOWkaIuf+YJ1Oe+Ydf4ffZZTlKAe7Y8ctbNu2jYMHD/KFL3yB9fV1fvizxzlx6jSvv/46y8vLRCoUOMuypD/sI9yoI05KydLS0shox1nKKjUlhgU+keisoNUrGcy2EOLavhkT3FhMSPe3jDqq0G6Ay9cw2SBs56OioIWVhM0ppaYsB+g4DdvGDHt8/OMfZ8vW+Sui2HeKcXVWUVrSNEZ6yde/+R2+8fW/Y229R6c7FXYxjhOoltq1UU2SJEx1O6ytrdFupXz6U5/gq1/9E7Zv3wqECNvasSgxufnNDzcbTXqnglKCdjul3U7Zvn0b+26/ndI4lpaWeOmll/jVU89w5MgRVldXiTtp5ZccotJWu8X6+jpJkpCVwTiIWBNLje6XFFMp8WpGdHKRwc4Z/NDStJBNcNMxId3rhKsVJMYfS8pV8mwZm60RVwTmrAfrEJGqmiJiiiJjJukQtVIOf+SRKiK+Psvw+vNIKSiNazxhPfDkM8/x5//X/8Naf8Dc/AKXL6808rJaE5xGcfCSLQuiSDHVafPwwXv5z/7sP2Xn9gVKY7EuEEwcx+T5sFFjvNdRz5ub93Kr1QtxHKO0ZOvcDJ959ON85tOPsrq6zokTJzhz5gyP/eTnXLx4kfMXFxlc6qF16GTLhwPiOCaTHmUFba+44EpavZz8+eOUH9yDshbkhHTfLZiQ7m8Bm31CvffI7CJyeBlfDhAyFJAsHolEeCitqdpBc4o849BDD7J713ZsnoN+5wOm3gOtLEt01Xaq4hgH/PrXL/O//x9/Tm8wJG23WFlZw4uguR0M+jhvQIbnJHHE2toK7TThi5//LF/9k69w644FnHNNNGcrM5Q6wn037JZ7szFOtuM51LoV2JoCpTWtdDRBzU51efiB+zn04P18/gtf4OTJU/zgR4/x13/zdbyoZGymNgoKFb4kSSiKdWZ1ytpLx0kuPYzotH+DDaEmuFF491Qyfk/RyHJ6i6hiDeUKhK8cwOoln/OUZRhAaRKjpeCzn/7EhjzsO4UzI1NyAFVV4td6Bd/45nd49bWjdLrTrPf6GGdpt9usr6+Pttn24bNkWUYrSfn44Y/wj/7oD7lt787gh+LqLYfCzftRHnFSGa+Lb6GpBULx0poimLA7M3JLc0GRYK0NHh0VP6dacOfte3n/Qw/RSmOG/UGwlEySoHaojJOLRBJ5QSIE7s0lpl89T/p2IusJbigmo+E64a1c8eubHVxGmD4RBuEtrrJKFKgN7bhCCO68/TYePPhA1Xl2fdILOorAg9YS5w2CsNXPN/7u2zz5zLO0OlOs9fsY45iamkJUfhD1Dr5151Or1eLjhz/CP/unf8pdt+8LRuOVSXpNFnW7a/heAqWu7RX8nkJ9nVQNEkqHTSnrx4XUjWxQyLA1UVEYjDVI4P777uHDH/5w2AnDe0pjGA6HlWRQsOILZqLgXsZgSPyrNyh71/bNmODGYkK6Nwgu7yO9afY2CwSrqvuiyX0WWc773vc+Zue6ABv2FHunyKrNF+sJ4tix43znO99hvddvdhqYnp2ltx4E/PPz8/T7fUQV5fZ6Pe64Yz9f/OIX2b9/d9g+O64NaxxKiw0ytfGJZ4Jgun7lgzUJhz3TIKRnSuOwNkS6OtZoKShNSRxH3Hv33cFZjFEaR3uB1jFrJqejQteftB7z8mmylfUb9A0n+E1ww0jXex8sBQGPI4kVebaOjhyCAufF7/RtSIQyXdJS4lgkj0uIt9DOBsxc+B5xuYJ0JVbGWJkCAokJO0koD1rRz4bs2rWdwx99BE0ovlyvyr8V4FXwe9UyIh+W/Nt/97+xst5naD1t1cJU277otqRf9CmdDZ6vToA37Ln1Fv6TP/0nfODQ/dUOxoD0GFMCGkFEKBOEHKMQo+3Wx20qr3ZteEKzh3Vs8AFwtqQssjH/9fo1HN4ZfJXWMKa4IsVRP14U2XU5hu8MMngGN+S6+fcRlFJEWqKVaCxtnQirB5zl8CMf4vbdu7G5pZ12GA4swhrWXUaqI3rO0C0F005gRc7Obz3H5QRmCkHHK5yWpC7sb9bT0C4nOt0biZtSSBsvJEgh8EKg5e92NKSFQWKDnpIEh0K7AmF7+HL4tqovKSVYy3333cfOnTuv++eT1XuE3Q3gBz96jPPnL9Lv94miiCzLmJ2dZTgc4gTMzsyxvrbG7NQ0K5eX2X7LPH/2Z3/Ggw8exPmN51BHb58+qHPT4ymXDVv/EExkRH0sxrbnkUptcFyrv5HzYft06UHpUIAyLux8ITyAu6anwu8SKiselFR0u10eeughjh4/hbcWqSCWCi+hwGFwGAHeecreAHNukekzq5QLcwy1x9mQ3rJ4YhSlt1x3XeIEb4kbekVWJYQNcM4hnENw/ZbRNwORLhEOrDUIr9HCIcsVyBZxxfLbLim8M3Q7LR754PtZmJ1qHi+K4rpIrpx3oR0XuHBhke9+7/usrq5ivSBJYvL1dbrTHVbWVlFxhHcO6T15NmR2psuX/+hLfPITH0GK0FwRR2FPriB3+s0uo/FId/POt+AQBPlUXWCs88NCSiIdQbWPm/OQG0e/P2AwzCjLEikc27ZtI01iBGFHBCUkUgqsLdH6d1u2Vm9RKQl+xx965IN894c/5uLSMrFOUdYSaUnhHQaPVRLpJaKfY88usfD8/8/emwfZdZ7lvr9vWMOeelC3ZFnWYMuWZUmOZ1uOSZw4gYwEQhICYSa5IZAcCHUv91adS51zqVuHew/UOZxbB3KYDxQBMhpwTCAhiSEjOJ7nQdZgzWq11MMe1vh93/3jW2t3t2xLCpZkifRTtap3q7R3r7X2Wu96v/d93uc5wOANI8yH0OpajFKUQAtFIUqWg+65w7kLulJ4KxVc1Wn1OqCmKKEsEC47Z7tyNuBMQVHmuFKBVIgih2wGOziESOeBkzsv2KLgqldtZfPmzf7zKoWxeun9cm8JWU1EdAcJ3/iXe9mxczdIRVRZvqhAk2UZQRAhlWZ+fp52o0GR9nnzG9/Mu37onTgLSZ7TbvgA5mliwWntXS1R+MJgW2W/hdehXZw1a1nXOB3Y0u9jXrJzz/Pc++0HePDhR5maPoa1lnVrV3HNtqvZvn07V1x+GUoHvhHpj/5lnr1XHgIvL4lSmLJk48aNbNu2hcP/9HXiSOLmc0QgUVJSOkOpfRmpaUrKXkH7wd10t6+naLcJpcJIibEFSkEu3fLoxDnEK7L2qtWUTFFi8gJRFtgLPNOV1mBMBi5C2QbapjA4hMiOoow95X2vMNx6y01MTox6nzPhz9GZGiywpkCqgKNHj3HPP32drChxSLQQFEVOMw7Jy4I4jimtJVQaU+Zs27KZt7/tzbQbIUmS0a6sdQb9Pq2WF1I5HbuXxdS3E8VWANSiYJulKQhFFEU4IC9y4jjkyad28LVvfJP7H3yIPXv3009SlA4I4yYHD+7nyad2sHP387zj7W/lxhuu8cftTi7beKHAe8UtjI63GyHf8+rt3Hvf/RSmBGe9CaSrHtjCIRGESCInUM/sx+w9RDBxGUHQwFjjP9M5xHmkgfHdgHN2NQrhM93hzWl9xuusxZUGEVzgyxujsSQ4WaIsiHKAyKeQZR8t4lOS01dNrmDb1i0V59Wi1IJl+JnEzl272Ll7DzqISPLcu7+qhUaXc440zRkf7ZANerzlzW/iqiuvwFlLGKiq9upoNpsAZFl22s2+xUH2xIw3SwdEsf/MIGpiBWQWZmfnOXr0KHfddTc7nt3Jszufoywt4xMruHh8giwrmO/1aHRGGSQp9973IINBSrfb5bZX30ojVBSFecEY7oUGaz3LZbHJ45YtV7F+/VqefPoZtAwIBQS2UkHD4qxDO4iExszMIB57nvaGi2G0hTOGUAZkziDPyFpqGaeLcxZ0nXPD73XxMlMLCUJyoUtyCBthVQbaQWlwZQ+bHwOTEIhRMk5ePrn55pt9A825Ie2qHiooLIQvMxlRSjHXnedf7ruPorSgFIGOKAovYpOmCVIqjPNOtc7A9ptvZPtNN6KEBSGRQgzlJ2ucrrFhLZN4oo167aYQxU2Mhaw09AcJBw4d4dHHn+D+++9nx3O7EM7TzzpjKzDGkKQZ890eWms/hVWCDmP6gz7fvv8BiizBGcutt9xMpx2/vJN3HsAYQ1CtFpSQWGeZnJzk5ltuZOfuXZRWh5dwVwAAIABJREFUVRKdlgABUmKcIceiJVgM4tE9NG/cRtoZxZWGuBHTswkt59ktyzg3OGdB1ziHFW5I+BfCi0C7alQndxd22JUu8FmpFDhhsSbHFQMCUyJkCKcIuttvuZkVo50qsXXkWUYYNXDAmVr9TU1Ncf/99+Mdfws6nQ79QZc4CL3HmhP+dVPQ7/d5y1vewspVE1CXfoRACp+zZ1nm9RjimCwvl0gfvhSWGmXmdLtdut0uSZKwc9centu9h1279zDfG5BkObNzXQZZ5j3LjKyCuyPUmjhuEoYlzliwDufAIGi0Orgy45HHnmDm2DTzszO88wffsYQ/fCEiCJS3CbLWe7AJCDXceP0NfOWef+LAsVlMaZHGEmiF1AEpUDqHUQKrFXLfUfTxLnlRIHNH1NbMGotycjnonkOcu/KCM2glccZijIVQUyLIbI6UJeoCb3ZY3QPbROQFUb6fON2Ny0uMaJDEDpk6EBKLJMly2u0mzljytM+Wq67kuqu3UOQZYRgihCSMGgsmhWegxFAieeLp3czM9mi1RymtH4ZohN6FwIkAqSDPe9iy4DXbb+CarVdisgIZhsNViqyeAItLClGoSYqcIAgQiCXfpHCe7SC1JClKgkBz4Mg0n/jkZ3ns8ScxwPxcj3Tw4lNTgfTBRooSpRVSegfcrDA0Gg10FNHr9QjCymW3LNE6QEjFzv1H+NTnvsC+6Xl+4afehw4kSjkcxjemLJQlhIFesro+MXP32hGv7FTdcIUhBYiFfdq0bg1XX7aOo1Pz5EWODCVCCvKkRyQ1nahBlqSkkWXNrODYF+5ncst6Zlqa49k87XYT2c240MsLgRBIpTCFwSo/xWlKR4DC5AbOo6HIC7/DcJ7AVvVqaQ029zP1Eu/o4EpvMKgDhRQSrb3HlTAlzUaD19/+OoIgWOIYcKaR54apqamh40CNYW1VKrI8IVKKRqPBddddx8hI2zdmrD1lsyUOQow1ZPXosA591i68vnc/y4mjkH++934+c+ffsGPnLpI0JytLtAqHk3ovhSCMhpziRrNFmqb0BglhGNIeGSXPU5RS5LmXOozjmFarxfHjx/na175G79hhPvzzH2J8vEO312Wk3UJKSRhIbzNXZfB1yWOxtdErHXBhsRayWXKdtNttbrrpJr796G6cM+goJMtz4jAGY8gr63ctFaUwiLkBdt8UbLmEXJToLMM4i7vAkx7jLM5JnKjKl9VUidISAo2jeKV3cYgL+0yfRygRCAzSJLh0HpsnCGdRODDZsA5a3zTOeL+w0U6bW266YWhGedb2ryw5ePDg0F78RCU0WJgEGxkZ4YYbbhj+e60adtLPLwq0VDTjBqEOcG5hOizPU6IoZOfuvfzN39zFt771La92pgNM7hkTp4KUGq1DjHEMBinOCdrtNkopjh07RpZlVQNSLbFQT5KEqakp7n3gYf7Tb/4Xnt31PK32qHfr9UeHEGbo2lDrRpxvDrmLmQuwoHUhhODmm29m5eQ41pS40htoBkGAEJWBpZYETpFLCKfmsI/uRlmDUxKZ+5/V8/GC3ZxzWOkqx/uFARzrHKU4vwavloPumYJQKEp00YN83nuO1RbolX13WZYUhSft60AicFxz9VZWTqx4Ue7qmbRSkVJy/PjxIQWtFrCp/9ZQ01UpxsbGWLVqFWmaA6dHuQqkoswLcF5JS0pJlg4oyoww1Ox8fj9/8clP8u37H6TZ7hBFnrfcaLQwxamzkGMzcwilaHU6SK3JyxInBI12g85YhyiKhp5htcmjD+yakZER5noDnnzmOX7zt/479973EDqIq+DtFvQPKixu9J5sfPlcon7wLW5E1g+FiYkJbr7hegIlyfNs+N3WGbvFN3eJNPFchntkN3qmR6wVsZM4KarB5At4k7IyAxA+8Na0VBzmPGvTLwfdMwQhHcqmBPk8QdZFugIhlNcfQGCtIzflgrusg9FOmzfe8XpG2mdfa9Y5R5IkL8hwF4tpR1GEtZZ2u11lSsKLtJxG4HfWDgcbkn7qZQfjJg7Jnr37+MM/+lP+5d4HEErTbLaZmZ0jTVPiOCaphHhOhotWTiCBPE3REu8VNujSnZ2lqD7HWktRFMMMMAxDtNa+3NBucXxujuf37ue3P/b7fObOu4gbTYTUOLnU6HGxx91iW/TzAUvGr7UeBuM77vge1qxehbPW1+kLn+krrSmKDFv417EDu+sQ7pn9xN0UUVqyQeaD8gW8mSLH5iWuKCEvcUWOM361db5Jiy7XdM8QpLDIvI/KjqPNwLvsonFC4LDgvMdVoL3sXlGkbNy0kau3bhkuj07EmbzZjfFLaGNMJWi+EFj8T4aBp7YBbzUChANrDPIUQuqFLQjxDakwjnxTA/jnbz/M3XffzWNP78I6gQ5i0qxqugk/StxqtTDm5Nnu8eljaC39stpatBKMjXrftdm548zOziKlpNHwDcgsq5uSnokRuJh2u02WZhw7PscnP/1XHD48xc/+zE/SbsUoFjL6WmrzfLpZF5ee6iba4v3bsPYSrrzicnY/v69qEJYIFkomzvqHvWsoVL+PfWAnYtU4vRUdeqYkOo8eLP8aGGcRgSYoBYkscVJiBgmmKM6rMhEsB90zBmkLbN6FZBZtByADcgRYiRQBTlq0CCvBGS8+vX37dprNGFOUS4La2cispJSMjIxUZQu5qENfW3CrYdY0PT3N9PQ0rXWrT/vzwyii15vHCUmz1ebQ0Rn+7gv/wD9+9WscPjyFCAIskAxSpJR0Oh3yPKe/aLLtZBgbbZPnXkls7SUX86Y3fy+33rqdHc89w5133sme56ex1g51fLMsoygKoijykpl5QtQeIWy16PUGCCH4wpf/kT379/FLv/gRVo20aLfbw3O1uI5dL9PPB9T7ceI+aeXYfvNN/PO/3E8/K9FS45Qjz3OCMMDkDpPlpFFIZALc43sxm9cyHwmsUGAu7IlQYy0yCnC5JZf+3Ig0x+UlwpqlxoCvMJaD7hmCcCWuSKBIkLbEKoVzAuscSOmFy43xgSBQTE5O8prX3FZxcNVJl/CnM3xwKoRhyMjIiF8uO4lZlFhbaxHKB90gCJienubQoUOMdpqMj454XugpUJqcVrvNzHyXL37pHr58z1d54qlnSdKMKGqQJFnFNBjBGUOWZZUvXEyR5Uh98uObGBvl6quv5uZbbuTSS9ezcuUkcaS5aOU4ay6a5E8+ftfQyLHZbA7LDXXTsNWIcKagnxSEYYRFkA4GPLdrN//Pb/wmv/SBn2bt2rWsWLFieM7PN9S0MSnl8Nigqmdqzc0338jq1avZs/8wQRhhbMFcb46RoOnZKf2EshXQajYoD83gpuZJL19BM2oTigt7Ys9pAUKhBRTOoanUC5UApfw8+HmC5aB7mghcQY6mdBopHJErPT1MBFipGes+RprNUIiINIBAOpTJkEIgg4jABJSiQEYBg6TL5s3b2XjpWp+xCDgZT/JMZL6xFqxfs5pQR8zOJ0gdobREBhqbZxQGRtotsjwh6fb4+Cc+zXvf8y5uvvEGtFboym7GGFcFZ40Q/vdut8sze6d45plneOCBB3juuefo9XoopSo+ryXAomyJtcWw1KG1rwHn1lJYz/0ts4xGHLJm9UWMj3ZYt/YSbrj2GrZu2Uyj0aDRaAzr0tYY2mHMtVtexa98dA1/+Md/wte//nWsyQmjBlmW0U8TrydR1OPVwbBBJqWm183Ykxzi//6vv82P/MiP8PrbX8toK6rsc2ylWSEp0ww95CZLT6OT8pzSWxc3NE/MvK0xjIy0ecMbb+cP/ufHEU7T6/eZmJgkHSRYWzLe6nA4S5EiYJWIOfTgLsY2TZJcGiMLRZHnCCkptCBzXuGtgUKmBWVw/mSKLw6BMSVS+Gm8wnmVPCE11rnzioW8HHRPE9avxCuPKzBCUimyewX/IkGYAkmJxCIqOx4rvKhIXpZYWxKHmnazxW2v3g4wXOKfbWRZxjXXXMNffOYu2u02TiqSpI+1jlarhVMRZe6nv3Qc89gTj7Nv3/Pc/prX8PrbX8uKiXHvVuAcg8GAmZkZDh06xK5du9i/fz97D8+QJAlJkuCq+nVN3SrLkk67UzXMBHGjQVmWdLtdlFJ0Oh0GgwHbb7yebdu2sXrVSi6/bAMTK8bBlF7MW4hhc+/F0Gm3+MjPf5CVE+P8w5fvoTdIhoHcWC8EXjMS6s9YvFTvdQd87GMf48C+vfzUj72PTtvXhgMVUBYZOo7AOfKsyjaV8qpfUpwXpQepFHlZsHXrViYmJuj2+qjq4WSMQQWa0hpiJ0BDGljoDdAHjhFddhGFcFgtCaQiVJVTtXVYTs3RXsZ3huWge5pwTiDxFyFCYYVGOAuuRBQFIuuhzADhCvzEqV30XjdspBV5zpYtm7jp+usBv2QM9Nkn30spueqqq7jy8o08/dwegjBGSskgmSfUbaz1dj5COtqNJoHWdLt9vvilL3HvvfdSVjVEYwx57qlkdVDN85y0cMNlrlJqyRLYWst8rz/M1JIkRWvN6Ogoxhjm5+d5x1veyDve8Q42X7nRC5lTJ5FBdS5feOMvDqCjrZgs0Lz/Z3+G1atX8xef+iyzc12MteggwlZUKiG8ieZSSp7jyJEjXLR6JX/7t3/Hrp07+PkPfpDNmzaSZCmNqIEtPe0vjBaW4XXZxZnzIzA5Y9l85Sauu+ZVfOUf/4k4Dsnz3O+bVuRZQRtJLh3dWKDmU+KnD8ONV9ANFE44BA4NREJVk4oWAu3HCpdxRvDKXykXCGojyXqE36CxUiFMjip7kPd8OcEZ6oC7mNBeL7WLLOW1r76VdqvhKVBVADvbCIKAKNK87a1vRknHoD9HFAhacYO0yCmynGazSaPhl+V5nhM1fC3w0JGjJLlhkJXDrTvImO+n9NOCrHS0220ajcaSQFyLkDcaDYKoQW+QkpclI+NjKKWYmZmh0+nwlre+iZ97/8+w9cqNaKBIU2yZ+/NocrBLm1qLa5nDf88TolASCMvb3/ZWfvmXfpGNl22oHnTpsIu/mAK22MVizbq19HsJZVny+BNP8ev/+T/zpa/8E2EUk5YGJ7wW1xJG/nkGrTVhILn9NbchcURhUIngx5R4e55GxRjJAkErN7R2TcMB34R0ArLKjVhb0JV1kpXn4cFewFgOuqeJWuZZVvbpVkhsNYGm02koM8SSbOAENS3nFbpWr17FjTdcjzMFgnM3YuqAsjTcdNMN3HrzTVhTMuj3GBsbHQbGmlCvlKI0ll5/gHWCFStXMT/fJUlSnIMwjGg0msRxgyiKiaKYNE1J03QYbOsssi4vSClpt5u0Gg2yQcKg32PVygne/L138KEPfIAV46NgS4o8JQr10JK88i0f7hsszXCHZzsIKNOEIAgIleS27Tfwv/7yR7nhmqtpx+GQDlfXk2sK1sLkWopSGlu5F08fm+P3//B/8rHf/WPm5vugBMbCICkw1SLGWbtQ233F4elhErhq8xVs3nQ5WbLgDVd/B0IISmuwwtEQkni2B0/vpZVDQ/hzXlQ85wCJsK6y81nGmcL5cLVcEChRSCFQGHClr+NaiyrmCZPDOOczCSEVDoV14BAoL61CWZakacptt97K2otXE2pFWWSEQXhOsiaHIwgUo5027/rBt3PZhkvod+fJ89wPFJicJO2TpikoSWukQ9xqUljL8bk54riJEIo8LxkMUvr9hCTJKAoDSMIwHA4j1B32uuFVliVZ0kc4Q783R39+lqu3bubf/fwH+dEffhcTow2ydOBtecLQN3PynCLPq2ExMZwSezHnCWu99KSOGxR5ipKgBWy+Yj2/8tGP8OY33E6n08Y5S7/fI8tSLwgnBUKAUp5CV1iDlIowamCMY9+Bw3zlq1/jj/704zz2xDOkeUnUCJDS26ILKSv79PMnKAmg02xwx+tur2yKdNVPsCihyYQfl22UFiFB4wifOUhrLmfEaYIgoFRQW8TXNeFlnDks13RPE7Z6PinnUM5irEG6DF3MEyRTGBROqcqxthrZrH2tnAOpaDXbvHr7LcRRAJTDppxzZ1yr/AUQVWSPQsnmzVfyzh94BxY4PD2NxVGkXlcXJcnSgkE/9SWJ0LMFbJkjhERKPVzWG2Oq0ebyRWlMtY5BEARoqej3ukyMj3H7a27jTd/3Bq7adAWBllhTEFX6C6bKRoXUw1XAAsPjJY5NCPK8IAxDgjCm2+3S6XTQWGJl+Xcfej/xyCSPPvoozzzzDHmeL6GEKaWQQiNChRKObrdHURRMrlxFaeCuz32eQwf28463v53XvfY1RIsXJ6IS4ZevLOXKsVDOCrTk1dtv4W/u+jxHjs1gkCihURbmRIFshLQSx6DMKZohI4fmmXl+ilazQTCi6OMohCPUEmGqVcX5w7i64LEcdE8TTlQZlrBVY8wgbYkyCarskwdjQx8y5/wABEjPZADiuMnKleOsW7cOsGRp6gONdYhzUDPzgwl92s0Wzpa88wfeRj/LufOuu3BKo6zwpYHSEQSRp29ZMxSGiYQPiEXVUKsDrxISpfx8fx0kF4uT1w0rR8mN113LG7/3Dm7dfhMTo21vn14WviFVaSUEYYyqrsqitCglX5TYvnhUVwhBEIb+/0vodFrgDCbPGBsbBWf5mZ/6YT7/9ytJkoSDBw8Oyx51OSXNEz9W6xxCBbTiJkVhSJKETqfDww8/Sr/fZ2bmGG98wx2sHB8B6+3n9TlohJ4K1la6y9YR6ICLL17NhkvXceDIUcJmA0yJKh2pMDSDiMZ8znSR4MKIznzJ/v1TxJeshE6bwlkKLCG+Iaql8n9gGWcEy0H3NLFCHKdvm/TlCsJAMFocQ/Wep0xn6IYtYgqU9B3fQGovqm0sSns9A1vO8Z53/zTtkQ4GSRS3K4uaGFdkiODsuhsIa2k3/eSXUl767kfe+TZWjsR87nOfY8eBmSqjFMOstchykIpms4mzOUIJhLUYayldZTRZPYxKp3HWoYSrslKHFoaxsTEmV4zx3h96B5s2baoeOrWThIJKP0ApRRAuzRaDk+g9nlhiECf+fwEqag5fR8C73vp6tmxcz1984pPce99DREGEDiNm5rqMdhqewiYkzWYTY0vyPENJiXSO5tgIT+zYwcGpw+w/coh3vP3tXLp+LUiFFA55YkwS4GrZinNQxFMCytJSlilxHGKd4V3v+kEefvwpirKAMqcIQ1a4BqZbMi0MutWm4QISXXLtN/bwzK2XcUhrNtIhS3KShiUKFFmawXnwYPm3guWge5oonao0cwGbY4sBokgRNkdg/fTZMLtb1EhyJQ7HqlWrWL92Dc1GMLwH62aOOIuSjjWGilPVhBb4KbU3velNXH311fz5Z+5m5+5d7NmzlyzpEcUNOq2GH9XtzQ73NwiCIV+2zhbLsiQMJUVRYPKCIApYvXol11y9lVffup0tW7bQjoOhhGOdYS5WPDvbyIscrUI2b97IRz7yC6y9++/5m8/dzeHDB1m9eg29Xo9Go4XWmixLKPOCVquFUoput0uep6xceRFZ0udv/vpzHDpwkPe9971cd+3Vw5W3Ky1ZWRAECqW1T9AFFYPg7FvAay1RMqxeayYnJ7niio08+tiTAEMxH6UUYRgOx6UBr8a14yCjq0fpupxQK5RzZMYiTqG7sYzvDMtB9zRRuMpaHYs0GSLvIfIesiyQzuCcWiSVaHwn3DqMKVBKcc3VW7ns0g0IIE1zGrFvOp2Tgi4L00x1c6So5BS11qxdu5Zf+eVf4ImnnuUb3/gGDz/8KAcOHSLPvBB7UyviZqMS6iko8nTB1UIptJLYIuOqjZdx/fXX86ptW9iwfh2T42PEsQ8Ci49wMXsAGC7xzybCQFOUGcIpLlo5zk/82Hu5+OKL+Pzn/57HnnicZmus0nbwU2uFMHT7A6IoIohibFGQ9hNAEgQNHnrwMY4cPMLb3vY2fvAH30ErlIhQEod+as05R5ZVbIrTsDJ6uaiZzJ5JYZEILlo1yS033cjjjz85rDnXpZ9aoyLPc+8xZ0vCR/ayYsulHJpsMBEFiKwgw9DQ+nzqFV7wWA66p4lSBKA02pnK6beLzPtoWyCFwAkFCJTiBMqUYWSkwy0330S7UQWgOsgOg+254UEudkSoNWfrfXUWrtl6JddsvZKjR4+xc8/z7N27l8OHDzM7M8fh6WPDTD4IAjqdDpOTk0xOTtLpdNi6+QrGxsYYbftsUQpX6TwYH6xLO8yUF0+CAWc94AI4VxJohUOQVZbz3//WN7JibIQvfGGU+x5+EhCY6oHkhCCK/QBJP0lpx5H/d+eIQ6/rsP/gFH/3hS+z/+AUP/Nj72J8fByt/YiwMbU4u8XYAiXPrnync3UzDUxpUVrSCAOuu+ZqLlq5gumZLuCvvaIohtdnnfmmpmDF3lkGu6bQKzf4zzIWF1YslOVO2hnDctA9XYgAhUC5AlXOo/I5pE0QmEq1i2r8VVXBxGGtQQeSS9ZezPWv2joMrXEUVHXMiqR/jriedXmhvtGEEMMM2NlsyHBYObmCiRVj3HrzDTgHRVEitM/cl2rNiiGrIO33aTQai0oFdZFToLT2timLUHNmzxVP2Wd0MQJBHGissyghufGGa9l0xWX82Sf+mvvuu499+/czMjJK1GyTDDKyIieOQ7K0IAj9YMEgzQiURumIg4cOc3T6GEXW5Q2vv4Obb7neG67aheM1xpwxc9GXgpRQmpJAVVY+zk/frV+3lmtftZUv3vNNwI9FF0UxFK2vSz0Ow2Sh2fv0fkav24CJDA5HFITYYrmJdiaxzNM9TQixUFqQRRdRdNGuQFZDq4u9x+oM0rqSkZE21113DaOjHYo89R17LOLEMeFzhDrwpmk6dEXwot8OZ8tKCS2r9tFnTmHox0AVjlBJIi0JlRdit6XFFIZWq4GPt96ixxrj6V9lSbnIGcLr+dqhdfq50i2IoybWOC/qgkQJCc7RDEMuXjnJB3/2J3jbm97Ixg1rERj68/NkSR9nSyQCIRcGRxqNBkJJeoM+SZYTRDFf+spX+fgnPs0XvviPDFKD0gGFseR5SXiWm6Tgz/qCpsQCi2SkFbP9lhtotVpDB+f6QVfzqf33XzVY9x1jZP8sIi1wgSKw4gXOGst4eVjOdE8X1iFcAUUPl/cQZQq2AKEonUQJRenKBY1aLEoJVq9ezW233QqwYCjo3CK5RDl02D2bWNzMqZthNRarVwkkoVJYC0WlsSqEIAzqPH1R1iNqe/hK3GeRpYxUSzPY+oH0YtNk58Zt11PblFpEaysdQoBQgvFOgx//kXdz+cYNfPbOv+KRx56g0xlBBxGHDx9mfHwFWutK1MdrAF900UX0k4Sjx44x3hnhgQcf4dChIxybnuFN33cHq1eNY1GexXIOelHqRK6wdQjleNW2LVx11VV8+9vfHnr01SsN8N9JoGCmTGn0IH3yAEy0oR0hU4fF4ZZHgc8YljPd04TEgjPeAqTMweZILE5IrFuY6V/srxUEAZOTK9i08VJckVclXLdEb+FcaS/U3erFnl91U8X/LjFmIVsXUqKVRiv90sT/Sl5xcSbrucwLzrV5YbzKVzUoUWsz1PVReGXcdgXCS1tWJR5T5kSh5vbvuZUP/8LP8+bveyN5mnD82FFWXTRJkWekaYJzliDwQevY7DHSbMDIaJteP2FychWDJOVTn/oMf/zHf8KePQdQUnhq3FlGuViEvC4BSb/iWjmxgq1bt9JsNocN0LopWme+SsBMkRCjKHcfQhYFaIXNCyTL7IUzieWge5oYSIW2PUayg7T6h9F5jhUxTgYILIPeDM1mk0FuvKZuAZGKePsddxDbwvNwhQahCaMGtaXeuaAS1aibaAsWPWLRWK3PArUOgaXusJXqxAs3oZAqQAcRPmMPEHIha1ZKEQYKJZfyauuG2ivqPXaCnWwtjINzXLVpIx/8Xz7AT/74+9iwbi0zx44TRjHGWEAhdYQTiihsEAYxva5nOcz3uqR5RiEc37j/Pn79v/0Wd33lHnIJNs+AEiiAAkdBkg1IS3dGbBMjpRfasVJUx6WqzF6z/YZrGGk1wFm6/YT2yBh5aWk0GuRZSpxJbCsCUdLZeYjxpw7QSFJMI6B3Er70Mr5zLJ/N00QgS5zJMWWOwwyXW37iytEe8XqxSinKPKURxVx11ZVs2LDhnFDClvHyYMrS6z4IQVEYJlaM8u53v5v3ve99XH/tNUxNTdFut2m1GuTpAFNkiIpHNTIyskTWstYc3rNnD1/+8j38+Sc+hQwjjh+fxz/CBEmaEkcxkRYk/eysH9+6S9Zw2223Yq0lblSSjxWTASARjrYMKLMcFSrskePIuQQjQZ5nFuYXOpZruqeJ2KWQ9zHZAI23ADFDfzEJzo/RNsPYswIk3HrrrUxMjp+XMoDLWArf0FtoiKpA0Yg1d7zutaxZvYoVkyt56KGHmJqaZsWKFSit6XZ7lM4iFSdoE3j2QK834PHHH2fv3r30js/y3vf+MIWTKCFoxC3KqpZdUwnPJpqtiNtuvZXP//0XEA6yyvrHOa8T3LOGcdtkPu8SdVqwdxpx8BiMt1DLudkZxfLZPE1Epo9I5zFZHyo1fVNNoQnpM5sw9BdxOw4YGx3hxuuvrehjy6f5fIeQeljjjqIIW1qKwhJHiuuu2cqHf+4DvOH217JqchxjCkyZEwSaOAgxxgxZGXWNPAgCmo02IJmf7/G3X7yH3/rv/4Odew5gEJTG852zNAVx9icPbFGy8dINbN50pVdZkwu0QecgVY6w9N5ipbNER+eRu6aIjEUtc3TPKJajwWkiyGc8N9dkCJYaRUoHSOEtv53BWcMN113L2jWrPDVpGec9Fk/YKelHauPAt5CyJGN8rMNHf/FDfPADP8P4WJuiyBBYjClptZrDgROox5y9w7IUGpyksJaHHn6M//c3/gtf/+Z9GOcZIlEckyfJWT++KNBMrujwxjteh5ISLTxVzFBJNwqFy0vN1MZDAAAgAElEQVTCMMTkBa3EovccpX18gF6WdjyjWI4Ip4vBcWTZQzvvaOAApNfQdc4Sx02vTZtnaCm4/TWeJlazBpZxfmPxQ9RVRob1ppUg1oosTfj+t72Z//irv8qVmy6nKAoazYijh48AFiEWLIAWlNYczvnmYeks+/Yf4Hd+5/f43N1/R5KW3iW5eWoL+pcPC85x/bXXsGrVZCXJWSBQoCSRUCSu9OwSY7z56pE5wt1TyPTs15y/m7AcdE8TLpklMDmBcJ4q5qoJ9+oGW+xscO2rtrHp8itwlc/5csw9/1HTqOqxaO8mkeBsSRhq8qxPM47AWTZfeQW/9h/+I29/+1sZDAZMTk4u+f6l1BVrQCKEQkpNUeQE0mfD3V6fP/2TP+d3f/+POD7fp9sfnP0DdAZTlEyunOCWm24eZvVCCJQMaDpFEkBpLZGQ5FgaSQFP78el6ak/fxmnjeWge5pweYJ0GUr6iataJxYYCsjEcUwcRrz2ta+l3VnIXhYPHyzj/IRjqRCPlJJGI6p0KSxhFGJNMfRrW7FihPe///18+MMfRukFl4W6pjvMdA0IFJ1WgyxLKwsdSV4avvgPX+b3fv8PODw1ffYPUAh0qGjFITfffDNRFBGG4XB6MnaSUilKa4h0QGpLmkZg9k1js+LUn7+M08YZDbr10qrO/up/q0Wv6yyiHkWFBdUrv05/6c0JedLND9a+9OacGW71qKpfDvoN2/ROD7JPRkkm2jjRRhezBN1H0KaPc45chBT4izVwJdpLg+CcodPp0Oo02bZtG4EWaCW8Jq2zYKHM3RK1Jmv9AMFym+JcwJIkvgnq3MKoc309eEulygwTO3QMRghKY7BWIVWEUgHSGgJgshXyfbfdxK//n7/C2nUX0Wr7cd962KMoMnRoUEFOv5cShhEq0N5hV0vm04xv3PsQv/n//T733/cIM8e7w32tJ/+sBVMumGguOaJFTh2nhNCAxFnD1VdeytZNGzBF6hvCKqCrCmRRIK1DO0lLhRwRCQEFm/7hSeZFycpMoZMME0siJUnLgqIV0i6Whye+E5yTTPe0tAWEO+nmnD3p5nOVk22eEuSng2p6kBi+lsI3UgQKLSShMARlD5UeR/WOnHL3gyBgamoKpQLm5+cBSNKcIPLDBgjQgUAo76/leZJVB/k7PqPL+E7R7XZpNBqA19HQQURhrH8gI3FISuvIihKHtyWqcbKJuYmJCS677DL+/f/xv7Px0g3EUQjW29R3Oh2kDukN0iGP10s+ZtW04iRCCHbt2sVv/Nff4pHHHmOQZAwW1VDLvECphUYfLNgk1QnM6aCeNPQGoW2uv/56wsAL+Ahn0U6ghcQCBd4ZWDpwg4zB1HHGjicUsSBvaFRukMZTJUVhyClP/seXsQRnPOgufiKfy4mjxVn2i204gUC+MIO2gBNYTNUe8xYligxtjhMlU8SnEXQHgwHj4+PMzMzwq//h/+Izd97FbLeHA5I0I80GlYWPJQzlcGn3HWUry/hXo9lsApJ+P1lYWUiNE5K0KJnt9gijGB14zmxhymol4oYOwsO3LVqpAXQ6HS7fsJZf+9V/z5ve8DriSNOIQvr9PmVpUDpapNXrs8KiKIai4kVRcOjocf7bx36PT9351/STzD8QrCWMAlzl5Lv47y/WsDidpKZeffrxdMVtr76VyclJTJn76904ZGXTXgiH05JIB6i0pDw6x9iOo3RlQdLUqLTElg4tJLqwLCe63xnOaLGxDrhnQzXrVE+HU4b3kz0AnCNHoAkQotKZzfvY1NPEYpuTnkIPdXxygumj04yNjnD02DR/+md/zhNPPMFP/NiPc/kVG5AUlKbwpRahK7eJBUHpZZxd1EI3YdTAAt1ewv0PPMjOnTs5NHWE8fFxrtl2NRs3bmRyYgVxpJF42U0pgyU1/DrgLc4etYSJ0TYf/ciHuPjii7j7819kemaW2W6XzsgISgfDem8Yem5vknhftk6ng3OCQ1NT/PknP8Px47O864d+gMvWrQHrsLZEiUWiRFUTrMbpiMDXmhdCCKwxbNiwjq1XbWbvvgNoYZClxQmBVQIjQApBrAJcXmLSEvH4Xg5evYKiM44y3otIaIksLXkg0Mt5w2lD/dqv/dqvnZmP8tSY47Pz/MOX76EozcLTFZDCkIsQKQShzTCqQd5ag41GwZqhGMxLbUJw0u2U5QV38rBcKo0WeCJ4kWCT47hkijCfJcZQnEK0JC8KlJDI2tImDHnmmed48sknCcMG6y+9BKk1QiisNZTGeb5krTy2jLOKuj4rpWDn7r18+jOf5R++fA/fuvc+nnr6WXbs2MnjTz7N/v0HaDYarFu7BoH3Hast2uugtSChuMgS3jl83TjhuuuuYe3atex+/nnKoiTpD2i322RZ5j30gsA7EC9yVe5nGSMjo2RpxjPP7OD4sWOsvmg1K1dNIKuHcp7n5Hm+aKjBDbWRT3n81A8Q6RXnAk1RGh546CGKvEAYh9QKpxeyeOUgoAqoScb0xhXo1RO0c4l1AqOkLzoHAnEeBF0nQDtBKbwTtygKxr+9B5ckp0zKWo2IN33f9zLSaSF8m6aCDzBn8g49o0HXWlcF3a+cEHTFaQTdkx+WrCS2X2obSue/xCaE5KV8pP2FawjKlCDroZJpbHIYl0yjyxSNxJ7qrEtBPxkQ6gAhJN15n+EkWc4/fvWfMEVJGDZZtWqVrylKhayXh7as9m8ZZwv19TUz3+dTn/4Md93993T7CTqIaI6MkBUl/d6AgwcPMTU1TRRFrF23nlBL8qwgCPQLGsRL4CwORxiFFEXGunVruXrbNvbt20eWpkwfnxkG3LqpXA9UpGlK1IgZ9BParTZKSp599jkOHjrIiomVrFp9Eaq2Rqo0cGvWAZy+x5yxoGR1T0rFyOgoTz31NAcPHfRSnLL29XO40uCMp0YiIC4N3RUR4SWr0HGT0lickpTOVIJIr3zi8F0ZdJ2DY1XQLctFRohnIOg665aUL07cTgXfKPPZyGJeA84gsARmBj2YIeoeJkoOEZZTKDOPcg5J5DvOJ4MQdDodsP5maLXa9AcDirJkZGSMBx94hLn5HkEQ0WmPEEdR5bpgK+eI5cLY2YS1fvvGN/+ZL3zxS8zM+xpuVhiStCCOmwRBSJalHDh4kJljxxgbGWX9urUElTHjggXTwrgvVBKZRniNZOHQStDv97ho5Upefct2jhw+xPwgHWaQQoglNV4hBFIJwjCgN99D6YDx8Qme2bGDJ595hhWTE0yMjhBF0fA+WWy9tLjJ9lKobxEpRfUeQ6PZoNvt8dhjj2GUwjiLMA5lfZJjncMKsFrSFIKkyHEXrySd6GAKi5CCUjliY7DnwfV7oQTds9JIW4zF2cHJYU+6OT+w+JIbwp58W0QTG37mIvqYTI4iu4dRcwdoDA7TMcfpyIRQutPSQ5VC0+12GWSpz4qVH7OM45j5+XniRpuvffWb/I+P/QGf//zfc+TI0YX3qlc+S/i3DinBGMvDDz/M/oOHGRkZwTiBcb7JVhQFSZbTaHVoNps8/OjjfPzjH+eee76Kc0t1jxdrA9clBqUleVEOr/VWM0Zg6bRj/reP/iJvfetbWbNmzVDDtg6gNY0yHfSQztFqtSgLy3y3T7szxuxcl9/53T/g4Ycf5vDhw8N9qINu/fp0oNTCvWitRQLbtm2j3W5TyoqCVhoCIYlUpZimJFZLnLAE+2awR2YZ2IKiTpS0Qi9PCX9HOKMdnFog2z9JMy8jF8fMz87h4gZh5ayQqybS5YxO/Qvi6L3+4lMnb1QZ8/JoKbXkHixlWNQ/I1GSZRm5NVitkW4EU5pKRSwhrN5fGk+nESqgqH3RwoAy6RFFEVp4K/I8SRgd6eCcJyX10ozm2BjT83P82Sc/zZPP7uA973on11+7DS0lzhRIpbzFjTGEkT8fZVH4Rts5EML+t47clEzPzmF1QGqc1511BZSCsBrpTgc9wjAkarZ5+KnnKD77OVIR8f1vuJWyyNDaZ7M2L3EqxClF6SCubY0qLDZakAre/6M/wIbJUe66+26eeW4PpfPNPeMEFkersYIszTDWK49pLbG2pCgseV7wn3779/iJH/0Rfujtb2O0GeEJ3wqcpzzmeT7UKK6NJ+sGbe3HVxQFWZbRbo+gKxrcuosv5g23v5bP3v0FrHVYCZnwAd1ZkNYhC0shFCPHB7hdx8iuK8iiEOMskY6YExnnThX6wscZLS8opTkyfZyvf/NbDCpuIkCgA2yV7C6hcdXvdI6iMNiyfMkNjB8yeJmbs+ZFf5a550a+WBYjpSQZJMSNBqX1ivtRI0JLKPIU4RxaBRR5SV6UIH05JE0zjLXeEFFqjPGOtFop9j6/h0cefogsLbj0sstpxJG3ddEBSgeUpUHgUFpXncLlbPjlIE0HNBsxu/ft49lnd6K0Is8KwNGIG6RpOrSxsdbS6XSIooi5uTmeffZZGnHI+vUbCAJNvzcgCCOU8qMxxjiCU9jZlKXlissvY/2Gy5jtznPg4GFPCQtDytKQ5wnNZoNWq4UxpvIzs8OVYqczwoMPPEA2GLB1yxYajQa29Gp3dcngRRt81e95NiAMA8IwAgTJICUINFEUkKU59z38GFDrQ7/w/iyl83QyremvaFCsHqVVCOJeRt4OkfZ0VrNnFxdKeeGMBl0QHDxylK9941v0B145yRiDri7OE7/I+qdzDrtoqf/iW+mf7i9jc4t/r8sNdW3XuiVKUXXdrra4kUojlaYs/fKwGUc045BIK0LlyMuF90uhUDpAByFCKvLCG/+ZovB2L9pnGf1uj4OHDvD4E0+wfu0ljK+YQEhBnvvgLKTCWocpC6RappW9HDhKpFQEYcxjTzzO9NFj4ATtVpvZ2TnA0Ww2h5liHbQGgwFzc3McOnQQhGDDpRtpt3wjSQiBEuKUARd8LRUBK1dOsHbtepRU7D9wgNmZmcrUU5DnGXmeIQRLGneecWCYm51jauooWZawauUqxsZGAc8pdlWNeTGHt7ZFKsuSKArAOcrSIJWsZCVztFbEcZMv3vPV4dCF398FXREhBLmwyGaM6uWYSMOVa2joEDU7IBmPkeUrT1/4rgu61pQIqZiemeNLX/lHSrPQNLDGvKjF+OJGmJYOJXjJ7UXMYv7VmxJiyWslBKJqStQi1nXNrL7w4maH2blZ2q0mURgw2mny/p/+Sd733vdQZAP27D2Iq76txRe8xaGDEAXEcYwQkCYpFkscN0izjD3P7+XJp54EqZhctZpWK64+w5/XIFhevL1cKAnWwcTkBALJ7l17mJ/vEsUNTOkIQz1cmdXsgppHu3LlSg4cOsKe5/cRBAFbrroSrRTJYICSVUA91W3pYG6+i7GO1RdNsGnTlRRFxtHpowx6XVQQYEyJMXY4vbYgrA55WrBmzRr6/R6PP/YYWZaz7tL1jHTaOBxaLex/ff3WSYDWmrLIkErh8M4YSmmsczz++FPceeed7N53kDzPh0G3xnBsvyxRoYYkJ3SKcO0Eg5VtZpVPTM6HucrvuqDrubSSXpLxd1/4Isb4YNpoNLzt9aLu5os11sQ5VCBYTHKvYU9gQmithxe+EIKiLGk3mwz6XS5dv46PfOiDvP61tzLaaXHj9dfSHJkg6ffZt/d58iyj2W4SRXGV4UOgNEVRUBQ5UimE85/phNdUPXrsGI889gRJknDxxWuIG00C7TviWbX0XcbLgWPQHxBHDTZv3oS1liNHppiaOsrY+BjOWbIsoygKwjAkCILhKgd8DX8w6LNnz/OkWcmVm66k1Ywocy+HeNLhG6DIc5qtJmGgKUtLqxmxdevVNBoh+/buYXpmhkYjJo4b3tAzL6os0wfSRhSRJilCCLKyZP+Bg+RZxpq163zvoJqaK8tyWBqrJ+mstSitcSicEDgnefypZ/jLv/wUd/7VX/PkU0+R26WawrDwu5R+8szgyDCEWUmE5vDGUabXNOkcy5D6le85fNcFXYGDKuh+9evfYGZ2bthIOzHovii95RQ+TM65U6ornO7GCT/r1/VFVg8slGU5HNWUzpFlA15/+2v54Ad+lqs2XeHdA7QkCEOu2nwFa1avQTjDselp+r0egdYESpFlBThDUXhfqiiKiOIYqSSlMRRFiQxCOqNjPPDAgzz86KNctmE9kytX0e/1aLWa50EecWGjLP3KIs8yAq25fONG4kbMc7t3cfTo0SX1T1g65ZVlGVpplNLMzMyxZ/ce8qJg06YraTZ9jfRUX5C1BUpqEJAM+gihiCPFmtWrWb1qJfuPHGF2dobZuVkajSZxHNPv9ymK0td5y9yXHpSm1eqQZBm79+wlyzIuvfQy2o0IKX0Tt75+ay4wQIkkKy3P7tjJX9/1OT71GW8zP9ftkZe+l1AH3MWKafU5aaIosBShJE4Nup9xfE2bYs0Eq/rivBgF/q4LutYahFQUxrFrz16ef37f8It31r2Ah3qiNoKUJ9dO8AnoycYjTn8TL9KYqjOCxYpoeZ6jtWZkZITRZoP3vPudvO+97+Gy9WvQWhEGAUIKTOln0S9Zs4pXbbuaKAyYOnKYo1NTlMYQRQF5ntNut4njmCTJ6PX6gCCMG4RRTGEF00eP0mg2SJOUb33rm/T7Pa7asoVGHC4H3ZcJZyVSeAEZaz2Vb+26tUStBvsP7CcZZEOX4vq7rx+Q3mw0x9iSRqNJfzBgz+49OGPZsH49cRgiTlHXVUp6mx/jP0MpSVkUNOKIyzdeRnNsjG63x9TUEaSSRFHsy1PWEYUN0sEco2Nj5EVJVhgazTZzc3McPniELC3YetXlxHG8pIFW3zvz8/McmZnn05/9K/78Lz/B/Q8+wux8DyE1KgiJWiO+ocxCI61+XX+OkpJMGgIlaVooixJXQltFyHaLXL/yV+h3XdB1zhP8hQ6Z6/Z48smn0VqTJIlvHJ2Q3Z4YVA32pG00UCDkv3oTcuH9zhvuLNmkWFBiqtWgpJRMTk6yYcMGPvxzP8sbXnc742MtytIT4J1z5HlBEAYoJSizgrgR86qrt3HZZRtJ04SjR4+SFxlREJPlOVmWoZSm2WyigoCiMKRpRhCFxI0meZbhjM+un336SZ5/fg+rV13EqpWTZ+Jr+q6FFFAWjjAMUFp56UYh2LplC1Ez5qknd5DnOcaY4ZguMKzNR4FGSYV1Dh3GGGN47rkdJIM+r9q2bQld7MXhFun1+vWVUtI3WE3Jug0bWLlqFb1en7179zE7O0e73aHRaDLoJzRiRZbnFNbihMQYCMMYZw07duwgkCWXXHIJ7XZ7yV89cuQId911F3/08U/y7fvup9/PEFpjAaVCDJIsL1CCoYg7vPD+TMucVDtGStBC0cUQHu4RpzC9YQxaJ6d8ngt81wVdIf1TMZCCyf+fvfcOkuS6znx/9950Zdv3mO7xHjMACIIEKXIpkhIlkgBIEJBIiZS0Mmte7D4f8eK92D829t9nIl7Ei914bzcU6+WlJZcUPQmJEugJAhy4wQzGz3RPT0+b6u4yaW7e+/64mdXVMDNDCcAC2PoiEpjurqrMyqo8ee53vvOdsTEUcO6Fc6y11qnVm+jcyXNKn4SX+CdQtPrara29pbujki4wvtJGoYd9pU1JcdO/C08hPYXRmlwn7Jie5EMf+Dv8yi9/gocf+EUO7NuN70kETmQODGghBWmaEYSBy9ixzOzYxoljh6mGioWrF1nruYDushFBHPfwlCQMPLI0Jk8TAiWRgDGuISPRlmuLy/z45LOEMmRqajtRxUfnGoQpzqUgz21RzHEYzFbeKkhxoUqnKUpaEDk2yxBKAgKyDJ3FSE8ABpNnCOlusEli8DzX9eW+XAIhFZ50M9COHTjA7OwMTz11kpXVFs3RCecM14sZqddIux1yKTGFjajvuZby1toaN1ZaXFm4wdvuPo5SspD+JmA0Qkl0DlYK5E1WXlIqPGB2eor9u3bR3tjgypXL5MYSViN6aUKgqujM1QbCwEfYnNymWGHR1nD58gIaxd6DB/ACn0vzC/zxf/oc/+H3/5Qf/PhpVlbXEEIhhGuoV8IdqxKugJ0Lic41YeCjpKXX6yClIqhUSHPjzpUGbQWxKNvyDZW1NruenmNxJKAiJV6zQlIRdLwcpCU0liDVJL5y3W2v0RaHHrnOUKEi0T0ma1V2LMYsPf0CY0sZ5hb3xDdl0M3SFJO7qmi1VmHP3v1s2z6NwbK6uoLOdUEhsPl/IfpBF2uRxTJGKYmnlONEfY/A97HWIIV4xU1JedOtvz/oP97zFEHgEwYBtWqVwwf387M/+z4+9sD9PPSxB/jgB97Pvt0zVCvRrU/kgOlIWcyo1+vs2rWLo0ePcunKPAjLxsYGWmdEUQWtNb00IQyrKCnw/U03q/LfWZaR5zmnnnmOTq/L1PQ042MjziFYuEvZyY3SF9Exmxz5WyEIp9riyUJ1Ugj3hedhkbR7KTa3BFEFhGBtfY2oUkEUocErg+1N0GiOsH/fPi5fvcqlS5eQymNsdJTW6irjExPY4jNxxVDXwOD7jjZaWlri0oXz3P22e4jCkNzkKN/djKWUtDc6hOGtFShJkjA5NcmefQdod7rMzc+RJinKU9g8x9jc6XOtZX19HT/wqdfrxHHMjWuLIAQry6v8+Imf8J8/93kee+y7LC+vUq3UMEWmDZsZbdnMBCCUwuQaJSVhGBT8sCbVzhck8FS/qCYHNMHlSmD78y3GLrWoXFvDLq0jeglSCowvyRQ0EkuozWu2iW5C1NNUspxso41NM/KVNdYvXSUUEi+9eXPV6xl0hX3VfBgNxriAIwuNqQWWllvMz88XWsiXVw680qSJl2ukeCUMtmm+El5cHBg0ENmzZw9RFBGGjj+V1uCpQvqVxkSVmw8PLC9ERzmk/dcGd06uXlviD/7gD/jRj58g1YaNTpfcWKr1JgaLjnv4vt8v3IVh2De8BvCkj80177zv7Xzqlx/injtPoKS7gCSiyPg2UV5QbyXVw2Dr68LCAhevXuPylatYoahHITtndnDgwD4a1QoCQ1760Fr5kvMDW1U0QghyA99//An+xb/8XRZvrKC1pl5vsra2hlKib8lYUhBlwBFCEIUe7/2Zd/Pf/P3fZvvkGDY3CAW9bpdKpX4bhbaiCwxHp127vswf/vGf8vVvPkqSamqVmpvZZm1hjE/fhMkYQyB8rM2xbEo1rbXk2pDnFuQmvTFInZSPTfLi+55lmLwoxkkPKyXKC9BxbwvXa+XmzT03htwqiHx0qMh8CZWAsBKhhMRkGmFfW0MnX/l4CIIoYDVtIyMPL9bEpy4yk4es3SJvmh5r8H/9H/87szunEMYWI75xgw7eqEFXa92/wFPtihV+MMDzGHfSXxw//0skYOU7flkRBUWmKnHaYluIvsXNg9dgpXjwd4MStF4v4evfeJQvfPmrXJ1fwAhXeFS+h46dJrSUKZUXR9mgoY1k27Yprs9fYXJ8hP/hH/8j3v++97gbQhiB2Fr4eMvB5sRxjJAezz73PN/66+/wgx8/wfUbKwRhRCUKCUOfD/3cB3nkoQeZGG24phcLYG75+enMOY4lqeH0uQv8n//3/8O1hRukujATLywPy1UI0L8hVqtVkiRDKcEH3vdefvs3PsPO7RNkcUIQ+RRp+S32nxT710Vg9bl2fZnf+4M/4rHHHqMbZ1QqFXrdGINlZGSEdq/LxnqH0dFRTJr1TXhKnS9sZrVeoLbwteXfysDrV6pOYRPHRKHLoNudHq2NDfwgIirNdbCbDRS+1y9Am8LClSxH5AYfie95COsSElur/A0+9NtHt93DFxCGIZ08xkQ+ypPkax0mwzob4uYGEa9n0H0VC2mbyxclhWuRtBqtU7ROCbwAWVJqP81W8rrc3Nrxb7UVDRFS2AH6o0wjcrIkQd2iQeHl7PUG24gxOVjLHcePcvTYMVZXV7l+YxEpJN12uy/VKYs4ZcAuM34V+CzeWKRSq2Jyw/d/8EM67TZHjx4jigKybHN44EtbrLPb8lx9o8PzQlqtdf7Nv/89vv/4k9xYXqVSrdHpJWx022x0upw/d4HFGzeYnZ1lfHQEawuv5ltYZ0oliHtdoiik2Why+PAhnj11itUVt48oDPqFtr4zWPHZ5HmOwEN5igsXLnJ9cZHZmVmmpycAS3utRRBVb7F/SRwnRGGA8hRJnDI62uDgwUP0uh3OnD2HEPQ9E3q9HsZaorDiVldxjO/7hEEFa6HXi4njBCFdHcHzVf+GvqkYct+3MAwxNsdkGaMjDe6+8wTH7ziGVK793Q98RNmxKdWmUsPavk11mOaERjj/XSXBk+SBIgklWUUR65SU/DXbbNXD+hKrXCHNeICU5J4kjzzELTrm3pSc7qDMSkpVFLgknvLwPR9rrEsxf9qtwK2tH81NX8eWSyn3YsWTBjri/E0z6sFlF1KifB9ucdpLf9PSRap8/pbjtmCxTE6Mc/fd96Ck5OrVy+Rak+WmHzhfHHQBcuvMtHWaE4YRWPjxkye5cPECe/btZ3J8dEv/vS6W1oPB4c0Mk2kyY/nGX/wVn/vzL7O+0SU3UK83yLWmUq9SrVToFNX/XqfDjpkZxsfGnJxxIOi+/LkweL7PxkabWq3C5PgE+/bt5ercHNevL/RvXGEYYowpVCiqz+sGKiRLMrzQ58zpM6ytr3Pw4AFqtQqVapVb8gtYsjTB9x29ZS14StKoVZjduZNumvLcc8+ysbHBxORU39ejXm/Q7XZpNuvkRpOkiSvARRGVWohUgkxnhfzM9I8Z6NcLhBBYnfKud7ydX/3kI3zylx7m/X/n3ezds4flpSWeOvkTAhWQafd4KSSi8OW1xiARtEPIfIkOFKmE1GjSXJMW+xjLfSIjX7MtsOBlBk/nRBqi1LpNC4g19mXopUG8KYNu/wWLjM0YUZjYWAQSqbZytD/tdivc1utIuZVT2PL7Tc5ByHKApdMH347BeL9zZ8Asp2wHdnTFZneb1gAR3pgAACAASURBVIYoDHj7206wb/cerl27ipUBcRz3g2VZ/Crla8Kz1CpVBJI4yRAocmtZWl7l2edPM9aICILAefpCP0jc7vl7o0NIRaotn/3PX2R+4QaNkdHi3KYEnsd6d4MkTqjUqlhtuHzlKlpnzMzO0hwZ2XLRvNz5cI5xhkoUkCYxQeCzY9s0O3fuZG7uKistN2w0CALHgSYJ5egdIQR5pqlVK7Q3ulRqVa7NX+P0mdPs37efqcmJ27hobcHpuyDoKVckzdKU8bFRdu7dw9UrcywsXMMLfDzPd363ucVaQZY53bfn+SjpaCqdZy7Tl+4x5Xe0rDsIIRgbG2NmZob/8R//Qz72wEe5644jVMMACUxNjtNsNhHWsLbeRWeaNHMz1TzlYexmM1GUWJQRKARGCvAU1vdQge9anK0hk7xmm8GSCMikwAQeRrrjMFFILOFW67w3ZdDNco03kGkJQV+JIP4GvMKLiWbxUz7+xSiPwVIkvxTPEeXrl6/gJEjOhMedcMSt85QXL+EHR7sopdC5QaryZ1lIy2Bqaop33/dOLs0v0uv1iOO4/zrl87XWWKH7RRslfayQ+EGEwXB9aZEzzzyJ53nMzMxQrVa3jAEqC3tvZuQ5GCH5wz/9LAs3ltDFFIQ0jlFSEFZc664wFuUF9Ho9nj/9PGvtNY4cOUyjurm8f7mga4Ug0zm+EijPI+518f2Qickpjhw5zOmzF2i1WnQ6HcIw7GeL5ZLdt4BUeH5App16YfHGdS5eOM/uPbvZdgudtfMuKb8XFl2YHCmlXEGv2WD7zp3cuLHE86fPIKVCeR69Xky1WkNKx7PmOnfBUBbSRuHGyXte2F+NOdeyBsePH+ehhx7iM5/5DHce2UsUhihhwGhs7uR4O3fs4OD+/Zy9cNWtZJMUUbia5QWH7Hme8ybGkuUGDHgGwsRS61lqXUPyGtuHhCgQkEuQniLX1mmafUXPZIS3uILflEFXvdzIkMHA+FOilJL1JWU/5eNf6fmv/PetByqE0wWL2wi4wEuC2osv7EEd7dbnSarVCu+9714qStJaWmRjbQNjLFYotBEYIRHWJ8ss1lgCTyGFJUu7YA3VMKKnLY//5GnmFhbZtWcPo6MjaJ2gdUIY+Og0RxZdgcYW77lkcG7zPd4Mudn0kC1vQG7SrNuRTjVSSLDO56JfCaeYQ3aLji5rcwyCF86e5blTzxOEIVIIrDH4nkeSZIjC31gpied79JKUldUNLl1Z4J573kZuLFHgobNeYRxvXIYrBB7gK0Vpi+T5rgjsSZgcG+GOY4eYv3qZpeVlrFXkGvLMYHPDSKVKWydYYclNhuc7HW+702Vlrc3psxeZnd3Jtm3TSAxxr43nu88i1sZRWHLryurFrnK+EIzWqmybmODZp55G69zRKlGEyXO0TvFUgNaGZm2EbruLJ33qlTpJL8Xgbva1aoVDB/bw0IMf5jc+/QjvuOsItdAUKzHhuG+pEMpJ3gQwMtLkrrtPUK0GrKwssrK6jDYWFfggPbI87ycx/SKeEGRYMk+QRd4tO0qdfvhvvuXCHbtCIsymOkkYS4iiFydElSrK88l0jkX0G6aU57NtJOD+j/4i1Uql8EuRJInrSO2rYF4lvOr0whB/M2SZ5ujRI2zbvoPl1RWWV1oF1SAIgpAk6VGrVamEYZ+fi6IQz/OIkxidacbGx5m7epWTPzlJo9Hg4P79eMqlGFK6i9j2G1MExpYF0L/9fVyKTde4PM8LKZ7rvsrSFN8P+3c4UdzJkiTDWvD925nM4YLp1bl5Tj1/pngpSbfXJYoqCLmpoy0NvcMwRGvNysoKZ04/z7vuu48wCJwRORZbrI8yrYuA+8qo1SocPny0P0NN+R7CCur1Gsurq1Rqlf7+0jQliiKq1Sp5ntNqtTh37gwjzVF2zc4QBAEUVqjGGCwSJTYHTcJLb9qlPKvZHGG1tc6Zs+ewCHJj8Dy/oKHcdAxf+ZTDBOJeFykFUsGhQ/v5+IP382uf/hXue8c91KoVMAZVBNibvv9qhcNHjrBn927A0mqtkiYx1hgynSEkhT1rEX6Fs2s1hYztlpt9bbd6o0amU3q9LmAJwwBjSj8U+PiHf5577713yyqz5MAHayuvBoZB9w0Cz3PLtdnZHdx119uwxnL5yhW6nQ6QU4lCrMlJ04Q814XKQvZlQmEQsbK8TG4N7XabU6dOEccpu/a4ZWOe6z6vvsn3ugz8dmZs3Q7KpWsQBP3XdJm0dFkuYMpiqxD4voeS8hWtP7fCdeCNT0yxvr7OufMXsAhXSOrF5LnuUyqDzSBlELyxvMK58xe48847CTwfz1MuQ8pTQt/jVpOrPCWoViOOHz/O0vIKL5x5gbASEffSYtbaJi00eD5LyVZrfZ1Tz59mdGSUA/v2YUtawvPIs7QIfJt4sY4d3E2tWq1Qqzd57Dvfw1hbWEG6z7L8d6lyVJ4gTXocu+Mov/arD/OJjz/Ie9/9DkbqVWyeu6YhJHHcxbuFOidNEoQ1zOzcwc+8590cO3oUKZwOOVDCZcrg3M5wlqmelASeR+B5/Z9fabvV3/+2W9LrYfMcXykqYYjNnSppYmyM48eO8elPfoLx8fH+jW/w/L/a1Nww6L5B0G13CCO3pK1VI44du4PJyXEWFxdZXVl2NpDWFIoQhegHXLc0c734Ab4f4iuf9XaHp596hsUbN9g5u4fJ8RGMNei8GOuCJUlitM7w/dJd+G+OwSxNSkkvTl2wVRIhJWnqAr0slv4lS1YGJXWrJg6R0+30mBgbY2bnLDeWllldbdGLY4TykWJzOu5gsUsIQa1WI9WW6wvXOXfhAvfe+3Z85eF5coAWu/n7t1mCHwSMNBrs2bOX1VaLc+fOE1YijLEgNuVY5U2n13NG/rVaDSMkcS/h/MVLgODY0SMoqei0N6hUKrgldlkPefnip5viK6nVmzz+xJOstlqumcJYsiwBBGEYkWdubqCUEAQ+Dz/8EL/08Q8zNlpH2kIWKZS72YGjUm5xz/U8V5+xxmK0Zmpyinvuvot77307J44fI0k1I80Gge85qsi65pRycwH+ldvwsTdp0X8VtnqtSiUKN2/yWLZvm+aDH/gAn/rkL3Ngz0z/OzxoaTnYkPNq4VXsSBvibwObG3JrXIOEdZNrhYJnnjtTdLKdLJbsHrk1Tm+vJNY6nWjgR9jckGYxUhYjWrKERq3Ovn17+PSnHuKOO+6g2agjgTSL8ZXH5vfp1elcS9MUPwhIU40XeHS6CRsbG5w/d4Hl5WWUgH379nHo4P5imkHxxFsm2pq4lxBValjg8tXr/N4f/Slf/NJXGR2fAAydTqdvJ9ovQBZZS14ISkcaNY4eOcg//J3fZHb7NJXIo72xRr0xdvPdWw1CFqPJJFfmFvmTz36ev/r2d1lcXGJ8fJRut4u1lihy7U+lfFApRWZywjAk7nSYndnBpz/5CD///vdRq0Zgc253Bl670yOqVvjn/9+/4Qtf/BLK88lzS547qWYUVYm7vWJYJkSBzz/7Z/+Uu47uQghRTC0pPvTBLrFbnH9TzPDbxOZzsyxDeD7r611WV1dpt9skSdL3Jwb6hcdXwmutsNnY2CAIAjzPw/d9qtUq1WqVsbExxscbKDYbvMqbd9mc9GoH3bdOj+ibHEKBZz2SJCHP80LbCbt2bON//u//Wz7351/he9/7HqeeO40fVWiOjJLlGq0NXhAgFbQ21vtC+E6nQ71eQ3iKb3/v+6wuX+fhhx/m/e//Weq1CpEfFdeZIe71iCp/u6+Cuyj94osKXuDR7qZ885uP8t3vfpezF+dor68RxzHTk+O8593v4hd+/uc4cuQIUeTdRiHPI6p4/Ytgz+w2fvWXH8Zkmr/+zndJiyyz1NFqrfuNJr1eD99303fb7TY/fuIk/yL9Xf6n/+4fMTHSoHmrgFvsv9dtu8/FWHbPTPM7v/kZjNH89be/w0a7izGmbw1ZUjiljtf3PNpr6zSbTW4srfKv/vW/o9dL+NQjD8JN5i6UN40yIARBgLWu4SHLMoRUbgyRX+nz2eB4cmNzhLBIBb63Ob49SRKMociwN1vYbwapNlvUyxboMpBViuLTaLPKSKPKqxyjXlUY4xpLoijqG1fB1nNQBlvgJXTVq4EhvfAGgTXuAvE8he8H9LpdfM+jWomo1aocOHgYrGCjvY4uqsVplpFb8P2Q9bUVJicnsNaQpgnNZpNuEtNaW2Pb9u1cv3adMy+c4/rCdSYnJtk2PYHOctI4oVKp/a3lC8bkrmBT8Iuptnzta1/nT/7sszz5k5O0Y019ZIx6vc5Ka41z5y/QWltjfHycHTu335Lc0LikWAmBNZo0TpmaGufggQPcWFxkaXVtix/yYIYlhCBQEqyl043JLSRxwjPPPcfRo0cZHR9D3eL9Z7khDEPSJEb5hWrC97jzxJ0kvS7Xri9tabNN07QfhKWUWJ0yMjJCkqRsdHpIIZmbv8bKyiq7d+/FL/xoB7OqwaJaWcyRSrKyusHXvvEoV+fmKbW5LiYUkyOkwvMUxmSMjTX5xV/8BSZHR+n1UvLcFjdmH2MNaZY6OuYW5z/TBs9TzuymOKYoitystcLkHWOKrtPN7lGMKfUJt6MUfc22zsYGviq4ZVVIWQFhLTY3GLtpWj9YOHstdO7DoPsGQfm5looFP3AXktYZSkmCIOD4iaPs2r2PheuLXL5yBd93HrydTpeRZo1Op9PnFNu9LuD4xCzLiMKIXrfHlUuXWbh2jUpUZdfMLsIouM1C1s0hpSi6/gQ6t1ydm+ff/4f/wMmnnmbfgYPIsMFKa5U004yMNsmyjIsXL9Ludhgbm2Rmx/RNXz/O3ZwzCmmaJwU6zRgdbXLH0WOcvzLP8vIya2trhGFYZIS2P2zRZinK8/CCEOV55MZy48Yi586d5eDBg0xN3Dzbza1ASlfwzHo91+ruOXrmxIkTXF9aY3l5mVar5SaDFIZFpXdGoBx/neqcWr1BEEZcvXKFa/Pz5HnOiWOHi/P40u7Bkl9MkgTleVy+Ms/nPv8FcmPRuZMCpmniimjW4nsexjjVwO5dMzz8yEMILQhDD1UoRbQ22ELeZbkNRl8KstwVQqXaLIrCAHUwwOvb4n3LFzck/RdCEIb97/ggZWCMcbRJUZguKaGSz30taI9h0H3DQBSFJ89pJYt7tCz0m9JahBXM7JjixB3HSeOYuStXSJOYMFD00sTZCUqBznMUAk8qMLbQcUpqjTraGs5dvMgLFy6grWV82zRRvYoiRWCxOAG8k1WVQwxvJyCLfueeUpInnnyKL37lUbxKA219dG+dSuAjMYUFqCLJNMsrLS5dnefgwQPUmyN4UpAmHZTnIux6t+emG8iibUUU/5ES6bnxN7V6hfvuuYuF+TkWrl8nTnMMConC6JxGtUqGLYqPeZGNWTqdLusbHc6cu8jhw4eoN5p4UmBNWjTLGJLc6Wh9WS4GhPPhkE6ipaQi8Dzefd89rLdWWb5+gzTJiOMUKSCqhGQ6IbcKrzCASuIeAkutViNOUk4+/Sxx3KM5OsbU9CQSSOIunicRGMgzumlGEEW0Nrr8wR//KSeffpZMWyq1JnGi8YVG+T5JbrFegFCCpL3BIw98hHecuMMFW7GZ+Skptgx8vRUkTrPczxB50Q1CgJBFN2fxf6nkptD9DbOJ4rjKY5b9gOu+u2pLlvtaYBh03zRwnUrdOGFkZITjJ05QqVW5cvUqrdVVgkoF3/fZ2NiA3DA+Pk6SpKyvrzMxMUmSZKRpQhD41GpVlpZu8Myzz7DeWmd8YoKJsRF6cUzghygvIE1KbvD2ud6y880Y+NHjT/CjHz9BrdZgvdMm9FXfujLLMqIockNLs4yVlRWeeeZpDh08yPTkROGQZUnShGpUIdPa3UBuApvn3HnX3SwtLzM/v4AQrqpfqVVYWVkljIL+/rXW/f1rrVldXeX086fYs2c301NTZFl504E4dnzwrQJTt9flvvvegTHwzDPPIj2F8j1arRbVRgNhecnFXKodoijiuWefodONGRsbZ3p6Es/zycuJ2p6P9ALOnb/En/2nz/LDHz1OnGR4vpvo64qHHnGcFr64eSHVknzykYeY2bGdNzTR+l8ZhkH3TQJjMoRyDQBCQhj5HD5yhO3bpmlvrHHx0lWEhUa9DgharTV8P6RebxSjX2okSdJfhkoh6fV6XF+8zvOnTjM6OsbM7B6Up+h1Y6JKhJLQ6bSLtuVbV9fLSr0QggsXLvLEEyfdlGVk36MV6HOf5bh7rTWrrTXOnD3LgQMHmJ6axOQ5vh+Q5ymBV3aKvTI8T1GpRNx599201tY5f+48XuDT7cRUa3WStLdl/2XgK9t4W+sbPHvqFFNTU+zfu4c0cecpDHx0FqPUzQtNYeAhERw5coTp7ds49fxpbiwvMzoxThzH2MLQyBjT1xOX1IMQgjiJmZu/xty1BTw/YnbXLpQn0Vax1lrjyadO8cUvfZlv/eVjrLbWCIpGjFxnKF+Ccb62snABy+IeB/fv4Zc+8XGq1Yjb8Q8Z4vXBMOi+SSCkpdvrFPyZs2v0fMWe3bs4duwoaZpz9cpV2u02I80RTJ6T65zQdzrSJEud81QU9quxQeC62xauX+f551+g0+6xY+csY2MNtHF8aCXyi461m1+0Wa7xS3MeIVBewFPPPMO1a9eJIifVKavfpW9wkiRIKWk0GgRRlevXr3Pm7Fm2bdvGrtkZcp05m0Bx6/2bLEZIQSUKOXL4CL045vTpM240uFJ4Smwxdh/cf7PZJNWG1dVVrs7PE0URB/YfQCnhgpqUCPnSjH+LIZPRRQXcY9++3TRHx1heWeX6wnWyXFOv1l7iZ1veoNI0JazVSHXOyvIKFy9d4fSZF3j+hQv86EeP8+3v/YDPf+HLnD13gSTN8EOnRMhNBhiUhCROqdZq5DYn8n3iXpeHP/4gd995nDC4tQ53iNcPw6D7poHB9zdbgH3fdx1AxjA2MsLxE3dRq0bMX5njxtIS1UqVIAxJs8SZyRdTJgwGJSVa58RF1Xl0dIy5uWtcvHyFpeUlGo0RpqYmiwyTwpP25plunrs23bJ6PjIyQmttnbXWGktLy9QajUKqZPp6yUEntl6cUG80uXL5CnPz82zfvp2ZHTuR0gU++TJBbxBu5qgiSVIa9SqHDh4i1Zorl6+wurrCyEhzy/5LVUPph9vtxTRHRpm7OseFS5fYtm2aHdu2k2UpQRhwS+Yz1yg/IElijLUcPLCPbdt3MH9tnhuLN6hWq1vaSsuRTmHoeN5UO8oFKVlaWeH8ufOcPn2G58+8wJkXzrG+3nG+BoXqwFiL5zv+0RVfnZxMpyk6S9k9O8Nv/vpnGB8dcZz8MOi+YTAMum8SuOGTqt8nL4R0xihC0u10aNZdb/zMzh2sr7e4dOUySZrge57ze62GpGnsph0IQeA5KZOxrgJeHxlFCHjqqac4ffo0k5PjTG/bjs0tvndriyipJFmW43sKgUBKwfT0NhrNJufOnmO51XJNHEHQ914ug245MTru9ZicnODS5StcuHiRXbtmmZ6cwvNuvrQHQCjyXOP7HmmS0qxXOX7iOBvra9y4schKa23L/gf9hrvdLqMjo6yurFCpRCRJxk9OnsT3fe6+8wQCyWAP0csWWIRHptN+150AZnZsY2bnDuLOBmfPX6Lb7fYDbTl9ofRO9gq+2RgnTasU06KN2yFRGDkPW6zrgCu8G6wBrMD3A7rdDlg3S+7B+z/C+973HnylbuXfPsTrjGHQfZNASsnKSosgCFHKyVniOMb3fALPRwiLzXP27d3N0WPHyLKM+flrpFlGFIa0NtapVKs06nUEijTTfW/LPDekuabb61KJKrTb65x88iesr62xZ/cemvVb63gNYHLTL0ABNOo1dszMIpVk7tr1LYLzUsdaZryBcvKlTrdHrdFkfm6eF86dY+/evUTVGpVbDHbcSJwBuMTiecJxwp7izuMnMLnm6sJin154pf0rKbFI4jRjY32DhevX0VqzY2Z2y/5f1o83B8/3nNNYnuEXk50btSrHjx5lbnGl36k1KGcrJ3tgDcJaPCkR1tKLY9rtdt88SBuNNkXbbjGBWuuyc8rre+TWaxXe955387EH7mdyzPkIG/PqmBoN8epgGHTfJNC582SQQtCLE5TyCAMXCLTWSOX8U3WuGR0d5Z63v51KtcrlK5fZaLeJqlW01s58u7C+c6oZhe8F5DYjjCKUJwn9gCRJePbpp7ly6Srj4xPs3LntpseXW2fvKYUzR1GF/jMKPQ4fPsZGN2Z5eZmlpaUt0wv6Pe6p8zbICtpjfGKS+fk5Tp78CXfccYyZ7TfX8YpSb2oNIs8RhblP0uty3zvvZWmtw9LS0pb9l/uWUtJdX6PeaJBmzte2OTLK/Pwc58+fQwrB2+46sbmvl8t0ZdnRbFCqdJaw+J5ipNnkyJ33sry8zNzcXD/YlvyyUgopDH4h08vzHE8pKrVav8OtlO0ZnL2nG7UTIoTE5hadZ9RqNabGx/md3/4tjhze58y+YEvn1RD/5TEMum8SyE3pLr5fep+6n6VSaG3Q2hD4zvXfV4LjRw+xb3YHq4vzLNxoIax1DlCFUba1BjBIBXmWEXgeRuekaYb0fDID8zeWePypZzAmp9EYpTFSRwC5Nk5apTwQBkWOFAZsXug0BUJY14dkMt52/DBZGjM/v0C700UoN8Ipz1xWmFmXLUsp8D03DlxKNyXjBz96gunpKbZt30HgSXSWIJXEWuhlGqEUPm46gBASZKF1FqIwEbK84+130t5Y58bCIr1ujLXCcdu5xpJjhCoM7o3bt4BKpUqqDc+cOsPqyjrT27YzMtrEWo21WeEPLMi1C7TuIxLk2oLwHQ8uFLmQjFQldx4/ytnTZ7k2v0jgV2h32gSVgF7aJqBCEjtPYDd1WKPT1OlhhcFmFmldX5fz2zDowqBDeh46XufO43fwa5/+FO+89y7nJZD2SNPYWWcOOYY3DIZB9y2C0rS5ROkpOz09zb59+8itZGlpkbXWGoHvI4TrclOBT7vTIwz8flVfCNHPBMus7Nlnn6Mbx0xNTeP5AZUowCuGjwrnXl24YKl+50+WpgjhaAOdJbzt7reBEJx8+lmSOKbZHEHnxlXvC2/gPM/77mCltCpJEk499yyTExPsnp0tfk/f+Dq3AiVemXMVQtBqrfKud72TNNGcOe1sGbNc0+l0iKpVpJBbdLTGuOOy1hKGIc899xzaaGZnZxlpNgEw2tlluq4r4ygCIYvM1fHw4JoQLNCLM5544iSXr8yR6RwhoVavui4y5eH5yhXKium6JZfd6yU0R5r9GWVB4BNFIViD1Rk213zwAz/LJz7+IO959zuQ4Pw0ohC/9O4dBt03DIZB9y2EsjgEm56unucxOTnJnXfehbSG5aUbJIkr+HS7PZJE0xhpkhdeBeWwwpLrLCvtaxtt5ubmOXnyKcbGxti7ezdgXMZddG2JYpKDNQZROvcLyNKE0FMIJdm7/yCe53Ft4TqLi4so5VGt1eh02sDm5IGS8yx54E63x09OPkW1WuXY0cMIRHFj8bYE3EEMBt9KFGJ0ztvuPsHI2DjPPPscG+020vfczUbn/eJW6ZdQ7t/Jswynnj/NtWvX2L59O9unpwHrbAsLdYC1brikpaQtRL8DtptmfOtbj/FXf/UYG+0eUTVyfrg6QypJp9MF4Tq5cgNpmqF1jqdC6rUmi0tLCASVSoQUsNFaw+iUY4cP8fM/97M88vBDHDt6uD9x2/HVxU041y77H+INgWHQfYvAGLNlGGUZMMsCS60acvedx9k1M8tSyS1a8IOw6PgS/YA9aORcmq3UR8dotdbp9ro8/fTTrLVa7N61CyUkfuTGxDizG4mQypmXW2d2ojwPJHTabWrVKidOHMNYOH/hImvra3hBSOA7hzWgmKzgbgBSSsIwRHoBvW6PcxcuYIzg2JEj+L6is9EmCP1CzfHSQab9Jog0dlOdLRw8uI96c4Rz5y+QJimdXpdqpfKyOtpS6ZALydT0FKdOnebJJ55g165d7N4164KcFM6gKAhQ3qY6wrUdQ7vd4Utf/wZf+vLXOPPCeaSniKIKxuYkSYznSWojTZLiWDw/oFavg1Ckhevc5PQk2NxNDFaSvbtneN973s0nPv4A93/kFxgfH0EYS64d/+t5HibXTiVyG9Osh3j9MAy6bxGUAzBh0w6wLBJZa5EmJ8s0u/fMcsfx46y11uh0O3jK48b164SF4N7zvD7N0DcvsZZunLJj53Y6Gxt0Oh0uXb7EmdNn2LZ9B2PjE3iBX/C8GuWVU4g3j8WanCCM+jre/fv3U280uHZtgfm5OSYnJ0mSpN9AUVoIliN4Ot0eY+MTrK+tc+r0GYQQHDp4iGotIukleK/Qrjw4KNVJ7SCOYw4cOMi+fft47tRzrK+t9xUFpdFJOWK9NCT3goDl5SUatTpa5zzx5JMIITh48BC5yYsg6oqcmc7xwgALPPGTp/jzL36JL37lGyzeuEGt3sBa6Mbdvl4ZK0hzje8Hham6RVpLrlOUlDQbdbqddcbGRrn7zhM88NEP8yuf+iXu//CHmJ3Z4Zyyisy2nCJsC1PwTXP4YdB9o2BoYv4WwaBzUrlM9rzNQKTjHl5YcdrdIMQK+NJXHuUP//iP2djosNbuorUmCAInsn+RjlT5Ia1Wi/GJURSCuNcFk7Njxw4eeOCjfOjnPsDkeLM4GMDm/aq5jnsIPyhuCoYkyQjDCqmBb3zzL/ijP/kzzl64gpSSWq0GQLfb7bt1ra+vU61WWV9v02w2yfMMJQW/+ssP82u/8ikCXwAv9TwdnMiMsRgcheAHARZBuxtz/sIl/u2//Xc8fvIUWmtqhWKg2+32s+w4jkE586Esy4j8gEoUkmcpR48e5sGP3c/xI0eKDN/SarU4c/Y8J0+e5PTp0ywtLdHuuZHn9XqDPM+J47jvMQGw/aC8BwAAEgZJREFU3ukwOjqK1RohBLt3zbBvz15mdmxjenqa3btnmZ6eZnLCycDKuioULyFM/33b0h1ry/kYcrpvFAyD7lsEg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kJUZC1ERdZCXLsWuGuYEzv3/IHFr6fgvY++MNoI1DldO8SJ3Xjn3C8YewZAih+NHP/Vlp+MHE6kXPdObfDxGwtMG35yjgSS7KddX9dzOPkuLFEAuGNIf3y+4mnccG2v8578S9K8SQM8Pf0+vDb/YYRVNfTcPXQdA/7+74YaAE2IUqcMleW7bbuwc88fRiKIlIuMqIG3Fk3Fg3cMVroBynm01oHM2HjngLI/SkSVkd1mw4Ipd2PqmOEIDfF8p+ArunfCtb09HrwLABACV6Fnz7+u/BtqAPY31LYBMHQJ89WVa40cTmQKTRO479br8e7iRxFdx9R5FdWlRGqMwzkLSUmmdhtE5H0P3jEYg/opmReFgkLDbxJH1C+o3ercvxgbw5Sc7BaAoasAb636GLv27jdUBpFZunRohU/eWIDe3TqauYwAMDlmX/EH9bsmcToWkZ/o3LYF7hqWoCyvS4cSh/qVm1vX/3rlyfAcRl1KQ7ufFbvcmDjnOb8eVkGVW62a1bF8wWRMuWeYybcExFVut/ub2M4DO5i4CBF5yZ1DBijdf6RvT4fxoVGaaPHX/zRakAwS7wEwdPbO3LoDDz7xgpkT2YgMEULgrmFOpDw/0+PNWspHXiA1fVO0w3mziYsQkcmEEOjVVW0vHxVZy/B8AgH9r6uMBkcBAwe/Sj0U43B+BuBKIznvrPkUJ07l4amp9yK8WlWjZRGZonPbFlj/+nyMnfksPt6YadYyoQJ4LcbhrNhcf/bPRD4jKjKiQk/7l9cFDWKxe+8Bj4+XEn+NPjXcAACAEHhFSmMNAACs3ZCOr7/7GbcMvBqdL26OyFqG99nwmqohIX4/dYvOiqgRjlfnPoQX316DOYuXo9jFkdZE9E9GnvgvPddYUyEg/torWkkDkNXQnhyzzzUbQBOjWcdOnMJTy5IVVOVdndpchDVLH7e6DPISIQRGD+6PLh1a447J87DvwEGrSyIiqhAlDQCSk92IT3wGUi5UkkdUSbRr2RQfvTYX42ctxtoN6VaXY9jIm/rh6svirS6j0hh073Q+wEyVlpoGAMAZFCyrgpDHAISoyiSqDGqEh+Hlxx/Asnc/xMxnX6/Ue4jXCA8z+SFH/6J+E18i7zH8FgAAxHRLbBQiQ1LBkz8FKCEERtx4Lda8/Bga14+xuhwiojIZbgBi4523wSV/kEBvFQURVWYXN2+Cj16bi36Xd7O6FCKiUhlpAESsI3G6lFgKoLqqgogqu+phVfHCnIlYNG2MaU8CExEZ5dEzAK1bJwXnVnO9KiFvUl0Qkb9IuqYnWjZthNGT5+G3P7KtLoeI6B8qfgUgKcl2tKorGQBP/kRlaNP8Aqx7fR4SrrzE6lKIiP6hwg1A9L7ihUKgvxnFEPmjsKqheG7mON4SICKfUqEGIDreOVFA3GtWMUT+LOmannh/6eOcGElEPqHcDUBs54EdNIk5ZhZD5O9aNmuE9a/Px5CEPlaXQkQBrlwNQLNmfatA6K9JIKjsTxNRaUKqBGPupDuxaNoYVA3l6Awiska5GoD82qFTpMDFZhdDFEiSrumJD5Y9geZNGlhdChEFoDJfA6wTlxQN6RrvjWKIAk3zJg3w4StPYtXHX8LlNrar4I879mB56npFlRGRvyuzAbCj+EFAVPVGMUSBKDSkCgb1u9xwzgf/28wGgIjKrdQGILJTYgyEHK1ywerh1dAoNgo1a4SpjLXchY3rW10CERFRuZXaAATb5M0SCFWxUFy7Frjn5kT06toBdptNRSQRVQIn807j0ade8fj4+tF1MO72GxRWRERAGQ2ABAYZXUDTBKaOGY7bb7wOmsbNM4kCTVFxETZ986PHx1/EhySJTFFiAxDTxdkSOtobCRdCYMGUe3DDtb2MxBAREZFiJb4GKHTj435vu+EanvyJiIh8UIkNgBRwGAmOjYrElHtuNhJBREREJil5EJBEnJHg0YP7o0owBwcSERH5ovM2AHXj+0cB8PjJG00TuL7vZR4XRUREROY6bwMghGhsJPTi5k0QUSPcSAQRERGZ6LwNgKbbaxoJbdmssZHDiYiIyGQlXAHQI4yE1qllqH8gIiIik523AZBS1DASKjjvh4iIyKed/y0AoUsjocdOnDJyOBEREZmspCsAhUZCsw8dNXI4ERERmez8DwFq4riR0C0//QIpDV1EICIiIhOdtwHQ3cg2EnrsxCns+PV3IxFERERkovM2AG7Y/jAavOL9z4xGEBERkUnOuxvg4czknBiH8yiA2p4GJ3+4AfePvgnVQkM8Lo6IKr+a4eF4Zd4kj48PDamisBoiOqfE7YAh8SMEenoafOzEKcx/6R1MHTPc0wgi8gN2uw0tmzWyugwi+peSNwPSxJdGw5eu+ADf/LDDaAwREREpVmIDIHTxudHwYpcbw+9/HHt+zzIaRURERAqV2ABk6bU/B3DM6AK5x0/imlsfwAf/22w0ioiIiBQp+RmALS8Ww+FcA+Bmo4uczDuNkQ/NRY/OF2NIQh+0vvAC1KltaNqwz7HZbAirGmp1GUREROVScgMAANBfBjTDDcA5X37zI7785kdVcT6lU5uLsGbp41aXQUREVC4lPwQIIDtj1UYh4Z9nbCIiogBWagNwlphtfhlERETkTWU2AFmZbZMl8IM3iiEiIiLvKOMZAACYrguZeC+E3ABAmF0QERGpEd+uJe6+2enx8W2aN1FYzX8NvPpSxLdv6fHxNcKrKazmn4LtdkP/7WrVrK6wGnOUowEA9DN5W7SQar8BMPdPAxERKdMjri16xLW1uowS3TLwaqtLKFFwcBAm3z3M6jJMVeYtgAbd+8dqIVU3gCd/IiIiv1HqFYC6nRKauorFBgD1vVMOEREReUOJDUA9h7O2DnwAnvyJiIj8znlvATRr1reKG1gDoLmX6yEiIiIvOG8DkFcr5FEBdPV2MUREROQd/2kA6jmcXQUw0YpiiIiIyDv+2QAkJdl0YCkAmzXlEBERkTf8owGI3ls8DIDnUxmIiIioUvj/BqDTqCAhxCMW1kJERERe8tdrgDHaoRsBoXTYj03TUDcyAsFB5Ro4WKlF1alldQlERETl9rczsxihKvSy+Pa4Y0h/dOvYBkEBcPInIiKqbOwAUL9r/2ZuNy4zGlYlOAizJtyOIQl9jFdGRGQCl8uNXXv34/cDB7E/5zAOHT2GvPwCFJ4pgq7rqBoagrBqoageVg0NY+uifkxdNGkYa+rGM0RWsAOA26UNgjC2059N0/DinInoc0mcmsqIiBT5/UAOPtv8PTK+/xk/79qLwjNFFTpeCIELGkSjY5vmiO/QCl07tobdxpelqHI7e31eiCuMBk26eyhP/kTkMwoKz2DthnSkfPQFdu/dbyhLSok9v2djz+/ZWPnhBkTWqoHreneFLqWiaom8zx7V9spqMDj1r/VFF2DUTf0UlURE5LnTBYV4a9UneGfNZ8jLP23KGkdyT+DVlR+Zkk3kLXZbaLVuUiLYSMikO4fwchgRWUpKiVUff4kX3lyNYydOWV0Okc+zS4k2RgIaxkahZ5cOquohIqqwrINHMGfxcnzzw06rSyGqNOyQogWE5/ex+l3RDZpm6PlBIiKPfbXlJ0xdsMy0y/1E/soOIVsYCXC04+RgIrLGm2kfY/HrKdB1PoxHVFF2ANFGAtq3aqaoFCKi8nvhrdV45d0PrS6DqNLSBBDu8cGaQK2a1VXWQ0RUppffeZ8nfyKDNGmgAageVg02TSv7g0REiqzdkI6lKz6wugyiSk8DEOrpwRWdpkVEZMQve/7AnMXLITmAh8gwDYDHj84WnilCQeEZheUQEZ1fcbELMxa9iuJil9WlEPkFDcBJIwG//ZGtqBQiopItS/4Qu/cdsLoMIr+hAcgzEpCxdYeiUoiIzu9w7nG8veoTq8sg8iuaBAztkvF5+veqaiEiOq9lKz7gM0dEitkBsR2Ql3sa8OmmLcg5nIvoOrVU1kVEBAA4lZ+PtRvSTcmOjKiBuHYt0Oni5qhbOwIRNcLhcrlw7MQp/JF9CBnfb8e323bxWSfyS3ZIbIeBSb4utxsvv/M+ptx7s7qqiIj+9P6nXyv/6b9Pj864eeDV6NWlQ5mjzPMLCpG2biOWvfshtv+6T2kdRFbSNImtRkNeWvE+dhncb5uI6Hw2bP5OWVaThrF499npeG3+w7i8W8dy7WNSLTQEQxL6YP0b8zFrwu0Ir1ZVWT1EVtKyZGQGAEN7ZxYXu3D31IXIO12gqCwiIuDEqXz8tHOPkqxLHG3x4StPokdcW4+Ot2kabrvhGqx6aQ5ioyKV1ERkJQ1bXiwG8JnRoJ92/oZRk+Yiv6BQQVlERMBPO/fAreuGc+LatcAbC6agepjxn95bNG2IlOdnomb1MMNZRFbSAEBKuU5F2Iavv0ff4ffj222/qIgjogD3q4L3/quHVcXzsyYgKMiuoKKzGsZGYf7ku5XlEVnBDgB2e9AKt9s1HwbGAp+ze+8BXHfbQ4hr1wKXOtqhcb1oBAcHGS7U10XUCEePzhdbXQaRX9l34KDhjMEDrkBM3doKqvmnvj3j0fqiC7Dtl9+UZxN5gx0A9m9Ozo11ON+VwC2qgjO37kBmAA0J6tTmIvRY+rjVZRD5lZOn8g1nDLz6MgWVlJzNBoAqq7+28nNr+hIrCyEi+jejzxQJIdCkYayiav6rWeN6pmUTme2vBuDg16vSBcA9NonIZxS73IaOD6kSjNCQKoqq+a+IGh7vpk5kOe0f/+bWJgLgVltERER+7h8NQNaW93YIiJesKoaIiIi8Q/v3L7gL8+4HsN2CWoiIiMhL/tMAHPxhfb6u4wYAHOtHRETkp/7TAADAwW9Sf4LESADGR3ARERGRzzlvAwAA2Zmpb0opR4BNABERkd8psQEAgJzMtFch5J0CKPZWQURERGS+UhsAAMhOT3sRAr2lRJY3CiIiIiLzldkAAEBWeuqXNk3EA+BWf0RERH6gXA0AALilvAlAiIm1EBERkZeUqwGoG98/SgBTzS6GiIiIvKNcDYBN1x4CEGZyLUREROQl9rI+UM/hrK0Do1Ut2Lh+NFo0bYTIiBqqIn1Cw9i6VpdARERUbmU2AG4phgshQ40u1LZFU0y+exh6xF0MIYTROCIiIjKgzAZACDnC6CJDE67ErAkjEBwcZDSKiIiIFCi1AYiOH9AKEi2NLNC/T3c88dBo/tRPRETkQ0p9CFCT4joj4c0a18PTU8fw5E9ERORjSm0AJHCZkfA594/iZX8iIiIfVFoDIADRzdNgR7uW6NH5Yk8PJyIiIhOV2ADUi0+sB6Cmp8FDnVd6eigRERGZrMQGQEJv5XGoJnDlJXGeHk5EREQmK+UWgKjvaWiLpo1QPayqp4cTERGRyUq+AiBFtKehLZs18vRQIiIi8oJSbgHA41m9tWtW9/RQIiIi8oISGwAhZRVPQ8Or8fI/ERGRLyv5CoCQ5dop8HxO5uV7eigRERF5QSknea3A09Cjx096eigRERF5QckNgJAe/xi/fdc+Tw8lIiIiLyhxMyABmQN4NsN/x57fcfxkHmpWD/O4MKJAsP3XfUgc/QhOnMqzuhQiCjAlXwGQNo9/jJdSYv3GTE8PJwoIe/fnYPCYGTz5E5ElSrwCoEvs0gxs4rc8dT1uuLaX5wFEfuzgkWO4acyjOHjkmNWlkAF2mw01wj2/0lk11OOXrYgMK7EBOJj53m8xDudRALU9Cd7y4058+tW3uLxbR4+LI/JHJ/NOY+i4Wdh34KDVpZBB7Vo2xfZPXre6DCKPlPaqnwRg6Dr+1AVLUXimyEgEkV8pKDyDoeNmYdsvv1ldChEFuLLe9f/YSPhvf2RjzPRF0HVpJIbILxS73Bg1aS6++WGH1aUQEZXeAAjd/YHRBd7/bDPGz3qWVwIooOm6xH2PPo1Pv/rW6lKIiACU8gwAAGR9s3pnTJzzWwgYupH/7gf/w48792DSXUPRu2tHaJrA1u2/4uQpTgz0d106tkaQ3WZ1GZZ7ZP7LSFu/0eoyypS5dTsKCgutLuMvh4/yIUkis5TaAACAEOJVCWn4Sb7tu/fh5vGzERsViRZNG+LHHXtwOPe40Vjycds/ed3QU9L+YP5LK/DKyrVWl1Eun6d/j8/Tv7e6DCLygjIbgOAiffmZYDELgJIt/rIOHkHWwSMqooh83rJ3P8T8l1dYXQYR0X+UueHP3u/TjktgiTeKIfInqes2YurCpVaXQUR0XuXb8a/YPh8Ar9cTmvrSngAAIABJREFUldOnX32LsTOe5hswROSzytUA5HyXfBjADJNrIfILW376BaMfnodil9vqUoiISlS+KwAAst11noUE32EiKsX23fswZOxMnC7wnSfpiYjOp9wNALa8WCw1fRgAfmcjOo+9+3Mw+L4ZfL2ViCqF8jcAAHLSV/0MIUaZVQxRZXXk2AkMHTeLm/sQUaVR5muA/5adnrI8Nj6hnZRighkFEVU2J07l4cZ7pmPP71nqw4WYJaX8obwf14DrJXCD+kKIyN9UuAEAgKz0tPtjHM4wAKMV10NUqRSeKcLwiY9j++59yrMFxKNZ6SnTK3JMdLyzpeCLB0RUDhW6BfA3Mjuj3V0CgvMBKGAVu9wY+dCTSP/+Z+XZAmJJVkbFTv5ERBXhaQMAYLqelZFylwQeAMD3nSig6LrE2BnmbO4jgHezMtreozyYiOhvDDQAZ+VkpM6FFL0B7FdQD1Gl8MiCpUhdZ8LmPkJ+EpFvHwZM19WHExH9P8MNAABkZ6Z8oRfmtxAQjwLgvr/k1+a/tAKvJH9oRnSmKzTIuW1bMv8OEZHplDQAAHDwh/X5WRkp04XubgvI5QBcqrKJfMUrySZt7iOwQ7hd1xzekJynPpyI6L88egugNFnfrN4J4Ob6XZMm6W7XUAkMAtAOgFC9FpWsdkR1NL+gIWpHKNnE0WNBduV/xCyTum4jHllgyuY++4XLflXWllRuk0lEXmPad+f9m5MPAHgCwBN14/tH2aRtJYAenmQNqAF0qqa0PJ+SXQwsOawmq0nDWDww+iZc06sL7DabmlAyc3Ofo1LoV2VvSf5dTZy+UkLbribLegLyAUB0troOIn/klR/PDqWvPhjjcHp8iutcFUiqqbIi3/LMITU51/XuigWP3IOwqqFqAgkAkLl1B0ZNmmvC5j4iXxe47mD6KmXvEeaczVL/XqJFYhzOIQDYABCZwH+uz1ZSbgArTxjPubxbRzw3azx/6lds++59uHnCHBQUnlEdXQSIgQfT3/tadTAR+a8zZ4qVZSl7CJA8s/U0cNDg17NedCSWzJ7Ak79i+w4cxOD7ZuDEKeXP5ekCcnh2xnvrVAcTkf/a/O02fPLlN8ry2ABYLOO08YwZ40bwsr9iR46dwJCxM83Z3EeICVkZaW+rDyYif3X8ZB7GPLoIbl3diBA2ABbLMLhzbMtmjdC3Z7yaYggAcDLvNAbfN8OUzX2ExIzs9JSnlAcTkd+SUmLsjGdwIEfti0JsACy2rdDY8TcnXqWmEAIAFBSewbDxs/DTzt/MiH8+KzN1mhnBROS/XnhrDdZvzFSeywbAQsdcwAmDD5b37dlFTTGEYpcbox+eh8ytO0xIF2nZjeyc709EFbJ1+694bMkbpmSzAbDQPoMP/13QIAZ1a/vx+5FepOsS42Y+g082bVEfLuQn1XILBiE5mZtmeVlRUbEpuzUSecPJvNO4Y/I8FBerG6yrS/Hjuf/NBsBC2QYnvre+sLGSOgiYunApUj76wozozGK7nrh791rl7xFS2dy6jpvGzDDl8imR2R54bAn2HTioLE9ApOfokY+f+3c2ABbKM/gwZ+2IGmoKCXDzX1qBZe+q39xHQux2C3e/I5tWn1IeTuVWeKYIIx54Am+t+sTqUojKbXnqeqz+ZJPKyDxNiGHY8uJf157ZAFjotMGpsuFhVdUUEsBeWbnWnM19gP3CjisOpa9W176Tx9y6jvsfW4Ilb66yuhSiMm3/dR+mLVymNlTKO/env7fr77/EBsBCLoMNQF6+giECASxt/UY8Mv9lM6JzpdCvyv4qZZ8Z4eQZKSVmPv0aZj3zOqRUvqcDkRIFhWdw5+QFKDyjcFdwgTeyM9P+8yQhGwAL1TY4iDn3OK8se+qzr77FfY+asbmPyNeFuDZH4Xx/Uuu5N9IwftZiuNx8JpN8z+R5L+OX3/5QlieAX8+g8O7z/R4bAAtFGpzcu+NXRRvIBZhvt/2CUQ/PU765jwCKpa4nHUxP4Xx/H7fi/c8w4sEn1f6URWTQ6k824Z01n6qMdAlgWG762pPn+002ABaqF2Ts+F179+PYCV4FqIjtv+7DkPtm4XSBwQlM/6UDYnjON2lrVQcHMgmYMZQBAPDxxkwMvm8GTubxVhpZb+/+bEyc/ZzaUCGnHshI3VzSb7MBsFDDKkCI8Px4KSVfb6qAfQcOYvAYUzb3gYAYm5WR8pby4ACXk5E6CcACs/K//u5nJN4xBYeOHjdrCaIyFRUVY/TD85F3ukBZpgD+l53e/onSPsMGwEI2AE2rGMt4I+1jJbX4u0NHj+OmMY+atbnPrKyMlGfUBxMAmZ2ROkFITAJgypN7P+/ai4RRD2Pv/hwz4onKNPPZ1/Hjzj0qI48KIW4Gppf6sjkbAIt1MPgm35Yfd+LLzB/UFOOnTp7Kx+D7Zpj1Df6F7PSUR8wIpv+XlZn6uIAcCcCUJ/f27s9BwqiH8fOuvWbEE5Vo/cZM5XNIBOTtB9JT9pf1OTYAFutSzXjGw/NeQlGRwbnCfqrwTBFunjDHrG/sK7Mb2c/7dC2pl5WRtlRAXg9A+QMcwNmrRIl3TMHX3/EFDvKOrINHMHbGM6pfS30+KyMtrTwfZANgMUc1IMjAcwAAsHvvAUyY8xzfbf6XYpcboybNRcbW7cqzJfBptdzCoZzv711ZGWlpUpN9BXDCjPxzW0Hz2Roym8vtxl2PLMTxk0qfSfrZZrOPL++H2QBYrLoGdFNwFeC9tZ9j0pMvKt00ojLTdYnxs541Z3MfyG9cQW4n5/tbI+frtA3QtV4ATJmyWHimCLc/+CRWvP+ZGfFEAM6OIFf8w0mhTWg37d+cXO4nCQ2+iV5+4fVa3gigpSfH9g4HWocqLsiH6AA+UfA239btv+KL9K24oEEMGsTUNR5YiU1duAxvmvOA5E5ZHHTFoYxUE54mpPI6lbU9p2bDi9KktPUDEKE6X//zDZvQkCqIa9tCdTwFuI0ZP+CBx5dA5UVbITHhQEbK6godo2750sU4nCkAnJ4cOzMGSFL+V9x3FOjAZb8AJw1uDvR3Fzauj1YXNka9qEgIzWtfZp+QdfAIUtdtNCP6AOyiO0f8+o7ITokxdpv8SABtzVrjziEDMOXemyFEYP09InMczj2OPkPHK331VAAfZGWk9kMF35QxOIyWVAjVgBsigJePqsvctXc/du0t8yFQKicBnBDAtQd48vcpR7akZDdun3DZmWCxGsAlZqyx5M1VOJx7HPOn3IMgu9cumpIf0nWJMdOfVj134lCxtN8OD16T5TMAPmJILeMPA5JZRL4A+h7ISN1qdSX0X3u/Tztus9mvEsD7Zq2xcu3nGPkQRweTMc8tT8Xn6d+rjNSFkMMOZyZ79I4zGwAfERME3OTHtzkqq3Pz/Usbp0nW2785uSCrkT0BEEvNWmP9xkwk3TWV47fJI99t24W5L76tNFNILMhKT1vv6fFsAHzIHZFAOK8w+hJdStzK+f6VRHKyOzsjZaQQcr5ZS2z56Rck3T3NnImS5LdOnMrDaPUbkG2JOG2fbCSADYAPqWUHJgb2w/u+ZmJ2ZuqbVhdBFSKz0tMmQsixMHF0cP/bJ+G3P7LNiCc/NH7WYuzPOawwUeQLtzZ027ZkQ/ek2AD4mBsigHiD44FJASFmZWekLrS6DPJMdnraIiFwO0waHfxH9iE4R0/Gtl9+MyOe/MgrK9di7YZ0pZlCyDFZW94zvFMmGwAfIwDMrQ/U5fsZVuJ8fz+QlZ66TEjdCUDdFmt/c+jocSSMmoyNGdyLg85v+6/7MPPp19SGSvFeVnrqMhVRbAB8UF07MK8eYOdbAVbgfH8/kpW5ao2UMG10cH5BIW6eMBvrvsgwI54qsfyCQox8aK7qN0f2VSnWb1cVxgbARzmqAXNigACb4WMpAfyP8/39T05m6uduHT2kRJYZ+WeKinH7g0+aNXmSKqlJT7yAPb8r/SOnS00O3/t9mrIhAmwAfFj/msC0aDYBXrKlKMg9gPP9/dPBb1J/stvdlwFQuun6OW5dxwOPP4/nlqeaEU+VzLsf/A8r136uNFNAzMz5Om2Dykw2AD7uxgjgqXpACJsA00iI3W7hvvbIptV8wduP7d+8erdL2rsDUDqJ5RwpJWY9uxyPLFgKXefOnIHqtz+yMWXey2pDBTZlNbLNVBvKBqBSuLI68GpjIDbI6kr80gFhxxWH0lebsrMc+ZbDmck5VYpkLwCmbBYBAEtXfIDxs56Fy807SYHmTFExRk6ai7zTSp87PQ6bGGLGrUk2AJVE+1BgVROgXw2rK/EruVJqV3Fzn8BybnSwhFhj1hrvfvA/jHiQo4MDzaOLXsHPu/YqzZRSjjbrexQbgEok3AbMrQcsbwy0DrG6mkrvtAZcl5P53jarCyHv2785uSCnam6iABS/o/X/Pt6YiUH3PoqTp/LNWoJ8yAf/24xXV36kNFNKvJKTmfau0tC/8fvtgHeeAX447dmxANC2KtC8iufHm0UC+CofeCMX+OKUSdNO/JQAinVdDuCIXwIgYuOcT0qBiWYt0LJZI7y1aCqiIrnZh7/6I/sQ+gyboLrZ+8VV1d7p8IbkPJWhf+f342a+zAPmGri7e3+UbzYAAkD3amf/OeYCPs8HNuWdbXh+OwMU8xmkknC+P/2dzMpMvT/WkXBYQjwOE34o2r57HxJGPYy3n56KxvVjVMeTxYpdbtw1ZYHqk38RhBxs5skfCIAGIBBE2IGEGmf/Ac6e/E+4gTwdyNetre3fcl3AyN+Npog0XYoJnhxp12TRgYyU/UYrIP+SlZH2ZGy887CUeBEmfF/cd+AgBoycjDefegRtml+gOp4s9MTzb2HLT78ozZQCk3PS07YoDT0PNgB+KEgAkXYg0upCzuOQS0WKfupgZpop73NT4MpKT30lNm7AESm0FQBCVecfzj0O5+jJWPrkg7jU0U51PFlgw9ff4/k309SGSnyck9FuAWD+TAk+BEhE9KeszFVrIMXV5o4OnoP3P9tsRjx50eHc4xg742nVMx8OF+viFmC6V67dsgEgIvqb7MyUL9w6egA4YEZ+UVEx7pw8n6ODKzFdl7hn2lM4dFTZVF4AkEITtx/ZkuK1fabZABAR/cvBb1J/0uG6BMAuM/LPjQ6e/9IKM+LJZIteWal+F0iBZ7K+TlmtNrR0bACIiM7jYMaa31zSfilMHB08/+UVHB1cyaR//zMWLlX+av42m2Z/SHVoWdgAEBGVwFujg++b8TSKXZzm4etOnMrDvdMWqR7zXGgT2uD9m5OVzg8uDzYARESl2Pt92vFquYV9IMV7Zq3x3trPcfuDT6CgkJtR+iopJcbOeBb7cw6rjh63P/09xfcTyocNABFRGXbvXnsmu7HtRgCKt3n7fx9/+Q2S7pqGYye4KaUveumd97HuiwylmQL4IDsj9QWloRXABoCIqDySk93ZGamjJPCEWUt8u+0XOEdPQfaho2YtQR74YcevmL14uerYAwK4BWcnu1uCDQARUfnJnIzUhyDkWJj0jfuX3/5AwqjJ2PN7lhnxVEH5BYW465GFKC5WMsXsHF2T8pYDGamWdnpsAIiIKig7PW0RhLhFAMVm5P+RfQjO0VPw404OvLTag48/b0Yz9viBzLRPVYdWFBsAIiIPZKenLIcmrgdgytPbh3OPY+qCZWZEUzm9teoTpHz0hdpQgYxsd53pakM9wwaAiMhDWV+nrDZzdLCUnA9gpQ83fK06Mk+TGIYtL5py5aii/H4zoA6hwB0GdsXpoHxLECLyJ9mZKV9Exw3sLoS+DkA9q+shHyblnQcy09RuHWiA3zcAHaue/YeIyCw5me9ti3L0u0SDfR2AC62uh3yPAN7Nykx7w+o6/o63AIiIFPjb6OCtVtdCPmdPoSgcaXUR/8YGgIhIkcOZyTmuqvYeEPITq2shn/JZbvrak1YX8W9sAIiIFDq8ITmv2tEz15k5OphIBTYARESK7d699kx2tdxBAA5ZXQtRSdgAEBGZYcMGF4Aiq8sgKgkbACIiogDEBoCIiCgAsQEgIiIKQGwAiIiIAhAbACIiogDEBoCIiCgAsQEgIiIKQGwAiIiIApDf7wZIRERkJQE0iXY4k8r7ealj+8FvUn8ysyaADQARkc/auz8bDzz2vNVlBKwdv/6uJEcCvQXQu7yfFxpmA5iiZPFSsAEgIvJQjCPhA0BElfKRukbyDx09jjfS1huJICoRGwAiIo+JiwE0sLoKIk/wIUAiIqIAxAaAiIgoALEBICIiCkCV4hmAR7LP/kNERERq8AoAERFRAGIDQEREFIDYABAREQUgNgBEREQBiA0AERFRAGIDQEREFIDYABAREQUgNgBEREQBiA0AERFRAGIDQEREFIAqxShgIiJ/1LaeDUuGhlhdBiky4d1CfP2b2+oyyo0NABGRRaoEAY1q8UKsvwgJFlaXUCH8k0dERBSA2AAQEREFIN4CIF9UBCC/5N8Wp71WCRGRn/JaAyAFvtIkXOX+PNAbQG1P1low5W4M6ne5J4eSQW5dR4Ou1xuNSc7OSB2qoh4iIjo/rzUAOemp8yry+RhH4iZAdjOrHiIiokDGZwCIiIgCEBsAIiKiAMQGgIiIKACxASAiIgpAbACIiIgCEBsAIiKiAMQGgIiIKACxASAiIgpAbACIiIgCEBsA8jkCCLK6BiIif8cGgHyOBAbGOBJHW10HEZE/88kGoH7XpFBAxlpdB1nGBsglMY7Eh60uhIjIX/lkA+ByuRYDaGx1HWQpAcjZ0Y7EZ4DpPvnnlIioMvO5b6wx8QmjhMCtVtdBvkFA3hPj+H45Oo3icwFERAp5bTvg8qjncLbTJZ6yug7yNWJwjP1QXVfPJOfhDcl5VldDRL7ri10u5OZ7fvxVrW0IDRLqCvJhPtMA1O+aVMvtdqcCMtRoVlRkLRUlkS+R4gr7afe6+l2T+u3fnJxrdTlE5JvmrivCd3+4PT7+60lhqF9TYUE+zEduAUzXdLfrdUBeYDTpxut6o1fXDiqKIg9oQqBrx9Ympctubrfr8/pdk+qZtAARUcDwiQYgOu6HyRK41mhOk4axmDlhhIqSyENCCLy9aCqu693VrCXauN2uTbGd+zc3awEiokBgeQMQ5XD2FkJOM5pTJTgIL86ZiLCqhu8gkEHBwUFYMnsChiT0MWuJRlKzfRUVn9jFrAWIiPydpQ1AbNyABhrwDgCb0azHHhiFVhc2Nl4UKWHTNDz50B2YcPuNZi1RS5Py49j4hCvNWoCIyJ9Z1wB0GhUkNe1tAHWMRvXv0x2D+l2uoChSSQiBCSNvxMzxI6BppjxVGyalWBMdl3CDGeFERP7MsgYgVju8EBLdjeZc0CAGcyfdqaIkMsmIG6/FoqljEGQ3fKHnfIKFEG9xdDARUcVY0gDExjsHSYG7jeZUCQ7CC7MnIrxaVRVlkYkG9r0MLz/xIEJDqpgRbwPk89EO5+NmhBMR+SOvNwCxnfs3lxIvqsiaMX4E2jQ3/OYgeUmfHp2R/NyjiKgRbkq+AB7k6GAiovLx6jfKOj2TwqRmSwFg+AzQ7/JuGObk81+VTcfWFyHl+VmIrmPOsCaODiYiKh+vNgBBp13LALQymtO4fjTmPnyXgorICs2bNMCql+agSUOzNnwUg6Nth9dGdu9vzqUGIiI/4LUGIMbhHC+BJKM5wcFBeGHORFQP433/yqxBTF2kvjDLtFs4Arg8yGX7JLZTv0hTFiAiquS80gD8ObDlMRVZ0+4bjoubN1ERRRarU6smUl+YjUsd7cxZQMIhbfYvYuMGNDBnASKiysv0BiCqm7OuJuVKAMFGs67t1RW3Xt9XQVXkK6qFhuD1+Q+bOTq4pRTaRo4OJiL6J3N3A0xKstn2ud6RgOHNWxrVi8L8ybzv74/OjQ4Of+x5vL36EzOWaCQ128YYx8BrsjPe+8aMBahyiolLnAMhJ1m1fuZeN+o/eMqq5UsUEgSEBQs0rKWhRayGdvU19G4ehJgagbFNbqAwtQGI+d01SwK9jOYEBdmxZNYEVA+vpqIs8kE2TcO8h+9ErZrhWPx6qhlL1AH0z+rFJTgPZKZ9asYCVLnEOJzjAOtO/r6ssBgoLJY4ku/Gt3+48VY6AJxB23o2DOliR2KHIIQG+WYzkNDBjs6NPR86Fm7KqBLfZNpXMDbeOUBKpKpY49Fxt2HkoOsUVEWVwQtvrcaMp1+DlNKM+DNSiKE56SkrzQinyiEmPnEYpHwNJn4P9Gc1QwXu7hWMEd2DEGznf8Jzhi4rwIadLhVRs7MzUqeoCCqNKc8A1O2U0FRKvAoFf7muutSB2280vFMwVSKjB/fHU1Pvhd1myujgKkLKdzg6OHBFOxKvFVIuBU/+HjteIDH7wzPoNf80Nv3qtroc8pDy77D1uyaFAnIdgMaGs6Lr4K1FjyCkSgBdkyEAQOsLG6NN8yb46PN0uNzKv8FoAK4Lj23hzsva8YXqcPJdsZ0Tu0PI1QBCrK7FH5wokEj5thini4CuTeywBfgMzpTvXNh7VFcRtTHvwI7PVASVRvmXy+VyLQbQ3miO3WbDczPHoUZ4mIKqqDLq06MzVi6ZYdroYAgxMzbO+SxHBweGqM7ONlKTqwFwiIhCugSWfF6EocsKcLLQlNt2ZBKl3/hi4pwjhcCtKrIm3T0Undu2UBFFlVjH1hdh5ZIZiIqMMCVfCtzN0cH+L7pLQmMhsA6AOTOoCZt2u3D986eRm88moLJQ1gDUczjbQWCRiqzLu3XEHYP7q4giP9CyaSOsfvkxjg4mj0R3SKojdPGREDDrDxD96edsHYNeOo0TBWwCKgMlDUD9rkm1dIhUAKFGs2KjIvH09PsgBJ/Pof/njdHB9mLbuvpdk/gToh+pFd+3urC7PgLAQVBe8nO2jpHLC+BSciuczKSiARBut2sZIA1/Zz5339+0e75UqZk9OlgAXd1u15ccHewfWrdOCq6ih6yEQEerawk0X/3qxmNrC60ug8pguAGIiUuYDGCAglrwwOib4GjXUkUU+alqoSF4bf7D6Nsz3qwlODrYHyQl2XLDXG9DoI/VpQSqFzcW45PtSt6JJ5MYagCiHM7eEGK6ikJ6de2Au4Y5VUSRn6sSHIQXH7sfQxOuNGuJRlKzfVXP4TRtgwIylYj53bUEEolWFxLIpAQeTCnk8wA+zOMGIDZuQAMNeAcKZglERdbC09Pug6bxvj+Vj03T8MRDo3HPLaZ9j6+lA+vqxSVcbtYCZI4Yh3MWJEZaXQcBB09KPLGuyOoyqASeNQCdRgVJTXsbQB3DBWgCz0wfg9oR1Y1GUYARQuDhu4Zi1oTbzWoew3UhPoyOS7jBjHBSLzreeTeAh62ug/7fm18XYdchPhHoizxqAKJtRxZAoruKAiaOHIQecW1VRFGAuu2Ga/DU1DEIspsyOjhYCPFWjMN5hxnhpE5MXMJQIfGM1XXQP7klMG/9GavLoPOocAMQG+8cJCDvUbF4905tcO/wgSqiKMBd3/cyLH3yIYSGmDI22gZgSbTD+bgZ4WRcbLyzD4TgfH8ftfYnF/Yc4VUAX1OhBiC2c//mUuJFFQvXqVUTz84YB5vGKaykxhXdOyH5uUdNe41UAA9ydLDvieoyIF5KkQog2Opa6Px0Cbz2VbHVZdC/lPsbWWT3/uHSZksDYPi7q6YJLJ45zrTxrhS4/n90sDnzfDg62LdEdXa20XRtLSCrWV0LlS55SzGKXHwjwJfYy/vB4GLbUgkoGc4/9tYk9Oh8sYooov9o2bQRVr00GzeNmYHf/sg2YQUxOMZ2uJbe9srrD/6wPt+EBagcorskNBY6PgKg/CeJqqEhmPfwnahds4bqaJ/mcrvxR/Yh/LDjV6St/xKnC9QN8zlZKLHhFzeubFXu0w6ZrFz3y2LiE8dCyoUqFuzWsQ1WLJ7OS/9kuiPHTmDIfTPx4849puQL4GvNZr92/+bkXFMWoBJFdXPW1VzYCOAi1dlBdhtenfcwenXtoDq6UjlxKg+PLnoN76z5VFnmgPZBWHyT/+7EPHRZATbsVDL8aHZ2RuoUFUGlKfMsHNs5sbuQ8kkVi0VG1MDimbzvT94RGVEDK5fMQPdObUzJl0AXt9u1sV58Yn1TFqDziuzeP1xz4UOYcPLXNIGnpo4J+JM/ANQID8OCKXdj9v3qRips3OWCzmcBfUapZ+K68f2jdCHflYDh+52aJvDMo/fxvj95VXi1qnjzqUfMHB3cSpf6Jo4O9o5mzfpWCS62pQHoZEb+jHEj4LzqEjOiK61br++LcSOSlGTl5ktsz3ErySLjSm4AkpJsdml7W9UWmvfcnIjL4turiCKqkOA/RwcPHnCFSSuIhlKzfRkdlxhn0gIEAElJtvyI0Dcl0NuM+LG3JeG2G64xI7rSm3D7ILRv1UxJVsZvvATgK0psAGJ+d82SQC8Vi8S3b4WJowapiCLyiE3TMHfSnbj3FtPmTkQKIT+LjRtoVpcR6ETMXvdzENKUL+Aw55V4YPRNZkT7BU0TmD1Rza2A7Qd5BcBXnLcBiI13DoDEgyoWqFWzOp6bOQ52mylT2ojKTQiBSXcNwaPjboMQpsyLCZNCfz/K4TTlJ9RAFuNwzoSQo8zIvq53V8x5wJRov9Kh9YWIb9/KcM7ObF4B8BX/aQDqdkpoKiVehYKJWkIILJo2BjF1axuNIlJm5KDrsGiaaaODq2gA73UpFBOfcB+AyWZk94hri2cfHcsHk8tpSILx3ZV/O8oGwFf84099457DQ2yaeBdATRXhdw5NwOXdOqqIIlLq+r6X4Y2Fj6BaqP++kuQPYh2JgyHFAjOy27VsimVPPojgYM50Kq/L4tsZzjh2WqKYdwF8wj8agKKCk0sgoOSM3bltCzx4x2AVUUSmuMTRFm8tmooa4WFWl0LnEd05oS8gX4WBbctL0rRRPbyx8BGEVQ1VHe3X6tSqicb1YwxlSAkcyeNEQF/w11+smPiEUVJDkGPoAAAgAElEQVTK4SpCa4SHYfGMsWZdYiVSJq5dC6x6aTZvU/mYqC4D4oWmJat4BfnfouvUwluLHuEW5B5qXD/KcIaUbAB8gQYA9RzOdpDiKRWBQggsfORuNIipqyKOyHQXXdAAq16agyYNlbzxSgZFxw1srenah2bM968eVhVvLJzC708G1KppvHEKC+Gmjb5Aq981qZYOkQpAybWwUTf1w9WXmTZ0hcgU9aPrIO3F2bi4eROrSwloMd0SGwmhrwOgfDen0JAqePOpR9DqwsaqowNKcJCxizJCANWqsAHwBZrudr0OyAtUhHVofSEevmuoiigir4uMqMHXwSwU3SGpDlxyPYB6qrOD7Da89Nj96HQxBzYalXv8pKHj64QJ2Hj+9wmaBK5VEVQ9vBqenz0BQUHc6YkqL82c+QBUhsju/cNFULFp8/0XPnIvevONJCUO5x43dPwFkXzl0lco+0osmMz7/kRUca1bJwUHFdtWAqKzGflTxwxH4tWXmhEdcIqKirFt115DGY1rswHwFUq+ErfdcA2u6dVFRRQRBZKkJFtuNdebAK40I/6+W6/HqJv6mREdkL77eReKiooNZbSOZQPgKwx/Jdq2aIqp996iohYiCjDR+4oXArjejOxhzis5i0SxlWs/N5zRtSlvE/sKQw2AEALPz57ASVpEVGEx8YkzBcS9ZmRf24vz/VU7eSofaes2GsqIqCrQvC6vAPgKgw0A0Lh+tKpaiChAxMY5x0DKKWZk9+h8MRbP4Hx/1ea9tAL5BYWGMq5oaQe/LL6DXwoi8qpYR+JgKbDQjOy2LZpi2dyHeFVSsW+3/YJXV641nHOTg18XX8KbMUTq1Y+JT+hkdRGqFNv1X45sWn1KRVZMnPNqs+b7N2kYizef4nx/1fYdOIhbJz4Ol9vYDj51wwVCgoAfDvjvTkCnCpWNOL4vxpHQQUixrhj2dw9nJueoCv47NgBE6o2DFOOsLkIVe5GtJwDDT39FxSd2gcRKCWnKfP+3n57K+f6KpX//M+6YvMDwu/8AcOiUxDVPn1ZQVUAIA8Q1UuAaO1zzo+OcH2rQn8jKXPWVykXYABCR6aLjBrYWUv8AgPL5/jWrh+GtRVM5h0SRoqJi/PjLb3ht5Vqkrf/S8E/+ZJhdCPSX0PrHxDk/Fro2JmvLezuUBKsIISIqSUy3xEZw6R/BpPn+r89/GC2aNlQdbYmt23/FHZPnWbZ+scuNg4dz4dZ1y2qgUgj0kTZ9a6wj8bGsqrmzsGGDy0gcGwAiMs3Z+f6udQDqq84Ostvw4mP3o3PbFqqjLXOmqAj7Dhy0ugzybcESclrM6YjL7d373/jHptVZngbxLQAiMkVsp35VEeRaBUD5DjxCCDw56U5czvn+FLh6FBfZMuvFJbT3NIANABEp16xZ3yrSblslgK5m5D867lbceF1vM6KJKg0hEKsL8Vls54EdPDmeDQARqZWUZDtdK2Q5pLjCjPgxwwfi9huvMyOaqDKKkJq+PqaLs2VFD+QzAERUKgFcFBOfkFfuA/a6R0mBJDNqGZLQh/P9if4rUuhYU79rkmP/5uTc8h7EBoCISifwIqSowOeVDUP5h2t6dcHjD46GEBWohShASKCp2+16G8DVZ/+1bLwFQEQ+r3unNlg8Yxzn+xOV7spYR+I95f0w/zYRkU87N9+/Cuf7E5VJQj4e2ympXIMx2AAQkc9qXD8GyxdORni1qlaXQlRZVNU11xPl+SAbACLySVGRtbDi2WmoU6um1aUQVSpC4MbybEjGBoCIfE71sKp4Y+Fkzvcn8oyArk0q60NsAIjIp4SGVMEbC6eg9UUXWF0KUeUlpDMqbmCT0j7C1wCJyGcE2W14Yc5Ev5rvXxG1alRHv8u7WV0GecHRYyexY8/vyD1+0qwlNJvmHg5gakkfYANApFhYo1qoGuU7+9If256D4lOFVpdRJiEEFky5B1d0L/PWpd9q1rgeXpgz0eoyyIt27vkDL769Bive/xS6rnaGhpRiGNgAEHlPrYtj8H/t3XlcVdXeBvBn7XOYZ0QZHFO7qeWUok1mvdktvSKieW+j9V6zTN/qlg1aWVY2mpWlWZaVNjiggJqpaaY5IKCkOQ8lKnAAEZCZwzl7vX9UXjNA5Kwz8nw/n/7w7L2f/cs0fmevtdeKGtDJ2WWcVT233C0agKn/+V+MHDzQ2WUQOdRlHdtixrPjMWzQNZjw/Luqnwh0iOybcEX+juS9dR3kHAAicrqH7x2JsbdzfX9qvgb274WvZ05BgJ+v0lyhYXB9x9gAEJFT3Rk/CJMe4vr+RD26dMLbUxq9kF+jCODa+o5xCIAc7vCxk1i3eUeTrtUMGh66K15xReQsQ268Cm9MGsf1/Yl+F3fTNVh8zZXYsC1TVWTf+g6wASCH23c4C6/M/qJJ13p7e7EB8BBc35+obs+Mv1tlA9C63XX/CDuxZVXx+Qf4N4+IHK5r5/aY98bTXN+fqA7dLu2APlf8TVme1exd594AbACIyKEuaRuNxbOmIjgowNmlELms+L9fpyxLSp0NABE5V2REOBa+9zwiwkKcXQqRS+vXs6u6MGEIqutjNgBE5BAhQYH4+r0paBcT6exSiFxet0s7wGgwKMmSUtb5uI2TAImoQfNnPIN+PW1fmtfLaIS/4neciTyV0WBAWEgQThWV2B4mUGcnwQaAiBoU6O+HkKBAZ5dB1OyEBgcqaQCklHUuBcohACIiIhdUVV2jJkiIiro+ZgNARETkgorPlCnJEdDz6vqcDQAREZGLyS8sRkWVmk28DAZpqutzNgBEREQuJn33AVVR5myf0qy6DrABICIicjE/pP6kJEdIHMLGjZa6jrEBICIiciGFxWeQvPZHJVlSiPT6jrEBICIiciHvf74MNeZaJVkScnN9x9gAEBERuYjvt2Vi3pJVquJ0Ly/ruvoOsgEgIiJyAYePncQjU2dC16WaQIHUk1tX5NZ3mA0AERGRk6Vm7sOIcVOUvfsPAJD4sqHDXAqYiIjISSqqqvHBF8mYNT8JtRaryuiyWi/rVw2dwAaAiIhcWmVVNZIUzYp3FbW1FhzNysE3G1LVbPhzHiHk3MKtKxp8nMAGgIiIXNqZsgo89dqHzi7DjYgKqwFvXugszgEgIiLyKPK1/G3JBRc6iw0AERGR58gKKKp+qzEnsgEgIiLyHO0rw/0mN+ZENgBERESeQ0jIF2L6D58NTG3wZzwbACIiIg8jpRgf3W/39IbOYQNARETkmR6P7j/iP/UdZANARETkqaScEd0vfkBdh9gAEBEReS4NMMyPuHZYUB0HiIiIyHPJS7wt2uvnf8oGgIiIyMNJKR6Mviqh67mfsQEgIiLyfAYp5QvnfsAGgNyKbtVh1XW75ddaLHbLJiJyJiHFqMi+CVf88Ws2AORWLFYrcvJO2S0/KzvP5gyh8a8VEbkkTWjygbO/cGYlRE2RmrnPbtnbdu61OcPgy002icg1CYg7O3ce7ANwO2ByQx8v+gajhtwITRNKc/MLi5GybovNOUY/LwXVENEfggL98eyEe5xdhkMUnC7G0eM52LJjD2pr7TIk2aI83HcYgEQ2AOR29h/JwszPluKxMaOUZeq6xGMvvw+zudbmLJ+IAAUVEdEfAv39MGF0grPLcKj8wmK8P38ZPktcDSml0mxNiCEAEjkEQG7p7U8WY9nqTUqyrLqO52Z8jI3bd9mcJYwafCMCFVRFRM1ZZEQYpk28H5+8/hR8vNU+VZRSDgI4B4DclFXX8ciL7+GtuYtQa7E2OaeopBRjJ03H50vXKKkroE0ohOKhCSJqvgbf0B8zX3hE9ZBnm+irErqyASC3JaXE2/OWYNBdj2Hdlh3Q9cY/JqusqsZnS1fjulH/hzWb0pTVFNYlUlkWEREADBt0Le67bbDaUCn6cg4Aub0jWdm4d+KraBvdCiMHD8RVvbuhT/fLEODn+6fzCk6XIOPnA9ic/jNSvtuM0vJK5bWEXh6lPJOI6KkH78TK9dtwqqhETaCUXdgAkMc4aSrAu58mnv11gJ8vwkODYbFaUVRSihoFE/waEtyxBfxa/WW/DSIimwUH+uPe227FW3MXqQkU6MIhAPJYFVXVOGkqgKngtN1/+ANAq2s62v0eRNR83RU/CEaDQU2YRCc2AEQK+EcHI+zyaGeXQUQeLDIiHN27qPqiIYLZABAp0C7uCgj+bSIiO+vXs+uFT2oUyQaAyFYRfdoiuFOEs8sgombgb5e0URUVxAaAyAY+Yf5oH3/FhU8kIlIgPDRYUZKoZQNA1ESatxGXjo6FwYdr/xORYwT4+ynJkVKeYQNA1ARCCHS+80r4x4Q4uxQiakbOlJUryRECJVwHgOgiCU2g47+uRGhXLvpDRI51urhUVdQpNgBEF0EYNXS640qEXxHj7FKIqBnae+iYkhwJsYcNAFEjeYf64dLRsQhoHersUoiomUrffUBJjgbsZgNA1AjhV8SgfUIPeAV6O7sUImqmDv5yAoePnVSSJSF2sQEgaoBXsC/axV2OFj1aO7sUImrmvkj+TlXUaZP/aTYARHUx+HkhZmBnRF7XEZqXorW3iYia6ERuPhauWK8qbhU2brSwASC349cqCEITqMxTNhv2LN+WgYi89hJE9GkHgzd/8BOR80kpMWXGPFTXmBUFYjkAsAEgtxPQJgQd/3Ulyk8W4/RP2Sg9WoiqgnJAyosPEwL+kYEI6RqF8O7RnOBHRC5n+txFWLdlh6q4IqFb1gBsAMiNBbYNQ2DbMABAbbkZFSeKUFVYgZrCctSUVMFSbobVbAGsOiAEDL5eMPga4dPCH77hAfCNCkZwhzAY/Dixj4hcj1XX8dbcRZj52VJlmRL42LRzZSXABoA8hFegN0K7RYHf34nI3Ukp8WP6brwzL1HZa3+/s2hW4wd//IINABERubTiM2WY9MZHzi7DIU4VleBIVrbKFf/OEsBXuTsTT/zxazYARETk0qprzFj5/TZnl+Huyg1e1mfO/YCbAREREXk88drJrStyz/2EDQAREZFn2x1QVDXj/A/ZABAREXmuSmi44+jR1TXnH2ADQERE5KGEwMOm7cl1vkrASYBEREQeSEi8lJue/Gl9x/kEgIiIyPN8lJuR/EJDJ7ABICIi8iQC75nSe46/0GkcAiAiIvIMVinwaF5a8mwg+YInu3QDUFVdgyNZ2U26tkvHdvD29lJc0X/tPXQMutSbfH3Xzh3gZbTPbnMWqxX7j2Q1+XpfH2/87ZK26goiIiJHkNKKTY092aUbgF+O5+LWe59s0rWpSXPQvnWk4or+K+7+Sagx1zb5+sxvPkFUy3CFFf1XaVlFk3/fAOCyjm3xw8KZCisiIiIHMGoaZgG4oTEncw4AERGR5xgY1X/kkMacyAaAiIjIgwipT23MeWwAiIiIPEtsTGz8NRc6iQ0AERGRh5FCjLvQOS49CZCIiCgiLARr5k93dhlKmc21OJadh9TMfUhe+6NNk8rrJoZ3uOE+36yNn1fXdwYbACIicmleXkb06NLJ2WUo17dHF4wacgMmj78LE6fNxvqtO1XGB5mrS/8OYEV9J3AIgIiIyIlahodi/oxnMPb2oUpzdV0f1NBxNgBEREROJoTAC4/+L+JuuuDcvcZnQlzf0HE2AERERC5A0wTenDwOEWEhqiIv73DDfb713k/VXYiaPSmh11qb/I+0Nn1paSLyDCFBgXh63J2q4oy1FSVd6j2o6i5EzV11USV+fvP7Jl8f2D4c3cZfp7AiInJHtw25Aa9+8CWKz5TZnGXVtM4AdtV1jE8AiIiIXIiPtxdG3jpQSZYmEV3vMSV3ICIiImUGxHZXkiMFWtV3jA0AERGRi+nbo96h+4sioPvVd4wNABERkYsJCwlCUIC/7UG64FsARERE7iQ8NMj2EIHK+g6xASAiInJBRqPtL+rpUtT7KgEbACIiIhd0uviMzRlC00vrO8YGgIiIyMVU15hRWl5hc47UUW8XwQaAiIjIxWTuOwxdlzbnCCF/qe8YGwAiIiIXk/bTASU5BoP3vvqOsQEgIiJyIVJKLFuzyeYcAZiyUxOL6jvOBoCIiMiFbErbjV9P5NqcIyF/bug4GwAiIiIXYbFaMW3WAkVpYm1DR9kAEBERuYjZC5Kx/0iWkiyD0L5p6Di3AyZSRDNo8Alr+tKd3sE+CqshInezYt1WTJ+7UFXcoey0ZUcaOoENAJEi3qF+6DlpkLPLICI3I6XE7AXJePOjhUpe/QMACXnBToINABERkYPpukRO/imkZu7DJ4u/wd5Dx1TGV0ujmHOhk9gAEBGRSzMVnEafuLHOLsONiK/ytyUVXOgsTgIkIiLyEAKohSZnNOZcPgEgogbN+HgxWoQF25Th6+ONiWP/hXYxkYqqIqI6Scw0bU9u1DKCbACIqEHbMvcqyVmzKR3TJz+EYTdfqySPiP7iZG2A8cXGnswhACJyiLKKSox7bgaem/EJzOZaZ5dD5Gl0KbRxpzYmljf2AjYARORQny75FnH3T0ZWtsnZpRB5EDElL23ZtxdzBRsAInK4PYd+xd/veQIp3212dilE7k8gyZSe9NrFXsYGgIicoryyCuOnvINJb3yEGg4JEDWJhFhp0Ix3A7joFYTYABCRUy1IWotbRj+BQ7+edHYpRO7mqzxrxMjs1MSqplzMBoCInO7wsZOIGzMJyWs5JEDUCFYh8ZIpPfke7Jzb5MdnbACIyCWUV1ZhwvPv4JGpM1FZVe3scohcVRak+J/cjOQX0ITH/udiA0BELmXp6k2IGzMZR7KynV0Kkaup8vI2X2nKSPpRRRgbACJyOQd+OY5bRj+Br1LWObsUIlfiV1vrM1RVmM0rAUopIYRQUctfs217ukEeqjAzG4WZ/Hbo6aprzHjytTnYsecQXnnifvj7+Tq7JCLnk/JxAF+oiLLpCYCuS5wqOqOijjrlFZy2WzYRuYfF32zArfc9iQNHjzu7FCJX0CsmduQgFUFGADpsaAQOHM1Cqxa9VNTyF/v5F56IABzNysHQMZPwyhP34/a4m5xdDjmYpgmEBAU6u4xGKy2vgJR2fIIt9LEA1tsaYxRAmQRCmhrwRfJ3GNhffQNg1XUsXPG98lwick9V1TV4fNpsbN25F68//SACOCTQbERGhOPA+gXOLqPRasy1OPTrCSxc8T2WrPoBVdU1SvMlMLTlDaMCL2bd/7oYJVAKGxqAtZvSkZq5D1dfebktdfzFp0u+xYncfKWZRNQ0AtiQm558UV+9Y2Lj20pNWwgJpdv/LVu9Cbv2H8VHr0xEt0s7qIwmUsLH2ws9unRCjy6d8O9/DsEDk6erXujK36uyNg7AQltCNABZtgRYdR3jp7yNnLxCW2L+JDVzH6a9P19ZHhE5Xm7G8pMmv+IbIPA6bHxf+Xy/HM/BP/79NL5I/k5lLJFyl3Zog+Ufv4aundorzZUQo2zN0ISQe2wNyS8sxtAxk7Dv8DFbo7Dqh1Tc9Z+XUWux2pxFRE62caPFlJY8GRJDAJxSGV1jrsXTr3+Icc/NQFlFpcpoIqWCA/3x5bvPISwkSGXsAAA2vYKnQSJTRSX5hUUYOmYSps9diOoa80Vff6qoBI9MnYkHJr/VpOuJyHWZMpLXGAzG3gA2qc5esW4rbhn9JPYc+lV1NJEy0a1a4NkJ96iMjGjVZ3hHWwI0zeC1Booez9WYa/HOvET0Hz4Or37wJY5m5TR4vlXXsXPvYUx85QNclfAQlq7eZN+Zk0TkNNmpiTmm9sabIMQ0/Pb2kTJZ2SbE3T8Zny1drTKWSKnb427CZR3bKsszGtDPpuuzUxNzomMTfoLAlaqKOlVUglnzkzBrfhJahoei9+WXIiayBcJDgmGxWnG6p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style="color:#000;clip-rule:nonzero;display:inline;overflow:visible;visibility:visible;opacity:1;isolation:auto;mix-blend-mode:normal;color-interpolation:sRGB;color-interpolation-filters:linearRGB;solid-color:#000;solid-opacity:1;fill:#afafde;fill-opacity:1;fill-rule:nonzero;stroke:#ccc;stroke-width:.07269443;stroke-linecap:butt;stroke-linejoin:bevel;stroke-miterlimit:4;stroke-dasharray:none;stroke-dashoffset:0;stroke-opacity:1;color-rendering:auto;image-rendering:auto;shape-rendering:auto;text-rendering:auto;enable-background:accumulate" transform="translate(583.104 -160.268) scale(2.92733)"/><path d="m-189.56 60.903 3.184 1.159 3.232-1.177-3.185-1.159z" style="fill:#f7f7f4;fill-opacity:1;stroke:#ccc;stroke-width:.07269443;stroke-linejoin:bevel;stroke-miterlimit:4;stroke-dasharray:none" transform="translate(583.104 -160.268) scale(2.92733)"/><path d="M-186.376 62.062v3.743l3.232-1.177v-3.743z" style="fill:#3775a9;fill-opacity:1;stroke:#ccc;stroke-width:.07269443;stroke-linejoin:bevel;stroke-miterlimit:4;stroke-dasharray:none" transform="translate(583.104 -160.268) scale(2.92733)"/><path d="m-189.56 60.903 3.184 1.159v3.743l-3.185-1.16z" style="fill:#efeeea;fill-opacity:1;stroke:#ccc;stroke-width:.07269443;stroke-linejoin:bevel;stroke-miterlimit:4;stroke-dasharray:none" transform="translate(583.104 -160.268) scale(2.92733)"/></g><g style="fill:#a29d86;fill-opacity:1;stroke:#ccc;stroke-width:.07269443;stroke-linejoin:bevel;stroke-miterlimit:4;stroke-dasharray:none"><path d="m-186.329 59.726 3.185 1.16v3.742l-3.185-1.159z" style="color:#000;clip-rule:nonzero;display:inline;overflow:visible;visibility:visible;opacity:1;isolation:auto;mix-blend-mode:normal;color-interpolation:sRGB;color-interpolation-filters:linearRGB;solid-color:#000;solid-opacity:1;fill:#e9e9ff;fill-opacity:1;fill-rule:nonzero;stroke:#ccc;stroke-width:.07269443;stroke-linecap:butt;stroke-linejoin:bevel;stroke-miterlimit:4;stroke-dasharray:none;stroke-dashoffset:0;stroke-opacity:1;color-rendering:auto;image-rendering:auto;shape-rendering:auto;text-rendering:auto;enable-background:accumulate" transform="translate(573.74 -145.798) scale(2.92733)"/><path d="M-189.56 60.903v3.743l3.231-1.177v-3.743z" style="color:#000;clip-rule:nonzero;display:inline;overflow:visible;visibility:visible;opacity:1;isolation:auto;mix-blend-mode:normal;color-interpolation:sRGB;color-interpolation-filters:linearRGB;solid-color:#000;solid-opacity:1;fill:#353564;fill-opacity:1;fill-rule:nonzero;stroke:#ccc;stroke-width:.07269443;stroke-linecap:butt;stroke-linejoin:bevel;stroke-miterlimit:4;stroke-dasharray:none;stroke-dashoffset:0;stroke-opacity:1;color-rendering:auto;image-rendering:auto;shape-rendering:auto;text-rendering:auto;enable-background:accumulate" transform="translate(573.74 -145.798) scale(2.92733)"/><path d="m-189.56 64.646 3.184 1.159 3.232-1.177-3.185-1.159z" style="color:#000;clip-rule:nonzero;display:inline;overflow:visible;visibility:visible;opacity:1;isolation:auto;mix-blend-mode:normal;color-interpolation:sRGB;color-interpolation-filters:linearRGB;solid-color:#000;solid-opacity:1;fill:#afafde;fill-opacity:1;fill-rule:nonzero;stroke:#ccc;stroke-width:.07269443;stroke-linecap:butt;stroke-linejoin:bevel;stroke-miterlimit:4;stroke-dasharray:none;stroke-dashoffset:0;stroke-opacity:1;color-rendering:auto;image-rendering:auto;shape-rendering:auto;text-rendering:auto;enable-background:accumulate" transform="translate(573.74 -145.798) scale(2.92733)"/><path d="m-189.56 60.903 3.184 1.159 3.232-1.177-3.185-1.159z" style="fill:#f7f7f4;fill-opacity:1;stroke:#ccc;stroke-width:.07269443;stroke-linejoin:bevel;stroke-miterlimit:4;stroke-dasharray:none" transform="translate(573.74 -145.798) scale(2.92733)"/><path d="M-186.376 62.062v3.743l3.232-1.177v-3.743z" 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style="color:#000;clip-rule:nonzero;display:inline;overflow:visible;visibility:visible;opacity:1;isolation:auto;mix-blend-mode:normal;color-interpolation:sRGB;color-interpolation-filters:linearRGB;solid-color:#000;solid-opacity:1;fill:#e9e9ff;fill-opacity:1;fill-rule:nonzero;stroke:#ccc;stroke-width:.07269443;stroke-linecap:butt;stroke-linejoin:bevel;stroke-miterlimit:4;stroke-dasharray:none;stroke-dashoffset:0;stroke-opacity:1;color-rendering:auto;image-rendering:auto;shape-rendering:auto;text-rendering:auto;enable-background:accumulate" transform="translate(573.74 -156.864) scale(2.92733)"/><path d="M-189.56 60.903v3.743l3.231-1.177v-3.743z" style="color:#000;clip-rule:nonzero;display:inline;overflow:visible;visibility:visible;opacity:1;isolation:auto;mix-blend-mode:normal;color-interpolation:sRGB;color-interpolation-filters:linearRGB;solid-color:#000;solid-opacity:1;fill:#353564;fill-opacity:1;fill-rule:nonzero;stroke:#ccc;stroke-width:.07269443;stroke-linecap:butt;stroke-linejoin:bevel;stroke-miterlimit:4;stroke-dasharray:none;stroke-dashoffset:0;stroke-opacity:1;color-rendering:auto;image-rendering:auto;shape-rendering:auto;text-rendering:auto;enable-background:accumulate" transform="translate(573.74 -156.864) scale(2.92733)"/><path d="m-189.56 64.646 3.184 1.159 3.232-1.177-3.185-1.159z" style="color:#000;clip-rule:nonzero;display:inline;overflow:visible;visibility:visible;opacity:1;isolation:auto;mix-blend-mode:normal;color-interpolation:sRGB;color-interpolation-filters:linearRGB;solid-color:#000;solid-opacity:1;fill:#afafde;fill-opacity:1;fill-rule:nonzero;stroke:#ccc;stroke-width:.07269443;stroke-linecap:butt;stroke-linejoin:bevel;stroke-miterlimit:4;stroke-dasharray:none;stroke-dashoffset:0;stroke-opacity:1;color-rendering:auto;image-rendering:auto;shape-rendering:auto;text-rendering:auto;enable-background:accumulate" transform="translate(573.74 -156.864) scale(2.92733)"/><path d="m-189.56 60.903 3.184 1.159 3.232-1.177-3.185-1.159z" style="fill:#2f6491;fill-opacity:1;stroke:#ccc;stroke-width:.07269443;stroke-linejoin:bevel;stroke-miterlimit:4;stroke-dasharray:none" transform="translate(573.74 -156.864) scale(2.92733)"/><path d="M-186.376 62.062v3.743l3.232-1.177v-3.743z" 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style="color:#000;clip-rule:nonzero;display:inline;overflow:visible;visibility:visible;opacity:1;isolation:auto;mix-blend-mode:normal;color-interpolation:sRGB;color-interpolation-filters:linearRGB;solid-color:#000;solid-opacity:1;fill:#e9e9ff;fill-opacity:1;fill-rule:nonzero;stroke:#ccc;stroke-width:.07269443;stroke-linecap:butt;stroke-linejoin:bevel;stroke-miterlimit:4;stroke-dasharray:none;stroke-dashoffset:0;stroke-opacity:1;color-rendering:auto;image-rendering:auto;shape-rendering:auto;text-rendering:auto;enable-background:accumulate" transform="translate(3477.852 524.633) scale(4.49135)"/><path d="M-189.56 60.903v3.743l3.231-1.177v-3.743z" style="color:#000;clip-rule:nonzero;display:inline;overflow:visible;visibility:visible;opacity:1;isolation:auto;mix-blend-mode:normal;color-interpolation:sRGB;color-interpolation-filters:linearRGB;solid-color:#000;solid-opacity:1;fill:#353564;fill-opacity:1;fill-rule:nonzero;stroke:#ccc;stroke-width:.07269443;stroke-linecap:butt;stroke-linejoin:bevel;stroke-miterlimit:4;stroke-dasharray:none;stroke-dashoffset:0;stroke-opacity:1;color-rendering:auto;image-rendering:auto;shape-rendering:auto;text-rendering:auto;enable-background:accumulate" transform="translate(3477.852 524.633) scale(4.49135)"/><path d="m-189.56 64.646 3.184 1.159 3.232-1.177-3.185-1.159z" 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style="fill:#3775a9;fill-opacity:1;stroke:#ccc;stroke-width:.07269443;stroke-linejoin:bevel;stroke-miterlimit:4;stroke-dasharray:none" transform="translate(3477.852 524.633) scale(4.49135)"/><path d="m-189.56 60.903 3.184 1.159v3.743l-3.185-1.16z" style="fill:#2f6491;fill-opacity:1;stroke:#ccc;stroke-width:.07269443;stroke-linejoin:bevel;stroke-miterlimit:4;stroke-dasharray:none" transform="translate(3477.852 524.633) scale(4.49135)"/></g><circle cx="2816.017" cy="1772.922" r="2.967" style="fill:#fff;fill-opacity:1" transform="skewY(-20) scale(.9397 1)"/></g></svg>
\ No newline at end of file
diff --git a/doc/rtd/content/00_start_page/images/variants/v10/welcome.png b/doc/rtd/content/00_start_page/images/variants/v10/welcome.png
new file mode 100755
index 000000000..77d26df7e
Binary files /dev/null and b/doc/rtd/content/00_start_page/images/variants/v10/welcome.png differ
diff --git a/doc/rtd/content/00_start_page/images/welcome.png b/doc/rtd/content/00_start_page/images/welcome.png
index 4c6b155c5..77d26df7e 100755
Binary files a/doc/rtd/content/00_start_page/images/welcome.png and b/doc/rtd/content/00_start_page/images/welcome.png differ
diff --git a/doc/rtd/content/03_machine_learning/mlpro_oa/main.rst b/doc/rtd/content/03_machine_learning/mlpro_oa/main.rst
index 61f716813..18cc6d7f7 100644
--- a/doc/rtd/content/03_machine_learning/mlpro_oa/main.rst
+++ b/doc/rtd/content/03_machine_learning/mlpro_oa/main.rst
@@ -2,9 +2,12 @@
 MLPro-OA - Online Adaptivity
 ============================
 
-This framework addresses topics of online machine learning and is already implemented and ready to be used by early adopters. The 
-documentation is still in preparation but the API description is already done. Just browse through the menu structure and follow 
-the links provided...
+This framework addresses the challenge of continuously adapting to changing conditions by processing new information in real time. Unlike traditional offline approaches, 
+which rely on predefined models trained on historical data, online-adaptive methods dynamically refine their behavior as new data becomes available. This enables 
+continuous learning, rapid adaptation to non-stationary environments, and increased robustness in uncertain or evolving scenarios. Such methods are essential in domains 
+requiring real-time decision-making and continuous model updates, including autonomous systems, predictive analytics, and self-optimizing processes.
+
+The following subfields are available in MLPro-OA:
 
 .. toctree::
    :maxdepth: 2
diff --git a/doc/rtd/content/03_machine_learning/mlpro_oa/sub/01_oa_stream_processing.rst b/doc/rtd/content/03_machine_learning/mlpro_oa/sub/01_oa_stream_processing.rst
new file mode 100644
index 000000000..b296ed995
--- /dev/null
+++ b/doc/rtd/content/03_machine_learning/mlpro_oa/sub/01_oa_stream_processing.rst
@@ -0,0 +1,31 @@
+.. _target_oa_streams:
+Online-adaptive data stream processing (OADSP)
+==============================================
+
+This sub-framework of MLPro-OA is directly related to the research topic of online machine learning (OML). It deals with
+online-adaptive stream tasks embedded in a stream workflow as part of an extended process model. This process model extends
+the non-adaptive DSP process model of sub-framework MLPro-BF-Streams by advanced adaptation mechanisms like 
+
+   - Event-oriented adaptation
+   - Cascaded adaptation
+   - Reverse adaptation
+
+which are explained in more detail below.
+
+The description is still under contruction but parts of the sub-menu and first howtos and API specifications are already available.
+Browse the menu and see Section 'Cross reference' for further details.
+
+
+**Learn more**
+
+.. toctree::
+   :maxdepth: 3
+   :glob:
+
+   streams/*
+
+
+**Cross reference**
+  - :ref:`Howtos MPro-OA-Streams <target_appendix1_OA_streams>`
+  - :ref:`API reference: MLPro-OA-Streams <target_api_oa_streams>`
+  - :ref:`Basics of data stream processing in MLPro <target_bf_streams>`
\ No newline at end of file
diff --git a/doc/rtd/content/03_machine_learning/mlpro_oa/sub/01_overview.rst b/doc/rtd/content/03_machine_learning/mlpro_oa/sub/01_overview.rst
deleted file mode 100644
index 5d5a4e930..000000000
--- a/doc/rtd/content/03_machine_learning/mlpro_oa/sub/01_overview.rst
+++ /dev/null
@@ -1,5 +0,0 @@
-.. _target_oa_overview:
-Overview
-========
-
-Further descriptions coming soon...
\ No newline at end of file
diff --git a/doc/rtd/content/03_machine_learning/mlpro_oa/sub/02_getstarted.rst b/doc/rtd/content/03_machine_learning/mlpro_oa/sub/02_getstarted.rst
deleted file mode 100644
index d164b5365..000000000
--- a/doc/rtd/content/03_machine_learning/mlpro_oa/sub/02_getstarted.rst
+++ /dev/null
@@ -1,6 +0,0 @@
-.. _target_oa_getstarted:
-Getting Started
-===============
-
-Further descriptions coming soon...
-
diff --git a/doc/rtd/content/03_machine_learning/mlpro_oa/sub/03_oa_stream_processing.rst b/doc/rtd/content/03_machine_learning/mlpro_oa/sub/03_oa_stream_processing.rst
deleted file mode 100644
index 28d62550d..000000000
--- a/doc/rtd/content/03_machine_learning/mlpro_oa/sub/03_oa_stream_processing.rst
+++ /dev/null
@@ -1,21 +0,0 @@
-.. _target_oa_streams:
-Online Adaptive Stream Processing
-=================================
-
-Description under contruction. Please consider the cross reference for first technical information.
-
-
-**Learn more**
-
-.. toctree::
-   :maxdepth: 3
-   :glob:
-
-   layer0_oa_stream_processing/*
-
-
-**Cross Reference**
-  - :ref:`Howtos MPro-OA <target_appendix1_OA>`
-  - :ref:`API Reference: MLPro-OA <target_api_oa_streams>`
-  - :ref:`API Reference: MLPro-OA Pool of Objects <target_api_pool_oa_streams>`
-  - :ref:`Basics of stream processing in MLPro <target_bf_streams>`
\ No newline at end of file
diff --git a/doc/rtd/content/03_machine_learning/mlpro_oa/sub/04_oa_systems.rst.off b/doc/rtd/content/03_machine_learning/mlpro_oa/sub/03_oa_systems.rst.off
similarity index 54%
rename from doc/rtd/content/03_machine_learning/mlpro_oa/sub/04_oa_systems.rst.off
rename to doc/rtd/content/03_machine_learning/mlpro_oa/sub/03_oa_systems.rst.off
index 6deb29d73..3838ed72c 100644
--- a/doc/rtd/content/03_machine_learning/mlpro_oa/sub/04_oa_systems.rst.off
+++ b/doc/rtd/content/03_machine_learning/mlpro_oa/sub/03_oa_systems.rst.off
@@ -1,6 +1,6 @@
 .. _target_oa_systems:
-Online Adaptive Systems
-=======================
+Online-adaptive state-based systems
+===================================
 
 Further descriptions coming soon...
 
@@ -8,4 +8,4 @@ Further descriptions coming soon...
    :maxdepth: 2
    :glob:
 
-   layer1_oa_systems/*
\ No newline at end of file
+   systems/*
\ No newline at end of file
diff --git a/doc/rtd/content/03_machine_learning/mlpro_oa/sub/layer0_oa_stream_processing/01_overview.rst b/doc/rtd/content/03_machine_learning/mlpro_oa/sub/streams/01_overview.rst
similarity index 100%
rename from doc/rtd/content/03_machine_learning/mlpro_oa/sub/layer0_oa_stream_processing/01_overview.rst
rename to doc/rtd/content/03_machine_learning/mlpro_oa/sub/streams/01_overview.rst
diff --git a/doc/rtd/content/03_machine_learning/mlpro_oa/sub/layer0_oa_stream_processing/01_overview/01_oa_stream_task.rst b/doc/rtd/content/03_machine_learning/mlpro_oa/sub/streams/01_overview/01_oa_stream_task.rst
similarity index 68%
rename from doc/rtd/content/03_machine_learning/mlpro_oa/sub/layer0_oa_stream_processing/01_overview/01_oa_stream_task.rst
rename to doc/rtd/content/03_machine_learning/mlpro_oa/sub/streams/01_overview/01_oa_stream_task.rst
index 915658a36..b11b06036 100644
--- a/doc/rtd/content/03_machine_learning/mlpro_oa/sub/layer0_oa_stream_processing/01_overview/01_oa_stream_task.rst
+++ b/doc/rtd/content/03_machine_learning/mlpro_oa/sub/streams/01_overview/01_oa_stream_task.rst
@@ -1,5 +1,5 @@
 .. _target_oa_stream_tasks:
-Online Adaptive Stream Tasks
+Online-adaptive stream tasks
 ============================
 
 ...
diff --git a/doc/rtd/content/03_machine_learning/mlpro_oa/sub/layer0_oa_stream_processing/01_overview/02_oa_stream_workflow.rst b/doc/rtd/content/03_machine_learning/mlpro_oa/sub/streams/01_overview/02_oa_stream_workflow.rst
similarity index 68%
rename from doc/rtd/content/03_machine_learning/mlpro_oa/sub/layer0_oa_stream_processing/01_overview/02_oa_stream_workflow.rst
rename to doc/rtd/content/03_machine_learning/mlpro_oa/sub/streams/01_overview/02_oa_stream_workflow.rst
index 15e56a1bf..19deb36af 100644
--- a/doc/rtd/content/03_machine_learning/mlpro_oa/sub/layer0_oa_stream_processing/01_overview/02_oa_stream_workflow.rst
+++ b/doc/rtd/content/03_machine_learning/mlpro_oa/sub/streams/01_overview/02_oa_stream_workflow.rst
@@ -1,5 +1,5 @@
 .. _target_oa_stream_workflows:
-Online Adaptive Stream Workflows
+Online-adaptive stream workflows
 ================================
 
 
diff --git a/doc/rtd/content/03_machine_learning/mlpro_oa/sub/streams/10_preprocessing.rst b/doc/rtd/content/03_machine_learning/mlpro_oa/sub/streams/10_preprocessing.rst
new file mode 100644
index 000000000..9431ca969
--- /dev/null
+++ b/doc/rtd/content/03_machine_learning/mlpro_oa/sub/streams/10_preprocessing.rst
@@ -0,0 +1,21 @@
+.. _target_oa_stream_preprocessing:
+Preprocessing
+=============
+
+Further descriptions coming soon... 
+
+
+.. toctree::
+   :maxdepth: 2
+   :glob:
+
+   10_preprocessing/*
+
+
+
+**Cross Reference**
+
+- BF: Stream Processing
+- Howto section
+- API
+- MLPro-Int-River
diff --git a/doc/rtd/content/03_machine_learning/mlpro_oa/sub/layer0_oa_stream_processing/02_oa_boundary_detector.rst b/doc/rtd/content/03_machine_learning/mlpro_oa/sub/streams/10_preprocessing/10_boundary_detector.rst
similarity index 87%
rename from doc/rtd/content/03_machine_learning/mlpro_oa/sub/layer0_oa_stream_processing/02_oa_boundary_detector.rst
rename to doc/rtd/content/03_machine_learning/mlpro_oa/sub/streams/10_preprocessing/10_boundary_detector.rst
index 301f3237c..a656d6d79 100644
--- a/doc/rtd/content/03_machine_learning/mlpro_oa/sub/layer0_oa_stream_processing/02_oa_boundary_detector.rst
+++ b/doc/rtd/content/03_machine_learning/mlpro_oa/sub/streams/10_preprocessing/10_boundary_detector.rst
@@ -1,5 +1,5 @@
 .. _target_oa_boundary_detector:
-Boundary Detection
+Boundary detection
 ==================
 
 Further descriptions coming soon...
diff --git a/doc/rtd/content/03_machine_learning/mlpro_oa/sub/layer0_oa_stream_processing/03_oa_normalization.rst b/doc/rtd/content/03_machine_learning/mlpro_oa/sub/streams/10_preprocessing/20_normalization.rst
similarity index 69%
rename from doc/rtd/content/03_machine_learning/mlpro_oa/sub/layer0_oa_stream_processing/03_oa_normalization.rst
rename to doc/rtd/content/03_machine_learning/mlpro_oa/sub/streams/10_preprocessing/20_normalization.rst
index 43a967297..8c2c678c2 100644
--- a/doc/rtd/content/03_machine_learning/mlpro_oa/sub/layer0_oa_stream_processing/03_oa_normalization.rst
+++ b/doc/rtd/content/03_machine_learning/mlpro_oa/sub/streams/10_preprocessing/20_normalization.rst
@@ -1,4 +1,4 @@
-.. _target_oa_normalizers:
+.. _target_oa_streams_normalization:
 Normalization
 =============
 
@@ -11,4 +11,4 @@ Further descriptions coming soon...
    :maxdepth: 2
    :glob:
 
-   03_oa_normalization/*
\ No newline at end of file
+   normalization/*
\ No newline at end of file
diff --git a/doc/rtd/content/03_machine_learning/mlpro_oa/sub/layer0_oa_stream_processing/03_oa_normalization/01_oa_minmax.rst b/doc/rtd/content/03_machine_learning/mlpro_oa/sub/streams/10_preprocessing/normalization/01_oa_minmax.rst
similarity index 100%
rename from doc/rtd/content/03_machine_learning/mlpro_oa/sub/layer0_oa_stream_processing/03_oa_normalization/01_oa_minmax.rst
rename to doc/rtd/content/03_machine_learning/mlpro_oa/sub/streams/10_preprocessing/normalization/01_oa_minmax.rst
diff --git a/doc/rtd/content/03_machine_learning/mlpro_oa/sub/layer0_oa_stream_processing/03_oa_normalization/02_oa_zscaling.rst b/doc/rtd/content/03_machine_learning/mlpro_oa/sub/streams/10_preprocessing/normalization/02_oa_zscaling.rst
similarity index 100%
rename from doc/rtd/content/03_machine_learning/mlpro_oa/sub/layer0_oa_stream_processing/03_oa_normalization/02_oa_zscaling.rst
rename to doc/rtd/content/03_machine_learning/mlpro_oa/sub/streams/10_preprocessing/normalization/02_oa_zscaling.rst
diff --git a/doc/rtd/content/03_machine_learning/mlpro_oa/sub/layer0_oa_stream_processing/10_cluster_analysis.rst b/doc/rtd/content/03_machine_learning/mlpro_oa/sub/streams/20_cluster_analysis.rst
similarity index 72%
rename from doc/rtd/content/03_machine_learning/mlpro_oa/sub/layer0_oa_stream_processing/10_cluster_analysis.rst
rename to doc/rtd/content/03_machine_learning/mlpro_oa/sub/streams/20_cluster_analysis.rst
index ec1059338..2861bf2f5 100644
--- a/doc/rtd/content/03_machine_learning/mlpro_oa/sub/layer0_oa_stream_processing/10_cluster_analysis.rst
+++ b/doc/rtd/content/03_machine_learning/mlpro_oa/sub/streams/20_cluster_analysis.rst
@@ -1,5 +1,5 @@
-.. _target_oa_cluster_analyzer:
-Cluster Analysis
+.. _target_oa_cluster_analysis:
+Cluster analysis
 ================
 
 Further descriptions coming soon...
@@ -11,7 +11,7 @@ Further descriptions coming soon...
    :maxdepth: 2
    :glob:
 
-   10_cluster_analysis/*
+   20_cluster_analysis/*
 
 
 **Cross Reference**
diff --git a/doc/rtd/content/03_machine_learning/mlpro_oa/sub/layer0_oa_stream_processing/10_cluster_analysis/.gitkeep b/doc/rtd/content/03_machine_learning/mlpro_oa/sub/streams/20_cluster_analysis/.gitkeep
similarity index 100%
rename from doc/rtd/content/03_machine_learning/mlpro_oa/sub/layer0_oa_stream_processing/10_cluster_analysis/.gitkeep
rename to doc/rtd/content/03_machine_learning/mlpro_oa/sub/streams/20_cluster_analysis/.gitkeep
diff --git a/doc/rtd/content/03_machine_learning/mlpro_oa/sub/layer0_oa_stream_processing/20_anomaly_detection.rst b/doc/rtd/content/03_machine_learning/mlpro_oa/sub/streams/30_anomaly_detection.rst
similarity index 96%
rename from doc/rtd/content/03_machine_learning/mlpro_oa/sub/layer0_oa_stream_processing/20_anomaly_detection.rst
rename to doc/rtd/content/03_machine_learning/mlpro_oa/sub/streams/30_anomaly_detection.rst
index 8acdea62a..4864d0ebf 100644
--- a/doc/rtd/content/03_machine_learning/mlpro_oa/sub/layer0_oa_stream_processing/20_anomaly_detection.rst
+++ b/doc/rtd/content/03_machine_learning/mlpro_oa/sub/streams/30_anomaly_detection.rst
@@ -1,19 +1,19 @@
 .. _target_oa_anomaly_detection:
-Anomaly Detection
+Anomaly detection
 =================
 
 Anomaly detection involves identifying instances that are structurally or dimensionally similar to non-anomalous data but deviate significantly from the normal data distribution or pattern. In real-world problems, anomaly detection helps uncover unusual activities in banking and finance, abnormalities in medical test results, uncommon behavior or sensor readings of machines, defective products in manufacturing lines, or malicious activities in network traffic monitoring. Detecting and analyzing these instances or behaviors is crucial for taking immediate action, preventing future occurrences of undesirable events, and ensuring data quality. Moreover, anomaly detection plays a pivotal role in making unbiased and accurate decisions across various domains.
 
 Anomaly detection techniques can be broadly classified into two categories based on the under- lying principles and methodologies. The two categories are Statistical anomaly detectors and Machine Learning anomaly detectors.
 
-**Types of Anomalies**
+**Types of anomalies**
 There are three main types of anomalies- Point anomalies, Contextual anomalies and Collective anomalies.
 
   (a) Point Anomalies : Type I anomalies or point anomalies are individual data instances that are significantly different from the rest of the dataset. Also known as global anomalies, these do not fit the normal distribution or pattern of the dataset.
   (b) Contextual Anomalies : Type II anomalies or contextual anomalies are data instances that are anomalies only in a particular context or subset of the dataset. Also known as conditional anomalies, these are not necessarily anomalies in the context of the whole dataset but anomalous within a specific context or condition.
   (c) Group Anomalies : Type III anomalies or group anomalies or collective anomalies are anomalous data instances when taken as a group or subset of the dataset. They may or may not be anomalies when considered individually. Also known as group anomalies, these occur when there is a deviation or unexpected relationship or behaviour among a group of data instances from the normal distribution of data.
 
-**Classification of Anomaly Detectors**
+**Classification of anomaly detectors**
 Anomaly detection techniques can be broadly classified into two categories based on the under- lying principles and methodologies. The two categories are Statistical anomaly detectors and Machine Learning anomaly detectors.
 
   (a) Statistical Anomaly Detectors : Statistical anomaly detectors use statistical methods to find data point deviations from the normal distribution. Common algorithms for this category of anomaly detectors are Z-score, Kernel Density Estimate, and Gaussian Mixture Models (GMM).
@@ -26,10 +26,10 @@ Anomaly detection techniques can be broadly classified into two categories based
    :maxdepth: 2
    :glob:
 
-   20_anomaly_detection/*
+   30_anomaly_detection/*
 
 
-**Cross Reference**
+**Cross reference**
 
 - Selected open access papers
 - Howtos
diff --git a/doc/rtd/content/03_machine_learning/mlpro_oa/sub/layer0_oa_stream_processing/20_anomaly_detection/01_cbad.rst b/doc/rtd/content/03_machine_learning/mlpro_oa/sub/streams/30_anomaly_detection/01_cbad.rst.off
similarity index 90%
rename from doc/rtd/content/03_machine_learning/mlpro_oa/sub/layer0_oa_stream_processing/20_anomaly_detection/01_cbad.rst
rename to doc/rtd/content/03_machine_learning/mlpro_oa/sub/streams/30_anomaly_detection/01_cbad.rst.off
index df676878b..e8db463eb 100644
--- a/doc/rtd/content/03_machine_learning/mlpro_oa/sub/layer0_oa_stream_processing/20_anomaly_detection/01_cbad.rst
+++ b/doc/rtd/content/03_machine_learning/mlpro_oa/sub/streams/30_anomaly_detection/01_cbad.rst.off
@@ -1,5 +1,5 @@
 .. _target_oa_cbad:
-Cluster-based Anomaly Detection
+Cluster-based anomaly detection
 ===============================
 
 Cluster-based anomaly detection uses clustering algorithms to form clustered data and identify anomolous behaviours of these clusters, which are flagged as anomalies.
@@ -18,7 +18,8 @@ Can uncover contextual anomalies (data points that are anomalous in one cluster
 Can identify global anomalies (points far from all clusters) and local anomalies (outliers within a cluster).
 Scalability with various clustering algorithms, such as k-means for simpler scenarios or DBSCAN for non-spherical and dense data distributions.
 
-**New Types of Anomalies**
+
+**New types of anomalies**
 
   Cluster-based methods introduce nuanced anomaly categorizations:
   
@@ -28,9 +29,10 @@ Scalability with various clustering algorithms, such as k-means for simpler scen
   
   (c) Cluster Structural Anomalies: Unusual clusters themselves, such as unexpected densities, shapes, or sizes, signaling broader irregularities.
 
-**Special Dependencies on Cluster Algorithms**
 
-  Cluster-based anomaly detection heavily depends on the choice of clustering algorithm, as it directly impacts the detection process:
+**Special dependencies on cluster algorithms**
+
+Cluster-based anomaly detection heavily depends on the choice of clustering algorithm, as it directly impacts the detection process:
 
 k-means: Effective for spherical clusters but may miss anomalies in datasets with non-convex shapes or varying densities.
 DBSCAN: Ideal for discovering density-based anomalies but sensitive to hyperparameter tuning (e.g., minPts, ε).
@@ -39,7 +41,8 @@ Gaussian Mixture Models (GMM): Handles soft clustering and detects probabilistic
 Spectral Clustering: Good for identifying anomalies in non-linear manifolds but computationally intensive for large datasets.
 Effective anomaly detection requires understanding the clustering algorithm’s limitations and ensuring it aligns with the data characteristics and problem context.
 
-**Cross Reference**
+
+**Cross reference**
 
 - Howtos
 - API
diff --git a/doc/rtd/content/03_machine_learning/mlpro_oa/sub/layer0_oa_stream_processing/30_anomaly_prediction.rst b/doc/rtd/content/03_machine_learning/mlpro_oa/sub/streams/31_anomaly_prediction.rst.off
similarity index 76%
rename from doc/rtd/content/03_machine_learning/mlpro_oa/sub/layer0_oa_stream_processing/30_anomaly_prediction.rst
rename to doc/rtd/content/03_machine_learning/mlpro_oa/sub/streams/31_anomaly_prediction.rst.off
index 273e957f2..d5cc86c18 100644
--- a/doc/rtd/content/03_machine_learning/mlpro_oa/sub/layer0_oa_stream_processing/30_anomaly_prediction.rst
+++ b/doc/rtd/content/03_machine_learning/mlpro_oa/sub/streams/31_anomaly_prediction.rst.off
@@ -1,5 +1,5 @@
 .. _target_oa_anomaly_prediction:
-Anomaly Prediction
+Anomaly prediction
 ==================
 
 Further descriptions coming soon...
@@ -11,10 +11,10 @@ Further descriptions coming soon...
    :maxdepth: 2
    :glob:
 
-   20_anomaly_prediction/*
+   31_anomaly_prediction/*
 
 
-**Cross Reference**
+**Cross reference**
 
 - Selected open access papers
 - Howtos
diff --git a/doc/rtd/content/03_machine_learning/mlpro_oa/sub/layer0_oa_stream_processing/30_anomaly_prediction/.gitkeep b/doc/rtd/content/03_machine_learning/mlpro_oa/sub/streams/31_anomaly_prediction/.gitkeep
similarity index 100%
rename from doc/rtd/content/03_machine_learning/mlpro_oa/sub/layer0_oa_stream_processing/30_anomaly_prediction/.gitkeep
rename to doc/rtd/content/03_machine_learning/mlpro_oa/sub/streams/31_anomaly_prediction/.gitkeep
diff --git a/doc/rtd/content/03_machine_learning/mlpro_oa/sub/streams/40_drift_detection.rst b/doc/rtd/content/03_machine_learning/mlpro_oa/sub/streams/40_drift_detection.rst
new file mode 100644
index 000000000..002563bdd
--- /dev/null
+++ b/doc/rtd/content/03_machine_learning/mlpro_oa/sub/streams/40_drift_detection.rst
@@ -0,0 +1,21 @@
+.. _target_oa_drift_detection:
+Drift detection
+===============
+
+Further descriptions coming soon...
+
+
+**Learn more**
+
+.. toctree::
+   :maxdepth: 2
+   :glob:
+
+   40_drift_detection/*
+
+
+**Cross reference**
+
+- Selected open access papers
+- Howtos
+- API
diff --git a/doc/rtd/content/03_machine_learning/mlpro_oa/sub/streams/40_drift_detection/01_cbdd.rst.off b/doc/rtd/content/03_machine_learning/mlpro_oa/sub/streams/40_drift_detection/01_cbdd.rst.off
new file mode 100644
index 000000000..81e8efda7
--- /dev/null
+++ b/doc/rtd/content/03_machine_learning/mlpro_oa/sub/streams/40_drift_detection/01_cbdd.rst.off
@@ -0,0 +1,21 @@
+.. _target_oa_cbdd:
+Cluster-based drift detection
+=============================
+
+Further descriptions coming soon...
+
+
+**New types of drift**
+
+...
+
+
+**Special dependencies on cluster algorithms**
+
+...
+
+
+**Cross reference**
+
+- Howtos
+- API
diff --git a/doc/rtd/content/03_machine_learning/mlpro_oa/sub/layer1_oa_systems/.gitkeep b/doc/rtd/content/03_machine_learning/mlpro_oa/sub/systems/.gitkeep
similarity index 100%
rename from doc/rtd/content/03_machine_learning/mlpro_oa/sub/layer1_oa_systems/.gitkeep
rename to doc/rtd/content/03_machine_learning/mlpro_oa/sub/systems/.gitkeep
diff --git a/doc/rtd/content/99_appendices/appendix1/sub/mlpro_oa/01_streams.rst b/doc/rtd/content/99_appendices/appendix1/sub/mlpro_oa/01_streams.rst
index ec0513982..741c03a73 100644
--- a/doc/rtd/content/99_appendices/appendix1/sub/mlpro_oa/01_streams.rst
+++ b/doc/rtd/content/99_appendices/appendix1/sub/mlpro_oa/01_streams.rst
@@ -1,8 +1,9 @@
-Online Adaptive Stream Processing
-=================================
+.. _target_appendix1_OA_streams:
+Online-adaptive data stream processing
+======================================
 
 .. toctree::
    :maxdepth: 1
    :glob:
    
-   oa_dsp/* 
+   streams/* 
diff --git a/doc/rtd/content/99_appendices/appendix1/sub/mlpro_oa/streams/howto_oa_pp_009.rst b/doc/rtd/content/99_appendices/appendix1/sub/mlpro_oa/streams/howto_oa_pp_009.rst
deleted file mode 100644
index fd5588ec5..000000000
--- a/doc/rtd/content/99_appendices/appendix1/sub/mlpro_oa/streams/howto_oa_pp_009.rst
+++ /dev/null
@@ -1,26 +0,0 @@
-.. _Howto_OA_PP_009:
-Howto OA-PP-009: Complex Preprocessing with Parallel Tasks
-==========================================================
-
-
-**Executable code**
-
-.. literalinclude:: ../../../../../../../../test/howtos/oa/howto_oa_pp_009_complex_preprocessing.py
-	:language: python
-
-
-
-**Results**
-
-After starting the Howto, the Workflow and Tasks windows appear. These can now be arranged on 
-the screen before the actual processing is started with a keystroke... 
-
-.. image::
-    images/howto_oa_pp_009.gif
-    :width: 700px
-
-
-
-**Cross Reference**
-
-- :ref:`API Reference - Online Adaptive Stream Processing <target_api_oa_streams>`
diff --git a/doc/rtd/content/99_appendices/appendix1/sub/mlpro_oa/streams/howto_oa_pp_121.rst b/doc/rtd/content/99_appendices/appendix1/sub/mlpro_oa/streams/howto_oa_pp_121.rst
new file mode 100644
index 000000000..07617824d
--- /dev/null
+++ b/doc/rtd/content/99_appendices/appendix1/sub/mlpro_oa/streams/howto_oa_pp_121.rst
@@ -0,0 +1,26 @@
+.. _Howto_OA_PP_121:
+Howto OA-PP-121: Complex preprocessing with parallel tasks
+==========================================================
+
+
+**Executable code**
+
+.. literalinclude:: ../../../../../../../../test/howtos/oa/streams/hybrid/howto_oa_streams_pp_121_complex_preprocessing.py
+	:language: python
+
+
+
+**Results**
+
+After starting the Howto, the Workflow and Tasks windows appear. These can now be arranged on 
+the screen before the actual processing is started with a keystroke... 
+
+.. image::
+    images/howto_oa_pp_121.gif
+    :width: 700px
+
+
+
+**Cross reference**
+
+- :ref:`API Reference - Online-adaptive data stream processing <target_api_oa_streams>`
diff --git a/doc/rtd/content/99_appendices/appendix1/sub/mlpro_oa/streams/images/howto_oa_pp_009.gif b/doc/rtd/content/99_appendices/appendix1/sub/mlpro_oa/streams/images/howto_oa_pp_121.gif
similarity index 100%
rename from doc/rtd/content/99_appendices/appendix1/sub/mlpro_oa/streams/images/howto_oa_pp_009.gif
rename to doc/rtd/content/99_appendices/appendix1/sub/mlpro_oa/streams/images/howto_oa_pp_121.gif
diff --git a/doc/rtd/content/99_appendices/appendix2/sub/01_core.rst b/doc/rtd/content/99_appendices/appendix2/sub/01_core.rst
deleted file mode 100644
index 14becf59f..000000000
--- a/doc/rtd/content/99_appendices/appendix2/sub/01_core.rst
+++ /dev/null
@@ -1,10 +0,0 @@
-Core Functions
-==============
-
-.. toctree::
-   :maxdepth: 2
-   :glob:
-   
-   core/*
-   
-
diff --git a/doc/rtd/content/99_appendices/appendix2/sub/02_pool.rst b/doc/rtd/content/99_appendices/appendix2/sub/02_pool.rst
deleted file mode 100644
index 48d9f356f..000000000
--- a/doc/rtd/content/99_appendices/appendix2/sub/02_pool.rst
+++ /dev/null
@@ -1,10 +0,0 @@
-.. _target_pool_objects:
-Pool Objects
-============
-
-
-.. toctree::
-   :maxdepth: 2
-   :glob:
-
-   pool/*
diff --git a/doc/rtd/content/99_appendices/appendix2/sub/core/10_mlpro.bf.rst b/doc/rtd/content/99_appendices/appendix2/sub/10_mlpro.bf.rst
similarity index 100%
rename from doc/rtd/content/99_appendices/appendix2/sub/core/10_mlpro.bf.rst
rename to doc/rtd/content/99_appendices/appendix2/sub/10_mlpro.bf.rst
diff --git a/doc/rtd/content/99_appendices/appendix2/sub/core/20_mlpro.sl.rst b/doc/rtd/content/99_appendices/appendix2/sub/20_mlpro.sl.rst
similarity index 100%
rename from doc/rtd/content/99_appendices/appendix2/sub/core/20_mlpro.sl.rst
rename to doc/rtd/content/99_appendices/appendix2/sub/20_mlpro.sl.rst
diff --git a/doc/rtd/content/99_appendices/appendix2/sub/core/30_mlpro.oa.rst b/doc/rtd/content/99_appendices/appendix2/sub/30_mlpro.oa.rst
similarity index 100%
rename from doc/rtd/content/99_appendices/appendix2/sub/core/30_mlpro.oa.rst
rename to doc/rtd/content/99_appendices/appendix2/sub/30_mlpro.oa.rst
diff --git a/doc/rtd/content/99_appendices/appendix2/sub/core/40_mlpro.rl.rst b/doc/rtd/content/99_appendices/appendix2/sub/40_mlpro.rl.rst
similarity index 100%
rename from doc/rtd/content/99_appendices/appendix2/sub/core/40_mlpro.rl.rst
rename to doc/rtd/content/99_appendices/appendix2/sub/40_mlpro.rl.rst
diff --git a/doc/rtd/content/99_appendices/appendix2/sub/core/50_mlpro.gt.rst b/doc/rtd/content/99_appendices/appendix2/sub/50_mlpro.gt.rst
similarity index 100%
rename from doc/rtd/content/99_appendices/appendix2/sub/core/50_mlpro.gt.rst
rename to doc/rtd/content/99_appendices/appendix2/sub/50_mlpro.gt.rst
diff --git a/doc/rtd/content/99_appendices/appendix2/sub/03_wrappers.rst b/doc/rtd/content/99_appendices/appendix2/sub/99_wrappers.rst
similarity index 100%
rename from doc/rtd/content/99_appendices/appendix2/sub/03_wrappers.rst
rename to doc/rtd/content/99_appendices/appendix2/sub/99_wrappers.rst
diff --git a/doc/rtd/content/99_appendices/appendix2/sub/core/mlpro_oa/01_streams.rst b/doc/rtd/content/99_appendices/appendix2/sub/core/mlpro_oa/01_streams.rst
deleted file mode 100644
index 0feed5d49..000000000
--- a/doc/rtd/content/99_appendices/appendix2/sub/core/mlpro_oa/01_streams.rst
+++ /dev/null
@@ -1,12 +0,0 @@
-.. _target_api_oa_streams:
-OA Stream Processing
-====================
-
-.. image:: streams/images/MLPro-OA-Stream-Processing_class_diagram.drawio.png
-   :scale: 50%
-   
-.. automodule:: mlpro.oa.streams.basics
-   :members:
-   :undoc-members:
-   :private-members:
-   :show-inheritance:
\ No newline at end of file
diff --git a/doc/rtd/content/99_appendices/appendix2/sub/core/images/bf/MLPro-BF-Systems-AdaptiveSystems_class_diagram.drawio b/doc/rtd/content/99_appendices/appendix2/sub/images/bf/MLPro-BF-Systems-AdaptiveSystems_class_diagram.drawio
similarity index 100%
rename from doc/rtd/content/99_appendices/appendix2/sub/core/images/bf/MLPro-BF-Systems-AdaptiveSystems_class_diagram.drawio
rename to doc/rtd/content/99_appendices/appendix2/sub/images/bf/MLPro-BF-Systems-AdaptiveSystems_class_diagram.drawio
diff --git a/doc/rtd/content/99_appendices/appendix2/sub/core/images/rl/01_environments/MLPro-RL-Env-Ada_class_diagram.drawio b/doc/rtd/content/99_appendices/appendix2/sub/images/rl/01_environments/MLPro-RL-Env-Ada_class_diagram.drawio
similarity index 100%
rename from doc/rtd/content/99_appendices/appendix2/sub/core/images/rl/01_environments/MLPro-RL-Env-Ada_class_diagram.drawio
rename to doc/rtd/content/99_appendices/appendix2/sub/images/rl/01_environments/MLPro-RL-Env-Ada_class_diagram.drawio
diff --git a/doc/rtd/content/99_appendices/appendix2/sub/core/mlpro_bf/01_layer0_elementary.rst b/doc/rtd/content/99_appendices/appendix2/sub/mlpro_bf/10_layer0_elementary.rst
similarity index 100%
rename from doc/rtd/content/99_appendices/appendix2/sub/core/mlpro_bf/01_layer0_elementary.rst
rename to doc/rtd/content/99_appendices/appendix2/sub/mlpro_bf/10_layer0_elementary.rst
diff --git a/doc/rtd/content/99_appendices/appendix2/sub/core/mlpro_bf/02_layer1_computation.rst b/doc/rtd/content/99_appendices/appendix2/sub/mlpro_bf/20_layer1_computation.rst
similarity index 100%
rename from doc/rtd/content/99_appendices/appendix2/sub/core/mlpro_bf/02_layer1_computation.rst
rename to doc/rtd/content/99_appendices/appendix2/sub/mlpro_bf/20_layer1_computation.rst
diff --git a/doc/rtd/content/99_appendices/appendix2/sub/core/mlpro_bf/03_layer2_mathematics.rst b/doc/rtd/content/99_appendices/appendix2/sub/mlpro_bf/30_layer2_mathematics.rst
similarity index 100%
rename from doc/rtd/content/99_appendices/appendix2/sub/core/mlpro_bf/03_layer2_mathematics.rst
rename to doc/rtd/content/99_appendices/appendix2/sub/mlpro_bf/30_layer2_mathematics.rst
diff --git a/doc/rtd/content/99_appendices/appendix2/sub/core/mlpro_bf/04_layer3_application_support.rst b/doc/rtd/content/99_appendices/appendix2/sub/mlpro_bf/40_layer3_application_support.rst
similarity index 100%
rename from doc/rtd/content/99_appendices/appendix2/sub/core/mlpro_bf/04_layer3_application_support.rst
rename to doc/rtd/content/99_appendices/appendix2/sub/mlpro_bf/40_layer3_application_support.rst
diff --git a/doc/rtd/content/99_appendices/appendix2/sub/core/mlpro_bf/05_layer4_machine_learning.rst b/doc/rtd/content/99_appendices/appendix2/sub/mlpro_bf/50_layer4_machine_learning.rst
similarity index 100%
rename from doc/rtd/content/99_appendices/appendix2/sub/core/mlpro_bf/05_layer4_machine_learning.rst
rename to doc/rtd/content/99_appendices/appendix2/sub/mlpro_bf/50_layer4_machine_learning.rst
diff --git a/doc/rtd/content/99_appendices/appendix2/sub/pool/10_mlpro.bf.rst b/doc/rtd/content/99_appendices/appendix2/sub/mlpro_bf/90_pool_objects.rst
similarity index 50%
rename from doc/rtd/content/99_appendices/appendix2/sub/pool/10_mlpro.bf.rst
rename to doc/rtd/content/99_appendices/appendix2/sub/mlpro_bf/90_pool_objects.rst
index 85983008a..62a064749 100644
--- a/doc/rtd/content/99_appendices/appendix2/sub/pool/10_mlpro.bf.rst
+++ b/doc/rtd/content/99_appendices/appendix2/sub/mlpro_bf/90_pool_objects.rst
@@ -1,9 +1,9 @@
 .. _target_api_pool_bf:
-MLPro-BF - Basic Functions
-==========================
+Pool objects
+============
 
 .. toctree::
    :maxdepth: 3
    :glob:
    
-   mlpro_bf/*
\ No newline at end of file
+   pool/*
\ No newline at end of file
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diff --git a/doc/rtd/content/99_appendices/appendix2/sub/pool/mlpro_bf/bf_systems/images/MLPro-BF-Systems-NativeDoublePendulumSystem_class_diagram.drawio b/doc/rtd/content/99_appendices/appendix2/sub/mlpro_bf/pool/bf_systems/images/MLPro-BF-Systems-NativeDoublePendulumSystem_class_diagram.drawio
similarity index 100%
rename from doc/rtd/content/99_appendices/appendix2/sub/pool/mlpro_bf/bf_systems/images/MLPro-BF-Systems-NativeDoublePendulumSystem_class_diagram.drawio
rename to doc/rtd/content/99_appendices/appendix2/sub/mlpro_bf/pool/bf_systems/images/MLPro-BF-Systems-NativeDoublePendulumSystem_class_diagram.drawio
diff --git a/doc/rtd/content/99_appendices/appendix2/sub/pool/mlpro_bf/bf_systems/images/MLPro-BF-Systems-NativeDoublePendulumSystem_class_diagram.drawio.png b/doc/rtd/content/99_appendices/appendix2/sub/mlpro_bf/pool/bf_systems/images/MLPro-BF-Systems-NativeDoublePendulumSystem_class_diagram.drawio.png
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diff --git a/doc/rtd/content/99_appendices/appendix2/sub/core/mlpro_gt/01_dg.rst b/doc/rtd/content/99_appendices/appendix2/sub/mlpro_gt/10_dg.rst
similarity index 100%
rename from doc/rtd/content/99_appendices/appendix2/sub/core/mlpro_gt/01_dg.rst
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diff --git a/doc/rtd/content/99_appendices/appendix2/sub/core/mlpro_gt/02_native.rst b/doc/rtd/content/99_appendices/appendix2/sub/mlpro_gt/20_native.rst
similarity index 100%
rename from doc/rtd/content/99_appendices/appendix2/sub/core/mlpro_gt/02_native.rst
rename to doc/rtd/content/99_appendices/appendix2/sub/mlpro_gt/20_native.rst
diff --git a/doc/rtd/content/99_appendices/appendix2/sub/pool/50_mlpro.gt.rst b/doc/rtd/content/99_appendices/appendix2/sub/mlpro_gt/90_pool_objects.rst
similarity index 53%
rename from doc/rtd/content/99_appendices/appendix2/sub/pool/50_mlpro.gt.rst
rename to doc/rtd/content/99_appendices/appendix2/sub/mlpro_gt/90_pool_objects.rst
index 6e5317380..3a1ba7aed 100644
--- a/doc/rtd/content/99_appendices/appendix2/sub/pool/50_mlpro.gt.rst
+++ b/doc/rtd/content/99_appendices/appendix2/sub/mlpro_gt/90_pool_objects.rst
@@ -1,10 +1,10 @@
 .. _target_api_pool_gt:
-MLPro-GT - Game Theory
-======================
+Pool objects
+============
 
 .. toctree::
    :maxdepth: 3
    :glob:
    
-   mlpro_gt/*
+   pool/*
 
diff --git a/doc/rtd/content/99_appendices/appendix2/sub/core/mlpro_gt/images/MLPro-GT-Native_class_diagram.drawio b/doc/rtd/content/99_appendices/appendix2/sub/mlpro_gt/images/MLPro-GT-Native_class_diagram.drawio
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rename from doc/rtd/content/99_appendices/appendix2/sub/core/mlpro_gt/images/MLPro-GT-Native_class_diagram.drawio
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diff --git a/doc/rtd/content/99_appendices/appendix2/sub/core/mlpro_gt/images/MLPro-GT-Native_class_diagram.drawio.png b/doc/rtd/content/99_appendices/appendix2/sub/mlpro_gt/images/MLPro-GT-Native_class_diagram.drawio.png
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rename from doc/rtd/content/99_appendices/appendix2/sub/core/mlpro_gt/images/MLPro-GT-Native_class_diagram.drawio.png
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diff --git a/doc/rtd/content/99_appendices/appendix2/sub/core/mlpro_gt/images/MLPro-GT_class_diagram.drawio b/doc/rtd/content/99_appendices/appendix2/sub/mlpro_gt/images/MLPro-GT_class_diagram.drawio
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diff --git a/doc/rtd/content/99_appendices/appendix2/sub/core/mlpro_gt/images/MLPro-GT_class_diagram.drawio.png b/doc/rtd/content/99_appendices/appendix2/sub/mlpro_gt/images/MLPro-GT_class_diagram.drawio.png
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diff --git a/doc/rtd/content/99_appendices/appendix2/sub/pool/mlpro_gt/01_dg.rst b/doc/rtd/content/99_appendices/appendix2/sub/mlpro_gt/pool/01_dg.rst
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diff --git a/doc/rtd/content/99_appendices/appendix2/sub/pool/mlpro_gt/02_native.rst b/doc/rtd/content/99_appendices/appendix2/sub/mlpro_gt/pool/02_native.rst
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diff --git a/doc/rtd/content/99_appendices/appendix2/sub/pool/mlpro_gt/dg/01_games.rst b/doc/rtd/content/99_appendices/appendix2/sub/mlpro_gt/pool/dg/01_games.rst
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diff --git a/doc/rtd/content/99_appendices/appendix2/sub/pool/mlpro_gt/dg/game_boards/bglp.rst b/doc/rtd/content/99_appendices/appendix2/sub/mlpro_gt/pool/dg/game_boards/bglp.rst
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diff --git a/doc/rtd/content/99_appendices/appendix2/sub/pool/mlpro_gt/dg/game_boards/multi_cartpole.rst b/doc/rtd/content/99_appendices/appendix2/sub/mlpro_gt/pool/dg/game_boards/multi_cartpole.rst
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rename from doc/rtd/content/99_appendices/appendix2/sub/pool/mlpro_gt/dg/game_boards/multi_cartpole.rst
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diff --git a/doc/rtd/content/99_appendices/appendix2/sub/pool/mlpro_gt/native/01_games.rst b/doc/rtd/content/99_appendices/appendix2/sub/mlpro_gt/pool/native/01_games.rst
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diff --git a/doc/rtd/content/99_appendices/appendix2/sub/pool/mlpro_gt/native/02_solvers.rst b/doc/rtd/content/99_appendices/appendix2/sub/mlpro_gt/pool/native/02_solvers.rst
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diff --git a/doc/rtd/content/99_appendices/appendix2/sub/pool/mlpro_gt/native/games/prisonersdilemma_2p.rst b/doc/rtd/content/99_appendices/appendix2/sub/mlpro_gt/pool/native/games/prisonersdilemma_2p.rst
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diff --git a/doc/rtd/content/99_appendices/appendix2/sub/pool/mlpro_gt/native/games/prisonersdilemma_3p.rst b/doc/rtd/content/99_appendices/appendix2/sub/mlpro_gt/pool/native/games/prisonersdilemma_3p.rst
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rename to doc/rtd/content/99_appendices/appendix2/sub/mlpro_gt/pool/native/games/prisonersdilemma_3p.rst
diff --git a/doc/rtd/content/99_appendices/appendix2/sub/pool/mlpro_gt/native/games/rockpaperscissors.rst b/doc/rtd/content/99_appendices/appendix2/sub/mlpro_gt/pool/native/games/rockpaperscissors.rst
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diff --git a/doc/rtd/content/99_appendices/appendix2/sub/pool/mlpro_gt/native/games/routingproblems_3p.rst b/doc/rtd/content/99_appendices/appendix2/sub/mlpro_gt/pool/native/games/routingproblems_3p.rst
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rename from doc/rtd/content/99_appendices/appendix2/sub/pool/mlpro_gt/native/games/routingproblems_3p.rst
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diff --git a/doc/rtd/content/99_appendices/appendix2/sub/pool/mlpro_gt/native/games/supplydemand_3p.rst b/doc/rtd/content/99_appendices/appendix2/sub/mlpro_gt/pool/native/games/supplydemand_3p.rst
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rename from doc/rtd/content/99_appendices/appendix2/sub/pool/mlpro_gt/native/games/supplydemand_3p.rst
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diff --git a/doc/rtd/content/99_appendices/appendix2/sub/pool/mlpro_gt/native/solvers/greedypolicy.rst b/doc/rtd/content/99_appendices/appendix2/sub/mlpro_gt/pool/native/solvers/greedypolicy.rst
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rename from doc/rtd/content/99_appendices/appendix2/sub/pool/mlpro_gt/native/solvers/greedypolicy.rst
rename to doc/rtd/content/99_appendices/appendix2/sub/mlpro_gt/pool/native/solvers/greedypolicy.rst
diff --git a/doc/rtd/content/99_appendices/appendix2/sub/pool/mlpro_gt/native/solvers/randomsolver.rst b/doc/rtd/content/99_appendices/appendix2/sub/mlpro_gt/pool/native/solvers/randomsolver.rst
similarity index 100%
rename from doc/rtd/content/99_appendices/appendix2/sub/pool/mlpro_gt/native/solvers/randomsolver.rst
rename to doc/rtd/content/99_appendices/appendix2/sub/mlpro_gt/pool/native/solvers/randomsolver.rst
diff --git a/doc/rtd/content/99_appendices/appendix2/sub/mlpro_oa/01_streams.rst b/doc/rtd/content/99_appendices/appendix2/sub/mlpro_oa/01_streams.rst
new file mode 100644
index 000000000..334dc24f5
--- /dev/null
+++ b/doc/rtd/content/99_appendices/appendix2/sub/mlpro_oa/01_streams.rst
@@ -0,0 +1,14 @@
+.. _target_api_oa_streams:
+Online-adaptive data stream processing
+======================================
+
+.. toctree::
+   :maxdepth: 2
+   :glob:
+   
+   streams/00_basics/*
+   streams/10_preprocessing/*
+   streams/20_cluster_analysis/*
+   streams/30_anomaly_detection/*
+   streams/31_anomaly_prediction/*
+   streams/40_drift_detection/*
\ No newline at end of file
diff --git a/doc/rtd/content/99_appendices/appendix2/sub/mlpro_oa/02_systems.rst.off b/doc/rtd/content/99_appendices/appendix2/sub/mlpro_oa/02_systems.rst.off
new file mode 100644
index 000000000..53516997f
--- /dev/null
+++ b/doc/rtd/content/99_appendices/appendix2/sub/mlpro_oa/02_systems.rst.off
@@ -0,0 +1,9 @@
+.. _target_api_oa_systems:
+Online-adaptive state-based systems
+===================================
+
+.. toctree::
+   :maxdepth: 2
+   :glob:
+   
+   systems/*
\ No newline at end of file
diff --git a/doc/rtd/content/99_appendices/appendix2/sub/mlpro_oa/streams/00_basics/01_basics.rst b/doc/rtd/content/99_appendices/appendix2/sub/mlpro_oa/streams/00_basics/01_basics.rst
new file mode 100644
index 000000000..a64ffdce5
--- /dev/null
+++ b/doc/rtd/content/99_appendices/appendix2/sub/mlpro_oa/streams/00_basics/01_basics.rst
@@ -0,0 +1,12 @@
+.. _target_api_oa_streams_basics:
+Basics
+======
+
+.. image:: images/MLPro-OA-Stream-Processing_class_diagram.drawio.png
+   :scale: 50%
+   
+.. automodule:: mlpro.oa.streams.basics
+   :members:
+   :undoc-members:
+   :private-members:
+   :show-inheritance:
\ No newline at end of file
diff --git a/doc/rtd/content/99_appendices/appendix2/sub/core/mlpro_oa/streams/images/MLPro-OA-Stream-Processing_class_diagram.drawio b/doc/rtd/content/99_appendices/appendix2/sub/mlpro_oa/streams/00_basics/images/MLPro-OA-Stream-Processing_class_diagram.drawio
similarity index 100%
rename from doc/rtd/content/99_appendices/appendix2/sub/core/mlpro_oa/streams/images/MLPro-OA-Stream-Processing_class_diagram.drawio
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diff --git a/doc/rtd/content/99_appendices/appendix2/sub/core/mlpro_oa/streams/images/MLPro-OA-Stream-Processing_class_diagram.drawio.png b/doc/rtd/content/99_appendices/appendix2/sub/mlpro_oa/streams/00_basics/images/MLPro-OA-Stream-Processing_class_diagram.drawio.png
similarity index 100%
rename from doc/rtd/content/99_appendices/appendix2/sub/core/mlpro_oa/streams/images/MLPro-OA-Stream-Processing_class_diagram.drawio.png
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diff --git a/doc/rtd/content/99_appendices/appendix2/sub/pool/mlpro_oa/streams/tasks/01_preprocessing_tasks.rst b/doc/rtd/content/99_appendices/appendix2/sub/mlpro_oa/streams/10_preprocessing/10_preprocessing_tasks.rst
similarity index 90%
rename from doc/rtd/content/99_appendices/appendix2/sub/pool/mlpro_oa/streams/tasks/01_preprocessing_tasks.rst
rename to doc/rtd/content/99_appendices/appendix2/sub/mlpro_oa/streams/10_preprocessing/10_preprocessing_tasks.rst
index 959b5f3c1..8638031f1 100644
--- a/doc/rtd/content/99_appendices/appendix2/sub/pool/mlpro_oa/streams/tasks/01_preprocessing_tasks.rst
+++ b/doc/rtd/content/99_appendices/appendix2/sub/mlpro_oa/streams/10_preprocessing/10_preprocessing_tasks.rst
@@ -1,12 +1,12 @@
 .. _target_api_oa_stream_tasks_prepro:
-OA Data Preprocessing
-=====================
+Preprocessing
+=============
 
 .. image:: images/MLPro-OA-Preprocessing-Tasks_class_diagram.drawio.png
    :scale: 50%
    
 
-Boundary Detector
+Boundary detector
 -----------------
 
 .. automodule:: mlpro.oa.streams.tasks.boundarydetectors
diff --git a/doc/rtd/content/99_appendices/appendix2/sub/pool/mlpro_oa/streams/tasks/images/MLPro-OA-Preprocessing-Tasks_class_diagram.drawio b/doc/rtd/content/99_appendices/appendix2/sub/mlpro_oa/streams/10_preprocessing/images/MLPro-OA-Preprocessing-Tasks_class_diagram.drawio
similarity index 100%
rename from doc/rtd/content/99_appendices/appendix2/sub/pool/mlpro_oa/streams/tasks/images/MLPro-OA-Preprocessing-Tasks_class_diagram.drawio
rename to doc/rtd/content/99_appendices/appendix2/sub/mlpro_oa/streams/10_preprocessing/images/MLPro-OA-Preprocessing-Tasks_class_diagram.drawio
diff --git a/doc/rtd/content/99_appendices/appendix2/sub/pool/mlpro_oa/streams/tasks/images/MLPro-OA-Preprocessing-Tasks_class_diagram.drawio.png b/doc/rtd/content/99_appendices/appendix2/sub/mlpro_oa/streams/10_preprocessing/images/MLPro-OA-Preprocessing-Tasks_class_diagram.drawio.png
similarity index 100%
rename from doc/rtd/content/99_appendices/appendix2/sub/pool/mlpro_oa/streams/tasks/images/MLPro-OA-Preprocessing-Tasks_class_diagram.drawio.png
rename to doc/rtd/content/99_appendices/appendix2/sub/mlpro_oa/streams/10_preprocessing/images/MLPro-OA-Preprocessing-Tasks_class_diagram.drawio.png
diff --git a/doc/rtd/content/99_appendices/appendix2/sub/pool/mlpro_oa/streams/tasks/02_cluster_analyzers.rst b/doc/rtd/content/99_appendices/appendix2/sub/mlpro_oa/streams/20_cluster_analysis/10_cluster_analysis.rst
similarity index 93%
rename from doc/rtd/content/99_appendices/appendix2/sub/pool/mlpro_oa/streams/tasks/02_cluster_analyzers.rst
rename to doc/rtd/content/99_appendices/appendix2/sub/mlpro_oa/streams/20_cluster_analysis/10_cluster_analysis.rst
index 13c65ec5e..7a5a73b58 100644
--- a/doc/rtd/content/99_appendices/appendix2/sub/pool/mlpro_oa/streams/tasks/02_cluster_analyzers.rst
+++ b/doc/rtd/content/99_appendices/appendix2/sub/mlpro_oa/streams/20_cluster_analysis/10_cluster_analysis.rst
@@ -1,12 +1,12 @@
 .. _target_api_oa_stream_tasks_clu:
-Cluster Analysis
+Cluster analyzer
 ================
 
 .. image:: images/MLPro-OA-Cluster_Analyzers_class_diagram.drawio.png
    :scale: 50%
    
    
-Template for Cluster Algorithms
+Template for cluster algorithms
 -------------------------------
 
 .. automodule:: mlpro.oa.streams.tasks.clusteranalyzers.basics
diff --git a/doc/rtd/content/99_appendices/appendix2/sub/pool/mlpro_oa/streams/tasks/images/MLPro-OA-Cluster_Analyzers_class_diagram.drawio b/doc/rtd/content/99_appendices/appendix2/sub/mlpro_oa/streams/20_cluster_analysis/images/MLPro-OA-Cluster_Analyzers_class_diagram.drawio
similarity index 100%
rename from doc/rtd/content/99_appendices/appendix2/sub/pool/mlpro_oa/streams/tasks/images/MLPro-OA-Cluster_Analyzers_class_diagram.drawio
rename to doc/rtd/content/99_appendices/appendix2/sub/mlpro_oa/streams/20_cluster_analysis/images/MLPro-OA-Cluster_Analyzers_class_diagram.drawio
diff --git a/doc/rtd/content/99_appendices/appendix2/sub/pool/mlpro_oa/streams/tasks/images/MLPro-OA-Cluster_Analyzers_class_diagram.drawio.png b/doc/rtd/content/99_appendices/appendix2/sub/mlpro_oa/streams/20_cluster_analysis/images/MLPro-OA-Cluster_Analyzers_class_diagram.drawio.png
similarity index 100%
rename from doc/rtd/content/99_appendices/appendix2/sub/pool/mlpro_oa/streams/tasks/images/MLPro-OA-Cluster_Analyzers_class_diagram.drawio.png
rename to doc/rtd/content/99_appendices/appendix2/sub/mlpro_oa/streams/20_cluster_analysis/images/MLPro-OA-Cluster_Analyzers_class_diagram.drawio.png
diff --git a/doc/rtd/content/99_appendices/appendix2/sub/pool/mlpro_oa/streams/tasks/anomaly_detectors/10_common_anomalies.rst b/doc/rtd/content/99_appendices/appendix2/sub/mlpro_oa/streams/30_anomaly_detection/10_common_anomalies.rst
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rename from doc/rtd/content/99_appendices/appendix2/sub/pool/mlpro_oa/streams/tasks/anomaly_detectors/10_common_anomalies.rst
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diff --git a/doc/rtd/content/99_appendices/appendix2/sub/pool/mlpro_oa/streams/tasks/anomaly_detectors/20_common_algorithms.rst b/doc/rtd/content/99_appendices/appendix2/sub/mlpro_oa/streams/30_anomaly_detection/20_common_algorithms.rst
similarity index 100%
rename from doc/rtd/content/99_appendices/appendix2/sub/pool/mlpro_oa/streams/tasks/anomaly_detectors/20_common_algorithms.rst
rename to doc/rtd/content/99_appendices/appendix2/sub/mlpro_oa/streams/30_anomaly_detection/20_common_algorithms.rst
diff --git a/doc/rtd/content/99_appendices/appendix2/sub/pool/mlpro_oa/streams/tasks/03_anomaly_detectors.rst b/doc/rtd/content/99_appendices/appendix2/sub/mlpro_oa/streams/30_anomaly_detection/30_anomaly_detectors.rst
similarity index 85%
rename from doc/rtd/content/99_appendices/appendix2/sub/pool/mlpro_oa/streams/tasks/03_anomaly_detectors.rst
rename to doc/rtd/content/99_appendices/appendix2/sub/mlpro_oa/streams/30_anomaly_detection/30_anomaly_detectors.rst
index bd4d77d36..72e54e2ef 100644
--- a/doc/rtd/content/99_appendices/appendix2/sub/pool/mlpro_oa/streams/tasks/03_anomaly_detectors.rst
+++ b/doc/rtd/content/99_appendices/appendix2/sub/mlpro_oa/streams/30_anomaly_detection/30_anomaly_detectors.rst
@@ -1,8 +1,8 @@
 .. _target_api_oa_stream_tasks_ad:
-Anomaly Detectors
+Anomaly detection
 =================
 
-.. image:: anomaly_detectors/images/MLPro-OA-Anomaly-Detectors_class_diagram.drawio.png
+.. image:: images/MLPro-OA-Anomaly-Detectors_class_diagram.drawio.png
    :scale: 50%
    
    
diff --git a/doc/rtd/content/99_appendices/appendix2/sub/pool/mlpro_oa/streams/tasks/anomaly_detectors/30_cluster_based.rst b/doc/rtd/content/99_appendices/appendix2/sub/mlpro_oa/streams/30_anomaly_detection/30_cluster_based.rst
similarity index 100%
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diff --git a/doc/rtd/content/99_appendices/appendix2/sub/mlpro_oa/streams/30_anomaly_detection/images/MLPro-OA-Anomaly-Detectors_class_diagram.drawio b/doc/rtd/content/99_appendices/appendix2/sub/mlpro_oa/streams/30_anomaly_detection/images/MLPro-OA-Anomaly-Detectors_class_diagram.drawio
new file mode 100644
index 000000000..c01e69720
--- /dev/null
+++ b/doc/rtd/content/99_appendices/appendix2/sub/mlpro_oa/streams/30_anomaly_detection/images/MLPro-OA-Anomaly-Detectors_class_diagram.drawio
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new file mode 100644
index 000000000..564cb3af5
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diff --git a/doc/rtd/content/99_appendices/appendix2/sub/mlpro_oa/streams/30_anomaly_detection/instance_based/images/MLPro-OA-Anomaly-Detectors-IB_class_diagram.drawio b/doc/rtd/content/99_appendices/appendix2/sub/mlpro_oa/streams/30_anomaly_detection/instance_based/images/MLPro-OA-Anomaly-Detectors-IB_class_diagram.drawio
new file mode 100644
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+        <mxCell id="0cJ1NAXk0Xtcf3CmwPfC-8" value="Extended template&amp;nbsp;&lt;div&gt;for anomaly detectors. Auto-detects and raises groups of point anomalies as group anomalies&lt;/div&gt;" style="rounded=0;whiteSpace=wrap;html=1;fillColor=#fff2cc;strokeColor=#d6b656;shadow=1;align=left;fontStyle=1" parent="WIyWlLk6GJQsqaUBKTNV-1" vertex="1">
+          <mxGeometry x="50" y="1160" width="130" height="101" as="geometry" />
+        </mxCell>
+        <mxCell id="7JkVl93vQoOUYRVFK4Rs-10" style="edgeStyle=orthogonalEdgeStyle;rounded=0;orthogonalLoop=1;jettySize=auto;html=1;endArrow=block;endFill=0;" edge="1" parent="WIyWlLk6GJQsqaUBKTNV-1" source="7JkVl93vQoOUYRVFK4Rs-1" target="4use4ZCXdNiZSNTW99xv-16">
+          <mxGeometry relative="1" as="geometry" />
+        </mxCell>
+        <mxCell id="7JkVl93vQoOUYRVFK4Rs-1" value="IBAnomaly (Anomaly)" style="swimlane;fontStyle=1;align=center;verticalAlign=top;childLayout=stackLayout;horizontal=1;startSize=26;horizontalStack=0;resizeParent=1;resizeLast=0;collapsible=1;marginBottom=0;rounded=0;shadow=1;strokeWidth=1;fillColor=#f8cecc;strokeColor=#b85450;swimlaneFillColor=#ffffff;fontFamily=Helvetica;fontSize=12;gradientColor=#F5F5F5;" vertex="1" parent="WIyWlLk6GJQsqaUBKTNV-1">
+          <mxGeometry x="620" y="741" width="290" height="380" as="geometry">
+            <mxRectangle x="230" y="140" width="160" height="26" as="alternateBounds" />
+          </mxGeometry>
+        </mxCell>
+        <mxCell id="K-uiR6WVb6SvZu_kqw6H-3" value="instances : list[Instance]" style="text;align=left;verticalAlign=top;spacingLeft=4;spacingRight=4;overflow=hidden;rotatable=0;points=[[0,0.5],[1,0.5]];portConstraint=eastwest;fontStyle=1" parent="7JkVl93vQoOUYRVFK4Rs-1" vertex="1">
+          <mxGeometry y="26" width="290" height="26" as="geometry" />
+        </mxCell>
+        <mxCell id="7JkVl93vQoOUYRVFK4Rs-2" value="" style="line;html=1;strokeWidth=1;align=center;verticalAlign=top;spacingTop=-1;spacingLeft=3;spacingRight=3;rotatable=0;labelPosition=right;points=[];portConstraint=eastwest;rounded=0;shadow=1;strokeColor=#6c8ebf;swimlaneFillColor=#ffffff;fontFamily=Helvetica;fontSize=12;fontColor=default;fontStyle=1;fillColor=#dae8fc;gradientColor=#ffffff;" vertex="1" parent="7JkVl93vQoOUYRVFK4Rs-1">
+          <mxGeometry y="52" width="290" height="8" as="geometry" />
+        </mxCell>
+        <mxCell id="7JkVl93vQoOUYRVFK4Rs-3" value="__init__( p_instances : list[Instance],&#xa;               p_visualize : bool = False,&#xa;               p_raising_object : object = None,&#xa;               p_det_time : str = None,&#xa;               p_deviation : float=None,&#xa;               **p_kwargs)" style="text;align=left;verticalAlign=top;spacingLeft=4;spacingRight=4;overflow=hidden;rotatable=0;points=[[0,0.5],[1,0.5]];portConstraint=eastwest;fontStyle=1" vertex="1" parent="7JkVl93vQoOUYRVFK4Rs-1">
+          <mxGeometry y="60" width="290" height="100" as="geometry" />
+        </mxCell>
+        <mxCell id="7JkVl93vQoOUYRVFK4Rs-4" value="_init_plot_2d( p_figure: Figure,&#xa;                       p_settings: PlotSettings,&#xa;                        **p_kwargs)" style="text;align=left;verticalAlign=top;spacingLeft=4;spacingRight=4;overflow=hidden;rotatable=0;points=[[0,0.5],[1,0.5]];portConstraint=eastwest;" vertex="1" parent="7JkVl93vQoOUYRVFK4Rs-1">
+          <mxGeometry y="160" width="290" height="50" as="geometry" />
+        </mxCell>
+        <mxCell id="7JkVl93vQoOUYRVFK4Rs-5" value="_init_plot_3d( p_figure: Figure,&#xa;                       p_settings: PlotSettings,&#xa;                        **p_kwargs)" style="text;align=left;verticalAlign=top;spacingLeft=4;spacingRight=4;overflow=hidden;rotatable=0;points=[[0,0.5],[1,0.5]];portConstraint=eastwest;" vertex="1" parent="7JkVl93vQoOUYRVFK4Rs-1">
+          <mxGeometry y="210" width="290" height="50" as="geometry" />
+        </mxCell>
+        <mxCell id="7JkVl93vQoOUYRVFK4Rs-6" value="_init_plot_nd( p_figure: Figure,&#xa;                       p_settings: PlotSettings,&#xa;                        **p_kwargs)" style="text;align=left;verticalAlign=top;spacingLeft=4;spacingRight=4;overflow=hidden;rotatable=0;points=[[0,0.5],[1,0.5]];portConstraint=eastwest;" vertex="1" parent="7JkVl93vQoOUYRVFK4Rs-1">
+          <mxGeometry y="260" width="290" height="50" as="geometry" />
+        </mxCell>
+        <mxCell id="7JkVl93vQoOUYRVFK4Rs-7" value="_remove_plot_2d()" style="text;align=left;verticalAlign=top;spacingLeft=4;spacingRight=4;overflow=hidden;rotatable=0;points=[[0,0.5],[1,0.5]];portConstraint=eastwest;" vertex="1" parent="7JkVl93vQoOUYRVFK4Rs-1">
+          <mxGeometry y="310" width="290" height="20" as="geometry" />
+        </mxCell>
+        <mxCell id="7JkVl93vQoOUYRVFK4Rs-8" value="_remove_plot_3d()" style="text;align=left;verticalAlign=top;spacingLeft=4;spacingRight=4;overflow=hidden;rotatable=0;points=[[0,0.5],[1,0.5]];portConstraint=eastwest;" vertex="1" parent="7JkVl93vQoOUYRVFK4Rs-1">
+          <mxGeometry y="330" width="290" height="20" as="geometry" />
+        </mxCell>
+        <mxCell id="7JkVl93vQoOUYRVFK4Rs-9" value="_remove_plot_nd()" style="text;align=left;verticalAlign=top;spacingLeft=4;spacingRight=4;overflow=hidden;rotatable=0;points=[[0,0.5],[1,0.5]];portConstraint=eastwest;" vertex="1" parent="7JkVl93vQoOUYRVFK4Rs-1">
+          <mxGeometry y="350" width="290" height="20" as="geometry" />
+        </mxCell>
+        <mxCell id="7JkVl93vQoOUYRVFK4Rs-11" value="Sub-type for instance-based anomalies" style="rounded=0;whiteSpace=wrap;html=1;fillColor=#fff2cc;strokeColor=#d6b656;shadow=1;align=left;fontStyle=1;verticalAlign=top;" vertex="1" parent="WIyWlLk6GJQsqaUBKTNV-1">
+          <mxGeometry x="890" y="700" width="100" height="51" as="geometry" />
+        </mxCell>
+        <mxCell id="7JkVl93vQoOUYRVFK4Rs-26" style="edgeStyle=orthogonalEdgeStyle;rounded=0;orthogonalLoop=1;jettySize=auto;html=1;endArrow=block;endFill=0;" edge="1" parent="WIyWlLk6GJQsqaUBKTNV-1" source="7JkVl93vQoOUYRVFK4Rs-12" target="4use4ZCXdNiZSNTW99xv-16">
+          <mxGeometry relative="1" as="geometry">
+            <Array as="points">
+              <mxPoint x="1175" y="680" />
+              <mxPoint x="765" y="680" />
+            </Array>
+          </mxGeometry>
+        </mxCell>
+        <mxCell id="7JkVl93vQoOUYRVFK4Rs-12" value="CBAnomaly (Anomaly)" style="swimlane;fontStyle=1;align=center;verticalAlign=top;childLayout=stackLayout;horizontal=1;startSize=26;horizontalStack=0;resizeParent=1;resizeLast=0;collapsible=1;marginBottom=0;rounded=0;shadow=1;strokeWidth=1;fillColor=#f8cecc;strokeColor=#b85450;swimlaneFillColor=#ffffff;fontFamily=Helvetica;fontSize=12;gradientColor=#F5F5F5;" vertex="1" parent="WIyWlLk6GJQsqaUBKTNV-1">
+          <mxGeometry x="1030" y="741" width="290" height="380" as="geometry">
+            <mxRectangle x="230" y="140" width="160" height="26" as="alternateBounds" />
+          </mxGeometry>
+        </mxCell>
+        <mxCell id="7JkVl93vQoOUYRVFK4Rs-14" value="" style="line;html=1;strokeWidth=1;align=center;verticalAlign=top;spacingTop=-1;spacingLeft=3;spacingRight=3;rotatable=0;labelPosition=right;points=[];portConstraint=eastwest;rounded=0;shadow=1;strokeColor=#6c8ebf;swimlaneFillColor=#ffffff;fontFamily=Helvetica;fontSize=12;fontColor=default;fontStyle=1;fillColor=#dae8fc;gradientColor=#ffffff;" vertex="1" parent="7JkVl93vQoOUYRVFK4Rs-12">
+          <mxGeometry y="26" width="290" height="8" as="geometry" />
+        </mxCell>
+        <mxCell id="7JkVl93vQoOUYRVFK4Rs-15" value="__init__( p_instances : list[Instance],&#xa;               p_visualize : bool = False,&#xa;               p_raising_object : object = None,&#xa;               p_det_time : str = None,&#xa;               p_deviation : float=None,&#xa;               **p_kwargs)" style="text;align=left;verticalAlign=top;spacingLeft=4;spacingRight=4;overflow=hidden;rotatable=0;points=[[0,0.5],[1,0.5]];portConstraint=eastwest;fontStyle=1" vertex="1" parent="7JkVl93vQoOUYRVFK4Rs-12">
+          <mxGeometry y="34" width="290" height="100" as="geometry" />
+        </mxCell>
+        <mxCell id="7JkVl93vQoOUYRVFK4Rs-16" value="_init_plot_2d( p_figure: Figure,&#xa;                       p_settings: PlotSettings,&#xa;                        **p_kwargs)" style="text;align=left;verticalAlign=top;spacingLeft=4;spacingRight=4;overflow=hidden;rotatable=0;points=[[0,0.5],[1,0.5]];portConstraint=eastwest;" vertex="1" parent="7JkVl93vQoOUYRVFK4Rs-12">
+          <mxGeometry y="134" width="290" height="50" as="geometry" />
+        </mxCell>
+        <mxCell id="7JkVl93vQoOUYRVFK4Rs-17" value="_init_plot_3d( p_figure: Figure,&#xa;                       p_settings: PlotSettings,&#xa;                        **p_kwargs)" style="text;align=left;verticalAlign=top;spacingLeft=4;spacingRight=4;overflow=hidden;rotatable=0;points=[[0,0.5],[1,0.5]];portConstraint=eastwest;" vertex="1" parent="7JkVl93vQoOUYRVFK4Rs-12">
+          <mxGeometry y="184" width="290" height="50" as="geometry" />
+        </mxCell>
+        <mxCell id="7JkVl93vQoOUYRVFK4Rs-18" value="_init_plot_nd( p_figure: Figure,&#xa;                       p_settings: PlotSettings,&#xa;                        **p_kwargs)" style="text;align=left;verticalAlign=top;spacingLeft=4;spacingRight=4;overflow=hidden;rotatable=0;points=[[0,0.5],[1,0.5]];portConstraint=eastwest;" vertex="1" parent="7JkVl93vQoOUYRVFK4Rs-12">
+          <mxGeometry y="234" width="290" height="50" as="geometry" />
+        </mxCell>
+        <mxCell id="7JkVl93vQoOUYRVFK4Rs-19" value="_remove_plot_2d()" style="text;align=left;verticalAlign=top;spacingLeft=4;spacingRight=4;overflow=hidden;rotatable=0;points=[[0,0.5],[1,0.5]];portConstraint=eastwest;" vertex="1" parent="7JkVl93vQoOUYRVFK4Rs-12">
+          <mxGeometry y="284" width="290" height="20" as="geometry" />
+        </mxCell>
+        <mxCell id="7JkVl93vQoOUYRVFK4Rs-20" value="_remove_plot_3d()" style="text;align=left;verticalAlign=top;spacingLeft=4;spacingRight=4;overflow=hidden;rotatable=0;points=[[0,0.5],[1,0.5]];portConstraint=eastwest;" vertex="1" parent="7JkVl93vQoOUYRVFK4Rs-12">
+          <mxGeometry y="304" width="290" height="20" as="geometry" />
+        </mxCell>
+        <mxCell id="7JkVl93vQoOUYRVFK4Rs-21" value="_remove_plot_nd()" style="text;align=left;verticalAlign=top;spacingLeft=4;spacingRight=4;overflow=hidden;rotatable=0;points=[[0,0.5],[1,0.5]];portConstraint=eastwest;" vertex="1" parent="7JkVl93vQoOUYRVFK4Rs-12">
+          <mxGeometry y="324" width="290" height="20" as="geometry" />
+        </mxCell>
+        <mxCell id="7JkVl93vQoOUYRVFK4Rs-22" value="Sub-type for cluster-based anomalies" style="rounded=0;whiteSpace=wrap;html=1;fillColor=#fff2cc;strokeColor=#d6b656;shadow=1;align=left;fontStyle=1;verticalAlign=top;" vertex="1" parent="WIyWlLk6GJQsqaUBKTNV-1">
+          <mxGeometry x="1300" y="700" width="100" height="51" as="geometry" />
+        </mxCell>
+      </root>
+    </mxGraphModel>
+  </diagram>
+  <diagram id="lryuDQluacSowWsRYUra" name="Page-2">
+    <mxGraphModel dx="1098" dy="828" grid="1" gridSize="10" guides="1" tooltips="1" connect="1" arrows="1" fold="1" page="1" pageScale="1" pageWidth="850" pageHeight="1100" math="0" shadow="0">
+      <root>
+        <mxCell id="0" />
+        <mxCell id="1" parent="0" />
+      </root>
+    </mxGraphModel>
+  </diagram>
+</mxfile>
diff --git a/doc/rtd/content/99_appendices/appendix2/sub/pool/mlpro_oa/streams/tasks/04_anomaly_predictors.rst b/doc/rtd/content/99_appendices/appendix2/sub/mlpro_oa/streams/31_anomaly_prediction/10_anomaly_predictors.rst.off
similarity index 78%
rename from doc/rtd/content/99_appendices/appendix2/sub/pool/mlpro_oa/streams/tasks/04_anomaly_predictors.rst
rename to doc/rtd/content/99_appendices/appendix2/sub/mlpro_oa/streams/31_anomaly_prediction/10_anomaly_predictors.rst.off
index d4c847a1a..0ce038b8b 100644
--- a/doc/rtd/content/99_appendices/appendix2/sub/pool/mlpro_oa/streams/tasks/04_anomaly_predictors.rst
+++ b/doc/rtd/content/99_appendices/appendix2/sub/mlpro_oa/streams/31_anomaly_prediction/10_anomaly_predictors.rst.off
@@ -1,5 +1,5 @@
 .. _target_api_oa_stream_tasks_ap:
-Anomaly Predictors
+Anomaly predictors
 ==================
 
 In progress...
diff --git a/doc/rtd/content/99_appendices/appendix2/sub/pool/mlpro_oa/streams/tasks/anomaly_predictors/images/MLPro-OA_Anomaly_Predictors_class_diagram.drawio b/doc/rtd/content/99_appendices/appendix2/sub/mlpro_oa/streams/31_anomaly_prediction/images/MLPro-OA_Anomaly_Predictors_class_diagram.drawio
similarity index 100%
rename from doc/rtd/content/99_appendices/appendix2/sub/pool/mlpro_oa/streams/tasks/anomaly_predictors/images/MLPro-OA_Anomaly_Predictors_class_diagram.drawio
rename to doc/rtd/content/99_appendices/appendix2/sub/mlpro_oa/streams/31_anomaly_prediction/images/MLPro-OA_Anomaly_Predictors_class_diagram.drawio
diff --git a/doc/rtd/content/99_appendices/appendix2/sub/pool/mlpro_oa/streams/tasks/anomaly_predictors/images/MLPro-OA_Anomaly_Predictors_class_diagram.drawio.png b/doc/rtd/content/99_appendices/appendix2/sub/mlpro_oa/streams/31_anomaly_prediction/images/MLPro-OA_Anomaly_Predictors_class_diagram.drawio.png
similarity index 100%
rename from doc/rtd/content/99_appendices/appendix2/sub/pool/mlpro_oa/streams/tasks/anomaly_predictors/images/MLPro-OA_Anomaly_Predictors_class_diagram.drawio.png
rename to doc/rtd/content/99_appendices/appendix2/sub/mlpro_oa/streams/31_anomaly_prediction/images/MLPro-OA_Anomaly_Predictors_class_diagram.drawio.png
diff --git a/doc/rtd/content/99_appendices/appendix2/sub/mlpro_oa/streams/40_drift_detection/10_common_drifts.rst b/doc/rtd/content/99_appendices/appendix2/sub/mlpro_oa/streams/40_drift_detection/10_common_drifts.rst
new file mode 100644
index 000000000..dcb3085f4
--- /dev/null
+++ b/doc/rtd/content/99_appendices/appendix2/sub/mlpro_oa/streams/40_drift_detection/10_common_drifts.rst
@@ -0,0 +1,10 @@
+.. _target_api_oa_stream_tasks_ad_common_drifts:
+Common drifts
+=============
+  
+
+.. toctree::
+   :maxdepth: 2
+   :glob:
+   
+   common_drifts/*
\ No newline at end of file
diff --git a/doc/rtd/content/99_appendices/appendix2/sub/mlpro_oa/streams/40_drift_detection/20_common_algorithms.rst b/doc/rtd/content/99_appendices/appendix2/sub/mlpro_oa/streams/40_drift_detection/20_common_algorithms.rst
new file mode 100644
index 000000000..ae5e1efb6
--- /dev/null
+++ b/doc/rtd/content/99_appendices/appendix2/sub/mlpro_oa/streams/40_drift_detection/20_common_algorithms.rst
@@ -0,0 +1,10 @@
+.. _target_api_oa_stream_tasks_ad_common_algorithms:
+Common algorithms
+=================
+  
+
+.. toctree::
+   :maxdepth: 2
+   :glob:
+   
+   common_algorithms/*
\ No newline at end of file
diff --git a/doc/rtd/content/99_appendices/appendix2/sub/mlpro_oa/streams/40_drift_detection/40_drift_detectors.rst b/doc/rtd/content/99_appendices/appendix2/sub/mlpro_oa/streams/40_drift_detection/40_drift_detectors.rst
new file mode 100644
index 000000000..227bbaec4
--- /dev/null
+++ b/doc/rtd/content/99_appendices/appendix2/sub/mlpro_oa/streams/40_drift_detection/40_drift_detectors.rst
@@ -0,0 +1,36 @@
+.. _target_api_oa_stream_tasks_dd:
+Drift detectors
+===============
+
+.. image:: drift_detectors/images/MLPro-OA-Drift-Detectors_class_diagram.drawio.png
+   :scale: 50%
+   
+   
+Template for drift detectors
+----------------------------
+
+.. automodule:: mlpro.oa.streams.tasks.driftdetectors.basics
+   :members:
+   :undoc-members:
+   :private-members:
+   :show-inheritance:
+
+
+Template for drift events
+-------------------------
+
+.. automodule:: mlpro.oa.streams.tasks.driftdetectors.drift.basics
+   :members:
+   :undoc-members:
+   :private-members:
+   :show-inheritance:
+
+
+Concrete drift events and algorithms
+------------------------------------
+
+.. toctree::
+   :maxdepth: 2
+   :glob:
+   
+   drift_detectors/*
diff --git a/doc/rtd/content/99_appendices/appendix2/sub/mlpro_oa/streams/40_drift_detection/cluster_based/30_cluster_based.rst b/doc/rtd/content/99_appendices/appendix2/sub/mlpro_oa/streams/40_drift_detection/cluster_based/30_cluster_based.rst
new file mode 100644
index 000000000..30d1b7a43
--- /dev/null
+++ b/doc/rtd/content/99_appendices/appendix2/sub/mlpro_oa/streams/40_drift_detection/cluster_based/30_cluster_based.rst
@@ -0,0 +1,14 @@
+.. _target_api_oa_stream_tasks_ad_cb:
+Cluster-based drifts and detectors
+=====================================
+
+
+.. image:: cluster_based/images/MLPro-OA-CB-Drift-Detectors_class_diagram.drawio.png
+   :scale: 50%
+
+
+.. toctree::
+   :maxdepth: 2
+   :glob:
+   
+   cluster_based/*
\ No newline at end of file
diff --git a/doc/rtd/content/99_appendices/appendix2/sub/mlpro_oa/streams/40_drift_detection/cluster_based/algorithms/.gitkeep b/doc/rtd/content/99_appendices/appendix2/sub/mlpro_oa/streams/40_drift_detection/cluster_based/algorithms/.gitkeep
new file mode 100644
index 000000000..8d1c8b69c
--- /dev/null
+++ b/doc/rtd/content/99_appendices/appendix2/sub/mlpro_oa/streams/40_drift_detection/cluster_based/algorithms/.gitkeep
@@ -0,0 +1 @@
+ 
diff --git a/doc/rtd/content/99_appendices/appendix2/sub/mlpro_oa/streams/40_drift_detection/cluster_based/anomalies/.gitkeep b/doc/rtd/content/99_appendices/appendix2/sub/mlpro_oa/streams/40_drift_detection/cluster_based/anomalies/.gitkeep
new file mode 100644
index 000000000..8d1c8b69c
--- /dev/null
+++ b/doc/rtd/content/99_appendices/appendix2/sub/mlpro_oa/streams/40_drift_detection/cluster_based/anomalies/.gitkeep
@@ -0,0 +1 @@
+ 
diff --git a/doc/rtd/content/99_appendices/appendix2/sub/mlpro_oa/streams/40_drift_detection/cluster_based/images/.$MLPro-OA-CB-Drift-Detectors_class_diagram.drawio.bkp b/doc/rtd/content/99_appendices/appendix2/sub/mlpro_oa/streams/40_drift_detection/cluster_based/images/.$MLPro-OA-CB-Drift-Detectors_class_diagram.drawio.bkp
new file mode 100644
index 000000000..3e94fcfa5
--- /dev/null
+++ b/doc/rtd/content/99_appendices/appendix2/sub/mlpro_oa/streams/40_drift_detection/cluster_based/images/.$MLPro-OA-CB-Drift-Detectors_class_diagram.drawio.bkp
@@ -0,0 +1,147 @@
+<mxfile host="Electron" agent="Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) draw.io/26.0.9 Chrome/128.0.6613.186 Electron/32.2.5 Safari/537.36" version="26.0.9" pages="2">
+  <diagram id="C5RBs43oDa-KdzZeNtuy" name="Page-1">
+    <mxGraphModel dx="2403" dy="1031" grid="1" gridSize="10" guides="1" tooltips="1" connect="1" arrows="1" fold="1" page="1" pageScale="1" pageWidth="2336" pageHeight="1654" math="0" shadow="0">
+      <root>
+        <mxCell id="WIyWlLk6GJQsqaUBKTNV-0" />
+        <mxCell id="WIyWlLk6GJQsqaUBKTNV-1" parent="WIyWlLk6GJQsqaUBKTNV-0" />
+        <mxCell id="fcJVb3HREh7NScvAmxeq-0" value="" style="group" parent="WIyWlLk6GJQsqaUBKTNV-1" vertex="1" connectable="0">
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diff --git a/doc/rtd/content/99_appendices/appendix2/sub/mlpro_oa/streams/40_drift_detection/cluster_based/images/MLPro-OA-CB-Drift-Detectors_class_diagram.drawio b/doc/rtd/content/99_appendices/appendix2/sub/mlpro_oa/streams/40_drift_detection/cluster_based/images/MLPro-OA-CB-Drift-Detectors_class_diagram.drawio
new file mode 100644
index 000000000..9249c1e80
--- /dev/null
+++ b/doc/rtd/content/99_appendices/appendix2/sub/mlpro_oa/streams/40_drift_detection/cluster_based/images/MLPro-OA-CB-Drift-Detectors_class_diagram.drawio
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diff --git a/doc/rtd/content/99_appendices/appendix2/sub/mlpro_oa/streams/40_drift_detection/images/.$MLPro-OA-Drift-Detectors_class_diagram.drawio.bkp b/doc/rtd/content/99_appendices/appendix2/sub/mlpro_oa/streams/40_drift_detection/images/.$MLPro-OA-Drift-Detectors_class_diagram.drawio.bkp
new file mode 100644
index 000000000..3e94fcfa5
--- /dev/null
+++ b/doc/rtd/content/99_appendices/appendix2/sub/mlpro_oa/streams/40_drift_detection/images/.$MLPro-OA-Drift-Detectors_class_diagram.drawio.bkp
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diff --git a/doc/rtd/content/99_appendices/appendix2/sub/mlpro_oa/streams/40_drift_detection/images/MLPro-OA-Drift-Detectors_class_diagram.drawio b/doc/rtd/content/99_appendices/appendix2/sub/mlpro_oa/streams/40_drift_detection/images/MLPro-OA-Drift-Detectors_class_diagram.drawio
new file mode 100644
index 000000000..cec6402bb
--- /dev/null
+++ b/doc/rtd/content/99_appendices/appendix2/sub/mlpro_oa/streams/40_drift_detection/images/MLPro-OA-Drift-Detectors_class_diagram.drawio
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diff --git a/doc/rtd/content/99_appendices/appendix2/sub/mlpro_oa/streams/40_drift_detection/instance_based/anomalies/.gitkeep b/doc/rtd/content/99_appendices/appendix2/sub/mlpro_oa/streams/40_drift_detection/instance_based/anomalies/.gitkeep
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diff --git a/doc/rtd/content/99_appendices/appendix2/sub/mlpro_oa/streams/40_drift_detection/instance_based/images/MLPro-OA-CB-Drift-Detectors_class_diagram.drawio b/doc/rtd/content/99_appendices/appendix2/sub/mlpro_oa/streams/40_drift_detection/instance_based/images/MLPro-OA-CB-Drift-Detectors_class_diagram.drawio
new file mode 100644
index 000000000..9249c1e80
--- /dev/null
+++ b/doc/rtd/content/99_appendices/appendix2/sub/mlpro_oa/streams/40_drift_detection/instance_based/images/MLPro-OA-CB-Drift-Detectors_class_diagram.drawio
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diff --git a/doc/rtd/content/99_appendices/appendix2/sub/core/mlpro_oa/02_systems.rst.off b/doc/rtd/content/99_appendices/appendix2/sub/mlpro_oa/systems/01_basics.rst
similarity index 54%
rename from doc/rtd/content/99_appendices/appendix2/sub/core/mlpro_oa/02_systems.rst.off
rename to doc/rtd/content/99_appendices/appendix2/sub/mlpro_oa/systems/01_basics.rst
index e9f6cde36..44d2b88e7 100644
--- a/doc/rtd/content/99_appendices/appendix2/sub/core/mlpro_oa/02_systems.rst.off
+++ b/doc/rtd/content/99_appendices/appendix2/sub/mlpro_oa/systems/01_basics.rst
@@ -1,8 +1,8 @@
-.. _target_api_oa_systems:
-OA Systems
-==========
+.. _target_api_oa_systems_basics:
+Basics
+======
 
-.. image:: systems/images/MLPro-OA-Systems_OASystems.drawio.png
+.. image:: images/MLPro-OA-Systems_OASystems.drawio.png
    :scale: 50%
    
 .. automodule:: mlpro.oa.systems.basics
diff --git a/doc/rtd/content/99_appendices/appendix2/sub/core/mlpro_oa/systems/images/MLPro-OA-Systems_OASystems.drawio.png b/doc/rtd/content/99_appendices/appendix2/sub/mlpro_oa/systems/images/MLPro-OA-Systems_OASystems.drawio.png
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rename from doc/rtd/content/99_appendices/appendix2/sub/core/mlpro_oa/systems/images/MLPro-OA-Systems_OASystems.drawio.png
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diff --git a/doc/rtd/content/99_appendices/appendix2/sub/core/mlpro_oa/systems/images/MLPro-OA-Systems_OASystems_class_diagram.drawio b/doc/rtd/content/99_appendices/appendix2/sub/mlpro_oa/systems/images/MLPro-OA-Systems_OASystems_class_diagram.drawio
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rename from doc/rtd/content/99_appendices/appendix2/sub/core/mlpro_oa/systems/images/MLPro-OA-Systems_OASystems_class_diagram.drawio
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diff --git a/doc/rtd/content/99_appendices/appendix2/sub/core/mlpro_rl/01_environments.rst b/doc/rtd/content/99_appendices/appendix2/sub/mlpro_rl/10_environments.rst
similarity index 100%
rename from doc/rtd/content/99_appendices/appendix2/sub/core/mlpro_rl/01_environments.rst
rename to doc/rtd/content/99_appendices/appendix2/sub/mlpro_rl/10_environments.rst
diff --git a/doc/rtd/content/99_appendices/appendix2/sub/core/mlpro_rl/02_env_models.rst b/doc/rtd/content/99_appendices/appendix2/sub/mlpro_rl/20_env_models.rst
similarity index 89%
rename from doc/rtd/content/99_appendices/appendix2/sub/core/mlpro_rl/02_env_models.rst
rename to doc/rtd/content/99_appendices/appendix2/sub/mlpro_rl/20_env_models.rst
index bbbdcdc80..401dd22a1 100644
--- a/doc/rtd/content/99_appendices/appendix2/sub/core/mlpro_rl/02_env_models.rst
+++ b/doc/rtd/content/99_appendices/appendix2/sub/mlpro_rl/20_env_models.rst
@@ -1,5 +1,5 @@
 .. _target_api_rl_env_ada:
-RL-ENV-ADA - Environment Models
+RL-ENV-ADA - Environment models
 ===============================
 
 .. image:: images/01_environments/MLPro-RL-Env-Ada_class_diagram.drawio.png
diff --git a/doc/rtd/content/99_appendices/appendix2/sub/core/mlpro_rl/11_agents.rst b/doc/rtd/content/99_appendices/appendix2/sub/mlpro_rl/30_agents.rst
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rename from doc/rtd/content/99_appendices/appendix2/sub/core/mlpro_rl/11_agents.rst
rename to doc/rtd/content/99_appendices/appendix2/sub/mlpro_rl/30_agents.rst
diff --git a/doc/rtd/content/99_appendices/appendix2/sub/core/mlpro_rl/21_scenarios_training.rst b/doc/rtd/content/99_appendices/appendix2/sub/mlpro_rl/40_scenarios_training.rst
similarity index 86%
rename from doc/rtd/content/99_appendices/appendix2/sub/core/mlpro_rl/21_scenarios_training.rst
rename to doc/rtd/content/99_appendices/appendix2/sub/mlpro_rl/40_scenarios_training.rst
index f532f9402..5f1fee645 100644
--- a/doc/rtd/content/99_appendices/appendix2/sub/core/mlpro_rl/21_scenarios_training.rst
+++ b/doc/rtd/content/99_appendices/appendix2/sub/mlpro_rl/40_scenarios_training.rst
@@ -1,5 +1,5 @@
 .. _target_api_rl_run_train:
-RL-TRAIN - Scenarios, Training and Tuning
+RL-TRAIN - Scenarios, training and tuning
 =========================================
 
 .. image:: images/03_run_and_train/MLPro-RL-Train_class_diagram.drawio.png
diff --git a/doc/rtd/content/99_appendices/appendix2/sub/mlpro_rl/90_pool_objects.rst b/doc/rtd/content/99_appendices/appendix2/sub/mlpro_rl/90_pool_objects.rst
new file mode 100644
index 000000000..fe082b168
--- /dev/null
+++ b/doc/rtd/content/99_appendices/appendix2/sub/mlpro_rl/90_pool_objects.rst
@@ -0,0 +1,10 @@
+.. _target_api_pool_rl:
+Pool objects
+============
+
+.. toctree::
+   :maxdepth: 3
+   :glob:
+   
+   pool/*
+
diff --git a/doc/rtd/content/99_appendices/appendix2/sub/core/mlpro_rl/images/01_environments/.$MLPro-RL-Env-Ada_class_diagram.drawio.bkp b/doc/rtd/content/99_appendices/appendix2/sub/mlpro_rl/images/01_environments/.$MLPro-RL-Env-Ada_class_diagram.drawio.bkp
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new file mode 100644
index 000000000..83ed19a79
--- /dev/null
+++ b/doc/rtd/content/99_appendices/appendix2/sub/mlpro_sl/90_pool_objects.rst
@@ -0,0 +1,9 @@
+.. _target_api_pool_sl:
+Pool objects
+============
+
+.. toctree::
+   :maxdepth: 3
+   :glob:
+   
+   pool/*
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diff --git a/doc/rtd/content/99_appendices/appendix2/sub/pool/20_mlpro.sl.rst b/doc/rtd/content/99_appendices/appendix2/sub/pool/20_mlpro.sl.rst
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index b86bba902..000000000
--- a/doc/rtd/content/99_appendices/appendix2/sub/pool/20_mlpro.sl.rst
+++ /dev/null
@@ -1,9 +0,0 @@
-.. _target_api_pool_sl:
-MLPro-SL - Supervised Learning
-==============================
-
-.. toctree::
-   :maxdepth: 3
-   :glob:
-   
-   mlpro_sl/*
diff --git a/doc/rtd/content/99_appendices/appendix2/sub/pool/30_mlpro.oa.rst b/doc/rtd/content/99_appendices/appendix2/sub/pool/30_mlpro.oa.rst
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index 143ed96db..000000000
--- a/doc/rtd/content/99_appendices/appendix2/sub/pool/30_mlpro.oa.rst
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@@ -1,9 +0,0 @@
-.. _target_api_pool_oa:
-MLPro-OA - Online Adaptivity
-============================
-
-.. toctree::
-   :maxdepth: 3
-   :glob:
-   
-   mlpro_oa/*
diff --git a/doc/rtd/content/99_appendices/appendix2/sub/pool/40_mlpro.rl.rst b/doc/rtd/content/99_appendices/appendix2/sub/pool/40_mlpro.rl.rst
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index c9b65d4ee..000000000
--- a/doc/rtd/content/99_appendices/appendix2/sub/pool/40_mlpro.rl.rst
+++ /dev/null
@@ -1,10 +0,0 @@
-.. _target_api_pool_rl:
-MLPro-RL - Reinforcement Learning
-=================================
-
-.. toctree::
-   :maxdepth: 3
-   :glob:
-   
-   mlpro_rl/*
-
diff --git a/doc/rtd/content/99_appendices/appendix2/sub/pool/mlpro_oa/10_streams.rst b/doc/rtd/content/99_appendices/appendix2/sub/pool/mlpro_oa/10_streams.rst
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index cfee65612..000000000
--- a/doc/rtd/content/99_appendices/appendix2/sub/pool/mlpro_oa/10_streams.rst
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@@ -1,9 +0,0 @@
-.. _target_api_pool_oa_streams:
-OA Stream Processing
-====================
-
-.. toctree::
-   :maxdepth: 2
-   :glob:
-   
-   streams/*
\ No newline at end of file
diff --git a/doc/rtd/content/99_appendices/appendix2/sub/pool/mlpro_oa/20_systems.rst b/doc/rtd/content/99_appendices/appendix2/sub/pool/mlpro_oa/20_systems.rst
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index ad50a5910..000000000
--- a/doc/rtd/content/99_appendices/appendix2/sub/pool/mlpro_oa/20_systems.rst
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@@ -1,11 +0,0 @@
-.. _target_api_pool_oa_systems:
-OA Systems
-==========
-
-Coming soon...
-
-.. toctree::
-   :maxdepth: 2
-   :glob:
-   
-   systems/*
\ No newline at end of file
diff --git a/doc/rtd/content/99_appendices/appendix2/sub/pool/mlpro_oa/streams/01_stream_tasks.rst b/doc/rtd/content/99_appendices/appendix2/sub/pool/mlpro_oa/streams/01_stream_tasks.rst
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index fd9c2ab0b..000000000
--- a/doc/rtd/content/99_appendices/appendix2/sub/pool/mlpro_oa/streams/01_stream_tasks.rst
+++ /dev/null
@@ -1,9 +0,0 @@
-.. _target_api_oa_streams_tasks:
-OA Stream Tasks
-===============
-
-.. toctree::
-   :maxdepth: 2
-   :glob:
-   
-   tasks/*
\ No newline at end of file
diff --git a/doc/rtd/content/99_appendices/appendix2/sub/pool/mlpro_oa/streams/tasks/anomaly_detectors/images/MLPro-OA-Anomaly-Detectors_class_diagram.drawio b/doc/rtd/content/99_appendices/appendix2/sub/pool/mlpro_oa/streams/tasks/anomaly_detectors/images/MLPro-OA-Anomaly-Detectors_class_diagram.drawio
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--- a/doc/rtd/content/99_appendices/appendix2/sub/pool/mlpro_oa/streams/tasks/anomaly_detectors/images/MLPro-OA-Anomaly-Detectors_class_diagram.drawio
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@@ -1 +0,0 @@
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diff --git a/doc/rtd/content/99_appendices/appendix2/sub/pool/mlpro_oa/streams/tasks/images/.$MLPro-OA-Preprocessing-Tasks_class_diagram.drawio.bkp b/doc/rtd/content/99_appendices/appendix2/sub/pool/mlpro_oa/streams/tasks/images/.$MLPro-OA-Preprocessing-Tasks_class_diagram.drawio.bkp
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diff --git a/doc/rtd/content/99_appendices/appendix2/sub/pool/mlpro_oa/streams/tasks/images/.$MLPro-OA_Anomaly_Predictors_class_diagram.drawio.bkp b/doc/rtd/content/99_appendices/appendix2/sub/pool/mlpro_oa/streams/tasks/images/.$MLPro-OA_Anomaly_Predictors_class_diagram.drawio.bkp
deleted file mode 100644
index cc0c28b5c..000000000
--- a/doc/rtd/content/99_appendices/appendix2/sub/pool/mlpro_oa/streams/tasks/images/.$MLPro-OA_Anomaly_Predictors_class_diagram.drawio.bkp
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-        <mxCell id="4use4ZCXdNiZSNTW99xv-2" value="C_TYPE = &#39;Anomaly Predictor AD&#39;" style="text;align=left;verticalAlign=top;spacingLeft=4;spacingRight=4;overflow=hidden;rotatable=0;points=[[0,0.5],[1,0.5]];portConstraint=eastwest;fontColor=default;" parent="4use4ZCXdNiZSNTW99xv-1" vertex="1">
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-        <mxCell id="gMNO8MBthjWmqtpiZwQ6-5" value="_run( p_inst : InstDict )" style="text;align=left;verticalAlign=top;spacingLeft=4;spacingRight=4;overflow=hidden;rotatable=0;points=[[0,0.5],[1,0.5]];portConstraint=eastwest;fontColor=#FF0000;" vertex="1" parent="4use4ZCXdNiZSNTW99xv-1">
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-        <mxCell id="gMNO8MBthjWmqtpiZwQ6-6" value="_adapt_on_event ( p_event_id : str, &#xa;                               p_event_object : Event ) -&gt; bool" style="text;align=left;verticalAlign=top;spacingLeft=4;spacingRight=4;overflow=hidden;rotatable=0;points=[[0,0.5],[1,0.5]];portConstraint=eastwest;fontColor=#FF0000;" vertex="1" parent="4use4ZCXdNiZSNTW99xv-1">
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-        <mxCell id="gMNO8MBthjWmqtpiZwQ6-7" value="AnomalyPredictor&#xa;(OATask)" style="swimlane;fontStyle=3;align=center;verticalAlign=top;childLayout=stackLayout;horizontal=1;startSize=41;horizontalStack=0;resizeParent=1;resizeLast=0;collapsible=1;marginBottom=0;rounded=0;shadow=1;strokeWidth=1;fillColor=#f8cecc;strokeColor=#b85450;swimlaneFillColor=#ffffff;gradientColor=#FFFFFF;" vertex="1" parent="WIyWlLk6GJQsqaUBKTNV-1">
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-        <mxCell id="gMNO8MBthjWmqtpiZwQ6-8" value="C_TYPE = &#39;Anomaly Predictor&#39;" style="text;align=left;verticalAlign=top;spacingLeft=4;spacingRight=4;overflow=hidden;rotatable=0;points=[[0,0.5],[1,0.5]];portConstraint=eastwest;fontColor=default;" vertex="1" parent="gMNO8MBthjWmqtpiZwQ6-7">
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-        <mxCell id="gMNO8MBthjWmqtpiZwQ6-20" style="edgeStyle=orthogonalEdgeStyle;rounded=0;orthogonalLoop=1;jettySize=auto;html=1;endArrow=block;endFill=0;" edge="1" parent="WIyWlLk6GJQsqaUBKTNV-1" source="gMNO8MBthjWmqtpiZwQ6-13" target="gMNO8MBthjWmqtpiZwQ6-17">
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-        </mxCell>
-        <mxCell id="gMNO8MBthjWmqtpiZwQ6-13" value="AnomalyPredictionAD&#xa;(AnomalyPrediction,&#xa;oa.streams.tasks.anomalydetectors.Anomaly)" style="swimlane;fontStyle=3;align=center;verticalAlign=top;childLayout=stackLayout;horizontal=1;startSize=60;horizontalStack=0;resizeParent=1;resizeLast=0;collapsible=1;marginBottom=0;rounded=0;shadow=1;strokeWidth=1;fillColor=#f8cecc;strokeColor=#b85450;swimlaneFillColor=#ffffff;gradientColor=#FFFFFF;" vertex="1" parent="WIyWlLk6GJQsqaUBKTNV-1">
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-  <diagram id="lryuDQluacSowWsRYUra" name="Page-2">
-    <mxGraphModel dx="1098" dy="828" grid="1" gridSize="10" guides="1" tooltips="1" connect="1" arrows="1" fold="1" page="1" pageScale="1" pageWidth="850" pageHeight="1100" math="0" shadow="0">
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diff --git a/doc/rtd/index.rst b/doc/rtd/index.rst
index 88993a62d..e40fdce92 100644
--- a/doc/rtd/index.rst
+++ b/doc/rtd/index.rst
@@ -3,63 +3,82 @@
    You can adapt this file completely to your liking, but it should at least
    contain the root `toctree` directive.
 
-MLPro - Machine Learning Professional
-#####################################
+MLPro - Elevate Your Machine Learning Journey
+#############################################
 
-Welcome to MLPro - the integrative middleware framework for standardized machine learning in Python!
+**Welcome to MLPro - the integrative middleware framework for standardized machine learning in Python!**
 
-MLPro
-   - enables hybrid ML applications in just one framework
-   - provides complete process landscapes and powerful ML templates on a scientific level
-   - integrates a growing number of :ref:`proven ML packages <target_wrappers>` 
-   - enables comparable and reproducible results in publications
-   - is open source and even commercially usable (`Apache License 2.0 <https://github.com/fhswf/MLPro/blob/main/LICENSE>`_)
+.. raw:: html
+
+    <div style="max-width: 840px; text-align: justify; margin: 0;">
+      💡 Machine learning frameworks often focus on specific algorithms or isolated tasks, leaving you to bridge
+      the gaps between tools, workflows, and real-world applications. MLPro takes a different approach: Instead 
+      of just another toolbox, it provides a structured foundation with powerful processes and reusable templates 
+      designed to bring standardization and productivity to your ML development.<br><br>
+
+      📐 Whether you're a researcher exploring new methods, a developer building intelligent systems, or a student 
+      discovering the world of machine learning, MLPro gives you the structure and flexibility to elevate your ML projects to the 
+      next level.<br><br>
+
+      🔓 MLPro is fully open source and licensed under <a href="https://github.com/fhswf/MLPro?tab=Apache-2.0-1-ov-file#readme" target="_blank">Apache 2.0</a>, giving you the freedom to innovate, customize, and contribute.<br><br>
+
+      ✨ Start exploring MLPro and take your machine learning journey further! <br><br>
+    </div>
 
 
 .. |welcome| image:: content/00_start_page/images/welcome.png
-   :scale: 65 %
+   :scale: 35 %
    :target: content/01_welcome/main.html
 
 .. |mlpro_bf| image:: content/00_start_page/images/mlpro_bf.png
-   :scale: 65 %
+   :scale: 35 %
    :target: content/02_basic_functions/mlpro_bf/main.html
    
 .. |mlpro_sl| image:: content/00_start_page/images/mlpro_sl.png
-   :scale: 65 %
+   :scale: 35 %
    :target: content/03_machine_learning/mlpro_sl/main.html
 
 .. |mlpro_rl| image:: content/00_start_page/images/mlpro_rl.png
-   :scale: 65 %
+   :scale: 35 %
    :target: content/03_machine_learning/mlpro_rl/main.html
 
 .. |mlpro_gt| image:: content/00_start_page/images/mlpro_gt.png
-   :scale: 65 %
+   :scale: 35 %
    :target: content/03_machine_learning/mlpro_gt/main.html
 
 .. |mlpro_oa| image:: content/00_start_page/images/mlpro_oa.png
-   :scale: 65 %
+   :scale: 35 %
    :target: content/03_machine_learning/mlpro_oa/main.html
 
 .. |examples| image:: content/00_start_page/images/example_pool.png
-   :scale: 65 %
+   :scale: 35 %
    :target: content/99_appendices/appendix1/main.html
 
+.. |podcast| image:: content/00_start_page/images/podcast.png
+   :scale: 35 %
+   :target: https://www.gitpodcast.com/fhswf/mlpro
+
+.. |chatgpt| image:: content/00_start_page/images/chatgpt.png
+   :scale: 35 %
+   :target: https://chatgpt.com/g/g-67a1b30e91d481918ee1d6fe2af91bb5-mlpro
+
 .. |mlpro_api| image:: content/00_start_page/images/api_reference.png
-   :scale: 65 %
+   :scale: 35 %
    :target: content/99_appendices/appendix2/main.html
 
+.. |extensions| image:: content/00_start_page/images/extension_hub.png
+   :scale: 35 %
+   :target: content/04_extensions/main.html
+
 .. |project| image:: content/00_start_page/images/project_mlpro.png
-   :scale: 65 %
+   :scale: 35 %
    :target: content/99_appendices/appendix3/main.html
 
-.. |extensions| image:: content/00_start_page/images/extension_hub.png
-   :scale: 65 %
-   :target: content/04_extensions/main.html
 
 
-|welcome| |mlpro_bf| |mlpro_sl| |mlpro_oa| |mlpro_rl| 
+|welcome| |mlpro_bf| |mlpro_sl| |mlpro_oa| |mlpro_rl| |mlpro_gt| 
 
-|mlpro_gt| |examples| |mlpro_api| |project| |extensions|
+|examples| |mlpro_api| |extensions| |podcast| |chatgpt| |project|
 
 
 .. |mlpro_pypi| image:: content/00_start_page/images/pypi.svg
@@ -68,24 +87,21 @@ MLPro
 
 .. |mlpro_anaconda| image:: content/00_start_page/images/anaconda.png
    :target: https://anaconda.org/mlpro/mlpro
-   :scale: 11 %
+   :scale: 10 %
 
 .. |mlpro_github| image:: content/00_start_page/images/github.png
    :target: https://github.com/fhswf/MLPro
    :scale: 2 %
 
-.. |mlpro_rg| image:: content/00_start_page/images/researchgate.jpeg
-   :target: https://www.researchgate.net/project/MLPro-A-Synoptic-Framework-for-Standardized-Machine-Learning-Tasks-in-Python
-   :scale: 6 %
-
 
-MLPro is also present on...
+🌐 MLPro is available on...
 
 .. list-table::
+   :widths: 33 33 33
 
-   * - |mlpro_pypi| `Python Package Index <https://pypi.org/project/mlpro>`_
-     - |mlpro_anaconda| `Anaconda.org <https://anaconda.org/mlpro/mlpro>`_
-     - |mlpro_github| `GitHub <https://github.com/fhswf/MLPro>`_
+   * - |mlpro_pypi|     `PyPI <https://pypi.org/project/mlpro>`_
+     - |mlpro_anaconda| `Anaconda <https://anaconda.org/mlpro/mlpro>`_
+     - |mlpro_github|   `GitHub <https://github.com/fhswf/MLPro>`_
 
 
 
diff --git a/src/mlpro/bf/math/normalizers/basics.py b/src/mlpro/bf/math/normalizers/basics.py
index e1be9bb5a..88dde747a 100644
--- a/src/mlpro/bf/math/normalizers/basics.py
+++ b/src/mlpro/bf/math/normalizers/basics.py
@@ -129,7 +129,7 @@ def denormalize(self, p_data: Union[Element, np.ndarray]):
 
         elif isinstance(p_data, np.ndarray):
             p_data = np.multiply(p_data, 1 / self._param[0]) + \
-                     (self._param[1] / self._param[0])
+                    (self._param[1] / self._param[0])
             p_data = np.nan_to_num(p_data)
         else:
             raise ParamError('Wrong datatype provided for denormalization')
diff --git a/src/mlpro/bf/plot/backends/__init__.py b/src/mlpro/bf/plot/backends/__init__.py
index 656f482f9..d5ef44714 100644
--- a/src/mlpro/bf/plot/backends/__init__.py
+++ b/src/mlpro/bf/plot/backends/__init__.py
@@ -1,3 +1,3 @@
 from mlpro.bf.plot.backends.basics import WindowState, WSMINIMIZED, WSMAXIMIZED, WSNORMAL, WindowGeometry, PlotBackend
 from mlpro.bf.plot.backends.tkagg import PlotBackendTkAgg
-from mlpro.bf.plot.backends.qtagg import PlotBackendqtagg
\ No newline at end of file
+from mlpro.bf.plot.backends.qtagg import PlotBackendqtagg, PlotBackendQtAgg
\ No newline at end of file
diff --git a/src/mlpro/bf/plot/backends/qtagg.py b/src/mlpro/bf/plot/backends/qtagg.py
index c810102f1..f285a9eef 100644
--- a/src/mlpro/bf/plot/backends/qtagg.py
+++ b/src/mlpro/bf/plot/backends/qtagg.py
@@ -88,3 +88,11 @@ def _figure_set_geometry_default(self, p_figure : Figure, p_geometry : WindowGeo
             state_qt = QtCore.Qt.WindowNoState
 
         window.setWindowState(state_qt)
+
+
+
+
+
+## -------------------------------------------------------------------------------------------------
+## -------------------------------------------------------------------------------------------------
+class PlotBackendQtAgg (PlotBackendqtagg): pass
\ No newline at end of file
diff --git a/src/mlpro/oa/streams/tasks/anomalydetectors/__init__.py b/src/mlpro/oa/streams/tasks/anomalydetectors/__init__.py
index c5978a189..d0b9e33b1 100644
--- a/src/mlpro/oa/streams/tasks/anomalydetectors/__init__.py
+++ b/src/mlpro/oa/streams/tasks/anomalydetectors/__init__.py
@@ -1,4 +1,4 @@
 from mlpro.oa.streams.tasks.anomalydetectors.basics import AnomalyDetector
 from mlpro.oa.streams.tasks.anomalydetectors.anomalies import *
-from mlpro.oa.streams.tasks.anomalydetectors.paga_detectors import AnomalyDetectorPAGA
-from mlpro.oa.streams.tasks.anomalydetectors.cb_detectors import *
+from mlpro.oa.streams.tasks.anomalydetectors.instancebased import *
+from mlpro.oa.streams.tasks.anomalydetectors.clusterbased import *
diff --git a/src/mlpro/oa/streams/tasks/anomalydetectors/anomalies/__init__.py b/src/mlpro/oa/streams/tasks/anomalydetectors/anomalies/__init__.py
index c495961d0..d6cf86def 100644
--- a/src/mlpro/oa/streams/tasks/anomalydetectors/anomalies/__init__.py
+++ b/src/mlpro/oa/streams/tasks/anomalydetectors/anomalies/__init__.py
@@ -1,6 +1,3 @@
 from mlpro.oa.streams.tasks.anomalydetectors.anomalies.basics import Anomaly
-from mlpro.oa.streams.tasks.anomalydetectors.anomalies.point import PointAnomaly
-from mlpro.oa.streams.tasks.anomalydetectors.anomalies.group import GroupAnomaly
-from mlpro.oa.streams.tasks.anomalydetectors.anomalies.drift import DriftAnomaly
-from mlpro.oa.streams.tasks.anomalydetectors.anomalies.contextual import ContextualAnomaly
+from mlpro.oa.streams.tasks.anomalydetectors.anomalies.instancebased import *
 from mlpro.oa.streams.tasks.anomalydetectors.anomalies.clusterbased import *
\ No newline at end of file
diff --git a/src/mlpro/oa/streams/tasks/anomalydetectors/anomalies/basics.py b/src/mlpro/oa/streams/tasks/anomalydetectors/anomalies/basics.py
index 32d6bdaa4..64ab695ea 100644
--- a/src/mlpro/oa/streams/tasks/anomalydetectors/anomalies/basics.py
+++ b/src/mlpro/oa/streams/tasks/anomalydetectors/anomalies/basics.py
@@ -14,27 +14,32 @@
 ## -- 2024-05-07  1.3.1     SK       Bug fix related to p_instances
 ## -- 2024-05-09  1.3.2     DA       Bugfix in method Anomaly._update_plot()
 ## -- 2024-05-22  1.4.0     SK       Refactoring
+## -- 2025-02-12  1.4.1     DA       Code reduction
+## -- 2025-02-18  2.0.0     DA       Class Anomaly:
+## --                                - refactoring and simplification
+## --                                - new attribute event_id
+## --                                - new parent Renormalizable
 ## -------------------------------------------------------------------------------------------------
 
 """
-Ver. 1.4.0 (2024-05-22)
+Ver. 2.0.0 (2025-02-18)
 
 This module provides a template class for anomalies to be used in anomaly detection algorithms.
 """
 
+from datetime import datetime
+
 from mlpro.bf.various import Id
 from mlpro.bf.plot import Plottable, PlotSettings
 from mlpro.bf.events import Event
-from mlpro.bf.streams import Instance
-from mlpro.bf.math.properties import PropertyDefinitions, Properties
-
+from mlpro.bf.math.normalizers import Renormalizable
 
 
 
 
 ## -------------------------------------------------------------------------------------------------
 ## -------------------------------------------------------------------------------------------------
-class Anomaly (Id, Event, Plottable):
+class Anomaly (Id, Event, Plottable, Renormalizable):
     """
     This is the base class for anomaly events which can be raised by the anomaly detectors when an
     anomaly is detected.
@@ -43,11 +48,7 @@ class Anomaly (Id, Event, Plottable):
     ----------
     p_id : int
         Anomaly ID. Default value = 0.
-    p_instances : Instance
-        List of instances. Default value = None.
-    p_ano_scores : list
-        List of anomaly scores of instances. Default = None.
-    p_det_time : str
+    p_tstamp : datetime
         Time of occurance of anomaly. Default = None.
     p_visualize : bool
         Boolean switch for visualisation. Default = False.
@@ -57,8 +58,6 @@ class Anomaly (Id, Event, Plottable):
         Further optional keyword arguments.
     """
 
-    C_TYPE                  = 'Anomaly'
-
     C_PLOT_ACTIVE           = True
     C_PLOT_STANDALONE       = False
     C_PLOT_VALID_VIEWS      = [ PlotSettings.C_VIEW_2D, 
@@ -69,58 +68,18 @@ class Anomaly (Id, Event, Plottable):
 ## -------------------------------------------------------------------------------------------------
     def __init__(self,
                  p_id : int = 0,
-                 p_instances: list[Instance] = None,
-                 p_ano_scores : list = None,
-                 p_det_time : str = None,
+                 p_tstamp : datetime = None,
                  p_visualize : bool = False,
                  p_raising_object : object = None,
                  **p_kwargs):
         
         Id.__init__( self, p_id = p_id )
-        Event.__init__( self, p_raising_object=p_raising_object,
-                        p_tstamp=p_det_time, **p_kwargs)
-        Plottable.__init__( self, p_visualize = p_visualize )
-
-        self._instances : list[Instance] = p_instances
-        self._ano_scores = p_ano_scores
-
-
-## -------------------------------------------------------------------------------------------------
-    def get_instances(self) -> list[Instance]:
-        """
-        Method that returns the instances associated with the anomaly.
-        
-        Returns
-        -------
-        list[Instance]
-            The list of instances.
-        """
-        return self._instances
-    
 
-## -------------------------------------------------------------------------------------------------
-    def get_detection_time(self) -> float:
-        """
-        Method that returns the time at which the anomaly/anomalies were detection.
+        Event.__init__( self, 
+                        p_raising_object=p_raising_object,
+                        p_tstamp=p_tstamp, 
+                        **p_kwargs )
         
-        Returns
-        -------
-        float
-            The time of detection.
-        """
-        return self._instances
-    
-
-## -------------------------------------------------------------------------------------------------
-    def get_ano_scores(self):
-        """
-        Method that returns the anomaly scores associated with the instances of the anomaly.
-        
-        Returns
-        -------
-        list
-            The list of anomaly scores.
-        """
-        return self._ano_scores
-    
+        Plottable.__init__( self, p_visualize = p_visualize )
 
+        self.event_id   = type(self).__name__
\ No newline at end of file
diff --git a/src/mlpro/oa/streams/tasks/anomalydetectors/anomalies/clusterbased/__init__.py b/src/mlpro/oa/streams/tasks/anomalydetectors/anomalies/clusterbased/__init__.py
index 134079f20..b5e76cf4f 100644
--- a/src/mlpro/oa/streams/tasks/anomalydetectors/anomalies/clusterbased/__init__.py
+++ b/src/mlpro/oa/streams/tasks/anomalydetectors/anomalies/clusterbased/__init__.py
@@ -1,7 +1,6 @@
 from mlpro.oa.streams.tasks.anomalydetectors.anomalies.clusterbased.basics import CBAnomaly
 from mlpro.oa.streams.tasks.anomalydetectors.anomalies.clusterbased.density import ClusterDensityVariation
 from mlpro.oa.streams.tasks.anomalydetectors.anomalies.clusterbased.disappearance import ClusterDisappearance
-from mlpro.oa.streams.tasks.anomalydetectors.anomalies.clusterbased.drift import ClusterDrift
 from mlpro.oa.streams.tasks.anomalydetectors.anomalies.clusterbased.enlargement import ClusterEnlargement
 from mlpro.oa.streams.tasks.anomalydetectors.anomalies.clusterbased.shrinkage import ClusterShrinkage
 from mlpro.oa.streams.tasks.anomalydetectors.anomalies.clusterbased.new_cluster import NewClusterAppearance
\ No newline at end of file
diff --git a/src/mlpro/oa/streams/tasks/anomalydetectors/anomalies/clusterbased/basics.py b/src/mlpro/oa/streams/tasks/anomalydetectors/anomalies/clusterbased/basics.py
index 75c75f3b5..c412aecce 100644
--- a/src/mlpro/oa/streams/tasks/anomalydetectors/anomalies/clusterbased/basics.py
+++ b/src/mlpro/oa/streams/tasks/anomalydetectors/anomalies/clusterbased/basics.py
@@ -25,15 +25,14 @@
 except:
     class Figure : pass
 
-from mlpro.oa.streams.basics import Instance
+from datetime import datetime
+
 from mlpro.oa.streams.tasks.anomalydetectors.anomalies.basics import Anomaly
 from mlpro.bf.mt import Figure, PlotSettings
 from mlpro.oa.streams.tasks.clusteranalyzers.clusters.basics import Cluster
 
 
 
-
-
 ## -------------------------------------------------------------------------------------------------
 ## -------------------------------------------------------------------------------------------------
 class CBAnomaly (Anomaly):
@@ -44,14 +43,10 @@ class CBAnomaly (Anomaly):
     ----------
     p_id : int
         Anomaly ID. Default value = 0.
-    p_instances : Instance
-        List of instances. Default value = None.
     p_clusters : dict[Cluster]
         Clusters associated with the anomaly. Default = None.
     p_properties : dict
         Poperties of clusters associated with the anomaly. Default = None.
-    p_ano_scores : list
-        List of anomaly scores of instances. Default = None.
     p_det_time : str
         Time of occurance of anomaly. Default = None.
     p_visualize : bool
@@ -62,7 +57,6 @@ class CBAnomaly (Anomaly):
         Further optional keyword arguments.
     """
     
-    C_NAME      = 'Cluster based Anomaly'
     C_PLOT_ACTIVE           = True
     C_PLOT_STANDALONE       = False
     C_PLOT_VALID_VIEWS      = [ PlotSettings.C_VIEW_2D, 
@@ -75,39 +69,23 @@ class CBAnomaly (Anomaly):
 ## -------------------------------------------------------------------------------------------------
     def __init__(self,
                  p_id : int = 0,
-                 p_instances : list[Instance] = None,
                  p_clusters : dict[Cluster] = None,
                  p_properties : dict = None,
-                 p_ano_scores : list = None,
-                 p_det_time : str = None,
+                 p_tstamp : datetime = None,
                  p_visualize : bool = False,
                  p_raising_object : object = None,
                  **p_kwargs):
         
-        super().__init__(p_id=p_id,
-                         p_instances=p_instances,
-                         p_ano_scores=p_ano_scores,
-                         p_visualize=p_visualize, 
-                         p_raising_object=p_raising_object,
-                         p_det_time=p_det_time,
-                         **p_kwargs)
+        super().__init__( p_id = p_id,
+                          p_tstamp = p_tstamp,
+                          p_visualize = p_visualize, 
+                          p_raising_object = p_raising_object,
+                          **p_kwargs )
         
-        self._colour_id = 0
-        self._clusters : dict[Cluster] = p_clusters
-        self._properties : dict = p_properties
+        self._colour_id               = 0
+        self.clusters : dict[Cluster] = p_clusters
+        self._properties : dict       = p_properties
 
-## -------------------------------------------------------------------------------------------------
-    def get_clusters(self) -> dict[Cluster]:
-        """
-        Method that returns the clusters associated with the anomaly.
-        
-        Returns
-        -------
-        dict[Cluster]
-            Dictionary of clusters.
-        """
-        return self._clusters
-    
 
 ## -------------------------------------------------------------------------------------------------
     def get_properties(self) -> dict:
@@ -128,7 +106,7 @@ def _init_plot_2d(self, p_figure: Figure, p_settings: PlotSettings):
 
         cluster : Cluster = None
 
-        for cluster in self._clusters.values(): 
+        for cluster in self.clusters.values(): 
 
             cluster.color = "red"
 
@@ -139,9 +117,6 @@ def _init_plot_3d(self, p_figure: Figure, p_settings: PlotSettings):
     
         cluster : Cluster = None
 
-        for cluster in self._clusters.values(): 
-
-            cluster.color = "red"
-
-
+        for cluster in self.clusters.values(): 
 
+            cluster.color = "red"
\ No newline at end of file
diff --git a/src/mlpro/oa/streams/tasks/anomalydetectors/anomalies/clusterbased/density.py b/src/mlpro/oa/streams/tasks/anomalydetectors/anomalies/clusterbased/density.py
index f16df5c96..2932df408 100644
--- a/src/mlpro/oa/streams/tasks/anomalydetectors/anomalies/clusterbased/density.py
+++ b/src/mlpro/oa/streams/tasks/anomalydetectors/anomalies/clusterbased/density.py
@@ -32,7 +32,6 @@ class ClusterDensityVariation (CBAnomaly):
     Event class to be raised when the density of a cluster changes.
     
     """
-
-    C_NAME      = 'Cluster density variation'
-
+    
+    pass
 
diff --git a/src/mlpro/oa/streams/tasks/anomalydetectors/anomalies/clusterbased/drift.py b/src/mlpro/oa/streams/tasks/anomalydetectors/anomalies/clusterbased/drift.py
deleted file mode 100644
index 0db1e1a6f..000000000
--- a/src/mlpro/oa/streams/tasks/anomalydetectors/anomalies/clusterbased/drift.py
+++ /dev/null
@@ -1,39 +0,0 @@
-## -------------------------------------------------------------------------------------------------
-## -- Project : MLPro - The integrative middleware framework for standardized machine learning
-## -- Package : mlpro.oa.tasks.anomalydetectors.anomalies.clusterbased
-## -- Module  : drift.py
-## -------------------------------------------------------------------------------------------------
-## -- History :
-## -- yyyy-mm-dd  Ver.      Auth.    Description
-## -- 2023-06-08  0.0.0     SK       Creation
-## -- 2023-09-12  1.0.0     SK       Release
-## -- 2023-11-21  1.0.1     SK       Time Stamp update
-## -- 2024-02-25  1.1.0     SK       Visualisation update
-## -- 2024-04-10  1.2.0     DA/SK    Refactoring
-## -- 2024-05-22  1.2.1     SK       Refactoring
-## -- 2024-05-28  1.3.0     SK       Refactoring
-## -------------------------------------------------------------------------------------------------
-
-"""
-Ver. 1.3.0 (2024-05-28)
-
-This module provides a template class for cluster drift to be used in anomaly detection algorithms.
-"""
-
-from mlpro.oa.streams.tasks.anomalydetectors.anomalies.clusterbased.basics import CBAnomaly
-
-
-
-
-
-## -------------------------------------------------------------------------------------------------
-## -------------------------------------------------------------------------------------------------
-class ClusterDrift (CBAnomaly):
-    """
-    Event class to be raised when cluster drift detected.
-    
-    """
-
-    C_NAME      = 'Cluster drift Anomaly'
-
-
diff --git a/src/mlpro/oa/streams/tasks/anomalydetectors/anomalies/instancebased/__init__.py b/src/mlpro/oa/streams/tasks/anomalydetectors/anomalies/instancebased/__init__.py
new file mode 100644
index 000000000..702e77b54
--- /dev/null
+++ b/src/mlpro/oa/streams/tasks/anomalydetectors/anomalies/instancebased/__init__.py
@@ -0,0 +1,3 @@
+from mlpro.oa.streams.tasks.anomalydetectors.anomalies.instancebased.point import PointAnomaly
+from mlpro.oa.streams.tasks.anomalydetectors.anomalies.instancebased.group import GroupAnomaly
+from mlpro.oa.streams.tasks.anomalydetectors.anomalies.instancebased.contextual import ContextualAnomaly
\ No newline at end of file
diff --git a/src/mlpro/oa/streams/tasks/anomalydetectors/anomalies/contextual.py b/src/mlpro/oa/streams/tasks/anomalydetectors/anomalies/instancebased/contextual.py
similarity index 100%
rename from src/mlpro/oa/streams/tasks/anomalydetectors/anomalies/contextual.py
rename to src/mlpro/oa/streams/tasks/anomalydetectors/anomalies/instancebased/contextual.py
diff --git a/src/mlpro/oa/streams/tasks/anomalydetectors/anomalies/group.py b/src/mlpro/oa/streams/tasks/anomalydetectors/anomalies/instancebased/group.py
similarity index 100%
rename from src/mlpro/oa/streams/tasks/anomalydetectors/anomalies/group.py
rename to src/mlpro/oa/streams/tasks/anomalydetectors/anomalies/instancebased/group.py
diff --git a/src/mlpro/oa/streams/tasks/anomalydetectors/anomalies/point.py b/src/mlpro/oa/streams/tasks/anomalydetectors/anomalies/instancebased/point.py
similarity index 100%
rename from src/mlpro/oa/streams/tasks/anomalydetectors/anomalies/point.py
rename to src/mlpro/oa/streams/tasks/anomalydetectors/anomalies/instancebased/point.py
diff --git a/src/mlpro/oa/streams/tasks/anomalydetectors/basics.py b/src/mlpro/oa/streams/tasks/anomalydetectors/basics.py
index 352c1a244..a132a6f87 100644
--- a/src/mlpro/oa/streams/tasks/anomalydetectors/basics.py
+++ b/src/mlpro/oa/streams/tasks/anomalydetectors/basics.py
@@ -15,35 +15,33 @@
 ## -- 2024-05-22  1.4.0     SK       Refactoring
 ## -- 2024-08-12  1.4.1     DA       Correction in AnomalyDetector.update_plot()
 ## -- 2024-12-11  1.4.2     DA       Pseudo classes if matplotlib is not installed
+## -- 2025-02-14  1.5.0     DA       Review and refactoring
 ## -------------------------------------------------------------------------------------------------
 
 """
-Ver. 1.4.2 (2024-12-11)
+Ver. 1.5.0 (2025-02-12)
 
 This module provides templates for anomaly detection to be used in the context of online adaptivity.
 """
 
-from typing import List
-
 try:
     from matplotlib.figure import Figure
 except:
     class Figure : pass
 
-from mlpro.bf.math.properties import *
+from mlpro.bf.various import Log
 from mlpro.bf.plot import PlotSettings
+from mlpro.bf.math.normalizers import Normalizer
 from mlpro.bf.streams import InstDict
-from mlpro.bf.various import *
-from mlpro.bf.plot import *
+
 from mlpro.oa.streams import OAStreamTask
-from mlpro.bf.math.normalizers import Normalizer
 from mlpro.oa.streams.tasks.anomalydetectors.anomalies import Anomaly
 
 
 
 ## -------------------------------------------------------------------------------------------------
 ## -------------------------------------------------------------------------------------------------
-class AnomalyDetector(OAStreamTask):
+class AnomalyDetector (OAStreamTask):
     """
     Base class for online anomaly detectors. It raises an event when an
     anomaly is detected.
@@ -66,40 +64,32 @@ class AnomalyDetector(OAStreamTask):
         Further optional named parameters.
     """
 
-    C_TYPE                  = 'Anomaly Detector'
-
-    C_EVENT_ANOMALY_ADDED   = 'ANOMALY_ADDED'
-    C_EVENT_ANOMALY_REMOVED = 'ANOMALY_REMOVED'
-
-    C_PLOT_ACTIVE           = True
-    C_PLOT_STANDALONE       = False
-
-## -------------------------------------------------------------------------------------------------
-    def __init__(self,
-                 p_name:str = None,
-                 p_range_max = OAStreamTask.C_RANGE_THREAD,
-                 p_ada : bool = True,
-                 p_duplicate_data : bool = False,
-                 p_visualize : bool = False,
-                 p_logging=Log.C_LOG_ALL,
-                 **p_kwargs):
-
-        super().__init__(p_name = p_name,
-                         p_range_max = p_range_max,
-                         p_ada = p_ada,
-                         p_duplicate_data = p_duplicate_data,
-                         p_visualize = p_visualize,
-                         p_logging = p_logging,
-                         **p_kwargs)
+    C_TYPE              = 'Anomaly Detector'
+    C_PLOT_ACTIVE       = True
+    C_PLOT_STANDALONE   = False
+
+## -------------------------------------------------------------------------------------------------
+    def __init__( self,
+                  p_name:str = None,
+                  p_range_max = OAStreamTask.C_RANGE_THREAD,
+                  p_ada : bool = True,
+                  p_duplicate_data : bool = False,
+                  p_visualize : bool = False,
+                  p_logging=Log.C_LOG_ALL,
+                  p_anomaly_buffer_size : int = 10,
+                  **p_kwargs ):
+
+        super().__init__( p_name = p_name,
+                          p_range_max = p_range_max,
+                          p_ada = p_ada,
+                          p_duplicate_data = p_duplicate_data,
+                          p_visualize = p_visualize,
+                          p_logging = p_logging,
+                          **p_kwargs )
         
-        self._ano_id = 0
-        self._anomalies = {}
-        self._ano_scores = []
-
-
-## -------------------------------------------------------------------------------------------------
-    def _run(self, p_inst : InstDict):
-        pass
+        self._ano_id : int          = 0
+        self.anomalies              = {}
+        self._ano_buffer_size : int = p_anomaly_buffer_size
 
 
 ## -------------------------------------------------------------------------------------------------
@@ -116,20 +106,6 @@ def _get_next_anomaly_id(self):
         return self._ano_id
 
 
-## -------------------------------------------------------------------------------------------------
-    def get_anomalies(self):
-        """
-        Method to return the current list of anomalies. 
-
-        Returns
-        -------
-        dict_of_anomalies : dict[Anomaly]
-            Current dictionary of anomalies.
-        """
-
-        return self._anomalies
-    
-
 ## -------------------------------------------------------------------------------------------------
     def _buffer_anomaly(self, p_anomaly:Anomaly):
         """
@@ -141,13 +117,23 @@ def _buffer_anomaly(self, p_anomaly:Anomaly):
             Anomaly object to be added.
         """
 
-        p_anomaly.set_id( p_id = self._get_next_anomaly_id() )
-        self._anomalies[p_anomaly.get_id()] = p_anomaly
-        return p_anomaly
+        # 1 Buffering turned on?
+        if self._ano_buffer_size <= 0: return
+
+        # 2 Buffer full?
+        if len( self.anomalies ) >= self._ano_buffer_size:
+            # 2.1 Remove oldest entry
+            oldest_key     = next(iter(self.anomalies))
+            oldest_anomaly = self.anomalies.pop(oldest_key)
+            oldest_anomaly.remove_plot()
+
+        # 3 Buffer new anomaly
+        p_anomaly.id = self._get_next_anomaly_id() 
+        self.anomalies[p_anomaly.id] = p_anomaly
 
 
 ## -------------------------------------------------------------------------------------------------
-    def remove_anomaly(self, p_anomaly):
+    def _remove_anomaly(self, p_anomaly:Anomaly):
         """
         Method to remove an existing anomaly. Please use as part of your algorithm.
 
@@ -158,11 +144,11 @@ def remove_anomaly(self, p_anomaly):
         """
 
         p_anomaly.remove_plot(p_refresh=True)
-        del self._anomalies[p_anomaly.get_id()]
+        del self.anomalies[p_anomaly.id]
 
 
 ## -------------------------------------------------------------------------------------------------
-    def _raise_anomaly_event(self, p_anomaly : Anomaly ):
+    def _raise_anomaly_event(self, p_anomaly:Anomaly, p_buffer: bool = True):
         """
         Method to raise an anomaly event.
 
@@ -170,14 +156,18 @@ def _raise_anomaly_event(self, p_anomaly : Anomaly ):
         ----------
         p_anomaly : Anomaly
             Anomaly object to be raised.
+        p_buffer : bool
+            Anomaly is buffered when set to True.
         """
 
-        p_anomaly = self._buffer_anomaly(p_anomaly=p_anomaly)
+        if p_buffer: self._buffer_anomaly( p_anomaly=p_anomaly )
 
         if self.get_visualization(): 
-            p_anomaly.init_plot( p_figure=self._figure, p_plot_settings=self.get_plot_settings() )
+            p_anomaly.init_plot( p_figure=self._figure, 
+                                 p_plot_settings=self.get_plot_settings() )
 
-        self._raise_event(p_anomaly.C_TYPE, p_anomaly)
+        self._raise_event( p_event_id = p_anomaly.event_id,
+                           p_event_object = p_anomaly )
 
                  
 ## -------------------------------------------------------------------------------------------------
@@ -191,7 +181,7 @@ def init_plot(self, p_figure: Figure = None, p_plot_settings: PlotSettings = Non
 
         super().init_plot( p_figure=p_figure, p_plot_settings=p_plot_settings)
 
-        for anomaly in self._anomalies.values():
+        for anomaly in self.anomalies.values():
             anomaly.init_plot(p_figure=p_figure, p_plot_settings = p_plot_settings)
     
 
@@ -222,7 +212,7 @@ def update_plot(self, p_inst : InstDict = None, **p_kwargs):
         self._plot_ax_ylim = ax_ylim_new
         self._plot_ax_zlim = ax_zlim_new
 
-        for anomaly in self._anomalies.values():
+        for anomaly in self.anomalies.values():
             anomaly.update_plot( p_axlimits_changed = axlimits_changed,
                                  p_xlim = ax_xlim_new,
                                  p_ylim = ax_ylim_new,
@@ -235,28 +225,23 @@ def remove_plot(self, p_refresh: bool = True):
 
         if not self.get_visualization(): return
 
-        super().remove_plot(p_refresh=p_refresh)
+        # super().remove_plot(p_refresh=p_refresh)
 
-        for anomaly in self._anomalies.values():
+        for anomaly in self.anomalies.values():
             anomaly.remove_plot(p_refresh=p_refresh)
 
 
 ## -------------------------------------------------------------------------------------------------
-    def _renormalize(self, p_normalizer: Normalizer):
+    def _renormalize(self, p_normalizer):
         """
-        Internal renormalization of all anomaly instances. See method OATask.renormalize_on_event() for further
-        information.
+        Internal renormalization of all buffered anomalies. See method OATask.renormalize_on_event() 
+        for further information.
 
         Parameters
         ----------
         p_normalizer : Normalizer
             Normalizer object to be applied on task-specific 
         """
-        anomaly : Anomaly = None
-
-        for anomaly in self._anomalies.values():
-            instances = anomaly.get_instances()
-
-            for inst in instances:
-                inst.remormalize( p_normalizer=p_normalizer)
 
+        for anomaly in self.anomalies.values():
+           anomaly.renormalize( p_normalizer=p_normalizer )
\ No newline at end of file
diff --git a/src/mlpro/oa/streams/tasks/anomalydetectors/cb_detectors/__init__.py b/src/mlpro/oa/streams/tasks/anomalydetectors/cb_detectors/__init__.py
deleted file mode 100644
index cc3af768a..000000000
--- a/src/mlpro/oa/streams/tasks/anomalydetectors/cb_detectors/__init__.py
+++ /dev/null
@@ -1,7 +0,0 @@
-from mlpro.oa.streams.tasks.anomalydetectors.cb_detectors.basics import AnomalyDetectorCB
-from mlpro.oa.streams.tasks.anomalydetectors.cb_detectors.disappearance_detector import ClusterDisappearanceDetector
-from mlpro.oa.streams.tasks.anomalydetectors.cb_detectors.drift_detector import ClusterDriftDetector
-from mlpro.oa.streams.tasks.anomalydetectors.cb_detectors.density_change_detector import ClusterDensityChangeDetector
-from mlpro.oa.streams.tasks.anomalydetectors.cb_detectors.geo_size_change_detector import ClusterGeometricSizeChangeDetector
-from mlpro.oa.streams.tasks.anomalydetectors.cb_detectors.new_cluster_detector import NewClusterDetector
-from mlpro.oa.streams.tasks.anomalydetectors.cb_detectors.size_change_detector import ClusterSizeChangeDetector
\ No newline at end of file
diff --git a/src/mlpro/oa/streams/tasks/anomalydetectors/clusterbased/__init__.py b/src/mlpro/oa/streams/tasks/anomalydetectors/clusterbased/__init__.py
new file mode 100644
index 000000000..95ef92665
--- /dev/null
+++ b/src/mlpro/oa/streams/tasks/anomalydetectors/clusterbased/__init__.py
@@ -0,0 +1 @@
+from mlpro.oa.streams.tasks.anomalydetectors.clusterbased.basics import AnomalyDetectorCB
diff --git a/src/mlpro/oa/streams/tasks/anomalydetectors/cb_detectors/basics.py b/src/mlpro/oa/streams/tasks/anomalydetectors/clusterbased/basics.py
similarity index 77%
rename from src/mlpro/oa/streams/tasks/anomalydetectors/cb_detectors/basics.py
rename to src/mlpro/oa/streams/tasks/anomalydetectors/clusterbased/basics.py
index 80d855d54..f6367cb51 100644
--- a/src/mlpro/oa/streams/tasks/anomalydetectors/cb_detectors/basics.py
+++ b/src/mlpro/oa/streams/tasks/anomalydetectors/clusterbased/basics.py
@@ -42,17 +42,16 @@ class AnomalyDetectorCB(AnomalyDetector):
     # List of cluster properties necessary for the algorithm
     C_REQ_CLUSTER_PROPERTIES : PropertyDefinitions = []
 
-
 ## -------------------------------------------------------------------------------------------------
-    def __init__(self,
-                 p_clusterer : ClusterAnalyzer = None,
-                 p_name : str = None,
-                 p_range_max = StreamTask.C_RANGE_THREAD,
-                 p_ada : bool = True,
-                 p_duplicate_data : bool = False,
-                 p_visualize : bool = False,
-                 p_logging=Log.C_LOG_ALL,
-                 **p_kwargs):
+    def __init__( self,
+                  p_clusterer : ClusterAnalyzer,
+                  p_name : str = None,
+                  p_range_max = StreamTask.C_RANGE_THREAD,
+                  p_ada : bool = True,
+                  p_duplicate_data : bool = False,
+                  p_visualize : bool = False,
+                  p_logging=Log.C_LOG_ALL,
+                  **p_kwargs):
 
         super().__init__(p_name = p_name,
                          p_range_max = p_range_max,
@@ -64,14 +63,7 @@ def __init__(self,
         
         self._clusterer = p_clusterer
 
-        for x in self.C_PROPERTY_DEFINITIONS:
-            if x not in self.C_REQ_CLUSTER_PROPERTIES:
-                self.C_REQ_CLUSTER_PROPERTIES.append(x)
-
         unknown_prop = self._clusterer.align_cluster_properties(p_properties=self.C_REQ_CLUSTER_PROPERTIES)
 
-        #if len(unknown_prop) >0:
-        #    raise RuntimeError("The following cluster properties need to be provided by the clusterer: ", unknown_prop)
-        
-        self._visualize = p_visualize
-
+        if len(unknown_prop) >0:
+            raise RuntimeError("The following cluster properties need to be provided by the clusterer: ", unknown_prop)
\ No newline at end of file
diff --git a/src/mlpro/oa/streams/tasks/anomalydetectors/cb_detectors/density_change_detector.py b/src/mlpro/oa/streams/tasks/anomalydetectors/clusterbased/density_change_detector.py
similarity index 100%
rename from src/mlpro/oa/streams/tasks/anomalydetectors/cb_detectors/density_change_detector.py
rename to src/mlpro/oa/streams/tasks/anomalydetectors/clusterbased/density_change_detector.py
diff --git a/src/mlpro/oa/streams/tasks/anomalydetectors/cb_detectors/disappearance_detector.py b/src/mlpro/oa/streams/tasks/anomalydetectors/clusterbased/disappearance_detector.py
similarity index 100%
rename from src/mlpro/oa/streams/tasks/anomalydetectors/cb_detectors/disappearance_detector.py
rename to src/mlpro/oa/streams/tasks/anomalydetectors/clusterbased/disappearance_detector.py
diff --git a/src/mlpro/oa/streams/tasks/anomalydetectors/cb_detectors/geo_size_change_detector.py b/src/mlpro/oa/streams/tasks/anomalydetectors/clusterbased/geo_size_change_detector.py
similarity index 100%
rename from src/mlpro/oa/streams/tasks/anomalydetectors/cb_detectors/geo_size_change_detector.py
rename to src/mlpro/oa/streams/tasks/anomalydetectors/clusterbased/geo_size_change_detector.py
diff --git a/src/mlpro/oa/streams/tasks/anomalydetectors/cb_detectors/new_cluster_detector.py b/src/mlpro/oa/streams/tasks/anomalydetectors/clusterbased/new_cluster_detector.py
similarity index 100%
rename from src/mlpro/oa/streams/tasks/anomalydetectors/cb_detectors/new_cluster_detector.py
rename to src/mlpro/oa/streams/tasks/anomalydetectors/clusterbased/new_cluster_detector.py
diff --git a/src/mlpro/oa/streams/tasks/anomalydetectors/cb_detectors/size_change_detector.py b/src/mlpro/oa/streams/tasks/anomalydetectors/clusterbased/size_change_detector.py
similarity index 100%
rename from src/mlpro/oa/streams/tasks/anomalydetectors/cb_detectors/size_change_detector.py
rename to src/mlpro/oa/streams/tasks/anomalydetectors/clusterbased/size_change_detector.py
diff --git a/src/mlpro/oa/streams/tasks/anomalydetectors/instancebased/__init__.py b/src/mlpro/oa/streams/tasks/anomalydetectors/instancebased/__init__.py
new file mode 100644
index 000000000..44730f5e1
--- /dev/null
+++ b/src/mlpro/oa/streams/tasks/anomalydetectors/instancebased/__init__.py
@@ -0,0 +1 @@
+from mlpro.oa.streams.tasks.anomalydetectors.instancebased.paga_detectors import AnomalyDetectorPAGA
\ No newline at end of file
diff --git a/src/mlpro/oa/streams/tasks/anomalydetectors/instancebased/paga_detectors.py b/src/mlpro/oa/streams/tasks/anomalydetectors/instancebased/paga_detectors.py
new file mode 100644
index 000000000..12d4dc078
--- /dev/null
+++ b/src/mlpro/oa/streams/tasks/anomalydetectors/instancebased/paga_detectors.py
@@ -0,0 +1,156 @@
+## -------------------------------------------------------------------------------------------------
+## -- Project : MLPro - The integrative middleware framework for standardized machine learning
+## -- Package : mlpro.oa.streams.tasks.anomalydetectors
+## -- Module  : paga_detectors.py
+## -------------------------------------------------------------------------------------------------
+## -- History :
+## -- yyyy-mm-dd  Ver.      Auth.    Description
+## -- 2023-06-08  0.0.0     SK       Creation
+## -- 2023-09-12  1.0.0     SK       Release
+## -- 2023-11-21  1.0.1     SK       Time Stamp update
+## -- 2024-02-25  1.1.0     SK       Visualisation update
+## -- 2024-04-10  1.2.0     DA/SK    Code review
+## -- 2024-05-07  1.2.1     SK       Bug fix on groupanomaly visualisation
+## -- 2024-08-12  1.3.0     DA       Review and adjustments on documentation
+## -- 2025-02-14  1.4.0     DA       Refactoring
+## -- 2025-02-17  1.5.0     DA       Review and generalization
+## -------------------------------------------------------------------------------------------------
+
+"""
+Ver. 1.5.0 (2025-02-17)
+
+This module provides an extended template for custom anomaly detectors that supports an optional
+group anomaly detection based on point anomalies.
+"""
+
+
+from mlpro.bf.various import Log
+from mlpro.bf.streams import Instance
+
+from mlpro.oa.streams.basics import StreamTask
+from mlpro.oa.streams.tasks.anomalydetectors.basics import AnomalyDetector
+from mlpro.oa.streams.tasks.anomalydetectors.anomalies import Anomaly, PointAnomaly, GroupAnomaly
+
+
+
+## -------------------------------------------------------------------------------------------------
+## -------------------------------------------------------------------------------------------------
+class AnomalyDetectorPAGA (AnomalyDetector):
+    """
+    This class is an extended template offering an optional group anomaly detection based on point 
+    anomalies. This detection can be turned on/off via the p_group_anomaly_det parameter. See 
+    method _buffer_anomaly() for further details. 
+
+    Parameters
+    ----------
+    p_group_anomaly_det : bool
+        Paramter to activate group anomaly detection. Default is True.
+    p_name : str
+        Optional name of the task. Default is None.
+    p_range_max : int
+        Maximum range of asynchonicity. See class Range. Default is Range.C_RANGE_PROCESS.
+    p_ada : bool
+        Boolean switch for adaptivitiy. Default = True.
+    p_duplicate_data : bool
+        If True, instances will be duplicated before processing. Default = False.
+    p_visualize : bool
+        Boolean switch for visualisation. Default = False.
+    p_logging
+        Log level (see constants of class Log). Default: Log.C_LOG_ALL
+    p_kwargs : dict
+        Further optional named parameters.
+    """
+
+    C_TYPE = 'Anomaly Detector PAGA'
+
+## -------------------------------------------------------------------------------------------------
+    def __init__( self,
+                  p_group_anomaly_det : bool = True,
+                  p_name:str = None,
+                  p_range_max = StreamTask.C_RANGE_THREAD,
+                  p_ada : bool = True,
+                  p_duplicate_data : bool = False,
+                  p_visualize : bool = False,
+                  p_logging = Log.C_LOG_ALL,
+                  **p_kwargs ):
+
+        super().__init__( p_name = p_name,
+                          p_range_max = p_range_max,
+                          p_ada = p_ada,
+                          p_duplicate_data = p_duplicate_data,
+                          p_visualize = p_visualize,
+                          p_logging = p_logging,
+                          **p_kwargs )
+        
+        self._group_anomalies : list[Anomaly] = []
+        self._group_anomalies_instances : list[Instance] = []
+        self._group_ano_scores  = []
+        self._group_anomaly_det = p_group_anomaly_det
+
+
+## -------------------------------------------------------------------------------------------------
+    def _buffer_anomaly(self, p_anomaly):
+        """
+        Method to be used to add a new anomaly. Please use as part of your algorithm. 
+
+        Parameters
+        ----------
+        p_anomaly : Anomaly
+            Anomaly object to be added.
+        """
+
+        if ( not self._group_anomaly_det ) or ( type(p_anomaly) != PointAnomaly ):
+            return super()._buffer_anomaly( p_anomaly = p_anomaly)
+
+
+        if self._group_anomaly_det:
+            self._group_anomalies.append(p_anomaly)
+            self._group_anomalies_instances.append(p_anomaly.get_instances()[-1])
+            self._group_ano_scores.append(p_anomaly.get_ano_scores())
+
+            if len(self._group_anomalies_instances) > 1:
+
+                inst_2 = self._group_anomalies_instances[-1]
+                second = inst_2.id
+                inst_1 = self._group_anomalies_instances[-2]
+                first  = inst_1.id
+                    
+                if int(second) - 1 == int(first):
+
+                    if len(self._group_anomalies_instances) == 3:
+
+                        for i in range(2):
+                            self._remove_anomaly(self._group_anomalies[i])
+
+                        self._ano_id -= 2
+                        groupanomaly = GroupAnomaly( p_instances = self._group_anomalies_instances,
+                                                     p_ano_scores = self._group_ano_scores, p_visualize=self._visualize,
+                                                     p_raising_object = self,
+                                                     p_tstampt = inst_2.tstamp )
+                            
+                        super()._buffer_anomaly( p_anomaly = groupanomaly )                    
+
+                        self._group_anomalies = []
+                        self._group_anomalies.append(groupanomaly)
+
+                    elif len(self._group_anomalies_instances) > 3:
+                        self._group_anomalies[0].set_instances(self._group_anomalies_instances, self._group_ano_scores)
+                        self._group_anomalies.pop(-1)
+                        return self._group_anomalies[0]
+                            
+                    else:
+                        super()._buffer_anomaly( p_anomaly = p_anomaly)                    
+
+                else:
+                    for groupanomaly in self._group_anomalies:
+                        if isinstance(groupanomaly, GroupAnomaly):
+                            groupanomaly.plot_update = False
+                    self._group_anomalies = []
+                    self._group_anomalies_instances = []
+                    self._group_ano_scores = []
+                    self._group_anomalies.append(p_anomaly)
+                    self._group_anomalies_instances.append(p_anomaly.get_instances()[-1])
+                    self._group_ano_scores.append(p_anomaly.get_ano_scores())
+                    super()._buffer_anomaly( p_anomaly = p_anomaly )
+            else:
+                super()._buffer_anomaly( p_anomaly = p_anomaly)            
\ No newline at end of file
diff --git a/src/mlpro/oa/streams/tasks/anomalydetectors/paga_detectors.py b/src/mlpro/oa/streams/tasks/anomalydetectors/paga_detectors.py
deleted file mode 100644
index 3af769917..000000000
--- a/src/mlpro/oa/streams/tasks/anomalydetectors/paga_detectors.py
+++ /dev/null
@@ -1,162 +0,0 @@
-## -------------------------------------------------------------------------------------------------
-## -- Project : MLPro - The integrative middleware framework for standardized machine learning
-## -- Package : mlpro.oa.streams.tasks.anomalydetectors
-## -- Module  : paga_detectors.py
-## -------------------------------------------------------------------------------------------------
-## -- History :
-## -- yyyy-mm-dd  Ver.      Auth.    Description
-## -- 2023-06-08  0.0.0     SK       Creation
-## -- 2023-09-12  1.0.0     SK       Release
-## -- 2023-11-21  1.0.1     SK       Time Stamp update
-## -- 2024-02-25  1.1.0     SK       Visualisation update
-## -- 2024-04-10  1.2.0     DA/SK    Code review
-## -- 2024-05-07  1.2.1     SK       Bug fix on groupanomaly visualisation
-## -- 2024-08-12  1.3.0     DA       Review and adjustments on documentation
-## -------------------------------------------------------------------------------------------------
-
-"""
-Ver. 1.3.0 (2024-08-12)
-
-This module provides a ready-to-use detector for point and group anomalies.
-"""
-
-from mlpro.oa.streams.basics import *
-from mlpro.oa.streams.tasks.anomalydetectors.basics import AnomalyDetector
-from mlpro.oa.streams.tasks.anomalydetectors.anomalies import *
-
-
-
-## -------------------------------------------------------------------------------------------------
-## -------------------------------------------------------------------------------------------------
-class AnomalyDetectorPAGA(AnomalyDetector):
-    """
-    This class implements a ready-to-use detector for point and group anomalies.
-
-    Parameters
-    ----------
-    p_group_anomaly_det : bool
-        Paramter to activate group anomaly detection. Default is True.
-    p_name : str
-        Optional name of the task. Default is None.
-    p_range_max : int
-        Maximum range of asynchonicity. See class Range. Default is Range.C_RANGE_PROCESS.
-    p_ada : bool
-        Boolean switch for adaptivitiy. Default = True.
-    p_duplicate_data : bool
-        If True, instances will be duplicated before processing. Default = False.
-    p_visualize : bool
-        Boolean switch for visualisation. Default = False.
-    p_logging
-        Log level (see constants of class Log). Default: Log.C_LOG_ALL
-    p_kwargs : dict
-        Further optional named parameters.
-
-    """
-
-    C_NAME          = 'Anomaly Detector'
-
-    C_PLOT_ACTIVE           = True
-    C_PLOT_STANDALONE       = False
-
-## -------------------------------------------------------------------------------------------------
-    def __init__(self,
-                 p_group_anomaly_det : bool = True,
-                 p_name:str = None,
-                 p_range_max = StreamTask.C_RANGE_THREAD,
-                 p_ada : bool = True,
-                 p_duplicate_data : bool = False,
-                 p_visualize : bool = False,
-                 p_logging=Log.C_LOG_ALL,
-                 **p_kwargs):
-
-        super().__init__(p_name = p_name,
-                         p_range_max = p_range_max,
-                         p_ada = p_ada,
-                         p_duplicate_data = p_duplicate_data,
-                         p_visualize = p_visualize,
-                         p_logging = p_logging,
-                         **p_kwargs)
-        
-        self.group_anomalies : list[Anomaly] = []
-        self.group_anomalies_instances : list[Instance] = []
-        self.group_ano_scores = []
-        self.group_anomaly_det = p_group_anomaly_det
-
-
-## -------------------------------------------------------------------------------------------------
-    def _buffer_anomaly(self, p_anomaly):
-        """
-        Method to be used to add a new anomaly. Please use as part of your algorithm.
-
-        Parameters
-        ----------
-        p_anomaly : Anomaly
-            Anomaly object to be added.
-
-        Returns
-        -------
-        p_anomaly : Anomaly
-            Modified Anomaly object.
-        """
-
-        if self.group_anomaly_det:
-            self.group_anomalies.append(p_anomaly)
-            self.group_anomalies_instances.append(p_anomaly.get_instances()[-1])
-            self.group_ano_scores.append(p_anomaly.get_ano_scores())
-
-            if len(self.group_anomalies_instances) > 1:
-
-                inst_2 = self.group_anomalies_instances[-1]
-                second = inst_2.get_id()
-                inst_1 = self.group_anomalies_instances[-2]
-                first  = inst_1.get_id()
-                
-                if int(second) - 1 == int(first):
-
-                    if len(self.group_anomalies_instances) == 3:
-
-                        for i in range(2):
-                            self.remove_anomaly(self.group_anomalies[i])
-                        self._ano_id -= 2
-                        anomaly = GroupAnomaly(p_instances=self.group_anomalies_instances,
-                                               p_ano_scores=self.group_ano_scores, p_visualize=self._visualize,
-                                               p_raising_object=self,
-                                               p_det_time=str(inst_2.get_tstamp()))
-                        anomaly.set_id( p_id = self._get_next_anomaly_id() )
-                        self._anomalies[anomaly.get_id()] = anomaly
-                        self.group_anomalies = []
-                        self.group_anomalies.append(anomaly)
-                        return anomaly
-
-                    elif len(self.group_anomalies_instances) > 3:
-                        self.group_anomalies[0].set_instances(self.group_anomalies_instances, self.group_ano_scores)
-                        self.group_anomalies.pop(-1)
-                        return self.group_anomalies[0]
-                        
-                    else:
-                        p_anomaly.set_id( p_id = self._get_next_anomaly_id() )
-                        self._anomalies[p_anomaly.get_id()] = p_anomaly
-                        return p_anomaly
-                    
-                else:
-                    for anomaly in self.group_anomalies:
-                        if isinstance(anomaly, GroupAnomaly):
-                            anomaly.plot_update = False
-                    self.group_anomalies = []
-                    self.group_anomalies_instances = []
-                    self.group_ano_scores = []
-                    self.group_anomalies.append(p_anomaly)
-                    self.group_anomalies_instances.append(p_anomaly.get_instances()[-1])
-                    self.group_ano_scores.append(p_anomaly.get_ano_scores())
-                    p_anomaly.set_id( p_id = self._get_next_anomaly_id() )
-                    self._anomalies[p_anomaly.get_id()] = p_anomaly
-                    return p_anomaly
-            else:
-                p_anomaly.set_id( p_id = self._get_next_anomaly_id() )
-                self._anomalies[p_anomaly.get_id()] = p_anomaly
-                return p_anomaly
-            
-        else:
-            p_anomaly.set_id( p_id = self._get_next_anomaly_id() )
-            self._anomalies[p_anomaly.get_id()] = p_anomaly
-            return p_anomaly
diff --git a/src/mlpro/oa/streams/tasks/anomalypredictors/__init__.py b/src/mlpro/oa/streams/tasks/anomalypredictors/__init__.py.off
similarity index 100%
rename from src/mlpro/oa/streams/tasks/anomalypredictors/__init__.py
rename to src/mlpro/oa/streams/tasks/anomalypredictors/__init__.py.off
diff --git a/src/mlpro/oa/streams/tasks/driftdetectors/__init__.py b/src/mlpro/oa/streams/tasks/driftdetectors/__init__.py
new file mode 100644
index 000000000..60bc06394
--- /dev/null
+++ b/src/mlpro/oa/streams/tasks/driftdetectors/__init__.py
@@ -0,0 +1 @@
+from mlpro.oa.streams.tasks.driftdetectors.basics import DriftDetector
\ No newline at end of file
diff --git a/src/mlpro/oa/streams/tasks/driftdetectors/basics.py b/src/mlpro/oa/streams/tasks/driftdetectors/basics.py
new file mode 100644
index 000000000..3c36d0b1e
--- /dev/null
+++ b/src/mlpro/oa/streams/tasks/driftdetectors/basics.py
@@ -0,0 +1,217 @@
+## -------------------------------------------------------------------------------------------------
+## -- Project : MLPro - The integrative middleware framework for standardized machine learning
+## -- Package : mlpro.oa.streams.tasks.driftdetectors
+## -- Module  : basics.py
+## -------------------------------------------------------------------------------------------------
+## -- History :
+## -- yyyy-mm-dd  Ver.      Auth.    Description
+## -- 2025-02-12  0.1.0     DA       Creation
+## -------------------------------------------------------------------------------------------------
+
+"""
+Ver. 0.1.0 (2025-02-12)
+
+This module provides templates for drift detection to be used in the context of online adaptivity.
+"""
+
+try:
+    from matplotlib.figure import Figure
+except:
+    class Figure : pass
+
+from mlpro.bf.plot import PlotSettings
+from mlpro.bf.streams import InstDict
+from mlpro.bf.various import Log
+from mlpro.oa.streams import OAStreamTask
+from mlpro.oa.streams.tasks.driftdetectors.drifts import Drift
+
+
+
+## -------------------------------------------------------------------------------------------------
+## -------------------------------------------------------------------------------------------------
+class DriftDetector (OAStreamTask):
+    """
+    Base class for online anomaly detectors. It raises an event whenever the beginning or the end 
+    of a drift is detected. Please describe in child classes which event classes are used. Always
+    use the _raise_drift_event() method when raising an event. 
+
+    Parameters
+    ----------
+    p_name : str
+        Optional name of the task. Default is None.
+    p_range_max : int
+        Maximum range of asynchonicity. See class Range. Default is Range.C_RANGE_PROCESS.
+    p_ada : bool
+        Boolean switch for adaptivitiy. Default = True.
+    p_duplicate_data : bool
+        If True, instances will be duplicated before processing. Default = False.
+    p_visualize : bool
+        Boolean switch for visualisation. Default = False.
+    p_logging
+        Log level (see constants of class Log). Default: Log.C_LOG_ALL
+    p_kwargs : dict
+        Further optional named parameters.
+    """
+
+    C_TYPE            = 'Drift Detector'
+    C_PLOT_ACTIVE     = True
+    C_PLOT_STANDALONE = False
+
+## -------------------------------------------------------------------------------------------------
+    def __init__( self,
+                  p_name:str = None,
+                  p_range_max = OAStreamTask.C_RANGE_THREAD,
+                  p_ada : bool = True,
+                  p_duplicate_data : bool = False,
+                  p_visualize : bool = False,
+                  p_logging=Log.C_LOG_ALL,
+                  **p_kwargs ):
+
+        super().__init__( p_name = p_name,
+                          p_range_max = p_range_max,
+                          p_ada = p_ada,
+                          p_duplicate_data = p_duplicate_data,
+                          p_visualize = p_visualize,
+                          p_logging = p_logging,
+                          **p_kwargs )
+        
+        self._drift_id = 0
+        self._drifts   = {}
+
+
+## -------------------------------------------------------------------------------------------------
+    def get_drifts(self):
+        """
+        Method to return the current list of drifts. 
+
+        Returns
+        -------
+        drifts : dict[Drift]
+            Current dictionary of drifts.
+        """
+
+        return self._drifts
+    
+
+## -------------------------------------------------------------------------------------------------
+    def _get_next_drift_id(self):
+        """
+        Methd that returns the id of the next drift. 
+
+        Returns
+        -------
+        drift_id : int
+        """
+
+        self._drift_id +=1
+        return self._drift_id
+
+
+## -------------------------------------------------------------------------------------------------
+    def _buffer_drift(self, p_drift:Drift):
+        """
+        Method to be used internally to add a new drift object. Please use as part of your algorithm.
+
+        Parameters
+        ----------
+        p_drift : Drift
+            Drift object to be added.
+        """
+
+        p_drift.id = self._get_next_drift_id()
+        self._drifts[p_drift.id] = p_drift
+
+
+## -------------------------------------------------------------------------------------------------
+    def _remove_drift(self, p_drift:Drift):
+        """
+        Method to remove an existing drift object. Please use as part of your algorithm.
+
+        Parameters
+        ----------
+        p_drift : Drift
+            Drift object to be removed.
+        """
+
+        p_drift.remove_plot(p_refresh=True)
+        del self._drifts[p_drift.id]
+
+
+## -------------------------------------------------------------------------------------------------
+    def _raise_drift_event( self, p_drift : Drift ):
+        """
+        Specialized method to raise drift events. 
+
+        Parameters
+        ----------
+        p_drift : Drift
+            Drift event object to be raised.
+        """
+
+        self._buffer_drift( p_drift = p_drift )
+
+        if self.get_visualization(): 
+            p_drift.init_plot( p_figure=self._figure, p_plot_settings=self.get_plot_settings() )
+
+        return super()._raise_event( p_event_id = p_drift.event_id, p_event_object = p_drift )
+    
+                 
+## -------------------------------------------------------------------------------------------------
+    def init_plot(self, p_figure: Figure = None, p_plot_settings: PlotSettings = None):
+
+        if not self.get_visualization(): return
+
+        self._plot_ax_xlim = None
+        self._plot_ax_ylim = None
+        self._plot_ax_zlim = None
+
+        super().init_plot( p_figure=p_figure, p_plot_settings=p_plot_settings)
+
+        for drift in self._drifts.values():
+            drift.init_plot(p_figure=p_figure, p_plot_settings = p_plot_settings)
+    
+
+## -------------------------------------------------------------------------------------------------
+    def update_plot(self, p_inst : InstDict = None, **p_kwargs):
+    
+        if not self.get_visualization(): return
+
+        # super().update_plot(p_inst, **p_kwargs)
+
+        axes = self._plot_settings.axes
+
+        ax_xlim_new = axes.get_xlim()
+        if self._plot_settings.view != PlotSettings.C_VIEW_ND:
+            axlimits_changed = ( self._plot_ax_xlim is None ) or ( self._plot_ax_xlim != ax_xlim_new )
+        else:
+            axlimits_changed = False
+
+        ax_ylim_new = axes.get_ylim()
+        axlimits_changed = axlimits_changed or ( self._plot_ax_ylim is None ) or ( self._plot_ax_ylim != ax_ylim_new )
+        try:
+            ax_zlim_new = axes.get_zlim()
+            axlimits_changed = axlimits_changed or ( self._plot_ax_zlim is None ) or ( self._plot_ax_zlim != ax_zlim_new )
+        except:
+            ax_zlim_new = None
+        
+        self._plot_ax_xlim = ax_xlim_new
+        self._plot_ax_ylim = ax_ylim_new
+        self._plot_ax_zlim = ax_zlim_new
+
+        for drift in self._drifts.values():
+            drift.update_plot( p_axlimits_changed = axlimits_changed,
+                               p_xlim = ax_xlim_new,
+                               p_ylim = ax_ylim_new,
+                               p_zlim = ax_zlim_new,
+                               **p_kwargs )
+    
+
+## -------------------------------------------------------------------------------------------------
+    def remove_plot(self, p_refresh: bool = True):
+
+        if not self.get_visualization(): return
+
+        # super().remove_plot( p_refresh = p_refresh )
+
+        for drift in self._drifts.values():
+            drift.remove_plot(p_refresh=p_refresh)
\ No newline at end of file
diff --git a/src/mlpro/oa/streams/tasks/driftdetectors/clusterbased/__init__.py b/src/mlpro/oa/streams/tasks/driftdetectors/clusterbased/__init__.py
new file mode 100644
index 000000000..a4f80c4ae
--- /dev/null
+++ b/src/mlpro/oa/streams/tasks/driftdetectors/clusterbased/__init__.py
@@ -0,0 +1,2 @@
+from mlpro.oa.streams.tasks.driftdetectors.clusterbased.basics import DriftDetectorCB
+from mlpro.oa.streams.tasks.driftdetectors.clusterbased.movement import DriftDetectorCBMovement
\ No newline at end of file
diff --git a/src/mlpro/oa/streams/tasks/driftdetectors/clusterbased/basics.py b/src/mlpro/oa/streams/tasks/driftdetectors/clusterbased/basics.py
new file mode 100644
index 000000000..6f0dc275b
--- /dev/null
+++ b/src/mlpro/oa/streams/tasks/driftdetectors/clusterbased/basics.py
@@ -0,0 +1,83 @@
+## -- ----------------------------------------------------------------------------------------------
+## -- Project : MLPro - The integrative middleware framework for standardized machine learning
+## -- Package : mlpro.oa.streams.tasks.driftdetectors.clusterbased
+## -- Module  : basics.py
+## -------------------------------------------------------------------------------------------------
+## -- History :
+## -- yyyy-mm-dd  Ver.      Auth.    Description
+## -- 2025-02-12  0.1.0     DA       Creation
+## -------------------------------------------------------------------------------------------------
+
+"""
+Ver. 0.1.0 (2025-02-12)
+
+This module provides template for cluster-based drift detection algorithms to be used in the context of online adaptivity.
+"""
+
+from mlpro.bf.various import Log
+from mlpro.bf.math.properties import PropertyDefinitions
+
+from mlpro.oa.streams.basics import OAStreamTask
+from mlpro.oa.streams.tasks.driftdetectors.basics import DriftDetector
+from mlpro.oa.streams.tasks.clusteranalyzers.basics import ClusterAnalyzer
+
+
+
+
+## -------------------------------------------------------------------------------------------------
+## -------------------------------------------------------------------------------------------------
+class DriftDetectorCB (DriftDetector):
+    """
+    This is the base class for online-adaptive cluster-based drift detectors. It raises an event 
+    when a drift is detected in a cluster dataset.
+
+    Parameters
+    ----------
+    p_clusterer : ClusterAnalyzer
+        Related cluster analyzer providing its clusters as the basis for drift detection
+    p_name : str
+        Optional name of the task. Default is None.
+    p_range_max : int
+        Maximum range of asynchonicity. See class Range. Default is Range.C_RANGE_PROCESS.
+    p_ada : bool
+        Boolean switch for adaptivitiy. Default = True.
+    p_duplicate_data : bool
+        If True, instances will be duplicated before processing. Default = False.
+    p_visualize : bool
+        Boolean switch for visualisation. Default = False.
+    p_logging
+        Log level (see constants of class Log). Default: Log.C_LOG_ALL
+    p_kwargs : dict
+        Further optional named parameters.
+    """
+
+    C_TYPE = 'Cluster-based Drift Detector'
+
+    # List of cluster properties necessary for the algorithm
+    C_REQ_CLUSTER_PROPERTIES : PropertyDefinitions = []
+
+## -------------------------------------------------------------------------------------------------
+    def __init__( self,
+                  p_clusterer : ClusterAnalyzer,
+                  p_name : str = None,
+                  p_range_max = OAStreamTask.C_RANGE_THREAD,
+                  p_ada : bool = True,
+                  p_duplicate_data : bool = False,
+                  p_visualize : bool = False,
+                  p_logging=Log.C_LOG_ALL,
+                  **p_kwargs ):
+
+        super().__init__( p_name = p_name,
+                          p_range_max = p_range_max,
+                          p_ada = p_ada,
+                          p_duplicate_data = p_duplicate_data,
+                          p_visualize = p_visualize,
+                          p_logging = p_logging,
+                          **p_kwargs )
+        
+        self._clusterer = p_clusterer
+        unknown_prop    = self._clusterer.align_cluster_properties(p_properties=self.C_REQ_CLUSTER_PROPERTIES)
+
+        if len(unknown_prop) > 0:
+           raise RuntimeError("The following cluster properties need to be provided by the clusterer: ", unknown_prop)
+        
\ No newline at end of file
diff --git a/src/mlpro/oa/streams/tasks/anomalydetectors/cb_detectors/drift_detector.py b/src/mlpro/oa/streams/tasks/driftdetectors/clusterbased/movement.py
similarity index 71%
rename from src/mlpro/oa/streams/tasks/anomalydetectors/cb_detectors/drift_detector.py
rename to src/mlpro/oa/streams/tasks/driftdetectors/clusterbased/movement.py
index a28ffd8ed..a25c363ec 100644
--- a/src/mlpro/oa/streams/tasks/anomalydetectors/cb_detectors/drift_detector.py
+++ b/src/mlpro/oa/streams/tasks/driftdetectors/clusterbased/movement.py
@@ -1,7 +1,7 @@
 ## -- ----------------------------------------------------------------------------------------------
 ## -- Project : MLPro - The integrative middleware framework for standardized machine learning
-## -- Package : mlpro.oa.tasks.anomalydetectors.cb_detectors
-## -- Module  : drift_detector.py
+## -- Package : mlpro.oa.tasks.driftdetectors.clusterbased
+## -- Module  : movement.py
 ## -------------------------------------------------------------------------------------------------
 ## -- History :
 ## -- yyyy-mm-dd  Ver.      Auth.    Description
@@ -9,74 +9,108 @@
 ## -- 2023-09-12  1.0.0     SK       Release
 ## -- 2024-04-10  1.1.0     DA/SK    Refactoring
 ## -- 2024-05-28  1.2.0     SK       Refactoring
+## -- 2025-02-12  2.0.0     DA       Relocation, refactoring, renaming
 ## -------------------------------------------------------------------------------------------------
 
 """
-Ver. 1.2.0 (2024-05-28)
+Ver. 2.0.0 (2025-02-12)
 
-This module provides cluster drift detector algorithm.
+This module provides a cluster-based drift detector algorithm determining cluster movement.
 """
 
-from mlpro.oa.streams.basics import *
-from mlpro.oa.streams.tasks.anomalydetectors.cb_detectors.basics import AnomalyDetectorCB
-from mlpro.oa.streams.tasks.anomalydetectors.anomalies.clusterbased.drift import ClusterDrift
-from mlpro.oa.streams.tasks.clusteranalyzers.basics import ClusterAnalyzer
-from mlpro.bf.streams import Instance, InstDict
+import time
+
+
+from mlpro.bf.various import Log
+from mlpro.bf.streams import InstDict
+from mlpro.bf.streams.tasks import StreamTask
 from mlpro.bf.math.properties import *
+
+from mlpro.oa.streams.tasks.driftdetectors.clusterbased import DriftDetectorCB
+from mlpro.oa.streams.tasks.driftdetectors.drifts.clusterbased import DriftCBMovement
+from mlpro.oa.streams.tasks.clusteranalyzers.basics import ClusterAnalyzer
 from mlpro.oa.streams.tasks.clusteranalyzers.clusters.properties.centroid import cprop_centroid2
-import time
+
 
 
 
 ## -------------------------------------------------------------------------------------------------
 ## -------------------------------------------------------------------------------------------------
-class ClusterDriftDetector(AnomalyDetectorCB):
+class DriftDetectorCBMovement (DriftDetectorCB):
     """
     This is the class for detecting change in velocity of clusters.
 
+    Parameters
+    ----------
+    p_clusterer : ClusterAnalyzer
+        Related cluster analyzer providing its clusters as the basis for drift detection
+    p_instantaneous_velocity_change_detection : bool = True
+        Boolean switch for instantaneous velocity change detection (Default: True)
+    p_min_velocity_threshold : float = 0.01
+        Threshold for minimum velocity (Default: 0.01)
+    p_state_change_detection : bool = False
+        Boolean switch for state change detection (Default: False)
+    p_min_acceleration_threshold : float = None
+        Threshold for minimum acceleration (Default: None)
+    p_name : str
+        Optional name of the task. Default is None.
+    p_range_max : int
+        Maximum range of asynchonicity. See class Range. Default is Range.C_RANGE_PROCESS.
+    p_ada : bool
+        Boolean switch for adaptivitiy. Default = True.
+    p_duplicate_data : bool
+        If True, instances will be duplicated before processing. Default = False.
+    p_visualize : bool
+        Boolean switch for visualisation. Default = False.
+    p_logging
+        Log level (see constants of class Log). Default: Log.C_LOG_ALL
+    p_kwargs : dict
+        Further optional named parameters.
+
     """
+
+    C_TYPE = 'Cluster-based Drift Detector (Movement)'
     C_PROPERTY_DEFINITIONS : PropertyDefinitions = [cprop_centroid2]
 
 ## -------------------------------------------------------------------------------------------------
-    def __init__(self,
-                 p_clusterer : ClusterAnalyzer = None,
-                 p_instantaneous_velocity_change_detection : bool = True,
-                 p_min_velocity_threshold : float = 0.01,
-                 p_state_change_detection : bool = False,
-                 p_min_acceleration_threshold : float = False,
-                 p_buffer_size = 5,
-                 p_ema_alpha : float = 0.7,
-                 p_with_time_calculation : bool = True, 
-                 p_initial_skip : int = 1,
-                 p_name:str = None,
-                 p_range_max = StreamTask.C_RANGE_THREAD,
-                 p_ada : bool = True,
-                 p_duplicate_data : bool = False,
-                 p_visualize : bool = False,
-                 p_logging=Log.C_LOG_ALL,
-                 **p_kwargs):
-
-        super().__init__(p_clusterer = p_clusterer,
-                         p_name = p_name,
-                         p_range_max = p_range_max,
-                         p_ada = p_ada,
-                         p_duplicate_data = p_duplicate_data,
-                         p_visualize = p_visualize,
-                         p_logging = p_logging,
-                         **p_kwargs)
+    def __init__( self,
+                  p_clusterer : ClusterAnalyzer,
+                  p_instantaneous_velocity_change_detection : bool = True,
+                  p_min_velocity_threshold : float = 0.01,
+                  p_state_change_detection : bool = False,
+                  p_min_acceleration_threshold : float = None,
+                  p_buffer_size = 5,
+                  p_ema_alpha : float = 0.7,
+                  p_with_time_calculation : bool = True, 
+                  p_initial_skip : int = 1,
+                  p_name:str = None,
+                  p_range_max = StreamTask.C_RANGE_THREAD,
+                  p_ada : bool = True,
+                  p_duplicate_data : bool = False,
+                  p_visualize : bool = False,
+                  p_logging=Log.C_LOG_ALL,
+                  **p_kwargs ):
+
+        super().__init__( p_clusterer = p_clusterer,
+                          p_name = p_name,
+                          p_range_max = p_range_max,
+                          p_ada = p_ada,
+                          p_duplicate_data = p_duplicate_data,
+                          p_visualize = p_visualize,
+                          p_logging = p_logging,
+                          **p_kwargs )
         
         # Parameters for dynamic threshold, EMA smoothing, and time-based calculation
-        self._inst_change_det = p_instantaneous_velocity_change_detection
-        self._state_change_det = p_state_change_detection
-        self._min_vel_thresh = p_min_velocity_threshold
-        self._min_acc_thresh = p_min_acceleration_threshold
-        self._buffer_size = p_buffer_size
-        self._ema_alpha = p_ema_alpha
-        self._with_time_calculation = p_with_time_calculation
-        self._init_skip = p_initial_skip
-        self._visualize = p_visualize
-        self._count = 0
-        self._count_change = {}
+        self._inst_change_det : bool        = p_instantaneous_velocity_change_detection
+        self._state_change_det : bool       = p_state_change_detection
+        self._min_vel_thresh : float        = p_min_velocity_threshold
+        self._min_acc_thresh : float        = p_min_acceleration_threshold
+        self._buffer_size                   = p_buffer_size
+        self._ema_alpha                     = p_ema_alpha
+        self._with_time_calculation : bool  = p_with_time_calculation
+        self._init_skip : int               = p_initial_skip
+        self._count : int                   = 0
+        self._count_change                  = {}
         
         # Data structures for storing previous states and buffers
         self._centroids_history = {}
@@ -145,13 +179,14 @@ def _run(self, p_inst : InstDict):
         # Raise Anomaly event
         if (self._count >= self._init_skip):
             if len(drifting_clusters) != 0:
-                anomaly = ClusterDrift(p_id = self._get_next_anomaly_id,
-                                 p_instances=new_instances,
-                                 p_clusters=drifting_clusters,
-                                 p_properties=properties,
-                                 p_det_time=str(inst.get_tstamp()),
-                                 p_visualize=self._visualize)
-                self._raise_anomaly_event(anomaly)
+                drift = DriftCBMovement( p_drift_status = True,
+                                         p_id = self._get_next_drift_id(),
+                                         p_instances=new_instances,
+                                         p_clusters=drifting_clusters,
+                                         p_properties=properties,
+                                         p_det_time=str(inst.get_tstamp()),
+                                         p_visualize=self._visualize )
+                self._raise_drift_event( p_drift = drift )
 
         if self._count < self._init_skip:
             self._count += 1
diff --git a/src/mlpro/oa/streams/tasks/driftdetectors/drifts/__init__.py b/src/mlpro/oa/streams/tasks/driftdetectors/drifts/__init__.py
new file mode 100644
index 000000000..7651d8c8f
--- /dev/null
+++ b/src/mlpro/oa/streams/tasks/driftdetectors/drifts/__init__.py
@@ -0,0 +1,2 @@
+from mlpro.oa.streams.tasks.driftdetectors.drifts.basics import Drift
+from mlpro.oa.streams.tasks.driftdetectors.drifts.clusterbased import *
\ No newline at end of file
diff --git a/src/mlpro/oa/streams/tasks/driftdetectors/drifts/basics.py b/src/mlpro/oa/streams/tasks/driftdetectors/drifts/basics.py
new file mode 100644
index 000000000..7b068a888
--- /dev/null
+++ b/src/mlpro/oa/streams/tasks/driftdetectors/drifts/basics.py
@@ -0,0 +1,98 @@
+## -------------------------------------------------------------------------------------------------
+## -- Project : MLPro - The integrative middleware framework for standardized machine learning
+## -- Package : mlpro.oa.tasks.driftdetectors.drifts
+## -- Module  : basics.py
+## -------------------------------------------------------------------------------------------------
+## -- History :
+## -- yyyy-mm-dd  Ver.      Auth.    Description
+## -- 2025-02-12  0.1.0     DA       Creation
+## -- 2025-02-13  0.2.0     DA       Class Drift: new attributes event_id, drift_status
+## -- 2025-02-19  0.3.0     DA       Class Drift: new parent Renomalizable
+## -------------------------------------------------------------------------------------------------
+
+"""
+Ver. 0.3.0 (2025-02-19)
+
+This module provides a template class for types of data drift to be used in drift detection algorithms.
+"""
+
+from datetime import datetime
+
+from mlpro.bf.various import Id
+from mlpro.bf.plot import Plottable
+from mlpro.bf.events import Event
+from mlpro.bf.math.normalizers import Renormalizable
+
+
+
+## -------------------------------------------------------------------------------------------------
+## -------------------------------------------------------------------------------------------------
+class Drift (Id, Event, Plottable, Renormalizable):
+    """
+    This is the base class for drift events which can be raised by drift detectors when the beginning
+    or end of a drift is detected.
+
+    Parameters
+    ----------
+    p_drift_status : bool
+        Determines whether a new drift starts (True) or an existing drift ends (False).
+    p_id : int
+        Drift ID. Default value = 0.
+    p_tstamp : datetime
+        Time stamp of drift detection. Default = None.
+    p_visualize : bool
+        Boolean switch for visualisation. Default = False.
+    p_raising_object : object
+        Reference of the object raised. Default = None.
+    **p_kwargs
+        Further optional keyword arguments.
+
+    Attributes
+    ----------
+    event_id : str
+        Event id to be used when raising a drift event object. It is a string consisting of the 
+        class name and one of the postfixes '(ON)', '(OFF)' depending on the drift status.
+    """
+
+    C_TYPE            = 'Drift'
+    C_PLOT_STANDALONE = False
+
+## -------------------------------------------------------------------------------------------------
+    def __init__( self,
+                  p_drift_status : bool,
+                  p_id : int = 0,
+                  p_tstamp : datetime = None,
+                  p_visualize : bool = False,
+                  p_raising_object : object = None,
+                  **p_kwargs):
+        
+        Id.__init__( self, p_id = p_id )
+
+        Event.__init__( self, 
+                        p_raising_object=p_raising_object,
+                        p_tstamp=p_tstamp, 
+                        **p_kwargs )
+        
+        Plottable.__init__( self, p_visualize = p_visualize )
+
+        self._drift_status : bool = p_drift_status
+
+        if p_drift_status:
+            self._event_id = type(self).__name__ + '(ON)'
+        else:
+            self._event_id = type(self).__name__ + '(OFF)'
+
+
+## -------------------------------------------------------------------------------------------------
+    def _get_drift_status(self) -> bool:
+        return self._drift_status
+    
+
+## -------------------------------------------------------------------------------------------------
+    def _get_event_id(self) -> str:
+        return self._event_id
+
+
+## -------------------------------------------------------------------------------------------------
+    drift_status = property( fget = _get_drift_status )
+    event_id     = property( fget = _get_event_id ) 
diff --git a/src/mlpro/oa/streams/tasks/driftdetectors/drifts/clusterbased/__init__.py b/src/mlpro/oa/streams/tasks/driftdetectors/drifts/clusterbased/__init__.py
new file mode 100644
index 000000000..620733649
--- /dev/null
+++ b/src/mlpro/oa/streams/tasks/driftdetectors/drifts/clusterbased/__init__.py
@@ -0,0 +1,2 @@
+from mlpro.oa.streams.tasks.driftdetectors.drifts.clusterbased.basics import DriftCB
+from mlpro.oa.streams.tasks.driftdetectors.drifts.clusterbased.movement import DriftCBMovement
\ No newline at end of file
diff --git a/src/mlpro/oa/streams/tasks/driftdetectors/drifts/clusterbased/basics.py b/src/mlpro/oa/streams/tasks/driftdetectors/drifts/clusterbased/basics.py
new file mode 100644
index 000000000..2b79382a2
--- /dev/null
+++ b/src/mlpro/oa/streams/tasks/driftdetectors/drifts/clusterbased/basics.py
@@ -0,0 +1,128 @@
+## -------------------------------------------------------------------------------------------------
+## -- Project : MLPro - The integrative middleware framework for standardized machine learning
+## -- Package : mlpro.oa.tasks.driftdetectors.drifts.clusterbased
+## -- Module  : basics.py
+## -------------------------------------------------------------------------------------------------
+## -- History :
+## -- yyyy-mm-dd  Ver.      Auth.    Description
+## -- 2025-02-12  1.0.0     DA       Creation
+## -------------------------------------------------------------------------------------------------
+
+"""
+Ver. 0.1.0 (2025-02-12)
+
+This module provides a template class for cluster-based drifts to be used in cluster-based drift 
+detection algorithms.
+"""
+
+from datetime import datetime
+
+try:
+    from matplotlib.figure import Figure
+except:
+    class Figure : pass
+
+from mlpro.bf.mt import PlotSettings
+from mlpro.oa.streams.tasks.driftdetectors.drifts.basics import Drift
+from mlpro.oa.streams.tasks.clusteranalyzers.clusters.basics import Cluster
+
+
+
+## -------------------------------------------------------------------------------------------------
+## -------------------------------------------------------------------------------------------------
+class DriftCB (Drift):
+    """
+    Sub-type for cluster-based drift events.
+    
+    Parameters
+    ----------
+    p_id : int
+        Drift ID. Default value = 0.
+    p_tstamp : datetime
+        Time stamp of drift detection. Default = None.
+    p_visualize : bool
+        Boolean switch for visualisation. Default = False.
+    p_raising_object : object
+        Reference of the object raised. Default = None.
+    p_clusters : dict[Cluster]
+        Clusters associated with the anomaly. Default = None.
+    p_properties : dict
+        Poperties of clusters associated with the anomaly. Default = None.
+    **p_kwargs
+        Further optional keyword arguments.
+    """
+
+    C_PLOT_ACTIVE   = True
+
+## -------------------------------------------------------------------------------------------------
+    def __init__(self,
+                 p_drift_status : bool,
+                 p_id : int = 0,
+                 p_tstamp : datetime = None,
+                 p_visualize : bool = False,
+                 p_raising_object : object = None,
+                 p_clusters : dict[Cluster] = None,
+                 p_properties : dict = None,
+                 **p_kwargs):
+        
+        super().__init__( p_drift_status = p_drift_status,
+                          p_id = p_id,
+                          p_tstamp = p_tstamp,
+                          p_visualize = p_visualize, 
+                          p_raising_object = p_raising_object,
+                          **p_kwargs )
+        
+        self._clusters : dict[Cluster] = p_clusters
+        self._properties : dict = p_properties
+
+
+## -------------------------------------------------------------------------------------------------
+    def get_clusters(self) -> dict[Cluster]:
+        """
+        Method that returns the clusters associated with the drift.
+        
+        Returns
+        -------
+        dict[Cluster]
+            Dictionary of clusters.
+        """
+        return self._clusters
+    
+
+## -------------------------------------------------------------------------------------------------
+    def get_properties(self) -> dict:
+        """
+        Method that returns the properties of clusters associated with the drift.
+        
+        Returns
+        -------
+        dict
+            Dictionary of properties.
+        """
+        return self._properties
+    
+
+## -------------------------------------------------------------------------------------------------
+    def _init_plot_2d(self, p_figure: Figure, p_settings: PlotSettings):
+        super()._init_plot_2d(p_figure=p_figure, p_settings=p_settings)
+
+        cluster : Cluster = None
+
+        for cluster in self._clusters.values(): 
+            if self.drift_status:
+                cluster.color = "red"
+            else:
+                raise NotImplementedError
+
+
+## -------------------------------------------------------------------------------------------------
+    def _init_plot_3d(self, p_figure: Figure, p_settings: PlotSettings):
+        super()._init_plot_3d(p_figure=p_figure, p_settings=p_settings)
+    
+        cluster : Cluster = None
+
+        for cluster in self._clusters.values(): 
+            if self.drift_status:
+                cluster.color = "red"
+            else:
+                raise NotImplementedError
diff --git a/src/mlpro/oa/streams/tasks/anomalydetectors/anomalies/drift.py b/src/mlpro/oa/streams/tasks/driftdetectors/drifts/clusterbased/movement.py
similarity index 56%
rename from src/mlpro/oa/streams/tasks/anomalydetectors/anomalies/drift.py
rename to src/mlpro/oa/streams/tasks/driftdetectors/drifts/clusterbased/movement.py
index 2a3efd241..6b00e92f5 100644
--- a/src/mlpro/oa/streams/tasks/anomalydetectors/anomalies/drift.py
+++ b/src/mlpro/oa/streams/tasks/driftdetectors/drifts/clusterbased/movement.py
@@ -1,34 +1,29 @@
 ## -------------------------------------------------------------------------------------------------
 ## -- Project : MLPro - The integrative middleware framework for standardized machine learning
-## -- Package : mlpro.oa.tasks.anomalydetectors.anomalies
-## -- Module  : drift.py
+## -- Package : mlpro.oa.tasks.driftdetectors.drifts.clusterbased
+## -- Module  : movement.py
 ## -------------------------------------------------------------------------------------------------
 ## -- History :
 ## -- yyyy-mm-dd  Ver.      Auth.    Description
-## -- 2023-06-08  0.0.0     SK       Creation
-## -- 2023-09-12  1.0.0     SK       Release
-## -- 2024-04-10  1.2.0     DA/SK    Refactoring
+## -- 2025-02-13  1.0.0     DA       Creation
 ## -------------------------------------------------------------------------------------------------
 
 """
-Ver. 1.2.0 (2024-04-10)
+Ver. 1.0.0 (2025-02-13)
 
-This module provides a template class for drift anomaly to be used in anomaly detection algorithms.
+This module provides a sub-typ of class DriftCB related to cluster movement.
 """
 
-from mlpro.oa.streams.tasks.anomalydetectors.anomalies.basics import Anomaly
+from mlpro.oa.streams.tasks.driftdetectors.drifts.clusterbased.basics import DriftCB
+
 
 
 
 ## -------------------------------------------------------------------------------------------------
 ## -------------------------------------------------------------------------------------------------
-class DriftAnomaly (Anomaly):
+class DriftCBMovement (DriftCB):
     """
-    Event class to be raised when drift is detected.
+    Sub-type indicating the begin or end of a cluster movement.
     """
 
-    C_NAME      = 'Drift'
-
-
-
-
+    C_PLOT_ACTIVE   = True
\ No newline at end of file
diff --git a/doc/rtd/content/99_appendices/appendix2/sub/pool/mlpro_oa/systems/.gitkeep b/src/mlpro/oa/streams/tasks/driftdetectors/drifts/instancebased/__init__.py
similarity index 100%
rename from doc/rtd/content/99_appendices/appendix2/sub/pool/mlpro_oa/systems/.gitkeep
rename to src/mlpro/oa/streams/tasks/driftdetectors/drifts/instancebased/__init__.py
diff --git a/doc/rtd/content/99_appendices/appendix2/sub/pool/mlpro_sl/.gitkeep b/src/mlpro/oa/streams/tasks/driftdetectors/instancebased/__init__.py
similarity index 100%
rename from doc/rtd/content/99_appendices/appendix2/sub/pool/mlpro_sl/.gitkeep
rename to src/mlpro/oa/streams/tasks/driftdetectors/instancebased/__init__.py
diff --git a/test/howtos/oa/howto_oa_streams_cbad_001_KMeans_ClusterDriftDetector_InstantaneousDetection_2d.py b/test/howtos/oa/howto_oa_streams_cbad_001_KMeans_ClusterDriftDetector_InstantaneousDetection_2d.py
deleted file mode 100644
index 90cc691b3..000000000
--- a/test/howtos/oa/howto_oa_streams_cbad_001_KMeans_ClusterDriftDetector_InstantaneousDetection_2d.py
+++ /dev/null
@@ -1,180 +0,0 @@
-## -------------------------------------------------------------------------------------------------
-## -- Project : MLPro - The integrative middleware framework for standardized machine learning
-## -- Package : test.howtos.oa
-## -- Module  : howto_oa_streams_cbad_002_KMeans_ClusterDriftDetector_InstantaneousDetection_2d.py
-## -------------------------------------------------------------------------------------------------
-## -- History :
-## -- yyyy-mm-dd  Ver.      Auth.    Description
-## -- 2024-08-04  1.0.0     SK       Creation
-## -- 2024-10-29  1.0.1     DA       Refactoring
-## -------------------------------------------------------------------------------------------------
-
-"""
-Ver. 1.0.1 (2024-10-29)
-
-This module is done as a part of the Master's Thesis named Online Adaptive Cluster-based Anomaly
-Detection, authored by Syamraj Purushamparambil Satheesh, as a part of the course
-"Systems Engineering and Engineering Management" in the Department of "Electrical Energy Engineering",
-supervised by Dipl.-Info. Detlef Arend.
-
-
-This module demonstrates cluster-based anomlay detection, on synthetic 2-dimensional data generated by
-the StreamMLProClusterGenerator, using the ClusterDriftDetector anomaly detection algorithm employing
-the KMeans clustering algorithm wrapped from River library, to detect ClusterDrift anomaly.
-
-You will learn:
-
-1. Generating synthetic data using the native StreamMLProClusterGenerator.
-
-2. Creating a workflow and tasks in MLPro-OA.
-
-3. Clustering the normalized streaming data using the WrRiverKMeans2MLPro, with normalizer as a predecessor.
-
-4. Detecting drift anomalies in the clustered data using the ClusterDriftDetector.
-
-In the visualization, the cross hair designating the cluster's centroid becomes'red' in colour when
-an anomaly is detected linked to that specific cluster. An overview of the anomalies is displayed on
-the screen following the run.
-
-"""
-from mlpro.bf.streams.streams import *
-from mlpro.bf.various import Log
-from mlpro.oa.streams import *
-from mlpro_int_river.wrappers.clusteranalyzers import WrRiverKMeans2MLPro
-from mlpro.oa.streams.tasks import ClusterDriftDetector
-
-
-
-
-## -------------------------------------------------------------------------------------------------
-## -------------------------------------------------------------------------------------------------
-# 1 Prepare a scenario
-class MyScenario(OAStreamScenario):
-    C_NAME = 'ClusterDriftScenario'
-
-    def _setup(self, p_mode, p_ada: bool, p_visualize: bool, p_logging):
-
-        # 1.1 Get MLPro benchmark Clutser Generator
-        stream = StreamMLProClusterGenerator(p_num_dim=2,
-                                             p_num_instances=5000,
-                                             p_num_clusters=3,
-                                             p_radii=[100],
-                                             p_velocities=[0.0],
-                                             p_change_velocities=True,
-                                             p_changed_velocities=[0.5, 0.6],
-                                             p_points_of_change_velocities=[1000, 1200],
-                                             p_num_clusters_for_change_velocities=2,
-                                             p_seed=12,
-                                             p_logging=p_logging)
-
-
-        # 1.2 Set up a stream workflow
-
-        # 1.2.1 Creation of a workflow
-        workflow = OAStreamWorkflow( p_name='Anomaly Detection',
-                               p_range_max=OAStreamWorkflow.C_RANGE_NONE,
-                               p_ada=p_ada,
-                               p_visualize=p_visualize,
-                               p_logging=p_logging )
-
-
-        # 1.2.2 Creation of tasks and add them to the workflow
-
-        # Cluster Analyzer
-        task_clusterer = WrRiverKMeans2MLPro( p_name='#1: KMeans@River',
-                                              p_n_clusters=3,
-                                              p_halflife=0.05, 
-                                              p_sigma=3, 
-                                              p_seed=42,
-                                              p_visualize=p_visualize,
-                                              p_logging=p_logging )
-
-        workflow.add_task(p_task = task_clusterer)
-
-        # Anomaly Detector
-        task_anomaly_detector = ClusterDriftDetector(p_clusterer=task_clusterer,
-                                                     p_with_time_calculation=False,
-                                                     p_instantaneous_velocity_change_detection=True,
-                                                     p_min_velocity_threshold=1,
-                                                     p_initial_skip=400,
-                                                     p_visualize=p_visualize,
-                                                     p_logging=p_logging)
-        
-        workflow.add_task(p_task=task_anomaly_detector, p_pred_tasks=[task_clusterer])
-
-        # 1.3 Return stream and workflow
-        return stream, workflow
-
-
-
-# 2 Prepare for test
-if __name__ == "__main__":
-    cycle_limit = 2000
-    logging     = Log.C_LOG_ALL
-    visualize   = True
-    step_rate   = 1
-else:
-    cycle_limit = 5
-    logging     = Log.C_LOG_NOTHING
-    visualize   = False
-    step_rate   = 1
-
-
-# 3 Instantiate the stream scenario
-myscenario = MyScenario( p_mode=Mode.C_MODE_SIM,
-                               p_cycle_limit=cycle_limit,
-                               p_visualize=visualize,
-                               p_logging=logging )
-
-# 4 Reset and run own stream scenario
-myscenario.reset()
-
-if __name__ == "__main__":
-    myscenario.init_plot( p_plot_settings=PlotSettings( p_view = PlotSettings.C_VIEW_2D,
-                                                            p_step_rate = step_rate ) )
-    input('\nPlease arrange all windows and press ENTER to start stream processing...')
-
-
-
-tp_before           = datetime.now()
-myscenario.run()
-tp_after            = datetime.now()
-tp_delta            = tp_after - tp_before
-duraction_sec       = ( tp_delta.seconds * 1000000 + tp_delta.microseconds + 1 ) / 1000000
-myscenario.log(Log.C_LOG_TYPE_W, 'Duration [sec]:', round(duraction_sec,2), ', Cycles/sec:', round(cycle_limit/duraction_sec,2))
-
-
-
-# 5 Summary
-anomalies         = myscenario.get_workflow()._tasks[1].get_anomalies()
-detected_anomalies= len(anomalies)
-
-myscenario.log(Log.C_LOG_TYPE_W, '-------------------------------------------------------')
-myscenario.log(Log.C_LOG_TYPE_W, '-------------------------------------------------------')
-myscenario.log(Log.C_LOG_TYPE_W, 'Here is the recap of the anomaly detector')
-myscenario.log(Log.C_LOG_TYPE_W, 'Number of anomalies: ', detected_anomalies )
-
-for anomaly in anomalies.values():
-     anomaly_name = anomaly.C_NAME
-     anomaly_id = str(anomaly.id)
-     clusters_affected = {}
-     clusters = anomaly.get_clusters()
-     properties = anomaly.get_properties()
-     for x in clusters.keys():
-        clusters_affected[x] = {}
-        clusters_affected[x]["velocity"] = properties[x]["velocity"]
-        clusters_affected[x]["acceleration"] = properties[x]["acceleration"]
-
-     
-     inst = anomaly.get_instances()[-1].get_id()
-     myscenario.log(Log.C_LOG_TYPE_W, 
-                    'Anomaly : ', anomaly_name,
-                    '\n Anomaly ID : ', anomaly_id,
-                    '\n Instance ID : ', inst,
-                    '\n Clusters : ', clusters_affected)
-
-myscenario.log(Log.C_LOG_TYPE_W, '-------------------------------------------------------')
-myscenario.log(Log.C_LOG_TYPE_W, '-------------------------------------------------------')
-
-if __name__ == "__main__":
-    input('Press ENTER to exit...')
\ No newline at end of file
diff --git a/test/howtos/oa/howto_oa_streams_cbad_002_KMeans_ClusterDriftDetector_InstantaneousDetection_normalization_2d.py b/test/howtos/oa/howto_oa_streams_cbad_002_KMeans_ClusterDriftDetector_InstantaneousDetection_normalization_2d.py
deleted file mode 100644
index 3bfc52da4..000000000
--- a/test/howtos/oa/howto_oa_streams_cbad_002_KMeans_ClusterDriftDetector_InstantaneousDetection_normalization_2d.py
+++ /dev/null
@@ -1,208 +0,0 @@
-## -------------------------------------------------------------------------------------------------
-## -- Project : MLPro - The integrative middleware framework for standardized machine learning
-## -- Package : test.howtos.oa
-## -- Module  : howto_oa_streams_cbad_002_KMeans_ClusterDriftDetector_InstantaneousDetection_normalization_2d.py
-## -------------------------------------------------------------------------------------------------
-## -- History :
-## -- yyyy-mm-dd  Ver.      Auth.    Description
-## -- 2024-08-04  1.0.0     SK       Creation
-## -- 2024-10-29  1.0.1     DA       Refactoring
-## -------------------------------------------------------------------------------------------------
-
-"""
-Ver. 1.0.1 (2024-10-29)
-
-This module is done as a part of the Master's Thesis named Online Adaptive Cluster-based Anomaly
-Detection, authored by Syamraj Purushamparambil Satheesh, as a part of the course
-"Systems Engineering and Engineering Management" in the Department of "Electrical Energy Engineering",
-supervised by Dipl.-Info. Detlef Arend.
-
-
-This module demonstrates cluster-based anomlay detection, on synthetic 2-dimensional data generated by
-the StreamMLProClusterGenerator, using the ClusterDriftDetector anomaly detection algorithm employing
-the KMeans clustering algorithm wrapped from River library, to detect ClusterDrift anomaly.
-
-You will learn:
-
-1. Generating synthetic data using the native StreamMLProClusterGenerator.
-
-2. Creating a workflow and tasks in MLPro-OA.
-
-3. Normalizing streaming data using the MinMax Normalizer, with boundary detector as a predecessor task.
-
-4. Clustering the normalized streaming data using the WrRiverKMeans2MLPro, with normalizer as a predecessor.
-
-5. Detecting drift anomalies in the clustered data using the ClusterDriftDetector.
-
-In the visualization, the cross hair designating the cluster's centroid becomes'red' in colour when
-an anomaly is detected linked to that specific cluster. An overview of the anomalies is displayed on
-the screen following the run.
-
-"""
-
-from mlpro.bf.streams.streams import *
-from mlpro.bf.various import Log
-from mlpro.oa.streams import *
-from mlpro_int_river.wrappers.clusteranalyzers.kmeans import WrRiverKMeans2MLPro
-from mlpro.oa.streams.tasks.anomalydetectors import ClusterDriftDetector
-from mlpro.oa.streams.tasks import NormalizerMinMax
-from mlpro.oa.streams.tasks import BoundaryDetector
-
-
-
-## -------------------------------------------------------------------------------------------------
-## -------------------------------------------------------------------------------------------------
-# 1 Prepare a scenario
-class MyScenario(OAStreamScenario):
-    C_NAME = 'ClusterDriftScenario'
-
-    def _setup(self, p_mode, p_ada: bool, p_visualize: bool, p_logging):
-
-        # 1.1 Get MLPro benchmark Clutser Generator
-        stream = StreamMLProClusterGenerator(p_num_dim=2,
-                                             p_num_instances=5000,
-                                             p_num_clusters=3,
-                                             p_radii=[100],
-                                             p_velocities=[0.0],
-                                             p_change_velocities=True,
-                                             p_changed_velocities=[0.5, 0.6],
-                                             p_points_of_change_velocities=[1000, 1200],
-                                             p_num_clusters_for_change_velocities=2,
-                                             p_seed=12,
-                                             p_logging=p_logging)
-
-
-        # 1.2 Set up a stream workflow
-
-        # 1.2.1 Creation of a workflow
-        workflow = OAStreamWorkflow( p_name='Anomaly Detection',
-                               p_range_max=OAStreamWorkflow.C_RANGE_NONE,
-                               p_ada=p_ada,
-                               p_visualize=p_visualize,
-                               p_logging=p_logging )
-
-
-        # 1.2.2 Creation of tasks and add them to the workflow
-
-        task_bd = BoundaryDetector( p_name='T1 - Boundary Detector', 
-                                    p_ada=p_ada, 
-                                    p_visualize=p_visualize,
-                                    p_logging=p_logging )
-        
-        workflow.add_task(p_task = task_bd)
-        
-        
-        task_norm = NormalizerMinMax( p_name='T2 - MinMax Normalizer', 
-                                      p_ada=p_ada, 
-                                      p_visualize=p_visualize,
-                                      p_logging=p_logging)
-        
-        workflow.add_task(p_task = task_norm, p_pred_tasks=[task_bd])
-
-        task_bd.register_event_handler(
-            p_event_id=BoundaryDetector.C_EVENT_ADAPTED,
-            p_event_handler=task_norm.adapt_on_event
-            )
-
-        # Cluster Analyzer
-        task_clusterer = WrRiverKMeans2MLPro( p_name='#1: KMeans@River',
-                                              p_n_clusters=3,
-                                              p_halflife=0.1, 
-                                              p_sigma=3, 
-                                              p_seed=42,
-                                              p_visualize=p_visualize,
-                                              p_logging=p_logging )
-
-        workflow.add_task(p_task = task_clusterer, p_pred_tasks=[task_norm])
-
-        task_norm.register_event_handler( p_event_id=NormalizerMinMax.C_EVENT_ADAPTED,
-                                                 p_event_handler=task_clusterer.renormalize_on_event )
-
-        # Anomaly Detector
-        task_anomaly_detector = ClusterDriftDetector(p_clusterer=task_clusterer,
-                                                     p_with_time_calculation=False,
-                                                     p_instantaneous_velocity_change_detection=True,
-                                                     p_min_velocity_threshold=0.015,
-                                                     p_ema_alpha=0.9,
-                                                     p_initial_skip=400,
-                                                     p_visualize=p_visualize,
-                                                     p_logging=p_logging)
-        workflow.add_task(p_task=task_anomaly_detector, p_pred_tasks=[task_clusterer])
-
-        # 1.3 Return stream and workflow
-        return stream, workflow
-
-
-
-# 2 Prepare for test
-if __name__ == "__main__":
-    cycle_limit = 2000
-    logging     = Log.C_LOG_ALL
-    visualize   = True
-    step_rate   = 1
-else:
-    cycle_limit = 5
-    logging     = Log.C_LOG_NOTHING
-    visualize   = False
-    step_rate   = 1
-
-
-# 3 Instantiate the stream scenario
-myscenario = MyScenario( p_mode=Mode.C_MODE_SIM,
-                               p_cycle_limit=cycle_limit,
-                               p_visualize=visualize,
-                               p_logging=logging )
-
-# 4 Reset and run own stream scenario
-myscenario.reset()
-
-if __name__ == "__main__":
-    myscenario.init_plot( p_plot_settings=PlotSettings( p_view = PlotSettings.C_VIEW_2D,
-                                                            p_step_rate = step_rate ) )
-    input('\nPlease arrange all windows and press ENTER to start stream processing...')
-
-
-
-tp_before           = datetime.now()
-myscenario.run()
-tp_after            = datetime.now()
-tp_delta            = tp_after - tp_before
-duraction_sec       = ( tp_delta.seconds * 1000000 + tp_delta.microseconds + 1 ) / 1000000
-myscenario.log(Log.C_LOG_TYPE_W, 'Duration [sec]:', round(duraction_sec,2), ', Cycles/sec:', round(cycle_limit/duraction_sec,2))
-
-
-
-# 5 Summary
-anomalies         = myscenario.get_workflow()._tasks[3].get_anomalies()
-detected_anomalies= len(anomalies)
-
-myscenario.log(Log.C_LOG_TYPE_W, '-------------------------------------------------------')
-myscenario.log(Log.C_LOG_TYPE_W, '-------------------------------------------------------')
-myscenario.log(Log.C_LOG_TYPE_W, 'Here is the recap of the anomaly detector')
-myscenario.log(Log.C_LOG_TYPE_W, 'Number of anomalies: ', detected_anomalies )
-
-for anomaly in anomalies.values():
-     anomaly_name = anomaly.C_NAME
-     anomaly_id = str(anomaly.id)
-     clusters_affected = {}
-     clusters = anomaly.get_clusters()
-     properties = anomaly.get_properties()
-     for x in clusters.keys():
-        clusters_affected[x] = {}
-        clusters_affected[x]["velocity"] = properties[x]["velocity"]
-        clusters_affected[x]["acceleration"] = properties[x]["acceleration"]
-
-     
-     inst = anomaly.get_instances()[-1].get_id()
-     myscenario.log(Log.C_LOG_TYPE_W, 
-                    'Anomaly : ', anomaly_name,
-                    '\n Anomaly ID : ', anomaly_id,
-                    '\n Instance ID : ', inst,
-                    '\n Clusters : ', clusters_affected)
-
-myscenario.log(Log.C_LOG_TYPE_W, '-------------------------------------------------------')
-myscenario.log(Log.C_LOG_TYPE_W, '-------------------------------------------------------')
-
-if __name__ == "__main__":
-    input('Press ENTER to exit...')
-
diff --git a/test/howtos/oa/howto_oa_streams_cbad_003_KMeans_ClusterDriftDetector_StateDetection_2d.py b/test/howtos/oa/howto_oa_streams_cbad_003_KMeans_ClusterDriftDetector_StateDetection_2d.py
deleted file mode 100644
index 0b1789aed..000000000
--- a/test/howtos/oa/howto_oa_streams_cbad_003_KMeans_ClusterDriftDetector_StateDetection_2d.py
+++ /dev/null
@@ -1,182 +0,0 @@
-## -------------------------------------------------------------------------------------------------
-## -- Project : MLPro - The integrative middleware framework for standardized machine learning
-## -- Package : test.howtos.oa
-## -- Module  : howto_oa_streams_cbad_003_KMeans_ClusterDriftDetector_StateDetection_2d.py
-## -------------------------------------------------------------------------------------------------
-## -- History :
-## -- yyyy-mm-dd  Ver.      Auth.    Description
-## -- 2024-08-04  1.0.0     SK       Creation
-## -- 2024-10-29  1.0.1     DA       Refactoring
-## -------------------------------------------------------------------------------------------------
-
-"""
-Ver. 1.0.1 (2024-10-29)
-
-This module is done as a part of the Master's Thesis named Online Adaptive Cluster-based Anomaly
-Detection, authored by Syamraj Purushamparambil Satheesh, as a part of the course
-"Systems Engineering and Engineering Management" in the Department of "Electrical Energy Engineering",
-supervised by Dipl.-Info. Detlef Arend.
-
-
-This module demonstrates cluster-based anomlay detection, on synthetic 2-dimensional data generated by
-the StreamMLProClusterGenerator, using the ClusterDriftDetector anomaly detection algorithm employing
-the KMeans clustering algorithm wrapped from River library, to detect ClusterDrift anomaly.
-
-You will learn:
-
-1. Generating synthetic data using the native StreamMLProClusterGenerator.
-
-2. Creating a workflow and tasks in MLPro-OA.
-
-3. Clustering the normalized streaming data using the WrRiverKMeans2MLPro, with normalizer as a predecessor.
-
-4. Detecting drift anomalies in the clustered data using the ClusterDriftDetector.
-
-In the visualization, the cross hair designating the cluster's centroid becomes'red' in colour when
-an anomaly is detected linked to that specific cluster. An overview of the anomalies is displayed on
-the screen following the run.
-
-"""
-
-from mlpro.bf.streams.streams import *
-from mlpro.bf.various import Log
-from mlpro.oa.streams import *
-from mlpro_int_river.wrappers.clusteranalyzers.kmeans import WrRiverKMeans2MLPro
-from mlpro.oa.streams.tasks.anomalydetectors import ClusterDriftDetector
-
-
-
-## -------------------------------------------------------------------------------------------------
-## -------------------------------------------------------------------------------------------------
-# 1 Prepare a scenario
-class MyScenario(OAStreamScenario):
-    C_NAME = 'ClusterDriftScenario'
-
-    def _setup(self, p_mode, p_ada: bool, p_visualize: bool, p_logging):
-
-        # 1.1 Get MLPro benchmark Clutser Generator
-        stream = StreamMLProClusterGenerator(p_num_dim=2,
-                                             p_num_instances=5000,
-                                             p_num_clusters=3,
-                                             p_radii=[100],
-                                             p_velocities=[0.0],
-                                             p_change_velocities=True,
-                                             p_changed_velocities=[0.5, 0.6],
-                                             p_points_of_change_velocities=[1000, 1200],
-                                             p_num_clusters_for_change_velocities=2,
-                                             p_seed=12,
-                                             p_logging=p_logging)
-
-
-        # 1.2 Set up a stream workflow
-
-        # 1.2.1 Creation of a workflow
-        workflow = OAStreamWorkflow( p_name='Anomaly Detection',
-                               p_range_max=OAStreamWorkflow.C_RANGE_NONE,
-                               p_ada=p_ada,
-                               p_visualize=p_visualize,
-                               p_logging=p_logging )
-
-
-        # 1.2.2 Creation of tasks and add them to the workflow
-
-        # Cluster Analyzer
-        task_clusterer = WrRiverKMeans2MLPro( p_name='#1: KMeans@River',
-                                              p_n_clusters=3,
-                                              p_halflife=0.05, 
-                                              p_sigma=3, 
-                                              p_seed=40,
-                                              p_visualize=p_visualize,
-                                              p_logging=p_logging )
-
-        workflow.add_task(p_task = task_clusterer)
-
-        # Anomaly Detector
-        task_anomaly_detector = ClusterDriftDetector(p_clusterer=task_clusterer,
-                                                     p_with_time_calculation=False,
-                                                     p_state_change_detection=True,
-                                                     p_instantaneous_velocity_change_detection=False,
-                                                     p_min_velocity_threshold=1,
-                                                     p_initial_skip=400,
-                                                     p_buffer_size=50,
-                                                     p_visualize=p_visualize,
-                                                     p_logging=p_logging)
-        
-        workflow.add_task(p_task=task_anomaly_detector, p_pred_tasks=[task_clusterer])
-
-        # 1.3 Return stream and workflow
-        return stream, workflow
-
-
-
-# 2 Prepare for test
-if __name__ == "__main__":
-    cycle_limit = 2000
-    logging     = Log.C_LOG_ALL
-    visualize   = True
-    step_rate   = 1
-else:
-    cycle_limit = 5
-    logging     = Log.C_LOG_NOTHING
-    visualize   = False
-    step_rate   = 1
-
-
-# 3 Instantiate the stream scenario
-myscenario = MyScenario( p_mode=Mode.C_MODE_SIM,
-                               p_cycle_limit=cycle_limit,
-                               p_visualize=visualize,
-                               p_logging=logging )
-
-# 4 Reset and run own stream scenario
-myscenario.reset()
-
-if __name__ == "__main__":
-    myscenario.init_plot( p_plot_settings=PlotSettings( p_view = PlotSettings.C_VIEW_2D,
-                                                            p_step_rate = step_rate ) )
-    input('\nPlease arrange all windows and press ENTER to start stream processing...')
-
-
-
-tp_before           = datetime.now()
-myscenario.run()
-tp_after            = datetime.now()
-tp_delta            = tp_after - tp_before
-duraction_sec       = ( tp_delta.seconds * 1000000 + tp_delta.microseconds + 1 ) / 1000000
-myscenario.log(Log.C_LOG_TYPE_W, 'Duration [sec]:', round(duraction_sec,2), ', Cycles/sec:', round(cycle_limit/duraction_sec,2))
-
-
-
-# 5 Summary
-anomalies         = myscenario.get_workflow()._tasks[1].get_anomalies()
-detected_anomalies= len(anomalies)
-
-myscenario.log(Log.C_LOG_TYPE_W, '-------------------------------------------------------')
-myscenario.log(Log.C_LOG_TYPE_W, '-------------------------------------------------------')
-myscenario.log(Log.C_LOG_TYPE_W, 'Here is the recap of the anomaly detector')
-myscenario.log(Log.C_LOG_TYPE_W, 'Number of anomalies: ', detected_anomalies )
-
-for anomaly in anomalies.values():
-     anomaly_name = anomaly.C_NAME
-     anomaly_id = str(anomaly.id)
-     clusters_affected = {}
-     clusters = anomaly.get_clusters()
-     properties = anomaly.get_properties()
-     for x in clusters.keys():
-        clusters_affected[x] = {}
-        clusters_affected[x]["velocity"] = properties[x]["velocity"]
-        clusters_affected[x]["acceleration"] = properties[x]["acceleration"]
-
-     
-     inst = anomaly.get_instances()[-1].get_id()
-     myscenario.log(Log.C_LOG_TYPE_W, 
-                    'Anomaly : ', anomaly_name,
-                    '\n Anomaly ID : ', anomaly_id,
-                    '\n Instance ID : ', inst,
-                    '\n Clusters : ', clusters_affected)
-
-myscenario.log(Log.C_LOG_TYPE_W, '-------------------------------------------------------')
-myscenario.log(Log.C_LOG_TYPE_W, '-------------------------------------------------------')
-
-if __name__ == "__main__":
-    input('Press ENTER to exit...')
\ No newline at end of file
diff --git a/test/howtos/oa/howto_oa_streams_cbad_004_KMeans_ClusterDriftDetector_StateDetection_normalization_2d.py b/test/howtos/oa/howto_oa_streams_cbad_004_KMeans_ClusterDriftDetector_StateDetection_normalization_2d.py
deleted file mode 100644
index e590a33e6..000000000
--- a/test/howtos/oa/howto_oa_streams_cbad_004_KMeans_ClusterDriftDetector_StateDetection_normalization_2d.py
+++ /dev/null
@@ -1,209 +0,0 @@
-## -------------------------------------------------------------------------------------------------
-## -- Project : MLPro - The integrative middleware framework for standardized machine learning
-## -- Package : test.howtos.oa
-## -- Module  : howto_oa_streams_cbad_004_KMeans_ClusterDriftDetector_StateDetection_normalization_2d.py
-## -------------------------------------------------------------------------------------------------
-## -- History :
-## -- yyyy-mm-dd  Ver.      Auth.    Description
-## -- 2024-08-04  1.0.0     SK       Creation
-## -- 2024-10-29  1.0.1     DA       Refactoring
-## -------------------------------------------------------------------------------------------------
-
-"""
-Ver. 1.0.1 (2024-10-29)
-
-This module is done as a part of the Master's Thesis named Online Adaptive Cluster-based Anomaly
-Detection, authored by Syamraj Purushamparambil Satheesh, as a part of the course
-"Systems Engineering and Engineering Management" in the Department of "Electrical Energy Engineering",
-supervised by Dipl.-Info. Detlef Arend.
-
-
-This module demonstrates cluster-based anomlay detection, on synthetic 2-dimensional data generated by
-the StreamMLProClusterGenerator, using the ClusterDriftDetector anomaly detection algorithm employing
-the KMeans clustering algorithm wrapped from River library, to detect ClusterDrift anomaly.
-
-You will learn:
-
-1. Generating synthetic data using the native StreamMLProClusterGenerator.
-
-2. Creating a workflow and tasks in MLPro-OA.
-
-3. Normalizing streaming data using the MinMax Normalizer, with boundary detector as a predecessor task.
-
-4. Clustering the normalized streaming data using the WrRiverKMeans2MLPro, with normalizer as a predecessor.
-
-5. Detecting drift anomalies in the clustered data using the ClusterDriftDetector.
-
-In the visualization, the cross hair designating the cluster's centroid becomes'red' in colour when
-an anomaly is detected linked to that specific cluster. An overview of the anomalies is displayed on
-the screen following the run.
-
-"""
-
-from mlpro.bf.streams.streams import *
-from mlpro.bf.various import Log
-from mlpro.oa.streams import *
-from mlpro_int_river.wrappers.clusteranalyzers import WrRiverKMeans2MLPro
-from mlpro.oa.streams.tasks import BoundaryDetector, NormalizerMinMax, ClusterDriftDetector
-
-
-
-
-## -------------------------------------------------------------------------------------------------
-## -------------------------------------------------------------------------------------------------
-# 1 Prepare a scenario
-class MyScenario(OAStreamScenario):
-    C_NAME = 'ClusterDriftScenario'
-
-    def _setup(self, p_mode, p_ada: bool, p_visualize: bool, p_logging):
-
-        # 1.1 Get MLPro benchmark Clutser Generator
-        stream = StreamMLProClusterGenerator(p_num_dim=2,
-                                             p_num_instances=5000,
-                                             p_num_clusters=3,
-                                             p_radii=[100],
-                                             p_velocities=[0.0],
-                                             p_change_velocities=True,
-                                             p_changed_velocities=[0.5, 0.6],
-                                             p_points_of_change_velocities=[1000, 1200],
-                                             p_num_clusters_for_change_velocities=2,
-                                             p_seed=12,
-                                             p_logging=p_logging)
-
-
-        # 1.2 Set up a stream workflow
-
-        # 1.2.1 Creation of a workflow
-        workflow = OAStreamWorkflow( p_name='Anomaly Detection',
-                               p_range_max=OAStreamWorkflow.C_RANGE_NONE,
-                               p_ada=p_ada,
-                               p_visualize=p_visualize,
-                               p_logging=p_logging )
-
-
-        # 1.2.2 Creation of tasks and add them to the workflow
-        task_bd = BoundaryDetector( p_name='T1 - Boundary Detector', 
-                                    p_ada=p_ada, 
-                                    p_visualize=p_visualize,
-                                    p_logging=p_logging )
-        
-        workflow.add_task(p_task = task_bd)
-        
-        
-        task_norm = NormalizerMinMax( p_name='T2 - MinMax Normalizer', 
-                                      p_ada=p_ada, 
-                                      p_visualize=p_visualize,
-                                      p_logging=p_logging)
-        
-        workflow.add_task(p_task = task_norm, p_pred_tasks=[task_bd])
-
-        task_bd.register_event_handler(
-            p_event_id=BoundaryDetector.C_EVENT_ADAPTED,
-            p_event_handler=task_norm.adapt_on_event
-            )
-
-
-        # Cluster Analyzer
-        task_clusterer = WrRiverKMeans2MLPro( p_name='#1: KMeans@River',
-                                              p_n_clusters=3,
-                                              p_halflife=0.05, 
-                                              p_sigma=3, 
-                                              p_seed=40,
-                                              p_visualize=p_visualize,
-                                              p_logging=p_logging )
-
-        workflow.add_task(p_task = task_clusterer)
-
-        task_norm.register_event_handler( p_event_id=NormalizerMinMax.C_EVENT_ADAPTED,
-                                                 p_event_handler=task_clusterer.renormalize_on_event )
-
-
-        # Anomaly Detector
-        task_anomaly_detector = ClusterDriftDetector(p_clusterer=task_clusterer,
-                                                     p_with_time_calculation=False,
-                                                     p_state_change_detection=True,
-                                                     p_instantaneous_velocity_change_detection=False,
-                                                     p_min_velocity_threshold=0.01,
-                                                     p_initial_skip=400,
-                                                     p_buffer_size=50,
-                                                     p_visualize=p_visualize,
-                                                     p_logging=p_logging)
-        
-        workflow.add_task(p_task=task_anomaly_detector, p_pred_tasks=[task_clusterer])
-
-        # 1.3 Return stream and workflow
-        return stream, workflow
-
-
-
-# 2 Prepare for test
-if __name__ == "__main__":
-    cycle_limit = 2000
-    logging     = Log.C_LOG_ALL
-    visualize   = True
-    step_rate   = 1
-else:
-    cycle_limit = 5
-    logging     = Log.C_LOG_NOTHING
-    visualize   = False
-    step_rate   = 1
-
-
-# 3 Instantiate the stream scenario
-myscenario = MyScenario( p_mode=Mode.C_MODE_SIM,
-                               p_cycle_limit=cycle_limit,
-                               p_visualize=visualize,
-                               p_logging=logging )
-
-# 4 Reset and run own stream scenario
-myscenario.reset()
-
-if __name__ == "__main__":
-    myscenario.init_plot( p_plot_settings=PlotSettings( p_view = PlotSettings.C_VIEW_2D,
-                                                            p_step_rate = step_rate ) )
-    input('\nPlease arrange all windows and press ENTER to start stream processing...')
-
-
-
-tp_before           = datetime.now()
-myscenario.run()
-tp_after            = datetime.now()
-tp_delta            = tp_after - tp_before
-duraction_sec       = ( tp_delta.seconds * 1000000 + tp_delta.microseconds + 1 ) / 1000000
-myscenario.log(Log.C_LOG_TYPE_W, 'Duration [sec]:', round(duraction_sec,2), ', Cycles/sec:', round(cycle_limit/duraction_sec,2))
-
-
-
-# 5 Summary
-anomalies         = myscenario.get_workflow()._tasks[3].get_anomalies()
-detected_anomalies= len(anomalies)
-
-myscenario.log(Log.C_LOG_TYPE_W, '-------------------------------------------------------')
-myscenario.log(Log.C_LOG_TYPE_W, '-------------------------------------------------------')
-myscenario.log(Log.C_LOG_TYPE_W, 'Here is the recap of the anomaly detector')
-myscenario.log(Log.C_LOG_TYPE_W, 'Number of anomalies: ', detected_anomalies )
-
-for anomaly in anomalies.values():
-     anomaly_name = anomaly.C_NAME
-     anomaly_id = str(anomaly.id)
-     clusters_affected = {}
-     clusters = anomaly.get_clusters()
-     properties = anomaly.get_properties()
-     for x in clusters.keys():
-        clusters_affected[x] = {}
-        clusters_affected[x]["velocity"] = properties[x]["velocity"]
-        clusters_affected[x]["acceleration"] = properties[x]["acceleration"]
-
-     
-     inst = anomaly.get_instances()[-1].get_id()
-     myscenario.log(Log.C_LOG_TYPE_W, 
-                    'Anomaly : ', anomaly_name,
-                    '\n Anomaly ID : ', anomaly_id,
-                    '\n Instance ID : ', inst,
-                    '\n Clusters : ', clusters_affected)
-
-myscenario.log(Log.C_LOG_TYPE_W, '-------------------------------------------------------')
-myscenario.log(Log.C_LOG_TYPE_W, '-------------------------------------------------------')
-
-if __name__ == "__main__":
-    input('Press ENTER to exit...')
\ No newline at end of file
diff --git a/test/howtos/oa/howto_oa_streams_cbad_005_KMeans_ClusterDriftDetector_InstantaneousDetection_normalization_3d.py b/test/howtos/oa/howto_oa_streams_cbad_005_KMeans_ClusterDriftDetector_InstantaneousDetection_normalization_3d.py
deleted file mode 100644
index ff54c0cf3..000000000
--- a/test/howtos/oa/howto_oa_streams_cbad_005_KMeans_ClusterDriftDetector_InstantaneousDetection_normalization_3d.py
+++ /dev/null
@@ -1,180 +0,0 @@
-## -------------------------------------------------------------------------------------------------
-## -- Project : MLPro - The integrative middleware framework for standardized machine learning
-## -- Package : test.howtos.oa
-## -- Module  : howto_oa_streams_cbad_005_KMeans_ClusterDriftDetector_InstantaneousDetection_normalization_3d.py
-## -------------------------------------------------------------------------------------------------
-## -- History :
-## -- yyyy-mm-dd  Ver.      Auth.    Description
-## -- 2024-08-04  1.0.0     SK       Creation
-## -- 2024-10-29  1.0.1     DA       Refactoring
-## -------------------------------------------------------------------------------------------------
-
-"""
-Ver. 1.0.1 (2024-10-29)
-
-This module is done as a part of the Master's Thesis named Online Adaptive Cluster-based Anomaly
-Detection, authored by Syamraj Purushamparambil Satheesh, as a part of the course
-"Systems Engineering and Engineering Management" in the Department of "Electrical Energy Engineering",
-supervised by Dipl.-Info. Detlef Arend.
-
-
-This module demonstrates cluster-based anomlay detection, on synthetic 2-dimensional data generated by
-the StreamMLProClusterGenerator, using the ClusterDriftDetector anomaly detection algorithm employing
-the KMeans clustering algorithm wrapped from River library, to detect ClusterDrift anomaly.
-
-You will learn:
-
-1. Generating synthetic data using the native StreamMLProClusterGenerator.
-
-2. Creating a workflow and tasks in MLPro-OA.
-
-3. Clustering the normalized streaming data using the WrRiverKMeans2MLPro, with normalizer as a predecessor.
-
-4. Detecting drift anomalies in the clustered data using the ClusterDriftDetector.
-
-In the visualization, the cross hair designating the cluster's centroid becomes'red' in colour when
-an anomaly is detected linked to that specific cluster. An overview of the anomalies is displayed on
-the screen following the run.
-
-"""
-
-from mlpro.bf.streams.streams import *
-from mlpro.bf.various import Log
-from mlpro.oa.streams import *
-from mlpro_int_river.wrappers.clusteranalyzers import WrRiverKMeans2MLPro
-from mlpro.oa.streams.tasks import ClusterDriftDetector
-
-
-
-## -------------------------------------------------------------------------------------------------
-## -------------------------------------------------------------------------------------------------
-# 1 Prepare a scenario
-class MyScenario(OAStreamScenario):
-    C_NAME = 'ClusterDriftScenario'
-
-    def _setup(self, p_mode, p_ada: bool, p_visualize: bool, p_logging):
-
-        # 1.1 Get MLPro benchmark Clutser Generator
-        stream = StreamMLProClusterGenerator(p_num_dim=3,
-                                             p_num_instances=5000,
-                                             p_num_clusters=3,
-                                             p_radii=[100],
-                                             p_velocities=[0.0],
-                                             p_change_velocities=True,
-                                             p_changed_velocities=[0.5, 0.6],
-                                             p_points_of_change_velocities=[1000, 1200],
-                                             p_num_clusters_for_change_velocities=2,
-                                             p_seed=23,
-                                             p_logging=p_logging)
-
-
-        # 1.2 Set up a stream workflow
-
-        # 1.2.1 Creation of a workflow
-        workflow = OAStreamWorkflow( p_name='Anomaly Detection',
-                               p_range_max=OAStreamWorkflow.C_RANGE_NONE,
-                               p_ada=p_ada,
-                               p_visualize=p_visualize,
-                               p_logging=p_logging )
-
-
-        # 1.2.2 Creation of tasks and add them to the workflow
-
-        # Cluster Analyzer
-        task_clusterer = WrRiverKMeans2MLPro( p_name='#1: KMeans@River',
-                                              p_n_clusters=3,
-                                              p_halflife=0.05, 
-                                              p_sigma=3, 
-                                              p_seed=42,
-                                              p_visualize=p_visualize,
-                                              p_logging=p_logging )
-
-        workflow.add_task(p_task = task_clusterer)
-
-        # Anomaly Detector
-        task_anomaly_detector = ClusterDriftDetector(p_clusterer=task_clusterer,
-                                                     p_with_time_calculation=False,
-                                                     p_instantaneous_velocity_change_detection=True,
-                                                     p_min_velocity_threshold=1,
-                                                     p_initial_skip=400,
-                                                     p_visualize=p_visualize,
-                                                     p_logging=p_logging)
-
-        workflow.add_task(p_task=task_anomaly_detector, p_pred_tasks=[task_clusterer])
-
-        # 1.3 Return stream and workflow
-        return stream, workflow
-
-
-
-# 2 Prepare for test
-if __name__ == "__main__":
-    cycle_limit = 2000
-    logging     = Log.C_LOG_ALL
-    visualize   = True
-    step_rate   = 1
-else:
-    cycle_limit = 5
-    logging     = Log.C_LOG_NOTHING
-    visualize   = False
-    step_rate   = 1
-
-
-# 3 Instantiate the stream scenario
-myscenario = MyScenario( p_mode=Mode.C_MODE_SIM,
-                               p_cycle_limit=cycle_limit,
-                               p_visualize=visualize,
-                               p_logging=logging )
-
-# 4 Reset and run own stream scenario
-myscenario.reset()
-
-if __name__ == "__main__":
-    myscenario.init_plot( p_plot_settings=PlotSettings( p_view = PlotSettings.C_VIEW_2D,
-                                                            p_step_rate = step_rate ) )
-    input('\nPlease arrange all windows and press ENTER to start stream processing...')
-
-
-
-tp_before           = datetime.now()
-myscenario.run()
-tp_after            = datetime.now()
-tp_delta            = tp_after - tp_before
-duraction_sec       = ( tp_delta.seconds * 1000000 + tp_delta.microseconds + 1 ) / 1000000
-myscenario.log(Log.C_LOG_TYPE_W, 'Duration [sec]:', round(duraction_sec,2), ', Cycles/sec:', round(cycle_limit/duraction_sec,2))
-
-
-
-# 5 Summary
-anomalies         = myscenario.get_workflow()._tasks[1].get_anomalies()
-detected_anomalies= len(anomalies)
-
-myscenario.log(Log.C_LOG_TYPE_W, '-------------------------------------------------------')
-myscenario.log(Log.C_LOG_TYPE_W, '-------------------------------------------------------')
-myscenario.log(Log.C_LOG_TYPE_W, 'Here is the recap of the anomaly detector')
-myscenario.log(Log.C_LOG_TYPE_W, 'Number of anomalies: ', detected_anomalies )
-
-for anomaly in anomalies.values():
-     anomaly_name = anomaly.C_NAME
-     anomaly_id = str(anomaly.id)
-     clusters_affected = {}
-     clusters = anomaly.get_clusters()
-     properties = anomaly.get_properties()
-     for x in clusters.keys():
-        clusters_affected[x] = {}
-        clusters_affected[x]["velocity"] = properties[x]["velocity"]
-        clusters_affected[x]["acceleration"] = properties[x]["acceleration"]
-
-     
-     inst = anomaly.get_instances()[-1].get_id()
-     myscenario.log(Log.C_LOG_TYPE_W, 
-                    'Anomaly : ', anomaly_name,
-                    '\n Anomaly ID : ', anomaly_id,
-                    '\n Instance ID : ', inst,
-                    '\n Clusters : ', clusters_affected)
-
-myscenario.log(Log.C_LOG_TYPE_W, '-------------------------------------------------------')
-myscenario.log(Log.C_LOG_TYPE_W, '-------------------------------------------------------')
-
-if __name__ == "__main__":
-    input('Press ENTER to exit...')
\ No newline at end of file
diff --git a/test/howtos/oa/howto_oa_streams_cbad_006_KMeans_ClusterDriftDetector_InstantaneousDetection_normalization_3d.py b/test/howtos/oa/howto_oa_streams_cbad_006_KMeans_ClusterDriftDetector_InstantaneousDetection_normalization_3d.py
deleted file mode 100644
index 1ae780b75..000000000
--- a/test/howtos/oa/howto_oa_streams_cbad_006_KMeans_ClusterDriftDetector_InstantaneousDetection_normalization_3d.py
+++ /dev/null
@@ -1,206 +0,0 @@
-## -------------------------------------------------------------------------------------------------
-## -- Project : MLPro - The integrative middleware framework for standardized machine learning
-## -- Package : test.howtos.oa
-## -- Module  : howto_oa_streams_cbad_006_KMeans_ClusterDriftDetector_InstantaneousDetection_normalization_3d.py
-## -------------------------------------------------------------------------------------------------
-## -- History :
-## -- yyyy-mm-dd  Ver.      Auth.    Description
-## -- 2024-08-04  1.0.0     SK       Creation
-## -- 2024-10-29  1.0.1     DA       Refactoring
-## -------------------------------------------------------------------------------------------------
-
-"""
-Ver. 1.0.1 (2024-10-29)
-
-This module is done as a part of the Master's Thesis named Online Adaptive Cluster-based Anomaly
-Detection, authored by Syamraj Purushamparambil Satheesh, as a part of the course
-"Systems Engineering and Engineering Management" in the Department of "Electrical Energy Engineering",
-supervised by Dipl.-Info. Detlef Arend.
-
-
-This module demonstrates cluster-based anomlay detection, on synthetic 2-dimensional data generated by
-the StreamMLProClusterGenerator, using the ClusterDriftDetector anomaly detection algorithm employing
-the KMeans clustering algorithm wrapped from River library, to detect ClusterDrift anomaly.
-
-You will learn:
-
-1. Generating synthetic data using the native StreamMLProClusterGenerator.
-
-2. Creating a workflow and tasks in MLPro-OA.
-
-3. Normalizing streaming data using the MinMax Normalizer, with boundary detector as a predecessor task.
-
-4. Clustering the normalized streaming data using the WrRiverKMeans2MLPro, with normalizer as a predecessor.
-
-5. Detecting drift anomalies in the clustered data using the ClusterDriftDetector.
-
-In the visualization, the cross hair designating the cluster's centroid becomes'red' in colour when
-an anomaly is detected linked to that specific cluster. An overview of the anomalies is displayed on
-the screen following the run.
-
-"""
-
-from mlpro.bf.streams.streams import *
-from mlpro.bf.various import Log
-from mlpro.oa.streams import *
-from mlpro_int_river.wrappers.clusteranalyzers.kmeans import WrRiverKMeans2MLPro
-from mlpro.oa.streams.tasks import BoundaryDetector, NormalizerMinMax, ClusterDriftDetector
-
-
-
-## -------------------------------------------------------------------------------------------------
-## -------------------------------------------------------------------------------------------------
-# 1 Prepare a scenario
-class MyScenario(OAStreamScenario):
-    C_NAME = 'ClusterDriftScenario'
-
-    def _setup(self, p_mode, p_ada: bool, p_visualize: bool, p_logging):
-
-        # 1.1 Get MLPro benchmark Clutser Generator
-        stream = StreamMLProClusterGenerator(p_num_dim=3,
-                                             p_num_instances=5000,
-                                             p_num_clusters=5,
-                                             p_radii=[100],
-                                             p_velocities=[0.0],
-                                             p_change_velocities=True,
-                                             p_changed_velocities=[0.3, 0.8, 0.6],
-                                             p_points_of_change_velocities=[1000, 1200, 1500],
-                                             p_num_clusters_for_change_velocities=3,
-                                             p_seed=23,
-                                             p_logging=p_logging)
-
-
-        # 1.2 Set up a stream workflow
-
-        # 1.2.1 Creation of a workflow
-        workflow = OAStreamWorkflow( p_name='Anomaly Detection',
-                               p_range_max=OAStreamWorkflow.C_RANGE_NONE,
-                               p_ada=p_ada,
-                               p_visualize=p_visualize,
-                               p_logging=p_logging )
-
-
-        # 1.2.2 Creation of tasks and add them to the workflow
-
-        task_bd = BoundaryDetector( p_name='T1 - Boundary Detector', 
-                                    p_ada=p_ada, 
-                                    p_visualize=p_visualize,
-                                    p_logging=p_logging )
-        
-        workflow.add_task(p_task = task_bd)
-        
-        
-        task_norm = NormalizerMinMax( p_name='T2 - MinMax Normalizer', 
-                                      p_ada=p_ada, 
-                                      p_visualize=p_visualize,
-                                      p_logging=p_logging)
-        
-        workflow.add_task(p_task = task_norm, p_pred_tasks=[task_bd])
-
-        task_bd.register_event_handler(
-            p_event_id=BoundaryDetector.C_EVENT_ADAPTED,
-            p_event_handler=task_norm.adapt_on_event
-            )
-
-        # Cluster Analyzer
-        task_clusterer = WrRiverKMeans2MLPro( p_name='#1: KMeans@River',
-                                              p_n_clusters=5,
-                                              p_halflife=0.1, 
-                                              p_sigma=3, 
-                                              p_seed=42,
-                                              p_visualize=p_visualize,
-                                              p_logging=p_logging )
-
-        workflow.add_task(p_task = task_clusterer)
-
-        task_norm.register_event_handler( p_event_id=NormalizerMinMax.C_EVENT_ADAPTED,
-                                                 p_event_handler=task_clusterer.renormalize_on_event )
-
-
-        # Anomaly Detector
-        task_anomaly_detector = ClusterDriftDetector(p_clusterer=task_clusterer,
-                                                     p_with_time_calculation=False,
-                                                     p_instantaneous_velocity_change_detection=True,
-                                                     p_min_velocity_threshold=0.01,
-                                                     p_initial_skip=400,
-                                                     p_visualize=p_visualize,
-                                                     p_logging=p_logging)
-
-        workflow.add_task(p_task=task_anomaly_detector, p_pred_tasks=[task_clusterer])
-
-        # 1.3 Return stream and workflow
-        return stream, workflow
-
-
-
-# 2 Prepare for test
-if __name__ == "__main__":
-    cycle_limit = 2000
-    logging     = Log.C_LOG_ALL
-    visualize   = True
-    step_rate   = 1
-else:
-    cycle_limit = 5
-    logging     = Log.C_LOG_NOTHING
-    visualize   = False
-    step_rate   = 1
-
-
-# 3 Instantiate the stream scenario
-myscenario = MyScenario( p_mode=Mode.C_MODE_SIM,
-                               p_cycle_limit=cycle_limit,
-                               p_visualize=visualize,
-                               p_logging=logging )
-
-# 4 Reset and run own stream scenario
-myscenario.reset()
-
-if __name__ == "__main__":
-    myscenario.init_plot( p_plot_settings=PlotSettings( p_view = PlotSettings.C_VIEW_2D,
-                                                            p_step_rate = step_rate ) )
-    input('\nPlease arrange all windows and press ENTER to start stream processing...')
-
-
-
-tp_before           = datetime.now()
-myscenario.run()
-tp_after            = datetime.now()
-tp_delta            = tp_after - tp_before
-duraction_sec       = ( tp_delta.seconds * 1000000 + tp_delta.microseconds + 1 ) / 1000000
-myscenario.log(Log.C_LOG_TYPE_W, 'Duration [sec]:', round(duraction_sec,2), ', Cycles/sec:', round(cycle_limit/duraction_sec,2))
-
-
-
-# 5 Summary
-anomalies         = myscenario.get_workflow()._tasks[3].get_anomalies()
-detected_anomalies= len(anomalies)
-
-myscenario.log(Log.C_LOG_TYPE_W, '-------------------------------------------------------')
-myscenario.log(Log.C_LOG_TYPE_W, '-------------------------------------------------------')
-myscenario.log(Log.C_LOG_TYPE_W, 'Here is the recap of the anomaly detector')
-myscenario.log(Log.C_LOG_TYPE_W, 'Number of anomalies: ', detected_anomalies )
-
-for anomaly in anomalies.values():
-     anomaly_name = anomaly.C_NAME
-     anomaly_id = str(anomaly.id)
-     clusters_affected = {}
-     clusters = anomaly.get_clusters()
-     properties = anomaly.get_properties()
-     for x in clusters.keys():
-        clusters_affected[x] = {}
-        clusters_affected[x]["velocity"] = properties[x]["velocity"]
-        clusters_affected[x]["acceleration"] = properties[x]["acceleration"]
-
-     
-     inst = anomaly.get_instances()[-1].get_id()
-     myscenario.log(Log.C_LOG_TYPE_W, 
-                    'Anomaly : ', anomaly_name,
-                    '\n Anomaly ID : ', anomaly_id,
-                    '\n Instance ID : ', inst,
-                    '\n Clusters : ', clusters_affected)
-
-myscenario.log(Log.C_LOG_TYPE_W, '-------------------------------------------------------')
-myscenario.log(Log.C_LOG_TYPE_W, '-------------------------------------------------------')
-
-if __name__ == "__main__":
-    input('Press ENTER to exit...')
\ No newline at end of file
diff --git a/test/howtos/oa/howto_oa_streams_cbad_007_KMeans_ClusterDriftDetector_StateDetection_3d.py b/test/howtos/oa/howto_oa_streams_cbad_007_KMeans_ClusterDriftDetector_StateDetection_3d.py
deleted file mode 100644
index 747ee214d..000000000
--- a/test/howtos/oa/howto_oa_streams_cbad_007_KMeans_ClusterDriftDetector_StateDetection_3d.py
+++ /dev/null
@@ -1,182 +0,0 @@
-## -------------------------------------------------------------------------------------------------
-## -- Project : MLPro - The integrative middleware framework for standardized machine learning
-## -- Package : test.howtos.oa
-## -- Module  : howto_oa_streams_cbad_007_KMeans_ClusterDriftDetector_StateDetection_3d.py
-## -------------------------------------------------------------------------------------------------
-## -- History :
-## -- yyyy-mm-dd  Ver.      Auth.    Description
-## -- 2024-08-04  1.0.0     SK       Creation
-## -- 2024-10-29  1.0.1     DA       Refactoring
-## -------------------------------------------------------------------------------------------------
-
-"""
-Ver. 1.0.1 (2024-10-29)
-
-This module is done as a part of the Master's Thesis named Online Adaptive Cluster-based Anomaly
-Detection, authored by Syamraj Purushamparambil Satheesh, as a part of the course
-"Systems Engineering and Engineering Management" in the Department of "Electrical Energy Engineering",
-supervised by Dipl.-Info. Detlef Arend.
-
-
-This module demonstrates cluster-based anomlay detection, on synthetic 2-dimensional data generated by
-the StreamMLProClusterGenerator, using the ClusterDriftDetector anomaly detection algorithm employing
-the KMeans clustering algorithm wrapped from River library, to detect ClusterDrift anomaly.
-
-You will learn:
-
-1. Generating synthetic data using the native StreamMLProClusterGenerator.
-
-2. Creating a workflow and tasks in MLPro-OA.
-
-3. Clustering the normalized streaming data using the WrRiverKMeans2MLPro, with normalizer as a predecessor.
-
-4. Detecting drift anomalies in the clustered data using the ClusterDriftDetector.
-
-In the visualization, the cross hair designating the cluster's centroid becomes'red' in colour when
-an anomaly is detected linked to that specific cluster. An overview of the anomalies is displayed on
-the screen following the run.
-
-"""
-
-from mlpro.bf.streams.streams import *
-from mlpro.bf.various import Log
-from mlpro.oa.streams import *
-from mlpro_int_river.wrappers.clusteranalyzers import WrRiverKMeans2MLPro
-from mlpro.oa.streams.tasks import ClusterDriftDetector
-
-
-
-## -------------------------------------------------------------------------------------------------
-## -------------------------------------------------------------------------------------------------
-# 1 Prepare a scenario
-class MyScenario(OAStreamScenario):
-    C_NAME = 'ClusterDriftScenario'
-
-    def _setup(self, p_mode, p_ada: bool, p_visualize: bool, p_logging):
-
-        # 1.1 Get MLPro benchmark Clutser Generator
-        stream = StreamMLProClusterGenerator(p_num_dim=3,
-                                             p_num_instances=5000,
-                                             p_num_clusters=3,
-                                             p_radii=[100],
-                                             p_velocities=[0.0],
-                                             p_change_velocities=True,
-                                             p_changed_velocities=[0.5, 0.6],
-                                             p_points_of_change_velocities=[1000, 1200, 1500],
-                                             p_num_clusters_for_change_velocities=2,
-                                             p_seed=23,
-                                             p_logging=p_logging)
-
-
-        # 1.2 Set up a stream workflow
-
-        # 1.2.1 Creation of a workflow
-        workflow = OAStreamWorkflow( p_name='Anomaly Detection',
-                               p_range_max=OAStreamWorkflow.C_RANGE_NONE,
-                               p_ada=p_ada,
-                               p_visualize=p_visualize,
-                               p_logging=p_logging )
-
-
-        # 1.2.2 Creation of tasks and add them to the workflow
-
-        # Cluster Analyzer
-        task_clusterer = WrRiverKMeans2MLPro( p_name='#1: KMeans@River',
-                                              p_n_clusters=3,
-                                              p_halflife=0.05, 
-                                              p_sigma=3, 
-                                              p_seed=42,
-                                              p_visualize=p_visualize,
-                                              p_logging=p_logging )
-
-        workflow.add_task(p_task = task_clusterer)
-
-        # Anomaly Detector
-        task_anomaly_detector = ClusterDriftDetector(p_clusterer=task_clusterer,
-                                                     p_with_time_calculation=False,
-                                                     p_state_change_detection=True,
-                                                     p_instantaneous_velocity_change_detection=False,
-                                                     p_min_velocity_threshold=1,
-                                                     p_initial_skip=400,
-                                                     p_buffer_size=50,
-                                                     p_visualize=p_visualize,
-                                                     p_logging=p_logging)
-        
-        workflow.add_task(p_task=task_anomaly_detector, p_pred_tasks=[task_clusterer])
-
-        # 1.3 Return stream and workflow
-        return stream, workflow
-
-
-
-# 2 Prepare for test
-if __name__ == "__main__":
-    cycle_limit = 2000
-    logging     = Log.C_LOG_ALL
-    visualize   = True
-    step_rate   = 1
-else:
-    cycle_limit = 5
-    logging     = Log.C_LOG_NOTHING
-    visualize   = False
-    step_rate   = 1
-
-
-# 3 Instantiate the stream scenario
-myscenario = MyScenario( p_mode=Mode.C_MODE_SIM,
-                               p_cycle_limit=cycle_limit,
-                               p_visualize=visualize,
-                               p_logging=logging )
-
-# 4 Reset and run own stream scenario
-myscenario.reset()
-
-if __name__ == "__main__":
-    myscenario.init_plot( p_plot_settings=PlotSettings( p_view = PlotSettings.C_VIEW_2D,
-                                                            p_step_rate = step_rate ) )
-    input('\nPlease arrange all windows and press ENTER to start stream processing...')
-
-
-
-tp_before           = datetime.now()
-myscenario.run()
-tp_after            = datetime.now()
-tp_delta            = tp_after - tp_before
-duraction_sec       = ( tp_delta.seconds * 1000000 + tp_delta.microseconds + 1 ) / 1000000
-myscenario.log(Log.C_LOG_TYPE_W, 'Duration [sec]:', round(duraction_sec,2), ', Cycles/sec:', round(cycle_limit/duraction_sec,2))
-
-
-
-# 5 Summary
-anomalies         = myscenario.get_workflow()._tasks[1].get_anomalies()
-detected_anomalies= len(anomalies)
-
-myscenario.log(Log.C_LOG_TYPE_W, '-------------------------------------------------------')
-myscenario.log(Log.C_LOG_TYPE_W, '-------------------------------------------------------')
-myscenario.log(Log.C_LOG_TYPE_W, 'Here is the recap of the anomaly detector')
-myscenario.log(Log.C_LOG_TYPE_W, 'Number of anomalies: ', detected_anomalies )
-
-for anomaly in anomalies.values():
-     anomaly_name = anomaly.C_NAME
-     anomaly_id = str(anomaly.id)
-     clusters_affected = {}
-     clusters = anomaly.get_clusters()
-     properties = anomaly.get_properties()
-     for x in clusters.keys():
-        clusters_affected[x] = {}
-        clusters_affected[x]["velocity"] = properties[x]["velocity"]
-        clusters_affected[x]["acceleration"] = properties[x]["acceleration"]
-
-     
-     inst = anomaly.get_instances()[-1].get_id()
-     myscenario.log(Log.C_LOG_TYPE_W, 
-                    'Anomaly : ', anomaly_name,
-                    '\n Anomaly ID : ', anomaly_id,
-                    '\n Instance ID : ', inst,
-                    '\n Clusters : ', clusters_affected)
-
-myscenario.log(Log.C_LOG_TYPE_W, '-------------------------------------------------------')
-myscenario.log(Log.C_LOG_TYPE_W, '-------------------------------------------------------')
-
-if __name__ == "__main__":
-    input('Press ENTER to exit...')
\ No newline at end of file
diff --git a/test/howtos/oa/howto_oa_streams_cbad_008_KMeans_ClusterDriftDetector_StateDetection_normalization_3d.py b/test/howtos/oa/howto_oa_streams_cbad_008_KMeans_ClusterDriftDetector_StateDetection_normalization_3d.py
deleted file mode 100644
index aae113b6f..000000000
--- a/test/howtos/oa/howto_oa_streams_cbad_008_KMeans_ClusterDriftDetector_StateDetection_normalization_3d.py
+++ /dev/null
@@ -1,208 +0,0 @@
-## -------------------------------------------------------------------------------------------------
-## -- Project : MLPro - The integrative middleware framework for standardized machine learning
-## -- Package : test.howtos.oa
-## -- Module  : howto_oa_streams_cbad_008_KMeans_ClusterDriftDetector_StateDetection_normalization_3d.py
-## -------------------------------------------------------------------------------------------------
-## -- History :
-## -- yyyy-mm-dd  Ver.      Auth.    Description
-## -- 2024-08-04  1.0.0     SK       Creation
-## -- 2024-10-29  1.0.1     DA       Refactoring
-## -------------------------------------------------------------------------------------------------
-
-"""
-Ver. 1.0.1 (2024-10-29)
-
-This module is done as a part of the Master's Thesis named Online Adaptive Cluster-based Anomaly
-Detection, authored by Syamraj Purushamparambil Satheesh, as a part of the course
-"Systems Engineering and Engineering Management" in the Department of "Electrical Energy Engineering",
-supervised by Dipl.-Info. Detlef Arend.
-
-
-This module demonstrates cluster-based anomlay detection, on synthetic 2-dimensional data generated by
-the StreamMLProClusterGenerator, using the ClusterDriftDetector anomaly detection algorithm employing
-the KMeans clustering algorithm wrapped from River library, to detect ClusterDrift anomaly.
-
-You will learn:
-
-1. Generating synthetic data using the native StreamMLProClusterGenerator.
-
-2. Creating a workflow and tasks in MLPro-OA.
-
-3. Normalizing streaming data using the MinMax Normalizer, with boundary detector as a predecessor task.
-
-4. Clustering the normalized streaming data using the WrRiverKMeans2MLPro, with normalizer as a predecessor.
-
-5. Detecting drift anomalies in the clustered data using the ClusterDriftDetector.
-
-In the visualization, the cross hair designating the cluster's centroid becomes'red' in colour when
-an anomaly is detected linked to that specific cluster. An overview of the anomalies is displayed on
-the screen following the run.
-
-"""
-
-from mlpro.bf.streams.streams import *
-from mlpro.bf.various import Log
-from mlpro.oa.streams import *
-from mlpro_int_river.wrappers.clusteranalyzers import WrRiverKMeans2MLPro
-from mlpro.oa.streams.tasks import BoundaryDetector, NormalizerMinMax, ClusterDriftDetector
-
-
-
-## -------------------------------------------------------------------------------------------------
-## -------------------------------------------------------------------------------------------------
-# 1 Prepare a scenario
-class MyScenario(OAStreamScenario):
-    C_NAME = 'ClusterDriftScenario'
-
-    def _setup(self, p_mode, p_ada: bool, p_visualize: bool, p_logging):
-
-        # 1.1 Get MLPro benchmark Clutser Generator
-        stream = StreamMLProClusterGenerator(p_num_dim=3,
-                                             p_num_instances=5000,
-                                             p_num_clusters=3,
-                                             p_radii=[100],
-                                             p_velocities=[0.0],
-                                             p_change_velocities=True,
-                                             p_changed_velocities=[0.5, 0.6],
-                                             p_points_of_change_velocities=[1000, 1200, 1500],
-                                             p_num_clusters_for_change_velocities=2,
-                                             p_seed=23,
-                                             p_logging=p_logging)
-
-
-        # 1.2 Set up a stream workflow
-
-        # 1.2.1 Creation of a workflow
-        workflow = OAStreamWorkflow( p_name='Anomaly Detection',
-                               p_range_max=OAStreamWorkflow.C_RANGE_NONE,
-                               p_ada=p_ada,
-                               p_visualize=p_visualize,
-                               p_logging=p_logging )
-
-
-        # 1.2.2 Creation of tasks and add them to the workflow
-
-        task_bd = BoundaryDetector( p_name='T1 - Boundary Detector', 
-                                    p_ada=p_ada, 
-                                    p_visualize=p_visualize,
-                                    p_logging=p_logging )
-        
-        workflow.add_task(p_task = task_bd)
-        
-        
-        task_norm = NormalizerMinMax( p_name='T2 - MinMax Normalizer', 
-                                      p_ada=p_ada, 
-                                      p_visualize=p_visualize,
-                                      p_logging=p_logging)
-        
-        workflow.add_task(p_task = task_norm, p_pred_tasks=[task_bd])
-
-        task_bd.register_event_handler(
-            p_event_id=BoundaryDetector.C_EVENT_ADAPTED,
-            p_event_handler=task_norm.adapt_on_event
-            )
-
-        # Cluster Analyzer
-        task_clusterer = WrRiverKMeans2MLPro( p_name='#1: KMeans@River',
-                                              p_n_clusters=3,
-                                              p_halflife=0.05, 
-                                              p_sigma=3, 
-                                              p_seed=42,
-                                              p_visualize=p_visualize,
-                                              p_logging=p_logging )
-
-        workflow.add_task(p_task = task_clusterer)
-
-        task_norm.register_event_handler( p_event_id=NormalizerMinMax.C_EVENT_ADAPTED,
-                                                 p_event_handler=task_clusterer.renormalize_on_event )
-
-
-        # Anomaly Detector
-        task_anomaly_detector = ClusterDriftDetector(p_clusterer=task_clusterer,
-                                                     p_with_time_calculation=False,
-                                                     p_state_change_detection=True,
-                                                     p_instantaneous_velocity_change_detection=False,
-                                                     p_min_velocity_threshold=0.01,
-                                                     p_initial_skip=400,
-                                                     p_buffer_size=50,
-                                                     p_visualize=p_visualize,
-                                                     p_logging=p_logging)
-        
-        workflow.add_task(p_task=task_anomaly_detector, p_pred_tasks=[task_clusterer])
-
-        # 1.3 Return stream and workflow
-        return stream, workflow
-
-
-
-# 2 Prepare for test
-if __name__ == "__main__":
-    cycle_limit = 2000
-    logging     = Log.C_LOG_ALL
-    visualize   = True
-    step_rate   = 1
-else:
-    cycle_limit = 5
-    logging     = Log.C_LOG_NOTHING
-    visualize   = False
-    step_rate   = 1
-
-
-# 3 Instantiate the stream scenario
-myscenario = MyScenario( p_mode=Mode.C_MODE_SIM,
-                               p_cycle_limit=cycle_limit,
-                               p_visualize=visualize,
-                               p_logging=logging )
-
-# 4 Reset and run own stream scenario
-myscenario.reset()
-
-if __name__ == "__main__":
-    myscenario.init_plot( p_plot_settings=PlotSettings( p_view = PlotSettings.C_VIEW_2D,
-                                                            p_step_rate = step_rate ) )
-    input('\nPlease arrange all windows and press ENTER to start stream processing...')
-
-
-
-tp_before           = datetime.now()
-myscenario.run()
-tp_after            = datetime.now()
-tp_delta            = tp_after - tp_before
-duraction_sec       = ( tp_delta.seconds * 1000000 + tp_delta.microseconds + 1 ) / 1000000
-myscenario.log(Log.C_LOG_TYPE_W, 'Duration [sec]:', round(duraction_sec,2), ', Cycles/sec:', round(cycle_limit/duraction_sec,2))
-
-
-
-# 5 Summary
-anomalies         = myscenario.get_workflow()._tasks[3].get_anomalies()
-detected_anomalies= len(anomalies)
-
-myscenario.log(Log.C_LOG_TYPE_W, '-------------------------------------------------------')
-myscenario.log(Log.C_LOG_TYPE_W, '-------------------------------------------------------')
-myscenario.log(Log.C_LOG_TYPE_W, 'Here is the recap of the anomaly detector')
-myscenario.log(Log.C_LOG_TYPE_W, 'Number of anomalies: ', detected_anomalies )
-
-for anomaly in anomalies.values():
-     anomaly_name = anomaly.C_NAME
-     anomaly_id = str(anomaly.id)
-     clusters_affected = {}
-     clusters = anomaly.get_clusters()
-     properties = anomaly.get_properties()
-     for x in clusters.keys():
-        clusters_affected[x] = {}
-        clusters_affected[x]["velocity"] = properties[x]["velocity"]
-        clusters_affected[x]["acceleration"] = properties[x]["acceleration"]
-
-     
-     inst = anomaly.get_instances()[-1].get_id()
-     myscenario.log(Log.C_LOG_TYPE_W, 
-                    'Anomaly : ', anomaly_name,
-                    '\n Anomaly ID : ', anomaly_id,
-                    '\n Instance ID : ', inst,
-                    '\n Clusters : ', clusters_affected)
-
-myscenario.log(Log.C_LOG_TYPE_W, '-------------------------------------------------------')
-myscenario.log(Log.C_LOG_TYPE_W, '-------------------------------------------------------')
-
-if __name__ == "__main__":
-    input('Press ENTER to exit...')
\ No newline at end of file
diff --git a/test/howtos/oa/howto_oa_streams_cbad_009_KMeans_ClusterDriftDetector_InstantaneousDetection_5d.py b/test/howtos/oa/howto_oa_streams_cbad_009_KMeans_ClusterDriftDetector_InstantaneousDetection_5d.py
deleted file mode 100644
index b0483ece0..000000000
--- a/test/howtos/oa/howto_oa_streams_cbad_009_KMeans_ClusterDriftDetector_InstantaneousDetection_5d.py
+++ /dev/null
@@ -1,180 +0,0 @@
-## -------------------------------------------------------------------------------------------------
-## -- Project : MLPro - The integrative middleware framework for standardized machine learning
-## -- Package : test.howtos.oa
-## -- Module  : howto_oa_streams_cbad_009_KMeans_ClusterDriftDetector_InstantaneousDetection_5d.py
-## -------------------------------------------------------------------------------------------------
-## -- History :
-## -- yyyy-mm-dd  Ver.      Auth.    Description
-## -- 2024-08-04  1.0.0     SK       Creation
-## -- 2024-10-29  1.0.1     DA       Refactoring
-## -------------------------------------------------------------------------------------------------
-
-"""
-Ver. 1.0.1 (2024-10-29)
-
-This module is done as a part of the Master's Thesis named Online Adaptive Cluster-based Anomaly
-Detection, authored by Syamraj Purushamparambil Satheesh, as a part of the course
-"Systems Engineering and Engineering Management" in the Department of "Electrical Energy Engineering",
-supervised by Dipl.-Info. Detlef Arend.
-
-
-This module demonstrates cluster-based anomlay detection, on synthetic 2-dimensional data generated by
-the StreamMLProClusterGenerator, using the ClusterDriftDetector anomaly detection algorithm employing
-the KMeans clustering algorithm wrapped from River library, to detect ClusterDrift anomaly.
-
-You will learn:
-
-1. Generating synthetic data using the native StreamMLProClusterGenerator.
-
-2. Creating a workflow and tasks in MLPro-OA.
-
-3. Clustering the normalized streaming data using the WrRiverKMeans2MLPro, with normalizer as a predecessor.
-
-4. Detecting drift anomalies in the clustered data using the ClusterDriftDetector.
-
-In the visualization, the cross hair designating the cluster's centroid becomes'red' in colour when
-an anomaly is detected linked to that specific cluster. An overview of the anomalies is displayed on
-the screen following the run.
-
-"""
-
-from mlpro.bf.streams.streams import *
-from mlpro.bf.various import Log
-from mlpro.oa.streams import *
-from mlpro_int_river.wrappers.clusteranalyzers import WrRiverKMeans2MLPro
-from mlpro.oa.streams.tasks import ClusterDriftDetector
-
-
-
-## -------------------------------------------------------------------------------------------------
-## -------------------------------------------------------------------------------------------------
-# 1 Prepare a scenario
-class MyScenario(OAStreamScenario):
-    C_NAME = 'ClusterDriftScenario'
-
-    def _setup(self, p_mode, p_ada: bool, p_visualize: bool, p_logging):
-
-        # 1.1 Get MLPro benchmark Clutser Generator
-        stream = StreamMLProClusterGenerator(p_num_dim=5,
-                                             p_num_instances=5000,
-                                             p_num_clusters=3,
-                                             p_radii=[100],
-                                             p_velocities=[0.0],
-                                             p_change_velocities=True,
-                                             p_changed_velocities=[0.8, 0.8],
-                                             p_points_of_change_velocities=[1000, 1200],
-                                             p_num_clusters_for_change_velocities=2,
-                                             p_seed=23,
-                                             p_logging=p_logging)
-
-
-        # 1.2 Set up a stream workflow
-
-        # 1.2.1 Creation of a workflow
-        workflow = OAStreamWorkflow( p_name='Anomaly Detection',
-                               p_range_max=OAStreamWorkflow.C_RANGE_NONE,
-                               p_ada=p_ada,
-                               p_visualize=p_visualize,
-                               p_logging=p_logging )
-
-
-        # 1.2.2 Creation of tasks and add them to the workflow
-
-        # Cluster Analyzer
-        task_clusterer = WrRiverKMeans2MLPro( p_name='#1: KMeans@River',
-                                              p_n_clusters=3,
-                                              p_halflife=0.05, 
-                                              p_sigma=3, 
-                                              p_seed=42,
-                                              p_visualize=p_visualize,
-                                              p_logging=p_logging )
-
-        workflow.add_task(p_task = task_clusterer)
-
-        # Anomaly Detector
-        task_anomaly_detector = ClusterDriftDetector(p_clusterer=task_clusterer,
-                                                     p_with_time_calculation=False,
-                                                     p_instantaneous_velocity_change_detection=True,
-                                                     p_min_velocity_threshold=1.5,
-                                                     p_initial_skip=400,
-                                                     p_visualize=p_visualize,
-                                                     p_logging=p_logging)
-
-        workflow.add_task(p_task=task_anomaly_detector, p_pred_tasks=[task_clusterer])
-
-        # 1.3 Return stream and workflow
-        return stream, workflow
-
-
-
-# 2 Prepare for test
-if __name__ == "__main__":
-    cycle_limit = 2000
-    logging     = Log.C_LOG_ALL
-    visualize   = True
-    step_rate   = 1
-else:
-    cycle_limit = 5
-    logging     = Log.C_LOG_NOTHING
-    visualize   = False
-    step_rate   = 1
-
-
-# 3 Instantiate the stream scenario
-myscenario = MyScenario( p_mode=Mode.C_MODE_SIM,
-                               p_cycle_limit=cycle_limit,
-                               p_visualize=visualize,
-                               p_logging=logging )
-
-# 4 Reset and run own stream scenario
-myscenario.reset()
-
-if __name__ == "__main__":
-    myscenario.init_plot( p_plot_settings=PlotSettings( p_view = PlotSettings.C_VIEW_2D,
-                                                            p_step_rate = step_rate ) )
-    input('\nPlease arrange all windows and press ENTER to start stream processing...')
-
-
-
-tp_before           = datetime.now()
-myscenario.run()
-tp_after            = datetime.now()
-tp_delta            = tp_after - tp_before
-duraction_sec       = ( tp_delta.seconds * 1000000 + tp_delta.microseconds + 1 ) / 1000000
-myscenario.log(Log.C_LOG_TYPE_W, 'Duration [sec]:', round(duraction_sec,2), ', Cycles/sec:', round(cycle_limit/duraction_sec,2))
-
-
-
-# 5 Summary
-anomalies         = myscenario.get_workflow()._tasks[1].get_anomalies()
-detected_anomalies= len(anomalies)
-
-myscenario.log(Log.C_LOG_TYPE_W, '-------------------------------------------------------')
-myscenario.log(Log.C_LOG_TYPE_W, '-------------------------------------------------------')
-myscenario.log(Log.C_LOG_TYPE_W, 'Here is the recap of the anomaly detector')
-myscenario.log(Log.C_LOG_TYPE_W, 'Number of anomalies: ', detected_anomalies )
-
-for anomaly in anomalies.values():
-     anomaly_name = anomaly.C_NAME
-     anomaly_id = str(anomaly.id)
-     clusters_affected = {}
-     clusters = anomaly.get_clusters()
-     properties = anomaly.get_properties()
-     for x in clusters.keys():
-        clusters_affected[x] = {}
-        clusters_affected[x]["velocity"] = properties[x]["velocity"]
-        clusters_affected[x]["acceleration"] = properties[x]["acceleration"]
-
-     
-     inst = anomaly.get_instances()[-1].get_id()
-     myscenario.log(Log.C_LOG_TYPE_W, 
-                    'Anomaly : ', anomaly_name,
-                    '\n Anomaly ID : ', anomaly_id,
-                    '\n Instance ID : ', inst,
-                    '\n Clusters : ', clusters_affected)
-
-myscenario.log(Log.C_LOG_TYPE_W, '-------------------------------------------------------')
-myscenario.log(Log.C_LOG_TYPE_W, '-------------------------------------------------------')
-
-if __name__ == "__main__":
-    input('Press ENTER to exit...')
\ No newline at end of file
diff --git a/test/howtos/oa/howto_oa_streams_cbad_010_KMeans_ClusterDriftDetector_InstantaneousDetection_normalization_5d.py b/test/howtos/oa/howto_oa_streams_cbad_010_KMeans_ClusterDriftDetector_InstantaneousDetection_normalization_5d.py
deleted file mode 100644
index 05d224c20..000000000
--- a/test/howtos/oa/howto_oa_streams_cbad_010_KMeans_ClusterDriftDetector_InstantaneousDetection_normalization_5d.py
+++ /dev/null
@@ -1,205 +0,0 @@
-## -------------------------------------------------------------------------------------------------
-## -- Project : MLPro - The integrative middleware framework for standardized machine learning
-## -- Package : test.howtos.oa
-## -- Module  : howto_oa_streams_cbad_010_KMeans_ClusterDriftDetector_InstantaneousDetection_normalization_5d.py
-## -------------------------------------------------------------------------------------------------
-## -- History :
-## -- yyyy-mm-dd  Ver.      Auth.    Description
-## -- 2024-08-04  1.0.0     SK       Creation
-## -- 2024-10-29  1.0.1     DA       Refactoring
-## -------------------------------------------------------------------------------------------------
-
-"""
-Ver. 1.0.1 (2024-10-29)
-
-This module is done as a part of the Master's Thesis named Online Adaptive Cluster-based Anomaly
-Detection, authored by Syamraj Purushamparambil Satheesh, as a part of the course
-"Systems Engineering and Engineering Management" in the Department of "Electrical Energy Engineering",
-supervised by Dipl.-Info. Detlef Arend.
-
-
-This module demonstrates cluster-based anomlay detection, on synthetic 2-dimensional data generated by
-the StreamMLProClusterGenerator, using the ClusterDriftDetector anomaly detection algorithm employing
-the KMeans clustering algorithm wrapped from River library, to detect ClusterDrift anomaly.
-
-You will learn:
-
-1. Generating synthetic data using the native StreamMLProClusterGenerator.
-
-2. Creating a workflow and tasks in MLPro-OA.
-
-3. Normalizing streaming data using the MinMax Normalizer, with boundary detector as a predecessor task.
-
-4. Clustering the normalized streaming data using the WrRiverKMeans2MLPro, with normalizer as a predecessor.
-
-5. Detecting drift anomalies in the clustered data using the ClusterDriftDetector.
-
-In the visualization, the cross hair designating the cluster's centroid becomes'red' in colour when
-an anomaly is detected linked to that specific cluster. An overview of the anomalies is displayed on
-the screen following the run.
-
-"""
-from mlpro.bf.streams.streams import *
-from mlpro.bf.various import Log
-from mlpro.oa.streams import *
-from mlpro_int_river.wrappers.clusteranalyzers.kmeans import WrRiverKMeans2MLPro
-from mlpro.oa.streams.tasks import BoundaryDetector, NormalizerMinMax, ClusterDriftDetector
-
-
-
-## -------------------------------------------------------------------------------------------------
-## -------------------------------------------------------------------------------------------------
-# 1 Prepare a scenario
-class MyScenario(OAStreamScenario):
-    C_NAME = 'ClusterDriftScenario'
-
-    def _setup(self, p_mode, p_ada: bool, p_visualize: bool, p_logging):
-
-        # 1.1 Get MLPro benchmark Clutser Generator
-        stream = StreamMLProClusterGenerator(p_num_dim=5,
-                                             p_num_instances=5000,
-                                             p_num_clusters=4,
-                                             p_radii=[100],
-                                             p_velocities=[0.0],
-                                             p_change_velocities=True,
-                                             p_changed_velocities=[0.6, 1.0, 0.3],
-                                             p_points_of_change_velocities=[1200, 1500, 1700],
-                                             p_num_clusters_for_change_velocities=3,
-                                             p_seed=23,
-                                             p_logging=p_logging)
-
-
-        # 1.2 Set up a stream workflow
-
-        # 1.2.1 Creation of a workflow
-        workflow = OAStreamWorkflow( p_name='Anomaly Detection',
-                               p_range_max=OAStreamWorkflow.C_RANGE_NONE,
-                               p_ada=p_ada,
-                               p_visualize=p_visualize,
-                               p_logging=p_logging )
-
-
-        # 1.2.2 Creation of tasks and add them to the workflow
-
-        task_bd = BoundaryDetector( p_name='T1 - Boundary Detector', 
-                                    p_ada=p_ada, 
-                                    p_visualize=p_visualize,
-                                    p_logging=p_logging )
-        
-        workflow.add_task(p_task = task_bd)
-        
-        
-        task_norm = NormalizerMinMax( p_name='T2 - MinMax Normalizer', 
-                                      p_ada=p_ada, 
-                                      p_visualize=p_visualize,
-                                      p_logging=p_logging)
-        
-        workflow.add_task(p_task = task_norm, p_pred_tasks=[task_bd])
-
-        task_bd.register_event_handler(
-            p_event_id=BoundaryDetector.C_EVENT_ADAPTED,
-            p_event_handler=task_norm.adapt_on_event
-            )
-
-        # Cluster Analyzer
-        task_clusterer = WrRiverKMeans2MLPro( p_name='#1: KMeans@River',
-                                              p_n_clusters=4,
-                                              p_halflife=0.1, 
-                                              p_sigma=3, 
-                                              p_seed=42,
-                                              p_visualize=p_visualize,
-                                              p_logging=p_logging )
-
-        workflow.add_task(p_task = task_clusterer)
-
-        task_norm.register_event_handler( p_event_id=NormalizerMinMax.C_EVENT_ADAPTED,
-                                                 p_event_handler=task_clusterer.renormalize_on_event )
-
-
-        # Anomaly Detector
-        task_anomaly_detector = ClusterDriftDetector(p_clusterer=task_clusterer,
-                                                     p_with_time_calculation=False,
-                                                     p_instantaneous_velocity_change_detection=True,
-                                                     p_min_velocity_threshold=0.01,
-                                                     p_initial_skip=400,
-                                                     p_visualize=p_visualize,
-                                                     p_logging=p_logging)
-
-        workflow.add_task(p_task=task_anomaly_detector, p_pred_tasks=[task_clusterer])
-
-        # 1.3 Return stream and workflow
-        return stream, workflow
-
-
-
-# 2 Prepare for test
-if __name__ == "__main__":
-    cycle_limit = 2000
-    logging     = Log.C_LOG_ALL
-    visualize   = True
-    step_rate   = 1
-else:
-    cycle_limit = 5
-    logging     = Log.C_LOG_NOTHING
-    visualize   = False
-    step_rate   = 1
-
-
-# 3 Instantiate the stream scenario
-myscenario = MyScenario( p_mode=Mode.C_MODE_SIM,
-                               p_cycle_limit=cycle_limit,
-                               p_visualize=visualize,
-                               p_logging=logging )
-
-# 4 Reset and run own stream scenario
-myscenario.reset()
-
-if __name__ == "__main__":
-    myscenario.init_plot( p_plot_settings=PlotSettings( p_view = PlotSettings.C_VIEW_2D,
-                                                            p_step_rate = step_rate ) )
-    input('\nPlease arrange all windows and press ENTER to start stream processing...')
-
-
-
-tp_before           = datetime.now()
-myscenario.run()
-tp_after            = datetime.now()
-tp_delta            = tp_after - tp_before
-duraction_sec       = ( tp_delta.seconds * 1000000 + tp_delta.microseconds + 1 ) / 1000000
-myscenario.log(Log.C_LOG_TYPE_W, 'Duration [sec]:', round(duraction_sec,2), ', Cycles/sec:', round(cycle_limit/duraction_sec,2))
-
-
-
-# 5 Summary
-anomalies         = myscenario.get_workflow()._tasks[3].get_anomalies()
-detected_anomalies= len(anomalies)
-
-myscenario.log(Log.C_LOG_TYPE_W, '-------------------------------------------------------')
-myscenario.log(Log.C_LOG_TYPE_W, '-------------------------------------------------------')
-myscenario.log(Log.C_LOG_TYPE_W, 'Here is the recap of the anomaly detector')
-myscenario.log(Log.C_LOG_TYPE_W, 'Number of anomalies: ', detected_anomalies )
-
-for anomaly in anomalies.values():
-     anomaly_name = anomaly.C_NAME
-     anomaly_id = str(anomaly.id)
-     clusters_affected = {}
-     clusters = anomaly.get_clusters()
-     properties = anomaly.get_properties()
-     for x in clusters.keys():
-        clusters_affected[x] = {}
-        clusters_affected[x]["velocity"] = properties[x]["velocity"]
-        clusters_affected[x]["acceleration"] = properties[x]["acceleration"]
-
-     
-     inst = anomaly.get_instances()[-1].get_id()
-     myscenario.log(Log.C_LOG_TYPE_W, 
-                    'Anomaly : ', anomaly_name,
-                    '\n Anomaly ID : ', anomaly_id,
-                    '\n Instance ID : ', inst,
-                    '\n Clusters : ', clusters_affected)
-
-myscenario.log(Log.C_LOG_TYPE_W, '-------------------------------------------------------')
-myscenario.log(Log.C_LOG_TYPE_W, '-------------------------------------------------------')
-
-if __name__ == "__main__":
-    input('Press ENTER to exit...')
\ No newline at end of file
diff --git a/test/howtos/oa/howto_oa_streams_cbad_011_KMeans_ClusterDriftDetector_StateDetection_5d.py b/test/howtos/oa/howto_oa_streams_cbad_011_KMeans_ClusterDriftDetector_StateDetection_5d.py
deleted file mode 100644
index 4a34d4269..000000000
--- a/test/howtos/oa/howto_oa_streams_cbad_011_KMeans_ClusterDriftDetector_StateDetection_5d.py
+++ /dev/null
@@ -1,182 +0,0 @@
-## -------------------------------------------------------------------------------------------------
-## -- Project : MLPro - The integrative middleware framework for standardized machine learning
-## -- Package : test.howtos.oa
-## -- Module  : howto_oa_streams_cbad_011_KMeans_ClusterDriftDetector_StateDetection_5d.py
-## -------------------------------------------------------------------------------------------------
-## -- History :
-## -- yyyy-mm-dd  Ver.      Auth.    Description
-## -- 2024-08-04  1.0.0     SK       Creation
-## -- 2024-10-29  1.0.1     DA       Refactoring
-## -------------------------------------------------------------------------------------------------
-
-"""
-Ver. 1.0.1 (2024-10-29)
-
-This module is done as a part of the Master's Thesis named Online Adaptive Cluster-based Anomaly
-Detection, authored by Syamraj Purushamparambil Satheesh, as a part of the course
-"Systems Engineering and Engineering Management" in the Department of "Electrical Energy Engineering",
-supervised by Dipl.-Info. Detlef Arend.
-
-
-This module demonstrates cluster-based anomlay detection, on synthetic 2-dimensional data generated by
-the StreamMLProClusterGenerator, using the ClusterDriftDetector anomaly detection algorithm employing
-the KMeans clustering algorithm wrapped from River library, to detect ClusterDrift anomaly.
-
-You will learn:
-
-1. Generating synthetic data using the native StreamMLProClusterGenerator.
-
-2. Creating a workflow and tasks in MLPro-OA.
-
-3. Clustering the normalized streaming data using the WrRiverKMeans2MLPro, with normalizer as a predecessor.
-
-4. Detecting drift anomalies in the clustered data using the ClusterDriftDetector.
-
-In the visualization, the cross hair designating the cluster's centroid becomes'red' in colour when
-an anomaly is detected linked to that specific cluster. An overview of the anomalies is displayed on
-the screen following the run.
-
-"""
-
-from mlpro.bf.streams.streams import *
-from mlpro.bf.various import Log
-from mlpro.oa.streams import *
-from mlpro_int_river.wrappers.clusteranalyzers import WrRiverKMeans2MLPro
-from mlpro.oa.streams.tasks import ClusterDriftDetector
-
-
-
-## -------------------------------------------------------------------------------------------------
-## -------------------------------------------------------------------------------------------------
-# 1 Prepare a scenario
-class MyScenario(OAStreamScenario):
-    C_NAME = 'ClusterDriftScenario'
-
-    def _setup(self, p_mode, p_ada: bool, p_visualize: bool, p_logging):
-
-        # 1.1 Get MLPro benchmark Clutser Generator
-        stream = StreamMLProClusterGenerator(p_num_dim=5,
-                                             p_num_instances=5000,
-                                             p_num_clusters=3,
-                                             p_radii=[100],
-                                             p_velocities=[0.0],
-                                             p_change_velocities=True,
-                                             p_changed_velocities=[0.8, 0.8],
-                                             p_points_of_change_velocities=[1000, 1200],
-                                             p_num_clusters_for_change_velocities=2,
-                                             p_seed=23,
-                                             p_logging=p_logging)
-
-
-        # 1.2 Set up a stream workflow
-
-        # 1.2.1 Creation of a workflow
-        workflow = OAStreamWorkflow( p_name='Anomaly Detection',
-                               p_range_max=OAStreamWorkflow.C_RANGE_NONE,
-                               p_ada=p_ada,
-                               p_visualize=p_visualize,
-                               p_logging=p_logging )
-
-
-        # 1.2.2 Creation of tasks and add them to the workflow
-
-        # Cluster Analyzer
-        task_clusterer = WrRiverKMeans2MLPro( p_name='#1: KMeans@River',
-                                              p_n_clusters=3,
-                                              p_halflife=0.05, 
-                                              p_sigma=3, 
-                                              p_seed=42,
-                                              p_visualize=p_visualize,
-                                              p_logging=p_logging )
-
-        workflow.add_task(p_task = task_clusterer)
-
-        # Anomaly Detector
-        task_anomaly_detector = ClusterDriftDetector(p_clusterer=task_clusterer,
-                                                     p_with_time_calculation=False,
-                                                     p_state_change_detection=True,
-                                                     p_instantaneous_velocity_change_detection=False,
-                                                     p_min_velocity_threshold=1.5,
-                                                     p_initial_skip=400,
-                                                     p_buffer_size=50,
-                                                     p_visualize=p_visualize,
-                                                     p_logging=p_logging)
-        
-        workflow.add_task(p_task=task_anomaly_detector, p_pred_tasks=[task_clusterer])
-
-        # 1.3 Return stream and workflow
-        return stream, workflow
-
-
-
-# 2 Prepare for test
-if __name__ == "__main__":
-    cycle_limit = 2000
-    logging     = Log.C_LOG_ALL
-    visualize   = True
-    step_rate   = 1
-else:
-    cycle_limit = 5
-    logging     = Log.C_LOG_NOTHING
-    visualize   = False
-    step_rate   = 1
-
-
-# 3 Instantiate the stream scenario
-myscenario = MyScenario( p_mode=Mode.C_MODE_SIM,
-                               p_cycle_limit=cycle_limit,
-                               p_visualize=visualize,
-                               p_logging=logging )
-
-# 4 Reset and run own stream scenario
-myscenario.reset()
-
-if __name__ == "__main__":
-    myscenario.init_plot( p_plot_settings=PlotSettings( p_view = PlotSettings.C_VIEW_2D,
-                                                            p_step_rate = step_rate ) )
-    input('\nPlease arrange all windows and press ENTER to start stream processing...')
-
-
-
-tp_before           = datetime.now()
-myscenario.run()
-tp_after            = datetime.now()
-tp_delta            = tp_after - tp_before
-duraction_sec       = ( tp_delta.seconds * 1000000 + tp_delta.microseconds + 1 ) / 1000000
-myscenario.log(Log.C_LOG_TYPE_W, 'Duration [sec]:', round(duraction_sec,2), ', Cycles/sec:', round(cycle_limit/duraction_sec,2))
-
-
-
-# 5 Summary
-anomalies         = myscenario.get_workflow()._tasks[1].get_anomalies()
-detected_anomalies= len(anomalies)
-
-myscenario.log(Log.C_LOG_TYPE_W, '-------------------------------------------------------')
-myscenario.log(Log.C_LOG_TYPE_W, '-------------------------------------------------------')
-myscenario.log(Log.C_LOG_TYPE_W, 'Here is the recap of the anomaly detector')
-myscenario.log(Log.C_LOG_TYPE_W, 'Number of anomalies: ', detected_anomalies )
-
-for anomaly in anomalies.values():
-     anomaly_name = anomaly.C_NAME
-     anomaly_id = str(anomaly.id)
-     clusters_affected = {}
-     clusters = anomaly.get_clusters()
-     properties = anomaly.get_properties()
-     for x in clusters.keys():
-        clusters_affected[x] = {}
-        clusters_affected[x]["velocity"] = properties[x]["velocity"]
-        clusters_affected[x]["acceleration"] = properties[x]["acceleration"]
-
-     
-     inst = anomaly.get_instances()[-1].get_id()
-     myscenario.log(Log.C_LOG_TYPE_W, 
-                    'Anomaly : ', anomaly_name,
-                    '\n Anomaly ID : ', anomaly_id,
-                    '\n Instance ID : ', inst,
-                    '\n Clusters : ', clusters_affected)
-
-myscenario.log(Log.C_LOG_TYPE_W, '-------------------------------------------------------')
-myscenario.log(Log.C_LOG_TYPE_W, '-------------------------------------------------------')
-
-if __name__ == "__main__":
-    input('Press ENTER to exit...')
\ No newline at end of file
diff --git a/test/howtos/oa/howto_oa_streams_cbad_012_KMeans_ClusterDriftDetector_StateDetection_normalization_5d.py b/test/howtos/oa/howto_oa_streams_cbad_012_KMeans_ClusterDriftDetector_StateDetection_normalization_5d.py
deleted file mode 100644
index ff17f43ae..000000000
--- a/test/howtos/oa/howto_oa_streams_cbad_012_KMeans_ClusterDriftDetector_StateDetection_normalization_5d.py
+++ /dev/null
@@ -1,208 +0,0 @@
-## -------------------------------------------------------------------------------------------------
-## -- Project : MLPro - The integrative middleware framework for standardized machine learning
-## -- Package : test.howtos.oa
-## -- Module  : howto_oa_streams_cbad_012_KMeans_ClusterDriftDetector_StateDetection_normalization_5d.py
-## -------------------------------------------------------------------------------------------------
-## -- History :
-## -- yyyy-mm-dd  Ver.      Auth.    Description
-## -- 2024-08-04  1.0.0     SK       Creation
-## -- 2024-10-29  1.0.1     DA       Refactoring
-## -------------------------------------------------------------------------------------------------
-
-"""
-Ver. 1.0.1 (2024-10-29)
-
-This module is done as a part of the Master's Thesis named Online Adaptive Cluster-based Anomaly
-Detection, authored by Syamraj Purushamparambil Satheesh, as a part of the course
-"Systems Engineering and Engineering Management" in the Department of "Electrical Energy Engineering",
-supervised by Dipl.-Info. Detlef Arend.
-
-
-This module demonstrates cluster-based anomlay detection, on synthetic 2-dimensional data generated by
-the StreamMLProClusterGenerator, using the ClusterDriftDetector anomaly detection algorithm employing
-the KMeans clustering algorithm wrapped from River library, to detect ClusterDrift anomaly.
-
-You will learn:
-
-1. Generating synthetic data using the native StreamMLProClusterGenerator.
-
-2. Creating a workflow and tasks in MLPro-OA.
-
-3. Normalizing streaming data using the MinMax Normalizer, with boundary detector as a predecessor task.
-
-4. Clustering the normalized streaming data using the WrRiverKMeans2MLPro, with normalizer as a predecessor.
-
-5. Detecting drift anomalies in the clustered data using the ClusterDriftDetector.
-
-In the visualization, the cross hair designating the cluster's centroid becomes'red' in colour when
-an anomaly is detected linked to that specific cluster. An overview of the anomalies is displayed on
-the screen following the run.
-
-"""
-
-from mlpro.bf.streams.streams import *
-from mlpro.bf.various import Log
-from mlpro.oa.streams import *
-from mlpro_int_river.wrappers.clusteranalyzers.kmeans import WrRiverKMeans2MLPro
-from mlpro.oa.streams.tasks import BoundaryDetector, NormalizerMinMax, ClusterDriftDetector
-
-
-
-## -------------------------------------------------------------------------------------------------
-## -------------------------------------------------------------------------------------------------
-# 1 Prepare a scenario
-class MyScenario(OAStreamScenario):
-    C_NAME = 'ClusterDriftScenario'
-
-    def _setup(self, p_mode, p_ada: bool, p_visualize: bool, p_logging):
-
-        # 1.1 Get MLPro benchmark Clutser Generator
-        stream = StreamMLProClusterGenerator(p_num_dim=5,
-                                             p_num_instances=5000,
-                                             p_num_clusters=4,
-                                             p_radii=[100],
-                                             p_velocities=[0.0],
-                                             p_change_velocities=True,
-                                             p_changed_velocities=[0.6, 1.0, 0.3],
-                                             p_points_of_change_velocities=[1200, 1500, 1700],
-                                             p_num_clusters_for_change_velocities=3,
-                                             p_seed=23,
-                                             p_logging=p_logging)
-
-
-        # 1.2 Set up a stream workflow
-
-        # 1.2.1 Creation of a workflow
-        workflow = OAStreamWorkflow( p_name='Anomaly Detection',
-                               p_range_max=OAStreamWorkflow.C_RANGE_NONE,
-                               p_ada=p_ada,
-                               p_visualize=p_visualize,
-                               p_logging=p_logging )
-
-
-        # 1.2.2 Creation of tasks and add them to the workflow
-
-        task_bd = BoundaryDetector( p_name='T1 - Boundary Detector', 
-                                    p_ada=p_ada, 
-                                    p_visualize=p_visualize,
-                                    p_logging=p_logging )
-        
-        workflow.add_task(p_task = task_bd)
-        
-        
-        task_norm = NormalizerMinMax( p_name='T2 - MinMax Normalizer', 
-                                      p_ada=p_ada, 
-                                      p_visualize=p_visualize,
-                                      p_logging=p_logging)
-        
-        workflow.add_task(p_task = task_norm, p_pred_tasks=[task_bd])
-
-        task_bd.register_event_handler(
-            p_event_id=BoundaryDetector.C_EVENT_ADAPTED,
-            p_event_handler=task_norm.adapt_on_event
-            )
-
-        # Cluster Analyzer
-        task_clusterer = WrRiverKMeans2MLPro( p_name='#1: KMeans@River',
-                                              p_n_clusters=4,
-                                              p_halflife=0.1, 
-                                              p_sigma=3, 
-                                              p_seed=42,
-                                              p_visualize=p_visualize,
-                                              p_logging=p_logging )
-
-        workflow.add_task(p_task = task_clusterer)
-
-        task_norm.register_event_handler( p_event_id=NormalizerMinMax.C_EVENT_ADAPTED,
-                                                 p_event_handler=task_clusterer.renormalize_on_event )
-
-
-        # Anomaly Detector
-        task_anomaly_detector = ClusterDriftDetector(p_clusterer=task_clusterer,
-                                                     p_with_time_calculation=False,
-                                                     p_state_change_detection=True,
-                                                     p_instantaneous_velocity_change_detection=False,
-                                                     p_min_velocity_threshold=0.01,
-                                                     p_initial_skip=400,
-                                                     p_buffer_size=50,
-                                                     p_visualize=p_visualize,
-                                                     p_logging=p_logging)
-        
-        workflow.add_task(p_task=task_anomaly_detector, p_pred_tasks=[task_clusterer])
-
-        # 1.3 Return stream and workflow
-        return stream, workflow
-
-
-
-# 2 Prepare for test
-if __name__ == "__main__":
-    cycle_limit = 2000
-    logging     = Log.C_LOG_ALL
-    visualize   = True
-    step_rate   = 1
-else:
-    cycle_limit = 5
-    logging     = Log.C_LOG_NOTHING
-    visualize   = False
-    step_rate   = 1
-
-
-# 3 Instantiate the stream scenario
-myscenario = MyScenario( p_mode=Mode.C_MODE_SIM,
-                               p_cycle_limit=cycle_limit,
-                               p_visualize=visualize,
-                               p_logging=logging )
-
-# 4 Reset and run own stream scenario
-myscenario.reset()
-
-if __name__ == "__main__":
-    myscenario.init_plot( p_plot_settings=PlotSettings( p_view = PlotSettings.C_VIEW_2D,
-                                                            p_step_rate = step_rate ) )
-    input('\nPlease arrange all windows and press ENTER to start stream processing...')
-
-
-
-tp_before           = datetime.now()
-myscenario.run()
-tp_after            = datetime.now()
-tp_delta            = tp_after - tp_before
-duraction_sec       = ( tp_delta.seconds * 1000000 + tp_delta.microseconds + 1 ) / 1000000
-myscenario.log(Log.C_LOG_TYPE_W, 'Duration [sec]:', round(duraction_sec,2), ', Cycles/sec:', round(cycle_limit/duraction_sec,2))
-
-
-
-# 5 Summary
-anomalies         = myscenario.get_workflow()._tasks[3].get_anomalies()
-detected_anomalies= len(anomalies)
-
-myscenario.log(Log.C_LOG_TYPE_W, '-------------------------------------------------------')
-myscenario.log(Log.C_LOG_TYPE_W, '-------------------------------------------------------')
-myscenario.log(Log.C_LOG_TYPE_W, 'Here is the recap of the anomaly detector')
-myscenario.log(Log.C_LOG_TYPE_W, 'Number of anomalies: ', detected_anomalies )
-
-for anomaly in anomalies.values():
-     anomaly_name = anomaly.C_NAME
-     anomaly_id = str(anomaly.id)
-     clusters_affected = {}
-     clusters = anomaly.get_clusters()
-     properties = anomaly.get_properties()
-     for x in clusters.keys():
-        clusters_affected[x] = {}
-        clusters_affected[x]["velocity"] = properties[x]["velocity"]
-        clusters_affected[x]["acceleration"] = properties[x]["acceleration"]
-
-     
-     inst = anomaly.get_instances()[-1].get_id()
-     myscenario.log(Log.C_LOG_TYPE_W, 
-                    'Anomaly : ', anomaly_name,
-                    '\n Anomaly ID : ', anomaly_id,
-                    '\n Instance ID : ', inst,
-                    '\n Clusters : ', clusters_affected)
-
-myscenario.log(Log.C_LOG_TYPE_W, '-------------------------------------------------------')
-myscenario.log(Log.C_LOG_TYPE_W, '-------------------------------------------------------')
-
-if __name__ == "__main__":
-    input('Press ENTER to exit...')
\ No newline at end of file
diff --git a/test/howtos/oa/howto_oa_streams_cbap_001_KMeans_ClusterDriftDetector_InstantaneousDetection_2d.py.off b/test/howtos/oa/howto_oa_streams_cbap_001_KMeans_ClusterDriftDetector_InstantaneousDetection_2d.py.off
deleted file mode 100644
index 9dad40ce2..000000000
--- a/test/howtos/oa/howto_oa_streams_cbap_001_KMeans_ClusterDriftDetector_InstantaneousDetection_2d.py.off
+++ /dev/null
@@ -1,199 +0,0 @@
-## -------------------------------------------------------------------------------------------------
-## -- Project : MLPro - The integrative middleware framework for standardized machine learning
-## -- Package : test.howtos.oa
-## -- Module  : howto_oa_streams_cbap_001_KMeans_ClusterDriftDetector_InstantaneousDetection_2d.py
-## -------------------------------------------------------------------------------------------------
-## -- History :
-## -- yyyy-mm-dd  Ver.      Auth.    Description
-## -- 2024-08-14  0.0.0     DA       Copied from howto_oa_streams_cbad_001_KMeans_....py
-## -------------------------------------------------------------------------------------------------
-
-"""
-Ver. 0.0.0 (2024-08-14)
-
-This module is done as a part of the Master's Thesis named Online Adaptive Cluster-based Anomaly
-Detection, authored by Syamraj Purushamparambil Satheesh, as a part of the course
-"Systems Engineering and Engineering Management" in the Department of "Electrical Energy Engineering",
-supervised by Dipl.-Info. Detlef Arend.
-
-
-This module demonstrates cluster-based anomlay detection, on synthetic 2-dimensional data generated by
-the StreamMLProClusterGenerator, using the ClusterDriftDetector anomaly detection algorithm employing
-the KMeans clustering algorithm wrapped from River library, to detect ClusterDrift anomaly.
-
-You will learn:
-
-1. Generating synthetic data using the native StreamMLProClusterGenerator.
-
-2. Creating a workflow and tasks in MLPro-OA.
-
-3. Clustering the normalized streaming data using the WrRiverKMeans2MLPro, with normalizer as a predecessor.
-
-4. Detecting drift anomalies in the clustered data using the ClusterDriftDetector.
-
-In the visualization, the cross hair designating the cluster's centroid becomes'red' in colour when
-an anomaly is detected linked to that specific cluster. An overview of the anomalies is displayed on
-the screen following the run.
-
-"""
-from mlpro.bf.math import Event
-from mlpro.bf.mt import Event
-from mlpro.bf.streams.streams import *
-from mlpro.bf.various import Log
-from mlpro.oa.streams import *
-from mlpro_int_river.wrappers.clusteranalyzers.kmeans import WrRiverKMeans2MLPro
-from mlpro.oa.streams.tasks.anomalydetectors.cb_detectors.drift_detector import ClusterDriftDetector
-from mlpro.oa.streams.tasks.anomalypredictors.tsf.ad_based import AnomalyPredictorAD
-
-
-
-
-class DevindisPredictor (AnomalyPredictorAD):
-
-    def _adapt_on_event(self, p_event_id: str, p_event_object: Event) -> bool:
-        
-        # Set breakpoint here...
-        pass
-
-
-
-
-## -------------------------------------------------------------------------------------------------
-## -------------------------------------------------------------------------------------------------
-# 1 Prepare a scenario
-class MyScenario(OAStreamScenario):
-    C_NAME = 'ClusterDriftScenario'
-
-    def _setup(self, p_mode, p_ada: bool, p_visualize: bool, p_logging):
-
-        # 1.1 Get MLPro benchmark Clutser Generator
-        stream = StreamMLProClusterGenerator(p_num_dim=2,
-                                             p_num_instances=5000,
-                                             p_num_clusters=3,
-                                             p_radii=[100],
-                                             p_velocities=[0.0],
-                                             p_change_velocities=True,
-                                             p_changed_velocities=[0.5, 0.6],
-                                             p_points_of_change_velocities=[1000, 1200],
-                                             p_num_clusters_for_change_velocities=2,
-                                             p_seed=12,
-                                             p_logging=p_logging)
-
-
-        # 1.2 Set up a stream workflow
-
-        # 1.2.1 Creation of a workflow
-        workflow = OAStreamWorkflow( p_name='Anomaly Detection',
-                                     p_range_max=OAStreamWorkflow.C_RANGE_NONE,
-                                     p_ada=p_ada,
-                                     p_visualize=p_visualize,
-                                     p_logging=p_logging )
-
-
-        # 1.2.2 Creation of tasks and add them to the workflow
-
-        # Cluster Analyzer
-        task_clusterer = WrRiverKMeans2MLPro( p_name='#1: KMeans@River',
-                                              p_n_clusters=3,
-                                              p_halflife=0.05, 
-                                              p_sigma=3, 
-                                              p_seed=42,
-                                              p_visualize=p_visualize,
-                                              p_logging=p_logging )
-
-        workflow.add_task(p_task = task_clusterer)
-
-        # Anomaly Detector
-        task_anomaly_detector = ClusterDriftDetector( p_clusterer=task_clusterer,
-                                                      p_with_time_calculation=False,
-                                                      p_instantaneous_velocity_change_detection=True,
-                                                      p_min_velocity_threshold=1,
-                                                      p_initial_skip=400,
-                                                      p_visualize=p_visualize,
-                                                      p_logging=p_logging ) 
-        
-        workflow.add_task(p_task=task_anomaly_detector, p_pred_tasks=[task_clusterer])
-
-        # Anomaly Predictor
-        task_anomaly_predictor = DevindisPredictor( p_visualize=p_visualize,
-                                                    p_logging=p_logging)
-        
-        task_anomaly_detector.register_event_handler( p_event_id = 'Anomaly',
-                                                      p_event_handler = task_anomaly_predictor.adapt_on_event )
-
-        # 1.3 Return stream and workflow
-        return stream, workflow
-
-
-
-# 2 Prepare for test
-if __name__ == "__main__":
-    cycle_limit = 2000
-    logging     = Log.C_LOG_ALL
-    visualize   = True
-    step_rate   = 1
-else:
-    cycle_limit = 5
-    logging     = Log.C_LOG_NOTHING
-    visualize   = False
-    step_rate   = 1
-
-
-# 3 Instantiate the stream scenario
-myscenario = MyScenario( p_mode=Mode.C_MODE_SIM,
-                               p_cycle_limit=cycle_limit,
-                               p_visualize=visualize,
-                               p_logging=logging )
-
-# 4 Reset and run own stream scenario
-myscenario.reset()
-
-if __name__ == "__main__":
-    myscenario.init_plot( p_plot_settings=PlotSettings( p_view = PlotSettings.C_VIEW_2D,
-                                                        p_step_rate = step_rate ) )
-    input('\nPlease arrange all windows and press ENTER to start stream processing...')
-
-
-
-tp_before           = datetime.now()
-myscenario.run()
-tp_after            = datetime.now()
-tp_delta            = tp_after - tp_before
-duraction_sec       = ( tp_delta.seconds * 1000000 + tp_delta.microseconds + 1 ) / 1000000
-myscenario.log(Log.C_LOG_TYPE_W, 'Duration [sec]:', round(duraction_sec,2), ', Cycles/sec:', round(cycle_limit/duraction_sec,2))
-
-
-
-# 5 Summary
-anomalies         = myscenario.get_workflow()._tasks[1].get_anomalies()
-detected_anomalies= len(anomalies)
-
-myscenario.log(Log.C_LOG_TYPE_W, '-------------------------------------------------------')
-myscenario.log(Log.C_LOG_TYPE_W, '-------------------------------------------------------')
-myscenario.log(Log.C_LOG_TYPE_W, 'Here is the recap of the anomaly detector')
-myscenario.log(Log.C_LOG_TYPE_W, 'Number of anomalies: ', detected_anomalies )
-
-for anomaly in anomalies.values():
-     anomaly_name = anomaly.C_NAME
-     anomaly_id = str(anomaly.id)
-     clusters_affected = {}
-     clusters = anomaly.get_clusters()
-     properties = anomaly.get_properties()
-     for x in clusters.keys():
-        clusters_affected[x] = {}
-        clusters_affected[x]["velocity"] = properties[x]["velocity"]
-        clusters_affected[x]["acceleration"] = properties[x]["acceleration"]
-
-     
-     inst = anomaly.get_instances()[-1].get_id()
-     myscenario.log(Log.C_LOG_TYPE_W, 
-                    'Anomaly : ', anomaly_name,
-                    '\n Anomaly ID : ', anomaly_id,
-                    '\n Instance ID : ', inst,
-                    '\n Clusters : ', clusters_affected)
-
-myscenario.log(Log.C_LOG_TYPE_W, '-------------------------------------------------------')
-myscenario.log(Log.C_LOG_TYPE_W, '-------------------------------------------------------')
-
-if __name__ == "__main__":
-    input('Press ENTER to exit...')
\ No newline at end of file
diff --git a/test/howtos/oa/howto_oa_streams_pp_105_rearranger_window_bd_normminmax_2d_3d_nD_multithreading.py.off b/test/howtos/oa/howto_oa_streams_pp_105_rearranger_window_bd_normminmax_2d_3d_nD_multithreading.py.off
deleted file mode 100644
index 756c2b248..000000000
--- a/test/howtos/oa/howto_oa_streams_pp_105_rearranger_window_bd_normminmax_2d_3d_nD_multithreading.py.off
+++ /dev/null
@@ -1,261 +0,0 @@
-## -------------------------------------------------------------------------------------------------
-## -- Project : MLPro - The integrative middleware framework for standardized machine learning
-## -- Package : mlpro.oa.examples
-## -- Module  : howto_oa_pp_007_rearranger_window_bd_normminmax_2d_3d_nD_multithreading.py
-## -------------------------------------------------------------------------------------------------
-## -- History :
-## -- yyyy-mm-dd  Ver.      Auth.    Description
-## -- 2023-01-09  1.0.0     DA       Creation
-## -- 2023-04-10  1.1.0     DA       Refactoring after changes on class OAScenario
-## -- 2023-05-20  1.1.1     DA       Registered handler of boundary detector to window
-## -------------------------------------------------------------------------------------------------
-
-"""
-Ver. 1.1.1 (2023-05-20)
-
-This module is an example of adaptive normalization of streaming data using MinMax normalizer. To 
-this regard, an online-adadptive custom scenario is set up. It combines a native 10-dimensional 
-sample stream with an online-adaptive workflow. The latter one consists of four tasks: a rearranger
-to reduce the stream data to 3 dimensions, a window that buffers the last 50 instances, a boundary
-detector and finally the MinMax normalizer. 
-
-You will learn:
-
-1. How to set up online-adaptive custom stream scenarios.
-
-2. How to set up online-adaptive workflows reusing various adaptive/non-adaptive MLPro stream tasks
-
-3. How to run and visualize your own custom stream scenario.
-"""
-
-
-
-from mlpro.bf.streams import *
-from mlpro.bf.streams.streams import *
-from mlpro.bf.streams.tasks import RingBuffer, Rearranger
-from mlpro.oa.streams import *
-
-
-
-## -------------------------------------------------------------------------------------------------
-## -------------------------------------------------------------------------------------------------
-class MyAdaptiveScenario (OAScenario):
-
-    C_NAME = 'Dummy'
-
-## -------------------------------------------------------------------------------------------------
-    def _setup(self, p_mode, p_ada: bool, p_visualize: bool, p_logging):
-
-        # 1 Prepare a native stream from MLPro
-        mlpro  = StreamProviderMLPro(p_logging=p_logging)
-        stream = mlpro.get_stream( p_name=StreamMLProRnd10D.C_NAME, 
-                                   p_mode=p_mode, 
-                                   p_visualize=p_visualize, 
-                                   p_logging=p_logging )
-
-
-        # 2 Set up a stream workflow based on a custom stream task
-
-        # 2.1 Creation of a workflow
-        workflow = OAWorkflow( p_name='wf',
-                               p_range_max=OAWorkflow.C_RANGE_THREAD,
-                               p_ada=p_ada,
-                               p_visualize=p_visualize, 
-                               p_logging=p_logging )
-
-
-        # 2.2 Creation of a task chain 2D
-
-        # 2.2.1 Rearranger to reduce the number of features
-        features     = stream.get_feature_space().get_dims()
-        features_new = [ ( 'F', features[1:3] ) ]
-
-        task_rearranger = Rearranger( p_name='Chain 2D, Task T1',
-                                      p_duplicate_data=True,
-                                      p_range_max=Task.C_RANGE_THREAD,
-                                      p_visualize=p_visualize,
-                                      p_logging=p_logging,
-                                      p_features_new=features_new )
-
-        workflow.add_task( p_task=task_rearranger )
-      
-        # 2.2.2 Window to buffer some data
-        task_window = RingBuffer( p_buffer_size=50, 
-                                  p_delay=True,
-                                  p_enable_statistics=True,
-                                  p_name='Chain 2D, Task T2',
-                                  p_duplicate_data=True,
-                                  p_visualize=p_visualize,
-                                  p_logging=p_logging )
-
-        workflow.add_task(p_task=task_window, p_pred_tasks=[task_rearranger])
-
-        # 2.2.3 Boundary detector 
-        task_bd = BoundaryDetector( p_name='Chain 2D, Task T3', 
-                                    p_ada=True, 
-                                    p_visualize=True,   
-                                    p_logging=p_logging )
-
-        task_window.register_event_handler( p_event_id=RingBuffer.C_EVENT_DATA_REMOVED, p_event_handler=task_bd.adapt_on_event )
-        workflow.add_task(p_task = task_bd, p_pred_tasks=[task_window])
-
-        # # 2.2.4 MinMax-Normalizer
-        task_norm_minmax = NormalizerMinMax( p_name='Chain 2D, Task T4', 
-                                             p_ada=True, 
-                                             p_visualize=p_visualize, 
-                                             p_logging=p_logging )
-
-        task_bd.register_event_handler( p_event_id=BoundaryDetector.C_EVENT_ADAPTED, p_event_handler=task_norm_minmax.adapt_on_event )
-
-        workflow.add_task(p_task = task_norm_minmax, p_pred_tasks=[task_bd])
-
-
-        # 2.3 Creation of a task chain 3D
-
-        # 2.3.1 Rearranger to reduce the number of features
-        features     = stream.get_feature_space().get_dims()
-        features_new = [ ( 'F', features[1:4] ) ]
-
-        task_rearranger = Rearranger( p_name='Chain 3D, Task T1',
-                                      p_range_max=Task.C_RANGE_THREAD,
-                                      p_duplicate_data=True,
-                                      p_visualize=p_visualize,
-                                      p_logging=p_logging,
-                                      p_features_new=features_new )
-
-        workflow.add_task( p_task=task_rearranger )
-      
-        # 2.3.2 Window to buffer some data
-        task_window = Window( p_buffer_size=50, 
-                              p_delay=True,
-                              p_enable_statistics=True,
-                              p_name='Chain 3D, Task T2',
-                              p_duplicate_data=True,
-                              p_visualize=p_visualize,
-                              p_logging=p_logging )
-
-        workflow.add_task(p_task=task_window, p_pred_tasks=[task_rearranger])
-
-        # 2.3.3 Boundary detector 
-        task_bd = BoundaryDetector( p_name='Chain 3D, Task T3', 
-                                    p_ada=True, 
-                                    p_visualize=True,   
-                                    p_logging=p_logging,
-                                    p_window=task_window )
-
-        workflow.add_task(p_task = task_bd, p_pred_tasks=[task_window])
-
-        # # 2.3.4 MinMax-Normalizer
-        task_norm_minmax = NormalizerMinMax( p_name='Chain 3D, Task T4', 
-                                             p_ada=True, 
-                                             p_visualize=p_visualize, 
-                                             p_logging=p_logging )
-
-        task_bd.register_event_handler( p_event_id=BoundaryDetector.C_EVENT_ADAPTED, p_event_handler=task_norm_minmax.adapt_on_event )
-
-        workflow.add_task(p_task = task_norm_minmax, p_pred_tasks=[task_bd])
-
-
-
-        # 2.5 Creation of a task chain nD
-
-        # 2.5.1 Rearranger to reduce the number of features
-        features     = stream.get_feature_space().get_dims()
-        features_new = [ ( 'F', features[1:5] ) ]
-
-        task_rearranger = Rearranger( p_name='Chain nD, Task T1',
-                                      p_range_max=Task.C_RANGE_THREAD,
-                                      p_duplicate_data=True,
-                                      p_visualize=p_visualize,
-                                      p_logging=p_logging,
-                                      p_features_new=features_new )
-
-        workflow.add_task( p_task=task_rearranger )
-      
-        # 2.5.2 Window to buffer some data
-        task_window = Window( p_buffer_size=50, 
-                              p_delay=True,
-                              p_enable_statistics=True,
-                              p_name='Chain nD, Task T2',
-                              p_duplicate_data=True,
-                              p_visualize=p_visualize,
-                              p_logging=p_logging )
-
-        workflow.add_task(p_task=task_window, p_pred_tasks=[task_rearranger])
-
-        # 2.5.3 Boundary detector 
-        task_bd = BoundaryDetector( p_name='Chain nD, Task T3', 
-                                    p_ada=True, 
-                                    p_visualize=True,   
-                                    p_logging=p_logging,
-                                    p_window=task_window )
-
-        workflow.add_task(p_task = task_bd, p_pred_tasks=[task_window])
-
-        # # 2.5.4 MinMax-Normalizer
-        task_norm_minmax = NormalizerMinMax( p_name='Chain nD, Task T4', 
-                                             p_ada=True, 
-                                             p_visualize=p_visualize, 
-                                             p_logging=p_logging )
-
-        task_bd.register_event_handler( p_event_id=BoundaryDetector.C_EVENT_ADAPTED, p_event_handler=task_norm_minmax.adapt_on_event )
-
-        workflow.add_task(p_task = task_norm_minmax, p_pred_tasks=[task_bd])
-
-
-        # 3 Return stream and workflow
-        return stream, workflow
-
-
-
-
-# 1 Preparation of demo/unit test mode
-if __name__ == "__main__":
-    # 1.1 Parameters for demo mode
-    logging     = Log.C_LOG_ALL
-    visualize   = True
-
-    try:
-        cycle_limit = min(1000, max(1, int(input('\nPlease enter number of cycles (1 - 1000, default = 200): '))))
-    except:
-        cycle_limit = 200
-
-    try:
-        step_rate   = max(1, int(input('\nPlease enter update step rate for visualization (1 = update after every cycle): ')))
-    except:
-        step_rate = 1
-
-else:
-    # 1.2 Parameters for internal unit test
-    cycle_limit = 2
-    logging     = Log.C_LOG_NOTHING
-    visualize   = False
-    step_rate   = 1
-
-
-# 2 Instantiate the stream scenario
-myscenario = MyAdaptiveScenario(p_mode=Mode.C_MODE_REAL,
-    p_cycle_limit=cycle_limit,
-    p_visualize=visualize,
-    p_logging=logging)
-
-
-
-
-# 3 Reset and run own stream scenario
-myscenario.reset()
-
-if __name__ == '__main__':
-    myscenario.init_plot( p_plot_settings=PlotSettings( p_view = PlotSettings.C_VIEW_ND,
-                                                        p_step_rate = step_rate ) )
-    input('\nPlease arrange all windows and press ENTER to start stream processing...')
-
-tp_before = datetime.now()
-myscenario.run()
-tp_after = datetime.now()
-tp_delta = tp_after - tp_before
-duraction_sec = ( tp_delta.seconds * 1000000 + tp_delta.microseconds + 1 ) / 1000000
-myscenario.log(Log.C_LOG_TYPE_S, 'Duration [sec]:', round(duraction_sec,2), ', Cycles/sec:', round(cycle_limit/duraction_sec,2))
-
-if __name__ == '__main__':
-    input('Press ENTER to exit...')
\ No newline at end of file
diff --git a/test/howtos/oa/streams/anomalydetection/.gitkeep b/test/howtos/oa/streams/anomalydetection/.gitkeep
new file mode 100644
index 000000000..e69de29bb
diff --git a/test/howtos/oa/streams/anomalyprediction/.gitkeep b/test/howtos/oa/streams/anomalyprediction/.gitkeep
new file mode 100644
index 000000000..e69de29bb
diff --git a/test/howtos/oa/streams/driftdetection/.gitkeep b/test/howtos/oa/streams/driftdetection/.gitkeep
new file mode 100644
index 000000000..e69de29bb
diff --git a/test/howtos/oa/howto_oa_streams_pp_121_complex_preprocessing.py b/test/howtos/oa/streams/hybrid/howto_oa_streams_pp_121_complex_preprocessing.py
similarity index 100%
rename from test/howtos/oa/howto_oa_streams_pp_121_complex_preprocessing.py
rename to test/howtos/oa/streams/hybrid/howto_oa_streams_pp_121_complex_preprocessing.py
diff --git a/test/howtos/oa/howto_oa_streams_pp_001_bd_normminmax_2d.py b/test/howtos/oa/streams/normalization/howto_oa_streams_pp_001_bd_normminmax_2d.py
similarity index 100%
rename from test/howtos/oa/howto_oa_streams_pp_001_bd_normminmax_2d.py
rename to test/howtos/oa/streams/normalization/howto_oa_streams_pp_001_bd_normminmax_2d.py
diff --git a/test/howtos/oa/howto_oa_streams_pp_002_bd_normminmax_3d.py b/test/howtos/oa/streams/normalization/howto_oa_streams_pp_002_bd_normminmax_3d.py
similarity index 100%
rename from test/howtos/oa/howto_oa_streams_pp_002_bd_normminmax_3d.py
rename to test/howtos/oa/streams/normalization/howto_oa_streams_pp_002_bd_normminmax_3d.py
diff --git a/test/howtos/oa/howto_oa_streams_pp_003_bd_normminmax_nd.py b/test/howtos/oa/streams/normalization/howto_oa_streams_pp_003_bd_normminmax_nd.py
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