UOP staff
ΓΙΑΚΟΥΜΑΤΟΣ ΣΤΕΦΑΝΟΣ
ΚΑΘΗΓΗΤΗΣ
iVBORw0KGgoAAAANSUhEUgAAASwAAAEsCAYAAAB5fY51AAAACXBIWXMAAC4jAAAuIwF4pT92AAAKT2lDQ1BQaG90b3Nob3AgSUNDIHByb2ZpbGUAAHjanVNnVFPpFj333vRCS4iAlEtvUhUIIFJCi4AUkSYqIQkQSoghodkVUcERRUUEG8igiAOOjoCMFVEsDIoK2AfkIaKOg6OIisr74Xuja9a89+bN/rXXPues852zzwfACAyWSDNRNYAMqUIeEeCDx8TG4eQuQIEKJHAAEAizZCFz/SMBAPh+PDwrIsAHvgABeNMLCADATZvAMByH/w/qQplcAYCEAcB0kThLCIAUAEB6jkKmAEBGAYCdmCZTAKAEAGDLY2LjAFAtAGAnf+bTAICd+Jl7AQBblCEVAaCRACATZYhEAGg7AKzPVopFAFgwABRmS8Q5ANgtADBJV2ZIALC3AMDOEAuyAAgMADBRiIUpAAR7AGDIIyN4AISZABRG8lc88SuuEOcqAAB4mbI8uSQ5RYFbCC1xB1dXLh4ozkkXKxQ2YQJhmkAuwnmZGTKBNA/g88wAAKCRFRHgg/P9eM4Ors7ONo62Dl8t6r8G/yJiYuP+5c+rcEAAAOF0ftH+LC+zGoA7BoBt/qIl7gRoXgugdfeLZrIPQLUAoOnaV/Nw+H48PEWhkLnZ2eXk5NhKxEJbYcpXff5nwl/AV/1s+X48/Pf14L7iJIEyXYFHBPjgwsz0TKUcz5IJhGLc5o9H/LcL//wd0yLESWK5WCoU41EScY5EmozzMqUiiUKSKcUl0v9k4t8s+wM+3zUAsGo+AXuRLahdYwP2SycQWHTA4vcAAPK7b8HUKAgDgGiD4c93/+8//UegJQCAZkmScQAAXkQkLlTKsz/HCAAARKCBKrBBG/TBGCzABhzBBdzBC/xgNoRCJMTCQhBCCmSAHHJgKayCQiiGzbAdKmAv1EAdNMBRaIaTcA4uwlW4Dj1wD/phCJ7BKLyBCQRByAgTYSHaiAFiilgjjggXmYX4IcFIBBKLJCDJiBRRIkuRNUgxUopUIFVIHfI9cgI5h1xGupE7yAAygvyGvEcxlIGyUT3UDLVDuag3GoRGogvQZHQxmo8WoJvQcrQaPYw2oefQq2gP2o8+Q8cwwOgYBzPEbDAuxsNCsTgsCZNjy7EirAyrxhqwVqwDu4n1Y8+xdwQSgUXACTYEd0IgYR5BSFhMWE7YSKggHCQ0EdoJNwkDhFHCJyKTqEu0JroR+cQYYjIxh1hILCPWEo8TLxB7iEPENyQSiUMyJ7mQAkmxpFTSEtJG0m5SI+ksqZs0SBojk8naZGuyBzmULCAryIXkneTD5DPkG+Qh8lsKnWJAcaT4U+IoUspqShnlEOU05QZlmDJBVaOaUt2ooVQRNY9aQq2htlKvUYeoEzR1mjnNgxZJS6WtopXTGmgXaPdpr+h0uhHdlR5Ol9BX0svpR+iX6AP0dwwNhhWDx4hnKBmbGAcYZxl3GK+YTKYZ04sZx1QwNzHrmOeZD5lvVVgqtip8FZHKCpVKlSaVGyovVKmqpqreqgtV81XLVI+pXlN9rkZVM1PjqQnUlqtVqp1Q61MbU2epO6iHqmeob1Q/pH5Z/YkGWcNMw09DpFGgsV/jvMYgC2MZs3gsIWsNq4Z1gTXEJrHN2Xx2KruY/R27iz2qqaE5QzNKM1ezUvOUZj8H45hx+Jx0TgnnKKeX836K3hTvKeIpG6Y0TLkxZVxrqpaXllirSKtRq0frvTau7aedpr1Fu1n7gQ5Bx0onXCdHZ4/OBZ3nU9lT3acKpxZNPTr1ri6qa6UbobtEd79up+6Ynr5egJ5Mb6feeb3n+hx9L/1U/W36p/VHDFgGswwkBtsMzhg8xTVxbzwdL8fb8VFDXcNAQ6VhlWGX4YSRudE8o9VGjUYPjGnGXOMk423GbcajJgYmISZLTepN7ppSTbmmKaY7TDtMx83MzaLN1pk1mz0x1zLnm+eb15vft2BaeFostqi2uGVJsuRaplnutrxuhVo5WaVYVVpds0atna0l1rutu6cRp7lOk06rntZnw7Dxtsm2qbcZsOXYBtuutm22fWFnYhdnt8Wuw+6TvZN9un2N/T0HDYfZDqsdWh1+c7RyFDpWOt6azpzuP33F9JbpL2dYzxDP2DPjthPLKcRpnVOb00dnF2e5c4PziIuJS4LLLpc+Lpsbxt3IveRKdPVxXeF60vWdm7Obwu2o26/uNu5p7ofcn8w0nymeWTNz0MPIQ+BR5dE/C5+VMGvfrH5PQ0+BZ7XnIy9jL5FXrdewt6V3qvdh7xc+9j5yn+M+4zw33jLeWV/MN8C3yLfLT8Nvnl+F30N/I/9k/3r/0QCngCUBZwOJgUGBWwL7+Hp8Ib+OPzrbZfay2e1BjKC5QRVBj4KtguXBrSFoyOyQrSH355jOkc5pDoVQfujW0Adh5mGLw34MJ4WHhVeGP45wiFga0TGXNXfR3ENz30T6RJZE3ptnMU85ry1KNSo+qi5qPNo3ujS6P8YuZlnM1VidWElsSxw5LiquNm5svt/87fOH4p3iC+N7F5gvyF1weaHOwvSFpxapLhIsOpZATIhOOJTwQRAqqBaMJfITdyWOCnnCHcJnIi/RNtGI2ENcKh5O8kgqTXqS7JG8NXkkxTOlLOW5hCepkLxMDUzdmzqeFpp2IG0yPTq9MYOSkZBxQqohTZO2Z+pn5mZ2y6xlhbL+xW6Lty8elQfJa7OQrAVZLQq2QqboVFoo1yoHsmdlV2a/zYnKOZarnivN7cyzytuQN5zvn//tEsIS4ZK2pYZLVy0dWOa9rGo5sjxxedsK4xUFK4ZWBqw8uIq2Km3VT6vtV5eufr0mek1rgV7ByoLBtQFr6wtVCuWFfevc1+1dT1gvWd+1YfqGnRs+FYmKrhTbF5cVf9go3HjlG4dvyr+Z3JS0qavEuWTPZtJm6ebeLZ5bDpaql+aXDm4N2dq0Dd9WtO319kXbL5fNKNu7g7ZDuaO/PLi8ZafJzs07P1SkVPRU+lQ27tLdtWHX+G7R7ht7vPY07NXbW7z3/T7JvttVAVVN1WbVZftJ+7P3P66Jqun4lvttXa1ObXHtxwPSA/0HIw6217nU1R3SPVRSj9Yr60cOxx++/p3vdy0NNg1VjZzG4iNwRHnk6fcJ3/ceDTradox7rOEH0x92HWcdL2pCmvKaRptTmvtbYlu6T8w+0dbq3nr8R9sfD5w0PFl5SvNUyWna6YLTk2fyz4ydlZ19fi753GDborZ752PO32oPb++6EHTh0kX/i+c7vDvOXPK4dPKy2+UTV7hXmq86X23qdOo8/pPTT8e7nLuarrlca7nuer21e2b36RueN87d9L158Rb/1tWeOT3dvfN6b/fF9/XfFt1+cif9zsu72Xcn7q28T7xf9EDtQdlD3YfVP1v+3Njv3H9qwHeg89HcR/cGhYPP/pH1jw9DBY+Zj8uGDYbrnjg+OTniP3L96fynQ89kzyaeF/6i/suuFxYvfvjV69fO0ZjRoZfyl5O/bXyl/erA6xmv28bCxh6+yXgzMV70VvvtwXfcdx3vo98PT+R8IH8o/2j5sfVT0Kf7kxmTk/8EA5jz/GMzLdsAAFMNaVRYdFhNTDpjb20uYWRvYmUueG1wAAAAAAA8P3hwYWNrZXQgYmVnaW49Iu+7vyIgaWQ9Ilc1TTBNcENlaGlIenJlU3pOVGN6a2M5ZCI/Pgo8eDp4bXBtZXRhIHhtbG5zOng9ImFkb2JlOm5zOm1ldGEvIiB4OnhtcHRrPSJBZG9iZSBYTVAgQ29yZSA1LjYtYzEzMiA3OS4xNTkyODQsIDIwMTYvMDQvMTktMTM6MTM6NDAgICAgICAgICI+CiAgIDxyZGY6UkRGIHhtbG5zOnJkZj0iaHR0cDovL3d3dy53My5vcmcvMTk5OS8wMi8yMi1yZGYtc3ludGF4LW5zIyI+CiAgICAgIDxyZGY6RGVzY3JpcHRpb24gcmRmOmFib3V0PSIiCiAgICAgICAgICAgIHhtbG5zOmRjPSJodHRwOi8vcHVybC5vcmcvZGMvZWxlbWVudHMvMS4xLyIKICAgICAgICAgICAgeG1sbnM6eG1wPSJodHRwOi8vbnMuYWRvYmUuY29tL3hhcC8xLjAvIgogICAgICAgICAgICB4bWxuczp4bXBNTT0iaHR0cDovL25zLmFkb2JlLmNvbS94YXAvMS4wL21tLyIKICAgICAgICAgICAgeG1sbnM6c3RSZWY9Imh0dHA6Ly9ucy5hZG9iZS5jb20veGFwLzEuMC9zVHlwZS9SZXNvdXJjZVJlZiMiCiAgICAgICAgICAgIHhtbG5zOnN0RXZ0PSJodHRwOi8vbnMuYWRvYmUuY29tL3hhcC8xLjAvc1R5cGUvUmVzb3VyY2VFdmVudCMiCiAgICAgICAgICAgIHhtbG5zOmlsbHVzdHJhdG9yPSJodHRwOi8vbnMuYWRvYmUuY29tL2lsbHVzdHJhdG9yLzEuMC8iCiAgICAgICAgICAgIHhtbG5zOnhtcFRQZz0iaHR0cDovL25zLmFkb2JlLmNvbS94YXAvMS4wL3QvcGcvIgogICAgICAgICAgICB4bWxuczpzdERpbT0iaHR0cDovL25zLmFkb2JlLmNvbS94YXAvMS4wL3NUeXBlL0RpbWVuc2lvbnMjIgogICAgICAgICAgICB4bWxuczp4bXBHPSJodHRwOi8vbnMuYWRvYmUuY29tL3hhcC8xLjAvZy8iCiAgICAgICAgICAgIHhtbG5zOnBkZj0iaHR0cDovL25zLmFkb2JlLmNvbS9wZGYvMS4zLyIKICAgICAgICAgICAgeG1sbnM6cGhvdG9zaG9wPSJodHRwOi8vbnMuYWRvYmUuY29tL3Bob3Rvc2hvcC8xLjAvIgogICAgICAgICAgICB4bWxuczp0aWZmPSJodHRwOi8vbnMuYWRvYmUuY29tL3RpZmYvMS4wLyIKICAgICAgICAgICAgeG1sbnM6ZXhpZj0iaHR0cDovL25zLmFkb2JlLmNvbS9leGlmLzEuMC8iPgogICAgICAgICA8ZGM6Zm9ybWF0PmltYWdlL3BuZzwvZGM6Zm9ybWF0PgogICAgICAgICA8ZGM6dGl0bGU+CiAgICAgICAgICAgIDxyZGY6QWx0PgogICAgICAgICAgICAgICA8cmRmOmxpIHhtbDpsYW5nPSJ4LWRlZmF1bHQiPnBlb3BsZTwvcmRmOmxpPgogICAgICAgICAgICA8L3JkZjpBbHQ+CiAgICAgICAgIDwvZGM6dGl0bGU+CiAgICAgICAgIDx4bXA6Q3JlYXRvclRvb2w+QWRvYmUgUGhvdG9zaG9wIENDIDIwMTUuNSAoV2luZG93cyk8L3htcDpDcmVhdG9yVG9vbD4KICAgICAgICAgPHhtcDpDcmVhdGVEYXRlPjIwMTUtMDUtMzBUMTY6MDg6MjIrMDc6MDA8L3htcDpDcmVhdGVEYXRlPgogICAgICAgICA8eG1wOk1vZGlmeURhdGU+MjAxNi0wOS0wMVQxNTowNjowMyswMTowMDwveG1wOk1vZGlmeURhdGU+CiAgICAgICAgIDx4bXA6TWV0YWRhdGFEYXRlPjIwMTYtMDktMDFUMTU6MDY6MDMrMDE6MDA8L3htcDpNZXRhZGF0YURhdGU+CiAgICAgICAgIDx4bXBNTTpSZW5kaXRpb25DbGFzcz5wcm9vZjpwZGY8L3htcE1NOlJlbmRpdGlvbkNsYXNzPgogICAgICAgICA8eG1wTU06T3JpZ2luYWxEb2N1bWVudElEPnV1aWQ6NjVFNjM5MDY4NkNGMTFEQkE2RTJEODg3Q0VBQ0I0MDc8L3htcE1NOk9yaWdpbmFsRG9jdW1lbnRJRD4KICAgICAgICAgPHhtcE1NOkRvY3VtZW50SUQ+YWRvYmU6ZG9jaWQ6cGhvdG9zaG9wOjI2YzA2OTZlLTcwNGQtMTFlNi05N2QyLTg5YjIzOGM4YjZjZTwveG1wTU06RG9jdW1lbnRJRD4KICAgICAgICAgPHhtcE1NOkluc3RhbmNlSUQ+eG1wLmlpZDpjNWE5MmE5Yy1hNDBlLWJmNGEtYTRjYy03OGUxNzhlMTgyYmE8L3htcE1NOkluc3RhbmNlSUQ+CiAgICAgICAgIDx4bXBNTTpEZXJpdmVkRnJvbSByZGY6cGFyc2VUeXBlPSJSZXNvdXJjZSI+CiAgICAgICAgICAgIDxzdFJlZjppbnN0YW5jZUlEPnhtcC5paWQ6ODA4OTY1MjgtYWRiZS02NjRjLTliNzEtYzQzYjhlYjUzZTMwPC9zdFJlZjppbnN0YW5jZUlEPgogICAgICAgICAgICA8c3RSZWY6ZG9jdW1lbnRJRD54bXAuZGlkOjVmZWZhYWFmLThhOTItZGQ0OC04MTYyLWE4ZmQ3ZWZkY2MwODwvc3RSZWY6ZG9jdW1lbnRJRD4KICAgICAgICAgICAgPHN0UmVmOm9yaWdpbmFsRG9jdW1lbnRJRD51dWlkOjY1RTYzOTA2ODZDRjExREJBNkUyRDg4N0NFQUNCNDA3PC9zdFJlZjpvcmlnaW5hbERvY3VtZW50SUQ+CiAgICAgICAgICAgIDxzdFJlZjpyZW5kaXRpb25DbGFzcz5wcm9vZjpwZGY8L3N0UmVmOnJlbmRpdGlvbkNsYXNzPgogICAgICAgICA8L3htcE1NOkRlcml2ZWRGcm9tPgogICAgICAgICA8eG1wTU06SGlzdG9yeT4KICAgICAgICAgICAgPHJkZjpTZXE+CiAgICAgICAgICAgICAgIDxyZGY6bGkgcmRmOnBhcnNlVHlwZT0iUmVzb3VyY2UiPgogICAgICAgICAgICAgICAgICA8c3RFdnQ6YWN0aW9uPnNhdmVkPC9zdEV2dDphY3Rpb24+CiAgICAgICAgICAgICAgICAgIDxzdEV2dDppbnN0YW5jZUlEPnhtcC5paWQ6QTQyQUQzNEVFRENGRTQxMThBQjJGNTYzNUFFMEE5OEM8L3N0RXZ0Omluc3RhbmNlSUQ+CiAgICAgICAgICAgICAgICAgIDxzdEV2dDp3aGVuPjIwMTUtMDMtMjJUMDA6MTE6MjYrMDc6MDA8L3N0RXZ0OndoZW4+CiAgICAgICAgICAgICAgICAgIDxzdEV2dDpzb2Z0d2FyZUFnZW50PkFkb2JlIElsbHVzdHJhdG9yIENTNTwvc3RFdnQ6c29mdHdhcmVBZ2VudD4KICAgICAgICAgICAgICAgICAgPHN0RXZ0OmNoYW5nZWQ+Lzwvc3RFdnQ6Y2hhbmdlZD4KICAgICAgICAgICAgICAgPC9yZGY6bGk+CiAgICAgICAgICAgICAgIDxyZGY6bGkgcmRmOnBhcnNlVHlwZT0iUmVzb3VyY2UiPgogICAgICAgICAgICAgICAgICA8c3RFdnQ6YWN0aW9uPnNhdmVkPC9zdEV2dDphY3Rpb24+CiAgICAgICAgICAgICAgICAgIDxzdEV2dDppbnN0YW5jZUlEPnhtcC5paWQ6NTM2YzUxOTEtZjRjZC1mZjQ2LTgxMjgtOTZiODBiN2FkZTRhPC9zdEV2dDppbnN0YW5jZUlEPgogICAgICAgICAgICAgICAgICA8c3RFdnQ6d2hlbj4yMDE1LTA1LTMwVDE2OjA4OjIyKzA3OjAwPC9zdEV2dDp3aGVuPgogICAgICAgICAgICAgICAgICA8c3RFdnQ6c29mdHdhcmVBZ2VudD5BZG9iZSBJbGx1c3RyYXRvciBDQyAyMDE0IChXaW5kb3dzKTwvc3RFdnQ6c29mdHdhcmVBZ2VudD4KICAgICAgICAgICAgICAgICAgPHN0RXZ0OmNoYW5nZWQ+Lzwvc3RFdnQ6Y2hhbmdlZD4KICAgICAgICAgICAgICAgPC9yZGY6bGk+CiAgICAgICAgICAgICAgIDxyZGY6bGkgcmRmOnBhcnNlVHlwZT0iUmVzb3VyY2UiPgogICAgICAgICAgICAgICAgICA8c3RFdnQ6YWN0aW9uPmNvbnZlcnRlZDwvc3RFdnQ6YWN0aW9uPgogICAgICAgICAgICAgICAgICA8c3RFdnQ6cGFyYW1ldGVycz5mcm9tIGFwcGxpY2F0aW9uL3Bvc3RzY3JpcHQgdG8gaW1hZ2UvZXBzZjwvc3RFdnQ6cGFyYW1ldGVycz4KICAgICAgICAgICAgICAgPC9yZGY6bGk+CiAgICAgICAgICAgICAgIDxyZGY6bGkgcmRmOnBhcnNlVHlwZT0iUmVzb3VyY2UiPgogICAgICAgICAgICAgICAgICA8c3RFdnQ6YWN0aW9uPnNhdmVkPC9zdEV2dDphY3Rpb24+CiAgICAgICAgICAgICAgICAgIDxzdEV2dDppbnN0YW5jZUlEPnhtcC5paWQ6NWMxYzZmNWUtNDZjNi03ODQxLTk5ODEtYjNlMmE5NDE4ZTIzPC9zdEV2dDppbnN0YW5jZUlEPgogICAgICAgICAgICAgICAgICA8c3RFdnQ6d2hlbj4yMDE2LTA5LTAxVDE1OjA1OjMzKzAxOjAwPC9zdEV2dDp3aGVuPgogICAgICAgICAgICAgICAgICA8c3RFdnQ6c29mdHdhcmVBZ2VudD5BZG9iZSBQaG90b3Nob3AgQ0MgMjAxNS41IChXaW5kb3dzKTwvc3RFdnQ6c29mdHdhcmVBZ2VudD4KICAgICAgICAgICAgICAgICAgPHN0RXZ0OmNoYW5nZWQ+Lzwvc3RFdnQ6Y2hhbmdlZD4KICAgICAgICAgICAgICAgPC9yZGY6bGk+CiAgICAgICAgICAgICAgIDxyZGY6bGkgcmRmOnBhcnNlVHlwZT0iUmVzb3VyY2UiPgogICAgICAgICAgICAgICAgICA8c3RFdnQ6YWN0aW9uPmNvbnZlcnRlZDwvc3RFdnQ6YWN0aW9uPgogICAgICAgICAgICAgICAgICA8c3RFdnQ6cGFyYW1ldGVycz5mcm9tIGFwcGxpY2F0aW9uL3Bvc3RzY3JpcHQgdG8gYXBwbGljYXRpb24vdm5kLmFkb2JlLnBob3Rvc2hvcDwvc3RFdnQ6cGFyYW1ldGVycz4KICAgICAgICAgICAgICAgPC9yZGY6bGk+CiAgICAgICAgICAgICAgIDxyZGY6bGkgcmRmOnBhcnNlVHlwZT0iUmVzb3VyY2UiPgogICAgICAgICAgICAgICAgICA8c3RFdnQ6YWN0aW9uPmRlcml2ZWQ8L3N0RXZ0OmFjdGlvbj4KICAgICAgICAgICAgICAgICAgPHN0RXZ0OnBhcmFtZXRlcnM+Y29udmVydGVkIGZyb20gaW1hZ2UvZXBzZiB0byBhcHBsaWNhdGlvbi92bmQuYWRvYmUucGhvdG9zaG9wPC9zdEV2dDpwYXJhbWV0ZXJzPgogICAgICAgICAgICAgICA8L3JkZjpsaT4KICAgICAgICAgICAgICAgPHJkZjpsaSByZGY6cGFyc2VUeXBlPSJSZXNvdXJjZSI+CiAgICAgICAgICAgICAgICAgIDxzdEV2dDphY3Rpb24+c2F2ZWQ8L3N0RXZ0OmFjdGlvbj4KICAgICAgICAgICAgICAgICAgPHN0RXZ0Omluc3RhbmNlSUQ+eG1wLmlpZDo1ZmVmYWFhZi04YTkyLWRkNDgtODE2Mi1hOGZkN2VmZGNjMDg8L3N0RXZ0Omluc3RhbmNlSUQ+CiAgICAgICAgICAgICAgICAgIDxzdEV2dDp3aGVuPjIwMTYtMDktMDFUMTU6MDU6MzMrMDE6MDA8L3N0RXZ0OndoZW4+CiAgICAgICAgICAgICAgICAgIDxzdEV2dDpzb2Z0d2FyZUFnZW50PkFkb2JlIFBob3Rvc2hvcCBDQyAyMDE1LjUgKFdpbmRvd3MpPC9zdEV2dDpzb2Z0d2FyZUFnZW50PgogICAgICAgICAgICAgICAgICA8c3RFdnQ6Y2hhbmdlZD4vPC9zdEV2dDpjaGFuZ2VkPgogICAgICAgICAgICAgICA8L3JkZjpsaT4KICAgICAgICAgICAgICAgPHJkZjpsaSByZGY6cGFyc2VUeXBlPSJSZXNvdXJjZSI+CiAgICAgICAgICAgICAgICAgIDxzdEV2dDphY3Rpb24+c2F2ZWQ8L3N0RXZ0OmFjdGlvbj4KICAgICAgICAgICAgICAgICAgPHN0RXZ0Omluc3RhbmNlSUQ+eG1wLmlpZDo4MDg5NjUyOC1hZGJlLTY2NGMtOWI3MS1jNDNiOGViNTNlMzA8L3N0RXZ0Omluc3RhbmNlSUQ+CiAgICAgICAgICAgICAgICAgIDxzdEV2dDp3aGVuPjIwMTYtMDktMDFUMTU6MDY6MDMrMDE6MDA8L3N0RXZ0OndoZW4+CiAgICAgICAgICAgICAgICAgIDxzdEV2dDpzb2Z0d2FyZUFnZW50PkFkb2JlIFBob3Rvc2hvcCBDQyAyMDE1LjUgKFdpbmRvd3MpPC9zdEV2dDpzb2Z0d2FyZUFnZW50PgogICAgICAgICAgICAgICAgICA8c3RFdnQ6Y2hhbmdlZD4vPC9zdEV2dDpjaGFuZ2VkPgogICAgICAgICAgICAgICA8L3JkZjpsaT4KICAgICAgICAgICAgICAgPHJkZjpsaSByZGY6cGFyc2VUeXBlPSJSZXNvdXJjZSI+CiAgICAgICAgICAgICAgICAgIDxzdEV2dDphY3Rpb24+Y29udmVydGVkPC9zdEV2dDphY3Rpb24+CiAgICAgICAgICAgICAgICAgIDxzdEV2dDpwYXJhbWV0ZXJzPmZyb20gYXBwbGljYXRpb24vdm5kLmFkb2JlLnBob3Rvc2hvcCB0byBpbWFnZS9wbmc8L3N0RXZ0OnBhcmFtZXRlcnM+CiAgICAgICAgICAgICAgIDwvcmRmOmxpPgogICAgICAgICAgICAgICA8cmRmOmxpIHJkZjpwYXJzZVR5cGU9IlJlc291cmNlIj4KICAgICAgICAgICAgICAgICAgPHN0RXZ0OmFjdGlvbj5kZXJpdmVkPC9zdEV2dDphY3Rpb24+CiAgICAgICAgICAgICAgICAgIDxzdEV2dDpwYXJhbWV0ZXJzPmNvbnZlcnRlZCBmcm9tIGFwcGxpY2F0aW9uL3ZuZC5hZG9iZS5waG90b3Nob3AgdG8gaW1hZ2UvcG5nPC9zdEV2dDpwYXJhbWV0ZXJzPgogICAgICAgICAgICAgICA8L3JkZjpsaT4KICAgICAgICAgICAgICAgPHJkZjpsaSByZGY6cGFyc2VUeXBlPSJSZXNvdXJjZSI+CiAgICAgICAgICAgICAgICAgIDxzdEV2dDphY3Rpb24+c2F2ZWQ8L3N0RXZ0OmFjdGlvbj4KICAgICAgICAgICAgICAgICAgPHN0RXZ0Omluc3RhbmNlSUQ+eG1wLmlpZDpjNWE5MmE5Yy1hNDBlLWJmNGEtYTRjYy03OGUxNzhlMTgyYmE8L3N0RXZ0Omluc3RhbmNlSUQ+CiAgICAgICAgICAgICAgICAgIDxzdEV2dDp3aGVuPjIwMTYtMDktMDFUMTU6MDY6MDMrMDE6MDA8L3N0RXZ0OndoZW4+CiAgICAgICAgICAgICAgICAgIDxzdEV2dDpzb2Z0d2FyZUFnZW50PkFkb2JlIFBob3Rvc2hvcCBDQyAyMDE1LjUgKFdpbmRvd3MpPC9zdEV2dDpzb2Z0d2FyZUFnZW50PgogICAgICAgICAgICAgICAgICA8c3RFdnQ6Y2hhbmdlZD4vPC9zdEV2dDpjaGFuZ2VkPgogICAgICAgICAgICAgICA8L3JkZjpsaT4KICAgICAgICAgICAgPC9yZGY6U2VxPgogICAgICAgICA8L3htcE1NOkhpc3Rvcnk+CiAgICAgICAgIDxpbGx1c3RyYXRvcjpTdGFydHVwUHJvZmlsZT5XZWI8L2lsbHVzdHJhdG9yOlN0YXJ0dXBQcm9maWxlPgogICAgICAgICA8eG1wVFBnOk5QYWdlcz4xPC94bXBUUGc6TlBhZ2VzPgogICAgICAgICA8eG1wVFBnOkhhc1Zpc2libGVUcmFuc3BhcmVuY3k+RmFsc2U8L3htcFRQZzpIYXNWaXNpYmxlVHJhbnNwYXJlbmN5PgogICAgICAgICA8eG1wVFBnOkhhc1Zpc2libGVPdmVycHJpbnQ+RmFsc2U8L3htcFRQZzpIYXNWaXNpYmxlT3ZlcnByaW50PgogICAgICAgICA8eG1wVFBnOk1heFBhZ2VTaXplIHJkZjpwYXJzZVR5cGU9IlJlc291cmNlIj4KICAgICAgICAgICAgPHN0RGltOnc+MTIwMC4wMDAwMDA8L3N0RGltOnc+CiAgICAgICAgICAgIDxzdERpbTpoPjEyMDAuMDAwMDAwPC9zdERpbTpoPgogICAgICAgICAgICA8c3REaW06dW5pdD5QaXhlbHM8L3N0RGltOnVuaXQ+CiAgICAgICAgIDwveG1wVFBnOk1heFBhZ2VTaXplPgogICAgICAgICA8eG1wVFBnOlBsYXRlTmFtZXM+CiAgICAgICAgICAgIDxyZGY6U2VxPgogICAgICAgICAgICAgICA8cmRmOmxpPkN5YW48L3JkZjpsaT4KICAgICAgICAgICAgICAgPHJkZjpsaT5NYWdlbnRhPC9yZGY6bGk+CiAgICAgICAgICAgICAgIDxyZGY6bGk+WWVsbG93PC9yZGY6bGk+CiAgICAgICAgICAgICAgIDxyZGY6bGk+QmxhY2s8L3JkZjpsaT4KICAgICAgICAgICAgPC9yZGY6U2VxPgogICAgICAgICA8L3htcFRQZzpQbGF0ZU5hbWVzPgogICAgICAgICA8eG1wVFBnOlN3YXRjaEdyb3Vwcz4KICAgICAgICAgICAgPHJkZjpTZXE+CiAgICAgICAgICAgICAgIDxyZGY6bGkgcmRmOnBhcnNlVHlwZT0iUmVzb3VyY2UiPgogICAgICAgICAgICAgICAgICA8eG1wRzpncm91cE5hbWU+RGVmYXVsdCBTd2F0Y2ggR3JvdXA8L3htcEc6Z3JvdXBOYW1lPgogICAgICAgICAgICAgICAgICA8eG1wRzpncm91cFR5cGU+MDwveG1wRzpncm91cFR5cGU+CiAgICAgICAgICAgICAgIDwvcmRmOmxpPgogICAgICAgICAgICAgICA8cmRmOmxpIHJkZjpwYXJzZVR5cGU9IlJlc291cmNlIj4KICAgICAgICAgICAgICAgICAgPHhtcEc6Z3JvdXBOYW1lPkNvbG9yIEdyb3VwIDE8L3htcEc6Z3JvdXBOYW1lPgogICAgICAgICAgICAgICAgICA8eG1wRzpncm91cFR5cGU+MTwveG1wRzpncm91cFR5cGU+CiAgICAgICAgICAgICAgICAgIDx4bXBHOkNvbG9yYW50cz4KICAgICAgICAgICAgICAgICAgICAgPHJkZjpTZXE+CiAgICAgICAgICAgICAgICAgICAgICAgIDxyZGY6bGkgcmRmOnBhcnNlVHlwZT0iUmVzb3VyY2UiPgogICAgICAgICAgICAgICAgICAgICAgICAgICA8eG1wRzpzd2F0Y2hOYW1lPlI9ODkgRz04OSBCPTg5PC94bXBHOnN3YXRjaE5hbWU+CiAgICAgICAgICAgICAgICAgICAgICAgICAgIDx4bXBHOm1vZGU+UkdCPC94bXBHOm1vZGU+CiAgICAgICAgICAgICAgICAgICAgICAgICAgIDx4bXBHOnR5cGU+UFJPQ0VTUzwveG1wRzp0eXBlPgogICAgICAgICAgICAgICAgICAgICAgICAgICA8eG1wRzpyZWQ+ODk8L3htcEc6cmVkPgogICAgICAgICAgICAgICAgICAgICAgICAgICA8eG1wRzpncmVlbj44OTwveG1wRzpncmVlbj4KICAgICAgICAgICAgICAgICAgICAgICAgICAgPHhtcEc6Ymx1ZT44OTwveG1wRzpibHVlPgogICAgICAgICAgICAgICAgICAgICAgICA8L3JkZjpsaT4KICAgICAgICAgICAgICAgICAgICAgICAgPHJkZjpsaSByZGY6cGFyc2VUeXBlPSJSZXNvdXJjZSI+CiAgICAgICAgICAgICAgICAgICAgICAgICAgIDx4bXBHOnN3YXRjaE5hbWU+Uj0yNTAgRz0yNTAgQj0yNTA8L3htcEc6c3dhdGNoTmFtZT4KICAgICAgICAgICAgICAgICAgICAgICAgICAgPHhtcEc6bW9kZT5SR0I8L3htcEc6bW9kZT4KICAgICAgICAgICAgICAgICAgICAgICAgICAgPHhtcEc6dHlwZT5QUk9DRVNTPC94bXBHOnR5cGU+CiAgICAgICAgICAgICAgICAgICAgICAgICAgIDx4bXBHOnJlZD4yNTA8L3htcEc6cmVkPgogICAgICAgICAgICAgICAgICAgICAgICAgICA8eG1wRzpncmVlbj4yNTA8L3htcEc6Z3JlZW4+CiAgICAgICAgICAgICAgICAgICAgICAgICAgIDx4bXBHOmJsdWU+MjUwPC94bXBHOmJsdWU+CiAgICAgICAgICAgICAgICAgICAgICAgIDwvcmRmOmxpPgogICAgICAgICAgICAgICAgICAgICA8L3JkZjpTZXE+CiAgICAgICAgICAgICAgICAgIDwveG1wRzpDb2xvcmFudHM+CiAgICAgICAgICAgICAgIDwvcmRmOmxpPgogICAgICAgICAgICA8L3JkZjpTZXE+CiAgICAgICAgIDwveG1wVFBnOlN3YXRjaEdyb3Vwcz4KICAgICAgICAgPHBkZjpQcm9kdWNlcj5BZG9iZSBQREYgbGlicmFyeSAxMS4wMDwvcGRmOlByb2R1Y2VyPgogICAgICAgICA8cGhvdG9zaG9wOkNvbG9yTW9kZT4zPC9waG90b3Nob3A6Q29sb3JNb2RlPgogICAgICAgICA8cGhvdG9zaG9wOklDQ1Byb2ZpbGU+c1JHQiBJRUM2MTk2Ni0yLjE8L3Bob3Rvc2hvcDpJQ0NQcm9maWxlPgogICAgICAgICA8dGlmZjpPcmllbnRhdGlvbj4xPC90aWZmOk9yaWVudGF0aW9uPgogICAgICAgICA8dGlmZjpYUmVzb2x1dGlvbj4zMDAwMDAwLzEwMDAwPC90aWZmOlhSZXNvbHV0aW9uPgogICAgICAgICA8dGlmZjpZUmVzb2x1dGlvbj4zMDAwMDAwLzEwMDAwPC90aWZmOllSZXNvbHV0aW9uPgogICAgICAgICA8dGlmZjpSZXNvbHV0aW9uVW5pdD4yPC90aWZmOlJlc29sdXRpb25Vbml0PgogICAgICAgICA8ZXhpZjpDb2xvclNwYWNlPjE8L2V4aWY6Q29sb3JTcGFjZT4KICAgICAgICAgPGV4aWY6UGl4ZWxYRGltZW5zaW9uPjMwMDwvZXhpZjpQaXhlbFhEaW1lbnNpb24+CiAgICAgICAgIDxleGlmOlBpeGVsWURpbWVuc2lvbj4zMDA8L2V4aWY6UGl4ZWxZRGltZW5zaW9uPgogICAgICA8L3JkZjpEZXNjcmlwdGlvbj4KICAgPC9yZGY6UkRGPgo8L3g6eG1wbWV0YT4KICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgIAogICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgCiAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAKICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgIAogICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgCiAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAKICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgIAogICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgCiAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAKICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgIAogICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgCiAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAKICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgIAogICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgCiAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAKICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgIAogICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgCiAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAKICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgIAogICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgCiAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAKICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgIAogICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgCiAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAKICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgIAogICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgCiAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAKICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgIAogICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgCiAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAKICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgIAogICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgCiAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAKICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgIAogICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgCiAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAKICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgIAogICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgCiAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAKICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgIAogICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgCiAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAKICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgIAogICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgCiAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAKICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgIAogICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgCiAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAKICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgIAogICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgCiAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAKICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgIAogICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgCiAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAKICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgIAogICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgCiAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAKICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgIAogICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgCiAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAKICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgIAogICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgCiAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAKICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgIAogICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgCiAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAKICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgIAogICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgCiAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAKICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgIAogICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgCiAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAKICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgIAogICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgCiAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAKICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgIAogICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgCiAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAKICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgIAogICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgCiAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAKICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgIAogICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgCiAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAKICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgIAogICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgCiAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAKICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgIAogICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgCiAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAKICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgIAogICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgCiAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAKICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgIAogICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgCiAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAKICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgIAogICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgCiAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAKICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgIAogICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgCiAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAKICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgIAogICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgCiAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAKICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgIAogICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgCiAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAKICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgIAogICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgCiAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAKICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgIAogICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgCiAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAKICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgIAogICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgCiAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAKICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgIAogICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgCiAgICAgICAgICAgICAgICAgICAgICAgICAgICAKPD94cGFja2V0IGVuZD0idyI/PjBdct0AAAAgY0hSTQAAeiUAAICDAAD5/wAAgOkAAHUwAADqYAAAOpgAABdvkl/FRgAAI0VJREFUeNrs3Xl4VPW9P/D3nGUmk0lmkpkkEwhZ2BKWnyhqFftwH6it2tZaq/Xai7Wt96krUuvtLbb+7m3delvlti4o2Kq3Vbyt+LMWUMAFRVYrdUEQAgGyTrZJMsnsyzlnzvn9QWYKFJRAQmYy79fz+MCDJGS+55z3+Xy+53vOMWmaZoCIKAsIHAIiYmARETGwiIiBRUTEwCIiYmAREQOLiIiBRUTEwCIiBhYREQOLiIiBRUQMLCIiBhYREQOLiBhYREQMLCIiBhYRMbCIiBhYREQMLCJiYBERMbCIiBhYRMTAIiJiYBERMbCIiIFFRMTAIiIGFhERA4uIiIFFRAwsIiIGFhERA4uIGFhERAwsIiIGFhExsIiIGFhERAwsImJgERExsIiIGFhExMAiImJgERExsIiIgUVExMAiImJgEREDi4iIgUVExMAiIgYWEREDi4joMIlDQMPFZDId988Nw+DgEAOLRieUBGF4CnNd1xlmxMCi4SEIwgmrpmg0Cl9fH0KRCOLxOGKxGELBIGKxGETp8G6VTCaRb7WioKAAVqsVeVYrCm02OF0u2Gy24wafYRjQdZ2DTwwsOrWQCgQCaDhwAK2treju6kJ7Rwd6e3vR19uLuKIgqWlIJpPQU9XS4K+p72MymSCKIkRJgsVsRklJCcrKylAxfjzc5eWoqa5G3bRpKHI40n+X4UXHrfA1TWNNTumQAABFVdHS0oL3338fe/bsQUdnJ7zd3YhEIjCZTDCbzZBkGZIoQhRFCIJwOJBE8R/CzjAMJJPJw4Gm60gmk9A0DZqmQVVV6LqO/Px8uMvLUTFuHGbMnIk5F16ImpoamM3m9PdJJpPcSMTAYjX194Cpr6/HX3fswLatW+HxeBCPxyGKIgrtdthsNoiDLdyJ2sRUQKX+/5G/P9HfBQ7PZUUiEQSDQWjJJKx5eZhQWYl/mjsXcy68EDNnzjzq6xheDCzKpY1+xMR5NBrFhjffxJZt2/Dxxx8jGosh32qF3W5HYWEhTCZTujoa6fAURRGGYSAcDiMQDCIaicCan4+zzzoLc+fOxWWXXYaCgoJ0yHHCnoFFORJUvb29WL9+PV57/XV4PB7IsoyioiJYrVbIsgxd10dt/kgQBAiCAFVVEY/H4ff7oSgKKioqcNmll+JrX/sa3G43g4uBRWNVao7K5/Nh9erVeHnVKvj6+uB0OlFSUpLRE9ypAOvr64PP54PL5cLXv/51XHXVVXCXlbFNZGDRWKuq9GQSL738Ml588UW0tbWhpKQEJSUlEEURqqpmxWdJVX59fX3w9vSgsrIS11x9NRYsWABJklhtMbBoLFRVu3bvxuOPP45du3bB5XLB5XJBEISsrUpSc12+/n709fZi5syZ+OEdd2D27NmsthhYlG1SV//iiQR+97vf4YWVK2Exm1FVVTWmDmhREGASBLR5PIjFYviXa6/FzTffDJvNxjVcDCzKig0qCBBMJuzduxe/eugh7N27F9VVVSgqKoKqqmOuZTKZTJBlGX6/H62trZg2bRru/ulPMWvWLIYWA4uyoQV8de1a/PLBB2Exm1E5YUJOtEmpqrKjowPRaBR3LV6Mq6++mi0iA4syOawee+wxPPP736OmpgYlLhcSiUROjYPFYkF/fz+amppw3YIFuPvuuxlaDCzKtLAKhUK45957sWnTJkyaNAk2mw2KonzqSvOxyDAMmM1mRKNRHDp0CHPnzsUDDzyAIoeDocXAokwIK4/Hgx/9+7+juaUF06dNY0WBv6/d2r9/PyoqKvDIww+jpqaGocXAotEMq46ODtxy663o7e3F9OnTx+TE+inv3CYTJEnCgQMH4Cgqwm+XL2doMbBotMKqq7sbCxcuRG9vL6ZMmcKwOkFoybKMpuZmOOx2PLl8OSZMmMDQytbKmUOQXQzDgCiK6OzsxE0334yenh5MmTIFiqIwrE4wXoqiYNLEiQj4/bjpxhvR1taWXnxKDCwaQZIkIRqNYvFdd6H3iLCiT6coCiZNngx/MIgfL16MYCgESZJy7qIEA4vOaBuo6zr+4z//Ew0NDZg+fTpbmyFQVRV1dXVobm7G3XffDU3Thu359MTAoiM31OA6q4cffhgbN25MT7BzJffQ28Pp06dj+/bteGjJkvSJgBhYNIxMAF5//XU89/zzmDJlSvoApFMLrqlTp+KFlSvx6tq1HJBsOg54lTA7WsGWlhZ874YbYLfb4XQ6s+aRMJlKlmUEAgH09fXhD//zP5haW8v2mhUWDUdYGYaB+x54AEldR0lJCcNqGKiqiuLiYoiiiPt/8QtomsbWkIFFp1X+Dl7B+sOzz+KjDz9EVWVlzt0bOJISiQQqKyuxe/duPP3000eNOTGwaKgbRxDQ2NiIZ555BtXV1byiNUInhYkTJ+K5FSvQ0NDAMWZg0amGFQA88uijMJlMcLlcvCI4ApLJJIqLiyHLMh559NF0G04MLBrimf/1N97Ajh07MHHiRC4OHUGKoqCmpgYffPAB1vKqIQOLhl5dxWIxPPvss3AMvr6dSxhGTmpsnS4X/vDsswiHw6yyGFh0spWVyWTCq6++ioMHD6K0tJSX289Qa1hSUoLm5masXrMmvS2IgUWfUV3F43GsfPHF9NwKnRmSKKKkpAQvvvgiotEoJ+AZWPRZYQUAa9etQ5vHg3HjxkHTNA7MGaJpGsrLy9Hd3Y01r7xy1DYhBhYdpx1MahrWrVuHoqIizqOM0jZwOp1Yv359Tj5imoFFQ7Jl61YcPHgQ7rIyXhkcBaqqorS0FI2Njdi0eTMHhIFFn9YOvvPOO+kzPY0eURSxceNGtoUMLDpRK+Lt7saHH36IoqIiLmMYRYZhoKioCDs/+ggej4cnDwYWHRtWALDt3XfRPzCAgoICBtYoB1ZBQQGCoRDee+89VrwMLDpeO7hp0yaYLRZYLBYOyigzm82wWCx4Z9MmtoUMLDqW1+tFU1MTihwO3jOYAXRdR1FREZqamtDe3s4BYWDRka3G+++/j2AwCLvdzpXtGSCZTMJutyMcDmPH+++zLWRg0ZGtxq7du6HrOtdeZeDJZM/u3WwLGViUomka2tvbYbfbubI9A6us9vZ2PjiRgUUpjY2NaG9vh81m4/xVBtF1HTabDe2dnTh06BAHhIFFANDU1IQBvx+SJHEwMowoiggFg2hqbORgMLAIAHr7+gDD4KRuJh4gggDDMODt7eVgMLB4MABAu8fD16ZnMLPZjM6OjnTFRQysnGQymWDoOrq9XsiyzMDK0G0kyzI87e28GZ2BRf5AAF6vF6IoMrAyNLAEQUAgFEIkEuGAMLByW2jwQGCrkblEUUQ0HEYgGORgMLByW0JRkNT19OQuZRbDMCBJEhLxOMKhEAeEgZXb4rEYVFVlhZXhFVZS17l4lIFFqqpCTyZ520cmHySDr1lTeRcCAytXpSbYY7EYDMNgYGX2xgIMA/pgYPHiCAMrZwVDId70nAUtoQ4gGA5zMBhYrLQoSw4WVsEMrFyVuiLosNsPT+ryGVgZK5lMQhQEFBYUHLXtiIGVc1KPQ+ZTGjL67MJ5RgYWAYfvU2OFldn0wRvTZT5Ng4GV66xWK2RZZoWV6S2hKMKSl8fBYGCxwhJFEbqucwI+A5lMJmiaBkteHux2OweEgZXbHA4HCgsLobElzOgKq8BmQ0FhIQeDgZXb7HY73G43NE3j1acMZBgG9GQSRUVF6auExMDK2YMBAMrLy5FkYGXsNlJUFZWVlXyENQMrt6Um2idMmABVVRlYGUpVVUyoqEi3h8TAymnusrL0xDtl3klFEASUl5dzMBhYBACTJ0+Gy+Xi0wAykKZpcDgcqKmp4WAwsAgAqquqUFFRgXA4zNXUmXRwCAJCoRAqKysxZcoUDggDiwBAEEVUV1cjHApxYjeDiKKIcDiMyspKPk2DgUXA3ydxzz33XMiyzFfVZ5DU/NXZ55xz1LYiBlbOm33OObA7HPD7/TybZ0h15ff74XA48LnzzuOAMLDoSE6nE/9nxgwEg0HeopMBTCYTgsEgaqdORVlZGQeEgUVHth4AMH/+fCiqing8zkEZZYlEAoqiYP78+UdtI2Jg5bzUgtHzzjsPZaWlCIfDrLJGubqKRCJwFhfjwgsvPGobEQOLBs/gJSUluGjOHAQCAQbWKAeWPxDABRdcALfbzeqKgUUnqrIuufRSyLLMK1IZ0KJfcsklrK4YWPRpzj/vPMyYMQNerxdms5kDcoaZzWZ4vV7MmDYNF110EQeEgUWfdWb/xpVXIhwOQ1EUtoZnuBVUVRXBYBBXXnklTCYT20EGFn1WW/jFL34RtbW16Ojo4Mr3M0gURXR2dmLSpEm47LLL2A4ysOizpJ4ffv23v41IOIxEIsFBOUMURUEoGMS3v/1tSJLEeUQGFp1spXXppZfinNmz4e3u5ttazgBJkuD1ejFz5kx89ctfZmXFwKKTlbqH7cYbb0Q0HoeqabxdZyQPAkGApmmIRCK48aabIPEtRgwsGnqVNefCC3HJl76ExsZGzmWNIFmW0dTUhHnz5uGf5s5ldZXBTJqmcetkKFEU4fP5cO23vgVZllFaWsp5lREY476+PsRiMbz4wgtwl5dzjFlh0am2hi6XC4sWLUJXVxc0VeUyh+E8Ww++c7CzsxOLbr8d7vJytoIMLDqdttAwDFz1jW/gS1/6EppbW7mYdBiZzWa0tLRg3vz5uOaaa9LjTWwJ6TTbloGBAVz/3e9CTSQwvqICiqJwYE4zrLq6umAymbBixQqUsd1mhUXDI5lMori4GPf87Gfo8/kwMDAASZJYDZzGCcDv98Pr9eLnP/sZw4qBRcNN13VccMEFuPPOO9Hc3AxN0/jCilPZ4QUByWQSjU1NuH3hQnz+859n8DOwaLilDqrvfuc7uG7BAuzduxeCIDC0hhhWgiCgvr4e//zNb+Kmm25KnwwoO3AOKwvbGQC44447sG37dsycORO6rrNKOAmSJKG+vh7nn38+li9blq62iIFFIxxakUgECxcuRP2+fZgxYwY0TWNonWgnN5kOh9W+fZg6dSp+u3w57HY7w4otIZ0JyWQSNpsNy5Ytw6xZs1BfXw9RFNkenqANlCQJ+/btw/Tp0xlWDCwardAqKCjAw7/5DWZMn469e/fCZDIxtI6prARBwN76ekydMgWPPvwww4qBRaNF0zQ4HA4sW7YMc+bMwSeffIJkMsn7DnF4vko3DHzyySc479xz8eSTT8LpdPJFtQwsGs0KIlVpPb50Ka655hrU19fD7/fDbDbn5JyWYRgwm80IBoPYu3cvrrjiCix74ol0ZcVbm7J8n+ek+9iQunr4xz/+EQ8/+ihcTifGjx8PVVVzJrhMJhNkWUZ3dzd6enqwcOFC3Pj976dbaGJgUQaG1gcffICf33MPBgYGUFVVBYvFMuZbIUmSoCgKWltbYbfbcd9992HO4DsFGVYMLMrw4Orp7cWSJUvw1ltvoby8HOXl5dA0bcwtkhQEAbIsw+v1orOzE/Pnz8ddixdj/PjxXJ/GwKJsaY1SVwv/smoVli9fjnA4jJqaGpjN5jFTbUmSBFVV0dLSAqvViltvuQXXXnstADCsGFiUrS1iR2cnnly+HG9v3Aiz2Yzy8nJYLBYkk8msO6hNJhNEUYSiKPB6vYjH45g3bx4W3X47JkyYwBaQgUWZWj2d7IGZCq6tW7dixfPPY+fOnSi02zFu3DiYZTlrHlWTqg47OzsRCAQwa9YsXH/99fjixRcPqapKjYdhGLyPkIFFI1ktAcChxkZUV1VBlmVomnZSl+qP/PrX33gDL6xcif379yPfakVZWRlkWU4fxJlSdZlMpvRn0zQNPT09iEajmDxpEhYsWICvfOUr6c91MuFtGAYkSYKmaWhqakJtbW36/7EqY2DRMIeV1+vFihUr8P9eegnz5s3Dr375S8iyPKSDLfW9VFXFxo0b8eq6ddjzySdQFAUOhwOFhYWwWq3QdX3UDuLUbUaxeBzhUOjwujKLBTNnzMDll1+Oi7/wBeTl5Q05aERRhKpp+L//8R/Y+PbbuPrqq3HDDTegYvx4hhYDi4ar/YvFYnjxpZfw8p//jD6fDy6nEz09PZgxYwbuv+8+VFVVQdd16Lp+0gsjj6y43tuxA1u2bMFf//pXeL1eyLKMwsJCOByO9AtFR7p1EgQBoigimUzC7/cjGAxCSyZRWlqKi+bMwfx58zBnzpwhV0SGYaQfK+PxePDze+/F3j174Ha74fP5UFxcjCuvvBLXLViAgoICtokMLDqVgzcVPBs3bsTTzzyDhgMHUDF+PJxOJ5LJJARBQHNzMwRBwI9//GNc8bWvDWke58hATOnz+fDRhx9i85YtaGhogM/ng6IosFqtKCwshCRJ6YP/yJ/xyHA40b9z7N8zDCMdhKqqIhwOIxaLwWw2o7i4GLW1tZg/bx7OO/98lJaUpL/2VD/fuvXr8d+//jWSmoaJEydC1/X0o6c7OjowadIk3Pj97x/1inoGFwOLTrLyaWtrw1NPPYU3N2yAzWZDZWVlupVLze2YzWb09PSgq6sLV1xxBX6waBFKBg/uobY3x4bXwMAAPv74Y+zatQttbW1obWuDz+c7/FLXwbVPqapIkqR0G3fszdepyi+ZTELTNCR1HXoyCVVVD9/3KMtwOZ2orq5GVWUlZs2ahdmzZ8PpdJ5SSB07jn6/H48tXYrVa9Zg/LhxKCsrg6Io6dBMzd11dHYi4Pfj4osvxs0334wpkyezTWRg0ckExuo1a7B8+XKEQiFUVFQgPz//hEsQJElCLBZDW1sbysrKcMcPfoBLLrnklA/0Ez3xobW1FR6PBx2dnejp6UFrSwv6+vsRjUQQiUSgDj6PS9d1IPVvmkwQBQEwmSCLIvJtNtgKCuAsLkZ1dTXGlZdj3LhxqKqqQnV19T/8m6f787+zaROWLl2Kzs5OVFZWIj8//7hr0FJLJeLxODweD6xWK2677Tb88zXXnPLPQQysMV9Vedrb8d9LlmDL1q2oqqqC0+k8qXsBUw+o6+vrQ7fXi4u/8AXcfNNNmDp16mm3N8dr/VKisRii0SjCoRDi8Ti0ZBJKIgHDMGAymWAAsFgskEQRFosFBYWFsOXnIz8//4TzTcPxczY1NeGpp5/G22+/DZfLBbfbfVIPOEzdizgwMIC2tjbMufBCLF68GJMmTWK1xcCi1GV2AFi7bh2WLVuG/v5+TJw4ERaLBYqiDOnpAmazGbFYDK2trXA4HPj6FVfguuuuQ3Fx8bAecJ8WYkP57MM1R5QK/IGBAbywciXWvPIK/AMDqK6qQr7NNqR1ZqmnPaiqiqamJhQWFuK2W2/F1VdfDQAnvYyEGFhjSuqgVxQFDz74INa88gpKS0vhdrtP66pcah4pEAig2+tFuduNf/nWt3D55ZfDbrcPe1iM9vgBQCQSwdq1a/GnF15Ad3c33G43ioqK0vNnp/r9RVFEb28vuru7cflXv4q77747XR2y2mJg5VwLuG/fPvzyV7/C3vp61NXWQpbl9KT6cMyJybKMvr4+eL1eTKypwWWXXYYrr7wyPTGfjQfekcsx/IEAVq9ejddeew1NTU0oKytDSUnJsC3DSE3Ka5qGAwcOoLa2Fj+56y6cffbZrLYYWLlVWb3xxht4aMkSxONxTJkyJX2Zf7ilQrCvrw+BYBATKiowd+5cfPUrX0FdXV3672XyxPKxFwIaGxux7rXXsG3rVrR5PCgsKEBpaWn6s47ENhNFEY2NjRAEAT+8805886qrWGkxsHKjslqxYgUefewxuMvLUVZaCk1VYYzwAZ+qBILBIPr6+mDNy8OsWbNwySWX4Nxzz0V5efk/fN1ornQ/ltfrxUc7d2LDhg3YvXs3ItEoXE4nioqK0tXQSAeuLMvo7+9Hm8eDW2+5BbfdeitDi4E1dsNK13Xcd//9WLVqFWpra1FQUIBEInHG2orUJL8kSQgEAumFoeVuN2bPno1Zs2bhgs99DuMrKo77tSM173WiSfyu7m78bccOfPLJJ/hw5050d3VBlmW4XC44HI702q4zOX4WiwXxWAz1+/bhy1/+Mu6//37kDT75ghhY2T/Ag+1MKBTC4sWL8d6OHairq4PFYhm2+apTDQlBEBCPxxGNRhEMBmEYBsrKylBVVYUZ06dj9uzZqK6uRmlp6acexMf7/bFjcLzfH6vP50NrSws++OgjNOzbB097O7xeLwDAbrcjPz8feXl5pzWZPlyhr2ka9jc04Jyzz8Yjv/kNioqLuV6LgTU2wqq1rQ13//SnOHToEGbMnJlxT/48smWMRKMI+P1QVRWSJKG4uBgVFRWoq6tD5YQJKC8vT1c4JS4XTEN8rZih6/D5fBgIBODr64O3pwcd7e3Y39CA9vZ2DAwMQNU0mGUZDocDNpvtjLV8Qw18URTR0NCACRUVeOihhzB58mRWWgys7JNaOCmKIpqamvDDO+9ET08Ppk+fnvEvhUgtQjUMA7FYDOFwGPF4/PCVN8OAWZZhs9lgt9tRVlaGwsJCWCwWyJKE/Px8yEe8rcdkMkHTNEQiEWiqingigVAohN7eXgQCAUQiESiKkn7ZqdlsTj8tIvW1mT5WZrMZ+/fvh6OoCI898gjq6urSdyXwCiIDKyvCKtVuNbe0YOFttyESjWLixIlZ9zr5VIWYuoKZmjPSdR2apkFV1fT9gcantEOpCk4QRciSBFmW0/cfHvmfyWTKurYqFfAtra2QZRnLly1D7dSpQ356BjGwRo0oitizZw/+7Uc/gqZpqKmpQSKRGHPt7vF+/ay5rmN/HSvy8vLQ2tYGQ9fxm1//GrNnz2Z7yMDKjjZwf0MDFi1aBEVRMGnSpKx5BDGdHovZjObmZgDA40uX4qxZs9geMrAyu7Jqam7GLbfeCj2ZRGVlJcMqx5jNZnR0dEDTNPz2ySc5pzXM+Kr6YQyr1tZW3H777VAUBVVVVQyrHKQoCioqKmAymbBo0SIcPHQoPT9HDKyMCatoNIrFP/kJ+gcGUFNdPebmrGhooVVVVYVINIq77roLgWCQocXAypyw0jQNP168GM3NzZg+bdqYfy08fTZVVVFXV4euri78249+hHg8ftwHIxID64wwDCN9z9sv/uu/8O6772JGFqyzojO3fyQSCUybNg07d+7Ez++5J32CIwbWmR+4wbPls88+iz+//DJqj5hcJUrRNA3T6uqwfv16PLFsGYDDS0C4nzCwzpjUgsotmzdj6eOPY3pdHfIsFr5lhf6BruuQJAlnnXUWnn76abz66qvHfVkHneSxx2UNQ5e6InjDv/4rLBYLxpWXI8ErgvQp7aHFYoHP50N/fz+efuopzJgxgwtLWWGdmbBSVBX33XcfYrEY3G43w4o+syJXFAVOpxMmkwn33nsvotEoRFFka8jAGsHBGizjH330UezctQu1tbUj8oRLGptUVcXkyZPR1NSEJUuWpE+AxMAakbOkyWTCpk2bsHLlStQOvkKLaCh0XUdtXR3WvPIK3njjjaMe7UMncRxyDuvk5iAkSUJvby+++73vQTcMlLvdrK7olEiShN6+PmiqihXPPYdx48bxhRassIYvrFJl+68efBA+nw/lbjdvu6HTag3LSksRDIXwwC9+kQ4xYmANWyu4bv16vPX22+mnSvJsSKezT2mahsmTJmHbtm34y6pV6T8ntoSnJfUyzeu/8x2Yzeb06+OJTpcsy/D7/QiHw1jx3HOoqKjgUgdWWKd3JgSAxx9/HP5AAC6Xi60gDRtFUVBcXIxIJIKlS5em9zkudWBgnVJYCYKA7du347XXXkPlhAlQFIVlOw3rPqYoCiorK/HW229j4zvvcBU8A+sUB0YQoCgKnnjiCeQXFCA/P5+DQiPCarWiqKgITyxbhkgkwsBiYJ1aK/jq2rXY39CAqspKzi3QiEkmk5gwYQKam5rwwsqV6RMmMbBOurry+/1YsWJF+umRnFegkZJ652J1dTVeeukldHV1ceqBgTW06ur5559HV1cXXC4XrwrSiFNVFQ6HA36/H7///e+P2heJgfWp1VVrayv+smoVytxurkCmM3ai1DQNbrcbb7z5Jg4ePMi2kIH12WEFAM//7/8iHA7DWVzMVpDOaGtYVFSEhKJgxfPPH7VPEgPruGe5lpYWbNiwAVVVVQwrOuN0XUdlZSU2btyIAw0NrO4ZWJ9eXf3pT39CQlFgt9t5ZZBGJbAKCwpgGAb++MILAPgIGgbWCaqrxsZGbHznHVSMH88V7TRqFEXB+PHjsWXrVuzbt48DwsD6x7ACgNVr1iAYCiE/P5/tII0awzCQl5eHaCSCVatXH7WPMrAIgiDA19+Pt996Cy6nk2FFGaGkpASbNm2Ct6eHk+8MrL+HFQCsX78eff39cDqdfPsNjTpd1+F0OhEIBPDq2rVH7asMrBxvBxVFweuvv44ih4MPUqOMOpk6nU5sePNNxGIxtoUMrMO2v/suWlpb4eZjjymDqKqK0tJSeDwebN6yhQOS64ElDpbYGzZsgMjHelCGks1mvLVhw+F9NseXOOT2EWoyoaW1FR/t3MkniVJG0jQNLqcTu3bvxqFDh1hh5W5WHZ4PePfddzEwMIC8vDweHZSRLBYLQqEQtm3bdvigzeFOIGc/uSAIMHQdmzdvhjUvj5PtlLEkSUJ+fj42bd6c8zfj53RLeKixEQ0NDSgqKuJRQRkrdVN0c3MzGg4cYEuYi9UVAGzevBmKosBht3PtFWUsXddRWFgIRVHwzsaNOd0W5uSnTpXUH3z4IaxWK9e3UFYoKCjArl27oOt6zu6zOdsSHjp0CO3t7YevDmoajwbKaKqqwuVywePx5PQN0TkbWDveew/9/f3Iy8vjvYOUFcxmM4KhEP72t78d1SkwsMbyBx7s/RsOHoTZYmFYUdZIJpOw5OWlJ94ZWDnAZDKhv78f9fX1KLDZGFiUVQpsNuzfvx/dXi8DK1ccOHAAPT09MJvNPAIo69rCPp8Phw4ezMnPn1OBlWoH9zc0QNd1WNgSUhYxDOPwSdYwsHfv3qP2aQbWGG0HDcPAxx9/DEmSuLqdso4kSZBlGR/n6PKGnGsJA4EA2traeO8gZWuZBavVio7OTgwMDLAlHOs8bW0IBoMoLCxkO0hZRzcMFBQUIBwKobmlhYE1Zj/oYK+/b/9+JBIJ5OXl8XYcysIC6/ALKhKJBPbu2XPUvs3AGkNSvf6hpibous6H9VFW78uCIKC1tfWofZuBNQb19vTAarWyuqLsbQt1HVarFT29vWwJx7L+/n709fXBZrMxsCirAys/Px8+nw9er5eBNVZ1dHRgwO/nglHKaoZhwGKxoN/nS7eFDKwxyOPxIBgMQpIkXiGk7D5wBQGxeDzn2sKcCKzUpKTf74eeTPL5VzQm9mld19Ez2BLmyj6dUxWWz+fj1UEaU1VWMBhkhTUWNywA9PT0QJIkVlg0JiosSZLSLWGunIhzptzQDQP+QID3D9KYIZvN6PF6EYvFWGGNNeFQCH6/H0KOvzmXxlbnEI5EEI1GGVhjTSAQgD8QyPlXfdMYOnhNJsRiMVZYY1E8HoemqhA4f0VjhCiKiMfjCIZCOfOZc2ZCJ55IpO8h5KQ7ZbvUpHskHEY0EmFgjcUKKxaPw2KxQNM0LhylrA8sXdehqCoSiQQDa6wZGBhAW1sbEmVlyLNaYfBeQsrmwBIEJOJxdHd3w+/3587n1jRtzJcagiCgzePB9u3bkW+1QhRFsL6irD5wcfi1X9FYDJ+/6CJUV1fnxA39ORFYAHh1kMa0ZDLJlnCsbVBOttNYlEvzsRI3LBFlC94JTEQMLCIiBhYRMbCIiBhYREQMLCJiYBERMbCIiBhYRMTAIiJiYBERMbCIiIFFRMTAIiJiYBERA4uIiIFFRMTAIiIGFhERA4uIiIFFRAwsIiIGFhERA4uIGFhERAwsIiIGFhExsIiIGFhExMDiEBARA4uIiIFFRAwsIiIGFhERA4uIGFhERAwsIiIGFhExsIiIGFhERAwsImJgERExsIiIGFhExMAiImJgERExsIiIgUVExMAiImJgEREDi4iIgUVExMAiIgYWEREDi4ho0P8fAOxFHZKKu63rAAAAAElFTkSuQmCC
Ε-mail: stefanos (at) uop (dot) gr
Phone: 27210-45-301
Office: Γ1.01 Νέο Κτήριο- Αντικάλαμος
Ώρες Γραφείου: Τετάρτη 11:00-12:00 Παρασκευή 11:00-12:00 - (Συνιστάται η προηγούμενη ειδοποίηση με e-mail)
Short CV

Ο Δρ. Στέφανος Γιακουμάτος είναι Καθηγητής Στατιστικής και Ποσοτικών Μεθόδων στο Τμήμα Λογιστικής και Χρηματοοικονομικής του Πανεπιστημίου Πελοποννήσου. Σπούδασε Στατιστική (Bachelor, Master και Ph.D.)  στο Τμήμα Στατιστικής του Οικονομικού Πανεπιστημίου Αθηνών.

Ο Δρ. Γιακουμάτος εργάστηκε ως Στατιστικός Αναλυτής στον ιδιωτικό τομέα (Intralot SA, TNS Metrisis) και στο δημόσιο τομέα (τμήμα ΕΛ.ΣΤΑΤ., Μεθοδολογία και Ανάλυση). Έχει ευρύ διδακτικό έργο σε θέματα στατιστικής, ανάλυσης δεδομένων και ποσοτικών μεθόδων στο ΤΕΙ της Πελοποννήσου καθώς και στο Πανεπιστήμιο Πελοποννήσου.

Έχει επίσης διδάξει Στατιστική, Ανάλυση Δεδομένων Παράγωγα και Λήψη Αποφάσεων σε προπτυχιακό και μεταπτυχιακό επίπεδο στο Πανεπιστήμιο Πελοποννήσου,στο  Ιόνιο Πανεπιστήμιο και στο Πάντειο Πανεπιστήμιο. Είναι ενεργό ΣΕΠ του ΕΑΠ. Επιπλέον, ήταν ερευνητής σε ευρωπαϊκά ερευνητικά προγράμματα και είναι μέλος της ερευνητικής επιτροπής της Διεθνούς Οργάνωσης Εργασίας (ILO).

Τα ερευνητικά του ενδιαφέροντα περιλαμβάνουν την Μοντελοποίηση Δεδομένων σε Οικονομικά και Κοινωνικά Θέματα, Ανάλυση Χρονικών Σειρών, Τεχνικές Δειγματοληψίας, Μετρήσεις Φτώχειας

Short CV

Stefanos Giakoumatos is Professor at the Department of Accounting and Finance of the University of Peloponnese, where he teaches Statistics, Quantitative Methods, Financial Derivatives and Decision Making at undergraduate and postgraduate level. He obtained his degrees (Bachelor, Master and Ph.D.) from the Department of Statistics in Athens University of Economics and Business. In the past, Prof. Giakoumatos has been employed as Statistical Analyst in private sector (Intralot SA, TNS Metrisis) and in public sector (EL.STAT), he was researcher in European research  programs and he is member of research committee of ILO (International Labor Organization).

His research interests, include Data Modelling in Finance and Social Silences, Markov Chain Monte Carlo Methods and Time Series Analysis.

knownledge Object: Στατιστική και Ποσοτικές Μέθοδοι στις Κοινωνικές Επιστήμες [ ΦΕΚ 344/Γ΄/20-03-2014 ]
Science Interest: Μοντελοποίηση Δεδομένων σε Οικονομικά και Κοινωνικά Θέματα, Λήψη απόφασης υπό καθεστώς αβεβαιότητας, Ανάλυση Χρονικών Σειρών, Τεχνικές Δειγματοληψίας, Μετρήσεις Φτώχειας
Science Interest:

Data Modelling in Finance and Social Silences, Markov Chain Monte Carlo Methods, Decision making under uncertainty, and Time Series Analysis.

Επιλεγμένες Δημοσιεύσεις:

1.     Theodoridis, D., and S. G. Giakoumatos (2025). Determining Poverty Factors Using Logit Models. International Journal of Accounting Finance and Social Science Research, Volume 3 Issue 6, November-December 2025, pp 01-10.  

2.     Sotiropoulou, T., Georgopoulos, A. ., & Giakoumatos, S., (2023). Financial development, economic growth, and income inequality: A Toda-Yamamoto panel causality test. Economics and Business Letters, Vol 12(2), 172-185.

3.     Kourkoutas, E.P., and S. G. Giakoumatos, (2023). Statistical analysis and evaluation of Greek students’ background determinants on Science literacy. Journal of Statistical and Econometric Methods, Vol. 12, No. 2, 2023, 17-42. https://doi.org/10.47260/jsem/122

4.     Garefalakis E., Giakoumatos S., and Rezitis A., (2023). The Usage of Markov Chain Monte Carlo (MCMC) Methods in Time-varying Volatility Models. Journal of Risk and Control, Vol. 10, No. 1, 2023, 1-14. https://doi.org/10.47260/jrc/1011

5.     Koutrafouri, Kl., and Giakoumatos, S., (2023). The Health Status of the Elderly Greeks and the Effect of the Economic Crisis. Journal of Statistical and Econometric Methods, Vol. 12, No. 2, 2023, 43-54. https://doi.org/10.47260/jsem/1223

6.     Giakoumatos S., Delis F., Mavridoglou G., Maalouf M., and Bhat S. (2022). Factors Leading to Student Attrition in the Public Vocational Training Institutes of Greece: A Lean Six Sigma perspective. To appear on the 6th International Conference on Lean Six Sigma in Higher Education, 14th and 15th November 2022, Abu Dhabi 

7.     Sotiropoulou, T., Georgopoulos, A. ., & Giakoumatos, S. . (2022). Causality between financial development, economic growth, and income inequality in EU countries . International Journal of Applied Research in Management and Economics, 5(1), 1–13. https://doi.org/10.33422/ijarme.v5i1.759

8.     Sotiropoulou Th., Giakoumatos S., and A. Georgopoulos (2022). “Causality Relationships between financial development, economic growth and income inequality”. Proceedings of the 2nd International Conference on Advanced Researchin Management, Economics and Accounting, 18-20 February 2022, Milan, Italy 

9.     Λάμπρου Ε, Κεσκίνη Ε, Σηματηράκη Ε, Γιακουμάτος Σ. (2022). «Εξ’ Αποστάσεως Εκπαίδευση στην Εποχή της Πανδημίας: Μία πρώτη αποτίμηση». Review of Counselling and Guidance- Επιθεώρηση Συμβουλευτικής και Προσανατολισμού, Vol 128-129, 69-83

10.  S. Giakoumatos, D. Nikolakopoulos and A. Athanasopoulos (2021). “Burnout Syndrome and Anxiety/Depressive Disorders in Healthcare Professionals: A Canonical Correlation Analysis”. International Journal Of Occupational Health and Public Health Nursing, Vol.7, No. 1, 2021, 21-33

11.  Theodora Sotiropoulou, Stefanos G. Giakoumatos (2021). “Multiple imputation for missing values with an empirical application”. Journal of Risk & Control, 2021, 8 (1), 1-18

12.  Stefanos G. Giakoumatos, Malapani Eleni (2020). “Estimating Poverty and Unemployment in Greece Using Small Area Estimation Methods”. ICODECON 2020 (http://www.icodecon.uop.gr/)

13.  Stefanos G. Giakoumatos, Stavros Loukas (2020). “The Determinants of Earnings Inequality in Greece”. ICODECON 2020 (http://www.icodecon.uop.gr/)

14.  Giakoumatos S.G.  and S. Loukas (2019). Discovering the factors for the impoverishment of the middle class in Greece. of Statistical and Econometric Methods, vol.8, no.4, 2019, pp 41-50.

15.  Giakoumatos S.G. and T. Sotiropoulou (2019). The impact of financial development on economic growth. To appear in Journal Economic alternatives

16.  Giakoumatos S.G., Sotiropoulou T. and D. Petropoulos (2019).  Financial development, financial stability and economic growth in European Union: a panel data approach. Advances in Management and Applied Economics, vol 9, 55-69

17.  Karamessini M, Giakoumatos S.G. (2018). ”Industrial relations, imposed flexibility and inequality during the Greek Great Depression”, in Reducing Inequalities in Europe (eds Daniel Vaughan-Whitehead), pages 257-290, ISBN: 978 1 78811 628 2, Edward Elgar Publishing, UK.

18.  Giakoumatos S.G and T. Sotiropoulou (2018). “Modelling Economic Growth in The EU”,  Proceedings of the 2st International conference on quantitative, social, biomedical and economic issues 2018, (ICQSBEI2018), pages 235-242, ISBN 978-618-82980-2-6, 2-3 March 2018, Athens.

19.  Giakoumatos S.G. and Malapani E. (2017b). “Poverty in Greece using Small Area Estimation Methods”, Journal of Finance and Investment Analysis, vol. 6, no. 3, 2017, 61-84. ISSN: 2241-0998.

20.  Giakoumatos S.G. and S. Loukas (2017). “The impoverishment of the middle class in Greece. An econometric analysis”. Proceedings of the 1st International conference on quantitative, social, biomedical and economic issues 2017, (ICQSBEI2017), pages 98-106, June29-30, 2017, Athens, Greece. ISBN 978-618-82890-0-2

21.  Giakoumatos S.G. and Malapani E. (2017a). “Estimating Poverty in Greece in Small Geographical Areas”. Journal of Statistical Science and Application, vol5, pages 16-29.

22.  Karamessini M, Giakoumatos S.G. (2017). “Imposed Flexibility and Growing Wages and Employement Inequalities under Austerity in Greece”, in Inequalities and the World of Work, Brussels 23-24/2/2017. Proceedings of Conference Organized by International Labour Office and European Commission, pages 181-206, ILO Publications, Switzerland

23.  Karamessini M, Giakoumatos S.G. (2016). “The middle classes in Greek Great Depression: Dissolution or resilience?”, in Europe's Disappearing Middle Class? (eds Daniel Vaughan-Whitehead), pages 244-278, ISBN: 978 1 78643 059 5, Edward Elgar Publishing, UK.

24.  Karamessini M, Giakoumatos S.G. (2016). “The Greek Middle Classes facing an unertain future”, in Long-Term trends in the world of work, Brussels 29/2-1/3/2016. Proceedings of Conference Organized by International Labour Office and European Commission, pages 165-188, ILO Publications, Switzerland 

25.  Maria Karamessini, Stefanos Giakoumatos (2016). “Public Sector and the Middle Classes in Bad Times: Factor of Erosion or Resilience During the Greek Great Depression?”. Economia and Lavoro, vol 2, pages 59-70.

26.  Giakoumatos et al. (2015). “Socio-demographic and health factors influencing to the number of admissions of Elderly”. Proceedings of the 4th International Conference “Quantitative and Qualitative Methodologies in the Economic and Administrative Sciences”, Athens 21-22/5/2015, pages 77-83. ISBN 978-960-98739-6-3

27.  Giakoumatos (2015). Advanced statistical methods for combining census and survey data. Workshop of Research uses of high-precision census data. Barcelona 25-27/7/2015

28.  Giakoumatos S.G. and Malapani E. (2015). “Small area estimation: Theory and Methods”.Proceedings of the 4th International Conference “Quantitative and Qualitative Methodologies in the Economic and Administrative Sciences”, Athens 21-22/5/2015, pages 231-240. ISBN 978-960-98739-6-3

29.  Stavropoulos I., Tripolotsioti A., Giakoumatos S.G. and A. Stergioulas (2014). “Competencies of The First and Second Division Track and Field Coaches”, International Journal of Sport Studies. Vol., 4 (5), 584-590. 

30.  Giakoumatos, S.G. (2013). Bayesian Stochastic Volatility Models. Archives of Economic History, Volume XXIV, No 2, pages 35-56.

31.  Giakoumatos (2010). "Bayesian Stochastic Volatility Models: Auxiliary Variable Methods For Stochastic Volatility And Other Time-Varying Volatility Models". LAP LAMBERT Academic Publishing, ISBN-10: 3838386337  

32.  Giakoumatos (2013). “Auxialiry Variable Sampler for ARCH and GARCH Models”: Proceedings of the 3rd International Conference: Quantitative and Qualitative Methodologies in the Economic and Administrative Sciences. Pages 203-210. ISBN 978-960-98739-4-9 

33.  Giakoumatos SG and Avgerinou V. (2011). “The Effect of Hooliganism on Greek Football Demand”,  in Violence and Aggression in Sporting Contests
Economics
, History and Policy (eds 
R. Todd Todd Jewell) pages 155-174, Springer, ISBN-13: 978-1441966292.

34.  Giakoumatos S.G. (2012). “Weighting Methods: An application to Greek-IPUMS data”. Modern Greek Studies Yearbook. V22/23, 163-173. 

35.  Giakoumatos S.G., Dellaportas P., and Politis D.M. (2005). Bayesian Analysis of the Unobserved ARCH Model. Statistics and Computing, vol. 15, pp. 103-111.

36.  Avgerinou, V., Giakoumatos, S., (2009). Price, Income & Unemployment Effects on Greek Professional Football, IASE/NAASE Working Paper Series, No. 09-07, http://ideas.repec.org/p/spe/wpaper/0907.html

37.  Vrontos I.D., Giakoumatos S.G., Dellaportas P., and Politis D.N.(2001). An application of three bivariate time varying volatility models. Applied stochastic models in business and industry,17, 121-133.

38.  Giakoumatos, S.G. (2007). Testing the Efficient Market Hypothesis in Greek Stock Exchange. Archives of Economic History, ΧΙΧ, 169-182.

39.  Giakoumatos S. G., Vrontos I. D., Dellaportas P. And D. N. Politis (1999). An MCMC Convergence Diagnostic using Subsampling.  Journal of Computational and Graphical Statistics, volume 8, number 3, 431-451.

40.  Giakoumatos S.G., Dellaportas P. and Politis D.N. (1999).  Conjugate Gibbs for some non-linear time series.  Hercma '98: 4th Hellenic European Conference on Computer Mathematics and its applications, E.A. Lipitakis (Ed), pp. 479-486.

41.  Giakoumatos S. G. (1997). Bayesian Stochastic Volatility Models. MSc Thesis, Athens University Of Economics, ISBN 960-7929-03-9, External Examiner: Prof. P. Mueller, Duke University.

42.  Giakoumatos S. G. (2004). Auxilary Variable Sampling Methods for some Time-Varying Volatility Models. PhD Thesis, Athens University οf Economics and Business.

43.  Giakoumatos S. G., P. Dellaportas and D.N. Politis (2008). Bayesian analysis of some Multivariate time varying volatility models. Submitted.

44.  Giakoumatos (2009). Stochastic Volatility Model using Gibbs Sampler. Submitted

45.  Avgerinou, V., Giakoumatos, S., Konstantinakos, P., Mountakis, K., (2008),
The Demand for Greek Professional Football, 16th European Association of
Sport Management (EASM) Congress, Sept. 10th-13th, Bayreuth/Heidelberg,
Germany.

46.  Αυγερινού, Β., Γιακουμάτος, Σ., Κωνσταντινάκος, Π., Μουντάκης, Κ., (2006)
Οικονομική προσέγγιση της βαθμολογικής κατάταξης στο ελληνικό ποδόσφαιρο:
σχέση βαθμολογίας και εξόδων, 7ο Συνέδριο Ελληνικής Εταιρίας Διοίκησης
Αθλητισμού (ΕΛΛΕΔΑ), 24-26 Νοεμβρίου, Θεσσαλονίκη.

47.  Giakoumatos S.G. and I. Nikolaidis (2005).Sample of Greek population census data. Workshop “Integrating European Census Microdata”, http://www.hist.umn.edu/~rmccaa/ipums-europe/greece/greece_workshop_report.pdf

48.  Avgerinou, V., Giakoumatos, S., (2009). Price, Income & Unemployment Effects on Greek Professional Football, IASE/NAASE Working Paper Series, No. 09-07, http://ideas.repec.org/p/spe/wpaper/0907.html

49.  Giakoumatos S.G. (2011) Official Statistics in Greece (Επίσημες Στατιστικές στο Ελληνικό Κράτος: Μία ιστορική Προσέγγιση). Πρακτικά Συνεδρίου "Θεσμοί στην Ελλάδα της Μεταπολίτευσης. Αποτίμηση μιας αντιφατικής περιόδου" (υπό έκδοση), Καλαμάτα 4-6/2011

50.  Γιακουμάτος Σ. et al (2007). Μεθοδολογία Της Στατιστικής Έρευνας Και Λήψης Αποφάσεων. Εθνική Σχολή Τοπικής Αυτοδιοίκησης

51.  Γιακουμάτος Σ. και Στ. Ζαχαρίου (2005). «ΣΗΜΕΙΩΣΕΙΣ ΣΤΙΣ ΤΕΧΝΙΚΕΣ ΔΕΙΓΜΑΤΟΛΗΨΙΑΣ», Ινστιτούτο Επιμόρφωσης Δημοσίων Υπαλλήλων, Σχολή Δημόσιας Διοίκησης.

 

Selected publications: