Ãîäèøåí àëìàíàõ "Íàó÷íè èçñëåäâàíèÿ íà äîêòîðàíòè"

Îùå ñòàòèè îò òîçè áðîé

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  LEVERAGING DIGITAL TWINS FOR ECOSYSTEM RESTORATION AND BIODIVERSITY CONSERVATION IN THE GREEN ECONOMY FRAMEWORK

  Digital twin (DT) technology is emerging as a pivotal tool for addressing critical environmental challenges, especially in the fields of ecosystem restoration and biodiversity conservation, within the broader framework of the green economy. Digital twins - sophisticated, data-driven virtual replicas of physical systems - offer a transformative ... Digital twin (DT) technology is emerging as a pivotal tool for addressing critical environmental challenges, especially in the fields of ecosystem restoration and biodiversity conservation, within the broader framework of the green economy. Digital twins - sophisticated, data-driven virtual replicas of physical systems - offer a transformative approach by enabling real-time monitoring, predictive modelling, and scenario-based planning. These capabilities allow for a nuanced understanding of complex ecological processes and the impacts of human activities on natural systems. The integration of DTs into ecological management introduces advanced tools for enhancing decision-making processes, improving predictive accuracy, and supporting sustainable practices that align with green economy objectives, such as carbon reduction, biodiversity protection, and efficient natural resource use. This paper employs qualitative methodologies to explore how DT technology can model species-specific behaviors and environmental conditions. This thesis focuses on integrating digital twin technology into environmental management practices, specifically for ecosystem restoration and biodiversity conservation. It explores how digital twins can model ecological changes, simulate the impact of various conservation strategies, and improve decision-making processes. Eventually, the paper examines how digital twin technology can be leveraged to support ecosystem restoration and biodiversity conservation while advancing a green economy. It also aims to demonstrate how this technology can help mitigate environmental costs associated with economic growth, in line with the principles of the Environmental Kuznets Curve (EKC), by enabling more informed and effective conservation strategies.

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  ÅÂÐÎÏÅÉÑÊÀ ÍÎÐÌÀÒÈÂÍÀ ÐÅÃËÀÌÅÍÒÀÖÈß ÍÀ ÏÐÈËÎÆÅÍÈÅÒÎ ÍÀ ÈÇÊÓÑÒÂÅÍÈß ÈÍÒÅËÅÊÒ

  Æåëàíèåòî íà Åâðîïåéñêèÿ ñúþç äà ñå íàëîæè êàòî ñâåòîâåí ëèäåð â îáëàñòòà íà èçêóñòâåíèÿ èíòåëåêò äîâåäå äî ïðèåìàíåòî íà ïúðâèÿ Àêò çà èçêóñòâåíèÿ èíòåëåêò (ÀÈÈ). Ñ òîçè èíîâàòèâåí íîðìàòèâåí òåêñò ÅÑ ñå ñòðåìè äà ïðîìåíè ìîäåëà íà óï-ðàâëåíèå íà ðèñêà îò ðåàêòèâåí êúì ïðîàêòèâåí. Öåëòà íà ñòàòèÿòà å äà ñå íàïðàâè îïèò çà î÷åðòàâàíå ïîòåíöèàëà íà ... Æåëàíèåòî íà Åâðîïåéñêèÿ ñúþç äà ñå íàëîæè êàòî ñâåòîâåí ëèäåð â îáëàñòòà íà èçêóñòâåíèÿ èíòåëåêò äîâåäå äî ïðèåìàíåòî íà ïúðâèÿ Àêò çà èçêóñòâåíèÿ èíòåëåêò (ÀÈÈ). Ñ òîçè èíîâàòèâåí íîðìàòèâåí òåêñò ÅÑ ñå ñòðåìè äà ïðîìåíè ìîäåëà íà óï-ðàâëåíèå íà ðèñêà îò ðåàêòèâåí êúì ïðîàêòèâåí. Öåëòà íà ñòàòèÿòà å äà ñå íàïðàâè îïèò çà î÷åðòàâàíå ïîòåíöèàëà íà çàêîíîäàòåëíèÿ àêò äà ñå íàëîæè êàòî ñâåòîâíà ðàì-êà çà ðåãóëèðàíå íà èçêóñòâåíèÿ èíòåëåêò íà áàçà íà àíàëèç íà ñúùåñòâóâàùèòå è âúçìîæíè ïðîáëåìè è åâåíòóàëíèòå ðåøåíèÿ íà çàêîíîäàòåëÿ. Ïðèëîæåíàòà ìåòîäîëîãèÿ íà èçñëåäâàíå å êîíòåíò-àíàëèç íà íîðìàòèâíè äîêóìåíòè, íàó÷íè ïóáëèêàöèè è íàó÷íè ñòàòèè.

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  ÈÇÊÓÑÒÂÅÍÈßÒ ÈÍÒÅËÅÊÒ: ÏÎÌÎÙÍÈÊ ÈËÈ ÇÀÏËÀÕÀ ÇÀ „ÌÈÄÚË ÌÅÍÈÄÆÌÚÍÒÀ“ ÏÐÈ ÂÍÅÄÐßÂÀÍÅ ÍÀ ÈÍÔÎÐÌÀÖÈÎÍÍÈ ÑÈÑÒÅÌÈ ÇÀ ÓÏÐÀÂËÅÍÈÅ ÍÀ ÀÃÐÎÁÈÇÍÅÑÀ

  Äèãèòàëèçàöèÿòà íà àãðîáèçíåñà å ïðîöåñ, êîéòî çíà÷èòåëíî ïðîìåíÿ íà÷èíà íà ðàáîòà è óïðàâëåíèå íà ñåëñêîñòîïàíñêèòå ïðåäïðèÿòèÿ. Èíòåãðèðàíåòî íà èçêóñòâåíèÿ èíòåëåêò (Artificial Intelligence, AI) â óïðàâëåíèåòî íà àãðîáèçíåñà ïðåäñòàâëÿâà êàêòî âúçìîæíîñòè, òàêà è ïðåäèçâèêàòåëñòâà, îñîáåíî çà ñðåäíîòî óïðàâëåíñêî íèâî. AI òåõíîëîãèèòå ìîãàò äà ... Äèãèòàëèçàöèÿòà íà àãðîáèçíåñà å ïðîöåñ, êîéòî çíà÷èòåëíî ïðîìåíÿ íà÷èíà íà ðàáîòà è óïðàâëåíèå íà ñåëñêîñòîïàíñêèòå ïðåäïðèÿòèÿ. Èíòåãðèðàíåòî íà èçêóñòâåíèÿ èíòåëåêò (Artificial Intelligence, AI) â óïðàâëåíèåòî íà àãðîáèçíåñà ïðåäñòàâëÿâà êàêòî âúçìîæíîñòè, òàêà è ïðåäèçâèêàòåëñòâà, îñîáåíî çà ñðåäíîòî óïðàâëåíñêî íèâî. AI òåõíîëîãèèòå ìîãàò äà ïîäîáðÿò ïðîöåñèòå íà âçåìàíå íà ðåøåíèÿ, äà ïîäîáðÿò îïåðàòèâíàòà åôåêòèâíîñò è äà ïîäêðåïÿò óñòîé÷èâè ïðàêòèêè â àãðîáèçíåñà. Òå ïðåäñòàâëÿâàò è ïîòåíöèàëíè çàïëàõè çà ñèãóðíîñòòà íà ðàáîòíèòå ìåñòà è òðàäèöèîííèòå óïðàâëåíñêè ðîëè. Öåëòà íà íàñòîÿùîòî èçñëåäâàíå å äà ñå àíàëèçèðà ðîëÿòà íà AI ïðè âíåäðÿâàíå-òî íà Èíôîðìàöèîííè ñèñòåìè çà óïðàâëåíèå (ÈÑÓ) â àãðîáèçíåñà è äà ñå îöåíè íåãîâîòî âúçäåéñòâèå âúðõó „ìèäúë ìåíèäæìúíòà“. Èçñëåäâàíåòî ñå ñòðåìè äà èäåíòèôèöèðà êàêòî ïîòåíöèàëíèòå ïîëçè, òàêà è ïðåäèçâèêàòåëñòâàòà, ñâúðçàíè ñ èçïîëçâàíåòî íà èçêóñòâåí èíòåëåêò, è äà ïðåäëîæè ñòðàòåãèè çà åôåêòèâíî èíòåãðèðàíå íà òåçè òåõíîëîãèè â óïðàâëåíñêèòå ïðîöåñè. Âúïðåêè òîâà âúçíèêâà âúïðîñúò, äàëè AI å ïî-ìîùíèê èëè çàïëàõà çà „ìèäúë ìåíèäæìúíòà“ – ñðåäíèÿ óïðàâëåíñêè ñëîé, êîéòî òðàäèöèîííî èçïúëíÿâà âàæíè ôóíêöèè â îðãàíèçàöèèòå.