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Durch die stetige Weiterentwicklung und die mediale Präsenz der künstlichen Intelligenz findet die Steigerung der Unternehmenssicherheit in Unternehmen besondere Bedeutung. Insbesondere aus dem Umfeld des Machine Learnings sind kontinuierlich Anwendungen zu verzeichnen, die dazu dienen, eine derartige Maßnahme zu unterstützen.
Im Rahmen dieser Arbeit wurde untersucht, ob eine potenzielle Steigerung der Unternehmenssicherheit durch den Einsatz eines Prototyps für die Objekterkennung basierend auf einem YOLOv5-Algorithmus erreicht werden kann. Es wurden Beispielszenarien definiert und die Wirksamkeit dieses Algorithmus bei der Erkennung und Identifizierung in Bezug auf die Sicherheitsanforderungen in einem Unternehmensumfeld evaluiert.
Die Forschungsmethodik umfasste die Entwicklung und den Aufbau des Prototyps, der auf einem YOLOv5-Algorithmus basiert und auf einem Trainingsdatensatz der Objekterkennung trainiert wurde. Der Prototyp wurde anschließend in einer Laborumgebung implementiert und auf seine Fähigkeit getestet, Objekte nach definierten Sicherheitsanforderungen zu erkennen.
Die Implementierung eines solchen Prototyps konnte dazu beitragen, die Sicherheitsmaßnahmen in Unternehmen zu unterstützen, die Sicherheitsreaktion zu beschleunigen und proaktivere Ansätze zur Gefahrenabwehr zu ermöglichen. Aus diesen Ergebnissen sind weitere Forschungen und praktische Anwendungen im Bereich der Unternehmenssicherheit denkbar.
Due to the global phenomenon of climate change the region of Mara Siana is projected to increasingly face extreme weather events that particularly comprise prolonged droughts and
heavier rainfalls. To be able to adequately adapt to these changing circumstances and maintain their livelihoods communities need to build respective capacities. As the main objective, this research aims at determining landowners’ climate change adaptative capacity (CCAC) across different villages in Mara Siana. Accordingly, a semi-quantitative approach was carried out including qualitative interviews and the subsequent quantitative calculation of CCAC based on a multidimensional indicator set and a respective coding
system. In addition to predominantly positive results of socio-cultural characteristics and the quality of natural resources, this work reveals clear weaknesses and potential for improvement in the areas of income security and financial stability, the expansion and resilience of infrastructure, and the relationship between communities and local authorities. Moreover, differences in capacity results are not only identified between the investigated villages as well as between individual households but also systemic disadvantage in capacity building affecting female landowners and community members can be indicated from the obtained interview data. Therefore, this research gives concrete recommendations for the implementation and verification of suitable adaptive measures that are particularly tailored for the improvement of low-performance indicators while following a gendertransformative approach and thus hold the potential to increase CCAC in the long-term.
Elaeis guineensis Jacq. or oil palm is a native species of West Africa. Its oils, extracted from the fruit mesocarp and the kernel are widely used in the food industry, industrial applications, and bioenergy production. Due to its versatility, profitability and growing demand, the global oil palm agroindustry raises concerns regarding deforestation, effects in biodiversity, contamination and related to social issues such as labor conditions, poverty, and social conflicts. In Mexico, the establishment and subsequent growth of the oil palm industry was promoted by past government policies and financial support. In Chiapas the current main producer of the country, the expansion can be also attributed to oil palm resilience to floods, hurricanes, and the economic profitability.
The objective of this study is to evaluate the sustainability status of the oil palm production system within Acapetahua and Villa Comaltitlán Municipalities by analyzing the indicators of sustainability. To achieve this, the Evaluation Framework for Natural Resource Management Systems (MESMIS), was adapted to measure the attributes status of productivity, stability, reliability, resilience, self-management, equity, and adaptability, of the different dimensions of sustainability (environmental, social, political, and economic).
It was identified that MESMIS is an appropriate framework to study oil palm system in Acapetahua and Villa Comaltitlán municipalities. The methodology allowed the identification of critical points, and relevant indicators that include land use and vegetation cover changes, oil palm cashflow, good agricultural practices, farmers´ training, level of participation and farmers´ well-being. As a result, it was identified that vegetation and land use changes were principally from pastures land and previous oil palm plantations, and a positive profitability in the last two years. Soil and water conservation practices are implemented, and farmers have received different trainings principally from social mills, but other good agricultural practices and awareness of social problems should be improved, while the social participation evaluation showed a weak status of the political dimension.