This paper focuses on the reliability and radiation effects of wide bandgap SiC MOSFET power devices. Based on the failure issues of SiC MOSFET power devices used in space solar power stations, the paper investigates the feasibility of applying SiC MOSFET power devices in space solar power stations. By examining the failure mechanisms of wide bandgap SiC MOSFET power devices, the paper proposes reinforcement methods for radiation resistance and high reliability from both device and circuit application perspectives, providing feasible solutions for the use of these devices in space solar power stations.

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This research examines energy-efficient solutions in industrial manufacturing through a Multi-Criteria Decision Analysis (MCDA) approach to discover optimal methods for improving energy consumption alongside cost-efficiency and environmental sustainability. The production activities within the industrial sector including chemical processing, metalworking and food manufacturing make up substantial portions of world energy usage. This study examines the role of advanced technologies including heat recovery systems and high-efficiency furnaces together with energy-efficient refrigeration systems in reducing energy consumption within industrial sectors. This research employs the MCDA framework which combines the Analytic Hierarchy Process (AHP), Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), and Simple Additive Weighting (SAW) methods. The research examines how energy-efficient solutions perform during a six-month period by measuring energy savings alongside cost reductions and environmental benefits. The study results reveal significant energy reductions alongside cost savings between 10% to 30% while chemical production achieved a 25% decrease in energy consumption. The observed environmental improvements that include a 30% decrease in carbon emissions from chemical production demonstrate how these technologies can advance sustainable industrial practices.

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As social media platforms have penetrated every aspect of people’s lives, many mental health problems have also arisen along with them. In today’s digital age, analyzing mental health trends through these platforms has become critical. In this study, we present a system designed to identify the mental health trends of Weibo users by extracting and analyzing the content posted by users on Weibo, China’s leading social media platform. The system is mainly composed of two parts: a data acquisition module and an analysis module. The data collection module uses the Python-based web scraping tool Scrapy to scrape comments from popular topics on Weibo. At the heart of the analysis module is a large language model fine-tuned from a psychological database. The module assesses the topic and specific content of the posts, scoring comments based on criteria such as positivity, alignment with mood disorders, and potential signs of psychoactive substance use. This data is stored and mediated using the relational database MySQL, and then analyzed and visualized using advanced data analysis tools. Through this method, we can timely and comprehensively monitor the mental health status of social media platforms, and provide a solid foundation for further academic research on public mental health.

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The zuozhen wood, a unique and precious Cudrania tricuspidataspecies native to Nantong, is an ecologically and economically versatile species. Historically, it has been used for furniture making, brewing wine, medicinal purposes, and more, playing a key role in the local ecological environment and culture of Nantong. This paper explores the potential for the ecological and social benefits of the Zuozhen and its industry under the new era context, as well as some current issues in its development, and offers suggestions for further development.

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