In Urban development, diversity respect is needed to prioritize and balance the urban development design for sustainable eco-city development. As a result, this research aimed to investigate the causal factor pathways of social network factors influencing sustainable eco-city development in the northeastern region of Thailand through a quantitative research approach. With the aim to survey insightful information, the analysis unit was conducted at the individual level with three hundred and eighty-three (383) samplings in Khon Kaen and Udon Thani provinces, including univariate analysis and multivariate analysis, using path analysis and multiple linear regression. The study results indicated that two pathways of social network factors influencing sustainable eco-city development were indirect influence factors. The indirect influence factor consists of information exchange, benefits exchange in the network, and members’ role in the social network. Additionally, the study revealed that the pathway has influences through social network types and the economic and social dimensions of sustainable cities (R² = 0.330). Therefore, this study concluded that sustainable eco-city development should be implemented through community networks and economic and social network development for environmental development through social network types.

The transition to sustainable agricultural practices is critical in the face of escalating climate challenges. Despite significant advances, the integration of green technologies within agribusiness remains underexplored. This study undertakes a comprehensive bibliometric analysis, utilizing data from the Web of Science Core Collection (1990–2023), to elucidate the integration of green technologies within agribusiness strategies. The research highlights key trends, influential authors, prominent journals, and significant thematic clusters, including biogas, biochar, biotech remediation, sustainable agriculture transition, low-carbon agriculture, and green strategies. By employing R, Bibliometrix, and VOSviewer, the study provides a nuanced understanding of the research landscape, emphasizing the critical role of strategic planning, policy frameworks, technological innovation, and interdisciplinary approaches in promoting sustainable agricultural development. The findings underscore the growing scholarly interest in sustainable practices, driven by global initiatives such as the UN’s 2030 Agenda and the Paris Agreement. This study contributes to the literature by offering qualitative insights and policy implications, highlighting the necessity for a holistic integration of green technologies to enhance the environmental and economic viability of agribusinesses.

This study evaluates the health and sustainability of higher education systems in nine countries: the USA, UK, Australia, Germany, Canada, China, Brazil, India, and South Africa. Using a multi-level analysis model and principal component analysis (PCA), nine key factors—such as international student numbers, academic levels, and graduate employment rates—were identified, capturing over 90% of the cumulative impact on higher education systems. India, scoring 6.2036 initially, shows significant room for improvement. The study proposes policies to increase graduate employment, promote international faculty collaboration, and enhance India’s educational expenditure, which surpasses 9.8% of GDP. Post-policy simulations suggest India’s score could rise to 8.7432. The paper also addresses the impact of COVID-19 on global education, recommending a hybrid model and increased graduate enrollment in China to reduce unemployment by 5.4%. The research aims to guide sustainable development in higher education globally.

Young people are a traditional risk group for radicalization and involvement in protest and extremist activities. The relevance of this topic is due to the growing threat of youth radicalization, the expansion of the activities of extremist organizations, and the need to organize high-quality preventive work in educational organizations at various levels. The article provides an overview of research on the topic under consideration and also presents the results of a series of surveys in general educational institutions and organizations of secondary vocational education (n = 11,052), universities (n = 3966) located in the Arctic zone of the Russian Federation. The results of the study on aspects of students’ ideas about extremism are presented in terms of assessing their own knowledge about extremism, the presence/absence of radically minded people around them, determining the degree of threat from the activities of extremist groups for themselves and their social environment, and identifying approaches to preventing the growth of extremism in society. Conclusions are drawn about the need to improve preventive work models in educational organizations towards a targeted (group) approach.

Credit policies for clean and renewable energy businesses play a crucial role in supporting carbon neutrality efforts to combat climate change. Clustering the credit capacity of these companies to prioritize lending is essential given the limited capital available. Support Vector Machine (SVM) and Artificial Neural Network (ANN) are two robust machine learning algorithms for addressing complex clustering problems. Additionally, hyperparameter selection within these models is effectively enhanced through the support of a robust heuristic optimization algorithm, Particle Swarm Optimization (PSO). To leverage the strength of these advanced machine learning techniques, this paper aims to develop SVM and ANN models, optimized with the PSO, for the clustering problem of green credit capacity in the renewable energy industry. The results show low Mean Square Error (MSE) values for both models, indicating high clustering accuracy. The credit capabilities of wind energy, clean fuel, and biomass pellet companies are illustrated in quadrant charts, providing stakeholders with a clear view to adjust their credit strategies. This helps ensure the efficient operation of banking green credit policies.

This paper explores the integration of Large Language Models (LLMs) and Software-Defined Resources (SDR) as innovative tools for enhancing cloud computing education in university curricula. The study emphasizes the importance of practical knowledge in cloud technologies such as Infrastructure as a Service (IaaS), Platform as a Service (PaaS), Software as a Service (SaaS), DevOps, and cloud-native environments. It introduces Lean principles to optimize the teaching framework, promoting efficiency and effectiveness in learning. By examining a comprehensive educational reform project, the research demonstrates that incorporating SDR and LLMs can significantly enhance student engagement and learning outcomes, while also providing essential hands-on skills required in today’s dynamic cloud computing landscape. A key innovation of this study is the development and application of the Entropy-Based Diversity Efficiency Analysis (EDEA) framework, a novel method to measure and optimize the diversity and efficiency of educational content. The EDEA analysis yielded surprising results, showing that applying SDR (i.e., using cloud technologies) and LLMs can each improve a course’s Diversity Efficiency Index (DEI) by approximately one-fifth. The integrated approach presented in this paper provides a structured tool for continuous improvement in education and demonstrates the potential for modernizing educational strategies to better align with the evolving needs of the cloud computing industry.