The rapid spread of new coronaviruses throughout China and the world in 2019–2020 has had a great impact on China’s economic and social development. As the backbone of Chinese society, Chinese universities have made significant contributions to emergency risk management. Such contributions have been made primarily in the following areas: alumni resource collection, medical rescue and emergency management, mental health maintenance, control of staff mobility, and innovation in online education models. Through the support of these methods, Chinese universities have played a positive role in the prevention and control of the epidemic situation. However, they also face the problems of alumni’s economic development difficulties, the risk of deadly infection to medical rescue teams and health workers, infection of teachers and students, and the unsatisfactory application of information technology in resolving the crisis. In response to these risks and emergency problems, we propose some corresponding solutions for public dissemination, including issues related to medical security, emergency research, professional assistance, positive communication, and hierarchical information-based teaching.

The ongoing outbreak of the novel coronavirus disease (COVID-19) that occurred in China is rapidly spreading globally. China’s bond and strict containment measures have been proved (in practice) to significantly reduce the spread of the epidemic. This was obtained through the use of emergency control measures in the epidemic areas and the integration of resources from multiple systems, including business, community, technology, education, and transportation, across the country. In order to better understand how China has managed to reduce the public health and economic impacts of the COVID-19 epidemic, this editorial systematically reviews the specific measures for infection prevention and control of the disease. The best practices for COVID-19 eradication in China provide evidence-based strategies that could be replicated in other countries.

In this study, activated carbon (AC) from coconut shell, as a widely available agricultural waste, was synthesised in a simple one-step procedure and used to produce a magnetic Fe₃O₄/AC/TiO₂ nano-catalyst for the degradation of methylene blue (MB) dye under UV light. Scanning electron microscopy revealed that TiO₂ nanoparticles, with an average particle size of 45 to 62 nm, covered the surface of the AC porous structure without a reunion of its structure, which according to the TGA results enhanced the stability of the photocatalyst at high temperatures. The photocatalytic activities of synthesised AC, commercial TiO₂, Fe₃O₄/AC, and Fe₃O₄/AC/TiO₂ were compared, with Fe₃O₄/AC/TiO₂ (1:2) exhibiting the highest catalytic activity (98%). Furthermore, evaluation of the recovery and reusability of the photocatalysts after treatment revealed that seven treatment cycles were possible without a significant reduction in the removal efficiency.

This study investigates the factors influencing undergraduate students’ self-directed learning (SDL) abilities in generative Artificial Intelligence (AI)-driven interactive learning environments. The advent of generative AI has revolutionized interactive learning environments, offering unprecedented opportunities for personalized and adaptive education. Generative AI supports teachers in delivering smart education, enhancing students’ acceptance of technology, and providing personalized, adaptive learning experiences. Nevertheless, the application of generative AI in higher education is underexplored. This study explores how these AI-driven platforms impact undergraduate students’ self-directed learning (SDL) abilities, focusing on the key factors of teacher support, learning strategies, and technology acceptance. Through a quantitative approach involving surveys of 306 undergraduates, we identified the key factors of motivation, technological familiarity, and the quality of AI interaction. The findings reveal the mediating roles of self-efficacy and learning motivation. Also, the findings confirmed that improvements in teacher support and learning strategies within generative AI-enhanced learning environments contribute to increasing students’ self-efficacy, technology acceptance, and learning motivation. This study contributes to uncovering the influencing factors that can inform the design of more effective educational technologies and strategies to enhance student autonomy and learning outcomes. Our theoretical model and research findings deepen the understanding of applying generative AI in higher education while offering important research contributions and managerial implications.