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.

Accurate drug-drug interaction (DDI) prediction is essential to prevent adverse effects, especially with the increased use of multiple medications during the COVID-19 pandemic. Traditional machine learning methods often miss the complex relationships necessary for effective DDI prediction. This study introduces a deep learning-based classification framework to assess adverse effects from interactions between Fluvoxamine and Curcumin. Our model integrates a wide range of drug-related data (e.g., molecular structures, targets, side effects) and synthesizes them into high-level features through a specialized deep neural network (DNN). This approach significantly outperforms traditional classifiers in accuracy, precision, recall, and F1-score. Additionally, our framework enables real-time DDI monitoring, which is particularly valuable in COVID-19 patient care. The model’s success in accurately predicting adverse effects demonstrates the potential of deep learning to enhance drug safety and support personalized medicine, paving the way for safer, data-driven treatment strategies.

Presently, there exists a burgeoning trend of female entrepreneurs worldwide, notably within the realm of small and medium-sized enterprises (SMEs), many of which manifest as family-run enterprises. The systematic literature review endeavors to construct an integrative framework concerning the practical ramifications of female involvement in family businesses by amalgamating extant global studies. The findings elucidate the practical implications inherent in female participation across global family businesses, concurrently furnishing a reservoir of prospects for prospective investigations. The deduction posits the imperative eradication of gender disparities, cognizant that gender parity underpins economic and financial advancement and is contingent upon female involvement. Furthermore, familial enterprises are urged to acknowledge and integrate women’s contributions in entrepreneurial decision-making processes.

Distributed biomass energy technology has strong adaptability to the types of raw materials, flexible project scale, can meet the needs of special users, better economy in small scale, easier commercial development, in line with the characteristics of biomass resources and China’s national conditions. The distributed utilization of biomass energy mainly includes biomass briquette fuel and biogas. The key technologies include biomass briquette fuel processing and combustion, large and medium-sized biogas engineering technology, biomass gasification pyrolysis and gas utilization. At present, China’s distributed biomass energy technology is mainly in the stage of technological improvement and application demonstration. It is expected that by 2030, most of the key technologies will be basically mature and have the conditions for industrialization. The main development direction of China’s distributed biomass energy industry is the replacement of traditional coal-fired gas, urban/rural clean living energy supply, and rural ecological environmental protection. The pollution caused by burning coal/fuel oil, and at the same time centering on the national new urbanization strategy, provide sustainable clean energy for the construction of new rural areas, and improve the level of rural ecological and environmental protection. At present, the main bottleneck restricting the development of distributed biomass energy industry is economy and reliability. The state should increase investment in technological innovation and policy support, convert the environmental and social benefits of biomass energy into cost benefits, and promote biomass energy. The development of the industry can be distributed and utilized.