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.

This study analyzes in a comparative way the psychological meanings that social science and basic science researchers assign to the term “research”. Using the Natural Semantic Networks technique with 127 participants from a Colombian public university, we sought to unravel the distinctive epistemological and methodological positions between these disciplines. The findings reveal that, although both groups closely associate research with knowledge, they differ in the lexical network and associated terms, reflecting their different epistemological approaches. Basic science researchers emphasize terms such as “innovation” and “experimentation,” while social science researchers lean toward “solving” and “learning.” Despite the variability in the associated words, “knowledge” remains the common core, suggesting a shared basis in the perception of research. These results show the importance of considering disciplinary differences in research training and knowledge generation. The study concludes that research contributes significantly to both the advancement of individual disciplines and social welfare, urging future research to explore these dynamics in broader contexts to enrich interdisciplinary understanding and foster cooperation in knowledge generation.

Accurate demand forecasting is key for companies to optimize inventory management and satisfy customer demand efficiently. This paper aims to Investigate on the application of generative AI models in demand forecasting. Two models were used: Long Short-Term Memory (LSTM) networks and Variational Autoencoder (VAE), and results were compared to select the optimal model in terms of performance and forecasting accuracy. The difference of actual and predicted demand values also ascertain LSTM’s ability to identify latent features and basic trends in the data. Further, some of the research works were focused on computational efficiency and scalability of the proposed methods for providing the guidelines to the companies for the implementation of the complicated techniques in demand forecasting. Based on these results, LSTM networks have a promising application in enhancing the demand forecasting and consequently helpful for the decision-making process regarding inventory control and other resource allocation.