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.

The article analyzes the process of formation of research universities as one of the elements of a strong innovation economy. The formation of a new university model is a global trend, successfully implemented in English-speaking countries. In Russia, the educational system is not yet ready to ensure the country’s effective competition in the innovation market. The Strategic Academic Leadership Program “Priority-2030” is designed to carry out the functional transformation of the entire infrastructure of human capital reproduction in a short period of time in Russia. The article presents an analysis of the main conditions for the development of a university with a research strategy, as well as an assessment of the implementation of this strategy by Moscow Polytechnic University. The methodological basis of the study was formed by qualitative methods: included observation and benchmarking of universities’ activities, which allowed to generalize the current global trends and best practices in the field of education. For the analysis we used the data of monitoring the activities of higher education organizations, data of official statistics, as well as data from reports and presentation materials of universities and online publications participating in the “5-100” and “Priority-2030” programs. The results of the study may be useful for researchers and practitioners engaged in the transformation of the Russian higher education system.

This study aims to evaluate the influence of population dependency ratio on the economic growth of Bangladesh, India, and Pakistan, the three members of the South Asian Association for Regional Cooperation (SAARC). The study covers the time from 1960 to 2021. It also analyses in detail how population aging and the youth dependency ratio affects the development of certain sectors, including industry, services and agriculture. This study uses panel data to determine the influence of population dependency ratios on economic growth. To estimate this effect, we use the Pooled Mean Group/Autoregressive Distributed Lag (PMG/ARDL) technique. Based on the results obtained from the ARDL analysis indicate the presence of a long-term relationship among these variables. These discoveries align with prior empirical research conducted by Lee and Shin, Mamun et al., and Rostiana and Rodesbi. Furthermore, the findings suggest that an increase in the old age population dependency ratio positively influences economic growth within these nations. The long-term relationship findings pertaining to the old and young dependency ratio and economic growth corroborate the conclusions of Bawazir et al., who proposed that the old population dependency ratio exerts a favorable impact, while the young population has an adverse effect on economic growth. Originality: This research focused on the population dependency ratio, a pivotal demographic metric that gauges the proportion of individuals relying on support (including children and the elderly) compared to those of working age. This investigation particularly explores the interconnection between the population dependency ratio and sectoral development, an essential aspect given that various sectors make distinct contributions to economic advancement. Examining how population dynamics affect sectoral development yields valuable insights into the overall economic performance of Pakistan, India, and Bangladesh.

There are a number of issues that can influence elderly life satisfaction, which can mirror their welfare. This study aims to explore the differences in elderly parents’ life satisfaction across socioeconomic characteristics and investigates how the traits of both children and parents associate with elderly parents’ life satisfaction in Thailand. This study uses individual data obtained from Thailand’s National Statistical Organization covering 2008–2015, 2018 and 2020, with a total sample size of 28,494. To investigate the association between children’s and parents’ characteristics, particularly formal education and parental life satisfaction, this study uses ordered logistic regression for the analysis. Our results show that male parents are more likely to have higher life satisfaction than their female counterparts. Parents who are employed, holding a bachelor’s degree, and living with female children are more satisfied with their life. Statistically, children’s formal education demonstrates its importance for their elderly parents’ life satisfaction. This documents the vital role of schooling in improving parental life satisfaction. Moreover, facing the challenge of entering an aging society, government agencies must take a proactive stance on creating jobs suitable for the elderly or retirees to maintain their sense of independence. The evidence of intergenerational mobility reaffirms the importance of children’s education along with their caring ability, which should be strengthened.

This paper provides a disaster resilience-based approach. For the definition of the approach, a three-step method (definition of components, analysis of the resilience pillars and definitions of resilience-based actions) has been followed. To validate the approach, an application scenario for mitigating the COVID-19 pandemic is provided in the paper. The proposed approach contributes to stimulating the co-responsibility quadruple helix of actors in the implementation of actions for disaster management. Moreover, the approach is adaptable and flexible, as it can be used to manage different kinds of disasters, adjusting or changing itself to meet specific needs.