Bibliometric analysis is a commonly used tool to assess scientific collaborations within the researchers, community, institution, regions and countries. The analysis of publication records can provide a wealth of information about scientific collaboration, including the number of publications, the impact of the publications, and the areas of research where collaborations are most common. By providing detailed information on the patterns and trends in scientific collaboration, these tools can help to inform policy decisions and promote the development of effective strategies to support and enhance scientific collaborations between countries. This study aimed to analyze and visualize the scientific collaboration between Japan and Russia, using bibliometric analysis of collaborative publications from the Web of Science (WoS) database. The analysis utilized the bibliometrix package within the R statistical program. The analysis covered a period of two decades, from 2000 to 2021. The results showed a slight decrease in co-authored publications, with an annual growth rate of −1.26%. The keywords and thematic trends analysis confirmed that physics is the most co-authored field between the two countries. The study also analyzed the collaboration network and research funding sources. Overall, the study provides valuable insights into the current state of scientific collaboration between Japan and Russia. The study also highlights the importance of research funding sources in promoting and sustaining scientific cooperation between countries. The analysis suggests that more efforts in government funding are needed to increase collaboration between the two countries in various fields.

This paper analyzes the characteristics and influence mechanisms of financial support for China’s strategic emerging industries. Using a sample of 356 listed companies across nine major industries, we conduct an in-depth analysis of the efficiency of financial support and its influencing factors. In addition, this paper analyzes the influence mechanism of financial support for strategic emerging industries based on the relevant theory of financial support for industry development. It clarifies the internal and external influencing factors. Based on the theoretical analysis, a two-stage empirical investigation was conducted: The data of 356 listed companies in strategic emerging industries from 2010 to 2022 were selected as a sample, and the data envelopment analysis (DEA) method was applied to measure efficiency. The influencing factors were then analyzed using a Tobit regression and an intermediate effects test.

The Malaysian dilemma presents a complex challenge in the wake of the COVID-19 pandemic, requiring a comprehensive statistical analysis for the formulation of a sustainable economic framework. This study delves into the multifaceted aspects of reconstructing Malaysia’s economy post-COVID-19, employing a data-driven approach to navigate the intricacies of the nation’s economic landscape. The research focuses on key statistical indicators, including GDP growth, unemployment rates, and inflation, to assess the immediate and long-term impacts of the pandemic. Additionally, it examines the effectiveness of government interventions and stimulus packages in mitigating economic downturns and fostering recovery. A comparative analysis with pre-pandemic data provides valuable insights into the extent of economic resilience and identifies sectors that require targeted support for sustained growth. Furthermore, the study explores the role of technology and digital transformation in building a resilient economy, considering the accelerated shift towards remote work and digital transactions during the pandemic. The analysis incorporates data on technological adoption rates, digital infrastructure development, and innovation ecosystems to gauge their contributions to economic sustainability. Addressing the Malaysian Dilemma also involves an examination of social and environmental dimensions. The study investigates the impact of economic policies on income distribution, social equity, and environmental sustainability, aiming to achieve sustainable economic growth. The study contributes a nuanced analysis to guide policymakers and stakeholders in constructing a sustainable post-COVID-19 economy in Malaysia.

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.

Virtual environments like the Metaverse have been gaining popularity in recent years. Live streaming has gained popularity as a favorite way to entertain among social network users, thanks to its real-time authenticity. This study will utilize the Extended Unified Theory of Acceptability and Use of Technology (UTAUT2) to examine the factors influencing the adoption of live streaming in the Metaverse, a new platform with greater immersion, among citizens in Vietnam. The research used a quantitative approach, collected data from a sample of participants through a structured questionnaire including Performance Expectancy (PEE), Effort Expectancy (EEF), Social Influence (SCI), Hedonic Motivation (HEM), and Experience (EXP). Additionally, technological Self-Efficacy (TSE) as an extended alternative is thought to influence that relationship as well. Results from the PLS-SEM technique was used to examine perception, acceptance, and adoption differences among demographic groups. Remarkably, the results show experience has a remarkable impact on the relationship between behavioral intention and the adoption use Metaverse for livestreaming. This study contributes theoretical value for investors and researchers on the entertainment and technology sectors due to the abilities of the live-streaming industry and the advanced features of metaverse in this digital world.

Over the last few decades, demographic growth combined with poorly controlled urbanization has confronted African cities with a variety of environmental protection challenges. As part of a gradual awareness-raising process, African countries have ratified conventions and adopted a series of laws to protect the environment. Since independence (1960), Gabon has adopted legal instruments to provide a better framework for environmental protection. Despite the existence of well-developed legislation, the Libreville conurbation faces difficulties in waste management. This situation contributes to the degradation of the coastal zone. This study aims to analyse stakeholders’ perceptions of environmental protection regulations in solid waste management practices along the coastline of the Libreville metropolitan area in Gabon. The methodology includes documentary research, field observations, and surveys of 300 study area participants. The results show that the degradation of the coastline is due to a lack of awareness and compliance with the laws governing environmental protection and waste management. As a result, waste disposal practices such as dumping in nature, waterways, illegal dumps, and gutters are commonplace among the population. To achieve sustainable coastal zone management, it is essential to apply regulatory texts and involve stakeholders in improving planning and the quality of the coastal environment.