Relational database models offer a pathway for the storage, standardization, and analysis of factors influencing national sports development. While existing research delves into the factors linked with sporting success, there remains an unexplored avenue for the design of databases that seamlessly integrate quantitative analyses of these factors. This study aims to design a relational database to store and analyse quantitative sport development data by employing information technology tools. The database design was carried out in three phases: (i) exploratory study for context analysis, identification, and delimitation of the data scope; (ii) data extraction from primary sources and cataloguing; (iii) database design to allow an integrated analysis of different dimensions and production of quantitative indicators. An entity-relationship diagram and an entity-relationship model were built to organize and store information relating to sports, organizations, people, investments, venues, facilities, materials, events, and sports results, enabling the sharing of data across tables and avoiding redundancies. This strategy demonstrated potential for future knowledge advancement by including the establishment of perpetual data updates through coding and web scraping. This, in turn, empowers the continuous evaluation and vigilance of organizational performance metrics and sports development policies, aligning seamlessly with the journal’s focus on cutting-edge methodologies in the realm of digital technology.

This article focuses on studying how transportation connectivity affects Vietnam’s trade with Association of Southeast Asian Nations (ASEAN) countries. By using a gravity model, the article applies fixed effects (FE) and random effects (RE) to analyze panel data on trade, GDP, tariffs, border effects, and indicators. The number represents Vietnam’s transport connectivity with ASEAN countries from 2004 to 2021. Research results show that transport connectivity hurts Vietnam’s trade with other countries. ASEAN. The article proposes solutions for the Government and Vietnamese export enterprises to promote intra-ASEAN trade in the direction of increasing the added value of Vietnam’s imported and exported goods within ASEAN countries and balancing between Developing intra-ASEAN and foreign trade.

The privacy of personal information is aimed at protecting human rights both under the international human rights regime and the Saudi Arabian constitution and other statutes and regulations, subject only to some exceptions that include the protection of public health. The coronavirus disease 2019 (COVID-19) pandemic has brought about certain challenges that necessitate strategies to augment the conventional surveillance of infectious diseases, contact tracing, isolation, reporting and vaccination. Several governments institutions, and agencies presently adopt mobile applications for collecting, analyzing, managing, and sharing critical personal data of individuals infected with or exposed to COVID-19. While the benefits of sharing private information for achieving public health needs may not be disputed, the risk of breach of personal privacy is enormous. This had forced the national governments into a dilemma of either succumbing to public health needs, strictly respecting and protecting the privacy of individuals, or alternatively, balancing the two conflicting demands. There is a massive body of literature on the security and privacy of such mobile applications, but none has adequately explored and discussed public interest justifications under Saudi Arabian laws for alleged privacy breaches. We examined the health surveillance mobile app technologies currently in use in Saudi Arabia with the aim of determining the potential risks of data breaches under extant data protection laws. The paper recommends, among others, that any potential risk of breach to right to privacy of personal information under the law must be (justified by) the public health needs to protect society during the COVID-19 pandemic.

The well-being of society can be realized through meeting basic needs, one of which is providing public infrastructure. This study examines the role of Natural Resource Revenue Sharing Funds (DBH SDA) on government investment in infrastructure in 491 regencies/cities in Indonesia. The testing in this research uses panel data regression analysis. The results show that per capita DBH SDA in Indonesia during the study period of 2010–2012 has a significant and positive influence on government investment in infrastructure. The selection of this period is based on the consideration that a resources boom has occurred, where there is an increased global demand for natural resource commodities followed by an increase in commodity prices, thereby positively impacting revenue for countries or regions abundant in natural resources. Despite DBH SDA having a significant and positive influence, regional spending on infrastructure tends to be more influenced by central government transfers such as General Allocation Fund (DAU), Special Allocation Fund (DAK), and Local Own-source Revenue (PAD). It was found that government investment in infrastructure tends to be influenced by transfer funds, indicating that the role of the central government remains significant in determining the infrastructure expenditure of regencies/cities in Indonesia.

With the wide application of the Internet and smart systems, data centers (DCs) have become a hot spot of global concern. The energy saving for data centers is at the core of the related works. The thermal performance of a data center directly affects its total energy consumption, as cooling consumption accounts for nearly 50% of total energy consumption. Superior power distribution is a reliable method to improve the thermal performance of DCs. Therefore, analyzing the effects of different power distribution on thermal performance is a challenge for DCs. This paper analyzes the thermal performance numerically and experimentally in DCs with different power distribution. First, it uses Fluent simulate the temperature distribution and flow field distribution in the room, taking the cloud computing room as the research object. Then, it summarizes a formula based on the computing power distribution in a certain range by the numerical and experimental analysis. Finally, it calculates an optimal cooling power by analyzing the cooling power distribution. The results shows that it reduces the maximum temperature difference between the highest temperature of the cabinet from 5-7k to within 1.2k. In addition, the cooling energy consumption is reduced by more than 5%.

In today’s fast-paced digital world, generative AI, especially OpenAI’s ChatGPT, has become a game-changing technology with significant effects on education. This study examines public sentiment and discourse surrounding ChatGPT’s role in higher education, as reflected on social media platform X (formerly Twitter). Employing a mixed-methods approach, we conducted a thematic analysis using Leximancer and Voyant Tools and sentiment analysis with SentiStrength on a dataset of 18,763 tweets, subsequently narrowed to 5655 through cleaning and preprocessing. Our findings identified five primary themes: Authenticity, Integrity, Creativity, Productivity, and Research. The sentiment analysis revealed that 46.6% of the tweets expressed positive sentiment, 38.5% were neutral, and 14.8% were negative. The results highlight a general openness to integrating AI in educational contexts, tempered by concerns about academic integrity and ethical considerations. This study underscores the need for ongoing dialogue and ethical frameworks to responsibly navigate AI’s incorporation into education. The insights gained provide a foundation for future research and policy-making, aiming to enhance learning outcomes while safeguarding academic values. Limitations include the focus on English-language tweets, suggesting future research should encompass a broader linguistic and platform scope to capture diverse global perspectives.