5G technology is transforming healthcare by enhancing precision, efficiency, and connectivity in diagnostics, treatments, and remote monitoring. Its integration with AI and IoT is set to revolutionize healthcare standards. This study aims to establish the state of the art in research on 5G technology and its impact on healthcare innovation. A systematic review of 79 papers from digital libraries such as IEEE Xplore, Scopus, Springer, ScienceDirect, and ResearchGate was conducted, covering publications from 2018 to 2024. Among the reviewed papers, China and India emerge as leaders in 5G health-related publications. Scopus, Springer Link, and IEEE Xplore house the majority of first-quartile (Q1) papers, whereas Science Direct and other sources show a higher proportion in the second quartile (Q2) and lower rankings. The predominance of Q1 papers in Scopus, Springer Link, and IEEE Xplore underscores these platforms’ influence and recognition, reflecting significant advancements in both practice and theory, and highlighting the expanding application of 5G technology in healthcare.

Background: Various studies have demonstrated the usefulness of Google search data for public health-monitoring systems. The aim of this study is to be estimated interest of public in infectious diseases in infectious diseases in South Korea, the five other countries. Methods: We conducted cross-country comparisons for queries related to the H1N1 virus and Middle East respiratory syndrome coronavirus (MERS-CoV). We analyzed queries related to the novel coronavirus disease (COVID-19) from 20 January to 13 April 2020, and performed time-descriptive and correlation analyses on trend patterns. Results: Trends in H1N1, MERS-CoV, and COVID-19 queries in South Korea matched those in the five other countries and worldwide. The relative search volume (RSV) for the MERS-CoV virus increased as the cumulative number of confirmed cases in South Korea increased and decreased significantly as the number of confirmed cases decreased. The volume of COVID-19 queries dramatically increased as South Korea’s confirmed COVID-19 cases grew significantly at the community level. However, RSV remained stable over time. Conclusions: Google Trends provides real-time data based on search patterns related to infectious diseases, allowing for continuous monitoring of public reactions, disease spread, and changes in perceptions or concerns. We can use this information to adjust their strategies of the prevention of epidemics or provide timely updates to the public.

Energy systems face serious difficulties due to economic policy uncertainty, which affects consumption trends and makes the shift to sustainability more difficult. While adjusting for economic growth and carbon emissions, this study examines the dynamic relationship between economic policy uncertainty and energy consumption (including renewable and nonrenewable) in China from 1985Q1 to 2023Q4. The research reveals the frequency-specific and time-varying relationships between these variables by employing sophisticated techniques such as Wavelet Cross-Quantile Correlation (WCQC) and Partial WCQC (PWCQC). Economic policy uncertainty and energy consumption do not significantly correlate in the short term; however, over the long term, economic policy uncertainty positively correlates with renewable energy consumption at medium-to-upper quantiles, indicating that it may play a role in encouraging investments in sustainable energy. On the other hand, EPU has a negative correlation with nonrenewable energy usage at lower quantiles, indicating a slow move away from fossil fuels. These results are confirmed by robustness testing with Spearman-based WCQC techniques. The study ends with policy recommendations to maximize economic policy uncertainty’s long-term impacts on renewable energy, reduce dependency on fossil fuels, and attain environmental and energy sustainability in China.

The major goal of decisions made by a business organization is to enhance business performance. These days, owners, managers and other stakeholders are seeking for opportunities of modelling and automating decisions by analysing the most recent data with the help of artificial intelligence (AI). This study outlines a simple theoretical model framework using internal and external information on current and potential clients and performing calculations followed by immediate updating of contracting probabilities after each sales attempt. This can help increase sales efficiency, revenues, and profits in an easily programmable way and serve as a basis for focusing on the most promising deals customising personal offers of best-selling products for each potential client. The search for new customers is supported by the continuous and systematic collection and analysis of external and internal statistical data, organising them into a unified database, and using a decision support model based on it. As an illustration, the paper presents a fictitious model setup and simulations for an insurance company considering different regions, age groups and genders of clients when analysing probabilities of contracting, average sales and profits per contract. The elements of the model, however, can be generalised or adjusted to any sector. Results show that dynamic targeting strategies based on model calculations and most current information outperform static or non-targeted actions. The process from data to decision-making to improve business performance and the decision itself can be easily algorithmised. The feedback of the results into the model carries the potential for automated self-learning and self-correction. The proposed framework can serve as a basis for a self-sustaining artificial business intelligence system.

The study examines the economic and social impacts of a Southeast Asian multinational company operating in the northwestern region of Hungary, with a particular focus on the local labor market and community responses. The research aims to explore the company’s location choice motivations, its integration process into the local economy, and its cooperation with the local government and communities. The research provides a comprehensive picture of the company’s impacts by employing qualitative and quantitative methodologies—including management interviews and household surveys. The findings indicate that the company has significantly increased employment, enhanced infrastructure, and promoted cultural diversity. However, challenges related to cultural integration persist. The study offers valuable guidance for policymakers and businesses on leveraging the economic benefits of foreign investments and fostering cultural cooperation. Future research could delve deeper into the long-term socio-economic impacts.

Molan, an intangible cultural heritage of the Zhuang nationality in China, faces a crisis due to traditional communication and inheritance models. In the digital era, leveraging advanced digital technology is crucial for revitalizing this ancient heritage. From a communication theory perspective, this paper uses field investigation and applies the classic 5W communication model by Lasswell to deeply analyze the crisis facing Molan culture. Integrating the media evolution theory of Levinson, it explores the benefits and methodologies of digital dissemination for ancient intangible cultural heritage and proposes a digital communication model. The paper emphasizes adopting the PGC (Professional Generated Content) + UGC (User Generated Content) production model and strictly adhering to the “Content is King” principle. It advocates for models such as “Social Media + Molan,” “Short Video + Molan,” and “Algorithm + Molan” to enhance communication effectiveness. These viewpoints aim to revitalize and preserve Molan culture in the digital age.

Urban mobility in Grand Lomé is affected by several negative externalities, including road congestion, insecurity and environmental pollution. Traffic jams cause considerable economic losses, estimated at more than 13,000 CFA francs per month for some public officials, and represent a financial drain of several million CFA francs per day on the Togolese economy. These challenges are accentuated by rapid urbanization and a dizzying increase in the number of vehicles, especially motorcycle taxis. These factors not only cause economic losses, but also to the deterioration of the quality of life of the inhabitants. On average, motorists lose up to 49.5 min per day in traffic jams, with fuel and time costs estimated at hundreds of thousands of CFA francs per year for each user of the main boulevards. Through an in-depth analysis of the impacts of these negative externalities on mobility and sustainable development, this study reveals that traffic congestion, combined with the lack of road infrastructure, generates considerable economic and environmental costs. These traffic jams also worsen air pollution, making the transport sector responsible for 80% of greenhouse gas emissions. These proposed solutions include: 1) The modernization of road infrastructure, culminating in the construction of new lanes entirely dedicated to public and non-motorized transport. 2) The regulation of motorcycle taxis, inspired by regional examples, to improve safety and efficiency. 3) The introduction of rapid transit systems, such as Bus Rapid Transit (BRT), to make travel more fluid. 4) The implementation of strict environmental standards and regular technical controls to reduce greenhouse gas emissions. These proposals aim to reduce social and economic costs, while promoting sustainable mobility and a better quality of life for residents.

Mediating role of artificial intelligence in the relationship between higher education quality and scientific research ethics among faculty members: A Study in carrying out the study, specific research objectives were derived, and based on the derived objectives, null hypotheses were formulated and tested for the study. This study, thus, employed survey research design. This study’s population comprised postgraduate students from Middle Eastern University, Jordan, with 1200 students. Using the population, a sample size of 291 respondents was selected based on Krecie and Morgan The students in the sample completed Google Forms questionnaires. The data were statistically processed, and the analysis’s most significant level was 0.25. The research questions were analyzed using descriptive statistics, and the null hypothesis was tested using Pearson Product Moment Correlational Analysis (PPMC). Also, the study showed a significant relationship between artificial intelligence and the quality of higher education and the relationship of significance between artificial intelligence and ethics in scientific research. The researcher suggested a need for ongoing education, cross-discipline cooperation, and the development of solid ethical frameworks for the integration ethics of AI academia.