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.

In Central and Eastern European countries, the labour shortage is becoming increasingly pronounced, posing a challenge for the economy. Labour shortages limit the potential national income as many positions remain unfilled, which could lead to a slowdown in economic growth. To address this issue, various solutions need to be explored. This research aims to analyze solutions for alleviating labour shortages, with particular emphasis on measures that encourage workforce participation. One strategy is introducing training and retraining programs that help workers develop skills and adapt to labour market demands. Another option is to promote part-time employment, which may be especially attractive to groups unable or unwilling to work full-time. Enhancing population mobility could also be crucial in addressing labour shortages, particularly in bridging regional disparities. Integrating certain inactive groups, such as retirees, homemakers, students, people with disabilities, and those with low education levels experiencing generational poverty, into the labour market could also yield significant benefits. The study employs quantitative analysis methods and includes a survey that examines citizens’ perspectives on the effectiveness of measures aimed at increasing labour market participation and their economic impact on the Slovak economy. The survey data were collected in 2023 in the region of Rožňava and its surrounding areas.

The potential of entrepreneurship to reduce poverty is closely tied to critical factors such as access to finance, training and education, networks and social capital, and supportive regulatory environments. Understanding and addressing these underlying issues through the lens of the Social Capital theory can help foster an entrepreneurial spirit in cities and mitigate poverty through business and community development. This paper explores the insights and standpoints of key stakeholders about poverty in Saint John and its impact on entrepreneurship. The study uses a quantitative method and analyzes data from surveys with stakeholders. The results show that social isolation, system inflexibility, individual issues, housing, and financial support programs are significant poverty challenges in Saint John, and these issues have implications for entrepreneurship. By integrating Social Capital Theory into policy initiatives, policymakers can enhance community resilience and empower vulnerable individuals. This application of social capital principles provides a holistic framework for designing effective poverty-reduction measures, offering transformative insights applicable not only to Saint John but also to diverse small cities. The study contributes a nuanced understanding of poverty’s impact on entrepreneurship, advocating for inclusive strategies that resonate with the social fabric of communities.

Credit policies for clean and renewable energy businesses play a crucial role in supporting carbon neutrality efforts to combat climate change. Clustering the credit capacity of these companies to prioritize lending is essential given the limited capital available. Support Vector Machine (SVM) and Artificial Neural Network (ANN) are two robust machine learning algorithms for addressing complex clustering problems. Additionally, hyperparameter selection within these models is effectively enhanced through the support of a robust heuristic optimization algorithm, Particle Swarm Optimization (PSO). To leverage the strength of these advanced machine learning techniques, this paper aims to develop SVM and ANN models, optimized with the PSO, for the clustering problem of green credit capacity in the renewable energy industry. The results show low Mean Square Error (MSE) values for both models, indicating high clustering accuracy. The credit capabilities of wind energy, clean fuel, and biomass pellet companies are illustrated in quadrant charts, providing stakeholders with a clear view to adjust their credit strategies. This helps ensure the efficient operation of banking green credit policies.

This study investigates the potential predictors of resource creation behaviours in the Shanxi merchant courtyard scenic areas based on resource dependence theory. The research was conducted in China using questionnaire survey, and data analysis employed structural equation modelling, including mediation and moderation effects. The model was tested using a sample of 376 individual managers from scenic areas. The results show that external resource integration, internal resource integration, and shared value significantly affect resource creation in scenic areas. The findings indicate that shared value plays a significant mediating role in the relationship between resource integration and resource creation, while environmental dynamism significantly moderates this relationship. This study clearly demonstrates the relationship among resource integration, shared value, and value creation in scenic areas. This research contributes to the tourism management literature by identifying gaps and offering a comprehensive perspective to understand resource creation behaviours in the tourism industry.

The Consumer Price Index (CPI) is a vital gauge of economic performance, reflecting fluctuations in the costs of goods, services, and other commodities essential to consumers. It is a cornerstone measure used to evaluate inflationary trends within an economy. In Saudi Arabia, forecasting the Consumer Price Index (CPI) relies on analyzing CPI data from 2013 to 2020, structured as an annual time series. Through rigorous analysis, the SARMA (0,1,0) (12,0,12) model emerges as the most suitable approach for estimating this dataset. Notably, this model stands out for its ability to accurately capture seasonal variations and autocorrelation patterns inherent in the CPI data. An advantageous feature of the chosen SARMA model is its self-sufficiency, eliminating the need for supplementary models to address outliers or disruptions in the data. Moreover, the residuals produced by the model adhere closely to the fundamental assumptions of least squares principles, underscoring the precision of the estimation process. The fitted SARMA model demonstrates stability, exhibiting minimal deviations from expected trends. This stability enhances its utility in estimating the average prices of goods and services, thus providing valuable insights for policymakers and stakeholders. Utilizing the SARMA (0,1,0) (12,0,12) model enables the projection of future values of the Consumer Price Index (CPI) in Saudi Arabia for the period from June 2020 to June 2021. The model forecasts a consistent upward trajectory in monthly CPI values, reflecting ongoing economic inflationary pressures. In summary, the findings underscore the efficacy of the SARMA model in predicting CPI trends in Saudi Arabia. This model is a valuable tool for policymakers, enabling informed decision-making in response to evolving economic dynamics and facilitating effective policies to address inflationary challenges.