The digital economy is booming in China and has become the world’s largest after the United States’. Since China entered the era of the digital economy, its digital technology has radiated into various fields. This study is to examine the impact of China’s digital economy on the provision efficiency of public health institutions and the mechanism of action between them. Specifically, it measures the development level of China’s digital economy, and the provision efficiency of public health institutions from 2009 to 2018. The research also explores the relationship between China’s digital economy and its provision efficiency, through the Tobit-DEA model. An analysis of the regional heterogeneity indicated that the performance of China’s digital economy in the eastern region has a significant positive effect on improving the efficiency of the public health sector. This further confirms that the digital economy has strengthened China’s ability to deal with public health crises during the COVID-19 pandemic. A further mediation effect analysis showed that China’s digital economy optimizes the efficiency of public health provision by improving governmental performance and regulatory quality. This shows that the development of the digital economy promotes the construction of digital government, and thus improves the quality of governmental supervision and governmental performance, which has a significant positive effect on the efficiency of the supply of public health services. During the COVID-19 pandemic especially, government delivery of public health services was critical in addressing public health crises. Therefore, based on the results of our empirical analysis, this study provides policy suggestions for improving the efficiency of public health service provision in the era of the digital economy.

Talent is one of the most significant factors to promote the development of sports undertakings. The present study aimed to explore the factors affecting the identification of sports talents in China's physical education curriculum. Based on the literature review, this study puts forward a model to examine the influencing factors of sports talent identification in China's physical education curriculum using structural equation modeling and uses the structural equation modeling and factor analysis method to verify the hypothesis combined with the results of 310 effective questionnaires. The article summarizes influencing factors from four aspects, namely, physical, psychological, coach, and environmental factors. On the basis of relevant literature, the hypothesis model was established by structural equation modeling. The results show that the main factors affecting the identification of sports talents in the physical education curriculum are personal physical quality performance, psychological quality, coach's knowledge, and the identification policies of schools to sports talents. The conclusion of this study can provide guidance for the reform of the physical education curriculum, the growth of sports talents, and the development of sports talents in China.

In the current discourse surrounding economic and societal growth, much emphasis has been placed on the role and impact of digitalization. Despite this trend, research exploring the ecological implications of the digital economy remains scarce. To fill this research gap, our study aimed to investigate the correlation between the digital economy and carbon emissions, specifically examining the moderating impact of environmental regulations. For empirical analysis, we utilize the CRITIC methodology to establish a thorough set of indicators that can evaluate the performance of China’s digital economy. According to our empirical results, the digital economy seems to exert a moderating influence on the levels of carbon dioxide (CO₂) emissions, and this negative impact is more pronounced in affluent and densely populated regions of China. The effectiveness of digitalization in reducing pollution can be enhanced by the enforcement of environmental regulations. This paper elucidates the potential mechanisms via which the digital economy affects carbon dioxide emissions, and constructs a framework for the mechanisms via which the digital economy affects the environment by influencing the carbon dioxide emissions, providing a new way for enterprises and governments to participate in environmental protection and expanding the content of research related to the digital economy.

In order to enhance the operational efficiency of the healthcare industry, this paper investigates a medical information diagnostic platform through the application of swarm and evolutionary algorithms. This paper begins with an analysis of the current development status of medical information diagnostic platforms based on Chat Generative Pre-trained Transformer (ChatGPT) and Internet of Things (IoT) technology. Subsequently, a comprehensive exploration of the advantages and disadvantages of swarm and evolutionary algorithms within the medical information diagnostic platform is presented. Further, the optimization of the swarm algorithm is achieved through reverse learning and Gaussian functions. The rationality and effectiveness of the proposed optimization algorithm are validated through horizontal comparative experiments. Experimental results demonstrate that the optimized model achieves favorable performance at the levels of minimum, average, and maximum algorithm fitness values. Additionally, preprocessing data in a 10 * 10 server configuration enhances the algorithm’s fitness values. The minimum fitness value obtained by the optimized algorithm is 3.56, representing a 3 % improvement compared to the minimum value without sorting. In comparative experiments on algorithm stability, the optimized algorithm exhibits the best stability, with further enhancement observed when using sorting algorithms. Therefore, this paper not only provides a new perspective for the field of medical information diagnostics but also offers effective technical support for practical applications in medical information processing.

Digital transformation in university research management is a strategic imperative for modern higher education, yet its implementation remains inconsistent across institutions due to an insufficient understanding of its catalytic elements. This study aimed to develop and validate a robust measurement scale to evaluate the driving factors behind this transition. Drawing on a mixed-methods design, the scale was constructed through qualitative interviews and then rigorously tested using a large-scale survey of 1,875 stakeholders, including administrators, research leaders, and faculty within the Chinese higher education sector. The resulting 23 items instrument identifies six critical dimensions: equipment and technical support, university leadership attitudes, digital cognition, government policy support, university-enterprise collaboration, and digital technology capabilities. Psychometric evaluation confirmed the scale's high internal consistency and strong construct validity. This study fills a gap by operationalizing the driving factors of digital transformation in university research management into a validated six-dimensional framework, thereby extending existing models to the context of academic research governance. The findings offer a diagnostic tool for administrators to identify institutional bottlenecks and provide policymakers with an evidence-based foundation for targeted interventions that foster an integrated and efficient research ecosystem.