How the integration of technology and finance empowers the collaborative development of digitalization and greenization of Chinese cities

Digital transformation as a catalyst for green innovation: An examination of SMEs from developing economies

Factors influencing farmer's adaptation potential towards post COVID-19 impacts: A systematic literature review.

From complexity to strength: How digital transformation and innovation enhances agricultural supply chain resilience?

Using 15 years of hospital survey data (2008-2023), we examined rural-urban trends in electronic health records (EHR) adoption and advanced capabilities for data sharing, public health reporting, and care improvements. We conducted a repeated cross-sectional study using the American Hospital Association Annual Survey Information Technology Supplement (2008-2018, 2020-2023). We constructed hospital-year measures of basic and comprehensive EHR capability, certified EHR technology (CEHRT) adoption, and advanced functions in patient engagement, interoperability, public health reporting, and social determinants of health (SDOH). We compared rural and urban adoption using descriptive analyses, Cochran-Armitage trend tests, and logistic regression with clustered standard errors, adjusted for bed size, ownership, and census division. The cohort included 41838 hospital-years (45% rural). Comprehensive capability rose from 2% in 2008 to 75% in 2020, and CEHRT was nearly universal by 2023. Rural hospitals lagged urban hospitals in adopting basic and comprehensive EHRs, with the gap peaking at 25% in 2016. By 2021-2023, most hospitals reported patient portals, interoperability, public health reporting, and SDOH capabilities. Rural hospitals remained 4%-15% behind urban peers in advanced functions, particularly interoperability, public health reporting, and SDOH data collection and use. Persistent rural-urban gaps in advanced EHR use may limit interoperability, data-driven care, and public health reporting in rural settings. EHR adoption is widespread, but substantial rural-urban gaps persist in advanced EHR use. Support for advanced capabilities that enable data sharing, reporting, and data-driven improvement is needed to ensure that rural hospitals and communities benefit from digital transformation beyond adoption.

Driving digital innovation through quality incentives: Evidence from Chinese cities in the digital transformation era

Decision-making and analysis on digital transformation of battery manufacturing enterprises under heterogeneous subsidies of governments in a complex network environment

Undergraduate nursing students' perceptions of using holopatient in learning and clinical training: An exploratory-descriptive qualitative study.

Measuring digital health competence among healthcare professionals: A rapid review of assessment tools.

Embodied learning-driven hospital simulation management training digital textbook

Impact of new energy demonstration city policy on digital transformation of enterprises in China

Digital literacy and scientific research ability among nursing postgraduates: The chain-mediating role of deep learning and research self-efficacy.

How does digital transformation impact the transfer of enterprise scientific, knowledge, and technological achievements?

Can you hear me? An exploratory study on the technological barriers experienced by Brazilian teleworkers.

Against the dual background of digital transformation and the development of New Medicine, traditional paper-based experiment reports increasingly show inherent limitations in efficiency, interactivity, archiving, and resource sharing. This study designed and developed an online electronic experiment report submission system tailored for medical universities, and further explored the application of artificial intelligence (AI) technology in automated grading and plagiarism detection. The system was built on the ThinkPHP 5.0 framework and MySQL database, with three role-based portals for administrators, teachers, and students. To meet the specific requirements of medical experiment teaching, core functions including copy-paste restriction, modular grading, a reusable comment library, one-click score aggregation, and batch PDF export were implemented. Furthermore, this study proposed a multi-algorithm similarity-based plagiarism detection scheme and an AI auto-grading concept based on a dual-layer evaluation model, providing a feasible path for the intelligent upgrade of experiment teaching. The application of this system is expected to effectively alleviate problems such as heavy grading workload, delayed feedback, and physical archiving difficulties, thereby improving the management efficiency and teaching quality of medical experiment education.

Advancing Digital Transformation through Virtual Project Management: An Evidence-Based Implementation Framework in the Built Environment Industry

ABSTRACT The rapid digital transformation in the fashion industry has increased the demand for graduates with industry-relevant digital fashion skills. This study aims to develop a data-driven Digital Fashion Skills Framework for Fashion Education, using Principal Component Analysis (PCA), Artificial Neural Networks (ANN), and cluster analysis. When PCA was applied, it was evident that Digital Design Skills, CAD Skills, and VR Proficiency were the most influential, with the first two components accounting for 30.2% of the total variance. Furthermore, ANN also demonstrated its relevance in predicting digital skills readiness. Then, cluster analysis separated students into three learner profiles (low, medium, and high skill groups), indicating different levels of readiness for the digital fashion workflow. These findings provide evidence that digital fashion skills development is not homogeneous and requires different learning pathways. The proposed framework offers practical guidance for curriculum development, digital learning design, and workforce preparation in contemporary fashion education.

Corporate digital transformation and M&A completion: Evidence from China

Emerging health leaders are no longer tasked with only clinical, financial, and workforce management, they must foster authentic human connection, steer a digital transformation, and steward sustainability into the future. This article explores the intersection of authentic leadership, digital fluency, and ecosystem thinking as the core competency triad for the next generation of health leaders. It offers fresh perspectives on what it will take to lead with heart, technical agility, and global conscientiousness-anchored in health quality-into an increasingly complex future.

Despite the digital transformation of society, handwritten documents continue to be collected in various investigations, such as fraud investigations and drug trafficking. Recent research highlighted that handwriting offers not only comparative value (i.e. helping address a source question), but it also has the potential to infer a writer's background profile (i.e. helping address other questions than source). This study compared binary logistic regression (BLR) and classification & regression tree (CRT) models to infer a writer's cultural background based on handwriting features. An experimental two-step modelling approach was employed to distinguish Australian, Korean, and Vietnamese writers (N = 196) using categorical handwriting features coded from scanned handwritten texts. The first step was classifying Australian from non-Australian writers, and the second step was further specifically classifying non-Australians into Korean and Vietnamese. The results demonstrated how a two-step modelling framework could be operationalised for early-stage writer classification and highlighted its practical strengths and limitations. The BLR model provided statistical depths for detailed interpretation, and it achieved higher classification accuracy, 93.4% and 97.8% in each step. The CRT model also achieved a high accuracy rate, but lower than BLR with 86.7% and 94.2%. Furthermore, blind test results reflected the practical challenges and strengths of each model. The CRT model correctly classified six out of seven blind specimens while the BLR correctly classified three out of seven blind specimens. Each model presented distinct strengths as the BLR model provided rich detailed statistical outputs, such as odds ratios and significance levels, while the CRT model offered greater accessibility and usability for non-statistical experts. These findings suggest that model selection should balance interpretability, robustness and accuracy. Although more work is required until such models can be applied in practice, this study highlights the potential to extract operational insights from handwriting beyond traditional comparison methods, supporting intelligence-led workflows even when no comparison material is available.