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In this study, based on the data of the Chinese listed firms, the effect of digital transformation on capital mismatch was examined. And the potential mechanism was also further discussed. It was found that digital transformation can significantly suppress capital mismatch, especially for non-state-owned enterprises, mature enterprises, and regions with high marketization and financial technology level. In addition, management capability and information environment are potential influencing mechanisms of digital transformation to suppress capital mismatch. These findings have important implications for revealing the relationship between enterprise digital transformation and capital mismatch, provides new ideas for improving the efficiency of capital allocation, and also provides important insights for enterprises to accelerate digital transformation and promote the high-quality development of enterprises.
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11790084 | PMC |
http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0313674 | PLOS |
Int J Med Inform
September 2025
School of Psychology & Public Health, La Trobe University, Melbourne, Victoria, Australia.
Background: As healthcare systems increasingly embrace digital transformation, the need for a specialised digital health workforce, distinct from general clinical or IT roles, has become paramount. This study offers a national review of digital health education (DHE) offerings in Australian universities, with a focus on how current curricula support the development of advanced, workforce-ready skills in areas such as health informatics, data analytics, digital implementation, and leadership.
Methods: A systematic web-based review was conducted across all 42 Australian universities, drawing on publicly available resources including official handbooks, course catalogues, and subject guides.
Pestic Biochem Physiol
November 2025
National Engineering Laboratory for Efficient Utilization of Soil and Fertilizer Resources, Key Laboratory of Agricultural Environment in Universities of Shandong, College of Resources and Environment, Shandong Agricultural University, 61 Daizong Road, Tai'an 271018, PR China. Electronic address: wj
Difenoconazole (DFC) is a commonly used triazole fungicide known for its high efficiency and environmental persistence. A thorough understanding of its environmental behavior, particularly sorption in soil, is critical to obtain a comprehensive assessment of the ecological risk of DFC. In this study, three soils with distinct physicochemical properties (brown soil, cinnamon soil, and fluvo-aquic soil) were used to elucidate the adsorption mechanisms of DFC on soil.
View Article and Find Full Text PDFMicrobes Infect
September 2025
Institute of Medical Microbiology, University of Zurich, Zurich, Switzerland; ESCMID study group on Molecular Diagnostics and Genomics. Electronic address:
Rapid advancements in artificial intelligence (AI) and machine learning (ML) offer significant potential to transform medical microbiology diagnostics, improving pathogen identification, antimicrobial susceptibility prediction and outbreak detection. To address these opportunities and challenges, the ESCMID workshop, "Artificial Intelligence and Machine Learning in Medical Microbiology Diagnostics", was held in Zurich, Switzerland, from June 2-5, 2025. The course featured expert lectures, practical sessions and panel discussions covering foundational ML concepts and deep learning architectures, data interoperability, quality control processes, model development and validation strategies.
View Article and Find Full Text PDFISA Trans
August 2025
Key Laboratory of Knowledge Automation for Industrial Processes of Ministry of Education, University of Science and Technology Beijing, Beijing, 100083, PR China; School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing, 100083, PR China. Electronic addr
With the deep digital transformation of traditional manufacturing industry and the continuous automation level improvement of production lines, it is more important to predict the Key Performance Indicators (KPIs) of processes in a timely and accurate manner. The traditional laboratory destructive test method for obtaining KPIs consumes a large amount of time and incurs high costs, which not only fails to provide timely and effective guidance for production processes but also results in significant losses for manufacturing enterprises. To address these issues, an online prediction soft sensor model for KPIs based on a serial-parallel gated recurrent unit with self-attention mechanism (SPGRU-SA) soft sensor model is proposed.
View Article and Find Full Text PDFHealth Serv Res
September 2025
Division of Clinical Informatics and Digital Transformation, Director, Center for Clinical Informatics and Improvement Research, University of California - San Francisco, San Francisco, CA, San Francisco, California, USA.
Objective: To analyze national rates of team-based ordering and evaluate changes in key outcomes following adoption.
Study Setting And Design: We conducted an observational pre-post intervention-comparison study of 249,463 ambulatory physicians across 401 organizations using the Epic EHR. Our intervention was the adoption of team-based ordering, measured as the proportion of orders involving team support.