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A novel MADM model integrating hybrid information for evaluating the development prospects of urban new energy vehicles.
PLoS One
January 2025
Business School, University of Shanghai for Science and Technology, Shanghai, China.
As an effective approach to mitigating urban environmental issues, New Energy Vehicles (NEVs) have become a focal point of research regarding their current development status and future prospects in China. Addressing the significant disparities in the development of the NEVs industry across different cities, this study focuses on ten typical Chinese cities and develops a novel multi-attribute decision-making (MADM) framework to evaluate the prospects of NEVs promotion in these cities. The study first establishes a comprehensive indicator system that covers key dimensions such as economy, policy support, infrastructure, technological innovation, and environment, encompassing five different types of evaluation information.
View Article and Find Full Text PDFZn(TFSI)-Mediated Ring-Opening Polymerization for Electrolyte Engineering Toward Stable Aqueous Zinc Metal Batteries.
Nanomicro Lett
January 2025
Department of Chemistry and Shanghai Key Laboratory of Molecular Catalysis and Innovative Materials, Institute of New Energy, iChEM (Collaborative Innovation Center of Chemistry for Energy Materials), Fudan University, Shanghai, 200433, People's Republic of China.
Practical Zn metal batteries have been hindered by several challenges, including Zn dendrite growth, undesirable side reactions, and unstable electrode/electrolyte interface. These issues are particularly more serious in low-concentration electrolytes. Herein, we design a Zn salt-mediated electrolyte with in situ ring-opening polymerization of the small molecule organic solvent.
View Article and Find Full Text PDFRole of artificial intelligence in predicting disease-related malnutrition - A narrative review.
Nutr Hosp
January 2025
Centro de Investigación de Endocrinología y Nutrición. Universidad de Valladolid.
Background: disease-related malnutrition (DRM) affects 30-50 % of hospitalized patients and is often underdiagnosed, increasing risks of complications and healthcare costs. Traditional DRM detection has relied on manual methods that lack accuracy and efficiency.
Objective: this narrative review explores how artificial intelligence (AI), specifically machine learning (ML) and deep learning (DL), can transform the prediction and management of DRM in clinical settings.
Gesture recognition from surface electromyography signals based on the SE-DenseNet network.
Biomed Tech (Berl)
January 2025
College of Ocean, Jiangsu University of Science and Technology, Zhenjiang, China.
Objectives: In recent years, significant progress has been made in the research of gesture recognition using surface electromyography (sEMG) signals based on machine learning and deep learning techniques. The main motivation for sEMG gesture recognition research is to provide more natural, convenient, and personalized human-computer interaction, which makes research in this field have considerable application prospects in rehabilitation technology. However, the existing gesture recognition algorithms still need to be further improved in terms of global feature capture, model computational complexity, and generalizability.
View Article and Find Full Text PDFValvular heart disease and heart-related disease: a bibliometric and visual analysis from 2000 to 2023.
Acta Cardiol
January 2025
Department of Cardiology, China Aerospace Science & Industry Corporation 731 Hospital, Beijing, China.
Background: Previous studies have shown that valvular heart disease (VHD) is closely related to the development of heart-related disease (HRD). However, the current research for the relationship between VHD and HRD is complex and poorly targeted. Meanwhile, these studies lack the support of bibliometric analysis results.
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