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Patient Perspectives on Conversational Artificial Intelligence for Atrial Fibrillation Self-Management: Qualitative Analysis.
J Med Internet Res
March 2025
Westmead Applied Research Centre, Faculty of Medicine and Health, The University of Sydney, Westmead, Australia.
Background: Conversational artificial intelligence (AI) allows for engaging interactions, however, its acceptability, barriers, and enablers to support patients with atrial fibrillation (AF) are unknown.
Objective: This work stems from the Coordinating Health care with AI-supported Technology for patients with AF (CHAT-AF) trial and aims to explore patient perspectives on receiving support from a conversational AI support program.
Methods: Patients with AF recruited for a randomized controlled trial who received the intervention were approached for semistructured interviews using purposive sampling.
On-Off Magnetism of Ferromagnetic Metals via Electrochemical Driven Band Filling.
Nano Lett
March 2025
College of Physics, Weihai Innovation Research Institute, College of Materials Science and Engineering, Qingdao University, Qingdao 266071, China.
Ferromagnetic metals, distinguished by high Curie temperatures and magnetization, are crucial in voltage-controlled magnetism for potential room-temperature applications in low-power multifunctional devices. Despite numerous attempts based on various mechanisms, achieving ideal magnetic modulation in metals remains challenging. This work proposes a new mechanism to control bulk metal magnetism by modulating valence electron filling in spin-polarized bands, leveraging the Slater-Pauling rule in alloys.
View Article and Find Full Text PDFHigh-performance magnesium organic batteries using renewable biomolecule-based cathode.
Chemistry
March 2025
Chongqing University, College of Materials Science and Engineering, CHINA.
Rechargeable magnesium batteries (RMBs) exhibit significant potential in large-scale energy storage due to their features of high volumetric capacity, resistance to dendrite formation, and abundant magnesium resources. However, the high polarity of divalent Mg2+ ions results in sluggish diffusion kinetics in conventional inorganic cathode materials, adversely affecting reversible capacity and rate performance. Organic materials such as pyrene-4,5,9,10-tetrone (PTO) and 3,4,9,10-perylenetetracarboxylic dianhydride (PTCDA), achieve rapid and reversible intercalation of magnesium ions through carbonyl enolization, but these materials are challenged by high cost, complex preparation, and poor environmental friendliness.
View Article and Find Full Text PDFAn Integrated Fuzzy Neural Network and Topological Data Analysis for Molecular Graph Representation Learning and Property Forecasting.
Mol Inform
March 2025
Faculty of Information Technology, HUTECH University, Ho Chi Minh City, Vietnam.
Within a recent decade, graph neural network (GNN) has emerged as a powerful neural architecture for various graph-structured data modelling and task-driven representation learning problems. Recent studies have highlighted the remarkable capabilities of GNNs in handling complex graph representation learning tasks, achieving state-of-the-art results in node/graph classification, regression, and generation. However, most traditional GNN-based architectures like GCN and GraphSAGE still faced several challenges related to the capability of preserving the multi-scaled topological structures.
View Article and Find Full Text PDFAJGM: Joint Learning of Heterogeneous Gene Networks with Adaptive Graphical Model.
Bioinformatics
March 2025
Department of Statistics, Hunan University, Changsha, 410000, China.
Motivation: Inferring gene networks provides insights into biological pathways and functional relationships among genes. When gene expression samples exhibit heterogeneity, they may originate from unknown subtypes, prompting the utilization of mixture Gaussian graphical model for simultaneous subclassification and gene network inference. However, this method overlooks the heterogeneity of network relationships across subtypes and does not sufficiently emphasize shared relationships.
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