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Exploring the purpose and stages of patient and public involvement and engagement (PPIE) in audiology research: a case study approach.
Res Involv Engagem
January 2025
Patient Researcher and PPI Lead, College of Health and Life Sciences, Aston University, Birmingham, UK.
Background: Patient and Public Involvement and Engagement (PPIE) has become an integral component of contemporary audiology research. It aims to capture diverse views and experiences, essential for evaluating the long-term impact of technological advancements and care models on individuals. Traditional inclusion methods, such as focus groups, may exclude individuals with additional needs or communication difficulties, necessitating the development of more inclusive approaches.
View Article and Find Full Text PDFAdvanced microgrid optimization using price-elastic demand response and greedy rat swarm optimization for economic and environmental efficiency.
Sci Rep
January 2025
Department of Theoretical Electrical Engineering and Diagnostics of Electrical Equipment, Institute of Electrodynamics, National Academy of Sciences of Ukraine, Beresteyskiy, 56, Kyiv-57, Kyiv, 03680, Ukraine.
In this paper, a comprehensive energy management framework for microgrids that incorporates price-based demand response programs (DRPs) and leverages an advanced optimization method-Greedy Rat Swarm Optimizer (GRSO) is proposed. The primary objective is to minimize the generation cost and environmental impact of microgrid systems by effectively scheduling distributed energy resources (DERs), including renewable energy sources (RES) such as solar and wind, alongside fossil-fuel-based generators. Four distinct demand response models-exponential, hyperbolic, logarithmic, and critical peak pricing (CPP)-are developed, each reflecting a different price elasticity of demand.
View Article and Find Full Text PDFUnderstanding the role of metabolic syndrome in prostate cancer risk: A UK Biobank prospective cohort study.
Sci Rep
January 2025
Department of Non-Communicable Disease Epidemiology, London School of Hygiene and Tropical Medicine, Keppel Street, London, WC1E 7HT, UK.
Predictive value of metabolic syndrome for prostate cancer risk is not clear. We aimed to assess the association between metabolic syndrome and its components with prostate cancer incidence. The primary outcome was prostate cancer incidence, i.
View Article and Find Full Text PDFDynamic hydrogen-bonding enables high-performance and mechanically robust organic solar cells processed with non-halogenated solvent.
Nat Commun
January 2025
CAS Key Laboratory of Organic Solids, Institute of Chemistry, Chinese Academy of Sciences, Beijing, China.
Developing active-layer systems with both high performance and mechanical robustness is a crucial step towards achieving future commercialization of flexible and stretchable organic solar cells (OSCs). Herein, we design and synthesize a series of acceptors BTA-C6, BTA-E3, BTA-E6, and BTA-E9, featuring the side chains of hexyl, and 3, 6, and 9 carbon-chain with ethyl ester end groups respectively. Benefiting from suitable phase separation and vertical phase distribution, the PM6:BTA-E3-based OSCs processed by o-xylene exhibit lower energy loss and improved charge transport characteristic and achieve a power conversion efficiency of 19.
View Article and Find Full Text PDFKernel representation-based End-to-End network-enabled decoding strategy for precise and medical diagnosis.
J Hazard Mater
January 2025
School of Computer Science and Technology, Wuhan University of Science and Technology, Wuhan 430070, China; Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System, Wuhan 430070, China. Electronic address:
Artificial intelligence-assisted imaging biosensors have attracted increasing attention due to their flexibility, allowing for the digital image analysis and quantification of biomarkers. While deep learning methods have led to advancements in biomarker identification, the diversity in the density and adherence of targets still poses a serious challenge. In this regard, we propose CellNet, a neural network model specifically designed for detecting dense targets.
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