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A participatory needs assessment to map the system dynamics of overweight related behaviours of 10-14-year-old adolescents living in an underserved neighbourhood in Amsterdam, the Netherlands.
BMC Public Health
January 2025
Amsterdam UMC location Vrije Universiteit Amsterdam, Public and Occupational Health, De Boelelaan 1117, Amsterdam, the Netherlands.
Background: Developing interventions along with the population of interest using systems thinking is a promising method to address the underlying system dynamics of overweight. The purpose of this study is twofold: to gain insight into the perspectives of adolescents regarding: (1) the system dynamics of energy balance-related behaviours (EBRBs) (physical activity, screen use, sleep behaviour and dietary behaviour); and (2) underlying mechanisms and overarching drivers of unhealthy EBRBs.
Methods: We conducted Participatory Action Research (PAR) to map the system dynamics of EBRBs together with adolescents aged 10-14 years old living in a lower socioeconomic, ethnically diverse neighbourhood in Amsterdam East, the Netherlands.
Decoding the suppressing effects of Pluronic triblock copolymers on copper electrodeposition.
J Colloid Interface Sci
April 2025
Shanghai Key Laboratory of Molecular Catalysis and Innovative Materials, Collaborative Innovation Center of Chemistry for Energy Materials, Department of Chemistry, Fudan University, Shanghai, China. Electronic address:
Triblock Pluronics of polyoxyethylene (PEO) and polyoxypropylene (PPO) are identified as competent suppressors for copper (Cu) electroplating in advanced electronics manufacturing. However, the specific interfacial roles of PEO and PPO blocks in Pluronic suppressors, are not yet fully understood, which is crucial for the rational design of effective suppressors. Herein, the influences of composition and block arrangement of such Pluronics on the inhibition against Cu plating are systematically investigated.
View Article and Find Full Text PDFEnhancing Activation Energy Predictions under Data Constraints Using Graph Neural Networks.
J Chem Inf Model
January 2025
Department of Chemical Engineering, National Taiwan University, No. 1, Section 4, Roosevelt Road, Taipei 10617, Taiwan.
Accurately predicting activation energies is crucial for understanding chemical reactions and modeling complex reaction systems. However, the high computational cost of quantum chemistry methods often limits the feasibility of large-scale studies, leading to a scarcity of high-quality activation energy data. In this work, we explore and compare three innovative approaches (transfer learning, delta learning, and feature engineering) to enhance the accuracy of activation energy predictions using graph neural networks, specifically focusing on methods that incorporate low-cost, low-level computational data.
View Article and Find Full Text PDFPatterns and Drivers of Surface Energy Flux in the Alpine Meadow Ecosystem in the Qilian Mountains, Northwest China.
Plants (Basel)
January 2025
Key Laboratory of Ecological Safety and Sustainable Development in Arid Lands, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China.
Alpine meadows are vital ecosystems on the Qinghai-Tibet Plateau, significantly contributing to water conservation and climate regulation. This study examines the energy flux patterns and their driving factors in the alpine meadows of the Qilian Mountains, focusing on how the meteorological variables of net radiation (), air temperature, vapor pressure deficit (), wind speed (), and soil water content () influence sensible heat flux () and latent heat flux (). Using the Bowen ratio energy balance method, we monitored energy changes during the growing and non-growing seasons from 2022 to 2023.
View Article and Find Full Text PDFDiet Quality and Caloric Accuracy in AI-Generated Diet Plans: A Comparative Study Across Chatbots.
Nutrients
January 2025
Division of Nutrition, Food & Dietetics, School of Biosciences, University of Nottingham, Leics LE12 5RD, UK.
With the rise of artificial intelligence (AI) in nutrition and healthcare, AI-driven chatbots are increasingly recognised as potential tools for generating personalised diet plans. This study aimed to evaluate the capabilities of three popular chatbots-Gemini, Microsoft Copilot, and ChatGPT 4.0-in designing weight-loss diet plans across varying caloric levels and genders.
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