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Multi-Agent Large Language Model Frameworks: Unlocking New Possibilities for Optimizing Wastewater Treatment Operation.
Environ Res
March 2025
School of Civil and Environmental Engineering, University of Connecticut, Storrs, Connecticut 06269, USA. Electronic address:
Wastewater treatment plants (WWTPs) are highly complex systems where biological, chemical, and physical processes interact dynamically, creating significant operational challenges. Traditional modeling approaches, such as Activated Sludge Models (ASMs) and machine learning algorithms (MLAs), struggle to process the unstructured and multimodal data generated in WWTPs, limiting their effectiveness. Large Language Models (LLMs) offer a promising solution by integrating diverse data sources, recognizing patterns, and enabling human-in-the-loop interactions for informed decision-making.
View Article and Find Full Text PDFDimensions of the functional degrees of freedom of the first serve in tennis.
J Sports Sci
March 2025
Cardiff School of Sport and Health Sciences, Cardiff Metropolitan University, Cardiff, UK.
We examined the influence of court side and target location of the ball on the coordination and control of the tennis serve. Five male and five female experienced players performed 10 'first-serves' to centre and wide targets. 3D kinematics of the service were analysed from two frames of reference: joint position (v) and joint angle (ω) with emphasis on the qualitative aspects of movement coordination.
View Article and Find Full Text PDFThe National Paediatric Applied Research Translation Initiative (N-PARTI): using implementation science to improve primary care for Australian children with asthma, type 1 diabetes, and infections.
BMC Health Serv Res
March 2025
Australian Institute of Health Innovation, Macquarie University, 75 Talavera Road, North Ryde 2113, Sydney, NSW, 2109, Australia.
General practice-based care for Australian children is facing low levels of clinical guideline adherence particularly in three key areas: asthma, type 1 diabetes and antibiotic use. We offer an implementation science-informed position paper, providing a broad overview of how we aim to address this issue. This is the co-designed National Paediatric Applied Research Translation Initiative (N-PARTI), a bespoke, three-phased research solution by deploying mixed methods, simulation and scale-up of evidence into practice.
View Article and Find Full Text PDFTiming of human rhythmic jumping with vertical floor vibrations tends towards mechanically efficient solutions.
Sci Rep
March 2025
Faculty of Health and Life Sciences, University of Exeter, Exeter, Devon, EX2 4TA, UK.
Jumping on vibrating platforms is described not only by the frequency of jumping (FoJ) but also by the timing of key events in a cycle of jumping relative to vibrations. This study aimed to capture timing and efficiency-related adaptations during jumping on vertically vibrating platforms. Whole body kinematic and kinetic data were collected as ten participants jumped on a sinusoidally vibrating platform of 2.
View Article and Find Full Text PDFMachine Learning Potential for Copper Hydride Clusters: A Neutron Diffraction-Independent Approach for Locating Hydrogen Positions.
J Am Chem Soc
March 2025
State Key Laboratory of Photoelectric Conversion and Utilization of Solar Energy, Qingdao New Energy Shandong Laboratory, Qingdao Institute of Bioenergy and Bioprocess Technology, Chinese Academy of Sciences, Qingdao 266101, China.
Determining hydrogen positions in metal hydride clusters remains a formidable challenge, which relies heavily on unaffordable neutron diffraction. While machine learning has shown promise, only one deep learning-based method has been proposed so far, which relies heavily on neutron diffraction data for training, limiting its general applicability. In this work, we present an innovative strategy─SSW-NN (stochastic surface walking with neural network)─a robust, non-neutron diffraction-dependent technique that accurately predicts hydrogen positions.
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