Australia's national citizen science program VegeSafe has collected and analysed over 26,000 residential garden soil samples for their trace metal concentrations, enabling a more comprehensive understanding of the factors influencing contamination. H...
Statistical modelling serves as a valuable tool for predicting biogas production during the anaerobic digestion (AD) of organic substrates. This study offers a comparative analysis of three widely used models, specifically, the first-order kinetic mo...
Estimating future water stress under socioeconomic and climate change is crucial for sustainable management. However, previous studies in China normally rely on assumptions, lack region-specific calibration, or apply inconsistent scenarios for water ...
Clinical pharmacology and therapeutics
Sep 1, 2025
In silico trials, utilizing mathematical models calibrated with clinical data, present a transformative approach to expedite drug development. We propose a virtual trial framework for chronic Hepatitis B, accurately simulating clinical protocols, pat...
A robust physics-informed neural network (PINN) approach is developed to accurately predict pressure and flow velocity during the water hammer event, while an experimental system is designed to validate the proposed approach further. Compared to forw...
Roundabout safety evaluation in non-lane-based, heterogeneous traffic conditions in low-middle-income countries brings challenges due to unavailable/unreliable crash data, thereby switching to the utilization of safety surrogates. This study employed...
The surface soil organic carbon (SOC) dynamics typically follow a trend of initial loss followed by subsequent accumulation after cropland abandonment. However, the timing of SOC stock increase (referred to as the threshold in this study) remains ins...
Conventional unimodal computer vision models, trained on limited bespoke waste datasets, face significant challenges in classifying waste images in material recovery facilities, where waste appears in diverse forms. Maintaining performance of these m...
In a rapidly changing world, uncontrolled climate change worsens water scarcity disrupting hydrological cycles and hindering sustainable development. Addressing water resources vulnerability requires holistic approaches to better understand complex s...
The application of machine learning methods to the groundwater pollution inversion problem has become a hot research topic in recent years. However, applying machine learning methods to achieve synergistic and rapid identification of pollution source...
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