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Contaminants stored in the low permeability sediments will continue to threaten the adjacent shallow groundwater system after the aquifer is remediated. Understanding the storage and discharge behavior of contaminants in the aquitards is essential for the efficient remediation of contaminated sites, but most of the previous analytical studies focused on nonreactive solutes in a single homogenous aquitard. This study presents novel analytical solutions for the forward and back diffusion of contaminants through multi-layer low permeability sediments considering abiotic and biotic environmental degradation. Three representative source depletion patterns (i.e., instantaneous, linear, and exponential patterns) were selected to describe the dissolution of dense non-aqueous phase liquids (DNAPL) in the aquifer more realistically. At the forward diffusion stage, the mass storage of contaminants in the aquitards with the instantaneous pattern is the largest, nearly twice that with the exponential pattern. A simple equivalent homogeneous model is generally adopted in the risk assessment. However, relative to the proposed multi-layer model, it will significantly underestimate the onset of the back-diffusion of heterogeneous aquitards and overestimate the persistence of aquifer plumes. The previously-reported semi-infinite boundary assumption is also not applicable, with a maximum error of over 200% in the long-term prediction of back diffusion behavior of a thin aquitard. Moreover, when the degradation half-life is less than 16 years, less than 10% of the contaminants stored in the aquitards will diffuse into the overlying aquifer, suggesting that biostimulation or bioaugmentation can effectively mitigate back-diffusion risk. Overall, the proposed diffusion-reaction coupled model with multi-layer media is of great value and high demand in predicting the back-diffusion behavior of heterogeneous aquitards and guiding the soil bioremediation.
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http://dx.doi.org/10.1016/j.watres.2022.118925 | DOI Listing |
Health Soc Care Deliv Res
September 2025
Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK.
Background: Remote services (in which the patient and staff member are not physically colocated) and digital services (in which a patient encounter is digitally mediated in some way) were introduced extensively when the COVID-19 pandemic began in 2020. We undertook a longitudinal qualitative study of the introduction, embedding, evolution and abandonment of remote and digital innovations in United Kingdom general practice. This synoptic paper summarises study design, methods, key findings, outputs and impacts to date.
View Article and Find Full Text PDFEur Phys J E Soft Matter
September 2025
Université Gustave Eiffel, ENPC, Institut Polytechnique de Paris, CNRS, Navier, 77454, Marne-la-Vallée, France.
We experimentally study the heterogeneity of strain in a granular medium subjected to oscillatory shear in a rotating drum. Two complementary methods are used. The first method relies on optical imaging and grain tracking, allowing us to compute some components of the strain tensor and their variance.
View Article and Find Full Text PDFFuture Med Chem
September 2025
College of Mathematics and Computer Science, Dali University, Dali Old City, China.
Aim: Generating molecules with specific chemical properties for target proteins can accelerate the drug development process and open new avenues for developing treatments for diseases with known pathogenic target proteins. However, current approaches to generate molecules with desired properties face several challenges, including prolonged generation time, complexity in learning parameters, and unqualified chemical properties.
Results/methodology: To address these issues, we proposed a structure-aware diffusion model, termed KGMG.
IEEE Trans Biomed Eng
September 2025
Objective: Quantitative susceptibility mapping (QSM) is a useful magnetic resonance imaging technique. We aim to propose a deep learning (DL)-based method for QSM reconstruction that is robust to data perturbations.
Methods: We developed Diffusion-QSM, a diffusion model-based method with a time-travel and resampling refinement module for high-quality QSM reconstruction.
Sensors (Basel)
August 2025
School of Computer Science and Mathematics, Kingston University, London KT1 2EE, UK.
Utilizing tactile sensors embedded in intelligent mats is an attractive non-intrusive approach for human motion analysis. Interpreting tactile pressure 2D maps for accurate posture estimation poses significant challenges, such as dealing with data sparsity, noise interference, and the complexity of mapping pressure signals. Our approach introduces a novel dual-diffusion signal enhancement (DDSE) architecture that leverages tactile pressure measurements from an intelligent pressure mat for precise prediction of 3D body joint positions, using a diffusion model to enhance pressure data quality and a convolutional-transformer neural network architecture for accurate pose estimation.
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