A two-layer model based on the integrated form of Richards' equation (RE) was recently developed to simulate the soil water movement in the roots layer and the vadose zone with a relatively shallow and dynamic water table. The model simulates thickness-averaged volumetric water content and matric suction as opposed to point values and was numerically verified for three soil textures using HYDRUS as a benchmark. However, the strengths and limitations of the two-layer model and its performance in stratified soils and under actual field conditions have not been tested. This study further examined the two-layer model using two numerical verification experiments and, most importantly, tested its performance at site-level under actual, highly variable hydroclimate conditions. Moreover, model parameters were estimated and uncertainty and sources of errors were quantified using a Bayesian framework. First, the two-layer model was evaluated for 231 soil textures under varying soil layer thicknesses with a uniform soil profile. Second, the two-layer model was assessed for stratified conditions where the top and bottom soil layers have contrasting hydraulic conductivities. The model was evaluated by comparing soil moisture and flux estimates to those from the HYDRUS model. Last, a case study of model application using data from a Soil Climate Analysis Network (SCAN) site was presented. Bayesian Monte Carlo (BMC) method was implemented for model calibration and quantifying sources of uncertainty under real hydroclimate and soil conditions. For a homogeneous soil profile, the two-layer model generally had excellent performance in estimating volumetric water content and fluxes, while the model performance slightly declined with increasing layer thickness and coarser textured soils. The model configurations regarding layer thicknesses and soil textures that generate accurate soil moisture and flux estimations were further suggested. With the two layers of contrasting permeability, model-simulated soil moisture contents and fluxes agreed well with those computed by HYDRUS, indicating that the two-layer model accurately handles the water flow dynamics around the layer interface. In the field application, given the highly variable hydroclimate conditions, the two-layer model combined with the BMC method showed good agreement with the observed average soil moisture of the root zone and the vadose zone below ( <0.021 during calibration and <0.023 during validation periods). The contribution of parametric uncertainty to the total model uncertainty was too small compared to other sources. The numerical tests and the site level application showed that the two-layer model can reliably simulate thickness-averaged soil moisture and estimate fluxes in the vadose zone under various soil and hydroclimate conditions. Results also indicated that the BMC method could be a robust framework for vadose zone hydraulic parameters identification and model uncertainty estimation.
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http://dx.doi.org/10.1016/j.jhydrol.2022.128327 | DOI Listing |
Colloids Surf B Biointerfaces
January 2025
Centre for Advanced Jet Engineering Technology (CaJET), Key Laboratory of High-efficiency and Clean Mechanical Manufacture (Ministry of Education), National Experimental Teaching Demonstration Center for Mechanical Engineering (Shandong University), School of Mechanical Engineering, Shandong University, Jinan 250061, China.
The in vitro blood-brain barrier (BBB) structures can offer advantages for studying cerebrovascular functions and developing neuroprotective drugs. However, currently developed BBB models are overly simplistic and inadequate for replicating the complex three-dimensional architecture of the in vivo BBB. In this study, a method is introduced for fabricating a three-layer vascular structure exhibiting BBB function using a coaxial extrusion bioprinting technique with a two-layer nozzle.
View Article and Find Full Text PDFNature
January 2025
Department of Materials Science and Engineering, The Pennsylvania State University, University Park, PA, USA.
Proximity ferroelectricity is an interface-associated phenomenon in electric-field-driven polarization reversal in a non-ferroelectric polar material induced by one or more adjacent ferroelectric materials. Here we report proximity ferroelectricity in wurtzite ferroelectric heterostructures. In the present case, the non-ferroelectric layers are AlN and ZnO, whereas the ferroelectric layers are AlBN, AlScN and ZnMgO.
View Article and Find Full Text PDFSensors (Basel)
December 2024
School of Digital Media & Design Arts, Beijing University of Posts and Telecommunications, No. 10 Xitucheng Road, Beijing 100876, China.
Trust is a crucial human factor in automated supervisory control tasks. To attain appropriate reliance, the operator's trust should be calibrated to reflect the system's capabilities. This study utilized eye-tracking technology to explore novel approaches, given the intrusive, subjective, and sporadic characteristics of existing trust measurement methods.
View Article and Find Full Text PDFJ Med Internet Res
January 2025
Department of Biomedical Informatics, School of Medicine, Emory University, Atlanta, GA, United States.
Background: The increasing use of social media to share lived and living experiences of substance use presents a unique opportunity to obtain information on side effects, use patterns, and opinions on novel psychoactive substances. However, due to the large volume of data, obtaining useful insights through natural language processing technologies such as large language models is challenging.
Objective: This paper aims to develop a retrieval-augmented generation (RAG) architecture for medical question answering pertaining to clinicians' queries on emerging issues associated with health-related topics, using user-generated medical information on social media.
Sci Rep
January 2025
School of Management, Shenyang University of Technology, Shenyang, 110870, China.
The production stage of an automated job shop is closely linked to the automated guided vehicle (AGV), which needs to be planned in an integrated manner to achieve overall optimization. In order to improve the collaboration between the production stages and the AGV operation system, a two-layer scheduling optimization model is proposed for simultaneous decision making of batching problems, job sequences and AGV obstacle avoidance. Under the AGV automatic path seeking mode, this paper adopts a data-driven Bayesian network method to portray the transportation time of AGVs based on the historical operation data to control the uncertainty of the transportation time of AGVs.
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