Sharing and integrating Remote Sensing (RS) and Geographic Information System/Science (GIS) models are critical for developing practical application systems. Facilitating model sharing and model integration is a problem for model publishers and model users, respectively. To address this problem, a framework based on a Web service for sharing and integrating RS and GIS models is proposed in this paper. The fundamental idea of the framework is to publish heterogeneous RS and GIS models into standard Web services for sharing and interoperation and then to integrate the RS and GIS models using Web services. For the former, a "black box" and a visual method are employed to facilitate the publishing of the models as Web services. For the latter, model integration based on the geospatial workflow and semantic supported marching method is introduced. Under this framework, model sharing and integration is applied for developing the Pearl River Delta water environment monitoring system. The results show that the framework can facilitate model sharing and model integration for model publishers and model users.
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http://dx.doi.org/10.1155/2014/354919 | DOI Listing |
J Control Release
January 2025
Bioprocessing Technology Institute (BTI), Agency for Science, Technology and Research (A*STAR), 20 Biopolis Way, #06-01 Centros, Singapore 138668, Republic of Singapore. Electronic address:
mRNA-loaded lipid nanoparticles (mRNA-LNPs) hold great potential for disease treatment and prevention. LNPs are normally made from four lipids including ionizable lipid, helper lipid, cholesterol, and PEGylated lipid (PEG-lipid). Although PEG-lipid has the lowest content, it plays a crucial role in the effective delivery of mRNA-LNPs.
View Article and Find Full Text PDFJ Hazard Mater
January 2025
University of Belgrade, Faculty of Technology and Metallurgy, Karnegijeva 4, Belgrade 11120, Serbia. Electronic address:
Effective protection of groundwater requires an accurate health risk assessment of contaminants; however, the diversity of pollution sources, variability, and uncertainties in exposure parameters present significant challenges in this assessment. In this study, groundwater risk estimates associated with NO, and F, along with fourteen heavy metal(loid)s (V, Cr, Mn, Fe, Ni, Cu, As, Co, Cd, Se, Pb, Hg, Zn, and Al) in an agricultural area were optimized by implementing positive matrix factorization (PMF), multilinear regression, and two-dimensional Monte Carlo simulations to characterize source-specific health risks. Groundwater pollution was analyzed considering regional variations, including differences in elevation, land use and land cover, and soil types.
View Article and Find Full Text PDFPLoS One
January 2025
State Key Laboratory of Automotive Safety and Energy, School of Vehicle and Mobility, Tsinghua University, Beijing, China.
This study tried to focus on the older drivers' group and explore the impact factors of injury severity involving older drivers from geo-spatial analysis. To reach the goal, a spatial analysis was proposed employing geographic information systems (GIS) with a case study application to two counties in Nevada. First, crash clusters were explored using Density-Based Spatial Clustering of Applications with Noise (DBSCAN) approach to investigate the spatial crash pattern for older drivers, and determine high risk locations of injury severity.
View Article and Find Full Text PDFJ Multidiscip Healthc
January 2025
Department of Rheumatology and Immunology, Wuhan Children's Hospital, Tongji Medical College, Huazhong University of Science &Technology, Wuhan, 430016, People's Republic of China.
Background: Henoch-Schönlein Purpura (HSP) is a common systemic vasculitis in children that often involves the gastrointestinal system (GIS). Identifying reliable predictive markers for GIS complications is crucial for early intervention and improved patient outcomes.
Objective: This study aims to identify laboratory markers predictive of GIS complications in children with HSP using a machine learning approach.
Sci Rep
January 2025
Department of Pathology, Microbiology, and Immunology, School of Veterinary Medicine, University of California, Davis, CA, 95616, USA.
This study utilizes an integrated Geographic Information System (GIS)-based Multi-Criteria Decision-Making (MCDM) approach to perform Solar Power Plant Site Selection (SPPSS) in Kermanshah Province, Iran. It introduces a novel group weighting method, the Dempster-based Best-Worst Method (DBWM), which combines weights vectors derived from experts' opinions. The study also conducts a comprehensive sensitivity analysis comparing four GIS-based models for SPPSS.
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