Groundwater quality is related to several uncertain factors. Using multidimensional normal cloud model to reduce the randomness and ambiguity of the integrated groundwater quality evaluation is important in environmental research. Previous optimizations of multidimensional normal cloud models have focused on improving the affiliation criteria of the evaluation results, neglecting the weighting scheme of multiple indicators. In this study, a new multidimensional normal cloud model was constructed for the existing one-dimensional normal cloud model (ONCM) by combining the projection-pursuit (PP) method and the Grey Wolf Optimization (GWO) algorithm. The effectiveness and robustness of the model were analyzed. The results showed that compared with ONCM, the new multidimensional normal cloud model (GWOPPC model) integrated multiple evaluation parameters, simplified the modeling process, and reduced the number of calculations for the affiliation degree. Compared with other metaheuristic optimization algorithms, the GWO algorithms converged within 20 iterations during 20 simulations showing faster convergence speed, and the convergence results of all objective functions satisfy the iteration accuracy of 0.001, which indicates that the algorithm is more stable. Compared to the traditional entropy weights (0.27, 0.23, 0.47, 0.44, 0.29, 0.59, 0.12) or principal component weights (0.38, 0.33, 0.42, 0.34, 0.47, 0.29, 0.38), the weight allocation scheme provided by the GWOPP method (0.50, 0.48, 0.05, 0.38, 0.02. 0.51 and 0.32) considers the density of the distribution of all samples in the data set space. Among all 55 groundwater samples, the GWOPPC model has 21 samples with lower evaluation ratings than the fuzzy evaluation method, and 28 samples lower than the Random Forest method or the WQI method, indicating that the GWOPPC model is more conservative under the conditions of considering fuzziness and randomness. This method can be used to evaluate groundwater quality in other areas to provide a basis for the planning and management of groundwater resources.
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http://dx.doi.org/10.1016/j.jenvman.2024.120279 | DOI Listing |
FEBS Open Bio
December 2024
Department of Hepatobiliary and Pancreatic Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei, China.
Hepatocellular carcinoma remains a significant threat to human health. Recent studies have found that the intake of cellular cholesterol contributes to the development and progression of hepatocellular carcinoma, although the exact mechanisms remain unclear. Our analysis of transcriptomic and proteomic databases has identified increased mRNA and protein expression levels of NPC1, a cholesterol intracellular transporter protein, in hepatocellular carcinoma tissues.
View Article and Find Full Text PDFJ Anat
December 2024
Department of Veterinary Anatomy, Physiology and Pathology, Institute of Infection, Veterinary and Ecological Sciences, University of Liverpool, Liverpool, UK.
Understanding normal structural and functional anatomy is critical for health professionals across various fields such as medicine, veterinary, and dental courses. The landscape of anatomical education has evolved tremendously due to several challenges and advancements in blended learning approaches, which have led to the adoption of the use of high-fidelity 3D digital models in anatomical education. Cost-effective methods such as photogrammetry, which creates digital 3D models from aligning 2D photographs, provide a viable alternative to expensive imaging techniques (i.
View Article and Find Full Text PDFDesigning dental crowns with computer-aided design software in dental laboratories is complex and time-consuming. Using real clinical datasets, we developed an end-to-end deep learning model that automatically generates personalized dental crown meshes. The input context includes the prepared tooth, its adjacent teeth, and the two closest teeth in the opposing jaw.
View Article and Find Full Text PDFData Brief
December 2024
Faculty of Engineering, Politécnico Colombiano Jaime Isaza Cadavid, 48th Av, 7-151, Medellín, Colombia.
This article presents a comprehensive dataset combining Synthetic Aperture Radar (SAR) imagery from the Sentinel-1 mission with optical imagery, including RGB and Normalized Difference Vegetation Index (NDVI), from the Sentinel-2 mission. The dataset consists of 8800 images, organized into four folders-SAR_VV, SAR_VH, RGB, and NDVI-each containing 2200 images with dimensions of 512 × 512 pixels. These images were collected from various global locations using random geographic coordinates and strict criteria for cloud cover, snow presence, and water percentage, ensuring high-quality and diverse data.
View Article and Find Full Text PDFSensors (Basel)
November 2024
College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China.
In smoggy and dusty environments, vision- and laser-based localization methods are not able to be used effectively for controlling the movement of a robot. Autonomous operation of a security robot can be achieved in such environments by using millimeter wave (MMW) radar for the localization system. In this study, an approximate center method under a sparse point cloud is proposed, and a security robot localization system based on millimeter wave radar is constructed.
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