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Assessing uncertainties in modeling the risk of geogenic groundwater contamination.
Environ Sci Pollut Res Int
January 2025
Department of Civil and Architectural Engineering, Sultan Qaboos University, Muscat, Oman.
Assessing groundwater contamination risk is a critical aspect of preventing and managing groundwater pollution. There was a research gap in the investigation of uncertainties in modeling groundwater contamination risks in aquifers. This study addresses this gap using Bayesian Model Averaging (BMA), with a novel focus on risk exposures from geogenic contaminants, such as lead (Pb).
View Article and Find Full Text PDFEffects of computer-assisted learning for removable partial denture design on learning outcomes and satisfaction.
BMC Med Educ
January 2025
Department of Prosthodontics, Faculty of Dentistry, Chiang Mai University, Chiang Mai, Thailand.
Background: Computer-assisted learning (CAL) has the potential to enhance learning outcomes and satisfaction. However, there are limited reports in the literature that describe or evaluate the implementation of this method to promote competency-based learning in removable partial denture (RPD) design. Therefore, this study aimed to: (1) compare the effectiveness of different learning methods using a 3D software-aided RPD design program, (2) evaluate the learning outcomes associated with these different methods following active learning, and (3) assess students' satisfaction.
View Article and Find Full Text PDFPeptideForest: Semisupervised Machine Learning Integrating Multiple Search Engines for Peptide Identification.
J Proteome Res
January 2025
Institute of Pharmacy and Molecular Biotechnology, Heidelberg University, 69120 Heidelberg, Germany.
The first step in bottom-up proteomics is the assignment of measured fragmentation mass spectra to peptide sequences, also known as peptide spectrum matches. In recent years novel algorithms have pushed the assignment to new heights; unfortunately, different algorithms come with different strengths and weaknesses and choosing the appropriate algorithm poses a challenge for the user. Here we introduce PeptideForest, a semisupervised machine learning approach that integrates the assignments of multiple algorithms to train a random forest classifier to alleviate that issue.
View Article and Find Full Text PDFMythicVision: a deep learning powered mobile application for understanding Indian mythological deities using weight centric decision approach.
Sci Rep
January 2025
School of Computer Science and Engineering (SCOPE), VIT-AP University, Amaravati, Andhra Pradesh, 522237, India.
Indian mythology is a treasure trove of divine tales, yet a gap in understanding still exists between foreign tourists and the rich cultural heritage of Indian deities. To address the problem, this paper presents a deep learning-driven mobile application named "MythicVision" designed to help foreign tourists better understand India's rich cultural heritage by recognizing and interpreting images of Indian mythological deities. At first, four state-of-the-art deep models have been trained and evaluated on a custom in-house dataset consists of 10,970 images of various Indian deities sourced from both natural scene and web images.
View Article and Find Full Text PDFAssessing Trainee Needs for Developing Response Scenarios and Training Manuals for Unknown Infectious Diseases: Insights From a Focus Group Interview.
J Korean Med Sci
January 2025
Division of Infectious Diseases, Department of Internal Medicine, Hanyang University Seoul Hospital, Seoul, Korea.
This study employed focus group interviews (FGIs) to evaluate the preparedness and training requirements for an emerging infectious disease response system in the Republic of Korea. Based on the FGIs, the critical role of interdepartmental cooperation in responding to emerging infectious diseases was identified, with agencies such as public health centers, police, and fire services playing key roles in scene control, decontamination, and patient transport. Frequent staff turnover and a lack of trained personnel at local government levels were significant challenges, necessitating the development of training materials for unskilled workers.
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