This paper is one in a series that describes results of a benchmarking analysis initiated by the Department of Energy (DOE) and the United States Environmental Protection Agency (EPA). An overview of the study is provided in a companion paper by Laniak et al. presented in this journal issue. The three models used in the study--RESRAD (DOE), MMSOILS (EPA), and MEPAS (DOE)--represent analytically-based tools that are used by the respective agencies for performing human exposure and health risk assessments. Both single media and multimedia benchmarking scenarios were developed and executed. In this paper, the multimedia scenario is examined. That scenario consists of a hypothetical landfill that initially contained uranium-238 and methylene chloride. The multimedia models predict the fate of these contaminants, plus the progeny of uranium-238, through the unsaturated zone, saturated zone, surface water, and atmosphere. Carcinogenic risks are calculated from exposure to the contaminants via multiple pathways. Results of the tests show that differences in model endpoint estimates arise from both differences in the models' mathematical formulations and assumptions related to the implementation of the scenarios.
Download full-text PDF |
Source |
---|---|
http://dx.doi.org/10.1111/j.1539-6924.1997.tb00858.x | DOI Listing |
Neural Netw
March 2025
Beijing Key Laboratory of Intelligent Telecommunications Software and Multimedia, School of Computer Science (National Pilot School of Software Engineering), Beijing University of Posts and Telecommunications, Beijing, 100876, China.
Multimodal Public Speaking Anxiety Detection (MPSAD), which aims to identify the anxiety states of learners, has attracted widespread attention. Unfortunately, the current MPSAD task inevitably suffers from the impact of latent different types of multimodal hybrid biases, such as context bias, label bias and keyword bias. Models may rely on these biases as shortcuts, preventing them from fully utilizing all three modalities to learn multimodal knowledge.
View Article and Find Full Text PDFMethodsX
June 2025
Department of Computing Technologies, School of Computing, SRM Institute of Science and Technology, Kattankulathur, Tamil Nadu 603203, India.
In recent days, Internet of Medical Things (IoMT) and Deep Learning (DL) techniques are broadly used in medical data processing in decision-making. A lung tumour, one of the most dangerous medical diseases, requires early diagnosis with a higher precision rate. With that concern, this work aims to develop an Integrated Model (IM- LTS) for Lung Tumor Segmentation using Neural Networks (NN) and the Internet of Medical Things (IoMT).
View Article and Find Full Text PDFInt J Neural Syst
March 2025
Alibaba Cloud, Hangzhou, P. R. China.
Multi-label zero-shot learning (ML-ZSL) strives to recognize all objects in an image, regardless of whether they are present in the training data. Recent methods incorporate an attention mechanism to locate labels in the image and generate class-specific semantic information. However, the attention mechanism built on visual features treats label embeddings equally in the prediction score, leading to severe semantic ambiguity.
View Article and Find Full Text PDFPLoS One
January 2025
Faculty of Engineering (FOE), Multimedia University (MMU), Cyberjaya, Selangor, Malaysia.
Cancer and its diverse variations pose one of the most significant threats to human health and well-being. One of the most aggressive forms is blood cancer, originating from bone marrow cells and disrupting the production of normal blood cells. The incidence of blood cancer is steadily increasing, driven by both genetic and environmental factors.
View Article and Find Full Text PDFIEEE Trans Comput Aided Des Integr Circuits Syst
January 2025
National Key Laboratory for Multimedia Information Processing, School of Computer Science, Peking University, Beijing, China. Dr. Luo is also with the Center for Energy-efficient Computing and Applications, Peking University, Beijing, China.
The feasibility-seeking approach offers a systematic framework for managing and resolving intricate constraints in continuous problems, making it a promising avenue to explore in the context of floorplanning problems with increasingly heterogeneous constraints. The classic legality constraints can be expressed as the union of convex sets. However, conventional projection-based algorithms for feasibility-seeking do not guarantee convergence in such situations, which are also heavily influenced by the initialization.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!