Networks on-chip (NoCs) provide enhanced performance, scalability, modularity, and design productivity as compared with previous communication architectures for VLSI systems on-chip (SoCs), such as buses and dedicated signal wires. Since the NoC design space is very large and high dimensional, evaluation methodologies rely heavily on analytical modeling and simulation. Unfortunately, there is no standard modeling framework. In this paper we illustrate how to design and evaluate NoCs by integrating the Discrete Event System Specification (DEVS) modeling framework and the simulation environment called DEUS. The advantage of such an approach is that both DEVS and DEUS support modularity-the former being a sound and complete modeling framework and the latter being an open, general-purpose platform, characterized by a steep learning curve and the possibility to simulate any system at any level of detail.
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http://dx.doi.org/10.1155/2014/982569 | DOI Listing |
Am J Public Health
January 2025
Alexia Couture, A. Danielle Iuliano, Ryan Threlkel, Matthew Gilmer, Alissa O'Halloran, Dawud Ujamaa, Matthew Biggerstaff, and Carrie Reed are with the National Center for Immunization and Respiratory Diseases, Centers for Disease Control and Prevention, Atlanta, GA. Howard H. Chang is with the Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, Atlanta, GA.
To develop a method leveraging hospital-based surveillance to estimate influenza-related hospitalizations by state, age, and month as a means of enhancing current US influenza burden estimation efforts. Using data from the Influenza Hospitalization Surveillance Network (FluSurv-NET), we extrapolated monthly FluSurv-NET hospitalization rates after adjusting for testing practices and diagnostic test sensitivities to non-FluSurv-NET states. We used a Poisson zero-inflated model with an overdispersion parameter within the Bayesian hierarchical framework and accounted for uncertainty and variability between states and across time.
View Article and Find Full Text PDFAnnu Rev Genomics Hum Genet
January 2025
1Department of Medical Genetics and Genomic Medicine, School of Medical Sciences, University of Campinas, Campinas, Brazil;
Integrating genomics into healthcare within the precision medicine (PM) framework poses distinct challenges in resource-limited regions like Latin America and the Caribbean (LAC). These challenges arise partly from the lack of PM models tailored for low- and middle-income countries. To address this, healthcare authorities in LAC should adopt predictive models to estimate costs and infrastructure needed for PM programs.
View Article and Find Full Text PDFPLoS One
January 2025
TETIS, Université de Montpellier, AgroParisTech, CIRAD, INRAE, Montpellier, France.
African swine fever (ASF) is a highly contagious disease affecting wild and domestic pigs, characterised by severe haemorrhagic symptoms and high mortality rates. Originally confined to Sub-Saharan Africa, ASF virus genotype II has spread to Europe since 2014, mainly affecting Eastern Europe, and progressing through wild boar migrations and human action. In January 2022, the first case of ASF, due to genotype II, was reported in North-western Italy, in a wild boar carcass.
View Article and Find Full Text PDFPLoS One
January 2025
Transfers, Interfaces and Processes, Université libre de Bruxelles, Brussels, Belgium.
In this paper, we present a new computational framework for the simulation of airway resistance, the fraction of exhaled nitric oxide, and the diffusion capacity for nitric oxide in healthy and unhealthy lungs. Our approach is firstly based on a realistic representation of the geometry of healthy lungs as a function of body mass, which compares well with data from the literature, particularly in terms of lung volume and alveolar surface area. The original way in which this geometry is created, including an individual definition of the airways in the first seven generations of the lungs, makes it possible to consider the heterogeneous nature of the lungs in terms of perfusion and ventilation.
View Article and Find Full Text PDFPLoS One
January 2025
College of Education for the Future, Beijing Normal University, Zhuhai, Guangdong, China.
Personalized sports training plans are essential for addressing individual athlete needs, but traditional methods often need to integrate diverse data types, limiting adaptability and effectiveness. Existing machine learning (ML) and rule-based approaches cannot dynamically generate context-specific training programs, reducing their applicability in real-world scenarios. This study aims to develop a Generative Adversarial Network (GAN)- based framework to create context-specific training plans by integrating numeric attributes (e.
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