In this paper, we study the dynamics of the transmission of respiratory syncytial virus (RSV) in the population using stochastic models. The stochastic models are developed introducing stochastic perturbations on the demographic parameter as well as on the transmission rate of the RSV. Numerical simulations of the deterministic and stochastic models are performed in order to understand the effect of fluctuating birth rate and transmission rate of the RSV on the population dynamics. The numerical solutions of stochastic models are calculated using Euler-Maruyama and Milstein schemes, and confidence intervals for stochastic solutions are given using Monte-Carlo method. Analysis of the numerical results reveals that perturbations on the transmission rate are more decisive in the dynamics of RSV than perturbations on demographic parameters. In addition, the stochastic models show the advantage of reproducing more effectively the noisy RSV hospitalization data. It is concluded that these stochastic models are a viable option to provide a realistic modeling of the RSV dynamics on the population.
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http://dx.doi.org/10.1016/j.biosystems.2009.01.007 | DOI Listing |
Radiat Res
January 2025
Department of Radiation Oncology, Mayo Clinic, Rochester, Minnesota.
Variable relative biological effectiveness (RBE) of carbon radiotherapy may be calculated using several models, including the microdosimetric kinetic model (MKM), stochastic MKM (SMKM), repair-misrepair-fixation (RMF) model, and local effect model I (LEM), which have not been thoroughly compared. In this work, we compared how these four models handle carbon beam fragmentation, providing insight into where model differences arise. Monoenergetic and spread-out Bragg peak carbon beams incident on a water phantom were simulated using Monte Carlo.
View Article and Find Full Text PDFSensors (Basel)
January 2025
School of Reliability and Systems Engineering, Beihang University, Beijing 100191, China.
Accurately predicting the remaining useful life (RUL) of critical mechanical components is a central challenge in reliability engineering. Stochastic processes, which are capable of modeling uncertainties, are widely used in RUL prediction. However, conventional stochastic process models face two major limitations: (1) the reliance on strict assumptions during model formulation, restricting their applicability to a narrow range of degradation processes, and (2) the inability to account for potential variations in the degradation mechanism during modeling and prediction.
View Article and Find Full Text PDFMaterials (Basel)
January 2025
Department of Strength of Materials, National University for Science and Technology POLITEHNICA Bucharest, Splaiul Independeţei 313, 060042 Bucharest, Romania.
Sandwich structures with triply periodic minimal surface (TPMS) cores have garnered research attention due to their potential to address challenges in lightweight solutions, high-strength designs, and energy absorption capabilities. This study focuses on performing finite element analyses (FEAs) on eight novel TPMS cores and one stochastic topology. It presents a method of analysis obtained through implicit modeling in simulations and examines whether the results obtained differ from a conventional method that uses a non-uniform rational B-spline (NURBS) approach.
View Article and Find Full Text PDFMicroorganisms
January 2025
Institute of Aquaculture Torre de la Sal (IATS-CSIC), 12595 Ribera de Cabanes, Spain.
The significant microbiota variability represents a key feature that makes the full comprehension of the functional interaction between microbiota and the host an ongoing challenge. To overcome this limitation, in this study, fish intestinal microbiota was analyzed through a meta-analysis, identifying the core microbiota and constructing stochastic Bayesian network (BN) models with SAMBA. We combined three experiments performed with gilthead sea bream juveniles of the same hatchery batch, reared at the same season/location, and fed with diets enriched on processed animal proteins (PAP) and other alternative ingredients (NOPAP-PP, NOPAP-SCP).
View Article and Find Full Text PDFFoods
January 2025
Guizhou Guotai Distillery Co., Ltd., Renhuai 564501, China.
Stacking fermentation is critical in sauce-flavor production, but winter production often sees abnormal fermentations, like Waistline and Sub-Temp fermentation, affecting yield and quality. This study used three machine learning models (Logistic Regression, KNN, and Random Forest) combined with multi-omics (metagenomics and flavoromics) to develop a classification model for abnormal fermentation. SHAP analysis identified 13 Sub-Temp Fermentation and 9 Waistline microbial biomarkers, along with 9 Sub-Temp Fermentation and 12 Waistline flavor biomarkers.
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