Results 101 to 110 of about 1,765,864 (313)
Hepatology, EarlyView., 2022 Graphical summary of obesity‐induced NASH progression by LCN2 targeted to HSC activation. Abstract Background and Aims In obesity and type 2 diabetes mellitus, leptin promotes insulin resistance and contributes to the progression of NASH via activation of hepatic stellate cells (HSCs).
Kyung Eun Kim +12 more
wiley +1 more source
IEEE Access, 2018 Research on the uncertainty of wind power has a significant influence on power system planning and decision-making. This paper proposes a novel method for wind power interval forecasting based on rough sets theory, weighted Markov chain, and kernel ...
Xiyun Yang +3 more
doaj +1 more source
Quasi-exact Approximation of Hidden Markov Chain Filters [PDF]
This paper studies the application of exact simulation methods for multi-dimensional multiplicative noise stochastic differential equations to filtering.
Eckhard Platen, Renata Rendek
core
Advanced Intelligent Discovery, EarlyView.Heat generation in lithium‐ion batteries affects performance, aging, and safety, requiring accurate thermal modeling. Traditional methods face efficiency and adaptability challenges. This article reviews machine learning‐based and hybrid modeling approaches, integrating data and physics to improve parameter estimation and temperature prediction ...
Qi Lin +4 more
wiley +1 more source
A Markov Chain Model for Contagion
Risks, 2014 We introduce a bivariate Markov chain counting process with contagion for modelling the clustering arrival of loss claims with delayed settlement for an insurance company.
Angelos Dassios, Hongbiao Zhao
doaj +1 more source
Publications de l'Institut Mathematique, 2008 Let {Xi, i _? 1} denote a sequence of variables that take values in {0, 1} and suppose that the sequence forms a Markov chain with transition matrix P and with initial distribution (q, p) = (P(X1 = 0), P(X1 = 1)). Several authors have studied the quantities Sn, Y (r) and AR(n), where Sn = ?n i=1 Xi denotes the number of successes, where Y (r) denotes ...
Omey, Edward, Van Gulck, Stefan
openaire +4 more sources
Advanced Intelligent Systems, EarlyView.A hybrid Reinforcement Learning–Explainable AI framework integrates SHAP and LIME explanations directly into a Deep Q‐Network inference loop for real‐time ICU decision support. Trained on 18 142 mechanically ventilated stays from the eICU database, the system attains 93.0% decision accuracy, 20% fewer errors than RL alone, and a 91% clinician trust ...
Jannatul Ferdaus Disha +2 more
wiley +1 more source
Accelerating Materials Discovery: A Review of Machine Learning in X‐Ray Absorption Spectroscopy
Advanced Intelligent Systems, EarlyView.This review systematically details how machine learning transforms X‐ray absorption spectroscopy (XAS) analysis. It covers advanced deep learning architectures for structure‐spectra mapping and inverse tasks, while discussing key challenges like the simulation‐to‐reality gap.
Melaku Lake Tegegne +5 more
wiley +1 more source
Density estimators through Zero Variance Markov Chain Monte Carlo [PDF]
A Markov Chain Monte Carlo method is proposed for the pointwise evaluation of a density whose normalizing constant is not known. This method was introduced in the physics literature by Assaraf et al (2007).
Daniele Imparato, Antonietta Mira
core
American Journal of Botany, EarlyView.Abstract Premise Desert plant assemblages in southern California provide an opportunity to link patterns of community structure with climate‐driven vulnerability in a rapidly changing environment. California sustains an exceptionally diverse flora of approximately 4300 plant species, with 31% identified as endemic.
Hector Zumbado‐Ulate +4 more
wiley +1 more source