Results 181 to 190 of about 1,963,594 (263)

A Systematic Review on the Applications of Uppaal. [PDF]

open access: yesSensors (Basel)
Grobelna I, Gajewski K, Karatkevich A.
europepmc   +1 more source

Electrospun Porous Materials for the Next‐Generation Proton Exchange Membrane Fuel Cells: Progress, Challenges, and AI‐Optimized Perspectives

open access: yesCarbon Energy, EarlyView.
Incorporating AI to optimize solution, process, and environment enables efficient prediction of structural properties and performance, allowing tailoring of GDL, CL, and PEM fabrication to guide PEMFC toward 9.0 kW L‒1 by 2040. ABSTRACT The performance and commercial viability of proton exchange membrane fuel cells (PEMFCs) are constrained by key ...
Shangfeng Jiang   +11 more
wiley   +1 more source

Harnessing machine learning and optimization for informed chemical engineering decisions: A styrene reactor analysis

open access: yesThe Canadian Journal of Chemical Engineering, EarlyView.
This study shows that integrating multiple machine learning models with optimization and decision‐making improves chemical process design, and that a consensus‐based strategy across models provides more robust and reliable operating recommendations than any single model, especially under limited or noisy data conditions.
Farough Agin   +2 more
wiley   +1 more source

Copula‐based joint modelling of emergency department visits with time‐varying dependence

open access: yesCanadian Journal of Statistics, EarlyView.
Abstract Jointly modelling multiple correlated count time series is essential in health services research, where outcomes like emergency visits for mental health and substance use often evolve together. Ignoring these dependencies can obscure meaningful trends and limit the effectiveness of policy evaluation.
Guanjie Lyu, Cindy Feng, Lihui Liu
wiley   +1 more source

Sparse maximum likelihood estimation of regression models

open access: yesCanadian Journal of Statistics, EarlyView.
Abstract For regression model selection and estimation, we study a small set of candidate models of maximum likelihood from which all information criteria such as the Akaike information criterion (AIC) and the Bayesian information criterion (BIC) choose their models.
Min Tsao
wiley   +1 more source

Model selection for causal inference in missing exposure problems

open access: yesCanadian Journal of Statistics, EarlyView.
Abstract In causal inference, properly selecting the propensity score (PS) model is an important topic and has been widely investigated in observational studies. However, there are very few studies investigating the model selection issue for causal inference when the exposure is missing at random (MAR). When the exposure variable contains missing data,
Yuliang Shi, Yeying Zhu, Joel A. Dubin
wiley   +1 more source

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