Results 221 to 230 of about 24,269,673 (319)

Self-reported mental distress in the United States: a Bayesian analysis of the spatial structure over the COVID-19 pandemic across age groups. [PDF]

Int J Health Geogr
Comas C, Martínez A, Blanch A.
europepmc   +1 more source

Bayesian Data Analysis, 2nd edn

, 2005
A. Brix
semanticscholar   +1 more source

A Machine Learning Model for Interpretable PECVD Deposition Rate Prediction

Advanced Intelligent Discovery, EarlyView.
This study develops six machine learning models (k‐nearest neighbors, support vector regression, decision tree, random forest, CatBoost, and backpropagation neural network) to predict SiNx deposition rates in plasma‐enhanced chemical vapor deposition using hybrid production and simulation data.
Yuxuan Zhai, Wenrui Wang, Rui Chen, Tong Yu, Xiaojuan Zheng, Hua Shao, Dashan Shang, Ling Li, Lado Filipovic   +8 more
wiley   +1 more source

The Impact of Sequencing and Genotyping Errors on Bayesian Analysis of Genomic Data under the Multispecies Coalescent Model. [PDF]

Mol Biol Evol
Ji J, Kapli P, Flouri T, Yang Z.
europepmc   +1 more source

Topology‐Aware Machine Learning for High‐Throughput Screening of MOFs in C8 Aromatic Separation

Advanced Intelligent Discovery, EarlyView.
We screened 15,335 Computation‐Ready, Experimental Metal–Organic Frameworks (CoRE‐MOFs) using a topology‐aware machine learning (ML) model that integrates structural, chemical, pore‐size, and topological descriptors. Top‐performing MOFs exhibit aromatic‐enriched cavities and open metal sites that enable π–π and C–H···π interactions, serving as ...
Yu Li, Honglin Li, Jialu Li, Wan‐Lu Li
wiley   +1 more source

Exploring the Interactions Between Psychotic Symptoms, Cognition, and Environmental Risk Factors: A Bayesian Analysis of Networks. [PDF]

Schizophr Bull
Bosma MJ, Marsman M, Vermeulen JM, Huth KBS, de Haan L, Alizadeh BZ, Simons CJP, Schirmbeck F.   +7 more
europepmc   +1 more source

Deep Learning‐Assisted Design of Mechanical Metamaterials

Advanced Intelligent Discovery, EarlyView.
This review examines the role of data‐driven deep learning methodologies in advancing mechanical metamaterial design, focusing on the specific methodologies, applications, challenges, and outlooks of this field. Mechanical metamaterials (MMs), characterized by their extraordinary mechanical behaviors derived from architected microstructures, have ...
Zisheng Zong, Pu Wen, Zhiping Chai, Dunxuan Wu, Han Ding, Zhigang Wu   +5 more
wiley   +1 more source

A Bayesian analysis of diagnostic timelines across Alzheimer's disease, frontotemporal dementia, and other neurodegenerative conditions. [PDF]

Alzheimers Dement (Amst)
Ambikairajah A, Foxe D, de Lange AG, Carrick J, Cheung SC, Srikanth VK, Hwang YT, Ahmed RM, Burrell JR, Piguet O.   +9 more
europepmc   +1 more source

Large Language Model in Materials Science: Roles, Challenges, and Strategic Outlook

Advanced Intelligent Discovery, EarlyView.
Large language models (LLMs) are reshaping materials science. Acting as Oracle, Surrogate, Quant, and Arbiter, they now extract knowledge, predict properties, gauge risk, and steer decisions within a traceable loop. Overcoming data heterogeneity, hallucinations, and poor interpretability demands domain‐adapted models, cross‐modal data standards, and ...
Jinglan Zhang, Xinyi Chen, Xu Ye, Yulin Yang, Bin Ai   +4 more
wiley   +1 more source