Results 151 to 160 of about 42,084 (260)

Time-varying dynamic Bayesian network learning for an fMRI study of emotion processing. [PDF]

Stat Med
Sun L, Zhang A, Liang F.
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

A dynamic decision-making approach for cabin unlawful interference emergency disposal using dynamic Bayesian network. [PDF]

Sci Rep
Wu Y, He S, Shi J.
europepmc   +1 more source

Dynamic Bayesian network in infectious diseases surveillance: a simulation study. [PDF]

Sci Rep, 2019
Zhang T, Ma Y, Xiao X, Lin Y, Zhang X, Yin F, Li X.   +6 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

Dynamic Bayesian network structure learning based on an improved bacterial foraging optimization algorithm. [PDF]

Sci Rep
Meng G, Cong Z, Li T, Wang C, Zhou M, Wang B.   +5 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

Dynamic risk assessment of a coal slurry preparation system based on the structure-variable Dynamic Bayesian Network. [PDF]

PLoS One
Liu M, Wu L, Hou M.
europepmc   +1 more source

A Dynamic Bayesian Network model for the simulation of Amyotrophic Lateral Sclerosis progression. [PDF]

BMC Bioinformatics, 2019
Zandonà A, Vasta R, Chiò A, Di Camillo B.   +3 more
europepmc   +1 more source

Advanced Experiment Design Strategies for Drug Development

Advanced Intelligent Discovery, EarlyView.
Wang et al. analyze 592 drug development studies published between 2020 and 2024 that applied design of experiments methodologies. The review surveys both classical and emerging approaches—including Bayesian optimization and active learning—and identifies a critical gap between advanced experimental strategies and their practical adoption in ...
Fanjin Wang, Anthony Harker, Mohan Edirisinghe, Maryam Parhizkar   +3 more
wiley   +1 more source