Results 221 to 230 of about 4,158,085 (300)

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

The Selectively Nontargeted Analysis of Halogenated Disinfection Byproducts in Tap Water by Micro-LC QTOFMS. [PDF]

Toxics
Wu J, Zhang Y, Zhang Q, Tan F, Liu Q, Yang X.   +5 more
europepmc   +1 more source

Smart Denture with Embedded Thin‐Film Temperature Sensors Patterned by Femtosecond Laser Pulse for Elderly Healthcare

Advanced Intelligent Systems, Volume 7, Issue 3, March 2025.
We demonstrate the direct‐laser patterning of a gold thin film on polymethyl methacrylate to fabricate a temperature sensor for dentures. The temperature sensor‐embedded smart dentures are evaluated in an oral environment, enabling in‐situ monitoring for elderly healthcare.
Han Ku Nam, Yong Kwon Chae, Seoung‐Jin Hong, Younggen Lee, Dongwook Yang, Byunggi Kim, Sang Wook Kang, Young‐Jin Kim   +7 more
wiley   +1 more source

Figure of merit for piezoelectric MEMS speakers. [PDF]

Microsyst Nanoeng
Sun M, Zhang M, Wang Y, Gong S, Sun C, Sun C, Liu C, Xu L, Pang W.   +8 more
europepmc   +1 more source

A Task and Motion Planning Framework for Partially Observable Household Manipulation Scenes

Advanced Intelligent Systems, EarlyView.
This paper presents a novel TAMP framework tackling partial observability, cluttered scenes, and vague goals via symbolic reasoning and occlusion‐aware planning. It outperforms prior learning, policy, and LLM‐based methods in six real and simulated household tasks.
Yuhong Ma, Yeqing Yuan, Shaoquan Wu, Han Yuan   +3 more
wiley   +1 more source

Study on Few-Shot Fault Diagnosis Method for Marine Fuel Systems Based on DT-SViT-KNN. [PDF]

Sensors (Basel)
Li S, Qi L, Shi J, Xiao H, Da B, Tang R, Zuo D.   +6 more
europepmc   +1 more source

IAR‐Net: Tabular Deep Learning Model for Interventionalist's Action Recognition

Advanced Intelligent Systems, EarlyView.
This study presents IAR‐Net, a deep‐learning framework for catheterization action recognition. To ensure optimality, this study quantifies interoperator similarities and differences using statistical tests, evaluates the distribution fidelity of synthetic data produced by six generative models, and benchmarks multiple deep‐learning models.
Toluwanimi Akinyemi, Olatunji Omisore, Wenjing Du, Wenke Duan, Chen Bailang, Liu Kun, Lei Wang, Minxin Wei   +7 more
wiley   +1 more source

Mollugin reacts with phenol thiol but not produce modification on cysteine discovered by a phenol thiol probe. [PDF]

Front Cell Dev Biol
Liu T, Qi R, Feng W, Li Z, Zhong Z, Bai C.   +5 more
europepmc   +1 more source

Paracomplete Logic Kl — Natural Deduction, its Automation, Complexity and Applications [PDF]

, 2018
Alexander Bolotov, Daniil Kozhemiachenko, Vasilyi Shangin   +2 more
openalex  

Co‐flowering with congeners does not affect buzz‐pollinator specialization and pollination performance in Rhexia mariana, but does affect floral trait variance

American Journal of Botany, EarlyView.
Abstract Premise Pollinator‐mediated plant‐plant interactions may be negative (i.e., competition, reproductive interference) or positive (i.e., facilitation). Especially when co‐flowering with close relatives (e.g., congeners), negative interactions through reproductive interference may be strong and result in floral trait divergence and increased ...
Agnes S. Dellinger, Karolina Gwardiak, Ash Kerber, Viktoria C. Wieser, Karen D. Pérez‐Arroyo   +4 more
wiley   +1 more source