Results 51 to 60 of about 461 (226)

Capacitive versus Faradaic Microelectrodes for Extracellular Stimulation: A Fully Coupled FEM–Hodgkin–Huxley Study of Thresholds and Current Redistribution

Advanced Electronic Materials, EarlyView.
A fully coupled FEM–HH model shows that ideally capacitive microelectrodes can achieve lower charge‐density thresholds than Faradaic contacts under current‐controlled stimulation. The advantage stems from the dynamics of surface current density on capacitive interfaces, which redirects current beneath adherent neurons.
Aleksandar Opančar, Eric Daniel Głowacki, Vedran Đerek   +2 more
wiley   +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

Minding the Intergenerational Gap: The Impact of Age on Knowledge Hiding and Organizational Justice

, 2023
Intergenerational workplaces contain multiple generations (Iweins et al., 2013) contributing to an organization’s climate with unique challenges as employees learn to work together despite generational barriers.
Baker, Courtney, PhD, Jones, Seth W
core  

Taguchi–Bayesian Sampling: A Roadmap for Polymer Database Construction Toward Small Representative Machine Learning

Advanced Intelligent Discovery, EarlyView.
This article establishes a Taguchi–Bayesian sampling strategy to reconstruct polymer processing–property landscape at minimal sampling cost, generically building the roadmap for materials database construction from sampling their vast design space. This sampling strategy is featured by an alternating lesson between uniformity and representativeness ...
Han Liu, Liantang Li
wiley   +1 more source

Harnessing Machine Learning to Understand and Design Disordered Solids

Advanced Intelligent Discovery, EarlyView.
This review maps the dynamic evolution of machine learning in disordered solids, from structural representations to generative modeling. It explores how deep learning and model explainability transform property prediction into profound physical insight.
Muchen Wang, Yue Fan
wiley   +1 more source

Knowledge hiding in academia: an empirical study of Indian higher education students

, 2021
Purpose: This study aims to investigate the role of knowledge hiding (KH) on academic performance, using three antecedents – relatedness with peers, territoriality of knowledge and performance motivation.
Garg, Neha, Ganguly, Anirban, Talukdar, Asim, Kumar, Chitresh   +3 more
core   +1 more source

Why Physics Still Matters: Improving Machine Learning Prediction of Material Properties With Phonon‐Informed Datasets

Advanced Intelligent Discovery, EarlyView.
Phonons‐informed machine‐learning predictive models are propitious for reproducing thermal effects in computational materials science studies. Machine learning (ML) methods have become powerful tools for predicting material properties with near first‐principles accuracy and vastly reduced computational cost.
Pol Benítez, Cibrán López, Edgardo Saucedo, Teruyasu Mizoguchi, Claudio Cazorla   +4 more
wiley   +1 more source

The dual effects of job design on knowledge hiding: expanding job demands–resources theory to employee rational-choice behaviour

The International Journal of Human Resource Management
Abstract.
Shujahat, Muhammad, Wang, Minhong, Ali, Murad, Zhu, Qinghua, Škerlavaj, Miha   +4 more
openaire   +2 more sources

Accelerating Discovery of Organic Molecular Crystals via Materials Informatics and Autonomous Experiments

Advanced Intelligent Discovery, EarlyView.
Materials informatics and autonomous experimentation are transforming the discovery of organic molecular crystals. This review presents an integrated molecule–crystal–function–optimization workflow combining machine learning, crystal structure prediction, and Bayesian optimization with robotic platforms.
Takuya Taniguchi, Kazuki Ishizaki, Ryo Fukasawa   +2 more
wiley   +1 more source

Interpersonal conflict at work and knowledge hiding in service organizations : the mediator role of employee well-being

, 2020
Purpose This paper aims to explore the effects of interpersonal conflicts in the social workplace on various rationalized, knowledge-hiding behaviors in service organizations.
Losada Otálora, Mauricio, Sánchez, Iván Darío, Peña García, Nathalie   +2 more
core   +1 more source