Results 211 to 220 of about 129,653 (280)

Information Dense and Industry Scalable Accelerated Formation

Advanced Intelligent Discovery, EarlyView.
Pulsed formation can reduce lithium‐ion battery formation time by over 50% while maintaining or enhancing performance. Validated on 25 Ah prismatic cells, this industry‐scalable method yields thinner, more homogeneous solid electrolyte interphases (SEIs).
Leon Merker, Markus Blessing, Bojing Zhang, Helge Sören Stein   +3 more
wiley   +1 more source

Effect of cognitive bias modification training on body image dissatisfaction in adolescents with anorexia nervosa or depression-a pilot feasibility randomized controlled crossover study. [PDF]

Front Psychol
Glombitza E, Cornelissen PL, Tovée MJ, Legenbauer T.   +3 more
europepmc   +1 more source

What to Make and How to Make It: Combining Machine Learning and Statistical Learning to Design New Materials

Advanced Intelligent Discovery, EarlyView.
Combining machine learning and probabilistic statistical learning is a powerful way to discover and design new materials. A variety of machine learning approaches can be used to identify promising candidates for target applications, and causal inference can help identify potential ways to make them a reality.
Jonathan Y. C. Ting, Amanda S. Barnard
wiley   +1 more source

Using an audiovisual feedback device improves cardiopulmonary resuscitation performance during day and night - a randomized controlled simulation study. [PDF]

BMC Emerg Med
Preuss M, Röhrig R, Hübel C, Vos C, Unterkofler J, Brokmann JC, Willmes K, Plata C.   +7 more
europepmc   +1 more source

Artificial Intelligence‐Driven Insights into Electrospinning: Machine Learning Models to Predict Cotton‐Wool‐Like Structure of Electrospun Fibers

Advanced Intelligent Discovery, EarlyView.
Electrospinning allows the fabrication of fibrous 3D cotton‐wool‐like scaffolds for tissue engineering. Optimizing this process traditionally relies on trial‐and‐error approaches, and artificial intelligence (AI)‐based tools can support it, with the prediction of fiber properties. This work uses machine learning to classify and predict the structure of
Paolo D’Elia, Giovanna Pappalardo, Aldo R. Boccaccini, Liliana Liverani   +3 more
wiley   +1 more source

Information uncertainty influences learning strategy from sequentially delayed rewards. [PDF]

PLoS Comput Biol
Maulhardt SR, Solway A, Charpentier CJ.
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

Sequential logic circuit gold codes for electronics and communication technologies. [PDF]

MethodsX
Devrari A, Kumar A, Kuchhal P, Illés Z, Verma C.   +4 more
europepmc   +1 more source

Evaluation of a structured breathing-based intervention to reduce burnout and enhance mental well-being among healthcare professionals in community-based practice settings: study protocol for a single-arm pilot feasibility trial. [PDF]

BMJ Open
Sharma P, Osman R, Nycklemoe M, Boos DM, Murphy CM, Flock C, Jacobson DN, Lim S, Nation JL, Wilson MA, Schuldt K, Min PH.   +11 more
europepmc   +1 more source

Reexamining Korea's successful forest restoration through the revised PDCA framework. [PDF]

Environ Manage
Park H, Han H, Kim D, Kim S.
europepmc   +1 more source