Results 171 to 180 of about 1,581,551 (302)

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

Herbal product use in urban slum communities of Freetown, Sierra Leone: Consumption patterns, predictors, and ethnomedicinal applications. [PDF]

J Ethnobiol Ethnomed
Vandy A, Turay BA, Lahai M, Bah N, Osborne A.   +4 more
europepmc   +1 more source

East Bay Energy Consortium Meeting Minutes, September 14, 2009 [PDF]

, 2009
East Bay Energy Consortium
core   +1 more source

Artificial Intelligence for Bone: Theory, Methods, and Applications

Advanced Intelligent Discovery, EarlyView.
Advances in artificial intelligence (AI) offer the potential to improve bone research. The current review explores the contributions of AI to pathological study, biomarker discovery, drug design, and clinical diagnosis and prognosis of bone diseases. We envision that AI‐driven methodologies will enable identifying novel targets for drugs discovery. The
Dongfeng Yuan, Haicang Zhang, Liping Tong, Di Chen   +3 more
wiley   +1 more source

Seasonal Variation of Essential Oil Quantity and Quality in Bay Laurel (<i>Laurus nobilis</i> L.) Leaves from Montenegro. [PDF]

Plants (Basel)
Ilić ZS, Stanojević L, Milenković L, Milenković A, Šunić L, Ilić D, Stanojević J, Cvetković D, Božović D, Kevrešan Ž.   +9 more
europepmc   +1 more source

East Bay Energy Consortium Meeting Minutes, September 21, 2009 [PDF]

, 2009
East Bay Energy Consortium
core   +1 more source

Advances in Thermal Modeling and Simulation of Lithium‐Ion Batteries with Machine Learning Approaches

Advanced Intelligent Discovery, EarlyView.
Heat generation in lithium‐ion batteries affects performance, aging, and safety, requiring accurate thermal modeling. Traditional methods face efficiency and adaptability challenges. This article reviews machine learning‐based and hybrid modeling approaches, integrating data and physics to improve parameter estimation and temperature prediction ...
Qi Lin, Kang Fu, Junjie Ding, Kai Sun, Peng Tan   +4 more
wiley   +1 more source

Temporal stability and spatial patterns of genetic diversity in populations of the climate-vulnerable fucoid Scytothalia dorycarpa. [PDF]

J Phycol
Edgeloe JM, Coleman MA, Wood GV, Starko S, Nimbs MJ, Batley J, Wernberg T.   +6 more
europepmc   +1 more source

East Bay Energy Consortium Joint Committee Workshop at the Corporate Offices of Applied Science Associates, February 12, 2010, Meeting Notes [PDF]

, 2010
East Bay Energy Consortium
core   +1 more source

Toward Predictable Nanomedicine: Current Forecasting Frameworks for Nanoparticle–Biology Interactions

Advanced Intelligent Discovery, EarlyView.
Predictive models successfully screen nanoparticles for toxicity and cellular uptake. Yet, complex biological dynamics and sparse, nonstandardized data limit their accuracy. The field urgently needs integrated artificial intelligence/machine learning, systems biology, and open‐access data protocols to bridge the gap between materials science and safe ...
Mariya L. Ivanova, Michael Nicholls, Nicola Russo, Gueorgui Mihaylov, Konstantin Nikolic   +4 more
wiley   +1 more source