Results 231 to 240 of about 790,487 (351)

Retraction Note: Recent advances in green technology and Industrial Revolution 4.0 for a sustainable future. [PDF]

open access: yesEnviron Sci Pollut Res Int
Bradu P   +10 more
europepmc   +1 more source

AWARENESS, APPLICATION AND USE OF FOURTH INDUSTRIAL REVOLUTION (4IR) TECHNOLOGIES IN SELECTED LIBRARIES IN OGUN STATE, NIGERIA

open access: green
Oluseun Mobolanle Sodipe   +3 more
openalex   +1 more source

Smart Flexible Tactile Sensors: Recent Progress in Device Designs, Intelligent Algorithms, and Multidisciplinary Applications

open access: yesAdvanced Intelligent Discovery, EarlyView.
Flexible tactile sensors have considerable potential for broad application in healthcare monitoring, human–machine interfaces, and bioinspired robotics. This review explores recent progress in device design, performance optimization, and intelligent applications. It highlights how AI algorithms enhance environmental adaptability and perception accuracy
Siyuan Wang   +3 more
wiley   +1 more source

Democratizing ownership and participation in the 4th Industrial Revolution: challenges and opportunities in cellular agriculture. [PDF]

open access: yesAgric Human Values, 2021
Chiles RM   +8 more
europepmc   +1 more source

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

open access: yesAdvanced 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   +4 more
wiley   +1 more source

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

open access: yesAdvanced 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   +4 more
wiley   +1 more source

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