Retraction Note: Recent advances in green technology and Industrial Revolution 4.0 for a sustainable future. [PDF]
Bradu P +10 more
europepmc +1 more source
The Opportunities and Challenges Associated with the Implementation of Fourth Industrial Revolution Technologies to Manage Health and Safety. [PDF]
Malomane R, Musonda I, Okoro CS.
europepmc +1 more source
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
Editorial: Cyber security in the wake of fourth industrial revolution: opportunities and challenges. [PDF]
Ukwandu E, Hewage C, Hindy H.
europepmc +1 more source
Democratizing ownership and participation in the 4th Industrial Revolution: challenges and opportunities in cellular agriculture. [PDF]
Chiles RM +8 more
europepmc +1 more source
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
Food Preservation in the Industrial Revolution Epoch: Innovative High Pressure Processing (HPP, HPT) for the 21st-Century Sustainable Society. [PDF]
Sojecka AA, Drozd-Rzoska A, Rzoska SJ.
europepmc +1 more source
BioInspired, BioDriven, BioMADE: The U.S. Bioindustrial Manufacturing and Design Ecosystem as a driver of the 4th Industrial Revolution. [PDF]
Rose PP, Friedman D.
europepmc +1 more source
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

