Results 191 to 200 of about 1,147,666 (317)

Design and Applications of Multi‐Frequency Programmable Metamaterials for Adaptive Stealth

Advanced Functional Materials, EarlyView.
This article provides a comprehensive overview of metamaterials, including their fundamental principles, properties, synthesis techniques, and applications in stealth, as well as their challenges and future prospects. It covers topics that are more advanced than those typically discussed in existing review articles, while still being closely connected ...
Jonathan Tersur Orasugh, Ankita Mohanty, Amit Malakar, Suryasarathi Bose, Suprakas Sinha Ray   +4 more
wiley   +1 more source

AI-Prediction of <i>Neisseria gonorrhoeae</i> Resistance at the Point of Care from Genomic and Epidemiologic Data. [PDF]

Healthcare (Basel)
Kolluru V, Hole SR, Sagar A, Chintakunta AN, R J, Salotagi S.   +5 more
europepmc   +1 more source

Smart, Bio‐Inspired Polymers and Bio‐Based Molecules Modified by Zwitterionic Motifs to Design Next‐Generation Materials for Medical Applications

Advanced Functional Materials, EarlyView.
Bio‐based and (semi‐)synthetic zwitterion‐modified novel materials and fully synthetic next‐generation alternatives show the importance of material design for different biomedical applications. The zwitterionic character affects the physiochemical behavior of the material and deepens the understanding of chemical interaction mechanisms within the ...
Theresa M. Lutz, Jonas De Breuck, Sahar Salehi, Meike N. Leiske   +3 more
wiley   +1 more source

Knowledge transfer, knowledge gaps, and knowledge silos in citation networks. [PDF]

PLoS One
Cunningham E, Greene D.
europepmc   +1 more source

Unleashing the Power of Machine Learning in Nanomedicine Formulation Development

Advanced Functional Materials, EarlyView.
A random forest machine learning model is able to make predictions on nanoparticle attributes of different nanomedicines (i.e. lipid nanoparticles, liposomes, or PLGA nanoparticles) based on microfluidic formulation parameters. Machine learning models are based on a database of nanoparticle formulations, and models are able to generate unique solutions
Thomas L. Moore, Cristiano Pesce, Antonietta Greco, Claudia Pisante, Greta Avancini, Valentina Di Francesco, Yosi Shamay, Paolo Decuzzi   +7 more
wiley   +1 more source

AI-driven analysis of diabetes risk determinants in U.S. adults: Exploring disease prevalence and health factors. [PDF]

PLoS One
Majcherek D, Ciesielski A, Sobczak P.
europepmc   +1 more source

Electron Compensation Enhanced Triboelectric Sensor Assisted by Machine Learning for Tactile Perception Recognition

Advanced Functional Materials, EarlyView.
Integrating polyethyleneimine and carbon black into polyurethane enhances electron transport and mechanical durability. The resulting sensor achieves significantly improved electrical signal and sensitivity, enabling efficient machine learning‐based tactile signal recognition in bionic applications.
Xiangkun Bo, Wei Wang, Hong Zhao, Ruiqin Zhang, Walid A. Daoud   +4 more
wiley   +1 more source

Correction: Use of machine learning to predict creativity among nurses: a multidisciplinary approach. [PDF]

BMC Nurs
Mudallal RH, Mrayyan MT, Kharabsheh M.
europepmc   +1 more source

Advanced Cellulose‐Based Gels for Wearable Physiological Monitoring: From Fiber Modification to Application Optimization

Advanced Functional Materials, EarlyView.
This review discusses cellulose‐based hydrogels technology, analyzes their application progress in physiological signal monitoring, and explores the effects of pretreatment, crosslinking, and molding methods on gel performance, to provide valuable insights into the efficient utilization of plant fibers and the environmentally friendly development of ...
Zhiming Wang, Jinxuan Jiang, Kexin Wei, Xiaojian Zhou, Muhammad Wakil Shahzad, Yifan Li, Xuehua Zhang, Ben Bin Xu, Shengbo Ge   +8 more
wiley   +1 more source

A comprehensive dataset of rice leaf images for disease detection using machine learning. [PDF]

Data Brief
Hasan A, Layes TA, Afridi AS, Rifat SH, Nur FN, Moon NN.   +5 more
europepmc   +1 more source