Results 271 to 280 of about 522,455 (311)

AI–Guided 4D Printing of Carnivorous Plants–Inspired Microneedles for Accelerated Wound Healing

Advanced Materials, EarlyView.
This work presents an artificial intelligence (AI)‐guided 4D‐printed microneedle platform inspired by carnivorous plants for wound healing. A thermo‐responsive shape memory polymer enables body temperature–triggered self‐coiling for autonomous wound closure.
Hyun Lee, Moon‐Jo Kim, DongEung Kim, Chan Ho Moon, Seojoon Bang, Hyeong Seok Kang, Ju Yeong Gwon, Jong Hwa Seo, Junhyub Jeon, Junhyuk Son, Munwon Lim, Minho Kang, Dong Yun Lee, Donghyun Lim, Jung‐Hoon Park, Gi Doo Cha, Soo‐Hong Lee, Tae‐Sik Jang, Kisuk Yang, Yunho Jeong, Youngho Eom, Hyun‐Do Jung   +21 more
wiley   +1 more source

Applications of artificial intelligence and machine learning for cardiovascular risk management in the public health policy context: a systematic review protocol. [PDF]

BMJ Open
Arias-Ortiz WA, Ibañez-Pinilla EA, Cala-Vitery FE.   +2 more
europepmc   +1 more source

Hidden in the Pangenome? Machine Learning-Driven Discovery of Antimicrobial Potential in <i>Corynebacterium glutamicum</i>. [PDF]

Bioinform Biol Insights
Islam SI, Shahed K, Parvin MM, Sanjida S.   +3 more
europepmc   +1 more source

Evaluating machine learning algorithms based on thermal imaging and milk-based parameters to identify subclinical mastitis in dairy cows. [PDF]

Vet Q
Paudyal S, Neupane R, Shrestha B, Chiang HI, Aryal A.   +4 more
europepmc   +1 more source

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Potential of edge machine learning for instrumentation

Applied Optics, 2022
New developments in radiation and photonic detectors improve resolution, sensitivity, size, and rate, all of which contribute to a gigantic increase in the data production rate. Moving data analysis and compression adjacent or even embedded within the detector hardware will reduce the data volumes generated, thereby reducing material cost, power, and ...
Audrey C. Therrien, Berthié Gouin-Ferland, Mohammad Mehdi Rahimifar   +2 more
openaire   +2 more sources

Pair Potentials as Machine Learning Features

Journal of Chemical Theory and Computation, 2020
Atom pairwise potential functions make up an essential part of many scoring functions for protein decoy detection. With the development of machine learning (ML) tools, there are multiple ways to combine potential functions to create novel ML models and methods.
Jun Pei, Lin Frank Song, Kenneth M. Merz
openaire   +2 more sources