Advanced Materials, EarlyView.A transparent, deformable stevia–PVA hydrogel triboelectric nanogenerator delivers significantly enhanced mechanical strength and electrical output through biomimetic hydrogen‐bonded networks. Coupled with machine learning–assisted signal recognition, the self‐powered hydrogel enables accurate human‐motion sensing for intelligent wearable and IoT ...
Thien Trung Luu +5 more
wiley +1 more source
Artificial intelligence (AI) and machine learning (ML) in ovarian cancer: transforming detection, treatment, and prevention. [PDF]
J Ovarian ResSingh M, Betgeri SN, Kakar SS.
europepmc +1 more source
Classification Framework for Healthy Hairs and Alopecia Areata: A Machine Learning (ML) Approach. [PDF]
Comput Math Methods Med, 2021 Shakeel CS +4 more
europepmc +1 more source
Advanced Materials, EarlyView.Template effect and kinetic control enable crystal‐phase engineering of Ru nanocrystals, granting access to either metastable fcc‐Ru or stable hcp‐Ru with distinct surface structures, thermal stabilities, and catalytic behaviors. Moreover, the hcp‐Ru can further serve as an epitaxial template to direct Pd and Rh nanocrystals into the metastable hcp ...
Jianlong He +3 more
wiley +1 more source
A Review on Sustainable Recycling of NdFeB Waste: Methodologies, Challenges, and the Integration of Machine Learning (ML). [PDF]
Materials (Basel)Ullah R +5 more
europepmc +1 more source
Why machine learning (ML) has failed physical activity research and how we can improve. [PDF]
BMJ Open Sport Exerc Med, 2022 Fuller D, Ferber R, Stanley K.
europepmc +1 more source
Advanced Materials, EarlyView.ABSTRACT Accurately knowing the frontier orbital energies of the structurally disordered small‐molecule organic semiconductors that are used in optoelectronic devices such as organic light‐emitting diodes is required to rationally improve their performance. Here, we show that these energies can be deduced with a large accuracy from the peak energies of
Christian B. McDonald +7 more
wiley +1 more source
New Analytical Strategies for Quality Control and Classification of Apple Juices Using Digital Image Processing (DIP) Combined with Machine Learning (ML). [PDF]
ACS OmegaKaczala S, de Lima VA, Felsner ML.
europepmc +1 more source
Phase Engineering of Nanomaterials (PEN): Evolution, Current Challenges, and Future Opportunities
Advanced Materials, EarlyView.This review summarizes the synthesis, phase transition, advanced characterization spanning ex situ to in situ and operando techniques, and diverse applications of phase engineering of nanomaterials (PEN). It further outlines key challenges and future opportunities, such as phase stability, architecture control, and artificial intelligence (AI)‐driven ...
Ye Chen +7 more
wiley +1 more source
Applications of Machine Learning (ML) in the context of marketing: a bibliometric approach. [PDF]
F1000ResCardona-Acevedo S +7 more
europepmc +1 more source