Results 221 to 230 of about 794,564 (269)

Bridging Nature and Technology: A Perspective on Role of Machine Learning in Bioinspired Ceramics

Advanced Engineering Materials, EarlyView.
Machine learning (ML) is revolutionizing the development of bioinspired ceramics. This article investigates how ML can be used to design new ceramic materials with exceptional performance, inspired by the structures found in nature. The research highlights how ML can predict material properties, optimize designs, and create advanced models to unlock a ...
Hamidreza Yazdani Sarvestani, Aditi Singh, Behnam Ashrafi   +2 more
wiley   +1 more source

Unsupervised Deep Feature Learning for Deformable Registration of MR Brain Images

, 2013
Guorong Wu, Minjeong Kim, Qian Wang, Yaozong Gao, Shu Liao, Dinggang Shen   +5 more
openalex   +1 more source

Advances in Hybrid Icing and Frosting Protection Strategies for Optics, Lens, and Photonics in Cold Environments Using Thin‐Film Acoustic Waves

Advanced Engineering Materials, EarlyView.
This article provides a comprehensive overview of fundamentals and recent advances of transparent thin‐film surface acoustic wave technologies on glass substrates for monitoring and prevention/elimination of fog, ice, and frost. Fogging, icing, or frosting on optical lenses, optics/photonics, windshields, vehicle/airplane windows, and solar panel ...
Hui Ling Ong, Zhangbin Ji, Luke Haworth, Yihao Guo, Jaime del Moral, Stefan Jacob, Ana Borras, Agustin R. Gonzalez‐Elipe, Jikai Zhang, Jian Zhou, Glen McHale, Yong‐Qing Fu   +11 more
wiley   +1 more source

DL-PRO: A novel deep learning method for protein model quality assessment [PDF]

, 2014
Son P. Nguyen, Yi Shang, Dong Xu
openalex   +1 more source

Static and Dynamic Behavior of Novel Y‐Shaped Sandwich Beams Subjected to Compressive Loadings: Integration of Supervised Learning and Experimentation

Advanced Engineering Materials, EarlyView.
In this study, the mechanical response of Y‐shaped core sandwich beams under compressive loading is investigated, using deep feed‐forward neural networks (DFNNs) for predictive modeling. The DFNN model accurately captures stress–strain behavior, influenced by design parameters and loading rates.
Ali Khalvandi, Saeed Kamarian, Mahdi Bodaghi, Saeed Saber‐Samandari, Jung‐il Song   +4 more
wiley   +1 more source

Consolidate Overview of Ribonucleic Acid Molecular Dynamics: From Molecular Movements to Material Innovations

Advanced Engineering Materials, EarlyView.
Molecular dynamics simulations are advancing the study of ribonucleic acid (RNA) and RNA‐conjugated molecules. These developments include improvements in force fields, long‐timescale dynamics, and coarse‐grained models, addressing limitations and refining methods.
Kanchan Yadav, Iksoo Jang, Jong Bum Lee
wiley   +1 more source

Nanoparticle‐Coated X2CrNiMo17‐12‐2 Powder for Additive Manufacturing – Part I: Surface, Flowability, and Optical Properties of SiC, Si, and Si3N4 Coated Metal Powders

Advanced Engineering Materials, EarlyView.
Herein, silicon‐based nanoparticle coatings on X2CrNiMo17‐12‐2 metal powder are presented. The coating process scale, process parameters, nanoparticle size (65–200 nm) as well as the coating amount are discussed regarding powder properties. The surface roughness affects the flowability, while reflectance depends on the coating material and surface ...
Arne Lüddecke, Nick Hantke, Harald Zetzener, Jan T. Sehrt, Arno Kwade   +4 more
wiley   +1 more source

A Deeper Lecturer and Student View of a Sustainable Learning Requirement in Tertiary Education in Malaysia

, 2013
Christian Kahl
openalex   +1 more source

Beyond Order: Perspectives on Leveraging Machine Learning for Disordered Materials

Advanced Engineering Materials, EarlyView.
This article explores how machine learning (ML) revolutionizes the study and design of disordered materials by uncovering hidden patterns, predicting properties, and optimizing multiscale structures. It highlights key advancements, including generative models, graph neural networks, and hybrid ML‐physics methods, addressing challenges like data ...
Hamidreza Yazdani Sarvestani, Surabhi Nadigotti, Erfan Fatehi, Derek Aranguren van Egmond, Behnam Ashrafi   +4 more
wiley   +1 more source

Active Hydrogen for Electrochemical Ammonia Synthesis

Advanced Functional Materials, EarlyView.
This review provides a comprehensive overview of the active hydrogen (H*) for electrochemical ammonia synthesis with particular attention given to the regulation of H* generation and consumption to suppress the competition of hydrogen evolution reaction and enhance the yield, selectivity, and Faradaic efficiency of ammonia.
Guoqiang Gan, Guo Hong, Wenjun Zhang
wiley   +1 more source