Results 161 to 170 of about 7,806,329 (300)

Entropy‐Driven Design of Low‐Melting‐Point Alloys via Compositionally Complex Strategy

Advanced Engineering Materials, EarlyView.
Conventional low‐melting‐point alloys (LMPAs) are limited by a narrow compositional space and inherent property trade‐offs. This review presents an entropy‐driven design strategy that overcomes these limitations, ushering in a new class of low‐melting‐point compositionally complex alloys (LMCCAs).
Yinghui Shang, Jianxiang Zhao, Zhaoqi Chen, Hao Gong, Yinsen Yan, Junlei Tao, Yong Yang   +6 more
wiley   +1 more source

Controlling Grain Growth in Powder Bed Fusion of Yttria‐Stabilized Zirconia Using Femtosecond Lasers: Challenges and Methodological Insights

Advanced Engineering Materials, EarlyView.
Binder‐free laser powder bed fusion of 8YSZ with a femtosecond laser is used to map process windows linking scan strategy, heat accumulation, and grain growth. Time‐resolved thermography and simulations reveal thermal regimes that enable continuous, vitrified, and fine‐grained 8YSZ surface layers without absorptive additives and demonstrate ...
Markus Kühn, Sarah Kaden, Andrea Barz, Daniel Störzner, Jens Bliedtner, Andrés Fabián Lasagni   +5 more
wiley   +1 more source

From Euclidean to Graph-Structured Data : A Survey of Collaborative Learning

The conventional approach to machine learning, that is, collecting data, training models, and performing inference in a single location, faces fundamental limitations, including scalability and privacy, that restrict its applicability.
Fodor, Viktória, Girdzijauskas, Sarunas, Bourgerie, Rémi   +2 more
core   +1 more source

Predicting freeway incident duration using machine learning. [PDF]

PLoS One
Ju Y, Liu J, Ardakani MK, Li W, Li G.
europepmc   +1 more source

Leveraging Symbolic Artificial Intelligence and Fuzzy Logic for Materials Science: A Review of Methods, Challenges, and Applications to Scarce and Imperfect Experimental Data

Advanced Engineering Materials, EarlyView.
This article explores the transformative potential of symbolic artificial intelligence (AI) in the field of materials science, particularly in leveraging experimental data. The article presents several symbolic AI models and discusses their applications in materials science.
Ahmed Amrani, Mohamed‐Ali Belloum, Laurence Boudet, Valentin Fouillard, Edwin Friedmann, Guillaume Lacharme, Ali Mahmoud, Jean‐Philippe Poli   +7 more
wiley   +1 more source

Latent mixed-effect models for high-dimensional longitudinal data

Publisher Copyright: © 2025, Transactions on Machine Learning Research. All rights reserved.Modelling longitudinal data is an important yet challenging task.
Lönnroth, Otto, Haußmann, Manuel, Lähdesmäki, Harri, Ong, Priscilla   +3 more
core  

Artificial intelligence for dysphagia screening: A machine learning approach. [PDF]

PLOS Digit Health
Rech RS, Lopes Becker CD, Bernieri Schiavon DE, Vieira DP, Hilgert JB, Hugo FN.   +5 more
europepmc   +1 more source

Investigating the Low‐Temperature Phase Stability of the Binary Ta–W System

Advanced Engineering Materials, EarlyView.
Atomistic simulations show that the binary Ta–W system forms ordered intermetallic phases, B2‐TaW and D03‐TaW3, as 0 K ground states. Configurational entropy, however, lowers the free energy of the disordered bcc solid solution, which becomes the stable phase above about 400 K.
Klemens Lechner, Anna Sophie Jelinek, Velislava Terziyska, Peter Wagatha, Wolfram Knabl, Christian Mitterer, Helmut Clemens, David Holec   +7 more
wiley   +1 more source

Machine Learning Did Not Outperform Conventional Competing Risk Modeling to Predict Revision Arthroplasty

BACKGROUND: Estimating the risk of revision after arthroplasty could inform patient and surgeon decision-making. However, there is a lack of well-performing prediction models assisting in this task, which may be due to current conventional modeling ...
Machine Learning Consortium
core  

Explainable Machine Learning for Predicting Student Depression Risk. [PDF]

Bioengineering (Basel)
Cernechez R, Hosseini SE, Nadeem M, Pervez S, Salah M, Shahbaz M.   +5 more
europepmc   +1 more source