Results 251 to 260 of about 650,861 (329)
Evolution problems with perturbed 1-Laplacian type operators on random walk spaces. [PDF]
Górny W, Mazón JM, Toledo J.
europepmc +1 more source
Hydrostatic bearings excel in high‐precision applications, but their performance hinges on a continuous external supply. This study evaluates various material combinations for sliding surfaces to mitigate damage during supply failures or misalignment and to discover the most effective materials identified for enhancing the reliability and efficiency of
Michal Michalec+6 more
wiley +1 more source
Bayesian network structure learning by opposition-based learning. [PDF]
Sun B, Zhang X, Jiang J, Gong J, Lin D.
europepmc +1 more source
A Different Perspective on the Solid Lubrication Performance of Black Phosphorous: Friend or Foe?
Researchers investigate black phosphorous (BP) as a standalone solid lubricant coating through ball‐on‐disc linear‐reciprocating sliding experiments in dry conditions. Testing on different metals shows BP doesn't universally reduce friction and wear. However, it achieves 33% friction reduction on rougher iron surfaces and 23% wear reduction on aluminum.
Matteo Vezzelli+5 more
wiley +1 more source
An Efficient Evolutionary Neural Architecture Search Algorithm Without Training. [PDF]
An Y, Zhang C, Shao J, Yan Y, Sun B.
europepmc +1 more source
Hybrid materials enable high‐performance components but are challenging to process. This study explores an inductive heating concept with spray cooling for steel–aluminum specimens in a two‐step process including friction welding and cup backward extrusion.
Armin Piwek+7 more
wiley +1 more source
A quantum-inspired neural fuzzy sliding mode control framework for fractional-order modeling of intraocular pressure regulation and optic nerve damage in glaucoma. [PDF]
Amilo D.
europepmc +1 more source
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+4 more
wiley +1 more source
Highest-Weight Vectors and Three-Point Functions in GKO Coset Decomposition. [PDF]
Bershtein M, Feigin B, Trufanov A.
europepmc +1 more source
Beyond Order: Perspectives on Leveraging Machine Learning for Disordered Materials
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+4 more
wiley +1 more source