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
I. On the Correction of the Mean Distance, Eccentricity, Epoch, and Longitude, of the Aphelion of the Orbit of Venus, by Errors of Heliocentric Longitude, derived from the Cambridge Observations of the Years 1833, 1834, and 1835, and the Greenwich Observations of 1836 [PDF]
openalex +1 more source
Correction to the paper “Note on Halperin's method of fitting straight lines when both variables are subject to error” [PDF]
Václav Fabian
openalex +1 more source
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
Correction of an Error in the English Life Tables (No. 1 Males). [PDF]
Tamizhselvan M Tamizhselvan M
openalex +1 more source
Deformation Behavior of La2O3‐Doped Copper during Equal Channel Angular Pressing
By additions of strengthening elements and/or structure optimization, the mechanical properties of copper can be increased while keeping favorable electric conductivity. By combining addition of La2O3 and processing by equal channel angular pressing, substructure development is achieved, leading to increase in microhardness to more than double the ...
Lenka Kunčická+2 more
wiley +1 more source
Coordinating sentence composition with error correction: A multilevel analysis
Error analysis involves detecting and correcting discrepancies between the 'text produced so far' (TPSF) and the writer's mental representation of what the text should be.
Van Waes, L., De Maeyer, S., Leijten, M.
doaj
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
UNOISE2: improved error-correction for Illumina 16S and ITS amplicon sequencing
Robert C. Edgar
semanticscholar +1 more source