Results 171 to 180 of about 410,031 (262)

Ontology‐Aligned Structuring and Reuse of Multimodal Materials Data and Workflows Toward Automatic Reproduction

Advanced Engineering Materials, EarlyView.
Reproduction of stacking fault energy calculations from literature with a semi‐automated large language model‐assisted extraction procedure: extraction of simulation protocol, atomistic structures, computational parameters, and reported results, ontology alignment, knowledge graph construction and, finally, recomputation forvalidation.
Sepideh Baghaee Ravari, Abril Azocar Guzman, Sarath Menon, Stefan Sandfeld, Tilmann Hickel, Markus Stricker   +5 more
wiley   +1 more source

An ambitious global goal on adaptation for heritage. [PDF]

NPJ Clim Action
Simpson N, Sabour S, Zvobgo L, Potts A, Williams PA, Clarke J, Trisos CH.   +6 more
europepmc   +1 more source

In Situ Micromechanical Study of Bimodal γ′–γ″ Precipitate Assemblies in Ni–Cr–Al–Nb Superalloy

Advanced Engineering Materials, EarlyView.
A Ni–Cr–Al–Nb superalloy with a bimodal γ′–γ″ precipitate distribution is developed. Composite precipitate assemblies form through heterogeneous nucleation, effectively impeding dislocation motion. Micropillar compression reveals high strength at room and elevated temperatures, governed by precipitate shearing, with coupled faulting mechanisms ...
Ujjval Bansal, Tae‐Hyeok Kang, Pyuck‐Pa Choi, Subin Lee, Christoph Kirchlechner   +4 more
wiley   +1 more source

Financial Performance of Medical Corporations in Japan From 2016 to 2022: A Nationwide Longitudinal Analysis. [PDF]

J Epidemiol
Funada S, Hiragi S, Ashizawa M, Goto R.
europepmc   +1 more source

Experimental Characterization and Constitutive Modeling of the Effect of Prior Inelastic Strain on the Viscoplastic Behavior of AD730 at 700 °C

Advanced Engineering Materials, EarlyView.
This study shows that superalloys used in aircraft engine disks become much more prone to deformation at high temperatures if they have been strained during manufacturing. This effect increases with the level of prior strain but eventually reaches a limit.
Fabio Machado Alves da Fonseca, Patrick Villechaise, Malik Durand, Arnaud Longuet, Florent Coudon, Loïc Signor, Julien De Jaeger, Dominique Eyidi, Nathalie Bozzolo, Jonathan Cormier   +9 more
wiley   +1 more source

Clustering-Conditioned Granger Causality Between GDP Growth and Private Financing. [PDF]

Entropy (Basel)
Flores-Nava R, Roman-Rangel E.
europepmc   +1 more source

Microstructure Reconstruction in Battery Electrodes Using Machine Learning Based on Low‐Voltage Focused Ion Beam–Scanning Electron Microscopy Tomography Images

Advanced Engineering Materials, EarlyView.
Low‐voltage FIB‐SEM tomography combined with a image preprocessing pipeline improves phase contrast and enables reliable machine‐learning segmentation of conductive networks in lithium‐ion battery electrodes. Structural descriptors are extracted from segmented images, done semimanually and automated, and compared.
Lisa Beran, Vincent Nebel, Dominik Perius, Kshitij Kumar, Yannis Heim, Laura Bies, Tobias Kraus   +6 more
wiley   +1 more source

The Price of a Whipple: Predicting Hospital Charges Using Preoperative Patient Characteristics. [PDF]

World J Surg
Bonthu SSR, Mukharjee S, Kong J, Malo J, Osman H, Jeyarajah DR.   +5 more
europepmc   +1 more source

Feasibility of Nitrogen as a Carrier Gas for Inconel Cold Spray in Hydropower Application

Advanced Engineering Materials, EarlyView.
N2, despite being nearly two orders of magnitude cheaper than He, is a feasible carrier gas for cold spray Inconel coatings in hydropower repair applications when higher gas temperature and pressure are used. Reducing powder size significantly improved cavitation resistance, while the addition of fine chromium carbide particles further enhanced erosion
Tianhao Wang, Mayur Pole, Tingkun Liu, Kenneth A. Ross, Christopher B. Smith   +4 more
wiley   +1 more source

Machine Learning‐Supported Analysis for Predicting and Visualizing Nonlinear Relationships Between Material Properties in Electroplated Chromium Layers

Advanced Engineering Materials, EarlyView.
This study applies machine learning regression to predict chromium layer thickness in decorative trivalent chromium electroplating, using 441 experiments from laboratory‐scale (1L) and pilot‐scale (14L) setups. Tree‐based models, particularly CatBoost, outperformed linear regression by capturing nonlinear parameter interactions (R2$R^2$ up to 0.77 ...
Christoph Baumer, Christopher Mai, Luca Eisentraut, Andreas Bund, Ricardo Buettner   +4 more
wiley   +1 more source