Results 221 to 230 of about 2,043,579 (311)

Enhancing Performance of the National Field Triage Guidelines Using Machine Learning: Development of a Prehospital Triage Model to Predict Severe Trauma. [PDF]

J Med Internet Res
Chen Q, Qin Y, Jin Z, Zhao X, He J, Wu C, Tang B.   +6 more
europepmc   +1 more source

Multimodal Data‐Driven Microstructure Characterization

Advanced Engineering Materials, EarlyView.
A self‐consistent autonomous workflow for EBSP‐based microstructure segmentation by integrating PCA, GMM clustering, and cNMF with information‐theoretic parameter selection, requiring no user input. An optimal ROI size related to characteristic grain size is identified.
Qi Zhang, Santiago Benito, Özge Özgün, Sebastian Weber, Markus Stricker   +4 more
wiley   +1 more source

Book review: Thorpe, J. (2024) AI for students: creative hacks for academic success: an illustrated guide. Glasgow: Promptly Books

Journal of Learning Development in Higher Education
Kim Pedersen, Anne-Marie Langford
doaj   +1 more source

Symbolic Regression and Multi‐Objective Optimization of the Flory–Huggins Interaction Parameter for Hydrogels

Advanced Engineering Materials, EarlyView.
We develop a data‐driven method to derive the mathematical expressions of the Flory–Huggins interaction parameter χ for the swelling behavior of temperature–responsive hydrogels. Starting from initial assumptions of χ, our workflow combines Bayesian optimization, Flory–Rehner theory, and symbolic regression to generate candidate χ expressions.
Yawen Wang, Adrian Ehrenhofer, Thomas Wallmersperger   +2 more
wiley   +1 more source

Microstructure Evolution of a VMnFeCoNi High‐Entropy Alloy After Synthesis, Swaging, and Annealing

Advanced Engineering Materials, EarlyView.
The synthesis and processing (rotary swaging and annealing) of the novel VMnFeCoNi alloy is investigated, alongside the estimation of the grain size effect on hardness. Analysis of a wide grain size range of recrystallized microstructures (12–210 µm) reveals a low annealing twin density.
Aditya Srinivasan Tirunilai, Zeki Ayrildi, Buse Şen, Maik Rajkowski, Mathias Redondo Garcia, Markus Stricker, Guillaume Laplanche   +6 more
wiley   +1 more source

A Lightweight Procedural Layer for Hybrid Experimental–Computational Workflows in Materials Science

Advanced Engineering Materials, EarlyView.
We unveil a prototype hybrid‐workflow framework that fuses automatedcomputation with hands‐on experiments. Built atop pyiron, a lightweight, parameterized layer translates procedure descriptions into executable manual steps, syncing instrument settings, human interventions, and data capture in real‐time today.
Steffen Brinckmann, Sarath Menon, Raphael Röske, Johannes Steinhülb, Michael Selzer, Jan Janssen, Tilmann Hickel, Ruth Schwaiger, Jörg Neugebauer   +8 more
wiley   +1 more source

Automatic Calculation of Cervical Spine Parameters Using Deep Learning: Development and Validation on an External Dataset. [PDF]

Global Spine J
Nakarai H, Cina A, Jutzeler C, Grob A, Haschtmann D, Loibl M, Fekete TF, Kleinstück F, Wilke HJ, Tao Y, Galbusera F.   +10 more
europepmc   +1 more source

PASTA‐ELN: Simplifying Research Data Management for Experimental Materials Science

Advanced Engineering Materials, EarlyView.
Research data management faces ongoing hurdles as many ELNs remain complex and restrictive. PASTA‐ELN offers an open‐source, cross‐platform solution that prioritizes simplicity, offline access, and user control. Its in tuitive folder structure, modular Python add‐ons, and open formats enable seamless documentation, FAIR data practices, and easy ...
S. Brinckmann, G. Winkens, R. Schwaiger
wiley   +1 more source

Comprehensive Assessment and Early Prediction of Gross Motor Performance in Toddlers With Graph Convolutional Networks-Based Deep Learning: Development and Validation Study. [PDF]

JMIR Form Res
Chun S, Jang S, Kim JY, Ko C, Lee J, Hong J, Park YR.   +6 more
europepmc   +1 more source