Results 191 to 200 of about 5,558,612 (273)

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

Novel approach to early prediction of alzheimer's disease progression using integrated deep regulatory genetic neural network and optimized deep belief networks. [PDF]

Sci Rep
Roobini S, Kavitha MS, Karthik S.
europepmc   +1 more source

Current Status and Challenges in Data Collection for Aerospace Coatings Deposited by Plasma Spraying

Advanced Engineering Materials, EarlyView.
An innovative approach has been integrated into the GRENAT project to optimize plasma spraying and coating performance. Raw materials are accelerated and melted in the plasma generated by torches, creating coatings. Monitoring sensors collect process data which are combined with ex situ characterization data.
Lila Randriamananjara, Vincent Rat, Quentin Dubreuil, Patrice Valorge, Simon Goutier, Alain Denoirjean, Olivier Messe, Céline Ruelle, Jean‐Philippe Poli   +8 more
wiley   +1 more source

Introduction to high throughput synthesis, characterisation and optimisation of nanomaterials. [PDF]

Nanoscale Adv
Howes PD, Minelli C, Thomas MR, Hansel CS.   +3 more
europepmc   +1 more source

Workflow for Design of Experiments‐Based Modeling of Species Transport and Growth Kinetics in GaN Hydride Vapor Phase Epitaxy

Advanced Engineering Materials, EarlyView.
A novel workflow for investigating hydride vapor phase epitaxy for GaN bulk crystal growth is proposed. It combines Design of experiments (DoE) with physical simulations of mass transport and crystal growth kinetics, serving as an intermediate step between DoE and experiments.
J. Tomkovič, G. Lukin, H. Torkashvand, A. Kerschbaum, M. Müller, S. Besendörfer, J. Heitmann, J. Friedrich   +7 more
wiley   +1 more source

Exploring epidemic control policies using nonlinear programming and mathematical models. [PDF]

PLoS Comput Biol
Montes-Olivas S, Kucharski AJ, Gravenor MB, Frost SDW.   +3 more
europepmc   +1 more source

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

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

Successful Management of a Distal Left Main Perforation Using the Jailed-balloon Technique and Covered Stent Fenestration. [PDF]

Interv Cardiol
Kákonyi K, Nagy FT, Andréka J, Ruzsa Z.   +3 more
europepmc   +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