Results 91 to 100 of about 126,627 (262)

microRNA‐7‐5p and α‐Synuclein SAA Predict Parkinson's Disease Phenoconversion

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Objective Corroborate blood neuron‐derived extracellular vesicle (NDEV) alpha‐synuclein (αSyn), the CSF αSyn seed amplification assay (αSyn‐SAA), and blood microRNA‐7‐5p (miR‐7‐5p) as markers for Parkinson's disease (PD) phenoconversion and determine if combining these markers would help select subjects who would be more likely to phenoconvert.
Shayan Zadegan, Praveen Velammal, Kanmani Muthiah, Madhu Jatty, Christopher Adams   +4 more
wiley   +1 more source

Employment Trajectories of Patients With Systemic Lupus Erythematosus and Factors Driving Transitions Between States

Arthritis Care &Research, EarlyView.
Objective Systemic lupus erythematosus (SLE) significantly impacts employment capacity. This study aimed to investigate the impact of burden of disease activity, damage, and treatment on employment outcomes and transitions in patients with SLE. Methods Using data from a single center, we analyzed employment transitions, adjusted mean disease activity ...
Javier Mencia‐Ledo, Zahi Touma, Laura Whittall Garcia, Dafna D. Gladman, Behdin Nowrouzi‐Kia   +4 more
wiley   +1 more source

What Do Large Language Models Know About Materials?

Advanced Engineering Materials, EarlyView.
If large language models (LLMs) are to be used inside the material discovery and engineering process, they must be benchmarked for the accurateness of intrinsic material knowledge. The current work introduces 1) a reasoning process through the processing–structure–property–performance chain and 2) a tool for benchmarking knowledge of LLMs concerning ...
Adrian Ehrenhofer, Thomas Wallmersperger, Gianaurelio Cuniberti   +2 more
wiley   +1 more source

Is an Apple an Orange? A Large Language Model Benchmark for Candidate Term Extraction and Subclass Decisions Against Upper Ontologies in Engineering and Materials Science

Advanced Engineering Materials, EarlyView.
Building machine‐readable vocabularies for materials science is slow, expert‐driven work. This study benchmarks 13 large language models on two of its first steps: finding candidate terms in engineering articles and deciding where they belong in a class hierarchy.
Thomas Bjarsch, Maximilian Holland, Klaus Drechsler, Johannes Schilp   +3 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

Inverse Identification of Energy‐Dependent Laser Absorptivity in NiTi Laser Powder‐Bed Fusion via Calibrated Melt Pool Simulation

Advanced Engineering Materials, EarlyView.
A combined experimental–computational framework identifies energy‐dependent laser absorptivity for NiTi in laser powder‐bed fusion, applicable to conduction and transition modes. Single‐track experiments and thermofluid smoothed particle hydrodynamics simulations are coupled through inverse analysis of melt pool geometry.
Mohamadreza Afrasiabi, Hadi Moutablaleh, Ignatius A. Setiawan, Mehrshad Mehrpouya   +3 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

HEMODYNAMIC STATUS IN AIRWAY MANAGEMENT DURING GENERAL ANESTHESIA: COMPARISON OF THREE METHODS

Journal of Research in Medical Sciences, 2000
Introduction. The laryngeal mask airway (LMA) was recently introduced in general anesthesia as an alternative to the face mask or tracheal intubation for airway maintenance. Methods.
K MONTAZERI, KH NAGHIBI, A.A AKHOUNDI
doaj  

High‐Resolution Corrosion Fingerprinting of Alloy Libraries via Microdroplet Spectroelectrochemistry

Advanced Engineering Materials, EarlyView.
Ionic‐liquid microdroplet spectroelectrochemistry enables stable, localized electrochemical measurements over extended timescales. By coupling impedance spectroscopy, wetting analysis, finite‐element simulations, and operando Raman spectroscopy, it disentangles geometrical and electrochemical effects, opening new opportunities for rapid corrosion ...
Ekaterina Kurchavova, Oumaïma Gharbi, Mireille Turmine, Vincent Vivier   +3 more
wiley   +1 more source

A Multi‐Scale Machine Learning Framework for the Inverse Design of High Entropy Alloys

Advanced Engineering Materials, EarlyView.
High‐entropy alloys offer vast potential for various applications, including electrocatalysis; however, their compositional complexity challenges conventional screening. We introduce an inverse‐design framework combining two neural networks to determine optimal compositions and reconstruct nanoparticle geometry from targeted properties and conventional
Mikael Takoutsin, Marta Campolucci, Nicolas de Andrade Ishiki, Nathan Mirgot, Nadir Zhamantay, Frédéric Kanoufi, Jennifer Peron, Jean Charlety, Emiliano Fonda, Valérie Briois, Marco Faustini, Maria‐Letizia De Marco, Christine Goyhenex, Ovidiu Ersen, Hervé Bulou   +14 more
wiley   +1 more source