Results 201 to 210 of about 7,480,106 (305)
A Workflow to Accelerate Microstructure‐Sensitive Fatigue Life Predictions
This study introduces a workflow to accelerate predictions of microstructure‐sensitive fatigue life. Results from frameworks with varying levels of simplification are benchmarked against published reference results. The analysis reveals a trade‐off between accuracy and model complexity, offering researchers a practical guide for selecting the optimal ...
Luca Loiodice +2 more
wiley +1 more source
Chemical Composition and Biochemical Changes during Fruit Development on Seed Quality in Hot Chili (<i>Capsicum annuum</i> L.). [PDF]
Pola W, Photchanachai S, Sangsawad P.
europepmc +1 more source
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 +3 more
wiley +1 more source
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 +7 more
wiley +1 more source
Genotype-Specific Responses to Drought During Seed Production in Carrot: Biochemical, Physiological, and Seed Quality Evaluation. [PDF]
Jagosz B +10 more
europepmc +1 more source
Quality garden seeds ; Grand Rapids Growers' quality seeds ; Seeds that grow [bulletin] /
openaire +2 more sources
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 +6 more
wiley +1 more source
More is not always merrier: Enhanced atmospheric CO2 reduces nutritional seed quality in oak trees. [PDF]
Sharma G.
europepmc +1 more source
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 +4 more
wiley +1 more source

