Results 231 to 240 of about 15,364,011 (346)
This perspective reframes additive manufacturing for electrical machines as a qualification‐limited materials and architecture design problem. It links process–structure–property–performance relationships to magnetic, conducting, dielectric, and thermal property windows, highlighting where AM can enable segmented magnetic circuits, permanent magnet ...
Dénes Fodor, Loránd Szabó
wiley +1 more source
Aging, inflammation, and oxidative stress converge on semen quality in Oravka roosters. [PDF]
Tvrdá E +9 more
europepmc +1 more source
Supporting AI Readiness Through Digital Workflows in Materials Science
Digitalization drives innovation in materials science by connecting data silos and turning heterogeneous processes into reusable research pipelines. Across 13 MaterialDigital projects, digital workflows reveal complementary pathways toward AI‐ready materials research, founded on structured data, persistent artifacts, executable orchestration, and ...
Marian Bruns +67 more
wiley +1 more source
Adenosine supplementation enhances chilled semen quality in Assaf rams: dose-dependent effects on sperm motility, membrane integrity, and morphology. [PDF]
Khalil MR, Abdallah JM.
europepmc +1 more source
PUF/ENB microcapsules were synthesized based on a sequential factorial design and incorporated into an epoxy matrix. Their anticorrosive action, associated with the self‐healing mechanism, demonstrated that the addition of microcapsules to the coating improved anticorrosive performance compared to pure epoxy coatings, particularly when the encapsulate ...
Vandicleya Alves Moreira +6 more
wiley +1 more source
Semen Quality and Risk Factors for Mortality
Batty, G. David +2 more
openaire +6 more sources
Avian β-defensin 1 as an antibiotic-sparing supplement for preserving rooster semen quality and fertility during chilled storage. [PDF]
Ratchamak R +3 more
europepmc +1 more source
Martin Blomberg Jensen +4 more
semanticscholar +1 more source
Detecting Anomalous Cell Behavior in Electrochemical Battery Testing Using Machine Learning
Machine‐learning‐based screening enables automated identification of anomalous battery cells from complementary electrochemical tests. A curated battery database supports configuration‐aware comparison of rate‐capability and impedance data. Supervised classification of rate‐test data achieves 90% accuracy, while CNN‐VAE‐based impedance analysis reaches
Minu Rose +7 more
wiley +1 more source
Modifiable key factors and semen quality in men undergoing preconception evaluation: a cross-sectional study. [PDF]
Yao HL +6 more
europepmc +1 more source

