Results 171 to 180 of about 122,547 (263)

Characterization of Defect Distribution in an Additively Manufactured AlSi10Mg as a Function of Processing Parameters and Correlations with Extreme Value Statistics

Advanced Engineering Materials, EarlyView.
Predicting extreme defects in additive manufacturing remains a key challenge limiting its structural reliability. This study proposes a statistical framework that integrates Extreme Value Theory with advanced process indicators to explore defect–process relationships and improve the estimation of critical defect sizes. The approach provides a basis for
Muhammad Muteeb Butt, Varma Kvvssn, Hossein Laieghi, Zia Uddin, Peyman Ansari, E. Kıvanç Sadak, Can Barış Toprak, Hüseyin Kızıl, Enrico Salvati   +8 more
wiley   +1 more source

Accelerated development of rice near-isogenic lines based on rapid generation advance (RGA) and target capture sequencing (TCS). [PDF]

Mol Breed
Jeong SK, Lee SM, Cha JK, Park H, Jo S, Kang JW, An YR, Kim WJ, Lee JH, Kwon Y.   +9 more
europepmc   +1 more source

Facilitating Expedited Drug Approval—the Past, Present, and Future of Clinical Trials for Pediatric Rheumatic Disease

Arthritis Care &Research, EarlyView.
Pamela F. Weiss, Hermine I. Brunner
wiley   +1 more source

On the Lightweight Potential of Laser Additive Manufactured NiTi Triply Periodic Minimal Sheet Lattices

Advanced Engineering Materials, EarlyView.
This study explores the lightweight potential of laser additive‐manufactured NiTi triply periodic minimal surface sheet lattices. It systematically investigates the effects of relative density and unit cell size on surface quality, deformation recovery, compression behavior, and energy absorption.
Haoming Mo, Shuo Qu, Shiming Gao, Xu Song   +3 more
wiley   +1 more source

Strategies to improve on selection based on estimated breeding values. [PDF]

Genet Sel Evol
Pook T, Hassanpour A, Niehoff TAM, Calus MPL.   +3 more
europepmc   +1 more source

A Workflow to Accelerate Microstructure‐Sensitive Fatigue Life Predictions

Advanced Engineering Materials, EarlyView.
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, Krzysztof S. Stopka, Michael D. Sangid   +2 more
wiley   +1 more source

Polyploidy and Mutagenic Germplasm Innovation in Minor Legumes: Paradigm Shift and Challenges from Model Crops to Mung Bean. [PDF]

Plants (Basel)
Guo F, Ma C, Liu Y, Wang L, Li C.
europepmc   +1 more source

NFDI MatWerk Ontology (MWO): A BFO‐Compliant Ontology for Research Data Management in Materials Science and Engineering

Advanced Engineering Materials, EarlyView.
This article presents the NFDI‐MatWerk Ontology (MWO), a Basic Formal Ontology‐based framework for interoperable research data management in materials science and engineering (MSE). Covering consortium structures, research data management resources, services, and instruments, MWO enables semantic integration, Findable, Accessible, Interoperable, and ...
Hossein Beygi Nasrabadi, Ebrahim Norouzi, Kostiantyn Hubaiev, Jörg Waitelonis, Harald Sack   +4 more
wiley   +1 more source

Environmental Variability Shapes Life-History Trade-Offs Within and Between Populations of a Long-Lived Seabird. [PDF]

Ecol Lett
Mohring B, Potts JR, Wilson AJ, Réale D, Phillips RA, Weimerskirch H, Barbraud C, Bennison A, Delord K, Wood AG, Peroteau S, Rouby E, Ventura F, Patrick SC.   +13 more
europepmc   +1 more source

Prediction of Surface Topography Parameters in Direct Laser Interference Patterning of Stainless Steel Using Infrared Monitoring and Convolutional Neural Networks

Advanced Engineering Materials, EarlyView.
This study presents an infrared monitoring approach for direct laser interference patterning (DLIP) combined with a convolutional neural network (CNN). Thermal emission data captured during structuring are used to predict surface topography parameters.
Lukas Olawsky, Marcelo Sallese, Leander Kläber, Clemens Kuhn, Keming Du, Andrés Fabián Lasagni   +5 more
wiley   +1 more source