Results 141 to 150 of about 629 (227)

Polygenic Prediction of Equestrian Sport Discipline Among Horses Bred for Jumping and Dressage

open access: yesAnimal Genetics, Volume 57, Issue 3, June 2026.
ABSTRACT Horses bred for different equestrian sports vary in physical, physiological and behavioural requirements. Characterising genetic markers associated with discipline provides an opportunity to improve identification of horses best suited to either jumping or dressage.
Emmeline W. Hill   +4 more
wiley   +1 more source

A dancing bear, a colleague, or a sharpened toolbox? The cautious adoption of generative artificial intelligence technologies in digital humanities research

open access: yesJournal of the Association for Information Science and Technology, Volume 77, Issue 6, Page 812-830, June 2026.
Abstract The emergence of generative artificial intelligence (GenAI) is reshaping the research landscape and carries significant implications for Digital Humanities (DH), a field long intertwined with computational methods and technologies. This study examines how DH scholars are adopting and critically evaluating GenAI in their research. Drawing on an
Rongqian Ma, Meredith Dedema, Andrew Cox
wiley   +1 more source

(Inner-Product) Functional Encryption with Updatable Ciphertexts. [PDF]

open access: yesJ Cryptol
Cini V   +4 more
europepmc   +1 more source

Data‐Driven Investigation of Aqueous Zinc Anode Degradation Using Design of Experiments and Bayesian Optimization

open access: yesBatteries &Supercaps, Volume 9, Issue 6, June 2026.
Optimizing reversible Zn electrodeposition is challenging due to tightly coupled degradation mechanisms. Here, we combine a symmetric cell design with fractional factorial screening and Bayesian optimization of pulsed electrodeposition to navigate this complexity, achieving a mean capacity loss of ∼1% per cycle.
Klara Neumayr   +2 more
wiley   +1 more source

Backpropagation Network‐Based Contrastive Learning for Unsupervised Domain Adaptation

open access: yesEngineering Reports, Volume 8, Issue 6, June 2026.
Unsupervised domain adaptation for contractive learning. ABSTRACT This study introduces a new method for domain adaptation for image classification tasks that aims to improve the model's performance on a target domain after being trained on a source domain.
Yushui Xiao, Yong Huang, Yujie Li
wiley   +1 more source

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