Results 201 to 210 of about 674,066 (256)

Physics‐Aware Machine‐Learning‐Driven Inverse Design of Broadband Ultra‐Open Acoustic Metamaterials

open access: yesAdvanced Science, EarlyView.
A physics‐aware machine‐learning framework enables inverse design of ultra‐open acoustic silencers by decoupling spectral and radial design spaces. The approach rapidly identifies broadband, compact, and highly ventilated architectures, while revealing hidden linear design rules that link geometry, impedance matching, and acoustic performance.
Zhiwei Yang   +5 more
wiley   +1 more source

Capturing infant and child growth dynamics with P-splines mixed effects models. [PDF]

open access: yesInt J Obes (Lond)
Hernandez MA   +5 more
europepmc   +1 more source

Further Evidences for the Sulfur Spillover on Carbon: Thermodynamic Aspects and Effects on Li‐S Solid‐State Batteries

open access: yesAdvanced Science, EarlyView.
ABSTRACT This study investigates sulfur spillover, the spontaneous and rapid spread of sulfur over carbon surfaces at ambient temperature. The process is studied by small‐angle X‐ray scattering (SAXS), X‐ray radiography, and microcalorimetry from which a number of physicochemical properties are derived.
Francesco Piccolo   +9 more
wiley   +1 more source

High‐Entropy Engineering and External Magnetic Field Modulation Synergistically Enhance the H2S Sensing Performance of Rare‐Earth Perovskite Ferrites

open access: yesAdvanced Science, EarlyView.
A novel high‐entropy perovskite oxide, (Gd0.2Tb0.2Dy0.2Ho0.2Er0.2)FeO3, was successfully synthesized for advanced hydrogen sulfide gas detection. By synergistically coupling high‐entropy‐induced local lattice distortion with weak magnetic field modulation, the tailored material generates abundant active sites. This innovative dual strategy dramatically
Yunfei Wang   +7 more
wiley   +1 more source

SEM mapping of sequence-specific protein-DNA interactions on long DNA molecules. [PDF]

open access: yesNucleic Acids Res
Noh C   +11 more
europepmc   +1 more source

SemanticST: A Scalable Multi‐Contextual Graph Learning Framework for Uncovering Spatial Niches and Robust Multi‐Sample Integration in Spatial Transcriptomics

open access: yesAdvanced Science, EarlyView.
Technical limitations often let dominant signals overshadow rare cell types and fine‐grained heterogeneity in spatial transcriptomics. SemanticST, a graph neural network using multi‐semantic graph fusion and a novel min‐cut loss, recovers these subtle patterns.
Roxana Zahedi   +7 more
wiley   +1 more source

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