Results 191 to 200 of about 25,499,806 (331)

Functionalized Biomimetic Scaffolds for Human‐Derived Auditory Neural Circuit Construction

open access: yesAdvanced Science, EarlyView.
In Vitro Auditory Circuit Model. ABSTRACT Damage to auditory circuits results in sensorineural hearing loss. However, the scarcity of human inner ear tissue significantly hinders the development of therapies to preserve auditory function, creating a critical need for reliable in vitro models.
Pan Feng   +12 more
wiley   +1 more source

Integration of Reconfigurable p‐Bit and 1R Crossbar Array for Memristive Probabilistic Computing

open access: yesAdvanced Science, EarlyView.
A memristive probabilistic computing system is demonstrated by integrating stochastic p‐bits based on volatile memristors with a 1R crossbar array encoding interaction weights. The system performs weighted‐sum operations across the array and updates p‐bits iteratively.
Keunho Soh   +7 more
wiley   +1 more source

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

Geometry‐Encoded Actuation as a Structural Interaction Layer in Origami Robots

open access: yesAdvanced Science, EarlyView.
We present geometry‐encoded actuation (GEA), a design framework that integrates multistable origami with liquid crystal elastomer actuators to encode environmental interactions into autonomous structural responses. GEA enables programmable shape transformation without continuous sensing or complex control, achieving robust adaptive manipulation and ...
Jianshu Zhou   +8 more
wiley   +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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