Results 171 to 180 of about 103,588 (268)

Memristive Physical Reservoir Computing

open access: yesAdvanced Science, EarlyView.
Memristors’ nonlinear dynamics and input‐dependent memory effects make them ideal candidates for high‐performance physical reservoir computing (RC). Based on their conductance modulation, memristors can be classified as electronic or optoelectronic types.
Dian Jiao   +9 more
wiley   +1 more source

Advanced Multiscale Modeling for Revealing Anomalous Fluid Transport Induced by Confinement Interfacial Layer Reconstruction in Sub‐10 nm Space

open access: yesAdvanced Science, EarlyView.
Integrating experimental insights with molecular dynamics simulations, we establish an advanced mathematical model for fluids confined in sub‐10‐nm channels, enabling quantitative prediction of interfacial layer thickness and viscosity by accounting for channel material, temperature, and fluid properties.
Xiang Zhang   +3 more
wiley   +1 more source

Developing Micro/Nanostructured Fluidic Mixing Technology for Biomedical Applications

open access: yesAdvanced Science, EarlyView.
This review critically evaluates how micro/nanostructured mixing technologies are redefining biomedical research. By synergizing fundamental analysis, numerical modeling, structural design, and external field manipulation, these systems attain unprecedented control over mass transport.
Junkai Wang   +3 more
wiley   +1 more source

Decoding Spatial Heterogeneity and Multi‐Omics Regulation with Hierarchical Graph Learning

open access: yesAdvanced Science, EarlyView.
ABSTRACT Recent advances in spatial multi‐omics technologies have enabled the simultaneous profiling of multiple molecular layers within the same tissue slice, providing unprecedented opportunities to investigate tissue spatial organization. However, most existing computational methods identify spatial domains in a purely data‐driven manner, rarely ...
Jiazhou Chen   +6 more
wiley   +1 more source

STAID: A Self‐Refining Deep Learning Framework for Spatial Cell‐Type Deconvolution with Biologically Informed Modeling

open access: yesAdvanced Science, EarlyView.
STAID is a unified deep learning framework that couples iterative pseudo‐spot refinement with neural network training through a feedback loop and exploits gene co‐expression information to model higher‐order interactions, achieving accurate and robust cell‐type deconvolution in spatial transcriptomics.
Jixin Liu   +5 more
wiley   +1 more source

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