Results 171 to 180 of about 66,013 (262)

Synergistic Dual Slip‐Link Toughening of a Water‐Rich Double Network Hydrogel Combining Slide‐Ring and Highly Entangled Networks

Advanced Science, EarlyView.
Two distinct slip‐link mechanisms – the slide‐ring pulley effect and highly entangled chains – are combined in a water‐rich double network hydrogel, achieving synergistic toughening, near‐complete reversibility, and exceptional friction tear resistance at >$>$91 wt.% water.
Subhankar Mandal, Saleh Assadi, Aseem Milind Visal, Ignacio Lorente Montero, Franck J. Vernerey, Carson J. Bruns   +5 more
wiley   +1 more source

Combining Spatial Multi‐Omics Data to Decipher Spatial Domains and Elucidate Cell Heterogeneity Based on Self‐Supervised Graph Learning

Advanced Science, EarlyView.
A self‐supervised multi‐view graph fusion framework integrates spatial multi‐omics, excelling in domain identification and denoising. It reconstructs spatial pseudo‐expression, jointly analyzes multi‐omics data, infers RNA velocity, predicts spatial omics features from single‐cell multi‐omics, and detects spatially dark genes and transcription factors,
Yuejing Lu, Rui Qiao, Ying Li, Junhong Li, Nuoya Yue, Jing Ge, Jiao Wang, Luonan Chen, Peiluan Li   +8 more
wiley   +1 more source

A Two-Stage Transformer Framework for Sparse-Array Direction-of-Arrival Estimation via Correlation Vector Recovery. [PDF]

Sensors (Basel)
He W, Shi Y, Zhao H, Zhu H, Bao C.
europepmc   +1 more source

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

Advanced 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, Jiahui Xie, Yi Liao, Junyu Li, Longqi Liu, Xin Gao, Hongmin Cai   +6 more
wiley   +1 more source

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

Advanced 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, Shuli Sun, Zhengliang Lv, Xinyu Liu, Yihua Wang, Bingqiang Liu   +5 more
wiley   +1 more source

Topologically Structured PLLA Fibers With Stress Concentration Effects for Health Monitoring

Advanced Science, EarlyView.
In this study, a multi‐path strategy was developed to enhance the piezoelectric properties of PLLA fibers through the stress concentration effect. The optimized devices generate piezoelectric output up to 14 times that of pure PLLA fibers. This piezoelectric enhancement strategy offers a new approach for high‐performance piezoelectric devices, with ...
Longfei Li, Juwei Yang, Yiqian Wang, Qiao Yu, Zhenmin Fan, Chang Zhu, Ming Yin, Wei Hua, Zhou Li   +8 more
wiley   +1 more source

A Robust Multivariate Thresholding Function for Sparse and Biomedical Signal Reconstruction. [PDF]

Sensors (Basel)
Ullah H, Gaire S, Graves CA.
europepmc   +1 more source

Metasurface‐Enabled Active‐Like Passive Radar

Advanced Science, EarlyView.
A programmable space‐time‐coding metasurface embeds distinct spatiotemporal tags into ambient wireless signals, allowing passive radar to operate in an active‐like manner. By enabling code‐correlated reconstruction under interference, the approach supports robust real‐time UAV tracking in complex environments and points to intelligent, low‐power ...
Mingyi Li, Jiawen Xu, Hanting Zhao, Xu Zhao, Yanjin Chen, Tie Jun Cui, Vincenzo Galdi, Lianlin Li   +7 more
wiley   +1 more source

Continuous Non-Invasive Assessment of Segmental Cervical Motion: A Narrative Review and Validation Framework. [PDF]

Bioengineering (Basel)
Burtovaja N, Burtovojs S, Dekhtyar Y, Hauser RA, Ribickis L.   +4 more
europepmc   +1 more source

Hadamard Matrix Slicing Single‐Pixel Imaging: Establishing a Linear‐Array‐Inspired Paradigm for N‐fold Acceleration Single‐Pixel Imaging

Advanced Science, EarlyView.
To overcome two‐dimensional modulation bottlenecks, Hadamard Matrix Slicing Single‐Pixel Imaging (HMS‐SPI) establishes an efficient one‐dimensional imaging paradigm. By slicing the traditional Hadamard matrix into one‐dimensional encoding vectors and spatially expanding them, the required measurement patterns decrease by a factor of N.
Xiaoxue Li, Jieting Hou, Qizheng Zhao, Yiqi Jia, Xiang Zhong, Mengchao Ma, Huaxia Deng, Xinglong Gong, Ziwei Wang   +8 more
wiley   +1 more source