Results 91 to 100 of about 4,990,305 (257)

Deciphering Anti‐Cancer Drug Efficacy Through Nanomechanical Vibrations in Living Gastric Cancer Organoids

Advanced Science, EarlyView.
This study developed an efficacy assessment platform that integrates patient‐derived gastric cancer organoids, atomic force microscopy (AFM)‐based nanomechanical vibration detection, deep learning analysis, and organoid mechanical modeling. It detects picomolar drug effects within 0.1 s signal, achieves 97% classification accuracy, and offers non ...
Ting Zhang, Qiubo Chen, Shihai Lan, Liang Zhu, Shuaiyu Liu, Yan Xia, Yalu Zhang, Hanhui Yao, Yongman Liu, Shangquan Wu, Qingchuan Zhang   +10 more
wiley   +1 more source

Bayesian graph convolutional network for traffic prediction [PDF]

Recently, adaptive graph convolutional network based traffic prediction methods, learning a latent graph structure from traffic data via various attention-based mechanisms, have achieved impressive performance.
Chen, Zhibo, Fu, Jun, Zhou, Wei
core   +1 more source

Groundwater Rise Sustains the World's Largest Alpine Water System Under Global Warming

Advanced Science, EarlyView.
Shallow groundwater depth (SGWD) across the non‐permafrost plains of the Qinghai–Xizang Plateau decreased at averagely 0.02 m year−1, adding 31.44 Gt of freshwater storage from 2000 to 2020 and sustaining ∼53 500 km2 of alpine ecosystems. A vadose‐zone capacity of 426.6 Gt reveals these aquifers as promising reservoirs, highlighting groundwater's ...
Jianqing Du, Zhixiang Niu, Yanfen Wang, Shirui Zhou, Youqing Yang, Xiang Wei, Haishan Niu, Yitao Zhang, Anquan Xia, Yali Liu, Yanbin Hao, Kai Xue, Bingfang Wu, Xinquan Zhao, Tandong Yao, Yiyu Chen   +15 more
wiley   +1 more source

AAGCN: a graph convolutional neural network with adaptive feature and topology learning

Scientific Reports
In recent years, there has been a growing prevalence of deep learning in various domains, owing to advancements in information technology and computing power.
Bin Wang, Bodong Cai, Jinfang Sheng, Wenzhe Jiao   +3 more
doaj   +1 more source

Sample and Aggregate Voronoi Neighborhood Weighted Graph Neural Network (SAGE-Voronoi) and Its Capability for City-Sized Vehicle Traffic Time Series Prediction

Applied Sciences
The application of graph convolutional neural networks for traffic prediction is a standard procedure; however, this approach is rarely used under the assumption that the exact city plan is unknown and the prediction area is a city-sized region.
Przemysław Bielecki, Tomasz Hachaj, Jarosław Wąs   +2 more
doaj   +1 more source

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

Advanced 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, Ahmadreza Argha, Nona Farbehi, Ivan Bakhshayeshi, Thantrira Porntaveetus, Youqiong Ye, Nigel H. Lovell, Hamid Alinejad‐Rokny   +7 more
wiley   +1 more source

Large‐Area Noise‐Resilient Multiplexing Triboelectric Biomechanical Sensing on Clothing for High‐Precision Full‐Body Motion Capture in Wearables

Advanced Science, EarlyView.
A large‐area, noise‐resilient multiplexed triboelectric sensing garment based on a multilayer shielded architecture and SnS2 nanoflower‐engineered hybrid materials enables interference‐suppressed, high‐fidelity full‐body motion tracking, achieving 0.13% signal misrecognition, 57 dB electromagnetic noise attenuation, and >1.5 N threshold force, while ...
Beibei Shao, Ming‐Han Lu, Wei‐Chen Peng, Wei‐Chun Yang, Yi‐Ting Chen, Jiun‐Wei Fong, Tzu‐Ching Lu, Tai‐Chen Wu, Zhi‐Xian Yan, Kai‐Yuan Hsiao, Jiann‐Yeu Chen, Ming‐Yen Lu, Heng‐Jui Liu, Ruiyuan Liu, Ying‐Chih Lai   +14 more
wiley   +1 more source

Application of Graph Spatio-Temporal Convolutional Neural Network in Motor Intention Recognition of Stroke Patients

康复学报
ObjectiveTo evaluate the decoding accuracy and model performance of a graph spatio-temporal convolutional neural network (G-STCNN) in motor intention recognition of stroke patients.MethodsWe developed a novel G-STCNN model by integrating graph ...
XU Hui, MA Jun, ZHANG Jingjing, ZHANG Tao, ZHAO Zhiwei, SHAN Chunlei   +5 more
doaj  

Graph Convolutional Recommendation System Based on Bilateral Attention Mechanism

Journal of Engineering
Collaborative Filtering Recommender Systems face data sparsity and cold-start issues, leading to a decrease in their recommendation performance. Therefore, numerous researchers have integrated knowledge graphs and graph convolutional networks into ...
Hui Yang, Changchun Yang
doaj   +1 more source

LGTCN: A Spatial–Temporal Traffic Flow Prediction Model Based on Local–Global Feature Fusion Temporal Convolutional Network

Applied Sciences
High-precision traffic flow prediction facilitates intelligent traffic control and refined management decisions. Previous research has built a variety of exquisite models with good prediction results.
Wei Ye, Haoxuan Kuang, Kunxiang Deng, Dongran Zhang, Jun Li   +4 more
doaj   +1 more source