Advanced Intelligent Systems, EarlyView.This study introduces Cellular Material Network (CM‐Net), a pioneering machine learning architecture integrating physical information, to predict the mechanical properties of cellular materials. Comprehensive validation through simulations and experiments demonstrates its accuracy in predicting nonlinear behaviors, including initial peak compression ...
Sicong Zhou +5 more
wiley +1 more source
MV-HAGCN: Prediction of miRNA-Disease Association Based on Multi-View Hybrid Attention Graph Convolutional Network. [PDF]
Int J Mol SciXing K, Zhang Y, Zhu W.
europepmc +1 more source
Advanced Intelligent Systems, EarlyView.Inspired by human touch, a tendon‐driven soft robotic finger combines multimodal tactile sensing and deep learning to simultaneously perceive texture and softness on deformable surfaces. A CNN‐LSTM model fuses pressure, accelerometer, and gyroscope signals to accurately classify material properties, achieving up to 95.4% texture and 97.0% softness ...
Gorkem Anil Al +3 more
wiley +1 more source
BCST-GCN: a skeleton-based spatiotemporal graph convolutional network with bidirectional cross-attention for pig behavior recognition. [PDF]
Front Vet SciChai H, Zhan W, Su J.
europepmc +1 more source
Behaviorally Adaptive and Inclusive Advanced Driver‐Assistance Systems
Advanced Intelligent Systems, EarlyView.Advanced driver‐assistance systems (ADASs) are mapped as evolving human‐centered, adaptive technologies linking sensing, driver monitoring, AR/HUD interfaces, patents, regulation, and inclusive design. The review identifies gaps in real‐world evidence, diverse‐driver validation, gaze metrics, and governance, outlining a roadmap for safer, behaviorally ...
Jana Skirnewskaja +2 more
wiley +1 more source
Exploiting Edge Semantics in Job Shop Scheduling Problem With Heterogeneous Graph Transformers
Advanced Intelligent Systems, EarlyView.A heterogeneous graph transformer (HGT) is introduced for reinforcement learning‐based job shop scheduling by explicitly distinguishing precedence and machine‐contention relations through edge‐type‐specific attention. The proposed framework learns richer scheduling representations, improves decision quality over homogeneous graph models, and highlights
Bulent Soykan, Fatih Kasimoglu
wiley +1 more source
DWGCN: distance-weighted graph convolutional network for robust spatial domain identification in spatial transcriptomics. [PDF]
Front GenetPeng C, Li G, Wu J, Fan Q, Guo X.
europepmc +1 more source
Accelerating Materials Discovery: A Review of Machine Learning in X‐Ray Absorption Spectroscopy
Advanced Intelligent Systems, EarlyView.This review systematically details how machine learning transforms X‐ray absorption spectroscopy (XAS) analysis. It covers advanced deep learning architectures for structure‐spectra mapping and inverse tasks, while discussing key challenges like the simulation‐to‐reality gap.
Melaku Lake Tegegne +5 more
wiley +1 more source
STransfer: a transfer learning-enhanced graph convolutional network for clustering spatial transcriptomics data. [PDF]
BioinformaticsWang C, Yu X.
europepmc +1 more source
Arthritis &Rheumatology, EarlyView.Objective Regular imaging by conventional radiography to assess for joint damage is a cornerstone in the management of rheumatoid arthritis. Scoring systems to quantify such damage, such as the widely used Sharp/van der Heijde (SvdH) score, are limited by the requirement of time and experienced staff as well as intra‐ and interrater variability.
Thomas Deimel +6 more
wiley +1 more source