graph neural networks

Multimodal Spatiotemporal Biomechanical Modeling for Musculoskeletal Injury Risk Screening of Body Movements Based on Cross-Modal Attention Network

Ethnic dance involves large-amplitude and repetitive movements that may impose substantial and uneven mechanical demands on the musculoskeletal system, potentially increasing the risk of joint and soft-tissue injuries. Conventional injury assessment relies largely on manual observation and clinical evaluation, which li...

Human Brain Connectome Mapping Using Graph Neural Networks Across Applications: A Systematic Review and Diagnostic-Accuracy Sub-study

This systematic review will examine the applications of graph neural networks (GNNs) in human brain connectome mapping, including the analysis of structural and functional brain connectivity derived from neuroimaging. It will synthesize the methodological characteristics of eligible studies, neuroimaging modalities, br...

Integrating multi-omics with machine learning: from genomic variant functional annotation to pharmacogenomics and biomarker discovery

Integrating heterogeneous multi-omics data with machine learning (ML) provides a powerful framework for interpreting genomic variants and advancing pharmacogenomic research in precision medicine. High-throughput technologies have generated vast datasets spanning genomics, transcriptomics, epigenomics, and proteomics, y...

Replication Package for "Graph Neural Networks for Cyber-Physical Intrusion and Anomaly Detection in Industrial Control Systems: A Systematic Review"

This replication package supports the systematic review “Graph Neural Networks for Cyber-Physical Intrusion and Anomaly Detection in Industrial Control Systems: A Systematic Review” (manuscript reference: COMPELECENG-D-26-07913). The final evidence base comprises 346 eligible primary studies, including 317 Direct and 2...

Multirobot collaborative visual SLAM and real-time map fusion method based on deep learning

Multi-robot cooperative visual SLAM schemes often suffer from narrow data transmission bandwidth, unsatisfactory runtime computation efficiency, poor anti-interference performance when dynamic objects exist, and unreliable alignment between multiple sub-maps. Against these practical engineering defects, this study puts...

Intelligent technological problem–solution recommendation using large language models and heterogeneous graph neural networks

Prediction of solvent aggregation in CO2-loaded aqueous amines using machine learning molecular dynamics simulations

Aggregation of ion pairs formed during CO2 capture may govern absorption thermodynamics and kinetics, but it is often overestimated by fixed-charge classical force fields. Here, we demonstrate the promise of graph neural network interatomic potentials, which offer quantum-level accuracy, for directly evaluating the agg...

PolyFKAN: Integrating Fourier-KAN layers into graph neural networks for polymer property prediction.

Graph neural networks have become a central tool for polymer property prediction, but their fixed activation functions can impose a low-pass spectral bias that smooths over the sharp, multi-scale structure-property variations that distinguish chemically similar polymers. We address this limitation with PolyFKAN, a mult...

Panvascular-JEPA: Unseen-Structure Evaluation of Factorized Predictive States for Hemodynamic Networks

Predictive representations for vascular simulation should be tested on unseen network structures and compared with the governing equations before being interpreted as general-purpose world models. We constructed a new, dimensioned but uncalibrated nodal resistance-inertance-compliance benchmark containing all 38 connec...

Functional MRI Biomarkers of Addiction-Related Neuroplasticity in Neurological Disorders: A Comprehensive Review of Functional Connectivity, Brain Network Reorganization, and Clinical Applications

Recent advances in resting-state functional MRI have enabled non-invasive characterization of large-scale functional networks, providing novel biomarkers for diagnosis, prognosis, and treatment monitoring.Functional magnetic resonance imaging (fMRI)-particularly resting-state functional connectivity (rsFC) and graphthe...

Machine learning prediction of electronic density of states using Lorentzian basis expansion

In this work we propose an approach for fast and reliable prediction of electronic density of states (DOS), which provides valuable information about physical properties of crystals and particularly important for discovery and screening of new materials. Presented method is based on graph neural networks (GNN) and repr...

Design and implementation of an AI-enabled English teaching support system

Traditional English teaching system has some problems, such as insufficient semantic understanding, low efficiency of personalized recommendation and high real-time interaction delay. This paper proposes an adaptive English teaching system based on AI. The system adopts a six layer microservice architecture, integratin...