Search Results (1,740)

The human microbiota is essential for maintaining physiological homeostasis, and microbial dysbiosis is increasingly implicated in the pathogenesis of complex diseases. Identifying potential microbe-disease associations (MDAs) can therefore facilitate mechanistic investigation, biomarker discovery, and therapeutic development. However, wet-laboratory validation is costly and time-consuming, while existing computational methods often struggle with sparse association networks and complex nonlinear interactions. We propose a novel deep learning approach named the Dual Dynamic Hypergraph Convolution Framework for Human Microbe-Disease Association Prediction (DDHMDA). Specifically, DDHMDA first utilizes graph convolutional networks to encode local topological features. Subsequently, it dynamically constructs a dual hypergraph architecture: a differentiable K-means similarity hypergraph to capture intra-modal global clustering patterns, and an attention-based cross-modal interaction hypergraph to model inter-modal interactions synergistically. Under leakage-free pair-level five-fold cross-validation (denoted as CV3), DDHMDA achieved AUC/AUPR values of 0.9789 ± 0.0177/0.9843 ± 0.0129 on HMDAD and 0.9651 ± 0.0042/0.9740 ± 0.0031 on Disbiome. DDHMDA also obtained the best overall CV3 performance among the eight evaluated methods. Furthermore, ablation experiments and case studies validate the practical effectiveness of individual modules and the biological interpretability in discovering novel MDAs. Therefore, DDHMDA would be a reliable tool for identifying potential MDAs. Full article

Fine-grained monitoring of aquatic vegetation in crab ponds is essential for regulating water quality, sustaining ecological balance, and optimizing Chinese mitten crab (Eriocheir sinensis) aquaculture. However, owing to the complex water environment, fragmented vegetation morphology, and the absence of dedicated annotated datasets, traditional remote sensing techniques struggle to achieve highly accurate semantic segmentation and classification. In this study, we construct the first unmanned aerial vehicle (UAV) multispectral dataset for crab pond aquatic vegetation, encompassing four species, Alternanthera philoxeroides, Vallisneria natans, Hydrilla verticillata, and Elodea nuttallii, with pixel-level annotations verified by field surveys across typical aquaculture sites in Jiangsu Province, China. Furthermore, we introduce the Multi-scale Frequency–Spatial Collaborative Network (MFSCNet), built upon a MedNeXt backbone and augmented with distributed modules, including Channel Reduction Attention, Spatial Frequency Selection, a spatial–frequency fusion module, and Mobile Graph Convolution that operate cooperatively across the encoder, skip connections, decoder, and output head. This design suppresses complex water-background interference, enhances vegetation texture representation, and preserves the spatial continuity of vegetation patches. Experimental results demonstrate that, with a lightweight parameter size of merely 19.38 M, MFSCNet achieves a remarkable mean Intersection over Union (mIoU) of 0.9044, outperforming various mainstream convolutional neural network (CNN) and Transformer-based architectures. This study not only provides a high-precision remote sensing technical framework for the accurate multi-class identification and quantitative assessment of aquatic vegetation in crab ponds but also establishes reliable data support for refined aquaculture management and aquatic ecological conservation. Full article

Medical image segmentation is critical for clinical diagnosis, yet existing methods face a persistent trade-off: CNN-based approaches are constrained by local receptive fields, while Transformer-based methods suffer from semantic dilution when modeling global context. To address these limitations, we propose SCAGC-UNet, a region-aware graph convolutional network that bridges local detail extraction and global dependency modeling through structured region-level reasoning. The architecture features a dual-layer residual encoder for hierarchical feature extraction and a Spatial-Channel Graph Convolution (SC-GCN) module at the bottleneck, which simultaneously captures inter-region spatial topology and intra-region channel semantics via dual-branch graph inference. Feature refinement in the decoder is further enhanced by Context-Corrected Modules and Backward-Aided Modules to reduce the semantic gap across skip connections. We validate SCAGC-UNet on three public benchmarks covering distinct imaging challenges. On Kvasir-SEG, the model achieves a Dice score of 92.28% and MIOU of 92.41%, surpassing the strongest CNN-based baseline CCBANet by 0.73% in DSC and outperforming TransUNet by 11.76% in DSC. On BUSI, it attains an IOU of 78.10% and MIOU of 87.68%, outperforming UNet by 2.82% in IOU and TransUNet by 6.91% in DSC. On COVID-19 CT, it achieves a DSC of 82.51%, surpassing UNet by 4.99% and TransUNet by 7.47%, demonstrating robust performance on irregular lesion morphologies. These results confirm that SCAGC-UNet achieves consistent and robust segmentation performance across three public benchmark datasets spanning distinct imaging modalities, suggesting its potential clinical relevance. Full article

Autism spectrum disorder (ASD) is a heterogeneous neurodevelopmental condition, and objective identification based on neuroimaging remains challenging due to inter-subject variability, multi-site heterogeneity, and the complex topology of brain functional networks. Resting-state functional magnetic resonance imaging (rs-fMRI) provides a non-invasive way to characterize intrinsic brain activity, but existing functional-connectivity-based methods often rely on single-scale static representations and insufficiently capture high-order topology, temporal evolution, and phenotypic heterogeneity. This study aims to develop a mathematical and AI-based brain-network modeling framework for ASD identification from rs-fMRI. The proposed method integrates low-order functional connectivity, high-order functional connectivity, phenotypic information, dynamic graph sequences, Transformer-based temporal attention, and static–dynamic gated fusion. Experiments were conducted on the ABIDE-I dataset, including 1112 subjects from 17 acquisition sites, with 539 ASD subjects and 573 typical controls. The proposed static multi-channel model achieved an accuracy of 75.8%, while the dynamic extension achieved a mean accuracy of 78.5% ± 0.7% and an AUC of 0.84 ± 0.01 over repeated runs. The results suggest that jointly modeling multi-scale static topology and dynamic temporal evolution may improve rs-fMRI-based ASD identification and offer a computationally interpretable framework for AI-assisted neuroimaging analysis. Full article

Safe-separation-and-collision-avoidance unmanned aerial vehicle (UAV) swarms are increasingly used for inspection, emergency response, environmental monitoring, and search-and-rescue support in cluttered airspace where communication links may be delayed, degraded, or intermittently unavailable. These applications require heterogeneous vehicles to maintain situational awareness, allocate tasks, and avoid hazards under partial observability and changing team topology. To address these challenges, this paper proposes a Hierarchical Graph-Attention Multi-Agent Reinforcement Learning architecture (HG-MARL) for safe-separation-and-collision-avoidance heterogeneous UAV swarm coordination. The proposed framework decomposes the task into high-level resource allocation and low-level local-control execution, uses graph attention for changing swarm topology, and applies Transformer memory, action masking, potential-field reward shaping, and domain-randomized simulation training. In the multi-scenario simulation summaries, HG-MARL achieves 92.9%, 89.8%, and 82.6% task success in Scenarios A–C, respectively, improving upon MAPPO by 15.1, 21.4, and 20.1 percentage points. Summary-statistic Welch tests show that all six HG-MARL comparisons against MAPPO and QMIX yield $p<0.01$ with large effect sizes. Fair-control, reward-sensitivity, communication-degradation, safety-ablation, training-stability, latency, and transfer-oriented stress tests further support the contributions of the integrated architecture. The validation scope is simulator-based, with platform-level flight/HIL evaluation discussed as future work. These results suggest that HG-MARL is a promising simulation-validated framework for civilian UAV swarm coordination in collision-and-separation-critical and communication-degraded environments. Full article

Low-slow-small (LSS) target recognition using multi-dimensional radar remains challenging due to weak signatures, similar kinematics, and overlapping short-term Doppler patterns. Digital-array radar provides continuous, complementary Doppler-spectrum and kinematic measurements; however, their heterogeneity in dimension, distribution, and physical meaning often makes direct fusion under-exploit discriminative complementarity and inadequately model temporal track evolution. To address this, we propose a Doppler-Kinematic Spatio-Temporal Graph Learning framework named Dual-Stream Spatio-Temporal Cross-Attention Graph Convolutional Network (DS-STCAGCN) for LSS target recognition using multi-dimensional radar observations. The method separately encodes Doppler-spectrum and kinematic features to preserve their modality-specific characteristics, fuses them through bidirectional cross-attention, captures long-range temporal dependencies via self-attention, and aggregates local frame-to-frame correlations through graph convolution on a time-ordered observation graph. On the public L-band digital-array dataset LSS-DAUR-1.0, DS-STCAGCN achieves 99.73% mean accuracy and maintains 98.64% at 5 dB signal-to-noise ratio (SNR). On the passive-radar dataset LSS-PR-1.0, it reaches 99.86% mean accuracy, demonstrating strong cross-modal generalization. This work provides an effective spatio-temporal modelling framework for multi-dimensional radar sensing and robust LSS target recognition. Full article

To address the residual lateral coil misalignment after an unmanned aerial vehicle (UAV) lands on a fixed wireless-charging platform, this study proposes a graph-attention-based knowledge distillation method for embedded self-alignment localization. Four detection-coil voltages form an induced-voltage fingerprint database organized as a multi-scale spatial graph. A graph attention network (GAT) teacher model is trained offline to learn neighborhood correlations in the voltage–position mapping, and its spatial knowledge is distilled into a lightweight Tiny-MLP student model for microcontroller unit (MCU)-based online inference. Experimental results show that the GAT teacher achieves a mean absolute error (MAE) of 0.589 cm, while the distilled Tiny-MLP reduces the MAE of the directly trained Tiny-MLP from 1.548 cm to 1.148 cm (a 25.8% reduction under a fixed seed). In 2000 closed-loop alignment trials with random initial positions, the system achieves an 85.5% success rate under a 0.5 cm threshold, indicating that the method supports low-complexity closed-loop self-alignment for UAV wireless charging. Full article

Automated assessment of coronary artery disease (CAD) severity from invasive X-ray angiography is important for diagnostic accuracy, but there are limitations due to limited label data and privacy issues in multi-institutional collaboration. This research proposes a Federated Graph-Transformer Network (FGTN) that models coronary vessel compositions as graphs and uses a transformer unit of measurement to encode global anatomic circumstances for severity scaling. The publicly available X-ray angiography images and SYNTAX-Score dataset will be used, consisting of 232 X-ray coronary angiography images with analogous clinically calculated SYNTAX tons and angiographic factors from 231 patients, manually annotated by a competent cardiologist. The vascular tree is a primary segment that transforms inside the node-edge graph representing bifurcation and vessel sections, continuing topological features, and then processes by graph convolutions integrated with transformer self-attention to capture simultaneously the local stenosis features and global vessel relationships. A Horizontal Federated Learning Strategy allowing collaborative model training on clinical sites without sharing raw data. The intended FGTN achieved overall accuracy of 99.4%, precision of 97.6%, recall of 98.8%, and F1-score of 98.2%, exceeding the usual CNNs, Attention-UNet, and Capsule Connection baselines by a margin of 4–7%. For non-obstructive, mild, moderate, and severe stenosis classes, the AUC values were 0.98, 0.97, 0.96, and 0.95, respectively. Moreover, the Federated Learning framework shows firm convergence with lower, compared to 1.8% performance degradation, when compared to centralized training, and confirms robustness via heterogeneous data distribution. These results show that the proposed solution automatically calculates the CAD severity grading from coronary angiography images. Full article

The accurate classification of seabed sediment and benthic covers in shallow-water environments remains a key challenge for marine activities and oceanographic research. However, coastal areas of shallow waters are influenced by complex dynamic environments, making it difficult to obtain authentic sediment and benthic-cover samples. Therefore, to address the problem of few-shot classification of seabed sediment and benthic covers, a few-shot classification algorithm of seabed sediment and benthic covers based on the fusion model of airborne LiDAR bathymetry (ALB) and multispectral images is proposed in this article. Based on the extracted features, a scale-invariant feature transform-progressive sample consensus (SIFT-PROSAC) algorithm and perspective transform model were constructed to achieve feature fusion. Then, multi-modal feature selection is realized using a formal concept analysis-Relief-F (FCA-Relief-F) algorithm. Finally, a graph attention network-prototype network (GAT-PN) model was established to classify five types of sediment and benthic cover (coral reef, stone, sand, vegetation, and coastal zone). To validate the effectiveness of the proposed method, experimental data from actual measurements at Ganquan Island in the Xisha Islands of China were used. Compared to other classical classifiers, the GAT-PN algorithm achieves a higher classification accuracy, with an overall accuracy (OA) and Kappa coefficient of 97.50% and 0.97, respectively. The findings of this study provide effective technical support for marine engineering and related fields. Full article

Forecasting multi-asset volatility spillovers and turning the forecasts into risk-aware portfolios requires methods that uncover directional information flow between assets, compress the state into a minimal sufficient representation, deliver calibrated uncertainty, and respect explicit tail-risk limits. We propose TDV (Transfer-entropy, Dynamic-graph-attention, Variational-information-bottleneck), an information-theoretic artificial intelligence framework that couples a time-varying transfer entropy network with a graph attention encoder regularised by a variational information bottleneck, and demonstrates the practical value of the calibrated predictive distribution through a downstream entropy-regulated, CVaR-constrained portfolio application. We establish three theoretical results: $L^2$ consistency of the k-nearest-neighbour transfer entropy estimator on $\alpha$-mixing returns with rate $O_P\left(n^{-2/(2+d)}\right)$, a PAC–Bayes generalisation bound of order $O\left(\sqrt{\left(I\right(X;Z)+log(1/\delta \left)\right)/n}\right)$ for the bottleneck-encoded forecaster, and asymptotic CVaR feasibility of the plug-in allocation. In simulations across sparse Granger networks, contagion DCC–GARCH ensembles, and regime-switching factor models, the framework cuts spillover forecasting errors by 24 to 42 percent against LSTM, vanilla GAT, and Transformer baselines, and it recovers 1.6 additional nats of mutual information with the realised connectedness matrix. On a 32-asset global panel covering 2014 to 2025, the model delivers an out-of-sample $R^2$ of 0.331, an annualised Sharpe ratio of 1.46 against 0.83 for an equally weighted benchmark, a maximum drawdown of 7.8 percent, and 95 percent CVaR reductions of 28 to 36 percent across sub-periods relative to a shrinkage minimum-variance baseline. Full article

To address the challenges of wind–solar output correlation, renewable-output uncertainty, transient stability, and economic optimization, this paper proposes a transient stability-constrained optimal power flow (TSCOPF) model considering wind–solar correlation. First, kernel density estimation (KDE) is employed to establish the marginal probability density functions of wind and photovoltaic outputs, and a Frank-Copula function is used to characterize the wind–solar correlation and construct a joint probability distribution model. A Wasserstein generative adversarial network with gradient penalty (WGAN-GP) is then used to generate wind–solar output scenarios, which are further reduced by K-means++ clustering. Second, a transient stability assessment method combining a graph convolutional network with attention mechanism (GCN-Attention) and conditional mutual information (CMI)-based feature selection is developed to extract key stability features, and a TSCOPF model considering renewable-energy integration is constructed. Third, an improved Coati Optimization Algorithm (ICOA) integrating refraction-based opposition learning, Levy flight, and spiral search strategies is proposed to enhance global optimization performance. Simulations on the modified Institute of Electrical and Electronics Engineers (IEEE) 39-bus system and the IEEE 118-bus system demonstrate the accuracy, effectiveness, and scalability of the proposed method. Full article

While MADRL has demonstrated significant potential in Unmanned Aerial Vehicle (UAV) swarm control, traditional architectures often rely on fixed-dimensional observation spaces. This rigid structural constraint severely limits the swarm’s adaptability in dynamic environments, particularly when facing sudden topological changes such as node failures or dynamic reinforcements. To overcome these limitations, this paper proposes an end-to-end UAV swarm motion control framework incorporating a state-modulated Graph Attention Network (GAT). By modeling the swarm as a dynamic interaction graph, the proposed method dynamically aggregates neighbor features using attention weights modulated by the agents’ real-time kinematic states. Furthermore, a virtual structure combined with an auction mechanism is introduced to achieve precise formation planning and target allocation. Evaluated in the Genesis 3D physics engine, the proposed Prioritized Experience Replay (PER)-MADDPG-Graph Attention Network (GAT) algorithm exhibits superior robustness and spatial adaptability. Extensive experiments, including dynamic node reduction and addition scenarios, confirm that the proposed framework seamlessly maintains swarm configurations without catastrophic policy degradation, outperforming baseline MADRL methods in both convergence speed and control precision. Full article

Sustainable pavement maintenance increasingly requires coordinated management of infrastructure condition, renewable-energy availability, carbon emissions, financial resources, and operational capacity. This study proposes a renewable-energy resource management framework for low-carbon network-level pavement maintenance using simulation-based pavement-energy modeling and multi-agent deep reinforcement learning. The proposed framework develops an AnyLogic-based pavement-energy simulation environment in which road sections, deterioration states, work zones, maintenance crews, equipment resources, photovoltaic generation, battery storage, grid support, diesel backup, carbon tracking, and budget consumption are represented within one integrated decision environment. To support adaptive maintenance control, pavement sections are modeled as interacting agents, while road connectivity, dispatch dependency, traffic interaction, and maintenance-route relationships are encoded through graph structures. A graph-based multi-agent deep reinforcement learning model, named Graph-MAPPO, is then used as the decision controller. The model integrates multi-head graph attention for spatial dependency learning, GRU-based temporal memory for deterioration-history representation, finite-element-assisted structural-risk indicators for hidden damage characterization, and constraint-aware action masking to prevent infeasible decisions under budget, carbon, energy, crew, and equipment constraints. Two calibrated datasets were generated to support the framework: a pavement network and maintenance dataset containing 4437 records and 55 features, and a renewable energy-carbon-budget dataset containing 9875 records and 38 features. The decision controller jointly selects the pavement section, treatment type, intervention timing, crew, equipment, and energy mode. Results from 20 experimental configurations show that the balanced Graph-MAPPO policy improves average $PCI$ from 69.4 to 78.9, achieves an $RSL$ gain of 6.8 years, reduces emissions to 58.3 tCO₂e, maintains a renewable-energy share of 74.6%, and limits the constraint-violation rate to 1.8%. Under high renewable-energy availability, the framework achieves the best overall performance, with an average $PCI$ of 80.2, renewable-energy share of 84.6%, emissions of 50.8 tCO₂e, and reward of 0.90. These findings demonstrate that integrating pavement-energy simulation, renewable-energy resource allocation, carbon-aware maintenance planning, structural-risk awareness, and multi-agent decision control can support more adaptive, low-carbon, and resource-efficient pavement maintenance management. Full article

Fault location in distribution networks is challenged by dynamic topology changes and heterogeneous equipment. This paper proposes a Dynamic and Hierarchical Multi-Head Graph Attention Network (DHMGAT) that overcomes the limitations of static graph assumptions. Unlike methods that treat network structure as fixed or neglect line parameters, DHMGAT employs a hierarchical multi-head attention mechanism to encode topology dynamically. An Edge Feature Encoding Module fuses physical line attributes—impedance and switch states—directly into node embeddings. A Topology-Gated Pooling mechanism adapts to radial structural variations, and a Physics-Constrained Data Augmentation strategy ensures robustness under limited-sample and anomalous-data conditions. Evaluated on the IEEE 33-node and IEEE 123-node systems under comprehensive fault scenarios, DHMGAT achieves localization accuracies of 96.70% and 94.31%, respectively, with near-perfect calibration (ECE = 0.066). It maintains accuracy above 92% under high-noise conditions and N-1 topological reconfiguration, and above 88% under severe feature loss (up to 30% missing data), substantially outperforming conventional graph neural networks. Full article

Floating liquefied natural gas (FLNG) facilities integrate natural gas liquefaction, storage, and offloading into a single vessel. During ship-to-ship (STS) side-by-side offloading, an LNG carrier (LNGC) moors alongside the FLNG to transfer liquefied cargo through a loading-arm system. The hydrodynamic interactions between the two vessels, combined with environmental loads, can lead to excessive relative motions that pose a risk of collision or damage to the loading arms and fenders. Accurate short-term prediction of vessel motions would provide operators with advance warning of potentially dangerous conditions, allowing preventive actions to be taken. This study presents a data-driven approach to short-term motion prediction using experimental data obtained from comprehensive basin model tests of an FLNG system. The model tests covered 15 environmental conditions, including survival conditions (100-year return period) and operating conditions (1-year return period), under both single-vessel and side-by-side configurations. Three prediction methods were evaluated: an autoregressive linear model, a single-degree-of-freedom multi-layer perceptron, and a multi-head attention cross-coupling network (MAC-Net) that leverages temporal attention, cross-DOF graph message passing, and multi-task learning with uncertainty-weighted loss. The results show that surge, sway, and yaw can be predicted with high skill scores at model-scale horizons of up to 4 s (32 s full-scale equivalent), while heave and pitch exhibit limited predictability beyond 2 s model scale. The MAC-Net model demonstrates particular advantages for roll prediction, achieving a skill score of 0.88 at a 4 s model-scale horizon compared to 0.76 for the conventional method, attributable to the physical coupling between roll and the horizontal-plane motions through the mooring system. These findings support a practical early warning concept in which horizontal-plane motions provide advance collision alerts and heave/pitch are treated as short-horizon monitoring quantities. Full article