Search Results (98)

Accurate identification of disease-associated microRNAs (miRNAs) is crucial for elucidating pathogenic mechanisms and advancing therapeutic discovery. Although computational methods, particularly those based on biological networks, have become essential tools for predicting miRNA-disease associations, existing approaches often struggle to comprehensively learn from heterogeneous data and optimize feature representations. To overcome these limitations, we propose the Multi-view Hybrid Attention Graph Convolutional Network (MV-HAGCN). This framework constructs a comprehensive heterogeneous network by integrating multi-source biological information, simultaneously capturing miRNA similarity and disease similarity. We design a hierarchical attention mechanism to enable refined feature learning: first, the Efficient Channel Attention (ECA) module prioritizes information-rich input features, ensuring the model focuses on high-value biological characteristics. Subsequently, the Multi-Head Self-Attention Graph Convolutional Network operates on these refined features. Through iterative message passing and multi-head self-attention, it captures not only direct first-order relationships between nodes but also explicitly models and infers complex, indirect higher-order relationships within the network. This hierarchical design progressively refines feature representations, from channel-level recalibration to global structural dependency modeling, enabling the model to capture both local and high-order relational patterns. Furthermore, a dynamic weight learning strategy adaptively integrates multi-perspective similarity matrices, achieving superior feature complementarity and synergy. Finally, the high-order node representations learned through multi-layer graph convolutions are fed into a multi-layer perceptron for integration and nonlinear transformation, enabling precise prediction of potential miRNA-disease associations. Comprehensive evaluation through five-fold cross-validation on HMDD v2.0 and v3.2 benchmark datasets demonstrates that MV-HAGCN consistently outperforms existing state-of-the-art methods in predictive performance. Case studies targeting key diseases such as breast cancer, lung tumors, and pancreatic disorders revealed that the top 50 miRNAs associated with each of these three conditions were all validated in databases, confirming the practical value of this model in screening candidate miRNAs with high biological relevance. Full article

The growing number of on-orbit spacecraft and the increasing volume of telemetry data have made intelligent anomaly detection in multi-channel telemetry essential for mission operations. Current spacecraft anomaly detection methods primarily rely on statistical models or time-series deep learning approaches, which often fail to explicitly model spatiotemporal dependencies across multiple telemetry channels. This shortcoming limits their ability to capture the dynamically evolving and intricately coupled relationships between variables. To overcome this limitation, a Progressive Spatiotemporal Graph (PSTG) model is proposed for anomaly detection in multi-channel spacecraft telemetry. PSTG employs a multi-scale patch embedding module to extract hierarchical semantic features from multi-channel time series, effectively reducing the dimensionality of the spatiotemporal graph. It constructs a sparse adjacency matrix using a multi-head attention mechanism that integrates intra-channel temporal dynamics, inter-channel spatial correlations, and cross-channel spatiotemporal interactions. An improved multi-head graph attention network then captures pairwise dependencies among nodes within the adjacency matrix. As a result, PSTG encodes rich spatiotemporal representations derived from intricate variable interactions, enabling accurate, real-time prediction of multi-channel telemetry. Furthermore, a dynamic thresholding mechanism is incorporated into PSTG to perform online anomaly detection based on prediction residuals. Extensive experiments on real-world spacecraft telemetry data collected over 84 months show that PSTG outperforms eleven state-of-the-art benchmark methods in almost all cases across multiple evaluation metrics. Finally, visualizations of the learned adjacency and attention matrices are presented to interpret the spatiotemporal modeling process, providing operators with actionable insights into the detected anomalies and facilitating root cause analysis. Full article

Dam deformation monitoring is essential for ensuring the safe operation of hydraulic structures, yet practical data are often compromised by missing values and noise, while spatial coupling among monitoring points further complicates prediction. To address these challenges, this study proposes a Spatio-Temporal Multi-Graph Attention Fusion Network (STMGAFN) for dam deformation prediction and risk early warning under incomplete data conditions. Data quality is enhanced through a data-quality-aware hierarchical adaptive imputation mechanism combined with a VMD–wavelet joint denoising strategy. A multi-graph spatial modeling framework integrating temporal similarity, spatial proximity, structural zoning, and measuring-line connectivity is constructed, and fuses multi-source spatial features through a lightweight adaptive attention mechanism. A parameter-sharing recursive probabilistic temporal modeling approach is adopted to jointly predict deformation values and their associated uncertainties. Based on the predicted confidence intervals, a four-level risk classification and early-warning scheme is established. Experimental results on real GNSS monitoring data from dam sites demonstrate that the proposed method achieves an RMSE of 0.3588 mm, an MAE of 0.1738 mm, and an R² of 0.9865, outperforming baseline models including LSTM, TCN, CNN-LSTM, and STGCN. Moreover, the correlation between predictive uncertainty and actual error reaches 0.892, verifying the effectiveness and reliability of the proposed method for dam safety monitoring under complex conditions. Full article

Fault diagnosis of rolling bearings in wind turbines is significantly challenged by strong noise, non-stationary signals, and multi-source interference. To address these issues, a Multi-Scale Attention Residual Graph Convolutional Network (MSAR-GCN) is proposed. First, a fully connected graph is constructed in the frequency domain using a temporal segmentation strategy, which preserves full spectral resolution and captures cross-frequency coupling features via node embeddings. Second, a multi-scale residual module with a cross-layer pyramid structure is designed to extract features at varying granularities, integrated with a dynamic multi-head attention mechanism to adaptively emphasize damage-sensitive frequency bands. Additionally, a hierarchical feature distillation mechanism is employed to compress high-dimensional features, ensuring model lightweighting while retaining critical fault information. Experimental validations on CWRU and JNU datasets demonstrate that MSAR-GCN achieves 97.02% and 92.5% accuracy under −10 dB Gaussian noise, respectively, outperforming existing methods by over 4%. Specifically, the model exhibits exceptional robustness, maintaining 93.09% accuracy under severe non-Gaussian impulsive noise. With verified feature separability and high computational efficiency, the proposed method offers a promising solution for high-precision, real-time industrial fault diagnosis. Full article

Machining feature recognition is a key enabling technology in intelligent manufacturing that extracts manufacturing semantics from the boundary representation (B-Rep) of 3D CAD models to bridge design and process planning. Recent advances in deep learning have accelerated data-driven feature recognition methods. Among these, graph neural networks (GNNs) have gained significant attention due to their natural compatibility with the non-Euclidean, hierarchical topological structure of B-Rep data, enabling efficient and lossless encoding of geometric and topological attributes. However, existing GNN-based methods primarily leverage the topological structure and geometric attributes of B-Rep models, often neglecting the inherent geometric relationships present in the B-Rep data structure. To address this gap, we propose a dual-graph fusion network (Topo-Geom DualGNN) that integrates a topological attribute adjacency graph and a geometric relationship graph. Our approach employs a GatedGCN-based graph encoder and an FiLM-based cross-stream fusion mechanism to jointly encode topological and geometric information from the B-Rep model. Evaluations on open-source synthetic datasets, including MFInstSeg and MFRCAD, demonstrate that the proposed method achieves competitive comprehensive recognition performance and exhibits promising capability in recognizing machining features in complex parts. Full article

With the rapid growth of fashion e-commerce, fashion attribute recognition has emerged as a critical research area in computer vision. Existing methods face two primary problems: (1) building multi-task models, leading to complex network architectures; (2) the overlooking of semantic relationships and spatial positional dependencies between fashion attributes. To address these issues, this paper proposes SLAR-Net, a novel hierarchical multi-label classification network that effectively fuses spatial and semantic information for improved recognition performance. Specifically, SLAR-Net adopts a progressive, hierarchical architecture. Firstly, we introduce a lightweight backbone network enhanced with a custom-designed attention mechanism to extract low-level image features. Secondly, we innovatively construct an adjacency matrix to represent the relative spatial orientations of attributes, which is then employed by a graph convolutional network to model mid-level spatial positional features. Thirdly, we design a graph embedding matrix that captures attribute dependency relationships, leveraging a neural network to learn high-level semantic representations. Finally, we propose a custom multi-head attention mechanism to fuse spatial and semantic features, facilitating enhanced feature interaction and improving recognition performance. Experimental results on fashion attribute and benchmark datasets demonstrate that SLAR-Net outperforms state-of-the-art methods in recognition accuracy, validating the effectiveness of the proposed hierarchical architecture and fusion strategy. Full article

Nested Named Entity Recognition (nested NER) frequently encounters challenges like boundary conflicts, complications in modeling long-distance dependencies, and inadequate representation of deep nested semantics resulting from overlapping spans and hierarchical inclusion relationships of entities. This research presents a multi-scale heterogeneous graph attention network to facilitate end-to-end recognition of nested entities through the collaborative modeling of structure and semantics. The model initially presents the structural integration mechanism, which consolidates the hierarchical restrictions of the syntactic tree and the inter-word relationships of the dependency tree within a singular heterogeneous graph space. It subsequently generates 1/2/3-hop multi-scale subgraphs and employs multi-scale subgraph attention to adaptively integrate information from various structural receptive fields, harmonizing the local cues of shallow entities with the global dependencies of deep entities. The experimental findings on the ACE2004, ACE2005, and GENIA benchmark datasets indicate that the proposed method surpasses several robust baselines regarding overall performance and nested entity recognition, particularly exhibiting notable advantages in identifying long entities and low-frequency entities. We further evaluate MHGAT on KBP2017 and GermEval2014 to validate generalization across datasets and languages. Full article

Motor imagery EEG decoding often relies on static functional connectivity graphs that cannot capture the dynamic, stage-wise reorganization of brain networks during tasks. This paper aims to develop a graph neural network that explicitly simulates this neurodynamic process to improve decoding and provide computational insights. This paper proposes a Structure-Feature Evolution Graph Attention Network (SFE-GAT). Its inter-layer evolution mechanism dynamically co-adapts graph topology and node features, mimicking functional network reorganization. Initialized with phase-locking value connectivity and spectral features, the model uses a graph autoencoder with Monte Carlo sampling to iteratively refine edges and embeddings. On the BCI Competition IV-2a dataset, SFE-GAT achieved 77.70% (subject-dependent) and 66.59% (subject-independent) accuracy, outperforming baselines. Evolved graphs showed sparsification and strengthening of task-critical connections, indicating hierarchical processing. This paper advances EEG decoding through a dynamic graph architecture, providing a computational framework for studying the hierarchical organization of motor cortex activity and linking adaptive graph learning with neural dynamics. Full article

Accurate forecasting of nutrition supply–demand dynamics is essential for reducing resource wastage and improving equitable allocation. However, this task remains challenging due to heterogeneous data sources, cold-start regions, and the risk of information leakage in spatiotemporal modeling. This study presents a leakage-aware multimodal machine learning framework for nutrition supply–demand forecasting. The framework integrates temporal, spatial, and contextual information within a unified architecture. It combines self-supervised temporal representation learning, causal time-lag modeling, and few-shot adaptation to improve generalization under limited or previously unseen data conditions. Heterogeneous inputs include epidemiological, environmental, demographic, sentiment, and biologically derived indicators. These signals are encoded using a PatchTST-inspired temporal backbone coupled with a feature-token transformer employing cross-modal attention. Spatial dependencies are explicitly modeled using graph neural networks. Hierarchical decoding enables multi-horizon forecasting with calibrated uncertainty estimates. Model evaluation is conducted under strict spatiotemporal hold-out protocols with explicit leakage detection. All synthetic signals are excluded from testing. Across geographically and temporally disjoint datasets, the proposed framework consistently outperforms strong unimodal and multimodal baselines. It achieves macro-F1 scores above 99.5% and stable early-warning lead times of approximately 9 days under distribution shift. Ablation studies indicate that causal time-lag enforcement and few-shot adaptation contribute most strongly to performance robustness. Closed-loop simulation experiments suggest potential reductions in nutrient wastage of approximately 38%, response latency of 19%, and operational costs of 16% when deployed as a decision-support tool. External validation on fully unseen regions confirms the generalizability of the framework under realistic forecasting constraints. Full article

The diagnosis of attention-deficit/hyperactivity disorder (ADHD) has long relied on subjective scales, lacking objective neuroimaging biomarkers. Static functional connectivity (sFC) and dynamic functional connectivity (dFC), as commonly used metrics in resting-state functional magnetic resonance imaging (rs-fMRI) analysis, provide important perspectives for related research. However, existing unimodal approaches struggle to effectively integrate the spatiotemporal characteristics of functional connectivity. To address this, this paper proposes the multimodal fusion framework D-SFANet, which synergistically models the static and dynamic features of brain functional connectivity through an attention mechanism: in the static path, it integrates a multi-scale convolutional network with phenotypic information extraction to extract hierarchical topological features; in the dynamic path, it combines graph theory with a bidirectional long short-term memory network (BiLSTM) to capture key state transition patterns in brain networks. Experimental validation demonstrates that D-SFANet achieves significantly higher classification accuracy than existing mainstream methods, robustly validating the effectiveness of its spatiotemporal fusion strategy. Full article

The construction sector contributes approximately 39% of global carbon emissions, with embodied carbon—emissions from material extraction, manufacturing, transportation, and construction—representing a systematically underestimated yet increasingly critical component of building life cycle environmental impacts. Traditional Life Cycle Assessment (LCA) methods suffer from static database dependencies, delayed feedback cycles, and limited integration with active construction decision-making, creating a fundamental gap between environmental assessment and construction operations. This paper presents the Digital Twin-Enabled Life Cycle Assessment Framework (DT-LCAF), a dynamic construction-phase embodied carbon accounting system aligned with the EN 15978 standard (stages A1–A5) that integrates Building Information Modeling (BIM), Internet of Things (IoT) sensor networks, and machine learning designed to support real-time sustainability decision-making during smart building construction, with computational performance validated through the offline processing of historical datasets. The framework introduces two enabling mechanisms: (1) a Multi-Scale Carbon Prediction Network (MSCPN) employing hierarchical graph attention networks to capture material interdependencies across component, system, and building scales; and (2) a Reinforcement Learning-based Carbon Optimization Engine (RL-COE) that generates constraint-aware recommendations for material substitution, supplier selection, and construction sequencing while respecting structural, economic, and temporal constraints. Experimental evaluation employs two complementary validation strategies using proxy embodied carbon labels (not ground-truth construction measurements): embodied carbon prediction accuracy is assessed using proxy carbon labels derived from the CBECS dataset (5900 commercial buildings) combined with the ICE Database v3.0 emission factors, achieving a 10.24% MAPE, representing a 23.7% improvement over the best-performing baseline in predicting these proxy estimates; temporal responsiveness and streaming data ingestion capabilities are validated using the Building Data Genome Project 2 (1636 buildings, 3053 m). The RL-COE optimization engine demonstrates an 18.4% mean carbon reduction rate within the proxy label framework across building types while maintaining cost and schedule feasibility. A BIM-based case study illustrates the framework’s construction-phase update loop, showing how embodied carbon estimates evolve dynamically as construction progresses. The limitations regarding the proxy-based nature of embodied carbon labels and the absence of ground-truth construction-phase measurements are explicitly discussed. The framework contributes to smart city sustainability by enabling scalable, data-driven embodied carbon intelligence across building portfolios. All quantitative results are based on proxy embodied carbon estimates derived from building characteristics and standard emission factor databases, rather than measured project data. The reported performance therefore demonstrates a proof-of-concept within the proxy system, and real-project, measurement-based validation remains future work. Full article

Hydraulic systems are pivotal components in safety-critical aerospace and industrial applications, making reliable health monitoring essential. However, traditional data-driven diagnosis methods typically rely on static graph structures that fail to capture evolving sensor correlations during different fault modes. Furthermore, existing grid-based models often struggle to extract multi-resolution features and maintain performance under data-limited conditions. To address these challenges, this paper proposes a novel Dynamic Multi-Scale Graph Neural Network (DMS-GNN) for hydraulic system fault diagnosis. The framework integrates a hierarchical multi-scale feature extraction module to capture diverse fault signatures across different frequency bands. Crucially, a self-attention-based dynamic graph learner is introduced to adaptively infer latent sensor topologies end-to-end, eliminating the reliance on predefined physical connections. Experimental validation on a dedicated electro-hydraulic test bench demonstrates that the proposed DMS-GNN achieves a superior diagnostic accuracy of 98.47%, outperforming state-of-the-art baselines such as GraphSAGE, Static GCN, and GAT. The result confirms the efficacy of combining multi-scale temporal learning with dynamic spatial reasoning for robust multi-sensor fusion diagnosis. Full article

Traffic forecasting in cellular networks is a challenging spatiotemporal prediction problem due to strong temporal dependencies, spatial heterogeneity across cells, and the need for scalability to large network deployments. Traditional cell-specific models incur prohibitive training and maintenance costs, while global models often fail to capture heterogeneous spatial dynamics. Recent spatiotemporal architectures based on attention or graph neural networks improve accuracy but introduce high computational overhead, limiting their applicability in large-scale or real-time settings. We propose HiSTM (Hierarchical SpatioTemporal Mamba), a spatiotemporal forecasting architecture built on state-space modeling. HiSTM combines spatial convolutional encoding for local neighborhood interactions with Mamba-based temporal modeling to capture long-range dependencies, followed by attention-based temporal aggregation for prediction. The hierarchical design enables representation learning with linear computational complexity in sequence length and supports both grid-based and correlation-defined spatial structures. Cluster-aware extensions incorporate spatial regime information to handle heterogeneous traffic patterns. Experimental evaluation on large-scale real-world cellular datasets demonstrates that HiSTM achieves better accuracy, outperforming strong baselines. On the Milan dataset, HiSTM reduces MAE by 29.4% compared to STN, while achieving the lowest RMSE and highest R² score among all evaluated models. In multi-step autoregressive forecasting, HiSTM maintains 36.8% lower MAE than STN and 11.3% lower than STTRE at the 6-step horizon, with a 58% slower error accumulation rate compared to STN. On the unseen Trentino dataset, HiSTM achieves 47.3% MAE reduction over STN and demonstrates better cross-dataset generalization. A single HiSTM model outperforms 10,000 independently trained cell-specific LSTMs, demonstrating the advantage of joint spatiotemporal learning. HiSTM maintains best-in-class performance with up to 30% missing data, outperforming all baselines under various missing data scenarios. The model achieves these results while being 45× smaller than PredRNNpp, 18× smaller than xLSTM, and maintaining competitive inference latency of 1.19 ms, showcasing its effectiveness for scalable 5/6G traffic prediction in resource-constrained environments. Full article

Large Language Models (LLMs) have demonstrated extraordinary capabilities in natural language generation; however, their real-world deployment is frequently hindered by the generation of factually incorrect or biased content, along with an inherent deficiency in transparency. To address these critical limitations and thereby enhance the reliability and explainability of LLM outputs, this study proposes a novel integrated framework, namely the Adaptive Knowledge-Driven Correction Network (AKDC-Net), which incorporates three core algorithmic innovations. Firstly, the Hierarchical Uncertainty-Aware Bias Detector (HUABD) performs multi-level linguistic analysis (lexical, syntactic, semantic, and pragmatic) and, for the first time, decomposes predictive uncertainty into epistemic and aleatoric components. This decomposition enables principled, interpretable bias detection with clear theoretical underpinnings. Secondly, the Neural-Symbolic Knowledge Graph Enhanced Corrector (NSKGEC) integrates a temporal graph neural network with a differentiable symbolic reasoning module, facilitating logically consistent and factually grounded corrections based on dynamically updated knowledge sources. Thirdly, the Contrastive Learning-driven Multimodal Explanation Generator (CLMEG) leverages a cross-modal attention mechanism within a contrastive learning paradigm to generate coherent, high-quality textual and visual explanations that enhance the interpretability of LLM outputs. Extensive evaluations were conducted on a challenging medical domain dataset to validate the effectiveness of the proposed AKDC-Net framework. Experimental results demonstrate significant improvements over state-of-the-art baselines: specifically, a 14.1% increase in the F1-score for bias detection, a 19.4% enhancement in correction quality, and a 31.4% rise in user trust scores. These findings establish a new benchmark for the development of more trustworthy and transparent artificial intelligence (AI) systems, laying a solid foundation for the broader and more reliable application of LLMs in high-stakes domains. Full article

Urbanization is intensifying congestion, emissions, and unequal mobility access in cities. This study aims to operationalize sustainability objectives—efficiency, environmental externalities, and service equity—in network-wide traffic system control. We propose SERL-H, a sustainability-aware hierarchical multi-agent reinforcement learning (MARL) controller. SERL-H separates fast intersection-level actuation from slower region-level coordination under a centralized-training decentralized-execution paradigm, and employs adaptive graph attention to capture time-varying interdependencies with bounded neighborhood communication. The learning reward explicitly balances delay/throughput, emissions/fuel, and an equity regularizer based on service dispersion across user groups. In a SUMO-based city-scale simulation with 100 signalized intersections, SERL-H reduces average delay from 45 s to 29 s and average travel time from 120 s to 88 s relative to fixed-time control, while increasing throughput and lowering total emissions (4800 kg to 3950 kg). A socio-economic assessment suggests higher annualized cost savings (e.g., $50.27 M/year to $65.91 M/year) and improved environmental quality indices. We also report, as supporting evidence, an optional sustainability-enhanced spatio-temporal graph predictor (SUT-GNN) that provides reliable short-horizon forecasts during peak-hour volatility. Full article