Search Results (789)

Industrial Control Systems (ICSs), which are essential components of critical infrastructures, are inherently complex and vulnerable to cyberattacks. Advanced Persistent Threats (APTs) that target these systems are multi-stage, coordinated attacks that can lead not only to information loss but also to physical damage and loss of life. Traditional threat modeling approaches fall short in adapting to the dynamic nature of ICSs, necessitating new methodologies to predict and prevent such complex attacks. This work presents a digital twin-assisted dynamic threat modeling framework for ICS environments. The framework leverages a knowledge graph that integrates system data and cyber threat intelligence to predict potential attacks. In addition, the digital twin environment enables the validation of mitigation strategies before deployment in the physical system, while also supporting adaptive response and real-time mitigation. To predict the attacker’s next move, we propose a Relational Graph Convolutional Network (RGCN)-based model that utilizes enriched relational data such as tactics, campaigns, groups, techniques, and assets. The proposed RGCN model achieves a recall of 0.887, an F1-score of 0.893, and an AUC of 0.957 in predicting potential attack sequences. These results demonstrate that the model provides reliable and well-balanced predictive performance. Full article

To address the limitations of existing vehicle trajectory prediction methods, including insufficient modeling of dynamic inter-vehicle interactions, weak temporal continuity of complex driving intentions such as lane-changing, and high uncertainty in future trajectory prediction, this paper proposes a vehicle trajectory prediction method that integrates Dynamic Graph Neural Networks (DyGNN) with Transformer. Specifically, a time-varying interaction graph is constructed to model the dynamically evolving topological interaction relationships among vehicles, while a Transformer encoder is employed to extract temporal dependency features from historical trajectory sequences. In this way, the joint representation of spatial interaction information and temporal evolution information is achieved, thereby improving the accuracy and continuity of driving intention recognition in complex traffic scenarios. On this basis, driving intention is further introduced into the trajectory prediction process as a prior constraint, which effectively reduces the uncertainty of future trajectory prediction. Comparative experiments on real-world traffic datasets demonstrate that the proposed method maintains low prediction errors across different prediction horizons, showing good effectiveness and robustness. Full article

Accurate energy consumption prediction is fundamental for enhancing range estimation and trip planning in battery electric vehicles (BEVs) under real-world conditions. This study develops a route-level benchmark utilizing 1 Hz data acquired via ECU/OBD-II interfaces (CAN 500 kbps) across ten diverse real-world driving routes. The input feature set comprises vehicle speed, longitudinal acceleration, estimated motor torque, road altitude, and accelerator pedal position. Ground truth energy consumption was derived from battery voltage and current, integrated via the trapezoidal rule. We performed a comparative analysis between five memoryless regressors (FNN, SVR, GPR, QRNN, and Bagged Trees) and three sequence models (LSTM, GRU, and BiLSTM) trained on 20-second temporal windows. The results indicate that the GRU model achieved the highest overall performance (mean RMSE = 0.1142 kWh, R² = 0.9545 and MAE = 0.072 kWh), while Bagged Trees emerged as the most robust static model (mean RMSE = 0.1587 kWh). Temporal models outperformed static ones on routes with high dynamic variability, whereas Bagged Trees excelled in five specific scenarios. These findings provide a controlled within-route benchmark for time-resolved cumulative energy estimation and highlight the need for chronological and cross-route validation before drawing deployment-oriented generalization claims. Full article

Driving behavior primitives serve as fundamental building blocks for modeling and semantically interpreting time-series driving behavior. Extracting behavior primitives is challenging due to the high dimensionality and complex interdependencies among behavioral variables, as well as the rich temporal dynamics of real-world driving maneuvers. This paper proposes an unsupervised two-stage framework that optimizes time-series segmentation and segment clustering to yield interpretable and context-aware behavior primitives. First, a Hierarchical Bayesian Model-based Agglomerative Sequence Segmentation (H-BMASS) method is introduced that decouples longitudinal and lateral driving behaviors and performs hierarchical segmentation. This design mitigates under-segmentation by ensuring that change points reflect genuine behavioral transitions. Second, to cluster driving segments of varying durations into a finite set of primitive types, an Integrating Numerical and Trend Discretization Latent Dirichlet Allocation (INT-LDA) model is developed. The model combines variables’ temporal trend discretization with numerical discretization to create symbolic representations of driving data, thereby preserving the essential time dependency of driving behavior and improving segment clustering accuracy. Evaluated on naturalistic driving data collected from a high-fidelity simulator, the proposed framework identifies five distinct behavior primitives with clear physical interpretations. The resulting primitives provide a compact, semantically rich representation of driving behavior, facilitating driver modeling, decision prediction, and scenario-based testing for autonomous vehicles. Full article

With the rapid growth of the global shipping industry and the increasing availability of Automatic Identification System (AIS) data, accurate vessel trajectory prediction has become crucial for ensuring navigational safety and optimizing maritime traffic management. This paper presents a systematic review of recent advances in deep learning-based methods for vessel trajectory prediction. We provide a comprehensive analysis of mainstream models, including Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs) such as Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) networks, Sequence-to-Sequence (Seq2Seq) models, and the Transformer architecture. Their performance is compared in terms of spatio-temporal data processing capability, prediction accuracy, and computational efficiency. Furthermore, this review examines practical applications of these methods in scenarios such as collision avoidance and route optimization. Despite notable progress, several challenges remain, including data quality issues, real-time prediction capability, and model interpretability. Future research directions may focus on multi-source data fusion and the development of lightweight model designs to further improve prediction performance. This survey aims to serve as a valuable reference for researchers and contribute to ongoing innovation in vessel trajectory prediction technology. Full article

Earthquake-induced landslide reactivation poses a sustained hazard for years following major seismic events, yet operational prediction remains constrained by heterogeneous multi-modal data, sparse supervision, and the absence of uncertainty-aware frameworks. This paper presents PIMSEL, a physically guided multi-modal semi-supervised framework for post-seismic landslide reactivation risk assessment. PIMSEL integrates satellite-derived morphological features, precipitation time series, and seismic hazard attributes through four components: entropy-regularized optimal transport for cross-modal semantic alignment without paired supervision; causally constrained hierarchical fusion enforcing domain-consistent modal weighting; scenario-based prototype mutation for semi-supervised learning from sparse expert annotations; and prototype-anchored variational graph clustering that simultaneously stratifies landslides into HIGH, MEDIUM, and LOW risk tiers and produces decomposed aleatoric and epistemic uncertainty estimates for operational triage. The HIGH risk tier operationally corresponds to predicted reactivation, validated against 598 documented reactivation events across 7482 co-seismic landslides from three Sichuan Province earthquake sequences: the 2013 Lushan (Mw 7.0), 2017 Jiuzhaigou (Mw 7.0), and 2022 Luding (Mw 6.8) events. PIMSEL achieves 82.5% reactivation recall and 66.4% precision, outperforming twelve baselines across clustering quality, classification, and uncertainty calibration metrics. Ablation studies confirm that optimal transport alignment contributes the largest individual performance gain. Current limitations include quarterly assessment frequency and dependence on optical imagery under cloud cover, which future integration of real-time meteorological triggers and SAR data should address. Full article

Azo dyes demonstrate dose-dependent carcinogenic and mutagenic effects in exposed cells. Among remediation approaches, microbial adsorption is the most sustainable and environmentally friendly method for eliminating azo dyes. A novel Aspergillus terreus silica composite was developed as a sustainable adsorbent for crystal violet dye (CVD) removal. The fungal strain was isolated from dye wastewater and was genetically identified by 18S rRNA gene sequencing. Dried mycelia of A. terreus (PX920301) were combined with SiO₂ (1:1 w/w) through iterative hydration-drying cycles, yielding a composite characterized by FTIR analyses. Removal CVD %, adsorption capacity, and CVD residual were calculated, and the adsorption process was optimized using Box–Behnken design (four factors, 25 runs). The biosafety of the composite was assessed for phytotoxicity and microbial toxicity. The composite was also applied to real dyes wastewater collected from the bacteriological laboratory. Aspergillus terreus-silica composite showed the highest CVD removal percentage by 85.4%, adsorption capacity (qe) 121.1 mg/L, and lowest CVD residual by 7.26 mg/L, followed by the dried active mycelia (DA-mycelia) with CVD removal 40.23%, adsorption capacity (qe) 57.05 mg/L, and CVD residual by 29.73 mg/L. Optimization data cleared that the maximum experimental values of CVD removal (%) was 99.59% (predicted value 100%) obtained in run number (4) using initial CVD concentration (200 mg/L), pH (8), adsorbent composite weight (0.1 g), and contact time (48 h). Biosafety evaluation demonstrated negligible phytotoxicity against Triticum aestivum seedlings post-treatment, with restored germination and growth comparable to controls. Microbial toxicity assays via well-diffusion to seven microbial isolates confirmed no toxic activities against the tested bacteria, yeast, and fungi, underscoring the composite’s environmental safety. The composite could decolorize the real dye wastewater of laboratories by 95.37%. In conclusion, A. terreus mycelial-silica composite offers a cost-effective, sustainable, and eco-friendly alternative solution for dye bioremediation. Full article

The ORCI project aims to develop an AI-based decision-support tool to assist air traffic controllers in complex TMA operations, taking Barcelona’s transitions as the primary use case. Using historical radar data, the tool has been trained to predict spacing between consecutive arrivals based on real-time vectoring commands. A data-processing pipeline was developed to clean, classify and validate flight trajectories, and synthetic samples were generated to enable a wider variety of situations. Explainable ML models achieved a mean absolute error of around 0.38 NM, demonstrating strong predictive capability. The results show the potential of ORCI to improve sequencing efficiency and runway throughput. Full article

Owing to the intricate mechanical coupling characteristics and the considerable difficulty in extracting synergistic spatio-temporal features from high-dimensional sensor data under fluctuating alternating loads, this study proposes a robust anomaly detection framework that combines Normalized Mutual Information (NMI) and Spatio-Temporal Graph Neural Networks (STGNN). First, NMI is utilized to quantify the nonlinear physical coupling intensity among multi-source sensors, thereby filtering out weakly correlated noise and reconstructing the spatial topological structure of the coupler system. Subsequently, a deep learning architecture incorporating Graph Convolutional Networks (GCN), Gated Recurrent Units (GRU), and one-dimensional convolutional residual connections is developed to capture the dynamic evolutionary characteristics of equipment states across both spatial interactions and temporal sequences. Finally, based on the model’s health-state predictions, a moving average algorithm is introduced to smooth the residual sequences, and an anomaly early-warning baseline is established in conjunction with the 3σ criterion. Experimental validation conducted using field service data from heavy-haul trains demonstrates that, compared to conventional serial CNN and Long Short-Term Memory (LSTM) models, the proposed method exhibits superior fitting performance and robustness against noise, effectively reducing the false alarm rate within normal working intervals. In a real-world case study, the method successfully identified variations in spatial linkage features induced by local damage and triggered timely alerts. Notably, the spatial alarm nodes were highly consistent with the fatigue crack initiation sites identified through on-site magnetic particle inspection. This study provides a viable data-driven analytical framework for the condition monitoring and anomaly identification of critical load-bearing components in heavy-haul trains. Full article

With the implementation of market-oriented electricity trading in an increasing number of countries, accurate electricity price forecasting can not only help participants in the electricity market to make more reasonable decisions but also enable regulators to have a more reliable regulatory basis. Therefore, it is necessary to propose an appropriate electricity price forecasting method. In view of the insufficiency of the traditional models in dealing with nonlinear and non-stationary data, to improve the detection ability of the model for hidden information in data and considering the high randomness of electricity price data, this paper proposes an electricity price forecasting method based on singular spectrum analysis (SSA) to decompose the original sequence and combines it with an extreme learning machine (ELM) optimized by the grey wolf optimizer (GWO). First, SSA is used to decompose the original sequence, and then the ELM is used to predict each subsequence and add them, in which the number of neurons in the hidden layer of each ELM is jointly optimized by the GWO. To verify the effectiveness of the SSA–GWO–ELM model, a total of 2106 days of electricity price data in Victoria, Australia, were selected for modeling. The results show that the prediction accuracy of the model proposed in this paper is significantly higher than that of the other comparison models, and the R² score is as high as 0.989, which is 0.017 higher than that of the suboptimal SSA–ELM. It can also maintain strong robustness and high prediction accuracy for heterogeneous data on power demand. SSA has the potential for real-time prediction, which can provide reliable data support for electricity market participants and supervisors. Full article

To address the limited accuracy of power demand prediction for hybrid electric vehicles under complex and dynamic driving conditions, this paper proposes a hybrid prediction approach based on the cascade correction of Long Short-Term Memory networks and Least Squares Support Vector Machines (LSTM-LSSVM-CC). The proposed method adopts a stage-wise modeling framework that exploits the least-squares optimality of LSSVM for low-frequency steady-state signals and the dynamic compensation capability of LSTM for high-frequency non-stationary residuals, thereby achieving complementary feature representation in the frequency domain. Specifically, an LSSVM is first used to construct a baseline regression model that captures stationary components, followed by an LSTM network that performs deep temporal modeling of the residual sequence to correct nonlinear prediction errors. Extensive experiments conducted on three standard driving cycles—CLTC-P, WLTP, and UDDS—demonstrate that the proposed model consistently outperforms conventional methods including LSSVM, RNN, ELMAN, and Random Forest in multi-step predictions, achieving an average RMSE reduction of 28–52% and maintaining correlation coefficients (R²) between 0.87 and 0.99. Particularly under highly dynamic and abrupt load conditions, the model exhibits superior real-time performance and stability while significantly mitigating cumulative prediction errors. These results demonstrate that the proposed LSTM-LSSVM-CC model achieves robust modeling performance of non-stationary time series while balancing prediction accuracy and computational efficiency, providing an effective technical foundation for hybrid vehicle energy management optimization and offering a transferable theoretical framework for time-series prediction in complex systems. Full article

Reliable short-term prediction of bus travel time on signalized urban arterials is essential for improving service reliability and may provide a useful forecasting basis for prediction-informed transit signal priority (TSP) and arterial coordination applications. However, bus operations on urban arterials are highly variable due to stop dwell times, signal delays, and interactions with mixed traffic, leading to nonlinear and nonstationary travel time patterns with strong spatiotemporal dependence. This study proposes a hybrid KNN–ConvLSTM framework for short-term arterial bus travel time prediction using real-world field data. A K-nearest neighbors (KNNs) module is first employed to retrieve historical operation sequences that are most similar to the current corridor state, thereby reducing interference from mismatched traffic regimes and improving robustness. Smart-card (IC card) transaction data are incorporated as demand-related features to represent passenger activity and its impact on dwell time and travel time variability. The selected sequences are then organized into a corridor-ordered spatiotemporal representation and further refined by lightweight temporal enhancement operations, including relevance gating, multi-scale aggregation, adaptive feature fusion, and residual enhancement, before being fed into the convolutional long short-term memory (ConvLSTM) predictor. The proposed approach is evaluated using weekday service-hour data extracted from 30 days of real-world bus operation records collected from a typical urban arterial corridor in Changchun, China, and is compared with several benchmark models, including ARIMA, KNN, LSTM, CNN, ConvLSTM, Transformer, and DCRNN. The results indicate that the proposed KNN–ConvLSTM framework achieves an MAE of 40.1 s, an RMSE of 55.8 s, a SMAPE of 10.7%, and an R² of 0.878, outperforming all benchmark models. Specifically, compared with the Transformer baseline, the proposed framework reduces MAE by 1.5%, RMSE by 5.1%, and SMAPE by 7.0%, while increasing R² by 0.014. Compared with the DCRNN baseline, it reduces MAE by 10.7%, RMSE by 1.9%, and SMAPE by 2.7%, while increasing R² by 0.008. These findings demonstrate that similarity-aware retrieval combined with spatiotemporal deep learning can substantially enhance short-term bus travel time prediction on signalized urban arterials. More accurate short-term forecasts may support prediction-informed transit signal priority and arterial coordination by providing more reliable downstream arrival-time estimates. However, the generalizability of the reported results is still constrained by the relatively short 30-day observation period and the single-corridor case setting, and the operational and environmental effects of downstream applications remain to be validated through dedicated closed-loop control evaluation in future work. Full article

Ramadan fasting substantially alters meal timing, sleep patterns, and daily activity, thereby increasing the risk of hypoglycaemia in adults with type 1 diabetes (T1D). Although continuous glucose monitoring (CGM) systems provide real-time alerts, these are largely reactive or limited to short prediction horizons, offering insufficient warning under fasting-related behavioural and circadian disruption. This study aims to evaluate whether behaviour-aware, temporally enriched recurrent deep learning models, leveraging multimodal CGM and wearable-derived signals, can forecast hypoglycaemia one hour ahead during Ramadan and the post-fasting period. In an observational, free-living cohort study conducted in Qatar, 33 adults with T1D were monitored using CGM and a wrist-worn wearable during Ramadan 2023 and the subsequent month. Multimodal data were aggregated into hourly features and organised into rolling 36 h sequences. In addition to physiological signals, explicit temporal and circadian proxy features were engineered, including cyclic time encodings, day–night indicators, and Ramadan-specific behavioural windows (e.g., pre-iftar, iftar, post-iftar, and fasting phases). Recurrent models, including LSTM and BiLSTM architectures, were trained using patient-wise, leak-free splits, with focal loss applied to address class imbalance. Model performance was evaluated on a held-out, naturally imbalanced test set using ROC AUC, precision–recall AUC, recall, and probability calibration, alongside cross-phase evaluation between Ramadan and post-fasting periods. Following quality control, 1164 participant-days were retained, with hypoglycaemia accounting for approximately 4% of hourly observations. Temporal feature enrichment and the use of a 36 h lookback window improved both discrimination and calibration, with performance stabilizing beyond this horizon. On the imbalanced test set, the best-performing multimodal model achieved an ROC AUC of 0.867 and a precision–recall AUC of 0.341, identifying 77% of next-hour hypoglycaemic events at a sensitivity-focused operating point (precision = 0.14). The selected BiLSTM model demonstrated good probability calibration (Brier score ≈ 0.03). Models trained using wearable-derived inputs alone achieved comparable discrimination and, in some configurations, higher precision–recall AUC than CGM-only baselines. Notably, models trained on the original imbalanced data outperformed resampled variants, suggesting that temporal and behavioural features provided sufficient discriminatory signal without requiring aggressive class balancing. Cross-phase evaluation indicated robust generalisation, particularly for the BiLSTM model. Overall, behaviour-aware, temporally enriched multimodal models can provide calibrated, hour-ahead hypoglycaemia risk estimates during Ramadan fasting in adults with T1D, enabling proactive intervention beyond reactive CGM alerts. Explicit modelling of circadian and behavioural dynamics enhances predictive performance under real-world class imbalance. Furthermore, integrating wearable-derived behavioural and physiological signals adds predictive value beyond CGM alone, supporting robustness across varying levels of contextual data availability. External validation and prospective clinical evaluation are required prior to deployment. Full article

Accurate vessel speed prediction is essential for maritime traffic supervision, navigational safety, and intelligent coastal management. However, due to the nonlinear, time-varying, and context-dependent characteristics of vessel motion in nearshore waters, conventional single-model approaches often fail to provide sufficiently accurate forecasts. To address this issue, this study proposes a hybrid deep learning framework for Automatic Identification System (AIS)-based nearshore vessel speed prediction and risk warning, integrating a temporal convolutional network (TCN), an attention mechanism, and a bidirectional long short-term memory network (BiLSTM) into a unified architecture. The core novelty of this framework is its task-oriented sequential design, in which TCN extracts local temporal patterns and multi-scale sequence features from historical AIS observations, the attention mechanism adaptively emphasizes informative representations, and BiLSTM models bidirectional contextual dependencies in vessel motion sequences; on this basis, a speed-risk warning process is constructed by combining the predicted speed with electronic-fence threshold constraints. Experiments conducted on real AIS data from coastal waters show that the proposed method obtains lower mean absolute error (MAE), mean squared error (MSE), and root mean square error (RMSE) as well as a higher coefficient of determination (R²) than several benchmark models. The results illustrate that the proposed framework effectively improves vessel speed prediction accuracy within the studied coastal area and provides practical support for proactive maritime supervision and nearshore safety management. Full article

Flexible deformation sensors inevitably suffer from sensitivity degradation and severe measurement errors during long-term cyclic stretching due to structural fatigue. Traditional material-level optimizations are costly and lack dynamic adaptability. Herein, we propose an artificial intelligence (AI)-driven predict-and-compensate framework for the online calibration of flexible sensors. To overcome training sample scarcity, a generative adversarial network (GAN) performs temporal data augmentation. Subsequently, a hybrid deep learning framework integrating long short-term memory (LSTM) networks and a Sequence Attention mechanism is employed. This architecture accurately captures both local signal fluctuations and multiscale long-term decay trends, enabling precise multi-step prediction and output compensation. Experimental evaluations validate that this strategy significantly suppresses sensor response drift. Under cyclic loading, an initially substantial relative measurement error of 48.63% plummets to 7.16% post-calibration, with typical errors consistently reduced to the ~1% level. Furthermore, when deployed in a smart glove gesture recognition system, this method successfully restores the recognition accuracy from a fatigue-induced low of 75.73% (after 200 stretch cycles) back to 97.70%. This generative and attention-based deep learning paradigm offers robust, real-time error calibration, providing a highly viable solution for extending the long-term reliability and stability of flexible sensor systems. Full article