Search Results (6,108)

Evacuation planning is increasingly challenged by compound hazards in which interacting threats degrade infrastructure, influence human behavior, and destabilize transportation systems. Although agent-based models and dynamic traffic simulations have advanced substantially, much of the evacuation literature remains hazard-specific, case-bound, or difficult to transfer across regions. In parallel, transportation resilience research shows that multi-hazard effects are often non-additive and that cascading infrastructure failures can amplify disruption beyond directly affected areas, raising important sustainability concerns related to community safety, infrastructure continuity, social equity, and long-term planning capacity. These realities motivate the development of evacuation modeling frameworks that are modular, adaptable, and capable of representing co-evolving behavioral and network processes under compound hazard conditions. This review synthesizes advances in evacuation agent-based modeling, dynamic traffic assignment, hazard-induced network degradation, and compound disaster research to propose an adaptable compound-hazard evacuation framework integrating three interdependent layers: hazard processes, transportation network dynamics, and agent decision-making. The proposed framework is organized around four principles: (1) modular hazard representation, (2) decoupling behavioral decision logic from hazard physics, (3) dynamic network state evolution, and (4) neighborhood-scale performance metrics. To support sustainable and equitable local planning, the framework prioritizes spatially resolved outputs, including neighborhood clearance time, isolation probability, accessibility loss, and shelter demand imbalance. By emphasizing modularity, configurability, and policy-relevant metrics, this review connects methodological advances in evacuation modeling to the broader sustainability goals of resilient infrastructure systems, inclusive disaster risk reduction, and locally informed emergency planning. Full article

Rapid expansion of electric vehicle adoption has led to increased dependence on a charging infrastructure that is tightly integrated with energy distribution systems and digital communication networks. As electric vehicle charging stations evolve into complex cyber–physical systems, cybersecurity risks pose a growing threat to grid reliability and user trust. This paper presents a hybrid decision-support framework for cybersecurity risk assessment in EV charging infrastructure that advances beyond prior multi-criteria decision-making approaches by combining interpretability with data-driven validation. Specifically, the framework integrates the Analytic Hierarchy Process (AHP) for expert-driven weighting of cybersecurity attributes with PROMETHEE for flexible threat prioritization, enabling transparent and auditable risk rankings. The framework categorizes cybersecurity criteria across four infrastructure layers—transmission, distribution, consumer, and electric vehicle charging stations—and assigns relative weights through expert-driven pairwise comparisons. PROMETHEE is then applied to rank potential cyber threats based on these weights, allowing for flexible prioritization of cybersecurity interventions. The methodology is validated using the real-world WUSTL-IIoT-2018 SCADA dataset, which includes simulated reconnaissance (network scanning), device identification, and exploitation attacks. While this dataset does not natively include OCPP 2.0 or ISO 15118 protocols, the experimental results demonstrate strong discrimination power (AUC = 0.99, recall = 95%) and provide a basis for extension to modern EVSE communication standards. The results identify critical metrics such as anomalous source packet behavior and encryption reliability as key vulnerability markers, aligning with documented EV charging attack scenarios. By bridging expert judgment with empirical traffic data, the proposed framework offers both technical robustness and explainability, supporting grid operators, SOC teams, and infrastructure planners in systematically assessing risks, allocating resources, and enhancing the resilience of EV charging ecosystems against evolving cyber threats. Full article

Tomato (Solanum lycopersicum) is a widely consumed vegetable crop and an established model system for plant functional genomics and genetic research in dicotyledons. Salt stress is a major abiotic factor limiting tomato productivity worldwide. The WRKY transcription factor family, one of the largest and most conserved plant-specific transcription factor families, plays pivotal roles in stress responses. This review summarizes recent advances in understanding the functions of tomato WRKY genes under salt stress, focusing on the genomic basis and evolutionary characteristics of the WRKY family, the roles of core WRKY members under salt stress, and the multi-layered regulatory networks mediating WRKY-dependent salt and alkali tolerance. To date, approximately 10 core SlWRKY genes have been functionally validated to regulate tomato salt tolerance, mainly by maintaining ion homeostasis, regulating reactive oxygen species (ROS) balance, facilitating osmotic adjustment, and integrating hormone signaling pathways. Despite this progress, systemic regulatory hierarchies and epigenetic modulation remain poorly resolved. Furthermore, we discuss how specific WRKY members directly regulate downstream effector genes, such as SlSOS1 and SlNHX4. However, direct experimental evidence for the coordination between tomato WRKYs and mitogen-activated protein kinase (MAPK) cascades, as well as epigenetic modifiers under salt stress, is still scarce in current studies. This review provides a theoretical framework and outlines potential technical pathways for translating fundamental insights into tomato salt tolerance into practical applications for sustainable agriculture. Full article

As a significant source of global carbon emissions, the construction industry (CI) urgently needs to promote green transformation with the help of digital twin (DT) against the backdrop of human–machine collaboration and sustainable development advocated by CI 5.0. However, there is still a lack of systematic research on its specific driving mechanism and carbon reduction path. This study uses a systematic literature review (SLR) to explore how five key DT-enabled capabilities, namely, resource management (RM), process optimization (PO), real-time monitoring (R-Tm), sustainable design (SD), and predictive maintenance (PM), influence three performance indicators: efficiency improvement (EI), energy optimization (EO), and cost control (CC). Data from 490 companies were analyzed using partial least squares structural equation modeling (PLS-SEM) and a multilayer perceptron (MLP) with Shapley additive explanation (SHAP). The results show that the PLS-SEM and MLP models showed consistent patterns, with EO exhibiting the strongest predictive performance (Q² = 0.372; R² = 0.3666), followed by EI (Q² = 0.307; R² = 0.3109) and CC (Q² = 0.305; R² = 0.2609); the SHAP results further indicated that RM contributed most to EI (0.242), while PO was the most important driver for both EO (0.304) and CC (0.259). Academically, it introduces a quantitative approach combining PLS-SEM and machine learning. Practically, it highlights the priority of key technologies with cross-dimensional effects and offers guidance for governments to optimize digital resource allocation and carbon performance evaluation, as well as for enterprises to apply DT more effectively. Full article

Traditional intrusion detection systems for IoT networks achieve high classification accuracy but lack interpretability and actionable incident-response capabilities, limiting their operational value in security-critical environments. This paper presents a graph-based multi-agent framework that integrates ensemble machine learning with Large Language Model (LLM)-powered incident report generation via Retrieval-Augmented Generation (RAG). The system employs a three-phase architecture: (1) a lightweight Random Forest binary pre-detection, achieving 99.49% accuracy with a 6 MB model size for edge deployment; (2) ensemble classification combining Multi-Layer Perceptron, Random Forest, and XGBoost with soft voting and SHAP-based feature attribution for explainability; and (3) a ReAct-based summary agent that synthesizes classification results with external threat intelligence from Web search and scholarly databases to generate evidence-grounded incident reports. To address the challenge of evaluating non-deterministic LLM outputs, we introduce custom RAG evaluation metrics—faithfulness and groundedness implemented via the LLM-as-Judge framework. Experimental validation on the ACI IoT Network Dataset 2023 demonstrates ensemble accuracy exceeding 99.8% across 11 attack classes; perfect groundedness scores (1.0), indicating all generated claims derive from the retrieved context; and moderate faithfulness (0.64), reflecting appropriate analytical synthesis. The ensemble approach mitigates individual model weaknesses, improving the UDP Flood F1 score from 48% (MLP alone) to 95% through soft voting. This work bridges the gap between high-accuracy detection and trustworthy, actionable security analysis for automated incident-response systems. Full article

The paper proposed an adaptive preference-based multi-objective optimization framework of intelligent energy management in smart microgrids that are dynamically adapted to operational priorities with regard to real-time grid conditions, stakeholder preferences, and environmental constraints. The suggested hierarchical algorithm combines an improved Non-dominated Sorting Genetic Algorithm II (NSGA-II) with an advanced dynamic preference weight distribution system that can trade off between minimization of operational cost. Reduction of carbon emission, enhancement of voltage stability, enhancement of power quality and maximization of system reliability and adaptability to different operational conditions, such as renewable energy intermittency, demand response schemes and emergencies. The framework presents a new multi-layered preference-learning module that represents the intricate stakeholder priorities in terms of more sophisticated fuzzy logic-based decision matrices, neural network preference prediction, and adaptive reinforcement learning methods and transforms them into dynamic optimization weights with feedback mechanisms. Large-scale simulations on a modified IEEE 33-bus test system coupled with various renewable energy sources, energy storage facilities, electric vehicle charging points, and smart appliances demonstrate superior improvements in performance: 23.7% operational costs reduction, 31.2% carbon emissions reduction, 18.5% system reliability improvement, 15.3% voltage stability increase and 12.8% reduction of deviations in power quality. The proposed system has an adaptive nature with better performance in a variety of operating conditions such as peak demand times, renewable energy intermittency events, grid-connected and islanded operations, emergency load shedding situations, and cyber–physical security risks. The framework is shown to be highly effective under different conditions of uncertainty and variation in parameters and communication delay through intense sensitivity analysis and robustness testing, thus demonstrating its practical applicability in real-world applications of smart grids. Full article

Modern automotive paints have a thin undercoat and color coat layer protected by a thick clear coat layer. All too often, only the clear coat layer of the automotive paint is recovered at the crime scene of a vehicle-related fatality. Searches for motor vehicle paint databases of clear coats using commercial software typically generate large hitlists that are difficult for a forensic paint examiner to work through unless additional information is provided for the search. To address this problem, deep learning has been applied to the infrared spectra of automotive clear coats to identify patterns in their spectra indicative of the motor vehicle manufacturer. An in-house automotive paint library of 2796 clear coat infrared spectra from six automotive manufacturers and 100 assembly plants was partitioned into training, validation, and prediction sets. Each spectrum has 1880 measurements over the spectral range of 4000 cm⁻¹ to 376 cm⁻¹. Several multilayer perceptron neural network models, each with three hidden layers, were developed that achieved high classification success rates for the training, validation, and prediction sets. The addition of convolutional layers to the deep learning neural network models did not improve the performance of these models. Full article

In addressing the issue of insufficient theoretical model accuracy for Cut-out type piezoelectric beams with limiters under the influence of contact-impact nonlinearity, this study utilizes the backpropagation neural network algorithm to develop a data-driven modeling approach based on experimental data from partial distance parameters. This approach aims to achieve accurate predictions of the output voltage and displacement responses of the energy harvester. For different parameter combinations of the limiter gap distance d and installation distance a, amplitude–frequency response data were first systematically collected through experiments, along with time–voltage response data corresponding to different load resistances. Using these data, a training sample set was constructed, and a multi-layer BP neural network prediction model was established with frequency or time as the input and voltage and displacement responses as the outputs. Validation against experimental data demonstrated that the BP neural network can accurately extrapolate and predict the amplitude–frequency response curves of voltage and displacement under various distance parameter combinations, as well as accurately predict the transient voltage outputs under different load conditions. Full article

The increasing deployment of Internet of Things (IoT) devices introduces significant security challenges, while privacy concerns limit centralized data aggregation for intrusion detection. Federated learning (FL) offers a decentralized alternative, yet the interaction between feature representation, model architecture, and data heterogeneity remains insufficiently understood in IoT malware detection. This study provides a controlled comparative analysis of centralized and federated learning, optionally using amino acid encoding, under IID and Non-IID conditions using a 10,000-sample subset of the CTU–IoT–Malware–Capture dataset. First, we evaluate raw tabular features versus amino acid-based feature encoding, followed by a lightweight multi-layer perceptron (2882 parameters) versus a deeper residual network (70,532 parameters), across binary and multi-class classification tasks. In the binary setting, centralized training achieved up to 98.6% accuracy, while federated IID training reached 98.6%, with differences within statistical variance. Under Non-IID conditions, performance decreased modestly (0.1–0.5 percentage points), and accuracy was consistently lower when using encoded features compared with raw features. The degradation is smaller in deeper architectures and may offer improved stability under highly skewed federated conditions. In the four-class setting, the complex network achieved up to 97.8% accuracy with raw features, while amino acid encoding achieves up to 93.3%. The results show that federated learning can achieve performance comparable to centralized training under moderate heterogeneity, that lightweight architectures are sufficient for low-dimensional IoT traffic features, and that feature compression via amino acid encoding does not inherently mitigate Non-IID effects. These findings clarify the relative impact of representation, heterogeneity, and architectural capacity in practical FL-based IoT intrusion detection systems. Full article

To address the issues of suboptimal recovery performance, low timeliness, and poor economic efficiency associated with traditional fault recovery methods following large-scale power outages in active distribution networks (ADNs) caused by extreme weather, this paper proposes a dynamic fault recovery strategy for ADNs based on a two-layer hybrid algorithm under extreme ice and snow conditions. First, a line fault rate model considering the thermal effect of current under extreme ice and snow conditions is constructed, and an information entropy-based typical scenario screening method is introduced to filter the fault scenarios. Second, a photovoltaic (PV) output model and a time-varying load model under the influence of extreme ice and snow conditions are established. Subsequently, a multi-objective dynamic fault recovery model is formulated, incorporating island partitioning and integration constraints based on the concept of single-commodity flow, alongside tightened relaxation constraints. To achieve an accurate and rapid solution for the fault recovery model, a two-layer hybrid algorithm is proposed. This algorithm combines an outer-layer improved binary grey wolf optimizer (IBGWO) and an inner-layer second-order cone relaxation (SOCR) algorithm to solve the discrete and continuous decision variables within the model, respectively. Finally, the effectiveness and superiority of the proposed method are verified using the PG&E 69-bus and IEEE 123-bus systems. Full article

Accurate trajectory tracking of redundant manipulators is difficult because the controller must simultaneously model local couplings between adjacent joints and global dependencies across the whole kinematic chain. Existing reinforcement learning methods typically employ multilayer perceptrons, which do not explicitly exploit manipulator structure and therefore show limited stability and representation ability in high-dimensional continuous control tasks. This paper proposes GT-TD3, a Graph Transformer-enhanced-Twin Delayed Deep Deterministic Policy Gradient framework, for redundant manipulator trajectory tracking. The proposed actor first converts the raw system state into joint-level node features and uses a graph neural network to extract local kinematic coupling information. A Transformer is then employed to capture long-range dependencies among joints. To strengthen the use of structural priors, topology- and distance-related bias terms are incorporated into the attention mechanism, enabling the network to encode manipulator structure during global feature learning. Experiments on a 7-DoF KUKA iiwa manipulator in PyBullet demonstrate that GT-TD3 outperforms MLP, pure GNN, and pure Transformer baselines in tracking performance. The proposed method achieves more stable training, faster convergence, and smoother and more accurate end-effector motion. The results show that the integration of local graph modeling and structure-aware global attention provides an effective solution for high-precision trajectory tracking of redundant manipulators. Full article

Dam monitoring data are nonlinear and nonstationary time series. Most existing data-driven dam displacement models are developed independently for each measuring point, disregarding the fact that a dam is a complex structure composed of various interconnected elements that form a unified whole. Regardless of the dam type, all points on the dam are exposed to the same external environmental influences. To account for the correlation between displacement time series at different points, this paper proposes a novel fine-tuned deep-learning nonlinear autoregressive (NAR) model based on a Long Short-Term Memory (LSTM) network for predicting dam tangential displacement, and a new method for generating source data to train the base model. The models for three measuring points were developed and tested on experimental data collected over a period of slightly more than twelve years. Compared with the model without fine-tuning, the proposed approach achieves an average mean square error (MSE) reduction of 80.68% on the training set and 65.79% on the test set, as well as an average mean absolute error (MAE) reduction of 51.05% and 52.62%, respectively. Furthermore, the proposed model outperforms Random Forest (RF), Support Vector Regression (SVR), and Multi-Layer Perceptron (MLP) models for dam displacement prediction. Full article

With the rapid evolution toward 6G networks, ensuring robust physical layer security (PLS) in highly dynamic and heterogeneous wireless environments has become a key challenge. Traditional security methods often struggle to adapt to time-varying channels, especially in the absence of perfect channel state information. Furthermore, the dynamic nature of node selection and power allocation in heterogeneous networks creates a complex hybrid action space operating across multiple timescales, significantly complicating the design of efficient and adaptive security strategies. To address this, this paper proposes a novel constrained hierarchical reinforcement learning (CHRL) framework for secure cooperative communications in next-generation wireless systems. The framework is designed to optimize secrecy performance within a hybrid action space comprising both discrete node selection and continuous power allocation, operating at different timescales. By hierarchically decoupling the joint optimization problem, the upper layer performs risk-aware node selection to maximize long-term secrecy capacity (SC) while guaranteeing a stable and secure link. At the lower layer, we develop a constrained MiniMax Multi-objective Deep Deterministic Policy Gradient (M3DDPG) algorithm that optimizes power allocation considering worst-case conditions. Lagrange multipliers are integrated to enforce a strictly positive SC constraint throughout transmission, effectively preventing security outages. Simulation results under time-varying Rayleigh fading channels demonstrate that the proposed CHRL framework outperforms existing HRL methods, achieving up to 17% improvement in SC while strictly maintaining security constraints. These results validate the effectiveness of the proposed approach for enhancing PLS in next-generation cooperative wireless networks. Full article

In many situations, the complete microscopic structure of a network is empirically inaccessible and has to be inferred from aggregate information using some probabilistic model. While several network reconstruction methods have been developed in the case of single-layer networks where nodes can be connected only by one type of link, the problem is still largely unexplored in the case of multiplex networks where several interdependent layers, each representing a distinct mode of connection, coexist. Even the most advanced network reconstruction techniques, when applied to each layer separately, may fail in replicating the inter-layer dependencies embodying the essence of multiplex networks. Here we develop a methodology to reconstruct a class of correlated multiplexes which includes, as a specific example that we study in detail, the multiplex network of international trade in different products. Our method starts from virtually any reconstruction model that successfully reproduces a set of desired marginal properties of each layer separately, i.e., node strengths and/or node degrees. It then introduces the minimal dependency structure required to replicate an additional set of higher-order properties, namely the portion of each node’s degree and each node’s strength that is shared and/or reciprocated across pairs of layers. These properties are found to provide empirically robust measures of inter-layer coupling, allowing for an accurate reconstruction of the world trade multiplex network. Our method allows for joint multi-layer connection probabilities to be reliably reconstructed from marginal ones, effectively bridging the gap between single-layer information and global multiplex properties. Full article

To meet the need for global coverage, space–air–ground integrated networks (SAGINs) are crucial, but the openness of wireless links makes communications vulnerable to eavesdropping. This paper investigates the physical layer security (PLS) of uplink transmissions in a cooperative dual-hop SAGIN. The system comprises a ground source with a directional antenna, an unmanned aerial vehicle (UAV) relay cluster, and a low Earth orbit (LEO) satellite. Utilizing stochastic geometry, we model the spatial randomness of terrestrial eavesdroppers and the multi-layered dual-orbital LEO destination. To combat mixed radio-frequency (RF) and free-space optical (FSO) fading, multiple relay selection and maximum ratio combining (MRC) are integrated into the UAV cluster. We analytically derive the piecewise probability density function for the FSO link distance, obtaining exact closed-form expressions for the end-to-end secrecy outage probability (SOP). Monte Carlo simulations strictly validate the derivations. The results demonstrate that while increasing available relays and antennas enhances PLS via spatial diversity, a security bottleneck restricts the RF-FSO architecture under high-transmit power regimes, generating asymptotic secrecy floors. These findings provide explicit theoretical guidelines for the secure design and parameter optimization of future SAGINs. Full article