Search Results (986)

In this work, a flexible and sustainable radar-absorbing material (RAM) based on porous carbon derived from raw Kraft black liquor was developed. The porous carbon filler was synthesized through a simple, eco-friendly one-pot polymerization route, thereby avoiding lignin extraction, purification, and chemical activation steps. Macroporosity was introduced by using poly(methyl methacrylate) microspheres as a hard template, yielding a lightweight carbon material with a foam-like morphology, low density, and high porosity. The carbon filler was incorporated into a silicone rubber matrix at different loadings (5–25 wt.%) to produce flexible composites. The structural, morphological, and textural properties of porous carbon were investigated by SEM, EDX, Raman spectroscopy, nitrogen adsorption, and mercury porosimetry. The electromagnetic properties of composites were measured in the X-band (8.2–12.4 GHz) using a vector network analyzer. The mechanical behavior was evaluated through Young’s modulus. The results show that increasing filler content enhances dielectric losses and attenuation capability. Among all composites, the sample containing 20 wt.% of porous carbon exhibited the best electromagnetic performance, achieving a reflection loss of −42.3 dB at 10.97 GHz with a thickness of 2.43 mm, corresponding to an absorption efficiency of 99.99%. This performance is attributed to a favorable combination of impedance matching and quarter-wavelength cancellation effects. The developed sustainable, lightweight, and flexible composites demonstrate potential as low-cost RAM for aerospace and electromagnetic interference mitigation applications. Full article

As nuclear energy expands, nuclear emergency response systems increasingly exhibit strong human–machine–environment (H–M–E) coupling, long-duration operations, and multi-department coordination, in which minor disturbances can be amplified by feedback loops into cascading failures and loss of situational control. To address the inability of conventional static and linear methods to represent dynamic risk evolution and chaotic uncertainty, this study proposes an integrated “risk network–chaotic evolution–synchronization control” framework. Based on 12-year-old on-site comprehensive drill reports from a Chinese nuclear power base, we construct a directed H–M–E risk network in a semi-quantitative, qualitative–quantitative manner and identify critical nodes using a composite betweenness–PageRank risk metric. We further abstract the system into a three-dimensional nonlinear coupled dynamical model; phase portraits, Lyapunov exponents, and bifurcation analysis confirm threshold effects, period-doubling routes, and chaotic attractors, revealing nonlinear amplification under strong coupling. Finally, an adaptive chaotic synchronization controller driven by network coupling strength is designed. Simulations show all strategies suppress chaos and achieve synchronization, while the machine-dominated strategy offers the best speed–energy trade-off for emergency resource allocation. Full article

Urban flood peak mitigation by green infrastructure (GI) is fundamentally a timing problem. Because GI storage is finite, interception occurs only within a brief active window; whether it reduces the outlet peak depends on GI placement in the network, routing lags, and rainfall timing. Here, we develop a timescale-based framework that links outlet peak reduction to the alignment among within-storm temporal structure, network response, and GI filling dynamics, providing a compact way to interpret when different network positions become most effective under a fixed GI design. Starting from a general convolution representation of runoff generation, interception, and routing, we show that peak reduction efficiency and location ranking can be organized by two nondimensional ratios—comparing storm duration and network response time to a characteristic GI filling time—plus simple descriptors of within-storm temporal structure. Under uniform rainfall, these ratios yield an interpretable regime diagram with analytical transition curves between downstream-, mid-network-, and upstream-optimal placement for a generic dispersive routing representation. Relaxing the uniform-rainfall assumption shows that within-storm variability can substantially reorganize these regimes because storm timing controls both how long GI storage remains available before it fills and which routed contributions overlap to form the outlet peak. Highly concentrated storms and storms with early internal peaks are especially likely to reorder the ranking of candidate locations relative to the uniform-rainfall baseline. Using 2351 observed hourly storm events evaluated across virtual catchments spanning fast to slow network responses, we quantify how often realistic event structure alters the optimal location and the regret associated with adopting a uniform design storm. The results motivate robustness-oriented placement strategies based on ensembles of plausible storm temporal structures, organized within the proposed timescale diagram rather than reliance on a single design hyetograph. Full article

This paper addresses the issues of blurred details, low contrast, and feature degradation in insulator images under harsh meteorological conditions, as well as the challenges of high computational complexity and insufficient real-time performance when deploying existing deep learning models on edge devices. It proposes a lightweight insulator defect detection method that integrates an improved image enhancement algorithm. The method introduces Mahalanobis distance-based modulation weight optimization for scene depth estimation and improves the color decay prior model to effectively enhance foggy insulator images. It further designs a lightweight detection network integrating region-aware routing attention mechanisms, utilizing multi-scale feature fusion strategies to achieve precise insulator identification and localization. Experimental results demonstrate that the proposed method significantly enhances inference speed while maintaining detection accuracy, effectively adapting to edge computing devices. This provides a viable technical solution for real-time deployment in intelligent transmission line inspection systems. Full article

Urban underground infrastructures are highly vulnerable to intense rainfall events, particularly access stairs, where preferential runoff paths and the most probable evacuation routes can conflict. This study presents a pluvial flood hazard assessment for underground access stairs in the Barcelona Metropolitan Area Metro network. It integrates the EU ICARIA project modeling framework and the hazard assessment criteria based on hydraulic parameters identified by the Spanish national research project FAVOUR. Both current and future climate change rainfall scenarios are considered. The results showed that out of 415 underground access points, 27 face a high risk of floods, while 35 more have potentially high-risk conditions. These figures could rise to 38 (40% increase) and 47 (74% increase) respectively by the end of the century since climate change is projected to increase rainfall intensity and frequency. By quantifying hazard levels across the network, this study allows the identification of points of the infrastructure where hazard conditions can be more critical. Furthermore, the results presented could potentially support targeted adaptation strategies such as entrance retrofitting, improved drainage design, and emergency planning to develop resilient and sustainable cities. The proposed methodology demonstrates how ICARIA’s modeling framework can effectively evaluate and anticipate flood hazards in complex urban environments at the asset level. Full article

Drone ad-hoc networks are inherently vulnerable to performance-degradation attacks such as jamming, packet disruption, and routing interference due to dynamic topology changes and unstable wireless channels. In such environments, conventional threshold-based detection schemes often fail to identify threats in their early stages because individual performance metrics remain within normal ranges despite emerging abnormal temporal patterns. To address this limitation, this study proposes an LSTM-based early threat detection method that learns the temporal dynamics of network performance indicators, including packet delivery ratio (PDR), connection reliability (CR), and delay. By modeling inter-metric correlations and evolving degradation trends, the proposed approach enables probabilistic inference of abnormal state transitions prior to explicit threshold violations. The proposed method is validated through simulation experiments conducted in a drone ad-hoc network environment under jamming attack scenarios, and its performance is compared with that of conventional threshold-based schemes. The results show that while the threshold-based approach first detected the attack at t = 65 s when predefined metric boundaries were exceeded, the proposed LSTM-based detector identified the attack at t = 45 s with an estimated attack probability of 0.63, achieving approximately 20 s earlier detection. This improvement is attributed to the LSTM’s capability to capture subtle temporal dependencies, directional trends, and cross-metric interactions that precede abrupt metric degradation. Furthermore, the LSTM output probabilities exhibited monotonic growth during the attack period and gradual decay during recovery, indicating robust tracking of network state transitions rather than isolated event detection. These results demonstrate that the proposed method not only enhances early threat awareness but also contributes to resilience-oriented operation by enabling proactive mitigation in drone ad-hoc networks. This study provides quantitative evidence that sequence learning over performance metrics can overcome the structural limitations of threshold-based detection and enable effective early threat detection in drone ad-hoc network environments. Full article

Graph Neural Networks (GNNs) have emerged as the standard for learning representations from graph-structured data. While traditional architectures relying on message-passing mechanisms excel in homophilic settings, they essentially function as fixed low-pass filters. However, this smoothing operation limits their ability to generalize to heterophilic graphs, where connected nodes often exhibit dissimilar labels and high-frequency signals are crucial for discrimination. Furthermore, existing Mixture-of-Experts (MoE) methods for graphs often suffer from local-view routing, failing to capture global structural context during expert selection. To address these challenges, this paper proposes SS-AdaMoE, a novel Spatio-Spectral Adaptive Mixture of Experts framework designed for robust node classification across diverse graph patterns. Specifically, a Dual-Domain Expert System is constructed, integrating heterogeneous spatial aggregators with learnable spectral filters based on Bernstein polynomials. This allows the model to adaptively capture arbitrary frequency responses—including high-pass and band-pass signals—which are overlooked by standard GNNs. To resolve the locality bias, a Hierarchical Global-Prior Gating Network augmented by a Linear Graph Transformer is introduced, ensuring that expert selection is guided by both local node features and global topological awareness. Extensive experiments are conducted on five benchmark datasets spanning both homophilic and heterophilic networks. The results demonstrate that SS-AdaMoE consistently outperforms baselines, achieving accuracy improvements of up to 2.65% on Chameleon and 1.41% on Roman-empire over the strongest MoE baseline, while surpassing traditional GCN architectures by margins exceeding 28% on heterophilic datasets such as Texas. These findings validate that the synergy of learnable spectral priors and global gating effectively bridges the gap between spatial aggregation and spectral filtering. Full article

During pandemic emergencies, demand for relief supplies in affected areas surges abruptly and evolves randomly and dynamically, resulting in highly asymmetric supply and demand. Ensuring timely and reliable supply requires robust decision-making under risk. This study addresses a stochastic multi-objective location-routing problem (LRP) that simultaneously considers demand uncertainty and travel time variability. A multi-scenario stochastic programming model is developed with three objectives: minimizing total system cost, minimizing total waiting time, and minimizing the composite conditional value at risk (CVaR–Rcomp) to capture tail risks under extreme scenarios. A novel regret-based risk mechanism is introduced to unify temporal and cost dimensions, enabling joint evaluation of uncertainties within a single framework. To solve this challenging high-dimensional problem, a reinforcement learning-enhanced NSGA-III (RL-NSGAIII) is proposed. Specifically, Q-learning generates high-quality initial solutions, which accelerate convergence and improve population diversity for NSGA-III. Case studies demonstrate that the proposed method outperforms traditional evolutionary algorithms in convergence efficiency and Pareto solution quality, while effectively revealing potential risk blind spots. The results provide quantitative decision support and robust optimization insights for emergency logistics networks operating under uncertain conditions. Full article

This paper proposes a Q-Learning-Based Distributed Energy-Efficient Routing (QDER) protocol for underwater acoustic sensor networks (UASNs). The routing problem is formulated as a Markov Decision Process (MDP) and a distributed Q-learning approach is proposed. Each sensor node is treated as an agent that independently selects its next-hop node based on a Q-table. The rewards function is designed that jointly considers node residual energy and depth information, enabling each node to learn an effective routing policy through distributed decision-making. Unlike centralized routing approaches that rely on extensive global information exchange, the proposed scheme allows nodes to make local decisions, thereby reducing communication overhead and energy consumption while maintaining efficient routing paths. In addition, link quality is designed in the reward to account for channel conditions, which improves the robustness of the routing strategy under noisy underwater acoustic environments. Simulation results demonstrate that the QDER achieves better system performance compared with Depth-Based Routing (DBR) and Deep Q-Network-Based Intelligent Routing (DQIR). Considering channel attenuation and noise, the proposed method with the link quality metric achieves improved network lifetime and energy efficiency. It also shows good robustness and adaptability under different signal-to-noise ratio (SNR) conditions. Full article

Machining process route planning (MPRP) is vital for autonomous manufacturing yet remains challenging under complex, multi-dimensional engineering constraints. This paper proposes an attention-augmented deep reinforcement learning (DRL) framework to achieve intelligent process orchestration. First, an Optional Process Attribute Adjacency Graph (OPAAG) is established to formally model the “feature–process–resource–constraint” coupling, enhancing the agent’s perception of manufacturing semantics. The architecture synergistically integrates Graph Attention Networks (GAT) to perceive spatial benchmark dependencies and a Transformer-based encoder to capture sequential resource correlations within variable-length machining chains. Furthermore, a dynamic action masking mechanism is integrated to guarantee a 100% constraint satisfaction rate during both training and inference stages. Experimental evaluations across diverse part geometries demonstrate that the proposed method offers significant advantages in cost optimization, inference efficiency, and topological stability compared to traditional heuristic algorithms and standard DRL models. By effectively distilling the search space and maintaining action feasibility, the framework provides an efficient and robust solution for autonomous process planning in complex industrial scenarios. Full article

This study is based on the practical context of the livestream e-commerce industry’s shift from “traffic competition” to “quality competition”. Addressing the limitations of existing research that predominantly focuses on streamers’ external traits while overlooking intrinsic qualities and frequently employs linear models that oversimplify the decision-making processes of consumer purchasing behavior (CPB), a theoretical framework grounded in the Elaboration Likelihood Model (ELM) is developed to explain how streamer traits drive consumer trust and identification through dual pathways. This study adopted a mixed-method approach combining structural equation modeling (SEM) and artificial neural networks (ANNs). By analyzing 408 valid questionnaires, it systematically investigated the driving mechanisms through which streamer traits affected consumers’ trust and identification. The study found that streamers’ integrity significantly enhanced perceived trust and perceived identification via the central route. While awareness could strengthen identification, it had no significant effect on trust building, revealing the inherent tension between “traffic” and “quality”. ANN analysis further demonstrated that the nonlinear combination of traits more effectively predicts consumer responses than traits. This study provided empirical support for the “quality transformation” of livestream e-commerce from both theoretical and methodological perspectives, offering important implications for platforms to develop a quality assessment system centered on trust and identification and to optimize the streamer cultivation mechanism. Full article

Low Earth orbit (LEO) satellite networks require real-time routing to cope with dynamic topology variations caused by continuous orbital motion. As an alternative to conventional routing approaches, deep reinforcement learning (DRL) has recently gained attention as an effective means for optimizing routing paths. To solve routing problems modeled as a grid-based Markov decision process (grid-based MDP), DRL methods such as CNN-based Dueling DQN have been proposed. However, these approaches are difficult to implement in practice. In particular, the substantial floating-point computation and memory traffic of CNN inference make real-time onboard inference challenging under the stringent power and resource constraints of satellite platforms. To address these constraints, this paper proposes an INT8 quantization and hardware–software co-design framework using heterogeneous SoC FPGA acceleration. We offload compute-intensive CNN inference to the programmable logic (PL), while the processing system (PS) orchestrates overall control and data movement, forming a collaborative PS–PL architecture. Furthermore, we integrate the NITI-style two-pass scaling with PS–PL exponent propagation to preserve end-to-end integer consistency without floating-point conversion. To demonstrate its practical onboard feasibility, we employ standard accelerator implementation choices—such as output-stationary scheduling and on-chip prefetching—and conduct an ablation study over independently tunable axes (PE array size and PS-side buffer reuse) to quantify their incremental contributions. Experimental results show that the proposed PS–PL cooperative scheme dramatically reduces computation time compared to a PS-only reference implementation on the same platform. Full article

With the development of 6G communication technology, Space–Air–Ground Integrated Networks (SAGINs) have become critical infrastructure for global intelligent collaborative computing. However, federated learning deployment in SAGINs faces three severe challenges: the high dynamics of satellite orbital motion, node resource heterogeneity, and privacy vulnerabilities in data transmission. This paper proposes an adaptive compressed sensing differential privacy federated learning framework based on orbital spatiotemporal characteristics. First, we design orbital periodicity-driven time-varying sparse sensing matrices that dynamically adjust compression strategies according to satellite orbital positions, achieving intelligent communication efficiency optimization. Second, we propose an orbital predictability-based privacy budget temporal allocation mechanism and perform differential privacy noise injection in the compressed domain, establishing a compression–privacy joint optimization algorithm. Furthermore, we construct an energy–communication–privacy ternary collaborative mechanism that achieves multi-objective dynamic balance through model predictive control. Finally, we design reinforcement learning-based dynamic routing scheduling and hierarchical aggregation strategies to effectively handle the time-varying characteristics of network topology. Simulation experiments demonstrate that compared to existing methods, the proposed approach achieves 3–12% improvement in model accuracy and 30–50% enhancement in communication efficiency while maintaining differential privacy protection with dynamic privacy budget $\epsilon \in [0.1,10.0]$ and compression ratio $\rho \in [0.2,0.8]$. Unlike static compressed sensing approaches that ignore orbital periodicity, the proposed orbital-driven time-varying sensing matrices reduce reconstruction error by up to 19.4% compared to fixed-matrix baselines, validating the synergistic effectiveness of integrating orbital spatiotemporal characteristics with federated learning in 6G SAGIN deployments. The framework assumes reliable orbital propagation via SGP4/SDP4 models and does not account for Doppler frequency shifts or inter-satellite link handover delays; future extensions include scalability to mega-constellations and integration of quantum-resistant privacy mechanisms. Full article

To address the challenges of facility–demand mismatch, aggravated congestion, and imbalanced benefit distribution caused by the interdependence between dynamic wireless charging systems (DWCS) and transportation networks, this study proposes an optimization scheme that coordinates DWCS planning, travel flow guidance for electric vehicle (EV) owners, and transportation network operations. We develop a bi-level dynamic collaborative optimization model. The upper-level model aims to maximize the annual net profit of DWCS operators and determines DWCS planning by optimizing the traffic flow distribution. The lower-level model, based on the user equilibrium principle, guides EV route choices via a traffic flow guidance mechanism to mitigate peak-hour congestion and minimize vehicle owners’ travel costs. We validate the model using a test network comprising 9 nodes and 13 links. Results indicate that, compared with a full-coverage planning scenario, the proposed bi-level optimization scheme significantly reduces operational losses by accounting for owners’ optimal travel flow distribution. Introducing a traffic flow guidance mechanism further improves traffic flow distribution, enhances operator revenue, and effectively reduces owners’ travel time costs. Sensitivity analysis reveals that increased battery capacity decreases construction and maintenance costs, thereby improving annual net profit, while lower energy consumption reduces charging demand and weakens dependence on charging infrastructure. These factors are interrelated; specifically, lower energy consumption implies reduced battery capacity requirements for the same driving range. Additionally, the effectiveness of the traffic flow guidance mechanism becomes more pronounced as traffic flow increases. Overall, the proposed framework integrates DWCS planning and traffic flow guidance to achieve a win–win outcome for both operators and owners. These findings demonstrate the practicality and economic feasibility of interactive optimization between DWCS and transportation networks. Full article

Real-time route guidance during disruptions in urban rail transit systems requires rapidly providing effective strategies that simultaneously alleviate congestion and account for passengers’ travel time. This study proposes an optimization framework that considers travel time, congestion perception time, and information costs, incorporating a Logit choice model with information bias to reflect passengers’ behavioral responses under disruptions. A bi-level simulation evaluation mechanism is employed to rapidly evaluate the objective functions under different guidance strategies, where a Physically Consistent Incremental Simulator, based on differential computation, achieves a 599-fold speedup while maintaining high fidelity with full-scale simulations (Pearson correlation > 0.96). A hybrid algorithm combining the Gray Wolf Optimizer and Adaptive Large Neighborhood Search is developed to solve the origin–destination level route guidance optimization problem. The algorithm embeds domain knowledge-based “destroy and repair” operators with a sequential repair mechanism to enable fast global search and precise local refinement. Case study results demonstrate that the framework reduces severely congested sections by 36%, shortens average travel time by 7.16 min, and improves solution quality by 12–30% over baseline algorithms. These findings confirm the practical applicability of integrating intelligent optimization with high-efficiency simulation for emergency route guidance in large-scale metro networks. Full article