Search Results (31)

Multi-label remote sensing scene classification requires identifying multiple land-cover categories from a single high-resolution aerial image. Existing methods strengthen visual features or model label dependencies, yet they apply a fixed calibration strategy regardless of the underlying label-density regime, leading to over-prediction on dense scenes or under-correction on sparse scenes. We propose Density-Adaptive CDG Calibration (DARC), a lightweight framework that explicitly conditions calibration on dataset label density. DARC comprises three modules: (1) Label-density Driven Profile Selection (DDP) automatically routes the calibration path based on training-set density statistics; (2) Label-token Correlative-Discriminative Graph Mixing (CDM) injects both co-occurrence and exclusivity relations into label semantic tokens through positive and negative graph propagation; (3) Density-aware Gated Calibration (DCM) applies cardinality-controlled gating for dense labels and EMA-stabilized graph calibration for sparse labels. Experiments on AID-ML and UCM-ML demonstrate that DARC achieves 90.12% and 89.05% sample-F1, respectively, outperforming six competitive baselines including SFIN, ASL, C-Tran, ML-Decoder, SPIN, and Two-Way Loss by 1.65–3.87%, while introducing only 1.42% additional parameters. Cross-regime routing analysis confirms that no single fixed strategy matches DARC’s adaptive approach, and sensitivity analysis shows the routing is robust across a wide threshold range. Ablation studies validate the necessity of each component, and visualization analyses demonstrate that the learned label graphs capture interpretable semantic patterns. Full article

Determining optimal process parameters for laser transmission welding (LTW) of solid/porous materials remains challenging due to the complexity of influencing factors. In this study, the welding of solid polycarbonate (PC) and porous polyethylene terephthalate (porous-PET) was chosen as an exemplary case and the relationship between parameters and welding quality was established using a Gaussian process regression (GPR) model. First, the experimental dataset, comprising welding power, welding speed, PC thickness, and porous-PET density, is established based on a flexible factor-level design. Then, the optimized GPR model trained based on the full experimental dataset achieved high predictive performance, significantly outperforming that trained with the averaged experimental dataset. Next, using the optimal prediction model as the objective function, three different optimization methods, genetic algorithm (GA), Bayesian optimization (BO), and covariance matrix adaptation evolution strategy (CMA-ES), are employed to optimize the process parameters, and the performance of the different optimization algorithms shows that CMA-ES has demonstrated the fastest convergence and the shortest runtime, while still converging to the same recommended parameters as GA and BO. Experimental validation confirms the accuracy of the recommended parameters, with a low relative error. Morphological analysis confirms that the weld seam is uniformly formed at recommended parameters. The proposed strategy provides an efficient route for achieving high-performance LTW joints and shows strong potential for improving process efficiency and reducing manufacturing cost in solid/porous materials joining. Full article

Silicon/carbon (Si/C) composites combine the high theoretical specific capacity of silicon with the electronic conductivity, structural stability, and volume-buffering capability of carbon, making them promising anode candidates for next-generation high-energy-density lithium-ion batteries. However, the substantial volume variation of silicon during repeated charge/discharge processes continuously perturbs the electrode/electrolyte interface, and the resulting interfacial instability remains a major barrier to practical application. Porous carbon host design and Si/C interface regulation have become key routes for improving structural robustness and electrochemical performance. Most existing reviews focus on the failure mechanisms of silicon-based anodes or the structural classification of Si/C composites, whereas the structural regulation role of porous carbon hosts has not been systematically summarized. This review places porous carbon hosts at the center of analysis and summarizes the main preparation strategies, including the hard-templating method, soft-templating method, combined hard- and soft-templating method, template-free synthesis, and etching strategies, with emphasis on their pore-forming mechanisms, structural regulation features, and industrialization potential. Building on this host-centered framework, silicon incorporation and carbon coating strategies are further discussed in terms of their effects on silicon distribution, Si/C interfacial stability, electronic transport, and volume-expansion accommodation. This review further evaluates recent advances in Si/C anodes for lithium-ion batteries from the perspectives of initial Coulombic efficiency, cycling stability, and practical electrode performance. Finally, key challenges related to scalable preparation, structural consistency, electrode-processing compatibility, and industrial adaptation are identified, and future directions for porous-carbon-host-based Si/C anodes are proposed. Full article

Foodborne pathogens rely on colonization, biofilm formation, virulence expression, and environmental adaptation as fundamental biological drivers of food safety risk. Quorum sensing (QS), a cell-density-dependent microbial communication mechanism, coordinates the expression of these key phenotypes by integrating intraspecies, interspecies, and host-derived signals, making QS an attractive intervention target in food microbial control. Although QS research has advanced considerably in recent years, existing reviews have largely focused on individual bacterial species or specific classes of signal molecules. A systematic integration of how QS coordinately drives both food spoilage and pathogen virulence remains lacking. In this review, we conceptualize the QS network as a central regulatory hub connecting microbial signal perception to hazardous phenotype expression. We systematically examine the mechanistic roles of QS in food spoilage, biofilm formation, host colonization and invasion, and toxin production. We also summarize current QS-targeted intervention strategies, including inhibition of signal synthesis, enzymatic signal degradation, receptor antagonism, and indirect regulation via beneficial microorganisms. Building on the available evidence, we further analyze the key challenges limiting practical application: signal system specificity, ecological safety, industrial-scale feasibility, and microbial adaptability. Overall, QS-based strategies offer a non-bactericidal route for food microbial control, although substantial barriers remain for translation into complex food matrices. Reframing QS function and intervention from the perspective of food safety risk formation provides an analytical framework that bridges mechanistic understanding with practical application. This framework also establishes a theoretical foundation for developing next-generation food preservation and foodborne disease control strategies. Full article

Indoor evacuation under emergency conditions remains a challenging problem due to dynamic hazards, uncertain infrastructure availability, and variability in human behavior. Traditional evacuation systems rely heavily on centralized architectures, making them vulnerable to communication failures and delayed global decision making. To address these limitations, this paper proposes a novel framework termed Federated Edge-Semantic Learning for Decentralized Resilient Evacuation (FESL-DRE). The proposed framework distributes evacuation intelligence across edge nodes, enabling autonomous decision making without dependence on a central controller. It integrates semantic reasoning to transform raw sensor data into interpretable environmental states, federated learning to model behavioral patterns in a privacy-preserving manner, and a gossip-based coordination mechanism to propagate hazard information across neighboring nodes. An adaptive routing strategy is developed to account for hazard levels, crowd density, and human behavioral variability. The framework is evaluated using a simulation-based environment under dynamic hazard conditions and varying levels of node failure. Experimental results demonstrate that FESL-DRE achieves superior performance compared to classical and centralized adaptive methods, with improvements in evacuation success rate, reduced blocked movement attempts, and enhanced resilience under moderate infrastructure degradation. Furthermore, the proposed approach maintains low communication overhead and demonstrates promising scalability characteristics within the evaluated simulation environment. The results highlight the potential of decentralized intelligence for evacuation support and provide a foundation for future validation in realistic smart building and IoT-enabled environments. Full article

Shape memory polymers (SMPs) can undergo reversible shape transformations, yet most conventional one-way SMPs recover only a single programmed shape. Reported bidirectional SMPs frequently rely on complex chemistries or continuous external loads or tolerate pronounced losses in mechanical robustness, largely because microphase separation, crystallization and internal stress are difficult to regulate in an integrated fashion. Here, we propose a UV-programmed internal-stress-locking strategy to construct a crosslinked polyurethane (UV-SMPU) that simultaneously achieves high toughness and stable, stress-free bidirectional actuation. Using polycaprolactone (PCL) as the soft segment, hexamethylene diisocyanate (HDI) as the hard segment and triallyl isocyanurate (TAIC) as a photocrosslinker, in-situ UV curing under pre-stretch fixes a tunable three-dimensional network while “freezing” the microphase-separated morphology and pre-oriented internal stress. Covalent crosslinks stabilize PCL crystallites as reversible actuation domains, whereas hydrogen-bonded hard segments provide elastic restoring force; the coordinated regulation of crosslink density, crystallinity and locked-in internal stress enables efficient CIE/MIC-type transitions without compromising mechanical integrity. The optimized UV-SMPU (3 wt% TAIC, 10 min UV) exhibits excellent thermal stability, a rare strength–ductility balance (26.6 MPa tensile strength; ~1700% elongation) and robust bidirectional actuation, with reversible strain stabilizing at 15.73% after six cycles. This work offers a simple, scalable route to tough bidirectional SMPUs and furnishes mechanistic design principles for next-generation adaptive and soft-actuated materials. Full article

Wireless Sensor Networks (WSNs) enable energy-efficient data collection in dynamic environments but continue to face the dual challenges of severely constrained node energy and the spatiotemporal heterogeneity of data traffic. Inspired by honeybee foraging behavior, this paper proposes a hybrid optimization framework that integrates mixed-integer linear programming (MILP) and Markov decision processes (MDP), utilizing Q-learning for adaptive decision-making. The proposed framework systematically maps the dual-layer decision-making mechanism of honeybee foraging onto a synergistic architecture combining MILP-based global planning and MDP-based local adaptation, offering a novel bio-inspired solution for mobile sink trajectory planning and adaptive routing. Specifically, the upper-level MILP module simulates a colony-level global assessment of distant nectar sources, generating an initial global trajectory by determining the optimal access sequence of cluster heads to minimize the movement cost of the mobile sink. The lower-level Q-learning module simulates the individual-level local adaptation, where bees adjust harvesting behavior in real-time based on nectar quality and distance. This module continuously optimizes routing parameters based on real-time network states, including residual energy, the ratio of surviving nodes, data queue lengths, and cluster head density. The algorithm employs an $ϵ$-greedy strategy to balance exploration and exploitation, while a periodic decision-update mechanism is introduced to harmonize computational efficiency with learning stability. Furthermore, a multi-objective reward function is designed to jointly optimize energy efficiency, network lifetime, end-to-end latency, and path length. Extensive simulation results demonstrate that the proposed MILP-MDP hybrid framework significantly outperforms several representative baseline algorithms in terms of network lifetime extension and energy balance. These findings validate that the integration of bio-inspired foraging strategies and reinforcement learning provides an efficient and robust solution for trajectory planning and adaptive routing in dynamic WSNs. Full article

The rising market penetration of new energy vehicles (NEVs) is transforming urban traffic into a heterogeneous mix of battery electric (BEVs), hybrid electric (HEVs), and conventional fuel vehicles (FVs). For analytical brevity, traditional internal combustion engine vehicles (ICEVs) are hereafter referred to as ‘fuel vehicles (FVs)’ in the discussion of New Energy Vehicle (NEV) networks. This research investigates the efficacy of centralized coordination for NEVs within a localized region, as opposed to individualized speed control, in enhancing the mitigation of traffic congestion. Evaluating traffic efficiency and decarbonization strategies in such settings often requires extensive random sampling and Monte Carlo simulations over a large set of parameter combinations. However, conventional microscopic traffic simulators, which rely on fine-grained modeling of vehicle dynamics and signal control, incur prohibitive computational time when scaled to large networks and numerous experimental scenarios. In this study, battery electric vehicles and hybrid electric vehicles are designed as density-aware vehicles, whose movement speed is adaptively adjusted according to the regional traffic density in their vicinity and the control parameter β. In contrast, fuel vehicles adopt a stochastic movement speed and, together with other vehicle types, exhibit either movement or stoppage in the lattice environment. This density-driven speed-adaptive control and lattice arbitration mechanism is intended to reproduce, in a simplified yet extensible manner, changes in mobility and traffic-flow stability under high-density traffic conditions. The simulation results indicate that, under the same Manhattan road network and vehicle-density conditions, tuning the β parameter of new energy vehicles to reduce their movement speed in high-density areas and to mitigate abrupt position changes can suppress traffic-flow oscillations, delay the onset of the congestion phase transition, and promote spatial equilibrium of traffic flow. Meanwhile, this study develops simplified energy-consumption and carbon emission models for battery electric vehicles, hybrid electric vehicles, and fuel vehicles, demonstrating that incorporating a speed-adaptive density strategy into mixed traffic flow not only helps alleviate abnormal congestion but also reduces potential energy use and carbon emissions caused by congestion and stop-and-go behavior. From a sensing and practical perspective, the proposed framework assumes that future connected and autonomous vehicles (CAVs) can estimate vehicle states and local traffic density through GNSS–IMU multi-sensor fusion and V2X communications, indicating methodological consistency between the proposed model and real-world CAV sensing capabilities and making it a suitable and effective experimental platform for investigating the relationships among new energy vehicle penetration, density-control strategies, and carbon footprint. Full article

Automatic Identification System (AIS) trajectories provide valuable spatiotemporal information for maritime route structure mining, but robust extraction of key route nodes remains difficult because raw data are noisy, turning behaviors are easily masked by local fluctuations, and conventional Density-Based Spatial Clustering of Applications with Noise (DBSCAN) is sensitive to fixed parameters and ignores heading differences. To address these issues, this study proposes a key route node extraction framework based on multi-constraint turning-point identification and heading-aware adaptive DBSCAN (HA-DBSCAN). Raw AIS data are first cleaned, segmented, and compressed using a heading-aware Douglas–Peucker strategy to reduce redundancy while preserving geometric and directional characteristics. Valid turning points are then identified by jointly considering heading change rate, geometric curvature, and temporal stability. Finally, HA-DBSCAN integrates a heading-aware distance metric, adaptive neighborhood estimation, and density-aware MinPts optimization to cluster turning points and extract representative route nodes. Experiments using AIS data from the Ningbo–Zhoushan Port area retained 287,614 valid records and 754 continuous trajectory segments, from which 1710 turning points were identified. The proposed method generated 45 stable clusters with a noise ratio of 0.0450 and route coverage of 95.5%. These results indicate that, within the current study setting, the framework can distinguish crossing routes, adapt to heterogeneous traffic densities, and provide an interpretable intermediate layer for subsequent maritime route-structure modeling. Supplementary validation on the same AIS dataset further showed that, compared with DBSCAN, Ordering Points To Identify the Clustering Structure (OPTICS), and HDBSCAN baselines as well as several pipeline ablations, the full framework achieved a more balanced performance in terms of coverage, noise suppression, and avoidance of cluster over-fragmentation. Full article

Remote sensing images are highly susceptible to spatially non-uniform haze under complex atmospheric conditions, leading to contrast degradation and structural detail loss. Moreover, remote sensing scenes usually exhibit complex spatial structures, highly uneven haze distribution, and significant statistical variability, which further increases the difficulty of haze removal. To address this issue, we revisit the haze degradation mechanism of remote sensing imagery and propose a dynamic histogram-sorted Transformer dehazing method from the perspectives of statistical distribution modeling and region-adaptive restoration. Specifically, a Histogram-Sorted Adaptive Attention is designed to map spatial features into the statistical distribution domain through a dynamic histogram sorting mechanism, enabling explicit discrimination and precise modeling of regions with different haze densities. Meanwhile, a Perception-Adaptive Feed-Forward Network is constructed, which incorporates a stable routing-based mixture-of-experts mechanism to adaptively select restoration strategies according to local texture characteristics and global haze density, thereby significantly enhancing the adaptability of the model in complex remote sensing scenarios. Extensive experimental results demonstrate that the proposed method achieves superior performance over existing approaches across multiple remote sensing benchmark datasets, effectively improving both visual quality and robustness of remote sensing imagery. Full article

Autonomous ground vehicles operating in structured and semi-structured environments—such as urban roads, parking lots, and logistics warehouses—require fast, reliable, and collision-free path planning on occupancy grid maps. Existing metaheuristic planners often suffer from premature convergence, insufficient population diversity, and poor feasibility maintenance, limiting their deployment in safety-critical vehicular navigation. This paper proposes a multi-strategy enhanced Crested Porcupine Optimizer (MSCPO) that systematically addresses these limitations through four coordinated enhancements: chaos-opposition initialization with feasibility repair to ensure high-quality and diverse initial routes; a diversity-coupled adaptive mechanism for dynamic strategy scheduling throughout the search; elite-guided differential Lévy perturbation to escape local optima and accelerate convergence; and a two-stage safety-aware objective with elite local refinement to sharpen final solution precision. Experiments on four representative grid maps with varying obstacle densities, conducted over 30 independent runs per algorithm, demonstrate that MSCPO consistently outperforms state-of-the-art metaheuristic planners and deterministic baselines in path length, smoothness, and convergence speed. Statistical analysis via Wilcoxon rank-sum and Friedman tests confirms the significance of the improvements. An ablation study quantifies the individual contribution of each enhancement module, confirming the practical effectiveness of MSCPO for autonomous vehicle navigation tasks. Full article

The increasing availability of high-resolution gridded meteorological data poses significant challenges for efficient storage and rapid data access. This study proposes a route-aware adaptive variable-resolution storage (AVRS) strategy for gridded meteorological datasets. The spatial domain is partitioned into fixed-size blocks and storage resolution is dynamically assigned based on radar reflectivity characteristics and air-route traffic density, prioritizing aviation-relevant regions while reducing redundancy elsewhere. Composite radar reflectivity (CREF) data are used as a case study to evaluate storage efficiency, reconstruction accuracy, and query performance. Experimental results indicate that AVRS approach reduces storage volume while maintaining high reconstruction fidelity and preserving key convective structures. In addition, route-oriented point-based queries are significantly accelerated compared with conventional uniform-resolution storage. The proposed AVRS framework provides a scalable and aviation-oriented storage solution for large-scale gridded meteorological data, with potential benefits for atmospheric research and air traffic operations. Full article

DUR (Deep Underground Railways) stations, such as Suseo Station in Korea, present unique evacuation challenges stemming from multi-level spatial depth, long vertical circulation paths, and rapid smoke spread dynamics. Conventional design guidelines often fail to capture these complexities, underscoring the need for advanced, simulation-driven safety evaluation frameworks. This study proposes a comprehensive Digital Twin-based methodology that integrates spatial topology modeling, agent-based evacuation simulation, and dynamic hazard-aware routing. A multi-layer map topology was constructed from high-fidelity architectural geometry, decomposing the station into functional regions and encoding connectivity across platforms, concourses, corridors, and vertical circulation elements. Real-time hazard conditions were reflected through dynamic adjustments to edge weights, allowing evacuation paths to adapt to blocked exits, fire shutter operations, and smoke-infiltrated domains. Ten evacuation scenarios were developed to assess sensitivity to fire origin, exit availability, vertical circulation failures, and onboard passenger loads. Simulation results reveal that evacuation performance is primarily constrained by vertical circulation bottlenecks, with emergency stairways (E1 and E2) serving as critical choke points under high-density conditions. Cases involving exit closures or fire-compartment failures produced significant delays, frequently exceeding NFPA 130 and KRCODE performance criteria. Conversely, guided evacuation strategies demonstrated marked improvements, reducing congestion and enabling compliance with platform evacuation thresholds even in full-load scenarios. These findings highlight the necessity of transitioning from static design evaluations toward Digital Twin-enabled, predictive safety management. The proposed framework enables real-time visualization, intervention testing, and operator decision support, offering a scalable foundation for next-generation evacuation planning in extreme-depth railway infrastructures. Full article

This review develops a materials-to-clinic framework for patient-specific, functionally graded (FG) NiTi shape memory alloy (SMA) rods as a complementary paradigm for scoliosis correction that targets durable alignment with motion preservation. The article synthesizes the thermomechanical basis of NiTi (thermoelastic martensitic transformation, near constant superelastic plateau, and hysteretic damping) while leveraging additive manufacturing (AM) capabilities to spatially program transformation temperatures (e.g., A_f), effective stiffness, and geometric inertia along the rod. Consolidated process–structure–property linkages are provided for the PBF-LB, DED, and BJAM routes, together with contamination and composition-control strategies (mitigation of Ni volatilization; management of O/C uptake; gradient heat treatments) and segment-level quality assurance (DSC mapping, micro-CT, EBSD/indentation, and bench bending/torsion in physiologic media). Building on clinical curve classification, the methodology formalizes a grading mask and target moment vector that drive multi-objective optimization of the segmental A_f, relative density/architecture, and cross-section, followed by route-specific build plans and acceptance tolerances. A phenomenological constitutive description provides the forward map from local design variables to temperature-dependent moment–curvature loops for finite element verification and uncertainty control. Surgical handling and activation policies are codified (cold shaping in martensite and controlled intra-/postoperative warming within tissue-safe bounds), and a translational roadmap is outlined, encompassing prospective calibration of classification-to-design mappings, AM process maps with in situ monitoring, digital twin planning, and long-horizon fatigue/corrosion protocols. The proposed graded structures provide an adaptive transformation temperature gradient and tunable mechanical response, representing an important design direction toward 3D-printed, patient-specific SMA rods for durable, adjustable, and efficient scoliosis correction. Full article

In automated warehousing scenarios, mobile UHF RFID robots typically operate along preset fixed paths to collect basic information from goods tags. They lack the ability to perceive shelf layouts and goods distribution, leading to problems such as missing reads and low inventory efficiency. To address this issue, this paper proposes a cross-layer sensing method for mobile UHF RFID robot reading states based on multiple linear regression-orthogonal least squares (MLR-OLS) and random forest. For shelf state sensing, a position sensing model is constructed based on the physical layer, and MLR-OLS is used to estimate shelf positions and interaction time. For good state sensing, combining physical layer and MAC layer features, a K-means-based tag density classification method and a missing tag count estimation algorithm based on frame states and random forest are proposed to realize the estimation of goods distribution and the number of missing goods. On this basis, according to the read state sensing results, this paper further proposes an adaptive reading strategy for RFID robots to perform targeted reading on missing goods. Experimental results show that when the robot is moving at medium and low speeds, the proposed method can achieve centimeter-level shelf positioning accuracy and exhibit high reliability in goods distribution sensing and missing goods count estimation, and the adaptive reading strategy can significantly improve the goods read rate. This paper realizes cross-layer sensing and read optimization of the RFID robot system, providing a theoretical basis and technical route for the application of mobile UHF RFID robot systems. Full article