Search Results (437)

Civilian communication systems often fail during armed conflicts, political unrest, and large-scale Internet disruptions—precisely when reliable communication is most needed. This paper presents HERMES, a resilient hybrid communication architecture that integrates HTTP/IP networking, Bluetooth Low Energy (BLE) mesh communication, and Delay-Tolerant Networking (DTN) within a unified adaptive routing framework. Unlike conventional approaches that treat alternative transports as backup solutions, HERMES dynamically selects the most efficient transport path based on current network conditions using a transport-aware forwarding policy whose cost function combines round-trip time, transport preference, and observed link risk. The architecture is built on distributed microservices that support topology discovery, shortest-path routing, and fault-tolerant message delivery. Reliability is enhanced through acknowledgments, bounded retransmissions, duplicate suppression, and graceful degradation mechanisms, while end-to-end authenticated encryption (Noise XX with a Double Ratchet) ensures secure communication across transport changes. A prototype implementation developed in C# on .NET 9 was evaluated on a five-node testbed, and a custom Network Simulator 3 (NS-3) module was used to extend the evaluation to networks of up to 500 nodes, under multiple failure scenarios, including node crashes, network partitioning, and complete Internet outages. Experimental results show that HERMES maintains perfect or near-perfect delivery in static topologies, including during a complete Internet blackout that disables IP-only messaging. Compared with the published Delay-Tolerant Networking protocols Epidemic and PRoPHET at one hundred nodes, HERMES exceeds their delivery ratio in static and failure scenarios and remains within 0.06 of them under pedestrian mobility during blackout, while transmitting roughly 35× fewer bytes– and about 21× fewer even relative to the more bandwidth-efficient MaxProp baseline. Under coordinated drop attacks by adversarial relays, HERMES degrades gracefully where flooding-based baselines collapse. This approach demonstrates that resilient civilian communication can be effectively achieved through metric-driven adaptive multi-transport routing, making it suitable for disaster recovery, contested environments, and connectivity-limited regions. Full article

Covert communication has emerged as a promising technique for protecting wireless transmissions by concealing the existence of legitimate communication from malicious wardens. However, achieving end-to-end covert communication in space–air–ground integrated networks (SAGINs) is challenging due to the coupled effects of satellite-to-ground relay access, ground multi-hop forwarding, and cooperative jamming. In this paper, we propose an incentive-aware end-to-end covert routing framework for SAGINs, where a low Earth orbit (LEO) satellite delivers information to a ground destination through a selected relay base station and a self-organizing ground route. We first establish a two-stage SAGIN model and characterize the satellite-to-ground covert capacity under satellite sidelobe interference, as well as the ground-route covert performance in the presence of multiple wardens and cooperative jammers. Since jammers are self-interested and incur power costs when generating artificial interference, we design an incentive mechanism to stimulate cooperative jamming for enhancing ground-route covertness. Specifically, the reward allocation and jamming-power response are jointly derived by considering both the route-dependent covertness gain and the power cost of jammers. Based on the resulting route-dependent utility, the ground routing problem is further transformed into a shortest-weighted path-finding problem. To improve the long-term stability of satellite-to-ground relay access, we model the repeated interaction between the LEO satellite transmitter and the satellite warden as a base-station selection process and develop a zero-determinant strategy to stabilize the long-term expected utility relation under different warden monitoring policies. Simulation results demonstrate that the proposed framework effectively balances satellite-to-ground covert capacity and ground-route utility, outperforms baseline relay selection schemes, and achieves stable long-term covert routing performance against uncertain warden behaviors. Full article

Truck–UAV collaborative delivery can improve last-mile logistics efficiency, but fixed-node rendezvous often causes waiting loss and service delay. To address this problem, this paper proposes a route optimization method integrating en route synchronization, pseudo-node insertion, and GAT-PPO. Pseudo-nodes are generated along truck travel arcs to provide flexible UAV recovery points, and a time-recursive simulation model is developed to evaluate makespan and total tardiness under soft time windows. In the proposed framework, GAT is used to capture spatial–temporal relationships among nodes, while PPO supports sequential routing decisions and UAV dispatch coordination. Experiments on Solomon VRPTW instances with clustered, random, and mixed customer distributions show that GAT-PPO achieves the shortest total travel distance, the lowest total tardiness, and the shortest completion time among Random, NN, NN+2-opt, MLP-PPO, ALNS, GA, and VNS. Ablation results further confirm the contributions of GAT, PPO, pseudo-node insertion, en route synchronization, and UAV collaboration. The results indicate that the proposed framework can effectively reduce synchronization waiting loss and improve the temporal efficiency of truck–UAV collaborative delivery. Full article

Renewable energy system (RES)-based electric vehicle (EV) charging infrastructure enhances energy security. The need for intelligent routing techniques that achieve low latency, high reliability and adaptive path selection under extremely dynamic traffic situations has increased owing to the quick growth of Internet of Things (IoT)-enabled vehicular networks. With an emphasis on recurrent neural networks (RNNs), deep belief networks (DBNs), radial basis function neural networks (RBFNNs), and long short-term memory (LSTM) networks, in addition to convolutional neural networks (CNNs), this analysis looks at cutting-edge AI-based models used for shortest-path routing in IoT-driven vehicular ad hoc networks (VANETs). The paper examines how various designs handle issues such as connection instability, heterogeneous sensor data, quick topological changes, and real-time decision making. A comparative analysis shows that DBN and CNN display strong feature learning for intricate mobility patterns and congestion recognition, while sequence-aware techniques like RNN and LSTM advance spatiotemporal traffic estimation. For low-latency route evaluation, RBFNN compromises rapid nonlinear representation. The examination shows that CNN models greatly improve the scalability, adaptability and optimality of routing, confirming AI-enabled structures as a promising path for next-generation IoT-based vehicular routing methods. Full article

This work examines the implementation and evaluation of shortest-path algorithm in a Geographic Information System environment integrating QGIS with a PostgreSQL/PostGIS spatial database. Spatial edge and junction layers stored in the PostgreSQL/PostGIS define a directed weighted network in which edge costs are derived from spatial distance and modified through a gradient-based cost function. The routing algorithm is implemented in Python using the PyQGIS library and a binary heap priority queue. Network data are loaded from the geodatabase and processed in memory to compute the optimal route between selected nodes. The resulting path is reconstructed as a dissolved polyline feature stored in the geodatabase and visualized within the GIS environment. The study also includes a theoretical comparison of several classical shortest-path algorithms—Dijkstra, $A^{*}$, Bellman–Ford, and Floyd–Warshall—with respect to their applicability to sparse spatial graphs typical of transportation networks. The analysis confirms the suitability of Dijkstra’s algorithm for such networks and demonstrates that routing outcomes depend directly on the definition of the edge cost function. The proposed workflow relies exclusively on open-source GISs and database technologies. 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

Metro-connected underground commercial spaces present complex fire safety challenges due to enclosed layouts, dense pedestrian flows, shared evacuation routes, and diverse combustible materials. This study conducts a scenario-based fire evacuation assessment of a typical metro-connected underground commercial space in Nanjing, China, using coupled FDS–Pathfinder simulations. Three fuel scenarios were considered: polyurethane (PU), polyvinyl chloride (PVC), and a combined PU–PVC fuel scenario. Available safe egress time (ASET) was evaluated using visibility, CO concentration, and smoke temperature thresholds, while smoke-affected evacuation performance was assessed using a visibility-based walking speed attenuation model. The results show that visibility reached the critical threshold earlier than CO concentration and smoke temperature in all scenarios. The PU-only scenario produced the fastest smoke spread and the shortest ASET, with Stair 2 reaching the visibility threshold at 244 s. Under smoke-affected conditions, the total evacuation time increased from 364 s to 378 s. More importantly, a local ASET/RSET mismatch was identified at Stair 2, where 62 occupants remained near the node after the local ASET threshold was reached and local clearance was completed at approximately 250 s. By contrast, Stairs 4 and 7 retained safety margins of 133 s and 113 s, respectively. These findings indicate that evacuation safety in the simulated case was controlled not only by smoke-induced walking speed reduction, but also by route convergence, local stair capacity, and uneven exit utilization. The study provides case-based evidence for node-level fire evacuation assessment and evacuation management in metro-connected underground commercial spaces. Full article

Biomimetic optimization transfers biological information-processing mechanisms into computational systems. Ant colony optimization (ACO) is a canonical example: artificial ants functionally abstract pheromone-mediated stigmergy, decentralized exploration, trail decay through algorithmic evaporation, and adaptive path reinforcement. Building on this functional biological analogue, we present a controlled cross-paradigm evaluation of routing-heuristic generation. A standardized interface embeds human-designed rules, the genetic programming hyper-heuristic GHPP, a resource-constrained DeepACO-MLP proxy, and an offline ReEvo-style proxy into the same ACO solver. The methods are evaluated on held-out TSP and CVRP instances in terms of solution quality, reported generation or training cost, interpretability, and cross-scale behavior under a matched distribution. GHPP yields the shortest routes at all tested scales; the ReEvo-offline proxy and strong human-designed rules generally form a second tier, whereas the resource-constrained neural proxy degrades markedly as problem size increases. These results do not establish an intrinsic ranking of full-capability paradigms. Instead, they show that method selection depends on the operating constraint and on evidence provenance: longer locally measured offline search favors GHPP, while auditable explicit rules characterize the human and ReEvo-offline proxies. By holding the ant-inspired execution mechanism fixed and varying the source of heuristic information, the benchmark clarifies how evolutionary, neural, and LLM-style design strategies interact with a common biomimetic substrate. Full article

Many multilayer service systems require a single unit of demand to use a mutually compatible tuple of layerwise routes. In these systems, layerwise reachability and layerwise maximum-flow values do not determine how much service can be jointly delivered. We introduce bundle-constrained multilayer flow networks, in which physical capacities remain on ordinary directed edges while admissible service units are compatible bundles generated by finite-state compatibility systems. The maximum bundle-flow problem is a finite path-packing linear program over accepting paths in product networks. Its dual is a fractional service-bundle cut problem whose edge and demand-cap weights must intersect every admissible bundle. We prove the resulting flow-cut duality, identify dual weights as capacity supergradients, give a product-network shortest-path oracle for separation and pricing, and establish an unbounded compatibility gap for systems with identical layerwise shadows. These results show that compatibility must be modelled explicitly, since layerwise data alone cannot recover joint service capacity. The same fractional-cut geometry is then applied to affine shocks, minimum restoration costs, and projected adjustment dynamics. Deterministic computations and a synthetic scalability experiment illustrate the compatibility gap, active-cut switching, restoration breakpoints, projected decision regimes, and the column-generation method. Full article

Apricot powder functionality after drying, grinding and sieving remains insufficiently understood. This study prepared powders from diluted Diaoganxing apricot pulp using hot-air drying (HAD), infrared drying (IRD), vacuum-pulsed drying (VPD) and vacuum freeze drying (VFD), followed by identical grinding and 60-mesh sieving. Moisture status, density, calculated porosity, particle characteristics, reconstitution, flowability, color, selected phytochemical indicators, ferric reducing antioxidant power (FRAP) and supplementary electronic-nose fingerprints were evaluated. The drying route markedly affected powder properties: moisture content and water activity ranged from 7.23 ± 0.38% to 9.85 ± 0.02% and 0.218 ± 0.002 to 0.370 ± 0.007, respectively. VFD gave the highest calculated porosity (59.09 ± 0.84%), shortest wettability time (46.05 ± 1.71 s), highest water-holding capacity (3.63 ± 0.07 g/g), smallest color difference (ΔE = 1.12 ± 0.47), and highest TPC, TCC, AAC and FRAP values. VPD showed the best handling indices, with the lowest angle of repose (28.44 ± 0.34°), Hausner ratio (1.07 ± 0.01) and Carr index (6.33 ± 0.66%). Correlation/PCA indicated treatment-level co-variation, not causality. Under the tested processing conditions, VFD may be preferable for rapid reconstitution and the measured quality indicators, whereas VPD may be more suitable for powder handling. Full article

Modern communication networks require routing protocols that can adapt to dynamic traffic conditions while accounting for topology-based structural risk. Conventional open shortest path first (OSPF) relies on static or linear link cost metrics, which are often inadequate for capturing the nonlinear behavior of network dynamics and structural risk. This paper proposes sigmoid-enhanced OSPF (SE-OSPF), which integrates topology-based structural risk into the OSPF routing metric through a nonlinear sigmoid function. The proposed framework employs two configurable sigmoid parameters, the midpoint ($x_0$) and the steepness (k), to provide smooth cost transitions and adaptive routing decisions under varying network conditions. Simulation results on a Barabási–Albert scale-free topology demonstrate that SE-OSPF reduces the average end-to-end delay by 19.7% and packet jitter by 8.6% compared with Standard OSPF. In addition, SE-OSPF increases the average number of successfully delivered packets by up to 16.6% compared with Linear-OSPF while reducing maximum link utilization (MLU), indicating more balanced traffic distribution, improved load balancing, and reduced congestion. These results demonstrate that the proposed sigmoid-based routing metric effectively balances routing efficiency, packet delivery reliability, and network load distribution, establishing SE-OSPF as an effective framework for topology-based structural risk-aware adaptive routing in modern communication networks. Full article

Urban heat island effects increasingly challenge pedestrian mobility by intensifying thermal stress and reducing the attractiveness of walking during hot periods. However, most pedestrian routing systems still prioritize distance or travel time, while environmental conditions such as heat exposure and shade are rarely incorporated into operational route generation. Existing comfort-aware approaches often rely on static maps, simulated microclimatic indicators, or descriptive greenery measures, limiting their direct integration into user-configurable pedestrian navigation. This study develops a thermal comfort-aware pedestrian routing framework that integrates heterogenic data sources including observed land surface temperature, pedestrian-perspective tree-canopy coverage, and network distance into a unified multi-criteria pathfinding model. The workflow proceeds in four steps: first, airborne thermal imagery is processed to derive a high-resolution land surface temperature layer; second, Google Street View images are sampled at street-segment locations and segmented using SegFormer to extract visible tree-canopy coverage; third, both environmental indicators are aggregated to a cleaned pedestrian network; and fourth, normalized distance, temperature, and canopy attributes are combined through a user-adjustable edge-cost formulation and solved using Dijkstra’s algorithm. The framework is implemented as an operational web-based routing tool for the historic center of Clermont-Ferrand, France. The routable graph includes 551 nodes and 796 edges, with 600 segments carrying GSV-derived canopy information and 623 segments carrying airborne-derived LST values. Across the network, we observed LST ranges from 19.5 °C to 39.1 °C, while canopy coverage ranged from 0 to 70.6%. For a representative origin–destination pair, the coolest route reduces average LST by nearly 5 °C and almost triples canopy coverage compared with the shortest path, although at the cost of a 72% longer distance. These results demonstrate that the framework can generate interpretable comfort–efficiency trade-offs and support user-comfort pathfinding as an operational approach for heat-resilient pedestrian navigation. Full article

To improve emergency response efficiency in passenger ship fire accidents and ensure the life safety of passengers and crew, this paper proposes a dynamic route planning model for passenger ship fire evacuation that accounts for fire spread. Firstly, a passenger ship fire spread model is established based on the field simulation theory, and an emergency evacuation network model is constructed by determining the evacuation network topology from the fire propagation process. Secondly, the factors affecting emergency evacuation route planning during fire spread are analyzed, and a multi-objective optimization model for dynamic evacuation routes is developed. Thirdly, an improved ant colony optimization algorithm is designed to solve the problem. Finally, using the RP1 vessel from the publicly available ship evacuation dataset of the EU Seventh Framework Program SAFEGUARD project as a case study, simulation and comparative experiments are conducted. The results show that, in medium- and large-scale evacuation problems, the proposed method consistently maintains higher computational efficiency compared with the DABD algorithm and the FOA. In terms of objective optimization, it demonstrates that the proposed method has better effectiveness and feasibility than the shortest-path evacuation strategy, as it minimizes evacuation cost while satisfying assembly station capacity constraints and achieves a more balanced utilization of all emergency exits. Full article

Electric-vehicle evacuation planning requires evacuation routes that are both road-capacity feasible and driving-range feasible. Existing evacuation routing methods typically account for road capacity but do not explicitly enforce electric-vehicle range constraints, whereas electric-vehicle routing models usually focus on individual vehicle routing and do not address large-scale evacuation scheduling under shared road-capacity limits. This paper studies the Resource Constraint Evacuation Route Planning (RC-ERP) problem, in which evacuees must be routed from source locations to safe destinations while satisfying road-capacity constraints and a maximum travel-distance constraint between consecutive charging-station visits. We propose the Time-Expanded Charge-Encoded Routing Algorithm (TE-CERA), a scalable constructive heuristic that combines charge-encoded route generation with time-expanded capacity-aware scheduling. The proposed Node-Encoded Shortest Path (NESP) procedure computes charging-feasible spatial routes by tracking the accumulated travel distance since the most recent charging-station visit, while the scheduling stage assigns feasible departure times using a time-indexed edge-occupancy table. Under the stated modeling assumptions, the framework guarantees charging-feasible and capacity-feasible evacuation schedules. Experiments on real-world transportation networks show that TE-CERA eliminates charging-constraint violations while maintaining comparable evacuation times to a capacity-constrained evacuation routing baseline. The results demonstrate the feasibility and scalability of integrating electric-vehicle range constraints into evacuation routing, while also highlighting future extensions involving charging duration, charger capacity, and station-level queueing. Full article

Wire harness assembly is a highly manual job performed on formboards. Augmented reality (AR)-assisted wiring operations can improve work efficiency and reduce operator workload. However, investigations into the effects of AR-assisted wiring assembly on operator performance remain in the preliminary stage. To investigate how different AR wire harness modes support novice operators in completing assembly tasks effectively, this exploratory laboratory study examined the impacts of AR instruction modes for single-route conditions on assembly performance (task time and number of assembly errors), gaze behavior using eye-tracking data, and subjective experience measured with the NASA-TLX (Task Load Index) as a post-experiment questionnaire in a controlled laboratory environment. Three wire path visualization modes, i.e., static color mode (SCM), dynamic color mode with flashing display (DCM-FD), and dynamic color mode with segment display (DCM-SD), were implemented for monitor-based, AR-assisted wiring instruction on a formboard. The results reveal a substantial influence of the wire path visualization modes on task time under our controlled experimental conditions: the SCM group achieved an 18% shorter task time than the other two groups, with a statistically significant difference. This finding contradicts the existing observations in the mechanical assembly domain. For gaze behavior, an analysis of the eye-tracking data indicated that the number of switches in the SCM group was the lowest among the three groups, with a marginally significant difference from the DCM-FD group for both low- and high-complexity wiring tasks during the laying phase. Additionally, the total fixation time of the three groups showed a significant difference for low- and high-complexity tasks with a large effect size; the SCM group exhibited the shortest total fixation time across all tasks. No significant differences in the number of assembly errors and users’ perceived workload were observed among the three groups. These findings can serve as a reference for guiding the visual style design in AR-assisted wiring systems for training novice operators in human-centric Industry 5.0 and achieving a decrease in overall workload and improved task performance. Full article