Search Results (2,451)

Against the backdrop of rising psychological stress and declining physical fitness in cities, how streetscape characteristics and Myers–Briggs Type Indicator (MBTI) personality traits jointly influence cycling willingness across different contexts remains underexplored. Using Shenzhen, China, as a case study, we integrated objective bicycle-sharing travel records from 2021 and subjective pairwise ratings of 1000 street-view images from 960 participants. Cycling willingness was extrapolated through the TrueSkill algorithm and a ResNet50-based model, while street view elements were extracted via DeepLabV3+ and summarized into five indicators. Multivariate regression and multifactor ANOVA were used to test main and moderating effects across six cycling contexts. Results show that (1) Objective cycling indicators and subjective willingness exhibit a pattern of lower values in the center and higher values in the periphery. (2) The Spatial Green Index, Sky Openness Index, Path Freedom Index, and Facility Accessibility Index are the main influencing factors, while the Interface Enclosure Index has the weakest and most context-dependent effect. (3) Intuition/Feeling traits are more salient in leisure and exploration, Judging/Thinking in fitness and transport, and Extraversion/Feeling in social and companion contexts. These findings provide evidence for optimizing urban street cycling spaces in a multi-context and personality-informed manner. Full article

Underwater dynamic target detection, classification, localization, and tracking (DCLT) is central to maritime surveillance and monitoring and increasingly relies on distributed AUV-based robotic sonar networks operating in passive listening and, when required, cooperative multistatic modes. Achieving a robust performance in realistic oceans remains challenging, because sensor placement must adapt to time-varying acoustic conditions and target priors while preserving acoustic communication connectivity, and because frequent reconfiguration under dynamic currents makes classical large-scale planning computationally expensive. This paper presents an integrated deep reinforcement learning (DRL)-based framework for passive-stage sonar placement and dynamic reconfiguration in distributed AUV networks. First, we cast placement as a constructive finite-horizon Markov decision process (MDP) and train a Proximal Policy Optimization (PPO) agent to sequentially build a collision-free layout on a discretized surveillance grid. The terminal reward is formulated to jointly optimize the environment-aware detection performance, computed from BELLHOP-based transmission loss models, and global network connectivity, quantified using algebraic connectivity. Second, to enable time-critical reconfiguration, we estimate flow-aware motion costs for all AUV–destination pairs using a PPO with a Long Short-Term Memory (LSTM) trajectory policy trained for partial observability. The learned policy can be deployed onboard, allowing each AUV to refine its path online using locally sensed currents, improving robustness to ocean-model uncertainty. The resulting cost matrix is solved via an efficient zero-element assignment method to obtain the optimal one-to-one reassignment. In the reported simulation studies, the proposed Sequential PPO placement method achieves a final reward 16–21% higher than Particle Swarm Optimization (PSO) and 2–3.7% higher than the Genetic Algorithm (GA), while the proposed PPO + LSTM planner reduces average travel time by 30.44% compared with A*. The proposed closed-loop architecture supports frequent re-optimization, scalable fleet operation, and a seamless transition to communication-supported cooperative multistatic tracking after detection, enabling efficient, adaptive DCLT in dynamic marine environments. Full article

With the rapid maturation of autonomous driving technology, the hybrid traffic of Internal Container Trucks (ICTs) and External Container Trucks (ECTs) has become a major trend in Automated Container Terminals (ACTs), imposing higher demands on the interaction efficiency between trucks and Yard Cranes (YCs). This paper proposes a comprehensive optimization strategy for the coordinated scheduling of ICTs, ECTs and YCs under hybrid traffic. First, a task combination strategy for ICTs is designed to improve ICT utilization by pairing delivery and retrieval tasks across yard blocks. Second, a Chebyshev-motion-based coordination strategy for YC gantry and trolley movements is developed to reduce travel time and optimize handover points. Third, a mixed-integer programming model is formulated to minimize total energy consumption. An Improved Hybrid Genetic Algorithm (IHGA) is then developed, incorporating chaotic initialization, simulated annealing-based mutation, and dual local search to enhance convergence and solution quality. Simulation results confirm that the proposed model and strategy effectively reduce the total energy consumption of task execution, and the designed algorithm outperforms comparative algorithms in both optimization capability and convergence speed. Overall, the research provides theoretical support for future automated terminal development and practical guidance for achieving efficient and sustainable port operations. Full article

High-speed photodetectors with extended dynamic ranges are critical for emerging optical systems like LiDAR. This paper presents a design methodology for a modified uni-traveling-carrier photodetector (MUTC-PD) that integrates a physics-based analytical model with numerical simulations. The existing analytical models for MUTC-PDs rely on approximations that may not hold under high injection levels and high frequencies, leading to discrepancies between theoretical predictions and practical observations. To address this limitation, we re-examine the governing equations and derive a corrected frequency response analytical model based on drift–diffusion theory by decomposing the device into distinct transport regions, enabling a physically meaningful optimization of the epitaxial layer structure to maximize theoretical intrinsic bandwidth. The calculated results closely match the simulated bandwidth (maximum error less than 6%), demonstrating consistent peak positions and trends. Subsequently, numerical simulations reveal the dynamic evolution of the device’s bandwidth under varying incident optical intensities. The results demonstrate that the intrinsic bandwidth initially increases to a peak of 325.82 GHz at $7\times {10}^4\mathrm{W}/{\mathrm{cm}}^2$ under −3.5 V, attributed to the drift-enhancement effect driven by the self-induced quasielectric field. Beyond this optimal regime, the space charge effect causes internal field collapse and significant bandwidth degradation. This study establishes bandwidth maintenance capability under high injection as a key metric for linearity, offering a transparent theoretical and practical framework for designing a high-speed MUTC-PD. Full article

To enhance fleet replenishment efficiency and ensure navigational safety, this paper investigates the Underway Replenishment Routing Problem (URRP), focusing on the dynamic characteristics of receiving ships. Mathematical models for replenishment ship travel time and formation vessel speed adjustment are formulated, explicitly considering navigational state transitions and formation stability (risk control). Consequently, a dynamic route optimization model is constructed to provide intelligent decision support for fleet commanders. An intelligent optimization algorithm, the Hybrid Genetic Algorithm with Adaptive Variable Neighborhood Search (HGA-AVNS), is proposed to solve this model. Computational results demonstrate that the proposed approach outperforms the traditional empirical replenishment strategy, validating its effectiveness in enhancing maritime safety and operational efficiency. Extensive sensitivity analyses further reveal that under the strict premise of maintaining formation stability, appropriately reducing the cruise speed can offset the increase in overall speed over ground (SOG) induced by following ocean currents, thereby preventing systematic time loss. Additionally, fine-tuning the execution timing of sudden tactical turning based on the replenishment ship’s real-time operational status can further maximize overall replenishment efficiency without compromising navigational safety. Full article

Automated Storage and Retrieval Systems (AS/RSs) are pivotal hubs in modern intelligent logistics, yet their operational efficiency is often constrained by the complex coupling of storage allocation, equipment scheduling, and path planning. This study proposes a systematic optimization framework to address these three critical control challenges. First, a multi-objective mathematical model for storage location allocation is established, considering efficiency, stability, and correlation. To solve this high-dimensional discrete problem, a Tabu Variable Neighborhood Search (TVNS) algorithm is proposed, integrating short-term memory mechanisms with multi-structure exploration to prevent premature convergence. Second, regarding stacker crane and forklift collaborative scheduling, a Pheromone-guided Artificial Hummingbird Algorithm (PT-AHA) is introduced. By incorporating pheromone feedback into foraging behavior, the algorithm significantly enhances global search capability to minimize total task completion time. Third, stacker crane path planning is modeled as a constrained Traveling Salesman Problem (TSP) and solved using a hybrid Simulated Annealing-Whale Optimization Algorithm (SA-WOA). Quantitative simulation results demonstrate that the TVNS algorithm improves storage allocation fitness by 1.1% over standard Genetic Algorithms, while the PT-AHA reduces task completion time (Makespan) by 21.9% for small-scale batches and consistently outperforms ACO by up to 3.6% in large-scale operations. Validation through an Intelligent Warehouse Management System (WMS) confirms that the integrated framework maintains high industrial resilience by triggering fault alarms and initiating recovery within 3.2 s during simulated equipment failures, providing a robust solution for enterprise-level deployments. Full article

Unmanned Aerial Vehicles (UAVs) present a promising solution for urban logistics, where an effective energy management strategy guided by optimal path planning is crucial for reducing operational costs and extending system lifespan. This study begins by analyzing the wind field distribution in a specific urban area of Chengdu using Computational Fluid Dynamics, and establishes a data-driven power prediction model to evaluate UAV energy consumption. A hybrid wind-field-aware A* with Ant Colony Optimization algorithm is subsequently proposed to compute the optimal flight path that balances energy consumption and distance, generating corresponding power demand profiles for the ensuing energy management strategy. Finally, a Deep Q-Learning (DQN)-based energy management strategy is implemented to regulate power distribution between the fuel cell and the battery, aiming to minimize hydrogen consumption and stabilize the power output of the primary source. Experimental results demonstrate that the proposed path planning method can effectively reduce energy consumption across different scenarios while causing only a marginal increase in travel distance. In addition, the DQN-based strategy significantly suppresses fuel cell power fluctuations at the cost of only a slight increase in hydrogen consumption, thereby demonstrating the effectiveness of the path-planning-informed energy management strategy. Full article

This research presents a comprehensive framework for optimizing Electric Vehicle (EV) charging infrastructure along the Lake Michigan circuit (LMC) in Michigan to support ecotourism, considering both slow charging at destinations and fast charging along the corridor. The framework identifies the optimum location and number of Level 2 chargers and Direct Current Fast Chargers (DCFC), using heuristic algorithms. The study evaluates infrastructure planning based on four key objectives: (1) minimizing overall charging infrastructure costs, (2) reducing grid network upgrade costs, (3) providing an acceptable level of service to long-distance travelers using DCFCs by minimizing queuing delays and deviations from their intended routes, and (4) minimizing unserved charging demand at Level 2 chargers, which reduces redirection to DCFC and consequently mitigates battery degradation. The integration of Level 2 and DCFC networks facilitates strategic investment by effectively managing charging demand, allowing unserved Level 2 demand to be accommodated at DCFC stations while adhering to budgetary constraints. The results show that increasing the budget from $15 to $20 million reduces user inconvenience by 47%, while a further increase to $25 million yields an additional 18% reduction. Additionally, increasing users’ value of time from $13 to $36 per hour results in a 50% reduction in average queuing time. Full article

Blind and visually impaired individuals face persistent challenges when navigating unfamiliar environments, where unseen obstacles compromise their safety and independence. Although many electronic travel aids have been proposed, most remain impractical for daily use—they often rely on bulky or costly hardware, require external processing, or provide unintuitive feedback. This work presents a wearable stereo-vision-based vibrotactile system for real-time obstacle detection and navigation assistance. The device combines an off-the-shelf stereo camera integrated with a simultaneous localization and mapping framework to perceive spatial geometry and detect obstacles in the user’s path. Two stereo-matching methods were implemented to estimate depth: a block-based algorithm optimized for low-latency performance and a semi-global approach providing denser depth maps. Detected obstacles are translated into distinct vibration patterns delivered through four skin-contact body-mounted actuators encoding both direction and distance. The system was evaluated with blindfolded sighted, visually impaired, and blind participants. Both stereo approaches supported reliable real-time guidance and high obstacle-avoidance rates, demonstrating robust performance on affordable, wearable hardware. These findings confirm the feasibility of real-time tactile guidance using commercially available components, marking a concrete step toward accessible navigation support that enhances safety and autonomy for blind and visually impaired individuals. Full article

Traffic signal control at urban intersections is one of the key determinants of the overall efficiency of the transportation system, given its direct impact on travel time, congestion levels, and emissions of exhaust fumes. This study proposes an integrated hybrid model that combines a metaheuristic Genetic Algorithm for generating potential signal timing plans with fuzzy multi-criteria decision-making (MCDM) for their evaluation and selection of the optimal solution. In order to determine the relative importance of criteria, the fuzzy methods F-AHP, F-FUCOM, and F-PIPRECIA were employed, thus providing stable assessments of criteria importance under conditions of uncertainty and expert subjectivity. The ranking of generated alternatives was performed by employing the F-TOPSIS, F-WASPAS, and F-ARAS methods, while the robust decision-making rule approach was employed to develop a robust decision-making rule by integrating multiple MCDM methods. The proposed model was tested using data collected from a real urban intersection. The results show that the integrated hybrid approach enables a significantly more reliable selection of the optimal signal timing plan and achieves higher traffic management efficiency compared to traditional methods. The proposed model provides a flexible and scalable framework that can be adapted to different types of intersections and traffic demand conditions, thereby significantly contributing to the development of modern intelligent traffic management systems. Full article

Wire and arc additive manufacturing (WAAM) offers high deposition efficiency for large-scale aluminum components; however, layer-by-layer thermal cycling often induces microstructural anisotropy and spatial heterogeneity, which compromise structural reliability. In this study, an ultrasonic-based quantitative framework is proposed to evaluate and optimize anisotropy and heterogeneity in WAAM 2319 aluminum alloy. Nine blocks were fabricated using an orthogonal design with three key process parameters: torch travel speed, arc current, and shielding gas flow rate. Ultrasonic velocity and attenuation were employed to construct anisotropy and heterogeneity indicators. Results show that velocity-based anisotropy remains below 0.53%, indicating nearly isotropic elastic stiffness, whereas attenuation-based anisotropy reaches up to 76%, revealing pronounced direction-dependent microstructural and porosity features. Metallographic analysis confirms that grain morphology variation and interlayer porosity jointly govern attenuation responses. Response surface surrogate models were established to correlate ultrasonic indicators with process parameters, and both single- and multi-objective optimizations were performed within the feasible process window. The proposed framework provides a non-destructive, volumetric approach for microstructure-informed process parameter optimization in WAAM aluminum alloys. Full article

The Traveling Salesman Problem (TSP) is a fundamental challenge in combinatorial optimization, with wide-ranging applications in logistics, manufacturing, and network design. In addition to the classical formulation, recent years have witnessed the emergence of complex variants, such as the TSP with Drones (TSP-D), TSP with Time Windows, and Prize-Collecting TSP, that incorporate novel constraints reflecting real-world requirements like last-mile delivery and multimodal logistics. This review presents a comprehensive survey of metaheuristic approaches for solving both the classical TSP and its emerging extensions, with particular emphasis on metaheuristic, hybrid methods, and machine learning-enhanced strategies. Recent algorithmic developments, benchmark datasets, and evaluation metrics are investigated, and critical challenges in addressing drone coordination, synchronization, and uncertainty are identified, as well. Bibliometric analysis is further provided to map research trends and the evolution of the field. By synthesizing foundational techniques and state-of-the-art innovations, this review outlines current progress and proposes future directions for metaheuristic optimization in increasingly dynamic and heterogeneous routing scenarios. Full article

To reduce charging time and improve operational efficiency at integrated energy stations (IESs) for electric vehicles (EVs), this paper develops a sustainability-oriented collaborative optimization model by coupling vehicle routing behavior with charging decision-making. Firstly, a dynamic road network model is established to simulate vehicle arrivals at IESs from different network nodes. Then, considering grid peak–valley electricity prices, station electricity procurement costs and EV charging demand, a dynamic pricing strategy for IESs is proposed to guide EVs to charge at off-peak hours so as to realize peak shaving and valley filling for the power grid. Meanwhile, the NSGA-III algorithm is improved through the introduction of Good Point Set initialization and an adaptive crossover mechanism, and the Good Point Set initialization and Adaptive Crossover NSGA-III (GPS-AC-NSGA-III) algorithm is proposed to solve the scheduling optimization problem. Finally, the CRITIC-based TOPSIS method is employed to identify the optimal compromise solution from the Pareto-optimal set. Case studies further prove the effectiveness of the proposed multi-objective collaborative optimization model for EVs and IESs. Compared with scenarios without dynamic Dijkstra-based navigation and dynamic pricing, the IES daily revenue increased by 39.83%, pollutant emissions decreased by 0.4%, and the peak-to-valley load difference ratio was reduced by 4.94%. The results indicate that dynamic Dijkstra-based vehicle routing improves travel efficiency, while the proposed dynamic pricing strategy enhances station profitability and smooths grid load fluctuations. Overall, the proposed framework contributes to sustainable transportation and energy systems by reducing pollutant emissions, improving energy efficiency, and enhancing the operational stability of integrated energy infrastructure, thereby supporting the transition toward low-carbon and sustainable urban energy systems. Full article

To develop a refined traffic diversion scheme for expressway reconstruction and expansion, this study establishes generalized link impedance functions for cars and trucks, considering their differences in road travel time, time value, and toll costs. Subsequently, a traffic diversion model is constructed based on user equilibrium theory, taking the heterogeneity between cars and trucks into consideration. A path-based solution algorithm using the method of successive averages is designed to solve the model. To evaluate the environmental impact of the traffic diversion, a vehicle exhaust emission (including CO₂, CO, HC, and NO_x) estimation method based on the COPERT model is proposed. The results of a case study show that the optimized traffic diversion scheme significantly reduces the average V/C ratio while increasing the average velocity of both cars and trucks on the reconstructed links, without substantially compromising the traffic efficiency of other links. Additionally, the diversion scheme reduces the exhaust pollutant emissions, but increases the CO₂ emissions within the network. The findings justify the effectiveness of the traffic diversion approach on alleviating the traffic congestion on the reconstructed expressway and its mixed impacts on the environment. Full article

Nature-inspired metaheuristic algorithms are commonly applied to complex combinatorial optimization problems where exact methods are computationally impractical. Tourist route optimization is a representative multi-objective problem characterized by realistic constraints such as travel time, cost, opening hours, and transportation modes. Although Mushroom Reproduction Optimization is computationally efficient, it often experiences premature convergence in complex search spaces. This paper proposes a novel hybrid algorithm, Mushroom–Weed Hybrid Reproduction Optimization (MWHRO), which integrates the colony-based local search of the Mushroom Reproduction algorithm with the fitness-proportional reproduction and competitive elimination mechanisms of Invasive Weed Optimization. Hybridization enhances population diversity and global exploration while preserving fast convergence. The proposed algorithm is evaluated based on a realistic tourist route optimization problem using real-world data from Zagreb, Croatia, across multiple transportation modes and objective-weight scenarios. Performance is compared against Ant Colony Optimization, Invasive Weed Optimization, Particle Swarm Optimization, and standard Mushroom Reproduction Optimization under equal evaluation budgets. Experimental results demonstrate that the proposed MWHRO algorithm consistently achieves high-quality solutions with significantly lower execution times, particularly in constrained and multimodal scenarios. Statistical analysis confirms the robustness and practical suitability of the proposed approach for real-world route optimization. Full article