Search Results (82)

Variable-rate pesticide spraying and fertilizer application require coordinated operation of heterogeneous agricultural machines, particularly in irregular fields where task demands, vehicle mobility, payload capacity, energy consumption, and resupply requirements vary spatially. However, most existing studies optimize aerial spraying or ground fertilization separately and do not jointly consider prescription-map demands, air–ground synchronization, pesticide-drift risk, and agricultural vehicle constraints. This study formulates collaborative UAV spraying and UGV fertilization as a multi-objective mixed-integer nonlinear programming problem with three objectives: minimizing system makespan, weighted energy consumption, and pesticide-drift penalty. A prescription-map-guided bi-level planning framework is proposed. At the upper level, the problem-specific TNSAOO solver determines UAV and UGV task sequences and collaborative resupply-point activation. At the lower level, adaptive Theta* and row-constrained Hybrid A* generate UAV spraying and UGV fertilization trajectories, respectively, while prescription-dependent application commands are assigned along active operation segments and a time-window mechanism detects and corrects residual air–ground conflicts. The framework was evaluated using 30 real farmland boundaries and 90 randomized prescription scenarios. Mean geometric coverage rates reached 98.82% for UAV spraying and 98.95% for UGV fertilization, while the mean prescription-compliance errors were 6.21% and 2.13%, respectively. In addition, 96.7% of the batch runs contained no more than one detected air–ground conflict, with a mean corrective waiting time of 1.07 s. Compared with traditional independent operation, collaborative planning reduced mean system makespan by 9.32%, weighted energy consumption by 7.21%, modeled drift penalty by 3.62%, and total path length by 5.79%. In the multi-objective comparison, TNSAOO obtained a mean hypervolume of 0.597 and a mean inverted generational distance of 0.375, showing competitive Pareto-search performance relative to established comparison algorithms, particularly NSGA-II. Additional terrain and drift sensitivity analyses produced systematic changes in energy, completion time, and modeled drift risk under controlled parameter perturbations. These findings demonstrate the simulation-based feasibility of jointly planning heterogeneous variable-rate spraying and fertilization under a shared prescription map. Physical field experiments remain necessary to validate spray deposition, fertilizer-distribution uniformity, terrain effects, and model calibration under environmental uncertainty. Full article

For decades, soilless media have been used as a growing medium in horticultural production, but issues with sustainability and the climate impact of some components, like peat moss, have driven the industry to search for alternative products or fillers that are more environmentally viable. Utility mulch, a byproduct of tree trimming companies, is an organic mulch containing various components of a tree, including ground and chipped wood, and is often available for free. Thus, this study aimed to evaluate whether utility mulch can be an economically and ecologically viable soilless media filler for growing petunia (Petunia Juss.) ‘Multiflora Prostrate’ and begonia (Begonia L.) ‘Viking XL Red On Chocolate’ plants in a greenhouse. The experiment was conducted in a split-plot design with two replications. The rate of mulch was considered the main plot and the type of mulch as the sub-plot. Utility mulch was divided into three types: fresh, aged, and decomposed, and was applied at rates of 10%, 20%, 30%, 40%, and 50% v/v, while 100% soilless media was used as a control. For growth media properties, decomposed mulch showed a greater bulk density and water-holding capacity compared to the control, while fresh and aged mulch showed reduced values for both properties. The highest air porosity values (30.2–31.2%) were observed in media with fresh mulch at a 30 to 50% rate. For begonia, up to 20% of any of the different mulch treatments showed similar plant growth (height, width, SPAD, number of flowers) as the control. Shoot dry weight was found to be the greatest with the control but presented similar results when measured with 10% of fresh and decomposed mulch, while water use efficiency was only greater with the control. For petunia, only 10% of any type of mulch showed similar growth and performance as the control, except for the number of flowers. The number of flowers was reduced at all rates of mulch in petunia. Greater rates of fresh and aged mulch reduced nitrate content in the media, while the pH increased with all types of mulch. For phosphorus, a greater rate of aged and decomposed mulch showed the lowest value compared to the control for petunia, whereas in begonia, decomposed mulch showed the lowest P content, which might be associated with the higher substrate pH level. This study suggests that as much as 20% of the different-aged mulches for begonia and 10% for petunia could be used as a cheap substrate replacement in soilless media. Full article

Air travel is widely regarded as a safe mode of transport, yet the aviation journey remains a significant source of psychological and physiological stress for many passengers. Existing research has largely focused on fear of flying during the flight itself, with limited synthesis of stress experienced across the entire passenger journey. This integrative review aimed to synthesize evidence on stressors affecting commercial airline passengers during the pre-flight, in-flight, and post-flight stages of travel. A comprehensive search of Google Scholar, Scopus, Web of Science, and PubMed identified peer-reviewed studies published between 2013 and 2025. Following screening and quality appraisal using an adaptation of Critical Appraisal Skills Program (CASP) principles, 46 studies met the inclusion criteria. The review identified 12 major categories of passenger stressors across the aviation journey. Pre-flight stress was primarily associated with procedural, socio-psychological, and financial factors, while in-flight stress centered on perceived loss of control, physical cabin constraints, environmental conditions, and disruptive passenger behavior. Post-flight stress was linked to physical fatigue, communication gaps, baggage-related challenges, immigration procedures, and ground transport difficulties. The review also revealed a substantial geographical imbalance, with most evidence originating from high-income countries. By providing a comprehensive temporal synthesis of passenger stressors, this review offers an evidence-based framework to inform passenger wellbeing initiatives, aviation policy, and future cross-cultural research. Full article

With the rapid proliferation of internet of vehicles applications, vehicle users in urban road scenarios face ever-increasing demands for low-latency and energy-efficient processing of computation-intensive tasks. The traditional fixed terrestrial infrastructure offers limited coverage in complex urban environments, making it difficult to satisfy the differentiated quality of service requirements of large-scale vehicle populations. To fully exploit the advantages of unmanned aerial vehicles (UAVs) in terms of flexible deployment and on-demand service provisioning, we propose a UAV-assisted mobile edge computing architecture tailored for urban road scenarios. By modeling realistic urban road terrain with varying elevations, we construct a two-tier cooperative network consisting of multiple rotary wing UAVs and ground vehicles. Aiming at maximizing the total system energy consumption, we formulate a mixed integer nonlinear programming problem that minimizes total system energy consumption through joint optimization of UAV flight trajectories and vehicle task offloading decisions while comprehensively accounting for task latency constraints, UAV flight velocity constraints, and the impact of three-dimensional terrain on air-to-ground channels. Considering the high-dimensional, non-convex, mixed integer, and strongly coupled nature of the problem, we design a genetic algorithm (GA)-based UAV trajectory design and offload allocation algorithm. The proposed approach encodes UAV trajectories as real-valued vectors and offloading decisions as binary vectors, employs a penalty function method to handle constraints, and achieves efficient global search through tournament selection, single-point crossover, and Gaussian mutation operators. Simulation results verify that the proposed algorithm converges reliably to feasible solutions under varying task data sizes and vehicle densities and achieves up to 20.6% energy savings compared to the benchmark schemes. The experimental results validate the necessity and effectiveness of jointly optimizing UAV trajectory and task offloading in urban road scenarios. Full article

Collaborative task allocation for multiple unmanned ground vehicles (UGVs) is a constrained combinatorial optimization problem in which symmetric vehicle resources must be coordinated with asymmetric task requirements. In delivery and inspection scenarios, homogeneous vehicles operate under identical rules, whereas task points differ in spatial distribution, demand, service time, and time window requirements. These asymmetries make compact, temporally feasible, and workload-balanced routing difficult. SACWDO-PSO is developed as a discrete particle swarm optimization framework that integrates Clarke–Wright savings initialization, adaptive parameter control, and simulated annealing local search. The savings strategy improves initial swarm quality, adaptive control adjusts exploration and exploitation during the search, and simulated annealing refines local route structures. The method is evaluated on Solomon VRPTW benchmark data under a soft time window penalty objective and insimulation scenarios developed using Unreal Engine 4.27 integrated with Microsoft AirSim 1.8.1. SACWDO-PSO obtains lower objective values and fewer time window violations than the compared swarm-intelligence baselines on most benchmark instances, while Wilcoxon signed-rank tests indicate statistically significant improvements over PSO, DPSO, and GA. Full article

Rising cooling demand in hot-arid climates requires passive, low-energy building strategies that can be compared across heterogeneous evidence. This study develops a PRISMA-informed systematic mapping review and an evidence-based energy-efficiency decision matrix for building-scale passive cooling strategies in hot-arid climates, while comparing evidence from both traditional and contemporary building contexts. Scopus and Web of Science Core Collection were searched for English-language journal articles and reviews published between 2010 and 2026. Rather than conducting statistical meta-analysis, the review uses qualitative and evidence-based synthesis to map, classify, and interpret heterogeneous performance evidence. After duplicate removal, 844 records were screened. A completed prioritized full-text synthesis assessed 92 reports and produced a core analytical evidence base of 78 studies, supported by 11 borderline or contextual studies, giving 89 mapped studies. The studies were coded by strategy cluster, climatic context, building typology, evidence type, performance metric, energy relevance, water dependency, implementation complexity, maintenance sensitivity, and evidence strength. Seven strategy clusters were identified: evaporative/windcatcher/solar-chimney systems; envelope/façade/shading strategies; courtyard/microclimate strategies; roof-based cooling; earth-to-air or ground-coupled cooling; natural ventilation/night flushing; and integrated passive cooling packages. The results show that passive cooling decisions require more than a thermal performance comparison. The proposed matrix distinguishes performance potential from implementation suitability and provides a structured design-support framework for low-energy hot-arid buildings. Full article

Low-altitude cellular services are increasingly demanded by unmanned aerial vehicle (UAV)-enabled logistics, inspection, and emergency missions, yet existing terrestrial-oriented configurations often lead to severe 3D coverage overlap, interference accumulation, and spatial serving-sector ambiguity in airspace. This paper develops a practical region-oriented framework to enhance air–ground cellular performance under existing deployments by jointly tuning sector electrical and mechanical downtilts. A unified evaluation pipeline is constructed to characterize received signal strength (RSS), signal-to-interference-plus-noise ratio (SINR), and spatial serving-sector patterns, and to quantify service reliability through threshold-based coverage rates and edge performance. By treating UAV service altitude as an adjustable planning parameter, the proposed framework captures height-dependent coverage behavior and provides altitude-specific downtilt optimization results for representative low-altitude service heights. A sampling-based regional evaluation scheme is further designed to approximate area-level performance with reduced computational cost, enabling repeated fitness evaluation within a genetic search procedure. Extensive evaluations show that the proposed approach consistently enhances low-altitude coverage reliability and edge experience while keeping the coupled impact on terrestrial performance within a controlled range. Full article

To address the limited constraints of ground-based lidar with few channels in retrieving aerosol microphysical parameters in urban atmospheres, this study developed a method to retrieve aerosol volume size distribution and effective radius from a 355/532/1064 nm triple-wavelength elastic-scattering, single-polarization lidar system. The method uses 3β + 2α optical quantities as input constraints, applies Mie scattering theory as the forward model, parameterizes the volume size distribution with B-spline functions, and achieves stable solutions through Tikhonov regularization and cross-validation. To reduce uncertainties in prior parameters, including the complex refractive index, particle size range, and lidar ratio, an optimization strategy based on parameter search, retrieval reconstruction, and error minimization was introduced. Numerical simulations showed that the method reproduced the main features of a bimodal lognormal aerosol volume size distribution with good feasibility and stability. Two case studies further showed fine-mode dominance and decreasing extinction coefficient, depolarization ratio, and effective radius with height under good air quality conditions, but enhanced coarse-mode contribution and effective radius in the upper cloud-influenced layer under lightly polluted conditions, as inferred from the combined variations in RSCS, extinction coefficient, depolarization ratio, and effective radius. Full article

Heterogeneous airground robotic systems, which integrate unmanned ground vehicles and unmanned aerial vehicles, have shown significant potential in complex autonomous missions. However, when deployed in urban canyons, dense high-rise buildings impose severe communication constraints on ground vehicles, necessitating the introduction of aerial vehicles as relays to maintain reliable connectivity. The resulting cooperative trajectory planning problem is challenging for three reasons. First, the kinematic and communication constraints are tightly coupled. Second, the optimization landscape is highly non-convex and non-differentiable. Third, the planner must balance topological exploration with real-time efficiency. To address these challenges, we propose a hierarchical cooperative trajectory planning framework for an air–ground robotic system. Specifically, in the upper layer, a heuristic-search-guided reinforcement learning mechanism is employed to narrow the search space and circumvent the sparse reward problem, rapidly generating an initial solution. Subsequently, the lower-layer planner utilizes an optimization-based solver, together with a corridor-based constraint formulation method, to refine the initial solution into a kinematically feasible cooperative trajectory. Ultimately, this strategy improves real-time efficiency while improving the quality of feasible cooperative trajectories. Extensive ablation studies and comparative experiments with representative baselines demonstrate that the proposed framework improves collision avoidance, communication reliability, trajectory smoothness, and computational efficiency in the tested urban canyon scenarios. Full article

The ability of uncrewed aerial vehicles (UAVs) to hover, recognize, and localize ground targets is crucial for efficient and accurate intelligent low-altitude operations, such as material delivery, emergency rescue, and firefighting. This paper presents a technical solution for low-altitude UAV target recognition and search localization. The core algorithm is a RepViT-enhanced detection model, which integrates the Re-Parameterization Vision Transformer (RepViT) lightweight neural network with an efficient object detection framework, further augmented by the Convolutional Block Attention Module (CBAM) to improve detection accuracy. The search localization strategy implements a tiered approach for exploring nearby areas from the current position, assigning targets to priority tiers and visiting them in order of priority. Experimental results demonstrate that the RepViT-enhanced model achieves a mean average precision (mAP) of 98.58% on a custom emergency rescue dataset, improving real-time detection speed by two frames per second (18.70 FPS vs. 16.70 FPS for the standard YOLOv4 baseline). Thus, the proposed method effectively enhances both detection accuracy and speed, enabling better target search and localization in complex environments. The search strategy was validated through simulations, confirming its feasibility. Full article

The widespread commercialization of wireless chargers for electric vehicles generally suffers from one main problem, which is the perfect alignment between the two coils, leading to a decrease in mutual inductance, which causes a drop in magnetic coupling and even a failure to transfer power. To address this persistent problem, this work proposes a comprehensive and integrated method for optimizing the coils and control architecture for reliable and safe battery charging. To address the challenges of a complex, nonlinear design space and the need for misalignment-tolerant geometries, we employ a memetic algorithm (MA) that hybridizes Particle Swarm Optimization (PSO) for broad global exploration with Mesh Adaptive Direct Search (MADS) for precise local refinement. This combination effectively avoids poor local solutions—a limitation of standalone PSO or GA approaches reported in recent studies—while efficiently converging to coil geometries that maintain strong magnetic coupling under misalignment. After the coils have been designed, electromagnetic validation is tested using finite element analysis (FEA), which allows the magnetic field distribution to be evaluated, as well as the coupling coefficient under different scenarios of misalignment and variation in the air gap between the ground side and the vehicle side. At the same time, a comprehensive control strategy for the primary side of the system has been developed. This control method ensures power management on the primary side, enabling system interoperability for charging multiple types of vehicles, as well as reducing vehicle weight for greater range. All this is combined with an innovative pulsed current charging method, chosen for its advantages in terms of thermal stability, ensuring safe and efficient recharging that is mindful of battery health. Simulation and experimental validation demonstrate that the proposed framework maintains stable wireless power transfer and achieves over 87% DC–DC efficiency under lateral misalignments up to 100 mm, fully complying with SAE J2954 alignment tolerance requirements. Full article

Pre-warehouse last-mile delivery is currently constrained by service radiuses and intense delivery pressures. Meanwhile, national policies are increasingly promoting a transition toward green logistics. By undertaking deliveries to remote or dispersed locations, UAVs can streamline truck routes and minimize the fuel consumption and emissions typically exacerbated by urban traffic congestion. Accordingly, this paper establishes a Ground-Vehicle and Drone Parallel Distribution Model with Priorities (PW-PDSVRP-P), quantifying customer priorities via delivery delay functions to align efficiency with social service requirements. A master–slave hybrid Large Neighborhood Search algorithm is developed and validated through a Hema Fresh case study in Xuzhou. Results define a clear “economic advantage zone” for drone adoption and reveal an adaptive assignment strategy: drones serve as mass-delivery tools in low-cost scenarios but act as “surgical tools” to prune inefficient truck segments in high-cost environments. These findings confirm that air–ground collaboration fosters a more resilient urban distribution system by balancing operational costs with environmental and social sustainability goals. Full article

Unmanned vehicles are becoming increasingly essential in executing high-risk missions in unknown environments such as search and rescue. As the complexity of operational environments escalates, carrying out unmanned tasks becomes cumbersome or even infeasible for a single vehicle, hampered by limited perception and operational constraints. Aiming at enhancing the flexibility of unmanned operations under complicated scenarios, this study introduces AGC-NeRF, an innovative air–ground collaborative exploration framework that harnesses the functional complementarity of UAVs and UGVs—enabling a UGV to navigate through a complex scenario with the assistance of a UAV via referencing a neural radiance map. First, a UAV is employed to collect aerial images for reconstructing the environment to be explored by a UGV, leveraging its aerial perspective to achieve wide-area coverage and global environmental perception that is unattainable for a single UGV. Concurrently, an innovative image saliency evaluation approach is introduced to meticulously select landmarks that are contributive to the UGV’s navigation system, yielding a pre-trained NeRF model of the operation scene. Then, a landmark-aware 6-DOF ego-motion estimator and collision-free trajectory optimizer are designed for the UGV based on the NeRF map. Finally, an online replanning architecture is established which relies on a ground station for NeRF training and state optimization by synergizing the trajectory planner and the state estimator, which forms a dual-agent vision-only navigation pipeline. Simulations and experiments validate that AGC-NeRF enables reliable UGV trajectory planning and state estimation in unknown environments, demonstrating superior efficacy and robustness of the air–ground collaborative paradigm. Full article

This paper presents a collaborative hazard-detection system that pairs a UAV running YOLOv9 for rapid aerial scanning with a UGV running Faster R-CNN for precise ground-level confirmation. The pipeline exploits complementary strengths, fast wide-area cueing from the air and high-precision verification on the ground, to reduce false alarms while maintaining responsiveness in complex environments. On the validation set, YOLOv9 reached mAP@0.5 = 0.969 with F1 = 0.95 at 41.7 FPS, enabling real-time scanning of large areas. Faster R-CNN attained mAP@0.5 = 0.979 with F1 = 0.95 at 1.72 FPS, providing reliable close-range confirmations where localization accuracy is critical. Together, these results show that the proposed UAV–UGV pipeline delivers a practical balance between rapid hazard identification and trustworthy validation, suitable for search and rescue, critical infrastructure monitoring, and operations in hazardous environments. Potential extensions include inference optimization on the ground platform, multi-sensor data fusion, and field trials to assess robustness under real-world conditions. Full article

To address the task-grouping problem for air–ground integrated Unmanned Aerial Vehicle (UAV) swarm missions in three-dimensional (3D) environments, this study proposes a data-preprocessing and hybrid initialization clustering method based on 3D spatial features. A dual-modal prototype meta-heuristic optimization model, Dual-Prototype Metaheuristic K-Means (DPM-Kmeans), is constructed accordingly. First, to overcome spatial information loss in high-dimensional task allocation, a 3D spatial task data preprocessing technique and a hybrid initialization strategy based on the golden spiral distribution are designed. This ensures the diversity and environmental adaptability of the initial solutions. Second, a dual-modal prototype optimization framework incorporating row prototypes (local refinement) and column prototypes (global combination) was constructed using meta-heuristics and clustering algorithms. The prototype-driven replacement update mechanism simultaneously performs global and local search, balancing the algorithm’s exploration and exploitation capabilities while expanding the solution space. This effectively addresses premature convergence issues in complex search spaces. Simultaneously, a collaborative multi-constraint, dynamically weighted optimization model was constructed, incorporating task requirements and flight distance constraints to ensure that the grouping scheme approximates the global optimum. Simulation results demonstrate that compared to traditional K-means and mainstream meta-heuristic optimization algorithms, DPM-Kmeans achieves an overall improvement of 2–10% in Sum of Squared Errors (SSE), Silhouette Coefficient (SC), and Davies–Bouldin Index (DB) metrics. It exhibits superior convergence speed and solution quality, proving the method’s excellent scalability and robustness in multi-constraint, large-scale 3D scenarios. Full article