Search Results (169)

Intelligent parking systems have been recognized as a core technological intervention for resolving parking garage shortages and advancing traffic safety. Nevertheless, it remains challenging to generate a smooth, accurate, and optimal parking trajectory when employing conventional intelligent path optimization algorithms. Hence, building upon a newly designed optimization model for intelligent vehicle parking path planning, this study develops an improved immune moth–flame optimization algorithm (IIMFO). Specifically, aiming at the shortest path length and smooth enough trajectory, we leverage a cubic spline interpolation-driven path planning model to resolve the complex automatic parking trajectory optimization problem. To significantly strengthen the optimization effect, we introduce immune concentration selection, nonlinear decaying adaptive inertia weight adjustments, and elite opposition-based learning mechanisms to improve the immune moth–flame algorithm. Based on the evaluation results of the test functions, as well as the simulation and semi-automatic experiments of the real-world scenario of intelligent vehicle parking path optimization, the results indicate that the improved strategy can achieve better parking trajectories. Full article

To address the computational-efficiency bottlenecks of Hybrid A* and its bidirectional variant in long-distance parking and narrow-slot scenarios, an improved bidirectional Hybrid A* algorithm is presented. First, the cohesion cost is reformulated in a vector-space representation. Distance and heading-consistency terms are evaluated using dot products, which eliminates trigonometric operations and reduces the overhead of node evaluation. Second, an RS (Reeds–Shepp) cost template is constructed on a sparse grid of key nodes. Neighborhood costs are approximated with Euclidean-distance correction. In addition, a geometry reachability-based trigger is designed for analytic RS connections to avoid redundant analytic linking and unnecessary RS curve computations. Third, a KD-tree spatial index is introduced to accelerate nearest-neighbor queries in the Voronoi potential field, and vehicle corner coordinates are updated in a vectorized manner to improve the efficiency of potential-field evaluation. Simulation results in parallel and perpendicular parking show that, compared with the baseline bidirectional Hybrid A* algorithm, RS computations are reduced by 98.7% and 97.8%, respectively, while total planning time is shortened by 63.2% and 57.5%, with stable path quality. These results indicate that the proposed method effectively mitigates the dominant computational costs of bidirectional Hybrid A* in complex parking tasks and improves the efficiency and real-time performance of automatic parking path planning. Full article

Urban street trees provide essential environmental and social benefits, yet their vitality is often challenged by adverse urban conditions such as traffic emissions, impervious surfaces, and limited soil moisture. Conventional street tree management relies heavily on labor-intensive field inspections, making large-scale and repeatable assessment difficult. To address this limitation, this study proposes a GeoAI-based framework that integrates high-resolution aerial imagery, multispectral satellite data, and deep learning–based semantic segmentation to automatically delineate individual street trees and derive a remote sensing-based vitality proxy. Street trees are detected from orthorectified aerial imagery using semantic segmentation models, and vegetation indices—including NDVI, NDRE, and NDMI—are extracted from multispectral satellite imagery. An area-weighted object–pixel matching strategy is applied to associate spectral indicators with individual crowns across multi-resolution datasets. A composite vitality proxy is then constructed by integrating chlorophyll- and moisture-related indices. The results reveal spatial variability in spectral vitality signals across different urban environments. Trees along major road corridors tended to exhibit lower chlorophyll- and moisture-related indicators, while those near parks, riverfront walkways, and recently developed residential areas generally showed higher values. NDMI provided complementary insights into moisture-related stress that were not fully reflected by chlorophyll-based indices. Although the proposed vitality proxy is not a substitute for field-based diagnosis, the overall framework offers a scalable approach for citywide screening and prioritization of potentially stressed trees, supporting data-informed urban green infrastructure management within smart-city planning contexts. Full article

Automatic parking in constrained environments, such as dead-end roads and narrow parallel spaces, remains a challenge due to the low success rate and poor real-time performance of conventional planning algorithms. The paper proposes an entry-point guided path planning method that integrates heuristic search with hybrid A* and reeds-shepp curve to address the above limitations. By rapidly identifying the optimal initial parking pose, the proposed method ensures the kinematic feasibility and smoothness of the resulting trajectories. To further improve efficiency and safety in tight spaces, a hybrid collision detection mechanism is developed by combining a rectangular envelope with multi-circle fitting. The hierarchical geometric modeling approach significantly reduces computational cost while maintaining high detection accuracy. The method is validated through both simulations and real-vehicle experiments in vertical and parallel parking scenarios. Results demonstrate that in typical constrained scenarios, the average planning time is only 0.543 s, and the number of direction changes is maintained between 1 and 6, demonstrating superior computational efficiency and improved trajectory smoothness. These attributes make the algorithm highly suitable for practical deployment in advanced driver assistance systems and autonomous vehicles. Full article

About 16% of typhoons making landfall in China strike Hainan Island, where near-surface extreme winds in dense urban areas exhibit a strong spatiotemporal heterogeneity that is difficult to capture with current observations and mesoscale models. Focusing on Haikou during Super Typhoon Yagi (2024)—the strongest autumn typhoon to hit China since 1949—we developed a multiscale ERA5–WRF–PALM framework to conduct 30 m resolution large-eddy simulations. PALM results are in reasonable agreement with most of the five automatic weather stations, while performance is weaker at the most sheltered park site. Mean near-surface wind speeds increased by 20–50% relative to normal conditions, showing a coastal–urban gradient: maps of weighted cumulative exposure to strong winds (≥Beaufort force 8) show much longer and more intense events along open coasts than within built-up urban cores. Urban morphology exerted nonlinear effects: wind speeds followed a U-shaped relation with both the open-space ratio and mean building height, with suppression zones at ~0.5–0.7 openness and ~20–40 m height, while clusters of super-tall buildings induced Venturi-like acceleration of 2–3 m s⁻¹. Spatiotemporal analysis revealed banded swaths of high winds, with open areas and islands sustaining longer, broader extremes, and dense street grids experiencing shorter, localized events. Methodologically, this study provides a rare, systematically evaluated application of a multiscale ERA5–WRF–PALM framework to a real typhoon case at 30 m resolution in a tropical coastal city. These findings clarify typhoon–city interactions, quantify morphological regulation of extreme winds, and support risk assessment, urban planning, and wind-resilient design in coastal megacities. Full article

This study investigates the fire behaviour of Battery Electric Vehicles (BEVs) and Internal Combustion Engine Vehicles (ICEVs) in confined environments such as underground parking facilities and tunnels. Using the Fire Dynamics Simulator (FDS), several scenarios were modelled to analyse the effects of ventilation and automatic suppression systems on fire growth, heat release, and smoke propagation. Three ventilation configurations—reduced, standard, and increased airflow—were evaluated to determine their influence on combustion dynamics and thermal development. Results show that BEV fires produce higher peak Heat Release Rates (up to 7 MW) and longer combustion durations than ICEVs, mainly due to self-sustained battery reactions. Increased ventilation enhances smoke removal but intensifies flames and radiant heat transfer, while limited airflow restricts combustion yet leads to hazardous smoke accumulation. The inclusion of a sprinkler system effectively reduced temperatures by over 60% within 100 s of activation, though residual heat in BEVs poses a risk of re-ignition. This underlines the need for tailored ventilation and suppression strategies in modern underground facilities to ensure safety in the transition toward electric mobility. Full article

In the context of rapid urbanization, the connection between humans and nature has progressively diminished. As an essential approach to fostering public ecological awareness and well-being, nature education requires greater integration into urban green space planning and management. This study examines 14 country parks, urban parks, and forest parks in Beijing, conducting questionnaire surveys in six representative parks and collecting 820 valid responses. Combining image semantic segmentation techniques, the research employs the PSPNet model trained on the ADE20K dataset to automatically extract landscape features of nature education spaces. These features are then integrated with visitor perception evaluations through univariate linear regression models to analyze the impact of spatial variables on visitor perceptions. Results indicate that building coverage, plant species density, interpretation sign density, number of artificial interpretations, and number of nature education activities offerings show significant positive correlations (p < 0.05) with visitor perceptions. In contrast, excessive artificial structures exert a negative influence. The R² values of each model ranged from 0.12 to 0.34, indicating that natural education space features possess explanatory power for visitor perceptions but remain influenced by multiple interacting factors. This study establishes a quantitative evaluation framework linking natural education space landscape features to visitor perceptions, providing a scientific basis for natural education planning and spatial optimization in parks within megacity contexts. Full article

Understanding and monitoring benthic habitat distribution is essential for implementing ecosystem-based fisheries management (EBFM). Satellite remote sensing offers a rapid and cost-effective approach to marine habitat assessments; however, its application requires context-specific adjustment to account for environmental variability and differing study aims. As such, predictor variables must be tailored to the specific site and target habitat. This study uses Sentinel-2 Level 2A surface reflectance satellite imagery and stability selection via Random Forest Recursive Feature Elimination to assess the importance of remote sensing indices for mapping moderately deep (<20 m) seagrass habitats in relation to the Marine Stewardship Council-certified Western Australia Enhanced Greenlip Abalone Fishery (WAEGAF). Of the seven indices tested, the Normalised Difference Aquatic Vegetation Index (NDAVI) and Depth Invariant Index for the blue and green bands were selected in the optimal model on every run. The kernelised NDAVI and Water-Adjusted Vegetation Index also scored highly (both 0.92) and were included in the final classification and regression models. Both models performed well and predicted a similar cover and distribution of seagrass within the fishery compared to the surrounding area, providing a baseline and supporting EBFM of the WAEGAF within the surrounding marine protected area. Importantly, the use of indices from freely accessible ready-to-use satellite products via Google Earth Engine workflows and expedited ground truth image annotation using highly accurate (0.96) automatic image annotation provides a rapidly repeatable method for delivering ecosystem information for this fishery. Full article

The importance of power quality has increased these days due to the growth in the use of renewable energies and nonlinear loads. Although the use of renewable energies provides power generation sources that help in reducing greenhouse gas emissions, they might have a detrimental effect on the power quality due to their intermittency and dependence on weather conditions. Due to the importance of keeping an optimal power quality, in this work, a novel methodology is developed whose main contribution relies on the use of entropy features and envelope analysis for the detection and quantification of power quality disturbances. The proposed method is implemented within a machine learning framework, where linear discriminant analysis (LDA) is employed to optimize entropy-based features. Subsequently, a neural network classifier performs an automatic classification and quantifies the magnitude of affectation associated with grid disturbances. The training is performed using synthetic signals, and validation is conducted with real signals from a photovoltaic park and from an IEEE working group. The results obtained are compared with those provided by other methodologies proving the accuracy and the viability of the proposed approach. Full article

Urban mediterranean forests are key components of urban ecosystems. Accurate, high-resolution data on forest structural attributes are essential for effective management. This study evaluates the efficiency of unmanned laser scanning systems (ULS) and terrestrial LiDAR (TLS) in deriving key tree attributes, diameter at breast height (DBH) and tree height, within a small urban park in Zadar, Croatia. Accuracy assessment of the ULS and TLS-derived DBH was conducted based on traditional ground-based measurement (TGBM) data. For ULS, an automatic Spatix workflow was applied that classified points into a Tree class, segmented trees using trunk-based logic, and estimated DBH by fitting a circle to a 1.3 m slice; tree height was computed from the ground-normalized cloud with the Output Tree Cells tool. A semi-automatic CloudCompare/ArcMap workflow used CSF ground filtering, Connected Components segmentation, extraction of a 10 cm slice, manual trunk vectorization, and DBH calculation via Minimum Bounding Geometry. TLS scans, processed in FARO SCENE, were then analyzed in Spatix using the same automatic trunk-fitting procedure to derive DBH and height. Accuracy for DBH was evaluated against TGBM; comparative performance was summarized with standard error metrics, while ULS and TLS tree heights were compared using Concordance Correlation Coefficient (CCC) and Bland–Altman statistics. Results indicate that the semi-automatic approach outperformed the automatic approach in deriving DBH. TLS-derived DBH values demonstrated higher consistency and agreement with TGBM, as evidenced by their strong linear correlation, minimal bias, and narrow residual spread, while ULS exhibited greater variability and systematic deviation. Tree height comparisons between ULS and TLS revealed that ULS consistently produced slightly higher and more uniform measurements. This study highlights limitations in the evaluated techniques and proposes a hybrid approach combining ULS scanning with personal laser scanning (PLS) systems to enhance data accuracy in urban forest assessments. Full article

In the domain of automatic parking systems, parking space detection and localization represent fundamental challenges that must be addressed. As a core research focus within the field of intelligent automatic parking, they constitute the essential prerequisite for the realization of fully autonomous parking. Accurate and effective detection of parking spaces is still the core problem that needs to be solved in automatic parking systems. In this study, building upon existing public parking space datasets, a comprehensive panoramic parking space dataset named PSEX (Parking Slot Extended) with complex environmental diversity was constructed by integrating the concept of GAN (Generative Adversarial Network)-based image style transfer. Meanwhile, an improved algorithm based on PP-Yoloe (Paddle-Paddle Yoloe) is used to detect the state (free or occupied) and angle (T-shaped or L-shaped) of the parking space in real-time. For the many and small labels of the parking space, the ResSpp in it is replaced by the ResSimSppf module, the SimSppf structure is introduced at the neck end, and Silu is replaced by Relu in the basic structure of the CBS (Conv-BN-SiLU), and finally an auxiliary detector head is added at the prediction head. Experimental results show that the proposed SimSppf_mepre-Yoloe model achieves an average improvement of 4.5% in mAP50 and 2.95% in mAP50:95 over the baseline PP-Yoloe across various parking space detection tasks. In terms of efficiency, the model maintains comparable inference latency with the baseline, reaching up to 33.7 FPS on the Jetson AGX Xavier platform under TensorRT optimization. And the improved enhancement algorithm can greatly enrich the diversity of parking space data. These results demonstrate that the proposed model achieves a better balance between detection accuracy and real-time performance, making it suitable for deployment in intelligent vehicle and robotic perception systems. Full article

In complex scenarios, such as industrial parks and underground parking lots, efficient and safe autonomous navigation is essential for driverless operation and automatic parking. However, conventional modular navigation methods, especially the A* algorithm, suffer from excessive node traversal and short paths that bring vehicles dangerously close to obstacles. To address these issues, we propose an autonomous navigation approach based on a layered terrain cost map and a nonlinear predictive control model, which ensures real-time performance, safety, and reduced computational cost. The global planner applies a two-stage A* strategy guided by the hierarchical terrain cost map, improving efficiency and obstacle avoidance, while the local planner combines linear interpolation with nonlinear model predictive control to adaptively adjust the vehicle speed under varying terrain conditions. Experiments conducted in simulated and real underground parking scenarios demonstrate that the proposed method significantly improves the computational efficiency and navigation safety, outperforming the traditional A* algorithm and other baseline approaches in overall performance. Full article

In recent years, antiviral therapy has proved crucial in the treatment of infectious diseases, particularly infections by highly variable viruses such as human immunodeficiency virus, hepatitis B, hepatitis C, SARS-CoV-2 or bacteria such as Mycobacterium tuberculosis. Under the effect of selection pressure, this variability induces mutations that lead to resistance to antiviral and antibacterial drugs, and thus to escape from treatment. The use of Advanced Biological Laboratories (ABL) assays technology combined with next-generation sequencing (NGS) and automatized software to detect majority and minority variants involved in treatment resistance has become a mainstay for establishing therapeutic strategies. The present study demonstrated high concordance between majority and minority subtypes and mutations identified in 15 samples across four NGS platforms: ISeq100 (Illumina (San Diego, CA, USA)), MiSeq (Illumina), DNBSEQ-G400 (MGI (Santa Clara, CA, USA)) and Mk1C MinION (Oxford Nanopore (Oxford Science Park, UK)). However, nanopore technology showed a higher number of minority mutations (<20%). The analysis also validated the pooling of microbiological samples as a method for detecting mutations and genotypes in viral and bacterial organisms, using the easy-to-use DeepChek^® bioinformatics software, compatible with all four sequencing platforms. This study underlines the constant evolution of microbiological diagnostic research and the need to adapt rapidly to improve patient care. Full article

The proximity of urban green spaces to residential areas has become a central principle in contemporary urban planning, with cities worldwide adopting “15-minute city” concepts that prioritize walking-distance access to parks. This study examined whether proximity to different types of parks influences park visitation, physical activity, and mental well-being in Bangkok, Thailand, where the government recently launched a 15-minute parks policy initiative to improve the proximity of urban residents to green spaces. Using a cross-sectional survey of 615 residents across Bangkok’s 50 districts, we measured proximity to six park types using GIS network analysis and assessed health outcomes through validated instruments (Global Physical Activity Questionnaire, GPAQ for physical activity GPAQ for physical activity, and WHO-5 for well-being). Our findings revealed that only proximity to community parks (5–20 ha) was significantly associated with park visitation, sufficient physical activity, and good well-being. Proximity to smaller parks, including the new 15-minute parks, pocket parks, and neighborhood parks, showed no significant associations with any health outcomes, despite being within walking distance. These results suggest a critical size threshold below which parks cannot generate health and well-being benefits in Bangkok’s environment. The findings challenge the argument commonly used in proximity-based green space policies that assume closer parks automatically improve park visitation and public health benefits, indicating that cities facing similar constraints should balance between providing small park networks and securing larger, functional parks to support meaningful recreational use or health improvements. Full article

Although SAR systems can provide high-resolution aerial view images all-day, all-weather, the aspect and pose-sensitivity of the SAR target signatures, which defies the Gestalt perceptual principles, sets a frustrating performance upper bound for SAR Automatic Target Recognition (ATR). Therefore, we propose a network to support context-guided ATR by using aligned Electro-Optical (EO)-SAR image pairs. To realize EO-SAR image scene grammar alignment, the stable context features highly correlated to the parking patterns of the vehicle and aircraft targets are extracted from the EO images as prior knowledge, which is used to assist SAR-ATR. The proposed network consists of a Scene Recognition Module (SRM) and an instance-level Cross-modality ATR Module (CATRM). The SRM is based on a novel light-condition-driven adaptive EO-SAR decision weighting scheme, and the Outlier Exposure (OE) approach is employed for SRM training to realize Out-of-Distribution (OOD) scene detection. Once the scene depicted in the cut of interest is identified with the SRM, the image cut is sent to the CATRM for ATR. Considering that the EO-SAR images acquired from diverse observation angles often feature unbalanced quality, a novel class-incremental learning method based on the Context-Guided Re-Identification (ReID)-based Key-view (CGRID-Key) exemplar selection strategy is devised so that the network is capable of continuous learning in the open-world deployment environment. Vehicle ATR experimental results based on the UNICORN dataset, which consists of 360-degree EO-SAR images of an army base, show that the CGRID-Key exemplar strategy offers a classification accuracy 29.3% higher than the baseline model for the incremental vehicle category, SUV. Moreover, aircraft ATR experimental results based on the aligned EO-SAR images collected over several representative airports and the Arizona aircraft boneyard show that the proposed network achieves an F1 score of 0.987, which is 9% higher than YOLOv8. Full article