Search Results (420)

With the strict requirements of national policies on truck dimensions, axle loads, and weight limits, along with the implementation of tolls based on vehicle types, rapid and accurate identification of vehicle axle types has become essential for toll station management. To address the limitations of existing methods in distinguishing between drive and driven axles, complex equipment setup, and image evidence retention, this article proposes a panoramic image detection technology for vehicle chassis based on enhanced ORB and YOLOv11. A portable vehicle chassis image acquisition system, based on area array cameras, was developed for rapid on-site deployment within 20 min, eliminating the requirement for embedded installation. The FeatureBooster (FB) module was employed to optimize the ORB algorithm’s feature matching, and combined with keyframe technology to achieve high-quality panoramic image stitching. After fine-tuning the FB model on a domain-specific area scan dataset, the number of feature matches increased to 151 ± 18, substantially outperforming both the pre-trained FB model and the baseline ORB. Experimental results on axle type recognition using the YOLOv11 algorithm combined with ORB and FB features demonstrated that the integrated approach achieved superior performance. On the overall test set, the model attained an mAP@50 of 0.989 and an mAP@50:95 of 0.780, along with a precision (P) of 0.98 and a recall (R) of 0.99. In nighttime scenarios, it maintained an mAP@50 of 0.977 and an mAP@50:95 of 0.743, with precision and recall both consistently at 0.98 and 0.99, respectively. The field verification shows that the real-time and accuracy of the system can provide technical support for the axle type recognition of toll stations. Full article

Historically, the Mediterranean Sea has been an area of cultural exchange and maritime commerce. One out of many submerged archaeological sites is the Roman shipwreck that was discovered in 2006 off the coast of Santo Stefano al Mare, in the Ligurian Sea, Italy. The wreck was dated to the 1st century B.C. and consists of a well-preserved cargo ship of Roman amphorae that were likely used for transporting wine. In this study, we present the results of the first underwater survey of the wreck using an Autonomous Underwater Vehicle (AUV) industrialized by Graal Tech. The AUV was equipped with a NORBIT WBMS multibeam sonar, a 450 kHz side-scan sonar, and inertial navigation systems. The AUV conducted multiple high-resolution surveys on the wreck site and the collected data were processed using geospatial analysis methods to highlight local anomalies directly related to the presence of the Roman shipwreck. The main feature was an accumulation of amphorae, covering an area of approximately 10 × 7 m with a maximum height of 1 m above the seabed. The results of this interdisciplinary work demonstrated the effectiveness of integrating AUV technologies with spatial analysis techniques for underwater archaeological applications. Furthermore, the success of this mission highlighted the potential for broader applications of AUVs in the study of the seafloor, such as monitoring seabed movements related to offshore underground energy storage or the identification of objects lying on the seabed, such as cables or pipelines. Full article

As crucial transportation hubs and economic nodes, the underwater security and infrastructure maintenance of harbors are of paramount importance. Harbors are characterized by high vessel traffic and complex underwater environments, where traditional underwater inspection methods, such as diver operations, face challenges of low efficiency, high risk, and limited operational range. This paper introduces a collaborative survey and disposal system that integrates a deformable unmanned surface vehicle (USV) with a lightweight remotely operated vehicle (ROV). The USV is equipped with a side-scan sonar (SSS) and a multibeam echo sounder (MBES), enabling rapid, large-area searches and seabed topographic mapping. The ROV, equipped with an optical camera system, forward-looking sonar (FLS), and a manipulator, is tasked with conducting close-range, detailed observations to confirm and dispose of abnormal objects identified by the USV. Field trials were conducted at an island harbor in the South China Sea, where simulated underwater objects, including an iron drum, a plastic drum, and a rubber tire, were deployed. The results demonstrate that the USV-ROV collaborative system effectively meets the demands for underwater environmental measurement, object localization, identification, and disposal in complex harbor environments. The USV acquired high-resolution (0.5 m × 0.5 m) three-dimensional topographic data of the harbor, effectively revealing its topographical features. The SSS accurately localized and preliminarily identified all deployed simulated objects, revealing their acoustic characteristics. Repeated surveys revealed a maximum positioning deviation of 2.2 m. The lightweight ROV confirmed the status and location of the simulated objects using an optical camera and an underwater positioning system, with a maximum deviation of 3.2 m when compared to the SSS locations. The study highlights the limitations of using either vehicle alone. The USV survey could not precisely confirm the attributes of the objects, whereas a full-area search of 0.36 km² by the ROV alone would take approximately 20 h. In contrast, the USV-ROV collaborative model reduced the total time to detect all objects to 9 h, improving efficiency by 55%. This research offers an efficient, reliable, and economical practical solution for applications such as underwater security, topographic mapping, infrastructure inspection, and channel dredging in harbor environments. Full article

The shoreline and seabed topography are key components of the coastal zone and are essential for hydrographic surveys, shoreline process modelling, and coastal infrastructure management. The development of unmanned aerial vehicles (UAVs) and optoelectronic sensors, such as photogrammetric cameras and airborne laser scanning (ALS) using light detection and ranging (LiDAR) technology, has enabled the acquisition of high-resolution bathymetric data with greater accuracy and efficiency than traditional methods using echo sounders on manned vessels. This article presents a spatial analysis of bathymetric data obtained from UAV photogrammetry and ALS LiDAR, focusing on shallow-water depth estimation and shoreline extraction. The study area is Lake Kłodno, an inland waterbody with moderate ecological status. Aerial imagery from the photogrammetric camera was used to model the lake bottom in shallow areas, while the LiDAR point cloud acquired through ALS was used to determine the shoreline. Spatial analysis of support vector regression (SVR)-based bathymetric data showed effective depth estimation down to 1 m, with a reported standard deviation of 0.11 m and accuracy of 0.22 m at the 95% confidence, as reported in previous studies. However, only 44.5% of 1 × 1 m grid cells met the minimum point density threshold recommended by the National Oceanic and Atmospheric Administration (NOAA) (≥5 pts/m²), while 43.7% contained no data. In contrast, ALS LiDAR provided higher and more consistent shoreline coverage, with an average density of 63.26 pts/m², despite 27.6% of grid cells being empty. The modified shoreline extraction method applied to the ALS data achieved a mean positional accuracy of 1.24 m and 3.36 m at the 95% confidence level. The results show that UAV photogrammetry and ALS laser scanning possess distinct yet complementary strengths, making their combined use beneficial for producing more accurate and reliable maps of shallow waters and shorelines. Full article

Accurate spatial data is essential for the effective planning and restoration of rural streets, which are linear elements within settlements. This study evaluates the applicability of digital street models to landscape architecture, focusing on the precision and efficiency of three data acquisition methods: terrestrial laser scanning (TLS), aerial photogrammetry using an unmanned aerial vehicle (UAV), and close-range photogrammetry (CRP) using a smartphone. TLS was used as the reference method due to its high local geometric accuracy, while UAV and CRP were assessed as low-cost alternatives. We conducted field data collection, digital model processing, and a comparative analysis of accuracy, cost, and time requirements. TLS achieved high precision, with 85% of measured points within ±0.5 cm; however, it produced data gaps due to scanning obstacles. UAV-derived models demonstrated 93% agreement with TLS and offered more complete coverage, making it a more efficient option for overall mapping. CRP models showed only 34% compliance with TLS but provided superior texture detail. However, their limited geometric accuracy and risk of deformation constrain their use in visualizing specific elements. Among the low-cost methods, the UAV is the most suitable for generating models usable in GIS and CAD environments. A combined approach—using a UAV for accurate geometry and CRP for detailed textures—offers a cost-effective strategy for enhancing model quality in landscape architectural applications. Full article

Traditional unmanned aerial vehicle (UAV) path planning methods for image-based 3D reconstruction often rely solely on geometric information from initial models, resulting in redundant data acquisition in non-architectural areas. This paper proposes a UAV path planning method via semantic segmentation of 3D reality mesh models to enhance efficiency and accuracy in complex scenarios. The scene is segmented into buildings, vegetation, ground, and water bodies. Lightweight polygonal surfaces are extracted for buildings, while planar segments in non-building regions are fitted and projected into simplified polygonal patches. These photography targets are further decomposed into point, line, and surface primitives. A multi-resolution image acquisition strategy is adopted, featuring high-resolution coverage for buildings and rapid scanning for non-building areas. To ensure flight safety, a Digital Surface Model (DSM)-based shell model is utilized for obstacle avoidance, and sky-view-based Real-Time Kinematic (RTK) signal evaluation is applied to guide viewpoint optimization. Finally, a complete weighted graph is constructed, and ant colony optimization is employed to generate a low-energy-cost flight path. Experimental results demonstrate that, compared with traditional oblique photogrammetry, the proposed method achieves higher reconstruction quality. Compared with the commercial software Metashape, it reduces the number of images by 30.5% and energy consumption by 37.7%, while significantly improving reconstruction results in both architectural and non-architectural areas. Full article

Reparative tertiary dentinogenesis requires the recruitment and odontogenic differentiation of dental pulp stem cells (DPSCs). Extracellular vesicles (EVs) as bioactive molecules have gained attention in regenerative medicine for their ability to mediate tissue repair through intercellular communication, influencing cell recruitment, proliferation, and differentiation. This study aimed to evaluate the effects of EVs on DPSC homing and odontogenic differentiation for dentin regeneration. DPSC-derived EVs were cultured in either growth (EV-G) or odontogenic differentiation (EV-O) conditions and isolated using a modified precipitation method. EVs were characterized by nanoparticle tracking analysis, scanning electron microscopy, antibody array, and cellular uptake assay. Treatment with 5 × 10⁸ EVs/mL significantly enhanced DPSC chemotaxis and proliferation compared with a no-treatment control and a lower dosage of EV (5 × 10⁷ EVs/mL). Gene expression and biochemical analyses revealed that EV-O up-regulated odontogenic markers including collagen type 1A1 (COL1A1), runt-related transcription factor 2 (RUNX2), and alkaline phosphatase (ALP). EV-O enhanced dentin regeneration by approximately 55% over vehicle controls in a rabbit partial dentinotomy/pulpotomy model. We identified key microRNAs (miR-21-5p, miR-221-3p, and miR-708-3p) in EV-O involved in cell homing and odontogenesis. In conclusion, our EV-based cell homing and odontogenic differentiation strategy has significant therapeutic potential for dentin regeneration. Full article

To achieve robust and accurate collaborative 3D measurement under complex noise conditions, a global calibration method for dual-arm hand–eye systems and multi-view 3D imaging is proposed. A multi-view 3D scanning approach based on ICP (M3DHE-ICP) integrates a multi-frequency heterodyne coding phase solution with ICP optimization, effectively correcting stitching errors caused by robotic arm attitude drift. After correction, the average 3D imaging error is 0.082 mm, reduced by 0.330 mm. A global calibration method based on encoded marker points (GCM-DHE) is also introduced. By leveraging spatial geometry constraints and a dynamic tracking model of marker points, the transformation between multi-coordinate systems of the dual arms is robustly solved. This reduces the average imaging error to 0.100 mm, 0.456 mm lower than that of traditional circular calibration plate methods. In actual engineering measurements, the average error for scanning a vehicle’s front mudguard is 0.085 mm, with a standard deviation of 0.018 mm. These methods demonstrate significant value for intelligent manufacturing and multi-robot collaborative measurement. Full article

Background/Objectives: Early diagnosis and oral or, in severe cases, intravenous antibiotics are usually effective for Lyme disease, but some patients have persistent symptoms unresponsive to standards of care, requiring alternative therapies. Disulfiram (DIS), a drug for alcoholism, is under investigation as a potential adjunctive treatment, but its low bioavailability, rapid metabolism, and safety concerns urge the development of improved formulations for clinical translation. Methods: Screening dissolution and permeation studies were investigated for vehicle and excipient selection, following the pharmacopeia perspectives to develop and optimize the low-dose DIS rectal suppository intended for application in post-treatment Lyme disease syndrome (PTLDS). Further characterizations were carried out by differential scanning calorimetry, X-ray diffraction, and infrared spectroscopy. Results: Cyclodextrin (CD) encapsulation was investigated to improve the aqueous solubility of the hydrophobic drug. The dissolution of DIS from fatty base suppository was very slow; it was remarkably improved by the molecular encapsulation of the drug with CDs. The dissolution of DIS from a water-soluble base was more favorable, but incomplete. In the polyethylene glycol (PEG) based suppositories, the addition of CDs already in a physical mixture ensured the dissolution of the drug. The presented drug delivery system relates to a novel preparation for rectal administration comprising a low-dose disulfiram with improved solubility and permeability by the PEG and hydroxypropyl-β-cyclodextrin (HPBCD) synergistic matrix. Conclusions: The rectal dosage form containing the drug and CD in the physical mixture is advantageous, avoiding the hepatic first-pass effect, minimizing dose-limiting toxicity, simplifying production, and fasting the availability of the repositioned drug. Full article

The vehicle scanning method (VSM) is widely used for bridge damage identification (BDI) because it relies solely on vehicle dynamic responses. The recently introduced contact point response, which is derived from vehicle dynamics but devoid of vehicle-related natural frequencies, shows great potential for application in the vehicle scanning method. However, its application in bridge damage detection remains understudied. The aim of this paper is to propose a new bridge damage identification method based on the contact point response. The method uses variational modal decomposition (VMD) to solve the problem of mode mixing and spurious frequencies in the signal. The continuous wavelet transform (CWT) is then utilized for damage identification. The introduction of variational modal decomposition makes the extracted signal more accurate, thus enabling more accurate damage identification. Numerical simulations validate the method’s robustness under varying conditions, including the vehicle speed, wavelet scale factors, the number of bridge spans, and pavement roughness. The results demonstrate that variational modal decomposition eliminates signal artifacts, producing smooth variational modal decomposition–continuous wavelet transform curves for accurate damage detection. In this study, we offer a robust and practical solution for bridge health monitoring using the vehicle scanning method. Full article

Thanks to light detection and ranging (LiDAR), unmanned ground vehicles (UGVs) are able to detect different objects in their environment and measure the distance between them. This device gives the ability to see its surroundings in real time. However, the accuracy of LiDAR can be reduced, especially in rainy weather, fog, urban smog and the like. These factors can have disastrous consequences as they increase the errors in the vehicle’s control computer. The aim of this research was to determine the most appropriate LiDAR frequency for static objects, depending on the distance to them and the scanning frequency in different weather conditions; therefore, it is based on empiric data obtained by using the RoboPeak A1M8 LiDAR. The results obtained in rainy conditions are compared with the same ones in clear weather, using stochastic methods. A direct influence of both the frequencies used and the rain on the accuracy of the LiDAR measurements was found. The range measurement errors increase in rainy weather; as the scanning frequency increases, the results become more accurate but capture a smaller number of object points. The higher frequencies lead to about five times less error at the farthest distances compared to the lower frequencies. Full article

Background: Methicillin-resistant Staphylococcus aureus (MRSA) considered under the category of serious threats by the Centers for Disease Control and Prevention (CDC), urges for new antibiotics or alternate strategies to control MRSA. Methods: Ethosome-like liposomes have been developed and characterized using dynamic light scattering (DLS), Fourier transform infrared spectroscopy (FTIR), and scanning electron microscopy (SEM). Liposomes were confirmed for antibiotics infusion by encapsulation efficiency and release kinetics as well. Further, the antimicrobial potential of liposomes was checked by determination of minimum inhibitory concentrations (MICs), crystal violet assay, and live/dead biofilm eradication assay. Results: The specially designed liposomes consist of amphiphilic molecules, tocopherol, conjugated with ampicillin and, another antibiotic amikacin, loaded in the core. The developed liposomes exhibited good encapsulation efficiency, and sustained release while serving as ideal antibiotic carriers for advanced efficacy along with anti-inflammatory benefits from tocopherol. Conclusively, newly designed liposomes displayed potential antimicrobial activity against MRSA and its complex biofilms. Conclusions: Overall, dual antibiotic-encapsulated liposomes demonstrate the potential to eradicate MRSA and its mature biofilms by dual-targeted action. This could be developed as an efficient anti-infective agent and delivery vehicle for conventional antibiotics to combat MRSA. Full article

The composite primary structures of railway vehicles endure not only mechanical loads including tension, compression, bending, and torsion, but also external impacts, such as by the crushed stone in ballast. In the present study, the low-velocity impact response of preloaded hybrid composite laminates with different thicknesses is examined using a finite element method based on a progressive damage model. The hybrid plate consists of carbon fiber-reinforced unidirectional and woven prepregs. The progressive damage model, based on the 3D Hashin model, is validated by experiments on hybrid laminate, and further compared with the post-impact appearance obtained from CT scans. Preloading, considered to be tensile, compressive, or shear, corresponds to different positions in a bending beam with flanges and a web. Finally, the effects of impact energy, preloading, thickness, and impact angle on the dynamic response are analyzed, with an emphasis on new results and failure mechanism analysis comparing the influence of preloads under a given impact energy and different thicknesses. Full article

Autonomous underwater vehicle inspection in 3D environments presents significant challenges in spatial mapping for obstacle avoidance and motion control. Current solutions rely on either 2D forward-looking sonar or expensive 3D sonar systems. To address these limitations, this study proposes a cost-effective 3D reconstruction method using an oscillatory forward-looking sonar with a pan-tilt mechanism that extends perception from a 2D plane to a 75-degree spatial range. Additionally, a polar coordinate-based frontier extraction method for sequential sonar images is introduced that captures more complete contour frontiers. Through bridge pier scanning validation, the system shows a maximum measurement error of 0.203 m. Furthermore, the method is integrated with the Ego-Planner path planning algorithm and nonlinear Model Predictive Control (MPC) algorithm, creating a comprehensive underwater 3D perception, planning, and control system. Gazebo simulations confirm that generated 3D point clouds effectively support the Ego-Planner method. Under localisation errors of 0 m, 0.25 m, and 0.5 m, obstacle avoidance success rates are 100%, 60%, and 30%, respectively, demonstrating the method’s potential for autonomous operations in complex underwater environments. Full article

This paper proposes a novel SDA-Mask R-CNN framework for precise seabed terrain edge feature extraction from Side-Scan Sonar (SSS) images to enhance Unmanned Underwater Vehicle (UUV) perception and navigation. The developed architecture addresses critical challenges in underwater image analysis, including low segmentation accuracy and ambiguous edge delineation, through three principal innovations. First, we introduce a Structural Synergistic Group-Attention Residual Network (SSGAR-Net) that integrates group convolution with an enhanced convolutional block attention mechanism, complemented by a layer-skipping architecture for optimized information flow and redundancy verification for computational efficiency. Second, a Depth-Weighted Hierarchical Fusion Network (DWHF-Net) incorporates depthwise separable convolution to minimize computational complexity while preserving model performance, which is particularly effective for high-resolution SSS image processing. This module further employs a weighted pyramid architecture to achieve multi-scale feature fusion, significantly improving adaptability to diverse object scales in dynamic underwater environments. Third, an Adaptive Synergistic Mask Optimization (ASMO) strategy systematically enhances mask generation through classification head refinement, adaptive post-processing, and progressive training protocols. Comprehensive experiments demonstrate that our method achieves 0.695 (IoU) segmentation accuracy and 1.0 (AP) edge localization accuracy. The proposed framework shows notable superiority in preserving topological consistency of seabed features, offering a reliable technical framework for underwater navigation and seabed mapping in marine engineering applications. Full article