Search Results (144)

Robustness falsification is a critical verification task for ensuring the safety of cyber-physical systems (CPS). Under partially observable conditions, where internal states are hidden and only input–output data is accessible, existing deep reinforcement learning (DRL) approaches for CPS robustness falsification face two key limitations: inadequate temporal modeling due to unidirectional network architectures, and sparse reward signals that impede efficient exploration. These limitations severely undermine the efficacy of DRL in black-box falsification, leading to low success rates and high computational costs. This study addresses these limitations by proposing DRL-BiT-MPR, a novel framework whose core innovation is the synergistic integration of a bidirectional temporal network with a multi-granularity reward function. Specifically, the bidirectional temporal network captures bidirectional temporal dependencies, remedies inadequate temporal modeling, and complements unobservable state information. The multi-granularity reward function includes fine-grained, medium-grained and coarse-grained layers, corresponding to single-step local feedback, phased progress feedback, and global result feedback, respectively, providing multi-time-scale incentives to resolve reward sparsity. Experiments are conducted on three benchmark CPS models: the continuous CARS model, the hybrid discrete-continuous AT model, and the controller-based PTC model. Results show that DRL-BiT-MPR increases the falsification success rate by an average of 39.6% compared to baseline methods and reduces the number of simulations by more than 50.2%. The framework’s robustness is further validated through theoretical analysis of convergence and soundness properties, along with systematic parameter sensitivity studies. Full article

Most electric vehicles use 400 V batteries, while some companies are moving to 800 V to reduce current in electric drives. More cars are expected to adopt 800 V at the DC terminals of the batteries, but 400 V will remain common for the duration of this transition, so future off-board chargers must support a wide voltage output range. Silicon carbide switches are used to keep the power–electronics interface compact and scalable. The AC/DC stage of a modular silicon carbide-based interface is designed using a T-type active front end and a dual active bridge. The T-type front end is optimized with a genetic algorithm. The resulting model is used to tune the inner current and outer voltage controllers. Bode analysis shows an inner current loop bandwidth of 4.25 kHz with a phase margin of ${53}^{°}$ and a gain margin of 30 dB. The outer voltage loop reaches 50 Hz with a phase margin of ${108}^{°}$ and a gain margin of 33 dB. The controller is implemented on a dSPACE MicroLabBox. Tests show peak efficiency of 98.5% in G2V mode and 98.3% V2G mode. THD stays under 5% above 4 kW and reaches 3% at peak power. Full article

The rapid development of autonomous driving systems has increased the demand for scalable frameworks capable of modeling vehicle motion patterns in complex traffic environments. This paper proposes a big data spatio-temporal modeling architecture that integrates Apache Spark version 4.0.1 (SparkML) with Long Short-Term Memory (LSTM) networks to analyze and classify vehicle trajectory patterns. The proposed SparkML–LSTM framework exploits Spark’s distributed processing capabilities and LSTM’s strength in sequential learning to handle large-scale traffic trajectory data efficiently. Experiments were conducted using the DETRAC dataset, which is a large-scale benchmark for vehicle detection and multi-object tracking consisting of more than 10 h of video captured at 24 different locations. The videos were recorded at 25 frames per second with a resolution of 960 × 540 pixels and annotated across more than 140,000 frames, covering 8.250 vehicles and approximately 1.21 million bounding box annotations. The dataset provides detailed annotations, including vehicle categories (Car, Bus, Van, Others), weather conditions (Sunny, Cloudy, Rainy, Night), occlusion ratio, truncation ratio, and vehicle scale. Based on the extracted trajectory features, vehicle motion patterns were categorized into predefined movement classes derived from trajectory dynamics. The experimental results demonstrate strong classification performance. These findings suggest that the proposed SparkML–LSTM architecture is effective for large-scale spatio-temporal trajectory modeling and traffic behavior analysis, and can serve as a foundation for higher-level decision-making modules in intelligent transportation system. Full article

Amid rapid advancements in artificial intelligence, the detection of abnormal human behaviors in complex traffic environments has garnered significant attention. However, detection errors frequently occur due to interference from complex backgrounds, small targets, and other factors. Therefore, this paper proposes a research methodology that integrates the anomaly detection YOLO-SGCF algorithm with the tracking BoT-SORT-ReID algorithm. The detection module uses YOLOv8 as the baseline model, incorporating Swin Transformer to enhance global feature modeling capabilities in complex scenes. CBAM and CA attention are embedded into the Neck and backbone, respectively: CBAM enables dual-dimensional channel-spatial weighting, while CA precisely captures object location features by encoding coordinate information. The Neck layer incorporates GSConv convolutional modules to reduce computational load while expanding feature receptive fields. The loss function is replaced with Focal-EIoU to address sample imbalance issues and precisely optimize bounding box regression. For tracking, to enhance long-term tracking stability, ReID feature distances are incorporated during the BoT-SORT data association phase. This integrates behavioral category information from YOLO-SGCF, enabling the identification and tracking of abnormal pedestrian behaviors in complex environments. Evaluations on our self-built dataset (covering four abnormal behaviors: Climb, Fall, Fight, Phone) show mAP@50%, precision, and recall reaching 92.2%, 90.75%, and 86.57% respectively—improvements of 3.4%, 4.4%, and 6% over the original model—while maintaining an inference speed of 328.49 FPS. Additionally, generalization testing on the UCSD Ped1 dataset (covering six abnormal behaviors: Biker, Skater, Car, Wheelchair, Lawn, Runner) yielded an mAP score of 92.7%, representing a 1.5% improvement over the original model and outperforming existing mainstream models. Furthermore, the tracking algorithm achieved an MOTA of 90.8% and an MOTP of 92.6%, with a 47.6% reduction in IDS, demonstrating superior tracking performance compared to existing mainstream algorithms. Full article

Background: Recent advancements in regenerative medicine have introduced mesenchymal stem cell–derived exosomes (MSC-Exos) as a novel therapeutic approach. Exosomes are extracellular vesicles containing proteins, lipids, and RNAs capable of modulating cellular behavior and promoting tissue regeneration. A systematic review of human studies is warranted to summarize outcomes, assess therapeutic value, and guide clinical applications. Objectives: This systematic review synthesizes current evidence on mesenchymal stem cell–derived exosomes for cutaneous scars, aging, and hyperpigmentation, with a focus on functional and aesthetic outcomes. Method: A comprehensive search of PubMed, Scopus, Embase, Web of Science, and Google Scholar (January 2010–July 2025) was performed following 2020 PRISMA guidelines. Eligible studies included studies that were randomized controlled trials, pilot studies, case series, and case reports involving human participants treated with MSC-Exos. Outcomes assessed were scar remodeling, pigmentation, skin regeneration, recurrence, and adverse events. Data extraction and bias assessment were conducted independently. Result: Six studies (n = 99; age 19–72 years) from diverse regions, including the United States, the Republic of Korea, and México, were included. MSC-Exos therapy showed promising improvements in reducing scar thickness (32.5% vs. 19.9%, p < 0.01), wrinkle parameters were reduced by 1 (2.4–14.4% vs. 6.6–7.1%, p < 0.05), and elasticity was enhanced (+11.3% vs. −3.3%, p = 0.002) Additional benefits included hydration (+6.5% vs. +4.5%, p = 0.37) and reduced melanin index (−9.9% vs. −1%, p = 0.44). The Global Aesthetic Improvement Scale score showed significant improvement (p = 0.005). Using the Investigator Global Assessment, 16 out of 25 areas treated with exosomes showed significant improvement (grade ≥ 2), compared to 12 out of 25 areas in the control group (p = 0.02), indicating that exosome treatment led to more visible improvement. Complete resolution of icepick scars, partial improvement of boxcar/rolling scars, and no recurrence of keloids (18/21) were reported. Adverse events were mild and transient. Conclusions: Early human evidence suggests that MSC-Exos may offer potential therapeutic benefits for scars, hyperpigmentation, and skin aging, with favorable short-term safety profiles. However, the current evidence remains preliminary due to small sample sizes, heterogeneous study designs, and limited follow-up durations. Larger, well-designed randomized trials are needed to confirm long-term efficacy and safety. Full article

This study addresses the critical issue of high casualty rates in frontal collisions by proposing structural optimization methods for the energy-absorbing box of lightweight electric vehicles. A small pure electric car was selected as the research object. A finite element model for frontal collision was established in HyperMesh and solved using the LS-DYNA explicit dynamics solver. The parameters such as the acceleration of the B-pillar of the vehicle, the compression distance of the energy absorption box and the energy absorption are analyzed in this study. Energy absorption was used as the primary crashworthiness indicator while ensuring that the peak collision force, compression distance of the energy-absorbing box, and acceleration of the B-pillar complied with safety standards. Results demonstrate that Scheme 2 (featuring reduced wall thickness and a single induced groove) outperformed other designs, increasing energy absorption by 3% and reducing mass by 17% compared to the baseline model. This conclusion can provide a reference basis for the subsequent vehicle collision analysis. Full article

MADS-box transcription factors play a vital role in the development of reproductive organs and fruits. However, the mechanisms by which MADS-box transcription factors participate in determining the size of organs remain incompletely understood. This study demonstrated that the overexpression of SlMADS48 results in elongated sepals and is accompanied by an elevated gibberellin content, compared with the wild type (WT). The interaction between SlMADS48 and several proteins (SlMC, SlMBP21, SlJOINTLESS, and SlFYFL) involved in sepal development was identified. In addition, the OE-SlMADS48 lines exhibited increased branches and total numbers of flowers. Molecular analysis revealed that SlMADS48 interacted with TM29, FUL1, FUL2, and MBP20, which are associated with inflorescence development. Moreover, SlMDS48 directly targeted the promoter of SlTM3 via the CArG-box motif, reducing its transcript levels. Additionally, the overexpression of SlMADS48 led to a reduction in the size of fruit, together with decreased contents of cytokinins and indole acetic acid (IAA) compared with the WT. Furthermore, SlMADS48 directly combined with the promoters of SlcycD6;1 and SlIAA29 in the cytokinin and auxin pathways, respectively. This research advanced our understanding of SlMADS48’s role in determining organ size and provided valuable insights into target gene selection in tomato breeding programs. Full article

Turnouts are critical components of railway infrastructure, ensuring operational flexibility but also representing a significant share of track maintenance costs. The frog, as the most vulnerable part of a turnout, is subject to severe wear and degradation, requiring frequent inspection and maintenance. Traditional manual inspection methods are costly, labour-intensive, and susceptible to subjectivity. This study explores a data-driven approach to condition monitoring of fixed-turnout frogs using standard track recording car measurements. By leveraging over 20 years of longitudinal level and rail surface signal data from the Austrian track-recording measurement car, we assess the feasibility of using existing measurement data for predictive maintenance. Six complementary approaches are proposed to evaluate frog condition, including track geometry assessment, ballast condition analysis, rail surface irregularity detection, and axle box acceleration-based monitoring. Results indicate that data-driven monitoring enhances maintenance decision-making by identifying deterioration trends, reducing reliance on manual inspections, and enabling predictive interventions. The integration of standardised measurement data with advanced analytical models offers a cost-effective and scalable solution for turnout maintenance. Full article

Background: Autism spectrum disorder (ASD) is a complex neurodevelopmental condition characterized by impaired social skills and communication. Forkhead box protein P1 (FOXP1) is involved in the development of the brain and the pathogenesis of ASD. However, the function of FOXP1 within the brain remains unclear. The aim of this case–control study was to evaluate whether FOXP1 could be used as a diagnostic biomarker for ASD. Method: Blood plasma was collected from children with ASD and age-matched controls. The enzyme-linked immunosorbent assay (ELISA) was used to determine the FOXP1 plasma levels in ASD and control groups. The behavioral and social impairments in children with ASD were assessed using the Childhood Autism Rating Scale (CARS) and the Social Responsiveness Scale (SRS). Spearman’s correlation coefficient (r) was used to determine the correlation between different variables. Results: The plasma FOXP1 protein level was significantly decreased in children with ASD compared to the controls (p < 0.001). CARS showed significant differences between the mild-to-moderate and severe subgroups, while the SRS showed no significant difference between the two subgroups. Moreover, both the mild-to-moderate and severe subgroups exhibited a substantial drop in plasma FOXP1 compared to the controls. ASD children older than six years old also showed a significantly decreased FOXP1 level, compared to those aged six years or less. Furthermore, no significant correlation between the FOXP1 level, CARS, and SRS was observed. However, a negative correlation was found between age and FOXP1 plasma level. Conclusions: We suggest that plasma FOXP1 may act as a potential biomarker for the prognosis of ASD severity and social communication impairment. Further research with a larger sample size is needed to clarify these associations and help diagnose or understand the underlying mechanism of ASD. Full article

As a pivotal component of the single-gear reducer, the casing of the miniature car reducer not only safeguards the internal transmission system but also interfaces seamlessly with the external structure. Currently, the structural design of domestic single-stage reducers primarily leans on experience and standardized specifications. To guarantee the reliable and stable operation of the casing, a high safety factor is often incorporated, which inevitably results in increased weight and necessitates secure bolting connections. This study presents an innovative scheme to design the flange with the box and realize the lightweight nature of the box by finite element analysis to reduce the manufacturing cost. Based on the working state of maximum torque and maximum speed, this study obtains the stress distribution of each bearing seat under different working conditions and carries out static and dynamic analysis combined with other coupling constraints. The analysis results show that the structure has high stiffness and strength, which is suitable for lightweight design, and that the first ten spontaneous vibration frequencies are far away from the excitation frequency of the inner and outer boundary, avoiding the resonance phenomenon. Moreover, this study proposes a new structure design method, which effectively improves the stiffness of the structure. Through the calculation of volume ratio before and after three optimizations, the optimal volume ratio of 30% is selected, unnecessary materials around the bearing seat are removed, and the layout of ribs is determined. After structural optimization, the weight of the shell is reduced by 10.2%, and both the static and dynamic characteristics meet the design requirements. Full article

Satellite videos typically employ image registration techniques for video stabilization in order to achieve persistent observation. However, existing methods largely neglect the residual stabilization offsets, particularly when exceeding the physical dimensions of target vehicles, which inevitably causes performance degradation. Furthermore, the detection pipeline struggles with hard-to-discriminate samples that exhibit low contrast, motion blur, or occlusion, while conventional sample assignment strategies fail to address the inherent annotation ambiguity for extremely small objects. We propose an end-to-end method called ViTrans for detecting moving vehicles in satellite video under stabilization offsets. ViTrans consists of three core modules: (1) a feature-aligned stabilization offset correction module (SCM) that mitigates feature misalignment by aligning features between the reference frame and the current frame; (2) a feature adaptive aggregation enhancement module (AAEM) based on vehicle trajectory consistency, which leverages the motion characteristics of objects across consecutive frames to eliminate dynamic clutter and false-alarm artifacts; and (3) a Gaussian distribution-based metric that dynamically adapts to bounding box dimensions, thereby providing more accurate positive sample feedback during model training. Extensive experiments on the VISO and SDM-Car datasets under simulated stabilization offsets demonstrate that ViTrans achieves state-of-the-art performance, improving F1-score by 14.4% on VISO and 6.9% on SDM-Car over existing methods. Full article

This paper explores the potential of public webcams as a source of data for transport research. Eight different open-source object detection models were tested on three publicly accessible webcams located in the city of Brunswick, Germany. Fifteen images at different lighting conditions (bright light, dusk, and night) were selected from each webcam and manually labelled with regard to the following six categories: cars, persons, bicycles, trucks, trams, and buses. The manual counts in these six categories were then compared to the number of counts found by the object detection models. The results show that public webcams constitute a useful source of data for transport research. In bright light conditions, applying out-of-the-box object detection models can yield reliable counts of cars or persons in public squares, streets, and junctions. However, the detection of cars and persons was not reliably accurate at dusk or night. Thus, different object detection models might have to be used to generate accurate counts in different lighting conditions. Furthermore, the object detection models worked less well for identifying trams, buses, bicycles, and trucks. Hence fine-tuning and adapting the models to the specific webcams might be needed to achieve satisfactory results for these four types of traffic participants. Full article

A previously buried paleosol was found on the continental shelf during a study of sea floor scour, nucleated by large artificial reef structures such as vessel hulks, barges, train cars, military vehicles, etc., called “scour nuclei”. It is a relic paleo-land surface of sapling-sized tree stumps, root systems, and fossil animal bone exhumed by scour processes active adjacent to the artificial reef structure. Over the span of five research cruises to the site in 2022–2024, soil samples were taken using hand excavation, PONAR grab samplers, split spoon, hollow tube auger, and a modified Shelby-style push box. High-definition (HD) video was taken using a Remotely Operated Vehicle (ROV) and diver-held cameras. Radiocarbon dating of wood samples returned ages of 42,015–43,417 calibrated years before present (cal yrBP). Pollen studies, together with the recovered macrobotanical remains, support our interpretation of the site as a freshwater forested wetland whose keystone tree species was Taxodium distichum—bald cypress. The paleosol was identified as an Aquult, a sub-order of Ultisols where water tables are at or near the surface year-round. A deep (0.25 m+) argillic horizon comprised the bulk of the preserved soil. Comparable Ultisols found in Georgia wetlands include Typic Paleaquult (Grady and Bayboro series) soils. Full article

Camera and LiDAR data fusion has been a popular research area, especially in the field of autonomous vehicles. This study evaluates the efficiency and accuracy of different depth point extraction methods, including Point-by-Point (PbyP), Complete Region Depth Extraction (CoRDE), Central Region Depth Extraction (CeRDE), and Grid Central Region Depth Extraction (GCRDE), across object categories such as person, bicycle, car, bus, and truck, and occlusion levels ranging from 0 to 3. The approaches are assessed based on extraction time, accuracy, and root mean squared error (RMSE). Bounding box-based methods, such as PbyP and CoRDE, consistently show slower extraction times compared to segmentation mask methods, with CeRDE being the most efficient in terms of computational speed. However, segmentation mask methods, particularly CeRDE and GCRDE, offer superior accuracy, especially for complex objects like trucks and cars, where bounding box methods struggle, particularly at higher occlusion levels. In terms of RMSE, segmentation mask methods consistently outperform bounding box methods, providing more precise depth estimations, particularly for larger and more occluded objects. Overall, segmentation mask methods are preferred for applications where accuracy is critical, despite their slower processing speed, while bounding box methods are suitable for real-time applications requiring faster depth extraction. GeRDE offers a balance between speed and accuracy, making it ideal for tasks needing both efficiency and precision. Full article

A motorcycle box intersection is a signalized intersection with advanced stop lines or stopping spaces intended for motorcycles, creating a waiting area in front of other vehicles. This study introduces the External Driver Model (EDM) with microscopic traffic simulation using PTV Vissim 2024 software, which replicates the filtering and stopping behavior of motorcycles in mixed traffic on intersection approaches. This research aims to evaluate the traffic performance of motorcycle boxes with respect to motorcycle departure times, headway intervals, lane-filtering rates, and vehicle movement patterns at 12 signalized urban intersections in Chiang Mai, Thailand. The results show that the motorcycle box intersection has improved traffic efficiency, reduced motorcycle departure time, and maintained a constant distance between cars and other vehicles. Signalized intersections with motorcycle boxes improved traffic flow efficiency by favoring motorcycles without affecting car delays. Spatial-temporal visualization further supported the clustering characteristics of motorcycles in motorcycle-stopping areas, contributing to more orderly and predictable behavior in traffic. Furthermore, the lane-filtering rates demonstrated significant improvement at intersections equipped with motorcycle boxes compared to conventional intersection designs. These findings indicated that motorcycle boxes are valuable for motorcycle traffic management and intersection safety in urban areas with high volumes of motorcycle traffic. Full article