Search Results (136)

High-temperature stress has become an important factor that has restricted the aquaculture industry. Huso dauricus is a high-economic-value fish that has faced the threat of thermal stress. Based on this point, our investigation aimed to explore the detailed mechanism of the negative impacts of heat stress on the liver metabolism functions in Huso dauricus. In this study, we set one control group (19 °C) and four high-temperature treatment groups (22 °C, 25 °C, 28 °C, 31 °C) with 40 fish in each group for continuous 53-day heat exposure. Histological analysis, biochemical detection, and transcriptome technology were used to explore the effects of heat stress on the liver structure and functions of juvenile Huso dauricus. It suggested heat-stress-induced obvious liver injury and reactive oxygen species accumulation in Huso dauricus with a time/temperature-dependent manner. Serum total protein, transaminase, and alkaline phosphatase activities showed significant changes under heat stress (p < 0.05). In addition, 6433 differentially expressed genes (DEGs) were identified based on the RNA-seq project. Gene Ontology enrichment analysis showed that various DEGs could be mapped to the lipid-metabolism-related terms. KEGG enrichment and immunohistochemistry analysis showed that ferroptosis and FoxO signaling pathways were significantly enriched (p < 0.05). These results demonstrated that thermal stress induced oxidative stress damage in the liver of juvenile Huso dauricus, which triggered lipid metabolism disorder and hepatocyte ferroptosis to disrupt normal liver functions. In conclusion, chronic thermal stress can cause antioxidant capacity imbalance in the liver of Huso dauricus to mediate the ferroptosis process, which would finally disturb the lipid metabolism homeostasis. In further research, it will be necessary to verify the detailed cellular signaling pathways that are involved in the heat-stress-induced liver function disorder response based on the in vitro experiment, while the multi-organ crosswalk mode under the thermal stress status is also essential for understanding the comprehensive mechanism of heat-stress-mediated negative effects on fish species. Full article

Simulation sickness (SS) remains a challenge in virtual reality (VR) applications, especially in pedestrian safety research. This study investigates SS symptoms in VR environments, focusing on gender differences, exposure time, and user perceptions. A total of 145 participants were exposed to two VR pedestrian scenarios: a crosswalk and a sidewalk. The Simulator Sickness Questionnaire (SSQ) was used to assess symptoms of nausea, oculomotor disturbance, and disorientation. Results showed that female participants reported significantly higher SS symptoms than males, with the sidewalk scenario inducing greater overall SS. Additionally, perceived realism in the VR environment was associated with reduced symptoms, while perceived disengagement led to increased discomfort. These findings highlight the importance of user perceptions in mitigating SS and suggest that VR scenarios should be designed with attention to gender differences and environmental realism to improve user experience and safety. Full article

Pedestrian safety at crosswalks remains a critical concern as traffic accidents frequently result from drivers’ failure to yield, leading to severe injuries or fatalities. In response, various jurisdictions have enacted pedestrian priority laws to regulate driver behavior. Nevertheless, intersections lacking clear traffic signage and environments with limited visibility continue to present elevated risks. The scarcity and difficulty of collecting data under such complex conditions pose significant challenges to the development of accurate detection systems. This study proposes a CyclicGAN-based loop-learning framework, in which the learning process begins with a set of manually annotated images used to train an initial labeling model. This model is then applied to automatically annotate newly generated synthetic images, which are incorporated into the training dataset for subsequent rounds of model retraining and image generation. Through this iterative process, the model progressively refines its ability to simulate and recognize diverse contextual features, thereby enhancing detection performance under varying environmental conditions. The experimental results show that environmental variations—such as daytime, nighttime, and rainy conditions—substantially affect the model performance in terms of F1-score. Training with a balanced mix of real and synthetic images yields an F1-score comparable to that obtained using real data alone. These results suggest that CycleGAN-generated images can effectively augment limited datasets and enhance model generalization. The proposed system may be integrated with in-vehicle assistance platforms as a supportive tool for pedestrian-crossing detection in data-scarce environments, contributing to improved driver awareness and road safety. Full article

The rapid advancement of computer vision technology is transforming how transportation agencies collect roadway characteristics inventory (RCI) data, yielding substantial savings in resources and time. Traditionally, capturing roadway data through image processing was seen as both difficult and error-prone. However, considering the recent improvements in computational power and image recognition techniques, there are now reliable methods to identify and map various roadway elements from multiple imagery sources. Notably, comprehensive geospatial data for pedestrian and bicycle lanes are still lacking across many state and local roadways, including those in the State of Florida, despite the essential role this information plays in optimizing traffic efficiency and reducing crashes. Developing fast, efficient methods to gather this data are essential for transportation agencies as they also support objectives like identifying outdated or obscured markings, analyzing pedestrian and bicycle lane placements relative to crosswalks, turning lanes, and school zones, and assessing crash patterns in the associated areas. This study introduces an innovative approach using deep neural network models in image processing and computer vision to detect and extract pedestrian and bicycle lane features from very high-resolution aerial imagery, with a focus on public roadways in Florida. Using YOLOv5 and MTRE-based deep learning models, this study extracts and segments bicycle and pedestrian features from high-resolution aerial images, creating a geospatial inventory of these roadway features. Detected features were post-processed and compared with ground truth data to evaluate performance. When tested against ground truth data from Leon County, Florida, the models demonstrated accuracy rates of 73% for pedestrian lanes and 89% for bicycle lanes. This initiative is vital for transportation agencies, enhancing infrastructure management by enabling timely identification of aging or obscured lane markings, which are crucial for maintaining safe transportation networks. Full article

Pedestrians, as the most vulnerable road users in traffic crashes, prompt transportation researchers and urban planners to prioritize pedestrian safety due to the elevated risk and growing incidence of injuries and fatalities. Thorough pedestrian crash data are indispensable for safety research, as the most detailed descriptions of crash scenes and pedestrian actions are typically found in crash narratives and diagrams. However, extracting and analyzing this information from police crash reports poses significant challenges. This study tackles these issues by introducing innovative image-processing techniques to analyze crash diagrams. By employing cutting-edge technological methods, the research aims to uncover and extract hidden features from pedestrian crash data in Michigan, thereby enhancing the understanding and prevention of such incidents. This study evaluates the effectiveness of three Convolutional Neural Network (CNN) architectures—VGG-19, AlexNet, and ResNet-50—in classifying multiple hidden features in pedestrian crash diagrams. These features include intersection type (three-leg or four-leg), road type (divided or undivided), the presence of marked crosswalk (yes or no), intersection angle (skewed or unskewed), the presence of Michigan left turn (yes or no), and the presence of nearby residentials (yes or no). The research utilizes the 2020–2023 Michigan UD-10 pedestrian crash reports, comprising 5437 pedestrian crash diagrams for large urbanized areas and 609 for rural areas. The CNNs underwent comprehensive evaluation using various metrics, including accuracy and F1-score, to assess their capacity for reliably classifying multiple pedestrian crash features. The results reveal that AlexNet consistently surpasses other models, attaining the highest accuracy and F1-score. This highlights the critical importance of choosing the appropriate architecture for crash diagram analysis, particularly in the context of pedestrian safety. These outcomes are critical for minimizing errors in image classification, especially in transportation safety studies. In addition to evaluating model performance, computational efficiency was also considered. In this regard, AlexNet emerged as the most efficient model. This understanding is precious in situations where there are limitations on computing resources. This study contributes novel insights to pedestrian safety research by leveraging image processing technology, and highlights CNNs’ potential use in detecting concealed pedestrian crash patterns. The results lay the groundwork for future research, and offer promise in supporting safety initiatives and facilitating countermeasures’ development for researchers, planners, engineers, and agencies. Full article

Background: Despite the popularity of the modified Harris Hip Score (mHHS) to monitor patient-reported outcome measures (PROMs) following Total Hip Arthroplasty (THA) over the last 5 decades, International Joint Registries have recently favoured the Hip disability and Osteoarthritis Outcome Score (HOOS). The ability to convert mHHS collected in historical and ongoing studies would be beneficial to benchmark more recent HOOS reports. Hence, this study aimed to create multi-directional crosswalks between mHHS and HOOS. Methods: Forty-nine patients undergoing primary THA prospectively completed both HHS and HOOS forms pre-operatively and at either 3, 6 and/or 12 months postoperatively. The Equipercentile (EQ) and Linear Regression (LR) crosswalk methodology were used. The Mean Absolute Error (MAE) of the crosswalk-derived scores was established against patient-derived (PD) scores. Results: There was a strong correlation between PD mHHS and HOOS (0.90) and HOOS-12 (0.90). The MAE of mHHS-to-HOOS-12 crosswalk was 10.4 (EQ) and 10.1 (LR). Subcategory activity had a larger contribution towards the error in the crosswalks than pain. Conclusions: This is the first crosswalk to facilitate conversion of mHHS and HOOS scores, which are required in long-term THA quality-assurance and research studies, which often span 2 decades of expected implant survivorship. Full article

Addressing the traffic safety issues caused by pedestrian–vehicle conflicts during street crossing, this study proposes optimization strategies from both theoretical and technical perspectives. A safety braking distance model is introduced, taking into account pedestrians’ psychological safety and vehicle braking processes. Additionally, an active safety warning system for crosswalks has been designed. This system features a modular design, including detection, control, alarm, and wireless communication modules. It can monitor, in real-time, the positions and speeds of pedestrians and vehicles, assess potential conflicts between them under various scenarios, and implement different warning strategies accordingly. Compared to mainstream variable message sign (VMS) warning systems, this proposed system shows significant advantages in terms of section-weighted total delay metrics. Through simulations involving 3000 pedestrian crossings and comparative analyses of vehicle speed, pedestrian speed, vehicle deceleration rate, and accident numbers before and after the application of the active safety warning system, it was found that the critical accident rate indicator decreased from 0.27% to 0.06%. The results demonstrate that the system effectively provides bidirectional warnings to pedestrians and vehicles, significantly enhancing the safety of pedestrian street crossings. This research offers new insights into addressing pedestrian crossing safety issues. Full article

This study investigates the association between aggressive driving behavior and elderly pedestrian traffic accidents using the Explainable Artificial Intelligence (XAI) method. This study focuses on Seoul, South Korea, where an aging population and urban challenges create a pressing need for pedestrian safety research. The analysis reveals that aggressive driving behaviors, particularly rapid acceleration, rapid deceleration, and speeding, are the most influential factors on the frequency of and deaths from elderly pedestrian traffic accidents. In addition, several built environments and demographic factors such as the number of crosswalks and elderly population play varying roles depending on the spatial match or mismatch between risky driving areas and accident spots. The findings of this study underscore the importance of tailored interventions including well-lit crosswalks, traffic calming measures, and driver education, to reduce the vulnerabilities of elderly pedestrians. The integration of XAI methods provides transparency and interpretability, enabling policymakers to make data-driven decisions. Expanding this approach to other urban contexts with diverse characteristics could validate and refine the findings, contributing to a comprehensive strategy for improving pedestrian safety globally. Full article

Pedestrian safety is a growing global concern, particularly in urban areas, where rapid urbanization and increased mobile device usage have led to an increase in distracted walking. This study investigates the impact of technological distractions, specifically mobile usage (MU), on pedestrian behavior and safety at signalized urban intersections. Data were collected from 11 signalized intersections in New Delhi, India, using video recordings. Key inputs to the modeling process include pedestrian demographics (age, gender, group size) and behavioral variables (crossing speed, waiting time, compliance behaviors). The outputs of the models focus on predicting mobile usage behavior and its association with compliance behaviors such as crosswalk and signal adherence. The results show that 6.9% of the pedestrians used mobile phones while crossing the road. Advanced machine learning models, including Convolutional Neural Networks (CNN), Long Short-Term Memory networks (LSTM), and Recurrent Neural Networks (RNN), have been applied to analyze and predict MU behavior. Key findings reveal that younger pedestrians and females are more likely to exhibit distracted behavior, with pedestrians crossing alone being the most prone to mobile usage. MU was significantly associated with increased levels of crosswalk violation. Among the machine learning models, the CNN demonstrated the highest prediction accuracy (94.93%). The findings of this study have a practical application in urban planning, traffic management, and policy formulation. Recommendations include infrastructure improvements, public awareness campaigns, and technology-based interventions to mitigate pedestrian distractions and to enhance road safety. These findings contribute to the development of data-driven strategies to improve pedestrian safety in rapidly urbanizing regions. Full article

This paper explores the development and testing of two Internet of Things (IoT) applications designed to leverage Vehicle-to-Infrastructure (V2I) communication for managing intelligent intersections. The first scenario focuses on enabling the rapid and safe passage of emergency vehicles through intersections by notifying approaching drivers via a mobile application. The second scenario enhances pedestrian safety by alerting drivers, through the same application, about the presence of pedestrians detected at crosswalks by a traffic sensor equipped with neural network capabilities. Both scenarios were tested at two distinct intelligent intersections in Lioni, Avellino, Italy, and demonstrated notable effectiveness. Results show a significant reduction in emergency vehicle response times and a measurable increase in driver awareness of pedestrians at crossings. The findings underscore the potential of V2I technologies to improve traffic flow, reduce risks for vulnerable road users, and contribute to the advancement of safer and smarter urban transportation systems. Full article

Public transport is an integral part of sustainable urban development when its use is promoted by setting bus priority routes (BPRs). BPRs provide clear mobility benefits, but they raise pedestrian safety concerns. In this study, observations were conducted at signalized intersections with two types of BPRs, center-lane and curbside, aiming to characterize pedestrian crossing behaviors, with a particular focus on red-light crossings. We found that at intersections with center-lane BPRs, 30% of pedestrians crossed at least one crosswalk on red, while at another type, 11% crossed on red. Multivariate analyses showed that the risk of crossing on red was substantially higher at intersections with center-lane vs. curbside BPRs; it was also higher among pedestrians crossing to/from the bus stop, males, and young people but lower under the presence of other waiting pedestrians. Furthermore, among pedestrians crossing on red at center-lane BPRs, over 10% did not check the traffic before crossing and another 10% checked the traffic in the wrong direction, thus further increasing the risk. At center-lane BPRs, infrastructure solutions are needed to reduce pedestrian intention to cross on red. Additionally, education and awareness programs for pedestrians should be promoted to emphasize the heightened risk of red-light crossing at intersections with BPRs. Full article

Pedestrian safety is a critical concern worldwide, as pedestrians account for nearly a quarter of all road crash deaths. In Poland, in the last decade, the number of pedestrians killed in road accidents varied from 25 to 30% of all road accident victims each year. A similar tendency is observed in EU countries, but the average number of pedestrian fatalities is lower and amounts to 20%. Numerous activities have been undertaken to improve the safety of vulnerable road users. Land planning plays a crucial role in enhancing pedestrian safety. Effective land-use planning can mitigate risks by integrating pedestrian-friendly infrastructure into urban design. Numerous measures have been implemented to improve the safety of vulnerable road users, including education campaigns, speed reduction measures, and infrastructure enhancements. One of the latest initiatives involves enhancing the visibility of pedestrian crossings through the installation of additional lighting systems. In order to assess the effects of the undertaken activities, a number of zebra crossings with and without additional luminance were investigated. Crash data gained from police statistics, along with the calculated crash rates (CRs), were utilized to evaluate changes in safety performance at selected crosswalks. For this purpose, a „before–after” method was applied. Importantly, the research results did not show a clear impact of additional lighting on reducing the number of road crashes and they highlight that other factors, including the geometric characteristics of crossings and their location and proximity to land uses generating significant pedestrian traffic, significantly influence crash rates. Full article

According to statistics from the World Health Organization, traffic accidents are one of the leading causes of death among children and young people, and statistical indicators are even worse for the elderly population. Preventive measures require an approach that includes analyses of traffic infrastructure and regulations, users’ traffic behavior, and their interactions. In this study, a methodology based on traffic microsimulations was developed to select the optimal reconstruction solution for urban traffic infrastructure from the perspective of traffic safety. Comprehensive analyses of local traffic conditions at the selected location, infrastructural properties, and properties related to traffic users were carried out. The developed methodology was applied and tested at a selected unsignalized pedestrian crosswalk located in Osijek, Croatia, where traffic safety issues had been detected. Analyses of the possible solutions for traffic safety improvements were carried out, taking into account the specificities of the chosen location and the traffic participants’ behaviors, which were recorded and measured. The statistical analysis showed that children had shorter reaction times and crossed the street faster than the analyzed group of adult pedestrians, which was dominated by elderly people in this case. Using microsimulation traffic modeling (VISSIM), an analysis was conducted on the incoming vehicle speeds for both the existing and the reconstructed conflict zone solutions under different traffic conditions. The results exhibited a decrease in average speeds for the proposed solution, and traffic volume was detected to have a great impact on incoming speeds. The developed methodology proved to be effective in selecting a traffic solution that respects the needs of both motorized traffic and pedestrians. Full article

This study derived effective driving behavior indicators to assess the driving safety of autonomous vehicles (AV). A variety of operation design domains (ODD) in urban road networks, which include intersections, illegal parking, bus stop, bicycle lanes, and pedestrian crossings, were taken into consideration in traffic simulation analyses. Both longitudinal and interaction driving indicators were investigated to identify the driving performance of AVs in terms of traffic safety in mixed traffic stream based on simulation experiments. As a result of identifying the appropriate evaluation indicator, time-varying stochastic volatility (VF) headway time was selected as a representative evaluation indicator for left turn and straight through signalized intersections among ODDs related to intersection types. VF headway time is suitable for evaluating driving ability by measuring the variation in driving safety in terms of interaction with the leading vehicle. In addition to ODDs associated with intersection type, U-turns, additional lane segments, illegal parking, bus stops, and merging lane have common characteristics that increase the likelihood of interactions with neighboring vehicles. The VF headway time for these ODDs was derived as driving safety in terms of interaction between vehicles. The results of this study would be valuable in establishing a guideline for driving performance evaluation of AVs. The study found that unsignalized left turns, signalized right turns, and roundabouts had the highest risk scores of 0.554, 0.525, and 0.501, respectively, indicating these as the most vulnerable ODDs for AVs. Additionally, intersection and mid-block crosswalks, as well as bicycle lanes, showed high risk scores due to frequent interactions with pedestrians and cyclists. These areas are particularly risky because they involve unpredictable movements from non-vehicular road users, which require AVs to make rapid adjustments in speed and trajectory. These findings provide a foundation for improving AV algorithms to enhance safety and establishing objective criteria for AV policy-making. Full article

Background: Comparing outcomes across different health measurement tools is essential where various patient-reported outcome measures (PROMs) are used. In spinal surgery, where recent studies show that over 30 different PROMs are applied, this need becomes even more pressing. Although several statistical transformations between the Oswestry Disability Index (ODI) and the PROMIS Profile 29 have been proposed, validation studies on conversion equations and cross-walk tables remain limited. In this study, we examined the agreement between observed ODI scores and those predicted from the PROMIS Profile 29 in a large sample of patients with low back pain, collected from routine clinical care. Methods: We compared the performance of regression and linking models at both the individual and group levels. Using Bland–Altman plots, we assessed the mean difference, 95% limits of agreement, root mean squared error (RMSE), and standardized mean differences (Cohen’s d) between predicted and observed ODI scores. Results: While group-level agreement was satisfactory, with negligible effect sizes, individual prediction accuracy was relatively poor. Additionally, regression models showed inconsistent performance across the ODI score range, though incorporating more domains marginally improved predictions. Conclusions: The equipercentile linking approach demonstrated stable agreement across all ODI scores, making it the preferred method. Future regression models should account for nonlinear relationships between PROMs to enhance prediction accuracy. Full article