Search Results (141)

This review provides a mechanistic framework to strategically design nanoparticles capable of efficiently crossing the blood–brain barrier (BBB), a critical limitation in neurological treatments. We systematically analyze nanoparticle–BBB transport mechanisms, including receptor-mediated transcytosis, adsorptive-mediated transcytosis, and transient barrier modulation. Essential nanoparticle parameters (size, shape, stiffness, surface charge, and biofunctionalization) are evaluated for their role in enhancing brain targeting. For instance, receptor-targeted nanoparticles can significantly enhance brain uptake, achieving levels of up to 17.2% injected dose per gram (ID/g) in preclinical glioma models. Additionally, validated preclinical models (human-derived in vitro systems, rodents, and non-human primates) and advanced imaging techniques crucial for assessing nanoparticle performance are discussed. Distinct from prior BBB nanocarrier reviews that primarily catalogue mechanisms, this work (i) derives quantitative ‘design windows’ (size 10–100 nm, aspect ratio ~2–5, near-neutral ζ) linked to transcytosis efficiency, (ii) cross-walks human-relevant in vitro/in vivo models (including TEER thresholds and NHP evidence) into a translational decision guide, and (iii) integrates regulatory/toxicology readiness (ISO 10993-4, FDA/EMA, ICH) into practical checklists. We also curate recent (2020–2025) %ID/g brain-uptake data across lipidic, polymeric, protein, inorganic, and hybrid vectors to provide actionable, evidence-based rules for BBB design. Full article

With the recent 5G communication technology deployment, Cellular Vehicle-to-Everything (C-V2X) significantly enhances road safety by enabling real-time exchange of critical traffic information among vehicles, pedestrians, infrastructure, and networks. However, further research is required to address real-time application latency and communication reliability challenges. This paper explores integrating cutting-edge C-V2X technology with environmental perception systems to enhance safety at intersections and crosswalks. We propose a multi-module architecture combining C-V2X with state-of-the-art perception technologies, GPS mapping methods, and the client–server module to develop a co-operative perception system for collision avoidance. The proposed system includes the following: (1) a hardware setup for C-V2X communication; (2) an advanced object detection module leveraging Deep Neural Networks (DNNs); (3) a client–server-based co-operative object detection framework to overcome computational limitations of edge computing devices; and (4) a module for mapping GPS coordinates of detected objects, enabling accurate and actionable GPS data for collision avoidance—even for detected objects not equipped with C-V2X devices. The proposed system was evaluated through real-time experiments at the GMMRC testing track at Kettering University. Results demonstrate that the proposed system enhances safety by broadcasting critical obstacle information with an average latency of 9.24 milliseconds, allowing for rapid situational awareness. Furthermore, the proposed system accurately provides GPS coordinates for detected obstacles, which is essential for effective collision avoidance. The technology integration in the proposed system offers high data rates, low latency, and reliable communication, which are key features that make it highly suitable for C-V2X-based applications. Full article

Effective communication between electric autonomous vehicles (EAVs) and pedestrians is critical for safety, yet the absence of a driver removes traditional cues such as eye contact or gestures. While external human–machine interfaces (eHMIs) have been proposed, few studies have systematically compared visual formats across demographic groups and validated findings in both simulation and real-world settings. This study addresses this gap by evaluating various eHMI designs using combinations of textual cues (“WALK” and “CROSS”), symbolic indicators (pedestrian and arrow icons), and display colors (white and green). Twenty simulated scenarios were developed in the CARLA simulator, where 100 participants observed an EAV equipped with eHMIs and responded by pressing a button upon understanding the vehicle’s intention. The results showed that green displays facilitated faster comprehension than white, “WALK” was understood more quickly than “CROSS,” and pedestrian symbols outperformed arrows in clarity. The fastest overall comprehension occurred with the green pedestrian symbol paired with the word “WALK.” A subsequent field experiment using a Level 3 autonomous vehicle with a smaller participant group and differing speed/distance conditions provided preliminary support for the consistency of these observed trends. The novelty of this work lies in combining simulation with preliminary field validation, using comprehension time as the primary metric, and comparing results across four age groups to derive evidence-based eHMI design recommendations. These findings offer practical guidance for enhancing pedestrian safety, comprehension, and trust in EAV–pedestrian interactions. Full article

As human–robot shared service environments become increasingly common, autonomous navigation in narrow space environments (NSEs), such as indoor corridors and crosswalks, becomes challenging. Mobile robots must go beyond reactive collision avoidance and interpret surrounding risks to proactively select safer routes in dynamic and spatially constrained environments. This study proposes a deep reinforcement learning (DRL)-based navigation framework that enables mobile robots to interact with pedestrians while identifying and traversing open and safe spaces. The framework fuses 3D LiDAR and RGB camera data to recognize individual pedestrians and estimate their position and velocity in real time. Based on this, a human-aware occupancy map (HAOM) is constructed, combining both static obstacles and dynamic risk zones, and used as the input state for DRL. To promote proactive and safe navigation behaviors, we design a state representation and reward structure that guide the robot toward less risky areas, overcoming the limitations of traditional approaches. The proposed method is validated through a series of simulation experiments, including straight, L-shaped, and cross-shaped layouts, designed to reflect typical narrow space environments. Various dynamic obstacle scenarios were incorporated during both training and evaluation. The results demonstrate that the proposed approach significantly improves navigation success rates and reduces collision incidents compared to conventional navigation planners across diverse NSE conditions. Full article

This article examines the evolution of the most extensively researched subjects in e-mobility during the previous two decades. The objective of this analysis is to identify the lessons that the State of Kuwait, which is falling behind other nations in terms of e-mobility, can learn from in its efforts to adopt electric vehicles (EVs). To strengthen the body of knowledge and determine the most effective and efficient route to an “EV-ready” nation, the authors compiled data on the latest developments in the EV industry. A bibliometric analysis was performed on 3962 articles using VOSviewer software, which identified six noteworthy clusters that warranted further discussion. Additionally, we examined the sequential progression of these clusters as follows: (1) the environmental ramifications of electric mobility; (2) advancements in EV technology, including range extension and soundless engines, as well as the capital expenditure (CAPEX) and operating expenditure (OPEX) of purchasing and operating EVs; (3) concerns regarding the effectiveness and durability of EV batteries; (4) the availability of EV charging stations and grid integration; (5) charging time; and, finally, (6) the origin and source of the energy used in the development of e-mobility. Delineating critical aspects in the development of e-mobility can help to equip policymakers and decision makers in Kuwait in formulating timely and economical choices pertaining to sustainable transportation. This study contributes by cross-walking six global bibliometric clusters to Kuwait’s ten EV adoption barriers and mapping each to actionable policy levers, linking evidence to deployment guidance for an emerging market grid. Unlike prior bibliometric overviews, our analysis is Kuwait-specific and heat-contextual, and it reports each cluster’s size and recency to show where the field is moving. Using Kuwait driving logs, we found that summer (avg 43.2 °C) reduced the effective full-charge range by 24% versus pre-winter (approximately 244 km vs. 321 km), underscoring the need for shaded PV-coupled hyper-hubs and active thermal management. Full article

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