Search Results (326)

Traffic variability along heavily congested signalised urban corridors undermines roadway safety, reduces energy efficiency, weakens operational reliability, and can hinder emergency response. Although many simulation-based studies have examined the impacts of Autonomous Vehicles (AVs), relatively few have combined high-resolution congestion observations with link-level microscopic calibration in a real urban network, particularly when evaluating implications for emergency mobility. This study develops and calibrates a microscopic Aimsun traffic simulation model for the Atakum district of Samsun, Türkiye, using a 10 min Google Traffic Index (GTI) observation stream converted into a four-level ordinal congestion scale. The calibration process began with an origin–destination (OD) matrix derived from 2020 traffic counts and was refined through link-level GTI synchronization, iterative OD scaling on mismatched corridors, and signal retiming at key intersections. GTI was validated as an ordinal congestion proxy through both categorical agreement and volumetric consistency, achieving 83% class agreement and GEH values below 5 for more than 90% of links. Five AV penetration scenarios (0%, 25%, 50%, 75%, and 100%) were simulated under peak-hour conditions. Network performance was evaluated using delay, stop time, mean speed, throughput, missed turns, and total journey time, while emergency mobility was assessed along a representative ambulance corridor on Atatürk Boulevard using seconds per kilometre. The results indicate that increasing AV penetration improves flow stability more clearly than nominal capacity. Mean speed increased from 36.2 to 39.2 km/h, delay and stop time declined steadily, and throughput remained nearly constant at 22.2–22.5 thousand vehicles/h. Along the ambulance corridor, travel time improved by 11.5%, from 112.4 to 99.4 s/km, between the baseline and full automation scenarios. These findings provide scenario-based evidence that, within a calibrated signalised urban network, increasing AV penetration can enhance operational stability and emergency response efficiency. More broadly, the study demonstrates the practical value of integrating GTI-based congestion observations with microscopic simulation for AV impact assessment in real urban networks. Full article

The large-scale deployment of autonomous vehicles (AVs) in mixed-traffic environments raises an important question: how do human drivers evaluate safety when interacting with AVs under real-world uncertainty? This study aims to examine how drivers’ objective knowledge of AVs shapes their perceived safety when sharing the road with AVs in mixed-traffic environments. Using survey data from 905 licensed drivers in Wuhan, China, this study treats perceived road-sharing safety as an interaction-level evaluative outcome rather than merely a precursor of adoption intention. Latent class analysis was first used to identify knowledge-based driver segments, structural equation modeling was then applied to estimate Theory of Planned Behavior (TPB)-related psychological constructs, and ordered logit regression was finally employed to examine the determinants of perceived safety across segments. The results indicate that behavioral intention consistently shows a positive association with perceived safety; however, attitude toward AVs exhibits a significant negative association among high-knowledge drivers. This attitudinal reversal challenges the implicit homogeneity assumption embedded in conventional TPB applications and suggests that cognitive familiarity may recalibrate, rather than amplify, technological optimism. Overall, the findings show that knowledge-based heterogeneity changes the psychological mechanisms underlying safety appraisal in mixed traffic. These insights carry important implications for differentiated communication strategies and trust calibration in transitional automated mobility systems. Full article

Adverse weather conditions significantly degrade mobility and safety at rural signalized intersections, where high approach speeds and limited driver expectancy amplify operational and crash risks. While autonomous vehicles (AVs) have the potential to improve traffic performance, it takes a significant duration to penetrate. During this period, mixed traffic with human drivers and AVs will dominate. In this mixed traffic, the impacts of AVs at low penetration levels on adverse weather remain insufficiently understood, particularly in rural contexts. This study presents a simulation-based assessment of the effects of low AV penetration on mobility and safety at a rural signalized intersection under varying weather conditions. A calibrated microsimulation model was developed using PTV VISSIM to represent clear, rain, and snow scenarios with autonomous vehicles introduced at low penetration rates within conventional traffic. Mobility performance was evaluated using delay, travel time, and average speed, while safety impacts were assessed through surrogate safety measures extracted using the Surrogate Safety Assessment Model (SSAM), including time-to-collision and post-encroachment time. Results indicate that low levels of AV penetration of 10% can improve overall mobility performance compared with conventional traffic, particularly under adverse weather conditions. Safety outcomes show a reduction in conflict frequency and severity under low AV penetration, with more pronounced benefits observed during degraded weather scenarios. Further AV penetration from 10% to 25% may not significantly improve in a rural environment. The findings suggest that early-stage AV deployment may offer measurable mobility and safety benefits at rural signalized intersections, even before widespread adoption. This study provides practical insights for transportation agencies and policymakers regarding the potential role of low-penetration AV integration in enhancing rural traffic operations and safety under adverse weather conditions. Full article

Background/Objectives: Professionalism reflects an individual’s connection, identity, and dedication to their profession. In nursing, it is associated with quality of care and professional respect, making its assessment essential for workforce development and management. However, valid and reliable instruments are needed to measure this construct across cultural contexts. Therefore, this study aims to evaluate the validity and reliability of the Portuguese version of the Nurses’ Professionalism Inventory (NPI). Methods: This methodological study used cross-sectional data collected from November 2024 to January 2025 in northern Portugal. Data were gathered from a convenience sample of 684 nurses who completed a sociodemographic questionnaire, the Portuguese NPI, the Conditions of Work Effectiveness Questionnaire II (CWEQ-II), and the Team Psychological Safety (TPS) scale. Confirmatory Factor Analysis (CFA) was conducted. Factor loadings and Average Variance Extracted (AVE) were used to assess validity. Internal consistency was evaluated using Composite Reliability, McDonald’s omega, and Cronbach’s alpha. Convergent validity was examined using Spearman correlations among NPI subscales, CWEQ-II dimensions, and TPS. Results: The Portuguese version of NPI preserved the original five-factor structure. The model showed acceptable fit indices (TLI = 0.90; CFI: 0.91; RMSEA = 0.10; SRMR = 0.08). All items had factor loadings above 0.50, except item 18 (0.42), which did not load significantly on any other factor; therefore, it was removed. This improved the AVE of the Professional Attitude subscale. The overall internal consistency was satisfactory, with all reliability coefficients ranging between 0.73 and 0.99. The correlations among the NPI subscales, CWEQ-II dimensions, and TPS were positive and statistically significant. Conclusions: This study demonstrates adequate measurement properties of the Portuguese version of NPI, supporting its use as a valid and reliable instrument. Full article

The rapid emergence of New Psychoactive Substances (NPS), particularly pyrrolidinophenone derivatives, poses a significant challenge for public health and forensic toxicology. While their neuropharmacological profiles as dopamine transporter inhibitors are well-documented, their cardiac toxicity remains poorly understood. This study employs a multiparametric New Approach Methodology (NAM) using zebrafish embryos to integrate neurobehavioral and cardiotoxic endpoints for comparative hazard prioritization. We evaluated nine pyrrolidine-containing cathinones, including α-PVP, MDPV, α-PiHP, MDPiHP, α-D2PV, 3-Cl-, 4-Cl-, and 3,4-Cl-α-PVP, and 4-F-3-Me-α-PVP, on locomotor activity and cardiac rhythmicity using high-speed video microscopy and dynamic pixel analysis. Across the series, compounds induced concentration-dependent negative chronotropy and, in most cases, locomotor suppression. Crucially, we identified a functional dissociation between atrial rate control and atrioventricular (AV) conduction. The 3,4-dichloro substitution (3,4-Cl-α-PVP) was the most potent inducer of negative chronotropy (EC₅₀ = 52.6 μM), whereas 4-Cl-α-PVP exhibited a distinct pro-arrhythmic liability, increasing the incidence of 2:1 AV block. Time-course locomotor profiling indicated that α-PVP and chlorinated analogs were among the most potent behavioral modifiers. Using a Functional Safety Index (AV block EC₅₀/locomotor EC₅₀-like), we show that most compounds exhibit wide separations between neurobehavioral inhibition and severe conduction impairment, while specific substitutions, particularly para-chlorination, are associated with comparatively reduced functional separation between these endpoints within the assay. Overall, these data demonstrate that subtle structural changes within the pyrrolidinophenone scaffold can shape distinct arrhythmic phenotypes and functional safety profiles, supporting zebrafish-based integrated screening as a rapid platform for prioritizing emerging synthetic cathinones with comparatively higher cardiac liability within this experimental framework. Full article

Autonomous vehicle technology has rapidly advanced in recent years. Such technology is increasingly viewed not merely as a technical innovation but also as a social and behavioural transformation shaped by how these systems are interpreted, trusted, and integrated into everyday life. There are mounting expectations regarding its potential to improve traffic safety, enhance energy efficiency, reduce congestion, and support sustainable mobility; however, key questions remain about how different groups and communities experience autonomous mobility. This review synthesizes equity, governance, and sustainability dimensions as they appear within the existing corpus of AV user acceptance research. A structured review of research on autonomous vehicles (AVs) and user acceptance was conducted using an initial database search followed by iterative literature refinement and structured thematic coding. Using this approach, the review identifies key thematic patterns, highlights structural research gaps, and explores regional differences, offering a framework that supports subsequent comparative analysis. AVs have the potential to shape accessibility, social relations, and sustainable lifestyles. By integrating technological advancement with local governance, community practices, and social equity considerations, automated public transit may serve as a catalyst for sustainable and inclusive urban transformation. Full article

Emergency Departments (EDs) are the primary hospital interface during disasters and mass-casualty incidents, yet surge capacity assessments predominantly emphasize workforce and logistics while overlooking measurable spatial determinants. Observations from five tertiary hospitals in India indicate that circulation bottlenecks, incompatible functional adjacencies, and contamination risks can compromise safety and operational performance. This study develops and validates the Spatial Surge Capacity Assessment Framework (SSCAF) to operationalize spatial resilience as a quantifiable dimension of healthcare infrastructure preparedness. A sequential mixed-methods design was applied across five tertiary hospitals using structured spatial walkthroughs; architectural and disaster-planning document review; and focus group discussions involving 81 clinicians, administrators, and facility planners. The outcome of this thematic analysis produced 42 spatial indicators, refined through three Delphi rounds with a multidisciplinary expert panel. Consensus retained 30 key performance indicators (median ≥ 4/5; IQR ≤ 1; Kendall’s W = 0.855; χ² = 297.42; p < 0.001). Content validity was strong (I-CVI 0.75–1.00; S-CVI/Ave = 0.93), and reliability was high (ICC 0.82–0.91), structured into eight operational domains. The resulting weighted scoring matrix standardizes the measurement of spatial surge preparedness. The SSCAF provides an evidence-based audit and planning tool supporting resilient hospital infrastructure. It aligns with the Sendai Framework, enabling governance audits, guiding ED retrofitting, and supporting performance-based evaluation for planners and architects. Full article

For Autonomous Vehicles (AVs), recognizing traffic lights and signs is critical for safety because perception errors directly affect navigation decisions. Real-world disturbances such as glare, rain, dirt, and graffiti, as well as digital adversarial attacks, can lead to dangerous misclassifications. Current research lacks (i) temporal continuity (stable detection across consecutive frames to prevent flickering misclassifications), (ii) multi-field-of-view (FoV) sensing, and (iii) integrated defenses against both digital and natural degradation. This paper presents two principal contributions: (1) a three-layer defense framework integrating feature squeezing, inference-time temperature scaling (softmax $\tau$ = 3 without distillation training), and entropy-based anomaly detection with sequence-level temporal voting; (2) a 500 sequence dual-FoV benchmark (30k base frames, 150k with perturbations) from aiMotive, Waymo, Udacity, and Texas sources across four operational design domains. The unified defense stack achieves 79.8% mAP on a 100-sequence test set (6k base frames, 30k with perturbations), reducing attack success rate from 37.4% to 18.2% (51% reduction) and high-risk misclassifications by 32%. Cross-FoV validation and temporal voting enhance stability under lighting changes (+3.5% mAP) and occlusions (+2.7% mAP). Defense improvements (+9.5–9.6% mAP) remain consistent across native 3D (aiMotive, Waymo) and projected 2D (Udacity, Texas) annotations. Preliminary recapture experiments (n = 15 scenarios) show 2.5% synthetic–physical ASR gap (p = 0.18), though larger validation is needed. Code, models, and dataset reconstruction tools are publicly available. Full article

Autonomous vehicles (AVs) must make predictable and socially compliant behavioral decisions to ensure safe and efficient interactions with other road users. To address this challenge, this paper proposes a game-theoretic behavioral decision-making model integrated with spatial motion planning to capture the interactive intentions between the ego vehicle (EV) and target vehicle (TV) in pairwise scenarios. First, the study defines an intention representation method that characterizes intentions using spatial area boundaries, feasible speed ranges, and a set of goal points (speed goal points, position-orientation goal points). Second, a spatial motion planning approach is adopted to evaluate the intention, which optimizes the driving scheme using a multi-objective cost function (incorporating pursuit precision, comfort, energy efficiency, and travel efficiency). Finally, the game-theoretic decision-making model is constructed. The Social Value Orientation (SVO) is introduced to quantify drivers’ social preferences, and the payoff function, which integrates safety rewards (based on inter-vehicle distance) and performance rewards (based on motion planning indices), is established. Simulation results verify that the proposed model can effectively address the interactive intention decision-making problem between the AV and other road users and handle different scenarios. Full article

Autonomous vehicles (AVs) are expected to significantly influence road safety, traffic efficiency, and urban mobility. However, their real-world impacts depend not only on vehicle-level automation but also on interactions within the broader traffic ecosystem, including human-driven vehicles, vulnerable road users, infrastructure, and governance frameworks. This review provides a system-level synthesis of recent research on the integration of autonomous and connected autonomous vehicles in mixed traffic environments. Following PRISMA 2020 guidelines, 51 peer-reviewed studies published between 2016 and 2025 were systematically reviewed and thematically analyzed. The review addresses technological foundations, safety impacts, traffic flow and network performance, mixed traffic dynamics, infrastructure and urban systems, and policy and governance challenges. The findings indicate that AV impacts are highly non-linear and sensitive to market penetration rates, control strategies, and human behavioral adaptation. While high levels of automation and connectivity can improve safety, capacity, and traffic stability, early-stage deployment may temporarily increase delays and traffic conflicts. Policy measures—such as pricing, shared mobility integration, and regulatory oversight—are therefore critical to ensuring that AV deployment delivers sustainable and equitable mobility outcomes. Full article

The successful integration of autonomous vehicle (AV) technologies into future mobility systems depends not only on technological maturity but also on user acceptance and perceived value. While existing research has identified several demographic determinants of AV acceptance, the role of educational background—particularly differences between humanities and STEM graduates—has received limited attention within the context of user-centred mobility research. This study examines how educational background and gender influence attitudes toward autonomous vehicle technologies using a large-scale survey conducted in Hungary (N = 8663). The analysis combines non-parametric statistical tests with effect size measures, exploratory factor analysis, and structural equation modelling (SEM) to capture both group differences and underlying attitudinal mechanisms. The results indicate no meaningful differences between humanities and STEM graduates in overall acceptance of autonomous vehicles or trust in the technology. Statistically significant differences are observed only in two dimensions: willingness to spend on autonomous driving features and expectations regarding improved travel speed. However, effect size analyses reveal that these differences are negligible in practical terms, indicating substantial overlap in user attitudes. SEM results show that educational background does not directly determine acceptance of autonomous vehicle technologies. Instead, its influence is mediated through three latent attitude dimensions relevant for electric and autonomous mobility adoption: willingness to invest, functional expectations (e.g., time savings and convenience), and safety orientation. Humanities graduates—especially men—exhibit slightly higher financial openness toward autonomous features, whereas STEM graduates place greater emphasis on functional performance. Safety-related attitudes play a central mediating role, with gender-specific patterns. By integrating large-sample effect size interpretation with SEM-based modelling, this study provides a nuanced understanding of user acceptance of autonomous vehicle technologies. The findings suggest that differences between educational groups reflect variations in attitudinal emphasis rather than fundamental divides, offering relevant insights for user-centred AV development, mobility policy design, and communication strategies in the transition toward automated and electric mobility systems. Full article

With the rise of smart cities, technology has enabled more efficient urban management. A key part of this is the Internet of Vehicles (IoVs), which connects vehicles to smart city systems to improve transportation safety and efficiency. This integrated system enables wireless connection between vehicles, allowing for the sharing of essential traffic information. However, with all this connectivity, there are growing concerns about IoV security and privacy. This paper presents a new privacy-preserving authentication scheme for Autonomous Vehicles (AVs) in the IoV field using physical unclonable functions (PUFs). This scheme employs a bilinear pairing-based encryption technique that supports search over encrypted data. The primary aim of this scheme is to authenticate AVs inside the IoV architecture. A novel PUF design generates random keys for our authentication technique, hence boosting security. This dual-layer security strategy safeguards against a range of cyber threats, including identity fraud, man-in-the-middle attacks, and unauthorized access to personal user data. The PUF design will guarantee the true randomness of the AVs’ users’ secret keys. To handle the large amount of data involved, we use hardware acceleration with different Field-Programmable Gate Arrays (FPGAs). Our examination of privacy and security demonstrates the achievement of the defined design goals. The proposed authentication framework was fully implemented and validated on FPGA platforms to demonstrate its hardware feasibility and efficiency. The integrated heterogeneous PUF achieves an average reliability exceeding $98.5\%$ across a wide temperature range, while maintaining near-ideal randomness with an average Hamming weight of $49.7\%$ over multiple challenge sets. Furthermore, the uniqueness metric approaches $49.9\%$, confirming strong inter-device distinguishability among different PUF instances. The complete authentication architecture was synthesized on Nexys-100T, Zynq-104, and Kintex-116 devices, where the design utilizes less than $80\%$ of slice Look-Up Tables (LUTs), under $27\%$ of on-chip memory resources, and below $16\%$ of DSP blocks, demonstrating low hardware overhead. Full article

Winter road safety is critically influenced by microclimatic factors that determine where frost and ice persist on pavement surfaces. Among these, shadow duration plays a decisive yet often under quantified role in mountainous regions, where complex topography and variable solar exposure create localized cold zones. This study presents a GIS-based methodology for detecting and characterizing shadow-prone areas along high-altitude roads, extending previous national-scale models of winter risk toward local, geometry-driven analysis. Using high-resolution Digital Terrain Models (DTM02) and solar radiation simulations, four representative mountain roads (CL-505, AV-501, and CA-820) were analyzed to evaluate how orientation, slope, and surrounding relief control solar incidence. The resulting shadow maps were validated through UAV-derived thermal orthophotos and ground-based temperature measurements, confirming strong correspondence between simulated low-irradiance areas and observed cold surfaces. The integration of geometric and radiometric data demonstrates that topographic shading is a reliable predictor of frost persistence and can be incorporated into winter maintenance planning. By combining high-resolution terrain analysis with empirical thermal validation, this approach not only enhances predictive accuracy but also provides actionable insights for prioritizing road sections at greatest risk. Ultimately, it offers a scalable, data-driven framework for improving infrastructure resilience, optimizing maintenance operations, and mitigating winter hazards in cold-climate mountainous environments, supporting both safety and cost-effectiveness in road management strategies. Full article

Vascular access remains the cornerstone of effective hemodialysis but also constitutes a major source of burden, including dysfunctions, infections, patient discomfort, and other access-related morbidities. As dialysis care evolves, there is a pressing need to move beyond conventional approaches, marked by repeated needle punctures and open connection systems, toward safer, more comfortable, and technologically advanced solutions. This narrative article presents a forward-looking vision of vascular access connectivity supported in current clinical and technological knowledge. It explores how emerging connectivity, particularly needle-free port systems, could reshape the future of dialysis care. We briefly review existing vascular access modalities, including central venous catheters (CVCs) and arteriovenous (AV) accesses, along with their associated limitations. Special focus is given to the burden of infection, patient-reported discomfort, and workflow inefficiencies. We then examine emerging closed-system technologies designed to reduce contamination risk, improve patient experience, and potentially support long-term clinical outcomes. Drawing on advances in material science, biomedical engineering, and infection prevention, we outline a forward-looking vision for vascular access that aligns with patient-centered care, facilitates home-based treatment and remote connectivity, and anticipates future developments, such as wearable artificial kidneys within a value-based healthcare framework. However, the clinical adoption of these new technologies will require careful evaluation of long-term safety, durability, cost-effectiveness, training requirements, and real-world performance, underscoring the need to balance innovation-driven benefits against practical, regulatory, and organizational challenges. Full article

Purpose: This study evaluates the association between supervising attending surgeons’ post-residency experience and complication rates during resident-performed phacoemulsification (cataract extraction) surgeries, and to determine whether this relationship changes as the academic year progresses. Methods: A retrospective analysis of 1263 cataract surgeries performed by eight PGY-4 residents under 14 board-certified attendings was conducted at a New York City residency program over two years. Attendings were divided into four groups based on years of post-residency experience. Primary complications included posterior capsule (PC) tears, anterior vitrectomy (AV), capsulorrhexis extensions (CE), and inability to place a one-piece intraocular lens (IOL). Chi-square analyses compared complication rates between attending groups overall, and between the first and second halves of the academic year. Results: A total of 167 primary complications (13.2%) were identified. Attendings with the fewest years of experience (Group 1) supervised significantly more cases with PC tears (χ² = 8.173, p = 0.004), AV usage (χ² = 7.748, p = 0.005), and inability to place a one-piece IOL (χ² = 4.753, p = 0.029), particularly during the first half of the academic year. Notably, supervising attending experience was not correlated with resident complications in the second half of the academic year. Conclusions: Early in the academic year, less experienced attendings supervised cases with higher complication rates, underscoring the critical role of strategic case assignment and targeted mentorship during early surgical training. These findings suggest that aligning resident progression with appropriate supervision can enhance outcomes and support skill development, optimizing both education and patient safety. Full article