Search Results (286)

Examining pedestrian crossing violations at high-risk road midblock crossings has become essential, particularly in high-speed corridors, as a result of accidents at crossings resulting in fatalities. Hence, this article investigates such behavior in Alexandria, Egypt, as a credible case study in a developing country. According to our research methodology, a comprehensive dataset of over 2400 field-observed video recordings was used for real-life data collection. Machine learning (ML) models, such as CatBoost and gradient boosting (GB), were employed to predict crossing decisions. The models showed that risky behavior is strongly influenced by waiting time, crossing time, and the number of crossing attempts. The highest predictive performance was achieved by CatBoost and gradient boosting, indicating strong interpersonal influence within small groups engaging in unsafe road-crossing behavior. In the same context, the Shapley additive explanation (SHAP) values for these variables were 3, 2, and 0.60, respectively. Subsequently, based on SHAP sensitivity analysis, the results show that pedestrian crossing time (s) had the highest tendency to push the model towards class 1 (e.g., crossing illegally), while total time (s) and age group (40–60 Y) had a significant negative influence on model prediction converging to class 0 (e.g., crossing illegally). The results also showed that shorter exposure times increase the likelihood of crossing illegally. This research work is among the few studies that employ a behavior-based approach to understanding pedestrian behavior at midblock crossings. This study offers actionable insights and valuable information for urban designers and transportation planners when considering the design of midblock crossings. Full article

Background: Among many deleterious effects on the well-being of children and youth, the COVID-19 pandemic contributed to a surge in youth mental health distress. This, coupled with pre-existing prolonged wait times for mental health care, highlighted the need for accessible community-based mental health supports. The Healthy Living Project (HELP) is a virtual lifestyle change support program aimed at promoting positive lifestyle changes and improved mental well-being among youth with mental distress. A pilot feasibility study explored youth engagement with HELP e-resources, and preliminary mental health and lifestyle measures over a 3-month period. Methods: Youth were enrolled in a 3-month pilot of the HELP e-resource. Feasibility metrics (recruitment, retention, and platform engagement) were documented, while exploratory self-reported data on emotional and behavioral difficulties, youth quality of life, sedentary behavior (screen time), sleep hygiene, and physical activity were assessed at baseline and 3 months. Results: Twenty-three youth (mean age 15.7 years, SD 1.7) completed baseline assessments and started the intervention, with ten participants retained by the end of the study. Compared with non-completers (n = 13), study completers (n = 10) tended to report higher quality of life and healthier habits (lower screen time, improved sleep hygiene, and higher activity). Ongoing access to HELP over 3 months was associated with suggestive trends toward improvement in emotional and behavioral difficulties and sleep hygiene. Engaged participants who received screen time education tended to report lower screen times as compared to unengaged counterparts. Conclusions: This study provides early insights into the implementation and acceptability of HELP e-resources among youth experiencing mental distress, with suggestive trends toward potential benefit. Low recruitment and high attrition preclude definitive conclusions, and the findings should be interpreted as exploratory. Lessons from this pilot will inform the design of a subsequent trial to more rigorously evaluate feasibility and the potential impact of HELP on youth with mental distress. Full article

Pedestrian safety in developing countries faces critical challenges from rapid urbanization and infrastructure deficiencies. This study investigates how visual context influences pedestrian crossing preferences through a controlled stated preference experiment in multiple Ecuadorian cities. A sample of 875 participants was randomly assigned to view either non-compliant (mid-block crossing) or compliant (signalized crosswalk) imagery before evaluating six hypothetical scenarios involving three crossing alternatives. Multinomial logit models reveal that waiting time, traveling with a minor, and walking distance are primary determinants of choice. Visual context showed systematic associations with choice patterns: compliant imagery was associated with increased preference for safer alternatives (50.5% versus 43.8% prediction accuracy) and larger safety-related parameter magnitudes. Principal Component Analysis identified two latent perception constructs, safety/security and bridge-specific convenience, providing behavioral interpretation of choice patterns. Substantial spatial heterogeneity emerged across cities (χ² = 124.10 and 84.74, p < 0.001), with larger urban centers showing stronger responsiveness to formal infrastructure cues. The findings demonstrate that visual stimuli systematically alter choice distributions and attribute sensitivities through normative activation and perceptual recalibration. This research contributes methodologically by establishing visual framing effects in stated preference frameworks and provides actionable insights for pedestrian infrastructure design, emphasizing alignment of objective safety improvements with perceived risk and contextual behavioral cues. Full article

In urban road networks, queuing delays at signalized intersections often account for over half of the total travel time. The complexity of traffic signals and vehicle queuing makes traditional shortest path algorithms insufficient for real-time optimal path finding. This study proposes a Time-Dependent Dijkstra’s algorithm to address these challenges. The network topology is redesigned to model vehicle turning behaviors accurately. A periodic queuing delay parameter matrix for signalized intersections is introduced, storing traffic flow and signal phase parameters. Additionally, a time-varying weight matrix tracks the vehicle’s position in the signal cycle upon intersection arrival. Using cumulative curve theory, a periodic queuing-delay model is constructed to capture delays for vehicles arriving at different times. The algorithm updates the network weight matrix in real-time based on vehicle arrival times at intersections, enabling FIFO-consistent time-dependent shortest path computation for a given departure time. Numerical and SUMO simulations on a real-world road network in Suzhou Industrial Park (comprising 15 signalized intersections and 22 road segments) demonstrate the algorithm’s effectiveness. Results show a 25.36% reduction in travel time compared to the traditional Dijkstra’s Algorithm and a 10.46% reduction compared to an algorithm considering only signalized intersection waiting time when departure times vary. The results highlight the impact of periodic queuing delays, with the algorithm reducing travel time and improving path planning. Full article

In recent years, payment schemes in healthcare have garnered attention for their potential impact on service delivery and cost management. This paper explores the impact of the bundled payment scheme (BP) on hospital outpatient services, focusing on the cost–speed tradeoff. Specifically, a higher service rate increases patient demand but also raises medical costs. We consider a queueing-game theoretical model to analyze servers’ service rate behaviors under different payment schemes (fee-for-service and BP) and the payer’s optimal payment scheme setting. Our study shows that achieving the first-best outcome under centralized decision making using the BP requires specific conditions. When the medical budget is sufficiently high, the payer can guide hospitals toward the first-best decision by setting an optimal price under the BP. However, when the budget is at an intermediate level, hospitals may set slower equilibrium service rates to control costs. To address this issue, the payer can implement service level regulation based on the BP scheme to achieve the first-best outcome. This scheme encourages hospitals to choose higher service rates by limiting expected waiting times. When the budget is too low, hospitals may be unwilling to provide service due to unprofitability. Moreover, as competition between hospitals intensifies, it becomes easier to maximize social welfare under the BP scheme. Full article

Obstructive sleep apnea (OSA) in children is a common disorder with significant effects on behavior, cognition, and quality of life. Its diagnosis is primarily based on clinical history and examination, supported by standardized questionnaires such as the Sleep-Related Breathing Disorder subscale of the Pediatric Sleep Questionnaire (SRDB-PSQ), which provides high diagnostic accuracy. Although polysomnography remains the gold standard, its use should be limited to high-risk patients or unclear cases due to availability and cost constraints. Adenotonsillar hypertrophy represents the main cause of pediatric OSA and is often self-limiting. For children with mild symptoms, a watchful waiting approach may be appropriate. Randomized controlled trials (e.g., CHAT, POSTA) demonstrate that spontaneous improvement in polysomnographic parameters occurs in some children, though clinical symptoms often persist. Patients with low apnea-hypopnea-index (AHI), mild obesity, and mild symptoms appear suitable for observation but require a close follow-up. Adenotonsillectomy remains the most effective treatment for clinically significant OSA, leading to marked improvements in sleep quality, daytime symptoms, and quality of life, largely independent of polysomnographic findings. Partial tonsillectomy offers similar efficacy with reduced postoperative morbidity. Management should be individualized and focus on clinical presentation more than on sleep recordings. Future research should focus on identifying which children benefit most from conservative or surgical strategies. Full article

To explore the mechanism of consumers’ battery electric vehicle (BEV) purchase behavior in depth and address research gaps related to insufficient consideration of psychological latent variables and neglect of consumer heterogeneity in existing studies, this study constructs a latent class model (LCM) that integrates personal attributes, vehicle attributes, and six psychological latent variables: perceived usefulness, perceived ease of use, perceived risk, environmental awareness, purchase attitude, and purchase intention. Based on 1044 valid questionnaires collected from Urumqi, latent profile analysis (LPA) is used to classify consumers. The results indicate that BEV consumers can be divided into five distinct latent profiles with significant differences in purchase preferences: the risk-avoidance type, the moderate–low intention wait-and-see type, the utility-oriented and low environmental concern type, the high utility cognition and low-risk proactive type, and the all-dimensional high-intention core type. Socioeconomic and vehicle-related factors exert heterogeneous impacts on the psychological variables and purchase decisions of each profile. This study clarifies the intrinsic psychological mechanism of BEV purchase behavior, providing a theoretical basis and targeted strategy references for the government and enterprises to promote BEV adoption and advance sustainable transportation development. Full article

Diabetes mellitus (DM) is a complex, heterogeneous, hyperglycemic chronic metabolic disorder. Type 2 diabetes mellitus (T2DM) is characterized by progressive loss of insulin secretion from pancreatic islet β-cells due to IR (insulin resistance), which is a feature of metabolic syndrome (MetS). Chronic hyperglycemia in patients with T2DM in synergy with other metabolic abnormalities causes complications such as diabetic ketoacidosis, osmotic diuresis and hyperglycemic diabetic coma, as well as chronic microvascular and macrovascular complications such as atherosclerotic cardiovascular disease (ASCVD), peripheral artery disease (PAD) and cerebrovascular events, which implicate the formation of reactive species and the promotion of inflammatory pathways. In these events, natural or synthetic antioxidants and minerals seem to have ameliorative effects and may serve as beneficial co-treatment options. In view of these terms, the aim of this study is to investigate the underlying mechanisms of T2DM, its clinical presentation, and its complications. Additionally, the association of the pathogenesis of T2DM and the occurrence of its complications with obesity, chronic inflammation, oxidative stress (OS), insulin resistance (IR), hepatic steatosis, and dyslipidemia is examined, whilst molecular pathways, such as NF-κB and JAK/STAT, are also summarized, under the scope of the effects of several antioxidant compounds and minerals on their progression. The interrelation of T2DM with these conditions, as well as the effects of antioxidant supplementation, seems to be bidirectional, and it is recommended that obese patients be screened for T2DM and adopt lifestyle changes, including exercise, diet modification, and weight loss, in addition to potentially taking multifunctional supplements that offer antioxidant and anti-inflammatory potential. However, many aspects of the protective mechanisms of such antioxidants remain to be elucidated, with more drawbacks in their pharmacokinetic behavior, such as their poor absorption and solubility, waiting to be resolved. Full article

For a no-wait flow shop with continuous-flow characteristics, this study simultaneously considers machine setup times and rated processing speed constraints, aiming to minimize the sum of the maximum completion time and the maximum tardiness. First, lower bounds for the maximum completion time, the maximum tardiness, and the total objective function are developed. Second, a mixed-integer programming (MIP) model is formulated for the problem, and nonlinear elements are subsequently linearized via time discretization. Due to the computational complexity of the problem, two algorithms are proposed: a heuristic algorithm with fixed machine links and greedy rules (HAFG) and a genetic algorithm based on altering machine combinations (GAAM) for solving large-scale instances. The Earliest Due Date (EDD) rule is used as baselines for algorithmic comparison. To better understand the behaviors of the two algorithms, we observe the two components of the objective function separately. The results show that, compared with the EDD rule and GAAM, the HAFG algorithm tends to focus more on optimizing the maximum completion time. The performance of both algorithms is evaluated using their relative deviations from the developed lower bounds and is compared against the EDD rule. Numerical experiments demonstrate that both HAFG and GAAM significantly outperform the EDD rule. In large-scale instances, the HAFG algorithm achieves a gap of about 4%, while GAAM reaches a gap of about 3%, which is very close to the lower bound. In contrast, the EDD rule shows a deviation of about 10%. Combined with a sensitivity analysis on the number of machines, the proposed framework provides meaningful managerial insights for continuous-flow production environments. Full article

Background: Autism spectrum disorder (ASD) and related neurodevelopmental conditions are a significant public health concern, with diagnostic delays hindering timely intervention. Traditional assessments often lead to waiting times exceeding a year. Advances in artificial intelligence (AI) and biomarker-based screening offer objective, efficient alternatives for early identification. Objective: This review synthesizes the latest evidence for AI-enabled technologies aimed at improving early ASD identification. Modalities covered include eye-tracking, acoustic analysis, video- and sensor-based behavioral screening, neuroimaging, molecular/genetic assays, electronic health record prediction, and home-based digital applications or apps. This manuscript critically evaluates their diagnostic accuracy, clinical feasibility, scalability, and implementation hurdles, while highlighting regulatory and ethical considerations. Findings: Across modalities, machine learning approaches demonstrate strong accuracy and specificity in ASD detection. Eye-tracking and voice-acoustic classifiers reliably differentiate for autistic children, while home-video analysis and Electronic Health Record (EHR)-based algorithms show promise for scalable screening. Multimodal integration significantly enhances predictive power. Several tools have received Food and Drug Administration clearance, signaling momentum for wider clinical deployment. Issues persist regarding equity, data privacy, algorithmic bias, and real-world performance. Conclusions: AI-enabled screeners and diagnostic aids have the potential to transform ASD detection and access to early intervention. Integrating these technologies into clinical workflows must safeguard equity, privacy, and clinician oversight. Ongoing longitudinal research and robust regulatory frameworks are essential to ensure these advances benefit diverse populations and deliver meaningful outcomes for children and families. Full article

Digital Cognitive Behavioral Therapy for Insomnia (dCBT-I) is an effective alternative to therapist-delivered CBT-I. However, there is a lack of meta-analyses assessing its effects on other sleep-related outcomes. We aimed to conduct a meta-analysis of randomized controlled trials (RCTs) evaluating dCBT-I in adults with insomnia through polysomnography (PSG) and sleep diary. Systematic searches were performed in PubMed and Web of Science. The outcomes considered were total sleep time (TST), sleep onset latency (SOL), sleep efficiency (SE), wake after sleep onset (WASO), and number of awakenings (NWAK). Meta-analyses were performed using random-effects models to compare dCBT-I with active (in-person or telehealth CBT-I) or inactive (waiting list, no treatment, or minimal intervention) control groups. Of the fourteen RCTs included, only three employed an active control. As no trials used PSG, the analyses relied solely on sleep diary data. DCBT-I showed no statistically significant differences from active controls, indicating comparable effects with therapist-delivered CBT-I. In contrast, it demonstrated statistically significant effects against inactive controls; TST increased by 0.20 h, SOL decreased by 15.53 min, SE improved by 7.91%, WASO reduced by 15.61 min, and NWAK decreased by 0.53. Future research should prioritize comparisons with therapist-delivered CBT-I and incorporate PSG for measuring these parameters. Full article

During peak hours, urban rail transit systems often face imbalanced spatial–temporal demands. Due to the limited transportation capacity, passengers departing from downstream stations often experience longer waiting times. Mostly traditional timetable and skip-stop strategies overlook passengers’ transfer behavior, which may impact the implementation of optimization strategies. This paper aims to take passengers’ transfer behavior into account and construct a coordinated optimization model of timetable and skip-stop patterns. We regulate passengers’ transfer strategies and design a genetic algorithm for solving the optimization model. In order to characterize feasible passenger travel patterns, strict FCFS rules and capacity constraints are incorporated into the model. Our result demonstrates that considering passengers’ transfer behavior, the coordinated optimization of timetable and skip-stop strategy can not only mitigate the unfairness of acquiring rail service among passengers but also reduce the average waiting time of the entire system. We validate the effectiveness of our algorithm using the dataset from Line 1 of Singapore’s urban rail transit system as a case study. Full article

Ensuring thermal comfort in waiting areas is essential for visitor satisfaction and well-being. In the context of nearly zero-energy buildings (nZEBs), these spaces—typically characterized by short-term occupancy, transient user behavior, and the need for rapid temperature adjustment—pose specific challenges for HVAC control in balancing comfort and energy demand. This study investigates the influence of a ceiling-mounted fan coil unit (FCU) operating in heating mode on thermal comfort conditions in an nZEB office waiting area. Measurements were conducted at multiple points within the space to assess microclimate parameters, followed by the calculation of the predicted mean vote (PMV) and predicted percentage of dissatisfied (PPD) indices, supported by occupant feedback collected through short interviews. The results showed that although the FCU effectively increased the average temperature, its intermittent operation and localized air jets during the heating phase caused temporary discomfort near the unit. Occupant feedback confirmed sensations of discomfort due to strong air movement during FCU operation but indicated slightly higher overall dissatisfaction and smaller variability compared to model-based PPD values, reflecting the averaging effect of occupant perception over time. These findings highlight the need for optimized FCU control strategies in waiting areas, such as operating at reduced fan speeds and preheating the heat exchanger, to enhance occupant comfort. This study contributes to improving HVAC control concepts for semi-transient spaces in nZEBs. Full article

This study investigated how Japanese speakers interpret structurally ambiguous wh-questions, testing whether filler–gap resolution is guided by syntactic resolution based on hierarchical structure or linear locality based on surface word order. We combined behavioral key-press responses with fine-grained eye-tracking data and applied cluster-based permutation analysis to capture the moment-by-moment time course of syntactic interpretation as sentences were processed in real time. Key-press responses revealed a preference for resolving the dependency at the main clause (MC) gap position. Eye-tracking data showed early predictive fixations to the MC picture, followed by shifts to the embedded clause (EC) picture as the embedded event was described. These shifts occurred prior to the appearance of syntactic cues that signal the presence of an EC structure, such as the complementizer -to, and were therefore most likely guided by referential alignment with the linguistic input rather than by syntactic reanalysis. A subsequent return of the gaze to the MC picture occurred when the clause-final question particle -ka became available, confirming the interrogative use of the wh-phrase. Both key-press and eye-tracking data showed that participants did not commit to the first grammatically available EC interpretation but instead waited until clause-final particle information confirmed the interrogative use of the wh-phrase, ultimately favoring the MC interpretation. This pattern supports the view that filler–gap resolution is guided by structural locality rather than linear locality. By using high-resolution temporal data and statistically robust analytic techniques, this study demonstrates that Japanese comprehenders engage in predictive yet structurally cautious parsing. These findings challenge earlier claims that filler–gap resolution in Japanese is primarily driven by linear locality and instead showed a preference for resolving dependencies at the structurally higher MC position, consistent with parsing biases previously observed in English, despite typological differences in word order between the two languages. This preference also reflects sensitivity to language-specific morpho-syntactic cues in Japanese, such as clause-final particles. Full article

Demand-responsive transit (DRT) is a flexible transportation service that adapts routes and schedules based on real-time passenger needs, offering greater convenience than traditional fixed-route systems. DRT systems are highly dynamic and complex. Customer requests are often rejected due to operational constraints. Therefore, it is essential to identify and rank the factors that determine request acceptance or rejection. This study develops a Decision Tree Model (DTM) for vehicle dispatching in DRT, using the Korea National University of Transportation (KNUT) Chungju Campus as the study area. Elecle bicycle origin–destination (OD) data were first used to simulate DRT operations, and the resulting outputs were employed to train the DTM to classify passenger requests as “assign” or “reject.” The model considers key factors such as vehicle capacity, access time, Estimated Time of Arrival (ETA), waiting time, detour factor, and egress time. Based on 5-fold cross-validation, the detour factor was identified as the most influential variable across all fleet configurations, with mean importance values of 0.582 ± 0.055, 0.550 ± 0.047, and 0.447 ± 0.073 for the 1-, 2-, and 3-vehicle scenarios, respectively. The model achieved accuracies of 0.73 ± 0.02, 0.82 ± 0.04, and 0.83 ± 0.07, indicating improved performance with increasing fleet size. Error analysis revealed conservative behavior for one vehicle, balanced performance for two, and liberal over-assignment for three vehicles. Full article