Search Results (78)

In this pilot study, the crossing behavior of elementary school students commuting on bicycles was investigated with the objective of enhancing safety around pedestrian crossings within school zones. With a noticeable increase in crashes involving young cyclists near schools, this research assessed the effectiveness of visual nudges in the form of red strips displaying “CYCLISTS DISMOUNT” instructions. Initial observations indicated a lack of compliance with dismounting regulations. After the initial observations, a specific elementary school was selected for the implementation of the nudging intervention and additional pre- (N = 91) and post-intervention (N = 71) observations. The pre-intervention observations again revealed poor adherence to the regulations requiring cyclists to dismount at specific points. Following our targeted intervention, the post-intervention observations marked an improvement in compliance. Indeed, the visual nudge effectively communicated the necessity of dismounting at a critical location, leading to a higher rate of adherence among cyclists (52.74% pre-intervention, 97.18% post-intervention). Although it also indirectly affected the behavior of the accompanying adult, who more often held hands with their children while crossing, this effect was weaker than the direct effect on dismounting behavior (20.88% pre-intervention, 39.44% post-intervention). The findings of the current pilot study underscore the possible impact of nudging on behavior and advocate for a combined approach utilizing physical nudges to bolster safety within school zones. Follow-up research, including, for instance, multiple sites, long-term effects, or children traveling alone, is called for. Full article

As cities globally prioritize sustainable transportation, understanding gender-differentiated responses to the urban built environment is critical for equitable mobility planning. This study combined the Social Ecological Model (SEM) with the theoretical perspective of Gendered Spatial Experience to explore the differentiated impacts of the Perceived Street Built Environment (PSBE) on the cycling behavior of men and women. Questionnaire data from 285 e-bike and traditional bicycle riders (236 e-bike riders and 49 traditional cyclists, 138 males and 147 females) from Gulou District, Nanjing, between May and October 2023, were used to investigate gender differences in cycling behavior and PSBE using the Mann–Whitney U-test and crossover analysis. Linear regression and logistic regression analyses examined the PSBE impact on gender differences in cycling probability and route choice. The cycling frequency of women was significantly higher than that of men, and their cycling behavior was obviously driven by family responsibilities. Greater gender differences were observed in the PSBE among e-bike riders. Women rated facility accessibility, road accessibility, sense of safety, and spatial comfort significantly lower than men. Clear traffic signals and zebra crossings positively influenced women’s cycling probability. Women were more sensitive to the width of bicycle lanes and street noise, while men’s detours were mainly driven by the convenience of bus connections. We recommend constructing a gender-inclusive cycling environment through intersection optimization, family-friendly routes, lane widening, and noise reduction. This study advances urban science by identifying gendered barriers in cycling infrastructure, providing actionable strategies for equitable transport planning and urban design. Full article

Unsupervised domain adaptation (UDA) enables training a model on labeled source data to perform well in a target domain without supervision, which is especially valuable in vision-based semantic segmentation. However, existing UDA methods often struggle with accurate semantic labeling at object boundaries and recognizing minor categories in the target domain. This paper introduces a novel UDA framework—SamDA—that incorporates the Segment Anything Model (SAM), a large-scale foundational vision model, as the mask generator to enhance edge segmentation performance. The framework comprises three core modules: a cross-domain image mixing module, a self-training module with a teacher–student network, and exponential moving average (EMA). It also includes a finetuning module that leverages SAM-generated masks for pseudo-label matching. Evaluations on the GTA5 and Cityscapes datasets demonstrate that SamDA achieves a mean IoU (mIoU) of 75.2, surpassing state-of-the-art methods such as MIC-DAFormer by 1.0 mIoU and outperforming all ResNet-based approaches by at least 15 mIoU. Moreover, SamDA significantly enhances the segmentation of small objects like bicycles, riders, and fences, with, respective, IoU improvements of 4.5, 5.2, and 3.8 compared to baseline models. Full article

Background: Limited research has examined how older adults’ lifestyles intersect with multimorbidity to influence mortality risk. Methods: In this community-dwelling prospective cohort, the Sydney Memory and Ageing Study, principal component analysis was used to identify lifestyle patterns using baseline self-reported data on nutrition, lifestyle factors, and social engagement activities. Multimorbidity was defined by self-reported physician diagnoses. Multivariable logistic regression was used to estimate odds ratios (ORs) for multimorbidity cross-sectionally, and Cox proportional hazards models were used to assess hazard ratios (HRs) for mortality risk longitudinally. Results: Of 895 participants (mean age: 78.2 years; 56.3% female) with complete lifestyle data, 597 had multimorbidity. Two distinct lifestyle patterns emerged: (i) a nutrition pattern characterised by higher intakes of protein, fibre, iron, zinc, magnesium, potassium, and folate, and (ii) an exercise-sleep-social pattern marked by weekly physical activities like bowling, bicycling, sleep quality (low snoring/sleepiness), and high social engagement. Neither pattern was associated with multimorbidity cross-sectionally. Over a median 5.8-year follow-up (n = 869; 140 deaths), participants in the upper tertiles for combined lifestyle pattern scores had a 20% lower mortality risk than those in the lowest tertile [adjusted HR: 0.80 (95% CI: 0.65–0.97); p-trend = 0.02]. This association was stronger in participants with multimorbidity, with a 29% lower risk [0.71 (0.56–0.89); p-trend = 0.01], likely due to multimorbidity modifying the relationship between nutrition and mortality risk (p-interaction < 0.05). While multimorbidity did not modify the relationship between the exercise-sleep-social pattern and risk of mortality, it was consistently associated with a 19–20% lower risk (p-trend < 0.03), regardless of the multimorbidity status. Conclusions: Older adults with multimorbidity may particularly benefit from adopting healthy lifestyles focusing on nutrition, physical activity, sleep quality, and social engagement to reduce their mortality risk. Full article

Planning in mass-customization supply and manufacturing processes is a complex process that requires continuous planning and optimization to minimize time and cost across a wide variety of choices in large production volumes. While soft computing techniques are widely used for optimizing mass-customization products, they face scalability issues when handling large datasets and rely heavily on manually defined rules, which are prone to errors. In contrast, machine learning techniques offer an opportunity to overcome these challenges by automating rule generation and improving scalability. However, their full potential has yet to be explored. This article proposes a machine learning-based approach to address this challenge, aiming to optimize both the supply and manufacturing planning phases as a practical solution for industry planning or optimization problems. The proposed approach examines supervised machine learning and deep learning techniques for manufacturing time and cost planning in various scenarios of a large-scale real-life pilot study in the bicycle manufacturing domain. This experimentation included K-Nearest Neighbors with regression and Random Forest from the machine learning family, as well as Neural Networks and Ensembles as deep learning approaches. Additionally, Reinforcement Learning was used in scenarios where real-world data or historical experiences were unavailable. The training performance of the pilot study was evaluated using cross-validation along with two statistical analysis methods: the t-test and the Wilcoxon test. These performance evaluation efforts revealed that machine learning techniques outperform deep learning methods and the reinforcement learning approach, with K-NN combined with regression yielding the best results. The proposed approach was validated by industry experts in bicycle manufacturing. It demonstrated up to a 37% reduction in both time and cost for orders compared to traditional expert estimates. Full article

Purpose: Cycling is a popular activity for children aged 5–14 years and has a notable risk of head injuries. Extensive evidence shows that bicycle helmets can reduce the severity of head injuries and prevent fatalities. The current study examines the prevalence of bicycle helmet use among children (aged 5–17 years) in Saudi Arabia, parents’ attitudes and safety perceptions toward children’s bicycle helmets, and factors that influence parents’ decisions regarding their children’s bicycle helmets. Methods: This study used an analytical cross-sectional design via a validated questionnaire to examine parents’ attitudes toward helmet use for their children (aged 5–17 years) in Saudi Arabia. The study, which was carried out from September 2023 to September 2024, involved 492 participants (69.5% mothers and 30.5% fathers), and they were recruited from all regions of Saudi Arabia. A validated and translated questionnaire was used to assess helmet usage attitudes, considering demographic factors and potential confounders. Results: Approximately 60% of children wear helmets while cycling, despite a high mean attitude score of 5.49 (SD = 0.91), with 93.3% of respondents expressing strong support for mandatory helmet laws. While belief (mean (M) = 5.45) and knowledge (M = 4.63) scores were also high, they did not correlate with actual helmet use. Strong helmet regulations significantly increased usage rates (>80%). Helmet ownership and parental helmet-wearing habits were associated with higher usage among children, with mothers showing greater usage rates for younger children than fathers. Regression analyses indicated that parents who wore helmets as children were 5.85 times more likely to have their children wear helmets and parents who wore helmets themselves were 7.98 times more likely to ensure that their oldest child did so. Conclusions: While parents have positive attitudes toward helmet safety, actual helmet usage among children measures at approximately 60%. Sustainable helmet regulations and parental modeling, especially for parents who wear helmets, are crucial for improving safety. Full article

The rapid spread of micromobility vehicles such as bicycles and electric scooters poses new challenges to urban transportation systems, particularly in terms of road safety and infrastructure integration. This study investigates the driving behavior of micromobility users at a mini-roundabout, focusing on their speed profiles and their position within the lane during the entry, circulation, and exit phases. A structured recruitment process was used to select 20 participants with previous micromobility experience. Participants performed crossing maneuvers at a mini-roundabout in Gravina di Catania, Italy, which were monitored using drone footage and analyzed with tracking software to extract trajectories and speed data. The results show significant differences between e-scooter and bicycle users, with bicycles showing less speed variability, especially during the crossing and exit phases, while e-scooters showed greater variability, especially during the entry and exit phases. The results highlight the influence of vehicle stability and user posture on riding behavior and emphasize the need for infrastructure adaptations to increase safety. Mini-roundabouts designed for moderate speed are identified as a promising solution to improve the coexistence of micromobility and motor vehicles. This research identifies key differences in speed profiles and behavioral patterns between e-scooter and bicycle users, offering actionable insights and recommendations for safer and more efficient urban infrastructure. These contributions provide valuable guidance for urban planners and policymakers in promoting safer and more sustainable urban mobility. Full article

Background: Ischemic heart disease (IHD) impacts the quality of life and is the most frequently reported cause of morbidity and mortality globally. Aims: To assess the changes in the exhaled volatile organic compounds (VOCs) in patients with vs. without ischemic heart disease (IHD) confirmed by stress computed tomography myocardial perfusion (CTP) imaging. Objectives: IHD early diagnosis and management remain underestimated due to the poor diagnostic and therapeutic strategies including the primary prevention methods. Materials and Methods: A single center observational study included 80 participants. The participants were aged ≥ 40 years and given an informed written consent to participate in the study and publish any associated figures. Both groups, G1 (n = 31) with and G2 (n = 49) without post stress-induced myocardial perfusion defect, passed cardiologist consultation, anthropometric measurements, blood pressure and pulse rate measurements, echocardiography, real time breathing at rest into PTR-TOF-MS-1000, cardio-ankle vascular index, bicycle ergometry, and immediately after performing bicycle ergometry repeating the breathing analysis into the PTR-TOF-MS-1000, and after three minutes from the end of the second breath, repeat the breath into the PTR-TOF-MS-1000, then performing CTP. LASSO regression with nested cross-validation was used to find the association between the exhaled VOCs and existence of myocardial perfusion defect. Statistical processing performed with R programming language v4.2 and Python v.3.10 [^R], STATISTICA program v.12, and IBM SPSS v.28. Results: The VOCs specificity 77.6% [95% confidence interval (CI); 0.666; 0.889], sensitivity 83.9% [95% CI; 0.692; 0.964], and diagnostic accuracy; area under the curve (AUC) 83.8% [95% CI; 0.73655857; 0.91493173]. Whereas the AUC of the bicycle ergometry 50.7% [95% CI; 0.388; 0.625], specificity 53.1% [95% CI; 0.392; 0.673], and sensitivity 48.4% [95% CI; 0.306; 0.657]. Conclusions: The VOCs analysis appear to discriminate individuals with vs. without IHD using machine learning models. Other: The exhaled breath analysis reflects the myocardiocytes metabolomic signature and related intercellular homeostasis changes and regulation perturbances. Exhaled breath analysis poses a promise result to improve the diagnostic accuracy of the physical stress tests using machine learning models. Full article

Introduction: Diabetic eye disease is the most common microvascular complication of diabetes mellitus. This complication has some direct impact on an individual’s well-being and health. Some lifestyle habits have been associated with the incidence of these co-morbidities. Objective: To classify the diabetic population into sufferers or non-sufferers of diabetes eye disease according to lifestyle and demographic variables, and to identify which of these variables are significant for this classification. Methods: The present cross-sectional study based on the NHANES 2011–2020 used the Classification and Regression Tree (CRT) analysis for classifying the diabetic population into sufferers and non-sufferers of diabetes eye disease. The odds ratio (OR) and relative risks (RR) of suffering this diabetes complication of the subgroups formed by the model were studied. The final sample formed 2657 individuals (1537 males and 1120 females). Results: A 79.4% accuracy was found for the CRT model. The independent variables of sleep hours (100.0%), physical activity (PA) group (92.8%), gender (76.2%), age (46.4%), education level (38.4%), sedentary time (38.1%), and diet (10.0%) were found to be significant for the classification of cases. The variable high alcohol consumption was not found significant. The analysis of the OR and RR of the subgroups formed by the model evidenced greater odds of suffering diabetes eye disease for diabetes sufferers from the inactive and walk/bicycle PA group compared to those from the Low, Moderate, and High PA groups (OR: 1.48 and RR: 1.36), for males compared to females (OR: 1.77 and RR: 1.61), for those sleeping less than 6 h or more than 9 compared to those who sleep between 6 and 8 h (OR: 1.61 and RR: 1.43), and for diabetes sufferers aged over 62 compared to younger ones (OR: 1.53 and RR: 1.40). Conclusions: sleep hours, PA group, gender, age, education level, sedentary time, and diet are significant variables for classifying the diabetic population into sufferers and non-sufferers of diabetes eye disease. Additionally, being in the inactive or walk/bicycle PA group, being a male, sleeping less than 6 or more than 9 h, and being aged over 62 were identified as risk factors for suffering this diabetes complication. 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

In traffic engineering, vehicle speed is a critical determinant of both the risk and severity of road crashes, a fact that holds particularly important for signalized intersections. Accurately selecting vehicle speeds is crucial not only for minimizing accident risks but also for ensuring the proper calculation of intergreen times, which directly influences the efficiency and safety of traffic flow. Traditionally, the design of signal programs relies on fixed speed parameters, such as the posted speed limit or the operational speed, typically represented by the 85th percentile speed from speed distribution data. Furthermore, many design guidelines allow for the selection of these critical speed values based on the designer’s own experience. However, such practices may lead to discrepancies in intergreen time calculations, potentially compromising safety and efficiency at intersections. Our research underscores the substantial variability in the speeds of passenger vehicles traveling intersections under free-flow conditions. This study encompassed numerous intersections with the highest number of accidents, using unmanned aerial vehicles to conduct surveys in three Polish cities: Toruń, Bydgoszcz, and Warsaw. The captured video footage of vehicle movements at predetermined measurement sections was analyzed to find appropriate speeds for various travel maneuvers through these sections, encompassing straight-through, left-turn, and right-turn relations. Our analysis focused on how specific infrastructure-related factors influence driver behavior. The following were evaluated: intersection type, traffic organization, approach lane width, number of lanes, longitudinal road gradient, trams or pedestrian or bicycle crossing presence, and even roadside obstacles such as buildings, barriers or trees, and others. The results reveal that these factors significantly affect drivers’ speed choices, particularly in turning maneuvers. Furthermore, it was observed that the average speeds chosen by drivers at signalized intersections did not reach the permissible speed limit of 50 km/h as established in typical Polish urban areas. A key outcome of our analysis is the recommendation for a more precise speed model that contributes to the design of signal programs, enhancing road safety, and aligning with sustainable transport development policies. Based on our statistical analyses, we propose adopting a more sophisticated model to determine actual vehicle speeds more accurately. It was proved that, using the developed model, the results of calculating the intergreen times are statistically significantly higher. This recommendation is particularly pertinent to the design of signal programs. Furthermore, by improving speed accuracy values in intergreen calculation models with a clear impact on increasing road safety, we anticipate reductions in operational costs for the transportation system, which will contribute to both economic and environmental goals. Full article

To predict the behavior of aromatic contaminants (ACs) in complex soil–plant systems, this study developed machine learning (ML) models to estimate the root concentration factor (RCF) of both traditional (e.g., polycyclic aromatic hydrocarbons, polychlorinated biphenyls) and emerging ACs (e.g., phthalate acid esters, aryl organophosphate esters). Four ML algorithms were employed, trained on a unified RCF dataset comprising 878 data points, covering 6 features of soil–plant cultivation systems and 98 molecular descriptors of 55 chemicals, including 29 emerging ACs. The gradient-boosted regression tree (GBRT) model demonstrated strong predictive performance, with a coefficient of determination (R²) of 0.75, a mean absolute error (MAE) of 0.11, and a root mean square error (RMSE) of 0.22, as validated by five-fold cross-validation. Multiple explanatory analyses highlighted the significance of soil organic matter (SOM), plant protein and lipid content, exposure time, and molecular descriptors related to electronegativity distribution pattern (GATS8e) and double-ring structure (fr_bicyclic). An increase in SOM was found to decrease the overall RCF, while other variables showed strong correlations within specific ranges. This GBRT model provides an important tool for assessing the environmental behaviors of ACs in soil–plant systems, thereby supporting further investigations into their ecological and human exposure risks. Full article

(1) Background: The psychological benefits of cycling have been identified such as the maintenance of low-stress levels. However, no studies have been found addressing the benefits of cycling on variables such as emotional intelligence (EI) and motivational climate (MC), which are important for holistic development in children. This study aimed to investigate the interrelationships between MC, EI, and cycling habits in schoolchildren. (2) Methods: A descriptive, comparative, cross-sectional study was conducted in a sample of 347 Spanish schoolchildren (46.4% boys; 53.6% girls; M_age = 10.55, S.D. = 0.97). A sociodemographic questionnaire, the Trait Meta-Mood Scale (TMMS-24), and the Perceived Motivational Climate in Sport Questionnaire (PMCSQ-2) were applied for data collection. (3) Results: Boys use bicycles more frequently than girls; task-oriented motivational climate (TC) is observed in girls and schoolchildren with moderate cycling habits; ego-involving motivational climate (EC) prevails in boys and students who cycle more than four times per week; no statistical association was found between EI and cycling use habits; and EI and its dimensions correlate with TC and some categories of EC. (4) Conclusions: Moderate cycling habits are linked to a task-oriented MC and have slightly higher scores on general EI and its dimensions. Full article

Endurance athletes often aim to improve their aerobic metabolism. The aim of this pilot study was to examine if malic acid and L-citrulline supplementation can improve aerobic metabolism and lactate elimination. Nine young (23.9 ± 1.9 years) recreational male athletes participated in this study. Following a standardised breakfast and a body composition analysis (InBody720), 6000 mg of citrulline and 3000 mg of malic acid or a placebo of 300 mL of water were consumed on three separate days in a cross-over design using a double-blind method. Sixty minutes after the supplementation, participants completed a ramp bicycle spiroergometer protocol (35 W/3 min) until reaching a respiratory exchange ratio (RER) of 1.1, followed by a 9 min active recovery. Cadence, heart rate (HR), rate of perceived exertion (RPE), respiratory parameters and lactate levels were registered. The RPExHR value was calculated to accurately characterise exhaustion. During the exercise protocol, citrulline supplementation induced significantly lower RER values at 70-105-140 W compared to malic acid and the placebo, respectively. There was no difference in lactate levels neither during rest nor at RER 1.1. RPExHR rate values were significantly lower after malic acid supplementation compared to placebo at 175 and 210 W. Power at RER 1.1 was higher after malic acid (+4 W) and citrulline (+5 W) supplementation. Although the supplementation failed to decrease lactate levels, lower RER and RPE values may indicate a performance-enhancing benefit. Full article

During cycling, prolonged compression by the bicycle saddle on the anatomical structures located in the perineum area occurs. An additional factor that may have a negative impact on organs located in the pelvic area may be a prolonged sitting position resulting in increased intraabdominal pressure. This situation has the potential to adversely affect pelvic floor function. Therefore, the aim of this study was to assess the incidence of lower urinary tract symptoms (LUTSs) in female competitive road cyclists and cross-country cyclists. The study included 76 female competitive road cyclists and cross-country cyclists and 76 women not practising competitive sport. The Core Lower Urinary Tract Symptom Score (CLSS) questionnaire was used to assess the lower urinary tract condition. Female competitive cyclists had a statistically significantly higher LUTSs score (95% CI: 3.12–4.2 vs. 2.31–3.16; p < 0.05) compared to women not practising competitive sports. Female cyclists had a statistically significantly higher overall CLSS score (95% CI: 3.99–5.61 vs. 2.79–3.97; p < 0.05). Female cyclists had a statistically significantly higher incidence and severity of urinary frequency (p < 0.05 and p < 0.02), urge (p < 0.001 and p < 0.02) and stress incontinence (p < 0.001 and p < 0.001), and pain in the bladder (p < 0.01 and p < 0.01), while physically inactive women recorded a statistically higher incidence of slow urinary stream (p < 0.01 and p < 0.04). A statistically significant association was recorded between the years of cycling and the number of hours per week spent on training and the number of symptoms and their severity. The number of natural births experienced by women involved in competitive cycling significantly affects the severity of LUT symptoms. Compared to women not practising competitive sports, competitive female cyclists are found to have a higher prevalence of LUTSs and a greater degree of severity. LUTSs in competitive female cyclists are negatively influenced by years of competitive career and weekly number of training hours and the number of natural births experienced. Full article