Search Results (17,626)

Background: Identifying the correlates of exercise behavior is essential to combating physical inactivity as a public health concern. The purpose of this study is to identify the correlates of physical activity among urban Chinese adults under the social-ecological theoretical model in order to facilitate targeted interventions to promote physical activity. Methods: Using the socio-ecological model, we conducted a questionnaire survey among 1459 urban residents in Zhejiang and Shaanxi provinces of China, collecting data on individual demographic factors, sociological factors, environmental perception, and exercise behavior. Binary logistic regression models were employed to analyze the relationships between exercise behavior and socio-ecological factors. Results: Male gender (p < 0.01), advanced age (p < 0.001), higher education level (p < 0.05), living independently from parents (p < 0.05), absence of childcare responsibilities (p < 0.01), residence in a county/prefecture-level city (p < 0.001), favorable neighborhood esthetics (p < 0.001), available greenways/parks (p < 0.001), and family support for exercise participation (p < 0.05) were significantly correlated with an increased likelihood of participating in physical activity. Male gender (p < 0.001), advanced age (p < 0.001), absence of childcare responsibilities (p < 0.05), good neighborhood vegetation (p < 0.01), availability of free neighborhood exercise facilities (p < 0.05), and support from friends for exercise participation (p < 0.01) were significantly correlated with an increased likelihood of engaging in physical activity for more than 150 min per week. BMI, community air quality, traffic safety, public safety, and level of social development were not major correlates. Conclusions: To promote exercise behavior, more attention should be paid to individuals who are female, young, have lower levels of education, bear childcare responsibilities, or reside in provincial capitals in China. Improving the habitat environment and providing convenient and affordable facilities should also be considered. Furthermore, support from family and friends can positively reinforce exercise behavior. Full article

To mitigate the impact of wind power uncertainty and power–communication coupling on the robustness of a new power system, a bi-level mixed-integer robust optimization strategy is proposed. Firstly, a coupled network model is constructed based on complex network theory, taking into account the coupled relationship of energy supply and control dependencies between the power and communication networks. Next, a bi-level mixed-integer robust optimization model is developed to improve power system resilience, incorporating constraints related to the coupling strength, electrical characteristics, and traffic characteristics of the information network. The upper-level model seeks to minimize load shedding by optimizing DC power flow using fuzzy chance constraints, thereby reducing the risk of power imbalances caused by random fluctuations in wind power generation. Furthermore, the deterministic power balance constraints are relaxed into inequality constraints that account for wind power forecasting errors through fuzzy variables. The lower-level model focuses on minimizing traffic load shedding by establishing a topology–function-constrained information network traffic model based on the maximum flow principle in graph theory, thereby improving the efficiency of network flow transmission. Finally, a modified IEEE 39-bus test system with intermittent wind power is used as a case study. Random attack simulations demonstrate that, under the highest link failure rate and wind power penetration, Model 2 outperforms Model 1 by reducing the load loss ratio by 23.6% and improving the node survival ratio by 5.3%. Full article

The monitoring of carbon emissions is increasingly becoming a sustainability issue worldwide. Despite being largely unnoticed, the toxic gas carbon monoxide (CO) is ubiquitous in mechanized tunnel driving, but the individual sources, release and enrichment mechanisms are often unknown. In this study, the generation of CO from organic matter containing sedimentary rocks was investigated during mechanized tunnel driving and by reacting claystone and sandstone with 10 mM NaCl solutions for 2 months at 70 °C and 140 °C. The mineralogical and geochemical evolution of the solids and fluids was assessed by CO measurements and the XRD, DTA, TOC, IC and ICP-OES methods. The CO concentration in the atmosphere reached up to 1920 ppm (100 ppm on average) during tunnel driving, which is more than three times higher than the legal daily average dose for tunnellers, thus requiring occupational safety operations. Mineral-specific dissolution processes and the rapid decomposition of labile organic matter upon thermal alteration contributed to the liberation of CO and also carbon dioxide (CO₂) from the host rocks. In mechanized tunnel driving, frictional heat and ‘cold’ combustion with temperatures reaching 50–70 °C at the drill head is an important mechanism for increased CO and CO₂ generation, especially during drilling in sedimentary rocks containing significant amounts of OM and when the ventilation of the tunnel atmosphere and air mixing are limited. Under such conditions, human health damage due to CO exposure (HHD_CO) can be 30 times higher compared to tunnel outlets, where CO is emitted from traffic. Full article

The automatic recognition and quantitative analysis of abnormal behavior in mice play a crucial role in behavioral observation experiments in neuroscience, pharmacology, and toxicology. Due to the challenging definition of abnormal behavior and difficulty in collecting training samples, directly applying behavior recognition methods to identify abnormal behavior is often infeasible. This paper proposes ABNet, an AI-empowered abnormal action recognition approach for mice. ABNet utilizes an enhanced Spatio-Temporal Graph Convolutional Network (ST-GCN) as an encoder; ST-GCN combines graph convolution and temporal convolution to efficiently capture and analyze spatio-temporal dynamic features in graph-structured data, making it suitable for complex tasks such as action recognition and traffic prediction. ABNet trains the encoding network with normal behavior samples, then employs unsupervised clustering to identify abnormal behavior in mice. Compared to the original ST-GCN network, the method significantly enhances the capabilities of feature extraction and encoding. We conduct comprehensive experiments on the Kinetics-Skeleton dataset and the mouse behavior dataset to evaluate and validate the performance of ABNet in behavior recognition and abnormal motion detection. In the behavior recognition experiments conducted on the Kinetics-Skeleton dataset, ABNet achieves an accuracy of 32.7% for the top one and 55.2% for the top five. Moreover, in the abnormal behavior analysis experiments conducted on the mouse behavior dataset, ABNet achieves an average accuracy of 83.1%. Full article

Travel time variability (TTV) is a crucial indicator of transportation network performance, assessing travel time reliability and delays. This study investigates TTV metrics within the context of shared mobility using probe data from transportation network companies (TNCs) in Chicago, Los Angeles, and Dallas–Fort Worth. Eight reliability metrics are analyzed and compared for each origin–destination (OD) pair in the network, including standard deviation (SD), the Planning Time Index (PTI), the Travel Time Index (TTI), the Buffer Index (BI), On-time Measures PR (alpha), and the Misery Index (MI), to evaluate their effectiveness in clustering OD pairs using K-means clustering. The findings confirm that SD, PTI, and MI are particularly effective in measuring travel time reliability and clustering within urban systems. This study identifies the most unbalanced supply–demand OD pairs/regions in each city, noting that low/medium-SD clusters around metropolitan airports indicate stable travel times even in high-demand zones, while high-SD clusters in downtown areas reveal significant traffic demands and unreliability. These patterns become more pronounced in study areas with multiple city centers. This study highlights the need for targeted strategies to enhance travel time reliability, particularly in regions like Dallas–Fort Worth, where public transportation alternatives are limited. Full article

Currently, the dominant gated communities (GCs) in Chinese cities have fragmented the urban road network, causing traffic congestion, energy consumption, carbon emissions, and environmental pollution. The morphological elements of GCs are key factors affecting road network connectivity. This paper aimed to explore the influence of the morphological elements of GCs on road network connectivity, to provide a quantitative basis for the evaluation and renovation of the connectivity of GCs, and to provide insights for urban planning and policy. This paper quantitatively analyzed the connectivity of GCs using 120 samples from the central districts of Jinan, China. Morphological elements were the independent variables, while route directness (RD) and the network distance (D) to the nearest entrance were the dependent variables. RD measured the internal connectivity, and D measured the connectivity between the internal and external road networks of GCs. GIS was used to measure RD and D, and SPSS was used to conduct a correlation analysis to identify significant variables. Multiple linear regression and LASSO regression were used to test the influence of these factors on RD and D. LASSO regression was employed to construct prediction models for RD and D. We found that intersection density had the greatest impact on RD, while the number of entrances and exits, and the scale of GCs, had the greatest impact on D. Using thresholds of D = 250 and RD = 1.3, the four types of GCs were classified and corresponding renovation measures were proposed. Full article

Compared to maritime vessel traffic accidents, there is a scarcity of available, and only incomplete, accident data for inland waterway accidents. Additionally, the characteristics of different waterway segments vary significantly, and the factors affecting navigation safety risks and their mechanisms may also differ. Meanwhile, in recent years, extreme weather events have been frequent in inland waterways, and there has been a clear trend towards larger vessels, bringing about new safety hazards and management challenges. Currently, research on inland waterway navigation safety risks mainly focuses on risk assessment, with scarce quantitative studies on risk mitigation measures. This paper proposes a new method for improving inland waterway traffic safety, based on a cost-effectiveness trade-off approach to mitigate the risk of vessel traffic accidents. The method links the effectiveness and cost of measures and constructs a comprehensive cost-benefit evaluation model using fuzzy Bayesian and quantification conversion techniques, considering the reduction effects of risk mitigation measures under uncertain conditions and the various costs they may incur. Taking the upper, middle, and lower reaches of the Yangtze River as examples, this research evaluates key risk mitigation measures for different waterway segments and provides the most cost-effective strategies. Findings reveal that, even if different waterways share the same key risk sources, the most cost-effective measures vary due to environmental differences. Moreover, there is no inherent correlation between the best-performing measures in terms of benefits and the lowest-cost measures, nor are they necessarily recommended. The proposed method and case studies provide theoretical support for scientifically formulating risk mitigation measures in complex environments and offer guidance for inland waterway management departments to determine future key work directions. Full article

In underwater communications for 6G, Doppler effects cause the coherent time to become similar to or shorter than the orthogonal frequency division multiplexing (OFDM) symbol length. Conventional time and frequency synchronization methods require additional training symbols for synchronization, which reduces the traffic data rate. This paper proposes the Zadoff–Chu sequence (ZCS) pilot-based OFDM for time and frequency synchronization. The proposed method transmits ZCS as a pilot for OFDM symbols and simultaneously transmits traffic data to increase the traffic data rate while estimating the CFO at each coherence time. For time–frequency synchronization, the correlation of the ZCS pilot is used to perform coarse and fine time and frequency synchronization in two stages. Since the traffic data cause interference with the correlation of ZCS pilots, we theoretically analyzed the relationship between the amount of traffic data and interference and verified it through computer simulations. The synchronization and BER performance of the proposed ZCS pilot-based OFDM were evaluated by conduction computer simulations and a practical ocean experiment. Compared to the methods of Ren, Yang, and Avrashi, the proposed method demonstrated a 6.3% to 14.3% increase in traffic data rate with similar BER performance and a 2 dB to 3.8 dB SNR gain for a 14.3% to 23.8% decrease in traffic data rate. Full article

The application of nanomaterials as modifiers in the field of asphalt is increasingly widespread, and this paper aims to systematically review research on the impact of nanomaterials on asphalt viscosity. The results find that nanomaterials tend to increase asphalt’s viscosity, enhancing its resistance to high-temperature rutting and low-temperature cracking. Zero-dimension nanomaterials firmly adhere to the asphalt surface, augmenting non-bonding interactions through van der Waals forces and engaging in chemical reactions to form a spatial network structure. One-dimensional nanomaterials interact with non-polar asphalt molecules, forming bonds between tube walls, thereby enhancing adhesion, stability, and resistance to cyclic loading. Meanwhile, these bundled materials act as reinforcement to transmit stress, preventing or delaying crack propagation. Two-dimensional nanomaterials, such as graphene and graphene oxide, participate in chemical interactions, forming hydrogen bonds and aromatic deposits with asphalt molecules, affecting asphalt’s surface roughness and aggregate movement, which exhibit strong adsorption capacity and increase the viscosity of asphalt. Polymers reduce thermal movement and compact asphalt structures, absorbing light components and promoting the formation of a cross-linked network, thus enhancing high-temperature deformation resistance. However, challenges such as poor compatibility and dispersion, high production costs, and environmental and health concerns currently hinder the widespread application of nanomaterial-modified asphalt. Consequently, addressing these issues through comprehensive economic and ecological evaluations is crucial before large-scale practical implementation. Full article

Abstract: The study proposes an active trailer steering control method for tractor semitrailers to promote the path tracking effect of the trailer portion as well as lateral stability during lane changing. Firstly, a simplified model of a tractor semitrailer is constructed, and the MAP map is formed based on the genetic algorithm for the identification of the key parameters, which improves the model’s accuracy. Then the tractor and trailer’s yaw rate and sideslip angle at CG are tracked as the control objective and the trailer angle distribution strategy is given. Then the LQR-based corner controller is designed to control the steering actuators of each axle of the trailer. Finally, the effectiveness of the designed control strategy is verified based on the Trucksim/Simulink joint simulation platform and the semi-physical HiL test platform. The simulation results show that the designed controller can effectively improve the path tracking effect of the tractor and the trailer, and at the same time, the lateral stability parameters of the tractor and the trailer are also significantly improved, which improves the driving stability of the tractor semitrailer. Full article

One-shot devices are products that can only be used once. Typical one-shot devices include airbags, fire extinguishers, inflatable life vests, ammo, and handheld flares. Most of them are life-saving products and should be highly reliable in an emergency. Quality control of those productions and predicting their reliabilities over time is critically important. To assess the reliability of the products, manufacturers usually test them in controlled conditions rather than user conditions. We may rely on public datasets that reflect their reliability in actual use, but the datasets often come with missing observations. The experimenter may lose information on covariate readings due to human errors. Traditional missing-data-handling methods may not work well in handling one-shot device data as they only contain their survival statuses. In this research, we propose Multiple Imputation with Unsupervised Learning (MIUL) to impute the missing data using Hierarchical Clustering, k-prototype, and density-based spatial clustering of applications with noise (DBSCAN). Our simulation study shows that MIUL algorithms have superior performance. We also illustrate the method using datasets from the Crash Report Sampling System (CRSS) of the National Highway Traffic Safety Administration (NHTSA). Full article

Road congestion in Amman City has been increasing yearly, due to the increase in private car ownership and traffic volumes. This study aims to (a) evaluate the toll road’s effects on society and the economy in Amman, Jordan, through a survey questionnaire using statistical software (SPSS), (b) assess the impact of the toll road on reducing congestion and delays using micro-simulation (VISUM), (c) identify the optimal toll price for a selected road using VISUM and (d) validate the simulated models with the optimal revenue. Traffic, geometric, and cost data about the toll technique of two sections on the Airport Highway (from the Ministry of Foreign Affairs to the Madaba Interchange; and from the Madaba Interchange to the Queen Alia International Airport (QAIA) Interchange) were used for simulation purposes. The toll road (across seven different scenarios at different prices) was evaluated for optimal revenue. The survey questionnaire was made based on all scenarios, including the AM peak hour. The operation cost for the toll road was determined based on the Greater Amman Municipality (GAM). The best scenario was determined based on the value of revenue (JOD). The results indicate that higher acceptance is achieved when applying road pricing during the AM peak hour and that users prefer the charging method based on travelled distance (54.02%). Additionally, the total cost of the manual toll collection (MTC) method is 126,935 JOD. Road pricing can reduce traffic delay (or speed up traffic flow) by 4.61 min in the southbound direction and by 9.52 min in the northbound direction. The optimal toll value is 0.25 JOD (34.08%), with revenues of 1089.6 JOD for 2024 and 1122.6 JOD for 2025. Eventually, applying road pricing on the airport road is shown to be effective and economically feasible only when using the manual method. Full article

This article presents the results of the automatic monitoring of the concentrations of gaseous impurities of sulfur and nitrogen oxides in the ground-level atmosphere of the urban and rural areas in the Southern Baikal region (East Siberia, Russia). The study was conducted from 2020 to 2023 at the urban Irkutsk station and the rural Listvyanka station located at a distance of 70 km from each other. We calculated the main statistical characteristics of the variations in the concentrations of nitrogen oxides and sulfur dioxide in the ground-level atmosphere and determined a nature of variability in their concentrations on various time scales: annual, weekly, and daily. Annual variabilities of gaseous pollutants in the ground-level atmosphere above the Irkutsk city and the Listvyanka settlement were similar and showed the highest values in winter and the lowest in summer. The daily and weekly dynamics of the nitrogen oxide concentrations in the urban area clearly depended on the increase in the road traffic during rush hours (morning and evening). In the rural area, there was no such dependence. In this area, the daily and weekly variability in the concentrations of nitrogen oxides and sulfur dioxide mainly depended on natural meteorological processes. The work systematizes the meteorological parameters at which the largest amount of anthropogenic impurities enters the air basin of Lake Baikal. The maximum values of acid-forming gas concentrations were observed when the air masses were transferred from the northwest direction, which corresponds to the location of sources in the territory of the Irkutsk–Cheremkhovo industrial hub—the largest concentration of anthropogenic objects in the Irkutsk region. Full article

Bus lanes are commonly implemented to ensure absolute priority for buses at signalized intersections. However, while prioritizing buses, existing bus lane management strategies often exacerbate traffic demand imbalances among lanes. To address this issue, this paper proposes a dynamic Multi-Function Lane (MFL) management strategy. The proposed strategy transforms traditional bus lanes into Multi-Function Lanes (MFLs) that permit access to Connected and Automated Vehicles (CAVs). By fully utilizing the idle right-of-way of the MFL, the proposed strategy can achieve traffic efficiency improvement. To evaluate the proposed strategy, some experiments are conducted under various demand levels and CAV penetration rates. The results reveal that the proposed strategy (i) improves the traffic intensity balance degree by up to 52.9 under high demand levels; (ii) reduces delay by up to 80.56% and stops by up to 89.35% with the increase in demand level and CAV penetration rate; (iii) guarantees absolute bus priority under various demand levels and CAV penetration rates. The proposed strategy performs well even when CAV penetration is low. This indicates that the proposed strategy has the potential for real-world application. Full article

Contamination with potentially toxic elements (PTEs) frequently occurs in surface water in coal mining areas. This study analyzed 34 surface water samples collected from the Yunnan–Guizhou Plateau for their hydrochemical characteristics, spatial distribution, source apportionment, and human health risks. Our statistical analysis showed that the average concentrations of PTEs in the surface water ranked as follows: Fe > Al > Zn > Mn > Ba > B> Ni > Li > Cd > Mo > Cu > Co > Hg > Se > As > Pb > Sb. The spatial analysis revealed that samples with high concentrations of Fe, Al, and Mn were predominantly distributed in the main stream, Xichong River, and Yangchang River. Positive matrix factorization (PMF) identified four sources of PTEs in the surface water. Hg, As, and Se originated from wastewater discharged by coal preparation plants and coal mines. Mo, Li, and B originated from the dissolution of clay minerals in coal seams. Elevated concentrations of Cu, Fe, Al, Mn, Co, and Ni were attributed to the dissolution of kaolinite, illite, chalcopyrite, pyrite, and minerals associated with Co and Ni in coal seams. Cd, Zn, and Pb were derived from coal melting and traffic release. The deterministic health risks assessment showed that 94.12% of the surface water samples presented non-carcinogenic risks below the health limit of 1. Meanwhile, 73.56% of the surface water samples with elevated As posed level III carcinogenic risk to the local populations. Special attention to drinking water safety for children is warranted due to their lower metabolic capacity for detoxifying PTEs. This study provides insight for PTE management in sustainable water environments. Full article