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Vessel big data play a significant role in understanding vessel behaviors and thus facilitating the prosperity of waterway transportation. However, relevant research regarding vessel trajectory recognition in a broad range of narrow channels still lacks, especially using VITS data. The major objective of this paper is to conduct vessel trajectory analysis based on the novel VITS data and examine its availability in inland waterway vessel transportation. An alternate aim is to develop a more comprehensive framework to extract the vessel trajectory of multiple narrow waterways. This paper utilized vessel trajectory information of multiple narrow channels belonging to Yangtze River captured by VITS. Four compression algorithms were conducted. Additionally, the performances of three clustering approaches were evaluated. Speed distribution analysis was also implemented. The results indicated that slide window (SW) algorithm outperforms its other counterparts. Relative to DBSCAN, K-means and hierarchical clustering analysis (HCA) tend to be more capable of balanced classification. This paper is the first to utilize VITS data in vessel trajectory feature extraction and can potentially provide useful insight for vessel trajectory extraction in multiple narrow channels. [ABSTRACT FROM AUTHOR]

Road traffic accidents regarding commercial vehicles have been demonstrated as an important culprit restricting the steady development of the social economy, which are closely related to the distracted behavior of drivers. However, the existing driver's distracted behavior surveillance systems for monitoring and preventing the distracted behavior of drivers still have some shortcomings such as fewer recognition objects and scenarios. This study aims to provide a more comprehensive methodological framework to demonstrate the significance of enlarging the recognition objects, scenarios and types of the existing driver's distracted behavior recognition systems. The driver's posture characteristics were primarily analyzed to provide the basis of the subsequent modeling. Five CNN sub-models were established for different posture categories and to improve the efficiency of recognition, accompanied by a holistic multi-cascaded CNN framework. To suggest the best model, image data sets of commercial vehicle driver postures including 117,410 daytime images and 60,480 night images were trained and tested. The findings demonstrate that compared to the non-cascaded models, both daytime and night cascaded models show better performance. Besides, the night models exhibit worse accuracy and better speed relative to their daytime model counterparts for both non-cascaded and cascaded models. This study could be used to develop countermeasures to improve driver safety and provide helpful information for the design of the driver's real-time monitoring and warning system as well as the automatic driving system. Future research could be implemented to combine the vehicle state parameters with the driver's microscopic behavior to establish a more comprehensive proactive surveillance system. [ABSTRACT FROM AUTHOR]

Background: Low concentrations of S100B have neurotrophic effects and can promote nerve growth and repair, which plays an essential role in the pathophysiological and histopathological alterations of major depressive disorder (MDD) during disease development. Studies have shown that plasma S100B levels are altered in patients with MDD. In this study, we investigated whether the plasma S100B levels in MDD differ between genders. Methods: We studied 235 healthy controls (HCs) (90 males and 145 females) and 185 MDD patients (65 males and 120 females). Plasma S100B levels were detected via multifactor assay. The Mahalanobis distance method was used to detect the outliers of plasma S100B levels in the HC and MDD groups. The Kolmogorov–Smirnov test was used to test the normality of six groups of S100B samples. The Mann–Whitney test and Scheirer-Ray-Hare test were used for the comparison of S100B between diagnoses and genders, and the presence of a relationship between plasma S100B levels and demographic details or clinical traits was assessed using Spearman correlation analysis. Results: All individuals in the HC group had plasma S100B levels that were significantly greater than those in the MDD group. In the MDD group, males presented significantly higher plasma S100B levels than females. In the male group, the plasma S100B levels in the HC group were significantly higher than those in the MDD group, while in the female group, no significant difference was found between the HC and MDD groups. In the male MDD subgroup, there was a positive correlation between plasma S100B levels and years of education. In the female MDD subgroup, there were negative correlations between plasma S100B levels and age and suicidal ideation. Conclusions: In summary, plasma S100B levels vary with gender and are decreased in MDD patients, which may be related to pathological alterations in glial cells. [ABSTRACT FROM AUTHOR]

• The "linkage" role of micro-kinetic parameters between road characteristics and accidents was discovered. • The longitudinal kinetic characteristics of tires can reflect the risk of rear-end accidents. • Demonstrates changes in vehicle operating on high-risk segments through micro-kinetic parameters. • Proposed a relatively novel modeling method. • Reveals the reliability of micro-kinetic parameters in the field of accident analysis. Freeway accidents are a leading cause of death in China, which also triggers substantial economic loss and an emotional burden to society. However, the internal mechanism of how microscopic kinetic parameters of vehicles influenced by road characteristics determine the occurrence of different types of accidents has not been explicitly studied. This research aimed to explore the "link role" of tire microscopic kinetic parameters in road characteristic variables and traffic accidents to aid in facilitating the traffic design and management, and thus to prevent traffic accident. Method: A mountain freeway in Zhejiang Province, China was used as the research object and the data used in this paper were obtained through a real-time vehicle experiment. Multiple estimation models, including the standard ordered logit (SOL) model, fixed parameters logit (FPL) model, and random parameters logit (RPL) model were established. Results: The findings show that road characteristics will affect the longitudinal kinetic characteristics of the vehicle and, consequently, map the level of risk of rear-end accidents. Driving compensation effects were also identified in this paper (i.e., the drivers tend to be more cautious in complicated driving circumstances). Another finding relating to the mountain freeway is that different tunnel characteristics (e.g., tunnel entrance and tunnel exit) have different effects on different types of traffic accidents. Practical Applications: The framework proposed in this article can provide new insight for researchers to enlarge the research subjects of both explanatory and outcome variables in accident analysis. Future research could be implemented to consider more driving conditions. [ABSTRACT FROM AUTHOR]

Investigating the relationship between the months and traffic crashes is a foremost task for the safety improvement of mountainous freeways. Taking a mountainous freeway located in China as an example, this paper proposed a combined modeling framework to identify the relationships between months and different crash types. K-means and Apriori were initially used to extract the monthly distribution patterns of different types of crashes. A graphical approach and a risk calculation equation were developed to assess the output of K-means and Apriori. Then, using the assessment results as the input, a logistic regression model was constructed to quantify the effects of each month on crashes. The results indicate that the monthly distribution patterns of different crash types are inconsistent, i.e., for a specific month, the high risk of a certain crash type may be covered up if experts only focus on the total number of crashes. Moreover, when identified as high-risk months by K-means and Apriori, the crash-proneness will significantly increase several times than months identified as high-risk by only one of K-means and Apriori, thereby illustrating the superior performance of the mix-method. The conclusions can assist local relevant organizations in formulating strategies for preventing different types of traffic crashes in different months (e.g., the risk of rear-end crashes in August, the risk of fixed-object hitting crashes in February, and the risk of overturning crashes in October) and provide a methodological reference for relevant studies in other regions. [ABSTRACT FROM AUTHOR]

Background: L-theanine, a non-protein amino acid was found principally in the green tea, has been previously shown to exhibit potent anti-obesity property and hepatoprotective effect. Herein, we investigated the effects of L-theanine on alleviating nonalcoholic hepatic steatosis in vitro and in vivo, and explored the underlying molecular mechanism. Methods: In vitro, HepG2 and AML12 cells were treated with 500 μM oleic acid (OA) or treated with OA accompanied by L-theanine. In vivo, C57BL/6J mice were fed with normal control diet (NCD), high‐fat diet (HFD), or HFD along with L-theanine for 16 weeks. The levels of triglycerides (TG), accumulation of lipid droplets and the expression of genes related to hepatocyte lipid metabolic pathways were detected in vitro and in vivo. Results: Our data indicated that, in vivo, L-theanine significantly reduced body weight, hepatic steatosis, serum levels of alanine transaminase (ALT), aspartate transaminase (AST), TG and LDL cholesterol (LDL-C) in HFD-induced nonalcoholic fatty liver disease (NAFLD) mice. In vitro, L-theanine also significantly alleviated OA induced hepatocytes steatosis. Mechanic studies showed that L-theanine significantly inhibited the nucleus translocation of sterol regulatory element binding protein 1c (SREBP-1c) through AMPK-mTOR signaling pathway, thereby contributing to the reduction of fatty acid synthesis. We also identified that L-theanine enhanced fatty acid β-oxidation by increasing the expression of peroxisome proliferator–activated receptor α (PPARα) and carnitine palmitoyltransferase-1 A (CPT1A) through AMP-activated protein kinase (AMPK). Furthermore, our study indicated that L-theanine can active AMPK through its upstream kinase Calmodulin-dependent protein kinase kinase-β (CaMKKβ). Conclusions: Taken together, our findings suggested that L-theanine alleviates nonalcoholic hepatic steatosis by regulating hepatocyte lipid metabolic pathways via the CaMKKβ-AMPK signaling pathway. [ABSTRACT FROM AUTHOR]

The tensile performance of ductile tubes can be enhanced by the application of fillers. Research studies on the mechanical performance of filled tensile tubes have mainly focused on experiments and numerical simulations on concrete-filled steel tube (CFST) components, while the effects of factors such as size, section pattern, and material of filled tensile tubes on their performance have rarely been studied. In this research, the effects of size, section pattern, and material on the tensile performance of filled tubes have been evaluated through theoretical studies, simulations, and experiments. The tensile strength reinforcement and deformation weakening coefficients of filled circular thin-walled tubes corresponding to hollow tubes were theoretically deduced, and the influencing factors of the two were parametrically evaluated. Tensile performances of filled tubes with circular and square sections were compared with each other through numerical methods. In the current research, the circular section was optimized and prestressed circular hollow support section was proposed. Tensile fracture tests were performed on circular thin-walled tubes made of six different materials to determine material effects on the tensile performance of these structures. It was also found that metallic materials with good ductility significantly enhanced the tensile performance, fracture toughness, and energy consumption of test components containing prestressed filler. [ABSTRACT FROM AUTHOR]

Despite the broad application of advanced fleet management systems (FMSs) in third-party logistics (3PL) companies, there is a marginally limited understanding of how to employ them to enhance transport energy efficiency. In a case study of a Chinese 3PL company, this paper analyzed data obtained from the online FMS to assess drivers' awareness of fuel-saving factors. A questionnaire was primarily designed to investigate the drivers' awareness of fuel-saving factors based on the reliability and validity test. Then, Extreme Gradient Boosting (XGBoost), a machine learning algorithm, was utilized to explore the intrinsic impacts of various factors on fuel consumption with the outputs providing the evaluation basis for individual awareness of the drivers. The results show a significant deviation in the driver's awareness of fuel-saving factors, among which the three indicators of engine speed, idling condition, and rolling without engine load are seriously underestimated, while the indicators related to the environment are seriously overestimated due to social expectations. In addition, the average speed was found to be the most important fuel-saving indicator besides the load. Based on these findings, this paper recommends that the 3PL companies choose a route with more freeways when planning, and the mileage should be controlled within 800 km as far as possible. [ABSTRACT FROM AUTHOR]

Crash severity prediction has been raised as a key problem in traffic accident studies. Thus, to progress in this area, in this study, a thorough artificial neural network combined with an improved metaheuristic algorithm was developed and tested in terms of its structure, training function, factor analysis, and comparative results. Data from I5, an interstate highway in the Washington State during the period of 2011–2015, were used for fitting and prediction, and after setting the theoretical three-layer neural network (NN), an improved Particle Swarm Optimization (PSO) method with adaptive inertial weight was offered to optimize the NN, and finally, a comparison among different adaptive strategies was conducted. The results showed that although the algorithms produced almost the same accuracy in their predictions, a backpropagation method combined with a nonlinear inertial weight setting in PSO produced fast global and accurate local optimal searching, thereby demonstrating a better understanding of the entire model explanation, which could best fit the model, and at last, the factor analysis showed that non-road-related factors, particularly vehicle-related factors, are more important than road-related variables. The method developed in this study can be applied to a big data analysis of traffic accidents and be used as a fast-useful tool for policy makers and traffic safety researchers. [ABSTRACT FROM AUTHOR]

Globally, the use of electric vehicles, and in particular the use of electric buses, has been increasing. The city of Nanjing leads China in the adoption of electric buses, supported by city policies and infrastructure. To lower costs and provide a better service, vehicle selection is crucial, however, existing selection methods are limited. Accordingly, Nanjing Bus Company developed a test method based on road tests to select a bus. This paper presents a detailed description of the test method and a case study of its application. The method included an organization structure, selection of eight test vehicles (four 10 m length, four 8 m length) from four brands (a total of 32 test vehicles), selection of indicators and selection of routes. Data was collected from repeated drives by 65 drivers over an 8-week period. Indicators included power consumption, charging duration, failure duration and driving distance. It is concluded that the road test method designed and conducted by the Nanjing Bus Company provides a good framework for the selection of pure electric buses. Furthermore, subsequent experience with selected buses supports the validity and value of the model. [ABSTRACT FROM AUTHOR]