Effects of insomnia on risky driving behavior among bus drivers: The mediating effect of mental health.

Crashes caused by problems with bus drivers' physical and mental health have increased in frequency in recent years. Insomnia, a common type of sleep problem, has significant positive relationships with both crash risk and mental health problems, especially anxiety and depression, which are themselves associated with driving behavior. However, few studies have conducted analysis on sleep-related problems and mental health exclusively on bus drivers, nor on how these problems influence driving performance. Thus, this study explored the effect of insomnia and mental health on bus drivers' risky driving behavior and evaluated the interaction of four variables: insomnia, anxiety, depression, and risky driving behavior. The survey-based investigation was conducted in a bus company in Suzhou, China, with 1,295 bus drivers participating. Insomnia, anxiety, and depression were self-reported based on professional mental health scales and risky driving behaviors were measured by the Driver Behavior Questionnaire. Two mediation models and a chain mediation model were developed to examine relationships among the bus drivers' insomnia, anxiety, depression, and risky driving behavior. Results revealed that (a) bus drivers less than 31 years old, drivers with more than 11 years' experience driving buses, and those with crash and violation involvement within three years demonstrated more severe degrees of insomnia, anxiety, depression, and risky driving behavior; (b) there were significant positive correlations and interactions among the four variables. Results specifically related to the interaction among variables include findings that (a) anxiety mediated between insomnia and risky driving behavior; (b) depression mediated between insomnia and risky driving behavior; and (c) anxiety affected bus drivers' risky driving behavior primarily though depression. The findings in this study indicate the importance of regular physical and mental health examination of bus drivers and suggest that interventions focused on insomnia and mental health problems may be helpful to reduce risky driving behaviors of bus drivers both directly and indirectly.

Revision of the driver behavior questionnaire for Chinese drivers' aberrant driving behaviors using naturalistic driving data.

The Manchester Driver Behavior Questionnaire (DBQ) is a widely used self-reported measure of aberrant driving behaviors. It provides a standardized way of evaluating drivers' safety awareness and motivation, but the effectiveness of the DBQ's application in different regions can be influenced by culture, social norms, and time period. Several studies have adjusted DBQ items to reflect driver behavior native to particular regions or times, but few have used objective measurements to make proper adjustments. A naturalistic driving study (NDS) provides vehicle kinematic data and in-vehicle videos that objectively capture actual driving behaviors. The gender, age, and driving experience characteristics of aberrant driving behaviors were analyzed, and, based on comparisons between the DBQ self-reported driving behaviors and those observed in the Shanghai, China, NDS, the existing items from the Manchester DBQ were subsequently adjusted. Sixty-two types of real-world aberrant driving behaviors were extracted from 490 valid crash and near crash events observed in the Shanghai NDS. Aberrant driving behavior rates were calculated for individual characteristics (gender, age, and driving experience), and factor rates were calculated based on the three DBQ factor types of violation, error, and lapse. Results revealed that (a) male drivers, drivers in their thirties, and those with three to five years of driving experience demonstrated higher rates of aberrant driving behaviors; and (b) there were weak correlations between observed NDS factor rates and self-reported DBQ scores, and only slight differences among drivers divided by factor rate level (e.g., high violation rate). The questionnaire calibrated for Chinese drivers includes 23 items. Five of the original 24 DBQ items were modified, eight were left unchanged, eleven were deleted, and ten field-observed combined behaviors were added. In addition to the importance of adjusting the DBQ for today's Chinese drivers, this study provides a method for objectively modifying DBQ items in the future in accord with observed driving behaviors in an NDS.

Precise preparation of biomass-based porous carbon with pore structure-dependent VOCs adsorption/desorption performance by bacterial pretreatment and its forming process.

Pore distribution characteristic is one of the most crucial factors for porous adsorption materials, and the variety of volatile organic compounds (VOCs) approaches about how to simply and accurately tailor practical porous carbons for VOCs adsorption has gradually attracted attention. Here, precursors with different lignocellulose mass ratios have been used to produce porous carbon for model experiments to investigate the influence of the precursor lignocellulose contents on the pore structure and distribution characteristics of porous carbon, and the applicability of these mechanisms to real biomass materials has been further verified through bacteria-targeted bagasse decomposition: the microvolumes of ultra-micropores have decreased with decrease in cellulose contents, while mesopores have followed the reverse trend. The dynamic toluene adsorption/desorption performances of the obtained samples have been tested. The BACs-36 exhibits high toluene adsorption performance in low concentration with 635 mg/g while the BACs-48 shows excellent reusability in 10 times cycles. Based on this the balance between the adsorptive and regenerative capacities has been observed which indicates that carbon materials with abundant micropores and narrow mesopores have much better adsorption performance than porous carbon with a hierarchical pore structure, while the latter show better regeneration abilities than the former, which resulting in less desorption as a counter-acting force at the pore wall. Furthermore, the porous carbon has been shaped by one-step co-pyrolysis method using phenolic resin, which can not only maintain the hardness but also can avoid pore plugging phenomenon.

Preparing hierarchical porous carbon with well-developed microporosity using alkali metal-catalyzed hydrothermal carbonization for VOCs adsorption.

Biomass-derived porous carbonaceous materials are efficient adsorbents for VOCs, but their traditional preparation method, pyrolysis combined with activation, suffers from high energy consumption, equipment corrosion, and low pore-making efficiency, which hinders their large-scale practical application. A novel method of alkali metal-catalyzed hydrothermal carbonization coupling with chemical activation for the preparation of microporous carbon is presented. Porous carbon with well-developed microporosity deriving from corn husk were prepared through the hydrothermal carbonization using potassium persulfate (K2S2O8) as a catalyst and programmed heating activation process. And the products were applied to removal of typical oxygen-containing VOCs, ethyl acetate. The addition of K2S2O8 in hydrothermal carbonization accelerated the biomass hydrolysis, decomposed the biopolymer, and formed functional hydrochars. Potassium salts introduced into the hydrochars, which acted as an activator in this programmed heating activation process, formed a great deal of micropores. The specific surface area of micropores increased by 81%, and the specific surface area of micropores less than 1 nm increased by 180%. The introduction of K2S2O8 in preparation improved the adsorption performance of CH-based porous carbons 16.46% and 60.00% respectively at different preparation temperatures (600 °C and 800 °C). Basing on these results, the improvement of micropores less than 1 nm is directly related to the adsorption performance. This indicates that pores (<1 nm) respond well to the adsorption of ethyl acetate.

Key factors and primary modification methods of activated carbon and their application in adsorption of carbon-based gases: A review.

To achieve carbon neutrality, it is necessary to control carbon-based gas emissions to the atmosphere. Among the various carbon-based gas removal technologies reported to date, adsorption is considered one of the most promising because of its economic efficiency, reusability, and low energy consumption. Activated carbon is widely used to treat different types of carbon-based gases owing to its large specific surface area, abundant functional groups, and strong adsorption capacity. This paper reviews the recent research progress into activated carbon as an adsorbent for carbon-based gases. The key factors (i.e., specific surface area, pore structure, and surface functional groups) affecting the adsorption of carbon-based gases by activated carbon were analyzed. The main methods employed to modify activated carbon (i.e., surface oxidation, surface reduction, loading materials, and plasma modification methods) to improve its adsorption capacity are also discussed herein, along with the targeted applications of such material in the adsorption of different types of carbon-based gases (such as aldehydes, ketones, aromatic hydrocarbons, halogenated hydrocarbons, and carbon-based greenhouse gases). Finally, the future development directions and challenges of activated carbon are discussed. Our work will be expected to benefit the development of activated carbon exhibiting selective adsorption properties, and reduce the production costs of adsorbents.

Analysis of safety climate and individual factors affecting bus drivers' crash involvement using a two-level logit model.

Although traffic crashes involving buses are less frequent than those involving other vehicle types, the consequences of bus crashes are high due to the potential for multiple injuries and casualties. As driver error is a primary factor affecting bus crashes, driver safety education is one of the main countermeasures used to mitigate crash risk. In China, however, safety education is not as focused as it should be, largely due to the limited research identifying the specific driver behaviors, and potential influences on those behaviors, that are correlated with crashes. The aim of this study is, therefore, to explore the fleet- and driver-level risk factors underlying bus drivers' self-reported crash involvement, including analyzing the effect of psychological distress on the most influential driver-level factors. A survey was conducted of 725 drivers from a large Shanghai bus company, and a random-effects two-level logit model was developed to integrate fleet and individual variables. Results showed that: 1) the fleet-level safety climate explained about 8.5% of the model's variance, indicating it was a valid predictor of self-reported crash involvement; 2) the driver-level factors of drivers' age, seniority, marital status, positive behavior, and driving anger influenced drivers' self-reported crash involvement, but ordinary violations, lapses, aggressive violations, and insomnia were the most influential variables; 3) psychological distress appeared to associate with the high frequency of risky driving behavior and the high severity of driving anger. This study's findings will help bus companies to give more attention to their safety climate and implement more targeted improvements to their driver safety education programs.

Preparation of porous carbon based on partially degraded raw biomass by Trichoderma viride to optimize its toluene adsorption performance.

Volatile organic compounds (VOCs), which are usually organic compounds with boiling point in the range of 50 to 260°C, pose a serious threat to human health and ecological environment. In order to find an adsorbent with excellent adsorption effect on VOCs, activated carbon was prepared from corn bran partially degraded by Trichoderma viride, and the adsorption performance of the optimized porous carbon materials on toluene was studied. Physical and chemical properties (such as specific surface area, pore size distribution, and surface functional groups) of the activated carbon were characterized by scanning electron microscope (SEM), N2 adsorption/desorption experiences, Fourier-transform infrared (FTIR), and Raman and X-ray diffraction (XRD). The results showed that the specific surface area of corn bran reached 1896 m2/g and the total pore volume was 1.04 cm3/g after 15 days of microbial pretreatment. Dynamic simulation of adsorption experiment found that the saturated adsorption capacity of the pretreated carbon material was 237 mg/g at 100 ppm toluene concentration, which was 1.58 times of that of corn bran without microbial pretreatment. Generally, the improvement of adsorption performance may be mainly attributed to the increase of specific surface area, pore volume and the decrease of surface acidic groups.

A Two-Phase Cross-Modality Fusion Network for Robust 3D Object Detection.

A two-phase cross-modality fusion detector is proposed in this study for robust and high-precision 3D object detection with RGB images and LiDAR point clouds. First, a two-stream fusion network is built into the framework of Faster RCNN to perform accurate and robust 2D detection. The visible stream takes the RGB images as inputs, while the intensity stream is fed with the intensity maps which are generated by projecting the reflection intensity of point clouds to the front view. A multi-layer feature-level fusion scheme is designed to merge multi-modal features across multiple layers in order to enhance the expressiveness and robustness of the produced features upon which region proposals are generated. Second, a decision-level fusion is implemented by projecting 2D proposals to the space of the point cloud to generate 3D frustums, on the basis of which the second-phase 3D detector is built to accomplish instance segmentation and 3D-box regression on the filtered point cloud. The results on the KITTI benchmark show that features extracted from RGB images and intensity maps complement each other, and our proposed detector achieves state-of-the-art performance on 3D object detection with a substantially lower running time as compared to available competitors.