Fatigue at the wheel: A non-visual approach to truck driver fatigue detection by multi-feature fusion.

BACKGROUND: Monitoring of long-haul truck driver fatigue state has attracted considerable interest. Conventional fatigue driving detection methods based on the physiological and visual features are scarcely applicable, due to the intrusiveness, reliability, and cost-effectiveness concerns. METHODS: We elaborately developed a fatigue driving detection method by fusion of non-visual features derived from the customized wristbands, vehicle-mounted equipment, and trip logs. To capture the spatiotemporal information within the sequential data, the bidirectional long short-term memory network with attention mechanism was proposed to determine whether the truck driver was fatigued within a fine-grained episode of one minute. The model was validated using a natural driving dataset with nine truck drivers on real-world roads in Guiyang, China during June and July 2021. RESULTS: Our approach yielded 99.21 %, 84.44 %, 82.01 %, 99.63 %, and 83.21 % in accuracy, precision, recall, specificity, and F1-score, respectively. Compared with the mainstream visual-based methods, our approach outperformed particularly in terms of precision and recall. Photoplethysmogram stood out as the most important feature for truck driver fatigue state detection. Vehicle load, driving forward angle, cumulative driving time, midnight, and recent working hours were found to be positively associated with the probability of fatigue driving, while the galvanic skin response, vehicle acceleration, current time, and recent rest hours had a negative relationship. Specifically, truck drivers were more likely to fatigue when driving at 20-40 km/h, braking abruptly at 5-10 m/s2, with vehicle loads over 70 tons, and driving more than 100 min consecutively. CONCLUSIONS: Our study is among the first to harness the natural driving dataset to delve into the real-life fatigue pattern of long-haul truck drivers without disruptions on routine driving tasks. The proposed method holds pragmatic prospects by providing a privacy-preserving, robust, real-time, and non-intrusive technical pathway for truck driver fatigue monitoring.

Analysis of the injury severity of motor vehicle-pedestrian crashes at urban intersections using spatiotemporal logistic regression models.

This paper conducted a comprehensive study on the injury severity of motor vehicle-pedestrian crashes at 489 urban intersections across a dense road network based on high-resolution accident data recorded by the police from 2010 to 2019 in Hong Kong. Given that accounting for the spatial and temporal correlations simultaneously among crash data can contribute to unbiased parameter estimations for exogenous variables and improved model performance, we developed spatiotemporal logistic regression models with various spatial formulations and temporal configurations. The results indicated that the model with the Leroux conditional autoregressive prior and random walk structure outperformed other alternatives in terms of goodness-of-fit and classification accuracy. According to the parameter estimates, pedestrian age, head injury, pedestrian location, pedestrian actions, driver maneuvers, vehicle type, first point of collision, and traffic congestion status significantly affected the severity of pedestrian injuries. On the basis of our analysis, a range of targeted countermeasures integrating safety education, traffic enforcement, road design, and intelligent traffic technologies were proposed to improve the safe mobility of pedestrians at urban intersections. The present study provides a rich and sound toolkit for safety analysts to deal with spatiotemporal correlations when modeling crashes aggregated at contiguous spatial units within multiple years.

Electrospinning Novel Sodium Alginate/MXene Nanofiber Membranes for Effective Adsorption of Methylene Blue.

Understanding how to develop highly efficient and robust adsorbents for the removal of organic dyes in wastewater is crucial in the face of the rapid development of industrialization. Herein, d-Ti3C2Tx nanosheets (MXene) were combined with sodium alginate (SA), followed by electrospinning and successive Ca2+-mediated crosslinking, giving rise to a series of SA/MXene nanofiber membranes (NMs). The effects of the MXene content of the NMs on the adsorption performance for methylene blue (MB) were investigated systemically. Under the optimum MXene content of 0.74 wt.%, SA/MXene NMs possessed an MB adsorption capacity of 440 mg/g, which is much higher than SA/MXene beads with the same MXene content, pristine MXene, or electrospinning SA NMs. Furthermore, the optimum SA/MXene NMs showed excellent reusability. After the adsorbent was reused ten times, both the MB adsorption capacity and removal rate could remain at 95% of the levels found in the fresh samples, which indicates that the electrospinning technique has great potential for developing biomass-based adsorbents with high efficiency.

Can egg yolk antibodies terminate the CSBV infection in apiculture?

Chinese sacbrood virus (CSBV) is the most severe pathogen of Apis cerana, which leads to serious fatal diseases in bee colonies and eventual catastrophe for the Chinese beekeeping industry. Additionally, CSBV can potentially infect Apis mellifera by bridging the species barrier and significantly affect the productivity of the honey industry. Although several approaches, such as feeding royal jelly, traditional Chinese medicine, and double-stranded RNA treatments, have been employed to suppress CSBV infection, their practical applicabilities are constrained due to their poor effectiveness. In recent years, specific egg yolk antibodies (EYA) have been increasingly utilized in passive immunotherapy for infectious diseases without any side effects. According to both laboratory research and practical use, EYA have demonstrated superior protection for bees against CSBV infection. This review provided an in-depth analysis of the issues and drawbacks in this field in addition to provide a thorough summary of current advancements in CSBV studies. Some promising strategies for the synergistic study of EYA against CSBV, including the exploitation of novel antibody drugs, novel TCM monomer/formula determination, and development of nucleotide drugs, are also proposed in this review. Furthermore, the prospects for the future perspectives of EYA research and applications are presented. Collectively, EYA would terminate CSBV infection soon, as well as will provide scientific guidance and references to control and manage other viral infections in apiculture.

An empirical analysis of factors contributing to roadway infrastructure damage from expressway accidents: A Bayesian random parameters Tobit approach.

This paper presents an empirical analysis of factors contributing to roadway infrastructure damage from expressway accidents, using a Bayesian random parameters Tobit model. The accident data collected from Kaiyang Expressway, China in 2014 and 2015 are used for the empirical analysis. The results of parameter estimation in the proposed model indicate that: the effects of vehicle types are significantly heterogeneous across observations, and that the effects of horizontal curvature, time of day, vehicle registered province, and accident type are also significant but homogeneous across observations. The marginal effects of these contributing factors are calculated to explicitly quantify their impacts on road infrastructure damage. According to the analysis results, some strategies pertaining to safety education, traffic enforcement, roadway design, and intelligence transportation technology are advocated to reduce road infrastructure damage from expressway accidents.

[Example of rectification for infusion pump on the radiated emission test].

This article describes the measure to decrease the emission of a domestic infusion pump which can't pass the radiated emission test when performing EMC tests. Then optimizes the measure and evaluates whether these measures affect the original electrical safety of the pump.