Truck model recognition for an automatic overload detection system based on the improved MMAL-Net

Efficient and reliable transportation of goods through trucks is crucial for road logistics. However, the overloading of trucks poses serious challenges to road infrastructure and traffic safety. Detecting and preventing truck overloading is of utmost importance for maintaining road conditions and ensuring the safety of both road users and goods transported. This paper introduces a novel method for detecting truck overloading. The method utilizes the improved MMAL-Net for truck model recognition. Vehicle identification involves using frontal and side truck images, while APPM is applied for local segmentation of the side image to recognize individual parts. The proposed method analyzes the captured images to precisely identify the models of trucks passing through automatic weighing stations on the highway. The improved MMAL-Net achieved an accuracy of 95.03% on the competitive benchmark dataset, Stanford Cars, demonstrating its superiority over other established methods. Furthermore, our method also demonstrated outstanding performance on a small-scale dataset. In our experimental evaluation, our method achieved a recognition accuracy of 85% when the training set consisted of 20 sets of photos, and it reached 100% as the training set gradually increased to 50 sets of samples. Through the integration of this recognition system with weight data obtained from weighing stations and license plates information, the method enables real-time assessment of truck overloading. The implementation of the proposed method is of vital importance for multiple aspects related to road traffic safety

Molecular Composition of Oxygenated Organic Molecules and Their Contributions to Organic Aerosol in Beijing

The understanding at a molecular level of ambient secondary organic aerosol (SOA) formation is hampered by poorly constrained formation mechanisms and insufficient analytical methods. Especially in developing countries, SOA related haze is a great concern due to its significant effects on climate and human health. We present simultaneous measurements of gas-phase volatile organic compounds (VOCs), oxygenated organic molecules (OOMs), and particle-phase SOA in Beijing. We show that condensation of the measured OOMs explains 26-39% of the organic aerosol mass growth, with the contribution of OOMs to SOA enhanced during severe haze episodes. Our novel results provide a quantitative molecular connection from anthropogenic emissions to condensable organic oxidation product vapors, their concentration in particle-phase SOA, and ultimately to haze formation.Peer reviewe

Utilizing MRBQ to investigate risky rider behavior in Chinese young riders: combining the effect of Big Five personality and sensation seeking

The motorcycle rider behavior questionnaire (MRBQ) is one of the most extensively used questionnaires to explore risky rider behavior worldwide. However, whether previous research adopted other scales or other versions of MRBQ, neither of them fully cover the typicality of the risky behavior in Chinese motorcyclists. Moreover, past research investigated the MRBQ while combining the joint effect of Big Five personality (BFP) and sensation seeking lacks. Our study aims to revise the Chinese version of MRBQ in young riders and explore the relationship among BFP, sensation seeking, MRBQ, and self-reported traffic violations. 278 online participants filled out the Big Five Inventory measuring BFP, the sensation seeking scale, MRBQ items selected from previous versions in other countries, and self-reported traffic violations from the traffic management system (crashes, traffic violation frequency, penalty points, and fines). Exploratory factor analysis suggested 7 factors (safety equipment, traffic errors, speed violations, control errors, stunts, traffic violations, and safety violations), and the internal consistency reliability ranged from 0.58&ndash;0.91. The hierarchical linear regression analysis showed that agreeableness and conscientiousness in BFP negatively predicted the total MRBQ score, while openness in BFP and sensation seeking positively predicted the total MRBQ score. In addition, the Poisson regression analysis suggested that all kinds of self-reported traffic violations could be positively predicted by the total MRBQ score. Path analysis suggested the fully mediating role of sensation seeking. In conclusion, the Chinese version of the MRBQ is useful for future studies and the sensation seeking plays a mediating role between the Big Five personality and MRBQ.</p

Trait anger causes risky driving behavior by influencing executive function and hazard cognition

Drivers with a high level of trait anger feel more intensity of anger on road, contributing to more risky driving behavior and further increasing the probability of collisions. It seems that trait anger directly correlates with risky driving behavior, but how it works in detail remains unknown and previous research indicated executive function and hazard cognition may play a mediation role in it. Our research aims to explore the relationship among these variables and test if there is a multiple mediation model. We sampled 302 valid participants and used online questionnaires, containing trait anger scale (TAS), executive function index (EFI), hazard cognition scale (HCS; representing attitudes towards risky driving behavior), driver behavior questionnaire (DBQ), and self-reported traffic violations (e.g., accidents, penalty points, fines). Hierarchical multiple linear regression of DBQ results show trait anger is a medium but statistically significant predictor of risky driving behavior and drivers&rsquo; attitude towards risky situations can significantly predict risky driving behavior at medium effect. But risky driving behavior cannot be predicted by executive function. Interestingly, opposing to prior research, zero-inflated Poisson regression analysis of self-reported traffic violations suggests trait anger negatively predicts accidents and fines in the zero-inflation model, and hazard cognition negatively predicts penalty points. Notably, the executive function negatively predicts penalty points and fines in the count model, which confirms our hypothetical direction. They all represent a small effect size in this nonlinear regression model. Path analysis suggested that trait anger influences risky driving behavior through executive function, and hazard cognition both separately and jointly. This study provides a theoretical framework for the transaction model of aggressive driving behavior and offers some possible interventions toward the effect of trait anger on risky driving behavior.</p

The effect of perceived global stress and altruism on prosocial driving behavior, yielding behavior, and yielding attitude

Objective: Traffic accidents are mainly caused by driver-to-pedestrian collisions or driver-to-driver collisions. Prosocial driving behavior indicates that drivers exhibit altruistic behavior toward other drivers on roads. Yielding behavior demonstrates that drivers grant the right of passage to pedestrians at unsignalized crossings, while yielding attitude presents the subjective emotional and cognitive inclination to yield to pedestrians at unsignalized crossings. This study aims to explore the effect of altruism and drivers&#39; perceived stress on prosocial driving behavior, yielding behavior, and yielding attitude. In addition, we endeavor to explore the effect of stress on prosocial driving behavior exhibiting an inverted &quot;U-type&quot; curve as Yerkes-Dodson&#39;s law suggests and test the moderating role of perceived stress on altruism and prosocial driving behavior/yielding behavior/yielding attitude.Methods: Using a survey method, we asked 454 participants to complete an altruism scale from the IPIT measuring altruism, a Perceived Stress Scale-10 measuring drivers&#39; perceived stress, a prosocial driving scale from the PADI measuring prosocial driving behavior, and items on yielding behavior and yielding attitude. Then, a correlational matrix, a hierarchical multiple nonparametric regression analysis, and a moderating analysis of perceived stress were employed in sequence to reach our objective.Results: The hierarchical multiple nonparametric regression analysis showed that altruism positively predicts yielding attitude (F = 41.56, p &lt; 0.001), yielding behavior (z = 8.46, p &lt; 0.001, odds ratio = 4.90) and prosocial driving behavior (F = 110.66, p &lt; 0.001), but stress predicts only prosocial driving behavior (F = 7.63, p &lt; 0.001), not yielding attitude (F = 0.51, p &gt; 0.05) or yielding behavior (z = 0.12, p &gt; 0.05), which exhibits an inverted &quot;U-type&quot; curve. Moderating analyses showed that stress only moderates the relationship between altruism and yielding attitude (B = -0.24, t = -2.62, p &lt; 0.01).Conclusions: Altruism is positively related to prosocial driving behavior, yielding behavior, and yielding attitude. Stress influences prosocial driving behavior only and exhibits an inverted &quot;U-type&quot; curve. Stress does not directly influence the yielding behavior. Instead, stress moderates the relationship between altruism and yielding attitude only and may further increase the possibility of yielding behavior.</p

An Approach to Integrated Scheduling of Flexible Job-Shop Considering Conflict-Free Routing Problems

This study proposes an approach to minimize the maximum makespan of the integrated scheduling problem in flexible job-shop environments, taking into account conflict-free routing problems. A hybrid genetic algorithm is developed for production scheduling, and the optimal ranges of crossover and mutation probabilities are also discussed. The study applies the proposed algorithm to 82 test problems and demonstrates its superior performance over the Sliding Time Window (STW) heuristic proposed by Bilge and the Genetic Algorithm proposed by Ulusoy (UGA). For conflict-free routing problems of Automated Guided Vehicles (AGVs), the genetic algorithm based on AGV coding is used to study the AGV scheduling problem, and specific solutions are proposed to solve different conflicts. In addition, sensors on the AGVs provide real-time data to ensure that the AGVs can navigate through the environment safely and efficiently without causing any conflicts or collisions with other AGVs or objects in the environment. The Dijkstra algorithm based on a time window is used to calculate the shortest paths for all AGVs. Empirical evidence on the feasibility of the proposed approach is presented in a study of a real flexible job-shop. This approach can provide a highly efficient and accurate scheduling method for manufacturing enterprises

The effect of perceived global stress and altruism on prosocial driving behavior, yielding behavior, and yielding attitude

Traffic accidents are mainly caused by driver-to-pedestrian collisions or driver-to-driver collisions. Prosocial driving behavior indicates that drivers exhibit altruistic behavior toward other drivers on roads. Yielding behavior demonstrates that drivers grant the right of passage to pedestrians at unsignalized crossings, while yielding attitude presents the subjective emotional and cognitive inclination to yield to pedestrians at unsignalized crossings. This study aims to explore the effect of altruism and drivers’ perceived stress on prosocial driving behavior, yielding behavior, and yielding attitude. In addition, we endeavor to explore the effect of stress on prosocial driving behavior exhibiting an inverted “U-type” curve as Yerkes-Dodson’s law suggests and test the moderating role of perceived stress on altruism and prosocial driving behavior/yielding behavior/yielding attitude. Using a survey method, we asked 454 participants to complete an altruism scale from the IPIT measuring altruism, a Perceived Stress Scale-10 measuring drivers’ perceived stress, a prosocial driving scale from the PADI measuring prosocial driving behavior, and items on yielding behavior and yielding attitude. Then, a correlational matrix, a hierarchical multiple nonparametric regression analysis, and a moderating analysis of perceived stress were employed in sequence to reach our objective. The hierarchical multiple nonparametric regression analysis showed that altruism positively predicts yielding attitude (F = 41.56, p z = 8.46, p F = 110.66, p F = 7.63, p F = 0.51, p > 0.05) or yielding behavior (z = 0.12, p > 0.05), which exhibits an inverted “U-type” curve. Moderating analyses showed that stress only moderates the relationship between altruism and yielding attitude (B = −0.24, t = −2.62, p  Altruism is positively related to prosocial driving behavior, yielding behavior, and yielding attitude. Stress influences prosocial driving behavior only and exhibits an inverted “U-type” curve. Stress does not directly influence the yielding behavior. Instead, stress moderates the relationship between altruism and yielding attitude only and may further increase the possibility of yielding behavior.</p

Transient Analysis of Grout Penetration With Time-Dependent Viscosity Inside 3D Fractured Rock Mass by Unified Pipe-Network Method

Grouting is widely used for mitigating the seepage of underground water and enhancing the stability of fractured rock mass. After injection, the viscosity of the grout gradually increases until solidification. Conventional multifield analysis models ignoring such effects greatly overestimate the penetration region of the grout and the stability of the grouted rock structures. Based on the 3D unified pipe-network method (UPM), we propose a novel numerical model considering the time-dependent viscosity of the grout, therein being a quasi-implicit approach of high efficiency. The proposed model is verified by comparing with analytical results and a time-wise method. Several large-scale 3D examples of fractured rock mass are considered in the numerical studies, demonstrating the effectiveness and robustness of the proposed method. The influence of the time-dependent viscosity, fracture properties, and grouting operation methods are discussed for the grout penetration process