Replacing conventional buses with electric buses is in line with the concept of sustainable development. However, electric buses have the disadvantages of short driving range and high purchase price. Many cities must implement a semi-electrification strategy for bus routes. In this paper, a bi-level, multi-objective programming model is established for the collaborative scheduling problem of vehicles and drivers on a bus route served by the mixed bus fleet. The upper-layer model minimizes the operation cost and economic cost of carbon emission to optimize the vehicle and charging scheme; while the lower-layer model tries to optimize the crew-scheduling scheme with the objective of minimizing driver wages and maximizing the degree of bus-driver specificity, considering the impact of drivers’ labor restrictions. Then, the improved multi-objective particle swarm algorithm based on an $\epsilon$-constraint processing mechanism is used to solve the problem. Finally, an actual bus route is taken as an example to verify the effectiveness of the model. The results show that the established model can reduce the impact of unbalanced vehicle scheduling in mixed fleets on crew scheduling, ensure the degree of driver–bus specificity to standardize operation, and save the operation cost and driver wage.

Traffic safety is a critical factor for the sustainable development of urban transportation systems. This study investigates the impact of historical accident information on the prediction of future traffic accident risks, as well as the interaction between this information and other features, such as driver violations and vehicle attributes. Using a comprehensive dataset of traffic accidents involving passenger vehicles in a western Chinese city, we developed two predictive models: Model 1, which is based on vehicle information and driver violations, and Model 2, which integrates historical accident data. The results indicate that the inclusion of historical accident information significantly enhances the predictive performance of the model, particularly in terms of AUC (Area Under the Curve) and AP (Average Precision) values. Furthermore, through feature importance analysis and SHAP (SHapley Additive exPlanations) value evaluation, this study reveals the interaction effects between historical accident data and other features, and how these interactions influence model decisions. The findings suggest that historical accident data play a positive role in predicting future accident risk, with varying effects on risk mitigation. These insights provide a scientific basis for developing strategies to ensure the sustainable development of urban transportation systems.

Charging piles in the bus depot provide charging services to multiple electric bus (EB) routes operating in the area. As charging needs may overlap between independently operated routes, EB fleets often have to wait in line for charging. However, affected by the ambient temperature, the length of the waiting time will cause the battery temperature to change at the beginning of each charging, thereby influencing the charging performance and charging time of the battery. To this end, this paper considers the influence of ambient temperature on battery charging performance, and collaboratively optimizes the number of charging piles in the bus depot and the scheduling problem of EB charging. Aiming at minimizing the cost of laying charging piles in bus stations and the charging costs of bus fleets, as well as minimizing the empty time of electric bus fleets and waiting time for charging in queues, a mixed-integer nonlinear programming model is established, and the immune algorithm is used to solve it. At last, an actual bus depot and four EB routes are taken as examples for verification. The results show that by optimizing the charging waiting time of the electric bus at the bus station, the rapid decline in charging performance caused by the sharp drop in battery temperature is avoided. Without increasing the charging cost of the electric bus fleet, the established method reduces the charging pile installation cost, improves the bus depot’s service efficiency, and ensures the punctuality and integrity of the regional bus route operation.

Peanuts are susceptible to defects such as insect damage, breakage, germinant, and mildew, leading to varying qualities of peanuts. The disparity in peanut kernel quality results in significant differences in their prices and economic value. Conducting real-time, accurate, and non-destructive quality inspections of peanut kernels can effectively increase the utilization rate and commercial value of peanuts. Manual inspections are inefficient and subjective, while photoelectric sorting is costly and less precise. Therefore, this study proposes a peanut kernel quality detection algorithm based on an enhanced YOLO v5 model. Compared to other models, this model is practical, highly accurate, lightweight, and easy to integrate. Initially, YOLO v5s was chosen as the foundational training model through comparison. Subsequently, the original backbone network was replaced with a lightweight ShuffleNet v2 network to improve the model’s ability to differentiate features among various types of peanut kernels and reduce the parameters. The ECA (Efficient Channel Attention) mechanism was introduced into the C3 module to enhance feature extraction capabilities, thereby improving average accuracy. The CIoU loss function was replaced with the alpha-IoU loss function to boost detection accuracy. The experimental results indicated that the improved model, SEA-YOLOv5, achieved an accuracy of 98.8% with a parameter count of 0.47 M and an average detection time of 11.2 ms per image. When compared to other detection models, there was an improvement in accuracy, demonstrating the effectiveness of the proposed peanut kernel quality detection model. Furthermore, this model is suitable for deployment on resource-limited embedded devices such as mobile terminals, enabling real-time and precise detection of peanut kernel quality.

Introduction: With the escalating demands for agricultural product quality in modern agriculture, peanuts, as a crucial economic crop, have their pod appearance quality directly influencing market value and consumer acceptance. Traditionally, the visual inspection of peanut pod appearance quality relies heavily on manual labor, which is not only labor-intensive and inefficient but also susceptible to subjective judgments from inspectors, thereby compromising the consistency and accuracy of inspection outcomes. Consequently, the development of a rapid, accurate, and automated inspection system holds significant importance for enhancing production efficiency and quality control in the peanut industry.Methods: This study introduces the optimization and iteration of the YOLOv5s model, aiming to swiftly and precisely identify high-quality peanuts, peanuts with mechanical damage, moldy peanuts, and germinated peanuts. The CSPDarkNet53 network of the YOLOv5s model was substituted with the ShuffleNetv2 backbone network to reduce the model’s weight. Various attention mechanisms were explored for integration and substitution with the backbone network to enhance model performance. Furthermore, the substitution of various loss functions was investigated, with the Focal-EIoU loss function employed as the regression loss term for predicting bounding boxes, thereby improving inference accuracy.Results: Compared to the YOLOv5s network model, SSE-YOLOv5s boasts a mere 6.7% of the original model’s parameters, 7.8% of the computation, and an FPS rate 115. 1% higher. Its weight size is a mere 7.6% of the original model’s, while the detection accuracy and mean average precision (mAP) reach 98.3% and 99.3%, respectively, representing improvements of 1.6 and 0.7 percentage points over the original YOLOv5s model.Discussion: The results underscore the superiority of the SSE-YOLOv5s model, which achieves a maximum mAP of 99.3% with a minimal model size of 1. 1MB and a peak FPS of 192.3. This optimized network model excels in rapidly, efficiently, and accurately detecting the appearance quality of mixed multi-target peanut pods, making it suitable for deployment on embedded devices. This study provides an essential reference for multi-target appearance quality inspection of peanut pods.

To improve its cleaning performance, the peanut sheller’s air–screen cleaning device was used as the research object, and a numerical simulation was carried out. A scheme was proposed to optimize the installation angle of the screen surface of the air–screen cleaning device, and the influence of the airflow velocity of the sieve surface before and after the structural improvement of the machine on the airflow distribution of the whole chamber was evaluated. At the same time, the computational fluid dynamics–discrete element method (CFD–DEM) of coupling simulation was used to carry out single-factor simulation tests on the improved air–screen cleaning device, analyze the movement trajectory, velocity, and displacement of the extrudates in the cleaning chamber, characterize the separation law of extrudates, and determine the parameter ranges for each factor. In addition, based on the central composite Box–Behnken design, an orthogonal simulation test was carried out with three factors, including fan speed, amplitude, and vibration frequency, and the influence of each factor on the kernel loss rate and impurity rate was investigated. Furthermore, the influencing factors were optimized, and the optimal parameter combination was obtained; when the fan speed was 1682.72 r/min, the amplitude was 3.74 mm, and the vibration frequency was 492.86 Hz, it was more conducive to the cleaning of kernels. Finally, the accuracy of the simulation and optimization methods was verified via the machine test, and the optimal parameter combination was obtained: at a fan speed of 1680 r/min, an amplitude of 3.7 mm and a vibration frequency of 490 Hz were achieved, and the kernel loss rate and impurity rate were 2.01% and 2.42%, 3.71 and 4.42 percentage points lower than those before optimization, respectively.