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

Transcriptomic evidence for involvement of reactive oxygen species in Rhizoctonia solani AG1 IA sclerotia maturation

Rhizoctonia solani AG1 IA is a soil-borne fungal phytopathogen that can significantly harm crops resulting in economic loss. This species overwinters in grass roots and diseased plants, and produces sclerotia that infect future crops. R. solani AG1 IA does not produce spores; therefore, understanding the molecular mechanism of sclerotia formation is important for crop disease control. To identify the genes involved in this process for the development of disease control targets, the transcriptomes of this species were determined at three important developmental stages (mycelium, sclerotial initiation, and sclerotial maturation) using an RNA-sequencing approach. A total of 5,016, 6,433, and 5,004 differentially expressed genes (DEGs) were identified in the sclerotial initiation vs. mycelial, sclerotial maturation vs. mycelial, and sclerotial maturation vs. sclerotial initiation stages, respectively. Moreover, gene ontology (GO) and kyoto encyclopedia of genes and genomes (KEGG) analyses showed that these DEGs were enriched in diverse categories, including oxidoreductase activity, carbohydrate metabolic process, and oxidation-reduction processes. A total of 12 DEGs were further verified using reverse transcription quantitative PCR. Among the genes examined, NADPH oxidase 1 (NOX1) and superoxide dismutase (SOD) were highly induced in the stages of sclerotial initiation and maturation. In addition, the highest reactive oxygen species (ROS) production levels were detected during sclerotial initiation, and enzyme activities of NOX1, SOD, and catalase (CAT) matched with the gene expression profiles. To further evaluate the role of ROS in sclerotial formation, R. solani AG1 IA was treated with the CAT inhibitor aminotriazole and H2O2, resulting in the early differentiation of sclerotia. Taken together, this study provides useful information toward understanding the molecular basis of R. solani AG1 IA sclerotial formation and maturation, and identified the important role of ROS in these processes

A numerical-analysis-based optimization method for location selection for planning residential areas in grid transportation networks

China’s economy has been rapidly growing over the past few decades; however, increasingly more modern cities are facing problems caused by poor city planning, such as traffic networks, resource distribution and routine management. Optimization methods are urgently needed during city development. From location planning perspective, the residential area issue is discussed in this paper. Based on this issue, a numerical-analysis-based method is proposed for selecting proper residential areas in grid transportation networks. First, a grid layout is introduced to formalize a real traffic network. Furthermore, to guarantee its approximation, a considerable amount of data are obtained from real-world scenarios based on the shortest routes to demonstrate the problem of traffic jams. Then, a quantitative evaluation system is proposed, which quantitatively evaluates the specified location with the traffic flow distribution index, traffic congestion index and infrastructure convenience index. Each index reflects the attitude of citizens from a unique perspective, which affects the final location planning and selection. Finally, an analytic hierarchy process is designed to analyse all these indices together for a comprehensive analysis, and case studies and experiments are conducted to demonstrate the effectiveness of the proposed method. The generated residential location can be considered as a technical reference for further city planning and development decisions

Digital Twin-Driven Rear Axle Assembly Torque Prediction and Online Control

During the assembly process of the rear axle, the assembly quality and assembly efficiency decrease due to the accumulation errors of rear axle assembly torque. To deal with the problem, we proposed a rear axle assembly torque online control method based on digital twin. First, the gray wolf-based optimization variational modal decomposition and long short-term memory network (GWO-VMD-LSTM) algorithm was raised to predict the assembly torque of the rear axle, which solves the shortcomings of unpredictable non-stationarity and nonlinear assembly torque, and the prediction accuracy reaches 99.49% according to the experimental results. Next, the evaluation indexes of support vector machine (SVM), recurrent neural network (RNN), LSTM, and SVM, RNN, and LSTM based on gray wolf optimized variational modal decomposition (GWO-VMD) were compared, and the performance of the GWO-VMD-LSTM is the best. For the purpose of solving the insufficient information interaction capability problem of the assembly line, we developed a digital twin system for the rear axle assembly line to realize the visualization and monitoring of the assembly process. Finally, the assembly torque prediction model is coupled with the digital twin system to realize real-time prediction and online control of assembly torque, and the experimental testing manifests that the response time of the system is about 1 s. Consequently, the digital twin-based rear axle assembly torque prediction and online control method can significantly improve the assembly quality and assembly efficiency, which is of great significance to promote the construction of intelligent production line

An Improved Entropy-Weighted Topsis Method for Decision-Level Fusion Evaluation System of Multi-Source Data

Due to the rapid development of industrial internet technology, the traditional manufacturing industry is in urgent need of digital transformation, and one of the key technologies to achieve this is multi-source data fusion. For this problem, this paper proposes an improved entropy-weighted topsis method for a multi-source data fusion evaluation system. It adds a fusion evaluation system based on the decision-level fusion algorithm and proposes a dynamic fusion strategy. The fusion evaluation system effectively solves the problem of data scale inconsistency among multi-source data, which leads to difficulties in fusing models and low fusion accuracy, and obtains optimal fusion results. The paper then verifies the effectiveness of the fusion evaluation system through experiments on the multilayer feature fusion of single-source data and the decision-level fusion of multi-source data, respectively. The results of this paper can be used in intelligent production and assembly plants in the discrete industry and provide the corresponding management and decision support with a certain practical value

Digital Twin-Driven Adaptive Scheduling for Flexible Job Shops

The traditional shop floor scheduling problem mainly focuses on the static environment, which is unrealistic in actual production. To solve this problem, this paper proposes a digital twin-driven shop floor adaptive scheduling method. Firstly, a digital twin model of the actual production line is established to monitor the operation of the actual production line in real time and provide a real-time data source for subsequent scheduling; secondly, to address the problem that the solution quality and efficiency of the traditional genetic algorithm cannot meet the actual production demand, the key parameters in the genetic algorithm are dynamically adjusted using a reinforcement learning enhanced genetic algorithm to improve the solution efficiency and quality. Finally, the digital twin system captures dynamic events and issues warnings when dynamic events occur in the actual production process, and adaptively optimizes the initial scheduling scheme. The effectiveness of the proposed method is verified through the construction of the digital twin system, extensive dynamic scheduling experiments, and validation in a laboratory environment. It achieves real-time monitoring of the scheduling environment, accurately captures abnormal events in the production process, and combines with the scheduling algorithm to effectively solve a key problem in smart manufacturing

Physiological basis for isoxadifen-ethyl induction of nicosulfuron detoxification in maize hybrids.

Isoxadifen-ethyl can effectively alleviate nicosulfuron injury in the maize. However, the effects of safener isoxadifen-ethyl on detoxifying enzymes in maize is unknown. The individual and combined effects of the sulfonylurea herbicide nicosulfuron and the safener isoxadifen-ethyl on the growth and selected physiological processes of maize were evaluated. Bioassays showed that the EC50 values of nicosulfuron and nicosulfuron plus isoxadifen-ethyl for maize cultivar Zhengdan958 were 18.87 and 249.28 mg kg-1, respectively, and were 24.8 and 275.51 mg kg-1, respectively, for Zhenghuangnuo No. 2 cultivar. Evaluations of the target enzyme of acetolactate synthase showed that the I50 values of nicosulfuron and nicosulfuron plus isoxadifen-ethyl for the ALS of Zhengdan958 were 15.46 and 28.56 μmol L-1, respectively, and were 0.57 and 2.17 μmol L-1, respectively, for the acetolactate synthase of Zhenghuangnuo No. 2. The safener isoxadifen-ethyl significantly enhanced tolerance of maize to nicosulfuron. The enhanced tolerance of maize to nicosulfuron in the presence of the safener, coupled with the enhanced injury observed in the presence of piperonyl butoxide, 1-aminobenzotriazole, and malathion, suggested cytochrome P450 monooxygenases may be involved in metabolism of nicosulfuron. We proposed that isoxadifen-ethyl increases plant metabolism of nicosulfuron through non-P450-catalyzed routes or through P450 monooxygenases not inhibited by piperonyl butoxide, 1-aminobenzotriazole, and malathion. Isoxadifen-ethyl, at a rate of 33 mg kg-1, completely reversed the effects of all doses (37.5-300 mg kg-1) of nicosulfuron on both of the maize cultivars. When the two compounds were given simultaneously, isoxadifen-ethyl enhanced activity of glutathione S-transferases (GSTs) and acetolactate synthase activity in maize. The free acid 4,5-dihydro-5,5-diphenyl-1,2-oxazole-3-carboxylic was equally effective at inducing GSTs as the parent ester and appeared to be the active safener. GST induction in the maize Zhenghuangnuo No. 2 was faster than in Zhengdan 958

Fault Diagnosis of Intelligent Production Line Based on Digital Twin and Improved Random Forest

Digital twin (DT) is a key technology for realizing the interconnection and intelligent operation of the physical world and the world of information and provides a new paradigm for fault diagnosis. Traditional machine learning algorithms require a balanced dataset. Training and testing sets must have the same distribution. Training a good generalization model is difficult in an actual production line operation process. Fault diagnosis technology based on the digital twin uses its ultrarealistic, multisystem, and high-precision characteristics to simulate fault data that are difficult to obtain in an actual production line to train a reliable fault diagnosis model. In this article, we first propose an improved random forest (IRF) algorithm, which reselects decision trees with high accuracy and large differences through hierarchical clustering and gives them weights. Digital twin technology is used to simulate a large number of balanced datasets to train the model, and the trained model can be transferred to a physical production line through transfer learning for fault diagnosis. Finally, the feasibility of our proposed algorithm is verified through a case study of an automobile rear axle assembly line, for which the accuracy of the proposed algorithm reaches 97.8%. The traditional machine learning plus digital twin fault diagnosis method proposed in this paper involves some generalization, and thus has practical value when extended to other fields

An Upper-Limb Power-Assist Exoskeleton Using Proportional Myoelectric Control

We developed an upper-limb power-assist exoskeleton actuated by pneumatic muscles. The exoskeleton included two metal links: a nylon joint, four size-adjustable carbon fiber bracers, a potentiometer and two pneumatic muscles. The proportional myoelectric control method was proposed to control the exoskeleton according to the user’s motion intention in real time. With the feature extraction procedure and the classification (back-propagation neural network), an electromyogram (EMG)-angle model was constructed to be used for pattern recognition. Six healthy subjects performed elbow flexion-extension movements under four experimental conditions: (1) holding a 1-kg load, wearing the exoskeleton, but with no actuation and for different periods (2-s, 4-s and 8-s periods); (2) holding a 1-kg load, without wearing the exoskeleton, for a fixed period; (3) holding a 1-kg load, wearing the exoskeleton, but with no actuation, for a fixed period; (4) holding a 1-kg load, wearing the exoskeleton under proportional myoelectric control, for a fixed period. The EMG signals of the biceps brachii, the brachioradialis, the triceps brachii and the anconeus and the angle of the elbow were collected. The control scheme’s reliability and power-assist effectiveness were evaluated in the experiments. The results indicated that the exoskeleton could be controlled by the user’s motion intention in real time and that it was useful for augmenting arm performance with neurological signal control, which could be applied to assist in elbow rehabilitation after neurological injury

The Effects of <i>Trichoderma viride</i> T23 on Rhizosphere Soil Microbial Communities and the Metabolomics of Muskmelon under Continuous Cropping

The continuous cropping can restrict the large scale and intensive cultivation of muskmelon, and the use of Trichoderma preparation to alleviate the negative effects is an effective mean. Although the impact on rhizosphere soil microbial communities and metabolites after applying Trichoderma are still unclear. In this study, we applied the fermentation broth of Trichoderma viride T23 to muskmelon under continuous cropping, collected rhizosphere soil samples at 60 days after transplantation, and investigated the changes in the microbial communities and metabolites of muskmelon by using high−throughput sequencing and metabolomic analysis, respectively. The results showed that T. viride T23 could effectively reduce the disease index of muskmelon wilt (65.86 to 18) and significantly increase the soil pH value (6.06 to 6.40). Trichoderma viride T23 induced drastic shifts in the richness, structure, and composition of rhizosphere microbial communities, and Proteobacteria, Bacteroidetes, and Actinobacteria were the dominant bacterial phyla. Bioactive substances such as scopoletin, erythronic acid, and palmitic acid were significantly upregulated in the rhizosphere soil, which enhanced soil activity. Overall, T. viride T23 resolves the continuous cropping limitation in muskmelon by improving soil physicochemical properties, elevating the biomass and diversity of soil microbial communities, and stimulating the production of soil active substances