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Objective Debris flow from the Hanjia gully develops on the left bank of the source area of the Fujiang River, Fenghe Village, Xiaohe Town, Songpan County, China. In recent years, debris flows have occurred frequently, and the largest debris flow occurred in August 2022, which seriously threatened the lives and properties of villagers in the Hanjia gully. Existing prevention and control engineering methods have decreased in effectiveness or even become ineffective. Currently, researchers have set a variety of extreme rainfall conditions and used FLO-2D to analyze the hazards of debris flow, based on which the governance effect of debris flow prevention and control engineering can be evaluated. However, there are few reports on how to improve the prevention and control engineering and evaluate the effect of the improved prevention and control engineering when the existing prevention and control engineering is ineffective. Methods To reduce damage to the Hanjia gully, the characteristics as well as prevention and control status of the debris flow in/from this gully were determined using remote sensing interpretation, field investigation, and FLO-2D numerical simulation; subsequently, improved prevention and control engineering was proposed. The hazard of debris flow before and after the improvements in prevention and control engineering under different rainfall frequencies were studied to analyze the effectiveness of the improved prevention and control engineering. Results The results show that the Hanjia gully is located in the "8.8" Jiuzhaigou earthquake disturbance area, the static reserves of post-earthquake landslides and collapses are about 49.79 × 104 m3, and the debris flow sources are abundant, which leads to frequent debris flow during heavy rainfall. The high-hazard area is concentrated in the No. 1 retaining dam, and Fenghe Village and Pingsong Highway are in the low-hazard area under a rainfall event occurring every 10 years, and the existing prevention and control engineering can effectively prevent the debris flow disaster. Under a rainfall event occurring once in 50 years, Fenghe Village is in the high-hazard area of debris flow. The debris flow rushes out of the drainage channel and destroys the Pingsong Highway. The maximum mud depth in the accumulation area increases from 1.41 m to 3.14 m, the maximum velocity increases from 2.4 m/s to 3.65 m/s, and the accumulation area increases from 0.28 × 104 m2 to 5.41 × 104 m2. However, the existing prevention and control engineering methods cannot meet these requirements. After adopting improved prevention and control engineering, such as multistage retaining dams and cutting and straightening of drainage channels, the flow velocity of the debris flow in front of the two additional retaining dams becomes lower than that before the improvement, and the depth of mud in front of the additional retaining dams becomes higher than that before the improvement. The maximum velocity of the debris flow within 100 m of Dam No. 3 decreases by 29%, and the maximum mud depth increases by 413%. The maximum flow velocity in the first 100 m of Dam No. 2 decreases by 21%, the maximum mud depth increases by 175%, the maximum mud depth in the accumulation area is 3.9 m, and the maximum flow velocity is 3.4 m/s. The accumulation volume of debris flows is reduced by 50.2%, and the accumulation area is reduced by 86%. Conclusion Improved prevention and control engineering can effectively reduce the solid mass of debris flows and guide debris flow to discharge along drainage channels. The high-hazard area of the debris flow is concentrated in the drainage channel, and the control effect of the debris flow is remarkable. Significance The research results provide a scientific method for evaluating the effectiveness of debris-flow control engineering improvements and offer technical support for local debris-flow early warning systems.

In this study, a desktop-class dual-axis rotational molder was employed to mold PA6 products. Notably, the morphology of the plastic powder during the rotational molding process was examined through visualization techniques. The research also aimed to delineate the causes of wall thickness variation in the products, while proposing a process method to enhance the uniformity of wall thickness. The implementation of a two-stage mold temperature setting method for the ends of the mold improved wall thickness uniformity of the products. Furthermore, the interrelationships between crystallinity, tensile strength, and impact strength of the products were explored by comparing different cooling rate conditions. Increasing the cooling rate resulted in a decrease in both crystallinity and tensile strength, while concurrently boosting the impact strength of the products. Highlights * A desktop-class, two-axis rotational molding equipment is designed. * A method for visualizing the rotational molding process is conducted. * Mold temperature and rotational speed affect product wall thickness. * Two-stage mold temperature improves wall thickness uniformity. * Mold cooling rate affects the tensile and impact strength of the product. KEYWORDS mechanical properties, PA6, process parameters, rotational molding, 1 | INTRODUCTION With the advancement of polymer materials, their applications have expanded in various sectors such as aerospace, transportation, medical, communication, and packaging industries. Packaging alone accounts for approximately [...]

Injection molding (IM) is one of the most essential forming methods for plastics. However, some potential risks which influence part quality may occur in the molding process. A non-return valve (NRV) is a major component on the screw head whose function is to seal during the injection process to prevent the backflow of the melt. The NRV will wear in this process and cause fluctuations in parameters and quality but the wear states of NRVs cannot be monitored without the disassembly of the injection barrel. In this study, we proposed an optimization method to compensate for the wear damage of the NRVs. The V/P switchover point in each molding cycle was recalculated and output to stabilize the part quality. As a result, the wear damage of the NRV on the current machine was able to be predicted and the part quality could be initially optimized in the condition that the NRV had a degree of wear. The experimental results reveal that our proposed compensation algorithm can monitor the type of wear of NRV online, and at the same time, it can compensate the axial wear of NRV and finally improve the consistency of product weight, which established a fundamental for further research in the future. KEYWORDS injection molding, monitoring, non-retum valve, wear compensation, wear states, 1 | INTRODUCTION Injection molding machines (IMMs) are commonly used in the polymer forming field. [11-3] The IMMs have advantages in molding plastic parts that have complicated shapes. However, the [...]

Due to the influence of the background of the era and its ideological and cognitive factors, some college students lack independent labor ability and awareness, and it is necessary to build a labor education system that meets the requirements of the new era to further improve the effectiveness of educating people. By updating the pheromone, the ant colony algorithm is introduced to improve the convergence speed of the basic clustering algorithm. A linear combination of average similarity and cosine distance function is used to cluster labor education-related data, and the probability conversion function is used to increase the accuracy of clustering. The dilemmas that confront labor education in colleges and universities become clustered after a finite number of iterations. And quantitative analysis is carried out from the two aspects of learning quality of labor education in colleges and the influence of learner characteristics on learning effect to explore the innovative road of labor education in colleges and universities. The results show that: in the analysis of learning quality, learners’ average learning readiness is the highest (M=3.89, S=0.49), and the cluster centers corresponding to the sub-dimensions of “knowledge reserve”, “learning attitude” and “learning skills” are 3.66, 4.33 and 3.98. The mean value of “learning skills” is the highest, with 0.875 learners above the medium level, indicating that most of the learners think they can conduct independent learning in labor technology. The development of a labor education model is given an innovative direction by this study.

This paper analyzes the factors that affect the mental health of college students, and the focus of this analysis is on emotional-emotional factors. The extroverted presentation of emotional affects is used as visual information to study the mental health status of college students. Based on the advantages of long and short memory neural networks based on deep learning models in processing two-dimensional images, a computer vision task is used to perform visual recognition, target detection, and expression image classification of college students’ facial expressions. The use of video facial expression recognition with multi-feature fusion is utilized to effectively identify the facial expression machine of college students in both laboratory-controlled and outdoor environments. The mental health status of college students was analyzed in terms of facial expression recognition and feature extraction. The recognition rate for general features was 80.3%, 89.3% for six specific facial emotions, and 84.4% for LBP features.