This review provides an overview of microcapsule self-healing technology and its application in the field of cement-based materials, as well as future prospects. The presence of cracks and damage in cement-based structures during service has a significant impact on their lifespan and safety performance. Microcapsule self-healing technology shows promise in achieving self-healing by encapsulating healing agents within microcapsules, which are released upon damage to the cement-based material. The review starts by explaining the fundamental principles of microcapsule self-healing technology and explores various methods for preparing and characterizing microcapsules. It also investigates the influence of incorporating microcapsules on the initial properties of cement-based materials. Additionally, the self-healing mechanisms and effectiveness of microcapsules are summarized. Finally, the review discusses the future development directions for microcapsule self-healing technology, outlining potential areas for further research and advancement.

Turbulent kinetic energy and turbulent dissipation are important aspects of the flow field characteristics, which can affect the wear and energy loss in mixing equipment. In order to increase equipment wear and energy loss in the mixing process, a series of fractal impellers were designed based on the fractal iteration method, and the effects of the fractal dimension and the number of iterations on the flow field characteristics were investigated. Firstly, the distribution characteristics of turbulent kinetic energy and its uniformity were studied. Then, the distribution characteristics of the turbulent dissipation rate were studied and interpreted using vortex analysis, and the mixing power of the device was further investigated. The results showed that: for the turbulent kinetic energy of the flow field, an increase in the fractal dimension and the number of iterations makes the turbulent kinetic energy intensity of the flow field decrease and the distribution more uniform, compared to the non-iterative impeller, specifically the rectangular secondary iterative impeller caused a 30% reduction in the turbulent kinetic energy intensity and a 50% increase in the uniformity; for the turbulent dissipation of the flow field, in general an increase in the fractal dimension reduces the turbulent dissipation in the flow field, and an increase in the number of iterations increases it slightly, this influence law is due to a change in the trailing vortex caused by the blade structure; and a change in the law of turbulent dissipation also causes a corresponding change in the stirring power, from the non-iterative impeller to a rectangular one iteration impeller, the power decreases by 20% while the average speed decreases by only 5%. In conclusion, the special boundary of the fractal iterative impeller can reduce the turbulent kinetic energy and turbulent dissipation of the flow field to a large extent, and its two characteristics, the fractal dimension and the number of iterations, affect the reduction effect. The results of the study can be used as a reference for the design of mixing equipment to reduce turbulent kinetic energy and turbulent dissipation.

Weed detection technology is of paramount significance in achieving automation and intelligence in weed control. Nevertheless, it grapples with several formidable challenges, including imprecise small target detection, high computational demands, inadequate real-time performance, and susceptibility to environmental background interference. In response to these practical issues, we introduce CCCS-YOLO, a lightweight weed detection algorithm, built upon enhancements to the Yolov5s framework. In this study, the Faster_Block is integrated into the C3 module of the YOLOv5s neck network, creating the C3_Faster module. This modification not only streamlines the network but also significantly amplifies its detection capabilities. Subsequently, the context aggregation module is enhanced in the head by improving the convolution blocks, strengthening the network’s ability to distinguish between background and targets. Furthermore, the lightweight Content-Aware ReAssembly of Feature (CARAFE) module is employed to replace the upsampling module in the neck network, enhancing the performance of small target detection and promoting the fusion of contextual information. Finally, Soft-NMS-EIoU is utilized to replace the NMS and CIoU modules in YOLOv5s, enhancing the accuracy of target detection under dense conditions. Through detection on a publicly available sugar beet weed dataset and sesame weed datasets, the improved algorithm exhibits significant improvement in detection performance compared to YOLOv5s and demonstrates certain advancements over classical networks such as YOLOv7 and YOLOv8.

The continuous development of information technology has increased the level of automation and informatization in the manufacturing industry, which makes it necessary for companies to effectively monitor their assembly lines. Aiming to visualize the monitoring challenges of the assembly line production process, taking the industrial detonator automatic assembly line as the research object and referring to the digital twin five-dimensional model, a visualization monitoring method that utilizes an assembly line based on a digital twin is proposed. First, the architecture of the assembly line visualization monitoring system based on digital twin is constructed, and its specific operation flow is studied. Then, three key implementation methods, including assembly line virtual entity model construction, data collection in the assembly process and complex equipment error detection, are studied. Finally, a visualization monitoring system for the industrial detonator automatic assembly line is designed and developed, which verifies that the proposed method is effective in the visualization monitoring of the assembly line.

The pollution caused by the chemical cleaning of polymer coatings when recycling printing screens can be alleviated through laser cleaning. The interaction mechanism between the laser beam and polymer coating is studied, a laser ablation threshold model of the cross-linked polymer is constructed, and principles of laser bonding and photoionization are discussed. When the laser energy density is 7.8 J / c m 2 , the laser pulse width and action energy influence the characterization after ablation. When the deposition energy is > 1.2 J / c m 2 , the polymer material undergoes carbonization. When the deposition energy is < 0.8 J / c m 2 , significant gasification and erosion are caused by the thermal decomposition of the resin. The findings provide a theoretical basis for research and reference for experiments and industrial applications.