Titanium alloys are extensively employed in the fabrication of various aviation structural parts, of which the most crucial processing step is hot working. In order to study the high-temperature deformation behavior of the TC21 titanium alloy, high-temperature tensile tests were performed. The results reveal that the flow stress of the material gradually decreases with an increased strain rate, and the stress increases rapidly with an increase in strain during the deformation of the alloy. Following this, flow stress gradually decreases. Flow stress decreases sharply, and the sample fractures when the appearance of necking and microvoids is observed. The Arrhenius and Radial basis function (RBF) neural network constitutive models are established in order to accurately describe the high-temperature deformation behavior of the material. In the modified Arrhenius model, strain rate indexes are expressed as a function of deformation temperature and strain rates; furthermore, the high prediction ability of the model was obtained. For the Radial basis function, the network parameters were obtained using the trial-and-error method. The established models could better forecast the flow stress of materials, and highly accurate results are obtained using the radial basis function model. The relationships between the stress index and the deformation activation energy with strain indicate that the primary deformation mechanism involves grain boundary slip and viscous slip of dislocations. The process of dynamic recrystallization primarily promotes the softening of the material.

The nickel powder brush plate is a core component of the direct contact between the cleaning machine and cathode plate of an electrolyzer, and its movement in the electrolytic cell will affect the energy consumption of the electrolyzer. In order to optimize the structure of the brush plate, a cleaning trolley brush plate was taken as the research object, a mathematical model of its electrolyzer was established, and the reliability was subsequently verified. The influence of the structural and operating parameters of the brush plate on the energy consumption of the electrolytic cell was studied. The research results show that additional energy consumption is the lowest in the process of cleaning a return grooved brush plate. Brush plates with a large slotting area have less impact on the energy consumption of the electrolyzer. The slotting method, where the anodes are arranged directly opposite each other and relatively concentrated, can be adapted to render a more uniform current density distribution on the anode surface, with lower energy consumption and less variation in voltage and current. With the increasing number of slots from one to three, the current density distribution on the anode surface became more uniform, with a reduction in the variation range of the slot voltage and current in the branch where the cathode plate was cleaned and a decreased energy consumption. With the linear increase in brush cleaning speed, the impact time of the brush plate on the electrolyzer decreased nonlinearly, and as the extent of this decrease gradually diminished, the additional energy consumption showed the same trend. These research results were then used as a basis for optimizing the existing commonly used empirical C-brush plates. Following optimization, the current density distribution on the anode surface was found to be more uniform, the variation amplitude of tank voltage was reduced by 34%, the current drop amplitude of the branch circuit where the brushed cathode plate was located was reduced by 39%, the impact time on the current field of the electrolytic tank was reduced by 40%, and the additional energy consumption was reduced by 50.9%. These results can be served as a reference for further theoretical research related to brush plates.

The seroprevalence of Mycoplasma bovis infection in dairy cows in Guangxi Zhuang Autonomous Region (GZAR) in subtropical southern China was surveyed between June 2009 and March 2010. A total of 455 serum samples of dairy cows were collected from 6 districts in 4 different cities, and examined for M. bovis antibodies with the indirect enzyme-linked immunosorbent assay (ELISA) using a commercially available kit. The overall seroprevalence of M. bovis infection in dairy cows was 7.69% (35/455). Three year-old dairy cows had the highest seroprevalence (15.0%), followed by dairy cows of 4 year-old (11.1%). Dairy cows with the history of 5 pregnancies had the highest seroprevalence (33.3%). However, no statistically significant association was found between M. bovis infection and age or number of pregnancies (p > 0.05). All the aborting dairy cows were negative for M. bovis antibodies, suggesting that bovine abortion may have no association with M. bovis infection in GZAR. These results indicate that M. bovis infection in dairy cows was widespread in GZAR, and integrated strategies and measures should be performed to control and prevent M. bovis infection and disease outbreak.

In this paper, deep learning and image processing technologies are combined, and an automatic sampling robot is proposed that can completely replace the manual method in the three-dimensional space when used for the autonomous location of sampling points. It can also achieve good localization accuracy, which solves the problems of the high labor intensity, low efficiency, and poor scientific accuracy of the manual sampling of mineral powder. To improve localization accuracy and eliminate non-linear image distortion due to wide-angle lenses, distortion correction was applied to the captured images. We solved the problem of low detection accuracy in some scenes of Single Shot MultiBox Detector (SSD) through data augmentation. A visual localization model has been established, and the image coordinates of the sampling point have been determined through color screening, image segmentation, and connected body feature screening, while coordinate conversion has been performed to complete the spatial localization of the sampling point, guiding the robot in performing accurate sampling. Field experiments were conducted to validate the intelligent sampling robot, which showed that the maximum visual positioning error of the robot is 36 mm in the x-direction and 24 mm in the y-direction, both of which meet the error range of less than or equal to 50 mm, and could meet the technical standards and requirements of industrial sampling localization accuracy.

The development of a battery-type loader is an important research direction in the field of industrial mining equipment. In the energy system, the battery will inevitably encounter the problem of heat dissipation when using high-power electricity. In this study, we took the power battery pack of a 3 m3 battery-type underground loader as the research object. The influence of single factors, such as the position of the air outlet of the battery pack, the size of the air outlet, the width of the separator, and the reverse plate, on the heat dissipation characteristics of the battery pack were studied. Then, a prediction model between the structural parameters and temperature was established using a radial basis function (RBF) neural network. This prediction model was then used as an adaptation evaluation model for global optimization through the multiobjective particle swarm optimization (PSO) algorithm, using which the optimal combination of structural parameters was obtained. The maximum temperature of the battery pack after optimization was reduced by 22%, compared to that before optimization, and the average temperature was reduced by 12.5%. Overall, the heat dissipation effect significantly improved. The optimization results indicate that the method proposed in this paper is feasible for use in optimizing battery heat dissipation systems in electric vehicles, thus providing a reference for research related to battery pack heat dissipation.