Research on the development and testing methods of physical education and agility training equipment in universities.

Introduction: Because of the problems of insufficient funds and traditional training methods in college sports agile training, an agile training system based on a wireless ad hoc network was developed to evaluate the effect of improving the sensitive quality of ordinary college students. Based on the ESP-MESH network, the lower computer realizes automatic networking between devices and tests the performance of the mesh network. Fourteen male college students received 9 weeks of agility training, with seven students in each of two groups: traditional agility training and agile equipment training. The researchers evaluated the performance of both groups in rapid disguise, body coordination, changing movements, and predictive decision-making. Results: There was no significant difference between the groups before training, but there were significant differences in the four abilities after training (p < 0.01). The experimental group had significant differences in rapid direction change and physical coordination (p < 0.05), and in changing movement and predictive decision-making ability (p < 0.01). Conclusion: Both traditional training and agile equipment training improve the agility quality of college students, and the latter shows better results in certain abilities. However, limited by other physical qualities, the improvement of motor changes and predictive decision-making ability is not as obvious as the other two abilities.

Soil Nitrogen Content Detection Based on Near-Infrared Spectroscopy.

Traditional soil nitrogen detection methods have the characteristics of being time-consuming and having an environmental pollution effect. We urgently need a rapid, easy-to-operate, and non-polluting soil nitrogen detection technology. In order to quickly measure the nitrogen content in soil, a new method for detecting the nitrogen content in soil is presented by using a near-infrared spectrum technique and random forest regression (RF). Firstly, the experiment took the soil by the Xunsi River in the area of Hubei University of Technology as the research object, and a total of 143 soil samples were collected. Secondly, NIR spectral data from 143 soil samples were acquired, and chemical and physical methods were used to determine the content of nitrogen in the soil. Thirdly, the raw spectral data of soil samples were denoised by preprocessing. Finally, a forecast model for the soil nitrogen content was developed by using the measured values of components and modeling algorithms. The model was optimized by adjusting the changes in the model parameters and Gini coefficient (∆Gini), and the model was compared with the back propagation (BP) and support vector machine (SVM) models. The results show that: the RF model modeling set prediction R2C is 0.921, the RMSEC is 0.115, the test set R2P is 0.83, and the RMSEP is 0.141; the detection of the soil nitrogen content can be realized by using a near-infrared spectrum technique and random forest algorithm, and its prediction accuracy is better than that of the BP and SVM models; using ∆ Gini to optimize the RF modeling data, the spectral information of the soil nitrogen content can be extracted, and the data redundancy can be reduced effectively.