Abstract With the increasing prominence of the problems of accelerated pace of life and aggravated academic pressure, psychological stress appears in many college students as a group. The article summarises the basic characteristics of PPG signals, implements the extraction of HRV features of psychological pressure in PPG signals, investigates the Transformer model in deep learning, and adapts the encoder in the model and optimises the parameters to be suitable for the analysis of psychological pressure signals. It is confirmed through relevant experiments that the psychological stress feature recognition and extraction method based on the one proposed in this paper has high consistency in the degree of psychological stress and can be used to assess the psychological stress of college students. The regression analysis of college students’ psychological stress and performance in physical education class shows that college students’ psychological stress significantly negatively predicts physical education class exercise β =−0.358 (t=−14.632,p<0.001). In summary, the deep learning technique proposed in the article is important for studying the psychological effects of college students’ stress on their performance in physical education classes.

Abstract This paper utilizes a keyword search of the library, Wanfang database and other databases. The elements of Civics teaching are summarized through expert interviews, questionnaire surveys, Delphi methods, and mathematical statistics. Analyzing the mean, standard deviation, coefficient of variation and Cronbach’s reliability coefficient of the data, etc., the elements of the study of Civics and Politics are screened, integrated and the appropriate indicators are extracted. Based on the indicators of civic and political elements, the intelligent teaching resource base of colleges and universities is constructed and analyzed empirically. The civic and political elements, including 4 first-level indicators and 12 second-level indicators, have been extracted. The construction of the overall system and organizational structure of the intelligent teaching resource base has been completed. The results of empirical analysis show that the intelligent teaching resource base can enhance ideological awareness (73.67%), enrich the knowledge reserve (84.33%), expand the perspective of vision (75.33%), collect teaching resources (74.67%) and so on.

Abstract Constructing a new development pattern and developing high-quality productivity cannot be separated from the innovative development of various industrial economies. What factors affect the development of industrial economy, according to which industrial economic development strategies can be summarized, is the focus of attention in this paper. Ten secondary indicators corresponding to the level of economic development, the level of urbanization, the standard of living of the population, the labor force and the role of the government are selected as variables and incorporated into the ordered Logit model for regression analysis, and innovative development strategies are proposed based on the results of the analysis. The significance p>0.05 of variables such as urban land utilization rate, number of people employed in the industrial economy, and cost of living index, and the regression coefficients of variables such as GDP, GDP per capita, proportion of the urban population, disposable income per capita, number of college graduates, and government financial expenditures and efficiencies were all found to be positive, which suggests that in order to realize the innovative development of the industrial economy, we should implement the following measures: strengthening the economy, expanding the industrial clusters, promoting consumption, attracting talents, increasing government support and other strategies should be implemented to realize the innovative development of industrial economy.

Abstract Optimizing training movements of basketball players can improve the game performance of basketball. The article firstly set up the human femur stress distribution model through Mimics and Ansus software, analyzed the mechanical indexes such as the bone volume fraction of femoral head trabeculae under different force directions, and adjusted the pitching postures according to the mechanical indexes to complete the optimization training of basketball players’ pitching target technology. Subsequently, simulation experiments were carried out on the model, and the core strength training program was proposed according to the experimental results, and 20 male basketball players from a university were selected as the research subjects to carry out core strength training. The results show that the finite element model proposed in this paper can adjust the model parameters according to the actual situation of basketball players, and quickly get the shooting trajectory and shooting score curve of different basketball players to provide an effective basis for the improvement of basketball players’ training technology. After using the core strength training method for basketball teaching, the indexes of basketball special skills of the experimental group after the experiment were better than those before the experiment (P<0.05), and other indexes were better than those of the control group except for the fixed-point shooting (P<0.05).

Abstract This paper collects and collates relevant information about school physical education teaching and physical education integration and proposes a reform path plan for physical education integration teaching of physical education majors from the individual level and the organizational level. It also uses multiple regression analysis to explore the influence of individual and organizational levels on the improvement of students’ physical fitness and health. The comparative experimental method was used to analyze the effect of teaching methods on physical fitness under the concept of integration of physical education on the improvement of physical fitness by taking a university student as the research object and setting up an intervention group and a control group. After analysis, it can be seen that factors at the individual level (sports motivation, health behavior P<0.01) and organizational level (environmental support, physical education faculty P<0.05, P<0.01) will have a significant positive impact on college students’ physical fitness and health. The intervention group’s performance on lung capacity index, test item of 50m, and minute sit-ups before and after the experiment was significantly improved (P<0.05).

Abstract Under the background of big data era, the traditional recruitment method can no longer adapt to the more and more severe employment situation, and there is an urgent need to improve the ability of the recruitment system to deal with massive data. This paper combines user image technology and decision tree algorithm to successfully optimize the design of recruitment screening models in personnel management systems. Specifically, this paper proposes a data mining-based user image talent label generation method, and uses the CART decision tree algorithm to construct an intelligent recruitment model, which achieves accurate recruitment screening. Taking the recruitment of editing positions as an example, this paper explores the construction of the talent portrait, and its results show that: different editing positions have obvious differentiation in the demand for professional knowledge of talents, and employers pay special attention to vocational skills and digital literacy in recruitment, and generally pay attention to soft skills. The constructed CART model’s training prediction value is more accurate than the linear model’s, with a better fitting effect, which is suitable for talent recruitment screening tasks. At the same time, compared with the algorithmic models such as logistic regression, plain Bayes, support vector machine, CART decision tree, and K-nearest neighbor classifier, the F1 value of the CART model reaches 0.857, and in the two models of whether the talent is delivered or not and whether the job recruiter is recognized or not, its AUC value is 0.704 and 0.787, respectively, which is better than the other models, which indicates that the recruitment screening model built in this paper can achieve better application results, and can assist HR to complete the recruitment process and improve work efficiency.

Abstract Gabion retaining walls have an important position in the application of maintaining the safety of gas pipelines in mountainous areas. This paper describes the specific role of the gabion retaining wall and finite element theory, and highlights the importance of studying the relevant values of the gabion retaining wall. Combined with the specific situation of the high-pressure natural gas transmission project in T city, the design of slope scour protection program is designed and appropriate parameters are set. The specific influence of the change of the elastic modulus of the reinforced body filler on the gabion retaining wall is investigated, and the stability of the gabion retaining wall under complex conditions is further analyzed. Six cases of 30 MPa-80 MPa were set up to verify that the horizontal displacement of DDK6+142.64 single-stage reinforced gabion retaining wall wall appeared to be the maximum horizontal displacement in the middle of the wall, and the rate of horizontal displacement changed with the change of elastic modulus of the reinforced body filler. By studying the changes brought by different filler elastic modulus to the 2nd, 10th, and 17th grids of single-stage reinforced gabion retaining wall, it is learned that the elastic modulus of the reinforced body filler has a small effect on the reinforcement force. When the cohesion of reinforced body filler is 5 kPa, the rate of change of the retaining wall is larger, and when the cohesion grows to more than 20 kPa, the retaining wall structure gradually tends to stabilize. The gabion retaining wall made of 7 layers of gabion mesh boxes stacked on top of each other has good stability.

Abstract Gas-liquid mixing is a commonly used technology for oil and gas pipeline transportation, and compared with single-phase pipelines, when a gas-liquid mixing pipeline leaks, the flow is complicated due to the interaction between gas and liquid phases. And among all flow types, segmental plug flow is the most difficult to study. In order to study the control strategy of segment plug flow in the case of unsteady flow multiphase flow in oil and gas pipelines, this paper uses computational fluid dynamics (CFD) to design the control equations and turbulence model, and selects the volume of fluid (VOF) model as the multiphase flow model, so as to analyze the pressure characteristics of the severe segment plug flow through numerical simulation, and based on which, we design the improved PID control system of the segment plug flow, and validate the validity of it. Based on this, an improved PID control system for blocked flow has been designed and its effectiveness has been verified. The results show that the numerical simulation model of unsteady flow in oil and gas pipelines established in this paper can well simulate the changes of various characteristic parameters in the section plug flow condition, and realize the mathematical model description of the pressure characteristics of the section plug flow condition. At the same time, the designed PID control system can effectively attenuate or eliminate the severe segmental plugging flow, control the pressure at the bottom of the riser at a stable value of 71.8 bara, control the pressure at the top of the riser near a stable value of 51 bara, control the mixing density of the flow at the top of the riser at a stable value of 577.2 kg/m3, and control the mass flow rate of the flow at the top of the riser at a stable value of 10.491 kg/s, which indicates that it can help the researchers to realize the pressure characteristics of segmental plugging flow conditions. This shows that it can help researchers to realize numerical simulations of unsteady multiphase flows in oil and gas pipelines and effectively control section plug flows.

Abstract With the continuous development of social Internet technology, machine learning plays an important role in different industries, and the current education field is also experiencing the transformation from traditional teaching mode to data-driven intelligent teaching mode. In this paper, we provide an overview of the current status of the application of machine learning and educational big data analytics in education, discuss the commonly used machine learning algorithms and key technologies for educational data analytics, and at the same time, analyse the problems and challenges of curriculum reform, and elucidate the potentials of machine learning and educational big data in the process of solving these problems. In addition, this paper uses machine learning models to analyse students’ learning behaviours, and also uses the models for personalised learning path recommendation, intelligent recommendation of teaching resources and other aspects. The application effect of the model in actual teaching activities is partially demonstrated through experimental research, and the effectiveness of the model is verified through data analysis. Finally, this paper summarises the research results and highlights the key role of machine learning-based big data analytics in education in promoting personalisation of education and improving teaching quality. The research in this paper not only provides educators with a new perspective on curriculum reform, but also provides educational decision makers with a reference for data-driven decision making, which is expected to promote the development of education in the direction of more intelligent and personalised education.

Abstract This paper adopts the questionnaire survey method to issue and collect formal questionnaires to vocational education students. Then, the quantitative analysis method was used to process the 200 recovered valid data by using SPSS software to verify the role of each factor in influencing the skill development of vocational education students through the Logistic binary model regression analysis established in this paper, followed by inputting the identified salient factors of employability skills into the ISM model to explain the logical relationship and hierarchical structure among the factors. Finally, a correlation analysis was conducted regarding the factors that affect students’ employability skills. The results showed that the mean values of general competence, professional competence, learning attitude, and career planning and confidence were 4.1895, 4.3231, 4.2255, and 4.5053 points, respectively, and it was clear that the factors had a significant effect on students’ employability skills training. In addition, the Pearson coefficients of the four dimensions related to the improvement of employment level ranged from 0.1924 to 0.2638, and their p-values were less than 0.05, which shows that there is a significant positive correlation between the improvement of vocational education students’ employability skills and the general competence, professional competence, attitude towards employment, and career planning and confidence.