This research proposes a new optimization technique for reinforcement concrete filled structural tubular columns using genetic algorithms and unified strength theory. A complete theoretical model to determine the axial bearing capacity of reinforced CFST columns incorporating modified confinement coefficients and enhanced steel section properties was developed. The optimization procedure deals with performance of the structure, materials usage and construction convenience as the optimization goals. Experimental validation for ultimate bearing capacity of five full scale specimens was carried out and the deviation was found out to be 5.2% which was found to be predicted by the theoretical model accurately. Internal stiffeners are likely to increase axial capacity by about 15.7%-23.4% over traditional CFST columns. The relationship between stiffener parameters and performance of the structure was found to be critical with optimal height to thickness of the stiffener to be in the range of 30 to 45 and space to diameter ratio no greater than 0.5. The problem sets out such mathematics as is nowadays simply necessary for the modern construction world to have at their disposal, as well as reasons for designing reinforced CFST columns.

This paper presents the design and implementation of the non-electric contacting power supply system for the electronic scale, which mainly focuses on improving power transfer and measurement accuracy. The whole system architecture includes electromagnetic coupling, an advanced algorithm of control, and safety. Simulation results have shown that, under standard conditions, it is possible to reach a high power transfer efficiency higher than 90% while keeping voltage regulations within ±1% and limiting current ripple to below ±1.8%. Therefore, this provides a measurement resolution of ±0.1g for the system while granting stable performance for a variety of conditions in both coupling and load. Protection mechanisms set within the system ensure reliable operation; fault detection time is less than 10μs. The proposed method represents a relatively good guide for non-contact power supply towards precision measurement, thus solving the challenge of WPT in an electronic scale system.

Under the background of globalization and knowledge economy, the importance of innovation and entrepreneurship education for college students is becoming more and more prominent. This paper combines fuzzy logic and decision tree algorithm to construct a cultural confidence recognition model of innovation and entrepreneurship education. Feature selection and classification are carried out on the salient features of the collected data information on innovation and entrepreneurship education. First, eight types of statistical features, such as the degree of integration of excellent traditional culture, the degree of value leadership and moral cultivation, the innovative power of grounded cultural knowledge, and the effect of social responsibility cultivation, are extracted as inputs to the C4.5 algorithm, and a decision tree is constructed for feature selection. Then, according to the constructed decision tree, the affiliation function and IF-THEN rule of the fuzzy inference model are designed. Finally, the designed fuzzy inference model is used to classify the degree of cultural confidence. The method achieves 100% accuracy in recognizing the lack of cultural self-confidence in innovation and entrepreneurship education, and more than 90% in recognizing the overall effect of general cultural self-confidence and rich cultural self-confidence. The experimental results show that the combination of decision tree and fuzzy inference modeling is feasible for the detection and classification of college students’ innovation and entrepreneurship education, and has strong practical application value.

The development of blockchain technology in modern business and finance is of great importance. The study delves into the blockchain-based shareholder voting system and the role of blockchain on corporate governance. On this basis, relevant research hypotheses are formulated. After completing the definition of research variables, the research model is constructed to empirically investigate the impact of blockchain-based shareholder voting system on corporate governance. The research hypotheses are tested through regression analysis and the robustness test is utilized to ensure the reliability of the research findings. The minimum value of blockchain-based shareholder voting and corporate governance level are both 0, the maximum value is 4.954, 0.624, and the average value is 0.821, 0.089, respectively. There is variability in shareholder voting and corporate governance level across companies. Before and after the control variables, the coefficients of blockchain-based shareholder voting system are 0.225 and 0.247 respectively, and both are significantly positive at 1% level. Blockchain-based shareholder voting system can improve corporate governance.

This paper constructs the key business index system of electric power system consisting of electric power supply, electric power transmission, electric power distribution, electric power equipment and electric power system management. By evaluating the validity optimization, reliability optimization, and redundant indicator removal based on the neural network analysis method of the indicator system, a new power system key business indicator system is formed, and the weights of the optimized indicators are calculated. The power system key business indicator control program is designed based on the weight parameters, and a new power system key business indicator control platform is developed. Extract power data using the weighted FCM clustering algorithm, and classify user power data on the cloud platform. Resource utilization and performance response analysis are performed on the power system key business index control platform. The power system key business index control platform designed by index weights developed in this paper is able to meet the transaction demand under different concurrent user numbers, and always maintains a memory utilization rate within 10, with good operating conditions.

In the context of the digital economy driven by the Internet of Everything, the dissemination of cultural heritage is facing the challenge of transitioning from traditional to digital media. The study develops an introduction to the visual SLAM system, models the binocular camera configuration and the indoor and outdoor dense 3D reconstruction process, and designs a complete set of algorithms based on the calibration of the actual binocular camera, image correction, binocular stereo matching algorithm (SELAS), and real-time dense point cloud 3D reconstruction. Based on the real laboratory scene, the original ELAS algorithm is compared with the improved method for experiments, and the results show that the mean value of the deviation of the optimized S-ELAS algorithm is -0.046m, and the algorithm accuracy is remarkable. Then a virtual cultural relics museum based on the combination of visual SELAM system and VR technology is designed to realize close interaction with S-ELAS stereo matching algorithm. In order to test the performance of the designed cultural relics museum system, the users are firstly acclimatized, and then the screened users are tested to experience the virtual museum system, and the MOS scores are made after the test. The MOS scores show that the virtual cultural relics museum system has better interactivity and experience.

In order to alleviate the problems of short supply of parking spaces and traffic congestion, intelligent driving solutions have emerged. Automatic parking has now become the first application scenario for driverless driving due to the more fixed scenario and lower traveling speed. In this study, the traditional A* algorithm is improved using the cost function, and the hybrid algorithm of parking space path search and planning is designed by combining the improved A* algorithm with the Reeds-Shepp curve, and then combined with the collision constraints to improve the algorithm’s path planning performance. The results of simulation experiments and in-loop test experiments show that the maximum lateral error and heading error are low in parallel and perpendicular parking scenarios, and it is found that the average lateral error during the whole parking process is only 0.177m in the in loop test, which is a good tracking effect for vehicles. The path search and planning algorithm designed in this paper can better realize the autonomous parking function and has high tracking accuracy and stability in the simulation scenario.

The process of innovative education is not only a purely intellectual activity process, it needs innovative emotion as a driving force, such as strong interest, strong passion, the motivational function of evaluation, harmonious teacher-student relationship and other non-intellectual factors cultivation, in order to obtain a comprehensive effect. This study is oriented to the intelligent distribution platform of journalism and communication content to study its teaching value and innovation emotion. The Information Adoption Model (IAM) was adopted as the theoretical basis for the study of content intelligent distribution platforms, the characteristics of the platforms were summarized, and the impact of the platforms on teaching value was studied using regression analysis. The result table of the study found that the content intelligent distribution platform’s exhaustiveness, readability, and objectivity had a significant positive correlation on the usefulness of educational value, and that the influence of interactivity on perception and participation did exist and had a certain impact on educational usefulness. Finally, this paper also takes S colleges and universities as an example to assess and calculate the innovative emotion and innovative ability of the platform’s teaching value, further analyzes the teaching value of the intelligent distribution platform, and provides suggestions for the cultivation of the innovative emotion in combination with practical research.

As one of the important stakeholders in ecotourism, community residents play a crucial role in ecotourism development. This study takes interactive emotional cognition, social exchange theory and the concept of psychological carrying capacity as the guiding theories, and designs the community residents’ questionnaire from the aspects of emotional cognition and psychological carrying capacity, respectively. Correlation analysis and regression modeling were used to test the influence of interactive emotional cognition on the psychological carrying capacity of ecotourism community residents. The calculation results show that the psychological carrying capacity of ecotourism community residents is positively correlated with positive interactive emotional cognition (r>0) and negatively correlated with negative interactive emotional cognition (r<0). It was also found that community residents' proud emotional perception of tourism development had the highest degree of influence on the psychological adjustment capacity variable (R²=0.299). This study verifies the mechanism of community residents' interactive emotional cognition on their psychological carrying capacity and enriches the theoretical research on promoting ecotourism development.

This paper improves the prediction accuracy of financial crisis of listed companies by optimizing the traditional Z-score model and taking the financial warning indicators as the input features of the neural network. The study selected the financial data of listed companies in a certain place from 2017 to 2023 as a sample, compared and analyzed the early warning performance of multiple traditional machine learning algorithms with this paper’s method, and assessed the reliability of this paper’s model in the early warning of financial quality by combining with cases. The neural network-based Zscore model has an AUC value of 0.914 on financial quality early warning, which is close to 1, and the prediction results are reliable. The model’s overall financial quality early warning accuracy in year t-1 is elevated by 16.61% to 19.35% compared with the comparison algorithm, and has a faster error has convergence speed. The Z-value calculation predicts that three companies will appear to have financial quality risk in 2017, which is consistent with the actual results. The algorithm of this paper predicts that company 9 has a Z-value of 3.79 in 2031, which may have financial quality risk. The results of this paper are reliable and show the early warning method of financial quality of listed companies in a new perspective, which is an important reference value for investors and managers.