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Based on the PSR model, the author proposes a research on news communication. PSR is a model proposed by OECD for system pressure, state and response analysis. On the basis of in-depth analysis of various factors in the process of coordinated development of the social, economic and natural environment composite system, the author constructs a kind of PSR composite model, by using the theory and method of system dynamics, the sustainable development of social, economic and environmental systems is studied, and the knowledge atlas is used to describe and evaluate the current situation and development trend of the education of news communication discipline, conduct a comprehensive and visual analysis of journalism and communication education research to show the frontier, hot spots, knowledge base and cooperation network of the discipline's education research, and try to clarify the relationship and interaction between various studies.

This paper establishes DID and PSM-DID models to examine the effect of extreme weather on enterprise productivity, using empirical evidence from natural experiments of torrential rainstorm in Zhengzhou of China. Based on analysis of empirical results, we find that extreme weather has a negative impact on enterprise productivity, psychological capital plays a regulatory role in this influence, and decrements of enterprise productivity with high psychological capital is smaller than enterprises with low psychological capital. The results of this study provide empirical data support to understand the negative effect of extreme weather upon enterprise productivity and the positive role of employee psychological capital.

Virtual power plants (VPPs) can aggregate different energy sources into a whole to participate in the electricity market and carbon trading market, and as the scale of VPPs continues to expand, they will also be transformed from the original price takers to price makers. The paper considers VPP as a price setter and suggests a two-tier bidding strategy for the VPP day-ahead electricity market that incorporates the impact of the carbon price. Among them, the upper-layer model aims to maximize the expected revenue of VPP, while the lower-layer model aims to minimize the system clearing cost. The dynamic carbon emission factor is used in the strategy to create a market incentive factor that corrects the offer price of each unit before clearing it again. Finally, the optimal bidding strategy, as well as the operation plan of the VPP, are given through an arithmetic example, and the impact of the uncertainty in the wind farm output forecast in the VPP on the expected return of the VPP is analyzed. The impact of carbon prices on power prices, output, and rates of energy demand change are further explored to provide a basis for the compensation mechanism for ecological protection of carbon values. The arithmetic example shows that VPP can influence the market price through strategic bidding decisions, and after considering the carbon price, the power market clearing price is 4.46 yuan/MWh, and the maximum gain is obtained as 225.54. The consumption rate of new energy units such as WT and PV is increased, the carbon emissions are reduced, and the total gain of VPP is increased. The increase in carbon prices and the offer of traditional generators increase the amount of electricity cleared by renewable energy units, while the increase in carbon quota ratio and the offer of new energy generators increase the average clearing price.

Financial shared services not only improve operational efficiency and reduce management costs but also realize auxiliary data analysis and management decision-making work, creating more value for enterprises. This paper builds a financial shared service system based on image recognition and extraction of financial bills to realize the functions of financial content recognition, voucher generation, application recognition, and administrative approval. We perform pre-processing tasks like binarization and edge detection on the financial bill image to identify the form lines. We then use the Hough transform to solve the function of the form lines, rotate, and correct the tilt angle. The form structure and financial text detection method are created based on the accurate form line detection. Analysis of two companies using the system of this paper reveals that the number of entered field information can be reduced by 91.48% of the entry workload after optimizing the system of this paper. The economic efficiency of labor costs can be significantly improved by reducing the time spent by reimbursement personnel in the reimbursement process by approximately 4,400 hours per month. By using the system in this paper, a new document can be created and submitted for approval in just 11 minutes, which reduces the document time by 86.25%. The constructed scores for the four first-level indicators, A, B, C, and D, range from 80 to 90 points, resulting in a total score of 87.417 points for the comprehensive evaluation. The construction of the financial shared service system in this paper consolidates the enterprise financial business into a centralized processing system, thereby enhancing the quality and efficiency of enterprise accounting management.

In group teaching activities, teacher-child interaction occupies a dominant position, and its quality has a significant impact on children’s interpersonal communication, language expression, and problem-handling. This study first designed a relevant questionnaire to assess the current quality of teacher-child interactions in preschools and then developed a stepwise regression algorithm based on decision trees to investigate the factors that influence the quality of these interactions. Firstly, we remove a certain number of irrelevant variables from all the input variables to reduce interference. Next, we establish stepwise regression equations between the input variables and output variables selected by decision tree pruning. Finally, we eliminate the variables with covariance. The study found that the average score of the quality of teacher-child interactions in preschools is 4.2381, which is relatively low, and the highest score in the dimension of “classroom organization” is only 4.8908. The highest score in the dimension of “classroom organization” was only 4.8908. The average scores of “emotional support”, “classroom management,” and “educational support” in the dimensions of small class and life activities were 4.76746, 4.10562, 2.8455, and 5.562, respectively, 2.8455) and (5.01564, 4.99567, 3.75402) respectively. Teacher-child interaction quality = 4.012 + 0.145 × emotional expression, i.e., emotional expression has a significant positive effect on the quality of teacher-student interaction, and the independent variable, emotional expression, can significantly and positively predict the dependent variable teacher-child interaction quality. This paper provides a useful exploration for improving the professional abilities level of kindergarten teachers and the quality of their interactions with children.

This paper proposes a resource integration model for film and television + professional clusters and builds a corresponding digital platform to realize the effective integration of film and television professional resources. Using XML technology of different modalities of film and television professional resources, the association of film and television professional resources is defined, and a multi-objective integration scheduling algorithm is incorporated to optimize the integration speed to ensure the operational efficiency of the integration model. The integrated film and television professional resources are stored in the digitization platform, the wavelet transform technology is used to decompose the image signals, and the classifiers trained by the learning algorithm and the information entropy are used to complete the classification of film and television professional resources. After applying the design model and platform of this paper in the practical course teaching Film and Television Production for the 2022 undergraduate students majoring in Film and Television Studies at University X, the quality of film and television works created by the students improved by 2.12 and 1.64 in the dimensions of artistry and innovativeness compared with the pre-practice dimension, which showed a significant difference (P

The study takes dynamic color design as the main research object, firstly, based on the atmospheric scattering model and the improved dark primary color a priori algorithm, designing the image defogging algorithm based on the ambient light variation, which is used to carry out the optimization processing of images. Aiming at the lack of the user’s visual cognitive semantics of color matching in the process of color design, it is proposed to construct a dynamic color design model based on the theoretical foundation of the image translation model, combined with the visual aesthetics data flow. The model obtains the user’s visual aesthetic cognition of the optimized image samples through eye tracking technology and, at the same time, applies the image translation model to the color design process to generate a color design scheme that is more in line with the human visual characteristics by learning the color matching law of the samples and their visual cognitive semantics. The experimental analysis shows that the image-defogging algorithm based on ambient light variation has a better image-processing effect and operation efficiency, exhibiting a higher average gradient (11.83), information entropy (7.86) and visual fidelity (2.99). Meanwhile, the color design scheme generated by the method of this paper is highly recognized by the subjects, with evaluation scores of more than 4 points in each dimension. The dynamic color design model constructed in this paper, by considering ambient light changes, can generate color schemes that are both rational and comfortable.

Entrepreneurship is one of the driving forces of economic development in the new era, but entrepreneurship is also highly risky behavior. The goal of constructing the entrepreneurial interactive decision-making model and entrepreneurial project system model is to provide a theoretical reference value for entrepreneurial teams through simulation experiments. The conceptual model of entrepreneurial team interaction decision-making is constructed based on multi-intelligence under the support of relevant theories, and the main influencing factors affecting the interaction decision-making are analyzed and the specific decision-making model is designed on the basis of this. Then, according to the ideas and principles of system dynamics, the boundary of the system is delineated, the scope of research is clarified, the structure of the system is analyzed, the levels of the system are delineated, and the feedback mechanism of the system is determined. The relationship between the system levels is analyzed, feedback loops and causality diagrams are established, and in this way, a mathematical and canonical system dynamics model is established, and finally, simulation prediction and hypothesis testing are carried out. The regression results show that there is a positive effect of mutual influence among the three variables of the interaction decision model: team initiative, team decision performance. The error between the values of this paper’s model for simulation and the actual values is less than 10%, and the improvement of entrepreneurial R&D investment and the conversion rate of scientific and technological achievements can promote the number of entrepreneurs and entrepreneurial structure to different degrees. Effective predictions of the influencing factors and development trends of entrepreneurship in the future have been made, which provides policy references for entrepreneurship to be carried out more effectively in practice.

Taking English microclasses as an example, this paper analyzes the practical operation of flipped classroom teaching in the reform of higher vocational English teaching from the three phases of pre-course, in-course and post-course. Comparing and analyzing the advantages of each fusion algorithm, the Stacking model fusion algorithm is selected to construct a multi-model fusion prediction model of students’ learning effectiveness, and the experimental process of students’ learning effectiveness prediction model based on Stacking fusion is summarized. The algorithmic performance of each machine learning prediction model is determined using each evaluation index. The multi-model fusion learning effectiveness prediction model is employed to predict and analyze the overall and individual effectiveness of English learning by organizing students’ English learning data. Combined with the prediction results of the flipped classroom platform data, the overall performance of the multi-model fusion prediction model is more stable, with a more balanced distribution in the range of 0.7~0.9, which can obtain better accuracy performance than LR, GBDT and XGBoost, and is more capable of predicting the students’ learning effectiveness in terms of the stages of learning (certified, grade, and total_time) in real life. Prediction.

The development of science and technology makes the cooperation and cultural exchanges between various countries more and more frequent. Machine translation has the advantages of high translation efficiency and low cost, and the research devoted to machine translation has become the current trend in the cross-cultural field. Based on the application form of cross-cultural perspective transformation in English text translation, this paper proposes cross-cultural text features using the Glove model and puts forward a neuro-machine translation strategy of optimizing the Transformer model with a multi-attention mechanism, which is used to solve the task of cross-cultural English text translation. In the Chinese translation task of cross-cultural English text, the translation accuracy of the model is negatively correlated with the sequence length. The average SER is 0.551, which has good anti-confusion performance. Furthermore, it appears to have a greater lexical richness and the number of idioms used when performing the translation task. Based on this study, the proposed English text translation technique achieves better results. It can replace manual translation to accomplish complex tasks and promote the development of translation cognition.

In the new journey of building a socialist modernized country, how to empower rural revitalization in ethnic areas with non-heritage cultural assets is an important topic that requires in-depth thinking. In this paper, the development level of rural revitalization and the degree of digital transformation of non-heritage cultural assets are taken as explanatory variables and explanatory variables, and the gray correlation between them is calculated using the gray correlation analysis method. Then, based on the differential evolution operator and the improved quantum particle swarm algorithm with weighted coefficients as a framework, the impact prediction model of digital transformation of non-heritage cultural assets on rural revitalization is constructed. The study shows that the grey correlation between X2 “reliability”, X4 “quality” and X7 “diversity” and the development level of the rural revitalization of the three variables of digital transformation of non-heritage cultural assets exceeds 0.8 percent. The grey correlation between X2 “reliability”, X4 “quality” and X7 “diversity” and the development level of rural revitalization among the three variables is more than 0.850, and the grey correlation of the other variables is above 0.70, which indicates that the digital transformation of NRM cultural assets is a possible way out of rural revitalization. In addition, the MAE value of the impact prediction model constructed in this paper is 1.63%, which is much lower than other comparative models, indicating that it can accurately predict the impact of digital transformation of non-heritage cultural assets on rural revitalization. This paper studies the impact mechanism of digital transformation of non-heritage cultural assets to drive the development of regional high-rural revitalization. It is of great significance for other ethnic regions and even backward regions.

Machine learning plays an increasingly important role in the field of disease risk prediction due to its optimization advantages. This paper proposes deep confidence network optimization based on the early warning model constructed by the neural network and chooses the restricted Boltzmann machine and backpropagation algorithm as the theoretical basis of deep confidence network construction. The deep confidence network is established through the construction and stacking of RBM, and backpropagation is used to fine-tune the network parameters to generate the model. Combined with the incidence rate data of sports injuries of physical education majors and the injury classification of the deep confidence network algorithm, the test data application results verify that the algorithm has a good effect of early warning in case of sports injuries. The survey data showed that the incidence of sports injuries was 228%, and the main risk indicators causing sports injuries were not drinking alcohol (95.31%) and incorrect sports knowledge (92.09%). The model correctly predicted 94.15% (95% CI: 0.9204, 0.9608) with sensitivity and specificity: 0.954 and 0.923, respectively.

The sports club industry has become an indispensable industry in improving the overall physical fitness of the residents, and the club operators are in urgent need of an intelligent management system to realize the intelligent management of the daily business. In this paper, the feasibility of an intelligent management system is firstly researched and analyzed, and the application system based on powerful database technology is established, which adopts B/S architecture, and good data interaction is carried out with the database through a web application. The development technology is JSP, and then the overall outline of the design, the realization of the detailed design of the system database, recommendation algorithms, and menus in three sections. The recommendation algorithm section has hidden semantic model optimization based on the user’s collaborative filtering algorithm. To complete the more complex sports recommendation algorithm design, the system’s functional modules are basically realized. The response time of the system meets the performance requirements of the intelligent management system. The sales of the sports club2, which applied the intelligent management system of this paper, reached the highest of 2.51 million, which is about 1 million more than the first month, which shows that the application of the intelligent management system has an improvement effect on the financial performance of the sports club, and the test results show that the system can improve the management efficiency, bring better services to the members, and indirectly improve the financial performance.

Accurate control of the gram weight of a single cigarette is of major significance for the improvement of product process quality and the reduction of production costs. This paper establishes a graph neural network (GNN) model to predict the equipment operation parameters and cigarette product weight indexes, based on data from the PROTOS1-8 fine cigarette rolling machine in a tobacco factory. Aiming at the lack of optimization ability of the GNNs model, using the excellent performance of the gradient boosting tree algorithm in nonlinear optimization ability and convergence speed, and using the cigarette gram weight index of the trained GNNs prediction model as the fitness function, we carry out the search for the optimal cigarette weight product indexes and come up with the set of operating parameters with the best cigarette weight indexes and optimize the cigarette equipment parameters in the reverse direction. Experiments and promotion tests have demonstrated that the method significantly enhances the optimization of fine cigarette equipment, with a prediction accuracy of 0.8942. Compared to the traditional multiple linear regression model of 0.8527, the prediction ability has improved by 4.15%. The method is useful for analyzing the parameters of different types of cigarette equipment and can meet practical needs.

In traditional learning situations, teachers mainly evaluate students’ behavioral changes, emotional changes, and interactions to ensure teaching quality. The Random Forest algorithm is employed in this paper to determine the characteristics of students’ body postures and observe their behavioral changes. The geometric analysis method is used to recognize students’ head posture, and the image test classification module is used to capture students’ facial expressions. Analyze the teacher-student learning interactions in the classroom by examining the student’s question-answering records from the interactive cloud platform. A multimodal information fusion model was constructed to combine students’ multimodal performance data to complete the evaluation of the preschool hygiene course. The results of the case study of five students showed that the fusion results of the ratings of students 1-5 under the multimodal information fusion model of the course evaluation were 0.56, 0.888, 0.083, 0.452, and 0.957, respectively. The results of the fusion of the ratings of students 2 and 5 were very serious in learning the course, and the percentage of their time spent in attentive behavior was 0.452. Student 2 and Student 5 studied the course very seriously, spent 90% and 89% of their time engaged in attentive behaviors, respectively, and maintained an emotion of interest for 66% and 70% of the time, respectively. Furthermore, Student 2 answered all the questions in the interactive session, while Student 5 answered all the questions correctly. The median integration rating of the 40 participating students was 0.706, and the majority of the students rated the integration of the course in the middle to high range, which means that the content and format of this pre-school hygiene course were excellent and stimulating for the students.

The chaos phenomenon is a seemingly random irregular motion occurring in a deterministic system, which is an inherent characteristic and prevalent phenomenon of amnesia-loaded DC-DC converter nonlinear systems. Nonlinear dynamics and chaos theory guide the text, and we create an amnesia-loaded DC-DC converter system model using an amnesia resistor. We analyze the stability of the system using Filippov theory, and analyze the nonlinear dynamic characteristic behavior of the amnesia-loaded DC-DC converter using the time-domain waveform diagram, bifurcation diagram, and phase-track diagram. This paper talks about the slope compensation method and the parametric resonance perturbation method for control analysis. These methods are used to stop the chaotic behavior that happens in the amnesia-loaded DCDC converter. The system maintains a stable state when the reference current is less than 4.732A and the supply voltage exceeds about 17.966V, maintaining an amplitude of the maximum eigenvalue below 1. When the load resistance is 50Ω, the capacitance is 43 uF, the inductance is 2.8 mH, and the reference voltage is 2.05 volts, the amnesia-loaded DC-DC converter suffers from obvious chaos. To restore the system to a steady state by applying the ramp compensation method, the slope of the ramp signal will need to increase as the reference current increases. Loading amnesia onto the DC-DC converter achieves a stable cycle 1 state when the perturbation phase’s fluctuation range falls between 4.35 and 6.15. A nonlinear dynamic analysis of the amnesia-loaded DC-DC converter helps find the stable parameters of the amnesia-loaded DC-DC converter and improve the design of the converter.

Willingness to teach is an important condition for improving pre-service teacher literacy to promote the professional development of preschool teachers. In this study, we randomly selected 600 students from six types of preschool education majors, and processed the data using a data-driven model that included an independent sample t-test, correlation analysis, and multiple linear regression model. The data results showed that the overall mean and standard deviation of preschool education students’ willingness to teach was 3.91±0.982, and preschool education students’ willingness to teach was high overall, but there were still students who expressed a lack of significant willingness to teach. Further regression analysis revealed that individual characteristics, family environment, school environment, and teacher career perception had explanatory power of 5.8%, 20.7%, 6%, and 3%, respectively, and the factors influencing preschool education majors’ willingness to teach were diverse. Measures at both the government and university levels are necessary to comprehensively improve the willingness to teach.

In this paper, by sorting out the principles followed by some scholars in constructing the evaluation index system of teacher literacy in colleges and universities, combined with the actual situation of teacher literacy in a college or university, the preliminary selection of teacher literacy evaluation indexes is made. The Delfel method must be used to screen the preliminary selection of evaluation indicators and then determine the teacher literacy evaluation index system. With the help of a hierarchical analysis algorithm, calculate the weight value of each index, formulate the evaluation factor set and comment set, and construct a fuzzy evaluation model. Adopt the model of this paper to explore the current situation of teacher literacy development. The comprehensive evaluation matrix of the three-level indicators in the teacher literacy evaluation index system of the school is 3.7519, 6.2918, 3.353, 3.407, and 3.1964. Based on the principle of maximum affiliation, the evaluation results of teacher literacy in the school are good, and at the same time, it maps out that there is a problem of teacher literacy in colleges and universities, and in order to promote the college and university teachers to have excellent literacy, it puts forward two optimization strategies, namely, teacher training and a sound appraisal mechanism. To promote the excellent quality of college teachers, two optimization strategies are proposed, namely, teacher training and sound assessment mechanisms.

This paper combines the theoretical knowledge of the current research on accounting information disclosure and ESG performance, selects seven first-level indicators, which are truthfulness, completeness, materiality, relevance, timeliness, comparability, and comprehensibility, and then subdivides these seven first-level indicators to obtain 22 second-level indicators, thus forming the evaluation index system of accounting information disclosure quality. Based on the high flexibility and robustness of the Random Forest (RF) model, it is proposed to use the Random Forest (RF) model to calculate the weights of the evaluation indicators. The evaluation index system’s comprehensive evaluation results are calculated using the TOPSIS method immediately. The comprehensive evaluation results of accounting information disclosure quality are taken as the explanatory variables of the fixed-effects model, and the data of the explanatory variables and control variables originated from a certain data system, and the fixed-effects model is chosen to clarify the relationship between the quality of accounting information disclosure and ESG performance. The regression coefficient for accounting disclosure quality is 0.0349, and there is a significant positive correlation between accounting disclosure quality and ESG performance at the 1% level. When the quantitative value of corporate accounting disclosure increases by 1 unit, the quantitative value of ESG performance improves by 0.0349 units.

This paper first introduces the lean supply chain international logistics management model and then determines the optimal conditions of the logistics supply chain for B2C enterprises through logistics service integrators and logistics service subcontractors. The paper then employs a combination of the factor analysis method and the factor rotation method to identify 17 key indicators that significantly influence the service quality of the international logistics management model for cross-border e-commerce. It then analyzes these relevant indicators and proposes targeted improvement countermeasures. The results revealed that the respondents have a high level of recognition for the 17 indicators that impact international logistics management, and their mean and standard deviation are stable. The results demonstrate that the first four factors can effectively explain the majority of the original data. Their eigenvalues are 8.157, 2.338, 2.132, and 1.045, and the variance contribution rates of their factors are 47.98%, 13.75%, 12.54%, and 6.14%, respectively, with the cumulative variance contribution rate explaining 80.41% of the original data. By combining the characteristics of the indicators, we identified four key factors: procurement, logistics and customs clearance, after-sale, and overall supply chain links. This study helps to better assist in the development of an international logistics supply chain management model oriented towards cross-border e-commerce.

The traditional microgrid electric energy system exhibits low damping and low inertia, making it susceptible to load fluctuations that can significantly impact the system. A new study suggests a way to control a microgrid virtual synchronous generator (VSG) using adaptive regulation of rotational inertia. This lowers the frequency steady-state offset of the VSG control system’s output by using a non-differential frequency regulation method. The virtual excitation regulator regulates the output voltage deviation. Meanwhile, we establish a small-signal model of the grid-connected inverter based on VSG control and explore the effects of rotational inertia and damping coefficient on the stability of the VSG. The MATLAB/Simulink simulation tool is used to simulate the microgrid differential power load mutation, and the effects of the VSG control and the traditional control strategy on several aspects of the system, such as frequency regulation, power regulation, and power quality, are analyzed by comparison. The findings show that the suggested adaptive control method lowers the voltage amplitude from ±25V to ±10V when the system is isolated, stops power fluctuations, and makes the output response curve as good as it can be. Under grid-connected conditions, the VSG adaptive control method still ensures better control performance, but it does not show a significant advantage in this experimental condition.

Traditional manufacturing equipment is developing in the direction of precision, intelligence and integration, and the precision requirements of CNC machine tools are getting higher and higher. Compensating and controlling the thermal error of the machine tool is an important part of improving machining accuracy. This paper uses a cuckoo search algorithm to optimize the BP neural network to get the CSBP neural network model and inputs the temperature value of the CNC machine tool into the CSBP model to get the thermal error prediction results. Combined with the thermal error values of each measurement point on the CNC machine table, the B-spline function is used to fit the thermal error prediction model for the entire working area. Finally, the thermal error compensation system of the CNC machine tool has been established. Simulation and empirical studies show that the fitting and prediction accuracy of the model in this paper are better, and the maximum error value of the prediction is reduced by 20.27% compared to the LSTM model. After the application of the thermal compensation system in this paper, the thermal error of the CNC machine tool is greatly reduced, and most of the offset caused by the thermal deformation of the machine tool is eliminated through compensation. In this paper, the design of a thermal error compensation system based on full work area modeling can effectively reduce the thermal error of CNC machine tools, which can be applied to the actual machining process of machine tools to improve the machining accuracy of CNC machine tools.

In the long history of five thousand years, various ethnic groups in China have created wonderful traditional sports programs, such as archery, horseback riding, martial arts, dragon boat racing, etc., which have formed a national sports culture with profound humanistic values and aesthetic connotations. The study first constructs a spatial diffusion model based on the GIS field model and object model and extracts the cultural spatial diffusion pattern. Then, taking Yang-style Taijiquan, a national traditional sports culture, as the research object, a spatiotemporal database of events is constructed to visualize the spread of this culture. Finally, we explored the differences in the evolution of Yang-style Taijiquan in cities of different sizes and aging levels through ANOVA. Yang-style Taijiquan has significantly more gyms in large cities than in small cities (P

In today’s context of rapid development of higher education, college cadres as the core force to promote the development of the school, its construction and management is particularly important. This paper adopts B/S architecture and MVC design, introduces the dynamic monitoring mechanism of the construction of college cadres, and builds a dynamic monitoring system. The assessment model adopting the multiple regression method is used to evaluate the cadre force’s response to changes in work efficiency through real-time and comprehensive data collection and analysis. The dynamic monitoring system’s online situation on Saturday and Sunday is not as good as it is on Monday to Friday, as found in this paper’s research. And dynamic monitoring can increase the willingness of cadres to learn different subjects and improve their knowledge. Dynamic monitoring can also enhance the average grade of the cadres, and the average grade after the implementation of dynamic monitoring is approximately 9.88 points higher than before monitoring. Overall, the cadre’s satisfaction with dynamic monitoring is high, with an evaluated satisfaction score of 4.92 on the nine indicators of the survey. A one-unit increase in “Monitoring management” is associated with a 0.694 increase in the work efficiency of the cadres, which is the most influential factor in dynamic monitoring. In conclusion, dynamic monitoring helps to provide strong data support for the construction of college cadres and to build high-quality, professional college cadres.

Physical fitness assessment is of great significance for the design and adjustment of youth basketball training, and talent selection and evaluation of training teams. In this paper, a dynamic assessment model of physical fitness and health is designed, and a convolutional neural network and autoencoder are used to achieve feature learning of raw body side data. According to the learning results, the Gaussian mixture model is selected for physical fitness assessment, and the quantitative evaluation method of physical fitness is established based on the parameter-solving results of the EM algorithm. The ablation experiment demonstrates that the model in this paper has a low feature loss and excellent convergence, and the mAP value is 89.12%, which is the most accurate performance. The comprehensive accuracy of the assessment reaches 97.5%, indicating that the assessment performance of the dynamic assessment model of physical fitness and health proposed in this paper is better and can provide help for youth basketball training.

The reasonable integration of natural elements in interior design needs to follow the principles of aesthetic color matching and visual perception theory. In this paper, we constructed an aesthetic assessment model for natural elements in interior design based on visual perception, using aesthetic color features, texture features, and higher-order semantic features. Firstly, we calculate the color histogram of the HSV distortion map to obtain the distortion’s description. The texture features are extracted using GLCM combined with LBP to capture the spatial relationships of different gray levels in the image within the global range. Smoothing the LBP features reduces the impact of noise on feature extraction, and the attention mechanism is employed to exclude redundant information in higher-order semantic features. The model is constructed and then tested on four datasets, including LIVE, to assess the aesthetic effect of visually perceived images, and it is found that the model in this paper performs at a better level in the four image libraries. Three practitioners applied the model to carry out interior design for a project, and the final work was rated by experts at 86 or above, with a mean score range of 82 to 92 for each dimension. In the process of using the model to evaluate the design draft for improvement and optimization, the model provides the designers with the functions of checking and filling the gaps in composition, light, and other aspects and optimizing the aesthetic color assessment so that they can complete a balanced and high-quality interior design work with natural elements, and all the dimensions of the aesthetic assessment have been paid attention to and optimized, and the overall interior design effect is more balanced, and this paper’s model meets the design expectations.

Tourism consumption, as a special type of daily consumption, has significant features that distinguish it from other types of consumption. The purpose of this paper is to summarize and extract the feature engineering in tourism consumption, encode the feature engineering in tourism consumption behaviors using the Transformer model, and predict consumer purchase intention. After the performance evaluation of the Transformer-based consumer purchase intention analysis model constructed in this paper, it is applied to the e-commerce platform, taking the dynamic pricing strategy of the e-commerce platform as the entry point and analyzing the application effect of the model in the optimal inventory time path, the optimal price of the platform, and the expected revenue. The transformer-based consumer purchase intention analysis model in this paper is significantly better than other prediction models in AUC, accuracy, precision, recall, and F1. When applied to e-commerce platforms, this paper’s model helps to realize the inventory equilibrium state between inbound enterprises and e-commerce platforms, which improves the overall revenue of both parties. When time t=40 or inventory n=30, the optimal online store price of the platform after the model is lower than the price before the model is applied, and consumers can enjoy a better purchasing experience. t=40 or n=30, the total expected revenue of both parties after the model is higher than the total expected revenue before the model is applied. This paper shows that the Transformer-based consumer purchase intention analysis model can positively impact the dynamic pricing of e-commerce platforms.

Intelligent recognition of instrument features plays an important role in automation management and overhaul and also facilitates the realization of accurate reading of key parameters in complex environments. The instrument dial intelligent recognition system proposed in this paper consists of geometry correction, pointer segmentation, and reading recognition modules. Combining the idea of the GhostNet model to improve the structure of the backbone network of the Mask RCNN model, the attention mechanism is introduced into the U-Net model, and the minimum outer rectangle method is used for reading recognition. Under different viewpoint rotation angles, the recognition errors of this paper’s method are relatively stable, and they are less than 1%. The region segmentation precision, recall, and accuracy are 99.39%, 99.05%, and 98.38%, respectively. The average error of the recognition results is only -0.04°C, which is satisfactory for instrument recognition.

The broadening of the teaching field and the innovation of the teaching form of the Civics course in colleges and universities are important links in the teaching reform of the Civics course. This paper relies on the reality support of red cultural resources integrated into the teaching of civic politics class of VR technology, selects the influencing factors to build a structural equation model, carries out correlation analysis and the measurement of model path coefficients, and mines the factors affecting the integration of red cultural resources into the teaching, of course, civic politics. On this basis, we explore the practical path of integrating red resources into the teaching of Civics courses in colleges and universities based on the VR model and combine the descriptive statistical analysis and multiple regression analysis to explore the effectiveness of integrating red cultural resources into the teaching of Civics based on the VR model in four dimensions. Learning motivation, self-efficacy, difficulty expectation, classroom environment, group influence, curriculum design, facilitation, and teaching ability all significantly affected the integration of red resources into Civics and Political Science teaching (P < 0.05). Based on the VR model, the red cultural resources have a better effect of integrating into the teaching of ideology and politics, and the scores of all dimensions are more than 4, among which the score of emotional identity is the highest, 4.566. The use of VR technology is an inevitable trend of education modernization, and colleges and universities should apply it to enrich and expand the content of “curriculum ideology and politics” and to promote the construction of information technology and teaching reform of the ideology and politics in colleges and universities. The modernization of education is inevitable due to the use of VR technology.

The student community in colleges and universities is based on students’ common living areas, and the community service model in colleges and universities should be student-oriented and centered on students’ development. This paper proposes a one-stop community service model from a student-oriented perspective, with the service community model and service recommendation model being the main component modules. In the service community model, a context-based association relationship mining algorithm is proposed to add time and location contexts to the collaborative filtering algorithm in order to obtain a collection of similar users and services. After constructing the one-stop service community, a service recommendation algorithm based on a trusted coalition is proposed to introduce student credibility and service usage frequency to achieve personalized recommendations of services. University H’s student community implemented the one-stop community service model. After the practice, the mean value of each dimension of the community’s service mode and content evaluation was greater than 3, and the overall satisfaction evaluation value of the community was 39.49, which was extremely significant compared with the evaluation value of University C (P

This paper integrates the learning characteristics of students in music classrooms, focusing on auditory and visual modalities to design a student-centered multimodal music teaching approach. The LMS filter is used to process the noise of traditional music, the constant Q transformation is used to specify the pitch, and the traditional music elements are presented visually. Next, we evaluate the combination of modes in the multimodal music teaching classroom and develop a multimodal music teaching program that incorporates traditional music elements. The evaluation of the multimodal music teaching design is based on the degree of integration of conventional music elements and the effectiveness of multimodal teaching. Briefly analyze the integration of conventional music elements in music teaching at the present stage, unite the advantages of the multimodal teaching mode, and put forward the teaching suggestions on the three levels of teaching principles, teaching methods, and teaching resources. In general, students’ acceptance of the integration of traditional music elements is good, and the percentage of auditory and visual modes used in multimodal music teaching is the highest, followed by action and visual modes.

While risk-oriented auditing has gradually emerged as a new trend in current internal auditing, the absence of specific risk assessment methods in related internal auditing standards has made it a common challenge for many enterprise internal auditing organizations and personnel to effectively implement and conduct risk assessments. This research focuses on integrating deep learning technology to construct an audit risk assessment model, first describing the problem of audit risk assessment and then validating it using the CNN-LSTM method. We select the appropriate indicators for audit risk assessment assignment after screening the sample data to ensure their qualitative and quantitative comparability. We construct a CNN-LSTM audit risk assessment model based on the above data. We use two input methods, the convolutional neural network and the long- and short-term memory network, to enhance the assessment model’s learning ability. We then choose 12 enterprise companies as data cases, with Company A serving as the representative for the CNN-LSTM-based assessment model’s performance simulation test and risk level assessment. We compare the four models, and the CNN-LSTM model’s AUC exceeds 0.5 in the confusion matrix and ROC curve graph, confirming the usefulness of CNN-LSTM in audit risk assessment.

How to carry out scientific and reasonable teaching design and then improve the teaching effect is a problem faced by many college English teachers. Aiming at such problems, this paper is based on the six elements of the BOPPPS teaching model to design a blended teaching mode of university English and finally designs a blended teaching mode of university English based on the BOPPPS teaching model. In order to confirm the practicality of the teaching model, research subjects and research materials are identified, and after collecting sample data through questionnaires, the independent sample t-test algorithm is used to analyze the effect of the teaching model designed in this paper. The data show that there is a significant difference between the experimental group and the control group in the five qualities of scientific thinking, with a p-value of less than 0.05, and in terms of English achievement, the corresponding t-test result is a sig (two-sided) of 0.007

The ADDIE model is highly compatible with teaching in terms of educational purposes, concepts, and processes and has a positive contribution to teaching. In this study, the ADDIE model is used to teach a comprehensive practice course for automobile majors in order to promote the systematic design and implementation of teaching. In this paper, the main problems to be solved are firstly extracted through the elemental analysis of the comprehensive practice course system of automobile majors, and the expected level of learning objectives to be achieved is pre-set. Then, a complete integration of educational media resources is carried out to test the effectiveness of the developed course instructional design. The fuzzy evaluation model is utilized to assess the quality of the course constructed by the ADDIE model. Specifically, the hierarchical analysis method is applied to calculate the index weights, and then the entropy weight method is used to correct the obtained index weights to obtain the final weights, determine the fuzzy operator, and make a comprehensive assessment of the teaching quality. It can be seen through the analysis that the method proposed in this paper makes the average final grade of the experimental class higher than that of the control class by 3.4524 points, and the final rating of teaching quality obtained by the method of this paper using the fuzzy comprehensive evaluation model is 3.7968 points, which is a good level. This indicates that the upgraded method of teaching practical courses is more effective than the original method, enhances the overall performance of students, and fulfills the requirements for teaching practice in automotive specialties.

In recent years, the accuracy and linguistic fluency of translation based on intelligent algorithms have been relatively close to that of human beings, and the application of intelligent algorithms has gradually become a research hotspot in the academic world. In this paper, it is found through relevant research that semantic retention, cultural appropriateness, linguistic fluency, content comprehensiveness and text readability are important factors in the application of intelligent algorithms for the translation of literary works for cross-cultural communication. To explore the degree of influence of these factors on the translation quality of literary works, this paper analyzes them using a multiple linear regression model. The first step in building the mathematical model for multiple linear regression is to construct it. Then, the formulas for calculating the goodness-of-fit and significance test of multiple linear regression equations are introduced. Finally, the regression model was tested using residual analysis and multicollinearity. The regression results show that the quality of literary translation = 0.165+0.124*Semantic retention + 0.356*Cultural appropriateness + 0.254*Linguistic fluency - 0.001*Content comprehensiveness + 0.256*Text accessibility. According to the results of the analysis, the impact of content comprehensiveness on the translation quality of literary works is not significant, and cultural appropriateness has the greatest impact on the translation quality of literary works.

The development of globalization has led to the rising demand of society and enterprises for the English application ability of students in colleges and universities, and the enhancement of the English application ability of non-English majors has become the key research direction of English teaching in colleges and universities under this trend. In this paper, we study this issue, highlighting the urgency of innovating the English teaching mode for non-English majors through the current situation of the passing rate of the English application ability examination and the main problems of the cultivation of English application ability of non-English majors. Gray correlation analysis is used to study the correlation between the English teaching mode, job requirements and other factors and the English application ability of non-English majors to provide data support for the introduction of ESP teaching mode. The ESP teaching mode is proposed, the teaching experiment is designed, and the results of the experimental and control classes are tested before and after participating in the experiment to assess the role of the ESP teaching mode in improving the English application ability of non-English majors as well. In the study of students’ satisfaction with the teaching of application abilities under the ESP mode, the overall satisfaction is high. Non-English majors are rated 11.87% more satisfied with the teaching effect under the ESP teaching model than English majors.

Classical poetry embodies the essence of traditional Chinese culture, and its lyricism and infectiousness provide an ideal platform for educating about emotions. However, due to the West’s influence in modern times, the teaching of classical poetry has not expanded to include the emotional aspect. Therefore, this paper establishes the JSA model as the research model for recognizing emotions in classical poetry, based on an analysis of existing methods for recognizing emotions in Chinese literature. Upon scrutinizing the JSA model’s construction, we discovered that it overly relies on the distribution of emotions for theme generation. Consequently, this paper enhances the JSA model by situating the emotion layer between the theme layer and the word layer, builds the reverse JSA model, and employs Bayesian estimation to estimate the model’s parameters. In this paper, we use classical poems as an example to demonstrate how to analyze the sentiment of classical poems by recognizing tone auxiliaries. The improved JSA model’s emotion recognition effect closely aligns with the actual expression effect of the poems, demonstrating the effective application of the advanced JSA model in this paper for emotion recognition of classical poems.

In this paper, according to the process of remote sensing monitoring of soil salinity and alkalinity process as well as the conditions, the remote sensing images were radiometrically corrected and aligned, and the remote sensing images were enhanced by using digital models to change the gray structure relationship of the image elements and change the gray value of the image elements, and then the changes in the patches of the remote sensing images were analyzed to extract the soil salinity and alkalinity data. In this paper, we also used statistical methods to analyze the acidity and salinity characteristics of soil samples, the soil spectral reflectance characteristics, and the sensitive bands for estimating the soil acidity and salinity characteristics, and we performed the multispectral inversion analysis of soil salinity on the basis of the spectral data. The results show that the remote sensing monitoring and early warning model of the soil salinization process established in this paper has a coefficient of determination R2 =0.697, RMSE=0.946, p=1.06*10-7 in the research calculations, and the root-mean-square error between predicted and measured values RMSE=2.33, which indicates that this model has a better performance in monitoring and prediction. The theoretical significance and practical value of this study are crucial for protecting the ecological environment and managing soil salinization.

It is especially important to solve the psychological pressure of college students’ employment if they want to complete their employment and get better development. This paper proposes a cognitive interaction model based on the full information emotion theory and develops the psychological analysis of employment and entrepreneurial pressure on college students on this basis. In the construction of the cognitive interaction model, the basic assumptions of the all-information affective theory are first proposed, the concepts of the individual’s a priori information, a posteriori information, actual information, and expected information are introduced, the individual’s emotional model is established, and the assumption of information-emotional equivalence is proposed. Combining the PDA model with the information theory of emotional motivation completes the model construction based on this basic assumption. In the model performance test, the accuracy of emotion dimension recognition in this paper’s model is more than 95%, which is better than other models. Taking college students in 2 general colleges and universities in Hunan Province, China, as the research object, the study shows that the total psychological mean score of college students’ employment and entrepreneurship stress is 2.75, and the stress psychology is at a medium level. In the data of the randomly sampled student samples, 70% of the student’s emotional tendencies are anxiety, and anxiety is the mainstream emotion of college students. College students’ feelings of employment and entrepreneurship stress and negative coping styles are both at a high-intensity level.

With a long history, profound cultural deposits, and diversified art types, Chinese painting is both an important part of traditional Chinese culture and a typical representative of traditional oriental art and culture. This article starts with the spirit of Chinese painting and aesthetic appreciation and then explores the significance of Chinese painting’s foreign dissemination and dissemination path. For the appreciation of Chinese paintings, the article designs an index system for assessing the appreciation ability of Chinese paintings, solves the weights of the indexes by using the optimal assignment method of distance estimation, and constructs a model for assessing the appreciation ability of Chinese paintings by combining with the cloud model. The Hidden Markov Model is used to establish an information diffusion model for Chinese painting dissemination, and the parameters of the model are estimated using the Great Likelihood Method. The quantitative analyses of Chinese painting appreciation ability, information diffusion effect, and satisfaction with communication effect were carried out with students from University S as the research subjects. The main factor of Chinese painting appreciation ability is emotional ability, which has a weight share of 26.74%. The comprehensive assessment cloud eigenvalue is (3.4714, 0.1462, 0.0348), which shows that the appreciation ability is good. When the state transfer probability is raised from 0.3 to 0.9, the increase in the number of Chinese painting communication nodes in 25s is 93.59%, and the satisfaction index of the audience for the communication effect of Chinese painting is 3.899. Combining with the information diffusion model, we can understand the change of communication nodes in Chinese paintings, which can provide a guide to enhance the communication effect of Chinese paintings.

Due to the improvement of national consumption levels, the Guizhou tourism industry plays an increasingly important role in the economy of Guizhou province, and the proportion of tourism revenue to GDP is increasing. We combine the improved FOA algorithm with an echo-state network, taking into account the characteristics of Guizhou tourism demand, to construct an AFOA-ESN prediction model for Guizhou tourism demand. The new algorithm fixes the problems with the old Drosophila algorithm by changing the number of Drosophila populations, the size of the search step, and the best place for the first iteration. This makes the new algorithm better at local searches and more efficient. We select the annual data on the number of travelers to Guizhou from ten provinces as the dataset and apply the simulation analysis method to test the effectiveness of the previously mentioned prediction model. The data show that among the ten sets of experiments, the AFOA-ESN prediction model has seven times the smallest MAPE value, while the AFOA-ESN model has six times the smallest MSE value. In addition, the consumption structure of Guizhou tourism tourists from 2010 to 2022 did not change much in this stage, showing the development of shock, sensitivity, and vulnerability. Based on the analysis results, we propose the tourism marketing strategy of “Ethnicity + Folklore + Folk Lodging.” This study selects forecast information suitable for the tourism industry and provides the necessary reference for decision-making in tourism-related departments.

The teaching quality of higher mathematics courses plays a pivotal role in the cultivation of students’ quality and the improvement of their abilities. In this paper, information entropy is added to the hierarchical analysis method to improve it and then combined with the fuzzy synthesis method to construct a model for evaluating the quality of mathematics teaching. The higher mathematics teaching course at a university is selected as the research object, and the constructed model for evaluating the quality of higher mathematics teaching is applied to the teaching of higher mathematics in classrooms. The evaluation index system for higher mathematics teaching quality is established by combining relevant literature, and the improved AHP is used for weighting. Then, the index data is collected through questionnaires to analyze the differences and assess the teaching quality of different genders and majors and then to propose strategies to improve the teaching quality of higher mathematics. The significant effects of teaching methods (0.184), teaching effectiveness (0.176), and teaching content (0.170) on the teaching quality of higher mathematics were revealed. The quality of teaching higher mathematics in the sample universities is located at a good level with scores between 8 and 9 on each evaluation index, with the best performance of 8.934 for teaching content. The evaluation model for higher mathematics teaching quality established by the research has validity and feasibility.

To further study the effect of different crop rotation patterns on the productivity of oilseed flax. In this study, the “Jinya No. 10” flax cultivar was used as the object, and a DSSAT model was constructed using 2017-2018 data to simulate the field trials (4-year rotation cycle) during the 30 years from 1994. The experiments were designed with six different crop rotation patterns including the following: flax→flax→flax→flax[(F)FFF], flax→wheat→maize→flax[(F)WMF], flax→maize→flax→wheat[(F)MFW], flax→flax→wheat→maize[(F)FWM], flax→wheat→flax→maize [(F)WFM], flax→wheat→maize→wheat[(F)WMW]. We investigated the effects of crop stubble, planting frequency of oilseed flax, years between plantings of oilseed flax, and years of continuous cropping on grain yield, aboveground biomass, and water use efficiency (WUE) in a semi-arid agricultural area. The results of the study showed that: (1) In the model calibration and validation, the RMSE of yield was 213.67 kg ha−1−217.43 kg ha−1, the RRMSE was 9.76%-10.79%; the RMSE of aboveground biomass was 796.24 kg ha−1−1527.81kg ha−1, the RRMSE was 13.46%-22.71%. The growing period of the cultivar was 95-97 d, indicating that the DSSAT model can simulate the productivity index of oilseed flax well. (2) Oilseed flax yield rotation than continuous mode significantly increased, the increase in 53.43%-54.28%, wheat, and maize stubble both significantly increased oilseed flax yield, compared with oilseed flax stubble increased by 31.04%, 31.95%. 25% oilseed flax frequency and 50% oilseed flax frequency than 100% oilseed flax planting frequency was significantly increased by 54.28% and 53.79%. The increase in grain yield of oilseed flax in the planting interval years was shown as 2a (32.97%) > 3a (31.51%) > 1a (30.84%). (3) The aboveground biomass of oilseed flax in the crop rotation compared to the continuous cropping pattern had a significant increase in the increase ranged from 60.49% to 100.31%. Wheat and maize stubble significantly increased the aboveground biomass of oilseed flax, compared with oilseed flax stubble increased by 25.67% and 22.56%. 25% oilseed flax frequency and 50% oilseed flax frequency than 100% oilseed flax planting frequency significantly increased by 92.02% and 72.81%. Oilseed flax aboveground biomass production in the planting intervals between the years of the increase in the performance of the 3a (39.8%) > 2a (28.99%) > 1a (19.45%). (4) The WUE of rotation compared to the continuous cropping mode increased between 45.78% and 58.54% for flax. The presence of wheat and maize stubble significantly increased the WUE of oilseed flax, compared to stubble of oilseed flax, which increased by 29.32% and 32.41%. 25% flax frequency and 50% flax frequency than 100% oilseed flax planting frequency significantly increased by 45.77% and 59.73%. Compared to the interval year 0a, the specific increase in WUE showed 2a (33.03%) > 1a (31.37%) > 3a (26.06%). Therefore, the continuous cultivation of oilseed flax will reduce productivity, should choose to rotate farming methods, replace different stubble, reduce the frequency of oilseed flax planting, and extend the interval between years of oilseed flax planting, and flax→wheat→maize→wheat is a suitable crop rotation mode in the northwestern dry zone.

Risks in the financial market are omnipresent, and the operations of listed companies are affected by various factors, so the study of the financial risks of listed companies is also of great significance. In this paper, the statement data of listed companies and the text data of annual reports are used separately. The XGBoost model is used to analyze its classification effect, and the confusion matrix and ROC curve evaluation methods are used to compare the accuracy of the prediction results between the XGBoost model and the GBDT model, which helps corporate managers to identify the financial risks of enterprises in advance, and at the same time, improves the level of operation control. The results show that the accuracy of the XGBoost model fluctuates around 0.85, and the highest accuracy of the model is 0.883 when the number of its features is 21. The results of the confusion matrix assessment show that the accuracy of the prediction results of the risk-free company of the XGBoost model reaches 94.95%, and the accuracy of the prediction results of the XGBoost model increases by 5.15% compared with that of the GBDT model. This is in accordance with the ROC curve evaluation results. Obviously, the XGBoost model has a better prediction effect and a more stable early warning performance, and the use of the XGBoost model can help the managers of listed companies to be informed of the deterioration of the company’s financial situation as early as possible so that they can implement the corresponding operational control measures to reduce losses in time.

In the era of aesthetic economy, users pay more and more attention to the aesthetic quality and emotional experience of products. The study discusses the intelligent design method of product model integrating computational aesthetics, combines the advantages of variational self-encoder and generative adversarial network, constructs a generative model based on VAE-GAN, and quantitatively analyzes the product images generated by the VAE-GAN network to explore the effect of the products it generates. Then, take the car form generated by the model as an example, establish the visual presentation quantitative index system of the product model form layout, and evaluate the visual presentation effect of the generated car model after applying gray correlation analysis to assign the indexes. The VAE-GAN model has a better generative effect, and its SSIM value is greater than 0.8 in most of the training phases. Among them, the product model generated after 2000 steps of training is the most excellent, with the highest evaluation score. The product model is the most outstanding, with the highest evaluation score of 1.92. The overall visual presentation effect of the product forms generated by the model is good, and the comprehensive evaluation scores are above 0.6. The VAE-GAN model can be used for product generation, and the visual presentation evaluation method that integrates computational aesthetics can realize the aesthetic evaluation of product forms, which can help designers make design creations and program decisions.

With the expansion of higher vocational colleges and universities, the difficulty of employment of graduates has become an increasingly serious problem in the development of society. The reason for this phenomenon, in addition to the total pressure, lies in the difference between the knowledge, ability, and quality of college students and the needs of employers. This paper crawls the employment information data of graduates of higher vocational colleges and universities from relevant websites, combines the text classification method based on the SVM analysis model to mine the system of graduates’ employment ability, and takes it as a survey scale. Then Jiangxi Province’s higher vocational graduates were selected as the research object. After testing the reliability and validity of the scale, the scale, combined with independent samples, T-test method regression analysis, and other mathematical and statistical methods to explore the factors affecting the employment ability of graduates of higher vocational colleges and universities in Jiangxi Province. Among them, there is no significant difference in the overall employability in terms of gender and specialty category (P>0.05), while there is a significant difference in terms of having or not having work experience (P

RBM, as an important structural component unit in the mainstream model of deep learning, is being widely used in various fields. The article first discusses the RBM deep neural network algorithm, gives an overview of the related function algorithm combining RBM and neural network, and then proposes a deep network method oriented to the prediction of advertisement click rate. At the end of the article, the algorithm proposed in this paper is compared with the model performance test. An advertising company is used as an object of empirical research to analyze the change in its market share and turnover in the years 2022-2023. Explore the significance and role of RBM-based deep neural networks in the innovation of advertising business models.

In this thesis, the Japanese language major in the College of Foreign Languages of University J was selected as an example, and the research method combining quantitative questionnaire and qualitative research was used to investigate the development trend and current situation of Japanese language teaching. Structural equation modeling was used to analyze and hypothesize factors that affect the quality of Japanese classroom teaching. Data was collected, processed, and analyzed with relevant regression statistics to study the statistical regularities among variables with correlations and to verify the research hypotheses proposed in this study, as well as the correlations. The results showed that there was an extremely significant causal relationship (Sig. < 0.01) between teacher level: professional ethics and teacher teaching, teacher professional knowledge, resource inputs and teaching content, and teacher professional competence, teaching content, and teacher teaching. Student level: there is a significant positive effect (Sig

As urbanization and informatization progress, human societies increasingly face unforeseeable public safety risks, necessitating a comprehensive governance system for early risk prevention. This paper introduces a model for community public safety resilience governance grounded in the adaptive cycle model and formulates research hypotheses. We derive a mathematical formula for calculating the safety disaster risk value using risk management theory, which aids in determining the resilience value and risk level of communities. The proposed governance model assesses community resilience through a four-faceted approach that encompasses natural disasters, accidents, public health events, and social security incidents. The public safety resilience of Community L in N city, Jiangsu Province, was evaluated using this model. Furthermore, it was determined that urban communities in NJ City, WX City, and XZ City had resilience governance scores of 77.655, 73.18, and 73.475, with scores exceeding 70 points. An analysis of 16 subject cities revealed that only five are currently in the renewal stage, representing an optimal state of high resilience and low risk. The remaining 11 cities face varying degrees of challenges. To prevent systemic decay, it is crucial to implement customized public safety governance strategies for different urban types.

With the continuous development of industrialized society, carbon emissions have become a significant global challenge. Carbon trading, as a crucial measure to mitigate carbon emissions, has garnered substantial attention in the context of market prediction analysis. Addressing the nonlinear and nonstationary nature of carbon trading prices, this study proposes a novel prediction model based on signal decomposition and deep learning. A GUR neural network model, integrated with an attention mechanism, is constructed within a deep learning framework. The model utilizes Ensemble Empirical Mode Decomposition (EEMD) to address the issue of non-smooth and nonlinear panel data, further enhanced by the Symbiotic Organism Search (SOA) algorithm. This approach culminates in an advanced price prediction model for China’s carbon trading market. Analysis of relevant data from 2014 to 2022 reveals several fluctuations in carbon trading prices, with transaction prices peaking at 68 yuan. The proposed method demonstrates superior performance metrics, with RMSE, MAE, and MAPE values of 0.512, 0.395, and 1.108%, respectively, outperforming other methods. This study offers an effective approach for predicting carbon trading market prices, providing valuable insights for optimizing and managing carbon market trading and development.