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Malaria, a lethal protozoan disease transmitted through the bites of female Anopheles mosquitoes infected with Plasmodium parasites, remains a significant global health concern. This study introduces a compartmental mathematical model to explore the impact of insecticide use and malaria treatment based on awareness initiatives. The model incorporates the influence of media-based awareness on the effectiveness of insecticide utilization for malaria control. Key mathematical properties, such as positivity, boundedness of solutions, feasibility, and stability of equilibria, are systematically investigated. Our analysis demonstrates that all solutions to the system are positive and bounded within a specified set of initial conditions, establishing the mathematical soundness and epidemiological relevance of the model. The basic reproduction number R 0 is determined through the next-generation matrix method. Stability analysis reveals that the disease-free equilibrium is globally asymptotically stable when R 0 is less than one, while it becomes unstable if R 0 exceeds one. Global stability of the endemic equilibrium is established using an appropriate quadratic Lyapunov function in cases where R 0 surpasses one. We identify the most sensitive parameters of the model through normalized forward sensitivity indices. In addition, numerical simulations employing the Runge–Kutta method in Python software further validate our findings. Both analytical and numerical results collectively suggest that the integration of awareness-based insecticide usage with malaria treatment holds the potential for malaria elimination. This comprehensive approach not only contributes to the mathematical rigor of the model but also underscores its practical implications for effective malaria control strategies. [ABSTRACT FROM AUTHOR]

Some nonlinear real-valued equations have no solution in the real number field, and only roots of this nature are of practical interest. However, complex roots associated with the solution may introduce an interpretation of the physical problem analysis. This paper investigates the solution of nonlinear equations exploiting fractional order derivative (FOD) calculus resources. The theory addresses a solver that considers the FOD and Newton-Raphson method. The problem is extended to a multivariate fractional order derivative (MFOD) method so that a set of nonlinear equations can be resolved iteratively. Applications to the computation of the power flow problem in energy systems are used to illustrate some types of equations and solutions. The MFOD uses a numerical limits technique to determine a Jacobian required for the fractional application. This work demonstrates how real-valued nonlinear equations with complex roots can be solved by the MFOD Newton-Raphson approach. The results indicate the potentiality of the method reaches complex-valued solutions despite the iterative process starting with a real-valued guess. The complex-valued results are interpreted considering the connection between the imaginary part of a root and the divergence of the classical Newton-Raphson (CNR) method. [ABSTRACT FROM AUTHOR]

We present an innovative approach to dimensional analysis, referred to as augmented dimensional analysis and based on a representation theorem for complete quantity functions with a scaling-covariant scalar representation. This new theorem, grounded in a purely algebraic theory of quantity spaces, allows the classical π theorem to be restated in an explicit and precise form and its prerequisites to be clarified and relaxed. Augmented dimensional analysis, in contrast to classical dimensional analysis, is guaranteed to take into account all relations among the quantities involved. Several examples are given to show that the information thus gained, together with symmetry assumptions, can lead to new or stronger results. We also explore the connection between dimensional analysis and matroid theory, elucidating combinatorial aspects of dimensional analysis. It is emphasized that dimensional analysis rests on a principle of covariance. [ABSTRACT FROM AUTHOR]

This paper develops a novel set of displacement temperature potential functions to solve the thermoelastodynamic problems in functionally graded transversely isotropic media subjected to thermal source. For this purpose, three-dimensional heat and wave equations are considered to obtain the displacement temperature equations of motion for functionally graded materials. In the present study, a systematic method is used to decouple the elasticity and heat equations. Hence one sixth-order differential equation and two second-order differential equations are obtained. Completeness of the solution is proved using a retarded logarithmic Newtonian potential function for functionally graded transversely isotropic domain. To verify the obtained solution, in a simpler case, potential functions are generated for homogeneous transversely isotropic media that coincide with respective equations. Presented potential functions can be used to solve the problems in various media like infinite and semi-infinite space, beams and columns, plates, shells, etc., with arbitrary boundary conditions and subjected to arbitrary mechanical and thermal loads. [ABSTRACT FROM AUTHOR]

Functionally graded materials (FGMs) are a novel class of composite materials that have attracted significant attention in the field of engineering due to their unique mechanical properties. This study aims to explore the dynamic behaviors of an FGM stepped beam with different boundary conditions based on an efficient solving method. Under the assumptions of the Euler-Bernoulli beam theory, the governing differential equations of an individual FGM beam are derived with Hamilton's principle and decoupled via the separation-of-variable approach. Then, the free and forced vibrations of the FGM stepped beam are solved with the transfer matrix method (TMM). Two models, i.e., a three-level FGM stepped beam and a five-level FGM stepped beam, are considered, and their natural frequencies and mode shapes are presented. To demonstrate the validity of the method in this paper, the simulation results by ABAQUS are also given. On this basis, the detailed parametric analyses on the frequencies and dynamic responses of the three-level FGM stepped beam are carried out. The results show the accuracy and efficiency of the TMM. [ABSTRACT FROM AUTHOR]

A novel phase-field model for the propagation of mixed-mode hydraulic fractures, characterized by the formation of mixed-mode fractures due to the interactions between fluids and solids, is proposed. In this model, the driving force for the phase field consists of both tensile and shear components, with the fluid contribution primarily manifesting in the tension driving force. The displacement and pressure are solved simultaneously by an implicit method. The numerical solution's iterative format is established by the finite element discretization and Newton-Raphson (NR) iterative methods. The correctness of the model is verified through the uniaxial compression physical experiments on fluid-pressurized rocks, and the limitations of the hydraulic fracture expansion phase-field model, which only considers mode I fractures, are revealed. In addition, the influence of matrix mode II fracture toughness value, natural fracture mode II toughness value, and fracturing fluid injection rate on the hydraulic fracture propagation in porous media with natural fractures is studied. [ABSTRACT FROM AUTHOR]

The globalization in business and tourism becomes crucial more and more for the economical sustainability of local communities. In the presence of an epidemic outbreak, there must be such a decision on the policy by the host community as whether to accept visitors or not, the number of acceptable visitors, or the condition for acceptable visitors. Making use of an SIRI type of mathematical model, we consider the influence of visitors on the spread of a reinfectious disease in a community, especially assuming that a certain proportion of accepted visitors are immune. The reinfectivity of disease here means that the immunity gained by either vaccination or recovery is imperfect. With the mathematical results obtained by our analysis on the model for such an epidemic dynamics of resident and visitor populations, we find that the acceptance of visitors could have a significant influence on the disease’s endemicity in the community, either suppressive or supportive. [ABSTRACT FROM AUTHOR]

More than half of the coronavirus disease 19 (COVID-19) related mortality rates in the United States and Europe are associated with long-term-care facilities (LTCFs) such as old-age organizations, nursing homes, and disability centers. These facilities are considered most vulnerable to spread of an pandemic like COVID-19 because of multiple reasons including high density of elderly population with a diverse range of medical requirements, limited resources, nursing activities/medications, and the role of external visitors. In this study, we aim to understand the role of visitor’s family members and specific interventions (such as use of face masks and restriction of visiting hours) on the dynamics of infection in a community using a mathematical model. The model considers two types of social contexts (community and LTCFs) with three different groups of interacting populations (non-mobile community individuals, mobile community individuals, and long-term facility residents). The goal of this work is to compare the outbreak burden between different centre of disease control (CDC) planning scenarios, which capture distinct types of intensity of diseases spread in LTCF observed during COVID-19 outbreak. The movement of community mobile members is captured via their average relative times in and out of the long-term facilities to understand the strategies that would work well in these facilities the CDC planning scenarios. Our results suggest that heterogeneous mixing worsens epidemic scenario as compared to homogeneous mixing and the epidemic burden is hundreds times greater for community spread than within the facility population.

In this paper, we formulate a population suppression model and a population replacement model with periodic impulsive releases of Nilaparvata lugens infected with wStri. The conditions for the stability of wild- $ N.\,lugens $ N. l u g e n s -eradication periodic solution of two systems are obtained by applying the Floquet theorem and comparison theorem. And the sufficient conditions for the persistence in the mean of wild $ N.\,lugens $ N. l u g e n s are also given. In addition, the sufficient conditions for the extinction and persistence of the wild $ N.\,lugens $ N. l u g e n s in the subsystem without wLug are also obtained. Finally, we give numerical analysis which shows that increasing the release amount or decreasing the release period are beneficial for controlling the wild $ N.\,lugens $ N. l u g e n s , and the efficiency of population replacement strategy in controlling wild populations is higher than that of population suppression strategy under the same release conditions. [ABSTRACT FROM AUTHOR]

The choice of an adequate mathematical model is a key step in solving problems in many different fields. When more than one model is available to represent a given phenomenon, a poor choice might result in loss of precision and efficiency. Well-known strategies for comparing mathematical models can be found in many previous works, but seldom regarding several models with uncertain parameters at once. In this work, we present a novel approach for measuring the similarity among any given number of mathematical models, so as to support decision making regarding model selection. The strategy consists in defining a new general model composed of all candidate models and a uniformly distributed random variable, whose sampling selects the candidate model employed to evaluate the response. Global Sensitivity Analysis (GSA) is then performed to measure the sensitivity of the response with respect to this random variable. The result indicates the level of discrepancy among the mathematical models in the stochastic context. We also demonstrate that the proposed approach is related to the Root Mean Square (RMS) error when only two models are compared. The main advantages of the proposed approach are: (i) the problem is cast in the sound framework of GSA, (ii) the approach also quantifies if the discrepancy among the mathematical models is significant in comparison to uncertainties/randomness of the parameters, an analysis that is not possible with RMS error alone. Numerical examples of different disciplines and degrees of complexity are presented, showing the kind of insight we can get from the proposed approach. [ABSTRACT FROM AUTHOR]

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