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To deeply analyze the performance of paper-cutting elements in interior space design, this paper explores the artistic expression of paper-cutting elements. The BIM model is constructed based on the TVL algorithm, and a noise-reducing style migration method with an additional total variable loss function is used to fuse the texture features of a specific paper-cutting style so that the paper-cutting texture is generated in the target object area more intensively. The algorithm is supplemented with a U-shaped network structure to obtain the mask map of the target object, modify and expand the ScanNet training data set, and input a content map and a style map to generate the stylized paper cutout image automatically. The results show that compared with other elements, the stability of paper-cut elements reaches 96.31%, the image smoothness reaches 89.34%, and the retention of significant regions reaches 93.26%. In modern design, the peak signal-noise of paper-cut elements improved by 13.418d on average, the structural similarity also improved by 0.34807, and the recognition rate of paper-cut elements in the furnishing category reached 95%. Applying paper-cut art in interior space design can give interior furnishing design better visual effects, provide inspiration and ideas for interior space design innovation, and contribute to improving aesthetics.

Paper-cut elements in the design of boat space can effectively improve the homogenisation tendency of existing boat space design, effectively enhance the characteristics of traditional Chinese culture and enrich the development ideas of boat space design. Therefore, based on the Chinese folk art of paper cutting, this paper combines theoretical analysis and empirical research to deeply analyse the artistic expression of paper-cut elements in the spatial design of boats. This paper mainly studies the artistic integration of paper-cut elements in the decorative design of boats and the artistic integration of paper-cut elements in the practical design of boats. Through the calculation and analysis of the sparse autoencoder algorithm model, it can be seen that the decorative integration of paper-cut elements in wallpaper is 82%, which is higher than that of traditional materials (24%). The integration of paper-cut elements in the space design of cabins and guest rooms is as high as 97% and 94%, respectively, which is far better than the integration of traditional materials in the space design of cabins and guest rooms. This shows that the application of folk paper-cut elements to the space design of boats not only highlights the theme of boats but also enhances the cultural value of boats.

English is a universal language and is widely used. In order to deepen the cognition of language, this paper proposes to analyse the variation of metadiscourse verb patterns in English academic papers from within and between disciplines. By comparing the metadiscourse chunks used in various disciplines, it is concluded that there are obvious differences in the verb types of the subject papers. Analysing various identification algorithms of verb types, it is obtained that the combination of the word movement distance (WMD) model and the R&L density peak clustering algorithm is the best. Using the R&L density peak clustering, identification parameters are easy to determine, and when combined with the WMD model to calculate the similarity of words, it improve the accuracy of verb pattern clustering. By comparison, it is proved that the accuracy of the algorithm combining the WMD model and R&L density peak clustering reaches 30.06%, and the effect of identifying verb pattern variation is the best.

In the realm of Chinese traditional arts, paper-cutting symbols embody deep-seated aesthetic and cultural values. Within the framework of artificial intelligence, the fusion of these conventional symbols with contemporary pattern design through intelligent reconstruction processes promotes innovation in modern design. This paper introduces a novel pattern feature extraction algorithm specifically designed for identifying and extracting the essence of traditional paper-cutting art. Utilizing Deep Convolutional Generative Adversarial Networks (DCGAN), an automated method for generating woven patterns is developed, integrating conventional motifs with modern design elements. Additionally, a segmentation model based on DeepLabV3+ semantics is constructed to facilitate this integration. The study focuses on the fusion of traditional paper-cutting symbols with fabric patterns, providing a new direction for textile design research. The empirical evaluation involved seven experimenters who predominantly favored patterns that blended abstract and traditional styles, with the average style rating exceeding 3.2. Moreover, the color performance analysis of the reconstructed fabric patterns across five regions showed color difference values greater than 4, indicating superior color fidelity. These findings underscore the potential of combining AI-driven techniques with traditional art forms to enhance and revolutionize modern design practices.

The study of metadiscourse word resources is an important way to reveal academic knowledge constructs and genre features in English papers. This paper selects intra- and interdisciplinary English academic papers as data samples, and the text representation is based on the vector space model. The text similarity is calculated using the word movement distance model to complete the clustering based on the density peak, and then the semantic annotation of metadiscourse verb patterns is performed using the conditional random field. Then, the intra- and interdisciplinary metadiscourse annotations are compared to analyze the variation of verb patterns. The intra-disciplinary variation of metadiscourse verb patterns was mainly found using attitude markers, where the log-likelihood ratios of 48.56, 30.25, and 35.64 were obtained for affirmative, critical, and critical declarative attitudes, respectively. In interdisciplinary studies, basic disciplines preferred to use verb patterns as transitions, while applied disciplines preferred to express the author’s point of view. The study of metadiscourse verb patterns effectively enriches the linguistic resources in academic English teaching and deepens the understanding of the characteristics of disciplinary essay genres.

This investigation delves into the evolving patterns and regional variances in industry-academia collaboration (IAC) within Functional Nucleic Acids (FNAs) research. Analyzing publications and patents from 1990 to 2021, we dissected IAC at individual and collective levels, documenting the count and share of IAC endeavors, key partners, and regional differences. Results indicate a rising trend in IAC publications and collaborating institutions, while patent trends display a mix of increases, decreases, and steady states. The United States leads in IAC activities, correlating with its prowess in scientific research and technological innovation. Notably, regional discrepancies in IAC are mainly due to intra-regional variations, with Asia and Europe experiencing the most pronounced disparities.

Leveraging artificial intelligence (AI), this study revolutionizes examination management and education in universities by developing an intelligent system encompassing comprehensive management, pre-examination activities, scheduling, and preparation. The system also features a quality management component for educational outcomes. An enhanced genetic algorithm introduces an adaptation function to optimize intelligent grouping, facilitating effective exam paper distribution. Applied at Y University, our innovative approach significantly refines exam paper difficulty (ranging between 0.5016 and 0.5581) and differentiation (0.3845 to 0.4596), showcasing the intelligent algorithm’s effectiveness in exam management and contributing valuable insights to educational research.

With the innovation and development of commercial shopping centers in China, the shopping environment is becoming more and more colorful. However, in today’s social environment, the central function of low-level consumption experience can no longer meet the needs of the masses. Therefore, in the design of indoor environment, we should start from a new perspective, focusing on the difference of internal space and the experience of consumption mode, so as to meet the development needs of commercial shopping in the future. Based on the research on the indoor environment of comprehensive commercial shopping center, comprehensive commercial shopping center in a certain area as the research target, the corresponding mathematical model was constructed, and using the measured method validation build the model of reliability and validity, thus obtains using CFD numerical simulation software of thermal and moisture environment of building an in-depth exploration, The effective countermeasures are put forward for the original air distribution mode. Comparing the improved scheme with the original scheme, it is found that the former can provide effective basis for the interior environment design of similar comprehensive commercial shopping malls.

Artificial intelligence (AI) and machine learning have become increasingly important in modern society and are poised to play an increasingly prominent role in education. This paper seeks to provide a theoretical framework for interrogating the integration of AI in education spaces. The paper argues that the eventual response of educators to recent developments in artificial intelligence is eerily like the earlier cycles of integrating ICT in education and, decades earlier, calculators into mathematics instruction. Premised on the argument that there are similarities between the calculator revolution in mathematics education and the ICT revolution in education several decades ago and the current ongoing developments in artificial intelligence, this paper offers a theoretical lens. The theoretical lens is composed of the Technology-Organization-Environment (TOE) framework, Technology Acceptance Model, Technological Pedagogical Content Knowledge, Socio-technical system theory, and Diffusion of Innovation theory. The paper concludes that despite spatial differences between the ICT revolution and the artificial intelligence revolution, there are shared similarities warranting adoption of a similar theoretical lens. Furthermore, factors that were considered pivotal in the integration of ICT are still relevant to the revolution of artificial intelligence. [ABSTRACT FROM AUTHOR]

Chaotic behaviour of dynamical systems, their routes to chaos, and the intermittency in particular are interesting and investigated subjects in nonlinear dynamics. The studying of these phenomena in non-smooth dynamical systems is of the special scientists’ interest. In this paper we study the type-III intermittency route to chaos in strongly nonlinear non-smooth discontinuous 2-DOF vibroimpact system. We apply relatively new mathematical tool – continuous wavelet transform CWT – for investigation this phenomenon. We show that CWT applying allows to detect and determine the chaotic motion and the intermittency with great confidence and reliability, gives the possibility to demonstrate intermittency route to chaos, to distinguish and analyze the laminar and turbulent phases.

In this article, we have studied the uniqueness problem of Dirichlet series, which is convergent in a right half-plane and having analytic continuation in the complex plane as a meromorphic function sharing some sets and values. Our first result partially improve a result of [Ann. Univ. Sci. Budapest., Sect. Comput., 48(2018), 117-128] by relaxing the sharing conditions. Most importantly, we have pointed out a number of big gaps in a recent paper [J. Contemp. Math. Anal., 56(2021), 80-86], which makes the existence of the paper under question. Finally, under a different approach, we have provided the corrected form of the result of [J. Contemp. Math. Anal., 56(2021), 80-86] as much as practicable. [ABSTRACT FROM AUTHOR]

Researching different solutions of nonlinear models has been interesting in different fields of science and application. In this study, we investigated different solutions of fourth-order nonlinear Ablowitz– Kaup–Newell–Segur wave equation. We have used the sine-Gordon expansion method (SGEM) during this research. We have given the 2D, 3D, and contour graphs acquired from the values of the solutions obtained using strong SGEM.

We extend the Boltzmann’s ideas that describe the evolution to the equilibrium of many body systems to the multifractal decomposition of the unitary interval 𝕀, in terms of sets Jα conformed by points with the same pointwise dimension, and obtain the D(α) singularity spectrum.

The study of everyday phenomena involving friction continues to maintain a high level of difficulty despite its long history. The causes of this problem lie in the different scale of the characteristics of the phenomenon, macroscopic and microscopic. Thus, very different models, valid in a narrow scope which prevents generalization, have been appearing. This survey presents the application of network simulation method to the numerical solution to the study of friction at very different scales. On the one hand, on a microscopic scale an atomic force microscope model has been studied, related to the analysis of soft surfaces at the atomic scale. Furthermore, on a macroscopic scale model related to the analysis of an industrial device, such as a brake mechanism has been studied. After presenting herein is a review of the different formulations of the friction force, the nature of the surfaces involved in the phenomenon, as well as the definition of the problems to be analyzed. The design of network models and the implementation of the initial conditions are explained. The results of the application of network models to selected problems are presented. In order to verify the reliability of the proposed models, their results are compared with the solutions obtained by other numerical methods or experimental results, one from a device developed during the preparation of this report.

We analyzed the first set of complete CCD light curves of the W UMa type eclipsing binary IK Boo in the BVRI bands by using the PHOEBE code and deduced its first photometric parameters with, mass ratio q = 0.648 and orbital inclination i = 63o. We have applied a spotted model due to the light curves asymmetry. The system shows a distinct O’Connell effect. The best solution fit to the light curves suggested the influence of star spot(s) on both components. Such presence of star spot(s) is common among the RS CVn and W UMa chromospheric active late type stars.

In this short paper, I recall the history of dealing with the lack of compactness of a sequence in the case of an unbounded domain and prove the vanishing Lions-type result for a sequence of Lebesgue-measurable functions. This lemma generalizes some results for a class of Orlicz–Sobolev spaces. What matters here is the behavior of the integral, not the space. [ABSTRACT FROM AUTHOR]

Let P (z) = ∑ j = 0 n a j z j be a polynomial of degree n such that an ≥ an−1 ≥... ≥ a1 ≥ a0 ≥ 0. Then according to Eneström-Kakeya theorem all the zeros of P (z) lie in |z| ≤ 1. This result has been generalized in various ways (see [1, 3, 4, 6, 7]). In this paper we shall prove some generalizations of the results due to Aziz and Zargar [1, 2] and Nwaeze [7]. [ABSTRACT FROM AUTHOR]

In this paper, we introduce one-parameter generalization of dual-hyperbolic Jacobsthal numbers – dual-hyperbolic r-Jacobsthal numbers. We present some properties of them, among others the Binet formula, Catalan, Cassini, and d'Ocagne identities. Moreover, we give the generating function and summation formula for these numbers. The presented results are a generalization of the results for the dual-hyperbolic Jacobsthal numbers. [ABSTRACT FROM AUTHOR]

In this paper, we present the Sugeno integral of Hermite–Hadamard inequality for the case of quasi-arithmetically convex (q-ac) functions which acts as a generator for all quasi-arithmetic means in the frame work of Sugeno integral. [ABSTRACT FROM AUTHOR]

In this paper we are focusing on finding the transcendental entire solution of Fermat-type trinomial and binomial equations, by restricting the hyper-order to be less than one. As the hyper-order is a crucial parameter that characterizes the growth of entire functions, it will be interesting to investigate this unexplored domain, as far as practible, with certain restriction on hyper order. Our results are the improvements of previous results reported in recent papers [12], [13]. We have provided a series of examples to demonstrate and validate the effectiveness of our proposed solutions.

In many digital systems, various sequential blocks are used. This paper is devoted to the case where the model of a Mealy finite state machine (FSM) represents the behaviour of a sequential block. The chip area occupied by an FSM circuit is one of the most important characteristics used in logic synthesis. In this paper, a method is proposed which aims at reducing LUT counts for FPGA-based Mealy FSMs with transformation of state codes into FSM outputs. This is done using the combined state codes. Such an approach allows excluding a block of transformation of binary state codes into extended state codes. The proposed method leads to LUT-based Mealy FSM circuits having exactly three levels of logic blocks. Under certain conditions, each function for any logic level is represented by a circuit including a single LUT. The proposed approach is illustrated with an example of synthesis. The results of experiments conducted using standard benchmarks show that the proposed method produces LUT-based FSM circuits with significantly smaller LUT counts than is the case for circuits produced by other investigated methods (Auto and One-hot of Vivado, JEDI, and transformation of binary codes into extended state codes). The LUT count is decreased by an average of 17.96 to 91.8%. Moreover, if some conditions are met, the decrease in the LUT count is accompanied with a slight improvement in the operating frequency compared with circuits based on extended state codes. The advantages of the proposed method multiply with increasing the numbers of FSM inputs and states.

A steady-state sinusoidal and distortion-free excitation source is very important for the accuracy and consistency of the calibration parameters of micro-electro-mechanical systems (MEMS) inertial sensors. To solve the problem that the current MEMS inertial measurement unit (IMU) calibration device is unable to reproduce the spatial motion of linear and angular vibration coupling, research topics on the coupling vibration characteristics and parameter identification for an electromagnetic linear-angular vibration exciter are proposed. This research paper used Ampere’s law and Lorentz force to establish the analytical expressions for the electromagnetic force and electromagnetic torque of the electromagnetic linear-angular vibration exciter. Then, the main purpose of this paper is to establish uniaxial and coupled vibration electromechanical analogy models containing mechanical parameters based on the admittance-type electromechanical analogy principle, and the parameter identification model is also obtained by combining the impedance formula with the additional mass method. Finally, the validity of the coupling vibration characteristics and the parameter identification model are verified by the frequency response simulation and the additional mass method, and the relative error of each parameter identification is within 5% in this paper.

In this paper we generalize a result of R. Urbański from paper [7] which states that for subsets A, B, C of topological vector space X the following implication holds A+B⊂B+C⇒A⊂CA + B \subset B + C \Rightarrow A \subset C provided that B is bounded and C is closed and convex. The generalization is given in Theorem 5 where we prove this result for k- convex subsets of a topological vector space. Also we introduce a notion of some abstract like-closure operation for subsets of linear space and we study its connections to order cancellation law.

This paper explores the concept of normal structure by introducing a generalized form known as P-proximal normal structure. Focusing on the framework of Hausdorff locally convex spaces, the study establishes optimal proximity outcomes for both cyclic and noncyclic relatively P-nonexpansive mappings. Furthermore, the paper presents in the realm of probabilistic normed spaces, considered as instances of Hausdorff locally convex spaces, some theorems that address the existence of best proximity points within this context.

A submetric space is a topological space with continuous metrics, generating a metric topology weaker than the original one (e.g. a separable Hilbert space with the weak topology). We demonstrate that on submetric spaces there exists a theory of convergence in probability, in law etc. equally effective as the Probability Theory on metric spaces. In the theory on submetric spaces the central role is played by a version of the Skorokhod almost sure representation, proved by the author some 25 years ago and in 2010 rediscovered by specialists in stochastic partial differential equations in the form of "stochastic compactness method". [ABSTRACT FROM AUTHOR]

With the deepening of higher vocational English teaching research and the continuous advancement of reform, this paper combines in-depth learning with higher vocational English teaching and makes an in-depth study of higher vocational English teaching practice based on in-depth learning theory and learning theory. First of all, the paper expounds on the concept of higher vocational English teaching and the research of English teaching. Explain the related concepts of psychological factors and the composition of psychological factors, then describe the model of in-depth learning, elaborate the related algorithms of in-depth learning optimization methods and the functions of various data of in-depth learning-related research, and finally, based on in-depth learning, explain the psychological factors of teachers and students in higher vocational English teaching. It reflects the importance of psychological factors in higher vocational English teaching. With the development of in-depth learning, we should apply in-depth learning to higher vocational English teaching and optimize the teaching mode of in-depth learning reasonably, including formulating comprehensive higher vocational English teaching objectives, adjusting students' psychological status reasonably, and carrying out cooperative learning.

The cultural hedging and fusion of the new era embodied in literary works is of great significance to the communication, development, and prosperity of Chinese and Western cultures. This paper selects the English works compiled and published by Lin Yutang as the research object and analyzes the communication value generated in the process of its compilation and publication. After designing a questionnaire qualified by reliability and relevance analysis, the questionnaire is used to investigate the audience’s exposure to English works and the degree of understanding of the newage cultural hedging embodied in the works. The results of the survey show that multimedia has become the main channel for audiences to access Lin Yutang’s English works. The audience’s understanding of the new age cultural hedges embodied in the works is poor; the acceptance of the new age cultural hedges is at a medium level (3.514 points), and there is a significant correlation between the acceptance level and the understanding effect. It is also found that the multimedia communication method consisting of video and text in a compiled publication has the best comprehension effect (4.857 points) on the new age cultural hedging of the audience. This paper provides a reference path for the compilation and publication of Lin Yutang’s English works under multimedia and lays a foundation for deepening the audience’s understanding of the new age cultural hedge.

With the rapid development of the information age, new technological means are constantly applied to the field of education and produce good results. This paper uses data mining algorithms to extract and analyze data on college English education and explore how to adjust the teaching methodology. On the basis of improving the inherent defects of the DPC algorithm, this paper determines the clustering center through a scoring function, uses the K-neighbor method to form the cluster backbone, and proposes a nearest-neighbor-based label propagation method to propagate the labels to their adjacent nodes to complete data mining. After mining and analyzing the current situation of English education, it was found that the age group over 30 years old accounted for 77.6%, female teachers accounted for 74.6% of the total number of teachers, teachers with the titles of lecturer and assistant professor accounted for 58%, and 33% of the students were not too satisfied or dissatisfied with the content of the English course. It shows that the English teaching team of universities in this province has shortcomings such as gender imbalance, insufficient young and senior teachers, and less than satisfactory content of English courses, which need to be improved in several aspects.

Migration learning is a kind of deep learning that plays an important role in risk prediction and warning. In this paper, we use the Triplet-loss representation learning technique to map data samples of the same category to adjacent spatial regions, map data samples of different categories to different spatial regions, and add domain adaptive computation to complete domain adaptation. XGBoost performance is also optimized to improve the prediction results. After completing the risk prediction model construction, this paper also incorporates indicator weights to propose a risk warning model based on a weighted knowledge graph. The analysis found that the model accuracy of the compliance risk prediction model is 1.155% higher than that of two traditional prediction models on average, and the indicators of accuracy, F1-score, and AUC are all over 0.99, which indicates that it has high accuracy. After applying the optimized risk management system in an enterprise, it is found that the weight of the indicators of the enterprise’s compliance management system reaches 25.71%, the combined weight of the legal audit system and the risk prevention and control mechanism is 16.23% and 14.10%, respectively, and the combined weight of the two indicators of the appraisal and evaluation of the performance of the compliance management and the accountability for the violation of the law exceeds 8%. The weights of the indicators related to the degree of business standardization are close to 16.51%, indicating that the enterprise’s daily production and operation activities are standardized and orderly, and the perfection of the compliance management system is in line with the real situation of the enterprise. It can be seen that the risk management system makes accurate judgments on the compliance risk of the selected enterprise, which reflects the feasibility of the application of the new model.

This paper builds a business English intelligent teaching network platform through cloud computing, virtual reality, corpus and automatic scoring system, and other information technologies. Educational information technology helps construct a linguistic corpus by utilizing vocabulary lookup and MI values to create a one-stop solution for multi-level language analysis and application. The essay scoring project in the Business English Smart Teaching Network Platform utilizes the bag-of-words vector model to extract the features of each word, text, and topic to complete automatic essay scoring. After analysis, it can be seen that the business skills and interests of the experimental group have significantly improved after the teaching of the Business English Wisdom Teaching Network Platform. After the corpus-scaffolded writing teaching training, the mean values of students’ vocabulary richness ranged from 3.869 to 4.108, and sentence variety ranged from 3.961 to 4.135. Automatic scoring of compositions resulted in a significant improvement in the writing quality of students (P=0.000). The smart teaching network platform mentioned in this paper can be utilized to improve business English teaching in the context of informationization.

Nautical talents are the first resource and fundamental guarantee for the development of marine careers, strengthening the construction of nautical talents and realizing the effective supply of nautical talents have become the urgent task of building a strong marine country. This paper combines the basic situation of the current nautical personnel training and the training requirements of nautical personnel, constructs the vocational literacy and ability enhancement path of nautical students based on the integration of post-course, race, and certificate under the guidance of the OBE concept, and constructs the corresponding index system. Subsequently, combining the hierarchical analysis method and entropy weight method, the weights of the indicators in the evaluation system of nautical students’ vocational quality and talent ability are calculated, and the cloud model is utilized to evaluate the vocational quality and talent ability of nautical talents after cultivation. The results of the study show that the comprehensive evaluation level of the vocational quality and talent ability of the maritime students of Jimei University after the implementation of the pathway is “good”. Among the secondary evaluation indexes, the vocational concept evaluation cloud has the highest degree of affiliation, which is 9.31. This indicates that the vocational concept of navigation students is being cultivated at a very high level. The evaluation result of “vocational behavior” also belongs to the “good” level. The cultivation path proposed in this paper provides a reference for the enhancement of the vocational quality and ability of nautical talents, and it can improve the cultivation of nautical talents through the synergistic approach of government, industry, enterprise, and school, and guarantee the enhancement of the vocational ability and career expansion of nautical talents.

Recent years have seen intensive research and widespread application of 6G wireless transmission technology, providing a technical bridge for the application of speech network analysis to spoken English teaching. Thus, this paper suggests a method for improving 6G technology. This paper primarily focuses on the following areas: First, to prevent overfitting, the dropout technique under 6G is added to the model, and the speech feature extracted from the data by the coding network is directly utilized. We then utilize the feature for either reconstruction or classification purposes. Initially, we perform unsupervised reconstruction using unlabeled data, and once we obtain the network parameters, we use the labeled data for classification. The next step involves feeding both data simultaneously into the model to calculate the error. Finally, we use gradient descent to minimize the error and optimize the parameters until the model converges. This model can convert sound-related symbols or information into visual symbols, and it can also convert speech into text. Comparative analysis shows that the improved method in this paper is far better than other methods in terms of noise reduction, and the signal-to-noise ratio of this paper is 3.76 dB higher than that of the LMS algorithm and 1.36 dB higher than that of the spectral subtraction method, which can stimulate the students’ interest in learning spoken English and increase the student’s interest in learning by 13.64% as a whole. It can be summarized through the comparison that this paper’s improved method increases the clarity of speech network analysis, which helps students teach spoken English.

In this paper, the convolutional neural network is applied to the processing of stylistic features in the images of landscape paintings of Wei Jin and North and South Dynasties. After convolution and pooling activation, rich and less parametric feature maps are generated to lay a solid foundation for generative adversarial networks. Meanwhile, in order to ensure the rigor of the image dataset of landscape painting images under scene transformation, an improvement is proposed on the basis of traditional GAN technology, and a function represents the improved CycleGAN model. The experimental dataset has been constructed, and the loss function, network training, and hyperparameters have been designed for the model. The visual and conversion outcomes of the model-generated images are evaluated through simulation experiments. The migration of all landscape painting style textures is within the range of (0.5,2.4) at a gray level of 8. The migration styles increase as the gray level increases, and the data between groups does not overlap. In the subjective evaluation of the landscape painting style transformation generated by the model used in this paper, the score of the generated image is 3.8135 points, while the number of high scores is more than 65% of the votes. The model constructed in this paper has a better effect on the transformation of the image style of the Wei, Jin, and North and South Dynasties, and it can satisfy people’s visual needs.

This paper takes green finance policy as the research object and collects local government policy texts about green finance so as to study its policy structure in depth. Then, it studies the theory of green finance to support the low-carbon economy, empirically analyzes it in terms of measurement using the panel vector autoregressive model, and tests its smoothness and covariance to explore the impact of green finance in the development of the low-carbon economy. According to the findings, the proportion of demand-based green financial policies is the lowest among the five provinces, with a mean value of 22%. The extreme differences in development levels of a low-carbon economy and green finance are 1.52 and 1.46, respectively, showing different characteristics. The model regression coefficient of the level of green financial development is 0.08, and the development of a low-carbon economy positively influences it. Although the effectiveness of green financial policies will fluctuate, the overall trend is upward. This paper analyzes the panel vector autoregressive model and the effectiveness of policy tools to provide suggestions for improving the policy system.

In the face of people’s growing spiritual civilization needs, the application of traditional art colors in modern visual communication design has become a hot spot of attention. In this paper, a model for extracting traditional art color features is constructed to provide a methodology and theory for applying traditional art color in modern visual communication design. In the model construction, the max-min clustering algorithm is proposed to achieve color segmentation, measure the distance between the clustering center and the samples, and improve computing speed. Based on the K-means algorithm, the initial clustering center selection and the number of class clusters are improved, and the color feature extraction method is optimized to improve the accuracy of color extraction. In the performance experiments of the model, the All, Noncc, and Disc parameters of this paper’s color segmentation algorithm are lower than those of other algorithms in color segmentation of different pictures, and the performance is good. The accuracy of the color feature extraction test in different color spaces is close to 100%, the average verification accuracy is as high as 99.8%, and it takes less time. The aesthetic, originality, symbolism, and spirituality evaluation indexes of the poster work “Lead,” designed by combining the model of this paper, have an average score of more than 4, which reaches a good level.

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

In the context of energy saving and emission reduction to protect the environment has become a social consensus, environmental benefit is a social responsibility that enterprises must actively undertake, and the technological advantages of information technology companies make them more equipped to enhance environmental benefits. Therefore, this paper proposes countermeasures for information technology companies to enhance their social responsibility and environmental benefits. To achieve practical countermeasures for energy savings and emission reduction, this paper proposes a method to improve the LSTM power prediction model using the Sparrow search algorithm for the company’s electricity management. The model optimizes the network structure of traditional LSTM by searching for parameters such as learning rate, iteration number, and the number of neurons in the two hidden layers of LSTM through the sparrow fitness function. The model proposed in this paper has an average prediction error rate of 3.32% in predicting the annual electricity consumption of a county for five years from 2018 to 2022. After an enterprise introduced the social responsibility practice countermeasures proposed in this paper, the company’s net profit margin increased by 12.2% on average, CO2 emission reduction was 963.2 tons, the average monthly electricity consumption was reduced by 24936.5 kWh, and the environmental performance score increased from 3.2 to 5.4.

In the context of practical education, deepening the reform of quality education and implementing the fundamental task of cultivating moral integrity have become the guidelines and the basis for the development of curriculum reform in colleges and universities. This study conducts data mining based on the association rule model on the influencing factors of the reform of the Civic and Political Theory Course in colleges and universities to obtain the key influencing factors in the process of the reform of the theory course. Then, under the perspective of deep learning, the idea of practical parenting is integrated, and the structural framework of Civic and Political Theory course reform is designed from four directions: learning theme, learning objectives, learning activities, and evaluation feedback, and finally, the course reform method of this paper is examined through teaching practice. The results show that the influence weights of self-factors, school education factors, family factors, and social factors are 0.3022, 0.3521, 0.1381, and 0.2076, respectively, with self-factors and school education as the key influencing factors. After the teaching practice, the P-value of each dimension of the students under the curriculum reform based on this paper is less than 0.05, and the grade of the Civics and Political Science Theory course is improved from 81.47 to 90.45, which is an increase of 7.49 points compared with the class of the regular course. The reform method based on deep learning in this study can promote students’ development and improve their academic performance, which provides a reference for the research on the reform and development of the Civics and Political Science Theory course.

This paper proposes a reform of the teaching methodology based on the exercise prescription course centered on intelligent exercise prescription recommendation and designs a specific implementation plan. A criterion function is used to determine and represent the constant terms of the user-set data using the K-means clustering algorithm. The personal data of users is added on this basis, and the attribute weighting is used to calculate user similarity and construct the user matrix. The core parameters of intelligentized exercise prescription are examined, and the relevant data of the exerciser group is obtained through modeling. The set of similar cases is determined, and the exercise effects in the set of similar cases are fused and calculated, and the effect level of the fused effects in the cases is discriminated. The model developed in this paper is implemented in the college physical education classroom, and the student’s attitudes toward physical exercise and fitness levels are analyzed. The results show that the attitude towards physical exercise behavior of students in group A is significantly better than that of those in group B. The significance test (F = 7.985, Sig = 0.005) is less than 0.05. Taking the 50-meter sprint as an example, the difference in the duration between male students and female students in groups A and B is 0.83s and 0.9s, respectively, which is a large enhancement, which shows that the intelligent exercise prescription has a positive impact on physical education teaching.

Ethnic music culture is a rare and precious artistic treasure in all national cultures, and the inheritance and promotion of ethnic music culture is an essential part of music education teaching in colleges and universities. This paper delves into the characteristics of the development and dissemination of ethnic music culture in the Internet era, with the aim of studying the integration of ethnic music culture into music education teaching in colleges and universities through the use of Internet technology. Through the collaborative filtering algorithm, students’ preferences for ethnic music works are mined so as to recommend accurate teaching resources for college students on the ethnic music online teaching platform constructed based on the collaborative filtering algorithm. In order to fully prove the effectiveness of the teaching platform designed in this paper, the rough set theory and neural network algorithm are used to construct the online teaching quality evaluation model for college ethnic music. The questionnaire survey method is used to study the status quo of ethnic music teaching in colleges and universities, and it is concluded that at present, there are problems in ethnic music teaching in colleges and universities, such as insufficient concepts of students learning ethnic music and insufficient attention to ethnic music in colleges and universities themselves. Through the analysis of the effect of the recommended algorithm, the design of the evaluation index system, and the practical application, the quantitative assessment value of the teaching quality of the ethnic music online teaching platform designed in this paper in the practical application is 92.11, which indicates that the comprehensive rating of the quality of the ethnic music online teaching in colleges and universities in this paper is excellent.

Cultivating innovative talents is an important goal of the talent cultivation strategy in the new era, and ideological and political education is an indispensable part of cultivating a correct outlook on life and the spirit of innovation. In order to fully leverage the supporting role of ideological and political education in cultivating innovative talents, this paper proposes an enhanced collaborative filtering algorithm and content recommendation algorithm. The traditional Civic and Political learning evaluation algorithm is improved by considering the forgetting factor and introducing disciplinary abilities and sequence dependence. The results of the experimental analysis point out that the average grade improvement of the seven Civics and Politics knowledge points of the students in the experimental class reaches 10.3%, the mean value of the four innovation dimensions reaches 14.95 points, and the average agreement rate between the self-assessment results of the learning effect and the results of the evaluation model is 90%. It shows that the optimized Civics personalized learning and evaluation method, based on this paper, has positive effects.

In recent years, with the interaction and integration of modern cultural and creative industries with Chinese excellent traditional culture, various innovative cultural and creative products have been introduced, ushering in new opportunities for the inheritance and development of Chinese excellent traditional culture. In order to integrate Chinese cultural elements into the design of cultural and creative products. This paper draws on the characteristics of DNA inheritance and mutation in living organisms to construct a multi-objective optimization model of DNA elements, and by applying the model to the analysis stage of cultural and creative product design, the designed products can be more completely integrated into the elements of Chinese characteristic culture to effectively guide the design practice of cultural and creative products. This paper takes Dunhuang Tang Dynasty Feitian cultural elements as an example for practical research through the integration of Dunhuang Tang Dynasty Feitian cultural elements of a series of cultural and creative products for the satisfaction survey from the symbolic perception, beautiful shape, functional and practical, cultural connotations, innovation and novelty of the five dimensions of the design effect assessment, the furniture category of cultural and creative products scored the highest, the average value of 4.766, the cultural and creative products in the symbolic perception of the dimensions of the score is high. It shows that in the Dunhuang Tang Dynasty, Feitian cultural and creative products can be perceived as symbols of the characteristic culture of the period.

The rapid development of the digital economy makes the center of gravity of enterprise development accelerate from the consumer field to the production and manufacturing field, penetrating all aspects of the daily operation and management of an enterprise. This paper presents relevant management strategies by exploring the impact of the ecosystem model of enterprise management innovation on the impact of enterprise management in the digital context. In order to measure the comprehensive efficiency value, the efficiency value of capital absorption, and the efficiency value of investment after enterprise management, the theory of antagonistic cross-efficiency is introduced, the antagonistic cross-DEA model is constructed, and the evaluation steps of enterprise management effect are given. Using this model to empirically analyze the management effectiveness of China’s construction industry, enterprise performance=4.563+0.352*digital innovation ecosystem index+0.236* periodic cost+0.362*number of R&D personnel, and the digital innovation ecosystem is significantly related to enterprise performance. Finally, this paper designs a corporate management strategy for the digital innovation ecosystem in the context of the digital economy from three perspectives.

The trend of our country into an aging society is becoming more and more obvious, and the elderly education service has become the focus of social attention. The integration of artificial intelligence into elderly education is one of the inspiring ideas in which the personalized recommendation algorithm can recommend educational resources according to the characteristics of older people, and it has the prospect of application. In this paper, we first provide a CTransD-GAT recommendation model based on a knowledge graph, which improves traditional problems such as data sparsity by setting weight preference and feature aggregation. A dynamic preference-capturing method is proposed based on contextual interaction to capture the variable user learning interests more accurately and flexibly. This paper examines the practical utility of personalized recommendation methods for educational resources based on these two improved techniques. The post-test mean score of each knowledge module test of the experimental group is improved by 1.83 points compared with the pre-test, 11.46 points improve the score of teaching ability, and the scores of perceived usefulness, ease of use, and intention to use are 3.82, 3.89, and 3.97, respectively. It shows that the improved educational resource recommendation model has an excellent effect on improving knowledge structure and teaching ability, and it is characterized by simplicity and ease of use.

This paper takes three colleges and universities in a province as an example, designs relevant questionnaires and distributes them, and analyzes the collected data by using statistical methods such as independent samples t-test and ANOVA one-way ANOVA. The paper analyzes the data collected using statistical methods such as independent sample T-tests and ANOVA to infer the overall situation of the current level of emotional identification with Marxist theory among Chinese college students. The data was examined in detail to determine the variations in the emotional recognition of Marxist theory among college students based on their gender, ethnicity, and professional classification. The results show that more than 50% of college students understand Marxism and strongly identify with it. The overall mean score of affective identification is 35.324, which is a good status for identification. In terms of different genders, the P-values of value identity, knowledge identity, social identity, and behavioral identity are less than 0.1, and there are significant differences. Emotional identity does not have significant differences in different ethnicities (P = 0.382 > 0.05) and different majors (P = 0.426 > 0.05).

Based on digital media technology, this paper defines Chinese art theory and, at the same time, combines digital media technology and the characteristics of Chinese art aesthetic discourse to construct an art aesthetic discourse system that covers the stage of sensory experience and the stage of “emotional experience”. In order to promote the digitalization of the art aesthetic discourse system, the MCFF algorithm is used to extract the features of art aesthetic discourse, match the features, and then merge the features of art aesthetic discourse based on the weighted summing algorithm so as to achieve the digitization of art aesthetic discourse features. The art aesthetic discourse system in digital media is analyzed based on the research data. The results show that compared with the traditional algorithm, the algorithm of this paper is better than the traditional algorithm in three aspects: pre-emphasis (growth rate of 62.70%), frame-plus-window (growth rate of 68.71%), and data conversion (growth rate of 72.14%). In addition, the aesthetic experiences of landscape painting and figure painting were 96.49±8.4 and 108.00±9.35 points, respectively, and F(1, 20)=20.746, p

In recent years, the number of psychological problems occurring in college students has been increasing day by day, and timely, convenient, and accurate psychological warning is an important way to prevent college students from psychological crises. Starting from the basic needs of the system and aiming at realizing the embedding of data mining technology in the psychological management system, this paper elaborates on the design and implementation of the data mining technology module suitable for the psychological management system. The clustering algorithm-K mean algorithm of data mining is used to distinguish groups of students with different categories of psychological problems, which provides data support for the next decision-making. Through simulation experiments between the system designed in this paper and four other mental health management systems, the clustering performance and system evaluation performance of this paper’s system are better than those of the other systems. The result of mining the mental health of college students through this system shows that 82.2% of the student’s assessment results show normal, and 8.35% of the students need to focus on, and this result is not much different from that obtained from the statistical survey of a personality questionnaire, which indicates that the use of clustering analysis in the system can be an effective prediction of the mental health of college students.

Carbon emission from building construction is an important issue in the construction industry, and the continuous application of management and assessment techniques forms a new trend of carbon emission reduction. This paper establishes a multi-objective optimization model for building construction carbon emission based on multidimensional data analysis of four objectives: schedule, cost, quality, and carbon emission. The particle swarm algorithm is improved by dynamically adjusting the inertia weight factor and penalty function to deal with constraints and is used to solve the multi-objective optimization model. Building construction cases are selected to predict and analyze the combinations of optimal execution modes under different teams, to compare the optimization results of configuration schemes and the efficiency of algorithms, and to further propose the practical path of carbon emission reduction in building construction. The study addresses the construction combination mode that meets the needs of various decision-makers, and the team 3 combination mode has the lowest carbon emission (228.41kg). The multi-objective optimization scheme under carbon emission constraints optimizes between 3.93% and 21.78% in the four objective dimensions. This paper further expands the scope of the multi-objective optimization model for building construction focusing on the “low carbon” objective, improves the feasibility of the configuration scheme, and then promotes the green and stable development of the construction industry.