bluuebunny/crossref_metadata_2025 huggingface dataset:
Analysis of the Design and Effectiveness Evaluation of Personalized English Teaching System for Mongolian College Students in Trilingual Context
Authors: Zhang Xiaohua
Published: September 2023

In order to promote the efficient development of English teaching in Mongolian colleges and universities, this paper constructs an English-personalized teaching system based on trilingual background. Firstly, BP neural network algorithm is used to manage and classify students’ learning data information and detect system teaching evaluation indexes by identifying students’ learning behavior characteristics. Secondly, the K-mean clustering algorithm is invoked in the detection process to improve the detection efficiency to ensure the accuracy of obtaining teaching evaluation indexes. Finally, to verify the effectiveness of the teaching system, a practical analysis of its teaching effect is conducted. The analysis results show that the completion rates of the teaching system constructed in this paper are 78.7% and 86% in course learning and course testing, respectively, and 81% and 75% in information management and knowledge management, respectively. And in the experimental group, the grade passing rate was generally improved by about 42.3%. It proves that the personalized English teaching system constructed in this paper effectively improves the teaching quality of Mongolian college students’ English and is beneficial to the long-term development of English teaching in Mongolian colleges and universities.
Dual Construction of “Composition” and “Analysis” in Composition and Technical Theory in the Context of Big Data
Authors: Huang Fei , Liao Meiqun
Published: September 2023

Based on big data technology, this paper firstly collects and pre-processes educational resources and builds a dual teaching system based on two basic concepts of “creation” and “analysis”, combining technology and theory. Secondly, the FCM clustering algorithm is used to cluster the student learning data and get the optimal clustering center, and the Lagrange multiplier method is used to optimize the objective function, identify the student learning characteristics, and conduct a dynamic pre-testing assessment. Finally, the quantum optimization algorithm dynamically assigns the assessment data to eliminate redundant data and construct a feature set library for individual student learning. The practical analysis and application results show that the effect feedback in the two courses of composition and orchestration reached 88% and 93%. In the music analysis and music composition assessments, the percentage of the number of students with performance range values of 100-90 averaged 43% and 42.75%, respectively. In addition, 72.97% of the students found the system helpful in independent learning ability. It indicates that the teaching system constructed in this paper can better serve the teaching of composition technique and further improve the teaching model and process.
Research on the quality of information disclosure of listed companies in the era of big data
Authors: Shen Chengrui
Published: September 2023

In order to further promote listed companies to achieve high-quality development, this paper constructs an information disclosure quality assessment system in the context of the big data era. Through big data technology, information disclosure data of listed companies are collected, stored as well as exported to promote the structuring and standardization of massive data. The KNN algorithm is used to classify and organize information features and define hierarchical categories, thus obtaining a more low-dimensional subset of data features. In evaluating and deciding the indicator weights among the levels, the class weights and maximum feature values are calculated using a stratified sampling algorithm, and the evaluation system is formed according to their average values as quantitative and qualitative indicators. To verify the effectiveness of this assessment system, the analysis results show that the system divides the quality of information disclosure into five assessment criteria, and the precision and smoothness can reach about 85.6% and 83.24% on average, respectively. It can be seen that the information disclosure quality assessment system constructed based on big data technology improves the authenticity, usefulness and comparability of information disclosed by enterprises.
Study on Regional Financial Innovation, Technological Progress Bias and High Quality Economic Development
Authors: Ma Li
Published: September 2023

This paper designs a coupled mechanism model of financial technology innovation and economic growth, aiming to analyze the impact path of technology finance and technological progress on economic development. Endogenous economic growth theory is adopted, with technological progress as an exogenous variable, and inputs of production factors and products are considered. The growth rate of capital accumulation is calculated using fuzzy principles taken into account from the likelihood distribution under the assumption that the output function is in generalized form. The value of the distance function is calculated considering the orientation efficiency to ensure profit maximization. After eliminating the effect of different magnitudes, the decision degree decomposition is clarified using the Marquist index. The decision unit boundary degrees are reassembled when the information contribution ratio is scaled up by a multiple. Production relative efficiency change index. According to the findings of the economic impact analysis, the eastern financial institutions’ loan growth rate is stable, the entropy index is steadily increasing, and the p-value for the regression coefficient of science and technology finance is less than 0.02. The standard deviation of regional economic growth variables is 1.0209. Consequently, the coupling mechanism model of financial innovation in science and technology and economic growth can encourage investment in such innovation, shorten the technological innovation cycle to speed up the transformation of outcomes and achieve high-quality economic development.
Research on the Market Positioning and Suitable Cargo Value Range of “Yiwu-Xinjiang-Europe” Train Based on Data Model
Authors: Cao Jingjing , Ye Lifang
Published: September 2023

A new globalization initiative for strengthening economic integration and policy coordination provides new opportunities for trade cooperation between China and countries along the route. In this paper, the normalization method is used to assign values, determine weights based on the coefficient of variation of indicators, and carry out dimensionless processing of each indicator. The degree of data dispersion is determined, the equivalent time distribution is assigned, and the time cost coefficient ratio is used as the corresponding time cost. The weighted average is used to calculate the value of goods, considering the annual holding cost coefficient. And combined with relying on indirect measures to measure the overall trade cost, randomly perturbed error proxy terms, and alternative utility function elasticity invariant. The residual variance is predicted by replacing the effect of multilateral resistance, and the data-based model of “Yiwu-Xinjiang-Europe” is constructed. To verify the practicality of the model, the cargo trade results show that the total export trade of the “Yiwu-Xinjiang-Europe” train has increased significantly, and the value of foreign trade exports has increased by 18.72%. The volume of China’s trade decreased by 56.1% year-on-year. The volume of “Yiwu-Xinjiang-Europe” trains increased by 15.467% year-on-year. From this, we can see the strategy to promote the high-quality development of the “Yiwu-Xinjiang-Europe” China-Europe train in terms of market positioning, expanding the value of import and export cargo sources and improving the service level of the train.
Innovation of fresh agricultural products e-commerce operation mode based on artificial intelligence and machine learning model
Authors: Cheng Jiamei
Published: September 2023

In recent years, there has been an increasing interest in emotional interaction models based on artificial intelligence technology, and researchers want to deal with real-life problems in daily life through artificial intelligence and emotional interaction technology. In this paper, based on artificial intelligence and the emotional interaction model, we analyze the model architecture of emotional interaction technology and the theory related to the e-commerce operation platform. Combining the two, we propose a collaborative filtering recommendation algorithm, define system performance indexes, and recommend fresh produce to target customers with similar users’ evaluation of fresh produce through artificial intelligence technology. Using the experiments of three different recommendation systems and user behavior data of different months in the past three years as reference, it is concluded that the e-commerce operation of fresh produce based on AI and emotional interaction model can effectively improve the browsing and transaction volume of the e-commerce platform. This shows that artificial intelligence and emotional interaction model can be applied to create a new e-commerce operation mode, which provides research directions and methods for the diversified development of future e-commerce operation modes.
Exploring the implementation path of teaching Civics in online courses in colleges and universities under the background of big data
Authors: Zhu Liyuan
Published: September 2023

In this paper, a web-based teaching platform is constructed based on the background of big data, which is modeled by using a neural network algorithm. First, the neural network structure is output, and the error function is minimized by dynamic iterations using the activation function as neurons. Then, the error values were trained with implicit nodes, the activation function was modeled nonlinearly, and the sample set was extracted to define the cost function. Finally, the optimal particle is trained iteratively using the gradient descent method to derive the optimal solution, thus completing the construction of a web-based teaching platform based on the big data background. The experimental results show that after using this platform for online ideological education teaching, the percentage of students visiting online courses every day is 35%, which is 29% higher than that before using it. Therefore, to improve the construction level of ideological and political education online courses, it is necessary to strengthen the construction of ideological and political education online course platform resources, improve the informatization level of ideological and political education teacher teams, and promote the integration of ideological and political education online courses and classroom teaching.
Research on face emotion recognition algorithm based on deep learning neural network
Authors: Chen Yan , Zhang Mengzi
Published: September 2023

This paper explores face emotion recognition based on deep learning neural networks in order to make the computer recognize facial emotion more accurately. Firstly, we introduce the convolutional neural network, which consists of a convolutional layer, pooling layer and fully connected layer in the deep learning neural network, and propose the variational self-encoder face emotion recognition algorithm based on the convolutional neural network in combination with the facial emotion recognition process. The correct recognition rates achieved by EVA using 32×32 pixels, LBP features and 50-dimensional Gabor wavelet features are 95.13%, 96.74% and 98.84%, respectively. In terms of emotion recognition ability, the recognition accuracy percentages were around 70% for large emotion fluctuations and around 30% for small emotion fluctuations. Since the neutral facial emotion is not well distinguished, the recognition degree of neutral facial emotion is only 80%, and the recognition degree using the self-encoder-based face emotion recognition algorithm in several facial features extraction is above 80%, and the recognition degree with neutral emotion removed is up to about 90%. Therefore, the algorithm in this paper has good classification performance and can recognize and analyze facial emotion features accurately.
Denoising complex background radar signals based on wavelet decomposition thresholding
Authors: Qiu Feng , Yuan Kee
Published: September 2023

The echo signals of the radar in complex backgrounds are often very unstable and thus require effective noise cancellation. In this paper, according to the characteristics of continuous wavelet variation and discrete wavelet variation, the decomposition effect of multi-resolution analysis and orthogonal Mallat algorithm on low-frequency and high-frequency non-smooth signals is studied, and the selection method of wavelet bases is explored. Then, the noise characteristics affecting the pulsed LIDAR system are analyzed, and the LIDAR pulse signal is simulated by MATLAB, while Gaussian white noise is introduced to obtain the noise-added echo signal, and then multiple wavelet threshold denoising methods are applied to denoise the echo signal. For the input signal-to-noise ratio of −10.57 dB, the output signal-to-noise ratios of db8, db9, db10, and bior3.5 wavelet bases under forced thresholding are −1.971, −2.178, −2.173, and −1.032, respectively. For different input signal-to-noise ratios, the average root mean square error of db8, db9, db10, and bior3.5 wavelet bases under default thresholding is 1.51. The denoising methods for radar signals using the properties of wavelet decomposition have obvious superiority compared to traditional filters, and the wavelet transforms threshold denoising methods have wide adaptability.
The use of color elements in graphic design based on convolutional neural network model
Authors: Song Shuhan
Published: September 2023

Exploring the effective application of color elements in graphic design based on a convolutional neural network model is beneficial to promote the innovative development of the graphic design. Starting from the convolutional neural network, this paper defines the quantification of geometric features of graphic images by convolutional operation and pooling operation and uses pixel and spatial distribution for color element extraction. The global color features and local color features are used for color matching perception feature quantification, and then a graphic image design feature model is constructed, and the application analysis of color elements is carried out for this model. From the primary design color, the average selection rate of this paper’s model is 41.58%, which is 13.4% and 9.6% higher than that of the DCGAN and PLS-MLP models, respectively. From the keyword color quality generation, the color design results of graphic images have significant differences (p < 0.05). This indicates that the use of CNN-based graphic image design feature models can realize the effective application of color elements and also provides a new research direction for intelligent graphic design.
Analysis of the Evolution Trend of Segmentation of Online Public Opinion Based on Dynamic Tracking Technology
Authors: Gong Zhen
Published: September 2023

In this paper, the dynamic tracking technique is applied to analyze the triggering factors of online public opinion, modify the feature subsets and establish a basic database of sensitive information. The test of statistical characteristics of the evolution process of public opinion is realized by using the prediction of time series by the ANOVA algorithm, and on this basis, the smoothness of the model is realized by testing the parameters. In the process of establishing the quantitative index system, the seasonal difference algorithm is introduced to predict the development trend of public opinion on hot topics to realize the visualization model design. Finally, in order to verify the validity of the model, an experiment was conducted with Event A as an example. The results show that users can set a total of 4 keywords with an average word frequency of 0.03 Hz. The prediction accuracy in different periods can reach 88.73% on average, and the adaptation value can reach 0.8 percentage points on average. Thus, it can be seen that the trend analysis model of online public opinion evolution constructed in this paper can quantitatively analyze the mechanism of public opinion segmentation evolution and the trend prediction problem and provide a theoretical reference for the government to govern public opinion.
The evolution of Chinese traditional ethnic clothing design style based on interactive dichroism algorithm
Authors: Zhao Jianzhang
Published: September 2023

In this paper, a traditional ethnic clothing design style model is proposed, aiming to promote the good development of clothing in artificial intelligence design. The model uses an interactive dichotomous meter algorithm for top-down information gain, differentiates sample attributes by selecting entropy values, and defines an average categorical fuzzy subset based on discrete attribute leaf node division for deep, circular arithmetic decision-making. After satisfying the clothing design evolution condition, the minimum prediction entropy value genus block is extended, and the feedback matrix random input variables are used to induce the regularized likelihood frequency classification value distribution. The results of the analysis of traditional ethnic clothing design show that there are {0, 0.5, 1} mathematical expressions on clothing design hues, the demand of clothing design value subject shows a positive state, the change of consumption wave that festival and style will bring, the knowledge value in the mean difference of clothing design orientation is as high as 0.9173 with a significant value of 0.00<0.05. Therefore, the design style of traditional ethnic clothing is not only the inheritance and promotion of national culture but also the enhancement and development of aesthetic consciousness.
First principles study of the electronic structure of antimony (Sb) and samarium (Sm) doped tin dioxide (SnO2) based on big data analysis
Authors: Liu Danjuan , He Wei , Fu Siyong
Published: September 2023

To study the electronic structure of Sb and Sm co-doped SnO2 materials, a lattice model of Sb and Sm co-doped SnO2 is designed in this paper based on a big data analysis algorithm. The physical properties of the SnO2 ground state are described by the particle density function using the density generalized function theory. The interactions between the particles are all subsumed into the exchange-correlation generalized function by the Kohn-Sham equation. A big data analysis algorithm is used to construct the electron wave function to reflect the luminescence mechanism of the spectrum produced by the electron leap between energy levels, which makes the computational effort significantly reduced. The results show that the enthalpy change of Sb and Sm co-doped SnO2 in the design model of this paper is −5.59918, and the energy interval of the density of states of s orbitals is [2.36, 31.45]. It can be seen that the co-doping of Sm and Sb can increase the electron polarization ability and electron leap probability of SnO2 in the infrared band and enhance the infrared reflectivity, and the co-doped system has the highest electron-binding ability reflectivity.
The value dimension and realization of online ideological and political education in colleges and universities based on deep learning
Authors: Gao Haiwei
Published: September 2023

This paper proposes a value dimension and realization way of online ideological and political education in colleges and universities based on deep learning. Firstly, a deep learning teaching platform is established, which is conducive to teacher-student communication, teachers can better grasp the focus of teaching, and students can be more clear about their learning status. The classroom teaching reform and auxiliary learning reform are carried out in three dimensions: theoretical classroom, practical classroom, and guided classroom. And a multi-dimensional response theory model is established to create conditions for the comprehensive and free development of college students so as to realize the value dimension of online ideological and political education in colleges and universities. The results show that more than 55% of students get political information through major portals and through students’ ideological and moral performance on the Internet, and then implement targeted education, which can effectively improve students’ online moral literacy.
Research on the integration path of cultural creative industry and tourism industry based on collaborative filtering recommendation algorithm
Authors: Yin Ying
Published: September 2023

In order to increase regional economic benefits and attract more tourists for local economic development. This paper adopts different ways to evolve industrial integration, enrich and expand the theoretical system of the industrial economy, establish new tourism strategies to meet personalized and diversified needs and make the tourism sector achieve industrial integration. Useful data are selected to filter cultural and creative, and tourism industry information by mining hidden information of coding sequences and finding support data items. Using collaborative filtering recommendation method to obtain the preference degree of fused data items, populate the data according to behavioral speculation, calculate the utility matrix similarity, and clarify the development of industry fusion path in the context of big data. The results show that the average error rate after fusion does not exceed 4%, and the fusion path of cultural and creative industries and the tourism industry improve the regional economy and the overall local living conditions.
Research on the development of literature practice teaching for college students in the context of computer technology
Authors: Liang Xueman
Published: September 2023

In this paper, intelligent information retrieval of relevant literary content through collaborative filtering algorithms in Internet technology is used to analyze the similarity between users and, thus, to select similar items in a weighted ranking. The Jacobian formula is used to calculate the common feature values and thus predict the user’s unrated item ratings. Word normalization is performed through information extraction of text keywords to effectively abstract the dimensionality of text features. The results showed that the influence of learning based on language literacy was 0.697, the preference for the literary genre of prose was as high as 49.4%, and students could accept infiltration teaching. It indicates that making full use of Internet technology means can break the time and space limitations in the traditional teaching of literature, provide effective support for students’ interactive communication, expand students’ cultural horizons, and improve their humanistic literacy.
Analysis of the construction and reform path of preschool education professional practice courses in the context of computer big data
Authors: Zheng Lixia
Published: September 2023

This paper first uses the big data adaptive learning system to facilitate students’ mastery of knowledge points, improve their learning performance, and stimulate their interest in learning. Then, the practical curriculum of preschool education is realized through curriculum facilities, curriculum objectives, curriculum content, curriculum organization and implementation, and curriculum evaluation. Next, the QFD entity model for quality assurance of preschool education personnel training is established. The realistic basis for the construction of the model is also elaborated, and the conceptual framework of the QFD model for quality assurance of higher education personnel training is proposed and comprehensively analyzed. The results show that the cooperative group learning approach is more favored by students, up to 64.44% in the practical courses. The number of graduates who chose to work in early education and other industries increased by 10%. It shows that improving students’ personal comprehensive quality can adapt to society’s comprehensive demand for professionals.
A study of algorithms for solving nonlinear two-level programming problems oriented to decision tree models
Authors: Lin Jinshan , Lin Min , Xu Hang
Published: September 2023

In this paper, the original two-level planning problem is transformed into a single-level optimization problem by combining the penalty function method for the large amount of data processing involved in the training process of the decision tree model, setting the output as a classification tree in the iterative process of the CART decision tree, and recursively building the CART classification tree with the training set to find the optimal solution set for the nonlinear two-level planning problem. It is verified that the proposed solution method is also stable at a convergence index of 1.0 with a maximum accuracy of 95.37%, which can provide an efficient solution method for nonlinear two-level programming problems oriented to decision tree models.
Dilemmas and Breakthroughs in the Legal Regulation of Artificial Intelligence Based on Deep Learning Models
Authors: Li Yanggui
Published: September 2023

In this paper, we use big data analysis techniques combined with the TF-IDF algorithm to weigh the frequently occurring word frequency vectors in text and reduce the document length to obtain keywords without destroying the original text feature information. The similarity of text features is combined with a Bayesian algorithm for label classification to facilitate data query and indexing. The results show that the running time of the system is kept around 14s, the recall and accuracy can be close to about 75% and 72% on average, and the number of keywords can reach 5971 with an F1 value of 0.9, which proves the effectiveness of the artificial intelligence legal regulation system based on big data analysis.
The main color structure setting of Chinese oil painting based on content analysis method
Authors: Chen Haiwei
Published: September 2023

Based on the content analysis method, this paper first uses the PCA technique to disassemble and transform the base color information to extract multiple feature values. Secondly, the sparse autoencoder is used to adjust the feature parameters by training, and the activation function pair is used to calculate the confidence to complete the effective classification. Finally, the SVM algorithm is introduced to optimize the color information parameters, and the corresponding color label values are calculated by constructing a decision function to accurately set the pixel positions. The results show that the color difference value of Chinese oil painting image keeps between 0.5nbs -1.5nbs, the standard deviation value of saturation is 165.55, and the resolution is 610ppi, which indicates that the content-based analysis method can better coordinate and integrate the relationship between various color structures.
The application of red music culture resources in the education of ecological civilization concept in colleges and universities under the background of multimedia
Authors: Wang Yijin
Published: September 2023

This paper uses a user-based collaborative filtering algorithm to predict the ratings of items by target users and to recommend items that target users have not interacted with. The red music and cultural resources were ranked according to their values, and the Bartholomew coefficient was introduced to calculate the similarity of items from the perspective of a probability distribution, and the content recommendation algorithm was used to construct a model of ecological civilization education concept. It is found that more than 50% of undergraduate college students pay regular attention to ecological environment information, and 30.56% of them will actively learn ecological civilization knowledge, which is not thorough and comprehensive enough for ecological civilization concept establishment and cultivation. It is suggested that universities should integrate red music culture into ecological civilization education to create an ecological civilization cultivation atmosphere.
Exploring the blended learning model of music teaching skills for teacher trainees based on teacher training professional certification in the context of big data
Authors: Liu Ke
Published: September 2023

The existing teaching model has the problems of a single learning mode and students’ interest in learning, so it is necessary to build a blended learning model scenario to promote students’ interest in learning. This paper proposes an educational data mining algorithm for the blended learning model of music teaching skills in the context of big data and proposes a blended learning model of teaching skills based on the PSO algorithm. Since the PSO particle swarm algorithm has the problem of low accuracy and overfitting, the XGBoost algorithm model is introduced based on the PSO particle swarm algorithm, and the Iris dataset is clustered. For the evaluation analysis of the blended learning model of music skills, 13 indicators were observed for three randomly selected students in this study. The accuracy of the optimized PSO-XGBoost algorithm was 0.95, which was 10% more accurate than the pre-optimized algorithm, and the overall accuracy was significantly improved. The three students scored 18.96, 18.97, and 19.61 in the music skills blended learning model evaluation learning assessment system evaluation, with the highest score reaching 19.61. The study showed that the music-teaching blended learning model is comprehensive and easy to implement in accordance with the existing teaching environment.
Research on unmanned vehicle obstacle avoidance technology based on LIDAR and depth camera fusion
Authors: Qiu Hao , Chen Weifeng , Ji Aihong , Hu Kai
Published: September 2023

To address the problems of poor accuracy of traditional EKF algorithm in estimating the position of unmanned vehicles and the deficiencies in accuracy and map completeness of the traditional map building method with single-line LiDAR, this paper proposes a method to create fused raster maps realized with multi-source data. Firstly, the combined data of the inertial measurement unit and wheel encoder are corrected by adding the positional information output from the visual odometer using the error-state SLAM algorithm, and the local raster constructed by LiDAR and depth camera is fused frame by frame using the idea of Bayesian estimation to finally generate the fused global map. Then, a four-wheeled mobile unmanned vehicle with a LiDAR sensor and depth camera is selected as the experimental object, and dynamic environment avoidance simulation experiments are conducted to draw conclusions. The simulation experiment results show that when γ = 5.99, the algorithm generates a new local target point p
g
2 (17.49, 13.49) and the corresponding getaway path and finally guides the unmanned vehicle to the specified target point, verifying that the method in this paper can achieve the avoidance capability of the unmanned vehicle in the process of getting away from the newly emerged obstacles. This study uses the scanned data of LiDAR for the estimation of the real-time position of the unmanned vehicle to realize obstacle avoidance and path planning of the unmanned vehicle.
A Study on Second Language Academic Writing Anxiety of Chinese Students Studying in Japan
Authors: Tian Jiayue
Published: September 2023

Anxiety is an important factor that affects the academic writing process and the quality of writing. In this paper, we first study the principle of emotional processing of EEG signals and discuss the pre-processing, feature extraction and emotional recognition of EEG signals. Then the signal features are extracted based on the EEG features of Daubechies wavelets, and the Mallat algorithm is improved by using the half-wavelet packet algorithm. The improved Mallat algorithm is then used to perform data downscaling of the resulting feature dimensions to achieve classification operations and feature selection. Finally, the model was used to assess the second language academic writing anxiety of college students in Japan, and the levels of second language academic writing anxiety of students in Japan with different levels of a second language, length of exposure to a second language, gender, and grade were analyzed. The mean score of academic writing anxiety was 68.04, and the overall level of academic writing anxiety among international students in Japan was moderate. The mean anxiety level of international students with a Level 1 second language level was 2.9, the mean anxiety level of international students with a Level 2 second language level was 3.07, and the mean anxiety level of international students with a Level 5 fifth language level was 3.15. The lower the second language level, the higher the anxiety level. This study provides insight and references for improving the academic writing skills of international students.
The application of BOPPPS teaching model in online and offline hybrid Civics teaching in universities
Authors: Xiong Ming
Published: September 2023

This study examines the hybrid online-offline teaching of civics in colleges and universities, with a particular emphasis on the analysis of the BOPPPS teaching model. The study also evaluates and analyzes the civics application under the BOPPS teaching model using the ID3 decision tree algorithm and the information gain method as attribute selection criteria. By integrating BOPPS teaching and online and offline hybrid Civics teaching, an online, hybrid teaching model platform based on BOPPPS is constructed. The teaching impact of the BOPPS online and offline hybrid Civics education was confirmed when combined with the empirical data. According to the findings, 66% of students anticipate learning about civics online, and 77% anticipate learning about civics both online and offline. The standard deviation for traditional instruction is 5.7819; for “online resources + offline classroom teaching,” it is 5.832; for BOPPPS teaching, it is 8.3988; and for BOPPPS teaching with online and offline integration, it is 6.0921. This study is helpful for fostering innovation and growth in both offline and online education at colleges and institutions. Hybrid civics education’s evolution and innovation.
Machining hardening and dislocation modeling of magnesium alloys based on Fields-Backofen equation
Authors: Zhang Congzheng , Liang Chen , Fan Yucheng , Jiang Jiahui , Sun Xuehan , Yuan Qingsong , Guo Haoyu
Published: September 2023

Magnesium alloys are now widely used, and the Fields-Backofen equation is combined with an investigation of the properties of common magnesium alloys in order to be able to better analyze them. In this paper, some properties, applications and superiority of magnesium alloys are first analyzed, in which the plastic deformation mechanism slip and twinning of magnesium alloys are particularly explored in detail. Focusing on the stages and model of machining hardening dislocations of magnesium alloy, and combined with Fields-Backofen equation, a model of machining hardening and dislocations of magnesium alloy based on Fields-Backofen optimization is constructed. Then the constructed model was applied to stage III of machining hardening dislocations in magnesium alloy for performance testing. The predicted and actual maximum values of the model were 132MPa and 30MPa, which were in basic agreement with 130MPa and 32MPa. The saturation stresses ranged from 50 MPa to 6 MPa for temperatures from 100°C to 500°C when the strain rate of the dislocation processing stage was 10−2 s−1, and from 55 MPa to 10 MPa for temperatures from 100°C to 500°C at a strain rate of 10−3
s
−1. Finally, only basal slip and twin initiation with higher saturation stresses were obtained at lower temperatures. As the temperature continues to increase, the dislocation density decreases, plastic deformation becomes easier, and then the saturation stress decreases.
Digital Intelligence Civilization Drives New Business Civilization: A Theoretical Framework for the Collaborative Ecosystem of Digital Culture and Tourism Industry
Authors: Huang Ping , Shi Yali , An Junxiu , Qiao Shaojie , Jin Liya
Published: September 2023

The promotion and utilization of information technology have promoted the transformation and upgrading of the industrial structure and economic structure of cultural tourism, making the traditional operation methods no longer applicable to the current operating environment. This paper analyzes the triple coupling mechanism of the cultural tourism industry, information technology and the ecological environment from the synergy of four elements: space, economy, culture and ecology. On this basis, the system dynamics concept is used to view the digital cultural tourism industry and ecological protection as a unified whole, to build a synergistic logic between the main factors in the whole, and to analyze the synergistic relationship between the two subsystems using the DPSIR framework. The results of the empirical analysis of the synergistic development efficiency of each region in China from 2011 to 2020 show that the coefficient of variation of the digital cultural tourism subsystem is the largest, which is between 1.1836 and 1.3705, indicating that the development level of digital cultural tourism varies greatly among Chinese provinces. The mean values of the integration synergy degree of the four-time nodes are 0.2226, 0.2678, 0.3549 and 0.4052, respectively, and the level of integration synergy is generally on the rise. In this paper, we effectively promote the integration of the cultural tourism industry and the ecological environment through information technology while being able to utilize ecological capital in perpetuity and realize the high-quality development of the cultural tourism industry.
A Study on the Evaluation System of Aesthetic Education in Guangdong Higher Education Institutions in the Context of Internet+ Education
Authors: Yang Qin
Published: September 2023

With the rapid development of the Internet and multimedia, aesthetic education in colleges and universities has an unprecedented strategic opportunity in the field of innovative education and teaching methods and the construction of high-quality network teaching resources. This paper firstly constructs the evaluation system of college aesthetic education network teaching, determines the index system according to the cultivation goal and education of college aesthetic education, the index system gets 3 primary indicators and 9 secondary indicators, and revises the name and connotation of each evaluation index through the agreement survey. Using hierarchical analysis and the entropy method to assign weights to indicators and calculate the weights of corresponding indicators, the relative weight system of university aesthetic education network teaching evaluation is constructed, and the weights are used to judge the discrete degree of each indicator of the evaluation system. The results show that the average comprehensive weight value of 23 secondary indicators is 0.6809, and the comprehensive weight of “Situational image Z
64” is 0.927, which indicates that the expert group agrees on the overall opinion of the evaluation indicators of college aesthetic education network teaching and verifies the practicality of the evaluation system, and the college aesthetic education teaching should start from many aspects to promote the overall development of students. Thereby promoting students’ all-around development. This study proves that the evaluation system is indeed reasonable, reliable and stable and provides teachers and students with intelligent decisions for improvement.
Research on New Liberal Arts Professional Talent Cultivation Model Based on OBE Concept
Authors: Lyu Bu , Chen Jianhui , Wang Na , Cao Jianlei
Published: September 2023

In this paper, an evaluation model is constructed to evaluate the new talent cultivation model of liberal arts under the OBE concept by analyzing the steps and ways of the model. The system of talent cultivation is determined by establishing the sample value and the whitening weight function of the gray class, and the system indicators are represented using the gray clustering weights, clustering coefficients, and clustering vectors. Then the system indexes are processed, and a multi-level DEA model is introduced on the basis of the gray clustering talent cultivation evaluation system. Finally, the multi-level gray clustering talent cultivation comprehensive evaluation model was constructed. The evaluation of the talent cultivation model is B=(0.4, 0.51, 0.09, 0, 0), and the overall evaluation result of the talent cultivation model is “excellent” according to the principle of maximum affiliation according to the calculation results.
Analysis of the integration development of enterprise refinement management and financial integration in the context of informationization
Authors: Yuan Fei
Published: September 2023

In the background of economic informationization, the integration and development of enterprise refinement management and financial integration have become the mainstream management direction of modern enterprise management. In this paper, the correlation function and judgment matrix are firstly determined by the entropy-weighted elementary topologizable evaluation model, while the initial judgment matrix V is standardized, and the differences in the correlation degree of indicators of different natures should be noted. The entropy power method is applied to calculate the change of relevant evaluation indexes, and the value of information utility in the evaluation system is determined through the change of entropy value. Then the initial data are obtained by means of questionnaires, and data analysis is performed on the first-level indicators in the evaluation system of enterprise financial management capability. The results show that the three indicators of financial risk control capability, financial management informationization level and financial personnel capability, and total budget management level have more room for improvement, but the score of financial risk control capability is only 5.04 points different from the total score, indicating that the specific situation of the enterprise itself for specific analysis. This study improves the level of financial management of enterprises and promotes their development of enterprises.
Research on the Construction of Innovation and Entrepreneurship Education Model System in Universities Based on Big Data Technology
Authors: Zhang Rui , Li Huajin
Published: September 2023

This paper aims to build a system of innovation and entrepreneurship education model in colleges and universities. Focusing on the analysis of measurement variables and structural variables in SEM, the evaluation model of college innovation and entrepreneurship education is constructed under the analysis process based on the structural equation model. The path diagram of the standardized solved structural equation model of practice teaching evaluation is outputted through software correction, and the standardized weights of evaluation are determined by combining the structural equation of college innovation and entrepreneurship practice teaching quality evaluation. Apply the evaluation principles to test whether the evaluation model is reasonable. By analyzing the form of innovation and entrepreneurship teaching, the evaluation is conducted according to the structural equation formula to find out the shortcomings in practice for improvement and refinement. The results show that in the dimension of practical teaching preparation, (H1) corresponds to effect coefficients of 0.47, 0.47, 0.60 and 0.53, and the percentages of students who receive innovation and entrepreneurship education courses very systematically are 3.5%, 5%, 6% and 9%. Most of the students were very satisfied and satisfied with the innovation and entrepreneurship courses, with 45% and 40%.
Analysis of the opportunities and challenges of information technology for enterprise development strategy based on big data technology
Authors: Yin Fangfang
Published: September 2023

Based on big data information technology, this paper analyzes the opportunities and challenges of business development strategies. The clustering algorithm and tree model algorithm in data mining is analyzed. In order to effectively solve the problem of big data classification in consumer-oriented enterprises, the Kmeans clustering algorithm and XGBoost algorithm in the two previous models are integrated to effectively avoid the problem of over-fitting when the models are used alone. The opportunities and challenges in the current stage of business development strategy are analyzed separately. The Kmeans-XGBoost algorithm is used to analyze the pricing and output of the enterprise for prediction. It is shown that the prediction curves of the Kmeans-XGBoost model basically match the actual values, and the confidence interval range is expanding from [3694.879,7202.897] to [2211.819,8406.462]. Meanwhile, the errors of enterprise output prediction under different algorithms are analyzed. The error rate weighted by Kmeans-XGBoost mean is 42.63, which is lower than the traditional model prediction error in 4.
The integration of traditional lacquerware and modern art design in the context of artificial intelligence
Authors: Xu Hang
Published: September 2023

This paper aims to promote the integration of traditional lacquerware and modern art design. AR technology is used for feature calculation of the appearance contour of traditional lacquerware. In this paper, the BRIEF algorithm is selected as the main algorithm for image feature calculation. The Steer BRIEF algorithm is used for key points, and the image feature points are extracted. The classical optical flow method of CMT is applied to track the appearance contour of traditional lacquerware, and the SSD and NCC are combined with filtering the measurement of tracking points. The results show that 90% of the traditional lacquerware can reflect the cultural connotation of the time, 80% of the traditional lacquerware can reflect the characteristics of the time, 80% of the traditional lacquerware has excellent craftsmanship, and the added value reflected by modern art design is much lower than that of the traditional lacquerware.
Research on the whole process monitoring and management of coal digital production based on IOT technology
Authors: Tang Juan
Published: September 2023

This paper firstly explores the method and monitoring process of coal SPC production quality monitoring based on IOT technology and simulates different control chart patterns using the Monte Carlo method. Then a control chart pattern recognition model based on PCA-PSO-SVM is established to downscale the collected data using the PCA method and quickly discriminate whether the control chart is abnormal or not based on the OC-SVM algorithm. By identifying abnormal control chart patterns, real-time monitoring of the coal production process is realized. Finally, comparative analysis and fault source analysis are conducted to verify the sensitivity and effectiveness of the monitoring method in this paper. The difference in methane concentration between the experimental group and the comparison group is less than 10ppm, CO concentration is less than 5ppm, and dust concentration is less than 0.05mg/m3. It shows that the method of this paper is very effective for coal production monitoring.