bluuebunny/crossref_metadata_2025 huggingface dataset:
Research on Construction and Application of Network Learning Space for Logistics Information Technology Specialized Courses on Higher Education Resource Platforms
Authors: Jian Xianghui
Published: May 2024

Network learning space can promote the informatization of logistics information technology professional education, and it has a unique function and value to facilitate the learning of students in colleges and universities. This paper constructs the network learning space for the logistics information technology professional class and analyzes the application effect of the network learning space using a questionnaire. The questionnaire for the analysis of students’ learning input in the online learning space under the educational resource platform is designed first. Then, the reliability and validity of each question item of the questionnaire are examined and rejected using a t-test, Cronbach’s alpha coefficient, KMO, and Bartlett’s spherical test. In the online learning space, students’ learning input was analyzed, and it was determined that the mean value of learning behavior input in each dimension was between 3.5 and 4.88 points, which was high. The analysis of the professional course scores found that the mean score of the logistics information technology work designed by the students in the experimental class (89.11 points) was significantly higher than that of the students in the control class (75.66 points), and there was a significant difference in the test scores (p=0.006<0.05). The educational resource platform network learning space constructed in this paper provides a reference path for the effective development of logistics information technology professional classes in colleges and universities. It lays a foundation for the construction of network learning space for other courses.
Distribution and clinical characterization of pathogenic bacteria in combined periodontal and endodontic lesions of periodontal origin by digital technology
Authors: Jing Zhixing , Mi Changjiang , Zhu Wanchun
Published: May 2024

In this paper, the Logistic regression model and χ2 automatic interaction test were used to study the distribution, number, and correlation of common pathogenic bacteria of periodontal origin with combined periodontal and endodontic lesions by sample training, extracting variable characteristics, and then these variables were modeled as characteristic variables of the regression model to test whether the independent variables were significantly correlated with the dependent variables. It is also necessary to perform χ2 automatic interaction test to compare with the normal distribution, and the results are merged into one group if they are the same and not incorporated into one group if they are different. After the merging was completed, the splitting was done. The study group had a detection rate of pathogenic bacteria of 50.70%, while the control group had a detection rate of 12.24%, according to the results. Campylobacter spp. was the main bacteria that infected periodontal pockets in the study group. And Clostridium spp. The root canal specimens were mainly from Corynebacterium spp. and Actinobacillus spp. There was a significant difference between the pathogenic species and the control group P<0.05. Campylobacter spp. was also a risk factor for co-morbidities (P=0.031). Intra-root canal tissue with its subgingival plaque pathogenic bacteria Ef, Pe (Ρ<0.05), Pg, Td, Tf (Ρ<0.01) played a more important role in the development of pulpitis. Before and after clinical treatment, Pgi showed the most significant decrease in bacterial counts (t=2.759, P=0.022).
Analysis of Machine Learning Application in Campus Network Traffic Anomaly Detection
Authors: Li Rongrong
Published: May 2024

In this paper, machine learning algorithms are first utilized to extract features of campus network traffic, and then the multi-attention mechanism is introduced to fuse the massive features extracted at different scales. Unsupervised learning is used to propose a method for detecting network traffic anomalies, and simulation experiments are conducted to verify the model’s performance. The results show that the detection rates of machine learning algorithms are all above 80%, the false alarm rate basically stays below 10%. The machine algorithms have higher accuracy than other algorithms in network data flow anomaly detection. This study has important reference value for campus network security research and verifies the important role of machine learning algorithms in detecting anomalies in campus network traffic.
A study on the impact of digital economy on driving up the income of rural residents in the context of big data
Authors: Li Yiqun , Fei Yun
Published: May 2024

The digital economy is promoting rural development and rural residents’ income growth and realizing the improvement of supply efficiency and the change of factor power. In this paper, we first construct the measurement system of rural residents’ income level and digital economy development level, respectively, and then use big data technology to collect relevant data from 2013 to 2020 and calculate the development level of the two by using principal component analysis and entropy weight method respectively. Then, the level of digital economy development was taken as an explanatory variable. Rural residents’ income was taken as an explanatory variable. Empirical regression analysis was carried out to explore the impact of digital economy on the improvement of rural residents’ income. The mediation effect and regional heterogeneity test were carried out. The results show that the coefficients of the digital economy are always significant and all positive at a 1% level when control variables are added gradually, and the Sobel statistic of entrepreneurial activity is 0.061 and 0.045. This study provides theoretical support for the analysis of how the rural digital economy can promote the revitalization of rural industries and promote the commonwealth of farmers and rural areas.
A Study on the Extension of Chinese Traditional Culture in College Art Education Combined with Gray Correlation Analysis Methods
Authors: Li Qiao
Published: May 2024

This study incorporates gray correlation analysis from gray statistical theory to assess the quality of traditional culture and art education. It establishes an evaluation index system and a gray correlation analysis model, examining the relationship between contemporary art education and traditional culture. The model is applied to identify the correlation, key characteristics, and principal influencing factors of traditional Chinese culture and art education in higher education institutions. Additionally, it examines the advantages of random sequences of factors through specific data processing techniques. The structural model tests the influencing factors on the quality of traditional culture and art education, with an analysis of path coefficients. The results showed that 97.4% of teachers chose traditional culture and art teaching resources, mainly by referring to the network aspect. The results of the weight distribution indicate that the teaching process U3 (0.3613) is the most important in traditional culture and art teaching, and the innovative activities in teaching content (0.8113) hold the largest weight. The top three gray correlation values of the indicators of the second-level factor layer are U43>U22>U32. The direct influence effect of excellent traditional culture permeating the teaching process is 0.232, which has the greatest influence on the quality of teaching. The direct impact of excellent conventional culture permeating teaching content is 0.184, which has the second largest influence on teaching quality. This paper successfully constructs a gray correlation model to analyze the relationship between traditional culture and contemporary art, which has high scientific validity.
Exploring the Teaching Reform Path of Ideological and Political Education in Colleges and Universities in the Context of Deep Learning
Authors: Liang Li
Published: May 2024

Deep learning significantly enhances the comprehension of ideological and political education (IPE) concepts and facilitates the advancement of IPE teaching in colleges and universities through bilinear blended teaching reforms. This innovative teaching approach employs association rules from big data and an enhanced Apriori algorithm to analyze students’ online learning data. An analytical model of student learning behaviors in IPE, utilizing a Support Vector Machine (SVM) for learner classification, forms the basis for developing an online learner profile. This profile integrates assessments of learning outcomes, group identification, and style classification to tailor learning activities to individual needs. The efficacy of this reform was tested over one year, comparing the impacts of IPE across four different regions. Results indicate that students in the experimental group significantly outperformed their counterparts in the control group. For instance, in Shanghai, scores from the experimental group averaged 89.63 compared to 83.25 in the control group, yielding a significant difference of 6.38 points. Statistical analysis showed a t-value of 6.21 and a p-value of 0.000, affirming the positive impact of the bilinear blended teaching reform on enhancing student ideological engagement and promoting educational reform in colleges.
Knowledge mapping analysis of Chinese traditional culture under the strategy of common wealth
Authors: Guo Han
Published: May 2024

With the continuous improvement of people’s living standards, their attention to the spiritual world and culture, especially traditional culture, is gradually increasing. With the help of the graph-based data structure of a knowledge graph, the connections between entities in conventional culture can be more intuitively displayed in the form of nodes and edges. Traditional knowledge is represented by the Trans series model in this paper, and the attention mechanism is integrated into the Graph Attention Network Model (GAT). The descriptive information of entities and relationships was introduced for pre-training initialization, and a Graph Attention Network Connection Prediction Model (BFGAT) was proposed to integrate the descriptive information and structural features and the BFGAT model was optimized by using information filtering operations. After the experimental study of the performance of the modified model, it is found that the score of the BFGAT model is 0.594 under the Hits@10 evaluation index in the dataset WN188RR, which is 0.01 and 0.018 higher than the two variant models, respectively, and the performance is better than that of other models. In the analysis of the ability of the knowledge graph to disseminate Chinese traditional culture, the number of searches for “Confucius” in Chinese traditional culture in the Americas is higher. The search frequency of Confucius has increased significantly from 2013 to 2014, from 830,000 times in 2013 to 2.95 million times in 2014, and the search frequency of Confucius in the Americas has reached 7.59 million times in 2023. It can be seen that the knowledge graph technology based on the knowledge graph attention network link prediction model has higher accuracy for keyword recognition and is also conducive to the wide dissemination of traditional Chinese culture.
3D Communication of Animated Character Image Visualization Based on Digital Media Perspectives
Authors: Huang Yan , Chen Sirui
Published: May 2024

In this study, we initiate the process by pre-processing the character image data and establishing coordinate axes. Utilizing the parallax principle, we construct a three-dimensional model of the animated character image and design a multidimensional, time-sequential visualization scheme based on clustering techniques. To enhance the effectiveness of cluster visualization, we incorporate color and transparency into the model and develop a three-dimensional communication framework to facilitate user interaction and improve animated image visualization. Subsequently, we evaluate the advantages and feasibility of this three-dimensional communication approach, focusing on user experience and user engagement. We also explore the visual perception capabilities of this method against contrasting background colors of light and dark. The findings demonstrate a credible expression of animated video images, with a credibility index of 4.690. Additionally, user perception is quantified at an average of 4.895, which not only exceeds the four-point benchmark but also outperforms alternative methods. This research offers a substantial reference for the advancement of visual communication studies concerning animated character images.
Modern graphic design application innovation based on graphic abstract processing method
Authors: Gao Xueqiang
Published: May 2024

The art of abstraction reflects the rich imagination of human beings to a certain extent and is now widely used in movies, games, advertisements, and other modern design fields. In this paper, a new edge tangential flow algorithm applicable to image abstraction is proposed, which first estimates the gradient strength and gradient direction of each point in a given input image by using the Sobel operator and then establishes the edge graph of the input image by using the edge information transfer strategy. On this basis, the application and value of abstract art and this graphical abstract processing method in modern graphic design are discussed, and the essence of abstract form art is perfectly embodied so as to make the two perfectly integrated. The results show that in the application of graphic abstract processing in graphic design, the average value of user attraction, understanding, and memory dimension scores are 0.53, 0.675, and 0.683, respectively, which verifies the effectiveness of this graphic design practice through users.
Classification of Styles and Expressive Characteristics in Higher Vocals Teaching - Based on Emotional Calculation Approach
Authors: Liu Lijuan
Published: May 2024

Higher vocational vocal music teaching not only lies in enhancing students’ grasp of notes but also in deep emotional expression and emotional transmission. How to realize the precise classification of emotions expressed by musical style and features is an urgent problem to be solved. In this paper, starting from the classification of acoustic features and music styles, the STFT algorithm is used to extract the music features, and the music emotion model is constructed by combining the two-dimensional emotional psychological model. Based on the Turkish music emotion dataset, the acoustic data are preprocessed by using methods such as frame splitting, pre-emphasis, windowing, and mute frames, a deep confidence network extracts the music features, and the SVM model is introduced to establish the emotion classification model of music style and expression features. To verify the effectiveness of the model, this paper uses the dataset to analyze the music style and emotion classification dimensions, respectively. It is found that the classification accuracy of the Renaissance music category in classical music reaches 97.94%, and the accuracy of the music emotion classification of the model when the music data preprocessing slice granularity is 5 s is 0.838. The DBN-SVM model can realize the emotion classification of the music style and expression characteristics, which can provide a reference for the teachers to carry out targeted vocal music teaching and for the students to realize the release of emotion and emotional resonance when singing in vocal music. It provides a reference for teachers to carry out targeted vocal music teaching and for students to realize emotional release and resonance during vocal singing.
Development Path of Teaching Method Innovation in Art Colleges and Universities under the Background of Internet
Authors: Xu Xueqian
Published: May 2024

The informatization teaching method is a big challenge to the traditional form of teaching in art college education. This paper studies the construction of an informatization teaching method combining multimedia technology and multimodal media information retrieval technology. In view of the demand and development conception of teaching methods in art colleges and universities under the background of the Internet, it examines the willingness of teachers of art colleges and universities to teach informatization as well as the degree of satisfaction of their teaching ability. It optimizes the retrieval of learning resources based on the multi-category classification method of ELM classifiers and proposes an integrated mode of informatization teaching in art disciplines. According to the factors affecting the informatization teaching ability of art teachers, teaching design and teacher implementation are carried out in a case-by-case manner to analyze and summarize the program effects of the informatization teaching approach. The correlation coefficient between teachers’ willingness to adopt informative teaching methods and their satisfaction with their information teaching ability is 0.614, which is moderately correlated. After the implementation of the teaching program, the total score of the teacher’s informatization teaching ability reaches 80.052, which improves the level of informatization teaching compared to the level of informatization in pre-preparation. The informatization teaching method’s program design can enhance the teacher’s informatization teaching level, which can have an impact on the innovative development of the teaching method in art colleges and universities through practical experience.
The Construction of a Shared Resource Base for Teaching Chinese Culture under the Architecture of Disciplinary Knowledge Mapping
Authors: Wang Youjing , Hou Yongguang
Published: May 2024

This paper proposes to use knowledge mapping to construct a shared resource base for Chinese culture teaching to realize the optimization and innovation of Chinese culture teaching. The internal data and external data of the Chinese teaching resource base are obtained through two ways textbook informatization and automatic semantic annotation. Then the data are preprocessed to ensure the accuracy of the data in the resource base. On this basis, the LSTM-CRF model is used to identify the entity naming of the knowledge points of Chinese teaching resources. Then, the named knowledge points of Chinese culture teaching resources are integrated and stored to complete the systematic construction of the Chinese culture teaching resource base. The empirical analysis of the proposed resource base is carried out. The data present that the average oral score of the students in the photo group is 37.2583, while that of the students in the experimental group is 43.1465, and the difference between the average scores of the two classes is about 5 points, which is a large score difference. The two-tailed significance sig value is 0.005 if equal variances are used, and the two-tailed significance sig value is 0.008 if equal variances are not used, both of which are less than 0.05, indicating that the method of this paper has a positive effect on improving the speaking scores of the students of the comprehensive Chinese culture class.
AIGC Enabling Non-Genetic Design Methods and Practices
Authors: Li Zujian , Ma Zhehao , Xu Boshen , Lei Shanshan , Cheng Yin , Xu Feng
Published: May 2024

Artificial Intelligence Generated Content (AIGC) technology aligns seamlessly with the design requirements of non-genetic heritage, offering a viable pathway for its modernization. This paper delineates the specific design needs of non-genetic heritage and utilizes a diffusion model to create themed images and animations related to this heritage. Additionally, AIGC is employed to enhance the creation of virtual reality interactive imagery. The Long Short-Term Memory (LSTM) network is deployed to classify time-series gesture data, facilitating the training and categorization of Chinese Sign Language (CSL) gestures for virtual interactive engagement with non-heritage themes. We have integrated the AIGC operation process into the theme of non-genetic inheritance, thereby constructing a robust development trajectory for AIGC-enhanced non-genetic heritage. The experimental setup is crafted to ascertain the optimal number of iterations and training durations through the control variable method. We evaluate the efficacy of the diffusion model for anti-implicit writing analysis and the performance of the speech recognition, text dialogue, and text response modules within the non-heritage multimodal interactive framework using Word Error Rate (WER) and Mean Opinion Score (MOS). A descriptive analysis of users’ interactive experiences with non-heritage content is also conducted. The results indicate that the speech recognition module achieved a WER of 0.365, while the text response module garnered an MOS of 4.49 with a standard deviation of 0.56. This multimodal, non-heritage virtual interaction, leveraging multiple modalities, enriches users’ experiences and deepens their understanding and appreciation of non-heritage content. Consequently, this enhances the high-quality development of non-genetic heritage.
Modeling of the accurate application of the Kernel Efficiency Method in the proportioning process of shield tunnel construction
Authors: Wang Yangxin , Li Jinkui
Published: May 2024

The existing control strategies for subway construction struggle to address the challenges posed by the impact of large-diameter shield-driven underpass subway tunnels on soil settlement. In this paper, based on the mechanism of ground settlement of the shield tunnel under the KNX method, the influence of the underpass tunnel on the existing tunnel and its surrounding soil stress-strain characteristics is calculated by the Peck superposition method. The KNX method is optimized by optimizing the ratio of KNX slurry and the grouting pressure to control the existing tunnel and the ground settlement. The geotechnical general finite element analysis software is being introduced to conduct numerical simulation and engineering application experiments to verify its effectiveness. The results show that when the bonding force of the grouting material is 350Pa-s, the maximum value of the variation is 3.91mm, and the mobility is more suitable, which can effectively fill the void of the shield machine and reduce the settlement of the foundation. When the grouting pressure is 280kN/m2, the maximum contraction of the tunnel is 6.25mm, which can ensure the safety of the existing tunnel on the basis of the tunnel so that the actual settlement of the soil in the tunnel is minimized. Comprehensively, the optimization of the ratio of the KNP method in the control of the deformation characteristics of the soil and structures under the shield tunnel has effectiveness and can effectively provide theoretical and technical guidance for the construction of subway tunnels under the large-diameter shield under the subway.
Transformation Research on the Teaching Mode of Ideological and Political Courses in Colleges and Universities under Big Data Environment
Authors: Yang Dan
Published: May 2024

The application of big data technology in the teaching of ideology and politics courses in colleges and universities can provide strong support for the precise reform and innovation of ideology and politics work. In this paper, we first sort out the status quo and dilemma of the teaching of ideology and politics courses in colleges and universities and analyze the advantages and feasibility of big data technology in the learning of ideology and politics. Then, based on the big data streaming computing Spark framework, a personalized learning service platform for teaching Civics and Politics courses in colleges and universities was established. Aiming at the online learning behavior of students on the platform, the K-mean algorithm is used to select their behavioral characteristics. The personalized recommendation of learning resources for Civics and Political Science courses is implemented based on the learning behavior. Data was utilized to analyze the effectiveness of the personalized learning platform in transforming the teaching of Civics and Political Science courses in colleges and universities using S University as the research object. The study found that the number of focused learners in category 1 of the online learning behavior reached 303, and the teaching of the Civic and Political Science course based on the personalized learning platform helped the students’ will cultivation score increase by 2.49 points and more than 95% of the students recognized the platform. The customized service learning platform for teaching Civics courses established based on big data can realize the innovative transformation of Civics courses in colleges and universities and improve the teaching quality and level of Civics courses in colleges and universities.
A Spectral Analytical Study of Melodic Innovations in the Opera “Aleko” and Rachmaninoff
Authors: Yang Dongdong , Li Ying
Published: May 2024

The opera “Aleko” is a graduation work composed by Rachmaninoff, for which the opera was born in Russia in the nineteenth century and has a strong literary and artistic value. This study tries to extract the time-frequency melodic characteristics of the opera “Aleko” by using the spectral analysis technique as a research tool combined with the Fourier transform and constant Q transform. From the perspective of quantitative analysis, the innovative laws of Rachmaninoff’s melodic features are summarized with the intention of revealing the great artistic charm of this opera work. The results of the spectral analysis show that the amplitude of the first part of “Aleko” is 27.5dB, which forms its unique “Rach’s style” through its innovative “stepped” meandering tones. At the same time, the rich melodic layers of “Aleko” emphasize the drama of the opera and make the internal structure of the phrases stronger.
Application of AI technology in personalized recommendation system for financial services
Authors: Yue Xiaowen
Published: May 2024

How to quickly and accurately retrieve the products that users are interested in from a huge amount of financial products has become a business pain point that financial institutions must solve. In this study, an interpretable EPRSA model for personalized financial service recommendations based on self-attention mechanisms is proposed by combining an LSTM model and an LDA topic model with AI technology support. A customized recommendation system for financial services is constructed by introducing the Nginx server into the Flask framework, and the design of the database and personalized recommendation module is interpreted. For the financial service personalized recommendation system proposed in this paper, its recommendation performance and system performance are tested, and the stock financial products are selected as the recommendation objects to explore its recommendation effect. It is found that the DNCG index of personalized recommendation of financial products of the EPRSA model is improved by 40.18%, the average response time of the system when the number of concurrent users is 1,000 is 1.96 s. The average quality of the personalized recommendation of the collection of stocks reaches 0.153. The customized recommendation of financial services using AI technology can select financial products based on the investor’s preference, help investors better understand the product returns, and improve the service quality of the financial industry.
An Exploration of Customer Experience Design Innovation in Tourism and Hospitality Industry Driven by Emerging Technologies
Authors: Liu Hui
Published: May 2024

With the rapid development of tourism hotels, there are many problems, such as uneven design quality and poor user experience. In response to the above issues, this paper proposes a digital design for tourism hotels that integrates VR technology. Using the network crawling technology in the network LDA model, the text data on customer experience design in the tourism hotel industry is obtained, and in order to avoid the problem of duplicate values and null values in the data, data processing is carried out to ensure the precision of the research results. Based on the principle of evaluation system construction, we determine the evaluation index system for tourism hotel customer experience design that allows the integration of VR technology and constructs the LDA-AHP model for hotel customer experience design. Based on the acquired research data, the customer experience design of the tourism hotel industry is analyzed by examples. The results indicate that the comprehensive evaluation score for tourism hotel user experience based on the LDA-AHP model is 3.3075, which suggests that the design of tourism hotel user experience based on the LDA-AHP model is at the 3-crown level. This study provides theoretical references for the design of tourist hotel customer experiences, which can help the tourist hotel industry improve and develop rapidly.
Ecological Network Construction of Putian Rural Landscape Based on Geospatial Analysis
Authors: Lu Xiaoxue , Chen Zhen , Cao Mengqi , Chen Zujian
Published: May 2024

As the countryside continues to expand and develop, ecological issues are becoming increasingly complex and critical. The creation of environmental networks plays a vital role in ensuring ecological protection and promoting sustainable development in these areas. This study selects the Putian countryside as the focal area for research, utilizing remote sensing technology to collect pertinent data. By integrating geospatial analysis methods, we constructed a landscape ecological network. The methodology involved several key steps: employing a landscape index to delineate the spatial pattern of the rural landscape, evaluating the service function value of the established ecological network, and applying the Minimum Cumulative Resistance (MCR) model to formulate the security pattern of the network. This approach underscores the importance of structured ecological planning in rural development initiatives. The analysis found that among the four types of rural green landscapes in the study area, the regional green space occupies a dominant position, with an LSI value of 116.1734, mainly large patches with uneven spatial distribution, and based on the Putian Rural Minimum Cumulative Resistance Surface Model = 0.41*Land Use + 0.33*Slope + 0.26*Distance from Water Bodies, the Putian Rural Landscape Ecological Network with a nucleus, a belt, five axes, and multiple points has been formed. The research in this paper provides a reference basis for the development of the Putian countryside and helps revitalize the Putian countryside.
Analysis of National Wave Brand Value and New Consumer Class Loyalty Combined with the HIROSE Model
Authors: Ma Lan
Published: May 2024

With the continuous development of China’s market economy and the gradual transformation of market competition to brand competition, the brand has an increasingly important role for enterprises. Based on the HIROSE model, this study constructs the M-HIROSE model, which is more suitable for the evaluation of the value of the National Tide brand by adjusting the brand revenue, brand expansion power, and period. To explore the relationship between the loyalty of the new consumer class and national brands, multiple regression is used for analysis. By empirically analyzing the basic financial data of China’s five national tide brand operators in the past three years, the results show that the ranking of the final brand value of the enterprises and the ranking order of the loyalty drivers are consistent, and the loyalty drivers basically determine the final brand value of the enterprises. The direct effect of new consumer class loyalty on the National Tide brand is significant (R2=0.235, p<0.001). This study, based on the M-HIROSE model of the National Tide brand value assessment, is feasible, and the new consumer class loyalty has a significant effect on the National Tide brand.
Meta-Universe Enabled Digital Modeling for Urban 3D Reality Visualization
Authors: Xie Qian , Jiang Chao
Published: May 2024

The rapid acquisition of 3D data with high accuracy and efficiency, along with the reduction of production cycles and costs, are pressing challenges in the field of 3D reality modeling. This paper introduces a novel approach for urban 3D real-view modeling from a metacosmic perspective. Utilizing tilt-shift photography technology, this method captures three-dimensional data of urban scenes under the meta-universe framework. The data are then processed through an integration principle, combining DOM (Digital Orthophoto Map), DEM (Digital Elevation Model), and vector data to filter out interference. Subsequently, a high-density digital point cloud is generated using tilted imagery combined with aerial triangulation. This point cloud, along with a TIN (Triangulated Irregular Network) model, facilitates the construction of a comprehensive three-dimensional visualization model of urban environments, enabling detailed digital analysis. Data presentation demonstrates that with an increase in the number of image control points, the planimetric error decreases from 0.0536 m to 0.0388 m, reflecting a 27.61% improvement. Similarly, elevation accuracy improves from 0.0927 m to 0.0539 m, marking a 41.86% enhancement. This methodology supports the creation of highly precise and cost-effective three-dimensional realistic models of urban built-up areas, providing robust data support for the development and management of smart cities.
Research on the Application of Northwest Traditional Patterns in Modern Design under the Background of Big Data
Authors: Guo Duhua
Published: May 2024

Northwest traditional patterns possess a rich history and a distinctive artistic style, offering immense potential for innovative design applications. This paper delves into the integration of these conventional motifs into contemporary design frameworks. Initially, we employ the semantic differential method to select vocabulary for the perceptual assessment of Northwest traditional patterns when incorporated into modern designs. We analyze the components of the design samples and utilize the partial least squares method to construct a correlation model between the perceptual assessment vocabulary and the design elements. This approach facilitates a detailed correlation analysis between the application of Northwest traditional motifs and their perceptual evaluation, leading to the formation of associative links between the imagery vocabulary and design features. Subsequently, we define theme, symbol, composition, and color as independent variables, with audience preference as the dependent variable, to conduct a multiple regression analysis. This analysis explores the impact of these dimensions on audience preference. The findings indicate that the significance levels of theme, symbolism, composition, and color are all below 0.05, demonstrating a positive influence on audience preferences. Notably, symbolism, with a regression coefficient of 0.356, exerts the most substantial impact on audience preference. The established correlations between design elements and perceptual evaluations, alongside the analysis of design dimensions’ influence on audience preferences, provide valuable insights for the application of Northwest traditional motifs in modern design contexts.
Research on Industry-Education Integration Model of Vocational Education Applying Automatic Coding
Authors: Pei Shuguang
Published: May 2024

Many applied undergraduate institutions in China have recognized that the integration of industry and education is essential for the advancement and innovation of educational practices. To expedite the development of this integration within vocational education, this paper introduces the Genetic Automatic Coding Assisted Integration of Industry and Education Teaching Mode. This approach begins with the cultivation of vocational education talents in higher education institutions, offering a comprehensive analysis of the industry-education fusion mode and delineating its implementation strategies and guiding principles. Employing the principles of automatic coding, the curriculum content of this integration mode is represented through genetic algorithm gene coding. The model for genetic automatic coding in industry-education integration for vocational education is established by assessing the compatibility of the integration mode and incorporating mechanisms for conflict detection and mutation crossover. The empirical analysis reveals that in genetic automatic coding, the eigenvalues stabilize after approximately 200 iterations. This suggests that this method enhances scheduling effectiveness in the vocational education-oriented industry-education fusion mode. Moreover, the results indicate a significant improvement in educational outcomes: students in the experimental group, where this model was applied, achieved an average assessment score of 88.38, compared to 78.47 in the control group. This substantial improvement confirms that the industry-education fusion mode, underpinned by genetic automatic coding, not only enhances the quality of education but also boosts students’ mastery of professional courses.
Modular Application of Evaluation System for Innovation and Entrepreneurship Education of College Students--Based on Big Data Platform Analysis
Authors: Wang Zhen
Published: May 2024

Based on the objectives of innovation and entrepreneurship education for college students, this paper constructs the evaluation index system, utilizes the improved Apriori algorithm to mine the association rules between the indicators, and reflects the relevant influence between the evaluation indicators through the association rules with higher support and confidence. Meanwhile, based on the big data platform, an evaluation system for innovation and entrepreneurship education for college students is established, and the level of innovation and entrepreneurship education for students is evaluated. The analysis of the correlation rules between the evaluation indicators found that the support degree of the rule that students with stronger independent ability are better at grasping innovation and entrepreneurship opportunities is 29.97%, which provides a reference for the teachers’ teaching objectives and content development. The results of the empirical analysis of the evaluation system of innovation and entrepreneurship education show that the modular application of the evaluation system created in this paper has a positive effect, and there is a highly significant difference in the evaluation of the “Z13 personal willingness” index before and after the application (P=0.000<0.001). The correlation rule of evaluation indexes in this study lays a theoretical foundation for the effective development of innovation and entrepreneurship teaching, and the modularized application of the innovation and entrepreneurship evaluation system constructed in this paper can improve the level of innovation and entrepreneurship education in colleges and universities to a certain extent.
Effects of the BRI on Sustainable Development Evidence from a Panel Difference-In-Differences Method
Authors: Huang Jinghan , Qin Peng , Zhao Xuan
Published: May 2024

The paper calculates the economic system coupling coordination level of the countries along the Belt and Road Initiative (BRI) from 2000 to 2020. On this basis, the paper analyzes the eco-economic effects and mechanisms of the BRI using the difference-in-difference method. The research shows that the “Belt and Road” initiative will significantly improve the eco-economic system coupling coordination level of participating countries. A series of robustness tests, including parallel trend test, placebo test, control of other policy impacts, and replacement of the “Belt and Road” occurrence time, all show that this promoting effect exists. Second, the “Belt and Road” initiative has improved the coupling coordination level of eco-economic systems through trade and investment behavior, but its driving role still needs to be strengthened. The study confirms the global value of the BRI and offers a reference for crafting appropriate policies to enhance the sustainable development level of countries in the BRI.
The etymology study of “fetish opposition” based on FTA analysis and the police PR avoidance approach
Authors: Ge Sichen , Liu Yuanshuo , Luo Yudong , Wang Haiyang , Li Mingshuo
Published: May 2024

In the contemporary post-truth era, there is a marked increase in the individual’s need for emotional expression and self-awareness. This paper introduces and defines the concept of “Fetish Opposition” through etymological research. Employing fault tree analysis, this study analyzes the risk identification and structural importance of “Fetish Opposition” in the context of “Public Security Prohibition” enforced by police departments. This is approached from the perspective of security system engineering, aiming to elucidate the underlying issues and delineate strategies to mitigate the phenomenon of “anti-public security.” This exploration seeks to provide theoretical guidance for enhancing the effectiveness of public security organs. The findings indicate that “lack of law enforcement transparency” holds structural importance of 0.186 within the fault tree of “Fetish Opposition,” making it the most significant factor affecting the entire system. Based on this insight, the police public relations department could develop targeted strategies to prevent such situations, thereby also opening new avenues for research in social psychology and the stabilization of police-community relations.
The impact of homestead system reform on farmers’ well-being: an empirical study based on the structural equation model
Authors: Liu Zhaojun , Zhang Yige
Published: May 2024

The reform of the homestead system is an important way to vacate inefficient and idle land, revitalize the stock, and visualize the asset properties of homesteads in the vast rural areas nowadays. In this study, we first carry out the selection of indicators for measuring the changes in farmers’ well-being under the reform of the homestead system and take a certain region that has carried out the reform of the rural land system as an example and use the fuzzy comprehensive evaluation to measure the changes in the overall well-being of the farmers in that region and the changes in the well-being of the farmers in different villages. The impact of the reform of the homestead system on farmers’ well-being is modeled using structural equations, and the corresponding hypotheses and validations are carried out. The analysis shows that farmers’ well-being in the study area improved by 0.254 overall and by 0.208, 0.204, and 0.350 in urban, peri-urban, and remote villages, respectively. The reform of the homestead system of uncompensated withdrawal, compensated withdrawal, compensated use, and construction of houses by farmers all positively affected the improvement of farmers’ well-being, with the compensated withdrawal policy having the greatest impact (0.315). It is necessary to innovate the homestead system, safeguard and expand farmers’ property rights, promote the reform of the expropriation system, and explore the establishment of a reasonable homestead withdrawal mechanism to encourage the further enhancement of farmers’ well-being.
Research on New Chinglish usage Mode based on emotion computing model: Acceptance and Preference of Chinese College Students
Authors: Liu Xiaoman
Published: May 2024

This study delves into the characteristics and emotional dimensions of New Chinglish, scrutinizing its expressive connotations. By examining the inner architecture of an emotion computation model, we propose a multi-layered cognitive affective computing model. This model integrates personality traits, mood states, and emotional conditions to refine the mapping relationship between PAD mood states and emotional responses. We introduce this multi-layer cognitive model as a novel method for preference mining, integrated with multi-objective decision-making processes. Additionally, we investigate the reception of New Chinglish among Chinese college students by examining various layers of engagement. We categorize the emotional types of New Chinglish utilizing the affective computing model, explore the students’ reception towards positive or negative expressions, and analyze preference patterns based on the attributes of New Chinglish. The findings indicate a favorable overall acceptance of New Chinglish among Chinese college students, with average scores for usage attitude and behavioral intention being 3.215 and 3.236, respectively. The three most favored aspects are humor (2.7986), phonetic appeal (2.5805), and innovativeness (2.4325).
Face orientation recognition based on Kinectv2 and an improved ELM model
Authors: Li Houcheng , Li Yanfei
Published: May 2024

Face orientation recognition is a crucial component of human behavior analysis. Challenges such as face occlusion and varying lighting conditions frequently degrade recognition accuracy. This study introduces an innovative approach using the Kinect sensor’s skeleton tracking technology to capture face orientation images across different lighting scenarios. To enhance image quality, techniques such as light attenuation and compensation are applied. Furthermore, multiple feature vectors based on face orientation are extracted and fused, followed by the optimization of the Extreme Learning Machine (ELM) model through the Archimedean Optimization Algorithm (AOA). The developed AOA-ELM model for face orientation recognition is compared with traditional models, including Back Propagation (BP), Radial Basis Function (RBF), standard ELM, and Genetic Algorithm enhanced ELM (GA-ELM). The AOA-ELM model exhibits superior performance in estimating face orientation amidst lighting disturbances, achieving an accuracy exceeding 95% in varied lighting conditions, thereby outperforming other classification models.
Combining the streamlined bilinear attention network of preschool teaching young children’s learning habit cultivation
Authors: Zhao Jianheng
Published: May 2024

In the preschool education period of young children, the cultivation of good learning habits is launched for young children, and the value is gradually increased along with the growth of young children’s age, which ultimately leads to the development of children. The DSBC dataset is manually completed after collecting and classifying the learning behaviors of young children in this paper. The dataset is expanded by using data enhancement, and a bilinear convolutional neural network-based model for learning behavior habit detection for young children is proposed. The embedded attention module is utilized to create a simplified bilinear attention network model, and the SLBAN model is employed in real preschool teaching work. After the experimental analysis, the model of cultivating young children’s learning habits proposed in this paper produced a 27.65% amount of change in young children’s self-efficacy. Meanwhile, the F-test value was 265.255, P<0.01, which showed a significant positive influence effect. In the analysis of young children’s interest level in learning, the overall interest level of student #1 in the classroom for 40 minutes remained around 0.5, which indicates a high level of interest in learning.