bluuebunny/crossref_metadata_2025 huggingface dataset:
Zero-sample text classification algorithm based on BERT and graph convolutional neural network
Authors: Qiao Ying , Li Yu , Zhou Liangzhi , Shang Xu
Published: June 2024

In this study, we undertake a comprehensive examination of zero-shot text classification and its associated implications. We propose the adoption of the BERT model as a method for text feature representation. Subsequently, we utilize the Pointwise Mutual Information (PMI) metric to adjust the weight values within a graph convolutional neural network, thereby facilitating the construction of a text graph. Additionally, we incorporate an attention mechanism to transform this text graph, enabling it to represent the output labels of zero-shot text classification effectively. The experimental environment is set up, and the comparison and ablation experiments of the text classification model based on BERT and graph convolutional neural network with the baseline models are carried out in several different types of datasets, and the parameter settings of λ are adjusted according to the experimental results, and the convergence of the BERT model is compared to test the robustness of the model performance and the classification effect. When λ was set to 0.60, the model achieved the best results in each dataset. When the task is set to 5-way-5-shot, the convergence rate of the model for the Snippets dataset using the penultimate layer of features can reach 74%-80% of the training accuracy at the 5,000th step. The training accuracy gradually flattens out in the first 10,000 steps, and the model achieves classification accuracy in all four learning scenarios, with good stability.
Optimization of the Integration Development Path of Party Building and Civic Management in Colleges and Universities--Application Based on the Kano Model
Authors: Zhao Yue , Zhao Hui , Guan Qi
Published: June 2024

Promoting Party building and Civic-Concern Management in colleges and universities is of great significance to the implementation of the fundamental task of establishing morality, firming students’ ideals, and constructing a good school spirit and learning style. This paper takes the integration and development of party building and ideological and political management in colleges and universities as the starting point, screens the demand element indicators, and explores the demographic differences by using one-way ANOVA after conducting a questionnaire survey. Then Kano model and Better-Worse satisfaction index are used to analyze the demand element indicators for the integration of party building and ideological and political management in colleges and universities. Finally, a practical optimization path is proposed based on the three dimensions of the obtained essential attribute elements, desired attribute elements, and charismatic attribute elements. The specific results are as follows: the evaluation mechanism for the integration of party building and Civic and political management in colleges and universities varies across genders, academic qualifications, and majors (p<0.05), and the satisfaction coefficient of linkage between party building activities and practical teaching of Civic and political courses is the highest (0.7706), while the dissatisfaction coefficient of optimization of incentive methods is the highest (0.9751). The integration and development of party building and civic management in colleges and universities can be optimized by strengthening the top-level design, improving team construction and evaluation mechanisms, and guiding external assistance.
Research on ESP Convergent College English Teaching - A Perspective of SMCR Communication Modeling
Authors: Wu Xian
Published: June 2024

Addressing the prevalent challenges in university-level English education, this study integrates the SMCR communication model’s attributes with the specifications of ESP (English for Specific Purposes)-integrated teaching, thereby devising an ESP-integrated model tailored for college English instruction from the SMCR perspective. The research objectives were clearly delineated, followed by the distribution of questionnaires to collect essential data for subsequent analysis. The gathered data underwent rigorous reliability and validity assessments to confirm the integrity of the research findings. Additionally, specialized data analysis software was employed to evaluate the effectiveness of the proposed instructional model. The analysis revealed that the average scores for the three dimensions of teacher knowledge—professional theoretical and practical knowledge, information technology knowledge and its applications, and knowledge of related disciplines and basic culture—were 3.947, 3.824, and 3.931, respectively. All dimensions demonstrated statistical significance with p-values less than 0.05 in the context of ESP-integrated English education. These results suggest a pressing need for university English instructors to enhance their understanding and proficiency in information technology, as well as to deepen their grasp of information technology teaching methodologies. Consequently, this investigation contributes novel approaches to university English teaching, leveraging information technology to enhance both educational outcomes and student engagement in the English learning process at the tertiary level.
Construction of Online Interactive Learning System for University English Driven by Artificial Intelligence
Authors: Wang Peipei
Published: June 2024

With the advent of rapid advancements in information technology, online learning has increasingly gained traction, prompting a multitude of colleges and universities to adopt innovative reforms in their educational delivery methods. This research undertakes the development of a college English online interactive learning system empowered by artificial intelligence. It primarily investigates the interactive behaviors of college students in online English learning environments. Initially, the study employs the K-means algorithm to categorize students based on their distinct learning behaviors. Subsequently, it leverages a recommendation algorithm to fine-tune the provision of English teaching resources online. Furthermore, the study integrates an enhanced Apriori algorithm to extract associations between student behaviors and learning outcomes. This analytical framework underpins the empirical evaluation of the system’s efficacy in actual educational settings, aiming to deepen the understanding of students’ online interactive behaviors. The findings reveal that over 95% of students consistently log into the learning platform on scheduled class days. The metrics for course recommendation success, such as the hit rate, the average inverse rank, and the range of normalized discounted cumulative gain, are reported between 18.37-21.94, 1.87-2.08, and 4.44-4.87, respectively. Additionally, system memory utilization remains stable at 57%-66%, corroborating the system’s operational effectiveness. This research contributes valuable insights and benchmarks for the development of robust online interactive learning systems.
Discussion on the Innovative Mode of Civic and Political Education by Integrating Students’ Behavioural Characteristics
Authors: Xu Dandan
Published: June 2024

How to effectively combine students’ behavioral characteristics and put forward an innovative civic education model is an important content of the current civic education module. In this paper, we collect relevant data from three aspects of students’ consumption, learning, and life behavioral characteristics and preprocess the data. Based on the data on students’ behavioral characteristics, a student profile is established, and a collection of their behavioral characteristics is constructed. Then, the entropy weight method is used to determine the weight value of each student’s behavioral characteristic, establish the stratification of student behavioral characteristics, and construct a model of student behavioral characteristics. In the student behavioral characteristic analysis, the students of University B in Guangdong Province, China, are used as the research object to develop the student behavioral characteristic analysis. In the cluster analysis of consumption behavior, 28% of students with low consumption levels may be experiencing economic poverty. During the winter and summer vacations, there was a decrease in the number of books borrowed, students entering the library, and seat borrowing appointments. The number of students who spent 14 hours a week on the internet and the frequency of accessing it more than three times was 62.01% and 89.76%, respectively. The average value of learning behaviors, except for information use behavior, is below 3.5, which is below average. Finally, by analyzing students’ behavioral characteristics, an innovative mode of civic education is proposed, which provides practical references to guarantee the creative development of civic education.
Application of zeroed neural networks to stability analysis of continuous dynamic systems
Authors: Huang Yun , Hu Jiaxin
Published: June 2024

Modern production processes frequently require steady-state analysis of continuous dynamic systems. Traditional numerical approaches, however, fall short in efficiency when tasked with addressing large-scale or dynamic problems. To tackle the inverse problem inherent in stability analysis, this study presents an innovative approach by integrating a combined excitation function into the foundational zeroing neural network (ZNN) model. This integration constrains the ZNN model, evolving it into an enhanced EZNN model specifically designed for solving the inverse of dynamic complex matrices. Additionally, this paper conducts a rigorous theoretical analysis of the robust performance of the EZNN model when excited by the combined function, both in the presence and absence of noise interference. The model solution process is promoted by using a class of high-dimensional continuous dynamic systems as an example, and numerical simulation experiments are used for validation. Considering the dynamic system satisfying

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1(x(t),t) obtained by the EZNN model with combinatorial function excitation always remains negative or tends rapidly to 0. The x (t) obtained by the model converges rapidly to an exact solution of the system. Through the discussion of parametric conditions, it is also found that increasing the value of parameter γ increases the rate of convergence of the ZNN model.
Analysis of Literature Education Strategies and Learning Behaviors in the Age of Big Data
Authors: Luo Chong
Published: June 2024

In the contemporary era of big data, the educational landscape is undergoing significant transformations. Literature education, as a vital component, faces both emerging opportunities and challenges. This study develops a framework for analyzing learning behaviors specific to literature education. It employs both an enhanced K-means clustering algorithm and a refined Apriori algorithm for mining data on student learning behaviors. Through cluster analysis and the investigation of association rules, this research explores the interconnections between students’ learning behaviors and their literary education. The findings categorize students into four distinct groups based on their learning behaviors. Students in Category 1 are identified as the most proficient learners, consistently achieving test scores above 85. Conversely, Category 2 students display the least motivation and effectiveness, with their examination scores not exceeding 70. Students in Categories 3 and 4 exhibit comparable levels of performance. Crucially, the analysis reveals that the most significant predictors of students’ literary achievement are their regular and examination scores, with correlation coefficients of 0.627 and 0.653, respectively. This segmentation and analysis of student behaviors facilitate the early detection of atypical learning patterns by educational practitioners, enabling timely intervention strategies to enhance academic outcomes.
Research on the Application of Image Feature Extraction in Mechanical Structure Recognition and Fault Diagnosis
Authors: Niu Zhenhua , Sun Shaokai
Published: June 2024

With the rapid development of modern industry and science and technology, in recent years the fault diagnosis method based on image processing has become a research hotspot in the field of mechanical fault diagnosis. In this paper, image characteristics are extracted from multiple aspects such as image texture, color, shape, etc. A grayscale symbiotic matrix image feature extraction method is proposed. On this basis, the algorithm for extracting gray symbiotic matrix time-frequency image features is designed. At the same time, the algorithm and parameters of mechanical structure identification are optimized to identify and diagnose mechanical faults. The results show that the grayscale symbiotic matrix time-frequency image feature extraction algorithm is able to accurately diagnose the wear-type faults, overwork-type faults, and short-circuit-type fault behavior of the mechanical equipment. All of them are able to obtain more than 80% accuracy, and all of them are able to reach 99.99% accurate detection of mechanical faults, which proves the effectiveness of the method of this research.
Design of online and offline teaching system for piano improvisation accompaniment course for preschool education majors integrating meta-learning recommendation algorithm and CDIO
Authors: She Tiantuo , Chen Chen
Published: June 2024

With the development of the Internet, educational data based on social networks and mobile applications are also emerging, and personalized teaching systems have become a topic that has gradually attracted attention. In this paper, after exploring the meta-learning algorithm based on deep learning, a dynamic graph neural network is used to predict user preference, and a recommendation algorithm model is constructed based on meta-learning. On this basis, combined with the teaching and training mechanism of CDIO, a teaching system for the piano improvisation accompaniment course of preschool education was built, and the performance of the construction system was tested to test its application effect in the piano improvisation accompaniment course of preschool education. It can be seen that the values of each index of the recommended algorithm model in this paper are between 0.382~0.956 under cold and warm start, and the effect is the best. And the MRR@10 accuracy of the model in this paper is up to 0.725. In the professional piano improvisation accompaniment course, the scores of all indicators of the system use effect in this paper are above 4 points, which are higher than those of the traditional system, which proves that the method can improve the use effect of the teaching system and provides practical value for the system design of professional piano improvisation accompaniment course.
Exploration and Practice of Digital Teaching Innovation Path for Art Majors in the Information Age
Authors: Sun Peng
Published: June 2024

This paper proposes a digital teaching innovation path for art majors and builds a digital teaching innovation technology framework with digital teaching resource management and online and offline blended teaching as the main modules. In digital teaching resource management, a SCORM digital teaching resource management model is constructed, and a resource recommendation algorithm is designed in combination with a collaborative filtering recommendation algorithm. In blended teaching, a meta-learning recommendation algorithm is utilized to complete the design of a teaching system for CDIO. Finally, experimental classes and control classes were set up to carry out digital teaching practices using the Fine Arts Painting major at University A in Zhejiang Province, China, as the research object. In the analysis of students’ learning behavior performance, the score of students in the experimental class was 4.373 points higher than that of the control class, with a significant difference (P<0.05). The total drawing performance score of the experimental class students was 78.45, and the overall drawing performance was satisfactory. In the analysis of students’ learning performance scale, all the index values of the experimental class were higher than those of the control class. In terms of teachers’ teaching behavior performance, except for teaching monitoring behavior, the experimental class showed significant differences from the control class in all other teaching behavior factors.
Integration and Innovation of Artificial Intelligence and Traditional English Translation Methods
Authors: Tang Hui
Published: June 2024

In this study, we employ autoregressive and neural machine translation models to process natural language. Our exploration of AI translation workflows encompasses three key aspects: the encoder-decoder framework, text feature representation, and translation derivation methodologies. Subsequently, we identify the core evaluation indices essential for assessing intelligent translation systems. An innovative translation model is constructed by amalgamating AI-driven and traditional English translation techniques. This model is scrutinized for its effectiveness through various lenses, including the quality of English-to-Chinese poetry translations and manual evaluations. Comparative analysis of the proposed translation method with other extant methods across diverse datasets reveals superior performance metrics: the BLEU score consistently exceeds 4.5 across all three test sets, and the METEOR score ranges between 4.3 and 4.6, surpassing competing methods. Additionally, translation accuracy for sentences of varying lengths in the source language is maintained between 85% and 96%. The objective of this research is to conduct a comprehensive examination of the integration of AI English translation with conventional methodologies, aiming to foster innovative theoretical contributions and practical advancements in the field.
Application of augmented reality technology in visual design of cultural product packaging
Authors: Tian Jing
Published: June 2024

Integrating intelligent terminal devices into product packaging to amplify brand influence represents a significant trend in contemporary packaging design research. This study introduces an augmented reality (AR) cultural product packaging design model predicated on the principles of AR packaging visual information interactivity and content interaction. This model facilitates a precise comprehension of shifts in user preferences for cultural product packaging. It primarily employs computer vision technology for AR tracking and registration, ensuring accurate overlay of virtual objects onto real-world spaces. Within the scope of this model, user experience metrics—ease of use, interactivity, and satisfaction—were quantitatively assessed, yielding scores of 14.523, 13.49, and 13.918, respectively, with variance values spanning from 0.031 to 0.1418. Notably, users rated the packaging design creativity of cultural product A, which utilizes this model, highest at 6.66. This demonstrates that the AR cultural product packaging design model delivers a stable and impactful user experience. Consequently, this research provides a robust and productive framework for advancing intelligent and interactive product packaging designs.
Construction and Application of University English Teaching Evaluation System - Based on the DPSIR Model
Authors: Wang Lixin
Published: June 2024

The establishment of the university English teaching evaluation system is to give full play to the guiding, diagnostic, and feedback role of evaluation, to realize the evaluation for teaching evaluation for learning, and to promote the overall development of students. In this paper, the drivers and pressures of teachers and students in the process of English teaching are taken as the basis of evaluation, and the evaluation index system of university English teaching is established in combination with the DPSIR model. Taking the English teaching data of Chinese universities from 2010 to 2023 as the research object, the entropy weighting method was used to calculate the weights of evaluation indexes, and the TOPSIS comprehensive evaluation model was combined to explore the comprehensive development level of university English teaching. The obstacle degree model was used to analyze the obstacle degree of university English teaching evaluation. It was found that the weight of the driving force subsystem in the evaluation system of university English teaching was 0.2036. The level of university English teaching increased nearly 1.3 times between 2010 and 2023. The highest cumulative degree of obstacle was the mean score of final English grades (R1), whose average degree of obstacle reached 18.15%. The construction of a university English teaching evaluation system requires teachers to optimize their teaching methods and enrich the teaching content so as to promote the internalization of students’ English knowledge and ensure a scientific and reasonable English teaching evaluation system.
The Role and Function of Artificial Intelligence and the Metaverse in Smart Libraries
Authors: Wang Xuetao , Gao Yan
Published: June 2024

The swift advancement of information technology has catalyzed the emergence of diverse new technical tools, presenting both significant opportunities and formidable challenges for university library service innovation. This study harnesses meta-universe technology to explore its integration with library services and to establish an architecture for an intelligent library system. It employs the trilateration positioning algorithm, among other technologies, to enhance the precision of user-targeted services within the smart library. Furthermore, the similarity of user preferences is calculated using a collaborative filtering algorithm, which is then utilized to refine the system’s recommendation mechanisms. A comprehensive research design was developed, incorporating empirical methods to evaluate the smart library system’s effectiveness from the perspectives of user satisfaction and willingness to engage with the technology. Among the ten higher education institutions evaluated, the ninth institution displayed the most favorable user experience, achieving a score of 0.757. Additionally, the study found a significant positive correlation between the variables measuring users’ willingness to use the system, ranging from 0.1 to 0.7. Notably, the strongest correlation was observed between concerns about privacy and behavioral intent, with a correlation coefficient of 0.6598. These findings underscore the critical importance of prioritizing user privacy in the development of intelligent library systems.
Study on the Relationship between Economic Growth and Environmental Pollution in Shanxi Province Based on EKC
Authors: Wang Yuxin
Published: June 2024

China’s rapid economic development since the reform and opening up has attracted the attention of the world, while a sharp deterioration of the environment accompanies the rapid economic development. Starting from the current situation of economic development and environmental pollution in Shanxi Province, the interaction between economic growth and environmental pollution in Shanxi Province is comprehensively analyzed, and the influence mechanism between the two is revealed. The EKC theoretical econometric model for the relationship between economic growth and environmental pollution in Shanxi Province is constructed and empirically analyzed by combining the multicollinearity test, correlation test, unit root test, and cointegration test to obtain the data required for this study, and at the same time, to determine the research indexes and parameters. The correlation coefficients between GDP per capita and industrial wastewater discharge per capita, industrial exhaust emissions per capita, and industrial solid waste generation per capita in Shanxi province reach more than 0.5, and the Sig values (0.001, 0.004, and 0.005) are less than 0.05, which is a significant correlation, and the R2 coefficient of determination of the fit of the EKC curves of GDP and exhaust emissions is 0.8532, and the expression is y = –0.1169 + 5×102
x – 1×103
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2 –1.6×103
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3. This study profoundly reveals the relationship between economic growth and ecological environment and promotes Shanxi Province to achieve the goals of environmental improvement and sustainable economic development.
Artistic Guidance and Emotional Expression Pathways in Piano Performance - Based on Deep Score Characterization Fusion
Authors: Wu Lingcao
Published: June 2024

In the evolving domain of artificial intelligence and deep learning, the pedagogical applications of piano performance— specifically its artistic instruction and emotional expressiveness—have garnered increasing scholarly attention. This research leverages MIDI music file formats to mine necessary data, engaging in a detailed examination of the structural and characteristic elements of MIDI files. To optimize the precision of mathematical models applied in this study, MIDI music data underwent preliminary preprocessing steps, including hexadecimal encoding and feature filtering, which facilitated the transformation of MIDI features into vectors representing emotional attributes. The study introduced the development of a BiLSTM_BLS model tailored to articulate the nuances of piano performance and emotional expression. This model synthesis integrates features reflective of emotional states induced by piano playing. A mathematically derived loss function was utilized to gauge the model’s fidelity, thereby enabling an assessment of its efficacy in the realms of artistic instruction and emotional conveyance in piano performance. Findings indicate a proximity between the emotional states of Anger (0.392) and Stress (0.385), with Depressed (0.422) presenting a slightly elevated distance from these states. Moreover, the BiLSTM_BLS model achieved a performance score of 3.214 in the evaluation of artistic expression, which corresponds to 64.28 on a standardized 100-point scale. This investigation contributes to the enhancement of musical skills and theoretical knowledge among pianists, promotes cognitive development, augments emotional expression, and nurtures creativity.
The Application of PDCA Cycle Theory Combined with Subjective and Objective Empowerment Method in Teaching Quality Assessment System of Higher Vocational Colleges and Universities
Authors: Xiao Zhiyong , Li Hongfeng , Yuan Xianyu , Zou Linfeng , Liu Yan , Jiang Biao , Qiu Tiegang , Yin Jieqin
Published: June 2024

As the expansion of higher vocational education hastens the massification of higher education, issues concerning the quality of teaching at higher vocational institutions have become increasingly apparent. This study addresses these concerns by integrating the PDCA (Plan-Do-Check-Act) cycle theory to develop a robust assessment system for evaluating teaching quality in higher vocational colleges. Utilizing a combination of hierarchical analysis and entropy weighting methods, the assessment system is enhanced both subjectively and objectively. By employing higher vocational colleges and universities labeled M1 to M6 as illustrative examples, we apply linear weighted summation to derive comprehensive evaluation scores from the tested teaching quality evaluation index system. The empirical results indicate that the cumulative variance contribution rate of the assessment indices reached 91.765%. In the quality assessment, M4 scored the highest with a composite score of 4.60, while M6 scored the lowest at 3.49. Comparative analysis across dimensions shows that the index scores for M6 were notably lower than those of other institutions, ranging between 3.20 and 3.80. Implementing the proposed teaching quality assurance system resulted in a substantial increase of 0.77 points in its comprehensive score. Thus, the assessment and assurance system developed in this study proves to be practically valuable, offering an effective means to enhance teaching quality and elevate the educational standards in higher vocational colleges.
Application of Virtual Reality Technology in Historical and Cultural Landscape Reproduction
Authors: Xu Yi , Xie Meiling , Xiong Shuhui
Published: June 2024

Virtual reality panoramic technology transcends the constraints of time and space, offering audiences immersive visual and auditory experiences that diminish the costs associated with exploring historical and cultural landscapes and enhance communication effectiveness. This study employs fisheye cameras to capture panoramic images of such landscapes. The collected images undergo correction via spherical projection and reflective planar unfolding mapping. Subsequently, an enhanced method for optimal stitching line selection is applied to ensure seamless transitions between spliced images. The process is completed by integrating image interpolation and projection algorithms to virtually recreate historical landscapes. Analytical assessment of the model’s reproduction effects focuses on point error and accuracy. A practical application involving a real historical and cultural landscape is examined, revealing a maximum control point deviation from the optical scan of 0.02m and a minimum of 0.0014m, which negligibly affects the landscape’s virtual representation. Reproducing an actual landscape image yielded a maximum measurement error of 0.02m, a minimum of 0.01m, and an average of 0.0175m, affirming the capability for precise replication. This paper introduces innovative approaches and methodologies for leveraging virtual reality technology in the accurate reproduction of historical and cultural landscapes, illustrating its practical utility and effectiveness.
Study on the Role of Public Sector Human Resource Management in Educational Management Reforms
Authors: Xu Lei
Published: June 2024

The principles and methods of human resource management have been introduced into the field of education for some time, but the analysis of the current state of research, specifically on educational management, is still insufficient. The first part of this paper analyzes the measures that are related to human resource management in education management reform in each sector and further investigates the factor analysis method in the public sector human resource management system. By constructing the factor analysis model and factor calculation method, we can provide an algorithmic basis for studying the role of public sector human resource management in education management reform. The article utilizes the factor analysis method to conduct an empirical study by using the educational resources of different schools in 11 cities of Province A as test objects. According to the study, the first principal component has seven-factor loadings that are higher than 0.72, which indicates that all seven factors have a positive impact on the allocation efficiency of higher vocational education. The second principal component has four-factor loadings greater than 0.80, suggesting that all four factors have a positive effect on the allocation efficiency output of higher vocational education. From the sample data of the school education resource allocation status of each city, it can be obtained that the school education resource allocation status of city A is the most favorable, with the highest overall score of 43,153.1, followed by city B and city D, with 25,431.9 and 12,136.4, respectively. This indicates that the education resource allocation effect of these three cities is better and belongs to the upstream level in province Z.
Innovative Applications of Intelligent Assistive Systems in Interior Design
Authors: Yang Zhen
Published: June 2024

With the development of smart homes, virtual reality, and other technologies, people’s demand for digital modeling and visualization of interior space is increasing. How to quickly and accurately estimate the layout of the interior space has become a matter of great concern. This paper develops a 3D virtual visual interior design platform structure based on this. By studying the indoor multi-sensor fusion localization system, the localization module in the navigation process was designed based on this fusion result. The visual localization algorithm, WiFi localization, PDR localization, and AMCL fusion localization algorithms are introduced, respectively. The extended Kalman filter algorithm is used to fuse the algorithm with the highest state value for localization, which is used for scene recognition according to the algorithm localization effect. To achieve the 3D virtual visual impact, a 3D virtual visual interior design platform is created based on 3D virtual vision. Experiments are conducted on the localization system in the platform, and the error of all the localization points of the localization method combining vision and AMCL is no more than 1.1m, with the maximum error of 1.071m and the average error of 0.481m, which is the lowest error among all the algorithms. The localization sensor constructed by the localization algorithm using vision and AMCL with extended Kalman filtering has an average error of 10.33cm, which allows for good scene recognition. The data analyzes designers’ satisfaction with the use of the platform, and the independent variable in the regression equation that contributes most significantly to total satisfaction is the platform’s time-saving, with a coefficient of 0.324. The platform’s focus should be on enhancing time efficiency.
Comparison of Grouping Paths for Improving Technological Innovation Efficiency in Financial Enterprises in the Context of Digital Economy
Authors: Yue Dazhi , Gao Shuanping
Published: June 2024

Technological innovation efficiency is an important index to measure the development level of financial enterprises, and scientific and reasonable assessment of technological innovation efficiency is of great significance to play the role of financial enterprises in economic development. The article takes the upstream and downstream financial enterprises in province A as the research object. It proposes the super-efficiency DEA model on the basis of the data envelopment analysis method to measure the technological innovation efficiency of the sample enterprises. On this basis, the fuzzy set qualitative comparative analysis method is utilized to compare group paths in five aspects: digital economy, government subsidies, enterprise scale, profitability, and operational capacity. The results of the study show that the overall consistency of the conditional grouping scheme (H1: 0PER*GOV*DEC*PROF, scheme H2: DEC*SIZE*0PER*GOV, scheme H3: DEC*0PER*GOV) that promotes the technological innovation efficiency of the upstream manufacturing enterprises reaches 0.78, and the total coverage rate reaches 0.51. The conditional grouping scheme (scheme H2: DEC*SIZE*0PER*GOV, scheme H3: DEC*0PER*GOV) that promotes the technological innovation efficiency of the downstream manufacturing enterprises reaches 0.51. The overall consistency of the conditional configuration scheme (Scheme L1: DEC*~SIZE*0PER, Scheme L2: ~SIZE*PROF*~GOV, Scheme L3: DEC*PROF) reaches 0.91, and the total coverage rate reaches 0.45. The research results reveal the differences between the current upstream and downstream technological innovation efficiency and driving paths in the financial industry and have some guiding significance for the choice of paths of the financial industry innovation drive. It has a certain relevance that is guiding.
A Study of Vocabulary Teaching Strategies in ESP Instruction and Their Effects on Students’ English Vocabulary Learning Using the Subsumption Sorting Algorithm
Authors: Zhang Jun , Liu Zhenqian
Published: June 2024

To facilitate the application of the subsumption sorting algorithm within vocabulary instruction, this study develops a novel Learning Management System (LMS). This system adopts the ESP vocabulary teaching model as its primary instructional framework delves into the requisites of English vocabulary acquisition, the system’s architecture, and its instructional content. It employs an enhanced DTW pattern-matching algorithm to dynamically refine English vocabulary resources. Furthermore, it integrates the merging and sorting algorithm to augment the efficiency of the LMS operations, enabling precise management of student learning progression and vocabulary retention. Comparative analyses of various sorting algorithms were conducted alongside assessments of the impacts of the LMS and ESP vocabulary teaching models on vocabulary learning outcomes. The findings from a semester-long study of vocabulary instruction indicate significant improvements within the experimental group: increases of 6.3, 5.32, and 9.33 points in word form, meaning, and usage accuracy, respectively. Notably, the usage of vocabulary saw the most substantial enhancement. The ESP model-based English vocabulary LMS effectively ameliorates students’ vocabulary usage errors and enhances accuracy in vocabulary application.
Student Management Perspectives on the Moderating Effects of Biological Clock Interventions on Mental Health
Authors: Zhao Chuang
Published: June 2024

In today’s fast-paced life and learning rhythm leads to the generalization of mental health problems in the student population, the biological clock, as one of the links that regulate the functioning of the human body, has been increasingly emphasized in the regulation of mental health. The study selected 545 students in School Z for the experiment on biological clock intervention in students’ mental health. It randomly divided them into an experimental group and a control group for comparison. The SDL-90 scale and SDS scale were used as the basis of the questionnaire design, and independent t-tests and pre-and post-test analyses were conducted on the collected data on students’ mental health to investigate the intervention effect of the biological clock on students’ mental health. The results showed that before the experiment, the mental health level of the experimental group (2.395±0.375) was slightly inferior to that of the control group (2.253±0.342). After the experiment, the mental health level of the experimental group was significantly improved; the total mean score on the SDL-90 scale was reduced from 2.395 to 1.932, and the total mean score of SDS was reduced from 52.242 to 36.769. The intervention of the biological clock had a significant positive effect on reducing the students’ depression and improving the students’ mental health levels.
Design of automatic style classification system of visual art works based on image processing
Authors: Dan Jin
Published: June 2024

The emergence of a vast array of visual artworks meets the growing public demand for cultural literacy while simultaneously presenting challenges in the understanding and selection of appropriate artistic styles. Leveraging advancements in image processing technology, this study successfully automates the classification of 158,652 visual art images by categorizing them into distinct artistic styles. A novel approach combining the Scale-Invariant Feature Transform (SIFT) feature extraction algorithm with the spatial pyramid matching technique is introduced. This paper details the development and evaluation of a classification system tailored for the automatic categorization of visual artworks. Comparative testing results demonstrate that this innovative combination surpasses the traditional SIFT algorithm in both execution speed and classification accuracy. Specifically, the Support Vector Machine (SVM) based classification system, utilizing the enhanced SIFT method, achieves an accuracy rate exceeding 90% in identifying five distinct styles, including PHPS, NOS, IS, TF, and AAS. The implementation of this automatic classification system proves to be a practical tool in aiding individuals to select the visual artwork style that best suits their preferences.
Landscape ecological network construction method supported by high-precision GIS data
Authors: Chen Lulu
Published: June 2024

With the acceleration of urbanization, the regional landscape pattern has undergone profound changes. This study combines green space ecology and explores the construction method of landscape ecological networks using the support of GIS data platform. After completing the extraction of landscape environmental information, it is fused and processed using principal component transformation, tasseled cap transformation, and vegetation index extraction. Different landscape pattern indices are utilized to construct a landscape analysis pathway based on the relevant data from high-precision GIS. Meanwhile, an ecological sensitivity evaluation method that includes soil erosion and rocky desertification was proposed. By comparing and analyzing the landscape pattern indices, it was concluded that the Shannon diversity index increased by 0.2927 and the Shannon evenness index increased by 0.0352, indicating that the landscape types of the study area gradually tend to be even in quantity and spatial distribution. In the evaluation of ecological sensitivity, the proportion of moderate and lower sensitivity reaches 69.28%, indicating that the ecological environment is relatively calm despite external disturbances.
Intelligent Analysis and Predictive Modeling of Tennis Match Data
Authors: Dong Yansong
Published: June 2024

In today’s sports environment, prediction has a significant effect on athletes to achieve good performance and improve training efficiency. This study explores the construction of a prediction model for tennis match data based on an intelligent analyzing system in combination with tennis match results. The probabilistic algorithm for predicting tennis tournament performance has been designed and implemented, and the Glicko ranking system has been optimized using the improved Apriori algorithm. After cleaning, integrating, and dimensional standardizing the data from previous tournaments, the player’s match ranking is predicted based on the Glicko ranking system. Players’ eigenvalues, technical averages, and tactical decisions are used to verify the accuracy of the model prediction. All 10 selected features have a certain degree of influence on the match results, as evidenced by the results, suggesting that the model has a certain reference value. According to the ranking prediction based on athletes’ technical averages, the prediction correct rate of both the Australian and French Open reaches no less than 80%, which proves that the model in this paper is able to effectively predict the performance of tennis tournaments.
The application of carbon footprint assessment under intelligent computing in green consumption decision-making
Authors: Du Yu , Zhou Qingna , Li Zhiwei , Zhang Ziying
Published: June 2024

Reducing carbon emissions is a basic policy long insisted on by the Chinese government, and the calculation of carbon footprint is of great application significance for reducing carbon emissions and realizing green consumption. Based on the multi-objective planning model this paper establishes a carbon footprint assessment model based on multi-objective planning from the economic, environmental, and social benefits of carbon footprint. It optimizes the particle swarm algorithm to solve the objective function of the carbon footprint multi-objective assessment model. The designed model is utilized in green consumption decision-making to forecast the carbon footprint and structure of green energy consumption in the future. The comparison shows that after the optimization of the carbon footprint assessment model based on multi-objective planning designed in this paper, the proportion of coal and oil consumption in the carbon footprint of energy consumption decreases by 1.2% and 0.5%, respectively, and the energy intensity can be optimized. The per capita carbon footprint of energy consumption of residents in eastern, central, and western China grows by 11.3%, 15.2%, and 2.7%, respectively, from 2024 to 2026. The carbon footprint of per capita energy consumption in the three regions is quite different. The results of the two projections show that the carbon footprint assessment model based on multi-objective planning designed in this paper is important for improving the energy consumption structure in different regions and realizing green consumption decisions.
The Design of Online Interactive Teaching Model of College English Combined with ICD Modeling
Authors: Duan Ting , Chen Mingming
Published: June 2024

Amidst the swift advancement and integration of science and technology in educational frameworks, the traditional pedagogical approaches to college English necessitate significant transformation and enhancement. This study introduces an online interactive teaching mode for college English, devised through the utilization of the Integrated Curriculum Design (ICD) model. This innovative model synergistically incorporates neural networks with backtracking search optimization to foster a dynamic educational environment. The preliminary establishment of the online interactive teaching mode, grounded in the ICD model, was thoroughly analyzed and researched. Subsequently, the strategic integration of the BP neural network with the backtracking search optimization algorithm furnishes robust technical support, thereby laying a solid foundation for extensive analytical undertakings. Empirical analysis reveals that students, teachers, and parents from varied geographical and socio-economic backgrounds demonstrate disparate performances across several metrics. Nonetheless, there remains a uniformly high level of satisfaction with the online interactive teaching modality, indicative of its effectiveness and acceptance. Notably, when juxtaposed with conventional offline teaching methodologies, the newly designed online mode evidences an enhancement in students’ auditory comprehension efficiency by approximately 28.74%. The advent of this online interactive teaching mode for college English, predicated on the ICD model, plays a pivotal role in catalyzing the burgeoning “Internet + education” paradigm, underscoring its potential to revolutionize traditional educational ecosystems.
The Role of Cloud Computing Platform in Improving Resource Allocation Efficiency of Training for Male Badminton Players
Authors: Gao Fei , Wang Ziya
Published: June 2024

In recent years, the deployment of computer vision technology across various applications for recognizing human posture and behavior has significantly advanced the field of sports training research and development. This study introduces the application of the OpenPose algorithm to estimate badminton training postures and identify critical points on the human skeleton. Following the identification of these points, we construct a human posture estimation model, which involves preprocessing the data through steps such as detecting key human points, removing redundant points, establishing a two-dimensional coordinate system, and substituting missing key points. The implementation of this algorithm is hosted on a cloud computing platform. Subsequently, we collect relevant data and execute feature extraction using the coordinate normalization technique. This paper analyzes the training postures and suggests an optimization strategy for the allocation of training resources aimed at enhancing the efficacy of training plans. The application of these optimized training schemes resulted in significant improvements in the players’ badminton skills, particularly in forehand high ball hits and serves. Notably, in the experimental tests of forehand high ball hits and serves, the skill levels were recorded at 16.56 and 15.29, respectively—outperforming the control group’s scores of 14.98 and 13.65.