bluuebunny/crossref_metadata_2025 huggingface dataset:
Research on the Construction and Application of Dynamic Early Warning Mechanism of Student Civics in Colleges and Universities
Authors: Liu Jia
Published: May 2024

Aiming at the problems of lagging information feedback, imperfect early warning and intervention mechanisms, and weak risk control ability that exist in the current work of ideological education in colleges and universities, this paper establishes a combination of a simple Bayesian algorithm of ideological dynamic early warning mechanism. This study takes school A as an example, collects a large volume of log data features based on user behavior, and after preprocessing, fuses the decision tree algorithm and the plain Bayesian algorithm and integrates them into the early warning classifier to establish a dynamic early warning model for civic education, and carries out experimental simulation and practical application to test its performance and early warning results. The results show that the prediction error indexes of this paper’s algorithm are no more than 0.1, and the abnormal rate of Civics behavior in each dimension is basically within 10%, which shows high adaptability. This study is beneficial for universities to carry out student civic education work and help civic workers better guide students and serve them.
Research on Network Security Technology of Internet of Things Intelligent Information Terminal under the Background of Information Technology
Authors: Liu Lijun , Qian Jin , Zhou Aiping , Zhang Bin
Published: May 2024

This paper explores the application of network security technology within intelligent information terminals of the Internet of Things (IoT). It examines existing security issues and proposes a technical framework to address them. The technical framework starts from two aspects of data encryption and intrusion detection technology, in the data encryption technology, the CCA-ANC encryption algorithm is proposed to change the attacker in ANC from pure ciphertext attack to using the method of selective ciphertext attack. In intrusion detection technology, the DAGMM algorithm has been improved and optimized. The WEFS-DAGMM improved intrusion detection algorithm has been proposed, and an intrusion detection firewall has been constructed. In the performance test, the CCA-ANC encryption algorithm completes the encryption after inputting 1024MB of data in 13.6s, and the average speed of encryption reaches 71.54MB/s. The optimized WEFS-DAGMM algorithm achieves an accuracy of 0.952, which is 0.03 higher than the original DAGMM algorithm. The recall and F1 values are 0.03 higher than the original DAGMM algorithm, respectively. The algorithms have been improved by 0.03 and 0.037, respectively. Simulation experiments are conducted on the technical framework constructed in this paper. The success rate of malicious attack detection is greater than 90%, and the security protection rate reaches more than 99% in all of them.
Convergence of Artificial Intelligence Algorithms and Education Fuels Educational Reform in Higher Education Programs
Authors: Nie Jingjing
Published: May 2024

In the realm of higher education management, data mining has emerged as a potent tool for elevating the quality of instruction and administrative operations. This study presents the development of a curriculum validation analysis model utilizing association rule clustering. We collected and processed data from design students at a university, addressing missing values and eliminating outliers. Through data mining, we explored the relationships between students’ coursework, skill acquisition, and the demands of potential employers in society. This analysis underpinned a series of reforms in higher education curricula. To assess the impact of these curricular changes, two instructors conducted both process and outcome evaluations of the revised programs. The evaluations revealed that five student groups achieved scores above 80, one group scored between 70 and 80, and three groups scored between 60 and 70. All student groups scored above 60, demonstrating their competence in fundamental job-related tasks and validating their readiness for professional employment. This research offers valuable insights for the revision of professional training programs and curriculum reform in higher education.
Research on Multi-objective Optimization Model of Physical Education Teaching Enhancement Path in Colleges and Universities
Authors: Shen Delei , Dai Fengjian
Published: May 2024

This study begins by assessing the current state of physical education (PE) teaching in higher education institutions. It establishes an optimization index system for PE teaching, utilizing the entropy weighting method to calculate the weights of the indices. These weights are then incorporated into the objective function to define the constraints for optimizing PE teaching, facilitating the construction of a multi-objective optimization model tailored to enhance PE instruction pathways. The model is solved using the NSGA-II optimization algorithm, and an illustrative analysis is conducted. The results indicate a smooth distribution of the superiority and inferiority degrees within the derived PE teaching optimization scheme, characterized by low overall dispersion (mean superiority and inferiority value of 74.2841) and minimal deviation compared to the current status (mean value of 0.09835). This confirms the effectiveness of the proposed model in improving PE teaching. The implications of this study are significant for elevating the standard and quality of PE teaching in colleges and universities, nurturing more professionals for China’s sports industry, and advancing the nation’s sports sector to unprecedented levels.
Simulation study of inventory cost control based on system dynamics
Authors: Wang Yanhong , Zhao Jun
Published: May 2024

In this study, we employ system dynamics to analyze the interplay between various factors influencing inventory cost control, resulting in a comprehensive subsystem model. The inventory strategy subsystem, based on the pipeline delay function, and the inventory cost subsystems, considering the actual situation of the enterprise, are combined to construct the inventory cost control system dynamics model. According to the validity test of the inventory cost control model, there are less than 10% simulation errors in the total transportation cost and total inventory cost data of the enterprise, and the model’s reliability is high. The simulation experiment of the model on the inventory cost control of the enterprise shows that the total inventory cost decreases to 4,398,330 yuan on the 100th day after shortening the order delay time, and the total inventory cost decreases by 1,160,450 yuan on 32 days after controlling the arrival delay time. This dynamic model proves highly effective and offers a dependable approach to managing inventory costs across various enterprises.
A Textual Logical Analysis of the Evolution of Marx’s Urban Critical Thought
Authors: Li Conglai
Published: May 2024

Focusing on elucidating Marx’s urban critical thought is of positive significance for understanding China’s urbanization path and shaping the socialist urban development model with Chinese characteristics in the new era. The main point of Marx’s urban critical thought is the starting point of this paper, and it examines the manifestation and evolution logic of Marx’s urban critical thought. For the textual logic of Marx’s urban critical thought, the Word2vec model is utilized to extract textual features. Then, the BERT model is introduced for text word vector construction, and a multiclassification model of Marx’s urban critical thought text is established based on the fusion of multiple semantic information features. After completing the construction of the method, relevant texts about Marx’s urban critical thought were collected to construct a dataset, and the classification effect was analyzed to explore the evolutionary development trend of Marx’s urban critical thought. The results show that the text classification model can achieve the optimal classification accuracy of 93.15% by applying the 15th layer. The optimal performance is obtained when the local text word length is 100, and the sustainability growth of Marx’s urban critical thought between 1950 and 2020 reaches 102.34%. An in-depth analysis of Marx’s urban critical thought evolutionary logic can provide theoretical guidance for the modernization of Chinese cities.
Deep Learning Based Algorithm for Detecting Errors in Mandarin Read-Aloud Backreading Omission Incremental Reading
Authors: Li Huixu , Yang Zhaozhao
Published: May 2024

With the continuous development of computer technology and the deepening of the popularization of Mandarin, the role of computer technology in language-assisted language learning and recognition has become more and more significant. In this study, the acoustic model and speech model based on the Hidden Markov Model are constructed for the error detection of reading aloud in Mandarin. Then the feature extraction of the acoustics of speech signals is carried out to build the error detection model of reading aloud in Mandarin based on pronunciation features. On this basis, a DNNHMM hybrid model is built by combining deep neural networks and Hidden Markov Models for detecting Mandarin read aloud keyword errors. Through the empirical analysis of Mandarin reading error detection, it can be seen that the average accuracy of the Mandarin reading error detection model constructed in this paper is 92.37%. Compared with the other models, the average accuracy improvement ranges from 4.69% to 8.19%, and the average accuracy of the vowel and consonant pronunciation features is 85.04% and 81.69%, respectively. In the detection of errors in Mandarin reading aloud, the F-score of misreading, back-reading, adding, changing, omitting, and other six error types is above 80%, and the accuracy rate is above 83%. It shows that the deep learning-based model for reading aloud in Mandarin performs well and provides an effective method for error detection in reading aloud in Mandarin.
Feature Extraction and Musical Personality Analysis of Piano Artistic Performances
Authors: Liu Shuai
Published: May 2024

Starting from the basic process of piano music recognition, this paper focuses on the feature extraction process of piano art performance. In the feature extraction of piano art performance, the fundamental frequency characteristics of piano music signals were analyzed by using short-time Fourier transform theory, focusing on the feature extraction of musical pitch, time value, and tonality, and the spectrum analysis of piano music signals and the reconstruction of piano timbre were completed. The piano performance work, “Autumn Moon on the Peaceful Lake,” is used as an example to analyze the musical characteristics and personality. The overall structure of “Autumn Moon on the Peaceful Lake” is a typical three-part structure: the key is the bD palace seven-tone mode, the key is single, and the marking range of performance strength is ppp-pp-p-mf-mf-f-ff. In the three versions played by Yin Chengzong, Lang Lang, and Chen Jie, the average tempo of the performance was 31.5 beats, 26.5 beats, and 26.8 beats, respectively. The performance process of different pianists is a “slow-fast-slow” layout, and other passages are added to the individual free processing to further enhance the charm of the work.
Cycle life and influencing factors of cathode materials for lithium-ion batteries--a case study of lithium-cobalt oxides
Authors: Shou Haofang , Cui Xiajing
Published: May 2024

Lithium-cobalt oxide has become a new generation of highly promising anode materials for lithium-ion batteries due to its low price, environmental friendliness, high platform voltage, and high theoretical capacity. In this paper, the working characteristics and related parameters of lithium-ion batteries are sorted out, and the influence factors of the decline mechanism of lithium-ion batteries are investigated from the perspective of chemical composition. Regarding the cycle life of lithium-ion battery cathode materials, this paper establishes a cycle life prediction model for lithium-ion batteries based on the LSTM model. It optimizes the hyperparameters of the model using the PSO algorithm. In addition, this paper also prepared lithium cobalt oxide cathode materials and carried out a validation analysis of the factors affecting their electrochemical performance. It is found that the cycle life prediction of lithium-ion battery based on LSTM has an RMSE of 3.27%, and the capacity of lithium cobalt oxide soft pack full battery decays from 249.81mAh to 137.04mAh at 26°C. The cycle life of its lithium cobalt oxide lithium-ion battery is around 250 cycles, and the average decay is 0.445mAh during one charge/discharge. Different charge/discharge cycles and diversity will have a significant effect on the cycle life of lithium-ion battery cathode materials, and it is necessary to pay attention to the parameter changes in the preparation of lithium cobalt oxide cathode materials in order to increase the cycle life of lithium-ion batteries.
Construction and Application of English teaching in vocational colleges Model under Output Orientation
Authors: Huang Yanhong
Published: May 2024

The basic idea of teaching reform and practice is the teaching mode. This research, which is based on POA, examines how higher vocational English is taught under the direction of a new teaching model and assesses the viability and application of using POA to teach English in vocational institutions. This paper establishes an English teaching in vocational colleges model under POA, which consists of three parts: “driving,” “facilitating,” and “evaluating.”Through the improvement of the BP neural network, this research develops the English teaching in vocational colleges quality assessment model under POA, thereby verifying the efficacy of the model. The changes in students’ English achievement are explored through teaching experiments and the two-sided sigmoid curve. A value of 0.007<0.05 in the paired-sample t-test of the experimental class proves that compared with the traditional English teaching in vocational colleges model, the English teaching in vocational colleges model under POA has a more obvious promotion effect on English achievement. The improved BP neural network model produced quantitative assessments of teaching quality that are mostly in line with actual findings. The model is used to evaluate the quality of English instruction in vocational colleges under POA.
Exploration and Reflection on the Development of Internationalized Industry-Education Integration of Vocational Education in the Context of Big Data
Authors: Kong Ming , Kuang Yan , Zhang Zhichao
Published: May 2024

The integration of industry and education serves as a potent mechanism to enhance the educational quality of higher vocational colleges and universities while simultaneously fostering the innovative vitality of enterprises. This study delineates a framework for internationalized industry-education integration in vocational education, grounded in the tripartite relationship among government, enterprises, and educational institutions. Building on this framework, the research initially constructs a performance evaluation index system for internationalized industry-education integration, which encompasses three dimensions: the input, process, and outcomes of industry-education integration. Subsequently, the weights of these indices are determined through hierarchical analysis, and the integration performance of four sample institutions is assessed using fuzzy analysis. The findings reveal that all sampled institutions surpass a ‘good’ performance level, with Institution X1 achieving the highest comprehensive evaluation score of 86.3593. Further, the study conducts an empirical analysis comparing the implementation effects of two models of industry-education integration: the ‘group-run school’ model and the ‘industrial park’ model. The study indicates that the ‘industrial park’ model is marginally superior by 0.13 points in its evaluation compared to the ‘group-run school’ model; however, this difference is statistically insignificant (P>0.05), suggesting that both models exhibit distinct characteristics yet achieve comparable outcomes in integrating industry and education. The research offers actionable insights and practical guidance to advance the development of internationalized industry-education integration within vocational education contexts.
Research on the application of multi-feature fusion in ceramic art design
Authors: Li Hua , Fang Xiao
Published: May 2024

In this paper, median filtering and linear stretching are used to preprocess the classified ceramic images, respectively, and the LoG operator is utilized to detect the edges of ceramic photos. Combining the Hough transform and TMOAA algorithm to measure the edge length, thickness, and diagonal dimension of ceramics to obtain the basic elements of ceramic art design. The FAST algorithm is used to make up for the shortcomings of the SIFT algorithm in real-time, to extract the features of the overall image of ceramics and the vessel image, and then combined with the thinking framework of ceramic art design to generate the best conceptual model of ceramic art design. Extract and detect the ceramic vessel image and carry out similarity rate calculation, quantify the enamel color characteristics to generate a color design scheme, calculate the color difference value, and evaluate the aesthetic value of ceramic art design works from the perspective of ceramic art design aesthetics. Porcelain enamel color difference ΔE = 2.0369, ΔE ≤ 5.00~6.00, in line with the color quality standards, indicating that the color design scheme generated based on the color semantic elements has a certain applicability. Evaluating the best conceptual model of ceramic art design by aesthetic standards, the works generated based on the seven ceramic patterns have different aesthetic values and different forms, which meet the various demands for ceramic artworks nowadays.
Research on the Application of Gray Prediction and BP Neural Network in English-Chinese Translation Quality Assessment
Authors: Wu Yitao
Published: May 2024

Gray prediction model and BP neural network are increasingly used in many fields as important prediction and analysis tools. The article first elaborates on the related algorithm of the gray prediction method. It constructs the gray prediction model, then constructs the BP neural network, and builds the gray BP neural network translation quality assessment model through the modeling of the simulation of the artificial neural network, and finally takes eight postgraduate students of translation majors of Beijing Foreign Studies University as the subjects, and the eight students translate two articles with similar word counts and difficulties, and score the translation quality of the subjects, and reasonably evaluate and scientifically predict these translations in order to validate the grey translation quality of subjects. The quality of translation is scored, and these translations are reasonably evaluated and scientifically predicted to verify the role and effect of the gray BP neural network in the quality of English-Chinese translation. The study concludes that the results based on the gray prediction model are that the prediction results are in the error range of 0%–7% compared with the original data, and the overall error is small. The prediction results of the compensatory fuzzy neural network prediction model for all 8 subjects before and after translation were more accurate than those predicted by the traditional fuzzy neural network method, with a good fit to the actual monitoring data and a small relative error. Only one subject out of the 8 spent more time on Chapter B than on Chapter A. Chapter B was more time-consuming than Chapter A. The time spent on Chapter A was more than the time spent on Chapter A. Chapter B was more time-consuming than Chapter A. Chapter B was more time-consuming than Chapter A. One subject spent equal time on both articles. The remaining 6 subjects did not spend as much time on chapter B as they did on chapter A.
Research on the Application of Data Mining Techniques in Early Warning Models for Financial Management
Authors: Su Wei
Published: May 2024

In the process of accelerating enterprise development in China, enterprises are prone to financial management crises while grasping development opportunities. This paper is oriented towards enterprise financial management data and proposes an adaptive neuro-fuzzy inference system to reason about risky data. The ANFIS model is integrated into K-mean clustering to provide early warning of enterprise risk by dividing different financial management risk indicators. The validation method uses both the comparison of similar algorithms and the practical test of A enterprise. Compared to other analysis models, the accuracy of the early warning model constructed by the K-Means algorithm is as high as 99.64%, which is suitable for enterprise financial management early warning. Using the model built in this paper to cluster the types of enterprise financial risk, the second cluster centers clusters of enterprises in financial difficulties, a number of indicators show negative results, and the net sales interest rate is as low as −94.23. This indicator is applied to the financial crisis of Company A’s financial situation analysis and found to be consistent with the characteristics of the second cluster center. This verifies the accuracy of the financial early warning model constructed in this paper.
Exploring the Path of Innovative College Dance Teaching Models with Motion Capture Technology
Authors: Sun Zhenyi , Cui Yunlong
Published: May 2024

The continuous development of artificial intelligence has brought new exploration paths and ideas for the teaching field, however, the current application and research of motion capture technology in college dance teaching mode is less. In this study, the human body and the appearance model are modeled in three dimensions: the simulated annealing particle swarm algorithm is used to optimize the human body modeling posture. Then, the feature vector matching algorithm is used to capture the human body’s movement posture, construct the college dance teaching mode, and carry out practical application. According to the study, the capture effect of the five movements in the dance teaching classroom video clip is between 0.865 and 0.945, and each movement is recognized with an accuracy of above 95%. After using this model, it was concluded by analyzing the effect of dance teaching that the number of excellent grades in the experimental group was 2 more than that in the control group, and the number of good grades was 2 more than that in the control group. This study provides a new development path for dance teaching in colleges and universities.
Optimization of Urban Science and Technology Innovation Performance Assessment Model - Based on VAR Methodology Perspective
Authors: Zhang Huimin
Published: May 2024

Conventional science and technology performance assessment methods and indicators can no longer meet the current needs of urban science and technology innovation performance assessment, and innovative research is urgently needed. In this study, the four elements of innovation input, innovation process, innovation output, and innovation guarantee are taken as the first-level indicators of the urban science and technology innovation performance assessment index system. The G1 entropy value correction method is used to assign weights to the urban science and technology innovation performance assessment index system to reduce subjective arbitrariness. Finally, the specific situation of innovation elements and innovation performance in S city was selected, and the influence relationship between innovation elements and innovation performance assessment was analyzed using a vector autoregressive model. The results show that the method of assessing the city’s science and technology innovation performance based on the VAR model is simple and easy to operate, the confidence coefficient of the assessment model is more than 0.7, and the mean value of the validity CVR is 0.703. The assessment results have a high degree of credibility and validity and effectively promote the region’s scientific and technological self-reliance and self-improvement.
Construction of Comprehensive Evaluation Model for Financial Risk Management in Colleges and Universities Based on Fuzzy Logic
Authors: Zhao Linglin
Published: May 2024

Among the many risks faced by colleges and universities, financial risk is one of the most important risks and has a large complexity. This paper evaluates the financial risk management level of colleges and universities using the fuzzy logic method based on the four dimensions of financial risk. The affiliation function is used to quantitatively describe fuzzy logic, and weighted summation is used to ensure the accuracy of fuzzy propositions. After selecting the evaluation indexes, the weights of the evaluation index system are determined, and a comprehensive evaluation system for financial risk management of colleges and universities is constructed. Taking Q colleges and universities in G city as the research object, the comprehensive evaluation coefficient of financial risk management of the subject colleges and universities is calculated, and the average value of the extensive risk coefficient of the subject colleges and universities in 2015–2017 is between 0 and 0.4, which belongs to the level of significant risk. The test university ranked second among 7 universities, but the comprehensive score index coefficient is 0.4898. However, there is still a large financial risk possibility. We need to formulate a corresponding risk management policy slightly.
A Study on the Integration and Practice of Integrated Transfer Learning Strategies in Japanese Language Teaching and Learning
Authors: Su Xiaoming
Published: May 2024

Addressing the prevalent issue of inefficiency in Japanese language learning, this study focuses on innovating classroom teaching methodologies for the Japanese language. Initially, the research employs a stochastic gradient descent algorithm to enhance the performance of a transfer learning algorithm, aiming to accurately identify student behaviors within the Japanese language classroom. Subsequently, these behaviors are quantified into integration weights using a predefined integration strategy. Behaviors demonstrating significant similarities are consolidated and analyzed to yield the results of the student behavior identification process. Furthermore, this behavior recognition approach is seamlessly incorporated into Japanese language pedagogy to develop a refined model for teaching, thereby potentially increasing the efficacy of language acquisition. Using this paper’s algorithm to identify students’ classroom behaviors and correlate them with their grades, it is found that when the number of positive behaviors in the classroom is higher, the student’s academic performance is also higher (r=0.9986, p=0.001<0.05). The average percentage of students’ attentive listening behaviors could reach 52.24% after the intervention, as determined by behavioral recognition results in the Japanese language teaching classroom. The behavior recognition algorithm in this paper provides an effective method for analyzing and intervening in students’ behavior in the classroom, and the teaching model constructed based on the algorithm offers a reference way to improve the effectiveness of Japanese language teaching.
Expansion of Innovative Diffusion and Communication Strategies of Chinese Opera Culture Based on ADDIE Modeling
Authors: Wang Youjing , Hou Yongguang
Published: May 2024

Based on the ADDIE model, this paper designs an innovative dissemination and communication strategy for Chinese opera culture and utilizes a questionnaire to explore the effects of opera culture dissemination and communication after the application of this strategy. The questionnaire is designed from the basic information of the audience, the cognition, attitude, and behavioral level of the opera culture communication, and the reliability and validity of the questionnaire are examined by using Cronbach’s Alpha reliability coefficient method and KMO test and Bartlett’s sphericity test, respectively. The survey results showed that only 16 people were exposed to opera culture for pure entertainment. The mean score of “I learned a lot of knowledge about opera” at the cognitive level is 3.48. In contrast, the mean scores of the audience’s attitude and behavior towards the dissemination of opera culture reach 3.79 and 4.03, respectively, which fully proves the positive application of this paper’s strategy. The innovative dissemination and communication strategy of opera culture constructed in this paper provides an effective path to improve the dissemination effect of opera culture. Also, it provides a reference basis for the inheritance and dissemination of other traditional Chinese cultures.
Virtual Reality-based Meta-Cosmic Perspective on Curriculum Integration Innovations in Art and Technology Programs
Authors: Wen Ling
Published: May 2024

At the end of the 20th century, the development of science and technology has made remarkable achievements, and some artists have begun to try to integrate science and technology into art creation. In this paper, based on DEM art space analysis, Unity technology is added to build a three-dimensional scene for teaching art and technology professional courses. At the same time, HoloLens2-based sensory interaction technology is introduced to enhance the integration of art and technology in the virtual classroom. Based on this basis, a teaching framework for VR art and technology fusion courses centered on students’ learning and interaction experience is designed and applied to actual teaching. In the sample correlation analysis, the experience of the relevant courses in the early stage had a significant impact on both the shadow students’ artistic self-perception (t=0.7854, p<0.01) and their learning behaviors in the later stage (t=0.6452, p<0.01). After the integration of art and technology teaching, the student’s interest in art learning and technology learning were both enhanced, with the mean values of 0.07398 and 0.0.20255, respectively, and the integration of art and technology professional course curriculum based on virtual reality technology effectively improved the students’ learning interest.
An Analysis of the Acceptance of SPOC Teaching Models in Foreign Language Courses in Colleges and Universities
Authors: Peng Fang , Bian Yuzhu
Published: May 2024

SPOC teaching effectively integrates online and traditional education, significantly enhancing the quality of basic education. In this study, we first conducted a 6-week teaching practice of SPOC mode, and then constructed an acceptance analysis model of SPOC teaching mode in foreign language courses in colleges and universities on the basis of UTAUT, defined the variables and measurement dimensions, and put forward four research hypotheses. A questionnaire is conducted for the students who use SPOC mode foreign language classroom in a university, and the relevant data obtained are subjected to exploratory factor analysis to construct the structural equation model that affects the acceptance. The research hypotheses were tested, and it was concluded that the p-values of the four paths were less than 0.010, with significant correlation, and all the paths showed positive and significant correlation, except for the significant negative correlation between effort expectation and intention to use. This study aims to conduct deeper research on the integration of the English SPOC teaching model to effectively and efficiently enhance students’ interest in learning foreign languages.
Innovative Development Path of Hands-on Ability Cultivation for College Students Combined with SPSS Statistical Analysis Modeling Based on Innovation and Entrepreneurship Education Orientation
Authors: Qi Xiaomeng , Chen Qi , Zhou Haijuan , Zhang Dongliang , Lu Liang , Huang Rong
Published: May 2024

In the context of China’s ongoing educational reform and the increasing diversity of employment options for college students, there is a growing emphasis on the practical skills of undergraduates. This study focuses on the development of these competencies, selecting a sample of 816 students from five universities to explore the requirements currently placed on Chinese college students. Utilizing factor analysis and ANOVA, we constructed a scale to assess the hands-on abilities of college students, as well as a scale for the influencing factors. The study was conducted using SPSS multiple linear regression to examine the correlations between the identified factors. The Hands-on Ability Scale consists of four primary indicators and 15 secondary indicators. Additionally, the analysis revealed that the influencing factors account for varying degrees of impact on key competencies: 23.4% on self-management ability, 13.3% on teamwork ability, 39.8% on innovation ability, and 25.5% on problem-solving ability. This study underscores the critical need for integrating practical skills training in the curriculum to better prepare students for the challenges of modern employment landscapes.
Parameter optimization of 3D printing process for ceramic sculptures
Authors: Shao Changzong
Published: May 2024

In this paper, for the problems of ceramic sculpture 3D printing process, such as the accuracy is difficult to predict and the process control parameters are difficult to choose, we propose to establish an accuracy prediction model using BP neural network optimized by particle swarm algorithm, and put forward the optimization algorithm of the printing process parameters based on genetic algorithm, and construct the fitness function by using the method of the weight coefficients, to achieve the search for the molding angle under the optimal surface quality and to improve the printing efficiency. In the simulation experiments of printing accuracy prediction, the average errors of dimensional error and surface roughness prediction of the PSO-BP network model proposed in this paper are 5.59% and 5.02%, respectively, and the prediction is accurate and reliable. In the 3D printing parameter optimization experiments, the average deviation of the Y and Z dimensions of the physical size printed with the optimized parameters is 0.0496mm and 0.058mm, respectively, which is smaller than the deviation before optimization. Parameter optimization has also reduced surface roughness in the molded parts of the porcelain sculpture. The extrusion head and hot bed temperature were adjusted to reach the temperature target after maintaining hollow finger precision within 3 degrees.
Emotional Characterization Mining in Music Performance and Its Guiding Role
Authors: Yan Jie , Hu Mingzhao
Published: May 2024

Emotional attributes are crucial in music performance, serving a pivotal role in guiding interpretation and execution. This study employs a Wavenet layer within a Waveform-Deep Neural Network (WLDNN) to extract emotional features from musical performances. An activation function is then applied to process and refine these features. Additionally, a Generative Adversarial Network (GAN) is utilized to identify and remove irrelevant features, thereby enhancing the quality of the emotional attributes extracted. The evaluation of these emotional features employs both music emotion language values and an emotion vector model. Our analysis demonstrates that this methodology achieves a 90% accuracy rate in identifying and extracting emotional features from music performances. Based on these findings, a stage lighting control system was developed, tailored to respond to the emotional cues within the music. The system was tested across ten different performances with a sampling frequency of 5 seconds, achieving an average synchronization rate of 94.01% with the emotional content of the music. This approach not only proves effective for stage lighting design but also offers valuable insights for enhancing the emotional expressiveness of musical performances.
Citespace-based Research on the Current Situation and Development Trend of the Art of “Sky Jumping” of the Zhuang on the Chinese-Vietnamese Border
Authors: Yang Feixue , Chen Wenying
Published: May 2024

China and Vietnam share both land and maritime borders, rendering the exchange and cooperation in ethnic arts along these frontiers crucial for their cultural evolution. This significance underscores the importance of organizing and analyzing the artistic expressions of the Zhuang people, particularly the “Tien Jumping” art form prevalent along the Sino-Vietnamese border. This study introduces an innovative method for text feature expansion utilizing Citespace, integrated with a deep learning-based text classification model, to establish a systematic framework for text classification within this cultural context. Furthermore, acknowledging the multidisciplinary nature of “Tien Jumping” art research, this paper develops a Citespace-based cross-collection system. This system harnesses a thematic network that amalgamates various disciplines to aggregate pertinent data efficiently. Empirical analysis validates the model’s robustness, evidencing an accuracy of 0.95, a recall of 0.87, and an F1 score of 0.91. The investigation into the current state and future trends of “Tien Jumping” research reveals a significant increase in publication volume, from 500 articles in 2013 to 1,108 in 2022— an increment of 608 articles. This growth highlights the strengthening focus on inheritance development, cultural preservation, and the artistry of the Zhuang people, with scores surpassing 7.5 in these emergent research areas. These findings affirm the effectiveness of the proposed methodologies in this study. The approach not only provides a rigorous analysis but also forecasts the trajectory of “Tien Jumping” art research along the Sino-Vietnamese border, offering a scientific foundation for future scholarly inquiries.
A Study of the Utility of Artificial Translation in English Reading Comprehension
Authors: Wang Shiyun
Published: May 2024

Human translation always has an incomparable role in the process of English learning, especially in the promotion of English reading comprehension. This paper centers on the research problem of selecting suitable research objects, combining questionnaire surveys and test methods to obtain relevant data on artificial translation and English reading comprehension. Then, it analyzes the correlation between the dimensions of artificial translation and the effect on English reading comprehension, constructs the multiple regression model of artificial intelligence on English reading comprehension, and proposes the corresponding significance test method. The results show that the correlation coefficients between the five dimensions of human translation and English reading comprehension scores are, in descending order: accuracy (0.824) > completeness (0.636) > fitness (0.579) > fluency (0.485) > consistency (0.372), which are all significantly positively correlated with English reading scores. The regression results showed that the level of manual translation dimension explained 26.7% of the variation in reading achievement. Among the human translation dimensions, accuracy, completeness, and consistency have significant positive effects on English reading comprehension. The study suggests that human translation accuracy, completeness, and consistency can be optimized to improve English reading comprehension.
The Construction of Online-Offline Blended Teaching Mode of Professional English Based on Java Web
Authors: Xu Mengyi
Published: May 2024

The “online + offline” blended teaching mode capitalizes on the Internet environment to seamlessly integrate traditional classroom instruction with online learning, representing a progressive trend in educational development. This study, grounded in Java Web technology, proposes a hybrid model for teaching professional English structured around three core components: preparation, instruction, and assessment. The model’s efficacy was assessed by devising a set of factors and rubrics to evaluate teaching quality, utilizing a fuzzy logic approach. Empirical validation was conducted using English majors from S colleges as participants. The overall teaching quality score was 87 points, meeting the established rubric standards, thus confirming the model’s applicability to professional English instruction. A controlled experimental analysis of students’ scores—pre-test, mid-test, and post-test—revealed a consistent improvement, with mid-test scores surpassing pre-test and post-test scores exceeding mid-test scores. This progression underscores the effectiveness of the Java Web-based online blended teaching model in enhancing students’ proficiency in English.
Research on Knowledge Graph Multiple Environment-Aware Recommendation Algorithm for Teaching Course Recommendation
Authors: Zhu Lina
Published: May 2024

With the rapid development of education informatization and intelligence, personalized teaching and course recommendations have gradually become an important research direction in the field of education technology. A knowledge graph, as a structured knowledge representation and organization, provides rich semantic information and a relationship network for teaching course recommendations. Based on this research background, this paper focuses on the research of knowledge graph multivariate environment-aware recommendation algorithms for teaching courses. The article firstly constructs the ontology of course resources, researches and analyzes the methods and steps of acquiring and processing data in order to build the knowledge graph, and then constructs the framework of the recommendation model combining multivariate perception in order to further analyze and explore the user’s course-aware representation learning and the user’s course-aware embedded learning. Embedded Learning, and finally, the teaching course of a school as an empirical study, it is shown that the performance of Ripple_mlp+ and Ripple_mlp in this article are higher than the benchmark algorithms through the experimental class and the control class of the pre and post-test scores have been sorted out and counted. The experimental students’ scores are steadily increasing, and the overall scores are better than the control class. In the experimental class, the significance value is 0.013, indicating that there is a significant difference between the midterm and final grades of the experimental class. Through the paired samples t-test on the midterm and final grades of the experimental class, after analyzing and comparing the midterm and final grades of students at different levels, it can be concluded that the t-value of the superior students is −3.326. The value of significance is 0.049, indicating that there is a significant difference between the midterm and final grades of the exceptional students. The T-value for intermediate students is −0.134, and the value of significance is 0.045, which indicates that there is a significant difference in the midterm final grades of intermediate students. The T-value of −1.623 and the value of significance is 0.534 for advanced students indicates that the difference in midterm final grades of advanced students is not significant.
The Practice of Intelligent Auxiliary Rib Equipment in the Flipped Classroom Teaching Mode of College Physical Education
Authors: Peng Xinxin , Wang Xue
Published: May 2024

This paper innovatively applies smart wearable devices to college sports flipped classrooms, using the photoelectric volumetric method to measure blood oxygen pulse and monitor heart rate during exercise, and realizing the calculation and measurement of exercise status by detecting the discrimination between peak exercise and normal exercise threshold. The sports-flipped classroom will improve the quality of physical education by using intelligent sports equipment to enable students to learn independently and teachers to guide them at the right time. The results show that there is a significant difference in the cognition of college students’ sports behavior in the duration of using smart wearable devices (P=0.044), and the frequency and time of using smart wearable devices will have a very significant impact on sports intensity (P<0.01). There were significant differences (P < 0.01) in height, 50-meter run, seated forward bend, and 800/1000 meters, and no significant differences in weight (P = 0.824), blood oxygen level, heart rate, and gait after smart device sports flipped classroom instruction. There were significant differences (P<0.05) in “novelty, negativity, attention, exploratory, enjoyment, and positivity” in the experimental group after the flipped classroom teaching of sports with intelligent devices, and the negativity showed a significant decreasing trend.
Collaborative Optimization Decision Support System for Biomass and Smart Energy Based on Virtual Power Plant
Authors: Wang Weirong , Chen Yangbo , Wang Zhongpeng , Li Jiawei
Published: May 2024

Energy is an important basic resource for the development of human society. This paper develops a decision support system for collaborative optimization of biomass energy and intelligence using biomass energy and smart energy management technology. Explain the various methods by which biomass can be transformed into usable energy for power plants. A smart energy management model is constructed using Internet of Things technology, and its performance prediction model is predicted using the gray prediction model. To study the optimal allocation of energy in the system constructed in this paper, the objective function and constraints are built using carbon capture technology, and the optimal allocation conditions of energy are calculated. After experimental analysis, it was found that in 2020, biomass energy power generation reached 29 million kilowatts, which was 020.1 million kilowatts higher than in 2015. The co-optimization strategy of biomass energy and smart energy constructed in this paper supports the energy utilization efficiency of the system to reach more than 60%. Compared to the traditional energy division system, the constructed system saves an annual cost of 52.71%. The payback period was shortened to 3.31 years, resulting in a net present value increase of 2.8991 million yuan. The energy synergy system’s power supply produces the least amount of carbon dioxide from an environmental perspective and has been stabilized at 50 kg.
Analysis of green art concept and interior design space utilization based on perception and behavioral association
Authors: Wu Fan
Published: May 2024

This paper summarizes 13 indoor space design indicators that affect perception and behavior from three aspects: physical elements, other elements, and spatial attributes. Then, through the K-means algorithm and FP-growth correlation rule algorithm, we obtain the user’s behavioral habit data and carry out the simple correlation analysis and curve estimation regression analysis of the user’s perception and behavior based on the indexes in turn. On this basis, it is proposed that the indoor space stay time factor of green art users and spatial perception have 10 highly significant correlation indexes with 0.01 boundaries (two-tailed). Under the quadratic function model of the time factor R2=0.3425, the width in the spatial scale and the significance of the time factor have a P-value of 0.0065. The peak value of indoor natural light intake of the neighborhoods adopting the green design is 2,667.3% higher than that of traditional neighborhoods on cloudy days. The cost is 2667.3951 more, but the green interior design that is based on perception and behavioral association is highly effective.