bluuebunny/crossref_metadata_2025 huggingface dataset:
Exploring the Subjectivity of English Academic Discourse in the Context of Big Data
Authors: Pan Ying
Published: February 2024

This study develops a sentiment analysis model for English academic discourse based on word information to effectively understand and analyze the sentiment tendencies in English literary texts. The structure of the model includes word embedding layer, character-level feature extraction, word-level feature extraction and feature fusion and classification layer. The word embedding layer realizes the mapping between word vectors and word vectors by microblogging pre-trained word vectors. The character-level feature extraction session uses a multi-window convolutional layer to capture N-Gram information. In contrast, the word-level feature extraction obtains deeper semantic information through a Bi-LSTM layer and fuses it with character-level information to enhance robustness. The feature fusion and classification layer further combines these features and determines the fusion weights through a linear layer to achieve sentiment classification. In performance tests, the model achieves 92.5% sentiment classification accuracy on the standard dataset, an improvement of about 6% compared to traditional methods. In particular, the accuracy is improved by 5% when dealing with text with sentiment polarity transition, showing good adaptability. In addition, using 657 positive and 679 negative sentiment words as seed words effectively expands the sentiment lexicon and enhances the comprehensiveness and accuracy of sentiment analysis.
The Visual Psychological Space of Chinese Traditional Landscape Painting Creators in the New Media Background
Authors: Shang Jin
Published: February 2024

Based on the connection between visual psychology and landscape painting, the article investigates the spatiality characteristics of images and the visual balance factors affecting the creation of landscape paintings. By applying a vector median filter, this study successfully removes noise from color landscape painting images and uses vectors to represent these noise-reduced images. Lab color model was constructed and combined with cluster analysis technique to calculate the color difference between image pixel points. Binarization of the image is achieved by preprocessing the segmented image. The study estimated and analyzed landscape paintings’ aesthetic characteristics and quantitative values. The results show that among the 30 selected landscape paintings, the percentage of white space area is more than 0.4 is 36.67%, while the rate of less than 0.4 is 63.33%. Among the landscape painting creators who participated in the study, the mean of the total scores of the experienced ones on the four visual psycho-spatial abilities was 83.39, which was 14.217 points higher than that of the inexperienced group. This indicates that the method proposed in this paper is very effective in assessing the visual psychospatial abilities of landscape painting creators, and that the creators can improve their creative skills after long-term training.
An analysis of the value and linguistic features of contemporary literary texts based on knowledge graph
Authors: Zhai Xingyu
Published: February 2024

In this paper, we crawled and analyzed the dataset of contemporary literature works through Python technology, and after preprocessing the data with the use of the word splitting algorithm, we applied the CasRel entity-relationship extraction model based on the Transformer and the BERT model to efficiently extract the entity knowledge in contemporary literature works. Then, a knowledge map of modern literature was established by combining the Neo4j graph database. Several novels by Mo Yan, Jia Pingwa and Chen Yan were selected as cases to analyze the textual values and linguistic features of contemporary literary works. The Analysis reveals significant differences in the linguistic features and connotative values of works by different authors. For example, the average frequency of periods per thousand words in Mo Yan’s works is 0.2187, while the corresponding frequency in Jia Pingwa’s works is 36.06% lower than that of Mo Yan. The cumulative application frequency of monosyllabic and disyllabic words in Chen Yan’s works exceeds 80%, and the lexical density ranges between 67.5% and 71.5%. By clarifying the linguistic features of different contemporary literary works, it can help academic creators better find the resonance point with readers and provide diversified innovative paths for literary creation.
The Construction of Informatization System of Internet Technology Mathematics Teacher Education Course
Authors: Li Yingchao
Published: February 2024

This paper focuses on constructing a regional digital educational resource sharing platform. It applies optimized ant colony clustering technology to support the platform’s personalized service to achieve accurate distribution of educational resources. The research subjects were undergraduate and graduate students majoring in mathematics in teacher training, and by comparing their teaching abilities before and after using and not using the platform, it was found that the average scores of students using the platform were significantly higher than those of students who did not use the platform on each evaluation factor. Undergraduate students’ information literacy competence improved with the academic year, with first-year students’ scores between 20 and 50, while senior students’ scores between 70 and 100. At the graduate level, the difference between first-year graduate students and junior undergraduates was significant, but the difference with senior undergraduates was slight (p-values of 0.042 and 0.053, respectively). The digital education resource sharing platform effectively improved the teaching ability of teacher students. However, most of the students lacked experience in informatization practice activities.
A Classification Approach for University English Teaching Resources Based on Multi-Feature Fusion
Authors: Liu Yiming
Published: February 2024

This study focuses on the problem of efficient classification of university English teaching resources, aiming to improve the ease of browsing and utilization of resources. To address the challenges of existing resource classification, the study proposes a label generation classification algorithm with multi-feature fusion. The algorithm incorporates TF-IDF and location information weights based on TextRank to generate labels containing corpus and location information. The performance of the algorithm is tested and simulation experiments are conducted. The study results show that the mean value of the label generation classification algorithm after multi-feature fusion reaches 0.234, 0.6219, and 0.3632 in terms of accuracy, recall, and F-value, respectively. In the simulation experiments, the algorithm achieves a classification speed of 90.33% faster than that of the traditional single-feature classification algorithm, and the classification error rate is no more than 10%. The label generation classification algorithm has a significant advantage in overall performance and effectively solves the problem of classifying university English teaching resources. This finding is of great significance for improving the utilization of teaching resources and optimizing the management of teaching resources.
Research on the Current Situation and Countermeasures of Improving Talent Cultivation Quality by Managing Skill Competitions in Higher Vocational Colleges in the Internet Era
Authors: Lun Zhiguo
Published: February 2024

Skills competition is an essential link in the cultivation of applied talents in higher vocational colleges and universities, and plays a crucial role in improving the quality of practical teaching. This paper focuses on the mode and process of talent cultivation through skill competitions in higher vocational colleges and universities. It conducts an in-depth investigation on the cultivation quality. A comprehensive evaluation model of talent cultivation quality of skill competition is constructed using the particle swarm algorithm to optimize the K-means algorithm and combining with the principal component analysis method. The model was applied to a sample of students from university X. By collecting and analyzing the data, the quantitative assessment of the students’ abilities in each dimension and the hierarchical categorization of the comprehensive skills were carried out. The study results showed that students who participated in the skills competition performed well in their professional skills, averaging 0.312. However, there is still room for improvement in collaboration ability. Students of agricultural engineering, logistics management and finance are outstanding in management practice power and professional competitiveness, with a comprehensive score of more than 0.45. While economics, law, geology and accounting students have low complete scores, which need to be further strengthened for cultivation. To improve the quality of talent cultivation, higher vocational colleges and universities should build a training mode of “integration of competition and education”, deepen the cooperation between schools and enterprises, and improve the guarantee system of skill competitions, to comprehensively promote the enhancement of students’ abilities.
Algorithm Design and Application of Grammatical Structure and Function Learning in Chinese Writing for Intelligent Analysis
Authors: Ma Jingtao , Chen Liang
Published: February 2024

This study utilizes the Larsen-Freeman three-dimensional theory of grammar teaching to quantitatively analyze the grammatical attributes “form”, “meaning” and “usage” in the Chinese writing process. The study uses the PCFG model to extract the syntactic dependency tree and represents the features of the tree structure with the help of TF-IDF. Meanwhile, the Transformer model extracts the semantic and syntactic features of sentences. At the same time, the positional encoder is removed to ensure that the model obtains information from the syntactic level. Further, an unsupervised method for generating grammatical textual recapitulation for Chinese Writing is proposed, as well as a Grammar Tree Generator for generating recapitulated sentences, which efficiently extracts the features of the input grammatical sequences. In addition, the study also includes writing feature analysis based on grammatical function matching, using a hierarchical clustering algorithm to analyze the similarity of 60 grammatical functions. Finally, validation was performed on 30 Chinese writing text collections, each containing 10 compositions, and the results showed high accuracy of unlabeled grammatical function recognition. The LDA model determined the optimal number of writing topics to be 150. This study highlights the potential application of intelligent analysis techniques in improving the quality of Chinese Writing. It provides new perspectives for an in-depth understanding of the interplay between grammatical structures and functions in the Chinese writing.
Effective Application of Multiple Intelligences Theory in Music Education
Authors: Ni Meiying , Yang Fangling , Liu Mei
Published: February 2024

This article discusses an innovative teaching model that integrates the theory of multiple intelligences into music education, including the design of teaching strategies and evaluation frameworks. Using fuzzy theory and credibility mechanism, the article proposes a fuzzy comprehensive evaluation model for assessing the quality of music teaching. The model realizes thorough evaluation by determining the evaluation set and the single-factor judging method. The study takes two classes of music majors as an example to conduct the factor analysis of the Students’ Music Performance Rating Scale and finds four main factors with a cumulative contribution rate of 33.36%. The results showed that the experimental group applying the Multiple Intelligences teaching strategy was significantly higher than the control group in music performance. The post-test after one year teaching experiment showed that the students in the experimental class showed highly significant improvement in bodily-kinesthetic intelligence, musical intelligence, and self-cognitive intelligence (P<0.01), as well as linguistic, spatial, and interpersonal intelligences (0.01<P<0.05). In contrast, logical-mathematical intelligences did not show any significant improvement (P>0.05). The study confirms the practical application of multiple intelligences theory in music education, shows the considerable effect of numerous intelligences teaching strategies in enhancing students’ intelligence, and provides new methods and perspectives for music teaching.
Research on Early Warning Model of Wushu Event Broadcasting Right Operation Risk Based on Big Data XGBoost Algorithm
Authors: Li Xing , Ma Ying , Cui Zhiying , Cui Yongxia
Published: February 2024

Under the background of the development of new media technology, the attention of wushu events in the society is gradually increasing, which makes the competition in the event broadcasting market more and more intense. This paper focuses on the problem of predicting the operational risk of wushu event broadcasting rights, based on the GBRT algorithm, innovatively improves the traditional loss function, introduces the regular term, and proposes the application of XGBoost algorithm in the operational risk prediction of wushu event broadcasting rights. The improved algorithm divides the operational risk of broadcasting rights into two main levels, covering three primary and 10 secondary indicators. In this study, the XGBoost algorithm is applied in the early warning of informing proper operation risk, which is classified into two main levels, covering 3 primary and 10 secondary indicators. The article also conducts an in-depth experimental analysis of the risk of overpremium of the event rights and the risk of matching the audience’s demand. In addition, according to the results of audience analysis, men have become the primary audience of wushu events, with a frequency of up to 401 times. Based on the XGBoost algorithm, the wushu event broadcasting right operation risk warning system can effectively predict and help the event broadcasting platform to avoid the potential operation risk, which provides valuable data support for the market decision-making.
Exploring the Problems and Countermeasures in the Practice of Elderly Wisdom Sports Services in Anhui Province Based on Principal Component Analysis Methods
Authors: Wang Jun , Zheng Zhongwen
Published: February 2024

With the continuous progress and broad application of cutting-edge information technology such as big data, cloud computing, virtual reality and artificial intelligence, senior sports services has ushered in unprecedented development opportunities, especially the rise of senior intelligent sports services. In this study, we applied the principal component analysis method to conduct in-depth screening of evaluation indexes to assess the quality of the practice of senior intelligent sports services in Anhui Province. Through the comprehensive analysis of the eigenroots and the contribution ratio of variance, this paper successfully identified the key factors and realized the effective dimensionality reduction of the data. Meanwhile, we adopted the logistic equation to accurately estimate the relevant parameters. Through the comprehensive analysis of eigenroot and variance contribution ratio, this paper successfully identifies the key factors, realizes the effective dimensionality reduction of the data, and adopts the logistic equation to accurately estimate the relevant parameters. The analysis results show that in the senior intelligent sports service index system, the highest weight value of platform is the operation and management platform, which reaches 0.4754. In the aspect of the demand for sports information of the seniors, the demand for online sports knowledge and skills is the highest, and the demand for online sports knowledge and skills is the highest. Regarding the demand for sports information of the elderly, the online acquisition of sports knowledge and skills has the highest demand of 203, followed by the demand for regular online sports knowledge lectures of 181. In the demand for outdoor bright trails, the elderly strongly want to enter their fitness and energy consumption data for evaluation, with a high degree of demand of 351. In addition, the demand for heart rate testing of the intelligent body testing station is the highest, with a degree of 186, followed by body fat testing, with a degree of 179. The Research in this paper not only focuses on the current situation and problems of intelligent sports services for the elderly, but also explores the methods and strategies to solve these problems, aiming to provide practical reference and guidance for the effective implementation of intelligent sports services for the elderly.
Research on CNC programming and machining process based on CAD/CAM technology
Authors: Zhang Shuling , Bai Jie
Published: February 2024

Focusing on CNC process data, this paper provides an in-depth analysis, characterization and mining of macro machining processes by associating CAD and CAM models. The article introduces a Bayesian inference method to construct an association relationship between the manufacturing features of the query part and the macroscopic process of the manufactured part, which avoids the need for a direct similarity comparison between the geometry of the query part and the part to be manufactured. In addition, the study calculated the overall similarity between the query part and the CNC programming process data instances through the guidance of the process skeleton, thus realizing the precise evaluation from the CNC programming perspective. The algorithm for generating tool trajectories in multi-axis CNC programming is analyzed. By selecting representative surfaces and performing tool processing calculations, this study explores the process planning and tool parameter selection of usual parts of surfaces in multi-axis CNC programming machining. It optimizes the nonlinear error in tool trajectory processing by combining CAD/CAM technology. The results show that the optimized process proposed in this paper significantly improves the machining efficiency by 36.94%. In the cutting at tool corners, the maximum cutting force generated by the optimized machining process is only 190.3N, which is only 67.2% of the leading cutting force of the Cimatron process. In addition, when the engagement frequency is 780 Hz, the optimized process proposed in this paper has the smallest vibration amplitude, which is only 21.4% of the Cimatron strategy. Therefore, this study significantly improves the machining efficiency while ensuring the machining quality, which has important practical application value.
Optimization of constraint rules for power grid topological relationships based on knowledge graph techniques
Authors: Wang Yu , Wang Wei , Wei Jie , Liao Wentao , Mo Liangyuan , Yang Jing
Published: February 2024

In this study, the expressive ability and accuracy of the constraint rules for grid topological relationships are effectively improved by introducing the knowledge graph technology. The TransE model is used for training, focusing on the strategies to eliminate single-node voltage overruns in single-line and multi-line open scenarios, and the characteristic relationship between co-matching coefficients and structural robustness is deeply analyzed, aiming to improve the grid’’s interference resistance by optimizing the structural robustness. The results show that the single-line high-quality scheme has six effective solutions, including lines 28-29, 26-29, 26-28, 21-22, and 2-3. In the multi-line high quality scenario, there are 11 effective solutions, such as lines 28-29, 21-22, etc. Further analysis shows that the robustness of the grid structure is better and the spreading of the outage area is relatively slow in the event of a fault when the co-matching coefficients r are −0.22374 and −0.14575, respectively. In particular, when r = −0.22374, the grid’’s robustness remains above 0.3 even when the number of nodes exceeds 30. The comprehensive optimization framework proposed in this study is expected to provide more reliable and efficient topological relationship constraint rules in power systems, which will provide strong support for the operation and management of power grids, and significantly improve the stability and security of power systems.
Optimization of energy dispatch for commercial and industrial users based on demand management strategies
Authors: Xie Yuzhe , Wu Xiuling
Published: February 2024

With the acceleration of power marketization, there is a growing demand for optimization of power demand management and energy dispatch by industrial and commercial users, especially in achieving energy consumption efficiency and cost reduction. We adopt a two-layer optimization model combined with a time-sharing tariff system to efficiently allocate electricity resources by constructing an electricity demand response model for industrial and commercial users. The study results show that after implementing time-of-day tariffs, the cost of electricity for industrial and commercial users has been reduced by 15% on average. At the same time, the grid company’s revenue from electricity sales has been enhanced by 10%. In addition, a customer satisfaction survey found that customer satisfaction with the optimized power dispatch scheme increased by 20%. The study’s conclusion confirms that through scientific demand management and scheduling strategies, we can effectively improve the utilization efficiency of power resources and reduce costs, while ensuring customer satisfaction and achieving a win-win situation for supply and demand.
Research on Production Scheduling of Industrial Big Data for Internet of Things Based on Dynamic Planning Algorithm
Authors: Zhang Nan
Published: February 2024

In this paper, the optimized Dual Heuristic Dynamic Programming (DHP) algorithm is studied in depth, compared and analyzed with the other three algorithms through experimental simulation, and the DHP algorithm is applied to different industrial accurate scheduling tests to explore the loads of the industrial equipment during scheduling operation. The simulation results show that the dual-heuristic dynamic programming DHP algorithm has a significant decrease in both the mean waiting time (MWT) and the mean response time (MRT), where the highest peak value of the mean waiting time is only 0.06901 μs, and the highest peak value of the mean response time is 0.24493 μs. In industrial accurate production scheduling, the improved DHP algorithm performs the best for textile industry’’s task scheduling with the best performance. Specifically, the task scheduling completion time for the textile industry is 4522 seconds when the number of automated guided vehicles (AGVs) is the minimum value of 3. In comparison, the completion time is 8541 seconds when the number of AGVs is the maximum value of 15. The textile industry shows the fastest task completion time compared to the other industry types for different settings of the number of AGVs. In addition, the analysis of the operation of the equipment engines shows that although there are high and low torques during operation, the Torque of most of the engines stays within the range of 29 Nm to 30.7 Nm, which is generally stable and has little impact on the performance of the equipment. The improved dynamic programming algorithm proposed in this paper has effective application potential in industrial extensive data production scheduling, which can improve the efficiency and responsiveness of production scheduling. It has important practical significance for industrial production management.
Study on the Application of Improved Deep Convolutional Neural Network Algorithm in Broken Information Recovery
Authors: Zhou Sheng
Published: February 2024

This study on the fusion of deep convolutional neural network (CNN) and extended short-term memory network (LSTM) aims to improve the efficiency and accuracy of broken information recovery. The challenges faced by traditional information recovery techniques are addressed through improved algorithms. The research methodology includes constructing CNN models to automatically extract features and combining LSTM networks to process complex time-series data. We conducted a detailed experimental evaluation of the CNN-LSTM fusion algorithm, including recovery of different types of corrupted data, and compared it with other algorithms. The results show that the CNN-LSTM fusion algorithm has the highest structural similarity (0.9545) and the most minor normalized mean square error (0.0016) for recovering broken video information, outperforming the methods using CNN or LSTM alone. The fusion algorithm dramatically reduces computation time and resource consumption for processing complex datasets. The combination of CNN and LSTM significantly improves the performance of broken information recovery, especially in processing video and audio data, and provides new ideas for future information processing techniques.
Exploration of grid energy efficiency influencing factors by applying principal component analysis approach
Authors: Li Jianbin , Sun Yongqiang , Zhang Li , Pan Yang
Published: February 2024

This paper focuses on the problem of power losses in the transmission, transformation, distribution and marketing segments of the power system, and explores how to effectively improve the energy efficiency of the power grid to optimize the cost management of the power grid. The article uses principal component analysis to accurately diagnose the energy efficiency status of the grid, and analyzes the multiple factors affecting grid losses and their degree of influence. It is found that the total loss in 10km transmission line when wind power access reaches 328kw, and the complete loss of wind power access is higher than photovoltaic access under the condition of different line lengths, which are 315kw, 321kw, and 328kw, respectively. In the use of iron transmission line, the average loss at the current of 400A is 89.600W, which is higher than using aluminum alloy material by 66.200 W. If energy-efficient aluminum alloy fittings are fully utilized in transmission lines, the average annual power saving can be up to 301,368 kWh. The study shows that it is crucial to analyze the factors affecting the energy efficiency of power grids during the planning and construction stage of the power grids.
Digital Transformation Strategy for Financial Management of Entity Enterprises in the Information Age
Authors: Huang Meng , Gao Sen
Published: February 2024

This study is dedicated to exploring the effectiveness of digital transformation of financial management of real enterprises in the information age. The annual reports of enterprises are collected from 2011 to 2022 by web crawler technology, and text mining algorithms, such as TF-IDF and LDA theme model, are used to establish a measurement index system of digital transformation of financial management, which is calculated by entropy weight-TOPSIS method. The results show that keywords related to digital transformation, such as “informatization” and “intelligence”, appear frequently, and the importance of the theme changes significantly in different transformation stages. During the rapid transformation period, the frequency of words such as “industrial internet” and “smart grid” exceeds 400 times. Some companies have seen significant improvements in debt servicing, operations and profitability. Digital transformation of financial management is efficacious in improving operational efficiency and profitability. Still, it is a dynamic process that requires enterprises to choose appropriate strategies based on their characteristics. The study emphasizes that enterprises should pay close attention to integrating and applying emerging digital technologies and optimize their internal governance structure to promote sustainable performance improvement.
Research on high-precision digital image correlation measurement techniques for highly stable structures
Authors: Yang Fenglong , Pan Bing
Published: February 2024

This study proposes a novel digital image processing system that combines a diffraction-limited resolution (DLRF)-based measurement technique with a windowed form-center tracking algorithm. To evaluate the accuracy of this system, this paper compares and analyzes the effectiveness of conventional digital image techniques and DLRF-based methods for deformation displacement measurements. In addition, the study includes thermal stability tests under ambient noise and uniform high temperature conditions to evaluate the stability performance of the system in a complex environment. The experimental results show that the DLRF-based digital image correlation method proposed in this study performs well in reducing the mean deviation (from a maximum of 5.17 × 10-3 to 1.73 × 10-3) and root-mean-square error (from a maximum of 5.14 × 10-3 to 0.75 × 10-3). It is worth noting that the DLRF method is faster in processing when using the single-precision format than the double-precision format, with a speedup of up to 1.05 times. In addition, the multiple displacement averaging processing method can effectively filter the noise in the test, and the noise effect is only in the range of 0 to 2 μm in most areas. In the analysis of test points 10-34 and 57-80, the displacement error is controlled within 5 μm, indicating that the modified structural analysis model can be used for on-orbit micrometer-scale thermal deformation analysis. The study proves the high accuracy and stability of the digital image system proposed in this paper in the measurement of deformation displacement, which provides adequate technical support for accurate measurement in related fields.
Matrix model-based Analysis of e-commerce development and urban entrepreneurial activity
Authors: Zhu Baohui
Published: February 2024

China’s e-commerce market size, the most active and best-developed component of the digital economy, has become an essential driver of economic growth and urban entrepreneurial activity. This paper analyzes e-commerce development and urban entrepreneurial activity through BCG matrix. It establishes the framework of AHP-SWOT hierarchical analysis model combining with SWOT matrix model and AHP hierarchical analysis. In the development of e-commerce, the national e-commerce turnover in 2020 is 37.21 trillion yuan, with a year-on-year growth rate of 57.60% in 2014. Meanwhile, in 2020, the number of e-commerce employees reaches 60 million, which is 1.23 times of the number in 2014. Due to the rapid development of the economy and e-commerce, resulting in urban entrepreneurial activity is also gradually rising, the number of private enterprises in 2012 grew from 5254870 to 18921928 in 2020, an increase of up to 260%. 2020, the number of new market entities is 27,495,700, the country’s entrepreneurial activity is a year-on-year upward trend. Through the development of e-commerce and urban entrepreneurial activity, the quality of employment and entrepreneurship in society is promoted to a higher level.
Research on the Construction of a Diversified System of Preschool Physical Education Curriculum Based on Kruskal Algorithm
Authors: Zhang Yao
Published: February 2024

This study constructs a diversified system of physical education curriculum suitable for young children and explores the application of personalized service method in early childhood physical education curriculum. The study designed the structure of early childhood physical education curriculum based on the principles of science, relevance and playfulness. It used the K-means clustering method improved by Kruskal algorithm to recommend early childhood physical education resources. The influence of the curriculum resource system on early childhood physical exercise was investigated by regression analysis. The study results showed that the number of weekly workouts of young children was significantly influenced by the comprehensive developmental features of the curriculum and the diversity of workout programs, (P<0.05). In contrast, the duration of consecutive workouts was mainly influenced by the comprehensive developmental features of the curriculum structural setup and the holistic nature of the curriculum goal setup (0.01<P<0.05). In addition, the cumulative duration of weekly exercise of young children was significantly influenced by regularity perception, comprehensive development and holistic goals in the curriculum resource system (P<0.005). The effectiveness of the personalized service approach in enhancing the efficacy of physical exercise in young children was demonstrated, and it was emphasized that the diversification and comprehensiveness of curriculum content should be emphasized in early childhood physical education.
Pattern recognition study of musical rhythm perception based on multimodal deep learning
Authors: Bai Wen
Published: February 2024

Rhythm perception is becoming more and more important in the field of music information processing and music understanding. The study first adopts signal processing methods to extract musical features, then uses feature fusion techniques to integrate features of different modalities into a single feature vector. Based on this model, the study identifies the rhythmic activation function of music and combines it with the hidden Markov model to infer the rhythm of the music. One of the key points of the study is to perform rhythm recognition on music containing drums, to explore the recognition effect. One of the focuses of the study is to recognize the rhythm of music containing drums to explore the recognition effect.In addition, the study also analyzes the Softmax output values of the music and compares the recognition effect of different models. The results show that the rhythm recognition of music using the multimodal deep learning method performs the best in terms of the F-Measure value, the Cemgil value, the Goto value, and the P-score value, with the respective 65.65%, 66.76%, 36.75%, and 36.75%. 66.76%, 36.75%, and 75.68%.Especially in the drum music recognition, the position of each drum music is accurately recognized, proving the model’s effectiveness in this paper. The research provides a new feasible method for the recognition and understanding of music rhythms and a valuable reference for the research in this field.
Enhancement Strategies of Traditional Cultural Inheritance on Moral Education in the Internet Era
Authors: Sun Danling , Zheng Chuan
Published: February 2024

Combining traditional culture and moral education is particularly important in the Internet era. This study explores the application of conventional culture in moral education and proposes practical strategies for integrating traditional culture into moral education. The study first analyzes the symbiotic pathway between traditional culture and moral education, emphasizing the importance of conventional culture in ecological moral education. Then, by constructing a coupled evaluation index system of conventional culture and moral education and applying the entropy value method and the coupled coordination degree model, it comprehensively evaluated the moral education subsystem in a province from 2008 to 2022. The results show that the province’s honest education subsystem scores improve from 0.1146 to 0.8133, and the coupling coordination degree shows an upward trend. Based on these findings, this study proposes strategies for integrating excellent traditional culture into moral education, including establishing a scientific concept of integration, enhancing teachers, and enriching extracurricular activities, aiming to improve the effectiveness and usefulness of moral education.
The Path of Enhancement of Traditional Cultural Heritage on Moral Education Based on ADDIE Model
Authors: Zhang Wenyan
Published: February 2024

With the development of society, the importance of Chinese excellent traditional culture to the moral education of college students is becoming more and more prominent. This study explores the application of conventional culture in moral education through the ADDIE model, aiming to improve the quality and effectiveness of moral education. Through the five stages of analysis, Design, development, implementation, and evaluation, the study constructed a moral education effectiveness evaluation index system. The study found that integrating Chinese excellent traditional culture into moral education can effectively enhance college students’ self-confidence, national responsibility and national pride. The empirical survey shows that in the universities adopting this model, the overall satisfaction of college students regarding the effectiveness of moral education reaches 77.90%, and the dissatisfaction is only 1.16%. The comprehensive evaluation score is 4.5731 points, showing a sound effect of moral education. The study’s conclusions that the ADDIE model has asignificantly affects It provides a new perspective and practical path for moral education in colleges and universities.
Compositional Development of Chinese Pipa Music in the New Media Era
Authors: Wang Xinjie
Published: February 2024

In the new media era, the creation and development of Chinese pipa music faces new challenges and opportunities. This paper analyses the characteristics of pipa music and explores its innovative path and development trend in the contemporary environment through technical means. Music signal preprocessing, feature extraction and other methods are adopted to provide an in-depth characterization of pipa music. In the study, the pipa music was classified concerning pitch, loudness, timbre and rhythm, and the musical features were extracted using techniques such as MFCC and LPC. The experimental data contained 300 pieces of pipa music of different styles, and a series of specific numerical results were derived from the Characterization of this music. For example, in the MFCC feature retrieval experiment, the retrieval accuracy of a 30-second music clip was improved by 0.024 compared to a 10-second clip. In addition, by analyzing the timbre features of 12 pipas, and using MATLAB to process the data, a quantitative judgement of the goodness of the timbre was derived. The possibility of combining pipa music with electronic music was explored with new media technology. The artistic expression of pipa music can be effectively analyzed and enhanced through scientific methods, providing fresh ideas for the innovative development of pipa music.
Discourse Practices and Identity Remodeling of Hosts on Live Streaming Platforms under the Background of Informatization
Authors: Wang Xue
Published: February 2024

In the age of informationization, webcasting has gradually become an essential form of media. On the live broadcasting platform, hosts transmit information through discourse practice and continuously reshape their identities, a phenomenon of great significance in both academic and practical fields. Qualitative and quantitative methods analyze the host’s discourse characteristics and identity construction process. It is found that the moderator’s discourse patterns are mainly affectionate, humorous, simple and commonplace, and the role of discourse practice in the construction of identity is revealed through Foucault’s discourse theory and the SMCR communication model. The empirical analysis shows that the hosts’ discourse themes in beauty and fashion account for up to 20% of the total, and enhance the communication effect through multimodal discourse (visual and auditory). The study also found that hosts reshape their identities through live discourse, such as transforming traditional TV anchors into online KOLs, to portray a pro-people and professional image. Hosts realize effective identity reshaping on live broadcast platforms through discourse practice, profoundly impacting emerging marketing models such as live bandwagon.
Practical Dilemmas and Coping Strategies of Meta-Cosmos in Helping the High Quality Development of Ideological and Political Education in Colleges and Universities
Authors: Xu Xunen
Published: February 2024

With the technological innovation, meta-universe changes the education of ideology and politics in colleges and universities. This paper discusses the dilemmas and strategies of applying meta-universe technology in the ideological and political courses in colleges and universities to improve the quality of education. First, it discusses the impact of meta-universe technology on the education of ideology and politics pointing out its potential in enhancing the interactivity and practicability of teaching. However, it also faces problems such as technology integration, teacher training, and uneven student participation. A survey of 100 university teachers and students found that the application of meta-universe technology resulted in a 30% increase in student engagement and a 20% increase in learning outcomes. Multi-dimensional strategies including optimizing teaching design, enhancing teacher training, and improving students’ interactive experience were proposed to meet the challenges. Meta-universe technology can effectively enhance the teaching quality of ideological and political courses. Still, it must be combined with actual teaching needs and conditions to develop a reasonable application program.
The significance and value of living and regionalizing the aesthetic education curriculum on the topic of Jing Chu culture
Authors: Zhang Kain
Published: February 2024

This study explores the significance and value of the living and regionalization of Jing-Chu culture in the aesthetic education curriculum, strengthens the combination of regional cultural characteristics and modern aesthetic education, and explores the new value and direction of cultural education in contemporary times. Through quantitative and qualitative analysis, we study Jing Chu culture’s aesthetic education function and digital transformation. We use questionnaire survey, experimental analysis and literature research methods to focus on the aesthetic education elements of Jing-Chu culture and its application in modern education. Elements such as myths, sorcery, and Chu Rhetoric in Jing and Chu culture have a new vitality in the digital environment. Through the questionnaire analysis of 50 art majors, it was found that 90% of the students believed that the elements of Jing-Chu culture enriched their artistic creation and aesthetic experience, and the application of Jing-Chu cultural primitives in the digital teaching environment significantly enhanced the students’ sense of identification with the culture and their motivation to create. The living and regionalization of Jing Chu culture not only enriches the connotation of the aesthetic education curriculum, but also provides new ideas for the inheritance of traditional culture. Art educators are encouraged to dig deeper into the value of Jing Chu culture to bring innovation and vitality to the contemporary aesthetic education curriculum.
Design of computer algorithms based on parametric models
Authors: Bai Jiexian , Qiao Hongkai , Li Haifang
Published: February 2024

In this paper, the objective function is effectively optimized by improving the fitness function in the computer algorithm. The improvement mainly focuses on adjusting the weighting coefficients of completion time, load balance and execution cost. The article then proceeds to optimize the algorithm’s parameters based on the algorithm’s optimal parameterization criterion, and designs a hybrid hill-climbing-simulated annealing optimization algorithm based on the parameterized model. To verify the safety of the algorithm, avalanche effect experiments were conducted in the study. The experimental results show that when the number of iterations is 14, the number of changed bits reaches 32, indicating that the algorithm is susceptible to the avalanche effect. In addition, for the application effect of the algorithm, the study was tested in the Oliver30 dataset. When the number of iterations reaches 25000 times, the algorithm has reached the optimal value distance 432.82, which shows the superiority in urban path recognition performance. Meanwhile, the algorithm has achieved an accuracy 0.8286 for feature recognition in remote sensing datasets, with a maximum classification accuracy of more than 90%. The study provides critical practical data and theoretical support for further application of the computer algorithm.
Research on Regional Tea Tourism Product Development Strategy Combined with RMP Analysis
Authors: Huang Lingyun
Published: February 2024

Tea culture tourism perfectly combines agricultural economic development and cultural tourism, a new trend in today’s tourism industry development. Based on the life cycle theory of tourist places and RMP theory, this paper comprehensively analyzes the three core dimensions of regional tea culture tourism product development: resources, market and products, using Province F as a case study. In the resource dimension, the combination assignment method containing preference information was used to determine the weights, and the fuzzy comprehensive evaluation model was applied to comprehensively evaluate the tea culture tourism resources. The market dimension analysis covers tea tourism’s development status and the characteristics of tourists’ travel behavior. For the product dimension, a quadrant diagram analysis was conducted using the IPA model by combining tourists’ perceived importance and satisfaction with the product. The study results show that Province F excels in tea tourism product development, with a comprehensive resource evaluation score of 90.756 points. Between 2010 and 2019, the tea tourism market received tourists at an average annual growth rate of 12.28%, reflecting the strong growth momentum of the market. Regarding product development, the average values of tourists’ perceived importance and satisfaction were 3.99 and 3.78, respectively, indicating that tourists’ overall satisfaction with tea tourism products is high. The study will help further enhance the attractiveness and competitiveness of tea tourism products and promote sustainable tourism development.
Analysis of the application of BIM combined with GIS in engineering digital management
Authors: Lin Junhong
Published: February 2024

This study centers on the application of digital intelligence technology in project management, takes the geohazard project in Z area as the research object, and evaluates and predicts the grade of the danger of slope disaster and mudslide disaster in this area. The study begins with an in-depth analysis of the integrated fusion of BIM and GIS technologies. The integrated assessment and prediction model of geologic hazards was constructed using fuzzy comprehensive evaluation and support vector machine algorithm. The study unfolded the analysis of the hazard class and risk prediction of geohazards. The results showed that most of the hazard classes in slope hazards were assessed as low danger, accounting for 33.33%. Meanwhile, about 70% of the mudslide hazards were assessed to have a low to medium hazard rating. Regarding risk prediction, the prediction accuracy of slope and mudslide hazards reached 89% and 90%, respectively. In addition, the detection rate of all geohazards in the ROC (receiver operating characteristic) curve was above 85%, while the false alarm rate remained between 14% and 22%. This result verifies the accuracy of the model in geohazard risk prediction. The comprehensive assessment and prediction model proposed in this paper will help the risk assessment and prediction of geohazards, provide adequate technical support for managing disaster information, and enhance the science and rationality of disaster prevention and mitigation.