bluuebunny/crossref_metadata_2025 huggingface dataset:
The Role of Data Fusion in Contemporary Urban Image Design and Communication in the Context of Big Data
Authors: Wang Bo
Published: August 2024

The urbanization process is accelerating, the competition between cities is getting more and more intense, and the problem of urban image management is getting more and more attention from urban image researchers and city managers. Taking Nanchang City as the case study, this paper selects the city government, economy, tourism, and people image, as well as three data platforms: the official website, news positioning, and tourists’ diary, and crawls the comments of the four types of city image elements on the platforms, and carries out the data fusion and pre-processing to get the comment dataset. Subsequently, the TF-IDF algorithm is used to extract high-frequency words from the city image, and it is combined with the SnowNLP model and LDA theme analysis model to analyze and supplement the overall image of Nanchang City. Finally, after studying the mechanism of city image under data (media) fusion, the communication effect of Nanchang’s image is explored in terms of communication heat, recognition, and participation. Government image and tourism image aspects of the words appear most often, and the highest and lowest percentage of positive emotions are the city’s economic image and government image, respectively, which should integrate the existing resources, improve the level of service, and further create the city’s characteristics, to expand the city’s visibility and communication effect.
Application of mathematical modeling in analyzing and optimizing English teaching methods in vocational education
Authors: Chen Liailun , Wang Junpeng
Published: August 2024

In the era of big data, mathematical and scientific methods significantly contribute to educational reform, continuously revitalizing precision teaching. In this paper, we use the new Hawkes process to collect and vectorize data on how students learn. Then, we use the unique thermal coding method to combine the behavioral feature vector with the teaching feature vector. Finally, we classify the data and use the softmax function to predict the learning effect. Based on the prediction results, a mathematical model for precise teaching optimization of vocational education English has been established, and the decision function is used to solve the problem and optimize teaching methods. It was found that all the indicators of learning behavior characteristics of students in the experimental class applying the teaching method optimization model were significantly improved, and there was a significant difference at the level of 0.05 (P = 0.000<0.05). Furthermore, the students in the experimental class achieved higher academic performance in vocational education English than those in the control class under traditional teaching. This paper provides a scientific basis for teaching English to students in vocational education and provides methodological references and ideas for unfolding individualized and precise learning in classroom teaching.
Construction of risk warning model of agitated behavior of the elderly in Zhejiang pension institutions
Authors: Du Rongbing , Xia Junli
Published: August 2024

Alzheimer’s disease is considered the epidemic of the twentieth century, particularly the radical behavior produced by the patients, which can easily lead to an increase in mortality. This paper focuses on optimizing the search path algorithm to forecast the likelihood of aggressive behavior, enabling the implementation of targeted preventive measures. Additionally, it performs preprocessing procedures like data cleansing on the health data of the elderly. We propose a feature extraction optimization model to calculate the feature contribution of elderly radical behavior data, filter the feature data associated with radical behavior based on this contribution, and establish an interpolation model for association rule learning. Using Logistic Regression, Simple Bayes, and Support Vector Machine classification models, the risk warning model for aggravated behavior is constructed. After one month’s intervention with the risk warning model, the aggressive behavior of the elderly decreased by 58.83%, 43.06%, and 67.94%, respectively, compared to the pre-intervention period, and the intervention effect of the model was good.
Evaluation Method of Teaching Quality of Foreign Language Teachers in Colleges and Universities Based on Decision Tree Algorithm
Authors: Huang Yumei , Xin Rui
Published: August 2024

Teaching evaluation is an important part of the teaching process in colleges and universities and plays a very important role in the development of higher education and the improvement of education quality. Applying a decision tree algorithm to teaching evaluation can uncover the hidden intrinsic connection between teacher information and course quality evaluation results. This study combines the radar graph with the decision tree C4.5 algorithm to construct a foreign language teaching quality evaluation model, which explores the correlation between teacher information and radar graph attributes on teaching quality evaluation grades on the one hand and evaluates the predictive effect of the model on the other hand. The data mined by the radar chart-C4.5 evaluation model show that if the center of gravity of the radar chart is in the first quadrant, then the evaluation grade passes and has the highest support, which is 53.33%. If the radargram area is large, then the evaluation grade good has the highest confidence level of 77.27%. The evaluation result of the model is that the number of error samples of the prediction grade and the expert evaluation grade are 3 excellent, 5 good, 1 average, 4 qualified, and 1 poor, which proves that the performance of the radar map-C4.5 evaluation model is better.
Design of a land use change prediction model incorporating neural networks
Authors: Chen Shuiying , Wang Haibin
Published: August 2024

Land use change has gradually developed into a core area of global environmental change research. In this paper, we use object-orientated modeling to construct a model that combines Markov models, neural networks, and cellular automata. We extend the Markov model to the traditional CA, fully utilizing the advantage of ANN in simplifying the definition of land use transformation rules and obtaining a large number of spatial variable parameters of the model. This successfully simplifies the structure of the model and the definition of transformation rules. We apply the constructed Ann-CA-Markov land use change analysis model to the evolution and prediction of land use in County A. It has been found that the proportion of arable land area in County A decreased from 23.3% to 12.1%, and the proportion of construction land increased from 28.07% to 50.87%. From 2000 to 2020, other land continued to converge into construction land in large quantities, so the land area of County A increased from 1224.73km² to 1295.15km² in 2020. The area of arable land converted out is the largest among the five types of land, with an arable land area of only 308.11km² by 2020. The probability of conversion of four land types, namely, arable land, forest land, grassland, and watershed, to construction land is 21.7%, 10.5%, 10.9%, and 9.2%, respectively, by 2030, while the probability of conversion of construction land to arable land is 21.7%, 10.5%, 10.9%, and 9.2%, respectively. The probability of converting land to cropland is 13.9%. The model constructed in this paper shows strong performance in the analysis of land use change evolution and prediction of County A, which is in line with the design expectation and makes an innovative exploration for realizing the effective simulation of spatial and temporal land use changes.
Research on the Application of SVM in Ideology Analysis and the Enhancement of Ideological and Political Education Effectiveness
Authors: Huang Yueqin , Shen Tingting , Wang Huaijing
Published: August 2024

The consciousness and behavior of students in the Ideological and Political Education curriculum generate massive amounts of data information, how to allow educators to quickly obtain information from the massive amount of text data is very important to improve the energy efficiency of classroom education. SVM technology is employed in this paper for data mining and text classification. Firstly, using the web crawler method, the text of messages related to the Ideological and Political Science course posted by students on social networks is collected. The short text was preprocessed before performing sentiment analysis, which mainly included interaction information filtering, word segmentation, and lexical labeling. Then, the SVM model suitable for students’ ideology analysis is constructed, using the Gauss radial basis kernel function to accurately depict the distribution structure of the data, and the L1-SVM model with more stable computational performance is also proposed. The extension method of the classification algorithm in the real number domain is summarized at the end. This algorithm’s accuracy is 78%, and its F1 value is 80%, which is higher than the other three algorithms. DAG-SVM and recall are both optimized to a lesser extent. Overall, the classification efficiency of the algorithm in this paper has been improved. The positive effect of this paper’s algorithm on improving the effectiveness of Ideological and Political Education can be seen in the significant increase in the learning interest of the experimental class.
Innovative Strategies and Practical Effects of Supply Chain Management of Business Enterprises under the Framework of Smart City
Authors: Jin Jun
Published: August 2024

The change in modern information technology has brought new development opportunities to all walks of life, and the supply chain management mode of industrial and commercial enterprises has gradually shifted from management based on historical experience to management relying on information data. This paper combines the problems of inventory cost optimization and vehicle route scheduling optimization in the supply chain management of industrial and commercial enterprises and develops the supply chain innovation management system of industrial and commercial enterprises under the framework of a smart city. The proposed DES_RSA hybrid encryption method is proposed to ensure the timeliness and security of the information transmission process of the smart supply chain management system. During the interface test of the purchase order paging request, the system test revealed that the supply chain management system had a 0.00% error rate. The results of the application practice of Company H show that after the application of the supply chain innovation management system, the growth rate of Company H’s operating costs continued to decline, the expense margin rose by 8.50% in two years, and the trend of customer satisfaction also shows a significant increase. The application value of the business enterprise supply chain management system designed in this paper is high, which can achieve efficient management of the business enterprise supply chain.
Dynamic Optimization Research on Regional Security Planning of College Dormitory under Neural Network Perspective
Authors: Li Lan
Published: August 2024

A dormitory is a place where students live and live for a long time, and the safety of the dormitory can directly affect the quality of students’ studies and lives. In light of this, the study constructs a dynamic security planning system for college dormitory areas, enhancing their security and intelligence through the application of intelligent technology and multi-directional management coordination. Taking the access control system and fire monitoring system in the dormitory security prevention system as an example, based on the neural network perspective, we design the face recognition method of HOG-YOLO and the fire detection algorithm of improved YOLOv5 to realize, train, and test the two models, and evaluate the face recognition performance and fire target detection effect of the models. The results show that the face recognition method established in this paper is highly accurate and rapid, with a recognition accuracy of 95.7%, and it can complete recognition and detection under different facial angles. The fire detection algorithm’s FPS has been improved by 14.75%. The overall accuracy has improved by more than 1%, which has a good effect on fire monitoring. Planning the security system for the dormitory areas in higher education is a significant area of research for ensuring campus security, and access control and fire monitoring systems have significant application potential.
Semantic analysis and construction of English discourse based on neural network
Authors: Liu Junjie
Published: August 2024

Accurate recognition and analysis of English semantics is of great significance in the application field of computer and artificial intelligence technology. Traditional text analysis technology has poor real-time analysis accuracy and efficiency. This paper constructs an English semantic analysis model based on neural networks. By designing an English semantic framework that integrates LSTM and RNN and optimizing it on the basis of LSTM and RNN models, an English semantic analysis model with improved attention mechanisms and Bi-LSTM is constructed. By comparing the Bi-LSTM+Attention model proposed in this paper with other semantic analysis models, the classification accuracy of the model constructed in this paper reaches 98%. Through the practical application of the model to analyze the semantic coherence of English, the average score of the semantic coherence quality measured by the model is 15.76, and the average score of the manual judgment is 16.03, which indicates that the application of the semantic analysis model constructed in this paper is close to the effect of the manual judgment.
A Study on Modeling and Quality Evaluation Methods of Assignment Revision in English Education
Authors: Lu Xiaochun , Tian Shang
Published: August 2024

Assignment revision is an important part of English assignment teaching, which is of great significance in guiding the direction of English assignment teaching as well as improving students’ writing ability, so it is of great value to establish a scientific and reasonable assignment revision model and quality evaluation method. This paper proposes a model of revision in English education by evaluating the English revision ability of students in College A and then proposing a revision evaluation system. An empirical study of English assignment teaching in College A is the article’s conclusion. The article concludes that the student’s overall assignment review ability scores are around 78%, which is the highest score, but the students’ English assignment revision ability scores are only 37%. This article’s evaluation index system of assignment revision has a composite calculated value of (0.146, 0.104, 0.092, 0.021, 0.001)
The Aesthetic Value of Caravaggio’s Light and Shadow Technique from a Geometric Perspective
Authors: Sun Hanying
Published: August 2024

Caravaggio was an influential Italian painter of the 17th century who made important contributions to the development of later European art, both in terms of light and shadow techniques and in terms of ideas. This study first explores the characteristics of Caravaggio’s paintings and briefly explains Caravaggio’s painting style, light and shadow processing techniques, and their significant influence. Secondly, based on the visual perception art understanding method and aesthetic law, the curvelet geometric transformation is utilized to interpret the characteristics of different styles of Caravaggio’s light and shadow techniques in terms of three geometric features: multi-scale amplitude, coefficient distribution curve, and image non-smoothness. Finally, the aesthetic value of Caravaggio’s light and shadow techniques is interpreted from the geometric point of view by using six works from different periods: Caravaggio’s The Doubt of St. Thomas, The Supper at Emmaus, The Basket of Fruits, The Diviner, Narcasus and Judith Cutting down Hierophant as the research materials. The results show that Caravaggio’s light and shadow technique in the early stage of his creative career is characterized by a high low-scale coefficient and a low high-scaled coefficient, which indicates that there are more thick lines and fewer details. The maximum smoothness value of the works is 0.4035 respectively. The light and shadow technique in the later stage of his creative career is characterized by a low scale coefficient and a high scale coefficient, which means that he uses more fine lines to depict the details and less thick lines, reflecting the fact that the light and shadow technique in the later stage of his creative career is characterized by a low scale coefficient and a high high scale coefficient. Caravaggio’s light and shadow technique is characterized by a low scale factor, a high scale factor, and the use of fine lines to depict details and fewer thick lines, reflecting the light and shadow characteristics of Caravaggio’s light and shadow technique, which is depicted by the use of brushes and mineral pigments, and highly demonstrating the aesthetic value of his realist painting.
A Study of Japanese Vocabulary Recognition Teaching Strategies Based on Deep Belief Networks
Authors: Tang Huiqin , Zhou Bin , Gu Weijie
Published: August 2024

Based on the deep belief network, this paper proposes a teaching strategy for Japanese vocabulary recognition with the main direction of realizing scientific classification of Japanese vocabulary and building a corpus. In order to improve the classification effect of Japanese vocabulary, a Japanese vocabulary classification model based on a deep belief network is constructed, and the TF-IDF and LSI features of the text are used as inputs to obtain the deep-level features and improve the final classification effect. A Japanese vocabulary corpus is constructed through the steps of corpus collection, database design, and corpus production, and a Markov number labeling model based on a deep belief network is constructed to transform the problem of sentence tense processing into the problem of labeling tense trees and propose a DBN-based method for translating the tense of the corpus. The teaching practice of Japanese vocabulary recognition was conducted with 100 first-year students at the College of Japanese Language at Dalian University of Foreign Languages as research subjects. The experimental class scored slightly higher than the control class in all six Japanese vocabulary tests, and the mean values of vocabulary utilization ability and vocabulary comprehension ability were higher than those of the control class by 2.94 and 1.06, respectively, which showed a significant difference (P<0.05). The experimental class also scored higher than the control class in all dimensions of Japanese vocabulary learning ability, showing significant differences (P<0.05).
A quantitative study of the relationship between attachment styles and psychological resilience in the context of social anxiety among college students
Authors: Wang Qinchao
Published: August 2024

The purpose of this paper is to reveal the effects of attachment style quality and psychological resilience on college students’ social anxiety so as to provide new ideas for subsequent research on social anxiety. The study polled 450 college students using the Intimate Relationship Experience Scale, the Adolescent Psychological Resilience Scale, and the Interaction Anxiety Scale. It then used statistical analysis to find out how attachment styles and psychological resilience are related in college students. It also looked at how attachment styles and psychological resilience affect social anxiety in college students. According to the findings, there was a significant difference in demographic variables and a significant correlation between attachment style and psychological resilience among college students. The mediating effect of psychological resilience between attachment style and social anxiety was 25.47% of the total effect, and psychological resilience mediated the effect of attachment style on social anxiety among college students.
Human Resource Management Optimization Strategies for Diverse Work Environments Based on Artificial Intelligence
Authors: Wu Min
Published: August 2024

Under the diversified work environment, the relationship between employees and enterprises has ushered in new and significant changes. How to adapt to changes, form a new human resource management model, and improve management performance is an urgent issue. In this paper, we constructed an employee turnover pre-analysis measurement model based on XGBoost artificial intelligence, iterated many times during training, generated a weak classifier in each iteration, trained on the basis of the residuals of the previous classifier, and finally combined all the weak classifiers in a weighted way, reduced the bias to improve the accuracy of the final classifier through continuous iteration, and completed the construction of the model. The historical data of employees in six branches of enterprise W are imported into the model for analysis, and the F1 values are all above 0.85, and the AUCs are all higher than 0.7, with good prediction performance. The top three important employee turnover influencing characteristics and their weights are overtime work 0.647, monthly income 0.618, and interpersonal relationship 0.579. Accordingly, human resource management optimization strategies are designed and applied in Enterprise W to implement human resource management reform. After the reform, the average monthly separation rate has been reduced from 2.23% to 0.19%, and the average number of monthly separations has been reduced to only 7, which is 91.86% lower than during the pre-reform period. This study proposes feasible paths for modern information technology and artificial intelligence-enabled human resource management, and optimization of human resource management in a diverse work environment.
Analysis of the Dynamic Relationship between Transgressive Innovation Behavior and Team Network Structure in High-Tech Firms
Authors: Xu Xingqiang , Youngwook KO
Published: August 2024

The current market economy has stepped into the stage of high-quality development led by innovation. How to effectively improve the innovation capacity of enterprises has become a widespread concern in the academic community. The article first analyzes the formation factors and consequences of transgressive innovation behavior and then combs through the dynamic collaboration and system performance generation in multi-team network structures. Based on the network centrality of social network analysis, two models based on sub-team type and team structure balance are constructed by combining structural balance theory. Regression analysis was conducted for Model 1 to clarify the effects of transgressive innovation behavior on network centrality and subteam type, and social network analysis was used to investigate the influencing factors of team structure balance in Model 2. Network centrality increases by 0.121 percentage points when the subteam type variable increases by 1 percentage point. The regression coefficient of subteam type on network centrality decreases from 0.138 to 0.114 after adding the variable of team members’ transgressive innovation behavior to Model 1. When the number of subteams is 3 and 4, the team network structure can reach a strong balance state at this time, while more than 5 will cause the growth of negative team relationships because of the transgressive innovation behaviors of team members, which will affect the balance of the team network structure. Therefore, there is a dynamic relationship between the transgressive innovative behavior of high-tech enterprises and the team network structure, and it is necessary to set up a reasonable multi-team structure in order to effectively ensure that the performance of high-tech enterprises can be improved.
Study on the transformation model of old neighborhoods oriented towards sustainable community economic development
Authors: Yang Shuyang , Ren Chongbao , Sun Xialing
Published: August 2024

Urban renewal now dominates the strategic stage of urbanization, shifting from the previous focus on “whether there is” to the pursuit of “good.” In the context of urban renewal, the transformation of old neighborhoods should be carried out reasonably. In this regard, this study focuses on exploring design ideas for the transformation of old neighborhoods in the context of sustainable community economic development, with the aim of proposing a set of comprehensive transformation programs. Then, based on the three perspectives of residents, community, and city, the evaluation indexes for the transformation of old neighborhoods are selected from two perspectives of input-output, and the transformation efficiency evaluation model is constructed by combining the model of DEA-BCC and the model of DEA-Malmquist index. Finally, taking 19 old neighborhoods in QD city as samples, we analyze the citizens’ satisfaction with the transformation effect of the neighborhoods, analyze the efficiency of the transformation investment in the old neighborhoods from the two perspectives of comprehensive efficiency value and scale efficiency value, and analyze the problems and the direction of improvement of the transformation of the old neighborhoods from the redundancy of the slack variables. The results show that the average investment efficiency of the old neighborhood transformation is 0.9185, and the pure technical efficiency and scale efficiency are 0.9239 and 0.9651, respectively.
Construction and Application of University English Knowledge Mapping Supported by the Concept of Outcome-Oriented Education
Authors: Gong Lin
Published: August 2024

The outcome-oriented e-education concept is one of the current international advanced education concepts, and its generation and development fit with the development of information technology and the social demand for the quality of talent cultivation. In this paper, a knowledge mapping system for university English courses based on the concept of outcome-orientated education is investigated to enable the integration of university English course knowledge and its visualization in the form of web pages. Knowledge points, concepts, definitions, and other information about the university English subject are extracted, and various data sources, which contain structured, semi-structured, and unstructured data, are collected, integrated, and extracted in the form of English knowledge triads. The system testing results indicate that, in comparison to the traditional DistMult method, this paper’s method enhances the classification accuracy of the three English course datasets by 0.93%, 1.68%, and 0.85%, respectively. The experimental results validate the effectiveness and superiority of the system. Meanwhile, the questionnaire survey found that the system can effectively help learners improve their confidence, interest, and initiative in learning, enhance their learning effectiveness, improve their learning efficiency, and provide them with high-quality learning resources that have good application value.
Research on Fault Detection and Localization Techniques for Distribution Networks Based on Edge Clustering
Authors: Lin Xiongfeng , Zhang Tuo , Li Shengyun , Qiu Junqi , Zhang Lihang , Su Lisha , Bai Yiming , Liang Jiehua
Published: August 2024

With the intelligent transformation of the power grid, the number and type of various terminals, sensors, and new types of loads in the distribution network increase, and the huge amount of information and noise information accessing the power grid brings great challenges for fault detection and localization. In this paper, we propose to combine wavelet transform and LSTM unit to form a novel neural network basic unit TFM and examine the fault detection and localization capability of TFM by combining them with a typical example system, IEEE33. Spectral clustering and the K-Means algorithm are utilized to cluster the edge nodes, and the number of nodes in the three partitions after partition correction is 11, 10, and 12 nodes in order. The data shows that the TFM system performs the fault diagnosis task with better test accuracy than LSTM in every dimension, and its accuracy improvement for fault localization is the largest, at 5.39%. Introducing two reconfiguration scenarios for three edge node partitioning models and retrograde fault localization detection, compared with the no reconfiguration scenario, their localization accuracies all produce different degrees of decline, but always not less than 80%. The localization accuracy of the MⅡ model is still not less than 98% in the fault resistance range of 500~1000Ω, which proves that the TFM system can effectively extract high-resistance fault features.
Semantic Network Analysis and Optimization Path Research on Medical English Terminology
Authors: Wang Dan , Yao Zhuang
Published: August 2024

Specialized terms are often longer words in medical English, and how to improve the effective memorization of specialized terms has become a research hotspot in medical English learning. The article proposes a method for creating a semantic network model for medical English terms using CRFs and the ATT-BiLSTM model. Firstly, on the basis of combing the characteristics and methods of medical English terminology composition, the CRF model is introduced to recognize the named entities of medical English terms and combined with the Viterbi decoding algorithm to obtain the lexical annotation result sequence of medical English terms. Secondly, an entity-relationship extraction model for medical English terms is established by combining the attention mechanism with the BiLSTM model, and ELMo vectors are added to improve the extraction efficiency of medical English terms. Finally, a semantic network model of medical English terms was constructed based on the Neo4j database by combining the named entity recognition and entity relationship extraction model, and the semantic network analysis was carried out by the semantic similarity algorithm based on information content. The results show that the CRFs model only needs a 14.2MB training corpus to co-represent the contextual information of medical English terms, and its F1 value is maximally improved by 10.22% on average after adding ELMo vectors to the ATT-BiLSTM model. The mean semantic similarity of medical English terms in the semantic network model is 1.391, and the result of the semantic similarity assessment is 0.803. Relying on the semantic network model can help learners understand the differences between medical English terms, find more similar terms to elucidate the standard terminology, and improve learners’ comprehension of medical English terms.
Analysis and Optimization of the Effectiveness of College Physical Education Courses Based on Fuzzy Cognitive Evaluation
Authors: Zhang Chunmiao
Published: August 2024

Cognitive diagnostic theory can provide a comprehensive and detailed objective evaluation of individual macro learning ability, thus providing an effective reference for targeted teaching and learning. In this paper, a fuzzy cognitive diagnostic model is used to evaluate the degree of mastery of a college student on the physical education course, calculate the probability of mastery of each attribute, and statistically analyze the degree of mastery of the attributes of the individual as well as the class as a whole. Then, a learning effect evaluation standard is constructed to evaluate the learning effects of the physical education course for all students. The empirical results show that the mean value of the evaluation of the students’ cognition of physical education in School X is 0.6587, and the average probability of mastery of physical education emotion is 0.6021, both of which have reached the degree of good mastery. The average probability of mastering sports operations was 0.5909, which indicates that university students have an average level of mastery in all sports skills through the physical education program. The outcome is in line with the current situation, which can serve as a reference for teachers to make targeted teaching decisions. The study has both theoretical value and practical significance in promoting the development of wisdom education.
Modern Transformation of Ancient Chinese Philosophical Thought in Ideological And Political Education in Colleges and Universities Based on Multiple Learning Styles Adaptation
Authors: Deng Haiman , Shao Jianxun , Li Tao
Published: August 2024

Ancient Chinese philosophical thinking, as an important part of Chinese civilization, has influenced thousands of years of Chinese history and also had a profound impact on the Ideological and Political Education in modern colleges and universities. Based on the philosophical nature of Ideological and Political Education, the article analyzes the learning styles of different categories and establishes a blended-tiered teaching model for Ideological and Political Education with the aid of digital technology. Based on students’ online Ideological and Political education learning behavior data, a Bayesian discriminant analysis algorithm is used to identify students’ learning styles. Then, the GRU neural network model is introduced to define the diagnosis of the knowledge level of Ideological and Political Education, and the Ideological and Political Knowledge Level Diagnosis Model is established. An example analysis was conducted to assess the learning style, cognitive level, and relevance of Ideological and Political Education. The study shows that when carrying out the identification of students’ learning styles, the Bayesian discrimination improves by 10.98% compared with the results of the style clustering algorithm, and the average absolute error of the diagnosis of students’ Civics knowledge level minimizes only 0.128. The scores for various learning styles are highly variable, and the extreme differences are all over 24 points. The standardized regression coefficients of learning interest in Ideological and Political courses and literacy learning styles are between 0.072 and 0.163, with a very significant correlation. Incorporating ancient Chinese philosophical ideas into ideological and political education is conducive to satisfying students’ multiple learning styles and also contributes to modernizing and preserving ancient philosophical ideas.
An Exploration of the Integration of Marx’s Philosophical Thought and Modern Ethical Decision-Making Models for Artificial Intelligence
Authors: Liu Jianning
Published: August 2024

Artificial Intelligence is involved in more and more fields, and the debate of whether human beings will be replaced by it is more and more intense. This paper explores the paradigm of ethical decision-making of artificial intelligence from the perspective of Marx’s philosophical thought. Based on the basic elements of ethical decision-making, three decision-making methods are proposed to complete the judgment of decision-making consequences, and the calculation formula for the consequences of these methods is derived. Using the Bayesian learning method, a utility-maximizing agent effect function is constructed to implement the moral embedding policy. Measure the ethical fairness of decision-making algorithms based on statistical fairness orientation to propose fair decision-making methods. Through the example analysis, the practical application effect of the model is analyzed from two perspectives: public policy implementation and education. More than 40% of the users are more satisfied with the public policy implementation made by AI, and they are relatively satisfied with the implementation of AI governance policy at this stage, but there is still room for improvement. There is a difference in the cognition of students of education-related majors and computer science majors on the ethics of identity intelligence, and education majors pay more attention to the educational level and social virtue issues, such as educational sovereignty, educational purpose, educational value, educational evaluation, educational fairness, as well as the development of students, and integrity issues.
A Study on Synergistic Improvement of Physical Fitness and Skills of Basketball Players Based on Big Data
Authors: Cai Ning , Zhang Jungang , Li Jinpeng , Luo Jian , Yu Ao
Published: August 2024

This paper proposes a strategy for synergistic improvement of physical fitness and skills of basketball players with the content of big data analysis of decision-making of physical fitness training programs and estimation of the human posture of basketball players. The data related to physical training and testing are processed using neural network methods, and a priori algorithms with continuous attributes discretize the data to realize data fragmentation and prevent data distortion caused by intrinsic correlation so as to build up a technical and methodological platform for physical training programs. At the same time, the basketball action posture estimation algorithm based on multi-scale spatiotemporal correlation features is proposed, and the human body temporal sequence feature capture module based on Transformer is constructed to improve the accuracy of basketball action posture estimation to meet the needs of skill training. The men’s basketball team of Shandong Agricultural University in China was used as a research object to carry out basketball physical fitness and skill training practices. The experimental group’s basketball players’ performance in barbell bench press and weighted squat was 11.06kg and 10.05kg more than that of the control group. Their performance in 3/4-court sprint running, and return running. Restriction-area footwork was 0.52s, 2.16s, and 1.97s faster than that of the control group, with significant differences in basketball skills, such as 14-meter round-trip straight-line dribbling around the pole and 30-second in situ spotting of two-handed chest pass (the first time in the experimental group). Basketball skills showed significant differences (P<0.05).
Research on the Path of Cost Control and Economic Benefit Enhancement in Construction Engineering Projects
Authors: Chen Liang , Tang Yu
Published: August 2024

The increasingly competitive market situation in the construction industry requires that construction enterprises strengthen the cost control of construction projects in order to improve the economic efficiency of enterprises. This paper is based on the improved genetic algorithm to realize the optimization of the cost control strategy of the construction project and effectively overcome the problems of weak cost control consciousness and high material procurement cost in the previous cost control. A construction company is selected as the object for the case study, and the genetic algorithm is used to calculate and analyze the cost control of the construction company. The return on investment, net present value, and internal rate of return are used as indicators to measure the economic benefits before and after optimizing cost control strategies. The improved genetic algorithm calculates that among the cost control factors of the construction company, procurement management, and field operation have a high probability of being selected in the genetic calculation, and the final project cost is 36.7849 million yuan through the genetic algorithm, which achieves the goal of project cost control. At the same time, this paper finds that cost control has a significant positive effect on the improvement of economic efficiency, and the economic efficiency of this construction company before and after the optimization of the cost control strategy is significantly improved.
Spatial Characterization of Sports Economic Linkages - Based on an Evolutionary Game Theory Perspective
Authors: Chen Ruikai , Wang Shuiquan
Published: August 2024

There exists an important connection between the development of the sports economy and the development of spatial characteristics, but the current development of spatial characteristics of sports economic linkages suffers from the lack of identification and screening of the subjects of interest and the inability to articulate the structure of the interests of each subject. The article adopts the research paradigm based on rooted theory to encode the game phenomenon of the spatial characteristics of sports economic linkages and constructs an evolutionary game model that integrates the sports administrative regulator, the supply side of community public sports services, and the demand side. The optimal equilibrium strategy to promote the sustainable development of urban community public sports services spatial governance is also explored by combining the Jacobian matrix and simulation experiments. Although the tripartite subjects play different dynamic roles at different stages of transformation, they all converge to the steady state of (1, 1, 1) in the end. In addition, the three have different evolutionary processes. First, the probability of choosing the support strategy in the economic core area decreases and then rises, while the probability of government support and mass recognition both increase. The economic core area, government support, and mass recognition are all experiencing a gradual increase. Finally, the probability of mass recognition shows a first decline and then a rise, while the probability of economic core area and government support continues to show an increase, and the three finally evolve into a (1, 1, 1) strategy steady state.
Analysis of Behavioral Motivation in a Self-Management Model of Physical Activity Supported by Information Technology
Authors: Feng Yingge
Published: August 2024

In recent years, the topic of scientific exercise has attracted widespread attention, and people realize that participating in physical exercise requires the management of physical exercise behavior in order to reap the desired results. In this paper, we propose an exercise prescription recommendation based on the K-means clustering algorithm and a self-adjustment mechanism based on NLP sentiment analysis. We aim to provide the public with more professional and planned exercise programs. A sample of 1000 online user data from exercise software is taken for simulation experiments to verify the recommended method’s iterative effect and effectiveness. The data show that the optimal number of clusters for both male and female users is five, and the clustered body types are analyzed according to the mean of the Z-Score standard score, which shows that the five boys’ body types are obese, thin, lean, strong, and athletic, and the five girls’ body types are lean, agile, thin, obese, and fat, respectively. The confusing heatmap of the NLP affective tendency reflects its affective. The classification method is effective, and the overall correct rate of sentiment analysis reaches 94.5%, of which the classification of users with positive emotional tendencies is the best, with a correct rate of 97.8%, while the classification of users with no emotional tendencies is poor, with a correct rate of 87.6%. The model’s AUC value in the ROC curve is 0.82, which means it has a better classification effect. In addition, taking a sample in the first category of male physique as an example, the NLP sentiment analysis method derives an exercise prescription adjustment that increases the intensity of this user’s exercise by 40% through his exercise data and sentiment feedback text.
An applied study of using deep learning technology to develop training programs for athletes in college physical education
Authors: Jia Chaofeng , Liang Changbin
Published: August 2024

This paper proposes the integration method of athlete training volume monitoring information based on deep learning, preprocessing the monitoring information using wavelet transform, obtaining the athlete training volume information after denoising and dimensionality reduction, combining with the theory of deep learning, utilizing convolutional neural network for feature extraction and integration of the processed information, and performing simulation test and analysis. The average Gini coefficient is 84.926, which proves the effectiveness of the method used in this paper. After exercising, the athletes’ weight and body fat rate were monitored to reduce a certain degree, and their lung capacity was improved. A deep learning algorithm generated personalized exercise program training achieved a heart rate interval of 94% for safe and effective workouts. The three stages of the test subjects’ training were tracked and analyzed, and the use of training monitoring can effectively promote the efficiency of training. The physical fitness program (P < 0.01) and physical fitness test (P < 0.05) in the first stage compared with the third stage showed significant improvement.
Research on the optimization model of personalized training for athletes based on machine learning in college physical education
Authors: Liang Changbin , Jia Chaofeng
Published: August 2024

We use deep learning to extract and identify the physiological indicators, sports, and data analysis parameters, providing scientific references for athletes’ personalized training. This paper aims to construct an optimization system for personalized training for athletes based on machine learning. Initially, we designed the system’s overall structure, hardware, and software scheme, and then we modeled the algorithms for measuring four physiological parameters. The NTC thermistor thermometry method was utilized to measure body temperature, the photoelectric volumetric method for determining blood oxygen and heart rate, and the ECG signal method for calculating blood pressure. The adaptive threshold method uses the human body’s steady-state recognition algorithm, based on a single triaxial acceleration sensor, to recognize steady-state motion states such as resting, running, and walking. After the construction of the system was completed, it was applied to the sports team of a university in Guangzhou, and a semester of personalized training optimization experiments for athletes was conducted. The system found a maximum error of 0.02 °C between the measured body temperature and the results from the medical temperature gun, ensuring stable performance during short-term body temperature changes like training. Except for B, C, D, and G errors of 1%, the rest of the athletes monitored by the system in this paper’s blood oxygen results are consistent with the oximeter. The accuracy of recognizing each sport is above 90%. In the eight swimming events that need to be assessed, the average score before the beginning of the semester was 65.43, and after the experiment, it was 87.14, an improvement of 21.71 points. The system in this paper has a great auxiliary effect on the optimization of personalized training for athletes in terms of physical function monitoring, sports status statistics, and training intensity setting, which provides new ideas and methods for the integration of personalized training for athletes with cutting-edge information technology.