bluuebunny/crossref_metadata_2025_split huggingface dataset:
Research on the innovation path of “one axis and two wheels” course ideology and politics of mechanical majors under the support of cloud computing
Authors: Li Yanfang 
Published: November 2024

Abstract
“Curriculum Civics” is a reform measure of human education put forward under the fundamental pursuit and urgent demand for human education in colleges and universities in the development of the country and society, and it is an important hand for colleges and universities to realize the fundamental task of establishing morality and educating people. This paper focuses on the problems that exist in the education and teaching of Civics and Politics in college and university mechanical courses, takes the core qualities of mechanical disciplines as the “axis” and takes the national courses and school-based courses as the “two-wheel drive” to build the “one-axis-two-wheels The curriculum system of “one axis and two wheels” is constructed with the national curriculum and school-based curriculum as the “two-wheel drive”. Based on the whole perspective learning theory and ARCS motivation model, combined with the “one axis and two wheels” curriculum system, the innovative model of Civic and Political Teaching for Mechanical Professional Courses is constructed. The teaching experiment method was utilized to implement the teaching design that included Civics elements in the experimental class. Analyzing the data of the control class and the experimental class, the conclusion shows that the ideological and political literacy of the experimental class (4.7119±0.9943) is significantly greater than that of the control class (4.1696±0.8406), and at the same time, the end-of-course averages of the “Fundamentals of Mechanical” course of the experimental class and the control class are 85.25 and 76.41, respectively, which are also significantly different (P<0.05). It indicates that the teaching mode of Civics and Politics of Mechanical Specialty Course can improve the students’ Civics and Politics level, indirectly contribute to the improvement of their grades in the specialty courses, so that the students can realize the moral and technical skills and cultivation.
Intelligent Optimization Method and Network Security Analysis for Power System Active Control
Authors: Ding Chaojie , Li Lanqing , Zhou Nan , Gu Dongjian 
Published: November 2024

Abstract
Optimizing the active control of the power system and improving the stability of the system in the face of cyber-attacks are necessary to secure the power supply and achieve energy saving and emission reduction. The article proposes an improved granular computing method applied to power system active control, which includes chaotic initialization, dynamic parameter adjustment, and a fast search strategy. And also provides two strategies for defense against FDIA and DDoS attacks. The minimum cost of the IGrC algorithm in 5, 10 and 30 unit systems are 43115.67$, 1017569.51$ and 10170321$, and it has good convergence and robustness. The F1 value of the vertical and horizontal prediction algorithm used is more than 90% in all environments, and the optimal marginal cost of this paper’s algorithm is 44.8357 regardless of whether or not it is facing DDoS attacks. Therefore, the active control optimization method and attack defense strategy proposed in this paper have practical application effects.
Advances in computer AI-assisted multimodal data fusion techniques
Authors: Fan Pan , Wu Qiang 
Published: November 2024

Abstract
Through the integration of multimodal data fusion technology and computer AI technology, people’s needs for intelligent life can be better met. This paper introduces the alignment and perception algorithm for multimodal data fusion, which is based on combining the multimodal data fusion model. Taking the air pollutant concentration prediction as an example, the time series of air pollutant concentration is obtained through the LSTM model for concentration prediction, and the attention mechanism is introduced to establish the numerical prediction model of air pollution. Different stations are also selected to acquire weather image data, and the TS-Conv-LSTM multimodal spatio-temporal fusion model of air quality images is constructed by utilizing the Conv-LSTM cell as an encoder, and then the TransConv-LSTM cell, which integrates the anti-convolution and the long-short-term memory network cell, as a decoder. The Gaussian regression model was then used to combine numerical prediction and image prediction models, thus achieving the multimodal synergistic prediction of air pollutant concentrations. The RMSE of the ATT-LSTM model on the dataset was reduced to 8.03 compared to the comparison model, and the predictive fit to the image dataset was above 0.75 for all R² values. The lowest MAE value obtained by the multimodal collaborative prediction model is only 3.815, and the highest R² value is up to 0.985. Introducing deep learning techniques into multimodal data fusion helps to explore the value of massive data more deeply and obtain more comprehensive and reliable information about it.
Behavioral Pattern Identification of E-commerce Consumers’ Purchase Intention in Big Data Environment
Authors: Gao Feng , Peng Caigen 
Published: November 2024

Abstract
Predicting user purchase behavior using shopping history data on e-commerce platforms helps to improve user experience and marketing effect. Our paper uses the time-sliding window method to construct features that mine users’ interest preferences in different periods based on the real interaction records between users and products in e-commerce scenarios. Then, a model for predicting user purchase behavior based on CNN-LSTM is proposed. By automatically extracting and selecting user attributes, product attributes, and user behavioral features, the model is used to predict user purchasing behavior. An online retail platform implements precision marketing using this model. The results show that the calculated values of the marketing effect in the Attention Stage, Interest Stage and Active Participation Stage are between [0.8-1.0], and the effect of Precision Marketing is “Excellent”. The calculated value of the marketing effect in the action stage and repeat purchase stage is between [0.6-0.8], and the effect of precision marketing is “good”. After the implementation of precision marketing, the operating income of e-commerce platform A is increasing, while the operating expense ratio remains stable. This paper’s model can effectively improve consumers’ purchase intention, as evidenced by its findings.
A study on the relationship between physical education teaching strategies and students’ physical fitness based on big data analysis
Authors: Guo Qiang , Liu Yamei , Guo Li 
Published: November 2024

Abstract
Physical education occupies a very important position in university programs. Cultivating students’ physical fitness and comprehensive ability is the important significance of the existence and development of university sports. In this paper, after collecting and processing data related to students’ physical fitness tests, the K-means clustering algorithm is improved to cluster and analyze the physical fitness data of different students. Then, the FP-growth algorithm based on association rules mines the relationship between the physical fitness indicators of different groups of students, obtains the optimization strategy of physical education teaching, and introduces the logistic regression model to analyze the correlation relationship between the physical education teaching strategy based on big data optimization and students’ physical fitness. The results showed that whether the 20m round-trip running (students’ speed) was qualified or not was positively correlated with the frequency of application of physical education teaching strategies (sometimes used, not used, or occasionally used) (OR values of 1.49 and 2.12, respectively, with p-values less than 0.05) and that it was conducive to promoting the normal development of college students’ physique and physical fitness through the application of physical education teaching strategies.
An Advanced Approach to Optimizing Teaching Strategies Using Game Theory in English Curriculum Ideology and Politics
Authors: Hu Zhihong 
Published: November 2024

Abstract
Ideology and politics education is both an innovative practice of professional talent training reform in colleges and universities and a realistic response to the cultivation of high-quality and high-level talents in the society of the new era. This paper creates an evolutionary game model that represents the ideology and politics of the English curriculum for teachers and students. Based on the evolutionary stability strategy, we put forward the game hypothesis, use the replication dynamic equation to construct the evolutionary game model for the optimization of the English course, and describe the model stability test principle and calculation process. Our exploration of the English course is based on game theory using the data simulation method. The cost directly impacts the likelihood of teachers’ ideologies and political education as well as students’ ability to take a serious attitude. When both sides of the benefit exceed 75, both sides of the evolutionary stability strategy adopt serious attitudes, leading to an increase in the speed of choosing a serious attitude. This increase is also reflected in the results of the overall game analysis, which determines the teaching strategy for the English curriculum’s ideology and politics. The complete interpretation of game theory regarding the English course optimization effect is necessary to enhance the level of ideological and political education in colleges and universities.
Personalized Recommendation Mechanism of College English Writing Teaching Content Based on OBE Concept in Big Data Environment
Authors: Huang Xuemei , Tan Haoliang 
Published: November 2024

Abstract
With the rapid development of education information technology today, more and more educators are beginning to consider how to implement more personalized education and better align teaching to students’ abilities. This paper explores personalized recommendations for college English writing instruction, utilizing the concept of Personalized Behavior Education (OBE). This paper first analyzed English-related data from 200 college students, including vocabulary, grammar, reading, and speaking abilities. Cluster analysis is used to classify the abilities of different groups in English writing. Collaborative filtering recommendation modeling is used to recommend writing methods for college students that are appropriate for them individually. This paper revealed that the first group of students utilizes 14% and 26% more quotations and advanced vocabulary, respectively, compared to the fifth group. Simulation experiments reveal that setting parameter 1 to 0.6 and parameter 2 to 0.2 results in a lower and more stable MAE value, thereby lessening its influence on the recommendation algorithm. Furthermore, the performance test results demonstrate that the precision value of this paper’s scheme surpasses that of the traditional CF algorithm by a range of 0.088 to 0.14, and the recall value of this paper’s algorithm surpasses that of the traditional CF method, with a maximum difference of 0.11. In addition, the actual accuracy of this paper’s personalized recommender system is 6.2% higher than the expected one, and it takes only 1s to provide the user with a personalized recommender’s response time. In summary, the experiments above reflect the high efficiency of this paper’s recommender system and the high acceptance of students.
Research on College Counselors’ Ability to Guide College Students’ Employment and Entrepreneurship Based on Analysis of Big Data Technology
Authors: Lei Yonghan 
Published: November 2024

Abstract
Employment is the basis of people’s livelihood, and in the face of the severe employment problems of college students, the guidance role and ability of college counselors are becoming more and more prominent. Based on the current situation of college students’ employment and entrepreneurship and the responsibilities of college counselors in this area, the author uses multiple linear regression analysis to explore the ability of college counselors to guide college students’ employment and entrepreneurship. After conducting factor analysis, descriptive statistical analysis, correlation analysis and regression analysis on the employment and entrepreneurship ability of college counselors through the example study, the influencing factors of the employment and entrepreneurship counseling ability of college counselors are identified. The four public factors extracted through principal component analysis have a total explained variance of over 98%. The mean and median of the overall employment and entrepreneurship counseling competence of the college counselors surveyed in this study were slightly greater than 4. Among the eight secondary indicators, the highest score was Q5 Entrepreneurship Policy Propaganda (4.28), while the lowest score was Q7 Innovation and Entrepreneurship Competition Guidance (3.67). The mean value of overall employment and entrepreneurship guidance competence is 4.13. There is a significant correlation between the employment and entrepreneurship guidance competence of college counselors and each sub-dimension. The correlation coefficients of overall employment and entrepreneurship guidance competence (EGA) with employment guidance competence (EGA1), entrepreneurship guidance competence (EGA2), internship guidance (Q2), and career planning and counseling (Q3) were 0.964, 0.902, 0.924, and 0.928, respectively. Counselors with more than 10 years of work experience were more skilled in career guidance than counselors with less than 5 years of experience.
Functional framework construction of blockchain technology in the information management of college students’ ideological and political education in colleges and universities
Authors: Li Lin 
Published: November 2024

Abstract
Currently, ideological and political education faces three major pain points: mismatch of information supply and demand, obstacles to resource sharing, and lack of trust between the subject and object of education. Based on this, this study applies blockchain technology to the informatization management of college students’ ideological and political education in colleges and universities and designs the functional framework and main functional modules of the informatization management platform of college and university ideological and political education. Teachers and students of a regional university are selected as the research samples, and after analyzing the application of the platform by teachers and students of the university, we construct the evaluation indexes of the platform’s system structure and system function and use the DBN model to evaluate the application effect of the platform. The results show that after the use of the Civic and Political Education Informatization Management Platform, the sample teachers and students have a better knowledge and use of it, and 65.8% of the teachers and students have a higher frequency of use. The ideological and political education informatization management platform obtains a better application effect, with evaluation scores of 75.3 and 85.7 for its system structure and system functions, and it obtains excellent performance in the quality and organization of teaching resources, the construction and sharing of high-quality teaching resources, and the improvement of management efficiency. The organic combination of blockchain technology and ideological and political education in colleges and universities can improve the level of ideological and political education and promote innovation and optimization of the education model.
Research on thermal error compensation strategy of CNC machine tools based on full working area modeling
Authors: Pei Xinglin 
Published: November 2024

Abstract
Traditional manufacturing equipment is developing in the direction of precision, intelligence and integration, and the precision requirements of CNC machine tools are getting higher and higher. Compensating and controlling the thermal error of the machine tool is an important part of improving machining accuracy. This paper uses a cuckoo search algorithm to optimize the BP neural network to get the CSBP neural network model and inputs the temperature value of the CNC machine tool into the CSBP model to get the thermal error prediction results. Combined with the thermal error values of each measurement point on the CNC machine table, the B-spline function is used to fit the thermal error prediction model for the entire working area. Finally, the thermal error compensation system of the CNC machine tool has been established. Simulation and empirical studies show that the fitting and prediction accuracy of the model in this paper are better, and the maximum error value of the prediction is reduced by 20.27% compared to the LSTM model. After the application of the thermal compensation system in this paper, the thermal error of the CNC machine tool is greatly reduced, and most of the offset caused by the thermal deformation of the machine tool is eliminated through compensation. In this paper, the design of a thermal error compensation system based on full work area modeling can effectively reduce the thermal error of CNC machine tools, which can be applied to the actual machining process of machine tools to improve the machining accuracy of CNC machine tools.
Intelligent Reconstruction and Effectiveness Evaluation of Nursing Workflow for Future Hospitals
Authors: Su Miao , Wang Dongmei 
Published: November 2024

Abstract
In this paper, the overall structure of the intelligent nursing system is designed in detail to realize the reconstruction of each process of nursing work by the intelligent nursing system. The data of nursing medical record documents of a tertiary hospital were collected as the basis of analysis, and statistical methods were used to compare and analyze the writing of medical records before and after the intelligent reconstruction of nursing workflow. Analysis of variance (ANOVA) and independent paired t-test were chosen to understand the clinical measurement differences between the intelligent nursing system and the traditional technology, and a self-designed questionnaire was used to explore the nurses’ intention to use the intelligent nursing system from their point of view. The results showed that the intelligent nursing system significantly reduced writing time (P≤0.001) and improved medical record writing ability compared with manual methods. The time efficiency of measuring physical sign data was improved by intelligent nursing systems by 74.16% and 69.8% respectively. Nurses’ knowledge of the intelligent nursing system was as high as 63.64%, and functionally, nurses’ intention to use the intelligent nursing system accounted for 85.29%, which was at a medium-high level.
Educational gamification: motivation and engagement strategies
Authors: Ma Junyuan 
Published: November 2024

Abstract
Gamified learning creates authentic situations for students to solve tasks, provides students with active learning opportunities, and is an effective way to cultivate problem-solving ability, critical thinking, creativity, etc. In this paper, we conduct an in-depth study of gamified learning and learning behavior modeling technology, choose the Hidden Markov Model as the technical support of the study, adopt the Bayesian Information Criterion to select the optimal number of hidden states of the model, and combine the K-means algorithm with the model optimization in model parameter estimation, which to a certain extent improves the speed of convergence in the process of learning behavior modeling. The game elements and learning activity elements have been integrated and migrated, and proposed gamified learning activity elements. Activity theory and self-determination theory were used to design a gamified learning activity process for the educational field. The game-based learning activity process was continuously revised and optimized by iterating the process with actual teaching cases. Subsequently, we merged qualitative and quantitative research to examine students’ gamified learning and the changes in their learning effects. The results showed that gamified learning produced significant facilitation (p<0.001) in all four dimensions of mind-flow experience, intrinsic motivation, knowledge retention, and transfer performance, with effect values of 1.05, 1.65, 1.00, and 1.08, respectively. This indicates that the gamified learning model can enable students to stimulate and maintain good motivation for learning, and further demonstrates that the process of designing gamified learning activities has effectiveness.
Research on the optimization of interior design visual elements and art design innovation method under the deep convolutional network framework
Authors: Miao Cuijie 
Published: November 2024

Abstract
As data science and computer science continue to advance, the application of deep learning methods to address technological innovation and optimize interior design has emerged as a significant area of research. In this paper, we propose a VR matching algorithm that utilizes deep convolutional networks to match the appropriate visual elements of the room layout, thereby realizing a panoramic scheme for interior design. A method of automatic extraction of features and labels of room layout based on a convolutional neural network is designed, trained, and tested with the help of a convolutional deep confidence network, and the labeled room layout feature map is classified using the Softmax classifier. A fast retrieval algorithm is being investigated with input in the form of images and combining text and content. Firstly, we input the room layout feature image, extract the feature vector using a convolutional neural network model, and then calculate the similarity between the feature vector in the visual element decorative design drawing and the feature vector of the input room layout image. The maximum similarity is selected as the visual element design scheme of the feature, and then the picture of the panoramic scheme is realized in order to let the designer get the customer’s feedback on the design effect in a more timely manner. The classification models developed in this paper achieve an accuracy rate exceeding 90%, effectively fulfilling the task of classifying images of room layout features. The user’s satisfaction with the interior design methodology experience projects proposed in this paper is also all higher than 70%, and the feedback from both designers and users is very good, which shows the excellent effect of deep convolutional networks in the optimization of interior design visual elements.