Pub Date : 2023-12-05DOI: 10.2478/amns.2023.2.01372
Lu Xu
Abstract In this paper, a Gaussian mixture network distribution finance has been carried out to assess the risk, which is used as a risk assessment tool for the visual platform of higher vocational financial education. Financial data is quantified and determined by determining the cumulative expected loss amount to establish the financial investment risk assessment function. The Activiti open-source workflow engine was utilized to remove complex financial data and configure the K-line as the platform’s data visualization tool. Finally, the financial education visualization platform was used to analyze the Gaussian distribution and K-line data of X stock, which verified the practicality of the platform, and the effectiveness of the platform was verified by taking the students of H higher vocational college as the sample of the teaching experiment. The results show that the influence coefficient of the platform teaching on the quality of the course is 0.856, and the influence coefficient on the learning interest is 0.887, which indicates that the visual platform teaching makes students interested and strengthens their cognitive level. The visual digital reform of teaching finance majors in colleges and universities is provided with a new reference direction by this paper.
{"title":"Construction of a Visual Platform for Higher Vocational Financial Education Combining Gaussian Hybrid Networks","authors":"Lu Xu","doi":"10.2478/amns.2023.2.01372","DOIUrl":"https://doi.org/10.2478/amns.2023.2.01372","url":null,"abstract":"Abstract In this paper, a Gaussian mixture network distribution finance has been carried out to assess the risk, which is used as a risk assessment tool for the visual platform of higher vocational financial education. Financial data is quantified and determined by determining the cumulative expected loss amount to establish the financial investment risk assessment function. The Activiti open-source workflow engine was utilized to remove complex financial data and configure the K-line as the platform’s data visualization tool. Finally, the financial education visualization platform was used to analyze the Gaussian distribution and K-line data of X stock, which verified the practicality of the platform, and the effectiveness of the platform was verified by taking the students of H higher vocational college as the sample of the teaching experiment. The results show that the influence coefficient of the platform teaching on the quality of the course is 0.856, and the influence coefficient on the learning interest is 0.887, which indicates that the visual platform teaching makes students interested and strengthens their cognitive level. The visual digital reform of teaching finance majors in colleges and universities is provided with a new reference direction by this paper.","PeriodicalId":52342,"journal":{"name":"Applied Mathematics and Nonlinear Sciences","volume":"52 11","pages":""},"PeriodicalIF":3.1,"publicationDate":"2023-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138600748","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-12-05DOI: 10.2478/amns.2023.2.01357
Lujuan Xin
Abstract In this paper, we first extracted the time-domain features, frequency-domain features and spatial-domain features of EEG signals, combined with the three-stage feature selection algorithm applicable to the binary classification problem and the multi-classification problem, and constructed the SEE model for emotion recognition based on EEG signals. Then, based on the three-level design model of emotion, emotion decoding and labeling are carried out on the instinctive layer, behavioral layer and reflective layer of product design, and the constructed model is combined to improve the product design emotionally. Finally, after analyzing the results of product emotion annotation, we explore the performance of the EEG-based emotion recognition model and the improvement effect of product design emotionalization. The results showed that the average accuracy of the EEG signal emotion recognition model for various emotion recognition was about 0.99, and the intensity of emotion intensity in Dahe was 0.32 and 0.25, respectively, accounting for 0.57 of the total sample, and the performance evaluation indicators of the eight emotions were greater than 0.85. Ninety percent of product experiencers had pre- and post-improvement differences between [0.12, 0.22] for happiness and [-0.20, -0.04] for dissatisfaction.
{"title":"Research on Emotional Improvement of Product Design Based on Emotion Recognition Technology","authors":"Lujuan Xin","doi":"10.2478/amns.2023.2.01357","DOIUrl":"https://doi.org/10.2478/amns.2023.2.01357","url":null,"abstract":"Abstract In this paper, we first extracted the time-domain features, frequency-domain features and spatial-domain features of EEG signals, combined with the three-stage feature selection algorithm applicable to the binary classification problem and the multi-classification problem, and constructed the SEE model for emotion recognition based on EEG signals. Then, based on the three-level design model of emotion, emotion decoding and labeling are carried out on the instinctive layer, behavioral layer and reflective layer of product design, and the constructed model is combined to improve the product design emotionally. Finally, after analyzing the results of product emotion annotation, we explore the performance of the EEG-based emotion recognition model and the improvement effect of product design emotionalization. The results showed that the average accuracy of the EEG signal emotion recognition model for various emotion recognition was about 0.99, and the intensity of emotion intensity in Dahe was 0.32 and 0.25, respectively, accounting for 0.57 of the total sample, and the performance evaluation indicators of the eight emotions were greater than 0.85. Ninety percent of product experiencers had pre- and post-improvement differences between [0.12, 0.22] for happiness and [-0.20, -0.04] for dissatisfaction.","PeriodicalId":52342,"journal":{"name":"Applied Mathematics and Nonlinear Sciences","volume":"20 3","pages":""},"PeriodicalIF":3.1,"publicationDate":"2023-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138600892","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-12-05DOI: 10.2478/amns.2023.2.01365
Yangjun Jing
Abstract This paper focuses on the design of an educational early warning mechanism based on the fusion of ideological education and multi-featured data so as to manage the educational situation of students in colleges and universities efficiently and accurately. In this paper, the wavelet transform, discrete Fourier transform, and lag sequence analysis algorithms are used to effectively extract temporal features of students’ behaviors. PageRank and Hit’s algorithms are employed to extract features related to student concept maps. The emotional tendencies recognition interface provided by Tencent Cloud was used to obtain the emotional features of students’ speeches. Following this, a multi-feature fusion was performed to depict the students’ learning. A Hive-based data warehouse is used to integrate heterogeneous data from multiple sources. Finally, the education early warning model based on multi-feature data fusion is introduced, and the operation mechanism of early warning mechanism for ideological and political education in colleges and universities is established. To verify the effect of this paper’s model against other algorithms, this paper’s model achieves the optimal performance in the F1 score in negative samples, which is 0.91, followed by the TPA-LSTM algorithm, which is 0.88. Before the optimization of the early warning mechanism, the average per capita absenteeism of the students was 1.32 sessions, and the rate of disciplinary actions was 0.0291. At the end of the academic year, the average per capita absence rate decreases to 1.24 sessions, and the disciplinary action rate decreases to 0.0245.
{"title":"Integration Development of Civic Education and Student Management in Colleges and Universities Based on Combining Data Fusion Model in the Context of Exquisite Parenting","authors":"Yangjun Jing","doi":"10.2478/amns.2023.2.01365","DOIUrl":"https://doi.org/10.2478/amns.2023.2.01365","url":null,"abstract":"Abstract This paper focuses on the design of an educational early warning mechanism based on the fusion of ideological education and multi-featured data so as to manage the educational situation of students in colleges and universities efficiently and accurately. In this paper, the wavelet transform, discrete Fourier transform, and lag sequence analysis algorithms are used to effectively extract temporal features of students’ behaviors. PageRank and Hit’s algorithms are employed to extract features related to student concept maps. The emotional tendencies recognition interface provided by Tencent Cloud was used to obtain the emotional features of students’ speeches. Following this, a multi-feature fusion was performed to depict the students’ learning. A Hive-based data warehouse is used to integrate heterogeneous data from multiple sources. Finally, the education early warning model based on multi-feature data fusion is introduced, and the operation mechanism of early warning mechanism for ideological and political education in colleges and universities is established. To verify the effect of this paper’s model against other algorithms, this paper’s model achieves the optimal performance in the F1 score in negative samples, which is 0.91, followed by the TPA-LSTM algorithm, which is 0.88. Before the optimization of the early warning mechanism, the average per capita absenteeism of the students was 1.32 sessions, and the rate of disciplinary actions was 0.0291. At the end of the academic year, the average per capita absence rate decreases to 1.24 sessions, and the disciplinary action rate decreases to 0.0245.","PeriodicalId":52342,"journal":{"name":"Applied Mathematics and Nonlinear Sciences","volume":"135 38","pages":""},"PeriodicalIF":3.1,"publicationDate":"2023-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138598948","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-12-05DOI: 10.2478/amns.2023.2.01345
Xiang Li
Abstract In this paper, a 3D model based on semantic annotation is constructed with the goal of improving the efficiency of building interior design. By analyzing the basic methods of semantic annotation for building interiors, the method of positioning and map construction is selected to obtain the indoor point cloud data. The distance between 3D spatial lines is calculated using the frame line extraction algorithm, and the target area of the frame line candidate is divided according to the distance. According to the principle of detecting raster circles using the Hough transform, an interior design structure recognition method is proposed for recognizing windows, doors, and walls in building interiors. The results show that the modeling time of the semantically annotated 3D model is 10 seconds faster than the other models on the wall; 9 seconds are saved on the door modeling, and 7 seconds are saved on the window modeling. The visualization effect of semantically annotated 3D models is mostly concentrated in (0.5-1), and a large number of data points are distributed in (0.6-0.9), which indicates that the visualization effect of semantically annotated 3D models is better. The semantically annotated 3D model proposed in this paper can improve the visualization of architectural interior design, which can improve the efficiency of designers to a certain extent.
{"title":"Visualization analysis of architectural interior design combined with virtual reality technology under new process conditions","authors":"Xiang Li","doi":"10.2478/amns.2023.2.01345","DOIUrl":"https://doi.org/10.2478/amns.2023.2.01345","url":null,"abstract":"Abstract In this paper, a 3D model based on semantic annotation is constructed with the goal of improving the efficiency of building interior design. By analyzing the basic methods of semantic annotation for building interiors, the method of positioning and map construction is selected to obtain the indoor point cloud data. The distance between 3D spatial lines is calculated using the frame line extraction algorithm, and the target area of the frame line candidate is divided according to the distance. According to the principle of detecting raster circles using the Hough transform, an interior design structure recognition method is proposed for recognizing windows, doors, and walls in building interiors. The results show that the modeling time of the semantically annotated 3D model is 10 seconds faster than the other models on the wall; 9 seconds are saved on the door modeling, and 7 seconds are saved on the window modeling. The visualization effect of semantically annotated 3D models is mostly concentrated in (0.5-1), and a large number of data points are distributed in (0.6-0.9), which indicates that the visualization effect of semantically annotated 3D models is better. The semantically annotated 3D model proposed in this paper can improve the visualization of architectural interior design, which can improve the efficiency of designers to a certain extent.","PeriodicalId":52342,"journal":{"name":"Applied Mathematics and Nonlinear Sciences","volume":"130 40","pages":""},"PeriodicalIF":3.1,"publicationDate":"2023-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138599057","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-12-05DOI: 10.2478/amns.2023.2.01350
Junchen Liu
Abstract The right of education and discipline is an important way of school education and teaching management, teachers to fulfill the teaching and educating people, the implementation of the fundamental task of moral education. This paper firstly discusses the dilemma of exercising the right to discipline teachers in education, and also analyzes the legal nature of the right to discipline in education and the impact on the emotional performance of teachers and students in the process of exercising the right. Secondly, cochlear filtering combined with CNN and LSTM network is introduced to extract the speech characteristics of teachers in the process of exercising the right of education and discipline, and a hybrid neural network model is used to realize the recognition and prediction of students’ auditory emotions. Finally, in order to verify the effectiveness of the method of this paper, experimental test analysis was carried out, and a comprehensive rule of law guarantee proposal was given in the process of exercising the right of teachers’ educational discipline. The results show that the maximum value of the intensity of the teacher’s speech signal after processing using the cochlear filter is 78.28dB, and the difference with the original signal is only 0.32%. The accuracy of recognizing students’ auditory emotions reached 90.48% after over 50 iterations. Under the background of big data, the right to discipline teachers in education needs to be analyzed with the help of technology for the data analysis of the appropriateness of exercise, and it is united in a number of aspects, such as strengthening the legislation, standardizing the implementation, strengthening the supervision, and perfecting the relief, as a way to help the comprehensive rule of law operation of the right to discipline teachers in education.
{"title":"Problem Analysis and Legal Protection of the Exercise of Teachers’ Educational Disciplinary Rights Based on the Background of Big Data","authors":"Junchen Liu","doi":"10.2478/amns.2023.2.01350","DOIUrl":"https://doi.org/10.2478/amns.2023.2.01350","url":null,"abstract":"Abstract The right of education and discipline is an important way of school education and teaching management, teachers to fulfill the teaching and educating people, the implementation of the fundamental task of moral education. This paper firstly discusses the dilemma of exercising the right to discipline teachers in education, and also analyzes the legal nature of the right to discipline in education and the impact on the emotional performance of teachers and students in the process of exercising the right. Secondly, cochlear filtering combined with CNN and LSTM network is introduced to extract the speech characteristics of teachers in the process of exercising the right of education and discipline, and a hybrid neural network model is used to realize the recognition and prediction of students’ auditory emotions. Finally, in order to verify the effectiveness of the method of this paper, experimental test analysis was carried out, and a comprehensive rule of law guarantee proposal was given in the process of exercising the right of teachers’ educational discipline. The results show that the maximum value of the intensity of the teacher’s speech signal after processing using the cochlear filter is 78.28dB, and the difference with the original signal is only 0.32%. The accuracy of recognizing students’ auditory emotions reached 90.48% after over 50 iterations. Under the background of big data, the right to discipline teachers in education needs to be analyzed with the help of technology for the data analysis of the appropriateness of exercise, and it is united in a number of aspects, such as strengthening the legislation, standardizing the implementation, strengthening the supervision, and perfecting the relief, as a way to help the comprehensive rule of law operation of the right to discipline teachers in education.","PeriodicalId":52342,"journal":{"name":"Applied Mathematics and Nonlinear Sciences","volume":"140 42","pages":""},"PeriodicalIF":3.1,"publicationDate":"2023-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138598698","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-12-05DOI: 10.2478/amns.2023.2.01348
Cong Li, Hui Li
Abstract This paper extracts the basic elements of the integration of industry and education under the perspective of symbiosis theory and builds a symbiosis model in the communication and cooperation between symbiosis units according to the symbiosis units in the system of integration of industry and education. Influential factors were screened from different levels, and the DEMATEL method was used to determine the importance degree of system factors in combination with the explanatory structural model so as to construct the structural framework of influential factors. The weights of the influencing factors were finally confirmed through analysis, and empirical research was conducted on three colleges and universities, S1, S2, and S3. The overall performance level of S1 college and university integration is excellent, and the comprehensive judgment value of the college and university integration performance of this college and university is 85.342. The overall performance level of the two colleges and universities, S2 and S3, is good, and the comprehensive judgment value of the college and university integration performance of the two colleges and universities is 77.933 and 81.930, respectively. The evaluation study on the integration of industry and education can provide better recommendations for the integration path.
{"title":"Research on Talent Cultivation and Industry-Education Integration Path Construction of College Education under the Perspective of Informatization","authors":"Cong Li, Hui Li","doi":"10.2478/amns.2023.2.01348","DOIUrl":"https://doi.org/10.2478/amns.2023.2.01348","url":null,"abstract":"Abstract This paper extracts the basic elements of the integration of industry and education under the perspective of symbiosis theory and builds a symbiosis model in the communication and cooperation between symbiosis units according to the symbiosis units in the system of integration of industry and education. Influential factors were screened from different levels, and the DEMATEL method was used to determine the importance degree of system factors in combination with the explanatory structural model so as to construct the structural framework of influential factors. The weights of the influencing factors were finally confirmed through analysis, and empirical research was conducted on three colleges and universities, S1, S2, and S3. The overall performance level of S1 college and university integration is excellent, and the comprehensive judgment value of the college and university integration performance of this college and university is 85.342. The overall performance level of the two colleges and universities, S2 and S3, is good, and the comprehensive judgment value of the college and university integration performance of the two colleges and universities is 77.933 and 81.930, respectively. The evaluation study on the integration of industry and education can provide better recommendations for the integration path.","PeriodicalId":52342,"journal":{"name":"Applied Mathematics and Nonlinear Sciences","volume":"75 6","pages":""},"PeriodicalIF":3.1,"publicationDate":"2023-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138600443","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-12-05DOI: 10.2478/amns.2023.2.01353
Chengjian Sheng, Chenxin Lian, Haolin Pang
Abstract In this paper, the human body posture estimation algorithm is used to locate the key points of the human body in the RGB screen, and two human body multi-objective algorithms are used to predict the posture trajectory, and they can overcome the influence of the errors contained in the information recorded by the sensors to a certain extent. Secondly, the spatio-temporal graph convolutional neural network is used to identify human behavior and extract behavioral action features, and through the analysis of the action features, we understand the basketball skill level of the students and put forward the reform strategy of college basketball teaching. Sixty students from the basketball minor class at University Q’s College of Physical Education were selected as research subjects for teaching practice. The results show that the average scores of the students in spot-up shooting, half-court folding dribbling and marching one-handed over-the-shoulder shooting after the reform are higher than those before the reform by 1.80, 1.08, and 1.85, which indicates that the reform of basketball teaching based on big data can improve the students’ interest in learning and their training scores, and enhance the students’ basketball skill level.
{"title":"A Practical Study of Basketball Teaching Reform in Colleges and Universities Based on Big Data","authors":"Chengjian Sheng, Chenxin Lian, Haolin Pang","doi":"10.2478/amns.2023.2.01353","DOIUrl":"https://doi.org/10.2478/amns.2023.2.01353","url":null,"abstract":"Abstract In this paper, the human body posture estimation algorithm is used to locate the key points of the human body in the RGB screen, and two human body multi-objective algorithms are used to predict the posture trajectory, and they can overcome the influence of the errors contained in the information recorded by the sensors to a certain extent. Secondly, the spatio-temporal graph convolutional neural network is used to identify human behavior and extract behavioral action features, and through the analysis of the action features, we understand the basketball skill level of the students and put forward the reform strategy of college basketball teaching. Sixty students from the basketball minor class at University Q’s College of Physical Education were selected as research subjects for teaching practice. The results show that the average scores of the students in spot-up shooting, half-court folding dribbling and marching one-handed over-the-shoulder shooting after the reform are higher than those before the reform by 1.80, 1.08, and 1.85, which indicates that the reform of basketball teaching based on big data can improve the students’ interest in learning and their training scores, and enhance the students’ basketball skill level.","PeriodicalId":52342,"journal":{"name":"Applied Mathematics and Nonlinear Sciences","volume":"44 10","pages":""},"PeriodicalIF":3.1,"publicationDate":"2023-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138600619","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-12-05DOI: 10.2478/amns.2023.2.01346
Ming Kong, Feilong Yu, Zhichao Zhang
Abstract This paper studies the use of artificial intelligence technology in the field of education and the way of empowering vocational education and constructs a wisdom teaching model of vocational education based on artificial intelligence. It also applies entropy weight and a fuzzy comprehensive evaluation model to determine evaluation indexes and weights, constructs a fuzzy relationship matrix, and synthesizes a fuzzy comprehensive evaluation model. Based on the model, the teaching effect of vocational education with artificial intelligence is studied, and the advantages of wisdom teaching in the creation of a learning environment and the triggering of students’ interest in learning, creative thinking and problem-solving ability are analyzed by comparing with traditional teaching methods. The results show that there is a significant difference between the effect of AI teaching and traditional teaching, p<0.05. For problem-solving ability, the average score of AI teaching students (M=4.049) is higher than the average score of traditional teaching (M=3.153), where t=14.745, p=0<0.05. The study is crucial for the utilization of artificial intelligence in education and the modernization and reform of teaching.
{"title":"Research on Artificial Intelligence Enabling High-Quality Development of Vocational Education","authors":"Ming Kong, Feilong Yu, Zhichao Zhang","doi":"10.2478/amns.2023.2.01346","DOIUrl":"https://doi.org/10.2478/amns.2023.2.01346","url":null,"abstract":"Abstract This paper studies the use of artificial intelligence technology in the field of education and the way of empowering vocational education and constructs a wisdom teaching model of vocational education based on artificial intelligence. It also applies entropy weight and a fuzzy comprehensive evaluation model to determine evaluation indexes and weights, constructs a fuzzy relationship matrix, and synthesizes a fuzzy comprehensive evaluation model. Based on the model, the teaching effect of vocational education with artificial intelligence is studied, and the advantages of wisdom teaching in the creation of a learning environment and the triggering of students’ interest in learning, creative thinking and problem-solving ability are analyzed by comparing with traditional teaching methods. The results show that there is a significant difference between the effect of AI teaching and traditional teaching, p<0.05. For problem-solving ability, the average score of AI teaching students (M=4.049) is higher than the average score of traditional teaching (M=3.153), where t=14.745, p=0<0.05. The study is crucial for the utilization of artificial intelligence in education and the modernization and reform of teaching.","PeriodicalId":52342,"journal":{"name":"Applied Mathematics and Nonlinear Sciences","volume":"18 6","pages":""},"PeriodicalIF":3.1,"publicationDate":"2023-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138598558","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-12-05DOI: 10.2478/amns.2023.2.01375
Bi Zhao
Abstract Keeping up with the development of the Internet era, it is imperative for colleges and universities to vigorously carry out the construction of a Chinese language and literature resource base to promote the healthy development of Chinese language and literature education. This paper starts with the construction of Chinese language literature resource base related technology, analyzes the basic model of the cognitive map and the construction of the cognitive map of Chinese language literature. The graph database technology is used to transform the data structure of the resource base and load data from the Chinese literature resource base. Based on the cognitive map and graph database, jointly constructed the Chinese language literature resource base and introduced the fuzzy C-mean integration algorithm to integrate the data resources for better access to Chinese language literature resources. To verify the effectiveness of the Chinese language and literature resource base constructed in this paper, it was tested and analyzed through practice. The results show that the overall average response time of the resource library in this paper is 718.50ms, which is 214.78ms lower than that of the online learning data platform, and the resource library developed in this paper can realize the loss in data sharing to be controlled to be less than 0.5MB. Utilizing the resource library to experiment with teaching Chinese language and literature, the average score of the experimental class increased from 88.96 to 95.23, which is an improvement of 6.27 points. The construction of the Chinese language and literature resource base under the cognitive mapping architecture can effectively enhance the common sharing of Chinese language and literature educational resources and prompt teachers to have richer teaching resources.
{"title":"The Construction of Chinese Language and Literature Resource Base in Colleges and Universities under the Construction of Cognitive Mapping","authors":"Bi Zhao","doi":"10.2478/amns.2023.2.01375","DOIUrl":"https://doi.org/10.2478/amns.2023.2.01375","url":null,"abstract":"Abstract Keeping up with the development of the Internet era, it is imperative for colleges and universities to vigorously carry out the construction of a Chinese language and literature resource base to promote the healthy development of Chinese language and literature education. This paper starts with the construction of Chinese language literature resource base related technology, analyzes the basic model of the cognitive map and the construction of the cognitive map of Chinese language literature. The graph database technology is used to transform the data structure of the resource base and load data from the Chinese literature resource base. Based on the cognitive map and graph database, jointly constructed the Chinese language literature resource base and introduced the fuzzy C-mean integration algorithm to integrate the data resources for better access to Chinese language literature resources. To verify the effectiveness of the Chinese language and literature resource base constructed in this paper, it was tested and analyzed through practice. The results show that the overall average response time of the resource library in this paper is 718.50ms, which is 214.78ms lower than that of the online learning data platform, and the resource library developed in this paper can realize the loss in data sharing to be controlled to be less than 0.5MB. Utilizing the resource library to experiment with teaching Chinese language and literature, the average score of the experimental class increased from 88.96 to 95.23, which is an improvement of 6.27 points. The construction of the Chinese language and literature resource base under the cognitive mapping architecture can effectively enhance the common sharing of Chinese language and literature educational resources and prompt teachers to have richer teaching resources.","PeriodicalId":52342,"journal":{"name":"Applied Mathematics and Nonlinear Sciences","volume":"139 30","pages":""},"PeriodicalIF":3.1,"publicationDate":"2023-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138598634","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-12-05DOI: 10.2478/amns.2023.2.01370
Hua’an Fu, Yang Gao
Abstract This paper explores how the synergistic effect of scale, structure and quality of Certain components can encourage postsecondary education for superior economic growth. Firstly, this study illustrates the synergistic effect of scale, structure, and quality factors in higher education by exploring the theoretical mechanisms of these elements in higher education to promote good economic development. Second, the conventional TOPSIS method is refined, and the entropy power-TO PSIS model is constructed by combining the entropy power method with the regression model built on the theoretical mechanism of the previous paper, and the empirical design is executed. The measurement of high-quality development of higher education and the economy is made possible by this. After building and finishing the evaluation index system for higher education and high economic, high-quality development, the development measurement is examined at the end. Based on the regression results, Analysis is done on the mediation and threshold effects of higher education for the establishment of high-quality economic growth, and pertinent policy recommendations are made. The scale structure and quality criteria of higher education are 40.1,303.1,9.9, and 60.2, respectively. The growth levels of the east, central, and west are 0.27,0.24, and 0.24, respectively, as is the proportion of the mediating influence to the total effect of 0.32 and 0.51. Through the factors of scale, structure, and educational quality, higher education works in concert to optimize technology and industry and, in turn, to foster the excellent growth of the economy.
{"title":"What makes higher education contribute to high-quality economic development - a synergy effect analysis based on scale, structure and quality elements","authors":"Hua’an Fu, Yang Gao","doi":"10.2478/amns.2023.2.01370","DOIUrl":"https://doi.org/10.2478/amns.2023.2.01370","url":null,"abstract":"Abstract This paper explores how the synergistic effect of scale, structure and quality of Certain components can encourage postsecondary education for superior economic growth. Firstly, this study illustrates the synergistic effect of scale, structure, and quality factors in higher education by exploring the theoretical mechanisms of these elements in higher education to promote good economic development. Second, the conventional TOPSIS method is refined, and the entropy power-TO PSIS model is constructed by combining the entropy power method with the regression model built on the theoretical mechanism of the previous paper, and the empirical design is executed. The measurement of high-quality development of higher education and the economy is made possible by this. After building and finishing the evaluation index system for higher education and high economic, high-quality development, the development measurement is examined at the end. Based on the regression results, Analysis is done on the mediation and threshold effects of higher education for the establishment of high-quality economic growth, and pertinent policy recommendations are made. The scale structure and quality criteria of higher education are 40.1,303.1,9.9, and 60.2, respectively. The growth levels of the east, central, and west are 0.27,0.24, and 0.24, respectively, as is the proportion of the mediating influence to the total effect of 0.32 and 0.51. Through the factors of scale, structure, and educational quality, higher education works in concert to optimize technology and industry and, in turn, to foster the excellent growth of the economy.","PeriodicalId":52342,"journal":{"name":"Applied Mathematics and Nonlinear Sciences","volume":"17 8","pages":""},"PeriodicalIF":3.1,"publicationDate":"2023-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138598165","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：