Revamped IPE confronts static material constraints and outdated pedagogy, warranting integration of web resources and big data analytics for instructional innovation. Digital IPE adoption in vocational education optimizes online resource use, enhancing teaching effectiveness. Introducing CUPMF, a personalized learning model, we conduct empirical assessments on a large dataset (364,617+ entries) from Smart Classroom's cloud platform and public datasets, reflecting varied IPE scenarios. Comparative experiments against association rule, content-, tag-based, and collaborative filtering algorithms show CUPMF's superiority. It achieves a 11.61% F1 score boost over four alternatives for basic recommendations and outperforms Que Rec by 1.975%. Complexity-wise, CUPMF registers an 11.52% mean F1 score increment over four methods and 1.875% over Que Rec. Proven, CUPMF markedly improves IPE resource recommendation accuracy and efficacy, poised to transform personalized online vocational learning.
改造后的 IPE 面临着静态材料的限制和过时的教学法，需要整合网络资源和大数据分析，以实现教学创新。在职业教育中采用数字化 IPE 可以优化网络资源的使用，提高教学效果。我们引入了个性化学习模型 CUPMF，在智慧课堂云平台的大型数据集（364,617+条目）和公共数据集上进行了实证评估，反映了不同的 IPE 场景。与关联规则算法、基于内容的算法、基于标签的算法和协同过滤算法的对比实验显示了 CUPMF 的优越性。在基本推荐方面，它比四种备选算法的 F1 分数提高了 11.61%，比 Que Rec 高出 1.975%。从复杂性来看，CUPMF 比四种方法的平均 F1 分数提高了 11.52%，比 Que Rec 高出 1.875%。事实证明，CUPMF 显著提高了 IPE 资源推荐的准确性和有效性，有望改变个性化在线职业学习。
{"title":"DNN-Based Resource Recommendation for Ideology Theory Courses Online","authors":"Jinrong Yu, Wenzhang Sun","doi":"10.4018/ijec.349976","DOIUrl":"https://doi.org/10.4018/ijec.349976","url":null,"abstract":"Revamped IPE confronts static material constraints and outdated pedagogy, warranting integration of web resources and big data analytics for instructional innovation. Digital IPE adoption in vocational education optimizes online resource use, enhancing teaching effectiveness. Introducing CUPMF, a personalized learning model, we conduct empirical assessments on a large dataset (364,617+ entries) from Smart Classroom's cloud platform and public datasets, reflecting varied IPE scenarios. Comparative experiments against association rule, content-, tag-based, and collaborative filtering algorithms show CUPMF's superiority. It achieves a 11.61% F1 score boost over four alternatives for basic recommendations and outperforms Que Rec by 1.975%. Complexity-wise, CUPMF registers an 11.52% mean F1 score increment over four methods and 1.875% over Que Rec. Proven, CUPMF markedly improves IPE resource recommendation accuracy and efficacy, poised to transform personalized online vocational learning.","PeriodicalId":46330,"journal":{"name":"International Journal of e-Collaboration","volume":null,"pages":null},"PeriodicalIF":0.2,"publicationDate":"2024-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141923425","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}
This paper makes a comprehensive investigation on the development and implementation of low-carbon student management platform in educational institutions. The platform adopts advanced information technology, including cloud computing and big data analysis, aiming at solving urgent environmental problems by analyzing the dynamic learning data and daily behavior data of college students. The implementation of the platform has greatly reduced carbon emissions, especially greenhouse effect variables and dormitory electricity consumption. The observed impact highlights the effectiveness of using academic management platform to promote carbon emission reduction. However, this study acknowledges the existing limitations and predicts the long-term challenges, and emphasizes the need for continuous innovation and research in the intersection of artificial intelligence and environmental sustainable development. Generally speaking, the development and deployment of low-carbon student management platform is a key step to promote the sustainable development of educational environment.
{"title":"Research on Student Management Platform Based on Big Data Under Low-Carbon Environment","authors":"Xiangang Hu, Chengyu Zhang","doi":"10.4018/ijec.348329","DOIUrl":"https://doi.org/10.4018/ijec.348329","url":null,"abstract":"This paper makes a comprehensive investigation on the development and implementation of low-carbon student management platform in educational institutions. The platform adopts advanced information technology, including cloud computing and big data analysis, aiming at solving urgent environmental problems by analyzing the dynamic learning data and daily behavior data of college students. The implementation of the platform has greatly reduced carbon emissions, especially greenhouse effect variables and dormitory electricity consumption. The observed impact highlights the effectiveness of using academic management platform to promote carbon emission reduction. However, this study acknowledges the existing limitations and predicts the long-term challenges, and emphasizes the need for continuous innovation and research in the intersection of artificial intelligence and environmental sustainable development. Generally speaking, the development and deployment of low-carbon student management platform is a key step to promote the sustainable development of educational environment.","PeriodicalId":46330,"journal":{"name":"International Journal of e-Collaboration","volume":null,"pages":null},"PeriodicalIF":0.2,"publicationDate":"2024-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141799811","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}
This paper discusses the application path and effect evaluation method of big data and artificial intelligence in college student management, aiming at promoting the intelligent and humanized development of management through technological innovation. A BP neural network model (IFOA-IAGA-BP) based on the combination of improved firefly optimization algorithm (IFOA) and improved artificial pigeon colony algorithm (IAGA) is studied and constructed, aiming at improving the accuracy and efficiency of management quality evaluation. This model can identify students' individual needs more accurately, optimize the allocation of teaching resources, improve teaching quality, predict students' learning risks through intelligent algorithms, intervene in time, and provide all-weather learning consultation services, so as to enhance the immediacy and effectiveness of student support services.
{"title":"Innovative Analysis of Student Management Path Based on Artificial Intelligence and Big Data Integration","authors":"Fangfang Zhang, Qiang Liu","doi":"10.4018/ijec.349566","DOIUrl":"https://doi.org/10.4018/ijec.349566","url":null,"abstract":"This paper discusses the application path and effect evaluation method of big data and artificial intelligence in college student management, aiming at promoting the intelligent and humanized development of management through technological innovation. A BP neural network model (IFOA-IAGA-BP) based on the combination of improved firefly optimization algorithm (IFOA) and improved artificial pigeon colony algorithm (IAGA) is studied and constructed, aiming at improving the accuracy and efficiency of management quality evaluation. This model can identify students' individual needs more accurately, optimize the allocation of teaching resources, improve teaching quality, predict students' learning risks through intelligent algorithms, intervene in time, and provide all-weather learning consultation services, so as to enhance the immediacy and effectiveness of student support services.","PeriodicalId":46330,"journal":{"name":"International Journal of e-Collaboration","volume":null,"pages":null},"PeriodicalIF":0.2,"publicationDate":"2024-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141806750","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}
Zero Knowledge Proof (ZKP) is a very effective method of preserving privacy as it hides the most confidential information throughout the transaction. In this paper, we present a security and privacy-preserving approach for blockchain that relies on account and multi-data asset models using the Zero Knowledge Proof (ZKP) mechanism. We provide options for transferring data assets and detecting duplicate expenditures, and we also develop transaction structures, anonymised addresses and anonymised metadata for the data assets. To create and validate the ZKP, we use the zk-SNARKs algorithm and specify validation criteria for masked transactions, and finally conduct experimental tests to validate it. Creating better algorithms for ZKP will be the focus of our future efforts.
{"title":"Research on ZKP Algorithm of Data Asset Security and Privacy Protection Based on Blockchain Technology","authors":"Fei Lan, Junjia Yang, Hao Feng, Wendi Xu, Wenxin Qiu, Zhang Zhao, Yanzuo Chen","doi":"10.4018/ijec.349211","DOIUrl":"https://doi.org/10.4018/ijec.349211","url":null,"abstract":"Zero Knowledge Proof (ZKP) is a very effective method of preserving privacy as it hides the most confidential information throughout the transaction. In this paper, we present a security and privacy-preserving approach for blockchain that relies on account and multi-data asset models using the Zero Knowledge Proof (ZKP) mechanism. We provide options for transferring data assets and detecting duplicate expenditures, and we also develop transaction structures, anonymised addresses and anonymised metadata for the data assets. To create and validate the ZKP, we use the zk-SNARKs algorithm and specify validation criteria for masked transactions, and finally conduct experimental tests to validate it. Creating better algorithms for ZKP will be the focus of our future efforts.","PeriodicalId":46330,"journal":{"name":"International Journal of e-Collaboration","volume":null,"pages":null},"PeriodicalIF":0.2,"publicationDate":"2024-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141809648","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}
With the development of information technology, virtual reality technology has been widely used in many industries. Compared with traditional tourism projects, virtual tourism created by virtual reality technology has many outstanding advantages. This paper presents a panoramic study based on ecotourism. In this paper, in the study of virtual reality technology, firstly, the relevant aspects of panoramic virtual reality are introduced, then a calculation formula is established, and its algorithmic techniques and principles are further studied and analysed through the formula, and then the natural environment and sustainable development are introduced and analysed through the establishment of data maps and other methods. The results of the study show that the integration of panoramic technology and ecotourism creates a favourable living environment for the future.
{"title":"Application Research and Analysis of Panoramic Virtual Reality Technology Based on Sustainable Development of the Ecological Environment","authors":"Rui Wu, Muyao Wu","doi":"10.4018/ijec.349568","DOIUrl":"https://doi.org/10.4018/ijec.349568","url":null,"abstract":"With the development of information technology, virtual reality technology has been widely used in many industries. Compared with traditional tourism projects, virtual tourism created by virtual reality technology has many outstanding advantages. This paper presents a panoramic study based on ecotourism. In this paper, in the study of virtual reality technology, firstly, the relevant aspects of panoramic virtual reality are introduced, then a calculation formula is established, and its algorithmic techniques and principles are further studied and analysed through the formula, and then the natural environment and sustainable development are introduced and analysed through the establishment of data maps and other methods. The results of the study show that the integration of panoramic technology and ecotourism creates a favourable living environment for the future.","PeriodicalId":46330,"journal":{"name":"International Journal of e-Collaboration","volume":null,"pages":null},"PeriodicalIF":0.2,"publicationDate":"2024-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141806648","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}
This paper discusses the integration of cutting-edge technologies, especially artificial intelligence (AI) and speech synthesis in UETL environment. By using methods based on artificial intelligence, such as Fuzzy Convolutional Neural Network (FCNN) and Improved Hidden Markov Model (MHMM), this study aims to reform the traditional teaching paradigm. Through the in-depth study of the experiment, it illustrates how these innovations can enhance students' autonomous learning, understanding and participation in English language education. The implementation of speech synthesis mechanism realizes the conversion from real-time speech to text, and promotes interactive learning experience and personalized feedback. The comparative analysis before and after adopting advanced teaching methods shows that students' learning achievements and the overall effectiveness of UETL process have been significantly improved. This study emphasizes the revolutionary potential of integrating artificial intelligence and speech synthesis technology to optimize college English education.
{"title":"A Teaching Mode of College English Listening in Intelligent Phonetic Environments","authors":"Xin Yan","doi":"10.4018/ijec.347986","DOIUrl":"https://doi.org/10.4018/ijec.347986","url":null,"abstract":"This paper discusses the integration of cutting-edge technologies, especially artificial intelligence (AI) and speech synthesis in UETL environment. By using methods based on artificial intelligence, such as Fuzzy Convolutional Neural Network (FCNN) and Improved Hidden Markov Model (MHMM), this study aims to reform the traditional teaching paradigm. Through the in-depth study of the experiment, it illustrates how these innovations can enhance students' autonomous learning, understanding and participation in English language education. The implementation of speech synthesis mechanism realizes the conversion from real-time speech to text, and promotes interactive learning experience and personalized feedback. The comparative analysis before and after adopting advanced teaching methods shows that students' learning achievements and the overall effectiveness of UETL process have been significantly improved. This study emphasizes the revolutionary potential of integrating artificial intelligence and speech synthesis technology to optimize college English education.","PeriodicalId":46330,"journal":{"name":"International Journal of e-Collaboration","volume":null,"pages":null},"PeriodicalIF":0.2,"publicationDate":"2024-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141821471","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}
Integrated learning has attracted much attention from industry and academia. In the new era, colleges and universities need to discuss information management in light of actual conditions, integrate different data in each information system into the same database, so as to form a data warehouse based on the integrated database which can truly reflect the historical changes of data and provides support for managers' decision-making. This paper analyzes the clustering effect of standard differential evolution algorithm, improved differential evolution algorithm and K-means algorithm. The algorithm is tested using Iris and Wine database marts, the results show that the K-means algorithm is a relatively poor algorithm and its accuracy is significantly lower than the other two. Based on big data, multi-factor interactive variance analysis technology is used to analyze different data indicators and influencing factors. Therefore, colleges and universities can use the database to better understand the problems and advantages in management, thus to improve management efficiency and teaching level.
{"title":"Application of Big Data in College Student Education Management Based on Data Warehouse Technology and Integrated Learning","authors":"Junping Zhou, Xueyuan Li","doi":"10.4018/ijec.346368","DOIUrl":"https://doi.org/10.4018/ijec.346368","url":null,"abstract":"Integrated learning has attracted much attention from industry and academia. In the new era, colleges and universities need to discuss information management in light of actual conditions, integrate different data in each information system into the same database, so as to form a data warehouse based on the integrated database which can truly reflect the historical changes of data and provides support for managers' decision-making. This paper analyzes the clustering effect of standard differential evolution algorithm, improved differential evolution algorithm and K-means algorithm. The algorithm is tested using Iris and Wine database marts, the results show that the K-means algorithm is a relatively poor algorithm and its accuracy is significantly lower than the other two. Based on big data, multi-factor interactive variance analysis technology is used to analyze different data indicators and influencing factors. Therefore, colleges and universities can use the database to better understand the problems and advantages in management, thus to improve management efficiency and teaching level.","PeriodicalId":46330,"journal":{"name":"International Journal of e-Collaboration","volume":null,"pages":null},"PeriodicalIF":0.2,"publicationDate":"2024-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141823722","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}
Rui Wang, Xin Liu, Yingxian Chang, Donglan Liu, Honglei Yao
Photovoltaic power generation system plays an important role in renewable energy. Therefore, accurately predicting the short-term output power of photovoltaic system has become a key challenge for real-time power grid management. This study focuses on Yingli's green energy photovoltaic system, and uses the convolution neural network and long-term and short-term memory network fusion model (CNN-LSTM) to predict the short-term power. The model integrates CNN's data feature extraction and LSTM's time series prediction ability, showing high accuracy and stability. The experimental results show that CNN-LSTM model has a low mean and variance of prediction error, and the prediction is stable and reliable, and it is consistent in different scenarios. This provides theoretical support for the output power prediction of photovoltaic system based on deep learning.
{"title":"Short-Term Photovoltaic System Output Power Prediction Based on Integrated Deep Learning Algorithms in the Clean Energy Sector","authors":"Rui Wang, Xin Liu, Yingxian Chang, Donglan Liu, Honglei Yao","doi":"10.4018/ijec.346979","DOIUrl":"https://doi.org/10.4018/ijec.346979","url":null,"abstract":"Photovoltaic power generation system plays an important role in renewable energy. Therefore, accurately predicting the short-term output power of photovoltaic system has become a key challenge for real-time power grid management. This study focuses on Yingli's green energy photovoltaic system, and uses the convolution neural network and long-term and short-term memory network fusion model (CNN-LSTM) to predict the short-term power. The model integrates CNN's data feature extraction and LSTM's time series prediction ability, showing high accuracy and stability. The experimental results show that CNN-LSTM model has a low mean and variance of prediction error, and the prediction is stable and reliable, and it is consistent in different scenarios. This provides theoretical support for the output power prediction of photovoltaic system based on deep learning.","PeriodicalId":46330,"journal":{"name":"International Journal of e-Collaboration","volume":null,"pages":null},"PeriodicalIF":0.2,"publicationDate":"2024-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141823185","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}
In view of the rapid development of information technology, the cultivation and promotion of digital literacy of higher vocational teachers has become an important issue in the field of education. The application of deep learning and soft computing technology provides strong technical support for this. This paper is to explore the construction and promotion path of digital literacy evaluation model of higher vocational teachers from the perspective of “AI+”. This study deeply analyzes the status quo of digital literacy of higher vocational teachers, and focuses on the combination and application potential of deep learning and intelligent algorithm in the evaluation model and promotion path of digital literacy of higher vocational teachers based on “AI+” perspective. This research plays an important role in promoting personalized education and cultivating talents with high-quality technical skills. Future research will further deepen relevant theories and promote the scientific, standardized and intelligent evaluation model of digital literacy of higher vocational teachers.
{"title":"Construction and Improvement Path of Digital Literacy Evaluation Model for Higher Vocational Teachers Based on Deep Learning and Soft Computing","authors":"Gan Chen","doi":"10.4018/ijec.347506","DOIUrl":"https://doi.org/10.4018/ijec.347506","url":null,"abstract":"In view of the rapid development of information technology, the cultivation and promotion of digital literacy of higher vocational teachers has become an important issue in the field of education. The application of deep learning and soft computing technology provides strong technical support for this. This paper is to explore the construction and promotion path of digital literacy evaluation model of higher vocational teachers from the perspective of “AI+”. This study deeply analyzes the status quo of digital literacy of higher vocational teachers, and focuses on the combination and application potential of deep learning and intelligent algorithm in the evaluation model and promotion path of digital literacy of higher vocational teachers based on “AI+” perspective. This research plays an important role in promoting personalized education and cultivating talents with high-quality technical skills. Future research will further deepen relevant theories and promote the scientific, standardized and intelligent evaluation model of digital literacy of higher vocational teachers.","PeriodicalId":46330,"journal":{"name":"International Journal of e-Collaboration","volume":null,"pages":null},"PeriodicalIF":0.2,"publicationDate":"2024-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141821389","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}
This study aims to explore the forecasting effect of smart tourism passenger flow supported by big data technology and improve the intelligence of smart tourism. In view of the differences in tourist traffic due to different times, the tourist traffic data in Xi'an from May 1, 2020, to April 1, 2021 are used as the sample period. Autoregressive Integrated Moving Average (ARIMA) is used to build a smart model of the tourism passenger flow prediction. The predictive performance of the constructed model is evaluated and analyzed. The results show that the prediction errors of the model algorithm Root Mean Squared Error (RMSE) and Mean Squared Error (MSE) are 2.22×10^1 and 4.95×10^2, respectively, which are smaller than other algorithms. The error is compared with the actual passenger flow with the highest accuracy. Therefore, the constructed model has high prediction accuracy in predicting and analyzing smart tourism passenger flow, which can provide a reference for the later tourist management and intelligent development of scenic spots.
{"title":"Forecasting Smart Tourism Visitor Flows Leveraging Big Data Technology Assistance","authors":"Guoqiang Tong","doi":"10.4018/ijec.346809","DOIUrl":"https://doi.org/10.4018/ijec.346809","url":null,"abstract":"This study aims to explore the forecasting effect of smart tourism passenger flow supported by big data technology and improve the intelligence of smart tourism. In view of the differences in tourist traffic due to different times, the tourist traffic data in Xi'an from May 1, 2020, to April 1, 2021 are used as the sample period. Autoregressive Integrated Moving Average (ARIMA) is used to build a smart model of the tourism passenger flow prediction. The predictive performance of the constructed model is evaluated and analyzed. The results show that the prediction errors of the model algorithm Root Mean Squared Error (RMSE) and Mean Squared Error (MSE) are 2.22×10^1 and 4.95×10^2, respectively, which are smaller than other algorithms. The error is compared with the actual passenger flow with the highest accuracy. Therefore, the constructed model has high prediction accuracy in predicting and analyzing smart tourism passenger flow, which can provide a reference for the later tourist management and intelligent development of scenic spots.","PeriodicalId":46330,"journal":{"name":"International Journal of e-Collaboration","volume":null,"pages":null},"PeriodicalIF":0.2,"publicationDate":"2024-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141641691","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}
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