Pub Date : 2017-01-31DOI: 10.14257/ijdta.2017.10.1.16
Li Pingquan
With the development of information technology and network education, as a kind of new teaching method, educational data mining has been widely concerned. In this paper, the author analyzes the data mining application in the college ideological and political education. Through big data mining, the author analyzes the present situation of Ideological and political education, and points out the key points of Ideological and political education reform, including theoretical reform, practical reform and examination reform. At the same time, we analyze the development of Ideological and political education in the context of new media. The result shows that the new media has played an important role in the cultivation of college students' ideological guidance, learning and aesthetic appreciation. Teachers should make full use of new media to strengthen ideological and political education.
{"title":"Research on the Network Data Mining Application in the College Ideological and Political Education","authors":"Li Pingquan","doi":"10.14257/ijdta.2017.10.1.16","DOIUrl":"https://doi.org/10.14257/ijdta.2017.10.1.16","url":null,"abstract":"With the development of information technology and network education, as a kind of new teaching method, educational data mining has been widely concerned. In this paper, the author analyzes the data mining application in the college ideological and political education. Through big data mining, the author analyzes the present situation of Ideological and political education, and points out the key points of Ideological and political education reform, including theoretical reform, practical reform and examination reform. At the same time, we analyze the development of Ideological and political education in the context of new media. The result shows that the new media has played an important role in the cultivation of college students' ideological guidance, learning and aesthetic appreciation. Teachers should make full use of new media to strengthen ideological and political education.","PeriodicalId":13926,"journal":{"name":"International journal of database theory and application","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2017-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85593870","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 : 2017-01-31DOI: 10.14257/ijdta.2017.10.1.12
Meimei Wang, Duoyong Zhang, Huimei Xu
In this paper, we conduct research on environmental quality evaluation model based on data mining and correlation analysis. Along with the application of multi-statistical analysis method, the big data analysis law by has been applied in the environmental quality evaluation. Reciprocities of this method among from many targets starts that changes into a few not related overall targets many targets and the merit lies in had considered the relevance among various targets that can maximum limit retain original information, carries on best comprehensive dimensionality reduction processing to the high dimensional data. Aside by using this feature, this paper proposes the data mining and correlation analysis based model. The basic task of the analytical grey incidence is the microscopic or macroscopic geometry of behavior based factor sequence is close, to analyze and contribution degree of influence or the factor between determination factors to main behavior, but the gray incidence space carries on the foundation of analytical grey incidence. We implement the model on the air and water quality evaluation which are assisted with the neural network and gray analysis. The experimental result reflect the effectiveness of our model, it can evaluate the environmental quality effectively.
{"title":"Construction and Simulation on Environmental Quality Evaluation Model Based on Data Mining and Correlation Analysis","authors":"Meimei Wang, Duoyong Zhang, Huimei Xu","doi":"10.14257/ijdta.2017.10.1.12","DOIUrl":"https://doi.org/10.14257/ijdta.2017.10.1.12","url":null,"abstract":"In this paper, we conduct research on environmental quality evaluation model based on data mining and correlation analysis. Along with the application of multi-statistical analysis method, the big data analysis law by has been applied in the environmental quality evaluation. Reciprocities of this method among from many targets starts that changes into a few not related overall targets many targets and the merit lies in had considered the relevance among various targets that can maximum limit retain original information, carries on best comprehensive dimensionality reduction processing to the high dimensional data. Aside by using this feature, this paper proposes the data mining and correlation analysis based model. The basic task of the analytical grey incidence is the microscopic or macroscopic geometry of behavior based factor sequence is close, to analyze and contribution degree of influence or the factor between determination factors to main behavior, but the gray incidence space carries on the foundation of analytical grey incidence. We implement the model on the air and water quality evaluation which are assisted with the neural network and gray analysis. The experimental result reflect the effectiveness of our model, it can evaluate the environmental quality effectively.","PeriodicalId":13926,"journal":{"name":"International journal of database theory and application","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2017-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77181418","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 : 2017-01-31DOI: 10.14257/IJDTA.2017.10.1.17
Yanjiao Xiao
Discovery in databases knowledge means obtain effective, implicit and potentially useful knowledge from a large number data of database. As China's higher education has been transferred to mass education, the scale of school and the number of students is increasing. By using data mining techniques, the author makes the score analysis of National English test (CET-4), mining useful information hidden in the performance data, then provides theoretical basis for the teaching design and management in English teaching. After K value clustering, we can effectively classify the students, so as to carry on the difference teaching, and this classified teaching will improve the quality of English teaching.
{"title":"Evaluation Method of College Students’ English Proficiency Based on Computer Aided Cluster Analysis","authors":"Yanjiao Xiao","doi":"10.14257/IJDTA.2017.10.1.17","DOIUrl":"https://doi.org/10.14257/IJDTA.2017.10.1.17","url":null,"abstract":"Discovery in databases knowledge means obtain effective, implicit and potentially useful knowledge from a large number data of database. As China's higher education has been transferred to mass education, the scale of school and the number of students is increasing. By using data mining techniques, the author makes the score analysis of National English test (CET-4), mining useful information hidden in the performance data, then provides theoretical basis for the teaching design and management in English teaching. After K value clustering, we can effectively classify the students, so as to carry on the difference teaching, and this classified teaching will improve the quality of English teaching.","PeriodicalId":13926,"journal":{"name":"International journal of database theory and application","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2017-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86298093","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 : 2017-01-31DOI: 10.14257/IJDTA.2017.10.1.18
L. J. Muhammad, S. Salisu, A. Yakubu, Y. M. Malgwi, E. Abdullahi, I. .. Mohammed, N. Muhammad
Road traffic accidents, the inadvertent crash involving at least one motor vehicle, occurring on a road open to public circulation, in which at least one person is injured or killed; intentional acts (murder, suicide) and natural disasters excluded, is indisputably one of the most frequent and most damaging calamities bedeviling human societies, in particular, Nigeria, today. It is therefore, of paramount importance to seek to identify the root causes of road traffic accidents in order to proffer mitigating solutions to address the menace. This research, aimed at predicting the likely causes of road accidents, its prone locations and time along Kano– Wudil highway in order to take all necessary counter measures is a step forward in this direction. In this study data mining decision tree algorithm was used to predict the causes of the accidents, its prone locations and time along Kano – Wudil Highway that links Kano State to Wudil Local Government Area Kano State for effective decision making. performance were analyzed using road accidents data set. The location is between the first 40 kilometers along the Ibadan-Lagos Express road. The work used Multilayer Perceptron as well as Radial Basis Function (RBF) Neural Networks, Id3 and Function Tree algorithms. that the tree algorithm performed with accuracy performed
{"title":"Using Decision Tree Data Mining Algorithm to Predict Causes of Road Traffic Accidents, its Prone Locations and Time along Kano –Wudil Highway","authors":"L. J. Muhammad, S. Salisu, A. Yakubu, Y. M. Malgwi, E. Abdullahi, I. .. Mohammed, N. Muhammad","doi":"10.14257/IJDTA.2017.10.1.18","DOIUrl":"https://doi.org/10.14257/IJDTA.2017.10.1.18","url":null,"abstract":"Road traffic accidents, the inadvertent crash involving at least one motor vehicle, occurring on a road open to public circulation, in which at least one person is injured or killed; intentional acts (murder, suicide) and natural disasters excluded, is indisputably one of the most frequent and most damaging calamities bedeviling human societies, in particular, Nigeria, today. It is therefore, of paramount importance to seek to identify the root causes of road traffic accidents in order to proffer mitigating solutions to address the menace. This research, aimed at predicting the likely causes of road accidents, its prone locations and time along Kano– Wudil highway in order to take all necessary counter measures is a step forward in this direction. In this study data mining decision tree algorithm was used to predict the causes of the accidents, its prone locations and time along Kano – Wudil Highway that links Kano State to Wudil Local Government Area Kano State for effective decision making. performance were analyzed using road accidents data set. The location is between the first 40 kilometers along the Ibadan-Lagos Express road. The work used Multilayer Perceptron as well as Radial Basis Function (RBF) Neural Networks, Id3 and Function Tree algorithms. that the tree algorithm performed with accuracy performed","PeriodicalId":13926,"journal":{"name":"International journal of database theory and application","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2017-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74812245","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 : 2017-01-31DOI: 10.14257/IJDTA.2017.10.1.10
Khawla El Bendadi, Y. Lakhdar, E. Sbai
A credal partition based on belief functions has been proposed in the literature for data clustering. It allows the objects to belong; with different masses of belief; not only to the specific classes, but also to the sets of classes called meta-class which correspond to the disjunction of several specific classes. In this paper, a kernel version of the credal classification rule (CCR) is proposed to perform the classification in feature space of higher dimension. Each singleton class or meta-class is characterized by a center that can be obtained using many way. The kernels based approaches have become popular for several years to solve supervised or unsupervised learning problems. In this paper, our method is extended to the CCR. It is realized by replacing the inner product with an appropriate positive definite function, implicitly perform a nonlinear mapping of the input data into a high-dimensional feature space, and the corresponding algorithm is called kernel Credal Classification Rule( KCCR). We present in this work KCCR algorithm to powerful corresponding nonlinear form using Mercer kernels. The approach is applied for the classification of experimental data collected from a system called VehicleInfrastructure-Driver (VID), based on several representative trajectories observations made in a bend, to obtain adequate results with data experimentally realized based on the instructions given to drivers. The test on real experimental data shows the value of the exploratory analysis method of data. Another experiments using the generated and real data form benchmark database are presented to evaluate and compare the performance of the KCCR method with other classification approaches.
{"title":"Kernel Credal Classification Rule – Application on Road Safety","authors":"Khawla El Bendadi, Y. Lakhdar, E. Sbai","doi":"10.14257/IJDTA.2017.10.1.10","DOIUrl":"https://doi.org/10.14257/IJDTA.2017.10.1.10","url":null,"abstract":"A credal partition based on belief functions has been proposed in the literature for data clustering. It allows the objects to belong; with different masses of belief; not only to the specific classes, but also to the sets of classes called meta-class which correspond to the disjunction of several specific classes. In this paper, a kernel version of the credal classification rule (CCR) is proposed to perform the classification in feature space of higher dimension. Each singleton class or meta-class is characterized by a center that can be obtained using many way. The kernels based approaches have become popular for several years to solve supervised or unsupervised learning problems. In this paper, our method is extended to the CCR. It is realized by replacing the inner product with an appropriate positive definite function, implicitly perform a nonlinear mapping of the input data into a high-dimensional feature space, and the corresponding algorithm is called kernel Credal Classification Rule( KCCR). We present in this work KCCR algorithm to powerful corresponding nonlinear form using Mercer kernels. The approach is applied for the classification of experimental data collected from a system called VehicleInfrastructure-Driver (VID), based on several representative trajectories observations made in a bend, to obtain adequate results with data experimentally realized based on the instructions given to drivers. The test on real experimental data shows the value of the exploratory analysis method of data. Another experiments using the generated and real data form benchmark database are presented to evaluate and compare the performance of the KCCR method with other classification approaches.","PeriodicalId":13926,"journal":{"name":"International journal of database theory and application","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2017-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79603904","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 : 2017-01-31DOI: 10.14257/ijdta.2017.10.1.14
Jiali Lin, Zhiqiang Wei, Z. Li
Li (lizhen0130@gmail.com) Abstract With the developing of the Internet technology, the information on the network is expanding at the speed of geometric progression. Facing such vast network information, quickly extracting the important information becomes the urgent needs. The subject extraction model is a good solution to the problem. In this paper, a new model based on Probabilistic Latent Semantic Analysis (PLSA) is proposed which is called Branch-combined PLSA (BPLSA). BPLSA divides training data into two subsets, and trains subsets separately first, then the global training is implemented. At the same time, Message Passing Interface (MPI) is used for parallel computing to speed up the proposed method. Through the parallelization of the BPLSA, the efficiency is
{"title":"Branch-combined PLSA for Topic Extraction","authors":"Jiali Lin, Zhiqiang Wei, Z. Li","doi":"10.14257/ijdta.2017.10.1.14","DOIUrl":"https://doi.org/10.14257/ijdta.2017.10.1.14","url":null,"abstract":"Li (lizhen0130@gmail.com) Abstract With the developing of the Internet technology, the information on the network is expanding at the speed of geometric progression. Facing such vast network information, quickly extracting the important information becomes the urgent needs. The subject extraction model is a good solution to the problem. In this paper, a new model based on Probabilistic Latent Semantic Analysis (PLSA) is proposed which is called Branch-combined PLSA (BPLSA). BPLSA divides training data into two subsets, and trains subsets separately first, then the global training is implemented. At the same time, Message Passing Interface (MPI) is used for parallel computing to speed up the proposed method. Through the parallelization of the BPLSA, the efficiency is","PeriodicalId":13926,"journal":{"name":"International journal of database theory and application","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2017-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80674722","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 the standard spectrum clustering algorithm, the metric based on Euclidean space can not represent the complicate space distribution feature of some data set, which might lead to the clustering result inaccuracy. While the geometric relationship between data can be describe more precise by manifold space. Considering Grassmann manifold is a entropy of Lie group, which not only has the smooth curved surface but also has the feature more fit for measuring the distance between data. All these can make the clustering result more accurate. The improved spectrum clustering analysis algorithm based on the distance metric under Graasmann manifold is proposed by this paper. The similarity between data is analyzed under manifold space. Experimental results show that the proposed algorithm can cluster data set either belonging the same or different subspace more accurately, further more, it can cluster data set with more complicate geometric structure under manifold space efficiently.
{"title":"Data Clustering Analysis on Grassmann Manifold Metric","authors":"Yinghong Xie, Yuqing He, Xiaosheng Yu, Xindong You, Q. Guo","doi":"10.14257/IJDTA.2017.10.1.20","DOIUrl":"https://doi.org/10.14257/IJDTA.2017.10.1.20","url":null,"abstract":"In the standard spectrum clustering algorithm, the metric based on Euclidean space can not represent the complicate space distribution feature of some data set, which might lead to the clustering result inaccuracy. While the geometric relationship between data can be describe more precise by manifold space. Considering Grassmann manifold is a entropy of Lie group, which not only has the smooth curved surface but also has the feature more fit for measuring the distance between data. All these can make the clustering result more accurate. The improved spectrum clustering analysis algorithm based on the distance metric under Graasmann manifold is proposed by this paper. The similarity between data is analyzed under manifold space. Experimental results show that the proposed algorithm can cluster data set either belonging the same or different subspace more accurately, further more, it can cluster data set with more complicate geometric structure under manifold space efficiently.","PeriodicalId":13926,"journal":{"name":"International journal of database theory and application","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2017-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91484373","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 : 2017-01-31DOI: 10.14257/IJDTA.2017.10.1.04
Shujuan Zhang, Zijing Wang
Massive trivial redundancy alarm information with high error alarm rate, generated by network security defense equipment, causes great difficulty in alarm analysis and understanding. In allusion to the research on this problem, an improved multi-source heterogeneous data fusion scheme is proposed in this paper to comprehensively analyze such attributes as alarm type, source IP, destination IP, destination port and time interval and summarize four rules, thus to dynamically update the time interval threshold value during the fusion process and improve the fusion accuracy. The experiment result shows that such method can efficiently reduce the quantity of the heterogeneous alarm information, and obtain accurate super-alarm data, as well as realize the ability for timely processing the alarm information.
{"title":"Multi-source Heterogeneous Data Fusion Method Considering Information Entropy in Large Data Environment","authors":"Shujuan Zhang, Zijing Wang","doi":"10.14257/IJDTA.2017.10.1.04","DOIUrl":"https://doi.org/10.14257/IJDTA.2017.10.1.04","url":null,"abstract":"Massive trivial redundancy alarm information with high error alarm rate, generated by network security defense equipment, causes great difficulty in alarm analysis and understanding. In allusion to the research on this problem, an improved multi-source heterogeneous data fusion scheme is proposed in this paper to comprehensively analyze such attributes as alarm type, source IP, destination IP, destination port and time interval and summarize four rules, thus to dynamically update the time interval threshold value during the fusion process and improve the fusion accuracy. The experiment result shows that such method can efficiently reduce the quantity of the heterogeneous alarm information, and obtain accurate super-alarm data, as well as realize the ability for timely processing the alarm information.","PeriodicalId":13926,"journal":{"name":"International journal of database theory and application","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2017-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84457325","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 : 2017-01-31DOI: 10.14257/ijdta.2017.10.1.02
Shivaprasad Km, T. H. Reddy
Over last few decades, we have witnessed the enormous accumulation and usage of the data. Major issues faced by this data are mismatch and overload. The mismatch is the some useful or interesting data has been overlooked and overload is nothing but the gathered data is not one the user needed. To overcome this issue a technique of text mining has been developed. Text mining extracts the useful and interesting data from the large unstructured data; it helps to cope up with the issues. A complex task in text mining is the analysis and categorization of the extracted data. For the efficient and effective extraction and analysis of the patterns of data, various techniques and methods like categorization, clustering, summarization, stemming etc. have been recently developed. Some of the techniques and methods are discussed in this paper.
{"title":"A Survey on Text Mining Techniques and Methods: A Review Approach","authors":"Shivaprasad Km, T. H. Reddy","doi":"10.14257/ijdta.2017.10.1.02","DOIUrl":"https://doi.org/10.14257/ijdta.2017.10.1.02","url":null,"abstract":"Over last few decades, we have witnessed the enormous accumulation and usage of the data. Major issues faced by this data are mismatch and overload. The mismatch is the some useful or interesting data has been overlooked and overload is nothing but the gathered data is not one the user needed. To overcome this issue a technique of text mining has been developed. Text mining extracts the useful and interesting data from the large unstructured data; it helps to cope up with the issues. A complex task in text mining is the analysis and categorization of the extracted data. For the efficient and effective extraction and analysis of the patterns of data, various techniques and methods like categorization, clustering, summarization, stemming etc. have been recently developed. Some of the techniques and methods are discussed in this paper.","PeriodicalId":13926,"journal":{"name":"International journal of database theory and application","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2017-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88218284","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 : 2017-01-31DOI: 10.14257/IJDTA.2017.10.1.11
Wang Yiwen
Based on the traditional data mining algorithm, a novel data mining algorithm is proposed. This algorithm consists of 5 steps: the first step, set the tree set; the second step, set the window third, subtree contribution; decision tree construction; the fourth step test, positive and negative examples set; the fifth step, expand the achievements window. The experimental study on open source data sets. The results showed that the five step proposed data mining method, not only can build a more concise decision tree, data mining and the accuracy is also higher than the traditional decision tree method.
{"title":"A Novel Five-Step Data Mining Algorithm","authors":"Wang Yiwen","doi":"10.14257/IJDTA.2017.10.1.11","DOIUrl":"https://doi.org/10.14257/IJDTA.2017.10.1.11","url":null,"abstract":"Based on the traditional data mining algorithm, a novel data mining algorithm is proposed. This algorithm consists of 5 steps: the first step, set the tree set; the second step, set the window third, subtree contribution; decision tree construction; the fourth step test, positive and negative examples set; the fifth step, expand the achievements window. The experimental study on open source data sets. The results showed that the five step proposed data mining method, not only can build a more concise decision tree, data mining and the accuracy is also higher than the traditional decision tree method.","PeriodicalId":13926,"journal":{"name":"International journal of database theory and application","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2017-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74865939","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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