Pub Date : 2008-12-30DOI: 10.1109/ISKE.2008.4731049
Limin Fang, M. Lin
For the rapid detection of the six kinds of acid in red wine, infrared (IR) spectra of 44 wine samples were analyzed. A new method of model construction based on back-propagation artificial neural networks (BP-ANN) regression and fast independent component analysis (FastICA) was proposed. This new chemometric method, named ICA-NNR, has been applied to detect the six kinds of acid in wine samples. Compared with the model built by the common used methods, such as PCR and PLS, ICA-NNR method has advantages in both the correlation coefficient and standard error of calibration. The correlation coefficients (R) between the referenced values and the model predicted values are 0.9833, 0.9759, 0.9585, 0.9989, 0.9643 and 0.9884, respectively. The results show the feasibility of establishing the models with ICA-NNR method for red wine samples¿ quantitative analysis and provide a foundation for the application and further development of IR on-line red wine analyzer.
{"title":"Detection of six kinds of acid in red wine with infrared spectroscopy based on FastICA and neural network","authors":"Limin Fang, M. Lin","doi":"10.1109/ISKE.2008.4731049","DOIUrl":"https://doi.org/10.1109/ISKE.2008.4731049","url":null,"abstract":"For the rapid detection of the six kinds of acid in red wine, infrared (IR) spectra of 44 wine samples were analyzed. A new method of model construction based on back-propagation artificial neural networks (BP-ANN) regression and fast independent component analysis (FastICA) was proposed. This new chemometric method, named ICA-NNR, has been applied to detect the six kinds of acid in wine samples. Compared with the model built by the common used methods, such as PCR and PLS, ICA-NNR method has advantages in both the correlation coefficient and standard error of calibration. The correlation coefficients (R) between the referenced values and the model predicted values are 0.9833, 0.9759, 0.9585, 0.9989, 0.9643 and 0.9884, respectively. The results show the feasibility of establishing the models with ICA-NNR method for red wine samples¿ quantitative analysis and provide a foundation for the application and further development of IR on-line red wine analyzer.","PeriodicalId":268667,"journal":{"name":"2008 3rd International Conference on Intelligent System and Knowledge Engineering","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116894481","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 : 2008-12-30DOI: 10.1109/ISKE.2008.4731150
Ramón González del Campo, L. Garmendia, J. Recasens
In this paper we define interval-valued relations. It is defined reflexive, symmetric and T-transitive properties of interval-valued relations, and the transitive closure of an interval-valued relation. Finally, we propose a algorithm to compute the transitive closure. Some examples are given and some properties are studied.
{"title":"Transitive closure of interval-valued relations","authors":"Ramón González del Campo, L. Garmendia, J. Recasens","doi":"10.1109/ISKE.2008.4731150","DOIUrl":"https://doi.org/10.1109/ISKE.2008.4731150","url":null,"abstract":"In this paper we define interval-valued relations. It is defined reflexive, symmetric and T-transitive properties of interval-valued relations, and the transitive closure of an interval-valued relation. Finally, we propose a algorithm to compute the transitive closure. Some examples are given and some properties are studied.","PeriodicalId":268667,"journal":{"name":"2008 3rd International Conference on Intelligent System and Knowledge Engineering","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125829633","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 : 2008-12-30DOI: 10.1109/ISKE.2008.4730902
Qiusun Ye
This paper bases itself upon the deep analyzing and discussing of both BFS (breadth-first searches) & DFS (depth-first searches) in searching without information, and points out that, merits and demerits of two methods on searching without information in FCN (fixed carrying numbers). And then, it introduces a novel problem-solving method which sometimes we must think simultaneously over the synthetic technique combining BFS & DFS in AI-searching technology of VCN (variable carrying numbers), and gives out an example of this practical problem-solving method, namely it such as catching fish with casting a net or boy-herder picks peaches by climbing up a tree.
{"title":"A novel computation for AI-Searching technology of VCN","authors":"Qiusun Ye","doi":"10.1109/ISKE.2008.4730902","DOIUrl":"https://doi.org/10.1109/ISKE.2008.4730902","url":null,"abstract":"This paper bases itself upon the deep analyzing and discussing of both BFS (breadth-first searches) & DFS (depth-first searches) in searching without information, and points out that, merits and demerits of two methods on searching without information in FCN (fixed carrying numbers). And then, it introduces a novel problem-solving method which sometimes we must think simultaneously over the synthetic technique combining BFS & DFS in AI-searching technology of VCN (variable carrying numbers), and gives out an example of this practical problem-solving method, namely it such as catching fish with casting a net or boy-herder picks peaches by climbing up a tree.","PeriodicalId":268667,"journal":{"name":"2008 3rd International Conference on Intelligent System and Knowledge Engineering","volume":"60 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125872333","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 : 2008-12-30DOI: 10.1109/ISKE.2008.4731005
Xiaobing Liu, Zhongkai Li, Xuewen Huang, Qingjie Song
To incorporate existing and new information tools, in particular optimization methods and software, an integrated resource management system was proposed for ferry companies. The system adopts a hierarchical system architecture which incorporates all the modules in the three layers: the supporting layer, the application layer, and the management and control layer. The blank ticket rolls were considered as critical resources, and a conceptual storage for blank ticket rolls was developed to complete a ticket lifecycle management approach. To balance the capacities of two or more sailings and enhance profits, a stowage optimization procedure oriented to Ro-Ro shipping was used. The system also provides cubic views for statistics as decision support methods. The system was tested as feasible and effective in two case studies conducted in Dalian, China.
{"title":"Study of an integrated resource management system oriented to ferry companies","authors":"Xiaobing Liu, Zhongkai Li, Xuewen Huang, Qingjie Song","doi":"10.1109/ISKE.2008.4731005","DOIUrl":"https://doi.org/10.1109/ISKE.2008.4731005","url":null,"abstract":"To incorporate existing and new information tools, in particular optimization methods and software, an integrated resource management system was proposed for ferry companies. The system adopts a hierarchical system architecture which incorporates all the modules in the three layers: the supporting layer, the application layer, and the management and control layer. The blank ticket rolls were considered as critical resources, and a conceptual storage for blank ticket rolls was developed to complete a ticket lifecycle management approach. To balance the capacities of two or more sailings and enhance profits, a stowage optimization procedure oriented to Ro-Ro shipping was used. The system also provides cubic views for statistics as decision support methods. The system was tested as feasible and effective in two case studies conducted in Dalian, China.","PeriodicalId":268667,"journal":{"name":"2008 3rd International Conference on Intelligent System and Knowledge Engineering","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123595576","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 : 2008-12-30DOI: 10.1109/ISKE.2008.4730952
Dedong Zhang, Renpu Li, X. Tang, Yongsheng Zhao
Attribute reduction is an important issue of data mining. In this paper an incremental reduct algorithm is proposed for incomplete decision tables. A reduct definition is firstly presented. And then based on the concept of generalized decision the different cases caused by adding a new object to an incomplete decision table are deeply analyzed and some important conclusions are proved by theorems. Finally an algorithm is proposed for incrementally computing the reducts of an incomplete decision table. An example shows that the proposed algorithm is very efficient because in many cases it can avoid recomputing the new reducts.
{"title":"An incremental reduct algorithm based on generalized decision for incomplete decision tables","authors":"Dedong Zhang, Renpu Li, X. Tang, Yongsheng Zhao","doi":"10.1109/ISKE.2008.4730952","DOIUrl":"https://doi.org/10.1109/ISKE.2008.4730952","url":null,"abstract":"Attribute reduction is an important issue of data mining. In this paper an incremental reduct algorithm is proposed for incomplete decision tables. A reduct definition is firstly presented. And then based on the concept of generalized decision the different cases caused by adding a new object to an incomplete decision table are deeply analyzed and some important conclusions are proved by theorems. Finally an algorithm is proposed for incrementally computing the reducts of an incomplete decision table. An example shows that the proposed algorithm is very efficient because in many cases it can avoid recomputing the new reducts.","PeriodicalId":268667,"journal":{"name":"2008 3rd International Conference on Intelligent System and Knowledge Engineering","volume":"101 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125288101","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 : 2008-12-30DOI: 10.1109/ISKE.2008.4730912
Jingwen Yan, Guide Yang, A. Zhang
A novel inter-scale correlation image denoising method based on dual-tree M-band wavelet (DTT) is proposed in this paper. Dual-tree M-band wavelet transform is a shift-invariant, multi-scale and multi-direction transform based on a Hilbert pair of wavelets initially proposed by N. Kingsbury. Improving upon Xu¿s denosing algorithm based on wavelet inter-scale correlation, a new correlation modeling is provided between each high frequency detail subimage and corresponding M subimages in adjacent lower frequency scale. In the new algorithm, signal and noise are distinguished by the strength of the correlation, and combined with threshold functions. The experiment result shows that comparing with the classical denoising methods, for example, wavelet denoising method, Dual-tree complex wavelet denoising method, contourlet denoising method and so on..., the proposed denoising method achieves an excellent balance between suppressing noise effectively and preserving as many image details and edges as possible.
{"title":"A novel inter-scale correlation image denoising method based on Dual-tree M-band wavelet","authors":"Jingwen Yan, Guide Yang, A. Zhang","doi":"10.1109/ISKE.2008.4730912","DOIUrl":"https://doi.org/10.1109/ISKE.2008.4730912","url":null,"abstract":"A novel inter-scale correlation image denoising method based on dual-tree M-band wavelet (DTT) is proposed in this paper. Dual-tree M-band wavelet transform is a shift-invariant, multi-scale and multi-direction transform based on a Hilbert pair of wavelets initially proposed by N. Kingsbury. Improving upon Xu¿s denosing algorithm based on wavelet inter-scale correlation, a new correlation modeling is provided between each high frequency detail subimage and corresponding M subimages in adjacent lower frequency scale. In the new algorithm, signal and noise are distinguished by the strength of the correlation, and combined with threshold functions. The experiment result shows that comparing with the classical denoising methods, for example, wavelet denoising method, Dual-tree complex wavelet denoising method, contourlet denoising method and so on..., the proposed denoising method achieves an excellent balance between suppressing noise effectively and preserving as many image details and edges as possible.","PeriodicalId":268667,"journal":{"name":"2008 3rd International Conference on Intelligent System and Knowledge Engineering","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125568300","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 : 2008-12-30DOI: 10.1109/ISKE.2008.4730940
Haiwei Pan, Qilong Han, Guisheng Yin, Wei Zhang, Jianzhong Li
Mining knowledge from large databases has been the focus of many recent studies and applications. Mining association rules in medical images is an important part in domain-specific application image mining because there are several technical aspects which make this problem challenging. In this paper, we firstly propose ROI extraction and clustering algorithm with domain knowledge constraint, then we extend the concept of association rule based on ROI and image in medical images, and propose two algorithms to discover frequent item-sets and mine association rules from medical images. Some interesting results are obtained by our program and we believe many of the problems we come across are likely to appear in other domains.
{"title":"Association rule mining with domain knowledge constraint","authors":"Haiwei Pan, Qilong Han, Guisheng Yin, Wei Zhang, Jianzhong Li","doi":"10.1109/ISKE.2008.4730940","DOIUrl":"https://doi.org/10.1109/ISKE.2008.4730940","url":null,"abstract":"Mining knowledge from large databases has been the focus of many recent studies and applications. Mining association rules in medical images is an important part in domain-specific application image mining because there are several technical aspects which make this problem challenging. In this paper, we firstly propose ROI extraction and clustering algorithm with domain knowledge constraint, then we extend the concept of association rule based on ROI and image in medical images, and propose two algorithms to discover frequent item-sets and mine association rules from medical images. Some interesting results are obtained by our program and we believe many of the problems we come across are likely to appear in other domains.","PeriodicalId":268667,"journal":{"name":"2008 3rd International Conference on Intelligent System and Knowledge Engineering","volume":"117 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126715360","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 : 2008-12-30DOI: 10.1109/ISKE.2008.4730955
Wei Yang, Su Zhang, Yazhu Chen, Yaqing Chen, Wenying Li, Hongtao Lu
In the medical diagnosis, the false negative prediction is more serious than the false positive prediction. We introduce the cost-sensitive rule ensemble method (RuleFit) to breast ultrasound, which can induce the interpretable scoring rules for malignancy assessment, and can be applied to tune the sensitivity and specificity of the predictive model by varying the cost weights of misclassification. The GentleCost boosting algorithm is proposed to generate the decision tree ensemble. Then, we use the modified RuleFit method with the cost-weighted loss function to select and fit the rules decomposing from the tree ensemble. Experiments results on a breast ultrasound image dataset (168 cases) with the varying cost weights demonstrate that the final rule ensemble contain only 22 (among total 600 decomposed rules) rules with the comparable performance to the tree ensemble. The examples of the rule ensemble for breast ultrasound and its interpretation are also illustrated.
{"title":"Mining diagnostic rules of breast tumor on ultrasound image using cost-sensitive RuleFit method","authors":"Wei Yang, Su Zhang, Yazhu Chen, Yaqing Chen, Wenying Li, Hongtao Lu","doi":"10.1109/ISKE.2008.4730955","DOIUrl":"https://doi.org/10.1109/ISKE.2008.4730955","url":null,"abstract":"In the medical diagnosis, the false negative prediction is more serious than the false positive prediction. We introduce the cost-sensitive rule ensemble method (RuleFit) to breast ultrasound, which can induce the interpretable scoring rules for malignancy assessment, and can be applied to tune the sensitivity and specificity of the predictive model by varying the cost weights of misclassification. The GentleCost boosting algorithm is proposed to generate the decision tree ensemble. Then, we use the modified RuleFit method with the cost-weighted loss function to select and fit the rules decomposing from the tree ensemble. Experiments results on a breast ultrasound image dataset (168 cases) with the varying cost weights demonstrate that the final rule ensemble contain only 22 (among total 600 decomposed rules) rules with the comparable performance to the tree ensemble. The examples of the rule ensemble for breast ultrasound and its interpretation are also illustrated.","PeriodicalId":268667,"journal":{"name":"2008 3rd International Conference on Intelligent System and Knowledge Engineering","volume":"115 Pt 1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128419216","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 : 2008-12-30DOI: 10.1109/ISKE.2008.4730887
Bo Zhang, Ling Zhang
One of the basic characteristics in human problem solving is the ability to conceptualize the world at different granularities and translate from one abstraction level to the others easily. But so far computers can only deal with one abstraction level in problem solving generally. It seems important to develop new techniques which will in some way enable the computers to represent the world at different granularities. So the multi-granular representation is the key to machine intelligence. In the talk, we first introduce the quotient space based problem solving theory. In the theory, a problem is represented by a triplet (X,F,T), where X - the universe with the finest grain-size, F -the attribute of X, and T- the structure of X. When we view the same problem at a coarser grain size, we have a coarse-grained universe denoted by [X]. Then we have a new representation ([X],[F],[T]) of the problem. The coarse universe [X] is defined by an equivalence relation R on X. Then, representation ([X],[F],[T]) is called a quotient space of(X,F,T), where [X] -the quotient set of X, [F] -the quotient attribute of F, and [T] -the quotient structure of T. Obviously, the set of representations of a problem at different granularities composes a complete semi-order lattice. That is, in the theory the concept, quotient space, in algebra is used as a mathematical model to represent the relationship between representations with different grain-sizes. Multi-granular representation methodology can be used both in problem solving and machine learning. In multi-granular problem solving, a problem is solved from the coarse grain-size to the fine one hierarchically. The aim of hierarchical problem solving is intended to reduce the computational complexity. Multi-granular machine learning is intended to learn the knowledge from representations with different grain-size, i.e., the so-called multi-information fusion. Generally speaking, the fine representation has more details but less robustness. Conversely, the coarse representation has more robustness but less expressiveness. They are complement so multi-granular learning can benefit from them. We also present some examples in hierarchical problem solving and machine learning to show the advantages of using multi-granular representation.
{"title":"Multi-granular representation-the key to machine intelligence","authors":"Bo Zhang, Ling Zhang","doi":"10.1109/ISKE.2008.4730887","DOIUrl":"https://doi.org/10.1109/ISKE.2008.4730887","url":null,"abstract":"One of the basic characteristics in human problem solving is the ability to conceptualize the world at different granularities and translate from one abstraction level to the others easily. But so far computers can only deal with one abstraction level in problem solving generally. It seems important to develop new techniques which will in some way enable the computers to represent the world at different granularities. So the multi-granular representation is the key to machine intelligence. In the talk, we first introduce the quotient space based problem solving theory. In the theory, a problem is represented by a triplet (X,F,T), where X - the universe with the finest grain-size, F -the attribute of X, and T- the structure of X. When we view the same problem at a coarser grain size, we have a coarse-grained universe denoted by [X]. Then we have a new representation ([X],[F],[T]) of the problem. The coarse universe [X] is defined by an equivalence relation R on X. Then, representation ([X],[F],[T]) is called a quotient space of(X,F,T), where [X] -the quotient set of X, [F] -the quotient attribute of F, and [T] -the quotient structure of T. Obviously, the set of representations of a problem at different granularities composes a complete semi-order lattice. That is, in the theory the concept, quotient space, in algebra is used as a mathematical model to represent the relationship between representations with different grain-sizes. Multi-granular representation methodology can be used both in problem solving and machine learning. In multi-granular problem solving, a problem is solved from the coarse grain-size to the fine one hierarchically. The aim of hierarchical problem solving is intended to reduce the computational complexity. Multi-granular machine learning is intended to learn the knowledge from representations with different grain-size, i.e., the so-called multi-information fusion. Generally speaking, the fine representation has more details but less robustness. Conversely, the coarse representation has more robustness but less expressiveness. They are complement so multi-granular learning can benefit from them. We also present some examples in hierarchical problem solving and machine learning to show the advantages of using multi-granular representation.","PeriodicalId":268667,"journal":{"name":"2008 3rd International Conference on Intelligent System and Knowledge Engineering","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129330216","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 : 2008-12-30DOI: 10.1109/ISKE.2008.4730981
Weijin Jiang
In order to improve the global ability of basic ACA(ant colony algorithm), a novel ACA algorithm which is based on adaptively adjusting pheromone decay parameter has been proposed, and it has been proved that for a sufficiently large number of iterations, the probability of finding the global best solution tends to 1. The simulations for TSP problem show that the improved ACA can find better routes than basic ACA.
{"title":"Research on analysis of convergence of an adaptive Ant Colony Optimization Algorithm","authors":"Weijin Jiang","doi":"10.1109/ISKE.2008.4730981","DOIUrl":"https://doi.org/10.1109/ISKE.2008.4730981","url":null,"abstract":"In order to improve the global ability of basic ACA(ant colony algorithm), a novel ACA algorithm which is based on adaptively adjusting pheromone decay parameter has been proposed, and it has been proved that for a sufficiently large number of iterations, the probability of finding the global best solution tends to 1. The simulations for TSP problem show that the improved ACA can find better routes than basic ACA.","PeriodicalId":268667,"journal":{"name":"2008 3rd International Conference on Intelligent System and Knowledge Engineering","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129200875","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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