This work proposes JECoLi - a novel Java-based library for the implementation of metaheuristic optimization algorithms with a focus on Genetic and Evolutionary Computation based methods. The library was developed based on the principles of flexibility, usability, adaptability, modularity, extensibility, transparency, scalability, robustness and computational efficiency. The project is open-source, so JECoLi is made available under the GPL license, together with extensive documentation and examples, all included in a community Wiki-based web site (http://darwin.di.uminho.pt/jecoli). JECoLi has been/is being used in several research projects that helped to shape its evolution, ranging application fields from Bioinformatics, to Data Mining and Computer Network optimization.
{"title":"Implementing Metaheuristic Optimization Algorithms with JECoLi","authors":"Pedro Evangelista, Paulo Maia, Miguel Rocha","doi":"10.1109/ISDA.2009.161","DOIUrl":"https://doi.org/10.1109/ISDA.2009.161","url":null,"abstract":"This work proposes JECoLi - a novel Java-based library for the implementation of metaheuristic optimization algorithms with a focus on Genetic and Evolutionary Computation based methods. The library was developed based on the principles of flexibility, usability, adaptability, modularity, extensibility, transparency, scalability, robustness and computational efficiency. The project is open-source, so JECoLi is made available under the GPL license, together with extensive documentation and examples, all included in a community Wiki-based web site (http://darwin.di.uminho.pt/jecoli). JECoLi has been/is being used in several research projects that helped to shape its evolution, ranging application fields from Bioinformatics, to Data Mining and Computer Network optimization.","PeriodicalId":330324,"journal":{"name":"2009 Ninth International Conference on Intelligent Systems Design and Applications","volume":"215 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123970109","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 deals with the problem of intracellular image segmentation. Our goal is to propose an algorithm selection framework that has the potential to be general enough to be used for a variety of intracellular image segmentation tasks. With this framework, an optimal algorithm suited to each segmentation task can be selected automatically by our proposed evaluation criteria derived from region similarity of image features and boundary shape. Furthermore, using our framework, we can rank different algorithms, as well as define each algorithm's parameters. We tested our prototype framework on confocal microscope images and showed that application of these criteria gave highly accurate segmentation results without missing any biologically important image characteristics.
{"title":"Algorithm Selection for Intracellular Image Segmentation Based on Region Similarity","authors":"S. Takemoto, H. Yokota","doi":"10.1109/ISDA.2009.205","DOIUrl":"https://doi.org/10.1109/ISDA.2009.205","url":null,"abstract":"This paper deals with the problem of intracellular image segmentation. Our goal is to propose an algorithm selection framework that has the potential to be general enough to be used for a variety of intracellular image segmentation tasks. With this framework, an optimal algorithm suited to each segmentation task can be selected automatically by our proposed evaluation criteria derived from region similarity of image features and boundary shape. Furthermore, using our framework, we can rank different algorithms, as well as define each algorithm's parameters. We tested our prototype framework on confocal microscope images and showed that application of these criteria gave highly accurate segmentation results without missing any biologically important image characteristics.","PeriodicalId":330324,"journal":{"name":"2009 Ninth International Conference on Intelligent Systems Design and Applications","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123623946","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 this ever changing business structure, Intelligent Business System (IBS) is one of the survivals of a company, and the functions of information technology (IT) are becoming increasingly important. Evaluating the appropriate IBS for required conditions is the critical strategic decisions in formulating a business strategy. Although a number of factors were found to be influential in the choice of IBS. IBS evaluation is an inherently uncertain activity. To deal with the uncertainty in decision making, a fuzzy multi criteria decision making (FMCDM) method is adopted. This study presents an empirical approach of BIS evaluation and a real life evaluation process is presented to illustrate the effectiveness of the approach.
{"title":"Evaluating an Intelligent Business System with a Fuzzy Multi-criteria Approach","authors":"Sinan Apak, Ö. Vayvay","doi":"10.1109/ISDA.2009.152","DOIUrl":"https://doi.org/10.1109/ISDA.2009.152","url":null,"abstract":"In this ever changing business structure, Intelligent Business System (IBS) is one of the survivals of a company, and the functions of information technology (IT) are becoming increasingly important. Evaluating the appropriate IBS for required conditions is the critical strategic decisions in formulating a business strategy. Although a number of factors were found to be influential in the choice of IBS. IBS evaluation is an inherently uncertain activity. To deal with the uncertainty in decision making, a fuzzy multi criteria decision making (FMCDM) method is adopted. This study presents an empirical approach of BIS evaluation and a real life evaluation process is presented to illustrate the effectiveness of the approach.","PeriodicalId":330324,"journal":{"name":"2009 Ninth International Conference on Intelligent Systems Design and Applications","volume":"12 4","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114120447","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}
Genetic Algorithms (GAs) are intelligent computational tools which their simplicity, accuracy and adaptable topology cause them to be used in globally minimum or maximum finding problems. Developing the GAs to increase their speed in finding the global minimum or maximum of a cost function has been a big challenge until now and many variants of GA has been evolved to accomplish this goal. This paper presents two new Sequential Mutation Method and Circular Gene Method to increase the speed of the GA. These methods attain a better final answer accompanied by lesser use of cost function evaluations in comparison with the original GA and some other known complementary methods. In addition, it speeds up reaching the minimum or maximum point regarding the number of generations. A number of common test functions with known minimum values and points are tested and the results are compared with some other algorithms such as original GA, Bacterial Evolutionary Algorithm, Jumping Gene and PSO. Simulation results show that the presented methods in this paper can reach the global minimum point through lesser generations and evaluations of the cost function in comparison with the traditional methods.
{"title":"Speeding Up the Genetic Algorithm Convergence Using Sequential Mutation and Circular Gene Methods","authors":"M. B. Nia, Y. Alipouri","doi":"10.1109/ISDA.2009.140","DOIUrl":"https://doi.org/10.1109/ISDA.2009.140","url":null,"abstract":"Genetic Algorithms (GAs) are intelligent computational tools which their simplicity, accuracy and adaptable topology cause them to be used in globally minimum or maximum finding problems. Developing the GAs to increase their speed in finding the global minimum or maximum of a cost function has been a big challenge until now and many variants of GA has been evolved to accomplish this goal. This paper presents two new Sequential Mutation Method and Circular Gene Method to increase the speed of the GA. These methods attain a better final answer accompanied by lesser use of cost function evaluations in comparison with the original GA and some other known complementary methods. In addition, it speeds up reaching the minimum or maximum point regarding the number of generations. A number of common test functions with known minimum values and points are tested and the results are compared with some other algorithms such as original GA, Bacterial Evolutionary Algorithm, Jumping Gene and PSO. Simulation results show that the presented methods in this paper can reach the global minimum point through lesser generations and evaluations of the cost function in comparison with the traditional methods.","PeriodicalId":330324,"journal":{"name":"2009 Ninth International Conference on Intelligent Systems Design and Applications","volume":"64 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114693685","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}
I. Boukhris, Zied Elouedi, T. Fober, Marco Mernberger, E. Hüllermeier
Protein binding sites are often represented by means of graphs capturing their most important geometrical and physicochemical properties. Searching for structural similarities and identifying functional relationships between them can thus be reduced to matching their corresponding graph descriptors. In this paper, we propose a method for the structural analysis of protein binding sites that makes use of such matching techniques to assess the similarity between proteins independently of sequence or fold homology. More specifically, we propose a similarity measure that generalizes the commonly used maximum common subgraph measure in two ways. First, using algorithms for so-called quasi-clique detection, our measure is based on maximum ‘approximately’ common subgraphs, a relaxation of maximum common subgraphs which is tolerant toward edge mismatches. Second, instead of focusing on equivalence, our measure is a compromise between a generalized equivalence and an inclusion measure. An experimental study is presented to illustrate the effectiveness of the method and to show that both types of relaxation are useful in the context of protein structure analysis.
{"title":"Similarity Analysis of Protein Binding Sites: A Generalization of the Maximum Common Subgraph Measure Based on Quasi-Clique Detection","authors":"I. Boukhris, Zied Elouedi, T. Fober, Marco Mernberger, E. Hüllermeier","doi":"10.1109/ISDA.2009.75","DOIUrl":"https://doi.org/10.1109/ISDA.2009.75","url":null,"abstract":"Protein binding sites are often represented by means of graphs capturing their most important geometrical and physicochemical properties. Searching for structural similarities and identifying functional relationships between them can thus be reduced to matching their corresponding graph descriptors. In this paper, we propose a method for the structural analysis of protein binding sites that makes use of such matching techniques to assess the similarity between proteins independently of sequence or fold homology. More specifically, we propose a similarity measure that generalizes the commonly used maximum common subgraph measure in two ways. First, using algorithms for so-called quasi-clique detection, our measure is based on maximum ‘approximately’ common subgraphs, a relaxation of maximum common subgraphs which is tolerant toward edge mismatches. Second, instead of focusing on equivalence, our measure is a compromise between a generalized equivalence and an inclusion measure. An experimental study is presented to illustrate the effectiveness of the method and to show that both types of relaxation are useful in the context of protein structure analysis.","PeriodicalId":330324,"journal":{"name":"2009 Ninth International Conference on Intelligent Systems Design and Applications","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124530847","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}
P. Lops, M. Degemmis, G. Semeraro, P. Gissi, C. Musto, F. Narducci
Basic content personalization consists in matching up the attributes of a user profile, in which preferences and interests are stored, against the attributes of a content object. This paper describes a content-based recommender system, called FIRSt, that integrates user generated content (UGC) with semantic analysis of content. The main contribution of FIRSt is an integrated strategy that enables a content-based recommender to infer user interests by applying machine learning techniques, both on official item descriptions provided by a publisher and on freely keywords which users adopt to annotate relevant items. Static content and dynamic content are preventively analyzed by advanced linguistic techniques in order to capture the semantics of the user interests, often hidden behind keywords. The proposed approach has been evaluated in the domain of cultural heritage personalization.
{"title":"Content-Based Filtering with Tags: The FIRSt System","authors":"P. Lops, M. Degemmis, G. Semeraro, P. Gissi, C. Musto, F. Narducci","doi":"10.1109/ISDA.2009.84","DOIUrl":"https://doi.org/10.1109/ISDA.2009.84","url":null,"abstract":"Basic content personalization consists in matching up the attributes of a user profile, in which preferences and interests are stored, against the attributes of a content object. This paper describes a content-based recommender system, called FIRSt, that integrates user generated content (UGC) with semantic analysis of content. The main contribution of FIRSt is an integrated strategy that enables a content-based recommender to infer user interests by applying machine learning techniques, both on official item descriptions provided by a publisher and on freely keywords which users adopt to annotate relevant items. Static content and dynamic content are preventively analyzed by advanced linguistic techniques in order to capture the semantics of the user interests, often hidden behind keywords. The proposed approach has been evaluated in the domain of cultural heritage personalization.","PeriodicalId":330324,"journal":{"name":"2009 Ninth International Conference on Intelligent Systems Design and Applications","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128185969","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}
Javier Bravo, Estefanía Martín, Alvaro Ortigosa, Rosa M. Carro
In the last years the development of learning environments, and particularly of Educational Adaptive Hypermedia (EAH) systems has increased significantly. However, it is important to complement this development with evaluation methods in order to improve EAH system performance. In this context, we propose to analyze the data from student interaction with EAH systems utilizing the GeSES method. This method has been specifically designed to work with student logs and is based on C4.5 rules. In particular, the work described in this paper aims to achieve the following two objectives: testing the method with different types of data in order to find out its reliability, and detecting symptoms of low performance in a specific adaptive learning environment, called CoMoLE.
{"title":"Checking the Reliability of GeSES: Method for Detecting Symptoms of Low Performance","authors":"Javier Bravo, Estefanía Martín, Alvaro Ortigosa, Rosa M. Carro","doi":"10.1109/ISDA.2009.213","DOIUrl":"https://doi.org/10.1109/ISDA.2009.213","url":null,"abstract":"In the last years the development of learning environments, and particularly of Educational Adaptive Hypermedia (EAH) systems has increased significantly. However, it is important to complement this development with evaluation methods in order to improve EAH system performance. In this context, we propose to analyze the data from student interaction with EAH systems utilizing the GeSES method. This method has been specifically designed to work with student logs and is based on C4.5 rules. In particular, the work described in this paper aims to achieve the following two objectives: testing the method with different types of data in order to find out its reliability, and detecting symptoms of low performance in a specific adaptive learning environment, called CoMoLE.","PeriodicalId":330324,"journal":{"name":"2009 Ninth International Conference on Intelligent Systems Design and Applications","volume":"1996 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128192156","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 presents a multiobjective optimization algorithm based on Particle Swarm Optimization (MOPSO-CDR) that uses a diversity mechanism called crowding distance to select the social leaders and the cognitive leader. We also use the same mechanism to delete solutions of the external archive. The performance of our proposal was evaluated in five well known benchmark functions using four metrics previously presented in the literature. Our proposal was compared to other four multi objective optimization algorithms based on Particle Swarm Optimization, called m-DNPSO, CSS-MOPSO, MOPSO and MOPSO-CDLS. The results showed that the proposed approach is competitive when compared to the other approaches and outperforms the other algorithms in many cases.
{"title":"A Multiple Objective Particle Swarm Optimization Approach Using Crowding Distance and Roulette Wheel","authors":"R. A. Santana, M. R. Pontes, C. J. A. B. Filho","doi":"10.1109/ISDA.2009.73","DOIUrl":"https://doi.org/10.1109/ISDA.2009.73","url":null,"abstract":"This paper presents a multiobjective optimization algorithm based on Particle Swarm Optimization (MOPSO-CDR) that uses a diversity mechanism called crowding distance to select the social leaders and the cognitive leader. We also use the same mechanism to delete solutions of the external archive. The performance of our proposal was evaluated in five well known benchmark functions using four metrics previously presented in the literature. Our proposal was compared to other four multi objective optimization algorithms based on Particle Swarm Optimization, called m-DNPSO, CSS-MOPSO, MOPSO and MOPSO-CDLS. The results showed that the proposed approach is competitive when compared to the other approaches and outperforms the other algorithms in many cases.","PeriodicalId":330324,"journal":{"name":"2009 Ninth International Conference on Intelligent Systems Design and Applications","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132522027","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}
Web 2.0 applications innovate traditional informative services providing Web users with a set of tools for publishing and sharing information. Social bookmarking systems are an interesting example of this trend where users generate new contents. Unfortunately, the growing amount of available resources makes hard the task of accessing to relevant information in these environments. Recommender systems face this problem filtering relevant resources connected to users' interests and preferences. In particular, collaborative filtering recommender systems produce suggestions using the opinions of similar users, called the neighbors. The task of finding neighbors is difficult in environment such as social bookmarking systems, since bookmarked resources belong to different domains. In this paper we propose a methodology for partitioning users, tags and resources into domains of interest. Filtering tags and resources in accordance to the specific domains we can select a different set of neighbors for each domain, improving the accuracy of recommendations.
Web 2.0应用程序革新了传统的信息服务，为Web用户提供了一组用于发布和共享信息的工具。社交书签系统是这种趋势的一个有趣的例子，用户可以生成新的内容。不幸的是，越来越多的可用资源使得在这些环境中访问相关信息的任务变得困难。推荐系统面临着过滤与用户兴趣和偏好相关的资源的问题。特别是，协同过滤推荐系统使用类似用户的意见(称为邻居)来产生建议。在社会书签系统这样的环境中，由于书签资源属于不同的域，查找邻居的任务比较困难。在本文中，我们提出了一种将用户、标签和资源划分到感兴趣的领域的方法。根据特定的域过滤标签和资源，我们可以为每个域选择一组不同的邻居，提高推荐的准确性。
{"title":"Neighbor Selection and Recommendations in Social Bookmarking Tools","authors":"A. Dattolo, Felice Ferrara, C. Tasso","doi":"10.1109/ISDA.2009.245","DOIUrl":"https://doi.org/10.1109/ISDA.2009.245","url":null,"abstract":"Web 2.0 applications innovate traditional informative services providing Web users with a set of tools for publishing and sharing information. Social bookmarking systems are an interesting example of this trend where users generate new contents. Unfortunately, the growing amount of available resources makes hard the task of accessing to relevant information in these environments. Recommender systems face this problem filtering relevant resources connected to users' interests and preferences. In particular, collaborative filtering recommender systems produce suggestions using the opinions of similar users, called the neighbors. The task of finding neighbors is difficult in environment such as social bookmarking systems, since bookmarked resources belong to different domains. In this paper we propose a methodology for partitioning users, tags and resources into domains of interest. Filtering tags and resources in accordance to the specific domains we can select a different set of neighbors for each domain, improving the accuracy of recommendations.","PeriodicalId":330324,"journal":{"name":"2009 Ninth International Conference on Intelligent Systems Design and Applications","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130818013","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}
Andréa Corrêa Flôres Albuquerque, José Laurindo Campos dos Santos, J. M. Netto
Convention on Biological Diversity (CBD) recognizes that biodiversity loss must be reduced to promote poverty alleviation and direct benefit of all live on Earth. To achieve that, we must consider robust strategies and action plans based on knowledge and state of art technology. Parallel to that, research is underway in universities and scientific organization aiming to develop semantic web as an additional resource associated to formal ontology and the avoidance of knowledge acquisition problems such as expertise dependence, tacit knowledge, experts’ availability and ideal time importance. Ontology can structure knowledge acquisition process for the purpose of comprehensive, portable machine understanding and knowledge extraction on the semantic web environment. These technologies applied to biodiversity domain can be a valuable resource for CBD. The paper presents a strategy for biodiversity knowledge acquisition based on a negotiation protocol which uses domain ontology to extract knowledge from data sources in the semantic web domain.
{"title":"A Strategy for Biodiversity Knowledge Acquisition Based on Domain Ontology","authors":"Andréa Corrêa Flôres Albuquerque, José Laurindo Campos dos Santos, J. M. Netto","doi":"10.1109/ISDA.2009.87","DOIUrl":"https://doi.org/10.1109/ISDA.2009.87","url":null,"abstract":"Convention on Biological Diversity (CBD) recognizes that biodiversity loss must be reduced to promote poverty alleviation and direct benefit of all live on Earth. To achieve that, we must consider robust strategies and action plans based on knowledge and state of art technology. Parallel to that, research is underway in universities and scientific organization aiming to develop semantic web as an additional resource associated to formal ontology and the avoidance of knowledge acquisition problems such as expertise dependence, tacit knowledge, experts’ availability and ideal time importance. Ontology can structure knowledge acquisition process for the purpose of comprehensive, portable machine understanding and knowledge extraction on the semantic web environment. These technologies applied to biodiversity domain can be a valuable resource for CBD. The paper presents a strategy for biodiversity knowledge acquisition based on a negotiation protocol which uses domain ontology to extract knowledge from data sources in the semantic web domain.","PeriodicalId":330324,"journal":{"name":"2009 Ninth International Conference on Intelligent Systems Design and Applications","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133028457","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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