This paper presents a novel system is to develop to determine the suitability of a location for installation of solar power stations. Necessary data including speed and direction of wind, solar radiation and rainfall are received from a meteorology station, and data acquired are then converted to the labels. Finally, the labels are evaluated in a Naive Bayes algorithm to determine the suitability of the location for the installation and axial structure of a Solar Power Plant. This helps to determine complicated calculations by means of the support system developed.
{"title":"Determining Suitability of Locations for Installation of Solar Power Station Based on Probabilistic Inference","authors":"I. Colak, Ş. Sağiroğlu, M. Demirtaş, H. Kahraman","doi":"10.1109/ICMLA.2010.169","DOIUrl":"https://doi.org/10.1109/ICMLA.2010.169","url":null,"abstract":"This paper presents a novel system is to develop to determine the suitability of a location for installation of solar power stations. Necessary data including speed and direction of wind, solar radiation and rainfall are received from a meteorology station, and data acquired are then converted to the labels. Finally, the labels are evaluated in a Naive Bayes algorithm to determine the suitability of the location for the installation and axial structure of a Solar Power Plant. This helps to determine complicated calculations by means of the support system developed.","PeriodicalId":336514,"journal":{"name":"2010 Ninth International Conference on Machine Learning and Applications","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129431267","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}
High levels of energy prices and the promise of international initiatives on decreasing the greenhouse gas emissions have regenerated the argument about the execution of energy conservation policies. This paper investigates the causal relationship between aggregated and disaggregated levels of energy production, energy demand, energy import and economic growth for Turkey for the period of 1975–2007. The relationship between the energy production, energy demand, energy import and Gross Domestic Product is examined. To this end, Engle-Granger cointegration, Error Correction Model and Granger causality tests are applied in order to determine the aforementioned relation. It is found that the energy production has direct relationship with the GDP and it has causality effects.
{"title":"Energy Production and Economic Growth: A Causality Analaysis for Turkey Based on Computer","authors":"O. Ozkan, M. Aktas, H. S. Kuyuk, S. Bayraktaroglu","doi":"10.1109/ICMLA.2010.103","DOIUrl":"https://doi.org/10.1109/ICMLA.2010.103","url":null,"abstract":"High levels of energy prices and the promise of international initiatives on decreasing the greenhouse gas emissions have regenerated the argument about the execution of energy conservation policies. This paper investigates the causal relationship between aggregated and disaggregated levels of energy production, energy demand, energy import and economic growth for Turkey for the period of 1975–2007. The relationship between the energy production, energy demand, energy import and Gross Domestic Product is examined. To this end, Engle-Granger cointegration, Error Correction Model and Granger causality tests are applied in order to determine the aforementioned relation. It is found that the energy production has direct relationship with the GDP and it has causality effects.","PeriodicalId":336514,"journal":{"name":"2010 Ninth International Conference on Machine Learning and Applications","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114913476","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}
The behavior of Hybrid Dynamic Systems (HDS) switches between several modes with different dynamics over time. Their identification aims at finding the model mapping the inputs to real-valued outputs. Generally, the identification is divided into tow steps: clustering and regression. In the clustering step, the discrete modes, i.e. classes, that each input-output data point belongs to as well as the switching sequence among these modes are estimated. The regression step aims at finding the models governing the continuous dynamic in each mode. In this paper, we propose an approach to achieve the clustering step of the identification of the switched HDS. In this approach, the number of discrete modes, classes, and the switching sequence among them are estimated using an unsupervised Pattern Recognition (PR) method. This estimation is achieved without the need to any prior information about these modes, e.g. their shape or distribution, or their number.
{"title":"Learning in Dynamic Environments: Application to the Identification of Hybrid Dynamic Systems","authors":"M. S. Mouchaweh","doi":"10.1109/ICMLA.2010.86","DOIUrl":"https://doi.org/10.1109/ICMLA.2010.86","url":null,"abstract":"The behavior of Hybrid Dynamic Systems (HDS) switches between several modes with different dynamics over time. Their identification aims at finding the model mapping the inputs to real-valued outputs. Generally, the identification is divided into tow steps: clustering and regression. In the clustering step, the discrete modes, i.e. classes, that each input-output data point belongs to as well as the switching sequence among these modes are estimated. The regression step aims at finding the models governing the continuous dynamic in each mode. In this paper, we propose an approach to achieve the clustering step of the identification of the switched HDS. In this approach, the number of discrete modes, classes, and the switching sequence among them are estimated using an unsupervised Pattern Recognition (PR) method. This estimation is achieved without the need to any prior information about these modes, e.g. their shape or distribution, or their number.","PeriodicalId":336514,"journal":{"name":"2010 Ninth International Conference on Machine Learning and Applications","volume":"110 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116628855","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}
Recently, the so-called Support Feature Machine (SFM) was proposed as a novel approach to feature selection for classification, based on minimisation of the zero norm of a separating hyper plane. We propose an extension for linearly non-separable datasets that allows a direct trade-off between the number of misclassified data points and the number of dimensions. Results on toy examples as well as real-world datasets demonstrate that this method is able to identify relevant features very effectively.
{"title":"A New Approach to Classification with the Least Number of Features","authors":"Sascha Klement, T. Martinetz","doi":"10.1109/ICMLA.2010.28","DOIUrl":"https://doi.org/10.1109/ICMLA.2010.28","url":null,"abstract":"Recently, the so-called Support Feature Machine (SFM) was proposed as a novel approach to feature selection for classification, based on minimisation of the zero norm of a separating hyper plane. We propose an extension for linearly non-separable datasets that allows a direct trade-off between the number of misclassified data points and the number of dimensions. Results on toy examples as well as real-world datasets demonstrate that this method is able to identify relevant features very effectively.","PeriodicalId":336514,"journal":{"name":"2010 Ninth International Conference on Machine Learning and Applications","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116806039","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}
Core Java is a framework which extends the programming language Java with built-in regression analysis, i.e., the capability to do parameter estimation for a function. Core Java is unique in that functional forms for regression analysis are expressed as first-class citizens, i.e., as Java programs, in which some parameters are not a priori known, but need to be learned from training sets provided as input. Typical applications of Core Java include calibration of parameters of computational processes, described as OO programs. If-then-else statements of Java language are naturally adopted to create piecewise functional forms of regression. Thus, minimization of the sum of least squared errors involves an optimization problem with a search space that is exponential to the size of learning set. In this paper, we propose a combinatorial restructuring algorithm which guarantees learning optimality and furthermore reduces the search space to be polynomial in the size of learning set, but exponential to the number of piece-wise bounds.
{"title":"An Optimal Regression Algorithm for Piecewise Functions Expressed as Object-Oriented Programs","authors":"Juan Luo, A. Brodsky","doi":"10.1109/ICMLA.2010.149","DOIUrl":"https://doi.org/10.1109/ICMLA.2010.149","url":null,"abstract":"Core Java is a framework which extends the programming language Java with built-in regression analysis, i.e., the capability to do parameter estimation for a function. Core Java is unique in that functional forms for regression analysis are expressed as first-class citizens, i.e., as Java programs, in which some parameters are not a priori known, but need to be learned from training sets provided as input. Typical applications of Core Java include calibration of parameters of computational processes, described as OO programs. If-then-else statements of Java language are naturally adopted to create piecewise functional forms of regression. Thus, minimization of the sum of least squared errors involves an optimization problem with a search space that is exponential to the size of learning set. In this paper, we propose a combinatorial restructuring algorithm which guarantees learning optimality and furthermore reduces the search space to be polynomial in the size of learning set, but exponential to the number of piece-wise bounds.","PeriodicalId":336514,"journal":{"name":"2010 Ninth International Conference on Machine Learning and Applications","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133563698","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}
Thanks to the numerous attempts that are being made to develop autonomous robots, increasingly intelligent and cognitive skills are allowed. This paper proposes an automatic presentation generator for a robot guide, which is considered one more cognitive skill. The presentations are made up of groups of paragraphs. The selection of the best paragraphs is based on a semantic understanding of the characteristics of the paragraphs, on the restrictions defined for the presentation and by the quality criteria appropriate for a public presentation. This work is part of the ROBONAUTA project of the Intelligent Control Research Group at the Universidad Politécnica de Madrid to create "awareness" in a robot guide. The software developed in the project has been verified on the tour-guide robot Urbano. The most important aspect of this proposal is that the design uses learning as the means to optimize the quality of the presentations. To achieve this goal, the system has to perform the optimized decision making, in different phases. The modeling of the quality index of the presentation is made using fuzzy logic and it represents the beliefs of the robot about what is good, bad, or indifferent about a presentation. This fuzzy system is used to select the most appropriate group of paragraphs for a presentation. The beliefs of the robot continue to evolving in order to coincide with the opinions of the public. It uses a genetic algorithm for the evolution of the rules. With this tool, the tour guide-robot shows the presentation, which satisfies the objectives and restrictions, and automatically it identifies the best paragraphs in order to find the most suitable set of contents for every public profile.
{"title":"Learning to Be a Good Tour-Guide Robot","authors":"J. J. Rainer, R. Galán","doi":"10.1109/ICMLA.2010.92","DOIUrl":"https://doi.org/10.1109/ICMLA.2010.92","url":null,"abstract":"Thanks to the numerous attempts that are being made to develop autonomous robots, increasingly intelligent and cognitive skills are allowed. This paper proposes an automatic presentation generator for a robot guide, which is considered one more cognitive skill. The presentations are made up of groups of paragraphs. The selection of the best paragraphs is based on a semantic understanding of the characteristics of the paragraphs, on the restrictions defined for the presentation and by the quality criteria appropriate for a public presentation. This work is part of the ROBONAUTA project of the Intelligent Control Research Group at the Universidad Politécnica de Madrid to create \"awareness\" in a robot guide. The software developed in the project has been verified on the tour-guide robot Urbano. The most important aspect of this proposal is that the design uses learning as the means to optimize the quality of the presentations. To achieve this goal, the system has to perform the optimized decision making, in different phases. The modeling of the quality index of the presentation is made using fuzzy logic and it represents the beliefs of the robot about what is good, bad, or indifferent about a presentation. This fuzzy system is used to select the most appropriate group of paragraphs for a presentation. The beliefs of the robot continue to evolving in order to coincide with the opinions of the public. It uses a genetic algorithm for the evolution of the rules. With this tool, the tour guide-robot shows the presentation, which satisfies the objectives and restrictions, and automatically it identifies the best paragraphs in order to find the most suitable set of contents for every public profile.","PeriodicalId":336514,"journal":{"name":"2010 Ninth International Conference on Machine Learning and Applications","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125163361","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 order to give the computer the ability to play against human opponents, one could utilize the Alpha-Beta algorithm. However, this algorithm has several limitations restricting its playing capabilities. Over the years, many variants of this algorithm were developed, among them a couple that make use of neural networks: a neural network to focus the search in the game tree, and a neural network trained without expert knowledge that substitutes the heuristic function in the Alpha-Beta algorithm. In this paper the weaknesses of the Alpha-Beta algorithm are reviewed alongside its variants that use neural networks. It is explained how each approach overcomes different limitations of the Alpha-Beta algorithm, and an attempt to overcome its weaknesses by the use of a combination of the neural network algorithms is presented. The proposed hybrid algorithm, which was developed using Evolutionary Strategies, still keeps the advantages of each of the individual neural algorithms, and shows a significant improvement in play against them.
{"title":"Overcoming Alpha-Beta Limitations Using Evolved Artificial Neural Networks","authors":"Y. Gal, M. Avigal","doi":"10.1109/ICMLA.2010.125","DOIUrl":"https://doi.org/10.1109/ICMLA.2010.125","url":null,"abstract":"In order to give the computer the ability to play against human opponents, one could utilize the Alpha-Beta algorithm. However, this algorithm has several limitations restricting its playing capabilities. Over the years, many variants of this algorithm were developed, among them a couple that make use of neural networks: a neural network to focus the search in the game tree, and a neural network trained without expert knowledge that substitutes the heuristic function in the Alpha-Beta algorithm. In this paper the weaknesses of the Alpha-Beta algorithm are reviewed alongside its variants that use neural networks. It is explained how each approach overcomes different limitations of the Alpha-Beta algorithm, and an attempt to overcome its weaknesses by the use of a combination of the neural network algorithms is presented. The proposed hybrid algorithm, which was developed using Evolutionary Strategies, still keeps the advantages of each of the individual neural algorithms, and shows a significant improvement in play against them.","PeriodicalId":336514,"journal":{"name":"2010 Ninth International Conference on Machine Learning and Applications","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129121989","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}
Functional Magnetic Resonance Imaging (fMRI) has become an important diagnostic tool for measuring brain haemodynamics. Previous research on analysing fMRI data mainly focuses on detecting low-level neuron activation from the ensued haemodynamic activities. An important recent advance is to show that the high-level cognitive status is recognisable from a period of fMRI records. Nevertheless, it would also be helpful to reveal dynamics of cognitive activities during the period. In this paper, we tackle the problem of discovering the dynamic cognitive activities by proposing an algorithm of boosted structure learning. We employ statistic model of random fields to represent the dynamics of the brain. To exploit the rich fMRI observations with reasonable model complexity, we build multiple models, where one model links the cognitive activities to only a fraction of the fMRI observations. We combine the simple models by using an altered AdaBoost scheme for multi-class structure learning and show theoretical justification of the proposed scheme. Empirical test shows the method effectively links the physiological and the psychological activities of the brain.
{"title":"Boosted Dynamic Cognitive Activity Recognition from Brain Images","authors":"Jun Li, D. Tao","doi":"10.1109/ICMLA.2010.60","DOIUrl":"https://doi.org/10.1109/ICMLA.2010.60","url":null,"abstract":"Functional Magnetic Resonance Imaging (fMRI) has become an important diagnostic tool for measuring brain haemodynamics. Previous research on analysing fMRI data mainly focuses on detecting low-level neuron activation from the ensued haemodynamic activities. An important recent advance is to show that the high-level cognitive status is recognisable from a period of fMRI records. Nevertheless, it would also be helpful to reveal dynamics of cognitive activities during the period. In this paper, we tackle the problem of discovering the dynamic cognitive activities by proposing an algorithm of boosted structure learning. We employ statistic model of random fields to represent the dynamics of the brain. To exploit the rich fMRI observations with reasonable model complexity, we build multiple models, where one model links the cognitive activities to only a fraction of the fMRI observations. We combine the simple models by using an altered AdaBoost scheme for multi-class structure learning and show theoretical justification of the proposed scheme. Empirical test shows the method effectively links the physiological and the psychological activities of the brain.","PeriodicalId":336514,"journal":{"name":"2010 Ninth International Conference on Machine Learning and Applications","volume":"74 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128041334","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}
Jinghua Gu, J. Xuan, Y. Wang, R. Riggins, R. Clarke
Network component analysis (NCA) and other methods based on the NCA model have become powerful bioinformatics tools to reconstruct underlying regulatory networks and recover hidden biological processes. However, due to the existence of experimental noises in micro array data and false information in network connectivity data (e.g., ChIP-on-chip binding data, motif information, etc.), it still remains challenging to reconstruct gene regulatory networks for real biomedical applications such as human cancer studies. In this paper, we model the relationship between the genes that share the same transcription factors (TF) from the angle of regression. We propose a statistic called outlier sum testing the conditional significance of the target genes. A Gibbs strategy is utilized in order to estimate the marginal value of outlier sum from its conditional function. Based on the outlier sum statistic we are able to extract the true target genes that carry information about transcription factor activities (TFAs) from the whole population. As a proof-of-concept, we demonstrated the efficiency and robustness of the proposed method on both simulation data and yeast cell cycle data.
{"title":"Identification of Transcriptional Regulatory Networks by Learning the Marginal Function of Outlier Sum Statistic","authors":"Jinghua Gu, J. Xuan, Y. Wang, R. Riggins, R. Clarke","doi":"10.1109/ICMLA.2010.48","DOIUrl":"https://doi.org/10.1109/ICMLA.2010.48","url":null,"abstract":"Network component analysis (NCA) and other methods based on the NCA model have become powerful bioinformatics tools to reconstruct underlying regulatory networks and recover hidden biological processes. However, due to the existence of experimental noises in micro array data and false information in network connectivity data (e.g., ChIP-on-chip binding data, motif information, etc.), it still remains challenging to reconstruct gene regulatory networks for real biomedical applications such as human cancer studies. In this paper, we model the relationship between the genes that share the same transcription factors (TF) from the angle of regression. We propose a statistic called outlier sum testing the conditional significance of the target genes. A Gibbs strategy is utilized in order to estimate the marginal value of outlier sum from its conditional function. Based on the outlier sum statistic we are able to extract the true target genes that carry information about transcription factor activities (TFAs) from the whole population. As a proof-of-concept, we demonstrated the efficiency and robustness of the proposed method on both simulation data and yeast cell cycle data.","PeriodicalId":336514,"journal":{"name":"2010 Ninth International Conference on Machine Learning and Applications","volume":"97 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123023699","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 paper, a novel control algorithm has been proposed to regulate the DC bus voltage of a single phase shunt active power filter using a fuzzy logic controller. The DC bus voltage of a shunt active power filter should be controlled to compensate the filter losses on the grid. In many industrial applications, a PI controller is generally used to regulate the DC bus voltage of shunt active power filters. In the novel control algorithm the error signal caused by the filter losses has been computed firstly. Then this error signal has been compensated using the fuzzy logic controller. Simulation model of the shunt active power filter with the proposed control algorithm has been designed using the Matlab/Simulink/Simpower and the Fuzzy Toolbox. The simulation results show that the fuzzy logic controller compensates filter losses on the grid and improves the power quality by reducing the total harmonic distortion (THD) of the supply current and increasing the power factor.
{"title":"DC Bus Voltage Regulation of an Active Power Filter Using a Fuzzy Logic Controller","authors":"I. Colak, R. Bayindir, O. Kaplan, Ferhat Tas","doi":"10.1109/ICMLA.2010.165","DOIUrl":"https://doi.org/10.1109/ICMLA.2010.165","url":null,"abstract":"In this paper, a novel control algorithm has been proposed to regulate the DC bus voltage of a single phase shunt active power filter using a fuzzy logic controller. The DC bus voltage of a shunt active power filter should be controlled to compensate the filter losses on the grid. In many industrial applications, a PI controller is generally used to regulate the DC bus voltage of shunt active power filters. In the novel control algorithm the error signal caused by the filter losses has been computed firstly. Then this error signal has been compensated using the fuzzy logic controller. Simulation model of the shunt active power filter with the proposed control algorithm has been designed using the Matlab/Simulink/Simpower and the Fuzzy Toolbox. The simulation results show that the fuzzy logic controller compensates filter losses on the grid and improves the power quality by reducing the total harmonic distortion (THD) of the supply current and increasing the power factor.","PeriodicalId":336514,"journal":{"name":"2010 Ninth International Conference on Machine Learning and Applications","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117285886","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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