The paper presents a Multi-class Support Vector Machine classifier and its application to hypothyroid detection and classification. Support Vector Machines (SVM) have been well known method in the machine learning community for binary classification problems. Multi-class SVMs (MCSVM) are usually implemented by combining several binary SVMs. The objective of this work is to show: first, robustness of various kind of kernels for Multi-class SVM classifier, second, a comparison of different constructing methods for Multi-class SVM, such as One-Against-One and One-Against-All, and finally comparing the classifiers' accuracy of Multi-class SVM classifier to AdaBoost and Decision Tree. The simulation results show that One-Against-All Support Vector Machines (OAASVM) are superior to One-Against-One Support Vector Machines (OAOSVM) with polynomial kernels. The accuracy of OAASVM is also higher than AdaBoost and Decision Tree classifier on hypothyroid disease datasets from UCI machine learning dataset.
{"title":"Multi-class Support Vector Machine (SVM) Classifiers -- An Application in Hypothyroid Detection and Classification","authors":"F. F. Chamasemani, Y. P. Singh","doi":"10.1109/BIC-TA.2011.51","DOIUrl":"https://doi.org/10.1109/BIC-TA.2011.51","url":null,"abstract":"The paper presents a Multi-class Support Vector Machine classifier and its application to hypothyroid detection and classification. Support Vector Machines (SVM) have been well known method in the machine learning community for binary classification problems. Multi-class SVMs (MCSVM) are usually implemented by combining several binary SVMs. The objective of this work is to show: first, robustness of various kind of kernels for Multi-class SVM classifier, second, a comparison of different constructing methods for Multi-class SVM, such as One-Against-One and One-Against-All, and finally comparing the classifiers' accuracy of Multi-class SVM classifier to AdaBoost and Decision Tree. The simulation results show that One-Against-All Support Vector Machines (OAASVM) are superior to One-Against-One Support Vector Machines (OAOSVM) with polynomial kernels. The accuracy of OAASVM is also higher than AdaBoost and Decision Tree classifier on hypothyroid disease datasets from UCI machine learning dataset.","PeriodicalId":211822,"journal":{"name":"2011 Sixth International Conference on Bio-Inspired Computing: Theories and Applications","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2011-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115668810","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}
Identifying the shortest Hamiltonian circuit is a task which appears in various types of industrial and logistics applications. It is a NP-hard problem [1]. This paper intends to find the shortest Hamiltonian circuit of the selected 68 towns/cities in Penang state, Malaysia using the generic Bee Colony Optimization (BCO) framework [2]. The proposed BCO framework realizes computationally the foraging process and waggle dance performed by bees and it is enriched with elitism, local optimization and adaptive pruning. A modification has been applied to the framework whereby a past solutions reinforcement policy is integrated. Also, the local optimization method is enhanced with the utilization of a Tabu list. The results from this study serve as an significant input to the preparation of logistics plan when a natural disaster occurs. Aiding resources can be delivered to affected areas, one after another, in a more appropriate and systematic manner and thus leads to cost and time saving. The results show that proposed BCO framework is able to produce a circuit (based on great-circle distance) with length of 263.332016km within 1.32s. The performance of the proposed BCO framework is comparable to the Genetic Algorithm and Lin-Ker heuristic.
{"title":"Finding the Shortest Hamiltonian Circuit of Selected Places in Penang Using a Generic Bee Colony Optimization Framework","authors":"L. Wong, M. Low, C. Chong","doi":"10.1109/BIC-TA.2011.5","DOIUrl":"https://doi.org/10.1109/BIC-TA.2011.5","url":null,"abstract":"Identifying the shortest Hamiltonian circuit is a task which appears in various types of industrial and logistics applications. It is a NP-hard problem [1]. This paper intends to find the shortest Hamiltonian circuit of the selected 68 towns/cities in Penang state, Malaysia using the generic Bee Colony Optimization (BCO) framework [2]. The proposed BCO framework realizes computationally the foraging process and waggle dance performed by bees and it is enriched with elitism, local optimization and adaptive pruning. A modification has been applied to the framework whereby a past solutions reinforcement policy is integrated. Also, the local optimization method is enhanced with the utilization of a Tabu list. The results from this study serve as an significant input to the preparation of logistics plan when a natural disaster occurs. Aiding resources can be delivered to affected areas, one after another, in a more appropriate and systematic manner and thus leads to cost and time saving. The results show that proposed BCO framework is able to produce a circuit (based on great-circle distance) with length of 263.332016km within 1.32s. The performance of the proposed BCO framework is comparable to the Genetic Algorithm and Lin-Ker heuristic.","PeriodicalId":211822,"journal":{"name":"2011 Sixth International Conference on Bio-Inspired Computing: Theories and Applications","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2011-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116693059","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 hypercube is a generalization of 3-cube to n dimensions, also called an n-cube or measure polytope. In this paper we define boundary edNCE recursive graph grammar and generate the language for the hypercube. For a graph grammar G with a graph-theoretical property, let be the language L(G) squeezed with i.e., This paper presents result on language-theoretic properties (such as membership and other decision properties) of NLC, B-edNCE languages squeezed with hyper cube graphs. We show that is in NP complete.
{"title":"Properties of NLC Languages Squeezed with Hypercube Graphs","authors":"S. Bharathi, M. Vadivu, K. Thiagarajan","doi":"10.1109/BIC-TA.2011.3","DOIUrl":"https://doi.org/10.1109/BIC-TA.2011.3","url":null,"abstract":"The hypercube is a generalization of 3-cube to n dimensions, also called an n-cube or measure polytope. In this paper we define boundary edNCE recursive graph grammar and generate the language for the hypercube. For a graph grammar G with a graph-theoretical property, let be the language L(G) squeezed with i.e., This paper presents result on language-theoretic properties (such as membership and other decision properties) of NLC, B-edNCE languages squeezed with hyper cube graphs. We show that is in NP complete.","PeriodicalId":211822,"journal":{"name":"2011 Sixth International Conference on Bio-Inspired Computing: Theories and Applications","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2011-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116993550","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}
Most of the biological processes such as the processes in liver cell have been modeled by using the approach of ordinary differential equation. Such conventional model has demonstrated drawbacks and limitations primarily in preserving the stochastic and nondeterministic behaviors of biological processes by characterizing them as continuous and deterministic processes. Membrane computing has been considered as an alternative to address these limitations by providing modeling capabilities in representing the structure and processes of biological systems essential for biological applications. This study was carried out to investigate the modeling of hormone-induced calcium oscillations in liver cell with membrane computing. Simulation strategy of Gillespie algorithm and the method of model checking with Probabilistic Symbolic Model Checker were used to verify and validate the membrane computing model. The results produced by membrane computing model were compared with the results from ordinary differential equation model. The simulation and model checking of membrane computing model of the hormone-induced calcium oscillations showed that the fundamental properties of the biological process were preserved. Membrane computing model has provided a better approach in accommodating the structure and processes of hormone-induced calcium oscillations system by sustaining the basic properties of the system compared with ordinary differential equation model. However there were some other issues such as the selection of kinetic constants according to the behavior of biological processes has to be addressed to strengthen membrane computing capability in modeling biological processes.
大多数生物过程，如肝细胞的过程，都是用常微分方程的方法来模拟的。这种传统模型主要表现在通过将生物过程描述为连续的和确定的过程来保留生物过程的随机和不确定性行为方面。膜计算被认为是解决这些限制的一种替代方法，它提供了表示生物系统的结构和过程的建模能力，这对生物应用至关重要。本研究采用膜计算方法研究激素诱导的肝细胞钙振荡模型。采用Gillespie算法的仿真策略和Probabilistic Symbolic model Checker的模型校验方法对膜计算模型进行了验证。将膜计算模型的计算结果与常微分方程模型的计算结果进行了比较。对激素诱导钙振荡的膜计算模型进行了仿真和模型检验，结果表明该生物过程的基本特性得以保留。与常微分方程模型相比，膜计算模型通过维持系统的基本性质，为适应激素诱导钙振荡系统的结构和过程提供了更好的方法。然而，为了增强膜计算在生物过程建模中的能力，还需要解决一些其他问题，如根据生物过程的行为选择动力学常数。
{"title":"Comparing Membrane Computing with Ordinary Differential Equation in Modeling a Biological Process in Liver Cell","authors":"R. C. Muniyandi, A. Zin","doi":"10.1109/BIC-TA.2011.68","DOIUrl":"https://doi.org/10.1109/BIC-TA.2011.68","url":null,"abstract":"Most of the biological processes such as the processes in liver cell have been modeled by using the approach of ordinary differential equation. Such conventional model has demonstrated drawbacks and limitations primarily in preserving the stochastic and nondeterministic behaviors of biological processes by characterizing them as continuous and deterministic processes. Membrane computing has been considered as an alternative to address these limitations by providing modeling capabilities in representing the structure and processes of biological systems essential for biological applications. This study was carried out to investigate the modeling of hormone-induced calcium oscillations in liver cell with membrane computing. Simulation strategy of Gillespie algorithm and the method of model checking with Probabilistic Symbolic Model Checker were used to verify and validate the membrane computing model. The results produced by membrane computing model were compared with the results from ordinary differential equation model. The simulation and model checking of membrane computing model of the hormone-induced calcium oscillations showed that the fundamental properties of the biological process were preserved. Membrane computing model has provided a better approach in accommodating the structure and processes of hormone-induced calcium oscillations system by sustaining the basic properties of the system compared with ordinary differential equation model. However there were some other issues such as the selection of kinetic constants according to the behavior of biological processes has to be addressed to strengthen membrane computing capability in modeling biological processes.","PeriodicalId":211822,"journal":{"name":"2011 Sixth International Conference on Bio-Inspired Computing: Theories and Applications","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2011-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120818373","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}
A unilateral graph representation is used for representing a network with a minimum of 3 nodes. A nearly closed network is identified from the various possible combinations of the formed network, with respect to the direction of communication. The possible number of bypass nodes are identified from the combination and tabulated. The same representation was carried out with the network having 4 nodes. A common word combination was observed in both the cases which can be extended to any number of nodes. Network at each level has been studied through finite state automaton along with its regular grammar.
{"title":"Study of Communication Network Using Unilateral Graph and Grammar","authors":"K. Thiagarajan, S. Bharathi, P. Natarajan","doi":"10.1109/BIC-TA.2011.9","DOIUrl":"https://doi.org/10.1109/BIC-TA.2011.9","url":null,"abstract":"A unilateral graph representation is used for representing a network with a minimum of 3 nodes. A nearly closed network is identified from the various possible combinations of the formed network, with respect to the direction of communication. The possible number of bypass nodes are identified from the combination and tabulated. The same representation was carried out with the network having 4 nodes. A common word combination was observed in both the cases which can be extended to any number of nodes. Network at each level has been studied through finite state automaton along with its regular grammar.","PeriodicalId":211822,"journal":{"name":"2011 Sixth International Conference on Bio-Inspired Computing: Theories and Applications","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2011-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127188482","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}
Fractal Image Compression is a well-known problem which is in the class of NP-Hard problems. Quantum Evolutionary Algorithm is a novel optimization algorithm which uses a probabilistic representation for solutions and is highly suitable for combinatorial problems like Knapsack problem. Genetic algorithms are widely used for fractal image compression problems, but QEA is not used for this kind of problems yet. This paper improves QEA whit change population size and used it in fractal image compression. Experimental results show that our method have a better performance than GA and conventional fractal image compression algorithms.
{"title":"Square Function for Population Size in Quantum Evolutionary Algorithm and its Application in Fractal Image Compression","authors":"Amin Qorbani, A. Nodehi, A. Ahmadi, S. Nodehi","doi":"10.1109/BIC-TA.2011.1","DOIUrl":"https://doi.org/10.1109/BIC-TA.2011.1","url":null,"abstract":"Fractal Image Compression is a well-known problem which is in the class of NP-Hard problems. Quantum Evolutionary Algorithm is a novel optimization algorithm which uses a probabilistic representation for solutions and is highly suitable for combinatorial problems like Knapsack problem. Genetic algorithms are widely used for fractal image compression problems, but QEA is not used for this kind of problems yet. This paper improves QEA whit change population size and used it in fractal image compression. Experimental results show that our method have a better performance than GA and conventional fractal image compression algorithms.","PeriodicalId":211822,"journal":{"name":"2011 Sixth International Conference on Bio-Inspired Computing: Theories and Applications","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2011-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115537707","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}
A novel design method is proposed to solve the problem of global asymptotic stabilization for the nonlinear systems in high-order lower-triangular form via recursive design procedure, with one common assumption. This new approach simplifies the design process of the class of complex lower-triangular systems. Furthermore, the practical example and simulation are provided to show the effectiveness of the design method.
{"title":"The Novel Design for Global Stabilization of a Class of Complex Lower-Triangular Systems","authors":"Bing Wang","doi":"10.1109/BIC-TA.2011.44","DOIUrl":"https://doi.org/10.1109/BIC-TA.2011.44","url":null,"abstract":"A novel design method is proposed to solve the problem of global asymptotic stabilization for the nonlinear systems in high-order lower-triangular form via recursive design procedure, with one common assumption. This new approach simplifies the design process of the class of complex lower-triangular systems. Furthermore, the practical example and simulation are provided to show the effectiveness of the design method.","PeriodicalId":211822,"journal":{"name":"2011 Sixth International Conference on Bio-Inspired Computing: Theories and Applications","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2011-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123202379","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 hybrid neural classifier combining the auto-encoder neural network and the Lattice Vector Quantization (LVQ) model is described. The auto-encoder network is used for dimensionality reduction by projecting high dimensional data into the 2D space. The LVQ model is used for data visualization by forming and adapting the granularity of a data map. The mapped data are employed to predict the target classes of new data samples. To improve classification accuracy, a majority voting scheme is adopted by the hybrid classifier. To demonstrate the applicability of the hybrid classifier, a series of experiments using simulated and real fault data from induction motors is conducted. The results show that the hybrid classifier is able to outperform the Multi-Layer Perceptron neural network, and to produce very good classification accuracy rates for various fault conditions of induction motors.
{"title":"A Hybrid Neural Classifier for Dimensionality Reduction and Data Visualization and Its Application to Fault Detection and Classification of Induction Motors","authors":"Mahnoosh Nadjarpoorsiyahkaly, C. Lim","doi":"10.1109/BIC-TA.2011.19","DOIUrl":"https://doi.org/10.1109/BIC-TA.2011.19","url":null,"abstract":"In this paper, a hybrid neural classifier combining the auto-encoder neural network and the Lattice Vector Quantization (LVQ) model is described. The auto-encoder network is used for dimensionality reduction by projecting high dimensional data into the 2D space. The LVQ model is used for data visualization by forming and adapting the granularity of a data map. The mapped data are employed to predict the target classes of new data samples. To improve classification accuracy, a majority voting scheme is adopted by the hybrid classifier. To demonstrate the applicability of the hybrid classifier, a series of experiments using simulated and real fault data from induction motors is conducted. The results show that the hybrid classifier is able to outperform the Multi-Layer Perceptron neural network, and to produce very good classification accuracy rates for various fault conditions of induction motors.","PeriodicalId":211822,"journal":{"name":"2011 Sixth International Conference on Bio-Inspired Computing: Theories and Applications","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2011-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121382619","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}
Machine Translation has been defined as the process that utilizes computer software to translate text from one natural language to another. This definition involves accounting for the grammatical structure of each language and using rules, examples and grammars to transfer the grammatical structure of the source language (SL) into the target language (TL). This paper presents English to Arabic approach for translating well-structured English sentences into well-structured Arabic sentences, using a Grammar based and example-translation techniques to handle the problems of ordering and agreement. The proposed methodology is flexible and scalable, the main advantages are: first, a hybrid-based approach combined advantages of rule-based (RBMT) with advantages example-based (EBMT), and second, it can be applied on some other languages with minor modifications. The OAK Parser is used to analyze the input English text to get the part of speech (POS) for each word in the text as a pre-translation process using the C# language, validation rules have been applied in both the database design and the programming code in order to ensure the integrity of data. A major design goal of this system is that it will be used as a stand-alone tool, and can be very well integrated with a general machine translation system for English sentences.
{"title":"Rule-Based and Example-Based Machine Translation from English to Arabic","authors":"M. Alawneh, T. Sembok","doi":"10.1109/BIC-TA.2011.76","DOIUrl":"https://doi.org/10.1109/BIC-TA.2011.76","url":null,"abstract":"Machine Translation has been defined as the process that utilizes computer software to translate text from one natural language to another. This definition involves accounting for the grammatical structure of each language and using rules, examples and grammars to transfer the grammatical structure of the source language (SL) into the target language (TL). This paper presents English to Arabic approach for translating well-structured English sentences into well-structured Arabic sentences, using a Grammar based and example-translation techniques to handle the problems of ordering and agreement. The proposed methodology is flexible and scalable, the main advantages are: first, a hybrid-based approach combined advantages of rule-based (RBMT) with advantages example-based (EBMT), and second, it can be applied on some other languages with minor modifications. The OAK Parser is used to analyze the input English text to get the part of speech (POS) for each word in the text as a pre-translation process using the C# language, validation rules have been applied in both the database design and the programming code in order to ensure the integrity of data. A major design goal of this system is that it will be used as a stand-alone tool, and can be very well integrated with a general machine translation system for English sentences.","PeriodicalId":211822,"journal":{"name":"2011 Sixth International Conference on Bio-Inspired Computing: Theories and Applications","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2011-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114825178","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}
F. Cabarle, H. Adorna, Miguel A. Martínez-del-Amor
Spiking Neural P (SNP) systems, variants of Psystems (under Membrane and Natural computing), are computing models that acquire abstraction and inspiration from the way neurons 'compute' or process information. Similar to other P system variants, SNP systems are Turing complete models that by nature compute non-deterministically and in a maximally parallel manner. P systems usually trade (often exponential) space for (polynomial to constant) time. Due to this nature, P system variants are currently limited to parallel simulations, and several variants have already been simulated in parallel devices. In this paper we present an improved SNP system simulator based on graphics processing units (GPUs). Among other reasons, current GPUs are architectured for massively parallel computations, thus making GPUs very suitable for SNP system simulation. The computing model, hardware/software considerations, and simulation algorithm are presented, as well as the comparisons of the CPU only and CPU-GPU based simulators.
{"title":"An Improved GPU Simulator for Spiking Neural P Systems","authors":"F. Cabarle, H. Adorna, Miguel A. Martínez-del-Amor","doi":"10.1109/BIC-TA.2011.37","DOIUrl":"https://doi.org/10.1109/BIC-TA.2011.37","url":null,"abstract":"Spiking Neural P (SNP) systems, variants of Psystems (under Membrane and Natural computing), are computing models that acquire abstraction and inspiration from the way neurons 'compute' or process information. Similar to other P system variants, SNP systems are Turing complete models that by nature compute non-deterministically and in a maximally parallel manner. P systems usually trade (often exponential) space for (polynomial to constant) time. Due to this nature, P system variants are currently limited to parallel simulations, and several variants have already been simulated in parallel devices. In this paper we present an improved SNP system simulator based on graphics processing units (GPUs). Among other reasons, current GPUs are architectured for massively parallel computations, thus making GPUs very suitable for SNP system simulation. The computing model, hardware/software considerations, and simulation algorithm are presented, as well as the comparisons of the CPU only and CPU-GPU based simulators.","PeriodicalId":211822,"journal":{"name":"2011 Sixth International Conference on Bio-Inspired Computing: Theories and Applications","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2011-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126568818","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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