Pub Date : 2017-03-07DOI: 10.1109/CSIEC.2017.7940160
Arman Balali Moghadam, M. Rafsanjani
In this article we used a genetic algorithm approach for generating and evaluating rhythms for creating levels of 2D runner platformer games. After generating rhythms, we used a grammar based approach to generate geometry based on these rhythms. We used a novel fitness function for the genetic algorithm in the area of PCG. This approach also minimizes the amount of the content that must be manually authored. Our results show that this method can produce a variety of levels with controlled difficulty between two levels and all generated levels are fully playable. We believe that the presented method is potentially applicable to commercial platformer games.
{"title":"A genetic approach in procedural content generation for platformer games level creation","authors":"Arman Balali Moghadam, M. Rafsanjani","doi":"10.1109/CSIEC.2017.7940160","DOIUrl":"https://doi.org/10.1109/CSIEC.2017.7940160","url":null,"abstract":"In this article we used a genetic algorithm approach for generating and evaluating rhythms for creating levels of 2D runner platformer games. After generating rhythms, we used a grammar based approach to generate geometry based on these rhythms. We used a novel fitness function for the genetic algorithm in the area of PCG. This approach also minimizes the amount of the content that must be manually authored. Our results show that this method can produce a variety of levels with controlled difficulty between two levels and all generated levels are fully playable. We believe that the presented method is potentially applicable to commercial platformer games.","PeriodicalId":166046,"journal":{"name":"2017 2nd Conference on Swarm Intelligence and Evolutionary Computation (CSIEC)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124700292","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-03-07DOI: 10.1109/CSIEC.2017.7940175
Pourya Farzi, R. Akbari, O. Bushehrian
Semantic web services represent the potential of the web and they have significant impact on the discovery process. Due to the high proliferation of web services, selecting the best web services from functional equivalent service providers have become a real challenge when a large number of services have been published in a registry. If these services have been functionally-equivalent, it is difficult for service requester to choose which one to be invoked. So the quality of the service plays a crucial role and it becomes a very important factor in discovery and selection of these candidates services to best meet users requirement. In this paper, a QOS method is designed and implemented to support web services of non-functional aspect. The proposed method is based on OWL-S expansion and adding needed information for acquiring non-functional parameters and it construct a better QoS metrics model. Furthermore, the experimental results show that the proposed method improve the accuracy of the discovery system.
{"title":"Improving semantic web service discovery method based on QoS ontology","authors":"Pourya Farzi, R. Akbari, O. Bushehrian","doi":"10.1109/CSIEC.2017.7940175","DOIUrl":"https://doi.org/10.1109/CSIEC.2017.7940175","url":null,"abstract":"Semantic web services represent the potential of the web and they have significant impact on the discovery process. Due to the high proliferation of web services, selecting the best web services from functional equivalent service providers have become a real challenge when a large number of services have been published in a registry. If these services have been functionally-equivalent, it is difficult for service requester to choose which one to be invoked. So the quality of the service plays a crucial role and it becomes a very important factor in discovery and selection of these candidates services to best meet users requirement. In this paper, a QOS method is designed and implemented to support web services of non-functional aspect. The proposed method is based on OWL-S expansion and adding needed information for acquiring non-functional parameters and it construct a better QoS metrics model. Furthermore, the experimental results show that the proposed method improve the accuracy of the discovery system.","PeriodicalId":166046,"journal":{"name":"2017 2nd Conference on Swarm Intelligence and Evolutionary Computation (CSIEC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130466883","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-03-07DOI: 10.1109/CSIEC.2017.7940159
Yaser Maghsoudi, M. Kamandar
Digital IIR filter design by optimizing a fitness function with respect to coefficients of a filter with rational transfer function by meta-heuristic algorithms has been considered recently. Most researchers use a fitness function consisted of difference between magnitude response of desired filter and designed filter and the constraints such as linear phase, minimum phase and stability of designed filter. In this paper, a comprehensive fitness function for IIR digital filter design with 6 terms is proposed. A new term is added to fitness function to get a filter with low delay. Low delay filters are desirable for real time signal processing. This term is weighted partial energy of the impulse response of designed causal filter. Maximizing this term leads to concentration of energy of impulse response at its beginning, consequently a low delay filter. Low delay property leads to fast decaying of transient response and low delay between input and output of designed filter. Proposed fitness function also includes some terms to meet linear phase, minimum phase and stability constraints. Meta-heuristic optimization algorithms GA, GSA and PSO are used to optimize proposed fitness function. To evaluate efficiency of the proposed method, it will be used to design a low delay low pass filter and a low delay differentiator. Reported results show lower delay of designed filters by proposed method than designed ones by traditional methods.
{"title":"Low delay digital IIR filter design using metaheuristic algorithms","authors":"Yaser Maghsoudi, M. Kamandar","doi":"10.1109/CSIEC.2017.7940159","DOIUrl":"https://doi.org/10.1109/CSIEC.2017.7940159","url":null,"abstract":"Digital IIR filter design by optimizing a fitness function with respect to coefficients of a filter with rational transfer function by meta-heuristic algorithms has been considered recently. Most researchers use a fitness function consisted of difference between magnitude response of desired filter and designed filter and the constraints such as linear phase, minimum phase and stability of designed filter. In this paper, a comprehensive fitness function for IIR digital filter design with 6 terms is proposed. A new term is added to fitness function to get a filter with low delay. Low delay filters are desirable for real time signal processing. This term is weighted partial energy of the impulse response of designed causal filter. Maximizing this term leads to concentration of energy of impulse response at its beginning, consequently a low delay filter. Low delay property leads to fast decaying of transient response and low delay between input and output of designed filter. Proposed fitness function also includes some terms to meet linear phase, minimum phase and stability constraints. Meta-heuristic optimization algorithms GA, GSA and PSO are used to optimize proposed fitness function. To evaluate efficiency of the proposed method, it will be used to design a low delay low pass filter and a low delay differentiator. Reported results show lower delay of designed filters by proposed method than designed ones by traditional methods.","PeriodicalId":166046,"journal":{"name":"2017 2nd Conference on Swarm Intelligence and Evolutionary Computation (CSIEC)","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128368207","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-03-07DOI: 10.1109/CSIEC.2017.7940165
M. Zangeneh, M. Ghazvini
The limited number of resources in Wireless sensor Networks (WSNs) and long communication distance between sensors and base station causes high energy consumption and consequently reduce the network lifetime. Therefore one of the important parameters in these networks is the optimized energy consumption. One way to reduce the energy consumption is to cluster the network. In this study, a dynamic clustering method is presented based on the artificial bee colony and the genetic algorithm. In fact, the genetic algorithm is used for determining the cluster heads and the artificial bee colony algorithm is used for determining member nodes in each cluster. The proposed algorithms were simulated by OMNeT++simulator. Simulation results showesome improvements.
{"title":"An energy-based clustering method for WSNs using artificial bee colony and genetic algorithm","authors":"M. Zangeneh, M. Ghazvini","doi":"10.1109/CSIEC.2017.7940165","DOIUrl":"https://doi.org/10.1109/CSIEC.2017.7940165","url":null,"abstract":"The limited number of resources in Wireless sensor Networks (WSNs) and long communication distance between sensors and base station causes high energy consumption and consequently reduce the network lifetime. Therefore one of the important parameters in these networks is the optimized energy consumption. One way to reduce the energy consumption is to cluster the network. In this study, a dynamic clustering method is presented based on the artificial bee colony and the genetic algorithm. In fact, the genetic algorithm is used for determining the cluster heads and the artificial bee colony algorithm is used for determining member nodes in each cluster. The proposed algorithms were simulated by OMNeT++simulator. Simulation results showesome improvements.","PeriodicalId":166046,"journal":{"name":"2017 2nd Conference on Swarm Intelligence and Evolutionary Computation (CSIEC)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131960736","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-03-07DOI: 10.1109/CSIEC.2017.7940167
Bahareh Nikpour, Mahin Shabani, H. Nezamabadi-pour
Classification of imbalanced data sets is one of the basic challenges in the field of machine learning and data mining. There have been many proposed methods for classification of such data sets up to now. In algorithmic level methods, new algorithms are created which are adapted to the nature of imbalanced data sets. Gravitational fixed radius nearest neighbor algorithm (GFRNN) is an algorithmic level method developed with the aim of enhancing k nearest neighbor classifier to acquire the ability of dealing with imbalanced data sets. This algorithm, utilizes the sum of gravitational forces on a query sample from its nearest neighbors in a fixed radius to determine its label. Simplicity and no need for parameter setting during the run of algorithm are the main advantages of this method. In this paper, a method is proposed for improving the performance of GFRNN algorithm in which mass assigning of each training sample is done based on the sum of gravitational forces from other training samples on it. The results obtained from applying the proposed method on 10 data sets prove the superiority of it compared with 5 other algorithms.
{"title":"Proposing new method to improve gravitational fixed nearest neighbor algorithm for imbalanced data classification","authors":"Bahareh Nikpour, Mahin Shabani, H. Nezamabadi-pour","doi":"10.1109/CSIEC.2017.7940167","DOIUrl":"https://doi.org/10.1109/CSIEC.2017.7940167","url":null,"abstract":"Classification of imbalanced data sets is one of the basic challenges in the field of machine learning and data mining. There have been many proposed methods for classification of such data sets up to now. In algorithmic level methods, new algorithms are created which are adapted to the nature of imbalanced data sets. Gravitational fixed radius nearest neighbor algorithm (GFRNN) is an algorithmic level method developed with the aim of enhancing k nearest neighbor classifier to acquire the ability of dealing with imbalanced data sets. This algorithm, utilizes the sum of gravitational forces on a query sample from its nearest neighbors in a fixed radius to determine its label. Simplicity and no need for parameter setting during the run of algorithm are the main advantages of this method. In this paper, a method is proposed for improving the performance of GFRNN algorithm in which mass assigning of each training sample is done based on the sum of gravitational forces from other training samples on it. The results obtained from applying the proposed method on 10 data sets prove the superiority of it compared with 5 other algorithms.","PeriodicalId":166046,"journal":{"name":"2017 2nd Conference on Swarm Intelligence and Evolutionary Computation (CSIEC)","volume":"61 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132773258","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-03-07DOI: 10.1109/CSIEC.2017.7940176
Seyyed-Farshad Kazemi, S. Motamedi, Saeed Sharifian
In recent years, real-time pricing has become so popular and give the residents the benefit of reducing the energy cost by scheduling appliances. However, scheduling appliances manually is so time taking and may not result in users' satisfaction. Automatic scheduler and Energy Management Systems are one of the solutions developed by the researchers in recent year. In this paper, authors propose a method based on Gray Wolf Optimization and Genetic Algorithm to achieve the optimal schedule for appliances in terms of cost and PAR.
{"title":"A home energy management system using Gray Wolf Optimizer in smart grids","authors":"Seyyed-Farshad Kazemi, S. Motamedi, Saeed Sharifian","doi":"10.1109/CSIEC.2017.7940176","DOIUrl":"https://doi.org/10.1109/CSIEC.2017.7940176","url":null,"abstract":"In recent years, real-time pricing has become so popular and give the residents the benefit of reducing the energy cost by scheduling appliances. However, scheduling appliances manually is so time taking and may not result in users' satisfaction. Automatic scheduler and Energy Management Systems are one of the solutions developed by the researchers in recent year. In this paper, authors propose a method based on Gray Wolf Optimization and Genetic Algorithm to achieve the optimal schedule for appliances in terms of cost and PAR.","PeriodicalId":166046,"journal":{"name":"2017 2nd Conference on Swarm Intelligence and Evolutionary Computation (CSIEC)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129137585","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-03-07DOI: 10.1109/CSIEC.2017.7940171
H. Nobahari, Ariyan Bighashdel
In this paper, an extension of the recently developed Crow Search Algorithm (CSA) to multi-objective optimization problems is presented. The proposed algorithm, called Multi-Objective Crow Search Algorithm (MOCSA), defines the fitness function using a set of determined weight vectors, employing the max-min strategy. In order to improve the efficiency of the search space, the performance space is regionalized using specific control points. A new chasing operator is also employed in order to improve the convergence process. Numerical results show that MOCSA is closely comparable to well-known multi-objective algorithms.
{"title":"MOCSA: A Multi-Objective Crow Search Algorithm for Multi-Objective optimization","authors":"H. Nobahari, Ariyan Bighashdel","doi":"10.1109/CSIEC.2017.7940171","DOIUrl":"https://doi.org/10.1109/CSIEC.2017.7940171","url":null,"abstract":"In this paper, an extension of the recently developed Crow Search Algorithm (CSA) to multi-objective optimization problems is presented. The proposed algorithm, called Multi-Objective Crow Search Algorithm (MOCSA), defines the fitness function using a set of determined weight vectors, employing the max-min strategy. In order to improve the efficiency of the search space, the performance space is regionalized using specific control points. A new chasing operator is also employed in order to improve the convergence process. Numerical results show that MOCSA is closely comparable to well-known multi-objective algorithms.","PeriodicalId":166046,"journal":{"name":"2017 2nd Conference on Swarm Intelligence and Evolutionary Computation (CSIEC)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121739871","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-03-07DOI: 10.1109/CSIEC.2017.7940155
Mostafa Meshkat, Mohsen Parhizgar
Recently, a novel bio-inspired optimization algorithm known as Multi-Verse Optimizer (MVO) has been proposed for solving optimization problems based on the fundamental multi-verse theory including concepts such as white holes, black holes, and wormholes. The objective of this study was to present an optimization algorithm using MVO as well as the stud selection and crossover (SSC) operator, namely the Stud Multi-Verse Algorithm (Stud MVO), in order to improve the performance of the MVO algorithm. The SCC operator is originated from the Stud Genetic Algorithm (Stud GA), by which the best search agent known as the stud provides optimal information for other search agents in the population using general genetic operators. In order to evaluate the performance of the Stud MVO, twenty-three benchmark functions including unimodal, multimodal and fixed-dimension multimodal benchmark functions were used. The comparison of the results indicated that Stud MVO outperformed the MVO algorithm in twenty benchmark functions.
{"title":"Stud Multi-Verse Algorithm","authors":"Mostafa Meshkat, Mohsen Parhizgar","doi":"10.1109/CSIEC.2017.7940155","DOIUrl":"https://doi.org/10.1109/CSIEC.2017.7940155","url":null,"abstract":"Recently, a novel bio-inspired optimization algorithm known as Multi-Verse Optimizer (MVO) has been proposed for solving optimization problems based on the fundamental multi-verse theory including concepts such as white holes, black holes, and wormholes. The objective of this study was to present an optimization algorithm using MVO as well as the stud selection and crossover (SSC) operator, namely the Stud Multi-Verse Algorithm (Stud MVO), in order to improve the performance of the MVO algorithm. The SCC operator is originated from the Stud Genetic Algorithm (Stud GA), by which the best search agent known as the stud provides optimal information for other search agents in the population using general genetic operators. In order to evaluate the performance of the Stud MVO, twenty-three benchmark functions including unimodal, multimodal and fixed-dimension multimodal benchmark functions were used. The comparison of the results indicated that Stud MVO outperformed the MVO algorithm in twenty benchmark functions.","PeriodicalId":166046,"journal":{"name":"2017 2nd Conference on Swarm Intelligence and Evolutionary Computation (CSIEC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131279954","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-03-07DOI: 10.1109/CSIEC.2017.7940168
A. Ebrahimi, Vajiheh Dehdeleh, A. Boroumandnia, V. Seydi
in the particle swarm optimization (PSO), each particle is enhanced based on its own best experience and one of the local or global best particle in local or global particle swarm optimization (LPSO or GPSO). In this paper, an orthogonal learning (OL) technique is proposed that mixes these experiences as a new combined algorithm that is named MOLPSO. MOLPSO is the result of mixed two algorithms OLPSO-L and OLPSO-G through orthogonal experimental design (OED). This technique can construct a more effective leadership vector to lead particles toward the best area by selecting better dimensions of these experiences. This technique is tested on a set of some benchmark functions that the results of tests confirm that the strategy significantly enhances the performance of PSO.
{"title":"Improved particle swarm optimization through orthogonal experimental design","authors":"A. Ebrahimi, Vajiheh Dehdeleh, A. Boroumandnia, V. Seydi","doi":"10.1109/CSIEC.2017.7940168","DOIUrl":"https://doi.org/10.1109/CSIEC.2017.7940168","url":null,"abstract":"in the particle swarm optimization (PSO), each particle is enhanced based on its own best experience and one of the local or global best particle in local or global particle swarm optimization (LPSO or GPSO). In this paper, an orthogonal learning (OL) technique is proposed that mixes these experiences as a new combined algorithm that is named MOLPSO. MOLPSO is the result of mixed two algorithms OLPSO-L and OLPSO-G through orthogonal experimental design (OED). This technique can construct a more effective leadership vector to lead particles toward the best area by selecting better dimensions of these experiences. This technique is tested on a set of some benchmark functions that the results of tests confirm that the strategy significantly enhances the performance of PSO.","PeriodicalId":166046,"journal":{"name":"2017 2nd Conference on Swarm Intelligence and Evolutionary Computation (CSIEC)","volume":"61 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127244123","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-03-07DOI: 10.1109/CSIEC.2017.7940173
A. Mahanipour, H. Nezamabadi-pour
Feature construction (FC) can improve the classification performance by creating powerful features from the original ones. Particle Swarm Optimization (PSO) is a global search technique that can construct features directly. We believe that using raw features may lead the PSO-based FC method to an inefficient feature, so in this paper, the aim is to select the prominent features before applying PSO-based FC method. The Forward Feature Selection (FFS) method is used for selecting more informative feature subset from original set and constructing feature by the selected ones. Experimental results show that the proposed method can increase the accuracy by constructing a new powerful feature.
{"title":"Improved PSO-based feature construction algorithm using Feature Selection Methods","authors":"A. Mahanipour, H. Nezamabadi-pour","doi":"10.1109/CSIEC.2017.7940173","DOIUrl":"https://doi.org/10.1109/CSIEC.2017.7940173","url":null,"abstract":"Feature construction (FC) can improve the classification performance by creating powerful features from the original ones. Particle Swarm Optimization (PSO) is a global search technique that can construct features directly. We believe that using raw features may lead the PSO-based FC method to an inefficient feature, so in this paper, the aim is to select the prominent features before applying PSO-based FC method. The Forward Feature Selection (FFS) method is used for selecting more informative feature subset from original set and constructing feature by the selected ones. Experimental results show that the proposed method can increase the accuracy by constructing a new powerful feature.","PeriodicalId":166046,"journal":{"name":"2017 2nd Conference on Swarm Intelligence and Evolutionary Computation (CSIEC)","volume":"239 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132555316","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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