Pub Date : 2004-10-20DOI: 10.1109/CEC.2003.1299817
Yen-Yen Joe, Arthur Tay, Zhao Yang Dong, H. Ng, Huan Xu
DNA microarray is a powerful tool to measure the level of a mixed population of nucleic acids at one time, which has great impact in many aspects of life sciences research. In order to distinguish nucleic acids with very similar composition by hybridization, it is necessary to design probes with high specificities, i.e. uniqueness, and also sensitivities, i.e., suitable melting temperature and no secondary structure. We make use of available biology tools to gain necessary sequence information of human chromosome 12, and combined with evolutionary strategy (ES) to find unique subsequences representing all predicted exons. The results are presented and discussed.
{"title":"Searching oligo sets of human chromosome 12 using evolutionary strategies","authors":"Yen-Yen Joe, Arthur Tay, Zhao Yang Dong, H. Ng, Huan Xu","doi":"10.1109/CEC.2003.1299817","DOIUrl":"https://doi.org/10.1109/CEC.2003.1299817","url":null,"abstract":"DNA microarray is a powerful tool to measure the level of a mixed population of nucleic acids at one time, which has great impact in many aspects of life sciences research. In order to distinguish nucleic acids with very similar composition by hybridization, it is necessary to design probes with high specificities, i.e. uniqueness, and also sensitivities, i.e., suitable melting temperature and no secondary structure. We make use of available biology tools to gain necessary sequence information of human chromosome 12, and combined with evolutionary strategy (ES) to find unique subsequences representing all predicted exons. The results are presented and discussed.","PeriodicalId":416243,"journal":{"name":"The 2003 Congress on Evolutionary Computation, 2003. CEC '03.","volume":"85 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2004-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123429339","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 : 2004-09-02DOI: 10.1109/CEC.2003.1299744
J. Imae, Y. Kikuchi, N. Ohtsuki, T. Kobayashi
Based on the differential genetic programming, a new design method is proposed for optimal and/or robust controllers of nonlinear systems. First we introduce a new type of the genetic programming (GP), so-called differential GP (DGP), combining GP with an automatic differentiation scheme, which could solve Hamilton-Jacobi-Bellman(HJB)/Hamilton-Jacobi-Isaacs(HJI)/Francis-Byrnes-Isidori (FBI) equations. Lastly, the effectiveness of a DGP based design method is demonstrated through some design examples of nonlinear systems.
{"title":"A nonlinear control system design based on HJB/HJI/FBI equations via differential genetic programming approach","authors":"J. Imae, Y. Kikuchi, N. Ohtsuki, T. Kobayashi","doi":"10.1109/CEC.2003.1299744","DOIUrl":"https://doi.org/10.1109/CEC.2003.1299744","url":null,"abstract":"Based on the differential genetic programming, a new design method is proposed for optimal and/or robust controllers of nonlinear systems. First we introduce a new type of the genetic programming (GP), so-called differential GP (DGP), combining GP with an automatic differentiation scheme, which could solve Hamilton-Jacobi-Bellman(HJB)/Hamilton-Jacobi-Isaacs(HJI)/Francis-Byrnes-Isidori (FBI) equations. Lastly, the effectiveness of a DGP based design method is demonstrated through some design examples of nonlinear systems.","PeriodicalId":416243,"journal":{"name":"The 2003 Congress on Evolutionary Computation, 2003. CEC '03.","volume":"94 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2004-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116267597","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 : 2003-12-23DOI: 10.1109/CEC.2003.1299888
T. Bartz-Beielstein, P. Limbourg, J. Mehnen, K. Schmitt, K. Parsopoulos, M. Vrahatis
During the last decade, numerous heuristic search methods for solving multi-objective optimization problems have been developed. Population oriented approaches such as evolutionary algorithms and particle swarm optimization can be distinguished into the class of archive-based algorithms and algorithms without archive. While the latter may lose the best solutions found so far, archive based algorithms keep track of these solutions. In this article, a new particle swarm optimization technique, called DOPS, for multi-objective optimization problems is proposed. DOPS integrates well-known archiving techniques from evolutionary algorithms into particle swarm optimization. Modifications and extensions of the archiving techniques are empirically analyzed and several test functions are used to illustrate the usability of the proposed approach. A statistical analysis of the obtained results is presented. The article concludes with a discussion of the obtained results as well as ideas for further research.
{"title":"Particle swarm optimizers for Pareto optimization with enhanced archiving techniques","authors":"T. Bartz-Beielstein, P. Limbourg, J. Mehnen, K. Schmitt, K. Parsopoulos, M. Vrahatis","doi":"10.1109/CEC.2003.1299888","DOIUrl":"https://doi.org/10.1109/CEC.2003.1299888","url":null,"abstract":"During the last decade, numerous heuristic search methods for solving multi-objective optimization problems have been developed. Population oriented approaches such as evolutionary algorithms and particle swarm optimization can be distinguished into the class of archive-based algorithms and algorithms without archive. While the latter may lose the best solutions found so far, archive based algorithms keep track of these solutions. In this article, a new particle swarm optimization technique, called DOPS, for multi-objective optimization problems is proposed. DOPS integrates well-known archiving techniques from evolutionary algorithms into particle swarm optimization. Modifications and extensions of the archiving techniques are empirically analyzed and several test functions are used to illustrate the usability of the proposed approach. A statistical analysis of the obtained results is presented. The article concludes with a discussion of the obtained results as well as ideas for further research.","PeriodicalId":416243,"journal":{"name":"The 2003 Congress on Evolutionary Computation, 2003. CEC '03.","volume":"118 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2003-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116010726","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 : 2003-12-08DOI: 10.1109/CEC.2003.1299758
Xiaolin Hu, Zhangcan Huang, Zhongfan Wang
It is known from single-objective optimization that hybrid variants of local search algorithms and evolutionary algorithms can outperform their pure counterparts. This holds, in particular, in continuous search spaces and for differentiable fitness functions. The same should be true in multiobjective optimization. An efficient gradient-based local algorithm, sequential quadratic programming (SQP) is combined with two well-known multiobjective evolutionary algorithms, strength Pareto evolutionary algorithm (SPEA) and nondominated sorting genetic algorithm (NSGA-II) respectively, by means of a modified /spl epsiv/-constraint method. The resulting two hybrid algorithms demonstrate great success over two sets of well-chosen functions regarding the convergence rate. In addition, from the simulation studies, the hybridization approach also enhances, at least does not ruin, the diversity of the solutions.
{"title":"Hybridization of the multi-objective evolutionary algorithms and the gradient-based algorithms","authors":"Xiaolin Hu, Zhangcan Huang, Zhongfan Wang","doi":"10.1109/CEC.2003.1299758","DOIUrl":"https://doi.org/10.1109/CEC.2003.1299758","url":null,"abstract":"It is known from single-objective optimization that hybrid variants of local search algorithms and evolutionary algorithms can outperform their pure counterparts. This holds, in particular, in continuous search spaces and for differentiable fitness functions. The same should be true in multiobjective optimization. An efficient gradient-based local algorithm, sequential quadratic programming (SQP) is combined with two well-known multiobjective evolutionary algorithms, strength Pareto evolutionary algorithm (SPEA) and nondominated sorting genetic algorithm (NSGA-II) respectively, by means of a modified /spl epsiv/-constraint method. The resulting two hybrid algorithms demonstrate great success over two sets of well-chosen functions regarding the convergence rate. In addition, from the simulation studies, the hybridization approach also enhances, at least does not ruin, the diversity of the solutions.","PeriodicalId":416243,"journal":{"name":"The 2003 Congress on Evolutionary Computation, 2003. CEC '03.","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2003-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115159999","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 : 2003-12-08DOI: 10.1109/CEC.2003.1299948
S. Y. Chong, D. C. Ku, Heng-Siong Lim, M. K. Tan, Jules White
Evolutionary computation was used to train neural networks to learn the play the game of Othello. Each neural network represents a strategy based on board evaluations of the game tree generated by a minimax search algorithm. Networks competed against each other in tournament play and selection used to eliminate those that performed poorly relative to other networks. Self-adaptation was used to mutate the weights and biases of surviving neural networks to generate offspring. By monitoring the evolutionary behavior over 1000 generations through game competitions with computer players playing at higher ply-depths using deterministic evaluations, the networks are shown to coevolve with the style of game play progressing from random to positional and finally to mobility strategy.
{"title":"Evolved neural networks learning Othello strategies","authors":"S. Y. Chong, D. C. Ku, Heng-Siong Lim, M. K. Tan, Jules White","doi":"10.1109/CEC.2003.1299948","DOIUrl":"https://doi.org/10.1109/CEC.2003.1299948","url":null,"abstract":"Evolutionary computation was used to train neural networks to learn the play the game of Othello. Each neural network represents a strategy based on board evaluations of the game tree generated by a minimax search algorithm. Networks competed against each other in tournament play and selection used to eliminate those that performed poorly relative to other networks. Self-adaptation was used to mutate the weights and biases of surviving neural networks to generate offspring. By monitoring the evolutionary behavior over 1000 generations through game competitions with computer players playing at higher ply-depths using deterministic evaluations, the networks are shown to coevolve with the style of game play progressing from random to positional and finally to mobility strategy.","PeriodicalId":416243,"journal":{"name":"The 2003 Congress on Evolutionary Computation, 2003. CEC '03.","volume":"75 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2003-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115237182","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 : 2003-12-08DOI: 10.1109/CEC.2003.1299933
Vitoantonio Bevilacqua, G. Mastronardi, G. Piscopo
A unified evolutionary approach to coplanar radiotherapy inverse planning is proposed. It consists of a genetic algorithm-based framework that solves with little modification treatment planning for three different kinds of radiation therapy: conformal, so-called aperture-based and intensity modulated. Thanks to evolutionary optimisation techniques we have been able to search for full beam configurations, that is, beam intensity, beam shape and especially beam orientation. Unlike some previous works found in literature, our proposed solution automatically determines exact beam angles not relaying solely on a geometrical basis but involving beam intensity profiles, thus considering the effective delivered dose. Our dose distribution model has been validated through comparison with commercial system: fixed the same beam configuration, both calculated beam shapes and the DVH have been compared. Then we have tested the optimisation algorithm with real clinical cases: these involved both simple (convex target, far OARs) and complex (concave target, close OARs) ones. As stated by physician and by simulation with the same commercial system, our tools found good solutions in both cases using corresponding correct therapy.
{"title":"A genetic algorithm approach to full beam configuration inverse planning in coplanar radiotherapy","authors":"Vitoantonio Bevilacqua, G. Mastronardi, G. Piscopo","doi":"10.1109/CEC.2003.1299933","DOIUrl":"https://doi.org/10.1109/CEC.2003.1299933","url":null,"abstract":"A unified evolutionary approach to coplanar radiotherapy inverse planning is proposed. It consists of a genetic algorithm-based framework that solves with little modification treatment planning for three different kinds of radiation therapy: conformal, so-called aperture-based and intensity modulated. Thanks to evolutionary optimisation techniques we have been able to search for full beam configurations, that is, beam intensity, beam shape and especially beam orientation. Unlike some previous works found in literature, our proposed solution automatically determines exact beam angles not relaying solely on a geometrical basis but involving beam intensity profiles, thus considering the effective delivered dose. Our dose distribution model has been validated through comparison with commercial system: fixed the same beam configuration, both calculated beam shapes and the DVH have been compared. Then we have tested the optimisation algorithm with real clinical cases: these involved both simple (convex target, far OARs) and complex (concave target, close OARs) ones. As stated by physician and by simulation with the same commercial system, our tools found good solutions in both cases using corresponding correct therapy.","PeriodicalId":416243,"journal":{"name":"The 2003 Congress on Evolutionary Computation, 2003. CEC '03.","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2003-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117180608","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 : 2003-12-08DOI: 10.1109/CEC.2003.1299782
Masataka Tokumaru, N. Muranaka, S. Imanishi
In this paper, we propose a system named "Virtual Stylist", which aims to help users find out their favorite clothes, which might fit them well. The system is composed of 3 parts as follows, 1) searching clothes in consideration of their color scheme harmonies and image sensations, 2) adopting rules for evaluating color scheme image sensations to a specific user's feeling of color images, 3) virtual fitting system. The system searches through clothes database for some clothes on the basis of the harmony and sensation of colors that are used in them. In the case that a user require a jacket and pants which she might wear with her own shirt, the system search for some jacket and pants whose colors are in harmony with the color of her shirt and with which the color scheme image sensation seems to fit her imagination of dressing. The system possesses some function so that the rules for evaluating color image sensations, which are controlled by some simple parameters are automatically changed and adjusted to the user's emotion. We achieved a way in which the system is real-time adapted to a user's subjectivity with interactive genetic algorithms.
{"title":"Virtual Stylist project - examination of adapting clothing search system to user's subjectivity with interactive genetic algorithms","authors":"Masataka Tokumaru, N. Muranaka, S. Imanishi","doi":"10.1109/CEC.2003.1299782","DOIUrl":"https://doi.org/10.1109/CEC.2003.1299782","url":null,"abstract":"In this paper, we propose a system named \"Virtual Stylist\", which aims to help users find out their favorite clothes, which might fit them well. The system is composed of 3 parts as follows, 1) searching clothes in consideration of their color scheme harmonies and image sensations, 2) adopting rules for evaluating color scheme image sensations to a specific user's feeling of color images, 3) virtual fitting system. The system searches through clothes database for some clothes on the basis of the harmony and sensation of colors that are used in them. In the case that a user require a jacket and pants which she might wear with her own shirt, the system search for some jacket and pants whose colors are in harmony with the color of her shirt and with which the color scheme image sensation seems to fit her imagination of dressing. The system possesses some function so that the rules for evaluating color image sensations, which are controlled by some simple parameters are automatically changed and adjusted to the user's emotion. We achieved a way in which the system is real-time adapted to a user's subjectivity with interactive genetic algorithms.","PeriodicalId":416243,"journal":{"name":"The 2003 Congress on Evolutionary Computation, 2003. CEC '03.","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2003-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121013519","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 : 2003-12-08DOI: 10.1109/CEC.2003.1299580
N. Franken, A. Engelbrecht
This paper investigates the effectiveness of various particle swarm optimiser structures to learn how to play the game of checkers. Co-evolutionary techniques are used to train the game playing agents. Performance is compared against a player making moves at random. Initial experimental results indicate definite advantages in using certain information sharing structures and swarm size configurations to successfully learn the game of checkers.
{"title":"Comparing PSO structures to learn the game of checkers from zero knowledge","authors":"N. Franken, A. Engelbrecht","doi":"10.1109/CEC.2003.1299580","DOIUrl":"https://doi.org/10.1109/CEC.2003.1299580","url":null,"abstract":"This paper investigates the effectiveness of various particle swarm optimiser structures to learn how to play the game of checkers. Co-evolutionary techniques are used to train the game playing agents. Performance is compared against a player making moves at random. Initial experimental results indicate definite advantages in using certain information sharing structures and swarm size configurations to successfully learn the game of checkers.","PeriodicalId":416243,"journal":{"name":"The 2003 Congress on Evolutionary Computation, 2003. CEC '03.","volume":"76 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2003-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121315959","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 : 2003-12-08DOI: 10.1109/CEC.2003.1299753
J. Woodward
Standard GP, chiefly concerned with evolving functions, which are mappings from inputs to output, is not Turing Complete. We raise issues resulting from attempts at extending standard GP to Turing Complete representations. Firstly, there is a problem when a contiguous piece of code is moved to a new location (in a different program) by crossover. In general its functionality will be altered if global memory is used, as other parts of the program may access the same piece of memory. Secondly, traditional crossover does not respect modules. Crossover can disrupt a group of instructions that were working together (e.g. in the body of a loop) in one parent, but end up separated in two different offspring after reproduction. A crossover operator is proposed that only operates at the boundaries of modules. The identification of module boundaries is made easy by using a representation in which explicit modules are denned, in contrast with other representations where the module boundaries would have to be identified by some other means. The halting problem is a central issue, however as a consequence of this crossover operator we are more likely to produce self terminating programs, thus saving time when testing.
{"title":"Evolving Turing Complete representations","authors":"J. Woodward","doi":"10.1109/CEC.2003.1299753","DOIUrl":"https://doi.org/10.1109/CEC.2003.1299753","url":null,"abstract":"Standard GP, chiefly concerned with evolving functions, which are mappings from inputs to output, is not Turing Complete. We raise issues resulting from attempts at extending standard GP to Turing Complete representations. Firstly, there is a problem when a contiguous piece of code is moved to a new location (in a different program) by crossover. In general its functionality will be altered if global memory is used, as other parts of the program may access the same piece of memory. Secondly, traditional crossover does not respect modules. Crossover can disrupt a group of instructions that were working together (e.g. in the body of a loop) in one parent, but end up separated in two different offspring after reproduction. A crossover operator is proposed that only operates at the boundaries of modules. The identification of module boundaries is made easy by using a representation in which explicit modules are denned, in contrast with other representations where the module boundaries would have to be identified by some other means. The halting problem is a central issue, however as a consequence of this crossover operator we are more likely to produce self terminating programs, thus saving time when testing.","PeriodicalId":416243,"journal":{"name":"The 2003 Congress on Evolutionary Computation, 2003. CEC '03.","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2003-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127174601","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 : 2003-12-08DOI: 10.1109/CEC.2003.1299448
S. Haque, A. Kabir, R. Sarker
The paper presents a genetic algorithm with fuzzy logic controller for determining opportunistic replacement policy for deteriorating components of an equipment or system. An opportunistic replacement model has been formulated by considering the dynamics of the decision process of such a policy. In order to reduce the computational burden involving complete enumeration of all possible policies, genetic algorithm has been used to find near optimal solution by maximizing net benefit to be gained from an opportunistic replacement. A fuzzy logic controller has been used to automatically adjust the fine-tuning structure of genetic algorithm parameters. The performance of the model and the solution procedure has been evaluated for a number of case problems, which clearly demonstrates that the proposed method is very effective.
{"title":"Optimization model for opportunistic replacement policy using genetic algorithm with fuzzy logic controller","authors":"S. Haque, A. Kabir, R. Sarker","doi":"10.1109/CEC.2003.1299448","DOIUrl":"https://doi.org/10.1109/CEC.2003.1299448","url":null,"abstract":"The paper presents a genetic algorithm with fuzzy logic controller for determining opportunistic replacement policy for deteriorating components of an equipment or system. An opportunistic replacement model has been formulated by considering the dynamics of the decision process of such a policy. In order to reduce the computational burden involving complete enumeration of all possible policies, genetic algorithm has been used to find near optimal solution by maximizing net benefit to be gained from an opportunistic replacement. A fuzzy logic controller has been used to automatically adjust the fine-tuning structure of genetic algorithm parameters. The performance of the model and the solution procedure has been evaluated for a number of case problems, which clearly demonstrates that the proposed method is very effective.","PeriodicalId":416243,"journal":{"name":"The 2003 Congress on Evolutionary Computation, 2003. CEC '03.","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2003-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124881765","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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