We consider self-organization problems, where agents try to agree about the value of a configuration space variable. Problems of consensus and synchronization belong to this category. These are the problems which would often be trivial to solve in a centralized setting, and non-trivial aspects are often directly induced by the process of self-organization itself. We discuss topological reasons as to why simple locally greedy algorithms are not able to create long-range order. The reason why greedy synchronization of a real-valued variable works in a straight forward manner, whereas greedy phase synchronization does not, is topological, in the latter non-trivial homotopy classes in mappings from the interaction graph of the agents to the configuration space exist. We identify higher dimensional configuration spaces with such non-trivial homotopy classes. However, we find that greedy self-organization is able to create long-range order for any higher-dimensional configuration space that does not possess circular components.
{"title":"Topological Aspects of Greedy Self-Organization","authors":"F. Ahmed, O. Tirkkonen","doi":"10.1109/SASO.2014.15","DOIUrl":"https://doi.org/10.1109/SASO.2014.15","url":null,"abstract":"We consider self-organization problems, where agents try to agree about the value of a configuration space variable. Problems of consensus and synchronization belong to this category. These are the problems which would often be trivial to solve in a centralized setting, and non-trivial aspects are often directly induced by the process of self-organization itself. We discuss topological reasons as to why simple locally greedy algorithms are not able to create long-range order. The reason why greedy synchronization of a real-valued variable works in a straight forward manner, whereas greedy phase synchronization does not, is topological, in the latter non-trivial homotopy classes in mappings from the interaction graph of the agents to the configuration space exist. We identify higher dimensional configuration spaces with such non-trivial homotopy classes. However, we find that greedy self-organization is able to create long-range order for any higher-dimensional configuration space that does not possess circular components.","PeriodicalId":6458,"journal":{"name":"2014 IEEE Eighth International Conference on Self-Adaptive and Self-Organizing Systems Workshops","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2014-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87476089","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 recent years, a number of different strands of research on self-organizing systems have come together to create a new "aggregate programming" approach to the engineering of distributed systems. Aggregate programming is motivated by a desire to avoid the notoriously intractable "local to global" problem, where the system designer must predict how to control individual devices to achieve a collective goal. Instead, the designer programs an abstraction of the collective, composing "building block" primitives from a library of special cases where the local-to-global problem is already solved. Unifying a number of the proposed aggregate programming approaches is the notion of a "computational field" that maps each device in the field's domain to a local value in its range. This concept was originally developed for spatial computers, in which communication and geometric position are closely linked, but can support effective aggregate programming of many non-spatial networks as well. A mathematical foundation for such approaches has been formalized recently with a minimal "field calculus" that appears to be an effective unifying model, covering a wide range of aggregate programming models, both continuous (e.g., geometry-based) and discrete (e.g., graph-based). On this foundation, restricted languages can ensure various desirable properties such as scalability, self-stabilization, and robustness to perturbation. By building up a sufficiently broad collection of composable "building block" distributed algorithms, it is possible to enable simple and rapid development of complex distributed systems that are implicitly scalable and resilient. The ultimate aim of this line of research is to make the programming of robust distributed systems as simple and widespread as single-processor programming, thereby enabling widespread increases in the reliability, efficiency, and democracy of our technological infrastructure.
{"title":"Predictable Self-Organization with Computational Fields","authors":"J. Beal, Mirko Viroli","doi":"10.1109/SASOW.2014.9","DOIUrl":"https://doi.org/10.1109/SASOW.2014.9","url":null,"abstract":"In recent years, a number of different strands of research on self-organizing systems have come together to create a new \"aggregate programming\" approach to the engineering of distributed systems. Aggregate programming is motivated by a desire to avoid the notoriously intractable \"local to global\" problem, where the system designer must predict how to control individual devices to achieve a collective goal. Instead, the designer programs an abstraction of the collective, composing \"building block\" primitives from a library of special cases where the local-to-global problem is already solved. Unifying a number of the proposed aggregate programming approaches is the notion of a \"computational field\" that maps each device in the field's domain to a local value in its range. This concept was originally developed for spatial computers, in which communication and geometric position are closely linked, but can support effective aggregate programming of many non-spatial networks as well. A mathematical foundation for such approaches has been formalized recently with a minimal \"field calculus\" that appears to be an effective unifying model, covering a wide range of aggregate programming models, both continuous (e.g., geometry-based) and discrete (e.g., graph-based). On this foundation, restricted languages can ensure various desirable properties such as scalability, self-stabilization, and robustness to perturbation. By building up a sufficiently broad collection of composable \"building block\" distributed algorithms, it is possible to enable simple and rapid development of complex distributed systems that are implicitly scalable and resilient. The ultimate aim of this line of research is to make the programming of robust distributed systems as simple and widespread as single-processor programming, thereby enabling widespread increases in the reliability, efficiency, and democracy of our technological infrastructure.","PeriodicalId":6458,"journal":{"name":"2014 IEEE Eighth International Conference on Self-Adaptive and Self-Organizing Systems Workshops","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2014-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76270216","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. Bucchiarone, C. A. Mezzina, M. Pistore, Heorhi Raik, G. Valetto
A collective adaptive system is composed of a set of heterogeneous, autonomous and self-adaptive entities that come into a collaboration with one another in order to improve the effectiveness with which they can accomplish their individual goals. In this paper, we offer a characterization of ensembles, as the main concept around which systems that exhibit collective adaptability can be built. Our conceptualization of ensembles enables to define a collective adaptive system as an emergent aggregation of autonomous and self-adaptive process-based elements. To elucidate our approach to ensembles and collective adaptation, we draw an example from a scenario in the urban mobility domain, we describe an architecture that enables that approach, and we show how our approach can address the problems posed by the motivating scenario.
{"title":"Collective Adaptation in Process-Based Systems","authors":"A. Bucchiarone, C. A. Mezzina, M. Pistore, Heorhi Raik, G. Valetto","doi":"10.1109/SASO.2014.28","DOIUrl":"https://doi.org/10.1109/SASO.2014.28","url":null,"abstract":"A collective adaptive system is composed of a set of heterogeneous, autonomous and self-adaptive entities that come into a collaboration with one another in order to improve the effectiveness with which they can accomplish their individual goals. In this paper, we offer a characterization of ensembles, as the main concept around which systems that exhibit collective adaptability can be built. Our conceptualization of ensembles enables to define a collective adaptive system as an emergent aggregation of autonomous and self-adaptive process-based elements. To elucidate our approach to ensembles and collective adaptation, we draw an example from a scenario in the urban mobility domain, we describe an architecture that enables that approach, and we show how our approach can address the problems posed by the motivating scenario.","PeriodicalId":6458,"journal":{"name":"2014 IEEE Eighth International Conference on Self-Adaptive and Self-Organizing Systems Workshops","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2014-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85019945","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}
Conventions are often used in multi-agent systems to achieve coordination amongst agents without creating additional system requirements. Encouraging the emergence of robust conventions via fixed strategy agents is one of the main methods of manipulating how conventions emerge. In this paper we demonstrate that fixed strategy agents can also be used to destabilise and remove established conventions. We examine the minimum level of intervention required to cause destabilisation, and explore the effect of different pricing mechanisms on the cost of interventions. We show that there is an inverse relationship between cost and the number of fixed strategy agents used. Finally, we investigate the effectiveness of placing fixed strategy agents by their cost, for different pricing mechanisms, as a mechanism for causing destabilisation. We show that doing so produces comparable results to placing by known metrics.
{"title":"Destabilising Conventions: Characterising the Cost","authors":"James Marchant, N. Griffiths, Matthew Leeke","doi":"10.1109/SASO.2014.26","DOIUrl":"https://doi.org/10.1109/SASO.2014.26","url":null,"abstract":"Conventions are often used in multi-agent systems to achieve coordination amongst agents without creating additional system requirements. Encouraging the emergence of robust conventions via fixed strategy agents is one of the main methods of manipulating how conventions emerge. In this paper we demonstrate that fixed strategy agents can also be used to destabilise and remove established conventions. We examine the minimum level of intervention required to cause destabilisation, and explore the effect of different pricing mechanisms on the cost of interventions. We show that there is an inverse relationship between cost and the number of fixed strategy agents used. Finally, we investigate the effectiveness of placing fixed strategy agents by their cost, for different pricing mechanisms, as a mechanism for causing destabilisation. We show that doing so produces comparable results to placing by known metrics.","PeriodicalId":6458,"journal":{"name":"2014 IEEE Eighth International Conference on Self-Adaptive and Self-Organizing Systems Workshops","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2014-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75196623","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}
Captive consumers of the current traditional and centralized power management systems will become proactive in the future of the smart grid. Their flexibilities will allow them to become prosumers. A prosumer (producer-consumer) is defined as a user that not only consumes electricity, but can also produce and store electricity. A new concept of aggregator has been introduced in the power market. The aggregator exploits the active participation of prosumers in order to provide commercial service in the power market. In this paper, we focus on power market models in which prosumers interact in a distributed environment during the purchase or sale of electric power. We propose a new aggregator which operates in the DEZENT power market model. The aggregator consists of a collection of prosumers who make use of reinforcement learning and of optimization techniques for the planning phase of their electricity production and consumption. In the paper we discuss the assumptions on which the aggregator design is based and we compare its behaviour with that of the aggregator proposed in the EU ADDRESS projects.
{"title":"Prosumers as Aggregators in the DEZENT Context of Regenerative Power Production","authors":"U. Montanari, Alain Tcheukam Siwe","doi":"10.1109/SASO.2014.30","DOIUrl":"https://doi.org/10.1109/SASO.2014.30","url":null,"abstract":"Captive consumers of the current traditional and centralized power management systems will become proactive in the future of the smart grid. Their flexibilities will allow them to become prosumers. A prosumer (producer-consumer) is defined as a user that not only consumes electricity, but can also produce and store electricity. A new concept of aggregator has been introduced in the power market. The aggregator exploits the active participation of prosumers in order to provide commercial service in the power market. In this paper, we focus on power market models in which prosumers interact in a distributed environment during the purchase or sale of electric power. We propose a new aggregator which operates in the DEZENT power market model. The aggregator consists of a collection of prosumers who make use of reinforcement learning and of optimization techniques for the planning phase of their electricity production and consumption. In the paper we discuss the assumptions on which the aggregator design is based and we compare its behaviour with that of the aggregator proposed in the EU ADDRESS projects.","PeriodicalId":6458,"journal":{"name":"2014 IEEE Eighth International Conference on Self-Adaptive and Self-Organizing Systems Workshops","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2014-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73124286","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 "local-to-global" issue in self-organisation is about finding a way to engineer local mechanisms according to the emergent, global behaviour desired for the system at hand. In this paper, we propose an approach to deal with such issue, by modelling the local mechanisms as artificial chemical reactions and by carefully designing their kinetic rates as "custom" functional expressions.
{"title":"On the \"Local-to-Global\" Issue in Self-Organisation: Chemical Reactions with Custom Kinetic Rates","authors":"S. Mariani","doi":"10.1109/SASOW.2014.14","DOIUrl":"https://doi.org/10.1109/SASOW.2014.14","url":null,"abstract":"The \"local-to-global\" issue in self-organisation is about finding a way to engineer local mechanisms according to the emergent, global behaviour desired for the system at hand. In this paper, we propose an approach to deal with such issue, by modelling the local mechanisms as artificial chemical reactions and by carefully designing their kinetic rates as \"custom\" functional expressions.","PeriodicalId":6458,"journal":{"name":"2014 IEEE Eighth International Conference on Self-Adaptive and Self-Organizing Systems Workshops","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2014-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75391998","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}
G. Anastasi, P. Cassará, Patrizio Dazzi, A. Gotta, M. Mordacchini, A. Passarella
The direct consequence of the rapid growth of the demand for computational power by cloud based-applications has been the creation of an increasing number of large-scale data centres. In such a competitive market, each Cloud vendor needs to lower the price of the offered resources in order to increase its shares. This is done by reducing the cost associated with the execution of the users' applications, but still maintaining an adequate quality of Service. To reach this goal, each Cloud infrastructure needs to self-organise, by efficiently allocating its own resources. The complexity of the problem (exact solutions are NP-complete) calls for new, adaptive and highly-automated approaches that, at the arrival of new resource requests, are able to autonomously estimate potential resource consumptions. Hence the resource management subsystem is tuned up just keeping the associated costs as low as possible. This paper represent our contribution to this problem. We propose an approach that exploits the Cross-Entropy minimisation method to forecast the impact of different resource allocations on a Cloud infrastructure, assuming that many objective functions need to be optimised. Yet, in order to select the best allocation among those presented here, we make use of an adaptive, fast, and low resource-demanding decision-making strategy, derived from models coming from the cognitive science field. Preliminary results show the effectiveness of the proposed solution.
{"title":"A Hybrid Cross-Entropy Cognitive-Based Algorithm for Resource Allocation in Cloud Environments","authors":"G. Anastasi, P. Cassará, Patrizio Dazzi, A. Gotta, M. Mordacchini, A. Passarella","doi":"10.1109/SASO.2014.13","DOIUrl":"https://doi.org/10.1109/SASO.2014.13","url":null,"abstract":"The direct consequence of the rapid growth of the demand for computational power by cloud based-applications has been the creation of an increasing number of large-scale data centres. In such a competitive market, each Cloud vendor needs to lower the price of the offered resources in order to increase its shares. This is done by reducing the cost associated with the execution of the users' applications, but still maintaining an adequate quality of Service. To reach this goal, each Cloud infrastructure needs to self-organise, by efficiently allocating its own resources. The complexity of the problem (exact solutions are NP-complete) calls for new, adaptive and highly-automated approaches that, at the arrival of new resource requests, are able to autonomously estimate potential resource consumptions. Hence the resource management subsystem is tuned up just keeping the associated costs as low as possible. This paper represent our contribution to this problem. We propose an approach that exploits the Cross-Entropy minimisation method to forecast the impact of different resource allocations on a Cloud infrastructure, assuming that many objective functions need to be optimised. Yet, in order to select the best allocation among those presented here, we make use of an adaptive, fast, and low resource-demanding decision-making strategy, derived from models coming from the cognitive science field. Preliminary results show the effectiveness of the proposed solution.","PeriodicalId":6458,"journal":{"name":"2014 IEEE Eighth International Conference on Self-Adaptive and Self-Organizing Systems Workshops","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2014-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73184261","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}
Embedded systems are becoming more and more complex because of the increasing chip integration density, larger number of chips in distributed applications and demanding application fields (e.g. in cars and in households). Bio-inspired techniques like self-organization are a key feature to handle this complexity. However, self organization needs a guideline for setting up and managing the system. In biology the structure and organization of a system is coded in its DNA. This concept can be adapted to embedded systems. Since many embedded systems can be composed from a limited number of basic elements, the structure and parameters of such systems can be stored in a compact way representing an artificial DNA deposited in each computation node. Based on the DNA, the self organization mechanisms can setup the system autonomously providing a self-building system. System repair and optimization at runtime are also possible, leading to higher robustness, dependability and flexibility. Since the system knows its own structure, the artificial DNA can also be a first step towards self-integrating systems. This paper introduces the basic concepts of the artificial DNA and presents a simulator to validate the approach.
{"title":"A Simulator to Validate the Concept of Artificial DNA for Self-Building Embedded Systems","authors":"U. Brinkschulte, Mathias Pacher, Benjamin Betting","doi":"10.1109/SASOW.2014.26","DOIUrl":"https://doi.org/10.1109/SASOW.2014.26","url":null,"abstract":"Embedded systems are becoming more and more complex because of the increasing chip integration density, larger number of chips in distributed applications and demanding application fields (e.g. in cars and in households). Bio-inspired techniques like self-organization are a key feature to handle this complexity. However, self organization needs a guideline for setting up and managing the system. In biology the structure and organization of a system is coded in its DNA. This concept can be adapted to embedded systems. Since many embedded systems can be composed from a limited number of basic elements, the structure and parameters of such systems can be stored in a compact way representing an artificial DNA deposited in each computation node. Based on the DNA, the self organization mechanisms can setup the system autonomously providing a self-building system. System repair and optimization at runtime are also possible, leading to higher robustness, dependability and flexibility. Since the system knows its own structure, the artificial DNA can also be a first step towards self-integrating systems. This paper introduces the basic concepts of the artificial DNA and presents a simulator to validate the approach.","PeriodicalId":6458,"journal":{"name":"2014 IEEE Eighth International Conference on Self-Adaptive and Self-Organizing Systems Workshops","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2014-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75109998","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}
J. Fernandez-Marquez, F. D. Angelis, G. Serugendo, Graeme Stevenson, G. Castelli
This paper presents The ONE-SAPERE simulator, the first simulator combining an opportunistic network environment simulator with a middleware for pervasive systems, the SAPERE Middleware, which has already been released for Android devices and PCs.
{"title":"The ONE-SAPERE Simulator: A Prototyping Tool for Engineering Self-Organisation in Pervasive Environments","authors":"J. Fernandez-Marquez, F. D. Angelis, G. Serugendo, Graeme Stevenson, G. Castelli","doi":"10.1109/SASO.2014.44","DOIUrl":"https://doi.org/10.1109/SASO.2014.44","url":null,"abstract":"This paper presents The ONE-SAPERE simulator, the first simulator combining an opportunistic network environment simulator with a middleware for pervasive systems, the SAPERE Middleware, which has already been released for Android devices and PCs.","PeriodicalId":6458,"journal":{"name":"2014 IEEE Eighth International Conference on Self-Adaptive and Self-Organizing Systems Workshops","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2014-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83987587","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}
Agents inhabiting large scale environments are faced with the problem of generating maps by which they can navigate. One solution to this problem is to use probabilistic roadmaps which rely on selecting and connecting a set of points that describe the interconnectivity of free space. However, the time required to generate these maps can be prohibitive, and agents do not typically know the environment in advance. In this paper we show that the optimal combination of different point selection methods used to create the map is dependent on the environment, no point selection method dominates. This motivates a novel self-adaptive approach for an agent to combine several point selection methods. The success rate of our approach is comparable to the state of the art and the generation cost is substantially reduced. Self-adaptation therefore enables a more efficient use of the agent's resources. Results are presented for both a set of archetypal scenarios and large scale virtual environments based in Second Life, representing real locations in London.
{"title":"Self-Adaptive Probabilistic Roadmap Generation for Intelligent Virtual Agents","authors":"Katrina Samperi, N. Bencomo, Peter R. Lewis","doi":"10.1109/SASO.2014.25","DOIUrl":"https://doi.org/10.1109/SASO.2014.25","url":null,"abstract":"Agents inhabiting large scale environments are faced with the problem of generating maps by which they can navigate. One solution to this problem is to use probabilistic roadmaps which rely on selecting and connecting a set of points that describe the interconnectivity of free space. However, the time required to generate these maps can be prohibitive, and agents do not typically know the environment in advance. In this paper we show that the optimal combination of different point selection methods used to create the map is dependent on the environment, no point selection method dominates. This motivates a novel self-adaptive approach for an agent to combine several point selection methods. The success rate of our approach is comparable to the state of the art and the generation cost is substantially reduced. Self-adaptation therefore enables a more efficient use of the agent's resources. Results are presented for both a set of archetypal scenarios and large scale virtual environments based in Second Life, representing real locations in London.","PeriodicalId":6458,"journal":{"name":"2014 IEEE Eighth International Conference on Self-Adaptive and Self-Organizing Systems Workshops","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2014-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90178418","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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