We propose a multiagent scheduling system (MSS) based on a resource schedule and execution matrix (RSEM) to tackle the issue of load balancing among photolithography machines in semiconductor manufacturing. This issue is derived from the dedicated photolithography machine constraint. It is one of the new challenges introduced in photolithography machinery due to natural bias. However, many scheduling policies or modeling methods proposed by previous research for the semiconductor manufacturing production have not addressed the load balancing issue and dedicated machine constraint. In this paper, we describe the design of the proposed MSS approach in detail, including the system architecture, coordination strategy, and its scheduling method on the RSEM. We also present the simulation results that validate the approach
{"title":"Load Balancing among Photolithography Machines in Semiconductor Manufacturing","authors":"Arthur M. D. Shr, Alan Liu, Peter P. Chen","doi":"10.1109/IS.2006.348439","DOIUrl":"https://doi.org/10.1109/IS.2006.348439","url":null,"abstract":"We propose a multiagent scheduling system (MSS) based on a resource schedule and execution matrix (RSEM) to tackle the issue of load balancing among photolithography machines in semiconductor manufacturing. This issue is derived from the dedicated photolithography machine constraint. It is one of the new challenges introduced in photolithography machinery due to natural bias. However, many scheduling policies or modeling methods proposed by previous research for the semiconductor manufacturing production have not addressed the load balancing issue and dedicated machine constraint. In this paper, we describe the design of the proposed MSS approach in detail, including the system architecture, coordination strategy, and its scheduling method on the RSEM. We also present the simulation results that validate the approach","PeriodicalId":116809,"journal":{"name":"2006 3rd International IEEE Conference Intelligent Systems","volume":"145 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129473323","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}
Various data mining methods are being considered. This paper examines the problem of extracting ratio rules. ratio rules are linear relationships in numeric attributes applicable to understanding data, filling missing attribute values, and related issues. Existing research for ratio rules, however, does not consider a concept used in association rule mining. This prevents us from extracting a ratio rule having a strong linear relationship in part. This also prevents us from measuring objective goodness of each ratio rule. We formulated ratio rule mining in analogy to association rule mining, and introduce support and confidence concepts to ratio rules. We propose a ratio rule extraction method based on support and confidence, and show the appropriateness of our proposed method using real and synthetic data
{"title":"Ratio Rule Mining with Support and Confidence Factors","authors":"M. Hamamoto, H. Kitagawa","doi":"10.1109/IS.2006.348470","DOIUrl":"https://doi.org/10.1109/IS.2006.348470","url":null,"abstract":"Various data mining methods are being considered. This paper examines the problem of extracting ratio rules. ratio rules are linear relationships in numeric attributes applicable to understanding data, filling missing attribute values, and related issues. Existing research for ratio rules, however, does not consider a concept used in association rule mining. This prevents us from extracting a ratio rule having a strong linear relationship in part. This also prevents us from measuring objective goodness of each ratio rule. We formulated ratio rule mining in analogy to association rule mining, and introduce support and confidence concepts to ratio rules. We propose a ratio rule extraction method based on support and confidence, and show the appropriateness of our proposed method using real and synthetic data","PeriodicalId":116809,"journal":{"name":"2006 3rd International IEEE Conference Intelligent Systems","volume":"168 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121251932","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 FP-tree is an effective data structure that facilitates the mining of frequent patterns from transactional databases. But, transactional databases are dynamic in general, and hence modifications on the database must be reflecting onto the FP-tree. Constructing the FP-tree from scratch and incrementally updating the FP-tree are two possible choices. However, from scratch construction turns unfeasible as the database size increases. So, this paper addresses incremental update by extending the FP-tree concepts and manipulation process. Our new approach is capable of handling all kinds of changes; include additions, deletions and modifications. The target is achieved by constructing and incrementally dealing with the complete FP-tree, i.e., with one minimum support. Constructing the complete FP-tree has the other advantage that it provides the freedom of mining for lower minimum support values without the need to reconstruct the tree. However, directly reflecting the changes onto the FP-tree may invalidate the basic FP-tree structure. Thus, we apply a sequence of shuffling and merging operations to validate and maintain the modified tree. The experiments conducted on synthetic and real datasets clearly highlight advantages of the proposed incremental approach over constructing the FP-tree from scratch
{"title":"Alternative Method for Increnentally Constructing the FP-Tree","authors":"Muhaimenul, R. Alhajj, K. Barker","doi":"10.1109/IS.2006.348469","DOIUrl":"https://doi.org/10.1109/IS.2006.348469","url":null,"abstract":"The FP-tree is an effective data structure that facilitates the mining of frequent patterns from transactional databases. But, transactional databases are dynamic in general, and hence modifications on the database must be reflecting onto the FP-tree. Constructing the FP-tree from scratch and incrementally updating the FP-tree are two possible choices. However, from scratch construction turns unfeasible as the database size increases. So, this paper addresses incremental update by extending the FP-tree concepts and manipulation process. Our new approach is capable of handling all kinds of changes; include additions, deletions and modifications. The target is achieved by constructing and incrementally dealing with the complete FP-tree, i.e., with one minimum support. Constructing the complete FP-tree has the other advantage that it provides the freedom of mining for lower minimum support values without the need to reconstruct the tree. However, directly reflecting the changes onto the FP-tree may invalidate the basic FP-tree structure. Thus, we apply a sequence of shuffling and merging operations to validate and maintain the modified tree. The experiments conducted on synthetic and real datasets clearly highlight advantages of the proposed incremental approach over constructing the FP-tree from scratch","PeriodicalId":116809,"journal":{"name":"2006 3rd International IEEE Conference Intelligent Systems","volume":"139 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124395689","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}
Rough sets, a tool for data mining, deal with the vagueness and granularity in information systems. This paper studies a type of covering generalized rough sets. After presenting their basic properties, this paper explores the inter dependency between the lower and the upper approximation operations, conditions under which two coverings generate a same upper approximation operation, and the axiomatic systems for these operations. In the end, this paper establishes the relationships between this type of covering rough sets and the other covering rough sets in literature
{"title":"A New Type of Covering Rough Set","authors":"William Zhu, Fei-Yue Wang","doi":"10.1109/IS.2006.348460","DOIUrl":"https://doi.org/10.1109/IS.2006.348460","url":null,"abstract":"Rough sets, a tool for data mining, deal with the vagueness and granularity in information systems. This paper studies a type of covering generalized rough sets. After presenting their basic properties, this paper explores the inter dependency between the lower and the upper approximation operations, conditions under which two coverings generate a same upper approximation operation, and the axiomatic systems for these operations. In the end, this paper establishes the relationships between this type of covering rough sets and the other covering rough sets in literature","PeriodicalId":116809,"journal":{"name":"2006 3rd International IEEE Conference Intelligent Systems","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124404677","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 we study an outer measure on F-sets with values in a complete Abelian l-group. First we introduce a G-valued measure on F-sets. Then we define attributes of an outer measure and we find an expression of this outer measure
{"title":"Outer measure on F-sets","authors":"A. Michalíková, V. Valencáková","doi":"10.1109/IS.2006.348509","DOIUrl":"https://doi.org/10.1109/IS.2006.348509","url":null,"abstract":"In this paper we study an outer measure on F-sets with values in a complete Abelian l-group. First we introduce a G-valued measure on F-sets. Then we define attributes of an outer measure and we find an expression of this outer measure","PeriodicalId":116809,"journal":{"name":"2006 3rd International IEEE Conference Intelligent Systems","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121756046","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}
C. Anagnostopoulos, Y. Ntarladimas, S. Hadjiefthymiades
Conceptual modeling is viewed as a promising means to represent contextual knowledge, which may be enriched with semantics. Such modeling is capable of describing situations context, as well as, reasoning about it. Moreover, situational reasoning is attained taking into consideration similarity-based approaches. This paper proposes approximate reasoning about situations similarity using ontological modeling, description logics representation, and fuzzy logic inference rules
{"title":"Reasoning about Situation Similarity","authors":"C. Anagnostopoulos, Y. Ntarladimas, S. Hadjiefthymiades","doi":"10.1109/IS.2006.348402","DOIUrl":"https://doi.org/10.1109/IS.2006.348402","url":null,"abstract":"Conceptual modeling is viewed as a promising means to represent contextual knowledge, which may be enriched with semantics. Such modeling is capable of describing situations context, as well as, reasoning about it. Moreover, situational reasoning is attained taking into consideration similarity-based approaches. This paper proposes approximate reasoning about situations similarity using ontological modeling, description logics representation, and fuzzy logic inference rules","PeriodicalId":116809,"journal":{"name":"2006 3rd International IEEE Conference Intelligent Systems","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116836486","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}
Steel industry is one of the pillar industries in Chinese national economy, and has made an active contribution to the national economy's sustained development. Therefore the study in prediction of steel output has become a very important task. In this paper, on the basis of reviewing the existing common prediction methods, we combine wavelet with neural network, put forward a data mining method based on self-adaptive wavelet neural network, and build a machine learning mechanism of data mining process to improve the capability of problem dealing. The demonstration results indicate that compared with general artificial neural network, data mining with self-adaptive wavelet neural network is not only effective but also feasible
{"title":"Forecast Method of Steel Output based on Self-Adaptive Wavelet Neural Network Model","authors":"L. Lanjuan, Shang Qingchen, Xie Meiping","doi":"10.1109/IS.2006.348529","DOIUrl":"https://doi.org/10.1109/IS.2006.348529","url":null,"abstract":"Steel industry is one of the pillar industries in Chinese national economy, and has made an active contribution to the national economy's sustained development. Therefore the study in prediction of steel output has become a very important task. In this paper, on the basis of reviewing the existing common prediction methods, we combine wavelet with neural network, put forward a data mining method based on self-adaptive wavelet neural network, and build a machine learning mechanism of data mining process to improve the capability of problem dealing. The demonstration results indicate that compared with general artificial neural network, data mining with self-adaptive wavelet neural network is not only effective but also feasible","PeriodicalId":116809,"journal":{"name":"2006 3rd International IEEE Conference Intelligent Systems","volume":"79 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115409609","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}
Many recent real-world applications, such as network traffic monitoring, intrusion detection systems, sensor network data analysis, click stream mining and dynamic tracing of financial transactions, call for studying a new kind of data. Called stream data, this model is, in fact, a continuous, potentially infinite flow of information as opposed to finite, statically stored data sets extensively studied by researchers of the data mining community. An important application is to mine data streams for interesting patterns or anomalies as they happen. For data stream applications, the volume of data is usually too huge to be stored on permanent devices, main memory or to be scanned thoroughly more than once. In this paper we propose a new approach, called SPEED (sequential patterns efficient extraction in data streams), to identify frequent maximal sequential patterns in a data stream. The main originality of our mining method is that we use a novel data structure to maintain frequent sequential patterns coupled with a fast pruning strategy. At any time, users can issue requests for frequent maximal sequences over an arbitrary time interval. Furthermore, our approach produces an approximate support answer with an assurance that it does not bypass a user-defined frequency error threshold. Finally the proposed method is analyzed by a series of experiments on different datasets
最近的许多现实应用，如网络流量监控、入侵检测系统、传感器网络数据分析、点击流挖掘和金融交易的动态跟踪，都需要研究一种新的数据。这种模型被称为流数据，实际上是一种连续的、潜在无限的信息流，而不是数据挖掘社区的研究人员广泛研究的有限的、静态存储的数据集。一个重要的应用是在数据流中挖掘有趣的模式或异常。对于数据流应用程序，数据量通常太大，无法存储在永久设备、主存储器或多次彻底扫描。本文提出了一种新的方法，称为SPEED (sequence patterns efficient extraction In data streams)，用于识别数据流中频繁出现的最大序列模式。我们的挖掘方法的主要独创性在于我们使用了一种新颖的数据结构来维护频繁的顺序模式，并结合了快速修剪策略。在任何时候，用户都可以在任意时间间隔内发出频繁最大序列的请求。此外，我们的方法产生了一个近似的支持答案，并保证它不会绕过用户定义的频率错误阈值。最后，通过在不同数据集上的一系列实验对所提出的方法进行了分析
{"title":"SPEED : Mining Maxirnal Sequential Patterns over Data Strearns","authors":"C. Raissi, P. Poncelet, M. Teisseire","doi":"10.1109/IS.2006.348478","DOIUrl":"https://doi.org/10.1109/IS.2006.348478","url":null,"abstract":"Many recent real-world applications, such as network traffic monitoring, intrusion detection systems, sensor network data analysis, click stream mining and dynamic tracing of financial transactions, call for studying a new kind of data. Called stream data, this model is, in fact, a continuous, potentially infinite flow of information as opposed to finite, statically stored data sets extensively studied by researchers of the data mining community. An important application is to mine data streams for interesting patterns or anomalies as they happen. For data stream applications, the volume of data is usually too huge to be stored on permanent devices, main memory or to be scanned thoroughly more than once. In this paper we propose a new approach, called SPEED (sequential patterns efficient extraction in data streams), to identify frequent maximal sequential patterns in a data stream. The main originality of our mining method is that we use a novel data structure to maintain frequent sequential patterns coupled with a fast pruning strategy. At any time, users can issue requests for frequent maximal sequences over an arbitrary time interval. Furthermore, our approach produces an approximate support answer with an assurance that it does not bypass a user-defined frequency error threshold. Finally the proposed method is analyzed by a series of experiments on different datasets","PeriodicalId":116809,"journal":{"name":"2006 3rd International IEEE Conference Intelligent Systems","volume":"171 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122690961","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}
Recent studies in data mining have proposed a new classification approach, called associative classification, which, according to several reports, such as Liu, B. et al (1998), achieves higher classification accuracy than traditional classification approaches such as C4.S However, the approach also suffers from two major deficiencies: (1) it generates a very large number of association rules, which leads to high processing overhead; and (2) its confidence-based rule evaluation measure may lead to overfitting. In comparison with associative classification, traditional rule-based classifiers, such as C4.5, FOIL and RIPPER, are substantially faster but their accuracy, in most cases, may not be as high. In this paper, we propose a new classification approach, CLoPAR (Classification based on Predictive Association Rules), which combines the advantages of both associative classification and traditional rule-based classification. Instead of generating a large number of candidate rules as in associative classification, CLoPAR adopts a greedy algorithm to generate rules directly from training data. Moreover, CLoPAR generates and tests more rules than traditional rule-based classifiers to avoid missing important rules. To avoid overfitting, CLoPAR uses expected accuracy to evaluate each rule and uses the best k rules in prediction
最近的数据挖掘研究提出了一种新的分类方法，称为关联分类，根据一些报道，如Liu, B. et al(1998)，它比传统的分类方法(如C4)实现了更高的分类精度。然而，该方法也存在两个主要缺陷:(1)生成大量关联规则，导致处理开销高;(2)基于置信度的规则评价方法可能导致过拟合。与关联分类相比，传统的基于规则的分类器，如C4.5、FOIL和RIPPER，速度要快得多，但在大多数情况下，它们的准确率可能没有那么高。本文提出了一种新的基于预测关联规则的分类方法CLoPAR (classification based on Predictive Association Rules)，它结合了关联分类和传统基于规则的分类的优点。CLoPAR不像关联分类那样生成大量的候选规则，而是采用贪心算法直接从训练数据中生成规则。此外，与传统的基于规则的分类器相比，CLoPAR生成和测试的规则更多，从而避免遗漏重要的规则。为了避免过拟合，CLoPAR使用预期精度来评估每个规则，并在预测中使用最佳的k条规则
{"title":"CLoPAR: Classification based on Predictive Association Rules","authors":"M. N. Dehkordi, M. H. Shenassa","doi":"10.1109/IS.2006.348467","DOIUrl":"https://doi.org/10.1109/IS.2006.348467","url":null,"abstract":"Recent studies in data mining have proposed a new classification approach, called associative classification, which, according to several reports, such as Liu, B. et al (1998), achieves higher classification accuracy than traditional classification approaches such as C4.S However, the approach also suffers from two major deficiencies: (1) it generates a very large number of association rules, which leads to high processing overhead; and (2) its confidence-based rule evaluation measure may lead to overfitting. In comparison with associative classification, traditional rule-based classifiers, such as C4.5, FOIL and RIPPER, are substantially faster but their accuracy, in most cases, may not be as high. In this paper, we propose a new classification approach, CLoPAR (Classification based on Predictive Association Rules), which combines the advantages of both associative classification and traditional rule-based classification. Instead of generating a large number of candidate rules as in associative classification, CLoPAR adopts a greedy algorithm to generate rules directly from training data. Moreover, CLoPAR generates and tests more rules than traditional rule-based classifiers to avoid missing important rules. To avoid overfitting, CLoPAR uses expected accuracy to evaluate each rule and uses the best k rules in prediction","PeriodicalId":116809,"journal":{"name":"2006 3rd International IEEE Conference Intelligent Systems","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129179783","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}
C. Lima, P. Castro, André L. V. Coelho, C. Junqueira, F. von Zuben
Projection pursuit learning (PPL) refers to a well-known constructive learning algorithm characterized by a very efficient and accurate computational procedure oriented to nonparametric regression. It has been employed as a means to counteract some problems related to the design of artificial neural network (ANN) models, namely, the estimation of a (usually large) number of free parameters, the proper definition of the model's dimension, and the choice of the sources of nonlinearities (activation functions). In this work, the potentials of PPL are exploited through a different perspective, namely, in designing one-hidden-layer feedforward ANNs for the adaptive control of nonlinear dynamic systems. For such purpose, the proposed methodology is divided into three stages. In the first, the model identification process is undertaken. In the second, the ANN structure is defined according to an offline control setting. In these two stages, the PPL algorithm estimates not only the optimal number of hidden neurons but also the best activation function for each node. The final stage is performed online and promotes a fine-tuning in the parameters of the identification model and the controller. Simulation results indicate that it is possible to design effective neural models based on PPL for the control of nonlinear multivariate systems, with superior performance when compared to benchmarks
{"title":"Controlling Nonlinear Dynamic Systems with Projection Pursuit Learning","authors":"C. Lima, P. Castro, André L. V. Coelho, C. Junqueira, F. von Zuben","doi":"10.1109/IS.2006.348441","DOIUrl":"https://doi.org/10.1109/IS.2006.348441","url":null,"abstract":"Projection pursuit learning (PPL) refers to a well-known constructive learning algorithm characterized by a very efficient and accurate computational procedure oriented to nonparametric regression. It has been employed as a means to counteract some problems related to the design of artificial neural network (ANN) models, namely, the estimation of a (usually large) number of free parameters, the proper definition of the model's dimension, and the choice of the sources of nonlinearities (activation functions). In this work, the potentials of PPL are exploited through a different perspective, namely, in designing one-hidden-layer feedforward ANNs for the adaptive control of nonlinear dynamic systems. For such purpose, the proposed methodology is divided into three stages. In the first, the model identification process is undertaken. In the second, the ANN structure is defined according to an offline control setting. In these two stages, the PPL algorithm estimates not only the optimal number of hidden neurons but also the best activation function for each node. The final stage is performed online and promotes a fine-tuning in the parameters of the identification model and the controller. Simulation results indicate that it is possible to design effective neural models based on PPL for the control of nonlinear multivariate systems, with superior performance when compared to benchmarks","PeriodicalId":116809,"journal":{"name":"2006 3rd International IEEE Conference Intelligent Systems","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128315422","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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