Pub Date : 2019-08-01DOI: 10.1109/QR2MSE46217.2019.9021154
H. Marius, Schlueter Nadine, Ansari Amirbabak
Digitalization provides us with more and more data about smart systems we develop and sell. While using feedback from the customer, gained in the use phase of a smart product, is a basic idea of quality improvement, a systematic and continuous handling of complaints information is still very rare. This paper describes a procedure on how to implement a workflow to use complaint information for improving the failure management of smart products. It points out what kind of an algorithm and which analysis steps are basically needed to manage complaint management and continuous improvement in production and products.
{"title":"Algorithm-Based Handling of Complaints Data from the Usage Phase","authors":"H. Marius, Schlueter Nadine, Ansari Amirbabak","doi":"10.1109/QR2MSE46217.2019.9021154","DOIUrl":"https://doi.org/10.1109/QR2MSE46217.2019.9021154","url":null,"abstract":"Digitalization provides us with more and more data about smart systems we develop and sell. While using feedback from the customer, gained in the use phase of a smart product, is a basic idea of quality improvement, a systematic and continuous handling of complaints information is still very rare. This paper describes a procedure on how to implement a workflow to use complaint information for improving the failure management of smart products. It points out what kind of an algorithm and which analysis steps are basically needed to manage complaint management and continuous improvement in production and products.","PeriodicalId":233855,"journal":{"name":"2019 International Conference on Quality, Reliability, Risk, Maintenance, and Safety Engineering (QR2MSE)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116160217","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 : 2019-08-01DOI: 10.1109/QR2MSE46217.2019.9021201
Zhi-qiang Lv, G. Jiao, Z. Tao, M.-L. Zhu, Ying-Hui Hua
In this paper, a three-dimensional turbine disc model is established. By using the FEM (finite element method), the critical stress and strain concentration regions are determined according to the static structural analysis results of the turbine disc. Besides, the stress gradient distributions of the assembly holes, the gear teeth region, and the hub region are identified. Then by introducing an improved SWT (Smith-Watson-Topper) parameter model which can take the stress gradient effect into consideration, the fatigue life of the hub region, the gear teeth region, and the assembly holes is obtained. Comparing the life prediction results of the critical regions, finally we get the turbine disc’s fatigue life.
{"title":"Fatigue Life Prediction of a Turbine Disc with Stress Gradient","authors":"Zhi-qiang Lv, G. Jiao, Z. Tao, M.-L. Zhu, Ying-Hui Hua","doi":"10.1109/QR2MSE46217.2019.9021201","DOIUrl":"https://doi.org/10.1109/QR2MSE46217.2019.9021201","url":null,"abstract":"In this paper, a three-dimensional turbine disc model is established. By using the FEM (finite element method), the critical stress and strain concentration regions are determined according to the static structural analysis results of the turbine disc. Besides, the stress gradient distributions of the assembly holes, the gear teeth region, and the hub region are identified. Then by introducing an improved SWT (Smith-Watson-Topper) parameter model which can take the stress gradient effect into consideration, the fatigue life of the hub region, the gear teeth region, and the assembly holes is obtained. Comparing the life prediction results of the critical regions, finally we get the turbine disc’s fatigue life.","PeriodicalId":233855,"journal":{"name":"2019 International Conference on Quality, Reliability, Risk, Maintenance, and Safety Engineering (QR2MSE)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123343900","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 : 2019-08-01DOI: 10.1109/QR2MSE46217.2019.9021119
B. Bai, Ze Li, Junyi Zhang
Based on fuzzy mathematics thought, a methodology combining the expert evaluation and multilevel hierarchy analysis (EE-MHA) is proposed, meanwhile, the non-electronic product reliability data, NPRD) of non-key parts is used to predict the reliability of RV reducer in six-axis industrial robots. First, the proportion of every component of RV reducer in the reliability prediction was calculated via expert scoring. Then the failures rates of main parts and RV reducer are obtained by the non-key part. Based on this, the reliability assessment is investigated. This method can quantify the cognition of engineers on RV reducer under the condition of processing and production, besides, the failure rate of RV reducer can be calculated, which provide theoretical basis for requirements of spare parts for manufacturers of industrial robots who is using RV reducer.
{"title":"Failure Rate Prediction and Reliability Assessment of RV Reducer","authors":"B. Bai, Ze Li, Junyi Zhang","doi":"10.1109/QR2MSE46217.2019.9021119","DOIUrl":"https://doi.org/10.1109/QR2MSE46217.2019.9021119","url":null,"abstract":"Based on fuzzy mathematics thought, a methodology combining the expert evaluation and multilevel hierarchy analysis (EE-MHA) is proposed, meanwhile, the non-electronic product reliability data, NPRD) of non-key parts is used to predict the reliability of RV reducer in six-axis industrial robots. First, the proportion of every component of RV reducer in the reliability prediction was calculated via expert scoring. Then the failures rates of main parts and RV reducer are obtained by the non-key part. Based on this, the reliability assessment is investigated. This method can quantify the cognition of engineers on RV reducer under the condition of processing and production, besides, the failure rate of RV reducer can be calculated, which provide theoretical basis for requirements of spare parts for manufacturers of industrial robots who is using RV reducer.","PeriodicalId":233855,"journal":{"name":"2019 International Conference on Quality, Reliability, Risk, Maintenance, and Safety Engineering (QR2MSE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121059480","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Machine learning is nowadays one of the most efficient and popular tool and theory which has influenced many of the engineering fields. The traditional failure analysis is also based on statistical learning and reliability data, these methods can be used to assess characteristics over the design life, predict reliability, assess the exchange effect, product life prognosis and help to failure analysis. These two subjects have the natural connection, so this paper presents a very general overview on reliability and machine learning, which will demonstrate how the machine learning tools used for classical reliability system and failure analysis. We especially state some algorithms such as Bayesian networks and its’ method to reliability area. Then we can see how a typical engineering area can benefit from the machine learning.
{"title":"An Overview of Failure Analysis Expert System Based on Machine Learning","authors":"Hongjian Wang, Liyuan Liu, Youliang Wang, Zeya Peng","doi":"10.1109/QR2MSE46217.2019.9021177","DOIUrl":"https://doi.org/10.1109/QR2MSE46217.2019.9021177","url":null,"abstract":"Machine learning is nowadays one of the most efficient and popular tool and theory which has influenced many of the engineering fields. The traditional failure analysis is also based on statistical learning and reliability data, these methods can be used to assess characteristics over the design life, predict reliability, assess the exchange effect, product life prognosis and help to failure analysis. These two subjects have the natural connection, so this paper presents a very general overview on reliability and machine learning, which will demonstrate how the machine learning tools used for classical reliability system and failure analysis. We especially state some algorithms such as Bayesian networks and its’ method to reliability area. Then we can see how a typical engineering area can benefit from the machine learning.","PeriodicalId":233855,"journal":{"name":"2019 International Conference on Quality, Reliability, Risk, Maintenance, and Safety Engineering (QR2MSE)","volume":"104 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122572477","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 : 2019-08-01DOI: 10.1109/QR2MSE46217.2019.9021150
Jize Chen, Dezhen Yang, Yun Xie, Lei Lin
Nowadays, the mainstream researches of FMEA (Failure Mode Effect Analysis) are mostly about products, the few process FMEA researches often ignore the influences of single process failure on the whole manufacture process, or are just failure modes arranging without considering the further influence of process failure. So, in this article, we firstly build a process mode and process failure mode knowledge database using ontology in order to deal with the complicated production process knowledge. Secondly, we build a blackboard structure, which can use our ontology database and semantics to execute process FMEA automatically. Being exemplified by the analysis of a aircraft skin, the present study demonstrates that all approaches are feasible, efficient, and could be applied in real engineering scenarios.
{"title":"An Approach for Process FMEA Based on Blackboard Structure and Semantics","authors":"Jize Chen, Dezhen Yang, Yun Xie, Lei Lin","doi":"10.1109/QR2MSE46217.2019.9021150","DOIUrl":"https://doi.org/10.1109/QR2MSE46217.2019.9021150","url":null,"abstract":"Nowadays, the mainstream researches of FMEA (Failure Mode Effect Analysis) are mostly about products, the few process FMEA researches often ignore the influences of single process failure on the whole manufacture process, or are just failure modes arranging without considering the further influence of process failure. So, in this article, we firstly build a process mode and process failure mode knowledge database using ontology in order to deal with the complicated production process knowledge. Secondly, we build a blackboard structure, which can use our ontology database and semantics to execute process FMEA automatically. Being exemplified by the analysis of a aircraft skin, the present study demonstrates that all approaches are feasible, efficient, and could be applied in real engineering scenarios.","PeriodicalId":233855,"journal":{"name":"2019 International Conference on Quality, Reliability, Risk, Maintenance, and Safety Engineering (QR2MSE)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128570495","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 : 2019-08-01DOI: 10.1109/QR2MSE46217.2019.9021109
Ran Yan, Yang Jian, L. Hao, Xinyu Han, Longli Tang
The traditional way of knowledge acquisition is that the knowledge engineer obtains the knowledge acquired from the knowledge source into the knowledge base through software. After the knowledge base is established, it is mainly updated by the deletion method and the completion method. Not only does it take time and effort, but it is not guaranteed for correctness and is inefficient for troubleshooting. Here is another way to gain knowledge - automatic knowledge acquisition. Establish an automated process of knowledge acquisition based on machine learning, realize the automatic acquisition process of software fault diagnosis knowledge, improve the human-computer interaction process of knowledge acquisition process, and realize the accuracy and reusability of knowledge.
{"title":"Research on Automatic Knowledge Acquisition Technology for Software Fault Diagnosis","authors":"Ran Yan, Yang Jian, L. Hao, Xinyu Han, Longli Tang","doi":"10.1109/QR2MSE46217.2019.9021109","DOIUrl":"https://doi.org/10.1109/QR2MSE46217.2019.9021109","url":null,"abstract":"The traditional way of knowledge acquisition is that the knowledge engineer obtains the knowledge acquired from the knowledge source into the knowledge base through software. After the knowledge base is established, it is mainly updated by the deletion method and the completion method. Not only does it take time and effort, but it is not guaranteed for correctness and is inefficient for troubleshooting. Here is another way to gain knowledge - automatic knowledge acquisition. Establish an automated process of knowledge acquisition based on machine learning, realize the automatic acquisition process of software fault diagnosis knowledge, improve the human-computer interaction process of knowledge acquisition process, and realize the accuracy and reusability of knowledge.","PeriodicalId":233855,"journal":{"name":"2019 International Conference on Quality, Reliability, Risk, Maintenance, and Safety Engineering (QR2MSE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128784913","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}
After analyzing the shortcomings of traditional fault tree analysis methods, a fuzzy Bayesian network reliability analysis method based on fault tree is proposed. This method of modeling uses the Bayesian method, the event polymorphism of complex systems is described by the node polymorphism expression feature of Bayesian network theory, and the uncertain logical relationship between events is described by the conditional probability table of Bayesian network. Based on the Bayesian model, the fuzzy set theory is introduced, and the experts fuzzy evaluation of event probability is described by triangular fuzzy numbers. In the evaluation information of the experts with uncertain weights, the expert evaluation information of the uncertain weights is calculated by using the uncertainty-ordered weighted average operator to calculate the expert weights, and finally the exact value of the occurrence probability of different states is obtained. Substituting it into the Bayesian network to calculate the probability of occurrence of different states of the leaf nodes, and then calculating the posterior probability of each root node and its importance.
{"title":"Reliability Analysis of Fuzzy Bayesian Networks Based on Uncertain Ordered Weighted Operators","authors":"Chunwei Li, Honghua Sun, Qing-yang Li, Xudong Chen","doi":"10.1109/QR2MSE46217.2019.9021264","DOIUrl":"https://doi.org/10.1109/QR2MSE46217.2019.9021264","url":null,"abstract":"After analyzing the shortcomings of traditional fault tree analysis methods, a fuzzy Bayesian network reliability analysis method based on fault tree is proposed. This method of modeling uses the Bayesian method, the event polymorphism of complex systems is described by the node polymorphism expression feature of Bayesian network theory, and the uncertain logical relationship between events is described by the conditional probability table of Bayesian network. Based on the Bayesian model, the fuzzy set theory is introduced, and the experts fuzzy evaluation of event probability is described by triangular fuzzy numbers. In the evaluation information of the experts with uncertain weights, the expert evaluation information of the uncertain weights is calculated by using the uncertainty-ordered weighted average operator to calculate the expert weights, and finally the exact value of the occurrence probability of different states is obtained. Substituting it into the Bayesian network to calculate the probability of occurrence of different states of the leaf nodes, and then calculating the posterior probability of each root node and its importance.","PeriodicalId":233855,"journal":{"name":"2019 International Conference on Quality, Reliability, Risk, Maintenance, and Safety Engineering (QR2MSE)","volume":"71 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125977791","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 : 2019-08-01DOI: 10.1109/QR2MSE46217.2019.9021224
Qidong You, S. Zeng, Jianbin Guo, Honghong Lv
The development of man-machine system poses a challenge to human’s information processing ability. Therefore, the human cognitive characteristics and the dynamic man-machine interaction (MMI) become the focus of the MMI research. This study takes the MMI process of complex system as the research object. According to multi-task and time-pressure scenarios, two kinds of MMI fault modes such as cognitive overload and cognitive confusion are proposed. In addition, this paper studies their failure mechanism and the uncertainty of MMI logic. And then a modeling method of the two faults based on Markov model are proposed. The corresponding quantitative calculation methods to complete the modeling and prediction of MMI reliability are introduced. At last, a case application proves the rationality and feasibility of the method.
{"title":"Man-Machine Interaction Reliability Modeling Method Based on Markov Model","authors":"Qidong You, S. Zeng, Jianbin Guo, Honghong Lv","doi":"10.1109/QR2MSE46217.2019.9021224","DOIUrl":"https://doi.org/10.1109/QR2MSE46217.2019.9021224","url":null,"abstract":"The development of man-machine system poses a challenge to human’s information processing ability. Therefore, the human cognitive characteristics and the dynamic man-machine interaction (MMI) become the focus of the MMI research. This study takes the MMI process of complex system as the research object. According to multi-task and time-pressure scenarios, two kinds of MMI fault modes such as cognitive overload and cognitive confusion are proposed. In addition, this paper studies their failure mechanism and the uncertainty of MMI logic. And then a modeling method of the two faults based on Markov model are proposed. The corresponding quantitative calculation methods to complete the modeling and prediction of MMI reliability are introduced. At last, a case application proves the rationality and feasibility of the method.","PeriodicalId":233855,"journal":{"name":"2019 International Conference on Quality, Reliability, Risk, Maintenance, and Safety Engineering (QR2MSE)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127562942","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 : 2019-08-01DOI: 10.1109/QR2MSE46217.2019.9021117
Baiqiao Huang, Guodong Qin, Peng Zhang
It is the consensus of people that improving product quality by improving management, but the management standards adopted in different fields are not the same, which is easy to be confused. As to the current status of different quality management standards and methods used in different business areas, this paper analyzes the development history of the general quality management system in the production field and the core concept of total quality management(TQM), compares it with the CMMI standard of quality management in the system development field, analyzes each other’s strengths and weaknesses, and proposes suggestions for improving the deficiencies of CMMI. Finally, the relationship between TQM, CMMI and system engineering (SE) is analyzed, and concludes that the integration with model-based system engineering(MBSE) will be the new direction of CMMI’s future development.
{"title":"Comparative Analysis of TQM and CMMI","authors":"Baiqiao Huang, Guodong Qin, Peng Zhang","doi":"10.1109/QR2MSE46217.2019.9021117","DOIUrl":"https://doi.org/10.1109/QR2MSE46217.2019.9021117","url":null,"abstract":"It is the consensus of people that improving product quality by improving management, but the management standards adopted in different fields are not the same, which is easy to be confused. As to the current status of different quality management standards and methods used in different business areas, this paper analyzes the development history of the general quality management system in the production field and the core concept of total quality management(TQM), compares it with the CMMI standard of quality management in the system development field, analyzes each other’s strengths and weaknesses, and proposes suggestions for improving the deficiencies of CMMI. Finally, the relationship between TQM, CMMI and system engineering (SE) is analyzed, and concludes that the integration with model-based system engineering(MBSE) will be the new direction of CMMI’s future development.","PeriodicalId":233855,"journal":{"name":"2019 International Conference on Quality, Reliability, Risk, Maintenance, and Safety Engineering (QR2MSE)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127267122","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 : 2019-08-01DOI: 10.1109/QR2MSE46217.2019.9021135
Yingchun Xu, Wen Yao, Xiaohu Zheng, Xiaoqian Chen
In recent years, there often exist multiple priors from experienced experts or historical experiments with the rapid development of system structure in engineering fields. Bayesian Melding Method is commonly used for integrating multiple priors, which is based on the deterministic system structure. However, if the system model cannot be described by an explicit expression, the traditional Bayesian Melding Method is not feasible for system reliability analysis anymore. In order to describe the structure relationship clearly, Bayesian Network is applied in this paper to construct the complex system structure model and the system reliability is calculated by node probability tables rather than explicit expressions. Combining the advantages of the Bayesian Melding Method and Bayesian Network, a multi-prior integration and updating algorithm is developed for the system reliability analysis of complex system structures. Finally, a satellite attitude control system is used to demonstrate the proposed method. The system is established by the Bayesian Network and the comparison between natural prior and updated prior is discussed at length.
{"title":"Multi-Prior Integration Method for System Reliability Analysis Based on Bayesian Network and Bayesian Melding Method","authors":"Yingchun Xu, Wen Yao, Xiaohu Zheng, Xiaoqian Chen","doi":"10.1109/QR2MSE46217.2019.9021135","DOIUrl":"https://doi.org/10.1109/QR2MSE46217.2019.9021135","url":null,"abstract":"In recent years, there often exist multiple priors from experienced experts or historical experiments with the rapid development of system structure in engineering fields. Bayesian Melding Method is commonly used for integrating multiple priors, which is based on the deterministic system structure. However, if the system model cannot be described by an explicit expression, the traditional Bayesian Melding Method is not feasible for system reliability analysis anymore. In order to describe the structure relationship clearly, Bayesian Network is applied in this paper to construct the complex system structure model and the system reliability is calculated by node probability tables rather than explicit expressions. Combining the advantages of the Bayesian Melding Method and Bayesian Network, a multi-prior integration and updating algorithm is developed for the system reliability analysis of complex system structures. Finally, a satellite attitude control system is used to demonstrate the proposed method. The system is established by the Bayesian Network and the comparison between natural prior and updated prior is discussed at length.","PeriodicalId":233855,"journal":{"name":"2019 International Conference on Quality, Reliability, Risk, Maintenance, and Safety Engineering (QR2MSE)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125970681","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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