Pub Date : 2016-04-19DOI: 10.1109/PACIFICVIS.2016.7465251
Q. Shu, Hanqi Guo, Jie Liang, Limei Che, Junfeng Liu, Xiaoru Yuan
This paper presents a novel visual analysis tool, EnsembleGraph, which aims at helping scientists understand spatiotemporal similarities across runs in time-varying ensemble simulation data. We abstract the input data into a graph, where each node represents a region with similar behaviors across runs and nodes in adjacent time frames are linked if their regions overlap spatially. The visualization of this graph, combined with multiple-linked views showing details, enables users to explore, select, and compare the extracted regions that have similar behaviors. The driving application of this paper is the study of regional emission influences over tropospheric ozone, based on the ensemble simulations conducted with different anthropogenic emission absences using MOZART-4. We demonstrate the effectiveness of our method by visualizing the MOZART-4 ensemble simulation data and evaluating the relative regional emission influences on tropospheric ozone concentrations.
{"title":"EnsembleGraph: Interactive visual analysis of spatiotemporal behaviors in ensemble simulation data","authors":"Q. Shu, Hanqi Guo, Jie Liang, Limei Che, Junfeng Liu, Xiaoru Yuan","doi":"10.1109/PACIFICVIS.2016.7465251","DOIUrl":"https://doi.org/10.1109/PACIFICVIS.2016.7465251","url":null,"abstract":"This paper presents a novel visual analysis tool, EnsembleGraph, which aims at helping scientists understand spatiotemporal similarities across runs in time-varying ensemble simulation data. We abstract the input data into a graph, where each node represents a region with similar behaviors across runs and nodes in adjacent time frames are linked if their regions overlap spatially. The visualization of this graph, combined with multiple-linked views showing details, enables users to explore, select, and compare the extracted regions that have similar behaviors. The driving application of this paper is the study of regional emission influences over tropospheric ozone, based on the ensemble simulations conducted with different anthropogenic emission absences using MOZART-4. We demonstrate the effectiveness of our method by visualizing the MOZART-4 ensemble simulation data and evaluating the relative regional emission influences on tropospheric ozone concentrations.","PeriodicalId":129600,"journal":{"name":"2016 IEEE Pacific Visualization Symposium (PacificVis)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130843390","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 : 2016-04-19DOI: 10.1109/PACIFICVIS.2016.7465256
Richen Liu, Hanqi Guo, Jiang Zhang, Xiaoru Yuan
We propose a longest common subsequence (LCSS)-based approach to compute the distance among vector field ensembles. By measuring how many common blocks the ensemble pathlines pass through, the LCSS distance defines the similarity among vector field ensembles by counting the number of shared domain data blocks. Compared with traditional methods (e.g., pointwise Euclidean distance or dynamic time warping distance), the proposed approach is robust to outliers, missing data, and the sampling rate of the pathline timesteps. Taking advantage of smaller and reusable intermediate output, visualization based on the proposed LCSS approach reveals temporal trends in the data at low storage cost and avoids tracing pathlines repeatedly. We evaluate our method on both synthetic data and simulation data, demonstrating the robustness of the proposed approach.
{"title":"Comparative visualization of vector field ensembles based on longest common subsequence","authors":"Richen Liu, Hanqi Guo, Jiang Zhang, Xiaoru Yuan","doi":"10.1109/PACIFICVIS.2016.7465256","DOIUrl":"https://doi.org/10.1109/PACIFICVIS.2016.7465256","url":null,"abstract":"We propose a longest common subsequence (LCSS)-based approach to compute the distance among vector field ensembles. By measuring how many common blocks the ensemble pathlines pass through, the LCSS distance defines the similarity among vector field ensembles by counting the number of shared domain data blocks. Compared with traditional methods (e.g., pointwise Euclidean distance or dynamic time warping distance), the proposed approach is robust to outliers, missing data, and the sampling rate of the pathline timesteps. Taking advantage of smaller and reusable intermediate output, visualization based on the proposed LCSS approach reveals temporal trends in the data at low storage cost and avoids tracing pathlines repeatedly. We evaluate our method on both synthetic data and simulation data, demonstrating the robustness of the proposed approach.","PeriodicalId":129600,"journal":{"name":"2016 IEEE Pacific Visualization Symposium (PacificVis)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122296252","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 percentage of individuals frequently using their smartphones in work and life is increasing steadily. The interactions between individuals and their smartphones can produce large amounts of usage data, which contain rich information about smartphone owners usage habits and their daily life. In this paper, a visual analytic tool is proposed to discover and understand individual behavior patterns in smartphone usage data. Four cooperated visualization views and many interactions are provided in this tool to visually explore the temporal features of various interactive events between smartphones and their users, the hierarchical associations among event types, and the detailed distributions of massive event sequences. In the case studies, plenty of interesting patterns are discovered by analyzing the data of two smartphone users with different usage styles.
{"title":"A visual analytics approach for exploring individual behaviors in smartphone usage data","authors":"Mingming Lu, Dan Meng, Yanni Peng, Yong Li, Ying Zhao, Xiaoping Fan, Fangfang Zhou","doi":"10.1109/PACIFICVIS.2016.7465275","DOIUrl":"https://doi.org/10.1109/PACIFICVIS.2016.7465275","url":null,"abstract":"The percentage of individuals frequently using their smartphones in work and life is increasing steadily. The interactions between individuals and their smartphones can produce large amounts of usage data, which contain rich information about smartphone owners usage habits and their daily life. In this paper, a visual analytic tool is proposed to discover and understand individual behavior patterns in smartphone usage data. Four cooperated visualization views and many interactions are provided in this tool to visually explore the temporal features of various interactive events between smartphones and their users, the hierarchical associations among event types, and the detailed distributions of massive event sequences. In the case studies, plenty of interesting patterns are discovered by analyzing the data of two smartphone users with different usage styles.","PeriodicalId":129600,"journal":{"name":"2016 IEEE Pacific Visualization Symposium (PacificVis)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116405656","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 : 2016-04-19DOI: 10.1109/PACIFICVIS.2016.7465276
Oky Purwantiningsih, A. Sallaberry, S. Andary, Antoine Seilles, J. Azé
The advancement of motion sensing input devices has enabled the collection of multivariate time-series body movement data. Analyzing such type of data is challenging due to the large amount of data and the task of mining for interesting temporal movement patterns. To address this problem, we propose an interface to visualize and analyze body movement data. This visualization enables users to navigate and explore the evolution of movement over time for different movement areas. We also propose a clustering method based on hierarchical clustering to group similar movement patterns. The proposed visualization is illustrated with a case study which demonstrates the ability of the interface to analyze body movements.
{"title":"Visual analysis of body movement in serious games for healthcare","authors":"Oky Purwantiningsih, A. Sallaberry, S. Andary, Antoine Seilles, J. Azé","doi":"10.1109/PACIFICVIS.2016.7465276","DOIUrl":"https://doi.org/10.1109/PACIFICVIS.2016.7465276","url":null,"abstract":"The advancement of motion sensing input devices has enabled the collection of multivariate time-series body movement data. Analyzing such type of data is challenging due to the large amount of data and the task of mining for interesting temporal movement patterns. To address this problem, we propose an interface to visualize and analyze body movement data. This visualization enables users to navigate and explore the evolution of movement over time for different movement areas. We also propose a clustering method based on hierarchical clustering to group similar movement patterns. The proposed visualization is illustrated with a case study which demonstrates the ability of the interface to analyze body movements.","PeriodicalId":129600,"journal":{"name":"2016 IEEE Pacific Visualization Symposium (PacificVis)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114877104","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 : 2016-04-19DOI: 10.1109/PACIFICVIS.2016.7465266
Robert Krüger, Guodao Sun, Fabian Beck, Ronghua Liang, T. Ertl
Geo-tagged microblog data covers billions of movement patterns on a global and local scale. Understanding these patterns could guide urban and traffic planning or help coping with disaster situations. We present a visual analytics system to investigate travel trajectories of people reconstructed from microblog messages. To analyze seasonal changes and events and to validate movement patterns against other data sources, we contribute highly interactive visual comparison methods that normalize and contrast trajectories as well as density maps within a single view. We also compute an adaptive hierarchical graph from the trajectories to abstract individual movements into higher-level structures. Specific challenges that we tackle are, among others, the spatio-temporal sparsity of the data, the volume of data varying by region, and a diverse mix of means of transportation. The applicability of our approach is presented in three case studies.
{"title":"TravelDiff: Visual comparison analytics for massive movement patterns derived from Twitter","authors":"Robert Krüger, Guodao Sun, Fabian Beck, Ronghua Liang, T. Ertl","doi":"10.1109/PACIFICVIS.2016.7465266","DOIUrl":"https://doi.org/10.1109/PACIFICVIS.2016.7465266","url":null,"abstract":"Geo-tagged microblog data covers billions of movement patterns on a global and local scale. Understanding these patterns could guide urban and traffic planning or help coping with disaster situations. We present a visual analytics system to investigate travel trajectories of people reconstructed from microblog messages. To analyze seasonal changes and events and to validate movement patterns against other data sources, we contribute highly interactive visual comparison methods that normalize and contrast trajectories as well as density maps within a single view. We also compute an adaptive hierarchical graph from the trajectories to abstract individual movements into higher-level structures. Specific challenges that we tackle are, among others, the spatio-temporal sparsity of the data, the volume of data varying by region, and a diverse mix of means of transportation. The applicability of our approach is presented in three case studies.","PeriodicalId":129600,"journal":{"name":"2016 IEEE Pacific Visualization Symposium (PacificVis)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125586434","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 : 2016-04-19DOI: 10.1109/PACIFICVIS.2016.7465246
Yueqi Hu, Tom Polk, J. Yang, Ye Zhao, Shixia Liu
Zoomable user interfaces are widely used in static visualizations and have many benefits. However, they are not well supported in animated visualizations due to problems such as change blindness and information overload. We propose the spot-tracking lens, a new zoomable user interface for animated bubble charts, to tackle these problems. It couples zooming with automatic panning and provides a rich set of auxiliary techniques to enhance its effectiveness. Our preliminary user studies suggested that, besides allowing users to examine detail information, it can be an engaging approach to exploratory analysis for dynamic data.
{"title":"Spot-tracking lens: A zoomable user interface for animated bubble charts","authors":"Yueqi Hu, Tom Polk, J. Yang, Ye Zhao, Shixia Liu","doi":"10.1109/PACIFICVIS.2016.7465246","DOIUrl":"https://doi.org/10.1109/PACIFICVIS.2016.7465246","url":null,"abstract":"Zoomable user interfaces are widely used in static visualizations and have many benefits. However, they are not well supported in animated visualizations due to problems such as change blindness and information overload. We propose the spot-tracking lens, a new zoomable user interface for animated bubble charts, to tackle these problems. It couples zooming with automatic panning and provides a rich set of auxiliary techniques to enhance its effectiveness. Our preliminary user studies suggested that, besides allowing users to examine detail information, it can be an engaging approach to exploratory analysis for dynamic data.","PeriodicalId":129600,"journal":{"name":"2016 IEEE Pacific Visualization Symposium (PacificVis)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131983212","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 : 2016-04-19DOI: 10.1109/PACIFICVIS.2016.7465260
Fangfang Zhou, Juncai Li, Wei Huang, Ying Zhao, Xiaoru Yuan, Xing Liang, Yang Shi
Subspace-based analysis has increasingly become the preferred method for clustering high-dimensional data. A visually interactive exploration of subspaces and clusters is a cyclic process. Every meaningful discovery will motivate users to re-search subspaces that can provide improved clustering results and reveal the relationships among clusters that can hardly coexist in the original subspaces. However, the combination of dimensions from the original subspaces is not always effective in finding the expected subspaces. In this study, we present an approach that enables users to reconstruct new dimensions from the data projections of subspaces to preserve interesting cluster information. The reconstructed dimensions are included into an analytical workflow with the original dimensions to help users construct target-oriented subspaces which clearly display informative cluster structures. We also provide a visualization tool that assists users in the exploration of subspace clusters by utilizing dimension reconstruction. Several case studies on synthetic and real-world data sets have been performed to prove the effectiveness of our approach. Lastly, further evaluation of the approach has been conducted via expert reviews.
{"title":"Dimension reconstruction for visual exploration of subspace clusters in high-dimensional data","authors":"Fangfang Zhou, Juncai Li, Wei Huang, Ying Zhao, Xiaoru Yuan, Xing Liang, Yang Shi","doi":"10.1109/PACIFICVIS.2016.7465260","DOIUrl":"https://doi.org/10.1109/PACIFICVIS.2016.7465260","url":null,"abstract":"Subspace-based analysis has increasingly become the preferred method for clustering high-dimensional data. A visually interactive exploration of subspaces and clusters is a cyclic process. Every meaningful discovery will motivate users to re-search subspaces that can provide improved clustering results and reveal the relationships among clusters that can hardly coexist in the original subspaces. However, the combination of dimensions from the original subspaces is not always effective in finding the expected subspaces. In this study, we present an approach that enables users to reconstruct new dimensions from the data projections of subspaces to preserve interesting cluster information. The reconstructed dimensions are included into an analytical workflow with the original dimensions to help users construct target-oriented subspaces which clearly display informative cluster structures. We also provide a visualization tool that assists users in the exploration of subspace clusters by utilizing dimension reconstruction. Several case studies on synthetic and real-world data sets have been performed to prove the effectiveness of our approach. Lastly, further evaluation of the approach has been conducted via expert reviews.","PeriodicalId":129600,"journal":{"name":"2016 IEEE Pacific Visualization Symposium (PacificVis)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133775326","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}
Streamline-based techniques play an important role in visualizing and analyzing uncertain steady vector fields. It is a challenging problem to generate accurate streamlines in uncertain vector fields due to the global uncertainty transportation. In this work, we present a novel probabilistic method for streamline computation on uncertain steady vector fields using a Bayesian framework. In our framework, a streamline is modeled as a state space model which captures the spatial coherence of integration steps and uncertainty in local distributions using the conditional prior density and the likelihood function. To approximate the posterior distribution for all the possible traces originating from a given seed position, a set of weighted samples are iteratively updated from which streamlines with higher likelihood can be derived. We qualitatively and quantitatively compare our method with alternative methods on different types of flow field data sets. Our method can generate possible streamlines with higher certainty and hence more accurate flow traces.
{"title":"A Bayesian approach for probabilistic streamline computation in uncertain flows","authors":"Wenbin He, Chun-Ming Chen, Xiaotong Liu, Han-Wei Shen","doi":"10.1109/PACIFICVIS.2016.7465273","DOIUrl":"https://doi.org/10.1109/PACIFICVIS.2016.7465273","url":null,"abstract":"Streamline-based techniques play an important role in visualizing and analyzing uncertain steady vector fields. It is a challenging problem to generate accurate streamlines in uncertain vector fields due to the global uncertainty transportation. In this work, we present a novel probabilistic method for streamline computation on uncertain steady vector fields using a Bayesian framework. In our framework, a streamline is modeled as a state space model which captures the spatial coherence of integration steps and uncertainty in local distributions using the conditional prior density and the likelihood function. To approximate the posterior distribution for all the possible traces originating from a given seed position, a set of weighted samples are iteratively updated from which streamlines with higher likelihood can be derived. We qualitatively and quantitatively compare our method with alternative methods on different types of flow field data sets. Our method can generate possible streamlines with higher certainty and hence more accurate flow traces.","PeriodicalId":129600,"journal":{"name":"2016 IEEE Pacific Visualization Symposium (PacificVis)","volume":"334 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116529082","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 : 2016-04-19DOI: 10.1109/PACIFICVIS.2016.7465253
R. Bujack, M. Hlawitschka, K. Joy
Vector field topology is one of the most powerful flow visualization tools, because it can break down huge amounts of data into a compact, sparse, and easy to read description with little information loss. It suffers from one main drawback though: The definition of critical points, which is the foundation of vector field topology, is highly dependent on the frame of reference. In this paper we propose to consider every point as a critical point and locally adjust the frame of reference to the most persistent ones, that means the extrema of the determinant of the Jacobian. The result is not the extraction of one well-suited frame of reference, but the simultaneous visualization of the dominating frames of reference in the different areas of the flow field. Each of them could individually be perceived by an observer traveling along these critical points. We show all important ones at once.
{"title":"Topology-inspired Galilean invariant vector field analysis","authors":"R. Bujack, M. Hlawitschka, K. Joy","doi":"10.1109/PACIFICVIS.2016.7465253","DOIUrl":"https://doi.org/10.1109/PACIFICVIS.2016.7465253","url":null,"abstract":"Vector field topology is one of the most powerful flow visualization tools, because it can break down huge amounts of data into a compact, sparse, and easy to read description with little information loss. It suffers from one main drawback though: The definition of critical points, which is the foundation of vector field topology, is highly dependent on the frame of reference. In this paper we propose to consider every point as a critical point and locally adjust the frame of reference to the most persistent ones, that means the extrema of the determinant of the Jacobian. The result is not the extraction of one well-suited frame of reference, but the simultaneous visualization of the dominating frames of reference in the different areas of the flow field. Each of them could individually be perceived by an observer traveling along these critical points. We show all important ones at once.","PeriodicalId":129600,"journal":{"name":"2016 IEEE Pacific Visualization Symposium (PacificVis)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132469544","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 : 2016-04-19DOI: 10.1109/PACIFICVIS.2016.7465281
Michaël Aupetit, Ehsan Ullah, Reda Rawi, H. Bensmail
Genome Wide Association Studies (GWAS) examine genetic variants in different individuals to detect variants associated to specific diseases. The 1000 Genomes project is such a collaborative research effort to sequence the genomes of at least 1000 participants of 26 different ethnicities, to establish a detailed summary of human genetic variation. The kinship information is a measure of individuals ancestor relationships within the considered populations. We study the design of kinship data visualizations allowing the experts to discover anomalies in GWAS data. The visual analysis of the 1000 Genomes Project kinship data reveals inconsistencies which call for a deeper analysis of the data quality within this project.
{"title":"A design study to identify inconsistencies in kinship information: The case of the 1000 Genomes project","authors":"Michaël Aupetit, Ehsan Ullah, Reda Rawi, H. Bensmail","doi":"10.1109/PACIFICVIS.2016.7465281","DOIUrl":"https://doi.org/10.1109/PACIFICVIS.2016.7465281","url":null,"abstract":"Genome Wide Association Studies (GWAS) examine genetic variants in different individuals to detect variants associated to specific diseases. The 1000 Genomes project is such a collaborative research effort to sequence the genomes of at least 1000 participants of 26 different ethnicities, to establish a detailed summary of human genetic variation. The kinship information is a measure of individuals ancestor relationships within the considered populations. We study the design of kinship data visualizations allowing the experts to discover anomalies in GWAS data. The visual analysis of the 1000 Genomes Project kinship data reveals inconsistencies which call for a deeper analysis of the data quality within this project.","PeriodicalId":129600,"journal":{"name":"2016 IEEE Pacific Visualization Symposium (PacificVis)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129790908","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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