Pub Date : 2018-08-01DOI: 10.1109/ICGCIOT.2018.8753088
B. Panigrahi, Bhagyashree Parija, Ruturaj Pattanayak, S. K. Tripathy
Introduction of distributed generators into the conventional power grid results, complexity in operation and control problem along with creating a challenge in identification of fault disturbances in electric power system. This paper describes a new technique for classification of fault disturbances like LLG under different operating conditions. The pattern recognition techniques namely support vector machines (SVM) and decision tree (DT) are used to classify faults disturbances. Based on the study of this paper, it is observed that SVM and DT provides the best possible accuracy as compared to other techniques, implying its robustness under different operating scenarios such as variation in load, solar insolation and presence of noise and harmonics in the system parameters.
{"title":"Faults classification In A Microgrid Using Decision Tree Technique And Support Vector Machine","authors":"B. Panigrahi, Bhagyashree Parija, Ruturaj Pattanayak, S. K. Tripathy","doi":"10.1109/ICGCIOT.2018.8753088","DOIUrl":"https://doi.org/10.1109/ICGCIOT.2018.8753088","url":null,"abstract":"Introduction of distributed generators into the conventional power grid results, complexity in operation and control problem along with creating a challenge in identification of fault disturbances in electric power system. This paper describes a new technique for classification of fault disturbances like LLG under different operating conditions. The pattern recognition techniques namely support vector machines (SVM) and decision tree (DT) are used to classify faults disturbances. Based on the study of this paper, it is observed that SVM and DT provides the best possible accuracy as compared to other techniques, implying its robustness under different operating scenarios such as variation in load, solar insolation and presence of noise and harmonics in the system parameters.","PeriodicalId":269682,"journal":{"name":"2018 Second International Conference on Green Computing and Internet of Things (ICGCIoT)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124522718","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 : 2018-08-01DOI: 10.1109/ICGCIOT.2018.8753103
R. Keerthi, P. Lakshmi
The exponential rise in technologies has opened up a gigantic scope to exploit the data for better decision making. The evolution of social media has contributed humungous information including ratings, reviews and comments. Considering the significance of an efficient predictive analysis model for the tourist destination prediction, in the proposed work robust technologies have been applied to perform the destination prediction. This work contributes to the technique of developing a novel Destination Prediction Model that corresponds to the tourist’s preferences.
{"title":"Predictive Analysis for Modeling Travel Decision Making","authors":"R. Keerthi, P. Lakshmi","doi":"10.1109/ICGCIOT.2018.8753103","DOIUrl":"https://doi.org/10.1109/ICGCIOT.2018.8753103","url":null,"abstract":"The exponential rise in technologies has opened up a gigantic scope to exploit the data for better decision making. The evolution of social media has contributed humungous information including ratings, reviews and comments. Considering the significance of an efficient predictive analysis model for the tourist destination prediction, in the proposed work robust technologies have been applied to perform the destination prediction. This work contributes to the technique of developing a novel Destination Prediction Model that corresponds to the tourist’s preferences.","PeriodicalId":269682,"journal":{"name":"2018 Second International Conference on Green Computing and Internet of Things (ICGCIoT)","volume":"102 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133640124","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 : 2018-08-01DOI: 10.1109/ICGCIOT.2018.8753051
Deepa Gupta, Vaibhav Sharma, Praveen Kumar
In the current scenario, ninety percent of the data present in the world right now is created in past two years only. As the world relentlessly turns out to be more associated with a consistently expanding number of electronic gadgets, which also results in exponential growth of data created, this is just set to become over the coming years. All in all, the Internet populace has developed by 7.5 percent since 2016 and now incorporates more than 3.75 billion people. As far as information utilization, that is one serious strain. By and large, the only us releases 2,657,700 gigabytes of Internet information consistently. The advancement of Internet-based media stages and organizations are having their day in the sun, yet not all. Amazon, YouTube, and Netflix are a segment of the best customers of Internet information transmission. While Amazon is getting a charge out of record benefits (around 258,751 deals every moment, up from 222,283 a year ago) and YouTube is gushing like never before (4.14 million recordings viewed every moment), Netflix has seen a 20 percent diminish in the quantity of "hours" their watchers watched demonstrates every moment contrasted with 2016.
{"title":"Beast to Beauty data in Virtual Analytics","authors":"Deepa Gupta, Vaibhav Sharma, Praveen Kumar","doi":"10.1109/ICGCIOT.2018.8753051","DOIUrl":"https://doi.org/10.1109/ICGCIOT.2018.8753051","url":null,"abstract":"In the current scenario, ninety percent of the data present in the world right now is created in past two years only. As the world relentlessly turns out to be more associated with a consistently expanding number of electronic gadgets, which also results in exponential growth of data created, this is just set to become over the coming years. All in all, the Internet populace has developed by 7.5 percent since 2016 and now incorporates more than 3.75 billion people. As far as information utilization, that is one serious strain. By and large, the only us releases 2,657,700 gigabytes of Internet information consistently. The advancement of Internet-based media stages and organizations are having their day in the sun, yet not all. Amazon, YouTube, and Netflix are a segment of the best customers of Internet information transmission. While Amazon is getting a charge out of record benefits (around 258,751 deals every moment, up from 222,283 a year ago) and YouTube is gushing like never before (4.14 million recordings viewed every moment), Netflix has seen a 20 percent diminish in the quantity of \"hours\" their watchers watched demonstrates every moment contrasted with 2016.","PeriodicalId":269682,"journal":{"name":"2018 Second International Conference on Green Computing and Internet of Things (ICGCIoT)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129339525","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 : 2018-08-01DOI: 10.1109/ICGCIOT.2018.8753030
R. Singh, V. K. Panchal, B. Singh
Genetic Algorithm is a soft computing technique which uses its special operators to solve an optimization problem. These algorithms can solve both minimization and maximization problems. This paper discusses the details of genetic algorithm i.e. how it works. The paper also discusses the application areas of genetic algorithm in various fields of science and engineering. We also discuss its current applications and how it can be useful to solve future problems or optimize the current procedures.
{"title":"A review on Genetic Algorithm and Its Applications","authors":"R. Singh, V. K. Panchal, B. Singh","doi":"10.1109/ICGCIOT.2018.8753030","DOIUrl":"https://doi.org/10.1109/ICGCIOT.2018.8753030","url":null,"abstract":"Genetic Algorithm is a soft computing technique which uses its special operators to solve an optimization problem. These algorithms can solve both minimization and maximization problems. This paper discusses the details of genetic algorithm i.e. how it works. The paper also discusses the application areas of genetic algorithm in various fields of science and engineering. We also discuss its current applications and how it can be useful to solve future problems or optimize the current procedures.","PeriodicalId":269682,"journal":{"name":"2018 Second International Conference on Green Computing and Internet of Things (ICGCIoT)","volume":"132 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131389897","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 : 2018-08-01DOI: 10.1109/ICGCIOT.2018.8753066
Sandeep S R, Rudranna Nandihalli
The paper proposes a novel method for fulfilling the load demands using the two renewable generation system such as wind and solar. The fuzzy logic controller is used to control the generation of the generating station. The power reference of each renewable source is calculated and provided to the fuzzy controller; it generates the control signals to alter the generation power. The controller is intelligent enough to make the decision for the dynamic change in the operating point of the solar and wind. MATLAB SIMULINK tool is used to implement the proposed system.
{"title":"Control of Stand-alone Hybrid Renewable Energy Generation System Using Fuzzy Controller","authors":"Sandeep S R, Rudranna Nandihalli","doi":"10.1109/ICGCIOT.2018.8753066","DOIUrl":"https://doi.org/10.1109/ICGCIOT.2018.8753066","url":null,"abstract":"The paper proposes a novel method for fulfilling the load demands using the two renewable generation system such as wind and solar. The fuzzy logic controller is used to control the generation of the generating station. The power reference of each renewable source is calculated and provided to the fuzzy controller; it generates the control signals to alter the generation power. The controller is intelligent enough to make the decision for the dynamic change in the operating point of the solar and wind. MATLAB SIMULINK tool is used to implement the proposed system.","PeriodicalId":269682,"journal":{"name":"2018 Second International Conference on Green Computing and Internet of Things (ICGCIoT)","volume":"105 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124659728","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 : 2018-08-01DOI: 10.1109/ICGCIOT.2018.8752998
Nallapaneni Manoj Kumar, P. Das, Jayanna Kanchikere
Wearable Devices and their relevant intelligent and integrated computing techniques are presently being discovered to promote extensive claims in many areas. Smart Glass is one such wearable device which attracted many sectors since its official launch as Google Glass in 2014. Currently, no resource exists in the literature that supports the use of Smart Glass for the solar industry. For the first time, this article seeks to expand the Smart Glass applications into the solar power industry especially for addressing the solar power plant operation and maintenance issues. Applicability and scope for possible operations were explored by studying the technology and integrated computing techniques. Various sensors were embedded in smart glass, and they are a camera, microphone, global navigation system (GPS), magnetometer, light sensor, and a tangible interface. These embedded sensors can do works that are most important in monitoring few relevant parameters and addressing the challenges in solar power plant and its system components. Few computing technologies which could be integrated with the smart glass specifically in the view of solar were proposed. The theoretical study was carried out in monitoring the feasibilities of capturing images of the photovoltaic (PV) module for addressing dust and temperature problems, identifying the location of the power plant, noise monitoring of the system components, detection of power cables using magnetometers, tracking of visually enriched images under light conditions etc. Also, with the help of tangible sensors, the operator can use and interact with any digital interface available for displaying the monitor parameters. Hence, it is felt that the smart glass could be a great assent for solar power plant operation and maintenance.
{"title":"Applicability of Wearable Smart Glass for Solar Power Plant Operation and Maintenance","authors":"Nallapaneni Manoj Kumar, P. Das, Jayanna Kanchikere","doi":"10.1109/ICGCIOT.2018.8752998","DOIUrl":"https://doi.org/10.1109/ICGCIOT.2018.8752998","url":null,"abstract":"Wearable Devices and their relevant intelligent and integrated computing techniques are presently being discovered to promote extensive claims in many areas. Smart Glass is one such wearable device which attracted many sectors since its official launch as Google Glass in 2014. Currently, no resource exists in the literature that supports the use of Smart Glass for the solar industry. For the first time, this article seeks to expand the Smart Glass applications into the solar power industry especially for addressing the solar power plant operation and maintenance issues. Applicability and scope for possible operations were explored by studying the technology and integrated computing techniques. Various sensors were embedded in smart glass, and they are a camera, microphone, global navigation system (GPS), magnetometer, light sensor, and a tangible interface. These embedded sensors can do works that are most important in monitoring few relevant parameters and addressing the challenges in solar power plant and its system components. Few computing technologies which could be integrated with the smart glass specifically in the view of solar were proposed. The theoretical study was carried out in monitoring the feasibilities of capturing images of the photovoltaic (PV) module for addressing dust and temperature problems, identifying the location of the power plant, noise monitoring of the system components, detection of power cables using magnetometers, tracking of visually enriched images under light conditions etc. Also, with the help of tangible sensors, the operator can use and interact with any digital interface available for displaying the monitor parameters. Hence, it is felt that the smart glass could be a great assent for solar power plant operation and maintenance.","PeriodicalId":269682,"journal":{"name":"2018 Second International Conference on Green Computing and Internet of Things (ICGCIoT)","volume":"389 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122878791","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 : 2018-08-01DOI: 10.1109/ICGCIOT.2018.8752982
S. Bharadwaj, Anil B.S., Abhiraj Pahargarh, Adhiraj Pahargarh, P. S. Gowra, Sharath Kumar
Customer relationship marketing is important since it provides a long standing relationship between the customer and the organization. Churn obstructs the growth of profitable customers and it is the biggest challenge to sustain a telecommunication network. We propose two models which predicts customer churn with a high degree of accuracy. Our first model is a logistic regression model which is a non-linear classifier with sigmoid as its activation function. The accuracy of the model is heightened by regularizing it with the regularizing parameter set to 0.01 and this gives an accuracy of 87.52% on our test dataset. Our second model is a full fledged Multilayer Perceptron(MLP) Neural Network with a normalized input feature vector which is stacked with three hidden layers and employs binary cross entropy as the loss function with a learning rate of 0.01. This model is split into a test-train set and achieves an accuracy of 94.19%. Using this predictive model the organization can conduct marketing research and study the needs of the particular customer in detail. Using that data they can produce goods according to the customer needs before the customer demands and present it to them. This helps to create brand loyalty which in turn leads to a sustainable network.
{"title":"Customer Churn Prediction in Mobile Networks using Logistic Regression and Multilayer Perceptron(MLP)","authors":"S. Bharadwaj, Anil B.S., Abhiraj Pahargarh, Adhiraj Pahargarh, P. S. Gowra, Sharath Kumar","doi":"10.1109/ICGCIOT.2018.8752982","DOIUrl":"https://doi.org/10.1109/ICGCIOT.2018.8752982","url":null,"abstract":"Customer relationship marketing is important since it provides a long standing relationship between the customer and the organization. Churn obstructs the growth of profitable customers and it is the biggest challenge to sustain a telecommunication network. We propose two models which predicts customer churn with a high degree of accuracy. Our first model is a logistic regression model which is a non-linear classifier with sigmoid as its activation function. The accuracy of the model is heightened by regularizing it with the regularizing parameter set to 0.01 and this gives an accuracy of 87.52% on our test dataset. Our second model is a full fledged Multilayer Perceptron(MLP) Neural Network with a normalized input feature vector which is stacked with three hidden layers and employs binary cross entropy as the loss function with a learning rate of 0.01. This model is split into a test-train set and achieves an accuracy of 94.19%. Using this predictive model the organization can conduct marketing research and study the needs of the particular customer in detail. Using that data they can produce goods according to the customer needs before the customer demands and present it to them. This helps to create brand loyalty which in turn leads to a sustainable network.","PeriodicalId":269682,"journal":{"name":"2018 Second International Conference on Green Computing and Internet of Things (ICGCIoT)","volume":"57 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125182868","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 : 2018-08-01DOI: 10.1109/ICGCIOT.2018.8753026
Shantanu V. Kulkarni, Sangeeta K
Eye tracking is the process of estimating as well as recording gaze positions and eye movements of an individual. Eye tracking technology has many statistical factors which are significant in generating knowledge and values. In most of the approaches an insight is presented with the help of traditional attention maps as well as gaze plots. There is no any single visualization type for all possible requirements. The appropriate choice of a visualization method depends on the format of the data, analysis task specific to the requirements. The objective of this work is to visualize eye tracking data using various visualization especially 3D visuals and animation of eye gazes. These implementations have respective benefits over the other methods of eye tracking visualizations and can be used to generate more knowledge and value extraction from eye tracking metrics.
{"title":"Techniques for Visual Analysis of Eye Tracking Data","authors":"Shantanu V. Kulkarni, Sangeeta K","doi":"10.1109/ICGCIOT.2018.8753026","DOIUrl":"https://doi.org/10.1109/ICGCIOT.2018.8753026","url":null,"abstract":"Eye tracking is the process of estimating as well as recording gaze positions and eye movements of an individual. Eye tracking technology has many statistical factors which are significant in generating knowledge and values. In most of the approaches an insight is presented with the help of traditional attention maps as well as gaze plots. There is no any single visualization type for all possible requirements. The appropriate choice of a visualization method depends on the format of the data, analysis task specific to the requirements. The objective of this work is to visualize eye tracking data using various visualization especially 3D visuals and animation of eye gazes. These implementations have respective benefits over the other methods of eye tracking visualizations and can be used to generate more knowledge and value extraction from eye tracking metrics.","PeriodicalId":269682,"journal":{"name":"2018 Second International Conference on Green Computing and Internet of Things (ICGCIoT)","volume":"56 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116059906","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 : 2018-08-01DOI: 10.1109/ICGCIOT.2018.8753039
Vinod H.C, S. Niranjan
Binarization of document images is a major phase in the handwritten text recognition process. Text recognition process gives best result and easy to archive recognition for printed documents, but more accurate and fast Binarization & segmentation methods are required to achieve high accuracy in handwritten character recognition. In this paper we presenting two modules, they are Document Binarization & Segmentation. In Document Binarization carried out using Haar wavelet decomposition, laplacian mask, maximum gradient difference, median filter and morphological operators. Segmentation is done by the projection profile method and paragraph skew correction recursively until height of the segmented line image is less than 7% of the input image, Connected Component Analysis is used to segment words. These segmented words can be feed to OCR for recognition; the proposed experimental results are encouraging.
{"title":"Binarization and Segmentation of Kannada Handwritten Document Images","authors":"Vinod H.C, S. Niranjan","doi":"10.1109/ICGCIOT.2018.8753039","DOIUrl":"https://doi.org/10.1109/ICGCIOT.2018.8753039","url":null,"abstract":"Binarization of document images is a major phase in the handwritten text recognition process. Text recognition process gives best result and easy to archive recognition for printed documents, but more accurate and fast Binarization & segmentation methods are required to achieve high accuracy in handwritten character recognition. In this paper we presenting two modules, they are Document Binarization & Segmentation. In Document Binarization carried out using Haar wavelet decomposition, laplacian mask, maximum gradient difference, median filter and morphological operators. Segmentation is done by the projection profile method and paragraph skew correction recursively until height of the segmented line image is less than 7% of the input image, Connected Component Analysis is used to segment words. These segmented words can be feed to OCR for recognition; the proposed experimental results are encouraging.","PeriodicalId":269682,"journal":{"name":"2018 Second International Conference on Green Computing and Internet of Things (ICGCIoT)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129666706","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 : 2018-08-01DOI: 10.1109/ICGCIOT.2018.8753058
Kiran V Parvatekar, Shebin M Zacharia, Shreya V Sheelvant, Tanya Nanaiah, K. Ambika
Our world has been polluted in many ways over the years and the most dangerous one is air pollution which causes depletion of the ozone layer leading to greenhouse effect and global warming. The developed product is a drone that aims at purifying air by first monitoring the amount of toxins in it and then filtering them out, thereby releasing relatively purer air. A single drone purifies a very small percentage of air. Therefore, to bring about a difference in the air conditions, they need to be used in swarm robotics.
{"title":"EnviDron — A drone that purifies air","authors":"Kiran V Parvatekar, Shebin M Zacharia, Shreya V Sheelvant, Tanya Nanaiah, K. Ambika","doi":"10.1109/ICGCIOT.2018.8753058","DOIUrl":"https://doi.org/10.1109/ICGCIOT.2018.8753058","url":null,"abstract":"Our world has been polluted in many ways over the years and the most dangerous one is air pollution which causes depletion of the ozone layer leading to greenhouse effect and global warming. The developed product is a drone that aims at purifying air by first monitoring the amount of toxins in it and then filtering them out, thereby releasing relatively purer air. A single drone purifies a very small percentage of air. Therefore, to bring about a difference in the air conditions, they need to be used in swarm robotics.","PeriodicalId":269682,"journal":{"name":"2018 Second International Conference on Green Computing and Internet of Things (ICGCIoT)","volume":"150 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123226037","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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