Pub Date : 2020-06-01DOI: 10.1109/INCET49848.2020.9154143
C. Kariya, P. Khodke
In this era of growing social media users, Twitter has significantly large number of daily users who post their opinions in the form of tweets. This paper presents an idea of extracting sentiments out of the tweet and an approach towards classifying a tweet into positive, negative or neutral. This approach can be in many ways useful to any organization, who gets mentioned or tagged in a tweet. Generally the tweets being unstructured in format, first of all the tweet needs to be converted into the structured format. In this paper, tweets are resolved using pre-processing phase and access of tweets has been accomplished via libraries using Twitter API. The datasets need to be trained using algorithms in a way, such that, it becomes capable of testing the tweets and it releases the required sentiments out of the feeded tweets.
{"title":"Twitter Sentiment Analysis","authors":"C. Kariya, P. Khodke","doi":"10.1109/INCET49848.2020.9154143","DOIUrl":"https://doi.org/10.1109/INCET49848.2020.9154143","url":null,"abstract":"In this era of growing social media users, Twitter has significantly large number of daily users who post their opinions in the form of tweets. This paper presents an idea of extracting sentiments out of the tweet and an approach towards classifying a tweet into positive, negative or neutral. This approach can be in many ways useful to any organization, who gets mentioned or tagged in a tweet. Generally the tweets being unstructured in format, first of all the tweet needs to be converted into the structured format. In this paper, tweets are resolved using pre-processing phase and access of tweets has been accomplished via libraries using Twitter API. The datasets need to be trained using algorithms in a way, such that, it becomes capable of testing the tweets and it releases the required sentiments out of the feeded tweets.","PeriodicalId":174411,"journal":{"name":"2020 International Conference for Emerging Technology (INCET)","volume":"56 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128623305","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 : 2020-06-01DOI: 10.1109/incet49848.2020.9154135
K. Sarath, J. Jacob
In this paper, hand position consensus control algorithm for wheeled mobile robots with disturbance observer is proposed. Hand position kinematics is described by single integrators using invertible transformation. Centroid consensus control strategy is proposed with disturbance observer, which ensures the asymptotic convergence of robotic hand positions. Simulation results demonstrates the robustness properties of the proposed controller in the presence of disturbance.
{"title":"Hand position consensus in wheeled mobile robots with disturbance observer","authors":"K. Sarath, J. Jacob","doi":"10.1109/incet49848.2020.9154135","DOIUrl":"https://doi.org/10.1109/incet49848.2020.9154135","url":null,"abstract":"In this paper, hand position consensus control algorithm for wheeled mobile robots with disturbance observer is proposed. Hand position kinematics is described by single integrators using invertible transformation. Centroid consensus control strategy is proposed with disturbance observer, which ensures the asymptotic convergence of robotic hand positions. Simulation results demonstrates the robustness properties of the proposed controller in the presence of disturbance.","PeriodicalId":174411,"journal":{"name":"2020 International Conference for Emerging Technology (INCET)","volume":"545 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123916423","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 : 2020-06-01DOI: 10.1109/incet49848.2020.9154165
Akash Mali, Rushikesh Sonawale, S. Gharat, Neha Ingle, R. D. Kulkarni, Sangita Nandurkar
Current sensor plays an important role in power industries, the information obtained from the current sensor is used for controlling, motoring and protection. The paper gives comprehensive review regarding various current measurement schemes used in industries and utilities for DC current measurement. Especially, the technical issues including difficulties in hardware implementation among all current sensing schemes like Hall Effect based current transducer, typical shunt resistor method, saturable core reactor technique and fiber optic current sensor have been extensively discussed. The comparative analysis of these methodologies have been presented.
{"title":"Design Methodologies for Measurement of KA DC Current: A Review","authors":"Akash Mali, Rushikesh Sonawale, S. Gharat, Neha Ingle, R. D. Kulkarni, Sangita Nandurkar","doi":"10.1109/incet49848.2020.9154165","DOIUrl":"https://doi.org/10.1109/incet49848.2020.9154165","url":null,"abstract":"Current sensor plays an important role in power industries, the information obtained from the current sensor is used for controlling, motoring and protection. The paper gives comprehensive review regarding various current measurement schemes used in industries and utilities for DC current measurement. Especially, the technical issues including difficulties in hardware implementation among all current sensing schemes like Hall Effect based current transducer, typical shunt resistor method, saturable core reactor technique and fiber optic current sensor have been extensively discussed. The comparative analysis of these methodologies have been presented.","PeriodicalId":174411,"journal":{"name":"2020 International Conference for Emerging Technology (INCET)","volume":"153 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116221753","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 : 2020-06-01DOI: 10.1109/incet49848.2020.9153981
G. Mutreja, Abhishek Aggarwal, Rohit Thakur, Shyam Sunder Tiwari, S. Deshpande
Object detection in satellite imagery is very important for a wide array of applications in surveillance system, monitoring tasks etc. The satellite images have lower resolution as compared to aerial images and hence detecting smaller objects such as vehicles, aircrafts in a remotely sensed image is a challenging task. In this paper, we focus on the comparative study of three different models namely YoloV3, SSD and RCNN. We have tested all the three models to find out which model performed best for the task of airplane detection when trained on aerial images and tested for small object detection (airplanes in our case) on satellite images. Finally, we illustrated the comparison of the three models on the basis of accuracy, losses etc.
{"title":"Comparative Assessment of Different Deep Learning Models for Aircraft Detection","authors":"G. Mutreja, Abhishek Aggarwal, Rohit Thakur, Shyam Sunder Tiwari, S. Deshpande","doi":"10.1109/incet49848.2020.9153981","DOIUrl":"https://doi.org/10.1109/incet49848.2020.9153981","url":null,"abstract":"Object detection in satellite imagery is very important for a wide array of applications in surveillance system, monitoring tasks etc. The satellite images have lower resolution as compared to aerial images and hence detecting smaller objects such as vehicles, aircrafts in a remotely sensed image is a challenging task. In this paper, we focus on the comparative study of three different models namely YoloV3, SSD and RCNN. We have tested all the three models to find out which model performed best for the task of airplane detection when trained on aerial images and tested for small object detection (airplanes in our case) on satellite images. Finally, we illustrated the comparison of the three models on the basis of accuracy, losses etc.","PeriodicalId":174411,"journal":{"name":"2020 International Conference for Emerging Technology (INCET)","volume":"335 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122267382","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 : 2020-06-01DOI: 10.1109/incet49848.2020.9154019
Pankaj Choudhary, Shriram R
A recent research study on the online discussion forum shows significant improvement in the learning growth of students. The discussion forum is a very useful pedagogical tool and plays a very important role in providing interaction among the participant in online courses. The study shows the use of discussion forums in the online course helps the participant in the better learning experience and improved critical thinking through collaborative learning, the course performance, and the cognitive presence is also increased. The researcher suggests that the discussion forum should be the part assessment tool. This paper investigates the use of a question answer type of discussion forum for the assessment of student posts based on the content analysis and the relevance of content to the discussion topic. A similarity measure is calculated for assigning the grade to the student answer and the result is compared with the teacher grade shows the significant results.
{"title":"Short Answer Type Discussion Forum Analysis and Assessment","authors":"Pankaj Choudhary, Shriram R","doi":"10.1109/incet49848.2020.9154019","DOIUrl":"https://doi.org/10.1109/incet49848.2020.9154019","url":null,"abstract":"A recent research study on the online discussion forum shows significant improvement in the learning growth of students. The discussion forum is a very useful pedagogical tool and plays a very important role in providing interaction among the participant in online courses. The study shows the use of discussion forums in the online course helps the participant in the better learning experience and improved critical thinking through collaborative learning, the course performance, and the cognitive presence is also increased. The researcher suggests that the discussion forum should be the part assessment tool. This paper investigates the use of a question answer type of discussion forum for the assessment of student posts based on the content analysis and the relevance of content to the discussion topic. A similarity measure is calculated for assigning the grade to the student answer and the result is compared with the teacher grade shows the significant results.","PeriodicalId":174411,"journal":{"name":"2020 International Conference for Emerging Technology (INCET)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132057697","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 : 2020-06-01DOI: 10.1109/incet49848.2020.9154060
Arit Kumar Bishwas, Ashish Mani, V. Palade
Supervised machine learning deals with developing complex non-linear models, which can be used later to predict the output for a known input. Clustering is usually treated as an unsupervised machine learning task, but we can formulate a solution to a clustering problem by using a supervised classification algorithm [1]. However, these classification algorithms are highly computationally intensive in nature, so the overall complexity in designing a clustering solution is often very costly from an implementation point of view. The more data we use, the more computational power is required too. Recent advancements in quantum computing show promising advantages in dealing with this kind of computational issues we face while training a complex machine-learning algorithm. In this paper, we do a theoretical investigation on the runtime complexity of algorithms, from classical to randomized, and then to quantum frameworks, when designing a clustering algorithm. The analysis shows significant computational advantages with a quantum framework as compared to the classical and randomized versions of the implementation.
{"title":"A Leap from Randomized to Quantum Clustering with Support Vector Machine - A Computation Complexity Analysis","authors":"Arit Kumar Bishwas, Ashish Mani, V. Palade","doi":"10.1109/incet49848.2020.9154060","DOIUrl":"https://doi.org/10.1109/incet49848.2020.9154060","url":null,"abstract":"Supervised machine learning deals with developing complex non-linear models, which can be used later to predict the output for a known input. Clustering is usually treated as an unsupervised machine learning task, but we can formulate a solution to a clustering problem by using a supervised classification algorithm [1]. However, these classification algorithms are highly computationally intensive in nature, so the overall complexity in designing a clustering solution is often very costly from an implementation point of view. The more data we use, the more computational power is required too. Recent advancements in quantum computing show promising advantages in dealing with this kind of computational issues we face while training a complex machine-learning algorithm. In this paper, we do a theoretical investigation on the runtime complexity of algorithms, from classical to randomized, and then to quantum frameworks, when designing a clustering algorithm. The analysis shows significant computational advantages with a quantum framework as compared to the classical and randomized versions of the implementation.","PeriodicalId":174411,"journal":{"name":"2020 International Conference for Emerging Technology (INCET)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129967777","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 : 2020-06-01DOI: 10.1109/incet49848.2020.9154150
Shilpa Lande, Punam Chabukswar, V. Bhope
The structuring of the underground coal mine monitoring system based on the ZigBee wireless sensor network evacuates the traditional underground coal mine monitoring system. In this monitoring system for wireless communication, ZigBee is utilized. So, there is a significant advancement in coal mine wellbeing production which is sheltered. Aside from this, it is inadmissible to lay the links which are exorbitant and consumes additional time. To take care of this issue there is have to plan and build up an underground coal mine monitoring system using WSN. The venture is isolated into two sections. The initial segment is the underground section which is inside the coal mines and the second is the ground section which is outside of coal mine. The sensor is set inside the underground section. This sensor detects all the physical parameters, for example, ascend in temperature, unsafe gases, vibration, and increment or fall in dampness. The controller converts this information into the computerized signal. The converted information is sent towards the ground section which is outside of coal mines. For the communication between the underground section and ground section, we utilized WSN which is Zigbee. The ground section consists of a server consisting of graphical UI (GUI) which is made by NeatBeans stage using Java programming. The camera likewise joined the server which checks the encompassing.
{"title":"An Efficient Implementation of Wireless Sensor Network for Performing Rescue & Safety Operation in Underground Coal Mines","authors":"Shilpa Lande, Punam Chabukswar, V. Bhope","doi":"10.1109/incet49848.2020.9154150","DOIUrl":"https://doi.org/10.1109/incet49848.2020.9154150","url":null,"abstract":"The structuring of the underground coal mine monitoring system based on the ZigBee wireless sensor network evacuates the traditional underground coal mine monitoring system. In this monitoring system for wireless communication, ZigBee is utilized. So, there is a significant advancement in coal mine wellbeing production which is sheltered. Aside from this, it is inadmissible to lay the links which are exorbitant and consumes additional time. To take care of this issue there is have to plan and build up an underground coal mine monitoring system using WSN. The venture is isolated into two sections. The initial segment is the underground section which is inside the coal mines and the second is the ground section which is outside of coal mine. The sensor is set inside the underground section. This sensor detects all the physical parameters, for example, ascend in temperature, unsafe gases, vibration, and increment or fall in dampness. The controller converts this information into the computerized signal. The converted information is sent towards the ground section which is outside of coal mines. For the communication between the underground section and ground section, we utilized WSN which is Zigbee. The ground section consists of a server consisting of graphical UI (GUI) which is made by NeatBeans stage using Java programming. The camera likewise joined the server which checks the encompassing.","PeriodicalId":174411,"journal":{"name":"2020 International Conference for Emerging Technology (INCET)","volume":"110 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134512736","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 : 2020-06-01DOI: 10.1109/incet49848.2020.9154008
S. Mane, R. Sapat, Pragati Kor, Jayesh Shelar, R. D. Kulkarni, J. Mundkar
Power quality is a key factor in all industrial and many more applications. An industry need to maintain certain power quality standard during day-to-day work for variety of applications. Power quality of electricity provided by utilities is also vital aspect. The best power quality helps to increase the overall production and gets rid of any sort of technical problems reducing cost of energy. The mains power factor is one of the important parameter which decides the quality of power. When the need of reactive power becomes more, power factor decreases, reducing the efficiency of power system. Therefore, there is need to add capacitance of required value when power factor goes below the specified value, preferably 0.92. Addition of required capacitors reduces the losses improving power factor. The paper proposes digitally controlled topology for performing Automatic Power Factor Correction to improve power quality. The design and simulation of Automatic Power Factor Correction system using Arduino UNO microcontroller has been presented in the paper. The system power factor has been monitored using power factor transducer followed by Arduino microcontroller which control the switching of capacitor banks in order to compensate reactive power and bring power factor near to unity enhancing power quality. The simulation results are also presented in the paper.
{"title":"Microcontroller based Automatic Power Factor Correction System for Power Quality Improvement","authors":"S. Mane, R. Sapat, Pragati Kor, Jayesh Shelar, R. D. Kulkarni, J. Mundkar","doi":"10.1109/incet49848.2020.9154008","DOIUrl":"https://doi.org/10.1109/incet49848.2020.9154008","url":null,"abstract":"Power quality is a key factor in all industrial and many more applications. An industry need to maintain certain power quality standard during day-to-day work for variety of applications. Power quality of electricity provided by utilities is also vital aspect. The best power quality helps to increase the overall production and gets rid of any sort of technical problems reducing cost of energy. The mains power factor is one of the important parameter which decides the quality of power. When the need of reactive power becomes more, power factor decreases, reducing the efficiency of power system. Therefore, there is need to add capacitance of required value when power factor goes below the specified value, preferably 0.92. Addition of required capacitors reduces the losses improving power factor. The paper proposes digitally controlled topology for performing Automatic Power Factor Correction to improve power quality. The design and simulation of Automatic Power Factor Correction system using Arduino UNO microcontroller has been presented in the paper. The system power factor has been monitored using power factor transducer followed by Arduino microcontroller which control the switching of capacitor banks in order to compensate reactive power and bring power factor near to unity enhancing power quality. The simulation results are also presented in the paper.","PeriodicalId":174411,"journal":{"name":"2020 International Conference for Emerging Technology (INCET)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131542548","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 : 2020-06-01DOI: 10.1109/INCET49848.2020.9154061
S. Ravikumar, Prasad Saraf
The stock market is an interesting industry to study. There are various variations present in it. Many experts have been studying and researching on the various trends that the stock market goes through. One of the major studies has been the attempt to predict the stock prices of various companies based on historical data. Prediction of stock prices will greatly help people to understand where and how to invest so that the risk of losing money is minimized. This application can also be used by companies during their Initial Public Offering (IPO) to know what value to target for and how many shares they should release. So far there have been significant developments in this field. Many researchers are looking at machine learning and deep learning as possible ways to predict stock prices. The proposed system works in two methods – Regression and Classification. In regression, the system predicts the closing price of stock of a company, and in classification, the system predicts whether the closing price of stock will increase or decrease the next day.
{"title":"Prediction of Stock Prices using Machine Learning (Regression, Classification) Algorithms","authors":"S. Ravikumar, Prasad Saraf","doi":"10.1109/INCET49848.2020.9154061","DOIUrl":"https://doi.org/10.1109/INCET49848.2020.9154061","url":null,"abstract":"The stock market is an interesting industry to study. There are various variations present in it. Many experts have been studying and researching on the various trends that the stock market goes through. One of the major studies has been the attempt to predict the stock prices of various companies based on historical data. Prediction of stock prices will greatly help people to understand where and how to invest so that the risk of losing money is minimized. This application can also be used by companies during their Initial Public Offering (IPO) to know what value to target for and how many shares they should release. So far there have been significant developments in this field. Many researchers are looking at machine learning and deep learning as possible ways to predict stock prices. The proposed system works in two methods – Regression and Classification. In regression, the system predicts the closing price of stock of a company, and in classification, the system predicts whether the closing price of stock will increase or decrease the next day.","PeriodicalId":174411,"journal":{"name":"2020 International Conference for Emerging Technology (INCET)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130956459","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 : 2020-06-01DOI: 10.1109/incet49848.2020.9154131
Divyaang Agarwal, Vishruti Gupta, Divyansh Jaiswal, A. K. Mandpura
The contribution of Solar Photovoltaic (PV) power in the energy sector has steadily grown over the past decades. However, the intermittent nature of PV power poses challenges to grid stability. Therefore, it is of significance for electrical utilities to obtain an accurate prediction of PV output power at a small time-scale so as to perform grid scheduling. In this paper, we propose a model based on machine learning to predict hourly-ahead PV output power. The model includes the effect of the sun’s position, the tilt angle of the array, PV array aging effect and the cloud cover. Simulations are performed to ascertain the efficacy of the model and to verify the accuracy of the model. Using the proposed model, nth-hour ahead PV output power forecasting is also performed.
{"title":"A Machine Learning-Based Approach for PV Power Forecasting","authors":"Divyaang Agarwal, Vishruti Gupta, Divyansh Jaiswal, A. K. Mandpura","doi":"10.1109/incet49848.2020.9154131","DOIUrl":"https://doi.org/10.1109/incet49848.2020.9154131","url":null,"abstract":"The contribution of Solar Photovoltaic (PV) power in the energy sector has steadily grown over the past decades. However, the intermittent nature of PV power poses challenges to grid stability. Therefore, it is of significance for electrical utilities to obtain an accurate prediction of PV output power at a small time-scale so as to perform grid scheduling. In this paper, we propose a model based on machine learning to predict hourly-ahead PV output power. The model includes the effect of the sun’s position, the tilt angle of the array, PV array aging effect and the cloud cover. Simulations are performed to ascertain the efficacy of the model and to verify the accuracy of the model. Using the proposed model, nth-hour ahead PV output power forecasting is also performed.","PeriodicalId":174411,"journal":{"name":"2020 International Conference for Emerging Technology (INCET)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133643053","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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