Pub Date : 2022-01-01DOI: 10.4236/jdaip.2022.102007
Abdelkhalek I. Alastal, Ashraf Hassan Shaqfa
Artificial intelligence has significantly altered many job workflows, hence ex-panding earlier notions of limitations, outcomes, size, and prices. GeoAI is a multidisciplinary field that encompasses computer science, engineering, sta-tistics, and spatial science. Because this subject focuses on real-world issues, it has a significant impact on society and the economy. A broad context incor-porating fundamental questions of theory, epistemology, and the scientific method is used to bring artificial intelligence (Al) and geography together. This connection has the potential to have far-reaching implications for the geographic study. GeoAI, or the combination of geography with artificial intelligence, offers unique solutions to a variety of smart city issues. This paper provides an overview of GeoAI technology, including the definition of GeoAI and the differences between GeoAI and traditional AI. Key steps to successful geographic data analysis include integrating AI with GIS and using GeoAI tools and technologies. Also shown are key areas of applications and models in GeoAI, likewise challenges to adopt GeoAI methods and technology as well as benefits. This article also included a case study on the use of GeoAI in Kuwait, as well as a number of recommendations.
{"title":"GeoAI Technologies and Their Application Areas in Urban Planning and Development: Concepts, Opportunities and Challenges in Smart City (Kuwait, Study Case)","authors":"Abdelkhalek I. Alastal, Ashraf Hassan Shaqfa","doi":"10.4236/jdaip.2022.102007","DOIUrl":"https://doi.org/10.4236/jdaip.2022.102007","url":null,"abstract":"Artificial intelligence has significantly altered many job workflows, hence ex-panding earlier notions of limitations, outcomes, size, and prices. GeoAI is a multidisciplinary field that encompasses computer science, engineering, sta-tistics, and spatial science. Because this subject focuses on real-world issues, it has a significant impact on society and the economy. A broad context incor-porating fundamental questions of theory, epistemology, and the scientific method is used to bring artificial intelligence (Al) and geography together. This connection has the potential to have far-reaching implications for the geographic study. GeoAI, or the combination of geography with artificial intelligence, offers unique solutions to a variety of smart city issues. This paper provides an overview of GeoAI technology, including the definition of GeoAI and the differences between GeoAI and traditional AI. Key steps to successful geographic data analysis include integrating AI with GIS and using GeoAI tools and technologies. Also shown are key areas of applications and models in GeoAI, likewise challenges to adopt GeoAI methods and technology as well as benefits. This article also included a case study on the use of GeoAI in Kuwait, as well as a number of recommendations.","PeriodicalId":71434,"journal":{"name":"数据分析和信息处理(英文)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70997839","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 : 2022-01-01DOI: 10.4236/jdaip.2022.104012
W. Lu, Peng Li, Xuhui Zhang
{"title":"Classification of Oil-Gas-Water Three-Phase Flow in a Pipeline Based on BP Neural Network Analysis","authors":"W. Lu, Peng Li, Xuhui Zhang","doi":"10.4236/jdaip.2022.104012","DOIUrl":"https://doi.org/10.4236/jdaip.2022.104012","url":null,"abstract":"","PeriodicalId":71434,"journal":{"name":"数据分析和信息处理(英文)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70997941","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 : 2022-01-01DOI: 10.4236/jdaip.2022.102006
Iraklis M. Spiliotis, Alexandros S. Peppas, Nikolaos D. Karampasis, Y. Boutalis
{"title":"Fast Object Extraction and Euler Number on Block Represented Images","authors":"Iraklis M. Spiliotis, Alexandros S. Peppas, Nikolaos D. Karampasis, Y. Boutalis","doi":"10.4236/jdaip.2022.102006","DOIUrl":"https://doi.org/10.4236/jdaip.2022.102006","url":null,"abstract":"","PeriodicalId":71434,"journal":{"name":"数据分析和信息处理(英文)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70997905","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 : 2022-01-01DOI: 10.4236/jdaip.2022.101001
Beilei He, Weiyi Han, Suet Ying Isabelle Hon
Predicting stock price movement direction is a challenging problem influenced by different factors and capricious events. The conventional stock price prediction machine learning models heavily rely on the internal financial features, especially the stock price history. However, there are many outside-of-com-pany features that deeply interact with the companies’ stock price performance, especially during the COVID period. In this study, we selected 9 COVID vaccine companies and collected their relevant features over the past 20 months. We add-ed handcrafted external information, including COVID-related statistics and company-specific vaccine progress information. We implemented, evaluated, and compared several machine learning models, including Multilayer Perceptron Neural Networks with logistic regression and decision trees with boosting and bagging algorithms. The results suggest that the application of feature engineering and data mining techniques can effectively enhance the performance of models predicting stock price movement during the COVID period. The results show that COVID-related handcrafted features help to increase the model prediction accuracy by 7.3% and AUROC by 6.5% on average. Further exploration showed that with data selection the decision tree model with gradient, boosting algorithm achieved 70% in AUROC and 66% in the accuracy.
{"title":"A Machine Learning Approach: Enhancing the Predictive Performance of Pharmaceutical Stock Price Movement during COVID","authors":"Beilei He, Weiyi Han, Suet Ying Isabelle Hon","doi":"10.4236/jdaip.2022.101001","DOIUrl":"https://doi.org/10.4236/jdaip.2022.101001","url":null,"abstract":"Predicting stock price movement direction is a challenging problem influenced by different factors and capricious events. The conventional stock price prediction machine learning models heavily rely on the internal financial features, especially the stock price history. However, there are many outside-of-com-pany features that deeply interact with the companies’ stock price performance, especially during the COVID period. In this study, we selected 9 COVID vaccine companies and collected their relevant features over the past 20 months. We add-ed handcrafted external information, including COVID-related statistics and company-specific vaccine progress information. We implemented, evaluated, and compared several machine learning models, including Multilayer Perceptron Neural Networks with logistic regression and decision trees with boosting and bagging algorithms. The results suggest that the application of feature engineering and data mining techniques can effectively enhance the performance of models predicting stock price movement during the COVID period. The results show that COVID-related handcrafted features help to increase the model prediction accuracy by 7.3% and AUROC by 6.5% on average. Further exploration showed that with data selection the decision tree model with gradient, boosting algorithm achieved 70% in AUROC and 66% in the accuracy.","PeriodicalId":71434,"journal":{"name":"数据分析和信息处理(英文)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70997867","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}
Online learning is a very important means of study, and has been adopted in many countries worldwide. However, only recently are researchers able to collect and analyze massive online learning datasets due to the COVID-19 epidemic. In this article, we analyze the difference between online learner groups by using an unsupervised machine learning technique, i.e., k-prototypes clustering. Specifically, we use a questionnaire designed by domain experts to collect various online learning data, and investigate students’ online learning behavior and learning outcomes through analyzing the collected questionnaire data. Our analysis results suggest that students with better learning media generally have better online learning behavior and learning results than those with poor online learning media. In addition, both in economically developed or undeveloped regions, the number of students with better learning media is less than the number of students with poor learning media. Finally, the results presented here show that whether in an economically developed or an economically undeveloped region, the number of students who are enriched with learning media available is an important factor that affects online learning behavior and learning outcomes.
{"title":"Analyzing Differences between Online Learner Groups during the COVID-19 Pandemic through K-Prototype Clustering","authors":"Guanggong Ge, Quanlong Guan, Lusheng Wu, Weiqi Luo, Xingyu Zhu","doi":"10.4236/jdaip.2022.101002","DOIUrl":"https://doi.org/10.4236/jdaip.2022.101002","url":null,"abstract":"Online learning is a very important means of study, and has been adopted in many countries worldwide. However, only recently are researchers able to collect and analyze massive online learning datasets due to the COVID-19 epidemic. In this article, we analyze the difference between online learner groups by using an unsupervised machine learning technique, i.e., k-prototypes clustering. Specifically, we use a questionnaire designed by domain experts to collect various online learning data, and investigate students’ online learning behavior and learning outcomes through analyzing the collected questionnaire data. Our analysis results suggest that students with better learning media generally have better online learning behavior and learning results than those with poor online learning media. In addition, both in economically developed or undeveloped regions, the number of students with better learning media is less than the number of students with poor learning media. Finally, the results presented here show that whether in an economically developed or an economically undeveloped region, the number of students who are enriched with learning media available is an important factor that affects online learning behavior and learning outcomes.","PeriodicalId":71434,"journal":{"name":"数据分析和信息处理(英文)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70997814","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 : 2022-01-01DOI: 10.4236/jdaip.2022.103010
Tariq Saeed Mian, F. Ghabban
{"title":"Competitive Advantage: A Study of Saudi SMEs to Adopt Data Mining for Effective Decision Making","authors":"Tariq Saeed Mian, F. Ghabban","doi":"10.4236/jdaip.2022.103010","DOIUrl":"https://doi.org/10.4236/jdaip.2022.103010","url":null,"abstract":"","PeriodicalId":71434,"journal":{"name":"数据分析和信息处理(英文)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70998150","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 : 2022-01-01DOI: 10.4236/jdaip.2022.102008
Ihar Yeuseyenka, Ihar Melnikau, I. Yemelyanov
The purpose of the article is to develop a methodology for automating the detection and selection of moving objects. The detection and separation of moving objects based on impulse and recurrence neural networks simulation. The result of the work is a developed motion detector based on impulse and recurrence neural networks and an automated system developed on the basis of this detector for detecting and separating moving objects and is ready for practical application. The feasibility of integrating the developed motion detector with Emgu CV (OpenCV) image processing package, multimedia framework functions, and DirectShow application programming interface were investigated. The proposed approach and software for the detection and separating of moving objects in video images using neural networks can be integrated into more sophisticated specialized computer-aided video surveillance systems, IoT (Internet of Things), IoV (Internet of Vehicles), etc.
{"title":"Detection and Selection of Moving Objects in Video Images Based on Impulse and Recurrent Neural Networks","authors":"Ihar Yeuseyenka, Ihar Melnikau, I. Yemelyanov","doi":"10.4236/jdaip.2022.102008","DOIUrl":"https://doi.org/10.4236/jdaip.2022.102008","url":null,"abstract":"The purpose of the article is to develop a methodology for automating the detection and selection of moving objects. The detection and separation of moving objects based on impulse and recurrence neural networks simulation. The result of the work is a developed motion detector based on impulse and recurrence neural networks and an automated system developed on the basis of this detector for detecting and separating moving objects and is ready for practical application. The feasibility of integrating the developed motion detector with Emgu CV (OpenCV) image processing package, multimedia framework functions, and DirectShow application programming interface were investigated. The proposed approach and software for the detection and separating of moving objects in video images using neural networks can be integrated into more sophisticated specialized computer-aided video surveillance systems, IoT (Internet of Things), IoV (Internet of Vehicles), etc.","PeriodicalId":71434,"journal":{"name":"数据分析和信息处理(英文)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70997509","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 : 2021-07-08DOI: 10.4236/jdaip.2021.93011
Ibrahim Ba’abbad, Thamer Althubiti, Abdulmohsen Alharbi, Khalid Alfarsi, S. Rasheed
Many business applications rely on their historical data to predict their business future. The marketing products process is one of the core processes for the business. Customer needs give a useful piece of information that helps to market the appropriate products at the appropriate time. Moreover, services are considered recently as products. The development of education and health services is depending on historical data. For the more, reducing online social media networks problems and crimes need a significant source of information. Data analysts need to use an efficient classification algorithm to predict the future of such businesses. However, dealing with a huge quantity of data requires great time to process. Data mining involves many useful techniques that are used to predict statistical data in a variety of business applications. The classification technique is one of the most widely used with a variety of algorithms. In this paper, various classification algorithms are revised in terms of accuracy in different areas of data mining applications. A comprehensive analysis is made after delegated reading of 20 papers in the literature. This paper aims to help data analysts to choose the most suitable classification algorithm for different business applications including business in general, online social media networks, agriculture, health, and education. Results show FFBPN is the most accurate algorithm in the business domain. The Random Forest algorithm is the most accurate in classifying online social networks (OSN) activities. Naïve Bayes algorithm is the most accurate to classify agriculture datasets. OneR is the most accurate algorithm to classify instances within the health domain. The C4.5 Decision Tree algorithm is the most accurate to classify students’ records to predict degree completion time.
{"title":"A Short Review of Classification Algorithms Accuracy for Data Prediction in Data Mining Applications","authors":"Ibrahim Ba’abbad, Thamer Althubiti, Abdulmohsen Alharbi, Khalid Alfarsi, S. Rasheed","doi":"10.4236/jdaip.2021.93011","DOIUrl":"https://doi.org/10.4236/jdaip.2021.93011","url":null,"abstract":"Many business applications rely on their historical data to predict their business future. The marketing products process is one of the core processes for the business. Customer needs give a useful piece of information that helps to market the appropriate products at the appropriate time. Moreover, services are considered recently as products. The development of education and health services is depending on historical data. For the more, reducing online social media networks problems and crimes need a significant source of information. Data analysts need to use an efficient classification algorithm to predict the future of such businesses. However, dealing with a huge quantity of data requires great time to process. Data mining involves many useful techniques that are used to predict statistical data in a variety of business applications. The classification technique is one of the most widely used with a variety of algorithms. In this paper, various classification algorithms are revised in terms of accuracy in different areas of data mining applications. A comprehensive analysis is made after delegated reading of 20 papers in the literature. This paper aims to help data analysts to choose the most suitable classification algorithm for different business applications including business in general, online social media networks, agriculture, health, and education. Results show FFBPN is the most accurate algorithm in the business domain. The Random Forest algorithm is the most accurate in classifying online social networks (OSN) activities. Naïve Bayes algorithm is the most accurate to classify agriculture datasets. OneR is the most accurate algorithm to classify instances within the health domain. The C4.5 Decision Tree algorithm is the most accurate to classify students’ records to predict degree completion time.","PeriodicalId":71434,"journal":{"name":"数据分析和信息处理(英文)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46038670","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 : 2021-07-08DOI: 10.4236/jdaip.2021.93012
O. T. Tasinda, Tian Ze, S. A. Imanche
The main purpose of this research was to analyze the impact the Chinese foreign direct investment (FDI), remittances, and foreign aid have had to human capital growth (HCG) and brain drain. The study data were collected from five African countries (Nigeria, Kenya, Ghana, South Africa, and Morocco) from 2009 to 2018. Secondary sources were used in data collection, then autoregressive distributed lag (ARDL) modeling was used in the analysis. Before modelling was done, co-integration tests and panel unit were applied. The results revealed that Chinese FDI, remittances, and foreign aid had a significant and positive impact on HCG in the long but not the short-run. Besides, remittances, Chinese FDI, and foreign aid demonstrated significant negative impacts on brain drain in the long term, not in the short term. This study makes important practical and theoretical contributions about the roles of Chinese FDI, remittances, and foreign aid in the reduction of brain drain and the growth of human capital.
{"title":"A Panel Data Analysis of the Impact of Chinese Foreign Direct Investment (FDI), Remittances and Foreign Aid on Human Capital Growth and Brain Drain in Africa","authors":"O. T. Tasinda, Tian Ze, S. A. Imanche","doi":"10.4236/jdaip.2021.93012","DOIUrl":"https://doi.org/10.4236/jdaip.2021.93012","url":null,"abstract":"The main purpose of this research was to analyze the impact the Chinese foreign direct \u0000investment (FDI), remittances, and foreign aid have had to human capital growth \u0000(HCG) and brain drain. The study data were collected from five African countries (Nigeria, Kenya, Ghana, South \u0000Africa, and Morocco) from 2009 to 2018. Secondary sources were used in data \u0000collection, then autoregressive distributed lag (ARDL) modeling was used in the \u0000analysis. Before modelling was done, co-integration tests and panel unit were \u0000applied. The results revealed that Chinese FDI, remittances, and foreign aid \u0000had a significant and positive impact on HCG in the long but not the short-run. \u0000Besides, remittances, Chinese FDI, and foreign aid demonstrated significant \u0000negative impacts on brain drain in the long term, not in the short term. This \u0000study makes important practical and theoretical contributions about the roles \u0000of Chinese FDI, remittances, and foreign aid in the reduction of brain drain \u0000and the growth of human capital.","PeriodicalId":71434,"journal":{"name":"数据分析和信息处理(英文)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41368597","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In this paper, we explore the multi-classification problem of acupuncture acupoints based on Bert model, i.e., we try to recommend the best main acupuncture point for treating the disease by classifying and predicting the main acupuncture point for the disease, and further explore its acupuncture point grouping to provide the medical practitioner with the optimal solution for treating the disease and improving the clinical decision-making ability. The Bert-Chinese-Acupoint model was constructed by retraining on the basis of the Bert model, and the semantic features in terms of acupuncture points were added to the acupuncture point corpus in the fine-tuning process to increase the semantic features in terms of acupuncture points, and compared with the machine learning method. The results show that the Bert-Chinese Acupoint model proposed in this paper has a 3% improvement in accuracy compared to the best performing model in the machine learning approach.
本文探讨了基于Bert模型的针灸穴位多分类问题，即通过对疾病的主要穴位进行分类和预测，尝试推荐治疗疾病的最佳主穴位，并进一步探索其穴位分组，为医生提供治疗疾病的最优方案，提高临床决策能力。在Bert模型的基础上通过再训练构建Bert- chinese -腧穴模型，并在微调过程中将穴位方面的语义特征添加到穴位语料库中，增加穴位方面的语义特征，并与机器学习方法进行比较。结果表明，与机器学习方法中表现最好的模型相比，本文提出的Bert-Chinese穴位模型的准确率提高了3%。
{"title":"Classification of Acupuncture Points Based on the Bert Model*","authors":"Xiong Zhong, Yangli Jia, Dekui Li, Xiangliang Zhang","doi":"10.4236/jdaip.2021.93008","DOIUrl":"https://doi.org/10.4236/jdaip.2021.93008","url":null,"abstract":"In this \u0000paper, we explore the multi-classification problem of acupuncture acupoints based on Bert model, i.e., we try to recommend the \u0000best main acupuncture point for treating the disease by classifying and \u0000predicting the main acupuncture point for the disease, and further explore its \u0000acupuncture point grouping to provide the medical practitioner with the optimal \u0000solution for treating the disease and improving the \u0000clinical decision-making ability. The Bert-Chinese-Acupoint model was \u0000constructed by retraining on the basis of the Bert model, and the semantic features in terms of acupuncture points were \u0000added to the acupuncture point corpus in the fine-tuning process to \u0000increase the semantic features in terms of acupuncture points, and compared with the machine learning method. The results \u0000show that the Bert-Chinese Acupoint model proposed in this paper has a 3% \u0000improvement in accuracy compared to the best \u0000performing model in the machine learning approach.","PeriodicalId":71434,"journal":{"name":"数据分析和信息处理(英文)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44481577","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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