Set intersection algorithms between sorted lists are important in triangles counting, community detection in graph analysis and in search engines where the intersection is computed between queries and inverted indexes. Many researches use GPU techniques for solving this intersection problem. The majority of these techniques focus on improving the level of parallelism by reducing redundant comparisons and distributing the workload among GPU threads. In this paper, we propose the GPU Test with Jumps (GTWJ) algorithm to compute the intersection between sorted lists using a new data structure. The idea of GTWJ is to group the data, of each sorted list, into a set of sequences. A sequence is identified by a key and is handled by a thread. Intersection is computed between sequences with the same key. This key allows skipping data packets in parallel if the keys do not match. A counter is used to avoid useless tests between cells of sequences with different lengths. Experiments on the data used in this filed show that GTWJ is better in terms of execution time and number of tests.
排序列表之间的集合交集算法在三角形计数、图分析中的社区检测以及在查询和倒排索引之间计算交集的搜索引擎中都很重要。许多研究使用GPU技术来解决这个交叉问题。这些技术中的大多数都侧重于通过减少冗余比较和在GPU线程之间分配工作负载来提高并行性水平。在本文中，我们提出了GPU Test with跳转(GTWJ)算法，该算法使用新的数据结构来计算排序列表之间的交集。GTWJ的思想是将每个排序列表的数据分组到一组序列中。序列由键标识，并由线程处理。在具有相同键的序列之间计算交集。如果密钥不匹配，此密钥允许并行跳过数据包。计数器用于避免在不同长度序列的细胞之间进行无用的测试。对该领域使用的数据进行的实验表明，GTWJ在执行时间和测试次数方面都优于GTWJ。
{"title":"A New Fast Intersection Algorithm for Sorted Lists on GPU","authors":"Faïza Manseur, Lougmiri Zekri, M. Senouci","doi":"10.4018/jitr.298325","DOIUrl":"https://doi.org/10.4018/jitr.298325","url":null,"abstract":"Set intersection algorithms between sorted lists are important in triangles counting, community detection in graph analysis and in search engines where the intersection is computed between queries and inverted indexes. Many researches use GPU techniques for solving this intersection problem. The majority of these techniques focus on improving the level of parallelism by reducing redundant comparisons and distributing the workload among GPU threads. In this paper, we propose the GPU Test with Jumps (GTWJ) algorithm to compute the intersection between sorted lists using a new data structure. The idea of GTWJ is to group the data, of each sorted list, into a set of sequences. A sequence is identified by a key and is handled by a thread. Intersection is computed between sequences with the same key. This key allows skipping data packets in parallel if the keys do not match. A counter is used to avoid useless tests between cells of sequences with different lengths. Experiments on the data used in this filed show that GTWJ is better in terms of execution time and number of tests.","PeriodicalId":296080,"journal":{"name":"J. Inf. Technol. Res.","volume":"57 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124848124","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}
S. Sastre-Merino, J. Martín-Núñez, A. Verdú-Vázquez
Training future programming teachers requires an innovative approach. Not only students need to handle the most current trends in technologies and teaching-learning methodologies, but also they must develop the capacity and criteria to search and select the most adequate to their context. This work analyzes the application of a collaborative Research-Based Learning methodology in the Programming subject of a master's degree in teacher training. The objective was to create a digital learning ecosystem and analyze the impact on the development of programming teaching skills. The results show that students perceive positive effects on the development of teaching skills, generating useful resources. However, teamwork has conditioned the quality of such resources. The digital ecosystem has allowed students to share knowledge with their peers and forthcoming students. Students who already had the generated ecosystem available valued it very positively. Future programming teachers require lifelong learning which can be supported by this living ecosystem.
{"title":"Creation of a Digital Learning Ecosystem Using Research-Based Learning for Future Programming Teachers","authors":"S. Sastre-Merino, J. Martín-Núñez, A. Verdú-Vázquez","doi":"10.4018/jitr.298324","DOIUrl":"https://doi.org/10.4018/jitr.298324","url":null,"abstract":"Training future programming teachers requires an innovative approach. Not only students need to handle the most current trends in technologies and teaching-learning methodologies, but also they must develop the capacity and criteria to search and select the most adequate to their context. This work analyzes the application of a collaborative Research-Based Learning methodology in the Programming subject of a master's degree in teacher training. The objective was to create a digital learning ecosystem and analyze the impact on the development of programming teaching skills. The results show that students perceive positive effects on the development of teaching skills, generating useful resources. However, teamwork has conditioned the quality of such resources. The digital ecosystem has allowed students to share knowledge with their peers and forthcoming students. Students who already had the generated ecosystem available valued it very positively. Future programming teachers require lifelong learning which can be supported by this living ecosystem.","PeriodicalId":296080,"journal":{"name":"J. Inf. Technol. Res.","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116144830","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}
Allotment of nurses to patients is a critical task in terms of better treatment. Nurses should be appointed according to a patient’s health condition, type of disease & financial condition. Again understaffing of nurses may hamper patient health & condition. Similarly, overstaffing of nurses is a waste of man powers. Adequate staffing of nurses is crucial. We propose a technique using game theory to meet over staffing and under staffing of nurses. Game theory plays a vital role to meet the exact requirement. Nash equilibrium can be used for taking all possible decisions, like appointment of nurses in different categories for smooth treatment of patients. However, final & most suitable decision can be taken using perfect Nash equilibrium. This technique is a perfect technique to implement in case of vital & critical decision points.
{"title":"Prediction of Nurses Allotment to Patient in Hospital through Game Theory","authors":"S. Dash, R. Sahu","doi":"10.4018/jitr.299916","DOIUrl":"https://doi.org/10.4018/jitr.299916","url":null,"abstract":"Allotment of nurses to patients is a critical task in terms of better treatment. Nurses should be appointed according to a patient’s health condition, type of disease & financial condition. Again understaffing of nurses may hamper patient health & condition. Similarly, overstaffing of nurses is a waste of man powers. Adequate staffing of nurses is crucial. We propose a technique using game theory to meet over staffing and under staffing of nurses. Game theory plays a vital role to meet the exact requirement. Nash equilibrium can be used for taking all possible decisions, like appointment of nurses in different categories for smooth treatment of patients. However, final & most suitable decision can be taken using perfect Nash equilibrium. This technique is a perfect technique to implement in case of vital & critical decision points.","PeriodicalId":296080,"journal":{"name":"J. Inf. Technol. Res.","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123011074","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}
Designing a system for analytics of high-frequency data (Big data) is a very challenging and crucial task in data science. Big data analytics involves the development of an efficient machine learning algorithm and big data processing techniques or frameworks. Today, the development of the data processing system is in high demand for processing high-frequency data in a very efficient manner. This paper proposes the processing and analytics of stochastic high-frequency stock market data using a modified version of suitable Gradient Boosting Machine (GBM). The experimental results obtained are compared with deep learning and Auto-Regressive Integrated Moving Average (ARIMA) methods. The results obtained using modified GBM achieves the highest accuracy (R2 = 0.98) and minimum error (RMSE = 0.85) as compared to the other two approaches.
{"title":"Gradient Boosting Machine and Deep Learning Approach in Big Data Analysis: A Case Study of the Stock Market","authors":"Lokesh Kumar Shrivastav, Ravinder Kumar","doi":"10.4018/jitr.2022010101","DOIUrl":"https://doi.org/10.4018/jitr.2022010101","url":null,"abstract":"Designing a system for analytics of high-frequency data (Big data) is a very challenging and crucial task in data science. Big data analytics involves the development of an efficient machine learning algorithm and big data processing techniques or frameworks. Today, the development of the data processing system is in high demand for processing high-frequency data in a very efficient manner. This paper proposes the processing and analytics of stochastic high-frequency stock market data using a modified version of suitable Gradient Boosting Machine (GBM). The experimental results obtained are compared with deep learning and Auto-Regressive Integrated Moving Average (ARIMA) methods. The results obtained using modified GBM achieves the highest accuracy (R2 = 0.98) and minimum error (RMSE = 0.85) as compared to the other two approaches.","PeriodicalId":296080,"journal":{"name":"J. Inf. Technol. Res.","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129275989","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}
This study explores the factors contributing to online users’ network centrality in a network on Twitter in the context of a social movement about the “clear the shelters” campaign across the United States. We performed a social network analysis on a network including 13,270 Twitter users and 24,354 relationships to reveal users’ betweenness, closeness, eigenvector, in-degree, and out-degree centralities before hypothesis testing. We applied a path analysis including users’ centrality measures and their user-related features. The path analysis discovered that the factors of the number of people a user follows, the number of followers a user has, and the number of years since a user had his account increased a user’s in-degree connections in the network. Together with the user’s out-degree connections along with in-degree links pushed a user to have a strategic place in the network. We also implemented a multi-group analysis to find whether the impact of these factors showed differences specifically in replies to, mentions, and retweets networks.
{"title":"Let's Get United and #ClearTheShelters: The Factors Contributing to Users' Network Centrality in Online Social Networks","authors":"Ezgi Akar","doi":"10.4018/jitr.299943","DOIUrl":"https://doi.org/10.4018/jitr.299943","url":null,"abstract":"This study explores the factors contributing to online users’ network centrality in a network on Twitter in the context of a social movement about the “clear the shelters” campaign across the United States. We performed a social network analysis on a network including 13,270 Twitter users and 24,354 relationships to reveal users’ betweenness, closeness, eigenvector, in-degree, and out-degree centralities before hypothesis testing. We applied a path analysis including users’ centrality measures and their user-related features. The path analysis discovered that the factors of the number of people a user follows, the number of followers a user has, and the number of years since a user had his account increased a user’s in-degree connections in the network. Together with the user’s out-degree connections along with in-degree links pushed a user to have a strategic place in the network. We also implemented a multi-group analysis to find whether the impact of these factors showed differences specifically in replies to, mentions, and retweets networks.","PeriodicalId":296080,"journal":{"name":"J. Inf. Technol. Res.","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130494356","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}
Stochastic time series analysis of high-frequency stock market data is a very challenging task for the analysts due to the lack availability of efficient tool and techniques for big data analytics. This has opened the door of opportunities for the developer and researcher to develop intelligent and machine learning based tools and techniques for data analytics. This paper proposed an ensemble for stock market data prediction using three most prominent machine learning based techniques. The stock market dataset with raw data size of 39364 KB with all attributes and processed data size of 11826 KB having 872435 instances. The proposed work implements an ensemble model comprises of Deep Learning, Gradient Boosting Machine (GBM) and distributed Random Forest techniques of data analytics. The performance results of the ensemble model are compared with each of the individual methods i.e. deep learning, Gradient Boosting Machine (GBM) and Random Forest. The ensemble model performs better and achieves the highest accuracy of 0.99 and lowest error (RMSE) of 0.1.
{"title":"An Ensemble of Random Forest Gradient Boosting Machine and Deep Learning Methods for Stock Price Prediction","authors":"Lokesh Kumar Shrivastav, Ravinder Kumar","doi":"10.4018/jitr.2022010102","DOIUrl":"https://doi.org/10.4018/jitr.2022010102","url":null,"abstract":"Stochastic time series analysis of high-frequency stock market data is a very challenging task for the analysts due to the lack availability of efficient tool and techniques for big data analytics. This has opened the door of opportunities for the developer and researcher to develop intelligent and machine learning based tools and techniques for data analytics. This paper proposed an ensemble for stock market data prediction using three most prominent machine learning based techniques. The stock market dataset with raw data size of 39364 KB with all attributes and processed data size of 11826 KB having 872435 instances. The proposed work implements an ensemble model comprises of Deep Learning, Gradient Boosting Machine (GBM) and distributed Random Forest techniques of data analytics. The performance results of the ensemble model are compared with each of the individual methods i.e. deep learning, Gradient Boosting Machine (GBM) and Random Forest. The ensemble model performs better and achieves the highest accuracy of 0.99 and lowest error (RMSE) of 0.1.","PeriodicalId":296080,"journal":{"name":"J. Inf. Technol. Res.","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121388613","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}
Prediction of the stock price is a crucial task as predicting it may lead to profits. Stock price prediction is a challenge owing to non-stationary and chaotic data. Thus, the projection becomes challenging among the investors and shareholders to invest the money to make profits. This paper is a review of stock price prediction, focusing on metrics, models, and datasets. It presents a detailed review of 30 research papers suggesting the methodologies, such as Support Vector Machine Random Forest, Linear Regression, Recursive Neural Network, and Long Short-Term Movement based on the stock price prediction. Aside from predictions, the limitations, and future works are discussed in the papers reviewed. The commonly used technique for achieving effective stock price prediction is the RF, LSTM, and SVM techniques. Despite the research efforts, the current stock price prediction technique has many limits. From this survey, it is observed that the stock market prediction is a complicated task, and other factors should be considered to accurately and efficiently predict the future.
{"title":"Intelligent Models for Stock Price Prediction: A Comprehensive Review","authors":"K. Ansah, Ismail Wafaa Denwar, J. K. Appati","doi":"10.4018/jitr.298616","DOIUrl":"https://doi.org/10.4018/jitr.298616","url":null,"abstract":"Prediction of the stock price is a crucial task as predicting it may lead to profits. Stock price prediction is a challenge owing to non-stationary and chaotic data. Thus, the projection becomes challenging among the investors and shareholders to invest the money to make profits. This paper is a review of stock price prediction, focusing on metrics, models, and datasets. It presents a detailed review of 30 research papers suggesting the methodologies, such as Support Vector Machine Random Forest, Linear Regression, Recursive Neural Network, and Long Short-Term Movement based on the stock price prediction. Aside from predictions, the limitations, and future works are discussed in the papers reviewed. The commonly used technique for achieving effective stock price prediction is the RF, LSTM, and SVM techniques. Despite the research efforts, the current stock price prediction technique has many limits. From this survey, it is observed that the stock market prediction is a complicated task, and other factors should be considered to accurately and efficiently predict the future.","PeriodicalId":296080,"journal":{"name":"J. Inf. Technol. Res.","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124123258","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}
Although the traditional convolutional neural network is applied to image segmentation successfully, it has some limitations. That's the context information of the long-range on the image is not well captured. With the success of the introduction of self-attentional mechanisms in the field of natural language processing (NLP), people have tried to introduce the attention mechanism in the field of computer vision. It turns out that self-attention can really solve this long-range dependency problem. This paper is a summary on the application of self-attention to image segmentation in the past two years. And think about whether the self-attention module in this field can replace convolution operation in the future.
{"title":"Research of Self-Attention in Image Segmentation","authors":"Fude Cao, Chunguang Zheng, Limin Huang, Aihua Wang, Jiong Zhang, Feng Zhou, Haoxue Ju, Haitao Guo, Yuxia Du","doi":"10.4018/jitr.298619","DOIUrl":"https://doi.org/10.4018/jitr.298619","url":null,"abstract":"Although the traditional convolutional neural network is applied to image segmentation successfully, it has some limitations. That's the context information of the long-range on the image is not well captured. With the success of the introduction of self-attentional mechanisms in the field of natural language processing (NLP), people have tried to introduce the attention mechanism in the field of computer vision. It turns out that self-attention can really solve this long-range dependency problem. This paper is a summary on the application of self-attention to image segmentation in the past two years. And think about whether the self-attention module in this field can replace convolution operation in the future.","PeriodicalId":296080,"journal":{"name":"J. Inf. Technol. Res.","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127716807","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}
Vehicular Ad Hoc Networks (VANETs) are part of Intelligent Transportation Systems (ITS) and their main objective is to provide communication between vehicles. As self-organizing and configuring networks, with decentralized control, their performance is totally dependent on the route duration times. This study proposes an analysis of the route duration times in vehicular networks, considering three influential factors: speed, density and travel orientation. Simulation experiments corroborate that the route duration times increases in denser networks and when vehicles travel in the same direction. However, contrary to common sense, unexpectedly, it is demonstrated that the route duration times in realistic vehicle environments do not decrease as the vehicles speed increases due to the mobility restrictions in this environments (stops at traffic lights and road crossings, braking to avoid collisions, acceleration an deceleration).
{"title":"An Analysis of Route Duration Times in Vehicular Networks Considering Influential Factors","authors":"Danilo Renato de Assis, J. A. Junior, E. Wille","doi":"10.4018/jitr.299927","DOIUrl":"https://doi.org/10.4018/jitr.299927","url":null,"abstract":"Vehicular Ad Hoc Networks (VANETs) are part of Intelligent Transportation Systems (ITS) and their main objective is to provide communication between vehicles. As self-organizing and configuring networks, with decentralized control, their performance is totally dependent on the route duration times. This study proposes an analysis of the route duration times in vehicular networks, considering three influential factors: speed, density and travel orientation. Simulation experiments corroborate that the route duration times increases in denser networks and when vehicles travel in the same direction. However, contrary to common sense, unexpectedly, it is demonstrated that the route duration times in realistic vehicle environments do not decrease as the vehicles speed increases due to the mobility restrictions in this environments (stops at traffic lights and road crossings, braking to avoid collisions, acceleration an deceleration).","PeriodicalId":296080,"journal":{"name":"J. Inf. Technol. Res.","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128182991","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 increasing prevalence of business cases utilizing Internet of Things (IoT) analytics, coupled with the diversity of IoT analytics platforms and their capabilities, poses an immense challenge for organizations seeking to make the best choice of IoT analytics platform for their specific use cases. Aiming to characterize the capabilities of IoT analytics, this article presents a reference architecture for IoT analytics platforms created through a qualitative content analysis of online reviews and published implementation architectures of IoT analytics platforms. A further contribution is a taxonomy of the functional and cross-functional capabilities of IoT analytics platforms derived from the analysis of published use cases and related business surveys. Both the reference architecture and the associated taxonomy provide a theoretical basis for further research into IoT analytics capabilities and should therefore facilitate the evaluation, selection and adoption of IoT analytics solutions through a unified description of their capabilities and functional requirements.
{"title":"Characterizing the Capabilities of Internet of Things Analytics Through Taxonomy and Reference Architecture: Insights From Content Analysis of the Voice of Practitioners","authors":"M. Daradkeh","doi":"10.4018/jitr.299929","DOIUrl":"https://doi.org/10.4018/jitr.299929","url":null,"abstract":"The increasing prevalence of business cases utilizing Internet of Things (IoT) analytics, coupled with the diversity of IoT analytics platforms and their capabilities, poses an immense challenge for organizations seeking to make the best choice of IoT analytics platform for their specific use cases. Aiming to characterize the capabilities of IoT analytics, this article presents a reference architecture for IoT analytics platforms created through a qualitative content analysis of online reviews and published implementation architectures of IoT analytics platforms. A further contribution is a taxonomy of the functional and cross-functional capabilities of IoT analytics platforms derived from the analysis of published use cases and related business surveys. Both the reference architecture and the associated taxonomy provide a theoretical basis for further research into IoT analytics capabilities and should therefore facilitate the evaluation, selection and adoption of IoT analytics solutions through a unified description of their capabilities and functional requirements.","PeriodicalId":296080,"journal":{"name":"J. Inf. Technol. Res.","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130896232","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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