Pub Date : 2020-12-11DOI: 10.1109/TOCS50858.2020.9339729
Hengxing Chen, Chun Liu
In recent years, the number of students receiving football training in colleges has been increasing, however, during football trainings in most colleges and universities, high-intensity sports are generally performed. Therefore, the athletes often experience knee injury during training, which is harmful to the health of athletes and affects football players' professional skills improvement. This paper analyzes the reasons for the knee injuries of college football trainees, based on the artificial neural network technology and through the method of data analysis. This paper finds that football muscle damage types mainly include the medial collateral ligament, meniscus injury, anterior cruciate ligament, the knee bursitis, enthesopathy of the patellar tendon, and the lateral collateral ligament and Sinding Larsen and chondromalacia patellae these seven types.
{"title":"Research on Knee Injuries in College Football Training Based on Artificial Neural Network","authors":"Hengxing Chen, Chun Liu","doi":"10.1109/TOCS50858.2020.9339729","DOIUrl":"https://doi.org/10.1109/TOCS50858.2020.9339729","url":null,"abstract":"In recent years, the number of students receiving football training in colleges has been increasing, however, during football trainings in most colleges and universities, high-intensity sports are generally performed. Therefore, the athletes often experience knee injury during training, which is harmful to the health of athletes and affects football players' professional skills improvement. This paper analyzes the reasons for the knee injuries of college football trainees, based on the artificial neural network technology and through the method of data analysis. This paper finds that football muscle damage types mainly include the medial collateral ligament, meniscus injury, anterior cruciate ligament, the knee bursitis, enthesopathy of the patellar tendon, and the lateral collateral ligament and Sinding Larsen and chondromalacia patellae these seven types.","PeriodicalId":373862,"journal":{"name":"2020 IEEE Conference on Telecommunications, Optics and Computer Science (TOCS)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121878787","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 : 2020-12-11DOI: 10.1109/TOCS50858.2020.9339745
Kun Qi
With the wide use of computer network, the network information security problem closely related to people has become the focus of attention in contemporary society. In today's society, e-banking, e-commerce and other network services are quietly entering people's lives. Along with it, cyber attacks are also increasing. Based on the gradual application of computing forms such as cloud computing and big data, the changes of data centralization, computing centralization and network complexity are gradually obvious. Based on the gradual application of computing forms such as cloud computing and big data, the changes of data centralization, computing centralization and network complexity are gradually obvious. If people's online life is not guaranteed, it will bring serious disadvantages to people's daily life. On the basis of analyzing the basic theory, this paper discusses the loopholes in network information security management at present, and then puts forward the improvement measures of network data security management based on improved wavelet neural network.
{"title":"Computer Network Information Security Analysis and Management Research Based on Improved Wavelet Neural Network","authors":"Kun Qi","doi":"10.1109/TOCS50858.2020.9339745","DOIUrl":"https://doi.org/10.1109/TOCS50858.2020.9339745","url":null,"abstract":"With the wide use of computer network, the network information security problem closely related to people has become the focus of attention in contemporary society. In today's society, e-banking, e-commerce and other network services are quietly entering people's lives. Along with it, cyber attacks are also increasing. Based on the gradual application of computing forms such as cloud computing and big data, the changes of data centralization, computing centralization and network complexity are gradually obvious. Based on the gradual application of computing forms such as cloud computing and big data, the changes of data centralization, computing centralization and network complexity are gradually obvious. If people's online life is not guaranteed, it will bring serious disadvantages to people's daily life. On the basis of analyzing the basic theory, this paper discusses the loopholes in network information security management at present, and then puts forward the improvement measures of network data security management based on improved wavelet neural network.","PeriodicalId":373862,"journal":{"name":"2020 IEEE Conference on Telecommunications, Optics and Computer Science (TOCS)","volume":"59 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125242113","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 : 2020-12-11DOI: 10.1109/TOCS50858.2020.9339749
Li Qing, Feng Weixi, Chen Huanbin
With the construction of digital city and the rapid development of large-scale 3D data acquisition technology, 3D laser scanning and dense matching of aerospace images have produced massive point cloud data. As a new digital representation method of 3D objects, 3D point cloud has gradually become a common processing object in various research and engineering applications because of its simplicity and flexibility. 3D point cloud data can build a real 3D city model for 3D geographic information system, simulation and virtual technology, and digital city construction. How to use the existing computer processing ability to efficiently organize and index the massive point cloud data and complete the 3D spatial visualization modeling of the point cloud data more quickly and accurately has become an important research topic. Massive point cloud data are collected by 3D laser scanning system, and finally saved to the computer. Through some software processing, the high-precision 3D model is reconstructed, and the 3D reconstruction and rapid visualization of point cloud data are realized.
{"title":"Research on Visualization Modeling Technology of Massive Laser Point Cloud 3D Data","authors":"Li Qing, Feng Weixi, Chen Huanbin","doi":"10.1109/TOCS50858.2020.9339749","DOIUrl":"https://doi.org/10.1109/TOCS50858.2020.9339749","url":null,"abstract":"With the construction of digital city and the rapid development of large-scale 3D data acquisition technology, 3D laser scanning and dense matching of aerospace images have produced massive point cloud data. As a new digital representation method of 3D objects, 3D point cloud has gradually become a common processing object in various research and engineering applications because of its simplicity and flexibility. 3D point cloud data can build a real 3D city model for 3D geographic information system, simulation and virtual technology, and digital city construction. How to use the existing computer processing ability to efficiently organize and index the massive point cloud data and complete the 3D spatial visualization modeling of the point cloud data more quickly and accurately has become an important research topic. Massive point cloud data are collected by 3D laser scanning system, and finally saved to the computer. Through some software processing, the high-precision 3D model is reconstructed, and the 3D reconstruction and rapid visualization of point cloud data are realized.","PeriodicalId":373862,"journal":{"name":"2020 IEEE Conference on Telecommunications, Optics and Computer Science (TOCS)","volume":"20 2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130208871","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 : 2020-12-11DOI: 10.1109/TOCS50858.2020.9339731
Chen Xu, Yuan Lei, Yuhang Zou
In order to improve the success rate and efficiency of automatic identification of low-voltage distribution topology, this paper proposes a record-based topology identification method. The topology recognition is divided into consumer-transformer relation recognition and hierarchical recognition. High-speed power line carrier (HPLC) automatic networking was used to obtain and judge the equipment information to generate the consumer-transformer relationship in the low voltage area. The topology terminal unit and low-voltage equipment with topology recognition function generate records through specific topology signal exchanging information. Then, the intelligent distribution transformer terminal unit summarizes these records and combines the relationship between power consumers and transformer to generate the hierarchical relationship. This topology identification method has the advantages of simple control logic, fast identification speed and high identification accuracy, which can provide the important basic data for real-time fault location, power line loss analysis and area capacity estimation and other advanced applications of low-voltage distribution.
{"title":"A Method of Low Voltage Topology Identification","authors":"Chen Xu, Yuan Lei, Yuhang Zou","doi":"10.1109/TOCS50858.2020.9339731","DOIUrl":"https://doi.org/10.1109/TOCS50858.2020.9339731","url":null,"abstract":"In order to improve the success rate and efficiency of automatic identification of low-voltage distribution topology, this paper proposes a record-based topology identification method. The topology recognition is divided into consumer-transformer relation recognition and hierarchical recognition. High-speed power line carrier (HPLC) automatic networking was used to obtain and judge the equipment information to generate the consumer-transformer relationship in the low voltage area. The topology terminal unit and low-voltage equipment with topology recognition function generate records through specific topology signal exchanging information. Then, the intelligent distribution transformer terminal unit summarizes these records and combines the relationship between power consumers and transformer to generate the hierarchical relationship. This topology identification method has the advantages of simple control logic, fast identification speed and high identification accuracy, which can provide the important basic data for real-time fault location, power line loss analysis and area capacity estimation and other advanced applications of low-voltage distribution.","PeriodicalId":373862,"journal":{"name":"2020 IEEE Conference on Telecommunications, Optics and Computer Science (TOCS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129525668","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 : 2020-12-11DOI: 10.1109/TOCS50858.2020.9339691
Bo Wang, Hu Liu, Xinyong Zhu, Xinzhu Zhang
Terahertz (THz) time-domain spectrum has been widely adopted as the de facto technique to inspect the physical characteristics of materials and to detect defects in bulks. However, the time series generated by scans are usually noisy and consist of minor peaks induced by the reflection of the defects inside the materials. To improve the quality of the data and to argument the frequency-domain spectrum of the signal, a Wiener filter modified by a time window is implemented to improve the dynamic range and the bandwidth of the frequency-domain spectrum. Software to make complex THz spectroscopy analysis and data visualization is developed, and the filters are expected to integrate into the numerical library of the software.
{"title":"A novel method to process the THz time-domain spectrum and software to make data visualization","authors":"Bo Wang, Hu Liu, Xinyong Zhu, Xinzhu Zhang","doi":"10.1109/TOCS50858.2020.9339691","DOIUrl":"https://doi.org/10.1109/TOCS50858.2020.9339691","url":null,"abstract":"Terahertz (THz) time-domain spectrum has been widely adopted as the de facto technique to inspect the physical characteristics of materials and to detect defects in bulks. However, the time series generated by scans are usually noisy and consist of minor peaks induced by the reflection of the defects inside the materials. To improve the quality of the data and to argument the frequency-domain spectrum of the signal, a Wiener filter modified by a time window is implemented to improve the dynamic range and the bandwidth of the frequency-domain spectrum. Software to make complex THz spectroscopy analysis and data visualization is developed, and the filters are expected to integrate into the numerical library of the software.","PeriodicalId":373862,"journal":{"name":"2020 IEEE Conference on Telecommunications, Optics and Computer Science (TOCS)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129687360","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 : 2020-12-11DOI: 10.1109/TOCS50858.2020.9339689
Cong Xu, Jiahao Fan, Lin Liu
Object detection is an extremely important part of computer vision. However, the object detection result of R-FCN is not good enough in terms of speed and accuracy. In this paper, a novel architecture called Enhanced R-FCN (ER-FCN) is proposed for object detection. Two improvements are presented in ER-FCN. Firstly, novel anchor boxes, 3 scales with box areas of 5122, 2562 and 1282 pixels, and 3 aspect ratios of 0.618:1, 1:1 and 1:0.618, are designed to suit the different scales object detection in RPN. Hence, the performance of object localization and detection speed are increased. Secondly, since the softmax classifier is not optimal to deal with the binary classification problem, a Whale Optimization Algorithm based on support vector machine, termed WOA-SVM, is introduced to improve the accuracy of classification. Extensive experimental results on PASCAL VOC 2007 and PASCAL VOC 2012 datasets show that the mean average precision of ER-FCN is improved by 3.9% compared with that of R-FCN.
{"title":"A novel object detection algorithm based on enhanced R-FCN and SVM","authors":"Cong Xu, Jiahao Fan, Lin Liu","doi":"10.1109/TOCS50858.2020.9339689","DOIUrl":"https://doi.org/10.1109/TOCS50858.2020.9339689","url":null,"abstract":"Object detection is an extremely important part of computer vision. However, the object detection result of R-FCN is not good enough in terms of speed and accuracy. In this paper, a novel architecture called Enhanced R-FCN (ER-FCN) is proposed for object detection. Two improvements are presented in ER-FCN. Firstly, novel anchor boxes, 3 scales with box areas of 5122, 2562 and 1282 pixels, and 3 aspect ratios of 0.618:1, 1:1 and 1:0.618, are designed to suit the different scales object detection in RPN. Hence, the performance of object localization and detection speed are increased. Secondly, since the softmax classifier is not optimal to deal with the binary classification problem, a Whale Optimization Algorithm based on support vector machine, termed WOA-SVM, is introduced to improve the accuracy of classification. Extensive experimental results on PASCAL VOC 2007 and PASCAL VOC 2012 datasets show that the mean average precision of ER-FCN is improved by 3.9% compared with that of R-FCN.","PeriodicalId":373862,"journal":{"name":"2020 IEEE Conference on Telecommunications, Optics and Computer Science (TOCS)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123955169","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 : 2020-12-11DOI: 10.1109/TOCS50858.2020.9339753
Dacheng Xing, Jinrong Li
In order to solve the problem that the safety control effect of the traditional electrical equipment circuit system is relatively poor, combined with the big data analysis method, the automatic switch-off technology of the circuit switch of the electrical equipment is studied. According to the power control principle of the peaking unit, a PID algorithm is used to optimize the circuit safety control parameter algorithm. The active power generated by the pulse signal input into the coil drives the automatic control of the circuit switch in the electrical equipment, and accurately detects and controls the operation of the electrical equipment power system, thereby avoiding problems such as circuit failure. Finally, through experimental analysis, it is verified that the effect of the automatic switch-off of the circuit switch of the electrical equipment on the control of the circuit system has been significantly improved based on the analysis of big data, which is an important reference for the development of the electrical industry.
{"title":"Circuit Switch Automatic Shutoff Technique for Electrical Equipment Based on Big Data Analysis","authors":"Dacheng Xing, Jinrong Li","doi":"10.1109/TOCS50858.2020.9339753","DOIUrl":"https://doi.org/10.1109/TOCS50858.2020.9339753","url":null,"abstract":"In order to solve the problem that the safety control effect of the traditional electrical equipment circuit system is relatively poor, combined with the big data analysis method, the automatic switch-off technology of the circuit switch of the electrical equipment is studied. According to the power control principle of the peaking unit, a PID algorithm is used to optimize the circuit safety control parameter algorithm. The active power generated by the pulse signal input into the coil drives the automatic control of the circuit switch in the electrical equipment, and accurately detects and controls the operation of the electrical equipment power system, thereby avoiding problems such as circuit failure. Finally, through experimental analysis, it is verified that the effect of the automatic switch-off of the circuit switch of the electrical equipment on the control of the circuit system has been significantly improved based on the analysis of big data, which is an important reference for the development of the electrical industry.","PeriodicalId":373862,"journal":{"name":"2020 IEEE Conference on Telecommunications, Optics and Computer Science (TOCS)","volume":"100 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115811921","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 : 2020-12-11DOI: 10.1109/TOCS50858.2020.9339720
Q. Zhuang, Fuming Lei
In the face of the increasing number of flights, the current AIDC operation mode is difficult to guarantee the success rate when managing a large number of flight handover management based on height and complex airway handover management. In 2018, CAAC released the research and Verification Technology Project Plan for the control transfer Technology between automated air traffic control systems based on flight data interaction, aiming to improve China's control transfer data communication technology. This paper expounds the project construction background, construction content and technical innovation in detail. The project results solves the problem of vertical transfer between automation systems that cannot be satisfied by AIDC protocol, and realizes the control transfer between heterogeneous systems of different manufacturers, which is helpful to further improve the localization level.
{"title":"Application of control transfer technology between automated air traffic control systems based on flight data interaction","authors":"Q. Zhuang, Fuming Lei","doi":"10.1109/TOCS50858.2020.9339720","DOIUrl":"https://doi.org/10.1109/TOCS50858.2020.9339720","url":null,"abstract":"In the face of the increasing number of flights, the current AIDC operation mode is difficult to guarantee the success rate when managing a large number of flight handover management based on height and complex airway handover management. In 2018, CAAC released the research and Verification Technology Project Plan for the control transfer Technology between automated air traffic control systems based on flight data interaction, aiming to improve China's control transfer data communication technology. This paper expounds the project construction background, construction content and technical innovation in detail. The project results solves the problem of vertical transfer between automation systems that cannot be satisfied by AIDC protocol, and realizes the control transfer between heterogeneous systems of different manufacturers, which is helpful to further improve the localization level.","PeriodicalId":373862,"journal":{"name":"2020 IEEE Conference on Telecommunications, Optics and Computer Science (TOCS)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116860299","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 : 2020-12-11DOI: 10.1109/TOCS50858.2020.9339719
Ying Ma
With the development of the times and the progress of society, the achievements of human civilization have been accumulated. Under the background of the rapid development of computer network and information technology, the traditional way of information transmission based on words can no longer meet the needs of people in the new era. Therefore, in this era of widespread data and image processing technology, image as a way of information dissemination has been more and more well known. In order to meet the challenges of the new era to the research of digital oil painting, this paper puts forward the method of applying digital image processing technology to the research of digital oil painting. Through the digital image processing technology to analyze the large amount of information contained in the digital oil painting, combined with the new requirements of the new era for digital oil painting research, a set of digital oil painting research and development most suitable for the new era is formulated The new plan of the exhibition. Through long-term research and analysis, it is found that digital image processing technology has a profound impact on the research of digital oil painting. The research method proposed in this paper successfully provides a new idea for the research and development of digital oil painting.
{"title":"Research on Digital Oil Painting Based on Digital Image Processing Technology","authors":"Ying Ma","doi":"10.1109/TOCS50858.2020.9339719","DOIUrl":"https://doi.org/10.1109/TOCS50858.2020.9339719","url":null,"abstract":"With the development of the times and the progress of society, the achievements of human civilization have been accumulated. Under the background of the rapid development of computer network and information technology, the traditional way of information transmission based on words can no longer meet the needs of people in the new era. Therefore, in this era of widespread data and image processing technology, image as a way of information dissemination has been more and more well known. In order to meet the challenges of the new era to the research of digital oil painting, this paper puts forward the method of applying digital image processing technology to the research of digital oil painting. Through the digital image processing technology to analyze the large amount of information contained in the digital oil painting, combined with the new requirements of the new era for digital oil painting research, a set of digital oil painting research and development most suitable for the new era is formulated The new plan of the exhibition. Through long-term research and analysis, it is found that digital image processing technology has a profound impact on the research of digital oil painting. The research method proposed in this paper successfully provides a new idea for the research and development of digital oil painting.","PeriodicalId":373862,"journal":{"name":"2020 IEEE Conference on Telecommunications, Optics and Computer Science (TOCS)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114182355","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 : 2020-12-11DOI: 10.1109/TOCS50858.2020.9339694
Jiaju Yu
Since the concept of “artificial intelligence” was first proposed in Dartmouth Conference in 1956, the discipline of artificial intelligence has entered an era of steady progress. Recently, the emergence of new technology such as big data, cloud computing and Internet of Things has promoted the rapid development of artificial intelligence technology represented by deep neural network.
{"title":"Application of machine learning in business district operation","authors":"Jiaju Yu","doi":"10.1109/TOCS50858.2020.9339694","DOIUrl":"https://doi.org/10.1109/TOCS50858.2020.9339694","url":null,"abstract":"Since the concept of “artificial intelligence” was first proposed in Dartmouth Conference in 1956, the discipline of artificial intelligence has entered an era of steady progress. Recently, the emergence of new technology such as big data, cloud computing and Internet of Things has promoted the rapid development of artificial intelligence technology represented by deep neural network.","PeriodicalId":373862,"journal":{"name":"2020 IEEE Conference on Telecommunications, Optics and Computer Science (TOCS)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121598404","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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