Pub Date : 2021-11-01DOI: 10.1109/ITME53901.2021.00012
Wei-Hua Liu, Ling Li
Complex q-rung orthopair fuzzy sets (Cq-ROFS) become one of the effective tools to deal with unresolved and complex information. In recent years, a variety of multi-attribute decision making(MADM) methods have been developed in the Cq-ROFS environment, such as complex intuitionistic fuzzy set (CIFS) and complex Pythagorean fuzzy sets(CPFS). Cq-ROFS is superior to CIFs and CPFS, which can describe a wider range of uncertain information spaces. Therefore, this paper studies MADM based on Cq-ROFS. However, decision making experts may have personal biases. In order to reduce the influence of individual preference on decision-making, the dependent Hamacher aggregation operators for Complex q-rung orthopair fuzzy sets(Cq-ROFDHA) are proposed based on the Hamacher operation and dependent method. The operator can improve personal bias by assigning lower weights to biased evaluations (unduly high or unduly low values) and higher weights to mid values. Firstly, the basic operation rules, score function, distance and properties of Cq-ROFDHA are described. We then applied Cq-ROFDHA to service utility decisions for online health communities. Finally, we compare it with other aggregation factors. The results show that the Cq-ROFDHA operator has the advantages of consistency and superiority.
{"title":"Dependent Hamacher Aggregation Operators for Complex q-Rung Orthopair Fuzzy Sets and Their Application","authors":"Wei-Hua Liu, Ling Li","doi":"10.1109/ITME53901.2021.00012","DOIUrl":"https://doi.org/10.1109/ITME53901.2021.00012","url":null,"abstract":"Complex q-rung orthopair fuzzy sets (Cq-ROFS) become one of the effective tools to deal with unresolved and complex information. In recent years, a variety of multi-attribute decision making(MADM) methods have been developed in the Cq-ROFS environment, such as complex intuitionistic fuzzy set (CIFS) and complex Pythagorean fuzzy sets(CPFS). Cq-ROFS is superior to CIFs and CPFS, which can describe a wider range of uncertain information spaces. Therefore, this paper studies MADM based on Cq-ROFS. However, decision making experts may have personal biases. In order to reduce the influence of individual preference on decision-making, the dependent Hamacher aggregation operators for Complex q-rung orthopair fuzzy sets(Cq-ROFDHA) are proposed based on the Hamacher operation and dependent method. The operator can improve personal bias by assigning lower weights to biased evaluations (unduly high or unduly low values) and higher weights to mid values. Firstly, the basic operation rules, score function, distance and properties of Cq-ROFDHA are described. We then applied Cq-ROFDHA to service utility decisions for online health communities. Finally, we compare it with other aggregation factors. The results show that the Cq-ROFDHA operator has the advantages of consistency and superiority.","PeriodicalId":6774,"journal":{"name":"2021 11th International Conference on Information Technology in Medicine and Education (ITME)","volume":"213 1","pages":"6-12"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76542912","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 : 2021-11-01DOI: 10.1109/itme53901.2021.00003
{"title":"[Copyright notice]","authors":"","doi":"10.1109/itme53901.2021.00003","DOIUrl":"https://doi.org/10.1109/itme53901.2021.00003","url":null,"abstract":"","PeriodicalId":6774,"journal":{"name":"2021 11th International Conference on Information Technology in Medicine and Education (ITME)","volume":"6 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87374758","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 : 2021-11-01DOI: 10.1109/ITME53901.2021.00049
Jinming Cao, Oren Katzir, Peng Jiang, D. Lischinski, D. Cohen-Or, Changhe Tu, Yangyan Li
Unsupervised domain adaptation aims at learning a shared model for two related domains by leveraging supervision from a source domain to an unsupervised target domain. A number of effective domain adaptation approaches rely on the ability to extract domain-invariant latent factors which are common to both domains. Extracting latent commonality is also useful for disentanglement analysis. It enables separation between the common and the domain-specific features of both domains, which can be recombined for synthesis. In this paper, we propose a strategy to boost the performance of domain adaptation and disentangled synthesis iteratively. The key idea is that by learning to separately extract both the common and the domain-specific features, one can synthesize more target domain data with supervision, thereby boosting the domain adaptation performance. Better common feature extraction, in turn, helps further improve the feature disentanglement and the following disentangled synthesis. We show that iterating between domain adaptation and disentangled synthesis can consistently improve each other on several unsupervised domain adaptation benchmark datasets and tasks, under various domain adaptation backbone models.
{"title":"DiDA: Iterative Boosting of Disentangled Synthesis and Domain Adaptation","authors":"Jinming Cao, Oren Katzir, Peng Jiang, D. Lischinski, D. Cohen-Or, Changhe Tu, Yangyan Li","doi":"10.1109/ITME53901.2021.00049","DOIUrl":"https://doi.org/10.1109/ITME53901.2021.00049","url":null,"abstract":"Unsupervised domain adaptation aims at learning a shared model for two related domains by leveraging supervision from a source domain to an unsupervised target domain. A number of effective domain adaptation approaches rely on the ability to extract domain-invariant latent factors which are common to both domains. Extracting latent commonality is also useful for disentanglement analysis. It enables separation between the common and the domain-specific features of both domains, which can be recombined for synthesis. In this paper, we propose a strategy to boost the performance of domain adaptation and disentangled synthesis iteratively. The key idea is that by learning to separately extract both the common and the domain-specific features, one can synthesize more target domain data with supervision, thereby boosting the domain adaptation performance. Better common feature extraction, in turn, helps further improve the feature disentanglement and the following disentangled synthesis. We show that iterating between domain adaptation and disentangled synthesis can consistently improve each other on several unsupervised domain adaptation benchmark datasets and tasks, under various domain adaptation backbone models.","PeriodicalId":6774,"journal":{"name":"2021 11th International Conference on Information Technology in Medicine and Education (ITME)","volume":"29 1","pages":"201-208"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77000892","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 : 2021-11-01DOI: 10.1109/ITME53901.2021.00041
Yanhua Liu, Jiaqi Li, Baoxu Liu, Xiaoling Gao, Ximeng Liu
In this paper, we propose a malware identification method employed by image analysis and generative adversarial networks, designed to solve the problems of increasingly sophisticated attack forms, insufficient sample data in malware. Specifically, we first generate fixed-size gray images of malware, which neither disassembly nor code execution is required for identification. Moreover, we introduce generative adversarial networks into malware identification for few samples scenarios and malware variants. Through the game training of generator and discriminator, the malware detection model is obtained from the discriminator and the samples are generated by the generator for data augment. Finally, we demonstrate that the proposed method is efficient and feasible using extensive experiments.
{"title":"Malware Identification Method Based on Image Analysis","authors":"Yanhua Liu, Jiaqi Li, Baoxu Liu, Xiaoling Gao, Ximeng Liu","doi":"10.1109/ITME53901.2021.00041","DOIUrl":"https://doi.org/10.1109/ITME53901.2021.00041","url":null,"abstract":"In this paper, we propose a malware identification method employed by image analysis and generative adversarial networks, designed to solve the problems of increasingly sophisticated attack forms, insufficient sample data in malware. Specifically, we first generate fixed-size gray images of malware, which neither disassembly nor code execution is required for identification. Moreover, we introduce generative adversarial networks into malware identification for few samples scenarios and malware variants. Through the game training of generator and discriminator, the malware detection model is obtained from the discriminator and the samples are generated by the generator for data augment. Finally, we demonstrate that the proposed method is efficient and feasible using extensive experiments.","PeriodicalId":6774,"journal":{"name":"2021 11th International Conference on Information Technology in Medicine and Education (ITME)","volume":"73 1","pages":"157-161"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76551256","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 : 2021-11-01DOI: 10.1109/ITME53901.2021.00069
Wan Yuqian, Ma Jianwei, Zang Shaofei
In order to solve the existing problems of low segmentation precision and obvious interference by background noise in the segmentation task of rectal cancer lesions, we propose an improved U-Net method based on feature fusion by U-Net network and weighted feature pyramid structure (W - FPN). First, the proportion of each pixel value in the final pixel is used to assign weights to strengthen the feature fusion ability and improve the segmentation effect by using the scale information in the fusion. Secondly, after the third network output layer, three serial depthwise separable dilated convolution layers with dilation rates of 1, 2 and 4 are added to enlarge the receptive field of feature image and make full use of image feature information. Finally, the improved model is compared with U-Net, SegNet and DeepLab segmentation models. The experimental results show that Our approach reaches good and stable results with a precision of 83.38% and the Dice similarity coefficient value of 92.56%.
{"title":"U -Net based on Feature Fusion for Rectal Cancer Image Segmentation","authors":"Wan Yuqian, Ma Jianwei, Zang Shaofei","doi":"10.1109/ITME53901.2021.00069","DOIUrl":"https://doi.org/10.1109/ITME53901.2021.00069","url":null,"abstract":"In order to solve the existing problems of low segmentation precision and obvious interference by background noise in the segmentation task of rectal cancer lesions, we propose an improved U-Net method based on feature fusion by U-Net network and weighted feature pyramid structure (W - FPN). First, the proportion of each pixel value in the final pixel is used to assign weights to strengthen the feature fusion ability and improve the segmentation effect by using the scale information in the fusion. Secondly, after the third network output layer, three serial depthwise separable dilated convolution layers with dilation rates of 1, 2 and 4 are added to enlarge the receptive field of feature image and make full use of image feature information. Finally, the improved model is compared with U-Net, SegNet and DeepLab segmentation models. The experimental results show that Our approach reaches good and stable results with a precision of 83.38% and the Dice similarity coefficient value of 92.56%.","PeriodicalId":6774,"journal":{"name":"2021 11th International Conference on Information Technology in Medicine and Education (ITME)","volume":"26 1","pages":"302-306"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76907018","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 : 2021-11-01DOI: 10.1109/ITME53901.2021.00019
L. Zhen, Lin Guanjun, Li Shusheng, Wang Weibin, L. Xiaoming
At present, the big data industry is developing rapidly in many fields around the world, and it brings opportunities for the transformation and upgradation of the traditional oil industry. The whole oil business chain is of large scale, and there are urgent needs to apply big data technologies in the fields of petroleum exploration and development, transportation, refining and other fields. However, the oil big data industry is still in its infancy and has encountered many challenges, including oil data storage and management being not standardized, technical standards being not unified, and security concerns. These issues further lead to the poor data sharing, repeated business deployment within the enterprise and the compromised of the systems. To solve the problems above, this paper proposes the overall architecture for the development of the oil big data industry. The architecture scheme integrates all the data and business of the oil industry chain, which allows the secure data sharing, effective business management and scientific allocation of resources. Therefore, the oil big data solution can provide an important research idea for the dynamic management of production process and industrial business, which improves the overall productivity of oil industry.
{"title":"Research on the future development scheme of the oil big data industry","authors":"L. Zhen, Lin Guanjun, Li Shusheng, Wang Weibin, L. Xiaoming","doi":"10.1109/ITME53901.2021.00019","DOIUrl":"https://doi.org/10.1109/ITME53901.2021.00019","url":null,"abstract":"At present, the big data industry is developing rapidly in many fields around the world, and it brings opportunities for the transformation and upgradation of the traditional oil industry. The whole oil business chain is of large scale, and there are urgent needs to apply big data technologies in the fields of petroleum exploration and development, transportation, refining and other fields. However, the oil big data industry is still in its infancy and has encountered many challenges, including oil data storage and management being not standardized, technical standards being not unified, and security concerns. These issues further lead to the poor data sharing, repeated business deployment within the enterprise and the compromised of the systems. To solve the problems above, this paper proposes the overall architecture for the development of the oil big data industry. The architecture scheme integrates all the data and business of the oil industry chain, which allows the secure data sharing, effective business management and scientific allocation of resources. Therefore, the oil big data solution can provide an important research idea for the dynamic management of production process and industrial business, which improves the overall productivity of oil industry.","PeriodicalId":6774,"journal":{"name":"2021 11th International Conference on Information Technology in Medicine and Education (ITME)","volume":"13 1","pages":"42-46"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75587094","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 : 2021-11-01DOI: 10.1109/ITME53901.2021.00061
T. Zhou, Yun Cheng
Most of the localization algorithms can achieve extremely high positioning accuracy in line of sight (LOS) environment. However, they are unable to obtain ideal accuracy due to the obstacles in non-line of sight (NLOS) environment. In order to reduce the influence of NLOS on positioning accuracy in indoor environment, Fang algorithm, Chan algorithm and Taylor algorithm based on TDOA in UWB indoor positioning technology are analyzed and tested. Through comparative simulation analysis, it can be concluded that in the case of Gaussian noise, regardless of the number of base stations, Chan algorithm has the best performance, Taylor algorithm is the second, and Fang algorithm has the worst performance. When the number of base stations reaches a certain number, Chan algorithm and Taylor algorithm are not sensitive to the number of base stations, but they can use all TDOA information to obtain more accurate parameter solutions, and can also be adapted to different measurement environments.
{"title":"Positioning Algorithm of UWB based on TDOA Technology in Indoor Environment","authors":"T. Zhou, Yun Cheng","doi":"10.1109/ITME53901.2021.00061","DOIUrl":"https://doi.org/10.1109/ITME53901.2021.00061","url":null,"abstract":"Most of the localization algorithms can achieve extremely high positioning accuracy in line of sight (LOS) environment. However, they are unable to obtain ideal accuracy due to the obstacles in non-line of sight (NLOS) environment. In order to reduce the influence of NLOS on positioning accuracy in indoor environment, Fang algorithm, Chan algorithm and Taylor algorithm based on TDOA in UWB indoor positioning technology are analyzed and tested. Through comparative simulation analysis, it can be concluded that in the case of Gaussian noise, regardless of the number of base stations, Chan algorithm has the best performance, Taylor algorithm is the second, and Fang algorithm has the worst performance. When the number of base stations reaches a certain number, Chan algorithm and Taylor algorithm are not sensitive to the number of base stations, but they can use all TDOA information to obtain more accurate parameter solutions, and can also be adapted to different measurement environments.","PeriodicalId":6774,"journal":{"name":"2021 11th International Conference on Information Technology in Medicine and Education (ITME)","volume":"22 1","pages":"261-266"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83056357","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 : 2021-11-01DOI: 10.1109/ITME53901.2021.00056
Ta Zhao, Caixin Fu, Gang Song, Lin Wang, Xiaotong Mu
With the increasing applications of information and communication technology in rail transit systems, the electronic and electrical devices in railway vehicles causes the serious electromagnetic interference (EMI) problem among any subsystems and devices. In this paper, an anti-EMI design scheme for railway vehicles is proposed, which can improve the anti-EMI ability of whole railway vehicles in terms of shielding, grounding, filtering and wiring. Based on Shanghai Metro Lines 4 and 6, the system implementation and testing are performed. The test results show that the proposed scheme can be effective and efficient to resolve the electromagnetic compatibility (EMC) problem.
{"title":"Anti-Electromagnetic Interference Design for Railway Vehicles in Complex Electromagnetic Environment","authors":"Ta Zhao, Caixin Fu, Gang Song, Lin Wang, Xiaotong Mu","doi":"10.1109/ITME53901.2021.00056","DOIUrl":"https://doi.org/10.1109/ITME53901.2021.00056","url":null,"abstract":"With the increasing applications of information and communication technology in rail transit systems, the electronic and electrical devices in railway vehicles causes the serious electromagnetic interference (EMI) problem among any subsystems and devices. In this paper, an anti-EMI design scheme for railway vehicles is proposed, which can improve the anti-EMI ability of whole railway vehicles in terms of shielding, grounding, filtering and wiring. Based on Shanghai Metro Lines 4 and 6, the system implementation and testing are performed. The test results show that the proposed scheme can be effective and efficient to resolve the electromagnetic compatibility (EMC) problem.","PeriodicalId":6774,"journal":{"name":"2021 11th International Conference on Information Technology in Medicine and Education (ITME)","volume":"30 1","pages":"01-05"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87752164","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 : 2021-11-01DOI: 10.1109/ITME53901.2021.00066
Huang Dong-xin, Jiao Yi, Shang Zhao-Xia
In order to select more accurate cancer treatment protocol and help decision makers to choose the best plan from all cancer treatment protocol more scientifically and more quickly, a cancer treatment plan evaluation algorithm based on multi-objective system is designed in this paper. Through intelligent method it can achieve a reasonable transforming between clinical data and treatment protocol evaluation data. At the same time, Multi-objective model analysis of typical cases and simulated calculation are carried out. By comparing with the original results, the objective of rapid sorting and evaluation of cancer treatment protocol is achieved effectively.
{"title":"Selection of cancer treatment protocol evaluation based on multi-objective","authors":"Huang Dong-xin, Jiao Yi, Shang Zhao-Xia","doi":"10.1109/ITME53901.2021.00066","DOIUrl":"https://doi.org/10.1109/ITME53901.2021.00066","url":null,"abstract":"In order to select more accurate cancer treatment protocol and help decision makers to choose the best plan from all cancer treatment protocol more scientifically and more quickly, a cancer treatment plan evaluation algorithm based on multi-objective system is designed in this paper. Through intelligent method it can achieve a reasonable transforming between clinical data and treatment protocol evaluation data. At the same time, Multi-objective model analysis of typical cases and simulated calculation are carried out. By comparing with the original results, the objective of rapid sorting and evaluation of cancer treatment protocol is achieved effectively.","PeriodicalId":6774,"journal":{"name":"2021 11th International Conference on Information Technology in Medicine and Education (ITME)","volume":"79 1","pages":"287-291"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89615248","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 : 2021-11-01DOI: 10.1109/ITME53901.2021.00017
Xiao Fu, Xinmin Song
This article focuses on the distributed state estimation problem for large-scale systems in the presence of data packet drops. The large-scale system is structured in several correlated subsystems in the physical space, and each subsystem only communicates with its neighbors. In particular, the states of different subsystems are measured by different sensors, and the sensor broadcasts measurement information to the subestimator and its neighbors through the lossy communication channel. Thus, subestimators obtain different local information in the presence of data packet drops. In this article, the distributed estimator is designed and the optimal gain is obtained under the minimum mean square error (MMSE) estimation criterion by using local information set. Finally, the effectiveness of the distributed estimator is illustrated by a simulation experiment.
{"title":"Distributed State Estimation for Large-Scale Systems in the Presence of Data Packet Drops","authors":"Xiao Fu, Xinmin Song","doi":"10.1109/ITME53901.2021.00017","DOIUrl":"https://doi.org/10.1109/ITME53901.2021.00017","url":null,"abstract":"This article focuses on the distributed state estimation problem for large-scale systems in the presence of data packet drops. The large-scale system is structured in several correlated subsystems in the physical space, and each subsystem only communicates with its neighbors. In particular, the states of different subsystems are measured by different sensors, and the sensor broadcasts measurement information to the subestimator and its neighbors through the lossy communication channel. Thus, subestimators obtain different local information in the presence of data packet drops. In this article, the distributed estimator is designed and the optimal gain is obtained under the minimum mean square error (MMSE) estimation criterion by using local information set. Finally, the effectiveness of the distributed estimator is illustrated by a simulation experiment.","PeriodicalId":6774,"journal":{"name":"2021 11th International Conference on Information Technology in Medicine and Education (ITME)","volume":"77 1","pages":"32-36"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89535060","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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