Pub Date : 2022-09-01DOI: 10.1109/ICNISC57059.2022.00069
Liangna Zou, Zhan Wu
In order to improve the visual quality of the original image, an image fusion and enhancement technology based on fuzzy theory is proposed in this paper. For the overexposed image, the corresponding membership function is designed to realize the fuzzy domain transformation of the original image, remove the noise and strong light interference of the original image and retain the details for the subsequent enhancement of the image contrast. The image fusion algorithm based on Laplace pyramid effectively combines the salient features of the blurred image and can provide high-quality spectral content. After verification, the image obtained by our method is better than the original image, Gamma correction image and histogram equalization image in visual effect, and the image processed by our method has higher definition, information entropy and peak signal-to-noise ratio, which verifies the superiority of our method.
{"title":"Exposure Image Correction Based on Fuzzy Theory","authors":"Liangna Zou, Zhan Wu","doi":"10.1109/ICNISC57059.2022.00069","DOIUrl":"https://doi.org/10.1109/ICNISC57059.2022.00069","url":null,"abstract":"In order to improve the visual quality of the original image, an image fusion and enhancement technology based on fuzzy theory is proposed in this paper. For the overexposed image, the corresponding membership function is designed to realize the fuzzy domain transformation of the original image, remove the noise and strong light interference of the original image and retain the details for the subsequent enhancement of the image contrast. The image fusion algorithm based on Laplace pyramid effectively combines the salient features of the blurred image and can provide high-quality spectral content. After verification, the image obtained by our method is better than the original image, Gamma correction image and histogram equalization image in visual effect, and the image processed by our method has higher definition, information entropy and peak signal-to-noise ratio, which verifies the superiority of our method.","PeriodicalId":286467,"journal":{"name":"2022 8th Annual International Conference on Network and Information Systems for Computers (ICNISC)","volume":"92 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133904588","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 : 2022-09-01DOI: 10.1109/ICNISC57059.2022.00102
Changfei Cui, Yongxin Shi, Shouzhao Sheng
Given the problem of unmanned helicopter (UH) path planning in complex mountain environment, an improved wolf pack algorithm has been proposed. Firstly, the mathematical model of three-dimensional environment is established and the track length, flight altitude and collision index are introduced into the fitness function. Then the traditional wolf pack algorithm is described, and the threat heuristic factor is introduced as the heuristic information to form part of the particle position update, which is used to guide the artificial wolf search and enhance the effectiveness and pertinence of particle search process. Then the adaptive step size is added to method to adjust global and local search capability of the algorithm. Finally, the smooth optimal path is fitted by B-spline interpolation. The simulation results proves that the improved algorithm can avoid falling into local optimization, shorten the search time and get the global optimal path faster comparing with the traditional wolf pack algorithm, which show the effectiveness of the improved algorithm
{"title":"Research on Unmanned helicopter Path Panning Based on Improved Wolf Pack Algorithm","authors":"Changfei Cui, Yongxin Shi, Shouzhao Sheng","doi":"10.1109/ICNISC57059.2022.00102","DOIUrl":"https://doi.org/10.1109/ICNISC57059.2022.00102","url":null,"abstract":"Given the problem of unmanned helicopter (UH) path planning in complex mountain environment, an improved wolf pack algorithm has been proposed. Firstly, the mathematical model of three-dimensional environment is established and the track length, flight altitude and collision index are introduced into the fitness function. Then the traditional wolf pack algorithm is described, and the threat heuristic factor is introduced as the heuristic information to form part of the particle position update, which is used to guide the artificial wolf search and enhance the effectiveness and pertinence of particle search process. Then the adaptive step size is added to method to adjust global and local search capability of the algorithm. Finally, the smooth optimal path is fitted by B-spline interpolation. The simulation results proves that the improved algorithm can avoid falling into local optimization, shorten the search time and get the global optimal path faster comparing with the traditional wolf pack algorithm, which show the effectiveness of the improved algorithm","PeriodicalId":286467,"journal":{"name":"2022 8th Annual International Conference on Network and Information Systems for Computers (ICNISC)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134100898","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 : 2022-09-01DOI: 10.1109/ICNISC57059.2022.00094
Angru Li, Jiajia Chen, Shaoliang Ling, Qi Liu, Ni Yan
At present, the main power generation method in my country is thermal power generation, which is the core pillar of my country's energy. Combustion efficiency is a key factor in thermal power generation. Reducing energy consumption and improving the combustion efficiency of boilers are the main issues of current research. However, the combustion efficiency of the boiler is a process involving multiple variables, nonlinearity and high complexity, and it is difficult to find suitable process parameters based on experience and theory. With the development of artificial intelligence technology, intelligent learning algorithms can now be used to analyze and study the historical combustion data of boilers, so as to improve the problem of low combustion efficiency. In this paper, steam volume prediction and improvement of combustion efficiency as the starting point, with the historical operation data of the power plant as the research object, using the improved model fusion method for tuning and prediction, compared with multiple linear regression, support vector machine, tree model, through experiments to verify the effectiveness of the fusion algorithm.
{"title":"Research on Prediction Algorithm of Thermal Power Generation Steam Volume Based on Model Fusion","authors":"Angru Li, Jiajia Chen, Shaoliang Ling, Qi Liu, Ni Yan","doi":"10.1109/ICNISC57059.2022.00094","DOIUrl":"https://doi.org/10.1109/ICNISC57059.2022.00094","url":null,"abstract":"At present, the main power generation method in my country is thermal power generation, which is the core pillar of my country's energy. Combustion efficiency is a key factor in thermal power generation. Reducing energy consumption and improving the combustion efficiency of boilers are the main issues of current research. However, the combustion efficiency of the boiler is a process involving multiple variables, nonlinearity and high complexity, and it is difficult to find suitable process parameters based on experience and theory. With the development of artificial intelligence technology, intelligent learning algorithms can now be used to analyze and study the historical combustion data of boilers, so as to improve the problem of low combustion efficiency. In this paper, steam volume prediction and improvement of combustion efficiency as the starting point, with the historical operation data of the power plant as the research object, using the improved model fusion method for tuning and prediction, compared with multiple linear regression, support vector machine, tree model, through experiments to verify the effectiveness of the fusion algorithm.","PeriodicalId":286467,"journal":{"name":"2022 8th Annual International Conference on Network and Information Systems for Computers (ICNISC)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133892532","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 : 2022-09-01DOI: 10.1109/ICNISC57059.2022.00150
Baohui Shi, Yannan Yin, Hai Lu
As the exponential growth of the Industrial Internet of Things(IoTs),a massive amount of data is generated gradually by multiple channels. It is not advisable to store all local raw data in an international IoTs device, which lies in the fact that the power and stora ge space of the end device are severely confined by self-organization. In this paper, research is conducted on the Internet of Things(IoTs) and cloud computing to provide solutions to problems in cloud compatibility and computing technologies to facilitate a stable transition from IoTs applications to cloud computing.
{"title":"Research on Problems, Challenges and Opportunities Based on Internet of Things (IoTs) and Cloud Computing","authors":"Baohui Shi, Yannan Yin, Hai Lu","doi":"10.1109/ICNISC57059.2022.00150","DOIUrl":"https://doi.org/10.1109/ICNISC57059.2022.00150","url":null,"abstract":"As the exponential growth of the Industrial Internet of Things(IoTs),a massive amount of data is generated gradually by multiple channels. It is not advisable to store all local raw data in an international IoTs device, which lies in the fact that the power and stora ge space of the end device are severely confined by self-organization. In this paper, research is conducted on the Internet of Things(IoTs) and cloud computing to provide solutions to problems in cloud compatibility and computing technologies to facilitate a stable transition from IoTs applications to cloud computing.","PeriodicalId":286467,"journal":{"name":"2022 8th Annual International Conference on Network and Information Systems for Computers (ICNISC)","volume":"62 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132003327","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 : 2022-09-01DOI: 10.1109/ICNISC57059.2022.00126
Li Ruan, Pengcheng Chen
We perform the gene set linkage analysis (GSLA) tool for interpreting the functional influences of genes with expression change in omics studies. It has been illustrated in several studies that the algorithm is useful for finding new clues for physiological coordination in transcriptome profile analyses, where traditional analysis tools cannot find similar results. The web tool of GSLA supports seven model organisms: including H. sapiens, M. musculus, A. thaliana et al.
{"title":"GSLA: A Tool for Deciphering Genotype Changes on Phenotype Level","authors":"Li Ruan, Pengcheng Chen","doi":"10.1109/ICNISC57059.2022.00126","DOIUrl":"https://doi.org/10.1109/ICNISC57059.2022.00126","url":null,"abstract":"We perform the gene set linkage analysis (GSLA) tool for interpreting the functional influences of genes with expression change in omics studies. It has been illustrated in several studies that the algorithm is useful for finding new clues for physiological coordination in transcriptome profile analyses, where traditional analysis tools cannot find similar results. The web tool of GSLA supports seven model organisms: including H. sapiens, M. musculus, A. thaliana et al.","PeriodicalId":286467,"journal":{"name":"2022 8th Annual International Conference on Network and Information Systems for Computers (ICNISC)","volume":"104 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133461676","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 : 2022-09-01DOI: 10.1109/ICNISC57059.2022.00042
Yuanpeng Duan, Zhiqi Zhang
With the deepening globalization of science and technology, the importance of artificial intelligence technology has become increasingly prominent, the competition for talents has become increasingly fierce, and many countries have made the cultivation of talents a vital force in promoting the development of artificial intelligence. However, the current model of cultivating intelligent talents is outdated, with problems such as weak discipline construction, single-course training, and lack of practical experience. In the pursuit of cultivating talents with the overall development of knowledge, ability, and quality, the “trinity” training model based on artificial intelligence is built to provide high-level, open, and complex, intelligent talents for economic and social development.
{"title":"Exploring the “Trinity” Model of Training Artificial Intelligence Talents","authors":"Yuanpeng Duan, Zhiqi Zhang","doi":"10.1109/ICNISC57059.2022.00042","DOIUrl":"https://doi.org/10.1109/ICNISC57059.2022.00042","url":null,"abstract":"With the deepening globalization of science and technology, the importance of artificial intelligence technology has become increasingly prominent, the competition for talents has become increasingly fierce, and many countries have made the cultivation of talents a vital force in promoting the development of artificial intelligence. However, the current model of cultivating intelligent talents is outdated, with problems such as weak discipline construction, single-course training, and lack of practical experience. In the pursuit of cultivating talents with the overall development of knowledge, ability, and quality, the “trinity” training model based on artificial intelligence is built to provide high-level, open, and complex, intelligent talents for economic and social development.","PeriodicalId":286467,"journal":{"name":"2022 8th Annual International Conference on Network and Information Systems for Computers (ICNISC)","volume":"66 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114534768","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 : 2022-09-01DOI: 10.1109/ICNISC57059.2022.00106
Haijun Liu
The decision support platform for the lower Yellow River integrates the models of hydrological, hydrodynamic, sediment transport, reservoir group optimal operation, beach area function planning in order to provide governance and management decision support for managers. To achieve this goal, the platform uses the stability and scalability of cloud computing technology to build a decision support platform for the lower Yellow River Based on cloud computing technology. The platform is composed of IaaS, PaaS and SaaS. The IaaS stores a large number of data resources. The PaaS layer provides services such as scheme management and data management for SaaS layer. Users can call web service interfaces to access required applications. This architecture provides a solution for the water conservancy industry to solve the integration of massive data and water conservancy models.
{"title":"Research on the framework of Decision Support Platform for the Lower Yellow River Based on Cloud Computing","authors":"Haijun Liu","doi":"10.1109/ICNISC57059.2022.00106","DOIUrl":"https://doi.org/10.1109/ICNISC57059.2022.00106","url":null,"abstract":"The decision support platform for the lower Yellow River integrates the models of hydrological, hydrodynamic, sediment transport, reservoir group optimal operation, beach area function planning in order to provide governance and management decision support for managers. To achieve this goal, the platform uses the stability and scalability of cloud computing technology to build a decision support platform for the lower Yellow River Based on cloud computing technology. The platform is composed of IaaS, PaaS and SaaS. The IaaS stores a large number of data resources. The PaaS layer provides services such as scheme management and data management for SaaS layer. Users can call web service interfaces to access required applications. This architecture provides a solution for the water conservancy industry to solve the integration of massive data and water conservancy models.","PeriodicalId":286467,"journal":{"name":"2022 8th Annual International Conference on Network and Information Systems for Computers (ICNISC)","volume":"63 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114962048","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 : 2022-09-01DOI: 10.1109/ICNISC57059.2022.00033
Fanjun Meng, Yuqing Pan, Renjun Feng
The rapid popularization of computer technology and Internet communication has not only brought convenience to people's life and work, but also brought many new network security challenges, such as malware, Trojan horse and spam. Among them, network spam is the preferred attack medium for network criminals to launch malicious activities. It usually includes phishing links, malicious warnings, and viruses. Therefore, fast and efficient spam detection technology has gradually become a research hotspot of network security. However, at present, the sending speed and scale of online mail are growing, the traditional network spam detection methods cannot meet the needs of users. With the in-depth development of machine learning, intelligent spam detection technology has been continuously applied, but the traditional machine learning methods often rely on the extraction of various features, which is time-consuming and difficult. To solve the problem, this paper, by taking advantage of the benefit of deep learning that can be completed automatically in feature extraction, proposes a CNN incorporated with attention model for network spam detection, including network spam collection, data preprocessing by using Glove model to train word vector, and model training. The experiments have verified the effectiveness of the proposed method.
{"title":"Network Spam Detection Based on CNN Incorporated with Attention Model","authors":"Fanjun Meng, Yuqing Pan, Renjun Feng","doi":"10.1109/ICNISC57059.2022.00033","DOIUrl":"https://doi.org/10.1109/ICNISC57059.2022.00033","url":null,"abstract":"The rapid popularization of computer technology and Internet communication has not only brought convenience to people's life and work, but also brought many new network security challenges, such as malware, Trojan horse and spam. Among them, network spam is the preferred attack medium for network criminals to launch malicious activities. It usually includes phishing links, malicious warnings, and viruses. Therefore, fast and efficient spam detection technology has gradually become a research hotspot of network security. However, at present, the sending speed and scale of online mail are growing, the traditional network spam detection methods cannot meet the needs of users. With the in-depth development of machine learning, intelligent spam detection technology has been continuously applied, but the traditional machine learning methods often rely on the extraction of various features, which is time-consuming and difficult. To solve the problem, this paper, by taking advantage of the benefit of deep learning that can be completed automatically in feature extraction, proposes a CNN incorporated with attention model for network spam detection, including network spam collection, data preprocessing by using Glove model to train word vector, and model training. The experiments have verified the effectiveness of the proposed method.","PeriodicalId":286467,"journal":{"name":"2022 8th Annual International Conference on Network and Information Systems for Computers (ICNISC)","volume":"80 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114811677","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 : 2022-09-01DOI: 10.1109/ICNISC57059.2022.00061
Xueyuan Li, Wentao Xie, Wentao Zhan
This paper studies the application of Lookup-Table reinforcement learning method into the continuous state space control of quadrotor simulator and designs a attitude controller for the quadrotor simulator based on Q-learning; for the improvement of defects concerning difficulty in the learning algorithm's convergence and low efficiency in learning when Q-learning is faced with large-scale and continuous-space optimized decision, the method of kernel approximate dynamic programming is introduced, Kernel-based Least-Squares Policy Iteration (KLSPI) is proposed, and a controller for the quadrotor simulator is designed based on this algorithm. The experiment shows that the reinforcement learning control method is of fast convergence speed, small steady-state error, strong adaptive ability and good control effect; when dealing with the problem of continuous state space, the Least-Squares Policy Iteration can converge better strategies with fewer training data compared with the traditional method of discretizing state space first.
{"title":"The Research of Quadrotor Flight Control Based on Reinforcement Learning and ADP","authors":"Xueyuan Li, Wentao Xie, Wentao Zhan","doi":"10.1109/ICNISC57059.2022.00061","DOIUrl":"https://doi.org/10.1109/ICNISC57059.2022.00061","url":null,"abstract":"This paper studies the application of Lookup-Table reinforcement learning method into the continuous state space control of quadrotor simulator and designs a attitude controller for the quadrotor simulator based on Q-learning; for the improvement of defects concerning difficulty in the learning algorithm's convergence and low efficiency in learning when Q-learning is faced with large-scale and continuous-space optimized decision, the method of kernel approximate dynamic programming is introduced, Kernel-based Least-Squares Policy Iteration (KLSPI) is proposed, and a controller for the quadrotor simulator is designed based on this algorithm. The experiment shows that the reinforcement learning control method is of fast convergence speed, small steady-state error, strong adaptive ability and good control effect; when dealing with the problem of continuous state space, the Least-Squares Policy Iteration can converge better strategies with fewer training data compared with the traditional method of discretizing state space first.","PeriodicalId":286467,"journal":{"name":"2022 8th Annual International Conference on Network and Information Systems for Computers (ICNISC)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134485211","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 : 2022-09-01DOI: 10.1109/ICNISC57059.2022.00179
Ke Xu, Haijie Hu, Song Lu, Yan Huang, Xinfang Zhang, Mustafa A. Al Sibahee
In order to solve the problem of the low accuracy of automatic scoring for programming questions on MOOC platform, this paper proposed a multi-granularity feature fusion automatic scoring method based on potential semantic analysis. Abstract syntax tree (AST) is used to extract the features of student evaluation programs and standard answer template program, and calculate the similarity of features. According to whether the program is compiled or not, the similarity of multi-granularity features is analyzed by different strategies to score automatically. The experimental results show that the average accuracy of the method proposed in this paper outperforms the dynamic test method and the traditional static method using the test case results only, and the automatic machine scoring results are highly consistent with the human score.
{"title":"Student Programs Performance Scoring Based on Probabilistic Latent Semantic Analysis and Multi-granularity Feature Fusion for MOOC","authors":"Ke Xu, Haijie Hu, Song Lu, Yan Huang, Xinfang Zhang, Mustafa A. Al Sibahee","doi":"10.1109/ICNISC57059.2022.00179","DOIUrl":"https://doi.org/10.1109/ICNISC57059.2022.00179","url":null,"abstract":"In order to solve the problem of the low accuracy of automatic scoring for programming questions on MOOC platform, this paper proposed a multi-granularity feature fusion automatic scoring method based on potential semantic analysis. Abstract syntax tree (AST) is used to extract the features of student evaluation programs and standard answer template program, and calculate the similarity of features. According to whether the program is compiled or not, the similarity of multi-granularity features is analyzed by different strategies to score automatically. The experimental results show that the average accuracy of the method proposed in this paper outperforms the dynamic test method and the traditional static method using the test case results only, and the automatic machine scoring results are highly consistent with the human score.","PeriodicalId":286467,"journal":{"name":"2022 8th Annual International Conference on Network and Information Systems for Computers (ICNISC)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129399351","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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