Peipei Xu, Lianxiang Jiang, Bingui Xu, Mingxiang Li, Fei Wang
Low-cost, intelligence and short development cycle has become its trend of small satellites. A hybrid on-board avionics topology based on CAN bus and router was proposed. The telemetry was collected by On-Board Computer (OBC) via CAN bus, while the router integrated RS422, LVDS, Ethernet, Camera Link and TLK2711 interfaces, which support data rate varying from 1Mbps to 10Gbps and usually used by payloads, so it makes regular payloads integrated into the avionics much easier. The OBC used the PowerPC MPC8548 processor, which run at 1GHz. Plug and play mechanism was adopted to make the OBC recognize the devices dynamically when they powered on, which accelerated the system integration; furthermore, the software modules were also allowed to install or uninstall dynamically on-line for flexibility. For the modular and various interfaces supported, payload modules such as GNSS-R receiver, ADS-B receiver and camera electronics was easily integrated into the avionics box, so the signaling were transferred via the backplane instead of cables.
{"title":"Highly integrated modular avionics from platform to payload for micro-satellites","authors":"Peipei Xu, Lianxiang Jiang, Bingui Xu, Mingxiang Li, Fei Wang","doi":"10.1117/12.2671425","DOIUrl":"https://doi.org/10.1117/12.2671425","url":null,"abstract":"Low-cost, intelligence and short development cycle has become its trend of small satellites. A hybrid on-board avionics topology based on CAN bus and router was proposed. The telemetry was collected by On-Board Computer (OBC) via CAN bus, while the router integrated RS422, LVDS, Ethernet, Camera Link and TLK2711 interfaces, which support data rate varying from 1Mbps to 10Gbps and usually used by payloads, so it makes regular payloads integrated into the avionics much easier. The OBC used the PowerPC MPC8548 processor, which run at 1GHz. Plug and play mechanism was adopted to make the OBC recognize the devices dynamically when they powered on, which accelerated the system integration; furthermore, the software modules were also allowed to install or uninstall dynamically on-line for flexibility. For the modular and various interfaces supported, payload modules such as GNSS-R receiver, ADS-B receiver and camera electronics was easily integrated into the avionics box, so the signaling were transferred via the backplane instead of cables.","PeriodicalId":227528,"journal":{"name":"International Conference on Artificial Intelligence and Computer Engineering (ICAICE 2022)","volume":"188 ","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120830357","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 examines the literature on AI and libraries, examines the significant roles AI has played recently in industries related to libraries, and briefly describes relevant technical functions and their application characteristics in the library field. It begins with six key technologies: OCR, data mining, natural language processing, face recognition, knowledge mapping, and machine learning, and then makes a thorough analysis of each. Detailed analysis and summary of the results achieved in the practical application of AI, an analytical overview of the business functions related to AI in the library field on the development and reform of libraries and the current application status of various technologies, and the problems that libraries may encounter in the practical implementation of AI-related technologies are pointed out.
{"title":"Research on the application of artificial intelligence in the library sector","authors":"Zihan Xu","doi":"10.1117/12.2671477","DOIUrl":"https://doi.org/10.1117/12.2671477","url":null,"abstract":"This study examines the literature on AI and libraries, examines the significant roles AI has played recently in industries related to libraries, and briefly describes relevant technical functions and their application characteristics in the library field. It begins with six key technologies: OCR, data mining, natural language processing, face recognition, knowledge mapping, and machine learning, and then makes a thorough analysis of each. Detailed analysis and summary of the results achieved in the practical application of AI, an analytical overview of the business functions related to AI in the library field on the development and reform of libraries and the current application status of various technologies, and the problems that libraries may encounter in the practical implementation of AI-related technologies are pointed out.","PeriodicalId":227528,"journal":{"name":"International Conference on Artificial Intelligence and Computer Engineering (ICAICE 2022)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128544848","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}
Zesheng Xi, Bo Zhang, Yuanyuan Ma, Chuan He, Yu-Na Wang
Serverless computing aims to handle all the system administration operations needed in cloud computing, thus, to provide a paradigm that greatly simplifies cloud programming. However, the security in serverless computing is regarded as an independent technology. The lack of security consideration in the initial design makes it difficult to handle the increasingly complicated attack scenario in serverless computing, especially for the vulnerabilities and backdoor based network attack. In this paper, we propose MDSC, a mimic defense enabled paradigm for serverless computing. Specifically, MDSC paradigm introduces Dynamic Heterogeneous Redundancy (DHR) structural model to serverless computing, and make fully use of features introduced by serverless computing to achieve an intrinsic security system with acceptable costs. We show the feasibility of MDSC paradigm by implementing a trial of MDSC paradigm based on Kubernetes and Knative. Analysis and experimental results show that MDSC paradigm can achieve high level security with acceptable cost.
{"title":"The MDSC paradigm design for serverless computing defense","authors":"Zesheng Xi, Bo Zhang, Yuanyuan Ma, Chuan He, Yu-Na Wang","doi":"10.1117/12.2671158","DOIUrl":"https://doi.org/10.1117/12.2671158","url":null,"abstract":"Serverless computing aims to handle all the system administration operations needed in cloud computing, thus, to provide a paradigm that greatly simplifies cloud programming. However, the security in serverless computing is regarded as an independent technology. The lack of security consideration in the initial design makes it difficult to handle the increasingly complicated attack scenario in serverless computing, especially for the vulnerabilities and backdoor based network attack. In this paper, we propose MDSC, a mimic defense enabled paradigm for serverless computing. Specifically, MDSC paradigm introduces Dynamic Heterogeneous Redundancy (DHR) structural model to serverless computing, and make fully use of features introduced by serverless computing to achieve an intrinsic security system with acceptable costs. We show the feasibility of MDSC paradigm by implementing a trial of MDSC paradigm based on Kubernetes and Knative. Analysis and experimental results show that MDSC paradigm can achieve high level security with acceptable cost.","PeriodicalId":227528,"journal":{"name":"International Conference on Artificial Intelligence and Computer Engineering (ICAICE 2022)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123167299","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}
When an abnormal flight occurs, if the previous flight cannot take off as planned, it will affect the subsequent flight, resulting in a downward impact. Therefore, airlines often adopt different recovery measures (including flight delays, flight cancellations, aircraft swaps, etc.) to eliminate or mitigate the downward impact. When evaluating the pros and cons of the recovery plan, the loss of delay, loss of flight cancellation and loss of aircraft exchange are generally considered. However, in fact, many complex factors are ignored when measuring these losses, such as food, transportation and accommodation costs of crew and passengers caused by flight delay, and compensation for delay, etc. Expert systems are suitable for situations where no or little data is available and the business logic is complex, and their introduction into flight disruption impact assessment is an exploration of artificial intelligence in civil aviation. The evaluation of the impact of flight disruptions by an expert system not only quantifies the benefits of recovery solutions, but also provides some reference for evaluating the advantages and disadvantages of existing models and algorithms.
{"title":"Flight disruption impact assessment based on expert system","authors":"Bingjie Liang, Fujun Wang, Jun Bi","doi":"10.1117/12.2671099","DOIUrl":"https://doi.org/10.1117/12.2671099","url":null,"abstract":"When an abnormal flight occurs, if the previous flight cannot take off as planned, it will affect the subsequent flight, resulting in a downward impact. Therefore, airlines often adopt different recovery measures (including flight delays, flight cancellations, aircraft swaps, etc.) to eliminate or mitigate the downward impact. When evaluating the pros and cons of the recovery plan, the loss of delay, loss of flight cancellation and loss of aircraft exchange are generally considered. However, in fact, many complex factors are ignored when measuring these losses, such as food, transportation and accommodation costs of crew and passengers caused by flight delay, and compensation for delay, etc. Expert systems are suitable for situations where no or little data is available and the business logic is complex, and their introduction into flight disruption impact assessment is an exploration of artificial intelligence in civil aviation. The evaluation of the impact of flight disruptions by an expert system not only quantifies the benefits of recovery solutions, but also provides some reference for evaluating the advantages and disadvantages of existing models and algorithms.","PeriodicalId":227528,"journal":{"name":"International Conference on Artificial Intelligence and Computer Engineering (ICAICE 2022)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126916251","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}
Yang Zhao, Qing Liu, Tong Shang, Yingqiang Shang, R. Xia
With the increasing scale of high-voltage cable equipment in domestic urban power grids, it is necessary to deepen the intelligent construction of transmission lines, solve the common problems encountered in big data processing and edge side application of high-voltage cables, and take edge IOT agent as the cutting point for technical research. By studying edge computing, AI image recognition and intelligent linkage control model of cable channel business application, intelligent management and control of high-voltage cable line status, risk early warning, differentiated operation and maintenance decision, etc. can be realized, and the intrinsic safety level and lean operation and maintenance management ability of cable lines and channel equipment can be improved.
{"title":"Research on multi-service local processing and application based on edge IoT agent","authors":"Yang Zhao, Qing Liu, Tong Shang, Yingqiang Shang, R. Xia","doi":"10.1117/12.2671189","DOIUrl":"https://doi.org/10.1117/12.2671189","url":null,"abstract":"With the increasing scale of high-voltage cable equipment in domestic urban power grids, it is necessary to deepen the intelligent construction of transmission lines, solve the common problems encountered in big data processing and edge side application of high-voltage cables, and take edge IOT agent as the cutting point for technical research. By studying edge computing, AI image recognition and intelligent linkage control model of cable channel business application, intelligent management and control of high-voltage cable line status, risk early warning, differentiated operation and maintenance decision, etc. can be realized, and the intrinsic safety level and lean operation and maintenance management ability of cable lines and channel equipment can be improved.","PeriodicalId":227528,"journal":{"name":"International Conference on Artificial Intelligence and Computer Engineering (ICAICE 2022)","volume":"2013 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127394509","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}
Neurological diseases, including Alzheimer's disease and brain tumors, are the leading causes of death and disability worldwide. However, it is difficult for scientists to quantify the response of these deadly diseases to treatment. Existing neuron-based solutions have limited accuracy. Neuroblastoma cell lines have unique, irregular and concave morphology, which makes them show low precision scores in different cancer cell types. Based on this, this study proposes a new cell semantic segmentation network model. The model first enhances the original cell map, and then introduces the residual module and attention mechanism based on the classical U-Net network structure, which alleviates the problem of network degradation and improves the efficiency and effect of network training. The experimental results on the neuroblastoma cell line data set provided by Sartorius show that the segmentation accuracy of the proposed model is about fifteen percentage points higher than that of the classical U-Net model and one percentage point higher than that of the U-Net++ model.
{"title":"Research on neural cell image segmentation based on improved U-Net model","authors":"Zhehao Xiao","doi":"10.1117/12.2671318","DOIUrl":"https://doi.org/10.1117/12.2671318","url":null,"abstract":"Neurological diseases, including Alzheimer's disease and brain tumors, are the leading causes of death and disability worldwide. However, it is difficult for scientists to quantify the response of these deadly diseases to treatment. Existing neuron-based solutions have limited accuracy. Neuroblastoma cell lines have unique, irregular and concave morphology, which makes them show low precision scores in different cancer cell types. Based on this, this study proposes a new cell semantic segmentation network model. The model first enhances the original cell map, and then introduces the residual module and attention mechanism based on the classical U-Net network structure, which alleviates the problem of network degradation and improves the efficiency and effect of network training. The experimental results on the neuroblastoma cell line data set provided by Sartorius show that the segmentation accuracy of the proposed model is about fifteen percentage points higher than that of the classical U-Net model and one percentage point higher than that of the U-Net++ model.","PeriodicalId":227528,"journal":{"name":"International Conference on Artificial Intelligence and Computer Engineering (ICAICE 2022)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122240802","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}
Path planning algorithm is the basis of unmanned ground platform to realize unmanned driving function. Traditional path planning algorithms mostly regard path planning as a geometric problem, which has great limitations on the work of unmanned platforms in the current complex environment. The reinforcement learning algorithm focuses on online planning and has the advantage of continuing to explore and find better solutions on the basis of effective actions. This paper studies path planning of unmanned ground platform based on reinforcement learning method. Aiming at the problems of low flexibility and slow convergence of the current reinforcement learning method in path planning, this paper improves the Q-learning algorithm based on the reinforcement learning algorithm and conducts simulation experiments and analyzes the experimental results. The analysis shows that the path planning algorithm of unmanned ground platform based on reinforcement learning has obvious advantages in performance.
{"title":"Research on path planning algorithm of unmanned ground platform based on reinforcement learning","authors":"Pei Zhang, Chengye Zhang, Weilong Gai","doi":"10.1117/12.2671690","DOIUrl":"https://doi.org/10.1117/12.2671690","url":null,"abstract":"Path planning algorithm is the basis of unmanned ground platform to realize unmanned driving function. Traditional path planning algorithms mostly regard path planning as a geometric problem, which has great limitations on the work of unmanned platforms in the current complex environment. The reinforcement learning algorithm focuses on online planning and has the advantage of continuing to explore and find better solutions on the basis of effective actions. This paper studies path planning of unmanned ground platform based on reinforcement learning method. Aiming at the problems of low flexibility and slow convergence of the current reinforcement learning method in path planning, this paper improves the Q-learning algorithm based on the reinforcement learning algorithm and conducts simulation experiments and analyzes the experimental results. The analysis shows that the path planning algorithm of unmanned ground platform based on reinforcement learning has obvious advantages in performance.","PeriodicalId":227528,"journal":{"name":"International Conference on Artificial Intelligence and Computer Engineering (ICAICE 2022)","volume":"159 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123452430","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In recent years, stock price prediction has become a research hotspot. The price of the stock market is unstable, which often rises or falls sharply due to the national policies, which makes it difficult for investors to achieve stable returns in the stock market. With the rapid rise of artificial intelligence, computers have become flexible in dealing with mathematical problems. Therefore, the extraordinary computing power of computers has been used to analyze and predict the trend of the stock market. More and more computer professionals began to enter the financial market and use neural network to study the trend of the stock market. This paper uses BP neural network and LSTM neural network to learn and predict the stock data of Shanghai Composite Index from January 2012 to June 2022. LSTM is a kind of RNN, but it is superior to other neural networks. It can effectively deal with data forgetting and gradient explosion problems and bring reliability to the prediction results of the model. The two models are evaluated by analyzing MAE, MSE and the time required for model training. The results show that LSTM model can not only learn longer time span than BP model, but also better than BP model in MAE and MSE indexes, which provides some reference and guidance for the prediction of medium and long-term stocks.
{"title":"A comparative study of stock price prediction based on BP and LSTM neural network","authors":"Shujia Huang, Ben Wang, Lingbo Hao, Zebin Si","doi":"10.1117/12.2671216","DOIUrl":"https://doi.org/10.1117/12.2671216","url":null,"abstract":"In recent years, stock price prediction has become a research hotspot. The price of the stock market is unstable, which often rises or falls sharply due to the national policies, which makes it difficult for investors to achieve stable returns in the stock market. With the rapid rise of artificial intelligence, computers have become flexible in dealing with mathematical problems. Therefore, the extraordinary computing power of computers has been used to analyze and predict the trend of the stock market. More and more computer professionals began to enter the financial market and use neural network to study the trend of the stock market. This paper uses BP neural network and LSTM neural network to learn and predict the stock data of Shanghai Composite Index from January 2012 to June 2022. LSTM is a kind of RNN, but it is superior to other neural networks. It can effectively deal with data forgetting and gradient explosion problems and bring reliability to the prediction results of the model. The two models are evaluated by analyzing MAE, MSE and the time required for model training. The results show that LSTM model can not only learn longer time span than BP model, but also better than BP model in MAE and MSE indexes, which provides some reference and guidance for the prediction of medium and long-term stocks.","PeriodicalId":227528,"journal":{"name":"International Conference on Artificial Intelligence and Computer Engineering (ICAICE 2022)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126628622","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}
A more accurate target detection model is proposed in this research based on Yolov5 target detection algorithm, aiming at its low regression accuracy to the target boundary box. Firstly, coordinate attention mechanism is added to the backbone network to improve the position information of the perceived target in the underlying feature information. Secondly, GIOU is replaced with EIOU to improve the convergence speed. Finally, the feature extraction network is replaced with BiFPN to more efficiently fuse different feature information. Using PASCAL VOC 2007 and 2012 datasets and redividing the training set and verification set, this algorithm is better than the original algorithm mAP@0.5 increased by 2.9%, mAP@0.5:0.95 increased by 1.4%.
{"title":"Object detection algorithm based on improved Yolov5","authors":"Hua Wang, Jiang Yin, Shuang Zhang, Daishuang Hou","doi":"10.1117/12.2672682","DOIUrl":"https://doi.org/10.1117/12.2672682","url":null,"abstract":"A more accurate target detection model is proposed in this research based on Yolov5 target detection algorithm, aiming at its low regression accuracy to the target boundary box. Firstly, coordinate attention mechanism is added to the backbone network to improve the position information of the perceived target in the underlying feature information. Secondly, GIOU is replaced with EIOU to improve the convergence speed. Finally, the feature extraction network is replaced with BiFPN to more efficiently fuse different feature information. Using PASCAL VOC 2007 and 2012 datasets and redividing the training set and verification set, this algorithm is better than the original algorithm mAP@0.5 increased by 2.9%, mAP@0.5:0.95 increased by 1.4%.","PeriodicalId":227528,"journal":{"name":"International Conference on Artificial Intelligence and Computer Engineering (ICAICE 2022)","volume":"117 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132755097","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}
Users of the simulation software not only need to model the capability of each unit, but also need to create a decision maker for the units in the simulation software, to control ships, aircrafts and ground units to cooperates to achieve one goal. In this paper a new approach is constructed to create the decision maker. We use reinforcement learning based on global critic and local actor. The invention constructs an air isomorphic formation command method based on multiagent PPO algorithm. The evaluation network uses global information, so that the algorithm has the ability to evaluate global information and guide the agent to select actions that are beneficial to the global environment state. The input of the action network is local information, so that the agent can focus on local information.
{"title":"Global critic and local actor for campaign-tactic combat management in the joint operation simulation software","authors":"Yabin Wang, Peng Cui, Youjiang Li","doi":"10.1117/12.2671217","DOIUrl":"https://doi.org/10.1117/12.2671217","url":null,"abstract":"Users of the simulation software not only need to model the capability of each unit, but also need to create a decision maker for the units in the simulation software, to control ships, aircrafts and ground units to cooperates to achieve one goal. In this paper a new approach is constructed to create the decision maker. We use reinforcement learning based on global critic and local actor. The invention constructs an air isomorphic formation command method based on multiagent PPO algorithm. The evaluation network uses global information, so that the algorithm has the ability to evaluate global information and guide the agent to select actions that are beneficial to the global environment state. The input of the action network is local information, so that the agent can focus on local information.","PeriodicalId":227528,"journal":{"name":"International Conference on Artificial Intelligence and Computer Engineering (ICAICE 2022)","volume":"36 ","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"113992860","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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