Pub Date : 2023-09-22DOI: 10.26599/TST.2023.9010028
Ming Guo;Feng Xu;Shunfei Wang;Zhibo Wang;Ming Lu;Xiufen Cui;Xiao Ling
As a popular kind of stylized face, cartoon faces have rich application scenarios. It is challenging to create personalized 3D cartoon faces directly from 2D real photos. Besides, in order to adapt to more application scenarios, automatic style editing, and animation of cartoon faces is also a crucial problem that is urgently needed to be solved in the industry, but has not yet had a perfect solution. To solve this problem, we first propose “3D face cartoonizer”, which can generate high-quality 3D cartoon faces with texture when fed into 2D facial images. We contribute the first 3D cartoon face hybrid dataset and a new training strategy which first pretrains our network with low-quality triplets in a reconstruction-then-generation manner, and then finetunes it with high-quality triplets in an adversarial manner to fully leverage the hybrid dataset. Besides, we implement style editing for 3D cartoon faces based on k-means, which can be easily achieved without retrain the neural network. In addition, we propose a new cartoon faces' blendshape generation method, and based on this, realize the expression animation of 3D cartoon faces, enabling more practical applications. Our dataset and code will be released for future research.
{"title":"Synthesis, Style Editing, and Animation of 3D Cartoon Face","authors":"Ming Guo;Feng Xu;Shunfei Wang;Zhibo Wang;Ming Lu;Xiufen Cui;Xiao Ling","doi":"10.26599/TST.2023.9010028","DOIUrl":"https://doi.org/10.26599/TST.2023.9010028","url":null,"abstract":"As a popular kind of stylized face, cartoon faces have rich application scenarios. It is challenging to create personalized 3D cartoon faces directly from 2D real photos. Besides, in order to adapt to more application scenarios, automatic style editing, and animation of cartoon faces is also a crucial problem that is urgently needed to be solved in the industry, but has not yet had a perfect solution. To solve this problem, we first propose “3D face cartoonizer”, which can generate high-quality 3D cartoon faces with texture when fed into 2D facial images. We contribute the first 3D cartoon face hybrid dataset and a new training strategy which first pretrains our network with low-quality triplets in a reconstruction-then-generation manner, and then finetunes it with high-quality triplets in an adversarial manner to fully leverage the hybrid dataset. Besides, we implement style editing for 3D cartoon faces based on k-means, which can be easily achieved without retrain the neural network. In addition, we propose a new cartoon faces' blendshape generation method, and based on this, realize the expression animation of 3D cartoon faces, enabling more practical applications. Our dataset and code will be released for future research.","PeriodicalId":60306,"journal":{"name":"Tsinghua Science and Technology","volume":"29 2","pages":"506-516"},"PeriodicalIF":6.6,"publicationDate":"2023-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/5971803/10258149/10258246.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"68027654","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-22DOI: 10.26599/TST.2023.9010029
Xi Luo;Yanfei Lin;Rongxiao Guo;Xirui Zhao;Shangen Zhang;Xiaorong Gao
In this study, the effect of presentation rates on pupil dilation is investigated for target recognition in the Rapid Serial Visual Presentation (RSVP) paradigm. In this experiment, the RSVP paradigm with five different presentation rates, including 50, 80, 100, 150, and 200 ms, is designed. The pupillometry data of 15 subjects are collected and analyzed. The pupillometry results reveal that the peak and average amplitudes for pupil size and velocity at the 80-ms presentation rate are considerably higher than those at other presentation rates. The average amplitude of pupil acceleration at the 80-ms presentation rate is significantly higher than those at the other presentation rates. The latencies under 50- and 80-ms presentation rates are considerably lower than those of 100-, 150-, and 200-ms presentation rates. Additionally, no considerable differences are observed in the peak, average amplitude, and latency of pupil size, pupil velocity, and acceleration under 100-, 150-, and 200-ms presentation rates. These results reveal that with the increase in the presentation rate, pupil dilation first increases, then decreases, and later reaches saturation. The 80-ms presentation rate results in the largest point of pupil dilation. No correlation is observed between pupil dilation and recognition accuracy under the five presentation rates.
{"title":"Pupillometry Analysis of Rapid Serial Visual Presentation at Five Presentation Rates","authors":"Xi Luo;Yanfei Lin;Rongxiao Guo;Xirui Zhao;Shangen Zhang;Xiaorong Gao","doi":"10.26599/TST.2023.9010029","DOIUrl":"https://doi.org/10.26599/TST.2023.9010029","url":null,"abstract":"In this study, the effect of presentation rates on pupil dilation is investigated for target recognition in the Rapid Serial Visual Presentation (RSVP) paradigm. In this experiment, the RSVP paradigm with five different presentation rates, including 50, 80, 100, 150, and 200 ms, is designed. The pupillometry data of 15 subjects are collected and analyzed. The pupillometry results reveal that the peak and average amplitudes for pupil size and velocity at the 80-ms presentation rate are considerably higher than those at other presentation rates. The average amplitude of pupil acceleration at the 80-ms presentation rate is significantly higher than those at the other presentation rates. The latencies under 50- and 80-ms presentation rates are considerably lower than those of 100-, 150-, and 200-ms presentation rates. Additionally, no considerable differences are observed in the peak, average amplitude, and latency of pupil size, pupil velocity, and acceleration under 100-, 150-, and 200-ms presentation rates. These results reveal that with the increase in the presentation rate, pupil dilation first increases, then decreases, and later reaches saturation. The 80-ms presentation rate results in the largest point of pupil dilation. No correlation is observed between pupil dilation and recognition accuracy under the five presentation rates.","PeriodicalId":60306,"journal":{"name":"Tsinghua Science and Technology","volume":"29 2","pages":"543-552"},"PeriodicalIF":6.6,"publicationDate":"2023-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/5971803/10258149/10258247.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"68027657","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-22DOI: 10.26599/TST.2023.9010072
Weiqi Zhang;Zhenzhen Xie;Akshita Maradapu Vera Venkata Sai;Qasim Zia;Zaobo He;Guisheng Yin
The widespread availability of GPS has opened up a whole new market that provides a plethora of location-based services. Location-based social networks have become very popular as they provide end users like us with several such services utilizing GPS through our devices. However, when users utilize these services, they inevitably expose personal information such as their ID and sensitive location to the servers. Due to untrustworthy servers and malicious attackers with colossal background knowledge, users' personal information is at risk on these servers. Unfortunately, many privacy-preserving solutions for protecting trajectories have significantly decreased utility after deployment. We have come up with a new trajectory privacy protection solution that contraposes the area of interest for users. Firstly, Staying Points Detection Method based on Temporal-Spatial Restrictions (SPDM-TSR) is an interest area mining method based on temporal-spatial restrictions, which can clearly distinguish between staying and moving points. Additionally, our privacy protection mechanism focuses on the user's areas of interest rather than the entire trajectory. Furthermore, our proposed mechanism does not rely on third-party service providers and the attackers' background knowledge settings. We test our models on real datasets, and the results indicate that our proposed algorithm can provide a high standard privacy guarantee as well as data availability.
{"title":"A Local Differential Privacy Trajectory Protection Method Based on Temporal and Spatial Restrictions for Staying Detection","authors":"Weiqi Zhang;Zhenzhen Xie;Akshita Maradapu Vera Venkata Sai;Qasim Zia;Zaobo He;Guisheng Yin","doi":"10.26599/TST.2023.9010072","DOIUrl":"https://doi.org/10.26599/TST.2023.9010072","url":null,"abstract":"The widespread availability of GPS has opened up a whole new market that provides a plethora of location-based services. Location-based social networks have become very popular as they provide end users like us with several such services utilizing GPS through our devices. However, when users utilize these services, they inevitably expose personal information such as their ID and sensitive location to the servers. Due to untrustworthy servers and malicious attackers with colossal background knowledge, users' personal information is at risk on these servers. Unfortunately, many privacy-preserving solutions for protecting trajectories have significantly decreased utility after deployment. We have come up with a new trajectory privacy protection solution that contraposes the area of interest for users. Firstly, Staying Points Detection Method based on Temporal-Spatial Restrictions (SPDM-TSR) is an interest area mining method based on temporal-spatial restrictions, which can clearly distinguish between staying and moving points. Additionally, our privacy protection mechanism focuses on the user's areas of interest rather than the entire trajectory. Furthermore, our proposed mechanism does not rely on third-party service providers and the attackers' background knowledge settings. We test our models on real datasets, and the results indicate that our proposed algorithm can provide a high standard privacy guarantee as well as data availability.","PeriodicalId":60306,"journal":{"name":"Tsinghua Science and Technology","volume":"29 2","pages":"617-633"},"PeriodicalIF":6.6,"publicationDate":"2023-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/5971803/10258149/10258267.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"68028922","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-22DOI: 10.26599/TST.2023.9010031
Fei Ming;Wenyin Gong
During the past decade, research efforts have been gradually directed to the widely existing yet less noticed multimodal multi-objective optimization problems (MMOPs) in the multi-objective optimization community. Recently, researchers have begun to investigate enhancing the decision space diversity and preserving valuable dominated solutions to overcome the shortage caused by a preference for objective space convergence. However, many existing methods still have limitations, such as giving unduly high priorities to convergence and insufficient ability to enhance decision space diversity. To overcome these shortcomings, this article aims to explore a promising region (PR) and enhance the decision space diversity for handling MMOPs. Unlike traditional methods, we propose the use of non-dominated solutions to determine a limited region in the PR in the decision space, where the Pareto sets (PSs) are included, and explore this region to assist in solving MMOPs. Furthermore, we develop a novel neighbor distance measure that is more suitable for the complex geometry of PSs in the decision space than the crowding distance. Based on the above methods, we propose a novel dual-population-based coevolutionary algorithm. Experimental studies on three benchmark test suites demonstrates that our proposed methods can achieve promising performance and versatility on different MMOPs. The effectiveness of the proposed neighbor distance has also been justified through comparisons with crowding distance methods.
{"title":"Exploring a Promising Region and Enhancing Decision Space Diversity for Multimodal Multi-Objective Optimization","authors":"Fei Ming;Wenyin Gong","doi":"10.26599/TST.2023.9010031","DOIUrl":"https://doi.org/10.26599/TST.2023.9010031","url":null,"abstract":"During the past decade, research efforts have been gradually directed to the widely existing yet less noticed multimodal multi-objective optimization problems (MMOPs) in the multi-objective optimization community. Recently, researchers have begun to investigate enhancing the decision space diversity and preserving valuable dominated solutions to overcome the shortage caused by a preference for objective space convergence. However, many existing methods still have limitations, such as giving unduly high priorities to convergence and insufficient ability to enhance decision space diversity. To overcome these shortcomings, this article aims to explore a promising region (PR) and enhance the decision space diversity for handling MMOPs. Unlike traditional methods, we propose the use of non-dominated solutions to determine a limited region in the PR in the decision space, where the Pareto sets (PSs) are included, and explore this region to assist in solving MMOPs. Furthermore, we develop a novel neighbor distance measure that is more suitable for the complex geometry of PSs in the decision space than the crowding distance. Based on the above methods, we propose a novel dual-population-based coevolutionary algorithm. Experimental studies on three benchmark test suites demonstrates that our proposed methods can achieve promising performance and versatility on different MMOPs. The effectiveness of the proposed neighbor distance has also been justified through comparisons with crowding distance methods.","PeriodicalId":60306,"journal":{"name":"Tsinghua Science and Technology","volume":"29 2","pages":"325-342"},"PeriodicalIF":6.6,"publicationDate":"2023-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/5971803/10258149/10258255.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"68027589","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-22DOI: 10.26599/TST.2023.9010041
Xing Wang;Ling Wang;Chenxin Dong;Hao Ren;Ke Xing
On-demand food delivery (OFD) is gaining more and more popularity in modern society. As a kernel order assignment manner in OFD scenario, order recommendation directly influences the delivery efficiency of the platform and the delivery experience of riders. This paper addresses the dynamism of the order recommendation problem and proposes a reinforcement learning solution method. An actor-critic network based on long short term memory (LSTM) unit is designed to deal with the order-grabbing conflict between different riders. Besides, three rider sequencing rules are accordingly proposed to match different time steps of the LSTM unit with different riders. To test the performance of the proposed method, extensive experiments are conducted based on real data from Meituan delivery platform. The results demonstrate that the proposed reinforcement learning based order recommendation method can significantly increase the number of grabbed orders and reduce the number of order-grabbing conflicts, resulting in better delivery efficiency and experience for the platform and riders.
{"title":"Reinforcement Learning-Based Dynamic Order Recommendation for On-Demand Food Delivery","authors":"Xing Wang;Ling Wang;Chenxin Dong;Hao Ren;Ke Xing","doi":"10.26599/TST.2023.9010041","DOIUrl":"https://doi.org/10.26599/TST.2023.9010041","url":null,"abstract":"On-demand food delivery (OFD) is gaining more and more popularity in modern society. As a kernel order assignment manner in OFD scenario, order recommendation directly influences the delivery efficiency of the platform and the delivery experience of riders. This paper addresses the dynamism of the order recommendation problem and proposes a reinforcement learning solution method. An actor-critic network based on long short term memory (LSTM) unit is designed to deal with the order-grabbing conflict between different riders. Besides, three rider sequencing rules are accordingly proposed to match different time steps of the LSTM unit with different riders. To test the performance of the proposed method, extensive experiments are conducted based on real data from Meituan delivery platform. The results demonstrate that the proposed reinforcement learning based order recommendation method can significantly increase the number of grabbed orders and reduce the number of order-grabbing conflicts, resulting in better delivery efficiency and experience for the platform and riders.","PeriodicalId":60306,"journal":{"name":"Tsinghua Science and Technology","volume":"29 2","pages":"356-367"},"PeriodicalIF":6.6,"publicationDate":"2023-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/5971803/10258149/10258252.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"68027601","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-22DOI: 10.26599/TST.2023.9010074
Shuang Xu;Xiaojie Liu;Dengao Li;Jumin Zhao
Working as aerial base stations, mobile robotic agents can be formed as a wireless robotic network to provide network services for on-ground mobile devices in a target area. Herein, a challenging issue is how to deploy these mobile robotic agents to provide network services with good quality for more users, while considering the mobility of on-ground devices. In this paper, to solve this issue, we decouple the coverage problem into the vertical dimension and the horizontal dimension without any loss of optimization and introduce the network coverage model with maximum coverage range. Then, we propose a hybrid deployment algorithm based on the improved quick artificial bee colony. The algorithm is composed of a centralized deployment algorithm and a distributed one. The proposed deployment algorithm deploy a given number of mobile robotic agents to provide network services for the on-ground devices that are independent and identically distributed. Simulation results have demonstrated that the proposed algorithm deploys agents appropriately to cover more ground area and provide better coverage uniformity.
{"title":"IQABC-Based Hybrid Deployment Algorithm for Mobile Robotic Agents Providing Network Coverage","authors":"Shuang Xu;Xiaojie Liu;Dengao Li;Jumin Zhao","doi":"10.26599/TST.2023.9010074","DOIUrl":"https://doi.org/10.26599/TST.2023.9010074","url":null,"abstract":"Working as aerial base stations, mobile robotic agents can be formed as a wireless robotic network to provide network services for on-ground mobile devices in a target area. Herein, a challenging issue is how to deploy these mobile robotic agents to provide network services with good quality for more users, while considering the mobility of on-ground devices. In this paper, to solve this issue, we decouple the coverage problem into the vertical dimension and the horizontal dimension without any loss of optimization and introduce the network coverage model with maximum coverage range. Then, we propose a hybrid deployment algorithm based on the improved quick artificial bee colony. The algorithm is composed of a centralized deployment algorithm and a distributed one. The proposed deployment algorithm deploy a given number of mobile robotic agents to provide network services for the on-ground devices that are independent and identically distributed. Simulation results have demonstrated that the proposed algorithm deploys agents appropriately to cover more ground area and provide better coverage uniformity.","PeriodicalId":60306,"journal":{"name":"Tsinghua Science and Technology","volume":"29 2","pages":"589-604"},"PeriodicalIF":6.6,"publicationDate":"2023-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/5971803/10258149/10258303.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"68028924","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-22DOI: 10.26599/TST.2023.9010018
Rand Kouatly;Talha Ali Khan
This paper studies a high-speed text-independent Automatic Speaker Recognition (ASR) algorithm based on a multicore system's Gaussian Mixture Model (GMM). The high speech is achieved using parallel implementation of the feature's extraction and aggregation methods during training and testing procedures. Shared memory parallel programming techniques using both OpenMP and PThreads libraries are developed to accelerate the code and improve the performance of the ASR algorithm. The experimental results show speed-up improvements of around 3.2 on a personal laptop with Intel i5-6300HQ (2.3 GHz, four cores without hyper-threading, and 8 GB of RAM). In addition, a remarkable 100% speaker recognition accuracy is achieved.
{"title":"Performance of Text-Independent Automatic Speaker Recognition on a Multicore System","authors":"Rand Kouatly;Talha Ali Khan","doi":"10.26599/TST.2023.9010018","DOIUrl":"https://doi.org/10.26599/TST.2023.9010018","url":null,"abstract":"This paper studies a high-speed text-independent Automatic Speaker Recognition (ASR) algorithm based on a multicore system's Gaussian Mixture Model (GMM). The high speech is achieved using parallel implementation of the feature's extraction and aggregation methods during training and testing procedures. Shared memory parallel programming techniques using both OpenMP and PThreads libraries are developed to accelerate the code and improve the performance of the ASR algorithm. The experimental results show speed-up improvements of around 3.2 on a personal laptop with Intel i5-6300HQ (2.3 GHz, four cores without hyper-threading, and 8 GB of RAM). In addition, a remarkable 100% speaker recognition accuracy is achieved.","PeriodicalId":60306,"journal":{"name":"Tsinghua Science and Technology","volume":"29 2","pages":"447-456"},"PeriodicalIF":6.6,"publicationDate":"2023-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/5971803/10258149/10258152.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"68027598","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Alzheimer's disease (AD) is an irreversible and neurodegenerative disease that slowly impairs memory and neurocognitive function, but the etiology of AD is still unclear. With the explosive growth of electronic health data, the application of artificial intelligence (Al) in the healthcare setting provides excellent potential for exploring etiology and personalized treatment approaches, and improving the disease's diagnostic and prognostic outcome. This paper first briefly introduces Al technologies and applications in medicine, and then presents a comprehensive review of Al in AD. In simple, it includes etiology discovery based on genetic data, computer-aided diagnosis (CAD), computer-aided prognosis (CAP) of AD using multi-modality data (genetic, neuroimaging and linguistic data), and pharmacological or non-pharmacological approaches for treating AD. Later, some popular publicly available AD datasets are introduced, which are important for advancing Al technologies in AD analysis. Finally, core research challenges and future research directions are discussed.
{"title":"The Application of Artificial Intelligence in Alzheimer's Research","authors":"Qing Zhao;Hanrui Xu;Jianqiang Li;Faheem Akhtar Rajput;Liyan Qiao","doi":"10.26599/TST.2023.9010037","DOIUrl":"https://doi.org/10.26599/TST.2023.9010037","url":null,"abstract":"Alzheimer's disease (AD) is an irreversible and neurodegenerative disease that slowly impairs memory and neurocognitive function, but the etiology of AD is still unclear. With the explosive growth of electronic health data, the application of artificial intelligence (Al) in the healthcare setting provides excellent potential for exploring etiology and personalized treatment approaches, and improving the disease's diagnostic and prognostic outcome. This paper first briefly introduces Al technologies and applications in medicine, and then presents a comprehensive review of Al in AD. In simple, it includes etiology discovery based on genetic data, computer-aided diagnosis (CAD), computer-aided prognosis (CAP) of AD using multi-modality data (genetic, neuroimaging and linguistic data), and pharmacological or non-pharmacological approaches for treating AD. Later, some popular publicly available AD datasets are introduced, which are important for advancing Al technologies in AD analysis. Finally, core research challenges and future research directions are discussed.","PeriodicalId":60306,"journal":{"name":"Tsinghua Science and Technology","volume":"29 1","pages":"13-33"},"PeriodicalIF":6.6,"publicationDate":"2023-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/5971803/10225032/10225294.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"68001430","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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