This study aims to investigate the effect of big data analytics capability on big data values and business performance from the organizations performing big data analytics, which is one of the leading technologies in the fourth industrial revolution. For this study, the values of big data include transactional, strategic, transformational, and informational values. We conducted a survey and analyzed data from 200 professionals in organizations who had the experience of performing big data analytics. Structural equation modeling is used to test the research hypotheses. The results suggest that big data analytics capability has positive relationships with values of big data analytics and business performance. Of the values of big data analytics, however, the informational value of big data does not affect business performance. The results of this research are expected to provide researchers and practitioners who are interested in big data with useful information.
{"title":"The Relationships between Capabilities and Values of Big Data Analytics","authors":"Byeonghwa Park, M. Noh, Choong Kwon Lee","doi":"10.1145/3426020.3426052","DOIUrl":"https://doi.org/10.1145/3426020.3426052","url":null,"abstract":"This study aims to investigate the effect of big data analytics capability on big data values and business performance from the organizations performing big data analytics, which is one of the leading technologies in the fourth industrial revolution. For this study, the values of big data include transactional, strategic, transformational, and informational values. We conducted a survey and analyzed data from 200 professionals in organizations who had the experience of performing big data analytics. Structural equation modeling is used to test the research hypotheses. The results suggest that big data analytics capability has positive relationships with values of big data analytics and business performance. Of the values of big data analytics, however, the informational value of big data does not affect business performance. The results of this research are expected to provide researchers and practitioners who are interested in big data with useful information.","PeriodicalId":305132,"journal":{"name":"The 9th International Conference on Smart Media and Applications","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128209881","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}
Atmospheric haze limits the performance of the camera sensor, which results in capturing degraded hazy images. Removal of this haze from the observed images is a complicated task because of its ill-posed nature. This study offers the Deep-Dehaze network to retrieve the haze-free image. For the given input, the proposed architecture uses four feature extraction module to perform nonlinear feature extraction. We improvise the traditional Unet architecture and the residual network to design our architecture. We also introduced the L1 spatial-edge loss function, which enables our system to achieve better performance over the typical L1 and L2 loss function. Unlike other learning-based approaches, our network does not use any fusion connection for image dehazing. The experimental results show that our proposed Deep-Dehaze architecture surpasses previous state-of-the-art single image dehazing methods quantitatively and qualitatively. Our network achieves outstanding average PSNR score 24.5 on the RESIDE dataset.
{"title":"Single Image Dehazing Using End-to-End Deep-Dehaze Network","authors":"Masud An Nur Islam Fahim, H. Jung","doi":"10.1145/3426020.3426058","DOIUrl":"https://doi.org/10.1145/3426020.3426058","url":null,"abstract":"Atmospheric haze limits the performance of the camera sensor, which results in capturing degraded hazy images. Removal of this haze from the observed images is a complicated task because of its ill-posed nature. This study offers the Deep-Dehaze network to retrieve the haze-free image. For the given input, the proposed architecture uses four feature extraction module to perform nonlinear feature extraction. We improvise the traditional Unet architecture and the residual network to design our architecture. We also introduced the L1 spatial-edge loss function, which enables our system to achieve better performance over the typical L1 and L2 loss function. Unlike other learning-based approaches, our network does not use any fusion connection for image dehazing. The experimental results show that our proposed Deep-Dehaze architecture surpasses previous state-of-the-art single image dehazing methods quantitatively and qualitatively. Our network achieves outstanding average PSNR score 24.5 on the RESIDE dataset.","PeriodicalId":305132,"journal":{"name":"The 9th International Conference on Smart Media and Applications","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116620161","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}
O. Rehman, Zaroon Farrukh, A. Al-Busaidi, KyungJin Cha, Simon Park, Ibrahim M. H. Rahman
Cancer is a global challenge and the second leading cause of death worldwide as reported by the World Health Organization. With the current global pandemic caused by the novel coronavirus, cancer patients are identified as having increased risk of mortality. With the growing number of cancer patients every year, the need for a continuous and round the clock observation system has become quite imperative. An Internet of Things (IoT) based system for monitoring cancer patients has the potential to timely detect cancer related symptoms in its early stages, to continuously monitor cancer diagnosed patients and to monitor those that got cured for post-treatment measures. This paper proposes a multi-layered architecture of an IoT-based cancer observation system that can be utilized as a platform to remotely diagnose and monitor cancer patients. An implementation framework of the proposed system is also presented is this work, along with a prototype design of a Patient Side Unit (PSU) represented by a wearable wrist band. The proposed system has the potential to be applied as a solution for reducing expensive and exhausting hospital visits, while gaining similar quality of medical services when residing at home.
{"title":"IoT Powered Cancer Observation System.","authors":"O. Rehman, Zaroon Farrukh, A. Al-Busaidi, KyungJin Cha, Simon Park, Ibrahim M. H. Rahman","doi":"10.1145/3426020.3426111","DOIUrl":"https://doi.org/10.1145/3426020.3426111","url":null,"abstract":"Cancer is a global challenge and the second leading cause of death worldwide as reported by the World Health Organization. With the current global pandemic caused by the novel coronavirus, cancer patients are identified as having increased risk of mortality. With the growing number of cancer patients every year, the need for a continuous and round the clock observation system has become quite imperative. An Internet of Things (IoT) based system for monitoring cancer patients has the potential to timely detect cancer related symptoms in its early stages, to continuously monitor cancer diagnosed patients and to monitor those that got cured for post-treatment measures. This paper proposes a multi-layered architecture of an IoT-based cancer observation system that can be utilized as a platform to remotely diagnose and monitor cancer patients. An implementation framework of the proposed system is also presented is this work, along with a prototype design of a Patient Side Unit (PSU) represented by a wearable wrist band. The proposed system has the potential to be applied as a solution for reducing expensive and exhausting hospital visits, while gaining similar quality of medical services when residing at home.","PeriodicalId":305132,"journal":{"name":"The 9th International Conference on Smart Media and Applications","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116696702","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. Marahatta, Yaju Rajbhandari, A. Shrestha, Ajay Singh, A. Thapa, Seokjoo Shin
Digital smart meters hold many advantages over traditional analogue meters as the smart meters can be easily associated with the concept of smart grid. However, smart meters still face many challenges due to problems linked with communication mechanism such as low range, low data rate, high deployment and operating cost, and less reliability. To address such issues, smart metering can also be implemented with the deployment of a dedicated network formed with Low Power Wide Area Platform devices also known as LoRa devices. This paper discusses, how the LoRa technology can be implemented to solve the problems associated with smart metering especially considering rural energy system which uses microgrids. A simulation-based study has been done to analyse the applicability of LoRa technology in different architecture for smart metering purposes and also to identify a cost-effective and reliable way to implement smart metering, especially in rural microgrids.
{"title":"LoRa Mesh Network for Smart Metering in Rural Electrification","authors":"A. Marahatta, Yaju Rajbhandari, A. Shrestha, Ajay Singh, A. Thapa, Seokjoo Shin","doi":"10.1145/3426020.3426112","DOIUrl":"https://doi.org/10.1145/3426020.3426112","url":null,"abstract":"Digital smart meters hold many advantages over traditional analogue meters as the smart meters can be easily associated with the concept of smart grid. However, smart meters still face many challenges due to problems linked with communication mechanism such as low range, low data rate, high deployment and operating cost, and less reliability. To address such issues, smart metering can also be implemented with the deployment of a dedicated network formed with Low Power Wide Area Platform devices also known as LoRa devices. This paper discusses, how the LoRa technology can be implemented to solve the problems associated with smart metering especially considering rural energy system which uses microgrids. A simulation-based study has been done to analyse the applicability of LoRa technology in different architecture for smart metering purposes and also to identify a cost-effective and reliable way to implement smart metering, especially in rural microgrids.","PeriodicalId":305132,"journal":{"name":"The 9th International Conference on Smart Media and Applications","volume":"90 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114563983","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}
Using an electric field or an EPIC sensor that can measure contactless potential, we recorded electric field dwarfism generated by the human body, and conducted a study to detect hand movements and extract gesture frames using the recorded signals. Signals from the EPIC sensors include a large amount of power line noise (PLN) indoors. Using the fact that the PLN is shielded by human access to the sensor, a signal from the hand has been detected using the change. PLN consists mainly of a composite signal of 60 Hz frequency and 60 Hz harmonics indoors, of which motion is detected using a 120 Hz signal that is easy to identify the increase or decrease of the signal and has a low zero base of the signal. To measure a 120 Hz signal, use FFT to measure a spectral-separated frequency signal. The 120 Hz signal obtained in this way is believed to have been detected as it passed the threshold. If an action is detected, determine the frame based on the threshold. The final motion detection rate was about 90% and the frame accuracy was about 85%, and very fine signals were difficult to detect. This type of motion detection using EPIC sensors is the first study attempted domestically and internationally, and the results show room for development into a promising technology in motion detection applications.
{"title":"Study of Hand Motion Signal Detection and Frame Extraction Using Frequency Analysis by Electric Field Sensors✱","authors":"Sun-Yong Jung, Young-Chul Kim","doi":"10.1145/3426020.3426177","DOIUrl":"https://doi.org/10.1145/3426020.3426177","url":null,"abstract":"Using an electric field or an EPIC sensor that can measure contactless potential, we recorded electric field dwarfism generated by the human body, and conducted a study to detect hand movements and extract gesture frames using the recorded signals. Signals from the EPIC sensors include a large amount of power line noise (PLN) indoors. Using the fact that the PLN is shielded by human access to the sensor, a signal from the hand has been detected using the change. PLN consists mainly of a composite signal of 60 Hz frequency and 60 Hz harmonics indoors, of which motion is detected using a 120 Hz signal that is easy to identify the increase or decrease of the signal and has a low zero base of the signal. To measure a 120 Hz signal, use FFT to measure a spectral-separated frequency signal. The 120 Hz signal obtained in this way is believed to have been detected as it passed the threshold. If an action is detected, determine the frame based on the threshold. The final motion detection rate was about 90% and the frame accuracy was about 85%, and very fine signals were difficult to detect. This type of motion detection using EPIC sensors is the first study attempted domestically and internationally, and the results show room for development into a promising technology in motion detection applications.","PeriodicalId":305132,"journal":{"name":"The 9th International Conference on Smart Media and Applications","volume":"23 2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116403577","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 complex physical pattern of connections has been widely observed in many real-world systems, including social, biological, and technological networks. This ubiquitous characteristic of complex systems has also been observed in functional/structural networks of the human brain. In this study, we examined complex network topology of EEG-based functional connections while subjects performed voluntary movements. The complex system of the brain networks revealed an ideal balance between efficient information processing of local specialization and global integration, as reflected by small-world index across a wide range of EEG frequency-bands, i.e., theta, alpha, beta, and gamma oscillations. Further, directing subjective experience towards inner processes to voluntary movement altered the information processing of the brain networks wired by gamma oscillations. Brain functional networks demonstrate distinct small-world property depending on the strategy initiating voluntary movements while preserving the ubiquitous network characteristics found in many complex systems. Estimation of this alteration could prove helpful for understanding the brain mechanism of voluntary movements as well as for the evaluation of the efficiency of brain network.
{"title":"Complex information processing networks of EEG oscillations during voluntary movements","authors":"Han-Gue Jo, L. Wagels, M. Votinov, A. Puszta","doi":"10.1145/3426020.3426167","DOIUrl":"https://doi.org/10.1145/3426020.3426167","url":null,"abstract":"A complex physical pattern of connections has been widely observed in many real-world systems, including social, biological, and technological networks. This ubiquitous characteristic of complex systems has also been observed in functional/structural networks of the human brain. In this study, we examined complex network topology of EEG-based functional connections while subjects performed voluntary movements. The complex system of the brain networks revealed an ideal balance between efficient information processing of local specialization and global integration, as reflected by small-world index across a wide range of EEG frequency-bands, i.e., theta, alpha, beta, and gamma oscillations. Further, directing subjective experience towards inner processes to voluntary movement altered the information processing of the brain networks wired by gamma oscillations. Brain functional networks demonstrate distinct small-world property depending on the strategy initiating voluntary movements while preserving the ubiquitous network characteristics found in many complex systems. Estimation of this alteration could prove helpful for understanding the brain mechanism of voluntary movements as well as for the evaluation of the efficiency of brain network.","PeriodicalId":305132,"journal":{"name":"The 9th International Conference on Smart Media and Applications","volume":"2011 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125640436","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}
Thanh-Hung Vo, Gueesang Lee, Hyung-Jeong Yang, Sae-Ryung Kang, I. Oh, Soohyung Kim
Due to the increase of lung cancer in Korea, survival analysis for this kind of cancer gets emerging in recent years. Statistical and traditional machine learning methods usually used by medical doctors for this task. Which the success of deep learning in many tasks of computer vision, natural language processing, some studies starting to use DL for this task. Differ than many fields, data in medicine is difficult to collect and process, then the number of samples usually small and a little bit difficult to apply deep learning approach. In this study, we apply variational autoencoder together with the normal task of survival analysis and analysis the effect of it’s it on the target task. The results show that when combine the VAE with the target task, the network architecture less sensitive with the training size, and then could be trained with small number of sample. The limit of this study is using the internal dataset, then it is difficult to compare to the others.
{"title":"Multi-Task with Variational Autoencoder for Lung Cancer Prognosis on Clinical Data","authors":"Thanh-Hung Vo, Gueesang Lee, Hyung-Jeong Yang, Sae-Ryung Kang, I. Oh, Soohyung Kim","doi":"10.1145/3426020.3426080","DOIUrl":"https://doi.org/10.1145/3426020.3426080","url":null,"abstract":"Due to the increase of lung cancer in Korea, survival analysis for this kind of cancer gets emerging in recent years. Statistical and traditional machine learning methods usually used by medical doctors for this task. Which the success of deep learning in many tasks of computer vision, natural language processing, some studies starting to use DL for this task. Differ than many fields, data in medicine is difficult to collect and process, then the number of samples usually small and a little bit difficult to apply deep learning approach. In this study, we apply variational autoencoder together with the normal task of survival analysis and analysis the effect of it’s it on the target task. The results show that when combine the VAE with the target task, the network architecture less sensitive with the training size, and then could be trained with small number of sample. The limit of this study is using the internal dataset, then it is difficult to compare to the others.","PeriodicalId":305132,"journal":{"name":"The 9th International Conference on Smart Media and Applications","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128906862","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}
Deep neural networks have seen new milestones after the introduction of attention. Attention is defined as a mechanism in deep learning in which more priority or focus is given to a certain part of the data. A different variation of attention has been introduced in recent years. In this paper, we have used Convolutional Block Attention Module and Squeeze and Excitation Networks attention module in the Resnet-18 model for acoustic scene classification. Acoustic scene classification is a variation of sound classification in which we identify the place where the sound is recorded. Our study shows that squeeze and Excitation Networks, followed by Convolutional Block Attention Module, gives 2.97% more than the baseline Resnet-18 network.
{"title":"Comparison of Attention Module for Acoustic Scene Classification","authors":"Nisan Aryal, Sang-Woong Lee","doi":"10.1145/3426020.3426100","DOIUrl":"https://doi.org/10.1145/3426020.3426100","url":null,"abstract":"Deep neural networks have seen new milestones after the introduction of attention. Attention is defined as a mechanism in deep learning in which more priority or focus is given to a certain part of the data. A different variation of attention has been introduced in recent years. In this paper, we have used Convolutional Block Attention Module and Squeeze and Excitation Networks attention module in the Resnet-18 model for acoustic scene classification. Acoustic scene classification is a variation of sound classification in which we identify the place where the sound is recorded. Our study shows that squeeze and Excitation Networks, followed by Convolutional Block Attention Module, gives 2.97% more than the baseline Resnet-18 network.","PeriodicalId":305132,"journal":{"name":"The 9th International Conference on Smart Media and Applications","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115474738","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 modern smartphone is equipped with various types of sensors and modules such as accelerometer, magnetometer, gyroscope, Wi-Fi, Bluetooth low energy (BLE), etc. These sensors and modules can be utilized for a better localization estimation. The Particle filter (PF) has been widely accepted as a multisensory data fusion tool in indoor navigation. In this paper, we deployed PF by restricting the particle propagation with map-constraints and updating the weight of particle with the BLE beacon proximity. The proposed technique is experimentally accomplished on a smartphone with the real field deployment of BLE beacons. The results demonstrated that our system achieved a promising mean accuracy of 1.87 m in the testbed. Further, we represent the advantage of the map-constraints based PF over a typical PF for multisensory data fusion.
{"title":"Smartphone-based Indoor Navigation System Using Particle Filter and Map-Constraints","authors":"Suhardi Azliy Junoh, S. Subedi, Jae-Young Pyun","doi":"10.1145/3426020.3426126","DOIUrl":"https://doi.org/10.1145/3426020.3426126","url":null,"abstract":"A modern smartphone is equipped with various types of sensors and modules such as accelerometer, magnetometer, gyroscope, Wi-Fi, Bluetooth low energy (BLE), etc. These sensors and modules can be utilized for a better localization estimation. The Particle filter (PF) has been widely accepted as a multisensory data fusion tool in indoor navigation. In this paper, we deployed PF by restricting the particle propagation with map-constraints and updating the weight of particle with the BLE beacon proximity. The proposed technique is experimentally accomplished on a smartphone with the real field deployment of BLE beacons. The results demonstrated that our system achieved a promising mean accuracy of 1.87 m in the testbed. Further, we represent the advantage of the map-constraints based PF over a typical PF for multisensory data fusion.","PeriodicalId":305132,"journal":{"name":"The 9th International Conference on Smart Media and Applications","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131218287","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}
Monte Carlo Tree Search is one of the best algorithms for solving board game problems. However, Monte Carlo Tree Search is not suitable for real-time game problem because the problems have uncertainty of opponent’s action and a lot of simulation when determining behavior. We propose a Genetic Optimizing Method to solving the problems encountered when applying Monte Carlo Tree Search to real-time games. Our method helps solve the dilemma of Real-time Monte Carlo Tree Search between simulation and the number of branching factors by utilizing genetic algorithms. Finally, we applied our method to the Real-time Fighting Game to verify its performance.
{"title":"Genetic Optimizing Method for Real-time Monte Carlo Tree Search Problem","authors":"Man-Je Kim, Jong-Hyun Lee, C. Ahn","doi":"10.1145/3426020.3426030","DOIUrl":"https://doi.org/10.1145/3426020.3426030","url":null,"abstract":"Monte Carlo Tree Search is one of the best algorithms for solving board game problems. However, Monte Carlo Tree Search is not suitable for real-time game problem because the problems have uncertainty of opponent’s action and a lot of simulation when determining behavior. We propose a Genetic Optimizing Method to solving the problems encountered when applying Monte Carlo Tree Search to real-time games. Our method helps solve the dilemma of Real-time Monte Carlo Tree Search between simulation and the number of branching factors by utilizing genetic algorithms. Finally, we applied our method to the Real-time Fighting Game to verify its performance.","PeriodicalId":305132,"journal":{"name":"The 9th International Conference on Smart Media and Applications","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131291406","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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