The growth and radiation detection performance of high efficiency perovskite single crystal is studied. First, high-quality perovskite single crystals were successfully prepared by optimizing the growth conditions. Secondly, the structure, optical and electrical properties are characterized in detail, and the single crystal is found to be excellent. Moreover, the application of perovskite single crystal in radiation detection was also studied, and the results showed its high sensitivity, low detection limit and rapid response. This paper provides theoretical basis and experimental support for the application of perovskite single crystal in the field of practical radiation detection.
{"title":"Study on the growth and radiation detection performance of high-efficiency perovskite single crystal","authors":"Yuanxiang Feng","doi":"10.54097/ejo4lmfbad","DOIUrl":"https://doi.org/10.54097/ejo4lmfbad","url":null,"abstract":"The growth and radiation detection performance of high efficiency perovskite single crystal is studied. First, high-quality perovskite single crystals were successfully prepared by optimizing the growth conditions. Secondly, the structure, optical and electrical properties are characterized in detail, and the single crystal is found to be excellent. Moreover, the application of perovskite single crystal in radiation detection was also studied, and the results showed its high sensitivity, low detection limit and rapid response. This paper provides theoretical basis and experimental support for the application of perovskite single crystal in the field of practical radiation detection.","PeriodicalId":475988,"journal":{"name":"Journal of Computing and Electronic Information Management","volume":"19 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140418826","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}
Autonomous driving is becoming increasingly prevalent nowadays. With the help of a number of images of car movement from the Kaggle self-driving dataset, we explore the feasibility of utilizing the images obtained to train a deep neural network to detect and predict the steering angle, which is the critical part of the car behavior. Since deep neural networks have emerged as powerful tools for training autonomous cars and learning about and improving their driving behaviors, we incorporate convolutional layers and additional layers in the deep neural network architecture so that it can capture the behaviors appropriately and provide effective results. We demonstrate that the implementation of this approach is successful and that the corresponding implementation highlights the potential of deep neural network in advancing autonomous car technology. Our comprehensive evaluation suggests that further research should concentrate on refining the network architecture and enhancing perception capabilities in order to deliver promising advances to the field.
{"title":"Autonomous Car Behavioral Training Using Deep Neural Network","authors":"Jiayi Gao","doi":"10.54097/mkny71cuq7","DOIUrl":"https://doi.org/10.54097/mkny71cuq7","url":null,"abstract":"Autonomous driving is becoming increasingly prevalent nowadays. With the help of a number of images of car movement from the Kaggle self-driving dataset, we explore the feasibility of utilizing the images obtained to train a deep neural network to detect and predict the steering angle, which is the critical part of the car behavior. Since deep neural networks have emerged as powerful tools for training autonomous cars and learning about and improving their driving behaviors, we incorporate convolutional layers and additional layers in the deep neural network architecture so that it can capture the behaviors appropriately and provide effective results. We demonstrate that the implementation of this approach is successful and that the corresponding implementation highlights the potential of deep neural network in advancing autonomous car technology. Our comprehensive evaluation suggests that further research should concentrate on refining the network architecture and enhancing perception capabilities in order to deliver promising advances to the field.","PeriodicalId":475988,"journal":{"name":"Journal of Computing and Electronic Information Management","volume":"22 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140421514","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 the face of the rapid development of modern science and technology, in order to ensure that the commercial level is in line with The Times, we need to change the traditional operation mode, and fully apply the science and technology to the e-commerce data analysis. Nowadays, driven by the era of big data development, social process, bring new changes to daily life, consumption, such as electricity is a new product, as the latest business model in modern society, has an important impact on market development, want to ensure electricity enterprises can meet the demand of social development, then from the perspective of electricity data analysis, effectively grasp the market changes, so as to more effective to carry out subsequent work, to fundamentally improve the comprehensive competitiveness of electricity enterprises in the modern market. In view of this, in order that the article wants to ensure the smooth progress of the e-commerce data analysis work, it should start with the business intelligence system under the background of big data, and combine it to ensure the steady development of the e-commerce industry.
{"title":"The application of business intelligence system based on big data in e-commerce data analysis","authors":"Xubo Ye, Mababa Jonilo","doi":"10.54097/51qmiveqpf","DOIUrl":"https://doi.org/10.54097/51qmiveqpf","url":null,"abstract":" In the face of the rapid development of modern science and technology, in order to ensure that the commercial level is in line with The Times, we need to change the traditional operation mode, and fully apply the science and technology to the e-commerce data analysis. Nowadays, driven by the era of big data development, social process, bring new changes to daily life, consumption, such as electricity is a new product, as the latest business model in modern society, has an important impact on market development, want to ensure electricity enterprises can meet the demand of social development, then from the perspective of electricity data analysis, effectively grasp the market changes, so as to more effective to carry out subsequent work, to fundamentally improve the comprehensive competitiveness of electricity enterprises in the modern market. In view of this, in order that the article wants to ensure the smooth progress of the e-commerce data analysis work, it should start with the business intelligence system under the background of big data, and combine it to ensure the steady development of the e-commerce industry.","PeriodicalId":475988,"journal":{"name":"Journal of Computing and Electronic Information Management","volume":"58 13","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140419750","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}
With the rapid development and application of technologies such as artificial intelligence, the Internet of Things, and cloud computing, data is showing explosive growth. In order to address the rising energy consumption due to the increasing number of devices in the traditional network architecture, software-defined networking and network function virtualization have been proposed. In this paper, we propose a reinforcement learning model based on actor-critic architecture. The service function chain deployment problem is mathematically modeled, and minimizing the total service function chain delay is taken as the optimization objective. The experimental results demonstrate that the service function chain deployment algorithm proposed in this paper is improved in terms of total system latency.
{"title":"The Optimized Deployment of Service Function Chain Based on Reinforcement Learning","authors":"Yibo Zhang","doi":"10.54097/ivvdqt8l76","DOIUrl":"https://doi.org/10.54097/ivvdqt8l76","url":null,"abstract":"With the rapid development and application of technologies such as artificial intelligence, the Internet of Things, and cloud computing, data is showing explosive growth. In order to address the rising energy consumption due to the increasing number of devices in the traditional network architecture, software-defined networking and network function virtualization have been proposed. In this paper, we propose a reinforcement learning model based on actor-critic architecture. The service function chain deployment problem is mathematically modeled, and minimizing the total service function chain delay is taken as the optimization objective. The experimental results demonstrate that the service function chain deployment algorithm proposed in this paper is improved in terms of total system latency.","PeriodicalId":475988,"journal":{"name":"Journal of Computing and Electronic Information Management","volume":"80 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140422633","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 is dedicated to exploring the optimization strategies for impurity dissolution during garment washing, aiming at minimizing the number of washes and water costs, while ensuring that the clothes are cleaned as expected. The proposed model was evaluated in a series of programming simulation experiments to obtain optimal solutions for several key problems. The study first applies an iterative algorithm to achieve the optimal rationing of the amount of washing and water consumption under specific conditions. Then, the variables were dynamically combined and resolved to deeply analyze the effects of initial solubility, decay coefficient, and initial contamination on the results. Finally, a mathematical model for detergent selection was developed through a linear programming model, which resulted in the optimal selection and lowest cost of different detergents based on the constraints and objective function.
{"title":"Research on Garment Washing Optimization Based on Linear Programming and Iterative Algorithm","authors":"Yuyuan Pan","doi":"10.54097/gxg3usoxhr","DOIUrl":"https://doi.org/10.54097/gxg3usoxhr","url":null,"abstract":"This study is dedicated to exploring the optimization strategies for impurity dissolution during garment washing, aiming at minimizing the number of washes and water costs, while ensuring that the clothes are cleaned as expected. The proposed model was evaluated in a series of programming simulation experiments to obtain optimal solutions for several key problems. The study first applies an iterative algorithm to achieve the optimal rationing of the amount of washing and water consumption under specific conditions. Then, the variables were dynamically combined and resolved to deeply analyze the effects of initial solubility, decay coefficient, and initial contamination on the results. Finally, a mathematical model for detergent selection was developed through a linear programming model, which resulted in the optimal selection and lowest cost of different detergents based on the constraints and objective function.","PeriodicalId":475988,"journal":{"name":"Journal of Computing and Electronic Information Management","volume":"15 10","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140418874","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}
Guocheng Li, Zhanying Li, Yinghao Zhang, Yang Xiao, Ming Chen
Accurate prediction of the state of charge (SOC) of lead-acid batteries is the key to ensuring battery life. In this paper, a new combined SOC prediction model IF-GRU (Isolation Forest, Gated Recurrent Unit) is proposed. The model combines the Isolation Forest anomaly detection algorithm and the Gated Recurrent Network. The Isolation Forest algorithm is used to detect anomalous and missing values in the raw data. Length dependence of the GRU network can be further utilized to perform high-accuracy SOC estimation by implementing a sliding window that takes into account the data's charging and discharging details. In addition, the conventional Adam optimizer is utilized to improve the convergence speed of model training. The experimental data demonstrate that the IF-GRU model proposed in this paper has higher prediction accuracy and convergence speed with a RMSE of 1.59% compared with traditional LSTM network, GRU network, and BP network.
{"title":"Prediction of State of Charge for Lead-acid Batteries Based on GRU Network and Isolated Forest","authors":"Guocheng Li, Zhanying Li, Yinghao Zhang, Yang Xiao, Ming Chen","doi":"10.54097/x5pmz998zq","DOIUrl":"https://doi.org/10.54097/x5pmz998zq","url":null,"abstract":"Accurate prediction of the state of charge (SOC) of lead-acid batteries is the key to ensuring battery life. In this paper, a new combined SOC prediction model IF-GRU (Isolation Forest, Gated Recurrent Unit) is proposed. The model combines the Isolation Forest anomaly detection algorithm and the Gated Recurrent Network. The Isolation Forest algorithm is used to detect anomalous and missing values in the raw data. Length dependence of the GRU network can be further utilized to perform high-accuracy SOC estimation by implementing a sliding window that takes into account the data's charging and discharging details. In addition, the conventional Adam optimizer is utilized to improve the convergence speed of model training. The experimental data demonstrate that the IF-GRU model proposed in this paper has higher prediction accuracy and convergence speed with a RMSE of 1.59% compared with traditional LSTM network, GRU network, and BP network.","PeriodicalId":475988,"journal":{"name":"Journal of Computing and Electronic Information Management","volume":"81 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140418063","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 paper starts with the literature review from Cleveland to Heer, and deeply discusses the role of perception in the visualization process, and the importance of understanding this role. The study points out that visualization is not only a process of conveying data, but also a process of interacting with the audience's perception. Effective visualization leads viewers to better understand and interpret the data, which also relies on a deep understanding of the perceptual process. By exploring the role of perception in the visualization, this paper highlights the importance of perception in the design process, and provides designers with suggestions on how to better utilize perceptual principles to improve the effectiveness and influence of data visualization.
{"title":"Discussion of the role of perception in visualization: From Cleveland to Heer, we understand how much and why it is important","authors":"Lingjuan Li","doi":"10.54097/s5xr5i9dmt","DOIUrl":"https://doi.org/10.54097/s5xr5i9dmt","url":null,"abstract":"This paper starts with the literature review from Cleveland to Heer, and deeply discusses the role of perception in the visualization process, and the importance of understanding this role. The study points out that visualization is not only a process of conveying data, but also a process of interacting with the audience's perception. Effective visualization leads viewers to better understand and interpret the data, which also relies on a deep understanding of the perceptual process. By exploring the role of perception in the visualization, this paper highlights the importance of perception in the design process, and provides designers with suggestions on how to better utilize perceptual principles to improve the effectiveness and influence of data visualization.","PeriodicalId":475988,"journal":{"name":"Journal of Computing and Electronic Information Management","volume":"101 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140422603","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}
The healthcare sector faces unprecedented challenges due to global population growth, aging trends, and the continuous outbreak of diseases. This paper explores the significance and potential of big data applications in healthcare. We discuss challenges such as population aging, chronic disease management, and infectious disease transmission, highlighting big data's role in addressing these issues. We examine big data application mining methods, including data collection, storage, preprocessing, cleaning, and analysis, with applications in disease prediction, early diagnosis, clinical decision support, and epidemiological research, illustrated through case studies. Challenges encompass data privacy, ethics, data cleaning, integration, and model interpretability, necessitating continuous technological innovation. Future trends include enhanced data privacy, technological innovation, and interdisciplinary collaboration. Collaboration among research institutions, healthcare organizations, and government agencies is encouraged to advance big data application mining and contribute to healthcare progress. Overcoming challenges and embracing opportunities promises a healthier and more prosperous future.
{"title":"Big Data Applications and Mining in the Healthcare Field","authors":"Fenglong Zhao","doi":"10.54097/d9u9iwdzcu","DOIUrl":"https://doi.org/10.54097/d9u9iwdzcu","url":null,"abstract":"The healthcare sector faces unprecedented challenges due to global population growth, aging trends, and the continuous outbreak of diseases. This paper explores the significance and potential of big data applications in healthcare. We discuss challenges such as population aging, chronic disease management, and infectious disease transmission, highlighting big data's role in addressing these issues. We examine big data application mining methods, including data collection, storage, preprocessing, cleaning, and analysis, with applications in disease prediction, early diagnosis, clinical decision support, and epidemiological research, illustrated through case studies. Challenges encompass data privacy, ethics, data cleaning, integration, and model interpretability, necessitating continuous technological innovation. Future trends include enhanced data privacy, technological innovation, and interdisciplinary collaboration. Collaboration among research institutions, healthcare organizations, and government agencies is encouraged to advance big data application mining and contribute to healthcare progress. Overcoming challenges and embracing opportunities promises a healthier and more prosperous future.","PeriodicalId":475988,"journal":{"name":"Journal of Computing and Electronic Information Management","volume":"43 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140419024","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 the case of supply chain security detection research, determining the component version number is a crucial task for the open source components of package-free management files. This paper aims to explore the new perspective of the determination of component version numbers based on various methods and to propose an effective method. First, by analyzing the source code of the component, you can try to determine the version number of the component by a specific mode, function, or variable in the code. This approach requires in-depth study and analysis of the source code to extract key code snippets that may contain version information. Second, the submission history of the component can be used to track the change of the version number. The modification content and update information for each version is obtained by viewing the submission records of the components in the version control system. Such an approach is relatively feasible for those components with a canonical versioning history. In addition, the metadata or metadata information of the component can be used to determine the version number. Some open-source components may contain version-related information in their code or documentation, such as release date, release instructions, version labels, etc. By parsing and extraction of these metadata, the version number of the components is obtained. In addition, the version number of the component can be obtained through communication with the community or the developer. Participate in the relevant open source community or contact component developers to consult them for information about the component version. This approach may require more time and resources, but is a viable option for those components that are difficult to determine the version number through other means. To sum up, the determination of the version number of open source components without package management files is an important link in supply chain security detection.
{"title":"Securing Supply Chains in Open Source Ecosystems: Methodologies for Determining Version Numbers of Components Without Package Management Files","authors":"Li Sun","doi":"10.54097/n8djwto1zb","DOIUrl":"https://doi.org/10.54097/n8djwto1zb","url":null,"abstract":"In the case of supply chain security detection research, determining the component version number is a crucial task for the open source components of package-free management files. This paper aims to explore the new perspective of the determination of component version numbers based on various methods and to propose an effective method. First, by analyzing the source code of the component, you can try to determine the version number of the component by a specific mode, function, or variable in the code. This approach requires in-depth study and analysis of the source code to extract key code snippets that may contain version information. Second, the submission history of the component can be used to track the change of the version number. The modification content and update information for each version is obtained by viewing the submission records of the components in the version control system. Such an approach is relatively feasible for those components with a canonical versioning history. In addition, the metadata or metadata information of the component can be used to determine the version number. Some open-source components may contain version-related information in their code or documentation, such as release date, release instructions, version labels, etc. By parsing and extraction of these metadata, the version number of the components is obtained. In addition, the version number of the component can be obtained through communication with the community or the developer. Participate in the relevant open source community or contact component developers to consult them for information about the component version. This approach may require more time and resources, but is a viable option for those components that are difficult to determine the version number through other means. To sum up, the determination of the version number of open source components without package management files is an important link in supply chain security detection.","PeriodicalId":475988,"journal":{"name":"Journal of Computing and Electronic Information Management","volume":"136 16","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140423319","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}
Qiaojuan Shan, Yusrita Binti Mohd Yusoff, Ariffin Bin Abdul Mutalib
The article conducted a comprehensive study on the development trends of animation. Through the collection of relevant literature on the subject, it examined the research's impact in various countries. The study utilized the CiteSpace analysis tool to conduct a detailed quantitative analysis of the evolution of the animation field in recent years, covering literature and academic publications from 1990 to 2022. In terms of analytical methods, the study employed various approaches, including visual network analysis, collaboration network analysis, keyword analysis, co-contribution network analysis, and co-citation network analysis. These methods were applied to detect visual networks within the animation field. Through these analytical techniques, the study showcased research hotspots, collaboration relationships, keyword trends, and academic contribution networks within the animation field. The findings revealed a steady increase in the number of publications and papers in the animation field in recent years, indicating that animation has become a highly researched topic. To delve deeper into the comparison between interactive animation and traditional animation, the study employed CiteSpace bibliometric technology and selected 4567 English-language and 1346 Chinese-language documents from the ScienceNet and China National Knowledge Infrastructure databases. Through a systematic evaluation, the study conducted a thorough analysis of the current status of interactive animation and traditional animation, highlighting their differences and projecting potential future trends for interactive animation. This comprehensive research provides a holistic perspective on academic studies in the animation field, contributing to a better understanding of the evolution and future directions of the animation industry.
{"title":"A comparative study of interactive animation and traditional animation","authors":"Qiaojuan Shan, Yusrita Binti Mohd Yusoff, Ariffin Bin Abdul Mutalib","doi":"10.54097/mubvx0s0vl","DOIUrl":"https://doi.org/10.54097/mubvx0s0vl","url":null,"abstract":"The article conducted a comprehensive study on the development trends of animation. Through the collection of relevant literature on the subject, it examined the research's impact in various countries. The study utilized the CiteSpace analysis tool to conduct a detailed quantitative analysis of the evolution of the animation field in recent years, covering literature and academic publications from 1990 to 2022. In terms of analytical methods, the study employed various approaches, including visual network analysis, collaboration network analysis, keyword analysis, co-contribution network analysis, and co-citation network analysis. These methods were applied to detect visual networks within the animation field. Through these analytical techniques, the study showcased research hotspots, collaboration relationships, keyword trends, and academic contribution networks within the animation field. The findings revealed a steady increase in the number of publications and papers in the animation field in recent years, indicating that animation has become a highly researched topic. To delve deeper into the comparison between interactive animation and traditional animation, the study employed CiteSpace bibliometric technology and selected 4567 English-language and 1346 Chinese-language documents from the ScienceNet and China National Knowledge Infrastructure databases. Through a systematic evaluation, the study conducted a thorough analysis of the current status of interactive animation and traditional animation, highlighting their differences and projecting potential future trends for interactive animation. This comprehensive research provides a holistic perspective on academic studies in the animation field, contributing to a better understanding of the evolution and future directions of the animation industry.","PeriodicalId":475988,"journal":{"name":"Journal of Computing and Electronic Information Management","volume":"41 10","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140422129","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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