Mass customization is essential for smart manufacturing. In particular, generating demand forecast is undoubtedly the most important part of any industry. Appropriate demand forecasts make S&OP quality, which greatly contributes to overall corporate management. In addition, proper stock can be maintained to save the costs of maintaining multiple warehouses. In this paper, we find out why mass customization is needed in smart manufacturing and find appropriate demand forecasting techniques by comparing the traditional time series technique ARIMA analysis with the nonlinear network model. Afterwards, the company develops an algorithm to evaluate the sales process by finalizing the production plan by evaluating the expected inventory through mathematical modelling.
{"title":"Demand Forecasting Based on Machine Learning for Mass Customization in Smart Manufacturing","authors":"Myungsoo Kim, Jongpil Jeong, Sang-Pil Bae","doi":"10.1145/3335656.3335658","DOIUrl":"https://doi.org/10.1145/3335656.3335658","url":null,"abstract":"Mass customization is essential for smart manufacturing. In particular, generating demand forecast is undoubtedly the most important part of any industry. Appropriate demand forecasts make S&OP quality, which greatly contributes to overall corporate management. In addition, proper stock can be maintained to save the costs of maintaining multiple warehouses. In this paper, we find out why mass customization is needed in smart manufacturing and find appropriate demand forecasting techniques by comparing the traditional time series technique ARIMA analysis with the nonlinear network model. Afterwards, the company develops an algorithm to evaluate the sales process by finalizing the production plan by evaluating the expected inventory through mathematical modelling.","PeriodicalId":396772,"journal":{"name":"Proceedings of the 2019 International Conference on Data Mining and Machine Learning","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121757088","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}
Waste incineration power generation, as a waste disposal method of "reduction, harmlessness and resource utilization", is an important measure to improve national well-being index and guarantee the achievement of overall well-off struggle. However, in the process of project promotion, it is faced with the problem of landing difficulties caused by "NIMBY". In order to solve this social problem scientifically and quantitatively, this paper innovatively constructs a network case analysis method based on reputation and benefit space, and abstracts a clustering center with scientific management significance by case clustering method to evolve reputation and benefit space. Based on this, a decision-making aided method based on similarity calculation is constructed to provide support for the transformation of "NIMBY" crisis.
{"title":"Assistant Decision-making Method of \"NIMBY\" Crisis Conversion in Waste Incineration Based on \"Reputation and Benefit Space\"","authors":"Enyuan Liu, Minxuan Li, Shengya Liu","doi":"10.1145/3335656.3335686","DOIUrl":"https://doi.org/10.1145/3335656.3335686","url":null,"abstract":"Waste incineration power generation, as a waste disposal method of \"reduction, harmlessness and resource utilization\", is an important measure to improve national well-being index and guarantee the achievement of overall well-off struggle. However, in the process of project promotion, it is faced with the problem of landing difficulties caused by \"NIMBY\". In order to solve this social problem scientifically and quantitatively, this paper innovatively constructs a network case analysis method based on reputation and benefit space, and abstracts a clustering center with scientific management significance by case clustering method to evolve reputation and benefit space. Based on this, a decision-making aided method based on similarity calculation is constructed to provide support for the transformation of \"NIMBY\" crisis.","PeriodicalId":396772,"journal":{"name":"Proceedings of the 2019 International Conference on Data Mining and Machine Learning","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130895066","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 studies the Online Judge System for assignments such as programming. Sometimes there are plagiarismsin codes submitted by students[1]. In addition to calculating the similarity degree between the codes, we also extract other features to determine whether there isplagiarismsuspicion of a submitted code or not. By using combination of Random Forest and Gradient Boosting Decision Tree, we also can getitssuspicion level. The model first calculates the similarity degree between the newly submitted code and all submitted codes, and determines plagiarism suspect. For some codes that are difficult to confirm whetherisplagiarismor not, we extract the programming style similarity degree, and the student's submission behavior pattern (such as similar target concentration degree) and other features, to create decision trees such as Random Forestand Gradient Boosting Decision Trees, which can help determine the level of plagiarism suspect. If the level is medium, the teacher will mark the code as plagiarized or not. Finally, the learning model is incrementally trained to improve the accuracy of the model and the classification results. Experiment results show that the accuracy rate can reach 95.9%. As a result, the model can prevent students from plagiarizing while minimizing the workload of the teacher.
{"title":"Research on Code Plagiarism Detection Model Based on Random Forest and Gradient Boosting Decision Tree","authors":"Huang Qiubo, Tang Jingdong, Fang Guo-zheng","doi":"10.1145/3335656.3335692","DOIUrl":"https://doi.org/10.1145/3335656.3335692","url":null,"abstract":"This paper studies the Online Judge System for assignments such as programming. Sometimes there are plagiarismsin codes submitted by students[1]. In addition to calculating the similarity degree between the codes, we also extract other features to determine whether there isplagiarismsuspicion of a submitted code or not. By using combination of Random Forest and Gradient Boosting Decision Tree, we also can getitssuspicion level. The model first calculates the similarity degree between the newly submitted code and all submitted codes, and determines plagiarism suspect. For some codes that are difficult to confirm whetherisplagiarismor not, we extract the programming style similarity degree, and the student's submission behavior pattern (such as similar target concentration degree) and other features, to create decision trees such as Random Forestand Gradient Boosting Decision Trees, which can help determine the level of plagiarism suspect. If the level is medium, the teacher will mark the code as plagiarized or not. Finally, the learning model is incrementally trained to improve the accuracy of the model and the classification results. Experiment results show that the accuracy rate can reach 95.9%. As a result, the model can prevent students from plagiarizing while minimizing the workload of the teacher.","PeriodicalId":396772,"journal":{"name":"Proceedings of the 2019 International Conference on Data Mining and Machine Learning","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114602273","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}
Qin Yu, Junliang Yu, Jie Hu, Kun Yang, Taijun Wang, Rongsheng Ding
With the rapid development of communication technology, the demand of rate of communication network is higher and higher, and the problem of energy consumption is becoming more and more serious. Data and Energy Integrated communication Networks (DEINs) can simultaneously transmit information and energy for the terminal, which greatly improves the convenience of the terminal and makes devices without batteries possible in future. This paper studies the joint design of transceivers in a full-duplex cloud access number-integrated network. The system model considers both upstream and downstream users. Considering the need for joint resource allocation for system uplink and downlink, full-duplex technology and self-interference caused by full-duplex technology are considered into the system. The optimization goal of this problem is to minimize the total power consumption under the uplink and downlink SINR and EH constraints. For this non-convex optimization problem, An algorithm combining ZF beamforming and MRT beamforming is proposed. In the hybrid beamforming algorithm, the zero-forcing (ZF) beamformer and MRT beamformer are linearly combined, which simplifies the optimization of the downlink beam vector to the optimization of the combination ratio. The proposed algorithm is simulated. Simulation results show that the power consumed in the half-duplex scenario is higher than that in the full duplex scenario. The time spent in the hybrid beamforming algorithm does not change with the increase in the number of RRH antennas.
{"title":"Joint Transceiver Design for Fully-Duplex Cloud-Access DEINs","authors":"Qin Yu, Junliang Yu, Jie Hu, Kun Yang, Taijun Wang, Rongsheng Ding","doi":"10.1145/3335656.3335691","DOIUrl":"https://doi.org/10.1145/3335656.3335691","url":null,"abstract":"With the rapid development of communication technology, the demand of rate of communication network is higher and higher, and the problem of energy consumption is becoming more and more serious. Data and Energy Integrated communication Networks (DEINs) can simultaneously transmit information and energy for the terminal, which greatly improves the convenience of the terminal and makes devices without batteries possible in future. This paper studies the joint design of transceivers in a full-duplex cloud access number-integrated network. The system model considers both upstream and downstream users. Considering the need for joint resource allocation for system uplink and downlink, full-duplex technology and self-interference caused by full-duplex technology are considered into the system. The optimization goal of this problem is to minimize the total power consumption under the uplink and downlink SINR and EH constraints. For this non-convex optimization problem, An algorithm combining ZF beamforming and MRT beamforming is proposed. In the hybrid beamforming algorithm, the zero-forcing (ZF) beamformer and MRT beamformer are linearly combined, which simplifies the optimization of the downlink beam vector to the optimization of the combination ratio. The proposed algorithm is simulated. Simulation results show that the power consumed in the half-duplex scenario is higher than that in the full duplex scenario. The time spent in the hybrid beamforming algorithm does not change with the increase in the number of RRH antennas.","PeriodicalId":396772,"journal":{"name":"Proceedings of the 2019 International Conference on Data Mining and Machine Learning","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128676020","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}
Fan Yan-ying, Zhang Zi-min, Chen Guan-ping, Zheng Shi-yong
We apply the rough set theory to the evaluation process of students. Firstly, we should create the information table of evaluation decision by using the discernibility matrix of rough set theory to do attribute reduction for the evaluation data and hence reduce unnecessary evaluation indicators. We will do value reduction and rule extraction algorithm based on this and then dig out the general rule of the evaluation for students from enormous evaluation data in order to provide decision basis for the work of students at school. This process of evaluation is totally about enabling the date talk and reduce the influence of human-dominated factors, as a result, the outcome of the evaluation will be more objective and fair.
{"title":"The extraction research on evaluation rules for students based on discernibility matrix","authors":"Fan Yan-ying, Zhang Zi-min, Chen Guan-ping, Zheng Shi-yong","doi":"10.1145/3335656.3335680","DOIUrl":"https://doi.org/10.1145/3335656.3335680","url":null,"abstract":"We apply the rough set theory to the evaluation process of students. Firstly, we should create the information table of evaluation decision by using the discernibility matrix of rough set theory to do attribute reduction for the evaluation data and hence reduce unnecessary evaluation indicators. We will do value reduction and rule extraction algorithm based on this and then dig out the general rule of the evaluation for students from enormous evaluation data in order to provide decision basis for the work of students at school. This process of evaluation is totally about enabling the date talk and reduce the influence of human-dominated factors, as a result, the outcome of the evaluation will be more objective and fair.","PeriodicalId":396772,"journal":{"name":"Proceedings of the 2019 International Conference on Data Mining and Machine Learning","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128934462","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 popularity of mobile internet accelerates the dissemination and communication of information and also changes the way tourists obtain information. Tourists no longer rely on the officially published travel brochures and TV programs to obtain tourism information. Through Twitter, Sina Weibo, Facebook and other We-Media channels, tourists can get first-hand information about the tourist destination. A large number of GPS trajectory data, such as taxi trajectory data and mobile signaling data, are generated through the widely existing GPS sensors and have been widely used in traffic and resident travel research. Since tourists are not familiar with the road distribution and traffic rules of the destination city, taxi car is an important travel method for non-local tourists to choose, and its OD(origin-destination) points reflect the travel needs and travel characteristics of tourists. Therefore, this paper applies the taxi data to the tourism research. In our study, CFSDPF clustering algorithm is adopted to cluster Sina Weibo data to form tourism ROI (region of interest), and the tourism ROI is used to cluster taxi OD data. The travel characteristics of tourists can be fully and accurately reflected through multi-source data. From two different scales of citywide and central city, we can comprehensively analyze the relationship between the travel characteristics of tourists in chengdu and the tourism ROI.
{"title":"Spatio-temporal Changes of Tourists Based on Multi-source data in Chengdu","authors":"R. Yuan","doi":"10.1145/3335656.3335696","DOIUrl":"https://doi.org/10.1145/3335656.3335696","url":null,"abstract":"The popularity of mobile internet accelerates the dissemination and communication of information and also changes the way tourists obtain information. Tourists no longer rely on the officially published travel brochures and TV programs to obtain tourism information. Through Twitter, Sina Weibo, Facebook and other We-Media channels, tourists can get first-hand information about the tourist destination. A large number of GPS trajectory data, such as taxi trajectory data and mobile signaling data, are generated through the widely existing GPS sensors and have been widely used in traffic and resident travel research. Since tourists are not familiar with the road distribution and traffic rules of the destination city, taxi car is an important travel method for non-local tourists to choose, and its OD(origin-destination) points reflect the travel needs and travel characteristics of tourists. Therefore, this paper applies the taxi data to the tourism research. In our study, CFSDPF clustering algorithm is adopted to cluster Sina Weibo data to form tourism ROI (region of interest), and the tourism ROI is used to cluster taxi OD data. The travel characteristics of tourists can be fully and accurately reflected through multi-source data. From two different scales of citywide and central city, we can comprehensively analyze the relationship between the travel characteristics of tourists in chengdu and the tourism ROI.","PeriodicalId":396772,"journal":{"name":"Proceedings of the 2019 International Conference on Data Mining and Machine Learning","volume":"14 4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130410592","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}
Crowdfunding has become an important channel for the transformation of innovation achievements. Exploring the healthy and rapid development of crowdfunding is a hot of academic research. This paper simulates the agglomeration effect in the development of crowdfunding mode through multi-agent system. And this paper findsthat properly supporting superior enterprises or high-quality projects and concentrating resources to stimulate innovation and transformation, are beneficial to improve the whole development level of the crowdfunding system without reducing the stability of the system operation.
{"title":"Simulation for Agglomeration Effect of Internet Crowdfunding Model","authors":"Yunjie Ji, YanXia Zhu","doi":"10.1145/3335656.3335682","DOIUrl":"https://doi.org/10.1145/3335656.3335682","url":null,"abstract":"Crowdfunding has become an important channel for the transformation of innovation achievements. Exploring the healthy and rapid development of crowdfunding is a hot of academic research. This paper simulates the agglomeration effect in the development of crowdfunding mode through multi-agent system. And this paper findsthat properly supporting superior enterprises or high-quality projects and concentrating resources to stimulate innovation and transformation, are beneficial to improve the whole development level of the crowdfunding system without reducing the stability of the system operation.","PeriodicalId":396772,"journal":{"name":"Proceedings of the 2019 International Conference on Data Mining and Machine Learning","volume":"190 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116532347","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}
Identifying the vehicle in front of road is an important research topic for active safety and intelligent driving of vehicles. A vehicle identification algorithm is proposed based on computer vision using supervised machine learning algorithm AdaBoost and Haar-like features. Firstly, in terms of feature selection, dimension reduction processing is performed from two aspects of feature type and feature size, and integral graph is applied to accelerate the calculation of Haar-like eigenvalues. Secondly, a more efficient classifier is constructed based on a small number of effective features, and a single strong classifier is used to identify and verify the vehicle in front. Finally, the whole vehicle identification algorithm is tested with the test data including 350 frames captured from the highway video set and 450 frames captured from the urban road video set. The result shows that the vehicle identification algorithm have a high detection rate and Lower detection error rate.
{"title":"Research on Vehicle Identification Method Based on Computer Vision","authors":"Zhou Yan, Deming Yuan, Zhou Jun","doi":"10.1145/3335656.3335700","DOIUrl":"https://doi.org/10.1145/3335656.3335700","url":null,"abstract":"Identifying the vehicle in front of road is an important research topic for active safety and intelligent driving of vehicles. A vehicle identification algorithm is proposed based on computer vision using supervised machine learning algorithm AdaBoost and Haar-like features. Firstly, in terms of feature selection, dimension reduction processing is performed from two aspects of feature type and feature size, and integral graph is applied to accelerate the calculation of Haar-like eigenvalues. Secondly, a more efficient classifier is constructed based on a small number of effective features, and a single strong classifier is used to identify and verify the vehicle in front. Finally, the whole vehicle identification algorithm is tested with the test data including 350 frames captured from the highway video set and 450 frames captured from the urban road video set. The result shows that the vehicle identification algorithm have a high detection rate and Lower detection error rate.","PeriodicalId":396772,"journal":{"name":"Proceedings of the 2019 International Conference on Data Mining and Machine Learning","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115182338","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 development of the Internet, the amount of daily output data is constantly increasing, and the value contained in the data is increasing as well; Meanwhile, the difficulty of data mining and the complexity of data analysis increases sharply. Developing a new data processing system is in urgent need especially in the macroeconomic field. Word cloud is a trendy way to visualize hot spot. At first, the design of a distributed batch-based website word cloud rendering platform will be explained, combining the processing mode of big data and the traditional web crawler design method to collect all the information of a website and present the data using word cloud. Then, this platform will be used for practice and applied to the measurement of systemic financial risks.
{"title":"The Design of Word Cloud Rendering Platform and Its Application on Measuring Systematic Financial Risks","authors":"Shifen Wang, Yining Sun","doi":"10.1145/3335656.3335698","DOIUrl":"https://doi.org/10.1145/3335656.3335698","url":null,"abstract":"With the development of the Internet, the amount of daily output data is constantly increasing, and the value contained in the data is increasing as well; Meanwhile, the difficulty of data mining and the complexity of data analysis increases sharply. Developing a new data processing system is in urgent need especially in the macroeconomic field. Word cloud is a trendy way to visualize hot spot. At first, the design of a distributed batch-based website word cloud rendering platform will be explained, combining the processing mode of big data and the traditional web crawler design method to collect all the information of a website and present the data using word cloud. Then, this platform will be used for practice and applied to the measurement of systemic financial risks.","PeriodicalId":396772,"journal":{"name":"Proceedings of the 2019 International Conference on Data Mining and Machine Learning","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125073815","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}
Public bicycles are a healthy and environmentally friendly means of transportation that facilitates people's travel. However, due to the uncertainty of urban travel, especially the tidal phenomenon, public bicycles often "difficult to borrow a car" and "return the car". This will result in unreasonable distribution of the site during the operation of the public bicycle system, unbalanced bicycle processes at various sites during peak hours, and unbalanced operation and management, which restricts the development of public bicycles. This paper uses the data of the San Francisco Bay Area as the experimental data of this paper, using Spark SQL and Spark Dataframe to analyze the use of public bicycle users and sites, according to the impact of different user types on the use of public bicycles, using K-means clustering algorithm Analyze the use of the site. Based on the Spark MLlib machine learning library, the gradient usage algorithm is used to predict daily usage.
{"title":"Analysis and Research on the Use Situation of Public Bicycles Based on Spark Machine Learning","authors":"Chengang Li, Yu Liu, Chengcheng Li","doi":"10.1145/3335656.3335704","DOIUrl":"https://doi.org/10.1145/3335656.3335704","url":null,"abstract":"Public bicycles are a healthy and environmentally friendly means of transportation that facilitates people's travel. However, due to the uncertainty of urban travel, especially the tidal phenomenon, public bicycles often \"difficult to borrow a car\" and \"return the car\". This will result in unreasonable distribution of the site during the operation of the public bicycle system, unbalanced bicycle processes at various sites during peak hours, and unbalanced operation and management, which restricts the development of public bicycles. This paper uses the data of the San Francisco Bay Area as the experimental data of this paper, using Spark SQL and Spark Dataframe to analyze the use of public bicycle users and sites, according to the impact of different user types on the use of public bicycles, using K-means clustering algorithm Analyze the use of the site. Based on the Spark MLlib machine learning library, the gradient usage algorithm is used to predict daily usage.","PeriodicalId":396772,"journal":{"name":"Proceedings of the 2019 International Conference on Data Mining and Machine Learning","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127294637","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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