Missing values often exist in scientific datasets. Therefore, practical methods for missing data imputation and classification are necessary for machine learning, data analysis. The k-Nearest Neighbor (KNN) algorithm is a simple and effective algorithm in missing data imputation and classification. This paper focuses on the missing data classification problem and proposes a new classification method based on the local mean k-nearest centroid neighbour. When making classification judgments, the proposed method examines the closeness and symmetrical arrangement of the k neighbours and adopts the local mean-based vector of the k centroid neighbours for each class. We run classification error experiments on six UCI datasets to see how well the proposed method performs when there is missing data. Experimental results show that the performance of our proposed method obtains a significant improvement compared to the most advanced KNN-based algorithms.
{"title":"An Improved k-Nearest Centroid Neighbor Classification Method for Incomplete Data","authors":"Yezhen Wang","doi":"10.1145/3558819.3565209","DOIUrl":"https://doi.org/10.1145/3558819.3565209","url":null,"abstract":"Missing values often exist in scientific datasets. Therefore, practical methods for missing data imputation and classification are necessary for machine learning, data analysis. The k-Nearest Neighbor (KNN) algorithm is a simple and effective algorithm in missing data imputation and classification. This paper focuses on the missing data classification problem and proposes a new classification method based on the local mean k-nearest centroid neighbour. When making classification judgments, the proposed method examines the closeness and symmetrical arrangement of the k neighbours and adopts the local mean-based vector of the k centroid neighbours for each class. We run classification error experiments on six UCI datasets to see how well the proposed method performs when there is missing data. Experimental results show that the performance of our proposed method obtains a significant improvement compared to the most advanced KNN-based algorithms.","PeriodicalId":373484,"journal":{"name":"Proceedings of the 7th International Conference on Cyber Security and Information Engineering","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129422564","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}
According to the control problem of lake water level, an intelligent water level control system based on Internet of Things is designed. The system takes the gateway controller with high performance and low power consumption as the core, combines ZigBee wireless sensor network and relay automatic control technology, realizes the monitoring and display of lake water environmental factors, intelligently controls the water pump according to the lake water level, and sends alarm information in abnormal situations.The framework of lake water intelligent management system based on Internet of things can also be divided into three layers: sensor layer, network layer and application layer. Through equipment selection and the development of wireless sensor network based on zstack, the set function is realized, and the operation test results are good.
{"title":"Implementation Scheme of Intelligent Water Level Control System based on Internet of Things","authors":"Fang-liang Liu","doi":"10.1145/3558819.3565200","DOIUrl":"https://doi.org/10.1145/3558819.3565200","url":null,"abstract":"According to the control problem of lake water level, an intelligent water level control system based on Internet of Things is designed. The system takes the gateway controller with high performance and low power consumption as the core, combines ZigBee wireless sensor network and relay automatic control technology, realizes the monitoring and display of lake water environmental factors, intelligently controls the water pump according to the lake water level, and sends alarm information in abnormal situations.The framework of lake water intelligent management system based on Internet of things can also be divided into three layers: sensor layer, network layer and application layer. Through equipment selection and the development of wireless sensor network based on zstack, the set function is realized, and the operation test results are good.","PeriodicalId":373484,"journal":{"name":"Proceedings of the 7th International Conference on Cyber Security and Information Engineering","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125419342","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 text of power grid business cost demand is complex and the description cannot be unified and standardized. As a single text description involves multiple business types, it is difficult to judge the business cost type. This paper presents a classification method of clustering specific cost types for business cost requirements text. Firstly, the business cost requirement text is transformed, and the key weight parameters in the Chinese word segmentation model are improved iteratively according to the cost representation report to obtain the global semantic vector. At the same time, the weights of recognition loss values of different samples were dynamically modified according to the difficulty of sample fitting. In this paper, the existing text clustering model is improved by k-means clustering algorithm model, and the cost types of 450 real business cost demand texts in the province are identified. The results show that the performance index value of the text classification method proposed in this paper is better than the commonly used text classification method, and the F1 value of the algorithm in this paper reaches more than 93%. The value of F1 is more than 3.5% higher than that of single BERT model.
{"title":"Research on classification method of business requirement text based on deep learning","authors":"Weibing Ding, S. Jin, Yan Ren, Fangzhou Liu","doi":"10.1145/3558819.3565082","DOIUrl":"https://doi.org/10.1145/3558819.3565082","url":null,"abstract":"The text of power grid business cost demand is complex and the description cannot be unified and standardized. As a single text description involves multiple business types, it is difficult to judge the business cost type. This paper presents a classification method of clustering specific cost types for business cost requirements text. Firstly, the business cost requirement text is transformed, and the key weight parameters in the Chinese word segmentation model are improved iteratively according to the cost representation report to obtain the global semantic vector. At the same time, the weights of recognition loss values of different samples were dynamically modified according to the difficulty of sample fitting. In this paper, the existing text clustering model is improved by k-means clustering algorithm model, and the cost types of 450 real business cost demand texts in the province are identified. The results show that the performance index value of the text classification method proposed in this paper is better than the commonly used text classification method, and the F1 value of the algorithm in this paper reaches more than 93%. The value of F1 is more than 3.5% higher than that of single BERT model.","PeriodicalId":373484,"journal":{"name":"Proceedings of the 7th International Conference on Cyber Security and Information Engineering","volume":"190 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132601353","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 key challenges of the financial industry are the volatility and complexity of the stock market, so how to make optimal trading strategy to maximize the total profit in all market conditions has become an important issue to the professional researchers and investors. This paper describes a hybrid stock trading strategy model based on long short-term memory (LSTM) networks. The Complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) algorithm and sample entropy (SE), combined with LSTM, are used to construct the integrated prediction model, which has dramatically improved the forecast precision. On the premise of accurate prediction, the extreme value theory (EVT) is introduced to improve the predictive ability of dynamic value at risk (VaR), which can manage the risk of portfolio. To forecast stock trends, the approach of analytic hierarchy process (AHP) is applied to assign weights to related factors. The final trading decisions are made by establishing trading signals and scoring models. Based on models above, the integrated trading strategy model is constructed as an automated trading decision tool. Taking Gold and Crude oil as examples, the profit results are proved to be decent through trading simulations.
{"title":"Algorithm Optimization Model of Trading Strategy based on CEEMDAN-SE-LSTM and Artificial Intelligence","authors":"Jingwen Zhang, Lei Fan, Kaijie Gu","doi":"10.1145/3558819.3565218","DOIUrl":"https://doi.org/10.1145/3558819.3565218","url":null,"abstract":"The key challenges of the financial industry are the volatility and complexity of the stock market, so how to make optimal trading strategy to maximize the total profit in all market conditions has become an important issue to the professional researchers and investors. This paper describes a hybrid stock trading strategy model based on long short-term memory (LSTM) networks. The Complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) algorithm and sample entropy (SE), combined with LSTM, are used to construct the integrated prediction model, which has dramatically improved the forecast precision. On the premise of accurate prediction, the extreme value theory (EVT) is introduced to improve the predictive ability of dynamic value at risk (VaR), which can manage the risk of portfolio. To forecast stock trends, the approach of analytic hierarchy process (AHP) is applied to assign weights to related factors. The final trading decisions are made by establishing trading signals and scoring models. Based on models above, the integrated trading strategy model is constructed as an automated trading decision tool. Taking Gold and Crude oil as examples, the profit results are proved to be decent through trading simulations.","PeriodicalId":373484,"journal":{"name":"Proceedings of the 7th International Conference on Cyber Security and Information Engineering","volume":"86 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133530955","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 social economy, the market mechanism tends to be perfect, which promotes the tourism industry to a new height. With the rapid development of tourism industry, management problems have become increasingly prominent. This paper makes an in-depth analysis of tourism planning and management from multiple perspectives. In view of the poor performance of the original optimal tourism route optimization model in obtaining the shortest route, this paper constructs the optimal tourism route optimization model based on ant colony optimization algorithm, sets the route selection process, and uses ant colony algorithm to complete the optimal route selection. According to the results of route selection, pheromone update rules and route model format are set to complete the construction of optimal route optimization model. Compared with the traditional model, the path chosen by the model is shorter and the cost is lower. At the same time, using BP neural network model and matlab calculation program to evaluate tourism resources can avoid the influence of subjective factors on the evaluation results to the greatest extent. On this basis, the evaluation model is designed, and the error value of the evaluation model is analyzed. This paper mainly focuses on the relevant measures of tourism management and puts forward a tourist flow forecasting model based on data mining. Firstly, the historical data of tourism flow are collected, and then the chaos algorithm is introduced to construct the learning sample of tourism flow prediction. Finally, the particle swarm optimization algorithm is introduced to optimize the parameters of the tourist flow forecasting model. The simulation results show that, compared with the BP neural network optimized by particle swarm optimization and support vector machine, this model can describe the changing characteristics of passenger flow in scenic spots more accurately, and the prediction error of passenger flow in scenic spots is much smaller than that of the contrast model, and a more ideal passenger flow prediction result is obtained, which can put forward a new solution strategy in the field of tourism management.
{"title":"Research on Optimization and Improvement of Intelligent Management System based on Big Data Mining and ant Colony Algorithm","authors":"Juncheng Ma","doi":"10.1145/3558819.3565090","DOIUrl":"https://doi.org/10.1145/3558819.3565090","url":null,"abstract":"With the development of social economy, the market mechanism tends to be perfect, which promotes the tourism industry to a new height. With the rapid development of tourism industry, management problems have become increasingly prominent. This paper makes an in-depth analysis of tourism planning and management from multiple perspectives. In view of the poor performance of the original optimal tourism route optimization model in obtaining the shortest route, this paper constructs the optimal tourism route optimization model based on ant colony optimization algorithm, sets the route selection process, and uses ant colony algorithm to complete the optimal route selection. According to the results of route selection, pheromone update rules and route model format are set to complete the construction of optimal route optimization model. Compared with the traditional model, the path chosen by the model is shorter and the cost is lower. At the same time, using BP neural network model and matlab calculation program to evaluate tourism resources can avoid the influence of subjective factors on the evaluation results to the greatest extent. On this basis, the evaluation model is designed, and the error value of the evaluation model is analyzed. This paper mainly focuses on the relevant measures of tourism management and puts forward a tourist flow forecasting model based on data mining. Firstly, the historical data of tourism flow are collected, and then the chaos algorithm is introduced to construct the learning sample of tourism flow prediction. Finally, the particle swarm optimization algorithm is introduced to optimize the parameters of the tourist flow forecasting model. The simulation results show that, compared with the BP neural network optimized by particle swarm optimization and support vector machine, this model can describe the changing characteristics of passenger flow in scenic spots more accurately, and the prediction error of passenger flow in scenic spots is much smaller than that of the contrast model, and a more ideal passenger flow prediction result is obtained, which can put forward a new solution strategy in the field of tourism management.","PeriodicalId":373484,"journal":{"name":"Proceedings of the 7th International Conference on Cyber Security and Information Engineering","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130494865","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 response to the current low efficiency and high energy consumption of the tinning process, we designed an ultrasonic automated tinning device based on the ellipsoidal focusing effect. The process control is carried out by a set program, so that the device has a high degree of automation, can accurately pick up, transfer and fix the workpiece to be processed (mainly SMT components), so that it can be fixed on the carrier plate, movement, and unmanned completion of large quantities of tinning, the device mainly uses the ellipsoidal focusing device to focus the ultrasonic wave to use the cavitation effect to remove the oxide film and complete the tinning, which can greatly improve the work efficiency, and avoid the harm of chemical and acoustic contamination during the tinning process. It can greatly improve the work efficiency and avoid the harm of chemical reagents and acoustic pollution to human body in the process of tinning.
{"title":"An automated ultrasonic tinning machine based on ellipsoidal focusing effect and intelligent computing","authors":"Shuai Yuan, Yanshen Huang","doi":"10.1145/3558819.3565101","DOIUrl":"https://doi.org/10.1145/3558819.3565101","url":null,"abstract":"In response to the current low efficiency and high energy consumption of the tinning process, we designed an ultrasonic automated tinning device based on the ellipsoidal focusing effect. The process control is carried out by a set program, so that the device has a high degree of automation, can accurately pick up, transfer and fix the workpiece to be processed (mainly SMT components), so that it can be fixed on the carrier plate, movement, and unmanned completion of large quantities of tinning, the device mainly uses the ellipsoidal focusing device to focus the ultrasonic wave to use the cavitation effect to remove the oxide film and complete the tinning, which can greatly improve the work efficiency, and avoid the harm of chemical and acoustic contamination during the tinning process. It can greatly improve the work efficiency and avoid the harm of chemical reagents and acoustic pollution to human body in the process of tinning.","PeriodicalId":373484,"journal":{"name":"Proceedings of the 7th International Conference on Cyber Security and Information Engineering","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130728932","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 order to reduce the power consumption of carbon dioxide sensor and meet the application needs of multi-sensor long-distance load of coal mine safety monitoring system, carbon dioxide gas molecules are used in 4.2 ∼ 4.32 μ A low-power carbon dioxide sensor based on led-pr optical structure is designed. Based on the analysis of the principle of infrared carbon dioxide detection, LED light source and PR detector are studied.The design principle of LED light source driving circuit and the working mechanism of realizing low-power measurement, photoelectric signal processing circuit and software program flow are introduced. The power consumption of infrared carbon dioxide sensor is reduced to 0.06 W, which meets the needs of low-power detection applications in coal mines.
{"title":"Research on carbon dioxide sensor based on non dispersive infrared technology","authors":"Zhixing Li, Xuemei Li, Yurong Wang, Peng Yu","doi":"10.1145/3558819.3565175","DOIUrl":"https://doi.org/10.1145/3558819.3565175","url":null,"abstract":"In order to reduce the power consumption of carbon dioxide sensor and meet the application needs of multi-sensor long-distance load of coal mine safety monitoring system, carbon dioxide gas molecules are used in 4.2 ∼ 4.32 μ A low-power carbon dioxide sensor based on led-pr optical structure is designed. Based on the analysis of the principle of infrared carbon dioxide detection, LED light source and PR detector are studied.The design principle of LED light source driving circuit and the working mechanism of realizing low-power measurement, photoelectric signal processing circuit and software program flow are introduced. The power consumption of infrared carbon dioxide sensor is reduced to 0.06 W, which meets the needs of low-power detection applications in coal mines.","PeriodicalId":373484,"journal":{"name":"Proceedings of the 7th International Conference on Cyber Security and Information Engineering","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116792421","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In recent years, with the rapid spread of Internet of Things technology, wireless communication technology has been developed and applied faster. Due to the rapid development of the Internet and modern communication technology, ZigBee wireless transmission technology based on IEEE802.15.4 has become another promising future after Bluetooth communication due to its advantages of short distance, less loss of functions, low cost and strong security. wireless transmission technology. In the future, ZigBee technology will become a short-range wireless transmission technology with competitive advantages. This paper introduces the definition and characteristics of ZigBee wireless transmission technology in detail, analyzes the application of ZigBee in some fields, and briefly discusses the application prospect. On the basis of understanding the knowledge of ZigBee technology wireless monitoring network system, and on the premise of studying its latest standard communication protocol, a wireless monitoring system based on this technology is designed, which provides a certain reference value for wide application.
{"title":"Zigbee Wireless Communication Technology and Its Application","authors":"Xueli Wang","doi":"10.1145/3558819.3561832","DOIUrl":"https://doi.org/10.1145/3558819.3561832","url":null,"abstract":"In recent years, with the rapid spread of Internet of Things technology, wireless communication technology has been developed and applied faster. Due to the rapid development of the Internet and modern communication technology, ZigBee wireless transmission technology based on IEEE802.15.4 has become another promising future after Bluetooth communication due to its advantages of short distance, less loss of functions, low cost and strong security. wireless transmission technology. In the future, ZigBee technology will become a short-range wireless transmission technology with competitive advantages. This paper introduces the definition and characteristics of ZigBee wireless transmission technology in detail, analyzes the application of ZigBee in some fields, and briefly discusses the application prospect. On the basis of understanding the knowledge of ZigBee technology wireless monitoring network system, and on the premise of studying its latest standard communication protocol, a wireless monitoring system based on this technology is designed, which provides a certain reference value for wide application.","PeriodicalId":373484,"journal":{"name":"Proceedings of the 7th International Conference on Cyber Security and Information Engineering","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131861379","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 ILS electromagnetic environment can easily be changed by large objects reflection, which result in degradation of the signal-in-space. Therefore, how to choose a suitable localizer antenna is very important. The higher the operating category of ILS is, the stricter requirements for the localizer course structure will be, especially for Category Ⅱ/III, In the paper, three typical antennas are compared by computer simulation, and the results show the scalloping of course structures under different types of antennas. Computer simulation is very helpful to study the selection of localizer antenna for ILS Category Ⅱ/III. Computer simulation can identify the structure disturbance before construction and help engineers find solutions. This can also effectively protect electromagnetic environment of the airport and ensure operation safety.
{"title":"Study on the type selection of localizer antenna in Category Ⅱ/III of instrument landing system","authors":"Mu-qiong Chen, Yanlong Sun","doi":"10.1145/3558819.3565121","DOIUrl":"https://doi.org/10.1145/3558819.3565121","url":null,"abstract":"The ILS electromagnetic environment can easily be changed by large objects reflection, which result in degradation of the signal-in-space. Therefore, how to choose a suitable localizer antenna is very important. The higher the operating category of ILS is, the stricter requirements for the localizer course structure will be, especially for Category Ⅱ/III, In the paper, three typical antennas are compared by computer simulation, and the results show the scalloping of course structures under different types of antennas. Computer simulation is very helpful to study the selection of localizer antenna for ILS Category Ⅱ/III. Computer simulation can identify the structure disturbance before construction and help engineers find solutions. This can also effectively protect electromagnetic environment of the airport and ensure operation safety.","PeriodicalId":373484,"journal":{"name":"Proceedings of the 7th International Conference on Cyber Security and Information Engineering","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124405919","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 campus second-hand commodity trading platform is a trading platform based on MVC architecture developed by using eclipse and SQL Sever database. The whole system is developed for campus C2C system between students (e-commerce between individuals). The campus C2C second-hand transaction system not only strengthens the exchange and purchase among students, but also provides students with better services.
{"title":"Design and implementation of intelligent second-hand platform based on big data MVC architecture and information processing","authors":"Shuo Zhang, Xiangrui Meng","doi":"10.1145/3558819.3565183","DOIUrl":"https://doi.org/10.1145/3558819.3565183","url":null,"abstract":"The campus second-hand commodity trading platform is a trading platform based on MVC architecture developed by using eclipse and SQL Sever database. The whole system is developed for campus C2C system between students (e-commerce between individuals). The campus C2C second-hand transaction system not only strengthens the exchange and purchase among students, but also provides students with better services.","PeriodicalId":373484,"journal":{"name":"Proceedings of the 7th International Conference on Cyber Security and Information Engineering","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127834350","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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