Pub Date : 2019-12-31DOI: 10.11648/J.IJDST.20190504.12
Hashnayne Ahmed, Shek Ahmed
The exception of considering uncertainty could be very detrimental to the outcomes of any systems or phenomena in the long run. Stochastic Process describes the way of considering uncertainty in different sectors of our life. We use Linear Programming for planning at its best. It is also considered as the best optimization technique for taking decisions or planning. But this planning tool disappoints us in optimization for unexpected risk or stochasticity. Consideration of stochasticity for a farmer to devote land on different crops for harvesting could be some insurance for the farmer with the best possible outcomes. Stochastic Programming studies these types of optimization techniques with risk consideration for better decisions in every step of our life. In this paper, we described the early starting of uncertainty calculation or stochastic approach and the evolution of stochastic optimization fields. Stochastic optimization is rather important in the sense of uncertainty calculation than sensitivity analysis and works through data gained from experience. We also present a stochastic model with some uncertainty issues in harvesting to make better outcomes. Some application areas are also discussed.
{"title":"A Comparative Study on Harvesting Plan Predicting Insurance with Two-Stage Stochastic Analysis","authors":"Hashnayne Ahmed, Shek Ahmed","doi":"10.11648/J.IJDST.20190504.12","DOIUrl":"https://doi.org/10.11648/J.IJDST.20190504.12","url":null,"abstract":"The exception of considering uncertainty could be very detrimental to the outcomes of any systems or phenomena in the long run. Stochastic Process describes the way of considering uncertainty in different sectors of our life. We use Linear Programming for planning at its best. It is also considered as the best optimization technique for taking decisions or planning. But this planning tool disappoints us in optimization for unexpected risk or stochasticity. Consideration of stochasticity for a farmer to devote land on different crops for harvesting could be some insurance for the farmer with the best possible outcomes. Stochastic Programming studies these types of optimization techniques with risk consideration for better decisions in every step of our life. In this paper, we described the early starting of uncertainty calculation or stochastic approach and the evolution of stochastic optimization fields. Stochastic optimization is rather important in the sense of uncertainty calculation than sensitivity analysis and works through data gained from experience. We also present a stochastic model with some uncertainty issues in harvesting to make better outcomes. Some application areas are also discussed.","PeriodicalId":281025,"journal":{"name":"International Journal on Data Science and Technology","volume":"29 3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131013297","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}
Pub Date : 2019-12-25DOI: 10.11648/j.ijdst.20190503.11
Fisha Haileslassie, Adane Leta, Gizatie Desalegn, Melese Kalayu
Classification of marble image according to usage purpose and quality is an important procedure for export. Discrimination between marble varieties is a difficult task during selection, since it requires trainings and experience. Therefore, the development of automatic prediction model based on image processing is a potential application area to support experts across the world. In this study an attempt has been made to develop marble variety classification model by comparing color, texture and ensemble of color and texture. In view of this, a digital image processing technique based on combined texture and color features have been explored good classification performance to classify varieties of marble image. On the average 60 images were taken from each of the three marble varieties (Grade A, Grade B, Grade C). The total number of images taken was 180. For the classification model we applied image preprocessing techniques; image acquisition, image conversion, noise removal, image enhancement, edge detection and image binarization. For texture extraction gray level co-occurrence matrix, for color extraction color histogram was applied. For classification five textures and six color features were extracted from each marble image. To build the classification models for prediction of marble varieties, K-Nearest Neighbors (KNN), Artificial Neural Network (ANN) are investigated. Based on experimental results, ANN outperforms KNN. Quantitatively, an average accuracy of 83.3% and 93.7% is achieved KNN and ANN respectively for Grade A, Grade B, Grade C varieties with the combined feature sets of color and texture. This shows an encouraging result to design an applicable marble classification model. Marble fractured and vines of the images affect greatly the performance of the classifier and hence they are the future research direction that needs an investigation of generic noise removal and feature extraction techniques.
{"title":"Classification of Marble Using Image Processing","authors":"Fisha Haileslassie, Adane Leta, Gizatie Desalegn, Melese Kalayu","doi":"10.11648/j.ijdst.20190503.11","DOIUrl":"https://doi.org/10.11648/j.ijdst.20190503.11","url":null,"abstract":"Classification of marble image according to usage purpose and quality is an important procedure for export. Discrimination between marble varieties is a difficult task during selection, since it requires trainings and experience. Therefore, the development of automatic prediction model based on image processing is a potential application area to support experts across the world. In this study an attempt has been made to develop marble variety classification model by comparing color, texture and ensemble of color and texture. In view of this, a digital image processing technique based on combined texture and color features have been explored good classification performance to classify varieties of marble image. On the average 60 images were taken from each of the three marble varieties (Grade A, Grade B, Grade C). The total number of images taken was 180. For the classification model we applied image preprocessing techniques; image acquisition, image conversion, noise removal, image enhancement, edge detection and image binarization. For texture extraction gray level co-occurrence matrix, for color extraction color histogram was applied. For classification five textures and six color features were extracted from each marble image. To build the classification models for prediction of marble varieties, K-Nearest Neighbors (KNN), Artificial Neural Network (ANN) are investigated. Based on experimental results, ANN outperforms KNN. Quantitatively, an average accuracy of 83.3% and 93.7% is achieved KNN and ANN respectively for Grade A, Grade B, Grade C varieties with the combined feature sets of color and texture. This shows an encouraging result to design an applicable marble classification model. Marble fractured and vines of the images affect greatly the performance of the classifier and hence they are the future research direction that needs an investigation of generic noise removal and feature extraction techniques.","PeriodicalId":281025,"journal":{"name":"International Journal on Data Science and Technology","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128110006","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}
Pub Date : 2019-08-28DOI: 10.11648/J.IJDST.20190502.12
Zhenhua Sui
Social media has become more and more widely used nowadays. As the most popular media, a lot of information spread through Twitter, especially given the fact that U.S. President Trump has used Twitter as his main official free news publication outlet. Therefore, social media platforms like Twitter have become the important sources to extract information and then the information could be further analyzed through text analytics models for decision-making problems. In this paper, we first investigate several text analytics methods and then multiple tweets retrieving methods/software will be investigated: Twitter Analytics, Application for Twitter, Python plus Tweepy, and Next Analytics. Seven criteria related to features are applied to compare the methods for ease of use, extraction timing and capability to accommodate big data. Given that our results may be approximate because we might not be able to observe all the capability and features of the software, our results show that Python plus Tweepy method is the most ideal one when applying to big data projects (millions of tweets or above) and real time text data extraction. Next Analytics is the software that could retrieve historical text message in a more convenient way through Excel and is able to trace back further in time period, which could give much better capabilities in social media analysis.
{"title":"Social Media Data Extraction Method Benchmarking Comparison","authors":"Zhenhua Sui","doi":"10.11648/J.IJDST.20190502.12","DOIUrl":"https://doi.org/10.11648/J.IJDST.20190502.12","url":null,"abstract":"Social media has become more and more widely used nowadays. As the most popular media, a lot of information spread through Twitter, especially given the fact that U.S. President Trump has used Twitter as his main official free news publication outlet. Therefore, social media platforms like Twitter have become the important sources to extract information and then the information could be further analyzed through text analytics models for decision-making problems. In this paper, we first investigate several text analytics methods and then multiple tweets retrieving methods/software will be investigated: Twitter Analytics, Application for Twitter, Python plus Tweepy, and Next Analytics. Seven criteria related to features are applied to compare the methods for ease of use, extraction timing and capability to accommodate big data. Given that our results may be approximate because we might not be able to observe all the capability and features of the software, our results show that Python plus Tweepy method is the most ideal one when applying to big data projects (millions of tweets or above) and real time text data extraction. Next Analytics is the software that could retrieve historical text message in a more convenient way through Excel and is able to trace back further in time period, which could give much better capabilities in social media analysis.","PeriodicalId":281025,"journal":{"name":"International Journal on Data Science and Technology","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130021242","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}
Pub Date : 2019-08-28DOI: 10.11648/J.IJDST.20190502.13
He Song, Shaolin Hu
Kalman filter (KF) is composed of a set of recursion algorithms which can be used to estimate the optimal state of the linear system, and widely used in the control system, signal processing and other fields. In the practical application of the KF, it is an unavoidable problem that how faults or anomalies are infectious to the estimation value of state vectors in the linear system, which must be paid much attention to and solved down. In this paper, the effect of sensor faults and control input anomalies on the Kalman filtering values of state vectors is discussed, the transmission relationship is established to analyze the estimation deviation of state vectors which comes from pulse or step faults/anomalies, and a sufficient condition is deduced for the convergence of the estimation deviation of state vectors; Four different system models with 3-dimension state vector and 2-dimension observation vector are selected for simulation calculation and comparative analysis, simulation results show that sensor faults and control input anomalies in linear systems may cause significant deviations in the estimation value of state vectors for a long time, and there are distinct differences in the estimation value of state vectors. The research results provide a certain theoretical reference for us to analyze system fault types and to identify fault.
{"title":"Effect of Faults on Kalman Filter of State Vectors in Linear Systems","authors":"He Song, Shaolin Hu","doi":"10.11648/J.IJDST.20190502.13","DOIUrl":"https://doi.org/10.11648/J.IJDST.20190502.13","url":null,"abstract":"Kalman filter (KF) is composed of a set of recursion algorithms which can be used to estimate the optimal state of the linear system, and widely used in the control system, signal processing and other fields. In the practical application of the KF, it is an unavoidable problem that how faults or anomalies are infectious to the estimation value of state vectors in the linear system, which must be paid much attention to and solved down. In this paper, the effect of sensor faults and control input anomalies on the Kalman filtering values of state vectors is discussed, the transmission relationship is established to analyze the estimation deviation of state vectors which comes from pulse or step faults/anomalies, and a sufficient condition is deduced for the convergence of the estimation deviation of state vectors; Four different system models with 3-dimension state vector and 2-dimension observation vector are selected for simulation calculation and comparative analysis, simulation results show that sensor faults and control input anomalies in linear systems may cause significant deviations in the estimation value of state vectors for a long time, and there are distinct differences in the estimation value of state vectors. The research results provide a certain theoretical reference for us to analyze system fault types and to identify fault.","PeriodicalId":281025,"journal":{"name":"International Journal on Data Science and Technology","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129327192","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}
Pub Date : 2019-05-20DOI: 10.11648/J.IJDST.20190501.13
M. Abdul-Hak, N. Al-Holou, Youssef A. Bazzi, M. A. Tamer
Through the adoption of dedicated short-range communication (DSRC) wireless communication technology, intelligent transportation systems (ITS) will spur a new revolution in the U.S. transportation system. This paper is structured around providing drivers with the least-congested transportation route choices enabled by the ITS-envisioned vehicle-to-vehicle (V2V), vehicle-to-infrastructure (V2I), and infrastructure-to-vehicle (I2V) communication platforms. Recent research in vehicle navigation systems has proposed energy consumption and emission optimized routing methodologies using historical traffic data modeling. More than 50% of congestion in U.S. cities is nonrecurring congestion. Nonrecurring congestion reduces the availability of the traffic network, thus rendering historical traffic data-based systems insufficient in more than 50% of the cases. Real-time traffic data modeling provides an enhanced performance in traffic congestion assessment; however, greater performance is expected with a predictive traffic congestion model with increased certainty. This paper compares the conventional shortest path and fastest path vehicle routing methodologies and establish the improvement for environmentally friendly routing in a dynamic and predictive cost dependent traffic network based on Petri Net Modeling. The proposed routing algorithm is validated using a computer-based tool of choice.
{"title":"Predictive Vehicle Route Optimization in Intelligent Transportation Systems","authors":"M. Abdul-Hak, N. Al-Holou, Youssef A. Bazzi, M. A. Tamer","doi":"10.11648/J.IJDST.20190501.13","DOIUrl":"https://doi.org/10.11648/J.IJDST.20190501.13","url":null,"abstract":"Through the adoption of dedicated short-range communication (DSRC) wireless communication technology, intelligent transportation systems (ITS) will spur a new revolution in the U.S. transportation system. This paper is structured around providing drivers with the least-congested transportation route choices enabled by the ITS-envisioned vehicle-to-vehicle (V2V), vehicle-to-infrastructure (V2I), and infrastructure-to-vehicle (I2V) communication platforms. Recent research in vehicle navigation systems has proposed energy consumption and emission optimized routing methodologies using historical traffic data modeling. More than 50% of congestion in U.S. cities is nonrecurring congestion. Nonrecurring congestion reduces the availability of the traffic network, thus rendering historical traffic data-based systems insufficient in more than 50% of the cases. Real-time traffic data modeling provides an enhanced performance in traffic congestion assessment; however, greater performance is expected with a predictive traffic congestion model with increased certainty. This paper compares the conventional shortest path and fastest path vehicle routing methodologies and establish the improvement for environmentally friendly routing in a dynamic and predictive cost dependent traffic network based on Petri Net Modeling. The proposed routing algorithm is validated using a computer-based tool of choice.","PeriodicalId":281025,"journal":{"name":"International Journal on Data Science and Technology","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116807130","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}
Effective communication occurs when the receiver and sender both understand and synchronize the flow of information across board. The utility of language extends beyond human to human interaction and includes also, the use of syntactically formed programming languages to interact with digital systems. Nigeria has an estimate of over 450 languages, which makes it cumbersome to harmonize and put all into a single large repository for data mining. The goal of this paper is to firmly establish the importance of Information Technology in galvanizing Nigerian Languages and Mining scientific data thereof. The purpose of applying Information and Communication Technology (ICT) is to codify the process of extracting various underlying meanings in a language, processing the various idioms, proverbs and quaint statements in such language with the view of bringing out the creativity behind them. The authors explore the developmental stages and techniques of applying an artificial Intelligence system that scans through a given indigenous linguistic system to bring out the hidden facts therein. It is recommended that stakeholders in the ‘digital humanities’ adopt such mining platforms which helps in achieving greater insight into the diverse cultures and languages, in turn, promoting easy learning experience for indigenous languages.
{"title":"Digital Language Mining Platform for Nigerian Languages (DLMP)","authors":"Emejulu Augustine Obiajulu, Okpala Izunna Udebuana, Nwakanma Ifeanyi Cosmas","doi":"10.11648/J.IJDST.20190501.11","DOIUrl":"https://doi.org/10.11648/J.IJDST.20190501.11","url":null,"abstract":"Effective communication occurs when the receiver and sender both understand and synchronize the flow of information across board. The utility of language extends beyond human to human interaction and includes also, the use of syntactically formed programming languages to interact with digital systems. Nigeria has an estimate of over 450 languages, which makes it cumbersome to harmonize and put all into a single large repository for data mining. The goal of this paper is to firmly establish the importance of Information Technology in galvanizing Nigerian Languages and Mining scientific data thereof. The purpose of applying Information and Communication Technology (ICT) is to codify the process of extracting various underlying meanings in a language, processing the various idioms, proverbs and quaint statements in such language with the view of bringing out the creativity behind them. The authors explore the developmental stages and techniques of applying an artificial Intelligence system that scans through a given indigenous linguistic system to bring out the hidden facts therein. It is recommended that stakeholders in the ‘digital humanities’ adopt such mining platforms which helps in achieving greater insight into the diverse cultures and languages, in turn, promoting easy learning experience for indigenous languages.","PeriodicalId":281025,"journal":{"name":"International Journal on Data Science and Technology","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126563725","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}
Pub Date : 1900-01-01DOI: 10.11648/j.ijdst.20210704.11
K. Karoui, M. Zribi, Rochdi Feki
: The neuronal approach has interested a large number of researchers for analysis and in various fields. In this article, we use Kohonen Self-Organizing Map (SOM) which is an unsupervised neural network algorithm that projects high-dimensional data to predict dimension classification of the gender inequality index. This study covers 145 countries, demonstrates the relevance of the neural approach in this field of research. It was possible to determine an “optimal map” which involves a classification of countries and a view of the situation of inequalities in order to draw several relevant conclusions. The classification was carried out by the level of evolution of each dimension of the gender inequality index. Each group of countries classified in the same cell implies that these countries have suffered similar effects for the inequality indicators or that they have applied the same strategy to fight inequality. Grouping countries by zone shows, on the one hand, that countries with high inequalities are characterized by a strong correlation between dimensions. Second, African and Asian countries have the greatest deficit in education, health and the labor market.
{"title":"Analysis of the Index of Gender Inequality in the World by a Neural Approach","authors":"K. Karoui, M. Zribi, Rochdi Feki","doi":"10.11648/j.ijdst.20210704.11","DOIUrl":"https://doi.org/10.11648/j.ijdst.20210704.11","url":null,"abstract":": The neuronal approach has interested a large number of researchers for analysis and in various fields. In this article, we use Kohonen Self-Organizing Map (SOM) which is an unsupervised neural network algorithm that projects high-dimensional data to predict dimension classification of the gender inequality index. This study covers 145 countries, demonstrates the relevance of the neural approach in this field of research. It was possible to determine an “optimal map” which involves a classification of countries and a view of the situation of inequalities in order to draw several relevant conclusions. The classification was carried out by the level of evolution of each dimension of the gender inequality index. Each group of countries classified in the same cell implies that these countries have suffered similar effects for the inequality indicators or that they have applied the same strategy to fight inequality. Grouping countries by zone shows, on the one hand, that countries with high inequalities are characterized by a strong correlation between dimensions. Second, African and Asian countries have the greatest deficit in education, health and the labor market.","PeriodicalId":281025,"journal":{"name":"International Journal on Data Science and Technology","volume":"84 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124330110","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}
Pub Date : 1900-01-01DOI: 10.11648/j.ijdst.20220801.13
Ameen Abdullah Qaid Aqlan
{"title":"Data Mining and Revealing Hidden Sentiment in Tweets Using Spark","authors":"Ameen Abdullah Qaid Aqlan","doi":"10.11648/j.ijdst.20220801.13","DOIUrl":"https://doi.org/10.11648/j.ijdst.20220801.13","url":null,"abstract":"","PeriodicalId":281025,"journal":{"name":"International Journal on Data Science and Technology","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133974910","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}
Pub Date : 1900-01-01DOI: 10.11648/j.ijdst.20210704.12
Moges Tariku Tegenu
{"title":"Development of Intensity Duration Frequency Curves for Wolkite Town","authors":"Moges Tariku Tegenu","doi":"10.11648/j.ijdst.20210704.12","DOIUrl":"https://doi.org/10.11648/j.ijdst.20210704.12","url":null,"abstract":"","PeriodicalId":281025,"journal":{"name":"International Journal on Data Science and Technology","volume":"88 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132376673","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}
Pub Date : 1900-01-01DOI: 10.11648/j.ijdst.20220801.14
Ritu Chaturvedi, Christie I. Ezeife
{"title":"Customized Learning in Online Tutoring Systems by Mining Learning Units from Tasks and Examples","authors":"Ritu Chaturvedi, Christie I. Ezeife","doi":"10.11648/j.ijdst.20220801.14","DOIUrl":"https://doi.org/10.11648/j.ijdst.20220801.14","url":null,"abstract":"","PeriodicalId":281025,"journal":{"name":"International Journal on Data Science and Technology","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116521538","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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