Pub Date : 2017-11-09DOI: 10.1142/S2424922X17500085
S. Raghukanth, B. Kavitha, J. Dhanya
This paper explores a new method to model and forecast the global earthquake energy release time series. The ISC-GEM catalogue of global events with magnitude Mw ≥ 6.4 is used in this study. The magnitudes of individual events are converted into seismic energy using an empirical relation. The annual earthquake energy time series is constructed by adding the energy releases of all the events in a particular year. Then, the energy time series is decomposed into finite number of intrinsic mode functions (IMFs) using empirical mode decomposition (EMD) technique. The periodicities of these IMF’s and their contribution to the total variance of the data are examined to identify the influence of natural phenomenon on earthquake energy release. The artificial neural network technique (ANN) is further used for modeling the energy-time series. The model is verified with an independent subset of data and validated using statistical parameters. The forecast of the annual earthquake energy release is provided for the y...
{"title":"Forecasting of Global Earthquake Energy Time Series","authors":"S. Raghukanth, B. Kavitha, J. Dhanya","doi":"10.1142/S2424922X17500085","DOIUrl":"https://doi.org/10.1142/S2424922X17500085","url":null,"abstract":"This paper explores a new method to model and forecast the global earthquake energy release time series. The ISC-GEM catalogue of global events with magnitude Mw ≥ 6.4 is used in this study. The magnitudes of individual events are converted into seismic energy using an empirical relation. The annual earthquake energy time series is constructed by adding the energy releases of all the events in a particular year. Then, the energy time series is decomposed into finite number of intrinsic mode functions (IMFs) using empirical mode decomposition (EMD) technique. The periodicities of these IMF’s and their contribution to the total variance of the data are examined to identify the influence of natural phenomenon on earthquake energy release. The artificial neural network technique (ANN) is further used for modeling the energy-time series. The model is verified with an independent subset of data and validated using statistical parameters. The forecast of the annual earthquake energy release is provided for the y...","PeriodicalId":47145,"journal":{"name":"Advances in Data Science and Adaptive Analysis","volume":"24 1","pages":"1750008:1-1750008:20"},"PeriodicalIF":0.6,"publicationDate":"2017-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87340469","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 : 2017-07-30DOI: 10.1142/S2424922X1750005X
A. Ivanescu
Inference methods are proposed for the bivariate mean function of a continuous stochastic process with a two-dimensional domain. Nonparametric bivariate estimation is facilitated by thresholded projection estimators. Estimators adapt to the sparsity of the bivariate function. Oracle inequality results are developed to describe the adaptive inference methods. The construction of nonparametric bivariate confidence bands is presented. Implementation results show the applicability of the methods in practice.
{"title":"Adaptive Inference for the Bivariate Mean Function in Functional Data","authors":"A. Ivanescu","doi":"10.1142/S2424922X1750005X","DOIUrl":"https://doi.org/10.1142/S2424922X1750005X","url":null,"abstract":"Inference methods are proposed for the bivariate mean function of a continuous stochastic process with a two-dimensional domain. Nonparametric bivariate estimation is facilitated by thresholded projection estimators. Estimators adapt to the sparsity of the bivariate function. Oracle inequality results are developed to describe the adaptive inference methods. The construction of nonparametric bivariate confidence bands is presented. Implementation results show the applicability of the methods in practice.","PeriodicalId":47145,"journal":{"name":"Advances in Data Science and Adaptive Analysis","volume":"80 1","pages":"1750005:1-1750005:29"},"PeriodicalIF":0.6,"publicationDate":"2017-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79099281","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 : 2017-07-30DOI: 10.1142/S2424922X17500061
J. Pierret, S. Shen
This paper develops a web database application to make space-time 4D visual delivery (4DVD) of big climate data. The delivery system shows climate data in a 4D space-time box and allows users to visualize the data. Users can zoom in or out to help identify desired information for particular locations. Data can then be downloaded for the spatial maps and historical climate time series of a given location after the maps and time series are identified to be useful. These functions enable a user to quickly reach the core interested features without downloading the entire dataset in advance, which saves both time and storage space. The 4DVD system has many graphical display options such as displaying data on a round globe or on a 2D map with detailed background topographic images. It can animate maps and show time series. The combination of these features makes the system a convenient and attractive multimedia tool for classrooms, museums, and households, in addition to climate research scientists, industrial ...
{"title":"4D Visual Delivery of Big Climate Data: A Fast Web Database Application System","authors":"J. Pierret, S. Shen","doi":"10.1142/S2424922X17500061","DOIUrl":"https://doi.org/10.1142/S2424922X17500061","url":null,"abstract":"This paper develops a web database application to make space-time 4D visual delivery (4DVD) of big climate data. The delivery system shows climate data in a 4D space-time box and allows users to visualize the data. Users can zoom in or out to help identify desired information for particular locations. Data can then be downloaded for the spatial maps and historical climate time series of a given location after the maps and time series are identified to be useful. These functions enable a user to quickly reach the core interested features without downloading the entire dataset in advance, which saves both time and storage space. The 4DVD system has many graphical display options such as displaying data on a round globe or on a 2D map with detailed background topographic images. It can animate maps and show time series. The combination of these features makes the system a convenient and attractive multimedia tool for classrooms, museums, and households, in addition to climate research scientists, industrial ...","PeriodicalId":47145,"journal":{"name":"Advances in Data Science and Adaptive Analysis","volume":"58 1","pages":"1750006:1-1750006:24"},"PeriodicalIF":0.6,"publicationDate":"2017-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89288034","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 : 2017-07-30DOI: 10.1142/S2424922X17500073
Mehreen Ahmed, H. Afzal, A. Majeed, Behram Khan
The information-based prediction models using machine learning techniques have gained massive popularity during the last few decades. Such models have been applied in a number of domains such as medical diagnosis, crime prediction, movies rating, etc. Similar is the trend in telecom industry where prediction models have been applied to predict the dissatisfied customers who are likely to change the service provider. Due to immense financial cost of customer churn in telecom, the companies from all over the world have analyzed various factors (such as call cost, call quality, customer service response time, etc.) using several learners such as decision trees, support vector machines, neural networks, probabilistic models such as Bayes, etc. This paper presents a detailed survey of models from 2000 to 2015 describing the datasets used in churn prediction, impacting features in those datasets and classifiers that are used to implement prediction model. A total of 48 studies related to churn prediction in tel...
{"title":"A Survey of Evolution in Predictive Models and Impacting Factors in Customer Churn","authors":"Mehreen Ahmed, H. Afzal, A. Majeed, Behram Khan","doi":"10.1142/S2424922X17500073","DOIUrl":"https://doi.org/10.1142/S2424922X17500073","url":null,"abstract":"The information-based prediction models using machine learning techniques have gained massive popularity during the last few decades. Such models have been applied in a number of domains such as medical diagnosis, crime prediction, movies rating, etc. Similar is the trend in telecom industry where prediction models have been applied to predict the dissatisfied customers who are likely to change the service provider. Due to immense financial cost of customer churn in telecom, the companies from all over the world have analyzed various factors (such as call cost, call quality, customer service response time, etc.) using several learners such as decision trees, support vector machines, neural networks, probabilistic models such as Bayes, etc. This paper presents a detailed survey of models from 2000 to 2015 describing the datasets used in churn prediction, impacting features in those datasets and classifiers that are used to implement prediction model. A total of 48 studies related to churn prediction in tel...","PeriodicalId":47145,"journal":{"name":"Advances in Data Science and Adaptive Analysis","volume":"2 1","pages":"1750007:1-1750007:35"},"PeriodicalIF":0.6,"publicationDate":"2017-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87106858","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 : 2017-06-14DOI: 10.1142/S2424922X17500024
Shu-Mei Guo, J. Tsai, Chin-Yu Chen, Tzu-Cheng Yang
In the sifting process of the traditional empirical mode decomposition (EMD), intermittence causes mode mixing phenomenon. The intrinsic mode function (IMF) with the mode mixing phenomenon loses its original real physical meaning. An improved EMD based on time scale allocation method and the two-dimensional (2D) version of our method has been extended to improve the decomposition of the mode mixing phenomenon in 2D image data. Experimental results show that the method not only improves the phenomenon correctly both for 1D signal and 2D image, but also exhibits great performance in quality and computation time.
{"title":"An Improved Empirical Mode Decomposition Based on Time Scale Allocation Method and the 2D Mode Mixing Phenomenon Judgement","authors":"Shu-Mei Guo, J. Tsai, Chin-Yu Chen, Tzu-Cheng Yang","doi":"10.1142/S2424922X17500024","DOIUrl":"https://doi.org/10.1142/S2424922X17500024","url":null,"abstract":"In the sifting process of the traditional empirical mode decomposition (EMD), intermittence causes mode mixing phenomenon. The intrinsic mode function (IMF) with the mode mixing phenomenon loses its original real physical meaning. An improved EMD based on time scale allocation method and the two-dimensional (2D) version of our method has been extended to improve the decomposition of the mode mixing phenomenon in 2D image data. Experimental results show that the method not only improves the phenomenon correctly both for 1D signal and 2D image, but also exhibits great performance in quality and computation time.","PeriodicalId":47145,"journal":{"name":"Advances in Data Science and Adaptive Analysis","volume":"360 ","pages":"1750002:1-1750002:46"},"PeriodicalIF":0.6,"publicationDate":"2017-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1142/S2424922X17500024","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72438066","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 : 2017-06-01DOI: 10.1142/S2424922X17500036
V. Vatchev
In this paper, we study a class of functions that exhibit properties expected from intrinsic mode functions. A type of an empirical instantaneous frequency, depending on the extrema scale, is introduced and its proximity to the classical analytic instantaneous frequency is discussed. We also obtain a sufficient condition for positiveness of the instantaneous frequency and introduce a method similar in nature to EMD but with an empirical frequency as guide in lieu of empirical envelopes. The method is illustrated in several numerical examples.
{"title":"Intrinsic Fourier Mode Functions","authors":"V. Vatchev","doi":"10.1142/S2424922X17500036","DOIUrl":"https://doi.org/10.1142/S2424922X17500036","url":null,"abstract":"In this paper, we study a class of functions that exhibit properties expected from intrinsic mode functions. A type of an empirical instantaneous frequency, depending on the extrema scale, is introduced and its proximity to the classical analytic instantaneous frequency is discussed. We also obtain a sufficient condition for positiveness of the instantaneous frequency and introduce a method similar in nature to EMD but with an empirical frequency as guide in lieu of empirical envelopes. The method is illustrated in several numerical examples.","PeriodicalId":47145,"journal":{"name":"Advances in Data Science and Adaptive Analysis","volume":"14 1","pages":"1750003:1-1750003:19"},"PeriodicalIF":0.6,"publicationDate":"2017-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89047780","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 : 2017-04-16DOI: 10.1142/S2424922X17500012
R. Efendi, M. M. Deris
Fuzzy time series has been implemented for data prediction in the various sectors, such as education, finance-economic, energy, traffic accident, others. Moreover, many proposed models have been presented to improve the forecasting accuracy. However, the interval-length adjustment and the out-sample forecast procedure are still issues in fuzzy time series forecasting, where both issues are yet clearly investigated in the previous studies. In this paper, a new adjustment of the interval-length and the partition number of the data set is proposed. Additionally, the determining of the out-sample forecast is also discussed. The yearly oil production (OP) and oil consumption (OC) of Malaysia and Indonesia from 1965 to 2012 are examined to evaluate the performance of fuzzy time series and the probabilistic time series models. The result indicates that the fuzzy time series model is better than the probabilistic models, such as regression time series, exponential smoothing in terms of the forecasting accuracy. This paper thus highlights the effect of the proposed interval length in reducing the forecasting error significantly, as well as the main differences between the fuzzy and probabilistic time series models.
{"title":"Prediction of Malaysian-Indonesian Oil Production and Consumption Using Fuzzy Time Series Model","authors":"R. Efendi, M. M. Deris","doi":"10.1142/S2424922X17500012","DOIUrl":"https://doi.org/10.1142/S2424922X17500012","url":null,"abstract":"Fuzzy time series has been implemented for data prediction in the various sectors, such as education, finance-economic, energy, traffic accident, others. Moreover, many proposed models have been presented to improve the forecasting accuracy. However, the interval-length adjustment and the out-sample forecast procedure are still issues in fuzzy time series forecasting, where both issues are yet clearly investigated in the previous studies. In this paper, a new adjustment of the interval-length and the partition number of the data set is proposed. Additionally, the determining of the out-sample forecast is also discussed. The yearly oil production (OP) and oil consumption (OC) of Malaysia and Indonesia from 1965 to 2012 are examined to evaluate the performance of fuzzy time series and the probabilistic time series models. The result indicates that the fuzzy time series model is better than the probabilistic models, such as regression time series, exponential smoothing in terms of the forecasting accuracy. This paper thus highlights the effect of the proposed interval length in reducing the forecasting error significantly, as well as the main differences between the fuzzy and probabilistic time series models.","PeriodicalId":47145,"journal":{"name":"Advances in Data Science and Adaptive Analysis","volume":"13 1","pages":"1750001:1-1750001:17"},"PeriodicalIF":0.6,"publicationDate":"2017-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90798286","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 : 2017-04-16DOI: 10.1142/S2424922X17500048
P. Rzeszucinski, Michal Juraszek, J. Ottewill
The paper introduces the concept of exploring the potential of Ensemble Empirical Mode Decomposition (EEMD) and Sparsity Measurement (SM) in enhancing the diagnostic information contained in the Time Synchronous Averaging (TSA) method used in the field of gearbox diagnostics. EEMD was created as a natural improvement of the Empirical Mode Decomposition which suffered from a so-called mode mixing problem. SM is heavily used in the field of ultrasound signal processing as a tool for assessing the degree of sparsity of a signal. A novel process of automatically finding the optimal parameters of EEMD is proposed by incorporating a Form Factor parameter, known from the field of electrical engineering. All these elements are combined and applied on a set of vibration data generated on a 2-stage gearbox under healthy and faulty conditions. The results suggest that combining these methods may increase the robustness of the condition monitoring routine, when compared to the standard TSA used alone.
{"title":"Ensemble Empirical Mode Decomposition and Sparsity Measurement as Tools Enhancing the Gear Diagnostic Capabilities of Time Synchronous Averaging","authors":"P. Rzeszucinski, Michal Juraszek, J. Ottewill","doi":"10.1142/S2424922X17500048","DOIUrl":"https://doi.org/10.1142/S2424922X17500048","url":null,"abstract":"The paper introduces the concept of exploring the potential of Ensemble Empirical Mode Decomposition (EEMD) and Sparsity Measurement (SM) in enhancing the diagnostic information contained in the Time Synchronous Averaging (TSA) method used in the field of gearbox diagnostics. EEMD was created as a natural improvement of the Empirical Mode Decomposition which suffered from a so-called mode mixing problem. SM is heavily used in the field of ultrasound signal processing as a tool for assessing the degree of sparsity of a signal. A novel process of automatically finding the optimal parameters of EEMD is proposed by incorporating a Form Factor parameter, known from the field of electrical engineering. All these elements are combined and applied on a set of vibration data generated on a 2-stage gearbox under healthy and faulty conditions. The results suggest that combining these methods may increase the robustness of the condition monitoring routine, when compared to the standard TSA used alone.","PeriodicalId":47145,"journal":{"name":"Advances in Data Science and Adaptive Analysis","volume":"161 1","pages":"1750004:1-1750004:19"},"PeriodicalIF":0.6,"publicationDate":"2017-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75975967","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 : 2017-01-01DOI: 10.1142/S2424922X1650011X
Sungkono, B. J. Santosa, A. S. Bahri, F. Santos, A. Iswahyudi
Very low-frequency electromagnetic (VLF-EM) method can be used for imaging the subsurface resistivity, where this image can be used directly to determine subsurface condition. VLF-EM data are generally contaminated with unwanted noise which often leads to a mistake in the resistivity imaging result. In this study, noise-assisted multivariate empirical mode decomposition (NA-MEMD) was applied to reject the unwanted noise contained within the VLF-EM data which produced NA-MEMD-filtered VLF-EM data. The resistivity imaging resulted by filtered VLF-EM data has been used for determining the position of underground rivers over the karst area of Gunung Kidul district, Central Java province, Indonesia. The results show that the NA-MEMD-filtered VLF-EM data were more accurate in determining underground river tracks of the Suci cave areas. The overall result was supported by qualitative analyses (Fraser and K–Hjelt filters) of observed VLF-EM data as well as the NA-MEMD-filtered VLF-EM data.
{"title":"Application of Noise-Assisted Multivariate Empirical Mode Decomposition in VLF-EM Data to Identify Underground River","authors":"Sungkono, B. J. Santosa, A. S. Bahri, F. Santos, A. Iswahyudi","doi":"10.1142/S2424922X1650011X","DOIUrl":"https://doi.org/10.1142/S2424922X1650011X","url":null,"abstract":"Very low-frequency electromagnetic (VLF-EM) method can be used for imaging the subsurface resistivity, where this image can be used directly to determine subsurface condition. VLF-EM data are generally contaminated with unwanted noise which often leads to a mistake in the resistivity imaging result. In this study, noise-assisted multivariate empirical mode decomposition (NA-MEMD) was applied to reject the unwanted noise contained within the VLF-EM data which produced NA-MEMD-filtered VLF-EM data. The resistivity imaging resulted by filtered VLF-EM data has been used for determining the position of underground rivers over the karst area of Gunung Kidul district, Central Java province, Indonesia. The results show that the NA-MEMD-filtered VLF-EM data were more accurate in determining underground river tracks of the Suci cave areas. The overall result was supported by qualitative analyses (Fraser and K–Hjelt filters) of observed VLF-EM data as well as the NA-MEMD-filtered VLF-EM data.","PeriodicalId":47145,"journal":{"name":"Advances in Data Science and Adaptive Analysis","volume":"1 1","pages":"1650011:1-1650011:23"},"PeriodicalIF":0.6,"publicationDate":"2017-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84092139","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 : 2017-01-01DOI: 10.1142/S2424922X16500121
D. Veljković, P. Todorovic
This paper presents and investigates recursive order tracking (OT) techniques based on the least mean-square (LMS) method and the Vold–Kalman (VK) algorithm with a one pole structural equation, both of which could be realized as real-time applications. Additionally, for comparisons, two common adaptive OT filters are considered: the recursive least-squares (RLS) method and the VK algorithm with a two pole structural equation. The numerical implementations of the considered methods, through simulations on a representative noisy synthetic signal, including both close and crossing orders spectral components, are performed. The results indicate a possible degradation in the tracking performance of the RLS algorithm and the effectiveness of the simple LMS method, as well as both considered VK algorithms, for OT and distinguishing. The influence of the sampling frequency on the choosing of a weighting factor for the VK recursive OT filters is further investigated to extend the guidelines from the literature for...
{"title":"A Comparative Study of Adaptive Order Tracking Techniques for Rotating Machinery Analysis: Computer Experiments and Practical Implementations","authors":"D. Veljković, P. Todorovic","doi":"10.1142/S2424922X16500121","DOIUrl":"https://doi.org/10.1142/S2424922X16500121","url":null,"abstract":"This paper presents and investigates recursive order tracking (OT) techniques based on the least mean-square (LMS) method and the Vold–Kalman (VK) algorithm with a one pole structural equation, both of which could be realized as real-time applications. Additionally, for comparisons, two common adaptive OT filters are considered: the recursive least-squares (RLS) method and the VK algorithm with a two pole structural equation. The numerical implementations of the considered methods, through simulations on a representative noisy synthetic signal, including both close and crossing orders spectral components, are performed. The results indicate a possible degradation in the tracking performance of the RLS algorithm and the effectiveness of the simple LMS method, as well as both considered VK algorithms, for OT and distinguishing. The influence of the sampling frequency on the choosing of a weighting factor for the VK recursive OT filters is further investigated to extend the guidelines from the literature for...","PeriodicalId":47145,"journal":{"name":"Advances in Data Science and Adaptive Analysis","volume":"12 1","pages":"1650012:1-1650012:28"},"PeriodicalIF":0.6,"publicationDate":"2017-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72644552","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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