Pub Date : 2018-08-15DOI: 10.1142/S2424922X18400016
C. Sweeney-Reed, S. Nasuto, Marcus Fraga Vieira, A. Andrade
Empirical mode decomposition (EMD) provides an adaptive, data-driven approach to time–frequency analysis, yielding components from which local amplitude, phase, and frequency content can be derived...
{"title":"Empirical Mode Decomposition and its Extensions Applied to EEG Analysis: A Review","authors":"C. Sweeney-Reed, S. Nasuto, Marcus Fraga Vieira, A. Andrade","doi":"10.1142/S2424922X18400016","DOIUrl":"https://doi.org/10.1142/S2424922X18400016","url":null,"abstract":"Empirical mode decomposition (EMD) provides an adaptive, data-driven approach to time–frequency analysis, yielding components from which local amplitude, phase, and frequency content can be derived...","PeriodicalId":47145,"journal":{"name":"Advances in Data Science and Adaptive Analysis","volume":"5 3 1","pages":"1840001:1-1840001:34"},"PeriodicalIF":0.6,"publicationDate":"2018-08-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78272974","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 : 2018-08-15DOI: 10.1142/S2424922X18400077
A. Rueda, S. Krishnan
This study focuses on the possibility of remote monitoring and screening of Parkinson’s and age-related voice impairment for the general public using self-recorded data on readily available or emer...
{"title":"Clustering Parkinson's and Age-Related Voice Impairment Signal Features for Unsupervised Learning","authors":"A. Rueda, S. Krishnan","doi":"10.1142/S2424922X18400077","DOIUrl":"https://doi.org/10.1142/S2424922X18400077","url":null,"abstract":"This study focuses on the possibility of remote monitoring and screening of Parkinson’s and age-related voice impairment for the general public using self-recorded data on readily available or emer...","PeriodicalId":47145,"journal":{"name":"Advances in Data Science and Adaptive Analysis","volume":"12 1","pages":"1840007:1-1840007:24"},"PeriodicalIF":0.6,"publicationDate":"2018-08-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87268689","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 : 2018-07-01DOI: 10.1142/S2424922X18500067
K. Sakai, M. Kaneyama, K. Oohara, H. Takahashi
The Hilbert–Huang transform (HHT) extracts the intrinsic oscillation modes of input data, and estimates instantaneous amplitude (IA) and frequency (IF) for each mode. The HHT is applied to detection of some anomaly structures of signals as well as to analysis of signals. However, only qualitative discussions have been conducted on the applications to the detections. To make more statistically-based arguments on the application of the HHT, we investigated the probability distribution of the means of IA and IF for white Gaussian noise and found that it fits the Pearson distribution rather than the normal distribution. We defined a feature value for an anomaly detection by using the probability density function estimated on the basis of the Pearson distribution. Our method does not require different models for different lengths of the segment over which the mean is calculated, and therefore it is useful especially for the case that the length cannot be fixed.
{"title":"Probability Distributions of Means of IA and IF for Gaussian Noise and Its Application to an Anomaly Detection","authors":"K. Sakai, M. Kaneyama, K. Oohara, H. Takahashi","doi":"10.1142/S2424922X18500067","DOIUrl":"https://doi.org/10.1142/S2424922X18500067","url":null,"abstract":"The Hilbert–Huang transform (HHT) extracts the intrinsic oscillation modes of input data, and estimates instantaneous amplitude (IA) and frequency (IF) for each mode. The HHT is applied to detection of some anomaly structures of signals as well as to analysis of signals. However, only qualitative discussions have been conducted on the applications to the detections. To make more statistically-based arguments on the application of the HHT, we investigated the probability distribution of the means of IA and IF for white Gaussian noise and found that it fits the Pearson distribution rather than the normal distribution. We defined a feature value for an anomaly detection by using the probability density function estimated on the basis of the Pearson distribution. Our method does not require different models for different lengths of the segment over which the mean is calculated, and therefore it is useful especially for the case that the length cannot be fixed.","PeriodicalId":47145,"journal":{"name":"Advances in Data Science and Adaptive Analysis","volume":"46 1","pages":"1850006:1-1850006:14"},"PeriodicalIF":0.6,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79104040","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 : 2018-07-01DOI: 10.1142/S2424922X18500043
G. Sheen
Wireless recording and real time classification of brain waves are essential steps towards future wearable devices to assist Alzheimer’s patients in conveying their thoughts. This work is concerned with efficient computation of a dimension-reduced neural network (NN) model on Alzheimer’s patient data recorded by a wireless headset. Due to much fewer sensors in wireless recording than the number of electrodes in a traditional wired cap and shorter attention span of an Alzheimer’s patient than a normal person, the data is much more restrictive than is typical in neural robotics and mind-controlled games. To overcome this challenge, an alternating minimization (AM) method is developed for network training. AM minimizes a nonsmooth and nonconvex objective function one variable at a time while fixing the rest. The sub-problem for each variable is piecewise convex with a finite number of minima. The overall iterative AM method is descending and free of step size (learning parameter) in the standard gradient descent method. The proposed model, trained by the AM method, significantly outperforms the standard NN model trained by the stochastic gradient descent method in classifying four daily thoughts, reaching accuracies around 90% for Alzheimer’s patient. Curved decision boundaries of the proposed model with multiple hidden neurons are found analytically to establish the nonlinear nature of the classification.
{"title":"Wireless Brain Wave Classification for Alzheimer's Patients via Efficient Neural Network Computation","authors":"G. Sheen","doi":"10.1142/S2424922X18500043","DOIUrl":"https://doi.org/10.1142/S2424922X18500043","url":null,"abstract":"Wireless recording and real time classification of brain waves are essential steps towards future wearable devices to assist Alzheimer’s patients in conveying their thoughts. This work is concerned with efficient computation of a dimension-reduced neural network (NN) model on Alzheimer’s patient data recorded by a wireless headset. Due to much fewer sensors in wireless recording than the number of electrodes in a traditional wired cap and shorter attention span of an Alzheimer’s patient than a normal person, the data is much more restrictive than is typical in neural robotics and mind-controlled games. To overcome this challenge, an alternating minimization (AM) method is developed for network training. AM minimizes a nonsmooth and nonconvex objective function one variable at a time while fixing the rest. The sub-problem for each variable is piecewise convex with a finite number of minima. The overall iterative AM method is descending and free of step size (learning parameter) in the standard gradient descent method. The proposed model, trained by the AM method, significantly outperforms the standard NN model trained by the stochastic gradient descent method in classifying four daily thoughts, reaching accuracies around 90% for Alzheimer’s patient. Curved decision boundaries of the proposed model with multiple hidden neurons are found analytically to establish the nonlinear nature of the classification.","PeriodicalId":47145,"journal":{"name":"Advances in Data Science and Adaptive Analysis","volume":"3 1","pages":"1850004:1-1850004:19"},"PeriodicalIF":0.6,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83052672","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 : 2018-07-01DOI: 10.1142/S2424922X18500079
Jia-Hua Lee, T. Hsiao, Chia-Chi Chang, H. Hsu
Arterial blood pressure (ABP) is one of the most crucial cardiovascular indicators in clinical practice. Hilbert–Huang transform (HHT) has been performed on ABP signals and resulted in ABP variability. The instantaneous P-wave interval variation had been further examined with baroreflex sensitivity. However, the instantaneous magnitude variation of ABP signal is still unclear with the pulse pressure (PP) variability. In 2016, Holo–Hilbert spectral analysis (HHSA) extended the HHT method for identifying the amplitude-modulated (AM) characteristics of signals. This method was applied to investigate the magnitude variation of ABP signal during different respiratory manipulations in this study. The results indicated that the AM parts were moderately correlated with PP series and corresponding respiratory patterns. The [Formula: see text]-values on PP series are [Formula: see text], and [Formula: see text] for spontaneous breathing, six-cycle breathing, and hyperventilation, respectively. The values on respiratory patterns are [Formula: see text], and [Formula: see text] for spontaneous breathing, six-cycle breathing, and hyperventilation, respectively. This study concludes that ABP signal with HHSA presents the corresponding PP series, the respiratory-related activities, and the respiratory effect on PP variability. This is the first demonstration of the magnitude variation of ABP signal and further research in this area is warranted.
{"title":"Magnitude Variation of Arterial Blood Pressure Measured Using Holo-Hilbert Spectral Analysis","authors":"Jia-Hua Lee, T. Hsiao, Chia-Chi Chang, H. Hsu","doi":"10.1142/S2424922X18500079","DOIUrl":"https://doi.org/10.1142/S2424922X18500079","url":null,"abstract":"Arterial blood pressure (ABP) is one of the most crucial cardiovascular indicators in clinical practice. Hilbert–Huang transform (HHT) has been performed on ABP signals and resulted in ABP variability. The instantaneous P-wave interval variation had been further examined with baroreflex sensitivity. However, the instantaneous magnitude variation of ABP signal is still unclear with the pulse pressure (PP) variability. In 2016, Holo–Hilbert spectral analysis (HHSA) extended the HHT method for identifying the amplitude-modulated (AM) characteristics of signals. This method was applied to investigate the magnitude variation of ABP signal during different respiratory manipulations in this study. The results indicated that the AM parts were moderately correlated with PP series and corresponding respiratory patterns. The [Formula: see text]-values on PP series are [Formula: see text], and [Formula: see text] for spontaneous breathing, six-cycle breathing, and hyperventilation, respectively. The values on respiratory patterns are [Formula: see text], and [Formula: see text] for spontaneous breathing, six-cycle breathing, and hyperventilation, respectively. This study concludes that ABP signal with HHSA presents the corresponding PP series, the respiratory-related activities, and the respiratory effect on PP variability. This is the first demonstration of the magnitude variation of ABP signal and further research in this area is warranted.","PeriodicalId":47145,"journal":{"name":"Advances in Data Science and Adaptive Analysis","volume":"68 1","pages":"1850007:1-1850007:18"},"PeriodicalIF":0.6,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77153816","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 : 2018-05-09DOI: 10.1142/S2424922X18400028
D. Laszuk, J. O. Cadenas, S. Nasuto
Physiological signalling is often oscillatory and shows nonlinearity due to complex interactions of underlying processes or signal propagation delays. This is particularly evident in case of brain activity which is subject to various feedback loop interactions between di erent brain structures, that coordinate their activity to support normal function. In order to understand such signalling in health and disease, methods are needed that can deal with such complex oscillatory phenomena. In this paper, a data-driven method for analysing anharmonic oscillations is introduced. The KurSL model incorporates two well-studied components, which in the past have been used separately to analyse oscillatory behaviour. The Sturm-Liouville equations describe a form of a general oscillation, and the Kuramoto coupling model represents a set of oscillators interacting in the phase domain. Integration of these components provides a flexible framework for capturing complex interactions of oscillatory processes of more general form than the most commonly used harmonic oscillators. The paper introduces a mathematical framework of the KurSL model and analyses its behaviour for a variety of parameter ranges. The signi cance of the model follows from its ability to provide information about coupled oscillators' phase dynamics directly from the time series. KurSL o ers a novel framework for analysing a wide range of complex oscillatory behaviours, such as encountered in physiological signals.
{"title":"KurSL: Model of Anharmonic Coupled Oscillations Based on Kuramoto Coupling and Sturm-Liouville Problem","authors":"D. Laszuk, J. O. Cadenas, S. Nasuto","doi":"10.1142/S2424922X18400028","DOIUrl":"https://doi.org/10.1142/S2424922X18400028","url":null,"abstract":"Physiological signalling is often oscillatory and shows nonlinearity due to complex interactions of underlying processes or signal propagation delays. This is particularly evident in case of brain activity which is subject to various feedback loop interactions between di erent brain structures, that coordinate their activity to support normal function. In order to understand such signalling in health and disease, methods are needed that can deal with such complex oscillatory phenomena. In this paper, a data-driven method for analysing anharmonic oscillations is introduced. The KurSL model incorporates two well-studied components, which in the past have been used separately to analyse oscillatory behaviour. The Sturm-Liouville equations describe a form of a general oscillation, and the Kuramoto coupling model represents a set of oscillators interacting in the phase domain. Integration of these components provides a flexible framework for capturing complex interactions of oscillatory processes of more general form than the most commonly used harmonic oscillators. The paper introduces a mathematical framework of the KurSL model and analyses its behaviour for a variety of parameter ranges. The signi cance of the model follows from its ability to provide information about coupled oscillators' phase dynamics directly from the time series. KurSL o ers a novel framework for analysing a wide range of complex oscillatory behaviours, such as encountered in physiological signals.","PeriodicalId":47145,"journal":{"name":"Advances in Data Science and Adaptive Analysis","volume":"82 1","pages":"1840002:1-1840002:23"},"PeriodicalIF":0.6,"publicationDate":"2018-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75792427","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 : 2018-01-08DOI: 10.1142/S2424922X1850002X
K. Kume, N. Nose-Togawa
Singular spectrum analysis (SSA) is a nonparametric spectral decomposition of a time series into arbitrary number of interpretable components. It involves a single parameter, window length L, which...
{"title":"An Adaptive Orthogonal SSA Decomposition Algorithm for a Time Series","authors":"K. Kume, N. Nose-Togawa","doi":"10.1142/S2424922X1850002X","DOIUrl":"https://doi.org/10.1142/S2424922X1850002X","url":null,"abstract":"Singular spectrum analysis (SSA) is a nonparametric spectral decomposition of a time series into arbitrary number of interpretable components. It involves a single parameter, window length L, which...","PeriodicalId":47145,"journal":{"name":"Advances in Data Science and Adaptive Analysis","volume":"36 1","pages":"1850002:1-1850002:15"},"PeriodicalIF":0.6,"publicationDate":"2018-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86772501","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 : 2018-01-08DOI: 10.1142/S2424922X18500018
Xinting Zhai, Jixin Wang, Yingying Li
Load spectrum is the basement of reliability analysis. Selecting a suitable operator for a certain model in a typical experiment field is of benefit to the acquisition of load spectrum. Different operating performances caused by different operators have the characteristics of multiple evaluation indicators. In this paper, a comprehensive evaluation method based on median damage is proposed. The method is applied to evaluate the actual operating performances in the experiment field of the excavator. Comparative results show that the proposed method is feasible, and will be more convenient when more indicators are involved. The proposed method provides a theoretical reference for the compiling of load spectrum acquisition specification, and proposes an additional method for the evaluation of the operating performances.
{"title":"Evaluation of Operating Performances Based on Median Damage Load Spectrum","authors":"Xinting Zhai, Jixin Wang, Yingying Li","doi":"10.1142/S2424922X18500018","DOIUrl":"https://doi.org/10.1142/S2424922X18500018","url":null,"abstract":"Load spectrum is the basement of reliability analysis. Selecting a suitable operator for a certain model in a typical experiment field is of benefit to the acquisition of load spectrum. Different operating performances caused by different operators have the characteristics of multiple evaluation indicators. In this paper, a comprehensive evaluation method based on median damage is proposed. The method is applied to evaluate the actual operating performances in the experiment field of the excavator. Comparative results show that the proposed method is feasible, and will be more convenient when more indicators are involved. The proposed method provides a theoretical reference for the compiling of load spectrum acquisition specification, and proposes an additional method for the evaluation of the operating performances.","PeriodicalId":47145,"journal":{"name":"Advances in Data Science and Adaptive Analysis","volume":"6 1","pages":"1850001:1-1850001:14"},"PeriodicalIF":0.6,"publicationDate":"2018-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90702235","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 : 2018-01-01DOI: 10.1142/S2424922X18500031
T. tucker, S. Shen
This research develops a toolkit for snow-cover area calculation and display (SACD) based on the Interactive Multisensor Snow and Ice Mapping System (IMS). The paper uses the Tibetan Plateau region as an example to describe the toolkit’s method, results, and usage. The National Snow and Ice Data Center (NSIDC) provides to the public IMS a well-used system for monitoring the snow and ice cover. The newly developed toolkit is based on a simple shoe-lace formula for a grid box area on a sphere and can be conveniently used to calculate the total area of snow cover given the IMS data. The toolkit has been made available as an open source Python software on GitHub. The toolkit generates the time series of the daily snow-covered area for any region over the Northern Hemisphere from 4 February 1997. The toolkit also creates maps showing snow and ice coverage with an elevation background. The Tibetan Plateau (TP) region (25∘–45∘N) × (65∘–105∘E) is used as an example to demonstrate our work on SACD. The IMS product...
{"title":"A Toolkit for Snow-Cover Area Calculation and Display Based on the Interactive Multisensor Snow and Ice Mapping System and an Example for the Tibetan Plateau Region","authors":"T. tucker, S. Shen","doi":"10.1142/S2424922X18500031","DOIUrl":"https://doi.org/10.1142/S2424922X18500031","url":null,"abstract":"This research develops a toolkit for snow-cover area calculation and display (SACD) based on the Interactive Multisensor Snow and Ice Mapping System (IMS). The paper uses the Tibetan Plateau region as an example to describe the toolkit’s method, results, and usage. The National Snow and Ice Data Center (NSIDC) provides to the public IMS a well-used system for monitoring the snow and ice cover. The newly developed toolkit is based on a simple shoe-lace formula for a grid box area on a sphere and can be conveniently used to calculate the total area of snow cover given the IMS data. The toolkit has been made available as an open source Python software on GitHub. The toolkit generates the time series of the daily snow-covered area for any region over the Northern Hemisphere from 4 February 1997. The toolkit also creates maps showing snow and ice coverage with an elevation background. The Tibetan Plateau (TP) region (25∘–45∘N) × (65∘–105∘E) is used as an example to demonstrate our work on SACD. The IMS product...","PeriodicalId":47145,"journal":{"name":"Advances in Data Science and Adaptive Analysis","volume":"14 1","pages":"1850003:1-1850003:18"},"PeriodicalIF":0.6,"publicationDate":"2018-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1142/S2424922X18500031","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72414886","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 : 2017-11-09DOI: 10.1142/S2424922X17500097
Kathrine Knai, G. Kulia, M. Molinas, N. K. Skjaervold
Continuous biological signals, like blood pressure recordings, exhibit nonlinear and nonstationary properties which must be considered during their analysis. Heart rate variability analyses have identified several frequency components and their autonomic origin. There is need for more knowledge on the time-changing properties of these frequencies. The power spectrum, continuous wavelet transform and Hilbert–Huang transform are applied on a continuous blood pressure signal to investigate how the different methods compare to each other. The Hilbert–Huang transform shows high ability to analyze such data, and can, by identifying instantaneous frequency shifts, provide new insights into the nature of these kinds of data.
{"title":"Instantaneous Frequencies of Continuous Blood Pressure a Comparison of the Power Spectrum, the Continuous Wavelet Transform and the Hilbert-Huang Transform","authors":"Kathrine Knai, G. Kulia, M. Molinas, N. K. Skjaervold","doi":"10.1142/S2424922X17500097","DOIUrl":"https://doi.org/10.1142/S2424922X17500097","url":null,"abstract":"Continuous biological signals, like blood pressure recordings, exhibit nonlinear and nonstationary properties which must be considered during their analysis. Heart rate variability analyses have identified several frequency components and their autonomic origin. There is need for more knowledge on the time-changing properties of these frequencies. The power spectrum, continuous wavelet transform and Hilbert–Huang transform are applied on a continuous blood pressure signal to investigate how the different methods compare to each other. The Hilbert–Huang transform shows high ability to analyze such data, and can, by identifying instantaneous frequency shifts, provide new insights into the nature of these kinds of data.","PeriodicalId":47145,"journal":{"name":"Advances in Data Science and Adaptive Analysis","volume":"60 1","pages":"1750009:1-1750009:9"},"PeriodicalIF":0.6,"publicationDate":"2017-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86895709","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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