Pub Date : 2019-01-01DOI: 10.18287/1613-0073-2019-2391-48-53
L. Shiripova, E. Myasnikov
The paper is devoted to the problem of recognizing human actions in videos recorded in the optical range of wavelengths. An approach proposed in this paper consists in the detection of a moving person on a video sequence with the subsequent size normalization, generation of subsequences and dimensionality reduction using the principal component analysis technique. The classification of human actions is carried out using a support vector machine classifier. Experimental studies performed on the Weizmann dataset allowed us to determine the best values of the method parameters. The results showed that with a small number of action classes, high classification accuracy can be achieved.
{"title":"Human action recognition using dimensionality reduction and support vector machine","authors":"L. Shiripova, E. Myasnikov","doi":"10.18287/1613-0073-2019-2391-48-53","DOIUrl":"https://doi.org/10.18287/1613-0073-2019-2391-48-53","url":null,"abstract":"The paper is devoted to the problem of recognizing human actions in videos recorded in the optical range of wavelengths. An approach proposed in this paper consists in the detection of a moving person on a video sequence with the subsequent size normalization, generation of subsequences and dimensionality reduction using the principal component analysis technique. The classification of human actions is carried out using a support vector machine classifier. Experimental studies performed on the Weizmann dataset allowed us to determine the best values of the method parameters. The results showed that with a small number of action classes, high classification accuracy can be achieved.","PeriodicalId":10486,"journal":{"name":"Collection of selected papers of the III International Conference on Information Technology and Nanotechnology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79308307","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 : 2019-01-01DOI: 10.18287/1613-0073-2019-2416-340-353
M. Bulygin, M. Gayanova, A. M. Vulfin, A. Kirillova, R. Gayanov
Object of the research are modern structures and architectures of neural networks for image processing. Goal of the work is improving the existing image processing algorithms based on the extraction and compression of features using neural networks using the colorization of black and white images as an example. The subject of the work is the algorithms of neural network image processing using heterogeneous convolutional networks in the colorization problem. The analysis of image processing algorithms with the help of neural networks is carried out, the structure of the neural network processing system for image colorization is developed, colorization algorithms are developed and implemented. To analyze the proposed algorithms, a computational experiment was conducted and conclusions were drawn about the advantages and disadvantages of each of the algorithms.
{"title":"Convolutional neural network in the images colorization problem","authors":"M. Bulygin, M. Gayanova, A. M. Vulfin, A. Kirillova, R. Gayanov","doi":"10.18287/1613-0073-2019-2416-340-353","DOIUrl":"https://doi.org/10.18287/1613-0073-2019-2416-340-353","url":null,"abstract":"Object of the research are modern structures and architectures of neural networks for image processing. Goal of the work is improving the existing image processing algorithms based on the extraction and compression of features using neural networks using the colorization of black and white images as an example. The subject of the work is the algorithms of neural network image processing using heterogeneous convolutional networks in the colorization problem. The analysis of image processing algorithms with the help of neural networks is carried out, the structure of the neural network processing system for image colorization is developed, colorization algorithms are developed and implemented. To analyze the proposed algorithms, a computational experiment was conducted and conclusions were drawn about the advantages and disadvantages of each of the algorithms.","PeriodicalId":10486,"journal":{"name":"Collection of selected papers of the III International Conference on Information Technology and Nanotechnology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75404798","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 : 2019-01-01DOI: 10.18287/1613-0073-2019-2416-209-218
O. Pavlov
The article explores the incentive problem of executors of the new products development project at the industrial enterprise in continuous time. In the process of developing new products, the learning curve effect manifests itself, which leads to a reduction in labor intensity, depending on the cumulative volume of production. The project for the new products development is considered as a managed hierarchical dynamic system, consisting of a project management board (principal) and executors (agents). The interaction of project participants is formalized as a hierarchical differential game. To solve the formulated dynamic problem of material incentives, the well-known principle of cost compensation was applied. The original problem is divided into the task of coordinated incentives and the task of coordinated planning. The study showed that the task of coordinated dynamic planning is for the principal to determine the optimal planned production volumes in order to minimize the labor cost of agents. The initial dynamic problem of material incentives was reduced to the optimal control problem. The problem of optimal control with continuous time was solved analytically using the Pontryagin maximum principle. The study identifies a condition to determine the optimal production volumes for coordination of the interests of the principal and agents.
{"title":"Dynamic game task of executors incentives in projects for the development of new production in continuous time","authors":"O. Pavlov","doi":"10.18287/1613-0073-2019-2416-209-218","DOIUrl":"https://doi.org/10.18287/1613-0073-2019-2416-209-218","url":null,"abstract":"The article explores the incentive problem of executors of the new products development project at the industrial enterprise in continuous time. In the process of developing new products, the learning curve effect manifests itself, which leads to a reduction in labor intensity, depending on the cumulative volume of production. The project for the new products development is considered as a managed hierarchical dynamic system, consisting of a project management board (principal) and executors (agents). The interaction of project participants is formalized as a hierarchical differential game. To solve the formulated dynamic problem of material incentives, the well-known principle of cost compensation was applied. The original problem is divided into the task of coordinated incentives and the task of coordinated planning. The study showed that the task of coordinated dynamic planning is for the principal to determine the optimal planned production volumes in order to minimize the labor cost of agents. The initial dynamic problem of material incentives was reduced to the optimal control problem. The problem of optimal control with continuous time was solved analytically using the Pontryagin maximum principle. The study identifies a condition to determine the optimal production volumes for coordination of the interests of the principal and agents.","PeriodicalId":10486,"journal":{"name":"Collection of selected papers of the III International Conference on Information Technology and Nanotechnology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91534378","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 : 2019-01-01DOI: 10.18287/1613-0073-2019-2391-160-168
A. Kornilov, I. Safonov, A. V. Goncharova, I. Yakimchuk
We present an algorithm for processing of X-ray microtomographic (micro-CT) images that allows automatic selection of a sub-volume having the best visual quality for further mathematical simulation, for example, flow simulation. Frequently, an investigated sample occupies only a part of a volumetric image or the sample can be into a holder; a part of the image can be cropped. For each 2D slice across the Z-axis of an image, the proposed method locates a region corresponding to the sample. We explored applications of several existing blind quality measures for an estimation of the visual quality of a micro-CT image slice. Some of these metrics can be applied to ranking the image regions according to their quality. Our method searches for a cubic area located inside regions belonging to the sample and providing the maximal sum of the quality measures of slices crossing the cube across the Z-axis. The proposed technique was tested on synthetic and real micro-CT images of rocks.
{"title":"Selection in a 3D microtomographic image the region with the highest quality","authors":"A. Kornilov, I. Safonov, A. V. Goncharova, I. Yakimchuk","doi":"10.18287/1613-0073-2019-2391-160-168","DOIUrl":"https://doi.org/10.18287/1613-0073-2019-2391-160-168","url":null,"abstract":"We present an algorithm for processing of X-ray microtomographic (micro-CT) images that allows automatic selection of a sub-volume having the best visual quality for further mathematical simulation, for example, flow simulation. Frequently, an investigated sample occupies only a part of a volumetric image or the sample can be into a holder; a part of the image can be cropped. For each 2D slice across the Z-axis of an image, the proposed method locates a region corresponding to the sample. We explored applications of several existing blind quality measures for an estimation of the visual quality of a micro-CT image slice. Some of these metrics can be applied to ranking the image regions according to their quality. Our method searches for a cubic area located inside regions belonging to the sample and providing the maximal sum of the quality measures of slices crossing the cube across the Z-axis. The proposed technique was tested on synthetic and real micro-CT images of rocks.","PeriodicalId":10486,"journal":{"name":"Collection of selected papers of the III International Conference on Information Technology and Nanotechnology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75849683","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 : 2019-01-01DOI: 10.18287/1613-0073-2019-2391-233-242
A Y Denisova, L. M. Kavelenova, E. Korchikov, A. Pomogaybin, N. Prokhorova, D. A. Terentyeva, V. Fedoseev, N. Yankov
The forest and shrub communities are important components of the environment and provide a wide spectrum of ecological services. In the Samara region the forest and shrub cover is dispersed on the territory what makes its monitoring difficult. The forest areas are limited by natural and anthropogenic reasons since Samara region is a forest-steppe territory with a high level of human activity. The shrub communities are mostly the secondary ecosystems incorporated in natural grassy communities, agricultural fields or enclosing to forests. These specific ecosystems can be recognized on remote sensing data including satellite images supported by preliminary ground surveys. In this article, we present the study of the forest and shrub communities recognition using remote sensing images and ground surveys in the Samara region. We describe a process of the test site selection for remote sensing data verification and discuss the results of applying the author’s classification technology for multispectral remote sensing composites to classify forest communities in the Samara region
{"title":"Recognition of forest and shrub communities on the base of remotely sensed data supported by ground studies","authors":"A Y Denisova, L. M. Kavelenova, E. Korchikov, A. Pomogaybin, N. Prokhorova, D. A. Terentyeva, V. Fedoseev, N. Yankov","doi":"10.18287/1613-0073-2019-2391-233-242","DOIUrl":"https://doi.org/10.18287/1613-0073-2019-2391-233-242","url":null,"abstract":"The forest and shrub communities are important components of the environment and provide a wide spectrum of ecological services. In the Samara region the forest and shrub cover is dispersed on the territory what makes its monitoring difficult. The forest areas are limited by natural and anthropogenic reasons since Samara region is a forest-steppe territory with a high level of human activity. The shrub communities are mostly the secondary ecosystems incorporated in natural grassy communities, agricultural fields or enclosing to forests. These specific ecosystems can be recognized on remote sensing data including satellite images supported by preliminary ground surveys. In this article, we present the study of the forest and shrub communities recognition using remote sensing images and ground surveys in the Samara region. We describe a process of the test site selection for remote sensing data verification and discuss the results of applying the author’s classification technology for multispectral remote sensing composites to classify forest communities in the Samara region","PeriodicalId":10486,"journal":{"name":"Collection of selected papers of the III International Conference on Information Technology and Nanotechnology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72679621","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 : 2019-01-01DOI: 10.18287/1613-0073-2019-2391-180-186
N. Abramov, А. А. Talalayev, V. Fralenko, O. Shishkin, V. Khachumov
The paper introduces how multi-class and single-class problems of searching and classifying target objects in remote sensing images of the Earth are solved. To improve the recognition efficiency, the preparation tools for training samples, optimal configuration and use of deep learning neural networks using high-performance computing technologies have been developed. Two types of CNN were used to process ERS images: a convolutional neural network from the nnForge library and a network of the Darknet type. A comparative analysis of the results is obtained. The research showed that the capabilities of convolutional neural networks allow solving simultaneously the problems of searching (localizing) and recognizing objects in ERS images with high accuracy and completeness.
{"title":"Neural network technology to search for targets in remote sensing images of the Earth","authors":"N. Abramov, А. А. Talalayev, V. Fralenko, O. Shishkin, V. Khachumov","doi":"10.18287/1613-0073-2019-2391-180-186","DOIUrl":"https://doi.org/10.18287/1613-0073-2019-2391-180-186","url":null,"abstract":"The paper introduces how multi-class and single-class problems of searching and classifying target objects in remote sensing images of the Earth are solved. To improve the recognition efficiency, the preparation tools for training samples, optimal configuration and use of deep learning neural networks using high-performance computing technologies have been developed. Two types of CNN were used to process ERS images: a convolutional neural network from the nnForge library and a network of the Darknet type. A comparative analysis of the results is obtained. The research showed that the capabilities of convolutional neural networks allow solving simultaneously the problems of searching (localizing) and recognizing objects in ERS images with high accuracy and completeness.","PeriodicalId":10486,"journal":{"name":"Collection of selected papers of the III International Conference on Information Technology and Nanotechnology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74702346","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 : 2019-01-01DOI: 10.18287/1613-0073-2019-2416-57-62
A. Agafonov, A. Yumaganov
The problem of predicting the movement of public transport is one of the most popular problems in the field of transport planning due to its practical significance. Various parametric and non-parametric models are used to solve this problem. In this paper, heterogeneous information affecting the prediction value is used to predict the arrival time of public transport, and a comparison of the main machine learning algorithms for the public transport arrival time forecasting is given: neural networks, support vector regression. An experimental analysis of the algorithms was carried out on real traffic information about bus routes in Samara, Russia.
{"title":"Performance comparison of machine learning methods in the bus arrival time prediction problem","authors":"A. Agafonov, A. Yumaganov","doi":"10.18287/1613-0073-2019-2416-57-62","DOIUrl":"https://doi.org/10.18287/1613-0073-2019-2416-57-62","url":null,"abstract":"The problem of predicting the movement of public transport is one of the most popular problems in the field of transport planning due to its practical significance. Various parametric and non-parametric models are used to solve this problem. In this paper, heterogeneous information affecting the prediction value is used to predict the arrival time of public transport, and a comparison of the main machine learning algorithms for the public transport arrival time forecasting is given: neural networks, support vector regression. An experimental analysis of the algorithms was carried out on real traffic information about bus routes in Samara, Russia.","PeriodicalId":10486,"journal":{"name":"Collection of selected papers of the III International Conference on Information Technology and Nanotechnology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75171574","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 : 2019-01-01DOI: 10.18287/1613-0073-2019-2391-103-108
G. Safina, A. Tashlinskii, M. Tsaryov
The paper is devoted to the analysis of the possibilities of using Markov chains for analyzing the accuracy of stochastic gradient relay estimation of image geometric deformations. One of the ways to reduce computational costs is to discretize the domain of studied parameters. This approach allows to choose the dimension of transition probabilities matrix a priori. However, such a matrix has a rather complicated structure. It does not significantly reduce the number of computations. A modification of the transition probabilities matrix is proposed, it’s dimension does not depend on the dimension of estimated parameters vector. In this case, the obtained relations determine a recurrent algorithm for calculating the matrix at the estimation iterations. For the one-step transitions matrix, the calculated expressions for the probabilities of image deformation parameters estimates drift are given.
{"title":"Adaptation of the mathematical apparatus of the Markov chain theory for the probabilistic analysis of recurrent estimation of image inter-frame geometric deformations","authors":"G. Safina, A. Tashlinskii, M. Tsaryov","doi":"10.18287/1613-0073-2019-2391-103-108","DOIUrl":"https://doi.org/10.18287/1613-0073-2019-2391-103-108","url":null,"abstract":"The paper is devoted to the analysis of the possibilities of using Markov chains for analyzing the accuracy of stochastic gradient relay estimation of image geometric deformations. One of the ways to reduce computational costs is to discretize the domain of studied parameters. This approach allows to choose the dimension of transition probabilities matrix a priori. However, such a matrix has a rather complicated structure. It does not significantly reduce the number of computations. A modification of the transition probabilities matrix is proposed, it’s dimension does not depend on the dimension of estimated parameters vector. In this case, the obtained relations determine a recurrent algorithm for calculating the matrix at the estimation iterations. For the one-step transitions matrix, the calculated expressions for the probabilities of image deformation parameters estimates drift are given.","PeriodicalId":10486,"journal":{"name":"Collection of selected papers of the III International Conference on Information Technology and Nanotechnology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75500190","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 : 2019-01-01DOI: 10.18287/1613-0073-2019-2391-366-370
V. Fedoseev, T. Androsova
In the paper, we propose two watermarking algorithms for semi-fragile data hiding in JPEG 2000 lossy compressed images. Both algorithms are based on the concept of quantization index modulation. These methods have a property of semi-fragility to the image quality. It means that the hidden information is preserved after high-quality compression, and is destroyed in the case of significant degradation. Experimental investigations confirm this property for both algorithms. They also show that the introduced embedding distortions in terms of PSNR and PSNR-HVS are in almost linear dependence on the quantization parameter. It allows us to control the quality at an acceptable level when information embedding
{"title":"Watermarking algorithms for JPEG 2000 lossy compressed images","authors":"V. Fedoseev, T. Androsova","doi":"10.18287/1613-0073-2019-2391-366-370","DOIUrl":"https://doi.org/10.18287/1613-0073-2019-2391-366-370","url":null,"abstract":"In the paper, we propose two watermarking algorithms for semi-fragile data hiding in JPEG 2000 lossy compressed images. Both algorithms are based on the concept of quantization index modulation. These methods have a property of semi-fragility to the image quality. It means that the hidden information is preserved after high-quality compression, and is destroyed in the case of significant degradation. Experimental investigations confirm this property for both algorithms. They also show that the introduced embedding distortions in terms of PSNR and PSNR-HVS are in almost linear dependence on the quantization parameter. It allows us to control the quality at an acceptable level when information embedding","PeriodicalId":10486,"journal":{"name":"Collection of selected papers of the III International Conference on Information Technology and Nanotechnology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90094765","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 : 2019-01-01DOI: 10.18287/1613-0073-2019-2416-260-270
A. P. Shuravin, S. Vologdin
The article substantiates the relevance of optimization algorithms research for solving various applied problems and for the science of artificial intelligence. The need to solve problems of optimizing the thermal-hydraulic modes of buildings (as part of the project "Smart City") is explained. The paper presents a mathematical formulation of the problem of optimizing the temperature mode of rooms using adjustable devices. Existing work provides two methods for solving the posed problem. They are the coordinates search method and the genetic algorithm. The article contains the description of the above mentioned algorithms (including the mathematical apparatus used). The results of the computational experiment (for the considered optimization methods) are presented. These experimental results show that the genetic algorithm provides better optimization results than the coordinates search method, but it has a large computational cost. The hypothesis was confirmed that in order to increase the efficiency of solving the considered class of problems it is necessary to combine the genetic algorithm and the coordinates search method.
{"title":"Comparison of the characteristics of the genetic algorithm and the method of coordinates search for optimization of temperature modes indoor areas","authors":"A. P. Shuravin, S. Vologdin","doi":"10.18287/1613-0073-2019-2416-260-270","DOIUrl":"https://doi.org/10.18287/1613-0073-2019-2416-260-270","url":null,"abstract":"The article substantiates the relevance of optimization algorithms research for solving various applied problems and for the science of artificial intelligence. The need to solve problems of optimizing the thermal-hydraulic modes of buildings (as part of the project \"Smart City\") is explained. The paper presents a mathematical formulation of the problem of optimizing the temperature mode of rooms using adjustable devices. Existing work provides two methods for solving the posed problem. They are the coordinates search method and the genetic algorithm. The article contains the description of the above mentioned algorithms (including the mathematical apparatus used). The results of the computational experiment (for the considered optimization methods) are presented. These experimental results show that the genetic algorithm provides better optimization results than the coordinates search method, but it has a large computational cost. The hypothesis was confirmed that in order to increase the efficiency of solving the considered class of problems it is necessary to combine the genetic algorithm and the coordinates search method.","PeriodicalId":10486,"journal":{"name":"Collection of selected papers of the III International Conference on Information Technology and Nanotechnology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84699518","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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