Pub Date : 2021-09-21DOI: 10.1109/ICRAMI52622.2021.9585972
Chorfi Nouar, S. Bendoukha, S. Abdelmalek
In this article, we consider the HIV AIDS system proposed by K.O. Okosun [4]. We study the local and global asymptotic stability of the model’s equilibria in the presence of a diffusion term. An optimal controller is presented that considers the use of three different measures to combat the spread of HIV/AIDS, namely: the use of condoms and the screening and treatment of unaware infective individuals. The objective of the optimal controller is to minimize the size of the susceptible and infected populations. The study starts with an investigation of the existence and uniqueness of solutions. Then, we establish estimates of the controlled system’s positive strong solution by means of the semigroup theory of operators, and make use of minimal sequence techniques to show the existence of an optimal control. In doing so, we establish the necessary optimality conditions of the developed scheme.
{"title":"The Optimal Control of an HIV/AIDS Reaction-Diffusion Epidemic Model","authors":"Chorfi Nouar, S. Bendoukha, S. Abdelmalek","doi":"10.1109/ICRAMI52622.2021.9585972","DOIUrl":"https://doi.org/10.1109/ICRAMI52622.2021.9585972","url":null,"abstract":"In this article, we consider the HIV AIDS system proposed by K.O. Okosun [4]. We study the local and global asymptotic stability of the model’s equilibria in the presence of a diffusion term. An optimal controller is presented that considers the use of three different measures to combat the spread of HIV/AIDS, namely: the use of condoms and the screening and treatment of unaware infective individuals. The objective of the optimal controller is to minimize the size of the susceptible and infected populations. The study starts with an investigation of the existence and uniqueness of solutions. Then, we establish estimates of the controlled system’s positive strong solution by means of the semigroup theory of operators, and make use of minimal sequence techniques to show the existence of an optimal control. In doing so, we establish the necessary optimality conditions of the developed scheme.","PeriodicalId":440750,"journal":{"name":"2021 International Conference on Recent Advances in Mathematics and Informatics (ICRAMI)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130766182","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 : 2021-09-21DOI: 10.1109/ICRAMI52622.2021.9585968
Leila Boussaad, Aldjia Boucetta
Recently deep learning has shown significant achievement in the performance of many tasks, like natural language processing, image and speech recognition. Also, this improvement concerns multiple biometrics recognition systems. In this work, we focus on biometrics recognition, we present a stacked auto-encoder-based approach for various biometrics recognition, including Iris, Ear, palm-print, and face recognition. The proposed method allows training a neural network that includes two hidden layers for biometrics tasks. It runs in two steps, in the first one, each layer is trained individually in an unsupervised manner by auto-encoders, then the layers are stacked and trained in a supervised way. Experimental results on images, obtained from publicly available biometrics databases clearly demonstrate the benefit of using stacked auto-encoders as feature extraction and dimension reduction tools for biometrics recognition, as significant high accuracy rates are obtained over the four databases.
{"title":"Stacked Auto-Encoders Based Biometrics Recognition","authors":"Leila Boussaad, Aldjia Boucetta","doi":"10.1109/ICRAMI52622.2021.9585968","DOIUrl":"https://doi.org/10.1109/ICRAMI52622.2021.9585968","url":null,"abstract":"Recently deep learning has shown significant achievement in the performance of many tasks, like natural language processing, image and speech recognition. Also, this improvement concerns multiple biometrics recognition systems. In this work, we focus on biometrics recognition, we present a stacked auto-encoder-based approach for various biometrics recognition, including Iris, Ear, palm-print, and face recognition. The proposed method allows training a neural network that includes two hidden layers for biometrics tasks. It runs in two steps, in the first one, each layer is trained individually in an unsupervised manner by auto-encoders, then the layers are stacked and trained in a supervised way. Experimental results on images, obtained from publicly available biometrics databases clearly demonstrate the benefit of using stacked auto-encoders as feature extraction and dimension reduction tools for biometrics recognition, as significant high accuracy rates are obtained over the four databases.","PeriodicalId":440750,"journal":{"name":"2021 International Conference on Recent Advances in Mathematics and Informatics (ICRAMI)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125584526","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 : 2021-09-21DOI: 10.1109/ICRAMI52622.2021.9585903
Abdeldjalil Ledmi, Makhlouf Ledmi, Mohammed El Habib Souidi
Using cloud computing services has many advantages, such as improving efficiency, reducing costs, compatibility with multiple formats, unlimited storage capacity, and easy access to services anytime and anywhere. It should be mentioned that the fault tolerance is the main restriction of all varieties of cloud computing services. Cloud service providers need to effectively handle performance-related reliability, availability, and throughput issues to maximize the potential of using cloud computing services.This paper provides a comprehensive overview of the issues related to fault tolerance in cloud computing. It focuses on important advanced technologies, and methods. Its purpose is to provide insight into traditional fault-tolerant approaches and the challenges that still need to be overcome. This investigation enumerates several promising methods that can be used for efficient solutions.
{"title":"Fault Tolerance in Cloud Computing: A Survey","authors":"Abdeldjalil Ledmi, Makhlouf Ledmi, Mohammed El Habib Souidi","doi":"10.1109/ICRAMI52622.2021.9585903","DOIUrl":"https://doi.org/10.1109/ICRAMI52622.2021.9585903","url":null,"abstract":"Using cloud computing services has many advantages, such as improving efficiency, reducing costs, compatibility with multiple formats, unlimited storage capacity, and easy access to services anytime and anywhere. It should be mentioned that the fault tolerance is the main restriction of all varieties of cloud computing services. Cloud service providers need to effectively handle performance-related reliability, availability, and throughput issues to maximize the potential of using cloud computing services.This paper provides a comprehensive overview of the issues related to fault tolerance in cloud computing. It focuses on important advanced technologies, and methods. Its purpose is to provide insight into traditional fault-tolerant approaches and the challenges that still need to be overcome. This investigation enumerates several promising methods that can be used for efficient solutions.","PeriodicalId":440750,"journal":{"name":"2021 International Conference on Recent Advances in Mathematics and Informatics (ICRAMI)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121811306","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The classification of liver disease is of paramount significance for an early diagnosis of patients. In this paper, suggesting a way for classifying the liver in two categories: normal and abnormal based on CT scans is the target. For this experiment, a special earlier focus for getting the best rate by using the Convolutional Neural Networks (CNN) is made. This process has been done by using many different layers to increase the accuracy and reduce the error probabilities by invoking training, validation, and test database, each of these contains a set of images under testing. The process followed through extracting the features and the characteristics found in the segmented liver led up to the classification of testing group into normal and abnormal categories. Initially, and in order to get the best results, the extraction of the liver as a mono-element in the classification there were a need to use Rayleigh, GMM, THRESHOLDING, and finally GVF. These latest results are used as CNN inputs. Experimental results show that CNN features have achieved a rating performance of up to 99.84 %.
{"title":"Convolutional Neural Networks for Segmented Liver Classification","authors":"Toureche Amina, Laimeche Lakhdar, Bendjenna Hakim, Meraoumia Abdallah","doi":"10.1109/ICRAMI52622.2021.9585986","DOIUrl":"https://doi.org/10.1109/ICRAMI52622.2021.9585986","url":null,"abstract":"The classification of liver disease is of paramount significance for an early diagnosis of patients. In this paper, suggesting a way for classifying the liver in two categories: normal and abnormal based on CT scans is the target. For this experiment, a special earlier focus for getting the best rate by using the Convolutional Neural Networks (CNN) is made. This process has been done by using many different layers to increase the accuracy and reduce the error probabilities by invoking training, validation, and test database, each of these contains a set of images under testing. The process followed through extracting the features and the characteristics found in the segmented liver led up to the classification of testing group into normal and abnormal categories. Initially, and in order to get the best results, the extraction of the liver as a mono-element in the classification there were a need to use Rayleigh, GMM, THRESHOLDING, and finally GVF. These latest results are used as CNN inputs. Experimental results show that CNN features have achieved a rating performance of up to 99.84 %.","PeriodicalId":440750,"journal":{"name":"2021 International Conference on Recent Advances in Mathematics and Informatics (ICRAMI)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123762415","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 : 2021-09-21DOI: 10.1109/icrami52622.2021.9585983
{"title":"[Copyright notice]","authors":"","doi":"10.1109/icrami52622.2021.9585983","DOIUrl":"https://doi.org/10.1109/icrami52622.2021.9585983","url":null,"abstract":"","PeriodicalId":440750,"journal":{"name":"2021 International Conference on Recent Advances in Mathematics and Informatics (ICRAMI)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121471310","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 : 2021-09-21DOI: 10.1109/ICRAMI52622.2021.9585948
Zhor Diffallah, H. Ykhlef, Hafida Bouarfa, F. Ykhlef
Acoustic scene classification (ASC) refers to the identification of the environment in which audio excerpts have been recorded. It associates a semantic label to each audio recording. This task has recently drawn a lot of attention as a result of electronics such as smartphones, autonomous robots, or security systems acquiring the ability to perceive sounds. State-of-the-art sound scene classification heavily relies on deep neural network models. However, the complexity of these models makes them more prone to overfitting. The most widely used approach to overcome this concern is data augmentation. In this paper, we design and analyze the behavior of multiple deep learning-based acoustic scene classification systems. These systems are built following two deep convolutional neural network architectures which are defined with different characteristics. Moreover, this work deeply explores the use of Mixup data augmentation method and the effects of varying its hyperparameters. The obtained results indicate that proper tuning of Mixup hyperparameter significantly improves the classification performance, while considering the network architecture being employed.
声学场景分类(Acoustic scene classification, ASC)是指对录制音频片段的环境进行识别。它将一个语义标签关联到每个音频记录。由于智能手机、自动机器人、安保系统等电子产品获得了感知声音的能力，这项任务最近受到了广泛关注。最先进的声音场景分类严重依赖于深度神经网络模型。然而，这些模型的复杂性使它们更容易过度拟合。克服这种担忧的最广泛使用的方法是数据增强。在本文中，我们设计并分析了多个基于深度学习的声学场景分类系统的行为。这些系统是根据两种具有不同特征的深度卷积神经网络架构构建的。此外，本工作还深入探讨了Mixup数据增强方法的使用及其超参数变化的影响。结果表明，在考虑网络结构的情况下，适当调整Mixup超参数可以显著提高分类性能。
{"title":"Impact of Mixup Hyperparameter Tunning on Deep Learning-based Systems for Acoustic Scene Classification","authors":"Zhor Diffallah, H. Ykhlef, Hafida Bouarfa, F. Ykhlef","doi":"10.1109/ICRAMI52622.2021.9585948","DOIUrl":"https://doi.org/10.1109/ICRAMI52622.2021.9585948","url":null,"abstract":"Acoustic scene classification (ASC) refers to the identification of the environment in which audio excerpts have been recorded. It associates a semantic label to each audio recording. This task has recently drawn a lot of attention as a result of electronics such as smartphones, autonomous robots, or security systems acquiring the ability to perceive sounds. State-of-the-art sound scene classification heavily relies on deep neural network models. However, the complexity of these models makes them more prone to overfitting. The most widely used approach to overcome this concern is data augmentation. In this paper, we design and analyze the behavior of multiple deep learning-based acoustic scene classification systems. These systems are built following two deep convolutional neural network architectures which are defined with different characteristics. Moreover, this work deeply explores the use of Mixup data augmentation method and the effects of varying its hyperparameters. The obtained results indicate that proper tuning of Mixup hyperparameter significantly improves the classification performance, while considering the network architecture being employed.","PeriodicalId":440750,"journal":{"name":"2021 International Conference on Recent Advances in Mathematics and Informatics (ICRAMI)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114062811","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 : 2021-09-21DOI: 10.1109/ICRAMI52622.2021.9585985
M. E. Djebbar, Mustapha Réda Senouci, Abdenour Amamra, Mohamed El Yazid Boudaren
Compressive sensing (CS) is a sampling theory that aims to reconstruct signals from fewer measurements than is done in the classical Nyquist-Shannon sampling scheme. Aside from image coding, CS has been recently leveraged successfully in several rendering acceleration tasks. In this work, we generalize the recent success of CS in 3D rendering to a multi-view setup. We formulate the problem as a joint reconstruction of partially rendered views using the CS. A dictionary learning approach is used to leverage signal sparsity condition for the multi-view reconstruction. The reconstruction process was guided by the depth of the scene, which constitutes valuable and computationally efficient information on the geometry of the 3D scene. Preliminary results showed a significant improvement in both the synthetic image quality and rendering time.
{"title":"Compressive Multi-View Rendering: Problem Formulation and Resolution","authors":"M. E. Djebbar, Mustapha Réda Senouci, Abdenour Amamra, Mohamed El Yazid Boudaren","doi":"10.1109/ICRAMI52622.2021.9585985","DOIUrl":"https://doi.org/10.1109/ICRAMI52622.2021.9585985","url":null,"abstract":"Compressive sensing (CS) is a sampling theory that aims to reconstruct signals from fewer measurements than is done in the classical Nyquist-Shannon sampling scheme. Aside from image coding, CS has been recently leveraged successfully in several rendering acceleration tasks. In this work, we generalize the recent success of CS in 3D rendering to a multi-view setup. We formulate the problem as a joint reconstruction of partially rendered views using the CS. A dictionary learning approach is used to leverage signal sparsity condition for the multi-view reconstruction. The reconstruction process was guided by the depth of the scene, which constitutes valuable and computationally efficient information on the geometry of the 3D scene. Preliminary results showed a significant improvement in both the synthetic image quality and rendering time.","PeriodicalId":440750,"journal":{"name":"2021 International Conference on Recent Advances in Mathematics and Informatics (ICRAMI)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122585360","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 : 2021-09-21DOI: 10.1109/ICRAMI52622.2021.9585939
Mohammed Khorchef, N. Ramou, Rabah Abdelkader, Y. Boutiche, N. Chetih
The aim of this work is to create an application that uses the ISO 643:2012 norm for the physical characterization of materials. This application, with its well adapted graphical interface offers the user a better processing of micrographic images, which allows an easy use; it will lead directly to reliable and reproducible results. In this paper, we are interested in determining the mean grain size in material using LSM (the level set method) based on FCM (fuzzy c-means clustering) to get the mean grains size of interest (types of surfaces) and to improve the precision of segmentation with a specified micrographic method. There are two steps in the proposed method. The first step involves using the fuzzy c-means algorithm to generate a clustered image. The second step is based on extracting the grains boundaries by using the appropriate class of the clustered image as an initial condition of the level set method. To achieve this objective, an application has been developed in the OpenCV library to make it easier for the expert to calculate grain sizes.
{"title":"Physical Characterization of Materials by Grain Size Measurement Based Micrographic Images LSM-FCM Segmentation","authors":"Mohammed Khorchef, N. Ramou, Rabah Abdelkader, Y. Boutiche, N. Chetih","doi":"10.1109/ICRAMI52622.2021.9585939","DOIUrl":"https://doi.org/10.1109/ICRAMI52622.2021.9585939","url":null,"abstract":"The aim of this work is to create an application that uses the ISO 643:2012 norm for the physical characterization of materials. This application, with its well adapted graphical interface offers the user a better processing of micrographic images, which allows an easy use; it will lead directly to reliable and reproducible results. In this paper, we are interested in determining the mean grain size in material using LSM (the level set method) based on FCM (fuzzy c-means clustering) to get the mean grains size of interest (types of surfaces) and to improve the precision of segmentation with a specified micrographic method. There are two steps in the proposed method. The first step involves using the fuzzy c-means algorithm to generate a clustered image. The second step is based on extracting the grains boundaries by using the appropriate class of the clustered image as an initial condition of the level set method. To achieve this objective, an application has been developed in the OpenCV library to make it easier for the expert to calculate grain sizes.","PeriodicalId":440750,"journal":{"name":"2021 International Conference on Recent Advances in Mathematics and Informatics (ICRAMI)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132540132","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 : 2021-09-21DOI: 10.1109/ICRAMI52622.2021.9585943
H. Ykhlef, F. Ykhlef, Bouchra Amirouche
Audio tagging is concerned with the development of systems that are able to recognize sound events. With the growing interest geared towards audio tagging for various applications, it has become of paramount importance to design systems that distinguish among events of different natures. To mend with this, ensembling many tagging system has become a successful strategy that lives-up to these emerging challenges. In this paper, we introduce a tagging system composed of an ensemble of deep learners. We propose to formulate the fusion strategy as a coalitional game. Our approach weighs these individual learners, while considering two crucial notions that affect the performance of an ensemble: accuracy and diversity. To demonstrate the efficiency of our approach, we have carried out experimental comparisons on a huge dataset made of sound recordings with annotations of varying reliability. The experimental results indicate that the proposed system provides a reliable ranking and outperforms some major state-of-the art ensemble learning approaches.
{"title":"Game Theory-based Ensemble of Deep Neural Networks for Large Scale Audio Tagging","authors":"H. Ykhlef, F. Ykhlef, Bouchra Amirouche","doi":"10.1109/ICRAMI52622.2021.9585943","DOIUrl":"https://doi.org/10.1109/ICRAMI52622.2021.9585943","url":null,"abstract":"Audio tagging is concerned with the development of systems that are able to recognize sound events. With the growing interest geared towards audio tagging for various applications, it has become of paramount importance to design systems that distinguish among events of different natures. To mend with this, ensembling many tagging system has become a successful strategy that lives-up to these emerging challenges. In this paper, we introduce a tagging system composed of an ensemble of deep learners. We propose to formulate the fusion strategy as a coalitional game. Our approach weighs these individual learners, while considering two crucial notions that affect the performance of an ensemble: accuracy and diversity. To demonstrate the efficiency of our approach, we have carried out experimental comparisons on a huge dataset made of sound recordings with annotations of varying reliability. The experimental results indicate that the proposed system provides a reliable ranking and outperforms some major state-of-the art ensemble learning approaches.","PeriodicalId":440750,"journal":{"name":"2021 International Conference on Recent Advances in Mathematics and Informatics (ICRAMI)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115541288","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 : 2021-09-21DOI: 10.1109/ICRAMI52622.2021.9585990
Ghenaiet Bahia, Ouannas Adel
The majority of strongly nonlinear oscillators of higher fractional order do not have accurate analytical solution. As a result, this work provides an approximate approach, known as the optimal homotopy Asymptotic Method (OHAM) to provide approximate analytic solution of strongly oscillators having fractional derivatives. We give an exemple to show that the OHAM is a reliable approach to control the convergence of approximate solution.
{"title":"Solution Of Strongly Nonlinear Fractional-Order Oscillators Problems By Using The Optimal Homotopy Asymptotic Method","authors":"Ghenaiet Bahia, Ouannas Adel","doi":"10.1109/ICRAMI52622.2021.9585990","DOIUrl":"https://doi.org/10.1109/ICRAMI52622.2021.9585990","url":null,"abstract":"The majority of strongly nonlinear oscillators of higher fractional order do not have accurate analytical solution. As a result, this work provides an approximate approach, known as the optimal homotopy Asymptotic Method (OHAM) to provide approximate analytic solution of strongly oscillators having fractional derivatives. We give an exemple to show that the OHAM is a reliable approach to control the convergence of approximate solution.","PeriodicalId":440750,"journal":{"name":"2021 International Conference on Recent Advances in Mathematics and Informatics (ICRAMI)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115721701","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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