E. Maclean, Felix G. Hamza-Lup, April Garrity, C. Keck, Michelle Smith
Problem-based instruction is an active learning instructional practice that requires students to use rational and critical thinking skills to generate reasonable solutions to problem-based scenarios. For complex medical conditions such as stroke, degenerative diseases, and traumatic brain injury, students must have a strong command of neuroanatomy and physiology. While virtual and synthetic dissection simulation tools alleviate the need for procuring and maintaining costly resources, like cadavers, these tools are costly, inaccessible to students online, and inadequate in the teaching of practical knowledge needed to solve real-life clinical problems. In the wake of the Covid-19 pandemic, many courses have switched to an online format surprising students and faculty. The web-based visualization repository presented is intended to provide medical students with a comprehensive, web-based visual and problem-based learning tool to assist their learning of anatomical and neurophysiological concepts as applied to various medical disorders. The application can be used for online learning, as well as for in-person learning.
{"title":"Web-based 3D visualization system for anatomy online instruction","authors":"E. Maclean, Felix G. Hamza-Lup, April Garrity, C. Keck, Michelle Smith","doi":"10.1145/3409334.3452080","DOIUrl":"https://doi.org/10.1145/3409334.3452080","url":null,"abstract":"Problem-based instruction is an active learning instructional practice that requires students to use rational and critical thinking skills to generate reasonable solutions to problem-based scenarios. For complex medical conditions such as stroke, degenerative diseases, and traumatic brain injury, students must have a strong command of neuroanatomy and physiology. While virtual and synthetic dissection simulation tools alleviate the need for procuring and maintaining costly resources, like cadavers, these tools are costly, inaccessible to students online, and inadequate in the teaching of practical knowledge needed to solve real-life clinical problems. In the wake of the Covid-19 pandemic, many courses have switched to an online format surprising students and faculty. The web-based visualization repository presented is intended to provide medical students with a comprehensive, web-based visual and problem-based learning tool to assist their learning of anatomical and neurophysiological concepts as applied to various medical disorders. The application can be used for online learning, as well as for in-person learning.","PeriodicalId":148741,"journal":{"name":"Proceedings of the 2021 ACM Southeast Conference","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128354310","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}
H. Tran, Chunhua Dong, M. Naghedolfeizi, Xiangyan Zeng
Detection of the face region is a key step in a face recognition system. Thermal images are widely used in many applications where normal visibility is reduced, impaired or ineffective, such as night surveillance and fugitive searches. However, low spatial resolution brings challenges to face detection in thermal images. Viola-Jones is an object detection method widely used for face detection. The algorithm suffers from missed faces and wrongly detected non-face objects due to low resolution of thermal images. To improve the face detection performance for thermal images, we propose to incorporate cross-examples into our framework. In addition to using negative samples of non-face thermal images, we utilize non-face visible images as part of the negative samples (cross-examples). Cross-examples effectively increase the discriminability between the positive samples and negative samples. Experimental results show that the proposed scheme can effectively reduce the non-face objects and thus improve accuracy of face detection.
{"title":"Using cross-examples in viola-jones algorithm for thermal face detection","authors":"H. Tran, Chunhua Dong, M. Naghedolfeizi, Xiangyan Zeng","doi":"10.1145/3409334.3452083","DOIUrl":"https://doi.org/10.1145/3409334.3452083","url":null,"abstract":"Detection of the face region is a key step in a face recognition system. Thermal images are widely used in many applications where normal visibility is reduced, impaired or ineffective, such as night surveillance and fugitive searches. However, low spatial resolution brings challenges to face detection in thermal images. Viola-Jones is an object detection method widely used for face detection. The algorithm suffers from missed faces and wrongly detected non-face objects due to low resolution of thermal images. To improve the face detection performance for thermal images, we propose to incorporate cross-examples into our framework. In addition to using negative samples of non-face thermal images, we utilize non-face visible images as part of the negative samples (cross-examples). Cross-examples effectively increase the discriminability between the positive samples and negative samples. Experimental results show that the proposed scheme can effectively reduce the non-face objects and thus improve accuracy of face detection.","PeriodicalId":148741,"journal":{"name":"Proceedings of the 2021 ACM Southeast Conference","volume":"106 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133160129","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}
Md Khurram Monir Rabby, A. Islam, S. Belkasim, M. Bikdash
In this research, a Principal Component Analysis (PCA) with Genetic Algorithm based Machine Learning (ML) approach is developed for the binary classification of epileptic seizures from the EEG dataset. The proposed approach utilizes PCA to reduce the number of features for binary classification of epileptic seizures and is applied to the existing machine learning models to evaluate the model performance in comparison to the higher number of features. Here, Genetic Algorithm (GA) is employed to tune the hyperparameters of the machine learning models for identifying the best ML model. The proposed approach is applied to the UCI epileptic seizure recognition dataset, which is originated from the EEG dataset of Bonn University. As a preliminary analysis of the proposed approach, the data analysis result shows a significant reduction in the number of features but has minimal impact on the ML performance parameters in comparison to the existing ML method.
{"title":"Epileptic seizures classification in EEG using PCA based genetic algorithm through machine learning","authors":"Md Khurram Monir Rabby, A. Islam, S. Belkasim, M. Bikdash","doi":"10.1145/3409334.3452065","DOIUrl":"https://doi.org/10.1145/3409334.3452065","url":null,"abstract":"In this research, a Principal Component Analysis (PCA) with Genetic Algorithm based Machine Learning (ML) approach is developed for the binary classification of epileptic seizures from the EEG dataset. The proposed approach utilizes PCA to reduce the number of features for binary classification of epileptic seizures and is applied to the existing machine learning models to evaluate the model performance in comparison to the higher number of features. Here, Genetic Algorithm (GA) is employed to tune the hyperparameters of the machine learning models for identifying the best ML model. The proposed approach is applied to the UCI epileptic seizure recognition dataset, which is originated from the EEG dataset of Bonn University. As a preliminary analysis of the proposed approach, the data analysis result shows a significant reduction in the number of features but has minimal impact on the ML performance parameters in comparison to the existing ML method.","PeriodicalId":148741,"journal":{"name":"Proceedings of the 2021 ACM Southeast Conference","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125995017","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 national STEM teacher shortage in public schools is no secret. A recent expansion and adoption Computer Science (CS) Education in the K-12 curriculum exacerbates the shortage. Many states have formed CS Education committees in charge of creating standards and regulations. Universities are creating CS teacher preparation programs to meet the demand. The success of these K-12 CS Education efforts requires a comprehensive understanding of all stakeholders' expectations. In this study, we attempt to identify factors that university students consider most in considering a CS teaching career, along with analyses of gender, major, and degree level, among others. Our results show that CS and Math majors should be the first target group for recruiting efforts as they express the highest interest in CS teaching careers, with males indicating more interest than females.
{"title":"A study on students' views toward K-12 computer science teaching career","authors":"D. Lo, B. Lawler","doi":"10.1145/3409334.3452075","DOIUrl":"https://doi.org/10.1145/3409334.3452075","url":null,"abstract":"The national STEM teacher shortage in public schools is no secret. A recent expansion and adoption Computer Science (CS) Education in the K-12 curriculum exacerbates the shortage. Many states have formed CS Education committees in charge of creating standards and regulations. Universities are creating CS teacher preparation programs to meet the demand. The success of these K-12 CS Education efforts requires a comprehensive understanding of all stakeholders' expectations. In this study, we attempt to identify factors that university students consider most in considering a CS teaching career, along with analyses of gender, major, and degree level, among others. Our results show that CS and Math majors should be the first target group for recruiting efforts as they express the highest interest in CS teaching careers, with males indicating more interest than females.","PeriodicalId":148741,"journal":{"name":"Proceedings of the 2021 ACM Southeast Conference","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132173634","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}
Containers, an emerging service to manage and deploy applications into isolated boxes, are quickly increasing in popularity in the cloud and edge computing. In order to provide connectivity among multiple hosts, cloud providers adopt overlay networks, which not only impose significant overhead in throughput and latency in containerized applications, but also consume more CPU resources of the system. Through profiling and code analysis, this paper reveals that the overwhelming interrupts, as well as its load imbalance in the kernel processing contribute to the inefficiency of the container overlay networks. Specifically, every packet in container networks might raise multiple software interrupts compared to that in VM networks. Our results indicate that the container network throughput drops 2/3 and the tail latency increases more than 37 times if the interrupt overhead is not well optimized.
{"title":"Characterizing networking performance and interrupt overhead of container overlay networks","authors":"Kun Suo, Yong Shi, Ahyoung Lee, S. Baidya","doi":"10.1145/3409334.3452040","DOIUrl":"https://doi.org/10.1145/3409334.3452040","url":null,"abstract":"Containers, an emerging service to manage and deploy applications into isolated boxes, are quickly increasing in popularity in the cloud and edge computing. In order to provide connectivity among multiple hosts, cloud providers adopt overlay networks, which not only impose significant overhead in throughput and latency in containerized applications, but also consume more CPU resources of the system. Through profiling and code analysis, this paper reveals that the overwhelming interrupts, as well as its load imbalance in the kernel processing contribute to the inefficiency of the container overlay networks. Specifically, every packet in container networks might raise multiple software interrupts compared to that in VM networks. Our results indicate that the container network throughput drops 2/3 and the tail latency increases more than 37 times if the interrupt overhead is not well optimized.","PeriodicalId":148741,"journal":{"name":"Proceedings of the 2021 ACM Southeast Conference","volume":"98 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127631647","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}
Recent research points to a severe shortage of cybersecurity professionals right now and in the near future. Universities are introducing cybersecurity programs to fill the gap between cybersecurity professionals' supply and demand. Although cybersecurity concepts/courses are being taught in many undergraduate programs for quite some time, but with the recent surge for cybersecurity professionals' demand, educators have realized the need to offer cybersecurity degree programs. Despite having a plethora of unstructured cybersecurity information available, faculty are struggling to find structured content for cybersecurity major courses. In this paper, we discuss our efforts of teaching a computer forensics course. We present our teaching modules and associated hands-on activities with the hope that our peers might find our course content or teaching methodology helpful in preparing similar courses.
{"title":"Teaching a computer forensics course","authors":"F. Ali","doi":"10.1145/3409334.3452069","DOIUrl":"https://doi.org/10.1145/3409334.3452069","url":null,"abstract":"Recent research points to a severe shortage of cybersecurity professionals right now and in the near future. Universities are introducing cybersecurity programs to fill the gap between cybersecurity professionals' supply and demand. Although cybersecurity concepts/courses are being taught in many undergraduate programs for quite some time, but with the recent surge for cybersecurity professionals' demand, educators have realized the need to offer cybersecurity degree programs. Despite having a plethora of unstructured cybersecurity information available, faculty are struggling to find structured content for cybersecurity major courses. In this paper, we discuss our efforts of teaching a computer forensics course. We present our teaching modules and associated hands-on activities with the hope that our peers might find our course content or teaching methodology helpful in preparing similar courses.","PeriodicalId":148741,"journal":{"name":"Proceedings of the 2021 ACM Southeast Conference","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117192465","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}
Theodore A. Richards V, Eric Gamess, David Thornton
In this paper, we survey network simulators and/or emulators with support for wireless networks. We selected six tools, OMNeT++/INET, ns-3, Packet Tracer, Mininet-WiFi, CORE and Komondor, and further investigate them in regards to their potential use in higher education. These simulators/emulators are readily available and have support for wireless networks in one form or another. The goal of the paper is to help instructors in choosing adequate software to assist online teaching of courses related to wireless networks, including laboratories, using virtual devices, with a minimum investment.
{"title":"A survey of wireless network simulation and/or emulation software for use in higher education","authors":"Theodore A. Richards V, Eric Gamess, David Thornton","doi":"10.1145/3409334.3452066","DOIUrl":"https://doi.org/10.1145/3409334.3452066","url":null,"abstract":"In this paper, we survey network simulators and/or emulators with support for wireless networks. We selected six tools, OMNeT++/INET, ns-3, Packet Tracer, Mininet-WiFi, CORE and Komondor, and further investigate them in regards to their potential use in higher education. These simulators/emulators are readily available and have support for wireless networks in one form or another. The goal of the paper is to help instructors in choosing adequate software to assist online teaching of courses related to wireless networks, including laboratories, using virtual devices, with a minimum investment.","PeriodicalId":148741,"journal":{"name":"Proceedings of the 2021 ACM Southeast Conference","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115829219","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}
Recently one deep learning technique, Convolutional Neural Networks (CNN), has gained immense popularity. Their success is particularly noticeable on image data, but falls short on non-image data. New methods have been developed to transform non-image data to exhibit image like local structures. That would enable the transformed data to take advantage of CNN architectures. Question then arises, how to measure the presence of local structures, the quality of those local structures, and how to know if there is any optimal shape of the local structures that might result in superior performance for CNN. In this paper, we answer these three questions. We present three methods to identify presence of local structures by measuring entropy. We show experimental results that provide intuitions about the quality of the local structures. Finally, we provide results showing that the performances of CNN models corresponding to the lowest entropy producing datasets were superior.
{"title":"Detection of local structures in images using local entropy information","authors":"Torumoy Ghoshal, Yixin Chen","doi":"10.1145/3409334.3452061","DOIUrl":"https://doi.org/10.1145/3409334.3452061","url":null,"abstract":"Recently one deep learning technique, Convolutional Neural Networks (CNN), has gained immense popularity. Their success is particularly noticeable on image data, but falls short on non-image data. New methods have been developed to transform non-image data to exhibit image like local structures. That would enable the transformed data to take advantage of CNN architectures. Question then arises, how to measure the presence of local structures, the quality of those local structures, and how to know if there is any optimal shape of the local structures that might result in superior performance for CNN. In this paper, we answer these three questions. We present three methods to identify presence of local structures by measuring entropy. We show experimental results that provide intuitions about the quality of the local structures. Finally, we provide results showing that the performances of CNN models corresponding to the lowest entropy producing datasets were superior.","PeriodicalId":148741,"journal":{"name":"Proceedings of the 2021 ACM Southeast Conference","volume":"77 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126230381","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}
Hyperspectral images (HSI) contain rich spectral information from a large portion of the electromagnetic spectrum. Using these images, it is possible to make pixel-level classification as each pixel holds hundreds of features. In this paper, we propose an efficient, three-layer hyperspectral image classification model by utilizing spectral/spatial features. The first layer of the system includes two classifiers that work in parallel. These classifiers generate probability scores that form the "new feature set" of the original dataset. The second layer is an ensemble classifier that combines the new features to generate the initial region classification. The third layer introduces a novel approach for enhancing the initial region classification's accuracy from the second layer by utilizing the spatial characteristics of the dataset. A new proximity-based 2D edge preserving order-statistic filtering called Guided Median Filter (GMF) is introduced with weights assigned to each neighboring pixel. Experimental results show that the proposed system improves our previously published results and reaches over 96% overall accuracy on Indian Pines dataset by exceeding some well-known traditional classifiers. Moreover, our GMF based system produced comparable results with the state-of-the-art neural network based methodologies without complex training stage and lack of interpretability of classification model.
{"title":"A three layer spatial-spectral hyperspectral image classification model using guided median filters","authors":"S. Dinç, Luis Alberto Cueva Parra","doi":"10.1145/3409334.3452045","DOIUrl":"https://doi.org/10.1145/3409334.3452045","url":null,"abstract":"Hyperspectral images (HSI) contain rich spectral information from a large portion of the electromagnetic spectrum. Using these images, it is possible to make pixel-level classification as each pixel holds hundreds of features. In this paper, we propose an efficient, three-layer hyperspectral image classification model by utilizing spectral/spatial features. The first layer of the system includes two classifiers that work in parallel. These classifiers generate probability scores that form the \"new feature set\" of the original dataset. The second layer is an ensemble classifier that combines the new features to generate the initial region classification. The third layer introduces a novel approach for enhancing the initial region classification's accuracy from the second layer by utilizing the spatial characteristics of the dataset. A new proximity-based 2D edge preserving order-statistic filtering called Guided Median Filter (GMF) is introduced with weights assigned to each neighboring pixel. Experimental results show that the proposed system improves our previously published results and reaches over 96% overall accuracy on Indian Pines dataset by exceeding some well-known traditional classifiers. Moreover, our GMF based system produced comparable results with the state-of-the-art neural network based methodologies without complex training stage and lack of interpretability of classification model.","PeriodicalId":148741,"journal":{"name":"Proceedings of the 2021 ACM Southeast Conference","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116598099","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}
Fabric density and distortion offer important information on fabric attributes and quality during the manufacturing process. However, most current procedures require human effort, which is often inefficient, time-consuming, and imprecise. In this paper, we propose to use an automatic method using the 2D Fast Fourier Transform (2D-FFT) to count the number of yarns and determine the angle rotation of weft yarns in fabric images. First, we explain the mathematical background of Fourier Transform and 2D-FFT. Then, we use a customized and optimized software package to apply a 2D-FFT to extract image magnitude, phase, and power spectrum. We apply the inverse 2D Fast Fourier Transform (2D-iFFT) on selected frequencies corresponding to periodic structures - basic weave patterns - to reconstruct the original image and extract warp and weft yarns separately. Finally, we use a local adaptive threshold process to convert reconstructed images into binary images for the counting and calculating process. For the weft rotation, we apply a mathematical calculation on the frequency domain to collect the angular distribution and then figure out the major rotation of weft yarns. Our experiments show that the proposed method is highly accurate and capable of inspecting different patterns of fabric. We also observe that the processing time of our proposal method is practical and time-efficient.
{"title":"Detecting fabric density and weft distortion in woven fabrics using the discrete fourier transform","authors":"Bach Le, David Troendle, Byunghyun Jang","doi":"10.1145/3409334.3452049","DOIUrl":"https://doi.org/10.1145/3409334.3452049","url":null,"abstract":"Fabric density and distortion offer important information on fabric attributes and quality during the manufacturing process. However, most current procedures require human effort, which is often inefficient, time-consuming, and imprecise. In this paper, we propose to use an automatic method using the 2D Fast Fourier Transform (2D-FFT) to count the number of yarns and determine the angle rotation of weft yarns in fabric images. First, we explain the mathematical background of Fourier Transform and 2D-FFT. Then, we use a customized and optimized software package to apply a 2D-FFT to extract image magnitude, phase, and power spectrum. We apply the inverse 2D Fast Fourier Transform (2D-iFFT) on selected frequencies corresponding to periodic structures - basic weave patterns - to reconstruct the original image and extract warp and weft yarns separately. Finally, we use a local adaptive threshold process to convert reconstructed images into binary images for the counting and calculating process. For the weft rotation, we apply a mathematical calculation on the frequency domain to collect the angular distribution and then figure out the major rotation of weft yarns. Our experiments show that the proposed method is highly accurate and capable of inspecting different patterns of fabric. We also observe that the processing time of our proposal method is practical and time-efficient.","PeriodicalId":148741,"journal":{"name":"Proceedings of the 2021 ACM Southeast Conference","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122928874","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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