Pub Date : 2022-10-06DOI: 10.1109/ICOA55659.2022.9934652
Kaoutar Boussaoud, Meryeme Ayache, A. En-Nouaary
Software-defined networking (SDN) improves the network management due to the separation of the network control plane from the packet forwarding plane. However, with the increase in data traffic, SDN architectures have raised several challenges in terms of traffic engineering, QoS, and network management. Therefore, it is crucial to develop an intelligent system to classify the flows and predict future traffic. Indeed, in order to propose an adequate forwarding strategy for various flow types (particularly elephant flows (EFs)) in an SDN environment, an accurate flow detection system is required. Hence, in this paper, we propose a model-based SDN controller that includes machine learning algorithms to detect large-size traffic and forward it. Moreover, we represent a comparative simulation to evaluate the performance of some supervised machine learning algorithms such as Naive Bayes (NB), K-Nearest neighbors (KNN), Logistics regression (RL), Support Vector Machine (SVM), and Decision Tree (DT), to detect the elephant flow. A decision tree (DT) and K-Nearest neighbors (KNN) are the best candidate machine learning algorithms in elephant flow detection with an accuracy of 99%.
SDN (Software-defined networking)通过网络控制平面和报文转发平面的分离，改善了网络管理。然而，随着数据流量的增加，SDN架构在流量工程、QoS和网络管理方面提出了一些挑战。因此，开发一种智能系统来进行流量分类和预测未来的交通是至关重要的。实际上，为了在SDN环境中针对各种流量类型(特别是象流)提出适当的转发策略，需要一个精确的流量检测系统。因此，在本文中，我们提出了一种基于模型的SDN控制器，其中包括机器学习算法来检测大流量并转发它。此外，我们还代表了一个比较模拟来评估一些监督机器学习算法的性能，如朴素贝叶斯(NB)、k近邻(KNN)、物流回归(RL)、支持向量机(SVM)和决策树(DT)，以检测大象流。决策树(DT)和k近邻(KNN)是大象流检测中最好的候选机器学习算法，准确率为99%。
{"title":"Performance Evaluation of Supervised ML Algorithms for Elephant Flow Detection in SDN","authors":"Kaoutar Boussaoud, Meryeme Ayache, A. En-Nouaary","doi":"10.1109/ICOA55659.2022.9934652","DOIUrl":"https://doi.org/10.1109/ICOA55659.2022.9934652","url":null,"abstract":"Software-defined networking (SDN) improves the network management due to the separation of the network control plane from the packet forwarding plane. However, with the increase in data traffic, SDN architectures have raised several challenges in terms of traffic engineering, QoS, and network management. Therefore, it is crucial to develop an intelligent system to classify the flows and predict future traffic. Indeed, in order to propose an adequate forwarding strategy for various flow types (particularly elephant flows (EFs)) in an SDN environment, an accurate flow detection system is required. Hence, in this paper, we propose a model-based SDN controller that includes machine learning algorithms to detect large-size traffic and forward it. Moreover, we represent a comparative simulation to evaluate the performance of some supervised machine learning algorithms such as Naive Bayes (NB), K-Nearest neighbors (KNN), Logistics regression (RL), Support Vector Machine (SVM), and Decision Tree (DT), to detect the elephant flow. A decision tree (DT) and K-Nearest neighbors (KNN) are the best candidate machine learning algorithms in elephant flow detection with an accuracy of 99%.","PeriodicalId":345017,"journal":{"name":"2022 8th International Conference on Optimization and Applications (ICOA)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125232747","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 : 2022-10-06DOI: 10.1109/ICOA55659.2022.9934479
Hasnaa Souabni, Houssam Benbrahim, A. Amine
The Odoo system integrates all business processes in the organization which includes company's invoicing data, accounting data, sales data, and so on. Therefore, the primary goal of Odoo system is to ensure data security and to achieve the security objectives of the CIA triad (Confidentiality, Integrity, and Availability). An analysis was performed to extract the type of vulnerabilities that often occur in the Odoo system and threaten the security of their customers' information. Most vulnerabilities identified were related to controlling access to data. In this paper, a hybrid access control model is proposed that combines Odoo's access control method and Attribute-based access control (ABAC). The proposed approach provides the least privileges in the Odoo system due to the addition of attributes which will enhance the security of the system.
{"title":"Secure Data Acces in Odoo System","authors":"Hasnaa Souabni, Houssam Benbrahim, A. Amine","doi":"10.1109/ICOA55659.2022.9934479","DOIUrl":"https://doi.org/10.1109/ICOA55659.2022.9934479","url":null,"abstract":"The Odoo system integrates all business processes in the organization which includes company's invoicing data, accounting data, sales data, and so on. Therefore, the primary goal of Odoo system is to ensure data security and to achieve the security objectives of the CIA triad (Confidentiality, Integrity, and Availability). An analysis was performed to extract the type of vulnerabilities that often occur in the Odoo system and threaten the security of their customers' information. Most vulnerabilities identified were related to controlling access to data. In this paper, a hybrid access control model is proposed that combines Odoo's access control method and Attribute-based access control (ABAC). The proposed approach provides the least privileges in the Odoo system due to the addition of attributes which will enhance the security of the system.","PeriodicalId":345017,"journal":{"name":"2022 8th International Conference on Optimization and Applications (ICOA)","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127045516","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 : 2022-10-06DOI: 10.1109/ICOA55659.2022.9934118
Rachid Fateh, A. Darif, S. Safi
Over the last years, the subject of non-linear system identification has attracted considerable interest due to the numerous applications that could be used and the broad multidisciplinary scope of the field. In this paper, we exploit a non-linear system with a linear finite impulse response (FIR) sub-element under the existence of Gaussian noise, while using an algorithm based on positive defined kernels to identify the channel model parameters. Firstly, we have used an algorithm based on the theory of positive definite kernels to estimate the parameters of the selective channel. Secondly, we have studied the influence of the nonlinearity function of modeled single-input single-output (SISO) communication systems with binary-valued output observations on the identification performance of the channel impulse responses. To show which nonlinear function can achieve the most efficient result for channel parameter identification, some examples of simulation results are provided in this works.
{"title":"Hyperbolic Functions Impact Evaluation on Channel Identification Based on Recursive Kernel Algorithm","authors":"Rachid Fateh, A. Darif, S. Safi","doi":"10.1109/ICOA55659.2022.9934118","DOIUrl":"https://doi.org/10.1109/ICOA55659.2022.9934118","url":null,"abstract":"Over the last years, the subject of non-linear system identification has attracted considerable interest due to the numerous applications that could be used and the broad multidisciplinary scope of the field. In this paper, we exploit a non-linear system with a linear finite impulse response (FIR) sub-element under the existence of Gaussian noise, while using an algorithm based on positive defined kernels to identify the channel model parameters. Firstly, we have used an algorithm based on the theory of positive definite kernels to estimate the parameters of the selective channel. Secondly, we have studied the influence of the nonlinearity function of modeled single-input single-output (SISO) communication systems with binary-valued output observations on the identification performance of the channel impulse responses. To show which nonlinear function can achieve the most efficient result for channel parameter identification, some examples of simulation results are provided in this works.","PeriodicalId":345017,"journal":{"name":"2022 8th International Conference on Optimization and Applications (ICOA)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120962256","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 : 2022-10-06DOI: 10.1109/ICOA55659.2022.9934144
Bouzaachane Khadija
Coronavirus has already been spread around the world, in many countries, and it has already claimed many lives. Further, the World Health Organization (WHO) has notified public health officials that COVID-19 has reached global epidemic status. Therefore, an early diagnosis using a chest CT scan can aid medical specialists in critical situations. This study aims to develop a web-based service for detecting COVID-19 online. To achieve our goal, we merged the convolutional neural network (CNN) model with the Firefly algorithm (FA). This combination ameliorate definitely the performance and efficiency of the CNN proposed model. Furthermore, the experiments revealed that the proposed FACNN framework enables us to reach high performance with regard to precision, accuracy, sensitivity, F-measure, recall and specificity (1.0%, 1.0%, 1.0%, 1.0%, 1.0% and 1.0%). In addition, a web-based interface was developed to identify and recogonize COVID-19 in chest radiographs in just few seconds. We anticipate that this web predictor will potentially save precious lives, and therefore contribute to society positively.
{"title":"Automatic detection of covid-19 using CNN model combined with Firefly algorithm","authors":"Bouzaachane Khadija","doi":"10.1109/ICOA55659.2022.9934144","DOIUrl":"https://doi.org/10.1109/ICOA55659.2022.9934144","url":null,"abstract":"Coronavirus has already been spread around the world, in many countries, and it has already claimed many lives. Further, the World Health Organization (WHO) has notified public health officials that COVID-19 has reached global epidemic status. Therefore, an early diagnosis using a chest CT scan can aid medical specialists in critical situations. This study aims to develop a web-based service for detecting COVID-19 online. To achieve our goal, we merged the convolutional neural network (CNN) model with the Firefly algorithm (FA). This combination ameliorate definitely the performance and efficiency of the CNN proposed model. Furthermore, the experiments revealed that the proposed FACNN framework enables us to reach high performance with regard to precision, accuracy, sensitivity, F-measure, recall and specificity (1.0%, 1.0%, 1.0%, 1.0%, 1.0% and 1.0%). In addition, a web-based interface was developed to identify and recogonize COVID-19 in chest radiographs in just few seconds. We anticipate that this web predictor will potentially save precious lives, and therefore contribute to society positively.","PeriodicalId":345017,"journal":{"name":"2022 8th International Conference on Optimization and Applications (ICOA)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131465460","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 : 2022-10-06DOI: 10.1109/ICOA55659.2022.9934635
Sara Rhouas, Norelislam El Hami
Packaging is one of the most important elements in the value chain of transportation and logistics requirements who is frequently overlooked. It has evolved from a basic cardboard box to a complicated, coordinated system that ensures items travel securely and affordably across the supply chain, and to assure that it need to be optimized using metaheuristics that solves complex issues of minimization or maximizing of a function in order to obtain nearly optimal solutions the fastest way. There are many metaheuristics, but in this research, we will only discuss three optimization algorithms that can help us reduce the cost of packaging in a company by programming them with MATLAB software. The first algorithm is the best-known particle swarm optimization in the optimization field, which is inspired by the simulation movement of a flock of birds. The second algorithm is simulated annealing, which is inspired by annealing in metallurgy, a heat treatment technique that affects both temperature and energy. Last but not least, there's the genetic algorithm, which relies on bio-inspired operators like mutation, crossover, and selection to produce high-quality outcomes for optimization issues. We'll use the test functions to compare their performance in terms of uptime and convergence, and then apply it to our industrial optimization problem.
{"title":"A packaging industry optimization based on three metaheuristics methods","authors":"Sara Rhouas, Norelislam El Hami","doi":"10.1109/ICOA55659.2022.9934635","DOIUrl":"https://doi.org/10.1109/ICOA55659.2022.9934635","url":null,"abstract":"Packaging is one of the most important elements in the value chain of transportation and logistics requirements who is frequently overlooked. It has evolved from a basic cardboard box to a complicated, coordinated system that ensures items travel securely and affordably across the supply chain, and to assure that it need to be optimized using metaheuristics that solves complex issues of minimization or maximizing of a function in order to obtain nearly optimal solutions the fastest way. There are many metaheuristics, but in this research, we will only discuss three optimization algorithms that can help us reduce the cost of packaging in a company by programming them with MATLAB software. The first algorithm is the best-known particle swarm optimization in the optimization field, which is inspired by the simulation movement of a flock of birds. The second algorithm is simulated annealing, which is inspired by annealing in metallurgy, a heat treatment technique that affects both temperature and energy. Last but not least, there's the genetic algorithm, which relies on bio-inspired operators like mutation, crossover, and selection to produce high-quality outcomes for optimization issues. We'll use the test functions to compare their performance in terms of uptime and convergence, and then apply it to our industrial optimization problem.","PeriodicalId":345017,"journal":{"name":"2022 8th International Conference on Optimization and Applications (ICOA)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116791646","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 : 2022-10-06DOI: 10.1109/ICOA55659.2022.9934684
S. Idrissi, Youssef El Fezazi, Nabil El Fezazi, E. Tissir
This paper focused on the stability analysis criteria for Takagi-Sugeno (T-S) fuzzy systems with two additive time-varying delay. By constructing an appropriate Lyapunov-Krasovskii functional using two additive delay components and combined with the state vector augmentation. Then, by employing the Finsler's lemma and Seuret-Wirtinger's integral inequality, some less conservative delay-dependent stability criteria are obtained in terms of linear matrix inequality (LMIs), which can be solved by using Matlab LMI toolbox. Finally, numerical results are provided to illustrate the effectiveness of the proposed stability criteria.
{"title":"Improved stability analysis for Takagi-Sugeno (T-S) fuzzy systems with two additive time-varying delays","authors":"S. Idrissi, Youssef El Fezazi, Nabil El Fezazi, E. Tissir","doi":"10.1109/ICOA55659.2022.9934684","DOIUrl":"https://doi.org/10.1109/ICOA55659.2022.9934684","url":null,"abstract":"This paper focused on the stability analysis criteria for Takagi-Sugeno (T-S) fuzzy systems with two additive time-varying delay. By constructing an appropriate Lyapunov-Krasovskii functional using two additive delay components and combined with the state vector augmentation. Then, by employing the Finsler's lemma and Seuret-Wirtinger's integral inequality, some less conservative delay-dependent stability criteria are obtained in terms of linear matrix inequality (LMIs), which can be solved by using Matlab LMI toolbox. Finally, numerical results are provided to illustrate the effectiveness of the proposed stability criteria.","PeriodicalId":345017,"journal":{"name":"2022 8th International Conference on Optimization and Applications (ICOA)","volume":"156 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123095990","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 : 2022-10-06DOI: 10.1109/ICOA55659.2022.9934128
Ali Amkor, N. E. Barbri
This article assesses potatoes using an electronic nose according to the nature of the original fields of their harvest: traditionally treated with manure from domestic sheep and donkeys or with manure from chicken farms. A network of five commercial metal oxide sensors, a data card acquisition, a personal computer, and a data analysis and processing approach make up our electronic nose tool. The method of principal component analysis (PCA) was used for the classification of data from both two potatoes kinds and revealed that the first three principal components (PC1, PC2, and PC3) may explain 99.20 percent of the variance by recording a spectacular visual separation allowing each group to be identified.
{"title":"Electronic nose based on gas sensors and a machine-learning algorithm to discriminate potatoes according to the cultivated field nature","authors":"Ali Amkor, N. E. Barbri","doi":"10.1109/ICOA55659.2022.9934128","DOIUrl":"https://doi.org/10.1109/ICOA55659.2022.9934128","url":null,"abstract":"This article assesses potatoes using an electronic nose according to the nature of the original fields of their harvest: traditionally treated with manure from domestic sheep and donkeys or with manure from chicken farms. A network of five commercial metal oxide sensors, a data card acquisition, a personal computer, and a data analysis and processing approach make up our electronic nose tool. The method of principal component analysis (PCA) was used for the classification of data from both two potatoes kinds and revealed that the first three principal components (PC1, PC2, and PC3) may explain 99.20 percent of the variance by recording a spectacular visual separation allowing each group to be identified.","PeriodicalId":345017,"journal":{"name":"2022 8th International Conference on Optimization and Applications (ICOA)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130980112","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 : 2022-10-06DOI: 10.1109/ICOA55659.2022.9934407
Hamza Ouamna, Z. Madini, Y. Zouine
The ever-growing connected objects is key driver behind the development of wireless communications in addition to many others needed use cases application all of these key drivers are behind the development of 6G wireless communications with the jump to the Thz bands 6G will be supported by: Programmable V2X Environment, artificial intelligence, and Quantum Computing, in addition to Brain-Vehicle Interfacing, also Large Scale Non-orthogonal Multiple Access, Internet of Space Things with CubeSats, and cell-free massive MIMO communication networks. Moreover, as a part of the connected objects, vehicles are included in this development; thus, these key drivers will also enable Vehicle-to-everything (V2X) communications powered by the 6G networks. In this paper, we will introduce these key drivers with open problems and possible solutions in relation with V2X.
{"title":"6G and V2X Communications: Applications, Features, and Challenges","authors":"Hamza Ouamna, Z. Madini, Y. Zouine","doi":"10.1109/ICOA55659.2022.9934407","DOIUrl":"https://doi.org/10.1109/ICOA55659.2022.9934407","url":null,"abstract":"The ever-growing connected objects is key driver behind the development of wireless communications in addition to many others needed use cases application all of these key drivers are behind the development of 6G wireless communications with the jump to the Thz bands 6G will be supported by: Programmable V2X Environment, artificial intelligence, and Quantum Computing, in addition to Brain-Vehicle Interfacing, also Large Scale Non-orthogonal Multiple Access, Internet of Space Things with CubeSats, and cell-free massive MIMO communication networks. Moreover, as a part of the connected objects, vehicles are included in this development; thus, these key drivers will also enable Vehicle-to-everything (V2X) communications powered by the 6G networks. In this paper, we will introduce these key drivers with open problems and possible solutions in relation with V2X.","PeriodicalId":345017,"journal":{"name":"2022 8th International Conference on Optimization and Applications (ICOA)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114525845","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 : 2022-10-06DOI: 10.1109/ICOA55659.2022.9934204
Reddad Hakima, Zemzami Maria, E. Norelislam, Hmina Nabil
This article presents a study of a recent metaheuristic optimization method, the search and rescue algorithm (SAR), against four known metaheuristic optimization algorithms, the Salp Swarm Algorithm (SSA), the Cuckoo Search Algorithm (CSA), the Firefly Algorithm (FA), and the Grey Wolf Optimization Algorithm (GWO). An evaluation of its performance against the other algorithms will be performed by the means of thirteen mathematical benchmarks functions, afterwards a study of the optimization of five multi-dimensional mathematical problems will be investigated, the optimization of the Dejoung function, the Cosine Mixture function, the Griewank function, the Rastrigin function, and the Rosenbrok function, while the dimension of these problems increases from five to thirty. Furthermore, a discussion and a conclusion about the results obtained by each algorithm face to the resolution of these complex multi-dimensional problems will be drawn.
{"title":"A comparative study of several metaheuristic algorithms for optimization problems","authors":"Reddad Hakima, Zemzami Maria, E. Norelislam, Hmina Nabil","doi":"10.1109/ICOA55659.2022.9934204","DOIUrl":"https://doi.org/10.1109/ICOA55659.2022.9934204","url":null,"abstract":"This article presents a study of a recent metaheuristic optimization method, the search and rescue algorithm (SAR), against four known metaheuristic optimization algorithms, the Salp Swarm Algorithm (SSA), the Cuckoo Search Algorithm (CSA), the Firefly Algorithm (FA), and the Grey Wolf Optimization Algorithm (GWO). An evaluation of its performance against the other algorithms will be performed by the means of thirteen mathematical benchmarks functions, afterwards a study of the optimization of five multi-dimensional mathematical problems will be investigated, the optimization of the Dejoung function, the Cosine Mixture function, the Griewank function, the Rastrigin function, and the Rosenbrok function, while the dimension of these problems increases from five to thirty. Furthermore, a discussion and a conclusion about the results obtained by each algorithm face to the resolution of these complex multi-dimensional problems will be drawn.","PeriodicalId":345017,"journal":{"name":"2022 8th International Conference on Optimization and Applications (ICOA)","volume":"406 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115953223","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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