The data classification method based on support vector machine (SVM) has been widely used in various studies as a non-linear, high precision, and good generalization ability machine learning method. Among them, the kernel function and its parameters have a great impact on the classification accuracy. In order to find the optimal parameters to improve the classification accuracy of SVM, this paper proposes a data multi-classification method based on gray wolf algorithm optimized SVM(GWO-SVM). In this paper, the iris data set is used to test the performance of GWO-SVM, and the classification result is compared with those based on genetic algorithm (GA), particle swarm optimization (PSO) and the original SVM model. The test results show that the GWO-SVM model has a higher recognition and classification accuracy than the other three models, and has the shortest running time, which has obvious advantages and can effectively improve the classification accuracy of SVM. This method has practical significance in image classification, text classification, and fault detection.
{"title":"Research on Data Classification Method of Optimized Support Vector Machine Based on Gray Wolf Algorithm","authors":"Jinqiang Ma, Linchang Fan, Weijia Tian, Z. Miao","doi":"10.4018/ijghpc.318408","DOIUrl":"https://doi.org/10.4018/ijghpc.318408","url":null,"abstract":"The data classification method based on support vector machine (SVM) has been widely used in various studies as a non-linear, high precision, and good generalization ability machine learning method. Among them, the kernel function and its parameters have a great impact on the classification accuracy. In order to find the optimal parameters to improve the classification accuracy of SVM, this paper proposes a data multi-classification method based on gray wolf algorithm optimized SVM(GWO-SVM). In this paper, the iris data set is used to test the performance of GWO-SVM, and the classification result is compared with those based on genetic algorithm (GA), particle swarm optimization (PSO) and the original SVM model. The test results show that the GWO-SVM model has a higher recognition and classification accuracy than the other three models, and has the shortest running time, which has obvious advantages and can effectively improve the classification accuracy of SVM. This method has practical significance in image classification, text classification, and fault detection.","PeriodicalId":43565,"journal":{"name":"International Journal of Grid and High Performance Computing","volume":"2 1","pages":"1-14"},"PeriodicalIF":1.0,"publicationDate":"2023-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82120157","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}
Previous research on wireless charging did not discuss the problem of using optimal allocation to improve the network life cycle. The authors designed and analyzed a variety of network area cutting methods: rectangular cutting, square cutting, concentric circle cutting, sector cutting, and mixed cutting. Through simulation experiments, the difference between the survival time and the number of received packets is compared. The experimental results show that mixed cutting can calculate the expected energy consumption according to the energy consumption rate, and then allocate chargers according to the expected energy consumption, making the energy consumption burden of each charger more equitable. Compared with other partition methods, the load capacity distribution of each charger is more uniform. In terms of survival time and receiving message packets, the network can have a longer survival time, receive more message packets, and use the power consumption of each block more evenly and effectively.
{"title":"Optimum Partition for Wireless Charging Scheduling in Wireless Sensor Networks With Applications","authors":"Zuoli Zhang, Wenfei Hu, Tung-Hsien Peng, Zexiang Zheng","doi":"10.4018/ijghpc.316155","DOIUrl":"https://doi.org/10.4018/ijghpc.316155","url":null,"abstract":"Previous research on wireless charging did not discuss the problem of using optimal allocation to improve the network life cycle. The authors designed and analyzed a variety of network area cutting methods: rectangular cutting, square cutting, concentric circle cutting, sector cutting, and mixed cutting. Through simulation experiments, the difference between the survival time and the number of received packets is compared. The experimental results show that mixed cutting can calculate the expected energy consumption according to the energy consumption rate, and then allocate chargers according to the expected energy consumption, making the energy consumption burden of each charger more equitable. Compared with other partition methods, the load capacity distribution of each charger is more uniform. In terms of survival time and receiving message packets, the network can have a longer survival time, receive more message packets, and use the power consumption of each block more evenly and effectively.","PeriodicalId":43565,"journal":{"name":"International Journal of Grid and High Performance Computing","volume":"87 1","pages":"1-17"},"PeriodicalIF":1.0,"publicationDate":"2023-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86738373","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}
Wireless power transfer technique provides a new and promising method for alleviating the limited energy capacity problem, thus receiving much attention. However, previous works usually consider temporal, spatial, or both factors of the current selected node greedily without taking the residual moving distance of the remaining nodes into consideration. Surely, it is not easy to precisely estimate the residual moving distance of the remaining nodes before knowing their exact order in the scheduling path. In this work, the authors are the first to propose the concept of the residual moving distance (cost) and create a mathematical model to roughly estimate the cost of a given node set. Moreover, they design a temporal and spatial priority charging scheduling algorithm with additional considering the global cost (TSPG). Simulation results demonstrate that TSPG outperforms earliest deadline first scheduling algorithm and revised earliest deadline first scheduling algorithm. Moreover, the proposed new model for estimating moving distance in the residual area has all relative error below 9%.
{"title":"A Temporal and Spatial Priority With Global Cost Recharging Scheduling in Wireless Rechargeable Sensor Networks","authors":"Jingjing Chen, Hongwei Chen, Wen Ouyang, Chang-Wu Yu","doi":"10.4018/ijghpc.316152","DOIUrl":"https://doi.org/10.4018/ijghpc.316152","url":null,"abstract":"Wireless power transfer technique provides a new and promising method for alleviating the limited energy capacity problem, thus receiving much attention. However, previous works usually consider temporal, spatial, or both factors of the current selected node greedily without taking the residual moving distance of the remaining nodes into consideration. Surely, it is not easy to precisely estimate the residual moving distance of the remaining nodes before knowing their exact order in the scheduling path. In this work, the authors are the first to propose the concept of the residual moving distance (cost) and create a mathematical model to roughly estimate the cost of a given node set. Moreover, they design a temporal and spatial priority charging scheduling algorithm with additional considering the global cost (TSPG). Simulation results demonstrate that TSPG outperforms earliest deadline first scheduling algorithm and revised earliest deadline first scheduling algorithm. Moreover, the proposed new model for estimating moving distance in the residual area has all relative error below 9%.","PeriodicalId":43565,"journal":{"name":"International Journal of Grid and High Performance Computing","volume":"51 1","pages":"1-31"},"PeriodicalIF":1.0,"publicationDate":"2023-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80754982","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}
In this paper, an online self-constructing fuzzy neural network (SCFNN) is proposed to solve four kinds of nonlinear dynamic system identification (NDSI) problems in the internet of things (IoTs). The SCFNN is capable of constructing a simple network without the need for knowledge of the NDSI. Thus, carefully setting conditions for the increased demands for fuzzy rules will make the architecture of the constructed SCFNN fairly simple. The applications of neural networks in IoTs are discussed. The authors also propose a new identification model for NDSI. Through an experimental example, it is proved that online learning can arrange membership functions in a more appropriate vector space. The performance of the online SCFNN is compared with both MLP and RBF through four extensive simulations. The comparison terms are convergence rate, training root mean square error (RMSE), test RMSE, and prediction accuracy (PA). The simulation results show that SCFNN is superior to MLP and RBF in NDSI problems.
{"title":"Nonlinear System Identification Based on an Online SCFNN With Applications in IoTs","authors":"Ye Lin, Yea-Shuan Huang, Rui-Chang Lin","doi":"10.4018/ijghpc.316153","DOIUrl":"https://doi.org/10.4018/ijghpc.316153","url":null,"abstract":"In this paper, an online self-constructing fuzzy neural network (SCFNN) is proposed to solve four kinds of nonlinear dynamic system identification (NDSI) problems in the internet of things (IoTs). The SCFNN is capable of constructing a simple network without the need for knowledge of the NDSI. Thus, carefully setting conditions for the increased demands for fuzzy rules will make the architecture of the constructed SCFNN fairly simple. The applications of neural networks in IoTs are discussed. The authors also propose a new identification model for NDSI. Through an experimental example, it is proved that online learning can arrange membership functions in a more appropriate vector space. The performance of the online SCFNN is compared with both MLP and RBF through four extensive simulations. The comparison terms are convergence rate, training root mean square error (RMSE), test RMSE, and prediction accuracy (PA). The simulation results show that SCFNN is superior to MLP and RBF in NDSI problems.","PeriodicalId":43565,"journal":{"name":"International Journal of Grid and High Performance Computing","volume":"28 1","pages":"1-22"},"PeriodicalIF":1.0,"publicationDate":"2023-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78184422","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}
In the current era of technology, the utilization of tablets and smart phones plays a major role in every situation. As the numbers of mobile users increase, the quality of service (QoS) and quality of experience (QoE) are facing the greater challenges. Thus, this can significantly reduce the latency and optimize the power consumed by the tasks executed locally. Most of the previous works are focused only on quality optimization in the dynamic service layouts. However, they ignored the significant impact of accurate access network selection and perfect service placement. This article performs the detailed survey of various MEC approaches with service provision and adoption. The survey also provides the analysis of various approaches for optimizing the QoS parameters and MEC resources. In this regarding, the survey classifies the approaches based on service placement, network selection, QoS, and QoE parameters, and resources such as latency, energy, bandwidth, memory, storage, and processing.
{"title":"Mobile Edge Computing Architecture Challenges, Applications, and Future Directions","authors":"B. TejaSree, G. Varma, Hemalatha Indukuri","doi":"10.4018/ijghpc.316837","DOIUrl":"https://doi.org/10.4018/ijghpc.316837","url":null,"abstract":"In the current era of technology, the utilization of tablets and smart phones plays a major role in every situation. As the numbers of mobile users increase, the quality of service (QoS) and quality of experience (QoE) are facing the greater challenges. Thus, this can significantly reduce the latency and optimize the power consumed by the tasks executed locally. Most of the previous works are focused only on quality optimization in the dynamic service layouts. However, they ignored the significant impact of accurate access network selection and perfect service placement. This article performs the detailed survey of various MEC approaches with service provision and adoption. The survey also provides the analysis of various approaches for optimizing the QoS parameters and MEC resources. In this regarding, the survey classifies the approaches based on service placement, network selection, QoS, and QoE parameters, and resources such as latency, energy, bandwidth, memory, storage, and processing.","PeriodicalId":43565,"journal":{"name":"International Journal of Grid and High Performance Computing","volume":"43 1","pages":"1-23"},"PeriodicalIF":1.0,"publicationDate":"2023-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89213117","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}
Ben Wang, Kun-Ming Yu, Nattawat Sodsong, Ken H. Chuang
With the large-scale deployment of solar PV installations, managing the efficiency of the generation system became essential. Generally, the power output is heavily influenced by solar irradiance and sky conditions which are consistently changing. Thus, the ability to accurately forecast the solar PV power is critical for optimizing the generation system, estimating revenue, sustaining profits, and ensuring the quality of service. In this paper, the authors propose a solar PV forecasting model using multiple blocks of GRUs and RNN in a cascade model combined with hierarchical clustering to improve the overall prediction accuracy of solar PV forecast. This proposed model is a combination of hierarchical clustering, the Pearson correlation coefficient for feature selection, and the cascade model with GRU layer from k-means clustering and hierarchical clustering. These results, which are evaluated using NRMSE, show that hierarchical clustering is more suitable for solar PV forecast than k-means clustering.
{"title":"Forecasting Short-Term Solar PV Using Hierarchical Clustering and Cascade Model","authors":"Ben Wang, Kun-Ming Yu, Nattawat Sodsong, Ken H. Chuang","doi":"10.4018/ijghpc.316154","DOIUrl":"https://doi.org/10.4018/ijghpc.316154","url":null,"abstract":"With the large-scale deployment of solar PV installations, managing the efficiency of the generation system became essential. Generally, the power output is heavily influenced by solar irradiance and sky conditions which are consistently changing. Thus, the ability to accurately forecast the solar PV power is critical for optimizing the generation system, estimating revenue, sustaining profits, and ensuring the quality of service. In this paper, the authors propose a solar PV forecasting model using multiple blocks of GRUs and RNN in a cascade model combined with hierarchical clustering to improve the overall prediction accuracy of solar PV forecast. This proposed model is a combination of hierarchical clustering, the Pearson correlation coefficient for feature selection, and the cascade model with GRU layer from k-means clustering and hierarchical clustering. These results, which are evaluated using NRMSE, show that hierarchical clustering is more suitable for solar PV forecast than k-means clustering.","PeriodicalId":43565,"journal":{"name":"International Journal of Grid and High Performance Computing","volume":"25 1","pages":"1-21"},"PeriodicalIF":1.0,"publicationDate":"2023-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80256694","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 growing number of vehicles in a densely populated smart city results in a significant lack of parking space. During the implementation of systems for visibility of parking space vacancies for drivers, the bulk of the systems are focused on expensive dedicated sensor devices, requiring high installation costs. The emergence of a relatively inexpensive internet of things (IoT) system allows embedded cameras to track parking spaces' utilisation. However, parking space positions' manual specification before drivers can use such devices after implementation is important even for camera-captured images. Hence in this paper, IoT assisted intelligent parking system (IoT-AIPS) with cloud platform has been proposed to reduce vehicle parking waiting time and enhance accurate vehicle position prediction. The proposed method utilizes the machine learning method to classify topologies in the parking space based on stationary location.
{"title":"Design of Intelligent Parking System Based on Internet of Things and Cloud Platform","authors":"Jie Yang, Jinbao He, Xiongwei Wang","doi":"10.4018/ijghpc.316836","DOIUrl":"https://doi.org/10.4018/ijghpc.316836","url":null,"abstract":"The growing number of vehicles in a densely populated smart city results in a significant lack of parking space. During the implementation of systems for visibility of parking space vacancies for drivers, the bulk of the systems are focused on expensive dedicated sensor devices, requiring high installation costs. The emergence of a relatively inexpensive internet of things (IoT) system allows embedded cameras to track parking spaces' utilisation. However, parking space positions' manual specification before drivers can use such devices after implementation is important even for camera-captured images. Hence in this paper, IoT assisted intelligent parking system (IoT-AIPS) with cloud platform has been proposed to reduce vehicle parking waiting time and enhance accurate vehicle position prediction. The proposed method utilizes the machine learning method to classify topologies in the parking space based on stationary location.","PeriodicalId":43565,"journal":{"name":"International Journal of Grid and High Performance Computing","volume":"64 1","pages":"1-18"},"PeriodicalIF":1.0,"publicationDate":"2023-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72867475","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}
Charging scheduling is an important issue of wireless rechargeable sensor networks. Previous research proposed to optimize the scheduling sequence by considering factors such as distance and remaining working time. However, packets are transmitted to the base station hop by hop, so that the burden on each sensor is not the same. The unbalancing nature of loading should also be taken into account when dealing with charging requests scheduling. In this paper, the authors have found, both through theoretical analysis on hypothetical model and simulation in more realistic environments, that the communication loading of sensors impacts power consumption of sensors in different tiers relative to the base station significantly. Accordingly, the proposed charging scheduling algorithm takes the loading factor into consideration so that sensors closer to the base station may be given higher priority for recharge. The simulation results show that the proposed method can significantly improve the data delivery rate and achieve higher network availability when compared to previous research.
{"title":"A Tier-Based Loading-Aware Charging Scheduling Algorithm for Wireless Rechargeable Sensor Networks","authors":"Rei-Heng Cheng, Tung-Kuang Wu, Chengjie Xu, Jingjing Chen","doi":"10.4018/ijghpc.316156","DOIUrl":"https://doi.org/10.4018/ijghpc.316156","url":null,"abstract":"Charging scheduling is an important issue of wireless rechargeable sensor networks. Previous research proposed to optimize the scheduling sequence by considering factors such as distance and remaining working time. However, packets are transmitted to the base station hop by hop, so that the burden on each sensor is not the same. The unbalancing nature of loading should also be taken into account when dealing with charging requests scheduling. In this paper, the authors have found, both through theoretical analysis on hypothetical model and simulation in more realistic environments, that the communication loading of sensors impacts power consumption of sensors in different tiers relative to the base station significantly. Accordingly, the proposed charging scheduling algorithm takes the loading factor into consideration so that sensors closer to the base station may be given higher priority for recharge. The simulation results show that the proposed method can significantly improve the data delivery rate and achieve higher network availability when compared to previous research.","PeriodicalId":43565,"journal":{"name":"International Journal of Grid and High Performance Computing","volume":"7 1","pages":"1-21"},"PeriodicalIF":1.0,"publicationDate":"2023-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88609553","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}
Wireless rechargeable sensor networks (WRSNs) have received a lot of attention due to the development of wireless charging technology. Recently, a new solution of wireless charging vehicle (WCV) for WRSNs with separable charger array equipped with multiple chargers was suggested. By this method, each charger can be unloaded to serve one sensor, while the WCV can work in a very efficient way because it needs not to stay on site and can continue to perform its assigned task. But this solution created a new problem that is how to recollect these chargers for reusing when their charging services are finished. In previous research, however, the recollecting strategy has seldom been considered. In this work, an effectively opportunistic charger recollection algorithm (OCRA) are proposed. Simulation results indicate that OCRA has outperformed previous algorithms in many aspects.
{"title":"An Opportunistic Charger Recollection Algorithm for Wireless Rechargeable Sensor Networks","authors":"Ronglin Hu, Xiaomin Chen, Chengjie Xu","doi":"10.4018/ijghpc.316151","DOIUrl":"https://doi.org/10.4018/ijghpc.316151","url":null,"abstract":"Wireless rechargeable sensor networks (WRSNs) have received a lot of attention due to the development of wireless charging technology. Recently, a new solution of wireless charging vehicle (WCV) for WRSNs with separable charger array equipped with multiple chargers was suggested. By this method, each charger can be unloaded to serve one sensor, while the WCV can work in a very efficient way because it needs not to stay on site and can continue to perform its assigned task. But this solution created a new problem that is how to recollect these chargers for reusing when their charging services are finished. In previous research, however, the recollecting strategy has seldom been considered. In this work, an effectively opportunistic charger recollection algorithm (OCRA) are proposed. Simulation results indicate that OCRA has outperformed previous algorithms in many aspects.","PeriodicalId":43565,"journal":{"name":"International Journal of Grid and High Performance Computing","volume":"70 1","pages":"1-21"},"PeriodicalIF":1.0,"publicationDate":"2023-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88099522","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}
Nowadays, medical diseases are one of the primary causes of death, and it is one the major concerns of developed countries. So, the disease identification process needs a lot of attention since if the diseases are idenfied at the early stage, the rate of death can be decreased. Machine learning techniques is one of the popular approaches that is used for identifying the diseases at the early stage. In this paper, two machine learning techniques, namely Naive Bayes classification algorithm and Laplace smoothing technique are used to predict the heart disease. Here, many medical details are used, such as gender, age, fasting blood sugar, blood pressure, cholesterol, etc. to predict the hearth disease of a patient. The proposed decision system supports avoiding unnecessary diagnosis test, which can be highly beneficial to start the treatment quickly. Thus, both time and money can be saved. Both the performance analysis and the experimental results show the efficiency of the proposed scheme over the existing schemes.
{"title":"Early Prediction of Heart Diseases using Naive Bayes Classification Algorithm and Laplace Smoothing Technique","authors":"Subhashini Narayan, Sathiyamoorthy E.","doi":"10.4018/ijghpc.316157","DOIUrl":"https://doi.org/10.4018/ijghpc.316157","url":null,"abstract":"Nowadays, medical diseases are one of the primary causes of death, and it is one the major concerns of developed countries. So, the disease identification process needs a lot of attention since if the diseases are idenfied at the early stage, the rate of death can be decreased. Machine learning techniques is one of the popular approaches that is used for identifying the diseases at the early stage. In this paper, two machine learning techniques, namely Naive Bayes classification algorithm and Laplace smoothing technique are used to predict the heart disease. Here, many medical details are used, such as gender, age, fasting blood sugar, blood pressure, cholesterol, etc. to predict the hearth disease of a patient. The proposed decision system supports avoiding unnecessary diagnosis test, which can be highly beneficial to start the treatment quickly. Thus, both time and money can be saved. Both the performance analysis and the experimental results show the efficiency of the proposed scheme over the existing schemes.","PeriodicalId":43565,"journal":{"name":"International Journal of Grid and High Performance Computing","volume":"29 1","pages":"1-14"},"PeriodicalIF":1.0,"publicationDate":"2023-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87200004","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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