Pub Date : 2019-07-15DOI: 10.1109/IISA.2019.8900731
Konstantinos Christantonis, Christos Tjortjis
Data analysis can be applied to power consumption data for predictions that allow for the efficient scheduling and operation of electricity generation. This work focuses on the parameterization and evaluation of predictive algorithms utilizing metered data on predefined time intervals. More specifically, electricity consumption as a total, but also as main usages/spaces breakdown and weather data are used to develop, train and test predictive models. A technical comparison between different classification algorithms and methodologies are provided. Several weather metrics, such as temperature and humidity are exploited, along with explanatory past consuming variables. The target variable is binary and expresses the volume of consumption regarding each individual residence. The analysis is conducted for two different time intervals during a day, and the outcomes showcase the necessity of weather data for predicting residential electrical consumption. The results also indicate that the size of dwellings affects the accuracy of model.
{"title":"Data Mining for Smart Cities: Predicting Electricity Consumption by Classification","authors":"Konstantinos Christantonis, Christos Tjortjis","doi":"10.1109/IISA.2019.8900731","DOIUrl":"https://doi.org/10.1109/IISA.2019.8900731","url":null,"abstract":"Data analysis can be applied to power consumption data for predictions that allow for the efficient scheduling and operation of electricity generation. This work focuses on the parameterization and evaluation of predictive algorithms utilizing metered data on predefined time intervals. More specifically, electricity consumption as a total, but also as main usages/spaces breakdown and weather data are used to develop, train and test predictive models. A technical comparison between different classification algorithms and methodologies are provided. Several weather metrics, such as temperature and humidity are exploited, along with explanatory past consuming variables. The target variable is binary and expresses the volume of consumption regarding each individual residence. The analysis is conducted for two different time intervals during a day, and the outcomes showcase the necessity of weather data for predicting residential electrical consumption. The results also indicate that the size of dwellings affects the accuracy of model.","PeriodicalId":371385,"journal":{"name":"2019 10th International Conference on Information, Intelligence, Systems and Applications (IISA)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116200458","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-07-15DOI: 10.1109/IISA.2019.8900778
Yongsung Cheon, Chulhee Lee
In this paper, a license plate extraction algorithm is proposed, which can be used in moving vehicles. First, the Haar cascade classifier was used to find candidate regions. Then, a DoG filter was used to detect the edges and connected component labeling was applied to obtain the candidate blocks. The license plate color characteristics were used to eliminate irrelevant blocks using histogram comparison and color quantization. The Bhattacharyya distance and the correlation metric were used to compare the histograms. Experiments with real data showed good performance. The dataset consists of various road and weather conditions including expressway, downtown, sunny days and rainy days. For our dataset, the recall was 0.72, the precision was 0.88 and the F-score was 0.79. For the Caltech dataset, the recall was 0.86, the precision was 0.96 and the F-score was 0.91.
{"title":"License Plate Extraction for Moving Vehicles","authors":"Yongsung Cheon, Chulhee Lee","doi":"10.1109/IISA.2019.8900778","DOIUrl":"https://doi.org/10.1109/IISA.2019.8900778","url":null,"abstract":"In this paper, a license plate extraction algorithm is proposed, which can be used in moving vehicles. First, the Haar cascade classifier was used to find candidate regions. Then, a DoG filter was used to detect the edges and connected component labeling was applied to obtain the candidate blocks. The license plate color characteristics were used to eliminate irrelevant blocks using histogram comparison and color quantization. The Bhattacharyya distance and the correlation metric were used to compare the histograms. Experiments with real data showed good performance. The dataset consists of various road and weather conditions including expressway, downtown, sunny days and rainy days. For our dataset, the recall was 0.72, the precision was 0.88 and the F-score was 0.79. For the Caltech dataset, the recall was 0.86, the precision was 0.96 and the F-score was 0.91.","PeriodicalId":371385,"journal":{"name":"2019 10th International Conference on Information, Intelligence, Systems and Applications (IISA)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116677168","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-07-15DOI: 10.1109/IISA.2019.8900718
A. Martínez-Ballesté, P. Gimeno, A. Mariné, Edgar Batista, A. Solanas
Early Mobilisation routines aim at improving the recovery process of critically ill patients that are hospitalised in intensive care units. Such routines consist of passive and active progressive mobilisations aiming at reducing the side-effects derived from ICU stays. In this paper, we present e-PEMICU, an e-health platform to support early mobilisation programmes in intensive care units. Our solution is founded on motion sensors and smartphones, hence, it is affordable and easy to deploy in real scenarios. We present the design and implementation of a prototype that has been created to address real users needs. Our platform paves the way for a better and sustainable application of current early mobilisation routines.
{"title":"e-PEMICU: an e-Health Platform to Support Early Mobilisation in Intensive Care Units","authors":"A. Martínez-Ballesté, P. Gimeno, A. Mariné, Edgar Batista, A. Solanas","doi":"10.1109/IISA.2019.8900718","DOIUrl":"https://doi.org/10.1109/IISA.2019.8900718","url":null,"abstract":"Early Mobilisation routines aim at improving the recovery process of critically ill patients that are hospitalised in intensive care units. Such routines consist of passive and active progressive mobilisations aiming at reducing the side-effects derived from ICU stays. In this paper, we present e-PEMICU, an e-health platform to support early mobilisation programmes in intensive care units. Our solution is founded on motion sensors and smartphones, hence, it is affordable and easy to deploy in real scenarios. We present the design and implementation of a prototype that has been created to address real users needs. Our platform paves the way for a better and sustainable application of current early mobilisation routines.","PeriodicalId":371385,"journal":{"name":"2019 10th International Conference on Information, Intelligence, Systems and Applications (IISA)","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128386568","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-07-15DOI: 10.1109/IISA.2019.8900726
Akrivi Krouska, C. Troussas, M. Virvou
With the ongoing penetration rate of smart devices, their use is becoming popular in many areas, such as in the field of education. Recently, there has been an increasing demand for digital learning based on mobile devices, namely m-Learning. The development of an effective mobile authoring tool which provides the ability to instructors to manage the learning content with the intention not only to facilitate the content delivery, but also to improve the learning process by adapting content to students’ needs, is a critical task. To this direction, this paper presents the development of a novel mobile content authoring tool which incorporated dynamic reports on refining teaching strategies according to Learning Analytics; i.e. the analysis of the large amount of information resulting from the interaction of students with the m-learning app. The goal of this authoring tool is to support an m-learning system and help teachers to improve their instruction through personalized suggestions on the design and revision of the provided learning content. The developed authoring tool was used by teachers in secondary schools and was evaluated by them. The results of the evaluation showed that the incorporation of Learning Analytics is indeed considered as useful for the tutors supporting them through efficient authoring tools in m-learning environments.
{"title":"Using Learning Analytics to Improve the Efficacy of Mobile Authoring Tools","authors":"Akrivi Krouska, C. Troussas, M. Virvou","doi":"10.1109/IISA.2019.8900726","DOIUrl":"https://doi.org/10.1109/IISA.2019.8900726","url":null,"abstract":"With the ongoing penetration rate of smart devices, their use is becoming popular in many areas, such as in the field of education. Recently, there has been an increasing demand for digital learning based on mobile devices, namely m-Learning. The development of an effective mobile authoring tool which provides the ability to instructors to manage the learning content with the intention not only to facilitate the content delivery, but also to improve the learning process by adapting content to students’ needs, is a critical task. To this direction, this paper presents the development of a novel mobile content authoring tool which incorporated dynamic reports on refining teaching strategies according to Learning Analytics; i.e. the analysis of the large amount of information resulting from the interaction of students with the m-learning app. The goal of this authoring tool is to support an m-learning system and help teachers to improve their instruction through personalized suggestions on the design and revision of the provided learning content. The developed authoring tool was used by teachers in secondary schools and was evaluated by them. The results of the evaluation showed that the incorporation of Learning Analytics is indeed considered as useful for the tutors supporting them through efficient authoring tools in m-learning environments.","PeriodicalId":371385,"journal":{"name":"2019 10th International Conference on Information, Intelligence, Systems and Applications (IISA)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128927547","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-07-15DOI: 10.1109/IISA.2019.8900728
Mersini Paschou, E. Sakkopoulos
Mobile applications apps in healthcare are becoming rapidly adopted as they can be used widely in a smartphone. In this work we present taxonomy of mobile health applications. In this paper we consider health and medical mobile applications as equal. This categorization intends to provide an approach to organize the view of the work that has been carried out in the area of mobile applications in the healthcare domain. Furthermore, the objective is to distinguish the applications according to their target user groups The top apps were organized into groups, based on their usage, user satisfaction and end-users. It is apparent that m-health apps still have plenty of room to grow, towards taking advantage of as many advances as possible, from all the available unique mobile platforms’ features. This work aims at including a list of references in categories and promote international work doneon m-health applications, both as developers and as end-users.
{"title":"Personalized assistant apps in healthcare: a Systematic Review","authors":"Mersini Paschou, E. Sakkopoulos","doi":"10.1109/IISA.2019.8900728","DOIUrl":"https://doi.org/10.1109/IISA.2019.8900728","url":null,"abstract":"Mobile applications apps in healthcare are becoming rapidly adopted as they can be used widely in a smartphone. In this work we present taxonomy of mobile health applications. In this paper we consider health and medical mobile applications as equal. This categorization intends to provide an approach to organize the view of the work that has been carried out in the area of mobile applications in the healthcare domain. Furthermore, the objective is to distinguish the applications according to their target user groups The top apps were organized into groups, based on their usage, user satisfaction and end-users. It is apparent that m-health apps still have plenty of room to grow, towards taking advantage of as many advances as possible, from all the available unique mobile platforms’ features. This work aims at including a list of references in categories and promote international work doneon m-health applications, both as developers and as end-users.","PeriodicalId":371385,"journal":{"name":"2019 10th International Conference on Information, Intelligence, Systems and Applications (IISA)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121672024","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-07-15DOI: 10.1109/IISA.2019.8900769
I. Mporas, I. Perikos, M. Paraskevas
In this paper we present an architecture for identification of pigmented skin lesions from dermatoscopic images. The architecture used a large number of image features and was evaluated with several classification algorithms on different feature subsets as extracted from feature ranking. The best performing classification algorithm was the support vector machines using polynomial kernel function with classification accuracy equal to 74.69% and the most precisely classified skin lesion type between seven different skin pathologies was nevus with accuracy equal to 94.38%.
{"title":"Pigmented Skin Lesions Classification Using Data Driven Subsets of Image Features","authors":"I. Mporas, I. Perikos, M. Paraskevas","doi":"10.1109/IISA.2019.8900769","DOIUrl":"https://doi.org/10.1109/IISA.2019.8900769","url":null,"abstract":"In this paper we present an architecture for identification of pigmented skin lesions from dermatoscopic images. The architecture used a large number of image features and was evaluated with several classification algorithms on different feature subsets as extracted from feature ranking. The best performing classification algorithm was the support vector machines using polynomial kernel function with classification accuracy equal to 74.69% and the most precisely classified skin lesion type between seven different skin pathologies was nevus with accuracy equal to 94.38%.","PeriodicalId":371385,"journal":{"name":"2019 10th International Conference on Information, Intelligence, Systems and Applications (IISA)","volume":"70 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122612090","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-07-15DOI: 10.1109/IISA.2019.8900723
Evgenia Psarra, D. Apostolou
Developing an educational institution timetable is a complex problem which requires finding a successful combination of all the parameters involved (courses, professors, students, classrooms, etc.). To address this problem we developed a prototype algorithm that is a hybrid form of the Particle Swarm Optimization (PSO) algorithm. The original PSO algorithm simulates the mode of a bird cluster movement into nature. In particular, as in this case the solution to a problem with discrete values is needed, we developed a hybrid form of this algorithm with local search, in the process of which we incorporated original methods. The main contribution of this paper is how to improve particles based on optimal Gbest (Global best) and Pbest (Particle best) values of the particles. Our work provides also a fully detailed description of the innovate solution on how to update the algorithm particles in each iteration of the optimization process (Local Search). Our algorithm achieves satisfactory results within seconds.
{"title":"Timetable Scheduling Using a Hybrid Particle Swarm Optimization with Local Search Approach","authors":"Evgenia Psarra, D. Apostolou","doi":"10.1109/IISA.2019.8900723","DOIUrl":"https://doi.org/10.1109/IISA.2019.8900723","url":null,"abstract":"Developing an educational institution timetable is a complex problem which requires finding a successful combination of all the parameters involved (courses, professors, students, classrooms, etc.). To address this problem we developed a prototype algorithm that is a hybrid form of the Particle Swarm Optimization (PSO) algorithm. The original PSO algorithm simulates the mode of a bird cluster movement into nature. In particular, as in this case the solution to a problem with discrete values is needed, we developed a hybrid form of this algorithm with local search, in the process of which we incorporated original methods. The main contribution of this paper is how to improve particles based on optimal Gbest (Global best) and Pbest (Particle best) values of the particles. Our work provides also a fully detailed description of the innovate solution on how to update the algorithm particles in each iteration of the optimization process (Local Search). Our algorithm achieves satisfactory results within seconds.","PeriodicalId":371385,"journal":{"name":"2019 10th International Conference on Information, Intelligence, Systems and Applications (IISA)","volume":"67 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116031933","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-07-15DOI: 10.1109/IISA.2019.8900705
Akrivi Krouska, C. Troussas, M. Virvou
Adults often have busy lives with little time to spare to join formal or non-formal adult educational programs. Technology can facilitate their engagement by making resources and support available in order adults to have access when and where they have time. Technology provides the possibility of including multimedia and interactive resources that can make adult learning more attractive and realistic, encouraging and even inspiring adults to develop their skills. Moreover, it is essential the online environment to follow the principles of adult education. SN-Learning is a new technology advancement that employs social networks for promoting learning. This technology has not been widely used in adult education yet. To this direction, this paper analyzes the main adult learning theory, namely Andragogy, and its application into SN-Learning in order to promote high-quality adult online education tailored to the needs and preferences of this kind of learners. A prototype system was developed and evaluated regarding students’ perspective and system’s specification. The results of evaluation show that SN-Learning can advance the potential of adult online education.
{"title":"Advancing Adult Online Education through a SN-Learning Environment","authors":"Akrivi Krouska, C. Troussas, M. Virvou","doi":"10.1109/IISA.2019.8900705","DOIUrl":"https://doi.org/10.1109/IISA.2019.8900705","url":null,"abstract":"Adults often have busy lives with little time to spare to join formal or non-formal adult educational programs. Technology can facilitate their engagement by making resources and support available in order adults to have access when and where they have time. Technology provides the possibility of including multimedia and interactive resources that can make adult learning more attractive and realistic, encouraging and even inspiring adults to develop their skills. Moreover, it is essential the online environment to follow the principles of adult education. SN-Learning is a new technology advancement that employs social networks for promoting learning. This technology has not been widely used in adult education yet. To this direction, this paper analyzes the main adult learning theory, namely Andragogy, and its application into SN-Learning in order to promote high-quality adult online education tailored to the needs and preferences of this kind of learners. A prototype system was developed and evaluated regarding students’ perspective and system’s specification. The results of evaluation show that SN-Learning can advance the potential of adult online education.","PeriodicalId":371385,"journal":{"name":"2019 10th International Conference on Information, Intelligence, Systems and Applications (IISA)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115675392","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-07-01DOI: 10.1109/IISA.2019.8900746
Athanasios Anagnostis, E. Papageorgiou, Vasileios Dafopoulos, D. Bochtis
Long Short-Term Memory (LSTM) algorithm encloses the characteristics of the advanced recurrent neural network methods and is used in this research study to forecast the natural gas demand in Greece in the short-term. LSTM is generally recognized by researchers as a key tool for time series prediction problems and has found important applicability in many different scientific domains over the last years. In this study, we apply the proposed LSTM for the purposes of a day-ahead natural gas demand prediction to three distribution points (cities) of Greece’s natural gas grid. A comparative analysis was conducted by different Artificial Neural Network (ANN) structures and the results offer a deeper understanding of the large urban centers characteristics, showing the efficacy of the proposed methodology on predicting natural gas demand in a daily basis.
{"title":"Applying Long Short-Term Memory Networks for natural gas demand prediction","authors":"Athanasios Anagnostis, E. Papageorgiou, Vasileios Dafopoulos, D. Bochtis","doi":"10.1109/IISA.2019.8900746","DOIUrl":"https://doi.org/10.1109/IISA.2019.8900746","url":null,"abstract":"Long Short-Term Memory (LSTM) algorithm encloses the characteristics of the advanced recurrent neural network methods and is used in this research study to forecast the natural gas demand in Greece in the short-term. LSTM is generally recognized by researchers as a key tool for time series prediction problems and has found important applicability in many different scientific domains over the last years. In this study, we apply the proposed LSTM for the purposes of a day-ahead natural gas demand prediction to three distribution points (cities) of Greece’s natural gas grid. A comparative analysis was conducted by different Artificial Neural Network (ANN) structures and the results offer a deeper understanding of the large urban centers characteristics, showing the efficacy of the proposed methodology on predicting natural gas demand in a daily basis.","PeriodicalId":371385,"journal":{"name":"2019 10th International Conference on Information, Intelligence, Systems and Applications (IISA)","volume":"102 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115006618","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-07-01DOI: 10.1109/IISA.2019.8900780
Vasileios Gkamas, M. Paraskevas, Emmanouel Varvarigos
The benefits of mobile devices, such as laptops, tablets and smartphones, has led a number of schools to define Bring Your Own Device (BYOD) policies in order to allow teachers and students to bring their own devices to school for teaching and learning. This paper presents the results of a survey conducted among teachers of primary and secondary Greek schools, on their opinion and concerns regarding the adoption of BYOD model in the educational process. BYOD seems to be a promising technology potentially adding long-term value in teaching and learning, when designed in a secure and efficient manner. On the other hand, it requires adequate infrastructure and support and to address the security concerns raised.
{"title":"BYOD for learning and teaching in Greek Schools: Challenges and constraints according to teachers’ point of view","authors":"Vasileios Gkamas, M. Paraskevas, Emmanouel Varvarigos","doi":"10.1109/IISA.2019.8900780","DOIUrl":"https://doi.org/10.1109/IISA.2019.8900780","url":null,"abstract":"The benefits of mobile devices, such as laptops, tablets and smartphones, has led a number of schools to define Bring Your Own Device (BYOD) policies in order to allow teachers and students to bring their own devices to school for teaching and learning. This paper presents the results of a survey conducted among teachers of primary and secondary Greek schools, on their opinion and concerns regarding the adoption of BYOD model in the educational process. BYOD seems to be a promising technology potentially adding long-term value in teaching and learning, when designed in a secure and efficient manner. On the other hand, it requires adequate infrastructure and support and to address the security concerns raised.","PeriodicalId":371385,"journal":{"name":"2019 10th International Conference on Information, Intelligence, Systems and Applications (IISA)","volume":"279 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122305975","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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