Pub Date : 2023-03-15DOI: 10.23919/SAIEE.2023.10071978
M. Omar;A. Arif;M. Usman;S. S. Khan;S. Larkin
The performance of photovoltaic panels is affected by the accumulation of dust particles on their surface. Regular cleaning of these photovoltaic panels is required, which increases the overall system cost and solution complexity. In remote areas, especially in water-stressed areas like deserts, water availability is an issue that double-folds the problem's complexity. Few automatic or manual dust cleaning methods through dry brushing are still there, which damages the glass layer at the top of photovoltaic panels. Here the availability of water for cleaning is not only a piece of the puzzle, but the required power to generate water in case of water harvesting is also equally important. This work proposes a novel artificial intelligence-enabled, wind turbine-driven air-water harvester. The air-water harvester is designed to operate in three different modes depending on the amount of dust on the surface of the solar panel. The system can produce more than two liters of water per day at the expense of a maximum of 100 W. In the end, the increase in the performance of the photovoltaic panel with and without the proposed cleaning solution is tested by cleaning its surface with water produced by the air-water harvester.
{"title":"Self-cleaning solution for solar panels","authors":"M. Omar;A. Arif;M. Usman;S. S. Khan;S. Larkin","doi":"10.23919/SAIEE.2023.10071978","DOIUrl":"10.23919/SAIEE.2023.10071978","url":null,"abstract":"The performance of photovoltaic panels is affected by the accumulation of dust particles on their surface. Regular cleaning of these photovoltaic panels is required, which increases the overall system cost and solution complexity. In remote areas, especially in water-stressed areas like deserts, water availability is an issue that double-folds the problem's complexity. Few automatic or manual dust cleaning methods through dry brushing are still there, which damages the glass layer at the top of photovoltaic panels. Here the availability of water for cleaning is not only a piece of the puzzle, but the required power to generate water in case of water harvesting is also equally important. This work proposes a novel artificial intelligence-enabled, wind turbine-driven air-water harvester. The air-water harvester is designed to operate in three different modes depending on the amount of dust on the surface of the solar panel. The system can produce more than two liters of water per day at the expense of a maximum of 100 W. In the end, the increase in the performance of the photovoltaic panel with and without the proposed cleaning solution is tested by cleaning its surface with water produced by the air-water harvester.","PeriodicalId":42493,"journal":{"name":"SAIEE Africa Research Journal","volume":"114 2","pages":"58-66"},"PeriodicalIF":1.4,"publicationDate":"2023-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/8475037/10071972/10071978.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42610409","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Currently, the smart cities, smart vehicles, and smart gadgets will improve the way of living standard. Cloud connectivity of IoT sensed devices will capture real-time data in the cloud which helps to improve the system performance and quick response to queries. Electric Vehicle battery health diagnosis plays an important role in the proper functioning of the battery management system, guarantees safety, and warranty claim. Society 5.0 develops with the advancement in the road, infrastructure, better connectivity, transportation, and options available to purchase. Battery health cannot be measured directly. There are internal and external factors that affect battery health such as State of Charge, model parameters, charging/discharging method, temperature, Depth of Discharge, C-rate, battery chemistry, form factor, thermal management, and load change effect. Battery degrades due to both calendar ageing and cyclic ageing. Artificial Intelligence plays a significant role in Battery management system due to the nonlinear behavior of lithium-ion battery. Prediction of battery health accurately and in due time will reduce the risk of recklessness. Timely maintenance will reduce the risk of fatal accidents. This paper presents different batteries analysis under different discharge voltage and capacity conditions. Different machine learning algorithms such as Neural Network, Modified Support Vector Machine (M-SVM) and Linear Regression are used to predict state of health. The proposed M-SVM performs well with less error for all four-battery discharge data.
{"title":"Electric Vehicle Lithium-ion Battery Ageing Analysis under Dynamic Condition: A Machine Learning Approach","authors":"Radhika Swarnkar;R. Harikrishnan;Prabhat Thakur;Ghanshyam Singh","doi":"10.23919/SAIEE.2023.9962788","DOIUrl":"10.23919/SAIEE.2023.9962788","url":null,"abstract":"Currently, the smart cities, smart vehicles, and smart gadgets will improve the way of living standard. Cloud connectivity of IoT sensed devices will capture real-time data in the cloud which helps to improve the system performance and quick response to queries. Electric Vehicle battery health diagnosis plays an important role in the proper functioning of the battery management system, guarantees safety, and warranty claim. Society 5.0 develops with the advancement in the road, infrastructure, better connectivity, transportation, and options available to purchase. Battery health cannot be measured directly. There are internal and external factors that affect battery health such as State of Charge, model parameters, charging/discharging method, temperature, Depth of Discharge, C-rate, battery chemistry, form factor, thermal management, and load change effect. Battery degrades due to both calendar ageing and cyclic ageing. Artificial Intelligence plays a significant role in Battery management system due to the nonlinear behavior of lithium-ion battery. Prediction of battery health accurately and in due time will reduce the risk of recklessness. Timely maintenance will reduce the risk of fatal accidents. This paper presents different batteries analysis under different discharge voltage and capacity conditions. Different machine learning algorithms such as Neural Network, Modified Support Vector Machine (M-SVM) and Linear Regression are used to predict state of health. The proposed M-SVM performs well with less error for all four-battery discharge data.","PeriodicalId":42493,"journal":{"name":"SAIEE Africa Research Journal","volume":"114 1","pages":"4-13"},"PeriodicalIF":1.4,"publicationDate":"2022-11-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/8475037/9962764/09962788.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48550967","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-11-23DOI: 10.23919/SAIEE.2023.9962789
Davy K. Uwizera;Charles Ruranga;Patrick McSharry
Traditional survey methods for gathering information, such as questionnaires and field visits, have long been used in East Africa to evaluate road conditions and prioritize their development. These surveys are time-consuming, expensive, and vulnerable to human error. Road building and maintenance, on the other hand, has long experienced multiple challenges due to a lack of accountability and validation of conventional approaches to determining which areas to prioritize. With the digital revolution, a lot of data is generated daily such as call detail record (CDR), that is likely to contain useful proxy data for spatial mobility distribution across different routes. In this research we focus on satellite imagery data with applications in East Africa and Google Maps suggested inter-city roads to assess road conditions and provide an approach for infrastructure prioritization given mobility patterns between cities. With increased urban population, East African cities have been expanding in multiple directions affecting the overall distribution of residential areas and consequently likely to impact the mobility trends across cities. We introduce a novel approach for infrastructure prioritization using deep learning and big data analytics. We apply deep learning to satellite imagery, to assess road conditions by area and big data analytics to CDR data, to rank which ones could be prioritized for construction given mobility trends. Among deep learning models considered for roads condition classification, EfficientNet-B3 outperforms them and achieves accuracy of 99%.
{"title":"Deep Learning Inter-city Road Conditions in East Africa Focusing on Rwanda for Infrastructure Prioritization using Satellite Imagery and Mobile Data","authors":"Davy K. Uwizera;Charles Ruranga;Patrick McSharry","doi":"10.23919/SAIEE.2023.9962789","DOIUrl":"10.23919/SAIEE.2023.9962789","url":null,"abstract":"Traditional survey methods for gathering information, such as questionnaires and field visits, have long been used in East Africa to evaluate road conditions and prioritize their development. These surveys are time-consuming, expensive, and vulnerable to human error. Road building and maintenance, on the other hand, has long experienced multiple challenges due to a lack of accountability and validation of conventional approaches to determining which areas to prioritize. With the digital revolution, a lot of data is generated daily such as call detail record (CDR), that is likely to contain useful proxy data for spatial mobility distribution across different routes. In this research we focus on satellite imagery data with applications in East Africa and Google Maps suggested inter-city roads to assess road conditions and provide an approach for infrastructure prioritization given mobility patterns between cities. With increased urban population, East African cities have been expanding in multiple directions affecting the overall distribution of residential areas and consequently likely to impact the mobility trends across cities. We introduce a novel approach for infrastructure prioritization using deep learning and big data analytics. We apply deep learning to satellite imagery, to assess road conditions by area and big data analytics to CDR data, to rank which ones could be prioritized for construction given mobility trends. Among deep learning models considered for roads condition classification, EfficientNet-B3 outperforms them and achieves accuracy of 99%.","PeriodicalId":42493,"journal":{"name":"SAIEE Africa Research Journal","volume":"114 1","pages":"14-24"},"PeriodicalIF":1.4,"publicationDate":"2022-11-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/8475037/9962764/09962789.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42262420","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-11-23DOI: 10.23919/SAIEE.2023.9962791
{"title":"Notes for authors","authors":"","doi":"10.23919/SAIEE.2023.9962791","DOIUrl":"https://doi.org/10.23919/SAIEE.2023.9962791","url":null,"abstract":"","PeriodicalId":42493,"journal":{"name":"SAIEE Africa Research Journal","volume":"114 1","pages":"35-35"},"PeriodicalIF":1.4,"publicationDate":"2022-11-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/8475037/9962764/09962791.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67809358","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-11-23DOI: 10.23919/SAIEE.2023.9962790
Mayank Sharma;Pradeep Kumar;Ranjeet Singh Tomar
In vehicular ad-hoc network (VANET), every vehicle node indicates a mobile node and it acts as a transmitter, receiver and router for the delivery of the information. VANET is a subgroup of mobile ad-hoc network (MANET) and is related to the dynamic topology. Dynamic network scenarios are more challenging issues as compared to MANET topologies, so finding a suitable algorithm for all VANET applications is the major challenge for the researchers. Routing protocols in VANET are divided into six parts i.e., cluster-based, geocast-based, topology-based, position-based, and broadcast-based. Autonomous robots and unmanned military vehicles (UMVs) become part of the advanced warfare strategy to execute dangerous war field operations and military combat missions. The military vehicles (MVs) transfer information to each other in order to achieve required military tasks collectively. In the proposed work, rhombus shaped area is divided into multiple clusters using a weight-based clustering algorithm for transmitting the event information to the vehicles. Intersection clustering with rhombus shaped area which are very effective for clustering. To choose cluster head (CH), the proposed method has used two weighted metrics, one is real time average speed and the other parameter is degree. This work is useful for choosing right CH in the network. Each vehicle in the same cluster transmits the data to the CH instead of broadcasting it. The simulation has been done in the SUMO and NETSIM simulator, which shows the network performance for the different protocols like Ad-hoc on-demand distance vector (AODV), dynamic source routing (DSR) in terms of packet delivery ratio, throughput, delay, overhead transmission, mean and standard deviation.
{"title":"Weight-Based Clustering Algorithm for Military Vehicles Communication in VANET","authors":"Mayank Sharma;Pradeep Kumar;Ranjeet Singh Tomar","doi":"10.23919/SAIEE.2023.9962790","DOIUrl":"10.23919/SAIEE.2023.9962790","url":null,"abstract":"In vehicular ad-hoc network (VANET), every vehicle node indicates a mobile node and it acts as a transmitter, receiver and router for the delivery of the information. VANET is a subgroup of mobile ad-hoc network (MANET) and is related to the dynamic topology. Dynamic network scenarios are more challenging issues as compared to MANET topologies, so finding a suitable algorithm for all VANET applications is the major challenge for the researchers. Routing protocols in VANET are divided into six parts i.e., cluster-based, geocast-based, topology-based, position-based, and broadcast-based. Autonomous robots and unmanned military vehicles (UMVs) become part of the advanced warfare strategy to execute dangerous war field operations and military combat missions. The military vehicles (MVs) transfer information to each other in order to achieve required military tasks collectively. In the proposed work, rhombus shaped area is divided into multiple clusters using a weight-based clustering algorithm for transmitting the event information to the vehicles. Intersection clustering with rhombus shaped area which are very effective for clustering. To choose cluster head (CH), the proposed method has used two weighted metrics, one is real time average speed and the other parameter is degree. This work is useful for choosing right CH in the network. Each vehicle in the same cluster transmits the data to the CH instead of broadcasting it. The simulation has been done in the SUMO and NETSIM simulator, which shows the network performance for the different protocols like Ad-hoc on-demand distance vector (AODV), dynamic source routing (DSR) in terms of packet delivery ratio, throughput, delay, overhead transmission, mean and standard deviation.","PeriodicalId":42493,"journal":{"name":"SAIEE Africa Research Journal","volume":"114 1","pages":"25-34"},"PeriodicalIF":1.4,"publicationDate":"2022-11-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/8475037/9962764/09962790.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48318705","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-11-23DOI: 10.23919/SAIEE.2023.9962766
{"title":"Editors and reviewers","authors":"","doi":"10.23919/SAIEE.2023.9962766","DOIUrl":"10.23919/SAIEE.2023.9962766","url":null,"abstract":"","PeriodicalId":42493,"journal":{"name":"SAIEE Africa Research Journal","volume":"114 1","pages":"2-2"},"PeriodicalIF":1.4,"publicationDate":"2022-11-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/8475037/9962764/09962766.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46425158","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-11-10DOI: 10.23919/SAIEE.2022.9945864
Davy K. Uwizera;Charles Ruranga;Patrick McSharry
Monitoring and assessing the distribution of economic areas in East Africa such as low and high income neighborhoods, has typically relied on the use of structured data and traditional survey approaches for collecting information such as questionnaires, interviews and field visits. These types of surveys are slow, costly and prone to human error. With the digital revolution, a lot of unstructured data is generated daily that is likely to contain useful proxy data for many economic variables. In this research we focus on satellite imagery data with applications in East Africa. Recently East African cities have been developing at a fast pace by building new infrastructure and constructing innovative economic zones. Moreover with increased urban population, cities have been expanding in multiple directions affecting the overall distribution of areas with economic activity. Automatic detection and classification of these areas could be used to inform a number of policies such as land usage and could also assist with policy enforcement monitoring. On the other hand, the distribution of different economic areas in a specific city could provide proxies for various economic development variables such as income distribution and poverty metrics. In this research, we apply deep learning techniques to satellite imagery to classify and assess the distribution of various economic areas of a specific region for urban planning. By benchmarking performance against various state-of-art models, results show that the proposed deep learning techniques yielded superior performance with an f1-score of 99%.
{"title":"Classifying Economic Areas for Urban Planning using Deep Learning and Satellite Imagery in East Africa","authors":"Davy K. Uwizera;Charles Ruranga;Patrick McSharry","doi":"10.23919/SAIEE.2022.9945864","DOIUrl":"https://doi.org/10.23919/SAIEE.2022.9945864","url":null,"abstract":"Monitoring and assessing the distribution of economic areas in East Africa such as low and high income neighborhoods, has typically relied on the use of structured data and traditional survey approaches for collecting information such as questionnaires, interviews and field visits. These types of surveys are slow, costly and prone to human error. With the digital revolution, a lot of unstructured data is generated daily that is likely to contain useful proxy data for many economic variables. In this research we focus on satellite imagery data with applications in East Africa. Recently East African cities have been developing at a fast pace by building new infrastructure and constructing innovative economic zones. Moreover with increased urban population, cities have been expanding in multiple directions affecting the overall distribution of areas with economic activity. Automatic detection and classification of these areas could be used to inform a number of policies such as land usage and could also assist with policy enforcement monitoring. On the other hand, the distribution of different economic areas in a specific city could provide proxies for various economic development variables such as income distribution and poverty metrics. In this research, we apply deep learning techniques to satellite imagery to classify and assess the distribution of various economic areas of a specific region for urban planning. By benchmarking performance against various state-of-art models, results show that the proposed deep learning techniques yielded superior performance with an f1-score of 99%.","PeriodicalId":42493,"journal":{"name":"SAIEE Africa Research Journal","volume":"113 4","pages":"138-151"},"PeriodicalIF":1.4,"publicationDate":"2022-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/8475037/9945860/09945864.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67832055","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-11-10DOI: 10.23919/SAIEE.2022.9945887
{"title":"Notes for authors","authors":"","doi":"10.23919/SAIEE.2022.9945887","DOIUrl":"https://doi.org/10.23919/SAIEE.2022.9945887","url":null,"abstract":"","PeriodicalId":42493,"journal":{"name":"SAIEE Africa Research Journal","volume":"113 4","pages":"171-171"},"PeriodicalIF":1.4,"publicationDate":"2022-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/8475037/9945860/09945887.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67992476","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-11-10DOI: 10.23919/SAIEE.2022.9945865
Priyanka Mishra;Prabhat Thakur;Ghanshyam Singh
With the growth of data traffic, demand of huge number of digital devices and their interconnection to establish a reliable communication, the internet has become a potential demand of the society. To develop a system that securely connects the internet to real-world space would aid in the advancement of a human-centered society that balances economic progress with the resolution of social issues. This paper provides a detailed aspect of Society 5.0 with its requirements, architecture, and components. We have proceeded extensively with the state-of-the-art Society 5.0 and its link with Industry 4.0/5.0. Furthermore, the role of Society 5.0 in the sustainable development goals of the United Nations is well elaborated. Several emerging communication and computing technologies such 5G/5G-Internet of Things (IoT), edge computing/ cloud computing/ fog computing, Internet of everything, blockchain, and beyond networks have been also well explored to fulfill the demands of Society 5.0. The potential application of super smart cities (Society 5.0) with some real-time experience of inhabitants is thoroughly discussed. Finally, we highlighted several open research challenges with opportunities.
{"title":"Sustainable Smart City to Society 5.0: State-of-the-Art and Research Challenges","authors":"Priyanka Mishra;Prabhat Thakur;Ghanshyam Singh","doi":"10.23919/SAIEE.2022.9945865","DOIUrl":"10.23919/SAIEE.2022.9945865","url":null,"abstract":"With the growth of data traffic, demand of huge number of digital devices and their interconnection to establish a reliable communication, the internet has become a potential demand of the society. To develop a system that securely connects the internet to real-world space would aid in the advancement of a human-centered society that balances economic progress with the resolution of social issues. This paper provides a detailed aspect of Society 5.0 with its requirements, architecture, and components. We have proceeded extensively with the state-of-the-art Society 5.0 and its link with Industry 4.0/5.0. Furthermore, the role of Society 5.0 in the sustainable development goals of the United Nations is well elaborated. Several emerging communication and computing technologies such 5G/5G-Internet of Things (IoT), edge computing/ cloud computing/ fog computing, Internet of everything, blockchain, and beyond networks have been also well explored to fulfill the demands of Society 5.0. The potential application of super smart cities (Society 5.0) with some real-time experience of inhabitants is thoroughly discussed. Finally, we highlighted several open research challenges with opportunities.","PeriodicalId":42493,"journal":{"name":"SAIEE Africa Research Journal","volume":"113 4","pages":"152-164"},"PeriodicalIF":1.4,"publicationDate":"2022-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/8475037/9945860/09945865.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47902960","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-11-10DOI: 10.23919/SAIEE.2022.9945862
{"title":"Editors and reviewers","authors":"","doi":"10.23919/SAIEE.2022.9945862","DOIUrl":"https://doi.org/10.23919/SAIEE.2022.9945862","url":null,"abstract":"","PeriodicalId":42493,"journal":{"name":"SAIEE Africa Research Journal","volume":"113 4","pages":"136-136"},"PeriodicalIF":1.4,"publicationDate":"2022-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/8475037/9945860/09945862.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67992475","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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