Pub Date : 2023-03-15DOI: 10.23919/SAIEE.2023.10071977
Shingirirai Chakoma;Philip Baron
There is a significant population of hearing-impaired people who reside in South Africa; however, South African Sign Language (SASL) has not yet been recognized as South Africa's 12 th official language, resulting in slow uptake of this important language. Since most people do not know SASL, there is a need for gesture recognition systems that convert Sign Language (SL) to verbal and/or text to reduce the communication barriers between the hearing and the hearing-impaired. This study presents an application for gesture recognition in converting SASL to both a verbal format and a textual format. By using gesture recognition from a single wearable glove, hand gestures were quantified, categorized, and then converted into an auditory format and played on a speaker, as well as the equivalent textual information displayed on an LCD screen. The complete prototype consists of a wearable glove with a transmitter and an associated receiver box which were all designed to cost less than $150. The glove consists of five flex sensors that measure the handshape and an inertial measurement unit which measures the hand motion. The handshape and motion data are processed and wirelessly transmitted to a receiver box. This then displays the associated English character on an LCD while also playing the audio on a speaker. The SL converter can convert the 26 letters of the SASL manual alphabet with an overall accuracy of 69%, It can also convert common words and phrases, as well as proper names when fingerspelled.
{"title":"Converting South African sign language to verbal","authors":"Shingirirai Chakoma;Philip Baron","doi":"10.23919/SAIEE.2023.10071977","DOIUrl":"10.23919/SAIEE.2023.10071977","url":null,"abstract":"There is a significant population of hearing-impaired people who reside in South Africa; however, South African Sign Language (SASL) has not yet been recognized as South Africa's 12\u0000<sup>th</sup>\u0000 official language, resulting in slow uptake of this important language. Since most people do not know SASL, there is a need for gesture recognition systems that convert Sign Language (SL) to verbal and/or text to reduce the communication barriers between the hearing and the hearing-impaired. This study presents an application for gesture recognition in converting SASL to both a verbal format and a textual format. By using gesture recognition from a single wearable glove, hand gestures were quantified, categorized, and then converted into an auditory format and played on a speaker, as well as the equivalent textual information displayed on an LCD screen. The complete prototype consists of a wearable glove with a transmitter and an associated receiver box which were all designed to cost less than $150. The glove consists of five flex sensors that measure the handshape and an inertial measurement unit which measures the hand motion. The handshape and motion data are processed and wirelessly transmitted to a receiver box. This then displays the associated English character on an LCD while also playing the audio on a speaker. The SL converter can convert the 26 letters of the SASL manual alphabet with an overall accuracy of 69%, It can also convert common words and phrases, as well as proper names when fingerspelled.","PeriodicalId":42493,"journal":{"name":"SAIEE Africa Research Journal","volume":null,"pages":null},"PeriodicalIF":1.4,"publicationDate":"2023-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/8475037/10071972/10071977.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47841482","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 : 2023-03-15DOI: 10.23919/SAIEE.2023.10071974
{"title":"Editors and reviewers","authors":"","doi":"10.23919/SAIEE.2023.10071974","DOIUrl":"https://doi.org/10.23919/SAIEE.2023.10071974","url":null,"abstract":"","PeriodicalId":42493,"journal":{"name":"SAIEE Africa Research Journal","volume":null,"pages":null},"PeriodicalIF":1.4,"publicationDate":"2023-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/8475037/10071972/10071974.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67968042","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 : 2023-03-15DOI: 10.23919/SAIEE.2023.10071976
Ali Ziryawulawo;Melissa Kirabo;Cosmas Mwikirize;Jonathan Serugunda;Edwin Mugume;Simon Peter Miyingo
With the ever-growing traffic density, the number of road accidents has continued to increase. Finding solutions to reduce road accidents and improve traffic safety has become a top priority for Kiira Motors Corporation, a Ugandan state-owned automotive company. The company seeks to develop intelligent driver assistance systems for its market entry product, the Kayoola EVS bus. A machine learning-based driver monitoring system that would monitor driver drowsiness and send out an alarm in case drowsiness is detected has been developed in an attempt to reduce drowsiness-related accidents. The system consists of a camera positioned in such a way as to keep track of the driver's face. The camera is interfaced with a Raspberry Pi minicomputer which carries out the computations and analysis and when drowsiness is detected, an alarm is triggered. Dangerous driver behavior including distraction and fatigue has long been recognized as the main contributing factor in traffic accidents. This paper therefore presents the development of a driver monitoring system for the Kayoola Electric City Bus to address the increasing occurrences of road accidents. The machine learning-based driver monitoring system is designed to be non-intrusive with continuous real-time operation.
{"title":"Machine learning based driver monitoring system: A case study for the Kayoola EVS","authors":"Ali Ziryawulawo;Melissa Kirabo;Cosmas Mwikirize;Jonathan Serugunda;Edwin Mugume;Simon Peter Miyingo","doi":"10.23919/SAIEE.2023.10071976","DOIUrl":"10.23919/SAIEE.2023.10071976","url":null,"abstract":"With the ever-growing traffic density, the number of road accidents has continued to increase. Finding solutions to reduce road accidents and improve traffic safety has become a top priority for Kiira Motors Corporation, a Ugandan state-owned automotive company. The company seeks to develop intelligent driver assistance systems for its market entry product, the Kayoola EVS bus. A machine learning-based driver monitoring system that would monitor driver drowsiness and send out an alarm in case drowsiness is detected has been developed in an attempt to reduce drowsiness-related accidents. The system consists of a camera positioned in such a way as to keep track of the driver's face. The camera is interfaced with a Raspberry Pi minicomputer which carries out the computations and analysis and when drowsiness is detected, an alarm is triggered. Dangerous driver behavior including distraction and fatigue has long been recognized as the main contributing factor in traffic accidents. This paper therefore presents the development of a driver monitoring system for the Kayoola Electric City Bus to address the increasing occurrences of road accidents. The machine learning-based driver monitoring system is designed to be non-intrusive with continuous real-time operation.","PeriodicalId":42493,"journal":{"name":"SAIEE Africa Research Journal","volume":null,"pages":null},"PeriodicalIF":1.4,"publicationDate":"2023-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/8475037/10071972/10071976.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47391089","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 : 2023-03-15DOI: 10.23919/SAIEE.2023.10071979
{"title":"Notes for authors","authors":"","doi":"10.23919/SAIEE.2023.10071979","DOIUrl":"https://doi.org/10.23919/SAIEE.2023.10071979","url":null,"abstract":"","PeriodicalId":42493,"journal":{"name":"SAIEE Africa Research Journal","volume":null,"pages":null},"PeriodicalIF":1.4,"publicationDate":"2023-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/8475037/10071972/10071979.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67967852","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 : 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":null,"pages":null},"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":null,"pages":null},"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":null,"pages":null},"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":null,"pages":null},"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":null,"pages":null},"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":null,"pages":null},"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}
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