Pub Date : 2020-02-01DOI: 10.1109/ICONC345789.2020.9117465
Vinod Maan, Jayati Vijaywargiya, M. Srivastava
The expanse of machine learning has reached physical and medical sciences. The testing that were done before by physically examining a person can now be efficiently predicted by using machine learning algorithm. In today's generation diabetes is a growing health problem that has heterogeneous effects. In a report by World Health Organization (WHO), it revealed that in 2015 close to 1.6 million people died due to diabetes. The report also predicts that by 2030 diabetes will be seventh leading cause of death. In the assessment by International Diabetes Federation, more than 150 million cases of diabetes are undiagnosed. Busy lifestyle, improper food consumption and lack of physical activity on daily basis for long time has given birth to many diseases. One such disease is diabetes. It is already labelled as a Global disease. Treatment of diabetes is available but millions of people live with diabetes unknowing the fact they are suffering from it. The aim of this work is to make an user friendly, accurate and efficient low cost diabetes diagnose software, a Graphical User Interface which can predict diabetes and can be used by NGO's to diagnose people belonging to economically weaker section. This paper refers to a project which aims to classify a person's data into two classes, 'Yes' and ‘No’, based on ten factors., namely age, family history, alcoholic, smoker, etc.. In this work the accumulated result is obtained from mode of the four outputs, from four machine learning algorithms namely, SVM, KNN, ANN and Naive Bayes.
{"title":"Diabetes Prognostication – An Aptness of Machine Learning","authors":"Vinod Maan, Jayati Vijaywargiya, M. Srivastava","doi":"10.1109/ICONC345789.2020.9117465","DOIUrl":"https://doi.org/10.1109/ICONC345789.2020.9117465","url":null,"abstract":"The expanse of machine learning has reached physical and medical sciences. The testing that were done before by physically examining a person can now be efficiently predicted by using machine learning algorithm. In today's generation diabetes is a growing health problem that has heterogeneous effects. In a report by World Health Organization (WHO), it revealed that in 2015 close to 1.6 million people died due to diabetes. The report also predicts that by 2030 diabetes will be seventh leading cause of death. In the assessment by International Diabetes Federation, more than 150 million cases of diabetes are undiagnosed. Busy lifestyle, improper food consumption and lack of physical activity on daily basis for long time has given birth to many diseases. One such disease is diabetes. It is already labelled as a Global disease. Treatment of diabetes is available but millions of people live with diabetes unknowing the fact they are suffering from it. The aim of this work is to make an user friendly, accurate and efficient low cost diabetes diagnose software, a Graphical User Interface which can predict diabetes and can be used by NGO's to diagnose people belonging to economically weaker section. This paper refers to a project which aims to classify a person's data into two classes, 'Yes' and ‘No’, based on ten factors., namely age, family history, alcoholic, smoker, etc.. In this work the accumulated result is obtained from mode of the four outputs, from four machine learning algorithms namely, SVM, KNN, ANN and Naive Bayes.","PeriodicalId":155813,"journal":{"name":"2020 International Conference on Emerging Trends in Communication, Control and Computing (ICONC3)","volume":"66 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114578870","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 : 2020-02-01DOI: 10.1109/ICONC345789.2020.9117511
Rajashree Taparia, S. Janardhanan, Rajeev Gupta
This paper describes the management of inventory systems with multiple products. Linear Quadratic Regulator (LQR) strategy is used for optimizing the profit on the sale and managing the warehouse capacity. While the warehouse capacity is always maintained the customer demand for the products, are also satisfied most of the times. The system model of the considered inventory system includes the supply states in it. The control law is designed to keep the warehouse capacity at the desired level and at the same time it takes into account profit maximization from the sale of the products in the inventory. Numerical example of an inventory system with two products is presented to validate the effectiveness of the designed control law.
{"title":"LQR Control of Multiple Product Inventory Systems for Profit and Warehouse Capacity Maximization","authors":"Rajashree Taparia, S. Janardhanan, Rajeev Gupta","doi":"10.1109/ICONC345789.2020.9117511","DOIUrl":"https://doi.org/10.1109/ICONC345789.2020.9117511","url":null,"abstract":"This paper describes the management of inventory systems with multiple products. Linear Quadratic Regulator (LQR) strategy is used for optimizing the profit on the sale and managing the warehouse capacity. While the warehouse capacity is always maintained the customer demand for the products, are also satisfied most of the times. The system model of the considered inventory system includes the supply states in it. The control law is designed to keep the warehouse capacity at the desired level and at the same time it takes into account profit maximization from the sale of the products in the inventory. Numerical example of an inventory system with two products is presented to validate the effectiveness of the designed control law.","PeriodicalId":155813,"journal":{"name":"2020 International Conference on Emerging Trends in Communication, Control and Computing (ICONC3)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121605625","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 : 2020-02-01DOI: 10.1109/ICONC345789.2020.9117551
Chetan Jalendra, B. K. Rout
Current work presents a vibration suppression strategies of a Non-Deformable Metal Strip which is induced by rapid action of an industrial robot. In this case an external controller is designed as outer controller without any interruption in the robot internal controller to suppress the residual vibration in NDMS. The proposed controller is simulated in MATLAB/Simulink environment and designed in Python IDLE to validate the controller. The external controller is a closed-loop feedback Proportional Integral Derivative (PID) controller designed especially for an industrial robot that does not have control over acceleration. The robustness of the proposed controller is tested experimentally through the vibration control of a NDMS which is supposed to perform peg in hole assembly operation in various operating conditions. The designed controller suppresses vibration in less than 5 seconds and the stability time is reduced by 95% for in a Peg-in-hole assembly task.
{"title":"Vibration Suppression of Non-Deformable Metal Strip for Robot Assisted Assembly Operation","authors":"Chetan Jalendra, B. K. Rout","doi":"10.1109/ICONC345789.2020.9117551","DOIUrl":"https://doi.org/10.1109/ICONC345789.2020.9117551","url":null,"abstract":"Current work presents a vibration suppression strategies of a Non-Deformable Metal Strip which is induced by rapid action of an industrial robot. In this case an external controller is designed as outer controller without any interruption in the robot internal controller to suppress the residual vibration in NDMS. The proposed controller is simulated in MATLAB/Simulink environment and designed in Python IDLE to validate the controller. The external controller is a closed-loop feedback Proportional Integral Derivative (PID) controller designed especially for an industrial robot that does not have control over acceleration. The robustness of the proposed controller is tested experimentally through the vibration control of a NDMS which is supposed to perform peg in hole assembly operation in various operating conditions. The designed controller suppresses vibration in less than 5 seconds and the stability time is reduced by 95% for in a Peg-in-hole assembly task.","PeriodicalId":155813,"journal":{"name":"2020 International Conference on Emerging Trends in Communication, Control and Computing (ICONC3)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125361474","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 : 2020-02-01DOI: 10.1109/ICONC345789.2020.9117320
Aditi Kajala, V. Jain
Breast cancer is one of the common diseases specifically in women now days. It has become the second main reason of cancer death in females. Every year 4.5-5% new cancer cases are recorded and increasing the morbidity at worldwide. It has proved that early detection of any cancer when followed up with appropriate diagnosis and treatment can increase the survival rate of the patients. Breast cancer is diagnosed by mammography. Mammograms are films generated by radiologist with a device. These mammograms are observed and diagnosed by the oncologist for further treatment. Since all general hospitals do not have the specialist and patients used to wait for their report. So waiting for diagnosing a breast cancer may take time. This delay may be responsible for cancer spreading and reducing the survival rate of the patient. Therefore machine learning can be used to diagnose breast cancer by a computer to make the diagnosing efficient and effective. This does not mean to replace expert or physician by computer but it means that computer can assist the expert for better understanding the particular case and the results can be produced early. This paper presents a brief summary on breast cancer diagnosis using machine learning algorithms used to increase the efficiency and effectiveness of predicting cancer. The correct diagnosis and accurate classification are the main objective of the reviewed papers
{"title":"Diagnosis of Breast Cancer using Machine Learning Algorithms-A Review","authors":"Aditi Kajala, V. Jain","doi":"10.1109/ICONC345789.2020.9117320","DOIUrl":"https://doi.org/10.1109/ICONC345789.2020.9117320","url":null,"abstract":"Breast cancer is one of the common diseases specifically in women now days. It has become the second main reason of cancer death in females. Every year 4.5-5% new cancer cases are recorded and increasing the morbidity at worldwide. It has proved that early detection of any cancer when followed up with appropriate diagnosis and treatment can increase the survival rate of the patients. Breast cancer is diagnosed by mammography. Mammograms are films generated by radiologist with a device. These mammograms are observed and diagnosed by the oncologist for further treatment. Since all general hospitals do not have the specialist and patients used to wait for their report. So waiting for diagnosing a breast cancer may take time. This delay may be responsible for cancer spreading and reducing the survival rate of the patient. Therefore machine learning can be used to diagnose breast cancer by a computer to make the diagnosing efficient and effective. This does not mean to replace expert or physician by computer but it means that computer can assist the expert for better understanding the particular case and the results can be produced early. This paper presents a brief summary on breast cancer diagnosis using machine learning algorithms used to increase the efficiency and effectiveness of predicting cancer. The correct diagnosis and accurate classification are the main objective of the reviewed papers","PeriodicalId":155813,"journal":{"name":"2020 International Conference on Emerging Trends in Communication, Control and Computing (ICONC3)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128843335","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 : 2020-02-01DOI: 10.1109/ICONC345789.2020.9117293
Avireni Srinivasulu, C. Ravariu
The future of artificial intelligence applications like machine learning, medical diagnosis, healthcare, remote sensing, robot control, transportation e.t.c depends on the hybrid architecture and software. It is mainly contingent on the nano-scale device technology and leads to the cost efficiency, software defined storage options etc. These requirements set rigorous constraints on less power, high speed, latency, and for certain data types, safety and solitude of computing platforms.
{"title":"Emerging Artificial Intelligence Devices and The Underlying Technology","authors":"Avireni Srinivasulu, C. Ravariu","doi":"10.1109/ICONC345789.2020.9117293","DOIUrl":"https://doi.org/10.1109/ICONC345789.2020.9117293","url":null,"abstract":"The future of artificial intelligence applications like machine learning, medical diagnosis, healthcare, remote sensing, robot control, transportation e.t.c depends on the hybrid architecture and software. It is mainly contingent on the nano-scale device technology and leads to the cost efficiency, software defined storage options etc. These requirements set rigorous constraints on less power, high speed, latency, and for certain data types, safety and solitude of computing platforms.","PeriodicalId":155813,"journal":{"name":"2020 International Conference on Emerging Trends in Communication, Control and Computing (ICONC3)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116111210","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 : 2020-02-01DOI: 10.1109/ICONC345789.2020.9117547
S. Choudhary, Anand Sharma
With fast turn of events and development of the web, malware is one of major digital dangers nowadays. Henceforth, malware detection is an important factor in the security of computer systems. Nowadays, attackers generally design polymeric malware [1], it is usually a type of malware [2] that continuously changes its recognizable feature to fool detection techniques that uses typical signature based methods [3]. That is why the need for Machine Learning based detection arises. In this work, we are going to obtain behavioral-pattern that may be achieved through static or dynamic analysis, afterward we can apply dissimilar ML techniques to identify whether it's malware or not. Behavioral based Detection methods [4] will be discussed to take advantage from ML algorithms so as to frame social-based malware recognition and classification model.
{"title":"Malware Detection & Classification using Machine Learning","authors":"S. Choudhary, Anand Sharma","doi":"10.1109/ICONC345789.2020.9117547","DOIUrl":"https://doi.org/10.1109/ICONC345789.2020.9117547","url":null,"abstract":"With fast turn of events and development of the web, malware is one of major digital dangers nowadays. Henceforth, malware detection is an important factor in the security of computer systems. Nowadays, attackers generally design polymeric malware [1], it is usually a type of malware [2] that continuously changes its recognizable feature to fool detection techniques that uses typical signature based methods [3]. That is why the need for Machine Learning based detection arises. In this work, we are going to obtain behavioral-pattern that may be achieved through static or dynamic analysis, afterward we can apply dissimilar ML techniques to identify whether it's malware or not. Behavioral based Detection methods [4] will be discussed to take advantage from ML algorithms so as to frame social-based malware recognition and classification model.","PeriodicalId":155813,"journal":{"name":"2020 International Conference on Emerging Trends in Communication, Control and Computing (ICONC3)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128758486","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 : 2020-02-01DOI: 10.1109/ICONC345789.2020.9117475
Sangram Keshari Das, Sabyasachi Dash, B. K. Rout
A shape aware path planning algorithm is necessary for real time execution of a task by a mobile robot. Current work proposes a shape-aware A* path planning approach to facilitate accurate path finding in a given environment to accommodate the shape of mobile robot for differential wheeled mobile robot. The real-time map allows to assign a favorable cost value for each grid location of the map which later used to develop a shape aware global path planning strategy by using the well-known A* algorithm. For implementation and validation, an overhead camera is used to capture the task space and the obstacles which work in Robot operating software platform. The proposed method was tested in a real-time environment and proved the algorithm is capable of moving in a path that minimizes the distractions to static obstacles.
{"title":"Development of a Shape Aware Path Planning Algorithm for a Mobile Robot","authors":"Sangram Keshari Das, Sabyasachi Dash, B. K. Rout","doi":"10.1109/ICONC345789.2020.9117475","DOIUrl":"https://doi.org/10.1109/ICONC345789.2020.9117475","url":null,"abstract":"A shape aware path planning algorithm is necessary for real time execution of a task by a mobile robot. Current work proposes a shape-aware A* path planning approach to facilitate accurate path finding in a given environment to accommodate the shape of mobile robot for differential wheeled mobile robot. The real-time map allows to assign a favorable cost value for each grid location of the map which later used to develop a shape aware global path planning strategy by using the well-known A* algorithm. For implementation and validation, an overhead camera is used to capture the task space and the obstacles which work in Robot operating software platform. The proposed method was tested in a real-time environment and proved the algorithm is capable of moving in a path that minimizes the distractions to static obstacles.","PeriodicalId":155813,"journal":{"name":"2020 International Conference on Emerging Trends in Communication, Control and Computing (ICONC3)","volume":"472 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128765353","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 correspondence is confronting the quickest progressive changes in innovation. The control plane 4G speeds are intended to surpass that of 3G. Current 3G speeds have an upper top at 14 Mbps downlink and 5.8Mbps uplink. On the off chance that we accomplish the speed of up to 100 Mbps for a moving client and 1 Gbps for stationary client is achieved, it is named 4G advancements. Seamless roaming and versatility the executives are the premier difficulties before heterogeneous 4G remote systems. The goal of this work is to discover the simple and helpful strategy for vertical handoff which spread all the vital parameters required for handoff with a rearranged way utilizing layered methodology so both programmed and client explicit handovers are conceivable. Here we propose the GPA strategy to be created on the three layered stage to give a straightforward design which performs both kind of handovers considering more extensive scope of elements for basic leadership.
{"title":"Vertical Handoff in Heterogeneous Mechanism for Wireless LTE Network - An Optimal Approach","authors":"Pushparaj Pal, Taranpreet Kaur, Dinesh Sethi, Anil Kumar, Sanjay Kumar, A. Lamba, Umang Rastogi","doi":"10.1109/ICONC345789.2020.9117281","DOIUrl":"https://doi.org/10.1109/ICONC345789.2020.9117281","url":null,"abstract":"Wireless correspondence is confronting the quickest progressive changes in innovation. The control plane 4G speeds are intended to surpass that of 3G. Current 3G speeds have an upper top at 14 Mbps downlink and 5.8Mbps uplink. On the off chance that we accomplish the speed of up to 100 Mbps for a moving client and 1 Gbps for stationary client is achieved, it is named 4G advancements. Seamless roaming and versatility the executives are the premier difficulties before heterogeneous 4G remote systems. The goal of this work is to discover the simple and helpful strategy for vertical handoff which spread all the vital parameters required for handoff with a rearranged way utilizing layered methodology so both programmed and client explicit handovers are conceivable. Here we propose the GPA strategy to be created on the three layered stage to give a straightforward design which performs both kind of handovers considering more extensive scope of elements for basic leadership.","PeriodicalId":155813,"journal":{"name":"2020 International Conference on Emerging Trends in Communication, Control and Computing (ICONC3)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128962407","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 : 2020-02-01DOI: 10.1109/ICONC345789.2020.9117523
K. Pramod Kumar, H. Bansal
This paper talks about the vehicle-to-grid (V2G) idea, which is a system wherein electric vehicles (EVs) communicate with the power grid to trade services by either returning electricity to the grid or by regulating the power grid. The paper deals with the theoretical incentives bringing together the four players of V2G: car owners, power firms, aggregators and the society; and the barriers which they face for the commercialization of V2G. The paper then goes on to focus on the Indian market and the specific obstacles faced by the Indian market for the incorporation of V2G.
{"title":"Commercial Sustainability of Vehicle-to-Grid Concept: An Overview","authors":"K. Pramod Kumar, H. Bansal","doi":"10.1109/ICONC345789.2020.9117523","DOIUrl":"https://doi.org/10.1109/ICONC345789.2020.9117523","url":null,"abstract":"This paper talks about the vehicle-to-grid (V2G) idea, which is a system wherein electric vehicles (EVs) communicate with the power grid to trade services by either returning electricity to the grid or by regulating the power grid. The paper deals with the theoretical incentives bringing together the four players of V2G: car owners, power firms, aggregators and the society; and the barriers which they face for the commercialization of V2G. The paper then goes on to focus on the Indian market and the specific obstacles faced by the Indian market for the incorporation of V2G.","PeriodicalId":155813,"journal":{"name":"2020 International Conference on Emerging Trends in Communication, Control and Computing (ICONC3)","volume":"364 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132791804","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 : 2020-02-01DOI: 10.1109/ICONC345789.2020.9117499
B. Kumari, Aavishkar Katti, P. A. Alvi
This paper presents a brief study of the GaAs/Al0.20 Ga0.80As material system based photo-detector via simulation of the optical absorption, which has been simulated by making the detailed calculations of the envelope wavefunctions associated with valence and conduction band and as well as calculation of the dispersed energies of electrons and holes within the respective bands of the heterostructure. The calculated absorption peak is found to congregate at the photonic wavelength ~ 0.76 µm, which confirm the utility of the designed heterostructure in photonic biosensors and luminescence scintillators.
{"title":"Absorption in Al0.20Ga0.80As-GaAs MQWs Heterostructure","authors":"B. Kumari, Aavishkar Katti, P. A. Alvi","doi":"10.1109/ICONC345789.2020.9117499","DOIUrl":"https://doi.org/10.1109/ICONC345789.2020.9117499","url":null,"abstract":"This paper presents a brief study of the GaAs/Al0.20 Ga0.80As material system based photo-detector via simulation of the optical absorption, which has been simulated by making the detailed calculations of the envelope wavefunctions associated with valence and conduction band and as well as calculation of the dispersed energies of electrons and holes within the respective bands of the heterostructure. The calculated absorption peak is found to congregate at the photonic wavelength ~ 0.76 µm, which confirm the utility of the designed heterostructure in photonic biosensors and luminescence scintillators.","PeriodicalId":155813,"journal":{"name":"2020 International Conference on Emerging Trends in Communication, Control and Computing (ICONC3)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133152696","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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