Pub Date : 2020-01-01DOI: 10.1109/iCoMET48670.2020.9073819
T. Abbasi, K. Lim, Toufique Ahmed Soomro, I. Ismail, Ahmed Ali
Oil and gas industry requires capital-intensive investment especially in rotating mechanical equipment acquisition and installation. Rotating mechanical equipment such as induction motor, compressors and pumps, are essential components in industrial processes. The recent crude oil price drop raises the concern of effective maintenance management across oil and gas industry. Condition-based maintenance (CBM) is the most cost-effective maintenance technique to prevent the downtime of equipment and increases the productivity in petroleum industry. In this paper, recent reviews on CBM techniques for rotating equipment are presented under three categories, i.e. (1) Signature extraction-based method predicts machinery parameter in time and frequency domain, (2) Modelbased approach analyses machinery behavior in mathematical model and (3) Knowledge-based approach uses data-driven algorithm to learn system signal in the past for future prediction. The advantages, limitations and practical implication of each category are highlighted for suggestions and selection in the oil and gas industry.
{"title":"Condition Based Maintenance of Oil and Gas Equipment: A Review","authors":"T. Abbasi, K. Lim, Toufique Ahmed Soomro, I. Ismail, Ahmed Ali","doi":"10.1109/iCoMET48670.2020.9073819","DOIUrl":"https://doi.org/10.1109/iCoMET48670.2020.9073819","url":null,"abstract":"Oil and gas industry requires capital-intensive investment especially in rotating mechanical equipment acquisition and installation. Rotating mechanical equipment such as induction motor, compressors and pumps, are essential components in industrial processes. The recent crude oil price drop raises the concern of effective maintenance management across oil and gas industry. Condition-based maintenance (CBM) is the most cost-effective maintenance technique to prevent the downtime of equipment and increases the productivity in petroleum industry. In this paper, recent reviews on CBM techniques for rotating equipment are presented under three categories, i.e. (1) Signature extraction-based method predicts machinery parameter in time and frequency domain, (2) Modelbased approach analyses machinery behavior in mathematical model and (3) Knowledge-based approach uses data-driven algorithm to learn system signal in the past for future prediction. The advantages, limitations and practical implication of each category are highlighted for suggestions and selection in the oil and gas industry.","PeriodicalId":431051,"journal":{"name":"2020 3rd International Conference on Computing, Mathematics and Engineering Technologies (iCoMET)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128414509","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 : 2020-01-01DOI: 10.1109/iCoMET48670.2020.9074067
Nouman Saeed, K. Long, A. Rehman
Scientists and researchers are focusing on generation electrical energy using maximum share of renewable energy resources. Wind turbines possess crucial part in generation of electrical energy from renewable energy sources. A lot of research work has been carried out to find out optimum mechanical design of wind turbines specially focused on reduction in weight of turbine blades. This research work presents an extensive review for existing topology optimization techniques specially used for wind turbine blades. At first an indepth review for solid isotropic material with penalization (SIMP) techniques including optimal criteria and filter sensitivity has been presented. After that a comparative analysis focusing advantages and disadvantages of latest optimization techniques that evolutionary structural optimization (ESO) and bidirectional evolutionary structural optimization (BESO) is presented.
{"title":"A Review of Structural Optimization Techniques for Wind Turbines","authors":"Nouman Saeed, K. Long, A. Rehman","doi":"10.1109/iCoMET48670.2020.9074067","DOIUrl":"https://doi.org/10.1109/iCoMET48670.2020.9074067","url":null,"abstract":"Scientists and researchers are focusing on generation electrical energy using maximum share of renewable energy resources. Wind turbines possess crucial part in generation of electrical energy from renewable energy sources. A lot of research work has been carried out to find out optimum mechanical design of wind turbines specially focused on reduction in weight of turbine blades. This research work presents an extensive review for existing topology optimization techniques specially used for wind turbine blades. At first an indepth review for solid isotropic material with penalization (SIMP) techniques including optimal criteria and filter sensitivity has been presented. After that a comparative analysis focusing advantages and disadvantages of latest optimization techniques that evolutionary structural optimization (ESO) and bidirectional evolutionary structural optimization (BESO) is presented.","PeriodicalId":431051,"journal":{"name":"2020 3rd International Conference on Computing, Mathematics and Engineering Technologies (iCoMET)","volume":"223 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123973390","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 : 2020-01-01DOI: 10.1109/iCoMET48670.2020.9074123
Liu Dunnan, Rana Faizan Gul, N. R. Jaffri, Noorul Hassan, Liu Mingguang, Fazal Hussain Awan
Pakistan considered as the leading agricultural country till late 1960s. The industrialization of major industries from 1970s to onward push country towards the major climate changes. Pakistan made significant progress especially in the textile industry. However, this progress results in bad impact on the climate because of poor implementation of climatic laws. The world carbon emission data of 2018 revealed that Pakistan contributed 195.71 M-Ton that was 24.43 M-ton in year 1969. Although, the global carbon emission has also shoot up from 19.87 G-Ton to 44 G-Ton. But the Pakistan’s carbon emission contribution to world has risen up four times in these years. Pakistan has a lesson to learn from China. Carbon emission was big problem in till early 2000s as approximately 70% of electric industry was coal based. Closing down industry in order to clean environment is never a good solution. Since, this will close the door to progress for country. However, proper implementation of climate laws and efficient use of the energy can slow down carbon emission. In this work an efficient model to manage energy resources has been given. The key-points to taken care are commitment with work, awareness of situation, proper energy auditing and knowledge. This eventually led towards low carbon emission.
{"title":"Energy Management to reduce carbon emission in Pakistan","authors":"Liu Dunnan, Rana Faizan Gul, N. R. Jaffri, Noorul Hassan, Liu Mingguang, Fazal Hussain Awan","doi":"10.1109/iCoMET48670.2020.9074123","DOIUrl":"https://doi.org/10.1109/iCoMET48670.2020.9074123","url":null,"abstract":"Pakistan considered as the leading agricultural country till late 1960s. The industrialization of major industries from 1970s to onward push country towards the major climate changes. Pakistan made significant progress especially in the textile industry. However, this progress results in bad impact on the climate because of poor implementation of climatic laws. The world carbon emission data of 2018 revealed that Pakistan contributed 195.71 M-Ton that was 24.43 M-ton in year 1969. Although, the global carbon emission has also shoot up from 19.87 G-Ton to 44 G-Ton. But the Pakistan’s carbon emission contribution to world has risen up four times in these years. Pakistan has a lesson to learn from China. Carbon emission was big problem in till early 2000s as approximately 70% of electric industry was coal based. Closing down industry in order to clean environment is never a good solution. Since, this will close the door to progress for country. However, proper implementation of climate laws and efficient use of the energy can slow down carbon emission. In this work an efficient model to manage energy resources has been given. The key-points to taken care are commitment with work, awareness of situation, proper energy auditing and knowledge. This eventually led towards low carbon emission.","PeriodicalId":431051,"journal":{"name":"2020 3rd International Conference on Computing, Mathematics and Engineering Technologies (iCoMET)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121282115","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 : 2020-01-01DOI: 10.1109/iCoMET48670.2020.9074056
J. Iqbal, M. Iqbal, Umair Khadam, Ali Nawaz
Heart diseases are one of the major causes of human deaths today. About 610000 human beings expire annually in the United States due to this fatal disease and the condition is more severe in the underdeveloped countries lacking medical experts. Accurate detection of heart disease in a human being can be helpful in proper medication against this lethal disease and considerably reduce this alarming death rate. Data mining and machine learning techniques are being widely used for medical diagnosis these days. This research paper employs Ordinary Learning Method for the accurate detection of heart disease using clinical data. The proposed method is tested on the Standard UCI(University of California, Irvine) Cleveland Heart Disease dataset using 14 attributes. The achieved accuracy of the proposed method is 98.4615% which is compared with other states of the art techniques such as C5.0 decision trees, Support vector machine, KNN and Neural Network. Comparison results show that the proposed OLM technique outperforms the previous data mining techniques proposed in literature for the detection of heart disease.
{"title":"Ordinary Learning Method for Heart Disease Detection using Clinical Data","authors":"J. Iqbal, M. Iqbal, Umair Khadam, Ali Nawaz","doi":"10.1109/iCoMET48670.2020.9074056","DOIUrl":"https://doi.org/10.1109/iCoMET48670.2020.9074056","url":null,"abstract":"Heart diseases are one of the major causes of human deaths today. About 610000 human beings expire annually in the United States due to this fatal disease and the condition is more severe in the underdeveloped countries lacking medical experts. Accurate detection of heart disease in a human being can be helpful in proper medication against this lethal disease and considerably reduce this alarming death rate. Data mining and machine learning techniques are being widely used for medical diagnosis these days. This research paper employs Ordinary Learning Method for the accurate detection of heart disease using clinical data. The proposed method is tested on the Standard UCI(University of California, Irvine) Cleveland Heart Disease dataset using 14 attributes. The achieved accuracy of the proposed method is 98.4615% which is compared with other states of the art techniques such as C5.0 decision trees, Support vector machine, KNN and Neural Network. Comparison results show that the proposed OLM technique outperforms the previous data mining techniques proposed in literature for the detection of heart disease.","PeriodicalId":431051,"journal":{"name":"2020 3rd International Conference on Computing, Mathematics and Engineering Technologies (iCoMET)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125456787","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 : 2020-01-01DOI: 10.1109/iCoMET48670.2020.9073892
Darya Khan Bhutto, J. Ansari, Halar Zameer
Systematic investigation of an Automatic Voltage Regulator (AVR) indicates one significant tradeoff in the effectiveness of Excitation System i.e. rapid response with high gain of the AVR induces undesirable damped oscillations in an Electrical power system, which slow down the rotor speed; To overcome this problem, Power system stabilizer (PSS) is used in parallel with excitation system (ES), by injecting extra stabilizing signals to minimize the side effect induced by AVR. The PSS must be self-tuned for adjusting parameters and managing different loading conditions. Therefore, this work is mainly focused on Multilayer Perceptron (MLP) feed-forward neural network and fuzzy logic system controllers to tune and adjust the PSS parameters to achieve better enhancement instability for varying load conditions. In this research work, PSS is designed with different controllers in MATLAB/ Simulink. The development of the PSS is achieved by using different controllers like ProportionIntegrator (PI), Proportion-Integrator-Differentiator (PID) and Artificial Intelligence (AI) based fuzzy and MLP controller. Simulation test results of Voltage and Frequency show the robustness of MLP type PSS as compared to PI, PID, and Fuzzy PSS in terms of minimized overshoot peak value, settling time and rise time for varying loading conditions.
{"title":"Implementation of AI Based Power Stabilizer Using Fuzzy and Multilayer Perceptron In MatLab","authors":"Darya Khan Bhutto, J. Ansari, Halar Zameer","doi":"10.1109/iCoMET48670.2020.9073892","DOIUrl":"https://doi.org/10.1109/iCoMET48670.2020.9073892","url":null,"abstract":"Systematic investigation of an Automatic Voltage Regulator (AVR) indicates one significant tradeoff in the effectiveness of Excitation System i.e. rapid response with high gain of the AVR induces undesirable damped oscillations in an Electrical power system, which slow down the rotor speed; To overcome this problem, Power system stabilizer (PSS) is used in parallel with excitation system (ES), by injecting extra stabilizing signals to minimize the side effect induced by AVR. The PSS must be self-tuned for adjusting parameters and managing different loading conditions. Therefore, this work is mainly focused on Multilayer Perceptron (MLP) feed-forward neural network and fuzzy logic system controllers to tune and adjust the PSS parameters to achieve better enhancement instability for varying load conditions. In this research work, PSS is designed with different controllers in MATLAB/ Simulink. The development of the PSS is achieved by using different controllers like ProportionIntegrator (PI), Proportion-Integrator-Differentiator (PID) and Artificial Intelligence (AI) based fuzzy and MLP controller. Simulation test results of Voltage and Frequency show the robustness of MLP type PSS as compared to PI, PID, and Fuzzy PSS in terms of minimized overshoot peak value, settling time and rise time for varying loading conditions.","PeriodicalId":431051,"journal":{"name":"2020 3rd International Conference on Computing, Mathematics and Engineering Technologies (iCoMET)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134014330","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 : 2020-01-01DOI: 10.1109/iCoMET48670.2020.9074117
S. M. Ali, Z. Ullah, I. Khan, M. Sarwar, B. Khan, U. Farid, C. A. Mehmood, M. Jawad
Modeling Energy Consumption (EC) system based on environmental drifts, consumer psychology, and consumer body dynamics is a demanding task. No prior works have modeled EC system with respect to the above features. The use of an optimized, intelligent, and accurate model with all above inputs will help electric grid policymakers for (a) lowering energy cost (b) accurate predicted and forecasted energy models, and (c) optimized energy utilization, and (d) increased consumer empowerment with pollutants free atmosphere. Considering the above motivation, we discussed in-depth various promising features of the environment, climate, and weather shaping the energy demand of consumers. Our work describes detailed taxonomies of the above parameters with their respective trends and inter-relationship to each other. We investigated critically the mutual effects of surroundings and consumers.
{"title":"Mutual Interactive Effects of Environment and Consumer Biological Dynamics on Energy Consumption","authors":"S. M. Ali, Z. Ullah, I. Khan, M. Sarwar, B. Khan, U. Farid, C. A. Mehmood, M. Jawad","doi":"10.1109/iCoMET48670.2020.9074117","DOIUrl":"https://doi.org/10.1109/iCoMET48670.2020.9074117","url":null,"abstract":"Modeling Energy Consumption (EC) system based on environmental drifts, consumer psychology, and consumer body dynamics is a demanding task. No prior works have modeled EC system with respect to the above features. The use of an optimized, intelligent, and accurate model with all above inputs will help electric grid policymakers for (a) lowering energy cost (b) accurate predicted and forecasted energy models, and (c) optimized energy utilization, and (d) increased consumer empowerment with pollutants free atmosphere. Considering the above motivation, we discussed in-depth various promising features of the environment, climate, and weather shaping the energy demand of consumers. Our work describes detailed taxonomies of the above parameters with their respective trends and inter-relationship to each other. We investigated critically the mutual effects of surroundings and consumers.","PeriodicalId":431051,"journal":{"name":"2020 3rd International Conference on Computing, Mathematics and Engineering Technologies (iCoMET)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133972625","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 : 2020-01-01DOI: 10.1109/iCoMET48670.2020.9074125
Anees Baqir, Sami Rehman, Sayyam Malik, Faizan ul Mustafa, Usman Ahmad
The constant growth in urbanization is a cause of significant social and economical transformations in urban areas. Areas where crime rates are above the normal level, are known as crime hot-spots. The increase in urban population is posing challenges related to the management, services and safety from criminal activities. It is important to keep an eye on criminal activities and for the law enforcement agencies, being able to provide much needed safety of public is an increasingly complex task. This complex task can be handled by new technologies which can help these agencies to effectively analyze and understand the different crime trends and patterns with respect to their geographic locations. This paper uses Hierarchical Density-based spatial clustering of applications with noise (HDBSCAN) to find spatio-temporal crime hot-spots by clustering and the results shows that this technique outperforms others.
{"title":"Evaluating the Performance of Hierarchical Clustering algorithms to Detect Spatio-Temporal Crime Hot-Spots","authors":"Anees Baqir, Sami Rehman, Sayyam Malik, Faizan ul Mustafa, Usman Ahmad","doi":"10.1109/iCoMET48670.2020.9074125","DOIUrl":"https://doi.org/10.1109/iCoMET48670.2020.9074125","url":null,"abstract":"The constant growth in urbanization is a cause of significant social and economical transformations in urban areas. Areas where crime rates are above the normal level, are known as crime hot-spots. The increase in urban population is posing challenges related to the management, services and safety from criminal activities. It is important to keep an eye on criminal activities and for the law enforcement agencies, being able to provide much needed safety of public is an increasingly complex task. This complex task can be handled by new technologies which can help these agencies to effectively analyze and understand the different crime trends and patterns with respect to their geographic locations. This paper uses Hierarchical Density-based spatial clustering of applications with noise (HDBSCAN) to find spatio-temporal crime hot-spots by clustering and the results shows that this technique outperforms others.","PeriodicalId":431051,"journal":{"name":"2020 3rd International Conference on Computing, Mathematics and Engineering Technologies (iCoMET)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134284513","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 : 2020-01-01DOI: 10.1109/iCoMET48670.2020.9073860
Mir Lodro, G. Gradoni, A. Vukovic, David W. P. Thomas, S. Greedy
In this work we present low cost time-domain wireless channel sounding using software defined radio (SDR). We performed SDR based time-domain wireless indoor channel sounding using random sequences. We used three random sequences for channel measurements because of their excellent auto-correlation and cross-correlation properties. More specifically PN code sequence, Gold code sequence, and Zadoff-Chu sequences are used. The channel measurements are performed in indoor laboratory environment at two locations at RF frequency of 5.2 GHz and 5.8 GHz respectively. Channel measurements are performed by transmitting sequences with software defined radio in indoor laboratory environment.
{"title":"Time-Domain Wireless Channel Sounding Using Software Defined Radio","authors":"Mir Lodro, G. Gradoni, A. Vukovic, David W. P. Thomas, S. Greedy","doi":"10.1109/iCoMET48670.2020.9073860","DOIUrl":"https://doi.org/10.1109/iCoMET48670.2020.9073860","url":null,"abstract":"In this work we present low cost time-domain wireless channel sounding using software defined radio (SDR). We performed SDR based time-domain wireless indoor channel sounding using random sequences. We used three random sequences for channel measurements because of their excellent auto-correlation and cross-correlation properties. More specifically PN code sequence, Gold code sequence, and Zadoff-Chu sequences are used. The channel measurements are performed in indoor laboratory environment at two locations at RF frequency of 5.2 GHz and 5.8 GHz respectively. Channel measurements are performed by transmitting sequences with software defined radio in indoor laboratory environment.","PeriodicalId":431051,"journal":{"name":"2020 3rd International Conference on Computing, Mathematics and Engineering Technologies (iCoMET)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132610019","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 : 2020-01-01DOI: 10.1109/iCoMET48670.2020.9073878
M. Marouf, R. Siddiqi, Fatima Bashir, Bilal Vohra
Bone Age Assessment (BAA) is a medical approach to predict the growth in any individual and for this gender the classification has immense importance in medical research and forensics. To the best of our knowledge we have introduced a novel framework, which classifies the gender and predict the age of that individual by using a single left-hand radiograph. Deep Convolutional Neural Network (DCNN) as a method of learning and predicting the results gave us the accuracy of 79.6% for gender classification and for age classification we have achieved MAD 0.50 years and RMS 0.67 years. We have studied the methods of transfer learning and trained our dataset with VGG-16 model to find the optimal solution.
{"title":"Automated Hand X-Ray Based Gender Classification and Bone Age Assessment Using Convolutional Neural Network","authors":"M. Marouf, R. Siddiqi, Fatima Bashir, Bilal Vohra","doi":"10.1109/iCoMET48670.2020.9073878","DOIUrl":"https://doi.org/10.1109/iCoMET48670.2020.9073878","url":null,"abstract":"Bone Age Assessment (BAA) is a medical approach to predict the growth in any individual and for this gender the classification has immense importance in medical research and forensics. To the best of our knowledge we have introduced a novel framework, which classifies the gender and predict the age of that individual by using a single left-hand radiograph. Deep Convolutional Neural Network (DCNN) as a method of learning and predicting the results gave us the accuracy of 79.6% for gender classification and for age classification we have achieved MAD 0.50 years and RMS 0.67 years. We have studied the methods of transfer learning and trained our dataset with VGG-16 model to find the optimal solution.","PeriodicalId":431051,"journal":{"name":"2020 3rd International Conference on Computing, Mathematics and Engineering Technologies (iCoMET)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132437302","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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