Pub Date : 2020-12-14DOI: 10.1109/IEEM45057.2020.9309978
A. Schenk, U. Clausen
The complexity of global distribution networks in the automotive industry and likewise the number of disruptions significantly increased throughout the last years. In order to monitor relevant processes and to optimize decision-making in case of disruptions, a concept for a decision support system (DSS) was introduced. For this purpose, the distribution process weaknesses of the German premium automotive company BMW were identified. The method used was a Failure Mode and Effect Analysis with operational managers and relevant process partners interviews. Based on the findings, performance indicators, thresholds, early warnings and options for action were specified. A big data platform supports the processing of the growing number of relevant data in real-time. In the long-term decision-making can be automated using machine learning algorithms. This paper proves that negative impacts of disruptions can be minimized, and the robustness of the process improved by anticipating and identifying deviations beforehand and in real-time. Hence, companies save money while strengthening customer satisfaction. The DSS can be seen as a necessary precursor of a digital twin.
{"title":"Creating Transparency in the Finished Vehicles Transportation Process Through the Implementation of a Real-Time Decision Support System","authors":"A. Schenk, U. Clausen","doi":"10.1109/IEEM45057.2020.9309978","DOIUrl":"https://doi.org/10.1109/IEEM45057.2020.9309978","url":null,"abstract":"The complexity of global distribution networks in the automotive industry and likewise the number of disruptions significantly increased throughout the last years. In order to monitor relevant processes and to optimize decision-making in case of disruptions, a concept for a decision support system (DSS) was introduced. For this purpose, the distribution process weaknesses of the German premium automotive company BMW were identified. The method used was a Failure Mode and Effect Analysis with operational managers and relevant process partners interviews. Based on the findings, performance indicators, thresholds, early warnings and options for action were specified. A big data platform supports the processing of the growing number of relevant data in real-time. In the long-term decision-making can be automated using machine learning algorithms. This paper proves that negative impacts of disruptions can be minimized, and the robustness of the process improved by anticipating and identifying deviations beforehand and in real-time. Hence, companies save money while strengthening customer satisfaction. The DSS can be seen as a necessary precursor of a digital twin.","PeriodicalId":226426,"journal":{"name":"2020 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130063259","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 : 2020-12-14DOI: 10.1109/IEEM45057.2020.9309821
Lubing Wang, Peng Wu, F. Chu
With global warming, the probability of forest fires is increasing greatly, and more research attention has been paid to forest fires emergency scheduling. This paper addresses a resource-constrained emergency scheduling problem for dealing with forest fires with priority disaster areas. It aims to determine an optimal fire-fighting scheduling plan for multiple forest fire points to minimize the total transport distance and the fire extinguishing rescue time, simultaneously. To effectively solve this problem, we formulate a multi-objective mixed-integer linear programming model, and an iterative and fuzzy logic decision-making based on ε-constraint method is designed to obtain a preferred emergency scheduling scheme. Finally, the computational results on benchmark and randomly generate test instances verify the effectiveness and feasibility of the proposed model and method.
{"title":"A Multi-objective Emergency Scheduling Model for Forest Fires with Priority Areas","authors":"Lubing Wang, Peng Wu, F. Chu","doi":"10.1109/IEEM45057.2020.9309821","DOIUrl":"https://doi.org/10.1109/IEEM45057.2020.9309821","url":null,"abstract":"With global warming, the probability of forest fires is increasing greatly, and more research attention has been paid to forest fires emergency scheduling. This paper addresses a resource-constrained emergency scheduling problem for dealing with forest fires with priority disaster areas. It aims to determine an optimal fire-fighting scheduling plan for multiple forest fire points to minimize the total transport distance and the fire extinguishing rescue time, simultaneously. To effectively solve this problem, we formulate a multi-objective mixed-integer linear programming model, and an iterative and fuzzy logic decision-making based on ε-constraint method is designed to obtain a preferred emergency scheduling scheme. Finally, the computational results on benchmark and randomly generate test instances verify the effectiveness and feasibility of the proposed model and method.","PeriodicalId":226426,"journal":{"name":"2020 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134116127","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 : 2020-12-14DOI: 10.1109/IEEM45057.2020.9309973
A. Sathyanarayana, N. Shukla, F. Taghikhah
Due to the current COVID-19 (SARS-CoV-2) outbreak, supply chains have been severely disrupted in long term globally. In this paper, we present the results of a simulation study conducted on a case of the global supply chain. We have discussed the impact of COVID19 on the supply chains by citing some recent examples in the retail sector in Australia. We demonstrate the use of simulation modelling to quickly and reliably model and analyze supply chain disruptions through the use of anyLogistix simulation software. In this paper, we have simulated a case of an Australian hardware retail supply chain that has a global supply network. We have investigated the impact of COVID19 disruptions on the supply chain performance. Our results highlighted the importance of waiting order cancellation strategy in the recovery period for reducing supply chain costs and maintaining service level. We also discussed the negative effect of distance between supplier and customer on the resiliency of delivery systems. This initial work was a proof of concept to simulate COVID19 disruptions on a retail supply chain.
{"title":"Modelling the Impact of COVID-19 Pandemic on a Hardware Retail Supply Chain","authors":"A. Sathyanarayana, N. Shukla, F. Taghikhah","doi":"10.1109/IEEM45057.2020.9309973","DOIUrl":"https://doi.org/10.1109/IEEM45057.2020.9309973","url":null,"abstract":"Due to the current COVID-19 (SARS-CoV-2) outbreak, supply chains have been severely disrupted in long term globally. In this paper, we present the results of a simulation study conducted on a case of the global supply chain. We have discussed the impact of COVID19 on the supply chains by citing some recent examples in the retail sector in Australia. We demonstrate the use of simulation modelling to quickly and reliably model and analyze supply chain disruptions through the use of anyLogistix simulation software. In this paper, we have simulated a case of an Australian hardware retail supply chain that has a global supply network. We have investigated the impact of COVID19 disruptions on the supply chain performance. Our results highlighted the importance of waiting order cancellation strategy in the recovery period for reducing supply chain costs and maintaining service level. We also discussed the negative effect of distance between supplier and customer on the resiliency of delivery systems. This initial work was a proof of concept to simulate COVID19 disruptions on a retail supply chain.","PeriodicalId":226426,"journal":{"name":"2020 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134178625","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 : 2020-12-14DOI: 10.1109/IEEM45057.2020.9309847
N. Hankla, Ganda Boonsothonsatit
In a highly competitive manufacturing industry, it is necessary to reduce logistics cost for remaining competitiveness and increasing business profitability. One of several causes primarily influencing logistics cost is inventory to support fluctuation of raw material price and decision makers when and how much raw material is purchased. These hence require time-series prediction of raw material price. For a small-sized manufacturing case, its main raw material of copper is predicted using Autoregressive Integrated Moving Average (ARIMA). It returns Mean Absolute Percentage Error (MAPE) less than 5 percent.
{"title":"Prediction of Raw Material Price Using Autoregressive Integrated Moving Average","authors":"N. Hankla, Ganda Boonsothonsatit","doi":"10.1109/IEEM45057.2020.9309847","DOIUrl":"https://doi.org/10.1109/IEEM45057.2020.9309847","url":null,"abstract":"In a highly competitive manufacturing industry, it is necessary to reduce logistics cost for remaining competitiveness and increasing business profitability. One of several causes primarily influencing logistics cost is inventory to support fluctuation of raw material price and decision makers when and how much raw material is purchased. These hence require time-series prediction of raw material price. For a small-sized manufacturing case, its main raw material of copper is predicted using Autoregressive Integrated Moving Average (ARIMA). It returns Mean Absolute Percentage Error (MAPE) less than 5 percent.","PeriodicalId":226426,"journal":{"name":"2020 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134310467","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 : 2020-12-14DOI: 10.1109/IEEM45057.2020.9309984
Zhe Hu, Hao Hu, W. Chan, F. Xu
Construction noise is harmful to not only workers’ occupational health but also their work safety and productivity. But, workers’ subjective evaluation of the impacts of construction noises have been less studied. This study investigated the adverse impact of construction noise from the perspective of the workers and analyzed their responses for the role of personal factor (i.e. noise sensitivity) in perceiving adverse impacts and the likelihood of making errors during work. The results showed that noise sensitivity was an important factor in the subjective evaluation of construction noise and the likelihood of making mistakes. With the increase of noise sensitivity, the relations among some factors are non-linear. It was found that psychological impacts were regarded more obvious than physiological impacts to workers. And there was a significant positive correlation between adverse impact and the likelihood of work error. Current noise control methods could be categorized into five types and mostly singly used. There were no significant differences among them indicating less efficiency. The findings can facilitate better working environment for on-site workers to promote both their physical and mental health.
{"title":"An Investigation of the Perceived Adverse Impacts and Control of Construction Noise in China","authors":"Zhe Hu, Hao Hu, W. Chan, F. Xu","doi":"10.1109/IEEM45057.2020.9309984","DOIUrl":"https://doi.org/10.1109/IEEM45057.2020.9309984","url":null,"abstract":"Construction noise is harmful to not only workers’ occupational health but also their work safety and productivity. But, workers’ subjective evaluation of the impacts of construction noises have been less studied. This study investigated the adverse impact of construction noise from the perspective of the workers and analyzed their responses for the role of personal factor (i.e. noise sensitivity) in perceiving adverse impacts and the likelihood of making errors during work. The results showed that noise sensitivity was an important factor in the subjective evaluation of construction noise and the likelihood of making mistakes. With the increase of noise sensitivity, the relations among some factors are non-linear. It was found that psychological impacts were regarded more obvious than physiological impacts to workers. And there was a significant positive correlation between adverse impact and the likelihood of work error. Current noise control methods could be categorized into five types and mostly singly used. There were no significant differences among them indicating less efficiency. The findings can facilitate better working environment for on-site workers to promote both their physical and mental health.","PeriodicalId":226426,"journal":{"name":"2020 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133672967","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 : 2020-12-14DOI: 10.1109/IEEM45057.2020.9309932
Cang Wu, Shubin Si
Multivariate Poisson (MP) counts are common in the course of manufacturing and service process. It is significant to monitor the MP counts and judge whether the process is in control or not. Most of the previous researches assumed that the variables of each univariate Poisson process are independent. Taking the temporal and spatial correlations into account, this article proposes an integrated model based on copula model and autoregressive (AR) process. Furthermore, the inference functions for margins (IFM) method and the expectation maximization (EM) algorithm accompanied by sequential importance resampling (SIR) method, provide satisfactory estimators in the proposed model.
{"title":"An Integrated Modeling Framework for Multivariate Poisson Process with Temporal and Spatial Correlations","authors":"Cang Wu, Shubin Si","doi":"10.1109/IEEM45057.2020.9309932","DOIUrl":"https://doi.org/10.1109/IEEM45057.2020.9309932","url":null,"abstract":"Multivariate Poisson (MP) counts are common in the course of manufacturing and service process. It is significant to monitor the MP counts and judge whether the process is in control or not. Most of the previous researches assumed that the variables of each univariate Poisson process are independent. Taking the temporal and spatial correlations into account, this article proposes an integrated model based on copula model and autoregressive (AR) process. Furthermore, the inference functions for margins (IFM) method and the expectation maximization (EM) algorithm accompanied by sequential importance resampling (SIR) method, provide satisfactory estimators in the proposed model.","PeriodicalId":226426,"journal":{"name":"2020 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131435752","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 : 2020-12-14DOI: 10.1109/IEEM45057.2020.9309920
Meygen D. Cruz, J. Keh, Ramiel G. Deticio, Carl Vincent T. Tan, John Anthony C. Jose, E. Sybingco, E. Dadios
Data on various key performance indicators (KPIs) are crucial in preventing problems and growing a business. In this paper, we propose the creation and analysis of the feasibility of using an intelligent video analytics (IVA) system to gather data on certain restaurant key performance indicators (KPIs). The main challenge lies in maximizing the use of an existing CCTV camera with a fixed viewpoint, which is tailored for security purposes instead of video analytics, by using its footage in the IVA. The researchers partnered with a restaurant in a high-traffic business district to create and test the system. The final system gathered data on foot traffic, customer gender classification, and customer group size. Neural networks such as YOLO, Deep SORT, and InceptionV3 were employed in the implementation. The results show that while it is possible to gather data on these three metrics through the system, the speed and accuracy can still be improved through downsizing the frames, down sampling the videos, and using other algorithms.
{"title":"Visual-based People Counting and Profiling System for Use in Retail Data Analytics","authors":"Meygen D. Cruz, J. Keh, Ramiel G. Deticio, Carl Vincent T. Tan, John Anthony C. Jose, E. Sybingco, E. Dadios","doi":"10.1109/IEEM45057.2020.9309920","DOIUrl":"https://doi.org/10.1109/IEEM45057.2020.9309920","url":null,"abstract":"Data on various key performance indicators (KPIs) are crucial in preventing problems and growing a business. In this paper, we propose the creation and analysis of the feasibility of using an intelligent video analytics (IVA) system to gather data on certain restaurant key performance indicators (KPIs). The main challenge lies in maximizing the use of an existing CCTV camera with a fixed viewpoint, which is tailored for security purposes instead of video analytics, by using its footage in the IVA. The researchers partnered with a restaurant in a high-traffic business district to create and test the system. The final system gathered data on foot traffic, customer gender classification, and customer group size. Neural networks such as YOLO, Deep SORT, and InceptionV3 were employed in the implementation. The results show that while it is possible to gather data on these three metrics through the system, the speed and accuracy can still be improved through downsizing the frames, down sampling the videos, and using other algorithms.","PeriodicalId":226426,"journal":{"name":"2020 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127864196","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 : 2020-12-14DOI: 10.1109/IEEM45057.2020.9309867
A. Susanty, N. B. Puspitasari, Oktivia Selvina, S. Jati
The purpose of this research is to examine the impact of internal factors on the implementation of halal logistics in the Indonesian Food and Beverage Industry. This research adopts the Partial Least Square (PLS) method to predict the dependent variable by involving a large number of independent variables. This research uses 100 food and beverage companies as the sample. The result of this research indicated that image and reputation, social responsibility, and integrity of halal has a significant impact on the implementation of halal logistics.
{"title":"Impact of Internal Factors on the Implementation of Halal Logistics","authors":"A. Susanty, N. B. Puspitasari, Oktivia Selvina, S. Jati","doi":"10.1109/IEEM45057.2020.9309867","DOIUrl":"https://doi.org/10.1109/IEEM45057.2020.9309867","url":null,"abstract":"The purpose of this research is to examine the impact of internal factors on the implementation of halal logistics in the Indonesian Food and Beverage Industry. This research adopts the Partial Least Square (PLS) method to predict the dependent variable by involving a large number of independent variables. This research uses 100 food and beverage companies as the sample. The result of this research indicated that image and reputation, social responsibility, and integrity of halal has a significant impact on the implementation of halal logistics.","PeriodicalId":226426,"journal":{"name":"2020 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131876778","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 : 2020-12-14DOI: 10.1109/IEEM45057.2020.9309782
Xuechen Feng, Ziqi Zhao, Canrong Zhang
In the era of Industry 4.0, to cope with the complex, volatile and fiercely competitive market environment, factories have to become more and more intelligent, flexible, and agile. This paper studies the reconfigurable machine deployment and adjustment problem in the multi-product job shop. This problem belongs to production process control and is part of the digital factory, as it forecasts the future performance for the adjustment of the reconfigurable machines in an online manner. To be more specific, we design an online simulation control system based on digital twin, which integrates the function of monitoring, decision-making and control. We use simulation optimization and design heuristic algorithm to solve the multi-objective capacity adjustment decision-making problem. The simulation results show the effectiveness and stability of the system and can be used to cope with the complex and ever-changing industrial environment such as machine breakdowns and rush orders.
{"title":"Simulation Optimization Framework for Online Deployment and Adjustment of Reconfigurable Machines in Job Shops","authors":"Xuechen Feng, Ziqi Zhao, Canrong Zhang","doi":"10.1109/IEEM45057.2020.9309782","DOIUrl":"https://doi.org/10.1109/IEEM45057.2020.9309782","url":null,"abstract":"In the era of Industry 4.0, to cope with the complex, volatile and fiercely competitive market environment, factories have to become more and more intelligent, flexible, and agile. This paper studies the reconfigurable machine deployment and adjustment problem in the multi-product job shop. This problem belongs to production process control and is part of the digital factory, as it forecasts the future performance for the adjustment of the reconfigurable machines in an online manner. To be more specific, we design an online simulation control system based on digital twin, which integrates the function of monitoring, decision-making and control. We use simulation optimization and design heuristic algorithm to solve the multi-objective capacity adjustment decision-making problem. The simulation results show the effectiveness and stability of the system and can be used to cope with the complex and ever-changing industrial environment such as machine breakdowns and rush orders.","PeriodicalId":226426,"journal":{"name":"2020 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133717883","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 : 2020-12-14DOI: 10.1109/IEEM45057.2020.9309966
E. Arica, Ottar Bakaas, P. Sriram
In this paper, we study a specific type of Engineer-to-Order (ETO) firms called Complex ETO characterized by one-of-a-kind products with high complexity and low volumes. Such firms are at high risk of encountering significant engineering changes due to their characteristics. The management of engineering changes have a large impact of time, cost, and quality of the project. The purpose of this paper is therefore to provide a holistic taxonomy for Engineering Change Management (ECM) that can guide the companies with set of actions to prevent, handle, and manage engineering changes. The study is based on literature and empirical findings from a single case study conducted in an offshore platform producer, which resulted in development and verification of the taxonomy.
{"title":"A Taxonomy for Engineering Change Management in Complex ETO Firms","authors":"E. Arica, Ottar Bakaas, P. Sriram","doi":"10.1109/IEEM45057.2020.9309966","DOIUrl":"https://doi.org/10.1109/IEEM45057.2020.9309966","url":null,"abstract":"In this paper, we study a specific type of Engineer-to-Order (ETO) firms called Complex ETO characterized by one-of-a-kind products with high complexity and low volumes. Such firms are at high risk of encountering significant engineering changes due to their characteristics. The management of engineering changes have a large impact of time, cost, and quality of the project. The purpose of this paper is therefore to provide a holistic taxonomy for Engineering Change Management (ECM) that can guide the companies with set of actions to prevent, handle, and manage engineering changes. The study is based on literature and empirical findings from a single case study conducted in an offshore platform producer, which resulted in development and verification of the taxonomy.","PeriodicalId":226426,"journal":{"name":"2020 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124328408","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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