Pub Date : 2020-12-14DOI: 10.1109/WSC48552.2020.9383929
Javiera Barrera, Guido Lagos
In this paper we approximate the last, close-to-first, and what we call quantile failure times of a system, when the system-components’ failure times are modeled according to a Levy-frailty Marshall-Olkin (LFMO) distribution. The LFMO distribution is a fairly recent model that can be used to model components failing simultaneously in groups. One of its prominent features is that the failure times of the components are conditionally iid; indeed, the failure times are iid exponential when conditioned on the path of a given Lévy subordinator process. We are motivated by further studying the order statistics of the LFMO distribution, as recently Barrera and Lagos (2020) showed an atypical behavior for the upper-order statistics. We are also motivated by approximating the system when it has an astronomically large number of components. We perform computational experiments that show significative variations in the convergence speeds of our approximations.
{"title":"Approximating the Lévy-Frailty Marshall-Olkin Model for Failure Times","authors":"Javiera Barrera, Guido Lagos","doi":"10.1109/WSC48552.2020.9383929","DOIUrl":"https://doi.org/10.1109/WSC48552.2020.9383929","url":null,"abstract":"In this paper we approximate the last, close-to-first, and what we call quantile failure times of a system, when the system-components’ failure times are modeled according to a Levy-frailty Marshall-Olkin (LFMO) distribution. The LFMO distribution is a fairly recent model that can be used to model components failing simultaneously in groups. One of its prominent features is that the failure times of the components are conditionally iid; indeed, the failure times are iid exponential when conditioned on the path of a given Lévy subordinator process. We are motivated by further studying the order statistics of the LFMO distribution, as recently Barrera and Lagos (2020) showed an atypical behavior for the upper-order statistics. We are also motivated by approximating the system when it has an astronomically large number of components. We perform computational experiments that show significative variations in the convergence speeds of our approximations.","PeriodicalId":6692,"journal":{"name":"2020 Winter Simulation Conference (WSC)","volume":"20 4 1","pages":"2389-2399"},"PeriodicalIF":0.0,"publicationDate":"2020-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80503871","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/WSC48552.2020.9384123
Kasey Jones, Emily C Hadley, Sarah K. Rhea, E. Lofgren
The ability of healthcare systems to provide patient care can become disrupted and overwhelmed during a major epidemic or pandemic. We adapted an existing hospitalization microsimulation of North Carolina to assess the impact of a localized epidemic of a fictitious pathogen on inpatient hospital bed availability in the same locale. As area hospital beds reach capacity, agents are turned away and seek treatment at different hospital locations. We explore how variability in the duration and severity of an epidemic affects hospital capacity in different North Carolina counties. We analyze various epidemic scenarios and provide insights into how many days counties and hospitals would have to prepare for a surge in capacity.
{"title":"Assessing Strain On Hospital Capacity During A Localized Epidemic Using A Calibrated Hospitalization Microsimulation","authors":"Kasey Jones, Emily C Hadley, Sarah K. Rhea, E. Lofgren","doi":"10.1109/WSC48552.2020.9384123","DOIUrl":"https://doi.org/10.1109/WSC48552.2020.9384123","url":null,"abstract":"The ability of healthcare systems to provide patient care can become disrupted and overwhelmed during a major epidemic or pandemic. We adapted an existing hospitalization microsimulation of North Carolina to assess the impact of a localized epidemic of a fictitious pathogen on inpatient hospital bed availability in the same locale. As area hospital beds reach capacity, agents are turned away and seek treatment at different hospital locations. We explore how variability in the duration and severity of an epidemic affects hospital capacity in different North Carolina counties. We analyze various epidemic scenarios and provide insights into how many days counties and hospitals would have to prepare for a surge in capacity.","PeriodicalId":6692,"journal":{"name":"2020 Winter Simulation Conference (WSC)","volume":"58 1","pages":"102-110"},"PeriodicalIF":0.0,"publicationDate":"2020-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79073904","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/WSC48552.2020.9383909
Yaileen M. Méndez-Vázquez, D. Nembhard, Mauricio Cabrera-Réos
Many organizations have considered implementing teamwork as an approach to improve organizational performance and boost the learning process of workers. Despite the benefits offered by teamwork, literature has also shown negative aspects of this kind of work setting, including the transient initial team underachievement known as process loss. Studies have been dedicated to investigate the effect of implementing teamwork strategies on team productivity. However most of these studies remain observational in nature, partially due to the complexity associated with performing physical experimentation in teamwork manufacturing settings and the study of human cognition. The current study proposes the use of simulation as a strategy to conduct experimentation in this kind of setting. This work capitalizes on simulation to investigate the joint effect of knowledge transfer and process loss on team productivity for manufacturing settings. The joint effect of these factors on team productivity still remains unknown in current literature of teamwork.
{"title":"Simulation-Aided Assessment of Team Performance: The Effects of Transient Underachievement and Knowledge Transfer","authors":"Yaileen M. Méndez-Vázquez, D. Nembhard, Mauricio Cabrera-Réos","doi":"10.1109/WSC48552.2020.9383909","DOIUrl":"https://doi.org/10.1109/WSC48552.2020.9383909","url":null,"abstract":"Many organizations have considered implementing teamwork as an approach to improve organizational performance and boost the learning process of workers. Despite the benefits offered by teamwork, literature has also shown negative aspects of this kind of work setting, including the transient initial team underachievement known as process loss. Studies have been dedicated to investigate the effect of implementing teamwork strategies on team productivity. However most of these studies remain observational in nature, partially due to the complexity associated with performing physical experimentation in teamwork manufacturing settings and the study of human cognition. The current study proposes the use of simulation as a strategy to conduct experimentation in this kind of setting. This work capitalizes on simulation to investigate the joint effect of knowledge transfer and process loss on team productivity for manufacturing settings. The joint effect of these factors on team productivity still remains unknown in current literature of teamwork.","PeriodicalId":6692,"journal":{"name":"2020 Winter Simulation Conference (WSC)","volume":"28 1","pages":"1652-1663"},"PeriodicalIF":0.0,"publicationDate":"2020-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81610156","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/WSC48552.2020.9384013
Derya Kilinc, Narges Shahraki, A. Degnim, T. Hoskin, Tiffany M. Horton, M. Sir, K. Pasupathy, E. Gel
Increased surgeon workload can result in prolonged access times for patients and may lead to surgeon burnout. Management of access times through investments in care capacity and hiring of providers require an understanding of the patient access times resulting from a given level of care capacity under different patient demand scenarios. We explore the effectiveness of a simulation-based framework in providing workforce planning insights. Our framework involves modeling of patient demand by considering different groups of surgical procedures, a simulation model that allows calibration of certain parameters through the use of data, and consideration of different demand and capacity scenarios to provide an understanding of the range of patient access times that can be expected over the immediate future during the time horizon. Our results show that such a simulation-based framework can help ground workforce planning and capacity investment decisions on operational data, and help healthcare institutions manage such costs.
{"title":"Simulation Modeling as a Decision Tool for Capacity Allocation in Breast Surgery","authors":"Derya Kilinc, Narges Shahraki, A. Degnim, T. Hoskin, Tiffany M. Horton, M. Sir, K. Pasupathy, E. Gel","doi":"10.1109/WSC48552.2020.9384013","DOIUrl":"https://doi.org/10.1109/WSC48552.2020.9384013","url":null,"abstract":"Increased surgeon workload can result in prolonged access times for patients and may lead to surgeon burnout. Management of access times through investments in care capacity and hiring of providers require an understanding of the patient access times resulting from a given level of care capacity under different patient demand scenarios. We explore the effectiveness of a simulation-based framework in providing workforce planning insights. Our framework involves modeling of patient demand by considering different groups of surgical procedures, a simulation model that allows calibration of certain parameters through the use of data, and consideration of different demand and capacity scenarios to provide an understanding of the range of patient access times that can be expected over the immediate future during the time horizon. Our results show that such a simulation-based framework can help ground workforce planning and capacity investment decisions on operational data, and help healthcare institutions manage such costs.","PeriodicalId":6692,"journal":{"name":"2020 Winter Simulation Conference (WSC)","volume":"14 1","pages":"806-817"},"PeriodicalIF":0.0,"publicationDate":"2020-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84326231","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/WSC48552.2020.9383956
R. Bemthuis, M. Mes, M. Iacob, P. Havinga
Traditional modeling approaches, based on predefined business logic, offer little support for today’s complex environments. In this paper, we propose a conceptual agent-based simulation framework to help not only discover complex business processes but also to analyze and learn from emergent behavior arising in cyber-physical systems. Techniques originating from agent-based modeling as well as from the process mining discipline are used to reinforce agent-based decision-making. Whereas agent-technology is used to orchestrate the integration and relationship between the environment and business logic activities, process mining capabilities are mainly used to discover and analyze emergent behavior. Using a functional decomposition approach, we specified three agent types: cyber-physical controller agent, business rule management agent, and emergent behavior detection agent. We use agent-based simulation of a logistics cold chain case study to demonstrate the feasibility of our approach.
{"title":"Using Agent-Based Simulation for Emergent Behavior Detection in Cyber-Physical Systems","authors":"R. Bemthuis, M. Mes, M. Iacob, P. Havinga","doi":"10.1109/WSC48552.2020.9383956","DOIUrl":"https://doi.org/10.1109/WSC48552.2020.9383956","url":null,"abstract":"Traditional modeling approaches, based on predefined business logic, offer little support for today’s complex environments. In this paper, we propose a conceptual agent-based simulation framework to help not only discover complex business processes but also to analyze and learn from emergent behavior arising in cyber-physical systems. Techniques originating from agent-based modeling as well as from the process mining discipline are used to reinforce agent-based decision-making. Whereas agent-technology is used to orchestrate the integration and relationship between the environment and business logic activities, process mining capabilities are mainly used to discover and analyze emergent behavior. Using a functional decomposition approach, we specified three agent types: cyber-physical controller agent, business rule management agent, and emergent behavior detection agent. We use agent-based simulation of a logistics cold chain case study to demonstrate the feasibility of our approach.","PeriodicalId":6692,"journal":{"name":"2020 Winter Simulation Conference (WSC)","volume":"23 1","pages":"230-241"},"PeriodicalIF":0.0,"publicationDate":"2020-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85014370","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/WSC48552.2020.9383879
B. Feng, Guangxin Jiang
Simulation experiments are sometimes conducted periodically, with updated parameters of the stochastic system being modeled. Storing and reusing the past simulation experiment data may be helpful for the current simulation experiment. In this paper, we consider reusing simulation data in repeated experiments to develop high-quality metamodels. Specifically, we propose a generalized least square regression metamodel whose input data include simulation outputs from the current and the past experiments. Moreover, the past simulation outputs are reused via the likelihood ratio method. Asymptotic variance analysis is provided to show the benefits of reusing past simulation data in prediction accuracy, and the numerical results show the effectiveness of the proposed method.
{"title":"Reusing Simulation Outputs of Repeated Experiments Via Likelihood Ratio Regression","authors":"B. Feng, Guangxin Jiang","doi":"10.1109/WSC48552.2020.9383879","DOIUrl":"https://doi.org/10.1109/WSC48552.2020.9383879","url":null,"abstract":"Simulation experiments are sometimes conducted periodically, with updated parameters of the stochastic system being modeled. Storing and reusing the past simulation experiment data may be helpful for the current simulation experiment. In this paper, we consider reusing simulation data in repeated experiments to develop high-quality metamodels. Specifically, we propose a generalized least square regression metamodel whose input data include simulation outputs from the current and the past experiments. Moreover, the past simulation outputs are reused via the likelihood ratio method. Asymptotic variance analysis is provided to show the benefits of reusing past simulation data in prediction accuracy, and the numerical results show the effectiveness of the proposed method.","PeriodicalId":6692,"journal":{"name":"2020 Winter Simulation Conference (WSC)","volume":"9 1","pages":"325-336"},"PeriodicalIF":0.0,"publicationDate":"2020-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85092948","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/WSC48552.2020.9383895
A. Juan, P. Copado, Javier Panadero, C. Laroque, R. D. L. Torre
Modern Manufacturing, known as Industrial Internet or Industry 4.0, is more than ever determined by customer-specific products, that are to be manufactured and delivered in given lead times and due-dates. Many of these manufacturing systems can be modeled as flow-shops where some of the processes can handle jobs on parallel machines. In addition, complex manufacturing environments contain specific machine loops or re-entry cycles where jobs might re-enter specific processes at some point of the flow-shop chain. A specific server is assigned to a job the first time it visits a machine, and it is quite usual that this job has to be processed by exactly the same server if it re-visits the machine due to quality issues. With the goal of minimizing the makespan, this paper analyzes this complex flow-shop setting and proposes an original discrete-event heuristic for solving it in short computing times. Our algorithm combines biased (non-uniform) randomization strategies with the use of a discrete-event list, which iteratively processes as the simulation clock advances. A series of computational experiments contribute to illustrate the performance of our methodology.
{"title":"A Discrete-Event Heuristic for Makespan Optimization in Multi-Server Flow-Shop Problems with Machine re-entering","authors":"A. Juan, P. Copado, Javier Panadero, C. Laroque, R. D. L. Torre","doi":"10.1109/WSC48552.2020.9383895","DOIUrl":"https://doi.org/10.1109/WSC48552.2020.9383895","url":null,"abstract":"Modern Manufacturing, known as Industrial Internet or Industry 4.0, is more than ever determined by customer-specific products, that are to be manufactured and delivered in given lead times and due-dates. Many of these manufacturing systems can be modeled as flow-shops where some of the processes can handle jobs on parallel machines. In addition, complex manufacturing environments contain specific machine loops or re-entry cycles where jobs might re-enter specific processes at some point of the flow-shop chain. A specific server is assigned to a job the first time it visits a machine, and it is quite usual that this job has to be processed by exactly the same server if it re-visits the machine due to quality issues. With the goal of minimizing the makespan, this paper analyzes this complex flow-shop setting and proposes an original discrete-event heuristic for solving it in short computing times. Our algorithm combines biased (non-uniform) randomization strategies with the use of a discrete-event list, which iteratively processes as the simulation clock advances. A series of computational experiments contribute to illustrate the performance of our methodology.","PeriodicalId":6692,"journal":{"name":"2020 Winter Simulation Conference (WSC)","volume":"89 1","pages":"1492-1502"},"PeriodicalIF":0.0,"publicationDate":"2020-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77006865","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/WSC48552.2020.9384075
Daniel Nåfors, B. Johansson, P. Gullander, Sven Erixon
As manufacturing companies make changes to their production system, changes to the factory layout usually follow. The layout of a factory considers the positioning of all elements in the production system, and can contribute to the overall efficiency of operations and the work environment. The process of planning factory layouts affects both installation of the changes and operation of the production system, so the effects can be utilized for a long period of time. By combining 3D laser scanning, Virtual Reality, CAD models, and simulation modelling in a hybrid digital twin, this planning process can be noticeably improved yielding benefits in all phases. This is exemplified via a novel longitudinal industrial study using participant observation to gather data. Findings from the study show that the factory layout planning process can be innovated by smart use of modern digital technologies, resulting in better solution and more informed decisions with reduced risk.
{"title":"Simulation in Hybrid Digital Twins for Factory Layout Planning","authors":"Daniel Nåfors, B. Johansson, P. Gullander, Sven Erixon","doi":"10.1109/WSC48552.2020.9384075","DOIUrl":"https://doi.org/10.1109/WSC48552.2020.9384075","url":null,"abstract":"As manufacturing companies make changes to their production system, changes to the factory layout usually follow. The layout of a factory considers the positioning of all elements in the production system, and can contribute to the overall efficiency of operations and the work environment. The process of planning factory layouts affects both installation of the changes and operation of the production system, so the effects can be utilized for a long period of time. By combining 3D laser scanning, Virtual Reality, CAD models, and simulation modelling in a hybrid digital twin, this planning process can be noticeably improved yielding benefits in all phases. This is exemplified via a novel longitudinal industrial study using participant observation to gather data. Findings from the study show that the factory layout planning process can be innovated by smart use of modern digital technologies, resulting in better solution and more informed decisions with reduced risk.","PeriodicalId":6692,"journal":{"name":"2020 Winter Simulation Conference (WSC)","volume":"28 1","pages":"1619-1630"},"PeriodicalIF":0.0,"publicationDate":"2020-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81073931","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/WSC48552.2020.9383916
Mohammed Farhan, Brett Göhre, Edward Junprung
Reinforcement Learning has recently gained a lot of exposure in the simulation industry. In this paper, we demonstrate the use of reinforcement learning in AnyLogic software models using Pathmind. A coffee shop simulation is built to train a barista to make correct operational decisions and improve efficiency that directly affects customer service time. The trained policy outperforms rule-based functions in terms of customer service time and throughput.
{"title":"Reinforcement Learning in Anylogic Simulation Models: A Guiding Example Using Pathmind","authors":"Mohammed Farhan, Brett Göhre, Edward Junprung","doi":"10.1109/WSC48552.2020.9383916","DOIUrl":"https://doi.org/10.1109/WSC48552.2020.9383916","url":null,"abstract":"Reinforcement Learning has recently gained a lot of exposure in the simulation industry. In this paper, we demonstrate the use of reinforcement learning in AnyLogic software models using Pathmind. A coffee shop simulation is built to train a barista to make correct operational decisions and improve efficiency that directly affects customer service time. The trained policy outperforms rule-based functions in terms of customer service time and throughput.","PeriodicalId":6692,"journal":{"name":"2020 Winter Simulation Conference (WSC)","volume":"40 1","pages":"3212-3223"},"PeriodicalIF":0.0,"publicationDate":"2020-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85705099","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/WSC48552.2020.9383970
Adam VanDeusen, Nicholas Zacharek, Emmett Springer, Advaidh Venkat, A. Cohn, Megan Adams, Jacob E. Kurlander, S. Saini
Healthcare providers have begun providing care to patients via remote appointments using web-based, synchronous video visits. As this appointment modality becomes increasingly prevalent, decision-makers must consider how to incorporate patient preference for an in-person versus virtual care modality when scheduling future visits. We present a discrete-event simulation that models several potential policies that these decision-makers could use to schedule patients, and demonstrate this simulation in the clinical context of patients with gastroesophageal reflux disease. This simulation provides key metrics for decision-makers, including provider utilization, patient lead time, and proportion of appointments that satisfy patients’ preferences for appointment modality.
{"title":"Discrete-Event Simulation with Consideration for Patient Preference When Scheduling Specialty Telehealth Appointments","authors":"Adam VanDeusen, Nicholas Zacharek, Emmett Springer, Advaidh Venkat, A. Cohn, Megan Adams, Jacob E. Kurlander, S. Saini","doi":"10.1109/WSC48552.2020.9383970","DOIUrl":"https://doi.org/10.1109/WSC48552.2020.9383970","url":null,"abstract":"Healthcare providers have begun providing care to patients via remote appointments using web-based, synchronous video visits. As this appointment modality becomes increasingly prevalent, decision-makers must consider how to incorporate patient preference for an in-person versus virtual care modality when scheduling future visits. We present a discrete-event simulation that models several potential policies that these decision-makers could use to schedule patients, and demonstrate this simulation in the clinical context of patients with gastroesophageal reflux disease. This simulation provides key metrics for decision-makers, including provider utilization, patient lead time, and proportion of appointments that satisfy patients’ preferences for appointment modality.","PeriodicalId":6692,"journal":{"name":"2020 Winter Simulation Conference (WSC)","volume":"10 1","pages":"888-899"},"PeriodicalIF":0.0,"publicationDate":"2020-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86056087","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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