Pub Date : 2020-12-14DOI: 10.1109/WSC48552.2020.9383910
Lei Lei, C. Alexopoulos, Yijie Peng, James R. Wilson
This article develops confidence intervals (CIs) and confidence regions (CRs) for quantiles based on independent realizations of a simulation response. The methodology uses a combination of conditional Monte Carlo (CMC) and the generalized likelihood ratio (GLR) method. While batching and sectioning methods partition the sample into nonoverlapping batches, and construct CIs and CRs by estimating the asymptotic variance using sample quantiles from each batch, the proposed techniques directly estimate the underlying probability density function of the response. Numerical results show that the CIs constructed by applying CMC, GLR, and sectioning lead to comparable coverage results, which are closer to the targets compared with batching alone for relatively small samples; and the coverage rates of the CRs constructed by applying CMC and GLR are closer to the targets than both sectioning and batching when the sample size is relatively small and the number of probability levels is relatively large.
{"title":"Confidence Intervals and Regions for Quantiles using Conditional Monte Carlo and Generalized Likelihood Ratios","authors":"Lei Lei, C. Alexopoulos, Yijie Peng, James R. Wilson","doi":"10.1109/WSC48552.2020.9383910","DOIUrl":"https://doi.org/10.1109/WSC48552.2020.9383910","url":null,"abstract":"This article develops confidence intervals (CIs) and confidence regions (CRs) for quantiles based on independent realizations of a simulation response. The methodology uses a combination of conditional Monte Carlo (CMC) and the generalized likelihood ratio (GLR) method. While batching and sectioning methods partition the sample into nonoverlapping batches, and construct CIs and CRs by estimating the asymptotic variance using sample quantiles from each batch, the proposed techniques directly estimate the underlying probability density function of the response. Numerical results show that the CIs constructed by applying CMC, GLR, and sectioning lead to comparable coverage results, which are closer to the targets compared with batching alone for relatively small samples; and the coverage rates of the CRs constructed by applying CMC and GLR are closer to the targets than both sectioning and batching when the sample size is relatively small and the number of probability levels is relatively large.","PeriodicalId":6692,"journal":{"name":"2020 Winter Simulation Conference (WSC)","volume":"23 1","pages":"2071-2082"},"PeriodicalIF":0.0,"publicationDate":"2020-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84832275","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/WSC48552.2020.9383986
Y. Turgut, C. Bozdag
In the last few decades, dynamic job scheduling problems (DJSPs) has received more attention from researchers and practitioners. However, the potential of reinforcement learning (RL) methods has not been exploited adequately for solving DJSPs. In this work deep Q-network (DQN) model is applied to train an agent to learn how to schedule the jobs dynamically by minimizing the delay time of jobs. The DQN model is trained based on a discrete event simulation experiment. The model is tested by comparing the trained DQN model against two popular dispatching rules, shortest processing time and earliest due date. The obtained results indicate that the DQN model has a better performance than these dispatching rules.
{"title":"Deep Q-Network Model for Dynamic Job Shop Scheduling Pproblem Based on Discrete Event Simulation","authors":"Y. Turgut, C. Bozdag","doi":"10.1109/WSC48552.2020.9383986","DOIUrl":"https://doi.org/10.1109/WSC48552.2020.9383986","url":null,"abstract":"In the last few decades, dynamic job scheduling problems (DJSPs) has received more attention from researchers and practitioners. However, the potential of reinforcement learning (RL) methods has not been exploited adequately for solving DJSPs. In this work deep Q-network (DQN) model is applied to train an agent to learn how to schedule the jobs dynamically by minimizing the delay time of jobs. The DQN model is trained based on a discrete event simulation experiment. The model is tested by comparing the trained DQN model against two popular dispatching rules, shortest processing time and earliest due date. The obtained results indicate that the DQN model has a better performance than these dispatching rules.","PeriodicalId":6692,"journal":{"name":"2020 Winter Simulation Conference (WSC)","volume":"1 1","pages":"1551-1559"},"PeriodicalIF":0.0,"publicationDate":"2020-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90719012","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/WSC48552.2020.9384102
Nour Ramzy, Christian James Martens, Shreya Singh, Thomas Ponsignon, H. Ehm
With diverse product mixes in fabs, high demand volatility, and numerous manufacturing steps spread across different facilities, it is impossible to analyze the combined impacts of multiple operations in semiconductor supply chains without a modeling tool like simulation. This paper explains how ontologies can be used to develop and deploy simulation applications, with interoperability and knowledge sharing at the semantic level. This paper proposes a concept to automatically build simulations using ontologies and its preliminary results. The proposed approach seeks to save time and effort expended in recreating the information for different use cases that already exists elsewhere. The use case provides first indications that with an enhancement of a so-called Digital Reference with Semantic Web Technologies, modeling and simulation of semiconductor supply chains will not only become much faster but also require less modeling efforts because of the reusability property.
{"title":"First Steps Towards Bridging Simulation and Ontology to Ease the Model Creation on the Example of Semiconductor Industry","authors":"Nour Ramzy, Christian James Martens, Shreya Singh, Thomas Ponsignon, H. Ehm","doi":"10.1109/WSC48552.2020.9384102","DOIUrl":"https://doi.org/10.1109/WSC48552.2020.9384102","url":null,"abstract":"With diverse product mixes in fabs, high demand volatility, and numerous manufacturing steps spread across different facilities, it is impossible to analyze the combined impacts of multiple operations in semiconductor supply chains without a modeling tool like simulation. This paper explains how ontologies can be used to develop and deploy simulation applications, with interoperability and knowledge sharing at the semantic level. This paper proposes a concept to automatically build simulations using ontologies and its preliminary results. The proposed approach seeks to save time and effort expended in recreating the information for different use cases that already exists elsewhere. The use case provides first indications that with an enhancement of a so-called Digital Reference with Semantic Web Technologies, modeling and simulation of semiconductor supply chains will not only become much faster but also require less modeling efforts because of the reusability property.","PeriodicalId":6692,"journal":{"name":"2020 Winter Simulation Conference (WSC)","volume":"15 1","pages":"1789-1800"},"PeriodicalIF":0.0,"publicationDate":"2020-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91221660","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/WSC48552.2020.9384124
Daniel Glake, N. Ritter, T. Clemen
Spatio-temporal properties strongly influence a large proportion of multi-agent simulations (MAS) in their application domains. Time-dependent simulations benefit from correct and time-sensitive input data that match the current simulated time or offer the possibility to take into account previous simulation states in their modelling perspective. In this paper, we present the concepts and semantics of data-driven simulations with vector and raster data and extend them by a time dimension that applies at run-time within the simulation execution or in conjunction with the definition of MAS models. We show that the semantics consider the evolution of spatio-temporal objects with their temporal relationships between spatial entities.
{"title":"Utilizing Spatio-Temporal Data in Multi-Agent Simulation","authors":"Daniel Glake, N. Ritter, T. Clemen","doi":"10.1109/WSC48552.2020.9384124","DOIUrl":"https://doi.org/10.1109/WSC48552.2020.9384124","url":null,"abstract":"Spatio-temporal properties strongly influence a large proportion of multi-agent simulations (MAS) in their application domains. Time-dependent simulations benefit from correct and time-sensitive input data that match the current simulated time or offer the possibility to take into account previous simulation states in their modelling perspective. In this paper, we present the concepts and semantics of data-driven simulations with vector and raster data and extend them by a time dimension that applies at run-time within the simulation execution or in conjunction with the definition of MAS models. We show that the semantics consider the evolution of spatio-temporal objects with their temporal relationships between spatial entities.","PeriodicalId":6692,"journal":{"name":"2020 Winter Simulation Conference (WSC)","volume":"49 1","pages":"242-253"},"PeriodicalIF":0.0,"publicationDate":"2020-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90923077","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/WSC48552.2020.9383860
Angie Ramírez-Villamil, J. Montoya-Torres, Anicia Jaegler
Two-echelon distribution systems are very common in last-mile supply chains and urban logistics systems. The problem consists of delivering goods from one depot to a set of satellites usually located outside urban areas and from there to a set of geographically dispersed customers. This problem is modeled as a two-echelon vehicle routing problem (2E-VRP), which is known to be computationally difficult to solve. This paper proposes a solution approach based on optimization-simulation to solve the 2E-VRP with stochastic travel times. For the objective function, this paper considers the minimization of travel times. The efficiency of the solution approach is analyzed against the solution of the deterministic counterpart, which is solved using both exact and approximation approaches. The impact of adding stochastic travel speeds as part of the objective function is evaluated through simulation. Experiments are run using real data from several convenience stores in the city of Bogota, Colombia.
{"title":"A Simheuristic for the Stochastic Two-Echelon Capacitated Vehicle Routing Problem","authors":"Angie Ramírez-Villamil, J. Montoya-Torres, Anicia Jaegler","doi":"10.1109/WSC48552.2020.9383860","DOIUrl":"https://doi.org/10.1109/WSC48552.2020.9383860","url":null,"abstract":"Two-echelon distribution systems are very common in last-mile supply chains and urban logistics systems. The problem consists of delivering goods from one depot to a set of satellites usually located outside urban areas and from there to a set of geographically dispersed customers. This problem is modeled as a two-echelon vehicle routing problem (2E-VRP), which is known to be computationally difficult to solve. This paper proposes a solution approach based on optimization-simulation to solve the 2E-VRP with stochastic travel times. For the objective function, this paper considers the minimization of travel times. The efficiency of the solution approach is analyzed against the solution of the deterministic counterpart, which is solved using both exact and approximation approaches. The impact of adding stochastic travel speeds as part of the objective function is evaluated through simulation. Experiments are run using real data from several convenience stores in the city of Bogota, Colombia.","PeriodicalId":6692,"journal":{"name":"2020 Winter Simulation Conference (WSC)","volume":"4 1","pages":"1276-1287"},"PeriodicalIF":0.0,"publicationDate":"2020-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90070721","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/WSC48552.2020.9384048
Muhammad Tariq Afridi, S. Isaza, H. Ehm, Thomas Ponsignon, Abdelgafar Hamed
Vendor Managed Inventory (VMI) is a mainstream supply chain collaboration model. Measurement approaches defining minimum and maximum inventory levels for avoiding product shortages and over-stocking are rampant. No approach undertakes the responsibility aspect concerning inventory level status, especially in semiconductor industry which is confronted with short product life cycles, long process times, and volatile demand patterns. In this work, a root-cause enabling VMI performance measurement approach to assign responsibilities for poor performance is undertaken. Additionally, a solution methodology based on reinforcement learning is proposed for determining optimal replenishment policy in a VMI setting. Using a simulation model, different demand scenarios are generated based on real data from Infineon Technologies AG and compared on the basis of key performance indicators. Results obtained by the proposed method show improved performance than the current replenishment decisions of the company.
{"title":"A Deep Reinforcement Learning Approach for Optimal Replenishment Policy in A Vendor Managed Inventory Setting For Semiconductors","authors":"Muhammad Tariq Afridi, S. Isaza, H. Ehm, Thomas Ponsignon, Abdelgafar Hamed","doi":"10.1109/WSC48552.2020.9384048","DOIUrl":"https://doi.org/10.1109/WSC48552.2020.9384048","url":null,"abstract":"Vendor Managed Inventory (VMI) is a mainstream supply chain collaboration model. Measurement approaches defining minimum and maximum inventory levels for avoiding product shortages and over-stocking are rampant. No approach undertakes the responsibility aspect concerning inventory level status, especially in semiconductor industry which is confronted with short product life cycles, long process times, and volatile demand patterns. In this work, a root-cause enabling VMI performance measurement approach to assign responsibilities for poor performance is undertaken. Additionally, a solution methodology based on reinforcement learning is proposed for determining optimal replenishment policy in a VMI setting. Using a simulation model, different demand scenarios are generated based on real data from Infineon Technologies AG and compared on the basis of key performance indicators. Results obtained by the proposed method show improved performance than the current replenishment decisions of the company.","PeriodicalId":6692,"journal":{"name":"2020 Winter Simulation Conference (WSC)","volume":"17 1","pages":"1753-1764"},"PeriodicalIF":0.0,"publicationDate":"2020-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89265172","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/WSC48552.2020.9383968
A. Tariq, K. Roosa, G. Chowell
Mathematical modeling provides a powerful analytic framework to investigate the transmission and control of infectious diseases. However, the reliability of the results stemming from modeling studies heavily depend on the validity of assumptions underlying the models as well as the quality of data that is employed to calibrate them. When substantial uncertainty about the epidemiology of newly emerging diseases (e.g. the generation interval, asymptomatic transmission) hampers the application of mechanistic models that incorporate modes of transmission and parameters characterizing the natural history of the disease, phenomenological growth models provide a starting point to make inferences about key transmission parameters, such as the reproduction number, and forecast the trajectory of the epidemic in order to inform public health policies. We describe in detail the methodology and application of three phenomenological growth models, the generalized-growth model, generalized logistic growth model and the Richards model in context of the COVID-19 epidemic in Pakistan.
{"title":"Using Simple Dynamic Analytic Framework To Characterize And Forecast Epidemics","authors":"A. Tariq, K. Roosa, G. Chowell","doi":"10.1109/WSC48552.2020.9383968","DOIUrl":"https://doi.org/10.1109/WSC48552.2020.9383968","url":null,"abstract":"Mathematical modeling provides a powerful analytic framework to investigate the transmission and control of infectious diseases. However, the reliability of the results stemming from modeling studies heavily depend on the validity of assumptions underlying the models as well as the quality of data that is employed to calibrate them. When substantial uncertainty about the epidemiology of newly emerging diseases (e.g. the generation interval, asymptomatic transmission) hampers the application of mechanistic models that incorporate modes of transmission and parameters characterizing the natural history of the disease, phenomenological growth models provide a starting point to make inferences about key transmission parameters, such as the reproduction number, and forecast the trajectory of the epidemic in order to inform public health policies. We describe in detail the methodology and application of three phenomenological growth models, the generalized-growth model, generalized logistic growth model and the Richards model in context of the COVID-19 epidemic in Pakistan.","PeriodicalId":6692,"journal":{"name":"2020 Winter Simulation Conference (WSC)","volume":"74 1","pages":"30-44"},"PeriodicalIF":0.0,"publicationDate":"2020-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78164361","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/WSC48552.2020.9384045
Guanting Chen
We apply the Multi-Level Monte Carlo technique to get an unbiased estimator for the gradient of an optimization function. This procedure requires four exact or noisy function evaluations and produces an unbiased estimator for the gradient at one point. We apply this estimator to a non-convex stochastic programming problem. Under mild assumptions, our algorithm achieves a complexity bound independent of the dimension, compared with the typical one that grows linearly with the dimension.
{"title":"Unbiased Gradient Simulation for Zeroth-Order Optimization","authors":"Guanting Chen","doi":"10.1109/WSC48552.2020.9384045","DOIUrl":"https://doi.org/10.1109/WSC48552.2020.9384045","url":null,"abstract":"We apply the Multi-Level Monte Carlo technique to get an unbiased estimator for the gradient of an optimization function. This procedure requires four exact or noisy function evaluations and produces an unbiased estimator for the gradient at one point. We apply this estimator to a non-convex stochastic programming problem. Under mild assumptions, our algorithm achieves a complexity bound independent of the dimension, compared with the typical one that grows linearly with the dimension.","PeriodicalId":6692,"journal":{"name":"2020 Winter Simulation Conference (WSC)","volume":"11 1","pages":"2947-2959"},"PeriodicalIF":0.0,"publicationDate":"2020-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75026789","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/WSC48552.2020.9383951
Jixiang Qing, Nicolas Knudde, I. Couckuyt, Tom Dhaene, Kohei Shintani
Identifying all designs satisfying a set of constraints is an important part of the engineering design process. With physics-based simulation codes, evaluating the constraints becomes considerable expensive. Active learning can provide an elegant approach to efficiently characterize the feasible region, i.e., the set of feasible designs. Although active learning strategies have been proposed for this task, most of them are dealing with adding just one sample per iteration as opposed to selecting multiple samples per iteration, also known as batch active learning. While this is efficient with respect to the amount of information gained per iteration, it neglects available computation resources. We propose a batch Bayesian active learning technique for feasible region identification by assuming that the constraint function is Lipschitz continuous. In addition, we extend current state-of-the-art batch methods to also handle feasible region identification. Experiments show better performance of the proposed method than the extended batch methods.
{"title":"Batch Bayesian Active Learning For Feasible Region Identification by Local Penalization","authors":"Jixiang Qing, Nicolas Knudde, I. Couckuyt, Tom Dhaene, Kohei Shintani","doi":"10.1109/WSC48552.2020.9383951","DOIUrl":"https://doi.org/10.1109/WSC48552.2020.9383951","url":null,"abstract":"Identifying all designs satisfying a set of constraints is an important part of the engineering design process. With physics-based simulation codes, evaluating the constraints becomes considerable expensive. Active learning can provide an elegant approach to efficiently characterize the feasible region, i.e., the set of feasible designs. Although active learning strategies have been proposed for this task, most of them are dealing with adding just one sample per iteration as opposed to selecting multiple samples per iteration, also known as batch active learning. While this is efficient with respect to the amount of information gained per iteration, it neglects available computation resources. We propose a batch Bayesian active learning technique for feasible region identification by assuming that the constraint function is Lipschitz continuous. In addition, we extend current state-of-the-art batch methods to also handle feasible region identification. Experiments show better performance of the proposed method than the extended batch methods.","PeriodicalId":6692,"journal":{"name":"2020 Winter Simulation Conference (WSC)","volume":"17 1","pages":"2779-2790"},"PeriodicalIF":0.0,"publicationDate":"2020-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75469495","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/WSC48552.2020.9383899
Illhoe Hwang, Y. Jang, Seol Hwang, S. Hong, Hyunsuk Baek, Kyuhun Hahn
We present a current automation trend, robot collaboration intelligence, to control and manage individual industrial robots to collaborate intelligently with advanced AI technologies. To increase the level of flexibility in manufacturing lines and warehouse/distribution centers, flexible agent–type robots such as automated guided vehicles have been adopted in many industries. As information technologies advance, these individual agent robots become smart and the fleet size of agents becomes larger. Robot collaboration intelligence is a newly emerging technology that allows intelligent robots to work in a more effective and efficient way. We introduce this emerging technology with industry cases and provide researchers with new research directions in automation and simulation with AI.
{"title":"Robot Collaboration Intelligence with AI","authors":"Illhoe Hwang, Y. Jang, Seol Hwang, S. Hong, Hyunsuk Baek, Kyuhun Hahn","doi":"10.1109/WSC48552.2020.9383899","DOIUrl":"https://doi.org/10.1109/WSC48552.2020.9383899","url":null,"abstract":"We present a current automation trend, robot collaboration intelligence, to control and manage individual industrial robots to collaborate intelligently with advanced AI technologies. To increase the level of flexibility in manufacturing lines and warehouse/distribution centers, flexible agent–type robots such as automated guided vehicles have been adopted in many industries. As information technologies advance, these individual agent robots become smart and the fleet size of agents becomes larger. Robot collaboration intelligence is a newly emerging technology that allows intelligent robots to work in a more effective and efficient way. We introduce this emerging technology with industry cases and provide researchers with new research directions in automation and simulation with AI.","PeriodicalId":6692,"journal":{"name":"2020 Winter Simulation Conference (WSC)","volume":"13 10","pages":"2649-2658"},"PeriodicalIF":0.0,"publicationDate":"2020-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72598089","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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