Pub Date : 2025-01-01DOI: 10.1016/j.ifacol.2025.06.025
Lara Hauser , John Bagterp Jørgensen , Heiko Peuscher
We provide an open-source implementation of the NMPC algorithm for closed-loop insulin therapy presented by Hovorka et. al. in the seminal paper from 2004. An extended Kalman filter is added as observer, since specifics on state estimation have been omitted in the original paper. The controller is now part of the TypeScript-based closed-loop diabetes simulator LT1, which also comprises a library of models describing CGM sensors, infusion pumps, and patient physiology. The controller can thus be extensively tested in various closed-loop configurations. Also, the graphical user interface enables its integration in educational tools.
{"title":"An Open-Source Browser-Based Nonlinear Model Predictive Controller for Type 1 Diabetes","authors":"Lara Hauser , John Bagterp Jørgensen , Heiko Peuscher","doi":"10.1016/j.ifacol.2025.06.025","DOIUrl":"10.1016/j.ifacol.2025.06.025","url":null,"abstract":"<div><div>We provide an open-source implementation of the NMPC algorithm for closed-loop insulin therapy presented by Hovorka et. al. in the seminal paper from 2004. An extended Kalman filter is added as observer, since specifics on state estimation have been omitted in the original paper. The controller is now part of the TypeScript-based closed-loop diabetes simulator LT1, which also comprises a library of models describing CGM sensors, infusion pumps, and patient physiology. The controller can thus be extensively tested in various closed-loop configurations. Also, the graphical user interface enables its integration in educational tools.</div></div>","PeriodicalId":37894,"journal":{"name":"IFAC-PapersOnLine","volume":"59 2","pages":"Pages 143-148"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144588475","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 : 2025-01-01DOI: 10.1016/j.ifacol.2025.06.003
Giada Lops, Vito Andrea Racanelli, Gioacchino Manfredi, Luca De Cicco, Saverio Mascolo
This study investigates the use of Reinforcement Learning (RL) to optimize insulin infusion in Automated Insulin Delivery (AID) systems. The Deep Deterministic Policy Gradient (DDPG) algorithm is evaluated as a potential control strategy and compared to Model Predictive Control (MPC) and PID controllers using a highly realistic in silico simulation model. Simulations for adult Type 1 Diabetes (T1D) patients demonstrate that the DDPG controller effectively reduces hypo- and hyperglycemic episodes and improves glucose stability.
{"title":"A Deep Deterministic Policy Gradient control algorithm for Automatic Insulin Delivery","authors":"Giada Lops, Vito Andrea Racanelli, Gioacchino Manfredi, Luca De Cicco, Saverio Mascolo","doi":"10.1016/j.ifacol.2025.06.003","DOIUrl":"10.1016/j.ifacol.2025.06.003","url":null,"abstract":"<div><div>This study investigates the use of Reinforcement Learning (RL) to optimize insulin infusion in Automated Insulin Delivery (AID) systems. The Deep Deterministic Policy Gradient (DDPG) algorithm is evaluated as a potential control strategy and compared to Model Predictive Control (MPC) and PID controllers using a highly realistic in silico simulation model. Simulations for adult Type 1 Diabetes (T1D) patients demonstrate that the DDPG controller effectively reduces hypo- and hyperglycemic episodes and improves glucose stability.</div></div>","PeriodicalId":37894,"journal":{"name":"IFAC-PapersOnLine","volume":"59 2","pages":"Pages 13-18"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144588566","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 : 2025-01-01DOI: 10.1016/j.ifacol.2025.06.006
Matteo Ragni , Paolo A. Mongini , Lalo Magni , Chiara Toffanin
This study introduces a personalized Model Predictive Control (MPC) strategy for managing type 1 diabetes, specifically addressing intra-day variability in glucose regulation. The proposed method uses a soft-switching mechanism combining multiple MPCs, each optimized for distinct day periods (breakfast, lunch, and dinner). Patient-specific models were identified using an enhanced impulse response technique, featuring a novel clinically-based cost function to better capture circadian dynamics. A switching mechanism ensures smooth transitions between controllers, minimizing disruptions. Performance was evaluated using the FDA-approved UVA/Padova simulator, demonstrating a 20% improvement in model fit and enhanced control performance compared to the state-of-art approach. The strategy consistently prevented hypoglycemia while improving time-in-target metrics.
{"title":"Multiple Constrained MPCs for glucose regulation in Type 1 Diabetes1","authors":"Matteo Ragni , Paolo A. Mongini , Lalo Magni , Chiara Toffanin","doi":"10.1016/j.ifacol.2025.06.006","DOIUrl":"10.1016/j.ifacol.2025.06.006","url":null,"abstract":"<div><div>This study introduces a personalized Model Predictive Control (MPC) strategy for managing type 1 diabetes, specifically addressing intra-day variability in glucose regulation. The proposed method uses a soft-switching mechanism combining multiple MPCs, each optimized for distinct day periods (breakfast, lunch, and dinner). Patient-specific models were identified using an enhanced impulse response technique, featuring a novel clinically-based cost function to better capture circadian dynamics. A switching mechanism ensures smooth transitions between controllers, minimizing disruptions. Performance was evaluated using the FDA-approved UVA/Padova simulator, demonstrating a 20% improvement in model fit and enhanced control performance compared to the state-of-art approach. The strategy consistently prevented hypoglycemia while improving time-in-target metrics.</div></div>","PeriodicalId":37894,"journal":{"name":"IFAC-PapersOnLine","volume":"59 2","pages":"Pages 31-36"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144588569","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}
This paper presents a comprehensive investigation into the capability of Large Language Models (LLMs) to successfully complete a semester-long undergraduate control systems course. Through evaluation of 115 course deliverables, we assess LLM performance using ChatGPT under a “minimal effort” protocol that simulates realistic student usage patterns. The investigation employs a rigorous testing methodology across multiple assessment formats, from auto-graded multiple choice questions to complex Python programming tasks and long-form analytical writing. Our analysis provides quantitative insights into AI’s strengths and limitations in handling mathematical formulations, coding challenges, and theoretical concepts in control systems engineering. The LLM achieved a B-grade performance (82.24%), approaching but not exceeding the class average (84.99%), with strongest results in structured assignments and greatest limitations in open-ended projects. The findings inform discussions about course design adaptation in response to AI advancement, moving beyond simple prohibition towards thoughtful integration of these tools in engineering education. Additional materials including syllabus, examination papers, design projects, and example responses can be found at the project website: https://gradegpt.github.io.
{"title":"The Lazy Student’s Dream: ChatGPT Passing an Engineering Course on Its Own","authors":"Gokul Puthumanaillam, Timothy Bretl, Melkior Ornik","doi":"10.1016/j.ifacol.2025.08.049","DOIUrl":"10.1016/j.ifacol.2025.08.049","url":null,"abstract":"<div><div>This paper presents a comprehensive investigation into the capability of Large Language Models (LLMs) to successfully complete a semester-long undergraduate control systems course. Through evaluation of 115 course deliverables, we assess LLM performance using ChatGPT under a “minimal effort” protocol that simulates realistic student usage patterns. The investigation employs a rigorous testing methodology across multiple assessment formats, from auto-graded multiple choice questions to complex Python programming tasks and long-form analytical writing. Our analysis provides quantitative insights into AI’s strengths and limitations in handling mathematical formulations, coding challenges, and theoretical concepts in control systems engineering. The LLM achieved a B-grade performance (82.24%), approaching but not exceeding the class average (84.99%), with strongest results in structured assignments and greatest limitations in open-ended projects. The findings inform discussions about course design adaptation in response to AI advancement, moving beyond simple prohibition towards thoughtful integration of these tools in engineering education. Additional materials including syllabus, examination papers, design projects, and example responses can be found at the project website: <span><span>https://gradegpt.github.io</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":37894,"journal":{"name":"IFAC-PapersOnLine","volume":"59 7","pages":"Pages 213-218"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144989757","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 : 2025-01-01DOI: 10.1016/j.ifacol.2025.08.056
Zhen Shen , Hongyu Li , Jing Yang , Xiaojun Wang , Daniel Horti , Martin Ferenc Dömény , Szatmáry Sára , Adina Chotbaeva , Jimei Ma , Zsombor Zrubka , Fei-Yue Wang
This paper presents a novel educational framework that integrates additive manufacturing (3D printing), social manufacturing platforms, large language models (LLMs), iSTREAMS, and iCDIOS to bridge academic learning, industrial practice, and research innovation. The framework adopts a “learning-by-doing” approach, where students engage in project-driven tasks that span 2D-to-3D modeling, material property optimization, and collaborative problem-solving. By leveraging AI tools for design iteration and real-time feedback, students develop competencies in critical thinking, technical execution, and interdisciplinary collaboration. The framework also emphasizes dynamic assessment mechanisms that combine human evaluation with AI-driven analytics to provide personalized feedback and inform curriculum updates. Through this integrated approach, students are equipped with the skills necessary to navigate the complexities of modern Cyber-Physical-Social Systems (CPSS)-driven industries. A case study is provided to demonstrate the framework’s implementation and its potential to foster interdisciplinary control research and curriculum development, highlighting the synergy between iSTREAMS and iCDIOS in enhancing educational outcomes.
{"title":"Interdisciplinary Control Research and Curriculum Development in CPSS: A Case Study with 3D Printing and Social Manufacturing","authors":"Zhen Shen , Hongyu Li , Jing Yang , Xiaojun Wang , Daniel Horti , Martin Ferenc Dömény , Szatmáry Sára , Adina Chotbaeva , Jimei Ma , Zsombor Zrubka , Fei-Yue Wang","doi":"10.1016/j.ifacol.2025.08.056","DOIUrl":"10.1016/j.ifacol.2025.08.056","url":null,"abstract":"<div><div>This paper presents a novel educational framework that integrates additive manufacturing (3D printing), social manufacturing platforms, large language models (LLMs), iSTREAMS, and iCDIOS to bridge academic learning, industrial practice, and research innovation. The framework adopts a “learning-by-doing” approach, where students engage in project-driven tasks that span 2D-to-3D modeling, material property optimization, and collaborative problem-solving. By leveraging AI tools for design iteration and real-time feedback, students develop competencies in critical thinking, technical execution, and interdisciplinary collaboration. The framework also emphasizes dynamic assessment mechanisms that combine human evaluation with AI-driven analytics to provide personalized feedback and inform curriculum updates. Through this integrated approach, students are equipped with the skills necessary to navigate the complexities of modern Cyber-Physical-Social Systems (CPSS)-driven industries. A case study is provided to demonstrate the framework’s implementation and its potential to foster interdisciplinary control research and curriculum development, highlighting the synergy between iSTREAMS and iCDIOS in enhancing educational outcomes.</div></div>","PeriodicalId":37894,"journal":{"name":"IFAC-PapersOnLine","volume":"59 7","pages":"Pages 255-260"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144989763","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 : 2025-01-01DOI: 10.1016/j.ifacol.2025.08.023
Gabriel Conde , Luis de la Torre , Raquel Dormido , Sebastián Dormido
Laboratory-based learning plays a crucial role in engineering education, especially in fields like automation and control. However, traditional industrial training labs often face challenges in terms of both realism and accessibility, limiting students’ exposure to real-world systems. In this paper, we present the transformation of a complex industrial process control plant into a fully remote-operable laboratory. By integrating new advanced hardware elements, we have enabled remote monitoring and control of the system. These modifications offer students industry-level realism and hands-on experience in industrial automation, accessible from any location. This work demonstrates the potential of the industrial remote lab to enhance engineering education by providing a flexible and realistic learning environment.
{"title":"Adapting an Industrial Control Laboratory for Remote Access⁎","authors":"Gabriel Conde , Luis de la Torre , Raquel Dormido , Sebastián Dormido","doi":"10.1016/j.ifacol.2025.08.023","DOIUrl":"10.1016/j.ifacol.2025.08.023","url":null,"abstract":"<div><div>Laboratory-based learning plays a crucial role in engineering education, especially in fields like automation and control. However, traditional industrial training labs often face challenges in terms of both realism and accessibility, limiting students’ exposure to real-world systems. In this paper, we present the transformation of a complex industrial process control plant into a fully remote-operable laboratory. By integrating new advanced hardware elements, we have enabled remote monitoring and control of the system. These modifications offer students industry-level realism and hands-on experience in industrial automation, accessible from any location. This work demonstrates the potential of the industrial remote lab to enhance engineering education by providing a flexible and realistic learning environment.</div></div>","PeriodicalId":37894,"journal":{"name":"IFAC-PapersOnLine","volume":"59 7","pages":"Pages 60-65"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144989879","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 : 2025-01-01DOI: 10.1016/j.ifacol.2025.08.085
Jiaqi Hu , Jie Qi , Jing Zhang
Control of distributed parameter systems affected by delays is a challenging task, particularly when the delays depend on spatial variables. The idea of integrating analytical control theory with learning-based control within a unified control scheme is becoming increasingly promising and advantageous. In this paper, we design a integrated control strategy combining PDE backstepping and deep reinforcement learning (RL) for an unstable first-order hyperbolic PDE with spatially-varying delays. This method eliminates extra constraint on the delay function required for the backstepping design. We embed a DeepONet, trained to learn the backstepping controller, into a soft actor-critic (SAC) framework as a feature extractor for both the actor and critic networks. Simulation results demonstrate that the proposed algorithm outperforms standard SAC in reducing steady-state error and surpasses the backstepping controller in mitigating overshoot.
{"title":"Neural Operator based Reinforcement Learning for Control of first-order PDEs with Spatially-Varying State Delay⁎","authors":"Jiaqi Hu , Jie Qi , Jing Zhang","doi":"10.1016/j.ifacol.2025.08.085","DOIUrl":"10.1016/j.ifacol.2025.08.085","url":null,"abstract":"<div><div>Control of distributed parameter systems affected by delays is a challenging task, particularly when the delays depend on spatial variables. The idea of integrating analytical control theory with learning-based control within a unified control scheme is becoming increasingly promising and advantageous. In this paper, we design a integrated control strategy combining PDE backstepping and deep reinforcement learning (RL) for an unstable first-order hyperbolic PDE with spatially-varying delays. This method eliminates extra constraint on the delay function required for the backstepping design. We embed a DeepONet, trained to learn the backstepping controller, into a soft actor-critic (SAC) framework as a feature extractor for both the actor and critic networks. Simulation results demonstrate that the proposed algorithm outperforms standard SAC in reducing steady-state error and surpasses the backstepping controller in mitigating overshoot.</div></div>","PeriodicalId":37894,"journal":{"name":"IFAC-PapersOnLine","volume":"59 8","pages":"Pages 167-172"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144913469","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 : 2025-01-01DOI: 10.1016/j.ifacol.2025.08.096
Yanan Sun , Qiong Zhang
This work studies the boundary feedback stabilization for an elastic thin plate, where one part of its boundary is clamped and the remaining free part is attached to a rigid body. The boundary feedback controls are applied to the free boundary. Given that the boundary of the domain may have corners, we introduce a proper abstract framework to analyze the well-posedness and estimate the resolvent. When the geometric control condition (GCC) holds, we prove a polynomial energy decay rate of t-1/2. For cases where GCC does not hold, we analyze the system on a rectangular domain with control applied to only one edge and obtain a slower decay rate t-1/3.
{"title":"Polynomial Stability of an Elastic Plate with Dynamical Boundary Control","authors":"Yanan Sun , Qiong Zhang","doi":"10.1016/j.ifacol.2025.08.096","DOIUrl":"10.1016/j.ifacol.2025.08.096","url":null,"abstract":"<div><div>This work studies the boundary feedback stabilization for an elastic thin plate, where one part of its boundary is clamped and the remaining free part is attached to a rigid body. The boundary feedback controls are applied to the free boundary. Given that the boundary of the domain may have corners, we introduce a proper abstract framework to analyze the well-posedness and estimate the resolvent. When the geometric control condition (GCC) holds, we prove a polynomial energy decay rate of t<sup>-1/2</sup>. For cases where GCC does not hold, we analyze the system on a rectangular domain with control applied to only one edge and obtain a slower decay rate t<sup>-1/3</sup>.</div></div>","PeriodicalId":37894,"journal":{"name":"IFAC-PapersOnLine","volume":"59 8","pages":"Pages 232-236"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144913521","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 : 2025-01-01DOI: 10.1016/j.ifacol.2025.08.064
Ming Xu , Yi Cheng , Xin Wang , Yuhu Wu
This article solves the boundary stabilization problem of variable coefficient Rayleigh beams by using nonlinear boundary feedback controls. The feedback mechanism incorporates a nonlinear function that adheres to a sector condition, thus encompassing a diverse array of nonlinear feedback scenarios. The well-posedness of the closed-loop system is completed by applying nonlinear semigroup theory. The exponential stability of the energy function of a closed-loop system is demonstrated by using the integral multiplier method.
{"title":"Stabilization for Variable Coefficients Rayleigh Beam Systems under Nonlinear Boundary Controls","authors":"Ming Xu , Yi Cheng , Xin Wang , Yuhu Wu","doi":"10.1016/j.ifacol.2025.08.064","DOIUrl":"10.1016/j.ifacol.2025.08.064","url":null,"abstract":"<div><div>This article solves the boundary stabilization problem of variable coefficient Rayleigh beams by using nonlinear boundary feedback controls. The feedback mechanism incorporates a nonlinear function that adheres to a sector condition, thus encompassing a diverse array of nonlinear feedback scenarios. The well-posedness of the closed-loop system is completed by applying nonlinear semigroup theory. The exponential stability of the energy function of a closed-loop system is demonstrated by using the integral multiplier method.</div></div>","PeriodicalId":37894,"journal":{"name":"IFAC-PapersOnLine","volume":"59 8","pages":"Pages 43-48"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144913605","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 : 2025-01-01DOI: 10.1016/j.ifacol.2025.08.073
Alexander Schaum , Stefan Koch
This paper addresses the design of a nonlinear observer for an undamped wave equation with distributed disturbance and pointwise position measurement at the boundary. Using the sliding-mode based robust exact differentiator, the velocity at the boundary is estimated. Based on this estimate, a standard Luenberger observer is designed for the nominal, i.e., undisturbed system, and robust convergence is ensured in the presence of bounded distributed disturbances that are constant in time, by adequately combining this observer with an algebraic correction. The proposed design approach achieves exponential convergence of the observer and, for special observer gains, even ensures finite-time convergence. The approach is illustrated using numerical simulations for a representative case study.
{"title":"Nonlinear Observer Design for an Undamped Wave Equation with Distributed Disturbance","authors":"Alexander Schaum , Stefan Koch","doi":"10.1016/j.ifacol.2025.08.073","DOIUrl":"10.1016/j.ifacol.2025.08.073","url":null,"abstract":"<div><div>This paper addresses the design of a nonlinear observer for an undamped wave equation with distributed disturbance and pointwise position measurement at the boundary. Using the sliding-mode based robust exact differentiator, the velocity at the boundary is estimated. Based on this estimate, a standard Luenberger observer is designed for the nominal, i.e., undisturbed system, and robust convergence is ensured in the presence of bounded distributed disturbances that are constant in time, by adequately combining this observer with an algebraic correction. The proposed design approach achieves exponential convergence of the observer and, for special observer gains, even ensures finite-time convergence. The approach is illustrated using numerical simulations for a representative case study.</div></div>","PeriodicalId":37894,"journal":{"name":"IFAC-PapersOnLine","volume":"59 8","pages":"Pages 96-101"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144913636","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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