Pub Date : 2025-12-08DOI: 10.1109/LCSYS.2025.3641880
Martin T. Köhler;Artemi Makarow;Christian Kirches
We exploit the convergence of graph sequences to some limit object, a graphon, to generate scalable initial estimates for optimal control of large-scale networks. We derive a state error bound under the assumptions of converging graph sequences and initial state functions and, given this bound, we propose an iterative scale-up approach to accelerate the time required for numerical solvers to reach the optimum. The idea is to scale up the optimal control solution for a network dynamical system and use it to initialize the optimal control problem for a larger structurally similar network system. The numerical results demonstrate a significant reduction of the number of optimization iterations and the optimization time for a nonlinear large-scale coupled network.
{"title":"Exploiting Graph Convergence for Accelerated Optimization in Optimal Control of Large-Scale Networks","authors":"Martin T. Köhler;Artemi Makarow;Christian Kirches","doi":"10.1109/LCSYS.2025.3641880","DOIUrl":"https://doi.org/10.1109/LCSYS.2025.3641880","url":null,"abstract":"We exploit the convergence of graph sequences to some limit object, a graphon, to generate scalable initial estimates for optimal control of large-scale networks. We derive a state error bound under the assumptions of converging graph sequences and initial state functions and, given this bound, we propose an iterative scale-up approach to accelerate the time required for numerical solvers to reach the optimum. The idea is to scale up the optimal control solution for a network dynamical system and use it to initialize the optimal control problem for a larger structurally similar network system. The numerical results demonstrate a significant reduction of the number of optimization iterations and the optimization time for a nonlinear large-scale coupled network.","PeriodicalId":37235,"journal":{"name":"IEEE Control Systems Letters","volume":"9 ","pages":"2747-2752"},"PeriodicalIF":2.0,"publicationDate":"2025-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11284794","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145778212","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-08DOI: 10.1109/LCSYS.2025.3641886
Mohammad Khajenejad
We introduce a guaranteed privacy-preserving controller for nonlinear discrete-time systems with bounded uncertainties. Moving beyond stochastic differential privacy, our design offers deterministic privacy through hard bounds on the proximity of set-valued estimates. The solution involves synthesizing a stabilizing controller for a perturbed framer system, where control gains and a privacy-inducing noise factor are co-optimized via semi-definite programming (SDP). This integrated approach ensures both input-to-s tate stable closed-loop dynamics and certified privacy. We also formalize the inherent performance-privacy trade-off by quantifying the accuracy loss due to privacy constraints. Simulations confirm that our method outperforms standard differential privacy techniques.
{"title":"Guaranteed Privacy-Preserving Control of Discrete-Time Systems","authors":"Mohammad Khajenejad","doi":"10.1109/LCSYS.2025.3641886","DOIUrl":"https://doi.org/10.1109/LCSYS.2025.3641886","url":null,"abstract":"We introduce a guaranteed privacy-preserving controller for nonlinear discrete-time systems with bounded uncertainties. Moving beyond stochastic differential privacy, our design offers deterministic privacy through hard bounds on the proximity of set-valued estimates. The solution involves synthesizing a stabilizing controller for a perturbed framer system, where control gains and a privacy-inducing noise factor are co-optimized via semi-definite programming (SDP). This integrated approach ensures both input-to-s tate stable closed-loop dynamics and certified privacy. We also formalize the inherent performance-privacy trade-off by quantifying the accuracy loss due to privacy constraints. Simulations confirm that our method outperforms standard differential privacy techniques.","PeriodicalId":37235,"journal":{"name":"IEEE Control Systems Letters","volume":"9 ","pages":"2759-2764"},"PeriodicalIF":2.0,"publicationDate":"2025-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145778145","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 : 2025-12-08DOI: 10.1109/LCSYS.2025.3641525
Muzaffar Qureshi;Tochukwu E. Ogri;Humberto Ramos;Wanjiku A. Makumi;Zachary I. Bell;Rushikesh Kamalapurkar
This letter considers an autonomous agent that intermittently acquires state measurements to maintain trajectory tracking performance. The objective is to minimize sensing needs by extending periods of sensor-denied operation. A Lyapunov-based adaptive switched systems approach is developed, where the agent uses the intermittently acquired state measurements to learn the system model. The learned system models are then used during sensor-denied intervals to extend their length while maintaining tracking performance. The design uses a modeling error-dependent bound on the duration of the sensor-denied intervals to progressively reduce sensing needs as the modeling error decreases. The effectiveness of the developed technique is verified in a simulation study.
{"title":"A Switched Adaptive Control Approach to Reduce Sensing Needs in Trajectory Tracking Problems","authors":"Muzaffar Qureshi;Tochukwu E. Ogri;Humberto Ramos;Wanjiku A. Makumi;Zachary I. Bell;Rushikesh Kamalapurkar","doi":"10.1109/LCSYS.2025.3641525","DOIUrl":"https://doi.org/10.1109/LCSYS.2025.3641525","url":null,"abstract":"This letter considers an autonomous agent that intermittently acquires state measurements to maintain trajectory tracking performance. The objective is to minimize sensing needs by extending periods of sensor-denied operation. A Lyapunov-based adaptive switched systems approach is developed, where the agent uses the intermittently acquired state measurements to learn the system model. The learned system models are then used during sensor-denied intervals to extend their length while maintaining tracking performance. The design uses a modeling error-dependent bound on the duration of the sensor-denied intervals to progressively reduce sensing needs as the modeling error decreases. The effectiveness of the developed technique is verified in a simulation study.","PeriodicalId":37235,"journal":{"name":"IEEE Control Systems Letters","volume":"9 ","pages":"2777-2782"},"PeriodicalIF":2.0,"publicationDate":"2025-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145778277","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 : 2025-12-08DOI: 10.1109/LCSYS.2025.3641881
Aakash Khandelwal;Ranjan Mukherjee
Planar juggling of a devil-stick using impulsive inputs is addressed using the concept of discrete virtual holonomic constraints (DVHC). The location of the center-of-mass of the devil-stick is specified in terms of its orientation at the discrete instants when impulsive control inputs are applied. The discrete zero dynamics (DZD) resulting from the choice of DVHC provides conditions for stable juggling. A control design that enforces the DVHC and an orbit stabilizing controller are presented. The approach is validated in simulation.
{"title":"Planar Juggling of a Devil-Stick Using Discrete VHCs","authors":"Aakash Khandelwal;Ranjan Mukherjee","doi":"10.1109/LCSYS.2025.3641881","DOIUrl":"https://doi.org/10.1109/LCSYS.2025.3641881","url":null,"abstract":"Planar juggling of a devil-stick using impulsive inputs is addressed using the concept of discrete virtual holonomic constraints (DVHC). The location of the center-of-mass of the devil-stick is specified in terms of its orientation at the discrete instants when impulsive control inputs are applied. The discrete zero dynamics (DZD) resulting from the choice of DVHC provides conditions for stable juggling. A control design that enforces the DVHC and an orbit stabilizing controller are presented. The approach is validated in simulation.","PeriodicalId":37235,"journal":{"name":"IEEE Control Systems Letters","volume":"9 ","pages":"2753-2758"},"PeriodicalIF":2.0,"publicationDate":"2025-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145778138","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 : 2025-12-05DOI: 10.1109/LCSYS.2025.3641127
Kooktae Lee;Ethan Brook
Multi-agent systems are widely used for area coverage tasks in applications such as search-and-rescue, environmental monitoring, and precision agriculture. Achieving non-uniform coverage, where certain regions are prioritized, requires coordinating agents while accounting for dynamic and communication constraints. Existing density-driven methods effectively distribute agents according to a reference density but typically do not guarantee connectivity, which can lead to disconnected agents and degraded coverage in practical deployments. This letter presents a connectivity-preserving approach within the Density-Driven Optimal Control (D2OC) framework. The coverage problem, expressed via the Wasserstein distance between agent distributions and a reference density, is formulated as a quadratic program. Communication constraints are incorporated through a smooth penalty function, ensuring strict convexity and global optimality while naturally maintaining inter-agent connectivity without rigid formations. Simulation results demonstrate that the proposed method effectively keeps agents within communication range, improving coverage quality and convergence speed compared to methods without explicit connectivity enforcement.
{"title":"Connectivity-Preserving Multi-Agent Area Coverage via Density-Driven Optimal Control (D²OC)","authors":"Kooktae Lee;Ethan Brook","doi":"10.1109/LCSYS.2025.3641127","DOIUrl":"https://doi.org/10.1109/LCSYS.2025.3641127","url":null,"abstract":"Multi-agent systems are widely used for area coverage tasks in applications such as search-and-rescue, environmental monitoring, and precision agriculture. Achieving non-uniform coverage, where certain regions are prioritized, requires coordinating agents while accounting for dynamic and communication constraints. Existing density-driven methods effectively distribute agents according to a reference density but typically do not guarantee connectivity, which can lead to disconnected agents and degraded coverage in practical deployments. This letter presents a connectivity-preserving approach within the Density-Driven Optimal Control (D2OC) framework. The coverage problem, expressed via the Wasserstein distance between agent distributions and a reference density, is formulated as a quadratic program. Communication constraints are incorporated through a smooth penalty function, ensuring strict convexity and global optimality while naturally maintaining inter-agent connectivity without rigid formations. Simulation results demonstrate that the proposed method effectively keeps agents within communication range, improving coverage quality and convergence speed compared to methods without explicit connectivity enforcement.","PeriodicalId":37235,"journal":{"name":"IEEE Control Systems Letters","volume":"9 ","pages":"2723-2728"},"PeriodicalIF":2.0,"publicationDate":"2025-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145729416","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 letter proposes a novel sufficient condition for the leaky-integrator echo state network controller design based on Incremental Input-to-State Stability criteria for discrete-time systems. The controller design conditions are derived via Linear Matrix Inequalities. A novel leaky-integrator echo state network-based controller structure is presented, demonstrating enhanced system performance. The simulation results highlight the performance improvements achieved by adopting the proposed controller structure.
{"title":"Echo State Network Controller Design for a Class of Leaky-Integrator Nonlinear Systems","authors":"Hao Deng;Cristina Stoica;Daniel Ossmann;Mohammed Chadli","doi":"10.1109/LCSYS.2025.3641125","DOIUrl":"https://doi.org/10.1109/LCSYS.2025.3641125","url":null,"abstract":"This letter proposes a novel sufficient condition for the leaky-integrator echo state network controller design based on Incremental Input-to-State Stability criteria for discrete-time systems. The controller design conditions are derived via Linear Matrix Inequalities. A novel leaky-integrator echo state network-based controller structure is presented, demonstrating enhanced system performance. The simulation results highlight the performance improvements achieved by adopting the proposed controller structure.","PeriodicalId":37235,"journal":{"name":"IEEE Control Systems Letters","volume":"9 ","pages":"2693-2698"},"PeriodicalIF":2.0,"publicationDate":"2025-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145729316","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 : 2025-12-05DOI: 10.1109/LCSYS.2025.3641128
Abdelrahman Ramadan;Sidney Givigi
We develop a learning-based framework for constructing shrinking disturbance-invariant tubes under state- and input-dependent uncertainty, intended as a building block for tube Model Predictive Control (MPC), and certify safety via a lifted, isotone (order-preserving) fixed-point map. Gaussian Process (GP) posteriors become $text {(}1-alpha text {)}$ credible ellipsoids, then polytopic outer sets for deterministic set operations. A two-time-scale scheme separates learning epochs, where these polytopes are frozen, from an inner, outside-in iteration that converges to a compact fixed point $Z^{star }subseteq {mathcal {G}}$ ; its state projection is RPI for the plant. As data accumulate, disturbance polytopes tighten, and the associated tubes nest monotonically, resolving the circular dependence between the set to be verified and the disturbance model while preserving hard constraints. A double-integrator study illustrates shrinking tube cross-sections in data-rich regions while maintaining invariance.
{"title":"Learning-Based Shrinking Disturbance-Invariant Tubes for State- and Input-Dependent Uncertainty","authors":"Abdelrahman Ramadan;Sidney Givigi","doi":"10.1109/LCSYS.2025.3641128","DOIUrl":"https://doi.org/10.1109/LCSYS.2025.3641128","url":null,"abstract":"We develop a learning-based framework for constructing shrinking disturbance-invariant tubes under state- and input-dependent uncertainty, intended as a building block for tube Model Predictive Control (MPC), and certify safety via a lifted, isotone (order-preserving) fixed-point map. Gaussian Process (GP) posteriors become <inline-formula> <tex-math>$text {(}1-alpha text {)}$ </tex-math></inline-formula> credible ellipsoids, then polytopic outer sets for deterministic set operations. A two-time-scale scheme separates learning epochs, where these polytopes are frozen, from an inner, outside-in iteration that converges to a compact fixed point <inline-formula> <tex-math>$Z^{star }subseteq {mathcal {G}}$ </tex-math></inline-formula>; its state projection is RPI for the plant. As data accumulate, disturbance polytopes tighten, and the associated tubes nest monotonically, resolving the circular dependence between the set to be verified and the disturbance model while preserving hard constraints. A double-integrator study illustrates shrinking tube cross-sections in data-rich regions while maintaining invariance.","PeriodicalId":37235,"journal":{"name":"IEEE Control Systems Letters","volume":"9 ","pages":"2699-2704"},"PeriodicalIF":2.0,"publicationDate":"2025-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145729388","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 : 2025-12-03DOI: 10.1109/LCSYS.2025.3640066
Yi-Ming Kao;Parham Razaei;Sina Masoumi Shahrbabak;Jeremy Alanano Pepino;Ian Sebastian Kirk Shogren;Yang Wang;Andrew Tomas Reisner;Jin-Oh Hahn
Blood pressure (BP) management is a critical component of blood transfusion, but no mature technology capable of predicting BP response to blood transfusion exists. This letter concerns the development and preliminary in vivo testing of a BP prediction method applicable to hemorrhage and blood transfusion. Key obstacles are (i) large inter-individual variability in the BP response to blood transfusion, (ii) unknown hemorrhage, and (iii) input/state-dependent observability. To cope with these challenges, we developed a multi-modal sequential inference-enabled BP prediction method built upon a mathematical model of patient physiology parameterized by population-informed prior. The method infers patient-specific physiological state and hemorrhage, and uses them to predict future BP in a patient receiving blood transfusion. The in vivo testing of the method using the data collected from large animals undergoing hemorrhage and blood transfusion showed that it could adequately predict mean arterial BP with median absolute errors for 5-min and 15-min predictions of 3.1 mmHg and 7.4 mmHg as well as adequately infer physiological state and hemorrhage: all the hemorrhage events were detected with <3.5 min delay, with median F1 score of 85%. In sum, the prediction of BP response to blood transfusion may be feasible, even in the presence of unknown hemorrhage.
{"title":"Blood Pressure Prediction During Blood Transfusion: A Population-Informed Multi-Modal Sequential Inference Approach","authors":"Yi-Ming Kao;Parham Razaei;Sina Masoumi Shahrbabak;Jeremy Alanano Pepino;Ian Sebastian Kirk Shogren;Yang Wang;Andrew Tomas Reisner;Jin-Oh Hahn","doi":"10.1109/LCSYS.2025.3640066","DOIUrl":"https://doi.org/10.1109/LCSYS.2025.3640066","url":null,"abstract":"Blood pressure (BP) management is a critical component of blood transfusion, but no mature technology capable of predicting BP response to blood transfusion exists. This letter concerns the development and preliminary in vivo testing of a BP prediction method applicable to hemorrhage and blood transfusion. Key obstacles are (i) large inter-individual variability in the BP response to blood transfusion, (ii) unknown hemorrhage, and (iii) input/state-dependent observability. To cope with these challenges, we developed a multi-modal sequential inference-enabled BP prediction method built upon a mathematical model of patient physiology parameterized by population-informed prior. The method infers patient-specific physiological state and hemorrhage, and uses them to predict future BP in a patient receiving blood transfusion. The in vivo testing of the method using the data collected from large animals undergoing hemorrhage and blood transfusion showed that it could adequately predict mean arterial BP with median absolute errors for 5-min and 15-min predictions of 3.1 mmHg and 7.4 mmHg as well as adequately infer physiological state and hemorrhage: all the hemorrhage events were detected with <3.5 min delay, with median F1 score of 85%. In sum, the prediction of BP response to blood transfusion may be feasible, even in the presence of unknown hemorrhage.","PeriodicalId":37235,"journal":{"name":"IEEE Control Systems Letters","volume":"9 ","pages":"2711-2716"},"PeriodicalIF":2.0,"publicationDate":"2025-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145778141","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 : 2025-12-03DOI: 10.1109/LCSYS.2025.3640065
Tuo Yang;Zhisheng Duan;Yunxiao Ren
This letter investigates the stability problem of the Consensus-on-Information-based Distributed Kalman Filter (CI-DKF), a widely applied method for state estimation in sensor networks. Unlike previous studies that often assume the system matrix A has full rank, this letter demonstrates that this assumption can be relaxed while still maintaining the stability. To justify that, we provide a more refined characterization of the state estimate dynamics, decomposing it into components associated with the nonzero and zero modes of the original system, and establishing the corresponding upper bounds for each part. Finally, the results are verified via numerical simulations.
{"title":"Stability of the Consensus-on-Information-Based Distributed Kalman Filter With Rank-Deficient Systems","authors":"Tuo Yang;Zhisheng Duan;Yunxiao Ren","doi":"10.1109/LCSYS.2025.3640065","DOIUrl":"https://doi.org/10.1109/LCSYS.2025.3640065","url":null,"abstract":"This letter investigates the stability problem of the Consensus-on-Information-based Distributed Kalman Filter (CI-DKF), a widely applied method for state estimation in sensor networks. Unlike previous studies that often assume the system matrix A has full rank, this letter demonstrates that this assumption can be relaxed while still maintaining the stability. To justify that, we provide a more refined characterization of the state estimate dynamics, decomposing it into components associated with the nonzero and zero modes of the original system, and establishing the corresponding upper bounds for each part. Finally, the results are verified via numerical simulations.","PeriodicalId":37235,"journal":{"name":"IEEE Control Systems Letters","volume":"9 ","pages":"2729-2734"},"PeriodicalIF":2.0,"publicationDate":"2025-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145729340","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 : 2025-12-03DOI: 10.1109/LCSYS.2025.3640534
Brendon G. Anderson
In this letter, we consider finite-strategy approximations of infinite-strategy evolutionary games. We prove that such approximations converge to the true dynamics over finite-time intervals, under mild regularity conditions which are satisfied by classical examples, e.g., the replicator dynamics. We identify and formalize novel characteristics in evolutionary games: choice mobility, and its complement choice paralysis. Choice mobility is shown to be a key sufficient condition for the long-time limiting behavior of finite-strategy approximations to coincide with that of the true infinite-strategy game. An illustrative example is constructed to showcase how choice paralysis may lead to the infinite-strategy game getting “stuck,” even though every finite approximation converges to equilibrium.
{"title":"Choice Paralysis in Evolutionary Games","authors":"Brendon G. Anderson","doi":"10.1109/LCSYS.2025.3640534","DOIUrl":"https://doi.org/10.1109/LCSYS.2025.3640534","url":null,"abstract":"In this letter, we consider finite-strategy approximations of infinite-strategy evolutionary games. We prove that such approximations converge to the true dynamics over finite-time intervals, under mild regularity conditions which are satisfied by classical examples, e.g., the replicator dynamics. We identify and formalize novel characteristics in evolutionary games: choice mobility, and its complement choice paralysis. Choice mobility is shown to be a key sufficient condition for the long-time limiting behavior of finite-strategy approximations to coincide with that of the true infinite-strategy game. An illustrative example is constructed to showcase how choice paralysis may lead to the infinite-strategy game getting “stuck,” even though every finite approximation converges to equilibrium.","PeriodicalId":37235,"journal":{"name":"IEEE Control Systems Letters","volume":"9 ","pages":"2717-2722"},"PeriodicalIF":2.0,"publicationDate":"2025-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145729311","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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