Pub Date : 2026-03-10DOI: 10.1109/tcyb.2026.3668815
Jiajin He,Min Xiao,Yang Liu,Wenwu Yu,Tingwen Huang,Ju H Park
The study of dynamics in complex systems has increasingly incorporated higher order interactions, which capture the collective influence among three or more units, extending beyond traditional pairwise connections. Although such interactions are observed in biological neural networks, their precise role in shaping network dynamics and the feasibility of controlling these dynamics remain unclear. This article proposes a controlled diffusion hub neural network model that explicitly includes higher order interactions. To regulate the resulting spatiotemporal dynamics, a cross-node associated delayed feedback control (CNADFC) method is further introduced. Our analysis establishes conditions for local stability, Turing instability, and Hopf bifurcation. We show that while Turing instability cannot arise, spatially periodic patterns emerge under specific parametric conditions. Numerical simulations confirm these theoretical findings and highlight the pronounced effects of self-feedback, control, and first-order interaction on stability and dynamic behaviors; in contrast, higher order interactions exert a comparatively modest influence. Furthermore, simulations illustrate how the CNADFC method can effectively optimize spatiotemporal dynamics. This work advances the understanding of diffusion neural network behavior under complex higher order interaction and provides a reference for the effective control of such networks.
{"title":"Higher Order Interactions in Hub Neural Networks: Spatiotemporal Dynamics Reshaping and Control.","authors":"Jiajin He,Min Xiao,Yang Liu,Wenwu Yu,Tingwen Huang,Ju H Park","doi":"10.1109/tcyb.2026.3668815","DOIUrl":"https://doi.org/10.1109/tcyb.2026.3668815","url":null,"abstract":"The study of dynamics in complex systems has increasingly incorporated higher order interactions, which capture the collective influence among three or more units, extending beyond traditional pairwise connections. Although such interactions are observed in biological neural networks, their precise role in shaping network dynamics and the feasibility of controlling these dynamics remain unclear. This article proposes a controlled diffusion hub neural network model that explicitly includes higher order interactions. To regulate the resulting spatiotemporal dynamics, a cross-node associated delayed feedback control (CNADFC) method is further introduced. Our analysis establishes conditions for local stability, Turing instability, and Hopf bifurcation. We show that while Turing instability cannot arise, spatially periodic patterns emerge under specific parametric conditions. Numerical simulations confirm these theoretical findings and highlight the pronounced effects of self-feedback, control, and first-order interaction on stability and dynamic behaviors; in contrast, higher order interactions exert a comparatively modest influence. Furthermore, simulations illustrate how the CNADFC method can effectively optimize spatiotemporal dynamics. This work advances the understanding of diffusion neural network behavior under complex higher order interaction and provides a reference for the effective control of such networks.","PeriodicalId":13112,"journal":{"name":"IEEE Transactions on Cybernetics","volume":"7 1","pages":""},"PeriodicalIF":11.8,"publicationDate":"2026-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147383566","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
For stochastic sampled-data systems characterized by unknown nonlinear dynamics (SSDUNSs), it is a great challenge to design an appropriate controller to achieve stable tracking control. In this article, a perceptron-based adaptive model predictive control (PAMPC) scheme is developed for SSDUNSs with multiple discrete stochastic sampling intervals. The activation frequency of each sampling interval can be statistically obtained, which can be described by the categorical distribution. First, a PAMPC structure is developed for the tracking control of SSDUNS. A perceptron with a cost function is designed to incorporate the exploration of the environmental state, encompassing the sampling interval, predictive error, and tracking error. Second, an adaptive predictive horizon (APH) is incorporated into the predictive model to provide the necessary predicting information for the controller. APH is adjusted based on the activation frequency of stochastic sampling intervals. Third, an optimal control problem (OCP) combined with the penalty of the perceptron is designed to stabilize SSDUNS. Then, the control law can be computed to achieve the stable tracking control of SSDUNSs. Finally, the stability of the proposed method is analyzed theoretically to ensure its reliability and robustness. In addition, the effectiveness of the designed method is verified by numerical simulations and real-world applications in the context of wastewater treatment processes (WWTPs).
{"title":"Perceptron-Based Adaptive Model Predictive Control for Stochastic Sampled-Data Unknown Nonlinear Systems.","authors":"Shi-Jia Fu,Hao-Yuan Sun,Hong-Gui Han,Chang-Chun Hua","doi":"10.1109/tcyb.2026.3670868","DOIUrl":"https://doi.org/10.1109/tcyb.2026.3670868","url":null,"abstract":"For stochastic sampled-data systems characterized by unknown nonlinear dynamics (SSDUNSs), it is a great challenge to design an appropriate controller to achieve stable tracking control. In this article, a perceptron-based adaptive model predictive control (PAMPC) scheme is developed for SSDUNSs with multiple discrete stochastic sampling intervals. The activation frequency of each sampling interval can be statistically obtained, which can be described by the categorical distribution. First, a PAMPC structure is developed for the tracking control of SSDUNS. A perceptron with a cost function is designed to incorporate the exploration of the environmental state, encompassing the sampling interval, predictive error, and tracking error. Second, an adaptive predictive horizon (APH) is incorporated into the predictive model to provide the necessary predicting information for the controller. APH is adjusted based on the activation frequency of stochastic sampling intervals. Third, an optimal control problem (OCP) combined with the penalty of the perceptron is designed to stabilize SSDUNS. Then, the control law can be computed to achieve the stable tracking control of SSDUNSs. Finally, the stability of the proposed method is analyzed theoretically to ensure its reliability and robustness. In addition, the effectiveness of the designed method is verified by numerical simulations and real-world applications in the context of wastewater treatment processes (WWTPs).","PeriodicalId":13112,"journal":{"name":"IEEE Transactions on Cybernetics","volume":"15 1","pages":""},"PeriodicalIF":11.8,"publicationDate":"2026-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147383563","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-03-10DOI: 10.1109/tcyb.2026.3671125
Dong Wang,Zidong Wang,Chuanbo Wen
This article is concerned with the recursive neural network (NN)-based state estimation problem for a class of stochastic discrete time-varying systems subjected to both unknown nonlinear dynamics and the token bucket communication protocol. The token bucket protocol is utilized to determine whether the sensor signal is granted access to the network at each transmission instant, wherein the transmission may fail due to an insufficient number of tokens in the bucket. The objective of the addressed problem is to design a recursive NN-based state estimator such that, under the influence of the unknown nonlinear dynamics and the token bucket communication protocol, certain upper bounds of both the state estimation error covariance and the NN-weight (NNW) error covariance are guaranteed, while the explicit expressions of the NN-based estimator gain and the NN tuning parameters are derived. By employing two sets of matrix difference equations, two upper bounds for the state estimation error covariance and the NNW error covariance are established, and these upper bounds are subsequently minimized by parameterizing the NN-based estimator gain in terms of the solutions to the matrix difference equations. Finally, an illustrative example is provided to demonstrate the feasibility and effectiveness of the proposed estimation approach.
{"title":"Neural-Network-Based State Estimation for Nonlinear Stochastic Systems Under Token Bucket Communication Protocol.","authors":"Dong Wang,Zidong Wang,Chuanbo Wen","doi":"10.1109/tcyb.2026.3671125","DOIUrl":"https://doi.org/10.1109/tcyb.2026.3671125","url":null,"abstract":"This article is concerned with the recursive neural network (NN)-based state estimation problem for a class of stochastic discrete time-varying systems subjected to both unknown nonlinear dynamics and the token bucket communication protocol. The token bucket protocol is utilized to determine whether the sensor signal is granted access to the network at each transmission instant, wherein the transmission may fail due to an insufficient number of tokens in the bucket. The objective of the addressed problem is to design a recursive NN-based state estimator such that, under the influence of the unknown nonlinear dynamics and the token bucket communication protocol, certain upper bounds of both the state estimation error covariance and the NN-weight (NNW) error covariance are guaranteed, while the explicit expressions of the NN-based estimator gain and the NN tuning parameters are derived. By employing two sets of matrix difference equations, two upper bounds for the state estimation error covariance and the NNW error covariance are established, and these upper bounds are subsequently minimized by parameterizing the NN-based estimator gain in terms of the solutions to the matrix difference equations. Finally, an illustrative example is provided to demonstrate the feasibility and effectiveness of the proposed estimation approach.","PeriodicalId":13112,"journal":{"name":"IEEE Transactions on Cybernetics","volume":"14 1","pages":""},"PeriodicalIF":11.8,"publicationDate":"2026-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147383562","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-03-09DOI: 10.1109/tcyb.2026.3667965
Wencheng Zou, Jingyi Zhu, Zhengrong Xiang
{"title":"Output Consensus of a Class of Multiple Heterogeneous-Dimensional Switched Nonlinear Systems","authors":"Wencheng Zou, Jingyi Zhu, Zhengrong Xiang","doi":"10.1109/tcyb.2026.3667965","DOIUrl":"https://doi.org/10.1109/tcyb.2026.3667965","url":null,"abstract":"","PeriodicalId":13112,"journal":{"name":"IEEE Transactions on Cybernetics","volume":"29 1","pages":""},"PeriodicalIF":11.8,"publicationDate":"2026-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147380603","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-03-05DOI: 10.1109/tcyb.2026.3668393
Yuchen Zhu,Kuang Zhou,Fabio Cuzzolin
Deep clustering has achieved remarkable success in handling various types of real-world data, but often suffers from overconfidence, forcing ambiguous samples into specific clusters even when the evidence is insufficient. To address this limitation, we propose trustworthy deep credal clustering, a novel framework for uncertainty that integrates deep neural networks with the Dempster-Shafer Theory of evidence (DST). This method leverages credal cluster structures to enhance the model's robustness against uncertain data. Our model can refrain from assigning uncertain samples to a specific cluster, thereby reducing errors and enhancing the model's trustworthiness. Theoretically, we derive closed-form solutions for updating cluster memberships and prototypes, employing a coordinate descent strategy to rigorously optimize the objective function. Experiments on various datasets confirm that our proposed trustworthy clustering method leads to enhanced overall clustering effectiveness. Code is available at https://github.com/H1nkik/Trustworthy-Clustering.
{"title":"TDCC: A Trustworthy Deep Credal Clustering Method for Uncertain Data.","authors":"Yuchen Zhu,Kuang Zhou,Fabio Cuzzolin","doi":"10.1109/tcyb.2026.3668393","DOIUrl":"https://doi.org/10.1109/tcyb.2026.3668393","url":null,"abstract":"Deep clustering has achieved remarkable success in handling various types of real-world data, but often suffers from overconfidence, forcing ambiguous samples into specific clusters even when the evidence is insufficient. To address this limitation, we propose trustworthy deep credal clustering, a novel framework for uncertainty that integrates deep neural networks with the Dempster-Shafer Theory of evidence (DST). This method leverages credal cluster structures to enhance the model's robustness against uncertain data. Our model can refrain from assigning uncertain samples to a specific cluster, thereby reducing errors and enhancing the model's trustworthiness. Theoretically, we derive closed-form solutions for updating cluster memberships and prototypes, employing a coordinate descent strategy to rigorously optimize the objective function. Experiments on various datasets confirm that our proposed trustworthy clustering method leads to enhanced overall clustering effectiveness. Code is available at https://github.com/H1nkik/Trustworthy-Clustering.","PeriodicalId":13112,"journal":{"name":"IEEE Transactions on Cybernetics","volume":"25 1","pages":""},"PeriodicalIF":11.8,"publicationDate":"2026-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147359436","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-03-05DOI: 10.1109/tcyb.2026.3666574
R Manivannan,K Vinothini,Jinde Cao
For the first time, this article presents a dissipativity-based observer design for accurate state-of-charge (SOC) estimation, essential for improving the safety, performance, and lifespan of lithium-ion batteries (LIBs) in electric vehicle (EV) battery management systems (BMSs). However, model uncertainties and measurement noise significantly affect estimation accuracy. To address this, a novel observer design based on ( $mathcal {Q}, mathcal {S}, mathcal {R}$ )- $gamma $ -dissipativity theory is developed, formulated within a linear matrix inequality (LMI) framework, and integrated with the Lyapunov-Krasovskii functional (LKF) approach. The proposed observer ensures robustness and stability in SOC estimation under uncertain and noisy conditions. A one-resistor capacitor (1-RC) equivalent circuit model (ECM) is adopted for battery modeling, with experimental validation performed on a Panasonic 18650PF cell. The proposed method is compared against the adaptive unscented Kalman filter (AUKF) under four drive cycles: the urban dynamometer driving schedule (UDDS), the aggressive US06 supplemental federal test procedure, the Los Angeles 92 (LA92), and the highway fuel economy test (HWFET). Results show that the proposed observer achieves root-mean-square errors (RMSEs) of 0.77%, 0.50%, 0.65%, and 0.48% and mean absolute errors (MAEs) of 0.59%, 0.42%, 0.50%, and 0.40% under UDDS, US06, LA92, and HWFET, respectively. This corresponds to RMSE reductions of 28.38%, 88.93%, 67.25%, and 38.35% compared with AUKF. Notably, the proposed method achieves a maximum accuracy of 99.23%, surpassing the latest reported accuracy of 98.50%.
{"title":"A Novel Approach for Accurate SOC Estimation of Lithium-Ion Electric Vehicle Batteries Using a (Q, S, R)-γ-Based Dissipativity Observer.","authors":"R Manivannan,K Vinothini,Jinde Cao","doi":"10.1109/tcyb.2026.3666574","DOIUrl":"https://doi.org/10.1109/tcyb.2026.3666574","url":null,"abstract":"For the first time, this article presents a dissipativity-based observer design for accurate state-of-charge (SOC) estimation, essential for improving the safety, performance, and lifespan of lithium-ion batteries (LIBs) in electric vehicle (EV) battery management systems (BMSs). However, model uncertainties and measurement noise significantly affect estimation accuracy. To address this, a novel observer design based on ( $mathcal {Q}, mathcal {S}, mathcal {R}$ )- $gamma $ -dissipativity theory is developed, formulated within a linear matrix inequality (LMI) framework, and integrated with the Lyapunov-Krasovskii functional (LKF) approach. The proposed observer ensures robustness and stability in SOC estimation under uncertain and noisy conditions. A one-resistor capacitor (1-RC) equivalent circuit model (ECM) is adopted for battery modeling, with experimental validation performed on a Panasonic 18650PF cell. The proposed method is compared against the adaptive unscented Kalman filter (AUKF) under four drive cycles: the urban dynamometer driving schedule (UDDS), the aggressive US06 supplemental federal test procedure, the Los Angeles 92 (LA92), and the highway fuel economy test (HWFET). Results show that the proposed observer achieves root-mean-square errors (RMSEs) of 0.77%, 0.50%, 0.65%, and 0.48% and mean absolute errors (MAEs) of 0.59%, 0.42%, 0.50%, and 0.40% under UDDS, US06, LA92, and HWFET, respectively. This corresponds to RMSE reductions of 28.38%, 88.93%, 67.25%, and 38.35% compared with AUKF. Notably, the proposed method achieves a maximum accuracy of 99.23%, surpassing the latest reported accuracy of 98.50%.","PeriodicalId":13112,"journal":{"name":"IEEE Transactions on Cybernetics","volume":"67 1","pages":""},"PeriodicalIF":11.8,"publicationDate":"2026-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147359080","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-03-05DOI: 10.1109/tcyb.2026.3667963
Faxiang Zhang,Yu Shi,Jing Na,Pak Kin Wong,Guanbin Gao,Jing Zhao,Yingbo Huang,Pengshuai Dai
This article proposes an adjustable-error neural network (NN) approximator and incorporates it into the adaptive neural tracking controller design of uncertain nonlinear systems. Noted that the error between the unknown nonlinear function and the NN approximator cannot be adjusted under the traditional NN control framework, as it is solely determined by the selection of neurons, basis functions, and the estimation of the ideal weight vector. This inherent constraint compromises the precision of the NN approximation and the convergence accuracy of the tracking error. To improve the approximation accuracy of unknown nonlinear functions in adaptive neural control systems, an adjustable-error NN approximator is designed, in which the error between the approximator and the unknown nonlinear function can be adjusted by designed parameters. Based on the proposed NN approximator, an adaptive neural tracking controller is designed for a class of uncertain nonlinear systems, which achieves higher accuracy of the tracking error compared with traditional methods. The stability of the resulting closed-loop system is proved in the Lyapunov sense, and the convergence of the tracking error is also analyzed. The effectiveness of the proposed scheme is verified by simulation and experiment.
{"title":"Adjustable-Error-Based Adaptive Neural Network Tracking Control for Uncertain Nonlinear Systems.","authors":"Faxiang Zhang,Yu Shi,Jing Na,Pak Kin Wong,Guanbin Gao,Jing Zhao,Yingbo Huang,Pengshuai Dai","doi":"10.1109/tcyb.2026.3667963","DOIUrl":"https://doi.org/10.1109/tcyb.2026.3667963","url":null,"abstract":"This article proposes an adjustable-error neural network (NN) approximator and incorporates it into the adaptive neural tracking controller design of uncertain nonlinear systems. Noted that the error between the unknown nonlinear function and the NN approximator cannot be adjusted under the traditional NN control framework, as it is solely determined by the selection of neurons, basis functions, and the estimation of the ideal weight vector. This inherent constraint compromises the precision of the NN approximation and the convergence accuracy of the tracking error. To improve the approximation accuracy of unknown nonlinear functions in adaptive neural control systems, an adjustable-error NN approximator is designed, in which the error between the approximator and the unknown nonlinear function can be adjusted by designed parameters. Based on the proposed NN approximator, an adaptive neural tracking controller is designed for a class of uncertain nonlinear systems, which achieves higher accuracy of the tracking error compared with traditional methods. The stability of the resulting closed-loop system is proved in the Lyapunov sense, and the convergence of the tracking error is also analyzed. The effectiveness of the proposed scheme is verified by simulation and experiment.","PeriodicalId":13112,"journal":{"name":"IEEE Transactions on Cybernetics","volume":"31 1","pages":""},"PeriodicalIF":11.8,"publicationDate":"2026-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147359081","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-03-05DOI: 10.1109/tcyb.2026.3668026
Haitao Wang,Qingshan Liu,Ju H Park
This article addresses the optimal state observation and leader-following consensus for a nonlinear multiagent system (MAS) with input saturation under the Stackelberg game framework. The dynamics and states of followers are unknown, the leader's dynamics is unknown, and the leader's state is accessible only to a subset of followers. First, a distributed estimation algorithm is developed for each follower to estimate the leader's state. Then, a game-based observer is designed to estimate the follower state, where the bidirectional interaction between the observer and follower dynamics is considered. The follower dynamics and observer are modeled as leader and follower players in the Stackelberg game, respectively. Based on the proposed structure, an optimal auxiliary controller for the observer and an optimal consensus controller are developed. Furthermore, a fuzzy reinforcement learning approach approximates the unknown dynamics and derives the optimal state observers and leader-following consensus controllers. All closed-loop signals are guaranteed to be uniformly ultimately bounded based on the Lyapunov method. Finally, simulations are provided to validate the effectiveness of the proposed approach.
{"title":"Distributed Optimal Leader-Following Consensus Control of MAS Under Input Saturation: A Stackelberg Game Approach.","authors":"Haitao Wang,Qingshan Liu,Ju H Park","doi":"10.1109/tcyb.2026.3668026","DOIUrl":"https://doi.org/10.1109/tcyb.2026.3668026","url":null,"abstract":"This article addresses the optimal state observation and leader-following consensus for a nonlinear multiagent system (MAS) with input saturation under the Stackelberg game framework. The dynamics and states of followers are unknown, the leader's dynamics is unknown, and the leader's state is accessible only to a subset of followers. First, a distributed estimation algorithm is developed for each follower to estimate the leader's state. Then, a game-based observer is designed to estimate the follower state, where the bidirectional interaction between the observer and follower dynamics is considered. The follower dynamics and observer are modeled as leader and follower players in the Stackelberg game, respectively. Based on the proposed structure, an optimal auxiliary controller for the observer and an optimal consensus controller are developed. Furthermore, a fuzzy reinforcement learning approach approximates the unknown dynamics and derives the optimal state observers and leader-following consensus controllers. All closed-loop signals are guaranteed to be uniformly ultimately bounded based on the Lyapunov method. Finally, simulations are provided to validate the effectiveness of the proposed approach.","PeriodicalId":13112,"journal":{"name":"IEEE Transactions on Cybernetics","volume":"53 1","pages":""},"PeriodicalIF":11.8,"publicationDate":"2026-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147359082","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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