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This paper is concerned with a hybrid reaction–diffusion mosquitoes model perturbed by impulsive control, where the model incorporates imperfect maternal transmission, incomplete cytoplasmic incompatibility (CI) and fitness effect of Wolbachia. The model is periodic and lacks monotonicity due to the integration of impulsive control and imperfect maternal transmission. We establish threshold conditions for the extinction and existence of mosquito populations by the suitable auxiliary problems and lower and upper solutions that can overcome obstacles caused by factors considered, demonstrating the four possible biological outcomes for mosquitoes control. Especially, our theoretical results can reveal the subtle relation between the invasion of Wolbachia and several important parameters including impulsive control rate, impulsive periodic, CI intensity, maternal transmission rate, fitness effect and the initial occupancy of Wolbachia infection. Furthermore, the maximal Wolbachia maternal leakage rate and impulsive control rate against Wolbachia-infected mosquitoes are given. Numerically, we perform simulations to give some interesting phenomena. Specifically, the combination of efficient impulsive control and high proportion of appropriate Wolbachia under certain cases will be more effective in controlling mosquitoes. Surprisingly, the lower initial occupancy, incomplete CI, imperfect maternal transmission and fitness cost of Wolbachia lead to a reversal of Wolbachia invasion from success to failure, but the frequent implementation of impulsive control could again achieve a perfect reversal even for a low initial infection frequency. This work provides new perspectives for further research on mosquitoes control. [ABSTRACT FROM AUTHOR]

This paper investigates the finite-time synchronization (FTS) of delayed neural networks on time scales via impulsive control. First, an impulsive controller is designed when system states are accessible. Based on the time scale theory and mathematical induction method, a sufficient condition for FTS is presented. Then, an observer is provided to estimate system states when partial states can not be available. An observer-based impulsive controller is devised to ensure that both the observer error system and the synchronization error system converges to zero in finite time. Furthermore, the explicit expression for settling time of the FTS is given. Finally, the validity of our methods is verified by a numerical simulation. [ABSTRACT FROM AUTHOR]

This paper investigates the exponential synchronization of time-delayed stochastic complex networks under deception attacks. Unlike traditional methods that rely on fixed or preset impulsive sequences, a state-dependent dynamic event-triggered impulsive control strategy is proposed, integrating the advantages of both event-triggered mechanisms and impulsive control. This strategy dynamically adjusts the impulsive moments by monitoring the system's dynamic state. Adaptive parameters are incorporated into the event-triggered conditions, resulting in a Lyapunov-based adaptive event-triggered mechanism driven by subsystem state information. This mechanism allows each subsystem to independently determine its trigger moments using only its state information, rather than the whole system information. Additionally, stochastic variables following the Bernoulli distribution are introduced to model deception attacks, making the control strategy more applicable to real-world scenarios. Based on the Lyapunov functions of subsystems and graph theory, sufficient conditions for ensuring exponential synchronization are provided, excluding Zeno behavior. Finally, numerical simulations confirm the effectiveness and practicality of the theoretical results. [ABSTRACT FROM AUTHOR]

This study is devoted to addressing the problem of robust Mittag-Leffler (ML) synchronization for generalized fractional-order reaction-diffusion networks (GFRDNs) with mixed delays and uncertainties. The proposed GFRDNs include local field GFRDNs and static GFRDNs as its special cases. An impulsive controller is intended to achieve synchronization in GFRDNs, which was previously unsolved in integer-order generalized reaction-diffusion neural networks. Novel synchronization criteria as linear matrix inequalities (LMIs) are developed to undertake the ML synchronization beneath investigation. Ensuring conditions can be efficiently solved by means of MATLAB LMI toolbox. Following that, simulations are offered for proving the impact of the findings achieved. [ABSTRACT FROM AUTHOR]

This paper is devoted to the study of optimal release strategies to control vector-borne diseases, such as dengue, Zika, chikungunya and malaria. Two techniques are considered: the sterile insect one (SIT), which consists in releasing sterilized males among wild vectors in order to perturb their reproduction, and the Wolbachia one (presently used mainly for mosquitoes), which consists in releasing vectors, that are infected with a bacterium limiting their vectorial capacity, in order to replace the wild population by one with reduced vectorial capacity. In each case, the time dynamics of the vector population is modeled by a system of ordinary differential equations in which the releases are represented by linear combinations of Dirac measures with positive coefficients determining their intensity. We introduce optimal control problems that we solve numerically using ad-hoc algorithms, based on writing first-order optimality conditions characterizing the best combination of Dirac measures. We then discuss the results obtained, focusing in particular on the complexity and efficiency of optimal controls and comparing the strategies obtained. Mathematical modeling can help testing a great number of scenarios that are potentially interesting in future interventions (even those that are orthogonal to the present strategies) but that would be hard, costly or even impossible to test in the field in present conditions. [ABSTRACT FROM AUTHOR]

This work introduces an impulsive neural control algorithm designed to mitigate the spread of epidemic diseases. The objective of this paper is the development of a vaccination strategy based on a PIN-type impulsive controller based on an online-trained neural identifier to control the spread of infectious diseases under a complex network approach with time-varying connections where each node represents a population of individuals whose dynamics are defined by the MSEIR epidemiological model. Considering an unknown model of the system, a neural identifier is designed that provides a nonlinear model for the complex network trained through an extended Kalman filter algorithm. Simulation results are presented by applying the proposed control scheme for a complex network parameterized as infectious diseases. [ABSTRACT FROM AUTHOR]

This study investigates the impulsive tracking synchronization control of networked robotic manipulator systems based on Lagrangian systems. In the task space, the end-effector positions of networked robotic manipulators can synchronize and track a desired trajectory by designing an impulsive controller. Based on impulsive control, some algebraic synchronization criteria are derived. As a direct application of the theoretical results, tracking synchronization of six double-link robotic manipulators in the task space is discussed in detail. Numerical simulations are given to demonstrate the effectiveness and feasibility of the proposed control strategy. [ABSTRACT FROM AUTHOR]

This paper investigates the exponential quasi-synchronization of fractional-order fuzzy cellular neural networks with parameters mismatch via impulsive control. Firstly, under the framework of the generalized Caputo fractional-order derivative, a new fractional-order impulsive differential inequality is established. Secondly, based on this fractional-order impulsive differential inequality, a general criterion for the quasi-synchronization of fractional-order systems is obtained. Then, specific to the fractional-order fuzzy cellular neural network model in this paper, the criteria and error estimation of the exponential quasi-synchronization of fractional-order fuzzy cellular neural networks can be obtained. Finally, two numerical examples are given to illustrate the effectiveness of the obtained results. [ABSTRACT FROM AUTHOR]

This paper addresses the secure quasi-synchronization issue of heterogeneous complex networks (HCNs) under aperiodic denial-of-service (DoS) attacks with dynamic event-triggered impulsive scheme (ETIS). The heterogeneity of networks and the aperiodic DoS attacks, which hinder communication channels and synchronization goals, present challenges to the analysis of secure quasi-synchronization. The ETIS leverages impulsive control and dynamic event-triggered scheme (ETS) to handle the network heterogeneity and the DoS attacks. We give specific bounds on the attack duration and frequency that the network can endure, and obtain synchronization criteria that relate to event parameters, attack duration, attack frequency, and impulsive gain by the variation of parameter formula and recursive methods. Moreover, we prove that the dynamic ETS significantly reduces the controller updates, saves energy without sacrificing the system decay rate, and prevents the Zeno phenomenon. Finally, we validate our control scheme with a numerical example. • A novel dynamic event-triggered impulsive scheme that involves forced event sequence is developed. • We provide specific bounds on DoS attack duration and frequency that the network can tolerate. • This paper handles the secure quasi-synchronization of heterogeneous complex networks under DoS attacks. • An exact expression for the synchronization error bound and the system decay rate is provided. • Zeno behavior is avoided. [ABSTRACT FROM AUTHOR]

This paper discusses a synchronization issue of uncertain time-delay systems via flexible delayed impulsive control. A new Razumikhin-type inequality is presented, considering adjustable parameters the ϖ (t) , which relies on flexible impulsive gain. For the uncertain time-delay systems where delay magnitude is not constrained to impulsive intervals, sufficient conditions for global exponential synchronization (GES) are established. Furthermore, based on Lyapunov theory, a new differential inequality and linear matrix inequality design, and a flexible impulsive control method is introduced through using the variable impulsive gain and time-varying delays. It is interesting to find that uncertain time-delay systems can maintain GES by adjusting the impulsive gain and impulsive delay. Finally, two simulations are given to illustrate the effectiveness of the derived results. [ABSTRACT FROM AUTHOR]

In this paper, the exponential synchronization problem of a class of neural networks with mixed delays under impulsive control is studied. Combining the impulsive comparison principle and the concept of an average impulsive interval, two impulsive differential inequalities with mixed delays are discussed, and the sufficient conditions for the existence of exponential decay are obtained. Based on two different impulsive control strategies, and then by means of the Lyapunov function, the inequality technique, and these two new inequalities, a set of sufficient conditions are derived to ensure the synchronization of the drive and response systems. In order to prove the effectiveness of the proposed control scheme, two numerical examples are given to prove its practicability and effectiveness.

This paper deals with the problem of the prediction and control of cholera outbreak using real data of Cameroon. We first develop and analyze a deterministic model with seasonality for the cholera, the novelty of which lies in the incorporation of undetected cases. We present the basic properties of the model and compute two explicit threshold parameters R¯0 and R̲0 that bound the effective reproduction number R0, from below and above, that is R̲0≤R0≤R¯0. We prove that cholera tends to disappear when R¯0≤1, while when R̲0>1, cholera persists uniformly within the population. After, assuming that the cholera transmission rates and the proportions of newly symptomatic are unknown, we develop the EnKf approach to estimate unmeasurable state variables and these unknown parameters using real data of cholera from 2014 to 2022 in Cameroon. We use this result to estimate the upper and lower bound of the effective reproduction number and reconstructed active asymptomatic and symptomatic cholera cases in Cameroon, and give a short-term forecasts of cholera in Cameroon until 2024. Numerical simulations show that (i) the transmission rate from free Vibrio cholerae in the environment is more important than the human transmission and begin to be high few week after May and in October, (ii) 90% of newly cholera infected cases that present the symptoms of cholera are not diagnosed and (iii) 60.36% of asymptomatic are detected at 14% and 86% of them recover naturally. The future trends reveals that an outbreak appeared from July to November 2023 with the number of cases reported monthly peaked in October 2023. An impulsive control strategy is incorporated in the model with the aim to avoid or prevent the cholera outbreak. In the first year of monitoring, we observed a reduction of more than 75% of incidences and the disappearance of the peaks when no control are available in Cameroon. A second monitoring of control led to a further reduction of around 60% of incidences the following year, showing how impulse control could be an effective means of eradicating cholera.

This paper discusses a synchronization issue of uncertain time-delay systems via flexible delayed impulsive control. A new Razumikhin-type inequality is presented, considering adjustable parameters the $ \varpi(t) $, which relies on flexible impulsive gain. For the uncertain time-delay systems where delay magnitude is not constrained to impulsive intervals, sufficient conditions for global exponential synchronization (GES) are established. Furthermore, based on Lyapunov theory, a new differential inequality and linear matrix inequality design, and a flexible impulsive control method is introduced through using the variable impulsive gain and time-varying delays. It is interesting to find that uncertain time-delay systems can maintain GES by adjusting the impulsive gain and impulsive delay. Finally, two simulations are given to illustrate the effectiveness of the derived results.

In this paper, the impulsive synchronization of a class of nonlinear multiple neural networks (MNNs) with multi-delays was considered under a dynamic event-based mechanism. To achieve a more comprehensive synchronization outcome and mitigate the conservativeness of impulsive control due to predetermined time sequences, we integrated a dynamic event-triggered strategy. This approach formed a novel control framework for generalized MNNs, where impulsive inputs were applied only under specific conditions governed by event-triggering rules. Towards the above objectives, the impulsive jumping system, resulting from dynamic component, and matrix measure method were invoked to directly increase the computational simplicity and extensibility of the study. As the outcome, the synchronization criteria for the MNNs could be achieved, and the exponential convergence rate is resolved by considering both the generalized comparison principle regarding impulsive systems and the variable parameter formula. Moreover, Zeno-freeness of the achieved triggering regulation is ensured. Finally, two numerical examples confirmed the validity of the designed approach.

This paper focused on the decision and regulation for the production-warehousing-selling (P-W-S) model. A novel event-triggered mechanism (ETM) was meticulously developed to determine when to impose control, alongside the development of the corresponding impulsive strategy. By applying the input-to-state stability (ISS) theory, some quantitative relationships between system parameters and ETM were integrated into the estimation of the state of the P-W-S model. It was shown that under the designed ETM and impulsive strategy, the warehouse was able to autonomously adjust inventory levels based on factory production efficiency and market selling trends, and hence the quantity of goods could be maintained within a reasonable range, avoiding excessive inventory while adequately meeting market requirements. At last, an example with numerical simulations was presented to validate our results.

In this paper, the tracking control problem of the fully actuated rigid body system defined on the matrix Lie groups with impulsive effects is considered. By embedding the impulsive control systems defined on manifolds into Euclidean space via a feedback integrator, we obtain sufficient conditions for the invariance and attractivity of the manifolds. Then the embedding technique is applied to the fully actuated rigid body system with impulsive effects. The study of such a system with impulsive effects can be divided into impulsive disturbance problems and impulsive control problems, both of which are addressed in this paper. In the presence of impulsive disturbances, we realize the tracking target by the Lyapunov functions as well as the generalized Barbalat's Lemma. For the impulsive control problem, a controller containing impulsive input is designed for tracking control based on the high‐order fully actuated system approach. Finally, several examples demonstrate the effectiveness of the results presented in this paper. [ABSTRACT FROM AUTHOR]

This study investigated an n-species cooperative Lotka-Volterra system with impulsive controls and time delays. In the case of constructing appropriate Lyapunov functionals and using inequality techniques, some conditions for the permanence and global attractiveness of the system are obtained. In addition, sufficient conditions for the existence of positive periodic solutions were established using the coincidence degree theory. Finally, numerical simulation results illustrate the effectiveness of our findings. [ABSTRACT FROM AUTHOR]

In this paper, the exponential synchronization problem of a class of neural networks with mixed delays under impulsive control is studied. Combining the impulsive comparison principle and the concept of an average impulsive interval, two impulsive differential inequalities with mixed delays are discussed, and the sufficient conditions for the existence of exponential decay are obtained. Based on two different impulsive control strategies, and then by means of the Lyapunov function, the inequality technique, and these two new inequalities, a set of sufficient conditions are derived to ensure the synchronization of the drive and response systems. In order to prove the effectiveness of the proposed control scheme, two numerical examples are given to prove its practicability and effectiveness. [ABSTRACT FROM AUTHOR]

This paper deals with the problem of the prediction and control of cholera outbreak using real data of Cameroon. We first develop and analyze a deterministic model with seasonality for the cholera, the novelty of which lies in the incorporation of undetected cases. We present the basic properties of the model and compute two explicit threshold parameters R0 and R0 that bound the effective reproduction number R0, from below and above, that is R0 ≤ R0 ≤ R0. We prove that cholera tends to disappear when R0 ≤ 1, while when R0 >1, cholera persists uniformly within the population. After, assuming that the cholera transmission rates and the proportions of newly symptomatic are unknown, we develop the EnKf approach to estimate unmeasurable state variables and these unknown parameters using real data of cholera from 2014 to 2022 in Cameroon. We use this result to estimate the upper and lower bound of the effective reproduction number and reconstructed active asymptomatic and symptomatic cholera cases in Cameroon, and give a short-term forecasts of cholera in Cameroon until 2024. Numerical simulations show that (i) the transmission rate from free Vibrio cholerae in the environment is more important than the human transmission and begin to be high few week after May and in October, (ii) 90% of newly cholera infected cases that present the symptoms of cholera are not diagnosed and (iii) 60.36% of asymptomatic are detected at 14% and 86% of them recover naturally. The future trends reveals that an outbreak appeared from July to November 2023 with the number of cases reported monthly peaked in October 2023. An impulsive control strategy is incorporated in the model with the aim to avoid or prevent the cholera outbreak. In the first year of monitoring, we observed a reduction of more than 75% of incidences and the disappearance of the peaks when no control are available in Cameroon. A second monitoring of control led to a further reduction of around 60% of incidences the following year, showing how impulse control could be an effective means of eradicating cholera. [ABSTRACT FROM AUTHOR]

We introduce discontinuous solutions to nonlinear impulsive control systems with state time delays in the dynamics and derive necessary optimality conditions in the form of a maximum principle for associated optimal control problems. In the case without delays, if the measure control is scalar valued, the corresponding discontinuous state trajectory, understood as a limit of classical state trajectories for absolutely continuous controls approximating the measure, is unique. For vector-valued measure controls, however, the limiting trajectory is not unique and a full description of the control must include additional "attached" controls affecting instantaneous state evolution at a discontinuity. For impulsive control systems with time delays we reveal a new phenomenon, namely, that the limiting state trajectory resulting from different approximations of a given measure control needs not to be unique, even in the scalar case. Correspondingly, our framework allows for additional attached controls, even though the measure control is scalar valued. [ABSTRACT FROM AUTHOR]

In this paper, the impulsive synchronization of a class of nonlinear multiple neural networks (MNNs) with multi-delays was considered under a dynamic event-based mechanism. To achieve a more comprehensive synchronization outcome and mitigate the conservativeness of impulsive control due to predetermined time sequences, we integrated a dynamic event-triggered strategy. This approach formed a novel control framework for generalized MNNs, where impulsive inputs were applied only under specific conditions governed by event-triggering rules. Towards the above objectives, the impulsive jumping system, resulting from dynamic component, and matrix measure method were invoked to directly increase the computational simplicity and extensibility of the study. As the outcome, the synchronization criteria for the MNNs could be achieved, and the exponential convergence rate is resolved by considering both the generalized comparison principle regarding impulsive systems and the variable parameter formula. Moreover, Zeno-freeness of the achieved triggering regulation is ensured. Finally, two numerical examples confirmed the validity of the designed approach. [ABSTRACT FROM AUTHOR]

This paper focused on the decision and regulation for the production-warehousing-selling (P-W-S) model. A novel event-triggered mechanism (ETM) was meticulously developed to determine when to impose control, alongside the development of the corresponding impulsive strategy. By applying the input-to-state stability (ISS) theory, some quantitative relationships between system parameters and ETM were integrated into the estimation of the state of the P-W-S model. It was shown that under the designed ETM and impulsive strategy, the warehouse was able to autonomously adjust inventory levels based on factory production efficiency and market selling trends, and hence the quantity of goods could be maintained within a reasonable range, avoiding excessive inventory while adequately meeting market requirements. At last, an example with numerical simulations was presented to validate our results. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Guangdong University of Technology is the property of Journal of Guangdong University of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Control Theory & Applications / Kongzhi Lilun Yu Yinyong is the property of Editorial Department of Control Theory & Applications and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This study investigates the impulsive tracking synchronization control of networked robotic manipulator systems based on Lagrangian systems. In the task space, the end-effector positions of networked robotic manipulators can synchronize and track a desired trajectory by designing an impulsive controller. Based on impulsive control, some algebraic synchronization criteria are derived. As a direct application of the theoretical results, tracking synchronization of six double-link robotic manipulators in the task space is discussed in detail. Numerical simulations are given to demonstrate the effectiveness and feasibility of the proposed control strategy.

This paper investigates the mean-square bounded consensus issue in a two-layer multi-agent network under deception attacks. The two-layer network is composed of the leader and follower layers with a switching topology. Employing an impulsive control method, the mean-square bounded consensus for the leader layer and the node-to-node mean-square bounded consensus of the two-layer network are analyzed. Based on the knowledge of graph theory, Lyapunov stability theory, and linear matrix inequalities, sufficient conditions for the mean-square bounded consensus of multi-agent systems in the two-layer network are derived. Finally, the practicability and efficacy of the theoretical outcomes are corroborated via the provided numerical simulations. [ABSTRACT FROM AUTHOR]

Therapeutic agents acting on RNA, including RNA modification, RNA editing and RNA interference (RNAi), play a vital role in gene function study, signaling pathway, drug discovery, disease treatment, vaccine development and so on. Therein, RNAi as an emerging gene therapy has been widely applied in many cancer studies by silencing oncogenes or specific mRNA in malignant tumor cells. Mechanism and efficiency of RNAi are the key issues of RNAi technology. RNA silencing involves dynamic modeling, analyses and optimal control of RNAi gene system. Physiological delay and Hill response describing off-target are considerable elements involving RNAi efficiency. In this paper, we first formulate a four-dimensional RNAi model with time delays and Hill functions, and then investigate the complex dynamic behaviors including the number, existence and stability of internal equilibria and Hopf bifurcations of single delay and two delays. Furthermore, based on the specific mRNA degradation adopted in impulsive patterns, we build an optimal problem by adding exogenous dsRNA at alterable time points in variable dosages in a treatment session. By the method of gradient formula, we can find the optimal impulsive time and proportion of dsRNA. Finally, simulation indicates that (1) physiological lags not only raise the oscillations of mRNA but also cut down the levels of cost; (2) smaller delays and larger rates of siRNA–mRNA complex formation and dsRNA synthesis imply the rapid composition of RISC and fast synthesis of dsRNA leading to more desirable therapeutic schedule, which affords evidence for gene regulation and RNAi; (3) a larger half-saturation coefficient characterizes a unique and stable higher targeted mRNA, whereas a smaller half-saturation coefficient generates bistability in which the higher and lower targeted mRNAs simultaneously emerge; and (4) the bistability will provide a good guidance to control, suppress and degrade targeted mRNA. [ABSTRACT FROM AUTHOR]

This paper studies the problem of event-triggered impulsive control for discrete-time systems. A novel periodic event-triggering scheme with two tunable parameters is presented to determine the moments of updating impulsive control signals which are called event times. Sufficient conditions are established to guarantee asymptotic stability of the resulting impulsive systems. It is worth mentioning that the event times are different from the impulse times, that is, the control signals are updated at each event time but the actuator performs the impulsive control tasks at a later time due to time delays. The effectiveness of our theoretical result with the proposed scheme is illustrated by three examples. [ABSTRACT FROM AUTHOR]

The main goal of the paper is to present and study models of multi-agent systems for which the dynamics of the agents are described by a Caputo fractional derivative of variable order and a kernel that depends on an increasing function. Also, the order of the fractional derivative changes at update times. We study a case for which the exchanged information between agents occurs only at initially given update times. Two types of linear variable-order Caputo fractional models are studied. We consider both multi-agent systems without a leader and multi-agent systems with a leader. In the case of multi-agent systems without a leader, two types of models are studied. The main difference between the models is the fractional derivative describing the dynamics of agents. In the first one, a Caputo fractional derivative with respect to another function and with a continuous variable order is applied. In the second one, the applied fractional derivative changes its constant order at each update time. Mittag–Leffler stability via impulsive control is defined, and sufficient conditions are obtained. In the case of the presence of a leader in the multi-agent system, the dynamic of the agents is described by a Caputo fractional derivative with respect to an increasing function and with a constant order that changes at each update time. The leader-following consensus via impulsive control is defined, and sufficient conditions are derived. The theoretical results are illustrated with examples. We show with an example the leader's influence on the consensus. [ABSTRACT FROM AUTHOR]

The planning, regulation and effectiveness of the product design process depend on various characteristics. Recently, bio-inspired collective intelligence approaches have been applied in this process in order to create more appealing product forms and optimize the design process. In fact, the use of neural network models in product form design analysis is a complex process, in which the type of network has to be determined, as well as the structure of the network layers and the neurons in them; the connection coefficients, inputs and outputs have to be explored; and the data have to be collected. In this paper, an impulsive discrete fractional neural network modeling approach is introduced for product design analysis. The proposed model extends and complements several existing integer-order neural network models to the generalized impulsive discrete fractional-order setting, which is a more flexible mechanism to study product form design. Since control and stability methods are fundamental in the construction and practical significance of a neural network model, appropriate impulsive controllers are designed, and practical Mittag-Leffler stability criteria are proposed. The Lyapunov function strategy is applied in providing the stability criteria and their efficiency is demonstrated via examples and a discussion. The established examples also illustrate the role of impulsive controllers in stabilizing the behavior of the neuronal states. The proposed modeling approach and the stability results are applicable to numerous industrial design tasks in which multi-agent systems are implemented. [ABSTRACT FROM AUTHOR]

The paper proposes a new model-based optimization approach to improve the clinical efficiency of compensatory insulin bolus treatment in diabetic patients, aiming to mitigate the consequences of diabetes. The most important contribution of this paper is a novel methodology for determining the optimal parameters of insulin treatment, namely the size and timing of insulin boluses, to effectively compensate for carbohydrate intake. This concept can be seen as the so-called optimal model-based bolus calculator. The presented theoretical framework deals with the problem of optimal disturbance rejection in impulsive systems by minimizing an integral quadratic cost function. The methodology considers a personalized empirical transfer function model with static gains and time constants as the only parameters assumed to be known, making the bolus calculator more straightforward to implement in clinical practice. Contrary to other techniques, the proposed methodology considers impulsive insulin administration in the form of boluses, which is more feasible than continuous infusion. In contrast to the conventional bolus calculator, the proposed algorithm allows for maximizing therapy performance by optimizing the relative time of insulin bolus administration with respect to carbohydrate intake. Another feature to highlight is that the solution of the optimization problem can be obtained analytically, hence no numerical iterative solvers are required. Additionally, the continuous-time domain approach allows for a much finer adjustments of the insulin administration timing compared to discrete-time models. The proposed approach was validated in an in-silico study, which demonstrated the importance of systematically determined insulin–carbohydrate ratio and the relative delay between disturbance and its compensation. The results showed that the proposed optimal bolus calculator outperforms the traditional suboptimal formula. [ABSTRACT FROM AUTHOR]

This article investigates the stabilization issue of highly non-linear hybrid stochastic delayed networks (HSDNs) via periodic self-triggered control under impulse (PS-TCI). Firstly, the existence of a unique global solution for highly non-linear HSDNs under PS-TCI is studied. Then, a stabilization criterion for highly non-linear HSDNs is established, by combining a graph-theoretic approach with a novel Lyapunov-based analysis, based on a 'genuine' Lyapunov function defined by introducing an auxiliary timer. Therein, the less conservative polynomial growth condition and local Lipschitz condition for the drift and diffusion coefficients are used than the linear growth condition and global Lipschitz condition. Meanwhile, the design idea of PS-TCI is based on the evolution of an upper bound of the mathematical expectation for Lyapunov function (not directly Lyapunov function or system state), which implies that the triggered instant of PS-TCI is not a random variable. Finally the theoretical results are employed to study the stability of a class of FitzHugh–Nagumo circuits networks and the central pattern generators networks of a hexapod robot, and correlative numerical simulations are provided for demonstration. • The stabilization of highly non-linear HSDNs is first investigated via PS-TCI. • Some existing stabilization conditions for PS-TCI in [30,34] are weakened. • By introducing an auxiliary timer, a novel Lyapunov-based analysis is proposed. • The theoretical results could be applied to FNCNs and CPGNs successfully. [ABSTRACT FROM AUTHOR]

The purpose of this study was to review the evidence for the potential of Tai Chi Chuan (TCC) as a model of meditative movement in benefiting people with impulsivity related disorders and provide guidance for future research. A scoping review of the literature was conducted in five databases. Eligibility criteria were original articles reporting TCC based interventions or included TCC techniques and provided any assessment on impulsivity or related measures, impulse control disorders, or other psychiatric disorders related to impulsivity (e.g., addictive disorders, ADHD, and other conduct disorders). Twenty-eight out of 304 studies initially retrieved were reviewed. The reports concentrated mostly on neurodegenerative conditions, cognitive decline, and substance use disorders (SUD). TCC had several positive effects in cognitive domains resulting in improvements in memory, executive functions, inhibitory control, attention, and verbal fluency. These improvements in memory, executive function, including inhibitory control and attention, and verbal fluency were associated with changes in the brain plasticity, resting activity, and other neurobiological markers. Albeit no study was found on the use of TCC in impulse control disorders or impulse related conditions, other than SUD, the findings suggest that considering the behavioral impact of TCC, especially the improvement of executive functions, it could be a valuable therapeutic tool for approaching impulse control related disorders. • The benefits of Tai Chi Chuan (TCC) as a form of meditational movement has been postulated as beneficial particularly for elders. • TCC movements support the development of mindfulness, generating better regulation of physical, emotional and social processes, contributing to better decision-making and self-care behaviors. • The effects of TCC on increasing mindfulness may also be beneficial for improving impulsive behaviors regardless of age. • In addition to the physical benefits, TCC has the added benefit of promoting psychological improvement, and its most robust effects seem to be centered on executive functions. • Studies have also found a correlation between the practice of TCC and stimulation of the prefrontal cortex and medial temporal lobe structures, which can be fundamental for decision-making and impulse control. [ABSTRACT FROM AUTHOR]

This paper studies the pth moment exponential stability of stochastic delayed differential systems with impulsive control involving delays. By using the Razumikhin method, average impulsive delay and average dwell-time, sufficient conditions for pth moment exponential stability of stochastic delayed differential systems with impulsive control involving delays are obtained. In addition, we apply the derived theoretical results to nonlinear impulsive stochastic delayed systems. Finally, the validity and generality of the obtained results are demonstrated by an example. [ABSTRACT FROM AUTHOR]

In this paper, the lag synchronization problem in mean square of a class of complex stochastic networks with multiple mixed delays is studied. The mixed delays involved in this paper not only include n non-identical nonlinear time-varying delays and a coupled delay, but also include a infinite distributed delay. Using the impulsive differential inequality and the Lyapunov method, it is proved that this kind of the network can reach to be lag synchronization. Finally, two examples are given to illustrated to verify the effectiveness of our proposed result. [ABSTRACT FROM AUTHOR]

This paper was concerned with the trajectory tracking control of wheeled mobile robots using aperiodic intermittent control. By establishing the corresponding motion model of the wheeled mobile robot, a tracking control strategy was proposed based on the intermittent control approach and backstepping method. Compared to the controllers using continuous state feedback, the proposed control strategy was activated only on separate time intervals, which combined the features of closed- and open-loop control. An example was given to illustrate the effectiveness of the obtained result.