Prediction and control of cholera outbreak: Study case of Cameroon

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 R _ 0 that bound the effective reproduction number R 0 , from below and above, that is R _ 0 ≤ R 0 ≤ R ¯ 0 . We prove that cholera tends to disappear when R ¯ 0 ≤ 1 , while when R _ 0 > 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.

Effects of impulse on prescribed-time synchronization of switching complex networks

The specified convergence time, designated by the user, is highly attractive for many high-demand applications such as industrial robot control, missile guidance, and autonomous vehicles. For the application of neural networks in the field of secure communication and power systems, the importance of prescribed-time synchronization(PTs) and stable performance of the system is more prominent. This paper introduces a prescribed-time controller without the fractional power function and sign function, which can reach synchronization at a prescribed time and greatly reduce the chattering phenomenon of neural networks. Additionally, by constructing synchronizing/desynchronizing impulse sequences, the PTs of switching complex networks(SCN) is achieved with impulse effects, where the time sequences of switching and impulse occurrences in the networks are constrained by the average dwell time. This approach effectively reduces the impact of frequent mode switching on network synchronization, and the synchronization time can be flexibly adjusted within any physically allowable range to accommodate different application requirements. Finally, the effectiveness of the proposed control strategy is demonstrated by two examples.

Tai Chi Chuan evidence related to impulsivity and impulse related disorders: A scoping review

BACKGROUND

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.

METHODS

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).

RESULTS

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.

CONCLUSION

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.

Periodic self-triggered strategy for highly non-linear hybrid stochastic delayed networks based on impulsive control

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.

Input-to-state stability of positive delayed neural networks via impulsive control

This paper is concerned with the positivity and impulsive stabilization of equilibrium points of delayed neural networks (DNNs) subject to bounded disturbances. With the aid of the continuous dependence theorem for impulsive delay differential equations, a relaxed positivity condition is derived, which allows the neuron interconnection matrix to be Metzler if the activation functions satisfy a certain condition. The notion of input-to-state stability (ISS) is introduced to characterize internal global stability and disturbance attenuation performance for impulsively controlled DNNs. The ISS property is analyzed by employing a time-dependent max-separable Lyapunov function which is able to capture the positivity characterization and hybrid structure of the considered DNNs. A ranged dwell-time-dependent ISS condition is obtained, which allows to design an impulsive control law via partial state variables. As a byproduct, an improved global exponential stability criterion for impulse-free positive DNNs is obtained. The applicability of the achieved results is illustrated through three numerical examples.

Bounded synchronization for uncertain master-slave neural networks: An adaptive impulsive control approach

This paper investigates the bounded synchronization of the discrete-time master-slave neural networks (MSNNs) with uncertainty. To deal with the unknown parameter in the MSNNs, a parameter adaptive law combined with the impulsive mechanism is proposed to improve the estimation efficiency. Meanwhile, the impulsive method also is applied to the controller design for saving the energy. In addition, a novel time-varying Lyapunov functional candidate is employed to depict the impulsive dynamical characteristic of the MSNNs, wherein a convex function related to the impulsive interval is used to obtain a sufficient condition for bounded synchronization of the MSNNs. Based on the above condition, the controller gain is calculated utilizing an unitary matrix. An algorithm is proposed to reduce the boundary of the synchronization error by optimizing its parameters. Finally, a numerical example is provided to illustrate the correctness and the superiority of the developed results.

Reliable impulsive synchronization for fuzzy neural networks with mixed controllers

This work studies the synchronization of the master-slave (MS) fuzzy neural networks (FNNs) with random actuator failure, where the state information of the master FNNs can not be obtained directly. To reduce the loads of the communication channel and the controller, the simultaneously impulsive driven strategy of the communication channel and the controller is proposed. On the basis of the received measurements of the master FNNs, the mixed controller consisting of observer based controller and the static controller is designed. The randomly occurred actuator failure is also considered. According to the Lyapunov method, the sufficient conditions are achieved to ensure the synchronization of the MS FNNs, and the controller gains are designed by using the obtained results. The validity of the derived results is illustrated by a numerical example.

Secure consensus of multi-agent systems with redundant signal and communication interference via distributed dynamic event-triggered control

This paper studies a class of multi-agent systems (MASs) subject to deception signal and communication interference. The objective of the present work is to establish a flexible and generalized distributed dynamic event-triggered control (DDETC) with impulsive signal to make the investigated MASs achieve secure consensus under redundant signal and communication interference. It is shown that Zeno behavior can be precluded with such a DDETC. The challenging but valuable new designed DDETC scheme shows the trigger is developed to achieve itself away from exceeding the data transmission load through parameter adjustment, to reduce redundant triggering, to flexibly adjust the triggered frequency, and even to replace sampled-data scheme as special cases. By the impulsive DDETC, anti-deception and anti-interference techniques, the secure consensus criteria of MASs are constructed cleverly. Numerical examples with simulations are given to illustrate the effectiveness of the proposed scheme and control protocol.

Time for a true display of skill: Top players in League of Legends have better executive control

Research into the effects of action video gaming on cognition has largely relied on self-reported action video game experience and extended video game training. Only a few studies have focused on participants' actual gaming skills. However, whether superior players and average players have different executive control is still not fully demonstrated. This study had top-ranking League of Legends players (global top 0.17%; N = 35) and average-ranking League of Legends players (N = 35) perform two cognitive tasks that aimed to measure three aspects of executive functioning: cognitive flexibility, interference control, and impulsive control. We controlled self-reported gaming experience, so that top-ranking players and average-ranking players had similar years of play and hours of play per week. We found that compared to a group of average players, top players showed smaller task-switching costs and smaller response-congruency effects in a Stroop-switching test. In a continuous performance test, top players indicated higher hit rates and lower false alarm rates as compared to average players. These findings suggest that top players have better cognitive flexibility and more accurate control of interference in the context of task-switching. Moreover, top players exhibit better impulsive control. The present study provides evidence that players' gaming skills rather than gaming experience are related to cognitive abilities, which may explain why previous studies on self-reported gaming experience and those assessing supervised training and cognitive performance have shown inconsistent results.

PK/PD-based adaptive tailoring of oseltamivir doses to treat within-host influenza viral infections

Influenza A virus (IAV) is a latent global threat to human health. In view of the risk of pandemics, prophylactic and curative treatments are essential. Oseltamivir is a neuraminidase inhibitor efficiently supporting recovery from influenza infections. Current common clinical practice is a constant drug dose (75 or 150 mg) administered at regular time intervals twice a day. We aim to use quantitative systems pharmacology to propose an efficient adaptive drug scheduling. We combined the mathematical model for IAV infections validated by murine data, which captures the viral dynamics and the dynamics of the immune host response, with a pharmacokinetic (PK)/pharmacodynamic (PD) model of oseltamivir. Next, we applied an adaptive impulsive feedback control method to systematically calculate the adaptive dose of oseltamivir in dependence on the viral load and the number of immune effectors at the time of drug administration. Our in silico results revealed that the treatment with adaptive control-based drug scheduling is able to either increase the drug virological efficacy or reduce the drug dose while keeping the same virological efficacy. Thus, adaptive adjustment of the drug dose would reduce not only the potential side effects but also the amount of stored oseltamivir required for the prevention of outbreaks.

Mittag-Leffler synchronization of fractional neural networks with time-varying delays and reaction-diffusion terms using impulsive and linear controllers

In this paper, we propose a fractional-order neural network system with time-varying delays and reaction-diffusion terms. We first develop a new Mittag-Leffler synchronization strategy for the controlled nodes via impulsive controllers. Using the fractional Lyapunov method sufficient conditions are given. We also study the global Mittag-Leffler synchronization of two identical fractional impulsive reaction-diffusion neural networks using linear controllers, which was an open problem even for integer-order models. Since the Mittag-Leffler stability notion is a generalization of the exponential stability concept for fractional-order systems, our results extend and improve the exponential impulsive control theory of neural network system with time-varying delays and reaction-diffusion terms to the fractional-order case. The fractional-order derivatives allow us to model the long-term memory in the neural networks, and thus the present research provides with a conceptually straightforward mathematical representation of rather complex processes. Illustrative examples are presented to show the validity of the obtained results. We show that by means of appropriate impulsive controllers we can realize the stability goal and to control the qualitative behavior of the states. An image encryption scheme is extended using fractional derivatives.

Practical stability analysis of fractional-order impulsive control systems

In this paper we obtain sufficient conditions for practical stability of a nonlinear system of differential equations of fractional order subject to impulse effects. Our results provide a design method of impulsive control law which practically stabilizes the impulse free fractional-order system.

Exponential synchronization of discontinuous neural networks with time-varying mixed delays via state feedback and impulsive control

This paper investigates drive-response synchronization for a class of neural networks with time-varying discrete and distributed delays (mixed delays) as well as discontinuous activations. Strict mathematical proof shows the global existence of Filippov solutions to neural networks with discontinuous activation functions and the mixed delays. State feedback controller and impulsive controller are designed respectively to guarantee global exponential synchronization of the neural networks. By using Lyapunov function and new analysis techniques, several new synchronization criteria are obtained. Moreover, lower bound on the convergence rate is explicitly estimated when state feedback controller is utilized. Results of this paper are new and some existing ones are extended and improved. Finally, numerical simulations are given to verify the effectiveness of the theoretical results.