Passivity and passification of fractional-order memristive neural networks with time delays

A class of fractional-order memristive neural networks (FMNNs) with time delays is studied. At first, the original network system is converted to fractional-order uncertain one to simplify the analysis by a variable transformation. Successively, some new LMIs-based passivity criteria are derived by differential inclusions, set-valued maps, inequality techniques and linear matrix inequality approach. Furthermore, a feedback control protocol is designed to solve the passification problem for the considered system, whose feedback control effect on different neurons can be changed artificially, which can be better applied to neural networks. The obtained results include some existing ones as special cases. A numerical example is proposed to illustrate the theoretical results.

Global stability of latency-age/stage-structured epidemic models with differential infectivity

In this paper, we first formulate a system of ODEs-PDE to model diseases with latency-age and differential infectivity. Then, based on the ways how latent individuals leave the latent stage, one ODE and two DDE models are derived. We only focus on the global stability of the models. All the models have some similarities in the existence of equilibria. Each model has a threshold dynamics for global stability, which is completely characterized by the basic reproduction number. The approach is the Lyapunov direct method. We propose an idea on constructing Lyapunov functionals for the two DDE and the original ODEs-PDE models. During verifying the negative (semi-)definiteness of derivatives of the Lyapunov functionals along solutions, a novel positive definite function and a new inequality are used. The idea here is also helpful in applying the Lyapunov direct method to prove the global stability of some epidemic models with age structure or delays.

Stability of a delayed SARS-CoV-2 reactivation model with logistic growth and adaptive immune response

This paper develops and analyzes a SARS-CoV-2 dynamics model with logistic growth of healthy epithelial cells, CTL immune and humoral (antibody) immune responses. The model is incorporated with four mixed (distributed/discrete) time delays, delay in the formation of latent infected epithelial cells, delay in the formation of active infected epithelial cells, delay in the activation of latent infected epithelial cells, and maturation delay of new SARS-CoV-2 particles. We establish that the model's solutions are non-negative and ultimately bounded. We deduce that the model has five steady states and their existence and stability are perfectly determined by four threshold parameters. We study the global stability of the model's steady states using Lyapunov method. The analytical results are enhanced by numerical simulations. The impact of intracellular time delays on the dynamical behavior of the SARS-CoV-2 is addressed. We noted that increasing the time delay period can suppress the viral replication and control the infection. This could be helpful to create new drugs that extend the delay time period.

Cost-effectiveness of improvement strategies for reperfusion treatments in acute ischemic stroke: a systematic review

BACKGROUND

Reducing delays along the acute stroke pathway significantly improves clinical outcomes for acute ischemic stroke patients eligible for reperfusion treatments. The economic impact of different strategies reducing onset to treatment (OTT) is crucial information for stakeholders in acute stroke management. This systematic review aimed to provide an overview on the cost-effectiveness of several strategies to reduce OTT.

METHODS

A comprehensive literature search was conducted in EMBASE, PubMed, and Web of Science until January 2022. Studies were included if they reported 1/ stroke patients treated with intravenous thrombolysis and/or endovascular thrombectomy, 2/ full economic evaluation, and 3/ strategies to reduce OTT. The Consolidated Health Economic Evaluation Reporting Standards statement was applied to assess the reporting quality.

RESULTS

Twenty studies met the inclusion criteria, of which thirteen were based on cost-utility analysis with the incremental cost-effectiveness ratio per quality-adjusted life year gained as the primary outcome. Studies were performed in twelve countries focusing on four main strategies: educational interventions, organizational models, healthcare delivery infrastructure, and workflow improvements. Sixteen studies showed that the strategies concerning educational interventions, telemedicine between hospitals, mobile stroke units, and workflow improvements, were cost-effective in different settings. The healthcare perspective was predominantly used, and the most common types of models were decision trees, Markov models and simulation models. Overall, fourteen studies were rated as having high reporting quality (79%-94%).

CONCLUSIONS

A wide range of strategies aimed at reducing OTT is cost-effective in acute stroke care treatment. Existing pathways and local characteristics need to be taken along in assessing proposed improvements.

IMC based robust PI/PID controllers for time-delayed inverse response processes

In the last decade, several works have been reported for stable and integrating processes to achieve a specified maximum sensitivity. Also, internal model control (IMC) is a popular controller design strategy as it has only one tuning parameter. IMC based controllers are available in literature for time-delayed inverse response processes but none of the reported works provide guidelines for selecting the tuning parameter. In the present work, IMC-PI/PID controllers for time-delayed inverse response processes are reported. To achieve a specified maximum sensitivity in the range of 1.4 to 2.0, set of tuning rules is proposed for the tuning parameter. Normalized form of the transfer functions are used in the present method which simplifies the design procedure. Novelty of the proposed approach is that the user can not only tune the tuning parameter for desired maximum sensitivity, but can also switch from smooth (M_s = 1.4) to tight (M_s = 2.0) control. Simulation results illustrate the effectiveness of the proposed tuning rules.

Dynamic analysis of rumor propagation model with media report and time delay on social networks

When a rumor appears on social networks, the media of relevant departments need reaction time to make an authoritative announcement. Considering the effects of the media report and time delay on a rumor spreading, and the different attitudes of individuals towards media reports. We proposed a susceptible-expose-infective-media-remover (SEIMR) rumor propagation model with media reports and time delay. Firstly, the basic reproduction number of the model is obtained. Secondly, the positivity, boundedness and existence of the solutions of the model are analyzed. Then, the local asymptotic stability of the rumor free equilibrium and the boundary equilibriums is proved, and the global asymptotic stability of the equilibriums is proved by constructing Lyapunov function when the time delay is zero. Besides, the prevention and control effects of the media report on rumor spreading and the effect of time delay are analyzed. The shorter time delay in media report and the greater the impact of the media report, the more effective the suppression of rumors will be. Finally, the accuracy of the theoretical results as well as the effects of different parameters of the model have been verified through numerical simulations, and the effectiveness of the SEIMR model has been verified via comparative experiments.

Guaranteed cost-based feedback control design for fractional-order neutral systems with input-delayed and nonlinear perturbations

Time delay in actuators is mainly caused by electrical and mechanical components. The effect is visible in the system response particularly when changing in the input command. Therefore, input delay is a problem in the control system design that must be taken into account. Besides, ignoring uncertainty in the dynamic models may compromise the controller design. Thus, how to mitigate the effect of this issue on the system stability and performance is a challenging topic. This article deals with the stabilization of fractional neutral systems considering input-delayed and nonlinear perturbations using the guaranteed cost-based feedback control technique. The main focus is to design the state- and output-feedback controllers to achieve a good performance. The stability criteria are formulated in the Lyapunov sense, which are described in terms of matrix inequalities. The proposed idea is validated using simulations.

Fixed and Distributed Gene Expression Time Delays in Reaction-Diffusion Systems

Time delays, modelling the process of intracellular gene expression, have been shown to have important impacts on the dynamics of pattern formation in reaction-diffusion systems. In particular, past work has shown that such time delays can shrink the Turing space, thereby inhibiting patterns from forming across large ranges of parameters. Such delays can also increase the time taken for pattern formation even when Turing instabilities occur. Here, we consider reaction-diffusion models incorporating fixed or distributed time delays, modelling the underlying stochastic nature of gene expression dynamics, and analyse these through a systematic linear instability analysis and numerical simulations for several sets of different reaction kinetics. We find that even complicated distribution kernels (skewed Gaussian probability density functions) have little impact on the reaction-diffusion dynamics compared to fixed delays with the same mean delay. We show that the location of the delay terms in the model can lead to changes in the size of the Turing space (increasing or decreasing) as the mean time delay, [Formula: see text], is increased. We show that the time to pattern formation from a perturbation of the homogeneous steady state scales linearly with [Formula: see text], and conjecture that this is a general impact of time delay on reaction-diffusion dynamics, independent of the form of the kinetics or location of the delayed terms. Finally, we show that while initial and boundary conditions can influence these dynamics, particularly the time-to-pattern, the effects of delay appear robust under variations of initial and boundary data. Overall, our results help clarify the role of gene expression time delays in reaction-diffusion patterning, and suggest clear directions for further work in studying more realistic models of pattern formation.

A resilient outlier-resistant recursive filtering approach to time-delayed spatial-temporal systems with energy harvesting sensors

This paper is concerned with a resilient outlier-resistant recursive filtering problem for a class of time-delayed spatial-temporal systems (STSs) with energy harvesting sensors. We consider a situation that the sensors are able to harvest energy from external environments and consume certain energy when the measurements are transmitted to filters. When the energy of the sensor is insufficient to maintain the normal communication between the sensors and the filters, the measurement will be regarded as missing. For the sake of obtaining a satisfactory filtering performance, the innovations influenced by the measurement outliers is constrained by introducing a saturation function in the filter. Furthermore, the resilient issue of the designed recursive filter is considered to resist the fluctuations of the filter parameters. Under the effects of sensor energy constraints, measurement outliers as well as parameter fluctuations, a resilient outlier-resistant recursive filter is designed where an upper bound (UB) is first obtained on the filtering error covariance (FEC). Then, by resorting to a matrix recursive equation, such a UB is minimized by the filter gain matrix. Finally, we exhibit a numerical example to verify the effectiveness of the proposed resilient outlier-resistant recursive filter scheme for time-delayed STSs.

Finite-time bipartite synchronization of switched competitive neural networks with time delay via quantized control

This article tackles the finite-time bipartite synchronization (FTBS) of coupled competitive neural networks (CNNs) with switching parameters and time delay. Quantized control is utilized to achieve the FTBS at a small control cost and with limited channel resources. Since the effects of the time delay and switching parameters, traditional finite-time techniques cannot be directly utilized to the FTBS. By constructing a novel multiple Lyapunov functional (MLF), a sufficient criterion formulated by linear programming (LP) is established for the FTBS and the estimation of the settling time. To further improve the accuracy of the settling time, another MLF is designed by dividing the dwell time. With the aid of convex combination, a new LP is provided, which removes the requirement that the increment coefficient of the MLF at switching instants has to be larger than 1. In addition, to obtain the more precise settling time, an optimal algorithm is provided. Two numerical examples are put forward to demonstrate the reasonableness of the theoretical analysis.

There exists the "smartest" movement rate to control the epidemic rather than "city lockdown"

The emergency outbreak and spread of coronavirus disease 2019 (COVID-19) has left great damage to individuals over most of the world. Population mobility is the primary reason for the spread of the epidemic. A delayed stochastic epidemic susceptible-infected-recovered (SIR) model with Gaussian white noise is introduced. Compared with traditional models,this model is characterized by time delay, environmental noise and population mobility among municipalities with the convenient transportation network. The stochastic dynamic behavior of the SIR model is analyzed and the existence of the stochastic bifurcation of the system is proved. The effect of time delay and movement rate are investigated. Numerical simulations are performed to support the theoretical results. It is worth mentioning that the movement rate is not as low as possible and appropriate population mobility is conducive to alleviating the epidemic. Through simulation, we demonstrate the existence of the best movement rate named the "smartest" κ , which is helpful to control the epidemic. This model is also useful to prevent other infectious diseases.

Cell site analysis: Changes to networks with time

Cell service areas may change over time as sites or cells are adjusted, decommissioned or introduced, and there may have been changes between the time of calls and the analysis undertaken. The manner in which survey data is used as part of an analysis is of particular relevance as the data gathered may not reflect the state of the network at the time of calls and thus potentially mislead. Overlaying "historic" data (potentially generated before the calls) with "targeted" surveys (usually generated after the calls) may enable an assessment of possible network changes, or whether additional cells may also have served at a given location at a previous time. This paper outlines a case in which there was a significant time gap between the analysis of call data records and the date on which they were generated.

Simple internal model control based modified Smith predictor for integrating time delayed processes with real-time verification

Internal model control (IMC) tuned simple modified Smith predictor structure for integrating time delayed processes (IPTD) is reported here. Pole position at origin implies its non-self-regulating behaviour. Processes like distillation column, liquid supply to large storage tank, superheated steam flow to turbine etc. are usually found IPTD processes. Reported modified Smith predictor (MSP) design with multiple controllers is adequate to exhibit anticipated closed loop performance for IPTD processes. Tuning complexity of the reported multi-controller based structure is mitigated by the sole tuning parameter (closed loop time constant) obtained from IMC design. Proposed scheme shows considerable performance improvement during set point tracing by zero overshoot. Additionally, smooth as well as reasonably fast load recovery is ensured. Eminence of the reported scheme is established in terms of performance indices along with stability margins in assessment through recently reported modified Smith predictor techniques. Real-time evaluation of the proposed design is demonstrated on an indigenous set up of level control loop considered as IPTD process.

Tuning Generalized Predictive PI controllers for process control applications

Predictive PI (PPI) controllers have demonstrated to exceed traditional PID controllers when they are applied to systems with long delays. This work proposes a new controller structure and tuning that we call Generalized Predictive PI (GPPI) controller which provides greater design flexibility than PI and PPI strategies. To realize a fair comparison, the design and tuning rules for discrete PI and PPI controllers were developed using optimal arguments based on the root-locus, for critically damped response before a step change in the reference. Experimental results, using industrial equipment, have illustrated the tuning methodology and the performance of the proposed controller under real conditions. Flow and water level process in a laboratory flume were considered. For these processes, First Order Plus Time Delay (FOPTD) models are used. The GPPI control results are encouraging, reducing the settling time plus a very small overshoot before step change in the reference regarding the PI and PPI strategies, up to 41.03% for the flow control loop and up to 54.21% for the level control loop. The discrete analysis of the strategies in the Z plane was performed, allowing for a direct translation to recursive equations that can then be programmed into a Programmable Logic Controller (PLC), other industrial controllers such as Distributed Control Systems (DSC), or microcontrollers, such as Arduino, Raspberry or FPGA. This is an important result, since it demonstrates that the increased complexity of the proposed controller does not hamper its implementation in industrial controller systems. In this work, we used a Rockwell ControlLogix ^{\protect \relax \special {t4ht=®}} PLC with Structured Text programming language.

Parameter identification of Hammerstein-Wiener nonlinear systems with unknown time delay based on the linear variable weight particle swarm optimization

This paper deals with the parameter estimation of Hammerstein-Wiener (H-W) nonlinear systems which have unknown time delay. The linear variable weight particle swarm method is formulated for such time delay systems. This algorithm transforms the nonlinear system identification issue into a function optimization issue in the parameter space, then utilizes the parallel searching ability of the particle swarm optimization and the iterative identification technique to realize the simultaneous estimation of all parameters and the unknown time delay. Finally, parameters in the linear submodule, nonlinear submodule and the time delay are separated from the optimum parameter. Moreover, two illustrative examples are exhibited to evaluate the effectiveness of the proposed method. The simulation results demonstrate that the derived method has fast convergence speed and high estimation accuracy for estimating H-W systems with unknown time delay, and it is applied to the identification of the bed temperature systems.

Associative anticipatory learning and control of the cerebellar cortex based on the spike-timing-dependent plasticity of the parallel fiber-Purkinje cell synapses

Time delays are inevitable in the neural processing of sensorimotor systems; small delays can cause severe damage to movement accuracy and stability. It is strongly suggested that the cerebellum compensates for delays in neural signal processing and performs predictive control. Neural computational theories have explored concepts of the internal models of control objects-believed to avoid delays by providing internal feedback information-although there has been no clear relevance to neural processing. The timing-dependent plasticity of parallel fiber-Purkinje cell synapses is well known. The long-term depression of the synapse is observed when parallel fiber activation precedes climbing fiber activation within -50-300 ms, and is the greatest within 50-200 ms. This paper presents a theory that this temporal difference of 50-200 ms is the basis for an associative anticipation of as many milliseconds. Associative learning can theoretically connect an input signal to a desired signal; therefore, a 50-200 ms earlier input signal can be connected to a desired output signal through temporary asymmetric plasticity. After learning is completed, an input signal generates a desired output signal that appears 50-200 ms later. For the associative learning of temporally continuous signals, this study integrates the universal function approximation capability of the cerebellar cortex model and temporally asymmetric synaptic plasticity to create the theory of associative anticipatory learning of the cerebellum. The effective motor control of this learning is demonstrated by adaptively stabilizing an inverted pendulum with a delay similar to that done by humans.

Exponential synchronization of coupled neural networks under stochastic deception attacks

In this paper, the issue of synchronization is investigated for coupled neural networks subject to stochastic deception attacks. Firstly, a general differential inequality with delayed impulses is given. Then, the established differential inequality is further extended to the case of delayed stochastic impulses, in which both the impulsive instants and impulsive intensity are stochastic. Secondly, by modeling the stochastic discrete-time deception attacks as stochastic impulses, synchronization criteria of the coupled neural networks under the corresponding attacks are given. Finally, two numerical examples are provided to demonstrate the correctness of the theoretical results.

Stochastic COVID-19 SEIQ epidemic model with time-delay

In this work, we consider an epidemic model for corona-virus (COVID-19) with random perturbations as well as time delay, composed of four different classes of susceptible population, the exposed population, the infectious population and the quarantine population. We investigate the proposed problem for the derivation of at least one and unique solution in the positive feasible region of non-local solution. For one stationary ergodic distribution, the necessary result of existence is developed by applying the Lyapunov function in the sense of delay-stochastic approach and the condition for the extinction of the disease is also established. Our obtained results show that the effect of Brownian motion and noise terms on the transmission of the epidemic is very high. If the noise is large the infection may decrease or vanish. For validation of our obtained scheme, the results for all the classes of the problem have been numerically simulated.

Quantization synchronization of chaotic neural networks with time delay under event-triggered strategy

This paper shows solicitude for the quantization synchronization of delayed chaotic master and slave neural networks under an dynamic event-triggered strategy. In virtue of a generalized Halanay-type inequality, a theoretical criterion for quasi-synchronization of master and slave neural networks is derived. Meanwhile, we can obtain an exact upper bound of synchronization error by using this criterion. Compared with output feedback controller with event triggering and quantization, the case where the controller only affected by quantization is also considered. Then, we exclude the Zeno behavior of the event-triggered controller. A sufficient criterion for the existence of the quantized output feedback controllers is also provided. A numerical example is cited to illustrate the efficiency of our theoretical criteria. In addition, some experiments of secure image communication are conducted under quasi-synchronization of master and slave neural networks.

Bilateral motion prediction and control for teleoperation under long time-varying delays

Bilateral controller design for the teleoperation system is studied in this paper based on a motion prediction approach. To compensate the known long time-varying delays, novel predictors are presented to reconstruct the positions and velocities of robots on both sides through using the delayed measurements. The proposed predictors consist of several sub-predictors in a cascade structure, each of which is to predict the states of the previous one. The estimations of the actual states can be obtained from the last sub-predictor. New prediction horizons of each sub-observers are designed to cope with the time-varying fractions of the time delay. Then through applying the predicted results, bilateral predictive controller is designed for the teleoperation. The errors of both position tracking and prediction can converge into the bounded regions under several sufficient conditions of the control gains, which are obtained by using the Lyapunov-Krasovskii approach. The effective capacity of the presented method can be verified through comparative simulations.

Stability behavior of a two-susceptibility SHIR epidemic model with time delay in complex networks

Taking two susceptible groups into account, we formulate a modified subhealthy-healthy-infected-recovered (SHIR) model with time delay and nonlinear incidence rate in networks with different topologies. Concretely, two dynamical systems are designed in homogeneous and heterogeneous networks by utilizing mean field equations. Based on the next-generation matrix and the existence of a positive equilibrium point, we derive the basic reproduction numbers R 0 1 and R 0 2 which depend on the model parameters and network structure. In virtue of linearized systems and Lyapunov functions, the local and global stabilities of the disease-free equilibrium points are, respectively, analyzed when R 0 1 < 1 in homogeneous networks and R 0 2 < 1 in heterogeneous networks. Besides, we demonstrate that the endemic equilibrium point is locally asymptotically stable in homogeneous networks in the condition of R 0 1 > 1 . Finally, numerical simulations are performed to conduct sensitivity analysis and confirm theoretical results. Moreover, some conjectures are proposed to complement dynamical behavior of two systems.

Vaccination control of an epidemic model with time delay and its application to COVID-19

This paper studies an SEIR-type epidemic model with time delay and vaccination control. The vaccination control is applied when the basic reproduction number R 0 > 1 . The vaccination strategy is expressed as a state delayed feedback which is related to the current and previous state of the epidemic model, and makes the model become a linear system in new coordinates. For the presence and absence of vaccination control, we investigate the nonnegativity and boundedness of the model, respectively. We obtain some sufficient conditions for the eigenvalues of the linear system such that the nonnegativity of the epidemic model can be guaranteed when the vaccination strategy is applied. In addition, we study the stability of disease-free equilibrium when R 0 < 1 and the persistent of disease when R 0 > 1 . Finally, we use the obtained theoretical results to simulate the vaccination strategy to control the spread of COVID-19.

The effect of pre-analytical handling on the stability of fractalkine, monocyte chemoattractant protein 1 (MCP1), interleukin 6 and interleukin 8 in samples of human cerebrospinal fluid

Cytokine networks in cerebrospinal fluid (CSF) are important to our understanding of several neuroinflammatory diseases. Knowledge about optimal handling of samples is limited but important to minimize bias and reduce costs in CSF biomarker studies. The aim of this study was to examine the effect of storage temperature and time delay from CSF sample collection until freezing on the concentration of 11 different cytokines thought to be associated with chronic pain. CSF samples from 21 individuals undergoing hip or knee arthroplasty under spinal anesthesia were divided between two tubes. One tube was stored and centrifuged (within 30 min) at room temperature, and one tube was stored in ice water and centrifuged (within 30 min) at 4 °C. Each tube was split into six vials that were frozen at -80 °C, 0.5, 1, 2, 3, 4, or 5 h after collection. Cytokines were analyzed using a multiplex panel. A random effect panel data regression was conducted for each biomarker including the variables of storage temperature until freezing and time delay. Four cytokines had detectable levels: Fractalkine, monocyte chemoattractant protein 1(MCP-1), interleukine 6 (IL-6), and interleukine 8 (IL-8). There was no significant effect of storage temperature and time delay on MCP-1, IL-6, or IL-8 concentrations. Fractalkine concentration showed no clear trend. No concentration differences were observed between samples kept in ice water and those at room temperature except at the 3-h time point, and there was no overall significant effect of time delay on fractalkine concentration. We found no clear effect of storage temperature and time delay up to five hours from sample collection until freezing on the CSF concentrations of fractalkine, MCP-1, IL-6, or IL-8.

Mathematical modeling and analysis for controlling the spread of infectious diseases

In this work, we present and discuss the approaches, that are used for modeling and surveillance of dynamics of infectious diseases by considering the early stage asymptomatic and later stage symptomatic infections. We highlight the conceptual ideas and mathematical tools needed for such infectious disease modeling. We compute the basic reproduction number of the proposed model and investigate the qualitative behaviours of the infectious disease model such as, local and global stability of equilibria for the non-delayed as well as delayed system. At the end, we perform numerical simulations to validate the effectiveness of the derived results.

Simplified filtered Smith predictor for high-order dead-time processes

This paper proposes a control structure suitable for high-order non-minimum phase (NMP) processes. In general, dead-time compensators (DTCs) firstly predict the process output with zero error at a steady-state and then a primary controller with an integrator is designed based on the delay-free model. The main advantage of the proposed structure is that the primary controller is only a state feedback gain with no integrators. This leads to fewer parameters to tune and lower order filters, while a robustness filter is used to reject disturbances and guarantee zero error at a steady state. Simulation results show better or equivalent performance compared to other recently published works, even kept the controller design simplicity.